Abstract. Preparing performance measurements of HPC applications is usually a tradeoff between accuracy and granularity of the measured data. When using direct instrumentation, that is, the insertion of extra code around performance-relevant functions, the measurement overhead increases with the rate at which these functions are visited. If applied indiscriminately, the measurement dilation can even be prohibitive. In this paper, we show how static code analysis in combination with binary rewriting can help eliminate unnecessary instrumentation points based on configurable filter rules. In contrast to earlier approaches, our technique does not rely on dynamic information, making extra runs prior to the actual measurement dispensable. Moreover, the rules can be applied and modified without re-compilation. We evaluate filter rules designed for the analysis of computation and communication performance and show that in most cases the measurement dilation can be reduced to a few percent while still retaining significant detail.
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