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Singular Value Decomposition on the Connection Machine CM-5/CM-5E

Part of the Lecture Notes in Computer Science book series (LNCS,volume 879)

Abstract

The Singular Value Decomposition (SVD) is an algorithm that plays an essential role in many applications. There is a need for fast SVD algorithms in applications such as signal processing that require the SVD to be obtained or updated in real time. One technique for obtaining the SVD of a real dense matrix is to first reduce the dense matrix to bidiagonal form and then compute the SVD of the bidiagonal matrix. In this paper we describe how this approach can be implemented efficiently on the Connection Machine CM-5/CM-5E. Timing results show that use of the described techniques yields up to 45% of peak performance in the reduction from dense to bidiagonal form. Numerical results regarding the SVD computation of bidiagonal matrices illustrate that the approach considered yields accurate singular values as well as good performance. We also discuss the dependence between the accuracy of the singular values and the accuracy of the singular vectors.

Keywords

  • Singular Value Decomposition
  • Singular Vector
  • Tridiagonal Matrix
  • Singular Value Decomposition Algorithm
  • Bidiagonal Matrix

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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© 1994 Springer-Verlag Berlin Heidelberg

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Balle, S.M., Pedersen, P.M. (1994). Singular Value Decomposition on the Connection Machine CM-5/CM-5E. In: Dongarra, J., Waśniewski, J. (eds) Parallel Scientific Computing. PARA 1994. Lecture Notes in Computer Science, vol 879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0030134

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  • DOI: https://doi.org/10.1007/BFb0030134

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58712-5

  • Online ISBN: 978-3-540-49050-0

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