Vijay Gopal Chilkuri <[email protected]> writes:
Dear developers,
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.
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.)
How sparse is your matrix, where does it come from (affects spectrum and thus convergence rate), how many eigenvalues did you request, and what preconditioner did you use? Sending the output of running with -eps_view -log_view is necessary to start understanding the performance.
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