  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.
