### Abstract

In this paper, we introduce a GPU implementation and evaluation of batch learning self-organizing maps (BL-SOM) algorithm, which improves Kohonen's original SOM algorithm by making input data sequence independent from learning process. We used CUDA provided by NVIDIA Corporation for parallel programming, profiling, and data flow optimization so as to exploit inherent datalevel parallelism of the algorithm. With various parameter combinations, implementation on GTX280 achieved 250 times higher performance compared to Intel's Core2Quad Q6600 2.40GHz when parameters of map size, dimension of vectors, learning size and iteration of learning were 960×960, 136, 70 and 1, respectively.

Original language | English |
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Title of host publication | Proceedings of the 9th IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2010 |

Pages | 96-104 |

Number of pages | 9 |

Publication status | Published - 2010 |

Event | 9th IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2010 - Innsbruck, Austria Duration: 2010 Feb 16 → 2010 Feb 18 |

### Publication series

Name | Proceedings of the 9th IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2010 |
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### Other

Other | 9th IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2010 |
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Country | Austria |

City | Innsbruck |

Period | 10/2/16 → 10/2/18 |

### Keywords

- CUDA
- GPGPU
- GPU
- Self-organizing map

### ASJC Scopus subject areas

- Computational Theory and Mathematics
- Computer Networks and Communications
- Software

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## Cite this

*Proceedings of the 9th IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2010*(pp. 96-104). (Proceedings of the 9th IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2010).