1 2 /* 3 Basic functions for basic parallel dense matrices. 4 */ 5 6 7 #include <../src/mat/impls/dense/mpi/mpidense.h> /*I "petscmat.h" I*/ 8 9 #undef __FUNCT__ 10 #define __FUNCT__ "MatDenseGetLocalMatrix" 11 /*@ 12 13 MatDenseGetLocalMatrix - For a MATMPIDENSE or MATSEQDENSE matrix returns the sequential 14 matrix that represents the operator. For sequential matrices it returns itself. 15 16 Input Parameter: 17 . A - the Seq or MPI dense matrix 18 19 Output Parameter: 20 . B - the inner matrix 21 22 Level: intermediate 23 24 @*/ 25 PetscErrorCode MatDenseGetLocalMatrix(Mat A,Mat *B) 26 { 27 Mat_MPIDense *mat = (Mat_MPIDense*)A->data; 28 PetscErrorCode ierr; 29 PetscBool flg; 30 31 PetscFunctionBegin; 32 ierr = PetscObjectTypeCompare((PetscObject)A,MATMPIDENSE,&flg);CHKERRQ(ierr); 33 if (flg) *B = mat->A; 34 else *B = A; 35 PetscFunctionReturn(0); 36 } 37 38 #undef __FUNCT__ 39 #define __FUNCT__ "MatGetRow_MPIDense" 40 PetscErrorCode MatGetRow_MPIDense(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v) 41 { 42 Mat_MPIDense *mat = (Mat_MPIDense*)A->data; 43 PetscErrorCode ierr; 44 PetscInt lrow,rstart = A->rmap->rstart,rend = A->rmap->rend; 45 46 PetscFunctionBegin; 47 if (row < rstart || row >= rend) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"only local rows"); 48 lrow = row - rstart; 49 ierr = MatGetRow(mat->A,lrow,nz,(const PetscInt**)idx,(const PetscScalar**)v);CHKERRQ(ierr); 50 PetscFunctionReturn(0); 51 } 52 53 #undef __FUNCT__ 54 #define __FUNCT__ "MatRestoreRow_MPIDense" 55 PetscErrorCode MatRestoreRow_MPIDense(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v) 56 { 57 PetscErrorCode ierr; 58 59 PetscFunctionBegin; 60 if (idx) {ierr = PetscFree(*idx);CHKERRQ(ierr);} 61 if (v) {ierr = PetscFree(*v);CHKERRQ(ierr);} 62 PetscFunctionReturn(0); 63 } 64 65 #undef __FUNCT__ 66 #define __FUNCT__ "MatGetDiagonalBlock_MPIDense" 67 PetscErrorCode MatGetDiagonalBlock_MPIDense(Mat A,Mat *a) 68 { 69 Mat_MPIDense *mdn = (Mat_MPIDense*)A->data; 70 PetscErrorCode ierr; 71 PetscInt m = A->rmap->n,rstart = A->rmap->rstart; 72 PetscScalar *array; 73 MPI_Comm comm; 74 Mat B; 75 76 PetscFunctionBegin; 77 if (A->rmap->N != A->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only square matrices supported."); 78 79 ierr = PetscObjectQuery((PetscObject)A,"DiagonalBlock",(PetscObject*)&B);CHKERRQ(ierr); 80 if (!B) { 81 ierr = PetscObjectGetComm((PetscObject)(mdn->A),&comm);CHKERRQ(ierr); 82 ierr = MatCreate(comm,&B);CHKERRQ(ierr); 83 ierr = MatSetSizes(B,m,m,m,m);CHKERRQ(ierr); 84 ierr = MatSetType(B,((PetscObject)mdn->A)->type_name);CHKERRQ(ierr); 85 ierr = MatDenseGetArray(mdn->A,&array);CHKERRQ(ierr); 86 ierr = MatSeqDenseSetPreallocation(B,array+m*rstart);CHKERRQ(ierr); 87 ierr = MatDenseRestoreArray(mdn->A,&array);CHKERRQ(ierr); 88 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 89 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 90 ierr = PetscObjectCompose((PetscObject)A,"DiagonalBlock",(PetscObject)B);CHKERRQ(ierr); 91 *a = B; 92 ierr = MatDestroy(&B);CHKERRQ(ierr); 93 } else *a = B; 94 PetscFunctionReturn(0); 95 } 96 97 #undef __FUNCT__ 98 #define __FUNCT__ "MatSetValues_MPIDense" 99 PetscErrorCode MatSetValues_MPIDense(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv) 100 { 101 Mat_MPIDense *A = (Mat_MPIDense*)mat->data; 102 PetscErrorCode ierr; 103 PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend,row; 104 PetscBool roworiented = A->roworiented; 105 106 PetscFunctionBegin; 107 if (v) PetscValidScalarPointer(v,6); 108 for (i=0; i<m; i++) { 109 if (idxm[i] < 0) continue; 110 if (idxm[i] >= mat->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large"); 111 if (idxm[i] >= rstart && idxm[i] < rend) { 112 row = idxm[i] - rstart; 113 if (roworiented) { 114 ierr = MatSetValues(A->A,1,&row,n,idxn,v+i*n,addv);CHKERRQ(ierr); 115 } else { 116 for (j=0; j<n; j++) { 117 if (idxn[j] < 0) continue; 118 if (idxn[j] >= mat->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large"); 119 ierr = MatSetValues(A->A,1,&row,1,&idxn[j],v+i+j*m,addv);CHKERRQ(ierr); 120 } 121 } 122 } else if (!A->donotstash) { 123 mat->assembled = PETSC_FALSE; 124 if (roworiented) { 125 ierr = MatStashValuesRow_Private(&mat->stash,idxm[i],n,idxn,v+i*n,PETSC_FALSE);CHKERRQ(ierr); 126 } else { 127 ierr = MatStashValuesCol_Private(&mat->stash,idxm[i],n,idxn,v+i,m,PETSC_FALSE);CHKERRQ(ierr); 128 } 129 } 130 } 131 PetscFunctionReturn(0); 132 } 133 134 #undef __FUNCT__ 135 #define __FUNCT__ "MatGetValues_MPIDense" 136 PetscErrorCode MatGetValues_MPIDense(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[]) 137 { 138 Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data; 139 PetscErrorCode ierr; 140 PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend,row; 141 142 PetscFunctionBegin; 143 for (i=0; i<m; i++) { 144 if (idxm[i] < 0) continue; /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row"); */ 145 if (idxm[i] >= mat->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large"); 146 if (idxm[i] >= rstart && idxm[i] < rend) { 147 row = idxm[i] - rstart; 148 for (j=0; j<n; j++) { 149 if (idxn[j] < 0) continue; /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column"); */ 150 if (idxn[j] >= mat->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large"); 151 ierr = MatGetValues(mdn->A,1,&row,1,&idxn[j],v+i*n+j);CHKERRQ(ierr); 152 } 153 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local values currently supported"); 154 } 155 PetscFunctionReturn(0); 156 } 157 158 #undef __FUNCT__ 159 #define __FUNCT__ "MatDenseGetArray_MPIDense" 160 PetscErrorCode MatDenseGetArray_MPIDense(Mat A,PetscScalar *array[]) 161 { 162 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 163 PetscErrorCode ierr; 164 165 PetscFunctionBegin; 166 ierr = MatDenseGetArray(a->A,array);CHKERRQ(ierr); 167 PetscFunctionReturn(0); 168 } 169 170 #undef __FUNCT__ 171 #define __FUNCT__ "MatGetSubMatrix_MPIDense" 172 static PetscErrorCode MatGetSubMatrix_MPIDense(Mat A,IS isrow,IS iscol,MatReuse scall,Mat *B) 173 { 174 Mat_MPIDense *mat = (Mat_MPIDense*)A->data,*newmatd; 175 Mat_SeqDense *lmat = (Mat_SeqDense*)mat->A->data; 176 PetscErrorCode ierr; 177 PetscInt i,j,rstart,rend,nrows,ncols,Ncols,nlrows,nlcols; 178 const PetscInt *irow,*icol; 179 PetscScalar *av,*bv,*v = lmat->v; 180 Mat newmat; 181 IS iscol_local; 182 183 PetscFunctionBegin; 184 ierr = ISAllGather(iscol,&iscol_local);CHKERRQ(ierr); 185 ierr = ISGetIndices(isrow,&irow);CHKERRQ(ierr); 186 ierr = ISGetIndices(iscol_local,&icol);CHKERRQ(ierr); 187 ierr = ISGetLocalSize(isrow,&nrows);CHKERRQ(ierr); 188 ierr = ISGetLocalSize(iscol,&ncols);CHKERRQ(ierr); 189 ierr = ISGetSize(iscol,&Ncols);CHKERRQ(ierr); /* global number of columns, size of iscol_local */ 190 191 /* No parallel redistribution currently supported! Should really check each index set 192 to comfirm that it is OK. ... Currently supports only submatrix same partitioning as 193 original matrix! */ 194 195 ierr = MatGetLocalSize(A,&nlrows,&nlcols);CHKERRQ(ierr); 196 ierr = MatGetOwnershipRange(A,&rstart,&rend);CHKERRQ(ierr); 197 198 /* Check submatrix call */ 199 if (scall == MAT_REUSE_MATRIX) { 200 /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size"); */ 201 /* Really need to test rows and column sizes! */ 202 newmat = *B; 203 } else { 204 /* Create and fill new matrix */ 205 ierr = MatCreate(PetscObjectComm((PetscObject)A),&newmat);CHKERRQ(ierr); 206 ierr = MatSetSizes(newmat,nrows,ncols,PETSC_DECIDE,Ncols);CHKERRQ(ierr); 207 ierr = MatSetType(newmat,((PetscObject)A)->type_name);CHKERRQ(ierr); 208 ierr = MatMPIDenseSetPreallocation(newmat,NULL);CHKERRQ(ierr); 209 } 210 211 /* Now extract the data pointers and do the copy, column at a time */ 212 newmatd = (Mat_MPIDense*)newmat->data; 213 bv = ((Mat_SeqDense*)newmatd->A->data)->v; 214 215 for (i=0; i<Ncols; i++) { 216 av = v + ((Mat_SeqDense*)mat->A->data)->lda*icol[i]; 217 for (j=0; j<nrows; j++) { 218 *bv++ = av[irow[j] - rstart]; 219 } 220 } 221 222 /* Assemble the matrices so that the correct flags are set */ 223 ierr = MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 224 ierr = MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 225 226 /* Free work space */ 227 ierr = ISRestoreIndices(isrow,&irow);CHKERRQ(ierr); 228 ierr = ISRestoreIndices(iscol_local,&icol);CHKERRQ(ierr); 229 ierr = ISDestroy(&iscol_local);CHKERRQ(ierr); 230 *B = newmat; 231 PetscFunctionReturn(0); 232 } 233 234 #undef __FUNCT__ 235 #define __FUNCT__ "MatDenseRestoreArray_MPIDense" 236 PetscErrorCode MatDenseRestoreArray_MPIDense(Mat A,PetscScalar *array[]) 237 { 238 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 239 PetscErrorCode ierr; 240 241 PetscFunctionBegin; 242 ierr = MatDenseRestoreArray(a->A,array);CHKERRQ(ierr); 243 PetscFunctionReturn(0); 244 } 245 246 #undef __FUNCT__ 247 #define __FUNCT__ "MatAssemblyBegin_MPIDense" 248 PetscErrorCode MatAssemblyBegin_MPIDense(Mat mat,MatAssemblyType mode) 249 { 250 Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data; 251 MPI_Comm comm; 252 PetscErrorCode ierr; 253 PetscInt nstash,reallocs; 254 InsertMode addv; 255 256 PetscFunctionBegin; 257 ierr = PetscObjectGetComm((PetscObject)mat,&comm);CHKERRQ(ierr); 258 /* make sure all processors are either in INSERTMODE or ADDMODE */ 259 ierr = MPI_Allreduce((PetscEnum*)&mat->insertmode,(PetscEnum*)&addv,1,MPIU_ENUM,MPI_BOR,comm);CHKERRQ(ierr); 260 if (addv == (ADD_VALUES|INSERT_VALUES)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Cannot mix adds/inserts on different procs"); 261 mat->insertmode = addv; /* in case this processor had no cache */ 262 263 ierr = MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);CHKERRQ(ierr); 264 ierr = MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);CHKERRQ(ierr); 265 ierr = PetscInfo2(mdn->A,"Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);CHKERRQ(ierr); 266 PetscFunctionReturn(0); 267 } 268 269 #undef __FUNCT__ 270 #define __FUNCT__ "MatAssemblyEnd_MPIDense" 271 PetscErrorCode MatAssemblyEnd_MPIDense(Mat mat,MatAssemblyType mode) 272 { 273 Mat_MPIDense *mdn=(Mat_MPIDense*)mat->data; 274 PetscErrorCode ierr; 275 PetscInt i,*row,*col,flg,j,rstart,ncols; 276 PetscMPIInt n; 277 PetscScalar *val; 278 InsertMode addv=mat->insertmode; 279 280 PetscFunctionBegin; 281 /* wait on receives */ 282 while (1) { 283 ierr = MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);CHKERRQ(ierr); 284 if (!flg) break; 285 286 for (i=0; i<n;) { 287 /* Now identify the consecutive vals belonging to the same row */ 288 for (j=i,rstart=row[j]; j<n; j++) { 289 if (row[j] != rstart) break; 290 } 291 if (j < n) ncols = j-i; 292 else ncols = n-i; 293 /* Now assemble all these values with a single function call */ 294 ierr = MatSetValues_MPIDense(mat,1,row+i,ncols,col+i,val+i,addv);CHKERRQ(ierr); 295 i = j; 296 } 297 } 298 ierr = MatStashScatterEnd_Private(&mat->stash);CHKERRQ(ierr); 299 300 ierr = MatAssemblyBegin(mdn->A,mode);CHKERRQ(ierr); 301 ierr = MatAssemblyEnd(mdn->A,mode);CHKERRQ(ierr); 302 303 if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) { 304 ierr = MatSetUpMultiply_MPIDense(mat);CHKERRQ(ierr); 305 } 306 PetscFunctionReturn(0); 307 } 308 309 #undef __FUNCT__ 310 #define __FUNCT__ "MatZeroEntries_MPIDense" 311 PetscErrorCode MatZeroEntries_MPIDense(Mat A) 312 { 313 PetscErrorCode ierr; 314 Mat_MPIDense *l = (Mat_MPIDense*)A->data; 315 316 PetscFunctionBegin; 317 ierr = MatZeroEntries(l->A);CHKERRQ(ierr); 318 PetscFunctionReturn(0); 319 } 320 321 /* the code does not do the diagonal entries correctly unless the 322 matrix is square and the column and row owerships are identical. 323 This is a BUG. The only way to fix it seems to be to access 324 mdn->A and mdn->B directly and not through the MatZeroRows() 325 routine. 326 */ 327 #undef __FUNCT__ 328 #define __FUNCT__ "MatZeroRows_MPIDense" 329 PetscErrorCode MatZeroRows_MPIDense(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b) 330 { 331 Mat_MPIDense *l = (Mat_MPIDense*)A->data; 332 PetscErrorCode ierr; 333 PetscInt i,*owners = A->rmap->range; 334 PetscInt *nprocs,j,idx,nsends; 335 PetscInt nmax,*svalues,*starts,*owner,nrecvs; 336 PetscInt *rvalues,tag = ((PetscObject)A)->tag,count,base,slen,*source; 337 PetscInt *lens,*lrows,*values; 338 PetscMPIInt n,imdex,rank = l->rank,size = l->size; 339 MPI_Comm comm; 340 MPI_Request *send_waits,*recv_waits; 341 MPI_Status recv_status,*send_status; 342 PetscBool found; 343 const PetscScalar *xx; 344 PetscScalar *bb; 345 346 PetscFunctionBegin; 347 ierr = PetscObjectGetComm((PetscObject)A,&comm);CHKERRQ(ierr); 348 if (A->rmap->N != A->cmap->N) SETERRQ(comm,PETSC_ERR_SUP,"Only handles square matrices"); 349 if (A->rmap->n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only handles matrices with identical column and row ownership"); 350 /* first count number of contributors to each processor */ 351 ierr = PetscMalloc(2*size*sizeof(PetscInt),&nprocs);CHKERRQ(ierr); 352 ierr = PetscMemzero(nprocs,2*size*sizeof(PetscInt));CHKERRQ(ierr); 353 ierr = PetscMalloc((N+1)*sizeof(PetscInt),&owner);CHKERRQ(ierr); /* see note*/ 354 for (i=0; i<N; i++) { 355 idx = rows[i]; 356 found = PETSC_FALSE; 357 for (j=0; j<size; j++) { 358 if (idx >= owners[j] && idx < owners[j+1]) { 359 nprocs[2*j]++; nprocs[2*j+1] = 1; owner[i] = j; found = PETSC_TRUE; break; 360 } 361 } 362 if (!found) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Index out of range"); 363 } 364 nsends = 0; 365 for (i=0; i<size; i++) nsends += nprocs[2*i+1]; 366 367 /* inform other processors of number of messages and max length*/ 368 ierr = PetscMaxSum(comm,nprocs,&nmax,&nrecvs);CHKERRQ(ierr); 369 370 /* post receives: */ 371 ierr = PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(PetscInt),&rvalues);CHKERRQ(ierr); 372 ierr = PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);CHKERRQ(ierr); 373 for (i=0; i<nrecvs; i++) { 374 ierr = MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);CHKERRQ(ierr); 375 } 376 377 /* do sends: 378 1) starts[i] gives the starting index in svalues for stuff going to 379 the ith processor 380 */ 381 ierr = PetscMalloc((N+1)*sizeof(PetscInt),&svalues);CHKERRQ(ierr); 382 ierr = PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);CHKERRQ(ierr); 383 ierr = PetscMalloc((size+1)*sizeof(PetscInt),&starts);CHKERRQ(ierr); 384 385 starts[0] = 0; 386 for (i=1; i<size; i++) starts[i] = starts[i-1] + nprocs[2*i-2]; 387 for (i=0; i<N; i++) svalues[starts[owner[i]]++] = rows[i]; 388 389 starts[0] = 0; 390 for (i=1; i<size+1; i++) starts[i] = starts[i-1] + nprocs[2*i-2]; 391 count = 0; 392 for (i=0; i<size; i++) { 393 if (nprocs[2*i+1]) { 394 ierr = MPI_Isend(svalues+starts[i],nprocs[2*i],MPIU_INT,i,tag,comm,send_waits+count++);CHKERRQ(ierr); 395 } 396 } 397 ierr = PetscFree(starts);CHKERRQ(ierr); 398 399 base = owners[rank]; 400 401 /* wait on receives */ 402 ierr = PetscMalloc2(nrecvs,PetscInt,&lens,nrecvs,PetscInt,&source);CHKERRQ(ierr); 403 count = nrecvs; 404 slen = 0; 405 while (count) { 406 ierr = MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);CHKERRQ(ierr); 407 /* unpack receives into our local space */ 408 ierr = MPI_Get_count(&recv_status,MPIU_INT,&n);CHKERRQ(ierr); 409 410 