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A Supernodal Approach to Sparse Partial Pivoting

Demmel, James W., Eisenstat, Stanley C., Gilbert, John R., Li, Xiaoye S., Liu, Joseph W. H.·1999·SIAM Journal on Matrix Analysis and Applications

Abstract

We investigate several ways to improve the performance of sparse LU factorization with partial pivoting, as used to solve unsymmetric linear systems. We introduce the notion of unsymmetric supernodes to perform most of the numerical computation in dense matrix kernels. We introduce unsymmetric supernode-panel updates and two-dimensional data partitioning to better exploit the memory hierarchy. We use Gilbert and Peierls's depth-first search with Eisenstat and Liu's symmetric structural reductions to speed up symbolic factorization. We have developed a sparse LU code using all these ideas. We present experiments demonstrating that it is significantly faster than earlier partial pivoting codes. We also compare its performance with UMFPACK, which uses a multifrontal approach; our code is very competitive in time and storage requirements, especially for large problems.

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Metadata

Authors
Demmel, J. W., Eisenstat, S. C., Gilbert, J. R., Li, X. S., & Liu, J. W. H.
Venue
SIAM Journal on Matrix Analysis and Applications
Published
1999-01
Volume
20
Issue
3
Pages
720-755
ISSN
  • 0895-4798 (ISSN-L)
  • 1095-7162
OpenAlex
W2038205735
Cited by
840
References
0
FWCI
25.24
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Supernodal Approach to Sparse Partial Pivoting. https://app.cochat.ai/papers/10.1137/s0895479895291765
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Demmel, J. W., Eisenstat, S. C., Gilbert, J. R., Li, X. S., & Liu, J. W. H. (1999). A Supernodal Approach to Sparse Partial Pivoting. SIAM Journal on Matrix Analysis and Applications. https://doi.org/10.1137/s0895479895291765

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