Manycore processing of repeated k-NN queries over massive moving objects observations

TL;DR

This paper introduces a hybrid CPU/GPU pipeline for efficiently processing repeated k-NN queries over large, continuously moving object datasets, achieving significant speedups over CPU-only methods.

Contribution

It presents the first GPU-based solution for repeated k-NN queries on massive, dynamic spatial data, combining novel data structures and memory access strategies.

Findings

Achieves 10x-20x speedup over CPU-based methods.

Effectively handles highly skewed spatial distributions.

Demonstrates efficiency with inexpensive GPUs.

Abstract

The ability to timely process significant amounts of continuously updated spatial data is mandatory for an increasing number of applications. In this paper we focus on a specific data-intensive problem concerning the repeated processing of huge amounts of k nearest neighbours (k-NN) queries over massive sets of moving objects, where the spatial extents of queries and the position of objects are continuously modified over time. In particular, we propose a novel hybrid CPU/GPU pipeline that significantly accelerate query processing thanks to a combination of ad-hoc data structures and non-trivial memory access patterns. To the best of our knowledge this is the first work that exploits GPUs to efficiently solve repeated k-NN queries over massive sets of continuously moving objects, even characterized by highly skewed spatial distributions. In comparison with state-of-the-art sequential CPU-based implementations, our method highlights significant speedups in the order of 10x-20x, depending on the datasets, even when considering cheap GPUs.