数据范围分析工具的建议和规范
Recommendations and specifications for data scope analysis tools
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。在当今的计算机体系结构中，移动数据比在此数据上进行计算要消耗更多的时间和能量。因此，任何应用程序的关键性能优化之一就是最大限度地减少数据移动并最大限度地提高数据重用。特别是在具有非常复杂和深的存储层次结构的现代超级计算机上，必须考虑数据局部性。特别是当使用OpenACC或OpenMP等指令系统定位加速器时，识别数据范围、访问类型和数据重用对于最大限度地减少来自加速器和向加速器的数据传输至关重要。不幸的是，手动识别复杂代码库中的数据局部性信息可能是一项耗时的任务，因此需要工具支持。在这份报告中，我们总结了目前可用的工具，支持软件支持软件开发人员和性能工程师的数据位置信息在复杂的代码库，如数值天气预报（NWP）或气候模拟应用程序的调查结果。根据调查结果，我们然后推荐一个工具，并指定一些扩展的工具，以解决在NWP应用程序中遇到的问题。
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. In today's computer architectures, moving data is considerably more time- and energy consuming than computing on this data. One of the key performance optimizations for any application is therefore to minimize data motion and maximize data reuse. Especially on modern supercomputers with very complex and deep memory hierarchies, it is mandatory to take data locality into account. Especially when targeting accelerators with directive systems like OpenACC or OpenMP, identifying data scope, access type and data reuse are critical to minimize the data transfers from and to the accelerator. Unfortunately, manually identifying data locality information in complex code bases can be a time consuming task and tool support is therefore desirable. In this report we summarize the results of a survey of currently available tools that support software developers and performance engineers with data locality information in complex code bases like numerical weather prediction (NWP) or climate simulation applications. Based on the survey results we then recommend a tool and specify some extensions for a tool to solve the problems encountered in an NWP application.
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。
