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

合作研究：SHF：中：近硬件程序修复和优化

• 批准号: 2211749
• 负责人: Westley Weimer
• 金额: $ 55万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211749&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; Near

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.

该项目解决了当今现实，即特殊用途的计算硬件和硬件加速器已成为支持数据分析，AI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。同时，教育和现有工具仍然要求计算机程序员对低级硬件注意事项和更高级别的应用程序逻辑有深入的了解。 更高水平的程序抽象对于人类和自动化程序改进方法更具探索性，因为它们将算法逻辑与实现细节分开，而较低的“近乎硬件”抽象水平很难使人难以理解和优化，因为许多关键的建筑和硬件细节通常与非企业相互作用的逻辑相互作用。该项目通过开发自动化方法来解决此差距，以实现近乎硬件的程序优化程序，错误维修和创建新程序的差距。 它包括一个具有交互式人类评估的评估，该评估研究了人类与该项目沿几个维度的自动化工具的互动。该项目旨在改善近硬件域软件工程任务的自动化。这需要解决基本问题，例如：哪些表示涵盖了多个级别的抽象？如何分析和选择尊重现实世界应用程序的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领域体系结构或特定于硬件细节的专业知识的用户？该项目将高级自动化程序改进方法调整为三个特定任务：自动找到减少通用GPU代码运行时间的优化；修复电路设计中的缺陷；并为硬件加速器综合可辩论的代码。每个任务都需要交叉抽象水平的表示和算法，每个任务都具有评估计划，该计划明确强调人类元素，测量自动提高优化和不同级别的人类专业知识之间的语义差距，从而测量人类专业工具的使用范围，并使用人的专业水平来调查多个元素绘制的元素，并且使用互动综合工具的使用范围。 该项目将使源级自动化计划改进的许多好处可用于近硬件领域。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影响审查标准，认为值得通过评估来获得支持。

项目成果

Digging into Semantics: Where Do Search-Based Software Repair Methods Search?

• DOI: 10.1007/978-3-031-14721-0_1
• 发表时间: 2022
• 作者: Hammad Ahmad;Padriac Cashin;S. Forrest;Westley Weimer

CirFix: Automated Hardware Repair and its Real-World Applications

• DOI: 10.1109/tse.2023.3269899
• 发表时间: 2023-07
• 期刊: IEEE Transactions on Software Engineering
• 影响因子: 7.4
• 作者: Priscila Santiesteban;Yu Huang;Westley Weimer;Hammad Ahmad

START: A Framework for Trusted and Resilient Autonomous Vehicles (Practical Experience Report)

• DOI: 10.1109/issre55969.2022.00018
• 发表时间: 2022-10
• 期刊: 2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE)
• 作者: Kevin Leach;C. Timperley;K. Angstadt;A. Nguyen-Tuong;Jason Hiser;Aaron M. Paulos;P. Pal;P. Hurley;Carl Thomas;J. Davidson;S. Forrest;Claire Le Goues;Westley Weimer

How Do We Read Formal Claims? Eye-Tracking and the Cognition of Proofs about Algorithms

• DOI: 10.1109/icse48619.2023.00029
• 发表时间: 2023-05
• 期刊: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
• 作者: Hammad Ahmad;Z. Karas;Kimberly Diaz;A. Kamil;Jean-Baptiste Jeannin;Westley Weimer

Synthesizing Legacy String Code for FPGAs Using Bounded Automata Learning

使用有界自动机学习合成 FPGA 的遗留字符串代码

• DOI: 10.1109/mm.2022.3178037
• 发表时间: 2022
• 期刊: IEEE Micro
• 影响因子: 3.6
• 作者: Angstadt, Kevin;Tracy, Tommy;Skadron, Kevin;Jeannin, Jean-Baptiste;Weimer, Westley
• 通讯作者: Weimer, Westley