1 2 /* 3 Defines the basic matrix operations for the AIJ (compressed row) 4 matrix storage format. 5 */ 6 7 8 #include <../src/mat/impls/aij/seq/aij.h> /*I "petscmat.h" I*/ 9 #include <petscblaslapack.h> 10 #include <petscbt.h> 11 #include <petsc/private/kernels/blocktranspose.h> 12 13 #undef __FUNCT__ 14 #define __FUNCT__ "MatGetColumnNorms_SeqAIJ" 15 PetscErrorCode MatGetColumnNorms_SeqAIJ(Mat A,NormType type,PetscReal *norms) 16 { 17 PetscErrorCode ierr; 18 PetscInt i,m,n; 19 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)A->data; 20 21 PetscFunctionBegin; 22 ierr = MatGetSize(A,&m,&n);CHKERRQ(ierr); 23 ierr = PetscMemzero(norms,n*sizeof(PetscReal));CHKERRQ(ierr); 24 if (type == NORM_2) { 25 for (i=0; i<aij->i[m]; i++) { 26 norms[aij->j[i]] += PetscAbsScalar(aij->a[i]*aij->a[i]); 27 } 28 } else if (type == NORM_1) { 29 for (i=0; i<aij->i[m]; i++) { 30 norms[aij->j[i]] += PetscAbsScalar(aij->a[i]); 31 } 32 } else if (type == NORM_INFINITY) { 33 for (i=0; i<aij->i[m]; i++) { 34 norms[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]),norms[aij->j[i]]); 35 } 36 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown NormType"); 37 38 if (type == NORM_2) { 39 for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]); 40 } 41 PetscFunctionReturn(0); 42 } 43 44 #undef __FUNCT__ 45 #define __FUNCT__ "MatFindOffBlockDiagonalEntries_SeqAIJ" 46 PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A,IS *is) 47 { 48 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 49 PetscInt i,m=A->rmap->n,cnt = 0, bs = A->rmap->bs; 50 const PetscInt *jj = a->j,*ii = a->i; 51 PetscInt *rows; 52 PetscErrorCode ierr; 53 54 PetscFunctionBegin; 55 for (i=0; i<m; i++) { 56 if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) { 57 cnt++; 58 } 59 } 60 ierr = PetscMalloc1(cnt,&rows);CHKERRQ(ierr); 61 cnt = 0; 62 for (i=0; i<m; i++) { 63 if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) { 64 rows[cnt] = i; 65 cnt++; 66 } 67 } 68 ierr = ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,is);CHKERRQ(ierr); 69 PetscFunctionReturn(0); 70 } 71 72 #undef __FUNCT__ 73 #define __FUNCT__ "MatFindZeroDiagonals_SeqAIJ_Private" 74 PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A,PetscInt *nrows,PetscInt **zrows) 75 { 76 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 77 const MatScalar *aa = a->a; 78 PetscInt i,m=A->rmap->n,cnt = 0; 79 const PetscInt *jj = a->j,*diag; 80 PetscInt *rows; 81 PetscErrorCode ierr; 82 83 PetscFunctionBegin; 84 ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr); 85 diag = a->diag; 86 for (i=0; i<m; i++) { 87 if ((jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) { 88 cnt++; 89 } 90 } 91 ierr = PetscMalloc1(cnt,&rows);CHKERRQ(ierr); 92 cnt = 0; 93 for (i=0; i<m; i++) { 94 if ((jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) { 95 rows[cnt++] = i; 96 } 97 } 98 *nrows = cnt; 99 *zrows = rows; 100 PetscFunctionReturn(0); 101 } 102 103 #undef __FUNCT__ 104 #define __FUNCT__ "MatFindZeroDiagonals_SeqAIJ" 105 PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows) 106 { 107 PetscInt nrows,*rows; 108 PetscErrorCode ierr; 109 110 PetscFunctionBegin; 111 *zrows = NULL; 112 ierr = MatFindZeroDiagonals_SeqAIJ_Private(A,&nrows,&rows);CHKERRQ(ierr); 113 ierr = ISCreateGeneral(PetscObjectComm((PetscObject)A),nrows,rows,PETSC_OWN_POINTER,zrows);CHKERRQ(ierr); 114 PetscFunctionReturn(0); 115 } 116 117 #undef __FUNCT__ 118 #define __FUNCT__ "MatFindNonzeroRows_SeqAIJ" 119 PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A,IS *keptrows) 120 { 121 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 122 const MatScalar *aa; 123 PetscInt m=A->rmap->n,cnt = 0; 124 const PetscInt *ii; 125 PetscInt n,i,j,*rows; 126 PetscErrorCode ierr; 127 128 PetscFunctionBegin; 129 *keptrows = 0; 130 ii = a->i; 131 for (i=0; i<m; i++) { 132 n = ii[i+1] - ii[i]; 133 if (!n) { 134 cnt++; 135 goto ok1; 136 } 137 aa = a->a + ii[i]; 138 for (j=0; j<n; j++) { 139 if (aa[j] != 0.0) goto ok1; 140 } 141 cnt++; 142 ok1:; 143 } 144 if (!cnt) PetscFunctionReturn(0); 145 ierr = PetscMalloc1(A->rmap->n-cnt,&rows);CHKERRQ(ierr); 146 cnt = 0; 147 for (i=0; i<m; i++) { 148 n = ii[i+1] - ii[i]; 149 if (!n) continue; 150 aa = a->a + ii[i]; 151 for (j=0; j<n; j++) { 152 if (aa[j] != 0.0) { 153 rows[cnt++] = i; 154 break; 155 } 156 } 157 } 158 ierr = ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,keptrows);CHKERRQ(ierr); 159 PetscFunctionReturn(0); 160 } 161 162 #undef __FUNCT__ 163 #define __FUNCT__ "MatDiagonalSet_SeqAIJ" 164 PetscErrorCode MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is) 165 { 166 PetscErrorCode ierr; 167 Mat_SeqAIJ *aij = (Mat_SeqAIJ*) Y->data; 168 PetscInt i,m = Y->rmap->n; 169 const PetscInt *diag; 170 MatScalar *aa = aij->a; 171 const PetscScalar *v; 172 PetscBool missing; 173 174 PetscFunctionBegin; 175 if (Y->assembled) { 176 ierr = MatMissingDiagonal_SeqAIJ(Y,&missing,NULL);CHKERRQ(ierr); 177 if (!missing) { 178 diag = aij->diag; 179 ierr = VecGetArrayRead(D,&v);CHKERRQ(ierr); 180 if (is == INSERT_VALUES) { 181 for (i=0; i<m; i++) { 182 aa[diag[i]] = v[i]; 183 } 184 } else { 185 for (i=0; i<m; i++) { 186 aa[diag[i]] += v[i]; 187 } 188 } 189 ierr = VecRestoreArrayRead(D,&v);CHKERRQ(ierr); 190 PetscFunctionReturn(0); 191 } 192 ierr = MatSeqAIJInvalidateDiagonal(Y);CHKERRQ(ierr); 193 } 194 ierr = MatDiagonalSet_Default(Y,D,is);CHKERRQ(ierr); 195 PetscFunctionReturn(0); 196 } 197 198 #undef __FUNCT__ 199 #define __FUNCT__ "MatGetRowIJ_SeqAIJ" 200 PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *m,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) 201 { 202 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 203 PetscErrorCode ierr; 204 PetscInt i,ishift; 205 206 PetscFunctionBegin; 207 *m = A->rmap->n; 208 if (!ia) PetscFunctionReturn(0); 209 ishift = 0; 210 if (symmetric && !A->structurally_symmetric) { 211 ierr = MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,ishift,oshift,(PetscInt**)ia,(PetscInt**)ja);CHKERRQ(ierr); 212 } else if (oshift == 1) { 213 PetscInt *tia; 214 PetscInt nz = a->i[A->rmap->n]; 215 /* malloc space and add 1 to i and j indices */ 216 ierr = PetscMalloc1(A->rmap->n+1,&tia);CHKERRQ(ierr); 217 for (i=0; i<A->rmap->n+1; i++) tia[i] = a->i[i] + 1; 218 *ia = tia; 219 if (ja) { 220 PetscInt *tja; 221 ierr = PetscMalloc1(nz+1,&tja);CHKERRQ(ierr); 222 for (i=0; i<nz; i++) tja[i] = a->j[i] + 1; 223 *ja = tja; 224 } 225 } else { 226 *ia = a->i; 227 if (ja) *ja = a->j; 228 } 229 PetscFunctionReturn(0); 230 } 231 232 #undef __FUNCT__ 233 #define __FUNCT__ "MatRestoreRowIJ_SeqAIJ" 234 PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) 235 { 236 PetscErrorCode ierr; 237 238 PetscFunctionBegin; 239 if (!ia) PetscFunctionReturn(0); 240 if ((symmetric && !A->structurally_symmetric) || oshift == 1) { 241 ierr = PetscFree(*ia);CHKERRQ(ierr); 242 if (ja) {ierr = PetscFree(*ja);CHKERRQ(ierr);} 243 } 244 PetscFunctionReturn(0); 245 } 246 247 #undef __FUNCT__ 248 #define __FUNCT__ "MatGetColumnIJ_SeqAIJ" 249 PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) 250 { 251 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 252 PetscErrorCode ierr; 253 PetscInt i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n; 254 PetscInt nz = a->i[m],row,*jj,mr,col; 255 256 PetscFunctionBegin; 257 *nn = n; 258 if (!ia) PetscFunctionReturn(0); 259 if (symmetric) { 260 ierr = MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,0,oshift,(PetscInt**)ia,(PetscInt**)ja);CHKERRQ(ierr); 261 } else { 262 ierr = PetscCalloc1(n+1,&collengths);CHKERRQ(ierr); 263 ierr = PetscMalloc1(n+1,&cia);CHKERRQ(ierr); 264 ierr = PetscMalloc1(nz+1,&cja);CHKERRQ(ierr); 265 jj = a->j; 266 for (i=0; i<nz; i++) { 267 collengths[jj[i]]++; 268 } 269 cia[0] = oshift; 270 for (i=0; i<n; i++) { 271 cia[i+1] = cia[i] + collengths[i]; 272 } 273 ierr = PetscMemzero(collengths,n*sizeof(PetscInt));CHKERRQ(ierr); 274 jj = a->j; 275 for (row=0; row<m; row++) { 276 mr = a->i[row+1] - a->i[row]; 277 for (i=0; i<mr; i++) { 278 col = *jj++; 279 280 cja[cia[col] + collengths[col]++ - oshift] = row + oshift; 281 } 282 } 283 ierr = PetscFree(collengths);CHKERRQ(ierr); 284 *ia = cia; *ja = cja; 285 } 286 PetscFunctionReturn(0); 287 } 288 289 #undef __FUNCT__ 290 #define __FUNCT__ "MatRestoreColumnIJ_SeqAIJ" 291 PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) 292 { 293 PetscErrorCode ierr; 294 295 PetscFunctionBegin; 296 if (!ia) PetscFunctionReturn(0); 297 298 ierr = PetscFree(*ia);CHKERRQ(ierr); 299 ierr = PetscFree(*ja);CHKERRQ(ierr); 300 PetscFunctionReturn(0); 301 } 302 303 /* 304 MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from 305 MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output 306 spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ() 307 */ 308 #undef __FUNCT__ 309 #define __FUNCT__ "MatGetColumnIJ_SeqAIJ_Color" 310 PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool *done) 311 { 312 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 313 PetscErrorCode ierr; 314 PetscInt i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n; 315 PetscInt nz = a->i[m],row,*jj,mr,col; 316 PetscInt *cspidx; 317 318 PetscFunctionBegin; 319 *nn = n; 320 if (!ia) PetscFunctionReturn(0); 321 322 ierr = PetscCalloc1(n+1,&collengths);CHKERRQ(ierr); 323 ierr = PetscMalloc1(n+1,&cia);CHKERRQ(ierr); 324 ierr = PetscMalloc1(nz+1,&cja);CHKERRQ(ierr); 325 ierr = PetscMalloc1(nz+1,&cspidx);CHKERRQ(ierr); 326 jj = a->j; 327 for (i=0; i<nz; i++) { 328 collengths[jj[i]]++; 329 } 330 cia[0] = oshift; 331 for (i=0; i<n; i++) { 332 cia[i+1] = cia[i] + collengths[i]; 333 } 334 ierr = PetscMemzero(collengths,n*sizeof(PetscInt));CHKERRQ(ierr); 335 jj = a->j; 336 for (row=0; row<m; row++) { 337 mr = a->i[row+1] - a->i[row]; 338 for (i=0; i<mr; i++) { 339 col = *jj++; 340 cspidx[cia[col] + collengths[col] - oshift] = a->i[row] + i; /* index of a->j */ 341 cja[cia[col] + collengths[col]++ - oshift] = row + oshift; 342 } 343 } 344 ierr = PetscFree(collengths);CHKERRQ(ierr); 345 *ia = cia; *ja = cja; 346 *spidx = cspidx; 347 PetscFunctionReturn(0); 348 } 349 350 #undef __FUNCT__ 351 #define __FUNCT__ "MatRestoreColumnIJ_SeqAIJ_Color" 352 PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool *done) 353 { 354 PetscErrorCode ierr; 355 356 PetscFunctionBegin; 357 ierr = MatRestoreColumnIJ_SeqAIJ(A,oshift,symmetric,inodecompressed,n,ia,ja,done);CHKERRQ(ierr); 358 ierr = PetscFree(*spidx);CHKERRQ(ierr); 359 PetscFunctionReturn(0); 360 } 361 362 #undef __FUNCT__ 363 #define __FUNCT__ "MatSetValuesRow_SeqAIJ" 364 PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[]) 365 { 366 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 367 PetscInt *ai = a->i; 368 PetscErrorCode ierr; 369 370 PetscFunctionBegin; 371 ierr = PetscMemcpy(a->a+ai[row],v,(ai[row+1]-ai[row])*sizeof(PetscScalar));CHKERRQ(ierr); 372 PetscFunctionReturn(0); 373 } 374 375 /* 376 MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions 377 378 - a single row of values is set with each call 379 - no row or column indices are negative or (in error) larger than the number of rows or columns 380 - the values are always added to the matrix, not set 381 - no new locations are introduced in the nonzero structure of the matrix 382 383 This does NOT assume the global column indices are sorted 384 385 */ 386 387 #include <petsc/private/isimpl.h> 388 #undef __FUNCT__ 389 #define __FUNCT__ "MatSeqAIJSetValuesLocalFast" 390 PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is) 391 { 392 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 393 PetscInt low,high,t,row,nrow,i,col,l; 394 const PetscInt *rp,*ai = a->i,*ailen = a->ilen,*aj = a->j; 395 PetscInt lastcol = -1; 396 MatScalar *ap,value,*aa = a->a; 397 const PetscInt *ridx = A->rmap->mapping->indices,*cidx = A->cmap->mapping->indices; 398 399 row = ridx[im[0]]; 400 rp = aj + ai[row]; 401 ap = aa + ai[row]; 402 nrow = ailen[row]; 403 low = 0; 404 high = nrow; 405 for (l=0; l<n; l++) { /* loop over added columns */ 406 col = cidx[in[l]]; 407 value = v[l]; 408 409 if (col <= lastcol) low = 0; 410 else high = nrow; 411 lastcol = col; 412 while (high-low > 5) { 413 t = (low+high)/2; 414 if (rp[t] > col) high = t; 415 else low = t; 416 } 417 for (i=low; i<high; i++) { 418 if (rp[i] == col) { 419 ap[i] += value; 420 low = i + 1; 421 break; 422 } 423 } 424 } 425 return 0; 426 } 427 428 #undef __FUNCT__ 429 #define __FUNCT__ "MatSetValues_SeqAIJ" 430 PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is) 431 { 432 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 433 PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N; 434 PetscInt *imax = a->imax,*ai = a->i,*ailen = a->ilen; 435 PetscErrorCode ierr; 436 PetscInt *aj = a->j,nonew = a->nonew,lastcol = -1; 437 MatScalar *ap,value,*aa = a->a; 438 PetscBool ignorezeroentries = a->ignorezeroentries; 439 PetscBool roworiented = a->roworiented; 440 441 PetscFunctionBegin; 442 for (k=0; k<m; k++) { /* loop over added rows */ 443 row = im[k]; 444 if (row < 0) continue; 445 #if defined(PETSC_USE_DEBUG) 446 