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research-article

Ginex: SSD-enabled billion-scale graph neural network training on a single machine via provably optimal in-memory caching

Authors:

Jae W. LeeAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 15, Issue 11

Pages 2626 - 2639

https://doi.org/10.14778/3551793.3551819

Published: 01 July 2022 Publication History

Abstract

Graph Neural Networks (GNNs) are receiving a spotlight as a powerful tool that can effectively serve various inference tasks on graph structured data. As the size of real-world graphs continues to scale, the GNN training system faces a scalability challenge. Distributed training is a popular approach to address this challenge by scaling out CPU nodes. However, not much attention has been paid to disk-based GNN training, which can scale up the single-node system in a more cost-effective manner by leveraging high-performance storage devices like NVMe SSDs. We observe that the data movement between the main memory and the disk is the primary bottleneck in the SSD-based training system, and that the conventional GNN training pipeline is sub-optimal without taking this overhead into account. Thus, we propose Ginex, the first SSD-based GNN training system that can process billion-scale graph datasets on a single machine. Inspired by the inspector-executor execution model in compiler optimization, Ginex restructures the GNN training pipeline by separating sample and gather stages. This separation enables Ginex to realize a provably optimal replacement algorithm, known as Belady's algorithm, for caching feature vectors in memory, which account for the dominant portion of I/O accesses. According to our evaluation with four billion-scale graph datasets and two GNN models, Ginex achieves 2.11X higher training throughput on average (2.67X at maximum) than the SSD-extended PyTorch Geometric.

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 15, Issue 11

July 2022

980 pages

ISSN:2150-8097

Editors:
Fatma Özcan
Google
,
Juliana Freire
New York University
,
Xuemin Lin
University of New South Wales

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VLDB Endowment

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Published: 01 July 2022

Published in PVLDB Volume 15, Issue 11

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Cited By

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https://dl.acm.org/doi/10.1145/3709669
Cheng DZou YXiang SJiang C(2025)Graph neural networks for financial fraud detection: a reviewFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-024-40474-y19:9Online publication date: 1-Sep-2025
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https://dl.acm.org/doi/10.1007/s00778-024-00897-2
Hang JHong ZFeng XWang GCao DQiao JWang HZhang D(2024)Complex-Path: Effective and Efficient Node Ranking with Paths in Billion-Scale Heterogeneous GraphsProceedings of the VLDB Endowment10.14778/3685800.368582017:12(3973-3986)Online publication date: 8-Nov-2024
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Sheng ZZhang WTao YCui B(2024)OUTRE: An OUT-of-Core De-REdundancy GNN Training Framework for Massive Graphs within A Single MachineProceedings of the VLDB Endowment10.14778/3681954.368197617:11(2960-2973)Online publication date: 30-Aug-2024
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Wang KXu YLuo S(2024)TIGER: Training Inductive Graph Neural Network for Large-Scale Knowledge Graph ReasoningProceedings of the VLDB Endowment10.14778/3675034.367503917:10(2459-2472)Online publication date: 1-Jun-2024
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Park JMailthody VQureshi ZHwu W(2024)Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage AccessesProceedings of the VLDB Endowment10.14778/3648160.364816617:6(1227-1240)Online publication date: 1-Feb-2024
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