Visualization of level-of-detail meshes on the GPU

Extensive research has been carried out in multiresolution models for many decades. The tendency in recent years has been to harness the potential of GPUs to perform the level-of-detail extraction on graphics hardware. The aim of this work is to present a new level-of-detail scheme based on triangles which is both simple and efficient. In this approach, the extraction process updates vertices instead of indices, thus providing a perfect framework for adapting the algorithms to work completely on GPU shaders. One of the key aspects of our proposal is the need for just a single rendering pass in order to obtain the desired geometry. Moreover, coherence among the different approximations is maximized by means of a symmetric extraction algorithm, which performs the same process when refining and coarsening the mesh. Lastly, we also introduce different uses of the scheme to offer continuous and view-dependent resolution.     

Stardust: Accessible and Transparent GPU Support for Information Visualization Rendering

Web‐based visualization libraries are in wide use, but performance bottlenecks occur when rendering, and especially animating, a large number of graphical marks. While GPU‐based rendering can drastically improve performance, that paradigm has a steep learning curve, usually requiring expertise in the computer graphics pipeline and shader programming. In addition, the recent growth of virtual and augmented reality poses a challenge for supporting multiple display environments beyond regular canvases, such as a Head Mounted Display (HMD) and Cave Automatic Virtual Environment (CAVE). In this paper, we introduce a new web‐based visualization library called Stardust, which provides a familiar API while leveraging GPU's processing power. Stardust also enables developers to create both 2D and 3D visualizations for diverse display environments using a uniform API. To demonstrate Stardust's expressiveness and portability, we present five example visualizations and a coding playground for four display environments. We also evaluate its performance by comparing it against the standard HTML5 Canvas, D3, and Vega.     

Zippy: A Framework for Computation and Visualization on a GPU Cluster

Due to its high performance/cost ratio, a GPU cluster is an attractive platform for large scale general‐purpose computation and visualization applications. However, the programming model for high performance general‐purpose computation on GPU clusters remains a complex problem. In this paper, we introduce the Zippy frame‐work, a general and scalable solution to this problem. It abstracts the GPU cluster programming with a two‐level parallelism hierarchy and a non‐uniform memory access (NUMA) model. Zippy preserves the advantages of both message passing and shared‐memory models. It employs global arrays (GA) to simplify the communication, synchronization, and collaboration among multiple GPUs. Moreover, it exposes data locality to the programmer for optimal performance and scalability. We present three example applications developed with Zippy: sort‐last volume rendering, Marching Cubes isosurface extraction and rendering, and lattice Boltzmann flow simulation with online visualization. They demonstrate that Zippy can ease the development and integration of parallel visualization, graphics, and computation modules on a GPU cluster.     

GPU在实时阴影绘制中的应用

实时阴影在增强三维场景真实感方面起着非常重要的作用。阴影体算法是实时阴影绘制中效果非常理想的一种方法。但是随着场景复杂度的增加,该算法计算量比较大,将导致绘制效率的降低。另一方面,随着可编程GPU技术的发展,GPU的渲染速度远远大于CPU,为提高三维场景的渲染效率提供了更大的空间。在此基础上,介绍了一种在GPU上生成阴影体的方法,加速实时阴影绘制。利用图形硬件的图形处理单元（GPU）的运算能力和可编程性,将生成阴影体的大量计算从CPU转移到GPU,从而有效地提高实时阴影的绘制效率。     

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.     

GPU在复杂场景的阴影绘制中的应用

通过有效利用图形硬件的图形处理单元（GPU）的运算能力和可编程性，将人量计算从CPU分离出来。在GPU上采用顶点和片元程序进行阴影计算，从而加速复杂场景阴影绘制。选择图像空间阴影算法进行GPU加速绘制。用Cg图形编程语言和OpenGL实现了算法的绘制过程，能够满足通用的复杂3D场景应用的需要，达到满意的实时绘制效果。     

一种超图的区域型可视化方法

针对超图绘制中超边表达困难、绘制算法复杂的问题,提出一种超图的快速可视化方法.该方法将超边节点沿其走势线垂线方向向两侧扩展,获得超边中各节点的扩展点;对扩展点根据位置关系重新组合,使用Catmull-Rom算法连接各扩展点,获得超边表示区域的平滑边界曲线;将超边表示区域划分为对偶子段和独立子段,并分别使用三角带和三角扇模式填充;最后根据色相环理论对超边表示区域进行着色,以增强各条超边的区分度.实验结果表明,文中方法能够对超图数据结构进行快速可视化,使用闭合区域包围所有超边节点,从而实现直观、有效的超边表示;对于100个节点/30条超边以内的超图,该方法的绘制效率能够满足实时交互的要求.     

