A parallel data clustering algorithm for Intel MIC accelerators

Authors

  • T.V. Rechkalov South Ural State University
  • M.L. Zymbler South Ural State University

DOI:

https://doi.org/10.26089/NumMet.v20r211

Keywords:

clustering, medoid, parallel algorithm, OpenMP, Intel Xeon Phi, data layout, vectorization of computations

Abstract

The PAM (Partitioning Around Medoids) is a partitioning clustering algorithm where each cluster is represented by an object from the input dataset (called a medoid). The medoid-based clustering is used in a wide range of applications: the segmentation of medical and satellite images, the analysis of DNA microarrays and texts, etc. Currently, there are parallel implementations of PAM for GPU and FPGA systems, but not for Intel Many Integrated Core (MIC) accelerators. In this paper, we propose a novel parallel PhiPAM clustering algorithm for Intel MIC systems. Computations are parallelized by the OpenMP technology. The algorithm exploits a sophisticated memory data layout and loop tiling technique, which allows one to efficiently vectorize computations with Intel MIC. Experiments performed on real data sets show a good scalability of the algorithm.

Author Biographies

T.V. Rechkalov

South Ural State University
• PhD Student

M.L. Zymbler

South Ural State University
• Head of Department

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Published

08-04-2019

How to Cite

Речкалов Т.В., Цымблер М.Л. A Parallel Data Clustering Algorithm for Intel MIC Accelerators // Numerical Methods and Programming (Vychislitel’nye Metody i Programmirovanie). 2019. 20. 104-115. doi 10.26089/NumMet.v20r211

Issue

Section

Section 1. Numerical methods and applications