SHF:Small:Collaborative Research:Adaptive Automatic Parallelization

SHF：小型：协作研究：自适应自动并行化

• 批准号: 1218530
• 负责人: Michael Spear
• 金额: $ 24.78万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218530&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Adaptive

To effectively exploit the power of multi-core processors, programs must be structured as a collection of independent tasks, where separate tasks execute on independent cores. The complexity of modern software makes it difficult for programmers to express their algorithms within this model, both due to the amount of program analysis needed to identify regions of code that can run in parallel, and the likelihood that different regions of code will be best suited by distinct, and possibly incompatible, models of parallel computing. In particular, some codes are best parallelized through speculative techniques, while others favor regular analysis, such as that provided by the polyhedral approach.The proposed research addresses fundamental issues in the creation of parallel programs through a novel combination of automatic and profile-driven techniques. The heart of the research is a robust system based on machine learning, through which a compilation tool can analyze a program, assess the suitability of a variety of parallelization techniques to that program, and then apply the most promising techniques automatically. At run-time, the program will also employ learning to adapt its behavior according to inputs and environment. Furthermore, the programmer will be given a profile-driven feedback mechanism, in order to guide the tool to refine its parallelization of the program, and guide the program's self-tuning behavior. In conjunction with the creation of this system, new algorithms and tools for speculative parallelization and large-scale program analysis will be invented. Prototypes and source code will be distributed as open-source software.

为了有效地利用多核处理器的能力，程序必须被构建为独立任务的集合，其中独立的任务在独立的核心上执行。现代软件的复杂性使得程序员很难在这个模型中表达他们的算法，这既是因为识别可以并行运行的代码区域所需的程序分析的量，也是因为不同的代码区域可能最适合不同的、可能不兼容的并行计算模型。特别是，一些代码通过推测技术得到了最好的并行化，而另一些代码则倾向于常规分析，如多面体方法提供的分析。这项研究的核心是一个基于机器学习的健壮系统，通过它，编译工具可以分析程序，评估各种并行化技术对该程序的适用性，然后自动应用最有前途的技术。在运行时，该程序还将利用学习来根据输入和环境调整其行为。此外，还将给程序员一种轮廓驱动的反馈机制，以引导工具细化其程序并行化，并引导程序的自调优行为。随着这个系统的创建，将发明用于投机性并行化和大规模程序分析的新算法和工具。原型和源代码将作为开源软件分发。