基于GPU编程的地形可视化

由于地形模型固有的复杂性,致使计算机硬件水平一直难以满足大规模地形模型的实时显示需求。为了在现有的硬件水平上实现地形模型的快速绘制,在对传统的ROAM算法进行改进的基础上,提出一种基于GPU编程的地形可视化算法,实现了视点依赖的大规模地形的快速可视化。该算法首先基于改进的ROAM(real-time optimallyadaptive meshes)算法生成视点依赖的优化连续LOD模型;然后用GPU编程计算顶点的变换、法向量、纹理坐标、纹理采样和面元光照;最后完成地形的着色。实验结果表明,利用GPU编程不仅能有效提高算法速度,而且能实现较大规模地形的实时漫游。     

基于GPU的海量城市管线高效建模与实时绘制

地理信息系统行业积累了海量基于二维矢量的管线数据,文中针对传统的基于CPU的多层次细节预处理三维建模及绘制方法存在质量和效率方面的不足,从管线二维矢量数据的形状特征出发,提出一种无需预处理的、可一次性在GPU中装载并处理城市规模管线数据的三维建模与实时绘制算法.该算法利用现代GPU可编程硬件的特性来实现,在GPU上对管线形状进行解码,在顶点着色器采用2个剪枝策略进行场景的可见性剔除计算,然后基于细分着色器对管线曲面进行多层次细节几何自动建模,全过程无需CPU干预.实验结果表明,文中算法适用于城市级别的海量管网数据,相比于已有的方法,绘制效果和多项性能指标得到了大幅提升.     

基于旋转矢量法的三维管线建模

管线三维可视化的一个重点问题是对于从原有二维管线中心线数据中计算提取出管线表面顶点的坐标,难点在于弯管处的表面处理。文中提出基于旋转矢量法的三维管线建模方法,能够生成连续的三维管线,并可以控制管线的精细程度。实验表明此方法便于理解,计算量小,生成的管线表面平滑。     

使用GPU编程的光线投射体绘制算法

将传统的光线投射体绘制算法在具有可编程管线的图形处理器(GPU)上重新实现.首先将体数据作为三维纹理保存在显存中,然后通过编写顶点程序和片段程序将光线进入点/离开点计算和光线遍历的计算移入GPU中执行,最后根据不同的采样点颜色混合公式实现不同的绘制效果.文中算法仅需绘制一个四边形即可完成三维重建.实验结果表明:在进行光照效果的重建时,该算法能够达到实时交互的绘制要求,并能实现半透明等复杂绘制效果.     

基于GPGPU的实体表面实时提取

为实时提取三维实体表面,提出一种基于GPGPU并行计算的实体表面实时提取方法。在分析深度剥离算法原理和GPU图形绘制管线的基础上,给出在GPU上利用深度剥离算法实现实时提取三维实体表面的算法;通过OpenGL的高级着色语言GLSL控制GPU的图形绘制管线实现了该算法,给出其伪代码。以龙、叶轮和刀具扫描体的模型为应用实例验证了该算法效果良好,特别是对于刀具扫描体表面的提取,可满足实时性要求。     

基于CUDA海量空间数据实时体绘制研究

针对海量空间科学数据的精细及实时三维绘制需求,提出并实现了一种基于CUDA语言的并行化光线投射体绘制加速算法,利用传统体绘制算法中光线投射法的可并行特点和GPU中高速的纹理查询的优点,通过一个实际坐标到纹理坐标的转换函数实现了对不规则采样数据的准确采样,并完成了绘制算法的CUDA并行化改造,通过CUDA语言利用GPU强大的并行计算能力实现了对海量空间数据的实时三维光线投射绘制.     

基于GPU的彩色显微光学图像快速三维可视化

彩色显微光学图像三维可视化计算量较大,针对基于CPU单线程串行计算的可视化方法无法满足实时显示要求,提出一种基于图形处理器(GPU)的显微彩色图像快速三维可视化算法。该方法采用最大密度投影函数(MIP)实现可视化,通过插值计算通过物体光线上等间距点的RGB值,取其最大亮度点的RGB作为该光线对应像素的颜色值。以上过程通过构筑的内核函数在GPU上以多线程方法完成,最终使用Open GL直接绘制投影图像。利用激光共聚焦显微镜获得的小鼠肾细胞彩色图像和多层细胞样本进行算法验证。实验结果表明,与基于CPU的单线程串行计算方法相比,基于GPU的可视化方法在显示效果一致的前提下,计算速度提高了90倍。该方法极大提升了显微图像处理过程中的实时显示性能。     

基于增强现实的地下管线真实感可视化方法

为便于城市地下管线的铺设与维护,提出一种基于增强现实的地下管线真实感可视化方法,将原本不可见的虚拟地下管线真实地呈现在视频画面中.首先将屏幕空间分割为对地下管线具有不同遮挡语义的4个区域;然后借鉴工程图学中的剖视图方法,采用沿地下管线走向的坑道式剖切和地表面的竖直剖切方法来展示被遮挡的地下管线;进而根据画面的遮挡语义,运用视频中地表遮挡物和地下管线的重要视觉特征,揭示地下管线与地表物体之间的正确遮挡关系;最后利用渐变融合对地下隐藏信息与真实场景视频进行合成.实验结果表明,该方法不依赖于三维场景的精确几何重建,可在GPU上实时实现.     

