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MTrainS: Improving DLRM training efficiency using heterogeneous memories

MTrainS: Improving DLRM training efficiency using heterogeneous memories

19 April 2023
H. Kassa
Paul Johnson
Jason B. Akers
Mrinmoy Ghosh
Andrew Tulloch
Dheevatsa Mudigere
Jongsoo Park
Xing Liu
R. Dreslinski
E. K. Ardestani
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Papers citing "MTrainS: Improving DLRM training efficiency using heterogeneous memories"

4 / 4 papers shown
Title
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
39
20
0
21 Oct 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
51
46
0
13 Jan 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
46
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
59
150
0
12 Mar 2020
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