Sketch-fusion: A gradient compression method with multi-layer fusion for communication-efficient distributed training-Reference-Cited by-同舟云学术

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Sketch-fusion: A gradient compression method with multi-layer fusion for communication-efficient distributed training

Published:2024-03 Issue: Volume:185 Page:104811
ISSN:0743-7315
Container-title:Journal of Parallel and Distributed Computing
language:en
Short-container-title:Journal of Parallel and Distributed Computing

Author:

Dai Lingfei,Gong Luqi,An Zhulin^ORCID,Xu Yongjun,Diao Boyu^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software

Reference57 articles.

1. Sparse communication for distributed gradient descent;Aji,2017

2. QSGD: communication-efficient SGD via gradient quantization and encoding;Alistarh;Adv. Neural Inf. Process. Syst.,2017

3. The convergence of sparsified gradient methods;Alistarh;Adv. Neural Inf. Process. Syst.,2018

4. Low overhead instruction latency characterization for nvidia gpgpus;Arafa,2019

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1. Asynchronous SGD with stale gradient dynamic adjustment for deep learning training;Information Sciences;2024-10

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