GPU Optimization of Lattice Boltzmann Method with Local Ensemble Transform Kalman Filter
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The ensemble data assimilation of computational fluid dynamics simulations based on the lattice Boltzmann method (LBM) and the local ensemble transform Kalman filter (LETKF) is implemented and optimized on a GPU supercomputer based on NVIDIA A100 GPUs. To connect the LBM and LETKF parts, data transpose communication is optimized by overlapping computation, file I/O, and communication based on data dependency in each LETKF kernel. In two dimensional forced isotropic turbulence simulations with the ensemble size of M=64 and the number of grid points of N_x=128^2, the optimized implementation achieved ×3.80 speedup from the naive implementation, in which the LETKF part is not parallelized. The main computing kernel of the local problem is the eigenvalue decomposition (EVD) of M× M real symmetric dense matrices, which is computed by a newly developed batched EVD in . The batched EVD in outperforms that in , and ×65.3 speedup was achieved.
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