Evaluation of Clustering Algorithms on HPC Platforms

Juan M. Cebrian, Baldomero Imbernón, Jesús Soto and José M. Cecilia
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Juan M. Cebrian: Computer Engineering Department (DITEC), University of Murcia, 30100 Murcia, Spain
Baldomero Imbernón: Computer Science Department, Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain
Jesús Soto: Computer Science Department, Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain
José M. Cecilia: Computer Engineering Department (DISCA), Universitat Politécnica de Valéncia (UPV), 46022 Valencia, Spain

Mathematics, 2021, vol. 9, issue 17, 1-20

Abstract: Clustering algorithms are one of the most widely used kernels to generate knowledge from large datasets. These algorithms group a set of data elements (i.e., images, points, patterns, etc.) into clusters to identify patterns or common features of a sample. However, these algorithms are very computationally expensive as they often involve the computation of expensive fitness functions that must be evaluated for all points in the dataset. This computational cost is even higher for fuzzy methods, where each data point may belong to more than one cluster. In this paper, we evaluate different parallelisation strategies on different heterogeneous platforms for fuzzy clustering algorithms typically used in the state-of-the-art such as the Fuzzy C-means (FCM), the Gustafson–Kessel FCM (GK-FCM) and the Fuzzy Minimals (FM). The experimental evaluation includes performance and energy trade-offs. Our results show that depending on the computational pattern of each algorithm, their mathematical foundation and the amount of data to be processed, each algorithm performs better on a different platform.

Keywords: clustering algorithms; performance evaluation; GPU computing; energy-efficiency; vector architectures (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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