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Solid voxelization represents the process of transforming a polygonal mesh into a voxel representation by associating each polygon of a mesh with the cells in the voxel grid. We introduce a novel approach for the voxelization of solid objects, designed for Graphics Processing Units (GPU). The method is based on a heuristic approach that computes an approximate distance field instead of using mesh...
During the past decade Graphics Processing Units (GPU) have been increasingly employed for speeding up compute intensive scientific applications. In this field, the geometric multigrid method (GMG) is one of the most efficient algorithms for solving large sparse linear systems of equations. Herein we analyze the performance of an optimized GPU based implementation of the GMG method on different state-of-the-art...
Scientific applications are typically compute intensive, often due to the requirement of solving large sparse linear systems of equations. The geometric multigrid method (GMG) is one of the most efficient algorithms for solving these systems and is well suited for parallelization. Herein we focus on an in-depth analysis of a GPU-based GMG implementation and compare the results against an optimized...
In this paper we introduce a methodology for performing one-way Fluid-Structure interaction (FSI), i.e. where the motion of the wall boundaries is imposed. We use a Graphics Processing Unit (GPU) accelerated Lattice-Boltzmann Method (LBM) implementation and present an efficient workflow for embedding the moving geometry, given as a set of polygonal meshes, in the LBM computation. The proposed method...
Stencil based algorithms are used intensively in scientific computations. Graphics Processing Units (GPU) based implementations of stencil computations speed-up the execution significantly compared to conventional CPU only systems. In this paper we focus on double precision stencil computations, which are required for meeting the high accuracy requirements, inherent for scientific computations. Starting...
We propose a numerical implementation based on a Graphics Processing Unit (GPU) for the acceleration of the execution time of the Lattice Boltzmann Method (LBM). The study focuses on the application of the LBM for patient-specific blood flow computations, and hence, to obtain higher accuracy, double precision computations are employed. The LBM specific operations are grouped into two kernels, whereas...
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