III: Small: Extending and Automating Dynamic Specialization of Database Management Systems

III：小型：扩展和自动化数据库管理系统的动态专业化

• 批准号: 1318343
• 负责人: Richard Snodgrass
• 金额: $ 49.68万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1318343&HistoricalAwards=false
• 关键词: III; Small; Extending; Automating; Dynamic

Database management systems (DBMSs) are ubiquitous in industry and their performance and functionality are crucially important in commercial IT infrastructure. There has been a lot of work on improving the performance of these critical systems. This project adopts a complementary and orthogonal approach to substantially increasing the performance of DBMSs. This is done by exploiting runtime information about the DBMS to specialize the DBMS code on the fly in order to eliminate as much unnecessary work as possible. This dynamic code optimization, which we term "micro-specialization", is highly aggressive and goes well beyond what can be achieved using existing compiler optimization technology. This focus on runtime code optimization is also very different from most of the current research on DBMS performance improvement. The project investigates algorithms for automatic identification of code sequences where micro-specialization may profitably be applied; algorithms that can micro-specialize such identified target code sequences; and techniques for ensuring the correctness of the resulting code. Preliminary studies on disparate DBMSes suggest that micro-specialization offers significant performance improvements when applied to mature, high-performance DBMSs and to industry-standard benchmarks, and that there are rich opportunities for other substantial performance gains, with minimal impact on the DBMS architecture.Broader impacts of this project include increased efficiency of the IT infrastructure used by a wide variety of companies, leading to improved overall productivity. Graduate and undergraduate students are involved in all aspects of the research activities as an integral part of the project. The PIs have an established track record of integrating research activities into the undergraduate curriculum and involving undergraduates, including women and members of underrepresented minorities, in research; this project continues this tradition.For further information see the web site at http://www.cs.arizona.edu/projects/microspecialization/ .

数据库管理系统（DBMS）在工业中无处不在，其性能和功能在商业IT基础设施中至关重要。在提高这些关键系统的性能方面做了大量工作。 该项目采用互补和正交的方法来大幅提高数据库管理系统的性能。 这是通过利用有关DBMS的运行时信息来动态地专门化DBMS代码以尽可能多地消除不必要的工作来完成的。 这种动态代码优化，我们称之为“微专业化”，是非常积极的，远远超出了使用现有编译器优化技术所能实现的。这种对运行时代码优化的关注也与当前大多数关于DBMS性能改进的研究有很大不同。 该项目研究了自动识别代码序列的算法，其中微专业化可以有益地应用;可以微专业化这种识别的目标代码序列的算法;以及确保所得代码正确性的技术。 对不同DBMS的初步研究表明，当应用于成熟的高性能DBMS和行业标准基准测试时，微专业化提供了显著的性能改进，并且有大量的机会获得其他实质性的性能增益，而对DBMS体系结构的影响最小。从而提高整体生产率。 研究生和本科生参与研究活动的各个方面，作为项目的一个组成部分。PI有一个既定的跟踪记录，将研究活动纳入本科课程，并让本科生，包括妇女和代表性不足的少数民族成员参与研究;该项目延续了这一传统。http://www.cs.arizona.edu/projects/microspecialization/