Parallel preconditioned conjugate gradient algorithm on GPU

An Improved Implementation of Preconditioned Conjugate Gradient Method on GPU

An improved implementation of the Preconditioned Conjugate Gradient method on GPU using CUDA (Compute Unified Device Architecture) is proposed. It aims to solving the Poisson equation arising in liquid animation with high efficiency. We consider the features of the linear system obtained from the Poisson equation and propose an optimization method to solve it. First, a novel storage format call...

Efficient Two-Level Preconditioned Conjugate Gradient Method on the GPU

Analysis of Parallel Preconditioned Conjugate Gradient Algorithms

The conjugate gradient method is an iterative technique used to solve systems of linear equations. The paper analyzes the performance of parallel preconditioned conjugate gradient algorithms. First, a theoretical model is proposed for estimation of the complexity of PPCG method and a scalability analysis is done for three different data decomposition cases. Computational experiments are done on...

Modified Incomplete Cholesky Preconditioned Conjugate Gradient Algorithm on GPU for the 3D Parabolic Equation

In this study, for solving the three-dimensional partial differential equation ut = uxx + uyy + uzz, an efficient parallel method based on the modified incomplete Cholesky preconditioned conjugate gradient algorithm (MICPCGA) on the GPU is presented. In our proposed method, for this case, we overcome the drawbacks that the MIC preconditioner is generally difficult to be parallelized on the GPU ...

GPU-Based Parallel Nonlinear Conjugate Gradient Algorithms

In this paper we describe some parallel algorithms for solving nonlinear systems using CUDA (Compute Unified Device Architecture) over a GPU (Graphics Processing Unit). The proposed algorithms are based on both the Fletcher-Reeves version of the nonlinear conjugate gradient method and a polynomial preconditioner type based on block two-stage methods. Several strategies of parallelization and di...