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For most practical image processing systems, people often make the tradeoff between image processing quality and algorithm time complexity, because the best algorithms cannot meet the real-time requirement However, with the introduction and development of CUDA (Compute Unified Device Architecture), GPU (Graphics Processing Unit) is widely used to implement parallel algorithms, which can speed up the...
This study provides a parallel algorithm for meshfree analysis based on the radial point interpolation method. This algorithm is designed to suit graphics hardware that support compute unified device architecture, NVIDIA's software development environment on graphics processing units (GPUs). The radial point interpolation method is a meshfree method that enables strict imposition of Dirichlet boundary...
Parallel computation is application-oriented, particularly for the GPU (Graphics Processing Unit) with the inherent parallelism. This paper shows the architecture of a GPU cluster based on MPI (Message Passing Interface) and CUDA (Compute Unified Device Architecture). Results show that the acceleration ratio is obviously improved but the acceleration effect seems decelerated in large-scale GPU cluster...
The characteristics of modern graphics processing unit (GPU) is programmable, high price / performance ratio and high speed . It has a strong ability to adapt the parallel calculation, Based on this, the article study the general method of GPU calculating and use compute unified device architecture (CUDA) to design new parallel algorithm to accelerate the matrix inversion and binarization algorithm...
Geometric Calibration of digital camera and video is useful for experimental work, such as video surveillance, virtual endoscope etc. High-performance computing holds the key to making biologically relevant calculations tractable without compromise. In this paper we present a new approach to high performance GC algorithm on graphics processing units. Using modern graphics processing units for high...
In this paper, a parallel algorithm to inspect defects of thin film transistor liquid crystal displays (TFT-LCD) panel is presented. The algorithm is implemented on a graphics processing unit (GPU) using the compute unified device architecture (CUDA) development environment. The result shows better performance compared to the one implemented on a CPU.
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