SHF: Small: Collaborative Research: Explore, Understand, and Build a New Profiling Framework for Managed Language Virtual Machines

程序分析是发现、理解和推理程序的动态或运行时行为的一种基本且强大的技术。用于托管语言的现有运行时系统(或虚拟机，VM)极大地未充分利用分析系统的潜力，这导致严重的性能损失，并增加了成本或限制了VM在许多领域中的适用性。本研究的目的是了解和解决制约程序剖析的适用性和有效性的局限性。其智力优势是全面了解程序剖析的基本特征、程序剖析的局限性及其对依赖优化有效性的影响，构建全面和结构化的剖析框架，以提高VM任务期间剖析机制的效率并简化其采用，并为在VM中增加就业和实现适应性优化的好处奠定基础。该项目更广泛的意义和重要性是在不同的域中部署托管运行时环境，包括Web/Internet、桌面、服务器、云计算和移动系统。研究的目的是：(A)深入了解不同配置文件策略的优点和局限性，以及它们对反馈导向优化(FDO)的有效性和性能的影响，(B)进行应用这种了解的研究和工程，以开发新的配置文件机制和VM中的机制，以及(C)构建更高级别的预测模型，以最大限度地方便和受益于在VM任务期间使用配置文件知识。为了进行这项基础研究，评估和演示其观察结果，并展示更有效的程序剖析的好处，以改进现有的VM优化，并支持创建新的自适应VM优化，这项工作将采用三组真实的VM自适应任务：(A)选择性编译和反馈导向的优化，以提高程序速度；(B)堆内存管理，以提高性能和电源效率；以及(C)代码高速缓存管理，以在较低的内存利用率下保持性能。这项研究承诺提高托管语言程序的性能和可访问性，这对未来的计算系统非常重要，因为它们在面对日益增长的软件复杂性时提供了理想的编程平台，为基于Internet的可移植应用程序提供了最佳的分发格式，并为安全可靠地执行不可信的Web服务提供了最有效的执行策略。

Program profiling is a fundamental and powerful technique to discover, understand and reason about the dynamic or run-time behavior of a program. Existing run-time systems (or Virtual Machines, VM) for managed languages vastly under-utilize the potential of profiling systems, which results in severe performance losses, and increases costs or curtails the suitability of VMs in many domains. The goal of this research is to understand and resolve the limitations that restrict the applicability and effectiveness of program profiling. The intellectual merits are to develop a complete understanding of the fundamental characteristics of program profiling, its limitations and its impact on the effectiveness of dependent optimizations, build a comprehensive and structured profiling framework to increase the efficiency and ease the adoption of profiling mechanisms during VM tasks, and set the stage for increased employment and realized benefits from adaptive optimizations in a VM. The project's broader significance and importance are to deploy managed run-time environments in diverse domains that include the web/Internet, desktop, server, cloud-computing, and mobile systems.The research aims to: (a) develop a deeper fundamental understanding of the benefits and limitations of different profiling strategies, and their impact on the effectiveness and performance of feedback-directed optimizations (FDOs), (b) conduct research and engineering that applies this understanding to develop new profiling mechanisms and machinery in the VM, and (c) construct higher-level predictive models that maximize the ease and benefit of using profile knowledge during VM tasks. To conduct this fundamental study, assess and demonstrate its observations, and show the benefits of more effective program profiling to improve existing VM optimizations, and to enable the creation of new adaptive VM optimizations, this work will employ three sets of real VM adaptive tasks: (a) selective compilation and feedback-directed optimizations to improve program speed, (b) heap memory management to increase performance and power efficiency, and (c) code cache management to maintain performance at lower memory utilization. This research promises to improve the performance and accessibility of managed language programs, which is very important for future computing systems as they provide an ideal programming platform in the face of growing software complexity, the best distribution format for portable Internet-based applications, and the most effective execution strategy for safe and secure execution of untrusted web services.