Automatic Multi-GPU Code Generation applied to Simulation of Electrical Machines

de Oliveira Rodrigues, Antonio Wendell; Guyomarch, Frédéric; Dekeyser, Jean-Luc; Le Menach, Yvonnick

Type de document :

Communication dans un congrès avec actes

Titre :

Automatic Multi-GPU Code Generation applied to Simulation of Electrical Machines

Auteur(s) :

de Oliveira Rodrigues, Antonio Wendell [Auteur]
Contributions of the Data parallelism to real time [DART]
Guyomarch, Frédéric [Auteur]

Contributions of the Data parallelism to real time [DART]
Dekeyser, Jean-Luc [Auteur]

Contributions of the Data parallelism to real time [DART]
Le Menach, Yvonnick [Auteur]

Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]

Titre de la manifestation scientifique :

Compumag 2011

Ville :

Sydney

Pays :

Australie

Date de début de la manifestation scientifique :

2011-07-12

Date de publication :

2011-07-12

Discipline(s) HAL :

Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]

Résumé en anglais : [en]

The electrical and electronic engineering has used parallel programming to solve its large scale complex problems for performance reasons. However, as parallel programming requires a non-trivial distribution of tasks and ...
Lire la suite >The electrical and electronic engineering has used parallel programming to solve its large scale complex problems for performance reasons. However, as parallel programming requires a non-trivial distribution of tasks and data, developers find it hard to implement their applications effectively. Thus, in order to reduce design complexity, we propose an approach to generate code for hybrid architectures (e.g. CPU + GPU) using OpenCL, an open standard for parallel programming of heterogeneous systems. This approach is based on Model Driven Engineering (MDE) and the MARTE profile, standard proposed by Object Management Group (OMG). The aim is to provide resources to non-specialists in parallel programming to implement their applications. Moreover, thanks to model reuse capacity, we can add/change functionalities or the target architecture. Consequently, this approach helps industries to achieve their time-to-market constraints and confirms by experimental tests, performance improvements using multi-GPU environments.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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https://hal.inria.fr/inria-00605645/document
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http://arxiv.org/pdf/1107.0538
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https://hal.inria.fr/inria-00605645/document
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https://hal.inria.fr/inria-00605645/document
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document
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compumag2011-2.pdf
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1107.0538
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