Fast computation of database operations using graphics processors

Naga K. Govindaraju, Brandon Lloyd, Wei Wang, Ming Lin, Dinesh Manocha
University of North Carolina at Chapel Hill
In SIGMOD ’04: Proceedings of the 2004 ACM SIGMOD international conference on Management of data (2004), pp. 215-226


   title={Fast computation of database operations using graphics processors},

   author={Govindaraju, N.K. and Lloyd, B. and Wang, W. and Lin, M. and Manocha, D.},

   booktitle={ACM SIGGRAPH 2005 Courses},





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We present new algorithms for performing fast computation of several common database operations on commodity graphics processors. Specifically, 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 applications. 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 processing functionality of GPUs, for evaluating boolean predicate combinations and semi-linear queries on attributes and executing database operations efficiently. Our algorithms take into account some of the limitations of the programming model of current GPUs and perform no data rearrangements. Our algorithms have been implemented on a programmable 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 implementation of CPU-based algorithms. Our experiments indicate that the graphics processor available on commodity computer systems is an effective co-processor for performing database operations.
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