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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

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How Cyberinfrastructure is Helping Hurricane Mitigation

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  1. 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

  2. Outline • Background and Motivation • Role of Cyber-infrastructure • Project Overview • Project Status • Cyber-infrastructure Contributions • Conclusion

  3. 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

  4. 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

  5. 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

  6. 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

  7. Hurricane Mitigation Project Overview • Goals • High-resolution forecasts with guaranteed simulation execution times • Human-friendly portal • High-resolution visualization modality

  8. 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

  9. WRF Portal

  10. WRF Portal

  11. 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

  12. 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

  13. Modeling WRF Behavior Mathematical Modeling An Incremental Process Profiling Code Inspection & Modeling Parameter Estimation

  14. 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

  15. 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

  16. Case in point – High resolution visualization

  17. 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!

  18. 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

  19. Enhancements to SAGE (cont.)‏ • Wii Remote input interface • A traditional mouse makes it difficult to work with a large display

  20. 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

  21. 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

  22. 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

  23. Thank You! Any Questions? Heidi Alvarez. Director, Center for Internet Augmented Research and Assessment. FIU (heidi@fiu.edu )‏ S. Masoud Sadjadi. Professor and Co-PI of Global Cyberbridges (sadjadi@cs.fiu.edu)‏ Javier Delgado, Research Assistant, FIU (javier.delgado@fiu.edu)‏ Zhao Juan, Research Assistant, CNIC (zhaojuan@cnic.cn)‏ Javier Figueroa, Research Assistant, FIU (figueroa7@gmail.com)‏ Shuren Bi, Research Assistant, CNIC (bishuren@hotmail.com)‏ Mark Joselli, Research Assistant, UFF (mjoselli@m1nd.com)‏ Silvio Luiz Stanzani, Research Assistant, USP (silvio_ls@yahoo.com.br)‏

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