Floreon+ Modules: A Real-World HARPA Application in the High-End HPC System Domain

This chapter is centered around uncertainty computation with on-demand resource allocation for run-off prediction in a High-Performance Computer environment. Our research stands on a runtime operating system that automatically adapts resource allocation with the computation to provide precise outcomes before the time deadline. In our case, input data comes from several gauging stations, and when newly updated data arrives, models must be re-executed to provide accurate results immediately. Since the models run continuously (24/7), their computational demand is different during various hydrological events (e.g. periods with heavy rain and without any rain) and therefore computational resources have to be balanced according to the event severity. Although these kinds of models should run constantly, they are very computationally demanding during discrete periods of time, for example in the case of heavy rain. Then, the accuracy of the results must be as close as possible to reality. The work relies on the HARPA runtime resource manager that adapts resource allocation to the runtime-variable performance demand of applications. The resource assignment is temperature-aware: the application execution is dynamically migrated to the coolest cores, and this has a positive impact on the system reliability.