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DTSTART:19700308T020000
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LOCATION:C140/142
DTSTART;TZID=America/Chicago:20181114T153000
DTEND;TZID=America/Chicago:20181114T160000
UID:submissions.supercomputing.org_SC18_sess210_pap277@linklings.com
SUMMARY:The Design, Deployment, and Evaluation of the CORAL Pre-Exascale S
 ystems
DESCRIPTION:Sudharshan S. Vazhkudai (Oak Ridge National Laboratory); Broni
 s R. de Supinski (Lawrence Livermore National Laboratory); Arthur S. Bland
  and Al Geist (Oak Ridge National Laboratory); James Sexton and Jim Kahle 
 (IBM); Christopher J. Zimmer, Scott Atchley, Sarp H. Oral, Don E. Maxwell,
  and Veronica G. Vergara Larrea (Oak Ridge National Laboratory); Adam Bert
 sch and Robin Goldstone (Lawrence Livermore National Laboratory); Wayne Jo
 ubert (Oak Ridge National Laboratory); Chris Chambreau (Lawrence Livermore
  National Laboratory); David Appelhans and Robert Blackmore (IBM); Ben Cas
 ses (Lawrence Livermore National Laboratory); George Chochia and Gene Davi
 son (IBM); Matthew A. Ezell (Oak Ridge National Laboratory); Tom Gooding (
 IBM); Elsa Gonsiorowski (Lawrence Livermore National Laboratory); Leopold 
 Grinberg, Bill Hanson, and Bill Hartner (IBM); Ian Karlin and Matthew L. L
 eininger (Lawrence Livermore National Laboratory); Dustin Leverman (Oak Ri
 dge National Laboratory); Chris Marroquin (IBM); Adam Moody (Lawrence Live
 rmore National Laboratory); Martin Ohmacht (IBM); Ramesh Pankajakshan (Law
 rence Livermore National Laboratory); Fernando Pizzano (IBM); James H. Rog
 ers (Oak Ridge National Laboratory); Bryan Rosenburg (IBM); Drew Schmidt, 
 Mallikarjun Shankar, and Feiyi Wang (Oak Ridge National Laboratory); Py Wa
 tson (Lawrence Livermore National Laboratory); Bob Walkup (IBM); Lance D. 
 Weems (Lawrence Livermore National Laboratory); and Junqi Yin (Oak Ridge N
 ational Laboratory)\n\nCORAL, the Collaboration of Oak Ridge, Argonne and 
 Livermore, is fielding two similar IBM systems, Summit and Sierra, with NV
 IDIA GPUs that will replace the existing Titan and Sequoia systems. Summit
  and Sierra are currently ranked No. 1 and No. 3, respectively, on the Top
 500 list. We discuss the design and key differences of the systems. Our ev
 aluation of the systems highlights the following. Applications that fit in
  HBM see the most benefit and may prefer more GPUs; however, for some appl
 ications, the CPU-GPU bandwidth is more important than the number of GPUs.
  The node-local burst buffer scales linearly, and can achieve a 4X improve
 ment over the parallel file system for large jobs; smaller jobs, however, 
 may benefit from writing directly to the PFS. Finally, several CPU, networ
 k and memory bound analytics and GPU-bound deep learning codes achieve up 
 to a 11X and 79X speedup/node, respectively over Titan.\n\nTag: Architectu
 res, Networks, Performance, Scientific Computing, State of the Practice, T
 ools\n\nRegistration Category: Tech Program Reg Pass\n\nSession Chair: Ash
 ley Barker (Oak Ridge National Laboratory (ORNL))\n\n
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