CHS: Small: Translating Compilers for Visual Computing in Dynamic Languages

CHS：小型：用动态语言翻译用于视觉计算的编译器

• 批准号: 1619123
• 负责人: Baishakhi Ray
• 金额: $ 45万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619123&HistoricalAwards=false
• 关键词: CHS; Small; Translating; Compilers; Visual

This collaborative project is developing technologies to enable students, scientists, and other non-expert developers to use computer languages that facilitate rapid prototyping, and yet still automatically convert such programs to have high performance. In this research, the PI and co-PIs focus on programs that operate over visual data, such as programs in computer graphics, computer vision, and visualization. Visual data is important because visual datasets are rapidly growing in size, due to the use of cell-phone cameras, photo and video sharing online, and in scientific and medical imaging. The intellectual merits are that specialized program optimizations are being developed specifically for visual computing and for languages that enable rapid prototyping, alongside techniques that allow the computer to automatically search through different candidate optimizations and choose the fastest one. The project's broader significance and importance are that it will make the writing of computer programs that operate over visual datasets more accessible to novice programmers, make visual computing more accessible to a broader audience, permit faster research and development over visual programs, and make such programs themselves be more efficient.More specifically, this research program is producing translating compilers that are specialized to handle programs that compute over visual data. The group led by the PI is researching new compilers that translate code from dynamic languages into highly efficient code in a target language. Dynamic languages are defined as those with a very dynamic run-time model, for example, MATLAB, Python, and Javascript. The target language is a language such as C that permits implementation of highly efficient programs. This research framework incorporates ideas from compilers, graphics, computer vision, visual perception, and formal and natural languages. The research will make a number of key intellectual contributions. First, new domain-specific translations and optimizations for visual computing will be formalized into manual rules that can be applied to any input program. Second, the team will research a novel approach of automatically learning translations, instead of using manually-coded rules. This can take the form of learning translation "suggestions" from humans, who can interactively suggest better output code. Third, a new search process based on offline auto-tuning will be used to select the translations that result in the fastest program. The success of the project will be verified against a comprehensive test suite of programs from computer vision and graphics.

该合作项目正在开发技术，使学生、科学家和其他非专家开发人员能够使用计算机语言来促进快速原型设计，同时仍然自动将此类程序转换为高性能。在这项研究中，PI 和 co-PI 重点关注对视觉数据进行操作的程序，例如计算机图形、计算机视觉和可视化中的程序。视觉数据非常重要，因为由于手机摄像头、在线照片和视频共享以及科学和医学成像的使用，视觉数据集的规模正在迅速增长。其智力优势在于，专门为视觉计算和支持快速原型设计的语言开发了专门的程序优化，以及允许计算机自动搜索不同候选优化并选择最快的优化的技术。该项目更广泛的意义和重要性在于，它将使得新手程序员更容易编写在视觉数据集上运行的计算机程序，使视觉计算更容易被更广泛的受众使用，允许对视觉程序进行更快的研究和开发，并使此类程序变得更容易。更具体地说，该研究计划正在生产专门用于处理视觉数据计算程序的翻译编译器。由 PI 领导的小组正在研究新的编译器，将动态语言的代码翻译成目标语言的高效代码。动态语言被定义为具有非常动态的运行时模型的语言，例如 MATLAB、Python 和 Javascript。目标语言是诸如C之类的允许执行高效程序的语言。该研究框架融合了编译器、图形、计算机视觉、视觉感知以及形式语言和自然语言的想法。该研究将做出许多关键的智力贡献。首先，视觉计算的新的特定领域翻译和优化将被形式化为可应用于任何输入程序的手动规则。其次，该团队将研究一种自动学习翻译的新方法，而不是使用手动编码的规则。这可以采取从人类那里学习翻译“建议”的形式，人类可以交互式地建议更好的输出代码。第三，将使用基于离线自动调整的新搜索过程来选择产生最快程序的翻译。该项目的成功将根据计算机视觉和图形程序的综合测试套件进行验证。