1 /*$Id: mpiaij.c,v 1.328 2001/03/09 20:10:12 bsmith Exp balay $*/ 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*,Scalar*,InsertMode); 10 EXTERN int MatGetRow_SeqAIJ(Mat,int,int*,int**,Scalar**); 11 EXTERN int MatRestoreRow_SeqAIJ(Mat,int,int*,int**,Scalar**); 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 __FUNC__ 22 #define __FUNC__ "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 Scalar *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(Scalar))+(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(Scalar));CHKERRQ(ierr); \ 88 ierr = PetscMemcpy(new_a+ai[row]+shift+nrow+CHUNKSIZE,aa+ai[row]+shift+nrow, \ 89 len*sizeof(Scalar));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(Scalar))); \ 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 Scalar *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(Scalar))+(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(Scalar));CHKERRQ(ierr); \ 162 ierr = PetscMemcpy(new_a+bi[row]+shift+nrow+CHUNKSIZE,ba+bi[row]+shift+nrow, \ 163 len*sizeof(Scalar));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(Scalar))); \ 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 __FUNC__ 193 #define __FUNC__ "MatSetValues_MPIAIJ" 194 int MatSetValues_MPIAIJ(Mat mat,int m,int *im,int n,int *in,Scalar *v,InsertMode addv) 195 { 196 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 197 Scalar 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 Scalar *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 Scalar *ba = b->a; 212 213 int *rp,ii,nrow,_i,rmax,N,col1,low,high,t; 214 int nonew = a->nonew,shift = a->indexshift; 215 Scalar *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 __FUNC__ 279 #define __FUNC__ "MatGetValues_MPIAIJ" 280 int MatGetValues_MPIAIJ(Mat mat,int m,int *idxm,int n,int *idxn,Scalar *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 __FUNC__ 322 #define __FUNC__ "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 __FUNC__ 349 #define __FUNC__ "MatAssemblyEnd_MPIAIJ" 350 int MatAssemblyEnd_MPIAIJ(Mat mat,MatAssemblyType mode) 351 { 352 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 353 int i,j,rstart,ncols,n,ierr,flg; 354 int *row,*col,other_disassembled; 355 Scalar *val; 356 InsertMode addv = mat->insertmode; 357 358 PetscFunctionBegin; 359 if (!aij->donotstash) { 360 while (1) { 361 ierr = MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);CHKERRQ(ierr); 362 if (!flg) break; 363 364 for (i=0; i<n;) { 365 /* Now identify the consecutive vals belonging to the same row */ 366 for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; } 367 if (j < n) ncols = j-i; 368 else ncols = n-i; 369 /* Now assemble all these values with a single function call */ 370 ierr = MatSetValues_MPIAIJ(mat,1,row+i,ncols,col+i,val+i,addv);CHKERRQ(ierr); 371 i = j; 372 } 373 } 374 ierr = MatStashScatterEnd_Private(&mat->stash);CHKERRQ(ierr); 375 } 376 377 ierr = MatAssemblyBegin(aij->A,mode);CHKERRQ(ierr); 378 ierr = MatAssemblyEnd(aij->A,mode);CHKERRQ(ierr); 379 380 /* determine if any processor has disassembled, if so we must 381 also disassemble ourselfs, in order that we may reassemble. */ 382 /* 383 if nonzero structure of submatrix B cannot change then we know that 384 no processor disassembled thus we can skip this stuff 385 */ 386 if (!((Mat_SeqAIJ*)aij->B->data)->nonew) { 387 ierr = MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,mat->comm);CHKERRQ(ierr); 388 if (mat->was_assembled && !other_disassembled) { 389 ierr = DisAssemble_MPIAIJ(mat);CHKERRQ(ierr); 390 } 391 } 392 393 if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) { 394 ierr = MatSetUpMultiply_MPIAIJ(mat);CHKERRQ(ierr); 395 } 396 ierr = MatAssemblyBegin(aij->B,mode);CHKERRQ(ierr); 397 ierr = MatAssemblyEnd(aij->B,mode);CHKERRQ(ierr); 398 399 if (aij->rowvalues) { 400 ierr = PetscFree(aij->rowvalues);CHKERRQ(ierr); 401 aij->rowvalues = 0; 402 } 403 PetscFunctionReturn(0); 404 } 405 406 #undef __FUNC__ 407 #define __FUNC__ "MatZeroEntries_MPIAIJ" 408 int MatZeroEntries_MPIAIJ(Mat A) 409 { 410 Mat_MPIAIJ *l = (Mat_MPIAIJ*)A->data; 411 int ierr; 412 413 PetscFunctionBegin; 414 ierr = MatZeroEntries(l->A);CHKERRQ(ierr); 415 ierr = MatZeroEntries(l->B);CHKERRQ(ierr); 416 PetscFunctionReturn(0); 417 } 418 419 #undef __FUNC__ 420 #define __FUNC__ "MatZeroRows_MPIAIJ" 421 int MatZeroRows_MPIAIJ(Mat A,IS is,Scalar *diag) 422 { 423 Mat_MPIAIJ *l = (Mat_MPIAIJ*)A->data; 424 int i,ierr,N,*rows,*owners = l->rowners,size = l->size; 425 int *procs,*nprocs,j,idx,nsends,*work,row; 426 int nmax,*svalues,*starts,*owner,nrecvs,rank = l->rank; 427 int *rvalues,tag = A->tag,count,base,slen,n,*source; 428 int *lens,imdex,*lrows,*values,rstart=l->rstart; 429 MPI_Comm comm = A->comm; 430 MPI_Request *send_waits,*recv_waits; 431 MPI_Status recv_status,*send_status; 432 IS istmp; 433 PetscTruth found; 434 435 PetscFunctionBegin; 436 ierr = ISGetLocalSize(is,&N);CHKERRQ(ierr); 437 ierr = ISGetIndices(is,&rows);CHKERRQ(ierr); 438 439 /* first count number of contributors to each processor */ 440 ierr = PetscMalloc(2*size*sizeof(int),&nprocs);CHKERRQ(ierr); 441 ierr = PetscMemzero(nprocs,2*size*sizeof(int));CHKERRQ(ierr); 442 procs = nprocs + size; 443 ierr = PetscMalloc((N+1)*sizeof(int),&owner);CHKERRQ(ierr); /* see note*/ 444 for (i=0; i<N; i++) { 445 idx = rows[i]; 446 found = PETSC_FALSE; 447 for (j=0; j<size; j++) { 448 if (idx >= owners[j] && idx < owners[j+1]) { 449 nprocs[j]++; procs[j] = 1; owner[i] = j; found = PETSC_TRUE; break; 450 } 451 } 452 if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range"); 453 } 454 nsends = 0; for (i=0; i<size; i++) { nsends += procs[i];} 455 456 /* inform other processors of number of messages and max length*/ 457 ierr = PetscMalloc(2*size*sizeof(int),&work);CHKERRQ(ierr); 458 ierr = MPI_Allreduce(nprocs,work,2*size,MPI_INT,PetscMaxSum_Op,comm);CHKERRQ(ierr); 459 nrecvs = work[size+rank]; 460 nmax = work[rank]; 461 ierr = PetscFree(work);CHKERRQ(ierr); 462 463 /* post receives: */ 464 ierr = PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(int),&rvalues);CHKERRQ(ierr); 465 ierr = PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);CHKERRQ(ierr); 466 for (i=0; i<nrecvs; i++) { 467 ierr = MPI_Irecv(rvalues+nmax*i,nmax,MPI_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);CHKERRQ(ierr); 468 } 469 470 /* do sends: 471 1) starts[i] gives the starting index in svalues for stuff going to 472 the ith processor 473 */ 474 ierr = PetscMalloc((N+1)*sizeof(int),&svalues);CHKERRQ(ierr); 475 ierr = PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);CHKERRQ(ierr); 476 ierr = PetscMalloc((size+1)*sizeof(int),&starts);CHKERRQ(ierr); 477 starts[0] = 0; 478 for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[i-1];} 479 for (i=0; i<N; i++) { 480 svalues[starts[owner[i]]++] = rows[i]; 481 } 482 ierr = ISRestoreIndices(is,&rows);CHKERRQ(ierr); 483 484 starts[0] = 0; 485 for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[i-1];} 486 count = 0; 487 for (i=0; i<size; i++) { 488 if (procs[i]) { 489 ierr = MPI_Isend(svalues+starts[i],nprocs[i],MPI_INT,i,tag,comm,send_waits+count++);CHKERRQ(ierr); 490 } 491 } 492 ierr = PetscFree(starts);CHKERRQ(ierr); 493 494 base = owners[rank]; 495 496 /* wait on receives */ 497 ierr = PetscMalloc(2*(nrecvs+1)*sizeof(int),&lens);CHKERRQ(ierr); 498 source = lens + nrecvs; 499 count = nrecvs; slen = 0; 500 while (count) { 501 ierr = MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);CHKERRQ(ierr); 502 /* unpack receives into our local space */ 503 ierr = MPI_Get_count(&recv_status,MPI_INT,&n);CHKERRQ(ierr); 504 source[imdex] = recv_status.MPI_SOURCE; 505 lens[imdex] = n; 506 slen += n; 507 count--; 508 } 509 ierr = PetscFree(recv_waits);CHKERRQ(ierr); 510 511 /* move the data into the send scatter */ 512 ierr = PetscMalloc((slen+1)*sizeof(int),&lrows);CHKERRQ(ierr); 513 count = 0; 514 for (i=0; i<nrecvs; i++) { 515 values = rvalues + i*nmax; 516 for (j=0; j<lens[i]; j++) { 517 lrows[count++] = values[j] - base; 518 } 519 } 520 ierr = PetscFree(rvalues);CHKERRQ(ierr); 521 ierr = PetscFree(lens);CHKERRQ(ierr); 522 ierr = PetscFree(owner);CHKERRQ(ierr); 523 ierr = PetscFree(nprocs);CHKERRQ(ierr); 524 525 /* actually zap the local rows */ 526 ierr = ISCreateGeneral(PETSC_COMM_SELF,slen,lrows,&istmp);CHKERRQ(ierr); 527 PetscLogObjectParent(A,istmp); 528 529 /* 530 Zero the required rows. If the "diagonal block" of the matrix 531 is square and the user wishes to set the diagonal we use seperate 532 code so that MatSetValues() is not called for each diagonal allocating 533 new memory, thus calling lots of mallocs and slowing things down. 534 535 Contributed by: Mathew Knepley 536 */ 537 /* must zero l->B before l->A because the (diag) case below may put values into l->B*/ 538 ierr = MatZeroRows(l->B,istmp,0);CHKERRQ(ierr); 539 if (diag && (l->A->M == l->A->N)) { 540 ierr = MatZeroRows(l->A,istmp,diag);CHKERRQ(ierr); 541 } else if (diag) { 542 ierr = MatZeroRows(l->A,istmp,0);CHKERRQ(ierr); 543 if (((Mat_SeqAIJ*)l->A->data)->nonew) { 544 SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options\n\ 545 MAT_NO_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR"); 546 } 547 for (i = 0; i < slen; i++) { 548 row = lrows[i] + rstart; 549 ierr = MatSetValues(A,1,&row,1,&row,diag,INSERT_VALUES);CHKERRQ(ierr); 550 } 551 ierr = MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 552 ierr = MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 553 } else { 554 ierr = MatZeroRows(l->A,istmp,0);CHKERRQ(ierr); 555 } 556 ierr = ISDestroy(istmp);CHKERRQ(ierr); 557 ierr = PetscFree(lrows);CHKERRQ(ierr); 558 559 /* wait on sends */ 560 if (nsends) { 561 ierr = PetscMalloc(nsends*sizeof(MPI_Status),&send_status);CHKERRQ(ierr); 562 ierr = MPI_Waitall(nsends,send_waits,send_status);CHKERRQ(ierr); 563 ierr = PetscFree(send_status);CHKERRQ(ierr); 564 } 565 ierr = PetscFree(send_waits);CHKERRQ(ierr); 566 ierr = PetscFree(svalues);CHKERRQ(ierr); 567 568 PetscFunctionReturn(0); 569 } 570 571 #undef __FUNC__ 572 #define __FUNC__ "MatMult_MPIAIJ" 573 int MatMult_MPIAIJ(Mat A,Vec xx,Vec yy) 574 { 575 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 576 int ierr,nt; 577 578 PetscFunctionBegin; 579 ierr = VecGetLocalSize(xx,&nt);CHKERRQ(ierr); 580 if (nt != A->n) { 581 SETERRQ2(PETSC_ERR_ARG_SIZ,"Incompatible partition of A (%d) and xx (%d)",A->n,nt); 582 } 583 ierr = VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);CHKERRQ(ierr); 584 ierr = (*a->A->ops->mult)(a->A,xx,yy);CHKERRQ(ierr); 585 ierr = VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);CHKERRQ(ierr); 586 ierr = (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);CHKERRQ(ierr); 587 PetscFunctionReturn(0); 588 } 589 590 #undef __FUNC__ 591 #define __FUNC__ "MatMultAdd_MPIAIJ" 592 int MatMultAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz) 593 { 594 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 595 int ierr; 596 597 PetscFunctionBegin; 598 ierr = VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);CHKERRQ(ierr); 599 ierr = (*a->A->ops->multadd)(a->A,xx,yy,zz);CHKERRQ(ierr); 600 ierr = VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);CHKERRQ(ierr); 601 ierr = (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);CHKERRQ(ierr); 602 PetscFunctionReturn(0); 603 } 604 605 #undef __FUNC__ 606 #define __FUNC__ "MatMultTranspose_MPIAIJ" 607 int MatMultTranspose_MPIAIJ(Mat A,Vec xx,Vec yy) 608 { 609 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 610 int ierr; 611 612 PetscFunctionBegin; 613 /* do nondiagonal part */ 614 ierr = (*a->B->ops->multtranspose)(a->B,xx,a->lvec);CHKERRQ(ierr); 615 /* send it on its way */ 616 ierr = VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);CHKERRQ(ierr); 617 /* do local part */ 618 ierr = (*a->A->ops->multtranspose)(a->A,xx,yy);CHKERRQ(ierr); 619 /* receive remote parts: note this assumes the values are not actually */ 620 /* inserted in yy until the next line, which is true for my implementation*/ 621 /* but is not perhaps always true. */ 622 ierr = VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);CHKERRQ(ierr); 623 PetscFunctionReturn(0); 624 } 625 626 #undef __FUNC__ 627 #define __FUNC__ "MatMultTransposeAdd_MPIAIJ" 628 int MatMultTransposeAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz) 629 { 630 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 631 int ierr; 632 633 PetscFunctionBegin; 634 /* do nondiagonal part */ 635 ierr = (*a->B->ops->multtranspose)(a->B,xx,a->lvec);CHKERRQ(ierr); 636 /* send it on its way */ 637 ierr = VecScatterBegin(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);CHKERRQ(ierr); 638 /* do local part */ 639 ierr = (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);CHKERRQ(ierr); 640 /* receive remote parts: note this assumes the values are not actually */ 641 /* inserted in yy until the next line, which is true for my implementation*/ 642 /* but is not perhaps always true. */ 643 ierr = VecScatterEnd(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);CHKERRQ(ierr); 644 PetscFunctionReturn(0); 645 } 646 647 /* 648 This only works correctly for square matrices where the subblock A->A is the 649 diagonal block 650 */ 651 #undef __FUNC__ 652 #define __FUNC__ "MatGetDiagonal_MPIAIJ" 653 int MatGetDiagonal_MPIAIJ(Mat A,Vec v) 654 { 655 int ierr; 656 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 657 658 PetscFunctionBegin; 659 if (A->M != A->N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block"); 660 if (a->rstart != a->cstart || a->rend != a->cend) { 661 SETERRQ(PETSC_ERR_ARG_SIZ,"row partition must equal col partition"); 662 } 663 ierr = MatGetDiagonal(a->A,v);CHKERRQ(ierr); 664 PetscFunctionReturn(0); 665 } 666 667 #undef __FUNC__ 668 #define __FUNC__ "MatScale_MPIAIJ" 669 int MatScale_MPIAIJ(Scalar *aa,Mat A) 670 { 671 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 672 int ierr; 673 674 PetscFunctionBegin; 675 ierr = MatScale(aa,a->A);CHKERRQ(ierr); 676 ierr = MatScale(aa,a->B);CHKERRQ(ierr); 677 PetscFunctionReturn(0); 678 } 679 680 #undef __FUNC__ 681 #define __FUNC__ "MatDestroy_MPIAIJ" 682 int MatDestroy_MPIAIJ(Mat mat) 683 { 684 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 685 int ierr; 686 687 PetscFunctionBegin; 688 #if defined(PETSC_USE_LOG) 689 PetscLogObjectState((PetscObject)mat,"Rows=%d, Cols=%d",mat->M,mat->N); 690 #endif 691 ierr = MatStashDestroy_Private(&mat->stash);CHKERRQ(ierr); 692 ierr = PetscFree(aij->rowners);CHKERRQ(ierr); 693 ierr = MatDestroy(aij->A);CHKERRQ(ierr); 694 ierr = MatDestroy(aij->B);CHKERRQ(ierr); 695 #if defined (PETSC_USE_CTABLE) 696 if (aij->colmap) {ierr = PetscTableDelete(aij->colmap);CHKERRQ(ierr);} 697 #else 698 if (aij->colmap) {ierr = PetscFree(aij->colmap);CHKERRQ(ierr);} 699 #endif 700 if (aij->garray) {ierr = PetscFree(aij->garray);CHKERRQ(ierr);} 701 if (aij->lvec) {ierr = VecDestroy(aij->lvec);CHKERRQ(ierr);} 702 if (aij->Mvctx) {ierr = VecScatterDestroy(aij->Mvctx);CHKERRQ(ierr);} 703 if (aij->rowvalues) {ierr = PetscFree(aij->rowvalues);CHKERRQ(ierr);} 704 ierr = PetscFree(aij);CHKERRQ(ierr); 705 PetscFunctionReturn(0); 706 } 707 708 #undef __FUNC__ 709 #define __FUNC__ "MatView_MPIAIJ_ASCIIorDraworSocket" 710 int MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer) 711 { 712 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 713 Mat_SeqAIJ* C = (Mat_SeqAIJ*)aij->A->data; 714 int ierr,shift = C->indexshift,rank = aij->rank,size = aij->size; 715 PetscTruth isdraw,isascii,flg; 716 PetscViewer sviewer; 717 PetscViewerFormat format; 718 719 PetscFunctionBegin; 720 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);CHKERRQ(ierr); 721 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);CHKERRQ(ierr); 722 if (isascii) { 723 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 724 if (format == PETSC_VIEWER_ASCII_INFO_LONG) { 725 MatInfo info; 726 ierr = MPI_Comm_rank(mat->comm,&rank);CHKERRQ(ierr); 727 ierr = MatGetInfo(mat,MAT_LOCAL,&info);CHKERRQ(ierr); 728 ierr = PetscOptionsHasName(PETSC_NULL,"-mat_aij_no_inode",&flg);CHKERRQ(ierr); 729 if (flg) { 730 ierr = PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %d nz %d nz alloced %d mem %d, not using I-node routines\n", 731 rank,mat->m,(int)info.nz_used,(int)info.nz_allocated,(int)info.memory);CHKERRQ(ierr); 732 } else { 733 ierr = PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %d nz %d nz alloced %d mem %d, using I-node routines\n", 734 rank,mat->m,(int)info.nz_used,(int)info.nz_allocated,(int)info.memory);CHKERRQ(ierr); 735 } 736 ierr = MatGetInfo(aij->A,MAT_LOCAL,&info);CHKERRQ(ierr); 737 ierr = PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %d \n",rank,(int)info.nz_used);CHKERRQ(ierr); 738 ierr = MatGetInfo(aij->B,MAT_LOCAL,&info);CHKERRQ(ierr); 739 ierr = PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %d \n",rank,(int)info.nz_used);CHKERRQ(ierr); 740 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 741 ierr = VecScatterView(aij->Mvctx,viewer);CHKERRQ(ierr); 742 PetscFunctionReturn(0); 743 } else if (format == PETSC_VIEWER_ASCII_INFO) { 744 PetscFunctionReturn(0); 745 } 746 } else if (isdraw) { 747 PetscDraw draw; 748 PetscTruth isnull; 749 ierr = PetscViewerDrawGetDraw(viewer,0,&draw);CHKERRQ(ierr); 750 ierr = PetscDrawIsNull(draw,&isnull);CHKERRQ(ierr); if (isnull) PetscFunctionReturn(0); 751 } 752 753 if (size == 1) { 754 ierr = MatView(aij->A,viewer);CHKERRQ(ierr); 755 } else { 756 /* assemble the entire matrix onto first processor. */ 757 Mat A; 758 Mat_SeqAIJ *Aloc; 759 int M = mat->M,N = mat->N,m,*ai,*aj,row,*cols,i,*ct; 760 Scalar *a; 761 762 if (!rank) { 763 ierr = MatCreateMPIAIJ(mat->comm,M,N,M,N,0,PETSC_NULL,0,PETSC_NULL,&A);CHKERRQ(ierr); 764 } else { 765 ierr = MatCreateMPIAIJ(mat->comm,0,0,M,N,0,PETSC_NULL,0,PETSC_NULL,&A);CHKERRQ(ierr); 766 } 767 PetscLogObjectParent(mat,A); 768 769 /* copy over the A part */ 770 Aloc = (Mat_SeqAIJ*)aij->A->data; 771 m = aij->A->m; ai = Aloc->i; aj = Aloc->j; a = Aloc->a; 772 row = aij->rstart; 773 for (i=0; i<ai[m]+shift; i++) {aj[i] += aij->cstart + shift;} 774 for (i=0; i<m; i++) { 775 ierr = MatSetValues(A,1,&row,ai[i+1]-ai[i],aj,a,INSERT_VALUES);CHKERRQ(ierr); 776 row++; a += ai[i+1]-ai[i]; aj += ai[i+1]-ai[i]; 777 } 778 aj = Aloc->j; 779 for (i=0; i<ai[m]+shift; i++) {aj[i] -= aij->cstart + shift;} 780 781 /* copy over the B part */ 782 Aloc = (Mat_SeqAIJ*)aij->B->data; 783 m = aij->B->m; ai = Aloc->i; aj = Aloc->j; a = Aloc->a; 784 row = aij->rstart; 785 ierr = PetscMalloc((ai[m]+1)*sizeof(int),&cols);CHKERRQ(ierr); 786 ct = cols; 787 for (i=0; i<ai[m]+shift; i++) {cols[i] = aij->garray[aj[i]+shift];} 788 for (i=0; i<m; i++) { 789 ierr = MatSetValues(A,1,&row,ai[i+1]-ai[i],cols,a,INSERT_VALUES);CHKERRQ(ierr); 790 row++; a += ai[i+1]-ai[i]; cols += ai[i+1]-ai[i]; 791 } 792 ierr = PetscFree(ct);CHKERRQ(ierr); 793 ierr = MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 794 ierr = MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 795 /* 796 Everyone has to call to draw the matrix since the graphics waits are 797 synchronized across all processors that share the PetscDraw object 798 */ 799 ierr = PetscViewerGetSingleton(viewer,&sviewer);CHKERRQ(ierr); 800 if (!rank) { 801 ierr = MatView(((Mat_MPIAIJ*)(A->data))->A,sviewer);CHKERRQ(ierr); 802 } 803 ierr = PetscViewerRestoreSingleton(viewer,&sviewer);CHKERRQ(ierr); 804 ierr = MatDestroy(A);CHKERRQ(ierr); 805 } 806 PetscFunctionReturn(0); 807 } 808 809 #undef __FUNC__ 810 #define __FUNC__ "MatView_MPIAIJ" 811 int MatView_MPIAIJ(Mat mat,PetscViewer viewer) 812 { 813 int ierr; 814 PetscTruth isascii,isdraw,issocket,isbinary; 815 816 PetscFunctionBegin; 817 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);CHKERRQ(ierr); 818 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);CHKERRQ(ierr); 819 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);CHKERRQ(ierr); 820 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);CHKERRQ(ierr); 821 if (isascii || isdraw || isbinary || issocket) { 822 ierr = MatView_MPIAIJ_ASCIIorDraworSocket(mat,viewer);CHKERRQ(ierr); 823 } else { 824 SETERRQ1(1,"Viewer type %s not supported by MPIAIJ matrices",((PetscObject)viewer)->type_name); 825 } 826 PetscFunctionReturn(0); 827 } 828 829 /* 830 This has to provide several versions. 