Scheduling strategies for mixed data and task parallelism on heterogeneous processor grids

Olivier Beaumont 1, 2 Arnaud Legrand 3, 4 Yves Robert 3, 4
2 CEPAGE - Algorithmics for computationally intensive applications over wide scale distributed platforms
Université Sciences et Technologies - Bordeaux 1, Inria Bordeaux - Sud-Ouest, École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), CNRS - Centre National de la Recherche Scientifique : UMR5800
3 REMAP - Regularity and massive parallel computing
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme
Abstract : 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.
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Soumis le : mercredi 3 avril 2013 - 14:53:46
Dernière modification le : vendredi 20 avril 2018 - 15:44:24


  • HAL Id : hal-00807405, version 1


Olivier Beaumont, Arnaud Legrand, Yves Robert. Scheduling strategies for mixed data and task parallelism on heterogeneous processor grids. [Research Report] 2002-20, 2002. 〈hal-00807405〉



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