Parallel and Distributed Computing Research at the Computing Research Institute

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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

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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

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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

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System Support for Parallel and Distributed
Computing

• MOBY A Wireless Peer- to- peer Network
• Scalable Resource Location in Service Networks
• Scheduling in Clustered Environments

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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.

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Parallel and Distributed Algorithms

• Scalable Load Balancing Techniques
• Parallel Programming Paradigms
• Metrics and Analysis Frameworks (Isoefficiency,
  Architecture Abstractions for Portability)

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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.

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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.

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State-of-the-art in Controlled Structures
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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.
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Control Timelines
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Control Strategy
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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.

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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.
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Technical Objectives
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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

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Technical Challenges
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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

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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