A One-Sided Jacobi Algorithm for the Symmetric Eigenvalue Problem

Implementation of parallel one-sided block Jacobi methods for the symmetric eigenvalue problem

A GPU-based hyperbolic SVD algorithm

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....

A PARALLEL ALGORITHM FOR THE REDUCTION TO TRIDIAGONAL FORM FOR EIGENDECOMPOSITIONy

A new algorithm for the orthogonal reduction of a symmetric matrix to tridiagonal form is developed and analysed. It uses a Cholesky factorization of the original matrix and the rotations are applied to the factors. The idea is similar to the one used for the one-sided Jacobi algorithms B. The algorithm uses little communication , accesses data with stride one and is to a large extent independe...

On the QR algorithm and updating the SVD and URV decomposition in Parallel

A Jacobi-type updating algorithm for the SVD or URV decomposition is developed, which is related to the QR algorithm for the symmetric eigenvalue problem. The algorithm employs one-sided transformations, and therefore provides a cheap alternative to earlier developed updating algorithms based on two-sided transformations. The present algorithm as well as the corresponding systolic implementatio...

Parallel Code for One-sided Jacobi-Method

One sided block Jacobi algorithm for the singular value decomposition (SVD) of matrix can be a method of choice to compute SVD efficiently and accurately in parallel. A given matrix is logically partitioned into block columns and is subjected to an iteration process. In each iteration step, for given two block columns, their Gram matrix is generated, its symmetric eigenvalue decomposition (EVD)...