geocomputation in the grid computing age · grid computing age qingfeng (gene) guan, tong zhang,...

GeoComputation in the Grid Computing Age

Qingfeng (Gene) Guan,Tong Zhang,

Keith C. ClarkeDepartment of Geography

University of California, Santa BarbaraSan Diego State University

Dec. 5th, 2006

2006 W2GIS, Hong Kong, China

Content

1. Introduction

2. GeoComputation: Challenges and Opportunities– The Capital G and C in GeoComputation– Challenges and Opportunites

3. Emerging Technologies to Promote GeoComputation– Grid Computing– Web Portals for the Grid– Grid-enabled Parallel Computing

4. Strategies and Approaches– A Grid-based Geospatial Problem-Solving Architecture– Case Study: Grid-enabled Geographic Cellular Automata

5. Conclusions

Introduction

GeoComputation: the “art and science of solving complex spatial problems with computers” (www.geocomputation.org)

http://www.geocomputation.org/

http://www.geocomputation.org/

Introduction (cont.)

GeoComputation: a “grab-bag” of computational techniques for geospatial problem-solving (Couclelis, H., 1998) lacking in interoperability, and extremely hard to organize to form an integrated geospatial problem-solving environment

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Introduction (cont.)

Question: How do we make GeoComputation less like a “grab-bag”, and more like a “art and science”?

More specifically: How do we integrate those computational techniques effectively and efficiently into a collaborative geospatial problem-solving environment?

GeoComputation- The Capital G and C

Couclelis (1998) identified the “core GeoComputation” as the innovative (or derived from other disciplines) computer-based geospatial modeling and analysis

– Contrasted against the traditional computer-supported spatial data analysis and geospatial modeling

– Based on the theory of computation – effective procedure

Openshaw (2000) also emphasized the computational science as the origin of GeoComputation (the Computation part) and the essential concerns about geographical and earth systems (the Geopart)

The capital G and C, as the core of GeoComputation, fundamentally explain the philosophical origins of GeoComputation

– The revolutionary application of computational science in geography or an even broader domain

GeoComputation- Challenges, in a computing perspective

The high diversity of GeoComputation and the complexities of geospatial models

GeoComputation- Challenges, in a computing perspective (cont.)

Computational intensity– Artificial Intelligence (AI), or Computational

Intelligence (CI)– Massive amount of high-resolution

geospatial data– More sophisticated and complicated

geospatal models

High costs of high-performance supercomputers

GeoComputation- Opportunities provided by emerging computing technologies


Grid Computing (Cyberinfrastructure)

– High performance– Widely accessible– Low cost




Web services and Web portals– Interoperability– User friendly




Web services and Web portals– Interoperability– User friendly

Problem: Grid-enabled GeoComputation is largely lacking

New Computing Technologies- Grid Computing (Cyberinfrastructure)

Virtual Supercomputer– An “ integrated suite of computational

engines, mass storage, networks, digital libraries and databases, sensors, software and services” (NSF, 2003)

Virtual Organization– Cross the administrative and institutional

boundaries for resource and services sharing

– Network-based dynamic communities

Decentralized control, common and universal protocols plus “quality services”

New Computing Technologies- Web Portals for the Grid

The web portal offers a centralized and uniform interface to access the distributed and heterogeneous resources and services.

New Computing Technologies- Grid-enabled Parallel Computing

A Computational Grid is “a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities” (Foster, I. and C. Kesselman, 1999)

Transparent to users

Strategies and Approaches- A Grid-based Geospatial Problem-Solving Architecture

Strategies and Approaches- A Grid-based Geospatial Problem-Solving Architecture (cont.)

Presentation Tier: GIS/GeoComputation Web Portals as user-Grid interfaces; serves as a Visual Problem-Solving Environment (VPSE)

Service Tier: to locate and fetch requested GIServices;

Model Tier: Grid-enabled parallel GeoComputation algorithms and models

Grid Tier: Grid Infrastructure

Strategies and Approaches- Case Study: Grid-enabled Geographic Cellular Automata

Geographic Cellular Automata (Geo-CA) are typical GeoComputation models, and have been widely used for simulating and forecasting complex spatial-temporal phenomena

Model parameters represent multiple geospatial factors and non-geospatial factors

Calibration processes are needed produce more realistic simulation results

Extremely computationally intensive

Prediction of urban development to the Prediction of urban development to the year 2050 over southeastern Pennsylvania year 2050 over southeastern Pennsylvania and part of Delaware using the SLEUTH and part of Delaware using the SLEUTH modelmodel

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Case Study: Grid-enabled Geo-CA- Parallel Algorithm Design

Case Study: Grid-enabled Geo-CA- Parallel Algorithm Design

CA models were born to be parallelized

Distinguishing characteristics of geospatial data

– irregular in location and geometric characteristics (shape, size)

– heterogeneous in domain, e.g., dense and sparse

Regular data-decomposition methods might not be efficient to produce evenly distributed partitions in terms of workloads of sub cell spaces

Case Study: Grid-enabled Geo-CA- Parallel Algorithm Design: Data Decomposition

Spatial-adaptive data decomposition– Workload oriented– Spatial indexing methods: grid hierarchy, quad-trees, R-

treesReducing the granularity of decomposition, and assigning multiple sub cell spaces onto a computing unit

The region quad-tree (Worboys and Duckham, 2004)

Workload oriented decomposition

Case Study: Grid-enabled Geo-CA- Parallel Algorithm Design: Neighborhood issue

Case Study: Grid-enabled Geo-CA- Parallel Algorithm Design: Neighborhood issue

Ghost Cells: A sub cell space’s overlapping cells with its neighboring sub cell spaces.

The update processes of ghost cells’ states cause communication overheads

Solution: update on change

Case Study: Grid-enabled Geo-CA- Parallel Algorithm Design: Object Model

Object-Oriented Approach

– To support data decomposition and data transfer among computing units

– Globally indexed coordinates for data communication

Case Study: Grid-enabled Geo-CA- Implementation on the Grid

Implemented in C++

Resides in the Model Tier of the architecture

Conclusions

GeoComputation implies the revolutionary application of computational science in geography or an even broader domain

New computing technologies, especially Grid Computing, offer an opportunity to promote GeoComputation in terms of usability, feasibility, applicability and availability

A Grid-based geospatial problem-solving architecture is proposed to provides a solution for building an easy-to-use, widely accessible and high-performance geospatial problem-solving environment integrating multiple complicated GeoComputational processes at an acceptable cost

Spatial-adaptive data decomposition and “update on change”technique should deliver better efficient workload distribution onto computing units.

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geocomputation in the grid computing age · grid computing age qingfeng (gene) guan, tong zhang,...

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