A GRASS GIS parallel module for radio-propagation predictions

Abstract

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.Comment: 13 pages, 12 figures and 2 tables. International Journal of Geographical Information Science, 201

An IO-efficient parallel implementation of an R2 viewshed algorithm for large terrain maps on a CUDA GPU

“…A variety of analysis algorithms could be based on the computationally efficient kernel that forms the core of the presented algorithm. The presented algorithm, r.cuda.visibility, is currently part of the GRASS-RaPlaT project (Hrovat et al 2010, Benedičič et al 2014.…”