RT-DBSCAN：使用光线追踪硬件加速DBSCAN
RT-DBSCAN: Accelerating DBSCAN using Ray Tracing Hardware
摘要：
图形处理单元（GPGPU）上的通用计算与CPU相比带来了前所未有的加速水平，允许程序员利用GPU着色器核心的计算能力来加速其他计算应用程序。但这种加速方式最适合常规计算（例如，线性代数）。最新的GPU采用了新的光线追踪（RT）内核，这些内核使用边界体层次来加速光线追踪的不规则过程。虽然这些核心在功能上似乎有限，但它们可以通过利用RT核心来加速所需的距离计算来加速n体问题。在这项工作中，我们提出了RT-DBSCAN，第一个RT加速DBSCAN实现。我们使用RT核心来加速基于密度的聚类应用程序与噪声（DBSCAN）的固定半径最近邻查询转换为光线追踪查询。我们表明，利用RT硬件的结果在1.3倍到4倍之间的加速比目前最先进的，基于GPU的DBSCAN实现。
英文摘要：
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.
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