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Understanding Capacity-Driven Scale-Out Neural Recommendation Inference

Understanding Capacity-Driven Scale-Out Neural Recommendation Inference

4 November 2020
Michael Lui
Yavuz Yetim
Özgür Özkan
Zhuoran Zhao
Shin-Yeh Tsai
Carole-Jean Wu
Mark Hempstead
    GNN
    BDL
    LRM
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Papers citing "Understanding Capacity-Driven Scale-Out Neural Recommendation Inference"

10 / 10 papers shown
Title
RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection
RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection
Tomomasa Yamasaki
Zhehui Wang
Tao Luo
Niangjun Chen
Bo Wang
32
0
0
26 Mar 2025
ElasticRec: A Microservice-based Model Serving Architecture Enabling
  Elastic Resource Scaling for Recommendation Models
ElasticRec: A Microservice-based Model Serving Architecture Enabling Elastic Resource Scaling for Recommendation Models
Yujeong Choi
Jiin Kim
Minsoo Rhu
32
1
0
11 Jun 2024
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
FEL: High Capacity Learning for Recommendation and Ranking via Federated
  Ensemble Learning
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning
Meisam Hejazinia
Dzmitry Huba
Ilias Leontiadis
Kiwan Maeng
Mani Malek
Luca Melis
Ilya Mironov
Milad Nasr
Kaikai Wang
Carole-Jean Wu
FedML
9
5
0
07 Jun 2022
Training Personalized Recommendation Systems from (GPU) Scratch: Look
  Forward not Backwards
Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwards
Youngeun Kwon
Minsoo Rhu
16
27
0
10 May 2022
Supporting Massive DLRM Inference Through Software Defined Memory
Supporting Massive DLRM Inference Through Software Defined Memory
E. K. Ardestani
Changkyu Kim
Seung Jae Lee
Luoshang Pan
Valmiki Rampersad
...
Krishnakumar Nair
Maxim Naumov
Christopher Peterson
M. Smelyanskiy
Vijay Rao
BDL
31
20
0
21 Oct 2021
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
Scaling Laws for Neural Language Models
Scaling Laws for Neural Language Models
Jared Kaplan
Sam McCandlish
T. Henighan
Tom B. Brown
B. Chess
R. Child
Scott Gray
Alec Radford
Jeff Wu
Dario Amodei
228
4,460
0
23 Jan 2020
RecNMP: Accelerating Personalized Recommendation with Near-Memory
  Processing
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
41
212
0
30 Dec 2019
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