1 /*$Id: mpiaij.c,v 1.344 2001/08/10 03:30:48 bsmith Exp $*/ 2 3 #include "src/mat/impls/aij/mpi/mpiaij.h" 4 #include "src/vec/vecimpl.h" 5 #include "src/inline/spops.h" 6 7 EXTERN int MatSetUpMultiply_MPIAIJ(Mat); 8 EXTERN int DisAssemble_MPIAIJ(Mat); 9 EXTERN int MatSetValues_SeqAIJ(Mat,int,int*,int,int*,PetscScalar*,InsertMode); 10 EXTERN int MatGetRow_SeqAIJ(Mat,int,int*,int**,PetscScalar**); 11 EXTERN int MatRestoreRow_SeqAIJ(Mat,int,int*,int**,PetscScalar**); 12 EXTERN int MatPrintHelp_SeqAIJ(Mat); 13 14 /* 15 Local utility routine that creates a mapping from the global column 16 number to the local number in the off-diagonal part of the local 17 storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at 18 a slightly higher hash table cost; without it it is not scalable (each processor 19 has an order N integer array but is fast to acess. 20 */ 21 #undef __FUNCT__ 22 #define __FUNCT__ "CreateColmap_MPIAIJ_Private" 23 int CreateColmap_MPIAIJ_Private(Mat mat) 24 { 25 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 26 int n = aij->B->n,i,ierr; 27 28 PetscFunctionBegin; 29 #if defined (PETSC_USE_CTABLE) 30 ierr = PetscTableCreate(n,&aij->colmap);CHKERRQ(ierr); 31 for (i=0; i<n; i++){ 32 ierr = PetscTableAdd(aij->colmap,aij->garray[i]+1,i+1);CHKERRQ(ierr); 33 } 34 #else 35 ierr = PetscMalloc((mat->N+1)*sizeof(int),&aij->colmap);CHKERRQ(ierr); 36 PetscLogObjectMemory(mat,mat->N*sizeof(int)); 37 ierr = PetscMemzero(aij->colmap,mat->N*sizeof(int));CHKERRQ(ierr); 38 for (i=0; i<n; i++) aij->colmap[aij->garray[i]] = i+1; 39 #endif 40 PetscFunctionReturn(0); 41 } 42 43 #define CHUNKSIZE 15 44 #define MatSetValues_SeqAIJ_A_Private(row,col,value,addv) \ 45 { \ 46 \ 47 rp = aj + ai[row] + shift; ap = aa + ai[row] + shift; \ 48 rmax = aimax[row]; nrow = ailen[row]; \ 49 col1 = col - shift; \ 50 \ 51 low = 0; high = nrow; \ 52 while (high-low > 5) { \ 53 t = (low+high)/2; \ 54 if (rp[t] > col) high = t; \ 55 else low = t; \ 56 } \ 57 for (_i=low; _i<high; _i++) { \ 58 if (rp[_i] > col1) break; \ 59 if (rp[_i] == col1) { \ 60 if (addv == ADD_VALUES) ap[_i] += value; \ 61 else ap[_i] = value; \ 62 goto a_noinsert; \ 63 } \ 64 } \ 65 if (nonew == 1) goto a_noinsert; \ 66 else if (nonew == -1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero into matrix"); \ 67 if (nrow >= rmax) { \ 68 /* there is no extra room in row, therefore enlarge */ \ 69 int new_nz = ai[am] + CHUNKSIZE,len,*new_i,*new_j; \ 70 PetscScalar *new_a; \ 71 \ 72 if (nonew == -2) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix"); \ 73 \ 74 /* malloc new storage space */ \ 75 len = new_nz*(sizeof(int)+sizeof(PetscScalar))+(am+1)*sizeof(int); \ 76 ierr = PetscMalloc(len,&new_a);CHKERRQ(ierr); \ 77 new_j = (int*)(new_a + new_nz); \ 78 new_i = new_j + new_nz; \ 79 \ 80 /* copy over old data into new slots */ \ 81 for (ii=0; ii<row+1; ii++) {new_i[ii] = ai[ii];} \ 82 for (ii=row+1; ii<am+1; ii++) {new_i[ii] = ai[ii]+CHUNKSIZE;} \ 83 ierr = PetscMemcpy(new_j,aj,(ai[row]+nrow+shift)*sizeof(int));CHKERRQ(ierr); \ 84 len = (new_nz - CHUNKSIZE - ai[row] - nrow - shift); \ 85 ierr = PetscMemcpy(new_j+ai[row]+shift+nrow+CHUNKSIZE,aj+ai[row]+shift+nrow, \ 86 len*sizeof(int));CHKERRQ(ierr); \ 87 ierr = PetscMemcpy(new_a,aa,(ai[row]+nrow+shift)*sizeof(PetscScalar));CHKERRQ(ierr); \ 88 ierr = PetscMemcpy(new_a+ai[row]+shift+nrow+CHUNKSIZE,aa+ai[row]+shift+nrow, \ 89 len*sizeof(PetscScalar));CHKERRQ(ierr); \ 90 /* free up old matrix storage */ \ 91 \ 92 ierr = PetscFree(a->a);CHKERRQ(ierr); \ 93 if (!a->singlemalloc) { \ 94 ierr = PetscFree(a->i);CHKERRQ(ierr); \ 95 ierr = PetscFree(a->j);CHKERRQ(ierr); \ 96 } \ 97 aa = a->a = new_a; ai = a->i = new_i; aj = a->j = new_j; \ 98 a->singlemalloc = PETSC_TRUE; \ 99 \ 100 rp = aj + ai[row] + shift; ap = aa + ai[row] + shift; \ 101 rmax = aimax[row] = aimax[row] + CHUNKSIZE; \ 102 PetscLogObjectMemory(A,CHUNKSIZE*(sizeof(int) + sizeof(PetscScalar))); \ 103 a->maxnz += CHUNKSIZE; \ 104 a->reallocs++; \ 105 } \ 106 N = nrow++ - 1; a->nz++; \ 107 /* shift up all the later entries in this row */ \ 108 for (ii=N; ii>=_i; ii--) { \ 109 rp[ii+1] = rp[ii]; \ 110 ap[ii+1] = ap[ii]; \ 111 } \ 112 rp[_i] = col1; \ 113 ap[_i] = value; \ 114 a_noinsert: ; \ 115 ailen[row] = nrow; \ 116 } 117 118 #define MatSetValues_SeqAIJ_B_Private(row,col,value,addv) \ 119 { \ 120 \ 121 rp = bj + bi[row] + shift; ap = ba + bi[row] + shift; \ 122 rmax = bimax[row]; nrow = bilen[row]; \ 123 col1 = col - shift; \ 124 \ 125 low = 0; high = nrow; \ 126 while (high-low > 5) { \ 127 t = (low+high)/2; \ 128 if (rp[t] > col) high = t; \ 129 else low = t; \ 130 } \ 131 for (_i=low; _i<high; _i++) { \ 132 if (rp[_i] > col1) break; \ 133 if (rp[_i] == col1) { \ 134 if (addv == ADD_VALUES) ap[_i] += value; \ 135 else ap[_i] = value; \ 136 goto b_noinsert; \ 137 } \ 138 } \ 139 if (nonew == 1) goto b_noinsert; \ 140 else if (nonew == -1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero into matrix"); \ 141 if (nrow >= rmax) { \ 142 /* there is no extra room in row, therefore enlarge */ \ 143 int new_nz = bi[bm] + CHUNKSIZE,len,*new_i,*new_j; \ 144 PetscScalar *new_a; \ 145 \ 146 if (nonew == -2) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix"); \ 147 \ 148 /* malloc new storage space */ \ 149 len = new_nz*(sizeof(int)+sizeof(PetscScalar))+(bm+1)*sizeof(int); \ 150 ierr = PetscMalloc(len,&new_a);CHKERRQ(ierr); \ 151 new_j = (int*)(new_a + new_nz); \ 152 new_i = new_j + new_nz; \ 153 \ 154 /* copy over old data into new slots */ \ 155 for (ii=0; ii<row+1; ii++) {new_i[ii] = bi[ii];} \ 156 for (ii=row+1; ii<bm+1; ii++) {new_i[ii] = bi[ii]+CHUNKSIZE;} \ 157 ierr = PetscMemcpy(new_j,bj,(bi[row]+nrow+shift)*sizeof(int));CHKERRQ(ierr); \ 158 len = (new_nz - CHUNKSIZE - bi[row] - nrow - shift); \ 159 ierr = PetscMemcpy(new_j+bi[row]+shift+nrow+CHUNKSIZE,bj+bi[row]+shift+nrow, \ 160 len*sizeof(int));CHKERRQ(ierr); \ 161 ierr = PetscMemcpy(new_a,ba,(bi[row]+nrow+shift)*sizeof(PetscScalar));CHKERRQ(ierr); \ 162 ierr = PetscMemcpy(new_a+bi[row]+shift+nrow+CHUNKSIZE,ba+bi[row]+shift+nrow, \ 163 len*sizeof(PetscScalar));CHKERRQ(ierr); \ 164 /* free up old matrix storage */ \ 165 \ 166 ierr = PetscFree(b->a);CHKERRQ(ierr); \ 167 if (!b->singlemalloc) { \ 168 ierr = PetscFree(b->i);CHKERRQ(ierr); \ 169 ierr = PetscFree(b->j);CHKERRQ(ierr); \ 170 } \ 171 ba = b->a = new_a; bi = b->i = new_i; bj = b->j = new_j; \ 172 b->singlemalloc = PETSC_TRUE; \ 173 \ 174 rp = bj + bi[row] + shift; ap = ba + bi[row] + shift; \ 175 rmax = bimax[row] = bimax[row] + CHUNKSIZE; \ 176 PetscLogObjectMemory(B,CHUNKSIZE*(sizeof(int) + sizeof(PetscScalar))); \ 177 b->maxnz += CHUNKSIZE; \ 178 b->reallocs++; \ 179 } \ 180 N = nrow++ - 1; b->nz++; \ 181 /* shift up all the later entries in this row */ \ 182 for (ii=N; ii>=_i; ii--) { \ 183 rp[ii+1] = rp[ii]; \ 184 ap[ii+1] = ap[ii]; \ 185 } \ 186 rp[_i] = col1; \ 187 ap[_i] = value; \ 188 b_noinsert: ; \ 189 bilen[row] = nrow; \ 190 } 191 192 #undef __FUNCT__ 193 #define __FUNCT__ "MatSetValues_MPIAIJ" 194 int MatSetValues_MPIAIJ(Mat mat,int m,const int im[],int n,const int in[],const PetscScalar v[],InsertMode addv) 195 { 196 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 197 PetscScalar value; 198 int ierr,i,j,rstart = aij->rstart,rend = aij->rend; 199 int cstart = aij->cstart,cend = aij->cend,row,col; 200 PetscTruth roworiented = aij->roworiented; 201 202 /* Some Variables required in the macro */ 203 Mat A = aij->A; 204 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 205 int *aimax = a->imax,*ai = a->i,*ailen = a->ilen,*aj = a->j; 206 PetscScalar *aa = a->a; 207 PetscTruth ignorezeroentries = (((a->ignorezeroentries)&&(addv==ADD_VALUES))?PETSC_TRUE:PETSC_FALSE); 208 Mat B = aij->B; 209 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 210 int *bimax = b->imax,*bi = b->i,*bilen = b->ilen,*bj = b->j,bm = aij->B->m,am = aij->A->m; 211 PetscScalar *ba = b->a; 212 213 int *rp,ii,nrow,_i,rmax,N,col1,low,high,t; 214 int nonew = a->nonew,shift=0; 215 PetscScalar *ap; 216 217 PetscFunctionBegin; 218 for (i=0; i<m; i++) { 219 if (im[i] < 0) continue; 220 #if defined(PETSC_USE_BOPT_g) 221 if (im[i] >= mat->M) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",im[i],mat->M-1); 222 #endif 223 if (im[i] >= rstart && im[i] < rend) { 224 row = im[i] - rstart; 225 for (j=0; j<n; j++) { 226 if (in[j] >= cstart && in[j] < cend){ 227 col = in[j] - cstart; 228 if (roworiented) value = v[i*n+j]; else value = v[i+j*m]; 229 if (ignorezeroentries && value == 0.0) continue; 230 MatSetValues_SeqAIJ_A_Private(row,col,value,addv); 231 /* ierr = MatSetValues_SeqAIJ(aij->A,1,&row,1,&col,&value,addv);CHKERRQ(ierr); */ 232 } else if (in[j] < 0) continue; 233 #if defined(PETSC_USE_BOPT_g) 234 else if (in[j] >= mat->N) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %d max %d",in[j],mat->N-1);} 235 #endif 236 else { 237 if (mat->was_assembled) { 238 if (!aij->colmap) { 239 ierr = CreateColmap_MPIAIJ_Private(mat);CHKERRQ(ierr); 240 } 241 #if defined (PETSC_USE_CTABLE) 242 ierr = PetscTableFind(aij->colmap,in[j]+1,&col);CHKERRQ(ierr); 243 col--; 244 #else 245 col = aij->colmap[in[j]] - 1; 246 #endif 247 if (col < 0 && !((Mat_SeqAIJ*)(aij->A->data))->nonew) { 248 ierr = DisAssemble_MPIAIJ(mat);CHKERRQ(ierr); 249 col = in[j]; 250 /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */ 251 B = aij->B; 252 b = (Mat_SeqAIJ*)B->data; 253 bimax = b->imax; bi = b->i; bilen = b->ilen; bj = b->j; 254 ba = b->a; 255 } 256 } else col = in[j]; 257 if (roworiented) value = v[i*n+j]; else value = v[i+j*m]; 258 if (ignorezeroentries && value == 0.0) continue; 259 MatSetValues_SeqAIJ_B_Private(row,col,value,addv); 260 /* ierr = MatSetValues_SeqAIJ(aij->B,1,&row,1,&col,&value,addv);CHKERRQ(ierr); */ 261 } 262 } 263 } else { 264 if (!aij->donotstash) { 265 if (roworiented) { 266 if (ignorezeroentries && v[i*n] == 0.0) continue; 267 ierr = MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);CHKERRQ(ierr); 268 } else { 269 if (ignorezeroentries && v[i] == 0.0) continue; 270 ierr = MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);CHKERRQ(ierr); 271 } 272 } 273 } 274 } 275 PetscFunctionReturn(0); 276 } 277 278 #undef __FUNCT__ 279 #define __FUNCT__ "MatGetValues_MPIAIJ" 280 int MatGetValues_MPIAIJ(Mat mat,int m,const int idxm[],int n,const int idxn[],PetscScalar v[]) 281 { 282 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 283 int ierr,i,j,rstart = aij->rstart,rend = aij->rend; 284 int cstart = aij->cstart,cend = aij->cend,row,col; 285 286 PetscFunctionBegin; 287 for (i=0; i<m; i++) { 288 if (idxm[i] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %d",idxm[i]); 289 if (idxm[i] >= mat->M) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",idxm[i],mat->M-1); 290 if (idxm[i] >= rstart && idxm[i] < rend) { 291 row = idxm[i] - rstart; 292 for (j=0; j<n; j++) { 293 if (idxn[j] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %d",idxn[j]); 294 if (idxn[j] >= mat->N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %d max %d",idxn[j],mat->N-1); 295 if (idxn[j] >= cstart && idxn[j] < cend){ 296 col = idxn[j] - cstart; 297 ierr = MatGetValues(aij->A,1,&row,1,&col,v+i*n+j);CHKERRQ(ierr); 298 } else { 299 if (!aij->colmap) { 300 ierr = CreateColmap_MPIAIJ_Private(mat);CHKERRQ(ierr); 301 } 302 #if defined (PETSC_USE_CTABLE) 303 ierr = PetscTableFind(aij->colmap,idxn[j]+1,&col);CHKERRQ(ierr); 304 col --; 305 #else 306 col = aij->colmap[idxn[j]] - 1; 307 #endif 308 if ((col < 0) || (aij->garray[col] != idxn[j])) *(v+i*n+j) = 0.0; 309 else { 310 ierr = MatGetValues(aij->B,1,&row,1,&col,v+i*n+j);CHKERRQ(ierr); 311 } 312 } 313 } 314 } else { 315 SETERRQ(PETSC_ERR_SUP,"Only local values currently supported"); 316 } 317 } 318 PetscFunctionReturn(0); 319 } 320 321 #undef __FUNCT__ 322 #define __FUNCT__ "MatAssemblyBegin_MPIAIJ" 323 int MatAssemblyBegin_MPIAIJ(Mat mat,MatAssemblyType mode) 324 { 325 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 326 int ierr,nstash,reallocs; 327 InsertMode addv; 328 329 PetscFunctionBegin; 330 if (aij->donotstash) { 331 PetscFunctionReturn(0); 332 } 333 334 /* make sure all processors are either in INSERTMODE or ADDMODE */ 335 ierr = MPI_Allreduce(&mat->insertmode,&addv,1,MPI_INT,MPI_BOR,mat->comm);CHKERRQ(ierr); 336 if (addv == (ADD_VALUES|INSERT_VALUES)) { 337 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added"); 338 } 339 mat->insertmode = addv; /* in case this processor had no cache */ 340 341 ierr = MatStashScatterBegin_Private(&mat->stash,aij->rowners);CHKERRQ(ierr); 342 ierr = MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);CHKERRQ(ierr); 343 PetscLogInfo(aij->A,"MatAssemblyBegin_MPIAIJ:Stash has %d entries, uses %d mallocs.