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13th IEEE International Conference on Signal Processing (ICSP)

作者： An, Jianjing Wang, Dong Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China

ISBN: (纸本)9781509013456

Singular value decomposition (SVD) an important part of numerical calculation, widely used in many areas such as biological medicine, meteorology and quantum mechanics. Improving the speed and accuracy of SVD algorithm becomes an important issue, so we study efficient parallel SVD algorithm on AMD GPU using OpenCL language. In recent years, there are many approaches for SVD hardware computation have been proposed, however, which are limited by speed and we propose an one-sided jacobi parallel algorithm on AMD Graphics Processing Unit by using OpenCL. In the front part of this paper, SVD algorithm and one-sided jacobi algorithm are introduced and by using the Ring jacobi Ordering we achieve our parallelism computation. The next we give SVD workflow and implement 8x8, 16x16, 32x32, 64x64,128x128 and 256x256 matrices on GPU. By comparing with MATLAB and other paper, our speedups are respective approximately 3.25x and 1.24x.

关键词： singular value decomposition GPU one-sided jacobi algorithm OpenCL

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Efficient one-sided jacobi SVD Computation on AMD GPU using OpenCL

Efficient One-Sided Jacobi SVD Computation on AMD GPU using ...

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2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Jianjing An Dong Wang Institute of Information Science Beijing Jiaotong University

Singular value decomposition(SVD) an important part of numerical calculation, widely used in many areas such as biological medicine, meteorology and quantum mechanics. Improving the speed and accuracy of SVD algorithm becomes an important issue, so we study efficient parallel SVD algorithm on AMD GPU using OpenC L language. In recent years, there are many approaches for SVD hardware computation have been proposed, however, which are limited by speed and we propose an one-sided jacobi parallel algorithm on AMD Graphics Processing Unit by using OpenC L. In the front part of this paper, SVD algorithm and one-sided jacobi algorithm are introduced and by using the Ring jacobi Ordering we achieve our parallelism computation. The next we give SVD workflow and implement 8×8, 16×16, 32×32, 64×64,128×128 and 256×256 matrices on GPU. By comparing with MATLAB and other paper, our speedups are respective approximately 3.25× and 1.24×.

关键词： singular value decomposition GPU one-sided jacobi algorithm OpenCL

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A GPU-based hyperbolic SVD algorithm

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BIT NUMERICAL MATHEMATICS 2011年第4期51卷 1009-1030页

作者： Novakovic, Vedran Singer, Sanja Univ Zagreb Fac Mech Engn & Naval Architecture Zagreb 10000 Croatia

A one-sided jacobi hyperbolic singular value decomposition (HSVD) algorithm, using a massively parallel graphics processing unit (GPU), is developed. The algorithm also serves as the final stage of solving a symmetric indefinite eigenvalue problem. Numerical testing demonstrates the gains in speed and accuracy over sequential and MPI-parallelized variants of similar jacobi-type HSVD algorithms. Finally, possibilities of hybrid CPU-GPU parallelism are discussed.

关键词： one-sided jacobi algorithm Hyperbolic singular value decomposition Symmetric indefinite eigenvalue problem GPU parallel programming

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A one-sided jacobi algorithm FOR COMPUTING THE SINGULAR VALUE DECOMPOSITION ON A VECTOR COMPUTER

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SIAM JOURNAL ON SCIENTIFIC AND STATISTICAL COMPUTING 1989年第2期10卷 359-371页

作者： DERIJK, PPM

An old algorithm for computing the singular value decomposition, which was first mentioned by Hestenes [SIAM J. Appl. Math., 6 (1958), pp. 51–90], has gained renewed interest because of its properties of parallelism and vectorizability. Some computational modifications are given and a comparison with the well-known Golub–Reinsch algorithm is made. Comparative experiments on a CYBER 205 are reported.

关键词： 65F15 65F20 65F25 15A18 singular value decomposition one-sided jacobi algorithm vector computing

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