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要求用户担心平凡的细节。 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。这种缺乏分离的情况使程序变得过于复杂。此外，程序员必须显式或隐式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结构或“砖块”来表达计算中的并行性。该研究探索了 PetaBricks 组合语言，其中自动并行提取和位置识别将变得易于处理。主要任务是构建编译器，将算法并行性和局部性映射到接近最佳的资源利用率。该语言将由基本案例和组合组成。它们可以递归地组合来解决大问题。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。 该研究引入了 Patlo：一种用于优化的模式转换语言，领域专家可以在其中对模式和相应的转换进行编程，以实现特定于算法和特定于架构的优化。