GPU-based large-scale visualization

M. Hadwiger, J. Krüger, J. Beyer, S. Bruckner
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引用次数: 3

Abstract

Recent advances in image and volume acquisition as well as computational advances in simulation have led to an explosion of the amount of data that must be visualized and analyzed. Modern techniques combine the parallel processing power of GPUs with out-of-core methods and data streaming to enable the interactive visualization of giga- and terabytes of image and volume data. A major enabler for interactivity is making both the computational and the visualization effort proportional to the amount of data that is actually visible on screen, decoupling it from the full data size. This leads to powerful display-aware multi-resolution techniques that enable the visualization of data of almost arbitrary size. The course consists of two major parts: An introductory part that progresses from fundamentals to modern techniques, and a more advanced part that discusses details of ray-guided volume rendering, novel data structures for display-aware visualization and processing, and the remote visualization of large online data collections. You will learn how to develop efficient GPU data structures and large-scale visualizations, implement out-of-core strategies and concepts such as virtual texturing that have only been employed recently, as well as how to use modern multi-resolution representations. These approaches reduce the GPU memory requirements of extremely large data to a working set size that fits into current GPUs. You will learn how to perform ray-casting of volume data of almost arbitrary size and how to render and process gigapixel images using scalable, display-aware techniques. We will describe custom virtual texturing architectures as well as recent hardware developments in this area. We will also describe client/server systems for distributed visualization, on-demand data processing and streaming, and remote visualization. We will describe implementations using OpenGL as well as CUDA, exploiting parallelism on GPUs combined with additional asynchronous processing and data streaming on CPUs.
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基于gpu的大规模可视化
图像和体积采集的最新进展以及模拟计算的进步导致了必须可视化和分析的数据量的爆炸式增长。现代技术将gpu的并行处理能力与外核方法和数据流相结合,实现了千兆字节和太字节的图像和卷数据的交互式可视化。交互性的一个主要支持因素是使计算和可视化工作与屏幕上实际可见的数据量成正比,从而将其与完整的数据大小分离。这导致了强大的显示感知多分辨率技术,使几乎任意大小的数据可视化。本课程包括两个主要部分:从基础到现代技术的入门部分,以及更高级的部分,讨论光线引导体绘制的细节,用于显示感知可视化和处理的新数据结构,以及大型在线数据集的远程可视化。您将学习如何开发高效的GPU数据结构和大规模可视化,实现核心外的策略和概念,如最近才采用的虚拟纹理,以及如何使用现代多分辨率表示。这些方法将极大数据的GPU内存需求降低到适合当前GPU的工作集大小。您将学习如何执行几乎任意大小的体数据的光线投射,以及如何使用可扩展的显示感知技术渲染和处理千兆像素的图像。我们将描述自定义虚拟纹理架构以及该领域最近的硬件发展。我们还将描述用于分布式可视化、按需数据处理和流以及远程可视化的客户机/服务器系统。我们将描述使用OpenGL和CUDA的实现,利用gpu上的并行性结合cpu上的额外异步处理和数据流。
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