Neighbor search is of fundamental important to many engineering and science fields such as physics simulation and computer graphics. This paper proposes to formulate neighbor search as a ray tracing problem and leverage the dedicated ray tracing hardware in recent GPUs for acceleration. We show that a naive mapping under-exploits the ray tracing hardware. We propose two performance optimizations, query scheduling and query partitioning, to tame the inefficiencies. Experimental results show 2.2X -- 65.0X speedups over existing neighbor search libraries on GPUs. The code is available at https://github.com/horizon-research/rtnn.

翻译：对许多工程和科学领域,例如物理学模拟和计算机图形,邻里搜索具有根本重要性。本文建议将邻里搜索设计成一个射线跟踪问题,并利用最近GPU中专门光追踪硬件加速。我们显示,天真地绘制地图会利用射线跟踪硬件;我们建议两种性能优化、查询时间安排和查询分隔,以降低效率。实验结果显示,在GPU上的现有邻里搜索图书馆上,2.2X - 65.0X加速了速度。该代码可在https://github.com/horizon-research/rtnn上查阅。