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# Modern C++ Parallel Task Programming

Start Your First Taskflow Program
indexpage_1ASimpleFirstProgram
Create a Subflow Graph
indexpage_1QuickStartCreateASubflowGraph
Integrate Control Flow into a Task Graph
indexpage_1QuickStartIntegrateControlFlowIntoATaskGraph
Offload Tasks to a GPU
indexpage_1QuickStartOffloadTasksToGPU
Compose Task Graphs
indexpage_1QuickStartComposeTaskGraphs
Launch Asynchronous Tasks
indexpage_1QuickStartLaunchAsyncTasks
Run a Taskflow through an Executor
indexpage_1QuickStartRunATaskflowThroughAnExecution
Leverage Standard Parallel Algorithms
indexpage_1QuickStartLeverageStandardParallelAlgorithms
Visualize Taskflow Graphs
indexpage_1QuickStartVisualizeATaskflow
Supported Compilers
indexpage_1SupportedCompilers
Get Involved
indexpage_1QuickStartGetInvolved
License
indexpage_1License

Taskflow helps you quickly write parallel and heterogeneous task programs with high performance and simultaneous high productivity. It is faster, more expressive, fewer lines of code, and easier for drop-in integration than many of existing task programming libraries. The source code is available in our Project GitHub.

# 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.

    #include<taskflow/taskflow.hpp>//Taskflowisheader-only

    intmain(){

    tf::Executorexecutor;
    tf::Taskflowtaskflow;

    auto[A,B,C,D]=taskflow.emplace(//createfourtasks
    [](){std::cout<<"TaskA\n";},
    [](){std::cout<<"TaskB\n";},
    [](){std::cout<<"TaskC\n";},
    [](){std::cout<<"TaskD\n";}
    );

    A.precede(B,C);//ArunsbeforeBandC
    D.succeed(B,C);//DrunsafterBandC

    executor.run(taskflow).wait();

    return0;
    }

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 under `taskflow/`.

    ~$gitclonehttps://github.com/taskflow/taskflow.git#cloneitonlyonce
    ~$g++-std=c++20simple.cpp-Itaskflow/-O2-pthread-osimple
    ~$./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.

    #runtheprogramwiththeenvironmentvariableTF_ENABLE_PROFILERenabled
    ~$TF_ENABLE_PROFILER=simple.json./simple
    ~$catsimple.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"}]}]}
    ]
    #pastetheprofilingjsondatatohttps://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::TaskA=taskflow.emplace([](){}).name("A");
    tf::TaskC=taskflow.emplace([](){}).name("C");
    tf::TaskD=taskflow.emplace([](){}).name("D");

    tf::TaskB=taskflow.emplace([](tf::Subflow&subflow){//subflowtaskB
    tf::TaskB1=subflow.emplace([](){}).name("B1");
    tf::TaskB2=subflow.emplace([](){}).name("B2");
    tf::TaskB3=subflow.emplace([](){}).name("B3");
    B3.succeed(B1,B2);//B3runsafterB1andB2
    }).name("B");

    A.precede(B,C);//ArunsbeforeBandC
    D.succeed(B,C);//DrunsafterBandC

# 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::Taskinit=taskflow.emplace([](){}).name("init");
    tf::Taskstop=taskflow.emplace([](){}).name("stop");

    //createsaconditiontaskthatreturnsarandombinary
    tf::Taskcond=taskflow.emplace([](){returnstd::rand()%2;}).name("cond");

    //createsafeedbackloop{0:cond,1:stop}
    init.precede(cond);
    cond.precede(cond,stop);//movesonto'cond'onreturning0,or'stop'on1

