Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 591-596.

• Interdiscipline & Application • Previous Articles     Next Articles

Implementation and Optimization of SOM Algorithm on Sunway Many-core Processors

YAO Qing, ZHENG Kai, LIU Yao, WANG Su, SUN Jun, XU Meng-xuan   

  1. College of Computer Science and Software Engineering,East China Normal University,Shanghai 200062,China;
    State Key Laboratory of Mathematical Engineering and Advanced Computing,Wuxi,Jiangsu 214215,China
  • Online:2019-02-26 Published:2019-02-26

Abstract: The self-organizing map(SOM) is a classical algorithm often used in machine learning,but the execution time of the algorithm increases sharply when dealing with complex data.The parallelization of SOM can solve this problem effectively.A parallel SOM algorithm was proposed based on the “Sunway TaihuLight” heterogeneous supercomputer ranked first in the latest TOP500 list,which is implemented on the single core group and the multi core groups in view of model parallelism and data parallelism.On the one hand,the main calculation steps of SOM are transformed into matrix operations through the program refactoring,and its parallelism is implemented by using the high performance extended math library.On the other hand,a variety of optimization methods especially based on supercomputing hardware are used to optimize the performance.By these methods,the performance of the algorithm is improved greatly.In the experiment,the maximum speedup ratio reaches over 10000 when using 64 core groups,and the CPEs speedup ratio can reach more than 900 at most which indicate that the designed algorithm can take full advantage of the power of “Sunway 26010” CPE.

Key words: Self-organizing neural network, Sunway TaihuLight, Parallel computing, MPI, Athread

CLC Number: 

  • TP311.52
[1]KOHONEN T.The self-organizing map[J].Neurocomputing,1990,21(1-3):1-6.
[2]YUAN J,KE-JIA C,ZHI-HUA Z.SOM based image-segmentation[J].Lecture Notes in Computer Science,2003,2639:640-643.
[3]KUMAR D,RAI C S,KUMAR S.Face Recognition using Self-organizing Map and Principal Component Analysis[C]∥International Conference on Neural Networks and Brain,2005.Icnn&b.IEEE,2005:1469-1473.
[4]JIN H,SHUM W H,LEUNG K S,et al.Expanding self-organizing map for data visualization and cluster analysis[J].Information Sciences,2004,163(1-3):157-173.
[5]LEUNG C S,CHAN L W.Transmission of vector quantized data over a noisy channel[M].IEEE Press,1997.
[6]OJA M,SPERBER G,BLOMBERG J,et al.Grouping and visua-lizing human endogenous retroviruses by bootstrapping median self-organizing maps[C]∥Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology,2004(CIBCB ’04).IEEE,2004:95-101.
[7]KOHONEN T,XING H.Contextually Self-Organized Maps of Chinese Words[M]∥Advances in Self-Organizing Maps.Springer Berlin Heidelberg,2010:16-29.
[8]SUL S J,TOVCHIGRECHKO A.Parallelizing BLAST and SOM Algorithms with MapReduce-MPI Library[C]∥IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum.IEEE,2011:481-489.
[9]WITTEK P.Somoclu:An Efficient Distributed Library for Self-Organizing Maps[J].Journal of Statistical Software,2013,78(9):1-21.
[10]XIAO Y,FENG R B,HAN Z F,et al.GPU Accelerated Self-Organizing Map for High Dimensional Data[J].Neural Processing Letters,2015,41(3):341-355.
[11]TAKATSUKA M,BUI M.Parallel Batch Training of the Self-Organizing Map Using OpenCL[C]∥International Conference on Neural Information Processing:MODELS and Applications.Springer-Verlag,2010:470-476.
[12]WANG Y,LIN J,CAI L,et al.Portingand optimizing gtc-p on taihulight supercomputer with sunway openacc[C]∥HPC China.2016.
[13]KOHONEN T.Essentials of the self-organizing map[J].Neural Networks the Official Journal of the International Neural Network Society,2013,37(1):52-65.
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