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 #include <../src/mat/impls/aij/mpi/mpiaij.h> 9 #include <petscblaslapack.h> 10 11 #undef __FUNCT__ 12 #define __FUNCT__ "MatDenseGetLocalMatrix" 13 /*@ 14 15 MatDenseGetLocalMatrix - For a MATMPIDENSE or MATSEQDENSE matrix returns the sequential 16 matrix that represents the operator. For sequential matrices it returns itself. 17 18 Input Parameter: 19 . A - the Seq or MPI dense matrix 20 21 Output Parameter: 22 . B - the inner matrix 23 24 Level: intermediate 25 26 @*/ 27 PetscErrorCode MatDenseGetLocalMatrix(Mat A,Mat *B) 28 { 29 Mat_MPIDense *mat = (Mat_MPIDense*)A->data; 30 PetscErrorCode ierr; 31 PetscBool flg; 32 33 PetscFunctionBegin; 34 ierr = PetscObjectTypeCompare((PetscObject)A,MATMPIDENSE,&flg);CHKERRQ(ierr); 35 if (flg) *B = mat->A; 36 else *B = A; 37 PetscFunctionReturn(0); 38 } 39 40 #undef __FUNCT__ 41 #define __FUNCT__ "MatGetRow_MPIDense" 42 PetscErrorCode MatGetRow_MPIDense(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v) 43 { 44 Mat_MPIDense *mat = (Mat_MPIDense*)A->data; 45 PetscErrorCode ierr; 46 PetscInt lrow,rstart = A->rmap->rstart,rend = A->rmap->rend; 47 48 PetscFunctionBegin; 49 if (row < rstart || row >= rend) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"only local rows"); 50 lrow = row - rstart; 51 ierr = MatGetRow(mat->A,lrow,nz,(const PetscInt**)idx,(const PetscScalar**)v);CHKERRQ(ierr); 52 PetscFunctionReturn(0); 53 } 54 55 #undef __FUNCT__ 56 #define __FUNCT__ "MatRestoreRow_MPIDense" 57 PetscErrorCode MatRestoreRow_MPIDense(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v) 58 { 59 PetscErrorCode ierr; 60 61 PetscFunctionBegin; 62 if (idx) {ierr = PetscFree(*idx);CHKERRQ(ierr);} 63 if (v) {ierr = PetscFree(*v);CHKERRQ(ierr);} 64 PetscFunctionReturn(0); 65 } 66 67 #undef __FUNCT__ 68 #define __FUNCT__ "MatGetDiagonalBlock_MPIDense" 69 PetscErrorCode MatGetDiagonalBlock_MPIDense(Mat A,Mat *a) 70 { 71 Mat_MPIDense *mdn = (Mat_MPIDense*)A->data; 72 PetscErrorCode ierr; 73 PetscInt m = A->rmap->n,rstart = A->rmap->rstart; 74 PetscScalar *array; 75 MPI_Comm comm; 76 Mat B; 77 78 PetscFunctionBegin; 79 if (A->rmap->N != A->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only square matrices supported."); 80 81 ierr = PetscObjectQuery((PetscObject)A,"DiagonalBlock",(PetscObject*)&B);CHKERRQ(ierr); 82 if (!B) { 83 ierr = PetscObjectGetComm((PetscObject)(mdn->A),&comm);CHKERRQ(ierr); 84 ierr = MatCreate(comm,&B);CHKERRQ(ierr); 85 ierr = MatSetSizes(B,m,m,m,m);CHKERRQ(ierr); 86 ierr = MatSetType(B,((PetscObject)mdn->A)->type_name);CHKERRQ(ierr); 87 ierr = MatDenseGetArray(mdn->A,&array);CHKERRQ(ierr); 88 ierr = MatSeqDenseSetPreallocation(B,array+m*rstart);CHKERRQ(ierr); 89 ierr = MatDenseRestoreArray(mdn->A,&array);CHKERRQ(ierr); 90 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 91 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 92 ierr = PetscObjectCompose((PetscObject)A,"DiagonalBlock",(PetscObject)B);CHKERRQ(ierr); 93 *a = B; 94 ierr = MatDestroy(&B);CHKERRQ(ierr); 95 } else *a = B; 96 PetscFunctionReturn(0); 97 } 98 99 #undef __FUNCT__ 100 #define __FUNCT__ "MatSetValues_MPIDense" 101 PetscErrorCode MatSetValues_MPIDense(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv) 102 { 103 Mat_MPIDense *A = (Mat_MPIDense*)mat->data; 104 PetscErrorCode ierr; 105 PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend,row; 106 PetscBool roworiented = A->roworiented; 107 108 PetscFunctionBegin; 109 for (i=0; i<m; i++) { 110 if (idxm[i] < 0) continue; 111 if (idxm[i] >= mat->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large"); 112 if (idxm[i] >= rstart && idxm[i] < rend) { 113 row = idxm[i] - rstart; 114 if (roworiented) { 115 ierr = MatSetValues(A->A,1,&row,n,idxn,v+i*n,addv);CHKERRQ(ierr); 116 } else { 117 for (j=0; j<n; j++) { 118 if (idxn[j] < 0) continue; 119 if (idxn[j] >= mat->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large"); 120 ierr = MatSetValues(A->A,1,&row,1,&idxn[j],v+i+j*m,addv);CHKERRQ(ierr); 121 } 122 } 123 } else if (!A->donotstash) { 124 mat->assembled = PETSC_FALSE; 125 if (roworiented) { 126 ierr = MatStashValuesRow_Private(&mat->stash,idxm[i],n,idxn,v+i*n,PETSC_FALSE);CHKERRQ(ierr); 127 } else { 128 ierr = MatStashValuesCol_Private(&mat->stash,idxm[i],n,idxn,v+i,m,PETSC_FALSE);CHKERRQ(ierr); 129 } 130 } 131 } 132 PetscFunctionReturn(0); 133 } 134 135 #undef __FUNCT__ 136 #define __FUNCT__ "MatGetValues_MPIDense" 137 PetscErrorCode MatGetValues_MPIDense(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[]) 138 { 139 Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data; 140 PetscErrorCode ierr; 141 PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend,row; 142 143 PetscFunctionBegin; 144 for (i=0; i<m; i++) { 145 if (idxm[i] < 0) continue; /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row"); */ 146 if (idxm[i] >= mat->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large"); 147 if (idxm[i] >= rstart && idxm[i] < rend) { 148 row = idxm[i] - rstart; 149 for (j=0; j<n; j++) { 150 if (idxn[j] < 0) continue; /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column"); */ 151 if (idxn[j] >= mat->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large"); 152 ierr = MatGetValues(mdn->A,1,&row,1,&idxn[j],v+i*n+j);CHKERRQ(ierr); 153 } 154 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local values currently supported"); 155 } 156 PetscFunctionReturn(0); 157 } 158 159 #undef __FUNCT__ 160 #define __FUNCT__ "MatDenseGetArray_MPIDense" 161 PetscErrorCode MatDenseGetArray_MPIDense(Mat A,PetscScalar *array[]) 162 { 163 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 164 PetscErrorCode ierr; 165 166 PetscFunctionBegin; 167 ierr = MatDenseGetArray(a->A,array);CHKERRQ(ierr); 168 PetscFunctionReturn(0); 169 } 170 171 #undef __FUNCT__ 172 #define __FUNCT__ "MatGetSubMatrix_MPIDense" 173 static PetscErrorCode MatGetSubMatrix_MPIDense(Mat A,IS isrow,IS iscol,MatReuse scall,Mat *B) 174 { 175 Mat_MPIDense *mat = (Mat_MPIDense*)A->data,*newmatd; 176 Mat_SeqDense *lmat = (Mat_SeqDense*)mat->A->data; 177 PetscErrorCode ierr; 178 PetscInt i,j,rstart,rend,nrows,ncols,Ncols,nlrows,nlcols; 179 const PetscInt *irow,*icol; 180 PetscScalar *av,*bv,*v = lmat->v; 181 Mat newmat; 182 IS iscol_local; 183 184 PetscFunctionBegin; 185 ierr = ISAllGather(iscol,&iscol_local);CHKERRQ(ierr); 186 ierr = ISGetIndices(isrow,&irow);CHKERRQ(ierr); 187 ierr = ISGetIndices(iscol_local,&icol);CHKERRQ(ierr); 188 ierr = ISGetLocalSize(isrow,&nrows);CHKERRQ(ierr); 189 ierr = ISGetLocalSize(iscol,&ncols);CHKERRQ(ierr); 190 ierr = ISGetSize(iscol,&Ncols);CHKERRQ(ierr); /* global number of columns, size of iscol_local */ 191 192 /* No parallel redistribution currently supported! Should really check each index set 193 to comfirm that it is OK. ... Currently supports only submatrix same partitioning as 194 original matrix! */ 195 196 ierr = MatGetLocalSize(A,&nlrows,&nlcols);CHKERRQ(ierr); 197 ierr = MatGetOwnershipRange(A,&rstart,&rend);CHKERRQ(ierr); 198 199 /* Check submatrix call */ 200 if (scall == MAT_REUSE_MATRIX) { 201 /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size"); */ 202 /* Really need to test rows and column sizes! */ 203 newmat = *B; 204 } else { 205 /* Create and fill new matrix */ 206 ierr = MatCreate(PetscObjectComm((PetscObject)A),&newmat);CHKERRQ(ierr); 207 ierr = MatSetSizes(newmat,nrows,ncols,PETSC_DECIDE,Ncols);CHKERRQ(ierr); 208 ierr = MatSetType(newmat,((PetscObject)A)->type_name);CHKERRQ(ierr); 209 ierr = MatMPIDenseSetPreallocation(newmat,NULL);CHKERRQ(ierr); 210 } 211 212 /* Now extract the data pointers and do the copy, column at a time */ 213 newmatd = (Mat_MPIDense*)newmat->data; 214 bv = ((Mat_SeqDense*)newmatd->A->data)->v; 215 216 for (i=0; i<Ncols; i++) { 217 av = v + ((Mat_SeqDense*)mat->A->data)->lda*icol[i]; 218 for (j=0; j<nrows; j++) { 219 *bv++ = av[irow[j] - rstart]; 220 } 221 } 222 223 /* Assemble the matrices so that the correct flags are set */ 224 ierr = MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 225 ierr = MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 226 227 /* Free work space */ 228 ierr = ISRestoreIndices(isrow,&irow);CHKERRQ(ierr); 229 ierr = ISRestoreIndices(iscol_local,&icol);CHKERRQ(ierr); 230 ierr = ISDestroy(&iscol_local);CHKERRQ(ierr); 231 *B = newmat; 232 PetscFunctionReturn(0); 233 } 234 235 #undef __FUNCT__ 236 #define __FUNCT__ "MatDenseRestoreArray_MPIDense" 237 PetscErrorCode MatDenseRestoreArray_MPIDense(Mat A,PetscScalar *array[]) 238 { 239 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 240 PetscErrorCode ierr; 241 242 PetscFunctionBegin; 243 ierr = MatDenseRestoreArray(a->A,array);CHKERRQ(ierr); 