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DTSTAMP:20211207T055413Z
LOCATION:264
DTSTART;TZID=America/Chicago:20211115T130000
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UID:submissions.supercomputing.org_SC21_sess194_tut150@linklings.com
SUMMARY:High-Performance Machine Learning, Deep Learning and Data Science
DESCRIPTION:Tutorial\n\nHigh-Performance Machine Learning, Deep Learning a
 nd Data Science\n\nPanda, Jain, Shafi\n\nRecent advances in machine and de
 ep learning (ML/DL) have led to many exciting challenges and opportunities
 .  This tutorial provides an overview of recent trends in ML/DL and the ro
 le of cutting-edge hardware architectures and interconnects in moving the 
 field forward.  We will also present an overview of different DNN architec
 tures and ML/DL frameworks with a special focus on parallelization strateg
 ies for model training. We highlight new challenges and opportunities for 
 communication runtimes to exploit high-performance CPU/GPU architectures t
 o efficiently support large-scale distributed training.  The tutorial cove
 rs training traditional ML models including K-Means, linear regression, ne
 arest neighbors, using the cuML framework accelerated using MVAPICH2-GDR. 
  Also, the tutorial presents accelerating GPU-based data science applicati
 ons using MPI4Dask, which is an MPI-based backend for Dask.  Throughout th
 e tutorial, we include hands-on exercises to enable attendees to gain firs
 t-hand experience running distributed ML/DL training and Dask on a modern 
 GPU cluster.\n\nTag: In-Person Only, Machine Learning and Artificial Intel
 ligence\n\nRegistration Category: Tutorial Reg Pass
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