This work introduces a methodology for reducing the execution time of the kriging interpolation method without losing the quality of the model results, as occurs in simplified moving neighborhood solutions. The proposed solution distributes the computation applying parallel programming using MPI (Message Passing Interface) libraries in a HPC (High Performance Computing) environment. For the solution to be automatic and adaptable to different spatial patterns the variogram was automatically fitted; this preliminary modeling step is usually interactive in this interpolation method. The experimental results show the validity of the implemented solution, as it significantly reduces (in one of the examples the execution time decreases from 2. h 38. min to only 3. min) the final execution time of the entire process. The proposed solution is not exclusive to a particular architecture or operating system and can be applied in various environments and spatial resolutions of the generated raster model as well as at different magnitudes of the data to be interpolated. © 2010 Elsevier Ltd.
Original languageEnglish
Pages (from-to)464-473
JournalComputers and Geosciences
Publication statusPublished - 1 Apr 2011


  • Kriging
  • MPI
  • Parallel programming
  • Variogram fitting


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