Skip to main content
SpringerLink
Log in

Performance Prediction for Parallel Iterative Solvers

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In this paper, an exhaustive parallel library of sparse iterative methods and preconditioners in HPF and MPI was developed, and a model for predicting the performance of these codes is presented. This model can be used both by users and by library developers to optimize the efficiency of the codes, as well as to simplify their use. The information offered by this model combines theoretical features of the methods and preconditioners in addition to certain practical considerations and predictions about aspects of the performance of their execution in distributed memory multiprocessors.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. R. J. Allan, J. Heggarty, M. Goodman, and R. R. Ward. Parallel application software on high performance computers. Survey of parallel performance tools and debuggers. www.cse.clrc.ac.uk/Activity/HPCI

  2. R. Barret, M. Berry, T. F. Chan, J. Demmel, J. M. Donato, J. Dongarra, V. Eij Khout, R. P. C. Romine, and H. van der Vorst. Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods, SIAM, 1994.

  3. V. Blanco, J. C. Cabaleiro, P. González, D. B. Heras, T. F. Pena, J. J. Pombo, and F. F. Rivera. Performance prediction for parallel iterative solvers In International Conference on Computational Science (ICCS'2002). Amsterdam, Netherlands, 2002.

  4. V. Blanco, J. C. Cabaleiro, P. González, D. B. Heras, T. F. Pena, J. J. Pombo, and F. F. Rivera. A performance visualization tool for HPF and MPI iterative solvers. In International Parallel and Distributed Processing Symposium (IPDPS'02). Workshop on Parallel and Distributed Scientific and Engineering Computing with Applications (PDSECA'02). Marriot Marina, Fort Lauderdale, Florida, USA, 2002.

  5. V. Blanco, J. C. Cabaleiro, P. González, D. B. Heras, T. F. Pena, J. J. Pombo, and F. F. Rivera. Una librera de métodos iterativos en HPF. In X Jornadas de Paralelismo, pp. 101–106, La Manga del Mar Menor, Murcia, Spain, 1999.

  6. V. Blanco, J. C. Cabaleiro, P. González, D. B. Heras, T. F. Pena, J. J. Pombo, and F. F. Rivera. A performance analysis tool for irregular codes in HPF. In Fifth European SGI/Cray MPP Workshop, Bologna, 1999.

  7. V. Blanco, J. C. Cabaleiro, P. González, D. B. Heras, T. F. Pena, J. J. Pombo, and F. F. Rivera. Paraiso project. www.ac.usc.es/~paraiso, 2000.

  8. S. Browne, J. Dongarra, and K. London. Review of performance analysis tools for MPI parallel programs. www.cs.utk.edu/~browne/perftools-review

  9. Daniel Reed. PABLO: Scalable performance tools. www-pablo.cs.uiuc.edu

  10. L. DeRose, Y. Zhang, and D. A. Reed. SvPablo: A multi-language performance analysis system. In 10th International Conference on Computer Performance Evaluation—modeling Techniques and Tools–Performance Tools'98, pp. 352–355. Palma de Mallorca, Spain, 1998.

  11. K. Dincer, K. A. Hawick, A. Choudary, and G. C. Fox. High performance Fortran and possible extensions to support conjugate gradient algorithms. Technical Report SCCS 703, Northeast Parallel Architectures Center, Syracuse, NY, 1995.

    Google Scholar 

  12. I. S. Duff and J. G. Lewis. Users guide for the Rutherford-Boeing sparse matrix collection (Release I). Technical report, CERFACS and Boeing Computer Services. ftp://ftp.cerfacs.fr/pub/algo/matrices/Rutherford-Boeing.

  13. M. Forum. The MPI standard. www.mpi-forum.org

  14. D. Heras, V. Blanco, J. Cabaleiro, and F. Rivera. Modeling and improving locality for the sparse matrix-vector product on cache memories. Future Generation Computer Systems, 18(1):55–67, 2001.

    Google Scholar 

  15. H. Ishihata, M. Takahashi, and H. Sato. Hardware of AP3000 scalar parallel server. Fujitsu Sci. Tech, pp. 24–30, 1997.

  16. J. Labarta, S. Girona, V. Pillet, T. Cortés, and L. Gregoris. DiP: A parallel program development environment. www.cepba.upc.es/dimemas

  17. D. Patterson and J. Hennessy. Computer Architecture: A Hardware Sofware Interface, San Franciso, California, Morgan Kaufmann, 1996.

    Google Scholar 

  18. Portland Group Inc. PGHPF: HPF compiler. www.pgroup.com, 1995.

  19. D. A. Reed, R. A.Z Aydt, R. J. Noe, P. C. Roth, K. A. Shields, B. Schwartz, and L. F. Tavera. Scalable performance analysis: The Pablo performance analysis environment. In Proceedings of the Scalable Parallel Libraries Conference, pp. 104–113, 1993.

  20. L. F. Romero and E. L. Zapata. Data distributions for sparse matrix vector multiplication. Parallel Computing, 21(4):583–605, 1995.

    Google Scholar 

  21. Y. Saad. Iterative Methods for Sparse Linear Systems, PWS Publishing Co., 1996.

  22. M. Simmons and R. Koskela. Performance instrumentation and visualization. ACM Press, 1990.

  23. E. Sturler and D. Loher. Parallel solution of irregular, sparse matrix problems using high performance fortran. Technical Report TR-96-39, Swiss Center for Scientific Computing, 1996.

  24. M. Ujaldon, E. Zapata, B. Chapman, and H. Zima. Vienna fortran/HPF extensions for sparse and irregular problems and their compilation. IEEE Transactions on Parallel and Distributed Systems, 8(10):1068–1083, 1997.

    Google Scholar 

  25. VAMPIR. Visualization and analysis of MPI programs. www.pallas.de

  26. Version 2.0: 1997. High performance fortran language specification. High Performance Fortran Forum.

  27. J. Yan, S. Sarukhai, and P. Mehra. Performance measurement, visualization and modeling of parallel and distributed programs using the AIMS toolkit. Software Practice and Experience, 1995.

Download references

Author information

Authors and Affiliations

  1. Department of Statistics and Computer Science, LaLaguna University, 38071, Tenerife, Spain

    V. Blanco

  2. Department of Electronics and Systems, A Coruña University, A Coruña, Spain

    P. González

  3. Department of Electronics and Computer Science, Santiogo de Compostela University, 15706, Santiago, Spain

    J. C. Cabaleiro, D. B. Heras, T. F. Pena, J. J. Pombo & F. F. Rivera

Authors
  1. V. Blanco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. González
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. C. Cabaleiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. D. B. Heras
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. T. F. Pena
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. J. J. Pombo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. F. F. Rivera
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Blanco, V., González, P., Cabaleiro, J.C. et al. Performance Prediction for Parallel Iterative Solvers. The Journal of Supercomputing 28, 177–191 (2004). https://doi.org/10.1023/B:SUPE.0000020177.21935.52

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/B:SUPE.0000020177.21935.52

Search

Navigation