Scheduling strategies for mixed data and task parallelism on heterogeneous processor grids
BEAUMONT, Olivier
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
LEGRAND, Arnaud
Regularity and massive parallel computing [REMAP]
Laboratoire de l'Informatique du Parallélisme [LIP]
Regularity and massive parallel computing [REMAP]
Laboratoire de l'Informatique du Parallélisme [LIP]
ROBERT, Yves
Regularity and massive parallel computing [REMAP]
Laboratoire de l'Informatique du Parallélisme [LIP]
Regularity and massive parallel computing [REMAP]
Laboratoire de l'Informatique du Parallélisme [LIP]
BEAUMONT, Olivier
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
LEGRAND, Arnaud
Regularity and massive parallel computing [REMAP]
Laboratoire de l'Informatique du Parallélisme [LIP]
Regularity and massive parallel computing [REMAP]
Laboratoire de l'Informatique du Parallélisme [LIP]
ROBERT, Yves
Regularity and massive parallel computing [REMAP]
Laboratoire de l'Informatique du Parallélisme [LIP]
< Réduire
Regularity and massive parallel computing [REMAP]
Laboratoire de l'Informatique du Parallélisme [LIP]
Langue
en
Rapport
Ce document a été publié dans
2002
Résumé en anglais
In this paper, we consider the execution of a complex application on a heterogeneous grid computing platform. The complex application consists of a suite of identical, independent problems to be solved. In turn, each problem ...Lire la suite >
In this paper, we consider the execution of a complex application on a heterogeneous grid computing platform. The complex application consists of a suite of identical, independent problems to be solved. In turn, each problem consists of a set of tasks. There are dependences (precedence constraints) between these tasks. A typical example is the repeated execution of the same algorithm on several distinct data samples. We use a non-oriented graph to model the grid platform, where resources have different speeds of computation and communication. We show how to determine the optimal steady-state scheduling strategy for each processor (the fraction of time spent computing and the fraction of time spent communicating with each neighbor). This result holds for a quite general framework, allowing for cycles and multiple paths in the platform graph.< Réduire
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