Acceleration of SVD routines in LAPACK

Singular Value Decomposition is widely used in many different engineering applications like Image Processing, Image Compression, Principle Component Analysis etc. It is also very useful in statistical analysis for Least Squares Approximations, and Data Clustering for reducing data dimensionality. Its power consists in its capability to serve as a single method for analysing several features in a matrix. Its computation involves a sequence of vector kind operations which makes it computationally intensive. This paper addresses optimisations of computation of Singular Value Decomposition as implemented in LAPACK library by parallelizing on the Graphics Processing Unit architecture using OpenCL. Its programmable architecture suits well because of its flexibility and native support for vector operations and OpenCL is a key enabler for making the implementation platform independent and portable.