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DTSTAMP:20230124T171526Z
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DTSTART;TZID=America/Chicago:20221117T083000
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UID:submissions.supercomputing.org_SC22_sess275_rpost183@linklings.com
SUMMARY:Analysis and Visualization of Important Performance Counters to En
 hance Interpretability of Autotuner Output
DESCRIPTION:Posters, Research Posters\n\nAnalysis and Visualization of Imp
 ortant Performance Counters to Enhance Interpretability of Autotuner Outpu
 t\n\nZaeed, Islam, Cho, Sherry Li, Luo...\n\nAutotuning is a widely used m
 ethod for guiding developers of large-scale applications to achieve high p
 erformance. However, autotuners typically employ black-box optimizations t
 o recommend parameter settings at the cost of users missing the opportunit
 y to identify performance bottlenecks. Performance analysis fills that gap
  and identifies problems and optimization opportunities that can result in
  better runtime and utilization of hardware resources. This work combines 
 the best of the both worlds by integrating a systematic performance analys
 is and visualization approach into a publicly available autotuning framewo
 rk, GPTune, to suggest users which configuration parameters are important 
 to tune, to what value, and how tuning the parameters affect hardware-appl
 ication interactions. Our experiments demonstrate that a subset of the tas
 k parameters impact the execution time of the Hypre application; the memor
 y traffic and page faults cause performance problems in the Plasma-DGEMM r
 outine on Cori-Haswell.\n\nRegistration Category: Tech Program Reg Pass, E
 xhibits Reg Pass
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