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Modern Accelerator Technologies for Geographic Information Science

MODERN ACCELERATOR TECHNOLOGIES GGRPHC INFORMATION SCI

Information Technology

Computing & Information Technology

Gt Product Group

Binding Format

Marketing Imprint Name

B2B GT

CL Carton Count

Imprint Name

B2B & B2C

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.

POST SECONDARY FOUR YEAR

PROFESSIONAL & REFERENCE

COLFR

COLJR

COLSO

COLSR

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Contents.
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&#x00AE; Xeon Phi&#x2122; 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.

Modern Accelerator Technologies for Geographic Information Science.css-1p8rvzy{position:absolute;padding-left:8px;}

Modern Accelerator Technologies for Geographic Information Science