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MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks

Authors:

Theodoros Rekatsinas,

Shivaram VenkataramanAuthors Info & Claims

EuroSys '23: Proceedings of the Eighteenth European Conference on Computer Systems

Pages 144 - 161

https://doi.org/10.1145/3552326.3567501

Published: 08 May 2023 Publication History

Abstract

We study training of Graph Neural Networks (GNNs) for large-scale graphs. We revisit the premise of using distributed training for billion-scale graphs and show that for graphs that fit in main memory or the SSD of a single machine, out-of-core pipelined training with a single GPU can outperform state-of-the-art (SoTA) multi-GPU solutions. We introduce MariusGNN, the first system that utilizes the entire storage hierarchy---including disk---for GNN training. MariusGNN introduces a series of data organization and algorithmic contributions that 1) minimize the end-to-end time required for training and 2) ensure that models learned with disk-based training exhibit accuracy similar to those fully trained in memory. We evaluate MariusGNN against SoTA systems for learning GNN models and find that single-GPU training in MariusGNN achieves the same level of accuracy up to 8× faster than multi-GPU training in these systems, thus, introducing an order of magnitude monetary cost reduction. MariusGNN is open-sourced at www.marius-project.org.

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Cited By

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
https://doi.org/10.14778/3681954.3681976
Huang KJiang HWang MXiao GWipf DSong XGan QHuang ZZhai JZhang Z(2024)FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network TrainingProceedings of the VLDB Endowment10.14778/3648160.364818417:6(1473-1486)Online publication date: 3-May-2024
https://dl.acm.org/doi/10.14778/3648160.3648184
Jiang QJia LWang C(2024)GNNDrive: Reducing Memory Contention and I/O Congestion for Disk-based GNN TrainingProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673063(650-659)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3673038.3673063
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Index Terms

MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks
1. Computer systems organization
  1. Architectures
2. Computing methodologies
  1. Machine learning

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cover image ACM Conferences

EuroSys '23: Proceedings of the Eighteenth European Conference on Computer Systems

May 2023

910 pages

ISBN:9781450394871

DOI:10.1145/3552326

General Co-chairs:
Giuseppe Antonio Di Luna
University of Rome La Sapienza
,
Leonardo Querzoni
University of Rome La Sapienza
,
Program Co-chairs:
Alexandra Fedorova
University of British Columbia
,
Dushyanth Narayanan
Microsoft Research

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Published: 08 May 2023

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Cited By

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
https://doi.org/10.14778/3681954.3681976
Huang KJiang HWang MXiao GWipf DSong XGan QHuang ZZhai JZhang Z(2024)FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network TrainingProceedings of the VLDB Endowment10.14778/3648160.364818417:6(1473-1486)Online publication date: 3-May-2024
https://dl.acm.org/doi/10.14778/3648160.3648184
Jiang QJia LWang C(2024)GNNDrive: Reducing Memory Contention and I/O Congestion for Disk-based GNN TrainingProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673063(650-659)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3673038.3673063
Song YChen PLu YAbrar NKalavri V(2024)In situ neighborhood sampling for large-scale GNN trainingProceedings of the 20th International Workshop on Data Management on New Hardware10.1145/3662010.3663443(1-5)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3662010.3663443
Zheng CJiang GYan XYin PZhou QCheng J(2024)GE2: A General and Efficient Knowledge Graph Embedding Learning SystemProceedings of the ACM on Management of Data10.1145/36549862:3(1-27)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654986
Wang YPan XAn YZhang JReinman G(2024)BeaconGNN: Large-Scale GNN Acceleration with Out-of-Order Streaming In-Storage Computing2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00033(330-344)Online publication date: 2-Mar-2024
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Li YYang TYang MShen ZLi B(2024)Celeritas: Out-of-Core Based Unsupervised Graph Neural Network via Cross-Layer Computing2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00018(91-107)Online publication date: 2-Mar-2024
https://doi.org/10.1109/HPCA57654.2024.00018
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Jeyaraj RBalasubramaniam TBalasubramaniam APaul A(2024)DeepWalk with Reinforcement Learning (DWRL) for node embeddingExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122819243:COnline publication date: 25-Jun-2024
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