Abstract
Spatial interpolation is a widely used GIS function for estimating values at locations where observed values are not available or adequate. One popular method for spatial interpolation is inverse distance weighted, which calculates estimated values based on a weighted sum of the values of a number of nearest neighbors that have observed values. This research focuses on solving a large-scale interpolation problem with a global coverage based on the inverse distance weighted method. Specifically, spherical distance is calculated instead of normal Euclidean distance commonly used in GIS software, which is necessary to find correct neighbors in the regions along the 180° longitude and in the polar areas. The computation of the global-scale interpolation based on spherical distance is intensive especially for achieving high-resolution results. This paper introduces how to accelerate such computation by exploiting massive parallelism provided by Graphic Processing Units (GPUs) with significant improvement of computational performance reported.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the ACM SIGSPATIAL 2nd International Workshop on High Performance and Distributed Geographic Information Systems, ACM SIGSPATIAL HPDGIS 2011 |
| Pages | 38-41 |
| Number of pages | 4 |
| DOIs | |
| State | Published - Dec 19 2011 |
| Event | ACM SIGSPATIAL 2nd International Workshop on High Performance and Distributed Geographic Information Systems, ACM SIGSPATIAL HPDGIS 2011 - Chicago, IL, United States Duration: Nov 1 2011 → Nov 1 2011 |
Other
| Other | ACM SIGSPATIAL 2nd International Workshop on High Performance and Distributed Geographic Information Systems, ACM SIGSPATIAL HPDGIS 2011 |
|---|---|
| Country | United States |
| City | Chicago, IL |
| Period | 11/1/11 → 11/1/11 |
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Keywords
- graphic processing units
- high performance computing
- inverse distance weighted
- spatial interpolation
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Networks and Communications
- Information Systems
Cite this
Spherical interpolation over graphic processing units. / Ye, Fei; Shi, Xuan; Wang, Shaowen; Liu, Yan; Han, Su Yeon.
Proceedings of the ACM SIGSPATIAL 2nd International Workshop on High Performance and Distributed Geographic Information Systems, ACM SIGSPATIAL HPDGIS 2011. 2011. p. 38-41.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Spherical interpolation over graphic processing units
AU - Ye, Fei
AU - Shi, Xuan
AU - Wang, Shaowen
AU - Liu, Yan
AU - Han, Su Yeon
PY - 2011/12/19
Y1 - 2011/12/19
N2 - Spatial interpolation is a widely used GIS function for estimating values at locations where observed values are not available or adequate. One popular method for spatial interpolation is inverse distance weighted, which calculates estimated values based on a weighted sum of the values of a number of nearest neighbors that have observed values. This research focuses on solving a large-scale interpolation problem with a global coverage based on the inverse distance weighted method. Specifically, spherical distance is calculated instead of normal Euclidean distance commonly used in GIS software, which is necessary to find correct neighbors in the regions along the 180° longitude and in the polar areas. The computation of the global-scale interpolation based on spherical distance is intensive especially for achieving high-resolution results. This paper introduces how to accelerate such computation by exploiting massive parallelism provided by Graphic Processing Units (GPUs) with significant improvement of computational performance reported.
AB - Spatial interpolation is a widely used GIS function for estimating values at locations where observed values are not available or adequate. One popular method for spatial interpolation is inverse distance weighted, which calculates estimated values based on a weighted sum of the values of a number of nearest neighbors that have observed values. This research focuses on solving a large-scale interpolation problem with a global coverage based on the inverse distance weighted method. Specifically, spherical distance is calculated instead of normal Euclidean distance commonly used in GIS software, which is necessary to find correct neighbors in the regions along the 180° longitude and in the polar areas. The computation of the global-scale interpolation based on spherical distance is intensive especially for achieving high-resolution results. This paper introduces how to accelerate such computation by exploiting massive parallelism provided by Graphic Processing Units (GPUs) with significant improvement of computational performance reported.
KW - graphic processing units
KW - high performance computing
KW - inverse distance weighted
KW - spatial interpolation
UR - http://www.scopus.com/inward/record.url?scp=83455177057&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=83455177057&partnerID=8YFLogxK
U2 - 10.1145/2070770.2070777
DO - 10.1145/2070770.2070777
M3 - Conference contribution
AN - SCOPUS:83455177057
SN - 9781450310406
SP - 38
EP - 41
BT - Proceedings of the ACM SIGSPATIAL 2nd International Workshop on High Performance and Distributed Geographic Information Systems, ACM SIGSPATIAL HPDGIS 2011
ER -