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DTSTAMP:20211207T055346Z
LOCATION:230-231-232
DTSTART;TZID=America/Chicago:20211116T133000
DTEND;TZID=America/Chicago:20211116T150000
UID:submissions.supercomputing.org_SC21_sess172@linklings.com
SUMMARY:Large Scale Neural Network Training: Part I
DESCRIPTION:Paper\n\nET: Re-Thinking Self-Attention for Transformer Models
  on GPUs\n\nChen, Huang, Pandey, Li, Gao...\n\nTransformer-based deep lear
 ning models have become a ubiquitous vehicle driving a variety of natural 
 language processing (NLP) -related tasks beyond their accuracy ceiling. Th
 ese models, however, also suffer from two pronounced challenges, that is, 
 gigantic model size and prolonged turnaround time. ...\n\n----------------
 -----\nChimera: Efficiently Training Large-Scale Neural Networks with Bidi
 rectional Pipelines\n\nLi, Hoefler\n\nTraining large deep learning models 
 at scale is very challenging. This paper proposes Chimera, a novel pipelin
 e parallelism scheme which combines bidirectional pipelines for efficientl
 y training large-scale models. Chimera is a synchronous approach and there
 fore no loss of accuracy, which is more co...\n\n---------------------\nPa
 rallel Construction of Module Networks\n\nSrivastava, Chockalingam, Aluru,
  Aluru\n\nModule networks (MoNets) are a parameter-sharing specialization 
 of Bayesian networks that are used for reasoning about multidimensional en
 tities with concerted interactions between groups of variables. Constructi
 on of MoNets is compute-intensive, with sequential methods requiring month
 s for learning...\n\n\nTag: Machine Learning and Artificial Intelligence\n
 \nRegistration Category: Tech Program Reg Pass
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