Automatic Instrumentation Refinement for Empirical Performance Modeling

Lehr, Jan-Patrick; Calotoiu, Alexandru; Bischof, Christian; Wolf, Felix

doi:10.1109/protools49597.2019.00011

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…It uses MetaCG as fundamental data structure, annotated with the number of statements per function. Its analyzer PGIS implements different heuristics to select functions for instrumentation, see [14,15]. The instrumentation is passed to Score-P [12] to insert the profiling hooks.…”

Section: Piramentioning

confidence: 99%

MetaCG: annotated call-graphs to facilitate whole-program analysis

Lehr

Hück

Fischler

et al. 2020

Proceedings of the 11th ACM SIGPLAN International Workshop on Tools for Automatic Program Analysis

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The paper presents the extendable C/C++ whole-program call-graph tool MetaCG. We introduce its graph library, the Clang-based tool CGCollector to construct the call graph and attach meta information, and CGValidate to check for missing edges given a particular execution. MetaCG offers extendability through its metadata function-annotation mechanism to transfer information between tools. It preserves inheritance hierarchies and can be serialized into JSON. We evaluate CG-Collector's ability to construct whole-program call-graphs for C/C++ code and, subsequently, present a performance profiler and a memory sanitizer that rely on MetaCG for whole-program call-graph information. CCS Concepts: • Software and its engineering → Software libraries and repositories; Software system structures; Compilers.

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Section: Piramentioning

confidence: 99%

MetaCG: annotated call-graphs to facilitate whole-program analysis

Lehr

Hück

Fischler

et al. 2020

Proceedings of the 11th ACM SIGPLAN International Workshop on Tools for Automatic Program Analysis

Self Cite

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Automatic Low-Overhead Load-Imbalance Detection in MPI Applications

Arzt

Fischler

Lehr

et al. 2021

Euro-Par 2021: Parallel Processing

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

Kreutzer

Iwainsky²,

Lehr

et al. 2022

Lecture Notes in Computer Science

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Code 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.

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Automatic Instrumentation Refinement for Empirical Performance Modeling

Cited by 5 publications

References 27 publications

MetaCG: annotated call-graphs to facilitate whole-program analysis

MetaCG: annotated call-graphs to facilitate whole-program analysis

Automatic Low-Overhead Load-Imbalance Detection in MPI Applications

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

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