\n",nstash,reallocs); 344 PetscFunctionReturn(0); 345 } 346 347 348 #undef __FUNCT__ 349 #define __FUNCT__ "MatAssemblyEnd_MPIAIJ" 350 int MatAssemblyEnd_MPIAIJ(Mat mat,MatAssemblyType mode) 351 { 352 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 353 Mat_SeqAIJ *a=(Mat_SeqAIJ *)aij->A->data,*b= (Mat_SeqAIJ *)aij->B->data; 354 int i,j,rstart,ncols,n,ierr,flg; 355 int *row,*col,other_disassembled; 356 PetscScalar *val; 357 InsertMode addv = mat->insertmode; 358 359 PetscFunctionBegin; 360 if (!aij->donotstash) { 361 while (1) { 362 ierr = MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);CHKERRQ(ierr); 363 if (!flg) break; 364 365 for (i=0; i<n;) { 366 /* Now identify the consecutive vals belonging to the same row */ 367 for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; } 368 if (j < n) ncols = j-i; 369 else ncols = n-i; 370 /* Now assemble all these values with a single function call */ 371 ierr = MatSetValues_MPIAIJ(mat,1,row+i,ncols,col+i,val+i,addv);CHKERRQ(ierr); 372 i = j; 373 } 374 } 375 ierr = MatStashScatterEnd_Private(&mat->stash);CHKERRQ(ierr); 376 } 377 378 ierr = MatAssemblyBegin(aij->A,mode);CHKERRQ(ierr); 379 ierr = MatAssemblyEnd(aij->A,mode);CHKERRQ(ierr); 380 381 /* determine if any processor has disassembled, if so we must 382 also disassemble ourselfs, in order that we may reassemble. */ 383 /* 384 if nonzero structure of submatrix B cannot change then we know that 385 no processor disassembled thus we can skip this stuff 386 */ 387 if (!((Mat_SeqAIJ*)aij->B->data)->nonew) { 388 ierr = MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,mat->comm);CHKERRQ(ierr); 389 if (mat->was_assembled && !other_disassembled) { 390 ierr = DisAssemble_MPIAIJ(mat);CHKERRQ(ierr); 391 } 392 } 393 394 if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) { 395 ierr = MatSetUpMultiply_MPIAIJ(mat);CHKERRQ(ierr); 396 } 397 ierr = MatAssemblyBegin(aij->B,mode);CHKERRQ(ierr); 398 ierr = MatAssemblyEnd(aij->B,mode);CHKERRQ(ierr); 399 400 if (aij->rowvalues) { 401 ierr = PetscFree(aij->rowvalues);CHKERRQ(ierr); 402 aij->rowvalues = 0; 403 } 404 405 /* used by MatAXPY() */ 406 a->xtoy = 0; b->xtoy = 0; 407 a->XtoY = 0; b->XtoY = 0; 408 409 PetscFunctionReturn(0); 410 } 411 412 #undef __FUNCT__ 413 #define __FUNCT__ "MatZeroEntries_MPIAIJ" 414 int MatZeroEntries_MPIAIJ(Mat A) 415 { 416 Mat_MPIAIJ *l = (Mat_MPIAIJ*)A->data; 417 int ierr; 418 419 PetscFunctionBegin; 420 ierr = MatZeroEntries(l->A);CHKERRQ(ierr); 421 ierr = MatZeroEntries(l->B);CHKERRQ(ierr); 422 PetscFunctionReturn(0); 423 } 424 425 #undef __FUNCT__ 426 #define __FUNCT__ "MatZeroRows_MPIAIJ" 427 int MatZeroRows_MPIAIJ(Mat A,IS is,const PetscScalar *diag) 428 { 429 Mat_MPIAIJ *l = (Mat_MPIAIJ*)A->data; 430 int i,ierr,N,*rows,*owners = l->rowners,size = l->size; 431 int *nprocs,j,idx,nsends,row; 432 int nmax,*svalues,*starts,*owner,nrecvs,rank = l->rank; 433 int *rvalues,tag = A->tag,count,base,slen,n,*source; 434 int *lens,imdex,*lrows,*values,rstart=l->rstart; 435 MPI_Comm comm = A->comm; 436 MPI_Request *send_waits,*recv_waits; 437 MPI_Status recv_status,*send_status; 438 IS istmp; 439 PetscTruth found; 440 441 PetscFunctionBegin; 442 ierr = ISGetLocalSize(is,&N);CHKERRQ(ierr); 443 ierr = ISGetIndices(is,&rows);CHKERRQ(ierr); 444 445 /* first count number of contributors to each processor */ 446 ierr = PetscMalloc(2*size*sizeof(int),&nprocs);CHKERRQ(ierr); 447 ierr = PetscMemzero(nprocs,2*size*sizeof(int));CHKERRQ(ierr); 448 ierr = PetscMalloc((N+1)*sizeof(int),&owner);CHKERRQ(ierr); /* see note*/ 449 for (i=0; i<N; i++) { 450 idx = rows[i]; 451 found = PETSC_FALSE; 452 for (j=0; j<size; j++) { 453 if (idx >= owners[j] && idx < owners[j+1]) { 454 nprocs[2*j]++; nprocs[2*j+1] = 1; owner[i] = j; found = PETSC_TRUE; break; 455 } 456 } 457 if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range"); 458 } 459 nsends = 0; for (i=0; i<size; i++) { nsends += nprocs[2*i+1];} 460 461 /* inform other processors of number of messages and max length*/ 462 ierr = PetscMaxSum(comm,nprocs,&nmax,&nrecvs);CHKERRQ(ierr); 463 464 /* post receives: */ 465 ierr = PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(int),&rvalues);CHKERRQ(ierr); 466 ierr = PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);CHKERRQ(ierr); 467 for (i=0; i<nrecvs; i++) { 468 ierr = MPI_Irecv(rvalues+nmax*i,nmax,MPI_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);CHKERRQ(ierr); 469 } 470 471 /* do sends: 472 1) starts[i] gives the starting index in svalues for stuff going to 473 the ith processor 474 */ 475 ierr = PetscMalloc((N+1)*sizeof(int),&svalues);CHKERRQ(ierr); 476 ierr = PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);CHKERRQ(ierr); 477 ierr = PetscMalloc((size+1)*sizeof(int),&starts);CHKERRQ(ierr); 478 starts[0] = 0; 479 for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];} 480 for (i=0; i<N; i++) { 481 svalues[starts[owner[i]]++] = rows[i]; 482 } 483 ierr = ISRestoreIndices(is,&rows);CHKERRQ(ierr); 484 485 starts[0] = 0; 486 for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];} 487 count = 0; 488 for (i=0; i<size; i++) { 489 if (nprocs[2*i+1]) { 490 ierr = MPI_Isend(svalues+starts[i],nprocs[2*i],MPI_INT,i,tag,comm,send_waits+count++);CHKERRQ(ierr); 491 } 492 } 493 ierr = PetscFree(starts);CHKERRQ(ierr); 494 495 base = owners[rank]; 496 497 /* wait on receives */ 498 ierr = PetscMalloc(2*(nrecvs+1)*sizeof(int),&lens);CHKERRQ(ierr); 499 source = lens + nrecvs; 500 count = nrecvs; slen = 0; 501 while (count) { 502 ierr = MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);CHKERRQ(ierr); 503 /* unpack receives into our local space */ 504 ierr = MPI_Get_count(&recv_status,MPI_INT,&n);CHKERRQ(ierr); 505 source[imdex] = recv_status.MPI_SOURCE; 506 lens[imdex] = n; 507 slen += n; 508 count--; 509 } 510 ierr = PetscFree(recv_waits);CHKERRQ(ierr); 511 512 /* move the data into the send scatter */ 513 ierr = PetscMalloc((slen+1)*sizeof(int),&lrows);CHKERRQ(ierr); 514 count = 0; 515 for (i=0; i<nrecvs; i++) { 516 values = rvalues + i*nmax; 517 for (j=0; j<lens[i]; j++) { 518 lrows[count++] = values[j] - base; 519 } 520 } 521 ierr = PetscFree(rvalues);CHKERRQ(ierr); 522 ierr = PetscFree(lens);CHKERRQ(ierr); 523 ierr = PetscFree(owner);CHKERRQ(ierr); 524 ierr = PetscFree(nprocs);CHKERRQ(ierr); 525 526 /* actually zap the local rows */ 527 ierr = ISCreateGeneral(PETSC_COMM_SELF,slen,lrows,&istmp);CHKERRQ(ierr); 528 PetscLogObjectParent(A,istmp); 529 530 /* 531 Zero the required rows. If the "diagonal block" of the matrix 532 is square and the user wishes to set the diagonal we use seperate 533 code so that MatSetValues() is not called for each diagonal allocating 534 new memory, thus calling lots of mallocs and slowing things down. 535 536 Contributed by: Mathew Knepley 537 */ 538 /* must zero l->B before l->A because the (diag) case below may put values into l->B*/ 539 ierr = MatZeroRows(l->B,istmp,0);CHKERRQ(ierr); 540 if (diag && (l->A->M == l->A->N)) { 541 ierr = MatZeroRows(l->A,istmp,diag);CHKERRQ(ierr); 542 } else if (diag) { 543 ierr = MatZeroRows(l->A,istmp,0);CHKERRQ(ierr); 544 if (((Mat_SeqAIJ*)l->A->data)->nonew) { 545 SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options\n\ 546 MAT_NO_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR"); 547 } 548 for (i = 0; i < slen; i++) { 549 row = lrows[i] + rstart; 550 ierr = MatSetValues(A,1,&row,1,&row,diag,INSERT_VALUES);CHKERRQ(ierr); 551 } 552 ierr = MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 553 ierr = MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 554 } else { 555 ierr = MatZeroRows(l->A,istmp,0);CHKERRQ(ierr); 556 } 557 ierr = ISDestroy(istmp);CHKERRQ(ierr); 558 ierr = PetscFree(lrows);CHKERRQ(ierr); 559 560 /* wait on sends */ 561 if (nsends) { 562 ierr = PetscMalloc(nsends*sizeof(MPI_Status),&send_status);CHKERRQ(ierr); 563 ierr = MPI_Waitall(nsends,send_waits,send_status);CHKERRQ(ierr); 564 ierr = PetscFree(send_status);CHKERRQ(ierr); 565 } 566 ierr = PetscFree(send_waits);CHKERRQ(ierr); 567 ierr = PetscFree(svalues);CHKERRQ(ierr); 568 569 PetscFunctionReturn(0); 570 } 571 572 #undef __FUNCT__ 573 #define __FUNCT__ "MatMult_MPIAIJ" 574 int MatMult_MPIAIJ(Mat A,Vec xx,Vec yy) 575 { 576 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 577 int ierr,nt; 578 579 PetscFunctionBegin; 580 ierr = VecGetLocalSize(xx,&nt);CHKERRQ(ierr); 581 if (nt != A->n) { 582 SETERRQ2(PETSC_ERR_ARG_SIZ,"Incompatible partition of A (%d) and xx (%d)",A->n,nt); 583 } 584 ierr = VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);CHKERRQ(ierr); 585 ierr = (*a->A->ops->mult)(a->A,xx,yy);CHKERRQ(ierr); 586 ierr = VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);CHKERRQ(ierr); 587 ierr = (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);CHKERRQ(ierr); 588 PetscFunctionReturn(0); 589 } 590 591 #undef __FUNCT__ 592 #define __FUNCT__ "MatMultAdd_MPIAIJ" 593 int MatMultAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz) 594 { 595 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 596 int ierr; 597 598 PetscFunctionBegin; 599 ierr = VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);CHKERRQ(ierr); 600 ierr = (*a->A->ops->multadd)(a->A,xx,yy,zz);CHKERRQ(ierr); 601 ierr = VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);CHKERRQ(ierr); 602 ierr = (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);CHKERRQ(ierr); 603 PetscFunctionReturn(0); 604 } 605 606 #undef __FUNCT__ 607 #define __FUNCT__ "MatMultTranspose_MPIAIJ" 608 int MatMultTranspose_MPIAIJ(Mat A,Vec xx,Vec yy) 609 { 610 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 611 int ierr; 612 613 PetscFunctionBegin; 614 /* do nondiagonal part */ 615 ierr = (*a->B->ops->multtranspose)(a->B,xx,a->lvec);CHKERRQ(ierr); 616 /* send it on its way */ 617 ierr = VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);CHKERRQ(ierr); 618 /* do local part */ 619 ierr = (*a->A->ops->multtranspose)(a->A,xx,yy);CHKERRQ(ierr); 620 /* receive remote parts: note this assumes the values are not actually */ 621 /* inserted in yy until the next line, which is true for my implementation*/ 622 /* but is not perhaps always true. */ 623 ierr = VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);CHKERRQ(ierr); 624 PetscFunctionReturn(0); 625 } 626 627 EXTERN_C_BEGIN 628 #undef __FUNCT__ 629 #define __FUNCT__ "MatIsSymmetric_MPIAIJ" 630 int MatIsSymmetric_MPIAIJ(Mat Amat,Mat Bmat,PetscTruth *f) 631 { 632 Mat_MPIAIJ *Aij = (Mat_MPIAIJ *) Amat->data, *Bij; 633 Mat Adia = Aij->A, Bdia, Aoff,Boff,*Aoffs,*Boffs; 634 IS Me,Notme; 635 int M,N,first,last,*notme,i, ierr; 636 637 PetscFunctionBegin; 638 639 /* Easy test: symmetric diagonal block */ 640 Bij = (Mat_MPIAIJ *) Bmat->data; Bdia = Bij->A; 641 ierr = MatIsSymmetric(Adia,Bdia,f); CHKERRQ(ierr); 642 if (!