Hao Guo and Youyou Lu, Tsinghua University
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.
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author = {Hao Guo and Youyou Lu},
title = {Achieving {Low-Latency} {Graph-Based} Vector Search via Aligning {Best-First} Search Algorithm with {SSD}},
booktitle = {19th USENIX Symposium on Operating Systems Design and Implementation (OSDI 25)},
year = {2025},
isbn = {978-1-939133-47-2},
address = {Boston, MA},
pages = {171--186},
url = {https://www.usenix.org/conference/osdi25/presentation/guo},
publisher = {USENIX Association},
month = jul
}
