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Keywords: Performance diagnosis, parallel models, master-worker, measurement, analysis.
Parallel performance tuning naturally involves a diagnosis process to locate and explain sources of program inefficiency. Proposed is an approach that exploits parallel computation patterns (models) for diagnosis discovery. Knowledge of performance problems and inference rules for hypothesis search are engineered from model semantics and analysis expertise. In this manner, the performance diagnosis process can be automated as well as adapted for parallel model variations. We demonstrate the implementation of model-based performance diagnosis on the classic Master-Worker pattern. Our results suggest that pattern- based performance knowledge can provide effective guidance for locating and explaining performance bugs at a high level of program abstraction.
Modified: Thu Sep 21 10:18:55 US/Pacific 2006
Created: Tue May 30 16:00:14 US/Pacific 2006
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