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hal.structure.identifierMiddleware efficiently scalable [MESCAL]
hal.structure.identifierLaboratoire d'Informatique de Grenoble [LIG]
dc.contributor.authorSTANISIC, Luka
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierEfficient runtime systems for parallel architectures [RUNTIME]
dc.contributor.authorTHIBAULT, Samuel
hal.structure.identifierMiddleware efficiently scalable [MESCAL]
hal.structure.identifierLaboratoire d'Informatique de Grenoble [LIG]
dc.contributor.authorLEGRAND, Arnaud
hal.structure.identifierLaboratoire d'Informatique de Grenoble [LIG]
dc.contributor.authorVIDEAU, Brice
hal.structure.identifierLaboratoire d'Informatique de Grenoble [LIG]
dc.contributor.authorMÉHAUT, Jean-François
dc.date.accessioned2024-04-15T09:58:13Z
dc.date.available2024-04-15T09:58:13Z
dc.date.issued2014-08-27
dc.date.conference2014-08-26
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/198982
dc.description.abstractEnMulti-core architectures comprising several GPUs have become mainstream in the field of High-Performance Computing. However, obtaining the maximum performance of such heterogeneous machines is challenging as it requires to carefully offload computations and manage data movements between the different processing units. The most promising and successful approaches so far rely on task-based runtimes that abstract the machine and rely on opportunistic scheduling algorithms. As a consequence, the problem gets shifted to choosing the task granularity, task graph structure, and optimizing the scheduling strategies. Trying different combinations of these different alternatives is also itself a challenge. Indeed, getting accurate measurements requires reserving the target system for the whole duration of experiments. Furthermore, observations are limited to the few available systems at hand and may be difficult to generalize. In this article, we show how we crafted a coarse-grain hybrid simulation/emulation of StarPU, a dynamic runtime for hybrid architectures, over SimGrid, a versatile simulator for distributed systems. This approach allows to obtain performance predictions accurate within a few percents on classical dense linear algebra kernels in a matter of seconds, which allows both runtime and application designers to quickly decide which optimization to enable or whether it is worth investing in higher-end GPUs or not.
dc.description.sponsorshipSimulation de systèmes de prochaine génération - ANR-11-INFR-0013
dc.description.sponsorshipSolveurs pour architectures hétérogènes utilisant des supports d'exécution - ANR-13-MONU-0007
dc.language.isoen
dc.publisherSpringer International Publishing Switzerland
dc.subject.enstarpu-simgrid
dc.title.enModeling and Simulation of a Dynamic Task-Based Runtime System for Heterogeneous Multi-Core Architectures
dc.typeCommunication dans un congrès
dc.identifier.doi10.1007/978-3-319-09873-9_5
dc.subject.halInformatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
bordeaux.page50-62
bordeaux.hal.laboratoriesLaboratoire Bordelais de Recherche en Informatique (LaBRI) - UMR 5800*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleEuro-par - 20th International Conference on Parallel Processing
bordeaux.countryPT
bordeaux.conference.cityPorto
bordeaux.peerReviewedoui
hal.identifierhal-01011633
hal.version1
hal.invitednon
hal.proceedingsoui
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01011633v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2014-08-27&rft.spage=50-62&rft.epage=50-62&rft.au=STANISIC,%20Luka&THIBAULT,%20Samuel&LEGRAND,%20Arnaud&VIDEAU,%20Brice&M%C3%89HAUT,%20Jean-Fran%C3%A7ois&rft.genre=unknown


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