High-Level Application Specific Performance Analysis Using the G-PM Tool

  • Roland Wismüller
  • Marian Bubak
  • Włodzimierz Funika
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3666)


The paper presents an approach to overcome a traditional problem of parallel performance analysis tools: performance data often is too low level and cannot easily be mapped to the application, e.g. its execution phases. The G-PM tool offers the user an easy but flexible means to define his own high-level, application specific metrics based on existing metrics and application events. In a case study based on a real world medical application from the CrossGrid project, we demonstrate this concept as well as its usefulness in practice.


Lattice Boltzmann Method Load Imbalance Output Phase Event Trace Virtual Time 
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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  1. 1.
    CrossGrid User Manual Guide: G-PM (November 2004),
  2. 2.
    Espinosa, A., Margalef, T., Luque, E.: Automatic Performance Analysis of PVM Applications. In: Dongarra, J., Kacsuk, P., Podhorszki, N. (eds.) PVM/MPI 2000. LNCS, vol. 1908, pp. 47–55. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Fahringer, T., Gerndt, M., Riley, G., Träff, J.L.: Knowledge Specification for Automatic Performance Analysis. APART Technical Report, ESPRIT IV Working Group on Automatic Performance Analysis (November 1999),
  4. 4.
    Fahringer, T., Seragiotto, C.: Modeling and Detecting Performance Problems for Distributed and Parallel Programs with JavaPSL. In: 9th IEEE High-Performance Networking and Computing Conference, SC 2001, Denver, CO (November 2001)Google Scholar
  5. 5.
    Gerndt, M., et al.: Performance Tools for the Grid: State of the Art and Future, January 2004. Shaker Verlag, Aachen (2004), Google Scholar
  6. 6.
    Gerndt, M., Schmidt, A., Schulz, M., Wismüller, R.: Automatic Performance Analysis on Hitachi SR8000. In: Wagner, S., et al. (eds.) High Performance Computing in Science and Engineering, Munich, Germany, pp. 443–452. Springer, Heidelberg (2003)Google Scholar
  7. 7.
    Hollingsworth, J.R., Miller, B.P., Gonçalves, M.J.R., Xu, Z., Naim, O., Zheng, L.: MDL: A Language and Compiler for Dynamic Program Instrumentation. In: Proc. International Conference on Parallel Architectures and Compilation Techniques, San Francisco, CA, USA (November 1997),
  8. 8.
    Miller, B.P., et al.: The Paradyn Parallel Performance Measurement Tools. IEEE Computer 28(11), 37–46 (1995), Google Scholar
  9. 9.
    Sloot, P., Tirado-Ramos, A., Hoekstra, A., Bubak, M.: An Interactive Grid Environment for Non-Invasive Vascular Reconstruction. In: 2nd Intl. Workshop on Biomedical Computations on the Grid (BioGrid 2004), Chicago, Illinois, USA, April 2004. IEEE, Los Alamitos (2004)Google Scholar
  10. 10.
    Truong, H.-L., Fahringer, T.: SCALEA: A Performance Analysis Tool for Distributed and Parallel Programs. In: Monien, B., Feldmann, R.L. (eds.) Euro-Par 2002. LNCS, vol. 2400, pp. 75–85. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Wismüller, R., Bubak, M., Funika, W., Arodz, T., Kurdziel, M.: Support for User-Defined Metrics in the On-line Performance Analysis Tool G-PM. In: Dikaiakos, M.D. (ed.) AxGrids 2004. LNCS, vol. 3165, pp. 159–168. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Wismüller, R., Bubak, M., Funika, W., Balis, B.: A Performance Analysis Tool for Interactive Applications on the Grid. Intl. Journal of High Performance Computing Applications 18(3), 305–316 (Fall 2004)CrossRefGoogle Scholar
  13. 13.
    Wismüller, R., Mehammed, H., Gerndt, M., Bode, A.: Performance Monitoring and Analysis for the Grid. In: Martino, B.D., et al. (eds.) Engineering the Grid, American Scientific Publishers (2005) (in print)Google Scholar
  14. 14.
    Wolf, F., Mohr, B.: EARL - A Programmable and Extensible Toolkit for Analyzing Event Traces of Message Passing Programs. In: Hoekstra, A., Hertzberger, B. (eds.) Proc. of the 7th International Conference on High- Performance Computing and Networking (HPCN 1999), Amsterdam, The Netherlands, pp. 503–512 (1999)Google Scholar
  15. 15.
    Wolf, F., Mohr, B.: Automatic Performance Analysis of MPI Applications Based on Event Traces (LNCS 1900). In: Bode, A., Ludwig, T., Karl, W.C., Wismüller, R. (eds.) Euro-Par 2000. LNCS, vol. 1900, pp. 123–132. Springer, Heidelberg (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Roland Wismüller
    • 1
  • Marian Bubak
    • 2
    • 3
  • Włodzimierz Funika
    • 2
  1. 1.University of SiegenSiegenGermany
  2. 2.Institute of Computer Science, AGHKrakówPoland
  3. 3.Academic Computer Centre – CYFRONETKrakówPoland

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