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DTSTART;TZID=America/Chicago:20181111T133000
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UID:submissions.supercomputing.org_SC18_sess262_tut175@linklings.com
SUMMARY:Tools and Best Practices for Distributed Deep Learning with Superc
 omputers
DESCRIPTION:Weijia Xu (Texas Advanced Computing Center) and Zhao Zhang and
  David Walling (University of Texas)\n\nThis tutorial is a practical guide
  on how to run distributed deep learning over distributed compute nodes ef
 fectively. Deep Learning (DL) has emerged as an effective analysis method 
 and has been adapted quickly across many scientific domains in recent year
 s.  Domain scientists are embracing DL as both a standalone data science m
 ethod, as well as an effective approach to reducing dimensionality in the 
 traditional simulation. However, due to its inherent high computational re
 quirement, application of DL is limited by the available computational res
 ources. \n\nRecently, we have seen the fusion of DL and HPC: supercomputer
 s show an unparalleled capacity to reduce DL training time from days to mi
 nutes; HPC techniques have been used to speed up parallel DL training. The
 refore distributed deep learning has great potential to augment DL applica
 tions by leveraging existing high performance computing cluster. This tuto
 rial consists of three sessions. First, we will give an overview of the st
 ate-of-art approaches to enabling deep learning at scale. The second sessi
 on is an interactive hands-on session to help attendees running distribute
 d deep learning with resources at the Texas Advanced Computing Center. In 
 the last session, we will focus on the best practices to evaluate and tune
  up performance.\n\nTag: Deep Learning, Machine Learning\n\nRegistration C
 ategory: Tutorial Reg Pass\n\n
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