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Parallelizing local search for CNF satisfiability using vectorization and PVM

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Published:31 December 2002Publication History
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The purpose of this paper is to speed up the local search algorithm for the CNF Satisfiability problem. Our basic strategy is to run some 105 independent search paths simultaneously using PVM on a vector supercomputer VPP800, which consists of 40 vector processors. Using the above parallelization and vectorization together with some improvement of data structure, we obtained 600-times speedup in terms of the number of flips the local search can make per second, compared to the original GSAT by Selman and Kautz. We ran our parallel GSAT for benchmark instances and compared the running time with those of existing SAT programs. We could observe an apparent benefit of parallelization: Especially, we were able to solve two instances that have never been solved before this paper. We also tested parallel local search for the SAT encoding of the class scheduling problem. Again we were able to get almost the best answer in reasonable time.

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