GPU-Based Algorithms for Processing the k Nearest-Neighbor Query on Disk-Resident Data

Abstract

Algorithms for answering the k Nearest-Neighbor (k-NN) query are widely used for queries in spatial databases and for distance classification of a group of query points against a reference dataset to derive the dominating feature class. GPU devices have much larger numbers of processing cores than CPUs and faster device memory than the main memory accessed by CPUs, thus, providing higher computing power for processing demanding queries like the k-NN one. However, since device and/or main memory may not be able to host an entire, rather big, reference dataset, storing this dataset in a fast secondary device, like a Solid State Disk (SSD) is, in many practical cases, a feasible solution. We propose and implement the first GPU-based algorithms for processing the k-NN query for big reference data stored on SSDs. Based on 3d synthetic big data, we experimentally compare these algorithms and highlight the most efficient algorithmic variation.