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Improving the performance and energy of Non-Dominated Sorting for evolutionary multiobjective optimization on GPU/CPU platforms

Author

Listed:
  • J. J. Moreno

    (University of Almería)

  • G. Ortega

    (University of Almería)

  • E. Filatovas

    (Vilnius University)

  • J. A. Martínez

    (University of Almería)

  • E. M. Garzón

    (University of Almería)

Abstract

Non-Dominated Sorting (NDS) is the most time-consuming procedure used in the majority of evolutionary multiobjective optimization algorithms that are based on Pareto dominance ranking without regard to the computation time of the objective functions. It can be accelerated by the exploitation of its parallelism on High Performance Computing systems, that provide heterogeneous processing units, such as multicore processors and GPUs. The optimization of energy efficiency of such systems is a challenge in scientific computation since it depends on the kind of processing which is performed. Our interest is to solve NDS in an efficient way concerning both runtime and energy consumption. In literature, performance improvement has been extensively studied. Recently, a sequential Best Order Sort (BOS) algorithm for NDS has been introduced as one of the most efficient one in terms of practical performance. This work is focused on the acceleration of the NDS on modern architectures. Two efficient parallel NDS algorithms based on Best Order Sort, are introduced, MC-BOS and GPU-BOS. Both algorithms start with the fast sorting of population by objectives. MC-BOS computes in parallel the analysis of the population by objectives on the multicore processors. GPU-BOS is based on the principles of Best Order Sort, with a new scheme designed to harness the massive parallelism provided by GPUs. A wide experimental study of both algorithms on several kinds of CPU and GPU platforms has been carried out. Runtime and energy consumption are analysed to identify the best platform/algorithm of the parallel NDS for every particular population size. The analysis of obtained results defines criteria to help the user when selecting the optimal parallel version/platform for particular dimensions of NDS. The experimental results show that the new parallel NDS algorithms overcome the sequential Best Order Sort in terms of the performance and energy efficiency in relevant factors.

Suggested Citation

  • J. J. Moreno & G. Ortega & E. Filatovas & J. A. Martínez & E. M. Garzón, 2018. "Improving the performance and energy of Non-Dominated Sorting for evolutionary multiobjective optimization on GPU/CPU platforms," Journal of Global Optimization, Springer, vol. 71(3), pages 631-649, July.
    • Handle: RePEc:spr:jglopt:v:71:y:2018:i:3:d:10.1007_s10898-018-0669-3
    DOI: 10.1007/s10898-018-0669-3
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    References listed on IDEAS

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    1. G. Ortega & E. Filatovas & E. M. Garzón & L. G. Casado, 2017. "Non-dominated sorting procedure for Pareto dominance ranking on multicore CPU and/or GPU," Journal of Global Optimization, Springer, vol. 69(3), pages 607-627, November.
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    Cited by:

    1. Sumit Mishra & Carlos A. Coello Coello, 2019. "Parallelism in divide-and-conquer non-dominated sorting: a theoretical study considering the PRAM-CREW model," Journal of Heuristics, Springer, vol. 25(3), pages 455-483, June.
    2. Ana Maria A. C. Rocha & M. Fernanda P. Costa & Edite M. G. P. Fernandes, 2018. "Preface to the Special Issue “GOW’16”," Journal of Global Optimization, Springer, vol. 71(3), pages 441-442, July.
    3. E. Filatovas & O. Kurasova & J. L. Redondo & J. Fernández, 2020. "A reference point-based evolutionary algorithm for approximating regions of interest in multiobjective problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 402-423, July.

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    1. E. Filatovas & O. Kurasova & J. L. Redondo & J. Fernández, 2020. "A reference point-based evolutionary algorithm for approximating regions of interest in multiobjective problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 402-423, July.
    2. Sumit Mishra & Carlos A. Coello Coello, 2019. "Parallelism in divide-and-conquer non-dominated sorting: a theoretical study considering the PRAM-CREW model," Journal of Heuristics, Springer, vol. 25(3), pages 455-483, June.

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