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UID:submissions.supercomputing.org_SC23_sess291_rpost205@linklings.com
SUMMARY:Pipit: Simplifying Analysis of Parallel Execution Traces
DESCRIPTION:Alexander Movsesyan, Rakrish Dhakal, Aditya Ranjan, Jordan Mar
 ry, Onur Cankur, and Abhinav Bhatele (University of Maryland)\n\nPer-proce
 ss per-thread traces enable in-depth analysis of parallel program executio
 n to identify various kinds of performance issues. Often times, trace coll
 ection 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, Projec
 tions, Nsight, etc.) and provide a uniform data structure in the form of a
  pandas DataFrame. Pipit provides operations to aggregate, filter, and tra
 nsform the events in a trace to present the data in different ways. We als
 o provide several functions to quickly identify performance issues in para
 llel executions.\n\nTag: Artificial Intelligence/Machine Learning, Archite
 cture and Networks, Heterogeneous Computing, I/O and File Systems, Perform
 ance Measurement, Modeling, and Tools, Post-Moore Computing, Programming F
 rameworks and System Software, Quantum Computing\n\nRegistration Category:
  Tech Program Reg Pass, Exhibits Reg Pass\n\n
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