PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization

Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

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Authors

Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi

Abstract

<p>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 low-rank gradient compressor 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.</p>