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DTSTAMP:20210402T160549Z
LOCATION:Track 3
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UID:submissions.supercomputing.org_SC20_sess180@linklings.com
SUMMARY:Distributed Deep Learning
DESCRIPTION:Paper\n\nOptimizing Deep Learning Recommender Systems Training
  on CPU Cluster Architectures\n\nKalamkar, Georganas, Srinivasan, Chen, Sh
 iryaev...\n\nDuring the last two years, the goal of many researchers has b
 een to squeeze the last bit of performance out of HPC systems for AI tasks
 . ResNet50 is no longer a representative workload in 2020. Thus, we focus 
 on Recommender Systems, specifically Facebook's DLRM benchmark, which acco
 unt for most of t...\n\n---------------------\nHerring: Rethinking the Par
 ameter Server at Scale for the Cloud\n\nThangakrishnan, Cavdar, Karakus, G
 hai, Selivonchyk...\n\nTraining large deep neural networks is time-consumi
 ng and may take days or even weeks to complete. Although parameter-server-
 based approaches were initially popular in distributed training, scalabili
 ty issues led the field to move towards all-reduce-based approaches. Recen
 t developments in cloud net...\n\n---------------------\nGEMS: GPU-Enabled
  Memory-Aware Model-Parallelism System for Distributed DNN Training\n\nJai
 n, Awan, Aljuhani, Hashmi, Anthony...\n\nData-parallelism has become an es
 tablished paradigm in which to train DNNs that fit the GPU memory on large
 -scale HPC systems. Model-parallelism, however, is required to train out-o
 f-core DNNs. In this paper, we deal with emerging requirements brought for
 ward by very-large DNNs being trained using h...\n\n\nTag: Accelerators, F
 PGA, and GPUs, Machine Learning, Deep Learning and Artificial Intelligence
 , Scalable Computing\n\nRegistration Category: Tech Program Reg Pass
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