Discrete Models of Physicochemical Processes and Their Parallel Implementation

  • Olga Bandman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6083)


Discrete simulation method of physicochemical kinetic processes is proposed and investigated. The method is based on formal representation of classical Von-Neumann’s Cellular Automaton (CA) extension, which allow all kind of discrete alphabets, probabilistic transition functions, and asynchronous mode of operation. Some techniques for simple CA composition are given for simulating complex processes. Transformation of asynchronous CA into block-synchronous type is used to provide high efficiency of parallel implementation.


Cellular Automaton Local Operator Parallel Implementation Physicochemical Process Asynchronous Mode 
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  1. 1.
    Toffolli, T.: Cellular Automata as an Alternative to (rather than Approximation of) Differential Equations in Modeling Physics. Physica D 10, 117–127 (1984)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Wolfram, S.: Statistical mechanics of Cellular automata. Review of Modern Physics 55, 607–664 (1993)MathSciNetGoogle Scholar
  3. 3.
    Bandman, O.: Composing Fine-Grained Parallel Algorithms for Spatial Dynamics Simulation. In: Malyshkin, V.E. (ed.) PaCT 2005. LNCS, vol. 3606, pp. 99–113. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Makeev, A.G.: Coarse bifurcation analysis of kinetic Monte Carlo simulations: a lattice-gas model with lateral interactions. Journ. of chemical physics 117(18), 8229–8240 (2002)CrossRefGoogle Scholar
  5. 5.
    Elokhin, V., Latkin, E., Matveev, A., Gorodetskii, V.: Application of Statistical Lattice Models to the Analysis of Oscillatory and Autowave Processes on the Reaction of Carbon Monoxide Oxidation over Platinum and Palladium Surfaces. Kinetics and Catalysis 44(5), 672–700 (2003)CrossRefGoogle Scholar
  6. 6.
    Neizvestny, I.G., Shwartz, N.L., Yanovitskaya, Z.S., Zverev, A.V.: 3D-model of epitaxial growth on porous 111 and 100 Si surfaces. Comp. Phys. Communications 147, 272–275 (2002)zbMATHCrossRefGoogle Scholar
  7. 7.
    Betz, G., Husinsky, W.: Surface erosion and film growth studied by a combined molecular dynamics and kinetic Monte Carlo code, Izvestia of Russian Academy of Sciences. Physical Series 66(4), 585–587 (2002)Google Scholar
  8. 8.
    Bandman, O.: Synchronous versus asynchronous cellular automata for simulating nano-systems kinetics. Bull Nov.Comp.Center, series Comp. Science (25), 1–12 (2006)Google Scholar
  9. 9.
    Achasova, S., Bandman, O., Markova, V., Piskunov, S.: Parallel Substitution Algorithm. Theory and Application. World Scientific, Singapore (1994)zbMATHGoogle Scholar
  10. 10.
    Bandman, O.: Parallel Simulation of Asynchronous Cellular Automata Evolution. In: El Yacoubi, S., Chopard, B., Bandini, S. (eds.) ACRI 2006. LNCS, vol. 4173, pp. 41–48. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Wolfram, S.: A new kind of science - Champain, Ill. Wolfram Media Inc., USA (2002)Google Scholar
  12. 12.
    Toffolli, T., Margolus, N.: Cellular Automata Machine. MIT Press, USA (1987)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

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

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