Start Your First Taskflow Program

The following program (simple.cpp) creates a taskflow of four tasks A, B, C, and D, where A runs before B and C, and D runs after B and C. When A finishes, B and C can run in parallel.

#include <taskflow/taskflow.hpp>  // Taskflow is header-only
 
int main(){
  
  tf::Executor executor;
  tf::Taskflow taskflow;
 
  auto [A, B, C, D] = taskflow.emplace(  // create four tasks
    [] () { std::cout << "TaskA\n"; },
    [] () { std::cout << "TaskB\n"; },
    [] () { std::cout << "TaskC\n"; },
    [] () { std::cout << "TaskD\n"; } 
  );                                  
                                      
  A.precede(B, C);  // A runs before B and C
  D.succeed(B, C);  // D runs after  B and C
                                      
  executor.run(taskflow).wait(); 
 
  return 0;
}

Taskflow is header-only and there is no struggle with installation. To compile the program, clone the Taskflow project and tell the compiler to include the headers under taskflow/.

~$ git clone https://github.com/taskflow/taskflow.git  # clone it only once
~$ g++ -std=c++20 simple.cpp -I taskflow/ -O2 -pthread -o simple
~$ ./simple
TaskA
TaskC 
TaskB 
TaskD

Taskflow comes with a built-in profiler, Taskflow Profiler, for you to profile and visualize taskflow programs in an easy-to-use web-based interface.

# run the program with the environment variable TF_ENABLE_PROFILER enabled
~$ TF_ENABLE_PROFILER=simple.json ./simple
~$ cat simple.json
[
{"executor":"0","data":[{"worker":0,"level":0,"data":[{"span":[172,186],"name":"0_0","type":"static"},{"span":[187,189],"name":"0_1","type":"static"}]},{"worker":2,"level":0,"data":[{"span":[93,164],"name":"2_0","type":"static"},{"span":[170,179],"name":"2_1","type":"static"}]}]}
]
# paste the profiling json data to https://taskflow.github.io/tfprof/

Create a Subflow Graph

Taskflow supports recursive tasking for you to create a subflow graph from the execution of a task to perform recursive parallelism. The following program spawns a task dependency graph parented at task B.

tf::Task A = taskflow.emplace([](){}).name("A");  
tf::Task C = taskflow.emplace([](){}).name("C");  
tf::Task D = taskflow.emplace([](){}).name("D");  
 
tf::Task B = taskflow.emplace([] (tf::Subflow& subflow) { // subflow task B
  tf::Task B1 = subflow.emplace([](){}).name("B1");  
  tf::Task B2 = subflow.emplace([](){}).name("B2");  
  tf::Task B3 = subflow.emplace([](){}).name("B3");  
  B3.succeed(B1, B2);  // B3 runs after B1 and B2
}).name("B");
 
A.precede(B, C);  // A runs before B and C
D.succeed(B, C);  // D runs after  B and C

Integrate Control Flow into a Task Graph

Taskflow supports conditional tasking for you to make rapid control-flow decisions across dependent tasks to implement cycles and conditions in an end-to-end task graph.

tf::Task init = taskflow.emplace([](){}).name("init");
tf::Task stop = taskflow.emplace([](){}).name("stop");
 
// creates a condition task that returns a random binary
tf::Task cond = taskflow.emplace([](){ return std::rand() % 2; }).name("cond");
 
// creates a feedback loop {0: cond, 1: stop}
init.precede(cond);
cond.precede(cond, stop);  // moves on to 'cond' on returning 0, or 'stop' on 1

Compose Task Graphs

Taskflow is composable. You can create large parallel graphs through composition of modular and reusable blocks that are easier to optimize at an individual scope.

tf::Taskflow f1, f2;
 
// create taskflow f1 of two tasks
tf::Task f1A = f1.emplace([]() { std::cout << "Task f1A\n"; }).name("f1A");
tf::Task f1B = f1.emplace([]() { std::cout << "Task f1B\n"; }).name("f1B");
 
// create taskflow f2 with one module task composed of f1
tf::Task f2A = f2.emplace([]() { std::cout << "Task f2A\n"; }).name("f2A");
tf::Task f2B = f2.emplace([]() { std::cout << "Task f2B\n"; }).name("f2B");
tf::Task f2C = f2.emplace([]() { std::cout << "Task f2C\n"; }).name("f2C");
tf::Task f1_module_task = f2.composed_of(f1).name("module");
 
f1_module_task.succeed(f2A, f2B)
              .precede(f2C);

Launch Asynchronous Tasks

Taskflow supports asynchronous tasking. You can launch tasks asynchronously to dynamically explore task graph parallelism.

tf::Executor executor;
 
// create asynchronous tasks directly from an executor
std::future<int> future = executor.async([](){ 
  std::cout << "async task returns 1\n";
  return 1;
}); 
executor.silent_async([](){ std::cout << "async task does not return\n"; });
 
// create asynchronous tasks with dynamic dependencies
tf::AsyncTask A = executor.silent_dependent_async([](){ printf("A\n"); });
tf::AsyncTask B = executor.silent_dependent_async([](){ printf("B\n"); }, A);
tf::AsyncTask C = executor.silent_dependent_async([](){ printf("C\n"); }, A);
tf::AsyncTask D = executor.silent_dependent_async([](){ printf("D\n"); }, B, C);
 
executor.wait_for_all();

Leverage Standard Parallel Algorithms

Taskflow defines algorithms for you to quickly express common parallel patterns using standard C++ syntaxes, such as parallel iterations, parallel reductions, and parallel sort.

