98 lines
4.2 KiB
Text
98 lines
4.2 KiB
Text
namespace tf {
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/** @page release-3-5-0 Release 3.5.0 (2023/01/05)
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%Taskflow 3.5.0 is the 6th release in the 3.x line!
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This release includes several new changes, such as pipeline parallelism,
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improved work-stealing performance, profiling, documentation, examples, and unit tests.
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@tableofcontents
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@section release-3-5-0_download Download
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%Taskflow 3.5.0 can be downloaded from <a href="https://github.com/taskflow/taskflow/releases/tag/v3.5.0">here</a>.
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@section release-3-5-0_system_requirements System Requirements
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To use %Taskflow v3.5.0, you need a compiler that supports C++17:
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@li GNU C++ Compiler at least v8.4 with -std=c++17
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@li Clang C++ Compiler at least v6.0 with -std=c++17
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@li Microsoft Visual Studio at least v19.27 with /std:c++17
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@li AppleClang Xcode Version at least v12.0 with -std=c++17
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@li Nvidia CUDA Toolkit and Compiler (nvcc) at least v11.1 with -std=c++17
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@li Intel C++ Compiler at least v19.0.1 with -std=c++17
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@li Intel DPC++ Clang Compiler at least v13.0.0 with -std=c++17 and SYCL20
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%Taskflow works on Linux, Windows, and Mac OS X.
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@section release-3-5-0_summary Release Summary
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This release introduces a new data-parallel pipeline programming model,
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solves the busy-waiting problem in our work-stealing scheduler,
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and adds a new text-based feature for profiler report.
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@section release-3-5-0_new_features New Features
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@subsection release-3-5-0_taskflow_core Taskflow Core
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+ Added tf::WorkerInterface to allow changing properties of workers upon their creations
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+ Added tf::Executor::loop_until to allow looping a worker with a custom stop predicate
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+ Added tf::DataPipeline to implement data-parallel algorithms
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+ See @ref DataParallelPipeline
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+ Extended tf::Executor to include tf::WorkerInterface
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+ Improved parallel algorithms (e.g., tf::Taskflow::for_each) with tail optimization
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+ Resolved the busy-waiting problem in our work-stealing algorithm ([#400](https://github.com/taskflow/taskflow/pull/440))
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@subsection release-3-5-0_cudaflow cudaFlow
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This release has no update on tf::cudaFlow.
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@subsection release-3-5-0_utilities Utilities
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+ Added tf::unroll to unroll loops using template techniques
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+ Added tf::CachelineAligned to create a cacheline-aligned object
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+ Replaced std::aligned_union (deprecated in C++23) with a custom byte type ([#445](https://github.com/taskflow/taskflow/issues/445))
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@subsection release-3-5-0_profiler Taskflow Profiler (TFProf)
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+ Added a new feature to generate a profile summary report
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+ See @ref ProfilerDisplayProfileSummary
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@section release-3-5-0_bug_fixes Bug Fixes
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+ Fixed the compilation error in taking move-only types for tf::Taskflow::transform_reduce
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+ Fixed the compilation error in the graph pipeline benchmark
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+ Fixed the compilation error in unknown OS (replaced with @c TF_OS_UNKNOWN)
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If you encounter any potential bugs, please submit an issue at @IssueTracker.
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@section release-3-5-0_breaking_changes Breaking Changes
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This release has no breaking changes.
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@section release-3-5-0_deprecated_items Deprecated and Removed Items
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This release has no deprecated and removed items.
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@section release-3-5-0_documentation Documentation
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+ Revised @ref ExecuteTaskflow
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+ Added @ref ExecuteATaskflowFromAnInternalWorker
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+ Added @ref DataParallelPipeline
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@section release-3-5-0_miscellaneous_items Miscellaneous Items
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We have published %Taskflow in the following venues:
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+ Tsung-Wei Huang and Leslie Hwang, "[Task-Parallel Programming with Constrained Parallelism](https://tsung-wei-huang.github.io/papers/hpec22-semaphore.pdf)," <em>IEEE High-Performance Extreme Computing Conference (HPEC)</em>, MA, 2022
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+ Tsung-Wei Huang, "[Enhancing the Performance Portability of Heterogeneous Circuit Analysis Programs](https://tsung-wei-huang.github.io/papers/hpec22-ot.pdf)," <em>IEEE High-Performance Extreme Computing Conference (HPEC)</em>, MA, 2022
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+ Dian-Lun Lin, Haoxing Ren, Yanqing Zhang, and Tsung-Wei Huang, "[From RTL to CUDA: A GPU Acceleration Flow for RTL Simulation with Batch Stimulus](https://doi.org/10.1145/3545008.3545091)," <em>ACM International Conference on Parallel Processing (ICPP)</em>, Bordeaux, France, 2022
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Please do not hesitate to contact @twhuang if you intend to collaborate with us
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on using %Taskflow in your scientific computing projects.
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*/
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}
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