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Parallelization of stochastic algorithm for boolean satisfiability on GPGPU architecture

机译:GPGPU架构上布尔可满足性的随机算法并行化

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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.
机译:布尔可满足性问题(SAT)是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已成为各种计算机工程应用程序的基石。存在许多用于解决SAT的算法,这些算法具有不同程度的完整性和复杂性。一类基于随机本地搜索(SLS)的SAT算法通常比回溯搜索过程更容易实现。本文讨论cwSAT,它是GPGPU架构上SLS过程WalkSAT的并行实现。在SAT11竞赛随机组中,使用200个基准比较了cwSAT和WalkSAT的性能。实验结果表明,cwSAT可以为超过99%的基准找到满意的分配,而cwSAT的平均改进比WalkSAT大约高33%至98%。

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