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Towards ParadisEO-MO-GPU: A Framework for GPU-Based Local Search Metaheuristics

  • N. Melab
  • T. V. Luong
  • K. Boufaras
  • E. G. Talbi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6691)

Abstract

This paper is a major step towards a pioneering software framework for the reusable design and implementation of parallel metaheuristics on Graphics Processing Units (GPU). The objective is to revisit the ParadisEO framework to allow its utilization on GPU accelerators. The focus is on local search metaheuristics and the parallel exploration of their neighborhood. The challenge is to make the GPU as transparent as possible for the user. The first release of the new GPU-based ParadisEO framework has been experimented on the Quadratic Assignment Problem (QAP). The preliminary results are convincing, both in terms of flexibility and easiness of reuse at implementation, and in terms of efficiency at execution on GPU.

Keywords

Software Framework Local Search Meta-heuristics Parallel Computing GPU Computing Neighborhood Exploration 

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References

  1. 1.
    Van Luong, T., Melab, N., Talbi, E.-G.: Local search algorithms on graphics processing units. A case study: The permutation perceptron problem. In: Cowling, P., Merz, P. (eds.) EvoCOP 2010. LNCS, vol. 6022, pp. 264–275. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Cahon, S., Melab, N., Talbi, E.-G.: ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics. J. of Heuristics 10(3), 357–380 (2004)CrossRefzbMATHGoogle Scholar
  3. 3.
    Melab, N., Cahon, S., Talbi, E.-G.: Grid computing for parallel bioinspired algorithms. J. Parallel Distributed Computing 66(8), 1052–1061 (2006)CrossRefzbMATHGoogle Scholar
  4. 4.
    Tantar, A.A., Melab, N., Demarey, C., Talbi, E.G.: Building a Virtual Globus Grid in a Reconfigurable Environment - A case study: Grid5000 (2007)Google Scholar
  5. 5.
    Nickolls, J., Buck, I., Garland, M., Skadron, K.: Scalable Parallel Programming with CUDA. ACM Queue 6(2), 40–53 (2008)CrossRefGoogle Scholar
  6. 6.
    Taillard, É.D.: Robust tabu search for the quadratic assignment problem. Parallel Computing 17(4-5), 443–455 (1991)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Luong, T.V., Melab, N., Talbi, E.G.: Parallel hybrid evolutionary algorithms on gpu. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • N. Melab
    • 1
  • T. V. Luong
    • 1
  • K. Boufaras
    • 1
  • E. G. Talbi
    • 1
  1. 1.Dolphin Project, INRIA Lille Nord Europe - LIFL/CNRS UMR 8022Université de Lille1Villeneuve d’Ascq CedexFrance

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