Capsule*: An Out-of-Core Training Mechanism for Colossal GNNs
Summary: Capsule is an out-of-core GNN training mechanism using GPU-resident memory and kernels for scalable training on massive graphs. Unlike CPU-based out-of-core systems with CPU kernels, Capsule preserves GPU acceleration and integrates with DGL and PyG. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yongan Xiang (Suzhou Institute for Advanced Research, University of Science and Technology of China; University of Science and Technology Beijing)
- 2. Zezhong Ding (Suzhou Institute for Advanced Research, University of Science and Technology of China; University of Science and Technology Beijing)
- 3. Rui Guo (Suzhou Institute for Advanced Research, University of Science and Technology of China; University of Science and Technology Beijing)
- 4. Shangyou Wang (Suzhou Institute for Advanced Research, University of Science and Technology of China; University of Science and Technology Beijing)
- 5. Xike Xie (Suzhou Institute for Advanced Research, University of Science and Technology of China; University of Science and Technology Beijing)
- 6. S. Kevin Zhou (Suzhou Institute for Advanced Research, University of Science and Technology of China; University of Science and Technology Beijing)
BibTeX Citation
@inproceedings{xiang_sigmod25,
title = {{Capsule*: An Out-of-Core Training Mechanism for Colossal GNNs}},
author = {Xiang, Yongan and Ding, Zezhong and Guo, Rui and Wang, Shangyou and Xie, Xike and Zhou, S. Kevin},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709669},
url = {https://dl.acm.org/doi/10.1145/3709669},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,900 | Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving | 2025 | SIGMOD | 5.7430032e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 223 | AliGraph: A Comprehensive Graph Neural Network Platform | 2019 | VLDB | 0.00024182473 |
| 2,640 | Scalable and Efficient Full-Graph GNN Training for Large Graphs | 2023 | SIGMOD | 8.3074486e-05 |
| 2,953 | Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching | 2022 | VLDB | 7.9237794e-05 |
| 3,752 | Distributed Edge Partitioning for Trillion-edge Graphs | 2019 | VLDB | 7.15217e-05 |
| 5,610 | Hybrid Edge Partitioner: Partitioning Large Power-Law Graphs under Memory Constraints | 2021 | SIGMOD | 6.1508076e-05 |
| 6,440 | DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training | 2025 | SIGMOD | 5.8782861e-05 |
| 6,566 | Play like a Vertex: A Stackelberg Game Approach for Streaming Graph Partitioning | 2024 | SIGMOD | 5.8381749e-05 |
| 7,629 | Bitlist: New Full-text Index for Low Space Cost and Efficient Keyword Search | 2013 | VLDB | 5.5786605e-05 |
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