CDS&E: Harnessing Graphical Processing Units (GPUs) to Accelerate the Computational Efficiency of Air Quality Modeling Systems for Four-Dimensional Air Pollution Predictions
CDS
基本信息
- 批准号:2053560
- 负责人:
- 金额:$ 45.65万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-08-01 至 2025-07-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
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.
空气污染是一个国家和全球性问题,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,空气质量模型模拟可能由于在大区域上模拟的案例研究的缓慢处理速度而在计算上昂贵(例如,一个区域性的大气盆地)。本研究的目标是探索使用图形处理单元(GPU)来加速社区多尺度空气质量(CMAQ)模型的计算密集型例程/模块,该模型是EPA和州机构在全国范围内采用的开源化学品运输模型,用于评估空气质量以进行监管决策。为了推进这一目标,该项目的主要研究人员(PI)建议进行一个综合的计算建模和模拟程序,其结构是为了模拟加州南海岸空气盆地(SCAB)的臭氧形成,并评估SCAB臭氧形成的气象驱动因素,最近的臭氧浓度在几十年的下降后反弹到1994年的水平。该项目的成功完成将通过开发和部署更快、计算效率更高的模型/软件来支持监管空气质量监测,从而造福社会。通过对学生的教育和培训,包括对两名博士生的指导,将进一步造福社会。空气质量监管建模和模拟需要大空间域偏微分方程(PDE)模型的高分辨率数值解。由于图形处理单元(GPU)可以以可比的成本以比中央处理单元(CPU)更高的速度执行浮点运算,因此它们可以为求解高维PDE系统提供显著的计算速度增强和节省。在这个项目中,PI建议调查GPU的利用率,以加速EPA和州机构用于评估空气质量的监管决策的社区多尺度空气质量(CMAQ)模型的数值密集型例程/模块。CMAQ控制方程求解使用的过程分裂的方法,其中过程模块被执行的系列。这种方法通过将瓶颈模块迁移到GPU上来改善瓶颈模块的仿真时间。为了推进该项目的总体目标,PI建议最初专注于CMAQ模型的GPU增强气相化学求解器(GPCS)的开发和硬件实现。这项工作的具体任务将包括:1)通过控制方程的并行化和矢量化加速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。成功完成拟议的研究可能会导致更快和更有效的计算CMAQ模型/软件,以支持空气质量的预测模拟(例如,臭氧和微粒形成)在未来气候情景和极端天气事件的气象条件下(例如,该奖项由NSF/ENG/CBET分部的环境工程和计算与数据支持科学与工程(CDS E)项目共同资助。该奖项反映了NSF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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
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Cesunica Ivey其他文献
Erratum to: On the potential of iPhone significant location data to characterize individual mobility for air pollution health studies
- DOI:
10.1007/s11783-022-1556-1 - 发表时间:
2022-07-11 - 期刊:
- 影响因子:6.400
- 作者:
Elizabeth Eastman;Kelly A. Stevens;Cesunica Ivey;Haofei Yu - 通讯作者:
Haofei Yu
On the potential of iPhone significant location data to characterize individual mobility for air pollution health studies
- DOI:
10.1007/s11783-022-1542-7 - 发表时间:
2022-04-28 - 期刊:
- 影响因子:6.400
- 作者:
Elizabeth Eastman;Kelly A. Stevens;Cesunica Ivey;Haofei Yu - 通讯作者:
Haofei Yu
Development and evaluation of a daily temporal interpolation model for fine particulate matter species concentrations and source apportionment
- DOI:
10.1016/j.atmosenv.2016.06.014 - 发表时间:
2016-09-01 - 期刊:
- 影响因子:
- 作者:
Jeremiah D. Redman;Heather A. Holmes;Sivaraman Balachandran;Marissa L. Maier;Xinxin Zhai;Cesunica Ivey;Kyle Digby;James A. Mulholland;Armistead G. Russell - 通讯作者:
Armistead G. Russell
Cesunica Ivey的其他文献
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