Post on 17-Feb-2016
description
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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