  Distributed Deep Learning (DDL) is essential for large-scale Deep Learning
(DL) training. Synchronous Stochastic Gradient Descent (SSGD) 1 is the de facto
DDL optimization method. Using a sufficiently large batch size is critical to
achieving DDL runtime speedup. In a large batch setting, the learning rate must
be increased to compensate for the reduced number of parameter updates.
However, a large learning rate may harm convergence in SSGD and training could
easily diverge. Recently, Decentralized Parallel SGD (DPSGD) has been proposed
to improve distributed training speed. In this paper, we find that DPSGD not
only has a system-wise run-time benefit but also a significant convergence
benefit over SSGD in the large batch setting. Based on a detailed analysis of
the DPSGD learning dynamics, we find that DPSGD introduces additional
landscape-dependent noise that automatically adjusts the effective learning
rate to improve convergence. In addition, we theoretically show that this noise
smoothes the loss landscape, hence allowing a larger learning rate. We conduct
extensive studies over 18 state-of-the-art DL models/tasks and demonstrate that
DPSGD often converges in cases where SSGD diverges for large learning rates in
the large batch setting. Our findings are consistent across two different
application domains: Computer Vision (CIFAR10 and ImageNet-1K) and Automatic
Speech Recognition (SWB300 and SWB2000), and two different types of neural
network models: Convolutional Neural Networks and Long Short-Term Memory
Recurrent Neural Networks.

Machine Learning