Optimization of SVD over GPGPU using OpenCL: Parallelization of SVD Modules in LAPACK

Many of the engineering applications require linear algebra to furnish the analysis. Singular Value Decomposition is one of the most powerful tool of linear algebra. This method alone serves many computational and analytical purposes. Although the computation of SVD of a matrix is bulky, the process involves a sequence of vector operations. This makes it a good candidate for parallelization of over Graphic Processors. This book proposes parallelization of SVD modules in LAPACK over GPGPU using OpenCL, which is platform independent and focuses on routines beyond BLAS.