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arXiv · 2603.21466

GateANN: I/O-Efficient Filtered Vector Search on SSDs

Nakyung Lee·Soobin Cho·Jiwoong Park·Gyuyeong Kim
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Abstract

We present GateANN, an I/O-efficient SSD-based graph ANNS system that supports filtered vector search on an unmodified graph index. Existing SSD-based systems either waste I/O by post-filtering, or require expensive filter-aware index rebuilds. GateANN avoids both by decoupling graph traversal from vector retrieval. Our key insight is that traversing a node requires only its neighbor list and an approximate distance, neither of which needs the full-precision vector on SSD. Based on this, GateANN introduces graph tunneling. It checks each node's filter predicate in memory before issuing I/O and routes through non-matching nodes entirely in memory, preserving graph connectivity without any SSD read for non-matching nodes. Our experimental results show that it reduces SSD reads by up to 10x and improves throughput by up to 7.6x.

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Nakyung Lee, Soobin Cho, Jiwoong Park, Gyuyeong Kim. 2026-03-23. GateANN: I/O-Efficient Filtered Vector Search on SSDs. https://arxiv.org/abs/2603.21466

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