Graph neural networks (GNNs) gain wide popularity. Large graphs with
high-dimensional features become common and training GNNs on them is
non-trivial on an ordinary machine. Given a gigantic graph, even sample-based
GNN training cannot work efficiently, since it is difficult to keep the graph's
entire data in memory during the training process. Leveraging a solid-state
drive (SSD) or other storage devices to extend the memory space has been
studied in training GNNs. Memory and I/Os are hence critical for effectual
disk-based training. We find that state-of-the-art (SoTA) disk-based GNN
training systems severely suffer from issues like the memory contention between
a graph's topological and feature data, and severe I/O congestion upon loading
data from SSD for training. We accordingly develop GNNDrive. GNNDrive 1)
minimizes the memory footprint with holistic buffer management across sampling
and extracting, and 2) avoids I/O congestion through a strategy of asynchronous
feature extraction. It also avoids costly data preparation on the critical path
and makes the most of software and hardware resources. Experiments show that
GNNDrive achieves superior performance. For example, when training with the
Papers100M dataset and GraphSAGE model, GNNDrive is faster than SoTA PyG+,
Ginex, and MariusGNN by 16.9x, 2.6x, and 2.7x, respectively.

GNNDrive 通过综合的缓冲区管理和异步特征提取策略来最小化内存占用，避免内存和 I/O 的竞争，并充分利用软件和硬件资源。实验证明 GNNDrive 在 Papers100M 数据集上训练 GraphSAGE 模型时，比 SoTA PyG+、Ginex 和 MariusGNN 分别快 16.9 倍、2.6 倍和 2.7 倍。