if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1); 447 #endif 448 rp = aj + ai[row]; ap = aa + ai[row]; 449 rmax = imax[row]; nrow = ailen[row]; 450 low = 0; 451 high = nrow; 452 for (l=0; l<n; l++) { /* loop over added columns */ 453 if (in[l] < 0) continue; 454 #if defined(PETSC_USE_DEBUG) 455 if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1); 456 #endif 457 col = in[l]; 458 if (roworiented) { 459 value = v[l + k*n]; 460 } else { 461 value = v[k + l*m]; 462 } 463 if ((value == 0.0 && ignorezeroentries) && (is == ADD_VALUES)) continue; 464 465 if (col <= lastcol) low = 0; 466 else high = nrow; 467 lastcol = col; 468 while (high-low > 5) { 469 t = (low+high)/2; 470 if (rp[t] > col) high = t; 471 else low = t; 472 } 473 for (i=low; i<high; i++) { 474 if (rp[i] > col) break; 475 if (rp[i] == col) { 476 if (is == ADD_VALUES) ap[i] += value; 477 else ap[i] = value; 478 low = i + 1; 479 goto noinsert; 480 } 481 } 482 if (value == 0.0 && ignorezeroentries) goto noinsert; 483 if (nonew == 1) goto noinsert; 484 if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col); 485 MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar); 486 N = nrow++ - 1; a->nz++; high++; 487 /* shift up all the later entries in this row */ 488 for (ii=N; ii>=i; ii--) { 489 rp[ii+1] = rp[ii]; 490 ap[ii+1] = ap[ii]; 491 } 492 rp[i] = col; 493 ap[i] = value; 494 low = i + 1; 495 A->nonzerostate++; 496 noinsert:; 497 } 498 ailen[row] = nrow; 499 } 500 PetscFunctionReturn(0); 501 } 502 503 504 #undef __FUNCT__ 505 #define __FUNCT__ "MatGetValues_SeqAIJ" 506 PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[]) 507 { 508 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 509 PetscInt *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j; 510 PetscInt *ai = a->i,*ailen = a->ilen; 511 MatScalar *ap,*aa = a->a; 512 513 PetscFunctionBegin; 514 for (k=0; k<m; k++) { /* loop over rows */ 515 row = im[k]; 516 if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */ 517 if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1); 518 rp = aj + ai[row]; ap = aa + ai[row]; 519 nrow = ailen[row]; 520 for (l=0; l<n; l++) { /* loop over columns */ 521 if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */ 522 if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1); 523 col = in[l]; 524 high = nrow; low = 0; /* assume unsorted */ 525 while (high-low > 5) { 526 t = (low+high)/2; 527 if (rp[t] > col) high = t; 528 else low = t; 529 } 530 for (i=low; i<high; i++) { 531 if (rp[i] > col) break; 532 if (rp[i] == col) { 533 *v++ = ap[i]; 534 goto finished; 535 } 536 } 537 *v++ = 0.0; 538 finished:; 539 } 540 } 541 PetscFunctionReturn(0); 542 } 543 544 545 #undef __FUNCT__ 546 #define __FUNCT__ "MatView_SeqAIJ_Binary" 547 PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer) 548 { 549 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 550 PetscErrorCode ierr; 551 PetscInt i,*col_lens; 552 int fd; 553 FILE *file; 554 555 PetscFunctionBegin; 556 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 557 ierr = PetscMalloc1(4+A->rmap->n,&col_lens);CHKERRQ(ierr); 558 559 col_lens[0] = MAT_FILE_CLASSID; 560 col_lens[1] = A->rmap->n; 561 col_lens[2] = A->cmap->n; 562 col_lens[3] = a->nz; 563 564 /* store lengths of each row and write (including header) to file */ 565 for (i=0; i<A->rmap->n; i++) { 566 col_lens[4+i] = a->i[i+1] - a->i[i]; 567 } 568 ierr = PetscBinaryWrite(fd,col_lens,4+A->rmap->n,PETSC_INT,PETSC_TRUE);CHKERRQ(ierr); 569 ierr = PetscFree(col_lens);CHKERRQ(ierr); 570 571 /* store column indices (zero start index) */ 572 ierr = PetscBinaryWrite(fd,a->j,a->nz,PETSC_INT,PETSC_FALSE);CHKERRQ(ierr); 573 574 /* store nonzero values */ 575 ierr = PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);CHKERRQ(ierr); 576 577 ierr = PetscViewerBinaryGetInfoPointer(viewer,&file);CHKERRQ(ierr); 578 if (file) { 579 fprintf(file,"-matload_block_size %d\n",(int)PetscAbs(A->rmap->bs)); 580 } 581 PetscFunctionReturn(0); 582 } 583 584 extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer); 585 586 #undef __FUNCT__ 587 #define __FUNCT__ "MatView_SeqAIJ_ASCII" 588 PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer) 589 { 590 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 591 PetscErrorCode ierr; 592 PetscInt i,j,m = A->rmap->n; 593 const char *name; 594 PetscViewerFormat format; 595 596 PetscFunctionBegin; 597 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 598 if (format == PETSC_VIEWER_ASCII_MATLAB) { 599 PetscInt nofinalvalue = 0; 600 if (m && ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-1))) { 601 /* Need a dummy value to ensure the dimension of the matrix. */ 602 nofinalvalue = 1; 603 } 604 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);CHKERRQ(ierr); 605 ierr = PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);CHKERRQ(ierr); 606 ierr = PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);CHKERRQ(ierr); 607 #if defined(PETSC_USE_COMPLEX) 608 ierr = PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,4);\n",a->nz+nofinalvalue);CHKERRQ(ierr); 609 #else 610 ierr = PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);CHKERRQ(ierr); 611 #endif 612 ierr = PetscViewerASCIIPrintf(viewer,"zzz = [\n");CHKERRQ(ierr); 613 614 for (i=0; i<m; i++) { 615 for (j=a->i[i]; j<a->i[i+1]; j++) { 616 #if defined(PETSC_USE_COMPLEX) 617 ierr = PetscViewerASCIIPrintf(viewer,"%D %D %18.16e %18.16e\n",i+1,a->j[j]+1,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 618 #else 619 ierr = PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",i+1,a->j[j]+1,(double)a->a[j]);CHKERRQ(ierr); 620 #endif 621 } 622 } 623 if (nofinalvalue) { 624 #if defined(PETSC_USE_COMPLEX) 625 ierr = PetscViewerASCIIPrintf(viewer,"%D %D %18.16e %18.16e\n",m,A->cmap->n,0.,0.);CHKERRQ(ierr); 626 #else 627 ierr = PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",m,A->cmap->n,0.0);CHKERRQ(ierr); 628 #endif 629 } 630 ierr = PetscObjectGetName((PetscObject)A,&name);CHKERRQ(ierr); 631 ierr = PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);CHKERRQ(ierr); 632 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);CHKERRQ(ierr); 633 } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) { 634 PetscFunctionReturn(0); 635 } else if (format == PETSC_VIEWER_ASCII_COMMON) { 636 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);CHKERRQ(ierr); 637 for (i=0; i<m; i++) { 638 ierr = PetscViewerASCIIPrintf(viewer,"row %D:",i);CHKERRQ(ierr); 639 for (j=a->i[i]; j<a->i[i+1]; j++) { 640 #if defined(PETSC_USE_COMPLEX) 641 if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) { 642 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 643 } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) { 644 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 645 } else if (PetscRealPart(a->a[j]) != 0.0) { 646 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));CHKERRQ(ierr); 647 } 648 #else 649 if (a->a[j] != 0.0) {ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);CHKERRQ(ierr);} 650 #endif 651 } 652 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 653 } 654 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);CHKERRQ(ierr); 655 } else if (format == PETSC_VIEWER_ASCII_SYMMODU) { 656 PetscInt nzd=0,fshift=1,*sptr; 657 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);CHKERRQ(ierr); 658 ierr = PetscMalloc1(m+1,&sptr);CHKERRQ(ierr); 659 for (i=0; i<m; i++) { 660 sptr[i] = nzd+1; 661 for (j=a->i[i]; j<a->i[i+1]; j++) { 662 if (a->j[j] >= i) { 663 #if defined(PETSC_USE_COMPLEX) 664 if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++; 665 #else 666 if (a->a[j] != 0.0) nzd++; 667 #endif 668 } 669 } 670 } 671 sptr[m] = nzd+1; 672 ierr = PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);CHKERRQ(ierr); 673 for (i=0; i<m+1; i+=6) { 674 if (i+4<m) { 675 ierr = PetscViewerASCIIPrintf(viewer," %D %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4],sptr[i+5]);CHKERRQ(ierr); 676 } else if (i+3<m) { 677 ierr = PetscViewerASCIIPrintf(viewer," %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);CHKERRQ(ierr); 678 } else if (i+2<m) { 679 ierr = PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);CHKERRQ(ierr); 680 } else if (i+1<m) { 681 ierr = PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);CHKERRQ(ierr); 682 } else if (i<m) { 683 ierr = PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);CHKERRQ(ierr); 684 } else { 685 ierr = PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);CHKERRQ(ierr); 686 } 687 } 688 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 689 ierr = PetscFree(sptr);CHKERRQ(ierr); 690 for (i=0; i<m; i++) { 691 for (j=a->i[i]; j<a->i[i+1]; j++) { 692 if (a->j[j] >= i) {ierr = PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);CHKERRQ(ierr);} 693 } 694 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 695 } 696 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 697 for (i=0; i<m; i++) { 698 for (j=a->i[i]; j<a->i[i+1]; j++) { 699 if (a->j[j] >= i) { 700 #if defined(PETSC_USE_COMPLEX) 701 if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) { 702 ierr = PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 703 } 704 #else 705 if (a->a[j] != 0.0) {ierr = PetscViewerASCIIPrintf(viewer," %18.16e ",(double)a->a[j]);CHKERRQ(ierr);} 706 #endif 707 } 708 } 709 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 710 } 711 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);CHKERRQ(ierr); 712 } else if (format == PETSC_VIEWER_ASCII_DENSE) { 713 PetscInt cnt = 0,jcnt; 714 PetscScalar value; 715 #if defined(PETSC_USE_COMPLEX) 716 PetscBool realonly = PETSC_TRUE; 717 718 for (i=0; i<a->i[m]; i++) { 719 if (PetscImaginaryPart(a->a[i]) != 0.0) { 720 realonly = PETSC_FALSE; 721 break; 722 } 723 } 724 #endif 725 726 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);CHKERRQ(ierr); 727 for (i=0; i<m; i++) { 728 jcnt = 0; 729 for (j=0; j<A->cmap->n; j++) { 730 if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) { 731 value = a->a[cnt++]; 732 jcnt++; 733 } else { 734 value = 0.0; 735 } 736 #if defined(PETSC_USE_COMPLEX) 737 if (realonly) { 738 ierr = PetscViewerASCIIPrintf(viewer," %7.5e ",(double)PetscRealPart(value));CHKERRQ(ierr); 739 } else { 740 ierr = PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",(double)PetscRealPart(value),(double)PetscImaginaryPart(value));CHKERRQ(ierr); 741 } 742 #else 743 ierr = PetscViewerASCIIPrintf(viewer," %7.5e ",(double)value);CHKERRQ(ierr); 744 #endif 745 } 746 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 747 } 748 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);CHKERRQ(ierr); 749 } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) { 750 PetscInt fshift=1; 751 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);CHKERRQ(ierr); 752 #if defined(PETSC_USE_COMPLEX) 753 ierr = PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate complex general\n");CHKERRQ(ierr); 754 #else 755 ierr = PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate real general\n");CHKERRQ(ierr); 756 #endif 757 ierr = PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);CHKERRQ(ierr); 758 for (i=0; i<m; i++) { 759 for (j=a->i[i]; j<a->i[i+1]; j++) { 760 #if defined(PETSC_USE_COMPLEX) 761 ierr = PetscViewerASCIIPrintf(viewer,"%D %D %g %g\n", i+fshift,a->j[j]+fshift,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 762 #else 763 ierr = PetscViewerASCIIPrintf(viewer,"%D %D %g\n", i+fshift, a->j[j]+fshift, (double)a->a[j]);CHKERRQ(ierr); 764 #endif 765 } 766 } 767 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);CHKERRQ(ierr); 768 } else { 769 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);CHKERRQ(ierr); 770 if (A->factortype) { 771 for (i=0; i<m; i++) { 772 ierr = PetscViewerASCIIPrintf(viewer,"row %D:",i);CHKERRQ(ierr); 773 /* L part */ 774 for (j=a->i[i]; j<a->i[i+1]; j++) { 775 #if defined(PETSC_USE_COMPLEX) 776 if (PetscImaginaryPart(a->a[j]) > 0.0) { 777 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 778 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 779 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));CHKERRQ(ierr); 780 } else { 781 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));CHKERRQ(ierr); 782 } 783 #else 784 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);CHKERRQ(ierr); 785 #endif 786 } 787 /* diagonal */ 788 j = a->diag[i]; 789 #if defined(PETSC_USE_COMPLEX) 790 if (PetscImaginaryPart(a->a[j]) > 0.0) { 791 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)PetscImaginaryPart(1.0/a->a[j]));CHKERRQ(ierr); 792 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 793 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)(-PetscImaginaryPart(1.0/a->a[j])));CHKERRQ(ierr); 794 } else { 795 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(1.0/a->a[j]));CHKERRQ(ierr); 796 } 797 #else 798 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)(1.0/a->a[j]));CHKERRQ(ierr); 799 #endif 800 801 /* U part */ 802 for (j=a->diag[i+1]+1; j<a->diag[i]; j++) { 803 #if defined(PETSC_USE_COMPLEX) 804 if (PetscImaginaryPart(a->a[j]) > 0.0) { 805 