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 ...
Processor: Intel(r) IVYBRIDGE 2,8 Ghz 10 (bisocket)
Ram : 64Gb
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.HongI 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: b33322eSLEPc Version: master branch commit: c596d1cBest,Vijay