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首页> 外文期刊>Computers & geosciences >Visualizing 3D/4D environmental data using many-core graphics processing units (GPUs) and multi-core central processing units (CPUs)
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Visualizing 3D/4D environmental data using many-core graphics processing units (GPUs) and multi-core central processing units (CPUs)

机译：使用多核图形处理单元（GPU）和多核中央处理单元（CPU）可视化3D / 4D环境数据

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摘要

Visualizing 3D/4D environmental data is critical to understanding and predicting environmental phenomena for relevant decision making. This research explores how to best utilize graphics process units (GPUs) and central processing units (CPUs) collaboratively to speed up a generic geovisualization process. Taking the visualization of dust storms as an example, we developed a systematic 3D/4D geovisualization framework including preprocessing, coordinate transformation interpolation, and rendering. To compare the potential speedup of using GPUs versus that of using CPUs, we have implemented visualization components based on both multi-core CPUs and many-core GPUs. We found that (1) multi-core CPUs and many-core GPUs can improve the efficiency of mathematical calculations and rendering using multithreading techniques; (2) given the same amount of data, when increasing the size of blocks of GPUs for coordinate transformation, the executing time of interpolation and rendering drops consistently after reaching a peak; (3) the best performances obtained by GPU-based implementations in all the three major processes, are usually faster than CPU-based implementations whereas the best performance of rendering with GPUs is very close to that with CPUs; and (4) as the GPU on-board memory limits the capabilities of processing large volume data, preprocessing data with CPUs is necessary when visualizing large volume data which exceed the on-board memory of GPUs. However, the efficiency may be significantly hampered by the relative high-latency of the data exchange between CPUs and GPUs. Therefore, visualization of median size 3D/4D environmental data using GPUs is a better solution than that of using CPUs.
机译：可视化3D / 4D环境数据对于理解和预测相关决策的环境现象至关重要。这项研究探索了如何最佳地协同利用图形处理单元（GPU）和中央处理单元（CPU）来加速通用地理可视化过程。以沙尘暴的可视化为例，我们开发了一个系统的3D / 4D地理可视化框架，其中包括预处理，坐标转换插值和渲染。为了比较使用GPU和使用CPU的潜在速度，我们已经基于多核CPU和多核GPU实施了可视化组件。我们发现（1）多核CPU和多核GPU可以使用多线程技术提高数学计算和渲染的效率； （2）在给定相同数据量的情况下，当增加用于坐标转换的GPU块的大小时，插值和渲染的执行时间在达到峰值后始终下降； （3）在所有三个主要过程中，基于GPU的实现所获得的最佳性能通常比基于CPU的实现要快，而使用GPU进行渲染的最佳性能与使用CPU的最佳性能非常接近； （4）由于GPU板载内存限制了处理大容量数据的能力，因此在可视化超过GPU板载内存的大容量数据时，必须使用CPU对数据进行预处理。但是，CPU和GPU之间的数据交换相对较高的延迟可能会极大地影响效率。因此，与使用CPU相比，使用GPU可视化中等大小的3D / 4D环境数据是一种更好的解决方案。

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  • 来源
    《Computers & geosciences》 |2013年第9期|78-89|共12页
  • 作者

    Jing Li; Yunfeng Jiang; Chaowei Yang; Qunying Huang; Matt Rice;

  • 作者单位

    Center for Intelligent Spatial Computing and Department of Geography and Geolnformation Science, College of Science, George Mason University, 4400 University Dr. Fairfax, VA 22030-4444, USA,Department of Geography and the Environment, University of Denver, 2050 E. Iliff Ave., Denver, CO 80208-0710, USA;

    Center for Intelligent Spatial Computing and Department of Geography and Geolnformation Science, College of Science, George Mason University, 4400 University Dr. Fairfax, VA 22030-4444, USA;

    Center for Intelligent Spatial Computing and Department of Geography and Geolnformation Science, College of Science, George Mason University, 4400 University Dr. Fairfax, VA 22030-4444, USA;

    Center for Intelligent Spatial Computing and Department of Geography and Geolnformation Science, College of Science, George Mason University, 4400 University Dr. Fairfax, VA 22030-4444, USA;

    Center for Intelligent Spatial Computing and Department of Geography and Geolnformation Science, College of Science, George Mason University, 4400 University Dr. Fairfax, VA 22030-4444, USA;

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  • 正文语种 eng
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  • 关键词

    Ceovisualization; Digital earth; EarthCube; CyberGIS; World Wind;

    机译：可视化;数字地球;EarthCube;Cyber​​GIS;世界风;

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