CDFGNN: a Systematic Design of Cache-based Distributed Full-Batch Graph Neural Network Training with Communication Reduction

TL;DR

This paper introduces CDFGNN, a cache-based distributed full-batch GNN training framework that significantly reduces communication overhead and improves training efficiency through adaptive caching and optimized communication strategies.

Contribution

The paper presents a novel cache-based framework with adaptive caching and communication optimization for efficient distributed full-batch GNN training.

Findings

Reduces remote vertex accesses by 63.14% on average.

Outperforms state-of-the-art frameworks by 30.39%.

Demonstrates potential for accelerating distributed GNN training.

Abstract

Graph neural network training is mainly categorized into mini-batch and full-batch training methods. The mini-batch training method samples subgraphs from the original graph in each iteration. This sampling operation introduces extra computation overhead and reduces the training accuracy. Meanwhile, the full-batch training method calculates the features and corresponding gradients of all vertices in each iteration, and therefore has higher convergence accuracy. However, in the distributed cluster, frequent remote accesses of vertex features and gradients lead to huge communication overhead, thus restricting the overall training efficiency. In this paper, we introduce the cached-based distributed full-batch graph neural network training framework (CDFGNN). We propose the adaptive cache mechanism to reduce the remote vertex access by caching the historical features and gradients of neighbor vertices. Besides, we further optimize the communication overhead by quantifying the messages and designing the graph partition algorithm for the hierarchical communication architecture. Experiments show that the adaptive cache mechanism reduces remote vertex accesses by 63.14% on average. Combined with communication quantization and hierarchical GP algorithm, CDFGNN outperforms the state-of-the-art distributed full-batch training frameworks by 30.39% in our experiments. Our results indicate that CDFGNN has great potential in accelerating distributed full-batch GNN training tasks.