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