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

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)363-384
Number of pages22
JournalInternational Journal of Geographical Information Science
Volume27
Issue number2
DOIs
StatePublished - Feb 1 2013

Keywords

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

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences

Fingerprint Dive into the research topics of 'A parallel computing approach to viewshed analysis of large terrain data using graphics processing units'. Together they form a unique fingerprint.

  • Cite this