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

被引：0
作者：
Cuomo S. [1 ]
Galletti A. [2 ]
Marcellino L. [2 ]
机构：
[1] Department of Mathematics and Applications, University of Naples Federico II, Via Cinthia, Naples
[2] Department of Science and Technology, University of Naples ‘Parthenope’, Centro Direzionale, Isola C4, Naples
来源：
International Journal of High Performance Computing and Networking | 2018年 / 11卷 / 04期
关键词：
Cloud systems; E-health; GPU computing; MRI denoising; NLMs; Non-local means;
D O I：
10.1504/IJHPCN.2018.093231
中图分类号：
学科分类号：
摘要：
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. Copyright © 2018 Inderscience Enterprises Ltd.
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收藏
页码：304 / 311
页数：7
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