Title:

Preconditioned Conjugate Gradient Method for Solution of Large Finite Element Problems on CPU and GPU, Journal of Telecommunications and Information Technology, 2016, nr 2

Creator:

Fialko, Sergiy Yu. ; Zeglen, Filip

Subject and Keywords:

incomplete Cholesky factorization ; NVIDIA CUDA ; preconditioned conjugate gradient ; conjugate gradient ; iterative solver

Description:

In this article the preconditioned conjugate gradient (PCG) method, realized on GPU and intended to solution of large finite element problems of structural mechanics, is considered. The mathematical formulation of problem results in solution of linear equation sets with sparse symmetrical positive definite matrices. The authors use incomplete Cholesky factorization by value approach, based on technique of sparse matrices, for creation of efficient preconditioning, which ensures a stable convergence for weakly conditioned problems mentioned above. The research focuses on realization of PCG solver on GPU with using of CUBLAS and CUSPARSE libraries. Taking into account a restricted amount of GPU core memory, the efficiency and reliability of GPU PCG solver are checked and these factors are compared with data obtained with using of CPU version of this solver, working on large amount of RAM. The real-life large problems, taken from SCAD Soft collection, are considered for such a comparison.

Publisher:

National Institute of Telecommunications

Date:

2016, nr 2

Resource Type:

artykuł

Format:

application/pdf

Resource Identifier:

ISSN 1509-4553, on-line: ISSN 1899-8852

Source:

Journal of Telecommunications and Information Technology

Language:

ang

Rights Management:

Biblioteka Naukowa Instytutu Łączności