Parallel block Jacobi-Davidson method for solving large generalized eigenvalue problems and it's application

This paper gives a method of parallel block Jacobi-Davidson for computing large generalized eigenvalue problem AX=λBX,in which matrix A and B is symmetric.The large eigenvalue problem is transformed into an eigenvalue problem in a low dimension subspace by using orthogonal projection technique,and the Neumann series is used in the correction equation for the purpose of precondition.The method can get several eigenpairs at one time include multiply eigenpairs.The new algorithm is successfully applied in dynamic analysis of a wing and a rack of a plane,the numerical experiments on the IBM-P650 show that under the condition of the same accuracy,the parallel block Jacobi-Davidson method can get the eigenpairs in less time and less iteration steps than that of parallel subspace iterative method and has a higher speedup and efficient.