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.

翻译：我们提出GateANN——一种基于SSD的I/O高效图近似最近邻搜索系统，支持在未修改图索引上执行过滤向量搜索。现有基于SSD的系统要么采用后过滤方式浪费I/O，要么需要高昂的过滤感知索引重建。GateANN通过将图遍历与向量检索解耦避免了上述两种缺陷。我们的核心洞察在于：遍历节点仅需其邻接列表和近似距离，两者均无需访问SSD上的全精度向量。基于此，GateANN引入图隧道技术：在发起I/O前于内存中检查各节点的过滤谓词，并完全在内存中路由穿过非匹配节点，从而在避免读取非匹配节点SSD数据的前提下保持图连通性。实验结果表明，该系统可将SSD读取量降低至1/10，吞吐量提升至7.6倍。