Date: 2015-10-02T22:12:10+00:00

Actual source code: axpy.c
petsc-3.6.2 2015-10-02
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      2: #include <petsc/private/matimpl.h>  /*I   "petscmat.h"  I*/

      6: /*@
      7:    MatAXPY - Computes Y = a*X + Y.

      9:    Logically  Collective on Mat

     11:    Input Parameters:
     12: +  a - the scalar multiplier
     13: .  X - the first matrix
     14: .  Y - the second matrix
     15: -  str - either SAME_NONZERO_PATTERN, DIFFERENT_NONZERO_PATTERN
     16:          or SUBSET_NONZERO_PATTERN (nonzeros of X is a subset of Y's)

     18:    Level: intermediate

     20: .keywords: matrix, add

     22: .seealso: MatAYPX()
     23:  @*/
     24: PetscErrorCode MatAXPY(Mat Y,PetscScalar a,Mat X,MatStructure str)
     25: {
     27:   PetscInt       m1,m2,n1,n2;

     33:   MatGetSize(X,&m1,&n1);
     34:   MatGetSize(Y,&m2,&n2);
     35:   if (m1 != m2 || n1 != n2) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Non conforming matrix add: %D %D %D %D",m1,m2,n1,n2);

     37:   PetscLogEventBegin(MAT_AXPY,Y,0,0,0);
     38:   if (Y->ops->axpy) {
     39:     (*Y->ops->axpy)(Y,a,X,str);
     40:   } else {
     41:     MatAXPY_Basic(Y,a,X,str);
     42:   }
     43:   PetscLogEventEnd(MAT_AXPY,Y,0,0,0);
     44: #if defined(PETSC_HAVE_CUSP)
     45:   if (Y->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
     46:     Y->valid_GPU_matrix = PETSC_CUSP_CPU;
     47:   }
     48: #endif
     49: #if defined(PETSC_HAVE_VIENNACL)
     50:   if (Y->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
     51:     Y->valid_GPU_matrix = PETSC_VIENNACL_CPU;
     52:   }
     53: #endif
     54:   return(0);
     55: }

     59: PetscErrorCode MatAXPY_Basic(Mat Y,PetscScalar a,Mat X,MatStructure str)
     60: {
     61:   PetscInt          i,start,end,j,ncols,m,n;
     62:   PetscErrorCode    ierr;
     63:   const PetscInt    *row;
     64:   PetscScalar       *val;
     65:   const PetscScalar *vals;

     68:   MatGetSize(X,&m,&n);
     69:   MatGetOwnershipRange(X,&start,&end);
     70:   if (a == 1.0) {
     71:     for (i = start; i < end; i++) {
     72:       MatGetRow(X,i,&ncols,&row,&vals);
     73:       MatSetValues(Y,1,&i,ncols,row,vals,ADD_VALUES);
     74:       MatRestoreRow(X,i,&ncols,&row,&vals);
     75:     }
     76:   } else {
     77:     PetscMalloc1(n+1,&val);
     78:     for (i=start; i<end; i++) {
     79:       MatGetRow(X,i,&ncols,&row,&vals);
     80:       for (j=0; j<ncols; j++) {
     81:         val[j] = a*vals[j];
     82:       }
     83:       MatSetValues(Y,1,&i,ncols,row,val,ADD_VALUES);
     84:       MatRestoreRow(X,i,&ncols,&row,&vals);
     85:     }
     86:     PetscFree(val);
     87:   }
     88:   MatAssemblyBegin(Y,MAT_FINAL_ASSEMBLY);
     89:   MatAssemblyEnd(Y,MAT_FINAL_ASSEMBLY);
     90:   return(0);
     91: }

     95: PetscErrorCode MatAXPY_BasicWithPreallocation(Mat B,Mat Y,PetscScalar a,Mat X,MatStructure str)
     96: {
     97:   PetscInt          i,start,end,j,ncols,m,n;
     98:   PetscErrorCode    ierr;
     99:   const PetscInt    *row;
    100:   PetscScalar       *val;
    101:   const PetscScalar *vals;

