Improving Resource-Unaware SAT Solvers

  • Steffen Hölldobler
  • Norbert Manthey
  • Ari Saptawijaya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6397)


The paper discusses cache utilization in state-of-the-art SAT solvers. The aim of the study is to show how a resource-unaware SAT solver can be improved by utilizing the cache sensibly. The analysis is performed on a CDCL-based SAT solver using a subset of the industrial SAT Competition 2009 benchmark. For the analysis, the total cycles, the resource stall cycles, the L2 cache hits and the L2 cache misses are traced using sample based profiling. Based on the analysis, several techniques – some of which have not been used in SAT solvers so far – are proposed resulting in a combined speedup up to 83% without affecting the search path of the solver. The average speedup on the benchmark is 60%. The new techniques are also applied to MiniSAT2.0 improving its runtime by 20% on average.


Unit Propagation Work Cycle Cache Performance Watcher List Processor Event 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
    Baldow, C., Gräter, F., Hölldobler, S., Manthey, N., Seelemann, M., Steinke, P., Wernhard, C., Winkler, K., Zenker, E.: HydraSAT 2009.3 solver description. SAT 2009 Competitive Event Booklet,
  5. 5.
    Béjar, R., Manyà, F.: Solving the round robin problem using propositional logic. In: Procs. 17th National Conf. on Artificial Intelligence and 12th Conf. on Innovative Applications of Artificial Intelligence (2000)Google Scholar
  6. 6.
    Bonwick, J.: The slab allocator: an object-caching kernel memory allocator. In: Proceedings of the USENIX Summer 1994 Technical Conference (1994)Google Scholar
  7. 7.
    Chu, G., Harwood, A., Stuckey, P.J.: Cache conscious data structures for boolean satisfiability solvers. JSAT 6, 99–120 (2009)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Cook, S.A.: The complexity of theorem-proving procedures. In: Procs. 3rd Annual ACM Symposium on Theory of Computing (1971)Google Scholar
  9. 9.
    Davis, M., Logemann, G., Loveland, D.: A machine program for theorem proving. Communications of the ACM 5(7), 394–397 (1962)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Eén, N., Sörensson, N.: An extensible SAT-solver. In: Giunchiglia, E., Tacchella, A. (eds.) SAT 2003. LNCS, vol. 2919, Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Eén, N., Sörensson, N.: MiniSAT - a SAT solver with conflict-clause minimization. In: Poster - 8th SAT (2005)Google Scholar
  12. 12.
    Fuhs, C., Giesl, J., Middeldorp, A., Schneider-Kamp, P., Thiemann, R., Zankl, H.: SAT solving for termination analysis with polynomial interpretations. In: Marques-Silva, J., Sakallah, K.A. (eds.) SAT 2007. LNCS, vol. 4501, Springer, Heidelberg (2007)Google Scholar
  13. 13.
    Hennessy, J., Patterson, D.: Computer Architecture - A Quantitative Approach. Morgan Kaufmann, San Francisco (1996)zbMATHGoogle Scholar
  14. 14.
    Kautz, H., Selman, B.: Planning as satisfiability. In: Procs. 10th European Conference on Artificial Intelligence (1992)Google Scholar
  15. 15.
    Lynce, I., Marques-Silva, J.: SAT in bioinformatics: making the case with haplotype inference. In: Biere, A., Gomes, C.P. (eds.) SAT 2006. LNCS, vol. 4121, Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Manthey, N.: riss 2010 solver description. SAT Race (2010) (submitted to),
  17. 17.
    Manthey, N., Saptawijaya, A.: Towards improving the resource usage of SAT-solvers. In: Pragmatics of SAT Workshop (2010) (to appear)Google Scholar
  18. 18.
    Marques-Silva, J.P., Sakallah, K.A.: GRASP: A new search algorithm for satisfiability. In: Alur, R., Henzinger, T.A. (eds.) CAV 1996. LNCS, vol. 1102. Springer, Heidelberg (1996)Google Scholar
  19. 19.
    Moskewicz, M., Madigan, C., Zhao, Y., Zhang, L., Malik, S.: Chaff: Engineering an efficient SAT solver. In: Design Automation Conference, pp. 530–535 (2001)Google Scholar
  20. 20.
    Pipatsrisawat, K., Darwiche, A.: RSat solver description for SAT competition, SAT, Competitive Event Booklet (2009),
  21. 21.
    Pipatsrisawat, K., Darwiche, A.: A lightweight component caching scheme for satisfiability solvers. In: Marques-Silva, J., Sakallah, K.A. (eds.) SAT 2007. LNCS, vol. 4501. Springer, Heidelberg (2007)Google Scholar
  22. 22.
    Ryan, L.O.: Efficient algorithms for clause learning SAT solvers. Master’s thesis, Simon Fraser University, Canada (2004)Google Scholar
  23. 23.
    Sörensson, N., Eén, N.: MiniSAT 2.1 and MiniSAT++ 1.0 - SAT race, editions. SAT, Competitive Event Booklet (2008),
  24. 24.
    van Gelder, A.: Generalizations of watched literals for backtracking search. In: Seventh International Symposium on AI and Mathematics (2002)Google Scholar
  25. 25.
    Zhang, L., Malik, S.: Cache performance of SAT solvers: a case study for efficient implementation of algorithms. In: Giunchiglia, E., Tacchella, A. (eds.) SAT 2003. LNCS, vol. 2919, pp. 287–298. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Steffen Hölldobler
    • 1
  • Norbert Manthey
    • 1
  • Ari Saptawijaya
    • 1
  1. 1.ICCL - International Center for Computational LogicTechnische Universität DresdenDresdenGermany

Personalised recommendations