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,int *im,int n,int *in,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) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Row too large"); 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) {SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Column too large");} 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,int *idxm,int n,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) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative row"); 289 if (idxm[i] >= mat->M) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Row too large"); 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) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative column"); 294 if (idxn[j] >= mat->N) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Column too large"); 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 #if defined(PETSC_HAVE_SUPERLUDIST) || defined(PETSC_HAVE_SPOOLES) || defined(PETSC_HAVE_MUMPS) 359 PetscTruth flag; 360 #endif 361 362 PetscFunctionBegin; 363 if (!aij->donotstash) { 364 while (1) { 365 ierr = MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);CHKERRQ(ierr); 366 if (!flg) break; 367 368 for (i=0; i<n;) { 369 /* Now identify the consecutive vals belonging to the same row */ 370 for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; } 371 if (j < n) ncols = j-i; 372 else ncols = n-i; 373 /* Now assemble all these values with a single function call */ 374 ierr = MatSetValues_MPIAIJ(mat,1,row+i,ncols,col+i,val+i,addv);CHKERRQ(ierr); 375 i = j; 376 } 377 } 378 ierr = MatStashScatterEnd_Private(&mat->stash);CHKERRQ(ierr); 379 } 380 381 ierr = MatAssemblyBegin(aij->A,mode);CHKERRQ(ierr); 382 ierr = MatAssemblyEnd(aij->A,mode);CHKERRQ(ierr); 383 384 /* determine if any processor has disassembled, if so we must 385 also disassemble ourselfs, in order that we may reassemble. */ 386 /* 387 if nonzero structure of submatrix B cannot change then we know that 388 no processor disassembled thus we can skip this stuff 389 */ 390 if (!((Mat_SeqAIJ*)aij->B->data)->nonew) { 391 ierr = MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,mat->comm);CHKERRQ(ierr); 392 if (mat->was_assembled && !other_disassembled) { 393 ierr = DisAssemble_MPIAIJ(mat);CHKERRQ(ierr); 394 } 395 } 396 397 if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) { 398 ierr = MatSetUpMultiply_MPIAIJ(mat);CHKERRQ(ierr); 399 } 400 ierr = MatAssemblyBegin(aij->B,mode);CHKERRQ(ierr); 401 ierr = MatAssemblyEnd(aij->B,mode);CHKERRQ(ierr); 402 403 if (aij->rowvalues) { 404 ierr = PetscFree(aij->rowvalues);CHKERRQ(ierr); 405 aij->rowvalues = 0; 406 } 407 408 /* used by MatAXPY() */ 409 a->xtoy = 0; b->xtoy = 0; 410 a->XtoY = 0; b->XtoY = 0; 411 412 #if defined(PETSC_HAVE_SUPERLUDIST) 413 ierr = PetscOptionsHasName(mat->prefix,"-mat_aij_superlu_dist",&flag);CHKERRQ(ierr); 414 if (flag) { ierr = MatUseSuperLU_DIST_MPIAIJ(mat);CHKERRQ(ierr); } 415 #endif 416 417 #if defined(PETSC_HAVE_SPOOLES) 418 ierr = PetscOptionsHasName(mat->prefix,"-mat_aij_spooles",&flag);CHKERRQ(ierr); 419 if (flag) { ierr = MatUseSpooles_MPIAIJ(mat);CHKERRQ(ierr); } 420 #endif 421 422 #if defined(PETSC_HAVE_MUMPS) 423 ierr = PetscOptionsHasName(mat->prefix,"-mat_aij_mumps",&flag);CHKERRQ(ierr); 424 if (flag) { ierr = MatUseMUMPS_MPIAIJ(mat);CHKERRQ(ierr); } 425 #endif 426 PetscFunctionReturn(0); 427 } 428 429 #undef __FUNCT__ 430 #define __FUNCT__ "MatZeroEntries_MPIAIJ" 431 int MatZeroEntries_MPIAIJ(Mat A) 432 { 433 Mat_MPIAIJ *l = (Mat_MPIAIJ*)A->data; 434 int ierr; 435 436 PetscFunctionBegin; 437 ierr = MatZeroEntries(l->A);CHKERRQ(ierr); 438 ierr = MatZeroEntries(l->B);CHKERRQ(ierr); 439 PetscFunctionReturn(0); 440 } 441 442 #undef __FUNCT__ 443 #define __FUNCT__ "MatZeroRows_MPIAIJ" 444 int MatZeroRows_MPIAIJ(Mat A,IS is,PetscScalar *diag) 445 { 446 Mat_MPIAIJ *l = (Mat_MPIAIJ*)A->data; 447 int i,ierr,N,*rows,*owners = l->rowners,size = l->size; 448 int *nprocs,j,idx,nsends,row; 449 int nmax,*svalues,*starts,*owner,nrecvs,rank = l->rank; 450 int *rvalues,tag = A->tag,count,base,slen,n,*source; 451 int *lens,imdex,*lrows,*values,rstart=l->rstart; 452 MPI_Comm comm = A->comm; 453 MPI_Request *send_waits,*recv_waits; 454 MPI_Status recv_status,*send_status; 455 IS istmp; 456 PetscTruth found; 457 458 PetscFunctionBegin; 459 ierr = ISGetLocalSize(is,&N);CHKERRQ(ierr); 460 ierr = ISGetIndices(is,&rows);CHKERRQ(ierr); 461 462 /* first count number of contributors to each processor */ 463 ierr = PetscMalloc(2*size*sizeof(int),&nprocs);CHKERRQ(ierr); 464 ierr = PetscMemzero(nprocs,2*size*sizeof(int));CHKERRQ(ierr); 465 ierr = PetscMalloc((N+1)*sizeof(int),&owner);CHKERRQ(ierr); /* see note*/ 466 for (i=0; i<N; i++) { 467 idx = rows[i]; 468 found = PETSC_FALSE; 469 for (j=0; j<size; j++) { 470 if (idx >= owners[j] && idx < owners[j+1]) { 471 nprocs[2*j]++; nprocs[2*j+1] = 1; owner[i] = j; found = PETSC_TRUE; break; 472 } 473 } 474 if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range"); 475 } 476 nsends = 0; for (i=0; i<size; i++) { nsends += nprocs[2*i+1];} 477 478 /* inform other processors of number of messages and max length*/ 479 ierr = PetscMaxSum(comm,nprocs,&nmax,&nrecvs);CHKERRQ(ierr); 480 481 /* post receives: */ 482 ierr = PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(int),&rvalues);CHKERRQ(ierr); 483 ierr = PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);CHKERRQ(ierr); 484 for (i=0; i<nrecvs; i++) { 485 ierr = MPI_Irecv(rvalues+nmax*i,nmax,MPI_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);CHKERRQ(ierr); 486 } 487 488 /* do sends: 489 1) starts[i] gives the starting index in svalues for stuff going to 490 the ith processor 491 */ 492 ierr = PetscMalloc((N+1)*sizeof(int),&svalues);CHKERRQ(ierr); 493 ierr = PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);CHKERRQ(ierr); 494 ierr = PetscMalloc((size+1)*sizeof(int),&starts);CHKERRQ(ierr); 495 starts[0] = 0; 496 for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];} 497 for (i=0; i<N; i++) { 498 svalues[starts[owner[i]]++] = rows[i]; 499 } 500 ierr = ISRestoreIndices(is,&rows);CHKERRQ(ierr); 501 502 starts[0] = 0; 503 for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];} 504 count = 0; 505 for (i=0; i<size; i++) { 506 if (nprocs[2*i+1]) { 507 ierr = MPI_Isend(svalues+starts[i],nprocs[2*i],MPI_INT,i,tag,comm,send_waits+count++);CHKERRQ(ierr); 508 } 509 } 510 ierr = PetscFree(starts);CHKERRQ(ierr); 511 512 base = owners[rank]; 513 514 /* wait on receives */ 515 ierr = PetscMalloc(2*(nrecvs+1)*sizeof(int),&lens);CHKERRQ(ierr); 516 source = lens + nrecvs; 517 count = nrecvs; slen = 0; 518 while (count) { 519 ierr = MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);CHKERRQ(ierr); 520 /* unpack receives into our local space */ 521 ierr = MPI_Get_count(&recv_status,MPI_INT,&n);CHKERRQ(ierr); 522 source[imdex] = recv_status.MPI_SOURCE; 523 lens[imdex] = n; 524 slen += n; 525 count--; 526 } 527 ierr = PetscFree(recv_waits);CHKERRQ(ierr); 528 529 /* move the data into the send scatter */ 530 ierr = PetscMalloc((slen+1)*sizeof(int),&lrows);CHKERRQ(ierr); 531 count = 0; 532 for (i=0; i<nrecvs; i++) { 533 values = rvalues + i*nmax; 534 for (j=0; j<lens[i]; j++) { 535 lrows[count++] = values[j] - base; 536 } 537 } 538 ierr = PetscFree(rvalues);CHKERRQ(ierr); 539 ierr = PetscFree(lens);CHKERRQ(ierr); 540 ierr = PetscFree(owner);CHKERRQ(ierr); 541 ierr = PetscFree(nprocs);CHKERRQ(ierr); 542 543 /* actually zap the local rows */ 544 ierr = ISCreateGeneral(PETSC_COMM_SELF,slen,lrows,&istmp);CHKERRQ(ierr); 545 PetscLogObjectParent(A,istmp); 546 547 /* 548 Zero the required rows. If the "diagonal block" of the matrix 549 is square and the user wishes to set the diagonal we use seperate 550 code so that MatSetValues() is not called for each diagonal allocating 551 new memory, thus calling lots of mallocs and slowing things down. 552 553 Contributed by: Mathew Knepley 554 */ 555 /* must zero l->B before l->A because the (diag) case below may put values into l->B*/ 556 ierr = MatZeroRows(l->B,istmp,0);CHKERRQ(ierr); 557 if (diag && (l->A->M == l->A->N)) { 558 ierr = MatZeroRows(l->A,istmp,diag);CHKERRQ(ierr); 559 } else if (diag) { 560 ierr = MatZeroRows(l->A,istmp,0);CHKERRQ(ierr); 561 if (((Mat_SeqAIJ*)l->A->data)->nonew) { 562 SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options\n\ 563 MAT_NO_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR"); 564 } 565 for (i = 0; i < slen; i++) { 566 row = lrows[i] + rstart; 567 ierr = MatSetValues(A,1,&row,1,&row,diag,INSERT_VALUES);CHKERRQ(ierr); 568 } 569 ierr = MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 570 ierr = MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 571 } else { 572 ierr = MatZeroRows(l->A,istmp,0);CHKERRQ(ierr); 573 } 574 ierr = ISDestroy(istmp);CHKERRQ(ierr); 575 ierr = PetscFree(lrows);CHKERRQ(ierr); 576 577 /* wait on sends */ 578 if (nsends) { 579 ierr = PetscMalloc(nsends*sizeof(MPI_Status),&send_status);CHKERRQ(ierr); 580 ierr = MPI_Waitall(nsends,send_waits,send_status);CHKERRQ(ierr); 581 ierr = PetscFree(send_status);CHKERRQ(ierr); 582 } 583 ierr = PetscFree(send_waits);CHKERRQ(ierr); 584 ierr = PetscFree(svalues);CHKERRQ(ierr); 585 586 PetscFunctionReturn(0); 587 } 588 589 #undef __FUNCT__ 590 #define __FUNCT__ "MatMult_MPIAIJ" 591 int MatMult_MPIAIJ(Mat A,Vec xx,Vec yy) 592 { 593 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 594 int ierr,nt; 595 596 PetscFunctionBegin; 597 ierr = VecGetLocalSize(xx,&nt);CHKERRQ(ierr); 598 if (nt != A->n) { 599 SETERRQ2(PETSC_ERR_ARG_SIZ,"Incompatible partition of A (%d) and xx (%d)",A->n,nt); 600 } 601 ierr = VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);CHKERRQ(ierr); 602 ierr = (*a->A->ops->mult)(a->A,xx,yy);CHKERRQ(ierr); 603 ierr = VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);CHKERRQ(ierr); 604 ierr = (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);CHKERRQ(ierr); 605 PetscFunctionReturn(0); 606 } 607 608 #undef __FUNCT__ 609 #define __FUNCT__ "MatMultAdd_MPIAIJ" 610 int MatMultAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz) 611 { 612 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 613 int ierr; 614 615 PetscFunctionBegin; 616 ierr = VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);CHKERRQ(ierr); 617 ierr = (*a->A->ops->multadd)(a->A,xx,yy,zz);CHKERRQ(ierr); 618 ierr = VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);CHKERRQ(ierr); 619 ierr = (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);CHKERRQ(ierr); 620 PetscFunctionReturn(0); 621 } 622 623 #undef __FUNCT__ 624 #define __FUNCT__ "MatMultTranspose_MPIAIJ" 625 int MatMultTranspose_MPIAIJ(Mat A,Vec xx,Vec yy) 626 { 627 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 628 int ierr; 629 630 PetscFunctionBegin; 631 /* do nondiagonal part */ 632 ierr = (*a->B->ops->multtranspose)(a->B,xx,a->lvec);CHKERRQ(ierr); 633 /* send it on its way */ 634 ierr = VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);CHKERRQ(ierr); 635 /* do local part */ 636 ierr = (*a->A->ops->multtranspose)(a->A,xx,yy);CHKERRQ(ierr); 637 /* receive remote parts: note this assumes the values are not actually */ 638 /* inserted in yy until the next line, which is true for my implementation*/ 639 /* but is not perhaps always true. */ 640 ierr = VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);CHKERRQ(ierr); 641 PetscFunctionReturn(0); 642 } 643 644 EXTERN_C_BEGIN 645 #undef __FUNCT__ 646 #define __FUNCT__ "MatIsSymmetric_MPIAIJ" 647 int MatIsSymmetric_MPIAIJ(Mat Amat,Mat Bmat,PetscTruth *f) 648 { 649 Mat_MPIAIJ *Aij = (Mat_MPIAIJ *) Amat->data, *Bij; 650 Mat A = Aij->A, B,Aoff = Aij->B,Boff; 651 MatType type; 652 IS Me,Notme; 653 int M,N,first,last,*notme,i, ierr; 654 655 PetscFunctionBegin; 656 657 /* Compatible types */ 658 ierr = MatGetType(Bmat,&type); CHKERRQ(ierr); 659 ierr = PetscStrcmp(type,MATMPIAIJ,f); CHKERRQ(ierr); 660 if (!*f) SETERRQ(1,"Second matrix needs to be MPIAIJ too"); 661 662 /* Easy test: symmetric diagonal block */ 663 Bij = (Mat_MPIAIJ *) Bmat->data; B = Bij->B; 664 ierr = MatIsSymmetric(A,B,f); CHKERRQ(ierr); 665 if (!*f) PetscFunctionReturn(0); 666 667 /* Hard test: off-diagonal block. This takes a MatGetSubMatrix. */ 668 ierr = MatGetSize(Amat,&M,&N); CHKERRQ(ierr); 669 ierr = MatGetOwnershipRange(Amat,&first,&last); CHKERRQ(ierr); 670 ierr = PetscMalloc((N-last+first)*sizeof(int),¬me); CHKERRQ(ierr); 671 for (i=0; i<first; i++) notme[i] = i; 672 for (i=last; i<M; i++) notme[i-last+first] = i; 673 ierr = ISCreateGeneral 674 (MPI_COMM_SELF,N-last+first,notme,&Notme); CHKERRQ(ierr); 675 ierr = ISCreateStride 676 (MPI_COMM_SELF,last-first,first,1,&Me); CHKERRQ(ierr); 677 ierr = MatGetSubMatrix 678 (B,Notme,Me,PETSC_DECIDE,MAT_INITIAL_MATRIX,&Boff); CHKERRQ(ierr); 679 ierr = MatIsSymmetric(Aoff,Boff,f); CHKERRQ(ierr); 680 ierr = MatDestroy(Boff); CHKERRQ(ierr); 681 ierr = ISDestroy(Me); CHKERRQ(ierr); 682 ierr = ISDestroy(Notme); CHKERRQ(ierr); 683 684 PetscFunctionReturn(0); 685 } 686 EXTERN_C_END 687 688 #undef __FUNCT__ 689 #define __FUNCT__ "MatMultTransposeAdd_MPIAIJ" 690 int MatMultTransposeAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz) 691 { 692 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 693 int ierr; 694 695 PetscFunctionBegin; 696 /* do nondiagonal part */ 697 ierr = (*a->B->ops->multtranspose)(a->B,xx,a->lvec);CHKERRQ(ierr); 698 /* send it on its way */ 699 ierr = VecScatterBegin(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);CHKERRQ(ierr); 700 /* do local part */ 701 ierr = (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);CHKERRQ(ierr); 702 /* receive remote parts: note this assumes the values are not actually */ 703 /* inserted in yy until the next line, which is true for my implementation*/ 704 /* but is not perhaps always true. */ 705 ierr = VecScatterEnd(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);CHKERRQ(ierr); 706 PetscFunctionReturn(0); 707 } 708 709 /* 710 This only works correctly for square matrices where the subblock A->A is the 711 diagonal block 712 */ 713 #undef __FUNCT__ 714 #define __FUNCT__ "MatGetDiagonal_MPIAIJ" 715 int MatGetDiagonal_MPIAIJ(Mat A,Vec v) 716 { 717 int ierr; 718 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 719 720 PetscFunctionBegin; 721 if (A->M != A->N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block"); 722 if (a->rstart != a->cstart || a->rend != a->cend) { 723 SETERRQ(PETSC_ERR_ARG_SIZ,"row partition must equal col partition"); 724 } 725 ierr = MatGetDiagonal(a->A,v);CHKERRQ(ierr); 726 PetscFunctionReturn(0); 727 } 728 729 #undef __FUNCT__ 730 #define __FUNCT__ "MatScale_MPIAIJ" 731 int MatScale_MPIAIJ(PetscScalar *aa,Mat A) 732 { 733 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 734 int ierr; 735 736 PetscFunctionBegin; 737 ierr = MatScale(aa,a->A);CHKERRQ(ierr); 738 ierr = MatScale(aa,a->B);CHKERRQ(ierr); 739 PetscFunctionReturn(0); 740 } 741 742 #undef __FUNCT__ 743 #define __FUNCT__ "MatDestroy_MPIAIJ" 744 int MatDestroy_MPIAIJ(Mat mat) 745 { 746 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 747 int ierr; 748 749 PetscFunctionBegin; 750 #if defined(PETSC_USE_LOG) 751 PetscLogObjectState((PetscObject)mat,"Rows=%d, Cols=%d",mat->M,mat->N); 752 #endif 753 ierr = MatStashDestroy_Private(&mat->stash);CHKERRQ(ierr); 754 ierr = PetscFree(aij->rowners);CHKERRQ(ierr); 755 ierr = MatDestroy(aij->A);CHKERRQ(ierr); 756 ierr = MatDestroy(aij->B);CHKERRQ(ierr); 757 #if defined (PETSC_USE_CTABLE) 758 if (aij->colmap) {ierr = PetscTableDelete(aij->colmap);CHKERRQ(ierr);} 759 #else 760 if (aij->colmap) {ierr = PetscFree(aij->colmap);CHKERRQ(ierr);} 761 #endif 762 if (aij->garray) {ierr = PetscFree(aij->garray);CHKERRQ(ierr);} 763 if (aij->lvec) {ierr = VecDestroy(aij->lvec);CHKERRQ(ierr);} 764 if (aij->Mvctx) {ierr = VecScatterDestroy(aij->Mvctx);CHKERRQ(ierr);} 765 if (aij->rowvalues) {ierr = PetscFree(aij->rowvalues);CHKERRQ(ierr);} 766 ierr = PetscFree(aij);CHKERRQ(ierr); 767 PetscFunctionReturn(0); 768 } 769 770 extern int MatMPIAIJFactorInfo_SuperLu(Mat,PetscViewer); 771 extern int MatFactorInfo_Spooles(Mat,PetscViewer); 772 extern int MatFactorInfo_MUMPS(Mat,PetscViewer); 773 774 #undef __FUNCT__ 775 #define __FUNCT__ "MatView_MPIAIJ_Binary" 776 int MatView_MPIAIJ_Binary(Mat mat,PetscViewer viewer) 777 { 778 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 779 Mat_SeqAIJ* A = (Mat_SeqAIJ*)aij->A->data; 780 Mat_SeqAIJ* B = (Mat_SeqAIJ*)aij->B->data; 781 int nz,fd,ierr,header[4],rank,size,*row_lengths,*range,rlen,i,tag = ((PetscObject)viewer)->tag; 782 int nzmax,*column_indices,j,k,col,*garray = aij->garray,cnt,cstart = aij->cstart,rnz; 783 PetscScalar *column_values; 784 785 PetscFunctionBegin; 786 ierr = MPI_Comm_rank(mat->comm,&rank);CHKERRQ(ierr); 787 ierr = MPI_Comm_size(mat->comm,&size);CHKERRQ(ierr); 788 nz = A->nz + B->nz; 789 if (rank == 0) { 790 header[0] = MAT_FILE_COOKIE; 791 header[1] = mat->M; 792 header[2] = mat->N; 793 ierr = MPI_Reduce(&nz,&header[3],1,MPI_INT,MPI_SUM,0,mat->comm);CHKERRQ(ierr); 794 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 795 ierr = PetscBinaryWrite(fd,header,4,PETSC_INT,1);CHKERRQ(ierr); 796 /* get largest number of rows any processor has */ 797 rlen = mat->m; 798 ierr = PetscMapGetGlobalRange(mat->rmap,&range);CHKERRQ(ierr); 799 for (i=1; i<size; i++) { 800 rlen = PetscMax(rlen,range[i+1] - range[i]); 801 } 802 } else { 803 ierr = MPI_Reduce(&nz,0,1,MPI_INT,MPI_SUM,0,mat->comm);CHKERRQ(ierr); 804 rlen = mat->m; 805 } 806 807 /* load up the local row counts */ 808 ierr = PetscMalloc((rlen+1)*sizeof(int),&row_lengths);CHKERRQ(ierr); 809 for (i=0; i<mat->m; i++) { 810 row_lengths[i] = A->i[i+1] - A->i[i] + B->i[i+1] - B->i[i]; 811 } 812 813 /* store the row lengths to the file */ 814 if (rank == 0) { 815 MPI_Status status; 816 ierr = PetscBinaryWrite(fd,row_lengths,mat->m,PETSC_INT,1);CHKERRQ(ierr); 817 for (i=1; i<size; i++) { 818 rlen = range[i+1] - range[i]; 819 ierr = MPI_Recv(row_lengths,rlen,MPI_INT,i,tag,mat->comm,&status);CHKERRQ(ierr); 820 ierr = PetscBinaryWrite(fd,row_lengths,rlen,PETSC_INT,1);CHKERRQ(ierr); 821 } 822 } else { 823 ierr = MPI_Send(row_lengths,mat->m,MPI_INT,0,tag,mat->comm);CHKERRQ(ierr); 824 } 825 ierr = PetscFree(row_lengths);CHKERRQ(ierr); 826 827 /* load up the local column indices */ 828 nzmax = nz; /* )th processor needs space a largest processor needs */ 829 ierr = MPI_Reduce(&nz,&nzmax,1,MPI_INT,MPI_MAX,0,mat->comm);CHKERRQ(ierr); 830 ierr = PetscMalloc((nzmax+1)*sizeof(int),&column_indices);CHKERRQ(ierr); 831 cnt = 0; 832 for (i=0; i<mat->m; i++) { 833 for (j=B->i[i]; j<B->i[i+1]; j++) { 834 if ( (col = garray[B->j[j]]) > cstart) break; 835 column_indices[cnt++] = col; 836 } 837 for (k=A->i[i]; k<A->i[i+1]; k++) { 838 column_indices[cnt++] = A->j[k] + cstart; 839 } 840 for (; j<B->i[i+1]; j++) { 841 column_indices[cnt++] = garray[B->j[j]]; 842 } 843 } 844 if (cnt != A->nz + B->nz) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: cnt = %d nz = %d",cnt,A->nz+B->nz); 845 846 /* store the column indices to the file */ 847 if (rank == 0) { 848 MPI_Status status; 849 ierr = PetscBinaryWrite(fd,column_indices,nz,PETSC_INT,1);CHKERRQ(ierr); 850 for (i=1; i<size; i++) { 851 ierr = MPI_Recv(&rnz,1,MPI_INT,i,tag,mat->comm,&status);CHKERRQ(ierr); 852 if (rnz > nzmax) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: nz = %d nzmax = %d",nz,nzmax); 853 ierr = MPI_Recv(column_indices,rnz,MPI_INT,i,tag,mat->comm,&status);CHKERRQ(ierr); 854 ierr = PetscBinaryWrite(fd,column_indices,rnz,PETSC_INT,1);CHKERRQ(ierr); 855 } 856 } else { 857 ierr = MPI_Send(&nz,1,MPI_INT,0,tag,mat->comm);CHKERRQ(ierr); 858 ierr = MPI_Send(column_indices,nz,MPI_INT,0,tag,mat->comm);CHKERRQ(ierr); 859 } 860 ierr = PetscFree(column_indices);CHKERRQ(ierr); 861 862 /* load up the local column values */ 863 ierr = PetscMalloc((nzmax+1)*sizeof(PetscScalar),&column_values);CHKERRQ(ierr); 864 cnt = 0; 865 for (i=0; i<mat->m; i++) { 866 for (j=B->i[i]; j<B->i[i+1]; j++) { 867 if ( garray[B->j[j]] > cstart) break; 868 column_values[cnt++] = B->a[j]; 869 } 870 for (k=A->i[i]; k<A->i[i+1]; k++) { 871 column_values[cnt++] = A->a[k]; 872 } 873 for (; j<B->i[i+1]; j++) { 874 column_values[cnt++] = B->a[j]; 875 } 876 } 877 if (cnt != A->nz + B->nz) SETERRQ2(1,"Internal PETSc error: cnt = %d nz = %d",cnt,A->nz+B->nz); 878 879 /* store the column values to the file */ 880 if (rank == 0) { 881 MPI_Status status; 882 ierr = PetscBinaryWrite(fd,column_values,nz,PETSC_SCALAR,1);CHKERRQ(ierr); 883 for (i=1; i<size; i++) { 884 ierr = MPI_Recv(&rnz,1,MPI_INT,i,tag,mat->comm,&status);CHKERRQ(ierr); 885 if (rnz > nzmax) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: nz = %d nzmax = %d",nz,nzmax); 886 ierr = MPI_Recv(column_values,rnz,MPIU_SCALAR,i,tag,mat->comm,&status);CHKERRQ(ierr); 887 ierr = PetscBinaryWrite(fd,column_values,rnz,PETSC_SCALAR,1);CHKERRQ(ierr); 888 } 889 } else { 890 ierr = MPI_Send(&nz,1,MPI_INT,0,tag,mat->comm);CHKERRQ(ierr); 891 ierr = MPI_Send(column_values,nz,MPIU_SCALAR,0,tag,mat->comm);CHKERRQ(ierr); 892 } 893 ierr = PetscFree(column_values);CHKERRQ(ierr); 894 PetscFunctionReturn(0); 895 } 896 897 #undef __FUNCT__ 898 #define __FUNCT__ "MatView_MPIAIJ_ASCIIorDraworSocket" 899 int MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer) 900 { 901 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 902 int ierr,rank = aij->rank,size = aij->size; 903 PetscTruth isdraw,isascii,flg,isbinary; 904 PetscViewer sviewer; 905 PetscViewerFormat format; 906 907 PetscFunctionBegin; 908 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);CHKERRQ(ierr); 909 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);CHKERRQ(ierr); 910 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);CHKERRQ(ierr); 911 if (isascii) { 912 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 913 if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) { 914 MatInfo info; 915 ierr = MPI_Comm_rank(mat->comm,&rank);CHKERRQ(ierr); 916 ierr = MatGetInfo(mat,MAT_LOCAL,&info);CHKERRQ(ierr); 917 ierr = PetscOptionsHasName(PETSC_NULL,"-mat_aij_no_inode",&flg);CHKERRQ(ierr); 918 if (flg) { 919 ierr = PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %d nz %d nz alloced %d mem %d, not using I-node routines\n", 920 rank,mat->m,(int)info.nz_used,(int)info.nz_allocated,(int)info.memory);CHKERRQ(ierr); 