Khanh Do, George Delic, Jose Rodríguez Borbón, Duncan Quevedo, Bryan M Wong, Cesunica Ivey 

The Earth’s atmosphere is extremely complex due to the presence of several dynamic processes, such as dispersion, diffusion, deposition, and chemical reactions. There is a pressing need to improve the predictability of air quality models by integrating more of these scientific processes with an increasing number of chemical species into the mechanisms. These enhancements degrade the computational efficiency of the most comprehensive modeling applications, leading to a significant increase in simulation time. Offline chemical transport models (CTM) spend considerable time simulating large atmospheric domains, primarily on solving for the gas-phase chemistry. To reduce the simulation time while maintaining the integrity of the models, we utilized graphics processing units (GPUs) to replace the central processing units (CPU) for computing the most expensive science processes by successfully migrating the gas-phase chemistry solver onto a GPU to reduce computational time. The actual kernel computing time for the solver is twice as fast as the CPU with the BLKSIZE of 8,000; however, the GPU solver incurs communication time costs due to the of moving data back and forth between the CPU host system memory to the GPU memory. In this paper, we focus on compiling of the Community Multiscale Air Quality (CMAQ) model with CUDA kernels, migrating the gas-phase CTM solver onto the GPU, and optimizing the solver to improve GPU computational efficiency. Our positive results from the migrated solver show significant promise for intensive parallel computing applications on GPU devices reduce simulation time and accelerating air quality research.