OUTRE: An OUT-of-Core De-REdundancy GNN Training Framework for Massive Graphs within A Single Machine

Sampling-based Graph Neural Networks (GNNs) have become the de facto standard for handling various graph learning tasks on large-scale graphs. As the graph size grows larger and even exceeds the standard host memory size of a single machine, out-of-core sampling-based GNN training has gained attention from the community. For out-of-core sampling-based GNN training, the performance bottleneck is the data preparation process that includes sampling neighbor lists and gathering node features from external storage. Based on this observation, existing out-of-core GNN training frameworks try to accomplish larger percentages of data requests without inquiring the external storage by designing better in-memory caches. However, the enormous overall requested data volume is unchanged under this approach. In this paper, we present a new perspective on reducing the overall requested data volume. Through a quantitative analysis, we find that Neighborhood Redundancy and Temporal Redundancy exist in out-of-core sampling-based GNN training. To reduce these two kinds of data redundancies, we propose OUTRE, an OUT-of-core de-REdundancy GNN training framework. OUTRE incorporates two new designs, partition-based batch construction and historical embedding cache , to reduce the corresponding data redundancies. Moreover, we propose automatic cache space management to automatically organize available memory for different caches. Evaluation results on four public large-scale graph datasets show that OUTRE achieves 1.52× to 3.51× speedup against the SOTA framework.