parallel and distributed computing research at the computing research institute ananth grama...
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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
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
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
System Support for Parallel and Distributed Computing:
• MOBY: A Wireless Peer- to- peer Network• Scalable Resource Location in Service Networks• Scheduling in Clustered Environments
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.
Parallel and Distributed Algorithms
• Scalable Load Balancing Techniques
• Parallel Programming Paradigms
• Metrics and Analysis Frameworks (Isoefficiency, Architecture Abstractions for Portability)
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.
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.
State-of-the-art in Controlled Structures
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.
Control Timelines
Control Strategy
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.
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.
Technical Objectives
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
Technical Challenges
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
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