HongTu: Scalable Full-Graph GNN Training on Multiple GPUs
Summary: HongTu scales full-graph GNN training on multi-GPU platforms with CPU-memory vertex storage, GPU offload, and recomputation caching. It minimizes host-GPU traffic with deduplicated communication and cost-guided reorganization, and delivers speedups over DistGNN. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Qiange Wang (National University of Singapore)
- 2. Yao Chen (National University of Singapore)
- 3. Weng-Fai Wong (National University of Singapore)
- 4. Bingsheng He (National University of Singapore)
BibTeX Citation
@inproceedings{wang_sigmod23,
title = {{HongTu: Scalable Full-Graph GNN Training on Multiple GPUs}},
author = {Wang, Qiange and Chen, Yao and Wong, Weng-Fai and He, Bingsheng},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3626733},
url = {https://dl.acm.org/doi/10.1145/3626733},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,601 | Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods | 2025 | VLDB | 5.5866563e-05 |
| 10,323 | NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters | 2026 | SIGMOD | 5.093636e-05 |
| 10,357 | DepCache: A KV Cache Management Framework for GraphRAG with Dependency Attention | 2026 | SIGMOD | 5.093636e-05 |
| 10,596 | NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments | 2026 | VLDB | 5.093636e-05 |
| 10,811 | Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch | 2025 | VLDB | 5.093636e-05 |
| 10,835 | NeutronTask: Scalable and Efficient Multi-GPU GNN Training with Task Parallelism | 2025 | VLDB | 5.093636e-05 |
| 10,975 | Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation | 2025 | VLDB | 5.093636e-05 |
| 11,236 | Improving Graph Compression for Efficient Resource-Constrained Graph Analytics | 2024 | VLDB | 5.093636e-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 |
| 1,132 | SANCUS: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks | 2022 | VLDB | 0.00012041292 |
| 1,234 | Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture | 2021 | VLDB | 0.00011549432 |
| 1,574 | Pipelined Query Processing in Coprocessor Environments | 2018 | SIGMOD | 0.00010321274 |
| 1,666 | HippogriffDB: Balancing I/O and GPU Bandwidth in Big Data Analytics | 2016 | VLDB | 0.00010068964 |
| 2,695 | NeutronStar: Distributed GNN Training with Hybrid Dependency Management | 2022 | SIGMOD | 8.2468134e-05 |
| 4,402 | LargeEA: Aligning Entities for Large-scale Knowledge Graphs | 2022 | VLDB | 6.7232199e-05 |
| 5,480 | EMOGI: Efficient Memory-access for Out-of-memory Graph-traversal in GPUs | 2021 | VLDB | 6.2040237e-05 |
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| 1 | 9,546 | NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism | 2025 | VLDB |
| 2 | 6,630 | Efficient Training of Graph Neural Networks on Large Graphs | 2024 | VLDB |
| 3 | 1,863 | ByteGNN: Efficient Graph Neural Network Training at Large Scale | 2022 | VLDB |
| 4 | 3,751 | G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs | 2020 | VLDB |
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| 6 | 10,835 | NeutronTask: Scalable and Efficient Multi-GPU GNN Training with Task Parallelism | 2025 | VLDB |
| 7 | 1,234 | Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture | 2021 | VLDB |
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| 9 | 9,729 | Scalable Graph Convolutional Network Training on Distributed-Memory Systems | 2023 | VLDB |
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