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Non-Portfolio Approaches for Distributed Maximum Satisfiability

机译：分布式最大可满足性的非组合方法

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The most successful parallel SAT and MaxSAT solvers follow a portfolio approach, where each thread applies a different algorithm (or the same algorithm configured differently) to solve a given problem instance. The main goal of building a portfolio is to diversify the search process being carried out by each thread. As soon as one thread finishes, the instance can be deemed solved. In this paper we present a new open source distributed solver for MaxSAT solving that addresses two issues commonly found in multicore parallel solvers, namely memory contention and scalability. Preliminary results show that our nonportfolio distributed MaxSAT solver outperforms its sequential version and is able to solve more instances in several instance sets as the number of processes increases.
机译：最成功的并行SAT和MaxSAT求解器遵循组合方法，其中每个线程应用不同的算法（或配置不同的同一算法）来解决给定的问题实例。建立项目组合的主要目标是使每个线程正在执行的搜索过程多样化。一旦一个线程完成，该实例就可以视为已解决。在本文中，我们提出了一种新的用于MaxSAT解决的开源分布式求解器，它解决了多核并行求解器中常见的两个问题，即内存争用和可伸缩性。初步结果表明，随着流程数量的增加，我们的非组合分布式MaxSAT求解器的性能优于其顺序版本，并且能够在多个实例集中求解更多实例。

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