CSR: Medium: Collaborative Research: Scaling the Implicitly Parallel Programming Model with Lifelong Thread Extraction and Dynamic Adaptation

CSR：中：协作研究：通过终身线程提取和动态适应扩展隐式并行编程模型

• 批准号: 0964627
• 负责人: Mary Lou Soffa
• 金额: $ 40万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0964627&HistoricalAwards=false
• 关键词: CSR; Medium; Collaborative; Research; Scaling

The microprocessor industry has moved toward multicore designs to leverage increasing transistor counts in the face of physical and micro-architectural limitations. Unfortunately, providing multiple cores does not translate into performance for most applications. Rather than pushing all the burden onto programmers, this project advocates the use of the implicitly parallel programming model to eliminate the laborious and error-prone process of explicit parallel programming. Implicit parallel programming leverages sequential languages to facilitate shorter development and debug cycles, and relies on automatic tools, both static compilers and run-time systems, to identify parallelism and customize it to the target platform. Implicit parallelism can be systematically extracted using: (1) decoupled softwarepipelining, a technique to extract the pipeline parallelism found in many sequential applications; (2) low-frequency and high-confidence speculation to overcome limitations of memory dependence analysis; (3) whole-program scope for parallelization to eliminate analysis boundaries; (4) simple extensions to the sequential programming model that give the programmer the power to refine the meaning of a program; (5) dynamic adaptation to ensure efficiency is maintained across changing environments. This project is developing the set of technologies to realize an implicitly parallel programming system with scalable, lifelong thread extraction and dynamic adaptation. At the broader level, the implicitly parallel programming approach will free programmers to consider the problems they are trying to solve, rather than forcing them to overcome the processor industry's failure to continue to scale performance. This approach will keep computers accessible, helping computing to have the same increasingly positive impact on other fields.

微处理器行业已经转向多核设计，以在面对物理和微架构限制时利用不断增加的晶体管数量。 不幸的是，对于大多数应用程序来说，提供多个内核并不能转化为性能。这个项目提倡使用隐式并行编程模型来消除显式并行编程的费力和容易出错的过程，而不是将所有的负担都推给程序员。 隐式并行编程利用顺序语言来缩短开发和调试周期，并依赖于静态编译器和运行时系统等自动工具来识别并行性并将其定制到目标平台。 隐式并行性可以通过以下方法系统地提取：（1）解耦软件流水，一种提取在许多顺序应用中发现的流水线并行性的技术;（2）低频和高置信度的推测，以克服存储器依赖分析的局限性;（3）整个程序范围的并行化，以消除分析边界;（4）对顺序编程模型的简单扩展，使程序员能够细化程序的含义;（5）动态适应，以确保在不断变化的环境中保持效率。该项目正在开发一套技术来实现具有可扩展、终身线程提取和动态适应的隐式并行编程系统。 在更广泛的层面上，隐式并行编程方法将使程序员能够自由地考虑他们试图解决的问题，而不是迫使他们克服处理器行业无法继续扩展性能的问题。 这种方法将保持计算机的可访问性，帮助计算对其他领域产生同样日益积极的影响。