Large Scale Coastal Modeling on the Grid
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1 Large Scale Coastal Modeling on the Grid Lavanya Ramakrishnan Renaissance Computing Institute Duke University North Carolina State University University of North Carolina - Chapel Hill SURA Grid Application Planning & Implementation, Austin, TX December 7, 2005
2 Acknowledgements SCOOP SURA Partners Philip Bogden, Joanne Bintz, Helen Conver and many others SCOOP North Carolina Participants UNC Marine Sciences Brian Blanton, Rick Luettich, Larry Mason (ITS) MCNC/GCNS Michael Garvin, Steve Thorpe, Chuck Kesler RENaissance Computing Institute (RENCI) Howard Lander, Brad Viviano, Alan Blatecky Dan Reed
3 Outline Motivation and Background SCOOP, Science Scenarios SCOOP ADCIRC Experiences prototype and experiences SCOOP Service Oriented Architecture modular, flexible Future work, Conclusions
4 Weather Strikes Daily! Hurricane Season named storms, 14 hurricanes, 3 with major impact billions of dollars economic losses We need to provide early and accurate forecasts, dissemination of information provide infrastructure to solve inter-disciplinary problems to be able to interact in real-time i.e. evaluate and adapt Source: NOAA Today Next year
5 Next Generation Cyberinfrastructure R R R R R R R R R R Examples SURA Coastal Ocean Observing and Prediction (SCOOP) Program U. Florida, U. Alabama, LSU, VIMS, Texas A& M, U. Maryland, U. of Miami, GoMOOS, UNC Linked Environments for Atmospheric Discovery Oklahoma, Indiana, UCAR, Colorado State, Howard, Alabama, Millersville, NCSA, North Carolina
6 SURA Coastal Ocean Observing and Prediction (SCOOP) Program Integrated Ocean Observing System (IOOS) rapidly assess, predict, and mitigate the impact make information widely available plug and play model for next generation research End users modelers, decision makers, resource providers, etc
7 Illustrative Science Scenarios Daily operational 24/7/365 forecasts continuous, ensure availability Real-time ensemble model prediction real-time data, increased accuracy Retrospective analysis evaluate results, innovate new mechanisms Interdisciplinary problems inundation affected by storm surge, terrestrial hydrology, precipitation
8 Characteristics and Challenges Application domain need integrated data and modeling environment adapt rapidly and automatically in response to weather real-time policy, different data sources (e.g. sensors) and formats Computer Science/IT data storage, resource selection multilevel monitoring and intelligent control policies, priorities based on urgency
9 Outline Motivation and Background SCOOP, Science Scenarios SCOOP ADCIRC Experiences prototype and experiences SCOOP Service Oriented Architecture modular, flexible Future work, Conclusions
10 ADCIRC Coastal Modeling Advanced Circulation Model (ADCIRC) Finite Element Hydrodynamic Model for Coastal Oceans, Inlets, Rivers and Floodplains Access to data stored at UNC OpenDAP access through portal interface Retrospective Model Runs a portal interface, access to grid resources Real-time operational ensemble modeling 5 wind data sources, event driven, access to distributed resources ETA NAH WANA WANA WANA
11 Technology Exposition Grid technologies (Globus) standard job submission: Gatekeeper file transfer: GridFTP queue status: Information Services/MDS credential repository: MyProxy Domain products Local Data Manager event driven data transport system OpenDAP format independent network data access protocol Portal Technologies NSF NMI Open Grid Computing Environment (OGCE)
12 SCOOP Portal: OpenDAP
13 SCOOP Portal: Hindcast Set Run Dates (Hurricane Ivan) Current ADCIRC grid 16 CPU Decomposition Submit Job To GRID
14 Hindcast Analysis on the Grid Specify model run parameters Create tarball of needed Archived Files Third-party transfer between Portal host and Compute host Execution of requested simulation 1 To UAH RENCI/UNC Portal LDM 2 MyProxy GridFTP Globus Gatekeeper 4 3 Globus Gatekeeper MCNC Grid GridFTP LSF Queue Globus Gatekeeper GridFTP UNC Experimental SCOOP Machine Mass Storage NFS Mounted UNC Production System OPeNDAP Server Daily Model Runs NCEP
