In this paper we present an efficient method to compute the signed distance field for a large triangle mesh, which can run interactively with GPU accelerated. Restricted by absence of flexible pointer addressing on GPU, we design a novel multi-layer hash table to organize the voxel/triangle overlap pairs as two-tuples, such strategy provides an efficient way to store and access. Based on the ge...

High Performance Computing (HPC) has recently been considerably improved, for instance General Purpose computation on Graphics Processing Units (GPGPU) has been developed to accelerate parallel computing by using hundreds of cores simultaneously. GPU computing with Compute Unified Device Architecture (CUDA) is a new approach to solve complex problems and transform the GPU into a massively paral...

Multi-GPU implementations of the Lattice Boltzmann method are of practical interest as they allow the study of turbulent flows on large-scale simulations at high Reynolds numbers. Although programming GPUs, and in general power-efficient accelerators, typically guarantees high performances, the lack of portability in their low-level programming models implies significant efforts for maintainabi...

Graphics Processing Units (GPUs) are high performance co-processors originally intended to improve the use and quality of computer graphics applications. Since researchers and practitioners realized the potential of using GPU for general purposes, their applications have been extended to other fields, out of computer graphics scope. The main objective of this paper is to evaluate the impact of ...

A parallel algorithm for image noise removal is proposed. The algorithm is based on peer group concept and uses a fuzzy metric. An optimization study on the use of the CUDA platform to remove impulsive noise using this algorithm is presented. Moreover, an implementation of the algorithm on multi-core platforms using OpenMP is presented. Performance is evaluated in terms of execution time and a ...