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Parallel SAT Solver Selection and Scheduling

  • Yuri Malitsky
  • Ashish Sabharwal
  • Horst Samulowitz
  • Meinolf Sellmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7514)

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.

Keywords

Column Generation Training Instance Static Schedule Execution Phase Integer Program Formulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
  2. 2.
    Biere, A.: Lingeling, plingeling, picosat and precosat at sat race 2010. Technical report, Johannes Kepler University, Linz, Austria (2010)Google Scholar
  3. 3.
    Biere, A.: Lingeling and friends at the sat competition 2011. Technical report, Johannes Kepler University, Altenbergerstr. 69, 4040 Linz, Austria (2011)Google Scholar
  4. 4.
    Dantzig, G.: Linear programming and extensions. Princeton University Press, Princeton (1963)zbMATHGoogle Scholar
  5. 5.
    Een, N., Sorensson, N.: An extensible sat-solver [ver 1.2] (2003)Google Scholar
  6. 6.
    Hamadi, Y., Jabbour, S., Lakhdar, S.: Manysat: a parallel sat solver. Journal on Satisfiability, Boolean Modeling and Computation 6, 245–262 (2009)zbMATHGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    Petrik, M., Zilberstein, S.: Learning static parallel portfolios of algorithms. In: Ninth International Symposium on Artificial Intelligence and Mathematics (2006)Google Scholar
  10. 10.
    Roussel, O.: Description of ppfolio (2011), http://www.cril.univ-artois.fr/~roussel/ppfolio/solver1.pdf
  11. 11.
    Soos, M.: Cryptominisat 2.9.0 (2011)Google Scholar
  12. 12.
    Stern, D., Samulowitz, H., Herbrich, R., Graepel, T., Pulina, L., Tacchella, A.: Collaborative expert portfolio management. In: AAAI (2010)Google Scholar
  13. 13.
    Streeter, M., Smith, S.: Using decision procedures efficiently for optimization. In: ICAPS, pp. 312–319 (2007)Google Scholar
  14. 14.
    Xu, L., Hutter, F., Hoos, H., Leyton-Brown, K.: Satzilla: Portfolio-based algorithm selection for sat. JAIR 32(1), 565–606 (2008)zbMATHGoogle Scholar
  15. 15.
    Yun, X., Epstein, S.: Learning algorithm portfolios for parallel execution. In: Workshop on Learning and Intelligent Optimization (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yuri Malitsky
    • 1
  • Ashish Sabharwal
    • 2
  • Horst Samulowitz
    • 2
  • Meinolf Sellmann
    • 2
  1. 1.Cork Constraint Computation CentreUniversity College CorkIreland
  2. 2.IBM Watson Research CenterYorktown HeightsUSA

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