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DTSTAMP:20260522T150117Z
LOCATION:D166
DTSTART;TZID=America/Chicago:20181112T153000
DTEND;TZID=America/Chicago:20181112T154500
UID:submissions.supercomputing.org_SC18_sess173_ws_espm104@linklings.com
SUMMARY:Portable and Reusable Deep Learning Infrastructure with Containers
  to Accelerate Cancer Studies
DESCRIPTION:George Zaki (Frederick National Laboratory for Cancer Research
 )\n\nAdvanced programming models, domain specific languages, and scripting
  toolkits have the potential to greatly accelerate the adoption of high pe
 rformance computing.  These complex software systems, however, are often d
 ifficult to install and maintain, especially on exotic high-end systems.  
 We consider deep learning workflows used on petascale systems and redeploy
 ment on research clusters using containers.  Containers are used to deploy
  the MPI-based infrastructure, but challenges in efficiency, usability, an
 d complexity must be overcome.  In this work, we address these challenges 
 through enhancements to a unified workflow system that manages interaction
  with the container abstraction, the cluster scheduler, and the programmin
 g tools.  We also report results from running the application on our syste
 m, harnessing 298~TFLOPS (single precision).\n\nTag: Accelerators, Exascal
 e, Parallel Programming Languages, Libraries, and Models\n\nRegistration C
 ategory: Workshop Reg Pass\n\nSession Chairs: Dhabaleswar K. (DK) Panda (T
 he Ohio State University), Karl Schulz (Advanced Micro Devices (AMD) Inc),
  and Hari Subramoni (The Ohio State University)\n\n
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