PARALLEL K-MEANS CLUSTERING WITH NAÏVE SHARDING FOR UNSUPERVISED IMAGE SEGMENTATION VIA MPI

Year 2020, Volume: 8 Issue: 3, 791 - 798, 24.09.2020

Ahmet Esad Top , Fahreddin Şükrü Torun , Hilal Kaya

https://doi.org/10.21923/jesd.748209

Cited By: 1

Abstract

In digital image processing, image segmentation is an essential step in which an image is partitioned into groups of pixels. k-means clustering algorithm, which is often considered as fast and efficient, is one of the most widely used clustering algorithms to segment an image. However, as the problem size gets larger, the k-means starts to spend a significant amount of time to process. At this point, parallelization techniques should be applied to reduce the required time. Designing an efficient parallel and distributed model is not a trivial job since it should correspond to the parallel computer architecture and take communication and load balancing among processors into account. In this study, we propose a parallel and distributed k-means clustering algorithm with naive sharding centroid initialization for image segmentation. The proposed algorithm adopts the Message Passing Interface (MPI) standard to take advantage of the computational power of distributed computing nodes in a High Performance Computing Cluster. We demonstrate the parallel scalability of the proposed algorithm using up to 128 cores that achieves approximately 104 times faster clustering time.

Keywords

clustering, image segmentation, K-means clustering, distributed memory, parallel computing

DENETİMSİZ GÖRÜNTÜ BÖLÜTLEME İÇİN NAÏVE SHARDING İLE MPI KULLANARAK PARALEL K-MEANS KÜMELEMESİ

Abstract

Dijital görüntü işlemede, görüntü bölütleme, görüntünün piksel gruplarına ayrıldığı önemli bir adımdır. Verimli bir kümeleme algoritması kabul edilen k-means algoritması, bir görüntüyü bölümlere ayırmak için en yaygın kullanılan kümeleme algoritmalarından birisidir. Bununla birlikte, problem boyutu büyüdükçe, k-means, görüntü işlemek için önemli miktarda zaman harcamaya başlar. Bu noktada, gerekli zamanı azaltmak için paralelleştirme teknikleri uygulanmalıdır. Verimli bir paralel ve dağıtılmış model tasarlamak, paralel bilgisayar mimarisini karşılayabilmesi ve işlemciler arasındaki iletişim ve yük dengelemesini dikkate alması nedeniyle önemli bir iştir. Bu çalışmada, görüntü bölütlemesi için naïve sharding kullanarak orta nokta belirleme ile paralel ve dağıtılmış bir k-means kümeleme algoritması öneriyoruz. Önerilen algoritma, Yüksek Performanslı Bilgi İşleme Kümesindeki dağıtılmış bilgi işleme düğümlerinin hesaplama gücünden yararlanmak için Mesaj Geçirme Arayüzü (MGA) standardını kullanır. 128 adede kadar çekirdek kullanarak 104.23 kat daha hızlı kümeleme süresi sağlayan önerilen algoritmanın paralel ölçeklenebilirliğini gösterdik.

Keywords

Kümeleme, Görüntü Bölütleme, K-means Kümeleme, Dağıtılmış Bellek, Paralel İşleme

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