  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.

Machine Learning;Computational Geometry;Performance