Data Parallel SPMD Programming Models from Fortran to Java

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)库和面向对象编程模型。在并行编译器运行时联盟(PCRC)项目早期研究的基础上，该项目将研究一种语言模型，该语言模型结合了高性能Fortran(HPF)的数据并行特性和显式并行SPMD编程风格。这种“HPspmd”模型允许直接调用许多已建立的库，以便对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，编译器的任务大大简化了。然而，良好的库绑定(例如类库)提供了编程所需的表现力。该项目将开发和优化语言模型的Java版本(HPJava)；设计和实现到并行库的绑定，如CHAOS、Dagh、Global Arrares、MPI和ScaLAPACK；并在结果框架内实现大型应用程序。预计这些实现将推动语言模型的进一步完善，特别是在支持不规则问题方面。

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.