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A Communication Optimization Scheme for Basis Computation of Krylov Subspace Methods on Multi-GPUs Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Accelerating Computation of Eigenvectors in the Dense Nonsymmetric Eigenvalue Problem

Authors : Mark Gates, Azzam Haidar, Jack Dongarra

Published in: High Performance Computing for Computational Science -- VECPAR 2014

Publisher: Springer International Publishing

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Abstract

In the dense nonsymmetric eigenvalue problem, work has focused on the Hessenberg reduction and QR iteration, using efficient algorithms and fast, Level 3 BLAS. Comparatively, computation of eigenvectors performs poorly, limited to slow, Level 2 BLAS performance with little speedup on multi-core systems. It has thus become a dominant cost in the solution of the eigenvalue problem. To address this, we present improvements for the eigenvector computation to use Level 3 BLAS and parallelize the triangular solves, achieving good parallel scaling and accelerating the overall eigenvalue problem more than three-fold.

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Metadata
Title
Accelerating Computation of Eigenvectors in the Dense Nonsymmetric Eigenvalue Problem
Authors
Mark Gates
Azzam Haidar
Jack Dongarra
Copyright Year
2015
Book
High Performance Computing for Computational Science -- VECPAR 2014

Print ISBN: 978-3-319-17352-8

Electronic ISBN: 978-3-319-17353-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-17353-5_16

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