research-article

High performance genetic programming on GPU

Authors:

Denis Robilliard,

Virginie Marion,

Cyril FonluptAuthors Info & Claims

BADS '09: Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems

Pages 85 - 94

https://doi.org/10.1145/1555284.1555299

Published: 19 June 2009 Publication History

Abstract

The availability of low cost powerful parallel graphics cards has stimulated the port of Genetic Programming (GP) on Graphics Processing Units (GPUs). Our work focuses on the possibilities offered by Nvidia G80 GPUs when programmed in the CUDA language. We compare two parallelization schemes that evaluate several GP programs in parallel. We show that the fine grain distribution of computations over the elementary processors greatly impacts performances. We also present memory and representation optimizations that further enhance computation speed, up to 2.8 billion GP operations per second. The code has been developed with the well known ECJ library.

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Cited By

Cai YWu LBao LWang Z(2023)Research on storage-computing fusion transistors for novel computing architecturesChinese Science Bulletin10.1360/TB-2023-075868:35(4862-4871)Online publication date: 18-Sep-2023
https://doi.org/10.1360/TB-2023-0758
Hrbek VBrandejský T(2023)Memetic Algorithm with GPU OptimizationData Science and Algorithms in Systems10.1007/978-3-031-21438-7_15(174-185)Online publication date: 4-Jan-2023
https://doi.org/10.1007/978-3-031-21438-7_15
Trujillo LMuñoz Contreras JHernandez DCastelli MTapia J(2022)GSGP-CUDA — A CUDA framework for Geometric Semantic Genetic ProgrammingSoftwareX10.1016/j.softx.2022.10108518(101085)Online publication date: Jun-2022
https://doi.org/10.1016/j.softx.2022.101085
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Published In

cover image ACM Conferences

BADS '09: Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems

June 2009

114 pages

ISBN:9781605585840

DOI:10.1145/1555284

Program Chairs:
Gianluigi Folino
ICAR-CNR, National Research Council, Italy
,
Natalio Krasnogor
University of Nottingham, United Kingdom
,
Carlo Mastroianni
ICAR-CNR, National Research Council, Italy
,
Franco Zambonelli
Università di Modena e Reggio Emilia, Italy

Copyright © 2009 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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Published: 19 June 2009

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ICAC '09

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ICAC '09: 6th International Conference on Autonomic Computing

June 19, 2009

Barcelona, Spain

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Cited By

Cai YWu LBao LWang Z(2023)Research on storage-computing fusion transistors for novel computing architecturesChinese Science Bulletin10.1360/TB-2023-075868:35(4862-4871)Online publication date: 18-Sep-2023
https://doi.org/10.1360/TB-2023-0758
Hrbek VBrandejský T(2023)Memetic Algorithm with GPU OptimizationData Science and Algorithms in Systems10.1007/978-3-031-21438-7_15(174-185)Online publication date: 4-Jan-2023
https://doi.org/10.1007/978-3-031-21438-7_15
Trujillo LMuñoz Contreras JHernandez DCastelli MTapia J(2022)GSGP-CUDA — A CUDA framework for Geometric Semantic Genetic ProgrammingSoftwareX10.1016/j.softx.2022.10108518(101085)Online publication date: Jun-2022
https://doi.org/10.1016/j.softx.2022.101085
Baeta FCorreia JMartins TMachado PKrawiec K(2021)Speed benchmarking of genetic programming frameworksProceedings of the Genetic and Evolutionary Computation Conference10.1145/3449639.3459335(768-775)Online publication date: 26-Jun-2021
https://dl.acm.org/doi/10.1145/3449639.3459335
Silva BBernardino HBarbosa H(2021)Human Activity Recognition Using Parallel Cartesian Genetic Programming2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504793(474-481)Online publication date: 28-Jun-2021
https://doi.org/10.1109/CEC45853.2021.9504793
de Melo VFazenda ÁSotto LIacca G(2020)A MIMD Interpreter for Genetic ProgrammingApplications of Evolutionary Computation10.1007/978-3-030-43722-0_41(645-658)Online publication date: 9-Apr-2020
https://doi.org/10.1007/978-3-030-43722-0_41
Russo IBernardino HBarbosa H(2017)A massively parallel Grammatical Evolution technique with OpenCLJournal of Parallel and Distributed Computing10.1016/j.jpdc.2017.06.017109:C(333-349)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.jpdc.2017.06.017
Hendricks DGebbie TWilcox D(2016)High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithmSouth African Journal of Science10.17159/sajs.2016/20140340Volume 112:Number 1/2Online publication date: 1-Feb-2016
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Dhaenens CJourdan L(2016)BibliographyMetaheuristics for Big Data10.1002/9781119347569.biblio(161-186)Online publication date: 17-Sep-2016
https://doi.org/10.1002/9781119347569.biblio
Da Silva CDias DBentes CPacheco MCupertino L(2015)Evolving GPU machine codeThe Journal of Machine Learning Research10.5555/2789272.283113616:1(673-712)Online publication date: 1-Jan-2015
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