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.

在当前计算机系统中，通过记忆层次结构的数据移动成本相对于执行算术操作的成本在时间和能源方面都非常高。 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，在未来几年中，优化数据访问成本将变得越来越重要。这对此类系统上的算法设计和实现具有很大的影响。 数据访问复杂性的表征仅在今天的几类算法（例如密集的线性代数）中才知道，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的新思想，以表征任意算法的固有数据访问复杂性。可自动的数据访问程序复杂性的自动通用方法将对得出高效的算法及其实现，了解未来体系结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。提案提高了新颖的想法，可以开发出增加重要性的基本问题 - 对工具和技术的表征访问的依次进行了复杂的数据，该问题是对数据访问的重要性。该作品开发了以下内容：（i）在共同基础理论模型下，CDAG固有的数据访问复杂性的上限和下限的第一种统一方法； （ii）一种消除流行重用距离分析度量的基本电流限制的方法； （iii）对应用程序开发人员，编译器作者和建筑设计师的使用的可扩展性动态分析基础架构。