About speedup improvement of classical genetic algoritms using CUDA environment

Autores
Mroginski, Javier Luis; Castro, Hugo Guillermo
Año de publicación
2016
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Due to the increasing computational cost required for the numerical solution of evolutionary systems and problems based on topological design, in the last years, many parallel algorithms have been developed in order to improve its performance. Perhaps, the main numerical tool used to solve heuristic problems is known as Genetic Algorithm (GA), deriving its name from the similarity to the evolutionary theory of Darwing. During the last decade, Graphic Processing Unit (GPU) has been used for computing acceleration due to the intrinsic vector-oriented design of the chip set. This gave race to a new programming paradigm: the General Purpose Computing on Graphics Processing Units (GPGPU). Which was replaced then by the Compute Unified Device Architecture (CUDA) environment in 2007. CUDA environment is probably the parallel computing platform and programming model that more heyday has had in recent years, mainly due to the low acquisition cost of the graphics processing units (GPUs) compared to a cluster with similar functional characteristics. Consequently, the number of GPU-CUDAs present in the top 500 fastest supercomputers in the world is constantly growing. In this work, a numerical algorithm developed in the NVIDIA CUDA platform capable of solving classical optimization functions usually employed as benchmarks (De Jong, Rastring and Ackley functions) is presented. The obtained results using a GeForce GTX 750 Ti GPU shown that the proposed code is a valuable tool for acceleration of GAs, improving its speedup in about 130%.
Fil: Mroginski, Javier Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Universidad Nacional del Nordeste. Facultad de Ingeniería; Argentina
Fil: Castro, Hugo Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; Argentina
XXII Congreso de Métodos Numéricos y sus Aplicaciones
Córdoba
Argentina
Universidad Tecnológica Nacional. Facultad Regional Córdoba
Asociación Argentina de Mecánica Computacional
Materia
METAHEURISTIC OPTIMIZATION
CUDA
C++
HPC
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/207249
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/207249
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling About speedup improvement of classical genetic algoritms using CUDA environmentMroginski, Javier LuisCastro, Hugo GuillermoMETAHEURISTIC OPTIMIZATIONCUDAC++HPChttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Due to the increasing computational cost required for the numerical solution of evolutionary systems and problems based on topological design, in the last years, many parallel algorithms have been developed in order to improve its performance. Perhaps, the main numerical tool used to solve heuristic problems is known as Genetic Algorithm (GA), deriving its name from the similarity to the evolutionary theory of Darwing. During the last decade, Graphic Processing Unit (GPU) has been used for computing acceleration due to the intrinsic vector-oriented design of the chip set. This gave race to a new programming paradigm: the General Purpose Computing on Graphics Processing Units (GPGPU). Which was replaced then by the Compute Unified Device Architecture (CUDA) environment in 2007. CUDA environment is probably the parallel computing platform and programming model that more heyday has had in recent years, mainly due to the low acquisition cost of the graphics processing units (GPUs) compared to a cluster with similar functional characteristics. Consequently, the number of GPU-CUDAs present in the top 500 fastest supercomputers in the world is constantly growing. In this work, a numerical algorithm developed in the NVIDIA CUDA platform capable of solving classical optimization functions usually employed as benchmarks (De Jong, Rastring and Ackley functions) is presented. The obtained results using a GeForce GTX 750 Ti GPU shown that the proposed code is a valuable tool for acceleration of GAs, improving its speedup in about 130%.Fil: Mroginski, Javier Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Universidad Nacional del Nordeste. Facultad de Ingeniería; ArgentinaFil: Castro, Hugo Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; ArgentinaXXII Congreso de Métodos Numéricos y sus AplicacionesCórdobaArgentinaUniversidad Tecnológica Nacional. Facultad Regional CórdobaAsociación Argentina de Mecánica ComputacionalAsociación Argentina de Mecánica Computacional2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectCongresoJournalhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/207249About speedup improvement of classical genetic algoritms using CUDA environment; XXII Congreso de Métodos Numéricos y sus Aplicaciones; Córdoba; Argentina; 2016; 3295-32952591-3522CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://cimec.org.ar/ojs/index.php/mc/article/view/5203Nacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:23:46Zoai:ri.conicet.gov.ar:11336/207249instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:23:46.629CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv About speedup improvement of classical genetic algoritms using CUDA environment
title About speedup improvement of classical genetic algoritms using CUDA environment
spellingShingle About speedup improvement of classical genetic algoritms using CUDA environment
