Leveraging the Performance of LBM-HPC for Large Sizes on GPUs using Ghost Cells

Today, we are living a growing demand of larger and more efficient computational resources from the scienti c community. On the other hand, the appearance of GPUs for general purpose computing supposed an important advance for covering such demand. These devices o er an impressive computational capa...

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Detalles Bibliográficos
Autor: Valero-Lara, P.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2016
País:España
Institución:Basque Center for Applied Mathematics (BCAM)
Repositorio:BIRD. BCAM's Institutional Repository Data
OAI Identifier:oai:bird.bcamath.org:20.500.11824/322
Acceso en línea:http://hdl.handle.net/20.500.11824/322
Access Level:acceso abierto
Palabra clave:Computational Fluid Dynamics
Lattice-Boltzmann Method
GPU
CUDA
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spelling Leveraging the Performance of LBM-HPC for Large Sizes on GPUs using Ghost CellsValero-Lara, P.Computational Fluid DynamicsLattice-Boltzmann MethodGPUCUDAToday, we are living a growing demand of larger and more efficient computational resources from the scienti c community. On the other hand, the appearance of GPUs for general purpose computing supposed an important advance for covering such demand. These devices o er an impressive computational capacity at low cost and an efficient power consumption. However, the memory available in these devices is (sometimes) not enough, and so it is necessary computationally expensive memory transfers from (to) CPU to (from) GPU, causing a dramatic fall in performance. Recently, the Lattice-Boltzmann Method has positioned as an e cient methodology for fluid simulations. Although this method presents some interesting features particularly amenable to be efficiently exploited on parallel computers, it requires a considerable memory capacity, which can suppose an important drawback, in particular, on GPUs. In the present paper, it is proposed a new GPU-based implementation, which minimizes such requirements with respect to other state-of-the-art implementations. It allows us to execute almost 2 bigger problems without additional memory transfers, achieving faster executions when dealing with large problems.201620162016info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/20.500.11824/322reponame:BIRD. BCAM's Institutional Repository Datainstname:Basque Center for Applied Mathematics (BCAM)InglésReconocimiento-NoComercial-CompartirIgual 3.0 Españahttp://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:bird.bcamath.org:20.500.11824/3222026-06-19T12:47:47Z
dc.title.none.fl_str_mv Leveraging the Performance of LBM-HPC for Large Sizes on GPUs using Ghost Cells
title Leveraging the Performance of LBM-HPC for Large Sizes on GPUs using Ghost Cells
spellingShingle Leveraging the Performance of LBM-HPC for Large Sizes on GPUs using Ghost Cells
Valero-Lara, P.
Computational Fluid Dynamics
Lattice-Boltzmann Method
GPU
CUDA
title_short Leveraging the Performance of LBM-HPC for Large Sizes on GPUs using Ghost Cells
title_full Leveraging the Performance of LBM-HPC for Large Sizes on GPUs using Ghost Cells
title_fullStr Leveraging the Performance of LBM-HPC for Large Sizes on GPUs using Ghost Cells
title_full_unstemmed Leveraging the Performance of LBM-HPC for Large Sizes on GPUs using Ghost Cells
title_sort Leveraging the Performance of LBM-HPC for Large Sizes on GPUs using Ghost Cells
dc.creator.none.fl_str_mv Valero-Lara, P.
author Valero-Lara, P.
author_facet Valero-Lara, P.
author_role author
dc.subject.none.fl_str_mv Computational Fluid Dynamics
Lattice-Boltzmann Method
GPU
CUDA
topic Computational Fluid Dynamics
Lattice-Boltzmann Method
GPU
CUDA
description Today, we are living a growing demand of larger and more efficient computational resources from the scienti c community. On the other hand, the appearance of GPUs for general purpose computing supposed an important advance for covering such demand. These devices o er an impressive computational capacity at low cost and an efficient power consumption. However, the memory available in these devices is (sometimes) not enough, and so it is necessary computationally expensive memory transfers from (to) CPU to (from) GPU, causing a dramatic fall in performance. Recently, the Lattice-Boltzmann Method has positioned as an e cient methodology for fluid simulations. Although this method presents some interesting features particularly amenable to be efficiently exploited on parallel computers, it requires a considerable memory capacity, which can suppose an important drawback, in particular, on GPUs. In the present paper, it is proposed a new GPU-based implementation, which minimizes such requirements with respect to other state-of-the-art implementations. It allows us to execute almost 2 bigger problems without additional memory transfers, achieving faster executions when dealing with large problems.
publishDate 2016
dc.date.none.fl_str_mv 2016
2016
2016
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 http://hdl.handle.net/20.500.11824/322
url http://hdl.handle.net/20.500.11824/322
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:BIRD. BCAM's Institutional Repository Data
instname:Basque Center for Applied Mathematics (BCAM)
instname_str Basque Center for Applied Mathematics (BCAM)
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collection BIRD. BCAM's Institutional Repository Data
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