921 } else { 922 ierr = PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %d nz %d nz alloced %d mem %d, using I-node routines\n", 923 rank,mat->m,(int)info.nz_used,(int)info.nz_allocated,(int)info.memory);CHKERRQ(ierr); 924 } 925 ierr = MatGetInfo(aij->A,MAT_LOCAL,&info);CHKERRQ(ierr); 926 ierr = PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %d \n",rank,(int)info.nz_used);CHKERRQ(ierr); 927 ierr = MatGetInfo(aij->B,MAT_LOCAL,&info);CHKERRQ(ierr); 928 ierr = PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %d \n",rank,(int)info.nz_used);CHKERRQ(ierr); 929 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 930 ierr = VecScatterView(aij->Mvctx,viewer);CHKERRQ(ierr); 931 PetscFunctionReturn(0); 932 } else if (format == PETSC_VIEWER_ASCII_INFO) { 933 PetscFunctionReturn(0); 934 } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) { 935 #if defined(PETSC_HAVE_SUPERLUDIST) && !defined(PETSC_USE_SINGLE) 936 ierr = MatMPIAIJFactorInfo_SuperLu(mat,viewer);CHKERRQ(ierr); 937 #endif 938 #if defined(PETSC_HAVE_SPOOLES) && !defined(PETSC_USE_SINGLE) 939 ierr = MatFactorInfo_Spooles(mat,viewer);CHKERRQ(ierr); 940 #endif 941 #if defined(PETSC_HAVE_MUMPS) && !defined(PETSC_USE_SINGLE) 942 ierr = MatFactorInfo_MUMPS(mat,viewer);CHKERRQ(ierr); 943 #endif 944 PetscFunctionReturn(0); 945 } 946 } else if (isbinary) { 947 if (size == 1) { 948 ierr = PetscObjectSetName((PetscObject)aij->A,mat->name);CHKERRQ(ierr); 949 ierr = MatView(aij->A,viewer);CHKERRQ(ierr); 950 } else { 951 ierr = MatView_MPIAIJ_Binary(mat,viewer);CHKERRQ(ierr); 952 } 953 PetscFunctionReturn(0); 954 } else if (isdraw) { 955 PetscDraw draw; 956 PetscTruth isnull; 957 ierr = PetscViewerDrawGetDraw(viewer,0,&draw);CHKERRQ(ierr); 958 ierr = PetscDrawIsNull(draw,&isnull);CHKERRQ(ierr); if (isnull) PetscFunctionReturn(0); 959 } 960 961 if (size == 1) { 962 ierr = PetscObjectSetName((PetscObject)aij->A,mat->name);CHKERRQ(ierr); 963 ierr = MatView(aij->A,viewer);CHKERRQ(ierr); 964 } else { 965 /* assemble the entire matrix onto first processor. */ 966 Mat A; 967 Mat_SeqAIJ *Aloc; 968 int M = mat->M,N = mat->N,m,*ai,*aj,row,*cols,i,*ct; 969 PetscScalar *a; 970 971 if (!rank) { 972 ierr = MatCreateMPIAIJ(mat->comm,M,N,M,N,0,PETSC_NULL,0,PETSC_NULL,&A);CHKERRQ(ierr); 973 } else { 974 ierr = MatCreateMPIAIJ(mat->comm,0,0,M,N,0,PETSC_NULL,0,PETSC_NULL,&A);CHKERRQ(ierr); 975 } 976 PetscLogObjectParent(mat,A); 977 978 /* copy over the A part */ 979 Aloc = (Mat_SeqAIJ*)aij->A->data; 980 m = aij->A->m; ai = Aloc->i; aj = Aloc->j; a = Aloc->a; 981 row = aij->rstart; 982 for (i=0; i<ai[m]; i++) {aj[i] += aij->cstart ;} 983 for (i=0; i<m; i++) { 984 ierr = MatSetValues(A,1,&row,ai[i+1]-ai[i],aj,a,INSERT_VALUES);CHKERRQ(ierr); 985 row++; a += ai[i+1]-ai[i]; aj += ai[i+1]-ai[i]; 986 } 987 aj = Aloc->j; 988 for (i=0; i<ai[m]; i++) {aj[i] -= aij->cstart;} 989 990 /* copy over the B part */ 991 Aloc = (Mat_SeqAIJ*)aij->B->data; 992 m = aij->B->m; ai = Aloc->i; aj = Aloc->j; a = Aloc->a; 993 row = aij->rstart; 994 ierr = PetscMalloc((ai[m]+1)*sizeof(int),&cols);CHKERRQ(ierr); 995 ct = cols; 996 for (i=0; i<ai[m]; i++) {cols[i] = aij->garray[aj[i]];} 997 for (i=0; i<m; i++) { 998 ierr = MatSetValues(A,1,&row,ai[i+1]-ai[i],cols,a,INSERT_VALUES);CHKERRQ(ierr); 999 row++; a += ai[i+1]-ai[i]; cols += ai[i+1]-ai[i]; 1000 } 1001 ierr = PetscFree(ct);CHKERRQ(ierr); 1002 ierr = MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1003 ierr = MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1004 /* 1005 Everyone has to call to draw the matrix since the graphics waits are 1006 synchronized across all processors that share the PetscDraw object 1007 */ 1008 ierr = PetscViewerGetSingleton(viewer,&sviewer);CHKERRQ(ierr); 1009 if (!rank) { 1010 ierr = PetscObjectSetName((PetscObject)((Mat_MPIAIJ*)(A->data))->A,mat->name);CHKERRQ(ierr); 1011 ierr = MatView(((Mat_MPIAIJ*)(A->data))->A,sviewer);CHKERRQ(ierr); 1012 } 1013 ierr = PetscViewerRestoreSingleton(viewer,&sviewer);CHKERRQ(ierr); 1014 ierr = MatDestroy(A);CHKERRQ(ierr); 1015 } 1016 PetscFunctionReturn(0); 1017 } 1018 1019 #undef __FUNCT__ 1020 #define __FUNCT__ "MatView_MPIAIJ" 1021 int MatView_MPIAIJ(Mat mat,PetscViewer viewer) 1022 { 1023 int ierr; 1024 PetscTruth isascii,isdraw,issocket,isbinary; 1025 1026 PetscFunctionBegin; 1027 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);CHKERRQ(ierr); 1028 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);CHKERRQ(ierr); 1029 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);CHKERRQ(ierr); 1030 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);CHKERRQ(ierr); 1031 if (isascii || isdraw || isbinary || issocket) { 1032 ierr = MatView_MPIAIJ_ASCIIorDraworSocket(mat,viewer);CHKERRQ(ierr); 1033 } else { 1034 SETERRQ1(1,"Viewer type %s not supported by MPIAIJ matrices",((PetscObject)viewer)->type_name); 1035 } 1036 PetscFunctionReturn(0); 1037 } 1038 1039 1040 1041 #undef __FUNCT__ 1042 #define __FUNCT__ "MatRelax_MPIAIJ" 1043 int MatRelax_MPIAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,int its,int lits,Vec xx) 1044 { 1045 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data; 1046 int ierr; 1047 Vec bb1; 1048 PetscScalar mone=-1.0; 1049 1050 PetscFunctionBegin; 1051 if (its <= 0 || lits <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %d and local its %d both positive",its,lits); 1052 1053 ierr = VecDuplicate(bb,&bb1);CHKERRQ(ierr); 1054 1055 if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP){ 1056 if (flag & SOR_ZERO_INITIAL_GUESS) { 1057 ierr = (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,lits,xx);CHKERRQ(ierr); 1058 its--; 1059 } 1060 1061 while (its--) { 1062 ierr = VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 1063 ierr = VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 1064 1065 /* update rhs: bb1 = bb - B*x */ 1066 ierr = VecScale(&mone,mat->lvec);CHKERRQ(ierr); 1067 ierr = (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);CHKERRQ(ierr); 1068 1069 /* local sweep */ 1070 ierr = (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,lits,xx); 1071 CHKERRQ(ierr); 1072 } 1073 } else if (flag & SOR_LOCAL_FORWARD_SWEEP){ 1074 if (flag & SOR_ZERO_INITIAL_GUESS) { 1075 ierr = (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,PETSC_NULL,xx);CHKERRQ(ierr); 1076 its--; 1077 } 1078 while (its--) { 1079 ierr = VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 1080 ierr = VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 1081 1082 /* update rhs: bb1 = bb - B*x */ 1083 ierr = VecScale(&mone,mat->lvec);CHKERRQ(ierr); 1084 ierr = (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);CHKERRQ(ierr); 1085 1086 /* local sweep */ 1087 ierr = (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_FORWARD_SWEEP,fshift,lits,PETSC_NULL,xx); 1088 CHKERRQ(ierr); 1089 } 1090 } else if (flag & SOR_LOCAL_BACKWARD_SWEEP){ 1091 if (flag & SOR_ZERO_INITIAL_GUESS) { 1092 ierr = (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,PETSC_NULL,xx);CHKERRQ(ierr); 1093 its--; 1094 } 1095 while (its--) { 1096 ierr = VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 1097 ierr = VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 1098 1099 /* update rhs: bb1 = bb - B*x */ 1100 ierr = VecScale(&mone,mat->lvec);CHKERRQ(ierr); 1101 ierr = (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);CHKERRQ(ierr); 1102 1103 /* local sweep */ 1104 ierr = (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_BACKWARD_SWEEP,fshift,lits,PETSC_NULL,xx); 1105 CHKERRQ(ierr); 1106 } 1107 } else { 1108 SETERRQ(PETSC_ERR_SUP,"Parallel SOR not supported"); 1109 } 1110 1111 ierr = VecDestroy(bb1);CHKERRQ(ierr); 1112 PetscFunctionReturn(0); 1113 } 1114 1115 #undef __FUNCT__ 1116 #define __FUNCT__ "MatGetInfo_MPIAIJ" 1117 int MatGetInfo_MPIAIJ(Mat matin,MatInfoType flag,MatInfo *info) 1118 { 1119 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data; 1120 Mat A = mat->A,B = mat->B; 1121 int ierr; 1122 PetscReal isend[5],irecv[5]; 1123 1124 PetscFunctionBegin; 1125 info->block_size = 1.0; 1126 ierr = MatGetInfo(A,MAT_LOCAL,info);CHKERRQ(ierr); 1127 isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded; 1128 isend[3] = info->memory; isend[4] = info->mallocs; 1129 ierr = MatGetInfo(B,MAT_LOCAL,info);CHKERRQ(ierr); 1130 isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded; 1131 isend[3] += info->memory; isend[4] += info->mallocs; 1132 if (flag == MAT_LOCAL) { 1133 info->nz_used = isend[0]; 1134 info->nz_allocated = isend[1]; 1135 info->nz_unneeded = isend[2]; 1136 info->memory = isend[3]; 1137 info->mallocs = isend[4]; 1138 } else if (flag == MAT_GLOBAL_MAX) { 1139 ierr = MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,matin->comm);CHKERRQ(ierr); 1140 info->nz_used = irecv[0]; 1141 info->nz_allocated = irecv[1]; 1142 info->nz_unneeded = irecv[2]; 1143 info->memory = irecv[3]; 1144 info->mallocs = irecv[4]; 1145 } else if (flag == MAT_GLOBAL_SUM) { 1146 ierr = MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,matin->comm);CHKERRQ(ierr); 1147 info->nz_used = irecv[0]; 1148 info->nz_allocated = irecv[1]; 1149 info->nz_unneeded = irecv[2]; 1150 info->memory = irecv[3]; 1151 info->mallocs = irecv[4]; 1152 } 1153 info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */ 1154 info->fill_ratio_needed = 0; 1155 info->factor_mallocs = 0; 1156 info->rows_global = (double)matin->M; 1157 info->columns_global = (double)matin->N; 1158 info->rows_local = (double)matin->m; 1159 info->columns_local = (double)matin->N; 1160 1161 PetscFunctionReturn(0); 1162 } 1163 1164 #undef __FUNCT__ 1165 #define __FUNCT__ "MatSetOption_MPIAIJ" 1166 int MatSetOption_MPIAIJ(Mat A,MatOption op) 1167 { 1168 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 1169 int ierr; 1170 1171 PetscFunctionBegin; 1172 switch (op) { 1173 case MAT_NO_NEW_NONZERO_LOCATIONS: 1174 case MAT_YES_NEW_NONZERO_LOCATIONS: 1175 case MAT_COLUMNS_UNSORTED: 1176 case MAT_COLUMNS_SORTED: 1177 case MAT_NEW_NONZERO_ALLOCATION_ERR: 1178 case MAT_KEEP_ZEROED_ROWS: 1179 case MAT_NEW_NONZERO_LOCATION_ERR: 1180 case MAT_USE_INODES: 1181 case MAT_DO_NOT_USE_INODES: 1182 case MAT_IGNORE_ZERO_ENTRIES: 1183 ierr = MatSetOption(a->A,op);CHKERRQ(ierr); 1184 ierr = MatSetOption(a->B,op);CHKERRQ(ierr); 1185 break; 1186 case MAT_ROW_ORIENTED: 1187 a->roworiented = PETSC_TRUE; 1188 ierr = MatSetOption(a->A,op);CHKERRQ(ierr); 1189 ierr = MatSetOption(a->B,op);CHKERRQ(ierr); 1190 break; 1191 case MAT_ROWS_SORTED: 1192 case MAT_ROWS_UNSORTED: 1193 case MAT_YES_NEW_DIAGONALS: 1194 PetscLogInfo(A,"MatSetOption_MPIAIJ:Option ignored\n"); 1195 break; 1196 case MAT_COLUMN_ORIENTED: 1197 a->roworiented = PETSC_FALSE; 1198 ierr = MatSetOption(a->A,op);CHKERRQ(ierr); 1199 ierr = MatSetOption(a->B,op);CHKERRQ(ierr); 1200 break; 1201 case MAT_IGNORE_OFF_PROC_ENTRIES: 1202 a->donotstash = PETSC_TRUE; 1203 break; 1204 case MAT_NO_NEW_DIAGONALS: 1205 SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS"); 1206 default: 1207 SETERRQ(PETSC_ERR_SUP,"unknown option"); 1208 } 1209 PetscFunctionReturn(0); 1210 } 1211 1212 #undef __FUNCT__ 1213 #define __FUNCT__ "MatGetRow_MPIAIJ" 1214 int MatGetRow_MPIAIJ(Mat matin,int