Reduction to condensed form for the Eigenvalue problem on distributed memory architectures

Jack J. Dongarra; Robert A. van de Geijn

Reduction to condensed form for the Eigenvalue problem on distributed memory architectures

Jack J. Dongarra, Robert A. van de Geijn

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, we describe a parallel implementation for the reduction of general and symmetric matrices to Hessenberg and tridiagonal form, respectively. The methods are based on LAPACK sequential codes and use a panel-wrapped mapping of matrices to nodes. Results from experiments on the Intel Touchstone Delta are given. © 1992.

Original language	English
Pages (from-to)	973-982
Number of pages	9
Journal	Parallel Computing
Volume	18
Issue number	9
Publication status	Published - Sept 1992

Keywords

distributed memory architecture
Eigenvalue problem
LAPACK
linear algebra

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abstract = "In this paper, we describe a parallel implementation for the reduction of general and symmetric matrices to Hessenberg and tridiagonal form, respectively. The methods are based on LAPACK sequential codes and use a panel-wrapped mapping of matrices to nodes. Results from experiments on the Intel Touchstone Delta are given. {\textcopyright} 1992.",

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AB - In this paper, we describe a parallel implementation for the reduction of general and symmetric matrices to Hessenberg and tridiagonal form, respectively. The methods are based on LAPACK sequential codes and use a panel-wrapped mapping of matrices to nodes. Results from experiments on the Intel Touchstone Delta are given. © 1992.

KW - distributed memory architecture

KW - Eigenvalue problem

KW - LAPACK

KW - linear algebra

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SP - 973

EP - 982

JO - Parallel Computing

JF - Parallel Computing

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ER -

Reduction to condensed form for the Eigenvalue problem on distributed memory architectures

Abstract

Keywords

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