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LOCATION:HG F 30
DTSTART;TZID=Europe/Stockholm:20190613T094500
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UID:submissions.pasc-conference.org_PASC19_sess195_pap_jan102@linklings.co
 m
SUMMARY:Accelerating High-Resolution Weather Models with Deep-Learning Har
 dware
DESCRIPTION:Paper, Plenary\nClimate and Weather\n\nAccelerating High-Resol
 ution Weather Models with Deep-Learning Hardware\n\nHatfield, Chantry, Due
 ben, Palmer\n\nThe next generation of weather and climate models will have
  an unprecedented level of resolution and model complexity, and running th
 ese models efficiently will require taking advantage of future supercomput
 ers and heterogeneous hardware. In this paper, we investigate the use of m
 ixed-precision hardware that supports floating-point operations at double-
 , single- and half-precision. In particular, we investigate the potential 
 use of the NVIDIA Tensor Core, a mixed-precision matrix-matrix multiplier 
 mainly developed for use in deep learning, to accelerate the calculation o
 f the Legendre transforms in the Integrated Forecasting System (IFS), one 
 of the leading global weather forecast models. In the IFS, the Legendre tr
 ansform is one of the most expensive model components and dominates the co
 mputational cost for simulations at a very high resolution. We investigate
  the impact of mixed-precision arithmetic in IFS simulations of operationa
 l complexity through software emulation. Through a targeted but minimal us
 e of double-precision arithmetic we are able to use either half-precision 
 arithmetic or mixed half/single-precision arithmetic for almost all of the
  calculations in the Legendre transform without affecting forecast skill.<
 br /><br />https://doi.org/10.1145/3324989.3325711<br />Video Recording: h
 ttps://www.video.ethz.ch/conferences/2019/pasc19/f6a2bdd0-6bf1-4cf0-bb4d-2
 eb91762b953.html
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