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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-182
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-182
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 27 Aug 2020

Submitted as: development and technical paper | 27 Aug 2020

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This preprint is currently under review for the journal GMD.

Advanced parallel implementation of the coupled ocean-ice model FEMAO with load balancing

Pavel Perezhogin1, Ilya Chernov2, and Nikolay Iakovlev1 Pavel Perezhogin et al.
  • 1Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences, Moscow, Russia
  • 2Institute of Applied Math Research, Karelian Research Centre of RAS, Petrozavodsk, Russia

Abstract. In this paper, we present a parallel version of the finite element model of the Arctic Ocean (FEMAO) configured for the White sea and based on the MPI technology. This model consists of two main parts: an ocean dynamics model and a surface ice dynamics model. These parts are very different in terms of the amount of computations because the complexity of the ocean part depends on the bottom depth, while that of the sea-ice component does not. In the first step, we decided to locate both submodels on the same CPU cores with the common horizontal partition of the computational domain. The model domain is divided into small blocks, which are distributed over the CPU cores using Hilbert-curve balancing. Partition of the model domain is static (i.e., computed during the initialization stage). There are three baseline options: single block per core, balancing of 2D computations and balancing of 3D computations. After showing parallel acceleration for particular ocean and ice procedures, we construct the common partition, which minimizes joint imbalance in both submodels. Our novelty is using arrays shared by all blocks that belong to a CPU core instead of allocating separate arrays for each block, as is usually done. Computations on a CPU core are restricted by the masks of not-land grid nodes and block-core correspondence. This approach allows us to implement parallel computations into the model that are as simple as when the usual decomposition to squares is used, though with advances of load balancing. We provide parallel acceleration of up to 996 cores for the model with resolution 500 × 500 × 39 in the ocean component and 43 sea-ice scalars, and we carry out detailed analysis of different partitions on the model runtime.

Pavel Perezhogin et al.

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Pavel Perezhogin et al.

Model code and software

FEMAO code Pavel Perezhogin, Ilya Chernov, and Nikolay Iakovlev https://doi.org/10.5281/zenodo.3977346

Parallel library alone Pavel Perezhogin, Ilya Chernov, and Nikolay Iakovlev https://doi.org/10.5281/zenodo.3873239

Pavel Perezhogin et al.

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Latest update: 19 Oct 2020
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Short summary
We describe the parallel implementation of the FEMAO model of an ice-covered sea with 2D Hilbert-curve domain decomposition. Load-balancing is crucial because performance depends on the local depth. We propose, compare, and discuss four approaches to load balancing. The parallel library allowed to modify the original sequential algorithm as little as possible. The performance increases almost linearly (tested up to 996 CPU cores) for the model of the shallow White Sea.
We describe the parallel implementation of the FEMAO model of an ice-covered sea with 2D...
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