*f) PetscFunctionReturn(0); 643 644 /* Hard test: off-diagonal block. This takes a MatGetSubMatrix. */ 645 ierr = MatGetSize(Amat,&M,&N); CHKERRQ(ierr); 646 ierr = MatGetOwnershipRange(Amat,&first,&last); CHKERRQ(ierr); 647 ierr = PetscMalloc((N-last+first)*sizeof(int),¬me); CHKERRQ(ierr); 648 for (i=0; i<first; i++) notme[i] = i; 649 for (i=last; i<M; i++) notme[i-last+first] = i; 650 ierr = ISCreateGeneral(MPI_COMM_SELF,N-last+first,notme,&Notme); CHKERRQ(ierr); 651 ierr = ISCreateStride(MPI_COMM_SELF,last-first,first,1,&Me); CHKERRQ(ierr); 652 ierr = MatGetSubMatrices(Amat,1,&Me,&Notme,MAT_INITIAL_MATRIX,&Aoffs); CHKERRQ(ierr); 653 Aoff = Aoffs[0]; 654 ierr = MatGetSubMatrices(Bmat,1,&Notme,&Me,MAT_INITIAL_MATRIX,&Boffs); CHKERRQ(ierr); 655 Boff = Boffs[0]; 656 ierr = MatIsSymmetric(Aoff,Boff,f); CHKERRQ(ierr); 657 ierr = MatDestroyMatrices(1,&Aoffs); CHKERRQ(ierr); 658 ierr = MatDestroyMatrices(1,&Boffs); CHKERRQ(ierr); 659 ierr = ISDestroy(Me); CHKERRQ(ierr); 660 ierr = ISDestroy(Notme); CHKERRQ(ierr); 661 662 PetscFunctionReturn(0); 663 } 664 EXTERN_C_END 665 666 #undef __FUNCT__ 667 #define __FUNCT__ "MatMultTransposeAdd_MPIAIJ" 668 int MatMultTransposeAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz) 669 { 670 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 671 int ierr; 672 673 PetscFunctionBegin; 674 /* do nondiagonal part */ 675 ierr = (*a->B->ops->multtranspose)(a->B,xx,a->lvec);CHKERRQ(ierr); 676 /* send it on its way */ 677 ierr = VecScatterBegin(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);CHKERRQ(ierr); 678 /* do local part */ 679 ierr = (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);CHKERRQ(ierr); 680 /* receive remote parts: note this assumes the values are not actually */ 681 /* inserted in yy until the next line, which is true for my implementation*/ 682 /* but is not perhaps always true. */ 683 ierr = VecScatterEnd(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);CHKERRQ(ierr); 684 PetscFunctionReturn(0); 685 } 686 687 /* 688 This only works correctly for square matrices where the subblock A->A is the 689 diagonal block 690 */ 691 #undef __FUNCT__ 692 #define __FUNCT__ "MatGetDiagonal_MPIAIJ" 693 int MatGetDiagonal_MPIAIJ(Mat A,Vec v) 694 { 695 int ierr; 696 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 697 698 PetscFunctionBegin; 699 if (A->M != A->N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block"); 700 if (a->rstart != a->cstart || a->rend != a->cend) { 701 SETERRQ(PETSC_ERR_ARG_SIZ,"row partition must equal col partition"); 702 } 703 ierr = MatGetDiagonal(a->A,v);CHKERRQ(ierr); 704 PetscFunctionReturn(0); 705 } 706 707 #undef __FUNCT__ 708 #define __FUNCT__ "MatScale_MPIAIJ" 709 int MatScale_MPIAIJ(const PetscScalar aa[],Mat A) 710 { 711 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 712 int ierr; 713 714 PetscFunctionBegin; 715 ierr = MatScale(aa,a->A);CHKERRQ(ierr); 716 ierr = MatScale(aa,a->B);CHKERRQ(ierr); 717 PetscFunctionReturn(0); 718 } 719 720 #undef __FUNCT__ 721 #define __FUNCT__ "MatDestroy_MPIAIJ" 722 int MatDestroy_MPIAIJ(Mat mat) 723 { 724 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 725 int ierr; 726 727 PetscFunctionBegin; 728 #if defined(PETSC_USE_LOG) 729 PetscLogObjectState((PetscObject)mat,"Rows=%d, Cols=%d",mat->M,mat->N); 730 #endif 731 ierr = MatStashDestroy_Private(&mat->stash);CHKERRQ(ierr); 732 ierr = PetscFree(aij->rowners);CHKERRQ(ierr); 733 ierr = MatDestroy(aij->A);CHKERRQ(ierr); 734 ierr = MatDestroy(aij->B);CHKERRQ(ierr); 735 #if defined (PETSC_USE_CTABLE) 736 if (aij->colmap) {ierr = PetscTableDelete(aij->colmap);CHKERRQ(ierr);} 737 #else 738 if (aij->colmap) {ierr = PetscFree(aij->colmap);CHKERRQ(ierr);} 739 #endif 740 if (aij->garray) {ierr = PetscFree(aij->garray);CHKERRQ(ierr);} 741 if (aij->lvec) {ierr = VecDestroy(aij->lvec);CHKERRQ(ierr);} 742 if (aij->Mvctx) {ierr = VecScatterDestroy(aij->Mvctx);CHKERRQ(ierr);} 743 if (aij->rowvalues) {ierr = PetscFree(aij->rowvalues);CHKERRQ(ierr);} 744 ierr = PetscFree(aij);CHKERRQ(ierr); 745 PetscFunctionReturn(0); 746 } 747 748 #undef __FUNCT__ 749 #define __FUNCT__ "MatView_MPIAIJ_Binary" 750 int MatView_MPIAIJ_Binary(Mat mat,PetscViewer viewer) 751 { 752 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 753 Mat_SeqAIJ* A = (Mat_SeqAIJ*)aij->A->data; 754 Mat_SeqAIJ* B = (Mat_SeqAIJ*)aij->B->data; 755 int nz,fd,ierr,header[4],rank,size,*row_lengths,*range,rlen,i,tag = ((PetscObject)viewer)->tag; 756 int nzmax,*column_indices,j,k,col,*garray = aij->garray,cnt,cstart = aij->cstart,rnz; 757 PetscScalar *column_values; 758 759 PetscFunctionBegin; 760 ierr = MPI_Comm_rank(mat->comm,&rank);CHKERRQ(ierr); 761 ierr = MPI_Comm_size(mat->comm,&size);CHKERRQ(ierr); 762 nz = A->nz + B->nz; 763 if (rank == 0) { 764 header[0] = MAT_FILE_COOKIE; 765 header[1] = mat->M; 766 header[2] = mat->N; 767 ierr = MPI_Reduce(&nz,&header[3],1,MPI_INT,MPI_SUM,0,mat->comm);CHKERRQ(ierr); 768 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 769 ierr = PetscBinaryWrite(fd,header,4,PETSC_INT,1);CHKERRQ(ierr); 770 /* get largest number of rows any processor has */ 771 rlen = mat->m; 772 ierr = PetscMapGetGlobalRange(mat->rmap,&range);CHKERRQ(ierr); 773 for (i=1; i<size; i++) { 774 rlen = PetscMax(rlen,range[i+1] - range[i]); 775 } 776 } else { 777 ierr = MPI_Reduce(&nz,0,1,MPI_INT,MPI_SUM,0,mat->comm);CHKERRQ(ierr); 778 rlen = mat->m; 779 } 780 781 /* load up the local row counts */ 782 ierr = PetscMalloc((rlen+1)*sizeof(int),&row_lengths);CHKERRQ(ierr); 783 for (i=0; i<mat->m; i++) { 784 row_lengths[i] = A->i[i+1] - A->i[i] + B->i[i+1] - B->i[i]; 785 } 786 787 /* store the row lengths to the file */ 788 if (rank == 0) { 789 MPI_Status status; 790 ierr = PetscBinaryWrite(fd,row_lengths,mat->m,PETSC_INT,1);CHKERRQ(ierr); 791 for (i=1; i<size; i++) { 792 rlen = range[i+1] - range[i]; 793 ierr = MPI_Recv(row_lengths,rlen,MPI_INT,i,tag,mat->comm,&status);CHKERRQ(ierr); 794 ierr = PetscBinaryWrite(fd,row_lengths,rlen,PETSC_INT,1);CHKERRQ(ierr); 795 } 796 } else { 797 ierr = MPI_Send(row_lengths,mat->m,MPI_INT,0,tag,mat->comm);CHKERRQ(ierr); 798 } 799 ierr = PetscFree(row_lengths);CHKERRQ(ierr); 800 801 /* load up the local column indices */ 802 nzmax = nz; /* )th processor needs space a largest processor needs */ 803 ierr = MPI_Reduce(&nz,&nzmax,1,MPI_INT,MPI_MAX,0,mat->comm);CHKERRQ(ierr); 804 ierr = PetscMalloc((nzmax+1)*sizeof(int),&column_indices);CHKERRQ(ierr); 805 cnt = 0; 806 for (i=0; i<mat->m; i++) { 807 for (j=B->i[i]; j<B->i[i+1]; j++) { 808 if ( (col = garray[B->j[j]]) > cstart) break; 809 column_indices[cnt++] = col; 810 } 811 for (k=A->i[i]; k<A->i[i+1]; k++) { 812 column_indices[cnt++] = A->j[k] + cstart; 813 } 814 for (; j<B->i[i+1]; j++) { 815 column_indices[cnt++] = garray[B->j[j]]; 816 } 817 } 818 if (cnt != A->nz + B->nz) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: cnt = %d nz = %d",cnt,A->nz+B->nz); 819 820 /* store the column indices to the file */ 821 if (rank == 0) { 822 MPI_Status status; 823 ierr = PetscBinaryWrite(fd,column_indices,nz,PETSC_INT,1);CHKERRQ(ierr); 824 for (i=1; i<size; i++) { 825 ierr = MPI_Recv(&rnz,1,MPI_INT,i,tag,mat->comm,&status);CHKERRQ(ierr); 826 if (rnz > nzmax) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: nz = %d nzmax = %d",nz,nzmax); 827 ierr = MPI_Recv(column_indices,rnz,MPI_INT,i,tag,mat->comm,&status);CHKERRQ(ierr); 828 ierr = PetscBinaryWrite(fd,column_indices,rnz,PETSC_INT,1);CHKERRQ(ierr); 829 } 830 } else { 831 ierr = MPI_Send(&nz,1,MPI_INT,0,tag,mat->comm);CHKERRQ(ierr); 832 ierr = MPI_Send(column_indices,nz,MPI_INT,0,tag,mat->comm);CHKERRQ(ierr); 833 } 834 ierr = PetscFree(column_indices);CHKERRQ(ierr); 835 836 /* load up the local column values */ 837 ierr = PetscMalloc((nzmax+1)*sizeof(PetscScalar),&column_values);CHKERRQ(ierr); 838 cnt = 0; 839 for (i=0; i<mat->m; i++) { 840 for (j=B->i[i]; j<B->i[i+1]; j++) { 841 if ( garray[B->j[j]] > cstart) break; 842 column_values[cnt++] = B->a[j]; 843 } 844 for (k=A->i[i]; k<A->i[i+1]; k++) { 845 column_values[cnt++] = A->a[k]; 846 } 847 for (; j<B->i[i+1]; j++) { 848 column_values[cnt++] = B->a[j]; 849 } 850 } 851 if (cnt != A->nz + B->nz) SETERRQ2(1,"Internal PETSc error: cnt = %d nz = %d",cnt,A->nz+B->nz); 852 853 /* store the column values to the file */ 854 if (rank == 0) { 855 MPI_Status status; 856 ierr = PetscBinaryWrite(fd,column_values,nz,PETSC_SCALAR,1);CHKERRQ(ierr); 857 for (i=1; i<size; i++) { 858 ierr = MPI_Recv(&rnz,1,MPI_INT,i,tag,mat->comm,&status);CHKERRQ(ierr); 859 if (rnz > nzmax) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: nz = %d nzmax = %d",nz,nzmax); 860 ierr = MPI_Recv(column_values,rnz,MPIU_SCALAR,i,tag,mat->comm,&status);CHKERRQ(ierr); 861 ierr = PetscBinaryWrite(fd,column_values,rnz,PETSC_SCALAR,1);CHKERRQ(ierr); 862 } 863 } else { 864 ierr = MPI_Send(&nz,1,MPI_INT,0,tag,mat->comm);CHKERRQ(ierr); 865 ierr = MPI_Send(column_values,nz,MPIU_SCALAR,0,tag,mat->comm);CHKERRQ(ierr); 866 } 867 ierr = PetscFree(column_values);CHKERRQ(ierr); 868 PetscFunctionReturn(0); 869 } 870 871 #undef __FUNCT__ 872 #define __FUNCT__ "MatView_MPIAIJ_ASCIIorDraworSocket" 873 int MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer) 874 { 875 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 876 int ierr,rank = aij->rank,size = aij->size; 877 PetscTruth isdraw,isascii,flg,isbinary; 878 PetscViewer sviewer; 879 PetscViewerFormat format; 880 881 PetscFunctionBegin; 882 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);CHKERRQ(ierr); 883 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);CHKERRQ(ierr); 884 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);CHKERRQ(ierr); 885 if (isascii) { 886 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 887 if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) { 888 MatInfo info; 889 ierr = MPI_Comm_rank(mat->comm,&rank);CHKERRQ(ierr); 890 ierr = MatGetInfo(mat,MAT_LOCAL,&info);CHKERRQ(ierr); 891 ierr = PetscOptionsHasName(PETSC_NULL,"-mat_aij_no_inode",&flg);CHKERRQ(ierr); 892 if (flg) { 893 ierr = PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %d nz %d nz alloced %d mem %d, not using I-node routines\n", 894 rank,mat->m,(int)info.nz_used,(int)info.nz_allocated,(int)info.memory);CHKERRQ(ierr); 895 } else { 896 ierr = PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %d nz %d nz alloced %d mem %d, using I-node routines\n", 897 rank,mat->m,(int)info.nz_used,(int)info.nz_allocated,(int)info.memory);CHKERRQ(ierr); 898 } 899 ierr = MatGetInfo(aij->A,MAT_LOCAL,&info);CHKERRQ(ierr); 900 ierr = PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %d \n",rank,(int)info.nz_used);CHKERRQ(ierr); 901 ierr = MatGetInfo(aij->B,MAT_LOCAL,&info);CHKERRQ(ierr); 902 ierr = PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %d \n",rank,(int)info.nz_used);CHKERRQ(ierr); 903 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 904 ierr = VecScatterView(aij->Mvctx,viewer);CHKERRQ(ierr); 905 PetscFunctionReturn(0); 906 } else if (format == PETSC_VIEWER_ASCII_INFO) { 907 PetscFunctionReturn(0); 908 } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) { 909 PetscFunctionReturn(0); 910 } 911 } else if (isbinary) { 912 if (size == 1) { 913 ierr = PetscObjectSetName((PetscObject)aij->A,mat->name);CHKERRQ(ierr); 914 ierr = MatView(aij->A,viewer);CHKERRQ(ierr); 915 } else { 916 ierr = MatView_MPIAIJ_Binary(mat,viewer);CHKERRQ(ierr); 917 } 918 PetscFunctionReturn(0); 919 } else if (isdraw) { 920 PetscDraw draw; 921 PetscTruth isnull; 922 ierr = PetscViewerDrawGetDraw(viewer,0,&draw);CHKERRQ(ierr); 923 ierr = PetscDrawIsNull(draw,&isnull);CHKERRQ(ierr); if (isnull) PetscFunctionReturn(0); 924 } 925 926 if (size == 1) { 927 ierr = PetscObjectSetName((PetscObject)aij->A,mat->name);CHKERRQ(ierr); 928 ierr = MatView(aij->A,viewer);CHKERRQ(ierr); 929 } else { 930 /* assemble the entire matrix onto first processor. */ 931 Mat A; 932 Mat_SeqAIJ *Aloc; 933 int M = mat->M,N = mat->N,m,*ai,*aj,row,*cols,i,*ct; 934 PetscScalar *a; 935 936 if (!rank) { 937 ierr = MatCreateMPIAIJ(mat->comm,M,N,M,N,0,PETSC_NULL,0,PETSC_NULL,&A);CHKERRQ(ierr); 938 } else { 939 ierr = MatCreateMPIAIJ(mat->comm,0,0,M,N,0,PETSC_NULL,0,PETSC_NULL,&A);CHKERRQ(ierr); 940 } 941 PetscLogObjectParent(mat,A); 942 943 /* copy over the A part */ 944 Aloc = (Mat_SeqAIJ*)aij->A->data; 945 m = aij->A->m; ai = Aloc->i; aj = Aloc->j; a = Aloc->a; 946 row = aij->rstart; 947 for (i=0; i<ai[m]; i++) {aj[i] += aij->cstart ;} 948 for (i=0; i<m; i++) { 949 ierr = MatSetValues(A,1,&row,ai[i+1]-ai[i],aj,a,INSERT_VALUES);CHKERRQ(ierr); 950 row++; a += ai[i+1]-ai[i]; aj += ai[i+1]-ai[i]; 951 } 952 aj = Aloc->j; 953 for (i=0; i<ai[m]; i++) {aj[i] -= aij->cstart;} 954 955 /* copy over the B part */ 956 Aloc = (Mat_SeqAIJ*)aij->B->data; 957 m = aij->B->m; ai = Aloc->i; aj = Aloc->j; a = Aloc->a; 958 row = aij->rstart; 959 ierr = PetscMalloc((ai[m]+1)*sizeof(int),&cols);CHKERRQ(ierr); 960 ct = cols; 961 for (i=0; i<ai[m]; i++) {cols[i] = aij->garray[aj[i]];} 962 for (i=0; i<m; i++) { 963 ierr = MatSetValues(A,1,&row,ai[i+1]-ai[i],cols,a,INSERT_VALUES);CHKERRQ(ierr); 964 row++; a += ai[i+1]-ai[i]; cols += ai[i+1]-ai[i]; 965 } 966 ierr = PetscFree(ct);CHKERRQ(ierr); 967 ierr = MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 968 ierr = MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 969 /* 970 Everyone has to call to draw the matrix since the graphics waits are 971 synchronized across all processors that share the PetscDraw object 972 */ 973 ierr = PetscViewerGetSingleton(viewer,&sviewer);CHKERRQ(ierr); 974 if (!rank) { 975 ierr = PetscObjectSetName((PetscObject)((Mat_MPIAIJ*)(A->data))->A,mat->name);CHKERRQ(ierr); 976 ierr = MatView(((Mat_MPIAIJ*)(A->data))->A,sviewer);CHKERRQ(ierr); 977 } 978 ierr = PetscViewerRestoreSingleton(viewer,&sviewer);CHKERRQ(ierr); 979 ierr = MatDestroy(A);CHKERRQ(ierr); 980 } 981 PetscFunctionReturn(0); 982 } 983 984 #undef __FUNCT__ 985 #define __FUNCT__ "MatView_MPIAIJ" 986 int MatView_MPIAIJ(Mat mat,PetscViewer viewer) 987 { 988 int ierr; 989 PetscTruth isascii,isdraw,issocket,isbinary; 990 991 PetscFunctionBegin; 992 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);CHKERRQ(ierr); 993 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);CHKERRQ(ierr); 994 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);CHKERRQ(ierr); 995 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);CHKERRQ(ierr); 