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Practical low-rank gradient compression for distributed optimization: https://arxiv.org/abs/1905.13727
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schedule/neurips19
tasks Code May 31, 2019
.gitignore Code May 31, 2019
LICENSE Initial commit May 31, 2019
README.md Update README.md Oct 19, 2019
gradient_reducers.py Code May 31, 2019
mean_accumulator.py Code May 31, 2019
timer.py Code May 31, 2019
timings.py Code May 31, 2019
train.py Code May 31, 2019

README.md

PowerSGD

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.

Reference

If you use this code, please cite the following paper

@inproceedings{vkj2019powerSGD,
  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 = {https://arxiv.org/abs/1905.13727}
}
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