CAREER: Generative Programming and DSLs for Safe Performance Critical Systems

职业：用于安全性能关键系统的生成式编程和 DSL

• 批准号: 1553471
• 负责人: Tiark Rompf
• 金额: $ 51.72万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-15 至 2022-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1553471&HistoricalAwards=false
• 关键词: CAREER; Generative; Programming; DSLs; Safe

Most performance critical software is developed using very low-level techniques, close to the underlying hardware. But low-level code in unsafe languages attracts security vulnerabilities, developer productivity suffers without the software engineering benefits of higher-level languages, and in the age of heterogeneous hardware and big data workloads, a single hand-optimized codebase may no longer provide good performance across different target platforms. Generative programming is a radical rethinking of the role of high-level languages and low-level languages. Instead of running whole systems in a high-level managed language runtime, the idea is to focus the abstraction power of high-level languages on composing pieces of low-level code, making runtime code generation and domain-specific optimization a fundamental part of the program logic. This project will conduct a fundamental study of generative design patterns, which will be extracted from existing and emerging program generators and domain-specific languages. The intellectual merits are a deeper understanding of how to develop software in a generative style. The project's broader significance and importance are to establish generative programming as a part of every performance-minded programmer?s toolbox, enabling the use of high-level programming in more situations than currently possible.Generative programming, and the shift in perspective that goes along with it, has been shown to be extremely effective in areas like databases (query compilation), protocol and data format parsers, hardware circuit generation, signal processing kernels, machine learning, and big data processing on heterogeneous computing devices?traditional strongholds of low-level languages. But while the general idea of program generation is well understood, the technique has remained esoteric?a black art, accessible only to the most skilled and daring of programmers. What is missing is a discipline of practical generative programming, including design patterns, best practices and so on. To achieve these broader goals, the project includes an education program, which, driven by the project?s research, will teach generative programming to a wide audience of students and developers in industry. This education effort will also serve as a large-scale usability study, closing the feedback loop into the research on generative programming techniques.

大多数性能关键软件都是使用非常低级的技术开发的，接近底层硬件。但是，不安全语言中的低级代码会引起安全漏洞，开发人员的生产力会受到影响，而没有高级语言的软件工程优势，并且在异构硬件和大数据工作负载的时代，单一手工优化的代码库可能不再能够在不同的目标平台上提供良好的性能。生成式编程是对高级语言和低级语言角色的彻底反思。不是在高级托管语言运行时中运行整个系统，而是将高级语言的抽象能力集中在组成低级代码片段上，使运行时代码生成和特定于域的优化成为程序逻辑的基本部分。 该项目将对生成式设计模式进行基础研究，这些模式将从现有和新兴的程序生成器和特定领域的语言中提取。智力上的优点是对如何以生成式风格开发软件有了更深入的理解。该项目更广泛的意义和重要性是将生成式编程作为每个注重性能的程序员的一部分。生成式编程以及与之相伴的视角转变沿着被证明在数据库（查询编译）、协议和数据格式解析器、硬件电路生成、信号处理内核、机器学习和异构计算设备上的大数据处理等领域非常有效。低级语言的传统据点但是，虽然程序生成的一般思想是很好地理解，技术仍然是深奥的？这是一种只有最熟练和最大胆的程序员才能接触到的黑色艺术。缺少的是实用生成式编程的学科，包括设计模式、最佳实践等。为了实现这些更广泛的目标，该项目包括一个教育计划，该计划由项目驱动？的研究，将教授生成编程，以广大观众的学生和开发人员在行业。这项教育工作也将作为一个大规模的可用性研究，关闭反馈循环到生成式编程技术的研究。