CyberInfrastructure and Geospatial Information Laboratory CIGI Department of Geography and Geographic Information Science Department of Computer Science Department of Urban and Regional Planning ID: 802826
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Slide1
State of CyberGIS
Shaowen Wang
CyberInfrastructure and Geospatial Information Laboratory (CIGI)
Department of Geography and Geographic Information Science
Department of Computer Science
Department of Urban and Regional Planning
National Center for Supercomputing Applications (NCSA)
University of Illinois at Urbana-Champaign
Seattle, WA, USA
September 16, 2013
Slide2NSF SI2-SSI: CyberGIS Project Team
Principal Investigator
Shaowen Wang
Project Staff
ASU: Wenwen Li and Rob PahleORNL: Ranga Raju VatsavaiSDSC: Choonhan YounUIUC: Yan Liu and Anand PadmanabhanGraduate and undergraduate students
Industrial Partner: EsriSteve Kopp and Dawn Wright
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Co-Principal Investigators Luc Anselin Budhendra Bhaduri Timothy Nyerges Nancy Wilkins-Diehr
Senior PersonnelMichael GoodchildSergio ReyXuan ShiMarc SnirE. Lynn Usery
Project Manager
Anand Padmanabhan
Chair of the Science Advisory Committee
Michael
Goodchild
Slide3DiscoveriesQuestionsPredictionsKiller Problems?
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Slide4Big Spatial Data
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Slide5Big Spatial SimulationImage created by Eric Shook
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Slide6Complex Spatial Decision Making
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Slide7Slide8Collaborative Knowledge Discovery
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Slide9Geodesign
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Image source:
http
://www.esri.com/news/arcwatch/0412/a-conversation-with-carl-steinitz.html
Slide10CyberGIS for What and Whom?
CyberGIS Gateway
CyberGIS Toolkit
Middleware
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Slide1111
Slide1212
Slide13Big
Spatial Data
Big
Spatial SimulationComplex Spatial Decision MakingCollaborative Knowledge DiscoveryGeo-DesignCyberGIS GatewayYesMaybeYesMaybeYesMaybeYes
MaybeYesMaybeCyberGIS ToolkitYesMaybeYesMaybeYesMaybeYesMaybeYesMaybeGISolve MiddlewareYesMaybeYesMaybeYesMaybeYesMaybeYesMaybe
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Slide1414
Slide15HeterogeneousSyntacticSemantic
Dynamic
Spatial and temporal
E.g. social mediaMassiveProduced by individualsAccessible to individuals
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Large-scaleGlobal coverageFine granularityIndividual-levelHigh-resolutionDistributed accessInteroperabilityPrivacySecurityTheory + Experiment + Computation + Big Data
Slide16Digital EnvironmentsParallelUsed to be regarded as a way for speeding up GIS functions and spatial analysis
Now becoming a must for GIS and spatial analysis to be built on
Multi- and many-core
GPU (graphics processing unit)Heterogeneous architectureMobileDistributedService-orientedClouds
16Extreme-scale computing, information, and communication systems
Slide17Computing 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 32
XK 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 TBOnline StorageTotal Usable Storage 26.4 PBAggregate I/O Bandwidth > 1 TB/sNear-line StorageAggregate Bandwidth to tape 58 GB/s5-year capacity 380 PB
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Slide18Image source:
http://gigaom.com/2010/12/14/facebook-draws-a-map-of-the-connected-world/
via Mike
Goodchild
Slide19Spatial Computational DomainSufficiently 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
19Wang, 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
Slide20A 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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Slide21Computational Intensity QuestionWhat is the nature
of computational intensity of
geographic analysis?
Why spatial is special? Comparable to “What is the nature of computational complexity of an algorithm?”
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Slide22Spatial Computational Principles/TheoriesSpatial
Distribution
Dependence
IntegrationRepresentationUncertaintyEtc.ComputationalComplexity vs. intensityUncertainty vs. validityPerformance vs. reliabilityEtc.
SCALE22
Slide23Scalability
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Slide24Usability
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Slide25Interoperability
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Slide26Slide27Reliability
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Slide28Reproducibility
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Slide29Understanding of Scientific Processes
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Slide30Education and Workforce DevelopmentCyberGIS Gateway
used
by hundreds of undergraduate and graduate students on multiple campuses
Graduated 6 graduate students and trained 4 postdoctoral fellowsCyberGIS’12 (http://www.cigi.illinois.edu/cybergis12/): The First International Conference on Space, Time, and CyberGISCyberGIS Symposium at the 2013 Annual Meeting of the Association of American Geographers – 17
sessionsTutorialsCyberGIS, GIScience, SC, TeraGrid/XSEDE
Slide31Curriculum and pedagogyPartnershipsOpen ecosystems
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Slide32CyberGIS
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
InfrastructureMiddlewarePortalGatewayPlatformServiceToolkit
AppsCloudGrid
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Slide33www.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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Slide34SustainabilityIntellectual frontiersFinancial
Science challenges are long term and multidisciplinary
Reward mechanisms
Accelerate scientific discoveriesReusability OpenStandardsTechnologiesSocial and organizationalCommunity engagementPartnershipsDepartment of Energy Oak Ridge National LaboratoryIndustryUS Geological Survey
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Slide35CyberGIS 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
Digital
Thinking
Integration and Synthesis
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Slide36Acknowledgments
Federal Agencies
US Geological Survey
Department
of
Energy’s Office of Science
National
Science Foundation
BCS-0846655EAR-1239603OCI-1047916PHY-0621704PHY-1148698TeraGrid/XSEDE SES070004US Geological Survey
Industry
Environmental Systems Research Institute (Esri)Silicon Graphics, Inc. (SGI)
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Slide37Acknowledgments – CIGI
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Slide38Thanks!Comments / Questions? Email:
shaowen@illinois.edu
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