Abstract

We focus on the Graphic Processor Unit (GPU) profiling of the Singular Value Decomposition (SVD) that is a basic task of the Overcomplete Local Principal Component Analysis (OLPCA) method. More in detail, we investigate the impact of the SVD on the OLPCA algorithm for the Magnetic Resonance Imaging (MRI) denoising application. We have resorted several parallel approaches based on scientific libraries in order to investigate the heavy computational complexity of the algorithm. The GPU implementation is based on two specific libraries: NVIDIA cuBLAS and CULA, in order to compare them. Our results show how the GPU library based solution could be adopted for improving the performance of same tasks in a denoising algorithm.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.