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arXiv · 2211.06385

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

Md Vasimuddin·Ramanarayan Mohanty·Sanchit Misra·Sasikanth Avancha
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Abstract

Training Graph Neural Networks, on graphs containing billions of vertices and edges, at scale using minibatch sampling poses a key challenge: strong-scaling graphs and training examples results in lower compute and higher communication volume and potential performance loss. DistGNN-MB employs a novel Historical Embedding Cache combined with compute-communication overlap to address this challenge. On a 32-node (64-socket) cluster of $3^{rd}$ generation Intel Xeon Scalable Processors with 36 cores per socket, DistGNN-MB trains 3-layer GraphSAGE and GAT models on OGBN-Papers100M to convergence with epoch times of 2 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, as compute nodes scale from 2 to 32.

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Md Vasimuddin, Ramanarayan Mohanty, Sanchit Misra, Sasikanth Avancha. 2022-11-11. DistGNN-MB: Distributed Large-Scale Graph Neural Network Training on x86 via Minibatch Sampling. https://arxiv.org/abs/2211.06385

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