Geographical information systems are ideal candidates for the application of parallel programming techniques, mainly because they usually handle large data sets. To help us deal with complex calculations over such data sets, we investigated the performance constraints of a classic master–worker parallel paradigm over a message-passing communication model. To this end, we present a new approach that employs an external database in order to improve the calculation–communication overlap, thus reducing the idle times for the worker processes. The presented approach is implemented as part of a parallel radio-coverage prediction tool for the Geographic Resources Analysis Support System GRASS environment. The prediction calculation employs digital elevation models and land-usage data in order to analyze the radio coverage of a geographical area. We provide an extended analysis of the experimental results, which are based on real data from an Long Term Evolution LTE network currently deployed in Slovenia. Based on the results of the experiments, which were performed on a computer cluster, the new approach exhibits better scalability than the traditional master–worker approach. We successfully tackled real-world-sized data sets, while greatly reducing the processing time and saturating the hardware utilization.
Also, in @cite_35 there is no calculation dependency among the spatial blocks. The experimental evaluation is made over multiple cores of one CPU and a GPU, communicated using a master-worker setup.
@cite_35
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This paper presents a new Geographic Information Systems (GIS) tool to compute the optimal solar-panel positioning maps on large high-resolution Digital Elevation Models (DEMs). In particular, this software finds out (1) the maximum solar energy input that can be captured on a surface located at a specific height on each point of the DEM, and then (2) the optimal tilt and orientation that allow capturing this amount of energy. The radiation and horizon algorithms we developed in previous works were used as baseline for this tool ( in Comput. Phys. Commun. 178(11):800---808, 2008; in Int. J. Geogr. Inf. Sci. 25(4):541---555, 2011). A multi-method approach is analyzed to make the hybrid implementation of this tool especially appropriate for heterogeneous multicore-GPU architectures. The experimental results show a high numerical accuracy with a linear scalability.