Date of Award
Doctor of Philosophy
Joshua S. Fu
Chris D. Cox, Jack J. Dongarra, John B. Drake
In this study, the perennial problem of overestimation of ozone concentration from the global chemistry-climate model (CAM4-Chem [Community Earth System Model with chemistry activated]) is investigated in the sense of numerics and computation. The high-order Rosenbrock-type solvers are implemented into CAM4-Chem, motivated by its higher order accuracy and better computational efficiency. The results are evaluated by comparing to the observation data and the ROS-2 [second-order Rosenbrock] solver can reduce the positive bias of ozone concentration horizontally and vertically at most regions. The largest reduce occurs at the mid-latitudes of north hemisphere where the bias is generally high, and the summertime when the photochemical reaction is most active. In addition, the ROS-2 solver can achieve ~2x speed-up compared to the original IMP [first-order implicit] solver. This improvement is mainly due to the reuse of the Jacobian matrix and LU [lower upper] factorization during its two-stage calculation. In order to gain further speed-up, we port the ROS-2 solver to the GPU [graphics processing unit] and compare the performance with CPU. The speed-up of the GPU version with the optimized configuration reaches a factor of ~11.7× for the computation alone and ~3.82× considering the data movement between CPU and GPU. The computational time of the GPU version increases more slowly than the CPU version as a function of the number of loop iterations, which makes the GPU version more attractive for a massive computation. Moreover, under the stochastic perturbation of initial input, we find the ROS-3 [third-order Rosenbrock] solver yields better convergence property than the ROS-2 and IMP solver. However, the ROS-3 solver generally provides a further overestimation of ozone concentration when it is implemented into CAM4-Chem. This is due to the fact that more frequent time step refinements are involved by the ROS-3 solver, which also makes the ROS-3 solver less computationally efficient than the IMP and ROS-2 solvers. We also investigate the effect of grid resolution and it shows that the fine resolution can provide relatively better pattern correlation than the coarse resolution, given the same chemical solver.
Sun, Jian, "Integration of Rosenbrock-type solvers into CAM4-Chem and evaluation of its performance in the perspectives of science and computation. " PhD diss., University of Tennessee, 2018.
Available for download on Wednesday, May 15, 2019