Exploring Fast and Communication-Efficient Algorithms in Large-Scale Distributed Networks
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:674-683, 2019.
The communication overhead has become a significant bottleneck in data-parallel network with the increasing of model size and data samples. In this work, we propose a new algorithm LPC-SVRG with quantized gradients and its acceleration ALPC-SVRG to effectively reduce the communication complexity while maintaining the same convergence as the unquantized algorithms. Specifically, we formulate the heuristic gradient clipping technique within the quantization scheme and show that unbiased quantization methods in related works [3, 33, 38] are special cases of ours. We introduce double sampling in the accelerated algorithm ALPC-SVRG to fully combine the gradients of full-precision and low-precision, and then achieve acceleration with fewer communication overhead. Our analysis focuses on the nonsmooth composite problem, which makes our algorithms more general. The experiments on linear models and deep neural networks validate the effectiveness of our algorithms.