Success of modern technology enterprises in a highly-competitive and fast-changing market relies on the efficient prototyping of high-performance and low-consumption digital devices. GPUs have become the key for reducing the time-to-market of these digital devices by providing a highly-reconfigurable parallel processing platform for implementing computationally expensive DSP algorithms. This paper exposes the benefits of using a GPU for accelerating DSP algorithms used in big data analysis and scientific computing. Particularly, a parallel-structured implementation of the One-Sided Jacobi algorithm for the SV decomposition is analyzed. Two contributions are highlighted, first, the highest reported speedup for the One-Sided SVD algorithm is achieved and, second, a mixed formal/intuitive analysis technique is applied to cyclic algorithms with the aim of adequate them to GPU platforms.
['R. I. Acosta-Quinonez', 'D. Torres-Roman', 'R. Rodriguez-Avila', 'Daniel Robles-Valdez']
A parallel implementation of One-Sided Jacobi SVD for non-symmetric squared matrices on a high-performance GPU