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GraVAC: Adaptive Compression for Communication-Efficient Distributed DL
  Training
v1v2 (latest)

GraVAC: Adaptive Compression for Communication-Efficient Distributed DL Training

20 May 2023
S. Tyagi
Martin Swany
ArXiv (abs)PDFHTML

Papers citing "GraVAC: Adaptive Compression for Communication-Efficient Distributed DL Training"

26 / 26 papers shown
Title
Scavenger: A Cloud Service for Optimizing Cost and Performance of ML
  Training
Scavenger: A Cloud Service for Optimizing Cost and Performance of ML Training
S. Tyagi
Prateek Sharma
63
5
0
12 Mar 2023
ScaDLES: Scalable Deep Learning over Streaming data at the Edge
ScaDLES: Scalable Deep Learning over Streaming data at the Edge
S. Tyagi
Martin Swany
42
6
0
21 Jan 2023
Pufferfish: Communication-efficient Models At No Extra Cost
Pufferfish: Communication-efficient Models At No Extra Cost
Hongyi Wang
Saurabh Agarwal
Dimitris Papailiopoulos
74
59
0
05 Mar 2021
On the Utility of Gradient Compression in Distributed Training Systems
On the Utility of Gradient Compression in Distributed Training Systems
Saurabh Agarwal
Hongyi Wang
Shivaram Venkataraman
Dimitris Papailiopoulos
77
47
0
28 Feb 2021
An Efficient Statistical-based Gradient Compression Technique for
  Distributed Training Systems
An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems
A. Abdelmoniem
Ahmed Elzanaty
Mohamed-Slim Alouini
Marco Canini
111
77
0
26 Jan 2021
Accordion: Adaptive Gradient Communication via Critical Learning Regime
  Identification
Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification
Saurabh Agarwal
Hongyi Wang
Kangwook Lee
Shivaram Venkataraman
Dimitris Papailiopoulos
78
25
0
29 Oct 2020
Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep
  Learning
Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning
Aurick Qiao
Sang Keun Choe
Suhas Jayaram Subramanya
Willie Neiswanger
Qirong Ho
Hao Zhang
G. Ganger
Eric Xing
VLM
69
181
0
27 Aug 2020
AdaScale SGD: A User-Friendly Algorithm for Distributed Training
AdaScale SGD: A User-Friendly Algorithm for Distributed Training
Tyler B. Johnson
Pulkit Agrawal
Haijie Gu
Carlos Guestrin
ODL
76
37
0
09 Jul 2020
PyTorch Distributed: Experiences on Accelerating Data Parallel Training
PyTorch Distributed: Experiences on Accelerating Data Parallel Training
Shen Li
Yanli Zhao
R. Varma
Omkar Salpekar
P. Noordhuis
...
Adam Paszke
Jeff Smith
Brian Vaughan
Pritam Damania
Soumith Chintala
OODMoE
63
187
0
28 Jun 2020
Is Network the Bottleneck of Distributed Training?
Is Network the Bottleneck of Distributed Training?
Zhen Zhang
Chaokun Chang
Yanghua Peng
Yida Wang
R. Arora
Xin Jin
89
71
0
17 Jun 2020
The Early Phase of Neural Network Training
The Early Phase of Neural Network Training
Jonathan Frankle
D. Schwab
Ari S. Morcos
87
174
0
24 Feb 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
553
42,639
0
03 Dec 2019
Understanding Top-k Sparsification in Distributed Deep Learning
Understanding Top-k Sparsification in Distributed Deep Learning
Shaoshuai Shi
Xiaowen Chu
Ka Chun Cheung
Simon See
226
101
0
20 Nov 2019
PowerSGD: Practical Low-Rank Gradient Compression for Distributed
  Optimization
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
Thijs Vogels
Sai Praneeth Karimireddy
Martin Jaggi
90
322
0
31 May 2019
Communication-Efficient Distributed Blockwise Momentum SGD with
  Error-Feedback
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback
Shuai Zheng
Ziyue Huang
James T. Kwok
54
115
0
27 May 2019
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
Sai Praneeth Karimireddy
Quentin Rebjock
Sebastian U. Stich
Martin Jaggi
72
503
0
28 Jan 2019
An Empirical Model of Large-Batch Training
An Empirical Model of Large-Batch Training
Sam McCandlish
Jared Kaplan
Dario Amodei
OpenAI Dota Team
74
280
0
14 Dec 2018
The Convergence of Sparsified Gradient Methods
The Convergence of Sparsified Gradient Methods
Dan Alistarh
Torsten Hoefler
M. Johansson
Sarit Khirirat
Nikola Konstantinov
Cédric Renggli
169
493
0
27 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
88
25
0
13 Aug 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
148
1,410
0
05 Dec 2017
Critical Learning Periods in Deep Neural Networks
Critical Learning Periods in Deep Neural Networks
Alessandro Achille
Matteo Rovere
Stefano Soatto
66
100
0
24 Nov 2017
Mixed Precision Training
Mixed Precision Training
Paulius Micikevicius
Sharan Narang
Jonah Alben
G. Diamos
Erich Elsen
...
Boris Ginsburg
Michael Houston
Oleksii Kuchaiev
Ganesh Venkatesh
Hao Wu
178
1,805
0
10 Oct 2017
Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond
Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond
Levent Sagun
Léon Bottou
Yann LeCun
UQCV
95
236
0
22 Nov 2016
An overview of gradient descent optimization algorithms
An overview of gradient descent optimization algorithms
Sebastian Ruder
ODL
209
6,203
0
15 Sep 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,510
0
10 Dec 2015
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAttMDE
1.7K
100,529
0
04 Sep 2014
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