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DTSTAMP:20181221T160905Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181114T083000
DTEND;TZID=America/Chicago:20181114T170000
UID:submissions.supercomputing.org_SC18_sess323_post127@linklings.com
SUMMARY:Understanding Potential Performance Issues Using Resource-Based al
 ongside Time Models
DESCRIPTION:Poster\nTech Program Reg Pass, Exhibits Reg Pass\n\nUnderstand
 ing Potential Performance Issues Using Resource-Based alongside Time Model
 s\n\nding, Lee, Xue, Zheng\n\nNumerous challenges and opportunities are in
 troduced by the complexity and enormous code legacy of HPC applications, t
 he diversity of HPC architectures, and the nonlinearity of interactions be
 tween applications and HPC systems. To address these issues, we propose th
 e Resource-based Alongside Time (RAT) modeling method to help to understan
 d the application run-time performance efficiently. First, we use hardware
  counter-assisted profiling to identify the key kernels and non-scalable k
 ernels in the application. Second, we show how to apply the resource-based
  profiling into performance models to understand the potential performance
  issues and predict performance in the regimes of interest to developers a
 nd performance analysts. Third, we propose an easy-to-use performance mode
 ling tool for scientists and performance analytics. Our evaluations demons
 trate that by only performing a few small-scale profilings, RAT is able to
  keep the average model error rate around 15% with average performance ove
 rheads of 3% in multiple scenarios.
URL:https://sc18.supercomputing.org/presentation/?id=post127&sess=sess323
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