Using Multi Core Computers for Implementing Cellular Automata Systems

  • Olga Bandman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6873)

Abstract

A concept of cellular automata system (CA-system) is introduced as a model of comp[lex phenomena in which several interacting species are involved. CA system suggests a common work of several CA where each processes its own cellular array using in its transition rules cell states of others CA of the system. Taking into account that multi core computers with shared memory are nowadays widely used, a temptation to accelerate the computation by allocating each CA of the system onto one of computer cores is quite natural. Hence, it would be helpful to know what speedup can be obtained by such a parallelization. The paper is aimed to get an answer to this question by determining the conditions, when multicore parallel implementation of CA systems is expedient and correct, and develop the parallelization algorithms for typical CA systems. The results are illustrated by simulation experiments.

Keywords

Cellular Automaton Shared Memory Local Operator Cellular Automaton Parallel Composition 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wolfram, S.: Cellular Automata abd Comlexity – Collected paopers. Addison Wesley, Reading (1994)Google Scholar
  2. 2.
    Hoekstra, A.G., Kroc, J., Sloot, P.M.A. (eds.): Simulating Complex Systems by Cellular Automata. Understanding complex Systems. Springer, Berlin (2010)MATHGoogle Scholar
  3. 3.
    Bandman, O.: Cellular Automarta Composition Techniques for Spatial Automata Simulation. In: Hoekstra, A.G., Kroc, J., Sloot, P.M.A. (eds.) Simulating Complex Systems by Cellular Automata. Understanding complex Systems, pp. 81–115. Springer, Berlin (2010)CrossRefGoogle Scholar
  4. 4.
    Achasova, S., Bandman, O., Markova, V., Piskunov, S.: Parallel Substitution Algorithm. Theory and Application. World Scientific, Singapore (1994)CrossRefMATHGoogle Scholar
  5. 5.
    Bandman, O.: Coarse-Grained Parallelization of Cellular-Automata Simulation Algorithms. In: Malyshkin, V.E. (ed.) PaCT 2007. LNCS, vol. 4671, pp. 370–384. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Toffolli, T., Margolus, N.: Cellular Automata Machine. MIT Press, USA (1987)Google Scholar
  7. 7.
    Deutsch, A., Dorman, S.: Cellular Automata Modeling of Biological Pattern Formation. Birkhäuser, Berlin (2005)Google Scholar
  8. 8.
    Cataneo, G., Dennunzio, A., Farina, F.: A Full Cellular Automaton to Simulate Predatory-Prey Systems. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 446–451. Springer, Heidelberg (2006)Google Scholar
  9. 9.
    Chua, L.: CNN: a paradigm of complexity. World Scientific, Singapore (2002)Google Scholar
  10. 10.
    Bandman, O.: Simulating Spatial Dynamics by Pribabilistic Cellular Automata. In: Bandini, S., Chopard, B., Tomassini, M. (eds.) ACRI 2002. LNCS, vol. 2493, pp. 10–16. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Olga Bandman
    • 1
  1. 1.Supercomputer Software Department, ICM&MG, Siberian BranchRussian Academy of SciencesNovosibirskRussia

Personalised recommendations