Slim-DP: A Light Communication Data Parallelism for DNN

27 Sep 2017 Shizhao Sun Wei Chen Jiang Bian Xiaoguang Liu Tie-Yan Liu

Data parallelism has emerged as a necessary technique to accelerate the training of deep neural networks (DNN). In a typical data parallelism approach, the local workers push the latest updates of all the parameters to the parameter server and pull all merged parameters back periodically... (read more)

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