Fast computation of database operations using graphics processors

We present new algorithms for performing fast computa-tion of several common database operations on commod-ity graphics processors. Specically, we consider operations such as conjunctive selections, aggregations, and semi-linear queries, which are essential computational components of typical database, data warehousing, and data mining appli-cations. While graphics processing units (GPUs) have been designed for fast display of geometric primitives, we utilize the inherent pipelining and parallelism, single instruction and multiple data (SIMD) capabilities, and vector process-ing functionality of GPUs, for evaluating boolean predicate combinations and semi-linear queries on attributes and exe-cuting database operations eciently. Our algorithms take into account some of the limitations of the programming model of current GPUs and perform no data rearrange-ments. Our algorithms have been implemented on a pro-grammable GPU (e.g. NVIDIA's GeForce FX 5900) and applied to databases consisting of up to a million records. We have compared their performance with an optimized im-plementation of CPU-based algorithms. Our experiments indicate that the graphics processor available on commodity computer systems is an eective co-processor for performing database operations