State of CyberGIS

Shaowen WangCyberInfrastructure and Geospatial Information Laboratory (CIGI)Department of Geography and Geographic Information Science

Department of Computer ScienceDepartment of Urban and Regional Planning

National Center for Supercomputing Applications (NCSA)University of Illinois at Urbana-Champaign

Seattle, WA, USASeptember 16, 2013

NSF SI2-SSI: CyberGIS Project Team

Principal Investigator– Shaowen Wang

Project Staff– ASU: Wenwen Li and Rob Pahle– ORNL: Ranga Raju Vatsavai– SDSC: Choonhan Youn– UIUC: Yan Liu and Anand

Padmanabhan– Graduate and undergraduate

studentsIndustrial Partner: Esri– Steve Kopp and Dawn Wright

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Co-Principal Investigators– Luc Anselin – Budhendra Bhaduri– Timothy Nyerges– Nancy Wilkins-Diehr

Senior Personnel– Michael Goodchild– Sergio Rey– Xuan Shi– Marc Snir– E. Lynn Usery

Project Manager– Anand Padmanabhan

Chair of the Science Advisory Committee – Michael Goodchild

DiscoveriesQuestions

PredictionsKiller Problems?

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Big Spatial Data

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Big Spatial Simulation

Image created by Eric Shook

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Complex Spatial Decision Making

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Collaborative Knowledge Discovery

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Geodesign

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Image source: http://www.esri.com/news/arcwatch/0412/a-conversation-with-carl-steinitz.html

CyberGIS for What and Whom?

CyberGIS Gateway

CyberGIS Toolkit

Middleware

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Big Spatial Data

Big Spatial Simulation

Complex Spatial Decision Making

Collaborative Knowledge Discovery

Geo-Design

CyberGIS Gateway

YesMaybe

YesMaybe

YesMaybe

YesMaybe

YesMaybe

CyberGIS Toolkit

YesMaybe

YesMaybe

YesMaybe

YesMaybe

YesMaybe

GISolve Middleware

YesMaybe

YesMaybe

YesMaybe

YesMaybe

YesMaybe

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Heterogeneous• Syntactic• Semantic

Dynamic• Spatial and temporal• E.g. social media

Massive• Produced by

individuals• Accessible to

individuals

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Large-scale• Global coverage

Fine granularity• Individual-level• High-resolution

Distributed access• Interoperability• Privacy• Security

Theory + Experiment + Computation + Big

Data

Digital Environments Parallel

o Used to be regarded as a way for speeding up GIS functions and spatial analysis

o Now becoming a must for GIS and spatial analysis to be built on

• Multi- and many-core• GPU (graphics processing unit)

Heterogeneous architecture Mobile Distributed

o Service-orientedo Clouds

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Extreme-scale

computing, information, and

communication systems

Computing ProfileTotal Peak Performance 11.61 PFTotal System Memory 1.476 PB XE Compute Cabinets 237XE Peak Performance 7.1 PFXE Compute Nodes 22,640XE Bulldozer Cores 362,240XE System Memory 1.382 PB XK Compute Cabinets 32XK Peak Performance (CPU+GPU) 4.51 PFXK Compute Nodes 3072XK Bulldozer Cores (CPU) 24,576XK Kepler Accelerators (GPU) 3072XK System Memory (CPU) 96 TBXK Accelerator Memory (GPU) 18 TB

Online StorageTotal Usable Storage 26.4 PBAggregate I/O Bandwidth > 1 TB/s

Near-line StorageAggregate Bandwidth to tape 58 GB/s5-year capacity 380 PB

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Image source: http://gigaom.com/2010/12/14/facebook-draws-a-map-of-the-connected-world/ via Mike Goodchild

Spatial Computational Domain

• Sufficiently coarse to ensure that the derivation and decomposition of the spatial computational domain is computationally inexpensive

• Sufficiently fine to allow domain decomposition to produce a large number of sub-domains that are executed concurrently to improve computational performance

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Wang, S., and Armstrong, M. P. 2009. “A Theoretical Approach to the Use of Cyberinfrastructure in Geographical Analysis.” International Journal of Geographical Information Science, 23 (2): 169-193

A Hierarchical Computational Framework for Agent-based Modeling

Tang, W. and Wang, S. 2009 “HPABM: A Hierarchical Parallel Simulation Framework for Spatially-Explicit Agent-Based Models.” Transactions in GIS, 13 (3): 315-333

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Computational Intensity Question

• What is the nature of computational intensity of geographic analysis?o Why spatial is special?

• Comparable to o “What is the nature of

computational complexity of an algorithm?”

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Spatial Computational Principles/Theories Spatial

• Distribution• Dependence• Integration• Representation• Uncertainty• Etc.

Computational• Complexity vs. intensity• Uncertainty vs. validity• Performance vs. reliability• Etc.

SCA

LE

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Scalability

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Usability

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Interoperability

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Reliability

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Reproducibility

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Understanding of Scientific Processes

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Education and Workforce Development• CyberGIS Gateway used by hundreds of

undergraduate and graduate students on multiple campuses

• Graduated 6 graduate students and trained 4 postdoctoral fellows

• CyberGIS’12 (http://www.cigi.illinois.edu/cybergis12/): The First International Conference on Space, Time, and CyberGIS

• CyberGIS Symposium at the 2013 Annual Meeting of the Association of American Geographers – 17 sessions

• Tutorials• CyberGIS, GIScience, SC, TeraGrid/XSEDE

• Curriculum and pedagogy• Partnerships• Open ecosystems

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CyberGIS

Discovery and Innovation

Advanced Technologies

Wang, S. 2013. “CyberGIS: Blueprint for Integrated and Scalable Geospatial Software Ecosystems.” International Journal of Geographical Information Science, 27 (11), in press

InfrastructureMiddleware

PortalGatewayPlatform

ServiceToolkitApps

CloudGrid

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www.opensciencegrid.org www.xsede.org http://lakjeewa.blogspot.com/

2011/09/what-is-cloud-computing.html

Integrated Digital and Spatial Sciences

CyberGIS Gateway

CyberGIS Toolkit

Space-Time Integration & Synthesis

GISolve Middleware

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Sustainability• Intellectual frontiers• Financial

o Science challenges are long term and multidisciplinaryo Reward mechanisms

• Accelerate scientific discoveries• Reusability

• Openo Standardso Technologies

• Social and organizationalo Community engagemento Partnerships

• Department of Energy Oak Ridge National Laboratory• Industry• US Geological Survey

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CyberGIS Center for Advanced Digital and Spatial Studies

CyberGIS

Geospatial Sciences and Technologies

Advanced Cyberinfrastructure

Data-Intensive Applications and Sciences

Arts, Emergency Management,

Energy, Health, Sustainability, etc.

GISolve

Spatial Computational Theories / Methods

Extreme-Scale Computing, NSF

XSEDE, Open Science Grid

Spatial

Thinking

Digi

tal

Thinking

Inte

grat

ion

and

Synt

hesi

s

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Acknowledgments Federal Agencies

US Geological Survey Department of Energy’s Office of Science National Science Foundation

– BCS-0846655– EAR-1239603– OCI-1047916– PHY-0621704– PHY-1148698– TeraGrid/XSEDE SES070004

US Geological Survey Industry

Environmental Systems Research Institute (Esri)

Silicon Graphics, Inc. (SGI)

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Acknowledgments – CIGI

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Thanks!• Comments / Questions?

• Email: shaowen@illinois.edu

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