source[imdex] = recv_status.MPI_SOURCE; 411 lens[imdex] = n; 412 slen += n; 413 count--; 414 } 415 ierr = PetscFree(recv_waits);CHKERRQ(ierr); 416 417 /* move the data into the send scatter */ 418 ierr = PetscMalloc((slen+1)*sizeof(PetscInt),&lrows);CHKERRQ(ierr); 419 count = 0; 420 for (i=0; i<nrecvs; i++) { 421 values = rvalues + i*nmax; 422 for (j=0; j<lens[i]; j++) { 423 lrows[count++] = values[j] - base; 424 } 425 } 426 ierr = PetscFree(rvalues);CHKERRQ(ierr); 427 ierr = PetscFree2(lens,source);CHKERRQ(ierr); 428 ierr = PetscFree(owner);CHKERRQ(ierr); 429 ierr = PetscFree(nprocs);CHKERRQ(ierr); 430 431 /* fix right hand side if needed */ 432 if (x && b) { 433 ierr = VecGetArrayRead(x,&xx);CHKERRQ(ierr); 434 ierr = VecGetArray(b,&bb);CHKERRQ(ierr); 435 for (i=0; i<slen; i++) { 436 bb[lrows[i]] = diag*xx[lrows[i]]; 437 } 438 ierr = VecRestoreArrayRead(x,&xx);CHKERRQ(ierr); 439 ierr = VecRestoreArray(b,&bb);CHKERRQ(ierr); 440 } 441 442 /* actually zap the local rows */ 443 ierr = MatZeroRows(l->A,slen,lrows,0.0,0,0);CHKERRQ(ierr); 444 if (diag != 0.0) { 445 Mat_SeqDense *ll = (Mat_SeqDense*)l->A->data; 446 PetscInt m = ll->lda, i; 447 448 for (i=0; i<slen; i++) { 449 ll->v[lrows[i] + m*(A->cmap->rstart + lrows[i])] = diag; 450 } 451 } 452 ierr = PetscFree(lrows);CHKERRQ(ierr); 453 454 /* wait on sends */ 455 if (nsends) { 456 ierr = PetscMalloc(nsends*sizeof(MPI_Status),&send_status);CHKERRQ(ierr); 457 ierr = MPI_Waitall(nsends,send_waits,send_status);CHKERRQ(ierr); 458 ierr = PetscFree(send_status);CHKERRQ(ierr); 459 } 460 ierr = PetscFree(send_waits);CHKERRQ(ierr); 461 ierr = PetscFree(svalues);CHKERRQ(ierr); 462 PetscFunctionReturn(0); 463 } 464 465 #undef __FUNCT__ 466 #define __FUNCT__ "MatMult_MPIDense" 467 PetscErrorCode MatMult_MPIDense(Mat mat,Vec xx,Vec yy) 468 { 469 Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data; 470 PetscErrorCode ierr; 471 472 PetscFunctionBegin; 473 ierr = VecScatterBegin(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 474 ierr = VecScatterEnd(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 475 ierr = MatMult_SeqDense(mdn->A,mdn->lvec,yy);CHKERRQ(ierr); 476 PetscFunctionReturn(0); 477 } 478 479 #undef __FUNCT__ 480 #define __FUNCT__ "MatMultAdd_MPIDense" 481 PetscErrorCode MatMultAdd_MPIDense(Mat mat,Vec xx,Vec yy,Vec zz) 482 { 483 Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data; 484 PetscErrorCode ierr; 485 486 PetscFunctionBegin; 487 ierr = VecScatterBegin(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 488 ierr = VecScatterEnd(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 489 ierr = MatMultAdd_SeqDense(mdn->A,mdn->lvec,yy,zz);CHKERRQ(ierr); 490 PetscFunctionReturn(0); 491 } 492 493 #undef __FUNCT__ 494 #define __FUNCT__ "MatMultTranspose_MPIDense" 495 PetscErrorCode MatMultTranspose_MPIDense(Mat A,Vec xx,Vec yy) 496 { 497 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 498 PetscErrorCode ierr; 499 PetscScalar zero = 0.0; 500 501 PetscFunctionBegin; 502 ierr = VecSet(yy,zero);CHKERRQ(ierr); 503 ierr = MatMultTranspose_SeqDense(a->A,xx,a->lvec);CHKERRQ(ierr); 504 ierr = VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);CHKERRQ(ierr); 505 ierr = VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);CHKERRQ(ierr); 506 PetscFunctionReturn(0); 507 } 508 509 #undef __FUNCT__ 510 #define __FUNCT__ "MatMultTransposeAdd_MPIDense" 511 PetscErrorCode MatMultTransposeAdd_MPIDense(Mat A,Vec xx,Vec yy,Vec zz) 512 { 513 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 514 PetscErrorCode ierr; 515 516 PetscFunctionBegin; 517 ierr = VecCopy(yy,zz);CHKERRQ(ierr); 518 ierr = MatMultTranspose_SeqDense(a->A,xx,a->lvec);CHKERRQ(ierr); 519 ierr = VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);CHKERRQ(ierr); 520 ierr = VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);CHKERRQ(ierr); 521 PetscFunctionReturn(0); 522 } 523 524 #undef __FUNCT__ 525 #define __FUNCT__ "MatGetDiagonal_MPIDense" 526 PetscErrorCode MatGetDiagonal_MPIDense(Mat A,Vec v) 527 { 528 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 529 Mat_SeqDense *aloc = (Mat_SeqDense*)a->A->data; 530 PetscErrorCode ierr; 531 PetscInt len,i,n,m = A->rmap->n,radd; 532 PetscScalar *x,zero = 0.0; 533 534 PetscFunctionBegin; 535 ierr = VecSet(v,zero);CHKERRQ(ierr); 536 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 537 ierr = VecGetSize(v,&n);CHKERRQ(ierr); 538 if (n != A->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming mat and vec"); 539 len = PetscMin(a->A->rmap->n,a->A->cmap->n); 540 radd = A->rmap->rstart*m; 541 for (i=0; i<len; i++) { 542 x[i] = aloc->v[radd + i*m + i]; 543 } 544 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 545 PetscFunctionReturn(0); 546 } 547 548 #undef __FUNCT__ 549 #define __FUNCT__ "MatDestroy_MPIDense" 550 PetscErrorCode MatDestroy_MPIDense(Mat mat) 551 { 552 Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data; 553 PetscErrorCode ierr; 554 555 PetscFunctionBegin; 556 #if defined(PETSC_USE_LOG) 557 PetscLogObjectState((PetscObject)mat,"Rows=%D, Cols=%D",mat->rmap->N,mat->cmap->N); 558 #endif 559 ierr = MatStashDestroy_Private(&mat->stash);CHKERRQ(ierr); 560 ierr = MatDestroy(&mdn->A);CHKERRQ(ierr); 561 ierr = VecDestroy(&mdn->lvec);CHKERRQ(ierr); 562 ierr = VecScatterDestroy(&mdn->Mvctx);CHKERRQ(ierr); 563 564 ierr = PetscFree(mat->data);CHKERRQ(ierr); 565 ierr = PetscObjectChangeTypeName((PetscObject)mat,0);CHKERRQ(ierr); 566 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C","",NULL);CHKERRQ(ierr); 567 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMPIDenseSetPreallocation_C","",NULL);CHKERRQ(ierr); 568 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMatMult_mpiaij_mpidense_C","",NULL);CHKERRQ(ierr); 569 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMatMultSymbolic_mpiaij_mpidense_C","",NULL);CHKERRQ(ierr); 570 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMatMultNumeric_mpiaij_mpidense_C","",NULL);CHKERRQ(ierr); 571 PetscFunctionReturn(0); 572 } 573 574 #undef __FUNCT__ 575 #define __FUNCT__ "MatView_MPIDense_Binary" 576 static PetscErrorCode MatView_MPIDense_Binary(Mat mat,PetscViewer viewer) 577 { 578 Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data; 579 PetscErrorCode ierr; 580 PetscViewerFormat format; 581 int fd; 582 PetscInt header[4],mmax,N = mat->cmap->N,i,j,m,k; 583 PetscMPIInt rank,tag = ((PetscObject)viewer)->tag,size; 584 PetscScalar *work,*v,*vv; 585 Mat_SeqDense *a = (Mat_SeqDense*)mdn->A->data; 586 587 PetscFunctionBegin; 588 if (mdn->size == 1) { 589 ierr = MatView(mdn->A,viewer);CHKERRQ(ierr); 590 } else { 591 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 592 ierr = MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);CHKERRQ(ierr); 593 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);CHKERRQ(ierr); 594 595 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 596 if (format == PETSC_VIEWER_NATIVE) { 597 598 if (!rank) { 599 /* store the matrix as a dense matrix */ 600 header[0] = MAT_FILE_CLASSID; 601 header[1] = mat->rmap->N; 602 header[2] = N; 603 header[3] = MATRIX_BINARY_FORMAT_DENSE; 604 ierr = PetscBinaryWrite(fd,header,4,PETSC_INT,PETSC_TRUE);CHKERRQ(ierr); 605 606 /* get largest work array needed for transposing array */ 607 mmax = mat->rmap->n; 608 for (i=1; i<size; i++) { 609 mmax = PetscMax(mmax,mat->rmap->range[i+1] - mat->rmap->range[i]); 610 } 611 ierr = PetscMalloc(mmax*N*sizeof(PetscScalar),&work);CHKERRQ(ierr); 612 613 /* write out local array, by rows */ 614 m = mat->rmap->n; 615 v = a->v; 616 for (j=0; j<N; j++) { 617 for (i=0; i<m; i++) { 618 work[j + i*N] = *v++; 