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Scalable Computing Laboratory (SCL)

Welcome to the Scalable Computing Laboratory (SCL) at The Pennsylvania State University. 

At SCL, we focus on 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. We design scalable parallel algorithms and study energy-aware performance scaling of supercomputers in the petascale regime and beyond. 

 Dr. Raghavan and her grad students.    

Scalable Computing Laboratory members (2011)

 

Dr. Padma Raghavan and Dr. Kamesh Madduri supervise students in SCL. The PEOPLE page introduces current members of the lab, supported by GRANTS from various agencies. The RESEARCH page gives a quick overview of the current and past research pursued by the lab. More information on the Penn State wide inter-disciplinary research related to computing is at ICS: Institute for CyberScience. With the tremendous potential in the area of computational science, well beyond the area of computer science, our research addresses computational challenges from 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 allow the growth of collaborative skills in students, thus preparing them for future real world challenges. Students who have graduated from the laboratory have been successfully placed at various places including Google, Cray Inc, Mathworks, and in academia.

Our lab has openings for new students and postdoctoral candidates

Please contact Dr. Raghavan and Dr. Madduri if you are interested in pursuing research in High Performance Computing and its applications to the fields of Life Sciences, Material Sciences, and Energy and the Environment.

Selected Publications

  • J.D. Booth, P. Raghavan. Hybrid Sparse Linear Solutions with Substituted Factorization. Springer Series Lecture Notes in Computer Science (VECPAR 2014).
  • S. Kirmani, P. Raghavan. Scalable Parallel Graph Partitioning. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2013). 
  • B. Wang, et al. Kinetic Turbulence Simulations at Extreme Scale on Leadership-class Systems. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2013). 
  • G. Slota, K. Madduri. Fast approximate subgraph counting and enumeration. Proceedings of the International Conference on Parallel Processing (ICPP 2013). To appear.
  • M. Shantharam, Y. Youn, P. Raghavan. Speedup-Aware Co-Schedules for Efficient Workload Management, Proceedings of the Parallel Processing Letters ( June 2013). 
  • M. Frasca, K. Madduri, P. Raghavan. NUMA-Aware Graph Mining Techniques for Performance and Energy Efficiency. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2012).
  • M. Shantharam, S. Srinivasmurthy, P. Raghavan. Fault Tolerant Preconditioned Conjugate Gradient for Sparse Linear System Solution, Proceedings of the 26th International Conference on Supercomputing (ICS 2012).
  • M. Frasca, P. Raghavan. Phase Partitioning Methods for I/O Cache Optimization. Proceedings of the International Conference on Parallel Processing (ICPP 2012).
  • M. Frasca, R. Prabhakar, P. Raghavan, M. Kandemir. Virtual I/O Caching: Effective Storage Cache Management for Concurrent Workloads. of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2011).
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