BDS-GCN: Efficient Full-Graph Training of Graph Convolutional Nets with Partition-Parallelism and Boundary Sampling

Cheng Wan, Youjie Li, Nam Sung Kim, Yingyan Lin

Keywords: Graph Neural Networks, Graph Convolutional Networks, Full-Graph Training, Large-Graph Training, Distributed Training, Partition Parallelism, Sampling

Abstract: Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art model for graph-based learning tasks. However, it is still challenging to train GCNs at scale, limiting their applications to real-world large graphs and hindering the exploration of deeper and more sophisticated GCN architectures. While it can be natural to leverage graph partition and distributed training for tackling this challenge, this direction has only been slightly touched on previously due to the unique challenge posed by the GCN structures, especially the excessive amount of boundary nodes in each partitioned subgraph, which can easily explode the required memory and communications for distributed training of GCNs. To this end, we propose BDS-GCN, a method that adopts unbiased boundary sampling strategy to enable efficient and scalable distributed GCN training while maintaining the full-graph accuracy. Empirical evaluations and ablation studies validate the effectiveness of the proposed BDS-GCN, e.g., boosting the throughput by up-to 500% and reducing the memory usage by up-to 58% for distributed GCN training, while achieving the same accuracy, as compared with the state-of-the-art methods. We believe our BDS-GCN would open up a new paradigm for enabling GCN training at scale. All code will be released publicly upon acceptance.

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