SC23 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Research Posters Archive

Pipit: Simplifying Analysis of Parallel Execution Traces

Authors: Alexander Movsesyan, Rakrish Dhakal, Aditya Ranjan, Jordan Marry, Onur Cankur, and Abhinav Bhatele (University of Maryland)

Abstract: Per-process per-thread traces enable in-depth analysis of parallel program execution to identify various kinds of performance issues. Often times, trace collection tools provide a graphical tool to analyze the trace output. However, these GUI-based tools only support specific file formats, are difficult to scale when the data is large, limit data exploration to the implemented graphical views, and do not support automated comparisons of two or more datasets. In this poster, we present a pandas-based Python library, Pipit, which can read traces in different file formats (OTF2, HPCToolkit, Projections, Nsight, etc.) and provide a uniform data structure in the form of a pandas DataFrame. Pipit provides operations to aggregate, filter, and transform the events in a trace to present the data in different ways. We also provide several functions to quickly identify performance issues in parallel executions.

Best Poster Finalist (BP): no

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