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)

Paper ID: h389a703125658dae
Venue: SIGMOD
Year: 2023
Pagerank: 5.6426299e-05
Overall Rank: 6,924 | 53.45%
DOI: 10.1145/3626733

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 9 of 9 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
7,522	NeutronTask: Scalable and Efficient Multi-GPU GNN Training with Task Parallelism	2025	VLDB	5.5025409e-05
7,708	Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods	2025	VLDB	5.4718627e-05
9,795	NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters	2026	SIGMOD	5.1257999e-05
10,257	Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch	2025	VLDB	5.050482e-05
10,273	Improving Graph Compression for Efficient Resource-Constrained Graph Analytics	2024	VLDB	5.0485061e-05
10,558	DepCache: A KV Cache Management Framework for GraphRAG with Dependency Attention	2026	SIGMOD	4.9793485e-05
10,785	MGI: A Communication Framework for Data Processing in Massive GPU Infrastructures	2026	VLDB	4.9793485e-05
11,043	NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments	2026	VLDB	4.9793485e-05
11,361	Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation	2025	VLDB	4.9793485e-05

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
211	AliGraph: A Comprehensive Graph Neural Network Platform	2019	VLDB	0.00024816965
1,134	SANCUS: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks	2022	VLDB	0.00011893521
1,224	Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture	2021	VLDB	0.000114497
1,533	Pipelined Query Processing in Coprocessor Environments	2018	SIGMOD	0.00010332035
1,541	HippogriffDB: Balancing I/O and GPU Bandwidth in Big Data Analytics	2016	VLDB	0.00010313459
2,279	NeutronStar: Distributed GNN Training with Hybrid Dependency Management	2022	SIGMOD	8.7062637e-05
4,454	LargeEA: Aligning Entities for Large-scale Knowledge Graphs	2022	VLDB	6.5927535e-05
5,521	EMOGI: Efficient Memory-access for Out-of-memory Graph-traversal in GPUs	2021	VLDB	6.0959947e-05

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