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

在当前的计算机系统中，相对于执行算术运算的成本，在时间和能量方面，通过内存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占据更大的主导地位。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的影响。数据访问复杂性的表征目前只针对一些算法（如密集线性代数），并且需要使用特定于问题的分析技术。该建议提出了开发自动化工具和技术的新思路，以表征任意算法固有的数据访问复杂性。对于程序的数据访问复杂性，一种可自动化的通用方法将对获得高效算法及其实现，理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。该提案提出了一些新颖的想法，以解决一个日益重要的基本问题——开发用于表征算法固有数据访问复杂性的工具和技术。这项工作发展了以下内容：(i)在一个共同的基础理论模型下，为CDAG的固有数据访问复杂性制定上界和下界的第一个统一方法；（ii）一种消除目前在使用广受欢迎的重用距离分析量度方面的基本限制的方法；（iii）为应用程序开发人员、编译器编写人员和架构设计人员提供一个可扩展和通用的动态分析基础设施。

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.