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DTSTAMP:20260924T172458Z
LOCATION:B206
DTSTART;TZID=America/New_York:20241122T103600
DTEND;TZID=America/New_York:20241122T104800
UID:submissions.supercomputing.org_SC24_sess769_ws_adugai120@linklings.com
SUMMARY:Training Large-Scale Vision Transformer Foundation Models for Scie
 nce and Engineering Applications
DESCRIPTION:Mohamed Wahib (RIKEN); Peng Chen (National Institute of Advanc
 ed Industrial Science and Technology (AIST), Japan); Jun Igarashi (RIKEN);
  and Isaac Lyngaas and Xiao Wang (Oak Ridge National Laboratory (ORNL))\n\
 nVision Transformer (ViT) is a powerful AI architecture for computer visio
 n that is used by most imaging foundation models due to its effectiveness 
 in discerning complex visual patterns across many tasks. However, training
  large-scale ViT foundation models requires considerable computing resourc
 es, leading to a significant energy footprint for training. For example, O
 pen-AI’s SORA video generator model was trained on more than 10,000 NVIDIA
  H100 GPUs and the training took more than a month on a supercomputer. The
  energy consumption for training SORA was equivalent to the total annual e
 nergy consumption of 300 US households. This project aims to co-design the
  scaling algorithm and the ViT architecture to achieve hardware-, modality
 -, and energy-conscious computing for ViT foundation models. We anticipate
  that our proposed training approaches can not only significantly improve 
 energy efficiency and reduce carbon footprint, but also significantly impr
 ove computing efficiency and scalability, fostering an accelerated AI deve
 lopment cycle.\n\nTag: Artificial Intelligence/Machine Learning\n\nRegistr
 ation Category: Workshop Reg Pass\n\nSession Chairs: Charlie Catlett (Argo
 nne National Laboratory (ANL), University of Chicago); Fabrizio Gagliardi 
 (Barcelona Supercomputing Center (BSC), Association for Computing Machiner
 y (ACM)); Neeraj Kumar (Pacific Northwest National Laboratory (PNNL)); Sat
 oshi Matsuoka (RIKEN, Tokyo Institute of Technology); Irina Rish (Universi
 ty of Montreal, Canada; Mila – Quebec AI Institute); Rick Stevens (Argonne
  National Laboratory (ANL), University of Chicago); and Valerie Taylor (Ar
 gonne National Laboratory (ANL), University of Chicago)\n\n
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