SHF: Medium: Hierarchical Tuning of Floating-Point Computations

该项目实施了一些方法，通过根据应用程序的需要自适应地降低数据精度，来提高从超级计算机到移动设备的各种计算机器上的数值计算的资源效率。有效地调整数据精度允许这些机器运行更大的计算，并通过减少计算负担和减少数据移动的组合来提高整体性能(包括能源消耗)。该项目的智力优势是研究和开发关键步骤，以了解应用程序和计算系统的性质，并通过有针对性的数据精度调整适当地减少计算需求。这项工作的更广泛影响包括培训研究生和发布社区可以在未来的硬件和软件产品中使用的工具，以帮助将整体能源消耗降至最低并提高性能。浮点计算中未使用的精度最终会浪费缓存中分配的空间，并导致不必要的数据移动。该项目的技术部分是确定并寻求最佳分配精度的机会，并在实际代码中有效地实施这种分配方法。除了开发借助自动学习方法调整精度的新算法外，该项目还开发了符号分析方法，以超级优化器的形式在浮点指令选择和优化中发挥新的作用。这些工具将被发布给一个研究社区，研究人员对致力于亿级计算，并在安全关键设备上部署应用程序感兴趣。这项工作代表了研究人员通过高性能计算、形式化方法和编译器技术的技能的协同组合。

The project implements methods to improve the resource-efficiency of numerical computations on a variety of computing machines, ranging from supercomputers to mobile devices, by adaptively reducing data-precision based on the needs of the application. Efficiently adapting data-precision permits these machines to run larger computations and also improves the overall performance (including energy consumption) made possible by a combination of reduced computational burden as well as reduced data movement. The intellectual merits of this project are to research and develop the key steps to understand the nature of applications and computing systems, and to suitably minimize computing demands through targeted data-precision adjustment. Broader impacts of the work include training graduate students and releasing tools that the community can employ in future hardware and software product, to help minimize overall energy consumption and improve performance.Unused precision in floating-point computations ends up wasting allocated space in caches, and also causes unnecessary data movement. The technical parts of the project are to identify as well as pursue opportunities for optimally allocating precision, and to efficiently implement such allocation methods in actual codes. In addition to developing new algorithms to tune precision assisted by automated learning methods, the project develops symbolic analysis methods to serve novel roles in floating-point instruction selection and optimization in the form of superoptimizers. These tools will be released to a community of researchers interested in working toward exascale computing, and deploying applications in safety-critical devices. This work represents a synergistic combination of the investigator's skills ranging through high performance computing, formal methods, and compiler technologies.