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How Cyberinfrastructure is Helping Hurricane Mitigation. Students Javier Delgado (FIU)‏ [presenter] Zhao Juan (CNIC)‏ [presenter] Bi Shuren (CNIC)‏ Silvio Luiz Stanzani (UniSantos)‏ Mark Eirik Scortegagna Joselli (UFF)‏ Javier Figueroa (FIU/UM)‏ Advisors S. Masoud Sadjadi

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How cyberinfrastructure is helping hurricane mitigation
How Cyberinfrastructure is Helping Hurricane Mitigation

Students

Javier Delgado (FIU)‏ [presenter]

Zhao Juan (CNIC)‏ [presenter]

Bi Shuren (CNIC)‏

Silvio Luiz Stanzani (UniSantos)‏

Mark Eirik Scortegagna Joselli (UFF)‏

Javier Figueroa (FIU/UM)‏

Advisors

S. Masoud Sadjadi

Heidi Alvarez

Universidade de São Paulo

Chinese American Networking Symposium. Oct. 20 – 22, 2008


Outline

  • Background and Motivation

  • Role of Cyber-infrastructure

  • Project Overview

  • Project Status

  • Cyber-infrastructure Contributions

  • Conclusion


Background of Global CyberBridges

  • Improves technology training for international collaboration

    • Software usage

    • Logistical issues (e.g. time zones, holidays, etc.)‏

  • Collaborate for the purpose of scientific advancement

    • Visualization Modalities

    • Weather Prediction

    • Bioinformatics


Hurricane Mitigation Background

  • Computationally Intensive

  • Improvement requires cross-disciplinary expertise

  • High Performance Computing

    • Meta-scheduling

    • Resource Allocation

    • Work flow Management

  • Weather Modeling

    • Weather Research and Forecasting (WRF)‏

Image Source: http://mls.jpl.nasa.gov


Motivation

  • Alarming Statistics

    • 40% of (small-medium sized) companies shut down within 36 months, if forced closed for 3 or more days after a hurricane

    • Local communities lose jobs and hundreds of millions of dollars to their economy

      • If 5% of businesses in South Florida recover one week earlier, then we can prevent $219,300,000 in non-property economic losses

  • Hurricanes cost coastal regions financial and personal damage

  • Damage can be mitigated, but

    • Impact area prediction is inaccurate

    • Simulation using commodity computers is not precise

Hurricane Andrew, Florida 1992

Ike, Cuba 2008

Katrina, New Orleans 2005


Why Apply Cyberinfrastructure to Research & Learning?

  • Preparation for a globalized workforce

    • Innovation is now driven by global collaboration

    • Diverse (and complementary) expertise

  • Enable transparent cyberinfrastructure

  • In Global CyberBridges, students are the bridges


Hurricane Mitigation Project Overview

  • Goals

    • High-resolution forecasts with guaranteed simulation execution times

    • Human-friendly portal

    • High-resolution visualization modality


High Resolution Hurricane Forecasting

  • We create:

    • A distributedsoftware model that can run on heterogeneous computing nodes at multiple sites simultaneously to improve

      • Speed of results

      • Resolution of the numerical model

      • Scalability of requests by interested parties

    • In other words, we need to grid-enable WRF

  • WRF Information: http://wrf-model.org/index.php




Modeling WRF Behavior

  • Paradox of computationally-intensive jobs:

    • Underestimated execution time = killed job

    • Overestimated execution time = prohibitive queue time

  • Grid computing drawbacks

    • Less reliable than cluster computing

    • No built in quality assurance mechanism

    • Hurricane prediction is time-sensitive, so it needs to work around this

An Incremental Process


Modeling WRF Behavior

  • Meta-scheduler addresses the quality assurance issue

  • To predict execution time, model the software

    • Pick a representative simulation domain

    • Execute it on various platforms with various configurations

    • Devise a model for execution time prediction and implement it in software

    • Test model

    • Adjust until prediction accuracy is within 10 percent


Modeling WRF Behavior

Mathematical

Modeling

An Incremental Process

Profiling

Code Inspection

& Modeling

Parameter

Estimation


Current Execution Prediction Accuracy

  • Adequate accuracy on multiple platforms

  • Cross-cluster:

    • 8-node, 32-bit Intel Cluster

    • 16-node, 64-bit Intel Cluster

    • Different (simulated) CPU speed and number-of-node executions

  • Inter-cluster on MareNostrum Supercomputer of Barcelona Supercomputing Center

    • Up to 128-nodes

MareNostrum Info: http://www.top500.org/system/8242


Visualization Platform

  • Collaboration

    • e-Learning

    • Cross-disciplinary video conferencing

    • Desktop sharing

  • High-resolution Visualization

  • Built on top of the Scalable Adaptive Graphics Environment (SAGE)‏

SAGE is developed by the cavern group at the Electronic Visualization Laboratory. # SCI-0225642

# ANI-0225642

  • http://www.evl.uic.edu/cavern/sage/index.php



SAGE

  • Scalable

    • Hundreds of Screens can be used

    • Built with high-performance applications in mind

  • Extensible

    • Provides API for creating custom SAGE applications

    • But this is also a problem

      • Porting an application is not trivial

      • There's a lot of applications out there!


Enhancements to SAGE

  • Porting the Mozilla Firefox Web browser

    • Many emerging applications are web-based

    • The web browser is the platform

    • Native SAGE Web Browser would give optimal performance

  • Remote Desktop Enhancement

    • A responsive remote desktop modality is essential for collaboration and e-Learning

    • Users can share their display for all collaborators to see

    • Non-portable applications can be displayed also


Enhancements to SAGE (cont.)‏

  • Wii Remote input interface

    • A traditional mouse makes it difficult to work with a large display


Global CyberBridges Overall Contributions

  • Weather Forecasting

    • Students in different scientific fields from 3 different continents exposed to the problem through a remote class

    • Grid-computing related methodologies for addressing these problems have been presented

    • Collaborative publications in progress

  • Visualization

    • Based on the difficulties we had in the class, we are trying to implement a cutting-edge e-Learning environment based on SAGE

    • We are working together to publish this work


Conclusion

  • e-Learning is difficult,

    • Primitive nature of videoconferencing software

    • Different time zones

    • Holiday and Vacation periods

  • Global collaboration

    • Learning to work with people around the world is essential. This has been the most valuable lesson

    • We have done important research in the process


Acknowledgments

  • Global CyberBridges NSF CI-TEAM OCI-0636031

  • MareNostrum Supercomputer support NSF-PIRE OISE-0730065

  • Scalable Adaptive Graphics Environment (SAGE) NSF SCI-0225642, ANI-0225642

  • NSF research assistance grants: HRD-0833093, CNS-0426125, CNS-052081, CNS-0540592, IIS-0308155


Thank You!

Any Questions?

Heidi Alvarez. Director, Center for Internet Augmented Research and Assessment. FIU ([email protected] )‏

S. Masoud Sadjadi. Professor and Co-PI of Global Cyberbridges ([email protected])‏

Javier Delgado, Research Assistant, FIU

([email protected])‏

Zhao Juan, Research Assistant, CNIC

([email protected])‏

Javier Figueroa, Research Assistant, FIU

([email protected])‏

Shuren Bi, Research Assistant, CNIC

([email protected])‏

Mark Joselli, Research Assistant, UFF

([email protected])‏

Silvio Luiz Stanzani, Research Assistant, USP

([email protected])‏


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