Can emerging general purpose graphics processing unit (GPGPU) technology be used to mitigate computational burdens in environmental models?

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。气体到气溶胶的分配是确定气溶胶颗粒的化学成分和数量的关键，因此会对环境造成影响(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体健康影响的性质，就需要开发新的技术应用。数以千计的单个气溶胶成分确保了这些性质的显式手动计算既费力又耗时；显式自动机制生成技术的出现预测了多达数百万个单独的成分。由于与这种复杂程度相关的大量计算要求，当试图开发适当的模拟框架来评估真实的环境影响时，这带来了两个广泛的问题：1)不可能在大尺度框架内包括气溶胶过程的全部复杂性表示，例如区域气候模型。因此，开发了降低复杂性的表示法，不可避免地在精确度和改进的性能之间进行了权衡。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，比较各种条件下的全代表和参数化。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多个核心，通常在HPC服务器节点上有16个核心。为了让游戏具有逼真的效果，图形业一直在开发具有数千个内核的显卡(GPU)。最近，通用GPU(GPGPU)，虽然现在通常被称为GPU，但已经成为计算密集型工作的“加速器”。GPGPU的成本只有更传统的高性能计算(HPC)设施的一小部分，而且研究小组通常负担得起(因此也可以获得)。GPGPU计算的出现是一项令人兴奋的新技术发展。一些供应商和学科领域已经开始将一些代码移植到GPGPU，但大气化学领域在这方面的工作很少或为零。在这个项目中，我们建议量化最先进的气体到气溶胶分配模型的性能，作为第一个例子，使用新出现的GPGPU范式相对于更传统的CPU实现。这种泵启动活动被设计为环境模型中化学方案计算效率的更普遍的潜在改进的跳板。这一提议的成功结果将不仅意味着更快的过程模式，而且这些模式可能被纳入区域空气质量和气象模式，为其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，在该项目期间学到的重要经验教训将由更广泛的研究界分享，量化如何应对提取接近峰值的GPU性能的挑战。为此，我们将使用在线设施和信息学工具，以确保实现更广泛的好处。

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.