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Empirical Analysis on Top-k Gradient Sparsification for Distributed Deep
  Learning in a Supercomputing Environment

Empirical Analysis on Top-k Gradient Sparsification for Distributed Deep Learning in a Supercomputing Environment

18 September 2022
Daegun Yoon
Sangyoon Oh
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Papers citing "Empirical Analysis on Top-k Gradient Sparsification for Distributed Deep Learning in a Supercomputing Environment"

2 / 2 papers shown
Title
ScaleCom: Scalable Sparsified Gradient Compression for
  Communication-Efficient Distributed Training
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Chia-Yu Chen
Jiamin Ni
Songtao Lu
Xiaodong Cui
Pin-Yu Chen
...
Naigang Wang
Swagath Venkataramani
Vijayalakshmi Srinivasan
Wei Zhang
K. Gopalakrishnan
27
66
0
21 Apr 2021
Megatron-LM: Training Multi-Billion Parameter Language Models Using
  Model Parallelism
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
M. Shoeybi
M. Patwary
Raul Puri
P. LeGresley
Jared Casper
Bryan Catanzaro
MoE
243
1,817
0
17 Sep 2019
1