gStar4D computes the 3D Delaunay triangulation on the GPU.

The gStar4D algorithm uses neighbourhood information in the 3D digital Voronoi diagram as an approximation of the 3D Delaunay triangulation. It uses this to perform massively parallel creation of stars of each input point lifted to 4D and employs an unique star splaying approach to splay these 4D stars in parallel and make them consistent. The result is the 3D Delaunay triangulation of the input constructed fully on the GPU.

Our CUDA implementation of gStar4D is robust and achieves a speedup of up to 5 times over the 3D Delaunay triangulator of CGAL.

This project has been primarily tested on Windows 7 64-bit OS using Visual Studio 2008 and CUDA 4.0. This is a workstation with Intel i7 2600K CPU and NVIDIA GTX 580 GPU.

Open the GDelaunay solution file using Visual Studio and compile with the Release build for maximum performance.

CMake is needed to build gStar4D on Linux. Make sure that the CUDA capability values in the CMakeLists.txt file matches that of your GPU.

To build and execute using CMake:

$ cd gstar4d
$ mkdir build
$ cd build
$ cmake ..
$ make
$ ./gstar4d --help

Chapter 6 of my PhD thesis describes the details of the gStar4D algorithm.

To refer to gStar4D, please use:

Ashwin Nanjappa, "Delaunay triangulation in R3 on the GPU", PhD thesis,
School of Computing, National University of Singapore, 2012.

To cite gStar4D, please use this BibTeX entry:

@phdthesis{Ashwin2012GPUDelaunay,
    author = {Nanjappa, Ashwin},
    school = {National University of Singapore},
    title = {Delaunay triangulation in R3 on the {GPU}},
    year = {2012}
}

Author: Ashwin Nanjappa

Project: gStar4D

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