CDS&E: Harnessing Graphical Processing Units (GPUs) to Accelerate the Computational Efficiency of Air Quality Modeling Systems for Four-Dimensional Air Pollution Predictions

Air pollution is a national and global problem with significant adverse impacts on human health and wellbeing. Air quality model simulations are essential for understanding historical pollution episodes and predicting future air quality trends. However, air quality model simulations can be computationally expensive due to slow processing speeds for case studies simulated over a large area (e.g., a regional air basin) at high spatial resolutions.

The goal of this research is to explore the use of graphical processing units (GPUs) to accelerate computationally intensive routines/modules of the Community Multiscale Air Quality (CMAQ) model, an open-source chemical transport model employed nationwide by EPA and state agencies to assess air quality for regulatory decision making. To advance this goal, the Principal Investigators (PIs) of this project propose to carry out an integrated computational modeling and simulation program structured to simulate ozone formation in the California South Coast Air Basin (SCAB) and evaluate the meteorological drivers of ozone formation in the SCAB where recent ozone concentrations have rebounded to 1994 levels after decades of decline.

The successful completion of this project will benefit society through the development and deployment of faster and more computationally efficient models/software to support regulatory air quality monitoring. Further benefits to society will be achieved through student education and training including the mentoring of two doctoral students.

Regulatory air quality modeling and simulations require high-resolution numerical solutions of the model governing partial differential equations (PDEs) over large spatial domains. Because graphical processing units (GPU) can carry out floating point operations at higher speeds than central processing units (CPUs) at comparable costs, they could provide significant computational speed enhancements and savings for solving systems of high-dimensional PDEs.

In this project, the PIs propose to investigate the utilization of GPUs to accelerate numerically intensive routines/modules of the Community Multiscale Air Quality (CMAQ) model used by EPA and state agencies to assess air quality for regulatory decision making. CMAQ governing equations are solved using a process splitting approach where process modules are executed in series.

This approach facilitates the improvement of simulation times for bottleneck modules by migrating them to GPUs. To advance the overarching goal of the project, the PIs propose to initially focus on the development and hardware implementation of the GPU enhanced gas phase chemical solver (GPCS) of the CMAQ model. Specific tasks for this effort will include 1) the acceleration of the CMAQ GPCS through parallelization and vectorization of the governing equations, 2) precision and sensitivity tests, 3) evaluations of the impact of GPU parallelization on GPCS reaction rates, and 4) validation and applications of the new CMAQ-GPU model using case studies based on available air quality datasets.

The successful completion of the proposed research could lead to a faster and more computationally efficient CMAQ model/software to support predictive simulations of air quality (e.g., ozone and particulate formations) under future climate scenarios and meteorological conditions from extreme weather events (e.g., heat waves and wildfires) that are expected to exacerbate air pollution nationwide.

This award is jointly funded by the Environmental Engineering and the Computational and Data-enabled Science and Engineering (CDS&E) programs of the NSF/ENG/CBET Division.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.