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StarPU: a Runtime System for Scheduling Tasks over Accelerator-Based Multicore Machines

hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierEfficient runtime systems for parallel architectures [RUNTIME]
dc.contributor.authorAUGONNET, Cédric
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierEfficient runtime systems for parallel architectures [RUNTIME]
dc.contributor.authorTHIBAULT, Samuel
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierEfficient runtime systems for parallel architectures [RUNTIME]
dc.contributor.authorNAMYST, Raymond
dc.date.accessioned2024-04-15T09:49:29Z
dc.date.available2024-04-15T09:49:29Z
dc.date.issued2010-03
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/198263
dc.description.abstractEnMulticore machines equipped with accelerators are becoming increasingly popular. The TOP500-leading RoadRunner machine is probably the most famous example of a parallel computer mixing IBM Cell Broadband Engines and AMD opteron processors. Other architectures, featuring GPU accelerators, are expected to appear in the near future. To fully tap into the potential of these hybrid machines, pure offloading approaches, in which the main core of the application runs on regular processors and offloads specific parts on accelerators, are not sufficient. The real challenge is to build systems where the application would permanently spread across the entire machine, that is, where parallel tasks would be dynamically scheduled over the full set of available processing units. To face this challenge, we propose a new runtime system capable of scheduling tasks over heterogeneous, accelerator-based machines. Our system features a software virtual shared memory that provides a weak consistency model. The system keeps track of data copies within accelerator embedded-memories and features a data-prefetching engine. Such facilities, together with a database of self-tuned per-task performance models, can be used to greatly improve the quality of scheduling policies in this context. We demonstrate the relevance of our approach by benchmarking various parallel numerical kernel implementations over our runtime system. We obtain significant speedups and a very high efficiency on various typical workloads over multicore machines equipped with multiple accelerators.
dc.description.sponsorshipProgrammation des technologies multicoeurs hétérogènes - ANR-08-COSI-0013
dc.language.isoen
dc.title.enStarPU: a Runtime System for Scheduling Tasks over Accelerator-Based Multicore Machines
dc.typeRapport
dc.subject.halInformatique [cs]/Système d'exploitation [cs.OS]
bordeaux.page33
bordeaux.hal.laboratoriesLaboratoire Bordelais de Recherche en Informatique (LaBRI) - UMR 5800*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.type.institutionINRIA
bordeaux.type.reportrr
hal.identifierinria-00467677
hal.version1
hal.audienceNon spécifiée
hal.origin.linkhttps://hal.archives-ouvertes.fr//inria-00467677v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2010-03&rft.spage=33&rft.epage=33&rft.au=AUGONNET,%20C%C3%A9dric&THIBAULT,%20Samuel&NAMYST,%20Raymond&rft.genre=unknown


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