A Framework for Speeding Up Mobile Code Execution in Embedded Systems Using Superoperators and Annotations

使用超级运算符和注释加速嵌入式系统中移动代码执行的框架

• 批准号: 0311738
• 负责人: Alexander Veidenbaum
• 金额: $ 25万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-15 至 2008-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0311738&HistoricalAwards=false
• 关键词: Framework; Speeding; Up; Mobile; Code

Embedded platforms are increasingly used to connect to the Web and are executing mobile code. These platforms are a resource-constrained environment in which interpreted execution of mobile codes is the norm because dynamic compilation is not feasible. At the same time, the performance of the executed code is of critical importance and is often a limiting factor in both the capabilities of the system and user perception. The goals of the research proposed here are 1) to significantly improve interpreter performance for mobile code on embedded platforms without increasing resource requirements and 2) to design a resource constrained dynamic compilation system to be used with an interpreter for adaptive optimization to further improve the performance. The goals will be achieved by using extensive compile-time analysis and by passing the results of the analysis to the interpreter running on a client system via code annotations. Annotations will identify super-operators, groups of instructions that can be executed as a unit and optimized together. This will allow a more efficient interpretation by minimizing communication overhead and dispatch costs. Annotations will also permit adaptive dynamic optimization requiring fewer resources and little or no overhead.

嵌入式平台越来越多地被用来连接网络并执行移动代码。这些平台是一个资源受限的环境，其中解释执行移动代码是常态，因为动态编译是不可行的。同时，执行代码的性能至关重要，通常是系统能力和用户感知的限制因素。本文提出的研究目标是：1)在不增加资源需求的情况下，显著提高嵌入式平台上移动代码的解释器性能；2)设计一个资源受限的动态编译系统，与解释器一起进行自适应优化，以进一步提高性能。这些目标将通过使用广泛的编译时分析并通过代码注释将分析结果传递给在客户端系统上运行的解释器来实现。注释将标识超级运算符，即可以作为一个单元执行并一起优化的指令组。这将使通信开销和派遣成本降至最低，从而实现更有效的口译。注释还将允许自适应动态优化，所需资源更少，开销更少或根本不需要。