Parallel eigenvalue calculation based on multiple shiftinvert Lanczos and contour integral based spectral projection method
We study the problem of finding a large number of eigenpairs for sparse matrices. A model for estimating the optimal size of a sub-interval is presented. An optimal partitioning gives significantly better performance than a poor one. Spectral partitioning methods exhibit good scaling on massively parallel machines. Shiftinvert Lanczos method performs better than contour integral-based method.
multiple shiftinvert lanczos contour integral based spectral projection method parallel eigenvalue computations spectral transformation strong scaling weak scaling