ADTopk: All-Dimension Top-k Compression for High-Performance Data-Parallel DNN Training

Author:

Ming Zhangqiang1ORCID,Hu Yuchong1ORCID,Zhou Wenxiang1ORCID,Zheng Xinjue1ORCID,Yao Chenxuan1ORCID,Feng Dan1ORCID

Affiliation:

1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, HuBei, China

Publisher

ACM

Reference56 articles.

1. Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, and Dimitris Papailiopoulos. 2021. Adaptive gradient communication via critical learning regime identification. In Proceedings of Machine Learning and Systems. 55--80.

2. Alham Fikri Aji and Kenneth Heafield. 2017. Sparse communication for distributed gradient descent. arXiv preprint arXiv:1704.05021 (2017).

3. Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic. 2017. QSGD: Communication-efficient SGD via gradient quantization and encoding. In Advances in Neural Information Processing Systems. 8024--8035.

4. Varuna

5. Gradient Compression Supercharged High-Performance Data Parallel DNN Training

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