基于深度学习的城市建筑三维可视化系统研究

为提升三维建模建模效率和可视化效果,在深度学习的基础上,设计一个基于Mask R-CNN的三维可视化模型。首先利用Mask R-CNN算法对图像进行分割,选择ThreeJS作为模型的Web三维可视化框架;然后构建基于Mask R-CNN算法三维可视化模型,对模型多次进行训练和测试后,得到相应的建模图像,将该图像进行配准和坐标定位后,将其代入至适宜的建模场景中,最后完成建筑的快速建模。结果表明,提出的Mask R-CNN方法对图像检出率为98.13%,误检率为0,漏检率为1.87%,算法性能优越,建模效率高,可降低建模成本,提升城市建筑三维可视化效果。     

基于GPU的实时光线投射算法

介绍了一种基于GPU（可编程图形处理单元）的快速实时光线投射算法。为满足大规模体数据集的绘制要求,利用当前GPU的新特性,直接将体数据作为纹理载入显存,采用预积分分类方法在GPU中对体数据进行重采样和分类,避免了计算机主内存与GPU纹理内存之间数据交换的瓶颈问题;利用硬件支持的三维纹理和片元着色器,实时计算每个体素的梯度,实现高质量的光照,保证高质量的图像绘制效果。实验结果表明该方法在医学三维数据场可视化中,能够实时、高效地生成高质量的交互式体可视化图像。     

通过GPU加速数据挖掘的研究进展和实践

将计算密度高的部分迁移到GPU上是加速经典数据挖掘算法的有效途径。首先介绍GPU特性和主要的GPU编程模型,随后针对数据挖掘主要任务类型分别介绍基于GPU加速的工作,包括分类、聚类、关联分析、时序分析和深度学习。最后分别基于CPU和GPU实现协同过滤推荐的两类经典算法,并基于经典的MovieLens数据集的实验验证GPU对加速数据挖掘应用的显著效果,进一步了解GPU加速的工作原理和实际意义。     

LU分解和Laplace算法在GPU上的实现

随着图形处理器(GPU)性能的大幅度提升以及可编程性的发展,已经有许多算法成功地移植到GPU上.LU分解和Laplace算法是科学计算的核心,但计算量往往很大,由此提出了一种在GPU上加速计算的方法.使用Nvidia公司的统一计算设备架构(CUDA)编程模型实现这两个算法,通过对CPU与GPU进行任务划分,同时利用GP...     

Solving path problems on the GPU

We consider the computation of shortest paths on Graphic Processing Units (GPUs). The blocked recursive elimination strategy we use is applicable to a class of algorithms (such as all-pairs shortest-paths, transitive closure, and LU decomposition without pivoting) having similar data access patterns. Using the all-pairs shortest-paths problem as an example, we uncover potential gains over this class of algorithms. The impressive computational power and memory bandwidth of the GPU make it an attractive platform to run such computationally intensive algorithms. Although improvements over CPU implementations have previously been achieved for those algorithms in terms of raw speed, the utilization of the underlying computational resources was quite low. We implemented a recursively partitioned all-pairs shortest-paths algorithm that harnesses the power of GPUs better than existing implementations. The alternate schedule of path computations allowed us to cast almost all operations into matrix–matrix multiplications on a semiring. Since matrix–matrix multiplication is highly optimized and has a high ratio of computation to communication, our implementation does not suffer from the premature saturation of bandwidth resources as iterative algorithms do. By increasing temporal locality, our implementation runs more than two orders of magnitude faster on an NVIDIA 8800 GPU than on an Opteron. Our work provides evidence that programmers should rethink algorithms instead of directly porting them to GPU.     

