The Architectural Implications of Facebook’s DNN-based Personalized Recommendation

International Symposium on High Performance Computer Architecture (HPCA)


The widespread application of deep learning has changed the landscape of computation in data centers. In particular, personalized recommendation for content ranking is now largely accomplished using deep neural networks. However, despite their importance and the amount of compute cycles they consume, relatively little research attention has been devoted to recommendation systems. To facilitate research and advance the understanding of these workloads, this paper presents a set of real-world, production-scale DNNs for personalized recommendation coupled with relevant performance metrics for evaluation. In addition to releasing a set of open-source workloads, we conduct in-depth analysis that underpins future system design and optimization for at-scale recommendation: Inference latency varies by 60% across three Intel server generations, batching and co-location of inference jobs can drastically improve latency-bounded throughput, and diversity across recommendation models leads to different optimization strategies.

Related Publications

All Publications

NAACL - June 6, 2021

Deep Learning on Graphs for Natural Language Processing

Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li

ICASSP - June 6, 2021

On the Predictability of HRTFs from Ear Shapes Using Deep Networks

Yaxuan Zhou, Hao Jiang, Vamsi Krishna Ithapu

CoRL - December 1, 2020

Auxiliary Tasks Speed Up Learning PointGoal Navigation

Joel Ye, Dhruv Batra, Erik Wijmans, Abhishek Das

ACL - July 7, 2020

CraftAssist Instruction Parsing: Semantic Parsing for a Voxel-World Assistant

Kavya Srinet, Yacine Jernite, Jonathan Gray, Arthur Szlam

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy