Cookbook » Composable Tasking

Compose a Taskflow

A powerful feature of Taskflow is its composable interface. You can break down a large parallel workload into smaller pieces each designed to run a specific task dependency graph. This largely facilitates the modularity of writing a parallel task program.

// f1 has three independent tasks
tf::Taskflow f1;
f1.name("F1");
tf::Task f1A = f1.emplace([&](){ std::cout << "F1 TaskA\n"; });
tf::Task f1B = f1.emplace([&](){ std::cout << "F1 TaskB\n"; });
tf::Task f1C = f1.emplace([&](){ std::cout << "F1 TaskC\n"; });

f1A.name("f1A");
f1B.name("f1B");
f1C.name("f1C");
f1A.precede(f1C);
f1B.precede(f1C);

// f2A ---
//        |----> f2C ----> f1_module_task ----> f2D
// f2B --- 
tf::Taskflow f2;
f2.name("F2");
tf::Task f2A = f2.emplace([&](){ std::cout << "  F2 TaskA\n"; });
tf::Task f2B = f2.emplace([&](){ std::cout << "  F2 TaskB\n"; });
tf::Task f2C = f2.emplace([&](){ std::cout << "  F2 TaskC\n"; });
tf::Task f2D = f2.emplace([&](){ std::cout << "  F2 TaskD\n"; });

f2A.name("f2A");
f2B.name("f2B");
f2C.name("f2C");
f2D.name("f2D");

f2A.precede(f2C);
f2B.precede(f2C);

tf::Task f1_module_task = f2.composed_of(f1).name("module");
f2C.precede(f1_module_task);
f1_module_task.precede(f2D);

f2.dump(std::cout);

The above example first constructs a taskflow consisting of three tasks, f1A, f1B, and f1C, where f1A and f1B execute before f1C. It then creates a second taskflow with four tasks, f2A, f2B, f2C, and f2D. The first taskflow is encapsulated as a module task using Taskflow::composed_of, allowing it to be embedded within the second taskflow. Dependencies are then established so that f2C must complete before the module task begins, and the module task must finish before f2D executes, thereby integrating the two taskflows into a single execution graph with well-defined ordering constraints.

Create a Module Task from a Taskflow

The task created from Taskflow::composed_of is a module task that runs on a pre-defined taskflow. A module task does not own the taskflow but maintains a soft mapping to the taskflow. You can create multiple module tasks from the same taskflow but only one module task can run at one time. For example, the following composition is valid. Even though the two module tasks module1 and module2 refer to the same taskflow F1, the dependency link prevents F1 from multiple executions at the same time.

However, the following composition is invalid. Both module tasks refer to the same taskflow. They can not run at the same time because they are associated with the same graph.

Create a Custom Composable Graph

Taskflow allows you to create a custom graph object that can participate in the scheduling using composition. To become a module task, your class 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 object that can be assembled in a taskflow through composition:

 1: struct CustomGraph {
 2:   tf::Graph graph;
 3:   CustomGraph() {
 4:     tf::FlowBuilder builder(graph);  // inherit all task builders in tf::Taskflow
 5:     tf::Task task = builder.emplace([](){
 6:       std::cout << "a task\n";  // static task
 7:     });
 8:   }
 9:   // returns a reference to the graph for taskflow composition
10:   Graph& graph() { return graph; }
11: };
12:
13: CustomGraph obj;
14: tf::Task comp = taskflow.composed_of(obj);

The above code defines a custom graph that can participate in taskflow composition. The graph object is represented using tf::Graph, and its constructor builds the internal task graph through tf::FlowBuilder. To support composition, the graph implements the required interface method (Graph& graph()) that exposes its internal structure to the Taskflow runtime. With this interface in place, the custom graph can then be instantiated as a module task within a larger taskflow, enabling it to be seamlessly composed and scheduled alongside other tasks.