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.

对于科学编程语言来说，性能是一件严肃的事情。在这一领域取得成功取决于培育一个由高度优化的数学库组成的丰富生态系统。Julia语言的基础是，它的用户可以编写高效的数字代码，而不必求助于C或Fortran。Julia程序员努力编写编译后具有可预测性能的代码。特别是，他们认为，对于性能关键部分，该语言提供的高级抽象将融入到与用c等性能语言编写的代码相当（或足够接近）的代码中。对于可预测的速度的需求与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。高级代码使用诸如动态绑定、泛型类型和自动内存管理之类的抽象。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高效的代码。不幸的是，为此所需的信息类型是非局部的，它可以通过整个程序静态分析或通过反馈导向的推测编译来积累。它们都对程序中的微小变化很敏感。因此，如果在每次代码更改后不进行广泛的测试，程序员可能无法预测代码的效率。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他们的代码更有信心，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将Julia中的现有概念形式化，以实现可预测的性能，并讨论它们的正确性。该项目的影响是更好地理解编程人员和工具构建者可以利用的语言。该奖项反映了美国国家科学基金会的法定使命，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