Combining Both a Component Model and a Task-based Model for HPC Applications: a Feasibility Study on GYSELA

Olivier Aumage 1 Julien Bigot 2 Hélène Coullon 3 Christian Pérez 3 Jérôme Richard 3
1 STORM - STatic Optimizations, Runtime Methods
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest
2 MDLS - Maison de la Simulation
3 AVALON - Algorithms and Software Architectures for Distributed and HPC Platforms
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme
Abstract : This paper studies the feasibility of efficiently combining both a software component model and a task-based model. Task based models are known to enable efficient executions on recent HPC computing nodes while component models ease the separation of concerns of application and thus improve their modularity and adaptability. This paper describes a prototype version of the COMET programming model combining concepts of task-based and component models, and a preliminary version of the COMET runtime built on top of StarPU and L2C. Evaluations of the approach have been conducted on a real-world use-case analysis of a subpart of the production application GYSELA . Results show that the approach is feasible and that it enables easy composition of independent software codes without introducing overheads. Performance results are equivalent to those obtained with a plain OpenMP based implementation.
Keywords : HPC Software Component Model Task-Based Model Task scheduling Multi-cores Shared-memory
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Conference papers
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https://hal.inria.fr/hal-01518730
Contributor : Christian Perez <>
Submitted on : Friday, May 5, 2017 - 11:51:24 AM
Last modification on : Friday, January 10, 2020 - 3:42:27 PM

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  • HAL Id : hal-01518730, version 1

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Olivier Aumage, Julien Bigot, Hélène Coullon, Christian Pérez, Jérôme Richard. Combining Both a Component Model and a Task-based Model for HPC Applications: a Feasibility Study on GYSELA. 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)., May 2017, Madrid, Spain. ⟨hal-01518730⟩

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