Abstract
This work studies the solving of challenging SAT problem instances in distributed computing environments that have massive amounts of parallel resources but place limits on individual computations. We present an abstract framework which extends a previously presented iterative partitioning approach with clause learning, a key technique applied in modern SAT solvers. In addition we present two techniques that alter the clause learning of modern SAT solvers to fit the framework. An implementation of the proposed framework is then analyzed experimentally using a well-known set of benchmark instances. The results are very encouraging. For example, the implementation is able to solve challenging SAT instances not solvable in reasonable time by state-of-the-art sequential and parallel SAT solvers.
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References
Asín, R., Nieuwenhuis, R., Oliveras, A., Rodríguez-Carbonell, E.: Practical algorithms for unsatisfiability proof and core generation in SAT solvers. AI Communications 23(2-3), 145–157 (2010)
Biere, A.: Lingeling, Plingeling, PicoSAT and PrecoSAT at SAT race 2010. Technical Report 10/1, Institute for Formal Models and Verification, Johannes Kepler University (2010)
Blochinger, W., Sinz, C., Küchlin, W.: Parallel propositional satisfiability checking with distributed dynamic learning. Parallel Computing 29(7), 969–994 (2003)
Böhm, M., Speckenmeyer, E.: A fast parallel SAT-solver: Efficient workload balancing. Annals of Mathematics and Artificial Intelligence 17(4-3), 381–400 (1996)
Chrabakh, W., Wolski, R.: GridSAT: a system for solving satisfiability problems using a computational grid. Parallel Computing 32(9), 660–687 (2006)
Chu, G., Schulte, C., Stuckey, P.J.: Confidence-based work stealing in parallel constraint programming. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 226–241. Springer, Heidelberg (2009)
Eén, N., Sörensson, N.: Temporal induction by incremental SAT solving. Electronic Notes in Theoretical Computer Science 89(4) (2003)
Fuhs, C., Schneider-Kamp, P.: Synthesizing shortest linear straight-line programs over GF(2) using SAT. In: Strichman, O., Szeider, S. (eds.) SAT 2010. LNCS, vol. 6175, pp. 71–84. Springer, Heidelberg (2010)
Hamadi, Y., Jabbour, S., Sais, L.: ManySAT: a parallel SAT solver. Journal on Satisfiability, Boolean Modeling and Computation 6, 245–262 (2009)
Hamadi, Y., Jabbour, S., Sais, L.: Constrol-based clause sharing in parallel SAT solving. In: Proc. IJCAI 2009, pp. 499–504 (2009)
Heule, M., van Maaren, H.: Look-ahead based SAT solvers. In: Handbook of Satisfiability. Frontiers in Artificial Intelligence and Applications, vol. 185, pp. 155–184. IOS Press, Amsterdam (2009)
Huberman, B.A., Lukose, R.M., Hogg, T.: An economics approach to hard computational problems. Science 275(5296), 51–54 (1997)
Hyvärinen, A.E.J., Junttila, T., Niemelä, I.: A distribution method for solving SAT in grids. In: Biere, A., Gomes, C.P. (eds.) SAT 2006. LNCS, vol. 4121, pp. 430–435. Springer, Heidelberg (2006)
Hyvärinen, A.E.J., Junttila, T., Niemelä, I.: Incorporating clause learning in grid-based randomized SAT solving. Journal on Satisfiability, Boolean Modeling and Computation 6, 223–244 (2009)
Hyvärinen, A.E.J., Junttila, T., Niemelä, I.: Partitioning SAT instances for distributed solving. In: Fermüller, C.G., Voronkov, A. (eds.) LPAR-17. LNCS, vol. 6397, pp. 372–386. Springer, Heidelberg (2010)
Hyvärinen, A.E.J., Junttila, T., Niemelä, I.: Partitioning search spaces of a randomized search. Fundamenta Informaticae 107(2-3), 289–311 (2011)
Jurkowiak, B., Li, C., Utard, G.: A parallelization scheme based on work stealing for a class of SAT solvers. Journal of Automated Reasoning 34(1), 73–101 (2005)
Luby, M., Sinclair, A., Zuckerman, D.: Optimal speedup of Las Vegas algorithms. Information Processing Letters 47(4), 173–180 (1993)
Marques-Silva, J., Lynce, I., Malik, S.: Conflict-driven clause learning SAT solvers. In: Handbook of Satisfiability. IOS Press, Amsterdam (2009)
Marques-Silva, J., Sakallah, K.: GRASP: A search algorithm for propositional satisfiability. IEEE Transactions on Computers 48(5), 506–521 (1999)
Martins, R., Manquinho, V., Lynce, I.: Improving search space splitting for parallel SAT solving. In: Proc. ICTAI 2010, pp. 336–343. IEEE Press, Los Alamitos (2010)
Michel, L., See, A., van Hentenryck, P.: Parallelizing constraint programs transparently. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 514–528. Springer, Heidelberg (2007)
Moskewicz, M., Madigan, C., Zhao, Y., Zhang, L., Malik, S.: Chaff: Engineering an efficient SAT solver. In: Proc. DAC 2001, pp. 530–535. ACM, New York (2001)
Schubert, T., Lewis, M., Becker, B.: PaMiraXT: Parallel SAT solving with threads and message passing. Journal on Satisfiability, Boolean Modeling and Computation 6, 203–222 (2009)
Speckenmeyer, E., Monien, B., Vornberger, O.: Superlinear speedup for parallel backtracking. In: Houstis, E.N., Polychronopoulos, C.D., Papatheodorou, T.S. (eds.) ICS 1987. LNCS, vol. 297, pp. 985–993. Springer, Heidelberg (1988)
Wieringa, S., Niemenmaa, M., Heljanko, K.: Tarmo: A framework for parallelized bounded model checking. In: Proc. PDMC 2009. EPTCS, vol. 14, pp. 62–76 (2009)
Zhang, H., Bonacina, M., Hsiang, J.: PSATO: A distributed propositional prover and its application to quasigroup problems. Journal of Symbolic Computation 21(4), 543–560 (1996)
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Hyvärinen, A.E.J., Junttila, T., Niemelä, I. (2011). Grid-Based SAT Solving with Iterative Partitioning and Clause Learning. In: Lee, J. (eds) Principles and Practice of Constraint Programming – CP 2011. CP 2011. Lecture Notes in Computer Science, vol 6876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23786-7_30
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