CISE Postdoctoral Research and Training in Advanced Compiler Optimizations (Operating Systems and Compilers Program/CCR/CISE)

CISE 高级编译器优化博士后研究和培训（操作系统和编译器项目/CCR/CISE）

• 批准号: 0103933
• 负责人: Jagannathan Ramanujam
• 金额: $ 6.6万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-01 至 2005-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0103933&HistoricalAwards=false
• 关键词: CISE; Postdoctoral; Research; Training; Advanced

0103933Ramanujam, JagannathanLouisiana State University & Agricultural and Mechanical CollegeCISE Postdoctoral Associates in Experimental Computer Science: CISE Postdoctoral Research and Training in Advanced Compiler OptimizationModern processors extensively use memory hierarchies with multiple levels of caches in order to cope with the widening gap between processor and memory speeds. As a result, the performance of programs depend critically on their memory access characteristics and how these are matched to the memory hierarchy of the processors. While several compiler transformations have been proposed towards enhancing locality, even for programs with regular memory access patterns such as those in dense linear algebra computations, the best compiler-optimized codes do not match the performance of library implementations.One goal of this research is to train a postdoctoral research associate in the area of developing compiler transformations (either data or computation or a combination of the two) to handle a larger class of programming constructs than perfect nests and regular memory accesses. Initially, the associate will build on data shackling, which has been recently proposed as a data-centric approach to the problem of optimizing locality. To accomplish this goal, the associate will be trained to design an optimization strategy that will integrate data shackling (a data-centric approach) and tiling (a control-centric approach), and implement it in a compiler and evaluate its effectiveness. The associate will:1) Study a large collection of applications with both regular and irregular memory access patterns, including scientific computing codes and multimedia applications,2) Design heuristics for improving the effectiveness and applicability of data shackling, develop methods to integrate data shackling and tiling, and implement these in a compiler, and3) Perform extensive experimental evaluation of the techniques on several benchmarking applications.

0103933Ramanujam，Jagannathanlouisiana州立大学以及实验计算机科学领域的农业和机械学院的学院后的博士后助理：CISE高级编译器优化的博士后研究和培训，以广泛的记忆层次的范围，可用于较宽的记忆和较宽的记忆范围，以延伸的记忆层次，并使用记忆范围。 结果，程序的性能严重取决于其内存访问特性以及如何匹配处理器的内存层次结构。 尽管已经提出了几种编译器转换以增强位置，即使对于具有定期记忆访问模式的程序，例如密集的线性代数计算的程序，最佳编译器优化的代码也不符合图书馆实现的性能。该研究的一个目标是训练在较大的数据或计算群体中（与数据构造的群体更大的数据）相比，该研究的培训（和常规内存访问。 最初，该助理将基于数据shak缩，最近已提出，作为以数据为中心的方法，用于优化局部性问题。 为了实现这一目标，将对合伙人进行培训，以设计一种优化策略，该策略将整合数据夹具（以数据为中心的方法）和平铺（一种以控制为中心的方法），并将其在编译器中实施并评估其有效性。 The associate will:1) Study a large collection of applications with both regular and irregular memory access patterns, including scientific computing codes and multimedia applications,2) Design heuristics for improving the effectiveness and applicability of data shackling, develop methods to integrate data shackling and tiling, and implement these in a compiler, and3) Perform extensive experimental evaluation of the techniques on several benchmarking applications.