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SuperLU DIST: A scalable distributed-memory sparse direct solver for unsymmetric linear systems (2003)
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| Venue: | ACM Trans. Mathematical Software |
| Citations: | 144 - 18 self |
BibTeX
@ARTICLE{Li03superludist:,
author = {Xiaoyes. Li and James W. Demmel},
title = {SuperLU DIST: A scalable distributed-memory sparse direct solver for unsymmetric linear systems},
journal = {ACM Trans. Mathematical Software},
year = {2003},
volume = {29},
pages = {110--140}
}
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Abstract
We present the main algorithmic features in the software package SuperLU DIST, a distributedmemory sparse direct solver for large sets of linear equations. We give in detail our parallelization strategies, with a focus on scalability issues, and demonstrate the software’s parallel performance and scalability on current machines. The solver is based on sparse Gaussian elimination, with an innovative static pivoting strategy proposed earlier by the authors. The main advantage of static pivoting over classical partial pivoting is that it permits a priori determination of data structures and communication patterns, which lets us exploit techniques used in parallel sparse Cholesky algorithms to better parallelize both LU decomposition and triangular solution on large-scale distributed machines.
Keyphrases
unsymmetric linear system scalable distributed-memory sparse direct solver superlu dist distributedmemory sparse direct solver main advantage software parallel performance scalability issue linear equation static pivoting software package superlu dist classical partial pivoting parallelization strategy large-scale distributed machine lu decomposition communication pattern data structure priori determination current machine sparse gaussian elimination main algorithmic feature large set parallel sparse cholesky algorithm innovative static pivoting strategy triangular solution
