Abstract
Today's massively parallel machines are typically message passing systems consisting of hundreds or thousands of processors. Implementing parallel applications efficiently in this environment is a challenging task. The Performance Prediction Tool (PerPreT) presented in this paper is useful for system designers and application developers. The system designers can use the tool to examine the effects of changes of architectural parameters on parallel applications (e.g., reduction of setup time, increase of link bandwidth, faster execution units). Application developers are interested in a fast evaluation of different parallelization strategies of their codes. PerPreT uses a relatively simple analytical model to predict speedup, execution time, computation time, and communication time for a parametrized application. Especially for large numbers of processors, PerPreT's analytical model is preferable to traditional models (e.g., Markov based approaches such as queueing and Petri net models). The applications are modelled through parameterized formulae for communication and computation. The parameters used by PerPreT include the problem size and the number of processors used to execute the program. The target systems are described by architectural parameters (e.g., setup times for communication, link bandwidth, and sustained computing performance per node).
While on leave at Vanderbilt University on a postdoc fellowship from the A. von Humboldt foundation. For copies of PerPreT please contact this author by email.
This work was partially supported by sub-contract 19X-SL131V from ORNL managed by Martin Marietta Energy Systems, Inc. for the U.S. Department of Energy under contract no. DE-AC05-84OR21400.
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Brehm, J., Madhukar, M., Smirni, E., Dowdy, L. (1995). PerPreT — A performance prediction tool for massively parallel systems. In: Beilner, H., Bause, F. (eds) Quantitative Evaluation of Computing and Communication Systems. TOOLS 1995. Lecture Notes in Computer Science, vol 977. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0024322
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DOI: https://doi.org/10.1007/BFb0024322
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