论文标题
RTNN：使用硬件射线跟踪加速邻居搜索
RTNN: Accelerating Neighbor Search Using Hardware Ray Tracing
论文作者
论文摘要
邻居搜索对于许多工程和科学领域（例如物理模拟和计算机图形）至关重要。本文建议将邻居搜索作为射线追踪问题，并利用最近GPU中专用的射线追踪硬件进行加速。我们表明，一个幼稚的映射在射线跟踪硬件下进行了探索。我们提出了两个性能优化，即查询调度和查询分区，以驯服效率低下。实验结果显示2.2倍-65.0倍的加速度在GPU上现有的邻居搜索库。该代码可在https://github.com/horizon-research/rtnn上找到。
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.
