Parallel algorithms for reduction of a general matrix to upper Hessenberg form on a shared memory multiprocessor

DOI10.1016/j.amc.2004.04.046zbMath1070.65029OpenAlexW2030909531MaRDI QIDQ1780495

Ken Wright, Doǧan Kaya

Publication date: 13 June 2005

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

Full work available at URL: https://doi.org/10.1016/j.amc.2004.04.046

zbMATH Keywords

algorithmEigenvalueC++Parallel computationHessenberg formShared memory multiprocessorsorthogonal reductionTHREADS package

Mathematics Subject Classification ID

Related Items (1)

An experience using different synchronisation mechanisms on a shared memory multiprocessors

Uses Software

• NAPACK

Cites Work

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• Finding eigenvalues and eigenvectors of unsymmetric matrices using a distributed-memory multiprocessor
• Two algorithms for the parallel computation of eigenvalues and eigenvectors of large symmetric matrices using the ICL DAP
• A parallel algorithm for the dense symmetric eigenvalue problem on a transputer array
• Reduction to condensed form for the eigenvalue problem on distributed memory architectures
• Block reduction of matrices to condensed forms for eigenvalue computations
• A Fully Parallel Algorithm for the Symmetric Eigenvalue Problem
• A Multiprocessor Algorithm for the Symmetric Tridiagonal Eigenvalue Problem
• A Parallel Algorithm for the Nonsymmetric Eigenvalue Problem
• Reducing a Matrix to Hessenberg Form

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