Hi Murat, Yes, that sounds great. I would like to have a try. Would you let me know how to use it on top of SLEPC and PETSC in more details?
Cheers, Jifeng Zhao On Wed, Jul 2, 2014 at 9:41 PM, murat keçeli <[email protected]> wrote: > Hi Jifeng, > > I think your application is suitable for the SIPs method, see attached > paper. We have improved it recently, so that it can handle very > large (500k by 500k with more than 3.e7 nonzeros) sparse matrices.Current > version of SIPs is basically adding a second layer of parallelism on top of > SLEPc's shift and invert method. Let me or Hong Zhang (cc, the developer of > SIPs) know, if you would like to give it a try. > > Murat Keceli > > > On Wed, Jul 2, 2014 at 4:46 PM, jifeng zhao < > [email protected]> wrote: > >> Hello all, >> >> I am working on solving a generalized eigenvalue problem with SLEPC and >> PETSC. >> >> *K* x = lamda *M* x >> >> I attached the sparsity pattern of matrix *M* (*K* is the same). It is a >> FEM model. It is so sparse is because of constraints. >> >> I have tried two things: >> >> 1. Krylov-Schur and exact shift-and-invert (I will try MUMPS in future). >> It works. But I am worrying that it is less parrallelable, when the problem >> contains millions of degree of freedom. >> >> 2. JD with Jacobi preconditioner. It could work, but a lot of tuning >> needs to be done in terms of RTOL, max_iteration_number. And sometimes I >> suffer from a stagnated solution, and can't obtain accurate result. >> >> Does anybody know that for my specific case of matrix sparsity, is there >> any thing I can do to speed up my direct solver (Krylov-Schur)? >> >> Is there any recommended preconditioners I could try on, for the case of >> JD? There are a lot of preconditioners in HYPRE library. >> >> Thank you in advance! >> [image: Inline image 1] >> -- >> Jifeng Zhao >> PhD candidate at Northwestern University, US >> Theoretical and Applied Mechanics Program >> > > -- Jifeng Zhao PhD candidate at Northwestern University, US Theoretical and Applied Mechanics Program