996 if (isascii || isdraw || isbinary || issocket) { 997 ierr = MatView_MPIAIJ_ASCIIorDraworSocket(mat,viewer);CHKERRQ(ierr); 998 } else { 999 SETERRQ1(1,"Viewer type %s not supported by MPIAIJ matrices",((PetscObject)viewer)->type_name); 1000 } 1001 PetscFunctionReturn(0); 1002 } 1003 1004 1005 1006 #undef __FUNCT__ 1007 #define __FUNCT__ "MatRelax_MPIAIJ" 1008 int MatRelax_MPIAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,int its,int lits,Vec xx) 1009 { 1010 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data; 1011 int ierr; 1012 Vec bb1; 1013 PetscScalar mone=-1.0; 1014 1015 PetscFunctionBegin; 1016 if (its <= 0 || lits <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %d and local its %d both positive",its,lits); 1017 1018 ierr = VecDuplicate(bb,&bb1);CHKERRQ(ierr); 1019 1020 if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP){ 1021 if (flag & SOR_ZERO_INITIAL_GUESS) { 1022 ierr = (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,lits,xx);CHKERRQ(ierr); 1023 its--; 1024 } 1025 1026 while (its--) { 1027 ierr = VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 1028 ierr = VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 1029 1030 /* update rhs: bb1 = bb - B*x */ 1031 ierr = VecScale(&mone,mat->lvec);CHKERRQ(ierr); 1032 ierr = (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);CHKERRQ(ierr); 1033 1034 /* local sweep */ 1035 ierr = (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,lits,xx); 1036 CHKERRQ(ierr); 1037 } 1038 } else if (flag & SOR_LOCAL_FORWARD_SWEEP){ 1039 if (flag & SOR_ZERO_INITIAL_GUESS) { 1040 ierr = (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,PETSC_NULL,xx);CHKERRQ(ierr); 1041 its--; 1042 } 1043 while (its--) { 1044 ierr = VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 1045 ierr = VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 1046 1047 /* update rhs: bb1 = bb - B*x */ 1048 ierr = VecScale(&mone,mat->lvec);CHKERRQ(ierr); 1049 ierr = (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);CHKERRQ(ierr); 1050 1051 /* local sweep */ 1052 ierr = (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_FORWARD_SWEEP,fshift,lits,PETSC_NULL,xx); 1053 CHKERRQ(ierr); 1054 } 1055 } else if (flag & SOR_LOCAL_BACKWARD_SWEEP){ 1056 if (flag & SOR_ZERO_INITIAL_GUESS) { 1057 ierr = (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,PETSC_NULL,xx);CHKERRQ(ierr); 1058 its--; 1059 } 1060 while (its--) { 1061 ierr = VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 1062 ierr = VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 1063 1064 /* update rhs: bb1 = bb - B*x */ 1065 ierr = VecScale(&mone,mat->lvec);CHKERRQ(ierr); 1066 ierr = (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);CHKERRQ(ierr); 1067 1068 /* local sweep */ 1069 ierr = (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_BACKWARD_SWEEP,fshift,lits,PETSC_NULL,xx); 1070 CHKERRQ(ierr); 1071 } 1072 } else { 1073 SETERRQ(PETSC_ERR_SUP,"Parallel SOR not supported"); 1074 } 1075 1076 ierr = VecDestroy(bb1);CHKERRQ(ierr); 1077 PetscFunctionReturn(0); 1078 } 1079 1080 #undef __FUNCT__ 1081 #define __FUNCT__ "MatGetInfo_MPIAIJ" 1082 int MatGetInfo_MPIAIJ(Mat matin,MatInfoType flag,MatInfo *info) 1083 { 1084 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data; 1085 Mat A = mat->A,B = mat->B; 1086 int ierr; 1087 PetscReal isend[5],irecv[5]; 1088 1089 PetscFunctionBegin; 1090 info->block_size = 1.0; 1091 ierr = MatGetInfo(A,MAT_LOCAL,info);CHKERRQ(ierr); 1092 isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded; 1093 isend[3] = info->memory; isend[4] = info->mallocs; 1094 ierr = MatGetInfo(B,MAT_LOCAL,info);CHKERRQ(ierr); 1095 isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded; 1096 isend[3] += info->memory; isend[4] += info->mallocs; 1097 if (flag == MAT_LOCAL) { 1098 info->nz_used = isend[0]; 1099 info->nz_allocated = isend[1]; 1100 info->nz_unneeded = isend[2]; 1101 info->memory = isend[3]; 1102 info->mallocs = isend[4]; 1103 } else if (flag == MAT_GLOBAL_MAX) { 1104 ierr = MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,matin->comm);CHKERRQ(ierr); 1105 info->nz_used = irecv[0]; 1106 info->nz_allocated = irecv[1]; 1107 info->nz_unneeded = irecv[2]; 1108 info->memory = irecv[3]; 1109 info->mallocs = irecv[4]; 1110 } else if (flag == MAT_GLOBAL_SUM) { 1111 ierr = MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,matin->comm);CHKERRQ(ierr); 1112 info->nz_used = irecv[0]; 1113 info->nz_allocated = irecv[1]; 1114 info->nz_unneeded = irecv[2]; 1115 info->memory = irecv[3]; 1116 info->mallocs = irecv[4]; 1117 } 1118 info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */ 1119 info->fill_ratio_needed = 0; 1120 info->factor_mallocs = 0; 1121 info->rows_global = (double)matin->M; 1122 info->columns_global = (double)matin->N; 1123 info->rows_local = (double)matin->m; 1124 info->columns_local = (double)matin->N; 1125 1126 PetscFunctionReturn(0); 1127 } 1128 1129 #undef __FUNCT__ 1130 #define __FUNCT__ "MatSetOption_MPIAIJ" 1131 int MatSetOption_MPIAIJ(Mat A,MatOption op) 1132 { 1133 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 1134 int ierr; 1135 1136 PetscFunctionBegin; 1137 switch (op) { 1138 case MAT_NO_NEW_NONZERO_LOCATIONS: 1139 case MAT_YES_NEW_NONZERO_LOCATIONS: 1140 case MAT_COLUMNS_UNSORTED: 1141 case MAT_COLUMNS_SORTED: 1142 case MAT_NEW_NONZERO_ALLOCATION_ERR: 1143 case MAT_KEEP_ZEROED_ROWS: 1144 case MAT_NEW_NONZERO_LOCATION_ERR: 1145 case MAT_USE_INODES: 1146 case MAT_DO_NOT_USE_INODES: 1147 case MAT_IGNORE_ZERO_ENTRIES: 1148 ierr = MatSetOption(a->A,op);CHKERRQ(ierr); 1149 ierr = MatSetOption(a->B,op);CHKERRQ(ierr); 1150 break; 1151 case MAT_ROW_ORIENTED: 1152 a->roworiented = PETSC_TRUE; 1153 ierr = MatSetOption(a->A,op);CHKERRQ(ierr); 1154 ierr = MatSetOption(a->B,op);CHKERRQ(ierr); 1155 break; 1156 case MAT_ROWS_SORTED: 1157 case MAT_ROWS_UNSORTED: 1158 case MAT_YES_NEW_DIAGONALS: 1159 PetscLogInfo(A,"MatSetOption_MPIAIJ:Option ignored\n"); 1160 break; 1161 case MAT_COLUMN_ORIENTED: 1162 a->roworiented = PETSC_FALSE; 1163 ierr = MatSetOption(a->A,op);CHKERRQ(ierr); 1164 ierr = MatSetOption(a->B,op);CHKERRQ(ierr); 1165 break; 1166 case MAT_IGNORE_OFF_PROC_ENTRIES: 1167 a->donotstash = PETSC_TRUE; 1168 break; 1169 case MAT_NO_NEW_DIAGONALS: 1170 SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS"); 1171 default: 1172 SETERRQ(PETSC_ERR_SUP,"unknown option"); 1173 } 1174 PetscFunctionReturn(0); 1175 } 1176 1177 #undef __FUNCT__ 1178 #define __FUNCT__ "MatGetRow_MPIAIJ" 1179 int MatGetRow_MPIAIJ(Mat matin,int row,int *nz,int **idx,PetscScalar **v) 1180 { 1181 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data; 1182 PetscScalar *vworkA,*vworkB,**pvA,**pvB,*v_p; 1183 int i,ierr,*cworkA,*cworkB,**pcA,**pcB,cstart = mat->cstart; 1184 int nztot,nzA,nzB,lrow,rstart = mat->rstart,rend = mat->rend; 1185 int *cmap,*idx_p; 1186 1187 PetscFunctionBegin; 1188 if (mat->getrowactive == PETSC_TRUE) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active"); 1189 mat->getrowactive = PETSC_TRUE; 1190 1191 if (!mat->rowvalues && (idx || v)) { 1192 /* 1193 allocate enough space to hold information from the longest row. 1194 */ 1195 Mat_SeqAIJ *Aa = (Mat_SeqAIJ*)mat->A->data,*Ba = (Mat_SeqAIJ*)mat->B->data; 1196 int max = 1,tmp; 1197 for (i=0; i<matin->m; i++) { 1198 tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i]; 1199 if (max < tmp) { max = tmp; } 1200 } 1201 ierr = PetscMalloc(max*(sizeof(int)+sizeof(PetscScalar)),&mat->rowvalues);CHKERRQ(ierr); 1202 mat->rowindices = (int*)(mat->rowvalues + max); 1203 } 1204 1205 if (row < rstart || row >= rend) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Only local rows") 1206 lrow = row - rstart; 1207 1208 pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB; 1209 if (!v) {pvA = 0; pvB = 0;} 1210 if (!idx) {pcA = 0; if (!v) pcB = 0;} 1211 ierr = (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);CHKERRQ(ierr); 1212 ierr = (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);CHKERRQ(ierr); 1213 nztot = nzA + nzB; 1214 1215 cmap = mat->garray; 1216 if (v || idx) { 1217 if (nztot) { 1218 /* Sort by increasing column numbers, assuming A and B already sorted */ 1219 int imark = -1; 1220 if (v) { 1221 *v = v_p = mat->rowvalues; 1222 for (i=0; i<nzB; i++) { 1223 if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i]; 1224 else break; 1225 } 1226 imark = i; 1227 for (i=0; i<nzA; i++) v_p[imark+i] = vworkA[i]; 1228 for (i=imark; i<nzB; i++) v_p[nzA+i] = vworkB[i]; 1229 } 1230 if (idx) { 1231 *idx = idx_p = mat->rowindices; 1232 if (imark > -1) { 1233 for (i=0; i<imark; i++) { 1234 idx_p[i] = cmap[cworkB[i]]; 1235 } 1236 } else { 1237 for (i=0; i<nzB; i++) { 1238 if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]]; 1239 else break; 1240 } 1241 imark = i; 1242 } 1243 for (i=0; i<nzA; i++) idx_p[imark+i] = cstart + cworkA[i]; 1244 for (i=imark; i<nzB; i++) idx_p[nzA+i] = cmap[cworkB[i]]; 1245 } 1246 } else { 1247 if (idx) *idx = 0; 1248 if (v) *v = 0; 1249 } 1250 } 1251 *nz = nztot; 1252 ierr = (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);CHKERRQ(ierr); 1253 ierr = (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);CHKERRQ(ierr); 1254 PetscFunctionReturn(0); 1255 } 1256 1257 #undef __FUNCT__ 1258 #define __FUNCT__ "MatRestoreRow_MPIAIJ" 1259 int MatRestoreRow_MPIAIJ(Mat mat,int row,int *nz,int **idx,PetscScalar **v) 1260 { 1261 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 1262 1263 PetscFunctionBegin; 1264 if (aij->getrowactive == PETSC_FALSE) { 1265 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow not called"); 1266 } 1267 aij->getrowactive = PETSC_FALSE; 1268 PetscFunctionReturn(0); 1269 } 1270 1271 #undef __FUNCT__ 1272 #define __FUNCT__ "MatNorm_MPIAIJ" 1273 int MatNorm_MPIAIJ(Mat mat,NormType type,PetscReal *norm) 1274 { 1275 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 1276 Mat_SeqAIJ *amat = (Mat_SeqAIJ*)aij->A->data,*bmat = (Mat_SeqAIJ*)aij->B->data; 1277 int ierr,i,j,cstart = aij->cstart; 1278 PetscReal sum = 0.0; 1279 PetscScalar *v; 1280 1281 PetscFunctionBegin; 1282 if (aij->size == 1) { 1283 ierr = MatNorm(aij->A,type,norm);CHKERRQ(ierr); 1284 } else { 1285 if (type == NORM_FROBENIUS) { 1286 v = amat->a; 1287 for (i=0; i<amat->nz; i++) { 1288 #if defined(PETSC_USE_COMPLEX) 1289 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 1290 #else 1291 sum += (*v)*(*v); v++; 1292 #endif 1293 } 1294 v = bmat->a; 1295 for (i=0; i<bmat->nz; i++) { 1296 #if defined(PETSC_USE_COMPLEX) 1297 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 1298 #else 1299 sum += (*v)*(*v); v++; 1300 #endif 1301 } 1302 ierr = MPI_Allreduce(&sum,norm,1,MPIU_REAL,MPI_SUM,mat->comm);CHKERRQ(ierr); 1303 *norm = sqrt(*norm); 1304 } else if (type == NORM_1) { /* max column norm */ 1305 PetscReal *tmp,*tmp2; 1306 int *jj,*garray = aij->garray; 1307 ierr = PetscMalloc((mat->N+1)*sizeof(PetscReal),&tmp);CHKERRQ(ierr); 1308 ierr = PetscMalloc((mat->N+1)*sizeof(PetscReal),&tmp2);CHKERRQ(ierr); 1309 ierr = PetscMemzero(tmp,mat->N*sizeof(PetscReal));CHKERRQ(ierr); 1310 *norm = 0.0; 1311 v = amat->a; jj = amat->j; 1312 for (j=0; j<amat->nz; j++) { 1313 tmp[cstart + *jj++ ] += PetscAbsScalar(*v); v++; 1314 } 1315 v = bmat->a; jj = bmat->j; 1316 for (j=0; j<bmat->nz; j++) { 1317 tmp[garray[*jj++]] += PetscAbsScalar(*v); v++; 1318 } 1319 ierr = MPI_Allreduce(tmp,tmp2,mat->N,MPIU_REAL,MPI_SUM,mat->comm);CHKERRQ(ierr); 1320 for (j=0; j<mat->N; j++) { 1321 if (tmp2[j] > *norm) *norm = tmp2[j]; 1322 } 1323 ierr = PetscFree(tmp);CHKERRQ(ierr); 1324 ierr = PetscFree(tmp2);CHKERRQ(ierr); 1325 } else if (type == NORM_INFINITY) { /* max row norm */ 1326 PetscReal ntemp = 0.0; 1327 for (j=0; j<aij->A->m; j++) { 1328 v = amat->a + amat->i[j]; 1329 sum = 0.0; 1330 for (i=0; i<amat->i[j+1]-amat->i[j]; i++) { 1331 sum += PetscAbsScalar(*v); v++; 1332 } 1333 v = bmat->a + bmat->i[j]; 1334 for (i=0; i<bmat->i[j+1]-bmat->i[j]; i++) { 1335 sum += PetscAbsScalar(*v); v++; 1336 } 1337 if (sum > ntemp) ntemp = sum; 1338 } 1339 ierr = MPI_Allreduce(&ntemp,norm,1,MPIU_REAL,MPI_MAX,mat->comm);CHKERRQ(ierr); 1340 } else { 1341 SETERRQ(PETSC_ERR_SUP,"No support for two norm"); 1342 } 1343 } 1344 PetscFunctionReturn(0); 1345 } 1346 1347 #undef __FUNCT__ 1348 #define __FUNCT__ "MatTranspose_MPIAIJ" 1349 int MatTranspose_MPIAIJ(Mat A,Mat *matout) 1350 { 1351 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 1352 Mat_SeqAIJ *Aloc = (Mat_SeqAIJ*)a->A->data; 1353 int ierr; 1354 int M = A->M,N = A->N,m,*ai,*aj,row,*cols,i,*ct; 1355 Mat B; 1356 PetscScalar *array; 1357 1358 PetscFunctionBegin; 1359 if (!matout && M != N) { 