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Abstract: Many linear algebra algorithms require explicit row/column swapping mainly when pivoting operations are involved. Assuming an MIMD distributed-memory environment, these pivoting operations imply a communication overhead between the processors which store the corresponding rows/columns to be swapped. These communications suppose an appreciable bottleneck in the algorithms and reduce parallelism. (Update)
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0.5: Sparse Matrix Operations in Vector and - Parallel Processors Doallo (Correct)
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BibTeX entry: (Update)
@misc{ tourino-local,
author = "Juan Tourino and Ramon Doallo",
title = "Local Pivoting for Sparse Factorizations on Multiprocessors",
url = "citeseer.ist.psu.edu/683743.html" }
Citations (may not include all citations):2441 The Johns Hopkins University Press (context) - Golub, Van Loan - 1989
88 User's Guide for the Harwell-Boeing Sparse Matrix Collection (context) - Duff, Grimes et al. - 1992
21 ScaLAPACK: a Portable Linear Algebra Library for Distributed.. - Choi, Demmel et al. - 1995
7 Parallel Sparse Modified Gram-Schmidt QR Decomposition - Doallo, Fraguela et al. - 1996
7 Exploiting Locality on Parallel Sparse Matrix Computations - Trabado, Zapata - 1995
2 Parallelization and Compilation Issues for Sparse QR Algorit.. (context) - no - 1997
2 An Approach to Decrease Fill-in in Sparse Orthogonalizations.. - no, Doallo - 1996
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