Research

Ads recommendation systems are often trained with a tremendous amount of data, and distributed training is the workhorse to shorten the training time. Meanwhile, a commonly used technique to prevent overfitting in Ads recommendation is one pass training. In this scenario, the total amount of data is fixed. When we express data parallelism on n workers, each worker only processes 1/n data. The larger the number of workers, the less data each worker observes. While the training throughput can be increased by simply adding more workers, it is also increasingly challenging to preserve the model quality.

To address this problem, we propose the ShadowSync framework, in which the model parameters are synchronized across workers, yet we isolate synchronization from training and run it in the background. In contrast to common strategies including synchronous SGD, asynchronous SGD, and model averaging on independently trained sub-models, where synchronization happens in the foreground, ShadowSync synchronization is neither part of the backward pass nor happens every k iterations.

ShadowSync is simple but effective. Our framework is generic to host various types of synchronization algorithms, and we propose 3 approaches under this theme. The superiority of ShadowSync is confirmed by experiments on training deep neural networks for click-through-rate prediction. Our methods all succeed in making the training throughput linearly scale with the number of trainers. Comparing to their foreground counterparts, our methods exhibit neutral to better model quality and better scalability when we keep the number of parameter servers the same. In our training system which expresses both replication and Hogwild parallelism, ShadowSync also accomplishes the highest example level parallelism number comparing to the prior arts.