Publication

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

International Symposium on High Performance Computer Architecture (HPCA)


Abstract

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

EMNLP - October 1, 2021

Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little

Koustuv Sinha, Robin Jia, Dieuwke Hupkes, Joelle Pineau, Adina Williams, Douwe Kiela

IROS - September 30, 2021

Learning Navigation Skills for Legged Robots with Learned Robot Embeddings

Joanne Truong, Denis Yarats, Tianyu Li, Franziska Meier, Sonia Chernova, Dhruv Batra, Akshara Rai

International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE) - September 26, 2021

Behavioural and Structural Imitation Models in Facebook’s WW Simulation System

John Ahlgren, Kinga Bojarczuk, Inna Dvortsova, Mark Harman, Rayan Hatout, Maria Lomeli, Erik Meijer, Silvia Sapora

IROS - September 27, 2021

Joint Sampling and Trajectory Optimization over Graphs for Online Motion Planning

Kalyan Vasudev Alwala, Mustafa Mukadam

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