244 PetscFunctionReturn(0); 245 } 246 247 #undef __FUNCT__ 248 #define __FUNCT__ "MatAssemblyBegin_MPIDense" 249 PetscErrorCode MatAssemblyBegin_MPIDense(Mat mat,MatAssemblyType mode) 250 { 251 Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data; 252 MPI_Comm comm; 253 PetscErrorCode ierr; 254 PetscInt nstash,reallocs; 255 InsertMode addv; 256 257 PetscFunctionBegin; 258 ierr = PetscObjectGetComm((PetscObject)mat,&comm);CHKERRQ(ierr); 259 /* make sure all processors are either in INSERTMODE or ADDMODE */ 260 ierr = MPIU_Allreduce((PetscEnum*)&mat->insertmode,(PetscEnum*)&addv,1,MPIU_ENUM,MPI_BOR,comm);CHKERRQ(ierr); 261 if (addv == (ADD_VALUES|INSERT_VALUES)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Cannot mix adds/inserts on different procs"); 262 mat->insertmode = addv; /* in case this processor had no cache */ 263 264 ierr = MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);CHKERRQ(ierr); 265 ierr = MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);CHKERRQ(ierr); 266 ierr = PetscInfo2(mdn->A,"Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);CHKERRQ(ierr); 267 PetscFunctionReturn(0); 268 } 269 270 #undef __FUNCT__ 271 #define __FUNCT__ "MatAssemblyEnd_MPIDense" 272 PetscErrorCode MatAssemblyEnd_MPIDense(Mat mat,MatAssemblyType mode) 273 { 274 Mat_MPIDense *mdn=(Mat_MPIDense*)mat->data; 275 PetscErrorCode ierr; 276 PetscInt i,*row,*col,flg,j,rstart,ncols; 277 PetscMPIInt n; 278 PetscScalar *val; 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,mat->insertmode);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 *sizes,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 = PetscCalloc1(2*size,&sizes);CHKERRQ(ierr); 352 ierr = PetscMalloc1(N+1,&owner);CHKERRQ(ierr); /* see note*/ 353 for (i=0; i<N; i++) { 354 idx = rows[i]; 355 found = PETSC_FALSE; 356 for (j=0; j<size; j++) { 357 if (idx >= owners[j] && idx < owners[j+1]) { 358 sizes[2*j]++; sizes[2*j+1] = 1; owner[i] = j; found = PETSC_TRUE; break; 359 } 360 } 361 if (!found) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Index out of range"); 362 } 363 nsends = 0; 364 for (i=0; i<size; i++) nsends += sizes[2*i+1]; 365 366 /* inform other processors of number of messages and max length*/ 367 ierr = PetscMaxSum(comm,sizes,&nmax,&nrecvs);CHKERRQ(ierr); 368 369 /* post receives: */ 370 ierr = PetscMalloc1((nrecvs+1)*(nmax+1),&rvalues);CHKERRQ(ierr); 371 ierr = PetscMalloc1(nrecvs+1,&recv_waits);CHKERRQ(ierr); 372 for (i=0; i<nrecvs; i++) { 373 ierr = MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);CHKERRQ(ierr); 374 } 375 376 /* do sends: 377 1) starts[i] gives the starting index in svalues for stuff going to 378 the ith processor 379 */ 380 ierr = PetscMalloc1(N+1,&svalues);CHKERRQ(ierr); 381 ierr = PetscMalloc1(nsends+1,&send_waits);CHKERRQ(ierr); 382 ierr = PetscMalloc1(size+1,&starts);CHKERRQ(ierr); 383 384 starts[0] = 0; 385 for (i=1; i<size; i++) starts[i] = starts[i-1] + sizes[2*i-2]; 386 for (i=0; i<N; i++) svalues[starts[owner[i]]++] = rows[i]; 387 388 starts[0] = 0; 389 for (i=1; i<size+1; i++) starts[i] = starts[i-1] + sizes[2*i-2]; 390 count = 0; 391 for (i=0; i<size; i++) { 392 if (sizes[2*i+1]) { 393 ierr = MPI_Isend(svalues+starts[i],sizes[2*i],MPIU_INT,i,tag,comm,send_waits+count++);CHKERRQ(ierr); 394 } 395 } 396 ierr = PetscFree(starts);CHKERRQ(ierr); 397 398 base = owners[rank]; 399 400 /* wait on receives */ 401 ierr = PetscMalloc2(nrecvs,&lens,nrecvs,&source);CHKERRQ(ierr); 402 count = nrecvs; 403 slen = 0; 404 while (count) { 405 ierr = MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);CHKERRQ(ierr); 406 /* unpack receives into our local space */ 407 ierr = MPI_Get_count(&recv_status,MPIU_INT,&n);CHKERRQ(ierr); 408 409 source[imdex] = recv_status.MPI_SOURCE; 410 lens[imdex] = n; 411 slen += n; 412 count--; 413 } 414 ierr = PetscFree(recv_waits);CHKERRQ(ierr); 415 416 /* move the data into the send scatter */ 417 ierr = PetscMalloc1(slen+1,&lrows);CHKERRQ(ierr); 418 count = 0; 419 for (i=0; i<nrecvs; i++) { 420 values = rvalues + i*nmax; 421 for (j=0; j<lens[i]; j++) { 422 lrows[count++] = values[j] - base; 423 } 424 } 425 ierr = PetscFree(rvalues);CHKERRQ(ierr); 426 ierr = PetscFree2(lens,source);CHKERRQ(ierr); 427 ierr = PetscFree(owner);CHKERRQ(ierr); 428 ierr = PetscFree(sizes);CHKERRQ(ierr); 429 430 /* fix right hand side if needed */ 431 if (x && b) { 432 ierr = VecGetArrayRead(x,&xx);CHKERRQ(ierr); 433 ierr = VecGetArray(b,&bb);CHKERRQ(ierr); 434 for (i=0; i<slen; i++) { 435 bb[lrows[i]] = diag*xx[lrows[i]]; 436 } 437 ierr = VecRestoreArrayRead(x,&xx);CHKERRQ(ierr); 438 ierr = VecRestoreArray(b,&bb);CHKERRQ(ierr); 439 } 440 441 /* actually zap the local rows */ 442 ierr = MatZeroRows(l->A,slen,lrows,0.0,0,0);CHKERRQ(ierr); 443 if (diag != 0.0) { 444 Mat_SeqDense *ll = (Mat_SeqDense*)l->A->data; 445 PetscInt m = ll->lda, i; 446 447 for (i=0; i<slen; i++) { 448 ll->v[lrows[i] + m*(A->cmap->rstart + lrows[i])] = diag; 449 } 450 } 451 ierr = PetscFree(lrows);CHKERRQ(ierr); 452 453 /* wait on sends */ 454 if (nsends) { 455 ierr = PetscMalloc1(nsends,&send_status);CHKERRQ(ierr); 456 ierr = MPI_Waitall(nsends,send_waits,send_status);CHKERRQ(ierr); 457 ierr = PetscFree(send_status);CHKERRQ(ierr); 458 } 459 ierr = PetscFree(send_waits);CHKERRQ(ierr); 460 ierr = PetscFree(svalues);CHKERRQ(ierr); 461 PetscFunctionReturn(0); 462 } 463 464 PETSC_INTERN PetscErrorCode MatMult_SeqDense(Mat,Vec,Vec); 465 PETSC_INTERN PetscErrorCode MatMultAdd_SeqDense(Mat,Vec,Vec,Vec); 466 PETSC_INTERN PetscErrorCode MatMultTranspose_SeqDense(Mat,Vec,Vec); 467 PETSC_INTERN PetscErrorCode MatMultTransposeAdd_SeqDense(Mat,Vec,Vec,Vec); 468 469 #undef __FUNCT__ 470 #define __FUNCT__ "MatMult_MPIDense" 471 PetscErrorCode MatMult_MPIDense(Mat mat,Vec xx,Vec yy) 472 { 473 Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data; 474 PetscErrorCode ierr; 475 476 PetscFunctionBegin; 477 ierr = VecScatterBegin(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 478 ierr = VecScatterEnd(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 479 ierr = MatMult_SeqDense(mdn->A,mdn->lvec,yy);CHKERRQ(ierr); 480 PetscFunctionReturn(0); 481 } 482 483 #undef __FUNCT__ 484 #define __FUNCT__ "MatMultAdd_MPIDense" 485 PetscErrorCode MatMultAdd_MPIDense(Mat mat,Vec xx,Vec yy,Vec zz) 486 { 487 Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data; 488 PetscErrorCode ierr; 489 490 PetscFunctionBegin; 491 ierr = VecScatterBegin(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 492 ierr = VecScatterEnd(mdn->Mvctx,xx,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 493 ierr = MatMultAdd_SeqDense(mdn->A,mdn->lvec,yy,zz);CHKERRQ(ierr); 494 PetscFunctionReturn(0); 495 } 496 497 #undef __FUNCT__ 498 #define __FUNCT__ "MatMultTranspose_MPIDense" 499 PetscErrorCode MatMultTranspose_MPIDense(Mat A,Vec xx,Vec yy) 500 { 501 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 502 PetscErrorCode ierr; 503 PetscScalar zero = 0.0; 504 505 PetscFunctionBegin; 506 ierr = VecSet(yy,zero);CHKERRQ(ierr); 507 ierr = MatMultTranspose_SeqDense(a->A,xx,a->lvec);CHKERRQ(ierr); 508 ierr = VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);CHKERRQ(ierr); 509 ierr = VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);CHKERRQ(ierr); 510 PetscFunctionReturn(0); 511 } 512 513 #undef __FUNCT__ 514 #define __FUNCT__ "MatMultTransposeAdd_MPIDense" 515 PetscErrorCode MatMultTransposeAdd_MPIDense(Mat A,Vec xx,Vec yy,Vec zz) 516 { 517 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 518 PetscErrorCode ierr; 519 520 PetscFunctionBegin; 521 ierr = VecCopy(yy,zz);CHKERRQ(ierr); 522 ierr = MatMultTranspose_SeqDense(a->A,xx,a->lvec);CHKERRQ(ierr); 523 ierr = VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);CHKERRQ(ierr); 524 ierr = VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);CHKERRQ(ierr); 525 PetscFunctionReturn(0); 526 } 527 528 #undef __FUNCT__ 529 #define __FUNCT__ "MatGetDiagonal_MPIDense" 530 PetscErrorCode MatGetDiagonal_MPIDense(Mat A,Vec v) 531 { 532 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 533 Mat_SeqDense *aloc = (Mat_SeqDense*)a->A->data; 534 PetscErrorCode ierr; 535 PetscInt len,i,n,m = A->rmap->n,radd; 536 PetscScalar *x,zero = 0.0; 537 538 PetscFunctionBegin; 539 ierr = VecSet(v,zero);CHKERRQ(ierr); 540 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 541 ierr = VecGetSize(v,&n);CHKERRQ(ierr); 542 if (n != A->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming mat and vec"); 543 len = PetscMin(a->A->rmap->n,a->A->cmap->n); 544 radd = A->rmap->rstart*m; 545 for (i=0; i<len; i++) { 546 x[i] = aloc->v[radd + i*m + i]; 547 } 548 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 549 