Actual source code: ex13f90.F90

      1:       module ex13f90module
      2: #include <petsc/finclude/petscksp.h>
      3:         use petscksp
      4:         type User
      5:           Vec x
      6:           Vec b
      7:           Mat A
      8:           KSP ksp
      9:           PetscInt m
     10:           PetscInt n
     11:         end type User
     12:       end module

     14:       program main
     15:       use ex13f90module
     16:       implicit none

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

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

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

     36:       external UserInitializeLinearSolver
     37:       external UserFinalizeLinearSolver
     38:       external UserDoLinearSolver

     40: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     41: !                   Variable declarations
     42: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

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

     57:       PetscReal hx2,hy2
     58:       common /param/ hx2,hy2

     60:       tmax = 2
     61:       m = 6
     62:       n = 7

     64: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     65: !                 Beginning of program
     66: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

     68:       PetscCallA(PetscInitialize(ierr))
     69:       PetscCallMPIA(MPI_Comm_size(PETSC_COMM_WORLD,size,ierr))
     70:       PetscCheckA(size .eq. 1,PETSC_COMM_WORLD,PETSC_ERR_WRONG_MPI_SIZE,'This is a uniprocessor example only')

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

     75:       PetscCallA(PetscOptionsGetInt(PETSC_NULL_OPTIONS,PETSC_NULL_CHARACTER,'-m',m,flg,ierr))
     76:       PetscCallA(PetscOptionsGetInt(PETSC_NULL_OPTIONS,PETSC_NULL_CHARACTER,'-n',n,flg,ierr))

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

     80:       PetscCallA(UserInitializeLinearSolver(m,n,userctx,ierr))

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

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

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

     93:        ALLOCATE (rho(m,n))

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

     98:       hx = 1.0/real(m+1)
     99:       hy = 1.0/real(n+1)
    100:       y  = hy
    101:       do 20 j=1,n
    102:          x = hx
    103:          do 10 i=1,m
    104:             rho(i,j)      = x
    105:             solution(i,j) = sin(2.*PETSC_PI*x)*sin(2.*PETSC_PI*y)
    106:             userb(i,j)    = -2.*PETSC_PI*cos(2.*PETSC_PI*x)*sin(2.*PETSC_PI*y) + 8*PETSC_PI*PETSC_PI*x*sin(2.*PETSC_PI*x)*sin(2.*PETSC_PI*y)
    107:            x = x + hx
    108:  10      continue
    109:          y = y + hy
    110:  20   continue

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

    117:       do 100 t=1,tmax
    118:          PetscCallA(UserDoLinearSolver(rho,userctx,userb,userx,ierr))

    120: !        Compute error: Note that this could (and usually should) all be done
    121: !        using the PETSc vector operations. Here we demonstrate using more
    122: !        standard programming practices to show how they may be mixed with
    123: !        PETSc.
    124:          cnorm = 0.0
    125:          do 90 j=1,n
    126:             do 80 i=1,m
    127:               cnorm = cnorm + PetscConj(solution(i,j)-userx(i,j))*(solution(i,j)-userx(i,j))
    128:  80         continue
    129:  90      continue
    130:          enorm =  PetscRealPart(cnorm*hx*hy)
    131:          write(6,115) m,n,enorm
    132:  115     format ('m = ',I2,' n = ',I2,' error norm = ',1PE11.4)
    133:  100  continue

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

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

    140:       PetscCallA(UserFinalizeLinearSolver(userctx,ierr))
    141:       PetscCallA(PetscFinalize(ierr))
    142:       end

    144: ! ----------------------------------------------------------------
    145:       subroutine UserInitializeLinearSolver(m,n,userctx,ierr)
    146:       use ex13f90module
    147:       implicit none

    149:       PetscInt m,n
    150:       PetscErrorCode ierr
    151:       type(User) userctx

    153:       common /param/ hx2,hy2
    154:       PetscReal hx2,hy2

    156: !  Local variable declararions
    157:       Mat     A
    158:       Vec     b,x
    159:       KSP    ksp
    160:       PetscInt Ntot,five,one

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

    167:       hx2 = (m+1)*(m+1)
    168:       hy2 = (n+1)*(n+1)
    169:       Ntot = m*n

    171:       five = 5
    172:       one = 1

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

    176:       PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF,Ntot,Ntot,five,PETSC_NULL_INTEGER_ARRAY,A,ierr))
    177: !
    178: !  Create vectors. Here we create vectors with no memory allocated.
    179: !  This way, we can use the data structures already in the program
    180: !  by using VecPlaceArray() subroutine at a later stage.
    181: !
    182:       PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF,one,Ntot,PETSC_NULL_SCALAR_ARRAY,b,ierr))
    183:       PetscCall(VecDuplicate(b,x,ierr))

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

    188:       PetscCall(KSPCreate(PETSC_COMM_SELF,ksp,ierr))

    190:       userctx%x = x
    191:       userctx%b = b
    192:       userctx%A = A
    193:       userctx%ksp = ksp
    194:       userctx%m = m
    195:       userctx%n = n

    197:       end
    198: ! -----------------------------------------------------------------------

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

    204:       subroutine UserDoLinearSolver(rho,userctx,userb,userx,ierr)
    205:       use ex13f90module
    206:       implicit none

    208:       PetscErrorCode ierr
    209:       type(User) userctx
    210:       PetscScalar rho(*),userb(*),userx(*)

