We present BatchGNN, a distributed CPU system that showcases techniques that can be used to efficiently train GNNs on terabyte-sized graphs. It reduces communication overhead with macrobatching in which multiple minibatches' subgraph sampling and feature fetching are batched into one communication relay to reduce redundant feature fetches when input features are static. BatchGNN provides integrated graph partitioning and native GNN layer implementations to improve runtime, and it can cache aggregated input features to further reduce sampling overhead. BatchGNN achieves an average $3\times$ speedup over DistDGL on three GNN models trained on OGBN graphs, outperforms the runtimes reported by distributed GPU systems $P^3$ and DistDGLv2, and scales to a terabyte-sized graph.

BatchGNN是一个分布式CPU系统，可用于高效地在千兆字节级别的图形上训练图神经网络，通过在一个通信中绑定多个子图采样和特征提取以减少冗余特征提取、提供整合图分割和本地GNN层实现来提高运行时间，可以缓存聚合输入特征以进一步减少采样开销，相对于DistDGL，GNN的平均速度加快了3倍，在三个OGBN图表上训练的GNN模型优于分布式GPU系统P3和DistDGLv2的运行时间，并且可以扩展到千兆字节级别的图形。

BatchGNN：在非常大的图上高效的基于CPU的分布式GNN训练