Collaborative Research: SHF: Medium: Near-Hardware Program Repair and Optimization

该项目解决了当今的现实，即专用计算硬件和硬件加速器实际上已成为支持用于数据分析、人工智能和机器学习、科学建模和社交媒体平台的大规模计算的必需品。与此同时，教育和现有工具仍然要求计算机程序员对低级硬件考虑和高级应用程序逻辑都有深入的了解。较高层次的程序抽象对于人类和自动化程序改进方法来说更容易处理，因为它们将算法逻辑与实现细节分开，而较低的“近硬件”抽象级别对于人类来说很难理解和优化，因为许多关键的架构和硬件细节经常以非琐碎的方式与应用程序级逻辑交互。该项目通过开发自动化方法来解决这一差距，这些方法用于程序的近硬件运行时优化、错误修复和新程序的创建。它包括一个以交互式人类评估为特征的评估，它沿着几个维度研究人类与项目自动化工具的交互。该项目旨在提高近硬件领域软件工程任务的自动化。这需要解决一些基本问题，例如：哪些表示跨越多个抽象级别？对于现实世界的应用程序，如何分析和选择同时考虑硬件和软件约束的优化？工具如何将其结果传达给可能缺乏特定于领域的体系结构或特定于硬件的细节方面的专业知识的用户？该项目将更高级别的自动化程序改进方法应用于三个特定任务：自动找到减少通用GPU代码运行时间的优化；修复电路设计缺陷；合成硬件加速器的可调试代码。每个任务都需要跨抽象级别的表示和算法，每个任务都有一个评估计划，明确强调人的因素，测量自动提升优化和不同级别的人类专业知识之间的语义差距，测量跨人类专业知识级别的交互式合成工具的易用性，并使用眼动追踪来调查多编辑补丁的哪些元素是最难理解的。该项目将使源代码级自动化程序改进的许多好处可用于近硬件领域。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。

The project addresses today's reality that special-purpose computing hardware and hardware accelerators have become de facto necessities for supporting the large-scale computations used for data analysis, AI and machine learning, scientific modeling, and social-media platforms. At the same time, education and existing tools still require computer programmers to have deep knowledge of both low-level hardware considerations and higher-level application logic. Higher levels of program abstraction are more tractable for humans and automated program improvement methods because they separate algorithm logic from implementation details, while lower 'near-hardware' levels of abstraction are difficult for humans to understand and optimize because of the many crucial architectural and hardware details that often interact with application-level logic in non-trivial ways. The project addresses this gap by developing automated methods for near-hardware run-time optimization of programs, bug repair, and creation of new programs. It includes an evaluation featuring interactive human evaluations, which studies human interactions with the project's automated tools along several dimensions.The project aims to improve the automation of software engineering tasks for near-hardware domains. This requires addressing fundamental questions such as: What representations span multiple levels of abstraction? How can one analyze and select optimizations respecting both hardware and software constraints for real-world applications? How can a tool communicate its results to users who may lack expertise in either domain-specific architecture or hardware-specific details? The project adapts higher-level automated program improvement methods to three specific tasks: automatically finding optimizations that reduce general-purpose GPU code runtimes; repairing defects in circuit designs; and synthesizing debuggable code for hardware accelerators. Each task requires representations and algorithms that cross abstraction levels, and each task features an evaluation plan that places explicit emphasis on the human element, measuring the semantic gap between automatically lifted optimizations and different levels of human expertise, measuring ease of use of interactive synthesis tools across human expertise levels, and using eye tracking to investigate which elements of a multi-edit patch are most difficult understand. The project will enable many of the benefits of source-level automated program improvement to be available to near-hardware domains.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.