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, up to 4.1× faster than the best competitor.

A graph, consisting of vertices and edges, has been widely adopted for network analysis. Recently, with the increasing size of real-world networks, many graph engines have been studied to efficiently process large-scale real-world graphs. RealGraph, one of the state-of-the-art single-machine-based graph engines, efficiently processes storage-to-memory I/Os by considering unique characteristics of real-world graphs. Via an in-depth analysis of RealGraph, however, we found that there is still a chance for more performance improvement in the computation part of RealGraph despite its great I/O processing ability. Motivated by this, in this paper, we propose RealGraphGPU, a GPU-based single-machine graph engine. We design the core components required for GPU-based graph processing and incorporate them into the architecture of RealGraph. Further, we propose two optimizations that successfully address the technical issues that could cause the performance degradation in the GPU-based graph engine: buffer pre-checking and edge-based workload allocation strategies. Through extensive evaluation with 6 real-world datasets, we demonstrate that (1) RealGraphGPU improves RealGraph by up to 546%, (2) RealGraphGPU outperforms existing state-of-the-art graph engines dramatically, and (3) the optimizations are all effective in large-scale graph processing.