PetaBricks: A Language and Compiler for Scalability and Robustness

PetaBricks：具有可扩展性和鲁棒性的语言和编译器

• 批准号: 0832997
• 负责人: Alan Edelman
• 金额: $ 68.56万
• 依托单位: Massachusetts Institute of Technology
• 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 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0832997&HistoricalAwards=false
• 关键词: PetaBricks; Language; Compiler; Scalability; Robustness

The most widely used parallel languages require users to worry about mundane details. High performance programs written in these languages unnecessarily require both algorithmic and architecture expertise interposed in the same program text. This lack of separation overly complicates programs. Furthermore, the programmers have to explicitly, or implicitly, choose a data and computation distribution. This reduces the compatibility, malleability, portability, and maintainability of the optimized programs. This research addresses how programmers can express parallelism in computation by directly allowing experts to create reusable software constructs or "bricks." The research explores the PetaBricks compositional language, in which automatic parallelism extraction and locality recognition will become tractable. The major task is the building of the compiler to map the algorithmic parallelism and locality to near optimal utilization of the resources. The language will be composed of base cases and compositions. They can be composed recursively to solve large problems. The ordering and granularity of these compositions will be managed by the compiler and runtime framework to allow programs to adapt. The research introduces Patlo: a pattern transformation language for optimization where domain experts can program patterns and corresponding transformations for algorithm-specific and architecture-specific optimizations.

使用最广泛的并行语言需要用户关心平凡的细节。 用这些语言编写的高性能程序不必要地需要在同一程序文本中插入算法和架构专业知识。这种分离的缺乏使程序过于复杂。此外，程序员必须显式或隐式地选择数据和计算分布。这降低了优化程序的兼容性、延展性、可移植性和可维护性。这项研究解决了程序员如何通过直接允许专家创建可重用的软件构造或“砖块”来表达计算中的并行性。“这项研究探索了PetaBricks组合语言，其中自动并行提取和局部识别将变得易于处理。主要任务是编译器的建设，映射算法的并行性和局部性，以接近最佳的资源利用率。该语言将由基本情况和组合组成。它们可以递归组合以解决大型问题。这些组合的顺序和粒度将由编译器和运行时框架管理，以允许程序进行调整。 该研究介绍了Patlo：一种用于优化的模式转换语言，领域专家可以为特定于算法和特定于体系结构的优化编程模式和相应的转换。