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Improving the Energy Efficiency of Evolutionary Multi-objective Algorithms

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Algorithms and Architectures for Parallel Processing (ICA3PP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10049))

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Abstract

Problems for which many objective functions have to be simultaneously optimized can be easily found in many fields of science and industry. Solving this kind of problems in a reasonable amount of time while taking into account the energy efficiency is still a relevant task. Most of the evolutionary multi-objective optimization algorithms based on parallel computing are focused only on performance. In this paper, we propose a parallel implementation of the most time consuming parts of the Evolutionary Multi-Objective algorithms with major attention to energy consumption. Specifically, we focus on the most computationally expensive part of the state-of-the-art evolutionary NSGA-II algorithm – the Non-Dominated Sorting (NDS) procedure. GPU platforms have been considered due to their high acceleration capacity and energy efficiency. A new version of NDS procedure is proposed (referred to as EFNDS). A made-to-measure data structure to store the dominance information has been designed to take advantage of the GPU architecture. NSGA-II based on EFNDS is comparatively evaluated with another state-of-art GPU version, and also with a widely used sequential version. In the evaluation we adopt a benchmark that is scalable in the number of objectives as well as decision variables (the DTLZ test suite) using a large number of individuals (from 500 up to 30000). The results clearly indicate that our proposal achieves the best performance and energy efficiency for solving large scale multi-objective optimization problems on GPU.

This work has been partially supported by the Spanish Ministry of Science throughout projects TIN2015-66680 and CAPAP-H5 network TIN2014-53522, by J. Andalucía through projects P12-TIC-301 and P11-TIC7176, and by the European Regional Development Fund (ERDF). Ernestas Filatovas has been partially granted by the European COST Action IC1305: Network for sustainable Ultrascale computing (NESUS).

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Notes

  1. 1.

    https://developer.nvidia.com/cuda-toolkit.

  2. 2.

    https://devblogs.nvidia.com/parallelforall/faster-parallel-reductions-kepler/.

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Author information

Authors and Affiliations

  1. Informatics Department, University of Almería, Agrifood Campus of Int. Excell., ceiA3, 04120, Almería, Spain

    J. J. Moreno, G. Ortega, J. A. Martínez & E. M. Garzón

  2. Institute of Mathematics and Informatics, Vilnius University, Vilnius, Lithuania

    E. Filatovas

Authors
  1. J. J. Moreno
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  2. G. Ortega
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  3. E. Filatovas
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  4. J. A. Martínez
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  5. E. M. Garzón
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Corresponding author

Correspondence to G. Ortega .

Editor information

Editors and Affiliations

  1. Carlos III University of Madrid, Getafe, Spain

    Jesus Carretero

  2. Carlos III University of Madrid, Getafe, Spain

    Javier Garcia-Blas

  3. Mathematical Support for Computers, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhniy Novgorod, Russia

    Victor Gergel

  4. Research Computing Center (RCC), Moscow State University, Moscow, Russia

    Vladimir Voevodin

  5. Research Computing Center (RCC), Moscow State University, Moscow, Russia

    Iosif Meyerov

  6. E.U. Politécnica, Universidad de Extremaddura, Cáceres, Spain

    Juan A. Rico-Gallego

  7. Ingenieria de Sistemas Informáticos, Universidad de Extremaddura, Cáceres, Spain

    Juan C. Díaz-Martín

  8. Universitat Politécnica de València, Valencia, Spain

    Pedro Alonso

  9. Distributed and Parallel Systems Group, Institute for Computer Science, Innsbruck, Austria

    Juan Durillo

  10. Carlos III University of Madrid, Getafe, Spain

    José Daniel Garcia Sánchez

  11. UCD School of Computer Science, University College Dublin, Dublin, Ireland

    Alexey L. Lastovetsky

  12. University of Calabria, Rende (CS), Italy

    Fabrizio Marozzo

  13. Information Science and Engineering, Central South University, Changsha, Hunan, China

    Qin Liu

  14. Information Science and Engineering, Central South University, Changsha, Hunan, China

    Zakirul Alam Bhuiyan

  15. Ludwig Maximilian University of Munich, Munich, Germany

    Karl Fürlinger

  16. Informatik 10 - Rechnertechnik, Technische Universität München, Munich, Germany

    Josef Weidendorfer

  17. High Performance Computing Center (HLRS), Stuttgart, Germany

    José Gracia

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Moreno, J.J., Ortega, G., Filatovas, E., Martínez, J.A., Garzón, E.M. (2016). Improving the Energy Efficiency of Evolutionary Multi-objective Algorithms. In: Carretero, J., et al. Algorithms and Architectures for Parallel Processing. ICA3PP 2016. Lecture Notes in Computer Science(), vol 10049. Springer, Cham. https://doi.org/10.1007/978-3-319-49956-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-49956-7_5

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  • Online ISBN: 978-3-319-49956-7

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