Parallel conjugate gradient-like algorithms for solving sparse nonsymmetric linear systems on a vector multiprocessor
type: Item
instance of: scholarly article
title: Parallel conjugate gradient-like algorithms for solving sparse nonsymmetric linear systems on a vector multiprocessor
zbMATH Open document ID: 0678.65017
DOI: 10.1016/0167-8191(89)90030-6
published in: Parallel Computing
publication date: 1989-01-01T00:00:00Z
review text: Two parallel modifications of the preconditioned conjugate gradient method for systems with a nonsymmetric matrix are presented. The algorithms are the conjugate gradient squared algorithm and the generalized minimal residual algorithm. In the algorithms preconditionings by a diagonal matrix and by an incomplete LU factorization are considered. A compressed-matrix representation storage scheme developed at the University of Texas in Austin is adopted. The global and local preconditioners in the incomplete LU factorization are used. The algorithms were implemented on the vector multiprocessor IBM 3090-600E VF and tested for two sets of test matrices.
Mathematics Subject Classification ID: 65F10
Mathematics Subject Classification ID: 65Y05
zbMATH DE Number: 4111894
zbMATH Keywords: parallel conjugate gradient methods
zbMATH Keywords: parallel vector computer
zbMATH Keywords: preconditioned conjugate gradient method
zbMATH Keywords: nonsymmetric matrix
zbMATH Keywords: minimal residual algorithm
zbMATH Keywords: incomplete LU factorization
zbMATH Keywords: vector multiprocessor
author: Giuseppe Radicati di Brozolo
author: Yves Robert
reviewed by: Marián Vajteršic
MaRDI profile type: Publication
full work available at URL: 0167-8191(89)90030-6
OpenAlex ID: W2090314242
description: scientific article; zbMATH DE number 4111894