Parallel and Distributed Computing Systems Lab.

Ananth Grama

Associate Professor of Computer Sciences

Purdue University

http://www.cs.purdue.edu/people/ayg

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.

Lab MembersPh.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.

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.

Teaching Interests

• Parallel and Distributed Computing (CS525, CS590D).

• Numerical Analysis (CS514).

• Data Structures (CS251) and Compilers (CS352).

Recent Awards and Honors

• National Science Foundation CAREER Award, 1998.

• Purdue University School of Science Outstanding Assistant Professor Award, 1999.

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.

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.

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.

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.

Parallel Algorithms and Applications

Large-scale particle dynamics simulations.

Molecular Dynamics

Simulating the behavior of large complex molecules.

Protein Structure Estimation

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.

Data Mining and Analysis

“People who buy diapers in the evening are also likely to buy beer!” -- Put them in the same aisle.

Image Compression.

Pattern Matching Compression

JPEG (Current Standard) at same compression

Video Compression

Real-time mobile media handlers.