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Keywords: performance data mining, PerfExplorer, TAU, PerfDMF, R, Weka
Parallel applications running on high-end computer systems manifest a complexity of performance phenomena. Tools to observe parallel performance attempt to capture these phenomena in measurement datasets rich with information relating multiple performance metrics to execution dynam- ics and parameters specific to the application-system exper- iment. However, the potential size of datasets and the need to assimilate results from multiple experiments makes it a daunting challenge to not only process the information, but discover and understand performance insights. In this pa- per, we present PerfExplorer, a framework for parallel per- formance data mining and knowledge discovery. The frame- work architecture enables the development and integration of data mining operations that will be applied to large-scale parallel performance profiles. PerfExplorer operates as a client-server system and is built on a robust parallel per- formance database (PerfDMF) to access the parallel profiles and save its analysis results. Examples are given demon- strating these techniques for performance analysis of ASCI applications.
Created: Wed Aug 3 14:45:28 US/Pacific 2005
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