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 806 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 807 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));CHKERRQ(ierr); 808 } else { 809 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));CHKERRQ(ierr); 810 } 811 #else 812 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);CHKERRQ(ierr); 813 #endif 814 } 815 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 816 } 817 } else { 818 for (i=0; i<m; i++) { 819 ierr = PetscViewerASCIIPrintf(viewer,"row %D:",i);CHKERRQ(ierr); 820 for (j=a->i[i]; j<a->i[i+1]; j++) { 821 #if defined(PETSC_USE_COMPLEX) 822 if (PetscImaginaryPart(a->a[j]) > 0.0) { 823 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 824 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 825 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 826 } else { 827 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));CHKERRQ(ierr); 828 } 829 #else 830 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);CHKERRQ(ierr); 831 #endif 832 } 833 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 834 } 835 } 836 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);CHKERRQ(ierr); 837 } 838 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 839 PetscFunctionReturn(0); 840 } 841 842 #include <petscdraw.h> 843 #undef __FUNCT__ 844 #define __FUNCT__ "MatView_SeqAIJ_Draw_Zoom" 845 PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa) 846 { 847 Mat A = (Mat) Aa; 848 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 849 PetscErrorCode ierr; 850 PetscInt i,j,m = A->rmap->n,color; 851 PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0; 852 PetscViewer viewer; 853 PetscViewerFormat format; 854 855 PetscFunctionBegin; 856 ierr = PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);CHKERRQ(ierr); 857 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 858 859 ierr = PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);CHKERRQ(ierr); 860 /* loop over matrix elements drawing boxes */ 861 862 if (format != PETSC_VIEWER_DRAW_CONTOUR) { 863 /* Blue for negative, Cyan for zero and Red for positive */ 864 color = PETSC_DRAW_BLUE; 865 for (i=0; i<m; i++) { 866 y_l = m - i - 1.0; y_r = y_l + 1.0; 867 for (j=a->i[i]; j<a->i[i+1]; j++) { 868 x_l = a->j[j]; x_r = x_l + 1.0; 869 if (PetscRealPart(a->a[j]) >= 0.) continue; 870 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 871 } 872 } 873 color = PETSC_DRAW_CYAN; 874 for (i=0; i<m; i++) { 875 y_l = m - i - 1.0; y_r = y_l + 1.0; 876 for (j=a->i[i]; j<a->i[i+1]; j++) { 877 x_l = a->j[j]; x_r = x_l + 1.0; 878 if (a->a[j] != 0.) continue; 879 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 880 } 881 } 882 color = PETSC_DRAW_RED; 883 for (i=0; i<m; i++) { 884 y_l = m - i - 1.0; y_r = y_l + 1.0; 885 for (j=a->i[i]; j<a->i[i+1]; j++) { 886 x_l = a->j[j]; x_r = x_l + 1.0; 887 if (PetscRealPart(a->a[j]) <= 0.) continue; 888 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 889 } 890 } 891 } else { 892 /* use contour shading to indicate magnitude of values */ 893 /* first determine max of all nonzero values */ 894 PetscInt nz = a->nz,count; 895 PetscDraw popup; 896 PetscReal scale; 897 898 for (i=0; i<nz; i++) { 899 if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]); 900 } 901 scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv; 902 ierr = PetscDrawGetPopup(draw,&popup);CHKERRQ(ierr); 903 if (popup) { 904 ierr = PetscDrawScalePopup(popup,0.0,maxv);CHKERRQ(ierr); 905 } 906 count = 0; 907 for (i=0; i<m; i++) { 908 y_l = m - i - 1.0; y_r = y_l + 1.0; 909 for (j=a->i[i]; j<a->i[i+1]; j++) { 910 x_l = a->j[j]; x_r = x_l + 1.0; 911 color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(a->a[count])); 912 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 913 count++; 914 } 915 } 916 } 917 PetscFunctionReturn(0); 918 } 919 920 #include <petscdraw.h> 921 #undef __FUNCT__ 922 #define __FUNCT__ "MatView_SeqAIJ_Draw" 923 PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer) 924 { 925 PetscErrorCode ierr; 926 PetscDraw draw; 927 PetscReal xr,yr,xl,yl,h,w; 928 PetscBool isnull; 929 930 PetscFunctionBegin; 931 ierr = PetscViewerDrawGetDraw(viewer,0,&draw);CHKERRQ(ierr); 932 ierr = PetscDrawIsNull(draw,&isnull);CHKERRQ(ierr); 933 if (isnull) PetscFunctionReturn(0); 934 935 ierr = PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);CHKERRQ(ierr); 936 xr = A->cmap->n; yr = A->rmap->n; h = yr/10.0; w = xr/10.0; 937 xr += w; yr += h; xl = -w; yl = -h; 938 ierr = PetscDrawSetCoordinates(draw,xl,yl,xr,yr);CHKERRQ(ierr); 939 ierr = PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);CHKERRQ(ierr); 940 ierr = PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);CHKERRQ(ierr); 941 PetscFunctionReturn(0); 942 } 943 944 #undef __FUNCT__ 945 #define __FUNCT__ "MatView_SeqAIJ" 946 PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer) 947 { 948 PetscErrorCode ierr; 949 PetscBool iascii,isbinary,isdraw; 950 951 PetscFunctionBegin; 952 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 953 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);CHKERRQ(ierr); 954 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);CHKERRQ(ierr); 955 if (iascii) { 956 ierr = MatView_SeqAIJ_ASCII(A,viewer);CHKERRQ(ierr); 957 } else if (isbinary) { 958 ierr = MatView_SeqAIJ_Binary(A,viewer);CHKERRQ(ierr); 959 } else if (isdraw) { 960 ierr = MatView_SeqAIJ_Draw(A,viewer);CHKERRQ(ierr); 961 } 962 ierr = MatView_SeqAIJ_Inode(A,viewer);CHKERRQ(ierr); 963 PetscFunctionReturn(0); 964 } 965 966 #undef __FUNCT__ 967 #define __FUNCT__ "MatAssemblyEnd_SeqAIJ" 968 PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode) 969 { 970 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 971 PetscErrorCode ierr; 972 PetscInt fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax; 973 PetscInt m = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0; 974 MatScalar *aa = a->a,*ap; 975 PetscReal ratio = 0.6; 976 977 PetscFunctionBegin; 978 if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(0); 979 980 if (m) rmax = ailen[0]; /* determine row with most nonzeros */ 981 for (i=1; i<m; i++) { 982 /* move each row back by the amount of empty slots (fshift) before it*/ 983 fshift += imax[i-1] - ailen[i-1]; 984 rmax = PetscMax(rmax,ailen[i]); 985 if (fshift) { 986 ip = aj + ai[i]; 987 ap = aa + ai[i]; 988 N = ailen[i]; 989 for (j=0; j<N; j++) { 990 ip[j-fshift] = ip[j]; 991 ap[j-fshift] = ap[j]; 992 } 993 } 994 ai[i] = ai[i-1] + ailen[i-1]; 995 } 996 if (m) { 997 fshift += imax[m-1] - ailen[m-1]; 998 ai[m] = ai[m-1] + ailen[m-1]; 999 } 1000 1001 /* reset ilen and imax for each row */ 1002 a->nonzerorowcnt = 0; 1003 for (i=0; i<m; i++) { 1004 ailen[i] = imax[i] = ai[i+1] - ai[i]; 1005 a->nonzerorowcnt += ((ai[i+1] - ai[i]) > 0); 1006 } 1007 a->nz = ai[m]; 1008 if (fshift && a->nounused == -1) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB, "Unused space detected in matrix: %D X %D, %D unneeded", m, A->cmap->n, fshift); 1009 1010 ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr); 1011 ierr = PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap->n,fshift,a->nz);CHKERRQ(ierr); 1012 ierr = PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);CHKERRQ(ierr); 1013 ierr = PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);CHKERRQ(ierr); 1014 1015 A->info.mallocs += a->reallocs; 1016 a->reallocs = 0; 1017 A->info.nz_unneeded = (PetscReal)fshift; 1018 a->rmax = rmax; 1019 1020 ierr = MatCheckCompressedRow(A,a->nonzerorowcnt,&a->compressedrow,a->i,m,ratio);CHKERRQ(ierr); 1021 ierr = MatAssemblyEnd_SeqAIJ_Inode(A,mode);CHKERRQ(ierr); 1022 ierr = MatSeqAIJInvalidateDiagonal(A);CHKERRQ(ierr); 1023 PetscFunctionReturn(0); 1024 } 1025 1026 #undef __FUNCT__ 1027 #define __FUNCT__ "MatRealPart_SeqAIJ" 1028 PetscErrorCode MatRealPart_SeqAIJ(Mat A) 1029 { 1030 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1031 PetscInt i,nz = a->nz; 1032 MatScalar *aa = a->a; 1033 PetscErrorCode ierr; 1034 1035 PetscFunctionBegin; 1036 for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]); 1037 ierr = MatSeqAIJInvalidateDiagonal(A);CHKERRQ(ierr); 1038 PetscFunctionReturn(0); 1039 } 1040 1041 #undef __FUNCT__ 1042 #define __FUNCT__ "MatImaginaryPart_SeqAIJ" 1043 PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A) 1044 { 1045 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1046 PetscInt i,nz = a->nz; 1047 MatScalar *aa = a->a; 1048 PetscErrorCode ierr; 1049 1050 PetscFunctionBegin; 1051 for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]); 1052 ierr = MatSeqAIJInvalidateDiagonal(A);CHKERRQ(ierr); 1053 PetscFunctionReturn(0); 1054 } 1055 1056 #undef __FUNCT__ 1057 #define __FUNCT__ "MatZeroEntries_SeqAIJ" 1058 PetscErrorCode MatZeroEntries_SeqAIJ(Mat A) 1059 { 1060 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1061 PetscErrorCode ierr; 1062 1063 PetscFunctionBegin; 1064 ierr = PetscMemzero(a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));CHKERRQ(ierr); 1065 ierr = MatSeqAIJInvalidateDiagonal(A);CHKERRQ(ierr); 1066 PetscFunctionReturn(0); 1067 } 1068 1069 #undef __FUNCT__ 1070 #define __FUNCT__ "MatDestroy_SeqAIJ" 1071 PetscErrorCode MatDestroy_SeqAIJ(Mat A) 1072 { 1073 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1074 PetscErrorCode ierr; 1075 1076 PetscFunctionBegin; 1077 #if defined(PETSC_USE_LOG) 1078 PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz); 1079 #endif 1080 ierr = MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);CHKERRQ(ierr); 1081 ierr = ISDestroy(&a->row);CHKERRQ(ierr); 1082 ierr = ISDestroy(&a->col);CHKERRQ(ierr); 1083 ierr = PetscFree(a->diag);CHKERRQ(ierr); 1084 ierr = PetscFree(a->ibdiag);CHKERRQ(ierr); 1085 ierr = PetscFree2(a->imax,a->ilen);CHKERRQ(ierr); 1086 ierr = PetscFree3(a->idiag,a->mdiag,a->ssor_work);CHKERRQ(ierr); 1087 ierr = PetscFree(a->solve_work);CHKERRQ(ierr); 1088 ierr = ISDestroy(&a->icol);CHKERRQ(ierr); 1089 ierr = PetscFree(a->saved_values);CHKERRQ(ierr); 1090 ierr = ISColoringDestroy(&a->coloring);CHKERRQ(ierr); 1091 ierr = PetscFree2(a->compressedrow.i,a->compressedrow.rindex);CHKERRQ(ierr); 1092 ierr = PetscFree(a->matmult_abdense);CHKERRQ(ierr); 1093 1094 ierr = MatDestroy_SeqAIJ_Inode(A);CHKERRQ(ierr); 1095 ierr = PetscFree(A->data);CHKERRQ(ierr); 1096 1097 ierr = PetscObjectChangeTypeName((PetscObject)A,0);CHKERRQ(ierr); 1098 ierr = PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetColumnIndices_C",NULL);CHKERRQ(ierr); 1099 ierr = PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);CHKERRQ(ierr); 1100 ierr = PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);CHKERRQ(ierr); 1101 ierr = PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsbaij_C",NULL);CHKERRQ(ierr); 1102 ierr = PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqbaij_C",NULL);CHKERRQ(ierr); 1103 ierr = PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijperm_C",NULL);CHKERRQ(ierr); 1104 #if defined(PETSC_HAVE_ELEMENTAL) 1105 ierr = PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_elemental_C",NULL);CHKERRQ(ierr); 1106 #endif 1107 ierr = PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqdense_C",NULL);CHKERRQ(ierr); 1108 ierr = PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);CHKERRQ(ierr); 1109 ierr = PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);CHKERRQ(ierr); 1110 ierr = PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);CHKERRQ(ierr); 1111 ierr = PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);CHKERRQ(ierr); 1112 PetscFunctionReturn(0); 1113 } 1114 1115 #undef __FUNCT__ 1116 #define __FUNCT__ "MatSetOption_SeqAIJ" 1117 PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg) 1118 { 1119 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1120 PetscErrorCode ierr; 1121 1122 PetscFunctionBegin; 1123 switch (op) { 1124 case MAT_ROW_ORIENTED: 1125 a->roworiented = flg; 1126 break; 1127 case MAT_KEEP_NONZERO_PATTERN: 1128 a->keepnonzeropattern = flg; 1129 break; 1130 case MAT_NEW_NONZERO_LOCATIONS: 1131 a->nonew = (flg ? 