Large-scale hydrological models are demanding both in term of memory allocation and CPU time, particularly when assessment of modeling uncertainty is required. High Performance Computing offers the opportunity to reach resolutions not achievable with standard serial coding. However, the advantages may be offset by poor scalability of the model due to components that have to be executed in series, such as to simulate the presence of hydraulic infrastructures. Driven by this motivation, we developed HYPERstreamHS, a model that adopts a holistic approach to simulate hydrological processes in large river basins with streamflow altered by hydraulic infrastructures. The model adopts a dual-layer parallelization strategy, where the paralleled version of the hydrological kernel is the first-layer, with the second layer taking care of inverse modeling. The results show that the processors should be carefully organized and grouped in order to achieve the best overall performance and suggests that this subdivision is problem specific.