9 citations · 35 across the 24 of their papers we have counts for
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cs.DC2022★ 2 cited
Mimose: An Input-Aware Checkpointing Planner for Efficient Training on GPU
Jianjin Liao, Mingzhen Li, Qingxiao Sun +8
Larger deep learning models usually lead to higher model quality with an ever-increasing GPU memory footprint. Although tensor checkpointing techniques have been proposed to enable…
cs.DC2022★ 4 cited
EasyScale: Accuracy-consistent Elastic Training for Deep Learning
Mingzhen Li, Wencong Xiao, Biao Sun +9
Distributed synchronized GPU training is commonly used for deep learning. The resource constraint of using a fixed number of GPUs makes large-scale training jobs suffer from long q…
cs.LG2022
FamilySeer: Towards Optimized Tensor Codes by Exploiting Computation Subgraph Similarity
Shanjun Zhang, Mingzhen Li, Hailong Yang +3
Deploying various deep learning (DL) models efficiently has boosted the research on DL compilers. The difficulty of generating optimized tensor codes drives DL compiler to ask for…