    212:       common /param/ hx2,hy2
    213:       PetscReal hx2,hy2

    215:       PC   pc
    216:       KSP ksp
    217:       Vec  b,x
    218:       Mat  A
    219:       PetscInt m,n,one
    220:       PetscInt i,j,II,JJ
    221:       PetscScalar  v

    223:       one  = 1
    224:       x    = userctx%x
    225:       b    = userctx%b
    226:       A    = userctx%A
    227:       ksp  = userctx%ksp
    228:       m    = userctx%m
    229:       n    = userctx%n

    231: !  This is not the most efficient way of generating the matrix,
    232: !  but let's not worry about it.  We should have separate code for
    233: !  the four corners, each edge and then the interior. Then we won't
    234: !  have the slow if-tests inside the loop.
    235: !
    236: !  Compute the operator
    237: !          -div rho grad
    238: !  on an m by n grid with zero Dirichlet boundary conditions. The rho
    239: !  is assumed to be given on the same grid as the finite difference
    240: !  stencil is applied.  For a staggered grid, one would have to change
    241: !  things slightly.

    243:       II = 0
    244:       do 110 j=1,n
    245:          do 100 i=1,m
    246:             if (j .gt. 1) then
    247:                JJ = II - m
    248:                v = -0.5*(rho(II+1) + rho(JJ+1))*hy2
    249:                PetscCall(MatSetValues(A,one,[II],one,[JJ],[v],INSERT_VALUES,ierr))
    250:             endif
    251:             if (j .lt. n) then
    252:                JJ = II + m
    253:                v = -0.5*(rho(II+1) + rho(JJ+1))*hy2
    254:                PetscCall(MatSetValues(A,one,[II],one,[JJ],[v],INSERT_VALUES,ierr))
    255:             endif
    256:             if (i .gt. 1) then
    257:                JJ = II - 1
    258:                v = -0.5*(rho(II+1) + rho(JJ+1))*hx2
    259:                PetscCall(MatSetValues(A,one,[II],one,[JJ],[v],INSERT_VALUES,ierr))
    260:             endif
    261:             if (i .lt. m) then
    262:                JJ = II + 1
    263:                v = -0.5*(rho(II+1) + rho(JJ+1))*hx2
    264:                PetscCall(MatSetValues(A,one,[II],one,[JJ],[v],INSERT_VALUES,ierr))
    265:             endif
    266:             v = 2*rho(II+1)*(hx2+hy2)
    267:             PetscCall(MatSetValues(A,one,[II],one,[II],[v],INSERT_VALUES,ierr))
    268:             II = II+1
    269:  100     continue
    270:  110  continue
    271: !
    272: !     Assemble matrix
    273: !
    274:       PetscCall(MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY,ierr))
    275:       PetscCall(MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY,ierr))

    277: !
    278: !     Set operators. Here the matrix that defines the linear system
    279: !     also serves as the preconditioning matrix. Since all the matrices
    280: !     will have the same nonzero pattern here, we indicate this so the
    281: !     linear solvers can take advantage of this.
    282: !
    283:       PetscCall(KSPSetOperators(ksp,A,A,ierr))

    285: !
    286: !     Set linear solver defaults for this problem (optional).
    287: !     - Here we set it to use direct LU factorization for the solution
    288: !
    289:       PetscCall(KSPGetPC(ksp,pc,ierr))
    290:       PetscCall(PCSetType(pc,PCLU,ierr))

    292: !
    293: !     Set runtime options, e.g.,
    294: !        -ksp_type <type> -pc_type <type> -ksp_monitor -ksp_rtol <rtol>
    295: !     These options will override those specified above as long as
    296: !     KSPSetFromOptions() is called _after_ any other customization
    297: !     routines.
    298: !
    299: !     Run the program with the option -help to see all the possible
    300: !     linear solver options.
    301: !
    302:       PetscCall(KSPSetFromOptions(ksp,ierr))

    304: !
    305: !     This allows the PETSc linear solvers to compute the solution
    306: !     directly in the user's array rather than in the PETSc vector.
    307: !
    308: !     This is essentially a hack and not highly recommend unless you
    309: !     are quite comfortable with using PETSc. In general, users should
    310: !     write their entire application using PETSc vectors rather than
    311: !     arrays.
    312: !
    313:       PetscCall(VecPlaceArray(x,userx,ierr))
    314:       PetscCall(VecPlaceArray(b,userb,ierr))

    316: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    317: !                      Solve the linear system
    318: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

    320:       PetscCall(KSPSolve(ksp,b,x,ierr))

    322:       PetscCall(VecResetArray(x,ierr))
    323:       PetscCall(VecResetArray(b,ierr))
    324:       end

    326: ! ------------------------------------------------------------------------

    328:       subroutine UserFinalizeLinearSolver(userctx,ierr)
    329:       use ex13f90module
    330:       implicit none

    332:       PetscErrorCode ierr
    333:       type(User) userctx

    335: !
    336: !     We are all done and don't need to solve any more linear systems, so
    337: !     we free the work space.  All PETSc objects should be destroyed when
    338: !     they are no longer needed.
    339: !
    340:       PetscCall(VecDestroy(userctx%x,ierr))
    341:       PetscCall(VecDestroy(userctx%b,ierr))
    342:       PetscCall(MatDestroy(userctx%A,ierr))
    343:       PetscCall(KSPDestroy(userctx%ksp,ierr))
    344:       end

    346: !
    347: !/*TEST
    348: !
    349: !   test:
    350: !      args: -m 19 -n 20 -ksp_gmres_cgs_refinement_type refine_always
    351: !      output_file: output/ex13f90_1.out
    352: !
    353: !TEST*/