RIA: Improving Cache Performance in Scientific Applications

RIA：提高科学应用程序中的缓存性能

• 批准号: 9409341
• 负责人: Steven Carr
• 金额: $ 9.12万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-07-01 至 1997-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9409341&HistoricalAwards=false
• 关键词: RIA; Improving; Cache; Performance; Scientific

This project explores approaches for automatically restructuring code to improve the performance of cache memories for scientific applications. Specifically, the project examines experimentally the following techniques: (1) loop optimization guided by a new performance metric that includes the effects of cache performance and floating-point computation on execution speed, (2) the enhancement of cache line replacement algorithms by using compiler generated knowledge of future reference patterns, and (3) the use of compiler generated cache hit and miss information by the instruction scheduler to hide latency on architectures having non-blocking caches. Each of these methods is implemented in an experimental compiler and evaluated on advanced microprocessors. The success of the project represents a significant step towards achieving fully automatic memory hierarchy management.

这个项目探索自动重组代码的方法，以提高高速缓存存储器的性能，用于科学应用。具体而言，该项目通过实验研究了以下技术：（1）由包括高速缓存性能和浮点计算对执行速度的影响的新性能度量引导的循环优化，（2）通过使用编译器生成的未来引用模式的知识来增强高速缓存行替换算法，以及（3）指令调度器使用编译器生成的高速缓存命中和未命中信息来隐藏具有非阻塞高速缓存的体系结构上的等待时间。 这些方法中的每一个都在实验编译器中实现，并在先进的微处理器上进行评估。该项目的成功代表了实现全自动内存层次管理的重要一步。