Accelerating geospatial analysis on GPUs using CUDA

被引:28
作者
Xia, Ying-jie [1 ,2 ,3 ]
Kuang, Li [1 ]
Li, Xiu-mei [1 ]
机构
[1] Hangzhou Normal Univ, Hangzhou Inst Serv Engn, Hangzhou 310012, Zhejiang, Peoples R China
[2] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Dept Automat, Shanghai 200240, Peoples R China
[3] Soochow Univ, Prov Key Lab Comp Informat Proc Technol, Suzhou 215006, Peoples R China
来源
JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE C-COMPUTERS & ELECTRONICS | 2011年 / 12卷 / 12期
基金
中国国家自然科学基金;
关键词
General purpose GPU; CUDA; Geospatial analysis; Parallelization;
D O I
10.1631/jzus.C1100051
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
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.
引用
收藏
页码:990 / 999
页数:10
相关论文
共 23 条
[1]  
[Anonymous], 2010, ATI STREAM COMP OPEN
[2]  
Blelloch GE., 1990, Vector Models for Data-Parallel Computing
[3]  
Clarke Keith.C., 1995, ANAL COMPUTER CARTOG
[4]  
Densham PJ, 1998, PARALLEL PROCESSING ALGORITHMS FOR GIS, P387
[5]  
ESRI, 2009, ARCGIS DESKT HELP
[6]  
Finkel R. A., 1974, Acta Informatica, V4, P1, DOI 10.1007/BF00288933
[7]   Multidimensional access methods [J].
Gaede, V ;
Gunther, O .
ACM COMPUTING SURVEYS, 1998, 30 (02) :170-231
[8]  
GUAN X, 2009, SPIE, V7146, DOI DOI 10.1117/12.813163
[9]  
Kidner D B, 2001, T GIS, V5, P19, DOI DOI 10.1111/1467-9671.00065
[10]  
Micikevicius P., 2009, Proceedings of Second Workshop on General Purpose Processing on Graphics Processing Units, V383, P79, DOI DOI 10.1145/1513895.1513905