row,int *nz,int **idx,PetscScalar **v) 1215 { 1216 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data; 1217 PetscScalar *vworkA,*vworkB,**pvA,**pvB,*v_p; 1218 int i,ierr,*cworkA,*cworkB,**pcA,**pcB,cstart = mat->cstart; 1219 int nztot,nzA,nzB,lrow,rstart = mat->rstart,rend = mat->rend; 1220 int *cmap,*idx_p; 1221 1222 PetscFunctionBegin; 1223 if (mat->getrowactive == PETSC_TRUE) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active"); 1224 mat->getrowactive = PETSC_TRUE; 1225 1226 if (!mat->rowvalues && (idx || v)) { 1227 /* 1228 allocate enough space to hold information from the longest row. 1229 */ 1230 Mat_SeqAIJ *Aa = (Mat_SeqAIJ*)mat->A->data,*Ba = (Mat_SeqAIJ*)mat->B->data; 1231 int max = 1,tmp; 1232 for (i=0; i<matin->m; i++) { 1233 tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i]; 1234 if (max < tmp) { max = tmp; } 1235 } 1236 ierr = PetscMalloc(max*(sizeof(int)+sizeof(PetscScalar)),&mat->rowvalues);CHKERRQ(ierr); 1237 mat->rowindices = (int*)(mat->rowvalues + max); 1238 } 1239 1240 if (row < rstart || row >= rend) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Only local rows") 1241 lrow = row - rstart; 1242 1243 pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB; 1244 if (!v) {pvA = 0; pvB = 0;} 1245 if (!idx) {pcA = 0; if (!v) pcB = 0;} 1246 ierr = (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);CHKERRQ(ierr); 1247 ierr = (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);CHKERRQ(ierr); 1248 nztot = nzA + nzB; 1249 1250 cmap = mat->garray; 1251 if (v || idx) { 1252 if (nztot) { 1253 /* Sort by increasing column numbers, assuming A and B already sorted */ 1254 int imark = -1; 1255 if (v) { 1256 *v = v_p = mat->rowvalues; 1257 for (i=0; i<nzB; i++) { 1258 if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i]; 1259 else break; 1260 } 1261 imark = i; 1262 for (i=0; i<nzA; i++) v_p[imark+i] = vworkA[i]; 1263 for (i=imark; i<nzB; i++) v_p[nzA+i] = vworkB[i]; 1264 } 1265 if (idx) { 1266 *idx = idx_p = mat->rowindices; 1267 if (imark > -1) { 1268 for (i=0; i<imark; i++) { 1269 idx_p[i] = cmap[cworkB[i]]; 1270 } 1271 } else { 1272 for (i=0; i<nzB; i++) { 1273 if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]]; 1274 else break; 1275 } 1276 imark = i; 1277 } 1278 for (i=0; i<nzA; i++) idx_p[imark+i] = cstart + cworkA[i]; 1279 for (i=imark; i<nzB; i++) idx_p[nzA+i] = cmap[cworkB[i]]; 1280 } 1281 } else { 1282 if (idx) *idx = 0; 1283 if (v) *v = 0; 1284 } 1285 } 1286 *nz = nztot; 1287 ierr = (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);CHKERRQ(ierr); 1288 ierr = (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);CHKERRQ(ierr); 1289 PetscFunctionReturn(0); 1290 } 1291 1292 #undef __FUNCT__ 1293 #define __FUNCT__ "MatRestoreRow_MPIAIJ" 1294 int MatRestoreRow_MPIAIJ(Mat mat,int row,int *nz,int **idx,PetscScalar **v) 1295 { 1296 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 1297 1298 PetscFunctionBegin; 1299 if (aij->getrowactive == PETSC_FALSE) { 1300 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow not called"); 1301 } 1302 aij->getrowactive = PETSC_FALSE; 1303 PetscFunctionReturn(0); 1304 } 1305 1306 #undef __FUNCT__ 1307 #define __FUNCT__ "MatNorm_MPIAIJ" 1308 int MatNorm_MPIAIJ(Mat mat,NormType type,PetscReal *norm) 1309 { 1310 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 1311 Mat_SeqAIJ *amat = (Mat_SeqAIJ*)aij->A->data,*bmat = (Mat_SeqAIJ*)aij->B->data; 1312 int ierr,i,j,cstart = aij->cstart; 1313 PetscReal sum = 0.0; 1314 PetscScalar *v; 1315 1316 PetscFunctionBegin; 1317 if (aij->size == 1) { 1318 ierr = MatNorm(aij->A,type,norm);CHKERRQ(ierr); 1319 } else { 1320 if (type == NORM_FROBENIUS) { 1321 v = amat->a; 1322 for (i=0; i<amat->nz; i++) { 1323 #if defined(PETSC_USE_COMPLEX) 1324 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 1325 #else 1326 sum += (*v)*(*v); v++; 1327 #endif 1328 } 1329 v = bmat->a; 1330 for (i=0; i<bmat->nz; i++) { 1331 #if defined(PETSC_USE_COMPLEX) 1332 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 1333 #else 1334 sum += (*v)*(*v); v++; 1335 #endif 1336 } 1337 ierr = MPI_Allreduce(&sum,norm,1,MPIU_REAL,MPI_SUM,mat->comm);CHKERRQ(ierr); 1338 *norm = sqrt(*norm); 1339 } else if (type == NORM_1) { /* max column norm */ 1340 PetscReal *tmp,*tmp2; 1341 int *jj,*garray = aij->garray; 1342 ierr = PetscMalloc((mat->N+1)*sizeof(PetscReal),&tmp);CHKERRQ(ierr); 1343 ierr = PetscMalloc((mat->N+1)*sizeof(PetscReal),&tmp2);CHKERRQ(ierr); 1344 ierr = PetscMemzero(tmp,mat->N*sizeof(PetscReal));CHKERRQ(ierr); 1345 *norm = 0.0; 1346 v = amat->a; jj = amat->j; 1347 for (j=0; j<amat->nz; j++) { 1348 tmp[cstart + *jj++ ] += PetscAbsScalar(*v); v++; 1349 } 1350 v = bmat->a; jj = bmat->j; 1351 for (j=0; j<bmat->nz; j++) { 1352 tmp[garray[*jj++]] += PetscAbsScalar(*v); v++; 1353 } 1354 ierr = MPI_Allreduce(tmp,tmp2,mat->N,MPIU_REAL,MPI_SUM,mat->comm);CHKERRQ(ierr); 1355 for (j=0; j<mat->N; j++) { 1356 if (tmp2[j] > *norm) *norm = tmp2[j]; 1357 } 1358 ierr = PetscFree(tmp);CHKERRQ(ierr); 1359 ierr = PetscFree(tmp2);CHKERRQ(ierr); 1360 } else if (type == NORM_INFINITY) { /* max row norm */ 1361 PetscReal ntemp = 0.0; 1362 for (j=0; j<aij->A->m; j++) { 1363 v = amat->a + amat->i[j]; 1364 sum = 0.0; 1365 for (i=0; i<amat->i[j+1]-amat->i[j]; i++) { 1366 sum += PetscAbsScalar(*v); v++; 1367 } 1368 v = bmat->a + bmat->i[j]; 1369 for (i=0; i<bmat->i[j+1]-bmat->i[j]; i++) { 1370 sum += PetscAbsScalar(*v); v++; 1371 } 1372 if (sum > ntemp) ntemp = sum; 1373 } 1374 ierr = MPI_Allreduce(&ntemp,norm,1,MPIU_REAL,MPI_MAX,mat->comm);CHKERRQ(ierr); 1375 } else { 1376 SETERRQ(PETSC_ERR_SUP,"No support for two norm"); 1377 } 1378 } 1379 PetscFunctionReturn(0); 1380 } 1381 1382 #undef __FUNCT__ 1383 #define __FUNCT__ "MatTranspose_MPIAIJ" 1384 int MatTranspose_MPIAIJ(Mat A,Mat *matout) 1385 { 1386 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 1387 Mat_SeqAIJ *Aloc = (Mat_SeqAIJ*)a->A->data; 1388 int ierr; 1389 int M = A->M,N = A->N,m,*ai,*aj,row,*cols,i,*ct; 1390 Mat B; 1391 PetscScalar *array; 1392 1393 PetscFunctionBegin; 1394 if (!matout && M != N) { 1395 SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place"); 1396 } 1397 1398 ierr = MatCreateMPIAIJ(A->comm,A->n,A->m,N,M,0,PETSC_NULL,0,PETSC_NULL,&B);CHKERRQ(ierr); 1399 1400 /* copy over the A part */ 1401 Aloc = (Mat_SeqAIJ*)a->A->data; 1402 m = a->A->m; ai = Aloc->i; aj = Aloc->j; array = Aloc->a; 1403 row = a->rstart; 1404 for (i=0; i<ai[m]; i++) {aj[i] += a->cstart ;} 1405 for (i=0; i<m; i++) { 1406 ierr = MatSetValues(B,ai[i+1]-ai[i],aj,1,&row,array,INSERT_VALUES);CHKERRQ(ierr); 1407 row++; array += ai[i+1]-ai[i]; aj += ai[i+1]-ai[i]; 1408 } 1409 aj = Aloc->j; 1410 for (i=0; i<ai[m]; i++) {aj[i] -= a->cstart ;} 1411 1412 /* copy over the B part */ 1413 Aloc = (Mat_SeqAIJ*)a->B->data; 1414 m = a->B->m; ai = Aloc->i; aj = Aloc->j; array = Aloc->a; 1415 row = a->rstart; 1416 ierr = PetscMalloc((1+ai[m])*sizeof(int),&cols);CHKERRQ(ierr); 1417 ct = cols; 1418 for (i=0; i<ai[m]; i++) {cols[i] = a->garray[aj[i]];} 1419 for (i=0; i<m; i++) { 1420 ierr = MatSetValues(B,ai[i+1]-ai[i],cols,1,&row,array,INSERT_VALUES);CHKERRQ(ierr); 1421 row++; array += ai[i+1]-ai[i]; cols += ai[i+1]-ai[i]; 1422 } 1423 ierr = PetscFree(ct);CHKERRQ(ierr); 1424 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1425 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1426 if (matout) { 1427 *matout = B; 1428 } else { 1429 ierr = MatHeaderCopy(A,B);CHKERRQ(ierr); 1430 } 1431 PetscFunctionReturn(0); 1432 } 1433 1434 #undef __FUNCT__ 1435 #define __FUNCT__ "MatDiagonalScale_MPIAIJ" 1436 int MatDiagonalScale_MPIAIJ(Mat mat,Vec ll,Vec rr) 1437 { 1438 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 1439 Mat a = aij->A,b = aij->B; 1440 int ierr,s1,s2,s3; 1441 1442 PetscFunctionBegin; 1443 ierr = MatGetLocalSize(mat,&s2,&s3);CHKERRQ(ierr); 1444 if (rr) { 1445 ierr = VecGetLocalSize(rr,&s1);CHKERRQ(ierr); 1446 if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size"); 1447 /* Overlap communication with computation. */ 1448 ierr = VecScatterBegin(rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD,aij->Mvctx);CHKERRQ(ierr); 1449 } 1450 if (ll) { 1451 ierr = VecGetLocalSize(ll,&s1);CHKERRQ(ierr); 1452 if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size"); 1453 ierr = (*b->ops->diagonalscale)(b,ll,0);CHKERRQ(ierr); 1454 } 1455 /* scale the diagonal block */ 1456 ierr = (*a->ops->diagonalscale)(a,ll,rr);CHKERRQ(ierr); 1457 1458 if (rr) { 1459 /* Do a scatter end and then right scale the off-diagonal block */ 1460 ierr = VecScatterEnd(rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD,aij->Mvctx);CHKERRQ(ierr); 1461 ierr = (*b->ops->diagonalscale)(b,0,aij->lvec);CHKERRQ(ierr); 1462 } 1463 1464 PetscFunctionReturn(0); 1465 } 1466 1467 1468 #undef __FUNCT__ 1469 #define __FUNCT__ "MatPrintHelp_MPIAIJ" 1470 int MatPrintHelp_MPIAIJ(Mat A) 1471 { 1472 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 1473 int ierr; 1474 1475 PetscFunctionBegin; 1476 if (!a->rank) { 1477 ierr = MatPrintHelp_SeqAIJ(a->A);CHKERRQ(ierr); 1478 } 1479 PetscFunctionReturn(0); 1480 } 1481 1482 #undef __FUNCT__ 1483 #define __FUNCT__ "MatGetBlockSize_MPIAIJ" 1484 int MatGetBlockSize_MPIAIJ(Mat A,int *bs) 1485 { 1486 PetscFunctionBegin; 1487 *bs = 1; 1488 PetscFunctionReturn(0); 1489 } 1490 #undef __FUNCT__ 1491 #define __FUNCT__ "MatSetUnfactored_MPIAIJ" 1492 int MatSetUnfactored_MPIAIJ(Mat A) 1493 { 1494 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 1495 int ierr; 1496 1497 PetscFunctionBegin; 1498 ierr = MatSetUnfactored(a->A);CHKERRQ(ierr); 1499 PetscFunctionReturn(0); 1500 } 1501 1502 #undef __FUNCT__ 1503 #define __FUNCT__ "MatEqual_MPIAIJ" 1504 int MatEqual_MPIAIJ(Mat A,Mat B,PetscTruth *flag) 1505 { 1506 Mat_MPIAIJ *matB = (Mat_MPIAIJ*)B->data,*matA = (Mat_MPIAIJ*)A->data; 1507 Mat a,b,c,d; 1508 PetscTruth flg; 1509 int ierr; 1510 1511 PetscFunctionBegin; 1512 ierr = PetscTypeCompare((PetscObject)B,MATMPIAIJ,&flg);CHKERRQ(ierr); 1513 if (!flg) SETERRQ(PETSC_ERR_ARG_INCOMP,"Matrices must be same type"); 1514 a = matA->A; b = matA->B; 1515 c = matB->A; d = matB->B; 1516 1517 ierr = MatEqual(a,c,&flg);CHKERRQ(ierr); 1518 if (flg == PETSC_TRUE) { 1519 ierr = MatEqual(b,d,&flg);CHKERRQ(ierr); 1520 } 1521 ierr = MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);CHKERRQ(ierr); 1522 PetscFunctionReturn(0); 1523 } 1524 1525 #undef __FUNCT__ 1526 #define __FUNCT__ "MatCopy_MPIAIJ" 1527 int MatCopy_MPIAIJ(Mat A,Mat B,MatStructure str) 1528 { 1529 int ierr; 1530 Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data; 1531 Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data; 1532 PetscTruth flg; 1533 1534 PetscFunctionBegin; 1535 ierr = PetscTypeCompare((PetscObject)B,MATMPIAIJ,&flg);CHKERRQ(ierr); 1536 if (str != SAME_NONZERO_PATTERN || !flg) { 1537 /* because of the column compression in the off-processor part of the matrix a->B, 1538 the number of columns in a->B and b->B may be different, hence we cannot call 1539 the MatCopy() directly on the two parts. If need be, we can provide a more 1540 efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices 1541 then copying the submatrices */ 1542 ierr = MatCopy_Basic(A,B,str);CHKERRQ(ierr); 1543 } else { 1544 ierr = MatCopy(a->A,b->A,str);CHKERRQ(ierr); 1545 ierr = MatCopy(a->B,b->B,str);CHKERRQ(ierr); 1546 } 1547 PetscFunctionReturn(0); 1548 } 1549 1550 #undef __FUNCT__ 1551 #define __FUNCT__ "MatSetUpPreallocation_MPIAIJ" 1552 int MatSetUpPreallocation_MPIAIJ(Mat A) 1553 { 1554 int ierr; 1555 1556 PetscFunctionBegin; 1557 ierr = MatMPIAIJSetPreallocation(A,PETSC_DEFAULT,0,PETSC_DEFAULT,0);CHKERRQ(ierr); 1558 PetscFunctionReturn(0); 1559 } 1560 1561 EXTERN int MatDuplicate_MPIAIJ(Mat,MatDuplicateOption,Mat *); 1562 EXTERN int MatIncreaseOverlap_MPIAIJ(Mat,int,IS *,int); 1563 EXTERN int MatFDColoringCreate_MPIAIJ(Mat,ISColoring,MatFDColoring); 1564 EXTERN int MatGetSubMatrices_MPIAIJ (Mat,int,IS *,IS *,MatReuse,Mat **); 1565 EXTERN int MatGetSubMatrix_MPIAIJ (Mat,IS,IS,int,MatReuse,Mat *); 1566 #if !