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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Denis, C., Couturier, R., Jézéquel, F. (2009). A Sparse Linear System Solver Used in a Distributed and Heterogenous Grid Computing Environment. In: Parallel Scientific Computing and Optimization. Springer Optimization and Its Applications, vol 27. Springer, New York, NY. https://doi.org/10.1007/978-0-387-09707-7_4
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DOI: https://doi.org/10.1007/978-0-387-09707-7_4
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