Scheduling Strategies for Mixed Data and Task Parallelism on Heterogeneous Clusters
BEAUMONT, Olivier
Algorithms and high performance computing for grand challenge applications [SCALAPPLIX]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithms and high performance computing for grand challenge applications [SCALAPPLIX]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
MARCHAL, Loris
Algorithms and Scheduling for Distributed Heterogeneous Platforms [GRAAL]
Laboratoire de l'Informatique du Parallélisme [LIP]
See more >
Algorithms and Scheduling for Distributed Heterogeneous Platforms [GRAAL]
Laboratoire de l'Informatique du Parallélisme [LIP]
BEAUMONT, Olivier
Algorithms and high performance computing for grand challenge applications [SCALAPPLIX]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithms and high performance computing for grand challenge applications [SCALAPPLIX]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
MARCHAL, Loris
Algorithms and Scheduling for Distributed Heterogeneous Platforms [GRAAL]
Laboratoire de l'Informatique du Parallélisme [LIP]
< Reduce
Algorithms and Scheduling for Distributed Heterogeneous Platforms [GRAAL]
Laboratoire de l'Informatique du Parallélisme [LIP]
Language
en
Article de revue
This item was published in
Parallel Processing Letters. 2003, vol. 13, p. 225―244
World Scientific Publishing
English 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 ...Read more >
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 and these dependences are organized as a tree. 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.Read less <
Origin
Hal imported