In the big data era, in order to relieve computational pressure on overloaded CPU caused by ever increasing amount of data, many researches focus on hardware acceleration using FPGA for data-intensive applications. In this paper, a novel FPGA-based storage engine is proposed for DBMS in the cloud with focus on data filtering operation. A hardware data filter is designed which can significantly speedup filtering operations by utilizing parallelism provided by FPGA. Meanwhile, it can support different queries without partial reconfiguration. This FPGA-based storage engine is integrated with DBMS to realize end-to-end acceleration. In addition, an intelligent filtering on/off switch is designed to adaptively decide whether the FPGA-based filter should be employed, based on selectivity estimation. Experimental results show that the proposed solution realizes on average 2.80x computation speedup for data filtering compared with the software baseline, and achieves up to 1.95x improvement in end-to-end evaluation compared with conventional storage engine in low-selectivity cases. Moreover, the FPGA-based solution achieves 2.87x improvement on energy efficiency compared with the similar GPU-based acceleration solution.

在大数据时代，为了缓解不断增加的数据量对超负荷CPU的计算压力，许多研究都集中在使用FPGA进行数据密集型应用的硬件加速上。在本文中，针对云中的DBMS提出了一种新颖的基于FPGA的存储引擎，重点是数据过滤操作。设计了一种硬件数据过滤器，它可以利用FPGA提供的并行性显着加快过滤操作。同时，它可以支持不同的查询，而无需部分重新配置。该基于FPGA的存储引擎与DBMS集成在一起，以实现端到端加速。此外，基于选择性估计，设计了智能滤波通/断开关，以自适应地决定是否应使用基于FPGA的滤波器。实验结果表明，与低基准情况下相比，与传统存储引擎相比，所提出的解决方案与软件基线相比，平均实现了2.80倍的数据过滤计算速度提升，端到端评估性能提高了1.95倍。此外，与类似的基于GPU的加速解决方案相比，基于FPGA的解决方案在能效方面提高了2.87倍。