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
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/152637
Ver los metadatos del registro completo
| id |
SEDICI_01a7fc4e33a8865a86d2e3a45a34b519 |
|---|---|
| oai_identifier_str |
oai:sedici.unlp.edu.ar:10915/152637 |
| network_acronym_str |
SEDICI |
| repository_id_str |
1329 |
| 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:UNLP2025-10-22T17:20:18Zoai: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:13292025-10-22 17:20:18.478SEDICI (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 |
| 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/10915/152637 |
| url |
http://sedici.unlp.edu.ar/handle/10915/152637 |
| dc.language.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://39jaiio.sadio.org.ar/sites/default/files/39jaiio-hpc-03.pdf info:eu-repo/semantics/altIdentifier/issn/1851-9326 |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
| eu_rights_str_mv |
openAccess |
| rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
| dc.format.none.fl_str_mv |
application/pdf 3211-3219 |
| 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 |
| _version_ |
1846783626796072960 |
| score |
12.982451 |