Vijay: The performance of eigenvalue computation depends on many factors - matrix features, location of eigenvalues, orthogonalization of eigenvectors - how many eigensolutions do you compute, largest/smallest spectrum, accuracy - algorithms used - computer used ...
I'm doing exact diagonalization studies of some phenomenological model Hamiltonian. In this study I have to diagonalize large sparse matrices in Hilbert space of Slater determinants many times.
Why do you carry out these experiments? For solving this type of problem, I would suggest searching related research publications and compare your results.
I've successfully used PETSc + SLEPc to get few smallest eigenvalues. For example I've been able to diagonalize a matrix of rank *91454220* with 990 processors. This diagonalization took *15328.695847 *Sec (or *4.25* Hrs.)
The matrix size 91M is quite amazing. Hong
I have two questions:
1. Is this time reasonable, if not, is it possible to optimize further ?
2. I've tried a quick google search but could not find a comprehensive benchmarking of the SLEPc library for sparse matrix diagonalization. Could you point me to a publication/resource which has such a benchmarking ?
Thanks for your help.
PETSc Version: master branch commit: b33322e SLEPc Version: master branch commit: c596d1c
Best, Vijay