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Found 2 papers, 2 papers with code
DeAR: Accelerating Distributed Deep Learning with Fine-Grained All-Reduce Pipelining
Communication scheduling has been shown to be effective in accelerating distributed training, which enables all-reduce communications to be overlapped with backpropagation computations.
A Quantitative Survey of Communication Optimizations in Distributed Deep Learning
In this article, we present a quantitative survey of communication optimization techniques for data parallel distributed DL.
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