PetscFunctionReturn(0); 550 } 551 552 #undef __FUNCT__ 553 #define __FUNCT__ "MatDestroy_MPIDense" 554 PetscErrorCode MatDestroy_MPIDense(Mat mat) 555 { 556 Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data; 557 PetscErrorCode ierr; 558 559 PetscFunctionBegin; 560 #if defined(PETSC_USE_LOG) 561 PetscLogObjectState((PetscObject)mat,"Rows=%D, Cols=%D",mat->rmap->N,mat->cmap->N); 562 #endif 563 ierr = MatStashDestroy_Private(&mat->stash);CHKERRQ(ierr); 564 ierr = MatDestroy(&mdn->A);CHKERRQ(ierr); 565 ierr = VecDestroy(&mdn->lvec);CHKERRQ(ierr); 566 ierr = VecScatterDestroy(&mdn->Mvctx);CHKERRQ(ierr); 567 568 ierr = PetscFree(mat->data);CHKERRQ(ierr); 569 ierr = PetscObjectChangeTypeName((PetscObject)mat,0);CHKERRQ(ierr); 570 571 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatDenseGetArray_C",NULL);CHKERRQ(ierr); 572 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatDenseRestoreArray_C",NULL);CHKERRQ(ierr); 573 #if defined(PETSC_HAVE_ELEMENTAL) 574 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpidense_elemental_C",NULL);CHKERRQ(ierr); 575 #endif 576 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMPIDenseSetPreallocation_C",NULL);CHKERRQ(ierr); 577 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMatMult_mpiaij_mpidense_C",NULL);CHKERRQ(ierr); 578 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMatMultSymbolic_mpiaij_mpidense_C",NULL);CHKERRQ(ierr); 579 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMatMultNumeric_mpiaij_mpidense_C",NULL);CHKERRQ(ierr); 580 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMult_mpiaij_mpidense_C",NULL);CHKERRQ(ierr); 581 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultSymbolic_mpiaij_mpidense_C",NULL);CHKERRQ(ierr); 582 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultNumeric_mpiaij_mpidense_C",NULL);CHKERRQ(ierr); 583 PetscFunctionReturn(0); 584 } 585 586 #undef __FUNCT__ 587 #define __FUNCT__ "MatView_MPIDense_Binary" 588 static PetscErrorCode MatView_MPIDense_Binary(Mat mat,PetscViewer viewer) 589 { 590 Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data; 591 PetscErrorCode ierr; 592 PetscViewerFormat format; 593 int fd; 594 PetscInt header[4],mmax,N = mat->cmap->N,i,j,m,k; 595 PetscMPIInt rank,tag = ((PetscObject)viewer)->tag,size; 596 PetscScalar *work,*v,*vv; 597 Mat_SeqDense *a = (Mat_SeqDense*)mdn->A->data; 598 599 PetscFunctionBegin; 600 if (mdn->size == 1) { 601 ierr = MatView(mdn->A,viewer);CHKERRQ(ierr); 602 } else { 603 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 604 ierr = MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);CHKERRQ(ierr); 605 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);CHKERRQ(ierr); 606 607 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 608 if (format == PETSC_VIEWER_NATIVE) { 609 610 if (!rank) { 611 /* store the matrix as a dense matrix */ 612 header[0] = MAT_FILE_CLASSID; 613 header[1] = mat->rmap->N; 614 header[2] = N; 615 header[3] = MATRIX_BINARY_FORMAT_DENSE; 616 ierr = PetscBinaryWrite(fd,header,4,PETSC_INT,PETSC_TRUE);CHKERRQ(ierr); 617 618 /* get largest work array needed for transposing array */ 619 mmax = mat->rmap->n; 620 for (i=1; i<size; i++) { 621 mmax = PetscMax(mmax,mat->rmap->range[i+1] - mat->rmap->range[i]); 622 } 623 ierr = PetscMalloc1(mmax*N,&work);CHKERRQ(ierr); 624 625 /* write out local array, by rows */ 626 m = mat->rmap->n; 627 v = a->v; 628 for (j=0; j<N; j++) { 629 for (i=0; i<m; i++) { 630 work[j + i*N] = *v++; 631 } 632 } 633 ierr = PetscBinaryWrite(fd,work,m*N,PETSC_SCALAR,PETSC_FALSE);CHKERRQ(ierr); 634 /* get largest work array to receive messages from other processes, excludes process zero */ 635 mmax = 0; 636 for (i=1; i<size; i++) { 637 mmax = PetscMax(mmax,mat->rmap->range[i+1] - mat->rmap->range[i]); 638 } 639 ierr = PetscMalloc1(mmax*N,&vv);CHKERRQ(ierr); 640 for (k = 1; k < size; k++) { 641 v = vv; 642 m = mat->rmap->range[k+1] - mat->rmap->range[k]; 643 ierr = MPIULong_Recv(v,m*N,MPIU_SCALAR,k,tag,PetscObjectComm((PetscObject)mat));CHKERRQ(ierr); 644 645 for (j = 0; j < N; j++) { 646 for (i = 0; i < m; i++) { 647 work[j + i*N] = *v++; 648 } 649 } 650 ierr = PetscBinaryWrite(fd,work,m*N,PETSC_SCALAR,PETSC_FALSE);CHKERRQ(ierr); 651 } 652 ierr = PetscFree(work);CHKERRQ(ierr); 653 ierr = PetscFree(vv);CHKERRQ(ierr); 654 } else { 655 ierr = MPIULong_Send(a->v,mat->rmap->n*mat->cmap->N,MPIU_SCALAR,0,tag,PetscObjectComm((PetscObject)mat));CHKERRQ(ierr); 656 } 657 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"To store a parallel dense matrix you must first call PetscViewerPushFormat(viewer,PETSC_VIEWER_NATIVE)"); 658 } 659 PetscFunctionReturn(0); 660 } 661 662 extern PetscErrorCode MatView_SeqDense(Mat,PetscViewer); 663 #include <petscdraw.h> 664 #undef __FUNCT__ 665 #define __FUNCT__ "MatView_MPIDense_ASCIIorDraworSocket" 666 static PetscErrorCode MatView_MPIDense_ASCIIorDraworSocket(Mat mat,PetscViewer viewer) 667 { 668 Mat_MPIDense *mdn = (Mat_MPIDense*)mat->data; 669 PetscErrorCode ierr; 670 PetscMPIInt rank = mdn->rank; 671 PetscViewerType vtype; 672 PetscBool iascii,isdraw; 673 PetscViewer sviewer; 674 PetscViewerFormat format; 675 676 PetscFunctionBegin; 677 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 678 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);CHKERRQ(ierr); 679 if (iascii) { 680 ierr = PetscViewerGetType(viewer,&vtype);CHKERRQ(ierr); 681 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 682 if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) { 683 MatInfo info; 684 ierr = MatGetInfo(mat,MAT_LOCAL,&info);CHKERRQ(ierr); 685 ierr = PetscViewerASCIIPushSynchronized(viewer);CHKERRQ(ierr); 686 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); 687 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 688 ierr = PetscViewerASCIIPopSynchronized(viewer);CHKERRQ(ierr); 689 ierr = VecScatterView(mdn->Mvctx,viewer);CHKERRQ(ierr); 690 PetscFunctionReturn(0); 691 } else if (format == PETSC_VIEWER_ASCII_INFO) { 692 PetscFunctionReturn(0); 693 } 694 } else if (isdraw) { 695 PetscDraw draw; 696 PetscBool isnull; 697 698 ierr = PetscViewerDrawGetDraw(viewer,0,&draw);CHKERRQ(ierr); 699 ierr = PetscDrawIsNull(draw,&isnull);CHKERRQ(ierr); 700 if (isnull) PetscFunctionReturn(0); 701 } 702 703 { 704 /* assemble the entire matrix onto first processor. */ 705 Mat A; 706 PetscInt M = mat->rmap->N,N = mat->cmap->N,m,row,i,nz; 707 PetscInt *cols; 708 PetscScalar *vals; 709 710 ierr = MatCreate(PetscObjectComm((PetscObject)mat),&A);CHKERRQ(ierr); 711 if (!rank) { 712 ierr = MatSetSizes(A,M,N,M,N);CHKERRQ(ierr); 713 } else { 714 ierr = MatSetSizes(A,0,0,M,N);CHKERRQ(ierr); 715 } 716 /* Since this is a temporary matrix, MATMPIDENSE instead of ((PetscObject)A)->type_name here is probably acceptable. */ 717 ierr = MatSetType(A,MATMPIDENSE);CHKERRQ(ierr); 718 ierr = MatMPIDenseSetPreallocation(A,NULL);CHKERRQ(ierr); 719 ierr = PetscLogObjectParent((PetscObject)mat,(PetscObject)A);CHKERRQ(ierr); 720 721 /* Copy the matrix ... This isn't the most efficient means, 722 but it's quick for now */ 723 A->insertmode = INSERT_VALUES; 724 725 row = mat->rmap->rstart; 726 m = mdn->A->rmap->n; 727 for (i=0; i<m; i++) { 728 ierr = MatGetRow_MPIDense(mat,row,&nz,&cols,&vals);CHKERRQ(ierr); 729 ierr = MatSetValues_MPIDense(A,1,&row,nz,cols,vals,INSERT_VALUES);CHKERRQ(ierr); 730 ierr = MatRestoreRow_MPIDense(mat,row,&nz,&cols,&vals);CHKERRQ(ierr); 731 row++; 732 } 733 734 ierr = MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 735 ierr = MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 736 ierr = PetscViewerGetSubViewer(viewer,PETSC_COMM_SELF,&sviewer);CHKERRQ(ierr); 737 if (!rank) { 738 ierr = PetscObjectSetName((PetscObject)((Mat_MPIDense*)(A->data))->A,((PetscObject)mat)->name);CHKERRQ(ierr); 739 ierr = MatView_SeqDense(((Mat_MPIDense*)(A->data))->A,sviewer);CHKERRQ(ierr); 740 } 741 ierr = PetscViewerRestoreSubViewer(viewer,PETSC_COMM_SELF,&sviewer);CHKERRQ(ierr); 742 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 743 ierr = MatDestroy(&A);CHKERRQ(ierr); 744 } 745 PetscFunctionReturn(0); 746 } 747 748 #undef __FUNCT__ 749 #define __FUNCT__ "MatView_MPIDense" 750 PetscErrorCode MatView_MPIDense(Mat mat,PetscViewer viewer) 751 { 752 PetscErrorCode ierr; 753 PetscBool iascii,isbinary,isdraw,issocket; 754 755 PetscFunctionBegin; 756 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 757 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);CHKERRQ(ierr); 758 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSOCKET,&issocket);CHKERRQ(ierr); 759 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);CHKERRQ(ierr); 760 761 if (iascii || issocket || isdraw) { 762 ierr = MatView_MPIDense_ASCIIorDraworSocket(mat,viewer);CHKERRQ(ierr); 