A tool framework for static and dynamic analysis of object-oriented software with templates
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Advances in the TAU performance system
Performance analysis and grid computing
A Portable Programming Interface for Performance Evaluation on Modern Processors
International Journal of High Performance Computing Applications
An API for Runtime Code Patching
International Journal of High Performance Computing Applications
The Tau Parallel Performance System
International Journal of High Performance Computing Applications
Observing Performance Dynamics Using Parallel Profile Snapshots
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Diagnosing performance bottlenecks in emerging petascale applications
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
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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 developments in the TAU performance system aimed at controlling the amount of instrumentation in performance experiments. 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.