Exploiting Simultaneous Communications to Accelerate Data Parallel Distributed Deep Learning-Reference-Cited by-同舟云学术

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Exploiting Simultaneous Communications to Accelerate Data Parallel Distributed Deep Learning

Published:2021-05-10 Issue: Volume: Page:
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Container-title:IEEE INFOCOM 2021 - IEEE Conference on Computer Communications
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Author:

Shi Shaohuai,Chu Xiaowen,Li Bo

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9488422/9488423/09488803.pdf?arnumber=9488803

Cited by 24 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Sparse Gradient Communication with AlltoAll for Accelerating Distributed Deep Learning;Proceedings of the 53rd International Conference on Parallel Processing;2024-08-12

2. ADTopk: All-Dimension Top-k Compression for High-Performance Data-Parallel DNN Training;Proceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing;2024-06-03

3. Centauri: Enabling Efficient Scheduling for Communication-Computation Overlap in Large Model Training via Communication Partitioning;Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3;2024-04-27

4. ScheMoE: An Extensible Mixture-of-Experts Distributed Training System with Tasks Scheduling;Proceedings of the Nineteenth European Conference on Computer Systems;2024-04-22

5. Sketch-fusion: A gradient compression method with multi-layer fusion for communication-efficient distributed training;Journal of Parallel and Distributed Computing;2024-03

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