// standard parallel CPU algorithms
tf::Task task1 = taskflow.for_each( // assign each element to 100 in parallel
  first, last, [] (auto& i) { i = 100; }    
);
tf::Task task2 = taskflow.reduce(   // reduce a range of items in parallel
  first, last, init, [] (auto a, auto b) { return a + b; }
);
tf::Task task3 = taskflow.sort(     // sort a range of items in parallel
  first, last, [] (auto a, auto b) { return a < b; }
);

Additionally, Taskflow provides composable graph building blocks for you to efficiently implement common parallel algorithms, such as parallel pipeline.

// create a pipeline to propagate five tokens through three serial stages
tf::Pipeline pl(num_lines,
  tf::Pipe{tf::PipeType::SERIAL, [](tf::Pipeflow& pf) {
    if(pf.token() == 5) {
      pf.stop();
    }
  }},
  tf::Pipe{tf::PipeType::SERIAL, [](tf::Pipeflow& pf) {
    printf("stage 2: input buffer[%zu] = %d\n", pf.line(), buffer[pf.line()]);
  }},
  tf::Pipe{tf::PipeType::SERIAL, [](tf::Pipeflow& pf) {
    printf("stage 3: input buffer[%zu] = %d\n", pf.line(), buffer[pf.line()]);
  }}
);
taskflow.composed_of(pl)
executor.run(taskflow).wait();

Run a Taskflow through an Executor

The executor provides several thread-safe methods to run a taskflow. You can run a taskflow once, multiple times, or until a stopping criteria is met. These methods are non-blocking with a tf::Future<void> return to let you query the execution status.

// runs the taskflow once
tf::Future<void> run_once = executor.run(taskflow); 
 
// wait on this run to finish
run_once.get();
 
// run the taskflow four times
executor.run_n(taskflow, 4);
 
// runs the taskflow five times
executor.run_until(taskflow, [counter=5](){ return --counter == 0; });
 
// blocks the executor until all submitted taskflows complete
executor.wait_for_all();

Offload Tasks to a GPU

Taskflow supports GPU tasking for you to accelerate a wide range of scientific computing applications by harnessing the power of CPU-GPU collaborative computing using Nvidia CUDA Graph.

__global__ void saxpy(int n, float a, float *x, float *y) {
  int i = blockIdx.x*blockDim.x + threadIdx.x;
  if (i < n) {
    y[i] = a*x[i] + y[i];
  }
}
// create a CUDA Graph task
tf::Task cudaflow = taskflow.emplace([&]() {
  tf::cudaGraph cg;
  tf::cudaTask h2d_x = cg.copy(dx, hx.data(), N);
  tf::cudaTask h2d_y = cg.copy(dy, hy.data(), N);
  tf::cudaTask d2h_x = cg.copy(hx.data(), dx, N);
  tf::cudaTask d2h_y = cg.copy(hy.data(), dy, N);
  tf::cudaTask saxpy = cg.kernel((N+255)/256, 256, 0, saxpy, N, 2.0f, dx, dy);
  saxpy.succeed(h2d_x, h2d_y)
       .precede(d2h_x, d2h_y);
  
  // instantiate an executable CUDA graph and run it through a stream
  tf::cudaGraphExec exec(cg);
  tf::cudaStream stream;
  stream.run(exec).synchronize();
}).name("CUDA Graph Task");

Visualize Taskflow Graphs

You can dump a taskflow graph to a DOT format and visualize it using a number of free GraphViz tools such as GraphViz Online.

tf::Taskflow taskflow;
 
tf::Task A = taskflow.emplace([](){}).name("A");
tf::Task B = taskflow.emplace([](){}).name("B");
tf::Task C = taskflow.emplace([](){}).name("C");
tf::Task D = taskflow.emplace([](){}).name("D");
tf::Task E = taskflow.emplace([](){}).name("E");
A.precede(B, C, E);
C.precede(D);
B.precede(D, E);
 
// dump the graph to a DOT file through std::cout
taskflow.dump(std::cout); 

Supported Compilers

To use Taskflow v4.0.0, you need a compiler that supports C++20:

GNU C++ Compiler at least v11.0 with -std=c++20
Clang C++ Compiler at least v12.0 with -std=c++20
Microsoft Visual Studio at least v19.29 (VS 2019) with /std:c++20
Apple Clang (Xcode) at least v13.0 with -std=c++20
NVIDIA CUDA Toolkit and Compiler (nvcc) at least v12.0 with host compiler supporting C++20
Intel oneAPI DPC++/C++ Compiler at least v2022.0 with -std=c++20

Taskflow works on Linux, Windows, and Mac OS X.

Get Involved

Visit our Project Website and showcase presentation to learn more about Taskflow. To get involved:

See release notes at Release Notes
Read the step-by-step tutorial at Cookbook
Submit an issue at issue tracker
Learn more about our technical details at References
Watch our 2020 CppCon Taskflow Talk and 2020 MUC++ Taskflow Talk

We are committed to support trustworthy developments for both academic and industrial research projects in parallel and heterogeneous computing. If you are using Taskflow, please cite the following paper we published at 2022 IEEE TPDS:

Tsung-Wei Huang, Dian-Lun Lin, Chun-Xun Lin, and Yibo Lin, "Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System," IEEE Transactions on Parallel and Distributed Systems (TPDS), vol. 33, no. 6, pp. 1303-1320, June 2022

More importantly, we appreciate all Taskflow Contributors and the following organizations for sponsoring the Taskflow project!

License

Taskflow is open-source the under permissive MIT license. You are completely free to use, modify, and redistribute any work on top of Taskflow. The source code is available in Project GitHub and is actively maintained by Dr. Tsung-Wei Huang and his research group at the University of Wisconsin at Madison.

Table of Contents