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DTSTART:19700308T020000
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DTSTAMP:20211207T055408Z
LOCATION:265-266
DTSTART;TZID=America/Chicago:20211115T080000
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UID:submissions.supercomputing.org_SC21_sess187_tut133@linklings.com
SUMMARY:GPU Code Optimization and Auto-Tuning Made Easy with Kernel Tuner:
  A Hands-On, Bring Your Own Code Tutorial
DESCRIPTION:Tutorial\n\nGPU Code Optimization and Auto-Tuning Made Easy wi
 th Kernel Tuner: A Hands-On, Bring Your Own Code Tutorial\n\nvan Werkhoven
 , Sclocco, Willemsen, Heldens\n\nGraphics Processing Units (GPUs) have rev
 olutionized the HPC landscape. The first generation of exascale supercompu
 ters is currently being built, and most of these systems will have GPUs as
  their main computing platform. The performance of GPU applications strong
 ly depends on how the software has been optimized for the hardware.  There
  are many different implementations and code optimizations to consider tha
 t can also be parameterized, creating vast search spaces that are infeasib
 le to search by hand.  As such, automated performance tuning (auto-tuning)
  techniques are crucial to optimize such applications.\n\nIn this tutorial
 , you will learn how to use Kernel Tuner, an easy-to-use tool for auto-tun
 ing GPU code using simple Python scripts.  Kernel Tuner supports OpenCL, C
 UDA, C++, and Fortran.  We take a step-by-step approach to explain the aut
 o-tuning basics, building up to more complex search spaces with many tunab
 le parameters and inter-dependencies.\n\nTag: In-Person Only, Accelerator-
 based Architectures, Performance\n\nRegistration Category: Tutorial Reg Pa
 ss
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