Can emerging general purpose graphics processing unit (GPGPU) technology be used to mitigate computational burdens in environmental models?
新兴的通用图形处理单元(GPGPU)技术能否用于减轻环境模型中的计算负担?
基本信息
- 批准号:NE/J013471/1
- 负责人:
- 金额:$ 6.2万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2012
- 资助国家:英国
- 起止时间:2012 至 无数据
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Aerosol particles remain one of the most uncertain contributors to climate change and air quality. Gas-to-aerosol partitioning is key to determining the chemical composition and amount of aerosol particles, thus environmental impacts (e.g. the amount is critical to predicting air quality). Owing to the complexity and diversity of atmospheric aerosol components, quantification of the properties that determine their highly uncertain climatic and human health impacts requires the development of novel technological applications. The many thousands of individual aerosol components ensure that explicit manual calculation of these properties is laborious and time-consuming; the emergence of explicit automatic mechanism generation techniques predicting up to many millions of individual components. Due to heavy computational demands associated with this level of complexity, this presents two broad problems when trying to develop appropriate modeling frameworks to assess true environmental impacts: 1) It is impossible to include full complexity representations of aerosol processes within large-scale frameworks, such as regional climate models. As a result, reduced complexity representations are developed with the inevitable tradeoff between accuracy and improved performance. 2) To determine whether a parameterization of aerosol processes is suitable it is necessary to first perform sensitivity studies, comparing full representaions with parameterizations under a wide variety of conditions. This requires considerable computational power and time.Traditionally, computer processors have been single core. Recently this has evolved to several cores on a processor, typically 16 in an HPC server node. The graphics industry has been creating graphics cards (GPUs) with thousands of cores in order for games to have realistic effects. Recently, General Purpose GPUs (GPGPUs), although now commonly called just GPUs, have become available as "accelerators" for compute-intensive work. GPGPUs are available at a fraction of the cost of more traditional high performance computing (HPC) facilities and generally affordable (and thus accessible to) research groups. The advent of GPGPU computing is a new and exciting technological development. Some vendors and discipline areas have begun porting some codes to GPGPUs, yet the atmospheric chemistry field has little/zero work in this area. In this project we propose to quantify the performance of state-of-the-art models of gas-to-aerosol partitioning, as a first example, using the newly emerging GPGPU paradigm against the more traditional CPU implementations. This pump-priming activity is designed to act as a springboard for more generalized potential improvements in computational efficiency of chemistry schemes in environmental models. The successful outcome of this proposal will mean not only faster process models but that these could potentially be incorporated in to regional air quality & meteorological models, bringing higher accuracy and cost effectiveness to their solutions whilst improving their time-to-solution. As the emergence of GPU technology is relatively new, it is important lessons learned during this project will be shared by the broader research community, quantifying how the challenges of extracting near peak GPU performance were met. To this end we will use online facilities and informatics tools to ensure wider benefits are realised.
气溶胶颗粒仍然是气候变化和空气质量最不确定的因素之一。气体与气溶胶的分离是确定气溶胶颗粒的化学成分和数量的关键,因此对环境的影响(例如,数量对预测空气质量至关重要)。由于大气气溶胶成分的复杂性和多样性,要量化决定其高度不确定的气候和人类健康影响的特性,就需要开发新的技术应用。数以千计的单个气溶胶组分确保了这些性质的显式手动计算是费力和耗时的;显式自动机制生成技术的出现预测了数百万个单独的组分。由于与这种复杂程度相关的大量计算需求,当试图开发适当的建模框架以评估真实的环境影响时,这提出了两个广泛的问题:1)不可能在大尺度框架内包括气溶胶过程的完整复杂性表示,例如区域气候模型。因此,降低复杂性的表示开发与准确性和提高性能之间的不可避免的权衡。2)为了确定气溶胶过程的参数化是否合适,有必要首先进行敏感性研究,在各种条件下比较完整的代表性与参数化。这需要相当大的计算能力和时间。传统上,计算机处理器一直是单核的。最近,这已经发展到一个处理器上有几个核心,通常在HPC服务器节点中有16个核心。图形行业一直在创造具有数千个核心的图形卡(GPU),以便游戏具有逼真的效果。最近,通用GPU(GPGPU),虽然现在通常被称为GPU,但已成为计算密集型工作的“加速器”。GPGPU的成本仅为传统高性能计算(HPC)设施的一小部分,并且通常负担得起(因此可以访问)研究小组。GPGPU计算的出现是一个新的和令人兴奋的技术发展。一些供应商和学科领域已经开始将一些代码移植到GPGPU上,但大气化学领域在这方面几乎没有工作。在这个项目中,我们建议量化最先进的气体气溶胶分区模型的性能,作为第一个例子,使用新出现的GPGPU范式对更传统的CPU实现。这种泵启动活动的目的是作为一个跳板,更广义的潜在改善计算效率的化学方案在环境模型。该提案的成功结果不仅意味着更快的过程模型,而且这些模型可能被纳入区域空气质量和气象模型,为解决方案带来更高的准确性和成本效益,同时缩短解决方案的时间。由于GPU技术的出现相对较新,因此更广泛的研究社区将分享在此项目中吸取的重要经验教训,量化如何满足提取接近峰值GPU性能的挑战。为此,我们将使用在线设施和信息工具,以确保实现更广泛的利益。
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)
专利数量(0)
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David Topping其他文献
David Topping的其他文献
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{{ truncateString('David Topping', 18)}}的其他基金
International network for coordinating work on the physicochemical properties of molecules and mixtures important for atmospheric particulate matter
协调对大气颗粒物重要的分子和混合物的物理化学性质工作的国际网络
- 批准号:
NE/N013794/1 - 财政年份:2016
- 资助金额:
$ 6.2万 - 项目类别:
Research Grant
Diffusion and Equilibration in Viscous Atmospheric Aerosol
粘性大气气溶胶的扩散和平衡
- 批准号:
NE/M003531/1 - 财政年份:2015
- 资助金额:
$ 6.2万 - 项目类别:
Research Grant
Novel approaches for quantifying the highly uncertain thermodynamics and kinetics of atmospheric gas-to-particle conversion
量化大气气体到颗粒转化的高度不确定的热力学和动力学的新方法
- 批准号:
NE/J02175X/1 - 财政年份:2013
- 资助金额:
$ 6.2万 - 项目类别:
Research Grant
Improvement of composition and property prediction techniques for for Secondary Organic Aerosol (SOA)
二次有机气溶胶(SOA)成分和性质预测技术的改进
- 批准号:
NE/J009202/1 - 财政年份:2012
- 资助金额:
$ 6.2万 - 项目类别:
Research Grant
Novel informatic software for automated aerosol component property predictions and ensemble predictions for direct model - measurement comparison
用于自动气溶胶成分特性预测和直接模型测量比较的整体预测的新型信息软件
- 批准号:
NE/H002588/1 - 财政年份:2010
- 资助金额:
$ 6.2万 - 项目类别:
Research Grant
Direct Validated Improvement of Atmospheric Aerosol Property Prediction Using Laboratory Measurements
使用实验室测量直接验证改进大气气溶胶特性预测
- 批准号:
NE/E018181/1 - 财政年份:2007
- 资助金额:
$ 6.2万 - 项目类别:
Research Grant
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