Modern Accelerator Technologies for Geographic Information Science, 1st Edition

  • Published By:
  • ISBN-10: 1461487455
  • ISBN-13: 9781461487456
  • DDC: 910.285
  • Grade Level Range: College Freshman - College Senior
  • 251 Pages | eBook
  • Original Copyright 2013 | Published/Released April 2014
  • This publication's content originally published in print form: 2013

  • Price:  Sign in for price



This book explores the impact of augmenting novel architectural designs with hardware‚Äźbased 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.

Table of Contents

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
1: Introduction.
2: Modern Accelerator Technologies for Geographic Information Science.
3: Overview of Modern Accelerator Technologies (MAT) for Scientific Computation.
4: A Brief History and Introduction to GPGPU.
5: Intel® Xeon Phi™ Coprocessors.
6: Accelerating Geocomputation with Cloud Computing.
7: MAT in GIScience Applications.
8: Parallel Primitives-Based Spatial Join of Geospatial Data on GPGPUs.
9: Utilizing CUDA-Enabled GPUs to Support 5D Scientific Geovisualization: A Case Study of Simulating Dust Storm Events.
10: A Parallel Algorithm to Solve Near-Shortest Path Problems on Raster Graphs.
11: CUDA-Accelerated HD-ODETLAP: Lossy High Dimensional Gridded Data Compression.
12: Accelerating Agent-Based Modeling Using Graphics Processing Units.
13: MAT in Remotely Sensed Data Processing and Analysis.
14: Large-Scale Pulse Compression for Costas Signal with GPGPU.
15: Parallelizing ISODATA Algorithm for Unsupervised Image Classification on GPU.
16: Accelerating Mean Shift Segmentation Algorithm on Hybrid CPU/GPU Platforms.
17: Multi-Core Technology for Geospatial Services.
18: Simulation and Analysis of Cluster-Based Caching Replacement Based on Temporal and Spatial Locality of Tiles Access.
19: A High-Concurrency Web Map Tile Service Built with Open-Source Software.
20: Improved Parallel Optimal Choropleth Map Classification.
21: Vision and Applicability of MAT for Geospatial Modeling and Spatiotemporal Data Analytics.
22: Pursuing Spatiotemporally Integrated Social Science Using Cyberinfrastructure.
23: Opportunities and Challenges for Urban Land-Use Change Modeling Using High-Performance Computing.
24: Modern Accelerator Technologies for Spatially-Explicit Integrated Environmental Modeling.