Turbocharging Vector Databases using Modern SSDs

Summary: SSD-aware suite for disk-resident graph-based ANN (HNSW): parallel io_uring I/O, spatially-aware insertion reordering, and locality-preserving colocation, implemented in pgvector. Yields up to 11.1× query throughput, 3.23× cache-hit, 98.4% faster index build. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 14079
Venue: VLDB
Year: 2025
Pagerank: 4.1945683e-05
Overall Rank: 10,760 | 25.15%
DOI: 10.14778/3749646.3749724

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Rank	Citing Paper	Year	Venue	Pagerank

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

Rank	Cited Paper	Year	Venue	Pagerank
812	A Modeling Study of the TPC-C Benchmark	1993	SIGMOD	0.00016408423
2,690	Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment	2024	SIGMOD	8.293714e-05

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