Accelerating Storage-Based Training for Graph Neural Networks

2026-01-04

arXiv.org

AI Summary

This work addresses the inefficiency of existing storage-based graph neural network (GNN) training systems, which are hindered by numerous small I/O operations that fail to saturate the bandwidth of high-performance storage devices. To overcome this bottleneck, the authors propose AGNES, a novel framework that systematically tackles the small-I/O problem through an innovative integration of block-level storage I/O and a hyperbatch graph data processing mechanism, with tailored optimizations for NVMe SSDs on a single machine. Experimental evaluation on five real-world large-scale graph datasets demonstrates that AGNES achieves up to 4.1× speedup over the state-of-the-art methods, substantially improving training throughput.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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.

Problem

• Graph Neural Networks
• Storage-based Training
• I/O Bottleneck
• Large-scale Graphs
• Data Preparation

Innovation

• block-wise I/O
• hyperbatch processing
• storage-based GNN training
• I/O optimization
• large-scale graph neural networks