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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.

References

  1. Afrati, F.N., Borkar, V., Carey, M., Polyzotis, N., Ullman, J.D.: Cluster computing, recursion and datalog. In: Proceedings of the First International Conference on Datalog Reloaded, Datalog’10, pp. 120–144. Springer, Berlin (2011)

  2. Beeri, C., Ramakrishnan, R.: On the power of magic. J. Log. Program. 10(3&4), 255–299 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  3. Bekkerman, R., Bilenko, M., Langford, J. (eds.): Scaling up Machine Learning: Parallel and Distributed Approaches. Cambridge University Press, Cambridge (2011)

    Google Scholar 

  4. Chakrabarti, D., Faloutsos, C.: Graph mining: laws, generators, and algorithms. ACM Comput. Surv. 38(1) (2006). doi:10.1145/1132952.1132954

  5. Collins, J.M.: The DTP AIDS antiviral screen program (1999). http://dtp.nci.nih.gov/docs/aids/aidsdata.html

  6. Côrte-Real, J., Dutra, I., Rocha, R.: A map-reduce constructor for prolog. In: Proceedings of the International Conference on Principles and Practice of Declarative Programming (PPDP) (2013)

  7. Costa, V.S., Sagonas, K., Lopes, R.: Demand-driven indexing of prolog clauses. In: Veronica D., Ilkka N. (eds.) Proceedings of the 23rd International Conference on Logic Programming, volume 4670 of Lecture Notes in Computer Science, pp. 305–409. Springer (2007)

  8. Costa, V.S., Srinivasan, A., Camacho, R., Blockeel, H., Demoen, B., Janssens, G., Struyf, J., Vandecasteele, H., Van Laer, W.: Query transformations for improving the efficiency of ilp systems. J. Mach. Learn. Res. 4, 465–491 (2003)

    MATH  Google Scholar 

  9. Costa, V.S., Rocha, R., Damas, L.: The yap prolog system. Theory Pract. Log. Program. 12(1–2), 5–34 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  10. CUDA C programming guide. http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html

  11. Dastgeer, U., Li, L., Kessler, C.: Smart containers and skeleton programming for GPU-based systems. In: Proceedings 7th International Symposium on High-Level Parallel Programming and Applications (HLPP’14), Amsterdam (2014)

  12. De Raedt, L.: Logical and Relational Learning. Springer, Berlin (2008)

    Book  MATH  Google Scholar 

  13. Dehaspe, L., De Raedt, L.: Parallel inductive logic programming. In: In Proceedings of the MLnet Familiarization Workshop on Statistics, Machine Learning and Knowledge Discovery in Databases, pp. 112–117 (1995)

  14. Diamos, G., Wu, H., Lele, A., Wang, J., Yalamanchili, S.: Efficient relational algebra algorithms and data structures for GPU. Technical report, Georgia Institute of Technology (2012)

  15. Diamos, G., Wu, H., Wang, J., Lele, A., Yalamanchili, S.: Relational algorithms for multi-bulk-synchronous processors. In: Proceedings of the 18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP ’13, New York, NY, USA, pp. 301–302. ACM (2013)

  16. Fonseca, N.A., Srinivasan, A., Silva, F.M.A., Camacho, R.: Parallel ILP for distributed-memory architectures. Mach. Learn. 74(3), 257–279 (2009)

    Article  Google Scholar 

  17. Gavanelli, M., Riguzzi, F., Milano, M., Cagnoli, P.: Constraint and optimization techniques for supporting policy making. In: Yu, T., Chawla, N., Simoff, S. (eds) Computational Intelligent Data Analysis for Sustainable Development, Data Mining and Knowledge Discovery Series, chap. 12, pp. 361–382. Chapman & Hall/CRC, Abingdon (2013)

  18. Green, T.J., Aref, M., Karvounarakis, G.: Logicblox, platform and language: a tutorial. In: Proceedings of the Second International Conference on Datalog in Academia and Industry, Datalog 2.0’12, pp. 1–8. Springer, Berlin (2012)

  19. Green, O., McColl, R., Bader, D.A.: GPU merge path: a GPU merging algorithm. In: Proceedings of the 26th ACM International Conference on Supercomputing, ICS ’12, New York, NY, USA, pp. 331–340. ACM (2012)

  20. He, B., Mian, L., Yang, K., Fang, R., Govindaraju, N.K., Luo, Q., Sander, P.V.: Relational query coprocessing on graphics processors. ACM Trans. Database Syst. 34(4), 21:1–21:39 (2009)

