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Modern Accelerator Technologies for Geographic Information Science

  • Book
  • © 2013

Overview

Editors:
  1. Xuan Shi

    1. Department of Geosciences, University of Arkansas, Fayetteville, USA

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  2. Volodymyr Kindratenko

    1. Department of Electrical and Computer Engineering, University of Illinois, Urbana, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Chaowei Yang

    1. Department of Geography and GeoInformation Sciences, George Mason University, Fairfax, USA

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    You can also search for this editor in PubMed   Google Scholar

  • Equips readers to handle complex technologies
  • Treats both theoretical and practical aspects of GIS applications
  • Shares tips and insights from leading researchers and professionals

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Table of contents (18 chapters)

  1. Front Matter

    Pages i-vii
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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. Modern Accelerator Technologies for Geographic Information Science

      • Xuan Shi, Volodymyr Kindratenko, Chaowei Yang
      Pages 3-6

  3. Overview of Modern Accelerator Technologies (MAT) for Scientific Computation

    1. Front Matter

      Pages 7-7
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    2. A Brief History and Introduction to GPGPU

      • Richard Vuduc, Jee Choi
      Pages 9-23

    3. Intel® Xeon Phi™ Coprocessors

      • Jim Jeffers
      Pages 25-39

    4. Accelerating Geocomputation with Cloud Computing

      • Qunying Huang, Zhenlong Li, Jizhe Xia, Yunfeng Jiang, Chen Xu, Kai Liu et al.
      Pages 41-51

  4. MAT in GIScience Applications

    1. Front Matter

      Pages 53-53
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    2. Parallel Primitives-Based Spatial Join of Geospatial Data on GPGPUs

      • Jianting Zhang
      Pages 55-67

    3. Utilizing CUDA-Enabled GPUs to Support 5D Scientific Geovisualization: A Case Study of Simulating Dust Storm Events

      • Jing Li, Yunfeng Jiang, Chaowei Yang, Qunying Huang
      Pages 69-82

    4. A Parallel Algorithm to Solve Near-Shortest Path Problems on Raster Graphs

      • F. Antonio Medrano, Richard L. Church
      Pages 83-94

    5. CUDA-Accelerated HD-ODETLAP: Lossy High Dimensional Gridded Data Compression

      • W. Randolph Franklin, You Li, Tsz-Yam Lau, Peter Fox
      Pages 95-111

    6. Accelerating Agent-Based Modeling Using Graphics Processing Units

      • Wenwu Tang
      Pages 113-129

  5. MAT in Remotely Sensed Data Processing and Analysis

    1. Front Matter

      Pages 131-131
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    2. Large-Scale Pulse Compression for Costas Signal with GPGPU

      • Bin Zhou, Chun-mao Yeh, Wen-wen Li, Wei-jie Zhang
      Pages 133-143

    3. Parallelizing ISODATA Algorithm for Unsupervised Image Classification on GPU

      • Fei Ye, Xuan Shi
      Pages 145-156

    4. Accelerating Mean Shift Segmentation Algorithm on Hybrid CPU/GPU Platforms

      • Miaoqing Huang, Liang Men, Chenggang Lai
      Pages 157-166

  6. Multi-core Technology for Geospatial Services

    1. Front Matter

      Pages 167-167
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    2. Simulation and Analysis of Cluster-Based Caching Replacement Based on Temporal and Spatial Locality of Tiles Access

      • Rui Li, Xinxing Wang, Jingjing Wang, Huayi Wu
      Pages 169-181

    3. A High-Concurrency Web Map Tile Service Built with Open-Source Software

      • Huayi Wu, Xuefeng Guan, Tianming Liu, Lan You, Zhenqiang Li
      Pages 183-195
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Keywords

About this book

This book explores the impact of augmenting novel architectural designs with hardware‐based application accelerators. The text covers comprehensive aspects of the applications in Geographic Information Science, remote sensing and deploying Modern Accelerator Technologies (MAT) for geospatial simulations and spatiotemporal analytics. MAT in GIS applications, MAT in remotely sensed data processing and analysis, heterogeneous processors, many-core and highly multi-threaded processors and general purpose processors are also presented. This book includes case studies and closes with a chapter on future trends. Modern Accelerator Technologies for GIS is a reference book for practitioners and researchers working in geographical information systems and related fields. Advanced-level students in geography, computational science, computer science and engineering will also find this book useful.

Editors and Affiliations

  • Department of Geosciences, University of Arkansas, Fayetteville, USA

    Xuan Shi

  • Department of Electrical and Computer Engineering, University of Illinois, Urbana, USA

    Volodymyr Kindratenko

  • Department of Geography and GeoInformation Sciences, George Mason University, Fairfax, USA

    Chaowei Yang

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