Modern distributed engines are increasingly deployed to accelerate large-scaled deep learning (DL) training jobs. While the parallelism of distributed workers/nodes promises the scalability, the computation and communication overheads of the underlying iterative solving algorithms, e.g., stochastic gradient decent, unfortunately become the bottleneck for distributed DL training jobs. Existing approaches address such limitations by designing more efficient synchronization algorithms and model compressing techniques, but do not adequately address issues relating to processing massive datasets. In this article, we propose ClipDL, which accelerates the deep learning systems by simultaneously decreasing the number of model parameters as well as reducing the computations on critical data only. The core component of ClipDL is the estimation of critical set based on the observation that large proportions of input data have little influence on model parameter updating in many prevalent DL algorithms. We implemented ClipDL on Spark (a popular distributed engine for big data) and BigDL (based on de-factor distributed DL training architecture, parameter server), and integrated it with representative model compression techniques. The exhaustive experiments on real DL applications and datasets show ClipDL accelerates model training process by an average of 2.32 times while only incurring accuracy losses of 1.86 percent.