0 : 1); 1132 break; 1133 case MAT_NEW_NONZERO_LOCATION_ERR: 1134 a->nonew = (flg ? -1 : 0); 1135 break; 1136 case MAT_NEW_NONZERO_ALLOCATION_ERR: 1137 a->nonew = (flg ? -2 : 0); 1138 break; 1139 case MAT_UNUSED_NONZERO_LOCATION_ERR: 1140 a->nounused = (flg ? -1 : 0); 1141 break; 1142 case MAT_IGNORE_ZERO_ENTRIES: 1143 a->ignorezeroentries = flg; 1144 break; 1145 case MAT_SPD: 1146 case MAT_SYMMETRIC: 1147 case MAT_STRUCTURALLY_SYMMETRIC: 1148 case MAT_HERMITIAN: 1149 case MAT_SYMMETRY_ETERNAL: 1150 /* These options are handled directly by MatSetOption() */ 1151 break; 1152 case MAT_NEW_DIAGONALS: 1153 case MAT_IGNORE_OFF_PROC_ENTRIES: 1154 case MAT_USE_HASH_TABLE: 1155 ierr = PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);CHKERRQ(ierr); 1156 break; 1157 case MAT_USE_INODES: 1158 /* Not an error because MatSetOption_SeqAIJ_Inode handles this one */ 1159 break; 1160 default: 1161 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op); 1162 } 1163 ierr = MatSetOption_SeqAIJ_Inode(A,op,flg);CHKERRQ(ierr); 1164 PetscFunctionReturn(0); 1165 } 1166 1167 #undef __FUNCT__ 1168 #define __FUNCT__ "MatGetDiagonal_SeqAIJ" 1169 PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v) 1170 { 1171 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1172 PetscErrorCode ierr; 1173 PetscInt i,j,n,*ai=a->i,*aj=a->j,nz; 1174 PetscScalar *aa=a->a,*x,zero=0.0; 1175 1176 PetscFunctionBegin; 1177 ierr = VecGetLocalSize(v,&n);CHKERRQ(ierr); 1178 if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector"); 1179 1180 if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) { 1181 PetscInt *diag=a->diag; 1182 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 1183 for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]]; 1184 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 1185 PetscFunctionReturn(0); 1186 } 1187 1188 ierr = VecSet(v,zero);CHKERRQ(ierr); 1189 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 1190 for (i=0; i<n; i++) { 1191 nz = ai[i+1] - ai[i]; 1192 if (!nz) x[i] = 0.0; 1193 for (j=ai[i]; j<ai[i+1]; j++) { 1194 if (aj[j] == i) { 1195 x[i] = aa[j]; 1196 break; 1197 } 1198 } 1199 } 1200 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 1201 PetscFunctionReturn(0); 1202 } 1203 1204 #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h> 1205 #undef __FUNCT__ 1206 #define __FUNCT__ "MatMultTransposeAdd_SeqAIJ" 1207 PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy) 1208 { 1209 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1210 PetscScalar *y; 1211 const PetscScalar *x; 1212 PetscErrorCode ierr; 1213 PetscInt m = A->rmap->n; 1214 #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ) 1215 const MatScalar *v; 1216 PetscScalar alpha; 1217 PetscInt n,i,j; 1218 const PetscInt *idx,*ii,*ridx=NULL; 1219 Mat_CompressedRow cprow = a->compressedrow; 1220 PetscBool usecprow = cprow.use; 1221 #endif 1222 1223 PetscFunctionBegin; 1224 if (zz != yy) {ierr = VecCopy(zz,yy);CHKERRQ(ierr);} 1225 ierr = VecGetArrayRead(xx,&x);CHKERRQ(ierr); 1226 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 1227 1228 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ) 1229 fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y); 1230 #else 1231 if (usecprow) { 1232 m = cprow.nrows; 1233 ii = cprow.i; 1234 ridx = cprow.rindex; 1235 } else { 1236 ii = a->i; 1237 } 1238 for (i=0; i<m; i++) { 1239 idx = a->j + ii[i]; 1240 v = a->a + ii[i]; 1241 n = ii[i+1] - ii[i]; 1242 if (usecprow) { 1243 alpha = x[ridx[i]]; 1244 } else { 1245 alpha = x[i]; 1246 } 1247 for (j=0; j<n; j++) y[idx[j]] += alpha*v[j]; 1248 } 1249 #endif 1250 ierr = PetscLogFlops(2.0*a->nz);CHKERRQ(ierr); 1251 ierr = VecRestoreArrayRead(xx,&x);CHKERRQ(ierr); 1252 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 1253 PetscFunctionReturn(0); 1254 } 1255 1256 #undef __FUNCT__ 1257 #define __FUNCT__ "MatMultTranspose_SeqAIJ" 1258 PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy) 1259 { 1260 PetscErrorCode ierr; 1261 1262 PetscFunctionBegin; 1263 ierr = VecSet(yy,0.0);CHKERRQ(ierr); 1264 ierr = MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);CHKERRQ(ierr); 1265 PetscFunctionReturn(0); 1266 } 1267 1268 #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h> 1269 1270 #undef __FUNCT__ 1271 #define __FUNCT__ "MatMult_SeqAIJ" 1272 PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy) 1273 { 1274 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1275 PetscScalar *y; 1276 const PetscScalar *x; 1277 const MatScalar *aa; 1278 PetscErrorCode ierr; 1279 PetscInt m=A->rmap->n; 1280 const PetscInt *aj,*ii,*ridx=NULL; 1281 PetscInt n,i; 1282 PetscScalar sum; 1283 PetscBool usecprow=a->compressedrow.use; 1284 1285 #if defined(PETSC_HAVE_PRAGMA_DISJOINT) 1286 #pragma disjoint(*x,*y,*aa) 1287 #endif 1288 1289 PetscFunctionBegin; 1290 ierr = VecGetArrayRead(xx,&x);CHKERRQ(ierr); 1291 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 1292 aj = a->j; 1293 aa = a->a; 1294 ii = a->i; 1295 if (usecprow) { /* use compressed row format */ 1296 ierr = PetscMemzero(y,m*sizeof(PetscScalar));CHKERRQ(ierr); 1297 m = a->compressedrow.nrows; 1298 ii = a->compressedrow.i; 1299 ridx = a->compressedrow.rindex; 1300 for (i=0; i<m; i++) { 1301 n = ii[i+1] - ii[i]; 1302 aj = a->j + ii[i]; 1303 aa = a->a + ii[i]; 1304 sum = 0.0; 1305 PetscSparseDensePlusDot(sum,x,aa,aj,n); 1306 /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */ 1307 y[*ridx++] = sum; 1308 } 1309 } else { /* do not use compressed row format */ 1310 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ) 1311 fortranmultaij_(&m,x,ii,aj,aa,y); 1312 #else 1313 for (i=0; i<m; i++) { 1314 n = ii[i+1] - ii[i]; 1315 aj = a->j + ii[i]; 1316 aa = a->a + ii[i]; 1317 sum = 0.0; 1318 PetscSparseDensePlusDot(sum,x,aa,aj,n); 1319 y[i] = sum; 1320 } 1321 #endif 1322 } 1323 ierr = PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);CHKERRQ(ierr); 1324 ierr = VecRestoreArrayRead(xx,&x);CHKERRQ(ierr); 1325 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 1326 PetscFunctionReturn(0); 1327 } 1328 1329 #undef __FUNCT__ 1330 #define __FUNCT__ "MatMultMax_SeqAIJ" 1331 PetscErrorCode MatMultMax_SeqAIJ(Mat A,Vec xx,Vec yy) 1332 { 1333 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1334 PetscScalar *y; 1335 const PetscScalar *x; 1336 const MatScalar *aa; 1337 PetscErrorCode ierr; 1338 PetscInt m=A->rmap->n; 1339 const PetscInt *aj,*ii,*ridx=NULL; 1340 PetscInt n,i,nonzerorow=0; 1341 PetscScalar sum; 1342 PetscBool usecprow=a->compressedrow.use; 1343 1344 #if defined(PETSC_HAVE_PRAGMA_DISJOINT) 1345 #pragma disjoint(*x,*y,*aa) 1346 #endif 1347 1348 PetscFunctionBegin; 1349 ierr = VecGetArrayRead(xx,&x);CHKERRQ(ierr); 1350 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 1351 aj = a->j; 1352 aa = a->a; 1353 ii = a->i; 1354 if (usecprow) { /* use compressed row format */ 1355 m = a->compressedrow.nrows; 1356 ii = a->compressedrow.i; 1357 ridx = a->compressedrow.rindex; 1358 for (i=0; i<m; i++) { 1359 n = ii[i+1] - ii[i]; 1360 aj = a->j + ii[i]; 1361 aa = a->a + ii[i]; 1362 sum = 0.0; 1363 nonzerorow += (n>0); 1364 PetscSparseDenseMaxDot(sum,x,aa,aj,n); 1365 /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */ 1366 y[*ridx++] = sum; 1367 } 1368 } else { /* do not use compressed row format */ 1369 for (i=0; i<m; i++) { 1370 n = ii[i+1] - ii[i]; 1371 aj = a->j + ii[i]; 1372 aa = a->a + ii[i]; 1373 sum = 0.0; 1374 nonzerorow += (n>0); 1375 PetscSparseDenseMaxDot(sum,x,aa,aj,n); 1376 y[i] = sum; 1377 } 1378 } 1379 ierr = PetscLogFlops(2.0*a->nz - nonzerorow);CHKERRQ(ierr); 1380 ierr = VecRestoreArrayRead(xx,&x);CHKERRQ(ierr); 1381 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 1382 PetscFunctionReturn(0); 1383 } 1384 1385 #undef __FUNCT__ 1386 #define __FUNCT__ "MatMultAddMax_SeqAIJ" 1387 PetscErrorCode MatMultAddMax_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz) 1388 { 1389 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1390 PetscScalar *y,*z; 1391 const PetscScalar *x; 1392 const MatScalar *aa; 1393 PetscErrorCode ierr; 1394 PetscInt m = A->rmap->n,*aj,*ii; 1395 PetscInt n,i,*ridx=NULL; 1396 PetscScalar sum; 1397 PetscBool usecprow=a->compressedrow.use; 1398 1399 PetscFunctionBegin; 1400 ierr = VecGetArrayRead(xx,&x);CHKERRQ(ierr); 1401 ierr = VecGetArrayPair(yy,zz,&y,&z);CHKERRQ(ierr); 1402 1403 aj = a->j; 1404 aa = a->a; 1405 ii = a->i; 1406 if (usecprow) { /* use compressed row format */ 1407 if (zz != yy) { 1408 ierr = PetscMemcpy(z,y,m*sizeof(PetscScalar));CHKERRQ(ierr); 1409 } 1410 m = a->compressedrow.nrows; 1411 ii = a->compressedrow.i; 1412 ridx = a->compressedrow.rindex; 1413 for (i=0; i<m; i++) { 1414 n = ii[i+1] - ii[i]; 1415 aj = a->j + ii[i]; 1416 aa = a->a + ii[i]; 1417 sum = y[*ridx]; 1418 PetscSparseDenseMaxDot(sum,x,aa,aj,n); 1419 z[*ridx++] = sum; 1420 } 1421 } else { /* do not use compressed row format */ 1422 for (i=0; i<m; i++) { 1423 n = ii[i+1] - ii[i]; 1424 aj = a->j + ii[i]; 1425 aa = a->a + ii[i]; 1426 sum = y[i]; 1427 PetscSparseDenseMaxDot(sum,x,aa,aj,n); 1428 z[i] = sum; 1429 } 1430 } 1431 ierr = PetscLogFlops(2.0*a->nz);CHKERRQ(ierr); 1432 ierr = VecRestoreArrayRead(xx,&x);CHKERRQ(ierr); 1433 ierr = VecRestoreArrayPair(yy,zz,&y,&z);CHKERRQ(ierr); 1434 PetscFunctionReturn(0); 1435 } 1436 1437 #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h> 1438 #undef __FUNCT__ 1439 #define __FUNCT__ "MatMultAdd_SeqAIJ" 1440 PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz) 1441 { 1442 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1443 PetscScalar *y,*z; 1444 const PetscScalar *x; 1445 const MatScalar *aa; 1446 PetscErrorCode ierr; 1447 const PetscInt *aj,*ii,*ridx=NULL; 1448 PetscInt m = A->rmap->n,n,i; 1449 PetscScalar sum; 1450 PetscBool usecprow=a->compressedrow.use; 1451 1452 PetscFunctionBegin; 1453 ierr = VecGetArrayRead(xx,&x);CHKERRQ(ierr); 1454 ierr = VecGetArrayPair(yy,zz,&y,&z);CHKERRQ(ierr); 1455 1456 aj = a->j; 1457 aa = a->a; 1458 ii = a->i; 1459 if (usecprow) { /* use compressed row format */ 1460 if (zz != yy) { 1461 ierr = PetscMemcpy(z,y,m*sizeof(PetscScalar));CHKERRQ(ierr); 1462 } 1463 m = a->compressedrow.nrows; 1464 ii = a->compressedrow.i; 1465 ridx = a->compressedrow.rindex; 1466 for (i=0; i<m; i++) { 1467 n = ii[i+1] - ii[i]; 1468 aj = a->j + ii[i]; 1469 aa = a->a + ii[i]; 1470 sum = y[*ridx]; 1471 PetscSparseDensePlusDot(sum,x,aa,aj,n); 1472 z[*ridx++] = sum; 1473 } 1474 } else { /* do not use compressed row format */ 1475 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ) 1476 fortranmultaddaij_(&m,x,ii,aj,aa,y,z); 1477 #else 1478 for (i=0; i<m; i++) { 1479 n = ii[i+1] - ii[i]; 1480 aj = a->j + ii[i]; 1481 aa = a->a + ii[i]; 1482 sum = y[i]; 1483 PetscSparseDensePlusDot(sum,x,aa,aj,n); 1484 z[i] = sum; 1485 } 1486 #endif 1487 } 1488 ierr = PetscLogFlops(2.0*a->nz);CHKERRQ(ierr); 1489 ierr = VecRestoreArrayRead(xx,&x);CHKERRQ(ierr); 1490 ierr = VecRestoreArrayPair(yy,zz,&y,&z);CHKERRQ(ierr); 1491 #if defined(PETSC_HAVE_CUSP) 1492 /* 1493 ierr = VecView(xx,0);CHKERRQ(ierr); 1494 ierr = VecView(zz,0);CHKERRQ(ierr); 1495 ierr = MatView(A,0);CHKERRQ(ierr); 1496 */ 1497 #endif 1498 PetscFunctionReturn(0); 1499 } 1500 1501 /* 1502 Adds diagonal pointers to sparse matrix structure. 1503 */ 1504 #undef __FUNCT__ 1505 #define __FUNCT__ "MatMarkDiagonal_SeqAIJ" 1506 PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A) 1507 { 1508 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1509 PetscErrorCode ierr; 1510 PetscInt i,j,m = A->rmap->n; 1511 1512 PetscFunctionBegin; 1513 if (!a->diag) { 1514 ierr = PetscMalloc1(m,&a->diag);CHKERRQ(ierr); 1515 ierr = PetscLogObjectMemory((PetscObject)A, m*sizeof(PetscInt));CHKERRQ(ierr); 1516 } 1517 for (i=0; i<A->rmap->n; i++) { 1518 a->diag[i] = a->i[i+1]; 1519 for (j=a->i[i]; j<a->i[i+1]; j++) { 1520 if (a->j[j] == i) { 1521 a->diag[i] = j; 1522 break; 1523 } 1524 } 1525 } 1526 PetscFunctionReturn(0); 1527 } 1528 1529 /* 1530 Checks for missing diagonals 1531 */ 1532 #undef __FUNCT__ 1533 #define __FUNCT__ "MatMissingDiagonal_SeqAIJ" 1534 PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool *missing,PetscInt *d) 1535 { 1536 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1537 PetscInt *diag,*ii = a->i,i; 1538 1539 PetscFunctionBegin; 1540 *missing = PETSC_FALSE; 1541 if (A->rmap->n > 0 && !ii) { 1542 *missing = PETSC_TRUE; 1543 if (d) *d = 0; 1544 PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n"); 1545 } else { 1546 diag = a->diag; 1547 for (i=0; i<A->rmap->n; i++) { 1548 if (diag[i] >= ii[i+1]) { 1549 *missing = PETSC_TRUE; 1550 if (d) *d = i; 1551 PetscInfo1(A,"Matrix is missing diagonal number %D\n",i); 1552 break; 1553 } 1554 } 1555 } 1556 PetscFunctionReturn(0); 1557 } 1558 1559 #undef __FUNCT__ 1560 #define __FUNCT__ "MatInvertDiagonal_SeqAIJ" 1561 /* 1562 Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways 1563 */ 1564 PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift) 1565 { 1566 Mat_SeqAIJ *a = (Mat_SeqAIJ*) A->data; 1567 PetscErrorCode ierr; 1568 PetscInt i,*diag,m = A->rmap->n; 1569 MatScalar *v = a->a; 1570 PetscScalar *idiag,*mdiag; 1571 1572 PetscFunctionBegin; 1573 if (a->idiagvalid) PetscFunctionReturn(0); 1574 ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr); 1575 diag = a->diag; 1576 if (!a->idiag) { 1577 ierr = PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);CHKERRQ(ierr); 1578 ierr = PetscLogObjectMemory((PetscObject)A, 3*m*sizeof(PetscScalar));CHKERRQ(ierr); 1579 v = a->a; 1580 } 1581 mdiag = a->mdiag; 1582 idiag = a->idiag; 1583 1584 if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) { 1585 for (i=0; i<m; i++) { 1586 mdiag[i] = v[diag[i]]; 1587 if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */ 1588 if (PetscRealPart(fshift)) { 1589 ierr = PetscInfo1(A,"Zero diagonal on row %D\n",i);CHKERRQ(ierr); 1590 A->errortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1591 } else { 1592 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i); 1593 } 1594 } 1595 idiag[i] = 1.0/v[diag[i]]; 1596 } 1597 ierr = PetscLogFlops(m);CHKERRQ(ierr); 1598 } else { 1599 for (i=0; i<m; i++) { 1600 mdiag[i] = v[diag[i]]; 1601 