基于GPU的球面深度图实时绘制

提出了一种GPU加速的实时基于图像的绘制算法.该算法利用极坐标系生成对物体全方位均匀采样的球面深度图像;然后根据推导的两个预变换公式将单幅球面深度图像预变换到物体包围球的一个与视点相关的切平面上,以生成中间图像;再利用纹理映射生成最终目标图像.利用现代图形硬件的可编程性和并行性,将预变换移植到Vertex Shader来加快绘制速度;利用硬件的光栅化功能来完成图像的插值,以得到连续无洞的结果图像.此外,还在Pixel Shader上进行逐像素的光照以及环境映射的计算,生成高质量的光照效果.最终,文章解决了算法的视点受限问题,并设计了一种动态LOD(Level of Details)算法,实现了一个实时漫游系统,保持了物体间正确的遮挡关系.

基于GPU的近似软影实时绘制

通过对阴影图算法进行扩展,提出一种完全基于GPU的近似软影实时绘制算法,它是一种3遍算法:第一遍从光源中心计算场景的深度图;第二遍采用几何着色器提取物体的轮廓边,同时在轮廓边上生成新的几何图元,利用硬件自动插值功能向外绘制线性近似半影图,并根据第一遍得到的深度图在像素着色器中对背面轮廓形成的半影区进行剔除;对于重叠的半影区设定片元的伪深度值,利用硬件进行自动融合.第三遍分别查询深度图和半影图,确定场景的本影区以及半影区中像素的亮度,从而得到面光源照射下场景的近似软影效果.

Parallel Fully-Implicit Computation of Magnetohydrodynamics Acceleration Experiments

A three-dimensional MHD solver is described in the paper. The solver simulates reacting flows with nonequilibrium between translational-rotational, vibrational and electron translational modes. The conservation equations are discretized with implicit time marching and the second-order modified Steger-Warming scheme, and the resulted linear system is solved iteratively with Newton-Krylov-Schwarz method that is implemented by PETS,: package. The results of convergence tests arc plotted, which show good scalability and convergence around twice faster when compared with the DPLR method. Then five test runs are conducted simulating the experiments done at the NASA Ames MHD channel, and the calculated pressures, temperatures, electrical conductivity, back EMF, load factors and flow accelerations are shown to agree with the experimental data. Our computation shows that the electrical conductivity distribution is not uniform in the powered section of the MHD channel, and that it is important to include Joule heating in order to calculate the correct conductivity and the MHD acceleration.

Single-walled carbon nanotubes binding to human telomeric i-motif DNA: significant acceleration of S1 nuclease cleavage rate

Single-walled carbon nanotubes (SWNTs) binding to human telomeric i-motif DNA can significantly accelerate S1 nuclease cleavage rate by increasing the enzyme turnover number.

Plasma acceleration above Martian magnetic anomalies

Grande, Manuel, et al., 'Plasma acceleration above Martian magnetic anomalies', Science (2006) 311(5763) pp.980-983 RAE2008

Understanding GPU programming for statistical computation: Studies in massively parallel massive mixtures

This article describes advances in statistical computation for large-scale data analysis in structured Bayesian mixture models via graphics processing unit (GPU) programming. The developments are partly motivated by computational challenges arising in fitting models of increasing heterogeneity to increasingly large datasets. An example context concerns common biological studies using high-throughput technologies generating many, very large datasets and requiring increasingly high-dimensional mixture models with large numbers of mixture components.We outline important strategies and processes for GPU computation in Bayesian simulation and optimization approaches, give examples of the benefits of GPU implementations in terms of processing speed and scale-up in ability to analyze large datasets, and provide a detailed, tutorial-style exposition that will benefit readers interested in developing GPU-based approaches in other statistical models. Novel, GPU-oriented approaches to modifying existing algorithms software design can lead to vast speed-up and, critically, enable statistical analyses that presently will not be performed due to compute time limitations in traditional computational environments. Supplementalmaterials are provided with all source code, example data, and details that will enable readers to implement and explore the GPU approach in this mixture modeling context. © 2010 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.