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Efficient and Scalable k‑Means on GPUs

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Abstract

k-Means is a versatile clustering algorithm widely used in practice. To cluster large data sets, state-of-the-art implementations use GPUs to shorten the data to knowledge time. These implementations commonly assign points on a GPU and update centroids on a CPU.

We identify two main shortcomings of this approach. First, it requires expensive data exchange between processors when switching between the two processing steps point assignment and centroid update. Second, even when processing both steps of k-means on the same processor, points still need to be read two times within an iteration, leading to inefficient use of memory bandwidth.

In this paper, we present a novel approach for centroid update that allows us to efficiently process both phases of k-means on GPUs. We fuse point assignment and centroid update to execute one iteration with a single pass over the points. Our evaluation shows that our k-means approach scales to very large data sets. Overall, we achieve up to 20 × higher throughput compared to the state-of-the-art approach.

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Notes

  1. We previously sketched our work as a two-page short paper [25].

  2. Note that the Cross-Processing strategy uses the GPU for point assignment, whereas Single-Pass and Multi-Pass are executed on CPU only. Therefore we include the Cross-Processing strategy in both plots.

References

  1. Amazon EC (2018) Amazon ec2 pricing. https://aws.amazon.com/ec2/pricing/on-demand. Accessed: 25 May 2018

    Google Scholar 

  2. Arthur D, Vassilvitskii S (2007) k‑means++: The advantages of careful seeding. In: ACM-SIAM, pp 1027–1035

    Google Scholar 

  3. Bai H et al (2009) k‑means on commodity GPUs with CUDA. In: WRI CSIE, pp 651–655

    Google Scholar 

  4. Breß S, Funke H, Teubner J (2016) Robust query processing in co-processor-accelerated databases. In: SIGMOD, pp 1891–1906

    Chapter  Google Scholar 

  5. Breß S et al (2017) Generating custom code for efficient query execution on heterogeneous processors. CoRR abs/1709.00700

    Google Scholar 

  6. Cao F, Tung AKH, Zhou A (2006) Scalable clustering using graphics processors. In: WAIM, pp 372–384

    Google Scholar 

  7. Cassou C (2008) Intraseasonal interaction between the madden–julian oscillation and the north atlantic oscillation. Nature 455(7212):523–527

    Article  Google Scholar 

  8. Che S et al (2009) Rodinia: a benchmark suite for heterogeneous computing. In: IISWC, pp 44–54

    Google Scholar 

  9. Dall M et al (2017) Arctic sea ice melt leads to atmospheric new particle formation. Sci Rep 7(1):3318

    Article  Google Scholar 

  10. Elkan C (2003) Using the triangle inequality to accelerate k‑means. In: ICML, pp 147–153

    Google Scholar 

  11. Fang W et al (2008) Parallel data mining on graphics processors. Tech. Rep. HKUST-CS08-07, HKUST

    Google Scholar 

  12. Farivar R et al (2008) A parallel implementation of k‑means clustering on GPUs. In: PDPTA, pp 340–345

    Google Scholar 

  13. Fernando R (2004) GPU gems: programming techniques, tips and tricks for real-time graphics. In: Pearson higher education (chap 37.2)

    Google Scholar 

  14. Funke H et al (2018) Pipelined query processing in coprocessor environments. In: SIGMOD, ACM

    Google Scholar 

  15. Hall J, Hart J (2004) GPU acceleration of iterative clustering. In: GPGPU, pp 45–52

    Google Scholar 

  16. He B et al (2009) Relational query coprocessing on graphics processors. ACM Trans Database Syst. https://doi.org/10.1145/1620585.1620588

    Article  Google Scholar 

  17. Heimel M et al (2013) Hardware-oblivious parallelism for in-memory column-stores. Proceedings VLDB Endowment 6(9):709–720

    Article  Google Scholar 

  18. Heintzman ND et al (2007) Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat Genet 39(3):311

    Article  Google Scholar 

  19. Hellerstein J et al (2012) The MADlib analytics library or MAD skills, the SQL. Proceedings VLDB Endowment 5(12):1700–1711

    Article  Google Scholar 

  20. Karnagel T, Müller R, Lohman GM (2015) Optimizing GPU-accelerated group-by and aggregation. In: ADMS, pp 13–24

    Google Scholar 

  21. Kleisner KM et al (2016) The effects of sub-regional climate velocity on the distribution and spatial extent of marine species assemblages. PLoS ONE 11:1–21

