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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
12880
Venue
VLDB
Year
2022
Pagerank
0.00012041292
Overall Rank
1,132 | 92.24%
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
2,640 Scalable and Efficient Full-Graph GNN Training for Large Graphs 2023 SIGMOD 8.3074486e-05
2,668 DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU 2023 SIGMOD 8.2750247e-05
4,878 NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams 2024 VLDB 6.4684388e-05
4,891 DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks 2023 SIGMOD 6.4581865e-05
5,079 NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments 2024 VLDB 6.3717342e-05
5,205 ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs 2024 VLDB 6.318626e-05
5,243 FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training 2024 VLDB 6.3018825e-05
5,316 Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective 2024 VLDB 6.2687017e-05
5,760 DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training 2024 VLDB 6.0961929e-05
6,588 Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines 2023 VLDB 5.833338e-05
6,630 Efficient Training of Graph Neural Networks on Large Graphs 2024 VLDB 5.8192133e-05
6,730 SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement 2024 SIGMOD 5.7895038e-05
6,806 HongTu: Scalable Full-Graph GNN Training on Multiple GPUs 2023 SIGMOD 5.7673207e-05
6,900 Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving 2025 SIGMOD 5.7430032e-05
7,087 DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning 2024 VLDB 5.7069166e-05
7,601 Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods 2025 VLDB 5.5866563e-05
8,454 D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks 2024 VLDB 5.4226e-05
9,546 NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism 2025 VLDB 5.2528121e-05
9,729 Scalable Graph Convolutional Network Training on Distributed-Memory Systems 2023 VLDB 5.2289669e-05
9,881 The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format 2024 SIGMOD 5.2040783e-05
10,323 NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters 2026 SIGMOD 5.093636e-05
10,521 Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling 2026 VLDB 5.093636e-05
10,555 FlareDTDG: Harnessing Temporal Recency for Scalable Discrete-Time Dynamic Graph Training 2026 VLDB 5.093636e-05
10,596 NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments 2026 VLDB 5.093636e-05
10,780 SWASH: A Flexible Communication Framework with Sliding Window-Based Cache Sharing for Scalable DGNN Training 2025 SIGMOD 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
10,890 Heta: Distributed Training of Heterogeneous Graph Neural Networks 2025 VLDB 5.093636e-05
10,899 Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study 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
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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 0.0012250108
223 AliGraph: A Comprehensive Graph Neural Network Platform 2019 VLDB 0.00024182473
956 Parallelizing Sequential Graph Computations 2017 SIGMOD 0.0001297452
1,048 AGL: A Scalable System for Industrial-purpose Graph Machine Learning 2020 VLDB 0.00012433693
1,234 Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture 2021 VLDB 0.00011549432
2,162 Heterogeneity-aware Distributed Parameter Servers 2017 SIGMOD 9.0581831e-05
2,386 Accelerating Large Scale Real-Time GNN Inference using Channel Pruning 2021 VLDB 8.6490185e-05
3,751 G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs 2020 VLDB 7.1531889e-05
3,850 Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization 2021 VLDB 7.0742748e-05
4,171 Accelerating Dynamic Graph Analytics on GPUs 2018 VLDB 6.8537202e-05
4,956 Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce 2021 SIGMOD 6.4290135e-05
6,066 GRAPE: Parallelizing Sequential Graph Computations 2017 VLDB 5.9890886e-05
6,550 Reliable Data Distillation on Graph Convolutional Network 2020 SIGMOD 5.8430465e-05
Previous Page 1 / 1 Next

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1 5,243 FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training 2024 VLDB
2 10,975 Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation 2025 VLDB
3 4,891 DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks 2023 SIGMOD
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