831 832 2) a) use only local smoothing updating outer values only once. 833 b) local smoothing updating outer values each inner iteration 834 3) color updating out values betwen colors. 835 */ 836 #undef __FUNC__ 837 #define __FUNC__ "MatRelax_MPIAIJ" 838 int MatRelax_MPIAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,int its,Vec xx) 839 { 840 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data; 841 Mat AA = mat->A,BB = mat->B; 842 Mat_SeqAIJ *A = (Mat_SeqAIJ*)AA->data,*B = (Mat_SeqAIJ *)BB->data; 843 Scalar *b,*x,*xs,*ls,d,*v,sum; 844 int ierr,*idx,*diag; 845 int n = matin->n,m = matin->m,i,shift = A->indexshift; 846 847 PetscFunctionBegin; 848 if (!A->diag) {ierr = MatMarkDiagonal_SeqAIJ(AA);CHKERRQ(ierr);} 849 diag = A->diag; 850 if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP){ 851 if (flag & SOR_ZERO_INITIAL_GUESS) { 852 ierr = (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,its,xx);CHKERRQ(ierr); 853 PetscFunctionReturn(0); 854 } 855 ierr = VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 856 ierr = VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 857 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 858 if (xx != bb) { 859 ierr = VecGetArray(bb,&b);CHKERRQ(ierr); 860 } else { 861 b = x; 862 } 863 ierr = VecGetArray(mat->lvec,&ls);CHKERRQ(ierr); 864 xs = x + shift; /* shift by one for index start of 1 */ 865 ls = ls + shift; 866 while (its--) { 867 /* go down through the rows */ 868 for (i=0; i<m; i++) { 869 n = A->i[i+1] - A->i[i]; 870 PetscLogFlops(4*n+3); 871 idx = A->j + A->i[i] + shift; 872 v = A->a + A->i[i] + shift; 873 sum = b[i]; 874 SPARSEDENSEMDOT(sum,xs,v,idx,n); 875 d = fshift + A->a[diag[i]+shift]; 876 n = B->i[i+1] - B->i[i]; 877 idx = B->j + B->i[i] + shift; 878 v = B->a + B->i[i] + shift; 879 SPARSEDENSEMDOT(sum,ls,v,idx,n); 880 x[i] = (1. - omega)*x[i] + omega*(sum + A->a[diag[i]+shift]*x[i])/d; 881 } 882 /* come up through the rows */ 883 for (i=m-1; i>-1; i--) { 884 n = A->i[i+1] - A->i[i]; 885 PetscLogFlops(4*n+3); 886 idx = A->j + A->i[i] + shift; 887 v = A->a + A->i[i] + shift; 888 sum = b[i]; 889 SPARSEDENSEMDOT(sum,xs,v,idx,n); 890 d = fshift + A->a[diag[i]+shift]; 891 n = B->i[i+1] - B->i[i]; 892 idx = B->j + B->i[i] + shift; 893 v = B->a + B->i[i] + shift; 894 SPARSEDENSEMDOT(sum,ls,v,idx,n); 895 x[i] = (1. - omega)*x[i] + omega*(sum + A->a[diag[i]+shift]*x[i])/d; 896 } 897 } 898 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 899 if (bb != xx) {ierr = VecRestoreArray(bb,&b);CHKERRQ(ierr); } 900 ierr = VecRestoreArray(mat->lvec,&ls);CHKERRQ(ierr); 901 } else if (flag & SOR_LOCAL_FORWARD_SWEEP){ 902 if (flag & SOR_ZERO_INITIAL_GUESS) { 903 ierr = (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,its,xx);CHKERRQ(ierr); 904 PetscFunctionReturn(0); 905 } 906 ierr = VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 907 ierr = VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 908 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 909 if (xx != bb) { 910 ierr = VecGetArray(bb,&b);CHKERRQ(ierr); 911 } else { 912 b = x; 913 } 914 ierr = VecGetArray(mat->lvec,&ls);CHKERRQ(ierr); 915 xs = x + shift; /* shift by one for index start of 1 */ 916 ls = ls + shift; 917 while (its--) { 918 for (i=0; i<m; i++) { 919 n = A->i[i+1] - A->i[i]; 920 PetscLogFlops(4*n+3); 921 idx = A->j + A->i[i] + shift; 922 v = A->a + A->i[i] + shift; 923 sum = b[i]; 924 SPARSEDENSEMDOT(sum,xs,v,idx,n); 925 d = fshift + A->a[diag[i]+shift]; 926 n = B->i[i+1] - B->i[i]; 927 idx = B->j + B->i[i] + shift; 928 v = B->a + B->i[i] + shift; 929 SPARSEDENSEMDOT(sum,ls,v,idx,n); 930 x[i] = (1. - omega)*x[i] + omega*(sum + A->a[diag[i]+shift]*x[i])/d; 931 } 932 } 933 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 934 if (bb != xx) {ierr = VecRestoreArray(bb,&b);CHKERRQ(ierr); } 935 ierr = VecRestoreArray(mat->lvec,&ls);CHKERRQ(ierr); 936 } else if (flag & SOR_LOCAL_BACKWARD_SWEEP){ 937 if (flag & SOR_ZERO_INITIAL_GUESS) { 938 ierr = (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,its,xx);CHKERRQ(ierr); 939 PetscFunctionReturn(0); 940 } 941 ierr = VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 942 ierr = VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);CHKERRQ(ierr); 943 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 944 if (xx != bb) { 945 ierr = VecGetArray(bb,&b);CHKERRQ(ierr); 946 } else { 947 b = x; 948 } 949 ierr = VecGetArray(mat->lvec,&ls);CHKERRQ(ierr); 950 xs = x + shift; /* shift by one for index start of 1 */ 951 ls = ls + shift; 952 while (its--) { 953 for (i=m-1; i>-1; i--) { 954 n = A->i[i+1] - A->i[i]; 955 PetscLogFlops(4*n+3); 956 idx = A->j + A->i[i] + shift; 957 v = A->a + A->i[i] + shift; 958 sum = b[i]; 959 SPARSEDENSEMDOT(sum,xs,v,idx,n); 960 d = fshift + A->a[diag[i]+shift]; 961 n = B->i[i+1] - B->i[i]; 962 idx = B->j + B->i[i] + shift; 963 v = B->a + B->i[i] + shift; 964 SPARSEDENSEMDOT(sum,ls,v,idx,n); 965 x[i] = (1. - omega)*x[i] + omega*(sum + A->a[diag[i]+shift]*x[i])/d; 966 } 967 } 968 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 969 if (bb != xx) {ierr = VecRestoreArray(bb,&b);CHKERRQ(ierr); } 970 ierr = VecRestoreArray(mat->lvec,&ls);CHKERRQ(ierr); 971 } else { 972 SETERRQ(PETSC_ERR_SUP,"Parallel SOR not supported"); 973 } 974 PetscFunctionReturn(0); 975 } 976 977 #undef __FUNC__ 978 #define __FUNC__ "MatGetInfo_MPIAIJ" 979 int MatGetInfo_MPIAIJ(Mat matin,MatInfoType flag,MatInfo *info) 980 { 981 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data; 982 Mat A = mat->A,B = mat->B; 983 int ierr; 984 PetscReal isend[5],irecv[5]; 985 986 PetscFunctionBegin; 987 info->block_size = 1.0; 988 ierr = MatGetInfo(A,MAT_LOCAL,info);CHKERRQ(ierr); 989 isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded; 990 isend[3] = info->memory; isend[4] = info->mallocs; 991 ierr = MatGetInfo(B,MAT_LOCAL,info);CHKERRQ(ierr); 992 isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded; 993 isend[3] += info->memory; isend[4] += info->mallocs; 994 if (flag == MAT_LOCAL) { 995 info->nz_used = isend[0]; 996 info->nz_allocated = isend[1]; 997 info->nz_unneeded = isend[2]; 998 info->memory = isend[3]; 999 info->mallocs = isend[4]; 1000 } else if (flag == MAT_GLOBAL_MAX) { 1001 ierr = MPI_Allreduce(isend,irecv,5,MPI_DOUBLE,MPI_MAX,matin->comm);CHKERRQ(ierr); 1002 info->nz_used = irecv[0]; 1003 info->nz_allocated = irecv[1]; 1004 info->nz_unneeded = irecv[2]; 1005 info->memory = irecv[3]; 1006 info->mallocs = irecv[4]; 1007 } else if (flag == MAT_GLOBAL_SUM) { 1008 ierr = MPI_Allreduce(isend,irecv,5,MPI_DOUBLE,MPI_SUM,matin->comm);CHKERRQ(ierr); 1009 info->nz_used = irecv[0]; 1010 info->nz_allocated = irecv[1]; 1011 info->nz_unneeded = irecv[2]; 1012 info->memory = irecv[3]; 1013 info->mallocs = irecv[4]; 1014 } 1015 info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */ 1016 info->fill_ratio_needed = 0; 1017 info->factor_mallocs = 0; 1018 info->rows_global = (double)matin->M; 1019 info->columns_global = (double)matin->N; 1020 info->rows_local = (double)matin->m; 1021 info->columns_local = (double)matin->N; 1022 1023 PetscFunctionReturn(0); 1024 } 1025 1026 #undef __FUNC__ 1027 #define __FUNC__ "MatSetOption_MPIAIJ" 1028 int MatSetOption_MPIAIJ(Mat A,MatOption op) 1029 { 1030 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 1031 int ierr; 1032 1033 PetscFunctionBegin; 1034 if (op == MAT_NO_NEW_NONZERO_LOCATIONS || 1035 op == MAT_YES_NEW_NONZERO_LOCATIONS || 1036 op == MAT_COLUMNS_UNSORTED || 1037 op == MAT_COLUMNS_SORTED || 1038 op == MAT_NEW_NONZERO_ALLOCATION_ERR || 1039 op == MAT_KEEP_ZEROED_ROWS || 1040 op == MAT_NEW_NONZERO_LOCATION_ERR || 1041 op == MAT_USE_INODES || 1042 op == MAT_DO_NOT_USE_INODES || 1043 op == MAT_IGNORE_ZERO_ENTRIES) { 1044 ierr = MatSetOption(a->A,op);CHKERRQ(ierr); 1045 ierr = MatSetOption(a->B,op);CHKERRQ(ierr); 1046 } else if (op == MAT_ROW_ORIENTED) { 1047 a->roworiented = PETSC_TRUE; 1048 ierr = MatSetOption(a->A,op);CHKERRQ(ierr); 1049 ierr = MatSetOption(a->B,op);CHKERRQ(ierr); 1050 } else if (op == MAT_ROWS_SORTED || 1051 op == MAT_ROWS_UNSORTED || 1052 op == MAT_SYMMETRIC || 1053 op == MAT_STRUCTURALLY_SYMMETRIC || 1054 