Code Generation for Specialized Hardware-Supported Functional Units

图形处理单元（GPU）的进步开启了行业向专用加速器发展的主要趋势。然后，这些加速器被重新用于其他领域，这些领域可以从类似的硬件架构设计中受益。有效地使用gpu来训练深度卷积网络导致了张量处理单元（tpu）的设计，在其他供应商的产品中也有不同的名称，它们具有用于矩阵操作的特定架构特征。这个建议是基于这样的想法：最初为tpu设计的功能单元也可以重新用于通用的数值计算，这样做可能会带来性能和能量/性能的提升。因此，本研究的主要目标包括：-研究最初为tpu设计的功能单元的重新用途，用于通用高性能数值计算。-研究用于执行数字计算循环巢的专用功能单元的多种可能配置。-确定哪些程序分析和基于编译器的代码转换需要移植在编程模型中编写的带有或不带有并行注释的代码，并使它们适合于在这些功能单元中执行。-设计盈利功能，可用于确定应将哪些代码转换应用于循环巢，以使其在功能单元中表现良好，并使运行时决定何时应在一个这样的功能单元中执行给定的计算。拟议的研究将支持解决方案的开发，这些解决方案可能会为科学和数据分析应用带来更高的性能。开发这些解决方案的过程还将尝试利用新兴的自动化学习技术，并将它们集成到代码生成过程中。

Advances in Graphics Processing Units (GPU) started a major trend in the industry towards specialized accelerators. These accelerators were then repurposed for extensive usage in other domains that can benefit from similar hardware architecture design. The effective use of GPUs for the training of Deep Convolution Networks led to the design of Tensor Processing Units (TPUs), also known by different names in offerings by other vendors, which have specific architecture features for matrix operations. This proposal is based on the idea that the functional units originally designed for TPUs can also be repurposed for general-purpose numerical computing and that there might be performance and energy/performance gains in doing so. Thus, the main objectives of the proposed research include: - To investigate the repurposing of functional units originally designed for TPUs for general-purpose high-performance numerical computation.- To study multiple possible configurations of specialized functional units for the execution of numerical-computing loop nests.- To determine which program analysis, and compiler-based code transformations, are necessary to port code written in programming models with, or without, parallel annotations and to make them suitable to benefit from execution in such functional units.- To design profitability functions that can be used to determine which code transformations should be applied to loop nests to make them performant in the functional units and also to enable runtime decisions of when a given computation should be executed in one such functional unit.The proposed research will support the development of solutions that may result in higher performance for scientific and data-analytics applications. The process of developing these solutions will also attempt to leverage emerging automated learning techniques and to integrate them in the code-generation process.