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Faster, Cheaper, Better – a Hybridization Methodology to Develop Linear Algebra Software for GPUs

hal.structure.identifierHigh-End Parallel Algorithms for Challenging Numerical Simulations [HiePACS]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorAGULLO, Emmanuel
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierEfficient runtime systems for parallel architectures [RUNTIME]
dc.contributor.authorAUGONNET, Cédric
hal.structure.identifierInnovative Computing Laboratory [Knoxville] [ICL]
dc.contributor.authorDONGARRA, Jack
hal.structure.identifierInnovative Computing Laboratory [Knoxville] [ICL]
dc.contributor.authorLTAIEF, Hatem
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierEfficient runtime systems for parallel architectures [RUNTIME]
dc.contributor.authorNAMYST, Raymond
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierEfficient runtime systems for parallel architectures [RUNTIME]
dc.contributor.authorTHIBAULT, Samuel
hal.structure.identifierInnovative Computing Laboratory [Knoxville] [ICL]
dc.contributor.authorTOMOV, Stanimire
dc.contributor.editorWen-mei W. Hwu
dc.date.accessioned2024-04-15T09:48:11Z
dc.date.available2024-04-15T09:48:11Z
dc.date.issued2010-09
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/198161
dc.description.abstractEnIn this chapter, we present a hybridization methodology for the development of linear algebra software for GPUs. The methodology is successfully used in MAGMA – a new generation of linear algebra libraries, similar in functionality to LAPACK, but extended for hybrid, GPU-based systems. Algorithms of interest are split into computational tasks. The tasks' execution is scheduled over the computational components of a hybrid system of multicore CPUs with GPU accelerators using StarPU – a runtime system for accelerator-based multicore architectures. StarPU enables to express parallelism through sequential-like code and schedules the different tasks over the hybrid processing units. The productivity becomes then fast and cheap as the development is high level, using existing software infrastructure. Moreover, the resulting hybrid algorithms are better performance-wise than corresponding homogeneous algorithms designed exclusively for either GPUs or homogeneous multicore CPUs.
dc.description.sponsorshipProgrammation des technologies multicoeurs hétérogènes - ANR-08-COSI-0013
dc.language.isoen
dc.publisherMorgan Kaufmann
dc.source.titleGPU Computing Gems
dc.title.enFaster, Cheaper, Better – a Hybridization Methodology to Develop Linear Algebra Software for GPUs
dc.typeChapitre d'ouvrage
dc.subject.halInformatique [cs]/Système d'exploitation [cs.OS]
dc.description.sponsorshipEuropePerformance Portability and Programmability for Heterogeneous Many-core Architectures
bordeaux.volume2
bordeaux.hal.laboratoriesLaboratoire Bordelais de Recherche en Informatique (LaBRI) - UMR 5800*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.title.proceedingGPU Computing Gems
hal.identifierinria-00547847
hal.version1
hal.popularnon
hal.audienceNon spécifiée
hal.origin.linkhttps://hal.archives-ouvertes.fr//inria-00547847v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=GPU%20Computing%20Gems&rft.date=2010-09&rft.volume=2&rft.au=AGULLO,%20Emmanuel&AUGONNET,%20C%C3%A9dric&DONGARRA,%20Jack&LTAIEF,%20Hatem&NAMYST,%20Raymond&rft.genre=unknown


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