CAREER: Enabling high-throughput data management in scientific domains

许多科学领域已经进入了一个数据驱动的时代，在这个时代，科学发现在很大程度上依赖于对湿台式实验或计算机模拟产生的大规模数据的有效和高效分析。当前的数据库管理系统（dbms）虽然在商业世界中非常流行，但在科学应用程序所需的高吞吐量数据处理方面却存在不足。该项目的目标是设计和实现一种新颖的数据管理软件架构，为一般科学界提供高通量的数据管理服务。该项目通过以下方式实现了这一目标：(1)基于对大型数据源的重复扫描的新颖的一次扫描适用于所有数据处理框架；(2)利用现代图形处理单元（GPU）硬件的强大计算能力的查询引擎；(3)在查询引擎的基础上设计和实现了三个科学领域的流行分析算法，以证明所提出架构的有效性和效率。该项目还旨在构建一个软件原型，并用真实世界的科学数据集和查询工作负载评估这个原型。该项目有望提供一个高效的解决方案，以满足广泛的科学领域的数据管理需求。为了提供可比较的性能，所建议的体系结构只需要现有系统所需的硬件和能源成本的一小部分。因此，它有可能使被认为困难或不可行的科学研究成为现实。将拟议的研究纳入教育努力，有助于扩大计算机科学的影响，培养下一代多学科科学家，促进少数族裔和女性学生在计算机科学和工程领域的成功，是计划中的其他更广泛的影响活动。

Many scientific domains have entered a data-driven era, in which scientific discovery depends heavily on effective and efficient analysis of large-scale data generated by wet-bench experiments or computer simulations. Current database management systems (DBMSs), while being very popular in the business world, fall short in high-throughput data processing required by scientific applications. The goal of this project is to design and implement a novel data management software architecture that enables high-throughput data management services for general scientific communities. The project achieves this goal via (1) a novel one-scan-fits-all data processing framework based on repetitive scans of large data sources; (2) a query engine that leverages the massive computing power of modern Graphics Processing Units (GPU) hardware; and (3) design and implementation of algorithms for popular analytics in three scientific domains on top of the query engine to demonstrate the effectiveness and efficiency of the proposed architecture. The project also aims at building a software prototype and evaluating this prototype with real-world scientific datasets and query workloads. The project is expected to provide a highly efficient solution to satisfy the data management needs of a wide range of scientific fields. To deliver comparable performance, the proposed architecture requires only a fraction of the hardware and energy costs needed by existing systems. As a result, it has the potential to make scientific studies that are regarded as difficult or infeasible a reality. Integration of proposed research into educational endeavors that contribute to broadening the influence of computer science, nurturing the next generation of multidisciplinary scientists, and boosting the success of minority and women students in the computer science and engineering field are other broader impact activities planned.