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. Duke University Cise：Tune：Tune：系统支持记忆友好的编程的系统支持在基于微处理器的机器中广泛使用多级记忆层次结构，使得主要由一项应用程序确定，并极为敏感要进行内存映射，并且需要记忆友好的编程：仔细的数据结构布局和代码重组以改善数据局部性。 改善内存系统性能的技术在各个层面上都表现出自我，从高级算法和数据结构，到编译时间分析和对程序的重组到允许应用程序更好地管理内存层次结构的特殊架构基础。 缺乏用于改进数据区域的自动工具目前迫使许多应用程序程序员手动重组其代码。 不幸的是，现代科学计算中看到的复杂算法需要同样复杂的重组技术。 这些技术需要在计算机体系结构方面的专业知识，为程序员带来了与编程正确性无关的特定于机器的细节，并降低了重组代码的可读性，可维护性和可移植性。 由于所有这些原因，任何显着尺寸的程序的手动重组是站不住脚的，并且需要某种系统支持。 该项目针对此问题的各个方面，从开发相关的数学技术来代表和操纵数据局部性，通过实施交互式和自动数据局部管理工具到为未来生成系统提出创新的内存体系结构。 将使用代表现代科学计算的程序来证明所得系统的功效，包括分裂和构成科学计算，例如N体求解器（例如FastMultipole）和许多线性代数核（例如，Strassens的Matrix Matrix乘法ALGO RITHM）。