Parallel Multiple Observer Siting on Terrain | W. Randolph Franklin, Rensselaer Polytechnic Institute, Troy NY USA

Wenli Li, W. Randolph Franklin, Daniel N. Benedetti, and Salles V. G. de Magalhães. Parallel multiple observer siting on terrain. In 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2014). Dallas, Texas, USA, 4–7 Nov 2014.
[full text] [poster] [BibTeX▼]

@inproceedings{li-acmgis-2014,
    author = "Li, Wenli and Franklin, W. Randolph and Benedetti, Daniel N. and de Magalhães, Salles V. G.",
    title = "Parallel Multiple Observer Siting on Terrain",
    booktitle = "22nd {ACM SIGSPATIAL} International Conference on Advances in Geographic Information Systems {(ACM SIGSPATIAL 2014)}",
    year = "2014",
    month = "4--7 Nov",
    href = "\bibhrefpp{182-acmgis2014-par-siting}{182-acmgis2014-par-siting-poster.pdf}",
    address = "Dallas, Texas, USA",
    customlinkposter = "https://wrfranklin.org/p/182-acmgis2014-par-siting-poster.pdf",
    mykey = "benedetti salles wenlili visviewshedsiting gpusiting parallelsiting parallel"
}

Abstract

This paper presents the optimization and parallelization of the multiple observer siting program, originally developed by Franklin and Vogt. Siting is a compute-intensive application with a large amount of inherent parallelism. The advantage of parallelization is not only a faster program but also the ability to solve bigger problems. We have parallelized the program using two different techniques: OpenMP, using multi-core CPUs, and CUDA, using a general purpose graphics processing unit (GPGPU). Experiment results show that both techniques are very effective. Using the OpenMP program, we are able to site tens of thousands of observers on a 16385 × 16385 terrain in less than 2 minutes, on our workstation with two CPUs and one GPU. The CUDA program achieves the same in about 30 seconds.

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