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Non-Portfolio Approaches for Distributed Maximum Satisfiability

机译：用于分布式最大可靠性的非投资组合方法

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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求解器遵循一个投资组合方法，其中每个线程应用不同的算法（或配置不同的相同算法）以解决给定的问题实例。构建投资组合的主要目标是使每个线程执行的搜索过程多样化。一旦一个线程完成，实例可以被视为解决。在本文中，我们为MaxSAT解决了一个新的开源分布式求解器，解决了多核并行求解器中常见的两个问题，即内存争用和可扩展性。初步结果表明，我们的非沃利奥分布式MaxSAT求解器优于其顺序版本，并且能够在几个实例集中解决更多实例，因为进程的数量增加。

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