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AdaComp : Adaptive Residual Gradient Compression for Data-Parallel
  Distributed Training

AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training

7 December 2017
Chia-Yu Chen
Jungwook Choi
D. Brand
A. Agrawal
Wei Zhang
K. Gopalakrishnan
    ODL
ArXiv (abs)PDFHTML

Papers citing "AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training"

15 / 65 papers shown
Gradient Sparification for Asynchronous Distributed Training
Gradient Sparification for Asynchronous Distributed Training
Zijie Yan
FedML
60
2
0
24 Oct 2019
Associative Convolutional Layers
Associative Convolutional Layers
H. Omidvar
Vahideh Akhlaghi
M. Franceschetti
Rajesh K. Gupta
149
1
0
10 Jun 2019
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass
  Error-Compensated Compression
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated CompressionInternational Conference on Machine Learning (ICML), 2019
Hanlin Tang
Xiangru Lian
Chen Yu
Tong Zhang
Ji Liu
242
232
0
15 May 2019
Priority-based Parameter Propagation for Distributed DNN Training
Priority-based Parameter Propagation for Distributed DNN TrainingUSENIX workshop on Tackling computer systems problems with machine learning techniques (SysML), 2019
Anand Jayarajan
Jinliang Wei
Garth A. Gibson
Alexandra Fedorova
Gennady Pekhimenko
AI4CE
140
189
0
10 May 2019
Realizing Petabyte Scale Acoustic Modeling
Realizing Petabyte Scale Acoustic Modeling
S. Parthasarathi
Nitin Sivakrishnan
Pranav Ladkat
N. Strom
117
11
0
24 Apr 2019
Distributed Deep Learning Strategies For Automatic Speech Recognition
Distributed Deep Learning Strategies For Automatic Speech Recognition
Wei Zhang
Xiaodong Cui
Ulrich Finkler
Brian Kingsbury
G. Saon
David S. Kung
M. Picheny
138
30
0
10 Apr 2019
A Distributed Synchronous SGD Algorithm with Global Top-$k$
  Sparsification for Low Bandwidth Networks
A Distributed Synchronous SGD Algorithm with Global Top-kkk Sparsification for Low Bandwidth Networks
Shaoshuai Shi
Qiang-qiang Wang
Kaiyong Zhao
Zhenheng Tang
Yuxin Wang
Xiang Huang
Xiaowen Chu
290
158
0
14 Jan 2019
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep
  Net Training
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net TrainingNeural Information Processing Systems (NeurIPS), 2018
Youjie Li
Hang Qiu
Songze Li
A. Avestimehr
Nam Sung Kim
Alex Schwing
FedML
326
108
0
08 Nov 2018
A Hitchhiker's Guide On Distributed Training of Deep Neural Networks
A Hitchhiker's Guide On Distributed Training of Deep Neural Networks
K. Chahal
Manraj Singh Grover
Kuntal Dey
3DHOOD
179
55
0
28 Oct 2018
Computation Scheduling for Distributed Machine Learning with Straggling
  Workers
Computation Scheduling for Distributed Machine Learning with Straggling Workers
Mohammad Mohammadi Amiri
Deniz Gunduz
FedML
255
4
0
23 Oct 2018
Sparsified SGD with Memory
Sparsified SGD with Memory
Sebastian U. Stich
Jean-Baptiste Cordonnier
Martin Jaggi
365
835
0
20 Sep 2018
RedSync : Reducing Synchronization Traffic for Distributed Deep Learning
RedSync : Reducing Synchronization Traffic for Distributed Deep Learning
Jiarui Fang
Haohuan Fu
Guangwen Yang
Cho-Jui Hsieh
GNN
208
29
0
13 Aug 2018
ATOMO: Communication-efficient Learning via Atomic Sparsification
ATOMO: Communication-efficient Learning via Atomic Sparsification
Hongyi Wang
Scott Sievert
Zachary B. Charles
Shengchao Liu
S. Wright
Dimitris Papailiopoulos
313
374
0
11 Jun 2018
Demystifying Parallel and Distributed Deep Learning: An In-Depth
  Concurrency Analysis
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency AnalysisACM Computing Surveys (CSUR), 2018
Tal Ben-Nun
Torsten Hoefler
GNN
318
770
0
26 Feb 2018
Deep Gradient Compression: Reducing the Communication Bandwidth for
  Distributed Training
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Chengyue Wu
Song Han
Huizi Mao
Yu Wang
W. Dally
618
1,545
0
05 Dec 2017
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