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

arXiv: https://arxiv.org/abs/2305.18356 · HTML: https://mlsanity.com/paper/2305.18356/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 7 of 7)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms](https://mlsanity.com/paper/1807.05614/) (2018)
- [Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence](https://mlsanity.com/paper/2002.04803/) (2020)
- [Efficient Large-scale Approximate Nearest Neighbor Search on the GPU](https://mlsanity.com/paper/1702.05911/) (2017)
- [RTNN: Accelerating Neighbor Search Using Hardware Ray Tracing](https://mlsanity.com/paper/2201.01366/) (2022)
- [Efficient Space Skipping and Adaptive Sampling of Unstructured Volumes Using Hardware Accelerated Ray Tracing](https://mlsanity.com/paper/1908.01906/) (2019)
- [Accelerating Force-Directed Graph Drawing with RT Cores](https://mlsanity.com/paper/2008.11235/) (2020)

## Built on by (most cited first, 2 of 2)

- [Affine-Equivariant Kernel Space Encoding for NeRF Editing](https://mlsanity.com/paper/2508.02831/) (2025)
- [Monitoring Robustness and Individual Fairness](https://mlsanity.com/paper/2506.00496/) (2025)

## Abstract (arXiv)

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.