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: 0964328
• 负责人: David August
• 金额: $ 40万
• 依托单位: Princeton University
• 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=0964328&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.

面对物理和微构造的局限性，微处理器行业已朝着多核心设计迈向多核心设计，以利用增加的晶体管计数。 不幸的是，提供多个内核不会转化为大多数应用程序的性能。该项目并没有将所有负担推向程序员，而是主张使用隐式并行编程模型来消除费力且容易出错的显式并行编程的过程。 隐式并行编程利用顺序语言来促进较短的开发和调试周期，并依赖于静态编译器和运行时系统的自动工具来识别并行性并将其自定义为目标平台。 可以使用以下方式系统地提取隐式并行性：（1）解耦的软毛皮层，这是一种提取在许多顺序应用中发现的管道并行性的技术； （2）克服记忆依赖分析的局限性的低频和高信猜测； （3）平行化的全程图范围，以消除分析边界； （4）对顺序编程模型的简单扩展，使程序员有能力完善程序的含义； （5）动态适应以确保在不断变化的环境中保持效率。该项目正在开发一组技术，以实现具有可扩展，终身线程提取和动态适应性的隐式并行编程系统。 在更广泛的层面上，隐式平行的编程方法将使程序员释放他们试图解决的问题，而不是强迫他们克服处理器行业未能继续扩展性能。 这种方法将使计算机易于访问，从而帮助计算对其他领域产生相同的积极影响。