DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning

Summary: DAHA introduces a lightweight data-/hardware-aware cost model for accurate GNN operation-time estimates. It uses the model for batch-preparation rewriting, intra-/inter-batch scheduling, and pipeline parallelism, reducing preparation/transfer bottlenecks across diverse hardware and message-passing GNNs. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 13569
Venue: VLDB
Year: 2024
Pagerank: 5.7069166e-05
Overall Rank: 7,087 | 51.38%
DOI: 10.14778/3648160.3648176

Incoming Non-self Citations Over Time

Authors

1. Zhiyuan Li (Hong Kong University of Science and Technology)
2. Xun Jian (Hong Kong University of Science and Technology)
3. Yue Wang (Shenzhen University)
4. Yingxia Shao (Beijing Institute of Technology)
5. Lei Chen (Hong Kong University of Science and Technology)

BibTeX Citation

@article{li_vldb24,
        title = {{DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning}},
        author = {Li, Zhiyuan and Jian, Xun and Wang, Yue and Shao, Yingxia and Chen, Lei},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {6},
        pages = {1364--1376},
        doi = {10.14778/3648160.3648176},
        url = {https://doi.org/10.14778/3648160.3648176},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
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
6,900	Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving	2025	SIGMOD	5.7430032e-05
8,503	Fight Fire with Fire: Towards Robust Graph Neural Networks on Dynamic Graphs via Actively Defense	2024	VLDB	5.4132367e-05
10,323	NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters	2026	SIGMOD	5.093636e-05
10,452	Enumerating Graph Pattern Matches with ML Oracles	2026	SIGMOD	5.093636e-05
10,521	Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling	2026	VLDB	5.093636e-05
10,691	Flux: Unifying Heterogeneous Infrastructure for Alibaba AnalyticDB	2025	SIGMOD	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

Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 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
1,048	AGL: A Scalable System for Industrial-purpose Graph Machine Learning	2020	VLDB	0.00012433693
1,132	SANCUS: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks	2022	VLDB	0.00012041292
1,863	ByteGNN: Efficient Graph Neural Network Training at Large Scale	2022	VLDB	9.5950349e-05
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
2,695	NeutronStar: Distributed GNN Training with Hybrid Dependency Management	2022	SIGMOD	8.2468134e-05
3,210	Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank	2023	VLDB	7.6352864e-05
4,436	Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures	2023	VLDB	6.7069035e-05

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