Research
ML Applications
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems
September 3, 2020
Abstract
Large-scale training is important to ensure high performance and accuracy of machine-learning models. At Facebook we use many different models, including computer vision, video and language models. However, in this paper we focus on the deep learning recommendation models (DLRMs), which are responsible for more than 50% of the training demand in our data centers. Recommendation models present unique challenges in training because they exercise not only compute but also memory capacity as well as memory and network bandwidth. As model size and complexity increase, efficiently scaling training becomes a challenge. To address it we design Zion - Facebook's next-generation large-memory training platform that consists of both CPUs and accelerators. Also, we discuss the design requirements of future scale-out training systems.
AUTHORS
Written by
Maxim Naumov
John Kim
Dheevatsa Mudigere
Srinivas Sridharan
Xiaodong Wang
Whitney Zhao
Serhat Yilmaz
Changkyu Kim
Hector Yuen
Mustafa Ozdal
Krishnakumar Nair
Isabel Gao
Mikhail Smelyanskiy
Publisher
arXiv
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