GPU Profiling of Singular Value Decomposition in OLPCA Method for Image Denoising

被引：1

作者：

Cuomo, Salvatore ^{[1
]}

De Michele, Pasquale ^{[1
]}

Maiorano, Francesco ^{[1
]}

Marcellino, Livia ^{[2
]}

机构：

[1] Univ Naples Federico II, Str Vicinale Cupa Cintia 21, I-80126 Naples, Italy

[2] Univ Naples Parthenope, Dept Sci & Technol, Ctr Direz Isola C3, I-80143 Naples, Italy

来源：

ADVANCES ON P2P, PARALLEL, GRID, CLOUD AND INTERNET COMPUTING | 2017年 / 1卷

关键词：

D O I：

10.1007/978-3-319-49109-7_68

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：707 / 716

页数：10

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