RTNN: accelerating neighbor search using hardware ray tracing

Neighbor search is of fundamental importance 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.