Abstract
Combining differing solution approaches by means of solver portfolios has proven as a highly effective technique for boosting solver performance. We consider the problem of generating parallel SAT solver portfolios. Our approach is based on a recently introduced sequential SAT solver portfolio that excelled at the last SAT competition. We show how the approach can be generalized for the parallel case, and how obstacles like parallel SAT solvers and symmetries induced by identical processors can be overcome. We compare different ways of computing parallel solver portfolios with the best performing parallel SAT approaches to date. Extensive experimental results show that the developed methodology very significantly improves our current parallel SAT solving capabilities.
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References
SAT Competition, http://www.satcomptition.org
Biere, A.: Lingeling, plingeling, picosat and precosat at sat race 2010. Technical report, Johannes Kepler University, Linz, Austria (2010)
Biere, A.: Lingeling and friends at the sat competition 2011. Technical report, Johannes Kepler University, Altenbergerstr. 69, 4040 Linz, Austria (2011)
Dantzig, G.: Linear programming and extensions. Princeton University Press, Princeton (1963)
Een, N., Sorensson, N.: An extensible sat-solver [ver 1.2] (2003)
Hamadi, Y., Jabbour, S., Lakhdar, S.: Manysat: a parallel sat solver. Journal on Satisfiability, Boolean Modeling and Computation 6, 245–262 (2009)
Kadioglu, S., Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Algorithm Selection and Scheduling. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 454–469. Springer, Heidelberg (2011)
O’Mahony, E., Hebrard, E., Holland, A., Nugent, C., O’Sullivan, B.: Using case-based reasoning in an algorithm portfolio for constraint solving. In: Irish Conference on Artificial Intelligence and Cognitive Science (2008)
Petrik, M., Zilberstein, S.: Learning static parallel portfolios of algorithms. In: Ninth International Symposium on Artificial Intelligence and Mathematics (2006)
Roussel, O.: Description of ppfolio (2011), http://www.cril.univ-artois.fr/~roussel/ppfolio/solver1.pdf
Soos, M.: Cryptominisat 2.9.0 (2011)
Stern, D., Samulowitz, H., Herbrich, R., Graepel, T., Pulina, L., Tacchella, A.: Collaborative expert portfolio management. In: AAAI (2010)
Streeter, M., Smith, S.: Using decision procedures efficiently for optimization. In: ICAPS, pp. 312–319 (2007)
Xu, L., Hutter, F., Hoos, H., Leyton-Brown, K.: Satzilla: Portfolio-based algorithm selection for sat. JAIR 32(1), 565–606 (2008)
Yun, X., Epstein, S.: Learning algorithm portfolios for parallel execution. In: Workshop on Learning and Intelligent Optimization (2012)
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Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M. (2012). Parallel SAT Solver Selection and Scheduling. In: Milano, M. (eds) Principles and Practice of Constraint Programming. CP 2012. Lecture Notes in Computer Science, vol 7514. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33558-7_38
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DOI: https://doi.org/10.1007/978-3-642-33558-7_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33557-0
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