RTNN：使用硬件光线追踪加速邻域搜索
RTNN: Accelerating Neighbor Search Using Hardware Ray Tracing
摘要：
邻域搜索在物理模拟、计算机图形学等许多工程和科学领域都有着重要的应用。本文提出将邻域搜索公式化为光线追踪问题，并利用最近GPU中的专用光线追踪硬件进行加速。我们表明，一个天真的映射下利用光线追踪硬件。我们提出了两个性能优化，查询调度和查询分区，驯服效率低下。实验结果表明，2.2倍-65.0倍的加速比现有的邻居搜索库的GPU上。该代码可在https://github.com/horizon-research/rtnn上获得。
英文摘要：
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.
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