1360 SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place"); 1361 } 1362 1363 ierr = MatCreateMPIAIJ(A->comm,A->n,A->m,N,M,0,PETSC_NULL,0,PETSC_NULL,&B);CHKERRQ(ierr); 1364 1365 /* copy over the A part */ 1366 Aloc = (Mat_SeqAIJ*)a->A->data; 1367 m = a->A->m; ai = Aloc->i; aj = Aloc->j; array = Aloc->a; 1368 row = a->rstart; 1369 for (i=0; i<ai[m]; i++) {aj[i] += a->cstart ;} 1370 for (i=0; i<m; i++) { 1371 ierr = MatSetValues(B,ai[i+1]-ai[i],aj,1,&row,array,INSERT_VALUES);CHKERRQ(ierr); 1372 row++; array += ai[i+1]-ai[i]; aj += ai[i+1]-ai[i]; 1373 } 1374 aj = Aloc->j; 1375 for (i=0; i<ai[m]; i++) {aj[i] -= a->cstart ;} 1376 1377 /* copy over the B part */ 1378 Aloc = (Mat_SeqAIJ*)a->B->data; 1379 m = a->B->m; ai = Aloc->i; aj = Aloc->j; array = Aloc->a; 1380 row = a->rstart; 1381 ierr = PetscMalloc((1+ai[m])*sizeof(int),&cols);CHKERRQ(ierr); 1382 ct = cols; 1383 for (i=0; i<ai[m]; i++) {cols[i] = a->garray[aj[i]];} 1384 for (i=0; i<m; i++) { 1385 ierr = MatSetValues(B,ai[i+1]-ai[i],cols,1,&row,array,INSERT_VALUES);CHKERRQ(ierr); 1386 row++; array += ai[i+1]-ai[i]; cols += ai[i+1]-ai[i]; 1387 } 1388 ierr = PetscFree(ct);CHKERRQ(ierr); 1389 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1390 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1391 if (matout) { 1392 *matout = B; 1393 } else { 1394 ierr = MatHeaderCopy(A,B);CHKERRQ(ierr); 1395 } 1396 PetscFunctionReturn(0); 1397 } 1398 1399 #undef __FUNCT__ 1400 #define __FUNCT__ "MatDiagonalScale_MPIAIJ" 1401 int MatDiagonalScale_MPIAIJ(Mat mat,Vec ll,Vec rr) 1402 { 1403 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 1404 Mat a = aij->A,b = aij->B; 1405 int ierr,s1,s2,s3; 1406 1407 PetscFunctionBegin; 1408 ierr = MatGetLocalSize(mat,&s2,&s3);CHKERRQ(ierr); 1409 if (rr) { 1410 ierr = VecGetLocalSize(rr,&s1);CHKERRQ(ierr); 1411 if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size"); 1412 /* Overlap communication with computation. */ 1413 ierr = VecScatterBegin(rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD,aij->Mvctx);CHKERRQ(ierr); 1414 } 1415 if (ll) { 1416 ierr = VecGetLocalSize(ll,&s1);CHKERRQ(ierr); 1417 if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size"); 1418 ierr = (*b->ops->diagonalscale)(b,ll,0);CHKERRQ(ierr); 1419 } 1420 /* scale the diagonal block */ 1421 ierr = (*a->ops->diagonalscale)(a,ll,rr);CHKERRQ(ierr); 1422 1423 if (rr) { 1424 /* Do a scatter end and then right scale the off-diagonal block */ 1425 ierr = VecScatterEnd(rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD,aij->Mvctx);CHKERRQ(ierr); 1426 ierr = (*b->ops->diagonalscale)(b,0,aij->lvec);CHKERRQ(ierr); 1427 } 1428 1429 PetscFunctionReturn(0); 1430 } 1431 1432 1433 #undef __FUNCT__ 1434 #define __FUNCT__ "MatPrintHelp_MPIAIJ" 1435 int MatPrintHelp_MPIAIJ(Mat A) 1436 { 1437 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 1438 int ierr; 1439 1440 PetscFunctionBegin; 1441 if (!a->rank) { 1442 ierr = MatPrintHelp_SeqAIJ(a->A);CHKERRQ(ierr); 1443 } 1444 PetscFunctionReturn(0); 1445 } 1446 1447 #undef __FUNCT__ 1448 #define __FUNCT__ "MatGetBlockSize_MPIAIJ" 1449 int MatGetBlockSize_MPIAIJ(Mat A,int *bs) 1450 { 1451 PetscFunctionBegin; 1452 *bs = 1; 1453 PetscFunctionReturn(0); 1454 } 1455 #undef __FUNCT__ 1456 #define __FUNCT__ "MatSetUnfactored_MPIAIJ" 1457 int MatSetUnfactored_MPIAIJ(Mat A) 1458 { 1459 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 1460 int ierr; 1461 1462 PetscFunctionBegin; 1463 ierr = MatSetUnfactored(a->A);CHKERRQ(ierr); 1464 PetscFunctionReturn(0); 1465 } 1466 1467 #undef __FUNCT__ 1468 #define __FUNCT__ "MatEqual_MPIAIJ" 1469 int MatEqual_MPIAIJ(Mat A,Mat B,PetscTruth *flag) 1470 { 1471 Mat_MPIAIJ *matB = (Mat_MPIAIJ*)B->data,*matA = (Mat_MPIAIJ*)A->data; 1472 Mat a,b,c,d; 1473 PetscTruth flg; 1474 int ierr; 1475 1476 PetscFunctionBegin; 1477 a = matA->A; b = matA->B; 1478 c = matB->A; d = matB->B; 1479 1480 ierr = MatEqual(a,c,&flg);CHKERRQ(ierr); 1481 if (flg == PETSC_TRUE) { 1482 ierr = MatEqual(b,d,&flg);CHKERRQ(ierr); 1483 } 1484 ierr = MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);CHKERRQ(ierr); 1485 PetscFunctionReturn(0); 1486 } 1487 1488 #undef __FUNCT__ 1489 #define __FUNCT__ "MatCopy_MPIAIJ" 1490 int MatCopy_MPIAIJ(Mat A,Mat B,MatStructure str) 1491 { 1492 int ierr; 1493 Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data; 1494 Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data; 1495 1496 PetscFunctionBegin; 1497 /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */ 1498 if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) { 1499 /* because of the column compression in the off-processor part of the matrix a->B, 1500 the number of columns in a->B and b->B may be different, hence we cannot call 1501 the MatCopy() directly on the two parts. If need be, we can provide a more 1502 efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices 1503 then copying the submatrices */ 1504 ierr = MatCopy_Basic(A,B,str);CHKERRQ(ierr); 1505 } else { 1506 ierr = MatCopy(a->A,b->A,str);CHKERRQ(ierr); 1507 ierr = MatCopy(a->B,b->B,str);CHKERRQ(ierr); 1508 } 1509 PetscFunctionReturn(0); 1510 } 1511 1512 #undef __FUNCT__ 1513 #define __FUNCT__ "MatSetUpPreallocation_MPIAIJ" 1514 int MatSetUpPreallocation_MPIAIJ(Mat A) 1515 { 1516 int ierr; 1517 1518 PetscFunctionBegin; 1519 ierr = MatMPIAIJSetPreallocation(A,PETSC_DEFAULT,0,PETSC_DEFAULT,0);CHKERRQ(ierr); 1520 PetscFunctionReturn(0); 1521 } 1522 1523 EXTERN int MatDuplicate_MPIAIJ(Mat,MatDuplicateOption,Mat *); 1524 EXTERN int MatIncreaseOverlap_MPIAIJ(Mat,int,IS *,int); 1525 EXTERN int MatFDColoringCreate_MPIAIJ(Mat,ISColoring,MatFDColoring); 1526 EXTERN int MatGetSubMatrices_MPIAIJ (Mat,int,const IS[],const IS[],MatReuse,Mat *[]); 1527 EXTERN int MatGetSubMatrix_MPIAIJ (Mat,IS,IS,int,MatReuse,Mat *); 1528 #if !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 1529 EXTERN int MatLUFactorSymbolic_MPIAIJ_TFS(Mat,IS,IS,MatFactorInfo*,Mat*); 1530 #endif 1531 1532 #include "petscblaslapack.h" 1533 extern int MatAXPY_SeqAIJ(const PetscScalar[],Mat,Mat,MatStructure); 1534 #undef __FUNCT__ 1535 #define __FUNCT__ "MatAXPY_MPIAIJ" 1536 int MatAXPY_MPIAIJ(const PetscScalar a[],Mat X,Mat Y,MatStructure str) 1537 { 1538 int ierr,one=1,i; 1539 Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data,*yy = (Mat_MPIAIJ *)Y->data; 1540 Mat_SeqAIJ *x,*y; 1541 1542 PetscFunctionBegin; 1543 if (str == SAME_NONZERO_PATTERN) { 1544 x = (Mat_SeqAIJ *)xx->A->data; 1545 y = (Mat_SeqAIJ *)yy->A->data; 1546 BLaxpy_(&x->nz,(PetscScalar*)a,x->a,&one,y->a,&one); 1547 x = (Mat_SeqAIJ *)xx->B->data; 1548 y = (Mat_SeqAIJ *)yy->B->data; 1549 BLaxpy_(&x->nz,(PetscScalar*)a,x->a,&one,y->a,&one); 1550 } else if (str == SUBSET_NONZERO_PATTERN) { 1551 ierr = MatAXPY_SeqAIJ(a,xx->A,yy->A,str);CHKERRQ(ierr); 1552 1553 x = (Mat_SeqAIJ *)xx->B->data; 1554 y = (Mat_SeqAIJ *)yy->B->data; 1555 if (y->xtoy && y->XtoY != xx->B) { 1556 ierr = PetscFree(y->xtoy);CHKERRQ(ierr); 1557 ierr = MatDestroy(y->XtoY);CHKERRQ(ierr); 1558 } 1559 if (!y->xtoy) { /* get xtoy */ 1560 ierr = MatAXPYGetxtoy_Private(xx->B->m,x->i,x->j,xx->garray,y->i,y->j,yy->garray,&y->xtoy);CHKERRQ(ierr); 1561 y->XtoY = xx->B; 1562 } 1563 for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += (*a)*(x->a[i]); 1564 } else { 1565 ierr = MatAXPY_Basic(a,X,Y,str);CHKERRQ(ierr); 1566 } 1567 PetscFunctionReturn(0); 1568 } 1569 1570 /* -------------------------------------------------------------------*/ 1571 static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ, 1572 MatGetRow_MPIAIJ, 1573 MatRestoreRow_MPIAIJ, 1574 MatMult_MPIAIJ, 1575 MatMultAdd_MPIAIJ, 1576 MatMultTranspose_MPIAIJ, 1577 MatMultTransposeAdd_MPIAIJ, 1578 0, 1579 0, 1580 0, 1581 0, 1582 0, 1583 0, 1584 MatRelax_MPIAIJ, 1585 MatTranspose_MPIAIJ, 1586 MatGetInfo_MPIAIJ, 1587 MatEqual_MPIAIJ, 1588 MatGetDiagonal_MPIAIJ, 1589 MatDiagonalScale_MPIAIJ, 1590 MatNorm_MPIAIJ, 1591 MatAssemblyBegin_MPIAIJ, 1592 MatAssemblyEnd_MPIAIJ, 1593 0, 1594 MatSetOption_MPIAIJ, 1595 MatZeroEntries_MPIAIJ, 1596 MatZeroRows_MPIAIJ, 1597 #if !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 1598 MatLUFactorSymbolic_MPIAIJ_TFS, 1599 #else 1600 0, 1601 #endif 1602 0, 1603 0, 1604 0, 1605 MatSetUpPreallocation_MPIAIJ, 1606 0, 1607 0, 1608 0, 1609 0, 1610 MatDuplicate_MPIAIJ, 1611 0, 1612 0, 1613 0, 1614 0, 1615 MatAXPY_MPIAIJ, 1616 MatGetSubMatrices_MPIAIJ, 1617 MatIncreaseOverlap_MPIAIJ, 1618 MatGetValues_MPIAIJ, 1619 MatCopy_MPIAIJ, 1620 MatPrintHelp_MPIAIJ, 1621 MatScale_MPIAIJ, 1622 0, 1623 0, 1624 0, 1625 MatGetBlockSize_MPIAIJ, 1626 0, 1627 0, 1628 0, 1629 0, 1630 MatFDColoringCreate_MPIAIJ, 1631 0, 1632 MatSetUnfactored_MPIAIJ, 1633 0, 1634 0, 1635 MatGetSubMatrix_MPIAIJ, 1636 MatDestroy_MPIAIJ, 1637 MatView_MPIAIJ, 1638 MatGetPetscMaps_Petsc, 1639 0, 1640 0, 1641 0, 1642 0, 1643 0, 1644 0, 1645 0, 1646 0, 1647 MatSetColoring_MPIAIJ, 1648 MatSetValuesAdic_MPIAIJ, 1649 MatSetValuesAdifor_MPIAIJ 1650 }; 1651 1652 /* ----------------------------------------------------------------------------------------*/ 1653 1654 EXTERN_C_BEGIN 1655 #undef __FUNCT__ 1656 #define __FUNCT__ "MatStoreValues_MPIAIJ" 1657 int MatStoreValues_MPIAIJ(Mat mat) 1658 { 1659 Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data; 1660 int ierr; 1661 1662 PetscFunctionBegin; 1663 ierr = MatStoreValues(aij->A);CHKERRQ(ierr); 1664 ierr = MatStoreValues(aij->B);CHKERRQ(ierr); 1665 PetscFunctionReturn(0); 1666 } 1667 EXTERN_C_END 1668 1669 EXTERN_C_BEGIN 1670 #undef __FUNCT__ 1671 #define __FUNCT__ "MatRetrieveValues_MPIAIJ" 1672 int MatRetrieveValues_MPIAIJ(Mat mat) 1673 { 1674 Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data; 1675 int ierr; 1676 1677 PetscFunctionBegin; 1678 ierr = MatRetrieveValues(aij->A);CHKERRQ(ierr); 1679 ierr = MatRetrieveValues(aij->B);CHKERRQ(ierr); 1680 PetscFunctionReturn(0); 1681 } 1682 EXTERN_C_END 1683 1684 #include "petscpc.h" 1685 EXTERN_C_BEGIN 1686 EXTERN int MatGetDiagonalBlock_MPIAIJ(Mat,PetscTruth *,MatReuse,Mat *); 1687 EXTERN int MatDiagonalScaleLocal_MPIAIJ(Mat,Vec); 1688 EXTERN_C_END 1689 1690 EXTERN_C_BEGIN 1691 #undef __FUNCT__ 1692 #define __FUNCT__ "MatMPIAIJSetPreallocation_MPIAIJ" 1693 int MatMPIAIJSetPreallocation_MPIAIJ(Mat B,int d_nz,const int d_nnz[],int o_nz,const int o_nnz[]) 1694 { 1695 Mat_MPIAIJ *b; 1696 int ierr,i; 1697 1698 PetscFunctionBegin; 1699 B->preallocated = PETSC_TRUE; 1700 if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5; 1701 if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2; 1702 if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %d",d_nz); 1703 if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %d",o_nz); 1704 if (d_nnz) { 1705 for (i=0; i<B->m; i++) { 1706 if (d_nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"d_nnz cannot be less than 0: local row %d value %d",i,d_nnz[i]); 1707 } 1708 } 1709 if (o_nnz) { 1710 for (i=0; i<B->m; i++) { 1711 if (o_nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"o_nnz cannot be less than 0: local row %d value %d",i,o_nnz[i]); 1712 } 1713 } 1714 b = (Mat_MPIAIJ*)B->data; 1715 1716 ierr = MatCreateSeqAIJ(PETSC_COMM_SELF,B->m,B->n,d_nz,d_nnz,&b->A);CHKERRQ(ierr); 1717 PetscLogObjectParent(B,b->A); 1718 ierr = MatCreateSeqAIJ(PETSC_COMM_SELF,B->m,B->N,o_nz,o_nnz,&b->B);CHKERRQ(ierr); 1719 PetscLogObjectParent(B,b->B); 1720 1721 PetscFunctionReturn(0); 1722 } 1723 EXTERN_C_END 1724 1725 EXTERN_C_BEGIN 1726 #undef __FUNCT__ 1727 #define __FUNCT__ "MatCreate_MPIAIJ" 1728 int MatCreate_MPIAIJ(Mat B) 1729 { 1730 Mat_MPIAIJ *b; 1731 int ierr,i,size; 1732 1733 PetscFunctionBegin; 1734 ierr = MPI_Comm_size(B->comm,&size);CHKERRQ(ierr); 1735 1736 ierr = PetscNew(Mat_MPIAIJ,&b);CHKERRQ(ierr); 1737 B->data = (void*)b; 1738 ierr = PetscMemzero(b,sizeof(Mat_MPIAIJ));CHKERRQ(ierr); 1739 ierr = PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 1740 B->factor = 0; 1741 B->assembled = PETSC_FALSE; 1742 B->mapping = 0; 1743 1744 B->insertmode = NOT_SET_VALUES; 1745 b->size = size; 1746 ierr = MPI_Comm_rank(B->comm,&b->rank);CHKERRQ(ierr); 1747 1748 ierr = PetscSplitOwnership(B->comm,&B->m,&B->M);CHKERRQ(ierr); 1749 ierr = PetscSplitOwnership(B->comm,&B->n,&B->N);CHKERRQ(ierr); 1750 1751 /* the information in the maps duplicates the information computed below, eventually 1752 we should remove the duplicate information that is not contained in the maps */ 1753 ierr = PetscMapCreateMPI(B->comm,B->m,B->M,&B->rmap);CHKERRQ(ierr); 1754 ierr = PetscMapCreateMPI(B->comm,B->n,B->N,&B->cmap);CHKERRQ(ierr); 1755 1756 /* build local table of row and column ownerships */ 1757 ierr = PetscMalloc(2*(b->size+2)*sizeof(int),&b->rowners);CHKERRQ(ierr); 