Abstract
The execution of an application on a high performance system requires parameters concerning the problem in hand, and those that determine the system mapping, to be specified by a user. The system parameters are typically used to minimise the execution time. However, by the coupling of a performance model with an application, system parameters can be determined without user intervention. In the work presented here, a novel performance prediction system has been used to provide suitable performance models which can determine application mapping parameters, code execution decisions, and system choices on-the-fly. An example compact application of a convolution is used to illustrate the approach for automatically choosing the actual code to be executed, and the number of workstations in a cluster to be utilised. The performance prediction system is shown to have a reasonable accuracy (approximately 10% error), with a rapid evaluation time (typically < 2s).
Preview
Unable to display preview. Download preview PDF.
References
D.A. Agard, Y. Hiraoka, P. Shaw, J.W. Sedat, Fluorescence Micrroscopy in Three Dimensions, in Fluorescence Microscopy of Living Cells in Culture, Elsevier, pp. 353–377, 1989
J.N.C. Arabe, A.B.B. Lowekamp, E. Saligman, M.Starkey, and P. Stephan, Dome Parallel programming environment in a heterogeneous multi-user environment, Supercomputing, 1995.
J. Gehring, A. Reinefeld, MARS-A framework for minizing the job execution time in a metacomputing environment, Future Generation Computer Systems, vol. 12, pp. 87–99, 1996
A. Geist, A. Beguelin, J. Dongarra, W. Jiang, R. Manchek, V. Sunderam, PVM-Parallel Virtual Machine, MIT Press., 1994
M.W. Hall, J.M. Anderson, et.al, Maximizing Multiprocessor Performance with the SUIF Compiler, IEEE Computer, Vol. 29 (12), December 1996
D.J. Kerbyson, E. Papaefstathiou, J.S. Harper, S.C. Perry, G.R. Nudd, Is Predictive Tracing Too Late for HPC Users?, in High Performance Computing, R.J. Allan, A. Simpson, D. A. Nicole (Eds), Plenum Press, 1998.
E. Papaefstathiou, D.J. Kerbyson, G.R. Nudd, T.J. Atherton, An overview of the CHIP3S Performance Prediction Toolset for Parallel Systems, in Proc of 8th ISCA Int. Conf. on Parallel and Distributed Computing Systems, pp. 527–533, Orlando, 1995.
E. Papaefstathiou, D. J. Kerbyson, G.R. Nudd, A Layered approach to Parallel Software Performance Prediction: A Case Study, in: L. Dekker, W. Smit, and J.C. Zuidervaart, eds., Massively Parallel Processing Applications & Development, pp. 617–624, North-Holland, 1994.
B. Quin, H.A. Scholl, R.A. Ammar, Micro Time Cosrt Analysis of Parallel Computation, IEEE Trans. on Computers, Vol. 40 (5), pp. 613–628, 1991.
R. Wolski, Dynamically Forecasting Network Performance Using the Network Weather Service, UCSD Technical Report, TR-CS96-494, 1996.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kerbyson, D.J., Papaefstathiou, E., Nudd, G.R. (1998). Application execution steering using on-the-fly performance prediction. In: Sloot, P., Bubak, M., Hertzberger, B. (eds) High-Performance Computing and Networking. HPCN-Europe 1998. Lecture Notes in Computer Science, vol 1401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0037199
Download citation
DOI: https://doi.org/10.1007/BFb0037199
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64443-9
Online ISBN: 978-3-540-69783-1
eBook Packages: Springer Book Archive