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Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling
Summary: MorphGL: GNN training for billion-edge graphs via collective batching that dynamically splits mini-batch prep across CPU/GPU instead of static binding. Dual-buffer scheduling co-optimizes CPU, PCIe, and GPU stages, boosting utilization and yielding up to 2.76x over SALIENT.
(summarized by gpt-5.4-mini on Apr 12 2026)
- Paper ID
- 14270
- Venue
- VLDB
- Year
- 2026
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,233 | 28.89%
- DOI
-
10.14778/3797919.3797927
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Incoming Citations (Sorted by Pagerank)
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| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 27 of 27 cited papers.
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| 271 |
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| 1,162 |
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5.5104776e-05 |
| 5,452 |
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5.4972958e-05 |
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5.1779507e-05 |
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4.4909322e-05 |
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