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58
A column approximate minimum degree ordering algorithm
, 2000
"... Sparse Gaussian elimination with partial pivoting computes the factorization PAQ = LU of a sparse matrix A, where the row ordering P is selected during factorization using standard partial pivoting with row interchanges. The goal is to select a column preordering, Q, based solely on the nonzero patt ..."
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Cited by 253 (50 self)
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Sparse Gaussian elimination with partial pivoting computes the factorization PAQ = LU of a sparse matrix A, where the row ordering P is selected during factorization using standard partial pivoting with row interchanges. The goal is to select a column preordering, Q, based solely on the nonzero pattern of A such that the factorization remains as sparse as possible, regardless of the subsequent choice of P. The choice of Q can have a dramatic impact on the number of nonzeros in L and U. One scheme for determining a good column ordering for A is to compute a symmetric ordering that reduces fillin in the Cholesky factorization of ATA. This approach, which requires the sparsity structure of ATA to be computed, can be expensive both in
SuperLU DIST: A scalable distributedmemory sparse direct solver for unsymmetric linear systems
 ACM Trans. Mathematical Software
, 2003
"... 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 sc ..."
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Cited by 87 (17 self)
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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 largescale distributed machines.
A column preordering strategy for the unsymmetricpattern multifrontal method
 ACM Transactions on Mathematical Software
, 2004
"... A new method for sparse LU factorization is presented that combines a column preordering strategy with a rightlooking unsymmetricpattern multifrontal numerical factorization. The column ordering is selected to give a good a priori upper bound on fillin and then refined during numerical factoriza ..."
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Cited by 57 (4 self)
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A new method for sparse LU factorization is presented that combines a column preordering strategy with a rightlooking unsymmetricpattern multifrontal numerical factorization. The column ordering is selected to give a good a priori upper bound on fillin and then refined during numerical factorization (while preserving the bound). Pivot rows are selected to maintain numerical stability and to preserve sparsity. The method analyzes the matrix and automatically selects one of three preordering and pivoting strategies. The number of nonzeros in the LU factors computed by the method is typically less than or equal to those found by a wide range of unsymmetric sparse LU factorization methods, including leftlooking methods and prior multifrontal methods.
Corotational Simulation of Deformable Solids
, 2004
"... The classical formulation of large displacement viscoelasticity requires the geometrically nonlinear Green tensor. Keeping track of the rotational part of strain permits alternative formulations, that allow the tensor to stay linear, and at the same time maintaining rotational invariance. We replac ..."
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Cited by 38 (2 self)
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The classical formulation of large displacement viscoelasticity requires the geometrically nonlinear Green tensor. Keeping track of the rotational part of strain permits alternative formulations, that allow the tensor to stay linear, and at the same time maintaining rotational invariance. We replace a recently proposed heuristical warping technique by the application of the polar decomposition. The polar decomposition exactly extracts rotations, thus enhances stability and accuracy. We combine it with a hierarchical finite element basis, which allows us to compute accurate rotations from a coarse level nonlinear simulation and use them with corotated tensors for finer detail.
Sparse Gaussian Elimination on High Performance Computers
, 1996
"... This dissertation presents new techniques for solving large sparse unsymmetric linear systems on high performance computers, using Gaussian elimination with partial pivoting. The efficiencies of the new algorithms are demonstrated for matrices from various fields and for a variety of high performan ..."
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Cited by 36 (6 self)
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This dissertation presents new techniques for solving large sparse unsymmetric linear systems on high performance computers, using Gaussian elimination with partial pivoting. The efficiencies of the new algorithms are demonstrated for matrices from various fields and for a variety of high performance machines. In the first part we discuss optimizations of a sequential algorithm to exploit the memory hierarchies that exist in most RISCbased superscalar computers. We begin with the leftlooking supernodecolumn algorithm by Eisenstat, Gilbert and Liu, which includes Eisenstat and Liu's symmetric structural reduction for fast symbolic factorization. Our key contribution is to develop both numeric and symbolic schemes to perform supernodepanel updates to achieve better data reuse in cache and floatingpoint register...
Recent Advances in Direct Methods for Solving Unsymmetric Sparse Systems of Linear Equations
, 2001
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Using Generalized Cayley Transformations Within An Inexact Rational Krylov Sequence Method
 SIAM J. MATRIX ANAL. APPL
"... The rational Krylov sequence (RKS) method is a generalization of Arnoldi's method. It constructs an orthogonal reduction of a matrix pencil into an upper Hessenberg pencil. The RKS method is useful when the matrix pencil may be efficiently factored. This article considers approximately solving the r ..."
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Cited by 22 (3 self)
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The rational Krylov sequence (RKS) method is a generalization of Arnoldi's method. It constructs an orthogonal reduction of a matrix pencil into an upper Hessenberg pencil. The RKS method is useful when the matrix pencil may be efficiently factored. This article considers approximately solving the resulting linear systems with iterative methods. We show that a Cayley transformation leads to a more efficient and robust eigensolver than the usual shiftinvert transformation when the linear systems are solved inexactly within the RKS method. A relationship with the recently introduced JacobiDavidson method is also established.
An Unsymmetrized Multifrontal LU Factorization
 SIAM Journal on Matrix Analysis and Applications
, 2000
"... A well known approach to compute the LU factorization of a general unsymmetric matrix A is to build the elimination tree associated with the pattern of the symmetric matrix A+A T and use it as a computational graph to drive the numerical factorization. This approach, although very efficient on a lar ..."
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Cited by 19 (4 self)
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A well known approach to compute the LU factorization of a general unsymmetric matrix A is to build the elimination tree associated with the pattern of the symmetric matrix A+A T and use it as a computational graph to drive the numerical factorization. This approach, although very efficient on a large range of unsymmetric matrices, does not capture the unsymmetric structure of the matrices. We introduce a new algorithm which detects and exploits the structural asymmetry of the submatrices involved during the processing of the elimination tree. We show that, with the new algorithm, significant gains both in memory and in time to perform the factorization can be obtained.
NestedDissection Orderings For Sparse Lu With Partial Pivoting
 SIAM J. Matrix Anal. Appl
, 2000
"... . We describe the implementation and performance of a novel fillminimization ordering technique for sparse LU factorization with partial pivoting. The technique was proposed by Gilbert and Schreiber in 1980 but never implemented and tested. Like other techniques for ordering sparse matrices for ..."
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Cited by 18 (5 self)
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. We describe the implementation and performance of a novel fillminimization ordering technique for sparse LU factorization with partial pivoting. The technique was proposed by Gilbert and Schreiber in 1980 but never implemented and tested. Like other techniques for ordering sparse matrices for LU with partial pivoting, our new method preorders the columns of the matrix (the row permutation is chosen by the pivoting sequence during the numerical factorization). Also like other methods, the column permutation Q that we select is a permutation that minimizes the fill in the Cholesky factor of Q T A T AQ. Unlike existing columnordering techniques, which all rely on minimumdegree heuristics, our new method is based on a nesteddissection ordering of A T A. Our algorithm, however, never computes a representation of A T A, which can be expensive. We only work with a representation of A itself. Our experiments demonstrate that the method is e#cient and that it can reduce fill significantly relative to the best existing methods. The method reduces the LU running time on some very large matrices (tens of millions of nonzeros in the factors) by more than a factor of 2. 1.