1758 PetscLogObjectMemory(B,2*(b->size+2)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_MPIAIJ)); 1759 b->cowners = b->rowners + b->size + 2; 1760 ierr = MPI_Allgather(&B->m,1,MPI_INT,b->rowners+1,1,MPI_INT,B->comm);CHKERRQ(ierr); 1761 b->rowners[0] = 0; 1762 for (i=2; i<=b->size; i++) { 1763 b->rowners[i] += b->rowners[i-1]; 1764 } 1765 b->rstart = b->rowners[b->rank]; 1766 b->rend = b->rowners[b->rank+1]; 1767 ierr = MPI_Allgather(&B->n,1,MPI_INT,b->cowners+1,1,MPI_INT,B->comm);CHKERRQ(ierr); 1768 b->cowners[0] = 0; 1769 for (i=2; i<=b->size; i++) { 1770 b->cowners[i] += b->cowners[i-1]; 1771 } 1772 b->cstart = b->cowners[b->rank]; 1773 b->cend = b->cowners[b->rank+1]; 1774 1775 /* build cache for off array entries formed */ 1776 ierr = MatStashCreate_Private(B->comm,1,&B->stash);CHKERRQ(ierr); 1777 b->donotstash = PETSC_FALSE; 1778 b->colmap = 0; 1779 b->garray = 0; 1780 b->roworiented = PETSC_TRUE; 1781 1782 /* stuff used for matrix vector multiply */ 1783 b->lvec = PETSC_NULL; 1784 b->Mvctx = PETSC_NULL; 1785 1786 /* stuff for MatGetRow() */ 1787 b->rowindices = 0; 1788 b->rowvalues = 0; 1789 b->getrowactive = PETSC_FALSE; 1790 1791 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C", 1792 "MatStoreValues_MPIAIJ", 1793 MatStoreValues_MPIAIJ);CHKERRQ(ierr); 1794 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C", 1795 "MatRetrieveValues_MPIAIJ", 1796 MatRetrieveValues_MPIAIJ);CHKERRQ(ierr); 1797 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C", 1798 "MatGetDiagonalBlock_MPIAIJ", 1799 MatGetDiagonalBlock_MPIAIJ);CHKERRQ(ierr); 1800 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsSymmetric_C", 1801 "MatIsSymmetric_MPIAIJ", 1802 MatIsSymmetric_MPIAIJ); CHKERRQ(ierr); 1803 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIAIJSetPreallocation_C", 1804 "MatMPIAIJSetPreallocation_MPIAIJ", 1805 MatMPIAIJSetPreallocation_MPIAIJ); CHKERRQ(ierr); 1806 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatDiagonalScaleLocal_C", 1807 "MatDiagonalScaleLocal_MPIAIJ", 1808 MatDiagonalScaleLocal_MPIAIJ); CHKERRQ(ierr); 1809 PetscFunctionReturn(0); 1810 } 1811 EXTERN_C_END 1812 1813 #undef __FUNCT__ 1814 #define __FUNCT__ "MatDuplicate_MPIAIJ" 1815 int MatDuplicate_MPIAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat) 1816 { 1817 Mat mat; 1818 Mat_MPIAIJ *a,*oldmat = (Mat_MPIAIJ*)matin->data; 1819 int ierr; 1820 1821 PetscFunctionBegin; 1822 *newmat = 0; 1823 ierr = MatCreate(matin->comm,matin->m,matin->n,matin->M,matin->N,&mat);CHKERRQ(ierr); 1824 ierr = MatSetType(mat,MATMPIAIJ);CHKERRQ(ierr); 1825 a = (Mat_MPIAIJ*)mat->data; 1826 ierr = PetscMemcpy(mat->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 1827 mat->factor = matin->factor; 1828 mat->assembled = PETSC_TRUE; 1829 mat->insertmode = NOT_SET_VALUES; 1830 mat->preallocated = PETSC_TRUE; 1831 1832 a->rstart = oldmat->rstart; 1833 a->rend = oldmat->rend; 1834 a->cstart = oldmat->cstart; 1835 a->cend = oldmat->cend; 1836 a->size = oldmat->size; 1837 a->rank = oldmat->rank; 1838 a->donotstash = oldmat->donotstash; 1839 a->roworiented = oldmat->roworiented; 1840 a->rowindices = 0; 1841 a->rowvalues = 0; 1842 a->getrowactive = PETSC_FALSE; 1843 1844 ierr = PetscMemcpy(a->rowners,oldmat->rowners,2*(a->size+2)*sizeof(int));CHKERRQ(ierr); 1845 ierr = MatStashCreate_Private(matin->comm,1,&mat->stash);CHKERRQ(ierr); 1846 if (oldmat->colmap) { 1847 #if defined (PETSC_USE_CTABLE) 1848 ierr = PetscTableCreateCopy(oldmat->colmap,&a->colmap);CHKERRQ(ierr); 1849 #else 1850 ierr = PetscMalloc((mat->N)*sizeof(int),&a->colmap);CHKERRQ(ierr); 1851 PetscLogObjectMemory(mat,(mat->N)*sizeof(int)); 1852 ierr = PetscMemcpy(a->colmap,oldmat->colmap,(mat->N)*sizeof(int));CHKERRQ(ierr); 1853 #endif 1854 } else a->colmap = 0; 1855 if (oldmat->garray) { 1856 int len; 1857 len = oldmat->B->n; 1858 ierr = PetscMalloc((len+1)*sizeof(int),&a->garray);CHKERRQ(ierr); 1859 PetscLogObjectMemory(mat,len*sizeof(int)); 1860 if (len) { ierr = PetscMemcpy(a->garray,oldmat->garray,len*sizeof(int));CHKERRQ(ierr); } 1861 } else a->garray = 0; 1862 1863 ierr = VecDuplicate(oldmat->lvec,&a->lvec);CHKERRQ(ierr); 1864 PetscLogObjectParent(mat,a->lvec); 1865 ierr = VecScatterCopy(oldmat->Mvctx,&a->Mvctx);CHKERRQ(ierr); 1866 PetscLogObjectParent(mat,a->Mvctx); 1867 ierr = MatDuplicate(oldmat->A,cpvalues,&a->A);CHKERRQ(ierr); 1868 PetscLogObjectParent(mat,a->A); 1869 ierr = MatDuplicate(oldmat->B,cpvalues,&a->B);CHKERRQ(ierr); 1870 PetscLogObjectParent(mat,a->B); 1871 ierr = PetscFListDuplicate(matin->qlist,&mat->qlist);CHKERRQ(ierr); 1872 *newmat = mat; 1873 PetscFunctionReturn(0); 1874 } 1875 1876 #include "petscsys.h" 1877 1878 EXTERN_C_BEGIN 1879 #undef __FUNCT__ 1880 #define __FUNCT__ "MatLoad_MPIAIJ" 1881 int MatLoad_MPIAIJ(PetscViewer viewer,MatType type,Mat *newmat) 1882 { 1883 Mat A; 1884 PetscScalar *vals,*svals; 1885 MPI_Comm comm = ((PetscObject)viewer)->comm; 1886 MPI_Status status; 1887 int i,nz,ierr,j,rstart,rend,fd; 1888 int header[4],rank,size,*rowlengths = 0,M,N,m,*rowners,maxnz,*cols; 1889 int *ourlens,*sndcounts = 0,*procsnz = 0,*offlens,jj,*mycols,*smycols; 1890 int tag = ((PetscObject)viewer)->tag,cend,cstart,n; 1891 1892 PetscFunctionBegin; 1893 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 1894 ierr = MPI_Comm_rank(comm,&rank);CHKERRQ(ierr); 1895 if (!rank) { 1896 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 1897 ierr = PetscBinaryRead(fd,(char *)header,4,PETSC_INT);CHKERRQ(ierr); 1898 if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object"); 1899 if (header[3] < 0) { 1900 SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix in special format on disk, cannot load as MPIAIJ"); 1901 } 1902 } 1903 1904 ierr = MPI_Bcast(header+1,3,MPI_INT,0,comm);CHKERRQ(ierr); 1905 M = header[1]; N = header[2]; 1906 /* determine ownership of all rows */ 1907 m = M/size + ((M % size) > rank); 1908 ierr = PetscMalloc((size+2)*sizeof(int),&rowners);CHKERRQ(ierr); 1909 ierr = MPI_Allgather(&m,1,MPI_INT,rowners+1,1,MPI_INT,comm);CHKERRQ(ierr); 1910 rowners[0] = 0; 1911 for (i=2; i<=size; i++) { 1912 rowners[i] += rowners[i-1]; 1913 } 1914 rstart = rowners[rank]; 1915 rend = rowners[rank+1]; 1916 1917 /* distribute row lengths to all processors */ 1918 ierr = PetscMalloc(2*(rend-rstart+1)*sizeof(int),&ourlens);CHKERRQ(ierr); 1919 offlens = ourlens + (rend-rstart); 1920 if (!rank) { 1921 ierr = PetscMalloc(M*sizeof(int),&rowlengths);CHKERRQ(ierr); 1922 ierr = PetscBinaryRead(fd,rowlengths,M,PETSC_INT);CHKERRQ(ierr); 1923 ierr = PetscMalloc(size*sizeof(int),&sndcounts);CHKERRQ(ierr); 1924 for (i=0; i<size; i++) sndcounts[i] = rowners[i+1] - rowners[i]; 1925 ierr = MPI_Scatterv(rowlengths,sndcounts,rowners,MPI_INT,ourlens,rend-rstart,MPI_INT,0,comm);CHKERRQ(ierr); 1926 ierr = PetscFree(sndcounts);CHKERRQ(ierr); 1927 } else { 1928 ierr = MPI_Scatterv(0,0,0,MPI_INT,ourlens,rend-rstart,MPI_INT,0,comm);CHKERRQ(ierr); 1929 } 1930 1931 if (!rank) { 1932 /* calculate the number of nonzeros on each processor */ 1933 ierr = PetscMalloc(size*sizeof(int),&procsnz);CHKERRQ(ierr); 1934 ierr = PetscMemzero(procsnz,size*sizeof(int));CHKERRQ(ierr); 1935 for (i=0; i<size; i++) { 1936 for (j=rowners[i]; j< rowners[i+1]; j++) { 1937 procsnz[i] += rowlengths[j]; 1938 } 1939 } 1940 ierr = PetscFree(rowlengths);CHKERRQ(ierr); 1941 1942 /* determine max buffer needed and allocate it */ 1943 maxnz = 0; 1944 for (i=0; i<size; i++) { 1945 maxnz = PetscMax(maxnz,procsnz[i]); 1946 } 1947 ierr = PetscMalloc(maxnz*sizeof(int),&cols);CHKERRQ(ierr); 1948 1949 /* read in my part of the matrix column indices */ 1950 nz = procsnz[0]; 1951 ierr = PetscMalloc(nz*sizeof(int),&mycols);CHKERRQ(ierr); 1952 ierr = PetscBinaryRead(fd,mycols,nz,PETSC_INT);CHKERRQ(ierr); 1953 1954 /* read in every one elses and ship off */ 1955 for (i=1; i<size; i++) { 1956 nz = procsnz[i]; 1957 ierr = PetscBinaryRead(fd,cols,nz,PETSC_INT);CHKERRQ(ierr); 1958 ierr = MPI_Send(cols,nz,MPI_INT,i,tag,comm);CHKERRQ(ierr); 1959 } 1960 ierr = PetscFree(cols);CHKERRQ(ierr); 1961 } else { 1962 /* determine buffer space needed for message */ 1963 nz = 0; 1964 for (i=0; i<m; i++) { 1965 nz += ourlens[i]; 1966 } 1967 ierr = PetscMalloc((nz+1)*sizeof(int),&mycols);CHKERRQ(ierr); 1968 1969 /* receive message of column indices*/ 1970 ierr = MPI_Recv(mycols,nz,MPI_INT,0,tag,comm,&status);CHKERRQ(ierr); 1971 ierr = MPI_Get_count(&status,MPI_INT,&maxnz);CHKERRQ(ierr); 1972 if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file"); 1973 } 1974 1975 /* determine column ownership if matrix is not square */ 1976 if (N != M) { 1977 n = N/size + ((N % size) > rank); 1978 ierr = MPI_Scan(&n,&cend,1,MPI_INT,MPI_SUM,comm);CHKERRQ(ierr); 1979 cstart = cend - n; 1980 } else { 1981 cstart = rstart; 1982 cend = rend; 1983 n = cend - cstart; 1984 } 1985 1986 /* loop over local rows, determining number of off diagonal entries */ 1987 ierr = PetscMemzero(offlens,m*sizeof(int));CHKERRQ(ierr); 1988 jj = 0; 1989 for (i=0; i<m; i++) { 1990 for (j=0; j<ourlens[i]; j++) { 1991 if (mycols[jj] < cstart || mycols[jj] >= cend) offlens[i]++; 1992 jj++; 1993 } 1994 } 1995 1996 /* create our matrix */ 1997 for (i=0; i<m; i++) { 1998 ourlens[i] -= offlens[i]; 1999 } 2000 ierr = MatCreate(comm,m,n,M,N,&A);CHKERRQ(ierr); 2001 ierr = MatSetType(A,type);CHKERRQ(ierr); 2002 ierr = MatMPIAIJSetPreallocation(A,0,ourlens,0,offlens);CHKERRQ(ierr); 2003 2004 ierr = MatSetOption(A,MAT_COLUMNS_SORTED);CHKERRQ(ierr); 2005 for (i=0; i<m; i++) { 2006 ourlens[i] += offlens[i]; 2007 } 2008 2009 if (!rank) { 2010 ierr = PetscMalloc(maxnz*sizeof(PetscScalar),&vals);CHKERRQ(ierr); 2011 2012 /* read in my part of the matrix numerical values */ 2013 nz = procsnz[0]; 2014 ierr = PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);CHKERRQ(ierr); 2015 2016 /* insert into matrix */ 2017 jj = rstart; 2018 smycols = mycols; 2019 svals = vals; 2020 for (i=0; i<m; i++) { 2021 ierr = MatSetValues(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);CHKERRQ(ierr); 2022 smycols += ourlens[i]; 2023 svals += ourlens[i]; 2024 jj++; 2025 } 2026 2027 /* read in other processors and ship out */ 2028 for (i=1; i<size; i++) { 2029 nz = procsnz[i]; 2030 ierr = PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);CHKERRQ(ierr); 2031 ierr = MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);CHKERRQ(ierr); 2032 } 2033 ierr = PetscFree(procsnz);CHKERRQ(ierr); 2034 } else { 2035 /* receive numeric values */ 2036 ierr = PetscMalloc((nz+1)*sizeof(PetscScalar),&vals);CHKERRQ(ierr); 2037 2038 /* receive message of values*/ 2039 ierr = MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);CHKERRQ(ierr); 2040 ierr = MPI_Get_count(&status,MPIU_SCALAR,&maxnz);CHKERRQ(ierr); 2041 if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file"); 2042 2043 /* insert into matrix */ 2044 jj = rstart; 2045 smycols = mycols; 2046 svals = vals; 2047 for (i=0; i<m; i++) { 2048 ierr = MatSetValues(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);CHKERRQ(ierr); 2049 smycols += ourlens[i]; 2050 svals += ourlens[i]; 2051 jj++; 2052 } 2053 } 2054 ierr = PetscFree(ourlens);CHKERRQ(ierr); 2055 ierr = PetscFree(vals);CHKERRQ(ierr); 2056 ierr = PetscFree(mycols);CHKERRQ(ierr); 2057 ierr = PetscFree(rowners);CHKERRQ(ierr); 2058 2059 ierr = MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2060 ierr = MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2061 *newmat = A; 2062 PetscFunctionReturn(0); 2063 } 2064 EXTERN_C_END 2065 2066 #undef __FUNCT__ 2067 #define __FUNCT__ "MatGetSubMatrix_MPIAIJ" 2068 /* 2069 Not great since it makes two copies of the submatrix, first an SeqAIJ 2070 in local and then by concatenating the local matrices the end result. 