Abstract: We present performance results of a new method for computing eigenvectors of a real symmetric tridiagonal matrix. The method is a variation of inverse iteration and can in most cases substantially reduce the time required to produce orthogonal eigenvectors. Our implementation of this algorithm has been quite effective in solving “degenerate” eigenproblems in computational chemistry. On a biphenyl example, the implementation is 46 times faster than an earlier PeIGS 2.0 code using 1 processor of the IBM SP. It reduces the time for computing eigenvectors of this 966 Theta 966 matrix to under 0.15 seconds using 64 processors of the IBM SP. We present performance results for calculations from the SGI PowerChallenge and the IBM SP.
- Application of a New Algorithm for the Symmetric Eigenproblem to Computational Quantum Chemistry (pdf)
I. Dhillon, G. Fann, B. Parlett.
In SIAM Conference on Parallel Processing for Scientific Computing, March 1997.