Mroginski, Javier Luis
METAHEURISTIC OPTIMIZATION
CUDA
C++
HPC
title_short About speedup improvement of classical genetic algoritms using CUDA environment
title_full About speedup improvement of classical genetic algoritms using CUDA environment
title_fullStr About speedup improvement of classical genetic algoritms using CUDA environment
title_full_unstemmed About speedup improvement of classical genetic algoritms using CUDA environment
title_sort About speedup improvement of classical genetic algoritms using CUDA environment
dc.creator.none.fl_str_mv Mroginski, Javier Luis
Castro, Hugo Guillermo
author Mroginski, Javier Luis
author_facet Mroginski, Javier Luis
Castro, Hugo Guillermo
author_role author
author2 Castro, Hugo Guillermo
author2_role author
dc.subject.none.fl_str_mv METAHEURISTIC OPTIMIZATION
CUDA
C++
HPC
topic METAHEURISTIC OPTIMIZATION
CUDA
C++
HPC
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Due to the increasing computational cost required for the numerical solution of evolutionary systems and problems based on topological design, in the last years, many parallel algorithms have been developed in order to improve its performance. Perhaps, the main numerical tool used to solve heuristic problems is known as Genetic Algorithm (GA), deriving its name from the similarity to the evolutionary theory of Darwing. During the last decade, Graphic Processing Unit (GPU) has been used for computing acceleration due to the intrinsic vector-oriented design of the chip set. This gave race to a new programming paradigm: the General Purpose Computing on Graphics Processing Units (GPGPU). Which was replaced then by the Compute Unified Device Architecture (CUDA) environment in 2007. CUDA environment is probably the parallel computing platform and programming model that more heyday has had in recent years, mainly due to the low acquisition cost of the graphics processing units (GPUs) compared to a cluster with similar functional characteristics. Consequently, the number of GPU-CUDAs present in the top 500 fastest supercomputers in the world is constantly growing. In this work, a numerical algorithm developed in the NVIDIA CUDA platform capable of solving classical optimization functions usually employed as benchmarks (De Jong, Rastring and Ackley functions) is presented. The obtained results using a GeForce GTX 750 Ti GPU shown that the proposed code is a valuable tool for acceleration of GAs, improving its speedup in about 130%.
Fil: Mroginski, Javier Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Universidad Nacional del Nordeste. Facultad de Ingeniería; Argentina
Fil: Castro, Hugo Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; Argentina
XXII Congreso de Métodos Numéricos y sus Aplicaciones
Córdoba
Argentina
Universidad Tecnológica Nacional. Facultad Regional Córdoba
Asociación Argentina de Mecánica Computacional
description Due to the increasing computational cost required for the numerical solution of evolutionary systems and problems based on topological design, in the last years, many parallel algorithms have been developed in order to improve its performance. Perhaps, the main numerical tool used to solve heuristic problems is known as Genetic Algorithm (GA), deriving its name from the similarity to the evolutionary theory of Darwing. During the last decade, Graphic Processing Unit (GPU) has been used for computing acceleration due to the intrinsic vector-oriented design of the chip set. This gave race to a new programming paradigm: the General Purpose Computing on Graphics Processing Units (GPGPU). Which was replaced then by the Compute Unified Device Architecture (CUDA) environment in 2007. CUDA environment is probably the parallel computing platform and programming model that more heyday has had in recent years, mainly due to the low acquisition cost of the graphics processing units (GPUs) compared to a cluster with similar functional characteristics. Consequently, the number of GPU-CUDAs present in the top 500 fastest supercomputers in the world is constantly growing. In this work, a numerical algorithm developed in the NVIDIA CUDA platform capable of solving classical optimization functions usually employed as benchmarks (De Jong, Rastring and Ackley functions) is presented. The obtained results using a GeForce GTX 750 Ti GPU shown that the proposed code is a valuable tool for acceleration of GAs, improving its speedup in about 130%.
publishDate 2016
dc.date.none.fl_str_mv 2016
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Congreso
Journal
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/207249
About speedup improvement of classical genetic algoritms using CUDA environment; XXII Congreso de Métodos Numéricos y sus Aplicaciones; Córdoba; Argentina; 2016; 3295-3295
2591-3522
CONICET Digital
CONICET
url http://hdl.handle.net/11336/207249
identifier_str_mv About speedup improvement of classical genetic algoritms using CUDA environment; XXII Congreso de Métodos Numéricos y sus Aplicaciones; Córdoba; Argentina; 2016; 3295-3295
2591-3522
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://cimec.org.ar/ojs/index.php/mc/article/view/5203
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.coverage.none.fl_str_mv Nacional
dc.publisher.none.fl_str_mv Asociación Argentina de Mecánica Computacional
publisher.none.fl_str_mv Asociación Argentina de Mecánica Computacional
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.48226

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