2071 Writing it directly would be much like MatGetSubMatrices_MPIAIJ() 2072 */ 2073 int MatGetSubMatrix_MPIAIJ(Mat mat,IS isrow,IS iscol,int csize,MatReuse call,Mat *newmat) 2074 { 2075 int ierr,i,m,n,rstart,row,rend,nz,*cwork,size,rank,j; 2076 int *ii,*jj,nlocal,*dlens,*olens,dlen,olen,jend,mglobal; 2077 Mat *local,M,Mreuse; 2078 PetscScalar *vwork,*aa; 2079 MPI_Comm comm = mat->comm; 2080 Mat_SeqAIJ *aij; 2081 2082 2083 PetscFunctionBegin; 2084 ierr = MPI_Comm_rank(comm,&rank);CHKERRQ(ierr); 2085 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 2086 2087 if (call == MAT_REUSE_MATRIX) { 2088 ierr = PetscObjectQuery((PetscObject)*newmat,"SubMatrix",(PetscObject *)&Mreuse);CHKERRQ(ierr); 2089 if (!Mreuse) SETERRQ(1,"Submatrix passed in was not used before, cannot reuse"); 2090 local = &Mreuse; 2091 ierr = MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_REUSE_MATRIX,&local);CHKERRQ(ierr); 2092 } else { 2093 ierr = MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&local);CHKERRQ(ierr); 2094 Mreuse = *local; 2095 ierr = PetscFree(local);CHKERRQ(ierr); 2096 } 2097 2098 /* 2099 m - number of local rows 2100 n - number of columns (same on all processors) 2101 rstart - first row in new global matrix generated 2102 */ 2103 ierr = MatGetSize(Mreuse,&m,&n);CHKERRQ(ierr); 2104 if (call == MAT_INITIAL_MATRIX) { 2105 aij = (Mat_SeqAIJ*)(Mreuse)->data; 2106 ii = aij->i; 2107 jj = aij->j; 2108 2109 /* 2110 Determine the number of non-zeros in the diagonal and off-diagonal 2111 portions of the matrix in order to do correct preallocation 2112 */ 2113 2114 /* first get start and end of "diagonal" columns */ 2115 if (csize == PETSC_DECIDE) { 2116 ierr = ISGetSize(isrow,&mglobal);CHKERRQ(ierr); 2117 if (mglobal == n) { /* square matrix */ 2118 nlocal = m; 2119 } else { 2120 nlocal = n/size + ((n % size) > rank); 2121 } 2122 } else { 2123 nlocal = csize; 2124 } 2125 ierr = MPI_Scan(&nlocal,&rend,1,MPI_INT,MPI_SUM,comm);CHKERRQ(ierr); 2126 rstart = rend - nlocal; 2127 if (rank == size - 1 && rend != n) { 2128 SETERRQ2(1,"Local column sizes %d do not add up to total number of columns %d",rend,n); 2129 } 2130 2131 /* next, compute all the lengths */ 2132 ierr = PetscMalloc((2*m+1)*sizeof(int),&dlens);CHKERRQ(ierr); 2133 olens = dlens + m; 2134 for (i=0; i<m; i++) { 2135 jend = ii[i+1] - ii[i]; 2136 olen = 0; 2137 dlen = 0; 2138 for (j=0; j<jend; j++) { 2139 if (*jj < rstart || *jj >= rend) olen++; 2140 else dlen++; 2141 jj++; 2142 } 2143 olens[i] = olen; 2144 dlens[i] = dlen; 2145 } 2146 ierr = MatCreateMPIAIJ(comm,m,nlocal,PETSC_DECIDE,n,0,dlens,0,olens,&M);CHKERRQ(ierr); 2147 ierr = PetscFree(dlens);CHKERRQ(ierr); 2148 } else { 2149 int ml,nl; 2150 2151 M = *newmat; 2152 ierr = MatGetLocalSize(M,&ml,&nl);CHKERRQ(ierr); 2153 if (ml != m) SETERRQ(PETSC_ERR_ARG_SIZ,"Previous matrix must be same size/layout as request"); 2154 ierr = MatZeroEntries(M);CHKERRQ(ierr); 2155 /* 2156 The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly, 2157 rather than the slower MatSetValues(). 2158 */ 2159 M->was_assembled = PETSC_TRUE; 2160 M->assembled = PETSC_FALSE; 2161 } 2162 ierr = MatGetOwnershipRange(M,&rstart,&rend);CHKERRQ(ierr); 2163 aij = (Mat_SeqAIJ*)(Mreuse)->data; 2164 ii = aij->i; 2165 jj = aij->j; 2166 aa = aij->a; 2167 for (i=0; i<m; i++) { 2168 row = rstart + i; 2169 nz = ii[i+1] - ii[i]; 2170 cwork = jj; jj += nz; 2171 vwork = aa; aa += nz; 2172 ierr = MatSetValues_MPIAIJ(M,1,&row,nz,cwork,vwork,INSERT_VALUES);CHKERRQ(ierr); 2173 } 2174 2175 ierr = MatAssemblyBegin(M,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2176 ierr = MatAssemblyEnd(M,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2177 *newmat = M; 2178 2179 /* save submatrix used in processor for next request */ 2180 if (call == MAT_INITIAL_MATRIX) { 2181 ierr = PetscObjectCompose((PetscObject)M,"SubMatrix",(PetscObject)Mreuse);CHKERRQ(ierr); 2182 ierr = PetscObjectDereference((PetscObject)Mreuse);CHKERRQ(ierr); 2183 } 2184 2185 PetscFunctionReturn(0); 2186 } 2187 2188 #undef __FUNCT__ 2189 #define __FUNCT__ "MatMPIAIJSetPreallocation" 2190 /*@C 2191 MatMPIAIJSetPreallocation - Creates a sparse parallel matrix in AIJ format 2192 (the default parallel PETSc format). For good matrix assembly performance 2193 the user should preallocate the matrix storage by setting the parameters 2194 d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately, 2195 performance can be increased by more than a factor of 50. 2196 2197 Collective on MPI_Comm 2198 2199 Input Parameters: 2200 + A - the matrix 2201 . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix 2202 (same value is used for all local rows) 2203 . d_nnz - array containing the number of nonzeros in the various rows of the 2204 DIAGONAL portion of the local submatrix (possibly different for each row) 2205 or PETSC_NULL, if d_nz is used to specify the nonzero structure. 2206 The size of this array is equal to the number of local rows, i.e 'm'. 2207 You must leave room for the diagonal entry even if it is zero. 2208 . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local 2209 submatrix (same value is used for all local rows). 2210 - o_nnz - array containing the number of nonzeros in the various rows of the 2211 OFF-DIAGONAL portion of the local submatrix (possibly different for 2212 each row) or PETSC_NULL, if o_nz is used to specify the nonzero 2213 structure. The size of this array is equal to the number 2214 of local rows, i.e 'm'. 2215 2216 The AIJ format (also called the Yale sparse matrix format or 2217 compressed row storage), is fully compatible with standard Fortran 77 2218 storage. That is, the stored row and column indices can begin at 2219 either one (as in Fortran) or zero. See the users manual for details. 2220 2221 The user MUST specify either the local or global matrix dimensions 2222 (possibly both). 2223 2224 The parallel matrix is partitioned such that the first m0 rows belong to 2225 process 0, the next m1 rows belong to process 1, the next m2 rows belong 2226 to process 2 etc.. where m0,m1,m2... are the input parameter 'm'. 2227 2228 The DIAGONAL portion of the local submatrix of a processor can be defined 2229 as the submatrix which is obtained by extraction the part corresponding 2230 to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the 2231 first row that belongs to the processor, and r2 is the last row belonging 2232 to the this processor. This is a square mxm matrix. The remaining portion 2233 of the local submatrix (mxN) constitute the OFF-DIAGONAL portion. 2234 2235 If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored. 2236 2237 By default, this format uses inodes (identical nodes) when possible. 2238 We search for consecutive rows with the same nonzero structure, thereby 2239 reusing matrix information to achieve increased efficiency. 2240 2241 Options Database Keys: 2242 + -mat_aij_no_inode - Do not use inodes 2243 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2244 - -mat_aij_oneindex - Internally use indexing starting at 1 2245 rather than 0. Note that when calling MatSetValues(), 2246 the user still MUST index entries starting at 0! 2247 2248 Example usage: 2249 2250 Consider the following 8x8 matrix with 34 non-zero values, that is 2251 assembled across 3 processors. Lets assume that proc0 owns 3 rows, 2252 proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown 2253 as follows: 2254 2255 .vb 2256 1 2 0 | 0 3 0 | 0 4 2257 Proc0 0 5 6 | 7 0 0 | 8 0 2258 9 0 10 | 11 0 0 | 12 0 2259 ------------------------------------- 2260 13 0 14 | 15 16 17 | 0 0 2261 Proc1 0 18 0 | 19 20 21 | 0 0 2262 0 0 0 | 22 23 0 | 24 0 2263 ------------------------------------- 2264 Proc2 25 26 27 | 0 0 28 | 29 0 2265 30 0 0 | 31 32 33 | 0 34 2266 .ve 2267 2268 This can be represented as a collection of submatrices as: 2269 2270 .vb 2271 A B C 2272 D E F 2273 G H I 2274 .ve 2275 2276 Where the submatrices A,B,C are owned by proc0, D,E,F are 2277 owned by proc1, G,H,I are owned by proc2. 2278 2279 The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 2280 The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 2281 The 'M','N' parameters are 8,8, and have the same values on all procs. 2282 2283 The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are 2284 submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices 2285 corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively. 2286 Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL 2287 part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ 2288 matrix, ans [DF] as another SeqAIJ matrix. 2289 2290 When d_nz, o_nz parameters are specified, d_nz storage elements are 2291 allocated for every row of the local diagonal submatrix, and o_nz 2292 storage locations are allocated for every row of the OFF-DIAGONAL submat. 2293 One way to choose d_nz and o_nz is to use the max nonzerors per local 2294 rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices. 2295 In this case, the values of d_nz,o_nz are: 2296 .vb 2297 proc0 : dnz = 2, o_nz = 2 2298 proc1 : dnz = 3, o_nz = 2 2299 proc2 : dnz = 1, o_nz = 4 2300 .ve 2301 We are allocating m*(d_nz+o_nz) storage locations for every proc. This 2302 translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10 2303 for proc3. i.e we are using 12+15+10=37 storage locations to store 2304 34 values. 2305 2306 When d_nnz, o_nnz parameters are specified, the storage is specified 2307 for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices. 2308 In the above case the values for d_nnz,o_nnz are: 2309 .vb 2310 proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2] 2311 proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1] 2312 proc2: d_nnz = [1,1] and o_nnz = [4,4] 2313 .ve 2314 Here the space allocated is sum of all the above values i.e 34, and 2315 hence pre-allocation is perfect. 2316 2317 Level: intermediate 2318 2319 .keywords: matrix, aij, compressed row, sparse, parallel 2320 2321 .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues() 2322 @*/ 2323 int MatMPIAIJSetPreallocation(Mat B,int d_nz,const int d_nnz[],int o_nz,const int o_nnz[]) 2324 { 2325 int ierr,(*f)(Mat,int,const int[],int,const int[]); 2326 2327 PetscFunctionBegin; 2328 ierr = PetscObjectQueryFunction((PetscObject)B,"MatMPIAIJSetPreallocation_C",(void (**)(void))&f);CHKERRQ(ierr); 2329 if (f) { 2330 ierr = (*f)(B,d_nz,d_nnz,o_nz,o_nnz);CHKERRQ(ierr); 2331 } 2332 PetscFunctionReturn(0); 2333 } 2334 2335 #undef __FUNCT__ 2336 #define __FUNCT__ "MatCreateMPIAIJ" 2337 /*@C 2338 MatCreateMPIAIJ - Creates a sparse parallel matrix in AIJ format 2339 (the default parallel PETSc format). For good matrix assembly performance 2340 the user should preallocate the matrix storage by setting the parameters 2341 d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately, 2342 performance can be increased by more than a factor of 50. 2343 2344 Collective on MPI_Comm 2345 2346 Input Parameters: 2347 + comm - MPI communicator 2348 . m - number of local rows (or PETSC_DECIDE to have calculated if M is given) 2349 This value should be the same as the local size used in creating the 2350 y vector for the matrix-vector product y = Ax. 2351 . n - This value should be the same as the local size used in creating the 2352 x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have 2353 calculated if N is given) For square matrices n is almost always m. 2354 . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given) 2355 . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given) 2356 . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix 2357 (same value is used for all local rows) 2358 . d_nnz - array containing the number of nonzeros in the various rows of the 2359 DIAGONAL portion of the local submatrix (possibly different for each row) 2360 or PETSC_NULL, if d_nz is used to specify the nonzero structure. 2361 The size of this array is equal to the number of local rows, i.e 'm'. 2362 You must leave room for the diagonal entry even if it is zero. 2363 . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local 2364 submatrix (same value is used for all local rows). 2365 - o_nnz - array containing the number of nonzeros in the various rows of the 2366 OFF-DIAGONAL portion of the local submatrix (possibly different for 2367 each row) or PETSC_NULL, if o_nz is used to specify the nonzero 2368 structure. The size of this array is equal to the number 2369 of local rows, i.e 'm'. 2370 2371 Output Parameter: 2372 . A - the matrix 2373 2374 Notes: 2375 m,n,M,N parameters specify the size of the matrix, and its partitioning across 2376 processors, while d_nz,d_nnz,o_nz,o_nnz parameters specify the approximate 2377 storage requirements for this matrix. 2378 2379 If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one 2380 processor than it must be used on all processors that share the object for 2381 that argument. 2382 2383 The AIJ format (also called the Yale sparse matrix format or 2384 compressed row storage), is fully compatible with standard Fortran 77 2385 storage. That is, the stored row and column indices can begin at 2386 either one (as in Fortran) or zero. See the users manual for details. 2387 2388 The user MUST specify either the local or global matrix dimensions 2389 (possibly both). 2390 2391 The parallel matrix is partitioned such that the first m0 rows belong to 2392 process 0, the next m1 rows belong to process 1, the next m2 rows belong 2393 to process 2 etc.. where m0,m1,m2... are the input parameter 'm'. 