在混合架构上，预测天气和气候矮型以及整个NWP应用的可实现性能
Projections of achievable performance for Weather & Climate Dwarfs, and for entire NWP applications, on hybrid architectures
摘要：
本文档是为ESCAPE项目创建的可交付成果报告之一。ESCAPE是Energy-efficient Scalable Algorithms for Weather Prediction at Exascale的缩写。该项目为欧洲业务数值天气预报和未来气候模型开发世界级的极端规模计算能力。这是通过识别天气和气候侏儒来完成的，这些侏儒是计算和通信方面的关键模式（本着伯克利侏儒的精神）。然后针对不同的硬件架构（单节点和多节点）优化这些侏儒，并探索替代算法。性能可移植性是通过使用领域特定的语言来解决的。该交付内容包含针对不同硬件架构（特别是带有GPU加速器的多节点）的选定天气和气候侏儒的性能和能耗模型的描述。所呈现的性能模型是对可扩展3.2中提供的模型的扩展。通过进一步的增强，它们被合并到DCworms模拟器中。特别是，扩展模型允许预测不同架构上的计算和能源性能：配备CPU和GPU加速器的单节点和多节点。这允许在系统规模上提供可行的性能预测。
英文摘要：
This document is one of the deliverable reports created for the ESCAPE project. ESCAPE stands for Energy-efficient Scalable Algorithms for Weather Prediction at Exascale. The project develops world-class, extreme-scale computing capabilities for European operational numerical weather prediction and future climate models. This is done by identifying Weather & Climate dwarfs which are key patterns in terms of computation and communication (in the spirit of the Berkeley dwarfs). These dwarfs are then optimised for different hardware architectures (single and multi-node) and alternative algorithms are explored. Performance portability is addressed through the use of domain specific languages. This deliverable contains the description of the performance and energy models for the selected Weather & Climate dwarfs for different hardware architectures, multinode with GPU accelerators in particular. Presented performance models are extension to model provided in Deliverable 3.2. With some further enhancements, they are incorporated in the DCworms simulator. In particular, extended models allow to predict computational and energy performance on different architectures: single and multinodes, equipped with CPUs and GPUs accelerators. This allows to provide feasible performance projection at system scale.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
