CAREER: Input-Centric Program Behavior Analysis and Adaptation

程序行为分析通过分析和预测程序的动态行为，为程序转换和资源管理提供基础支持。它的有效性对于最大化计算效率至关重要。本研究建议将程序输入-一个迄今几乎被忽视的维度-纳入程序行为分析的重点，培养一种新的范式，即以输入为中心的程序行为分析和适应。以输入为中心的程序行为分析与适配技术由三部分组成，即以输入为中心的程序行为分析与适应技术。前两个部分，程序输入特征和输入行为建模，解决了程序输入的复杂性，提取重要特征，并识别特征输入特征和程序行为之间的相关性。第三个组成部分，即以投入为中心的适应，利用前两个组成部分创造的新机会，使动态优化具有主动性和整体性，但不会丧失对投入和环境变化的适应性。这三个组件加在一起，使可演化编程系统比以前更可行。在这样的系统中，输入行为模型体现了中央知识库，该知识库在程序生产运行中以增量方式增长。随着知识库变得更大，行为预测变得更加准确，从而刺激更好的软硬件匹配，并使程序和运行时系统的性能越来越好。由于程序行为分析在软硬件匹配中的基础作用，本研究有助于为推进软件执行堆栈(编译器、虚拟机、操作系统等)中各个层的优化铺平道路。

By analyzing and predicting program dynamic behaviors, program behavior analysis offers the fundamental support for program transformations and resource management. Its effectiveness is crucial for the maximization of computing efficiency. This research proposes to include program inputs---a so far virtually ignored dimension---into the focus of program behavior analysis, cultivating a new paradigm, namely input-centric program behavior analysis and adaptation. This input-centric paradigm will create many new opportunities for enhancing the matching between software and hardware, hence significantly improving the performance and power efficiency in modern computing.The proposed technique, input-centric program behavior analysis and adaptation, consists of three components. The first two components, program input characterization and input-behavior modeling, resolve the complexities of program inputs, extract important features, and recognize the correlations between characterized input features and program behaviors. The third component, input-centric adaptation, capitalizes on the novel opportunities that the first two components create, making dynamic optimizations proactive and holistic, but without losing the adaptivity to inputs and environmental changes. Together, the three components make evolvable programming systems more feasible than before. In such a system, the input-behavior models embody the central knowledge base, which grows incrementally across program production runs. As the knowledge base becomes larger, behavior prediction becomes more accurate, stimulating better software-hardware matching and making the program and runtime systems perform increasingly better. Because of the fundamental role of program behavior analysis in software-hardware matching, this research helps pave the way for advancing the optimizations in various layers in the software execution stack (compilers, virtual machines, OS, etc.).