Reverse engineering for reduction parallelization via semiring polynomials

Akimasa Morihata, Shigeyuki Sato
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引用次数: 1

Abstract

Parallel reduction, which summarizes a given dataset, e.g., the total, average, and maximum, plays a crucial role in parallel programming. This paper presents a new approach, reverse engineering, to automatically discovering nontrivial parallel reductions in sequential programs. The body of the sequential reduction loop is regarded as a black box, and its input-output behaviors are sampled. If the behaviors correspond to a set of linear polynomials over a semiring, a divide-and-conquer parallel reduction is generated. Auxiliary reverse-engineering methods enable a long and nested loop body to be decomposed, which makes our parallelization scheme applicable to various types of reduction loops. This approach is not only simple and efficient but also agnostic to the details of the input program. Its potential is demonstrated through several use case scenarios. A proof-of-concept implementation successfully inferred linear polynomials for nearly all of the 74 benchmarks exhaustively collected from the literature. These characteristics and experimental results demonstrate the promise of the proposed approach, despite its inherent unsoundness.
通过半环多项式减少并行化的逆向工程
并行约简(Parallel reduction)是对给定数据集的总结,例如,总数、平均值和最大值,在并行编程中起着至关重要的作用。本文提出了一种新的方法——逆向工程,来自动发现顺序程序中的非平凡并行约简。将序列约简回路的主体视为一个黑盒,对其输入-输出行为进行采样。如果行为对应于半环上的一组线性多项式,则生成分治并行约简。辅助的逆向工程方法可以分解长而嵌套的循环体,这使得我们的并行化方案适用于各种类型的还原循环。这种方法不仅简单有效,而且与输入程序的细节无关。通过几个用例场景演示了它的潜力。概念验证实现成功地为从文献中详尽收集的几乎所有74个基准推断出线性多项式。这些特征和实验结果证明了所提出的方法的前景,尽管其固有的不健全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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