基于GPU的COONS曲面片加速计算

随着可编程图形处理单元(GPU)性能的增强,许多基于GPU的三维几何造型系统的应用也与日俱增。Coons曲面片作为三维建模中重要的基本单元,在农作物的建模仿真以及曲面造型中是重要的。为了适应于GPU的通用计算,对传统Coons曲面片方程进行分解,从而并行构造出Coons曲面片。通过实验表明,随着插值点数的增加,片元程序执行的时间依然保持一定程度的稳定,并没有出现明显的增加;GPU上执行时间的增长速度明显低于在CPU上的增长速度。     

Towards Automatic Band‐Limited Procedural Shaders

Procedural shaders are a vital part of modern rendering systems. Despite their prevalence, however, procedural shaders remain sensitive to aliasing any time they are sampled at a rate below the Nyquist limit. Antialiasing is typically achieved through numerical techniques like supersampling or precomputing integrals stored in mipmaps. This paper explores the problem of analytically computing a band‐limited version of a procedural shader as a continuous function of the sampling rate. There is currently no known way of analytically computing these integrals in general. We explore the conditions under which exact solutions are possible and develop several approximation strategies for when they are not. Compared to supersampling methods, our approach produces shaders that are less expensive to evaluate and closer to ground truth in many cases. Compared to mipmapping or precomputation, our approach produces shaders that support an arbitrary bandwidth parameter and require less storage. We evaluate our method on a range of spatially‐varying shader functions, automatically producing antialiased versions that have comparable error to 4×4 multisampling but can be over an order of magnitude faster. While not complete, our approach is a promising first step toward this challenging goal and indicates a number of interesting directions for future work.     

基于GPU平台的有效字典压缩与解压缩技术

压缩技术被广泛应用于数据存储和传输中,然而由于其内在的串行特性,大多数已有的基于字典的压缩与解压缩算法被设计在CPU上串行执行。为了探究使用图形处理器（graphic processing unit,GPU）对压缩与解压缩过程潜在性能的提升,结合合并内存访问与并行组装的技术,基于CUDA（compute unified device archi-tecture）平台研究了两种并行压缩与解压缩方法：基于字典的无状态压缩和基于字典的LZW压缩。实验结果表明,与传统的单核实现比较,所提方法能够显著改善已有的基于字典的串行压缩与解压缩算法的性能。     

Route Visualization Using Detail Lenses

We present a method designed to address some limitations of typical route map displays of driving directions. The main goal of our system is to generate a printable version of a route map that shows the overview and detail views of the route within a single, consistent visual frame. Our proposed visualization provides a more intuitive spatial context than a simple list of turns. We present a novel multifocus technique to achieve this goal, where the foci are defined by points of interest (POI) along the route. A detail lens that encapsulates the POI at a finer geospatial scale is created for each focus. The lenses are laid out on the map to avoid occlusion with the route and each other, and to optimally utilize the free space around the route. We define a set of layout metrics to evaluate the quality of a lens layout for a given route map visualization. We compare standard lens layout methods to our proposed method and demonstrate the effectiveness of our method in generating aesthetically pleasing layouts. Finally, we perform a user study to evaluate the effectiveness of our layout choices.     

Information Visualization Evaluation Using Crowdsourcing

Visualization researchers have been increasingly leveraging crowdsourcing approaches to overcome a number of limitations of controlled laboratory experiments, including small participant sample sizes and narrow demographic backgrounds of study participants. However, as a community, we have little understanding on when, where, and how researchers use crowdsourcing approaches for visualization research. In this paper, we review the use of crowdsourcing for evaluation in visualization research. We analyzed 190 crowdsourcing experiments, reported in 82 papers that were published in major visualization conferences and journals between 2006 and 2017. We tagged each experiment along 36 dimensions that we identified for crowdsourcing experiments. We grouped our dimensions into six important aspects: study design & procedure, task type, participants, measures & metrics, quality assurance, and reproducibility. We report on the main findings of our review and discuss challenges and opportunities for improvements in conducting crowdsourcing studies for visualization research.     

Using Convex Sets for Exploratory Data Analysis and Visualization

In this paper a new, abstract method for analysis and visualization of multidimensional data sets in pattern recognition problems is introduced. It can be used to determine the properties of an unknown, complex data set and to assist in finding the most appropriate recognition algorithm. Additionally, it can be employed to design layers of a feedforward artificial neural network or to visualize the higher-dimensional problems in 2-D and 3-D without losing relevant data set information. The method is derived from the convex set theory and works by considering convex subsets within the data and analyzing their respective positions in the original dimension. Its ability to describe certain set features that cannot be explicitly projected into lower dimensions sets it apart from many other visualization techniques. Two classical multidimensional problems are analyzed and the results show the usefulness of the presented method and underline its strengths and weaknesses.     

