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

标题：SHF：Small：通过元跟踪加速器的即时配置缩小生产率/性能差距动态编程语言在从智能手机到服务器再到超级计算机的计算范围内越来越受欢迎。动态编程语言，如Python、JavaScript和MatLab，现在都跻身十大最受欢迎的编程语言之列。这些“生产力级别”的语言使得快速开发、验证和维护应用程序变得很容易，但不幸的是，这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，以缩小这一生产率-性能差距。该项目的智力优势在于，在专门为动态编程语言设计的新硬件加速器方面取得根本性进展的潜力，以及让应用程序利用这些加速器的新软件技术。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，以及显著提高这些高生产率语言的性能的潜力。人们对缩小“生产率-性能差距”的软件和硬件技术非常感兴趣。在软件方面，高性能解释器越来越多地利用基于实时(JIT)方法或跟踪的编译技术。在硬件方面，在硬件中直接支持高级语言的历史由来已久。不幸的是，高级语言的直接执行排除了JIT编译可能实现的那种激进优化，并可能阻止替代高级语言的有效执行。一个重要的见解是，未来动态编程语言的硬件加速不应该取代JIT编译，而应该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。该项目使用垂直集成的研究方法来探索以下四个研究方向：(1)开发和优化新的方案解释器(称为RLISPy)以创建最先进的软件基准；(2)探索用于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；以及(4)将这些技术应用于更流行的Python动态编程语言。

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.