High-performance computing (HPC) techniques are still considered an esoteric research branch of GI processing. They are complex to use, deterring both academic modellers and commercial software developers. Yet the use of many environmental models is constrained by computation times. Furthermore, as remote sensing, environmental modelling and GIS converge, so the need for parallel computing becomes more apparent. Several case studies, parallelising the processing of raster, grid and vector-topology, demonstrate that scope exists for encapsulating the complexity of the parallelism in software frameworks, with strategies of spatial decomposition into sub-areas maximising the re-use of code from sequential algorithms. We show that parallel software frameworks can speed both the development and the execution of new applications. Based upon these case studies, the parallelisation of both interpolation and modelling in one software system is considered, with reference to pest infestation models, using both task and data
Geographical Information Systems (GIS) are able to manipulate spatial data. Such spatial data can be available in a variety of formats, one of the most important of which is the vector-topological. This format retains the topological relationships between geographical features and is commonly used in a range of geographical data analyses. This paper describes the implementation and performance of a parallel data partitioning algorithm for the input of vector-topological data to parallel processes.
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