op == MAT_YES_NEW_DIAGONALS) { 1055 PetscLogInfo(A,"MatSetOption_MPIAIJ:Option ignored\n"); 1056 } else if (op == MAT_COLUMN_ORIENTED) { 1057 a->roworiented = PETSC_FALSE; 1058 ierr = MatSetOption(a->A,op);CHKERRQ(ierr); 1059 ierr = MatSetOption(a->B,op);CHKERRQ(ierr); 1060 } else if (op == MAT_IGNORE_OFF_PROC_ENTRIES) { 1061 a->donotstash = PETSC_TRUE; 1062 } else if (op == MAT_NO_NEW_DIAGONALS){ 1063 SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS"); 1064 } else { 1065 SETERRQ(PETSC_ERR_SUP,"unknown option"); 1066 } 1067 PetscFunctionReturn(0); 1068 } 1069 1070 #undef __FUNC__ 1071 #define __FUNC__ "MatGetOwnershipRange_MPIAIJ" 1072 int MatGetOwnershipRange_MPIAIJ(Mat matin,int *m,int *n) 1073 { 1074 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data; 1075 1076 PetscFunctionBegin; 1077 if (m) *m = mat->rstart; 1078 if (n) *n = mat->rend; 1079 PetscFunctionReturn(0); 1080 } 1081 1082 #undef __FUNC__ 1083 #define __FUNC__ "MatGetRow_MPIAIJ" 1084 int MatGetRow_MPIAIJ(Mat matin,int row,int *nz,int **idx,Scalar **v) 1085 { 1086 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data; 1087 Scalar *vworkA,*vworkB,**pvA,**pvB,*v_p; 1088 int i,ierr,*cworkA,*cworkB,**pcA,**pcB,cstart = mat->cstart; 1089 int nztot,nzA,nzB,lrow,rstart = mat->rstart,rend = mat->rend; 1090 int *cmap,*idx_p; 1091 1092 PetscFunctionBegin; 1093 if (mat->getrowactive == PETSC_TRUE) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active"); 1094 mat->getrowactive = PETSC_TRUE; 1095 1096 if (!mat->rowvalues && (idx || v)) { 1097 /* 1098 allocate enough space to hold information from the longest row. 1099 */ 1100 Mat_SeqAIJ *Aa = (Mat_SeqAIJ*)mat->A->data,*Ba = (Mat_SeqAIJ*)mat->B->data; 1101 int max = 1,tmp; 1102 for (i=0; i<matin->m; i++) { 1103 tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i]; 1104 if (max < tmp) { max = tmp; } 1105 } 1106 ierr = PetscMalloc(max*(sizeof(int)+sizeof(Scalar)),&mat->rowvalues);CHKERRQ(ierr); 1107 mat->rowindices = (int*)(mat->rowvalues + max); 1108 } 1109 1110 if (row < rstart || row >= rend) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Only local rows") 1111 lrow = row - rstart; 1112 1113 pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB; 1114 if (!v) {pvA = 0; pvB = 0;} 1115 if (!idx) {pcA = 0; if (!v) pcB = 0;} 1116 ierr = (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);CHKERRQ(ierr); 1117 ierr = (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);CHKERRQ(ierr); 1118 nztot = nzA + nzB; 1119 1120 cmap = mat->garray; 1121 if (v || idx) { 1122 if (nztot) { 1123 /* Sort by increasing column numbers, assuming A and B already sorted */ 1124 int imark = -1; 1125 if (v) { 1126 *v = v_p = mat->rowvalues; 1127 for (i=0; i<nzB; i++) { 1128 if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i]; 1129 else break; 1130 } 1131 imark = i; 1132 for (i=0; i<nzA; i++) v_p[imark+i] = vworkA[i]; 1133 for (i=imark; i<nzB; i++) v_p[nzA+i] = vworkB[i]; 1134 } 1135 if (idx) { 1136 *idx = idx_p = mat->rowindices; 1137 if (imark > -1) { 1138 for (i=0; i<imark; i++) { 1139 idx_p[i] = cmap[cworkB[i]]; 1140 } 1141 } else { 1142 for (i=0; i<nzB; i++) { 1143 if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]]; 1144 else break; 1145 } 1146 imark = i; 1147 } 1148 for (i=0; i<nzA; i++) idx_p[imark+i] = cstart + cworkA[i]; 1149 for (i=imark; i<nzB; i++) idx_p[nzA+i] = cmap[cworkB[i]]; 1150 } 1151 } else { 1152 if (idx) *idx = 0; 1153 if (v) *v = 0; 1154 } 1155 } 1156 *nz = nztot; 1157 ierr = (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);CHKERRQ(ierr); 1158 ierr = (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);CHKERRQ(ierr); 1159 PetscFunctionReturn(0); 1160 } 1161 1162 #undef __FUNC__ 1163 #define __FUNC__ "MatRestoreRow_MPIAIJ" 1164 int MatRestoreRow_MPIAIJ(Mat mat,int row,int *nz,int **idx,Scalar **v) 1165 { 1166 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 1167 1168 PetscFunctionBegin; 1169 if (aij->getrowactive == PETSC_FALSE) { 1170 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow not called"); 1171 } 1172 aij->getrowactive = PETSC_FALSE; 1173 PetscFunctionReturn(0); 1174 } 1175 1176 #undef __FUNC__ 1177 #define __FUNC__ "MatNorm_MPIAIJ" 1178 int MatNorm_MPIAIJ(Mat mat,NormType type,PetscReal *norm) 1179 { 1180 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 1181 Mat_SeqAIJ *amat = (Mat_SeqAIJ*)aij->A->data,*bmat = (Mat_SeqAIJ*)aij->B->data; 1182 int ierr,i,j,cstart = aij->cstart,shift = amat->indexshift; 1183 PetscReal sum = 0.0; 1184 Scalar *v; 1185 1186 PetscFunctionBegin; 1187 if (aij->size == 1) { 1188 ierr = MatNorm(aij->A,type,norm);CHKERRQ(ierr); 1189 } else { 1190 if (type == NORM_FROBENIUS) { 1191 v = amat->a; 1192 for (i=0; i<amat->nz; i++) { 1193 #if defined(PETSC_USE_COMPLEX) 1194 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 1195 #else 1196 sum += (*v)*(*v); v++; 1197 #endif 1198 } 1199 v = bmat->a; 1200 for (i=0; i<bmat->nz; i++) { 1201 #if defined(PETSC_USE_COMPLEX) 1202 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 1203 #else 1204 sum += (*v)*(*v); v++; 1205 #endif 1206 } 1207 ierr = MPI_Allreduce(&sum,norm,1,MPI_DOUBLE,MPI_SUM,mat->comm);CHKERRQ(ierr); 1208 *norm = sqrt(*norm); 1209 } else if (type == NORM_1) { /* max column norm */ 1210 PetscReal *tmp,*tmp2; 1211 int *jj,*garray = aij->garray; 1212 ierr = PetscMalloc((mat->N+1)*sizeof(PetscReal),&tmp);CHKERRQ(ierr); 1213 ierr = PetscMalloc((mat->N+1)*sizeof(PetscReal),&tmp2);CHKERRQ(ierr); 1214 ierr = PetscMemzero(tmp,mat->N*sizeof(PetscReal));CHKERRQ(ierr); 1215 *norm = 0.0; 1216 v = amat->a; jj = amat->j; 1217 for (j=0; j<amat->nz; j++) { 1218 tmp[cstart + *jj++ + shift] += PetscAbsScalar(*v); v++; 1219 } 1220 v = bmat->a; jj = bmat->j; 1221 for (j=0; j<bmat->nz; j++) { 1222 tmp[garray[*jj++ + shift]] += PetscAbsScalar(*v); v++; 1223 } 1224 ierr = MPI_Allreduce(tmp,tmp2,mat->N,MPI_DOUBLE,MPI_SUM,mat->comm);CHKERRQ(ierr); 1225 for (j=0; j<mat->N; j++) { 1226 if (tmp2[j] > *norm) *norm = tmp2[j]; 1227 } 1228 ierr = PetscFree(tmp);CHKERRQ(ierr); 1229 ierr = PetscFree(tmp2);CHKERRQ(ierr); 1230 } else if (type == NORM_INFINITY) { /* max row norm */ 1231 PetscReal ntemp = 0.0; 1232 for (j=0; j<aij->A->m; j++) { 1233 v = amat->a + amat->i[j] + shift; 1234 sum = 0.0; 1235 for (i=0; i<amat->i[j+1]-amat->i[j]; i++) { 1236 sum += PetscAbsScalar(*v); v++; 1237 } 1238 v = bmat->a + bmat->i[j] + shift; 1239 for (i=0; i<bmat->i[j+1]-bmat->i[j]; i++) { 1240 sum += PetscAbsScalar(*v); v++; 1241 } 1242 if (sum > ntemp) ntemp = sum; 1243 } 1244 ierr = MPI_Allreduce(&ntemp,norm,1,MPI_DOUBLE,MPI_MAX,mat->comm);CHKERRQ(ierr); 1245 } else { 1246 SETERRQ(PETSC_ERR_SUP,"No support for two norm"); 1247 } 1248 } 1249 PetscFunctionReturn(0); 1250 } 1251 1252 #undef __FUNC__ 1253 #define __FUNC__ "MatTranspose_MPIAIJ" 1254 int MatTranspose_MPIAIJ(Mat A,Mat *matout) 1255 { 1256 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 1257 Mat_SeqAIJ *Aloc = (Mat_SeqAIJ*)a->A->data; 1258 int ierr,shift = Aloc->indexshift; 1259 int M = A->M,N = A->N,m,*ai,*aj,row,*cols,i,*ct; 1260 Mat B; 1261 Scalar *array; 1262 1263 PetscFunctionBegin; 1264 if (!matout && M != N) { 1265 SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place"); 1266 } 1267 1268 ierr = MatCreateMPIAIJ(A->comm,A->n,A->m,N,M,0,PETSC_NULL,0,PETSC_NULL,&B);CHKERRQ(ierr); 1269 1270 /* copy over the A part */ 1271 Aloc = (Mat_SeqAIJ*)a->A->data; 1272 m = a->A->m; ai = Aloc->i; aj = Aloc->j; array = Aloc->a; 1273 row = a->rstart; 1274 for (i=0; i<ai[m]+shift; i++) {aj[i] += a->cstart + shift;} 1275 for (i=0; i<m; i++) { 1276 ierr = MatSetValues(B,ai[i+1]-ai[i],aj,1,&row,array,INSERT_VALUES);CHKERRQ(ierr); 1277 row++; array += ai[i+1]-ai[i]; aj += ai[i+1]-ai[i]; 1278 } 1279 aj = Aloc->j; 1280 for (i=0; i<ai[m]+shift; i++) {aj[i] -= a->cstart + shift;} 1281 1282 /* copy over the B part */ 1283 Aloc = (Mat_SeqAIJ*)a->B->data; 1284 m = a->B->m; ai = Aloc->i; aj = Aloc->j; array = Aloc->a; 1285 row = a->rstart; 1286 ierr = PetscMalloc((1+ai[m]-shift)*sizeof(int),&cols);CHKERRQ(ierr); 1287 ct = cols; 1288 for (i=0; i<ai[m]+shift; i++) {cols[i] = a->garray[aj[i]+shift];} 1289 for (i=0; i<m; i++) { 1290 ierr = MatSetValues(B,ai[i+1]-ai[i],cols,1,&row,array,INSERT_VALUES);CHKERRQ(ierr); 1291 row++; array += ai[i+1]-ai[i]; cols += ai[i+1]-ai[i]; 1292 } 1293 ierr = PetscFree(ct);CHKERRQ(ierr); 1294 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1295 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1296 if (matout) { 1297 *matout = B; 1298 } else { 1299 ierr = MatHeaderCopy(A,B);CHKERRQ(ierr); 1300 } 1301 PetscFunctionReturn(0); 1302 } 1303 1304 #undef __FUNC__ 1305 #define __FUNC__ "MatDiagonalScale_MPIAIJ" 1306 int MatDiagonalScale_MPIAIJ(Mat mat,Vec ll,Vec rr) 1307 { 1308 Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data; 1309 Mat a = aij->A,b = aij->B; 1310 int ierr,s1,s2,s3; 1311 1312 PetscFunctionBegin; 1313 ierr = MatGetLocalSize(mat,&s2,&s3);CHKERRQ(ierr); 1314 if (rr) { 1315 ierr = VecGetLocalSize(rr,&s1);CHKERRQ(ierr); 1316 if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size"); 1317 /* Overlap communication with computation. */ 1318 ierr = VecScatterBegin(rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD,aij->Mvctx);CHKERRQ(ierr); 1319 } 1320 if (ll) { 1321 ierr = VecGetLocalSize(ll,&s1);CHKERRQ(ierr); 1322 if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size"); 1323 ierr = (*b->ops->diagonalscale)(b,ll,0);CHKERRQ(ierr); 1324 } 1325 /* scale the diagonal