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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20260422T143140Z
LOCATION:B203
DTSTART;TZID=America/New_York:20241118T083000
DTEND;TZID=America/New_York:20241118T170000
UID:submissions.supercomputing.org_SC24_sess424_tut154@linklings.com
SUMMARY:Deep Learning at Scale
DESCRIPTION:Shashank Subramanian (National Energy Research Scientific Comp
 uting Center (NERSC)), Josh Romero and Thorsten Kurth (NVIDIA Corporation)
 , Junqi Yin and Aristeidis Tsaris (Oak Ridge National Laboratory (ORNL)), 
 Steven Farrell (National Energy Research Scientific Computing Center (NERS
 C)), and Wahid Bhimji and Wenbin Xu (NERSC at LBNL (Lawrence Berkeley Nati
 onal Laboratory))\n\nDeep learning is rapidly and fundamentally transformi
 ng the way science and industry use data to solve problems. Deep neural ne
 twork models have been shown to be powerful tools for extracting insights 
 from data across a large number of domains, from large language models (LL
 Ms) to protein folding. As these models grow in complexity to solve increa
 singly challenging problems with larger and larger datasets, the need for 
 scalable methods and software to train them grows accordingly.\n\nThe Deep
  Learning at Scale tutorial aims to provide attendees with a working knowl
 edge of deep learning on HPC-class systems, including core concepts, scien
 tific applications, performance optimization, tips, and techniques for sca
 ling. We will provide training accounts on some of the worlds largest GPU 
 systems, example code, and datasets to allow attendees to experiment hands
 -on with optimized, scalable distributed training of deep neural network m
 achine learning models from real scientific computing applications.\n\nTag
 : Artificial Intelligence/Machine Learning\n\nRegistration Category: Tutor
 ial Reg Pass\n\n
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