Scheduling Strategies for Mixed Data and Task Parallelism on Heterogeneous Clusters and Grids

Olivier Beaumont 1, 2 Arnaud Legrand 3 Yves Robert 3
1 SCALAPPLIX - Algorithms and high performance computing for grand challenge applications
INRIA Futurs, Université Bordeaux Segalen - Bordeaux 2, Université Sciences et Technologies - Bordeaux 1, École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), CNRS - Centre National de la Recherche Scientifique : UMR5800
3 GRAAL - Algorithms and Scheduling for Distributed Heterogeneous Platforms
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme
Abstract : 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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https://hal.inria.fr/hal-00789453
Contributor : Arnaud Legrand <>
Submitted on : Monday, February 18, 2013 - 11:51:44 AM
Last modification on : Friday, April 20, 2018 - 3:44:24 PM

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Olivier Beaumont, Arnaud Legrand, Yves Robert. Scheduling Strategies for Mixed Data and Task Parallelism on Heterogeneous Clusters and Grids. PDP'2003, 11th Euromicro Workshop on Parallel, Distributed and Network-Based Processing, Feb 2003, Gênes, Italy. pp.209―216, ⟨10.1109/EMPDP.2003.1183590⟩. ⟨hal-00789453⟩

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