XPS:FULL: New Abstractions and Applications for Automata Computing

XPS:FULL：自动机计算的新抽象和应用

• 批准号: 1629450
• 负责人: Kevin Skadron
• 金额: $ 87.5万
• 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 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629450&HistoricalAwards=false
• 关键词: XPS; FULL; New; Abstractions; Applications

As society collects more and more data about the world around us, and digitizes more and more artifacts, "big data" promises unparalleled potential, but also poses new and unique computational challenges. Turning data into useful knowledge at or near real-time can have significant impacts, such as enabling timely intervention in healthcare and fast response in cybersecurity. As technology constraints limit CPU performance, researchers and practitioners are increasingly looking to specialized processors to accelerate data analytics. The ability to extract patterns from unstructured data is an especially important task. This research project carries out a cross-stack investigation to evaluate the effectiveness of the automata computing paradigm to accelerate pattern mining of unstructured data. Specifically, by leveraging the industry's new Automata Processor (developed by Micron Technology), this project is (1) developing benchmark suites of truly diverse automata for performance comparison of real and simulated, existing and future automata engines, (2) developing new tools, including programming languages, systems, and architectural enhancement to make automata computing intuitive and easy to adopt, (3) evaluating automata computing solutions to address real-world big-data applications, and (4) developing a set of educational and community-building activities to maximize the broader impact of the project outcome. Successful implementation of this project enable new automata-based abstractions to shed light on the performance of AP technology for various applications, such as pattern mining. This project will build the the intellectual foundations to support and catalyze research, education, training, and adoption of automata-based solutions to address big-data challenges in industry, government, and society.

随着社会收集越来越多关于我们周围世界的数据，并将越来越多的文物数字化，“大数据”带来了无与伦比的潜力，但也带来了新的、独特的计算挑战。将数据在实时或接近实时地转化为有用的知识可能会产生重大影响，例如在医疗保健方面实现及时干预，在网络安全方面实现快速响应。由于技术限制限制了CPU性能，研究人员和从业者越来越多地希望使用专门的处理器来加速数据分析。从非结构化数据中提取模式的能力是一项特别重要的任务。该研究项目开展了一项跨栈调查，以评估自动机计算范式对加速非结构化数据模式挖掘的有效性。具体地说，通过利用行业新的自动机处理器(由美光科技开发)，该项目正在(1)开发真正多样化的自动机基准套件，用于真实和模拟的、现有的和未来的自动机引擎的性能比较，(2)开发新的工具，包括编程语言、系统和架构增强，使自动机计算变得直观和易于采用，(3)评估自动机计算解决方案，以应对现实世界的大数据应用，以及(4)开发一套教育和社区建设活动，以最大限度地扩大项目成果的更广泛影响。该项目的成功实施使新的基于自动机的抽象能够阐明AP技术在各种应用中的性能，例如模式挖掘。该项目将建立智力基础，以支持和促进研究、教育、培训和采用基于自动机的解决方案，以应对行业、政府和社会中的大数据挑战。

项目成果

A Deep Learning Architecture for Psychometric Natural Language Processing

• DOI: 10.1145/3365211
• 发表时间: 2020-02
• 期刊: ACM Transactions on Information Systems (TOIS)
• 作者: Faizan Ahmad;A. Abbasi;Jingjing Li;David G. Dobolyi;Richard G. Netemeyer;G. Clifford;Hsinchun Chen