    104:   MatGetSize(X,&m,&n);
    105:   MatGetOwnershipRange(X,&start,&end);
    106:   if (a == 1.0) {
    107:     for (i = start; i < end; i++) {
    108:       MatGetRow(Y,i,&ncols,&row,&vals);
    109:       MatSetValues(B,1,&i,ncols,row,vals,ADD_VALUES);
    110:       MatRestoreRow(Y,i,&ncols,&row,&vals);

    112:       MatGetRow(X,i,&ncols,&row,&vals);
    113:       MatSetValues(B,1,&i,ncols,row,vals,ADD_VALUES);
    114:       MatRestoreRow(X,i,&ncols,&row,&vals);
    115:     }
    116:   } else {
    117:     PetscMalloc1(n+1,&val);
    118:     for (i=start; i<end; i++) {
    119:       MatGetRow(Y,i,&ncols,&row,&vals);
    120:       MatSetValues(B,1,&i,ncols,row,vals,ADD_VALUES);
    121:       MatRestoreRow(Y,i,&ncols,&row,&vals);

    123:       MatGetRow(X,i,&ncols,&row,&vals);
    124:       for (j=0; j<ncols; j++) {
    125:         val[j] = a*vals[j];
    126:       }
    127:       MatSetValues(B,1,&i,ncols,row,val,ADD_VALUES);
    128:       MatRestoreRow(X,i,&ncols,&row,&vals);
    129:     }
    130:     PetscFree(val);
    131:   }
    132:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    133:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
    134:   return(0);
    135: }

    139: /*@
    140:    MatShift - Computes Y =  Y + a I, where a is a PetscScalar and I is the identity matrix.

    142:    Neighbor-wise Collective on Mat

    144:    Input Parameters:
    145: +  Y - the matrices
    146: -  a - the PetscScalar

    148:    Level: intermediate

    150:    Notes: If the matrix Y is missing some diagonal entries this routine can be very slow. To make it fast one should initially
    151:    fill the matrix so that all diagonal entries have a value (with a value of zero for those locations that would not have an
    152:    entry).

    154:    Developers Note: If the local "diagonal part" of the matrix Y has no entries then the local diagonal part is
    155:     preallocated with 1 nonzero per row for the to be added values. This allows for fast shifting of an empty matrix.

    157: .keywords: matrix, add, shift

    159: .seealso: MatDiagonalSet()
    160:  @*/
    161: PetscErrorCode  MatShift(Mat Y,PetscScalar a)
    162: {

    167:   if (!Y->assembled) SETERRQ(PetscObjectComm((PetscObject)Y),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
    168:   if (Y->factortype) SETERRQ(PetscObjectComm((PetscObject)Y),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    169:   MatCheckPreallocated(Y,1);

    171:   (*Y->ops->shift)(Y,a);

    173: #if defined(PETSC_HAVE_CUSP)
    174:   if (Y->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
    175:     Y->valid_GPU_matrix = PETSC_CUSP_CPU;
    176:   }
    177: #endif
    178: #if defined(PETSC_HAVE_VIENNACL)
    179:   if (Y->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
    180:     Y->valid_GPU_matrix = PETSC_VIENNACL_CPU;
    181:   }
    182: #endif
    183:   return(0);
    184: }

    188: PetscErrorCode  MatDiagonalSet_Default(Mat Y,Vec D,InsertMode is)
    189: {
    191:   PetscInt       i,start,end;
    192:   PetscScalar    *v;

    195:   MatGetOwnershipRange(Y,&start,&end);
    196:   VecGetArray(D,&v);
    197:   for (i=start; i<end; i++) {
    198:     MatSetValues(Y,1,&i,1,&i,v+i-start,is);
    199:   }
    200:   VecRestoreArray(D,&v);
    201:   MatAssemblyBegin(Y,MAT_FINAL_ASSEMBLY);
    202:   MatAssemblyEnd(Y,MAT_FINAL_ASSEMBLY);
    203:   return(0);
    204: }

    208: /*@
    209:    MatDiagonalSet - Computes Y = Y + D, where D is a diagonal matrix
    210:    that is represented as a vector. Or Y[i,i] = D[i] if InsertMode is
    211:    INSERT_VALUES.

