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Keywords: TAU, Performance measurement and analysis, parallel computing, profiling, phases
Parallel scientific applications are designed based on structural, logical, and numerical models of computation and correctness. When studying the performance of these applications, especially on large-scale parallel systems, there is a strong preference among developers to view performance information with respect to their “mental model” of the application, formed from the model semantics used in the program. If the developer can relate performance data measured during execution to what they know about the application, more effective program optimization may be achieved. This paper considers the concept of “phases” and its support in parallel performance measurement and analysis as a means to bridge the gap between high- level application semantics and low-level performance data. In particular, this problem is studied in the context of parallel performance profiling. The implementation of phase-based parallel profiling in the TAU parallel performance system is described and demonstrated for the NAS parallel benchmarks and MFIX application.
Created: Tue Aug 2 10:08:41 US/Pacific 2005
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