XPS:FULL: New Abstractions and Applications for Automata Computing

随着社会收集越来越多关于我们周围世界的数据，并将越来越多的人工制品数字化，“大数据”承诺了无与伦比的潜力，但也提出了新的和独特的计算挑战。将数据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，例如能够及时干预医疗保健和快速响应网络安全。由于技术限制限制了CPU性能，研究人员和从业人员越来越多地寻求专用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。本研究计划进行跨堆栈调查，以评估自动机计算范例的有效性，以加速模式挖掘的非结构化数据。具体来说，通过利用业界新的自动处理器（由美光科技开发），该项目是（1）开发真正多样化的自动机的基准套件，用于真实的和模拟的，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，使自动机计算直观，易于采用，（3）评估自动计算解决方案，以解决现实世界的大数据应用，（4）开发一系列教育和社区建设活动，以最大限度地扩大项目成果的广泛影响。 该项目的成功实施，使新的自动机为基础的抽象，以阐明各种应用程序，如模式挖掘的AP技术的性能。该项目将建立知识基础，以支持和促进研究，教育，培训和采用基于自动化的解决方案，以解决工业，政府和社会中的大数据挑战。

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.