Implementation and Evaluation of Large Scale SOM in GPU Cluster

DOI

Bibliographic Information

Other Title
  • 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

Details 詳細情報について

  • CRID
    1390846609786369792
  • NII Article ID
    130007773121
  • DOI
    10.14864/fss.35.0_286
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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