CuDB : a Relational Database Engine Boosted by Graphics Processing Units

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ISBN: 978-84-608-6309-0
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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.
Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.
Relational Database, In-Memory, SQLite, GPU
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Carretero Pérez, Jesús; (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.