Implementation and Evaluation of Large Scale SOM in GPU Cluster
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- Kato Satoru
- National Institute of Technology,Matsue College
Bibliographic Information
- Other Title
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- GPU クラスタにおける大規模 SOM の実装とその評価
Abstract
<p>When the SOM learning algorithm applies to a large amount of data, it can be an effective approach that a parallel and distributed processing is adopted. There are two approaches in parallelization of SOM learning algorithms. One is by dividing the learning dataset and another is by dividing a SOM’s competitive layer. Furthermore, GPGPU approach is also adopted for vector operations under SOM learning process and we have confirmed an effectiveness of these implementation methods with respect to a computation time. In this paper, we present a way of implementation of each two kind of parallelized SOM and its evaluation.</p>
Journal
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- Proceedings of the Fuzzy System Symposium
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Proceedings of the Fuzzy System Symposium 35 (0), 286-287, 2019
Japan Society for Fuzzy Theory and Intelligent Informatics
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Details 詳細情報について
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- CRID
- 1390846609786369792
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- NII Article ID
- 130007773121
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed