Efficient eigenvalue and singular value computations on shared memory machines
type: Item
instance of: scholarly article
title: Efficient eigenvalue and singular value computations on shared memory machines
zbMATH Open document ID: 0945.65036
DOI: 10.1016/S0167-8191(99)00021-6
author: N. E. Zubov
published in: Parallel Computing
publication date: 1999-01-01T00:00:00Z
review text: We describe two techniques for speeding up eigenvalue and singular value computations on shared memory parallel computers. Depending on the information that is required, different steps in the overall process can be made more efficient. If only the eigenvalues or singular values are sought then the reduction to condensed form may be done in two or more steps to make best use of optimized level-3 BLAS. If eigenvectors and/or singular vectors are required, too, then their accumulation can be speeded up by another blocking technique. The efficiency of the blocked algorithms depends heavily on the values of certain control parameters. We also present a very simple performance model that allows selecting these parameters automatically.
Mathematics Subject Classification ID: 65F15
Mathematics Subject Classification ID: 65F20
Mathematics Subject Classification ID: 65Y05
Mathematics Subject Classification ID: 65Y20
zbMATH DE Number: 4219147
zbMATH Keywords: parallel computation
zbMATH Keywords: eigenvalues
zbMATH Keywords: reduction to condensed form
zbMATH Keywords: Hessenberg QR iteration
zbMATH Keywords: blocked algorithms
zbMATH Keywords: singular values
zbMATH Keywords: eigenvectors
zbMATH Keywords: singular vectors
zbMATH Keywords: performance
describes a project that uses: LAPACK
describes a project that uses: BLAS
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 4219147