A Steel Bridge Design System Architecture using VR-CAD and Web Service-based Multi-Agents

This paper presents a new design environment based on Multi-Agents and Virtual Reality (VR). In this research, a design system with a virtual reality function was developed. The virtual world was realized by using GL4Java, liquid crystal shutter glasses, sensor systems, etc. And the Multi-Agent CAD system with product models, which had been developed before, was integrated with the VR design system. A prototype system was developed for highway steel plate girder bridges, and was applied to a design problem. The application verified the effectiveness of the developed system

Accelerating the Performance of a Novel Meshless Method Based on Collocation With Radial Basis Functions By Employing a Graphical Processing Unit as a Parallel Coprocessor

In recent times, a variety of industries, applications and numerical methods including the meshless method have enjoyed a great deal of success by utilizing the graphical processing unit (GPU) as a parallel coprocessor. These benefits often include performance improvement over the previous implementations. Furthermore, applications running on graphics processors enjoy superior performance per dollar and performance per watt than implementations built exclusively on traditional central processing technologies. The GPU was originally designed for graphics acceleration but the modern GPU, known as the General Purpose Graphical Processing Unit (GPGPU) can be used for scientific and engineering calculations. The GPGPU consists of massively parallel array of integer and floating point processors. There are typically hundreds of processors per graphics card with dedicated high-speed memory. This work describes an application written by the author, titled GaussianRBF to show the implementation and results of a novel meshless method that in-cooperates the collocation of the Gaussian radial basis function by utilizing the GPU as a parallel co-processor. Key phases of the proposed meshless method have been executed on the GPU using the NVIDIA CUDA software development kit. Especially, the matrix fill and solution phases have been carried out on the GPU, along with some post processing. This approach resulted in a decreased processing time compared to similar algorithm implemented on the CPU while maintaining the same accuracy