| Taskflow: A General-purpose Task-parallel Programming System

Loading...
Searching...
No Matches
Matrix Multiplication with CUDA GPU

Following Matrix Multiplication, we accelerate matrix multiplication on a CUDA GPU using tf::cudaGraph. The GPU's massive thread-level parallelism reduces large problem runtimes from minutes to milliseconds.

# CUDA Kernel

Unlike the CPU version where each task processes one row, on the GPU we assign one CUDA thread to each element of `C`. We store all matrices in 1D row-major layout to simplify host-to-device transfers: element `(x, y)` in a matrix of width `W` is stored at index `x * W + y`.

The CUDA kernel is:

__global__ void matmul(int* A, int* B, int* C, int M, int K, int N) {
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
int sum = 0;
if(row < M && col < N) {
for(int i = 0; i < K; i++) {
sum += A[row * K + i] * B[i * N + col];
}
C[row * N + col] = sum;
}
}

Each thread computes one element of `C` by iterating over the full inner dimension `K`.

# CUDA Graph Task

We build a Taskflow that allocates GPU memory in parallel, runs the CUDA graph, and frees GPU memory when done:

void matrix_multiplication(int* A, int* B, int* C, int M, int K, int N) {
tf::Taskflow taskflow;
tf::Executor executor;
int *da, *db, *dc;
// allocate GPU memory for A, B, and C in parallel
tf::Task allocate_a = taskflow.emplace([&]() {
cudaMalloc(&da, M * K * sizeof(int));
}).name("allocate_a");
tf::Task allocate_b = taskflow.emplace([&]() {
cudaMalloc(&db, K * N * sizeof(int));
}).name("allocate_b");
tf::Task allocate_c = taskflow.emplace([&]() {
cudaMalloc(&dc, M * N * sizeof(int));
}).name("allocate_c");
// build and execute the CUDA graph
tf::Task cuda = taskflow.emplace([&]() {
tf::cudaGraph cg;
// H2D transfers for A and B
tf::cudaTask copy_da = cg.copy(da, A, M * K);
tf::cudaTask copy_db = cg.copy(db, B, K * N);
// kernel: one thread per element of C
dim3 grid ((N + 15) / 16, (M + 15) / 16);
dim3 block(16, 16);
tf::cudaTask kmatmul = cg.kernel(grid, block, 0,
matmul, da, db, dc, M, K, N
);
// D2H transfer for C
tf::cudaTask copy_hc = cg.copy(C, dc, M * N);
kmatmul.succeed(copy_da, copy_db)
.precede(copy_hc);
tf::cudaStream stream;
tf::cudaGraphExec exec(cg);
stream.run(exec).synchronize();
}).name("cuda");
// free GPU memory
tf::Task free_mem = taskflow.emplace([&]() {
cudaFree(da);
cudaFree(db);
cudaFree(dc);
}).name("free");
cuda.succeed(allocate_a, allocate_b, allocate_c)
.precede(free_mem);
executor.run(taskflow).wait();
}
tf::Executor
class to create an executor
Definition executor.hpp:62
tf::Executor::run
tf::Future< void > run(Taskflow &taskflow)
runs a taskflow once
tf::FlowBuilder::emplace
Task emplace(C &&callable)
creates a static task
Definition flow_builder.hpp:1738
tf::Task
class to create a task handle over a taskflow node
Definition task.hpp:569
tf::Task::succeed
Task & succeed(Ts &&... tasks)
adds precedence links from other tasks to this
Definition task.hpp:1313
tf::Task::precede
Task & precede(Ts &&... tasks)
adds precedence links from this to other tasks
Definition task.hpp:1305
tf::Taskflow
class to create a taskflow object
Definition taskflow.hpp:64
tf::cudaGraphBase::copy
cudaTask copy(T *tgt, const T *src, size_t num)
creates a memcopy task that copies typed data
Definition cuda_graph.hpp:1075
tf::cudaGraphBase::kernel
cudaTask kernel(dim3 g, dim3 b, size_t s, F f, ArgsT... args)
creates a kernel task
Definition cuda_graph.hpp:1010
tf::cudaStreamBase::synchronize
cudaStreamBase & synchronize()
synchronizes the associated stream
Definition cuda_stream.hpp:232
tf::cudaStreamBase::run
cudaStreamBase & run(const cudaGraphExecBase< C, D > &exec)
runs the given executable CUDA graph
tf::cudaTask::succeed
cudaTask & succeed(Ts &&... tasks)
adds precedence links from other tasks to this
Definition cuda_graph.hpp:418
tf::cudaTask::precede
cudaTask & precede(Ts &&... tasks)
adds precedence links from this to other tasks
Definition cuda_graph.hpp:407
tf::cudaGraphExec
cudaGraphExecBase< cudaGraphExecCreator, cudaGraphExecDeleter > cudaGraphExec
default smart pointer type to manage a cudaGraphExec_t object with unique ownership
Definition cudaflow.hpp:23
tf::cudaGraph
cudaGraphBase< cudaGraphCreator, cudaGraphDeleter > cudaGraph
default smart pointer type to manage a cudaGraph_t object with unique ownership
Definition cudaflow.hpp:18
tf::cudaStream
cudaStreamBase< cudaStreamCreator, cudaStreamDeleter > cudaStream
default smart pointer type to manage a cudaStream_t object with unique ownership
Definition cuda_stream.hpp:340

The outer Taskflow manages CPU-side orchestration: the three allocation tasks run in parallel, then the CUDA graph task runs, and finally GPU memory is freed. Inside the CUDA graph, two H2D copy tasks feed the kernel and the kernel feeds the D2H copy task. The CPU taskflow graph is shown below:

After execution, the full task graph including the CUDA sub-graph can be visualised:

# Benchmarking

We compare three versions — sequential CPU, parallel CPU, and one GPU — on a 12-core Intel i7-8700 at 3.20 GHz and a Nvidia RTX 2080:

Matrix size | CPU sequential | CPU parallel | GPU
--- | --- | --- | ---
10×10 | 0.142 ms | 0.414 ms | 82 ms
100×100 | 1.641 ms | 0.733 ms | 83 ms
1000×1000 | 1532 ms | 504 ms | 85 ms
2000×2000 | 25688 ms | 4387 ms | 133 ms
3000×3000 | 104838 ms | 16170 ms | 214 ms
4000×4000 | 250133 ms | 39646 ms | 427 ms

For small matrices the GPU's data transfer overhead dominates and CPU solutions are faster. As problem size grows, the GPU's thread-level parallelism dominates completely. At 4000×4000, the GPU is 585× faster than the sequential CPU and 92× faster than the parallel CPU solution.