base on A General-purpose Task-parallel Programming System using Modern C++ # Taskflow <img align="right" width="10%" src="image/taskflow_logo.png"> <!--[](https://travis-ci.com/taskflow/taskflow)--> [](https://github.com/taskflow/taskflow/actions?query=workflow%3AUbuntu) [](https://github.com/taskflow/taskflow/actions?query=workflow%3AmacOS) [](https://github.com/taskflow/taskflow/actions?query=workflow%3AWindows) [][documentation] [](https://taskflow.github.io/tfprof/) [][TPDS22] Taskflow helps you quickly write parallel and heterogeneous task programs in modern C++ # Why Taskflow? Taskflow is faster, more expressive, and easier for drop-in integration than many of existing task programming frameworks in handling complex parallel workloads.  Taskflow lets you quickly implement task decomposition strategies that incorporate both regular and irregular compute patterns, together with an efficient *work-stealing* scheduler to optimize your multithreaded performance. | [Static Tasking](#start-your-first-taskflow-program) | [Subflow Tasking](#create-a-subflow-graph) | | :------------: | :-------------: | |  | <img align="right" src="image/dynamic_graph.svg" width="100%"> | Taskflow supports conditional tasking for you to make rapid control-flow decisions across dependent tasks to implement cycles and conditions that were otherwise difficult to do with existing tools. | [Conditional Tasking](#integrate-control-flow-to-a-task-graph) | | :-----------------: | |  | 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. | [Taskflow Composition](#compose-task-graphs) | | :---------------: | || Taskflow supports heterogeneous tasking for you to accelerate a wide range of scientific computing applications by harnessing the power of CPU-GPU collaborative computing. | [Concurrent CPU-GPU Tasking](#offload-a-task-to-a-gpu) | | :-----------------: | |  | Taskflow provides visualization and tooling needed for profiling Taskflow programs. | [Taskflow Profiler](https://taskflow.github.io/tfprof) | | :-----------------: | |  | We are committed to support trustworthy developments for both academic and industrial research projects in parallel computing. Check out [Who is Using Taskflow](https://taskflow.github.io/#tag_users) and what our users say: + *"Taskflow is the cleanest Task API I've ever seen." [Damien Hocking @Corelium Inc](http://coreliuminc.com)* + *"Taskflow has a very simple and elegant tasking interface. The performance also scales very well." [Glen Fraser][totalgee]* + *"Taskflow lets me handle parallel processing in a smart way." [Hayabusa @Learning](https://cpp-learning.com/cpp-taskflow/)* + *"Taskflow improves the throughput of our graph engine in just a few hours of coding." [Jean-Michaël @KDAB](https://ossia.io/)* + *"Best poster award for open-source parallel programming library." [Cpp Conference 2018][Cpp Conference 2018]* + *"Second Prize of Open-source Software Competition." [ACM Multimedia Conference 2019](https://tsung-wei-huang.github.io/img/mm19-ossc-award.jpg)* See a quick [presentation](https://taskflow.github.io/) and visit the [documentation][documentation] to learn more about Taskflow. Technical details can be referred to our [IEEE TPDS paper][TPDS21]. # Start Your First Taskflow Program The following program (`simple.cpp`) creates 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. Try it live on [Compiler Explorer (godbolt)](https://godbolt.org/z/j8hx3xnnx)! ```cpp #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 wrangle with installation. To compile the program, clone the Taskflow project and tell the compiler to include the [headers](./taskflow/). ```bash ~$ git clone https://github.com/taskflow/taskflow.git # clone it only once ~$ g++ -std=c++20 examples/simple.cpp -I. -O2 -pthread -o simple ~$ ./simple TaskA TaskC TaskB TaskD ``` # Visualize Your First Taskflow Program Taskflow comes with a built-in profiler, [TFProf](https://taskflow.github.io/tfprof/), for you to profile and visualize taskflow programs in an easy-to-use web-based interface.  ```bash # 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/ ``` In addition to execution diagram, you can dump the graph to a DOT format and visualize it using a number of free [GraphViz][GraphViz] tools. ``` // dump the taskflow graph to a DOT format through std::cout taskflow.dump(std::cout); ``` <p align="center"><img src="doxygen/images/simple.svg"></p> # Express Task Graph Parallelism Taskflow empowers users with both static and dynamic task graph constructions to express end-to-end parallelism in a task graph that embeds in-graph control flow. 1. [Create a Subflow Graph](#create-a-subflow-graph) 2. [Integrate Control Flow to a Task Graph](#integrate-control-flow-to-a-task-graph) 3. [Offload a Task to a GPU](#offload-a-task-to-a-gpu) 4. [Compose Task Graphs](#compose-task-graphs) 5. [Launch Asynchronous Tasks](#launch-asynchronous-tasks) 6. [Execute a Taskflow](#execute-a-taskflow) 7. [Leverage Standard Parallel Algorithms](#leverage-standard-parallel-algorithms) ## Create a Subflow Graph Taskflow supports *dynamic tasking* for you to create a subflow graph from the execution of a task to perform dynamic parallelism. The following program spawns a task dependency graph parented at task `B`. ```cpp 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) { 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 ``` <p align="center"><img src="doxygen/images/subflow_join.svg"></p> ## Integrate Control Flow to 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. ```cpp 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"); init.precede(cond); // creates a feedback loop {0: cond, 1: stop} cond.precede(cond, stop); ``` <p align="center"><img src="doxygen/images/conditional-tasking-1.svg"></p> ## Offload a Task 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 CUDA. ```cpp __global__ void saxpy(size_t N, float alpha, float* dx, float* dy) { int i = blockIdx.x*blockDim.x + threadIdx.x; if (i < n) { y[i] = a*x[i] + y[i]; } } tf::Task cudaflow = taskflow.emplace([&](tf::cudaFlow& cf) { // data copy tasks tf::cudaTask h2d_x = cf.copy(dx, hx.data(), N).name("h2d_x"); tf::cudaTask h2d_y = cf.copy(dy, hy.data(), N).name("h2d_y"); tf::cudaTask d2h_x = cf.copy(hx.data(), dx, N).name("d2h_x"); tf::cudaTask d2h_y = cf.copy(hy.data(), dy, N).name("d2h_y"); // kernel task with parameters to launch the saxpy kernel tf::cudaTask saxpy = cf.kernel( (N+255)/256, 256, 0, saxpy, N, 2.0f, dx, dy ).name("saxpy"); saxpy.succeed(h2d_x, h2d_y) .precede(d2h_x, d2h_y); }).name("cudaFlow"); ``` <p align="center"><img src="doxygen/images/saxpy_1_cudaflow.svg"></p> ## 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. ```cpp 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); ``` <p align="center"><img src="doxygen/images/composition.svg"></p> ## Launch Asynchronous Tasks Taskflow supports *asynchronous* tasking. You can launch tasks asynchronously to dynamically explore task graph parallelism. ```cpp 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(); ``` ## Execute a Taskflow 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. ```cpp // 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; }); // block the executor until all submitted taskflows complete 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. ```cpp // 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; } ); // standard parallel GPU algorithms tf::cudaTask cuda1 = cudaflow.for_each( // assign each element to 100 on GPU dfirst, dlast, [] __device__ (auto i) { i = 100; } ); tf::cudaTask cuda2 = cudaflow.reduce( // reduce a range of items on GPU dfirst, dlast, init, [] __device__ (auto a, auto b) { return a + b; } ); tf::cudaTask cuda3 = cudaflow.sort( // sort a range of items on GPU