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

程序剖析是一种发现、理解和推理程序的动态或运行时行为的基本而强大的技术。现有的运行时系统（或虚拟机，VM）的托管语言大大利用不足的剖析系统的潜力，这会导致严重的性能损失，并增加成本或削减VM在许多领域的适用性。本研究的目的是了解和解决限制程序剖析的适用性和有效性的局限性。知识产权的优点是开发一个完整的理解程序剖析的基本特征，其局限性和依赖优化的有效性，建立一个全面的和结构化的剖析框架，以提高效率和简化采用的剖析机制在VM任务，并设置阶段增加就业和实现的好处，从自适应优化的VM。该项目更广泛的意义和重要性是在不同的领域部署托管运行时环境，包括web/Internet、桌面、服务器、云计算和移动的系统。（a）对不同剖析策略的益处和局限性及其对反馈导向优化（FDO）的有效性和性能的影响有更深入的基本理解，（B）进行研究和工程，应用这种理解来开发VM中的新的剖析机制和机器，以及（c）构建更高级别的预测模型，最大化在VM任务期间使用剖析知识的容易性和益处。为了进行这项基础研究，评估和展示其观察结果，并展示更有效的程序分析对改善现有VM优化的好处，并能够创建新的自适应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优化以提高程序速度，（B）堆存储器管理以提高性能和功率效率，以及（c）代码高速缓存管理以在较低存储器利用率下维持性能。这项研究有望提高性能和可访问性的托管语言程序，这是非常重要的，为未来的计算系统，因为它们提供了一个理想的编程平台，在面对日益增长的软件复杂性，最好的分布式格式，便携式基于互联网的应用程序，和最有效的执行策略，安全和可靠的执行不可信的Web服务。

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.