Parallel Implementation of a Cellular Automaton Model for the Simulation of Laser Dynamics

  • J. L. Guisado
  • F. Fernández de Vega
  • F. Jiménez-Morales
  • K. A. Iskra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


A parallel implementation for distributed-memory MIMD systems of a 2D discrete model of laser dynamics based on cellular automata is presented.The model has been implemented on a PC cluster using a message passing library. A good performance has been obtained, allowing us to run realistic simulations of laser systems in clusters of workstations, which could not be afforded on an individual machine due to the extensive runtime and memory size needed.


Cellular Automaton Cellular Automaton Parallel Implementation Photon State Cellular Automaton Model 
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.


  1. 1.
    Chopard, B., Droz, M.: Cellular automata modeling of physical systems. Cambridge University Press, Cambridge (1998)MATHCrossRefGoogle Scholar
  2. 2.
    Bandini, S., Mauri, G., Serra, R.: Cellular automata: from a theoretical parallel computational model to its application to complex system. Parallel Computing 27(5), 539–553 (2001)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Sloot, P.M.A., Kaandorp, J.A., Hoekstra, A.G., Overeinder, B.J.: Distributed simulation with cellular automata: Architecture and applications. In: Bartosek, M., Tel, G., Pavelka, J. (eds.) SOFSEM 1999. LNCS, vol. 1725, pp. 203–248. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  4. 4.
    Bandini, S., Magagnini, M.: Parallel processing simulation of dynamic properties of filled rubber compounds based on cellular automata. Parallel Computing 27(5), 643–661 (2001)MATHCrossRefGoogle Scholar
  5. 5.
    Dattilo, G., Spezzano, G.: Simulation of a cellular landslide model with CAMELOT on high performance computers. Parallel Computing 29(10), 1403–1418 (2003)CrossRefGoogle Scholar
  6. 6.
    Love, P.J., Nekovee, M., Coveney, P.V., Chin, J., González-Segredo, N., Martin, J.M.R.: Simulations of amphiphilic fluids using mesoscale lattice-Boltzmann and lattice-gas methods. Computer Physics Communications 153, 340–358 (2003)CrossRefMATHGoogle Scholar
  7. 7.
    Talia, D.: Cellular processing tools for high-performance simulation. IEEE Computer 33(9), 44–52 (2000)Google Scholar
  8. 8.
    Hecker, C., Roytenberg, D., Sack, J.R., Wang, Z.: System development for parallel cellular automata and its applications. Fut. Gen. Comp. Sys. 16, 235–247 (1999)CrossRefGoogle Scholar
  9. 9.
    Guisado, J.L., Jiménez-Morales, F., Guerra, J.M.: Cellular automaton model for the simulation of laser dynamics. Physical Review E 67(6), 066708 (2003)CrossRefGoogle Scholar
  10. 10.
    Guisado, J.L., Jiménez-Morales, F., Guerra, J.M.: Computational simulation of laser dynamics as a cooperative phenomenon. Physica Scripta T118, 148–152 (2005)CrossRefGoogle Scholar
  11. 11.
    Guisado, J.L., Jiménez-Morales, F., Guerra, J.M.: Simulation of the dynamics of pulsed pumped lasers based on cellular automata. In: Sloot, P.M.A., Chopard, B., Hoekstra, A.G. (eds.) ACRI 2004. LNCS, vol. 3305, pp. 278–285. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Worsch, T.: Simulation of cellular automata. Future Generation Computer Systems 16(2-3), 157–170 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • J. L. Guisado
    • 1
  • F. Fernández de Vega
    • 1
  • F. Jiménez-Morales
    • 2
  • K. A. Iskra
    • 3
  1. 1.Centro Universitario de MéridaUniversidad de ExtremaduraMérida (Badajoz)Spain
  2. 2.Departamento de Física de la Materia CondensadaUniversidad de SevillaSevillaSpain
  3. 3.Section Computational Science, Faculty of ScienceUniversiteit van AmsterdamAmsterdamThe Netherlands

Personalised recommendations