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.
GPU Profiling of Singular Value Decomposition in OLPCA Method for Image Denoising / Cuomo, Salvatore; Salvatore and De, Michele; Pasquale and, Maiorano; Francesco and, Marcellino; Livia,. - 1:(2017), pp. 707-716. [10.1007/978-3-319-49109-7\_68]
GPU Profiling of Singular Value Decomposition in OLPCA Method for Image Denoising
Cuomo
;
2017
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
