SHF: Small: Closing the Productivity/Performance Gap with Just-in-Time Configuration of Meta-Trace Accelerators

SHF：小型：通过及时配置 Meta-Trace 加速器缩小生产力/性能差距

• 批准号: 1527065
• 负责人: Christopher Batten
• 金额: $ 50万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527065&HistoricalAwards=false
• 关键词: SHF; Small; Closing; Productivity; Performance

Title: SHF: Small: Closing the Productivity/Performance Gap with Just-in-Time Configuration of Meta-Trace AcceleratorsDynamic programming languages are growing in popularity across the computing spectrum from smartphones, to servers, to supercomputers. Dynamic programming languages such as Python, JavaScript, and MATLAB are all now among the top-ten most popular programming languages. These "productivity-level" languages make it easy to quickly develop, verify, and maintain applications, but unfortunately, the performance of such applications is usually quite low. This project is exploring a new hardware/software co-design approach for closing this productivity-performance gap. The intellectual merits of this project are the potential for fundamental advances in new hardware accelerators specifically designed for dynamic programming languages, and new software techniques to let applications take advantage of these accelerators. The project's broader significance and importance are rooted in the popularity of dynamic programming languages, and the potential to significantly improve the performance of these highly productive languages.There has been great interest in software and hardware techniques to close the "productivity-performance gap". On the software side, high-performance interpreters are increasingly leveraging just-in-time (JIT) method- or trace-based compilation techniques. On the hardware-side, there is a long history of directly supporting high-level languages in hardware. Unfortunately, direct execution of high-level languages precludes the kinds of aggressive optimizations possible with JIT compilation and can prevent efficient execution of alternative high-level languages. A key insight is that future hardware acceleration for dynamic programming languages should not replace JIT compilation, but should instead elegantly complement JIT compilation for a variety of different dynamic programming languages. This project is investigating a new approach using just-in-time configuration of meta-trace accelerators. The project is using a vertically integrated research methodology to explore the following four research thrusts: (1) developing and optimizing a new Scheme interpreter (called RLisPy) to create a state-of-the-art software baseline; (2) exploring various novel hardware meta-trace accelerators for RLisPy; (3) exploring JIT configuration to accelerate RLisPy using these meta-trace accelerators; and (4) applying these techniques to the more popular Python dynamic programming language.

标题：SHF：小型：通过及时配置 Meta-Trace 加速器来缩小生产力/性能差距动态编程语言在从智能手机、服务器到超级计算机的计算领域中越来越受欢迎。 Python、JavaScript 和 MATLAB 等动态编程语言现已跻身十大最流行编程语言之列。这些“生产力级”语言可以轻松快速地开发、验证和维护应用程序，但不幸的是，此类应用程序的性能通常相当低。该项目正在探索一种新的硬件/软件协同设计方法，以缩小生产力与性能之间的差距。该项目的智力优点是专为动态编程语言设计的新硬件加速器的根本性进步的潜力，以及让应用程序利用这些加速器的新软件技术的潜力。该项目的更广泛的意义和重要性植根于动态编程语言的流行，以及显着提高这些高生产力语言的性能的潜力。人们对缩小“生产力与性能差距”的软件和硬件技术抱有极大的兴趣。在软件方面，高性能解释器越来越多地利用即时（JIT）方法或基于跟踪的编译技术。在硬件方面，在硬件中直接支持高级语言有着悠久的历史。不幸的是，高级语言的直接执行排除了 JIT 编译可能进行的各种积极优化，并且可能阻止替代高级语言的有效执行。一个关键的见解是，未来动态编程语言的硬件加速不应取代 JIT 编译，而应完美地补充各种不同动态编程语言的 JIT 编译。该项目正在研究一种使用元跟踪加速器的即时配置的新方法。该项目正在使用垂直整合的研究方法来探索以下四个研究重点：（1）开发和优化新的Scheme解释器（称为RLisPy）以创建最先进的软件基线； (2) 探索RLisPy的各种新颖的硬件元跟踪加速器； (3) 探索 JIT 配置以使用这些元跟踪加速器来加速 RLisPy； (4)将这些技术应用到更流行的Python动态编程语言中。