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Parallel spectral division using the matrix sign function for the generalized eigenproblem

Publication ,  Journal Article
Huss-Lederman, S; Quintana-ORTÍ, ES; Sun, X; Wu, YJY
Published in: International Journal of High Speed Computing
December 1, 2000

In this paper we demonstrate the parallelism of the spectral division using the matrix sign function for the generalized nonsymmetric eigenproblem. We employ the so-called generalized Newton iterative scheme in order to compute the sign function of the matrix pair. A recent study showed a considerable reduction (by 75%) in the computational cost of this iteration, making this approach competitive when compared to the traditional QZ algorithm. The experimental results on an IBM SP3 multicomputer report the parallel performance (efficiency around 60-80%) and scalability of this approach.

Duke Scholars

Published In

International Journal of High Speed Computing

DOI

ISSN

0129-0533

Publication Date

December 1, 2000

Volume

11

Issue

1

Start / End Page

1 / 14

Related Subject Headings

  • Distributed Computing
  • 4606 Distributed computing and systems software
  • 0805 Distributed Computing
 

Citation

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ICMJE
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Huss-Lederman, S., Quintana-ORTÍ, E. S., Sun, X., & Wu, Y. J. Y. (2000). Parallel spectral division using the matrix sign function for the generalized eigenproblem. International Journal of High Speed Computing, 11(1), 1–14. https://doi.org/10.1142/S0129053300000084
Huss-Lederman, S., E. S. Quintana-ORTÍ, X. Sun, and Y. J. Y. Wu. “Parallel spectral division using the matrix sign function for the generalized eigenproblem.” International Journal of High Speed Computing 11, no. 1 (December 1, 2000): 1–14. https://doi.org/10.1142/S0129053300000084.
Huss-Lederman S, Quintana-ORTÍ ES, Sun X, Wu YJY. Parallel spectral division using the matrix sign function for the generalized eigenproblem. International Journal of High Speed Computing. 2000 Dec 1;11(1):1–14.
Huss-Lederman, S., et al. “Parallel spectral division using the matrix sign function for the generalized eigenproblem.” International Journal of High Speed Computing, vol. 11, no. 1, Dec. 2000, pp. 1–14. Scopus, doi:10.1142/S0129053300000084.
Huss-Lederman S, Quintana-ORTÍ ES, Sun X, Wu YJY. Parallel spectral division using the matrix sign function for the generalized eigenproblem. International Journal of High Speed Computing. 2000 Dec 1;11(1):1–14.
Journal cover image

Published In

International Journal of High Speed Computing

DOI

ISSN

0129-0533

Publication Date

December 1, 2000

Volume

11

Issue

1

Start / End Page

1 / 14

Related Subject Headings

  • Distributed Computing
  • 4606 Distributed computing and systems software
  • 0805 Distributed Computing