1 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 1 cited
CD-SGD: Distributed Stochastic Gradient Descent with Compression and Delay Compensation
Enda Yu, Dezun Dong, Yemao Xu +2
Communication overhead is the key challenge for distributed training. Gradient compression is a widely used approach to reduce communication traffic. When combining with parallel c…
cs.LG2020★ 1 cited
OD-SGD: One-step Delay Stochastic Gradient Descent for Distributed Training
Yemao Xu, Dezun Dong, Weixia Xu +1
The training of modern deep learning neural network calls for large amounts of computation, which is often provided by GPUs or other specific accelerators. To scale out to achieve…
cs.DC2020
Communication optimization strategies for distributed deep neural network training: A survey
Shuo Ouyang, Dezun Dong, Yemao Xu +1
Recent trends in high-performance computing and deep learning have led to the proliferation of studies on large-scale deep neural network training. However, the frequent communicat…