Run-Time Optimization Using Dynamic Performance Prediction

  • A. M. Alkindi
  • D. J. Kerbyson
  • E. Papaefstathiou
  • G. R. Nudd
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1823)


With the rapid expansion in the use of distributed systems the need for optimisation and the steering of application execution has become more important. The unquestionable aim to overcome bottle-neck problems, allocation, and performance degradation due to shared CPU time has prompted many investigations into the best way in which the performance of an application can be enhanced. In this work, we demonstrate the impact of using a Performance Prediction Toolset, PACE, which can be used in Dynamic (On-The-Fly) decision making for optimising application execution. An example application, the FFTW (The Fastest Fourier Transform in the West), is used to illustrate the approach which itself is a novel method that optimises the execution of an FFT. It is shown that performance prediction can provide the same quality of information as a measurement process for application optimisation but in a fraction of the time and thus improving the overall application performance.


Performance Optimisation Dynamic Performance Prediction Performance Modeling Application Steering FFTW 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Foster, I., Kesselman, C.: The Grid, Morgan Kaufmann, (1998).Google Scholar
  2. 2.
    Gu, W., Eisenhauer, G., Schwan, K.: On-line Monitoring and Steering of Parallel Programs, Concurrency: Practice and Experience 109 (1998) 699–736.zbMATHCrossRefGoogle Scholar
  3. 3.
    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 2811 (1995) 37–46.Google Scholar
  4. 4.
    DeRose, L., Zhang, Y., Reed, D.A.: SvPablo: A Multi-Language Performance Analysis System, in Proc. 10th Int. Conf. on Computer Performance, Spain (1998) 352–355.Google Scholar
  5. 5.
    Ramachandran, U., Venkateswaran, H., Sivasubramaniam, A., Singla, A.: Issues in Understanding the Scalability of Parallel Systems, in Proc. of the 1st Int. Workshop on Parallel Processing, Bangalore, India (December, 1994) 399–404.Google Scholar
  6. 6.
    Van Gemund, A.J.C., Reijns, G.L.: Predicting Parallel System Performance with Pamela, in Proc. 1st Annual Conf. of the Advanced School for Computing and Imaging,, Heijen, The Netherlands (1995) 422–431.Google Scholar
  7. 7.
    Balakrishnan, S., Nandy, S.K., van Gemund, A.J.C.: Modeling Multi-threaded Architectures in PAMELA for Real-time High-Performance Applications, in Proc. 4th Int. Conf. on High-Performance Computing, Los Alamitos, California, IEEE Computer Society 407–414 (December, 1997).Google Scholar
  8. 8.
    Frigo, M., Johnson. S.G.: FFTW: An adaptive software architecture for FFT, In Proc. of the IEEE Int. Conf. on Acoustics Speech, and Signal Processing, 3, Seattle (1998) 1381–1384.Google Scholar
  9. 9.
    Frigo. M.: A fast Fourier Transform Compiler. in Proc.. of the ACM SIGPLAN Conf. on Programming Language Design and Implementation (PLDI’99), Atlanta (1999).Google Scholar
  10. 10.
    Nudd, G.R., Papaefstathiou, E.,, A layered Approach to the Characterization of Parallel Systems for Performance Prediction, in Proc. of Performance Evaluation of Parallel Systems, Warwick (1993) 26–34.Google Scholar
  11. 11.
    Papaefstathiou, E., Kerbyson, D.J., Nudd, G.R., Atherton, T.J.: An overview of the CHIP3S performance prediction toolset for parallel systems, in: 8th ISCA Int. Conf. on Parallel and Distributed Computing Systems, Florida (1995) 527–533.Google Scholar
  12. 12.
    Perry, S.C., Kerbyson, D.J., Papaefstathiou, E., Grimwood, R., Nudd, G.R.: Performance Optimisation of Financial Option Calculations, To Appear in Parallel Computing, Elsvier North Holland (2000).Google Scholar
  13. 13.
    Smith, C.U.: Performance Engineering of Software Systems, Addison Wesley (1990).Google Scholar
  14. 14.
    Harper, J.S., Kerbyson, D.J., Nudd, G.R.: Analytical Modeling of Set-Associative Cache Behavior, IEEE Transactions on Computers 4810 (1999) 1009–1024.CrossRefGoogle Scholar
  15. 15.
    Wilson, R., French, R., Wilson, C., An Overview of the SUIF Compiler System, Technical Report, Computer Systems Lab Stanford University (1993).Google Scholar
  16. 16.
    Kerbyson, D.J., Papaefstathiou, E., Harper, J.S., Perry, S.C., Nudd, G.R.: Is Predictive Tracing Too Late for HPC Users?, in High Performance Computing, Kluwer Academic (1999) 57–67.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • A. M. Alkindi
    • 1
  • D. J. Kerbyson
    • 1
  • E. Papaefstathiou
    • 2
  • G. R. Nudd
    • 1
  1. 1.High Performance Systems Laboratory, Department of Computer ScienceUniversity of WarwickUK
  2. 2.Microsoft ResearchCambridgeUK

Personalised recommendations