International Collaboration Towards Net Zero Computational Modelling and Simulation (CONTINENTS)

实现净零计算建模和仿真的国际合作（大陆）

• 批准号: EP/Z531170/1
• 负责人: Mark Parsons
• 金额: $ 161.41万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FZ531170%2F1
• 关键词: International; Collaboration; Towards; Net; Zero

The UK's Net Zero strategy is for all sectors of the economy to meet the Net Zero target by 2050. Similar long-term aims to move towards Net Zero emissions on the same timescales exist in other countries, including the USA. High-performance computing (HPC) is not exempt from needing to adapt to these strategic challenges, and pushing HPC towards sustainability and Net Zero is crucial if the scientific community is to keep justifying the use and cost of large-scale HPC resources in the face of climate change. Electricity consumption of data centres and systems is by far the largest contributor to the carbon footprint of operational HPC and minimising the energy that is consumed, and reducing/reusing waste, is therefore key in achieving Net Zero.The International Collaboration Towards Net Zero Computational Modelling and Simulation (CONTINENTS) project will build on the expertise of EPCC, the supercomputing centre at the University of Edinburgh, the National Centre for Atmospheric Science in the UK and the National Centre for Atmospheric Research in the USA to transform the state-of-the-art in sustainability and power/energy efficiency of computational modelling and simulation through an ambitious programme of research that will drive innovations in: data centre and HPC system operation; optimal use of hardware; machine learning applied to data analysis and numerical modelling; and software design and development strategies.As a specific use case, CONTINENTS will focus the application of its research on weather and climate modelling. These are both scientifically and computationally challenging, and compute resource intensive, domains that are vital to furthering our understanding of climate change and the factors that influence it. CONTINENTS is a unique interdisciplinary collaboration between leading centres of HPC research and service provision, atmospheric science experts, and numerical and machine learning application developers, providing a comprehensive approach to addressing the Net Zero challenge from the data centre all the way through to individual applications using both computational and climate science expertise.The projects objectives include: establishing a long-term collaboration between leading supercomputing researchers and centres in the UK and USA; pushing the boundaries of sustainable operation of data centres; exploring the use of novel and specialised hardware; developing new methods for performance, power and energy efficient software development and deployment; minimising the resources needed for moving, processing, analysing and storing data; and creating a collaborative research environment that encourages sharing of expertise and knowledge.

英国的零净战略是在2050年到2050年经济的所有部门都实现净零目标。在包括美国在内的其他国家，还有类似的长期目标，以朝着同一时间尺度上的净零排放。高性能计算（HPC）并非不需要适应这些战略挑战，如果科学界要继续证明面对气候变化的大规模HPC资源的使用和成本是合理的，将HPC推向可持续性至关重要。 Electricity consumption of data centres and systems is by far the largest contributor to the carbon footprint of operational HPC and minimising the energy that is consumed, and reducing/reusing waste, is therefore key in achieving Net Zero.The International Collaboration Towards Net Zero Computational Modelling and Simulation (CONTINENTS) project will build on the expertise of EPCC, the supercomputing centre at the University of Edinburgh, the National Centre for Atmospheric Sc​​ience in the UK and美国国家大气研究中心通过雄心勃勃的研究计划来改变计算建模和模拟的可持续性和能力/能源效率的最先进，该计划将推动以下方面的创新：数据中心和HPC系统运营；最佳使用硬件；机器学习应用于数据分析和数值建模；以及软件设计和开发策略。作为特定用例，大陆将将其研究的应用集中在天气和气候建模上。这些在科学和计算上都具有挑战性，也是计算资源密集的领域，这些领域对于进一步了解我们对气候变化及其影响因素的理解至关重要。大陆是HPC研究和服务提供和服务提供，大气科学专家以及数值和机器学习应用程序开发人员之间的独特跨学科合作，提供了一种全面的方法，可以全面应对从数据中心到各个应用程序的净零挑战，直至使用计算和气候专家。突破数据中心可持续运行的界限；探索新颖和专业硬件的使用；为性能，功率和节能软件开发和部署开发新的方法；最大程度地减少移动，处理，分析和存储数据所需的资源；并创建一个协作研究环境，以鼓励分享专业知识和知识。