Cluster Computing

, Volume 10, Issue 2, pp 155–166 | Cite as

Predictive performance modelling of parallel component compositions

Article

Abstract

Large-scale scientific computing applications frequently make use of closely-coupled distributed parallel components. The performance of such applications is therefore dependent on the component parts and their interaction at run-time. This paper describes a methodology for predictive performance modelling and evaluation of parallel applications composed of multiple interacting components. In this paper, the fundamental steps and required operations involved in the modelling and evaluation process are identified—including component decomposition, component model combination, M×N communication modelling, dataflow analysis and overall performance evaluation. A case study is presented to illustrate the modelling process and the methodology is verified through experimental analysis.

Keywords

Performance modelling Parallel component composition M×N communication 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Beckman, P., Fasel, P., Humphrey, W., Mniszewski, S.: Efficient coupling of parallel applications using PAWS. In: Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing, July 1998 Google Scholar
  2. 2.
    Geist, G.A., Kohl, J.A., Papadopoulos, P.M.: CUMULVS: Providing fault-tolerance, visualization and steering of parallel applications. Int. J. High Perform. Comput. Appl. 11(3), 224–236 (1997) CrossRefGoogle Scholar
  3. 3.
    Edjlali, G., Sussman, A., Saltz, J.: Interoperability of data parallel runtime libraries. In: Proceedings of the 11th International Parallel Processing Symposium, IEEE Computer Society Press, Washington (1997) Google Scholar
  4. 4.
    Larson, J.W., Jacob, R., Foster, I., Guo, J.: The model coupling toolkit. In: Proceedings of International Conference on Computational Science, 2001 Google Scholar
  5. 5.
    Common Component Architecture (CCA) Forum, http://www.cca-forum.org/
  6. 6.
    Furmento, N., Mayer, A., McGough, S., Newhouse, S., Darlington, J.: A component framework for HPC applications. In: 7th International Euro-Par Conference, LNCS 2150, August 2001, pp. 540–548 Google Scholar
  7. 7.
    Foster, I., Kesselman, C., Nick, J., Tuecke, S.: The physiology of the grid: an open grid services architecture for distributed systems integration, Open Grid Service Infrastructure WG, Global Grid Forum, June 2002 Google Scholar
  8. 8.
    Govindaraju, M., Krishnan, S., Chiu, K., Slominski, A., Gannon, D., Bramley, R.: Merging the CCA component model with the OGSI framework. In: Proceedings of CCGrid2003, 3rd International Symposium on Cluster Computing and the Grid, May 2003 Google Scholar
  9. 9.
    Mayer, A., McGough, S., Furmento, N., Lee, W., Newhouse, S., Darlington, J.: ICENI dataflow and workflow: Composition and scheduling in space and time. In: UK e-Science All Hands Meeting, Nottingham, UK, September 2003 Google Scholar
  10. 10.
    Nudd, G., Kerbyson, D., Papaefstathiou, E., Perry, S., Harper, J., Wilcox, D.: PACE: a toolset for the performance prediction of parallel and distributed systems. Int. J. High Perform. Comput. Appl. 14(3), 228–251 (2000) CrossRefGoogle Scholar
  11. 11.
    Qin, X., Jiang, H., Zhu, Y., Swanson, D.R.: Towards load balancing support for I/O-intensive parallel jobs in a cluster of workstations. In: Proceedings of the 5th IEEE International Conference on Cluster Computing (Cluster 2003), December 2003, pp. 100–107 Google Scholar
  12. 12.
    Rosti, E., Serazzi, G., Smirini, E., Squillante, M.S.: Models of parallel applications with large computation and IO requirements. IEEE Trans. Softw. Eng. 28(3), 286–307 (2002) CrossRefGoogle Scholar
  13. 13.
    Adve, V.S., Vernon, M.K.: Parallel program performance prediction using deterministic task graph analysis. ACM Trans. Comput. Syst. 22(1), 94–136 (2004) CrossRefGoogle Scholar
  14. 14.
    Yan, Y., Zhang, X., Song, Y.: An effective and practical performance prediction model for parallel computing on non-dedicated heterogeneous NOW. J. Parallel Distributed Comput. 38(1), 63–80 (1996) CrossRefGoogle Scholar
  15. 15.
    Qin, X., Jiang, H., Zhu, Y., Swanson, D.R.: Dynamic load balancing for I/O-intensive tasks on heterogeneous clusters. In: Proceedings of the 10th International Conference on High Performance Computing (HiPC 2003), Dec. 2003, pp. 300–309 Google Scholar
  16. 16.
    Bertrand, F., Bramley, R.: DCA: a distributed CCA framework based on MPI. In: Proceedings of the 9th International Workshop on High-Level Parallel Programming Models and Supportive Environments, April 2004 Google Scholar
  17. 17.
    Keahey, K., Fasel, P., Mniszewski, S.: PAWS: Collective interactions and data transfers. In: Proceedings of the 10th IEEE High Performance Distributed Computing, August 2001 Google Scholar
  18. 18.
    Lee, J., Sussman, A.: Efficient communication between parallel programs with interComm, Technical Report CS-TR-4557 and UMIACS-TR-2004-04, University of Maryland, Department of Computer Science and UMIACS, January 2004 Google Scholar
  19. 19.
    Damevski, K.: Parallel RMI and M-by-N data redistribution using an IDL compiler. Master’s Thesis, The University of Utah, May 2003 Google Scholar
  20. 20.
    GridFTP Protocol Specification, Global Grid Forum Recommendation GFD.20, March 2003, http://www.globus.org/research/papers/GFD-R.0201.pdf
  21. 21.
    Bertrand, F., Yuan, Y., Chiu, K., Bramley, R.: An approach to parallel M×N communication. In: Proceedings of the Los Alamos Computer Science Institute Symposium, October 2003 Google Scholar
  22. 22.
    Marsan, M.A., Conte, G., Balbo, G.: A class of generalised stochastic Petri nets for the performance evaluation of multiprocessor systems. ACM Trans. Comput. Syst. 2(2), 93–122 (1984) CrossRefGoogle Scholar
  23. 23.
    Hillston, J.: A Compositional Approach to Performance Modelling. Cambridge University Press, New York (1996) Google Scholar
  24. 24.
    Papaefstathiou, E., Kerbyson, D.J., Nudd, G.R., Atherton, T.J., Harper, J.S.: An introduction to the layered characterisation for high performance systems, Research Report RR335, Department of Computer Science, University of Warwick, December 1997 Google Scholar
  25. 25.
    Geist, A., Beguelin, A., Dongarra, J., Jiand, W., Manchek, R., Sunderam, V.: PVM: Parallel virtual machine: a user’s guide and tutorial for networked parallel computing. In: Scientific and Engineering Computation Series. MIT Press, Cambridge (1994) Google Scholar
  26. 26.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, second edn. MIT Press, Cambridge (2001) MATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.High Performance Systems Group, Department of Computer ScienceUniversity of WarwickCoventryUK

Personalised recommendations