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DTSTAMP:20211207T055400Z
LOCATION:220-221
DTSTART;TZID=America/Chicago:20211119T083100
DTEND;TZID=America/Chicago:20211119T085500
UID:submissions.supercomputing.org_SC21_sess339_ws_pehc105@linklings.com
SUMMARY:GenMAT:  A General-Purpose Machine Learning-Driven Auto-Tuner for 
 Heterogeneous Platforms
DESCRIPTION:Workshop\n\nGenMAT:  A General-Purpose Machine Learning-Driven
  Auto-Tuner for Heterogeneous Platforms\n\nZhang, Srivastava, Kannan, Pras
 anna\n\nTo assist the end-users in optimally deploying workloads on the he
 terogeneous environment with high productivity, a fundamental problem is t
 o automatically find the best “variant” of an application—the implementati
 on with the optimal configurations on the most suitable hardware resource 
 resulting in the minimum runtime. We propose GenMAT, a portable tool for i
 dentifying the best variant of any application specified as a meta-program
  with exposed tunable parameters on any hardware. GenMAT automatically pro
 files the application by varying the exposed tunable parameters and trains
  a compact machine learning model to quickly predict the runtimes of numer
 ous candidate variants to identify the best variant. We show that the GenM
 AT-selected variant has a runtime deviation within 3.5% of the true best v
 ariant in determining the best linear algebra library for matrix operation
 s. GenMAT correctly ranks the runtimes of thousands of Halide schedules wi
 th an average Spearman’s rank correlation coefficient of 0.95.\n\nTag: Het
 erogeneous Systems, Parallel Programming Languages and Models, Parallel Pr
 ogramming Systems, Productivity Tools, System Software and Runtime Systems
 \n\nRegistration Category: Workshop Reg Pass
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