Release Notes » Release 3.7.0 (Master)

Taskflow 3.7.0 is the newest developing line to new features and improvements we continue to support. It is also where this documentation is generated. Many things are considered experimental and may change or break from time to time. While it may be difficult to be keep all things consistent when introducing new features, we continue to try our best to ensure backward compatibility.

Download

To download the newest version of Taskflow, please clone the master branch from Taskflow's GitHub.

System Requirements

To use Taskflow v3.7.0, you 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.

Release Summary

This release introduces a new exception interface to help identify C++ errors in taskflow programs. Additionally, this release enhances the scheduling performance through integration of C++20 atomic-wait into scheduler, executor, and notifier.

New Features

Taskflow Core

Improved scheduling performance of dependent asynchronous tasks
Improved scheduling performance of module task by removing busy looping
Improved tf::Executor::wait_for_all using C++20 atomic wait
Improved tf::Notifier using C++20 atomic wait
Improved worker-thread ID mapping performance using C++20 atomic wait
Added -Wshadow to the compilation check
Added tf::AsyncTask::is_done to query the completion status of an async task
Added tf::Taskflow::remove_dependency to remove dependencies from the graph
Added support for exception in tf::Taskflow and tf::Executor

tf::Executor executor;
tf::Taskflow taskflow;
taskflow.emplace([](){ throw std::runtime_error("exception"); });
try {
  executor.run(taskflow).get();
}
catch(const std::runtime_error& e) {
  std::cerr << e.what() << std::endl;
}

Modified the CI to exclude exception test under sanitizers
Modified the tf::PartitionerBase to allow defining custom closure wrappers

std::atomic<int> count = 0;
tf::Taskflow taskflow;
taskflow.for_each_index(0, 100, 1, 
  [](){                 
    printf("%d\n", i); 
  },
  tf::StaticPartitioner(0, [](auto&& closure){
    // do something before invoking the partitioned task
    // ...
    
    // invoke the partitioned task
    closure();

    // do something else after invoking the partitioned task
    // ...
  }
);
executor.run(taskflow).wait();

Utilities

Bug Fixes

Fixed compilation error of CUDA examples caused by not including for_each.hpp
Fixed the runtime error of tf::Taskflow::for_each_index when the range invalid

Breaking Changes

Renamed tf::Runtime::join to tf::Runtime::corun_all
Removed tf::WorkerInterface due to the support of exception

Documentation

Revised Asynchronous Tasking with Dependencies
- Added Query the Completion Status of Dependent Async Tasks
Revised Exception Handling
Revised Executor
- Removed the section of tf::WorkerInterface
Revised Partitioning Algorithm

Miscellaneous Items

We have published Taskflow in the following venues:

Cheng-Hsiang Chiu, Zhicheng Xiong, Zizheng Guo, Tsung-Wei Huang, and Yibo Lin, "An Efficient Task-parallel Pipeline Programming Framework," ACM International Conference on High-performance Computing in Asia-Pacific Region (HPC Asia), Nagoya, Japan, 2024
Cheng-Hsiang Chiu, Dian-Lun Lin, and Tsung-Wei Huang,, "Programming Dynamic Task Parallelism for Heterogeneous EDA Algorithms," IEEE/ACM International Conference on Computer-aided Design (ICCAD), San Francisco, CA, 2023

Please do not hesitate to contact Dr. Tsung-Wei Huang if you intend to collaborate with us on using Taskflow in your scientific computing projects.