Variance-based Gradient Compression for Efficient Distributed Deep Learning

TL;DR

This paper introduces a variance-based gradient compression method that significantly reduces communication overhead in distributed deep learning, maintaining accuracy and enabling efficient training in low-bandwidth environments.

Contribution

The paper proposes a novel gradient compression technique based on gradient variance, achieving high compression ratios without sacrificing model accuracy.

Findings

High compression ratios achieved

Maintains model accuracy

Enables efficient distributed training in low-bandwidth settings

Abstract

Due to the substantial computational cost, training state-of-the-art deep neural networks for large-scale datasets often requires distributed training using multiple computation workers. However, by nature, workers need to frequently communicate gradients, causing severe bottlenecks, especially on lower bandwidth connections. A few methods have been proposed to compress gradient for efficient communication, but they either suffer a low compression ratio or significantly harm the resulting model accuracy, particularly when applied to convolutional neural networks. To address these issues, we propose a method to reduce the communication overhead of distributed deep learning. Our key observation is that gradient updates can be delayed until an unambiguous (high amplitude, low variance) gradient has been calculated. We also present an efficient algorithm to compute the variance with negligible additional cost. We experimentally show that our method can achieve very high compression ratio while maintaining the result model accuracy. We also analyze the efficiency using computation and communication cost models and provide the evidence that this method enables distributed deep learning for many scenarios with commodity environments.