III: Small: Database Processing on GPUs

现代图形处理单元（gpu）提供比cpu更多的并行性和更高的内存带宽。该项目旨在通过开发一个系统来有效地处理gpu驻留数据集上的数据库查询，从而利用这些属性。为了实现这一目标，本项目采用以下方法：(a)开发新的索引技术，将多维分区与面向块的位图结合起来，其参数对查询分布敏感；(b)线程间内存库争用和值争用的优化；(c)有效地执行一整套关系数据库操作符，包括聚合、连接和索引选择；(d)使用实际应用程序和标准基准评价系统在查询密集型工作负载方面的性能。数据库系统性能的改进将对许多使用数据库系统进行分析的企业的效率产生广泛的影响。该项目支持从事数据库系统实现技术研究的博士生。在项目期间创造的创新和软件将用于加强哥伦比亚大学数据库系统实施课程的课程。出版物、软件和其他项目数据将通过我们的项目网站（http://www.cs.columbia.edu/~kar/gpuproject.html）发布。

Modern Graphic Processing Units (GPUs) offer more parallelism and higher memory bandwidth than CPUs. This project aims to take advantage of these properties by developing a system to efficiently process database queries over GPU-resident datasets. To achieve this goal this project employs the following approaches: (a) The development of novel indexing techniques that combine multidimensional partitioning with block-oriented bitmaps, and whose parameters are sensitive to the query distribution;(b) The optimization of memory bank contention and value contention between threads; (c) The efficient implementation of a complete set of relational database operators, including aggregation, joins, and indexed selections; and (d) The evaluation of the performance of the system on query-intensive workloads, using real applications and standard benchmarks. Improvements in database system performance would have wide-ranging impact on the efficiency of many enterprises that employ database systems for analytics. The project supports PhD students working on database system implementation techniques. The innovations and software created during the project will be used to enhance the curriculum of the Database Systems Implementation course at Columbia University. Publications, software, and other project data will be disseminated via the web at our project web site (http://www.cs.columbia.edu/~kar/gpuproject.html).