Multifrontal multithreaded rank-revealing sparse QR factorization
| Citations: | 5 - 2 self |
BibTeX
@MISC{Davis_multifrontalmultithreaded,
author = {Timothy A. Davis},
title = {Multifrontal multithreaded rank-revealing sparse QR factorization},
year = {}
}
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Abstract
SuiteSparseQR is a sparse QR factorization package based on the multifrontal method. Within each frontal matrix, LAPACK and the multithreaded BLAS enable the method to obtain high performance on multicore architectures. Parallelism across different frontal matrices is handled with Intel’s Threading Building Blocks library. The symbolic analysis and ordering phase preeliminates singletons by permuting the input matrix into the form [R11 R12; 0 A22] where R11 is upper triangular with diagonal entries above a given tolerance. Next, the fill-reducing ordering, column elimination tree, and frontal matrix structures are found without requiring the formation of the pattern of A T A. Rank-detection is performed within each frontal matrix using Heath’s method, which does not require column pivoting. The resulting sparse QR factorization obtains a substantial fraction of the theoretical peak performance of a multicore computer.
