Authors:
Samuel Cremer
;
Michel Bagein
;
Saïd Mahmoudi
and
Pierre Manneback
Affiliation:
University of Mons, Belgium
Keyword(s):
In-memory Database Systems, Embedded Databases, Relational Database Management Systems, GPU.
Related
Ontology
Subjects/Areas/Topics:
Architectural Concepts
;
Data Engineering
;
Data Management and Quality
;
Database Architecture and Performance
;
Databases and Data Security
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Mobile Databases
Abstract:
Concurrently, with the rise of Big Data systems, relational database management systems (RDBMS) are still
widely exploited in servers, client devices, and even embedded inside end-user applications. In this paper,
it is suggest to improve the performance of SQLite, the most deployed embedded RDBMS. The proposed
solution, named CuDB, is an ”In-Memory” Database System (IMDB) which attempts to exploit specificities
of modern CPU / GPU architectures. In this study massively parallel processing was combined with strategic
data placement, closer to computing units. According to content and selectivity of queries, the measurements
reveal an acceleration range between 5 to 120 times - with peak up to 411 - with one GPU GTX770 compared
to SQLite standard implementation on a Core i7 CPU.