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DTSTAMP:20250626T233531Z
LOCATION:B302-B305
DTSTART;TZID=America/New_York:20241121T100000
DTEND;TZID=America/New_York:20241121T170000
UID:submissions.supercomputing.org_SC24_sess534_post182@linklings.com
SUMMARY:Integrating HPCToolkit with Tools for Automated Analysis
DESCRIPTION:Dragana Grbic (Lawrence Livermore National Laboratory (LLNL), 
 Rice University); Matthew LeGendre and Olga Pearce (Lawrence Livermore Nat
 ional Laboratory (LLNL)); and John Mellor-Crummey (Rice University)\n\nHPC
 Toolkit enables users to gather detailed information about application per
 formance. Users can capture fine-grained measurement data, which may inclu
 de instruction-level samples on CPUs and GPUs. Collected data can be huge,
  making manual inspection using GUI tools difficult and time-consuming. We
  explored existing tools Hatchet and Thicket for programmatic analysis of 
 performance data to automate this process. However, they were not designed
  to handle data as large as HPCToolkit's. HPCToolkit's calling context tre
 es are difficult to interpret and visualize using these tools because of t
 heir overwhelming detail. Moreover, importing multiple trees into Thicket 
 can be slow, as unifying trees is costly when trees are large. To reduce t
 he size of large trees, we implemented heuristics that would automatically
  detect and remove specific code regions. After creating smaller trees tha
 t we believe contain all the meaningful information about the program's be
 havior, we used Thicket to analyze multiple performance profiles measured 
 by HPCToolkit.\n\nRegistration Category: Tech Program Reg Pass, Exhibits R
 eg Pass\n\nSession Chairs: Ayesha Afzal (Friedrich-Alexander University, E
 rlangen-Nuremberg; Erlangen National High Performance Computing Center); S
 ally Ellingson (University of Kentucky); and Alan Sussman (University of M
 aryland)\n\n
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