RTNN: accelerating neighbor search using hardware ray tracing
RTNN: accelerating neighbor search using hardware ray tracing
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DOI：
10.1145/3503221.3508409
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Yuhao Zhu
中科院分区：
文献类型：
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作者：
Yuhao Zhu
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中具有重要意义。本文提出将邻居搜索公式化为光线跟踪问题，并利用最近GPU中的专用光线跟踪硬件进行加速。我们表明，朴素的映射没有充分利用光线跟踪硬件。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，在GPU上现有的邻居搜索库的2.2倍-65.0倍的加速比。该代码可从https://github.com/horizon-research/rtnn获得。
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.