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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)

Paper ID
13370
Venue
VLDB
Year
2024
Pagerank
5.4332062e-05
Overall Rank
5,561 | 61.32%
DOI
10.14778/3648160.3648166

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