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RayJoin

RayJoin utilizes the ray tracing hardware in modern GPUs (e.g., NVIDIA RT Cores) as accelerators to achieve high-performance spatial join processing. Specifically, RayJoin consists of a high-performance and high-precision spatial join framework that accelerates two vital spatial join queries: line segment intersection (LSI) and point-in-polygon test (PIP). Polygon overlay analysis is also supported by combining the query results of LSI and PIP. Besides these ray tracing-backed algorithms, RayJoin also contains new solutions to address two challenging technical issues: (1) how to meet the high precision requirement of spatial data analysis with the insufficient precision support by the underlying hardware, and (2) how to reduce the high buildup cost of the hardware-accelerated index, namely Bounding Volume Hierarchy (BVH), while maintaining optimal query exe- cution times. RayJoin achieves speedups from 3.0x to 28.3x over any existing highly optimized methods in high precision. To the best of our knowledge, RayJoin stands as the sole solution capable of meeting the real-time requirements of diverse workloads, taking under 460ms to join millions of polygons.

Build

Install Dependencies

(1) gflags

(2) glog

(3) NVIDIA Optix 8.0

(4) NVIDIA CUDA 12.3+

(5) NVIDIA Driver 530+

Install gflags and glog: sudo apt install libgflags-dev libgoogle-glog-dev

Install Optix:

export OPTIX_HOME=~/optix

mkdir -p $OPTIX_HOME
./NVIDIA-OptiX-SDK-8.0.0-linux64-x86_64.sh --prefix=$OPTIX_HOME --exclude-subdir --skip-license

Building Instructions

  • Debug (Building the project under the debug mode enables some counter to profile RayJoin)
mkdir debug
cd debug
cmake -DCMAKE_BUILD_TYPE=Debug -DCMAKE_PREFIX_PATH=$OPTIX_HOME ..
make
  • Release
mkdir release
cd release
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_PREFIX_PATH=$OPTIX_HOME ..
make

After the project is successfully built, two binary called polyover_exec and query_exec will be generated under the bin of the building path.

Dataset Preparation

Download Datasets

We do not provide preprocessed datasets for now, because uploading datasets will expose our identification. You need to download and process by yourself. A very small sample dataset is included under the test folder, which allows you to try out RayJoin.

Datasets format

RayJoin requires CDB format to work, which is described in this paper. We may support more formats in the future, but for now, only CDB format is supported. This format allows polygons storing in chains to save space. The chain also carries neighboring information of polygons, which makes point-in-polygon (PIP) test easier.

<chain id> <number of points in the chain> <first point id> <last point id> <left face id> <right face id>
<point coordinates>

Example:

1 2 0 1 1 8
-3.6580000000e+01 -4.6636000000e+00
-3.6094300000e+01 -1.3593000000e+00
2 2 0 16657 925 4
-3.6580000000e+01 -4.6636000000e+00
-3.6594300000e+01 -4.8691000000e+00

We provide preprocessed datasets . You can also generate CDB datasets with the following steps:

  1. If you use the datasets from ArcGIS hub, you download and save them into shapefile. If the datasets are from SpatialHadoop website, they are in the Well-known Text (WKT) format, and you need to convert them into shapefile with misc/wkt2shp.py input.wkt output.shp.
  2. Load the shapefile in ArcGIS, and go through the two following steps. We need to convert polygons in shapefile into polylines with neighboring information.
  • Polygon To Line. Make sure "Identify and store polygon neighboring information" is checked.
  • Feature Class To Shapefile. This step dumps polylines to a shapefile.
  1. Run the script misc/shp2cdb.py input.shp output.cdb

Evaluate RayJoin

Parameters

  • -poly1 path of the base map (R) in CDB format
  • -poly2 path of the query map (S) in CDB format
  • -mode implementation used to run the query, including grid, lbvh, rt
  • -xsect_factor reserve a queue to store LSI results. The queue capacity is calculated by |R|*|S|*xsect_factor
  • -query query type, which can be lsi or pip. This parameter only works for query_exec
  • -check compare results with the grid implementation. Only works when the mode is rt
  • -output output path of polygon overlay results. Only works for polyover_exec

Run LSI and PIP Queries

./query_exec -poly1 base_map.cdb \
    -poly2 query_map.cdb \
    -mode=grid/lbvh/rt \
    -xsect_factor=0.1 \ 
    -warmup=5 \ # warmup rounds
    -repeat=5 \ # number of rounds to evaluate. Average time is reported
    -query=lsi/pip

Run Overlay Analysis

./polyover_exec -poly1 dataset1.cdb \
    -poly2 dataset2.cdb \
    -serialize=/dev/shm \ # Serialize CDB to binary format, which saves loading time.
    -mode=grid/rt \
    -check=true \ # Compare results with the uniform grid implementation
    -xsect_factor=0.5

Examples

We provided a sample dataset and test script under test, which allows you to try out RayJoin without figuring out what these parameters work for. Be sure you have built the project in debug and release mode before run the script.

Trouble Shooting

  1. Fix weird bug after changing rt code: rm -rf /var/tmp/OptixCache_${USER}
  2. To enable printf in Optix Kernel: export OPTIX_FORCE_DEPRECATED_LAUNCHER=1

Publication

Please cite this paper if you are using RayJoin.

@inproceedings{geng2024rayjoin,
author = {Geng, Liang and Lee, Rubao and Zhang, Xiaodong},
title = {RayJoin: Fast and Precise Spatial Join},
year = {2024},
isbn = {9798400706103},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3650200.3656610},
doi = {10.1145/3650200.3656610},
booktitle = {Proceedings of the 38th ACM International Conference on Supercomputing},
pages = {124–136},
numpages = {13},
keywords = {Hardware Acceleration, Ray Tracing, Spatial Join, Spatial- and Geo-Databases},
location = {<conf-loc>, <city>Kyoto</city>, <country>Japan</country>, </conf-loc>},
series = {ICS '24}
}

References

  1. OVERPROP helps us a lot for the polygon overlay implementation.
  2. We use this library as the LBVH implementation.

cuSpatial Evaluation (Updated on Aug 2024)

As we prepare the ICS paper, the latest version of cuSpatial is 23.12.00, which contains a bug that causes out-of-memory errors when processing large datasets. This issue arises due to the conservative memory allocation implementation in cuSpatial. To work around this bug, we process the queries in a batch fashion. However, this workaround significantly increases running time due to additional I/O, index construction, and other overheads. This bug has been fixed in cuSpatial 24.06. Recently, we have reevaluated cuSpatial on PIP queries. Below are the updated results.

PIP Running Time County ⋈ Zipcode Block ⋈ Water LKAF ⋈ PKAF LKAS ⋈ PKAS LKAU ⋈ PKAU LKEU ⋈ PKEU LKNA ⋈ PKNA LKSA ⋈ PKSA
cuSpatial 24.06 6572.397ms 3527.945ms 351.548ms 2035.707ms 236.781ms 6830.831ms 38799.309ms 324.018ms