Hatchet: Pruning the Overgrowth in Parallel Profiles

Authors: Abhinav Bhatele (University of Maryland, Lawrence Livermore National Laboratory), Stephanie Brink (Lawrence Livermore National Laboratory), Todd Gamblin (Lawrence Livermore National Laboratory)

Abstract: Performance analysis is critical for eliminating scalability bottlenecks in parallel codes. There are many profiling tools that can instrument codes and gather performance data, but general, easy to use, and programmable analytics and visualization tools are limited. In this paper, we focus on the analytics of structured profiling data, such as that obtained from calling context trees or nested region timers in code. We present a set of techniques and operations that build on the pandas data analysis library to enable parallel profile analysis. We have implemented these techniques in a Python-based library called Hatchet, which allows structured data to be filtered, aggregated, and pruned. Using performance datasets obtained from profiling parallel codes, we demonstrate how common performance analyses can be performed reproducibly with only a few lines of Hatchet code. Hatchet brings the power of modern data science tools to bear on performance analysis.


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