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Training Graph Neural Networks, on graphs containing billions of vertices andedges, at scale using minibatch sampling poses a key challenge: strong-scalinggraphs and training examples results in lower compute and higher communicationvolume and potential performance loss. DistGNN-MB employs a novel HistoricalEmbedding Cache combined with compute-communication overlap to address thischallenge. On a 32-node (64-socket) cluster of $3^{rd}$ generation Intel XeonScalable Processors with 36 cores per socket, DistGNN-MB trains 3-layerGraphSAGE and GAT models on OGBN-Papers100M to convergence with epoch times of2 seconds and 4.9 seconds, respectively, on 32 compute nodes. At this scale,DistGNN-MB trains GraphSAGE 5.2x faster than the widely-used DistDGL.DistGNN-MB trains GraphSAGE and GAT 10x and 17.2x faster, respectively, ascompute nodes scale from 2 to 32.

DistGNN-MB: Distributed Large-Scale Graph Neural Network Training on x86 via Minibatch Sampling