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PARALLELIZING THREE DIMENSIONAL CELLULAR AUTOMATA WITH OpenMP

    This paper describes our research on using Genetic Programming to obtain transition rules for Cellular Automata, which are one type of massively parallel computing system. Our purpose is to determine the existence of a limit of chaos for three dimensional Cellular Automata, empirically demonstrated for the two dimensional case. To do so, we must study statistical properties of 3D Cellular Automata over long simulation periods. When dealing with big three dimensional meshes, applying the transition rule to the whole structure can become a extremely slow task. In this work we decompose the Automata into pieces and use OpenMp to parallelize the process. Results show that using a decomposition procedure, and distributing the mesh between a set of processors, 3D Cellular Automata can be studied without having long execution times.

    References

    • OpenMP Architecture Review Board. OpenMP C and C++ Application Program Interface. OpenMP Architecture Review Board, 2005 . Google Scholar
    • Nichael Lynn Cramer, A representation for the adaptive generation of simple sequential programs, Proceedings of an International Conference on Genetic Algorithms and the Applications, ed. John J. Grefenstette (Carnegie-Mellon University, Pittsburgh, PA, USA) pp. 183–187. Google Scholar
    • R. M. Friedberg, IBM J. Research and Development 2(1), 2 (1958). Crossref, ISIGoogle Scholar
    • S. Garcia and F. Gonzlez, Genetic Programming and Evolvable Machines 2(2), 111 (2001). Google Scholar
    • J. R.   Koza , Genetic Programming: On the Programming of Computers by Means of Natural Selection ( MIT Press , Cambridge, MA, USA , 1992 ) . Google Scholar
    • C. Langton. pages 1-2, 1988 . Google Scholar
    • C. G. Langton, Physica D 42, 12 (1990), DOI: 10.1016/0167-2789(90)90064-V. Crossref, ISIGoogle Scholar
    • Claude E. Shannon and Warren Weaver, The mathematical theory of communication (The University of Illinois Press, 1949) pp. 1–2. Google Scholar
    • Mosh   Sipper , Machine nature: The Coming Age of Bio-inspired Computing ( McGraw-Hill , 2002 ) . Google Scholar
    • T.   Toffoli and N.   Margolus , Cellular Automata Machines ( The MIT Press , Cambridge, Massachusetts , 1987 ) . CrossrefGoogle Scholar
    • J.   von Neumann , Theory of Self-Reproducing Automata ( University of Illinois Press , Illinois, Illinois , 1966 ) . Google Scholar
    • S. Wolfram, Physica D 10, 1 (1984), DOI: 10.1016/0167-2789(84)90245-8. Crossref, ISIGoogle Scholar
    • S. Wolfram and N. H. Packard, J. Stat. Phys. 38, 901 (1985). ISIGoogle Scholar