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Relational Learning with GPUs: Accelerating Rule Coverage

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Abstract

Relational learning algorithms mine complex databases for interesting patterns. Usually, the search space of patterns grows very quickly with the increase in data size, making it impractical to solve important problems. In this work we present the design of a relational learning system, that takes advantage of graphics processing units (GPUs) to perform the most time consuming function of the learner, rule coverage. To evaluate performance, we use four applications: a widely used relational learning benchmark for predicting carcinogenesis in rodents, an application in chemo-informatics, an application in opinion mining, and an application in mining health record data. We compare results using a single and multiple CPUs in a multicore host and using the GPU version. Results show that the GPU version of the learner is up to eight times faster than the best CPU version.

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Notes

  1. CUDA is NVIDIA’s General-Purpose Parallel Computing Platform and Programming Model [10].

  2. The Aleph modified version is available upon request to the authors.

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Acknowledgments

The authors gratefully acknowledge the comments from all reviewers, which highly improved the quality of this paper. We would also like to thank Martínez-Angeles’ M.Sc. and qualification committee members for their helpful comments.

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Authors and Affiliations

  1. Departamento de Computación, CINVESTAV-IPN, Av. Instituto Politécnico Nacional 2508, 07360, Mexico, DF, Mexico

    Carlos Alberto Martínez-Angeles & Jorge Buenabad-Chávez

  2. Departmento de Ciência de Computadores, CRACS INESC-TEC LA and Universidade do Porto, Rua do Campo Alegre, 1021, 4169-007, Porto, Portugal

    Inês Dutra & Vítor Santos Costa

  3. Georgia Institute of Technology, 266 Ferst Drive, Atlanta, GA, 30332, USA

    Haicheng Wu

Authors
  1. Carlos Alberto Martínez-Angeles
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  2. Haicheng Wu
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  3. Inês Dutra
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  4. Vítor Santos Costa
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  5. Jorge Buenabad-Chávez
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Corresponding author

Correspondence to Inês Dutra.

Additional information

CMA was supported by the University of Porto, the Centre for Research and Postgraduate Studies of the National Polytechnic Institute (CINVESTAV-IPN) of Mexico, and the Council of Science and Technology (CONACyT) of Mexico. ICD and VSC were partially supported by: the European Regional Development Fund (ERDF), COMPETE Programme; the Portuguese Foundation for Science and Technology (FCT), projects ADE (PTDC/EIA-EIA/121686/2010 (FCOMP-01-0124-FEDER-020575)), and ABLe PTDC/EEI-SII/2094/2012.

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Martínez-Angeles, C.A., Wu, H., Dutra, I. et al. Relational Learning with GPUs: Accelerating Rule Coverage. Int J Parallel Prog 44, 663–685 (2016). https://doi.org/10.1007/s10766-015-0364-7

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  • DOI: https://doi.org/10.1007/s10766-015-0364-7

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