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

新的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，多程序和多线程的普遍存在使得程序运行在资源竞争激烈的环境中。其次，作为代码优化目标的执行环境在程序的生命周期中总是不断变化的。该项目采用了一种针对多个运行时上下文的系统优化和运行时适配方法--上下文感知和上下文自适应优化(Cacao)。Cacao在程序优化中利用了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)运行时环境特征，它表征程序将在其中执行的上下文，并用上下文扩充编译器模型；(2)新的剖析技术，它同时报告程序性能和上下文信息；(3)上下文感知代码优化，它使用剖析信息来识别和预测上下文，针对不同的上下文优化程序，并集成程序的上下文版本；以及(4)动态代码适配，它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。该项目直接导致在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好处，特别是对需要新的通用高性能计算机计算能力的人。该项目预计将提供新的代码优化技术，以加速新的高性能计算机中的程序。优化技术和资源将作为开放源码软件工具和包分发。

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.