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Methodology and optimization for implementing cluster-based parallel geospatial algorithms with a case study

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Abstract

Cluster-based parallel computing technology has been widely used in the geosciences. However, how to implement the corresponding parallel algorithm in a simple way, and how to make parallel algorithms more efficient and effective, are still of great value to this research area, especially to beginners who are new to parallel computing. In this research, a contour line generation algorithm is paralleled with a message passing interface-based parallel computing as a case study to illustrate the improvement and optimization methods for the aforementioned problems to high performance geo-computation newcomers. Through experiments it can be concluded that: (1) In order to implement parallel algorithms in a simple way, we adopt the single program/multiple data mode, which is a method that evenly distributes tasks. We make the processes independent by reading the input files simultaneously, while writing results in parallel and gathering them with the master. (2) Even small hotspots should also be considered in the optimization procedure in order to get an efficient parallel algorithm. (3) Implementing a suitable parallel algorithm should consider many factors, such as the state of the network, I/O throughput, and also the desired application and user requirements.

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Notes

  1. https://grass.osgeo.org/.

  2. http://www.mpich.org/.

  3. https://www.open-mpi.org/.

  4. SuperMike-II, HPC in the Center for Computation & Technology, Louisianan State University. It has a 146 TFlops peak performance supplied by 440 computing nodes and 50 GPU nodes. Detailed information can be found on the website: http://www.hpc.lsu.edu/resources/hpc/system.php?system=SuperMike-II.

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Acknowledgements

This study was supported mainly by the National Key R&D Program of China (No. 2018YFC1505205), Engineering Research Center of Geospatial Information and Digital Technology (NASG) (Wuhan University) (Grant No. SIDT20170601), the National Natural Science Foundation of China (Grant No. 41871312), and Hubei Provincial Key Laboratory of Intelligent Geo-Information Processing (China University of Geosciences) (Grant Nos. KLIGIP-2017A06, KLIGIP-2017A07, and KLIGIP-2017A09).

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Authors and Affiliations

  1. School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China

    Fang Huang & Bo Tie

  2. Texas A&M Engineering Experiment Station, Texas A&M High Performance Research Computing, and Texas A&M Institute of Data Science, Texas A&M University, College Station, TX, 77843, USA

    Jian Tao

  3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, People’s Republic of China

    Xicheng Tan

  4. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 10094, People’s Republic of China

    Yan Ma

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Fang Huang conceived of and designed the experiments and revised the paper. Bo Tie performed the experiments and wrote the paper. Jian Tao also performed the experiments. Xicheng Tan and Yan Ma analyzed the data and made key modifications to the paper.

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Huang, F., Tie, B., Tao, J. et al. Methodology and optimization for implementing cluster-based parallel geospatial algorithms with a case study. Cluster Comput 23, 673–704 (2020). https://doi.org/10.1007/s10586-019-02944-y

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