CPA-CPL-T: Collaborative Research: Revisiting the Sequential Programming Model for Multicore Systems

CPA-CPL-T：协作研究：重新审视多核系统的顺序编程模型

• 批准号: 0811580
• 负责人: David August
• 金额: $ 5万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811580&HistoricalAwards=false
• 关键词: CPA; CPL; Collaborative; Research; Revisiting

Recently, the microprocessor industry has moved toward multicore microprocessor designs as a means of utilizing the increasing transistor counts in the face of physical and micro-architectural limitations. Unfortunately, providing multiple cores does not directly translate into performance for most codes. To make use of multicore, many new languages have been proposed to ease the burden of writing parallel programs, yet the programming effort involved in creating correct and efficient parallel programs is still far more substantial than writing the equivalent single-threaded version. A more attractive approach is to rely on tools, both compilers and runtime optimizers, to automatically extract threads from sequential applications. Unfortunately, despite decades of research on automatic parallelization, most techniques have only been effective in the scientific and data-parallel domains. With recently gained insight, the investigators showed that the limits of prior thread-extraction approaches are not fundamental. By applying known and new compilation techniques in a systematic manner, the investigators found that SPEC CINT2000, among the most sequential of codes, has abundant scalable parallelism.In this project, the team of investigators is taking the initial steps toward developing the techniques necessary to build an automatic thread extraction framework. These techniques include developing static transformations that extract parallelism and quantifying the opportunities for dynamic optimization. The system will ultimately consist of a series of static transformations and compiler-inserted hints combined with a run-time optimization component. This framework will address the multicore challenge by reliably extracting parallelism from a wide range of applications without burdening the programmer with what should remain to be low-level implementation details.

最近，微处理器行业已朝着多核心微处理器设计迈进，作为面对物理和微构造限制，利用增加的晶体管计数的一种手段。不幸的是，提供多个内核并不能直接转化为大多数代码的性能。为了利用多项式，已经提出了许多新语言来减轻并行程序的负担，但是创建正确和高效的并行程序所涉及的编程工作仍然比编写等效的单线程版本要大得多。一种更具吸引力的方法是依靠编译器和运行时优化器的工具来自动从连续应用程序中提取线程。不幸的是，尽管进行了数十年的自动并行研究，但大多数技术仅在科学和数据并行域中有效。随着最近获得的见识，研究人员表明，先前的螺纹萃取方法的局限性不是基本的。通过以系统的方式应用已知和新的汇编技术，研究人员发现，在最顺序的代码中，规格CINT2000具有丰富的可扩展并行性。在该项目中，研究人员正在采取最初的步骤来开发建立自动线程提取框架所必需的技术。这些技术包括开发静态转换，以提取并行性并量化动态优化的机会。该系统最终将包括一系列静态转换和编译器插入的提示，并结合运行时优化组件。 该框架将通过可靠地从广泛的应用程序中提取并行性来解决多项挑战，而不必为程序员负担，而应该是低级实施细节。