Petri net performance models of parallel systems — Methodology and case study

  • H. Wabnig
  • G. Haring
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 817)


In the PAPS — parallel program performance prediction toolset — parallel systems are specified by the structure of the parallel program, the multiprocessor hardware, and the mapping of the program elements to processor nodes. The task scheduling strategy and the communication network behaviour is described in terms of timed Petri nets. Examples of Petri nets, reflecting different task scheduling strategies, are presented. A detailed Petri net performance model for the Virtual Channel Router (VCR) which is a software implementation of a packet switching communication kernel built upon a store & forward communication network is elaborated and validated. Resource parameters for an actual multiprocessor computer system running the VCR communication software are determined. A case study shows the applicability and accuracy of the presented Petri net performance models for VCR based packet switching communication networks.


Performance evaluation simulation parallel processing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Craig M. Chase, Alex L. Cheung, Anthony P. Reeves, and Mark R. Smith. Paragon: A Parallel Programming Environment for Scientific Applications Using Communication Structures. Journal of Parallel and Distributed Computing, 16:79–91, 1992.Google Scholar
  2. 2.
    Mark Debbage, Mark Hill, and Denis Nicole. Virtual Channel Router Version 2.0 User Guide. Department of Electronics & Computer Science, University of Southampton, June 1991.Google Scholar
  3. 3.
    J. B. Dugan, K. S. Trivedi, R. M. Geist, and V. F. Nicola. Extended stochastic Petri nets: Applications and analysis, pages 507–519. Performance 84, Paris, France, December 1984.Google Scholar
  4. 4.
    A. Ferscha. A Petri Net Approach for Performance Oriented Parallel Program Design. Journal of Parallel and Distributed Computing, (15):188–206, 1992.MathSciNetGoogle Scholar
  5. 5.
    A. Ferscha and G. Haring. On Performance Oriented Environments for the Development of Parallel Programs. Kybernetika a Informatika, Proceedings of the 15th Symposium on Cybernetics and Informatics '91, April 3–5 1991, Smolenice Castle, čSFR, 4(1/2), 1991.Google Scholar
  6. 6.
    Alois Ferscha. Modellierung und Leistungsanalyse paralleler Systeme mit dem PRM-Netz Modell. Ph.D. Thesis, University of Vienna. To be published in OCG-Schriftenreihe, Oldenbourg Verlag, 65, 1990.Google Scholar
  7. 7.
    Institut für Informatik. TOPSYS User's Overview Version 1.0. Technische Universität München, 1990.Google Scholar
  8. 8.
    G. A. Geist, M. T. Heath, B. W. Peyton, and P. H. Worley. PICL: A Portable Instrumented Communication Library. Technical Report ORNL/TM-11130, Oak Ridge National Laboratory, July 1990.Google Scholar
  9. 9.
    G. A. Geist, M. T. Heath, B. W. Peyton, and P. H. Worley. A users' guide to PICL: a portable instrumented communication library. Technical Report ORNL/TM-11616, Oak Ridge National Laboratory, August 1990.Google Scholar
  10. 10.
    Erol Gelenbe. Multiprocessor Performance, Series in Parallel Computing. John Wiley & Sons Ltd., 1989.Google Scholar
  11. 11.
    Leana Golubchik, Gary D. Rozenblat, William C. Cheng, and Richard R. Muntz. The Tangram Modeling Environment. In Proc. of the 5th Int. Conf. on Modelling Techniques and Tools for Computer Performance Evaluation. Torino, Italy, Feb. 13–15, 1991, pages 421–435, 1991.Google Scholar
  12. 12.
    V.A. Guarna Jr., D. Gannon, D. Jablonowski, A.D. Mallony, and Y. Gaur. FAUST: An Integrated Environment for Parallel Programming. IEEE Software, 6(4), 1989.Google Scholar
  13. 13.
    Michael T. Heath and Jennifer A. Etheridge. Visualizing Performance of Parallel Programs. Technical Report ORNL/TM-11813, Oak Ridge National Laboratory, May 1991.Google Scholar
  14. 14.
    Michael T. Heath and Jennifer A. Etheridge. Visualizing the Performance of Parallel Programs. IEEE Software, 8(5):29–39, September 1991.CrossRefGoogle Scholar
  15. 15.
    R. E. Lord, J. S. Kowalik, and S. P. Kumar. Solving linear algebraic equations on an MIMD computer. Journal of the ACM, 30(1):103–117, January 1983.CrossRefGoogle Scholar
  16. 16.
