An Approximate Minimum Degree Ordering Algorithm
SIAM Journal on Matrix Analysis and Applications
An Unsymmetric-Pattern Multifrontal Method for Sparse LU Factorization
SIAM Journal on Matrix Analysis and Applications
Recent advances in direct methods for solving unsymmetric sparse systems of linear equations
ACM Transactions on Mathematical Software (TOMS)
On Algorithms For Permuting Large Entries to the Diagonal of a Sparse Matrix
SIAM Journal on Matrix Analysis and Applications
A Fully Asynchronous Multifrontal Solver Using Distributed Dynamic Scheduling
SIAM Journal on Matrix Analysis and Applications
Improved Symbolic and Numerical Factorization Algorithms for Unsymmetric Sparse Matrices
SIAM Journal on Matrix Analysis and Applications
SuperLU_DIST: A scalable distributed-memory sparse direct solver for unsymmetric linear systems
ACM Transactions on Mathematical Software (TOMS)
On the lu factorization of sequences of identically structured sparse matrices within a distributed memory environment
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In this paper, we describe a parallel direct solver for general sparse systems of linear equations that has recently been included in the Watson Sparse Matrix Package (WSMP) [7]. This solver utilizes both shared- and distributed- memory parallelism in the same program and is designed for a hierarchical parallel computer with network-interconnected SMP nodes. We compare the WSMP solver with two similar well known solvers: MUMPS [2] and Super_LUDist [10]. We show that the WSMP solver achieves significantly better performance than both these solvers based on traditional algorithms and is more numerically robust than Super_LUDist. We had earlier shown [8] that MUMPS and Super_LUDist are amongst the fastest distributed-memory general sparse solvers available.