763 } else if (isbinary) { 764 ierr = MatView_MPIDense_Binary(mat,viewer);CHKERRQ(ierr); 765 } 766 PetscFunctionReturn(0); 767 } 768 769 #undef __FUNCT__ 770 #define __FUNCT__ "MatGetInfo_MPIDense" 771 PetscErrorCode MatGetInfo_MPIDense(Mat A,MatInfoType flag,MatInfo *info) 772 { 773 Mat_MPIDense *mat = (Mat_MPIDense*)A->data; 774 Mat mdn = mat->A; 775 PetscErrorCode ierr; 776 PetscReal isend[5],irecv[5]; 777 778 PetscFunctionBegin; 779 info->block_size = 1.0; 780 781 ierr = MatGetInfo(mdn,MAT_LOCAL,info);CHKERRQ(ierr); 782 783 isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded; 784 isend[3] = info->memory; isend[4] = info->mallocs; 785 if (flag == MAT_LOCAL) { 786 info->nz_used = isend[0]; 787 info->nz_allocated = isend[1]; 788 info->nz_unneeded = isend[2]; 789 info->memory = isend[3]; 790 info->mallocs = isend[4]; 791 } else if (flag == MAT_GLOBAL_MAX) { 792 ierr = MPIU_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)A));CHKERRQ(ierr); 793 794 info->nz_used = irecv[0]; 795 info->nz_allocated = irecv[1]; 796 info->nz_unneeded = irecv[2]; 797 info->memory = irecv[3]; 798 info->mallocs = irecv[4]; 799 } else if (flag == MAT_GLOBAL_SUM) { 800 ierr = MPIU_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));CHKERRQ(ierr); 801 802 info->nz_used = irecv[0]; 803 info->nz_allocated = irecv[1]; 804 info->nz_unneeded = irecv[2]; 805 info->memory = irecv[3]; 806 info->mallocs = irecv[4]; 807 } 808 info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */ 809 info->fill_ratio_needed = 0; 810 info->factor_mallocs = 0; 811 PetscFunctionReturn(0); 812 } 813 814 #undef __FUNCT__ 815 #define __FUNCT__ "MatSetOption_MPIDense" 816 PetscErrorCode MatSetOption_MPIDense(Mat A,MatOption op,PetscBool flg) 817 { 818 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 819 PetscErrorCode ierr; 820 821 PetscFunctionBegin; 822 switch (op) { 823 case MAT_NEW_NONZERO_LOCATIONS: 824 case MAT_NEW_NONZERO_LOCATION_ERR: 825 case MAT_NEW_NONZERO_ALLOCATION_ERR: 826 MatCheckPreallocated(A,1); 827 ierr = MatSetOption(a->A,op,flg);CHKERRQ(ierr); 828 break; 829 case MAT_ROW_ORIENTED: 830 MatCheckPreallocated(A,1); 831 a->roworiented = flg; 832 ierr = MatSetOption(a->A,op,flg);CHKERRQ(ierr); 833 break; 834 case MAT_NEW_DIAGONALS: 835 case MAT_KEEP_NONZERO_PATTERN: 836 case MAT_USE_HASH_TABLE: 837 ierr = PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);CHKERRQ(ierr); 838 break; 839 case MAT_IGNORE_OFF_PROC_ENTRIES: 840 a->donotstash = flg; 841 break; 842 case MAT_SYMMETRIC: 843 case MAT_STRUCTURALLY_SYMMETRIC: 844 case MAT_HERMITIAN: 845 case MAT_SYMMETRY_ETERNAL: 846 case MAT_IGNORE_LOWER_TRIANGULAR: 847 ierr = PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);CHKERRQ(ierr); 848 break; 849 default: 850 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %s",MatOptions[op]); 851 } 852 PetscFunctionReturn(0); 853 } 854 855 856 #undef __FUNCT__ 857 #define __FUNCT__ "MatDiagonalScale_MPIDense" 858 PetscErrorCode MatDiagonalScale_MPIDense(Mat A,Vec ll,Vec rr) 859 { 860 Mat_MPIDense *mdn = (Mat_MPIDense*)A->data; 861 Mat_SeqDense *mat = (Mat_SeqDense*)mdn->A->data; 862 PetscScalar *l,*r,x,*v; 863 PetscErrorCode ierr; 864 PetscInt i,j,s2a,s3a,s2,s3,m=mdn->A->rmap->n,n=mdn->A->cmap->n; 865 866 PetscFunctionBegin; 867 ierr = MatGetLocalSize(A,&s2,&s3);CHKERRQ(ierr); 868 if (ll) { 869 ierr = VecGetLocalSize(ll,&s2a);CHKERRQ(ierr); 870 if (s2a != s2) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector non-conforming local size, %d != %d.", s2a, s2); 871 ierr = VecGetArray(ll,&l);CHKERRQ(ierr); 872 for (i=0; i<m; i++) { 873 x = l[i]; 874 v = mat->v + i; 875 for (j=0; j<n; j++) { (*v) *= x; v+= m;} 876 } 877 ierr = VecRestoreArray(ll,&l);CHKERRQ(ierr); 878 ierr = PetscLogFlops(n*m);CHKERRQ(ierr); 879 } 880 if (rr) { 881 ierr = VecGetLocalSize(rr,&s3a);CHKERRQ(ierr); 882 if (s3a != s3) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vec non-conforming local size, %d != %d.", s3a, s3); 883 ierr = VecScatterBegin(mdn->Mvctx,rr,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 884 ierr = VecScatterEnd(mdn->Mvctx,rr,mdn->lvec,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 885 ierr = VecGetArray(mdn->lvec,&r);CHKERRQ(ierr); 886 for (i=0; i<n; i++) { 887 x = r[i]; 888 v = mat->v + i*m; 889 for (j=0; j<m; j++) (*v++) *= x; 890 } 891 ierr = VecRestoreArray(mdn->lvec,&r);CHKERRQ(ierr); 892 ierr = PetscLogFlops(n*m);CHKERRQ(ierr); 893 } 894 PetscFunctionReturn(0); 895 } 896 897 #undef __FUNCT__ 898 #define __FUNCT__ "MatNorm_MPIDense" 899 PetscErrorCode MatNorm_MPIDense(Mat A,NormType type,PetscReal *nrm) 900 { 901 Mat_MPIDense *mdn = (Mat_MPIDense*)A->data; 902 Mat_SeqDense *mat = (Mat_SeqDense*)mdn->A->data; 903 PetscErrorCode ierr; 904 PetscInt i,j; 905 PetscReal sum = 0.0; 906 PetscScalar *v = mat->v; 907 908 PetscFunctionBegin; 909 if (mdn->size == 1) { 910 ierr = MatNorm(mdn->A,type,nrm);CHKERRQ(ierr); 911 } else { 912 if (type == NORM_FROBENIUS) { 913 for (i=0; i<mdn->A->cmap->n*mdn->A->rmap->n; i++) { 914 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 915 } 916 ierr = MPIU_Allreduce(&sum,nrm,1,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));CHKERRQ(ierr); 917 *nrm = PetscSqrtReal(*nrm); 918 ierr = PetscLogFlops(2.0*mdn->A->cmap->n*mdn->A->rmap->n);CHKERRQ(ierr); 919 } else if (type == NORM_1) { 920 PetscReal *tmp,*tmp2; 921 ierr = PetscMalloc2(A->cmap->N,&tmp,A->cmap->N,&tmp2);CHKERRQ(ierr); 922 ierr = PetscMemzero(tmp,A->cmap->N*sizeof(PetscReal));CHKERRQ(ierr); 923 ierr = PetscMemzero(tmp2,A->cmap->N*sizeof(PetscReal));CHKERRQ(ierr); 924 *nrm = 0.0; 925 v = mat->v; 926 for (j=0; j<mdn->A->cmap->n; j++) { 927 for (i=0; i<mdn->A->rmap->n; i++) { 928 tmp[j] += PetscAbsScalar(*v); v++; 929 } 930 } 931 ierr = MPIU_Allreduce(tmp,tmp2,A->cmap->N,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));CHKERRQ(ierr); 932 for (j=0; j<A->cmap->N; j++) { 933 if (tmp2[j] > *nrm) *nrm = tmp2[j]; 934 } 935 ierr = PetscFree2(tmp,tmp2);CHKERRQ(ierr); 936 ierr = PetscLogFlops(A->cmap->n*A->rmap->n);CHKERRQ(ierr); 937 } else if (type == NORM_INFINITY) { /* max row norm */ 938 PetscReal ntemp; 939 ierr = MatNorm(mdn->A,type,&ntemp);CHKERRQ(ierr); 940 ierr = MPIU_Allreduce(&ntemp,nrm,1,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)A));CHKERRQ(ierr); 941 } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"No support for two norm"); 942 } 943 PetscFunctionReturn(0); 944 } 945 946 #undef __FUNCT__ 947 #define __FUNCT__ "MatTranspose_MPIDense" 948 PetscErrorCode MatTranspose_MPIDense(Mat A,MatReuse reuse,Mat *matout) 949 { 950 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 951 Mat_SeqDense *Aloc = (Mat_SeqDense*)a->A->data; 952 Mat B; 953 PetscInt M = A->rmap->N,N = A->cmap->N,m,n,*rwork,rstart = A->rmap->rstart; 954 PetscErrorCode ierr; 955 PetscInt j,i; 956 PetscScalar *v; 957 958 PetscFunctionBegin; 959 if (reuse == MAT_REUSE_MATRIX && A == *matout && M != N) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Supports square matrix only in-place"); 960 if (reuse == MAT_INITIAL_MATRIX || A == *matout) { 961 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 962 ierr = MatSetSizes(B,A->cmap->n,A->rmap->n,N,M);CHKERRQ(ierr); 963 ierr = MatSetType(B,((PetscObject)A)->type_name);CHKERRQ(ierr); 964 ierr = MatMPIDenseSetPreallocation(B,NULL);CHKERRQ(ierr); 965 } else { 966 B = *matout; 967 } 968 969 m = a->A->rmap->n; n = a->A->cmap->n; v = Aloc->v; 970 ierr = PetscMalloc1(m,&rwork);CHKERRQ(ierr); 971 for (i=0; i<m; i++) rwork[i] = rstart + i; 972 for (j=0; j<n; j++) { 973 ierr = MatSetValues(B,1,&j,m,rwork,v,INSERT_VALUES);CHKERRQ(ierr); 974 v += m; 975 } 976 ierr = PetscFree(rwork);CHKERRQ(ierr); 977 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 978 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 979 if (reuse == MAT_INITIAL_MATRIX || *matout != A) { 980 *matout = B; 981 } else { 982 ierr = MatHeaderMerge(A,&B);CHKERRQ(ierr); 983 } 984 PetscFunctionReturn(0); 985 } 986 987 988 static PetscErrorCode MatDuplicate_MPIDense(Mat,MatDuplicateOption,Mat*); 989 extern PetscErrorCode MatScale_MPIDense(Mat,PetscScalar); 990 991 #undef __FUNCT__ 992 #define __FUNCT__ "MatSetUp_MPIDense" 993 PetscErrorCode MatSetUp_MPIDense(Mat A) 994 { 995 PetscErrorCode ierr; 996 997 PetscFunctionBegin; 998 ierr = MatMPIDenseSetPreallocation(A,0);CHKERRQ(ierr); 999 PetscFunctionReturn(0); 1000 } 1001 1002 #undef __FUNCT__ 1003 #define __FUNCT__ "MatAXPY_MPIDense" 1004 PetscErrorCode MatAXPY_MPIDense(Mat Y,PetscScalar alpha,Mat X,MatStructure str) 1005 { 1006 PetscErrorCode ierr; 1007 Mat_MPIDense *A = (Mat_MPIDense*)Y->data, *B = (Mat_MPIDense*)X->data; 1008 1009 PetscFunctionBegin; 1010 ierr = MatAXPY(A->A,alpha,B->A,str);CHKERRQ(ierr); 1011 ierr = PetscObjectStateIncrease((PetscObject)Y);CHKERRQ(ierr); 1012 PetscFunctionReturn(0); 1013 } 1014 1015 #undef __FUNCT__ 1016 #define __FUNCT__ "MatConjugate_MPIDense" 1017 PetscErrorCode MatConjugate_MPIDense(Mat mat) 1018 { 1019 Mat_MPIDense *a = (Mat_MPIDense*)mat->data; 1020 PetscErrorCode ierr; 1021 1022 PetscFunctionBegin; 1023 ierr = MatConjugate(a->A);CHKERRQ(ierr); 1024 PetscFunctionReturn(0); 1025 } 1026 1027 #undef __FUNCT__ 1028 #define __FUNCT__ "MatRealPart_MPIDense" 1029 PetscErrorCode MatRealPart_MPIDense(Mat A) 1030 { 1031 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 1032 PetscErrorCode ierr; 1033 1034 PetscFunctionBegin; 1035 ierr = MatRealPart(a->A);CHKERRQ(ierr); 1036 PetscFunctionReturn(0); 1037 } 1038 1039 #undef __FUNCT__ 1040 #define __FUNCT__ "MatImaginaryPart_MPIDense" 1041 PetscErrorCode MatImaginaryPart_MPIDense(Mat A) 1042 { 1043 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 1044 PetscErrorCode ierr; 1045 1046 PetscFunctionBegin; 1047 ierr = MatImaginaryPart(a->A);CHKERRQ(ierr); 1048 PetscFunctionReturn(0); 1049 } 1050 1051 extern PetscErrorCode MatGetColumnNorms_SeqDense(Mat,NormType,PetscReal*); 1052 #undef __FUNCT__ 1053 #define __FUNCT__ "MatGetColumnNorms_MPIDense" 1054 PetscErrorCode MatGetColumnNorms_MPIDense(Mat A,NormType type,PetscReal *norms) 1055 { 1056 PetscErrorCode ierr; 1057 PetscInt i,n; 1058 Mat_MPIDense *a = (Mat_MPIDense*) A->data; 1059 PetscReal *work; 1060 1061 PetscFunctionBegin; 1062 ierr = MatGetSize(A,NULL,&n);CHKERRQ(ierr); 1063 ierr = PetscMalloc1(n,&work);CHKERRQ(ierr); 1064 ierr = MatGetColumnNorms_SeqDense(a->A,type,work);CHKERRQ(ierr); 1065 if (type == NORM_2) { 1066 for (i=0; i<n; i++) work[i] *= work[i]; 1067 } 1068 if (type == NORM_INFINITY) { 1069 ierr = MPIU_Allreduce(work,norms,n,MPIU_REAL,MPIU_MAX,A->hdr.comm);CHKERRQ(ierr); 1070 } else { 1071 ierr = MPIU_Allreduce(work,norms,n,MPIU_REAL,MPIU_SUM,A->hdr.comm);CHKERRQ(ierr); 1072 } 1073 ierr = PetscFree(work);CHKERRQ(ierr); 1074 if (type == NORM_2) { 1075 for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]); 1076 } 1077 PetscFunctionReturn(0); 1078 } 1079 1080 #undef __FUNCT__ 1081 #define __FUNCT__ "MatSetRandom_MPIDense" 1082 static PetscErrorCode MatSetRandom_MPIDense(Mat x,PetscRandom rctx) 1083 { 1084 Mat_MPIDense *d = (Mat_MPIDense*)x->data; 1085 PetscErrorCode ierr; 1086 PetscScalar *a; 1087 PetscInt m,n,i; 1088 1089 PetscFunctionBegin; 1090 ierr = MatGetSize(d->A,&m,&n);CHKERRQ(ierr); 1091 ierr = MatDenseGetArray(d->A,&a);CHKERRQ(ierr); 1092 for (i=0; i<m*n; i++) { 1093 ierr = PetscRandomGetValue(rctx,a+i);CHKERRQ(ierr); 1094 } 1095 ierr = MatDenseRestoreArray(d->A,&a);CHKERRQ(ierr); 1096 PetscFunctionReturn(0); 1097 } 1098 1099 extern PetscErrorCode MatMatMultNumeric_MPIDense(Mat A,Mat,Mat); 1100 1101 #undef __FUNCT__ 1102 #define __FUNCT__ "MatMissingDiagonal_MPIDense" 1103 static PetscErrorCode MatMissingDiagonal_MPIDense(Mat A,PetscBool *missing,PetscInt *d) 1104 { 1105 PetscFunctionBegin; 1106 *missing = PETSC_FALSE; 1107 PetscFunctionReturn(0); 1108 } 1109 1110 /* -------------------------------------------------------------------*/ 1111 static struct _MatOps MatOps_Values = { MatSetValues_MPIDense, 1112 MatGetRow_MPIDense, 1113 MatRestoreRow_MPIDense, 1114 MatMult_MPIDense, 1115 /* 4*/ MatMultAdd_MPIDense, 1116 MatMultTranspose_MPIDense, 1117 MatMultTransposeAdd_MPIDense, 1118 0, 1119 0, 1120 0, 1121 /* 10*/ 0, 1122 0, 1123 0, 1124 0, 1125 MatTranspose_MPIDense, 1126 /* 15*/ MatGetInfo_MPIDense, 1127 MatEqual_MPIDense, 1128 MatGetDiagonal_MPIDense, 1129 MatDiagonalScale_MPIDense, 1130 MatNorm_MPIDense, 1131 /* 20*/ MatAssemblyBegin_MPIDense, 1132 MatAssemblyEnd_MPIDense, 1133 MatSetOption_MPIDense, 1134 MatZeroEntries_MPIDense, 1135 /* 24*/ MatZeroRows_MPIDense, 1136 0, 1137 0, 1138 0, 1139 0, 1140 /* 29*/ MatSetUp_MPIDense, 1141 0, 1142 0, 1143 MatGetDiagonalBlock_MPIDense, 1144 0, 1145 /* 34*/ MatDuplicate_MPIDense, 1146 0, 1147 0, 1148 0, 1149 0, 1150 /* 39*/ MatAXPY_MPIDense, 1151 MatGetSubMatrices_MPIDense, 1152 0, 1153 MatGetValues_MPIDense, 1154 0, 1155 /* 44*/ 0, 1156 MatScale_MPIDense, 1157 MatShift_Basic, 1158 0, 1159 0, 1160 /* 49*/ MatSetRandom_MPIDense, 1161 0, 1162 0, 1163 0, 1164 0, 1165 /* 54*/ 0, 1166 0, 1167 0, 1168 0, 1169 0, 1170 /* 59*/ MatGetSubMatrix_MPIDense, 1171 MatDestroy_MPIDense, 1172 MatView_MPIDense, 1173 0, 1174 0, 1175 /* 64*/ 0, 1176 0, 1177 0, 1178 0, 1179 0, 1180 /* 69*/ 0, 1181 0, 1182 0, 1183 0, 1184 0, 1185 /* 74*/ 0, 1186 0, 1187 0, 1188 0, 1189 0, 1190 /* 79*/ 0, 1191 0, 1192 0, 1193 0, 1194 /* 83*/ MatLoad_MPIDense, 1195 0, 1196 0, 1197 0, 1198 0, 1199 0, 1200 #if defined(PETSC_HAVE_ELEMENTAL) 1201 /* 89*/ MatMatMult_MPIDense_MPIDense, 1202 MatMatMultSymbolic_MPIDense_MPIDense, 1203 #else 1204 /* 89*/ 0, 1205 0, 1206 #endif 1207 MatMatMultNumeric_MPIDense, 1208 0, 1209 0, 1210 /* 94*/ 0, 1211 0, 1212 0, 1213 0, 1214 0, 1215 /* 99*/ 0, 1216 0, 1217 0, 1218 MatConjugate_MPIDense, 1219 0, 1220 /*104*/ 0, 1221 MatRealPart_MPIDense, 1222 MatImaginaryPart_MPIDense, 1223 0, 1224 0, 1225 /*109*/ 0, 1226 0, 1227 0, 1228 0, 1229 MatMissingDiagonal_MPIDense, 1230 /*114*/ 0, 1231 0, 1232 0, 1233 0, 1234 0, 1235 /*119*/ 0, 1236 0, 1237 0, 1238 0, 1239 0, 1240 /*124*/ 0, 1241 MatGetColumnNorms_MPIDense, 1242 0, 1243 0, 1244 0, 1245 /*129*/ 0, 1246 MatTransposeMatMult_MPIDense_MPIDense, 1247 MatTransposeMatMultSymbolic_MPIDense_MPIDense, 1248 MatTransposeMatMultNumeric_MPIDense_MPIDense, 1249 0, 1250 /*134*/ 0, 1251 0, 1252 0, 1253 0, 1254 0, 1255 /*139*/ 0, 1256 0, 1257 0 1258 }; 1259 1260 #undef __FUNCT__ 1261 #define __FUNCT__ "MatMPIDenseSetPreallocation_MPIDense" 1262 PetscErrorCode MatMPIDenseSetPreallocation_MPIDense(Mat mat,PetscScalar *data) 1263 { 1264 Mat_MPIDense *a; 1265 PetscErrorCode ierr; 1266 1267 PetscFunctionBegin; 1268 mat->preallocated = PETSC_TRUE; 1269 /* Note: For now, when data is specified above, this assumes the user correctly 1270 allocates the local dense storage space. We should add error checking. */ 1271 1272 a = (Mat_MPIDense*)mat->data; 1273 ierr = PetscLayoutSetUp(mat->rmap);CHKERRQ(ierr); 1274 ierr = PetscLayoutSetUp(mat->cmap);CHKERRQ(ierr); 1275 a->nvec = mat->cmap->n; 1276 1277 ierr = MatCreate(PETSC_COMM_SELF,&a->A);CHKERRQ(ierr); 1278 ierr = MatSetSizes(a->A,mat->rmap->n,mat->cmap->N,mat->rmap->n,mat->cmap->N);CHKERRQ(ierr); 1279 ierr = MatSetType(a->A,MATSEQDENSE);CHKERRQ(ierr); 1280 ierr = MatSeqDenseSetPreallocation(a->A,data);CHKERRQ(ierr); 1281 ierr = PetscLogObjectParent((PetscObject)mat,(PetscObject)a->A);CHKERRQ(ierr); 1282 PetscFunctionReturn(0); 1283 } 1284 1285 #if defined(PETSC_HAVE_ELEMENTAL) 1286 #undef __FUNCT__ 1287 #define __FUNCT__ "MatConvert_MPIDense_Elemental" 1288 PETSC_INTERN PetscErrorCode MatConvert_MPIDense_Elemental(Mat A, MatType newtype,MatReuse reuse,Mat *newmat) 1289 { 1290 Mat mat_elemental; 1291 PetscErrorCode ierr; 1292 PetscScalar *v; 1293 PetscInt m=A->rmap->n,N=A->cmap->N,rstart=A->rmap->rstart,i,*rows,*cols; 1294 1295 PetscFunctionBegin; 1296 if (reuse == MAT_REUSE_MATRIX) { 1297 mat_elemental = *newmat; 1298 ierr = MatZeroEntries(*newmat);CHKERRQ(ierr); 1299 } else { 1300 ierr = MatCreate(PetscObjectComm((PetscObject)A), &mat_elemental);CHKERRQ(ierr); 1301 ierr = MatSetSizes(mat_elemental,PETSC_DECIDE,PETSC_DECIDE,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 1302 ierr = MatSetType(mat_elemental,MATELEMENTAL);CHKERRQ(ierr); 1303 ierr = MatSetUp(mat_elemental);CHKERRQ(ierr); 1304 ierr = MatSetOption(mat_elemental,MAT_ROW_ORIENTED,PETSC_FALSE);CHKERRQ(ierr); 1305 } 1306 1307 ierr = PetscMalloc2(m,&rows,N,&cols);CHKERRQ(ierr); 1308 for (i=0; i<N; i++) cols[i] = i; 1309 for (i=0; i<m; i++) rows[i] = rstart + i; 1310 1311 /* PETSc-Elemental interaface uses axpy for setting off-processor entries, only ADD_VALUES is allowed */ 1312 ierr = MatDenseGetArray(A,&v);CHKERRQ(ierr); 1313 ierr = MatSetValues(mat_elemental,m,rows,N,cols,v,ADD_VALUES);CHKERRQ(ierr); 1314 ierr = MatAssemblyBegin(mat_elemental, MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1315 ierr = MatAssemblyEnd(mat_elemental, MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1316 ierr = MatDenseRestoreArray(A,&v);CHKERRQ(ierr); 1317 ierr = PetscFree2(rows,cols);CHKERRQ(ierr); 1318 1319 if (reuse == MAT_INPLACE_MATRIX) { 1320 ierr = MatHeaderReplace(A,&mat_elemental);CHKERRQ(ierr); 1321 } else { 1322 *newmat = mat_elemental; 1323 } 1324 PetscFunctionReturn(0); 1325 } 1326 #endif 1327 1328 #undef __FUNCT__ 1329 #define __FUNCT__ "MatCreate_MPIDense" 1330 PETSC_EXTERN PetscErrorCode MatCreate_MPIDense(Mat mat) 1331 { 1332 Mat_MPIDense *a; 1333 