CPU-GPU ACCELERATION OF THE AIR POLLUTION FORECAST SYSTEM WITH AN EFFICIENT PARALLEL CHEMICAL SOLVER

Abstract

Chemistry-Transport Model (CTM) plays an important role in the air pollution prevention and control. Their general applications such as forecast and prevention of heavy pollution demand highly efficient CTM simulations. The gas-phase chemistry module is always the most computationally intensive module of a CTM. The main reason is that solving the stiff chemical ordinary differential equations (ODEs) of the gas-phase chemistry module consumes most of the computation time. Here we use the Nested Air Quality Prediction Modelling System (NAQPMS) as the CTM and CBM-Z mechanism as its gasphase chemistry module. CBM-Z adopts the popular Livermore Solver for Ordinary Differential Equations (LSODE) to solve the chemical ODEs. However, LSODE does not adapt to the parallel acceleration due to its complicated matrix iteration and complex code. In our previous work, we have designed an efficient chemical solver Modified-Backward-Euler (MBE) to improve the simulation speed and precision. In this paper, we review MBE algorithm, show its intrinsic parallelism and port the CBM-Z module with MBE solver on the CPU-GPU architecture to accelerate the NAQPMS further.