117 citations · 196 across the 10 of their papers we have counts for
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cs.DC2019
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning
Aritra Dutta, El Houcine Bergou, Ahmed M. Abdelmoniem +4
Compressed communication, in the form of sparsification or quantization of stochastic gradients, is employed to reduce communication costs in distributed data-parallel training of…
cs.DC2019★ 117 cited
Scaling Distributed Machine Learning with In-Network Aggregation
Amedeo Sapio, Marco Canini, Chen-Yu Ho +7
Training machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a p…