Exploiting potential of deep neural networks by layer-wise fine-grained parallelism

Deep neural networks (DNNs) have become more and more important for big data analysis. They usually use data parallelism or model parallelism for extreme scale computing. However, the two approaches realize the performance improvement mainly by using coarse-grained parallelization schemes. Neither can fully exploit the potentials of the parallelism of many-core systems (such as GPUs) for neural network models. Here, a new fine−grained...