提出AGNES框架，加速图神经网络存储训练中的小规模I/O瓶颈。
Accelerating Storage-Based Training for Graph Neural Networks
- 采用分块处理I/O，充分挖掘高性能存储带宽
- 利用真实图数据特性，通过超批次策略提升每次I/O效率
- 在五个真实图上比最优基线快4.1倍，适合大规模图训练场景
图神经网络（GNN）因其强大的表达能力在众多实际任务中取得突破。随着真实世界图数据规模持续增长，基于外部存储（如NVMe SSD）的单机存储式GNN训练方法受到关注。然而，现有方法因忽视大量小规模存储I/O带来的严重数据准备瓶颈，限制了性能。为此，本文提出新型存储式GNN训练框架AGNES，采用分块式存储I/O处理以充分利用高性能存储设备的带宽；同时，结合真实图数据特性，引入基于超批次的高效处理策略，进一步提升每次存储I/O的效率。在五个真实图上的全面实验表明，AGNES在所有测试场景下均优于四种先进方法，最快比最佳基线快4.1倍。代码已开源：https://github.com/Bigdasgit/agnes-kdd26。
原文摘要 · Abstract (English)
Graph neural networks (GNNs) have achieved breakthroughs in various real-world downstream tasks due to their powerful expressiveness. As the scale of real-world graphs has been continuously growing, a storage-based approach to GNN training has been studied, which leverages external storage (e.g., NVMe SSDs) to handle such web-scale graphs on a single machine. Although such storage-based GNN training methods have shown promising potential in large-scale GNN training, we observed that they suffer from a severe bottleneck in data preparation since they overlook a critical challenge: how to handle a large number of small storage I/Os. To address the challenge, in this paper, we propose a novel storage-based GNN training framework, named AGNES, that employs a method of block-wise storage I/O processing to fully utilize the I/O bandwidth of high-performance storage devices. Moreover, to further enhance the efficiency of each storage I/O, AGNES employs a simple yet effective strategy, hyperbatch-based processing based on the characteristics of real-world graphs. Comprehensive experiments on five real-world graphs reveal that AGNES consistently outperforms four state-of-the-art methods, by up to 4.1X faster than the best competitor. Our code is available at https://github.com/Bigdasgit/agnes-kdd26.
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