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，Jagannathan 路易斯安那州立大学和农业与机械学院CISE 实验计算机科学博士后研究员：高级编译器优化方面的 CISE 博士后研究和培训现代处理器广泛使用具有多级缓存的内存层次结构，以应对处理器和内存速度之间不断扩大的差距。 因此，程序的性能主要取决于它们的内存访问特性以及这些特性如何与处理器的内存层次结构相匹配。 虽然已经提出了几种编译器转换来增强局部性，但即使对于具有常规内存访问模式的程序（例如密集线性代数计算中的程序），最好的编译器优化代码也无法与库实现的性能相匹配。这项研究的一个目标是在开发编译器转换（数据或计算或两者的组合）领域培训一名博士后研究员，以处理比完美嵌套和常规内存访问更大的编程结构。 最初，该员工将建立在数据束缚的基础上，数据束缚最近被提议作为一种以数据为中心的方法来解决优化局部性问题。 为了实现这一目标，员工将接受培训，设计一种优化策略，该策略将集成数据束缚（一种以数据为中心的方法）和平铺（一种以控制为中心的方法），并在编译器中实施并评估其有效性。助理将：1）研究具有规则和不规则内存访问模式的大量应用程序，包括科学计算代码和多媒体应用程序，2）设计启发式方法以提高数据束缚的有效性和适用性，开发集成数据束缚和平铺，并在编译器中实现这些，以及 3) 在多个基准测试应用程序上对这些技术进行广泛的实验评估。