docs/ModuleAlgorithm.html
| | Taskflow: A General-purpose Task-parallel Programming System |
Loading...
Searching...
No Matches
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.
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>
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;
}
class to create an executor
Definition executor.hpp:62
void wait_for_all()
waits for all tasks to complete
auto async(P &¶ms, F &&func)
creates a parameterized asynchronous task to run the given function
Task emplace(C &&callable)
creates a static task
Definition flow_builder.hpp:1571
class to create a taskflow object
Definition taskflow.hpp:64
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:
Taskflow B
Taskflow C
Taskflow A
Taskflow D
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::silent_dependent_async
tf::AsyncTask silent_dependent_async(F &&func, Tasks &&... tasks)
runs the given function asynchronously when the given predecessors finish
auto dependent_async(F &&func, Tasks &&... tasks)
runs the given function asynchronously when the given predecessors finish
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));
class to create a task handle over a taskflow node
Definition task.hpp:569
Task & composed_of(T &object)
creates a module task from a taskflow
Definition task.hpp:1290
NoteSimilar 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.
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));
class to build a task dependency graph
Definition flow_builder.hpp:22
class to create a graph object
Definition graph.hpp:47
NoteUsers are responsible for ensuring the given custom graph remains valid throughout its execution. The executor does not assume ownership of the custom graph.