GPU-accelerated Database Systems: Survey and Open Challenges

Sebastian Bress, Max Heimel, Norbert Siegmund, Ladjel Bellatreche, Gunter Saake
University of Magdeburg
University of Magdeburg, 2014


   title={GPU-accelerated Database Systems: Survey and Open Challenges},

   author={Bre{ss}, Sebastian and Heimel, Max and Siegmund, Norbert and Bellatreche, Ladjel and Saake, Gunter},



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The vast amount of processing power and memory bandwidth provided by modern graphics cards make them an interesting platform for data-intensive applications. Unsurprisingly, the database research community identified GPUs as effective co-processors for data processing several years ago. In the past years, there were many approaches to make use of GPUs at different levels of a database system. In this paper, we explore the design space of GPU-accelerated database management systems. Based on this survey, we present key properties, important trade-offs and typical challenges of GPU-aware database architectures, and identify major open challenges. Additionally, we survey existing GPU-accelerated DBMSs and classify their architectural properties. Then, we summarize typical optimizations implemented in GPU-accelerated DBMSs. Finally, we propose a reference architecture, indicating how GPU acceleration can be integrated in existing DBMSs.
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