block */ 1326 ierr = (*a->ops->diagonalscale)(a,ll,rr);CHKERRQ(ierr); 1327 1328 if (rr) { 1329 /* Do a scatter end and then right scale the off-diagonal block */ 1330 ierr = VecScatterEnd(rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD,aij->Mvctx);CHKERRQ(ierr); 1331 ierr = (*b->ops->diagonalscale)(b,0,aij->lvec);CHKERRQ(ierr); 1332 } 1333 1334 PetscFunctionReturn(0); 1335 } 1336 1337 1338 #undef __FUNC__ 1339 #define __FUNC__ "MatPrintHelp_MPIAIJ" 1340 int MatPrintHelp_MPIAIJ(Mat A) 1341 { 1342 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 1343 int ierr; 1344 1345 PetscFunctionBegin; 1346 if (!a->rank) { 1347 ierr = MatPrintHelp_SeqAIJ(a->A);CHKERRQ(ierr); 1348 } 1349 PetscFunctionReturn(0); 1350 } 1351 1352 #undef __FUNC__ 1353 #define __FUNC__ "MatGetBlockSize_MPIAIJ" 1354 int MatGetBlockSize_MPIAIJ(Mat A,int *bs) 1355 { 1356 PetscFunctionBegin; 1357 *bs = 1; 1358 PetscFunctionReturn(0); 1359 } 1360 #undef __FUNC__ 1361 #define __FUNC__ "MatSetUnfactored_MPIAIJ" 1362 int MatSetUnfactored_MPIAIJ(Mat A) 1363 { 1364 Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data; 1365 int ierr; 1366 1367 PetscFunctionBegin; 1368 ierr = MatSetUnfactored(a->A);CHKERRQ(ierr); 1369 PetscFunctionReturn(0); 1370 } 1371 1372 #undef __FUNC__ 1373 #define __FUNC__ "MatEqual_MPIAIJ" 1374 int MatEqual_MPIAIJ(Mat A,Mat B,PetscTruth *flag) 1375 { 1376 Mat_MPIAIJ *matB = (Mat_MPIAIJ*)B->data,*matA = (Mat_MPIAIJ*)A->data; 1377 Mat a,b,c,d; 1378 PetscTruth flg; 1379 int ierr; 1380 1381 PetscFunctionBegin; 1382 ierr = PetscTypeCompare((PetscObject)B,MATMPIAIJ,&flg);CHKERRQ(ierr); 1383 if (!flg) SETERRQ(PETSC_ERR_ARG_INCOMP,"Matrices must be same type"); 1384 a = matA->A; b = matA->B; 1385 c = matB->A; d = matB->B; 1386 1387 ierr = MatEqual(a,c,&flg);CHKERRQ(ierr); 1388 if (flg == PETSC_TRUE) { 1389 ierr = MatEqual(b,d,&flg);CHKERRQ(ierr); 1390 } 1391 ierr = MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);CHKERRQ(ierr); 1392 PetscFunctionReturn(0); 1393 } 1394 1395 #undef __FUNC__ 1396 #define __FUNC__ "MatCopy_MPIAIJ" 1397 int MatCopy_MPIAIJ(Mat A,Mat B,MatStructure str) 1398 { 1399 int ierr; 1400 Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data; 1401 Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data; 1402 PetscTruth flg; 1403 1404 PetscFunctionBegin; 1405 ierr = PetscTypeCompare((PetscObject)B,MATMPIAIJ,&flg);CHKERRQ(ierr); 1406 if (str != SAME_NONZERO_PATTERN || !flg) { 1407 /* because of the column compression in the off-processor part of the matrix a->B, 1408 the number of columns in a->B and b->B may be different, hence we cannot call 1409 the MatCopy() directly on the two parts. If need be, we can provide a more 1410 efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices 1411 then copying the submatrices */ 1412 ierr = MatCopy_Basic(A,B,str);CHKERRQ(ierr); 1413 } else { 1414 ierr = MatCopy(a->A,b->A,str);CHKERRQ(ierr); 1415 ierr = MatCopy(a->B,b->B,str);CHKERRQ(ierr); 1416 } 1417 PetscFunctionReturn(0); 1418 } 1419 1420 #undef __FUNC__ 1421 #define __FUNC__ "MatSetUpPreallocation_MPIAIJ" 1422 int MatSetUpPreallocation_MPIAIJ(Mat A) 1423 { 1424 int ierr; 1425 1426 PetscFunctionBegin; 1427 ierr = MatMPIAIJSetPreallocation(A,PETSC_DEFAULT,0,PETSC_DEFAULT,0);CHKERRQ(ierr); 1428 PetscFunctionReturn(0); 1429 } 1430 1431 EXTERN int MatDuplicate_MPIAIJ(Mat,MatDuplicateOption,Mat *); 1432 EXTERN int MatIncreaseOverlap_MPIAIJ(Mat,int,IS *,int); 1433 EXTERN int MatFDColoringCreate_MPIAIJ(Mat,ISColoring,MatFDColoring); 1434 EXTERN int MatGetSubMatrices_MPIAIJ (Mat,int,IS *,IS *,MatReuse,Mat **); 1435 EXTERN int MatGetSubMatrix_MPIAIJ (Mat,IS,IS,int,MatReuse,Mat *); 1436 1437 /* -------------------------------------------------------------------*/ 1438 static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ, 1439 MatGetRow_MPIAIJ, 1440 MatRestoreRow_MPIAIJ, 1441 MatMult_MPIAIJ, 1442 MatMultAdd_MPIAIJ, 1443 MatMultTranspose_MPIAIJ, 1444 MatMultTransposeAdd_MPIAIJ, 1445 0, 1446 0, 1447 0, 1448 0, 1449 0, 1450 0, 1451 MatRelax_MPIAIJ, 1452 MatTranspose_MPIAIJ, 1453 MatGetInfo_MPIAIJ, 1454 MatEqual_MPIAIJ, 1455 MatGetDiagonal_MPIAIJ, 1456 MatDiagonalScale_MPIAIJ, 1457 MatNorm_MPIAIJ, 1458 MatAssemblyBegin_MPIAIJ, 1459 MatAssemblyEnd_MPIAIJ, 1460 0, 1461 MatSetOption_MPIAIJ, 1462 MatZeroEntries_MPIAIJ, 1463 MatZeroRows_MPIAIJ, 1464 0, 1465 0, 1466 0, 1467 0, 1468 MatSetUpPreallocation_MPIAIJ, 1469 0, 1470 MatGetOwnershipRange_MPIAIJ, 1471 0, 1472 0, 1473 0, 1474 0, 1475 MatDuplicate_MPIAIJ, 1476 0, 1477 0, 1478 0, 1479 0, 1480 0, 1481 MatGetSubMatrices_MPIAIJ, 1482 MatIncreaseOverlap_MPIAIJ, 1483 MatGetValues_MPIAIJ, 1484 MatCopy_MPIAIJ, 1485 MatPrintHelp_MPIAIJ, 1486 MatScale_MPIAIJ, 1487 0, 1488 0, 1489 0, 1490 MatGetBlockSize_MPIAIJ, 1491 0, 1492 0, 1493 0, 1494 0, 1495 MatFDColoringCreate_MPIAIJ, 1496 0, 1497 MatSetUnfactored_MPIAIJ, 1498 0, 1499 0, 1500 MatGetSubMatrix_MPIAIJ, 1501 MatDestroy_MPIAIJ, 1502 MatView_MPIAIJ, 1503 MatGetMaps_Petsc}; 1504 1505 /* ----------------------------------------------------------------------------------------*/ 1506 1507 EXTERN_C_BEGIN 1508 #undef __FUNC__ 1509 #define __FUNC__ "MatStoreValues_MPIAIJ" 1510 int MatStoreValues_MPIAIJ(Mat mat) 1511 { 1512 Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data; 1513 int ierr; 1514 1515 PetscFunctionBegin; 1516 ierr = MatStoreValues(aij->A);CHKERRQ(ierr); 1517 ierr = MatStoreValues(aij->B);CHKERRQ(ierr); 1518 PetscFunctionReturn(0); 1519 } 1520 EXTERN_C_END 1521 1522 EXTERN_C_BEGIN 1523 #undef __FUNC__ 1524 #define __FUNC__ "MatRetrieveValues_MPIAIJ" 1525 int MatRetrieveValues_MPIAIJ(Mat mat) 1526 { 1527 Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data; 1528 int ierr; 1529 1530 PetscFunctionBegin; 1531 ierr = MatRetrieveValues(aij->A);CHKERRQ(ierr); 1532 ierr = MatRetrieveValues(aij->B);CHKERRQ(ierr); 1533 PetscFunctionReturn(0); 1534 } 1535 EXTERN_C_END 1536 1537 #include "petscpc.h" 1538 EXTERN_C_BEGIN 1539 EXTERN int MatGetDiagonalBlock_MPIAIJ(Mat,PetscTruth *,MatReuse,Mat *); 1540 EXTERN_C_END 1541 1542 EXTERN int MatUseXXT_MPIAIJ(Mat); 1543 EXTERN int MatUseXYT_MPIAIJ(Mat); 1544 1545 EXTERN_C_BEGIN 1546 #undef __FUNC__ 1547 #define __FUNC__ "MatCreate_MPIAIJ" 1548 int MatCreate_MPIAIJ(Mat B) 1549 { 1550 Mat_MPIAIJ *b; 1551 int ierr,i,size; 1552 PetscTruth flg; 1553 1554 PetscFunctionBegin; 1555 ierr = MPI_Comm_size(B->comm,&size);CHKERRQ(ierr); 1556 1557 ierr = PetscNew(Mat_MPIAIJ,&b);CHKERRQ(ierr); 1558 B->data = (void*)b; 1559 ierr = PetscMemzero(b,sizeof(Mat_MPIAIJ));CHKERRQ(ierr); 1560 ierr = PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 1561 B->factor = 0; 1562 B->assembled = PETSC_FALSE; 1563 B->mapping = 0; 1564 1565 B->insertmode = NOT_SET_VALUES; 1566 b->size = size; 1567 ierr = MPI_Comm_rank(B->comm,&b->rank);CHKERRQ(ierr); 1568 1569 ierr = PetscSplitOwnership(B->comm,&B->m,&B->M);CHKERRQ(ierr); 1570 ierr = PetscSplitOwnership(B->comm,&B->n,&B->N);CHKERRQ(ierr); 1571 1572 /* the information in the maps duplicates the information computed below, eventually 1573 we should remove the duplicate information that is not contained in the maps */ 1574 ierr = MapCreateMPI(B->comm,B->m,B->M,&B->rmap);CHKERRQ(ierr); 1575 ierr = MapCreateMPI(B->comm,B->n,B->N,&B->cmap);CHKERRQ(ierr); 1576 1577 /* build local table of row and column ownerships */ 1578 ierr = PetscMalloc(2*(b->size+2)*sizeof(int),&b->rowners);CHKERRQ(ierr); 1579 PetscLogObjectMemory(B,2*(b->size+2)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_MPIAIJ)); 1580 b->cowners = b->rowners + b->size + 2; 1581 ierr = MPI_Allgather(&B->m,1,MPI_INT,b->rowners+1,1,MPI_INT,B->comm);CHKERRQ(ierr); 1582 b->rowners[0] = 0; 1583 for (i=2; i<=b->size; i++) { 1584 b->rowners[i] += b->rowners[i-1]; 1585 } 1586 b->rstart = b->rowners[b->rank]; 1587 b->rend = b->rowners[b->rank+1]; 1588 ierr = MPI_Allgather(&B->n,1,MPI_INT,b->cowners+1,1,MPI_INT,B->comm);CHKERRQ(ierr); 1589 b->cowners[0] = 0; 1590 for (i=2; i<=b->size; i++) { 1591 b->cowners[i] += b->cowners[i-1]; 1592 } 1593 b->cstart = b->cowners[b->rank]; 1594 b->cend = b->cowners[b->rank+1]; 1595 1596 /* build cache for off array entries formed */ 1597 ierr = MatStashCreate_Private(B->comm,1,&B->stash);CHKERRQ(ierr); 1598 b->donotstash = PETSC_FALSE; 1599 b->colmap = 0; 1600 b->garray = 0; 1601 b->roworiented = PETSC_TRUE; 1602 1603 /* stuff used for matrix vector multiply */ 1604 b->lvec = PETSC_NULL; 1605 b->Mvctx = PETSC_NULL; 1606 1607 /* stuff for MatGetRow() */ 1608 b->rowindices = 0; 1609 b->rowvalues = 0; 1610 b->getrowactive = PETSC_FALSE; 1611 1612 ierr = PetscOptionsHasName(PETSC_NULL,"-mat_mpiaij_xxt",&flg);CHKERRQ(ierr); 1613 if (flg) { ierr = MatUseXXT_MPIAIJ(B);CHKERRQ(ierr); } 1614 ierr = PetscOptionsHasName(PETSC_NULL,"-mat_mpiaij_xyt",&flg);CHKERRQ(ierr); 1615 if (flg) { ierr = MatUseXYT_MPIAIJ(B);CHKERRQ(ierr); } 1616 1617 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C", 1618 "MatStoreValues_MPIAIJ", 1619 MatStoreValues_MPIAIJ);CHKERRQ(ierr); 1620 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C", 1621 "MatRetrieveValues_MPIAIJ", 1622 MatRetrieveValues_MPIAIJ);CHKERRQ(ierr); 1623 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C", 1624 "MatGetDiagonalBlock_MPIAIJ", 1625 MatGetDiagonalBlock_MPIAIJ);CHKERRQ(ierr); 1626 PetscFunctionReturn(0); 1627 } 1628 EXTERN_C_END 1629 1630 #undef __FUNC__ 1631 #define __FUNC__ "MatDuplicate_MPIAIJ" 1632 int MatDuplicate_MPIAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat) 