An extended set of FORTRAN basic linear algebra subprograms
ACM Transactions on Mathematical Software (TOMS)
ACM Transactions on Mathematical Software (TOMS)
ACM Transactions on Mathematical Software (TOMS)
A set of level 3 basic linear algebra subprograms
ACM Transactions on Mathematical Software (TOMS)
A Supernodal Approach to Sparse Partial Pivoting
SIAM Journal on Matrix Analysis and Applications
Introduction to Algorithms
The design and implementation of a new out-of-core sparse cholesky factorization method
ACM Transactions on Mathematical Software (TOMS)
A column pre-ordering strategy for the unsymmetric-pattern multifrontal method
ACM Transactions on Mathematical Software (TOMS)
Parallel unsymmetric-pattern multifrontal sparse LU with column preordering
ACM Transactions on Mathematical Software (TOMS)
Analysis of the solution phase of a parallel multifrontal approach
Parallel Computing
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We present an out-of-core sparse direct solver for unsymmetric linear systems. The solver factors the coefficient matrix A into A = PLU using Gaussian elimination with partial pivoting. It assumes that A fits within main memory, but it stores the L and U factors on disk (that is, in files). Experimental results indicate that on small to moderately-large matrices (whose factors fit or almost fit in main memory), our code achieves high performance, comparable to that of SuperLU. In some of these cases it is somewhat slower than SuperLU due to overheads associated with the out-of-core behavior of the algorithm (in particular the fact that it always writes the factors to files), but not by a large factor. But in other cases it is faster than SuperLU, probably due to more efficient use of the cache. However, it is able to factor matrices whose factors are much larger than main memory, although at lower computational rates.