Partitioning SAT Instances for Distributed Solving

In this paper we study the problem of solving hard propositional satisfiability problem (SAT) instances in a computing grid or cloud, where run times and communication between parallel running computations are limited. We study analytically an approach where the instance is partitioned iteratively into a tree of subproblems and each node in the tree is solved in parallel. We present new methods for constructing partitions which combine clause learning and lookahead. The methods are incorporated into the iterative approach and its performance is demonstrated with an extensive comparison against the best sequential solvers in the SAT competition 2009 as well as against two efficient parallel solvers.

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