Rethinking the Value of Asynchronous Solvers for Distributed Deep Learning

Abstract

In recent years, the field of machine learning has seen significant advances as data becomes more abundant and deep learning models become larger and more complex. However, these improvements in accuracy [2] have come at the cost of longer training time. As a result, state-of-the-art models like OpenAI's GPT-2 [18] or AlphaZero [20] require the use of distributed systems or clusters in order to speed up training. Currently, there exist both asynchronous and synchronous solvers for distributed training. In this paper, we implement state-of-the-art asynchronous and synchronous solvers, then conduct a comparison between them to help readers pick the most appropriate solver for their own applications. We address three main challenges: (1) implementing asynchronous solvers that can outperform six common algorithm variants, (2) achieving state-of-the-art distributed performance for various applications with different computational patterns, and (3) maintaining accuracy for large-batch asynchronous training. For asynchronous algorithms, we implement an algorithm called EA-wild, which combines the idea of non-locking wild updates from Hogwild! [19] with EASGD. Our implementation is able to scale to 217,600 cores and finish 90 epochs of training the ResNet-50 model on ImageNet in 15 minutes (the baseline takes 29 hours on eight NVIDIA P100 GPUs). We conclude that more complex models (e.g., ResNet-50) favor synchronous methods, while our asynchronous solver outperforms the synchronous solver for models with a low computation-communication ratio. The results are documented in this paper; for more results, readers can refer to our supplemental website 1.