EAGER: Application-driven Data Precision Selection Methods

在网络物理系统、决策系统、金融处理和其他使用真实的数字的HPC应用程序中使用的数值算法很容易引入计算错误，这是因为一个众所周知的原因：计算机中不存在真实的数字，我们必须使用浮点数据类型来近似计算。由于数据移动会消耗能量，因此必须在不影响计算完整性的情况下分配最低精度的浮点数据。该项目实施了各种方法，以减少在各种规模的计算设备上运行的数值计算所消耗的能量，包括用于科学研究的超级计算机，一直到嵌入式和移动的设备，这些设备在真实的生活的许多行业中找到用途，包括医疗设备和机器人。 这项工作的一个关键推动力是通过减少计算单元之间的传输来实现节能。该项目研究了用于表示数据的位数如何在计算中引入错误，以及这些错误是否会影响结果的正确性。PI建议开发新的形式化方法工具来自动估计误差范围，开发自动调优编译器以仔细选择精度，并构建新的超级优化器以生成更有效的代码。这些新技术将用于改进机器学习和高性能计算领域的软件。PI应针对高性能计算系统和机器学习系统中的错误制定适当的标准。他们将开发工具，以最佳方式分配精度，同时保持在可接受的答案范围内。他们的工具将发布给有兴趣致力于exascale计算的研究人员社区，并在安全关键设备中部署机器学习应用程序。这项工作代表了PI技能的协同组合，包括高性能计算，机器学习，形式化方法和编译器技术。

Numerical algorithms used in Cyber Physical Systems, decision-making systems, financial processing, and other HPC applications that use real numbers are prone to introduce computational errors because of a well-known reason: real numbers do not exist in computers, and we must use floating-point data types to approximate such computations. As data movement costs energy, the lowest precision of floating-point data must be allocated without compromising the computational integrity. This project implements methods to reduce the amount of energy consumed by numerical computations running on computing devices at all scales including supercomputers for scientific research all the way to embedded and mobile devices finding uses in many walks of real life including medical devices and robots. A key thrust of the work is to perform energy reduction through reduced transfers between computing units. The project studies how the number of bits used to represent data introduce errors in computations, and whether these errors affect the correctness of results.The PIs propose to develop new formal methods tools to automatically estimate error bounds, develop auto-tuning compilers to carefully select precision, and build new superoptimizers to generate more efficient code. These new technologies will be applied to improve software in the domains of machine learning and high-performance computing. The PIs shall develop suitable criteria for errors in high performance computing systems and machine learning systems. They will develop tools that allocate precision optimally while staying within the bounds of acceptable answers. Their tools will be released to a community of researchers interested in working toward exascale computing, and deploying machine learning applications in safety-critical devices. This work represents a synergistic combination of PI skills ranging through high performance computing, machine learning, formal methods, and compiler technologies.