@inproceedings{adaab64f8eb644949ad591a2f2ad67e3,
title = "Hybrid (CPU/GPU) Exact Nearest Neighbors Search in High-Dimensional Spaces",
abstract = "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.",
keywords = "CPU, Exact, GPU, Hybrid, k-NN, Nearest neighbors",
author = "David Muhr and Michael Affenzeller",
note = "Publisher Copyright: {\textcopyright} 2022, IFIP International Federation for Information Processing.; 18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022 ; Conference date: 17-06-2022 Through 20-06-2022",
year = "2022",
doi = "10.1007/978-3-031-08337-2_10",
language = "English",
isbn = "9783031083365",
series = "IFIP Advances in Information and Communication Technology",
publisher = "Springer",
pages = "112--123",
editor = "Ilias Maglogiannis and Lazaros Iliadis and John Macintyre and Paulo Cortez",
booktitle = "Artificial Intelligence Applications and Innovations - 18th IFIP WG 12.5 International Conference, AIAI 2022, Proceedings",
address = "Germany",
}