高分辨率业务洪水预报的混合并行化方法

Swati Singhal, L. V. Real, Thomas George, Sandhya Aneja, Yogish Sabharwal
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引用次数: 2

摘要

由于过去几年与洪水有关的灾害发生率增加,准确和及时的洪水预报变得非常重要。这种预报需要高分辨率的综合洪水模拟方法。在本文中,我们提出了一个集成的洪水预报系统,该系统具有天气建模,地表径流估算和水路由组件的自动化工作流程。我们主要关注计算最密集的水路由过程,并提出了两种并行化策略来将其扩展到大网格规模。具体来说,我们采用了基于自然的流域模拟域分解方法,并提出了一种用于流域分布式处理的并行化主从模型。我们还提出了一种使用OpenMP的盆内共享内存并行化方法。对所提出的并行化策略的经验评估表明,在某些类型的场景中,可能会有很高的加速(例如,在里约热内卢大型盆地中,使用OpenMP并行化,16个线程的速度提高了13倍)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid parallelization approach for high resolution operational flood forecasting
Accurate and timely flood forecasts are becoming highly essential due to the increased incidence of flood related disasters over the last few years. Such forecasts require a high resolution integrated flood modeling approach. In this paper, we present an integrated flood forecasting system with an automated workflow over the weather modeling, surface runoff estimation and water routing components. We primarily focus on the water routing process which is the most compute intensive phase and present two parallelization strategies to scale it up to large grid sizes. Specifically, we employ nature-inspired decomposition of a simulation domain into watershed basins and propose a master slave model of parallelization for distributed processing of the basins. We also propose an intra-basin shared memory parallelization approach using OpenMP. Empirical evaluation of the proposed parallelization strategies indicates a potential for high speedups for certain types of scenarios (e.g., speedup of 13× with 16 threads using OpenMP parallelization for the large Rio de Janeiro basin).
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