dfirst, dlast, [] __device__ (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. ```cpp // create a pipeline to propagate five tokens through three serial stages tf::Pipeline pl(num_parallel_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(); ``` # Supported Compilers To use Taskflow, you only need a compiler that supports C++17: + GNU C++ Compiler at least v8.4 with -std=c++17 + Clang C++ Compiler at least v6.0 with -std=c++17 + Microsoft Visual Studio at least v19.27 with /std:c++17 + AppleClang Xcode Version at least v12.0 with -std=c++17 + Nvidia CUDA Toolkit and Compiler (nvcc) at least v11.1 with -std=c++17 + Intel C++ Compiler at least v19.0.1 with -std=c++17 + Intel DPC++ Clang Compiler at least v13.0.0 with -std=c++17 and SYCL20 Taskflow works on Linux, Windows, and Mac OS X. Although %Taskflow supports primarily C++17, you can enable C++20 compilation through `-std=c++20` to achieve better performance due to new C++20 features. # Learn More about Taskflow Visit our [project website][Project Website] and [documentation][documentation] to learn more about Taskflow. To get involved: + See [release notes][release notes] to stay up-to-date with newest versions + Read the step-by-step tutorial at [cookbook][cookbook] + Submit an issue at [GitHub issues][GitHub issues] + Find out our technical details at [references][references] + Watch our technical talks at YouTube | [CppCon20 Tech Talk][cppcon20 talk] | [MUC++ Tech Talk](https://www.youtube.com/watch?v=u8Mc_WgGwVY) | | :------------: | :-------------: | |  | <img align="right" src="doxygen/images/muc++20-thumbnail.jpg" width="100%"> | 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 2021 IEEE TPDS: + Tsung-Wei Huang, Dian-Lun Lin, Chun-Xun Lin, and Yibo Lin, &quot;[Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System](https://tsung-wei-huang.github.io/papers/tpds21-taskflow.pdf),&quot; <i>IEEE Transactions on Parallel and Distributed Systems (TPDS)</i>, vol. 33, no. 6, pp. 1303-1320, June 2022 More importantly, we appreciate all Taskflow [contributors][contributors] and the following organizations for sponsoring the Taskflow project! | <!-- --> | <!-- --> | <!-- --> | <!-- --> | |:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:| |<img src="doxygen/images/utah-ece-logo.png">|<img src="doxygen/images/nsf.png"> | <img src="doxygen/images/darpa.png"> | <img src="doxygen/images/NumFocus.png">| |<img src="doxygen/images/nvidia-logo.png"> | | | | # License Taskflow is licensed with the [MIT License](./LICENSE). You are completely free to re-distribute your work derived from Taskflow. * * * [Tsung-Wei Huang]: https://tsung-wei-huang.github.io/ [GitHub releases]: https://github.com/taskflow/taskflow/releases [GitHub issues]: https://github.com/taskflow/taskflow/issues [GitHub insights]: https://github.com/taskflow/taskflow/pulse [GitHub pull requests]: https://github.com/taskflow/taskflow/pulls [GraphViz]: https://www.graphviz.org/ [Project Website]: https://taskflow.github.io/ [cppcon20 talk]: https://www.youtube.com/watch?v=MX15huP5DsM [contributors]: https://taskflow.github.io/taskflow/contributors.html [totalgee]: https://github.com/totalgee [NSF]: https://www.nsf.gov/ [UIUC]: https://illinois.edu/ [CSL]: https://csl.illinois.edu/ [UofU]: https://www.utah.edu/ [documentation]: https://taskflow.github.io/taskflow/index.html [release notes]: https://taskflow.github.io/taskflow/Releases.html [cookbook]: https://taskflow.github.io/taskflow/pages.html [references]: https://taskflow.github.io/taskflow/References.html [PayMe]: https://www.paypal.me/twhuang/10 [email me]: mailto:[email protected] [Cpp Conference 2018]: https://github.com/CppCon/CppCon2018 [TPDS22]: https://tsung-wei-huang.github.io/papers/tpds21-taskflow.pdf ", Assign "at most 3 tags" to the expected json: {"id":"2424","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"