Efficient one-sided Jacobi SVD computation on AMD GPU using OpenCL

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 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×.