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

## TL;DR

CRouting is a novel routing strategy that significantly reduces distance computations in graph-based approximate nearest neighbor search, enhancing efficiency with minimal modifications to existing algorithms.

## Contribution

It introduces CRouting, a plugin that exploits high-dimensional angle distributions to bypass unnecessary distance calculations in graph-based ANNS methods.

## Key findings

- Reduces distance computations by up to 41.5%.
- Increases queries per second by up to 1.48×.
- Applicable to HNSW and NSG graph indexes.

## 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.