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Supporting Very Large Models using Automatic Dataflow Graph Partitioning

Supporting Very Large Models using Automatic Dataflow Graph Partitioning

24 July 2018
Minjie Wang
Chien-chin Huang
Jinyang Li
ArXivPDFHTML

Papers citing "Supporting Very Large Models using Automatic Dataflow Graph Partitioning"

13 / 63 papers shown
Title
TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale
  Language Models
TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models
Zhuohan Li
Siyuan Zhuang
Shiyuan Guo
Danyang Zhuo
Hao Zhang
D. Song
Ion Stoica
MoE
19
121
0
16 Feb 2021
Scaling Distributed Deep Learning Workloads beyond the Memory Capacity
  with KARMA
Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA
M. Wahib
Haoyu Zhang
Truong Thao Nguyen
Aleksandr Drozd
Jens Domke
Lingqi Zhang
Ryousei Takano
Satoshi Matsuoka
OODD
34
23
0
26 Aug 2020
A Computational-Graph Partitioning Method for Training
  Memory-Constrained DNNs
A Computational-Graph Partitioning Method for Training Memory-Constrained DNNs
Fareed Qararyah
M. Wahib
Douga Dikbayir
M. E. Belviranli
D. Unat
28
8
0
19 Aug 2020
DAPPLE: A Pipelined Data Parallel Approach for Training Large Models
DAPPLE: A Pipelined Data Parallel Approach for Training Large Models
Shiqing Fan
Yi Rong
Chen Meng
Zongyan Cao
Siyu Wang
...
Jun Yang
Lixue Xia
Lansong Diao
Xiaoyong Liu
Wei Lin
21
232
0
02 Jul 2020
HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU
  Clusters through Integration of Pipelined Model Parallelism and Data
  Parallelism
HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism
Jay H. Park
Gyeongchan Yun
Chang Yi
N. T. Nguyen
Seungmin Lee
Jaesik Choi
S. Noh
Young-ri Choi
MoE
25
128
0
28 May 2020
TensorOpt: Exploring the Tradeoffs in Distributed DNN Training with
  Auto-Parallelism
TensorOpt: Exploring the Tradeoffs in Distributed DNN Training with Auto-Parallelism
Zhenkun Cai
Kaihao Ma
Xiao Yan
Yidi Wu
Yuzhen Huang
James Cheng
Teng Su
F. Yu
19
42
0
16 Apr 2020
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
Samyam Rajbhandari
Jeff Rasley
Olatunji Ruwase
Yuxiong He
ALM
AI4CE
17
807
0
04 Oct 2019
Heterogeneity-Aware Asynchronous Decentralized Training
Heterogeneity-Aware Asynchronous Decentralized Training
Qinyi Luo
Jiaao He
Youwei Zhuo
Xuehai Qian
19
8
0
17 Sep 2019
Training on the Edge: The why and the how
Training on the Edge: The why and the how
Navjot Kukreja
Alena Shilova
Olivier Beaumont
Jan Huckelheim
N. Ferrier
P. Hovland
Gerard Gorman
16
33
0
13 Feb 2019
HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
Linghao Song
Jiachen Mao
Youwei Zhuo
Xuehai Qian
Hai Helen Li
Yiran Chen
24
97
0
07 Jan 2019
Wireless Network Intelligence at the Edge
Wireless Network Intelligence at the Edge
Jihong Park
S. Samarakoon
M. Bennis
Mérouane Debbah
21
518
0
07 Dec 2018
Parallax: Sparsity-aware Data Parallel Training of Deep Neural Networks
Parallax: Sparsity-aware Data Parallel Training of Deep Neural Networks
Soojeong Kim
Gyeong-In Yu
Hojin Park
Sungwoo Cho
Eunji Jeong
Hyeonmin Ha
Sanha Lee
Joo Seong Jeong
Byung-Gon Chun
23
73
0
08 Aug 2018
Google's Neural Machine Translation System: Bridging the Gap between
  Human and Machine Translation
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Yonghui Wu
M. Schuster
Z. Chen
Quoc V. Le
Mohammad Norouzi
...
Alex Rudnick
Oriol Vinyals
G. Corrado
Macduff Hughes
J. Dean
AIMat
716
6,746
0
26 Sep 2016
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