InforLorV4, Main, Merge, bibRecord, 005400

Modeling, Predicting and Optimizing Redistribution between Clusters on Low Latency Networks

Identifieur interne : 005400 ( Main/Merge ); précédent : 005399; suivant : 005401

Modeling, Predicting and Optimizing Redistribution between Clusters on Low Latency Networks

Auteurs : Emmanuel Jeannot [France] ; Frédéric Wagner [France]

Source :

RBID : Hal:inria-00097260

English descriptors

mix :
- Code-coupling, cluster computing, message scheduling, modelization.

Abstract

In this paper we study the problem of scheduling messages between two parallelmachines connected by a low latency network during a data redistribution. We compare two approaches. In the first approach no scheduling is performed. Since all the messages cannot be transmitted at the same time, the transport layer has to manage the congestion. In the second approach we use two higher-level scheduling algorithms proposed in our previous work [10] called GGP and OGGP. The contribution of this paper is the following: We show that the redistribution time with scheduling is always better than the brute-force approach (up to 30%). As this speedup depends on the input redistribution pattern, we propose a modelization of the behavior of both approaches and show that we are able to accurately predict the redistribution time with or without scheduling and thus able to choose for each pattern whether or not to schedule the communications.

Url:

https://hal.inria.fr/inria-00097260

DOI: 10.1109/AINA.2006.232

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Hal:inria-00097260

Le document en format XML

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<front><div type="abstract" xml:lang="en">In this paper we study the problem of scheduling messages between two parallelmachines connected by a low latency network during a data redistribution. We compare two approaches. In the first approach no scheduling is performed. Since all the messages cannot be transmitted at the same time, the transport layer has to manage the congestion. In the second approach we use two higher-level scheduling algorithms proposed in our previous work [10] called GGP and OGGP. The contribution of this paper is the following: We show that the redistribution time with scheduling is always better than the brute-force approach (up to 30%). As this speedup depends on the input redistribution pattern, we propose a modelization of the behavior of both approaches and show that we are able to accurately predict the redistribution time with or without scheduling and thus able to choose for each pattern whether or not to schedule the communications.</div>
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{{Explor lien
   |wiki=    Wicri/Lorraine
   |area=    InforLorV4
   |flux=    Main
   |étape=   Merge
   |type=    RBID
   |clé=     Hal:inria-00097260
   |texte=   Modeling, Predicting and Optimizing Redistribution between Clusters on Low Latency Networks
}}

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Modeling, Predicting and Optimizing Redistribution between Clusters on Low Latency Networks

Modeling, Predicting and Optimizing Redistribution between Clusters on Low Latency Networks

Source :

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri