57 citations · 194 across the 19 of their papers we have counts for
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cs.LG2022★ 57 cited
Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism
Xupeng Miao, Yujie Wang, Youhe Jiang +4
Transformer models have achieved state-of-the-art performance on various domains of applications and gradually becomes the foundations of the advanced large deep learning (DL) mode…
cs.DC2022
OSDP: Optimal Sharded Data Parallel for Distributed Deep Learning
Youhe Jiang, Fangcheng Fu, Xupeng Miao +2
Large-scale deep learning models contribute to significant performance improvements on varieties of downstream tasks. Current data and model parallelism approaches utilize model re…
cs.DC2022★ 13 cited
HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System
Xiaonan Nie, Pinxue Zhao, Xupeng Miao +2
As giant dense models advance quality but require large amounts of GPU budgets for training, the sparsely gated Mixture-of-Experts (MoE), a kind of conditional computation architec…