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Performance improvement of data mining in Weka through multi-core and GPU acceleration: opportunities and pitfalls

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Abstract

Data mining tools may be computationally demanding, which leads to an increasing interest on parallel computing strategies in order to improve their performance. While multi-core processors and Graphics Processing Units (GPUs) accelerators increased the computing power of current desktop computers, we observe that desktop-based data mining tools do not take full advantage of these architectures yet. This paper investigates strategies to improve the performance of Weka, a popular data mining tool, through multi-core and GPU acceleration. Using performance profiling of Weka, we identify operations that could improve the data mining performance when parallelized. We selected two of these operations, and analyze the impact of their parallel execution on Weka’s performance. These experiments demonstrate that while significant speedups can be achieved, all operations are not prone to be parallelized, which reinforces the need for a careful and well-studied selection of the candidates.

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Notes

  1. See KDNuggets polls at http://www.kdnuggets.com/polls/2013/analytics-big-data-mining-data-science-software.html.

  2. http://romeo.univ-reims.fr.

  3. http://romeo.univ-reims.fr.

  4. http://www.top500.org.

  5. http://www.green500.org.

  6. http://cosy.univ-reims.fr/PER-MARE.

References

  • Andriole SJ, Bojanova I (2014) Optimizing operational and strategic it. IT Prof 16(5):12–15. doi:10.1109/MITP.2014.74

    Article  Google Scholar 

  • Aparapi (2013) Aparapi team. https://code.google.com/p/aparapi/

  • Bache K, Lichman M (2013) UCI Machine Learning Repository. http://archive.ics.uci.edu/ml

  • Banerjee DS, Sakurikar P, Kothapalli K (2014) Comparison sorting on hybrid multicore architectures for fixed and variable length keys. Int J High Perform Comput Appl 28(3):267–284. doi:10.1177/1094342014526906. http://hpc.sagepub.com/content/28/3/267.full.pdf

  • Barry W (2006) Parallel Programming: wTechniques and applications using Networked Workstations And Parallel Computers, 2/E (Pearson Education, 2006), pp. 341–347. ISBN: 9788131702390. http://books.google.co.in/books?id=U_LlqRYYtl0C

  • Cederman D, Tsigas P (2010) Gpu-quicksort: A practical quicksort algorithm for graphics processors. J Exp Algorithmics 14:4–144124. doi:10.1145/1498698.1564500

  • Celis S, Musicant DR (2002) Weka-Parallel: Machine Learning in Parallel. Technical report. Carleton College, CS TR

  • CUDPP (2014) CUDPP: CUDA Data Parallel Primitives Library. http://cudpp.github.io/

  • De Wael M, Marr S, Van Cutsem T (2014) Fork/Join Parallelism in the Wild: Documenting Patterns and Anti-patterns in Java Programs Using the Fork/Join Framework. In: Proceedings of the 2014 International Conference on Principles and Practices of Programming on the Java Platform: Virtual Machines, Languages, and Tools. PPPJ ’14. ACM, New York, pp 39–50. ISBN: 978-1-4503-2926-2. doi:10.1145/2647508.2647511

  • Dotzler G, Veldema R, Klemm M (2010) JCudaMP: OpenMP/Java on CUDA. In: Proceedings of the 3rd International Workshop on Multicore Software Engineering. IWMSE ’10. ACM, New York, pp 10–17. ISBN: 978-1-60558-964-0. doi:10.1145/1808954.1808959

  • Engel TA, Charão AS, Kirsch-Pinheiro M, Steffenel LA (2014) Performance Improvement of Data Mining in Weka through GPU Acceleration. In: Proceedings of the 5th International Conference on Ambient Systems, Networks and Technologies (ANT 2014), the 5th International Conference on Sustainable Energy Information Technology (SEIT-2014). Hasselt, Belgium, pp 93–100. doi:10.1016/j.procs.2014.05.402

  • Fang W, Lau KK, Lu M, Xiao X, Lam CK, Yang PY, He B, Luo Q, Yang PVSK (2008) Parallel data mining on graphics processors, Technical report, Department of Computer Science and Engineering, Hong Kong University of Science and Technology

  • Fire M, Kagan D, Elyashar A, Elovici Y (2014) Friend or foe? fake profile identification in online social networks. Soc Netw Anal Mining 4(1). doi:10.1007/s13278-014-0194-4

  • Ghoting A, Kambadur P, Pednault E, Kannan R (2011) NIMBLE: A Toolkit for the Implementation of Parallel Data Mining and Machine Learning Algorithms on Mapreduce. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’11. ACM, New York, pp 334–342. ISBN: 978-1-4503-0813-7. doi:10.1145/2020408.2020464

  • Graf F, Kriegel HP, Pölsterl S, Schubert M, Cavallaro A (2011a) Position Prediction in CT Volume Scans. In: Proceedings of the 28th International Conference on Machine Learning (ICML) Workshop on Learning for Global Challenges. Bellevue, Washington

  • Graf F, Kriegel HP, Schubert M, Pölsterl S, Cavallaro A (2011b) 2D Image Registration in CT Images Using Radial Image Descriptors, in MICCAI (2):607–614

  • Hailemariam G, Hill S, Demissie S (2012) Exploring Data Mining Techniques and Algorithms for Predicting Customer Loyalty and Loan Default Risk Scenarios at Wisdom Microfinance, Addis Ababa, Ethiopia. In: Proceedings of the International Conference on Management of Emergent Digital EcoSystems. MEDES ’12. ACM, New York, pp 183–184. ISBN: 978-1-4503-1755-9. doi:10.1145/2457276.2457310

  • Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. SIGKDD Explor Newsl 11(1):10–18

    Article  Google Scholar 

  • JCublas (2013) Java bindings for CUBLAS. http://www.jcuda.org/jcuda/jcublas/JCublas.html

  • JCuda (2013) JCuda: Java bindings for CUDA. http://www.jcuda.org/

  • JCudpp (2014) JCudpp: Java bindings for CUDPP. http://www.jcuda.org/jcuda/jcudpp/JCudpp.html

  • Jiang W, Agrawal G (2010) MATE-CG: A Map Reduce-Like Framework for Accelerating Data-Intensive Computations on Heterogeneous Clusters. In: Parallel Distributed Processing Symposium (IPDPS), IEEE 26th. International. pp 644–655. doi:10.1109/IPDPS.2012.65

  • Jones S (2012) How Tesla K20 speeds QuickSort. http://blogs.nvidia.com/blog/2012/09/12/how-tesla-k20-speeds-up-quicksort-a-familiar-comp-sci-code/

  • JProbe (2013) JProbe: Java Profiler. http://www.ej-technologies.com/products/jprofiler/overview.html

  • JProfiler (2013) JProfiler: Java Profiler. http://www.ej-technologies.com/products/jprofiler/overview.html

  • Khoussainov R, Zuo X, Kushmerick N (2004) Grid-enabled weka: A toolkit for machine learning on the grid. ERCIM News, no. 59. http://www.ercim.eu/publication/Ercim_News/enw59/khussainov.html

  • Kirschenhofer P, Prodinger H, Martnez C (1997) Analysis of hoare’s find algorithm with median-of-three partition. Random Str Algorithms 10:143–156

    Article  MATH  Google Scholar 

  • Kumar P, Ozisikyilmaz B, Liao WK, Memik G, Choudhary A (2011) High Performance Data Mining Using R on Heterogeneous Platforms. In: Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on. pp 1720–1729

  • Ma W, Agrawal G (2010) AUTO-GC: automatic translation of data mining applications to GPU clusters. In: 24th IEEE International Symposium on Parallel and Distributed Processing—Workshop Proceedings (IEEE Computer Society. pp 1–8

  • Machado KS, Winck AT, Ruiz DD, Norberto de Souza O (2010) Mining flexible-receptor docking experiments to select promising protein receptor snapshots. BMC Genomics 11(5):1–13

    Google Scholar 

  • Markov Z, Russell I (2006) An Introduction to the WEKA Data Mining System. In: Proceedings of the 11th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education. ITICSE ’06. ACM, New York, pp 367–368. ISBN 1-59593-055-8. doi:10.1145/1140124.1140127

  • Murthy SK (1998) Automatic construction of decision trees from data: A multi-disciplinary survey. Data Mining Knowl Discov 2(4):345–389. doi:10.1023/A:1009744630224

    Article  Google Scholar 

  • Mytkowicz T, Diwan A, Hauswirth M, Sweeney PF (2010) Evaluating the accuracy of Java profilers. In: Proceedings of the 2010 ACM SIGPLAN conference on Programming language design and implementation. PLDI ’10. ACM, New York, pp 187–197. ISBN 978-1-4503-0019-3. doi:10.1145/1806596.1806618

  • Nickolls J, Buck I, Garland M, Skadron K (2008) Scalable parallel programming with cuda. Queue 6(2):40–53. doi:10.1145/1365490.1365500

