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，绩效是严肃的业务。成功取决于促进高度优化的数学库的丰富生态系统。预测朱莉娅的语言是，其用户可以编写有效的数值代码而无需诉诸C或Fortran。朱莉娅（Julia）程序员努力编写将以可预测性能编译的代码。他们特别认为，对于至关重要的部分，该语言提供的高级抽象将融合到代码等效的代码（或足够接近）中，与C的表演语言所写的。高级代码使用摘要，例如动态绑定，通用类型和自动内存管理。编译器配备了足够的有关特定程序预期行为的信息，确实可以将这些抽象汇编为有效的代码。不幸的是，它所需的信息是非本地的，并且可以通过全程静态分析或反馈指导的投机汇编来吸收它。任何一个对程序的小变化都敏感。因此，程序员可能无法预测其代码的效率，而无需在每个代码更改之后对其进行广泛测试。为了避免这种情况，朱莉娅的设计师在其语言设计中包括了两个新颖的功能：世界时代和类型稳定性。这些功能使程序员可以对其代码具有更大的信心，并且是编译器和程序员之间对话的一部分。该项目的新颖性是为了使朱莉娅的现有概念形式化，以进行可预测的绩效，并争论其正确性。该项目的影响是对程序员和工具构建者可以利用的语言的更好理解。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的审查标准来评估值得获得支持。

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