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Title: Relational joins on graphics processors
Authors: He, Bingsheng
Yang, Ke
Fang, Rui
Lu, Mian
Govindaraju, Naga K.
Luo, Qiong
Sander, Pedro V.
Keywords: Relational database
Parallel processing
Graphics processors
Issue Date: 2008
Citation: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, 9-12 June 2008, p. 511-514
Abstract: We present a novel design and implementation of relational join algorithms for new-generation graphics processing units (GPUs). The most recent GPU features include support for writing to random memory locations, efficient inter-processor communication, and a programming model for general-purpose computing. Taking advantage of these new features, we design a set of data-parallel primitives such as split and sort, and use these primitives to implement indexed or non-indexed nested-loop, sort-merge and hash joins. Our algorithms utilize the high parallelism as well as the high memory bandwidth of the GPU, and use parallel computation and memory optimizations to effectively reduce memory stalls. We have implemented our algorithms on a PC with an NVIDIA G80 GPU and an Intel quad-core CPU. Our GPU-based join algorithms are able to achieve a performance improvement of 2-7X over their optimized CPU-based counterparts.
Rights: © ACM, 2008. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, {9-12 June 2008}
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