Actual source code: ex13f90.F90

      1: !
      2: !
      3: ! -----------------------------------------------------------------------

      5:       module UserModule
      6: #include <petsc/finclude/petscksp.h>
      7:         use petscksp
      8:         type User
      9:           Vec x
     10:           Vec b
     11:           Mat A
     12:           KSP ksp
     13:           PetscInt m
     14:           PetscInt n
     15:         end type User
     16:       end module

     18:       program main
     19:       use UserModule
     20:       implicit none

     22: !    User-defined context that contains all the data structures used
     23: !    in the linear solution process.

     25: !   Vec    x,b      /* solution vector, right hand side vector and work vector */
     26: !   Mat    A        /* sparse matrix */
     27: !   KSP   ksp     /* linear solver context */
     28: !   int    m,n      /* grid dimensions */
     29: !
     30: !   Since we cannot store Scalars and integers in the same context,
     31: !   we store the integers/pointers in the user-defined context, and
     32: !   the scalar values are carried in the common block.
     33: !   The scalar values in this simplistic example could easily
     34: !   be recalculated in each routine, where they are needed.
     35: !
     36: !   Scalar hx2,hy2  /* 1/(m+1)*(m+1) and 1/(n+1)*(n+1) */

     38: !  Note: Any user-defined Fortran routines MUST be declared as external.

     40:       external UserInitializeLinearSolver
     41:       external UserFinalizeLinearSolver
     42:       external UserDoLinearSolver

     44: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     45: !                   Variable declarations
     46: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

     48:       PetscScalar  hx,hy,x,y
     49:       type(User) userctx
     50:       PetscErrorCode ierr
     51:       PetscInt m,n,t,tmax,i,j
     52:       PetscBool  flg
     53:       PetscMPIInt size
     54:       PetscReal  enorm
     55:       PetscScalar cnorm
     56:       PetscScalar,ALLOCATABLE :: userx(:,:)
     57:       PetscScalar,ALLOCATABLE :: userb(:,:)
     58:       PetscScalar,ALLOCATABLE :: solution(:,:)
     59:       PetscScalar,ALLOCATABLE :: rho(:,:)

     61:       PetscReal hx2,hy2
     62:       common /param/ hx2,hy2

     64:       tmax = 2
     65:       m = 6
     66:       n = 7

     68: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     69: !                 Beginning of program
     70: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

     72:       call PetscInitialize(PETSC_NULL_CHARACTER,ierr)
     73:       if (ierr .ne. 0) then
     74:         print*,'Unable to initialize PETSc'
     75:         stop
     76:       endif
     77:       call MPI_Comm_size(PETSC_COMM_WORLD,size,ierr)
     78:       if (size .ne. 1) then; SETERRA(PETSC_COMM_WORLD,PETSC_ERR_WRONG_MPI_SIZE,'This is a uniprocessor example only'); endif

     80: !  The next two lines are for testing only; these allow the user to
     81: !  decide the grid size at runtime.

     83:       call PetscOptionsGetInt(PETSC_NULL_OPTIONS,PETSC_NULL_CHARACTER,'-m',m,flg,ierr);CHKERRA(ierr)
     84:       call PetscOptionsGetInt(PETSC_NULL_OPTIONS,PETSC_NULL_CHARACTER,'-n',n,flg,ierr);CHKERRA(ierr)

     86: !  Create the empty sparse matrix and linear solver data structures

     88:       call UserInitializeLinearSolver(m,n,userctx,ierr);CHKERRA(ierr)

     90: !  Allocate arrays to hold the solution to the linear system.  This
     91: !  approach is not normally done in PETSc programs, but in this case,
     92: !  since we are calling these routines from a non-PETSc program, we
     93: !  would like to reuse the data structures from another code. So in
     94: !  the context of a larger application these would be provided by
     95: !  other (non-PETSc) parts of the application code.

     97:       ALLOCATE (userx(m,n),userb(m,n),solution(m,n))

     99: !  Allocate an array to hold the coefficients in the elliptic operator

    101:        ALLOCATE (rho(m,n))

    103: !  Fill up the array rho[] with the function rho(x,y) = x; fill the
    104: !  right-hand-side b[] and the solution with a known problem for testing.

    106:       hx = 1.0/real(m+1)
    107:       hy = 1.0/real(n+1)
    108:       y  = hy
    109:       do 20 j=1,n
    110:          x = hx
    111:          do 10 i=1,m
    112:             rho(i,j)      = x
    113:             solution(i,j) = sin(2.*PETSC_PI*x)*sin(2.*PETSC_PI*y)
    114:             userb(i,j)    = -2.*PETSC_PI*cos(2.*PETSC_PI*x)*sin(2.*PETSC_PI*y) +                                &
    115:      &                      8*PETSC_PI*PETSC_PI*x*sin(2.*PETSC_PI*x)*sin(2.*PETSC_PI*y)
    116:            x = x + hx
    117:  10      continue
    118:          y = y + hy
    119:  20   continue

    121: !  Loop over a bunch of timesteps, setting up and solver the linear
    122: !  system for each time-step.
    123: !  Note that this loop is somewhat artificial. It is intended to
    124: !  demonstrate how one may reuse the linear solvers in each time-step.