1633 { 1634 Mat mat; 1635 Mat_MPIAIJ *a,*oldmat = (Mat_MPIAIJ*)matin->data; 1636 int ierr; 1637 1638 PetscFunctionBegin; 1639 *newmat = 0; 1640 ierr = MatCreate(matin->comm,matin->m,matin->n,matin->M,matin->N,&mat);CHKERRQ(ierr); 1641 ierr = MatSetType(mat,MATMPIAIJ);CHKERRQ(ierr); 1642 a = (Mat_MPIAIJ*)mat->data; 1643 ierr = PetscMemcpy(mat->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 1644 mat->factor = matin->factor; 1645 mat->assembled = PETSC_TRUE; 1646 mat->insertmode = NOT_SET_VALUES; 1647 mat->preallocated = PETSC_TRUE; 1648 1649 a->rstart = oldmat->rstart; 1650 a->rend = oldmat->rend; 1651 a->cstart = oldmat->cstart; 1652 a->cend = oldmat->cend; 1653 a->size = oldmat->size; 1654 a->rank = oldmat->rank; 1655 a->donotstash = oldmat->donotstash; 1656 a->roworiented = oldmat->roworiented; 1657 a->rowindices = 0; 1658 a->rowvalues = 0; 1659 a->getrowactive = PETSC_FALSE; 1660 1661 ierr = PetscMemcpy(a->rowners,oldmat->rowners,2*(a->size+2)*sizeof(int));CHKERRQ(ierr); 1662 ierr = MatStashCreate_Private(matin->comm,1,&mat->stash);CHKERRQ(ierr); 1663 if (oldmat->colmap) { 1664 #if defined (PETSC_USE_CTABLE) 1665 ierr = PetscTableCreateCopy(oldmat->colmap,&a->colmap);CHKERRQ(ierr); 1666 #else 1667 ierr = PetscMalloc((mat->N)*sizeof(int),&a->colmap);CHKERRQ(ierr); 1668 PetscLogObjectMemory(mat,(mat->N)*sizeof(int)); 1669 ierr = PetscMemcpy(a->colmap,oldmat->colmap,(mat->N)*sizeof(int));CHKERRQ(ierr); 1670 #endif 1671 } else a->colmap = 0; 1672 if (oldmat->garray) { 1673 int len; 1674 len = oldmat->B->n; 1675 ierr = PetscMalloc((len+1)*sizeof(int),&a->garray);CHKERRQ(ierr); 1676 PetscLogObjectMemory(mat,len*sizeof(int)); 1677 if (len) { ierr = PetscMemcpy(a->garray,oldmat->garray,len*sizeof(int));CHKERRQ(ierr); } 1678 } else a->garray = 0; 1679 1680 ierr = VecDuplicate(oldmat->lvec,&a->lvec);CHKERRQ(ierr); 1681 PetscLogObjectParent(mat,a->lvec); 1682 ierr = VecScatterCopy(oldmat->Mvctx,&a->Mvctx);CHKERRQ(ierr); 1683 PetscLogObjectParent(mat,a->Mvctx); 1684 ierr = MatDuplicate(oldmat->A,cpvalues,&a->A);CHKERRQ(ierr); 1685 PetscLogObjectParent(mat,a->A); 1686 ierr = MatDuplicate(oldmat->B,cpvalues,&a->B);CHKERRQ(ierr); 1687 PetscLogObjectParent(mat,a->B); 1688 ierr = PetscFListDuplicate(matin->qlist,&mat->qlist);CHKERRQ(ierr); 1689 *newmat = mat; 1690 PetscFunctionReturn(0); 1691 } 1692 1693 #include "petscsys.h" 1694 1695 EXTERN_C_BEGIN 1696 #undef __FUNC__ 1697 #define __FUNC__ "MatLoad_MPIAIJ" 1698 int MatLoad_MPIAIJ(PetscViewer viewer,MatType type,Mat *newmat) 1699 { 1700 Mat A; 1701 Scalar *vals,*svals; 1702 MPI_Comm comm = ((PetscObject)viewer)->comm; 1703 MPI_Status status; 1704 int i,nz,ierr,j,rstart,rend,fd; 1705 int header[4],rank,size,*rowlengths = 0,M,N,m,*rowners,maxnz,*cols; 1706 int *ourlens,*sndcounts = 0,*procsnz = 0,*offlens,jj,*mycols,*smycols; 1707 int tag = ((PetscObject)viewer)->tag,cend,cstart,n; 1708 1709 PetscFunctionBegin; 1710 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 1711 ierr = MPI_Comm_rank(comm,&rank);CHKERRQ(ierr); 1712 if (!rank) { 1713 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 1714 ierr = PetscBinaryRead(fd,(char *)header,4,PETSC_INT);CHKERRQ(ierr); 1715 if (header[0] != MAT_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object"); 1716 if (header[3] < 0) { 1717 SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix in special format on disk, cannot load as MPIAIJ"); 1718 } 1719 } 1720 1721 ierr = MPI_Bcast(header+1,3,MPI_INT,0,comm);CHKERRQ(ierr); 1722 M = header[1]; N = header[2]; 1723 /* determine ownership of all rows */ 1724 m = M/size + ((M % size) > rank); 1725 ierr = PetscMalloc((size+2)*sizeof(int),&rowners);CHKERRQ(ierr); 1726 ierr = MPI_Allgather(&m,1,MPI_INT,rowners+1,1,MPI_INT,comm);CHKERRQ(ierr); 1727 rowners[0] = 0; 1728 for (i=2; i<=size; i++) { 1729 rowners[i] += rowners[i-1]; 1730 } 1731 rstart = rowners[rank]; 1732 rend = rowners[rank+1]; 1733 1734 /* distribute row lengths to all processors */ 1735 ierr = PetscMalloc(2*(rend-rstart+1)*sizeof(int),&ourlens);CHKERRQ(ierr); 1736 offlens = ourlens + (rend-rstart); 1737 if (!rank) { 1738 ierr = PetscMalloc(M*sizeof(int),&rowlengths);CHKERRQ(ierr); 1739 ierr = PetscBinaryRead(fd,rowlengths,M,PETSC_INT);CHKERRQ(ierr); 1740 ierr = PetscMalloc(size*sizeof(int),&sndcounts);CHKERRQ(ierr); 1741 for (i=0; i<size; i++) sndcounts[i] = rowners[i+1] - rowners[i]; 1742 ierr = MPI_Scatterv(rowlengths,sndcounts,rowners,MPI_INT,ourlens,rend-rstart,MPI_INT,0,comm);CHKERRQ(ierr); 1743 ierr = PetscFree(sndcounts);CHKERRQ(ierr); 1744 } else { 1745 ierr = MPI_Scatterv(0,0,0,MPI_INT,ourlens,rend-rstart,MPI_INT,0,comm);CHKERRQ(ierr); 1746 } 1747 1748 if (!rank) { 1749 /* calculate the number of nonzeros on each processor */ 1750 ierr = PetscMalloc(size*sizeof(int),&procsnz);CHKERRQ(ierr); 1751 ierr = PetscMemzero(procsnz,size*sizeof(int));CHKERRQ(ierr); 1752 for (i=0; i<size; i++) { 1753 for (j=rowners[i]; j< rowners[i+1]; j++) { 1754 procsnz[i] += rowlengths[j]; 1755 } 1756 } 1757 ierr = PetscFree(rowlengths);CHKERRQ(ierr); 1758 1759 /* determine max buffer needed and allocate it */ 1760 maxnz = 0; 1761 for (i=0; i<size; i++) { 1762 maxnz = PetscMax(maxnz,procsnz[i]); 1763 } 1764 ierr = PetscMalloc(maxnz*sizeof(int),&cols);CHKERRQ(ierr); 1765 1766 /* read in my part of the matrix column indices */ 1767 nz = procsnz[0]; 1768 ierr = PetscMalloc(nz*sizeof(int),&mycols);CHKERRQ(ierr); 1769 ierr = PetscBinaryRead(fd,mycols,nz,PETSC_INT);CHKERRQ(ierr); 1770 1771 /* read in every one elses and ship off */ 1772 for (i=1; i<size; i++) { 1773 nz = procsnz[i]; 1774 ierr = PetscBinaryRead(fd,cols,nz,PETSC_INT);CHKERRQ(ierr); 1775 ierr = MPI_Send(cols,nz,MPI_INT,i,tag,comm);CHKERRQ(ierr); 1776 } 1777 ierr = PetscFree(cols);CHKERRQ(ierr); 1778 } else { 1779 /* determine buffer space needed for message */ 1780 nz = 0; 1781 for (i=0; i<m; i++) { 1782 nz += ourlens[i]; 1783 } 1784 ierr = PetscMalloc((nz+1)*sizeof(int),&mycols);CHKERRQ(ierr); 1785 1786 /* receive message of column indices*/ 1787 ierr = MPI_Recv(mycols,nz,MPI_INT,0,tag,comm,&status);CHKERRQ(ierr); 1788 ierr = MPI_Get_count(&status,MPI_INT,&maxnz);CHKERRQ(ierr); 1789 if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file"); 1790 } 1791 1792 /* determine column ownership if matrix is not square */ 1793 if (N != M) { 1794 n = N/size + ((N % size) > rank); 1795 ierr = MPI_Scan(&n,&cend,1,MPI_INT,MPI_SUM,comm);CHKERRQ(ierr); 1796 cstart = cend - n; 1797 } else { 1798 cstart = rstart; 1799 cend = rend; 1800 n = cend - cstart; 1801 } 1802 1803 /* loop over local rows, determining number of off diagonal entries */ 1804 ierr = PetscMemzero(offlens,m*sizeof(int));CHKERRQ(ierr); 1805 jj = 0; 1806 for (i=0; i<m; i++) { 1807 for (j=0; j<ourlens[i]; j++) { 1808 if (mycols[jj] < cstart || mycols[jj] >= cend) offlens[i]++; 1809 jj++; 1810 } 1811 } 1812 1813 /* create our matrix */ 1814 for (i=0; i<m; i++) { 1815 ourlens[i] -= offlens[i]; 1816 } 1817 ierr = MatCreateMPIAIJ(comm,m,n,M,N,0,ourlens,0,offlens,newmat);CHKERRQ(ierr); 1818 A = *newmat; 1819 ierr = MatSetOption(A,MAT_COLUMNS_SORTED);CHKERRQ(ierr); 1820 for (i=0; i<m; i++) { 1821 ourlens[i] += offlens[i]; 1822 } 1823 1824 if (!rank) { 1825 ierr = PetscMalloc(maxnz*sizeof(Scalar),&vals);CHKERRQ(ierr); 1826 1827 /* read in my part of the matrix numerical values */ 1828 nz = procsnz[0]; 1829 ierr = PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);CHKERRQ(ierr); 1830 1831 /* insert into matrix */ 1832 jj = rstart; 1833 smycols = mycols; 1834 svals = vals; 1835 for (i=0; i<m; i++) { 1836 ierr = MatSetValues(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);CHKERRQ(ierr); 1837 smycols += ourlens[i]; 1838 svals += ourlens[i]; 1839 jj++; 1840 } 1841 1842 /* read in other processors and ship out */ 1843 for (i=1; i<size; i++) { 1844 nz = procsnz[i]; 1845 ierr = PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);CHKERRQ(ierr); 1846 ierr = MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);CHKERRQ(ierr); 1847 } 1848 ierr = PetscFree(procsnz);CHKERRQ(ierr); 1849 } else { 1850 /* receive numeric values */ 1851 ierr = PetscMalloc((nz+1)*sizeof(Scalar),&vals);CHKERRQ(ierr); 1852 1853 /* receive message of values*/ 1854 ierr = MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);CHKERRQ(ierr); 1855 ierr = MPI_Get_count(&status,MPIU_SCALAR,&maxnz);CHKERRQ(ierr); 1856 if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file"); 1857 1858 /* insert into matrix */ 1859 jj = rstart; 1860 smycols = mycols; 1861 svals = vals; 1862 for (i=0; i<m; i++) { 1863 ierr = MatSetValues(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);CHKERRQ(ierr); 1864 smycols += ourlens[i]; 1865 svals += ourlens[i]; 1866 jj++; 1867 } 1868 } 1869 ierr = PetscFree(ourlens);CHKERRQ(ierr); 1870 ierr = PetscFree(vals);CHKERRQ(ierr); 1871 ierr = PetscFree(mycols);CHKERRQ(ierr); 1872 ierr = PetscFree(rowners);CHKERRQ(ierr); 1873 1874 ierr = MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1875 ierr = MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1876 PetscFunctionReturn(0); 1877 } 1878 EXTERN_C_END 1879 1880 #undef __FUNC__ 1881 #define __FUNC__ "MatGetSubMatrix_MPIAIJ" 1882 /* 1883 Not great since it makes two copies of the submatrix, first an SeqAIJ 1884 in local and then by concatenating the local matrices the end result. 