A Survey on Optimization and Parallelization of Conjugate Gradient Solver

Conjugate Gradient (CG) Solver is a well-known iterative techniques for solving sparse symmetric positive definite (SPD) systems of linear equations. The aim of this paper is to introduce rarely used technique to optimize and parallelize the currently available Conjugate Gradient Solver on GPU using CUDA which stands for Compute Unified Device Architecture. Existing Conjugate Gradient Solver can be optimized with the help of some techniques available for sparse matrix storage like Compressed Sparse Vector (CSV). CUDA is a parallel computing platform and application programming interface (API) model created by NVIDIA. Threads can be used for parallel execution of the iterative part of CG solver. This parallelization will definitely speedup the performance of CG solver which can be used in many compute fluid dynamic (CFD) computations.