GPU-Accelerated Quantification Filters for Analytical Queries in Multidimensional Databases

Abstract

In online analytical processing (OLAP), filtering elements of a given dimensional attribute according to the value of a measure attribute is an essential operation, for example in top-k evaluation. Such filters can involve extremely large amounts of data to be processed, in particular when the filter condition includes "quantification" such as ANY or ALL, where large slices of an OLAP cube have to be computed and inspected. Due to the sparsity of OLAP cubes, the slices serving as input to the filter are usually sparse as well, presenting a challenge for GPU approaches which need to work with a limited amount of memory for holding intermediate results. Our CUDA solution involves a hashing scheme specifically designed for frequent and parallel updates, including several optimizations exploiting architectural features of Nvidia's Fermi and Kepler GPUs. © Springer International Publishing Switzerland 2015.