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dc.contributor.authorGonzález-Domínguez, Jorge
dc.contributor.authorMartín, María J.
dc.contributor.authorLópez Taboada, Guillermo
dc.contributor.authorTouriño, Juan
dc.contributor.authorDoallo, Ramón
dc.contributor.authorMallón, Damián A.
dc.contributor.authorWibecan, Brian
dc.date.accessioned2019-02-11T19:46:25Z
dc.date.available2019-02-11T19:46:25Z
dc.date.issued2012-09-25
dc.identifier.citationGonzález‐Domínguez, J. , Martín, M. J., Taboada, G. L., Touriño, J. , Doallo, R. , Mallón, D. A. and Wibecan, B. (2012), UPCBLAS: a library for parallel matrix computations in Unified Parallel C. Concurrency Computat.: Pract. Exper., 24: 1645-1667. doi:10.1002/cpe.1914es_ES
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.urihttp://hdl.handle.net/2183/21712
dc.descriptionThis is the peer reviewed version of the following article: González‐Domínguez, J. , Martín, M. J., Taboada, G. L., Touriño, J. , Doallo, R. , Mallón, D. A. and Wibecan, B. (2012), UPCBLAS: a library for parallel matrix computations in Unified Parallel C. Concurrency Computat.: Pract. Exper., 24: 1645-1667. doi:10.1002/cpe.1914, which has been published in final form at https://doi.org/10.1002/cpe.1914. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.es_ES
dc.description.abstract[Abstract] The popularity of Partitioned Global Address Space (PGAS) languages has increased during the last years thanks to their high programmability and performance through an efficient exploitation of data locality, especially on hierarchical architectures such as multicore clusters. This paper describes UPCBLAS, a parallel numerical library for dense matrix computations using the PGAS Unified Parallel C language. The routines developed in UPCBLAS are built on top of sequential basic linear algebra subprograms functions and exploit the particularities of the PGAS paradigm, taking into account data locality in order to achieve a good performance. Furthermore, the routines implement other optimization techniques, several of them by automatically taking into account the hardware characteristics of the underlying systems on which they are executed. The library has been experimentally evaluated on a multicore supercomputer and compared with a message‐passing‐based parallel numerical library, demonstrating good scalability and efficiency.es_ES
dc.description.sponsorshipMinisterio de Ciencia e Innovación; TIN2010-16735es_ES
dc.description.sponsorshipMinisterio de Educación; AP2008-01578es_ES
dc.language.isoenges_ES
dc.publisherJohn Wiley & Sons Ltd.es_ES
dc.relation.urihttps://doi.org/10.1002/cpe.1914es_ES
dc.subjectParallel Libraryes_ES
dc.subjectmatrix computationses_ES
dc.subjectPGASes_ES
dc.subjectUPCes_ES
dc.subjectBLASes_ES
dc.titleUPCBLAS: a library for parallel matrix computations in Unified Parallel Ces_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleConcurrency and Computation: Practice & Experiencees_ES
UDC.volume24es_ES
UDC.issue14es_ES
UDC.startPage1645es_ES
UDC.endPage1667es_ES
dc.identifier.doi10.1002/cpe.1914


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