    Article  Google Scholar 

  22. Lee S et al (2016) Evaluation of k‑means data clustering algorithm on intel xeon phi. In: BigData, pp 2251–2260

    Google Scholar 

  23. Li Y et al (2010) Speeding up k‑means algorithm by GPUs. In: IEEE CIT, pp 115–122

    Google Scholar 

  24. Lloyd S (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129–136

    Article  MathSciNet  Google Scholar 

  25. Lutz C et al (2018) Efficient k‑means on GPUs. In: DaMoN https://doi.org/10.1145/3211922.3211925

    Chapter  Google Scholar 

  26. MacQueen J et al (1967) Some methods for classification and analysis of multivariate observations. In: Proc. Fifth Berkeley Symp. on Math. Statist. and Prob., vol 1, pp 281–297

    Google Scholar 

  27. Mhembere D et al (2017) knor: A NUMA-optimized in-memory, distributed and semi-external-memory k‑means library. In: HPDC

    Google Scholar 

  28. Müller I et al (2015) Cache-efficient aggregation: hashing is sorting. In: SIGMOD, pp 1123–1136

    Google Scholar 

  29. Nugteren C et al (2011) High performance predictable histogramming on GPUs: exploring and evaluating algorithm trade-offs. In: GPGPU, p 1

    Google Scholar 

  30. Nvidia (2017a) CUDA C programming guide. Tech. Rep. PG-02829-001_v8.0. http://docs.nvidia.com/pdf/CUDA_C_Programming_Guide.pdf. Accessed: 20 Jan 2017

    Google Scholar 

  31. Nvidia (2017b) Tuning CUDA applications for maxwell. Tech. Rep. DA-07173-001_v9.0. http://docs.nvidia.com/cuda/pdf/Maxwell_Tuning_Guide.pdf. Accessed: 20 Jan 2017

    Google Scholar 

  32. Passing L et al (2017) SQL- and operator-centric data analytics in relational main-memory databases. In: EDBT, pp 84–95

    Google Scholar 

  33. Pirk H, Manegold S, Kersten ML (2014) Waste not…efficient co-processing of relational data. In: ICDE, pp 508–519

    Google Scholar 

  34. Pirk H et al (2016) Voodoo – A vector algebra for portable database performance on modern hardware. Proceedings VLDB Endowment 9(14):1707–1718

    Article  Google Scholar 

  35. Sanderson C, Curtin R (2016) Armadillo: a template-based c++ library for linear algebra. J Open Source Softw. https://doi.org/10.21105/joss.00026

    Article  Google Scholar 

  36. Shalom A, Dash M, Tue M (2008) Efficient k‑means clustering using accelerated graphics processors. In: DaWaK, pp 166–175

    Google Scholar 

  37. Shindler M, Wong A, Meyerson AW (2011) Fast and accurate k‑means for large datasets. In: NIPS, pp 2375–2383

    Google Scholar 

  38. Sitaridi EA, Ross KA (2013) Optimizing select conditions on gpus. In: DaMoN, p 4

    Google Scholar 

  39. Stehle E, Jacobsen H (2017) A memory bandwidth-efficient hybrid radix sort on GPUs. In: SIGMOD, pp 417–432

    Google Scholar 

  40. TPC-H (2017) Transaction processing performance council. http://www.tpc.org/tpch. Accessed: 29 Sep 2017

    Google Scholar 

  41. Vitak SA et al (2017) Sequencing thousands of single-cell genomes with combinatorial indexing. Nat Methods 14(3):302

    Article  Google Scholar 

  42. Wu F et al (2013) A vectorized k‑means algorithm for intel many integrated core architecture. In: APPT, pp 277–294

    Google Scholar 

  43. Zang C et al (2016) High-dimensional genomic data bias correction and data integration using mancie. Nat Commun 7:11305

    Article  Google Scholar 

  44. Zhang T, Ramakrishnan R, Livny M (1996) Birch: an efficient data clustering method for very large databases. In: SIGMOD, pp 103–114

    Chapter  Google Scholar 

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Acknowledgements

This work was funded by the EU projects SAGE (671500) and E2Data (780245), DFG Priority Program “Scalable Data Management for Future Hardware” (MA4662-5), and the German Ministry for Education and Research as BBDC (01IS14013A).

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Authors and Affiliations

  1. DFKI GmbH, Berlin, Germany

    Clemens Lutz, Sebastian Breß & Steffen Zeuch

  2. TU Berlin, Berlin, Germany

    Tilmann Rabl & Volker Markl

Authors
  1. Clemens Lutz
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  2. Sebastian Breß
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  3. Tilmann Rabl
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  4. Steffen Zeuch
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  5. Volker Markl
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Correspondence to Clemens Lutz.

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Lutz, C., Breß, S., Rabl, T. et al. Efficient and Scalable k‑Means on GPUs. Datenbank Spektrum 18, 157–169 (2018). https://doi.org/10.1007/s13222-018-0293-x

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