RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor Search

Vani Nagarajan, D. Mandarapu, Milind Kulkarni
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Abstract

The problem of identifying the k-Nearest Neighbors (kNNS) of a point has proven to be very useful both as a standalone application and as a subroutine in larger applications. Given its far-reaching applicability in areas such as machine learning and point clouds, extensive research has gone into leveraging GPU acceleration to solve this problem. Recent work has shown that using Ray Tracing cores in recent GPUs to accelerate kNNS is much more efficient compared to traditional acceleration using shader cores. However, the existing translation of kNNS to a ray tracing problem imposes a constraint on the search space for neighbors. Due to this, we can only use RT cores to accelerate fixed-radius kNNS, which requires the user to set a search radius a priori and hence can miss neighbors. In this work, we propose TrueKNN, the first unbounded RT-accelerated neighbor search. TrueKNN adopts an iterative approach where we incrementally grow the search space until all points have found their k neighbors. We show that our approach is orders of magnitude faster than existing approaches and can even be used to accelerate fixed-radius neighbor searches.
RT- knns Unbound:使用RT内核加速无限制邻居搜索
识别点的k近邻(kNNS)的问题已被证明是非常有用的,无论是作为独立的应用程序还是作为大型应用程序中的子例程。鉴于其在机器学习和点云等领域的广泛适用性,广泛的研究已经开始利用GPU加速来解决这个问题。最近的研究表明,在最近的gpu中使用光线追踪内核来加速kNNS比使用着色器内核的传统加速要有效得多。然而,现有的kNNS转换为光线追踪问题对邻居的搜索空间施加了限制。因此,我们只能使用RT核来加速固定半径的kNNS,这需要用户先验地设置搜索半径,因此可能会错过邻居。在这项工作中,我们提出了TrueKNN,这是第一个无界rt加速邻居搜索。TrueKNN采用了一种迭代的方法,我们逐渐增加搜索空间,直到所有的点都找到了它们的k个邻居。我们表明,我们的方法比现有的方法快几个数量级,甚至可以用来加速固定半径的邻居搜索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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