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.) 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