A parallel computing approach to viewshed analysis of large terrain data using graphics processing units

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.

Keywords:

• viewshed analysis
• general-purpose computation on graphics processing units
• parallel computing
• spatial data analysis

Acknowledgments

This work is supported in part by the National Science Foundation grants: BCS-0846655 and OCI-1047916, and a fellowship from the China Scholarship Council. The authors thank Dr. Wen-mei W. Hwu and John Stratton for their kind assistance on accessing computational resources. They are also grateful for helpful comments by Guofeng Cao and Eric Shook within the CyberInfrastructure and Geospatial Information Laboratory at the University of Illinois at Urbana-Champaign.

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