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 1567 EXTERN int MatLUFactorSymbolic_MPIAIJ_TFS(Mat,IS,IS,MatFactorInfo*,Mat*); 1568 #endif 1569 1570 #include "petscblaslapack.h" 1571 extern int MatAXPY_SeqAIJ(PetscScalar *,Mat,Mat,MatStructure); 1572 #undef __FUNCT__ 1573 #define __FUNCT__ "MatAXPY_MPIAIJ" 1574 int MatAXPY_MPIAIJ(PetscScalar *a,Mat X,Mat Y,MatStructure str) 1575 { 1576 int ierr,one=1,i; 1577 Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data,*yy = (Mat_MPIAIJ *)Y->data; 1578 Mat_SeqAIJ *x,*y; 1579 1580 PetscFunctionBegin; 1581 if (str == SAME_NONZERO_PATTERN) { 1582 x = (Mat_SeqAIJ *)xx->A->data; 1583 y = (Mat_SeqAIJ *)yy->A->data; 1584 BLaxpy_(&x->nz,a,x->a,&one,y->a,&one); 1585 x = (Mat_SeqAIJ *)xx->B->data; 1586 y = (Mat_SeqAIJ *)yy->B->data; 1587 BLaxpy_(&x->nz,a,x->a,&one,y->a,&one); 1588 } else if (str == SUBSET_NONZERO_PATTERN) { 1589 ierr = MatAXPY_SeqAIJ(a,xx->A,yy->A,str);CHKERRQ(ierr); 1590 1591 x = (Mat_SeqAIJ *)xx->B->data; 1592 y = (Mat_SeqAIJ *)yy->B->data; 1593 if (y->xtoy && y->XtoY != xx->B) { 1594 ierr = PetscFree(y->xtoy);CHKERRQ(ierr); 1595 ierr = MatDestroy(y->XtoY);CHKERRQ(ierr); 1596 } 1597 if (!y->xtoy) { /* get xtoy */ 1598 ierr = MatAXPYGetxtoy_Private(xx->B->m,x->i,x->j,xx->garray,y->i,y->j,yy->garray,&y->xtoy);CHKERRQ(ierr); 1599 y->XtoY = xx->B; 1600 } 1601 for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += (*a)*(x->a[i]); 1602 } else { 1603 ierr = MatAXPY_Basic(a,X,Y,str);CHKERRQ(ierr); 1604 } 1605 PetscFunctionReturn(0); 1606 } 1607 1608 /* -------------------------------------------------------------------*/ 1609 static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ, 1610 MatGetRow_MPIAIJ, 1611 MatRestoreRow_MPIAIJ, 1612 MatMult_MPIAIJ, 1613 MatMultAdd_MPIAIJ, 1614 MatMultTranspose_MPIAIJ, 1615 MatMultTransposeAdd_MPIAIJ, 1616 0, 1617 0, 1618 0, 1619 0, 1620 0, 1621 0, 1622 MatRelax_MPIAIJ, 1623 MatTranspose_MPIAIJ, 1624 MatGetInfo_MPIAIJ, 1625 MatEqual_MPIAIJ, 1626 MatGetDiagonal_MPIAIJ, 1627 MatDiagonalScale_MPIAIJ, 1628 MatNorm_MPIAIJ, 1629 MatAssemblyBegin_MPIAIJ, 1630 MatAssemblyEnd_MPIAIJ, 1631 0, 1632 MatSetOption_MPIAIJ, 1633 MatZeroEntries_MPIAIJ, 1634 MatZeroRows_MPIAIJ, 1635 #if !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 1636 MatLUFactorSymbolic_MPIAIJ_TFS, 1637 #else 1638 0, 1639 #endif 1640 0, 1641 0, 1642 0, 1643 MatSetUpPreallocation_MPIAIJ, 1644 0, 1645 0, 1646 0, 1647 0, 1648 MatDuplicate_MPIAIJ, 1649 0, 1650 0, 1651 0, 1652 0, 1653 MatAXPY_MPIAIJ, 1654 MatGetSubMatrices_MPIAIJ, 1655 MatIncreaseOverlap_MPIAIJ, 1656 MatGetValues_MPIAIJ, 1657 MatCopy_MPIAIJ, 1658 MatPrintHelp_MPIAIJ, 1659 MatScale_MPIAIJ, 1660 0, 1661 0, 1662 0, 1663 MatGetBlockSize_MPIAIJ, 1664 0, 1665 0, 1666 0, 1667 0, 1668 MatFDColoringCreate_MPIAIJ, 1669 0, 1670 MatSetUnfactored_MPIAIJ, 1671 0, 1672 0, 1673 MatGetSubMatrix_MPIAIJ, 1674 MatDestroy_MPIAIJ, 1675 MatView_MPIAIJ, 1676 MatGetPetscMaps_Petsc, 1677 0, 1678 0, 1679 0, 1680 0, 1681 0, 1682 0, 1683 0, 1684 0, 1685 MatSetColoring_MPIAIJ, 1686 MatSetValuesAdic_MPIAIJ, 1687 MatSetValuesAdifor_MPIAIJ 1688 }; 1689 1690 /* ----------------------------------------------------------------------------------------*/ 1691 1692 EXTERN_C_BEGIN 1693 #undef __FUNCT__ 1694 #define __FUNCT__ "MatStoreValues_MPIAIJ" 1695 int MatStoreValues_MPIAIJ(Mat mat) 1696 { 1697 Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data; 1698 int ierr; 1699 1700 PetscFunctionBegin; 1701 ierr = MatStoreValues(aij->A);CHKERRQ(ierr); 1702 ierr = MatStoreValues(aij->B);CHKERRQ(ierr); 1703 PetscFunctionReturn(0); 1704 } 1705 EXTERN_C_END 1706 1707 EXTERN_C_BEGIN 1708 #undef __FUNCT__ 1709 #define __FUNCT__ "MatRetrieveValues_MPIAIJ" 1710 int MatRetrieveValues_MPIAIJ(Mat mat) 1711 { 1712 Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data; 1713 int ierr; 1714 1715 PetscFunctionBegin; 1716 ierr = MatRetrieveValues(aij->A);CHKERRQ(ierr); 1717 ierr = MatRetrieveValues(aij->B);CHKERRQ(ierr); 1718 PetscFunctionReturn(0); 1719 } 1720 EXTERN_C_END 1721 1722 #include "petscpc.h" 1723 EXTERN_C_BEGIN 1724 EXTERN int MatGetDiagonalBlock_MPIAIJ(Mat,PetscTruth *,MatReuse,Mat *); 1725 EXTERN_C_END 1726 1727 EXTERN_C_BEGIN 1728 #undef __FUNCT__ 1729 #define __FUNCT__ "MatCreate_MPIAIJ" 1730 int MatCreate_MPIAIJ(Mat B) 1731 { 1732 Mat_MPIAIJ *b; 1733 int ierr,i,size; 1734 1735 PetscFunctionBegin; 1736 ierr = MPI_Comm_size(B->comm,&size);CHKERRQ(ierr); 1737 1738 ierr = PetscNew(Mat_MPIAIJ,&b);CHKERRQ(ierr); 1739 B->data = (void*)b; 1740 ierr = PetscMemzero(b,sizeof(Mat_MPIAIJ));CHKERRQ(ierr); 1741 ierr = PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 1742 B->factor = 0; 1743 B->assembled = PETSC_FALSE; 1744 B->mapping = 0; 1745 1746 B->insertmode = NOT_SET_VALUES; 1747 b->size = size; 1748 ierr = MPI_Comm_rank(B->comm,&b->rank);CHKERRQ(ierr); 1749 1750 ierr = PetscSplitOwnership(B->comm,&B->m,&B->M);CHKERRQ(ierr); 1751 ierr = PetscSplitOwnership(B->comm,&B->n,&B->N);CHKERRQ(ierr); 1752 1753 /* the information in the maps duplicates the information computed below, eventually 1754 we should remove the duplicate information that is not contained in the maps */ 1755 ierr = PetscMapCreateMPI(B->comm,B->m,B->M,&B->rmap);CHKERRQ(ierr); 1756 ierr = PetscMapCreateMPI(B->comm,B->n,B->N,&B->cmap);CHKERRQ(ierr); 1757 1758 /* build local table of row and column ownerships */ 1759 ierr = PetscMalloc(2*(b->size+2)*sizeof(int),&b->rowners);CHKERRQ(ierr); 1760 PetscLogObjectMemory(B,2*(b->size+2)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_MPIAIJ)); 1761 b->cowners = b->rowners + b->size + 2; 1762 ierr = MPI_Allgather(&B->m,1,MPI_INT,b->rowners+1,1,MPI_INT,B->comm);CHKERRQ(ierr); 1763 b->rowners[0] = 0; 1764 for (i=2; i<=b->size; i++) { 1765 b->rowners[i] += b->rowners[i-1]; 1766 } 1767 b->rstart = b->rowners[b->rank]; 1768 b->rend = b->rowners[b->rank+1]; 1769 ierr = MPI_Allgather(&B->n,1,MPI_INT,b->cowners+1,1,MPI_INT,B->comm);CHKERRQ(ierr); 1770 b->cowners[0] = 0; 1771 for (i=2; i<=b->size; i++) { 1772 b->cowners[i] += b->cowners[i-1]; 1773 } 1774 b->cstart = b->cowners[b->rank]; 1775 b->cend = b->cowners[b->rank+1]; 1776 1777 /* build cache for off array entries formed */ 1778 ierr = MatStashCreate_Private(B->comm,1,&B->stash);CHKERRQ(ierr); 1779 b->donotstash = PETSC_FALSE; 1780 b->colmap = 0; 1781 b->garray = 0; 1782 b->roworiented = PETSC_TRUE; 1783 1784 /* stuff used for matrix vector multiply */ 1785 b->lvec = PETSC_NULL; 1786 b->Mvctx = PETSC_NULL; 1787 1788 /* stuff for MatGetRow() */ 1789 b->rowindices = 0; 1790 b->rowvalues = 0; 1791 b->getrowactive = PETSC_FALSE; 1792 1793 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C", 1794 "MatStoreValues_MPIAIJ", 1795 MatStoreValues_MPIAIJ);CHKERRQ(ierr); 1796 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C", 1797 "MatRetrieveValues_MPIAIJ", 1798 MatRetrieveValues_MPIAIJ);CHKERRQ(ierr); 1799 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C", 1800 "MatGetDiagonalBlock_MPIAIJ", 1801 MatGetDiagonalBlock_MPIAIJ);CHKERRQ(ierr); 1802 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsSymmetric_C", 1803 "MatIsSymmetric_MPIAIJ", 1804 MatIsSymmetric_MPIAIJ); CHKERRQ(ierr); 1805 PetscFunctionReturn(0); 1806 } 1807 EXTERN_C_END 1808 1809 #undef __FUNCT__ 1810 #define __FUNCT__ "MatDuplicate_MPIAIJ" 1811 int MatDuplicate_MPIAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat) 1812 { 1813 Mat mat; 1814 Mat_MPIAIJ *a,*oldmat = (Mat_MPIAIJ*)matin->data; 1815 int ierr; 1816 1817 PetscFunctionBegin; 1818 *newmat = 0; 1819 ierr = MatCreate(matin->comm,matin->m,matin->n,matin->M,matin->N,&mat);CHKERRQ(ierr); 1820 ierr = MatSetType(mat,MATMPIAIJ);CHKERRQ(ierr); 1821 a = (Mat_MPIAIJ*)mat->data; 1822 ierr = PetscMemcpy(mat->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 1823 mat->factor = matin->factor; 1824 mat->assembled = PETSC_TRUE; 1825 mat->insertmode = NOT_SET_VALUES; 1826 mat->preallocated = PETSC_TRUE; 1827 1828 a->rstart = oldmat->rstart; 1829 a->rend = oldmat->rend; 1830 a->cstart = oldmat->cstart; 1831 a->cend = oldmat->cend; 1832 a->size = oldmat->size; 1833 a->rank = oldmat->rank; 1834 a->donotstash = oldmat->donotstash; 1835 a->roworiented = oldmat->roworiented; 1836 a->rowindices = 0; 1837 a->rowvalues = 0; 1838 a->getrowactive = PETSC_FALSE; 1839 1840 ierr = PetscMemcpy(a->rowners,oldmat->rowners,2*(a->size+2)*sizeof(int));CHKERRQ(ierr); 1841 ierr = MatStashCreate_Private(matin->comm,1,&mat->stash);CHKERRQ(ierr); 1842 if (oldmat->colmap) { 1843 #if defined (PETSC_USE_CTABLE) 1844 ierr = PetscTableCreateCopy(oldmat->colmap,&a->colmap);CHKERRQ(ierr); 1845 #else 1846 ierr = PetscMalloc((mat->N)*sizeof(int),&a->colmap);CHKERRQ(ierr); 1847 PetscLogObjectMemory(mat,(mat->N)*sizeof(int)); 1848 ierr = PetscMemcpy(a->colmap,oldmat->colmap,(mat->N)*sizeof(int));CHKERRQ(ierr); 1849 #endif 1850 } else a->colmap = 0; 1851 if (oldmat->garray) { 1852 int len; 1853 len = oldmat->B->n; 1854 ierr = PetscMalloc((len+1)*sizeof(int),&a->garray);CHKERRQ(ierr); 1855 PetscLogObjectMemory(mat,len*sizeof(int)); 1856 if (len) { ierr = PetscMemcpy(a->garray,oldmat->garray,len*sizeof(int));CHKERRQ(ierr); } 1857 } else a->garray = 0; 1858 1859 ierr = VecDuplicate(oldmat->lvec,&a->lvec);CHKERRQ(ierr); 1860 PetscLogObjectParent(mat,a->lvec); 1861 ierr = VecScatterCopy(oldmat->Mvctx,&a->Mvctx);CHKERRQ(ierr); 1862 PetscLogObjectParent(mat,a->Mvctx); 1863 ierr = MatDuplicate(oldmat->A,cpvalues,&a->A);CHKERRQ(ierr); 1864 PetscLogObjectParent(mat,a->A); 1865 ierr = MatDuplicate(oldmat->B,cpvalues,&a->B);CHKERRQ(ierr); 1866 PetscLogObjectParent(mat,a->B); 1867 ierr = PetscFListDuplicate(matin->qlist,&mat->qlist);CHKERRQ(ierr); 1868 *newmat = mat; 1869 PetscFunctionReturn(0); 1870 } 1871 1872 #include "petscsys.h" 1873 1874 EXTERN_C_BEGIN 1875 #undef __FUNCT__ 1876 #define __FUNCT__ "MatLoad_MPIAIJ" 1877 int MatLoad_MPIAIJ(PetscViewer viewer,MatType type,Mat *newmat) 1878 { 1879 Mat A; 1880 PetscScalar *vals,*svals; 1881 MPI_Comm comm = ((PetscObject)viewer)->comm; 1882 MPI_Status status; 1883 int i,nz,ierr,j,rstart,rend,fd; 1884 int header[4],rank,size,*rowlengths = 0,M,N,m,*rowners,maxnz,*cols; 1885 int *ourlens,*sndcounts = 0,*procsnz = 0,*offlens,jj,*mycols,*smycols; 1886 int tag = ((PetscObject)viewer)->tag,cend,cstart,n; 1887 #if defined(PETSC_HAVE_SPOOLES) || defined(PETSC_HAVE_SUPERLUDIST) || defined(PETSC_HAVE_MUMPS) 