Efficient CUDA polynomial preconditioned conjugate gradient solver for finite element computation of elasticity problems
type: Item
instance of: scholarly article
title: Efficient CUDA polynomial preconditioned conjugate gradient solver for finite element computation of elasticity problems
zbMATH Open document ID: 1296.74133
author: Lei Zhang
author: Jianfei Zhang
published in: Mathematical Problems in Engineering
publication date: 2014-10-13T00:00:00Z
review text: Summary: Graphics processing unit (GPU) has obtained great success in scientific computations for its tremendous computational horsepower and very high memory bandwidth. This paper discusses the efficient way to implement polynomial preconditioned conjugate gradient solver for the finite element computation of elasticity on NVIDIA GPUs using compute unified device architecture (CUDA). Sliced block ELLPACK (SBELL) format is introduced to store sparse matrix arising from finite element discretization of elasticity with fewer padding zeros than traditional ELLPACK-based formats. Polynomial preconditioning methods have been investigated both in convergence and running time. From the overall performance, the least-squares (L-S) polynomial method is chosen as a preconditioner in PCG solver to finite element equations derived from elasticity for its best results on different example meshes. In the PCG solver, mixed precision algorithm is used not only to reduce the overall computational, storage requirements and bandwidth but to make full use of the capacity of the GPU devices. With SBELL format and mixed precision algorithm, the GPU-based L-S preconditioned CG can get a speedup of about 7-9 to CPU-implementation.
Mathematics Subject Classification ID: 74S05
Mathematics Subject Classification ID: 74-04
Mathematics Subject Classification ID: 65N22
Mathematics Subject Classification ID: 65N30
Mathematics Subject Classification ID: 65Y10
zbMATH DE Number: 6354278
describes a project that uses: CUDA
describes a project that uses: ELLPACK
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 6354278