Parallel and Distributed Computing Systems Lab.

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Parallel and Distributed Computing Systems Lab.

• Ananth Grama
• Associate Professor of Computer Sciences
• Purdue University
• http//www.cs.purdue.edu/people/ayg

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Research Agenda

• Software infrastructure for large-scale parallel
  and distributed computing.
• Algorithms for system as well as application
  kernels.
• Compute-intensive applications in scientific and
  commercial domains.
• Techniques for compression and analysis of
  extremely large data-sets generated from
  simulations and other sources.

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Lab Members

• Ph.D. Students
• Ioannis Ioannidis,
• Paul Ruth,
• Lei Shan,
• Robert Light,
• Mehmet Koyuturk.
• M.S. Students
• Ramakrishna Muralikrishna,
• Tzvetan Horozov,
• Min Li.

• Undergraduate Research Students
• Chris Daniels.

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Sources of Research Funding

• Six current National Science Foundation research
  projects (as PI or Co-PI) totaling over 1.6M.
• Equipment support from National Science
  Foundation and Intel Corp. totaling over 2M.
• Research support from National Institutes of
  Health (140K) for medical data analysis.
• Research grant from CERIAS/Lilly Foundation,
  50K.
• Fellowship grants from the Department of Energy
  and Department of Education.

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Teaching Interests

• Parallel and Distributed Computing (CS525,
  CS590D).
• Numerical Analysis (CS514).
• Data Structures (CS251) and Compilers (CS352).

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Recent Awards and Honors

• National Science Foundation CAREER Award, 1998.
• Purdue University School of Science Outstanding
  Assistant Professor Award, 1999.

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Interdisciplinary Collaborations

• Prof. Mete Sozen, Civil Engg., Active Structures.
• Prof. Thomas Downar, Nuclear Engg., Reactor
  Simulations.
• Prof. Bruce Craig, Statistics, Medical Data
  Analysis.
• Profs. Kent Fuchs and Rudolf Eigenmann, Elect.
  Engg., Systems Infrastructure.
• Prof. Morry Levy (Biology) and Jun Xie
  (Statistics), Curriculum Development for
  Bioinformatics.

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Professional Activities and Affiliations

• Member, Sigma Xi, American Association for the
  Advancement of Sciences.
• Conference and workshop program committees and
  organization.
• Referee for international journals and funding
  agencies.
• Guest editor and author of journals and books,
  respectively.

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Technical Contributions

• How do you use a large number of computers to
  solve a single large problem?
• Parallel Algorithms.
• How do you program such computers?
• System software development.
• How do you solve specific problems in parallel?
• Application development, molecular dynamics,
  astrophysical simulations, VLSI modeling,
  scattering and inverse scattering problems.

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Technical Contributions

• How do you handle extremely large data-sets
  generated from these simulations and other
  sources?
• Data compression and analysis.
• What are emerging paradigms in parallel and
  distributed computing?
• Peer-to-peer networks for sharing data,
  computation, and resources.

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Parallel Algorithms and Applications

• Large-scale particle dynamics simulations.

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Molecular Dynamics
Simulating the behavior of large complex
molecules.
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Protein Structure Estimation
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Software Development

• Sharing data, services, and resources over the
  network.
• Clients such as Napster provide mechanisms for
  data sharing.
• Distributed clients such as Gnutella and Limewire
  do not have a centralized server and therefore
  are more scalable.
• How do we make a better Gnutella? Improved
  resource location, adaptive (content-based)
  network topologies, mechanisms for location and
  mapping of services, support for offloading
  computations and remote services.

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Data Mining and Analysis
People who buy diapers in the evening are also
likely to buy beer! -- Put them in the same
aisle.
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Image Compression.
Pattern Matching Compression
JPEG (Current Standard) at same compression
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Video Compression
Real-time mobile media handlers.