Data Parallel SPMD Programming Models from Fortran to Java

从 Fortran 到 Java 的数据并行 SPMD 编程模型

• 批准号: 9872125
• 负责人: Geoffrey Fox
• 金额: $ 34.68万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-12-01 至 2000-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9872125&HistoricalAwards=false
• 关键词: Data; Parallel; SPMD; Programming; Models

This project brings together several important research areas, includingparallel compilers, Single-Program Multiple-Data (SPMD) libraries, andobject-oriented programming models. The project combines these ideas toachieve high performance on parallel machines in a robust fashion over arange of languages.Building on earlier research in the Parallel Compiler Runtime Consortium(PCRC) project, this project will investigate a language model thatcombines data-parallel features from High Performance Fortran (HPF) with anexplicitly parallel SPMD programming style. This "HPspmd" model allowsdirect calls to many established libraries for parallel programming withdistributed data. By relaxing HPF's single-threaded semantics and havingthe programmer explicitly manage non-local references, the task of thecompiler is hugely simplified. However, good library bindings (e.g. classlibraries) supply the required expressiveness for programming. Thisproject will develop and optimize a Java version of the language model(HPJava); design and implement bindings to parallel libraries such asCHAOS, DAGH, Global Arrays, MPI,and ScaLAPACK; and implement largeapplications within the resulting framework. The implementations areexpected to drive further refinement of the language model, especially insupport of irregular problems.

该项目汇集了几个重要的研究领域，包括并行编译器，单程序多数据（SPMD）库，和面向对象的编程模型。 该项目结合了这些想法，以实现高性能的并行机在一个强大的方式在arangeoflanguage.Building在并行编译器联盟（PCRC）项目的早期研究，该项目将调查一个语言模型，结合数据并行功能，从高性能Fortran（HPF）与显式并行SPMD编程风格。 这种“HPspmd”模型允许直接调用许多已建立的库，以便使用分布式数据进行并行编程。通过放松HPF的单线程语义，并让程序员显式地管理非本地引用，编译器的任务得到了极大的简化。然而，好的库绑定（例如类库）提供了编程所需的表达能力。 该项目将开发和优化语言模型的Java版本（HPJava）;设计和实现与并行库（如CHAOS、DAGH、Global Arrays、MPI和ScaLAPACK）的绑定;并在最终框架内实现大型应用程序。 这些实现有望推动语言模型的进一步细化，特别是在支持不规则问题方面。