Performance optimization of the MGB hydrological model for multi-core and GPU architectures

Large-scale hydrological models simulate watershed processes with applications in water resources, climate change, land use, and forecast systems. The quality of the simulations mainly depends on calibrating optimal sets of watershed parameters, a time-consuming task that highly demands computational resources from repeated simulations. This work aims at performance optimizations on the MGB (“Modelo de Grandes Bacias”) hydrological model and the MOCOM-UA (Multi-Objective Complex Evolution) calibration method for two watersheds. The optimizations target state-of-the-art CPU/GPU systems, exploiting techniques that include AVX-512 vectorization, and multi-core (CPU) and many-core (GPU) parallelisms. Significant speedups of up to 20 × (CPU) were achieved for calibration, while the scalability analysis indicated 24 × (CPU) and 65 × (GPU) for simulations with larger problem sizes. The roofline analysis confirmed more effective use of the hardware resources, and the quantitative accuracy evaluation of the optimized implementations reached maximum relative errors of approximately 6% for discharges and objective functions.