The new SCIARA-fv3 numerical model and acceleration by GPGPU strategies

This paper presents the parallel implementation, using the Compute Unified Device Architecture (CUDA) architecture, of the SCIARA-fv3 Complex Cellular Automata model for simulating lava flows. The computational model is based on a Bingham-like rheology and both flow velocity and the physical time corresponding to a computational step have been made explicit. The parallelization design has involved, among other issues, the application of strategies that can avoid incorrect computation results due to race conditions and achieving the best performance and occupancy of the underlying available hardware. Two hardware types were adopted for testing different versions of the CUDA implementations of the SCIARA-fv3 model, namely the GTX 580 and GTX 680 graphic processors. Despite its computational complexity, carried out experiments of the model parallelization have shown significant performance improvements, confirming that graphic hardware can represent a valid solution for the implementation of Cellular Automata models.

Using Graphic Hardware to Accelerate Pocketing Tool-Path Generation

In this paper, we propose a new approach to accelerate the pocketing tool-path generation by using graphic hardware (graphic processing units, GPU). The intersections among tool-path elements can be eliminated with higher efficiency from GPU-based Voronoi diagrams. According to our experimental results, the GPU-based computation speed was seven to eight times faster than that of CPU-based computation. In addition, the difference of tool-path geometry between the CPU-based and GPU-based methods was insignificant. Therefore, the GPU-method can be efficiently used to accelerate the computation while the precision is assured for the tool-path generation in pocketing machining.

An Efficient Compressed Volume Rendering Algorithm Based on GPU

An efficient compressed volume rendering algorithm is presented. Firstly, the original volume data is compressed by a content-based classified hierarchical vector quantization algorithm. Secondly, the compressed volume data is then transferred to Graphic Processing Unit and decompressed in real time, subsequently, the decompressed data is rendered by a three-dimensional textures mapping method to accelerate the speed of rendering. Experimental results show that, in addition to reasonable fidelity and faster rendering speed, the presented algorithm can obtain multiple levels of detail on the off-the-shelf graphic hardware.