PetscErrorCode ierr; 1334 1335 PetscFunctionBegin; 1336 ierr = PetscNewLog(mat,&a);CHKERRQ(ierr); 1337 mat->data = (void*)a; 1338 ierr = PetscMemcpy(mat->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 1339 1340 mat->insertmode = NOT_SET_VALUES; 1341 ierr = MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&a->rank);CHKERRQ(ierr); 1342 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)mat),&a->size);CHKERRQ(ierr); 1343 1344 /* build cache for off array entries formed */ 1345 a->donotstash = PETSC_FALSE; 1346 1347 ierr = MatStashCreate_Private(PetscObjectComm((PetscObject)mat),1,&mat->stash);CHKERRQ(ierr); 1348 1349 /* stuff used for matrix vector multiply */ 1350 a->lvec = 0; 1351 a->Mvctx = 0; 1352 a->roworiented = PETSC_TRUE; 1353 1354 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatDenseGetArray_C",MatDenseGetArray_MPIDense);CHKERRQ(ierr); 1355 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatDenseRestoreArray_C",MatDenseRestoreArray_MPIDense);CHKERRQ(ierr); 1356 #if defined(PETSC_HAVE_ELEMENTAL) 1357 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpidense_elemental_C",MatConvert_MPIDense_Elemental);CHKERRQ(ierr); 1358 #endif 1359 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMPIDenseSetPreallocation_C",MatMPIDenseSetPreallocation_MPIDense);CHKERRQ(ierr); 1360 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMatMult_mpiaij_mpidense_C",MatMatMult_MPIAIJ_MPIDense);CHKERRQ(ierr); 1361 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMatMultSymbolic_mpiaij_mpidense_C",MatMatMultSymbolic_MPIAIJ_MPIDense);CHKERRQ(ierr); 1362 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatMatMultNumeric_mpiaij_mpidense_C",MatMatMultNumeric_MPIAIJ_MPIDense);CHKERRQ(ierr); 1363 1364 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMult_mpiaij_mpidense_C",MatTransposeMatMult_MPIAIJ_MPIDense);CHKERRQ(ierr); 1365 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultSymbolic_mpiaij_mpidense_C",MatTransposeMatMultSymbolic_MPIAIJ_MPIDense);CHKERRQ(ierr); 1366 ierr = PetscObjectComposeFunction((PetscObject)mat,"MatTransposeMatMultNumeric_mpiaij_mpidense_C",MatTransposeMatMultNumeric_MPIAIJ_MPIDense);CHKERRQ(ierr); 1367 ierr = PetscObjectChangeTypeName((PetscObject)mat,MATMPIDENSE);CHKERRQ(ierr); 1368 PetscFunctionReturn(0); 1369 } 1370 1371 /*MC 1372 MATDENSE - MATDENSE = "dense" - A matrix type to be used for dense matrices. 1373 1374 This matrix type is identical to MATSEQDENSE when constructed with a single process communicator, 1375 and MATMPIDENSE otherwise. 1376 1377 Options Database Keys: 1378 . -mat_type dense - sets the matrix type to "dense" during a call to MatSetFromOptions() 1379 1380 Level: beginner 1381 1382 1383 .seealso: MatCreateMPIDense,MATSEQDENSE,MATMPIDENSE 1384 M*/ 1385 1386 #undef __FUNCT__ 1387 #define __FUNCT__ "MatMPIDenseSetPreallocation" 1388 /*@C 1389 MatMPIDenseSetPreallocation - Sets the array used to store the matrix entries 1390 1391 Not collective 1392 1393 Input Parameters: 1394 . B - the matrix 1395 - data - optional location of matrix data. Set data=NULL for PETSc 1396 to control all matrix memory allocation. 1397 1398 Notes: 1399 The dense format is fully compatible with standard Fortran 77 1400 storage by columns. 1401 1402 The data input variable is intended primarily for Fortran programmers 1403 who wish to allocate their own matrix memory space. Most users should 1404 set data=NULL. 1405 1406 Level: intermediate 1407 1408 .keywords: matrix,dense, parallel 1409 1410 .seealso: MatCreate(), MatCreateSeqDense(), MatSetValues() 1411 @*/ 1412 PetscErrorCode MatMPIDenseSetPreallocation(Mat B,PetscScalar *data) 1413 { 1414 PetscErrorCode ierr; 1415 1416 PetscFunctionBegin; 1417 ierr = PetscTryMethod(B,"MatMPIDenseSetPreallocation_C",(Mat,PetscScalar*),(B,data));CHKERRQ(ierr); 1418 PetscFunctionReturn(0); 1419 } 1420 1421 #undef __FUNCT__ 1422 #define __FUNCT__ "MatCreateDense" 1423 /*@C 1424 MatCreateDense - Creates a parallel matrix in dense format. 1425 1426 Collective on MPI_Comm 1427 1428 Input Parameters: 1429 + comm - MPI communicator 1430 . m - number of local rows (or PETSC_DECIDE to have calculated if M is given) 1431 . n - number of local columns (or PETSC_DECIDE to have calculated if N is given) 1432 . M - number of global rows (or PETSC_DECIDE to have calculated if m is given) 1433 . N - number of global columns (or PETSC_DECIDE to have calculated if n is given) 1434 - data - optional location of matrix data. Set data=NULL (PETSC_NULL_SCALAR for Fortran users) for PETSc 1435 to control all matrix memory allocation. 1436 1437 Output Parameter: 1438 . A - the matrix 1439 1440 Notes: 1441 The dense format is fully compatible with standard Fortran 77 1442 storage by columns. 1443 1444 The data input variable is intended primarily for Fortran programmers 1445 who wish to allocate their own matrix memory space. Most users should 1446 set data=NULL (PETSC_NULL_SCALAR for Fortran users). 1447 1448 The user MUST specify either the local or global matrix dimensions 1449 (possibly both). 1450 1451 Level: intermediate 1452 1453 .keywords: matrix,dense, parallel 1454 1455 .seealso: MatCreate(), MatCreateSeqDense(), MatSetValues() 1456 @*/ 1457 PetscErrorCode MatCreateDense(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscScalar *data,Mat *A) 1458 { 1459 PetscErrorCode ierr; 1460 PetscMPIInt size; 1461 1462 PetscFunctionBegin; 1463 ierr = MatCreate(comm,A);CHKERRQ(ierr); 1464 ierr = MatSetSizes(*A,m,n,M,N);CHKERRQ(ierr); 1465 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 1466 if (size > 1) { 1467 ierr = MatSetType(*A,MATMPIDENSE);CHKERRQ(ierr); 1468 ierr = MatMPIDenseSetPreallocation(*A,data);CHKERRQ(ierr); 1469 if (data) { /* user provided data array, so no need to assemble */ 1470 ierr = MatSetUpMultiply_MPIDense(*A);CHKERRQ(ierr); 1471 (*A)->assembled = PETSC_TRUE; 1472 } 1473 } else { 1474 ierr = MatSetType(*A,MATSEQDENSE);CHKERRQ(ierr); 1475 ierr = MatSeqDenseSetPreallocation(*A,data);CHKERRQ(ierr); 1476 } 1477 PetscFunctionReturn(0); 1478 } 1479 1480 #undef __FUNCT__ 1481 #define __FUNCT__ "MatDuplicate_MPIDense" 1482 static PetscErrorCode MatDuplicate_MPIDense(Mat A,MatDuplicateOption cpvalues,Mat *newmat) 1483 { 1484 Mat mat; 1485 Mat_MPIDense *a,*oldmat = (Mat_MPIDense*)A->data; 1486 PetscErrorCode ierr; 1487 1488 PetscFunctionBegin; 1489 *newmat = 0; 1490 ierr = MatCreate(PetscObjectComm((PetscObject)A),&mat);CHKERRQ(ierr); 1491 ierr = MatSetSizes(mat,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 1492 ierr = MatSetType(mat,((PetscObject)A)->type_name);CHKERRQ(ierr); 1493 a = (Mat_MPIDense*)mat->data; 1494 ierr = PetscMemcpy(mat->ops,A->ops,sizeof(struct _MatOps));CHKERRQ(ierr); 1495 1496 mat->factortype = A->factortype; 1497 mat->assembled = PETSC_TRUE; 1498 mat->preallocated = PETSC_TRUE; 1499 1500 a->size = oldmat->size; 1501 a->rank = oldmat->rank; 1502 mat->insertmode = NOT_SET_VALUES; 1503 a->nvec = oldmat->nvec; 1504 a->donotstash = oldmat->donotstash; 1505 1506 ierr = PetscLayoutReference(A->rmap,&mat->rmap);CHKERRQ(ierr); 1507 ierr = PetscLayoutReference(A->cmap,&mat->cmap);CHKERRQ(ierr); 1508 1509 ierr = MatSetUpMultiply_MPIDense(mat);CHKERRQ(ierr); 1510 ierr = MatDuplicate(oldmat->A,cpvalues,&a->A);CHKERRQ(ierr); 1511 ierr = PetscLogObjectParent((PetscObject)mat,(PetscObject)a->A);CHKERRQ(ierr); 1512 1513 *newmat = mat; 1514 PetscFunctionReturn(0); 1515 } 1516 1517 #undef __FUNCT__ 1518 #define __FUNCT__ "MatLoad_MPIDense_DenseInFile" 1519 PetscErrorCode MatLoad_MPIDense_DenseInFile(MPI_Comm comm,PetscInt fd,PetscInt M,PetscInt N,Mat newmat) 1520 { 1521 PetscErrorCode ierr; 1522 PetscMPIInt rank,size; 1523 const PetscInt *rowners; 1524 PetscInt i,m,n,nz,j,mMax; 1525 PetscScalar *array,*vals,*vals_ptr; 1526 Mat_MPIDense *a = (Mat_MPIDense*)newmat->data; 1527 1528 PetscFunctionBegin; 1529 ierr = MPI_Comm_rank(comm,&rank);CHKERRQ(ierr); 1530 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 1531 1532 /* determine ownership of rows and columns */ 1533 m = (newmat->rmap->n < 0) ? PETSC_DECIDE : newmat->rmap->n; 1534 n = (newmat->cmap->n < 0) ? PETSC_DECIDE : newmat->cmap->n; 1535 1536 ierr = MatSetSizes(newmat,m,n,M,N);CHKERRQ(ierr); 1537 if (!a->A || !