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AGCM3D: A Highly Scalable Finite-Difference Dynamical Core of Atmospheric General Circulation Model Based on 3D Decomposition

It is commonly recognized that the dynamical core of the atmospheric model based on latitude-longitude mesh has poor parallel scalability, since it has to perform the costly polar or high-latitude filtering to dump out the unwanted modes. To parallelize the algorithm, only two dimensions can be partitioned even for a 3-dimensional mesh because of the costly filtering, which hinders the scalability of the algorithm. In this paper, we develop a highly scalable finite-difference dynamical core based on the latitude-longitude mesh using a 3D decomposition method, named as AGCM3D. Different from the traditional methods, our method releases the parallelism in all three dimensions, namely latitude, longitude, and level. To replace the costly Fast Fourier Transform (FFT) filtering, we propose a novel adaptive Gaussian filtering scheme, whose filtering strength increases as the latitude increases. Compared with the parallel FFT filtering, the parallel adaptive Gaussian filtering is far more efficient. In addition, we use the techniques of communication avoiding and message aggregation to further reduce the communication overhead. Experiments are conducted on Tianhe-2 supercomputer, and the resolution of the model is set as 0.5°x0.5°(50 km). Results show that our implementation scales up to 32,768 CPU cores in strong scaling and achieves the maximal simulation speed of 15.6 simulation-year-per-day (SYPD).