N-Body simulation using GP-GPU: evaluating host/device memory transference overhead

Autores
Martín, Sergio; Casas, Nicanor; De Luca, Graciela; Giulianelli, Daniel Alberto; Tinetti, Fernando Gustavo
Año de publicación
2013
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión enviada
Descripción
N-Body simulation algorithms are amongst the most commonly used within the field of scientific computing. Especially in computational astrophysics, they are used to simulate gravitational scenarios for solar systems or galactic collisions. Parallel versions of such N-Body algorithms have been extensively designed and optimized for multicore and distributed computing schemes. However, N-Body algorithms are still a novelty in the field of GPGPU computing. Although several N-body algorithms have been proved to harness the potential of a modern GPU processor, there are additional complexities that this architecture presents that could be analyzed for possible optimizations. In this article, we introduce the problem of host to device (GPU) – and vice versa – data transferring overhead and analyze a way to estimate its impact in the performance of simulations.
WPDP- XIII Workshop procesamiento distribuido y paralelo
Materia
Ciencias de la Información y Bioinformática
N-Body simulation
GPU optimization
data transference overhead
Simulation
Optimization
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/3356
Acceder
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oai_identifier_str oai:digital.cic.gba.gob.ar:11746/3356
network_acronym_str CICBA
repository_id_str 9441
network_name_str CIC Digital (CICBA)
spelling N-Body simulation using GP-GPU: evaluating host/device memory transference overheadMartín, SergioCasas, NicanorDe Luca, GracielaGiulianelli, Daniel AlbertoTinetti, Fernando GustavoCiencias de la Información y BioinformáticaN-Body simulationGPU optimizationdata transference overheadSimulationOptimizationN-Body simulation algorithms are amongst the most commonly used within the field of scientific computing. Especially in computational astrophysics, they are used to simulate gravitational scenarios for solar systems or galactic collisions. Parallel versions of such N-Body algorithms have been extensively designed and optimized for multicore and distributed computing schemes. However, N-Body algorithms are still a novelty in the field of GPGPU computing. Although several N-body algorithms have been proved to harness the potential of a modern GPU processor, there are additional complexities that this architecture presents that could be analyzed for possible optimizations. In this article, we introduce the problem of host to device (GPU) – and vice versa – data transferring overhead and analyze a way to estimate its impact in the performance of simulations.WPDP- XIII Workshop procesamiento distribuido y paralelo2013-10-01info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/submittedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/3356enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-11-06T09:35:20Zoai:digital.cic.gba.gob.ar:11746/3356Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-11-06 09:35:21.645CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv N-Body simulation using GP-GPU: evaluating host/device memory transference overhead
title N-Body simulation using GP-GPU: evaluating host/device memory transference overhead
spellingShingle N-Body simulation using GP-GPU: evaluating host/device memory transference overhead
Martín, Sergio
Ciencias de la Información y Bioinformática
N-Body simulation
GPU optimization
data transference overhead
Simulation
Optimization
title_short N-Body simulation using GP-GPU: evaluating host/device memory transference overhead
title_full N-Body simulation using GP-GPU: evaluating host/device memory transference overhead
title_fullStr N-Body simulation using GP-GPU: evaluating host/device memory transference overhead
title_full_unstemmed N-Body simulation using GP-GPU: evaluating host/device memory transference overhead
title_sort N-Body simulation using GP-GPU: evaluating host/device memory transference overhead
dc.creator.none.fl_str_mv Martín, Sergio
Casas, Nicanor
De Luca, Graciela
Giulianelli, Daniel Alberto
Tinetti, Fernando Gustavo
author Martín, Sergio
author_facet Martín, Sergio
Casas, Nicanor
De Luca, Graciela
Giulianelli, Daniel Alberto
Tinetti, Fernando Gustavo
author_role author
author2 Casas, Nicanor
De Luca, Graciela
Giulianelli, Daniel Alberto
Tinetti, Fernando Gustavo
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias de la Información y Bioinformática
N-Body simulation
GPU optimization
data transference overhead
Simulation
Optimization
topic Ciencias de la Información y Bioinformática
N-Body simulation
GPU optimization
data transference overhead
Simulation
Optimization
dc.description.none.fl_txt_mv N-Body simulation algorithms are amongst the most commonly used within the field of scientific computing. Especially in computational astrophysics, they are used to simulate gravitational scenarios for solar systems or galactic collisions. Parallel versions of such N-Body algorithms have been extensively designed and optimized for multicore and distributed computing schemes. However, N-Body algorithms are still a novelty in the field of GPGPU computing. Although several N-body algorithms have been proved to harness the potential of a modern GPU processor, there are additional complexities that this architecture presents that could be analyzed for possible optimizations. In this article, we introduce the problem of host to device (GPU) – and vice versa – data transferring overhead and analyze a way to estimate its impact in the performance of simulations.
WPDP- XIII Workshop procesamiento distribuido y paralelo
description N-Body simulation algorithms are amongst the most commonly used within the field of scientific computing. Especially in computational astrophysics, they are used to simulate gravitational scenarios for solar systems or galactic collisions. Parallel versions of such N-Body algorithms have been extensively designed and optimized for multicore and distributed computing schemes. However, N-Body algorithms are still a novelty in the field of GPGPU computing. Although several N-body algorithms have been proved to harness the potential of a modern GPU processor, there are additional complexities that this architecture presents that could be analyzed for possible optimizations. In this article, we introduce the problem of host to device (GPU) – and vice versa – data transferring overhead and analyze a way to estimate its impact in the performance of simulations.
publishDate 2013
dc.date.none.fl_str_mv 2013-10-01
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/submittedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str submittedVersion
dc.identifier.none.fl_str_mv https://digital.cic.gba.gob.ar/handle/11746/3356
url https://digital.cic.gba.gob.ar/handle/11746/3356
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/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:CIC Digital (CICBA)
instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron:CICBA
reponame_str CIC Digital (CICBA)
collection CIC Digital (CICBA)
instname_str Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron_str CICBA
institution CICBA
repository.name.fl_str_mv CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
repository.mail.fl_str_mv marisa.degiusti@sedici.unlp.edu.ar
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score 13.087074

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