Padma Raghavan

Padma Raghavan received her Ph.D. in computer science from Penn State. Prior to joining Penn State in August 2000, as she served as an associate professor in the Department of Computer Science at the University of Tennessee. Raghavan's research is in the area of parallel scientific computing and she directs the Scalable Computing Laboratory in the department. Raghavan is also the Director of the Penn State Institute for CyberScience, the coordinating unit on campus for multidisciplinary research to enable discovery and design through computing.

Raghavan is currently involved in three research projects funded by the Department of Energy and the National Science Foundation. The "SuperSolvers" project concerns developing robust, limited-memory adaptive and composite sparse solvers. The "MatCase" project involves the design of an e-laboratory for computational materials science to predict macroscopic properties of alloys using multiscale multicomponent modeling. More recently, she initiated the "PxP" (Performance x Power) project at the intersection of scientific computing and power-aware computer system design. This project concerns modeling and simulation of multiprocessor systems to co-optimize designs for scientific workloads for multiple objectives including performance, power and reliability. 

Raghavan received an NSF CAREER Award (1995-1998) for her research on sparse matrix computations. In 2002, she received Maria Goeppert-Mayer Distinguished Scholar Award from the University of Chicago and the Argonne National Laboratory, in recognition of her contributions to scientific computing. Raghavan recently co-edited Parallel Processing for Scientific Computing, published in the SIAM Software, Environments, and Tools series. Raghavan also serves on the editorial board of the SIAM Journal on Scientific Computing and on the program committees of IEEE/ACM Supercomputing and IEEE International Parallel and Distributed Processing Symposium.

Research Interests:

Scientific Computing, Parallel Sparse Computations, Energy-Aware High-Performance Computing, Computational Modeling and Simulation

Selected Publications:

1. Malkowski, K., P. Raghavan M. Kandemir, M. J. Irwin. August 27-29, 2007. Phase-Aware Hardware Adaptivity for Energy-Aware High Performance Computing. Proceedings of the International Symposium on Low Power Electronics and Design (ISLPED 2007). pp. 403-406.  Portland, OR.
   
2. Teranishi, P. Raghavan. 2007. A Hybrid Parallel Preconditioner Using Incomplete Cholesky Factorization and Sparse Approximate Inversion. Proceedings of the Sixteenth International Conference on Domain Decomposition Methods (DD16). Springer-Verlag LNCS 55(1):757-764. New York, NY.
   
3. Lee, I., P. Raghavan, E. G. Ng. 2006. Effective Preconditioning Through Ordering Interleaved with Incomplete Factorization. SIAM Journal on Matrix Analysis and Applications 27(4):1069-1088.
   
4. Malkowski, K., P. Raghavan. May 2005. Multi-Pass Mapping Schemes for Parallel Sparse Matrix Computations. Proceedings of the International Conference on Computational Science (ICCS 2005). Springer-Verlag LNCS 3514(1):245-255. Atlanta, GA.
   
Liu, Z. K., L. Q. Chen, P. Raghavan, Q. Du, J. O. Sofo, S. Langer, C. Wolverton. January 2004. An Integrated Framework for Multi-Scale Materials Simulation and Design. Journal of Computer-Aided Materials Design, Special Issue on Design of Heterogeneous Materials 11(2-3):183-199.