  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 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 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 data sets, while greatly reducing the
processing time and saturating the hardware utilization.
