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Towards a HPC Framework for Integrated Processing of Geographical Data: Encapsulating the Complexity of Parallel Algorithms

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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 parallelism. We discuss some of the requirements of a parallel software framework to underpin the integrated analysis of geographical data and environmental models.
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Document Type: Original Article

Affiliations: University of Edinburgh

Publication date: 2000-06-01

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