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An efficient implementation of parallel simulated annealing algorithm in GPUs

Author

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  • A. Ferreiro
  • J. García
  • J. López-Salas
  • C. Vázquez

Abstract

In this work we propose a highly optimized version of a simulated annealing (SA) algorithm adapted to the more recently developed graphic processor units (GPUs). The programming has been carried out with compute unified device architecture (CUDA) toolkit, specially designed for Nvidia GPUs. For this purpose, efficient versions of SA have been first analyzed and adapted to GPUs. Thus, an appropriate sequential SA algorithm has been developed as starting point. Next, a straightforward asynchronous parallel version has been implemented and then a specific and more efficient synchronous version has been developed. A wide appropriate benchmark to illustrate the performance properties of the implementation has been considered. Among all tests, a classical sample problem provided by the minimization of the normalized Schwefel function has been selected to compare the behavior of the sequential, asynchronous and synchronous versions, the last one being more advantageous in terms of balance between convergence, accuracy and computational cost. Also the implementation of a hybrid method combining SA with a local minimizer method has been developed. Note that the generic feature of the SA algorithm allows its application in a wide set of real problems arising in a large variety of fields, such as biology, physics, engineering, finance and industrial processes. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jglopt:v:57:y:2013:i:3:p:863-890
    DOI: 10.1007/s10898-012-9979-z
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    Citations

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    Cited by:

    1. Ana M. Ferreiro & Enrico Ferri & José A. García & Carlos Vázquez, 2021. "Global Optimization for Automatic Model Points Selection in Life Insurance Portfolios," Mathematics, MDPI, vol. 9(5), pages 1-19, February.
    2. Jinlong Yuan & Lei Wang & Xu Zhang & Enmin Feng & Hongchao Yin & Zhilong Xiu, 2015. "Parameter identification for a nonlinear enzyme-catalytic dynamic system with time-delays," Journal of Global Optimization, Springer, vol. 62(4), pages 791-810, August.
    3. Chandra Ade Irawan & Said Salhi & Kusmaningrum Soemadi, 2020. "The continuous single-source capacitated multi-facility Weber problem with setup costs: formulation and solution methods," Journal of Global Optimization, Springer, vol. 78(2), pages 271-294, October.
    4. 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.
    5. Ana Maria Ferreiro-Ferreiro & José Antonio García-Rodríguez & Luis A. Souto & Carlos Vázquez, 2020. "Efficient Model Points Selection in Insurance by Parallel Global Optimization Using Multi CPU and Multi GPU," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(1), pages 5-20, February.
    6. Konstantin Barkalov & Victor Gergel, 2016. "Parallel global optimization on GPU," Journal of Global Optimization, Springer, vol. 66(1), pages 3-20, September.
    7. Ferreiro-Ferreiro, Ana María & García-Rodríguez, José A. & Souto, Luis & Vázquez, Carlos, 2020. "A new calibration of the Heston Stochastic Local Volatility Model and its parallel implementation on GPUs," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 177(C), pages 467-486.
    8. Ferreiro, Ana M. & García-Rodríguez, José Antonio & Vázquez, Carlos & e Silva, E. Costa & Correia, A., 2019. "Parallel two-phase methods for global optimization on GPU," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 156(C), pages 67-90.
    9. Yue Sun & Alfredo Garcia, 2017. "Interactive model-based search with reactive resource allocation," Journal of Global Optimization, Springer, vol. 67(1), pages 135-149, January.
    10. Schryen, Guido, 2020. "Parallel computational optimization in operations research: A new integrative framework, literature review and research directions," European Journal of Operational Research, Elsevier, vol. 287(1), pages 1-18.

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