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Pipit: Simplifying Analysis of Parallel Execution Traces

DescriptionPer-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.

Authors

Alexander Movsesyan

University of Maryland

Rakrish Dhakal

University of Maryland

Aditya Ranjan

University of Maryland

Jordan Marry

University of Maryland

Onur Cankur

University of Maryland