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）在行业中无处不在，其性能和功能在商业IT基础架构中至关重要。在改善这些关键系统的性能方面，已经进行了很多工作。 该项目采用一种补充和正交的方法来大大提高DBMS的性能。 这是通过利用有关DBMS的运行时信息来实现的，以便消除尽可能多的不必要工作的DBMS代码。 我们称其为“微专业化”的动态代码优化是高度侵略性的，并且远远超出了使用现有的编译器优化技术可以实现的目标。对运行时代码优化的重点也与当前关于DBMS性能改进的大多数研究都大不相同。 该项目调查了可以自动识别可能可利用微专业化的代码序列的算法；可以将这种确定的目标代码序列微观化的算法；以及确保最终代码正确性的技术。 对不同DBMSE的初步研究表明，当应用于成熟，高性能DBMS和行业标准的基准时，微型特殊化提供了显着的性能提高，并且在DBMS的影响方面的影响不大的效率促进了IS的效率，并且具有最小的影响力的影响力，并且具有最小的影响。 研究生和本科生都参与研究活动的各个方面，这是该项目不可或缺的一部分。 PI在将研究活动纳入本科课程中并涉及包括妇女和人为少数群体不足的成员在内的本科生的既定记录；该项目延续了这一传统。有关更多信息，请参见网站http://www.cs.arizona.edu/projects/microspecialization/。