A sparse linear system solver used in a distributed and heterogeneous grid computing environment

Abstract : Many scientific applications need to solve very large sparse linear systems in order to simulate phenomena close to the reality. Grid computing is an answer to the growing demand of computational power. In a grid computing environment, communication times are significant and the bandwidth is variable, therefore frequent synchronizations slow down performances. Thus it is desirable to reduce the number of synchronizations in a parallel direct algorithm. Inspired from multisplitting techniques, the GREMLINS (GRid Efficient Methods for LINear Systems) solver we developed consists of solving several linear problems obtained by splitting. The principle of the balancing algorithm is presented, and experimental results are given.
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Raimondas Ciegis and David Henty and Bo Kagstrom and Julius Zilinskas. Parallel Scientific Computing and Optimization, 27, Springer, pp.47-56, 2009, Springer Optimization and Its Applications, 〈10.1007/978-0-387-09707-7_4〉
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https://hal.inria.fr/hal-00694484
Contributeur : Ist Rennes <>
Soumis le : vendredi 4 mai 2012 - 14:10:46
Dernière modification le : mercredi 21 mars 2018 - 18:57:59

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Christophe Denis, Raphaël Couturier, Fabienne Jézéquel. A sparse linear system solver used in a distributed and heterogeneous grid computing environment. Raimondas Ciegis and David Henty and Bo Kagstrom and Julius Zilinskas. Parallel Scientific Computing and Optimization, 27, Springer, pp.47-56, 2009, Springer Optimization and Its Applications, 〈10.1007/978-0-387-09707-7_4〉. 〈hal-00694484〉

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