The variety of memory devices in modern com- puter systems holds opportunities as well as challenges for data management systems. In particular, the exploitation of Graphics Processing Units (GPUs) and their fast memory has been studied quite intensively. However, current approaches treat GPUs as systems in their own right and fail to provide a generic strategy for efficient CPU/GPU cooperation. We propose such a strategy for relational query processing: calculating an approximate result based on lossily compressed, GPU-resident data and refine the result using residuals, i.e., the lost data, on the CPU. We developed the required algorithms, implemented the strategy in an existing DBMS and found up to 8 times performance improvement, even for datasets larger than the available GPU memory.
IEEE International Conference on Data Engineering
Database Architectures

Pirk, H., Manegold, S., & Kersten, M. (2014). Waste Not..
Efficient Co-Processing of Relational Data. IEEE. doi:10.1109/ICDE.2014.6816677