A control-structure splitting optimization for GPGPU
Proceedings of the 6th ACM conference on Computing frontiers
Parallel 3D Image Segmentation of Large Data Sets on a GPU Cluster
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
A lattice Boltzmann method for image denoising
IEEE Transactions on Image Processing
Proceedings of the 24th ACM International Conference on Supercomputing
Proceedings of the 19th international conference on Parallel architectures and compilation techniques
On-the-fly elimination of dynamic irregularities for GPU computing
Proceedings of the sixteenth international conference on Architectural support for programming languages and operating systems
Volumetric nonlinear anisotropic diffusion on GPUs
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Application of Lattice Boltzmann Method to Image Filtering
Journal of Mathematical Imaging and Vision
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Recent progress and challenges in exploiting graphics processors in computational fluid dynamics
The Journal of Supercomputing
Use of GPU computing for uncertainty quantification in computational mechanics: A case study
Scientific Programming
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In this paper, we propose a hardware-accelerated PDE (partial differential equation) solver based on the lattice Boltzmann model (LBM). The LBM is initially designed to solve fluid dynamics by constructing simplified microscopic kinetic models. As an explicit numerical scheme with only local operations, it has the advantage of being easy to implement and especially suitable for graphics hardware (GPU) acceleration. Beyond the Navier–Stokes equation of fluid mechanics, a typical LBM can be modified to solve the parabolic diffusion equation, which is further used to solve the elliptic Laplace and Poisson equations with a diffusion process. These PDEs are widely used in modeling and manipulating images, surfaces and volumetric data sets. Therefore, the LBM scheme can be used as an GPU-based numerical solver to provide a fast and convenient alternative to traditional implicit iterative solvers. We apply this method to several examples in volume smoothing, surface fairing and image editing, achieving outstanding performance on contemporary graphics hardware. It has the great potential to be used as a general GPU computing framework for efficiently solving PDEs in image processing, computer graphics and visualization.