G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs

Summary: G3 is a GPU-based GNN training framework derived from graph-processing systems for parallel graph operations. Users implement GNN layers in C/C++ via flexible APIs; the runtime auto-schedules on GPUs with graph-centric optimizations, delivering superior performance vs PyTorch/TensorFlow. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12136
Venue: VLDB
Year: 2020
Pagerank: 6.5611714e-05
Overall Rank: 3,986 | 72.28%
DOI: 10.14778/3415478.3415482

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,160	Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks	2022	VLDB	0.00013586221
3,025	NeutronStar: Distributed GNN Training with Hybrid Dependency Management	2022	SIGMOD	7.6906935e-05
5,007	Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning	2022	VLDB	5.763689e-05
6,884	Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines	2023	VLDB	4.8955332e-05
8,510	Fight Fire with Fire: Towards Robust Graph Neural Networks on Dynamic Graphs via Actively Defense	2024	VLDB	4.4952414e-05
9,596	Scalable Graph Convolutional Network Training on Distributed-Memory Systems	2023	VLDB	4.319218e-05
10,647	Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study	2025	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
4	Pregel: A System for Large-Scale Graph Processing	2010	SIGMOD	0.0019005923
4,577	Accelerating Dynamic Graph Analytics on GPUs	2018	VLDB	6.0709631e-05

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