idiag[i] = omega/(fshift + v[diag[i]]); 1602 } 1603 ierr = PetscLogFlops(2.0*m);CHKERRQ(ierr); 1604 } 1605 a->idiagvalid = PETSC_TRUE; 1606 PetscFunctionReturn(0); 1607 } 1608 1609 #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h> 1610 #undef __FUNCT__ 1611 #define __FUNCT__ "MatSOR_SeqAIJ" 1612 PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx) 1613 { 1614 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1615 PetscScalar *x,d,sum,*t,scale; 1616 const MatScalar *v = a->a,*idiag=0,*mdiag; 1617 const PetscScalar *b, *bs,*xb, *ts; 1618 PetscErrorCode ierr; 1619 PetscInt n = A->cmap->n,m = A->rmap->n,i; 1620 const PetscInt *idx,*diag; 1621 1622 PetscFunctionBegin; 1623 its = its*lits; 1624 1625 if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */ 1626 if (!a->idiagvalid) {ierr = MatInvertDiagonal_SeqAIJ(A,omega,fshift);CHKERRQ(ierr);} 1627 a->fshift = fshift; 1628 a->omega = omega; 1629 1630 diag = a->diag; 1631 t = a->ssor_work; 1632 idiag = a->idiag; 1633 mdiag = a->mdiag; 1634 1635 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 1636 ierr = VecGetArrayRead(bb,&b);CHKERRQ(ierr); 1637 /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */ 1638 if (flag == SOR_APPLY_UPPER) { 1639 /* apply (U + D/omega) to the vector */ 1640 bs = b; 1641 for (i=0; i<m; i++) { 1642 d = fshift + mdiag[i]; 1643 n = a->i[i+1] - diag[i] - 1; 1644 idx = a->j + diag[i] + 1; 1645 v = a->a + diag[i] + 1; 1646 sum = b[i]*d/omega; 1647 PetscSparseDensePlusDot(sum,bs,v,idx,n); 1648 x[i] = sum; 1649 } 1650 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1651 ierr = VecRestoreArrayRead(bb,&b);CHKERRQ(ierr); 1652 ierr = PetscLogFlops(a->nz);CHKERRQ(ierr); 1653 PetscFunctionReturn(0); 1654 } 1655 1656 if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented"); 1657 else if (flag & SOR_EISENSTAT) { 1658 /* Let A = L + U + D; where L is lower trianglar, 1659 U is upper triangular, E = D/omega; This routine applies 1660 1661 (L + E)^{-1} A (U + E)^{-1} 1662 1663 to a vector efficiently using Eisenstat's trick. 1664 */ 1665 scale = (2.0/omega) - 1.0; 1666 1667 /* x = (E + U)^{-1} b */ 1668 for (i=m-1; i>=0; i--) { 1669 n = a->i[i+1] - diag[i] - 1; 1670 idx = a->j + diag[i] + 1; 1671 v = a->a + diag[i] + 1; 1672 sum = b[i]; 1673 PetscSparseDenseMinusDot(sum,x,v,idx,n); 1674 x[i] = sum*idiag[i]; 1675 } 1676 1677 /* t = b - (2*E - D)x */ 1678 v = a->a; 1679 for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i]; 1680 1681 /* t = (E + L)^{-1}t */ 1682 ts = t; 1683 diag = a->diag; 1684 for (i=0; i<m; i++) { 1685 n = diag[i] - a->i[i]; 1686 idx = a->j + a->i[i]; 1687 v = a->a + a->i[i]; 1688 sum = t[i]; 1689 PetscSparseDenseMinusDot(sum,ts,v,idx,n); 1690 t[i] = sum*idiag[i]; 1691 /* x = x + t */ 1692 x[i] += t[i]; 1693 } 1694 1695 ierr = PetscLogFlops(6.0*m-1 + 2.0*a->nz);CHKERRQ(ierr); 1696 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1697 ierr = VecRestoreArrayRead(bb,&b);CHKERRQ(ierr); 1698 PetscFunctionReturn(0); 1699 } 1700 if (flag & SOR_ZERO_INITIAL_GUESS) { 1701 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) { 1702 for (i=0; i<m; i++) { 1703 n = diag[i] - a->i[i]; 1704 idx = a->j + a->i[i]; 1705 v = a->a + a->i[i]; 1706 sum = b[i]; 1707 PetscSparseDenseMinusDot(sum,x,v,idx,n); 1708 t[i] = sum; 1709 x[i] = sum*idiag[i]; 1710 } 1711 xb = t; 1712 ierr = PetscLogFlops(a->nz);CHKERRQ(ierr); 1713 } else xb = b; 1714 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) { 1715 for (i=m-1; i>=0; i--) { 1716 n = a->i[i+1] - diag[i] - 1; 1717 idx = a->j + diag[i] + 1; 1718 v = a->a + diag[i] + 1; 1719 sum = xb[i]; 1720 PetscSparseDenseMinusDot(sum,x,v,idx,n); 1721 if (xb == b) { 1722 x[i] = sum*idiag[i]; 1723 } else { 1724 x[i] = (1-omega)*x[i] + sum*idiag[i]; /* omega in idiag */ 1725 } 1726 } 1727 ierr = PetscLogFlops(a->nz);CHKERRQ(ierr); /* assumes 1/2 in upper */ 1728 } 1729 its--; 1730 } 1731 while (its--) { 1732 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) { 1733 for (i=0; i<m; i++) { 1734 /* lower */ 1735 n = diag[i] - a->i[i]; 1736 idx = a->j + a->i[i]; 1737 v = a->a + a->i[i]; 1738 sum = b[i]; 1739 PetscSparseDenseMinusDot(sum,x,v,idx,n); 1740 t[i] = sum; /* save application of the lower-triangular part */ 1741 /* upper */ 1742 n = a->i[i+1] - diag[i] - 1; 1743 idx = a->j + diag[i] + 1; 1744 v = a->a + diag[i] + 1; 1745 PetscSparseDenseMinusDot(sum,x,v,idx,n); 1746 x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */ 1747 } 1748 xb = t; 1749 ierr = PetscLogFlops(2.0*a->nz);CHKERRQ(ierr); 1750 } else xb = b; 1751 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) { 1752 for (i=m-1; i>=0; i--) { 1753 sum = xb[i]; 1754 if (xb == b) { 1755 /* whole matrix (no checkpointing available) */ 1756 n = a->i[i+1] - a->i[i]; 1757 idx = a->j + a->i[i]; 1758 v = a->a + a->i[i]; 1759 PetscSparseDenseMinusDot(sum,x,v,idx,n); 1760 x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i]; 1761 } else { /* lower-triangular part has been saved, so only apply upper-triangular */ 1762 n = a->i[i+1] - diag[i] - 1; 1763 idx = a->j + diag[i] + 1; 1764 v = a->a + diag[i] + 1; 1765 PetscSparseDenseMinusDot(sum,x,v,idx,n); 1766 x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */ 1767 } 1768 } 1769 if (xb == b) { 1770 ierr = PetscLogFlops(2.0*a->nz);CHKERRQ(ierr); 1771 } else { 1772 ierr = PetscLogFlops(a->nz);CHKERRQ(ierr); /* assumes 1/2 in upper */ 1773 } 1774 } 1775 } 1776 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1777 ierr = VecRestoreArrayRead(bb,&b);CHKERRQ(ierr); 1778 PetscFunctionReturn(0); 1779 } 1780 1781 1782 #undef __FUNCT__ 1783 #define __FUNCT__ "MatGetInfo_SeqAIJ" 1784 PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info) 1785 { 1786 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1787 1788 PetscFunctionBegin; 1789 info->block_size = 1.0; 1790 info->nz_allocated = (double)a->maxnz; 1791 info->nz_used = (double)a->nz; 1792 info->nz_unneeded = (double)(a->maxnz - a->nz); 1793 info->assemblies = (double)A->num_ass; 1794 info->mallocs = (double)A->info.mallocs; 1795 info->memory = ((PetscObject)A)->mem; 1796 if (A->factortype) { 1797 info->fill_ratio_given = A->info.fill_ratio_given; 1798 info->fill_ratio_needed = A->info.fill_ratio_needed; 1799 info->factor_mallocs = A->info.factor_mallocs; 1800 } else { 1801 info->fill_ratio_given = 0; 1802 info->fill_ratio_needed = 0; 1803 info->factor_mallocs = 0; 1804 } 1805 PetscFunctionReturn(0); 1806 } 1807 1808 #undef __FUNCT__ 1809 #define __FUNCT__ "MatZeroRows_SeqAIJ" 1810 PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b) 1811 { 1812 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1813 PetscInt i,m = A->rmap->n - 1,d = 0; 1814 PetscErrorCode ierr; 1815 const PetscScalar *xx; 1816 PetscScalar *bb; 1817 PetscBool missing; 1818 1819 PetscFunctionBegin; 1820 if (x && b) { 1821 ierr = VecGetArrayRead(x,&xx);CHKERRQ(ierr); 1822 ierr = VecGetArray(b,&bb);CHKERRQ(ierr); 1823 for (i=0; i<N; i++) { 1824 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]); 1825 bb[rows[i]] = diag*xx[rows[i]]; 1826 } 1827 ierr = VecRestoreArrayRead(x,&xx);CHKERRQ(ierr); 1828 ierr = VecRestoreArray(b,&bb);CHKERRQ(ierr); 1829 } 1830 1831 if (a->keepnonzeropattern) { 1832 for (i=0; i<N; i++) { 1833 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]); 1834 ierr = PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));CHKERRQ(ierr); 1835 } 1836 if (diag != 0.0) { 1837 ierr = MatMissingDiagonal_SeqAIJ(A,&missing,&d);CHKERRQ(ierr); 1838 if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d); 1839 for (i=0; i<N; i++) { 1840 a->a[a->diag[rows[i]]] = diag; 1841 } 1842 } 1843 } else { 1844 if (diag != 0.0) { 1845 for (i=0; i<N; i++) { 1846 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]); 1847 if (a->ilen[rows[i]] > 0) { 1848 a->ilen[rows[i]] = 1; 1849 a->a[a->i[rows[i]]] = diag; 1850 a->j[a->i[rows[i]]] = rows[i]; 1851 } else { /* in case row was completely empty */ 1852 ierr = MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);CHKERRQ(ierr); 1853 } 1854 } 1855 } else { 1856 for (i=0; i<N; i++) { 1857 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]); 1858 a->ilen[rows[i]] = 0; 1859 } 1860 } 1861 A->nonzerostate++; 1862 } 1863 ierr = MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1864 PetscFunctionReturn(0); 1865 } 1866 1867 #undef __FUNCT__ 1868 #define __FUNCT__ "MatZeroRowsColumns_SeqAIJ" 1869 PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b) 1870 { 1871 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1872 PetscInt i,j,m = A->rmap->n - 1,d = 0; 1873 PetscErrorCode ierr; 1874 PetscBool missing,*zeroed,vecs = PETSC_FALSE; 1875 const PetscScalar *xx; 1876 PetscScalar *bb; 1877 1878 PetscFunctionBegin; 1879 if (x && b) { 1880 ierr = VecGetArrayRead(x,&xx);CHKERRQ(ierr); 1881 ierr = VecGetArray(b,&bb);CHKERRQ(ierr); 1882 vecs = PETSC_TRUE; 1883 } 1884 ierr = PetscCalloc1(A->rmap->n,&zeroed);CHKERRQ(ierr); 1885 for (i=0; i<N; i++) { 1886 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]); 1887 ierr = PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));CHKERRQ(ierr); 1888 1889 zeroed[rows[i]] = PETSC_TRUE; 1890 } 1891 for (i=0; i<A->rmap->n; i++) { 1892 if (!zeroed[i]) { 1893 for (j=a->i[i]; j<a->i[i+1]; j++) { 1894 if (zeroed[a->j[j]]) { 1895 if (vecs) bb[i] -= a->a[j]*xx[a->j[j]]; 1896 a->a[j] = 0.0; 1897 } 1898 } 1899 } else if (vecs) bb[i] = diag*xx[i]; 1900 } 1901 if (x && b) { 1902 ierr = VecRestoreArrayRead(x,&xx);CHKERRQ(ierr); 1903 ierr = VecRestoreArray(b,&bb);CHKERRQ(ierr); 1904 } 1905 ierr = PetscFree(zeroed);CHKERRQ(ierr); 1906 if (diag != 0.0) { 1907 ierr = MatMissingDiagonal_SeqAIJ(A,&missing,&d);CHKERRQ(ierr); 1908 if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d); 1909 for (i=0; i<N; i++) { 1910 a->a[a->diag[rows[i]]] = diag; 1911 } 1912 } 1913 ierr = MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1914 PetscFunctionReturn(0); 1915 } 1916 1917 #undef __FUNCT__ 1918 #define __FUNCT__ "MatGetRow_SeqAIJ" 1919 PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v) 1920 { 1921 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1922 PetscInt *itmp; 1923 1924 PetscFunctionBegin; 1925 if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row); 1926 1927 *nz = a->i[row+1] - a->i[row]; 1928 if (v) *v = a->a + a->i[row]; 1929 if (idx) { 1930 itmp = a->j + a->i[row]; 1931 if (*nz) *idx = itmp; 1932 else *idx = 0; 1933 } 1934 PetscFunctionReturn(0); 1935 } 1936 1937 /* remove this function? */ 1938 #undef __FUNCT__ 1939 #define __FUNCT__ "MatRestoreRow_SeqAIJ" 1940 PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v) 1941 { 1942 PetscFunctionBegin; 1943 PetscFunctionReturn(0); 1944 } 1945 1946 #undef __FUNCT__ 1947 #define __FUNCT__ "MatNorm_SeqAIJ" 1948 PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm) 1949 { 1950 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1951 MatScalar *v = a->a; 1952 PetscReal sum = 0.0; 1953 PetscErrorCode ierr; 1954 PetscInt i,j; 1955 1956 PetscFunctionBegin; 1957 if (type == NORM_FROBENIUS) { 1958 for (i=0; i<a->nz; i++) { 1959 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 1960 } 1961 *nrm = PetscSqrtReal(sum); 1962 } else if (type == NORM_1) { 1963 PetscReal *tmp; 1964 PetscInt *jj = a->j; 1965 ierr = PetscCalloc1(A->cmap->n+1,&tmp);CHKERRQ(ierr); 1966 *nrm = 0.0; 1967 for (j=0; j<a->nz; j++) { 1968 tmp[*jj++] += PetscAbsScalar(*v); v++; 1969 } 1970 for (j=0; j<A->cmap->n; j++) { 1971 if (tmp[j] > *nrm) *nrm = tmp[j]; 1972 } 1973 ierr = PetscFree(tmp);CHKERRQ(ierr); 1974 } else if (type == NORM_INFINITY) { 1975 *nrm = 0.0; 1976 for (j=0; j<A->rmap->n; j++) { 1977 v = a->a + a->i[j]; 1978 sum = 0.0; 1979 for (i=0; i<a->i[j+1]-a->i[j]; i++) { 1980 sum += PetscAbsScalar(*v); v++; 1981 } 1982 if (sum > *nrm) *nrm = sum; 1983 } 1984 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm"); 1985 PetscFunctionReturn(0); 1986 } 1987 1988 /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */ 1989 #undef __FUNCT__ 1990 #define __FUNCT__ "MatTransposeSymbolic_SeqAIJ" 1991 PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B) 1992 { 1993 PetscErrorCode ierr; 1994 PetscInt i,j,anzj; 1995 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data,*b; 1996 PetscInt an=A->cmap->N,am=A->rmap->N; 1997 PetscInt *ati,*atj,*atfill,*ai=a->i,*aj=a->j; 1998 1999 PetscFunctionBegin; 2000 /* Allocate space for symbolic transpose info and work array */ 2001 ierr = PetscCalloc1(an+1,&ati);CHKERRQ(ierr); 2002 ierr = PetscMalloc1(ai[am],&atj);CHKERRQ(ierr); 2003 ierr = PetscMalloc1(an,&atfill);CHKERRQ(ierr); 2004 2005 /* Walk through aj and count ## of non-zeros in each row of A^T. */ 2006 /* Note: offset by 1 for fast conversion into csr format. */ 2007 for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1; 2008 /* Form ati for csr format of A^T. */ 2009 for (i=0;i<an;i++) ati[i+1] += ati[i]; 2010 2011 /* Copy ati into atfill so we have locations of the next free space in atj */ 2012 ierr = PetscMemcpy(atfill,ati,an*sizeof(PetscInt));CHKERRQ(ierr); 2013 2014 /* Walk through A row-wise and mark nonzero entries of A^T. */ 2015 for (i=0;i<am;i++) { 2016 anzj = ai[i+1] - ai[i]; 2017 for (j=0;j<anzj;j++) { 2018 atj[atfill[*aj]] = i; 2019 atfill[*aj++] += 1; 2020 } 2021 } 2022 2023 /* Clean up temporary space and complete requests. */ 2024 ierr = PetscFree(atfill);CHKERRQ(ierr); 2025 ierr = MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);CHKERRQ(ierr); 2026 ierr = MatSetBlockSizes(*B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));CHKERRQ(ierr); 2027 2028 b = (Mat_SeqAIJ*)((*B)->data); 2029 b->free_a = PETSC_FALSE; 2030 b->free_ij = PETSC_TRUE; 2031 b->nonew = 0; 2032 PetscFunctionReturn(0); 2033 } 2034 2035 #undef __FUNCT__ 2036 #define __FUNCT__ "MatTranspose_SeqAIJ" 2037 PetscErrorCode MatTranspose_SeqAIJ(Mat A,MatReuse reuse,Mat *B) 2038 { 2039 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2040 Mat C; 2041 PetscErrorCode ierr; 2042 PetscInt i,*aj = a->j,*ai = a->i,m = A->rmap->n,len,*col; 2043 MatScalar *array = a->a; 2044 2045 PetscFunctionBegin; 2046 if (reuse == MAT_REUSE_MATRIX && A == *B && m != