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Despite the recent success of Graph Neural Networks, it remains challengingto train a GNN on large graphs with millions of nodes and billions of edges,which are prevalent in many graph-based applications. Traditionalsampling-based methods accelerate GNN training by dropping edges and nodes,which impairs the graph integrity and model performance. Differently,distributed GNN algorithms accelerate GNN training by utilizing multiplecomputing devices and can be classified into two types: "partition-based"methods enjoy low communication costs but suffer from information loss due todropped edges, while "propagation-based" methods avoid information loss butsuffer from prohibitive communication overhead caused by the neighborexplosion. To jointly address these problems, this paper proposes DIGEST(DIstributed Graph reprEsentation SynchronizaTion), a novel distributed GNNtraining framework that synergizes the complementary strength of bothcategories of existing methods. We propose to allow each device to utilize thestale representations of its neighbors in other subgraphs during subgraphparallel training. This way, our method preserves global graph information fromneighbors to avoid information loss and reduce communication costs. Ourconvergence analysis demonstrates that DIGEST enjoys a state-of-the-artconvergence rate. Extensive experimental evaluation on large, real-world graphdatasets shows that DIGEST achieves up to 21.82 speedups without compromisingperformance compared to state-of-the-art distributed GNN training frameworks.

Distributed Graph Neural Network Training with Periodic Stale Representation Synchronization