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dc.date.accessioned 2023-05-09T13:55:08Z
dc.date.available 2023-05-09T13:55:08Z
dc.date.issued 2010
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/152637
dc.description.abstract 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. en
dc.format.extent 3211-3219 es
dc.language en es
dc.subject GPU es
dc.subject CPU es
dc.subject Efficiency es
dc.title Improving the performance of matrix inversion with a Tesla GPU en
dc.type Objeto de conferencia es
sedici.identifier.uri http://39jaiio.sadio.org.ar/sites/default/files/39jaiio-hpc-03.pdf es
sedici.identifier.issn 1851-9326 es
sedici.creator.person Ezzatti, Pablo es
sedici.creator.person Quintana Ortí, Enrique S. es
sedici.creator.person Remón, Alfredo es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Sociedad Argentina de Informática e Investigación Operativa es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/4.0/
sedici.date.exposure 2010
sedici.relation.event High-Performance Computing Symposium (HPC 2010) - JAIIO 39 (UADE, 30 de agosto al 3 de septiembre de 2010) es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)