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Parallel global optimization on GPU

Author

Listed:
  • Konstantin Barkalov

    (Lobachevsky State University of Nizhni Novgorod)

  • Victor Gergel

    (Lobachevsky State University of Nizhni Novgorod)

Abstract

This work considers a parallel algorithm for solving multidimensional multiextremal optimization problems. This algorithm uses Peano-type space filling curves for dimension reduction. Conditions of non-redundant parallelization of the algorithm are considered. Efficiency of the algorithm on modern computing systems with the use of graphics processing units (GPUs) is investigated. Speedup of the algorithm using GPU as compared with the same algorithm implemented on CPU only is demonstrated experimentally. Computational experiments are carried out on a series of several hundred multidimensional multiextremal problems.

Suggested Citation

  • Konstantin Barkalov & Victor Gergel, 2016. "Parallel global optimization on GPU," Journal of Global Optimization, Springer, vol. 66(1), pages 3-20, September.
    • Handle: RePEc:spr:jglopt:v:66:y:2016:i:1:d:10.1007_s10898-016-0411-y
    DOI: 10.1007/s10898-016-0411-y
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    References listed on IDEAS

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    1. A. Ferreiro & J. García & J. López-Salas & C. Vázquez, 2013. "An efficient implementation of parallel simulated annealing algorithm in GPUs," Journal of Global Optimization, Springer, vol. 57(3), pages 863-890, November.
    2. Weihang Zhu, 2011. "Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems," Journal of Global Optimization, Springer, vol. 50(3), pages 417-437, July.
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    Cited by:

    1. Kristian Sabo & Rudolf Scitovski & Šime Ungar & Zoran Tomljanović, 2024. "A method for searching for a globally optimal k-partition of higher-dimensional datasets," Journal of Global Optimization, Springer, vol. 89(3), pages 633-653, July.
    2. Grishagin, Vladimir & Israfilov, Ruslan & Sergeyev, Yaroslav, 2018. "Convergence conditions and numerical comparison of global optimization methods based on dimensionality reduction schemes," Applied Mathematics and Computation, Elsevier, vol. 318(C), pages 270-280.
    3. Daniela Lera & Yaroslav D. Sergeyev, 2018. "GOSH: derivative-free global optimization using multi-dimensional space-filling curves," Journal of Global Optimization, Springer, vol. 71(1), pages 193-211, May.
    4. Konstantin Barkalov & Roman Strongin, 2018. "Solving a set of global optimization problems by the parallel technique with uniform convergence," Journal of Global Optimization, Springer, vol. 71(1), pages 21-36, May.
    5. Ferreiro-Ferreiro, Ana M. & García-Rodríguez, José A. & Souto, Luis & Vázquez, Carlos, 2019. "Basin Hopping with synched multi L-BFGS local searches. Parallel implementation in multi-CPU and GPUs," Applied Mathematics and Computation, Elsevier, vol. 356(C), pages 282-298.
    6. Kvasov, Dmitri E. & Mukhametzhanov, Marat S., 2018. "Metaheuristic vs. deterministic global optimization algorithms: The univariate case," Applied Mathematics and Computation, Elsevier, vol. 318(C), pages 245-259.

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