LibRTS: A Spatial Indexing Library by Ray Tracing

The Ray-Tracing (RT) core has become a widely integrated feature in modern GPUs to accelerate ray-tracing rendering. Recent research has shown that RT cores can also be repurposed to accelerate non-rendering workloads. Since the RT core essentially serves as a hardware accelerator for Bounding Volume Hierarchy (BVH) tree traversal, it holds the potential to significantly improve the performance of spatial workloads. However, the specialized RT programming model poses challenges for using RT cores in these scenarios. Inspired by the core functionality of RT cores, we designed and implemented LibRTS, a spatial index library that leverages RT cores to accelerate spatial queries. LibRTS supports both point and range queries and remains mutable to accommodate changing data. Instead of relying on a case-by-case approach, LibRTS provides a general, highperformance spatial indexing framework for spatial data processing. By formulating spatial queries as RT-suitable problems and overcoming load-balancing challenges, LibRTS delivers superior query performance through RT cores without requiring developers to master complex programming on this specialized hardware. Compared to CPU and GPU spatial libraries, LibRTS achieves speedups of up to 85.1x for point queries, 94.0x for range-contains queries, and 11.0x for range-intersects queries. In a real-world application, pointin-polygon testing, LibRTS also surpasses the state-of-the-art RT method by up to 3.8x.

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Publication Details

Published
2025-02-28
DOI
https://doi.org/10.1145/3710848.3710850
Citations
2
Primary Topic
Data Management and Algorithms
Type
article
Field-Weighted Citation Impact
2.97
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article

LibRTS: A Spatial Indexing Library by Ray Tracing

Liang Geng, Xiaodong Zhang, Rubao Lee
2 citations
Data Management and Algorithms
2.97
article

LibRTS: A Spatial Indexing Library by Ray Tracing

Liang Geng, Xiaodong Zhang, Rubao Lee
article en
2 citations

Abstract

The Ray-Tracing (RT) core has become a widely integrated feature in modern GPUs to accelerate ray-tracing rendering. Recent research has shown that RT cores can also be repurposed to accelerate non-rendering workloads. Since the RT core essentially serves as a hardware accelerator for Bounding Volume Hierarchy (BVH) tree traversal, it holds the potential to significantly improve the performance of spatial workloads. However, the specialized RT programming model poses challenges for using RT cores in these scenarios. Inspired by the core functionality of RT cores, we designed and implemented LibRTS, a spatial index library that leverages RT cores to accelerate spatial queries. LibRTS supports both point and range queries and remains mutable to accommodate changing data. Instead of relying on a case-by-case approach, LibRTS provides a general, highperformance spatial indexing framework for spatial data processing. By formulating spatial queries as RT-suitable problems and overcoming load-balancing challenges, LibRTS delivers superior query performance through RT cores without requiring developers to master complex programming on this specialized hardware. Compared to CPU and GPU spatial libraries, LibRTS achieves speedups of up to 85.1x for point queries, 94.0x for range-contains queries, and 11.0x for range-intersects queries. In a real-world application, pointin-polygon testing, LibRTS also surpasses the state-of-the-art RT method by up to 3.8x.

The Ohio State University (US)
Openalex Percentile: Top 11%
Data Management and Algorithms
2.97
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