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.
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References
R. Barret, M. Berry, et al. Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods. SIAM, 1994.
V. Blanco, J. C. Cabaleiro, P. González, D. B. Heras, T. F. Pena, J. J. Pombo, and F. F. Rivera. A performance analysis tool for irregular codes in HPF. In Fifth European SGI/Cray MPP Workshop, Bologna, 1999.
V. Blanco, J. C. Cabaleiro, P. González, D. B. Heras, T. F. Pena, J. J. Pombo, and F. F. Rivera. Paraiso project. http://www.ac.usc.es/~paraiso, jun 2000.
S. Browne, J. Dongarra, and K. London. Review of performance analysis tools for mpi parallel programs. http://www.cs.utk.edu/~browne/perftools-review.
I. Duff, R. Grimes, and J. Lewis. Users guide for the harwell-boeing sparse matrix collection. Technical report, CERFACS, 1992.
D. Heras, V. Blanco, J. Cabaleiro, and F. Rivera. Modeling and improving locality for the sparse matrix-vector product on cache memories. High Performance Numerical Methods and Applications, 2000. special issue in Future Generation Computer Systems.
H. Ishihata, M. Takahashi, and H. Sato. Hardware of ap3000 scalar parallel server. Fujitsu Sci. Tech., pages 24–30, 1997.
L. F. Romero and E. L. Zapata. Data distributions for sparse matrix vector multiplication. Parallel Computing, 21(4):583–605, April 1995.
Y. Saad. Iterative Methods for Sparse Linear Systems. PWS Publishing Co., 1996.
E. Sturler and D. Loher. Parallel solution of irregular, sparse matrix problems using High Performance Fortran. Technical Report TR-96-39, Swiss Center for Scientific Computing, 1996.
M. Ujaldon, E. Zapata, B. Chapman, and H. Zima. Vienna Fortran/HPF extensions for sparse and irregular problems and their compilation. IEEE Transactions on Parallel and Distributed Systems, 8(10):1068–1083, Oct. 1997.
Vampir. Visualization and analysis of mpi programs. http://www.pallas.de.
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© 2002 Springer-Verlag Berlin Heidelberg
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Blanco, V. et al. (2002). Performance Prediction for Parallel Iterative Solvers. In: Sloot, P.M.A., Hoekstra, A.G., Tan, C.J.K., Dongarra, J.J. (eds) Computational Science — ICCS 2002. ICCS 2002. Lecture Notes in Computer Science, vol 2330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46080-2_97
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DOI: https://doi.org/10.1007/3-540-46080-2_97
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