@InProceedings{FreitasMendIlic:2020:PeOpSc,
               author = "Freitas, Henrique Renn{\'o} de Azeredo and Mendes, Celso Luiz and 
                         Ilic, Aleksandar",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade de 
                         Lisboa}",
                title = "Performance Optimization and Scalability Analysis of the MGB 
                         Hydrological Model",
            booktitle = "Proceedings...",
                 year = "2020",
         organization = "International Conference on High Performance Computing, Data, and 
                         Analytics, 27.",
            publisher = "IEEE",
             keywords = "MGB modelperformancescalabilityminiapprooflineCPU/GPU.",
             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.",
  conference-location = "Online",
      conference-year = "16-18 Dec.",
                  doi = "10.1109/HiPC50609.2020.00017",
                  url = "http://dx.doi.org/10.1109/HiPC50609.2020.00017",
             language = "en",
        urlaccessdate = "12 maio 2024"
}