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Parallel global optimization on GPU

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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.

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Acknowledgments

The authors of the paper express their gratitude to Ilya Lebedev for assistance in carrying out the computational experiments.

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Authors and Affiliations

  1. Computational Mathematics and Cybernetics Faculty, Lobachevsky State University of Nizhni Novgorod, Nizhni Novgorod, Russia

    Konstantin Barkalov & Victor Gergel

Authors
  1. Konstantin Barkalov
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  2. Victor Gergel
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Corresponding author

Correspondence to Konstantin Barkalov.

Additional information

The revised version of the paper was supported by the Russian Science Foundation, Project No 15-11-30022 “Global optimization, supercomputing computations, and applications”.

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Barkalov, K., Gergel, V. Parallel global optimization on GPU. J Glob Optim 66, 3–20 (2016). https://doi.org/10.1007/s10898-016-0411-y

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  • DOI: https://doi.org/10.1007/s10898-016-0411-y

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