Parallel implementation of a cellular automata in a hybrid CPU/GPU environment

Autores
Millán, Emmanuel N.; Martínez, Paula; Gil Costa, Graciela Verónica; Piccoli, María Fabiana; Printista, Alicia Marcela; Bederian, Carlos; García Garino, Carlos; Bringa, Eduardo M.
Año de publicación
2013
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Cellular Automata (CA) simulations can be used to model multiple systems, in fields like biology, physics and mathematics. In this work, a possible framework to execute a popular CA in hybrid CPU and GPUs (Graphics Processing Units) environments is presented. The inherently parallel nature of CA and the parallelism offered by GPUs makes their combination attractive. Benchmarks are conducted in several hardware scenarios. The use of MPI /OMP is explored for CPUs, together with the use of MPI in GPU clusters. Speed-ups up to 20 x are found when comparing GPU implementations to the serial CPU version of the code.
WPDP- XIII Workshop procesamiento distribuido y paralelo
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
general purpose GPU
cellular automata
multi-GPU
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/31730
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/31730
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network_name_str SEDICI (UNLP)
spelling Parallel implementation of a cellular automata in a hybrid CPU/GPU environmentMillán, Emmanuel N.Martínez, PaulaGil Costa, Graciela VerónicaPiccoli, María FabianaPrintista, Alicia MarcelaBederian, CarlosGarcía Garino, CarlosBringa, Eduardo M.Ciencias Informáticasgeneral purpose GPUcellular automatamulti-GPUCellular Automata (CA) simulations can be used to model multiple systems, in fields like biology, physics and mathematics. In this work, a possible framework to execute a popular CA in hybrid CPU and GPUs (Graphics Processing Units) environments is presented. The inherently parallel nature of CA and the parallelism offered by GPUs makes their combination attractive. Benchmarks are conducted in several hardware scenarios. The use of MPI /OMP is explored for CPUs, together with the use of MPI in GPU clusters. Speed-ups up to 20 x are found when comparing GPU implementations to the serial CPU version of the code.WPDP- XIII Workshop procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI)2013-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/31730enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-11-12T10:22:54Zoai:sedici.unlp.edu.ar:10915/31730Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-11-12 10:22:54.902SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Parallel implementation of a cellular automata in a hybrid CPU/GPU environment
title Parallel implementation of a cellular automata in a hybrid CPU/GPU environment
spellingShingle Parallel implementation of a cellular automata in a hybrid CPU/GPU environment
Millán, Emmanuel N.
Ciencias Informáticas
general purpose GPU
cellular automata
multi-GPU
title_short Parallel implementation of a cellular automata in a hybrid CPU/GPU environment
title_full Parallel implementation of a cellular automata in a hybrid CPU/GPU environment
title_fullStr Parallel implementation of a cellular automata in a hybrid CPU/GPU environment
title_full_unstemmed Parallel implementation of a cellular automata in a hybrid CPU/GPU environment
title_sort Parallel implementation of a cellular automata in a hybrid CPU/GPU environment
dc.creator.none.fl_str_mv Millán, Emmanuel N.
Martínez, Paula
Gil Costa, Graciela Verónica
Piccoli, María Fabiana
Printista, Alicia Marcela
Bederian, Carlos
García Garino, Carlos
Bringa, Eduardo M.
author Millán, Emmanuel N.
author_facet Millán, Emmanuel N.
Martínez, Paula
Gil Costa, Graciela Verónica
Piccoli, María Fabiana
Printista, Alicia Marcela
Bederian, Carlos
García Garino, Carlos
Bringa, Eduardo M.
author_role author
author2 Martínez, Paula
Gil Costa, Graciela Verónica
Piccoli, María Fabiana
Printista, Alicia Marcela
Bederian, Carlos
García Garino, Carlos
Bringa, Eduardo M.
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
general purpose GPU
cellular automata
multi-GPU
topic Ciencias Informáticas
general purpose GPU
cellular automata
multi-GPU
dc.description.none.fl_txt_mv Cellular Automata (CA) simulations can be used to model multiple systems, in fields like biology, physics and mathematics. In this work, a possible framework to execute a popular CA in hybrid CPU and GPUs (Graphics Processing Units) environments is presented. The inherently parallel nature of CA and the parallelism offered by GPUs makes their combination attractive. Benchmarks are conducted in several hardware scenarios. The use of MPI /OMP is explored for CPUs, together with the use of MPI in GPU clusters. Speed-ups up to 20 x are found when comparing GPU implementations to the serial CPU version of the code.
WPDP- XIII Workshop procesamiento distribuido y paralelo
Red de Universidades con Carreras en Informática (RedUNCI)
description Cellular Automata (CA) simulations can be used to model multiple systems, in fields like biology, physics and mathematics. In this work, a possible framework to execute a popular CA in hybrid CPU and GPUs (Graphics Processing Units) environments is presented. The inherently parallel nature of CA and the parallelism offered by GPUs makes their combination attractive. Benchmarks are conducted in several hardware scenarios. The use of MPI /OMP is explored for CPUs, together with the use of MPI in GPU clusters. Speed-ups up to 20 x are found when comparing GPU implementations to the serial CPU version of the code.
publishDate 2013
dc.date.none.fl_str_mv 2013-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/31730
url http://sedici.unlp.edu.ar/handle/10915/31730
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
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instname_str Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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