一个高效且可组合的并行任务编程库

Chun-Xun Lin, Tsung-Wei Huang, Guannan Guo, Martin D. F. Wong
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引用次数: 7

摘要

在编写快速扩展市场的应用程序(如并行机器学习算法和大数据分析)时,可组合性是提高程序员生产力的关键组成部分。这些应用程序展示了规则和不规则的计算模式,并且经常与其他函数或库组合在一起组成一个更大的程序。然而,在许多现有的并行编程库中,可组合并行处理已经退居次要地位,这使得在大规模并行程序中实现模块化变得困难。本文介绍了一种新的基于可组合任务图的并行任务编程库。我们的库有效地支持任务并行性,以及直观的任务图构造和灵活的执行API集,以支持可重用和可组合的任务依赖图。开发人员可以从小型和模块化的并行构建块快速组成大型并行程序,并轻松地将程序部署在多核机器上。我们已经在实际应用程序中评估了我们的库。实验结果表明,我们的库能够以更少的编码工作量达到与Intel线程构建块相当的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Efficient and Composable Parallel Task Programming Library
Composability is a key component to improve programmers’ productivity in writing fast market-expanding applications such as parallel machine learning algorithms and big data analytics. These applications exhibit both regular and irregular compute patterns, and are often combined with other functions or libraries to compose a larger program. However, composable parallel processing has taken a back seat in many existing parallel programming libraries, making it difficult to achieve modularity in large-scale parallel programs. In this paper, we introduce a new parallel task programming library using composable tasking graphs. Our library efficiently supports task parallelism together with an intuitive task graph construction and flexible execution API set to enable reusable and composable task dependency graphs. Developers can quickly compose a large parallel program from small and modular parallel building blocks, and easily deploy the program on a multicore machine. We have evaluated our library on real-world applications. Experimental results showed our library can achieve comparable performance to Intel Threading Building Blocks with less coding effort.
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