Fast tree-based algorithms for DBSCAN for low-dimensional data on GPUs

TL;DR

This paper introduces a GPU-accelerated framework for DBSCAN clustering using tree-based algorithms that efficiently handle low-dimensional data, reducing memory usage and improving performance over existing methods.

Contribution

The paper presents two novel tree-based algorithms for GPU-accelerated DBSCAN that fuse neighbor search with cluster updates, optimizing performance and memory efficiency.

Findings

Algorithms are at most twice as slow as neighbor determination.

Proposed methods outperform existing CPU and GPU implementations.

Memory usage is reduced by dynamic neighbor processing.

Abstract

DBSCAN is a well-known density-based clustering algorithm to discover arbitrary shape clusters. While conceptually simple in serial, the algorithm is challenging to efficiently parallelize on manycore GPU architectures. Common pitfalls, such as asynchronous range query calls, result in high thread execution divergence in many implementations. In this paper, we propose a new framework for GPU-accelerated DBSCAN, and describe two tree-based algorithms within that framework. Both algorithms fuse the search for neighbors with updating cluster information, but differ in their treatment of dense regions of the data. We show that the time taken to compute clusters is at most twice that of determination of the neighbors. We compare the proposed algorithms with existing CPU and GPU implementations, and demonstrate their competitiveness and performance using a fast traversal structure (bounding volume hierarchy) for low dimensional data. We also show that the memory usage can be reduced by processing object neighbors dynamically without storing them.