ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2104.08364
  4. Cited By
Sync-Switch: Hybrid Parameter Synchronization for Distributed Deep
  Learning
v1v2 (latest)

Sync-Switch: Hybrid Parameter Synchronization for Distributed Deep Learning

IEEE International Conference on Distributed Computing Systems (ICDCS), 2021
16 April 2021
Shijian Li
Oren Mangoubi
Lijie Xu
Tian Guo
ArXiv (abs)PDFHTML

Papers citing "Sync-Switch: Hybrid Parameter Synchronization for Distributed Deep Learning"

7 / 7 papers shown
OSP: Boosting Distributed Model Training with 2-stage Synchronization
OSP: Boosting Distributed Model Training with 2-stage SynchronizationInternational Conference on Parallel Processing (ICPP), 2023
Zixuan Chen
Lei Shi
Xuandong Liu
Jiahui Li
Sen Liu
Yang Xu
327
5
0
29 Jun 2023
GBA: A Tuning-free Approach to Switch between Synchronous and
  Asynchronous Training for Recommendation Model
GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation ModelNeural Information Processing Systems (NeurIPS), 2022
Yuchi Xu
Yuanxing Zhang
Yufeng Cai
Kaixu Ren
Pengjie Wang
...
Jing Chen
Hongbo Deng
Jian Xu
Lin Qu
Bo Zheng
210
5
0
23 May 2022
FuncPipe: A Pipelined Serverless Framework for Fast and Cost-efficient
  Training of Deep Learning Models
FuncPipe: A Pipelined Serverless Framework for Fast and Cost-efficient Training of Deep Learning ModelsProceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS), 2022
Yunzhuo Liu
Bo Jiang
Tian Guo
Zimeng Huang
Wen-ping Ma
Xinbing Wang
Chenghu Zhou
345
13
0
28 Apr 2022
CGX: Adaptive System Support for Communication-Efficient Deep Learning
CGX: Adaptive System Support for Communication-Efficient Deep Learning
I. Markov
Hamidreza Ramezanikebrya
Dan Alistarh
GNN
436
6
0
16 Nov 2021
On the Future of Cloud Engineering
On the Future of Cloud Engineering
David Bermbach
A. Chandra
C. Krintz
A. Gokhale
Aleksander Slominski
L. Thamsen
Everton Cavalcante
Tian Guo
Ivona Brandić
R. Wolski
171
28
0
19 Aug 2021
Quantifying and Improving Performance of Distributed Deep Learning with
  Cloud Storage
Quantifying and Improving Performance of Distributed Deep Learning with Cloud Storage
Nicholas Krichevsky
M. S. Louis
Tian Guo
215
9
0
13 Aug 2021
Xception: Deep Learning with Depthwise Separable Convolutions
Xception: Deep Learning with Depthwise Separable ConvolutionsComputer Vision and Pattern Recognition (CVPR), 2016
François Chollet
MDEBDLPINN
3.6K
17,346
0
07 Oct 2016
1
Page 1 of 1