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This paper presents GPU parallelization for a computational fluid dynamics solver which works on a mesh consisting of polyhedral cells, where each cell has an arbitrary number of faces and each face has an arbitrary number of vertices. The parallelization is achieved using NVIDIAs compute unified device architecture (CUDA). The developed code specifically targets performance improvement on NVIDIA...
This paper presents an approach for parallel implementation of cross-correlation using the graphics processing unit (GPU). Cross-correlation is a central digital signal processing (DSP) algorithm with applications in many areas. In many cases in real time systems, a sequential implementation of the cross-correlation creates a performance bottleneck and prevents the systems from reaching the real time...
In this paper, we propose to investigate the compromise of the relative gains between the computation time and the energy consumption on Graphics Processing Unit (GPU).The energy consumed by GPU can not be neglected anymore, even if code acceleration is the main target. We aim to propose a prediction model of both time computation and energy consumption of linear algebra operations. Numerical experiments...
The use of the GPU as a general purpose processor is becoming more popular and there are different approaches for this kind of programming. In this paper we present a comparison between different implementations of the OpenGL and CUDA approaches for solving our test case, a weighted Jacobi iteration with a structured matrix originating from a finite element discretization of the elliptic PDE part...
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