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arXiv · 2509.00365

CRouting: Reducing Expensive Distance Calls in Graph-Based Approximate Nearest Neighbor Search

Zhenxin Li·Shuibing He·Jiahao Guo·Xuechen Zhang·Xian-He Sun·Gang Chen
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Abstract

Approximate nearest neighbor search (ANNS) is a crucial problem in information retrieval and AI applications. Recently, there has been a surge of interest in graph-based ANNS algorithms due to their superior efficiency and accuracy. However, the repeated computation of distances in high-dimensional spaces constitutes the primary time cost of graph-based methods. To accelerate the search, we propose a novel routing strategy named CRouting, which bypasses unnecessary distance computations by exploiting the angle distributions of high-dimensional vectors. CRouting is designed as a plugin to optimize existing graph-based search with minimal code modifications. Our experiments show that CRouting reduces the number of distance computations by up to 41.5% and boosts queries per second by up to 1.48$\times$ on two predominant graph indexes, HNSW and NSG. Code is publicly available at https://github.com/ISCS-ZJU/CRouting.

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Zhenxin Li, Shuibing He, Jiahao Guo, Xuechen Zhang, Xian-He Sun, Gang Chen. 2025-08-30. CRouting: Reducing Expensive Distance Calls in Graph-Based Approximate Nearest Neighbor Search. https://arxiv.org/abs/2509.00365

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