Development methodologies for GPU and cluster of GPUs
Identifieur interne : 001343 ( Hal/Checkpoint ); précédent : 001342; suivant : 001344Development methodologies for GPU and cluster of GPUs
Auteurs : Sylvain Contassot-Vivier [France] ; Stéphane Vialle [France] ; Jens Gustedt [France]Source :
Abstract
This book chapter proposes to draw several development methodologies to obtain efficient codes in classical scientific applications. Those methodologies are based on the feedback from several research works involving GPUs, either alone in a single machine or in a cluster of machines. Indeed, our past collaborations with industries have allowed us to point out that in their economical context, they can adopt a parallel technology only if its implementation and maintenance costs are small according to the potential benefits (performance, accuracy,...). So, in such contexts, GPU programming is still regarded with some distance according to its specific field of applicability (SIMD/SIMT model) and its still higher programming complexity and maintenance. In the academic domain, things are a bit different but studies for efficiently integrating GPU computations in multi-core clusters with maximal overlapping of computations with communications and/or other computations, are still rare. For these reasons, the major aim of that chapter is to propose as simple as possible general programming patterns that can be followed or adapted in practical implementations of parallel scientific applications. Also, we propose in a third part, a prospect analysis together with a particular programming tool that is intended to ease multi-core GPU cluster programming.
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For these reasons, the major aim of that chapter is to propose as simple as possible general programming patterns that can be followed or adapted in practical implementations of parallel scientific applications. 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Development methodologies for GPU and cluster of GPUs, Chapman & Hall/CRC, 2013, 978-1-4665-7162-4</idno><idno type="halRef">Raphaël Couturier. Development methodologies for GPU and cluster of GPUs, Chapman & Hall/CRC, 2013, 978-1-4665-7162-4</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="UNIV-FCOMTE">Université de Franche-Comté</idno><idno type="stamp" n="UNIV-METZ">Université Paul Verlaine - Metz</idno><idno type="stamp" n="UNIV-STRASBG">Université de Strasbourg</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-NSS" p="LORIA">Réseaux, systèmes et services</idno><idno type="stamp" n="SUPELEC">SUPELEC</idno><idno type="stamp" n="SUP_IMS" p="SUPELEC">IMS - Equipe Information, Multimodalité et Signal</idno><idno type="stamp" n="INSA-STRASBOURG">Institut National des Sciences Appliquées de Strasbourg</idno><idno type="stamp" n="CENTRALESUPELEC">Ecole CentraleSupélec</idno><idno type="stamp" n="UMI-GTL" p="CENTRALESUPELEC">Unité Mixte Internationale - Georgia Tech Lorraine</idno><idno type="stamp" n="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="popular" n="0">No</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Development methodologies for GPU and cluster of GPUs</title><author role="aut"><persName><forename type="first">Sylvain</forename><surname>Contassot-Vivier</surname></persName><idno type="halAuthorId">661613</idno><orgName ref="#struct-300291"></orgName><affiliation ref="#struct-205117"></affiliation></author><author role="aut"><persName><forename type="first">Stéphane</forename><surname>Vialle</surname></persName><email>Stephane.Vialle@supelec.fr</email><idno type="idHal">stephane-vialle</idno><idno type="halAuthorId">944590</idno><orgName ref="#struct-300812"></orgName><affiliation ref="#struct-205117"></affiliation><affiliation ref="#struct-24541"></affiliation></author><author role="aut"><persName><forename type="first">Jens</forename><surname>Gustedt</surname></persName><email>Jens.Gustedt@inria.fr</email><ptr type="url" target="http://www.loria.fr/~gustedt/"></ptr><idno type="idHal">jens-gustedt</idno><idno type="halAuthorId">944591</idno><idno type="ORCID">http://orcid.org/0000-0003-0779-1944</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-205117"></affiliation><affiliation ref="#struct-217648"></affiliation></author></analytic><monogr><idno type="isbn">978-1-4665-7162-4</idno><title level="m">Development methodologies for GPU and cluster of GPUs</title><editor>Raphaël Couturier</editor><imprint><publisher>Chapman & Hall/CRC</publisher><date type="datePub">2013</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><classCode scheme="halDomain" n="info.info-dc">Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]</classCode><classCode scheme="halTypology" n="COUV">Book section</classCode></textClass><abstract xml:lang="en">This book chapter proposes to draw several development methodologies to obtain efficient codes in classical scientific applications. Those methodologies are based on the feedback from several research works involving GPUs, either alone in a single machine or in a cluster of machines. Indeed, our past collaborations with industries have allowed us to point out that in their economical context, they can adopt a parallel technology only if its implementation and maintenance costs are small according to the potential benefits (performance, accuracy,...). So, in such contexts, GPU programming is still regarded with some distance according to its specific field of applicability (SIMD/SIMT model) and its still higher programming complexity and maintenance. In the academic domain, things are a bit different but studies for efficiently integrating GPU computations in multi-core clusters with maximal overlapping of computations with communications and/or other computations, are still rare. For these reasons, the major aim of that chapter is to propose as simple as possible general programming patterns that can be followed or adapted in practical implementations of parallel scientific applications. Also, we propose in a third part, a prospect analysis together with a particular programming tool that is intended to ease multi-core GPU cluster programming.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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