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DTSTAMP:20211207T054807Z
LOCATION:230-231-232
DTSTART;TZID=America/Chicago:20211117T140000
DTEND;TZID=America/Chicago:20211117T143000
UID:submissions.supercomputing.org_SC21_sess174_pap464@linklings.com
SUMMARY:ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep
  Learning
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 th
 e limited CPU or NVMe bandwidth. ZeRO-Infinity can fit models with tens an
 d even hundreds of trillions of parameters for training on current generat
 ion GPU clusters. It can be used to fine-tune trillion parameter models on
  a single NVIDIA DGX-2 node, making large models more accessible. In terms
  of training throughput and scalability, it sustains over 25 petaFLOPS on 
 512 NVIDIA V100 GPUs (40% of peak), while also demonstrating superlinear s
 calability.\n\nTag: Reproducibility Badge, Machine Learning and Artificial
  Intelligence\n\nRegistration Category: Tech Program Reg Pass\n\nReproduci
 bility Badges: Artifact Available, Artifact Functional
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