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DTSTART:19700308T020000
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DTSTAMP:20181221T160728Z
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:Workshop\nAccelerators, Exascale, Parallel Programming Languag
 es, Libraries, and Models, Workshop Reg Pass\n\nPortable and Reusable Deep
  Learning Infrastructure with Containers to Accelerate Cancer Studies\n\nZ
 aki\n\nAdvanced programming models, domain specific languages, and scripti
 ng toolkits have the potential to greatly accelerate the adoption of high 
 performance computing.  These complex software systems, however, are often
  difficult to install and maintain, especially on exotic high-end systems.
   We consider deep learning workflows used on petascale systems and redepl
 oyment on research clusters using containers.  Containers are used to depl
 oy the MPI-based infrastructure, but challenges in efficiency, usability, 
 and complexity must be overcome.  In this work, we address these challenge
 s through enhancements to a unified workflow system that manages interacti
 on with the container abstraction, the cluster scheduler, and the programm
 ing tools.  We also report results from running the application on our sys
 tem, harnessing 298~TFLOPS (single precision).
URL:https://sc18.supercomputing.org/presentation/?id=ws_espm104&sess=sess1
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