General Purpose computing on Graphical Processing Units (GPGPU) has resulted in unprecedented levels of speedup over its CPU counterparts, allowing programmers to harness the computational power of GPU shader cores to accelerate other computing applications. But this style of acceleration is best suited for regular computations (e.g., linear algebra). Recent GPUs feature new Ray Tracing (RT) cores that instead speed up the irregular process of ray tracing using Bounding Volume Hierarchies. While these cores seem limited in functionality, they can be used to accelerate n-body problems by leveraging RT cores to accelerate the required distance computations. In this work, we propose RT-DBSCAN, the first RT-accelerated DBSCAN implementation. We use RT cores to accelerate Density-Based Clustering of Applications with Noise (DBSCAN) by translating fixed-radius nearest neighbor queries to ray tracing queries. We show that leveraging the RT hardware results in speedups between 1.3x to 4x over current state-of-the-art, GPU-based DBSCAN implementations.

翻译：图形处理单元（GPU）上的通用计算（GPGPU）相较于CPU实现了前所未有的加速比，使程序员得以利用GPU着色核的计算能力来加速其他应用。但这种加速模式最适用于规则计算（如线性代数）。近期推出的GPU配备了新型光线追踪（RT）核，通过层级包围盒技术专门加速追迹这种不规则过程。虽然这些核的功能看似受限，但可利用RT核加速所需的距离计算，进而用于加速n体问题。本文提出首个基于RT加速的DBSCAN实现——RT-DBSCAN。通过将固定半径最近邻查询转化为光线追踪查询，我们利用RT核加速了带噪声的基于密度的聚类算法（DBSCAN）。研究表明，与当前最先进的基于GPU的DBSCAN实现相比，利用RT硬件可获得1.3倍至4倍的加速比。