Open source computational science and engineering (CSE) software is an integral part of methodology-oriented computational research and a priority in the group. Due to the ongoing transformation of e-infrastructure to clouds, methods and workflows that promote horizontal scalability and elasticity for cloud applications are needed, and this may in many cases require re-thinking of how we best make use of computational resources. Other important questions include reproducibility and handling of large and complex data.
Selected recent publications:
- B. Drawert, A. Hellander, B. Bales, D. Banerjee, G. Bellesia, B.J. Daigle, Jr. G. Douglas, M. Gu, A. Gupta, S. Hellander, C. Horuk, D. Nath, A. Takkar, S. Wu, P. Lötstedt, C. Krintz, L. R. Petzold (2016) Stochastic Simulation Service: Bridging the gap between the computational expert and the biologist, PloS Comp. Bio. (to appear)
- B. Drawert, M. Trogdon, S. Toor, L. Petzold and A. Hellander (2016) MOLNs: A cloud appliance for interactive, reproducible and scalable spatial stochastic computational experiments, SIAM J. Sci. Comput. 38(3), C179–C202.
- J. H. Abel, B. Drawert, A. Hellander, and L. R. Petzold (2015). GillesPy: A Python package for stochastic model building and simulation, IEEE LSL (to appear)
- C. Horuk, G. Douglas, A. Gupta, C. Krintz, B. Bales, G. Bellesia, B. Drawert, R. Wolski, L. Petzold, and A. Hellander, Automatic and Portable Cloud Deployment for Scientific Simulations, IEEE/ACM International Conference on High Performance Computing and Simulation, July 2014.