Advertisement

Cray Performance Analysis Tools

  • Luiz DeRose
  • Bill Homer
  • Dean Johnson
  • Steve Kaufmann
  • Heidi Poxon

Abstract

The basic purpose of application performance tools, are to help the user identify whether or not their application is running efficiently on the computing resources available. However, the increasing system software and architecture complexity, as well as the scale of the current and future high end supercomputers, bring a new set of challenges to today’s performance tools. In order to be able to achieve high performance on these peta-scale computing systems, users need a new infrastructure for performance analysis that can handle the challenges associated with heterogeneous architectures with multiple levels of parallelism, hundreds of thousands of computing elements, and novel programming paradigms. In this paper we present the Cray Performance Analysis Tools, which is set on an evolutionary path to address the application performance analysis challenges associated with these massive computing systems.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bell, R., Malony, A.D., Shende, S.: A Portable, Extensible, and Scalable Tool for Parallel Performance Profile Analysis. In: Proceedings of Euro-Par 2003, pp. 17–26 (2003) Google Scholar
  2. 2.
    DeRose, L.: The Hardware Performance Monitor Toolkit. In: Proceedings of Euro-Par 2001, pp. 122–131 (August 2001) Google Scholar
  3. 3.
    DeRose, L., Ekanadham, K., Hollingsworth, J.K., Sbaraglia, S.: SIGMA: A Simulator Infrastructure to Guide Memory Analysis. In: Proceedings of SC2002. Baltimore, Maryland (2002) Google Scholar
  4. 4.
    DeRose, L., Homer, B., Johnson, D.: Detecting Application Load Imbalance on High End Massively Parallel Systems. In: Proceedings of Euro-Par 2007, pp. 151–159 (August 2001) Google Scholar
  5. 5.
    DeRose, L., Reed, D.: Svpablo: A Multi-Language Architecture-Independent Performance Analysis System. In: Proceedings of the International Conference on Parallel Processing, pp. 311–318 (1999) Google Scholar
  6. 6.
    European Center for Parallelism of Barcelona (CEPBA): Paraver - Parallel Program Visualization and Analysis Tool - Reference Manual (2000). Http://www.cepba.upc.es/paraver
  7. 7.
    Graham, S., Kessler, P., McKusick, M.: gprof: A Call Graph Execution Profiler. In: Proceedings of the SIGPLAN ’82 Symposium on Compiler Construction, pp. 120–126. Association for Computing Machinery, Boston, MA (1982) Google Scholar
  8. 8.
    Kim, S., Kuhn, B., Voss, M., Hoppe, H.C., Nagel, W.: VGV: Supporting Performance Analysis of Object-Oriented Mixed MPI/OpenMP Parallel Applications. In: Proceedings of the International Parallel and Distributed Processing Symposium (April 2002) Google Scholar
  9. 9.
    Mellor-Crummey, J., Fowler, R., Marin, G., Tallent, N.: HPCView: A tool for top-down analysis of node performance. The Journal of Supercomputing 23(1), 81–104 (2002) zbMATHCrossRefGoogle Scholar
  10. 10.
    Miller, B.P., Callaghan, M.D., Cargille, J.M., Hollingsworth, J.K., Irvin, R.B., Karavanic, K.L., Kunchithapadam, K., Newhall, T.: The Paradyn Parallel Performance Measurement Tools. IEEE Computer 28(11), 37–46 (1995) Google Scholar
  11. 11.
    Nagel, W., Arnold, A., Weber, M., Hoppe, H.C., Solchenbach, K.: Vampir: Visualization and Analysis of MPI Resources. Supercomputer 12, 69–80 (1996) Google Scholar
  12. 12.
    Wolf, F., Mohr, B.: Automatic Performance Analysis of Hybrid MPI/OpenMP Applications. Journal of Systems Architecture, Special Issue ‘Evolutions in parallel distributed and network-based processing’ 49(10–11), 421–439 (2003) CrossRefGoogle Scholar
  13. 13.
    Wu, C., Bolmarcich, A., Snir, M., Wootton, D., Parpia, F., Chan, A., Lusk, E., Gropp, W.: From trace generation to visualization: A performance framework for distributed parallel systems. In: Proceedings of Supercomputing 2000 (2000) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Luiz DeRose
    • 1
  • Bill Homer
    • 1
  • Dean Johnson
    • 1
  • Steve Kaufmann
    • 1
  • Heidi Poxon
    • 1
  1. 1.Cray Inc.Mendota HeightsUSA

Personalised recommendations