1885 Writing it directly would be much like MatGetSubMatrices_MPIAIJ() 1886 */ 1887 int MatGetSubMatrix_MPIAIJ(Mat mat,IS isrow,IS iscol,int csize,MatReuse call,Mat *newmat) 1888 { 1889 int ierr,i,m,n,rstart,row,rend,nz,*cwork,size,rank,j; 1890 int *ii,*jj,nlocal,*dlens,*olens,dlen,olen,jend; 1891 Mat *local,M,Mreuse; 1892 Scalar *vwork,*aa; 1893 MPI_Comm comm = mat->comm; 1894 Mat_SeqAIJ *aij; 1895 1896 1897 PetscFunctionBegin; 1898 ierr = MPI_Comm_rank(comm,&rank);CHKERRQ(ierr); 1899 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 1900 1901 if (call == MAT_REUSE_MATRIX) { 1902 ierr = PetscObjectQuery((PetscObject)*newmat,"SubMatrix",(PetscObject *)&Mreuse);CHKERRQ(ierr); 1903 if (!Mreuse) SETERRQ(1,"Submatrix passed in was not used before, cannot reuse"); 1904 local = &Mreuse; 1905 ierr = MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_REUSE_MATRIX,&local);CHKERRQ(ierr); 1906 } else { 1907 ierr = MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&local);CHKERRQ(ierr); 1908 Mreuse = *local; 1909 ierr = PetscFree(local);CHKERRQ(ierr); 1910 } 1911 1912 /* 1913 m - number of local rows 1914 n - number of columns (same on all processors) 1915 rstart - first row in new global matrix generated 1916 */ 1917 ierr = MatGetSize(Mreuse,&m,&n);CHKERRQ(ierr); 1918 if (call == MAT_INITIAL_MATRIX) { 1919 aij = (Mat_SeqAIJ*)(Mreuse)->data; 1920 if (aij->indexshift) SETERRQ(PETSC_ERR_SUP,"No support for index shifted matrix"); 1921 ii = aij->i; 1922 jj = aij->j; 1923 1924 /* 1925 Determine the number of non-zeros in the diagonal and off-diagonal 1926 portions of the matrix in order to do correct preallocation 1927 */ 1928 1929 /* first get start and end of "diagonal" columns */ 1930 if (csize == PETSC_DECIDE) { 1931 nlocal = n/size + ((n % size) > rank); 1932 } else { 1933 nlocal = csize; 1934 } 1935 ierr = MPI_Scan(&nlocal,&rend,1,MPI_INT,MPI_SUM,comm);CHKERRQ(ierr); 1936 rstart = rend - nlocal; 1937 if (rank == size - 1 && rend != n) { 1938 SETERRQ(1,"Local column sizes do not add up to total number of columns"); 1939 } 1940 1941 /* next, compute all the lengths */ 1942 ierr = PetscMalloc((2*m+1)*sizeof(int),&dlens);CHKERRQ(ierr); 1943 olens = dlens + m; 1944 for (i=0; i<m; i++) { 1945 jend = ii[i+1] - ii[i]; 1946 olen = 0; 1947 dlen = 0; 1948 for (j=0; j<jend; j++) { 1949 if (*jj < rstart || *jj >= rend) olen++; 1950 else dlen++; 1951 jj++; 1952 } 1953 olens[i] = olen; 1954 dlens[i] = dlen; 1955 } 1956 ierr = MatCreateMPIAIJ(comm,m,nlocal,PETSC_DECIDE,n,0,dlens,0,olens,&M);CHKERRQ(ierr); 1957 ierr = PetscFree(dlens);CHKERRQ(ierr); 1958 } else { 1959 int ml,nl; 1960 1961 M = *newmat; 1962 ierr = MatGetLocalSize(M,&ml,&nl);CHKERRQ(ierr); 1963 if (ml != m) SETERRQ(PETSC_ERR_ARG_SIZ,"Previous matrix must be same size/layout as request"); 1964 ierr = MatZeroEntries(M);CHKERRQ(ierr); 1965 /* 1966 The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly, 1967 rather than the slower MatSetValues(). 1968 */ 1969 M->was_assembled = PETSC_TRUE; 1970 M->assembled = PETSC_FALSE; 1971 } 1972 ierr = MatGetOwnershipRange(M,&rstart,&rend);CHKERRQ(ierr); 1973 aij = (Mat_SeqAIJ*)(Mreuse)->data; 1974 if (aij->indexshift) SETERRQ(PETSC_ERR_SUP,"No support for index shifted matrix"); 1975 ii = aij->i; 1976 jj = aij->j; 1977 aa = aij->a; 1978 for (i=0; i<m; i++) { 1979 row = rstart + i; 1980 nz = ii[i+1] - ii[i]; 1981 cwork = jj; jj += nz; 1982 vwork = aa; aa += nz; 1983 ierr = MatSetValues_MPIAIJ(M,1,&row,nz,cwork,vwork,INSERT_VALUES);CHKERRQ(ierr); 1984 } 1985 1986 ierr = MatAssemblyBegin(M,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1987 ierr = MatAssemblyEnd(M,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1988 *newmat = M; 1989 1990 /* save submatrix used in processor for next request */ 1991 if (call == MAT_INITIAL_MATRIX) { 1992 ierr = PetscObjectCompose((PetscObject)M,"SubMatrix",(PetscObject)Mreuse);CHKERRQ(ierr); 1993 ierr = PetscObjectDereference((PetscObject)Mreuse);CHKERRQ(ierr); 1994 } 1995 1996 PetscFunctionReturn(0); 1997 } 1998 1999 #undef __FUNC__ 2000 #define __FUNC__ "MatMPIAIJSetPreallocation" 2001 /*@C 2002 MatMPIAIJSetPreallocation - Creates a sparse parallel matrix in AIJ format 2003 (the default parallel PETSc format). For good matrix assembly performance 2004 the user should preallocate the matrix storage by setting the parameters 2005 d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately, 2006 performance can be increased by more than a factor of 50. 2007 2008 Collective on MPI_Comm 2009 2010 Input Parameters: 2011 + A - the matrix 2012 . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix 2013 (same value is used for all local rows) 2014 . d_nnz - array containing the number of nonzeros in the various rows of the 2015 DIAGONAL portion of the local submatrix (possibly different for each row) 2016 or PETSC_NULL, if d_nz is used to specify the nonzero structure. 2017 The size of this array is equal to the number of local rows, i.e 'm'. 2018 You must leave room for the diagonal entry even if it is zero. 2019 . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local 2020 submatrix (same value is used for all local rows). 2021 - o_nnz - array containing the number of nonzeros in the various rows of the 2022 OFF-DIAGONAL portion of the local submatrix (possibly different for 2023 each row) or PETSC_NULL, if o_nz is used to specify the nonzero 2024 structure. The size of this array is equal to the number 2025 of local rows, i.e 'm'. 2026 2027 The AIJ format (also called the Yale sparse matrix format or 2028 compressed row storage), is fully compatible with standard Fortran 77 2029 storage. That is, the stored row and column indices can begin at 2030 either one (as in Fortran) or zero. See the users manual for details. 2031 2032 The user MUST specify either the local or global matrix dimensions 2033 (possibly both). 2034 2035 The parallel matrix is partitioned such that the first m0 rows belong to 2036 process 0, the next m1 rows belong to process 1, the next m2 rows belong 2037 to process 2 etc.. where m0,m1,m2... are the input parameter 'm'. 2038 2039 The DIAGONAL portion of the local submatrix of a processor can be defined 2040 as the submatrix which is obtained by extraction the part corresponding 2041 to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the 2042 first row that belongs to the processor, and r2 is the last row belonging 2043 to the this processor. This is a square mxm matrix. The remaining portion 2044 of the local submatrix (mxN) constitute the OFF-DIAGONAL portion. 2045 2046 If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored. 2047 2048 By default, this format uses inodes (identical nodes) when possible. 2049 We search for consecutive rows with the same nonzero structure, thereby 2050 reusing matrix information to achieve increased efficiency. 2051 2052 Options Database Keys: 2053 + -mat_aij_no_inode - Do not use inodes 2054 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2055 - -mat_aij_oneindex - Internally use indexing starting at 1 2056 rather than 0. Note that when calling MatSetValues(), 2057 the user still MUST index entries starting at 0! 2058 2059 Example usage: 2060 2061 Consider the following 8x8 matrix with 34 non-zero values, that is 2062 assembled across 3 processors. Lets assume that proc0 owns 3 rows, 2063 proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown 2064 as follows: 2065 2066 .vb 2067 1 2 0 | 0 3 0 | 0 4 2068 Proc0 0 5 6 | 7 0 0 | 8 0 2069 9 0 10 | 11 0 0 | 12 0 2070 ------------------------------------- 2071 13 0 14 | 15 16 17 | 0 0 2072 Proc1 0 18 0 | 19 20 21 | 0 0 2073 0 0 0 | 22 23 0 | 24 0 2074 ------------------------------------- 2075 Proc2 25 26 27 | 0 0 28 | 29 0 2076 30 0 0 | 31 32 33 | 0 34 2077 .ve 2078 2079 This can be represented as a collection of submatrices as: 2080 2081 .vb 2082 A B C 2083 D E F 2084 G H I 2085 .ve 2086 2087 Where the submatrices A,B,C are owned by proc0, D,E,F are 2088 owned by proc1, G,H,I are owned by proc2. 2089 2090 The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 2091 The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 2092 The 'M','N' parameters are 8,8, and have the same values on all procs. 2093 2094 The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are 2095 submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices 2096 corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively. 2097 Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL 2098 part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ 2099 matrix, ans [DF] as another SeqAIJ matrix. 2100 2101 When d_nz, o_nz parameters are specified, d_nz storage elements are 2102 allocated for every row of the local diagonal submatrix, and o_nz 2103 storage locations are allocated for every row of the OFF-DIAGONAL submat. 2104 One way to choose d_nz and o_nz is to use the max nonzerors per local 2105 rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices. 2106 In this case, the values of d_nz,o_nz are: 2107 .vb 2108 proc0 : dnz = 2, o_nz = 2 2109 proc1 : dnz = 3, o_nz = 2 2110 proc2 : dnz = 1, o_nz = 4 2111 .ve 2112 We are allocating m*(d_nz+o_nz) storage locations for every proc. This 2113 translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10 2114 for proc3. i.e we are using 12+15+10=37 storage locations to store 2115 34 values. 2116 2117 When d_nnz, o_nnz parameters are specified, the storage is specified 2118 for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices. 2119 In the above case the values for d_nnz,o_nnz are: 2120 .vb 2121 proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2] 2122 proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1] 2123 proc2: d_nnz = [1,1] and o_nnz = [4,4] 2124 .ve 2125 Here the space allocated is sum of all the above values i.e 34, and 2126 hence pre-allocation is perfect. 