1888 PetscTruth flag; 1889 #endif 1890 1891 PetscFunctionBegin; 1892 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 1893 ierr = MPI_Comm_rank(comm,&rank);CHKERRQ(ierr); 1894 if (!rank) { 1895 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 1896 ierr = PetscBinaryRead(fd,(char *)header,4,PETSC_INT);CHKERRQ(ierr); 1897 if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object"); 1898 if (header[3] < 0) { 1899 SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix in special format on disk, cannot load as MPIAIJ"); 1900 } 1901 } 1902 1903 ierr = MPI_Bcast(header+1,3,MPI_INT,0,comm);CHKERRQ(ierr); 1904 M = header[1]; N = header[2]; 1905 /* determine ownership of all rows */ 1906 m = M/size + ((M % size) > rank); 1907 ierr = PetscMalloc((size+2)*sizeof(int),&rowners);CHKERRQ(ierr); 1908 ierr = MPI_Allgather(&m,1,MPI_INT,rowners+1,1,MPI_INT,comm);CHKERRQ(ierr); 1909 rowners[0] = 0; 1910 for (i=2; i<=size; i++) { 1911 rowners[i] += rowners[i-1]; 1912 } 1913 rstart = rowners[rank]; 1914 rend = rowners[rank+1]; 1915 1916 /* distribute row lengths to all processors */ 1917 ierr = PetscMalloc(2*(rend-rstart+1)*sizeof(int),&ourlens);CHKERRQ(ierr); 1918 offlens = ourlens + (rend-rstart); 1919 if (!rank) { 1920 ierr = PetscMalloc(M*sizeof(int),&rowlengths);CHKERRQ(ierr); 1921 ierr = PetscBinaryRead(fd,rowlengths,M,PETSC_INT);CHKERRQ(ierr); 1922 ierr = PetscMalloc(size*sizeof(int),&sndcounts);CHKERRQ(ierr); 1923 for (i=0; i<size; i++) sndcounts[i] = rowners[i+1] - rowners[i]; 1924 ierr = MPI_Scatterv(rowlengths,sndcounts,rowners,MPI_INT,ourlens,rend-rstart,MPI_INT,0,comm);CHKERRQ(ierr); 1925 ierr = PetscFree(sndcounts);CHKERRQ(ierr); 1926 } else { 1927 ierr = MPI_Scatterv(0,0,0,MPI_INT,ourlens,rend-rstart,MPI_INT,0,comm);CHKERRQ(ierr); 1928 } 1929 1930 if (!rank) { 1931 /* calculate the number of nonzeros on each processor */ 1932 ierr = PetscMalloc(size*sizeof(int),&procsnz);CHKERRQ(ierr); 1933 ierr = PetscMemzero(procsnz,size*sizeof(int));CHKERRQ(ierr); 1934 for (i=0; i<size; i++) { 1935 for (j=rowners[i]; j< rowners[i+1]; j++) { 1936 procsnz[i] += rowlengths[j]; 1937 } 1938 } 1939 ierr = PetscFree(rowlengths);CHKERRQ(ierr); 1940 1941 /* determine max buffer needed and allocate it */ 1942 maxnz = 0; 1943 for (i=0; i<size; i++) { 1944 maxnz = PetscMax(maxnz,procsnz[i]); 1945 } 1946 ierr = PetscMalloc(maxnz*sizeof(int),&cols);CHKERRQ(ierr); 1947 1948 /* read in my part of the matrix column indices */ 1949 nz = procsnz[0]; 1950 ierr = PetscMalloc(nz*sizeof(int),&mycols);CHKERRQ(ierr); 1951 ierr = PetscBinaryRead(fd,mycols,nz,PETSC_INT);CHKERRQ(ierr); 1952 1953 /* read in every one elses and ship off */ 1954 for (i=1; i<size; i++) { 1955 nz = procsnz[i]; 1956 ierr = PetscBinaryRead(fd,cols,nz,PETSC_INT);CHKERRQ(ierr); 1957 ierr = MPI_Send(cols,nz,MPI_INT,i,tag,comm);CHKERRQ(ierr); 1958 } 1959 ierr = PetscFree(cols);CHKERRQ(ierr); 1960 } else { 1961 /* determine buffer space needed for message */ 1962 nz = 0; 1963 for (i=0; i<m; i++) { 1964 nz += ourlens[i]; 1965 } 1966 ierr = PetscMalloc((nz+1)*sizeof(int),&mycols);CHKERRQ(ierr); 1967 1968 /* receive message of column indices*/ 1969 ierr = MPI_Recv(mycols,nz,MPI_INT,0,tag,comm,&status);CHKERRQ(ierr); 1970 ierr = MPI_Get_count(&status,MPI_INT,&maxnz);CHKERRQ(ierr); 1971 if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file"); 1972 } 1973 1974 /* determine column ownership if matrix is not square */ 1975 if (N != M) { 1976 n = N/size + ((N % size) > rank); 1977 ierr = MPI_Scan(&n,&cend,1,MPI_INT,MPI_SUM,comm);CHKERRQ(ierr); 1978 cstart = cend - n; 1979 } else { 1980 cstart = rstart; 1981 cend = rend; 1982 n = cend - cstart; 1983 } 1984 1985 /* loop over local rows, determining number of off diagonal entries */ 1986 ierr = PetscMemzero(offlens,m*sizeof(int));CHKERRQ(ierr); 1987 jj = 0; 1988 for (i=0; i<m; i++) { 1989 for (j=0; j<ourlens[i]; j++) { 1990 if (mycols[jj] < cstart || mycols[jj] >= cend) offlens[i]++; 1991 jj++; 1992 } 1993 } 1994 1995 /* create our matrix */ 1996 for (i=0; i<m; i++) { 1997 ourlens[i] -= offlens[i]; 1998 } 1999 ierr = MatCreateMPIAIJ(comm,m,n,M,N,0,ourlens,0,offlens,newmat);CHKERRQ(ierr); 2000 A = *newmat; 2001 ierr = MatSetOption(A,MAT_COLUMNS_SORTED);CHKERRQ(ierr); 2002 for (i=0; i<m; i++) { 2003 ourlens[i] += offlens[i]; 2004 } 2005 2006 if (!rank) { 2007 ierr = PetscMalloc(maxnz*sizeof(PetscScalar),&vals);CHKERRQ(ierr); 2008 2009 /* read in my part of the matrix numerical values */ 2010 nz = procsnz[0]; 2011 ierr = PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);CHKERRQ(ierr); 2012 2013 /* insert into matrix */ 2014 jj = rstart; 2015 smycols = mycols; 2016 svals = vals; 2017 for (i=0; i<m; i++) { 2018 ierr = MatSetValues(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);CHKERRQ(ierr); 2019 smycols += ourlens[i]; 2020 svals += ourlens[i]; 2021 jj++; 2022 } 2023 2024 /* read in other processors and ship out */ 2025 for (i=1; i<size; i++) { 2026 nz = procsnz[i]; 2027 ierr = PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);CHKERRQ(ierr); 2028 ierr = MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);CHKERRQ(ierr); 2029 } 2030 ierr = PetscFree(procsnz);CHKERRQ(ierr); 2031 } else { 2032 /* receive numeric values */ 2033 ierr = PetscMalloc((nz+1)*sizeof(PetscScalar),&vals);CHKERRQ(ierr); 2034 2035 /* receive message of values*/ 2036 ierr = MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);CHKERRQ(ierr); 2037 ierr = MPI_Get_count(&status,MPIU_SCALAR,&maxnz);CHKERRQ(ierr); 2038 if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file"); 2039 2040 /* insert into matrix */ 2041 jj = rstart; 2042 smycols = mycols; 2043 svals = vals; 2044 for (i=0; i<m; i++) { 2045 ierr = MatSetValues(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);CHKERRQ(ierr); 2046 smycols += ourlens[i]; 2047 svals += ourlens[i]; 2048 jj++; 2049 } 2050 } 2051 ierr = PetscFree(ourlens);CHKERRQ(ierr); 2052 ierr = PetscFree(vals);CHKERRQ(ierr); 2053 ierr = PetscFree(mycols);CHKERRQ(ierr); 2054 ierr = PetscFree(rowners);CHKERRQ(ierr); 2055 2056 ierr = MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2057 ierr = MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2058 #if defined(PETSC_HAVE_SPOOLES) 2059 ierr = PetscOptionsHasName(A->prefix,"-mat_aij_spooles",&flag);CHKERRQ(ierr); 2060 if (flag) { 2061 if (size == 1) { 2062 ierr = MatUseSpooles_SeqAIJ(A);CHKERRQ(ierr); 2063 } else { 2064 ierr = MatUseSpooles_MPIAIJ(A);CHKERRQ(ierr); 2065 } 2066 } 2067 #endif 2068 #if defined(PETSC_HAVE_SUPERLUDIST) 2069 ierr = PetscOptionsHasName(A->prefix,"-mat_aij_superlu_dist",&flag);CHKERRQ(ierr); 2070 if (flag) { ierr = MatUseSuperLU_DIST_MPIAIJ(A);CHKERRQ(ierr); } 2071 #endif 2072 #if defined(PETSC_HAVE_MUMPS) 2073 ierr = PetscOptionsHasName(A->prefix,"-mat_aij_mumps",&flag);CHKERRQ(ierr); 2074 if (flag) { ierr = MatUseMUMPS_MPIAIJ(A);CHKERRQ(ierr); } 2075 #endif 2076 PetscFunctionReturn(0); 2077 } 2078 EXTERN_C_END 2079 2080 #undef __FUNCT__ 2081 #define __FUNCT__ "MatGetSubMatrix_MPIAIJ" 2082 /* 2083 Not great since it makes two copies of the submatrix, first an SeqAIJ 2084 in local and then by concatenating the local matrices the end result. 2085 Writing it directly would be much like MatGetSubMatrices_MPIAIJ() 2086 */ 2087 int MatGetSubMatrix_MPIAIJ(Mat mat,IS isrow,IS iscol,int csize,MatReuse call,Mat *newmat) 2088 { 2089 int ierr,i,m,n,rstart,row,rend,nz,*cwork,size,rank,j; 2090 int *ii,*jj,nlocal,*dlens,*olens,dlen,olen,jend,mglobal; 2091 Mat *local,M,Mreuse; 2092 PetscScalar *vwork,*aa; 2093 MPI_Comm comm = mat->comm; 2094 Mat_SeqAIJ *aij; 2095 2096 2097 PetscFunctionBegin; 2098 ierr = MPI_Comm_rank(comm,&rank);CHKERRQ(ierr); 2099 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 2100 2101 if (call == MAT_REUSE_MATRIX) { 2102 ierr = PetscObjectQuery((PetscObject)*newmat,"SubMatrix",(PetscObject *)&Mreuse);CHKERRQ(ierr); 2103 if (!Mreuse) SETERRQ(1,"Submatrix passed in was not used before, cannot reuse"); 2104 local = &Mreuse; 2105 ierr = MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_REUSE_MATRIX,&local);CHKERRQ(ierr); 2106 } else { 2107 ierr = MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&local);CHKERRQ(ierr); 2108 Mreuse = *local; 2109 ierr = PetscFree(local);CHKERRQ(ierr); 2110 } 2111 2112 /* 2113 m - number of local rows 2114 n - number of columns (same on all processors) 2115 rstart - first row in new global matrix generated 2116 */ 2117 ierr = MatGetSize(Mreuse,&m,&n);CHKERRQ(ierr); 2118 if (call == MAT_INITIAL_MATRIX) { 2119 aij = (Mat_SeqAIJ*)(Mreuse)->data; 2120 ii = aij->i; 2121 jj = aij->j; 2122 2123 /* 2124 Determine the number of non-zeros in the diagonal and off-diagonal 2125 portions of the matrix in order to do correct preallocation 2126 */ 2127 2128 /* first get start and end of "diagonal" columns */ 2129 if (csize == PETSC_DECIDE) { 2130 ierr = ISGetSize(isrow,&mglobal);CHKERRQ(ierr); 2131 if (mglobal == n) { /* square matrix */ 2132 nlocal = m; 2133 } else { 2134 nlocal = n/size + ((n % size) > rank); 2135 } 2136 } else { 2137 nlocal = csize; 2138 } 2139 ierr = MPI_Scan(&nlocal,&rend,1,MPI_INT,MPI_SUM,comm);CHKERRQ(ierr); 2140 rstart = rend - nlocal; 2141 if (rank == size - 1 && rend != n) { 2142 SETERRQ2(1,"Local column sizes %d do not add up to total number of columns %d",rend,n); 2143 } 2144 2145 /* next, compute all the lengths */ 2146 ierr = PetscMalloc((2*m+1)*sizeof(int),&dlens);CHKERRQ(ierr); 2147 olens = dlens + m; 2148 for (i=0; i<m; i++) { 2149 jend = ii[i+1] - ii[i]; 2150 olen = 0; 2151 dlen = 0; 2152 for (j=0; j<jend; j++) { 2153 if (*jj < rstart || *jj >= rend) olen++; 2154 else dlen++; 2155 jj++; 2156 } 2157 olens[i] = olen; 2158 dlens[i] = dlen; 2159 } 2160 ierr = MatCreateMPIAIJ(comm,m,nlocal,PETSC_DECIDE,n,0,dlens,0,olens,&M);CHKERRQ(ierr); 2161 ierr = PetscFree(dlens);CHKERRQ(ierr); 2162 } else { 2163 int ml,nl; 2164 2165 M = *newmat; 2166 ierr = MatGetLocalSize(M,&ml,&nl);CHKERRQ(ierr); 2167 if (ml != m) SETERRQ(PETSC_ERR_ARG_SIZ,"Previous matrix must be same size/layout as request"); 2168 ierr = MatZeroEntries(M);CHKERRQ(ierr); 2169 /* 2170 The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly, 2171 rather than the slower MatSetValues(). 2172 */ 2173 M->was_assembled = PETSC_TRUE; 2174 M->assembled = PETSC_FALSE; 2175 } 2176 ierr = MatGetOwnershipRange(M,&rstart,&rend);CHKERRQ(ierr); 2177 aij = (Mat_SeqAIJ*)(Mreuse)->data; 2178 ii = aij->i; 2179 jj = aij->j; 2180 aa = aij->a; 2181 for (i=0; i<m; i++) { 2182 row = rstart + i; 2183 nz = ii[i+1] - ii[i]; 2184 cwork = jj; jj += nz; 2185 vwork = aa; aa += nz; 2186 ierr = MatSetValues_MPIAIJ(M,1,&row,nz,cwork,vwork,INSERT_VALUES);CHKERRQ(ierr); 2187 } 2188 2189 ierr = MatAssemblyBegin(M,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2190 ierr = MatAssemblyEnd(M,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2191 *newmat = M; 2192 2193 /* save submatrix used in processor for next request */ 2194 if (call == MAT_INITIAL_MATRIX) { 2195 ierr = PetscObjectCompose((PetscObject)M,"SubMatrix",(PetscObject)Mreuse);CHKERRQ(ierr); 2196 ierr = PetscObjectDereference((PetscObject)Mreuse);CHKERRQ(ierr); 2197 } 2198 2199 PetscFunctionReturn(0); 2200 } 2201 2202 #undef __FUNCT__ 2203 #define __FUNCT__ "MatMPIAIJSetPreallocation" 2204 /*@C 2205 MatMPIAIJSetPreallocation - Creates a sparse parallel matrix in AIJ format 2206 (the default parallel PETSc format). For good matrix assembly performance 2207 the user should preallocate the matrix storage by setting the parameters 2208 d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately, 2209 performance can be increased by more than a factor of 50. 