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Performance analysis is a critical step in the oft-repeated, iterativeprocess of performance tuning of parallel programs. Per-process, per-threadtraces (detailed logs of events with timestamps) enable in-depth analysis ofparallel program execution to identify different kinds of performance issues.Often times, trace collection tools provide a graphical tool to analyze thetrace output. However, these GUI-based tools only support specific fileformats, are challenging to scale to large trace sizes, limit data explorationto the implemented graphical views, and do not support automated comparisons oftwo or more datasets. In this paper, we present a programmatic approach toanalyzing parallel execution traces by leveraging pandas, a powerfulPython-based data analysis library. We have developed a Python library, Pipit,on top of pandas that can read traces in different file formats (OTF2,HPCToolkit, Projections, Nsight Systems, etc.) and provides a uniform datastructure in the form of a pandas DataFrame. Pipit provides operations toaggregate, filter, and transform the events in a trace to present the data indifferent ways. We also provide several functions to quickly and easilyidentify performance issues in parallel executions. More importantly, the APIis easily extensible to support custom analyses by different end users.

Pipit: Scripting the analysis of parallel execution traces