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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20260522T150153Z
LOCATION:A2 Ballroom
DTSTART;TZID=America/Chicago:20181114T153000
DTEND;TZID=America/Chicago:20181114T170000
UID:submissions.supercomputing.org_SC18_sess466@linklings.com
SUMMARY:Gordon Bell Prize Finalist Session 1
DESCRIPTION:Exascale Deep Learning for Climate Analytics\n\nWe extract pix
 el-level masks of extreme weather patterns using variants of Tiramisu and 
 DeepLabv3+ neural networks. We describe improvements to the software frame
 works, input pipeline, and the network training algorithms necessary to ef
 ficiently scale deep learning on the Piz Daint and Summit system...\n\n\nT
 horsten Kurth (Lawrence Berkeley National Laboratory), Sean Treichler and 
 Joshua Romero (Nvidia Corporation), Mayur Mudigonda (Lawrence Berkeley Nat
 ional Laboratory), Nathan Luehr and Everett Phillips (Nvidia Corporation),
  Ankur Mahesh (Lawrence Berkeley National Laboratory), Michael Matheson (O
 ak Ridge National Laboratory), Jack Deslippe (Lawrence Berkeley National L
 aboratory), Massimiliano Fatica (Nvidia Corporation), Mr Prabhat (Lawrence
  Berkeley National Laboratory), and Michael Houston (Nvidia Corporation)\n
 ---------------------\n167-PFlops Deep Learning for Electron Microscopy: F
 rom Learning Physics to Atomic Manipulation\n\nAn artificial intelligence 
 system called MENNDL, which used 25,200 Nvidia Volta GPUs on Oak Ridge Nat
 ional Laboratory’s Summit machine, automatically designed an optimal deep 
 learning network in order to extract structural information from raw atomi
 c-resolution microscopy data. In a few hours, MENND...\n\n\nRobert M. Patt
 on, J. Travis Johnston, Steven R. Young, Catherine D. Schuman, Don D. Marc
 h, Thomas E. Potok, Derek C. Rose, Seung-Hwan Lim, Thomas P. Karnowski, Ma
 xim A. Ziatdinov, and Sergei V. Kalinin (Oak Ridge National Laboratory)\n-
 --------------------\nA Fast Scalable Implicit Solver for Nonlinear Time-E
 volution Earthquake City Problem on Low-Ordered Unstructured Finite Elemen
 ts with Artificial Intelligence and Transprecision Computing\n\nTo address
  problems that occur due to earthquakes in urban areas, we propose a metho
 d that utilizes artificial intelligence (AI) and transprecision computing 
 to accelerate a nonlinear dynamic low-order unstructured finite-element so
 lver. The AI is used to improve the convergence of iterative solver ...\n\
 n\nTsuyoshi Ichimura, Kohei Fujita, and Takuma Yamaguchi (University of To
 kyo); Akira Naruse (Nvidia Corporation); Jack C. Wells (Oak Ridge National
  Laboratory); Thomas C. Schulthess (Swiss National Supercomputing Centre);
  Tjerk P. Straatsma and Christopher J. Zimmer (Oak Ridge National Laborato
 ry); Maxime Martinasso (Swiss National Supercomputing Centre); and Kengo N
 akajima, Muneo Hori, and Lalith Maddegedara (University of Tokyo)
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