GPGPU架构下布尔可满足性随机算法的并行化

S. Nimnon, M. Phadoongsidhi, N. Utamaphethai
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引用次数: 1

摘要

布尔可满足性问题(SAT)是一个np完全决策问题,用于确定是否存在使布尔表达式可满足(TRUE)的变量赋值。SAT已经成为各种计算机工程应用的基石。求解SAT的算法有很多,它们的完备程度和复杂程度各不相同。一类基于随机局部搜索(SLS)的SAT算法通常比回溯搜索过程更容易实现。本文讨论了在GPGPU架构上并行实现SLS程序WalkSAT的cwSAT。在SAT11比赛的随机班级中,使用200个基准将cwSAT的性能与WalkSAT的性能进行了比较。实验结果表明,cwSAT可以在99%以上的基准测试中找到满意的任务,而cwSAT比WalkSAT的平均改进约为33%至98%。
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Parallelization of stochastic algorithm for boolean satisfiability on GPGPU architecture
Boolean satisfiability problem (SAT) is an NP-complete decision problem for determining whether there exists a variable assignment making a Boolean expression satisfiable (TRUE). SAT has been the cornerstone for various Computer Engineering applications. Numerous algorithms for solving SAT exist with varying degrees of completeness and complexity. A class of SAT algorithms based on stochastic local search (SLS) is generally easier to implement than backtracking search procedures. This paper discusses cwSAT, a parallel implementation of an SLS procedure, WalkSAT, on a GPGPU architecture. The performance of cwSAT is compared with that of WalkSAT using 200 benchmarks in Random class of SAT11 Competition. Experimental results showed that cwSAT can find satisfiable assignments for over 99% of the benchmarks while the average improvement of cwSAT is approximately 33% to 98% over WalkSAT.
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