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

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.Comment: This paper has been accepted at the International Conference on Supercomputing 2023 (ICS'23

Generalized Neighbor Search using Commodity Hardware Acceleration

Tree-based Nearest Neighbor Search (NNS) is hard to parallelize on GPUs. However, newer Nvidia GPUs are equipped with Ray Tracing (RT) cores that can build a spatial tree called Bounding Volume Hierarchy (BVH) to accelerate graphics rendering. Recent work proposed using RT cores to implement NNS, but they all have a hardware-imposed constraint on the type of distance metric, which is the Euclidean distance. We propose and implement two approaches for generalized distance computations: filter-refine, and monotone transformation, each of which allows non-euclidean nearest neighbor queries to be performed in terms of Euclidean distances. We find that our reductions improve the time taken to perform distance computations during the search, thereby improving the overall performance of the NNS