    M. A. Marsan, G. Conte, and G. Balbo. A class of generalized stochastic Petri nets for the performance evaluation of multiprocessor systems. ACM Trans. Comput. Syst., 2(2):93–122, 1984.CrossRefGoogle Scholar
  17. 17.
    M. Molloy. On the integration of delay and throughput measures in distributed processing models. Technical report, Ph.D. dissertation, Univ. California, Los Angeles, 1981.Google Scholar
  18. 18.
    M. Molloy. Performance modeling using stochastic Petri nets. IEEE Trans. Comput., C-31:913–917, September 1982.Google Scholar
  19. 19.
    T. Murata. Petri Nets: Properties, Analysis and Applications. Proceedings of the IEEE, 77(4):541–580, April 1989.CrossRefGoogle Scholar
  20. 20.
    Lionel M. Ni and Philip K. McKinley. A Survey of Wormhole Routing Techniques in Direct Networks. IEEE Computer, February 1993.Google Scholar
  21. 21.
    K. Nichols. Performance Tools. IEEE Software, 7(3):21–30, May 1990.CrossRefGoogle Scholar
  22. 22.
    OACIS. Parallel Programming Support Environment Research. Technical Report TR-PPSE-89-1, Oregon Advanced Computing Institute, 1989.Google Scholar
  23. 23.
    P. Oman. CASE Analysis and Design Tools. IEEE Software, 7(3):37–43, May 1990.CrossRefGoogle Scholar
  24. 24.
    P. Oman. Tools for Multiple CPU Environments. IEEE Software, 7(3):45–51, May 1990.Google Scholar
  25. 25.
    PARSYTEC. PARIX Release 1.2 Software Documentation. PARSYTEC Computer GmbH, March 1993.Google Scholar
  26. 26.
    C. A. Petri. Communication with Automata. Technical Report RADC-TR-65-377, vol.1, Suppl. 1, New York: Griffiss Air Force Base, 1966.Google Scholar
  27. 27.
    R. J. Pooley. The Integrated Modelling Support Environment, a new generation of performance modelling tools. In Proc. of the 5th Int. Conf. on Modelling Techniques and Tools for Computer Performance Evaluation. Torino, Italy, Feb. 13–15, 1991, pages 1–15, 1991.Google Scholar
  28. 28.
    M. J. Quinn. Designing Efficient Algorithms for Parallel Computers. McGraw-Hill International Publishers, New York, 1987.Google Scholar
  29. 29.
    C. V. Ramamoorthy and G. S. Ho. Performance evaluation of asynchronous concurrency systems using Petri nets. IEEE Trans. Software Eng., SE-6:440–449, September 1984.Google Scholar
  30. 30.
    R. R. Razouk and C. V. Phelps. Performance analysis using timed Petri nets. pages 126–129. Proc. 1984 Int. Conf. Parallel Processing, August 1984.Google Scholar
  31. 31.
    Z. Segall and L. Rudolph. PIE: A Programming and Instrumentation Environment for Parallel Programming. IEEE Software, 2:22–37, November 1985.Google Scholar
  32. 32.
    J. Sifakis. Petri nets for performance evaluation. In Measuring, Modeling, and Evaluating Computer Systems, pages 75–93. H. Beilner and E. Gelenbe, Eds. North-Holland, 1977.Google Scholar
  33. 33.
    L. Snyder and D. Socha. Poker on the Cosmic Cube: The first retargetable parallel programming language and environment. In K. Hwang, S.M. Jacobs, E.E. Swartzlander (Editor): Proceedings of Int'l Conf. on Parallel Processing, IEEE Computer Society Press, Washington D.C., pages 628–635, August 1986.Google Scholar
  34. 34.
    H. Wabnig and G. Haring. PAPS — The Parallel Program Performance Prediction Toolset. In 7th International Conference on Modelling Techniques and Tools for Computer Performance Evaluation, (Vienna, Austria, May 4–6, 1994), to be published in Lecture Notes in Computer Science 794, pages 284–30. Springer Verlag, 1994.Google Scholar
  35. 35.
    H. Wabnig and G. Haring. Performance Prediction of Transputer Applications. Submitted to TRANSPUTERS'94, (Saline Royale d' Arc et Senans, France, September 21–23, 1994), 1994.Google Scholar
  36. 36.
    H. Wabnig, G. Haring, D. Kranzlmüller, and J. Volkert. Communication Pattern based Performance Prediction on the nCUBE 2 Multiprocessor System. Submitted to CONPAR'94 — VAPP VI, (Linz, Austria, September 6–8, 1994), 1994.Google Scholar
  37. 37.
    H. Wabnig, G. Kotsis, and G. Haring. Performance Prediction of Parallel Programs. In Proc. of the 7th GI/ITG Conference on Measurement, Modelling and Performance Evaluation of Computer Systems, 21–23 September 1993, Aachen, Germany, pages 64–76. Springer Verlag, New York, 1993.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • H. Wabnig
    • 1
  • G. Haring
    • 1
  1. 1.Institute of Applied Computer Science and Information Systems Department of Advanced Computer EngineeringUniversity of ViennaViennaAustria

Personalised recommendations