619 } 620 } 621 ierr = PetscBinaryWrite(fd,work,m*N,PETSC_SCALAR,PETSC_FALSE);CHKERRQ(ierr); 622 /* get largest work array to receive messages from other processes, excludes process zero */ 623 mmax = 0; 624 for (i=1; i<size; i++) { 625 mmax = PetscMax(mmax,mat->rmap->range[i+1] - mat->rmap->range[i]); 626 } 627 ierr = PetscMalloc(mmax*N*sizeof(PetscScalar),&vv);CHKERRQ(ierr); 628 for (k = 1; k < size; k++) { 629 v = vv; 630 m = mat->rmap->range[k+1] - mat->rmap->range[k]; 631 ierr = MPIULong_Recv(v,m*N,MPIU_SCALAR,k,tag,PetscObjectComm((PetscObject)mat));CHKERRQ(ierr); 632 633 for (j = 0; j < N; j++) { 634 for (i = 0; i < m; i++) { 635 work[j + i*N] = *v++; 636 } 637 } 638 ierr = PetscBinaryWrite(fd,work,m*N,PETSC_SCALAR,PETSC_FALSE);CHKERRQ(ierr); 639 } 640 ierr = PetscFree(work);CHKERRQ(ierr); 641 ierr = PetscFree(vv);CHKERRQ(ierr); 642 } else { 643 ierr = MPIULong_Send(a->v,mat->rmap->n*mat->cmap->N,MPIU_SCALAR,0,tag,PetscObjectComm((PetscObject)mat));CHKERRQ(ierr); 644 } 645 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"To store a parallel dense matrix you must first call PetscViewerSetFormat(viewer,PETSC_VIEWER_NATIVE)"); 646 } 647 PetscFunctionReturn(0); 648 } 649 650 #include <petscdraw.h> 651 #undef __FUNCT__ 652 #define __FUNCT__ "MatView_MPIDense_ASCIIorDraworSocket" 653 static PetscErrorCode MatView_MPIDense_ASCIIorDraworSocket(Mat mat,PetscViewer viewer) 654 { 655 Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data; 656 PetscErrorCode ierr; 657 PetscMPIInt size = mdn->size,rank = mdn->rank; 658 PetscViewerType vtype; 659 PetscBool iascii,isdraw; 660 PetscViewer sviewer; 661 PetscViewerFormat format; 662 663 PetscFunctionBegin; 664 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 665 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);CHKERRQ(ierr); 666 if (iascii) { 667 ierr = PetscViewerGetType(viewer,&vtype);CHKERRQ(ierr); 668 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 669 if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) { 670 MatInfo info; 671 ierr = MatGetInfo(mat,MAT_LOCAL,&info);CHKERRQ(ierr); 672 ierr = PetscViewerASCIISynchronizedAllow(viewer,PETSC_TRUE);CHKERRQ(ierr); 673 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] local rows %D nz %D nz alloced %D mem %D \n",rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);CHKERRQ(ierr); 674 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 675 ierr = PetscViewerASCIISynchronizedAllow(viewer,PETSC_FALSE);CHKERRQ(ierr); 676 ierr = VecScatterView(mdn->Mvctx,viewer);CHKERRQ(ierr); 677 PetscFunctionReturn(0); 678 } else if (format == PETSC_VIEWER_ASCII_INFO) { 679 PetscFunctionReturn(0); 680 } 681 } else if (isdraw) { 682 PetscDraw draw; 683 PetscBool isnull; 684 685 ierr = PetscViewerDrawGetDraw(viewer,0,&draw);CHKERRQ(ierr); 686 ierr = PetscDrawIsNull(draw,&isnull);CHKERRQ(ierr); 687 if (isnull) PetscFunctionReturn(0); 688 } 689 690 if (size == 1) { 691 ierr = MatView(mdn->A,viewer);CHKERRQ(ierr); 692 } else { 693 /* assemble the entire matrix onto first processor. */ 694 Mat A; 695 PetscInt M = mat->rmap->N,N = mat->cmap->N,m,row,i,nz; 696 PetscInt *cols; 697 PetscScalar *vals; 698 699 ierr = MatCreate(PetscObjectComm((PetscObject)mat),&A);CHKERRQ(ierr); 700 if (!rank) { 701 ierr = MatSetSizes(A,M,N,M,N);CHKERRQ(ierr); 702 } else { 703 ierr = MatSetSizes(A,0,0,M,N);CHKERRQ(ierr); 704 } 705 /* Since this is a temporary matrix, MATMPIDENSE instead of ((PetscObject)A)->type_name here is probably acceptable. */ 706 ierr = MatSetType(A,MATMPIDENSE);CHKERRQ(ierr); 707 ierr = MatMPIDenseSetPreallocation(A,NULL);CHKERRQ(ierr); 708 ierr = PetscLogObjectParent(mat,A);CHKERRQ(ierr); 709 710 /* Copy the matrix ... This isn't the most efficient means, 711 but it's quick for now */ 712 A->insertmode = INSERT_VALUES; 713 714 row = mat->rmap->rstart; 715 m = mdn->A->rmap->n; 716 for (i=0; i<m; i++) { 717 ierr = MatGetRow_MPIDense(mat,row,&nz,&cols,&vals);CHKERRQ(ierr); 718 ierr = MatSetValues_MPIDense(A,1,&row,nz,cols,vals,INSERT_VALUES);CHKERRQ(ierr); 719 ierr = MatRestoreRow_MPIDense(mat,row,&nz,&cols,&vals);CHKERRQ(ierr); 720 row++; 721 } 722 723 ierr = MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 724 ierr = MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 725 ierr = PetscViewerGetSingleton(viewer,&sviewer);CHKERRQ(ierr); 726 if (!rank) { 727 ierr = PetscObjectSetName((PetscObject)((Mat_MPIDense*)(A->data))->A,((PetscObject)mat)->name);CHKERRQ(ierr); 728 /* Set the type name to MATMPIDense so that the correct type can be printed out by PetscObjectPrintClassNamePrefixType() in MatView_SeqDense_ASCII()*/ 729 PetscStrcpy(((PetscObject)((Mat_MPIDense*)(A->data))->A)->type_name,MATMPIDENSE); 730 ierr = MatView(((Mat_MPIDense*)(A->data))->A,sviewer);CHKERRQ(ierr); 731 } 732 ierr = PetscViewerRestoreSingleton(viewer,&sviewer);CHKERRQ(ierr); 733 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 734 ierr = MatDestroy(&A);CHKERRQ(ierr); 735 } 736 PetscFunctionReturn(0); 737 } 738 739 #undef __FUNCT__ 740 #define __FUNCT__ "MatView_MPIDense" 741 PetscErrorCode MatView_MPIDense(Mat mat,PetscViewer viewer) 742 { 743 PetscErrorCode ierr; 744 PetscBool iascii,isbinary,isdraw,issocket; 745 746 PetscFunctionBegin; 747 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 748 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);CHKERRQ(ierr); 749 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSOCKET,&issocket);CHKERRQ(ierr); 750 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);CHKERRQ(ierr); 751 752 if (iascii || issocket || isdraw) { 753 ierr = MatView_MPIDense_ASCIIorDraworSocket(mat,viewer);CHKERRQ(ierr); 754 } else if (isbinary) { 755 ierr = MatView_MPIDense_Binary(mat,viewer);CHKERRQ(ierr); 756 } 757 PetscFunctionReturn(0); 758 } 759 760 #undef __FUNCT__ 761 #define __FUNCT__ "MatGetInfo_MPIDense" 762 PetscErrorCode MatGetInfo_MPIDense(Mat A,MatInfoType flag,MatInfo *info) 763 { 764 Mat_MPIDense *mat = (Mat_MPIDense*)A->data; 765 Mat mdn = mat->A; 766 PetscErrorCode ierr; 767 PetscReal isend[5],irecv[5]; 768 769 PetscFunctionBegin; 770 info->block_size = 1.0; 771 772 ierr = MatGetInfo(mdn,MAT_LOCAL,info);CHKERRQ(ierr); 773 774 isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded; 775 isend[3] = info->memory; isend[4] = info->mallocs; 776 if (flag == MAT_LOCAL) { 777 info->nz_used = isend[0]; 778 info->nz_allocated = isend[1]; 779 info->nz_unneeded = isend[2]; 780 info->memory = isend[3]; 781 info->mallocs = isend[4]; 782 } else if (flag == MAT_GLOBAL_MAX) { 783 ierr = MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)A));CHKERRQ(ierr); 784 785 info->nz_used = irecv[0]; 786 info->nz_allocated = irecv[1]; 787 info->nz_unneeded = irecv[2]; 788 info->memory = irecv[3]; 789 info->mallocs = irecv[4]; 790 } else if (flag == MAT_GLOBAL_SUM) { 791 ierr = MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));CHKERRQ(ierr); 792 793 info->nz_used = irecv[0]; 794 info->nz_allocated = irecv[1]; 795 info->nz_unneeded = irecv[2]; 796 info->memory = irecv[3]; 797 info->mallocs = irecv[4]; 798 } 799 info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */ 800 info->fill_ratio_needed = 0; 801 info->factor_mallocs = 0; 802 PetscFunctionReturn(0); 803 } 804 805 #undef __FUNCT__ 806 #define __FUNCT__ "MatSetOption_MPIDense" 807 PetscErrorCode MatSetOption_MPIDense(Mat A,MatOption op,PetscBool flg) 808 { 809 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 810 PetscErrorCode ierr; 811 812 PetscFunctionBegin; 813 switch (op) { 814 case MAT_NEW_NONZERO_LOCATIONS: 815 case MAT_NEW_NONZERO_LOCATION_ERR: 816 case MAT_NEW_NONZERO_ALLOCATION_ERR: 817 ierr = MatSetOption(a->A,op,flg);CHKERRQ(ierr); 818 break; 819 case MAT_ROW_ORIENTED: 820 a->roworiented = flg; 821 822 ierr = MatSetOption(a->A,op,flg);CHKERRQ(ierr); 823 break; 824 case MAT_NEW_DIAGONALS: 825 case MAT_KEEP_NONZERO_PATTERN: 826 case MAT_USE_HASH_TABLE: 827 ierr = PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);CHKERRQ(ierr); 828 break; 829 case MAT_IGNORE_OFF_PROC_ENTRIES: 830 a->donotstash = flg; 831 break; 832 case MAT_SYMMETRIC: 833 case MAT_STRUCTURALLY_SYMMETRIC: 834 case MAT_HERMITIAN: 835 case MAT_SYMMETRY_ETERNAL: 836 case MAT_IGNORE_LOWER_TRIANGULAR: 837 ierr = PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);CHKERRQ(ierr); 838 break; 839 default: 840 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %s",MatOptions[op]); 841 } 842 PetscFunctionReturn(0); 843 } 844 845 846 #undef __FUNCT__ 847 #define __FUNCT__ "MatDiagonalScale_MPIDense" 848 PetscErrorCode MatDiagonalScale_MPIDense(Mat A,Vec ll,Vec rr) 849 { 850 Mat_MPIDense *mdn = (Mat_MPIDense*)A->data; 851 Mat_SeqDense *mat = (Mat_SeqDense*)mdn->A->data; 852 PetscScalar *l,*r,x,*v; 853 PetscErrorCode ierr; 854 PetscInt i,j,s2a,s3a,s2,s3,m=mdn->A->rmap->n,n=mdn->A->cmap->n; 855 856 PetscFunctionBegin; 857 ierr = MatGetLocalSize(A,&s2,&s3);CHKERRQ(ierr); 858 if (ll) { 859 ierr = VecGetLocalSize(ll,&s2a);CHKERRQ(ierr); 860 if (s2a != s2) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector non-conforming local size, %d != %d.", s2a, s2); 861 ierr = VecGetArray(ll,&l);CHKERRQ(ierr); 862 for (i=0; i<m; i++) { 863 x = l[i]; 864 v = mat->v + i; 865 for (j=0; j<n; j++) { (*v) *= x; v+= m;} 866 } 867 ierr = VecRestoreArray(ll,&l);CHKERRQ(ierr); 868 ierr = PetscLogFlops(n*m);CHKERRQ(ierr); 869 } 870 if (rr) { 871 ierr = VecGetLocalSize(rr,&s3a);CHKERRQ(ierr); 872 if (s3a != s3) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vec non-conforming local size, %d != %d.", s3a, s3); 873 ierr = VecScatterBegin(mdn->Mvctx,rr,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 874 ierr = VecScatterEnd(mdn->Mvctx,rr,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 875 ierr = VecGetArray(mdn->lvec,&r);CHKERRQ(ierr); 876 for (i=0; i<n; i++) { 877 x = r[i]; 878 v = mat->v + i*m; 879 for (j=0; j<m; j++) (*v++) *= x; 880 } 881 ierr = VecRestoreArray(mdn->lvec,&r);CHKERRQ(ierr); 882 ierr = PetscLogFlops(n*m);CHKERRQ(ierr); 883 } 884 PetscFunctionReturn(0); 885 } 886 887 #undef __FUNCT__ 888 #define __FUNCT__ "MatNorm_MPIDense" 889 PetscErrorCode MatNorm_MPIDense(Mat A,NormType type,PetscReal *nrm) 890 { 891 Mat_MPIDense *mdn = (Mat_MPIDense*)A->data; 892 Mat_SeqDense *mat = (Mat_SeqDense*)mdn->A->data; 893 PetscErrorCode ierr; 894 PetscInt i,j; 895 PetscReal sum = 0.0; 896 PetscScalar *v = mat->v; 897 898 PetscFunctionBegin; 899 if (mdn->size == 1) { 900 ierr = MatNorm(mdn->A,type,nrm);CHKERRQ(ierr); 901 } else { 902 if (type == NORM_FROBENIUS) { 903 for (i=0; i<mdn->A->cmap->n*mdn->A->rmap->n; i++) { 904 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 905 } 906 ierr = MPI_Allreduce(&sum,nrm,1,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));CHKERRQ(ierr); 907 *nrm = PetscSqrtReal(*nrm); 908 ierr = PetscLogFlops(2.0*mdn->A->cmap->n*mdn->A->rmap->n);CHKERRQ(ierr); 909 } else if (type == NORM_1) { 910 PetscReal *tmp,*tmp2; 911 ierr = PetscMalloc2(A->cmap->N,PetscReal,&tmp,A->cmap->N,PetscReal,&tmp2);CHKERRQ(ierr); 912 ierr = PetscMemzero(tmp,A->cmap->N*sizeof(PetscReal));CHKERRQ(ierr); 913 ierr = PetscMemzero(tmp2,A->cmap->N*sizeof(PetscReal));CHKERRQ(ierr); 914 *nrm = 0.0; 915 v = mat->v; 916 for (j=0; j<mdn->A->cmap->n; j++) { 917 for (i=0; i<mdn->A->rmap->n; i++) { 918 tmp[j] += PetscAbsScalar(*v); v++; 919 } 920 } 921 ierr = MPI_Allreduce(tmp,tmp2,A->cmap->N,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));CHKERRQ(ierr); 922 for (j=0; j<A->cmap->N; j++) { 923 if (tmp2[j] > *nrm) *nrm = tmp2[j]; 924 } 925 ierr = PetscFree2(tmp,tmp);CHKERRQ(ierr); 926 ierr = PetscLogFlops(A->cmap->n*A->rmap->n);CHKERRQ(ierr); 927 } else if (type == NORM_INFINITY) { /* max row norm */ 928 PetscReal ntemp; 929 ierr = MatNorm(mdn->A,type,&ntemp);CHKERRQ(ierr); 930 ierr = MPI_Allreduce(&ntemp,nrm,1,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)A));CHKERRQ(ierr); 931 } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for two norm"); 932 } 933 PetscFunctionReturn(0); 934 } 935 936 #undef __FUNCT__ 937 #define __FUNCT__ "MatTranspose_MPIDense" 938 PetscErrorCode MatTranspose_MPIDense(Mat A,MatReuse reuse,Mat *matout) 939 { 940 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 941 Mat_SeqDense *Aloc = (Mat_SeqDense*)a->A->data; 942 Mat B; 943 PetscInt M = A->rmap->N,N = A->cmap->N,m,n,*rwork,rstart = A->rmap->rstart; 944 PetscErrorCode ierr; 945 PetscInt j,i; 946 PetscScalar *v; 947 948 PetscFunctionBegin; 949 if (reuse == MAT_REUSE_MATRIX && A == *matout && M != N) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Supports square matrix only in-place"); 950 if (reuse == MAT_INITIAL_MATRIX || A == *matout) { 951 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 952 ierr = MatSetSizes(B,A->cmap->n,A->rmap->n,N,M);CHKERRQ(ierr); 953 ierr = MatSetType(B,((PetscObject)A)->type_name);CHKERRQ(ierr); 954 ierr = MatMPIDenseSetPreallocation(B,NULL);CHKERRQ(ierr); 955 } else { 956 B = *matout; 957 } 958 959 m = a->A->rmap->n; n = a->A->cmap->n; v = Aloc->v; 960 ierr = PetscMalloc(m*sizeof(PetscInt),&rwork);CHKERRQ(ierr); 961 for (i=0; i<m; i++) rwork[i] = rstart + i; 962 for (j=0; j<n; j++) { 963 ierr = MatSetValues(B,1,&j,m,rwork,v,INSERT_VALUES);CHKERRQ(ierr); 964 v += m; 965 } 966 ierr = PetscFree(rwork);CHKERRQ(ierr); 967 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 968 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 969 if (reuse == MAT_INITIAL_MATRIX || *matout != A) { 970 *matout = B; 971 } else { 972 ierr = MatHeaderMerge(A,B);CHKERRQ(ierr); 973 } 974 PetscFunctionReturn(0); 975 } 976 977 978 static PetscErrorCode MatDuplicate_MPIDense(Mat,MatDuplicateOption,Mat*); 979 extern PetscErrorCode MatScale_MPIDense(Mat,PetscScalar); 980 981 #undef __FUNCT__ 982 #define __FUNCT__ "MatSetUp_MPIDense" 983 PetscErrorCode MatSetUp_MPIDense(Mat A) 984 { 985 PetscErrorCode ierr; 986 987 PetscFunctionBegin; 988 ierr = MatMPIDenseSetPreallocation(A,0);CHKERRQ(ierr); 989 PetscFunctionReturn(0); 990 } 991 992 #undef __FUNCT__ 993 #define __FUNCT__ "MatAXPY_MPIDense" 994 PetscErrorCode MatAXPY_MPIDense(Mat Y,PetscScalar alpha,Mat X,MatStructure str) 995 { 996 PetscErrorCode ierr; 997 Mat_MPIDense *A = (Mat_MPIDense*)Y->data, *B = (Mat_MPIDense*)X->data; 998 999 PetscFunctionBegin; 1000 ierr = MatAXPY(A->A,alpha,B->A,str);CHKERRQ(ierr); 1001 PetscFunctionReturn(0); 1002 } 1003 1004 #undef __FUNCT__ 1005 #define __FUNCT__ "MatConjugate_MPIDense" 1006 PetscErrorCode MatConjugate_MPIDense(Mat mat) 1007 { 1008 Mat_MPIDense *a = (Mat_MPIDense*)mat->data; 1009 PetscErrorCode ierr; 1010 1011 PetscFunctionBegin; 1012 ierr = MatConjugate(a->A);CHKERRQ(ierr); 1013 PetscFunctionReturn(0); 1014 } 1015 1016 #undef __FUNCT__ 1017 #define __FUNCT__ "MatRealPart_MPIDense" 1018 PetscErrorCode MatRealPart_MPIDense(Mat A) 1019 { 1020 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 1021 PetscErrorCode ierr; 1022 1023 PetscFunctionBegin; 1024 ierr = MatRealPart(a->A);CHKERRQ(ierr); 1025 PetscFunctionReturn(0); 1026 } 1027 1028 #undef __FUNCT__ 1029 #define __FUNCT__ "MatImaginaryPart_MPIDense" 1030 PetscErrorCode MatImaginaryPart_MPIDense(Mat A) 1031 { 1032 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 1033 PetscErrorCode ierr; 1034 