Achieving Low-Latency Graph-Based Vector Search via Aligning Best-First Search Algorithm with SSD
Abstract
We propose PipeANN , an on-disk graph-based approximate nearest neighbor search (ANNS) system, which significantly bridges the latency gap with in-memory ones. We achieve this by aligning the best-first search algorithm with SSD characteristics, avoiding strict compute-I/O order across search steps. Experiments show that PipeANN has 1.14×–2.02× search latency compared to in-memory Vamana, and 35.0% of the latency of on-disk DiskANN in billion-scale datasets, without sacrificing search accuracy. PipeANN is open-source at https://github.com/thustorage/PipeANN .
Fields of Study & Subjects
Advanced Data Storage Technologies
Advanced Image and Video Retrieval Techniques
Algorithms and Data Compression
Publication details
- Journal / source
- ACM Transactions on Storage
- Publisher
- Association for Computing Machinery (ACM)
- Volume
- 22
- Issue
- 2
- Pages
- 1–30
- Publication date
- 13 February 2026
- Language
- EN