article · 1996

Analyzing Data Structures for Parallel Sparse Direct Solvers: Pivoting And Fill-In

J. Tourino, R. Doallo, R. Asenjo, O. Plata, E. L. Zapata

Cited by 0Abstract and details only

Abstract

This paper addresses the problem of the parallelization of sparse direct methods for the solution of linear systems in distributed memory multiprocessors. Sparse direct solvers include pivoting operations and suffer from fill-in, problems that turn the efficient parallelization into a challenging task. We present some data structures to store the sparse matrices that permit to deal in a efficient way with both problems. These data structures have been evaluated on a Cray T3D, implementing, in particular, LU and QR factorizations as examples of direct solvers. Any of the data representations considered enforces the handling of indirections for data accesses, pointer referencing and dynamic data creation. All of

Cite this paper

APA 7

J. Tourino, R. D., Asenjo, R., Plata, O., & Zapata, E. L. (1996). Analyzing Data Structures for Parallel Sparse Direct Solvers: Pivoting And Fill-In.

MLA 9

J. Tourino, R. Doallo, et al. “Analyzing Data Structures for Parallel Sparse Direct Solvers: Pivoting And Fill-In.” 1996.

Chicago

J. Tourino, R. Doallo, R. Asenjo, O. Plata, and E. L. Zapata. 1996. “Analyzing Data Structures for Parallel Sparse Direct Solvers: Pivoting And Fill-In.”

Harvard

J. Tourino, R.D. et al. (1996) ‘Analyzing Data Structures for Parallel Sparse Direct Solvers: Pivoting And Fill-In’.

BibTeX

@article{jtourino1996analyzing,
  title = {{Analyzing Data Structures for Parallel Sparse Direct Solvers: Pivoting And Fill-In}},
  author = {J. Tourino, R. Doallo and Asenjo, R. and Plata, O. and Zapata, E. L.},
  year = {1996}
}

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