Abstract
In this paper, an exhaustive parallel library of sparse iterative methods and preconditioners in HPF and MPI was developed, and a model for predicting the performance of these codes is presented. This model can be used both by users and by library developers to optimize the efficiency of the codes, as well as to simplify their use. The information offered by this model combines theoretical features of the methods and preconditioners in addition to certain practical considerations and predictions about aspects of the performance of their execution in distributed memory multiprocessors. © Springer-Verlag Berlin Heidelberg 2002.
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CITATION STYLE
Blanco, V., González, P., Cabaleiro, J. C., Heras, D. B., Pena, T. F., Pombo, J. J., & Rivera, F. F. (2002). Performance prediction for parallel iterative solvers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2330 LNCS, pp. 923–932). Springer Verlag. https://doi.org/10.1007/3-540-46080-2_97
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