2394 2395 The DIAGONAL portion of the local submatrix of a processor can be defined 2396 as the submatrix which is obtained by extraction the part corresponding 2397 to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the 2398 first row that belongs to the processor, and r2 is the last row belonging 2399 to the this processor. This is a square mxm matrix. The remaining portion 2400 of the local submatrix (mxN) constitute the OFF-DIAGONAL portion. 2401 2402 If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored. 2403 2404 When calling this routine with a single process communicator, a matrix of 2405 type SEQAIJ is returned. If a matrix of type MPIAIJ is desired for this 2406 type of communicator, use the construction mechanism: 2407 MatCreate(...,&A); MatSetType(A,MPIAIJ); MatMPIAIJSetPreallocation(A,...); 2408 2409 By default, this format uses inodes (identical nodes) when possible. 2410 We search for consecutive rows with the same nonzero structure, thereby 2411 reusing matrix information to achieve increased efficiency. 2412 2413 Options Database Keys: 2414 + -mat_aij_no_inode - Do not use inodes 2415 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2416 - -mat_aij_oneindex - Internally use indexing starting at 1 2417 rather than 0. Note that when calling MatSetValues(), 2418 the user still MUST index entries starting at 0! 2419 2420 2421 Example usage: 2422 2423 Consider the following 8x8 matrix with 34 non-zero values, that is 2424 assembled across 3 processors. Lets assume that proc0 owns 3 rows, 2425 proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown 2426 as follows: 2427 2428 .vb 2429 1 2 0 | 0 3 0 | 0 4 2430 Proc0 0 5 6 | 7 0 0 | 8 0 2431 9 0 10 | 11 0 0 | 12 0 2432 ------------------------------------- 2433 13 0 14 | 15 16 17 | 0 0 2434 Proc1 0 18 0 | 19 20 21 | 0 0 2435 0 0 0 | 22 23 0 | 24 0 2436 ------------------------------------- 2437 Proc2 25 26 27 | 0 0 28 | 29 0 2438 30 0 0 | 31 32 33 | 0 34 2439 .ve 2440 2441 This can be represented as a collection of submatrices as: 2442 2443 .vb 2444 A B C 2445 D E F 2446 G H I 2447 .ve 2448 2449 Where the submatrices A,B,C are owned by proc0, D,E,F are 2450 owned by proc1, G,H,I are owned by proc2. 2451 2452 The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 2453 The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 2454 The 'M','N' parameters are 8,8, and have the same values on all procs. 2455 2456 The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are 2457 submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices 2458 corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively. 2459 Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL 2460 part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ 2461 matrix, ans [DF] as another SeqAIJ matrix. 2462 2463 When d_nz, o_nz parameters are specified, d_nz storage elements are 2464 allocated for every row of the local diagonal submatrix, and o_nz 2465 storage locations are allocated for every row of the OFF-DIAGONAL submat. 2466 One way to choose d_nz and o_nz is to use the max nonzerors per local 2467 rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices. 2468 In this case, the values of d_nz,o_nz are: 2469 .vb 2470 proc0 : dnz = 2, o_nz = 2 2471 proc1 : dnz = 3, o_nz = 2 2472 proc2 : dnz = 1, o_nz = 4 2473 .ve 2474 We are allocating m*(d_nz+o_nz) storage locations for every proc. This 2475 translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10 2476 for proc3. i.e we are using 12+15+10=37 storage locations to store 2477 34 values. 2478 2479 When d_nnz, o_nnz parameters are specified, the storage is specified 2480 for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices. 2481 In the above case the values for d_nnz,o_nnz are: 2482 .vb 2483 proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2] 2484 proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1] 2485 proc2: d_nnz = [1,1] and o_nnz = [4,4] 2486 .ve 2487 Here the space allocated is sum of all the above values i.e 34, and 2488 hence pre-allocation is perfect. 2489 2490 Level: intermediate 2491 2492 .keywords: matrix, aij, compressed row, sparse, parallel 2493 2494 .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues() 2495 @*/ 2496 int MatCreateMPIAIJ(MPI_Comm comm,int m,int n,int M,int N,int d_nz,const int d_nnz[],int o_nz,const int o_nnz[],Mat *A) 2497 { 2498 int ierr,size; 2499 2500 PetscFunctionBegin; 2501 ierr = MatCreate(comm,m,n,M,N,A);CHKERRQ(ierr); 2502 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 2503 if (size > 1) { 2504 ierr = MatSetType(*A,MATMPIAIJ);CHKERRQ(ierr); 2505 ierr = MatMPIAIJSetPreallocation(*A,d_nz,d_nnz,o_nz,o_nnz);CHKERRQ(ierr); 2506 } else { 2507 ierr = MatSetType(*A,MATSEQAIJ);CHKERRQ(ierr); 2508 ierr = MatSeqAIJSetPreallocation(*A,d_nz,d_nnz);CHKERRQ(ierr); 2509 } 2510 PetscFunctionReturn(0); 2511 } 2512 2513 #undef __FUNCT__ 2514 #define __FUNCT__ "MatMPIAIJGetSeqAIJ" 2515 int MatMPIAIJGetSeqAIJ(Mat A,Mat *Ad,Mat *Ao,int *colmap[]) 2516 { 2517 Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data; 2518 PetscFunctionBegin; 2519 *Ad = a->A; 2520 *Ao = a->B; 2521 *colmap = a->garray; 2522 PetscFunctionReturn(0); 2523 } 2524 2525 #undef __FUNCT__ 2526 #define __FUNCT__ "MatSetColoring_MPIAIJ" 2527 int MatSetColoring_MPIAIJ(Mat A,ISColoring coloring) 2528 { 2529 int ierr,i; 2530 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 2531 2532 PetscFunctionBegin; 2533 if (coloring->ctype == IS_COLORING_LOCAL) { 2534 ISColoringValue *allcolors,*colors; 2535 ISColoring ocoloring; 2536 2537 /* set coloring for diagonal portion */ 2538 ierr = MatSetColoring_SeqAIJ(a->A,coloring);CHKERRQ(ierr); 2539 2540 /* set coloring for off-diagonal portion */ 2541 ierr = ISAllGatherColors(A->comm,coloring->n,coloring->colors,PETSC_NULL,&allcolors);CHKERRQ(ierr); 2542 ierr = PetscMalloc((a->B->n+1)*sizeof(ISColoringValue),&colors);CHKERRQ(ierr); 2543 for (i=0; i<a->B->n; i++) { 2544 colors[i] = allcolors[a->garray[i]]; 2545 } 2546 ierr = PetscFree(allcolors);CHKERRQ(ierr); 2547 ierr = ISColoringCreate(MPI_COMM_SELF,a->B->n,colors,&ocoloring);CHKERRQ(ierr); 2548 ierr = MatSetColoring_SeqAIJ(a->B,ocoloring);CHKERRQ(ierr); 2549 ierr = ISColoringDestroy(ocoloring);CHKERRQ(ierr); 2550 } else if (coloring->ctype == IS_COLORING_GHOSTED) { 2551 ISColoringValue *colors; 2552 int *larray; 2553 ISColoring ocoloring; 2554 2555 /* set coloring for diagonal portion */ 2556 ierr = PetscMalloc((a->A->n+1)*sizeof(int),&larray);CHKERRQ(ierr); 2557 for (i=0; i<a->A->n; i++) { 2558 larray[i] = i + a->cstart; 2559 } 2560 ierr = ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,a->A->n,larray,PETSC_NULL,larray);CHKERRQ(ierr); 2561 ierr = PetscMalloc((a->A->n+1)*sizeof(ISColoringValue),&colors);CHKERRQ(ierr); 2562 for (i=0; i<a->A->n; i++) { 2563 colors[i] = coloring->colors[larray[i]]; 2564 } 2565 ierr = PetscFree(larray);CHKERRQ(ierr); 2566 ierr = ISColoringCreate(PETSC_COMM_SELF,a->A->n,colors,&ocoloring);CHKERRQ(ierr); 2567 ierr = MatSetColoring_SeqAIJ(a->A,ocoloring);CHKERRQ(ierr); 2568 ierr = ISColoringDestroy(ocoloring);CHKERRQ(ierr); 2569 2570 /* set coloring for off-diagonal portion */ 2571 ierr = PetscMalloc((a->B->n+1)*sizeof(int),&larray);CHKERRQ(ierr); 2572 ierr = ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,a->B->n,a->garray,PETSC_NULL,larray);CHKERRQ(ierr); 2573 ierr = PetscMalloc((a->B->n+1)*sizeof(ISColoringValue),&colors);CHKERRQ(ierr); 2574 for (i=0; i<a->B->n; i++) { 2575 colors[i] = coloring->colors[larray[i]]; 2576 } 2577 ierr = PetscFree(larray);CHKERRQ(ierr); 2578 ierr = ISColoringCreate(MPI_COMM_SELF,a->B->n,colors,&ocoloring);CHKERRQ(ierr); 2579 ierr = MatSetColoring_SeqAIJ(a->B,ocoloring);CHKERRQ(ierr); 2580 ierr = ISColoringDestroy(ocoloring);CHKERRQ(ierr); 2581 } else { 2582 SETERRQ1(1,"No support ISColoringType %d",coloring->ctype); 2583 } 2584 2585 PetscFunctionReturn(0); 2586 } 2587 2588 #undef __FUNCT__ 2589 #define __FUNCT__ "MatSetValuesAdic_MPIAIJ" 2590 int MatSetValuesAdic_MPIAIJ(Mat A,void *advalues) 2591 { 2592 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 2593 int ierr; 2594 2595 PetscFunctionBegin; 2596 ierr = MatSetValuesAdic_SeqAIJ(a->A,advalues);CHKERRQ(ierr); 2597 ierr = MatSetValuesAdic_SeqAIJ(a->B,advalues);CHKERRQ(ierr); 2598 PetscFunctionReturn(0); 2599 } 2600 2601 #undef __FUNCT__ 2602 #define __FUNCT__ "MatSetValuesAdifor_MPIAIJ" 2603 int MatSetValuesAdifor_MPIAIJ(Mat A,int nl,void *advalues) 2604 { 2605 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 2606 int ierr; 2607 2608 PetscFunctionBegin; 2609 ierr = MatSetValuesAdifor_SeqAIJ(a->A,nl,advalues);CHKERRQ(ierr); 2610 ierr = MatSetValuesAdifor_SeqAIJ(a->B,nl,advalues);CHKERRQ(ierr); 2611 PetscFunctionReturn(0); 2612 } 2613 2614 #undef __FUNCT__ 2615 #define __FUNCT__ "MatMerge" 2616 /*@C 2617 MatMerge - Creates a single large PETSc matrix by concatinating sequential 2618 matrices from each processor 2619 2620 Collective on MPI_Comm 2621 2622 Input Parameters: 2623 + comm - the communicators the parallel matrix will live on 2624 - inmat - the input sequential matrices 2625 2626 Output Parameter: 2627 . outmat - the parallel matrix generated 2628 2629 Level: advanced 2630 2631 Notes: The number of columns of the matrix in EACH of the seperate files 2632 MUST be the same. 2633 2634 @*/ 2635 int MatMerge(MPI_Comm comm,Mat inmat, Mat *outmat) 2636 { 2637 int ierr,m,n,i,rstart,*indx,nnz,I,*dnz,*onz; 2638 PetscScalar *values; 2639 PetscMap columnmap,rowmap; 2640 2641 PetscFunctionBegin; 2642 2643 ierr = MatGetSize(inmat,&m,&n);CHKERRQ(ierr); 2644 2645 /* count nonzeros in each row, for diagonal and off diagonal portion of matrix */ 2646 ierr = PetscMapCreate(comm,&columnmap);CHKERRQ(ierr); 2647 ierr = PetscMapSetSize(columnmap,n);CHKERRQ(ierr); 2648 ierr = PetscMapSetType(columnmap,MAP_MPI);CHKERRQ(ierr); 2649 ierr = PetscMapGetLocalSize(columnmap,&n);CHKERRQ(ierr); 2650 ierr = PetscMapDestroy(columnmap);CHKERRQ(ierr); 2651 2652 ierr = PetscMapCreate(comm,&rowmap);CHKERRQ(ierr); 2653 ierr = PetscMapSetLocalSize(rowmap,m);CHKERRQ(ierr); 2654 ierr = PetscMapSetType(rowmap,MAP_MPI);CHKERRQ(ierr); 2655 ierr = PetscMapGetLocalRange(rowmap,&rstart,0);CHKERRQ(ierr); 2656 ierr = PetscMapDestroy(rowmap);CHKERRQ(ierr); 2657 2658 ierr = MatPreallocateInitialize(comm,m,n,dnz,onz);CHKERRQ(ierr); 2659 for (i=0;i<m;i++) { 2660 ierr = MatGetRow(inmat,i,&nnz,&indx,&values);CHKERRQ(ierr); 2661 ierr = MatPreallocateSet(i+rstart,nnz,indx,dnz,onz);CHKERRQ(ierr); 2662 ierr = MatRestoreRow(inmat,i,&nnz,&indx,&values);CHKERRQ(ierr); 2663 } 2664 ierr = MatCreateMPIAIJ(comm,m,n,PETSC_DETERMINE,PETSC_DETERMINE,0,dnz,0,onz,outmat);CHKERRQ(ierr); 2665 ierr = MatPreallocateFinalize(dnz,onz);CHKERRQ(ierr); 2666 2667 for (i=0;i<m;i++) { 2668 ierr = MatGetRow(inmat,i,&nnz,&indx,&values);CHKERRQ(ierr); 2669 I = i + rstart; 2670 ierr = MatSetValues(*outmat,1,&I,nnz,indx,values,INSERT_VALUES);CHKERRQ(ierr); 2671 ierr = MatRestoreRow(inmat,i,&nnz,&indx,&values);CHKERRQ(ierr); 2672 } 2673 ierr = MatDestroy(inmat);CHKERRQ(ierr); 2674 ierr = MatAssemblyBegin(*outmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2675 ierr = MatAssemblyEnd(*outmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2676 2677 PetscFunctionReturn(0); 2678 } 2679 2680 #undef __FUNCT__ 2681 #define __FUNCT__ "MatFileSplit" 2682 int MatFileSplit(Mat A,char *outfile) 2683 { 2684 int ierr,rank,len,m,N,i,rstart,*indx,nnz; 2685 PetscViewer out; 2686 char *name; 2687 Mat B; 2688 PetscScalar *values; 2689 2690 PetscFunctionBegin; 2691 2692 ierr = MatGetLocalSize(A,&m,0);CHKERRQ(ierr); 2693 ierr = MatGetSize(A,0,&N);CHKERRQ(ierr); 2694 ierr = MatCreateSeqAIJ(PETSC_COMM_SELF,m,N,0,0,&B);CHKERRQ(ierr); 2695 ierr = MatGetOwnershipRange(A,&rstart,0);CHKERRQ(ierr); 2696 for (i=0;i<m;i++) { 2697 ierr = MatGetRow(A,i+rstart,&nnz,&indx,&values);CHKERRQ(ierr); 2698 ierr = MatSetValues(B,1,&i,nnz,indx,values,INSERT_VALUES);CHKERRQ(ierr); 2699 ierr = MatRestoreRow(A,i+rstart,&nnz,&indx,&values);CHKERRQ(ierr); 2700 } 2701 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2702 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2703 2704 ierr = MPI_Comm_rank(A->comm,&rank);CHKERRQ(ierr); 2705 ierr = PetscStrlen(outfile,&len);CHKERRQ(ierr); 2706 ierr = PetscMalloc((len+5)*sizeof(char),&name);CHKERRQ(ierr); 2707 sprintf(name,"%s.%d",outfile,rank); 2708 ierr = PetscViewerBinaryOpen(PETSC_COMM_SELF,name,PETSC_BINARY_CREATE,&out);CHKERRQ(ierr); 2709 ierr = PetscFree(name); 2710 ierr = MatView(B,out);CHKERRQ(ierr); 2711 ierr = PetscViewerDestroy(out);CHKERRQ(ierr); 2712 ierr = MatDestroy(B);CHKERRQ(ierr); 2713 PetscFunctionReturn(0); 2714 } 2715