((Mat_SeqDense*)(a->A->data))->user_alloc) { 1538 ierr = MatMPIDenseSetPreallocation(newmat,NULL);CHKERRQ(ierr); 1539 } 1540 ierr = MatDenseGetArray(newmat,&array);CHKERRQ(ierr); 1541 ierr = MatGetLocalSize(newmat,&m,NULL);CHKERRQ(ierr); 1542 ierr = MatGetOwnershipRanges(newmat,&rowners);CHKERRQ(ierr); 1543 ierr = MPI_Reduce(&m,&mMax,1,MPIU_INT,MPI_MAX,0,comm);CHKERRQ(ierr); 1544 if (!rank) { 1545 ierr = PetscMalloc1(mMax*N,&vals);CHKERRQ(ierr); 1546 1547 /* read in my part of the matrix numerical values */ 1548 ierr = PetscBinaryRead(fd,vals,m*N,PETSC_SCALAR);CHKERRQ(ierr); 1549 1550 /* insert into matrix-by row (this is why cannot directly read into array */ 1551 vals_ptr = vals; 1552 for (i=0; i<m; i++) { 1553 for (j=0; j<N; j++) { 1554 array[i + j*m] = *vals_ptr++; 1555 } 1556 } 1557 1558 /* read in other processors and ship out */ 1559 for (i=1; i<size; i++) { 1560 nz = (rowners[i+1] - rowners[i])*N; 1561 ierr = PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);CHKERRQ(ierr); 1562 ierr = MPIULong_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)(newmat))->tag,comm);CHKERRQ(ierr); 1563 } 1564 } else { 1565 /* receive numeric values */ 1566 ierr = PetscMalloc1(m*N,&vals);CHKERRQ(ierr); 1567 1568 /* receive message of values*/ 1569 ierr = MPIULong_Recv(vals,m*N,MPIU_SCALAR,0,((PetscObject)(newmat))->tag,comm);CHKERRQ(ierr); 1570 1571 /* insert into matrix-by row (this is why cannot directly read into array */ 1572 vals_ptr = vals; 1573 for (i=0; i<m; i++) { 1574 for (j=0; j<N; j++) { 1575 array[i + j*m] = *vals_ptr++; 1576 } 1577 } 1578 } 1579 ierr = MatDenseRestoreArray(newmat,&array);CHKERRQ(ierr); 1580 ierr = PetscFree(vals);CHKERRQ(ierr); 1581 ierr = MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1582 ierr = MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1583 PetscFunctionReturn(0); 1584 } 1585 1586 #undef __FUNCT__ 1587 #define __FUNCT__ "MatLoad_MPIDense" 1588 PetscErrorCode MatLoad_MPIDense(Mat newmat,PetscViewer viewer) 1589 { 1590 Mat_MPIDense *a; 1591 PetscScalar *vals,*svals; 1592 MPI_Comm comm; 1593 MPI_Status status; 1594 PetscMPIInt rank,size,tag = ((PetscObject)viewer)->tag,*rowners,*sndcounts,m,n,maxnz; 1595 PetscInt header[4],*rowlengths = 0,M,N,*cols; 1596 PetscInt *ourlens,*procsnz = 0,jj,*mycols,*smycols; 1597 PetscInt i,nz,j,rstart,rend; 1598 int fd; 1599 PetscErrorCode ierr; 1600 1601 PetscFunctionBegin; 1602 /* force binary viewer to load .info file if it has not yet done so */ 1603 ierr = PetscViewerSetUp(viewer);CHKERRQ(ierr); 1604 ierr = PetscObjectGetComm((PetscObject)viewer,&comm);CHKERRQ(ierr); 1605 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 1606 ierr = MPI_Comm_rank(comm,&rank);CHKERRQ(ierr); 1607 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 1608 if (!rank) { 1609 ierr = PetscBinaryRead(fd,(char*)header,4,PETSC_INT);CHKERRQ(ierr); 1610 if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object"); 1611 } 1612 ierr = MPI_Bcast(header+1,3,MPIU_INT,0,comm);CHKERRQ(ierr); 1613 M = header[1]; N = header[2]; nz = header[3]; 1614 1615 /* If global rows/cols are set to PETSC_DECIDE, set it to the sizes given in the file */ 1616 if (newmat->rmap->N < 0) newmat->rmap->N = M; 1617 if (newmat->cmap->N < 0) newmat->cmap->N = N; 1618 1619 if (newmat->rmap->N != M) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of rows:Matrix in file has (%D) and input matrix has (%D)",M,newmat->rmap->N); 1620 if (newmat->cmap->N != N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of cols:Matrix in file has (%D) and input matrix has (%D)",N,newmat->cmap->N); 1621 1622 /* 1623 Handle case where matrix is stored on disk as a dense matrix 1624 */ 1625 if (nz == MATRIX_BINARY_FORMAT_DENSE) { 1626 ierr = MatLoad_MPIDense_DenseInFile(comm,fd,M,N,newmat);CHKERRQ(ierr); 1627 PetscFunctionReturn(0); 1628 } 1629 1630 /* determine ownership of all rows */ 1631 if (newmat->rmap->n < 0) { 1632 ierr = PetscMPIIntCast(M/size + ((M % size) > rank),&m);CHKERRQ(ierr); 1633 } else { 1634 ierr = PetscMPIIntCast(newmat->rmap->n,&m);CHKERRQ(ierr); 1635 } 1636 if (newmat->cmap->n < 0) { 1637 n = PETSC_DECIDE; 1638 } else { 1639 ierr = PetscMPIIntCast(newmat->cmap->n,&n);CHKERRQ(ierr); 1640 } 1641 1642 ierr = PetscMalloc1(size+2,&rowners);CHKERRQ(ierr); 1643 ierr = MPI_Allgather(&m,1,MPI_INT,rowners+1,1,MPI_INT,comm);CHKERRQ(ierr); 1644 rowners[0] = 0; 1645 for (i=2; i<=size; i++) { 1646 rowners[i] += rowners[i-1]; 1647 } 1648 rstart = rowners[rank]; 1649 rend = rowners[rank+1]; 1650 1651 /* distribute row lengths to all processors */ 1652 ierr = PetscMalloc1(rend-rstart,&ourlens);CHKERRQ(ierr); 1653 if (!rank) { 1654 ierr = PetscMalloc1(M,&rowlengths);CHKERRQ(ierr); 1655 ierr = PetscBinaryRead(fd,rowlengths,M,PETSC_INT);CHKERRQ(ierr); 1656 ierr = PetscMalloc1(size,&sndcounts);CHKERRQ(ierr); 1657 for (i=0; i<size; i++) sndcounts[i] = rowners[i+1] - rowners[i]; 1658 ierr = MPI_Scatterv(rowlengths,sndcounts,rowners,MPIU_INT,ourlens,rend-rstart,MPIU_INT,0,comm);CHKERRQ(ierr); 1659 ierr = PetscFree(sndcounts);CHKERRQ(ierr); 1660 } else { 1661 ierr = MPI_Scatterv(0,0,0,MPIU_INT,ourlens,rend-rstart,MPIU_INT,0,comm);CHKERRQ(ierr); 1662 } 1663 1664 if (!rank) { 1665 /* calculate the number of nonzeros on each processor */ 1666 ierr = PetscMalloc1(size,&procsnz);CHKERRQ(ierr); 1667 ierr = PetscMemzero(procsnz,size*sizeof(PetscInt));CHKERRQ(ierr); 1668 for (i=0; i<size; i++) { 1669 for (j=rowners[i]; j< rowners[i+1]; j++) { 1670 procsnz[i] += rowlengths[j]; 1671 } 1672 } 1673 ierr = PetscFree(rowlengths);CHKERRQ(ierr); 1674 1675 /* determine max buffer needed and allocate it */ 1676 maxnz = 0; 1677 for (i=0; i<size; i++) { 1678 maxnz = PetscMax(maxnz,procsnz[i]); 1679 } 1680 ierr = PetscMalloc1(maxnz,&cols);CHKERRQ(ierr); 1681 1682 /* read in my part of the matrix column indices */ 1683 nz = procsnz[0]; 1684 ierr = PetscMalloc1(nz,&mycols);CHKERRQ(ierr); 1685 ierr = PetscBinaryRead(fd,mycols,nz,PETSC_INT);CHKERRQ(ierr); 1686 1687 /* read in every one elses and ship off */ 1688 for (i=1; i<size; i++) { 1689 nz = procsnz[i]; 1690 ierr = PetscBinaryRead(fd,cols,nz,PETSC_INT);CHKERRQ(ierr); 1691 ierr = MPI_Send(cols,nz,MPIU_INT,i,tag,comm);CHKERRQ(ierr); 1692 } 1693 ierr = PetscFree(cols);CHKERRQ(ierr); 1694 } else { 1695 /* determine buffer space needed for message */ 1696 nz = 0; 1697 for (i=0; i<m; i++) { 1698 nz += ourlens[i]; 1699 } 1700 ierr = PetscMalloc1(nz+1,&mycols);CHKERRQ(ierr); 1701 1702 /* receive message of column indices*/ 1703 ierr = MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);CHKERRQ(ierr); 1704 ierr = MPI_Get_count(&status,MPIU_INT,&maxnz);CHKERRQ(ierr); 1705 if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file"); 1706 } 1707 1708 ierr = MatSetSizes(newmat,m,n,M,N);CHKERRQ(ierr); 1709 a = (Mat_MPIDense*)newmat->data; 1710 if (!a->A || !((Mat_SeqDense*)(a->A->data))->user_alloc) { 1711 ierr = MatMPIDenseSetPreallocation(newmat,NULL);CHKERRQ(ierr); 1712 } 1713 1714 if (!rank) { 1715 ierr = PetscMalloc1(maxnz,&vals);CHKERRQ(ierr); 1716 1717 /* read in my part of the matrix numerical values */ 1718 nz = procsnz[0]; 1719 ierr = PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);CHKERRQ(ierr); 1720 1721 /* insert into matrix */ 1722 jj = rstart; 1723 smycols = mycols; 1724 svals = vals; 1725 for (i=0; i<m; i++) { 1726 ierr = MatSetValues(newmat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);CHKERRQ(ierr); 1727 smycols += ourlens[i]; 1728 svals += ourlens[i]; 1729 jj++; 1730 } 1731 1732 /* read in other processors and ship out */ 1733 for (i=1; i<size; i++) { 1734 nz = procsnz[i]; 1735 ierr = PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);CHKERRQ(ierr); 1736 ierr = MPI_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)newmat)->tag,comm);CHKERRQ(ierr); 1737 } 1738 ierr = PetscFree(procsnz);CHKERRQ(ierr); 1739 } else { 1740 /* receive numeric values */ 1741 ierr = PetscMalloc1(nz+1,&vals);CHKERRQ(ierr); 1742 1743 /* receive message of values*/ 1744 ierr = MPI_Recv(vals,nz,MPIU_SCALAR,0,((PetscObject)newmat)->tag,comm,&status);CHKERRQ(ierr); 1745 ierr = MPI_Get_count(&status,MPIU_SCALAR,&maxnz);CHKERRQ(ierr); 1746 if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file"); 1747 1748 /* insert into matrix */ 1749 jj = rstart; 1750 smycols = mycols; 1751 svals = vals; 1752 for (i=0; i<m; i++) { 1753 ierr = MatSetValues(newmat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);CHKERRQ(ierr); 1754 smycols += ourlens[i]; 1755 svals += ourlens[i]; 1756 jj++; 1757 } 1758 } 1759 ierr = PetscFree(ourlens);CHKERRQ(ierr); 1760 ierr = PetscFree(vals);CHKERRQ(ierr); 1761 ierr = PetscFree(mycols);CHKERRQ(ierr); 1762 ierr = PetscFree(rowners);CHKERRQ(ierr); 1763 1764 ierr = MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1765 ierr = MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1766 PetscFunctionReturn(0); 1767 } 1768 1769 #undef __FUNCT__ 1770 #define __FUNCT__ "MatEqual_MPIDense" 1771 PetscErrorCode MatEqual_MPIDense(Mat A,Mat B,PetscBool *flag) 1772 { 1773 Mat_MPIDense *matB = (Mat_MPIDense*)B->data,*matA = (Mat_MPIDense*)A->data; 1774 Mat a,b; 1775 PetscBool flg; 1776 PetscErrorCode ierr; 1777 1778 