2210 2211 Collective on MPI_Comm 2212 2213 Input Parameters: 2214 + A - the matrix 2215 . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix 2216 (same value is used for all local rows) 2217 . d_nnz - array containing the number of nonzeros in the various rows of the 2218 DIAGONAL portion of the local submatrix (possibly different for each row) 2219 or PETSC_NULL, if d_nz is used to specify the nonzero structure. 2220 The size of this array is equal to the number of local rows, i.e 'm'. 2221 You must leave room for the diagonal entry even if it is zero. 2222 . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local 2223 submatrix (same value is used for all local rows). 2224 - o_nnz - array containing the number of nonzeros in the various rows of the 2225 OFF-DIAGONAL portion of the local submatrix (possibly different for 2226 each row) or PETSC_NULL, if o_nz is used to specify the nonzero 2227 structure. The size of this array is equal to the number 2228 of local rows, i.e 'm'. 2229 2230 The AIJ format (also called the Yale sparse matrix format or 2231 compressed row storage), is fully compatible with standard Fortran 77 2232 storage. That is, the stored row and column indices can begin at 2233 either one (as in Fortran) or zero. See the users manual for details. 2234 2235 The user MUST specify either the local or global matrix dimensions 2236 (possibly both). 2237 2238 The parallel matrix is partitioned such that the first m0 rows belong to 2239 process 0, the next m1 rows belong to process 1, the next m2 rows belong 2240 to process 2 etc.. where m0,m1,m2... are the input parameter 'm'. 2241 2242 The DIAGONAL portion of the local submatrix of a processor can be defined 2243 as the submatrix which is obtained by extraction the part corresponding 2244 to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the 2245 first row that belongs to the processor, and r2 is the last row belonging 2246 to the this processor. This is a square mxm matrix. The remaining portion 2247 of the local submatrix (mxN) constitute the OFF-DIAGONAL portion. 2248 2249 If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored. 2250 2251 By default, this format uses inodes (identical nodes) when possible. 2252 We search for consecutive rows with the same nonzero structure, thereby 2253 reusing matrix information to achieve increased efficiency. 2254 2255 Options Database Keys: 2256 + -mat_aij_no_inode - Do not use inodes 2257 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2258 - -mat_aij_oneindex - Internally use indexing starting at 1 2259 rather than 0. Note that when calling MatSetValues(), 2260 the user still MUST index entries starting at 0! 2261 2262 Example usage: 2263 2264 Consider the following 8x8 matrix with 34 non-zero values, that is 2265 assembled across 3 processors. Lets assume that proc0 owns 3 rows, 2266 proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown 2267 as follows: 2268 2269 .vb 2270 1 2 0 | 0 3 0 | 0 4 2271 Proc0 0 5 6 | 7 0 0 | 8 0 2272 9 0 10 | 11 0 0 | 12 0 2273 ------------------------------------- 2274 13 0 14 | 15 16 17 | 0 0 2275 Proc1 0 18 0 | 19 20 21 | 0 0 2276 0 0 0 | 22 23 0 | 24 0 2277 ------------------------------------- 2278 Proc2 25 26 27 | 0 0 28 | 29 0 2279 30 0 0 | 31 32 33 | 0 34 2280 .ve 2281 2282 This can be represented as a collection of submatrices as: 2283 2284 .vb 2285 A B C 2286 D E F 2287 G H I 2288 .ve 2289 2290 Where the submatrices A,B,C are owned by proc0, D,E,F are 2291 owned by proc1, G,H,I are owned by proc2. 2292 2293 The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 2294 The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 2295 The 'M','N' parameters are 8,8, and have the same values on all procs. 2296 2297 The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are 2298 submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices 2299 corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively. 2300 Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL 2301 part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ 2302 matrix, ans [DF] as another SeqAIJ matrix. 2303 2304 When d_nz, o_nz parameters are specified, d_nz storage elements are 2305 allocated for every row of the local diagonal submatrix, and o_nz 2306 storage locations are allocated for every row of the OFF-DIAGONAL submat. 2307 One way to choose d_nz and o_nz is to use the max nonzerors per local 2308 rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices. 2309 In this case, the values of d_nz,o_nz are: 2310 .vb 2311 proc0 : dnz = 2, o_nz = 2 2312 proc1 : dnz = 3, o_nz = 2 2313 proc2 : dnz = 1, o_nz = 4 2314 .ve 2315 We are allocating m*(d_nz+o_nz) storage locations for every proc. This 2316 translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10 2317 for proc3. i.e we are using 12+15+10=37 storage locations to store 2318 34 values. 2319 2320 When d_nnz, o_nnz parameters are specified, the storage is specified 2321 for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices. 2322 In the above case the values for d_nnz,o_nnz are: 2323 .vb 2324 proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2] 2325 proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1] 2326 proc2: d_nnz = [1,1] and o_nnz = [4,4] 2327 .ve 2328 Here the space allocated is sum of all the above values i.e 34, and 2329 hence pre-allocation is perfect. 2330 2331 Level: intermediate 2332 2333 .keywords: matrix, aij, compressed row, sparse, parallel 2334 2335 .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues() 2336 @*/ 2337 int MatMPIAIJSetPreallocation(Mat B,int d_nz,int *d_nnz,int o_nz,int *o_nnz) 2338 { 2339 Mat_MPIAIJ *b; 2340 int ierr,i; 2341 PetscTruth flg2; 2342 2343 PetscFunctionBegin; 2344 ierr = PetscTypeCompare((PetscObject)B,MATMPIAIJ,&flg2);CHKERRQ(ierr); 2345 if (!flg2) PetscFunctionReturn(0); 2346 B->preallocated = PETSC_TRUE; 2347 if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5; 2348 if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2; 2349 if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %d",d_nz); 2350 if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %d",o_nz); 2351 if (d_nnz) { 2352 for (i=0; i<B->m; i++) { 2353 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]); 2354 } 2355 } 2356 if (o_nnz) { 2357 for (i=0; i<B->m; i++) { 2358 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]); 2359 } 2360 } 2361 b = (Mat_MPIAIJ*)B->data; 2362 2363 ierr = MatCreateSeqAIJ(PETSC_COMM_SELF,B->m,B->n,d_nz,d_nnz,&b->A);CHKERRQ(ierr); 2364 PetscLogObjectParent(B,b->A); 2365 ierr = MatCreateSeqAIJ(PETSC_COMM_SELF,B->m,B->N,o_nz,o_nnz,&b->B);CHKERRQ(ierr); 2366 PetscLogObjectParent(B,b->B); 2367 2368 PetscFunctionReturn(0); 2369 } 2370 2371 #undef __FUNCT__ 2372 #define __FUNCT__ "MatCreateMPIAIJ" 2373 /*@C 2374 MatCreateMPIAIJ - Creates a sparse parallel matrix in AIJ format 2375 (the default parallel PETSc format). For good matrix assembly performance 2376 the user should preallocate the matrix storage by setting the parameters 2377 d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately, 2378 performance can be increased by more than a factor of 50. 2379 2380 Collective on MPI_Comm 2381 2382 Input Parameters: 2383 + comm - MPI communicator 2384 . m - number of local rows (or PETSC_DECIDE to have calculated if M is given) 2385 This value should be the same as the local size used in creating the 2386 y vector for the matrix-vector product y = Ax. 2387 . n - This value should be the same as the local size used in creating the 2388 x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have 2389 calculated if N is given) For square matrices n is almost always m. 2390 . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given) 2391 . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given) 2392 . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix 2393 (same value is used for all local rows) 2394 . d_nnz - array containing the number of nonzeros in the various rows of the 2395 DIAGONAL portion of the local submatrix (possibly different for each row) 2396 or PETSC_NULL, if d_nz is used to specify the nonzero structure. 2397 The size of this array is equal to the number of local rows, i.e 'm'. 2398 You must leave room for the diagonal entry even if it is zero. 2399 . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local 2400 submatrix (same value is used for all local rows). 2401 - o_nnz - array containing the number of nonzeros in the various rows of the 2402 OFF-DIAGONAL portion of the local submatrix (possibly different for 2403 each row) or PETSC_NULL, if o_nz is used to specify the nonzero 2404 structure. The size of this array is equal to the number 2405 of local rows, i.e 'm'. 2406 2407 Output Parameter: 2408 . A - the matrix 2409 2410 Notes: 2411 m,n,M,N parameters specify the size of the matrix, and its partitioning across 2412 processors, while d_nz,d_nnz,o_nz,o_nnz parameters specify the approximate 2413 storage requirements for this matrix. 2414 2415 If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one 2416 processor than it must be used on all processors that share the object for 2417 that argument. 2418 2419 The AIJ format (also called the Yale sparse matrix format or 2420 compressed row storage), is fully compatible with standard Fortran 77 2421 storage. That is, the stored row and column indices can begin at 2422 either one (as in Fortran) or zero. See the users manual for details. 2423 2424 The user MUST specify either the local or global matrix dimensions 2425 (possibly both). 2426 2427 The parallel matrix is partitioned such that the first m0 rows belong to 2428 process 0, the next m1 rows belong to process 1, the next m2 rows belong 2429 to process 2 etc.. where m0,m1,m2... are the input parameter 'm'. 2430 2431 The DIAGONAL portion of the local submatrix of a processor can be defined 2432 as the submatrix which is obtained by extraction the part corresponding 2433 to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the 2434 first row that belongs to the processor, and r2 is the last row belonging 2435 to the this processor. This is a square mxm matrix. The remaining portion 2436 of the local submatrix (mxN) constitute the OFF-DIAGONAL portion. 2437 2438 If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored. 2439 2440 By default, this format uses inodes (identical nodes) when possible. 2441 We search for consecutive rows with the same nonzero structure, thereby 2442 reusing matrix information to achieve increased efficiency. 