Skip to main content

A Study on the Efficient Parallel Block Lanczos Method

  • Conference paper
Computational and Information Science (CIS 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3314))

Included in the following conference series:

Abstract

In order to use parallel computers in specific applications, algorithms need to be developed and mapped onto parallel computer architectures. Main memory access for shared memory system or global communication in message passing system deteriorate the computation speed. In this paper, it is found that the m-step generalization of the block Lanczos method enhances parallel properties by forming m simultaneous search direction vector blocks. QR factorization, which lowers the speed on parallel computers, is not necessary in the m-step block Lanczos method. The m-step method has the minimized synchronization points, which resulted in the minimized global communications and main memory accesses compared to the standard method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bendtsen, C., Hansen, P., Madsen, K., Nielsen, H., Pinar, M.: Implementation of QR up- and downdating on a massively parallel computer. Parallel Computing 21, 49–61 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  2. Cullum, J., Willoughby, R.: Lanczos Algorithms for Large Symmetric Eigenvalues Computation. Birkhauser Boston, Inc., Basel (1985)

    Google Scholar 

  3. Dave, A., Duff, I.: Sparse Matrix Calculations on the CRAY-2. Parallel Computing 5, 55–64 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  4. Demmel, J.: Applied Numerical Linear Algebra. The Society for Industrial and Applied Mathematics Press (1997)

    Google Scholar 

  5. Golub, G., Van Loan, C.: MATRIX Computations. Johns Hopkins University Press, Baltimore (1996)

    Google Scholar 

  6. Gutheil, I., Krotz-Vogel, W.: Performance of a Parallel Matrix Multiplication Routine on Intel iPSC/860. Parallel Computing 20, 953–974 (1994)

    Article  MATH  Google Scholar 

  7. Kim, S., Chronopoulos, A.: A Class of Lanczos Algorithms Implemented on Parallel Computers. Parallel Computing 17, 763–778 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  8. Mathur, K., Johnsson, S.: Multiplication of Matrices of Arbitrary Shape on a Data Parallel Computers. Parallel Computing 20, 919–951 (1994)

    Article  MATH  Google Scholar 

  9. Matstoms, P.: Parallel sparse QR factorization on shared memory architectures. Parallel Computing 21, 473–486 (1995)

    Article  MATH  Google Scholar 

  10. Ranka, S., Won, Y., Sahni, S.: Programming the NCUBE Hypercube. Tech. Rep. Csci No bf 88-13 Univ. of Minnesota (1988)

    Google Scholar 

  11. Saylor, P.: Leapfrog Variants of Iterative Methods for Linear Algebraic Equations. Journal of Computational and Applied Mathematics 24, 169–193 (1988)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kim, S.K., Kim, T.H. (2004). A Study on the Efficient Parallel Block Lanczos Method. In: Zhang, J., He, JH., Fu, Y. (eds) Computational and Information Science. CIS 2004. Lecture Notes in Computer Science, vol 3314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30497-5_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30497-5_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24127-0

  • Online ISBN: 978-3-540-30497-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics