Did you find out how to change option to use parallel symbolic factorization? Perhaps PETSc team can help.
Sherry On Tue, Jul 7, 2015 at 3:58 PM, Xiaoye S. Li <[email protected]> wrote: > 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 <[email protected]> > 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 <[email protected]> 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 <[email protected]> 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 <[email protected]> >>>> 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 <[email protected]> >>>> 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> >>>> >>>> >>> >> >
