SI2-SSE: Improving Vectorization

随着处理器和加速器中矢量指令集宽度的增加，有效的矢量化变得越来越重要。尽管编译器自动向量化的主题在过去几十年中受到了极大的关注，但目前的向量化编译器通常只能实现处理器峰值性能的一小部分。这个项目将探索几种编译器优化方法来生成高性能的向量化代码。先进的矢量化技术将通过Polly/LLVM模块整合到开源的LLVM/Clang编译器中。还将开发一个基准套件，旨在测试向量化编译器的有效性。产品编译器限制它们的优化搜索空间，以便控制编译程序所花费的时间。这是因为大多数用户期望快速的编译时间。然而，高性能应用程序的开发人员通常非常愿意忍受更长的程序编译等待时间，以换取编译后代码的性能提升。这个项目的一个重要重点是为那些优先考虑高应用程序性能而不是短编译时间的用户开发向量化方法。该项目将研究语义驱动的基于模式的矢量优化方法，对矢量化友好的平铺方法，以及积极的矢量指令调度方法，这些方法以可能的高编译时间为代价，保证了更高的性能。

The increasing width of vector instruction sets in processors and accelerators raises the importance of effective vectorization. Although the topic of automatic vectorization by compilers has received significant attention over the last few decades, current vectorizing compilers can typically realize only a small fraction of a processor's peak performance. This project will explore several compiler optimization approaches for generating high-performance vectorized code. Advanced vectorization techniques will be incorporated in the open-source LLVM/Clang compiler through the Polly/LLVM module. A benchmark suite will also be developed, aimed at testing the effectiveness of vectorizing compilers.Production compilers limit their optimization search space in order to control the time taken to compile programs. This is because the majority of users expect rapid compile times. However, the developers of high-performance applications are generally very willing to tolerate a much longer wait for program compilation, in return for a boost in the performance of the compiled code. A significant focus of this project will be the development of vectorization approaches for such users who prioritize high application performance over short compile times. The project will investigate semantically-driven pattern-based approaches to vector optimization, a vectorization-friendly approach to tiling, and aggressive vector instruction scheduling approaches that promise higher performance at the expense of possibly high compile times.