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QMR and TFQMR Methods for Sparse Nonsymmetric Problems on Massively Parallel Systems



1996
American Mathematical Society Providence, RI
ISBN: 0-8218-0530-4

The Mathematics of Numerical Analysis
AMS-SIAM Summer Seminar in Applied Mathematics, Park CityPark City, USA, 17 Jul 1995 - 11 Aug 19951995-07-171995-08-11
Providence, RI : American Mathematical Society, Lectures in applied mathematics 32, 59-76 ()

Abstract: Much of the supercomputer research so far has concentrated on implementations of iterative methods for sparse symmetric positive definite matrices, in particular the preconditioned conjugate gradient algorithm, and an understanding of the parallel issues of this algorithm is emerging. Much less work has been done regarding iterative methods for nonsymmetric problems. In this article, parallel implementations of two important algorithms for nonsymmetric systems of equations, namely, the quasi-minimal residual (QMR) and transpose-free QMR (TFQMR) algorithms for solving sparse nonsymmetric systems of linear equations are investigated. The developed data distribution and communication scheme for multiprocessors with distributed memory are based on the analysis of the indices of the non-zero matrix elements. On a PARAGON XP/S 10 with 140 processors, the parallel variants of both QMR and TFQMR show an advantageous scaling behavior for matrices with different sparsity patterns stemming from real finite element applications.

Keyword(s): Numerische Mathematik

Classification:

Contributing Institute(s):
  1. Zentralinstitut für Angewandte Mathematik (ZAM)
  2. Jülich Supercomputing Center (JSC)
Research Program(s):
  1. 899 - ohne Topic (POF2-899) (POF2-899)

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