Actual source code: ex2.c

  1: /*$Id: ex2.c,v 1.92 2001/03/23 23:23:55 balay Exp $*/

  3: /* Program usage:  mpirun -np <procs> ex2 [-help] [all PETSc options] */

  5: static char help[] = "Solves a linear system in parallel with SLES.n
  6: Input parameters include:n
  7:   -random_exact_sol : use a random exact solution vectorn
  8:   -view_exact_sol   : write exact solution vector to stdoutn
  9:   -m <mesh_x>       : number of mesh points in x-directionn
 10:   -n <mesh_n>       : number of mesh points in y-directionnn";

 12: /*T
 13:    Concepts: SLES^basic parallel example;
 14:    Concepts: SLES^Laplacian, 2d
 15:    Concepts: Laplacian, 2d
 16:    Processors: n
 17: T*/

 19: /* 
 20:   Include "petscsles.h" so that we can use SLES solvers.  Note that this file
 21:   automatically includes:
 22:      petsc.h       - base PETSc routines   petscvec.h - vectors
 23:      petscsys.h    - system routines       petscmat.h - matrices
 24:      petscis.h     - index sets            petscksp.h - Krylov subspace methods
 25:      petscviewer.h - viewers               petscpc.h  - preconditioners
 26: */
 27:  #include petscsles.h

 29: int main(int argc,char **args)
 30: {
 31:   Vec         x,b,u;  /* approx solution, RHS, exact solution */
 32:   Mat         A;        /* linear system matrix */
 33:   SLES        sles;     /* linear solver context */
 34:   PetscRandom rctx;     /* random number generator context */
 35:   double      norm;     /* norm of solution error */
 36:   int         i,j,I,J,Istart,Iend,ierr,m = 8,n = 7,its;
 37:   PetscTruth  flg;
 38:   Scalar      v,one = 1.0,neg_one = -1.0;
 39:   KSP         ksp;

 41:   PetscInitialize(&argc,&args,(char *)0,help);
 42:   PetscOptionsGetInt(PETSC_NULL,"-m",&m,PETSC_NULL);
 43:   PetscOptionsGetInt(PETSC_NULL,"-n",&n,PETSC_NULL);

 45:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
 46:          Compute the matrix and right-hand-side vector that define
 47:          the linear system, Ax = b.
 48:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
 49:   /* 
 50:      Create parallel matrix, specifying only its global dimensions.
 51:      When using MatCreate(), the matrix format can be specified at
 52:      runtime. Also, the parallel partitioning of the matrix is
 53:      determined by PETSc at runtime.

 55:      Performance tuning note:  For problems of substantial size,
 56:      preallocation of matrix memory is crucial for attaining good 
 57:      performance.  Since preallocation is not possible via the generic
 58:      matrix creation routine MatCreate(), we recommend for practical 
 59:      problems instead to use the creation routine for a particular matrix
 60:      format, e.g.,
 61:          MatCreateMPIAIJ() - parallel AIJ (compressed sparse row)
 62:          MatCreateMPIBAIJ() - parallel block AIJ
 63:      See the matrix chapter of the users manual for details.
 64:   */
 65:   MatCreate(PETSC_COMM_WORLD,PETSC_DECIDE,PETSC_DECIDE,m*n,m*n,&A);
 66:   MatSetFromOptions(A);

 68:   /* 
 69:      Currently, all PETSc parallel matrix formats are partitioned by
 70:      contiguous chunks of rows across the processors.  Determine which
 71:      rows of the matrix are locally owned. 
 72:   */
 73:   MatGetOwnershipRange(A,&Istart,&Iend);

 75:   /* 
 76:      Set matrix elements for the 2-D, five-point stencil in parallel.
 77:       - Each processor needs to insert only elements that it owns
 78:         locally (but any non-local elements will be sent to the
 79:         appropriate processor during matrix assembly). 
 80:       - Always specify global rows and columns of matrix entries.

 82:      Note: this uses the less common natural ordering that orders first
 83:      all the unknowns for x = h then for x = 2h etc; Hence you see J = I +- n
 84:      instead of J = I +- m as you might expect. The more standard ordering
 85:      would first do all variables for y = h, then y = 2h etc.

 87:    */
 88:   for (I=Istart; I<Iend; I++) {
 89:     v = -1.0; i = I/n; j = I - i*n;
 90:     if (i>0)   {J = I - n; MatSetValues(A,1,&I,1,&J,&v,INSERT_VALUES);}
 91:     if (i<m-1) {J = I + n; MatSetValues(A,1,&I,1,&J,&v,INSERT_VALUES);}
 92:     if (j>0)   {J = I - 1; MatSetValues(A,1,&I,1,&J,&v,INSERT_VALUES);}
 93:     if (j<n-1) {J = I + 1; MatSetValues(A,1,&I,1,&J,&v,INSERT_VALUES);}
 94:     v = 4.0; MatSetValues(A,1,&I,1,&I,&v,INSERT_VALUES);
 95:   }

