Hybrid (CPU/GPU) Exact Nearest Neighbors Search in High-Dimensional Spaces

In this paper, we propose a hybrid algorithm for exact nearest neighbors queries in high-dimensional spaces. Indexing structures typically used for exact nearest neighbors search become less efficient in high-dimensional spaces, effectively requiring brute-force search. Our method uses a massively-parallel approach to brute-force search that efficiently splits the computational load between CPU and GPU. We show that the performance of our algorithm scales linearly with the dimensionality of the data, improving upon previous approaches for high-dimensional datasets. The algorithm is implemented in Julia, a high-level programming language for numerical and scientific computing. It is openly available at https://github.com/davnn/ParallelNeighbors.jl.

Automated summary

This paper proposes a hybrid algorithm for exact nearest neighbors queries in high-dimensional spaces. By efficiently splitting the computational load between CPU and GPU, our method improves upon previous approaches for high-dimensional datasets, achieving linear scalability with the dimensionality of the data.