# RTNN: Accelerating Neighbor Search Using Hardware Ray Tracing

arXiv: https://arxiv.org/abs/2201.01366 · HTML: https://mlsanity.com/paper/2201.01366/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2022.

## Built on by (most cited first, 4 of 4)

- [TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval](https://mlsanity.com/paper/2502.20969/) (2025)
- [Thales: Formulating and Estimating Architectural Vulnerability Factors for DNN Accelerators](https://mlsanity.com/paper/2212.02649/) (2022)
- [RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor Search](https://mlsanity.com/paper/2305.18356/) (2023)
- [GRTX: Efficient Ray Tracing for 3D Gaussian-Based Rendering](https://mlsanity.com/paper/2601.20429/) (2026)

## Abstract (arXiv)

Neighbor search is of fundamental important 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.