Title: A GPU parallel optimised blockwise NLM algorithm in a distributed computing system

Authors: Salvatore Cuomo; Ardelio Galletti; Livia Marcellino

Addresses: Department of Mathematics and Applications, University of Naples Federico II, Via Cinthia, 80126 Naples, Italy ' Department of Science and Technology, University of Naples 'Parthenope', Centro Direzionale, Isola C4, 80143 Naples, Italy ' Department of Science and Technology, University of Naples 'Parthenope', Centro Direzionale, Isola C4, 80143 Naples, Italy

Abstract: Recently, advanced computing systems are widely adopted in order to intensively elaborate a huge amount of biomedical data in the e-health field. An interesting challenge is to perform real-time diagnosis by means of complex computational environments. In this paper, we suggest to deal the most computationally expensive processing steps of a distributed cloud e-health system by the use of graphics processing units (GPUs). In the case study of the magnetic resonance imaging (MRI), for improving the quality of denoising and helping the real-time diagnosis, we have implemented a GPU parallel algorithm based on the optimised blockwise non-local means (OB-NLM) method. Experimental results have shown a significant improvement of healthcare processing practice in terms of execution time.

Keywords: cloud systems; e-health; MRI denoising; non-local means; NLMs; GPU computing.

DOI: 10.1504/IJHPCN.2018.093231

International Journal of High Performance Computing and Networking, 2018 Vol.11 No.4, pp.304 - 311

Received: 11 Dec 2015
Accepted: 13 Mar 2016
Published online: 24 Jul 2018 *

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