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General Purpose computing on Graphical Processing Units (GPGPU) has resultedin unprecedented levels of speedup over its CPU counterparts, allowingprogrammers to harness the computational power of GPU shader cores toaccelerate other computing applications. But this style of acceleration is bestsuited for regular computations (e.g., linear algebra). Recent GPUs feature newRay Tracing (RT) cores that instead speed up the irregular process of raytracing using Bounding Volume Hierarchies. While these cores seem limited infunctionality, they can be used to accelerate n-body problems by leveraging RTcores to accelerate the required distance computations. In this work, wepropose RT-DBSCAN, the first RT-accelerated DBSCAN implementation. We use RTcores to accelerate Density-Based Clustering of Applications with Noise(DBSCAN) by translating fixed-radius nearest neighbor queries to ray tracingqueries. We show that leveraging the RT hardware results in speedups between1.3x to 4x over current state-of-the-art, GPU-based DBSCAN implementations.

RT-DBSCAN: Accelerating DBSCAN using Ray Tracing Hardware