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Keywords: Instrument optimization, selective instrumentation, measurement, Performance measurement and analysis, parallel computing
Tools to observe the performance of parallel programs typically employ profiling and tracing as the two main forms of event-based measurement models. In both of these approaches, the volume of performance data generated and the corresponding perturbation encountered in the program depend upon the amount of instrumentation in the program. To produce accurate performance data, tools need to control the granularity of instrumentation. In this paper, we describe our experiences in the TAU performance system for improving the accuracy of performance data by limiting the amount of instrumentation. A range of options are provided to optimize instrumentation based on the structure of the program, event generation rates, and historical performance data gathered from prior executions.
Modified: Fri Oct 13 9:29:44 US/Pacific 2006
Created: Tue Jun 20 11:11:06 US/Pacific 2006
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