A scalable RBF-FD method for atmospheric flow

作者：Tillenius Martin; Larsson Elisabeth^*; Lehto Erik; Flyer Natasha

来源：Journal of Computational Physics, 2015, 298: 406-422.

DOI：10.1016/j.jcp.2015.06.003

• Shallow water
• Scattered node
• Task parallel
• Distributed memory
• Multicore
• Radial basis function
• RBF-FD

Radial basis function-generated finite difference (RBF-FD) methods have recently been proposed as very interesting for global scale geophysical simulations, and have been shown to outperform established pseudo-spectral and discontinuous Galerkin methods for shallow water test problems. In order to be competitive for very large scale simulations, the RBF-FD methods needs to be efficiently implemented for modern multicore based computer architectures. This is a challenging assignment, because the main computational operations are unstructured sparse matrix-vector multiplications, which in general scale poorly on multicore computers due to bandwidth limitations. However, with the task parallel implementation described here we achieve 60-100% of theoretical speedup within a shared memory node, and 80-100% of linear speedup across nodes. We present results for global shallow water benchmark problems with a 30 km resolution.

• 出版日期2015-10-1