XGNN: Boosting Multi-GPU GNN Training via Global GNN Memory Store

Summary: XGNN introduces GGMS, a Global GNN Memory Store that partitions graph topology and features across GPU and host memory and leverages high‑speed interconnects for multi‑GPU training. GGMS’s transparent APIs enable out‑of‑core access and deliver up to 15.7× speedups vs DGL/Quiver/DGL+C. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13360
Venue: VLDB
Year: 2024
Pagerank: 4.714889e-05
Overall Rank: 7,545 | 47.52%
DOI: 10.14778/3641204.3641219

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,027	NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters	2026	SIGMOD	4.1945683e-05
10,082	Gem: Scalable Monotonic Graph Processing Beyond Billion-Scale on a Single Machine	2026	SIGMOD	4.1945683e-05
10,570	NeutronTask: Scalable and Efficient Multi-GPU GNN Training with Task Parallelism	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
278	AliGraph: A Comprehensive Graph Neural Network Platform	2019	VLDB	0.00029230623
1,103	Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture	2021	VLDB	0.00014025101

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