1035 PetscFunctionBegin; 1036 ierr = MatImaginaryPart(a->A);CHKERRQ(ierr); 1037 PetscFunctionReturn(0); 1038 } 1039 1040 extern PetscErrorCode MatGetColumnNorms_SeqDense(Mat,NormType,PetscReal*); 1041 #undef __FUNCT__ 1042 #define __FUNCT__ "MatGetColumnNorms_MPIDense" 1043 PetscErrorCode MatGetColumnNorms_MPIDense(Mat A,NormType type,PetscReal *norms) 1044 { 1045 PetscErrorCode ierr; 1046 PetscInt i,n; 1047 Mat_MPIDense *a = (Mat_MPIDense*) A->data; 1048 PetscReal *work; 1049 1050 PetscFunctionBegin; 1051 ierr = MatGetSize(A,NULL,&n);CHKERRQ(ierr); 1052 ierr = PetscMalloc(n*sizeof(PetscReal),&work);CHKERRQ(ierr); 1053 ierr = MatGetColumnNorms_SeqDense(a->A,type,work);CHKERRQ(ierr); 1054 if (type == NORM_2) { 1055 for (i=0; i<n; i++) work[i] *= work[i]; 1056 } 1057 if (type == NORM_INFINITY) { 1058 ierr = MPI_Allreduce(work,norms,n,MPIU_REAL,MPIU_MAX,A->hdr.comm);CHKERRQ(ierr); 1059 } else { 1060 ierr = MPI_Allreduce(work,norms,n,MPIU_REAL,MPIU_SUM,A->hdr.comm);CHKERRQ(ierr); 1061 } 1062 ierr = PetscFree(work);CHKERRQ(ierr); 1063 if (type == NORM_2) { 1064 for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]); 1065 } 1066 PetscFunctionReturn(0); 1067 } 1068 1069 #undef __FUNCT__ 1070 #define __FUNCT__ "MatSetRandom_MPIDense" 1071 static PetscErrorCode MatSetRandom_MPIDense(Mat x,PetscRandom rctx) 1072 { 1073 Mat_MPIDense *d = (Mat_MPIDense*)x->data; 1074 PetscErrorCode ierr; 1075 PetscScalar *a; 1076 PetscInt m,n,i; 1077 1078 PetscFunctionBegin; 1079 ierr = MatGetSize(d->A,&m,&n);CHKERRQ(ierr); 1080 ierr = MatDenseGetArray(d->A,&a);CHKERRQ(ierr); 1081 for (i=0; i<m*n; i++) { 1082 ierr = PetscRandomGetValue(rctx,a+i);CHKERRQ(ierr); 1083 } 1084 ierr = MatDenseRestoreArray(d->A,&a);CHKERRQ(ierr); 1085 PetscFunctionReturn(0); 1086 } 1087 1088 /* -------------------------------------------------------------------*/ 1089 static struct _MatOps MatOps_Values = { MatSetValues_MPIDense, 1090 MatGetRow_MPIDense, 1091 MatRestoreRow_MPIDense, 1092 MatMult_MPIDense, 1093 /* 4*/ MatMultAdd_MPIDense, 1094 MatMultTranspose_MPIDense, 1095 MatMultTransposeAdd_MPIDense, 1096 0, 1097 0, 1098 0, 1099 /* 10*/ 0, 1100 0, 1101 0, 1102 0, 1103 MatTranspose_MPIDense, 1104 /* 15*/ MatGetInfo_MPIDense, 1105 MatEqual_MPIDense, 1106 MatGetDiagonal_MPIDense, 1107 MatDiagonalScale_MPIDense, 1108 MatNorm_MPIDense, 1109 /* 20*/ MatAssemblyBegin_MPIDense, 1110 MatAssemblyEnd_MPIDense, 1111 MatSetOption_MPIDense, 1112 MatZeroEntries_MPIDense, 1113 /* 24*/ MatZeroRows_MPIDense, 1114 0, 1115 0, 1116 0, 1117 0, 1118 /* 29*/ MatSetUp_MPIDense, 1119 0, 1120 0, 1121 0, 1122 0, 1123 /* 34*/ MatDuplicate_MPIDense, 1124 0, 1125 0, 1126 0, 1127 0, 1128 /* 39*/ MatAXPY_MPIDense, 1129 MatGetSubMatrices_MPIDense, 1130 0, 1131 MatGetValues_MPIDense, 1132 0, 1133 /* 44*/ 0, 1134 MatScale_MPIDense, 1135 0, 1136 0, 1137 0, 1138 /* 49*/ MatSetRandom_MPIDense, 1139 0, 1140 0, 1141 0, 1142 0, 1143 /* 54*/ 0, 1144 0, 1145 0, 1146 0, 1147 0, 1148 /* 59*/ MatGetSubMatrix_MPIDense, 1149 MatDestroy_MPIDense, 1150 MatView_MPIDense, 1151 0, 1152 0, 1153 /* 64*/ 0, 1154 0, 1155 0, 1156 0, 1157 0, 1158 /* 69*/ 0, 1159 0, 1160 0, 1161 0, 1162 0, 1163 /* 74*/ 0, 1164 0, 1165 0, 1166 0, 1167 0, 1168 /* 79*/ 0, 1169 0, 1170 0, 1171 0, 1172 /* 83*/ MatLoad_MPIDense, 1173 0, 1174 0, 1175 0, 1176 0, 1177 0, 1178 /* 89*/ 1179 0, 1180 0, 1181 0, 1182 0, 1183 0, 1184 /* 94*/ 0, 1185 0, 1186 0, 1187 0, 1188 0, 1189 /* 99*/ 0, 1190 0, 1191 0, 1192 MatConjugate_MPIDense, 1193 0, 1194 /*104*/ 0, 1195 MatRealPart_MPIDense, 1196 MatImaginaryPart_MPIDense, 1197 0, 1198 0, 1199 /*109*/ 0, 1200 0, 1201 0, 1202 0, 1203 0, 1204 /*114*/ 0, 1205 0, 1206 0, 1207 0, 1208 0, 1209 /*119*/ 0, 1210 0, 1211 0, 1212 0, 1213 0, 1214 /*124*/ 0, 1215 MatGetColumnNorms_MPIDense 1216 }; 1217 1218 #undef __FUNCT__ 1219 #define __FUNCT__ "MatMPIDenseSetPreallocation_MPIDense" 1220 PetscErrorCode MatMPIDenseSetPreallocation_MPIDense(Mat mat,PetscScalar *data) 1221 { 1222 Mat_MPIDense *a; 1223 PetscErrorCode ierr; 1224 1225 PetscFunctionBegin; 1226 mat->preallocated = PETSC_TRUE; 1227 /* Note: For now, when data is specified above, this assumes the user correctly 1228 allocates the local dense storage space. We should add error checking. */ 1229 1230 a = (Mat_MPIDense*)mat->data; 1231 ierr = PetscLayoutSetUp(mat->rmap);CHKERRQ(ierr); 1232 ierr = PetscLayoutSetUp(mat->cmap);CHKERRQ(ierr); 1233 a->nvec = mat->cmap->n; 1234 1235 ierr = MatCreate(PETSC_COMM_SELF,&a->A);CHKERRQ(ierr); 1236 ierr = MatSetSizes(a->A,mat->rmap->n,mat->cmap->N,mat->rmap->n,mat->cmap->N);CHKERRQ(ierr); 1237 ierr = MatSetType(a->A,MATSEQDENSE);CHKERRQ(ierr); 1238 ierr = MatSeqDenseSetPreallocation(a->A,data);CHKERRQ(ierr); 1239 ierr = PetscLogObjectParent(mat,a->A);CHKERRQ(ierr); 1240 PetscFunctionReturn(0); 1241 } 1242 1243 #undef __FUNCT__ 1244 #define __FUNCT__ "MatCreate_MPIDense" 1245 PETSC_EXTERN PetscErrorCode MatCreate_MPIDense(Mat mat) 1246 { 1247 Mat_MPIDense *a; 1248 PetscErrorCode ierr; 1249 1250 PetscFunctionBegin; 1251 ierr = PetscNewLog(mat,Mat_MPIDense,&a);CHKERRQ(ierr); 1252 mat->data = (void*)a; 1253 ierr = PetscMemcpy(mat->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 1254 1255 mat->insertmode = NOT_SET_VALUES; 1256 ierr = MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&a->rank);CHKERRQ(ierr); 1257 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)mat),&a->size);CHKERRQ(ierr); 1258 1259 /* build cache for off array entries formed */ 1260 a->donotstash = PETSC_FALSE; 1261 1262 ierr = MatStashCreate_Private(PetscObjectComm((PetscObject)mat),1,&mat->stash);CHKERRQ(ierr); 1263 1264 /* stuff used for matrix vector multiply */ 1265 a->lvec = 0; 1266 a->Mvctx = 0; 1267 a->roworiented = PETSC_TRUE; 1268 1269 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatDenseGetArray_C","MatDenseGetArray_MPIDense",MatDenseGetArray_MPIDense);CHKERRQ(ierr); 1270 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatDenseRestoreArray_C","MatDenseRestoreArray_MPIDense",MatDenseRestoreArray_MPIDense);CHKERRQ(ierr); 1271 1272 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C","MatGetDiagonalBlock_MPIDense",MatGetDiagonalBlock_MPIDense);CHKERRQ(ierr); 1273 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMPIDenseSetPreallocation_C","MatMPIDenseSetPreallocation_MPIDense",MatMPIDenseSetPreallocation_MPIDense);CHKERRQ(ierr); 1274 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMatMult_mpiaij_mpidense_C","MatMatMult_MPIAIJ_MPIDense",MatMatMult_MPIAIJ_MPIDense);CHKERRQ(ierr); 1275 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMatMultSymbolic_mpiaij_mpidense_C","MatMatMultSymbolic_MPIAIJ_MPIDense",MatMatMultSymbolic_MPIAIJ_MPIDense);CHKERRQ(ierr); 1276 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMatMultNumeric_mpiaij_mpidense_C","MatMatMultNumeric_MPIAIJ_MPIDense",MatMatMultNumeric_MPIAIJ_MPIDense);CHKERRQ(ierr); 1277 ierr = PetscObjectChangeTypeName((PetscObject)mat,MATMPIDENSE);CHKERRQ(ierr); 1278 PetscFunctionReturn(0); 1279 } 1280 1281 /*MC 1282 MATDENSE - MATDENSE = "dense" - A matrix type to be used for dense matrices. 1283 1284 This matrix type is identical to MATSEQDENSE when constructed with a single process communicator, 1285 and MATMPIDENSE otherwise. 1286 1287 Options Database Keys: 1288 . -mat_type dense - sets the matrix type to "dense" during a call to MatSetFromOptions() 1289 1290 Level: beginner 1291 1292 1293 .seealso: MatCreateMPIDense,MATSEQDENSE,MATMPIDENSE 1294 M*/ 1295 1296 #undef __FUNCT__ 1297 #define __FUNCT__ "MatMPIDenseSetPreallocation" 1298 /*@C 1299 MatMPIDenseSetPreallocation - Sets the array used to store the matrix entries 1300 1301 Not collective 1302 1303 Input Parameters: 1304 . A - the matrix 1305 - data - optional location of matrix data. Set data=NULL for PETSc 1306 to control all matrix memory allocation. 