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DTSTART;TZID=America/Denver:20231112T114200
DTEND;TZID=America/Denver:20231112T120600
UID:submissions.supercomputing.org_SC23_sess419_ws_prot104@linklings.com
SUMMARY:Filtering and Ranking of Code Regions for Parallelization via Hots
 pot Detection and OpenMP Overhead Analysis
DESCRIPTION:Workshop\n\nSeyed Ali Mohammadi, Lukas Rothenberger, Gustavo d
 e Morais, Bertin Nico Görlich, Erik Lille, Hendrik Rüthers, and Felix Wolf
  (Technical University of Darmstadt)\n\nMany high-performance computing ap
 plications reach millions of code lines and hundreds of code regions. Anal
 yzing all code regions for parallelization with OpenMP is neither efficien
 t nor necessary. To facilitate this task and minimize the effort by the us
 er, the code regions of the application need to be filtered and ranked. We
  provide a simple filtering method to detect the critical code regions by 
 clearly defining a hotspot. Afterward, we identify parallelizable loops by
  analyzing their data dependencies using an automatic tool. As the number 
 of parallel opportunities can be high and the users must verify these para
 llel suggestions, we suggest a ranking strategy based on parallelization o
 verhead to help them prioritize their endeavors and present a set of OpenM
 P microbenchmarks for overhead analysis. We calculate optimistic expected 
 benefits using overhead estimations as ranking metrics and show how our ra
 nking provides an improvement on the ranking based on serial runtime.\n\nT
 ag: Performance Measurement, Modeling, and Tools, Programming Frameworks a
 nd System Software\n\nRegistration Category: Workshop Reg Pass\n\nSession 
 Chairs: David Boehme (Lawrence Livermore National Laboratory (LLNL)); Anth
 ony Danalis (University of Tennessee); and Josef Weidendorfer (Leibniz Sup
 ercomputing Centre, Technical University of Munich)
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