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

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Module Algorithm

Taskflow provides template methods that let users create reusable building blocks called modules. Users can connect modules together to build more complex parallel algorithms.

# Include the Header

You need to include the header file, `taskflow/algorithm/module.hpp`, for creating a module task over a schedulable graph target.

#include <taskflow/algorithm/module.hpp>

# What is a Module Task

Similar to Composable Tasking, but in a more general setting, the template function tf::make_module_task allows you to create a task over a Taskflow graph that can be executed by an executor. This provides a flexible mechanism to encapsulate and reuse complex task logic within your Taskflow applications. The following example demonstrates how to create and launch multiple Taskflow graphs in parallel using asynchronous tasking:

#include <taskflow/taskflow.hpp>
#include <taskflow/algorithm/module.hpp>
int main() {
tf::Executor executor;
tf::Taskflow A;
tf::Taskflow B;
tf::Taskflow C;
tf::Taskflow D;
A.emplace([](){ printf("Taskflow A\n"); });
B.emplace([](){ printf("Taskflow B\n"); });
C.emplace([](){ printf("Taskflow C\n"); });
D.emplace([](){ printf("Taskflow D\n"); });
// launch the four taskflows using asynchronous tasking
executor.async(tf::make_module_task(A));
executor.async(tf::make_module_task(B));
executor.async(tf::make_module_task(C));
executor.async(tf::make_module_task(D));
executor.wait_for_all();
return 0;
}
tf::Executor
class to create an executor
Definition executor.hpp:62
tf::Executor::wait_for_all
void wait_for_all()
waits for all tasks to complete
tf::Executor::async
auto async(P &&params, F &&func)
creates a parameterized asynchronous task to run the given function
tf::FlowBuilder::emplace
Task emplace(C &&callable)
creates a static task
Definition flow_builder.hpp:1738
tf::Taskflow
class to create a taskflow object
Definition taskflow.hpp:64
tf::make_module_task
auto make_module_task(T &target)
creates a module task using the given graph
Definition module.hpp:74

Since the four taskflows are launched asynchronously without any dependencies between them, we can observe any order of the output message:

# one possible output
Taskflow B
Taskflow C
Taskflow A
Taskflow D
# another possible output
Taskflow D
Taskflow A
Taskflow B
Taskflow C

If you need to enforce dependencies among these four taskflows, you can use dependent-async tasks. The example below launches the four taskflows one by one in sequential:

tf::Executor executor;
tf::Taskflow A;
tf::Taskflow B;
tf::Taskflow C;
tf::Taskflow D;
A.emplace([](){ printf("Taskflow A\n"); });
B.emplace([](){ printf("Taskflow B\n"); });
C.emplace([](){ printf("Taskflow C\n"); });
D.emplace([](){ printf("Taskflow D\n"); });
auto TA = executor.silent_dependent_async(tf::make_module_task(A));
auto TB = executor.silent_dependent_async(tf::make_module_task(B), TA);
auto TC = executor.silent_dependent_async(tf::make_module_task(C), TB);
auto [TD, FD] = executor.dependent_async(tf::make_module_task(D), TC);
FD.get();
tf::Executor::dependent_async
auto dependent_async(F &&func, Tasks &&... tasks)
runs the given function asynchronously when the given predecessors finish
tf::Executor::silent_dependent_async
tf::AsyncTask silent_dependent_async(F &&func, Tasks &&... tasks)
runs the given function asynchronously when the given predecessors finish
# dependent-async tasks enforce a sequential execution of the four taskflows
Taskflow A
Taskflow B
Taskflow C
Taskflow D

The module task maker, tf::make_module_task, operates similarly to tf::Taskflow::composed_of, but provides a more general interface that can be used beyond Taskflow. Specifically, the following two approaches achieve equivalent functionality:

// approach 1: composition using composed_of
tf::Task m1 = taskflow1.composed_of(taskflow2);
// approach 2: composition using make_module_task
tf::Task m1 = taskflow1.emplace(tf::make_module_task(taskflow2));
tf::Task
class to create a task handle over a taskflow node
Definition task.hpp:569
tf::Task::composed_of
Task & composed_of(T &object)
creates a module task from a taskflow
Definition task.hpp:1337

Note
  Similar to tf::Taskflow::composed_of, tf::make_module_task does not assume ownership of the provided taskflow but a soft reference. You are responsible for ensuring that the encapsulated taskflow remains valid throughout its execution.

# Create a Module Task over a Custom Graph

In addition to encapsulate taskflow graphs, you can create a module task to schedule a custom graph target. A schedulable target (of type `T`) must define the method `T::graph()` that returns a reference to the tf::Graph object managed by `T`. The following example defines a custom graph that can be scheduled through making module tasks:

struct CustomGraph {
tf::Graph graph;
CustomGraph() {
// use flow builder to inherit all task creation methods in tf::Taskflow
tf::FlowBuilder builder(graph);
tf::Task task = builder.emplace([](){
std::cout << "a task\n"; // static task
});
}
// returns a reference to the graph for taskflow composition
Graph& graph() { return graph; }
};
CustomGraph target;
executor.async(tf::make_module_task(target));
tf::FlowBuilder
class to build a task dependency graph
Definition flow_builder.hpp:114
tf::Graph
class to create a graph object
Definition graph.hpp:47

Note
  Users are responsible for ensuring the given custom graph remains valid throughout its execution. The executor does not assume ownership of the custom graph.