PFI AIR-TT: Improving Data Base Management System Performance Through Micro-Specialization

PFI AIR-TT：通过微专业化提高数据库管理系统性能

• 批准号: 1413780
• 负责人: Richard Snodgrass
• 金额: $ 20万
• 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: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1413780&HistoricalAwards=false
• 关键词: PFI; AIR; TT; Improving; Data

This PFI: AIR Technology Translation project focuses on translating the novel technology of micro-specialization to fill the need for improving the speed of database management systems (DBMSes) and enterprise application software (EAS). The next step necessary for commercialization is to demonstrate how the process can be automated. The project will result in a proof-of-concept set of software tools that will take a collection of identified invariants and micro-specialize the DBMS source code automatically.The enhanced version of those tools will have the following unique features: it will be (i) scalable, i.e., can handle realistic DBMS code bases, up to millions of lines of source code, with acceptable performance; (ii) robust, i.e., able to handle language features that can be tricky for compilers; and (iii) effective, i.e., able to generate high-quality specialized code that realizes the desired performance improvements. These features provide the following two main advantages. First, it does not require an expensive and time-consuming rewrite of the DBMS source code, but can be applied to existing DBMS code. Second, it is independent of, and orthogonal to, other approaches to DBMS performance improvement measures. This means that any investments a DBMS vendor may have made in developing other performance improvement technologies are not nullified by our approach; rather, our approach further enhances the effects of those technologies by producing additional improvements in performance.This project addresses the following technology gap as it translates from research discovery toward commercial application, that of automating micro-specialization. It is not practical for these vendors to manually perform micro-specialization on the end-user DBMSes since micro-specialization tailors the DBMS code to the specifics of an installation and so is different for different installations. Rather, what the vendors will want, and the product we will provide, is a software development environment that allows developers to quickly, efficiently, and reliably perform micro-specialization on their customers' DBMSes, that they then license to their customers: the DBMS end users. In order to commercialize our idea, therefore, we next have to show that the process can be largely automated.The project engages Dataware Ventures to be responsible for the development side of the project, focusing on large-scale testing and refinement of the functionality and efficacy of the code specialization algorithms developed by researchers at the University of Arizona. Specifically, Dataware Ventures will perform software development, carry out large-scale testing and evaluation, and provide relevant feedback to UA researchers, aiding in this technology translation effort from research discovery toward commercial reality.DBMS micro-specialization is important because most organizations with non-trivial data processing needs, including virtually all medium and large businesses and corporations, rely on enterprise application systems for their data processing and analysis. These applications generally run on top of a DBMS, which handles the actual storage and retrieval of data. The performance of the underlying DBMS, i.e., its ability to process large amounts of data within an acceptable amount of time, is therefore of crucial importance for providing timely and accurate information to business decision makers. In addition, the potential economic impact is expected to grow to $157B in the next three years, which will contribute to the U.S. competitiveness in enterprise application systems and in the underlying technology of database management systems.

PFI：AIR 技术翻译项目的重点是翻译微型专业化的新技术，以满足提高数据库管理系统 (DBMS) 和企业应用软件 (EAS) 速度的需求。 商业化所需的下一步是演示如何实现该过程的自动化。该项目将产生一套概念验证的软件工具，该工具将采用一组已识别的不变量并自动对 DBMS 源代码进行微观专业化。这些工具的增强版本将具有以下独特功能：它将是（ i) 可扩展，即可以处理实际的 DBMS 代码库，多达数百万行源代码，并且具有可接受的性能； (ii) 稳健，即能够处理对编译器来说可能棘手的语言功能； (iii) 有效，即能够生成高质量的专用代码来实现所需的性能改进。这些功能提供了以下两个主要优点。首先，它不需要对 DBMS 源代码进行昂贵且耗时的重写，而是可以应用于现有的 DBMS 代码。其次，它独立于其他 DBMS 性能改进措施，并且与之正交。这意味着 DBMS 供应商在开发其他性能改进技术方面可能进行的任何投资都不会因我们的方法而失效；相反，我们的方法通过产生额外的性能改进来进一步增强这些技术的效果。该项目解决了以下技术差距，因为它从研究发现转化为商业应用，即自动化微型专业化。对于这些供应商来说，在最终用户 DBMS 上手动执行微专业化是不切实际的，因为微专业化根据安装的具体情况定制 DBMS 代码，因此对于不同的安装来说是不同的。相反，供应商想要的以及我们将提供的产品是一个软件开发环境，允许开发人员快速、高效、可靠地在客户的 DBMS 上执行微专业化，然后将其许可给客户：DBMS最终用户。因此，为了将我们的想法商业化，我们接下来必须证明该过程可以在很大程度上实现自动化。该项目聘请 Dataware Ventures 负责项目的开发方面，重点关注大规模测试和功能的细化。亚利桑那大学研究人员开发的代码专业化算法的有效性。具体来说，Dataware Ventures 将进行软件开发，进行大规模测试和评估，并向 UA 研究人员提供相关反馈，帮助这项技术从研究发现到商业现实的转化工作。DBMS 微观专业化非常重要，因为大多数组织都没有非- 琐碎的数据处理需求，包括几乎所有大中型企业和公司，都依赖企业应用系统进行数据处理和分析。这些应用程序通常在 DBMS 之上运行，DBMS 处理数据的实际存储和检索。因此，底层 DBMS 的性能，即其在可接受的时间内处理大量数据的能力，对于向业务决策者提供及时、准确的信息至关重要。此外，未来三年潜在的经济影响预计将增长至$157B，这将有助于增强美国在企业应用系统和数据库管理系统底层技术方面的竞争力。