vasileios.karakasis

My personal web page at CSLab.


research.info

My current research interests lie in the field of High Performance Computing. Specifically, I study the performance problems of the well-known Sparse Matrix-Vector Multiplication (SpMV) kernel on modern microarchitectures. Currently, I study statistics and machine learning techniques for predicting the correct optimizations in terms of performance and/or energy consumption for a specific matrix and underlying architecture. A list of publications in this research field can be found here.

In my earlier research life ;-), I have also been interested in intelligent systems and pattern recognition algorithms. In particular, I have worked on data classification algorithms using concepts from artificial immune systems. A list of publications in this field can be found here.

publications.hpc.list

  • V. Karakasis, G. Goumas, K. Nikas, N. Koziris, J. Ruokolainen, and P. Råback, "Using State-of-the-Art Sparse Matrix Optimizations for Accelerating the Performance of Multiphysics Simulations" PARA 2012: Workshop on State-of-the-Art in Scientific and Parallel Computing Helsinki, Finland, June 10–13, 2012 (pdf).To Appear.
  • V. Karakasis, G. Goumas, and N. Koziris, "Exploring the Performance-Energy Tradeoffs in Sparse Matrix-Vector Multiplication," Workshop on Emerging Supercomputing Technologies (ICS'11) Tucson, AZ, USA, May 31, 2011 (pdf).
  • K. Kourtis, V. Karakasis, G. Goumas, and N. Koziris, "CSX: An extended compression format for SpMV on shared memory systems," 16th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP'11) San Antonio, TX, USA, February 12–16, 2011 (pdf).
  • V. Karakasis, G. Goumas, and N. Koziris, "Performance models for blocked Sparse Matrix-Vector multiplication kernels," International Conference on Parallel Processing (ICPP), Vienna, Austria, September 22–25, 2009 (pdf).
  • V. Karakasis, G. Goumas, and N. Koziris, "A comparative study of blocking storage methods for sparse matrices on multicore architectures," IEEE International Conference on Computational Science and Engineering (CSE), Vancouver, BC, Canada, August 29–31, 2009 (pdf).
  • V. Karakasis, G. Goumas, and N. Koziris, "Exploring the effect of block shapes on the performance of sparse kernels," Proceedings of the 23rd International Parallel and Distributed Processing Symposium (IPDPS-PDSEC), Rome, Italy, May 25–29, 2009 (pdf).
  • G. Goumas, K. Kourtis, N. Anastopoulos, V. Karakasis, and N. Koziris, "Performance evaluation of the Sparse Matrix-Vector Multiplication on modern architectures," The Journal of Supercomputing, 50(1):36–77,2009 (pdf).
  • G. Goumas, K. Kourtis, N. Anastopoulos, V. Karakasis, and N. Koziris, "Understanding the Performance of Sparse-Matrix Vector Multiplication," Proceedings of the 16-th Euromicro International Conference on Parallel, Distributed and network-based Processing (PDP 2008), Toulouse, France, February 13–15, 2008 (pdf).
  • G. Goumas, N. Drosinos, V. Karakasis, and N. Koziris, "Coarse-grain Parallel Execution for 2-dimensional PDE problems," Proceedings of 21st International Parallel and Distributed Processing Symposium (IPDPS-PDSEC), Long Beach, CA, U.S.A., March 26–30, 2007 (pdf).

publications.isys.list

  • V. Karakasis and A. Stafylopatis, "Efficient Evolution of Accurate Classification Rules Using a Combination of Gene Expression Programming and Clonal Selection," IEEE Transactions on Evolutionary Computation, 12(6):662–678, December 2008 (pdf).
  • A. Lanaridis, V. Karakasis, and A. Stafylopatis, "Clonal Selection-Based Neural Classifier," Proceedings of Eighth International Conference on Hybrid Intelligent Systems (HIS), Barcelona, Spain, September 10–12, 2008 (pdf).
  • V. Karakasis and A. Stafylopatis, "Data Mining based on Gene Expression Programming and Clonal Selection," Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada, July 16–21, 2006 (pdf).