Atmospheric models demand a lot of computational power and solving the chemical processes is one of its most computationally intensive components. This work shows how to improve the computational performance of the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH), a chemical weather prediction system developed by the Barcelona Supercomputing Center. The model implements the new flexible external package Chemistry Across Multiple Phases (CAMP) for the solving of gas- and aerosol-phase chemical processes, that allows multiple chemical processes to be solved simultaneously as a single system. We introduce a novel strategy to simultaneously solve multiple instances of a chemical mechanism, represented in the model as grid-cells, obtaining a speedup up to 9x using thousands of cells. In addition, we present a GPU strategy for the most time-consuming function of CAMP. The GPU version achieves up to 1.2x speedup compared to CPU. Also, we optimize the memory access in the GPU to increase its speedup up to 1.7x.

翻译：大气模型需要大量的计算资源，而求解化学过程是其中计算最密集的组成部分之一。本研究展示了如何提升由巴塞罗那超级计算中心开发的化学天气预测系统——多尺度在线非静力大气化学模型（MONARCH）的计算性能。该模型采用了新的灵活外部包——跨多相化学（CAMP）来求解气相和气溶胶相化学过程，该包允许多个化学过程作为一个单一系统同时求解。我们引入了一种新颖的策略，可同时求解化学机制的多个实例（在模型中表示为网格单元），在使用数千个单元时获得了高达9倍的加速比。此外，我们针对CAMP中最耗时的函数提出了一种GPU策略。与CPU版本相比，GPU版本实现了高达1.2倍的加速比。同时，我们优化了GPU中的内存访问，使其加速比进一步提升至1.7倍。