A parallel computing approach to viewshed analysis of large terrain data using graphics processing units |
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| Authors: | Yanli Zhao Anand Padmanabhan |
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| Institution: | 1. CyberInfrastructure and Geospatial Information Laboratory , University of Illinois at Urbana-Champaign , Urbana , IL , USA;2. Department of Geography and Geographic Information Science , University of Illinois at Urbana-Champaign , Urbana , IL , USA;3. Department of Geography and Geographic Information Science , University of Illinois at Urbana-Champaign , Urbana , IL , USA;4. National Center for Supercomputing Applications , University of Illinois at Urbana-Champaign , Urbana , IL , USA |
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| Abstract: | Viewshed analysis, often supported by geographic information system, is widely used in many application domains. However, as terrain data continue to become increasingly large and available at high resolutions, data-intensive viewshed analysis poses significant computational challenges. General-purpose computation on graphics processing units (GPUs) provides a promising means to address such challenges. This article describes a parallel computing approach to data-intensive viewshed analysis of large terrain data using GPUs. Our approach exploits the high-bandwidth memory of GPUs and the parallelism of massive spatial data to enable memory-intensive and computation-intensive tasks while central processing units are used to achieve efficient input/output (I/O) management. Furthermore, a two-level spatial domain decomposition strategy has been developed to mitigate a performance bottleneck caused by data transfer in the memory hierarchy of GPU-based architecture. Computational experiments were designed to evaluate computational performance of the approach. The experiments demonstrate significant performance improvement over a well-known sequential computing method, and an enhanced ability of analyzing sizable datasets that the sequential computing method cannot handle. |
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| Keywords: | viewshed analysis general-purpose computation on graphics processing units parallel computing spatial data analysis |
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