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 a system called DiskGNN, which achieves
high I/O efficiency and thus fast training without hurting model accuracy. The
key technique used by DiskGNN is offline sampling, which helps decouple graph
sampling from model computation. In particular, by conducting graph sampling
beforehand, DiskGNN acquires the node features that will be accessed by model
computation, and such information is utilized to pack the target node features
contiguously on disk to avoid read amplification. Besides, \name{} also adopts
designs including four-level feature store to fully utilize the memory
hierarchy to cache node features and reduce disk access, batched packing to
accelerate the feature packing process, and pipelined training to overlap disk
access with other operations. We compare DiskGNN with Ginex and MariusGNN,
which are state-of-the-art systems for out-of-core GNN training. The results
show that DiskGNN can speed up the baselines by over 8x while matching their
best model accuracy.

研究使用离线采样的 DiskGNN 系统，在训练大规模图神经网络时达到高的 I/O 效率和快速训练，同时保持模型的准确性。与其他现有系统相比，DiskGNN 能够将性能提高 8 倍以上，并且具有相同的模型精确度。