Data parallelism is an approach towards parallel processing that depends on being able to break up data between multiple compute units (which could be cores in a processor, processors in a computer, ...
Distributed deep learning has emerged as an essential approach for training large-scale deep neural networks by utilising multiple computational nodes. This methodology partitions the workload either ...
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