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An Efficient GPU Parallelization of the Jaya Optimization Algorithm and Its Application for Solving Large Systems of Nonlinear Equations

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Optimization, Learning Algorithms and Applications (OL2A 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1982 ))

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Abstract

This paper presents a new GPU-accelerated parallel version of Jaya, a simple and efficient population-based optimization algorithm that has attracted increasing interest in different areas of science and engineering. Jaya has recently been demonstrated to be relatively effective at solving nonlinear equation systems, a class of complex, challenging problems that are hard to solve using conventional numerical methods, especially as the size of the systems increases. This class of problems was chosen to illustrate the application of the proposed GPU-based parallel Jaya algorithm and its efficiency in solving difficult large-scale problems. The GPU parallelization of Jaya was implemented and tested on a GeForce RTX 3090 GPU with 10 496 CUDA cores and 24 GB VRAM, using a set of scalable nonlinear equation system problems with dimensions ranging from 500 to 2000. When compared with the Jaya sequential algorithm, the parallel implementation provides significant acceleration, with average speedup factors between 70.4 and 182.9 in computing time for the set of problems considered. This result highlights the efficiency of the proposed GPU-based massively parallel version of Jaya.

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Acknowledgement

The authors would like to thank Emiliano Gonçalves for kindly providing access to the GPUs used in the experiments.

Author information

Authors and Affiliations

  1. Doctoral Program in Informatics Engineering, University of Madeira, Funchal, Madeira Is., Portugal

    Bruno Silva

  2. Regional Secretariat for Education, Science and Technology, Regional Government of Madeira, Funchal, Portugal

    Bruno Silva

  3. Faculty of Exact Sciences and Engineering, University of Madeira, 9020-105, Funchal, Madeira Is., Portugal

    Luiz Guerreiro Lopes

Authors
  1. Bruno Silva
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  2. Luiz Guerreiro Lopes
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Corresponding author

Correspondence to Luiz Guerreiro Lopes .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. University of Azores, Ponta Delgada, Portugal

    Armando Mendes

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Coelho

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    José Lima

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Silva, B., Lopes, L.G. (2024). An Efficient GPU Parallelization of the Jaya Optimization Algorithm and Its Application for Solving Large Systems of Nonlinear Equations. In: Pereira, A.I., Mendes, A., Fernandes, F.P., Pacheco, M.F., Coelho, J.P., Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1982 . Springer, Cham. https://doi.org/10.1007/978-3-031-53036-4_26

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  • DOI: https://doi.org/10.1007/978-3-031-53036-4_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53035-7

  • Online ISBN: 978-3-031-53036-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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