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

空气污染是一个国家和全球问题，对人类健康和福祉产生重大不利影响。空气质量模型模拟对于了解历史污染事件和预测未来空气质量趋势至关重要。然而，由于在高空间分辨率下模拟大面积（例如，区域空气盆地）的案例研究的处理速度较慢，空气质量模型模拟的计算成本可能很高。本研究的目标是探索图形处理单元（gpu）的使用，以加速社区多尺度空气质量（CMAQ）模型的计算密集型例程/模块，CMAQ是一种开放源代码的化学传输模型，被EPA和州机构在全国范围内采用，用于评估空气质量，以进行监管决策。为了实现这一目标，该项目的主要研究者（pi）建议实施一个综合的计算建模和模拟程序，以模拟加利福尼亚南海岸空气盆地（SCAB）的臭氧形成，并评估SCAB中臭氧形成的气象驱动因素，其中最近的臭氧浓度在几十年的下降之后反弹到1994年的水平。这项计划的成功完成，将有助我们开发和部署速度更快、计算效率更高的模型/软件，以支援规管性的空气质素监测，从而造福社会。通过学生教育和培训，包括指导两名博士生，将进一步造福社会。调节空气质量的建模和模拟需要大空间域上控制偏微分方程（PDEs）模型的高分辨率数值解。由于图形处理单元（GPU）可以在同等成本下以比中央处理单元（cpu）更高的速度执行浮点运算，因此它们可以显著提高计算速度，并为解决高维pde系统节省成本。在这个项目中，pi建议研究gpu的使用，以加速社区多尺度空气质量（CMAQ）模型的数字密集型例程/模块，该模型被EPA和州机构用于评估空气质量以进行监管决策。CMAQ控制方程采用进程分裂方法求解，其中进程模块串联执行。通过将瓶颈模块迁移到gpu，这种方法有助于改进瓶颈模块的模拟时间。为了推进该项目的总体目标，pi建议最初将重点放在CMAQ模型的GPU增强型气相化学求解器（GPCS）的开发和硬件实现上。这项工作的具体任务将包括1)通过并行化和向量化控制方程来加速CMAQ GPCS， 2)精度和灵敏度测试，3)评估GPU并行化对GPCS反应速率的影响，以及4)使用基于可用空气质量数据集的案例研究验证和应用新的CMAQ-GPU模型。这项研究的成功完成可能会带来一个更快、计算效率更高的CMAQ模型/软件，以支持在未来气候情景和极端天气事件（如热浪和野火）的气象条件下对空气质量（如臭氧和颗粒形成）的预测模拟，这些极端天气事件预计会加剧全国范围内的空气污染。该奖项由NSF/ENG/CBET部门的环境工程和计算与数据驱动科学与工程（CDS&amp；E）项目共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.