1. Parallel and Distributed Computing Research at the Computing Research Institute Ananth Grama Computing Research Institute and Department of Computer Sciences Purdue University http://www.cs.purdue.edu/people/ayg

2. Areas of Research High Performance Computing Applications Large-Scale Data Handling, Compression, and Data Mining System Support for Parallel and Distributed Computing Parallel and Distributed Algorithms

3. High Performance Computing Applications Fast Multipole Methods –Particle Dynamics (Molecular Dynamics, Materials Simulations) –Fast Solvers and Preconditioners for Integral Equation Formulations –Error Control –Preconditioning Sparse Linear Systems Discrete Optimization Visualization

4. System Support for Parallel and Distributed Computing: MOBY: A Wireless Peer- to- peer Network Scalable Resource Location in Service Networks Scheduling in Clustered Environments

5. Large-Scale Data Handling, Compression, and Mining Bounded Distortion Compression of Particle Data Highly Asymmetric Compression of Multimedia Data Data Classification and Clustering Using Semi-Discrete Matrix Decompositions.

6. Parallel and Distributed Algorithms Scalable Load Balancing Techniques Parallel Programming Paradigms Metrics and Analysis Frameworks (Isoefficiency, Architecture Abstractions for Portability)

7. Computational Elements of Robust Civil Infrastructure Civil infrastructure represents the single largest investment in the United States, valued at over $20 trillion. While these systems are in a constant state of renewal, they are often required to withstand extreme loads caused by natural disasters or human intervention. High-rise structures, long-span bridges, dams, and pipelines are particularly vulnerable. The serviceability and safety of these structures can be vastly improved if damage can be detected and controlled in real-time.

8. Computational Elements of Robust Civil Infrastructure With the availability of reliable inexpensive sensors, large-scale actuation devices, and computing and communication elements, the technology for active control of large structures exists, in principle. The goal of this ambitious project is to: –Enable effective design and economical construction of highly robust smart structures. –Enhance robustness of existing structures by suitably retrofitting them. –Predict and mitigate impact of catastrophic events, –Provide support for area-wide disaster management plans.

9. State-of-the-art in Controlled Structures

10. Building Blocks of Smart Structures Magnetorheostatic dampers can change their load bearing characteristics from fully solid to fully damping in milliseconds when exposed to magnetic fields. Sensing/Computation/Communication elements - designed by part of our research team at Dartmouth. These units cost under $200 and are the size of a deck of cards. This is a rapidly evolving field and efforts are on to develop the next generation of devices here at Purdue.

11. Control Timelines

12. Control Strategy

13. Outstanding Challenges Building reliable inexpensive sensing/computation/communication/actuation (SCCA) units. Building a reliable network of SCCA units. Structural modeling and model reduction. Execution of the distributed control algorithm with tight real-time constraints. Supporting an area-wide disaster management information network.

14. Computational Aspects of Multi- scale Modeling of NEMS Efficient Numerical Algorithms Parallel and Distributed Computing Software and Libraries Interfaces to Experimental Data Acquisition and Design Components Interfaces to Application Servers The overall goal is to develop a comprehensive simulation environment built upon novel algorithms and parallelism for multi- scale modeling of NEMS.

15. Technical Objectives

16. Technical Challenges Diversity of phenomena - multiphysics Variance in spatial scales - nm to cm Variance in temporal scales - fs to s Variety of modeling phenomena Self consistency between scales and phenomena

17. Technical Challenges

18. Computational and Mathematical Challenges Novel problems in linear algebra Special functions and approximations Self consistency between scales and phenomena Highly dynamic geometries and interfaces Extremely large number of degrees of freedom Need for scalable parallelism

19. Collaborations Structures: Mete Sozen, Robert Frosch NEMS, Networks and Control: Mark Lundstrom, Supriyo Datta, Kent Fuchs, Jim Krogmeier, Mark Bell, Ness Shroff, Rudi Eigenman Laser Ablation: Jayathi Murthy, Xianfan Xu Algorithms and Software: Ahmed Sameh, Chris Hoffmann, Sonia Fahmy, Zhiyuan Li