13859

GPU Accelerated Randomized Singular Value Decomposition and Its Application in Image Compression

Hao Ji, Yaohang Li
Department of Computer Science, Old Dominion University
Modeling, Simulation, and Visualization Student Capstone Conference, 2014

@article{ji2014gpu,

   title={GPU Accelerated Randomized Singular Value Decomposition and Its Application in Image Compression},

   author={Ji, Hao and Li, Yaohang},

   journal={Modeling, Simulation, and Visualization Student Capstone Conference},

   volume={4},

   year={2014}

}

Download Download (PDF)   View View   Source Source   

858

views

In this paper, we present a GPU-accelerated implementation of randomized Singular Value Decomposition (SVD) algorithm on a large matrix to rapidly approximate the top-k dominating singular values and correspondent singular vectors. The fundamental idea of randomized SVD is to condense a large matrix into a small dense matrix by random sampling while keeping the important information. Then performing traditional deterministic SVD on this small dense matrix reveals the top-k dominating singular values/singular vectors approximation. The randomized SVD algorithm is suitable for the GPU architecture; however, our study finds that the key bottleneck lies on the SVD computation of the small matrix. Our solution is to modify the randomized SVD algorithm by applying SVD to a derived small square matrix instead as well as a hybrid GPU-CPU scheme. Our GPU-accelerated randomized SVD implementation is around 6~7 times faster than the corresponding CPU version. Our experimental results demonstrate that the GPU-accelerated randomized SVD implementation can be effectively used in image compression.
VN:F [1.9.22_1171]
Rating: 1.0/5 (1 vote cast)
GPU Accelerated Randomized Singular Value Decomposition and Its Application in Image Compression, 1.0 out of 5 based on 1 rating

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: