Local Search Algorithms on Graphics Processing Units. A Case Study: The Permutation Perceptron Problem

  • Thé Van Luong
  • Nouredine Melab
  • El-Ghazali Talbi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6022)

Abstract

Optimization problems are more and more complex and their resource requirements are ever increasing. Although metaheuristics allow to significantly reduce the computational complexity of the search process, the latter remains time-consuming for many problems in diverse domains of application. As a result, the use of GPU has been recently revealed as an efficient way to speed up the search. In this paper, we provide a new methodology to design and implement efficiently local search methods on GPU. The work has been experimented on the permuted perceptron problem and the experimental results show that the approach is very efficient especially for large problem instances.

Keywords

GPU-based metaheuristics local search algorithms on GPU 

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References

  1. 1.
    Che, S., Boyer, M., Meng, J., Tarjan, D., Sheaffer, J.W., Skadron, K.: A performance study of general-purpose applications on graphics processors using cuda. J. Parallel Distributed Computing 68(10), 1370–1380 (2008)CrossRefGoogle Scholar
  2. 2.
    Chitty, D.M.: A data parallel approach to genetic programming using programmable graphics hardware. In: GECCO, pp. 1566–1573 (2007)Google Scholar
  3. 3.
    Fok, K.L., Wong, T.T., Wong, M.L.: Evolutionary computing on consumer graphics hardware. IEEE Intelligent Systems 22(2), 69–78 (2007)CrossRefGoogle Scholar
  4. 4.
    Banzhaf, W., Harding, S.: Accelerating evolutionary computation with graphics processing units. In: GECCO (Companion), pp. 3237–3286 (2009)Google Scholar
  5. 5.
    Pointcheval, D.: A new identification scheme based on the perceptrons problem. In: Guillou, L.C., Quisquater, J.-J. (eds.) EUROCRYPT 1995. LNCS, vol. 921, pp. 319–328. Springer, Heidelberg (1995)Google Scholar
  6. 6.
    Ryoo, S., Rodrigues, C.I., Stone, S.S., Stratton, J.A., Ueng, S.Z., Baghsorkhi, S.S., Wen-mei, W.H.: Program optimization carving for gpu computing. J. Parallel Distribributed Computing 68(10), 1389–1401 (2008)CrossRefGoogle Scholar
  7. 7.
    Talbi, E.G.: Metaheuristics: From design to implementation. Wiley, Chichester (2009)MATHGoogle Scholar
  8. 8.
    NVIDIA: CUDA Programming Guide Version 2.1 (2009)Google Scholar
  9. 9.
    Luong, T.V., Melab, N., Talbi, E.G.: Parallel Local Search on GPU. Research Report RR-6915, INRIA (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Thé Van Luong
    • 1
  • Nouredine Melab
    • 1
  • El-Ghazali Talbi
    • 1
  1. 1.INRIA Dolphin Project / Opac LIFL CNRSVilleneuve d’Ascq CedexFrance

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