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

Autores
Martín, Sergio; Tinetti, Fernando Gustavo; Casas, Nicanor; De Luca, Graciela; Giulianelli, Daniel Alberto
Año de publicación
2013
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
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
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Simulation
Optimization
N-Body simulation
GPU optimization
data transference overhead
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:10915/31704
Acceder
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling N-Body simulation using GP-GPU: evaluating host/device memory transference overheadMartín, SergioTinetti, Fernando GustavoCasas, NicanorDe Luca, GracielaGiulianelli, Daniel AlbertoCiencias InformáticasSimulationOptimizationN-Body simulationGPU optimizationdata transference overheadN-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 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/10915/31704enginfo: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:UNLP2025-11-05T12:37:51Zoai:sedici.unlp.edu.ar:10915/31704Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-11-05 12:37:52.17SEDICI (UNLP) - Universidad Nacional de La Platafalse
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 Informáticas
Simulation
Optimization
N-Body simulation
GPU optimization
data transference overhead
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
Tinetti, Fernando Gustavo
Casas, Nicanor
De Luca, Graciela
Giulianelli, Daniel Alberto
author Martín, Sergio
author_facet Martín, Sergio
Tinetti, Fernando Gustavo
Casas, Nicanor
De Luca, Graciela
Giulianelli, Daniel Alberto
author_role author
author2 Tinetti, Fernando Gustavo
Casas, Nicanor
De Luca, Graciela
Giulianelli, Daniel Alberto
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Simulation
Optimization
N-Body simulation
GPU optimization
data transference overhead
topic Ciencias Informáticas
Simulation
Optimization
N-Body simulation
GPU optimization
data transference overhead
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
Red de Universidades con Carreras en Informática (RedUNCI)
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
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
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/31704
url http://sedici.unlp.edu.ar/handle/10915/31704
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
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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