Implementing a Preconditioned Iterative Linear Solver Using Massively Parallel Graphics Processing Units

• Main Author: Asgari Kamiabad, Amirhassan
• Other Authors: Tate, Zeb
• Language: en_ca
• Published: 2011
• Subjects: Energy Systems; Power Systems; Transient Stability; Parallel Programming; GPU; 0544
• Online Access: http://hdl.handle.net/1807/27321

The research conducted in this thesis provides a robust implementation of a preconditioned iterative linear solver on programmable graphic processing units (GPUs). Solving a large, sparse linear system is the most computationally demanding part of many widely used power system analysis. This thesis presents a detailed study of iterative linear solvers with a focus on Krylov-based methods. Since the ill-conditioned nature of power system matrices typically requires substantial preconditioning to ensure robustness of Krylov-based methods, a polynomial preconditioning technique is also studied in this thesis. Implementation of the Chebyshev polynomial preconditioner and biconjugate gradient solver on a programmable GPU are presented and discussed in detail. Evaluation of the performance of the GPU-based preconditioner and linear solver on a variety of sparse matrices shows significant computational savings relative to a CPU-based implementation of the same preconditioner and commonly used direct methods.