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.
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