1307 1308 Notes: 1309 The dense format is fully compatible with standard Fortran 77 1310 storage by columns. 1311 1312 The data input variable is intended primarily for Fortran programmers 1313 who wish to allocate their own matrix memory space. Most users should 1314 set data=NULL. 1315 1316 Level: intermediate 1317 1318 .keywords: matrix,dense, parallel 1319 1320 .seealso: MatCreate(), MatCreateSeqDense(), MatSetValues() 1321 @*/ 1322 PetscErrorCode MatMPIDenseSetPreallocation(Mat mat,PetscScalar *data) 1323 { 1324 PetscErrorCode ierr; 1325 1326 PetscFunctionBegin; 1327 ierr = PetscTryMethod(mat,"MatMPIDenseSetPreallocation_C",(Mat,PetscScalar*),(mat,data));CHKERRQ(ierr); 1328 PetscFunctionReturn(0); 1329 } 1330 1331 #undef __FUNCT__ 1332 #define __FUNCT__ "MatCreateDense" 1333 /*@C 1334 MatCreateDense - Creates a parallel matrix in dense format. 1335 1336 Collective on MPI_Comm 1337 1338 Input Parameters: 1339 + comm - MPI communicator 1340 . m - number of local rows (or PETSC_DECIDE to have calculated if M is given) 1341 . n - number of local columns (or PETSC_DECIDE to have calculated if N is given) 1342 . M - number of global rows (or PETSC_DECIDE to have calculated if m is given) 1343 . N - number of global columns (or PETSC_DECIDE to have calculated if n is given) 1344 - data - optional location of matrix data. Set data=NULL (NULL_SCALAR for Fortran users) for PETSc 1345 to control all matrix memory allocation. 1346 1347 Output Parameter: 1348 . A - the matrix 1349 1350 Notes: 1351 The dense format is fully compatible with standard Fortran 77 1352 storage by columns. 1353 1354 The data input variable is intended primarily for Fortran programmers 1355 who wish to allocate their own matrix memory space. Most users should 1356 set data=NULL (NULL_SCALAR for Fortran users). 1357 1358 The user MUST specify either the local or global matrix dimensions 1359 (possibly both). 1360 1361 Level: intermediate 1362 1363 .keywords: matrix,dense, parallel 1364 1365 .seealso: MatCreate(), MatCreateSeqDense(), MatSetValues() 1366 @*/ 1367 PetscErrorCode MatCreateDense(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscScalar *data,Mat *A) 1368 { 1369 PetscErrorCode ierr; 1370 PetscMPIInt size; 1371 1372 PetscFunctionBegin; 1373 ierr = MatCreate(comm,A);CHKERRQ(ierr); 1374 ierr = MatSetSizes(*A,m,n,M,N);CHKERRQ(ierr); 1375 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 1376 if (size > 1) { 1377 ierr = MatSetType(*A,MATMPIDENSE);CHKERRQ(ierr); 1378 ierr = MatMPIDenseSetPreallocation(*A,data);CHKERRQ(ierr); 1379 } else { 1380 ierr = MatSetType(*A,MATSEQDENSE);CHKERRQ(ierr); 1381 ierr = MatSeqDenseSetPreallocation(*A,data);CHKERRQ(ierr); 1382 } 1383 PetscFunctionReturn(0); 1384 } 1385 1386 #undef __FUNCT__ 1387 #define __FUNCT__ "MatDuplicate_MPIDense" 1388 static PetscErrorCode MatDuplicate_MPIDense(Mat A,MatDuplicateOption cpvalues,Mat *newmat) 1389 { 1390 Mat mat; 1391 Mat_MPIDense *a,*oldmat = (Mat_MPIDense*)A->data; 1392 PetscErrorCode ierr; 1393 1394 PetscFunctionBegin; 1395 *newmat = 0; 1396 ierr = MatCreate(PetscObjectComm((PetscObject)A),&mat);CHKERRQ(ierr); 1397 ierr = MatSetSizes(mat,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 1398 ierr = MatSetType(mat,((PetscObject)A)->type_name);CHKERRQ(ierr); 1399 a = (Mat_MPIDense*)mat->data; 1400 ierr = PetscMemcpy(mat->ops,A->ops,sizeof(struct _MatOps));CHKERRQ(ierr); 1401 1402 mat->factortype = A->factortype; 1403 mat->assembled = PETSC_TRUE; 1404 mat->preallocated = PETSC_TRUE; 1405 1406 a->size = oldmat->size; 1407 a->rank = oldmat->rank; 1408 mat->insertmode = NOT_SET_VALUES; 1409 a->nvec = oldmat->nvec; 1410 a->donotstash = oldmat->donotstash; 1411 1412 ierr = PetscLayoutReference(A->rmap,&mat->rmap);CHKERRQ(ierr); 1413 ierr = PetscLayoutReference(A->cmap,&mat->cmap);CHKERRQ(ierr); 1414 1415 ierr = MatSetUpMultiply_MPIDense(mat);CHKERRQ(ierr); 1416 ierr = MatDuplicate(oldmat->A,cpvalues,&a->A);CHKERRQ(ierr); 1417 ierr = PetscLogObjectParent(mat,a->A);CHKERRQ(ierr); 1418 1419 *newmat = mat; 1420 PetscFunctionReturn(0); 1421 } 1422 1423 #undef __FUNCT__ 1424 #define __FUNCT__ "MatLoad_MPIDense_DenseInFile" 1425 PetscErrorCode MatLoad_MPIDense_DenseInFile(MPI_Comm comm,PetscInt fd,PetscInt M,PetscInt N,Mat newmat,PetscInt sizesset) 1426 { 1427 PetscErrorCode ierr; 1428 PetscMPIInt rank,size; 1429 PetscInt *rowners,i,m,nz,j; 1430 PetscScalar *array,*vals,*vals_ptr; 1431 1432 PetscFunctionBegin; 1433 ierr = MPI_Comm_rank(comm,&rank);CHKERRQ(ierr); 1434 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 1435 1436 /* determine ownership of all rows */ 1437 if (newmat->rmap->n < 0) m = M/size + ((M % size) > rank); 1438 else m = newmat->rmap->n; 1439 ierr = PetscMalloc((size+2)*sizeof(PetscInt),&rowners);CHKERRQ(ierr); 1440 ierr = MPI_Allgather(&m,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);CHKERRQ(ierr); 1441 rowners[0] = 0; 1442 for (i=2; i<=size; i++) { 1443 rowners[i] += rowners[i-1]; 1444 } 1445 1446 if (!sizesset) { 1447 ierr = MatSetSizes(newmat,m,PETSC_DECIDE,M,N);CHKERRQ(ierr); 1448 } 1449 ierr = MatMPIDenseSetPreallocation(newmat,NULL);CHKERRQ(ierr); 1450 ierr = MatDenseGetArray(newmat,&array);CHKERRQ(ierr); 1451 1452 if (!rank) { 1453 ierr = PetscMalloc(m*N*sizeof(PetscScalar),&vals);CHKERRQ(ierr); 1454 1455 /* read in my part of the matrix numerical values */ 1456 ierr = PetscBinaryRead(fd,vals,m*N,PETSC_SCALAR);CHKERRQ(ierr); 1457 1458 /* insert into matrix-by row (this is why cannot directly read into array */ 1459 vals_ptr = vals; 1460 for (i=0; i<m; i++) { 1461 for (j=0; j<N; j++) { 1462 array[i + j*m] = *vals_ptr++; 1463 } 1464 } 1465 1466 /* read in other processors and ship out */ 1467 for (i=1; i<size; i++) { 1468 nz = (rowners[i+1] - rowners[i])*N; 1469 ierr = PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);CHKERRQ(ierr); 1470 ierr = MPIULong_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)(newmat))->tag,comm);CHKERRQ(ierr); 1471 } 1472 } else { 1473 /* receive numeric values */ 1474 ierr = PetscMalloc(m*N*sizeof(PetscScalar),&vals);CHKERRQ(ierr); 1475 1476 /* receive message of values*/ 1477 ierr = MPIULong_Recv(vals,m*N,MPIU_SCALAR,0,((PetscObject)(newmat))->tag,comm);CHKERRQ(ierr); 1478 1479 /* insert into matrix-by row (this is why cannot directly read into array */ 1480 vals_ptr = vals; 1481 for (i=0; i<m; i++) { 1482 for (j=0; j<N; j++) { 1483 array[i + j*m] = *vals_ptr++; 1484 } 1485 } 1486 } 1487 ierr = MatDenseRestoreArray(newmat,&array);CHKERRQ(ierr); 1488 ierr = PetscFree(rowners);CHKERRQ(ierr); 1489 ierr = PetscFree(vals);CHKERRQ(ierr); 1490 ierr = MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1491 ierr = MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1492 PetscFunctionReturn(0); 1493 } 1494 1495 #undef __FUNCT__ 1496 #define __FUNCT__ "MatLoad_MPIDense" 1497 PetscErrorCode MatLoad_MPIDense(Mat newmat,PetscViewer viewer) 1498 { 1499 PetscScalar *vals,*svals; 1500 MPI_Comm comm; 1501 MPI_Status status; 1502 PetscMPIInt rank,size,tag = ((PetscObject)viewer)->tag,*rowners,*sndcounts,m,maxnz; 1503 PetscInt header[4],*rowlengths = 0,M,N,*cols; 1504 PetscInt *ourlens,*procsnz = 0,*offlens,jj,*mycols,*smycols; 1505 PetscInt i,nz,j,rstart,rend,sizesset=1,grows,gcols; 1506 int fd; 1507 PetscErrorCode ierr; 1508 1509 PetscFunctionBegin; 1510 ierr = PetscObjectGetComm((PetscObject)viewer,&comm);CHKERRQ(ierr); 1511 