Benchmarking has proven to be crucial for the investigation of system behavior and performances. 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 profiles reflecting benchmarks’ duration, function repartition, stability, CPU efficiency, parallelization and memory usage. It identifies the needed system information for profile computation, collects it from execution traces and produces profiles through automatic and reproducible trace analysis. The paper presents the design, the implementation and the evaluation of the approach.
CITATION STYLE
Martin, A., & Marangozova-Martin, V. (2016). Automatic benchmark profiling through advanced trace analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9833 LNCS, pp. 63–74). Springer Verlag. https://doi.org/10.1007/978-3-319-43659-3_5
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