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How Cyberinfrastructure is Helping Hurricane Mitigation Students Javier Delgado (FIU)‏ [presenter] Zhao Juan (CNIC)‏ [presenter] Bi Shuren (CNIC)‏ Silvio.

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Presentation on theme: "How Cyberinfrastructure is Helping Hurricane Mitigation Students Javier Delgado (FIU)‏ [presenter] Zhao Juan (CNIC)‏ [presenter] Bi Shuren (CNIC)‏ Silvio."— Presentation transcript:

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:

5 Motivation 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 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 Hurricane Andrew, Florida 1992 Katrina, New Orleans 2005 Ike, Cuba 2008

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 distributed software 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:

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10 WRF Portal

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12 Modeling WRF Behavior An Incremental Process 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

13 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

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

15 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:

16 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 # ANI

17 Case in point – High resolution visualization

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19 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!

20 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

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

22 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

23 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

24 Acknowledgments Global CyberBridges NSF CI-TEAM OCI MareNostrum Supercomputer support NSF-PIRE OISE Scalable Adaptive Graphics Environment (SAGE) NSF SCI , ANI NSF research assistance grants: HRD , CNS , CNS , CNS , IIS

25 Thank You! Any Questions? Heidi Alvarez. Director, Center for Internet Augmented Research and Assessment. FIU )‏ S. Masoud Sadjadi. Professor and Co-PI of Global Cyberbridges Javier Delgado, Research Assistant, FIU Zhao Juan, Research Assistant, CNIC Javier Figueroa, Research Assistant, FIU Shuren Bi, Research Assistant, CNIC Mark Joselli, Research Assistant, UFF Silvio Luiz Stanzani, Research Assistant, USP


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