Iterative-method performance evaluation for multiple vectors associated with a large-scale sparse matrix
| Autor: | Kenji Ono, Seigo Imamura, Mitsuo Yokokawa |
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| Rok vydání: | 2016 |
| Předmět: |
Mathematical optimization
Matrix-free methods Scale (ratio) Iterative method Computer science Mechanical Engineering Linear system Computational Mechanics Energy Engineering and Power Technology Aerospace Engineering Condensed Matter Physics Supercomputer 01 natural sciences 010305 fluids & plasmas Mechanics of Materials 0103 physical sciences Convergence (routing) 010306 general physics Coefficient matrix Algorithm Sparse matrix |
| Zdroj: | International Journal of Computational Fluid Dynamics. 30:395-401 |
| ISSN: | 1029-0257 1061-8562 |
| Popis: | Ensemble computing, which is an instance of capacity computing, is an effective computing scenario for exascale parallel supercomputers. In ensemble computing, there are multiple linear systems associated with a common coefficient matrix. We improve the performance of iterative solvers for multiple vectors by solving them at the same time, that is, by solving for the product of the matrices. We implemented several iterative methods and compared their performance. The maximum performance on Sparc VIIIfx was 7.6 times higher than that of a naive implementation. Finally, to deal with the different convergence processes of linear systems, we introduced a control method to eliminate the calculation of already converged vectors. |
| Databáze: | OpenAIRE |
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