    Article  Google Scholar 

  21. Huang, S.S., Green, T.J., Loo, B.T.: Datalog and emerging applications: an interactive tutorial. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, SIGMOD ’11, New York, NY, USA, pp. 1213–1216. ACM (2011)

  22. Martínez-Angeles, C.A., Dutra, I., Costa, V.S., Buenabad-Chávez, J.: A datalog engine for GPUs. In: WFLP-2013: 22nd International Workshop on Functional and (Constraint) Logic Programming, Kiel, Germany, 11–13 Sept, pp. 239–253 (2013)

  23. Muggleton, S.: Inverse entailment and progol. New Gener. Comput. 13, 245–286 (1995)

    Article  Google Scholar 

  24. Odeh, S., Green, O., Mwassi, Z., Shmueli, O., Birk, Y.: Merge path—parallel merging made simple. In: Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum, IPDPSW ’12, Washington, DC, USA, IEEE Computer Society, pp. 1611–1618 (2012)

  25. Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2012)

    Google Scholar 

  26. Red fox: a compilation environment for data warehousing. http://gpuocelot.gatech.edu/projects/red-fox-a-compilation-environment-for-data-warehousing/

  27. Ryan, P.B., Schuemie, M.J.: Evaluating performance of risk identification methods through a large-scale simulation of observational data. Drug Saf. 36(1), 171–180 (2013)

    Article  Google Scholar 

  28. Sean Baxter: modern GPU library—tutorial. http://nvlabs.github.io/moderngpu/index.html (visited in Jan 2015) (2013)

  29. Srinivasan, A.: The Aleph manual. University of Oxford, England (2001). http://www.cs.ox.ac.uk/activities/machlearn/Aleph/aleph.html

  30. Srinivasan, A., King, R.D., Muggleton, S.H., Sternberg, M.J.E.: Carcinogenesis predictions using ILP. In: Lavrac, N., Dszeroski, S. (eds.) Inductive Logic Programming, volume 1297 of Lecture Notes in Computer Science, pp. 273–287. Springer, Berlin (1997)

    Google Scholar 

  31. Srinivasan, A., Faruquie, T.A., Joshi, S.: Data and task parallelism in ILP using MapReduce. Mach. Learn. 86(1), 141–168 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  32. Taskar, B., Getoor, L.: Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)

    MATH  Google Scholar 

  33. Tekle, K.T., Liu, Y.A.: More efficient datalog queries: subsumptive tabling beats magic sets. In: SIGMOD Conference, pp. 661–672 (2011)

  34. Thrust: a parallel template library. http://thrust.github.io/

  35. TPC-H transaction processing performance council benchmark H. http://www.tpc.org/tpch/

  36. Ullman, J.D.: Principles of Database and Knowledge-Base Systems, vol. I. Computer Science Press, Rockville (1988)

    Google Scholar 

  37. Ullman, J.D.: Principles of Database and Knowledge-Base Systems, vol. II. Computer Science Press, Rockville (1989)

    Google Scholar 

  38. Weislow, O.S., Kiser, R., Fine, D.L., Bader, J., Shoemaker, R.H., Boyd, M.R.: New soluble-formazan assay for hiv-1 cytopathic effects: application to high-flux screening of synthetic and natural products for aids-antiviral activity. J. Natl. Cancer Inst. 81(8), 577–586 (1989)

    Article  Google Scholar 

  39. Wu, H., Diamos, G., Cadambi, S., Yalamanchili, S.: Kernel weaver: automatically fusing database primitives for efficient GPU computation. In: Proceedings of the 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO-45, Washington, DC, USA, IEEE Computer Society, pp. 107–118 (2012)

  40. Wu, H., Diamos, G., Sheard, T., Aref, M., Baxter, S., Garland, M., Yalamanchili, S.: Red fox: an execution environment for relational query processing on gpus. In: International Symposium on Code Generation and Optimization (CGO) (2014)

  41. Wu, H., Diamos, G., Wang, J., Cadambi, S., Yalamanchili, S., Chakradhar, S.: Optimizing data warehousing applications for gpus using kernel fusion/fission. In: Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum, IPDPSW ’12, Washington, DC, USA, IEEE Computer Society, pp. 2433–2442 (2012)

  42. Young, J., Wu, H., Yalamanchili, S.: Satisfying data-intensive queries using GPU clusters. In: 2012 SC Companion High Performance Computing, Networking, Storage and Analysis (SCC), pp. 1314–1314 (2012)

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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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