用节点嵌入驱动图划分，实现快速高质量分布式训练
EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training
- 利用GNN训练时的节点嵌入进行聚类划分
- 相比Metis提升100倍以上速度，保持良好分区质量
- 支持图更新与快速重划分，适合大规模动态图
图神经网络（GNN）广泛用于图结构数据学习，但扩展到超大规模图的训练仍具挑战。为实现可扩展的分布式训练，需将图划分为较小分区并分布于多台机器，以最小化跨机通信并平衡计算负载。现有方法在划分开销与质量间存在根本权衡。本文提出EmbedPart，一种基于嵌入的划分方法，不直接处理不规则图结构，而是利用实际GNN训练过程中生成的节点嵌入，并对这些稠密嵌入进行聚类以获得划分结果。EmbedPart相比Metis实现超过100倍的速度提升，同时保持竞争力的分区质量，显著加速分布式GNN训练。此外，EmbedPart天然支持图更新和快速重划分，还可用于图重排序以改善数据局部性，加速单机GNN训练。通过将划分任务从不规则图结构转向稠密嵌入，EmbedPart实现了可扩展且高质量的图数据优化。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) are widely used for learning on graph-structured data, but scaling GNN training to massive graphs remains challenging. To enable scalable distributed training, graphs are divided into smaller partitions that are distributed across multiple machines such that inter-machine communication is minimized and computational load is balanced. In practice, existing partitioning approaches face a fundamental trade-off between partitioning overhead and partitioning quality. We propose EmbedPart, an embedding-driven partitioning approach that achieves both speed and quality. Instead of operating directly on irregular graph structures, EmbedPart leverages node embeddings produced during the actual GNN training workload and clusters these dense embeddings to derive a partitioning. EmbedPart achieves more than 100x speedup over Metis while maintaining competitive partitioning quality and accelerating distributed GNN training. Moreover, EmbedPart naturally supports graph updates and fast repartitioning, and can be applied to graph reordering to improve data locality and accelerate single-machine GNN training. By shifting partitioning from irregular graph structures to dense embeddings, EmbedPart enables scalable and high-quality graph data optimization.
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