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Advanced parallel implementation of the coupled ocean–ice model FEMAO (version 2.0) with load balancing

机译:高级平行实现耦合的海洋冰型模型Femao(版本2.0),负载平衡

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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 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 number 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 a 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. Partitioning of the model domain is static (i.e., computed during the initialization stage). There are three baseline options: a 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 non-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 in load balancing. We provide parallel acceleration of up to 996?cores for the model with a resolution of 500 × 500 × 39 in the ocean component and 43?sea-ice scalars, and we carry out a detailed analysis of different partitions on the model runtime.
机译:在本文中,我们介绍了为白海配置的北极海洋(Femao)的有限元模型的平行版本,并基于MPI技术。该模型由两个主要部分组成:海洋动力学模型和地表冰动力学模型。这些部件在计算的数量方面非常不同,因为海洋部件的复杂性取决于底部深度,而海冰组件的复杂性则不取决于底部深度。在第一步中,我们决定使用计算域的公共水平分区定位同一CPU内核上的两个子模型。模型域分为小块,使用Hilbert-Curve平衡分布在CPU内核上。模型域的划分是静态的(即,在初始化阶段计算)。有三种基线选项:每核的单个块,2D计算平衡,以及3D计算的平衡。在显示特定海洋和冰手术的平行加速后,我们构建公共分区,这最大限度地减少了两个子模型中的关节不平衡。我们的新颖性是使用属于CPU内核的所有块共享的数组,而不是通常完成的每个块分配单独的数组。 CPU核心的计算受非陆地网格节点的掩码和块核心对应的限制。这种方法允许我们将并行计算实施到模型中,就像使用通常的分解时一样简单,但随着负载平衡的进步。我们提供高达996的平行加速度为996个核心,该模型具有500×500×39的海洋组件和43个?海冰标量,我们对模型运行时进行了详细分析了不同分区。

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