GPU Optimization of Lattice Boltzmann Method with Local Ensemble Transform Kalman Filter
SessionWorkshop on Latest Advances in Scalable Algorithms for Large-Scale Heterogeneous Systems (ScalAH'22)
DescriptionThe 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 x3.80 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 x 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 x65.3 speedup was achieved.
Event Type
Workshop
TimeSunday, 13 November 20229:40am - 10:05am CST
LocationC146
W
Algorithms
Exascale Computing
Extreme Scale Computing
Heterogeneous Systems
Post-Moore Computing
Quantum Computing
Recorded