Complexity Results and Heuristics for Pipelined Multicast Operations on Heterogeneous Platforms
BEAUMONT, Olivier
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
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]
MARCHAL, Loris
Algorithms and Scheduling for Distributed Heterogeneous Platforms [GRAAL]
Laboratoire de l'Informatique du Parallélisme [LIP]
Leer más >
Algorithms and Scheduling for Distributed Heterogeneous Platforms [GRAAL]
Laboratoire de l'Informatique du Parallélisme [LIP]
BEAUMONT, Olivier
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
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]
MARCHAL, Loris
Algorithms and Scheduling for Distributed Heterogeneous Platforms [GRAAL]
Laboratoire de l'Informatique du Parallélisme [LIP]
< Leer menos
Algorithms and Scheduling for Distributed Heterogeneous Platforms [GRAAL]
Laboratoire de l'Informatique du Parallélisme [LIP]
Idioma
en
Communication dans un congrès
Este ítem está publicado en
2004 International Conference on Parallel Processing (ICPP\'2004), 2004, Unknown. 2004p. 267―274
IEEE Computer Society Press
Resumen en inglés
We consider the communications involved by the execution of a complex application deployed on a heterogeneous platform. Such applications extensively use macro-communication schemes, such as multicast operations, where ...Leer más >
We consider the communications involved by the execution of a complex application deployed on a heterogeneous platform. Such applications extensively use macro-communication schemes, such as multicast operations, where messages are broadcast to a set of predefined targets. We assume that there are a large number of messages to be multicast in pipeline fashion, and we seek to maximize the throughput of the steady-state operation. We target heterogeneous platforms, modeled by a graph where links have different communication speeds. We show that the problem of computing the best throughput for a multicast operation is NP-hard, whereas the best throughput to broadcast a message to every node in a graph can be computed in polynomial time. Thus, we introduce several heuristics to deal with this problem and prove that some of them are approximation algorithms. We perform, simulations to test these heuristics and show that their results are close to a theoretical upper bound on the throughput that we obtain with a linear programming approach.< Leer menos
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