Publication: CuDB : a Relational Database Engine Boosted by Graphics Processing Units
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2016-02
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Abstract
GPUs benefit from much more computation power with the same order of energy consumption than CPUs. Thanks
to their massive data parallel architecture, GPUs can outperform CPUs, especially on Single Program Multiple
Data (SPMD) programming paradigm on a large amount of data. Database engines are now everywhere, from
different sizes and complexities, for multiple usages, embedded or distributed; in 2012, 500 million of SQLite
active instances were estimated over the world. Our goal is to exploit the computation power of GPUs to improve
performance of SQLite, which is a key software component of many applications and systems. In this paper, we
introduce CuDB, a GPU-boosted in-memory database engine (IMDB) based on SQLite. The SQLite API remains
unchanged, allowing developers to easily upgrade database engine from SQlite to CuDB even on already existing
applications. Preliminary results show significant speedups of 70x with join queries on datasets of 1 million records.
We also demonstrate the "memory bounded" character of GPU-databases and show the energy efficiency of our
approach.
Description
Proceedings of the First PhD Symposium on Sustainable Ultrascale
Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.
Keywords
Relational Database, In-Memory, SQLite, GPU
Bibliographic citation
Carretero Pérez, Jesús; et.al. (eds.). (2016). Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016). Timisoara, Romania. Universidad Carlos III de Madrid, ARCOS. Pp. 13-16.