    213:    Input Parameters:
    214: +  Y - the input matrix
    215: .  D - the diagonal matrix, represented as a vector
    216: -  i - INSERT_VALUES or ADD_VALUES

    218:    Neighbor-wise Collective on Mat and Vec

    220:    Notes: If the matrix Y is missing some diagonal entries this routine can be very slow. To make it fast one should initially
    221:    fill the matrix so that all diagonal entries have a value (with a value of zero for those locations that would not have an
    222:    entry).

    224:    Level: intermediate

    226: .keywords: matrix, add, shift, diagonal

    228: .seealso: MatShift()
    229: @*/
    230: PetscErrorCode  MatDiagonalSet(Mat Y,Vec D,InsertMode is)
    231: {
    233:   PetscInt       matlocal,veclocal;

    238:   MatGetLocalSize(Y,&matlocal,NULL);
    239:   VecGetLocalSize(D,&veclocal);
    240:   if (matlocal != veclocal) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Number local rows of matrix %D does not match that of vector for diagonal %D",matlocal,veclocal);
    241:   if (Y->ops->diagonalset) {
    242:     (*Y->ops->diagonalset)(Y,D,is);
    243:   } else {
    244:     MatDiagonalSet_Default(Y,D,is);
    245:   }
    246:   return(0);
    247: }

    251: /*@
    252:    MatAYPX - Computes Y = a*Y + X.

    254:    Logically on Mat

    256:    Input Parameters:
    257: +  a - the PetscScalar multiplier
    258: .  Y - the first matrix
    259: .  X - the second matrix
    260: -  str - either SAME_NONZERO_PATTERN, DIFFERENT_NONZERO_PATTERN or SUBSET_NONZERO_PATTERN

    262:    Level: intermediate

    264: .keywords: matrix, add

    266: .seealso: MatAXPY()
    267:  @*/
    268: PetscErrorCode  MatAYPX(Mat Y,PetscScalar a,Mat X,MatStructure str)
    269: {
    270:   PetscScalar    one = 1.0;
    272:   PetscInt       mX,mY,nX,nY;

    278:   MatGetSize(X,&mX,&nX);
    279:   MatGetSize(X,&mY,&nY);
    280:   if (mX != mY || nX != nY) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Non conforming matrices: %D %D first %D %D second",mX,mY,nX,nY);

    282:   MatScale(Y,a);
    283:   MatAXPY(Y,one,X,str);
    284:   return(0);
    285: }

    289: /*@
    290:     MatComputeExplicitOperator - Computes the explicit matrix

    292:     Collective on Mat

    294:     Input Parameter:
    295: .   inmat - the matrix

    297:     Output Parameter:
    298: .   mat - the explict preconditioned operator

    300:     Notes:
    301:     This computation is done by applying the operators to columns of the
    302:     identity matrix.

    304:     Currently, this routine uses a dense matrix format when 1 processor
    305:     is used and a sparse format otherwise.  This routine is costly in general,
    306:     and is recommended for use only with relatively small systems.

