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DTSTART:19700308T020000
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DTSTAMP:20211207T055347Z
LOCATION:Online
DTSTART;TZID=America/Chicago:20211115T163000
DTEND;TZID=America/Chicago:20211115T170000
UID:submissions.supercomputing.org_SC21_sess422_ws_pmbsf103@linklings.com
SUMMARY:Bayesian Optimization for Auto-Tuning GPU kernels
DESCRIPTION:Workshop\n\nBayesian Optimization for Auto-Tuning GPU kernels\
 n\nWillemsen, van Nieuwpoort, van Werkhoven\n\nFinding optimal parameter c
 onfigurations for tunable GPU kernels is a non-trivial exercise for large 
 search spaces, even when automated. This poses an optimization task on a n
 on-convex search space, using an expensive-to-evaluate function with unkno
 wn derivative.  These characteristics make a good candidate for Bayesian o
 ptimization, which previously has not been applied to this problem. \n\nTh
 e application of Bayesian optimization to this problem, however, is challe
 nging. We demonstrate how to deal with the rough, discrete, constrained se
 arch spaces, containing invalid configurations.  We introduce a novel cont
 extual variance exploration factor, as well as new  acquisition functions 
 with improved scalability, combined with an informed acquisition function 
 selection mechanism. By comparing the performance of our Bayesian optimiza
 tion implementation on various test cases to the existing search strategie
 s in Kernel Tuner, as well as other Bayesian optimization implementations,
  we demonstrate that our search strategies generalize well and consistentl
 y outperform other search strategies by a wide margin.\n\nTag: Online Only
 , Accelerator-based Architectures, Applications, Computational Science, Em
 erging Technologies, Extreme Scale Computing, File Systems and I/O, Hetero
 geneous Systems, Parallel Programming Languages and Models, Performance, S
 cientific Computing, Software Engineering\n\nRegistration Category: Worksh
 op Reg Pass
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