Parallelization of Regional Operation Algorithm Using Parallel Raster-based Geocomputation Operators

  • 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
    3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application and School of Geography, Nanjing Normal University, Nanjing 210097, China;
    4. Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA

Received date: 2014-10-28

  Revised date: 2014-12-06

  Online published: 2015-05-10


Parallel raster- based programming libraries have been proposed to make the details of parallel programming and the parallel hardware architecture to be transparent to users in some degrees. Thus these libraries can facilitate the development of parallel programs of raster-based geocomputation. Among the existing parallel programming libraries, parallel raster-based geocomputation operators (PaRGO), which is recently proposed by Qin et al, shows great advantages. This is not only because PaRGO encapsulates the general steps in parallel raster- based geocomputation, but also because PaRGO is compatible with multiple commonly used parallel computing platforms. Currently, PaRGO is designed for supporting local operation, focal operation and global operation directly. However, the availability of PaRGO for supporting regional operation in raster-based geocomputation has not been evaluated. In this paper, we evaluate PaRGO to testify its performance in this circumstance by using a multiple-flow-direction algorithm as a representation of the regional operation. Different versions of PaRGObased parallel programs for this algorithm are tested on a symmetrical multiprocessing (SMP) cluster and evaluated from two aspects: the performability and the parallel efficiency. The experimental results show that the current PaRGO cannot directly support the parallelization of regional operations. But it can be supportive when the regional operation is transformed into an iteration process of focal operation. On a SMP cluster, MPI-version parallel program performs better than MPI/OpenMP-version parallel program.

Cite this article

AI Beibei, QIN Chengzhi, ZHU Axing . Parallelization of Regional Operation Algorithm Using Parallel Raster-based Geocomputation Operators[J]. Journal of Geo-information Science, 2015 , 17(5) : 562 -567 . DOI: 10.3724/SP.J.1047.2015.00562


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