Is there an inquiry function that tells you all the available options? Sherry
On Tue, Jul 7, 2015 at 3:25 PM, Anthony Paul Haas <a...@email.arizona.edu> wrote: > Hi Sherry, > > Thanks for your message. I have used superlu_dist default options. I did > not realize that I was doing serial symbolic factorization. That is > probably the cause of my problem. > Each node on Garnet has 60GB usable memory and I can run with 1,2,4,8,16 > or 32 core per node. > > So I should use: > > -mat_superlu_dist_r 20 > -mat_superlu_dist_c 32 > > How do you specify the parallel symbolic factorization option? is it > -mat_superlu_dist_matinput 1 > > Thanks, > > Anthony > > > On Tue, Jul 7, 2015 at 3:08 PM, Xiaoye S. Li <x...@lbl.gov> wrote: > >> For superlu_dist failure, this occurs during symbolic factorization. >> Since you are using serial symbolic factorization, it requires the entire >> graph of A to be available in the memory of one MPI task. How much memory >> do you have for each MPI task? >> >> It won't help even if you use more processes. You should try to use >> parallel symbolic factorization option. >> >> Another point. You set up process grid as: >> Process grid nprow 32 x npcol 20 >> For better performance, you show swap the grid dimension. That is, it's >> better to use 20 x 32, never gives nprow larger than npcol. >> >> >> Sherry >> >> >> On Tue, Jul 7, 2015 at 1:27 PM, Barry Smith <bsm...@mcs.anl.gov> wrote: >> >>> >>> I would suggest running a sequence of problems, 101 by 101 111 by 111 >>> etc and get the memory usage in each case (when you run out of memory you >>> can get NO useful information out about memory needs). You can then plot >>> memory usage as a function of problem size to get a handle on how much >>> memory it is using. You can also run on more and more processes (which >>> have a total of more memory) to see how large a problem you may be able to >>> reach. >>> >>> MUMPS also has an "out of core" version (which we have never used) >>> that could in theory anyways let you get to large problems if you have lots >>> of disk space, but you are on your own figuring out how to use it. >>> >>> Barry >>> >>> > On Jul 7, 2015, at 2:37 PM, Anthony Paul Haas <a...@email.arizona.edu> >>> wrote: >>> > >>> > Hi Jose, >>> > >>> > In my code, I use once PETSc to solve a linear system to get the >>> baseflow (without using SLEPc) and then I use SLEPc to do the stability >>> analysis of that baseflow. This is why, there are some SLEPc options that >>> are not used in test.out-superlu_dist-151x151 (when I am solving for the >>> baseflow with PETSc only). I have attached a 101x101 case for which I get >>> the eigenvalues. That case works fine. However If i increase to 151x151, I >>> get the error that you can see in test.out-superlu_dist-151x151 (similar >>> error with mumps: see test.out-mumps-151x151 line 2918 ). If you look a the >>> very end of the files test.out-superlu_dist-151x151 and >>> test.out-mumps-151x151, you will see that the last info message printed is: >>> > >>> > On Processor (after EPSSetFromOptions) 0 memory: >>> 0.65073152000E+08 =====> (see line 807 of module_petsc.F90) >>> > >>> > This means that the memory error probably occurs in the call to >>> EPSSolve (see module_petsc.F90 line 810). I would like to evaluate how much >>> memory is required by the most memory intensive operation within EPSSolve. >>> Since I am solving a generalized EVP, I would imagine that it would be the >>> LU decomposition. But is there an accurate way of doing it? >>> > >>> > Before starting with iterative solvers, I would like to exploit as >>> much as I can direct solvers. I tried GMRES with default preconditioner at >>> some point but I had convergence problem. What solver/preconditioner would >>> you recommend for a generalized non-Hermitian (EPS_GNHEP) EVP? >>> > >>> > Thanks, >>> > >>> > Anthony >>> > >>> > On Tue, Jul 7, 2015 at 12:17 AM, Jose E. Roman <jro...@dsic.upv.es> >>> wrote: >>> > >>> > El 07/07/2015, a las 02:33, Anthony Haas escribió: >>> > >>> > > Hi, >>> > > >>> > > I am computing eigenvalues using PETSc/SLEPc and superlu_dist for >>> the LU decomposition (my problem is a generalized eigenvalue problem). The >>> code runs fine for a grid with 101x101 but when I increase to 151x151, I >>> get the following error: >>> > > >>> > > Can't expand MemType 1: jcol 16104 (and then [NID 00037] >>> 2015-07-06 19:19:17 Apid 31025976: OOM killer terminated this process.) >>> > > >>> > > It seems to be a memory problem. I monitor the memory usage as far >>> as I can and it seems that memory usage is pretty low. The most memory >>> intensive part of the program is probably the LU decomposition in the >>> context of the generalized EVP. Is there a way to evaluate how much memory >>> will be required for that step? I am currently running the debug version of >>> the code which I would assume would use more memory? >>> > > >>> > > I have attached the output of the job. Note that the program uses >>> twice PETSc: 1) to solve a linear system for which no problem occurs, and, >>> 2) to solve the Generalized EVP with SLEPc, where I get the error. >>> > > >>> > > Thanks >>> > > >>> > > Anthony >>> > > <test.out-superlu_dist-151x151> >>> > >>> > In the output you are attaching there are no SLEPc objects in the >>> report and SLEPc options are not used. It seems that SLEPc calls are >>> skipped? >>> > >>> > Do you get the same error with MUMPS? Have you tried to solve linear >>> systems with a preconditioned iterative solver? >>> > >>> > Jose >>> > >>> > >>> > >>> <module_petsc.F90><test.out-mumps-151x151><test.out_superlu_dist-101x101><test.out-superlu_dist-151x151> >>> >>> >> >