Improving Performances of an Embedded Relational Database Management System with a Hybrid CPU/GPU Processing Engine

Abstract

End-user systems are increasingly impacted by the exponential growth of data volumes and their processing. Moreover, post-processing operations, essentially dedicated to ergonomic features, require more and more resources. Improving overall performances of embedded relational database management systems (RDBMS) can contribute to deliver better responsiveness of end-user systems while increasing the energy efficiency. In this paper, it is proposed to upgrade SQLite, the most-spreaded embedded RDBMS, with a hybrid CPU/GPU processing engine combined with appropriate data management. With the proposed solution, named CuDB, massively parallel processing is combined with strategic data placement, closer to computing units. Experimental results revealed, in all cases, better performances and power efficiency compared to SQLite with an in-memory database.