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