Beyond Data and Model Parallelism for Deep Neural Networks

14 Jul 2018Zhihao JiaMatei ZahariaAlex Aiken

The computational requirements for training deep neural networks (DNNs) have grown to the point that it is now standard practice to parallelize training. Existing deep learning systems commonly use data or model parallelism, but unfortunately, these strategies often result in suboptimal parallelization performance... (read more)

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