使用图形处理单元实现具有BLU加速的DB2快速查询处理的混合设计:技术演示

S. Meraji, Berni Schiefer, Lan Pham, Lee Chu, Peter Kokosielis, Adam J. Storm, Wayne Young, Chang Ge, Geoffrey Ng, Kajan Kanagaratnam
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引用次数: 9

摘要

在本文中,我们将展示如何使用Nvidia gpu和主机CPU内核在DB2数据库中使用BLU加速(DB2的列存储技术)来实现更快的查询处理。此外,我们还展示了在实际的商业关系数据库管理系统(RDBMS)中使用硬件加速器(更具体地说是gpu)的好处和问题。我们研究了将特定数据库操作卸载到GPU上的效果,并展示了这样做是如何显著提高性能的。然后,我们演示了对于某些查询,仅使用CPU来执行整个操作更为有益。虽然我们使用Nvidia的一些快速内核来进行排序等操作,但我们也开发了自己的高性能内核来进行分组和聚合等操作。最后,我们将展示如何使用动态设计,该设计可以利用优化器元数据来智能地选择要运行的GPU内核。在文献中,我们第一次使用代表客户环境的基准测试来衡量原型的性能,其结果表明,使用一组真实的查询,我们可以将速度提高2倍以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards a Hybrid Design for Fast Query Processing in DB2 with BLU Acceleration Using Graphical Processing Units: A Technology Demonstration
In this paper, we show how we use Nvidia GPUs and host CPU cores for faster query processing in a DB2 database using BLU Acceleration (DB2's column store technology). Moreover, we show the benefits and problems of using hardware accelerators (more specifically GPUs) in a real commercial Relational Database Management System(RDBMS).We investigate the effect of off-loading specific database operations to a GPU, and show how doing so results in a significant performance improvement. We then demonstrate that for some queries, using just CPU to perform the entire operation is more beneficial. While we use some of Nvidia's fast kernels for operations like sort, we have also developed our own high performance kernels for operations such as group by and aggregation. Finally, we show how we use a dynamic design that can make use of optimizer metadata to intelligently choose a GPU kernel to run. For the first time in the literature, we use benchmarks representative of customer environments to gauge the performance of our prototype, the results of which show that we can get a speed increase upwards of 2x, using a realistic set of queries.
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