Try -st_pc_type gamg: Doing this with the existing eigensolver,linear solver : ./RunPart -eps_type jd -eps_nev 3 -st_ksp_type cg -st_ksp_rtol 0.001 -eps_tol 0.001 -st_pc_type gamg -eps_smallest_real results in the following issues:- PETSC ERROR: Object is in wrong state PETSC ERROR: Must call EPSSolve() first: Parameter #1
Try -st_pc_type sor Doing this again with the existing eigensolver,linear solver returns me the eigenvalues but it takes more time. (106 seconds) for a Laplacian matrix of 200K size. The bjacobi was giving me the same eigenvalues but in 60 seconds. Run with -log_view to see where the computation is taking the most time: EPSSolve and KSPSolve take the most time to finish computation. Since the matrix is symmetric are you using the sbaij format instead of AIJ? I am using the sbaij format but with block size 1. I guess that is equivalent to the AIJ format itself. Thanks, Bodhi On Sat, Mar 25, 2017 at 10:22 PM, Barry Smith <[email protected]> wrote: > > > On Mar 25, 2017, at 8:52 PM, Bodhisatta Pramanik <[email protected]> > wrote: > > > > Hi, > > > > I apologize for the slepc question. I could not find any user lists so > I'm hoping someone on here might be able to offer some guidance. > > > > Problem Definition: > > I am working on a graph partitioning problem. I have the laplacian of a > large graph(500,000 nodes) and am interested in extracting its global > information in order to find good partitioning. An approach is to compute > the first few(4-5) eigenvalues and use that information to formulate the > partition algorithm. > > > > I am leveraging the EPS solvers of the Slepc library. It appears that > the Jacobi-davidson eigen solver gives me the eigenvalues in the shortest > period of time compared to others (Krylov-Schur, Rayleigh quotient, > Lanczos, etc). I use this eigensolver with the conjugate gradient linear > solver and the block-jacobi preconditioner. So this is what I am basically > passing through the command line: > > > > ./RunPart -eps_type jd -eps_nev 4 -st_ksp_type cg -st_ksp_rtol 0.001 > -eps_tol 0.001 -st_pc_type bjacobi -eps_smallest_real > > Try -st_pc_type gamg > > One one process default bjacobi results in ILU which is not a great > preconditioner. > > Also try -st_pc_type sor > > > > > Question: > > The time it takes to compute the first 4-5 eigenvectors of a matrix of > size (200k) is near about 60 seconds. CPU config: Intel Xeon 2GHz. I am > using a single processor to run my code. Is there any way I can gain major > speedup than what I am getting? > > Run with -log_view to see where the computation is taking the most time. > > > > > Is it possible to obtain the eigenvalues inside 10-15 seconds of such > huge matrices even if I do not use multiple processor?? > > > > Can someone provide me with some valuable guidance?? > > Since the matrix is symmetric are you using the sbaij format instead of > AIJ? > > > > > Thanks, > > Bodhi > > -- *Bodhisatta Pramanik,* *Graduate Student,* *Department of Electrical and Computer Engineering,* *301 Durham,* *Iowa State University,* *Ames,Iowa 50011,* [email protected] *515-735-6300*
