Computer Science

Graduate Research Forum Details

PerfExplorer: A Performance Data Mining Framework For Large- Scale Parallel Computing

Abstract

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 dynamics and parameters specific to the application-system experiment. 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 paper, we present PerfExplorer, a framework for parallel performance data mining and knowledge discovery. The framework 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 performance database (PerfDMF) to access the parallel profiles and save its analysis results. Examples are given demonstrating these techniques for performance analysis of ASC applications.