Skip to main content
SpringerLink
Log in

Some preliminary experiences with sparse BLAS in parallel iterative solvers

  • Conference paper
  • First Online:
Applied Parallel Computing Computations in Physics, Chemistry and Engineering Science (PARA 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1041))

Included in the following conference series:

  • 188 Accesses

Abstract

A proposal has been put forward for standardization of sparse matrix operatons in [10]; we describe here some early experiments in adapting the proposed standard to parallel operations in distributed memory environments.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agarwal, R. C., Gustavson, F. G., and Zubair, M. A high performance algorithm using pre-processing for the sparse matrix-vector multiplication. In Proceedings of Supercomputing '92, Minneapolis, MN. Nov 16–20, 1992.

    Google Scholar 

  2. Amestoy, P. R., Daydé, M., and Duff, I. S. Use of Level 3 BLAS in the solution of full and sparse linear equations. In High Performance Computing. Edited by J-L. Delhaye and E. Gelenbe. North-Holland, 19–31, 1989.

    Google Scholar 

  3. Barret, R., Berry, M., CHan, T., Demmel, J., Donato, J., Dongarra, J., Eijkhout, V., Pozo, R., Romine, C. and van der Vorst, H. Templates for the Solution of Linear Systems SIAM, 1993.

    Google Scholar 

  4. Bercovier, M. and Schlesinger, A. Random methods for creating disjoint sets of elements for parallel sparse matrix-vector product Technical report R93024, Université Pierre et Marie Curie, Paris, 1993.

    Google Scholar 

  5. Choi, J., Demmel, J., Dhillon, J., Dongarra, J., Ostrouchov, S., Petitet, A., Stanley, K., Walker, D. and Whalley, R. C. ScaLAPACK: A Portable Linear Algebra Library for Distributed Memory Computers LAPACK working note 95, available from http://www.netlib.org/

    Google Scholar 

  6. Dodson, D. S., Grimes, R. G., and Lewis, J. G. Sparse extensions to the Fortran Basic Linear Algebra Subprograms. ACM Trans. Math. Softw., 17:253–263, 1991.

    Google Scholar 

  7. Dongarra, J. J., Du Croz, J., Duff, I. S., and Hammarling, S. A set of level 3 Basic Linear Algebra Subprograms. ACM Trans. Math. Softw., 16:1–17, 1990.

    Google Scholar 

  8. Dongarra, J. J., Du Croz, J., Hammarling, S., and Hanson, R. J. An extended set of Fortran Basic Linear Algebra Subprograms. ACM Trans. Math. Softw., 14:1–17, 1988.

    Google Scholar 

  9. Duff, I. S., Grimes, R. G., and Lewis, J. G. Sparse matrix test problems. ACM Trans. Math. Softw., 15:1–14, 1989.

    Google Scholar 

  10. Duff, I., Marrone, M. and Radicati, G. A proposal for user level sparse BLAS Technical report TR/PA/92/85, CERFACS, Toulouse, 1992.

    Google Scholar 

  11. Eijkhout, V. Distributed sparse data structures for linear algebra operations Technical report CS-92-169, University of Tennessee, Knoxville, 1992.

    Google Scholar 

  12. Erhel, J. Sparse matrix multiplication on vector computers. Int J High Speed Comput, 2:101–116, 1990.

    Google Scholar 

  13. Filippone, S. Marrone, M. and Radicati di Brozolo, G. Parallel preconditioned conjugate-gradient type algorithms for general sparsity structures Intern. J. of Computer Math., Vol. 40, pp. 159–167, 1992.

    Google Scholar 

  14. Hendrickson, B. and Leland, R. An improved spectral graph partitioning algorithm for mapping parallel computations SIAM J. Sci. Computing, Vol. 16, No. 2, pp. 452–469, 1995.

    Google Scholar 

  15. Heroux, M. A proposal for a sparse BLAS toolkit. Technical Report TR/PA/92/90, CERFACS, Toulouse, France, 1992.

    Google Scholar 

  16. E. Anderson, Z. Bai, C. Bischof, J. Demmel, J. Dongarra, J. Du Croz, A. Greenbaum, S. Hammarling, A. McKenney, S. Ostrouchov, and D. Sorensen. LAPACK Users' Guide. SIAM Pub., 1992.

    Google Scholar 

  17. Lawson, C. L., Hanson, R. J., Kincaid, D. R., and Krogh, F. T. Basic linear algebra subprograms for Fortran usage. A CM Trans. Math. Softw., 5:308–323, 1979.

    Google Scholar 

  18. Notay, Y. An efficient parallel discrete PDE solver, Technical report NM-IBM 94-01, Université Libre de Bruxelles, 1994.

    Google Scholar 

  19. Romero, L. F. and Zapata, E. L. Data distributions for sparse matrix vector multiplication Parallel Computing, Vol. 21, pp. 583–605, 1995.

    Google Scholar 

  20. Van der Vorst, H. A. BiCGSTAB: a fast and smoothly converging variant of BICG for the solution of nonsymmetric linear systems, SIAM J. Sci. Stat. Comput., Vol. 13, No. 2, pp. 631–644, 1992.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IBM ECSEC Rome, Italy

    Salvatore Filippone

  2. IBM Cagliari, Italy

    Carlo Vittoli

Authors
  1. Salvatore Filippone
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlo Vittoli
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Jack Dongarra Kaj Madsen Jerzy Waśniewski

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Filippone, S., Vittoli, C. (1996). Some preliminary experiences with sparse BLAS in parallel iterative solvers. In: Dongarra, J., Madsen, K., Waśniewski, J. (eds) Applied Parallel Computing Computations in Physics, Chemistry and Engineering Science. PARA 1995. Lecture Notes in Computer Science, vol 1041. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60902-4_24

Download citation

  • DOI: https://doi.org/10.1007/3-540-60902-4_24

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60902-5

  • Online ISBN: 978-3-540-49670-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation