FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models

8 January 2023

Papers citing "FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models"

6 / 6 papers shown

Title
TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings N. Jouppi George Kurian Sheng Li Peter C. Ma R. Nagarajan ... Brian Towles C. Young Xiaoping Zhou Zongwei Zhou David A. Patterson BDL VLM 55 341 0 04 Apr 2023
RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation Geet Sethi Bilge Acun Niket Agarwal Christos Kozyrakis Caroline Trippel Carole-Jean Wu 47 67 0 25 Jan 2022
Learning to Embed Categorical Features without Embedding Tables for Recommendation Wang-Cheng Kang D. Cheng Tiansheng Yao Xinyang Yi Ting-Li Chen Lichan Hong Ed H. Chi LMTD CML DML 50 68 0 21 Oct 2020
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems Maxim Naumov John Kim Dheevatsa Mudigere Srinivas Sridharan Xiaodong Wang ... Krishnakumar Nair Isabel Gao Bor-Yiing Su Jiyan Yang M. Smelyanskiy GNN 46 83 0 20 Mar 2020
Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems Weijie Zhao Deping Xie Ronglai Jia Yulei Qian Rui Ding Mingming Sun P. Li MoE 59 151 0 12 Mar 2020
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing Liu Ke Udit Gupta Carole-Jean Wu B. Cho Mark Hempstead ... Dheevatsa Mudigere Maxim Naumov Martin D. Schatz M. Smelyanskiy Xiaodong Wang 49 216 0 30 Dec 2019