8 citations · 36 across the 18 of their papers we have counts for
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cs.DC2021★ 2 cited
Sync-Switch: Hybrid Parameter Synchronization for Distributed Deep Learning
Shijian Li, Oren Mangoubi, Lijie Xu +1
Stochastic Gradient Descent (SGD) has become the de facto way to train deep neural networks in distributed clusters. A critical factor in determining the training throughput and mo…
cs.DC2020★ 4 cited
Characterizing and Modeling Distributed Training with Transient Cloud GPU Servers
Shijian Li, Robert J. Walls, Tian Guo
Cloud GPU servers have become the de facto way for deep learning practitioners to train complex models on large-scale datasets. However, it is challenging to determine the appropri…
cs.DC2019
Perseus: Characterizing Performance and Cost of Multi-Tenant Serving for CNN Models
Matthew LeMay, Shijian Li, Tian Guo
Deep learning models are increasingly used for end-user applications, supporting both novel features such as facial recognition, and traditional features, e.g. web search. To accom…