2127 2128 Level: intermediate 2129 2130 .keywords: matrix, aij, compressed row, sparse, parallel 2131 2132 .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues() 2133 @*/ 2134 int MatMPIAIJSetPreallocation(Mat B,int d_nz,int *d_nnz,int o_nz,int *o_nnz) 2135 { 2136 Mat_MPIAIJ *b; 2137 int ierr,i; 2138 PetscTruth flg2; 2139 2140 PetscFunctionBegin; 2141 ierr = PetscTypeCompare((PetscObject)B,MATMPIAIJ,&flg2);CHKERRQ(ierr); 2142 if (!flg2) PetscFunctionReturn(0); 2143 B->preallocated = PETSC_TRUE; 2144 if (d_nz < -2) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than -2: value %d",d_nz); 2145 if (o_nz < -2) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than -2: value %d",o_nz); 2146 if (d_nnz) { 2147 for (i=0; i<B->m; i++) { 2148 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]); 2149 } 2150 } 2151 if (o_nnz) { 2152 for (i=0; i<B->m; i++) { 2153 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]); 2154 } 2155 } 2156 b = (Mat_MPIAIJ*)B->data; 2157 2158 if (d_nz == PETSC_DEFAULT) d_nz = 5; 2159 ierr = MatCreateSeqAIJ(PETSC_COMM_SELF,B->m,B->n,d_nz,d_nnz,&b->A);CHKERRQ(ierr); 2160 PetscLogObjectParent(B,b->A); 2161 if (o_nz == PETSC_DEFAULT) o_nz = 0; 2162 ierr = MatCreateSeqAIJ(PETSC_COMM_SELF,B->m,B->N,o_nz,o_nnz,&b->B);CHKERRQ(ierr); 2163 PetscLogObjectParent(B,b->B); 2164 2165 PetscFunctionReturn(0); 2166 } 2167 2168 #undef __FUNC__ 2169 #define __FUNC__ "MatCreateMPIAIJ" 2170 /*@C 2171 MatCreateMPIAIJ - Creates a sparse parallel matrix in AIJ format 2172 (the default parallel PETSc format). For good matrix assembly performance 2173 the user should preallocate the matrix storage by setting the parameters 2174 d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately, 2175 performance can be increased by more than a factor of 50. 2176 2177 Collective on MPI_Comm 2178 2179 Input Parameters: 2180 + comm - MPI communicator 2181 . m - number of local rows (or PETSC_DECIDE to have calculated if M is given) 2182 This value should be the same as the local size used in creating the 2183 y vector for the matrix-vector product y = Ax. 2184 . n - This value should be the same as the local size used in creating the 2185 x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have 2186 calculated if N is given) For square matrices n is almost always m. 2187 . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given) 2188 . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given) 2189 . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix 2190 (same value is used for all local rows) 2191 . d_nnz - array containing the number of nonzeros in the various rows of the 2192 DIAGONAL portion of the local submatrix (possibly different for each row) 2193 or PETSC_NULL, if d_nz is used to specify the nonzero structure. 2194 The size of this array is equal to the number of local rows, i.e 'm'. 2195 You must leave room for the diagonal entry even if it is zero. 2196 . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local 2197 submatrix (same value is used for all local rows). 2198 - o_nnz - array containing the number of nonzeros in the various rows of the 2199 OFF-DIAGONAL portion of the local submatrix (possibly different for 2200 each row) or PETSC_NULL, if o_nz is used to specify the nonzero 2201 structure. The size of this array is equal to the number 2202 of local rows, i.e 'm'. 2203 2204 Output Parameter: 2205 . A - the matrix 2206 2207 Notes: 2208 m,n,M,N parameters specify the size of the matrix, and its partitioning across 2209 processors, while d_nz,d_nnz,o_nz,o_nnz parameters specify the approximate 2210 storage requirements for this matrix. 2211 2212 If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one 2213 processor than it must be used on all processors that share the object for 2214 that argument. 2215 2216 The AIJ format (also called the Yale sparse matrix format or 2217 compressed row storage), is fully compatible with standard Fortran 77 2218 storage. That is, the stored row and column indices can begin at 2219 either one (as in Fortran) or zero. See the users manual for details. 2220 2221 The user MUST specify either the local or global matrix dimensions 2222 (possibly both). 2223 2224 The parallel matrix is partitioned such that the first m0 rows belong to 2225 process 0, the next m1 rows belong to process 1, the next m2 rows belong 2226 to process 2 etc.. where m0,m1,m2... are the input parameter 'm'. 2227 2228 The DIAGONAL portion of the local submatrix of a processor can be defined 2229 as the submatrix which is obtained by extraction the part corresponding 2230 to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the 2231 first row that belongs to the processor, and r2 is the last row belonging 2232 to the this processor. This is a square mxm matrix. The remaining portion 2233 of the local submatrix (mxN) constitute the OFF-DIAGONAL portion. 2234 2235 If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored. 2236 2237 By default, this format uses inodes (identical nodes) when possible. 2238 We search for consecutive rows with the same nonzero structure, thereby 2239 reusing matrix information to achieve increased efficiency. 2240 2241 Options Database Keys: 2242 + -mat_aij_no_inode - Do not use inodes 2243 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2244 - -mat_aij_oneindex - Internally use indexing starting at 1 2245 rather than 0. Note that when calling MatSetValues(), 2246 the user still MUST index entries starting at 0! 2247 2248 2249 Example usage: 2250 2251 Consider the following 8x8 matrix with 34 non-zero values, that is 2252 assembled across 3 processors. Lets assume that proc0 owns 3 rows, 2253 proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown 2254 as follows: 2255 2256 .vb 2257 1 2 0 | 0 3 0 | 0 4 2258 Proc0 0 5 6 | 7 0 0 | 8 0 2259 9 0 10 | 11 0 0 | 12 0 2260 ------------------------------------- 2261 13 0 14 | 15 16 17 | 0 0 2262 Proc1 0 18 0 | 19 20 21 | 0 0 2263 0 0 0 | 22 23 0 | 24 0 2264 ------------------------------------- 2265 Proc2 25 26 27 | 0 0 28 | 29 0 2266 30 0 0 | 31 32 33 | 0 34 2267 .ve 2268 2269 This can be represented as a collection of submatrices as: 2270 2271 .vb 2272 A B C 2273 D E F 2274 G H I 2275 .ve 2276 2277 Where the submatrices A,B,C are owned by proc0, D,E,F are 2278 owned by proc1, G,H,I are owned by proc2. 2279 2280 The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 2281 The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 2282 The 'M','N' parameters are 8,8, and have the same values on all procs. 2283 2284 The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are 2285 submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices 2286 corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively. 2287 Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL 2288 part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ 2289 matrix, ans [DF] as another SeqAIJ matrix. 2290 2291 When d_nz, o_nz parameters are specified, d_nz storage elements are 2292 allocated for every row of the local diagonal submatrix, and o_nz 2293 storage locations are allocated for every row of the OFF-DIAGONAL submat. 2294 One way to choose d_nz and o_nz is to use the max nonzerors per local 2295 rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices. 2296 In this case, the values of d_nz,o_nz are: 2297 .vb 2298 proc0 : dnz = 2, o_nz = 2 2299 proc1 : dnz = 3, o_nz = 2 2300 proc2 : dnz = 1, o_nz = 4 2301 .ve 2302 We are allocating m*(d_nz+o_nz) storage locations for every proc. This 2303 translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10 2304 for proc3. i.e we are using 12+15+10=37 storage locations to store 2305 34 values. 2306 2307 When d_nnz, o_nnz parameters are specified, the storage is specified 2308 for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices. 2309 In the above case the values for d_nnz,o_nnz are: 2310 .vb 2311 proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2] 2312 proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1] 2313 proc2: d_nnz = [1,1] and o_nnz = [4,4] 2314 .ve 2315 Here the space allocated is sum of all the above values i.e 34, and 2316 hence pre-allocation is perfect. 2317 2318 Level: intermediate 2319 2320 .keywords: matrix, aij, compressed row, sparse, parallel 2321 2322 .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues() 2323 @*/ 2324 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) 2325 { 2326 int ierr,size; 2327 2328 PetscFunctionBegin; 2329 ierr = MatCreate(comm,m,n,M,N,A);CHKERRQ(ierr); 2330 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 2331 if (size > 1) { 2332 ierr = MatSetType(*A,MATMPIAIJ);CHKERRQ(ierr); 2333 ierr = MatMPIAIJSetPreallocation(*A,d_nz,d_nnz,o_nz,o_nnz);CHKERRQ(ierr); 2334 } else { 2335 ierr = MatSetType(*A,MATSEQAIJ);CHKERRQ(ierr); 2336 ierr = MatSeqAIJSetPreallocation(*A,d_nz,d_nnz);CHKERRQ(ierr); 2337 } 2338 PetscFunctionReturn(0); 2339 } 2340 2341 #undef __FUNC__ 2342 #define __FUNC__ "MatMPIAIJGetSeqAIJ" 2343 int MatMPIAIJGetSeqAIJ(Mat A,Mat *Ad,Mat *Ao,int **colmap) 2344 { 2345 Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data; 2346 PetscFunctionBegin; 2347 *Ad = a->A; 2348 *Ao = a->B; 2349 *colmap = a->garray; 2350 PetscFunctionReturn(0); 2351 } 2352