Accelerating Synchronous Distributed Data Parallel Training with Small Batch Sizes

Abstract

Synchronous distributed data parallel (SDDP) training is widely employed in distributed deep learning systems to train DNN models on large datasets. The performance of SDDP training essentially depends on the communication overhead and the statistical efficiency. However, existing approaches only optimize either the communication overhead or the statistical efficiency to accelerate SDDP training. In this paper, we adopt the advantages of those approaches and design a new approach, namely SkipSMA, that benefits from both low communication overhead and high statistical efficiency. In particular, we exploit the skipping strategy with an adaptive interval to decrease the communication frequency, which guarantees low communication overhead. Moreover, we employ the correction technique to mitigate the divergence while keeping small batch sizes, which ensures high statistical efficiency. To demonstrate the performance of SkipSMA, we integrate it into TensorFlow. Our experiments show that SkipSMA outperforms the state-of-the-art solutions for SDDP training, e.g., 6.88x speedup over SSGD.