A rapid and flexible parallel approach for viewshed computation on large digital elevation models is presented. Our work is focused on the implementation of a derivate of the R2 viewshed algorithm. Emphasis has been placed on input/output (IO) efficiency that can be achieved by memory segmentation and coalesced memory access. An implementation of the parallel viewshed algorithm on the Compute Unified Device Architecture (CUDA), which exploits the high parallelism of the graphics processing unit, is presented. This version is referred to as r.cuda.visibility. The accuracy of our algorithm is compared to the r.los R3 algorithm (integrated into the open-source Geographic Resources Analysis Support System geographic information system environment) and other IO-efficient algorithms. Our results demonstrate that the proposed implementation of the R2 algorithm is faster and more IO efficient than previously presented IO-efficient algorithms, and that it achieves moderate calculation precision compared to the R3 algorithm. Thus, to the best of our knowledge, the algorithm presented here is the most efficient viewshed approach, in terms of computational speed, for large data sets.
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
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