Distributed Training of Large Graph Neural Networks with Variable Communication Rates

TL;DR

This paper introduces a variable compression scheme for distributed GNN training that reduces communication costs without sacrificing model accuracy, outperforming fixed compression methods across various budgets.

Contribution

The paper proposes a novel variable compression method with theoretical guarantees, enabling efficient distributed GNN training without accuracy loss.

Findings

Achieves comparable accuracy to full communication methods.

Outperforms fixed compression schemes at all communication budgets.

Theoretically converges to full communication solutions.

Abstract

Training Graph Neural Networks (GNNs) on large graphs presents unique challenges due to the large memory and computing requirements. Distributed GNN training, where the graph is partitioned across multiple machines, is a common approach to training GNNs on large graphs. However, as the graph cannot generally be decomposed into small non-interacting components, data communication between the training machines quickly limits training speeds. Compressing the communicated node activations by a fixed amount improves the training speeds, but lowers the accuracy of the trained GNN. In this paper, we introduce a variable compression scheme for reducing the communication volume in distributed GNN training without compromising the accuracy of the learned model. Based on our theoretical analysis, we derive a variable compression method that converges to a solution equivalent to the full communication case, for all graph partitioning schemes. Our empirical results show that our method attains a comparable performance to the one obtained with full communication. We outperform full communication at any fixed compression ratio for any communication budget.