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Square matrix only for in-place"); 2047 2048 if (reuse == MAT_INITIAL_MATRIX || *B == A) { 2049 ierr = PetscCalloc1(1+A->cmap->n,&col);CHKERRQ(ierr); 2050 2051 for (i=0; i<ai[m]; i++) col[aj[i]] += 1; 2052 ierr = MatCreate(PetscObjectComm((PetscObject)A),&C);CHKERRQ(ierr); 2053 ierr = MatSetSizes(C,A->cmap->n,m,A->cmap->n,m);CHKERRQ(ierr); 2054 ierr = MatSetBlockSizes(C,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));CHKERRQ(ierr); 2055 ierr = MatSetType(C,((PetscObject)A)->type_name);CHKERRQ(ierr); 2056 ierr = MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);CHKERRQ(ierr); 2057 ierr = PetscFree(col);CHKERRQ(ierr); 2058 } else { 2059 C = *B; 2060 } 2061 2062 for (i=0; i<m; i++) { 2063 len = ai[i+1]-ai[i]; 2064 ierr = MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);CHKERRQ(ierr); 2065 array += len; 2066 aj += len; 2067 } 2068 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2069 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2070 2071 if (reuse == MAT_INITIAL_MATRIX || *B != A) { 2072 *B = C; 2073 } else { 2074 ierr = MatHeaderMerge(A,&C);CHKERRQ(ierr); 2075 } 2076 PetscFunctionReturn(0); 2077 } 2078 2079 #undef __FUNCT__ 2080 #define __FUNCT__ "MatIsTranspose_SeqAIJ" 2081 PetscErrorCode MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f) 2082 { 2083 Mat_SeqAIJ *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) A->data; 2084 PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr; 2085 MatScalar *va,*vb; 2086 PetscErrorCode ierr; 2087 PetscInt ma,na,mb,nb, i; 2088 2089 PetscFunctionBegin; 2090 bij = (Mat_SeqAIJ*) B->data; 2091 2092 ierr = MatGetSize(A,&ma,&na);CHKERRQ(ierr); 2093 ierr = MatGetSize(B,&mb,&nb);CHKERRQ(ierr); 2094 if (ma!=nb || na!=mb) { 2095 *f = PETSC_FALSE; 2096 PetscFunctionReturn(0); 2097 } 2098 aii = aij->i; bii = bij->i; 2099 adx = aij->j; bdx = bij->j; 2100 va = aij->a; vb = bij->a; 2101 ierr = PetscMalloc1(ma,&aptr);CHKERRQ(ierr); 2102 ierr = PetscMalloc1(mb,&bptr);CHKERRQ(ierr); 2103 for (i=0; i<ma; i++) aptr[i] = aii[i]; 2104 for (i=0; i<mb; i++) bptr[i] = bii[i]; 2105 2106 *f = PETSC_TRUE; 2107 for (i=0; i<ma; i++) { 2108 while (aptr[i]<aii[i+1]) { 2109 PetscInt idc,idr; 2110 PetscScalar vc,vr; 2111 /* column/row index/value */ 2112 idc = adx[aptr[i]]; 2113 idr = bdx[bptr[idc]]; 2114 vc = va[aptr[i]]; 2115 vr = vb[bptr[idc]]; 2116 if (i!=idr || PetscAbsScalar(vc-vr) > tol) { 2117 *f = PETSC_FALSE; 2118 goto done; 2119 } else { 2120 aptr[i]++; 2121 if (B || i!=idc) bptr[idc]++; 2122 } 2123 } 2124 } 2125 done: 2126 ierr = PetscFree(aptr);CHKERRQ(ierr); 2127 ierr = PetscFree(bptr);CHKERRQ(ierr); 2128 PetscFunctionReturn(0); 2129 } 2130 2131 #undef __FUNCT__ 2132 #define __FUNCT__ "MatIsHermitianTranspose_SeqAIJ" 2133 PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f) 2134 { 2135 Mat_SeqAIJ *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) A->data; 2136 PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr; 2137 MatScalar *va,*vb; 2138 PetscErrorCode ierr; 2139 PetscInt ma,na,mb,nb, i; 2140 2141 PetscFunctionBegin; 2142 bij = (Mat_SeqAIJ*) B->data; 2143 2144 ierr = MatGetSize(A,&ma,&na);CHKERRQ(ierr); 2145 ierr = MatGetSize(B,&mb,&nb);CHKERRQ(ierr); 2146 if (ma!=nb || na!=mb) { 2147 *f = PETSC_FALSE; 2148 PetscFunctionReturn(0); 2149 } 2150 aii = aij->i; bii = bij->i; 2151 adx = aij->j; bdx = bij->j; 2152 va = aij->a; vb = bij->a; 2153 ierr = PetscMalloc1(ma,&aptr);CHKERRQ(ierr); 2154 ierr = PetscMalloc1(mb,&bptr);CHKERRQ(ierr); 2155 for (i=0; i<ma; i++) aptr[i] = aii[i]; 2156 for (i=0; i<mb; i++) bptr[i] = bii[i]; 2157 2158 *f = PETSC_TRUE; 2159 for (i=0; i<ma; i++) { 2160 while (aptr[i]<aii[i+1]) { 2161 PetscInt idc,idr; 2162 PetscScalar vc,vr; 2163 /* column/row index/value */ 2164 idc = adx[aptr[i]]; 2165 idr = bdx[bptr[idc]]; 2166 vc = va[aptr[i]]; 2167 vr = vb[bptr[idc]]; 2168 if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) { 2169 *f = PETSC_FALSE; 2170 goto done; 2171 } else { 2172 aptr[i]++; 2173 if (B || i!=idc) bptr[idc]++; 2174 } 2175 } 2176 } 2177 done: 2178 ierr = PetscFree(aptr);CHKERRQ(ierr); 2179 ierr = PetscFree(bptr);CHKERRQ(ierr); 2180 PetscFunctionReturn(0); 2181 } 2182 2183 #undef __FUNCT__ 2184 #define __FUNCT__ "MatIsSymmetric_SeqAIJ" 2185 PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool *f) 2186 { 2187 PetscErrorCode ierr; 2188 2189 PetscFunctionBegin; 2190 ierr = MatIsTranspose_SeqAIJ(A,A,tol,f);CHKERRQ(ierr); 2191 PetscFunctionReturn(0); 2192 } 2193 2194 #undef __FUNCT__ 2195 #define __FUNCT__ "MatIsHermitian_SeqAIJ" 2196 PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool *f) 2197 { 2198 PetscErrorCode ierr; 2199 2200 PetscFunctionBegin; 2201 ierr = MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);CHKERRQ(ierr); 2202 PetscFunctionReturn(0); 2203 } 2204 2205 #undef __FUNCT__ 2206 #define __FUNCT__ "MatDiagonalScale_SeqAIJ" 2207 PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr) 2208 { 2209 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2210 PetscScalar *l,*r,x; 2211 MatScalar *v; 2212 PetscErrorCode ierr; 2213 PetscInt i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz,*jj; 2214 2215 PetscFunctionBegin; 2216 if (ll) { 2217 /* The local size is used so that VecMPI can be passed to this routine 2218 by MatDiagonalScale_MPIAIJ */ 2219 ierr = VecGetLocalSize(ll,&m);CHKERRQ(ierr); 2220 if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length"); 2221 ierr = VecGetArray(ll,&l);CHKERRQ(ierr); 2222 v = a->a; 2223 for (i=0; i<m; i++) { 2224 x = l[i]; 2225 M = a->i[i+1] - a->i[i]; 2226 for (j=0; j<M; j++) (*v++) *= x; 2227 } 2228 ierr = VecRestoreArray(ll,&l);CHKERRQ(ierr); 2229 ierr = PetscLogFlops(nz);CHKERRQ(ierr); 2230 } 2231 if (rr) { 2232 ierr = VecGetLocalSize(rr,&n);CHKERRQ(ierr); 2233 if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length"); 2234 ierr = VecGetArray(rr,&r);CHKERRQ(ierr); 2235 v = a->a; jj = a->j; 2236 for (i=0; i<nz; i++) (*v++) *= r[*jj++]; 2237 ierr = VecRestoreArray(rr,&r);CHKERRQ(ierr); 2238 ierr = PetscLogFlops(nz);CHKERRQ(ierr); 2239 } 2240 ierr = MatSeqAIJInvalidateDiagonal(A);CHKERRQ(ierr); 2241 PetscFunctionReturn(0); 2242 } 2243 2244 #undef __FUNCT__ 2245 #define __FUNCT__ "MatGetSubMatrix_SeqAIJ" 2246 PetscErrorCode MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B) 2247 { 2248 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*c; 2249 PetscErrorCode ierr; 2250 PetscInt *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens; 2251 PetscInt row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi; 2252 const PetscInt *irow,*icol; 2253 PetscInt nrows,ncols; 2254 PetscInt *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen; 2255 MatScalar *a_new,*mat_a; 2256 Mat C; 2257 PetscBool stride; 2258 2259 PetscFunctionBegin; 2260 2261 ierr = ISGetIndices(isrow,&irow);CHKERRQ(ierr); 2262 ierr = ISGetLocalSize(isrow,&nrows);CHKERRQ(ierr); 2263 ierr = ISGetLocalSize(iscol,&ncols);CHKERRQ(ierr); 2264 2265 ierr = PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);CHKERRQ(ierr); 2266 if (stride) { 2267 ierr = ISStrideGetInfo(iscol,&first,&step);CHKERRQ(ierr); 2268 } else { 2269 first = 0; 2270 step = 0; 2271 } 2272 if (stride && step == 1) { 2273 /* special case of contiguous rows */ 2274 ierr = PetscMalloc2(nrows,&lens,nrows,&starts);CHKERRQ(ierr); 2275 /* loop over new rows determining lens and starting points */ 2276 for (i=0; i<nrows; i++) { 2277 kstart = ai[irow[i]]; 2278 kend = kstart + ailen[irow[i]]; 2279 starts[i] = kstart; 2280 for (k=kstart; k<kend; k++) { 2281 if (aj[k] >= first) { 2282 starts[i] = k; 2283 break; 2284 } 2285 } 2286 sum = 0; 2287 while (k < kend) { 2288 if (aj[k++] >= first+ncols) break; 2289 sum++; 2290 } 2291 lens[i] = sum; 2292 } 2293 /* create submatrix */ 2294 if (scall == MAT_REUSE_MATRIX) { 2295 PetscInt n_cols,n_rows; 2296 ierr = MatGetSize(*B,&n_rows,&n_cols);CHKERRQ(ierr); 2297 if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size"); 2298 ierr = MatZeroEntries(*B);CHKERRQ(ierr); 2299 C = *B; 2300 } else { 2301 PetscInt rbs,cbs; 2302 ierr = MatCreate(PetscObjectComm((PetscObject)A),&C);CHKERRQ(ierr); 2303 ierr = MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);CHKERRQ(ierr); 2304 ierr = ISGetBlockSize(isrow,&rbs);CHKERRQ(ierr); 2305 ierr = ISGetBlockSize(iscol,&cbs);CHKERRQ(ierr); 2306 ierr = MatSetBlockSizes(C,rbs,cbs);CHKERRQ(ierr); 2307 ierr = MatSetType(C,((PetscObject)A)->type_name);CHKERRQ(ierr); 2308 ierr = MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);CHKERRQ(ierr); 2309 } 2310 c = (Mat_SeqAIJ*)C->data; 2311 2312 /* loop over rows inserting into submatrix */ 2313 a_new = c->a; 2314 j_new = c->j; 2315 i_new = c->i; 2316 2317 for (i=0; i<nrows; i++) { 2318 ii = starts[i]; 2319 lensi = lens[i]; 2320 for (k=0; k<lensi; k++) { 2321 *j_new++ = aj[ii+k] - first; 2322 } 2323 ierr = PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));CHKERRQ(ierr); 2324 a_new += lensi; 2325 i_new[i+1] = i_new[i] + lensi; 2326 c->ilen[i] = lensi; 2327 } 2328 ierr = PetscFree2(lens,starts);CHKERRQ(ierr); 2329 } else { 2330 ierr = ISGetIndices(iscol,&icol);CHKERRQ(ierr); 2331 ierr = PetscCalloc1(oldcols,&smap);CHKERRQ(ierr); 2332 ierr = PetscMalloc1(1+nrows,&lens);CHKERRQ(ierr); 2333 for (i=0; i<ncols; i++) { 2334 #if defined(PETSC_USE_DEBUG) 2335 if (icol[i] >= oldcols) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Requesting column beyond largest column icol[%D] %D <= A->cmap->n %D",i,icol[i],oldcols); 2336 #endif 2337 smap[icol[i]] = i+1; 2338 } 2339 2340 /* determine lens of each row */ 2341 for (i=0; i<nrows; i++) { 2342 kstart = ai[irow[i]]; 2343 kend = kstart + a->ilen[irow[i]]; 2344 lens[i] = 0; 2345 for (k=kstart; k<kend; k++) { 2346 if (smap[aj[k]]) { 2347 lens[i]++; 2348 } 2349 } 2350 } 2351 /* Create and fill new matrix */ 2352 if (scall == MAT_REUSE_MATRIX) { 2353 PetscBool equal; 2354 2355 c = (Mat_SeqAIJ*)((*B)->data); 2356 if ((*B)->rmap->n != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size"); 2357 ierr = PetscMemcmp(c->ilen,lens,(*B)->rmap->n*sizeof(PetscInt),&equal);CHKERRQ(ierr); 2358 if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros"); 2359 ierr = PetscMemzero(c->ilen,(*B)->rmap->n*sizeof(PetscInt));CHKERRQ(ierr); 2360 C = *B; 2361 } else { 2362 PetscInt rbs,cbs; 2363 ierr = MatCreate(PetscObjectComm((PetscObject)A),&C);CHKERRQ(ierr); 2364 ierr = MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);CHKERRQ(ierr); 2365 ierr = ISGetBlockSize(isrow,&rbs);CHKERRQ(ierr); 2366 ierr = ISGetBlockSize(iscol,&cbs);CHKERRQ(ierr); 2367 ierr = MatSetBlockSizes(C,rbs,cbs);CHKERRQ(ierr); 2368 ierr = MatSetType(C,((PetscObject)A)->type_name);CHKERRQ(ierr); 2369 ierr = MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);CHKERRQ(ierr); 2370 } 2371 c = (Mat_SeqAIJ*)(C->data); 2372 for (i=0; i<nrows; i++) { 2373 row = irow[i]; 2374 kstart = ai[row]; 2375 kend = kstart + a->ilen[row]; 2376 mat_i = c->i[i]; 2377 mat_j = c->j + mat_i; 2378 mat_a = c->a + mat_i; 2379 mat_ilen = c->ilen + i; 2380 for (k=kstart; k<kend; k++) { 2381 if ((tcol=smap[a->j[k]])) { 2382 *mat_j++ = tcol - 1; 2383 *mat_a++ = a->a[k]; 2384 (*mat_ilen)++; 2385 2386 } 2387 } 2388 } 2389 /* Free work space */ 2390 ierr = ISRestoreIndices(iscol,&icol);CHKERRQ(ierr); 2391 ierr = PetscFree(smap);CHKERRQ(ierr); 2392 ierr = PetscFree(lens);CHKERRQ(ierr); 2393 /* sort */ 2394 for (i = 0; i < nrows; i++) { 2395 PetscInt ilen; 2396 2397 mat_i = c->i[i]; 2398 mat_j = c->j + mat_i; 2399 mat_a = c->a + mat_i; 2400 ilen = c->ilen[i]; 2401 ierr = PetscSortIntWithMatScalarArray(ilen,mat_j,mat_a);CHKERRQ(ierr); 2402 } 2403 } 2404 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2405 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2406 2407 ierr = ISRestoreIndices(isrow,&irow);CHKERRQ(ierr); 2408 *B = C; 2409 PetscFunctionReturn(0); 2410 } 2411 2412 #undef __FUNCT__ 2413 #define __FUNCT__ "MatGetMultiProcBlock_SeqAIJ" 2414 PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat) 2415 { 2416 PetscErrorCode