PetscFunctionBegin; 1779 a = matA->A; 1780 b = matB->A; 1781 ierr = MatEqual(a,b,&flg);CHKERRQ(ierr); 1782 ierr = MPIU_Allreduce(&flg,flag,1,MPIU_BOOL,MPI_LAND,PetscObjectComm((PetscObject)A));CHKERRQ(ierr); 1783 PetscFunctionReturn(0); 1784 } 1785 1786 #undef __FUNCT__ 1787 #define __FUNCT__ "MatDestroy_MatTransMatMult_MPIDense_MPIDense" 1788 PetscErrorCode MatDestroy_MatTransMatMult_MPIDense_MPIDense(Mat A) 1789 { 1790 PetscErrorCode ierr; 1791 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 1792 Mat_TransMatMultDense *atb = a->atbdense; 1793 1794 PetscFunctionBegin; 1795 ierr = PetscFree3(atb->sendbuf,atb->atbarray,atb->recvcounts);CHKERRQ(ierr); 1796 ierr = (atb->destroy)(A);CHKERRQ(ierr); 1797 ierr = PetscFree(atb);CHKERRQ(ierr); 1798 PetscFunctionReturn(0); 1799 } 1800 1801 #undef __FUNCT__ 1802 #define __FUNCT__ "MatTransposeMatMultNumeric_MPIDense_MPIDense" 1803 PetscErrorCode MatTransposeMatMultNumeric_MPIDense_MPIDense(Mat A,Mat B,Mat C) 1804 { 1805 Mat_MPIDense *a=(Mat_MPIDense*)A->data, *b=(Mat_MPIDense*)B->data, *c=(Mat_MPIDense*)C->data; 1806 Mat_SeqDense *aseq=(Mat_SeqDense*)(a->A)->data, *bseq=(Mat_SeqDense*)(b->A)->data; 1807 Mat_TransMatMultDense *atb = c->atbdense; 1808 PetscErrorCode ierr; 1809 MPI_Comm comm; 1810 PetscMPIInt rank,size,*recvcounts=atb->recvcounts; 1811 PetscScalar *carray,*atbarray=atb->atbarray,*sendbuf=atb->sendbuf; 1812 PetscInt i,cN=C->cmap->N,cM=C->rmap->N,proc,k,j; 1813 PetscScalar _DOne=1.0,_DZero=0.0; 1814 PetscBLASInt am,an,bn,aN; 1815 const PetscInt *ranges; 1816 1817 PetscFunctionBegin; 1818 ierr = PetscObjectGetComm((PetscObject)A,&comm);CHKERRQ(ierr); 1819 ierr = MPI_Comm_rank(comm,&rank);CHKERRQ(ierr); 1820 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 1821 1822 /* compute atbarray = aseq^T * bseq */ 1823 ierr = PetscBLASIntCast(a->A->cmap->n,&an);CHKERRQ(ierr); 1824 ierr = PetscBLASIntCast(b->A->cmap->n,&bn);CHKERRQ(ierr); 1825 ierr = PetscBLASIntCast(a->A->rmap->n,&am);CHKERRQ(ierr); 1826 ierr = PetscBLASIntCast(A->cmap->N,&aN);CHKERRQ(ierr); 1827 PetscStackCallBLAS("BLASgemm",BLASgemm_("T","N",&an,&bn,&am,&_DOne,aseq->v,&aseq->lda,bseq->v,&bseq->lda,&_DZero,atbarray,&aN)); 1828 1829 ierr = MatGetOwnershipRanges(C,&ranges);CHKERRQ(ierr); 1830 for (i=0; i<size; i++) recvcounts[i] = (ranges[i+1] - ranges[i])*cN; 1831 1832 /* arrange atbarray into sendbuf */ 1833 k = 0; 1834 for (proc=0; proc<size; proc++) { 1835 for (j=0; j<cN; j++) { 1836 for (i=ranges[proc]; i<ranges[proc+1]; i++) sendbuf[k++] = atbarray[i+j*cM]; 1837 } 1838 } 1839 /* sum all atbarray to local values of C */ 1840 ierr = MatDenseGetArray(c->A,&carray);CHKERRQ(ierr); 1841 ierr = MPI_Reduce_scatter(sendbuf,carray,recvcounts,MPIU_SCALAR,MPIU_SUM,comm);CHKERRQ(ierr); 1842 ierr = MatDenseRestoreArray(c->A,&carray);CHKERRQ(ierr); 1843 PetscFunctionReturn(0); 1844 } 1845 1846 #undef __FUNCT__ 1847 #define __FUNCT__ "MatTransposeMatMultSymbolic_MPIDense_MPIDense" 1848 PetscErrorCode MatTransposeMatMultSymbolic_MPIDense_MPIDense(Mat A,Mat B,PetscReal fill,Mat *C) 1849 { 1850 PetscErrorCode ierr; 1851 Mat Cdense; 1852 MPI_Comm comm; 1853 PetscMPIInt size; 1854 PetscInt cm=A->cmap->n,cM,cN=B->cmap->N; 1855 Mat_MPIDense *c; 1856 Mat_TransMatMultDense *atb; 1857 1858 PetscFunctionBegin; 1859 ierr = PetscObjectGetComm((PetscObject)A,&comm);CHKERRQ(ierr); 1860 if (A->rmap->rstart != B->rmap->rstart || A->rmap->rend != B->rmap->rend) { 1861 SETERRQ4(comm,PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, A (%D, %D) != B (%D,%D)",A->rmap->rstart,A->rmap->rend,B->rmap->rstart,B->rmap->rend); 1862 } 1863 1864 /* create matrix product Cdense */ 1865 ierr = MatCreate(comm,&Cdense);CHKERRQ(ierr); 1866 ierr = MatSetSizes(Cdense,cm,B->cmap->n,PETSC_DECIDE,PETSC_DECIDE);CHKERRQ(ierr); 1867 ierr = MatSetType(Cdense,MATMPIDENSE);CHKERRQ(ierr); 1868 ierr = MatMPIDenseSetPreallocation(Cdense,NULL);CHKERRQ(ierr); 1869 ierr = MatAssemblyBegin(Cdense,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1870 ierr = MatAssemblyEnd(Cdense,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1871 *C = Cdense; 1872 1873 /* create data structure for reuse Cdense */ 1874 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 1875 ierr = PetscNew(&atb);CHKERRQ(ierr); 1876 cM = Cdense->rmap->N; 1877 ierr = PetscMalloc3(cM*cN,&atb->sendbuf,cM*cN,&atb->atbarray,size,&atb->recvcounts);CHKERRQ(ierr); 1878 1879 c = (Mat_MPIDense*)Cdense->data; 1880 c->atbdense = atb; 1881 atb->destroy = Cdense->ops->destroy; 1882 Cdense->ops->destroy = MatDestroy_MatTransMatMult_MPIDense_MPIDense; 1883 PetscFunctionReturn(0); 1884 } 1885 1886 #undef __FUNCT__ 1887 #define __FUNCT__ "MatTransposeMatMult_MPIDense_MPIDense" 1888 PetscErrorCode MatTransposeMatMult_MPIDense_MPIDense(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 1889 { 1890 PetscErrorCode ierr; 1891 1892 PetscFunctionBegin; 1893 if (scall == MAT_INITIAL_MATRIX) { 1894 ierr = MatTransposeMatMultSymbolic_MPIDense_MPIDense(A,B,fill,C);CHKERRQ(ierr); 1895 } 1896 ierr = MatTransposeMatMultNumeric_MPIDense_MPIDense(A,B,*C);CHKERRQ(ierr); 1897 PetscFunctionReturn(0); 1898 } 1899 1900 #undef __FUNCT__ 1901 #define __FUNCT__ "MatDestroy_MatMatMult_MPIDense_MPIDense" 1902 PetscErrorCode MatDestroy_MatMatMult_MPIDense_MPIDense(Mat A) 1903 { 1904 PetscErrorCode ierr; 1905 Mat_MPIDense *a = (Mat_MPIDense*)A->data; 1906 Mat_MatMultDense *ab = a->abdense; 1907 1908 PetscFunctionBegin; 1909 ierr = MatDestroy(&ab->Ce);CHKERRQ(ierr); 1910 ierr = MatDestroy(&ab->Ae);CHKERRQ(ierr); 1911 ierr = MatDestroy(&ab->Be);CHKERRQ(ierr); 1912 1913 ierr = (ab->destroy)(A);CHKERRQ(ierr); 1914 ierr = PetscFree(ab);CHKERRQ(ierr); 1915 PetscFunctionReturn(0); 1916 } 1917 1918 #if defined(PETSC_HAVE_ELEMENTAL) 1919 #undef __FUNCT__ 1920 #define __FUNCT__ "MatMatMultNumeric_MPIDense_MPIDense" 1921 PetscErrorCode MatMatMultNumeric_MPIDense_MPIDense(Mat A,Mat B,Mat C) 1922 { 1923 PetscErrorCode ierr; 1924 Mat_MPIDense *c=(Mat_MPIDense*)C->data; 1925 Mat_MatMultDense *ab=c->abdense; 1926 1927 PetscFunctionBegin; 1928 ierr = MatConvert_MPIDense_Elemental(A,MATELEMENTAL,MAT_REUSE_MATRIX, &ab->Ae);CHKERRQ(ierr); 1929 ierr = MatConvert_MPIDense_Elemental(B,MATELEMENTAL,MAT_REUSE_MATRIX, &ab->Be);CHKERRQ(ierr); 1930 ierr = MatMatMultNumeric(ab->Ae,ab->Be,ab->Ce);CHKERRQ(ierr); 1931 ierr = MatConvert(ab->Ce,MATMPIDENSE,MAT_REUSE_MATRIX,&C);CHKERRQ(ierr); 1932 PetscFunctionReturn(0); 1933 } 1934 1935 #undef __FUNCT__ 1936 #define __FUNCT__ "MatMatMultSymbolic_MPIDense_MPIDense" 1937 PetscErrorCode MatMatMultSymbolic_MPIDense_MPIDense(Mat A,Mat B,PetscReal fill,Mat *C) 1938 { 1939 PetscErrorCode ierr; 1940 Mat Ae,Be,Ce; 1941 Mat_MPIDense *c; 1942 Mat_MatMultDense *ab; 1943 1944 PetscFunctionBegin; 1945 if (A->cmap->rstart != B->rmap->rstart || A->cmap->rend != B->rmap->rend) { 1946 SETERRQ4(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, A (%D, %D) != B (%D,%D)",A->rmap->rstart,A->rmap->rend,B->rmap->rstart,B->rmap->rend); 1947 } 1948 1949 /* convert A and B to Elemental matrices Ae and Be */ 1950 ierr = MatConvert(A,MATELEMENTAL,MAT_INITIAL_MATRIX, &Ae);CHKERRQ(ierr); 1951 ierr = MatConvert(B,MATELEMENTAL,MAT_INITIAL_MATRIX, &Be);CHKERRQ(ierr); 1952 1953 /* Ce = Ae*Be */ 1954 ierr = MatMatMultSymbolic(Ae,Be,fill,&Ce);CHKERRQ(ierr); 1955 ierr = MatMatMultNumeric(Ae,Be,Ce);CHKERRQ(ierr); 1956 1957 /* convert Ce to C */ 1958 ierr = MatConvert(Ce,MATMPIDENSE,MAT_INITIAL_MATRIX,C);CHKERRQ(ierr); 1959 1960 /* create data structure for reuse Cdense */ 1961 ierr = PetscNew(&ab);CHKERRQ(ierr); 1962 c = (Mat_MPIDense*)(*C)->data; 1963 c->abdense = ab; 1964 1965 ab->Ae = Ae; 1966 ab->Be = Be; 1967 ab->Ce = Ce; 1968 ab->destroy = (*C)->ops->destroy; 1969 (*C)->ops->destroy = MatDestroy_MatMatMult_MPIDense_MPIDense; 1970 (*C)->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIDense; 1971 PetscFunctionReturn(0); 1972 } 1973 1974 #undef __FUNCT__ 1975 #define __FUNCT__ "MatMatMult_MPIDense_MPIDense" 1976 PETSC_INTERN PetscErrorCode MatMatMult_MPIDense_MPIDense(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 1977 { 1978 PetscErrorCode ierr; 1979 1980 PetscFunctionBegin; 1981 if (scall == MAT_INITIAL_MATRIX) { /* simbolic product includes numeric product */ 1982 ierr = PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 1983 ierr = MatMatMultSymbolic_MPIDense_MPIDense(A,B,fill,C);CHKERRQ(ierr); 1984 ierr = PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 1985 } else { 1986 ierr = PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 1987 ierr = MatMatMultNumeric_MPIDense_MPIDense(A,B,*C);CHKERRQ(ierr); 1988 ierr = PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 1989 } 1990 PetscFunctionReturn(0); 1991 } 1992 #endif 1993