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DTSTART;TZID=America/Chicago:20211117T133000
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UID:submissions.supercomputing.org_SC21_sess174@linklings.com
SUMMARY:Large Scale Neural Network Training: Part II
DESCRIPTION:Paper\n\nZeRO-Infinity: Breaking the GPU Memory Wall for Extre
 me Scale Deep Learning\n\nRajbhandari, Ruwase, Rasley, Smith, He\n\nWe pre
 sent ZeRO-Infinity, a novel heterogeneous system technology that leverages
  GPU, CPU and NVMe memory to allow for unprecedented model scale on limite
 d resources without requiring model code refactoring. At the same time it 
 achieves excellent training throughput and scalability, unencumbered by ..
 .\n\n---------------------\nEfficient Large-Scale Language Model Training 
 on GPU Clusters Using Megatron-LM\n\nNarayanan, Shoeybi, Casper, LeGresley
 , Patwary...\n\nLarge language models have led to state-of-the-art accurac
 ies across several tasks. However, training these models efficiently is ch
 allenging because: a) GPU memory capacity is limited, making it impossible
  to fit large models on even a multi-GPU server, and b) the number of comp
 ute operations requi...\n\n---------------------\nFedAT: A High-Performanc
 e and Communication-Efficient Federated Learning System with Asynchronous 
 Tiers\n\nChai, Chen, Anwar, Zhao, Cheng...\n\nFederated learning (FL) invo
 lves training a model over massive distributed devices, while keeping the 
 training data localized and private. This form of collaborative learning e
 xposes new tradeoffs among model convergence speed, model accuracy, balanc
 e across clients and communication cost, with new ...\n\n\nTag: Machine Le
 arning and Artificial Intelligence\n\nRegistration Category: Tech Program 
 Reg Pass
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