Performance Optimization and Scalability Analysis of the MGB Hydrological Model

Abstract

Hydrological models are extensively used in applications such as water resources, climate change, land use, and forecast systems. The focus of this paper is performance optimization of the MGB hydrological model, which is widely employed to simulate water flows in large-scale watersheds. The optimization strategies that we selected include AVX-512 vectorization, thread-parallelism on multi-core CPUs (OpenMP), and data-parallelism on many-core GPUs (CUDA). We conducted experiments for real-world input datasets on state-of-the-art HPC systems based on Intel's Skylake CPUs and NVIDIA GPUs. In addition, a Roofline model characterization for these datasets confirmed performance improvements of up to 37.5x on the most time-consuming part of the code and 8.6x on the full MGB model. The work proposed herein shows that careful optimizations are needed for hydrological models to achieve a significant fraction of the performance potential in modern processors.