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SANCUS: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks

Summary: SANCUS is a decentralized full-graph GNN trainer that uses bounded embedding-staleness metrics, cached historical embeddings, and adaptive broadcast skipping to avoid communication. It provides convergence guarantees and achieves up to 74% less communication and 1.86× higher throughput without accuracy loss. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
h33e4d0ef20b4aa07
Venue
VLDB
Year
2022
Pagerank
0.00011893521
Overall Rank
1,134 | 92.38%
DOI
10.14778/3538598.3538614

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{peng_vldb22,
        title = {{SANCUS: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks}},
        author = {Peng, Jingshu and Chen, Zhao and Shao, Yingxia and Shen, Yanyan and Chen, Lei and Cao, Jiannong},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {9},
        pages = {1937--1950},
        doi = {10.14778/3538598.3538614},
        url = {https://doi.org/10.14778/3538598.3538614},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 30 of 30 citing papers.

Rank Citing Paper Year Venue Pagerank
2,506 DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU 2023 SIGMOD 8.3774747e-05
2,596 Scalable and Efficient Full-Graph GNN Training for Large Graphs 2023 SIGMOD 8.2419456e-05
4,502 NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams 2024 VLDB 6.5740722e-05
4,764 ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs 2024 VLDB 6.4276208e-05
4,825 DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning 2024 VLDB 6.3923554e-05
4,853 NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments 2024 VLDB 6.3805655e-05
4,898 Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective 2024 VLDB 6.365824e-05
4,993 DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks 2023 SIGMOD 6.32357e-05
5,310 DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training 2024 VLDB 6.1857447e-05
5,339 FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training 2024 VLDB 6.1731674e-05
5,899 Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines 2023 VLDB 5.9545431e-05
6,166 Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving 2025 SIGMOD 5.8631131e-05
6,669 Efficient Training of Graph Neural Networks on Large Graphs 2024 VLDB 5.7147311e-05
6,826 SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement 2024 SIGMOD 5.6700316e-05
6,924 HongTu: Scalable Full-Graph GNN Training on Multiple GPUs 2023 SIGMOD 5.6426299e-05
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
8,152 NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism 2025 VLDB 5.3896512e-05
8,623 D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks 2024 VLDB 5.3009314e-05
9,233 Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study 2025 VLDB 5.2056825e-05
9,795 NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters 2026 SIGMOD 5.1257999e-05
9,900 Scalable Graph Convolutional Network Training on Distributed-Memory Systems 2023 VLDB 5.1116429e-05
10,043 The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format 2024 SIGMOD 5.0921006e-05
10,257 Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch 2025 VLDB 5.050482e-05
10,706 Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling 2026 VLDB 4.9793485e-05
10,737 FlareDTDG: Harnessing Temporal Recency for Scalable Discrete-Time Dynamic Graph Training 2026 VLDB 4.9793485e-05
11,043 NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments 2026 VLDB 4.9793485e-05
11,198 SWASH: A Flexible Communication Framework with Sliding Window-Based Cache Sharing for Scalable DGNN Training 2025 SIGMOD 4.9793485e-05
11,289 Heta: Distributed Training of Heterogeneous Graph Neural Networks 2025 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
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Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
3 Pregel: A System for Large-Scale Graph Processing 2010 SIGMOD 0.0012092602
211 AliGraph: A Comprehensive Graph Neural Network Platform 2019 VLDB 0.00024816965
947 Parallelizing Sequential Graph Computations 2017 SIGMOD 0.00012914714
1,033 AGL: A Scalable System for Industrial-purpose Graph Machine Learning 2020 VLDB 0.00012397734
1,224 Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture 2021 VLDB 0.000114497
2,184 Heterogeneity-aware Distributed Parameter Servers 2017 SIGMOD 8.8958335e-05
2,439 Accelerating Large Scale Real-Time GNN Inference using Channel Pruning 2021 VLDB 8.4652234e-05
3,752 G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs 2020 VLDB 7.0517024e-05
3,899 Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization 2021 VLDB 6.9358695e-05
4,237 Accelerating Dynamic Graph Analytics on GPUs 2018 VLDB 6.7107028e-05
4,963 Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce 2021 SIGMOD 6.3384091e-05
6,179 GRAPE: Parallelizing Sequential Graph Computations 2017 VLDB 5.8595506e-05
6,674 Reliable Data Distillation on Graph Convolutional Network 2020 SIGMOD 5.7125691e-05
Previous Page 1 / 1 Next

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3 6,669 Efficient Training of Graph Neural Networks on Large Graphs 2024 VLDB
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