ierr; 2417 Mat B; 2418 2419 PetscFunctionBegin; 2420 if (scall == MAT_INITIAL_MATRIX) { 2421 ierr = MatCreate(subComm,&B);CHKERRQ(ierr); 2422 ierr = MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);CHKERRQ(ierr); 2423 ierr = MatSetBlockSizesFromMats(B,mat,mat);CHKERRQ(ierr); 2424 ierr = MatSetType(B,MATSEQAIJ);CHKERRQ(ierr); 2425 ierr = MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);CHKERRQ(ierr); 2426 *subMat = B; 2427 } else { 2428 ierr = MatCopy_SeqAIJ(mat,*subMat,SAME_NONZERO_PATTERN);CHKERRQ(ierr); 2429 } 2430 PetscFunctionReturn(0); 2431 } 2432 2433 #undef __FUNCT__ 2434 #define __FUNCT__ "MatILUFactor_SeqAIJ" 2435 PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info) 2436 { 2437 Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data; 2438 PetscErrorCode ierr; 2439 Mat outA; 2440 PetscBool row_identity,col_identity; 2441 2442 PetscFunctionBegin; 2443 if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu"); 2444 2445 ierr = ISIdentity(row,&row_identity);CHKERRQ(ierr); 2446 ierr = ISIdentity(col,&col_identity);CHKERRQ(ierr); 2447 2448 outA = inA; 2449 outA->factortype = MAT_FACTOR_LU; 2450 2451 ierr = PetscObjectReference((PetscObject)row);CHKERRQ(ierr); 2452 ierr = ISDestroy(&a->row);CHKERRQ(ierr); 2453 2454 a->row = row; 2455 2456 ierr = PetscObjectReference((PetscObject)col);CHKERRQ(ierr); 2457 ierr = ISDestroy(&a->col);CHKERRQ(ierr); 2458 2459 a->col = col; 2460 2461 /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */ 2462 ierr = ISDestroy(&a->icol);CHKERRQ(ierr); 2463 ierr = ISInvertPermutation(col,PETSC_DECIDE,&a->icol);CHKERRQ(ierr); 2464 ierr = PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);CHKERRQ(ierr); 2465 2466 if (!a->solve_work) { /* this matrix may have been factored before */ 2467 ierr = PetscMalloc1(inA->rmap->n+1,&a->solve_work);CHKERRQ(ierr); 2468 ierr = PetscLogObjectMemory((PetscObject)inA, (inA->rmap->n+1)*sizeof(PetscScalar));CHKERRQ(ierr); 2469 } 2470 2471 ierr = MatMarkDiagonal_SeqAIJ(inA);CHKERRQ(ierr); 2472 if (row_identity && col_identity) { 2473 ierr = MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);CHKERRQ(ierr); 2474 } else { 2475 ierr = MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);CHKERRQ(ierr); 2476 } 2477 PetscFunctionReturn(0); 2478 } 2479 2480 #undef __FUNCT__ 2481 #define __FUNCT__ "MatScale_SeqAIJ" 2482 PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha) 2483 { 2484 Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data; 2485 PetscScalar oalpha = alpha; 2486 PetscErrorCode ierr; 2487 PetscBLASInt one = 1,bnz; 2488 2489 PetscFunctionBegin; 2490 ierr = PetscBLASIntCast(a->nz,&bnz);CHKERRQ(ierr); 2491 PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&oalpha,a->a,&one)); 2492 ierr = PetscLogFlops(a->nz);CHKERRQ(ierr); 2493 ierr = MatSeqAIJInvalidateDiagonal(inA);CHKERRQ(ierr); 2494 PetscFunctionReturn(0); 2495 } 2496 2497 #undef __FUNCT__ 2498 #define __FUNCT__ "MatGetSubMatrices_SeqAIJ" 2499 PetscErrorCode MatGetSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[]) 2500 { 2501 PetscErrorCode ierr; 2502 PetscInt i; 2503 2504 PetscFunctionBegin; 2505 if (scall == MAT_INITIAL_MATRIX) { 2506 ierr = PetscMalloc1(n+1,B);CHKERRQ(ierr); 2507 } 2508 2509 for (i=0; i<n; i++) { 2510 ierr = MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);CHKERRQ(ierr); 2511 } 2512 PetscFunctionReturn(0); 2513 } 2514 2515 #undef __FUNCT__ 2516 #define __FUNCT__ "MatIncreaseOverlap_SeqAIJ" 2517 PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov) 2518 { 2519 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2520 PetscErrorCode ierr; 2521 PetscInt row,i,j,k,l,m,n,*nidx,isz,val; 2522 const PetscInt *idx; 2523 PetscInt start,end,*ai,*aj; 2524 PetscBT table; 2525 2526 PetscFunctionBegin; 2527 m = A->rmap->n; 2528 ai = a->i; 2529 aj = a->j; 2530 2531 if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used"); 2532 2533 ierr = PetscMalloc1(m+1,&nidx);CHKERRQ(ierr); 2534 ierr = PetscBTCreate(m,&table);CHKERRQ(ierr); 2535 2536 for (i=0; i<is_max; i++) { 2537 /* Initialize the two local arrays */ 2538 isz = 0; 2539 ierr = PetscBTMemzero(m,table);CHKERRQ(ierr); 2540 2541 /* Extract the indices, assume there can be duplicate entries */ 2542 ierr = ISGetIndices(is[i],&idx);CHKERRQ(ierr); 2543 ierr = ISGetLocalSize(is[i],&n);CHKERRQ(ierr); 2544 2545 /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */ 2546 for (j=0; j<n; ++j) { 2547 if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j]; 2548 } 2549 ierr = ISRestoreIndices(is[i],&idx);CHKERRQ(ierr); 2550 ierr = ISDestroy(&is[i]);CHKERRQ(ierr); 2551 2552 k = 0; 2553 for (j=0; j<ov; j++) { /* for each overlap */ 2554 n = isz; 2555 for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */ 2556 row = nidx[k]; 2557 start = ai[row]; 2558 end = ai[row+1]; 2559 for (l = start; l<end; l++) { 2560 val = aj[l]; 2561 if (!PetscBTLookupSet(table,val)) nidx[isz++] = val; 2562 } 2563 } 2564 } 2565 ierr = ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));CHKERRQ(ierr); 2566 } 2567 ierr = PetscBTDestroy(&table);CHKERRQ(ierr); 2568 ierr = PetscFree(nidx);CHKERRQ(ierr); 2569 PetscFunctionReturn(0); 2570 } 2571 2572 /* -------------------------------------------------------------- */ 2573 #undef __FUNCT__ 2574 #define __FUNCT__ "MatPermute_SeqAIJ" 2575 PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B) 2576 { 2577 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2578 PetscErrorCode ierr; 2579 PetscInt i,nz = 0,m = A->rmap->n,n = A->cmap->n; 2580 const PetscInt *row,*col; 2581 PetscInt *cnew,j,*lens; 2582 IS icolp,irowp; 2583 PetscInt *cwork = NULL; 2584 PetscScalar *vwork = NULL; 2585 2586 PetscFunctionBegin; 2587 ierr = ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);CHKERRQ(ierr); 2588 ierr = ISGetIndices(irowp,&row);CHKERRQ(ierr); 2589 ierr = ISInvertPermutation(colp,PETSC_DECIDE,&icolp);CHKERRQ(ierr); 2590 ierr = ISGetIndices(icolp,&col);CHKERRQ(ierr); 2591 2592 /* determine lengths of permuted rows */ 2593 ierr = PetscMalloc1(m+1,&lens);CHKERRQ(ierr); 2594 for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i]; 2595 ierr = MatCreate(PetscObjectComm((PetscObject)A),B);CHKERRQ(ierr); 2596 ierr = MatSetSizes(*B,m,n,m,n);CHKERRQ(ierr); 2597 ierr = MatSetBlockSizesFromMats(*B,A,A);CHKERRQ(ierr); 2598 ierr = MatSetType(*B,((PetscObject)A)->type_name);CHKERRQ(ierr); 2599 ierr = MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);CHKERRQ(ierr); 2600 ierr = PetscFree(lens);CHKERRQ(ierr); 2601 2602 ierr = PetscMalloc1(n,&cnew);CHKERRQ(ierr); 2603 for (i=0; i<m; i++) { 2604 ierr = MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 2605 for (j=0; j<nz; j++) cnew[j] = col[cwork[j]]; 2606 ierr = MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);CHKERRQ(ierr); 2607 ierr = MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 2608 } 2609 ierr = PetscFree(cnew);CHKERRQ(ierr); 2610 2611 (*B)->assembled = PETSC_FALSE; 2612 2613 ierr = MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2614 ierr = MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2615 ierr = ISRestoreIndices(irowp,&row);CHKERRQ(ierr); 2616 ierr = ISRestoreIndices(icolp,&col);CHKERRQ(ierr); 2617 ierr = ISDestroy(&irowp);CHKERRQ(ierr); 2618 ierr = ISDestroy(&icolp);CHKERRQ(ierr); 2619 PetscFunctionReturn(0); 2620 } 2621 2622 #undef __FUNCT__ 2623 #define __FUNCT__ "MatCopy_SeqAIJ" 2624 PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str) 2625 { 2626 PetscErrorCode ierr; 2627 2628 PetscFunctionBegin; 2629 /* If the two matrices have the same copy implementation, use fast copy. */ 2630 if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) { 2631 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2632 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 2633 2634 if (a->i[A->rmap->n] != b->i[B->rmap->n]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different"); 2635 ierr = PetscMemcpy(b->a,a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));CHKERRQ(ierr); 2636 } else { 2637 ierr = MatCopy_Basic(A,B,str);CHKERRQ(ierr); 2638 } 2639 PetscFunctionReturn(0); 2640 } 2641 2642 #undef __FUNCT__ 2643 #define __FUNCT__ "MatSetUp_SeqAIJ" 2644 PetscErrorCode MatSetUp_SeqAIJ(Mat A) 2645 { 2646 PetscErrorCode ierr; 2647 2648 PetscFunctionBegin; 2649 ierr = MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);CHKERRQ(ierr); 2650 PetscFunctionReturn(0); 2651 } 2652 2653 #undef __FUNCT__ 2654 #define __FUNCT__ "MatSeqAIJGetArray_SeqAIJ" 2655 PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[]) 2656 { 2657 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2658 2659 PetscFunctionBegin; 2660 *array = a->a; 2661 PetscFunctionReturn(0); 2662 } 2663 2664 #undef __FUNCT__ 2665 #define __FUNCT__ "MatSeqAIJRestoreArray_SeqAIJ" 2666 PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[]) 2667 { 2668 PetscFunctionBegin; 2669 PetscFunctionReturn(0); 2670 } 2671 2672 /* 2673 Computes the number of nonzeros per row needed for preallocation when X and Y 2674 have different nonzero structure. 2675 */ 2676 #undef __FUNCT__ 2677 #define __FUNCT__ "MatAXPYGetPreallocation_SeqX_private" 2678 PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *yi,const PetscInt *yj,PetscInt *nnz) 2679 { 2680 PetscInt i,j,k,nzx,nzy; 2681 2682 PetscFunctionBegin; 2683 /* Set the number of nonzeros in the new matrix */ 2684 for (i=0; i<m; i++) { 2685 const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i]; 2686 nzx = xi[i+1] - xi[i]; 2687 nzy = yi[i+1] - yi[i]; 2688 nnz[i] = 0; 2689 for (j=0,k=0; j<nzx; j++) { /* Point in X */ 2690 for (; k<nzy && yjj[k]<xjj[j]; k++) nnz[i]++; /* Catch up to X */ 2691 if (k<nzy && yjj[k]==xjj[j]) k++; /* Skip duplicate */ 2692 nnz[i]++; 2693 } 2694 for (; k<nzy; k++) nnz[i]++; 2695 } 2696 PetscFunctionReturn(0); 2697 } 2698 2699 #undef __FUNCT__ 2700 #define __FUNCT__ "MatAXPYGetPreallocation_SeqAIJ" 2701 PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz) 2702 { 2703 PetscInt m = Y->rmap->N; 2704 Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data; 2705 Mat_SeqAIJ *y = (Mat_SeqAIJ*)Y->data; 2706 PetscErrorCode ierr; 2707 2708 PetscFunctionBegin; 2709 /* Set the number of nonzeros in the new matrix */ 2710 ierr = MatAXPYGetPreallocation_SeqX_private(m,x->i,x->j,y->i,y->j,nnz);CHKERRQ(ierr); 2711 PetscFunctionReturn(0); 2712 } 2713 2714 #undef __FUNCT__ 2715 #define __FUNCT__ "MatAXPY_SeqAIJ" 2716 PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str) 2717 { 2718 PetscErrorCode ierr; 2719 Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data; 2720 PetscBLASInt one=1,bnz; 2721 2722 PetscFunctionBegin; 2723 ierr = PetscBLASIntCast(x->nz,&bnz);CHKERRQ(ierr); 2724 if (str == SAME_NONZERO_PATTERN) { 2725 PetscScalar alpha = a; 2726 PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one)); 2727 ierr = MatSeqAIJInvalidateDiagonal(Y);CHKERRQ(ierr); 2728 ierr = PetscObjectStateIncrease((PetscObject)Y);CHKERRQ(ierr); 2729 } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */ 2730 ierr = MatAXPY_Basic(Y,a,X,str);CHKERRQ(ierr); 2731 } else { 2732 Mat B; 2733 PetscInt *nnz; 2734 ierr = PetscMalloc1(Y->rmap->N,&nnz);CHKERRQ(ierr); 2735 ierr = MatCreate(PetscObjectComm((PetscObject)Y),&B);CHKERRQ(ierr); 2736 ierr = PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);CHKERRQ(ierr); 2737 ierr = MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);CHKERRQ(ierr); 2738 ierr = MatSetBlockSizesFromMats(B,Y,Y);CHKERRQ(ierr); 2739 ierr = MatSetType(B,(MatType) ((PetscObject)Y)->type_name);CHKERRQ(ierr); 2740 ierr = MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);CHKERRQ(ierr); 2741 ierr = MatSeqAIJSetPreallocation(B,0,nnz);CHKERRQ(ierr); 2742 ierr = MatAXPY_BasicWithPreallocation(B,Y,a,X,str);CHKERRQ(ierr); 2743 ierr = MatHeaderReplace(Y,&B);CHKERRQ(ierr); 2744 ierr = PetscFree(nnz);CHKERRQ(ierr); 2745 } 2746 PetscFunctionReturn(0); 2747 } 2748 2749 #undef __FUNCT__ 2750 #define __FUNCT__ "MatConjugate_SeqAIJ" 2751 PetscErrorCode MatConjugate_SeqAIJ(Mat mat) 2752 { 2753 #if defined(PETSC_USE_COMPLEX) 2754 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data; 2755 PetscInt i,nz; 2756 PetscScalar *a; 2757 2758 PetscFunctionBegin; 2759 nz = aij->nz; 2760 a = aij->a; 2761 for (i=0; i<nz; i++) a[i] = PetscConj(a[i]); 2762 #else 2763 PetscFunctionBegin; 2764 #endif 2765 PetscFunctionReturn(0); 2766 } 2767 2768 #undef __FUNCT__ 2769 #define __FUNCT__ "MatGetRowMaxAbs_SeqAIJ" 2770 PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[]) 2771 { 2772 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2773 PetscErrorCode ierr; 2774 PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n; 2775 PetscReal atmp; 2776 PetscScalar *x; 2777 MatScalar *aa; 2778 2779 PetscFunctionBegin; 2780 if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2781 aa = a->a; 2782 ai = a->i; 2783 aj = a->j; 2784 2785 ierr = VecSet(v,0.0);CHKERRQ(ierr); 2786 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 2787 ierr = VecGetLocalSize(v,&n);CHKERRQ(ierr); 2788 if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector"); 2789 for (i=0; i<m; i++) { 