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Time Parallel Simulation and hv-Monotonicity

  • J. M. Fourneau
  • I. Kadi
  • F. Quessette
Conference paper

Abstract

We show how we can make more efficient the time parallel simulation of monotone systems adapting Nicol’s approach. We use the monotonicity of a model related to the initial state of the simulation and we prove an algorithm with fix-up computations which minimises the number of runs before we get a consistent sample-path. We obtain proved upper or lower bounds of the sample-path of the simulation and bounds of some estimates as well.

Keywords

Shared Memory Input Sequence Logical Process Output Sequence Random Input 
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.

Notes

Acknowledgments

We thank UVSQ for a partial support (grant BQR 2010).

References

  1. 1.
    Andradottir, S., Hosseini-Nasab, M.: Parallel simulation of transfer lines by time segmentation. Eur. J. Oper. Res. 159(2), 449–469 (2004)CrossRefMATHMathSciNetGoogle Scholar
  2. 2.
    Fourneau, J.M., Kadi, I., Pekergin, N.: Improving time parallel simulation for monotone systems. In: Turner, S.J., Roberts, D., Cai, W., El-Saddik, A. (eds.) DS-RT, pp. 231–234. IEEE Computer Society, Singapore (2009)Google Scholar
  3. 3.
    Fourneau, J.M., Pekergin, N.: An algorithmic approach to stochastic bounds. In: Calzarossa, M., Tucci, S. (eds.) Performance Evaluation of Complex Systems: Techniques and Tools, Performance 2002, Tutorial Lectures, Rome, Italy. LNCS, vol. 2459, pp. 64–88. Springer, London (2002)Google Scholar
  4. 4.
    Fujimoto, R.M.: Parallel and Distributed Simulation Systems. Wiley Series on Parallel and Distributed Computing. John Wiley & Sons, New York (2000)Google Scholar
  5. 5.
    Fujimoto, R.M., Cooper, C.A., Nikolaidis, I.: Parallel simulation of statistical multiplexers. J. Discret. Event Dyn. Syst. 5, 115–140 (1994)CrossRefGoogle Scholar
  6. 6.
    Greenberg, A.G., Lubachevsky, B.D., Mitrani, I.: Algorithms for unboundedly parallel simulations. ACM Trans. Comput. Syst. 9(3), 201–221 (1991)CrossRefGoogle Scholar
  7. 7.
    Nicol, D., Greenberg, A., Lubachevsky, B.: Massively parallel algorithms for trace-driven cache simulations. IEEE Trans. Parallel Distrib. Syst. 5(8), 849–859 (1994)CrossRefGoogle Scholar
  8. 8.
    Turgut, D., Wang, G., Boloni, L., Marinescu, D.C.: Speedup-precision tradeoffs in time-parallel simulation of wireless ad hoc networks. In: DS-RT ’06: Proceedings of the 10th IEEE International Symposium on Distributed Simulation and Real-Time Applications, pp. 265–268. IEEE Computer Society, Malaga (2006)Google Scholar

Copyright information

© Springer-Verlag London Limited  2011

Authors and Affiliations

  1. 1.PRiSM, Université de Versailles-Saint-Quentin, CNRS UMR 8144VersaillesFrance

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