Advertisement

Lattice gas: An efficient and reusable parallel library based on a graph partitioning technique

  • Alexandre Dupuis
  • Bastien Chopard
Track C2: Computational Science
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1593)

Abstract

We present a parallel library which can be used for any lattice gas (LG) application. A highly reusable implementation, as well as a general parallelization scheme based on graph partitioning techniques are developed. We show that the performance we obtain with our approach compares favorably with the plain, classical implementation of LG models on regular domains whereas on irregular domains, it can even be better. We propose a theoretical expression for the execution time and we validate our analysis in the case of the problem of wave propagation in urban areas.

Keywords

Execution Time Cellular Automaton Lattice Boltzmann Object Orient Model Interprocessor Communication 
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.
    D. Rothman and S. Zaleski. Lattice-Gas Cellular Automata: Simple Models of Complex Hydrodynamics. Collection Aléa. Cambridge University Press, 1997.Google Scholar
  2. 2.
    B. Chopard and M. Droz. Cellular Automata Modeling of Physical Systems. Cambridge University Press, 1998.Google Scholar
  3. 3.
    Pascal O. Luthi. Lattice Wave Automata. PhD thesis, University of Geneva, 1998.Google Scholar
  4. 4.
    Frédéric Guidec, Patrice Calégari, and Pierre Kuonen. Parallel irregular software for wave propagation simulation. In HPCN'97 High-Parallel Computing and Networking, Lecture Notes in Computer Science, pages 84–94 Springer-Verlag, 1997.Google Scholar
  5. 5.
    S. Di Gregorio, R. Ringo, W. Spataro, Giandomenico Spezzano, and Domenico Talia. A parellel cellular environment for high performance scientific computing. In H. Liddell at al., editor, HPCN'96 High-Performance Computing and Networking, pages 514–521, Berlin, 1996. Springer-Verlag.CrossRefGoogle Scholar
  6. 6.
    M. Garey, D. Johnson, and L. Stockmeyer. Some simplified NP-complete graph problems. Theoritical Computer Science, 1:237–267, 1976.CrossRefMathSciNetzbMATHGoogle Scholar
  7. 7.
    B. W. Kernighan and S. Lin. An efficient heuristic procedure for partitioning graphs. The Bell system technical journal, 49(1):291–307, 1970.Google Scholar
  8. 8.
    Roberto Battiti, Alan Bertossi, and R. Rizzi. Randomized greedy algorithms for the hypergraph partitioning problem. In DIMACS Workshop on Randomization Methods in Algorithm Design, October 1997.Google Scholar
  9. 9.
    Roberto Battiti and Alan Bertossi. Greedy, prohibition, and reactive heuristics for graph partitioning. IEEE Transactions on Computers, to appear.Google Scholar
  10. 10.
    M. Laguna, T. A. feo, and H.C. Elrod. A greedy randomized adaptative search procedure for the two-partition problem. Operations Research, 42:667–687, 1994.Google Scholar
  11. 11.
    Thang Nguyen Bui and Byung Ro Moon. Genetic algorithm and graph partitioning. IEEE Transactions on Computers, 45(7):841–855, July 1996.CrossRefMathSciNetzbMATHGoogle Scholar
  12. 12.
    Gregor von Laszewski and Heinz Mühlenbein. Partitioning a graph with a parallel genetic algorithm. In Parallel problem solving from nature, pages 165–169, 1991.Google Scholar
  13. 13.
    D.S. Johnson, C.R. Aragon, L.A. McGeoch, and C. Schevon. Optimization by simulated annealing: An experimental evaluation, Operations Research, 37:865–892, 1989.CrossRefzbMATHGoogle Scholar
  14. 14.
    Alex Pothen, H.D. Simon, Lien Wang, and Stephen T. Bernard. Towards a fast implementation of spectral nested dissection. In Supercomputing '92, pages 42–51, 1992.Google Scholar
  15. 15.
    George Karypis and Vipin Kumar. A fast and highly quality multilevel scheme for partitioning irregular graphs. Technical Report 95–035, Departement of Computer Science, University of Minnesota, 1995.Google Scholar
  16. 16.
    Robert Leland and Bruce Hendrickson. An, empirical study of static load balancing algorithms. In Scalable High-Performance Computing Conference (SHPCC'94), pages 682–685, 1994.Google Scholar
  17. 17.
    http://www-users.cs.umn.edu/~karypis/metis/.Google Scholar
  18. 18.
    George Karypis and Vipin Kumar. A Software Package for Partitioning Unstructured Graphs, Partitioning Meshes, and Computing Fill-Reducing Orderings of Sparse Matrices, November 1997.Google Scholar
  19. 19.
    http://www.cs.sandia.gov/CRF/chac.html.Google Scholar
  20. 20.
    http://www.uni-paderborn.de/cs/robsy/party.html.Google Scholar
  21. 21.
    http://www.labri.u-bordeaux.fr/Equipe/ALiENor/membre/pelegrin/scotch/Google Scholar
  22. 22.
    B. Chopard, P.O. Luthi, and Jean-Frédéric Wagen. A lattice boltzmann method for wave propagation in urban microcells. IEEE Proceedings-Microwaves, Antennas and Propagation, 144:251–255, 1997.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 1999

Authors and Affiliations

  • Alexandre Dupuis
    • 1
  • Bastien Chopard
    • 1
  1. 1.CUIUniversity of GenevaGenevaSwitzerland

Personalised recommendations