  Deep learning frameworks have been widely deployed on GPU servers for deep
learning applications in both academia and industry. In training deep neural
networks (DNNs), there are many standard processes or algorithms, such as
convolution and stochastic gradient descent (SGD), but the running performance
of different frameworks might be different even running the same deep model on
the same GPU hardware. In this study, we evaluate the running performance of
four state-of-the-art distributed deep learning frameworks (i.e., Caffe-MPI,
CNTK, MXNet, and TensorFlow) over single-GPU, multi-GPU, and multi-node
environments. We first build performance models of standard processes in
training DNNs with SGD, and then we benchmark the running performance of these
frameworks with three popular convolutional neural networks (i.e., AlexNet,
GoogleNet and ResNet-50), after that, we analyze what factors that result in
the performance gap among these four frameworks. Through both analytical and
experimental analysis, we identify bottlenecks and overheads which could be
further optimized. The main contribution is that the proposed performance
models and the analysis provide further optimization directions in both
algorithmic design and system configuration.
