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 > > >
