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GPU optimization of lattice Boltzmann method with local ensemble transform Kalman filter

Hasegawa, Yuta   ; Imamura, Toshiyuki*; Ina, Takuya ; Onodera, Naoyuki   ; Asahi, Yuichi   ; Idomura, Yasuhiro   

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 $$times3.85$$ 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 $$Mtimes M$$ real symmetric dense matrices, which is computed by a newly developed batched EVD in EigenG. The batched EVD in EigenG outperforms that in cuSolver, and $$times64$$ speedup was achieved.



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