research-article

libCudaOptimize: an open source library of GPU-based metaheuristics

Authors:

Youssef S.G. Nashed,

Roberto Ugolotti,

Stefano CagnoniAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

Pages 117 - 124

https://doi.org/10.1145/2330784.2330803

Published: 07 July 2012 Publication History

Abstract

Evolutionary Computation techniques and other metaheuristics have been increasingly used in the last years for solving many real-world tasks that can be formulated as optimization problems. Among their numerous strengths, a major one is their natural predisposition to parallelization. In this paper, we introduce libCudaOptimize, an open source library which implements some metaheuristics for continuous optimization: presently Particle Swarm Optimization, Differential Evolution, Scatter Search, and Solis&Wets local search. This library allows users either to apply these metaheuristics directly to their own fitness function or to extend it by implementing their own parallel optimization techniques. The library is written in CUDA-C to make extensive use of parallelization, as allowed by Graphics Processing Units.

After describing the library, we consider two practical case studies: the optimization of a fitness function for the automatic localization of anatomical brain structures in histological images, and the parallel implementation of Simulated Annealing as a new module, which extends the library while keeping code compatibility with it, so that the new method can be readily available for future use within the library as an alternative optimization technique.

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Cited By

Santos MNogueira BPinheiro RGuimaraes ALima AAndrade E(2021)A comparative study of GPU metaheuristics for data clustering2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC52423.2021.9658803(1387-1392)Online publication date: 17-Oct-2021
https://doi.org/10.1109/SMC52423.2021.9658803
Martinez ADel Ser JVillar-Rodriguez EOsaba EPoyatos JTabik SMolina DHerrera F(2021)Lights and shadows in Evolutionary Deep Learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challengesInformation Fusion10.1016/j.inffus.2020.10.01467(161-194)Online publication date: Mar-2021
https://doi.org/10.1016/j.inffus.2020.10.014
Nogueira BTavares EAraujo JCallou G(2019)Accelerating continuous GRASP with a GPUThe Journal of Supercomputing10.1007/s11227-019-02833-6Online publication date: 3-Apr-2019
https://doi.org/10.1007/s11227-019-02833-6
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Index Terms

libCudaOptimize: an open source library of GPU-based metaheuristics
1. Software and its engineering
  1. Software notations and tools
    1. Context specific languages
    2. Software libraries and repositories

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cover image ACM Conferences

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

July 2012

1586 pages

ISBN:9781450311786

DOI:10.1145/2330784

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

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Published: 07 July 2012

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GECCO '12

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GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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

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Cited By

Santos MNogueira BPinheiro RGuimaraes ALima AAndrade E(2021)A comparative study of GPU metaheuristics for data clustering2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC52423.2021.9658803(1387-1392)Online publication date: 17-Oct-2021
https://doi.org/10.1109/SMC52423.2021.9658803
Martinez ADel Ser JVillar-Rodriguez EOsaba EPoyatos JTabik SMolina DHerrera F(2021)Lights and shadows in Evolutionary Deep Learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challengesInformation Fusion10.1016/j.inffus.2020.10.01467(161-194)Online publication date: Mar-2021
https://doi.org/10.1016/j.inffus.2020.10.014
Nogueira BTavares EAraujo JCallou G(2019)Accelerating continuous GRASP with a GPUThe Journal of Supercomputing10.1007/s11227-019-02833-6Online publication date: 3-Apr-2019
https://doi.org/10.1007/s11227-019-02833-6
Tan YDing K(2016)A Survey on GPU-Based Implementation of Swarm Intelligence AlgorithmsIEEE Transactions on Cybernetics10.1109/TCYB.2015.246026146:9(2028-2041)Online publication date: Sep-2016
https://doi.org/10.1109/TCYB.2015.2460261
Mesejo PIbáñez ÓCordón ÓCagnoni S(2016)A survey on image segmentation using metaheuristic-based deformable modelsApplied Soft Computing10.1016/j.asoc.2016.03.00444:C(1-29)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.03.004
Iturriaga SNesmachnow SLuna FAlba E(2015)A parallel local search in CPU/GPU for scheduling independent tasks on large heterogeneous computing systemsThe Journal of Supercomputing10.1007/s11227-014-1315-671:2(648-672)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1007/s11227-014-1315-6
Hassannejad HMatrella GMordonini MCagnoni S(2015)Using Stochastic Optimization to Improve the Detection of Small CheckerboardsAI*IA 2015 Advances in Artificial Intelligence10.1007/978-3-319-24309-2_6(75-86)Online publication date: 17-Oct-2015
https://doi.org/10.1007/978-3-319-24309-2_6
Cagnoni SArnold D(2014)Evolutionary image analysis and signal processingProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2605359(795-818)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2605359
Tsotskas CKipouros TSavill A(2014)The Design and Implementation of a GPU-enabled Multi-objective Tabu-search Intended for Real World and High-dimensional ApplicationsProcedia Computer Science10.1016/j.procs.2014.05.20029(2152-2161)Online publication date: 2014
https://doi.org/10.1016/j.procs.2014.05.200
Krömer PPlatoš JSnášel V(2014)Nature-Inspired Meta-Heuristics on Modern GPUsInternational Journal of Parallel Programming10.1007/s10766-013-0292-342:5(681-709)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1007/s10766-013-0292-3
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