RTNN

Overview

RTNN is a research prototype system and codebase that accelerates neighbor search by mapping it onto Nvidia GPU hardware ray tracing units (RT cores). It focuses on low-dimensional spaces (≤3D), as commonly found in engineering and scientific simulations (e.g., particles, surface samples in CFD, graphics, and vision). The implementation is primarily based on the OptiX ray tracing API with CUDA used for parallel helper routines.

• Category: SDKs & Libraries
• Use cases: Vector / spatial similarity search, neighbor search in 2D/3D simulations and analysis
• Source: https://github.com/horizon-research/rtnn

Features

Neighbor Search Capabilities

• Supports fixed-radius search (range search).
• Supports K-nearest neighbor (KNN) search.
• Interface assumes:
  • A search radius r for both range and KNN searches.
  • A maximum neighbor count K for both KNN and range searches.
• Can emulate unbounded KNN by using a very large r.
• Can emulate unbounded range search by using a very large K.
• Designed for low-dimensional (2D/3D) spatial neighbor search, common in physical simulations and geometric data.

Performance & Acceleration

• Uses Nvidia RT cores (hardware ray tracing units) to accelerate neighbor search.
• Demonstrates order-of-magnitude speedups over traditional GPU (CUDA-only) neighbor search implementations, as reported in an associated PPoPP 2022 paper.
• Explores ray tracing hardware as an alternative acceleration path to standard CPU/CUDA approximate nearest neighbor (ANN) indexes for similarity search workloads.

Implementation & Technology

• Built primarily on Nvidia OptiX (ray tracing programming interface).
• Uses CUDA to parallelize many non-ray-tracing helper functions.
• Includes required OptiX SDK 7.1.0 headers directly in the repository (no separate SDK install required for those headers).
• Repository structure:
  • include/ – OptiX headers (copied from OptiX SDK 7.1.0, unmodified).
  • src/ – Source code for RTNN implementation.

Design Considerations

• Search radius r even in KNN:
  • Reflects practical need to discard far-away neighbors whose influence (e.g., physical forces) is negligible.
• Bound on K even in range search:
  • Controls memory usage.
  • Conforms to downstream tasks that expect a fixed or bounded number of neighbors.

Research Context

• Technical design and evaluation are discussed in a PPoPP 2022 paper: https://horizon-lab.org/pubs/ppopp22.pdf.
• Performance has been significantly improved since the version described in the paper.

Requirements

• Nvidia GPU with RT cores (supports hardware ray tracing).
• OptiX 7.1.0 headers are included; external installation of the SDK is not strictly required for those headers.
• CUDA environment for building and running helper kernels.

Pricing

• Open-source library (license included in LICENSE file in the repository).
• No pricing information is provided; usage is governed by the repository’s open-source license.