A low level component model easing performance portability of HPC applications - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Computing Année : 2014

A low level component model easing performance portability of HPC applications

Résumé

Scientific applications are getting increasingly complex, e.g., to improve their accuracy by taking into account more phenomena. Meanwhile, computing infrastructures are continuing their fast evolution. Thus, software engineering is becoming a major issue to offer ease of development, portability and maintainability while achieving high performance. Component based software engineering offers a promising approach that enables the manipulation of the software architecture of applications. However, existing models do not provide an adequate support for performance portability of HPC applications. This paper proposes a low level component model (L²C) that supports inter-component interactions for typical scenarios of high performance computing, such as process-local shared memory and function invocation (C++ and Fortran), MPI, and Corba. To study the benefits of using L²C, this paper walks through an example of stencil computation, i.e. a structured mesh Jacobi implementation of the 2D heat equation parallelized through domain decomposition. The experimental results obtained on the Grid'5000 testbed and on the Curie supercomputer show that L²C can achieve performance similar to that of native implementations, while easing performance portability.
Fichier principal
Vignette du fichier
paper.pdf (276.09 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00911231 , version 1 (29-11-2013)

Identifiants

Citer

Julien Bigot, Zhengxiong Hou, Christian Pérez, Vincent Pichon. A low level component model easing performance portability of HPC applications. Computing, 2014, 96 (12), pp.1115-1130. ⟨10.1007/s00607-013-0368-3⟩. ⟨hal-00911231⟩
417 Consultations
416 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More