  • Oracle Corporation (2013) VisualVM. http://visualvm.java.net

  • Pérez MS, Sánchez A, Herrero P, Robles V, Peña JM (2005) Adapting the Weka Data Mining Toolkit to a Grid Based Environment. In: Advances in Web Intelligence (AWIC). Lecture Notes in Computer Science, vol. 3528. Springer, Lodz, Polonia, pp 492–497. ISSN: 0302–9743. http://www.gmrv.es/Publications/2005/PSHRP05

  • Schadt EE, Linderman MD, Sorenson J, Lee L, Nolan GP (2010) Computational solutions to large-scale data management and analysis. Nature Rev Genetics 11(9):647–657. doi:10.1038/nrg2857. http://www.nature.com/nrg/journal/v11/n9/abs/nrg2857.html

  • Senger H, Hruschka ER, Silva FAB, Sato LM, Bianchini CP, Jerosch BF (2007) Exploiting idle cycles to execute data mining applications on clusters of pcs. J Syst Softw 80(5):778–790. doi:10.1016/j.jss.2006.05.035

    Article  Google Scholar 

  • Talia D, Trunfio P, Verta O (2005) Weka4WS: a WSRFenabled Weka Toolkit for Distributed Data Mining on Grids. In: Proc. of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD). Springer, pp 309–320

  • Tan G, Li L, Triechle S, Phillips E, Bao Y, Sun N (2011) Fast implementation of DGEMM on Fermi GPU. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis. SC ’11. ACM, New York, pp 35–13511. ISBN 978-1-4503-0771-0. doi:10.1145/2063384.2063431

  • Wang D, Irani D, Pu C (2014) Spade: a social-spam analytics and detection framework. Soc Netw Anal Mining 4(1). doi:10.1007/s13278-014-0189-1

  • Wang Y, Witten IH (1997) Induction of model trees for predicting continuous classes. In: Poster papers of the 9th European Conference on Machine Learning. Springer

  • Witten IH, Frank E, Trigg L, Hall M, Holmes G, Cunningham SJ (1999) Weka: Practical Machine Learning Tools and Techniques with Java Implementations. In: Proceedings of the ICONIP/ANZIIS/ANNES’99 Workshop on Emerging Knowledge Engineering and Connectionist-Based Information Systems. pp 192–196

  • Wu R, Zhang B, Hsu M (2009) GPU-Accelerated Large Scale Analytics, Technical Report HPL-2009-38, HP Labs. http://www.hpl.hp.com/techreports/2009/HPL-2009-38.html

  • Xu M, Watanachaturaporn P, Varshney PK, Arora MK (2005) Decision tree regression for soft classification of remote sensing data. Remote Sensing Env 97(3):322–336. doi:10.1016/j.rse.2005.05.008. http://www.sciencedirect.com/science/article/pii/S0034425705001604

  • Zaremba W, Lin Y, Grover V (2012) JaBEE: framework for object-oriented Java bytecode compilation and execution on graphics processor units. In: Proceedings of the 5th Annual Workshop on General Purpose Processing with Graphics Processing Units. GPGPU-5. ACM, New York, pp 74–83. ISBN 978-1-4503-1233-2

  • Zhu M, Cao T, Jiang X (2014) Using clustering coefficient to construct weighted networks for supervised link prediction. Soc Netw Anal Mining 4(1). doi:10.1007/s13278-014-0215-3

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Acknowledgments

The authors would like to thank the LSC Laboratory and the ROMEO Computing Center for the access to their resources. This project is partially financed by the STIC-AmSud PER-MARE project\(^{6}\) (project number 13STIC07), an international collaboration program supported by CAPES/MAEE/ANII agencies.

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Authors and Affiliations

  1. Laboratório de Sistemas de Computação, Universidade Federal de Santa Maria, Santa Maria, Brazil

    Tiago Augusto Engel & Andrea Schwertner Charão

  2. Centre de Recherche en Informatique, Université Paris 1 Panthéon-Sorbonne, Paris, France

    Manuele Kirsch-Pinheiro

  3. Laboratoire CReSTIC, Équipe SysCom, Université de Reims Champagne-Ardenne, Reims, France

    Luiz-Angelo Steffenel

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  1. Tiago Augusto Engel
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Correspondence to Tiago Augusto Engel.

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Engel, T.A., Charão, A.S., Kirsch-Pinheiro, M. et al. Performance improvement of data mining in Weka through multi-core and GPU acceleration: opportunities and pitfalls. J Ambient Intell Human Comput 6, 377–390 (2015). https://doi.org/10.1007/s12652-015-0292-9

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