A performance-oriented comparative study ...
Type de document :
Communication dans un congrès avec actes
DOI :
Titre :
A performance-oriented comparative study of the Chapel high-productivity language to conventional programming environments
Auteur(s) :
Helbecque, Guillaume [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Inria Lille - Nord Europe
Optimisation de grande taille et calcul large échelle [BONUS]
Gmys, Jan [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Inria Lille - Nord Europe
Optimisation de grande taille et calcul large échelle [BONUS]
Carneiro, Tiago [Auteur]
Université du Luxembourg [Uni.lu]
Melab, Nouredine [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Inria Lille - Nord Europe
Optimisation de grande taille et calcul large échelle [BONUS]
Bouvry, Pascal [Auteur]
Université du Luxembourg [Uni.lu]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Inria Lille - Nord Europe
Optimisation de grande taille et calcul large échelle [BONUS]
Gmys, Jan [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Inria Lille - Nord Europe
Optimisation de grande taille et calcul large échelle [BONUS]
Carneiro, Tiago [Auteur]
Université du Luxembourg [Uni.lu]
Melab, Nouredine [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Inria Lille - Nord Europe
Optimisation de grande taille et calcul large échelle [BONUS]
Bouvry, Pascal [Auteur]
Université du Luxembourg [Uni.lu]
Titre de la manifestation scientifique :
13th International Workshop on Programming Models and Applications for Multicores and Manycores (PMAM'22)
Ville :
Séoul
Pays :
Corée du Sud
Date de début de la manifestation scientifique :
2022-04-02
Titre de l’ouvrage :
PMAM '22: Proceedings of the Thirteenth International Workshop on Programming Models and Applications for Multicores and Manycores
Date de publication :
2022-04-18
Mot(s)-clé(s) en anglais :
Chapel
MPI
Multi-core
OpenMP
Parallel computing
Productivity-awareness
MPI
Multi-core
OpenMP
Parallel computing
Productivity-awareness
Discipline(s) HAL :
Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
Informatique [cs]/Langage de programmation [cs.PL]
Informatique [cs]/Langage de programmation [cs.PL]
Résumé en anglais : [en]
The increase in complexity, diversity and scale of high performance computing environments, as well as the increasing sophistication of parallel applications and algorithms call for productivity-aware programming languages ...
Lire la suite >The increase in complexity, diversity and scale of high performance computing environments, as well as the increasing sophistication of parallel applications and algorithms call for productivity-aware programming languages for high-performance computing. Among them, the Chapel programming language stands out as one of the more successful approaches based on the Partitioned Global Address Space programming model. Although Chapel is designed for productive parallel computing at scale, the question of its competitiveness with well-established conventional parallel programming environments arises. To this end, this work compares the performance of Chapel-based fractal generation on shared-and distributed-memory platforms with corresponding OpenMP and MPI+X implementations. The parallel computation of the Mandelbrot set is chosen as a test-case for its high degree of parallelism and its irregular workload. Experiments are performed on a cluster composed of 192 cores using the French national testbed Grid'5000. Chapel as well as its default tasking layer demonstrate high performance in shared-memory context, while Chapel competes with hybrid MPI+OpenMP in distributed-memory environment.Lire moins >
Lire la suite >The increase in complexity, diversity and scale of high performance computing environments, as well as the increasing sophistication of parallel applications and algorithms call for productivity-aware programming languages for high-performance computing. Among them, the Chapel programming language stands out as one of the more successful approaches based on the Partitioned Global Address Space programming model. Although Chapel is designed for productive parallel computing at scale, the question of its competitiveness with well-established conventional parallel programming environments arises. To this end, this work compares the performance of Chapel-based fractal generation on shared-and distributed-memory platforms with corresponding OpenMP and MPI+X implementations. The parallel computation of the Mandelbrot set is chosen as a test-case for its high degree of parallelism and its irregular workload. Experiments are performed on a cluster composed of 192 cores using the French national testbed Grid'5000. Chapel as well as its default tasking layer demonstrate high performance in shared-memory context, while Chapel competes with hybrid MPI+OpenMP in distributed-memory environment.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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