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 1512 ierr = MPI_Comm_rank(comm,&rank);CHKERRQ(ierr); 1513 if (!rank) { 1514 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 1515 ierr = PetscBinaryRead(fd,(char*)header,4,PETSC_INT);CHKERRQ(ierr); 1516 if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object"); 1517 } 1518 if (newmat->rmap->n < 0 && newmat->rmap->N < 0 && newmat->cmap->n < 0 && newmat->cmap->N < 0) sizesset = 0; 1519 1520 ierr = MPI_Bcast(header+1,3,MPIU_INT,0,comm);CHKERRQ(ierr); 1521 M = header[1]; N = header[2]; nz = header[3]; 1522 1523 /* If global rows/cols are set to PETSC_DECIDE, set it to the sizes given in the file */ 1524 if (sizesset && newmat->rmap->N < 0) newmat->rmap->N = M; 1525 if (sizesset && newmat->cmap->N < 0) newmat->cmap->N = N; 1526 1527 /* If global sizes are set, check if they are consistent with that given in the file */ 1528 if (sizesset) { 1529 ierr = MatGetSize(newmat,&grows,&gcols);CHKERRQ(ierr); 1530 } 1531 if (sizesset && newmat->rmap->N != grows) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of rows:Matrix in file has (%d) and input matrix has (%d)",M,grows); 1532 if (sizesset && newmat->cmap->N != gcols) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of cols:Matrix in file has (%d) and input matrix has (%d)",N,gcols); 1533 1534 /* 1535 Handle case where matrix is stored on disk as a dense matrix 1536 */ 1537 if (nz == MATRIX_BINARY_FORMAT_DENSE) { 1538 ierr = MatLoad_MPIDense_DenseInFile(comm,fd,M,N,newmat,sizesset);CHKERRQ(ierr); 1539 PetscFunctionReturn(0); 1540 } 1541 1542 /* determine ownership of all rows */ 1543 if (newmat->rmap->n < 0) { 1544 ierr = PetscMPIIntCast(M/size + ((M % size) > rank),&m);CHKERRQ(ierr); 1545 } else { 1546 ierr = PetscMPIIntCast(newmat->rmap->n,&m);CHKERRQ(ierr); 1547 } 1548 ierr = PetscMalloc((size+2)*sizeof(PetscMPIInt),&rowners);CHKERRQ(ierr); 1549 ierr = MPI_Allgather(&m,1,MPI_INT,rowners+1,1,MPI_INT,comm);CHKERRQ(ierr); 1550 rowners[0] = 0; 1551 for (i=2; i<=size; i++) { 1552 rowners[i] += rowners[i-1]; 1553 } 1554 rstart = rowners[rank]; 1555 rend = rowners[rank+1]; 1556 1557 /* distribute row lengths to all processors */ 1558 ierr = PetscMalloc2(rend-rstart,PetscInt,&ourlens,rend-rstart,PetscInt,&offlens);CHKERRQ(ierr); 1559 if (!rank) { 1560 ierr = PetscMalloc(M*sizeof(PetscInt),&rowlengths);CHKERRQ(ierr); 1561 ierr = PetscBinaryRead(fd,rowlengths,M,PETSC_INT);CHKERRQ(ierr); 1562 ierr = PetscMalloc(size*sizeof(PetscMPIInt),&sndcounts);CHKERRQ(ierr); 1563 for (i=0; i<size; i++) sndcounts[i] = rowners[i+1] - rowners[i]; 1564 ierr = MPI_Scatterv(rowlengths,sndcounts,rowners,MPIU_INT,ourlens,rend-rstart,MPIU_INT,0,comm);CHKERRQ(ierr); 1565 ierr = PetscFree(sndcounts);CHKERRQ(ierr); 1566 } else { 1567 ierr = MPI_Scatterv(0,0,0,MPIU_INT,ourlens,rend-rstart,MPIU_INT,0,comm);CHKERRQ(ierr); 1568 } 1569 1570 if (!rank) { 1571 /* calculate the number of nonzeros on each processor */ 1572 ierr = PetscMalloc(size*sizeof(PetscInt),&procsnz);CHKERRQ(ierr); 1573 ierr = PetscMemzero(procsnz,size*sizeof(PetscInt));CHKERRQ(ierr); 1574 for (i=0; i<size; i++) { 1575 for (j=rowners[i]; j< rowners[i+1]; j++) { 1576 procsnz[i] += rowlengths[j]; 1577 } 1578 } 1579 ierr = PetscFree(rowlengths);CHKERRQ(ierr); 1580 1581 /* determine max buffer needed and allocate it */ 1582 maxnz = 0; 1583 for (i=0; i<size; i++) { 1584 maxnz = PetscMax(maxnz,procsnz[i]); 1585 } 1586 ierr = PetscMalloc(maxnz*sizeof(PetscInt),&cols);CHKERRQ(ierr); 1587 1588 /* read in my part of the matrix column indices */ 1589 nz = procsnz[0]; 1590 ierr = PetscMalloc(nz*sizeof(PetscInt),&mycols);CHKERRQ(ierr); 1591 ierr = PetscBinaryRead(fd,mycols,nz,PETSC_INT);CHKERRQ(ierr); 1592 1593 /* read in every one elses and ship off */ 1594 for (i=1; i<size; i++) { 1595 nz = procsnz[i]; 1596 ierr = PetscBinaryRead(fd,cols,nz,PETSC_INT);CHKERRQ(ierr); 1597 ierr = MPI_Send(cols,nz,MPIU_INT,i,tag,comm);CHKERRQ(ierr); 1598 } 1599 ierr = PetscFree(cols);CHKERRQ(ierr); 1600 } else { 1601 /* determine buffer space needed for message */ 1602 nz = 0; 1603 for (i=0; i<m; i++) { 1604 nz += ourlens[i]; 1605 } 1606 ierr = PetscMalloc((nz+1)*sizeof(PetscInt),&mycols);CHKERRQ(ierr); 1607 1608 /* receive message of column indices*/ 1609 ierr = MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);CHKERRQ(ierr); 1610 ierr = MPI_Get_count(&status,MPIU_INT,&maxnz);CHKERRQ(ierr); 1611 if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file"); 1612 } 1613 1614 /* loop over local rows, determining number of off diagonal entries */ 1615 ierr = PetscMemzero(offlens,m*sizeof(PetscInt));CHKERRQ(ierr); 1616 jj = 0; 1617 for (i=0; i<m; i++) { 1618 for (j=0; j<ourlens[i]; j++) { 1619 if (mycols[jj] < rstart || mycols[jj] >= rend) offlens[i]++; 1620 jj++; 1621 } 1622 } 1623 1624 /* create our matrix */ 1625 for (i=0; i<m; i++) ourlens[i] -= offlens[i]; 1626 1627 if (!sizesset) { 1628 ierr = MatSetSizes(newmat,m,PETSC_DECIDE,M,N);CHKERRQ(ierr); 1629 } 1630 ierr = MatMPIDenseSetPreallocation(newmat,NULL);CHKERRQ(ierr); 1631 for (i=0; i<m; i++) ourlens[i] += offlens[i]; 1632 1633 if (!rank) { 1634 ierr = PetscMalloc(maxnz*sizeof(PetscScalar),&vals);CHKERRQ(ierr); 1635 1636 /* read in my part of the matrix numerical values */ 1637 nz = procsnz[0]; 1638 ierr = PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);CHKERRQ(ierr); 1639 1640 /* insert into matrix */ 1641 jj = rstart; 1642 smycols = mycols; 1643 svals = vals; 1644 for (i=0; i<m; i++) { 1645 ierr = MatSetValues(newmat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);CHKERRQ(ierr); 1646 smycols += ourlens[i]; 1647 svals += ourlens[i]; 1648 jj++; 1649 } 1650 1651 /* read in other processors and ship out */ 1652 for (i=1; i<size; i++) { 1653 nz = procsnz[i]; 1654 ierr = PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);CHKERRQ(ierr); 1655 ierr = MPI_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)newmat)->tag,comm);CHKERRQ(ierr); 1656 } 1657 ierr = PetscFree(procsnz);CHKERRQ(ierr); 1658 } else { 1659 /* receive numeric values */ 1660 ierr = PetscMalloc((nz+1)*sizeof(PetscScalar),&vals);CHKERRQ(ierr); 1661 1662 /* receive message of values*/ 1663 ierr = MPI_Recv(vals,nz,MPIU_SCALAR,0,((PetscObject)newmat)->tag,comm,&status);CHKERRQ(ierr); 1664 ierr = MPI_Get_count(&status,MPIU_SCALAR,&maxnz);CHKERRQ(ierr); 1665 if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file"); 1666 1667 /* insert into matrix */ 1668 jj = rstart; 1669 smycols = mycols; 1670 svals = vals; 1671 for (i=0; i<m; i++) { 1672 ierr = MatSetValues(newmat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);CHKERRQ(ierr); 1673 smycols += ourlens[i]; 1674 svals += ourlens[i]; 1675 jj++; 1676 } 1677 } 1678 ierr = PetscFree2(ourlens,offlens);CHKERRQ(ierr); 1679 ierr = PetscFree(vals);CHKERRQ(ierr); 1680 ierr = PetscFree(mycols);CHKERRQ(ierr); 1681 ierr = PetscFree(rowners);CHKERRQ(ierr); 1682 1683 ierr = MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1684 ierr = MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1685 PetscFunctionReturn(0); 1686 } 1687 1688 #undef __FUNCT__ 1689 #define __FUNCT__ "MatEqual_MPIDense" 1690 PetscErrorCode MatEqual_MPIDense(Mat A,Mat B,PetscBool *flag) 1691 { 1692 Mat_MPIDense *matB = (Mat_MPIDense*)B->data,*matA = (Mat_MPIDense*)A->data; 1693 Mat a,b; 1694 PetscBool flg; 1695 PetscErrorCode ierr; 1696 1697 PetscFunctionBegin; 1698 a = matA->A; 1699 b = matB->A; 1700 ierr = MatEqual(a,b,&flg);CHKERRQ(ierr); 1701 ierr = MPI_Allreduce(&flg,flag,1,MPIU_BOOL,MPI_LAND,PetscObjectComm((PetscObject)A));CHKERRQ(ierr); 1702 PetscFunctionReturn(0); 1703 } 1704 1705