Community-based Layerwise Distributed Training of Graph Convolutional Networks

TL;DR

This paper introduces a community-based, layerwise distributed training algorithm for Graph Convolutional Networks that significantly accelerates training by parallelizing layers and dividing the graph into communities, outperforming existing methods.

Contribution

The paper presents a novel ADMM-based distributed training approach that combines layer parallelism and community division to efficiently train large-scale GCNs.

Findings

Over three times speedup in training time

Achieves state-of-the-art performance

Effective reduction of computational resources

Abstract

The Graph Convolutional Network (GCN) has been successfully applied to many graph-based applications. Training a large-scale GCN model, however, is still challenging: Due to the node dependency and layer dependency of the GCN architecture, a huge amount of computational time and memory is required in the training process. In this paper, we propose a parallel and distributed GCN training algorithm based on the Alternating Direction Method of Multipliers (ADMM) to tackle the two challenges simultaneously. We first split GCN layers into independent blocks to achieve layer parallelism. Furthermore, we reduce node dependency by dividing the graph into several dense communities such that each of them can be trained with an agent in parallel. Finally, we provide solutions for all subproblems in the community-based ADMM algorithm. Preliminary results demonstrate that our proposed community-based ADMM training algorithm can lead to more than triple speedup while achieving the best performance compared with state-of-the-art methods.