Dynamic performance analysis of long-running programs in the high performance computing community increasingly relies on statistical profiling techniques to provide performance measurement results. Systematic sampling rates used to generate the statistical data are typically selected in an ad hoc manner with little formal regard for the context provided by the program being analyzed and the underlying system on which it is run. In an effort to provide a more effective statistical profiling process and additional rigor we argue in favor of the general principle of deliberate sampling rate selection. We present our idea for a methodology of systematic sample rate selection based on a performance measurement model incorporating the effect of sampling on both measurement precision and perturbation effects.
['Geoff Stoker', 'Jeffrey K. Hollingsworth']
Towards a Methodology for Deliberate Sample-Based Statistical Performance Analysis