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. Our code is available at this https URL

本文研究了梯度压缩方法以缓解数据并行分布式优化中的通信瓶颈，提出了一种基于幂迭代的新型低秩梯度压缩器，在快速压缩梯度、高效聚合压缩梯度并且在测试性能上与随机梯度下降（SGD）相当的情况下实现了持续的加速，该算法是唯一评估的方法，与具有优化的通信后端的常规 SGD 进行基准测试时实现了一致的挂钟加速，并且在常见数据集上展示了卷积网络以及 LSTM 的训练时间缩短。