DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training

arXiv:2405.05231v1 Announce Type: new
Abstract: Graph neural networks (GNNs) are machine learning models specialized for graph data and widely used in many applications. To train GNNs on large graphs that exceed CPU memory, several systems store data on disk and conduct out-of-core processing. However, these systems suffer from either read amplification when reading node features that are usually smaller than a disk page or degraded model accuracy by treating the graph as disconnected partitions. To close this gap, we build …

abstract accuracy applications arxiv core cpu cs.lg data efficiency gnn gnns graph graph data graph neural networks graphs however machine machine learning machine learning models memory model accuracy networks neural networks processing store systems train training type