Actual source code: axpy.c

petsc-3.9.2 2018-05-20
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  2:  #include <petsc/private/matimpl.h>

  4: /*@
  5:    MatAXPY - Computes Y = a*X + Y.

  7:    Logically  Collective on Mat

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

 16:    Level: intermediate

 18: .keywords: matrix, add

 20: .seealso: MatAYPX()
 21:  @*/
 22: PetscErrorCode MatAXPY(Mat Y,PetscScalar a,Mat X,MatStructure str)
 23: {
 25:   PetscInt       m1,m2,n1,n2;
 26:   PetscBool      sametype;

 32:   MatGetSize(X,&m1,&n1);
 33:   MatGetSize(Y,&m2,&n2);
 34:   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);

 36:   PetscStrcmp(((PetscObject)X)->type_name,((PetscObject)Y)->type_name,&sametype);
 37:   PetscLogEventBegin(MAT_AXPY,Y,0,0,0);
 38:   if (Y->ops->axpy && sametype) {
 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_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
 45:   if (Y->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
 46:     Y->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
 47:   }
 48: #endif
 49:   return(0);
 50: }

 52: PetscErrorCode MatAXPY_Basic(Mat Y,PetscScalar a,Mat X,MatStructure str)
 53: {
 54:   PetscInt          i,start,end,j,ncols,m,n;
 55:   PetscErrorCode    ierr;
 56:   const PetscInt    *row;
 57:   PetscScalar       *val;
 58:   const PetscScalar *vals;

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

 86: PetscErrorCode MatAXPY_BasicWithPreallocation(Mat B,Mat Y,PetscScalar a,Mat X,MatStructure str)
 87: {
 88:   PetscInt          i,start,end,j,ncols,m,n;
 89:   PetscErrorCode    ierr;
 90:   const PetscInt    *row;
 91:   PetscScalar       *val;
 92:   const PetscScalar *vals;

 95:   MatGetSize(X,&m,&n);
 96:   MatGetOwnershipRange(X,&start,&end);
 97:   if (a == 1.0) {
 98:     for (i = start; i < end; i++) {
 99:       MatGetRow(Y,i,&ncols,&row,&vals);
100:       MatSetValues(B,1,&i,ncols,row,vals,ADD_VALUES);
101:       MatRestoreRow(Y,i,&ncols,&row,&vals);

103:       MatGetRow(X,i,&ncols,&row,&vals);
104:       MatSetValues(B,1,&i,ncols,row,vals,ADD_VALUES);
105:       MatRestoreRow(X,i,&ncols,&row,&vals);
106:     }
107:   } else {
108:     PetscMalloc1(n+1,&val);
109:     for (i=start; i<end; i++) {
110:       MatGetRow(Y,i,&ncols,&row,&vals);
111:       MatSetValues(B,1,&i,ncols,row,vals,ADD_VALUES);
112:       MatRestoreRow(Y,i,&ncols,&row,&vals);

114:       MatGetRow(X,i,&ncols,&row,&vals);
115:       for (j=0; j<ncols; j++) {
116:         val[j] = a*vals[j];
117:       }
118:       MatSetValues(B,1,&i,ncols,row,val,ADD_VALUES);
119:       MatRestoreRow(X,i,&ncols,&row,&vals);
120:     }
121:     PetscFree(val);
122:   }
123:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
124:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
125:   return(0);
126: }

128: /*@
129:    MatShift - Computes Y =  Y + a I, where a is a PetscScalar and I is the identity matrix.

131:    Neighbor-wise Collective on Mat

133:    Input Parameters:
134: +  Y - the matrices
135: -  a - the PetscScalar

137:    Level: intermediate

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

143:    To form Y = Y + diag(V) use MatDiagonalSet()

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

148: .keywords: matrix, add, shift

150: .seealso: MatDiagonalSet(), MatScale(), MatDiagonalScale()
151:  @*/
152: PetscErrorCode  MatShift(Mat Y,PetscScalar a)
153: {

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

162:   if (Y->ops->shift) {
163:     (*Y->ops->shift)(Y,a);
164:   } else {
165:     MatShift_Basic(Y,a);
166:   }

168: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
169:   if (Y->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
170:     Y->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
171:   }
172: #endif
173:   return(0);
174: }

176: PetscErrorCode  MatDiagonalSet_Default(Mat Y,Vec D,InsertMode is)
177: {
179:   PetscInt       i,start,end;
180:   PetscScalar    *v;

183:   MatGetOwnershipRange(Y,&start,&end);
184:   VecGetArray(D,&v);
185:   for (i=start; i<end; i++) {
186:     MatSetValues(Y,1,&i,1,&i,v+i-start,is);
187:   }
188:   VecRestoreArray(D,&v);
189:   MatAssemblyBegin(Y,MAT_FINAL_ASSEMBLY);
190:   MatAssemblyEnd(Y,MAT_FINAL_ASSEMBLY);
191:   return(0);
192: }

194: /*@
195:    MatDiagonalSet - Computes Y = Y + D, where D is a diagonal matrix
196:    that is represented as a vector. Or Y[i,i] = D[i] if InsertMode is
197:    INSERT_VALUES.

