PageANN: Scalable I/O-Efficient Disk-Based Approximate Nearest Neighbor Search with Disk Page-Aligned Graph
Winchester
Tue Sep 29 | 1:30pm
Abstract
Approximate Nearest Neighbor Search (ANNS) is a critical component of modern AI/ML systems, particularly in retrieval-augmented generation (RAG). While graph-based ANNS methods provide high query efficiency, their scalability is limited by host memory. Disk-based approaches mitigate this by offloading data to SSDs, but they suffer from a key bottleneck: as datasets grow and memory shrinks, they incur high I/O latency or fail due to insufficient memory.
We present PageANN, a scalable and I/O-efficient disk-based ANNS framework that maintains high performance across varying memory budgets. PageANN aligns graph node granularity with SSD page granularity, treating each page as a search unit. By grouping nearby vectors within the same page and constructing shared neighbor lists, each I/O operation retrieves only relevant data, expanding the effective search scope while reducing redundant accesses. PageANN further incorporates a lightweight in-memory hash-based routing index to reduce search hops and improve navigation efficiency. By packing all necessary information for each search step within a single page, it enables on-demand data fetching with low memory overhead, while still benefiting from additional memory when available.
Experiments show that PageANN outperforms state-of-the-art disk-based ANNS methods, achieving up to 1.94× higher throughput and 66.8% lower latency across datasets and memory settings, without sacrificing recall accuracy.
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