Linear algebra on high performance computers

DOI10.1016/0096-3003(86)90126-8zbMath0628.65016OpenAlexW1998318713MaRDI QIDQ1093318

Jack J. Dongarra, Danny C. Sorensen

Publication date: 1986

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://digital.library.unt.edu/ark:/67531/metadc282778/

zbMATH Keywords

surveyparallel computationdense matricesnumerical softwarebanded systemsEISPACKhigh performance computersLINPACKsparse QR- factorizationsymmetric eigenvalue problems

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Research exposition (monographs, survey articles) pertaining to numerical analysis (65-02) Parallel numerical computation (65Y05) Direct numerical methods for linear systems and matrix inversion (65F05) Numerical linear algebra (65Fxx)

Related Items

A systolic algorithm for Riccati and Lyapunov equations, Efficient FORTRAN implementation of the gaussian elimination and Householder reduction algorithms on the IBM 3090 vector multiprocessor, Block reduction of matrices to condensed forms for eigenvalue computations, Designing linear algebra algorithms on the IBM 3090 vector multiprocessor with a hierarchical memory system, An Implicit Representation and Iterative Solution of Randomly Sketched Linear Systems

Uses Software

• EISPACK
• LINPACK
• symrcm

Cites Work

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• Error analysis of QR decompositions by Givens transformations
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• Matrix eigensystem routines. EISPACK guide extension
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• Smoothing by spline functions.
• Solution of Large-Scale Sparse Least Squares Problems Using Auxiliary Storage
• On Stable Parallel Linear System Solvers
• Basic Linear Algebra Subprograms for Fortran Usage
• Some Modified Matrix Eigenvalue Problems