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Statistical Performance Analysis and Estimation for Parallel Multimedia Processing

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Abstract

When parallelizing complex multimedia processing on multiple processors, the stochastic timing behavior should be carefully studied. Although there are already many papers on the performance analysis of stochastic parallel system, they are not targeted on multimedia processing. In this paper, first we study H.264/AVC encoder (running on x86) and QSDPCM encoder (running on TI TMS32C62) to characterize important aspects of the stochastic timing behavior in complex multimedia processing applications. It is shown that the variation and correlation are indeed very significant. In order to make systematic analysis feasible, we apply Stochastic Timed Marked Graph (STMG) as a formal model to capture essential timing related behaviors of parallel multimedia processing systems. Then, we show how the local timing variations and correlations interact and propagate to the global timing behavior; from this we conclude general parallelization guidelines. Furthermore, we develop an analytical performance estimation technique to derive the probability distribution of timing behavior for parallel multimedia processing systems that have correlated stochastic timing behaviors inside. The estimation technique is based on principal component analysis and approximations.

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References

  1. Chen, K., Malik, S., August, D. I. (2001). Retargetable Static Timing Analysis for Embedded Software. Proceedings of the International Symposium on System Synthesis (ISSS), October.

  2. Leinbaugh, D. W. (1980). Guaranteed response time in a hard real-time environment. IEEE Trans. Software Engineering, 1139–1144, Jan.

  3. Zhou, T., & Sha, E. H.-M. (2001). Estimating probabilistic timing performance for real-time embedded systems. IEEE Transactions on VLSI System, 9(6), 833–844.

    Article  Google Scholar 

  4. Li, K. (1997). Stochastic bounds for parallel program execution times with processor constraints. IEEE Transactions on Computers, 46(5), 630–636 May.

    Article  Google Scholar 

  5. http://iphome.hhi.de/suehring/tml/download/

  6. Catthoor, F., et al. (2000). Data Transfer and Storage Architecture Issues and Exploration in Multimedia Processors. In Y. H. Yu (Ed.), Programmable Digital Signal Processors: Architecture, Programming, and Applications. New York: Marcel Dekker.

    Google Scholar 

  7. Brockwell, P. J., & Davis, R. (1986). A. Time Series Theory and Methods, Springer Series in Statistics.

  8. Proth, J.-M., & Xie, X. L. (1994). Cycle time of stochastic event graphs—evaluation and marking optimization. IEEE Trans. on Automatic Control, 39(7), 1482–1486.

    Article  MATH  MathSciNet  Google Scholar 

  9. Marsan, M. A., Balbo, G., & Conte, G. A. (1984). class of generalized stochastic petri nets for the performance evaluation of multiprocessor systems. ACM Trans. on Comp. sys.

  10. Molloy, M. K. (1982). Performance analysis using stochastic Petri nets. IEEE Trans Computers, C131(9), 913–17 September.

    Article  Google Scholar 

  11. Matteo, S. (1996). Approximate mean value analysis for stochastic marked graphs. IEEE Transactions on Software Engineering, 22(9), 654–664.

    Article  Google Scholar 

  12. Campos, J., Colom, J. M., Jungnitz, H., & Silva, M. (1994). Approximate throughput computation of stochastic marked graphs. IEEE Transactions on Software Engineering, 20(7)(July).

  13. Buchholz, P., & Kemper, P. Numerical analysis of stochastic marked graph nets, Proceedings of the Sixth International Workshop on Petri Nets and Performance Models.

  14. Lindemann C., & Lindemann C., Performance Modelling with Deterministic and Stochastic Petri Nets, byWiley.

  15. Rajsbaum, S. (1994). Upper and lower bounds for stochastic marked graphs. Information Processing Letters, 49(6), 291–295.

    Article  MATH  MathSciNet  Google Scholar 

  16. Lorch, J., & Smith, A. J. (2004). PACE: a new approach to dynamic voltage scaling. IEEE Transactions on Computers, 53(7), 856–869 July.

    Article  Google Scholar 

  17. Ross, A. (2003) “Useful Bounds on the Expected Maximum of Correlated Normal Variables", August 2003. Lehigh ISE Working Paper 03W-004

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Authors and Affiliations

  1. IMEC, Kapeldreef 75, B-3000, Leuven, Belgium

    Min Li, Tanja Van Achteren, Erik Brockmeyer & Francky Catthoor

Authors
  1. Min Li
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  2. Tanja Van Achteren
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  3. Erik Brockmeyer
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  4. Francky Catthoor
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Correspondence to Francky Catthoor.

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Li, M., Achteren, T.V., Brockmeyer, E. et al. Statistical Performance Analysis and Estimation for Parallel Multimedia Processing. J Sign Process Syst Sign Image Video Technol 58, 105–116 (2010). https://doi.org/10.1007/s11265-008-0318-z

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  • DOI: https://doi.org/10.1007/s11265-008-0318-z

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