Abstract
The K-Means algorithms is one of the most popular and effective clustering algorithms for many practical applications. However, direct K-Means methods, taking objects as processing unit, is computationally expensive especially in Objects-Assignment phase on Single-Instruction Single-Data (SISD) processors, typically as CPUs. In this paper, we propose a vectorized K-Means algorithm for Intel Many Integrated Core (MIC) coprocessor, a newly released product from Intel for highly parallel workloads. This new algorithm is able to achieve fine-grained Single-Instruction Multiple-Data (SIMD) parallelism by taking each dimension of all objects as a long vector. This vectorized algorithm is suitable for any-dimensional objects, which is little taken into consideration in preceding works. We also parallelize the vectorized K-Means algorithm on MIC coprocessor to achieve coarse-grained thread-level parallelism. Finally, we implement and evaluate the vectorized method on the first generation of Intel MIC product. Measurements show that this algorithm based on MIC coprocessor gets desired speedup to sequential algorithm on CPU and demonstrate that MIC coprocessor owns highly parallel computational power as well as scalability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Dayan, P.: Unsupervised Learning. The MIT Encyclopedia of the Cognitive Sciences
MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.-H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14, 1–37 (2007)
Lloyd, S.: Least Squares Quantization in PCM. IEEE Transactions on Information Theory 28(2), 129–136 (1982)
Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: SODA (2007)
Bahman, B., Moseley, B., Vattani, A.: Scalable K-Means++. Proceedings of the VLDB Endowment 5(7)
Ailon, N., Jaiswal, R., Monteleoni, C.: Streaming K-Means approximation. In: NIPS (2009)
Che, S., Boyer, M., Meng, J., Tarjan, D.: A performance study of general-purpose applications on graphics processors using CUDA. Journal of Parallel and Distributed Computing 68(10), 1370–1380 (2008)
Bai, H.-T., He, L.-L., Ouyang, D.-T., Li, Z.-S., Li, H.: K-Means on commodity GPUs with CUDA. In: World Congress on Computer Science and Information Engineering (2009)
Farivar, R., Rebolledo, D., Chan, E., Campbell, R.: A parallel implementation of K-Means clustering on GPUs. In: Proceeding of International Conference on Parallel and Distributed Processing Techniques and Applications (2008)
Mahout, http://mahout.apache.org/
Hadoop, http://hadoop.apache.org/
Dean, J., Ghemawat, S.: MapReduce: Simplified data processing on large clusters. In: OSDI, pp. 137–150 (2004)
Feldman, M.: TACC Steps Up to the MIC. HPCwire (April 21, 2011), http://www.hpcwire.com/hpcwire/2011-04-21/tacc_steps_up_to_the_mic.html
Nvidia. Oak Ridge National Lab Turns to NVIDIA Tesla GPUs to Deploy World’s Leading Supercomputer. HPCwire (October 11, 2011), http://www.hpcwire.com/hpcwire/2011-10-11/oak_ridge_national_lab_turns_to_Nvidia_tesla_gpus_to_deploy_world_s_leading_supercomputer.html
Intel. Introducing Intel Many Integrated Core Architecture. Press release (2011), http://www.intel.com/technology/architecture-silicon/mic/index.htm
Seiler, L., et al.: Larrabee: A Many-Core x86 Architecture for visual Computing. ACM Trans. Graphics 27(3), 18:1–18:15 (2008)
Saule, E., Catalyurek, U.V.: An early evaluation of the scalability of graph algorithms on the Intel MIC Architecture. In: IEEE IPDPSW (2012), doi:10.1109
McFarlin, D.S., Arbatov, V., Franchetti, F., Puschel, M., Zurich, E.: Automatic SIMD vectorization of fast fourier transforms for the Larrabee and AVX Instruction sets. In: ACM ICS (2011)
Owens, J.D., Houston, M., Luebke, D., Green, S., Stone, J.E., Phillips, J.C.: GPU Computing. Proceedings of the IEEE 96(5) (May 2008)
Aloise, D., Deshpande, A., Hansen, P., Popat, P.: NP-hardness of Euclidean sum-of-squares clustering. Machine Learning 75(2), 245–248 (2009)
Henretty, T., Stock, K., Pouchet, L.-N., Franchetti, F., Ramanujam, J., Sadayappan, P.: Data Layout Transformation for Stencil Computations on Short-Vector SIMD Architectures. In: Knoop, J. (ed.) CC 2011. LNCS, vol. 6601, pp. 225–245. Springer, Heidelberg (2011)
He, B.S., Govindaraju, N.K., Luo, Q., Smith, B.: Efficient Gather and Scatter operations on graphics processors. In: Proceeding of the 2007 ACM/IEEE Conference on Supercomputing, p. 46. ACM (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, F., Wu, Q., Tan, Y., Wei, L., Shao, L., Gao, L. (2013). A Vectorized K-Means Algorithm for Intel Many Integrated Core Architecture. In: Wu, C., Cohen, A. (eds) Advanced Parallel Processing Technologies. APPT 2013. Lecture Notes in Computer Science, vol 8299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45293-2_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-45293-2_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-45292-5
Online ISBN: 978-3-642-45293-2
eBook Packages: Computer ScienceComputer Science (R0)