199:    Input Parameters:
200: +  Y - the input matrix
201: .  D - the diagonal matrix, represented as a vector
202: -  i - INSERT_VALUES or ADD_VALUES

204:    Neighbor-wise Collective on Mat and Vec

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

210:    Level: intermediate

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

214: .seealso: MatShift(), MatScale(), MatDiagonalScale()
215: @*/
216: PetscErrorCode  MatDiagonalSet(Mat Y,Vec D,InsertMode is)
217: {
219:   PetscInt       matlocal,veclocal;

224:   MatGetLocalSize(Y,&matlocal,NULL);
225:   VecGetLocalSize(D,&veclocal);
226:   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);
227:   if (Y->ops->diagonalset) {
228:     (*Y->ops->diagonalset)(Y,D,is);
229:   } else {
230:     MatDiagonalSet_Default(Y,D,is);
231:   }
232:   return(0);
233: }

235: /*@
236:    MatAYPX - Computes Y = a*Y + X.

238:    Logically on Mat

240:    Input Parameters:
241: +  a - the PetscScalar multiplier
242: .  Y - the first matrix
243: .  X - the second matrix
244: -  str - either SAME_NONZERO_PATTERN, DIFFERENT_NONZERO_PATTERN or SUBSET_NONZERO_PATTERN

246:    Level: intermediate

248: .keywords: matrix, add

250: .seealso: MatAXPY()
251:  @*/
252: PetscErrorCode  MatAYPX(Mat Y,PetscScalar a,Mat X,MatStructure str)
253: {
254:   PetscScalar    one = 1.0;
256:   PetscInt       mX,mY,nX,nY;

262:   MatGetSize(X,&mX,&nX);
263:   MatGetSize(X,&mY,&nY);
264:   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);

266:   MatScale(Y,a);
267:   MatAXPY(Y,one,X,str);
268:   return(0);
269: }

271: /*@
272:     MatComputeExplicitOperator - Computes the explicit matrix

274:     Collective on Mat

276:     Input Parameter:
277: .   inmat - the matrix

279:     Output Parameter:
280: .   mat - the explict  operator

282:     Notes:
283:     This computation is done by applying the operators to columns of the
284:     identity matrix.

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

290:     Level: advanced

292: .keywords: Mat, compute, explicit, operator
293: @*/
294: PetscErrorCode  MatComputeExplicitOperator(Mat inmat,Mat *mat)
295: {
296:   Vec            in,out;
298:   PetscInt       i,m,n,M,N,*rows,start,end;
299:   MPI_Comm       comm;
300:   PetscScalar    *array,zero = 0.0,one = 1.0;
301:   PetscMPIInt    size;


307:   PetscObjectGetComm((PetscObject)inmat,&comm);
308:   MPI_Comm_size(comm,&size);

310:   MatGetLocalSize(inmat,&m,&n);
311:   MatGetSize(inmat,&M,&N);
312:   MatCreateVecs(inmat,&in,&out);
313:   VecSetOption(in,VEC_IGNORE_OFF_PROC_ENTRIES,PETSC_TRUE);
314:   VecGetOwnershipRange(out,&start,&end);
315:   PetscMalloc1(m,&rows);
316:   for (i=0; i<m; i++) rows[i] = start + i;

318:   MatCreate(comm,mat);
319:   MatSetSizes(*mat,m,n,M,N);
320:   if (size == 1) {
321:     MatSetType(*mat,MATSEQDENSE);
322:     MatSeqDenseSetPreallocation(*mat,NULL);
323:   } else {
324:     MatSetType(*mat,MATMPIAIJ);
325:     MatMPIAIJSetPreallocation(*mat,n,NULL,N-n,NULL);
326:   }

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

330:     VecSet(in,zero);
331:     VecSetValues(in,1,&i,&one,INSERT_VALUES);
332:     VecAssemblyBegin(in);
333:     VecAssemblyEnd(in);

335:     MatMult(inmat,in,out);

337:     VecGetArray(out,&array);
338:     MatSetValues(*mat,m,rows,1,&i,array,INSERT_VALUES);
339:     VecRestoreArray(out,&array);

341:   }
342:   PetscFree(rows);
343:   VecDestroy(&out);
344:   VecDestroy(&in);
345:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
346:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
347:   return(0);
348: }

350: /*@
351:     MatComputeExplicitOperatorTranspose - Computes the explicit matrix representation of
352:         a give matrix that can apply MatMultTranspose()

354:     Collective on Mat

356:     Input Parameter:
357: .   inmat - the matrix

359:     Output Parameter:
360: .   mat - the explict  operator transposed

362:     Notes:
363:     This computation is done by applying the transpose of the operator to columns of the
364:     identity matrix.

