Release Notes » Release 3.9.0 (2025/01/02) | Taskflow QuickStart

# Release Notes » Release 3.9.0 (2025/01/02)

## Release Summary

This release improves scheduling performance with a decentralized work-stealing strategy and enhances exception handling across all task types.

## Download

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

## System Requirements

To use Taskflow v3.9.0, 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

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

#### Attention

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.

## New Features

### Taskflow Core

- improved the core scheduling algorithm using a decentralized work-stealing strategy
  - tf::BoundedTaskQueue to optimize per-thread work-stealing latency
  - tf::UnboundedTaskQueue to handle overflowed tasks
- enhanced tf::Runtime to support preemptible execution flows
- optimized task storage by storing detached tasks in their original subflows
- optimized the query efficiency for strong dependencies by embedding their values in node states
- updated tf::Graph to derive from a vector of unique pointers to nodes
  - Graph node lifetimes are managed by std::unique_ptr
  - Asynchronous task node lifetimes are managed by tf::Executor.
- expanded unit tests to include more exception handling scenarios
- decoupled tf::Runtime from static task to accommodate distinct execution logic
- removed the blocking behavior to avoid underutilized threads for the following tasks:
  - module task (#649)
  - subflow task
  - all parallel algorithms (through preemptible async tasks)
- removed std::bind from asynchronous tasks to ensure proper constexpr switch
- added compile-time macros to enable specific features
  - `TF_ENABLE_TASK_POOL` to enable the use of task pool
- added taskflow execution through asynchronous tasking with tf::make_module_task
  - details can be referred to Module Algorithm
- added tf::WorkerInterface for users to configure the behaviors of workers
  - details can be referred to Executor
- added worker interface example and unit tests

### Utilities

- added `tf::pause` to relax CPU during busy spinning loop
- added `tf::seed` to generate a random seed based on calling time point
- added `tf::atomic_min` to update an atomic variable with the minimum value
- added `tf::atomic_max` to update an atomic variable with the maximum value
- added `TF_CPP20` and `TF_CPP17` macro for testing cpp versions

## Bug Fixes

- fixed AppleClang compile error in tsq.hpp (#651)
- fixed wrong range in uuid test (#632)
- fixed the exception bug in tf::Subflow::join (#602)
- fixed the wrong prefix of target when running benchmark.py
- fixed a bug in the join counter reset logic for scheduling condition tasks (#652)

## Breaking Changes

- decoupled tf::Subflow from inheriting tf::Runtime to accommodate distinct execution logic
  - tf::Subflow no longer supports tf::Runtime-specific features
- removed tf::Runtime::corun_until as it duplicates tf::Executor::corun_until
- removed tf::Runtime-based semaphore interface due to significant flaws of blocking corun (#647)
  - details can be referred to Limit the Maximum Concurrency

## Documentation

- fixed missing documentation of tf::Executor due to Doxygen bugs (#625)
- fixed benchmark instance names in documentation (#621)
- revised Exception Handling
- revised Asynchronous Tasking
- revised Limit the Maximum Concurrency
- added Module Algorithm

## Miscellaneous Items

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.

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