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Graph neural network (GNN) has been demonstrated to be a powerful model inmany domains for its effectiveness in learning over graphs. To scale GNNtraining for large graphs, a widely adopted approach is distributed trainingwhich accelerates training using multiple computing nodes. Maximizing theperformance is essential, but the execution of distributed GNN training remainspreliminarily understood. In this work, we provide an in-depth analysis ofdistributed GNN training on GPUs, revealing several significant observationsand providing useful guidelines for both software optimization and hardwareoptimization.

Characterizing and Understanding Distributed GNN Training on GPUs