CDCL SAT解算器中细粒度嵌套并行性的研究

Pub Date : 2021-09-20 DOI:10.1145/3470639

J. Edwards, U. Vishkin

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引用次数: 3

摘要

在许多问题领域中，布尔可满足性(SAT)是一个重要的对性能要求很高的问题。然而，大多数关于并行化SAT求解器的工作都集中在粗粒度的、令人尴尬的并行性上。在这里，我们研究了细粒度的并行性，它可以加速现有的顺序SAT解决方案，这些解决方案都是所谓的冲突导向子句学习。我们展示了在各种问题实例中加速高达382倍的潜力。我们希望这些结果将刺激未来的研究，特别是关于我们提出的计算机体系结构开放问题。

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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Study of Fine-grained Nested Parallelism in CDCL SAT Solvers

Boolean satisfiability (SAT) is an important performance-hungry problem with applications in many problem domains. However, most work on parallelizing SAT solvers has focused on coarse-grained, mostly embarrassing, parallelism. Here, we study fine-grained parallelism that can speed up existing sequential SAT solvers, which all happen to be of the so-called Conflict-Directed Clause Learning variety. We show the potential for speedups of up to 382× across a variety of problem instances. We hope that these results will stimulate future research, particularly with respect to a computer architecture open problem we present.

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