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Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (12): 990-999    DOI: 10.1631/jzus.C1100051
    
Accelerating geospatial analysis on GPUs using CUDA
Ying-jie Xia*,1,2,3, Li Kuang1, Xiu-mei Li1
1 Hangzhou Institute of Service Engineering, Hangzhou Normal University, Hangzhou 310012, China 2 Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 3 Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Soochow 215006, China
Accelerating geospatial analysis on GPUs using CUDA
Ying-jie Xia*,1,2,3, Li Kuang1, Xiu-mei Li1
1 Hangzhou Institute of Service Engineering, Hangzhou Normal University, Hangzhou 310012, China 2 Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 3 Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Soochow 215006, China
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摘要： Inverse distance weighting (IDW) interpolation and viewshed are two popular algorithms for geospatial analysis. IDW interpolation assigns geographical values to unknown spatial points using values from a usually scattered set of known points, and viewshed identifies the cells in a spatial raster that can be seen by observers. Although the implementations of both algorithms are available for different scales of input data, the computation for a large-scale domain requires a mass amount of cycles, which limits their usage. Due to the growing popularity of the graphics processing unit (GPU) for general purpose applications, we aim to accelerate geospatial analysis via a GPU based parallel computing approach. In this paper, we propose a generic methodological framework for geospatial analysis based on GPU and its programming model Compute Unified Device Architecture (CUDA), and explore how to map the inherent parallelism degrees of IDW interpolation and viewshed to the framework, which gives rise to a high computational throughput. The CUDA-based implementations of IDW interpolation and viewshed indicate that the architecture of GPU is suitable for parallelizing the algorithms of geospatial analysis. Experimental results show that the CUDA-based implementations running on GPU can lead to dataset dependent speedups in the range of 13–33-fold for IDW interpolation and 28–925-fold for viewshed analysis. Their computation time can be reduced by an order of magnitude compared to classical sequential versions, without losing the accuracy of interpolation and visibility judgment.
关键词： General purpose GPU;  CUDA;  Geospatial analysis;  Parallelization    
Abstract: Inverse distance weighting (IDW) interpolation and viewshed are two popular algorithms for geospatial analysis. IDW interpolation assigns geographical values to unknown spatial points using values from a usually scattered set of known points, and viewshed identifies the cells in a spatial raster that can be seen by observers. Although the implementations of both algorithms are available for different scales of input data, the computation for a large-scale domain requires a mass amount of cycles, which limits their usage. Due to the growing popularity of the graphics processing unit (GPU) for general purpose applications, we aim to accelerate geospatial analysis via a GPU based parallel computing approach. In this paper, we propose a generic methodological framework for geospatial analysis based on GPU and its programming model Compute Unified Device Architecture (CUDA), and explore how to map the inherent parallelism degrees of IDW interpolation and viewshed to the framework, which gives rise to a high computational throughput. The CUDA-based implementations of IDW interpolation and viewshed indicate that the architecture of GPU is suitable for parallelizing the algorithms of geospatial analysis. Experimental results show that the CUDA-based implementations running on GPU can lead to dataset dependent speedups in the range of 13–33-fold for IDW interpolation and 28–925-fold for viewshed analysis. Their computation time can be reduced by an order of magnitude compared to classical sequential versions, without losing the accuracy of interpolation and visibility judgment.
Key words: General purpose GPU    CUDA    Geospatial analysis    Parallelization
收稿日期: 2011-03-02 出版日期: 2011-11-30
CLC:  TP391  
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Ying-jie Xia, Li Kuang, Xiu-mei Li. Accelerating geospatial analysis on GPUs using CUDA. Front. Inform. Technol. Electron. Eng., 2011, 12(12): 990-999.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1100051    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2011/V12/I12/990

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