> 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
