Next Article in Journal
A Hybrid Genetic Algorithm for the Simple Assembly Line Balancing Problem with a Fixed Number of Workstations
Next Article in Special Issue
Representing Integer Sequences Using Piecewise-Affine Loops
Previous Article in Journal
A Method of Image Quality Assessment for Text Recognition on Camera-Captured and Projectively Distorted Documents
Previous Article in Special Issue
OpenCNN: A Winograd Minimal Filtering Algorithm Implementation in CUDA
 
 
Article

Evaluation of Clustering Algorithms on HPC Platforms

1
Computer Engineering Department (DITEC), University of Murcia, 30100 Murcia, Spain
2
Computer Science Department, Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain
3
Computer Engineering Department (DISCA), Universitat Politécnica de Valéncia (UPV), 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Gabriel Rodríguez and Juan Touriño
Mathematics 2021, 9(17), 2156; https://doi.org/10.3390/math9172156
Received: 29 June 2021 / Revised: 17 August 2021 / Accepted: 31 August 2021 / Published: 4 September 2021
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. View Full-Text
Keywords: clustering algorithms; performance evaluation; GPU computing; energy-efficiency; vector architectures clustering algorithms; performance evaluation; GPU computing; energy-efficiency; vector architectures
Show Figures

Figure 1

MDPI and ACS Style

Cebrian, J.M.; Imbernón, B.; Soto, J.; Cecilia, J.M. Evaluation of Clustering Algorithms on HPC Platforms. Mathematics 2021, 9, 2156. https://doi.org/10.3390/math9172156

AMA Style

Cebrian JM, Imbernón B, Soto J, Cecilia JM. Evaluation of Clustering Algorithms on HPC Platforms. Mathematics. 2021; 9(17):2156. https://doi.org/10.3390/math9172156

Chicago/Turabian Style

Cebrian, Juan M., Baldomero Imbernón, Jesús Soto, and José M. Cecilia. 2021. "Evaluation of Clustering Algorithms on HPC Platforms" Mathematics 9, no. 17: 2156. https://doi.org/10.3390/math9172156

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop