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

EAGER：矢量化编译器中高性能代码生成的局限性研究

• 批准号: 1251185
• 负责人: Franz Franchetti
• 金额: $ 8.34万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1251185&HistoricalAwards=false
• 关键词: EAGER; Study; Limitations; Performance; Code

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.

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。 PI将研究高级理想化矢量代码与真实的硬件上的特殊矢量指令之间的映射问题。 PI计划使用Spiral代码生成和自动调优系统来生成大量测试用例，以评估现有的自动向量化编译器如何管理此类映射。 这项研究将有可能开发更好的自动向量化编译器，通过识别程序转换所需的生成向量化代码的真实的硬件。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。