Towards a Combined Grouping and Aggregation Algorithm for Fast Query Processing in Columnar Databases with GPUs

Column-store in-memory databases have received a lot of attention because of their fast query processing response times on modern multi-core machines. Among different database operations, group by/aggregate is an important and potentially costly operation. Moreover, sort-based and hash-based algorithms are the most common ways of processing group by/aggregate queries. While sort-based algorithms are used in traditional Data Base Management Systems (DBMS), hash based algorithms can be applied for faster query processing in new columnar databases. Besides, Graphical Processing Units (GPU) can be utilized as fast, high bandwidth co-processors to improve the query processing performance of columnar databases. The focus of this article is on the prototype for group by/aggregate operations that we created to exploit GPUs. We show different hash based algorithms to improve the performance of group by/aggregate operations on GPU. One of the parameters that affect the performance of the group by/aggregate algorithm is the number of groups and hashing algorithm. We show that we can get up to 7.6x improvement in kernel performance compared to a multi-core CPU implementation when we use a partitioned multi-level hash algorithm using GPU shared and global memories.