SHF: Small: Predictable Performance for Just-in-Time Compilation

SHF：小型：可预测的即时编译性能

• 批准号: 2139612
• 负责人: Jan Vitek
• 金额: $ 49.96万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2139612&HistoricalAwards=false
• 关键词: SHF; Small; Predictable; Performance; Just

Performance is serious business for a scientific programming language. Success in that niche hinges on fostering a rich ecosystem of highly-optimized mathematical libraries. The Julia language is predicated on the bet that its users can write efficient numerical code without having to resort to C or Fortran. Julia programmers strive to write code that will be compiled with predictable performance. In particular, they believe that, for performance-critical sections, the high-level abstractions provided by the language will melt into code equivalent (or close enough) to what one would have written in a performance language like C. There is an inherent tension between the need for predictable speed and the desire of developers to write generic and reusable high-level code. High-level code uses abstractions such as dynamic binding, generic types, and automatic memory management. Equipped with sufficient information about the expected behavior of a particular program, a compiler can indeed compile these abstractions down to efficient code. Unfortunately, the kind of information needed for this is non-local, and it can be accrued either by whole-program static analysis or by feedback-directed speculative compilation. Either one is sensitive to small changes in the program. Programmers may thus not be able to predict how efficient their code will be without testing it extensively after each code change. To avoid this, the designers of Julia have included two novel features in their language design: world age and type stability. These features allow programmers to have greater confidence in their code, and are part of a dialogue between the compiler and programmers. This project's novelties are to formalize existing concepts in Julia for predictable performance and to argue about their correctness. The project’s impacts are a better understanding of the language that can be leveraged by programmers and tool-builders.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

对于一门科学编程语言来说，性能是一件严肃的事情。在这个利基市场的成功取决于培育一个高度优化的数学库的丰富生态系统。Julia语言的前提是它的用户可以编写有效的数字代码，而不必求助于C或Fortran。Julia程序员努力编写将以可预测的性能编译的代码。特别是，他们认为，对于性能关键的部分，语言提供的高级抽象将融入代码等效（或足够接近）的代码中，以像C这样的性能语言编写。在对可预测速度的需求和开发人员编写通用和可重用的高级代码的愿望之间存在固有的紧张关系。高级代码使用动态绑定、泛型类型和自动内存管理等抽象。有了关于特定程序的预期行为的足够信息，编译器确实可以将这些抽象编译成有效的代码。 不幸的是，这类所需的信息是非本地的，它可以通过整个程序的静态分析或反馈导向的推测性编译来获得。任何一个都对程序中的微小变化敏感。因此，程序员可能无法预测他们的代码在每次代码更改后没有广泛测试的情况下将有多高效。为了避免这种情况，Julia的设计者在他们的语言设计中加入了两个新颖的特性：世界年龄和类型稳定性。这些特性使程序员对自己的代码有更大的信心，并且是编译器和程序员之间对话的一部分。 这个项目的新颖之处在于形式化Julia中现有的概念，以获得可预测的性能，并讨论它们的正确性。这个奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Deoptless: speculation with dispatched on-stack replacement and specialized continuations

Deoptless：通过调度堆栈替换和专门的延续进行推测

• DOI: 10.1145/3519939.3523729
• 发表时间: 2022
• 期刊: ACM SIGPLAN International Conference on Programming Language Design and Implementation
• 作者: Flückiger, Olivier;Ječmen, Jan;Krynski, Sebastián;Vitek, Jan
• 通讯作者: Vitek, Jan