Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel). Mower, R., Gutmann, E. D., Liston, G. E., Lundquist, J., & Rasmussen, S. Geoscientific Model Development, 17(10):4135–4154, May, 2024. Publisher: Copernicus GmbH![Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel) [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex SnowModel, a spatially distributed snow-evolution modeling system, was parallelized using Coarray Fortran for high-performance computing architectures to allow high-resolution (1 m to hundreds of meters) simulations over large regional- to continental-scale domains. In the parallel algorithm, the model domain was split into smaller rectangular sub-domains that are distributed over multiple processor cores using one-dimensional decomposition. All the memory allocations from the original code were reduced to the size of the local sub-domains, allowing each core to perform fewer computations and requiring less memory for each process. Most of the subroutines in SnowModel were simple to parallelize; however, there were certain physical processes, including blowing snow redistribution and components within the solar radiation and wind models, that required non-trivial parallelization using halo-exchange patterns. To validate the parallel algorithm and assess parallel scaling characteristics, high-resolution (100 m grid) simulations were performed over several western United States domains and over the contiguous United States (CONUS) for a year. The CONUS scaling experiment had approximately 70 % parallel efficiency; runtime decreased by a factor of 1.9 running on 1800 cores relative to 648 cores (the minimum number of cores that could be used to run such a large domain because of memory and time limitations). CONUS 100 m simulations were performed for 21 years (2000–2021) using 46 238 and 28 260 grid cells in the x and y dimensions, respectively. Each year was simulated using 1800 cores and took approximately 5 h to run.
@article{mower_parallel_2024,
title = {Parallel {SnowModel} (v1.0): a parallel implementation of a distributed snow-evolution modeling system ({SnowModel})},
volume = {17},
issn = {1991-959X},
shorttitle = {Parallel {SnowModel} (v1.0)},
url = {https://gmd.copernicus.org/articles/17/4135/2024/},
doi = {10.5194/gmd-17-4135-2024},
abstract = {SnowModel, a spatially distributed snow-evolution modeling system, was parallelized using Coarray Fortran for high-performance computing architectures to allow high-resolution (1 m to hundreds of meters) simulations over large regional- to continental-scale domains. In the parallel algorithm, the model domain was split into smaller rectangular sub-domains that are distributed over multiple processor cores using one-dimensional decomposition. All the memory allocations from the original code were reduced to the size of the local sub-domains, allowing each core to perform fewer computations and requiring less memory for each process. Most of the subroutines in SnowModel were simple to parallelize; however, there were certain physical processes, including blowing snow redistribution and components within the solar radiation and wind models, that required non-trivial parallelization using halo-exchange patterns. To validate the parallel algorithm and assess parallel scaling characteristics, high-resolution (100 m grid) simulations were performed over several western United States domains and over the contiguous United States (CONUS) for a year. The CONUS scaling experiment had approximately 70 \% parallel efficiency; runtime decreased by a factor of 1.9 running on 1800 cores relative to 648 cores (the minimum number of cores that could be used to run such a large domain because of memory and time limitations). CONUS 100 m simulations were performed for 21 years (2000–2021) using 46 238 and 28 260 grid cells in the x and y dimensions, respectively. Each year was simulated using 1800 cores and took approximately 5 h to run.},
language = {English},
number = {10},
urldate = {2024-06-03},
journal = {Geoscientific Model Development},
author = {Mower, Ross and Gutmann, Ethan D. and Liston, Glen E. and Lundquist, Jessica and Rasmussen, Soren},
month = may,
year = {2024},
note = {Publisher: Copernicus GmbH},
keywords = {NALCMS},
pages = {4135--4154},
}
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The CONUS scaling experiment had approximately 70 % parallel efficiency; runtime decreased by a factor of 1.9 running on 1800 cores relative to 648 cores (the minimum number of cores that could be used to run such a large domain because of memory and time limitations). CONUS 100 m simulations were performed for 21 years (2000–2021) using 46 238 and 28 260 grid cells in the x and y dimensions, respectively. 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