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

最近，微处理器工业已经转向多核微处理器设计，作为在面对物理和微架构限制时利用增加的晶体管数量的手段。不幸的是，提供多个核心并不能直接转化为大多数代码的性能。为了利用多核，许多新的语言已经被提出来减轻编写并行程序的负担，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多得多。一种更有吸引力的方法是依赖于工具（编译器和运行时优化器）来自动从顺序应用程序中提取线程。不幸的是，尽管对自动并行化进行了数十年的研究，但大多数技术仅在科学和数据并行领域有效。通过最近获得的洞察力，研究人员表明，以前的线程提取方法的限制并不是根本性的。通过系统地应用已知的和新的编译技术，研究人员发现，SPEC CINT 2000，其中最顺序的代码，具有丰富的可扩展的parallelism.在这个项目中，研究小组正在采取的初步步骤，开发必要的技术，建立一个自动线程提取框架。这些技术包括开发提取并行性的静态转换和量化动态优化的机会。该系统最终将由一系列静态转换和编译器插入的提示以及运行时优化组件组成。 该框架将通过从广泛的应用程序中可靠地提取并行性来解决多核挑战，而不会给程序员带来应该保留的低级别实现细节的负担。

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.