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.

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AUTHORS

Written by

Maxim Naumov

Jiyan Yang

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

Research Topics

Artificial Intelligence

Human and Machine Learning

Systems Research

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