CAREER: Context-Aware and Context- Adaptive Code Optimization for New General High-Performance Computers

职业：新型通用高性能计算机的上下文感知和上下文自适应代码优化

• 批准号: 0746034
• 负责人: Xiaoming Li
• 金额: $ 40万
• 依托单位: University of Delaware
• 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: 2008-03-01 至 2013-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0746034&HistoricalAwards=false
• 关键词: CAREER; Context; Aware; Adaptive; Code

New general high-performance computing platforms simultaneously run multiple programs and a large number of threads. This presents unprecedented challenges for code optimization. First, the ubiquitous existence of multi-program and multi-thread makes a program run in an environment that has extensive resource competition. Second, the execution environment, which is the target of code optimization, is always changing during the life of a program. This project employs a systematic approach of optimization and runtime adaptation for multiple runtime contexts - Context-aware and Context-adaptive Optimization (CACAO). CACAO exploits both computer architectural features and runtime contexts in program optimizations and integrates four synergistic approaches: (1) Runtime environment characterization, which characterizes contexts in which a program will execute and augments compiler models with context; (2) New profiling techniques, which report both program performance and context information; (3) Context aware code optimization, which uses profiling information to identify and predict contexts, optimizes programs for different contexts, and integrates contextualized versions of a program; and (4) Dynamic code adaptation, which adapts programs at runtime by selecting the most suitable version of a program for the current context. The project directly leads to more effective compilation and code generation in all domains, thus bringing broad benefits to society, in particular for people who require the computation power from the new general high-performance computers. The project is expected to provide new code optimization techniques that accelerate programs in the new high-performance computers. The optimization techniques and resources will be disseminated as open-source software tools and packages.

新型通用高性能计算平台同时运行多个程序和大量线程。这给代码优化带来了前所未有的挑战。首先，多程序、多线程的普遍存在，使得程序运行在资源竞争广泛的环境中。其次，执行环境是代码优化的目标，在程序的生命周期中总是在变化。该项目采用了针对多个运行时上下文的优化和运行时适应的系统方法 - 上下文感知和上下文自适应优化（CACAO）。 CACAO在程序优化中利用计算机体系结构特征和运行时上下文，并集成了四种协同方法：（1）运行时环境表征，它表征程序将在其中执行的上下文，并用上下文增强编译器模型； (2) 新的分析技术，报告程序性能和上下文信息； (3) 上下文感知代码优化，使用分析信息来识别和预测上下文，针对不同上下文优化程序，并集成程序的上下文化版本； (4)动态代码适配，通过选择最适合当前上下文的程序版本来在运行时适配程序。该项目直接导致所有领域更有效的编译和代码生成，从而为社会带来广泛的利益，特别是对于需要新型通用高性能计算机计算能力的人们。该项目预计将提供新的代码优化技术，以加速新型高性能计算机中的程序。优化技术和资源将作为开源软件工具和软件包进行传播。