    126:       do 100 t=1,tmax
    127:          call UserDoLinearSolver(rho,userctx,userb,userx,ierr);CHKERRA(ierr)

    129: !        Compute error: Note that this could (and usually should) all be done
    130: !        using the PETSc vector operations. Here we demonstrate using more
    131: !        standard programming practices to show how they may be mixed with
    132: !        PETSc.
    133:          cnorm = 0.0
    134:          do 90 j=1,n
    135:             do 80 i=1,m
    136:               cnorm = cnorm + PetscConj(solution(i,j)-userx(i,j))*(solution(i,j)-userx(i,j))
    137:  80         continue
    138:  90      continue
    139:          enorm =  PetscRealPart(cnorm*hx*hy)
    140:          write(6,115) m,n,enorm
    141:  115     format ('m = ',I2,' n = ',I2,' error norm = ',1PE11.4)
    142:  100  continue

    144: !  We are finished solving linear systems, so we clean up the
    145: !  data structures.

    147:       DEALLOCATE (userx,userb,solution,rho)

    149:       call UserFinalizeLinearSolver(userctx,ierr);CHKERRA(ierr)
    150:       call PetscFinalize(ierr)
    151:       end

    153: ! ----------------------------------------------------------------
    154:       subroutine UserInitializeLinearSolver(m,n,userctx,ierr)
    155:       use UserModule
    156:       implicit none

    158:       PetscInt m,n
    159:       PetscErrorCode ierr
    160:       type(User) userctx

    162:       common /param/ hx2,hy2
    163:       PetscReal hx2,hy2

    165: !  Local variable declararions
    166:       Mat     A
    167:       Vec     b,x
    168:       KSP    ksp
    169:       PetscInt Ntot,five,one

    171: !  Here we assume use of a grid of size m x n, with all points on the
    172: !  interior of the domain, i.e., we do not include the points corresponding
    173: !  to homogeneous Dirichlet boundary conditions.  We assume that the domain
    174: !  is [0,1]x[0,1].

    176:       hx2 = (m+1)*(m+1)
    177:       hy2 = (n+1)*(n+1)
    178:       Ntot = m*n

    180:       five = 5
    181:       one = 1

    183: !  Create the sparse matrix. Preallocate 5 nonzeros per row.

    185:       call MatCreateSeqAIJ(PETSC_COMM_SELF,Ntot,Ntot,five,PETSC_NULL_INTEGER,A,ierr);CHKERRQ(ierr)
    186: !
    187: !  Create vectors. Here we create vectors with no memory allocated.
    188: !  This way, we can use the data structures already in the program
    189: !  by using VecPlaceArray() subroutine at a later stage.
    190: !
    191:       call VecCreateSeqWithArray(PETSC_COMM_SELF,one,Ntot,PETSC_NULL_SCALAR,b,ierr);CHKERRQ(ierr)
    192:       call VecDuplicate(b,x,ierr);CHKERRQ(ierr)

    194: !  Create linear solver context. This will be used repeatedly for all
    195: !  the linear solves needed.

    197:       call KSPCreate(PETSC_COMM_SELF,ksp,ierr);CHKERRQ(ierr)

    199:       userctx%x = x
    200:       userctx%b = b
    201:       userctx%A = A
    202:       userctx%ksp = ksp
    203:       userctx%m = m
    204:       userctx%n = n

    206:       return
    207:       end
    208: ! -----------------------------------------------------------------------

    210: !   Solves -div (rho grad psi) = F using finite differences.
    211: !   rho is a 2-dimensional array of size m by n, stored in Fortran
    212: !   style by columns. userb is a standard one-dimensional array.

    214:       subroutine UserDoLinearSolver(rho,userctx,userb,userx,ierr)
    215:       use UserModule
    216:       implicit none

    218:       PetscErrorCode ierr
    219:       type(User) userctx
    220:       PetscScalar rho(*),userb(*),userx(*)

    222:       common /param/ hx2,hy2
    223:       PetscReal hx2,hy2

    225:       PC   pc
    226:       KSP ksp
    227:       Vec  b,x
    228:       Mat  A
    229:       PetscInt m,n,one
    230:       PetscInt i,j,II,JJ
    231:       PetscScalar  v

    233:       one  = 1
    234:       x    = userctx%x
    235:       b    = userctx%b
    236:       A    = userctx%A
    237:       ksp  = userctx%ksp
    238:       m    = userctx%m
    239:       n    = userctx%n

    241: !  This is not the most efficient way of generating the matrix,
    242: !  but let's not worry about it.  We should have separate code for
    243: !  the four corners, each edge and then the interior. Then we won't
    244: !  have the slow if-tests inside the loop.
    245: !
    246: !  Compute the operator
    247: !          -div rho grad
    248: !  on an m by n grid with zero Dirichlet boundary conditions. The rho
    249: !  is assumed to be given on the same grid as the finite difference
    250: !  stencil is applied.  For a staggered grid, one would have to change
    251: !  things slightly.

    253:       II = 0
    254:       do 110 j=1,n
    255:          do 100 i=1,m
    256:             if (j .gt. 1) then
    257:                JJ = II - m
    258:                v = -0.5*(rho(II+1) + rho(JJ+1))*hy2
    259:                call MatSetValues(A,one,II,one,JJ,v,INSERT_VALUES,ierr);CHKERRQ(ierr)
    260:             endif
    261:             if (j .lt. n) then
    262:                JJ = II + m
    263:                v = -0.5*(rho(II+1) + rho(JJ+1))*hy2
    264:                call MatSetValues(A,one,II,one,JJ,v,INSERT_VALUES,ierr);CHKERRQ(ierr)
    265:             endif
    266:             if (i .gt. 1) then
    267:                JJ = II - 1
    268:                v = -0.5*(rho(II+1) + rho(JJ+1))*hx2
    269:                call MatSetValues(A,one,II,one,JJ,v,INSERT_VALUES,ierr);CHKERRQ(ierr)
    270:             endif
    271:             if (i .lt. m) then
    272:                JJ = II + 1
    273:                v = -0.5*(rho(II+1) + rho(JJ+1))*hx2
    274:                call MatSetValues(A,one,II,one,JJ,v,INSERT_VALUES,ierr);CHKERRQ(ierr)
    275:             endif
    276:             v = 2*rho(II+1)*(hx2+hy2)
    277:             call MatSetValues(A,one,II,one,II,v,INSERT_VALUES,ierr);CHKERRQ(ierr)
    278:             II = II+1
    279:  100     continue
    280:  110  continue
    281: !
    282: !     Assemble matrix
    283: !
    284:       call MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY,ierr);CHKERRQ(ierr)
    285:       call MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY,ierr);CHKERRQ(ierr)

    287: !
    288: !     Set operators. Here the matrix that defines the linear system
    289: !     also serves as the preconditioning matrix. Since all the matrices
    290: !     will have the same nonzero pattern here, we indicate this so the
    291: !     linear solvers can take advantage of this.
    292: !
    293:       call KSPSetOperators(ksp,A,A,ierr);CHKERRQ(ierr)

    295: !
    296: !     Set linear solver defaults for this problem (optional).
    297: !     - Here we set it to use direct LU factorization for the solution
    298: !
    299:       call KSPGetPC(ksp,pc,ierr);CHKERRQ(ierr)
    300:       call PCSetType(pc,PCLU,ierr);CHKERRQ(ierr)

    302: !
    303: !     Set runtime options, e.g.,
    304: !        -ksp_type <type> -pc_type <type> -ksp_monitor -ksp_rtol <rtol>
    305: !     These options will override those specified above as long as
    306: !     KSPSetFromOptions() is called _after_ any other customization
    307: !     routines.
    308: !
    309: !     Run the program with the option -help to see all the possible
    310: !     linear solver options.
    311: !
    312:       call KSPSetFromOptions(ksp,ierr);CHKERRQ(ierr)

    314: !
    315: !     This allows the PETSc linear solvers to compute the solution
    316: !     directly in the user's array rather than in the PETSc vector.
    317: !
    318: !     This is essentially a hack and not highly recommend unless you
    319: !     are quite comfortable with using PETSc. In general, users should
    320: !     write their entire application using PETSc vectors rather than
    321: !     arrays.
    322: !
    323:       call VecPlaceArray(x,userx,ierr);CHKERRQ(ierr)
    324:       call VecPlaceArray(b,userb,ierr);CHKERRQ(ierr)

    326: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    327: !                      Solve the linear system
    328: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

    330:       call KSPSolve(ksp,b,x,ierr);CHKERRQ(ierr)

    332:       call VecResetArray(x,ierr);CHKERRQ(ierr)
    333:       call VecResetArray(b,ierr);CHKERRQ(ierr)

    335:       return
    336:       end

    338: ! ------------------------------------------------------------------------

    340:       subroutine UserFinalizeLinearSolver(userctx,ierr)
    341:       use UserModule
    342:       implicit none

    344:       PetscErrorCode ierr
    345:       type(User) userctx

    347: !
    348: !     We are all done and don't need to solve any more linear systems, so
    349: !     we free the work space.  All PETSc objects should be destroyed when
    350: !     they are no longer needed.
    351: !
    352:       call VecDestroy(userctx%x,ierr);CHKERRQ(ierr)
    353:       call VecDestroy(userctx%b,ierr);CHKERRQ(ierr)
    354:       call MatDestroy(userctx%A,ierr);CHKERRQ(ierr)
    355:       call KSPDestroy(userctx%ksp,ierr);CHKERRQ(ierr)

    357:       return
    358:       end

    360: !
    361: !/*TEST
    362: !
    363: !   test:
    364: !      args: -m 19 -n 20 -ksp_gmres_cgs_refinement_type refine_always
    365: !      output_file: output/ex13f90_1.out
    366: !
    367: !TEST*/