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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
0.0001188789
Overall Rank
1,134 | 92.38%
DOI
10.14778/3538598.3538614
PDF
Download (CC BY-NC-ND 4.0)

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 8.3735089e-05
2,598 Scalable and Efficient Full-Graph GNN Training for Large Graphs 2023 SIGMOD 8.238044e-05
4,504 NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams 2024 VLDB 6.5709602e-05
4,767 ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs 2024 VLDB 6.4245781e-05
4,827 DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning 2024 VLDB 6.3893293e-05
4,855 NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments 2024 VLDB 6.377545e-05
4,899 Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective 2024 VLDB 6.3628105e-05
4,997 DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks 2023 SIGMOD 6.3205765e-05
5,315 DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training 2024 VLDB 6.1828165e-05
5,346 FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training 2024 VLDB 6.1702451e-05
5,901 Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines 2023 VLDB 5.9517243e-05
6,168 Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving 2025 SIGMOD 5.8603375e-05
6,673 Efficient Training of Graph Neural Networks on Large Graphs 2024 VLDB 5.7120259e-05
6,831 SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement 2024 SIGMOD 5.6673474e-05
6,927 HongTu: Scalable Full-Graph GNN Training on Multiple GPUs 2023 SIGMOD 5.6399587e-05
7,527 NeutronTask: Scalable and Efficient Multi-GPU GNN Training with Task Parallelism 2025 VLDB 5.499936e-05
7,715 Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods 2025 VLDB 5.4692724e-05
8,158 NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism 2025 VLDB 5.3870998e-05
8,630 D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks 2024 VLDB 5.298422e-05
9,243 Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study 2025 VLDB 5.2032182e-05
9,802 NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters 2026 SIGMOD 5.1233734e-05
9,907 Scalable Graph Convolutional Network Training on Distributed-Memory Systems 2023 VLDB 5.1092231e-05
10,048 The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format 2024 SIGMOD 5.0896901e-05
10,263 Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch 2025 VLDB 5.0480912e-05
10,716 Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling 2026 VLDB 4.9769913e-05
10,747 FlareDTDG: Harnessing Temporal Recency for Scalable Discrete-Time Dynamic Graph Training 2026 VLDB 4.9769913e-05
11,052 NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments 2026 VLDB 4.9769913e-05
11,207 SWASH: A Flexible Communication Framework with Sliding Window-Based Cache Sharing for Scalable DGNN Training 2025 SIGMOD 4.9769913e-05
11,297 Heta: Distributed Training of Heterogeneous Graph Neural Networks 2025 VLDB 4.9769913e-05
11,368 Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation 2025 VLDB 4.9769913e-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 0.0012087459
211 AliGraph: A Comprehensive Graph Neural Network Platform 2019 VLDB 0.00024805216
948 Parallelizing Sequential Graph Computations 2017 SIGMOD 0.00012908602
1,033 AGL: A Scalable System for Industrial-purpose Graph Machine Learning 2020 VLDB 0.00012391866
1,227 Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture 2021 VLDB 0.0001144428
2,186 Heterogeneity-aware Distributed Parameter Servers 2017 SIGMOD 8.8916253e-05
2,440 Accelerating Large Scale Real-Time GNN Inference using Channel Pruning 2021 VLDB 8.461216e-05
3,753 G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs 2020 VLDB 7.0483642e-05
3,899 Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization 2021 VLDB 6.9325861e-05
4,237 Accelerating Dynamic Graph Analytics on GPUs 2018 VLDB 6.707526e-05
4,965 Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce 2021 SIGMOD 6.3354086e-05
6,182 GRAPE: Parallelizing Sequential Graph Computations 2017 VLDB 5.8567767e-05
6,678 Reliable Data Distillation on Graph Convolutional Network 2020 SIGMOD 5.7098648e-05
Previous Page 1 / 1 Next

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1 5,346 FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training 2024 VLDB
2 11,368 Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation 2025 VLDB
3 4,997 DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks 2023 SIGMOD
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