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Exploring graphics processing units as parallel coprocessors for online aggregation

Tobias Lauer, Amitava Datta, Zurab Khadikov, Christoffer Anselm
University of Freiburg, Freiburg, Germany
Proceedings of the ACM 13th international workshop on Data warehousing and OLAP, DOLAP ’10, 2010

@inproceedings{lauer2010exploring,

   title={Exploring graphics processing units as parallel coprocessors for online aggregation},

   author={Lauer, T. and Datta, A. and Khadikov, Z. and Anselm, C.},

   booktitle={Proceedings of the ACM 13th international workshop on Data warehousing and OLAP},

   pages={77–84},

   year={2010},

   organization={ACM}

}

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Multidimensional aggregation is one of the most important computational building blocks and hence also a potential performance bottleneck in Online Analytic Processing (OLAP). In order to deliver fast query responses for interactive operations such as slicing, dicing, roll-up and drill-down, it is essential that aggregates along the relevant dimensions of a data cube can be calculated as efficiently as possible. General-purpose computing on graphics processing units (GPGPU) is a recent trend used in many computing domains with the potential for tremendous speedups through the massively data-parallel computation available on such devices. We present a GPU-based cube data structure and algorithms for fast multidimensional aggregation, implemented using Nvidia’s CUDA framework. Our experimental tests show a substantial speedup over state-of-the-art sequential algorithms. Moreover, the performance gain is particularly high in cases exposing the weaknesses of traditional algorithms, i.e. when the number of base cells involved in an aggregation is large.
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