MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training-Reference-Cited by-同舟云学术

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MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training

Published:2023-12-18 Issue: Volume:34 Page:87-96
ISSN:
Container-title:2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics (HiPC)
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Author:

Yoon Daegun¹,Oh Sangyoon¹

Affiliation:

1. Ajou University

Funder

National Research Foundation of Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10487008/10487042/10487098.pdf?arnumber=10487098

Reference25 articles.

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

2. Learned Gradient Compression for Distributed Deep Learning

3. Errorcompensatedx: error compensation for variance reduced algorithms;Tang;Advances in Neural Information Processing Systems,2021

4. Natural compression for distributed deep learning;Horvóth,2022

5. Adaptive gradient communication via critical learning regime identification;Agarwal

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