基于GPU加速的多物体碰撞检测方法

为了在虚拟环境中更加真实地模拟现实环境中物体的运动,需要在仿真系统中加入碰撞检测模块。现有的碰撞检测算法虽然能够快速检测两个物体是否相交,但在物体数量非常多的场景中,因需要对物体两两进行判断,所以仍无法达到较高的检测速度。利用GPU并行计算的特性,在GPU上增加一个预先剔除的过程,大幅度地快速排除不相交的物体,提高了检测的速度。     

Multi-level graph layout on the GPU

This paper presents a new algorithm for force directed graph layout on the GPU. The algorithm, whose goal is to compute layouts accurately and quickly, has two contributions. The first contribution is proposing a general multi-level scheme, which is based on spectral partitioning. The second contribution is computing the layout on the GPU. Since the GPU requires a data parallel programming model, the challenge is devising a mapping of a naturally unstructured graph into a well-partitioned structured one. This is done by computing a balanced partitioning of a general graph. This algorithm provides a general multi-level scheme, which has the potential to be used not only for computation on the GPU, but also on emerging multi-core architectures. The algorithm manages to compute high quality layouts of large graphs in a fraction of the time required by existing algorithms of similar quality. An application for visualization of the topologies of ISP (Internet Service Provider) networks is presented.     

Parallel Marching Blocks: A Practical Isosurfacing Algorithm for Large Data on Many‐Core Architectures

Interactive isosurface visualisation has been made possible by mapping algorithms to GPU architectures. However, current state‐of‐the‐art isosurfacing algorithms usually consume large amounts of GPU memory owing to the additional acceleration structures they require. As a result, the continued limitations on available GPU memory mean that they are unable to deal with the larger datasets that are now increasingly becoming prevalent. This paper proposes a new parallel isosurface‐extraction algorithm that exploits the blocked organisation of the parallel threads found in modern many‐core platforms to achieve fast isosurface extraction and reduce the associated memory requirements. This is achieved by optimising thread co‐operation within thread‐blocks and reducing redundant computation; ultimately, an indexed triangular mesh can be produced. Experiments have shown that the proposed algorithm is much faster (up to 10×) than state‐of‐the‐art GPU algorithms and has a much smaller memory footprint, enabling it to handle much larger datasets (up to 64×) on the same GPU.     

基于Irrlicht引擎的实时浅水效果模拟

本文在Irrlicht引擎的基础上结合GLSL语言,对浅水的水面波动、反射与折射特性及菲涅尔现象进行了实时模拟。将基于Gestner波的水面波动与基于纹理波的动态法线贴图相结合,真实地模拟了水面的波动与波纹效果。同时,采用渲染到纹理技术实时生成反射贴图,准确地模拟了水面的反射效果,解决了采用环境贴图渲染时的反射失真问题。在不考虑水深的情况下,本文将菲涅尔权值与材质的alpha通道相结合,去掉了实时渲染中折射贴图的生成,在满足实时浅水效果渲染的视觉需求条件下,减小了CPU与GPU的计算量。     

图形处理器对ADI-FDTD算法的加速作用研究

随着图形处理器(GPU)性能的大幅度提高以及可编程特性的发展,将通用数值算法的某些处理阶段从 CPU 迁移到 GPU 上已成为可能,从而达到加速计算的目的。首先简要介绍了一种常见的数值计算方法：交变方向隐式时域有限差分法（ADI-FDTD);然后详细论述了利用GPU加速ADI-FDTD计算的基本原理与关键技术,并给出了在GPU上求解ADI-FDTD线性方程组的共轭梯度法实现框架;最后,通过具体的计算实例和相关的性能比较验证了这种加速算法的精确性与效率特点。     

Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform

The introduction of NVidia’s powerful Tesla GPU hardware and Compute Unified Device Architecture (CUDA) platform enable many-core parallel programming. As a result, existing algorithms implemented on a GPU can run many times faster than on modern CPUs. Relatively little research has been done so far on GPU implementations of discrete optimisation algorithms. In this paper, two approaches to parallel GPU evaluation of the Permutation Flowshop Scheduling Problem, with makespan and total flowtime criteria, are proposed. These methods can be employed in most population-based algorithms, e.g. genetic algorithms, Ant Colony Optimisation, Particle Swarm Optimisation, and Tabu Search. Extensive computational experiments, on Tabu Search for Flowshop with both criteria, followed by statistical analysis, confirm great computational capabilities of GPU hardware. A GPU implementation of Tabu Search runs up to 89 times faster than its CPU counterpart.     

Monte Carlo simulation of X-ray and gamma-ray photon transport on a graphics-processing unit

Graphics-processing units (GPUs) suitable for general-purpose numerical computation are now available with performances in excess of 1 Teraflops, faster by one to two orders of magnitude than conventional desktop CPUs. Monte Carlo particle transport algorithms are ideally suited to parallel processing architectures and so are good candidates for acceleration using a GPU. We have developed a general-purpose code that computes the transport of high energy (>1 keV) photons through arbitrary 3-dimensional geometry models, simulates their physical interactions and performs tallying and variance reduction. We describe a new algorithm, the particle-per-block technique, that provides a good match with the underlying GPU multiprocessor hardware design. Benchmarking against an existing CPU-based simulation running on a single-core of a commodity desktop CPU demonstrates that our code can accurately model X-ray transport, with an approximately 35-fold speed-up factor.