Parallel Implementation of Fuzzy Joint Points Clustering Algorithm on GPU

ATILGAN C., TEZEL B. T., NASİBOĞLU E.

4th International Conference on Computer Science and Engineering (UBMK), Samsun, Türkiye, 11 - 15 Eylül 2019, ss.516-519 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/ubmk.2019.8907166
  • Basıldığı Şehir: Samsun
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.516-519
  • Anahtar Kelimeler: Clustering, Fuzzy joint points (FJP), Parallel Com-puting
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Fuzzy joint points (FJP) is a fully unsupervised neighborhood-based clustering method that uses a fuzzy neighborhood relationship and overcomes the parameter selection problem of classical neighborhood based clustering algorithms. The present work introduces a paralel implementation of the FJP algorithm on GPU using CUDA in order to reducing the processing time. Provided experimental results confirm a speed up of around 8 times over serial implementation is achieved in the GPU-parallel implementation of the FJP algorithm. So, the work shows that the GPU implementation of the FJP algorithm is a viable option if a speedup is needed.