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MEMORYBRIDGE: Leveraging Cloud Resource Characteristics for Cost-Efficient Disk-based GNN Training via Two-Level Architecture
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- Authors
- Yoochan Kim, Weikuan Yu, Hong-Yeon Kim, Youngjae Kim
- Issue Date
- 2025-02
- Citation
- International Symposium on Cluster, Cloud and Internet Computing (CCGrid) 2025, pp.1-10
- Language
- English
- Type
- Conference Paper
- Abstract
- Graph Neural Networks (GNNs) are machine learning models that process graph-structured data by learning relationships between vertices and edges, as well as graph-level characteristics. Recently, with the emergence of large graph datasets on a TB scale, dataset sizes have exceeded the memory capacity of single machines. As a result, traditional methods that load all graph data into memory have become unusable, leading to the emergence of disk-based GNN training that uses storage as a memory extension. Recent research has focused on reducing disk I/O bottlenecks in disk-based GNNs. However, disk-based GNNs face new challenges in cloud environments due to two main characteristics. First, compared to node-local environments, the significantly slower cloud storage I/O speed becomes the main bottleneck of the entire training process. Second, pre-defined virtual machines prevent users from freely utilizing desired memory sizes, bandwidth, and the latest GPU technologies. These limitations have made existing disk-based GNN research unusable in cloud environments. To overcome this, we propose MEMORYBRIDGE, a system that cost-effectively accelerates GNN training in cloud environments through a novel two-level architecture that utilizes affordable GPU resources as training nodes and remote memory resources without GPUs as memory nodes, instead of using a single expensive GPU resource. This architecture consists of two key components: (i) a mathematical solver that recommends the most cost-effective resource combination, and (ii) a cloud-specialized GNN framework that implements graphaware fixed caching and batch pipelining optimization. The experimental results show that MEMORYBRIDGE achieved a speed improvement of up to 32.7x compared to existing GNN training frameworks and a cost efficiency of 9.9x compared to alternative resource configuration strategies, effectively handling the unique problems that arise from the combination of cloud environments and GNN training.