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

Viewshed analysis, often supported by Geographic Information Systems (GIS), 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 (GPGPU) provides a promising means to address such challenges. This paper describes a parallel computing approach to data-intensive viewshed analysis of large terrain data using GPUs. Our approach exploits high-bandwidth memory of GPUs and parallelism of massive spatial data to enable memory-intensive and computeintensive tasks while CPUs (Central Processing Units) are used to achieve efficient Input/Output (I/O) management. Furthermore, a two-level spatial domain decomposition strategy is 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 enhanced ability of analyzing sizable datasets that the sequential computing method cannot handle.