SCALABLE COMPUTING LAB (SCL)
Welcome to the Scalable Computing Laboratory (SCL) at The Pennsylvania State University.
At SCL, we focus on the research at the frontiers of computer science towards cyber-enabled discovery and design across disciplines. In particular we focus on advanced algorithms, software and systems for computational modeling, simulation and knowledge abstraction. Our focus areas concern scalable parallel algorithms and the energy aware performance scaling of supercomputers in the Petascale regimes and beyond.
 Graduate students with their adviser Dr.Padma Raghavan and Dr. Kamesh Madduri
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Dr. Padma Raghavan, Director of SCL, Dr. Suzanne Shontz and Dr. Kamesh Madduri together supervise over 15 students.The PEOPLE page introduces the members of the lab supported by GRANTS from various agencies. More information on the Penn State wide inter-disciplinary research related to computing is at ICS: Institute for CyberScience
The CURRENT RESEARCH AT SCL page gives a quick overview of the current and past research pursued by the lab.
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Graduate students with their advisor Dr.Suzanne Shontz
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The Scalable Scientific Computing Laboratory is a well funded research lab with students pursuing their research in High Performance Computing. With the tremendous potential in the area of computational science well beyond the area of computer science the research addresses computational challenges from the fields including bioinformatics, medicine, science, engineering, visual computing and social networking sciences. With a continuous zeal to excel with high quality research, the lab has an upbeat team of students working towards this goal. The projects pursued at the lab allows the growth of collaborative skills in the students thus preparing them for future real world challenges. Students who have graduated from this lab have been successfully placed at various places including Google, Cray Inc, Mathworks, and in academia.
Our lab has openings for new students and post doctoral candidates.
Contact Dr. Raghavan if interested in pursuing a program of research in High Performance Computing and its applications to the field of Life Sciences, Material Sciences, and Energy and the Environment.
Selected Publications
- Virtual I/O Caching: Effective Storage Cache Management for Concurrent Workloads. M. Frasca, R. Prabhakar, P Raghavan, M. Kandemir., Proceedings of the International Conference on Computational Science, SC 2011
- Exploiting dense substructures for fast sparse matrix vector multiplication. M. Shantharam, A. Chatterjee, P. Raghavan, Proceedings of the 25th International Journal of High Performance Computing Applications: 328-341 (2011)
- Can models of scientific software-hardware interactions be predictive? M. Frasca, A. Chatterjee, P Raghavan, Proceedings of the International Conference on Computational Science, ICCS 2011
- Feature Subspace Transformations for Enhancing K-Means Clustering, A.Chatterjee, S. Bhowmick, P. Raghavan, In proceedings International Conference on Information and Knowledge Management (CIKM), Toronto, Canada, 2010
- Shontz, S., S. Vavasis. October 2010. Analysis of and Workarounds for Element Reversal for a Finite Element-based Algorithm for Warping Triangular and Tetrahedral Meshes. BIT, Numerical Mathematics. Published online. 23 pages.
- Kim, J., S. Sastry, S. Shontz. October 2010. Efficient Solution of Elliptic Partial Differential Equations via Effective Combination of Mesh Quality Metrics, Preconditioners, and Sparse Linear Solvers. Proceedings of the Nineteenth International Meshing Roundtable. pp. 103-120. Chattanooga, TN.
- Park, J., S. Shontz. May 31-June 2, 2010. Two Derivative-Free Optimization Algorithms for Mesh Quality Improvement. Proceedings of the 2010 International Conference on Computational Science (ICCS 2010). pp.
387-396. Amsterdam, Netherlands. - Hybrid Techniques for Fast Multicore Simulation, M. Shantharam, P. Raghavan and M. Kandemir, Euro-Par 09: Proceedings of the 15th International Euro-Par Conference on Parallel Processing, pp. 122–134, Springer Verlag, 2009.
- Towards Low-Cost, High-Accuracy Classifiers for Linear Solver Selection, S. Bhowmick, B. Toth and P. Raghavan, Proceedings of International Conference on Computational Science 2009, pp. 463–472. Lecture Notes in Computer Science, Vol, 5544, Springer, 2009.
- Energy-Aware Scheduling for Scalable Matrix Computations, P. Raghavan, Scheduling for large-scale systems, Knoxville, May 13-115, 2009.
