p- taskflow:使用现代c++快速的基于任务的并行编程

Tsung-Wei Huang, Chun-Xun Lin, Guannan Guo, Martin D. F. Wong
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引用次数: 51

摘要

在本文中,我们介绍了一个新的c++任务库Cpp-Taskflow,它可以帮助开发人员使用任务依赖图快速编写并行程序。Cpp-Taskflow利用现代c++和基于任务的方法的强大功能来实现并行分解策略的有效实现。我们的编程模型不仅可以快速处理传统的循环级并行,还可以处理不规则模式,如图算法、增量流和动态数据结构。与现有库相比,Cpp-Taskflow在性能扩展和软件集成方面更具成本效益。我们已经在微基准测试和具有百万级任务的实际应用程序上评估了cp - taskflow。在一个机器学习的例子中,cp - taskflow实现了1.5 - 2.7倍的编码复杂性和14-38%的速度比两个工业强度的库OpenMP Tasking和Intel Threading Building Blocks (TBB)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cpp-Taskflow: Fast Task-Based Parallel Programming Using Modern C++
In this paper we introduce Cpp-Taskflow, a new C++ tasking library to help developers quickly write parallel programs using task dependency graphs. Cpp-Taskflow leverages the power of modern C++ and task-based approaches to enable efficient implementations of parallel decomposition strategies. Our programming model can quickly handle not only traditional loop-level parallelism, but also irregular patterns such as graph algorithms, incremental flows, and dynamic data structures. Compared with existing libraries, Cpp-Taskflow is more cost efficient in performance scaling and software integration. We have evaluated Cpp-Taskflow on both micro-benchmarks and real-world applications with million-scale tasking. In a machine learning example, Cpp-Taskflow achieved 1.5–2.7× less coding complexity and 14–38% speed-up over two industrial-strength libraries OpenMP Tasking and Intel Threading Building Blocks (TBB).
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