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DTSTART:19700308T020000
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DTSTAMP:20260522T150111Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181115T083000
DTEND;TZID=America/Chicago:20181115T170000
UID:submissions.supercomputing.org_SC18_sess324_post127@linklings.com
SUMMARY:Understanding Potential Performance Issues Using Resource-Based al
 ongside Time Models
DESCRIPTION:Nan ding (Lawrence Berkeley National Laboratory), Victor W. Le
 e (Intel Corporation), and Wei Xue and Weimin Zheng (Tsinghua University)\
 n\nNumerous challenges and opportunities are introduced by the complexity 
 and enormous code legacy of HPC applications, the diversity of HPC archite
 ctures, and the nonlinearity of interactions between applications and HPC 
 systems. To address these issues, we propose the Resource-based Alongside 
 Time (RAT) modeling method to help to understand the application run-time 
 performance efficiently. First, we use hardware counter-assisted profiling
  to identify the key kernels and non-scalable kernels in the application. 
 Second, we show how to apply the resource-based profiling into performance
  models to understand the potential performance issues and predict perform
 ance in the regimes of interest to developers and performance analysts. Th
 ird, we propose an easy-to-use performance modeling tool for scientists an
 d performance analytics. Our evaluations demonstrate that by only performi
 ng a few small-scale profilings, RAT is able to keep the average model err
 or rate around 15% with average performance overheads of 3% in multiple sc
 enarios.\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pas
 s\n\n
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