Compiler-Assisted Instrumentation Selection for Large-Scale C++ Codes

Abstract

AbstractCode instrumentation is the primary method for collecting fine-grained performance data. As instrumentation introduces an inherent runtime overhead, it is essential to measure only those regions of the code which are most relevant to the analysis. In practice, the typical approach is to define filter lists manually. Prior projects aim to automate this process using static analysis. Specifically, InstRO enables tailored instrumentation via sophisticated user-defined selection of code regions. However, due to the need for whole-program call-graph analysis, its application on large-scale scientific codes is currently impractical. In this work, we present the new instrumentation tool CaPI (short for “Compiler-assisted Performance Instrumentation”), which is targeted towards such large-scale applications. We demonstrate its application on the CFD framework OpenFOAM. Our evaluation shows that a hybrid approach of CaPI and existing profile-guided filtering outperforms profile-guided filtering alone. Furthermore, we identify correctness and usability issues and propose possible avenues to improve CaPI, as well as compiler-assisted instrumentation tools in general.