EAGER: A Study of the Limitations of High Performance Code Generation in Vectorizing Compilers

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自变量的小向量。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，自动向量化的技术水平远远没有达到这一目标，在相同的代码上只实现了20%-30%的改进。尽管研究表明，当前的自动向量化编译器并没有识别出所有的向量化机会，但人们对它们未能做到这一点的原因知之甚少。PI将研究高级理想化向量代码与实际硬件上发现的特殊向量指令之间的映射问题。PI计划使用螺旋代码生成和自动调优系统来生成大量测试用例，以评估现有的自动矢量化编译器管理此类映射的情况。这项研究将通过识别为实际硬件生成向量化代码所需的程序转换来开发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各种应用程序的性能。

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. The PI will study the problem of mapping between high-level idealized vector code and the idiosyncratic vector instructions found on real hardware. The PI plans to use the Spiral code generation and autotuning system to generate a large set of test cases for evaluating how well existing autovectorizing compilers manage such mappings. This research will make it possible to develop better autovectorizing compilers by identifying the program transformations that are required to generate vectorized code for real hardware. The performance benefits of such compilers will improve the performance of applications ranging from multimedia software to scientific computing.