The Role of Interactive Super-Computing in Using HPC for Urgent Decision Making
Technological advances are creating exciting new opportunities that have the potential to move HPC well beyond traditional computational workloads. In this paper we focus on the potential for HPC to be instrumental in responding to disasters such as wildfires, hurricanes, extreme flooding, earthquakes, tsunamis, winter weather conditions, and accidents. Driven by the VESTEC EU funded H2020 project, our research looks to prove HPC as a tool not only capable of simulating disasters once they have happened, but also one which is able to operate in a responsive mode, supporting disaster response teams making urgent decisions in real-time. Whilst this has the potential to revolutionise disaster response, it requires the ability to drive HPC interactively, both from the user’s perspective and also based upon the arrival of data. As such interactivity is a critical component in enabling HPC to be exploited in the role of supporting disaster response teams so that urgent decision makers can make the correct decision first time, every time.
KeywordsUrgent decision making Disaster response Interactive HPC VESTEC
This work was funded under the EU FET VESTEC H2020 project, grant agreement number 800904.
- 1.Beckman, P., Nadella, S., Trebon, N., Beschastnikh, I.: SPRUCE: a system for supporting urgent high-performance computing. In: Gaffney, P.W., Pool, J.C.T. (eds.) Grid-Based Problem Solving Environments. ITIFIP, vol. 239, pp. 295–311. Springer, Boston, MA (2007). https://doi.org/10.1007/978-0-387-73659-4_16CrossRefGoogle Scholar
- 3.Meurdesoif, Y.: XIOS: an efficient and highly configurable parallel output library for climate modelling. In: The Second Workshop on Coupling Technologies for Earth System Models (2013)Google Scholar
- 6.Dorier, M., Antoniu, G., Cappello, F., Snir, M., Orf, L.: Damaris: how to efficiently leverage multicore parallelism to achieve scalable, Jitter-free I/O. In: CLUSTER - IEEE International Conference on Cluster Computing, Beijing, China, pp. 155–163 (2012)Google Scholar
- 7.Ayachit, U.: The Paraview Guide: a Parallel Visualization Application. Kitware Inc., Clifton Park (2015) Google Scholar
- 8.Ayachit, U., et al.: Paraview catalyst: enabling in situ data analysis and visualization. In: Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, pp. 25–29. ACM, November 2015Google Scholar
- 10.Amstutz, P., et al.: Common workflow language, v1. 0 (2016)Google Scholar