On selecting a satisfying truth assignment (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Efficient local search for very large-scale satisfiability problems
ACM SIGART Bulletin
On the greedy algorithm for satisfiability
Information Processing Letters
Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993
Vectorized Symbolic Model Checking of Computation Tree Logic for Sequential Machine Verification
CAV '91 Proceedings of the 3rd International Workshop on Computer Aided Verification
A Probabilistic Algorithm for k-SAT and Constraint Satisfaction Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Ten challenges in propositional reasoning and search
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Performance test of local search algorithms using new types of random CNF formulas
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Local search algorithms for partial MAXSAT
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Adding new clauses for faster local search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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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.