Advanced Languages for Scientific Computation Environments

科学计算环境的高级语言

• 批准号: 9706572
• 负责人: Guy Blelloch
• 金额: $ 159.43万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-15 至 2002-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9706572&HistoricalAwards=false
• 关键词: Advanced; Languages; Scientific; Computation; Environments

The goal of the project is to demonstrate the feasibility of a broad-based language for scientific and engineering computation. Such a language should serve equally well for developing production scientific code (which is now typically written in FORTRAN or C) and as the scripting language for interactive scientific computing environments (such as Mathematica or Matlab). This breadth would greatly simplify the development of code by allowing a clean transition from prototyping and small- scale experiments to full production code, and would better support the evolution of production code. As a broad-based language it should support both numerical and symbolic computation, should include constructs r parallel programming, and should include the high-level language features found in systems such as Mathematica (e.g. pattern matching, higher-order functions, and automatic memory management). The project involves demonstrating that it is possible to support these features while maintaining good code efficiency. As such, the project includes work on compiler techniques, on language design, on the development of scientific codes, and experimentation on the codes using the compiler and language. The project is using ML extended with the parallel constructs from NESL as a testbed.

该项目的目标是演示科学和工程计算的基础广泛的语言的可行性。这种语言应该同样适用于开发生产科学代码（现在通常用FORTRAN或C编写）和交互式科学计算环境（如Mathematica或Matlab）的脚本语言。通过允许从原型和小规模实验到完整的生产代码的干净过渡，这种广度将极大地简化代码的开发，并且将更好地支持生产代码的发展。作为一种基础广泛的语言，它应该支持数值和符号计算，应该包括并行编程的结构，应该包括在Mathematica等系统中发现的高级语言特性（例如模式匹配、高阶函数和自动内存管理）。该项目包括证明在保持良好代码效率的同时支持这些特性是可能的。因此，该项目包括编译器技术、语言设计、科学代码的开发以及使用编译器和语言对代码进行实验。该项目使用由NESL的并行结构扩展的ML作为测试平台。