    308:     Level: advanced

    310: .keywords: Mat, compute, explicit, operator
    311: @*/
    312: PetscErrorCode  MatComputeExplicitOperator(Mat inmat,Mat *mat)
    313: {
    314:   Vec            in,out;
    316:   PetscInt       i,m,n,M,N,*rows,start,end;
    317:   MPI_Comm       comm;
    318:   PetscScalar    *array,zero = 0.0,one = 1.0;
    319:   PetscMPIInt    size;

    325:   PetscObjectGetComm((PetscObject)inmat,&comm);
    326:   MPI_Comm_size(comm,&size);

    328:   MatGetLocalSize(inmat,&m,&n);
    329:   MatGetSize(inmat,&M,&N);
    330:   MatCreateVecs(inmat,&in,&out);
    331:   VecSetOption(in,VEC_IGNORE_OFF_PROC_ENTRIES,PETSC_TRUE);
    332:   VecGetOwnershipRange(out,&start,&end);
    333:   PetscMalloc1(m,&rows);
    334:   for (i=0; i<m; i++) rows[i] = start + i;

    336:   MatCreate(comm,mat);
    337:   MatSetSizes(*mat,m,n,M,N);
    338:   if (size == 1) {
    339:     MatSetType(*mat,MATSEQDENSE);
    340:     MatSeqDenseSetPreallocation(*mat,NULL);
    341:   } else {
    342:     MatSetType(*mat,MATMPIAIJ);
    343:     MatMPIAIJSetPreallocation(*mat,n,NULL,N-n,NULL);
    344:   }

    346:   for (i=0; i<N; i++) {

    348:     VecSet(in,zero);
    349:     VecSetValues(in,1,&i,&one,INSERT_VALUES);
    350:     VecAssemblyBegin(in);
    351:     VecAssemblyEnd(in);

    353:     MatMult(inmat,in,out);

    355:     VecGetArray(out,&array);
    356:     MatSetValues(*mat,m,rows,1,&i,array,INSERT_VALUES);
    357:     VecRestoreArray(out,&array);

    359:   }
    360:   PetscFree(rows);
    361:   VecDestroy(&out);
    362:   VecDestroy(&in);
    363:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
    364:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
    365:   return(0);
    366: }

    370: /*@
    371:   MatChop - Set all values in the matrix less than the tolerance to zero

    373:   Input Parameters:
    374: + A   - The matrix
    375: - tol - The zero tolerance

    377:   Output Parameters:
    378: . A - The chopped matrix

    380:   Level: intermediate

    382: .seealso: MatCreate(), MatZeroEntries()
    383:  @*/
    384: PetscErrorCode MatChop(Mat A, PetscReal tol)
    385: {
    386:   PetscScalar    *newVals;
    387:   PetscInt       *newCols;
    388:   PetscInt       rStart, rEnd, numRows, maxRows, r, colMax = 0;

    392:   MatGetOwnershipRange(A, &rStart, &rEnd);
    393:   for (r = rStart; r < rEnd; ++r) {
    394:     PetscInt ncols;

    396:     MatGetRow(A, r, &ncols, NULL, NULL);
    397:     colMax = PetscMax(colMax, ncols);
    398:     MatRestoreRow(A, r, &ncols, NULL, NULL);
    399:   }
    400:   numRows = rEnd - rStart;
    401:   MPI_Allreduce(&numRows, &maxRows, 1, MPIU_INT, MPI_MAX, PetscObjectComm((PetscObject)A));
    402:   PetscMalloc2(colMax,&newCols,colMax,&newVals);
    403:   for (r = rStart; r < rStart+maxRows; ++r) {
    404:     const PetscScalar *vals;
    405:     const PetscInt    *cols;
    406:     PetscInt           ncols, newcols, c;

    408:     if (r < rEnd) {
    409:       MatGetRow(A, r, &ncols, &cols, &vals);
    410:       for (c = 0; c < ncols; ++c) {
    411:         newCols[c] = cols[c];
    412:         newVals[c] = PetscAbsScalar(vals[c]) < tol ? 0.0 : vals[c];
    413:       }
    414:       newcols = ncols;
    415:       MatRestoreRow(A, r, &ncols, &cols, &vals);
    416:       MatSetValues(A, 1, &r, newcols, newCols, newVals, INSERT_VALUES);
    417:     }
    418:     MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY);
    419:     MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY);
    420:   }
    421:   PetscFree2(newCols,newVals);
    422:   return(0);
    423: }