A Survey on Optimization and Parallelization of Conjugate Gradient Solver

Abstract

Conjugate Gradient Solver is a well-known iterative technique for solving sparse symmetric positive definite (SPD) systems of linear equations. The aim of this paper is to optimize and parallelize the currently available Conjugate Gradient Solver for OpenFOAM (Open source Field Operation and Manipulation) on GPU using CUDA which stands for Compute Unified Device Architecture, is a parallel computing platform and application programming interface (API) model created by NVIDIA. OpenFOAM is a C++ toolbox for development of customized numerical solvers of continuum mechanics problems, including Computational Fluid Dynamics. Existing Conjugate Gradient Solver can be optimized with the help of some techniques available for sparse matrix storage like Compressed Sparse Vecto (CSV).