15 Ensemble Modeling V1: Hindcast runs V2: Forecast status ETA NAH UF-WANA LDM LDM RENCI SCOOP Portal (dante1) RENCI LDM Node (dante2) Resource Selection Application Management Globus (Gatekeeper, GridFTP, MDS) MCNC LSF (scoop) RENCI Maui /PBS (dante0) UF (sura-uf-d4600-2) UAH (beaker) LSU (hugo, hilda ) GRID core Partner GRID Establishing connections LDM Results pushed to RENCI VizWall
16 Real-time Resource Selection Given a set of resources, which is the best resource I should run on? If MDS available How many resources can I get (queue)? MDS else Run a probe job to find no of cpus and rough estimate on time Gatekeeper GridFTP Resource Management Are services up? MDS Policy MDS Gatekeeper GridFTP Meta-scheduling Monitoring Gatekeeper GridFTP
17 SCOOP Portal: Forecast Status
18 Lessons Learnt from Hurricane Season 2005 Forecast mode debugging is hard Resource selection performance, reliability Fault Tolerance when jobs fail Unexplored territory verification data management catalog and archive access Left: ADCIRC max water level for 72 hr forecast starting 29 Aug 2005,driven by the "usual, alwaysavailable ETA winds. Right: ADCIRC max water level over ALL of UFL ensemble wind fields for 72 hr forecast starting 29 Aug 2005, driven by UFL always-available ETA winds. Images credit: Brian O. Blanton, Dept of Marine Sciences, UNC Chapel Hill
19 Grid Testbed Experiences Components at every site Globus gatekeeper, GridFTP, PBS/LSF (optional), MDS (optional) Globus setup at compute sites hours of testing, firewall problems MDS not setup Resource availability and setup e.g: uudecode was not installed TAMU UNC, MCNC UAHb LSU UF
20 It is all about partnerships! SURAGrid V1: Hindcast runs V2: Forecast status Bioportal Gateway SCOOP Grid NCSCOOP Globus MCNC LSF cluster UF UAH LSU Archive TAMU WANA Archive Globus LDM Node RENCI LDM Maui /PBS Cluster Viz Wall Health Sciences Library SCOOP Portal ETA NAH Archive UNC Marine Sciences Ad-hoc collaboration on campus
21 Outline Motivation and Background SCOOP, Science Scenarios SCOOP ADCIRC Experiences prototype and experiences SCOOP Service Oriented Architecture modular, flexible Future work, Conclusions
22 Cross-cutting Components User Interface Layer portal (resource access, workflow interfaces, interactive search services, etc.), visualization tools, software libraries Directories Security (GSI, etc) Models & Analysis Tools Application and Tools Layer Workflow Tools Data Visualization Data Translation Management Layer Monitoring Archive Management Data Management Resource Management Application Management Resource Access Layer Data transport LDM, GridFTP, scp, etc Web service protocols SOAP, XML, WSRF, etc Virtualization Resources (compute, storage, network)
23 Retrospective Analysis User Interface Translation Coastal Model Verification / Validation Analysis Application Catalog Services Archive Services Data Management Archive Services Archive Services Winds Application Env. Resource Selection Catalog Services Model Results Catalog Services Observations Resource Access Layer
24 Forecast Analysis User Interface Visualization OpenIOOS Translation Coastal Model Verification / Validation Catalog Services Archive Services Data Management Archive Services Archive Services Winds Application Env. Resource Selection Catalog Services Model Results Catalog Services Observations Resource Access Layer
25 Next Steps Multi-level Monitoring and Resource Management resource level, web service, etc optimal resource selection based on data movement costs Science Problems ensemble wind generation enhancement statistically based, recomputation of cases for sensitivity analysis. SCOOP product translation service coupling between ADCIRC and SWAN Integrated Workflow Environment application planning and optimization data catalog and archive service external APIs and documentation external model joining SCOOP grid
26 Conclusions SCOOP: A Service Oriented Architecture modular, composable, event-driven instruments, streaming data Grid programming and deployment specifying resource requirements higher level requirements performability of grid environments managing resource environments specialized set of requirements for each application cost, accountability, auditing
27 Questions?
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