ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2208.06399
  4. Cited By
AutoShard: Automated Embedding Table Sharding for Recommender Systems

AutoShard: Automated Embedding Table Sharding for Recommender Systems

12 August 2022
Daochen Zha
Louis Feng
Bhargav Bhushanam
Dhruv Choudhary
Jade Nie
Yuandong Tian
Jay Chae
Yi-An Ma
A. Kejariwal
Xia Hu
ArXivPDFHTML

Papers citing "AutoShard: Automated Embedding Table Sharding for Recommender Systems"

11 / 11 papers shown
Title
Scaling User Modeling: Large-scale Online User Representations for Ads
  Personalization in Meta
Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta
Wei Zhang
Dai Li
Chen Liang
Fang Zhou
Zhongke Zhang
...
Huayu Li
Yunnan Wu
Zhan Shu
Mindi Yuan
Sri Reddy
22
7
0
16 Nov 2023
CoRTX: Contrastive Framework for Real-time Explanation
CoRTX: Contrastive Framework for Real-time Explanation
Yu-Neng Chuang
Guanchu Wang
Fan Yang
Quan-Gen Zhou
Pushkar Tripathi
Xuanting Cai
Xia Hu
46
19
0
05 Mar 2023
Towards Personalized Preprocessing Pipeline Search
Towards Personalized Preprocessing Pipeline Search
Diego Martinez
Daochen Zha
Qiaoyu Tan
Xia Hu
AI4TS
26
2
0
28 Feb 2023
DreamShard: Generalizable Embedding Table Placement for Recommender
  Systems
DreamShard: Generalizable Embedding Table Placement for Recommender Systems
Daochen Zha
Louis Feng
Qiaoyu Tan
Zirui Liu
Kwei-Herng Lai
Bhargav Bhushanam
Yuandong Tian
A. Kejariwal
Xia Hu
LMTD
OffRL
15
28
0
05 Oct 2022
RecShard: Statistical Feature-Based Memory Optimization for
  Industry-Scale Neural Recommendation
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
66
0
25 Jan 2022
Learnable Embedding Sizes for Recommender Systems
Learnable Embedding Sizes for Recommender Systems
Siyi Liu
Chen Gao
Yihong Chen
Depeng Jin
Yong Li
59
82
0
19 Jan 2021
FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference
FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference
D. Khudia
Jianyu Huang
Protonu Basu
Summer Deng
Haixin Liu
Jongsoo Park
M. Smelyanskiy
FedML
MQ
49
46
0
13 Jan 2021
Learning to Embed Categorical Features without Embedding Tables for
  Recommendation
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
45
68
0
21 Oct 2020
Deep Learning Training in Facebook Data Centers: Design of Scale-up and
  Scale-out Systems
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
41
83
0
20 Mar 2020
Distributed Hierarchical GPU Parameter Server for Massive Scale Deep
  Learning Ads Systems
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
57
150
0
12 Mar 2020
Neural Architecture Search with Reinforcement Learning
Neural Architecture Search with Reinforcement Learning
Barret Zoph
Quoc V. Le
269
5,326
0
05 Nov 2016
1