research-article

An efficient GPU multiple-observer siting method based on sparse-matrix multiplication

Authors:

Guilherme C. Pena,

Salles V. G. Magalhães,

Marcus V. A. Andrade,

W. Randolph Franklin,

Chaulio R. Ferreira,

Wenli LiAuthors Info & Claims

BigSpatial '14: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data

Pages 54 - 63

https://doi.org/10.1145/2676536.2676544

Published: 04 November 2014 Publication History

Abstract

This paper proposes an efficient parallel heuristic for siting observers on raster terrains. More specifically, the goal is to choose the smallest set of points on a terrain such that observers located in these points are able to visualize at least a given percentage of the terrain. This problem is NP-Hard and has several applications such as determining the best places to position (site) communication or monitoring towers on a terrain. Since siting observers is a massive operation, its solution requires a huge amount of processing time even to obtain an approximate solution using a heuristic. This is still more evident when processing high resolution terrains that have become available due to modern data acquiring technologies such as LIDAR and IFSAR.

Our new implementation uses dynamic programming and CUDA to accelerate the swap local search heuristic, which was proposed in previous works. Also, to efficiently use the parallel computing resources of GPUs, we adapted some techniques previously developed for sparse-dense matrix multiplication.

We compared this new method with previous parallel implementations and the new method is much more efficient than the previous ones. It can process much larger terrains (the older methods are restrictive about terrain size) and it is faster.

References

[1]

M. V. A. Andrade, S. V. G. Magalhães, M. A. Magalhães, W. R. Franklin, and B. M. Cutler. Efficient viewshed computation on terrain in external memory. GeoInformatica, 15(2):381--397, 2011.

Digital Library

[2]

B. Ben-Moshe. Geometric Facility Location Optimization. PHD thesis, Ben-Gurion University, Israel, Department of Computer Science, 2005.

[3]

Y. Ben-Shimol, B. Ben-Moshe, Y. Ben-Yehezkel, A. Dvir, and M. Segal. Automated antenna positioning algorithms for wireless fixed-access networks. Journal of Heuristics, 13(3):243--263, 2007.

Digital Library

[4]

A. Buluç, J. R. Gilbert, and C. Budak. Solving path problems on the GPU. Parallel Comput., 36(5-6):241--253, June 2010.

Digital Library

[5]

W. R. Franklin. Siting observers on terrain. In Springer-Verlag, editor, In D. Richardson and P. van Oosterom editors, Advances in Spatial Data Handling: 10th International Symposium on Spatial Data Handling, pages 109--120, 2002.

[6]

W. R. Franklin and C. Ray. Higher isn't necessarily better: Visibility algorithms and experiments. In Advances in GIS research: sixth international symposium on spatial data handling, volume 2, pages 751--770. Edinburgh, 1994.

[7]

Z. Li, Q. Zhu, and C. Gold. Digital terrain modeling: principles and methodology. CRC Press, 2005.

[8]

G. O. López, F. Vázquez, I. García, and E. M. Garzón. Fastspmm: An efficient library for sparse matrix matrix product on GPUs. Comput. J., 57(7):968--979, 2014.

[9]

S. V. G. Magalhães, M. V. A. Andrade, and C. Ferreira. Heuristics to site observers in a terrain represented by a digital elevation matrix. In GeoInfo, pages 110--121, 2010.

[10]

S. V. G. Magalhães, M. V. A. Andrade, and R. S. Ferreira. Using GPU to accelerate heuristics to site observers in DEM terrains. In IADIS Applied Computing (AC 2011), pages 127--133. 2011.

[11]

S. V. G. Magalhães, M. V. A. Andrade, and W. R. Franklin. An optimization heuristic for siting observers in huge terrains stored in external memory. In Hybrid Intelligent Systems (HIS), 2010 10th International Conference on, pages 135--140. IEEE, 2010.

[12]

G. Nagy. Terrain visibility. Computers & graphics, 18(6):763--773, 1994.

[13]

J. P. L. NASA. Shuttle Radar Topography Mission., 2013. Available at http://www2.jpl.nasa.gov/srtm (accessed on July 2014).

[14]

NVIDIA. CUDA C programming guide. NVIDIA Corporation, July, 2014. Available at http://docs.nvidia.com/cuda (accessed on July 2014).

[15]

G. C. Pena, M. V. A. Andrade, S. V. G. Magalhães, W. R. Franklin, and C. R. Ferreira. An improved parallel algorithm using GPU for siting observers on terrain. In 16th International Conference on Enterprise Information Systems (ICEIS-2014), pages 367--375, Lisbon, Portugal, 2014.

Cited By

Soliman KShenawy MFarag AAbuarqoub AAdebisi BHammoudeh MMurad SArioua M(2018)Loop unrolling effect on parallel code optimizationProceedings of the 2nd International Conference on Future Networks and Distributed Systems10.1145/3231053.3231060(1-6)Online publication date: 26-Jun-2018
https://dl.acm.org/doi/10.1145/3231053.3231060
Koanantakool PAzad ABuluc AMorozov DOh SOliker LYelick K(2016)Communication-Avoiding Parallel Sparse-Dense Matrix-Matrix Multiplication2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS.2016.117(842-853)Online publication date: May-2016
https://doi.org/10.1109/IPDPS.2016.117

Index Terms

An efficient GPU multiple-observer siting method based on sparse-matrix multiplication
1. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Computational geometry

Recommendations

An Improved Parallel Algorithm Using GPU for Siting Observers on Terrain
ICEIS 2014: Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1

This paper presents an efficient method to determine a set of observers (that is, where to site them) such that a given percentage of

a terrain is visually covered. Our method extends the method proposed in (Franklin, 2002) including a local search ...
Parallel multiple observer siting on terrain
SIGSPATIAL '14: Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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

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 ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

BigSpatial '14: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data

November 2014

69 pages

ISBN:9781450331326

DOI:10.1145/2676536

Editors:
Varun Chandola
State University of New York at Buffalo, NY
,
Ranga Raju Vatsavai
North Carolina State University, NC

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

ESRI
Yandex
Google Inc.
NVIDIA
Microsoft: Microsoft
ORACLE: ORACLE
Facebook: Facebook
SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Conference

SIGSPATIAL '14

Sponsor:

Microsoft
ORACLE
Facebook
SIGSPATIAL

SIGSPATIAL '14: 22nd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 4, 2014

Texas, Dallas

Acceptance Rates

BigSpatial '14 Paper Acceptance Rate 8 of 13 submissions, 62%;

Overall Acceptance Rate 32 of 58 submissions, 55%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
95
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)1

Reflects downloads up to 23 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Soliman KShenawy MFarag AAbuarqoub AAdebisi BHammoudeh MMurad SArioua M(2018)Loop unrolling effect on parallel code optimizationProceedings of the 2nd International Conference on Future Networks and Distributed Systems10.1145/3231053.3231060(1-6)Online publication date: 26-Jun-2018
https://dl.acm.org/doi/10.1145/3231053.3231060
Koanantakool PAzad ABuluc AMorozov DOh SOliker LYelick K(2016)Communication-Avoiding Parallel Sparse-Dense Matrix-Matrix Multiplication2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS.2016.117(842-853)Online publication date: May-2016
https://doi.org/10.1109/IPDPS.2016.117

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents