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Found 2 papers, 2 papers with code
Auto-Precision Scaling for Distributed Deep Learning
Our experimental results show that for many applications, APS can train state-of-the-art models by 8-bit gradients with no or only a tiny accuracy loss (<0. 05%).
Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes
To address this problem, we propose a communication backend named GradientFlow for distributed DNN training, and employ a set of network optimization techniques.
Distributed, Parallel, and Cluster Computing
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