# 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 CUDA.

    __global__voidsaxpy(intn,floata,float*x,float*y){
    inti=blockIdx.x*blockDim.x+threadIdx.x;
    if(i<n){
    y[i]=a*x[i]+y[i];
    }
    }
    tf::Taskcudaflow=taskflow.emplace([&](tf::cudaFlow&cf){
    tf::cudaTaskh2d_x=cf.copy(dx,hx.data(),N).name("h2d_x");
    tf::cudaTaskh2d_y=cf.copy(dy,hy.data(),N).name("h2d_y");
    tf::cudaTaskd2h_x=cf.copy(hx.data(),dx,N).name("d2h_x");
    tf::cudaTaskd2h_y=cf.copy(hy.data(),dy,N).name("d2h_y");
    tf::cudaTasksaxpy=cf.kernel((N+255)/256,256,0,saxpy,N,2.0f,dx,dy)
    .name("saxpy");//parameterstothesaxpykernel
    saxpy.succeed(h2d_x,h2d_y)
    .precede(d2h_x,d2h_y);
    }).name("cudaFlow");

# 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::Taskflowf1,f2;

    //createtaskflowf1oftwotasks
    tf::Taskf1A=f1.emplace([](){std::cout<<"Taskf1A\n";}).name("f1A");
    tf::Taskf1B=f1.emplace([](){std::cout<<"Taskf1B\n";}).name("f1B");

    //createtaskflowf2withonemoduletaskcomposedoff1
    tf::Taskf2A=f2.emplace([](){std::cout<<"Taskf2A\n";}).name("f2A");
    tf::Taskf2B=f2.emplace([](){std::cout<<"Taskf2B\n";}).name("f2B");
    tf::Taskf2C=f2.emplace([](){std::cout<<"Taskf2C\n";}).name("f2C");
    tf::Taskf1_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::Executorexecutor;

    //createasynchronoustasksdirectlyfromanexecutor
    std::future<int>future=executor.async([](){
    std::cout<<"asynctaskreturns1\n";
    return1;
    });
    executor.silent_async([](){std::cout<<"asynctaskdoesnotreturn\n";});

    //createasynchronoustaskswithdynamicdependencies
    tf::AsyncTaskA=executor.silent_dependent_async([](){printf("A\n");});
    tf::AsyncTaskB=executor.silent_dependent_async([](){printf("B\n");},A);
    tf::AsyncTaskC=executor.silent_dependent_async([](){printf("C\n");},A);
    tf::AsyncTaskD=executor.silent_dependent_async([](){printf("D\n");},B,C);

    executor.wait_for_all();

# 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.

    //runsthetaskflowonce
    tf::Future<void>run_once=executor.run(taskflow);

    //waitonthisruntofinish
    run_once.get();

    //runthetaskflowfourtimes
    executor.run_n(taskflow,4);

    //runsthetaskflowfivetimes
    executor.run_until(taskflow,[counter=5](){return--counter==0;});

    //blockstheexecutoruntilallsubmittedtaskflowscomplete
    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.

    //standardparallelCPUalgorithms
    tf::Tasktask1=taskflow.for_each(//assigneachelementto100inparallel
    first,last,[](auto&i){i=100;}
    );
    tf::Tasktask2=taskflow.reduce(//reducearangeofitemsinparallel
    first,last,init,[](autoa,autob){returna+b;}
    );
    tf::Tasktask3=taskflow.sort(//sortarangeofitemsinparallel
    first,last,[](autoa,autob){returna<b;}
    );

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

    //createapipelinetopropagatefivetokensthroughthreeserialstages
    tf::Pipelinepl(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("stage2:inputbuffer[%zu]=%d\n",pf.line(),buffer[pf.line()]);
    }},
    tf::Pipe{tf::PipeType::SERIAL,[](tf::Pipeflow&pf){
    printf("stage3:inputbuffer[%zu]=%d\n",pf.line(),buffer[pf.line()]);
    }}
    );
    taskflow.composed_of(pl)
    executor.run(taskflow).wait();

# 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::Taskflowtaskflow;

    tf::TaskA=taskflow.emplace([](){}).name("A");
    tf::TaskB=taskflow.emplace([](){}).name("B");
    tf::TaskC=taskflow.emplace([](){}).name("C");
    tf::TaskD=taskflow.emplace([](){}).name("D");
    tf::TaskE=taskflow.emplace([](){}).name("E");
    A.precede(B,C,E);
    C.precede(D);
    B.precede(D,E);

    //dumpthegraphtoaDOTfilethroughstd::cout
    taskflow.dump(std::cout);

# 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.

# 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 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.