小数据集的并行运行和算法

B. Videau, Erik Saule, J. Méhaut
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引用次数: 2

摘要

在本文中,我们提出了一个专门用于并行算法的引擎PaSTeL。PaSTeL提供了一个编程模型,用于构建并行算法和一个基于工作窃取的执行模型。特别注意使用优化的线程激活和同步机制。为了说明PaSTeL的使用,实现了STL算法的一个子集,这些算法也用于性能实验。PaSTeL的性能在一台使用两核的笔记本电脑上进行了评估,但也在16核的平台上进行了评估。PaSTeL比STL的其他实现表现出更好的性能,特别是在小数据集上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PaSTeL: Parallel Runtime and Algorithms for Small Datasets
In this paper, we put forward PaSTeL, an engine dedicated to parallel algorithms. PaSTeL offers both a programming model, to build parallel algorithms and an execution model based on work-stealing. Special care has been taken on using optimized thread activation and synchronization mechanisms. In order to illustrate the use of PaSTeL a subset of the STL's algorithms was implemented, which were also used on performance experiments. PaSTeL's performance is evaluated on a laptop computer using two cores, but also on a 16 cores platform. PaSTeL shows better performance than other implementations of the STL, especially on small datasets.
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