Abstracting the environment: automating geoscientific simulation

该项目将使地球科学家实现计算机模拟的能力得到革命性的提高，特别是在合并并行硬件和处理模拟结果方面。地球系统过程的计算机模拟已经成为科学的关键工具之一。在大气层和海洋中，从冻结的冰盖到地幔的熔融岩石，流体和固体的模拟是无处不在的和必不可少的工具。这些是关键的过程：世界上许多最大的计算机都在从事模拟它们。用于生成这些模型的数值方法正在迅速变得更加复杂。与此同时，大规模并行计算机硬件的出现为前所未有的分辨率水平提供了机会。然而，计数器和新硬件的复杂性使得研究人员编写正确、足够高性能和足够可用的计算机代码变得非常困难。传统的软件开发基本上需要同时是地球科学家、数学家和计算机科学家的超人开发人员。本质上，这些困难的出现是因为传统的计算机代码混合了数字和并行实现。相反，模型可以通过指定的数值方法在一个高级计算机语言类似的数学，并行实现可以自动生成。这将使数值模拟专家能够指定他们的算法，并以当前开发成本的一小部分获得正确的并行代码。这种模型的自动生成是今天的科学家和工程师在小规模和简化的仿真问题工作的现实。然而，地球的曲率，地球物理领域的极端平坦性和所涉及的领域的规模意味着地球科学家有额外的需求，这需要在模拟代码生成系统的深刻变化。我将扩展代码生成技术以满足这些特殊的挑战，从而为地球科学模型开发提供自动化。许多科学不仅仅依赖于模拟过程：它还依赖于研究系统的敏感性，优化输入和参数，稳定性分析和误差分析。所有这些过程都需要一个伴随模型：本质上是原始模拟的梯度。开发伴随模型是如此复杂，只有最大的国家中心通常可以负担得起开发它们。通过使用代码生成，我已经证明了对于某些类型的模型，这几乎是自动的。我将把这种能力扩展到地球科学中更常见的其他离散化，从而把伴随这一强大的工具交到从事前沿地球科学研究的科学家和学生手中。最大的模拟，特别是气候系统的模拟，产生了如此复杂的数据，以至于许多重要的科学都是通过研究存档模拟的输出而产生的。对于来自许多模型的大量数据集合，即使是计算统计数据的过程也是费力且容易出错的。目前也无法验证已发表的数据分析是否正确计算。我将扩展模拟软件的自动生成，以允许自动数据查询语言。这使得这种形式的数据科学的劳动密集度大大降低，将允许数据科学使用正确发布的方法，将减少错误的来源，并将允许科学家有效地使用未来的海量数据集。

This project will deliver a revolutionary increase in the ability ofgeoscientists to implement computer simulations, especially foremerging parallel hardware, and to work with the results of simulations.Computer simulation of processes in the Earth system has become one ofthe key tools if science. In the atmosphere and ocean and from frozenice sheets to the molten rock of the Earth's mantle, simulations offluids and solids are ubiquitous and essential tools. These arecritical processes: many of the world's largest computers are engagedin simulating them.The numerical methods used to produce these models are becomingrapidly more sophisticated. At the same time the emergence ofmassively parallel computer hardware presents the opportunity forunprecedented levels of resolution. However, the complexity of thenumerics and the new hardware is such that it is becoming verydifficult for researchers to write computer code which is correct,sufficiently high performance and sufficiently usable. Conventionalsoftware development essentially requires superhuman developers whoare simultaneously geoscientists, mathematicians and computerscientists.In essence these difficulties occur because conventional computer codemixes the numerics and the parallel implementation. Instead, modelscould be developed by specifying the numerical methods in a high-levelcomputer language similar to the maths, and the parallelimplementation could be generated automatically. This would enableexperts in numerical modelling to specify their algorithm, and arriveat correct, parallel code at a tiny fraction of current developmentcosts. This automatic generation of models is a reality today for scientistsand engineers working on smaller-scale and simplified simulationproblems. However the curvature of the earth, the extreme flatness ofgeophysical domains and the scale of the domains involved mean thatgeoscientists have additional needs which require deep changes insimulation code generation systems. I will extend code generationtechniques to meet these special challenges, and therefore deliverautomation to geoscientific model development.Much science does not just depend on simulating processes: it alsodepends on studying the sensitivity of systems, optimising inputs andparameters, stability analysis and error analysis. All of theseprocesses require an adjoint model: essentially the gradient of theoriginal simulation. Developing adjoint models is so complex that onlythe largest national centres can typically afford to developthem. Using code generation, I have already demonstrated that this canbe made almost automatic for some types of model. I will extend thiscapability to other discretisations which are more common in thegeosciences, and thereby put the powerful tool that adjoints are intothe hands of the individual scientists and students who conduct muchof the cutting edge geoscience.The largest simulations, particularly of the climate system, produceso much complex data that much important science occurs by studyingthe output of archived simulations. For large collections of data frommany models, even the process of calculating statistics is labouriousand error-prone. It is also currently impossible to verify ifpublished data analyses are correctly calculated. I will extend theautomated generation of simulation software to allow for an automateddata query language. This make this form of data science far less labour-intensive, will allow data science with properly published methods, will reduce sources of error and will allow scientists to work effectively with the massive data sets of the future.