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

程序分析是一种发现、理解和推断程序动态或运行时行为的基本而强大的技术。现有的托管语言运行时系统（或虚拟机，VM）在很大程度上没有充分利用分析系统的潜力，这导致了严重的性能损失，并增加了成本或限制了VM在许多领域的适用性。本研究的目标是理解和解决限制程序分析的适用性和有效性的限制。智力上的优点是对程序分析的基本特征、局限性及其对依赖优化有效性的影响有一个完整的理解，建立一个全面和结构化的分析框架，以提高VM任务中分析机制的效率和易用性，并为增加VM中的就业和实现自适应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、云计算和移动系统。本研究旨在：(a)深入了解不同分析策略的优点和局限性，以及它们对反馈导向优化（FDOs）的有效性和性能的影响；(b)开展研究和工程，将这种理解应用于VM中开发新的分析机制和机器；(c)构建更高层次的预测模型，最大限度地提高VM任务中使用分析知识的便利性和效益。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的好处，以改进现有的VM优化，并使创建新的自适应VM优化成为可能，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)堆内存管理，以提高性能和电源效率；(c)代码缓存管理，以在较低内存利用率下保持性能。本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，因为它们为面对日益增长的软件复杂性提供了理想的编程平台，为可移植的基于internet的应用程序提供了最佳分发格式，并为安全可靠地执行不受信任的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.