2443 2444 Options Database Keys: 2445 + -mat_aij_no_inode - Do not use inodes 2446 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2447 - -mat_aij_oneindex - Internally use indexing starting at 1 2448 rather than 0. Note that when calling MatSetValues(), 2449 the user still MUST index entries starting at 0! 2450 2451 2452 Example usage: 2453 2454 Consider the following 8x8 matrix with 34 non-zero values, that is 2455 assembled across 3 processors. Lets assume that proc0 owns 3 rows, 2456 proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown 2457 as follows: 2458 2459 .vb 2460 1 2 0 | 0 3 0 | 0 4 2461 Proc0 0 5 6 | 7 0 0 | 8 0 2462 9 0 10 | 11 0 0 | 12 0 2463 ------------------------------------- 2464 13 0 14 | 15 16 17 | 0 0 2465 Proc1 0 18 0 | 19 20 21 | 0 0 2466 0 0 0 | 22 23 0 | 24 0 2467 ------------------------------------- 2468 Proc2 25 26 27 | 0 0 28 | 29 0 2469 30 0 0 | 31 32 33 | 0 34 2470 .ve 2471 2472 This can be represented as a collection of submatrices as: 2473 2474 .vb 2475 A B C 2476 D E F 2477 G H I 2478 .ve 2479 2480 Where the submatrices A,B,C are owned by proc0, D,E,F are 2481 owned by proc1, G,H,I are owned by proc2. 2482 2483 The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 2484 The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 2485 The 'M','N' parameters are 8,8, and have the same values on all procs. 2486 2487 The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are 2488 submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices 2489 corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively. 2490 Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL 2491 part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ 2492 matrix, ans [DF] as another SeqAIJ matrix. 2493 2494 When d_nz, o_nz parameters are specified, d_nz storage elements are 2495 allocated for every row of the local diagonal submatrix, and o_nz 2496 storage locations are allocated for every row of the OFF-DIAGONAL submat. 2497 One way to choose d_nz and o_nz is to use the max nonzerors per local 2498 rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices. 2499 In this case, the values of d_nz,o_nz are: 2500 .vb 2501 proc0 : dnz = 2, o_nz = 2 2502 proc1 : dnz = 3, o_nz = 2 2503 proc2 : dnz = 1, o_nz = 4 2504 .ve 2505 We are allocating m*(d_nz+o_nz) storage locations for every proc. This 2506 translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10 2507 for proc3. i.e we are using 12+15+10=37 storage locations to store 2508 34 values. 2509 2510 When d_nnz, o_nnz parameters are specified, the storage is specified 2511 for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices. 2512 In the above case the values for d_nnz,o_nnz are: 2513 .vb 2514 proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2] 2515 proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1] 2516 proc2: d_nnz = [1,1] and o_nnz = [4,4] 2517 .ve 2518 Here the space allocated is sum of all the above values i.e 34, and 2519 hence pre-allocation is perfect. 2520 2521 Level: intermediate 2522 2523 .keywords: matrix, aij, compressed row, sparse, parallel 2524 2525 .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues() 2526 @*/ 2527 int MatCreateMPIAIJ(MPI_Comm comm,int m,int n,int M,int N,int d_nz,int *d_nnz,int o_nz,int *o_nnz,Mat *A) 2528 { 2529 int ierr,size; 2530 2531 PetscFunctionBegin; 2532 ierr = MatCreate(comm,m,n,M,N,A);CHKERRQ(ierr); 2533 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 2534 if (size > 1) { 2535 ierr = MatSetType(*A,MATMPIAIJ);CHKERRQ(ierr); 2536 ierr = MatMPIAIJSetPreallocation(*A,d_nz,d_nnz,o_nz,o_nnz);CHKERRQ(ierr); 2537 } else { 2538 ierr = MatSetType(*A,MATSEQAIJ);CHKERRQ(ierr); 2539 ierr = MatSeqAIJSetPreallocation(*A,d_nz,d_nnz);CHKERRQ(ierr); 2540 } 2541 PetscFunctionReturn(0); 2542 } 2543 2544 #undef __FUNCT__ 2545 #define __FUNCT__ "MatMPIAIJGetSeqAIJ" 2546 int MatMPIAIJGetSeqAIJ(Mat A,Mat *Ad,Mat *Ao,int **colmap) 2547 { 2548 Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data; 2549 PetscFunctionBegin; 2550 *Ad = a->A; 2551 *Ao = a->B; 2552 *colmap = a->garray; 2553 PetscFunctionReturn(0); 2554 } 2555 2556 #undef __FUNCT__ 2557 #define __FUNCT__ "MatSetColoring_MPIAIJ" 2558 int MatSetColoring_MPIAIJ(Mat A,ISColoring coloring) 2559 { 2560 int ierr,i; 2561 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 2562 2563 PetscFunctionBegin; 2564 if (coloring->ctype == IS_COLORING_LOCAL) { 2565 ISColoringValue *allcolors,*colors; 2566 ISColoring ocoloring; 2567 2568 /* set coloring for diagonal portion */ 2569 ierr = MatSetColoring_SeqAIJ(a->A,coloring);CHKERRQ(ierr); 2570 2571 /* set coloring for off-diagonal portion */ 2572 ierr = ISAllGatherColors(A->comm,coloring->n,coloring->colors,PETSC_NULL,&allcolors);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] = allcolors[a->garray[i]]; 2576 } 2577 ierr = PetscFree(allcolors);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 if (coloring->ctype == IS_COLORING_GHOSTED) { 2582 ISColoringValue *colors; 2583 int *larray; 2584 ISColoring ocoloring; 2585 2586 /* set coloring for diagonal portion */ 2587 ierr = PetscMalloc((a->A->n+1)*sizeof(int),&larray);CHKERRQ(ierr); 2588 for (i=0; i<a->A->n; i++) { 2589 larray[i] = i + a->cstart; 2590 } 2591 ierr = ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,a->A->n,larray,PETSC_NULL,larray);CHKERRQ(ierr); 2592 ierr = PetscMalloc((a->A->n+1)*sizeof(ISColoringValue),&colors);CHKERRQ(ierr); 2593 for (i=0; i<a->A->n; i++) { 2594 colors[i] = coloring->colors[larray[i]]; 2595 } 2596 ierr = PetscFree(larray);CHKERRQ(ierr); 2597 ierr = ISColoringCreate(PETSC_COMM_SELF,a->A->n,colors,&ocoloring);CHKERRQ(ierr); 2598 ierr = MatSetColoring_SeqAIJ(a->A,ocoloring);CHKERRQ(ierr); 2599 ierr = ISColoringDestroy(ocoloring);CHKERRQ(ierr); 2600 2601 /* set coloring for off-diagonal portion */ 2602 ierr = PetscMalloc((a->B->n+1)*sizeof(int),&larray);CHKERRQ(ierr); 2603 ierr = ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,a->B->n,a->garray,PETSC_NULL,larray);CHKERRQ(ierr); 2604 ierr = PetscMalloc((a->B->n+1)*sizeof(ISColoringValue),&colors);CHKERRQ(ierr); 2605 for (i=0; i<a->B->n; i++) { 2606 colors[i] = coloring->colors[larray[i]]; 2607 } 2608 ierr = PetscFree(larray);CHKERRQ(ierr); 2609 ierr = ISColoringCreate(MPI_COMM_SELF,a->B->n,colors,&ocoloring);CHKERRQ(ierr); 2610 ierr = MatSetColoring_SeqAIJ(a->B,ocoloring);CHKERRQ(ierr); 2611 ierr = ISColoringDestroy(ocoloring);CHKERRQ(ierr); 2612 } else { 2613 SETERRQ1(1,"No support ISColoringType %d",coloring->ctype); 2614 } 2615 2616 PetscFunctionReturn(0); 2617 } 2618 2619 #undef __FUNCT__ 2620 #define __FUNCT__ "MatSetValuesAdic_MPIAIJ" 2621 int MatSetValuesAdic_MPIAIJ(Mat A,void *advalues) 2622 { 2623 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 2624 int ierr; 2625 2626 PetscFunctionBegin; 2627 ierr = MatSetValuesAdic_SeqAIJ(a->A,advalues);CHKERRQ(ierr); 2628 ierr = MatSetValuesAdic_SeqAIJ(a->B,advalues);CHKERRQ(ierr); 2629 PetscFunctionReturn(0); 2630 } 2631 2632 #undef __FUNCT__ 2633 #define __FUNCT__ "MatSetValuesAdifor_MPIAIJ" 2634 int MatSetValuesAdifor_MPIAIJ(Mat A,int nl,void *advalues) 2635 { 2636 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 2637 int ierr; 2638 2639 PetscFunctionBegin; 2640 ierr = MatSetValuesAdifor_SeqAIJ(a->A,nl,advalues);CHKERRQ(ierr); 2641 ierr = MatSetValuesAdifor_SeqAIJ(a->B,nl,advalues);CHKERRQ(ierr); 2642 PetscFunctionReturn(0); 2643 } 2644 2645 #undef __FUNCT__ 2646 #define __FUNCT__ "MatMerge" 2647 /*@C 2648 MatMerge - Creates a single large PETSc matrix by concatinating sequential 2649 matrices from each processor 2650 2651 Collective on MPI_Comm 2652 2653 Input Parameters: 2654 + comm - the communicators the parallel matrix will live on 2655 - inmat - the input sequential matrices 2656 2657 Output Parameter: 2658 . outmat - the parallel matrix generated 2659 2660 Level: advanced 2661 2662 Notes: The number of columns of the matrix in EACH of the seperate files 2663 MUST be the same. 2664 2665 @*/ 2666 int MatMerge(MPI_Comm comm,Mat inmat, Mat *outmat) 2667 { 2668 int ierr,m,n,i,rstart,*indx,nnz,I,*dnz,*onz; 2669 PetscScalar *values; 2670 PetscMap columnmap,rowmap; 2671 2672 PetscFunctionBegin; 2673 2674 ierr = MatGetSize(inmat,&m,&n);CHKERRQ(ierr); 2675 2676 /* count nonzeros in each row, for diagonal and off diagonal portion of matrix */ 2677 ierr = PetscMapCreate(comm,&columnmap);CHKERRQ(ierr); 2678 ierr = PetscMapSetSize(columnmap,n);CHKERRQ(ierr); 2679 ierr = PetscMapSetType(columnmap,MAP_MPI);CHKERRQ(ierr); 2680 ierr = PetscMapGetLocalSize(columnmap,&n);CHKERRQ(ierr); 2681 ierr = PetscMapDestroy(columnmap);CHKERRQ(ierr); 2682 2683 ierr = PetscMapCreate(comm,&rowmap);CHKERRQ(ierr); 2684 ierr = PetscMapSetLocalSize(rowmap,m);CHKERRQ(ierr); 2685 ierr = PetscMapSetType(rowmap,MAP_MPI);CHKERRQ(ierr); 2686 ierr = PetscMapGetLocalRange(rowmap,&rstart,0);CHKERRQ(ierr); 2687 ierr = PetscMapDestroy(rowmap);CHKERRQ(ierr); 2688 2689 ierr = MatPreallocateInitialize(comm,m,n,dnz,onz);CHKERRQ(ierr); 2690 for (i=0;i<m;i++) { 2691 ierr = MatGetRow(inmat,i,&nnz,&indx,&values);CHKERRQ(ierr); 2692 ierr = MatPreallocateSet(i+rstart,nnz,indx,dnz,onz);CHKERRQ(ierr); 2693 ierr = MatRestoreRow(inmat,i,&nnz,&indx,&values);CHKERRQ(ierr); 2694 } 2695 ierr = MatCreateMPIAIJ(comm,m,n,PETSC_DETERMINE,PETSC_DETERMINE,0,dnz,0,onz,outmat);CHKERRQ(ierr); 2696 ierr = MatPreallocateFinalize(dnz,onz);CHKERRQ(ierr); 2697 2698 for (i=0;i<m;i++) { 2699 ierr = MatGetRow(inmat,i,&nnz,&indx,&values);CHKERRQ(ierr); 2700 I = i + rstart; 2701 ierr = MatSetValues(*outmat,1,&I,nnz,indx,values,INSERT_VALUES);CHKERRQ(ierr); 2702 ierr = MatRestoreRow(inmat,i,&nnz,&indx,&values);CHKERRQ(ierr); 2703 } 2704 ierr = MatDestroy(inmat);CHKERRQ(ierr); 2705 ierr = MatAssemblyBegin(*outmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2706 ierr = MatAssemblyEnd(*outmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2707 2708 PetscFunctionReturn(0); 2709 } 2710 2711 #undef __FUNCT__ 2712 #define __FUNCT__ "MatFileSplit" 2713 int MatFileSplit(Mat A,char *outfile) 2714 { 2715 int ierr,rank,len,m,N,i,rstart,*indx,nnz; 2716 PetscViewer out; 2717 char *name; 2718 Mat B; 2719 PetscScalar *values; 2720 2721 PetscFunctionBegin; 2722 2723 ierr = MatGetLocalSize(A,&m,0);CHKERRQ(ierr); 2724 ierr = MatGetSize(A,0,&N);CHKERRQ(ierr); 2725 ierr = MatCreateSeqAIJ(PETSC_COMM_SELF,m,N,0,0,&B);CHKERRQ(ierr); 2726 ierr = MatGetOwnershipRange(A,&rstart,0);CHKERRQ(ierr); 2727 for (i=0;i<m;i++) { 2728 ierr = MatGetRow(A,i+rstart,&nnz,&indx,&values);CHKERRQ(ierr); 2729 ierr = MatSetValues(B,1,&i,nnz,indx,values,INSERT_VALUES);CHKERRQ(ierr); 2730 ierr = MatRestoreRow(A,i+rstart,&nnz,&indx,&values);CHKERRQ(ierr); 2731 } 2732 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2733 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2734 2735 ierr = MPI_Comm_rank(A->comm,&rank);CHKERRQ(ierr); 2736 ierr = PetscStrlen(outfile,&len);CHKERRQ(ierr); 2737 ierr = PetscMalloc((len+5)*sizeof(char),&name);CHKERRQ(ierr); 2738 sprintf(name,"%s.%d",outfile,rank); 2739 ierr = PetscViewerBinaryOpen(PETSC_COMM_SELF,name,PETSC_BINARY_CREATE,&out);CHKERRQ(ierr); 2740 ierr = PetscFree(name); 2741 ierr = MatView(B,out);CHKERRQ(ierr); 2742 ierr = PetscViewerDestroy(out);CHKERRQ(ierr); 2743 ierr = MatDestroy(B);CHKERRQ(ierr); 2744 PetscFunctionReturn(0); 2745 } 2746