Fast k Nearest Neighbor Search using GPU

TL;DR

This paper demonstrates how leveraging GPU architecture with NVIDIA CUDA API can significantly accelerate k nearest neighbor searches, reducing computation time by up to 120 times for large datasets.

Contribution

The paper introduces a GPU-accelerated method for k nearest neighbor search that achieves substantial speedups over traditional CPU-based approaches.

Findings

GPU implementation accelerates KNN up to 120 times

Significant reduction in computation time for large datasets

Potential for real-time applications in computer vision

Abstract

The recent improvements of graphics processing units (GPU) offer to the computer vision community a powerful processing platform. Indeed, a lot of highly-parallelizable computer vision problems can be significantly accelerated using GPU architecture. Among these algorithms, the k nearest neighbor search (KNN) is a well-known problem linked with many applications such as classification, estimation of statistical properties, etc. The main drawback of this task lies in its computation burden, as it grows polynomially with the data size. In this paper, we show that the use of the NVIDIA CUDA API accelerates the search for the KNN up to a factor of 120.