Abstract
In this paper, we propose a method for parallel top-k query processing on GPU(s). We employ a novel partitioning strategy which splits the posting lists according to document ID numbers. Individual GPU threads simultaneously perform top-k query processing within their allocated subsets of posting lists, the results of the query are merged to give the final top-k results. We further design a CPU-GPU cooperative query processing method, where a majority of queries involving shorter posting lists are processed on the GPU side. We experiment with AND, OR, WAND, and Block-Max WAND (BMW) queries, with experimental results showing a promising improvement in query throughput, particularly in the case of BMW queries.
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Acknowledgment
This work is partially supported by NSF of China (grant numbers: 61373018, 61602266 11550110491), Science and Technology Development Plan of Tianjin (17JCYBJC15300, 16JCYBJC41900) and the Fundamental Research Funds for the Central Universities (Grant number: 65141020).
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Huang, H. et al. (2017). GPU-Accelerated Block-Max Query Processing. In: Ibrahim, S., Choo, KK., Yan, Z., Pedrycz, W. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2017. Lecture Notes in Computer Science(), vol 10393. Springer, Cham. https://doi.org/10.1007/978-3-319-65482-9_15
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DOI: https://doi.org/10.1007/978-3-319-65482-9_15
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