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hal.structure.identifierSTatic Optimizations, Runtime Methods [STORM]
dc.contributor.authorSAKKA, Chiheb
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierSTatic Optimizations, Runtime Methods [STORM]
dc.contributor.authorGUERMOUCHE, Amina
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierSTatic Optimizations, Runtime Methods [STORM]
dc.contributor.authorAUMAGE, Olivier
hal.structure.identifierSTatic Optimizations, Runtime Methods [STORM]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorSAILLARD, Emmanuelle
hal.structure.identifierIHU-LIRYC
hal.structure.identifierModélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorPOTSE, Mark
hal.structure.identifierModélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierIHU-LIRYC
dc.contributor.authorCOUDIÈRE, Yves
hal.structure.identifierSTatic Optimizations, Runtime Methods [STORM]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorBARTHOU, Denis
dc.date.accessioned2024-04-04T02:36:13Z
dc.date.available2024-04-04T02:36:13Z
dc.date.conference2022-09-05
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190707
dc.description.abstractEnRealistic simulation of cardiac electrophysiology requires both high resolution and computationally expensive models of membrane dynamics. Optimization of membrane models can therefore have a large impact on time, hardware, and energy usage. We tested both CPU-based and GPU-based optimization techniques for a human heart model with Ten Tusscher-Panfilov 2006 dynamics. Compared to a multithreaded code running on 64 CPU cores, the tested NVIDIA Tesla P100 GPU proved about 3 times faster. Effective use of the CPU's SIMD capabilities allowed a similar performance gain. GPU performance was bounded by the data transfer rate between GPU and main memory. Optimal SIMD use required explicit vectorization and an adapted data structure. We conclude that on mixed CPU-GPU systems the best results are obtained by optimizing both CPU and GPU code and using a runtime system that balances CPU and GPU load.
dc.description.sponsorshipSimulation exascale de l'électrophysiologie cardiaque pour la recherche sur les arythmies - ANR-18-CE46-0010
dc.description.sponsorshipL'Institut de Rythmologie et modélisation Cardiaque - ANR-10-IAHU-0004
dc.language.isoen
dc.title.enA comparison of multithreading, vectorization, and GPU computing for the acceleration of cardiac electrophysiology models
dc.typeCommunication dans un congrès
dc.subject.halInformatique [cs]
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleComputing in Cardiology 2022
bordeaux.countryFI
bordeaux.conference.cityTampere
bordeaux.peerReviewedoui
hal.identifierhal-03936903
hal.version1
hal.invitednon
hal.proceedingsnon
hal.conference.end2022-09-07
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03936903v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=SAKKA,%20Chiheb&GUERMOUCHE,%20Amina&AUMAGE,%20Olivier&SAILLARD,%20Emmanuelle&POTSE,%20Mark&rft.genre=unknown


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