第149回 第4部

Solving large-scale systems of linear equations which has sparse coefficient matrices derived from generalized eigenvalue problems are challenging for iterative solvers. Although the coefficient matrices have ill-conditioned, eigenvalue solvers require higher accuracy of approximate solutions. In this study, we evaluate an effect of a higher precision of a Krylov subspace method with a parallel ILU preconditioner. The ILU preconditioner with regularizations and multi-coloring parallelism showed higher robustness and good performance. To achieve higher accuracy and convergence, we applied quad precision to an IC-COCG method. In this talk, we will have a discussion about the accuracy, convergence ratio and computational time among single, double and quad precisions of the IC-COCG method with well and ill-conditioned problems.