SHF: Small: Automated Analysis and User Feedback on Data Movement Bottlenecks in Programs

SHF：小型：程序中数据移动瓶颈的自动分析和用户反馈

• 批准号: 1619303
• 负责人: Jagannathan Ramanujam
• 金额: $ 40万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619303&HistoricalAwards=false
• 关键词: SHF; Small; Automated; Analysis; User

The cost of data movement through the memory hierarchy is very high in current computer systems, relative to the cost of performing arithmetic operations, both in terms of time and energy. It is expected to become even more dominant in future computer systems. Therefore optimizing data access costs will become increasingly critical in the coming years. This has great impact on algorithm design and implementation on such systems. A characterization of data access complexity is only known today for a few classes of algorithms, such as dense linear algebra, and has required the use of problem-specific analysis techniques. This proposal presents novel ideas for developing automated tools and techniques to characterize the inherent data access complexity of arbitrary algorithms. An automatable general approach for data access complexity of programs will have significant impact on deriving highly effective algorithms and their implementations, understanding the impact of future architectures on algorithm design, and on advances in compilers aimed at improving data access costs of programs.The proposal develops novel ideas to address a fundamental problem of increasing importance -- developing tools and techniques for characterization of the inherent data access complexity of algorithms. The work develops the following: (i) the first unifying approach for developing both upper and lower bounds for the inherent data access complexity of a CDAG under a common underlying theoretical model; (ii) an approach to remove a fundamental current limitation in the use of the popular reuse distance analysis metric; (iii) a scalable and versatile dynamic analysis infrastructure of use to application developers, compiler writers and architecture designers.

在当前的计算机系统中，相对于执行算术运算的时间和能量成本，通过存储器层次结构移动数据的成本非常高。 预计它将在未来的计算机系统中变得更加占主导地位。因此，优化数据访问成本在未来几年将变得越来越重要。这对此类系统的算法设计和实现有很大影响。 目前，数据访问复杂性的表征仅针对少数几类算法（例如密集线性代数）而为人所知，并且需要使用特定于问题的分析技术。 该提案提出了开发自动化工具和技术来表征任意算法固有的数据访问复杂性的新颖想法。针对程序数据访问复杂性的自动化通用方法将对导出高效算法及其实现、了解未来架构对算法设计的影响以及旨在提高程序数据访问成本的编译器的进步产生重大影响。该提案制定解决日益重要的基本问题的新颖想法——开发用于表征算法固有数据访问复杂性的工具和技术。这项工作开发了以下内容：（i）第一个统一方法，用于在通用基础理论模型下开发 CDAG 固有数据访问复杂性的上限和下限； (ii) 一种消除当前使用流行的重用距离分析指标的基本限制的方法； (iii) 可供应用程序开发人员、编译器编写人员和架构设计人员使用的可扩展且多功能的动态分析基础设施。