Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses
Summary: GIDS: a GPU‑initiated direct storage access dataloader that lets GPU threads fetch node features from storage, bypassing CPU sampling/aggregation and OS page‑fault overhead for large-scale GNN training. Uses a dynamic storage access accumulator, constant CPU buffer, and GPU software cache with window buffering to balance CPU/storage/GPU, achieving up to 582× speedup vs DGL dataloader on terabyte graphs. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,980 | OUTRE: An OUT-of-core De-REdundancy GNN Training Framework for Massive Graphs within A Single Machine | 2024 | VLDB | 4.8744298e-05 |
| 7,108 | DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training | 2025 | SIGMOD | 4.8297805e-05 |
| 10,233 | Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling | 2026 | VLDB | 4.1945683e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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