Made a Rust DB run spatial queries on gaming GPU RT cores, beating an H100

# SedonaDB 0.4: GPU-Accelerated Spatial Joins Source: [https://sedona.apache.org/latest/blog/2026/06/26/sedonadb-04-gpu-accelerated-spatial-joins/](https://sedona.apache.org/latest/blog/2026/06/26/sedonadb-04-gpu-accelerated-spatial-joins/) [https://github.com/apache/sedona/edit/master/docs/blog/posts/raybooster-gpu-spatial-join.md](https://github.com/apache/sedona/edit/master/docs/blog/posts/raybooster-gpu-spatial-join.md)In SedonaDB 0\.4, we taught this Rust database to run spatial joins on your $1,500 gaming GPU's ray tracing cores, and it beats an H100\.  The Apache Sedona community released[SedonaDB](https://sedona.apache.org/sedonadb)0\.4\.0, resolving 187 issues and adding 26 new functions from 15 contributors\. SedonaDB is the first open\-source, single\-node analytical database that treats spatial data as a first\-class citizen — the counterpart to the distributed Sedona engines for small\-to\-medium datasets running on a single machine\. This is the first in a series of posts diving into what's new in SedonaDB 0\.4\. We'll be covering more of the release — the Python DataFrame API, the R dplyr interface, Geography support, GeoParquet write support, N\-dimensional rasters and Zarr, and more — in the posts to come; for the full rundown, see the[0\.4\.0 release blog post](https://sedona.apache.org/latest/blog/2026/06/19/sedonadb-040-release/)\. We're kicking things off with the feature we're most excited about: GPU\-accelerated spatial joins\. ## GPU\-Accelerated Spatial Joins[¶](https://sedona.apache.org/latest/blog/2026/06/26/sedonadb-04-gpu-accelerated-spatial-joins/#gpu-accelerated-spatial-joins)  Gaming GPUs contain dedicated ray tracing cores designed for video game lighting — and they sit idle during database queries\. Spatial joins are about finding intersecting geometries, which maps naturally onto ray tracing primitives\. We built**RayBooster**, an extension that brings ray tracing core acceleration into SedonaDB\. The accompanying research paper,[*"RayBooster: A Ray Tracing Engine to Accelerate SedonaDB,"*](https://jiayuasu.github.io/files/paper/sedona_db_gpu_vldb_2026.pdf)was accepted to**VLDB 2026**\(Industry Track\), developed in collaboration with The Ohio State University\. ### How it works: four components[¶](https://sedona.apache.org/latest/blog/2026/06/26/sedonadb-04-gpu-accelerated-spatial-joins/#how-it-works-four-components)  **1\. GPU\-friendly storage layout\.**Instead of the stream\-oriented WKB format, RayBooster uses a Structure of Arrays organization that separates offsets, vertices, and types, enabling O\(1\) random access to any geometry\. **2\. A single monolithic index\.**Rather than building millions of tiny index trees, it uses*Z\-stacking*— encoding each geometry's ID into the unused Z\-axis of the ray tracing scene and building one global BVH for the entire batch\. **3\. A universal predicate engine\.**`RelateEngine`computes the DE\-9IM matrix \(a topological descriptor\) on RT cores, giving one code path that resolves any geometry/predicate combination instead of hardcoding 500\+ kernel variants\. **4\. Memory\-aware execution\.**A scheduling and spilling layer keeps joins within GPU memory budgets on irregular real\-world workloads, preventing out\-of\-memory failures\.  ## Performance[¶](https://sedona.apache.org/latest/blog/2026/06/26/sedonadb-04-gpu-accelerated-spatial-joins/#performance) Testing on SpatialBench: - **Up to 5\.93x speedup**on heavy joins, with a 59\.02% cost reduction on AWS - **Q11 cross\-zone trip join**: 7\.51s \(CPU\) → 1\.61s on a consumer RTX 3090 — a 4\.66x speedup - **10x scale**: 53\.34s reduced to under 7s - **Heavy joins at scale**: 4\.93x to 9\.68x speedups across GPU models - **Consumer RTX 3090 vs\. H100**: on some queries the gaming card actually beat the H100 \(1\.26s vs 1\.77s on Q10\), despite the H100 lacking RT cores  ## Using it[¶](https://sedona.apache.org/latest/blog/2026/06/26/sedonadb-04-gpu-accelerated-spatial-joins/#using-it) On a machine with an NVIDIA GPU, pull the official Docker image and enable the feature with a single command: ``` ctx.sql("SET gpu.enable = true") ``` The[GPU Acceleration guide](https://sedona.apache.org/sedonadb/latest/gpu-acceleration/)walks through launching the Docker image on NVIDIA GPU machines and lists the supported compute capabilities\. ## Citation[¶](https://sedona.apache.org/latest/blog/2026/06/26/sedonadb-04-gpu-accelerated-spatial-joins/#citation) Liang Geng, Rubao Lee, Dewey Dunnington, Feng Zhang, Jia Yu, and Xiaodong Zhang\.["RayBooster: A Ray Tracing Engine to Accelerate SedonaDB\."](https://jiayuasu.github.io/files/paper/sedona_db_gpu_vldb_2026.pdf)PVLDB, 2026 \(Industry Track\)\.