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A general-purpose parallel raster processing programming library test application using a geographic cellular automata model

delete2010-04-19
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关庆锋 (Qingfeng Guan) *
K
Keith Clarke
DOI:10.1080/13658810902984228delete
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Abstract

Abstract

En 中文
A general-purpose parallel raster processing programming library (pRPL) was developed and applied to speed up a commonly used cellular automaton model with known tractability limitations. The library is suitable for use by geographic information scientists with basic programming skills, but who lack knowledge and experience of parallel computing and programming. pRPL is a general-purpose programming library that provides generic support for raster processing, including local-scope, neighborhood-scope, regional-scope, and global-scope algorithms as long as they are parallelizable. The library also supports multilayer algorithms. Besides the standard data domain decomposition methods, pRPL provides a spatially adaptive quad-tree-based decomposition to produce more evenly distributed workloads among processors. Data parallelism and task parallelism are supported, with both static and dynamic load-balancing. By grouping processors, pRPL also supports data-task hybrid parallelism, i.e., data parallelism within a processor group and task parallelism among processor groups. pSLEUTH, a parallel version of a well-known cellular automata model for simulating urban land-use change ( SLEUTH), was developed to demonstrate full utilization of the advanced features of pRPL. Experiments with real-world data sets were conducted and the performance of pSLEUTH measured. We conclude not only that pRPL greatly reduces the development complexity of implementing a parallel raster-processing algorithm, it also greatly reduces the computing time of computationally intensive raster-processing algorithms, as demonstrated with pSLEUTH.
Keywords:
parallel computing
raster
cellular automata
SLEUTH
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Journal

International Journal of Geographical Information Science cover
International Journal of Geographical Information Science
IF:
5.1
Papers:
2.7K
Citations:
9.3K

Organization

U
University of Nebraska Lincoln
Scholars:
7.8K
Papers: 6.5K
Citations: 2.0W
University of Nebraska System cover
University of Nebraska System
Scholars:
2.7W
Papers: 2.3W
Citations: 58
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