A GPU-based hyperbolic SVD algorithm

Abstract

A one-sided Jacobi hyperbolic singular value decomposition (HSVD) algorithm, using a massively parallel graphics processing unit (GPU), is developed. The algorithm also serves as the final stage of solving a symmetric indefinite eigenvalue problem. Numerical testing demonstrates the gains in speed and accuracy over sequential and MPI-parallelized variants of similar Jacobi-type HSVD algorithms. Finally, possibilities of hybrid CPU--GPU parallelism are discussed.

Accepted for publication in BIT Numerical Mathematics

References in corpus (1)

• Novel Modifications of Parallel Jacobi Algorithms

Cited by in corpus (6)

• A hierarchically blocked Jacobi SVD algorithm for single and multiple graphics processing units
• A note on the hyperbolic singular value decomposition without hyperexchange matrices
• Vectorization of a thread-parallel Jacobi singular value decomposition method
• Implicit Hari--Zimmermann algorithm for the generalized SVD on the GPUs
• Arithmetical enhancements of the Kogbetliantz method for the SVD of order two
• A Kogbetliantz-type algorithm for the hyperbolic SVD