### 抜粋

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.

元の言語 | English |
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ホスト出版物のタイトル | Proceedings of the 9th IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2010 |

出版者 | Acta Press |

ページ | 96-104 |

ページ数 | 9 |

ISBN（印刷物） | 9780889868205 |

DOI | |

出版物ステータス | Published - 2010 |

イベント | 9th IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2010 - Innsbruck, Austria 継続期間: 2010 2 16 → 2010 2 18 |

### 出版物シリーズ

名前 | 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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国 | Austria |

市 | Innsbruck |

期間 | 10/2/16 → 10/2/18 |

### ASJC Scopus subject areas

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

## フィンガープリント Preliminary evaluation of batch-learning self-organizing map algorithm on a graphic processor' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

## これを引用

*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). Acta Press. https://doi.org/10.2316/p.2010.676-089