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Journal Articles Software: Practice and Experience Year : 2018

Automatic benchmark profiling through advanced workflow-based trace analysis

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Abstract

Benchmarking has proven to be crucial for the investigation of the behavior and performances of a system. However, the choice of relevant benchmarks still remains a challenge. To help the process of comparing and choosing among benchmarks, we propose a solution for automatic benchmark profiling. It computes unified benchmark profiles reflecting benchmarks' duration, function repartition, stability, CPU efficiency, parallelization, and memory usage. Our approach identifies the needed system information for profile computation and collects it from execution traces captured without benchmark code modifications. It structures profile computation as a reproducible workflow for automatic trace analysis, which efficiently manages important trace volumes. In this paper, we report on the design and the implementation of our approach, which involves the collection and analysis of about 500 GB of trace data coming from 2 different platforms (an x86 desktop machine and the Juno SoC board). The computed benchmark profiles provide valuable insights about the benchmarks' behavior and help compare different benchmarks on the same platform as well as the behavior of the same benchmark on different platforms.
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Dates and versions

hal-02047273 , version 1 (06-03-2019)

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Alexis Martin, Vania Marangozova-Martin. Automatic benchmark profiling through advanced workflow-based trace analysis. Software: Practice and Experience, 2018, 48 (6), pp.1195-1217. ⟨10.1002/spe.2570⟩. ⟨hal-02047273⟩
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