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

370:     Level: advanced

372: .keywords: Mat, compute, explicit, operator
373: @*/
374: PetscErrorCode  MatComputeExplicitOperatorTranspose(Mat inmat,Mat *mat)
375: {
376:   Vec            in,out;
378:   PetscInt       i,m,n,M,N,*rows,start,end;
379:   MPI_Comm       comm;
380:   PetscScalar    *array,zero = 0.0,one = 1.0;
381:   PetscMPIInt    size;


387:   PetscObjectGetComm((PetscObject)inmat,&comm);
388:   MPI_Comm_size(comm,&size);

390:   MatGetLocalSize(inmat,&m,&n);
391:   MatGetSize(inmat,&M,&N);
392:   MatCreateVecs(inmat,&in,&out);
393:   VecSetOption(in,VEC_IGNORE_OFF_PROC_ENTRIES,PETSC_TRUE);
394:   VecGetOwnershipRange(out,&start,&end);
395:   PetscMalloc1(m,&rows);
396:   for (i=0; i<m; i++) rows[i] = start + i;

398:   MatCreate(comm,mat);
399:   MatSetSizes(*mat,m,n,M,N);
400:   if (size == 1) {
401:     MatSetType(*mat,MATSEQDENSE);
402:     MatSeqDenseSetPreallocation(*mat,NULL);
403:   } else {
404:     MatSetType(*mat,MATMPIAIJ);
405:     MatMPIAIJSetPreallocation(*mat,n,NULL,N-n,NULL);
406:   }

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

410:     VecSet(in,zero);
411:     VecSetValues(in,1,&i,&one,INSERT_VALUES);
412:     VecAssemblyBegin(in);
413:     VecAssemblyEnd(in);

415:     MatMultTranspose(inmat,in,out);

417:     VecGetArray(out,&array);
418:     MatSetValues(*mat,m,rows,1,&i,array,INSERT_VALUES);
419:     VecRestoreArray(out,&array);

421:   }
422:   PetscFree(rows);
423:   VecDestroy(&out);
424:   VecDestroy(&in);
425:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
426:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
427:   return(0);
428: }

430: /*@
431:   MatChop - Set all values in the matrix less than the tolerance to zero

433:   Input Parameters:
434: + A   - The matrix
435: - tol - The zero tolerance

437:   Output Parameters:
438: . A - The chopped matrix

440:   Level: intermediate

442: .seealso: MatCreate(), MatZeroEntries()
443:  @*/
444: PetscErrorCode MatChop(Mat A, PetscReal tol)
445: {
446:   PetscScalar    *newVals;
447:   PetscInt       *newCols;
448:   PetscInt       rStart, rEnd, numRows, maxRows, r, colMax = 0;

452:   MatGetOwnershipRange(A, &rStart, &rEnd);
453:   for (r = rStart; r < rEnd; ++r) {
454:     PetscInt ncols;

456:     MatGetRow(A, r, &ncols, NULL, NULL);
457:     colMax = PetscMax(colMax, ncols);
458:     MatRestoreRow(A, r, &ncols, NULL, NULL);
459:   }
460:   numRows = rEnd - rStart;
461:   MPIU_Allreduce(&numRows, &maxRows, 1, MPIU_INT, MPI_MAX, PetscObjectComm((PetscObject)A));
462:   PetscMalloc2(colMax,&newCols,colMax,&newVals);
463:   for (r = rStart; r < rStart+maxRows; ++r) {
464:     const PetscScalar *vals;
465:     const PetscInt    *cols;
466:     PetscInt           ncols, newcols, c;

468:     if (r < rEnd) {
469:       MatGetRow(A, r, &ncols, &cols, &vals);
470:       for (c = 0; c < ncols; ++c) {
471:         newCols[c] = cols[c];
472:         newVals[c] = PetscAbsScalar(vals[c]) < tol ? 0.0 : vals[c];
473:       }
474:       newcols = ncols;
475:       MatRestoreRow(A, r, &ncols, &cols, &vals);
476:       MatSetValues(A, 1, &r, newcols, newCols, newVals, INSERT_VALUES);
477:     }
478:     MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY);
479:     MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY);
480:   }
481:   PetscFree2(newCols,newVals);
482:   return(0);
483: }