Skip to main content

Accelerating k-NN Algorithm with Hybrid MPI and OpenSHMEM

  • Conference paper
  • First Online:
OpenSHMEM and Related Technologies. Experiences, Implementations, and Technologies (OpenSHMEM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9397))

Included in the following conference series:

Abstract

Machine learning algorithms are benefiting from the continuous improvement of programming models, including MPI, MapReduce and PGAS. k-Nearest Neighbors (k-NN) algorithm is a widely used machine learning algorithm, applied to supervised learning tasks such as classification. Several parallel implementations of k-NN have been proposed in the literature and practice. However, on high-performance computing systems with high-speed interconnects, it is important to further accelerate existing designs of the k-NN algorithm through taking advantage of scalable programming models. To improve the performance of k-NN on large-scale environment with InfiniBand network, this paper proposes several alternative hybrid MPI+OpenSHMEM designs and performs a systemic evaluation and analysis on typical workloads. The hybrid designs leverage the one-sided memory access to better overlap communication with computation than the existing pure MPI design, and propose better schemes for efficient buffer management. The implementation based on k-NN program from MaTEx toolkit with MVAPICH2-X (Unified MPI+PGAS Communication Runtime over InfiniBand) shows up to 9.0 % time reduction for training KDD Cup 2010 workload over 512 cores, and 27.6 % time reduction for small workload with balanced communication and computation. Experiments of running with varied number of cores show that our design can maintain good scalability.

This research is supported in part by National Science Foundation grants #OCI-1148371, #CCF-1213084, #IIS-1447804 and #CNS-1419123.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 34.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 44.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Altman, N.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)

    MathSciNet  Google Scholar 

  2. Apache Software Foundation: Apache Hadoop. http://hadoop.apache.org/

  3. Apache Software Foundation: Apache Mahout. http://mahout.apache.org/

  4. Aparício, G., Blanquer, I., Hernández, V.: A parallel implementation of the K nearest neighbours classifier in three levels: threads, MPI processes and the grid. In: Daydé, M., Palma, J.M.L.M., Coutinho, Á.L.G.A., Pacitti, E., Lopes, J.C. (eds.) VECPAR 2006. LNCS, vol. 4395, pp. 225–235. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  5. Arefin, A.S., Riveros, C., Berretta, R., Moscato, P.: GPU-FS-kNN: a software tool for fast and scalable kNN computation using GPUs. PLoS ONE 7, e44000 (2012)

    Article  Google Scholar 

  6. Carlson, W., Draper, J., Culler, D., Yelick, K., Brooks, E., Warren, K.: Introduction to UPC and Language Specification. Center for Computing Sciences, Institute for Defense Analyses (1999)

    Google Scholar 

  7. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011)

    Article  Google Scholar 

  8. Chapman, B., Curtis, T., Pophale, S., Poole, S., Kuehn, J., Koelbel, C., Smith, L.: Introducing openSHMEM: SHMEM for the PGAS community. In: Proceedings of the 4th Conference on Partitioned Global Address Space Programming Model, p. 2 (2010)

    Google Scholar 

  9. Chu, C.T., Kim, S., Lin, Y.a., Yu, Y., Bradski, G., Olukotun, K., Ng, A.: Map-reduce for machine learning on multicore. In: Advances in Neural Information Processing Systems, vol. 19 (2007)

    Google Scholar 

  10. Dongarra, J., Beckman, P., Moore, T., Aerts, P., et al.: The international exascale software project roadmap. Int. J. High Perform. Comput. Appl. 25(1), 3–60 (2011)

    Article  Google Scholar 

  11. Ghoting, A., Krishnamurthy, R., Pednault, E., Reinwald, B., Sindhwani, V., Tatikonda, S., Tian, Y., Vaithyanathan, S.: SystemML: declarative machine learning on mapreduce. In: Proceedings of IEEE 27th International Conference on Data Engineering (2011)

    Google Scholar 

  12. Jose, J., Potluri, S., Subramoni, H., Lu, X., Hamidouche, K., Schulz, K., Sundar, H., Panda, D.K.: Designing scalable out-of-core sorting with hybrid MPI+PGAS programming models. In: Proceedings of the 8th International Conference on Partitioned Global Address Space Programming Models (2014)

    Google Scholar 

  13. Jose, J., Potluri, S., Tomko, K., Panda, D.K.: Designing scalable graph500 benchmark with hybrid MPI+OpenSHMEM programming models. In: Kunkel, J.M., Ludwig, T., Meuer, H.W. (eds.) ISC 2013. LNCS, vol. 7905, pp. 109–124. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  14. Li, M., Lin, J., Lu, X., Hamidouche, K., Tomko, K., Panda, D.K.: Scalable MiniMD design with hybrid MPI and OpenSHMEM. In: Proceedings of the 8th International Conference on Partitioned Global Address Space Programming Models, p. 24 (2014)

    Google Scholar 

  15. Moon, L., Long, D., Joshi, S., Tripathi, V., Xiao, B., Biros, G.: Parallel algorithms for clustering and nearest neighbor search problems in high dimensions. In: Proceedings of the 2011 ACM/IEEE Conference on Supercomputing (2011)

    Google Scholar 

  16. Network Based Computing Lab, The Ohio State University: MVAPICH2-X: Unified MPI+PGAS Communication Runtime over OpenFabrics/Gen2 for Exascale Systems. http://mvapich.cse.ohio-state.edu/

  17. Numrich, R., Reid, J.: Co-Array Fortran for Parallel Programming. Technical Report RAL-TR-1998-060, Rutheford Appleton Laboratory (1998)

    Google Scholar 

  18. Pacific Northwest National Laboratory: Global Arrays Programming Models. http://hpc.pnl.gov/globalarrays/

  19. Pacific Northwest National Laboratory: MaTEx: Machine Learning Toolkit for Extreme Scale. http://hpc.pnl.gov/matex/

  20. Pophale, S., Jin, H., Poole, S., Kuehn, J.: OpenSHMEM performance and potential: A NPB experimental study. In: Proceedings of the 1st Workshop on OpenSHMEM (2013)

    Google Scholar 

  21. Yu, H.F., Lo, H.Y., Hsieh, H.P., Lou, J.K., Mckenzie, T.G., Chou, J.W., Chung, P.H., Ho, C.H., Chang, C.F., Weng, J.Y., et al.: Feature engineering and classifier ensemble for KDD cup 2010. In: JMLR Workshop and Conference Proceedings (2011)

    Google Scholar 

  22. Zhang, C., Li, F., Jestes, J.: Efficient parallel kNN joins for large data in MapReduce. In: Proceedings the 15th International Conference on Extending Database Technology (2012)

    Google Scholar 

  23. Zhang, Q., Li, C., He, P., Li, X., Zou, H.: Irregular partitioning method based K-nearest neighbor query algorithm using mapreduce. In: Proceedings of 2015 International Symposium on Computers & Informatics (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jian Lin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Lin, J., Hamidouche, K., Zhang, J., Lu, X., Vishnu, A., Panda, D. (2015). Accelerating k-NN Algorithm with Hybrid MPI and OpenSHMEM. In: Gorentla Venkata, M., Shamis, P., Imam, N., Lopez, M. (eds) OpenSHMEM and Related Technologies. Experiences, Implementations, and Technologies. OpenSHMEM 2014. Lecture Notes in Computer Science(), vol 9397. Springer, Cham. https://doi.org/10.1007/978-3-319-26428-8_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26428-8_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26427-1

  • Online ISBN: 978-3-319-26428-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics