[petsc-users] Benchmarking

[Hong]

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
>
>
>
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