Memory-efficient implementation of a graphics processor-based cluster detection algorithm for large spatial databases

Abstract

Numerous approaches have been proposed for detecting clusters, groups of data in spatial databases. Of these, the algorithm known as Density Based Spatial Clustering of Applications with Noise (DBSCAN) is a recent approach which has proven efficient for larger databases. Graphical Processing Units (GPUs), used originally to aid in the processing of high intensity graphics, have been found to be highly effective as general purpose parallel computing platforms. In this project, a GPU-based DBSCAN program has been implemented: the enhancement in this program allows for better memory scalability for use with very large databases. Algorithm performance, as compared to the original sequential program and to an initial GPU implementation, is investigated and analyzed.