Developing Efficient Discrete Simulations on Multicore and GPU Architectures

In this paper we show how to efficiently implement parallel discrete simulations on multicoreandGPUarchitecturesthrougharealexampleofanapplication: acellularautomatamodel of laser dynamics. We describe the techniques employed to build and optimize the implementations using OpenMP and CUDA frameworks....

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Autores: Cagigas Muñiz, Daniel, Díaz del Río, Fernando, López-Torres, Manuel Ramón, Jiménez-Morales, Francisco de Paula, Guisado Lizar, José Luis
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/94070
Acceso en línea:https://hdl.handle.net/11441/94070
https://doi.org/10.3390/electronics9010189
Access Level:acceso abierto
Palabra clave:Laser dynamics
Parallel computing
Cellular automata
GPUs and multi-core processors performance
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spelling Developing Efficient Discrete Simulations on Multicore and GPU ArchitecturesCagigas Muñiz, DanielDíaz del Río, FernandoLópez-Torres, Manuel RamónJiménez-Morales, Francisco de PaulaGuisado Lizar, José LuisLaser dynamicsParallel computingCellular automataGPUs and multi-core processors performanceIn this paper we show how to efficiently implement parallel discrete simulations on multicoreandGPUarchitecturesthrougharealexampleofanapplication: acellularautomatamodel of laser dynamics. We describe the techniques employed to build and optimize the implementations using OpenMP and CUDA frameworks. We have evaluated the performance on two different hardware platforms that represent different target market segments: high-end platforms for scientific computing, using an Intel Xeon Platinum 8259CL server with 48 cores, and also an NVIDIA Tesla V100GPU,bothrunningonAmazonWebServer(AWS)Cloud;and on a consumer-oriented platform, using an Intel Core i9 9900k CPU and an NVIDIA GeForce GTX 1050 TI GPU. Performance results were compared and analyzed in detail. We show that excellent performance and scalability can be obtained in both platforms, and we extract some important issues that imply a performance degradation for them. We also found that current multicore CPUs with large core numbers can bring a performance very near to that of GPUs, and even identical in some cases.Ministerio de Economía, Industria y Competitividad, Gobierno de España (MINECO), and the Agencia Estatal de Investigación (AEI) of Spain, cofinanced by FEDER funds (EU) TIN2017-89842PMDPIArquitectura y Tecnología de ComputadoresFísica de la Materia CondensadaTEP108: Robótica y Tecnología de ComputadoresFQM122: Fenómenos no-Lineales2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/94070https://doi.org/10.3390/electronics9010189reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésElectronics, 9 (1), 189-.TIN2017-89842Phttps://www.mdpi.com/2079-9292/9/1/189info:eu-repo/semantics/openAccessoai:idus.us.es:11441/940702026-06-17T12:51:07Z
dc.title.none.fl_str_mv Developing Efficient Discrete Simulations on Multicore and GPU Architectures
title Developing Efficient Discrete Simulations on Multicore and GPU Architectures
spellingShingle Developing Efficient Discrete Simulations on Multicore and GPU Architectures
Cagigas Muñiz, Daniel
Laser dynamics
Parallel computing
Cellular automata
GPUs and multi-core processors performance
title_short Developing Efficient Discrete Simulations on Multicore and GPU Architectures
title_full Developing Efficient Discrete Simulations on Multicore and GPU Architectures
title_fullStr Developing Efficient Discrete Simulations on Multicore and GPU Architectures
title_full_unstemmed Developing Efficient Discrete Simulations on Multicore and GPU Architectures
title_sort Developing Efficient Discrete Simulations on Multicore and GPU Architectures
dc.creator.none.fl_str_mv Cagigas Muñiz, Daniel
Díaz del Río, Fernando
López-Torres, Manuel Ramón
Jiménez-Morales, Francisco de Paula
Guisado Lizar, José Luis
author Cagigas Muñiz, Daniel
author_facet Cagigas Muñiz, Daniel
Díaz del Río, Fernando
López-Torres, Manuel Ramón
Jiménez-Morales, Francisco de Paula
Guisado Lizar, José Luis
author_role author
author2 Díaz del Río, Fernando
López-Torres, Manuel Ramón
Jiménez-Morales, Francisco de Paula
Guisado Lizar, José Luis
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnología de Computadores
Física de la Materia Condensada
TEP108: Robótica y Tecnología de Computadores
FQM122: Fenómenos no-Lineales
dc.subject.none.fl_str_mv Laser dynamics
Parallel computing
Cellular automata
GPUs and multi-core processors performance
topic Laser dynamics
Parallel computing
Cellular automata
GPUs and multi-core processors performance
description In this paper we show how to efficiently implement parallel discrete simulations on multicoreandGPUarchitecturesthrougharealexampleofanapplication: acellularautomatamodel of laser dynamics. We describe the techniques employed to build and optimize the implementations using OpenMP and CUDA frameworks. We have evaluated the performance on two different hardware platforms that represent different target market segments: high-end platforms for scientific computing, using an Intel Xeon Platinum 8259CL server with 48 cores, and also an NVIDIA Tesla V100GPU,bothrunningonAmazonWebServer(AWS)Cloud;and on a consumer-oriented platform, using an Intel Core i9 9900k CPU and an NVIDIA GeForce GTX 1050 TI GPU. Performance results were compared and analyzed in detail. We show that excellent performance and scalability can be obtained in both platforms, and we extract some important issues that imply a performance degradation for them. We also found that current multicore CPUs with large core numbers can bring a performance very near to that of GPUs, and even identical in some cases.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/94070
https://doi.org/10.3390/electronics9010189
url https://hdl.handle.net/11441/94070
https://doi.org/10.3390/electronics9010189
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Electronics, 9 (1), 189-.
TIN2017-89842P
https://www.mdpi.com/2079-9292/9/1/189
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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