[Libmesh-users] [RE]Re: Re: Using multi-core in execution files

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Thank you, David.I solved this problem.The command "mpirun" in Gmsh ran instead of "mpich" I installed in PETSc.So I removed Gmsh and all mpi* and rebuilt libMesh.I also fixed my mistake that did not set the path rightly when I installed PTESc.Thank youagain for your help.Best regards,Kang----- Original Message -----From : David Knezevic <dav...@ak...>To : "강신성" <ss...@pu...>Cc : "Libmesh user group" <lib...@li...>Sent : 2018-02-13 11:30:41Subject : Re: Re: [Libmesh-users] Using multi-core in execution filesThis has happened to me before when I had two MPIs installed on my system (e.g. mpich and openmpi) so that libMesh links to one during configuration and the other is used during "mpirun".I suggest that you make sure there is only one MPI on your system and then rebuild libMesh and try again.DavidOn Mon, Feb 12, 2018 at 9:19 PM, 강신성 <ss...@pu...> wrote:

  Thanks for your reply, David. 

  I tried to the command "mpirun -np N" in the RB example 5, but there was a problemthat the same code is running repeatedly as follows. 
  ==============================<wbr>== 
  $ mpirun -np 3 ./example-opt 
  ... 
  (skip) 
  ... 

  ---- Performing Greedy basis enrichment ---- 

  ---- Basis dimension: 0 ---- 
  Performing RB solves on training set 
  Maximum error bound is 90.1602 

  Performing truth solve at parameter: 
  load_Fx: -3.274396e+00 
  load_Fy: -4.127732e+00 
  load_Fz: 9.887087e-01 
  point_load_Fx: -4.856018e+00 
  point_load_Fy: -4.622528e+00 
  point_load_Fz: 4.774231e+00 
  x_scaling: 1.146354e+00 

  ---- Performing Greedy basis enrichment ---- 

  ---- Basis dimension: 0 ---- 
  Performing RB solves on training set 
  Maximum error bound is 90.1602 

  Performing truth solve at parameter: 
  load_Fx: -3.274396e+00 
  load_Fy: -4.127732e+00 
  load_Fz: 9.887087e-01 
  point_load_Fx: -4.856018e+00 
  point_load_Fy: -4.622528e+00 
  point_load_Fz: 4.774231e+00 
  x_scaling: 1.146354e+00 

  ---- Performing Greedy basis enrichment ---- 

  ---- Basis dimension: 0 ---- 
  Performing RB solves on training set 
  Maximum error bound is 90.1602 

  Performing truth solve at parameter: 
  load_Fx: -3.274396e+00 
  load_Fy: -4.127732e+00 
  load_Fz: 9.887087e-01 
  point_load_Fx: -4.856018e+00 
  point_load_Fy: -4.622528e+00 
  point_load_Fz: 4.774231e+00 
  x_scaling: 1.146354e+00 

  Enriching the RB space 
  Enriching the RB space 
  Updating RB matrices 
  Updating RB matrices 
  Updating RB residual terms 
  Updating RB residual terms 
  Enriching the RB space 
  Updating RB matrices 
  Updating RB residual terms 

  ---- Basis dimension: 1 ---- 
  Performing RB solves on training set 

  ---- Basis dimension: 1 ---- 
  Performing RB solves on training set 
  Maximum error bound is 337.302 

  Performing truth solve at parameter: 
  load_Fx: -1.545951e-01 
  load_Fy: -2.327825e+00 
  load_Fz: 3.578962e+00 
  point_load_Fx: 8.882769e-01 
  point_load_Fy: 5.864229e-01 
  point_load_Fz: 4.982374e+00 
  x_scaling: 1.228777e+00 

  Maximum error bound is 337.302 

  Performing truth solve at parameter: 
  load_Fx: -1.545951e-01 
  load_Fy: -2.327825e+00 
  load_Fz: 3.578962e+00 
  point_load_Fx: 8.882769e-01 
  point_load_Fy: 5.864229e-01 
  point_load_Fz: 4.982374e+00 
  x_scaling: 1.228777e+00 

  ---- Basis dimension: 1 ---- 
  Performing RB solves on training set 
  Maximum error bound is 337.302 

  Performing truth solve at parameter: 
  load_Fx: -1.545951e-01 
  load_Fy: -2.327825e+00 
  load_Fz: 3.578962e+00 
  point_load_Fx: 8.882769e-01 
  point_load_Fy: 5.864229e-01 
  point_load_Fz: 4.982374e+00 
  x_scaling: 1.228777e+00 

  Enriching the RB space 
  Updating RB matrices 
  Enriching the RB space 
  Updating RB matrices 
  Updating RB residual terms 
  Updating RB residual terms 
  Enriching the RB space 
  Updating RB matrices 
  Updating RB residual terms 

  ---- Basis dimension: 2 ---- 
  Performing RB solves on training set 

  ---- Basis dimension: 2 ---- 
  Performing RB solves on training set 
  Maximum error bound is 469.21 

   ... 
   (skip) 
   ... 

  ---- Basis dimension: 15 ---- 
  Performing RB solves on training set 

  ---- Basis dimension: 15 ---- 
  Performing RB solves on training set 

  ---- Basis dimension: 15 ---- 
  Performing RB solves on training set 
  Maximum error bound is 2.50754 

  Maximum number of basis functions reached: Nmax = 15 
  *** Warning, This code is deprecated, and likely to be removed in future library versions! ./include/libmesh/mesh_tools.<wbr>h, line 70, compiled Aug 25 2017 at 02:15:21 *** 
  Maximum error bound is 2.50754 

  Maximum number of basis functions reached: Nmax = 15 
  *** Warning, This code is deprecated, and likely to be removed in future library versions! ./include/libmesh/mesh_tools.<wbr>h, line 70, compiled Aug 25 2017 at 02:15:21 *** 

    (end) 

  ==============================<wbr>============================ 
  Other examples had the same problem, too. 
  Did I type the command wrong? If not, I want to know what is wrong. 

  Best regards, 
  Kang 

   ----- Original Message -----From: David Knezevic <dav...@ak...>To: "S. Kang" <ss...@pu...>Cc: Libmesh user group <libmesh-users@lists.<wbr>sourceforge.net>Date: Mon, 12 Feb 2018 22:28:09 +0900 (GMT)subject: Re: [Libmesh-users] Using multi-core in execution files 

     Hello, 

      You can run the reduced basis code in parallel. You do it just the same way that you run any other libMesh code in parallel, i.e. just run with "mpirun -np N" where N specifies the number of processors that you want to use. 

      If you want more info on the parallelization approach that is used, please refer to 
     this paper. 

      Best, 

      David 

       On Sun, Feb 11, 2018 at 9:30 PM, S. Kang 
      <ss...@pu...> wrote: 

         Hello everyone, 

         I am solving RB problems, but it takes too long because of many basis vectors. 
         So, similar to "make -j 10", I want to know how to use multi-core in execution files, not parallel programming. 

         Best regards, 

         S. Kang. 

        ------------------------------ 
       ------------------------------ 
        Shinseong Kang 
        Graduate Student 
        Pusan National University, South Korea 
        H.P.: 010-9770-6595 
        E-mail: 
       ss...@pu... 
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