Implementation of algebraic procedures on the GPU using CUDA architecture on the example of generalized eigenvalue problem

Abstract

The ready to use set of functions to facilitate solving a generalized eigenvalue problem for symmetric matrices in order to efficiently calculate eigenvalues and eigenvectors, using Compute Unified Device Architecture (CUDA) technology from NVIDIA, is provided. An integral part of the CUDA is the high level programming environment enabling tracking both code executed on Central Processing Unit and on Graphics Processing Unit. The presented matrix structures allow for the analysis of the advantages of using graphics processors in such calculations.

Keywords

• eigenvalue problem
• cuda
• gpgpu computing
• cublas library
• lapack library