CPA-CPL: Addressing the Memory Bottleneck in High-Level Programming Systems

CPA-CPL：解决高级编程系统中的内存瓶颈

• 批准号: 0811703
• 负责人: Arun Chauhan
• 金额: $ 6万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811703&HistoricalAwards=false
• 关键词: CPA; CPL; Addressing; Memory; Bottleneck

A combination of factors has resulted in an industry-wide move to build multiple cores on a single chip to sustain the rate of growth in the computing power of processors. At the same time, high-level programming systems (e.g., scripting languages) have gained tremendous popularity motivated by dramatically higher programming productivity that such systems afford compared to traditional programming languages, such as C/C++ or Java. Unfortunately, these two trends combine to increase the gap between application software and the underlying hardware, which negatively impacts hardware utilization. An important reason is the critical nature of memory subsystems on the modern multi-core processors and a lack of any systematic methods to derive the memory behavior of programs written in high-level programming systems. This research aims at addressing the problem by developing a theoretical model to estimate the memory behavior of programs written in MATLAB and implementing the model in a prototype compiler.A quantitative metric that past researchers have found useful in memory-related optimizations is reuse distance. Defined as the volume of data accessed between two successive references to a memory location, the goal is to transform programs to lower most of their reuse distances to below a threshold (usually, related to cache size).By restricting the computation of reuse distances to locations accessible at the source-level it is possible to define source-level reuse distance. The metric, even though approximate, enables crucial analysis for high-level programming systems that often benefit remarkably from source-level transformations. This research develops efficient algorithms for source-level reuse distance analysis, validates it against the actual program behavior, and implements it in a MATLAB compiler to drive memory-related optimizations, especially those related to array accesses.

各种因素的结合导致了全行业在单个芯片上构建多个核心的趋势，以维持处理器计算能力的增长速度。与此同时，高级编程系统（例如，脚本语言）已经获得了极大的普及，这是由这样的系统与传统编程语言（例如C/C ++或Java）相比所提供的显著更高的编程生产率所驱动的。不幸的是，这两种趋势联合收割机增加了应用软件和底层硬件之间的差距，这对硬件利用率产生了负面影响。一个重要的原因是现代多核处理器上的存储器子系统的关键性质，以及缺乏任何系统的方法来导出在高级编程系统中编写的程序的存储器行为。为了解决这一问题，提出了一个理论模型来估计MATLAB程序的内存行为，并在原型编译器中实现了该模型。定义为在两次连续引用内存位置之间访问的数据量，目标是将程序转换为将大多数重用距离降低到阈值以下（通常与缓存大小有关）。通过将重用距离的计算限制在源代码级别可访问的位置，可以定义源代码级别重用距离。这个度量标准，即使是近似的，也可以对高级编程系统进行关键的分析，这些系统通常从源代码级别的转换中受益匪浅。本研究开发了有效的算法源代码级重用距离分析，验证它对实际的程序行为，并实现在MATLAB编译器中驱动内存相关的优化，特别是那些与数组访问。