Deakin University
Browse

File(s) under permanent embargo

Parallelization and optimization of spatial analysis for large scale environmental model data assembly

journal contribution
posted on 2012-11-01, 00:00 authored by G Zhao, Brett BryanBrett Bryan, D King, X Song, Q Yu
Spatial-temporal modelling of environmental systems such as agriculture, forestry, and water resources requires high resolution input data. Assembling and summarizing this data in the appropriate format for model input often requires a series of spatial analyses which can be extremely time-consuming, especially when many large data sets are involved. In this paper we investigated the ability of high-performance computing techniques to improve the efficiency of spatial analysis for model data assembly. We implemented an array-based algorithm to calculate summary statistics for long time-series daily grid climate data sets for 11,575 climate-soil zones across the Australian wheat-growing regions for input into a crop simulation model. We developed a zonal statistics algorithm using Python's Numpy module then parallelized it and processed it using a shared memory, multi-processor system. We assessed algorithm performance with a varying number of CPU cores, and assessed the influence of load balancing on the efficiency of parallel processing. Compared with traditional desktop GIS software, the serial and parallel (32 cores) implementation achieved about 180 and 1440 times speed-up, respectively. We also found that the most efficient computation occurred when not all of the available CPU cores were used, and the chunk size of jobs also had an important influence on computing efficiency. The algorithm and the parallel processing scheme provides a useful approach to address computing challenges posed by spatial analysis of numerous large data sets for large scale environmental modelling.

History

Journal

Computers and electronics in agriculture

Volume

89

Pagination

94-99

Location

Amsterdam, The Netherlands

ISSN

0168-1699

Language

eng

Publication classification

C Journal article, C1.1 Refereed article in a scholarly journal

Copyright notice

2012, Elsevier B.V.

Publisher

Elsevier

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC