Challenges in CISE: TUNE: System Support for Memory-Friendly Programming

CISE 中的挑战：TUNE：内存友好型编程的系统支持

• 批准号: 9726370
• 负责人: Kishor Trivedi
• 金额: $ 154.79万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-15 至 2001-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9726370&HistoricalAwards=false
• 关键词: Challenges; CISE; TUNE; System; Support

9726370 Trivedi, Kishor Board, John A. Duke University Challenges in CISE: TUNE: System Support for Memory-Friendly Programming The pervasive use of multi-level memory hierarchies in microprocessor-based machines makes the performance of an application primarily determined by and extremely sensitive to its memory mapping, and requires memory-friendly programming: careful layout of data structures and restructuring of code to improve data locality. Techniques for improving memory system performance manifest themselves at various levels, from high-level algorithms and data structures, through compile-time analysis and restructuring of programs, to special architectural primitives that allow an application to better manage the memory hierarchy. The lack of automatic tools for improving data locality currently forces many application programmers to manually restructure their codes. Unfortunately, the sophisticated algorithms seen in modern scientific computing require equally sophisticated restructuring techniques. These techniques require expertise in computer architecture, burden the programmer with tedious machine-specific details unrelated to program correctness, and reduce the readability, maintainability, and portability of the restructured code. For all these reasons, manual restructuring of programs of any significant size is untenable, and some kind of system support is necessary. This project targets all aspects of this problem, from developing the relevant mathematical techniques for representing and manipulating data locality, through implementing interactive and automatic data locality management tools, to proposing innovative memory architectures for future-generation systems. The efficacy of the resulting system will be demonstrated using programs representative of modern scientific computations including divide-and-conquer scientific computations such as N-body solvers (e.g., fastmultipole) and many linear algebra kernels (e.g., Strassens's matrix multiplication algo rithm).

9726370 Trivedi，Kishor Board，John A. 杜克大学 CISE中的挑战：DAE：对内存友好编程的系统支持 在基于微处理器的机器中普遍使用多级存储器层次结构使得应用程序的性能主要由其存储器映射决定并且对其非常敏感，并且需要存储器友好的编程：仔细布局数据结构和重构代码以提高数据局部性。 用于提高存储器系统性能的技术在各个级别上表现出来，从高级算法和数据结构，通过编译时分析和程序重构，到允许应用程序更好地管理存储器层次结构的特殊架构原语。 由于缺乏用于提高数据局部性的自动化工具，目前许多应用程序编程人员不得不手动重构代码。 不幸的是，现代科学计算中的复杂算法需要同样复杂的重组技术。 这些技术需要计算机体系结构方面的专业知识，给程序员带来与程序正确性无关的繁琐的机器特定细节的负担，并降低了重构代码的可读性，可维护性和可移植性。 由于所有这些原因，对任何重要规模的程序进行手动重组都是站不住脚的，需要某种系统支持。 该项目针对这个问题的各个方面，从开发相关的数学技术来表示和操作数据局部性，通过实施交互式和自动数据局部性管理工具，为下一代系统提出创新的内存架构。 所得到的系统的功效将使用代表现代科学计算的程序来证明，包括分而治之的科学计算，例如N体求解器（例如，快速多极）和许多线性代数核（例如，Strassens矩阵乘法算法）。