Modern Accelerator Technologies for Geographic Information Science

244 Pages·2013·4.72 MB·English

by  Xuan Shi

Xuan Shi · Volodymyr Kindratenko
Chaowei Yang Editors
Modern Accelerator
Technologies
for Geographic
Information Science
Modern Accelerator Technologies
for Geographic Information Science

Xuan Shi (cid:129) Volodymyr Kindratenko
Chaowei Y ang
Editors
Modern Accelerator
Technologies for Geographic
Information Science
Editors
Xuan Shi Volodymyr Kindratenko
Department of Geosciences Department of Electrical
University of Arkansas and Computer Engineering
Fayetteville , AR , USA University of Illinois
Urbana , IL , USA
Chaowei Yang
Department of Geography
and GeoInformation Sciences
George Mason University
Fairfax , VA , USA
ISBN 978-1-4614-8744-9 ISBN 978-1-4614-8745-6 (eBook)
DOI 10.1007/978-1-4614-8745-6
Springer New York Heidelberg Dordrecht London
Library of Congress Control Number: 2013950907
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Contents
Part I Introduction
1 Modern Accelerator Technologies
for Geographic Information Science ..................................................... 3
Xuan Shi, Volodymyr Kindratenko, and Chaowei Yang
Part II Overview of Modern Accelerator Technologies (MAT)
for Scientifi c Computation
2 A Brief History and Introduction to GPGPU ....................................... 9
Richard Vuduc and Jee Choi
3 Intel® Xeon Phi™ Coprocessors ............................................................ 25
Jim Jeffers
4 Accelerating Geocomputation with Cloud Computing ....................... 41
Qunying Huang, Zhenlong Li, Jizhe Xia, Yunfeng Jiang,
Chen Xu, Kai Liu, Manzhu Yu, and Chaowei Yang
Part III MAT in GIScience Applications
5 Parallel Primitives-Based Spatial Join
of Geospatial Data on GPGPUs ............................................................. 55
Jianting Zhang
6 Utilizing CUDA-Enabled GPUs to Support 5D
Scientifi c Geovisualization: A Case Study
of Simulating Dust Storm Events .......................................................... 69
Jing Li, Yunfeng Jiang, Chaowei Yang, and Qunying Huang
v
vi Contents
7 A Parallel Algorithm to Solve Near-Shortest Path
Problems on Raster Graphs ................................................................... 83
F. Antonio Medrano and Richard L. Church
8 CUDA-Accelerated HD-ODETLAP: Lossy High Dimensional
Gridded Data Compression.................................................................... 95
W. Randolph Franklin, You Li, Tsz-Yam Lau, and Peter Fox
9 Accelerating Agent-Based Modeling Using Graphics
Processing Units ...................................................................................... 113
Wenwu Tang
Part IV MAT in Remotely Sensed Data Processing and Analysis
10 Large-Scale Pulse Compression for Costas Signal with GPGPU ....... 133
Bin Zhou, Chun-mao Yeh, Wen-wen Li, and Wei-jie Zhang
11 Parallelizing ISODATA Algorithm for Unsupervised
Image Classifi cation on GPU ................................................................. 145
Fei Ye and Xuan Shi
12 Accelerating Mean Shift Segmentation Algorithm
on Hybrid CPU/GPU Platforms ............................................................ 157
Miaoqing Huang, Liang Men, and Chenggang Lai
Part V Multi-core Technology for Geospatial Services
13 Simulation and Analysis of Cluster-Based Caching Replacement
Based on Temporal and Spatial Locality of Tiles Access .................... 169
Rui Li, Xinxing Wang, Jingjing Wang, and Huayi Wu
14 A High-Concurrency Web Map Tile Service Built
with Open-Source Software ................................................................... 183
Huayi Wu, Xuefeng Guan, Tianming Liu, Lan You, and Zhenqiang Li
15 Improved Parallel Optimal Choropleth Map Classifi cation ............... 197
Jason Laura and Sergio J. Rey
Part VI Vision and Applicability of MAT for Geospatial Modeling
and Spatiotemporal Data Analytics
16 Pursuing Spatiotemporally Integrated Social Science
Using Cyberinfrastructure ..................................................................... 215
Xinyue Ye and Xuan Shi
Contents vii
17 Opportunities and Challenges for Urban Land-Use Change
Modeling Using High- Performance Computing .................................. 227
Qingfeng Guan and Xuan Shi
18 Modern Accelerator Technologies for Spatially-Explicit
Integrated Environmental Modeling ..................................................... 237
Dali Wang and Shujiang Kang
Part I
Introduction
Chapter 1
Modern Accelerator Technologies
for Geographic Information Science
Xuan Shi , Volodymyr Kindratenko , and Chaowei Yang
Keywords Modern Accelerator Technologies (cid:129) GIScience
Geographic Information System (GIS) enables heterogeneous geospatial data
integration, processing, analysis, and visualization. With a variety of software tools,
GIS makes substantial contribution to the advancements of science, engineering and
decision-making in geospatial-related natural and social sciences, public safety and
emergency response, spatial intelligence analytics and military operations, ecologi-
cal and environmental science and engineering, and public health. Geospatial data
represents real-world geographic features or objects using either vector or raster
data models. In the vector model, features are captured as discrete geometric objects
and represented as points, lines or polygons with non-spatial attributes. In the raster
model, features are represented on a grid, or as a multidimensional matrix, includ-
ing satellite imagery and other remotely sensed data.
When geospatial data is increasingly available along with an accelerating
increase in data volume, it has been a grand challenge to manipulate large-scale data
and complete data processing and analytics using traditional GIS software tools.
Emerging computer architectures and advanced computing technologies provide a
promising solution to employ massive parallelism to achieve scalability with high
performance for data intensive computing over big geospatial data.
X. Shi (*)
Department of Geosciences , University of Arkansas , Fayetteville , AR 72701 , USA
e-mail: [email protected]
V. Kindratenko
National Center for Supercomputing Applications , University of Illinois
at Urbana-C hampaign , Urbana , IL 61801 , USA
C. Yang
Department of Geography and GeoInformation Science , College of Science,
George Mason University , Fairfax , VA 22030 , USA
X. Shi et al. (eds.), Modern Accelerator Technologies for Geographic Information Science, 3
DOI 10.1007/978-1-4614-8745-6_1, © Springer Science+Business Media New York 2013