Improving the performance of matrix inversion with a Tesla GPU

Autores
Ezzatti, Pablo; Quintana Ortí, Enrique S.; Remón, Alfredo
Año de publicación
2010
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
We study two different techniques for the computation of a matrix inverse, the traditional approach based on Gaussian factorization and the Gauss-Jordan elimination alternative more suitable for parallel architectures. The target architecture is a current general-purpose multi-core processor (CPU) connected to a graphics processor (GPU). Parallelism is obtained from the use of libraries MKL (for the CPU) and CUBLAS (for the GPU), as well as, performing simultaneously operations in both architectures. Numerical experiments performed on a system equipped with two Intel QuadCore processors and a Tesla C1060 GPU, illustrate the efficiency attained by the Gauss-Jordan elimination implementation.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
GPU
CPU
Efficiency
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/152637

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network_name_str SEDICI (UNLP)
spelling Improving the performance of matrix inversion with a Tesla GPUEzzatti, PabloQuintana Ortí, Enrique S.Remón, AlfredoCiencias InformáticasGPUCPUEfficiencyWe study two different techniques for the computation of a matrix inverse, the traditional approach based on Gaussian factorization and the Gauss-Jordan elimination alternative more suitable for parallel architectures. The target architecture is a current general-purpose multi-core processor (CPU) connected to a graphics processor (GPU). Parallelism is obtained from the use of libraries MKL (for the CPU) and CUBLAS (for the GPU), as well as, performing simultaneously operations in both architectures. Numerical experiments performed on a system equipped with two Intel QuadCore processors and a Tesla C1060 GPU, illustrate the efficiency attained by the Gauss-Jordan elimination implementation.Sociedad Argentina de Informática e Investigación Operativa2010info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf3211-3219http://sedici.unlp.edu.ar/handle/10915/152637enginfo:eu-repo/semantics/altIdentifier/url/http://39jaiio.sadio.org.ar/sites/default/files/39jaiio-hpc-03.pdfinfo:eu-repo/semantics/altIdentifier/issn/1851-9326info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:39:27Zoai:sedici.unlp.edu.ar:10915/152637Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:39:27.231SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Improving the performance of matrix inversion with a Tesla GPU
title Improving the performance of matrix inversion with a Tesla GPU
spellingShingle Improving the performance of matrix inversion with a Tesla GPU
Ezzatti, Pablo
Ciencias Informáticas
GPU
CPU
Efficiency
title_short Improving the performance of matrix inversion with a Tesla GPU
title_full Improving the performance of matrix inversion with a Tesla GPU
title_fullStr Improving the performance of matrix inversion with a Tesla GPU
title_full_unstemmed Improving the performance of matrix inversion with a Tesla GPU
title_sort Improving the performance of matrix inversion with a Tesla GPU
dc.creator.none.fl_str_mv Ezzatti, Pablo
Quintana Ortí, Enrique S.
Remón, Alfredo
author Ezzatti, Pablo
author_facet Ezzatti, Pablo
Quintana Ortí, Enrique S.
Remón, Alfredo
author_role author
author2 Quintana Ortí, Enrique S.
Remón, Alfredo
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
GPU
CPU
Efficiency
topic Ciencias Informáticas
GPU
CPU
Efficiency
dc.description.none.fl_txt_mv We study two different techniques for the computation of a matrix inverse, the traditional approach based on Gaussian factorization and the Gauss-Jordan elimination alternative more suitable for parallel architectures. The target architecture is a current general-purpose multi-core processor (CPU) connected to a graphics processor (GPU). Parallelism is obtained from the use of libraries MKL (for the CPU) and CUBLAS (for the GPU), as well as, performing simultaneously operations in both architectures. Numerical experiments performed on a system equipped with two Intel QuadCore processors and a Tesla C1060 GPU, illustrate the efficiency attained by the Gauss-Jordan elimination implementation.
Sociedad Argentina de Informática e Investigación Operativa
description We study two different techniques for the computation of a matrix inverse, the traditional approach based on Gaussian factorization and the Gauss-Jordan elimination alternative more suitable for parallel architectures. The target architecture is a current general-purpose multi-core processor (CPU) connected to a graphics processor (GPU). Parallelism is obtained from the use of libraries MKL (for the CPU) and CUBLAS (for the GPU), as well as, performing simultaneously operations in both architectures. Numerical experiments performed on a system equipped with two Intel QuadCore processors and a Tesla C1060 GPU, illustrate the efficiency attained by the Gauss-Jordan elimination implementation.
publishDate 2010
dc.date.none.fl_str_mv 2010
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