Abstract
We propose a method to partition training vectors into clusters for a parallel implementation of Self-Organizing Map (SOM) algorithm. The proposed algorithm assigns a cluster to a processor such that, in updating weights, the neighborhoods of a winning node in a cluster do not overlap the neighboring nodes of some winning nodes in other clusters. It reduces the overheads caused by synchronization (i.e. maintaining coherency) of the weight matrices in the processors since the proposed algorithm allows multiple vectors to find their winning nodes and update weights in parallel. Our experimental results show that an average speedup of 3.15 for a parallel implementation of a four-processor simulation.
| Original language | English (US) |
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| Pages | 1929-1933 |
| Number of pages | 5 |
| State | Published - 1999 |
| Event | International Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA Duration: Jul 10 1999 → Jul 16 1999 |
Other
| Other | International Joint Conference on Neural Networks (IJCNN'99) |
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| City | Washington, DC, USA |
| Period | 7/10/99 → 7/16/99 |
ASJC Scopus subject areas
- Software
- Artificial Intelligence