EAGER: A Study of the Limitations of Program Analysis for Autovectorization

大多数现代微处理器支持某种形式的向量运算，允许将相同的运算同时应用于小的参数向量。 研究表明，使用这些指令可以将许多科学代码的性能提高2倍或更多。 不幸的是，自动向量化的最新技术水平福尔斯远未达到这一目标，仅在相同的代码上实现了20-30%的改进。虽然研究表明，当前的自动向量化编译器没有识别所有的向量化机会，但很少有人知道为什么它们不能这样做。 具体来说，该项目将侧重于识别导致向量化失败的编译器分析中的弱点。 这项研究的目的是确定一系列导致绝大多数这些失败的原因。 这项研究将有可能开发更好的编译器分析算法，这将导致更好的自动向量化编译器。 这种编译器的性能优势将提高从多媒体软件到科学计算的应用程序的性能。

Most modern microprocessors support some form of vector operations that allow the same operation to be applied to small vectors of arguments simultaneously. Studies have shown that use of these instructions can improve the performance of many scientific codes by a factor of 2 or more. Unfortunately, the state of the art in autovectorization falls far short of this goal, only achieving improvements of 20-30% on the same codes.While studies have shown that current autovectorizing compilers do not identify all of the opportunities for vectorization, little is known about why they fail to do so. Specifically, the project will focus on identifying weaknesses in compiler analyses that cause failures to vectorize. The goal of this research is to identify a list of causes that are responsible for the vast majority of these failures. This research will make it possible to develop better compiler analysis algorithms that will result in better autovectorizing compilers. The performance benefits of such compilers will improve the performance of applications ranging from multimedia software to scientific computing.