Using a GPU to Accelerate Die and Mold Fabrication

The authors present a GPU-based method for generating and verifying cutter paths for numerically controlled milling. A CAM system based on this technology is now employed in production at Mazda Motor Corporation for manufacturing stamping dies. This system can compute cutter paths more than 20 times faster than previous methods     

使用GPU加速BLAST算法初探

应用GPU通用高性能编程技术实现了一种加速BLAST算法的新方法。BLAST是目前最常用的用于生物序列查询比对的算法和软件包,其处理速度受到串行化执行和磁盘I/O等因素的影响。本文通过实验分析了BLAST软件包中的典型程序BLASTN的运行热点,并选定关键热点模块,应用CUDA编程技术对其进行并行化改造。对比实验结果表明,对于平均序列长度较大的序列库,应用GPGPU并行化可明显缩短该模块的运行时间,获得超过35倍的加速比。这说明,我们可以利用GPGPU对BLAST进行并行化加速,以满足高性能生物序列查询的需求。     

Visualization of Anomalies Using Mixture Models

Anomaly detection is important to learn from major past events and to prepare for future crises. We propose a new anomaly detection method that visualizes multivariate data in a 2- or 3-dimensional space based on the probability of belonging to a mixture component and the probability of not belonging to any components. It helps to visually understand not only the magnitude of anomalies but also the relationships among anomalous and normal samples. This may provide new knowledge in the data, since we can see it from a different viewpoint. We show the validity of the proposed method by using both an artificial and an economic time series.     

Addressing GPU On-Chip Shared Memory Bank Conflicts Using Elastic Pipeline

One of the major problems with the GPU on-chip shared memory is bank conflicts. We analyze that the throughput of the GPU processor core is often constrained neither by the shared memory bandwidth, nor by the shared memory latency (as long as it stays constant), but is rather due to the varied latencies caused by memory bank conflicts. This results in conflicts at the writeback stage of the in-order pipeline and causes pipeline stalls, thus degrading system throughput. Based on this observation, we investigate and propose a novel Elastic Pipeline design that minimizes the negative impact of on-chip memory bank conflicts on system throughput, by decoupling bank conflicts from pipeline stalls. Simulation results show that our proposed Elastic Pipeline together with the co-designed bank-conflict aware warp scheduling reduces the pipeline stalls by up to 64.0 % (with 42.3 % on average) and improves the overall performance by up to 20.7 % (on average 13.3 %) for representative benchmarks, at trivial hardware overhead.     

基于Matlab的地表温度反演可视化系统

陆地表面温度(LST)是区域和全球尺度上陆地表面物理过程的一个关键参数,它综合了地表和大气相互作用以及能量交换的结果,研究地表温度对全球能量平衡的研究有着重要的意义.在项目已采用的昼/夜法和遗传算法基础上,利用Matlab强大的科学运算能力和高质量的图形用户界面设计能力,设计开发了地表温度反演可视化系统.系统主要由文件、反演地表温度、反演地表组分温度和帮助四大功能模块组成,实现了大气校正、温度反演和图像显示的一体化,提高了单纯利用文件读取进行温度反演的效率和可视化程度.最后选取温带半干旱地区的河北省怀来县Terra、Aqua卫星上的MODIS数据为例,通过系统完成了温度反演的全过程,验证了反演系统的可行性.     

Constrained Visualization Using the Shepard Interpolation Family

This paper discusses the problem of visualizing data where there are underlying constraints that must be preserved. For example, we may know that the data are inherently positive. We show how the Modified Quadratic Shepard method, which interpolates scattered data of any dimensionality, can be constrained to preserve positivity. We do this by forcing the quadratic basis functions to be positive. The method can be extended to handle other types of constraints, including lower bound of 0 and upper bound of 1—as occurs with fractional data. A further extension allows general range restrictions, creating an interpolant that lies between any two specified functions as the lower and upper bounds.     

Using Social Visualization to Motivate Social Production

In this paper we argue that social visualization can motivate contributors to social production projects, such as Wikipedia and open source development. As evidence, we present CodeSaw, a social visualization of open source software development that we studied with real open source communities. CodeSaw mines open source archives to visualize group dynamics that currently lie buried in textual databases. Furthermore, CodeSaw becomes an active social space itself by supporting comments directly inside the visualization. To demonstrate CodeSaw, we apply it to a popular open source project, showing how the visualization reveals group dynamics and individual roles. The paper concludes by presenting evidence that CodeSaw, and social visualization more generally, can motivate contributors to social production projects if the visualization leaves the laboratory and makes it to the community visualized.