Fast Deep Neural Network Training on Distributed Systems and Cloud TPUs

Since its creation, the ImageNet-1k benchmark set has played a significant role as a benchmark for ascertaining the accuracy of different deep neural net (DNN) models on the image classification problem. Moreover, in recent years it has also served as the principal benchmark for assessing different approaches to DNN training. Finishing a 90-epoch ImageNet-1k training with ResNet-50 on a NVIDIA M40 GPU takes 14 days. This training requires $10^{18}$1018 single precision operations in total. On the other hand, the world's current fastest supercomputer can finish $3 \times 10^{17}$3×1017 single precision operations per second (according to the Nov 2018 Top 500 results). If we can make full use of the computing capability of the fastest supercomputer, we should be able to finish the training in several seconds. Over the last two years, researchers have focused on closing this significant performance gap through scaling DNN training to larger numbers of processors. Most successful approaches to scaling ImageNet training have used the synchronous mini-batch stochastic gradient descent (SGD). However, to scale synchronous SGD one must also increase the batch size used in each iteration. Thus, for many researchers, the focus on scaling DNN training has translated into a focus on developing training algorithms that enable increasing the batch size in data-parallel synchronous SGD without losing accuracy over a fixed number of epochs. In this paper, we investigate supercomputers’ capability of speeding up DNN training. Our approach is to use a large batch size, powered by the Layer-wise Adaptive Rate Scaling (LARS) algorithm, for efficient usage of massive computing resources. Our approach is generic, as we empirically evaluate the effectiveness on five neural networks: AlexNet, AlexNet-BN, GNMT, ResNet-50, and ResNet-50-v2 trained with large datasets while preserving the state-of-the-art test accuracy. Compared to the baseline of a previous study from Goyal et al. [1] , our approach shows higher test accuracy on batch sizes that are larger than 16K. When we use the same baseline, our results are better than Goyal et al. for all the batch sizes (Fig. 20 ). Using 2,048 Intel Xeon Platinum 8160 processors, we reduce the 100-epoch AlexNet training time from hours to 11 minutes. With 2,048 Intel Xeon Phi 7250 Processors, we reduce the 90-epoch ResNet-50 training time from hours to 20 minutes. Our implementation is open source and has been released in the Intel distribution of Caffe, Facebook's PyTorch, and Google's TensorFlow. The difference between this paper and the conference-version of our work [2] includes: (1) we implement our approach on Google's cloud Tensor Processing Unit (TPU) platform, which verifies our previous success on CPUs and GPUs. (2) we scale the batch size of ResNet-50-v2 to 32K and achieve 76.3 percent accuracy, which is better than the 75.3 percent accuracy achieved in our conference paper. (3) we apply our approach to Google's Neural Machine Translation (GNMT) application, which helps us to achieves 4× speedup on the cloud TPUs.