abstract

First results of performance comparisons on many-core processors in solving QAP with ACO: kepler GPU versus xeon PHI

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Mitaro NamikiAuthors Info & Claims

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 1477 - 1478

https://doi.org/10.1145/2598394.2602274

Published: 12 July 2014 Publication History

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Abstract

This paper compares the performance of parallel computation on two types of many-core processors, Tesla K20c GPU and Xeon Phi 5110P, in solving the quadratic assignment problem (QAP) with ant colony optimization (ACO). The results show that the performance on Xeon Phi 5110P is not so promising compared to the Tesla K20c GPU on these problems. Further efficient implementation methods must be investigated for Xeon Phi.

References

[1]

S. Tsutsui and P. Collet (Ed). Massively parallel evolutionary computation on GPGPU, Natural Computing Series, Springer, 2013.

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[2]

Intel. http://blogs.intel.com/technology/2012/06/intel-xeon-phi-coprocessors-accelerate-discovery-and-innovation/

Google Scholar

[3]

QAPLIB - a quadratic assignment problem library, 2009. www.seas.upenn.edu/qaplib.

Google Scholar

[4]

T. Aoki. http://www.ocw.titech.ac.jp/index.php?module=General & action=T0300 & GakubuCD=226 & GakkaCD=226717 & KougiCD=77065 & Nendo=2013 & Gakki=1 & lang=JA & vid=05

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[5]

S. Tsutsui and N. Fujimoto. ACO with Tabu Search on a GPU for Solving QAPs using Move-Cost Adjusted Thread Assignment, Genetic and Evolutionary Computation Conference, pp. 1547--1554, ACM, 2011

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Almeida ALima JCarvalho M(2022)Systematic Literature Review on Parallel Trajectory-based MetaheuristicsACM Computing Surveys10.1145/355048455:8(1-34)Online publication date: 23-Dec-2022
https://dl.acm.org/doi/10.1145/3550484
Dzalbs IKalganova T(2020)Accelerating supply chains with Ant Colony Optimization across a range of hardware solutionsComputers & Industrial Engineering10.1016/j.cie.2020.106610(106610)Online publication date: Jun-2020
https://doi.org/10.1016/j.cie.2020.106610
Tirado FBarrientos RGonzález PMora M(2017)Efficient exploitation of the Xeon Phi architecture for the Ant Colony Optimization (ACO) metaheuristicThe Journal of Supercomputing10.1007/s11227-017-2124-573:11(5053-5070)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1007/s11227-017-2124-5
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First results of performance comparisons on many-core processors in solving QAP with ACO: kepler GPU versus xeon PHI
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
    2. Search methodologies
      1. Heuristic function construction

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GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1524 pages

ISBN:9781450328814

DOI:10.1145/2598394

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 12 July 2014

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GECCO '14

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SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

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GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Almeida ALima JCarvalho M(2022)Systematic Literature Review on Parallel Trajectory-based MetaheuristicsACM Computing Surveys10.1145/355048455:8(1-34)Online publication date: 23-Dec-2022
https://dl.acm.org/doi/10.1145/3550484
Dzalbs IKalganova T(2020)Accelerating supply chains with Ant Colony Optimization across a range of hardware solutionsComputers & Industrial Engineering10.1016/j.cie.2020.106610(106610)Online publication date: Jun-2020
https://doi.org/10.1016/j.cie.2020.106610
Tirado FBarrientos RGonzález PMora M(2017)Efficient exploitation of the Xeon Phi architecture for the Ant Colony Optimization (ACO) metaheuristicThe Journal of Supercomputing10.1007/s11227-017-2124-573:11(5053-5070)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1007/s11227-017-2124-5
Lloyd HAmos M(2016)A highly parallelized and vectorized implementation of Max-Min Ant System on Intel® Xeon Phi™2016 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2016.7850085(1-6)Online publication date: Dec-2016
https://doi.org/10.1109/SSCI.2016.7850085
Tirado FUrrutia ABarrientos R(2015)Using a coprocessor to solve the Ant Colony Optimization algorithm2015 34th International Conference of the Chilean Computer Science Society (SCCC)10.1109/SCCC.2015.7416584(1-6)Online publication date: Nov-2015
https://doi.org/10.1109/SCCC.2015.7416584
Deng LBai HZhao DWang F(2015)Kepler GPU vs. Xeon Phi: Performance case study with a high-order CFD application2015 IEEE International Conference on Computer and Communications (ICCC)10.1109/CompComm.2015.7387546(87-94)Online publication date: Oct-2015
https://doi.org/10.1109/CompComm.2015.7387546

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ACO with tabu search on a GPU for solving QAPs using move-cost adjusted thread assignment

Efficient exploitation of the Xeon Phi architecture for the Ant Colony Optimization (ACO) metaheuristic

Mapping of option pricing algorithms onto heterogeneous many-core architectures

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ACO with tabu search on a GPU for solving QAPs using move-cost adjusted thread assignment

Efficient exploitation of the Xeon Phi architecture for the Ant Colony Optimization (ACO) metaheuristic

Mapping of option pricing algorithms onto heterogeneous many-core architectures

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