 97:   /* 
 98:      Assemble matrix, using the 2-step process:
 99:        MatAssemblyBegin(), MatAssemblyEnd()
100:      Computations can be done while messages are in transition
101:      by placing code between these two statements.
102:   */
103:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
104:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);

106:   /* 
107:      Create parallel vectors.
108:       - We form 1 vector from scratch and then duplicate as needed.
109:       - When using VecCreate() and VecSetFromOptions() in this example, we specify only the
110:         vector's global dimension; the parallel partitioning is determined
111:         at runtime. 
112:       - When solving a linear system, the vectors and matrices MUST
113:         be partitioned accordingly.  PETSc automatically generates
114:         appropriately partitioned matrices and vectors when MatCreate()
115:         and VecCreate() are used with the same communicator.  
116:       - The user can alternatively specify the local vector and matrix
117:         dimensions when more sophisticated partitioning is needed
118:         (replacing the PETSC_DECIDE argument in the VecCreate() statement
119:         below).
120:   */
121:   VecCreate(PETSC_COMM_WORLD,PETSC_DECIDE,m*n,&u);
122:   VecSetFromOptions(u);
123:   VecDuplicate(u,&b);
124:   VecDuplicate(b,&x);

126:   /* 
127:      Set exact solution; then compute right-hand-side vector.
128:      By default we use an exact solution of a vector with all
129:      elements of 1.0;  Alternatively, using the runtime option
130:      -random_sol forms a solution vector with random components.
131:   */
132:   PetscOptionsHasName(PETSC_NULL,"-random_exact_sol",&flg);
133:   if (flg) {
134:     PetscRandomCreate(PETSC_COMM_WORLD,RANDOM_DEFAULT,&rctx);
135:     VecSetRandom(rctx,u);
136:     PetscRandomDestroy(rctx);
137:   } else {
138:     VecSet(&one,u);
139:   }
140:   MatMult(A,u,b);

142:   /*
143:      View the exact solution vector if desired
144:   */
145:   PetscOptionsHasName(PETSC_NULL,"-view_exact_sol",&flg);
146:   if (flg) {VecView(u,PETSC_VIEWER_STDOUT_WORLD);}

148:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
149:                 Create the linear solver and set various options
150:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

152:   /* 
153:      Create linear solver context
154:   */
155:   SLESCreate(PETSC_COMM_WORLD,&sles);

157:   /* 
158:      Set operators. Here the matrix that defines the linear system
159:      also serves as the preconditioning matrix.
160:   */
161:   SLESSetOperators(sles,A,A,DIFFERENT_NONZERO_PATTERN);

163:   /* 
164:      Set linear solver defaults for this problem (optional).
165:      - By extracting the KSP and PC contexts from the SLES context,
166:        we can then directly call any KSP and PC routines to set
167:        various options.
168:      - The following two statements are optional; all of these
169:        parameters could alternatively be specified at runtime via
170:        SLESSetFromOptions().  All of these defaults can be
171:        overridden at runtime, as indicated below.
172:   */

174:   SLESGetKSP(sles,&ksp);
175:   KSPSetTolerances(ksp,1.e-2/((m+1)*(n+1)),1.e-50,PETSC_DEFAULT,PETSC_DEFAULT);

177:   /* 
178:     Set runtime options, e.g.,
179:         -ksp_type <type> -pc_type <type> -ksp_monitor -ksp_rtol <rtol>
180:     These options will override those specified above as long as
181:     SLESSetFromOptions() is called _after_ any other customization
182:     routines.
183:   */
184:   SLESSetFromOptions(sles);

186:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
187:                       Solve the linear system
188:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

190:   SLESSolve(sles,b,x,&its);

192:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
193:                       Check solution and clean up
194:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

196:   /* 
197:      Check the error
198:   */
199:   VecAXPY(&neg_one,u,x);
200:   VecNorm(x,NORM_2,&norm);

202:   /* Scale the norm */
203:   /*  norm *= sqrt(1.0/((m+1)*(n+1))); */

205:   /*
206:      Print convergence information.  PetscPrintf() produces a single 
207:      print statement from all processes that share a communicator.
208:      An alternative is PetscFPrintf(), which prints to a file.
209:   */
210:   PetscPrintf(PETSC_COMM_WORLD,"Norm of error %A iterations %dn",norm,its);

212:   /*
213:      Free work space.  All PETSc objects should be destroyed when they
214:      are no longer needed.
215:   */
216:   SLESDestroy(sles);
217:   VecDestroy(u);  VecDestroy(x);
218:   VecDestroy(b);  MatDestroy(A);

220:   /*
221:      Always call PetscFinalize() before exiting a program.  This routine
222:        - finalizes the PETSc libraries as well as MPI
223:        - provides summary and diagnostic information if certain runtime
224:          options are chosen (e.g., -log_summary). 
225:   */
226:   PetscFinalize();
227:   return 0;
228: }