Skip to content
Practical low-rank gradient compression for distributed optimization:
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.
tasks Code May 31, 2019
.gitignore Code May 31, 2019
LICENSE Initial commit May 31, 2019 Update Oct 19, 2019 Code May 31, 2019 Code May 31, 2019 Code May 31, 2019 Code May 31, 2019 Code May 31, 2019


Practical Low-Rank Gradient Compression for Distributed Optimization

Abstract: We study gradient compression methods to alleviate the communication bottleneck in data-parallel distributed optimization. Despite the significant attention received, current compression schemes either do not scale well or fail to achieve the target test accuracy. We propose a new low-rank gradient compressor based on power iteration that can i) compress gradients rapidly, ii) efficiently aggregate the compressed gradients using all-reduce, and iii) achieve test performance on par with SGD. The proposed algorithm is the only method evaluated that achieves consistent wall-clock speedups when benchmarked against regular SGD with an optimized communication backend. We demonstrate reduced training times for convolutional networks as well as LSTMs on common datasets.


If you use this code, please cite the following paper

  author = {Vogels, Thijs and Karimireddy, Sai Praneeth and Jaggi, Martin},
  title = "{{PowerSGD}: Practical Low-Rank Gradient Compression for Distributed Optimization}",
  booktitle = {NeurIPS 2019 - Advances in Neural Information Processing Systems},
  year = 2019,
  url = {}
You can’t perform that action at this time.