Multi-core Symbolic Bisimulation Minimisation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9636)

Abstract

Bisimulation minimisation alleviates the exponential growth of transition systems in model checking by computing the smallest system that has the same behavior as the original system according to some notion of equivalence. One popular strategy to compute a bisimulation minimisation is signature-based partition refinement. This can be performed symbolically using binary decision diagrams to allow models with larger state spaces to be minimised.

This paper studies strong and branching symbolic bisimulation for labeled transition systems, continuous-time markov chains, and interactive markov chains. We introduce the notion of partition refinement with partial signatures. We extend the parallel BDD library Sylvan to parallelize the signature refinement algorithm, and develop a new parallel BDD algorithm to refine a partition, which conserves previous block numbers and uses a parallel data structure to store block assignments. We also present a specialized BDD algorithm for the computation of inert transitions. The experimental evaluation, based on benchmarks from the literature, demonstrates a speedup of up to 95x sequentially. In addition, we find parallel speedups of up to 17x due to parallelisation with 48 cores. Finally, we present the implementation of these algorithms as a versatile tool that can be customized for bisimulation minimisation in various contexts.

Keywords

Multi-core Parallel Binary decision diagrams Bisimulation minimisation Labeled transition systems Continuous-time Markov chains Interactive Markov chains 

References

  1. 1.
    Bahar, R.I., Frohm, E.A., Gaona, C.M., Hachtel, G.D., Macii, E., Pardo, A., Somenzi, F.: Algebraic decision diagrams and their applications. In: Lightner, M.R., Jess, J.A.G. (eds.) ICCAD, pp. 188–191. IEEE Computer Society / ACM (1993)Google Scholar
  2. 2.
    Blom, S., Haverkort, B.R., Kuntz, M., van de Pol, J.: Distributed markovian bisimulation reduction aimed at CSL model checking. ENTCS 220(2), 35–50 (2008)MATHGoogle Scholar
  3. 3.
    Blom, S., Orzan, S.: Distributed branching bisimulation reduction of state spaces. ENTCS 89(1), 99–113 (2003)MATHGoogle Scholar
  4. 4.
    Blumofe, R.D.: Scheduling multithreaded computations by work stealing. In: FOCS, pp. 356–368. IEEE Computer Society (1994)Google Scholar
  5. 5.
    Bouali, A., de Simone, R.: Symbolic bisimulation minimisation. In: von Bochmann, G., Probst, D.K. (eds.) CAV 1992. LNCS, vol. 663, pp. 96–108. Springer, Heidelberg (1992)CrossRefGoogle Scholar
  6. 6.
    Bryant, R.E.: Graph-based algorithms for boolean function manipulation. IEEE Trans. Comput. C–35(8), 677–691 (1986)CrossRefMATHGoogle Scholar
  7. 7.
    Burch, J., Clarke, E., Long, D., McMillan, K., Dill, D.: Symbolic model checking for sequential circuit verification. IEEE Trans. Comput. Aided Des. Integr. Circ. Syst. 13(4), 401–424 (1994)CrossRefGoogle Scholar
  8. 8.
    Clarke, E.M., McMillan, K.L., Zhao, X., Fujita, M., Yang, J.: Spectral transforms for large boolean functions with applications to technology mapping. In: DAC, pp. 54–60 (1993)Google Scholar
  9. 9.
    De Nicola, R., Vaandrager, F.W.: Three logics for branching bisimulation. J. ACM 42(2), 458–487 (1995)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Derisavi, S.: A symbolic algorithm for optimal markov chain lumping. In: Grumberg, O., Huth, M. (eds.) TACAS 2007. LNCS, vol. 4424, pp. 139–154. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Derisavi, S.: Signature-based symbolic algorithm for optimal markov chain lumping. In: QEST 2007, pp. 141–150. IEEE Computer Society (2007)Google Scholar
  12. 12.
    van Dijk, T., Laarman, A.W., van de Pol, J.C.: Multi-Core BDD operations for symbolic reachability. In: 11th International Workshop on Parallel and Distributed Methods in verifiCation. ENTCS. Elsevier (2012)Google Scholar
  13. 13.
    van Dijk, T., van de Pol, J.C.: Lace: non-blocking split deque for work-stealing. In: Lopes, L., et al. (eds.) Euro-Par 2014, Part II. LNCS, vol. 8806, pp. 206–217. Springer, Heidelberg (2014)Google Scholar
  14. 14.
    van Dijk, T., van de Pol, J.C.: Sylvan: multi-core decision diagrams. In: Baier, C., Tinelli, C. (eds.) Tools and Algorithms for the Construction and Analysis of Systems. LNCS, vol. 9035, pp. 677–691. Springer, Heidelberg (2015)Google Scholar
  15. 15.
    Hermanns, H., Katoen, J.-P.: The how and why of interactive markov chains. In: de Boer, F.S., Bonsangue, M.M., Hallerstede, S., Leuschel, M. (eds.) FMCO 2009. LNCS, vol. 6286, pp. 311–337. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Kant, G., Laarman, A., Meijer, J., van de Pol, J.C., Blom, S., van Dijk, T.: LTSmin: high-performance language-independent model checking. TACAS 2015. LNCS, vol. 9035, pp. 692–707. Springer, Heidelberg (2015)Google Scholar
  17. 17.
    Kulakowski, K.: Concurrent bisimulation algorithm. CoRR abs/1311.7635 (2013)Google Scholar
  18. 18.
    Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Mumme, M., Ciardo, G.: An efficient fully symbolic bisimulation algorithm for non-deterministic systems. Int. J. Found. Comput. Sci. 24(2), 263–282 (2013)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Paige, R., Tarjan, R.E.: Three partition refinement algorithms. SIAM J. Comput. 16(6), 973–989 (1987)MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Pugh, W.: Skip lists: a probabilistic alternative to balanced trees. Commun. ACM 33(6), 668–676 (1990)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Wijs, A.: GPU accelerated strong and branching bisimilarity checking. In: Baier, C., Tinelli, C. (eds.) TACAS 2015. LNCS, vol. 9035, pp. 368–383. Springer, Heidelberg (2015)Google Scholar
  23. 23.
    Wimmer, R., Becker, B.: Correctness issues of symbolic bisimulation computation for markov chains. In: Müller-Clostermann, B., Echtle, K., Rathgeb, E.P. (eds.) MMB & DFT 2010. LNCS, vol. 5987, pp. 287–301. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  24. 24.
    Wimmer, R., Derisavi, S., Hermanns, H.: Symbolic partition refinement with automatic balancing of time and space. Perform. Eval. 67(9), 816–836 (2010)CrossRefGoogle Scholar
  25. 25.
    Wimmer, R., Herbstritt, M., Becker, B.: Optimization techniques for BDD-based bisimulation computation. In: 17th GLSVLSI, pp. 405–410. ACM (2007)Google Scholar
  26. 26.
    Wimmer, R., Herbstritt, M., Hermanns, H., Strampp, K., Becker, B.: SIGREF – a symbolic bisimulation tool box. In: Graf, S., Zhang, W. (eds.) ATVA 2006. LNCS, vol. 4218, pp. 477–492. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  27. 27.
    Wimmer, R., Hermanns, H., Herbstritt, M., Becker, B.: Towards Symbolic Stochastic Aggregation. Technical report, SFB/TR 14 AVACS (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Formal Methods and ToolsUniversity of TwenteEnschedeThe Netherlands

Personalised recommendations