CLEANN: Lock-Free Augmented Trees for Low-Dimensional k-Nearest Neighbor Search
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3694906.3743339.pdf
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4.35 MB
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0f2f8217edadd80bab74fa68a9a9d10a
Author(s)
Manohar, Magdalen
Wei, Yuanhao
Blelloch, Guy
Date Issued
July 16, 2025
Publisher
ACM|37th ACM Symposium on Parallelism in Algorithms and Architectures
Citation
Magdalen Dobson Manohar, Yuanhao Wei, and Guy E. Blelloch. 2025. CLEANN: Lock-Free Augmented Trees for Low-Dimensional κ-Nearest Neighbor Search. In Proceedings of the 37th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA '25). Association for Computing Machinery, New York, NY, USA, 131–143.
Version
Final published version
Abstract
We develop a linearizable lock-free data structure, the CLEANN-tree (Concurrent Linearizable Efficient Augmented Nearest Neighbor tree), for low dimensional κ-nearest-neighbor searching. The data structure maintains a set of points P in d dimensions under insertion and deletion, while supporting queries that, given a point, return the k nearest points in P. The CLEANN-tree is constructed by modifying a kd-tree, a type of spatial decomposition commonly used for κ-nearest neighbor searching, for the concurrent environment. It is the first such concurrent structure---two previous structures were either not linearizable or only supported κ=1. Furthermore CLEANN-tree stores an augmented value (more specifically, a bounding box) in each internal node of the kd-tree. These bounding boxes significantly improve query performance by allowing more aggresive pruning. However, correctly and efficiently maintaining these augmented values is challenging in the linearizable lock-free setting because queries can examine large parts of the structure, which might be changing, and an insert or delete can require updating all the augmented values from the leaf to the root.
We develop new approaches for maintaining concurrent augmented trees which leverage recent work on lock-free locks and snapshotting. Based on these, we implement two variations of the CLEANN-tree and present experimental results for both. Both variations significantly outperform previous concurrent κ-nearest-neighbor search structures and get near linear speedup over optimized sequential structures.
Description
SPAA ’25, July 28–August 1, 2025, Portland, OR, USA
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3694906.3743339