2790 ncols = ai[1] - ai[0]; ai++; 2791 x[i] = 0.0; 2792 for (j=0; j<ncols; j++) { 2793 atmp = PetscAbsScalar(*aa); 2794 if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;} 2795 aa++; aj++; 2796 } 2797 } 2798 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 2799 PetscFunctionReturn(0); 2800 } 2801 2802 #undef __FUNCT__ 2803 #define __FUNCT__ "MatGetRowMax_SeqAIJ" 2804 PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[]) 2805 { 2806 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2807 PetscErrorCode ierr; 2808 PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n; 2809 PetscScalar *x; 2810 MatScalar *aa; 2811 2812 PetscFunctionBegin; 2813 if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2814 aa = a->a; 2815 ai = a->i; 2816 aj = a->j; 2817 2818 ierr = VecSet(v,0.0);CHKERRQ(ierr); 2819 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 2820 ierr = VecGetLocalSize(v,&n);CHKERRQ(ierr); 2821 if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector"); 2822 for (i=0; i<m; i++) { 2823 ncols = ai[1] - ai[0]; ai++; 2824 if (ncols == A->cmap->n) { /* row is dense */ 2825 x[i] = *aa; if (idx) idx[i] = 0; 2826 } else { /* row is sparse so already KNOW maximum is 0.0 or higher */ 2827 x[i] = 0.0; 2828 if (idx) { 2829 idx[i] = 0; /* in case ncols is zero */ 2830 for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */ 2831 if (aj[j] > j) { 2832 idx[i] = j; 2833 break; 2834 } 2835 } 2836 } 2837 } 2838 for (j=0; j<ncols; j++) { 2839 if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;} 2840 aa++; aj++; 2841 } 2842 } 2843 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 2844 PetscFunctionReturn(0); 2845 } 2846 2847 #undef __FUNCT__ 2848 #define __FUNCT__ "MatGetRowMinAbs_SeqAIJ" 2849 PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[]) 2850 { 2851 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2852 PetscErrorCode ierr; 2853 PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n; 2854 PetscReal atmp; 2855 PetscScalar *x; 2856 MatScalar *aa; 2857 2858 PetscFunctionBegin; 2859 if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2860 aa = a->a; 2861 ai = a->i; 2862 aj = a->j; 2863 2864 ierr = VecSet(v,0.0);CHKERRQ(ierr); 2865 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 2866 ierr = VecGetLocalSize(v,&n);CHKERRQ(ierr); 2867 if (n != A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector, %D vs. %D rows", A->rmap->n, n); 2868 for (i=0; i<m; i++) { 2869 ncols = ai[1] - ai[0]; ai++; 2870 if (ncols) { 2871 /* Get first nonzero */ 2872 for (j = 0; j < ncols; j++) { 2873 atmp = PetscAbsScalar(aa[j]); 2874 if (atmp > 1.0e-12) { 2875 x[i] = atmp; 2876 if (idx) idx[i] = aj[j]; 2877 break; 2878 } 2879 } 2880 if (j == ncols) {x[i] = PetscAbsScalar(*aa); if (idx) idx[i] = *aj;} 2881 } else { 2882 x[i] = 0.0; if (idx) idx[i] = 0; 2883 } 2884 for (j = 0; j < ncols; j++) { 2885 atmp = PetscAbsScalar(*aa); 2886 if (atmp > 1.0e-12 && PetscAbsScalar(x[i]) > atmp) {x[i] = atmp; if (idx) idx[i] = *aj;} 2887 aa++; aj++; 2888 } 2889 } 2890 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 2891 PetscFunctionReturn(0); 2892 } 2893 2894 #undef __FUNCT__ 2895 #define __FUNCT__ "MatGetRowMin_SeqAIJ" 2896 PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[]) 2897 { 2898 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2899 PetscErrorCode ierr; 2900 PetscInt i,j,m = A->rmap->n,ncols,n; 2901 const PetscInt *ai,*aj; 2902 PetscScalar *x; 2903 const MatScalar *aa; 2904 2905 PetscFunctionBegin; 2906 if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2907 aa = a->a; 2908 ai = a->i; 2909 aj = a->j; 2910 2911 ierr = VecSet(v,0.0);CHKERRQ(ierr); 2912 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 2913 ierr = VecGetLocalSize(v,&n);CHKERRQ(ierr); 2914 if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector"); 2915 for (i=0; i<m; i++) { 2916 ncols = ai[1] - ai[0]; ai++; 2917 if (ncols == A->cmap->n) { /* row is dense */ 2918 x[i] = *aa; if (idx) idx[i] = 0; 2919 } else { /* row is sparse so already KNOW minimum is 0.0 or lower */ 2920 x[i] = 0.0; 2921 if (idx) { /* find first implicit 0.0 in the row */ 2922 idx[i] = 0; /* in case ncols is zero */ 2923 for (j=0; j<ncols; j++) { 2924 if (aj[j] > j) { 2925 idx[i] = j; 2926 break; 2927 } 2928 } 2929 } 2930 } 2931 for (j=0; j<ncols; j++) { 2932 if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;} 2933 aa++; aj++; 2934 } 2935 } 2936 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 2937 PetscFunctionReturn(0); 2938 } 2939 2940 #include <petscblaslapack.h> 2941 #include <petsc/private/kernels/blockinvert.h> 2942 2943 #undef __FUNCT__ 2944 #define __FUNCT__ "MatInvertBlockDiagonal_SeqAIJ" 2945 PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values) 2946 { 2947 Mat_SeqAIJ *a = (Mat_SeqAIJ*) A->data; 2948 PetscErrorCode ierr; 2949 PetscInt i,bs = PetscAbs(A->rmap->bs),mbs = A->rmap->n/bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j; 2950 MatScalar *diag,work[25],*v_work; 2951 PetscReal shift = 0.0; 2952 PetscBool zeropivotdetected; 2953 2954 PetscFunctionBegin; 2955 if (a->ibdiagvalid) { 2956 if (values) *values = a->ibdiag; 2957 PetscFunctionReturn(0); 2958 } 2959 ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr); 2960 if (!a->ibdiag) { 2961 ierr = PetscMalloc1(bs2*mbs,&a->ibdiag);CHKERRQ(ierr); 2962 ierr = PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));CHKERRQ(ierr); 2963 } 2964 diag = a->ibdiag; 2965 if (values) *values = a->ibdiag; 2966 /* factor and invert each block */ 2967 switch (bs) { 2968 case 1: 2969 for (i=0; i<mbs; i++) { 2970 ierr = MatGetValues(A,1,&i,1,&i,diag+i);CHKERRQ(ierr); 2971 diag[i] = (PetscScalar)1.0 / (diag[i] + shift); 2972 } 2973 break; 2974 case 2: 2975 for (i=0; i<mbs; i++) { 2976 ij[0] = 2*i; ij[1] = 2*i + 1; 2977 ierr = MatGetValues(A,2,ij,2,ij,diag);CHKERRQ(ierr); 2978 ierr = PetscKernel_A_gets_inverse_A_2(diag,shift,!A->erroriffailure,&zeropivotdetected);CHKERRQ(ierr); 2979 if (zeropivotdetected) break; 2980 ierr = PetscKernel_A_gets_transpose_A_2(diag);CHKERRQ(ierr); 2981 diag += 4; 2982 } 2983 break; 2984 case 3: 2985 for (i=0; i<mbs; i++) { 2986 ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2; 2987 ierr = MatGetValues(A,3,ij,3,ij,diag);CHKERRQ(ierr); 2988 ierr = PetscKernel_A_gets_inverse_A_3(diag,shift,!A->erroriffailure,&zeropivotdetected);CHKERRQ(ierr); 2989 if (zeropivotdetected) break; 2990 ierr = PetscKernel_A_gets_transpose_A_3(diag);CHKERRQ(ierr); 2991 diag += 9; 2992 } 2993 break; 2994 case 4: 2995 for (i=0; i<mbs; i++) { 2996 ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3; 2997 ierr = MatGetValues(A,4,ij,4,ij,diag);CHKERRQ(ierr); 2998 ierr = PetscKernel_A_gets_inverse_A_4(diag,shift,!A->erroriffailure,&zeropivotdetected);CHKERRQ(ierr); 2999 if (zeropivotdetected) break; 3000 ierr = PetscKernel_A_gets_transpose_A_4(diag);CHKERRQ(ierr); 3001 diag += 16; 3002 } 3003 break; 3004 case 5: 3005 for (i=0; i<mbs; i++) { 3006 ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4; 3007 ierr = MatGetValues(A,5,ij,5,ij,diag);CHKERRQ(ierr); 3008 ierr = PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,!A->erroriffailure,&zeropivotdetected);CHKERRQ(ierr); 3009 if (zeropivotdetected) break; 3010 ierr = PetscKernel_A_gets_transpose_A_5(diag);CHKERRQ(ierr); 3011 diag += 25; 3012 } 3013 break; 3014 case 6: 3015 for (i=0; i<mbs; i++) { 3016 ij[0] = 6*i; ij[1] = 6*i + 1; ij[2] = 6*i + 2; ij[3] = 6*i + 3; ij[4] = 6*i + 4; ij[5] = 6*i + 5; 3017 ierr = MatGetValues(A,6,ij,6,ij,diag);CHKERRQ(ierr); 3018 ierr = PetscKernel_A_gets_inverse_A_6(diag,shift,!A->erroriffailure,&zeropivotdetected);CHKERRQ(ierr); 3019 if (zeropivotdetected) break; 3020 ierr = PetscKernel_A_gets_transpose_A_6(diag);CHKERRQ(ierr); 3021 diag += 36; 3022 } 3023 break; 3024 case 7: 3025 for (i=0; i<mbs; i++) { 3026 ij[0] = 7*i; ij[1] = 7*i + 1; ij[2] = 7*i + 2; ij[3] = 7*i + 3; ij[4] = 7*i + 4; ij[5] = 7*i + 5; ij[5] = 7*i + 6; 3027 ierr = MatGetValues(A,7,ij,7,ij,diag);CHKERRQ(ierr); 3028 ierr = PetscKernel_A_gets_inverse_A_7(diag,shift,!A->erroriffailure,&zeropivotdetected);CHKERRQ(ierr); 3029 if (zeropivotdetected) break; 3030 ierr = PetscKernel_A_gets_transpose_A_7(diag);CHKERRQ(ierr); 3031 diag += 49; 3032 } 3033 break; 3034 default: 3035 ierr = PetscMalloc3(bs,&v_work,bs,&v_pivots,bs,&IJ);CHKERRQ(ierr); 3036 for (i=0; i<mbs; i++) { 3037 for (j=0; j<bs; j++) { 3038 IJ[j] = bs*i + j; 3039 } 3040 ierr = MatGetValues(A,bs,IJ,bs,IJ,diag);CHKERRQ(ierr); 3041 ierr = PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work);CHKERRQ(ierr); 3042 ierr = PetscKernel_A_gets_transpose_A_N(diag,bs);CHKERRQ(ierr); 3043 diag += bs2; 3044 } 3045 ierr = PetscFree3(v_work,v_pivots,IJ);CHKERRQ(ierr); 3046 } 3047 a->ibdiagvalid = PETSC_TRUE; 3048 if (zeropivotdetected) { 3049 A->errortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3050 } 3051 PetscFunctionReturn(0); 3052 } 3053 3054 #undef __FUNCT__ 3055 #define __FUNCT__ "MatSetRandom_SeqAIJ" 3056 static PetscErrorCode MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx) 3057 { 3058 PetscErrorCode ierr; 3059 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)x->data; 3060 PetscScalar a; 3061 PetscInt m,n,i,j,col; 3062 3063 PetscFunctionBegin; 3064 if (!x->assembled) { 3065 ierr = MatGetSize(x,&m,&n);CHKERRQ(ierr); 3066 for (i=0; i<m; i++) { 3067 for (j=0; j<aij->imax[i]; j++) { 3068 ierr = PetscRandomGetValue(rctx,&a);CHKERRQ(ierr); 3069 col = (PetscInt)(n*PetscRealPart(a)); 3070 ierr = MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);CHKERRQ(ierr); 3071 } 3072 } 3073 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not yet coded"); 3074 ierr = MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 3075 ierr = MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 3076 PetscFunctionReturn(0); 3077 } 3078 3079 #undef __FUNCT__ 3080 #define __FUNCT__ "MatShift_SeqAIJ" 3081 PetscErrorCode MatShift_SeqAIJ(Mat Y,PetscScalar a) 3082 { 3083 PetscErrorCode ierr; 3084 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)Y->data; 3085 3086 PetscFunctionBegin; 3087 if (!Y->preallocated || !aij->nz) { 3088 ierr = MatSeqAIJSetPreallocation(Y,1,NULL);CHKERRQ(ierr); 3089 } 3090 ierr = MatShift_Basic(Y,a);CHKERRQ(ierr); 3091 PetscFunctionReturn(0); 3092 } 3093 3094 /* -------------------------------------------------------------------*/ 3095 static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ, 3096 MatGetRow_SeqAIJ, 3097 MatRestoreRow_SeqAIJ, 3098 MatMult_SeqAIJ, 3099 /* 4*/ MatMultAdd_SeqAIJ, 3100 MatMultTranspose_SeqAIJ, 3101 MatMultTransposeAdd_SeqAIJ, 3102 0, 3103 0, 3104 0, 3105 /* 10*/ 0, 3106 MatLUFactor_SeqAIJ, 3107 0, 3108 MatSOR_SeqAIJ, 3109 MatTranspose_SeqAIJ, 3110 /*1 5*/ MatGetInfo_SeqAIJ, 3111 MatEqual_SeqAIJ, 3112 MatGetDiagonal_SeqAIJ, 3113 MatDiagonalScale_SeqAIJ, 3114 MatNorm_SeqAIJ, 3115 /* 20*/ 0, 3116 MatAssemblyEnd_SeqAIJ, 3117 MatSetOption_SeqAIJ, 3118 MatZeroEntries_SeqAIJ, 3119 /* 24*/ MatZeroRows_SeqAIJ, 3120 0, 3121 0, 3122 0, 3123 0, 3124 /* 29*/ MatSetUp_SeqAIJ, 3125 0, 3126 0, 3127 0, 3128 0, 3129 /* 34*/ MatDuplicate_SeqAIJ, 3130 0, 3131 0, 3132 MatILUFactor_SeqAIJ, 3133 0, 3134 /* 39*/ MatAXPY_SeqAIJ, 3135 MatGetSubMatrices_SeqAIJ, 3136 MatIncreaseOverlap_SeqAIJ, 3137 MatGetValues_SeqAIJ, 3138 MatCopy_SeqAIJ, 3139 /* 44*/ MatGetRowMax_SeqAIJ, 3140 MatScale_SeqAIJ, 3141 MatShift_SeqAIJ, 3142 MatDiagonalSet_SeqAIJ, 3143 MatZeroRowsColumns_SeqAIJ, 3144 /* 49*/ MatSetRandom_SeqAIJ, 3145 MatGetRowIJ_SeqAIJ, 3146 MatRestoreRowIJ_SeqAIJ, 3147 MatGetColumnIJ_SeqAIJ, 3148 MatRestoreColumnIJ_SeqAIJ, 3149 /* 54*/ MatFDColoringCreate_SeqXAIJ, 3150 0, 3151 0, 3152 MatPermute_SeqAIJ, 3153 0, 3154 /* 59*/ 0, 3155 MatDestroy_SeqAIJ, 3156 MatView_SeqAIJ, 3157 0, 3158 MatMatMatMult_SeqAIJ_SeqAIJ_SeqAIJ, 3159 /* 64*/ MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ, 3160 MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ, 3161 0, 3162 0, 3163 0, 3164 /* 69*/ MatGetRowMaxAbs_SeqAIJ, 3165 MatGetRowMinAbs_SeqAIJ, 3166 0, 3167 MatSetColoring_SeqAIJ, 3168 0, 3169 /* 74*/ MatSetValuesAdifor_SeqAIJ, 3170 MatFDColoringApply_AIJ, 3171 0, 3172 0, 3173 0, 3174 /* 79*/ MatFindZeroDiagonals_SeqAIJ, 3175 0, 3176 0, 3177 0, 3178 MatLoad_SeqAIJ, 3179 /* 84*/ MatIsSymmetric_SeqAIJ, 3180 MatIsHermitian_SeqAIJ, 3181 0, 3182 0, 3183 0, 3184 /* 89*/ MatMatMult_SeqAIJ_SeqAIJ, 3185 MatMatMultSymbolic_SeqAIJ_SeqAIJ, 3186 MatMatMultNumeric_SeqAIJ_SeqAIJ, 3187 MatPtAP_SeqAIJ_SeqAIJ, 3188 MatPtAPSymbolic_SeqAIJ_SeqAIJ_DenseAxpy, 3189 /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ, 3190 MatMatTransposeMult_SeqAIJ_SeqAIJ, 3191 MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ, 3192 MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ, 3193 0, 3194 /* 99*/ 0, 3195 0, 3196 0, 3197 MatConjugate_SeqAIJ, 3198 0, 3199 /*104*/ MatSetValuesRow_SeqAIJ, 3200 MatRealPart_SeqAIJ, 3201 MatImaginaryPart_SeqAIJ, 3202 0, 3203 0, 3204 /*109*/ MatMatSolve_SeqAIJ, 3205 0, 3206 MatGetRowMin_SeqAIJ, 3207 0, 3208 MatMissingDiagonal_SeqAIJ, 3209 /*114*/ 0, 3210 0, 3211 0, 3212 0, 3213 0, 3214 /*119*/ 0, 3215 0, 3216 0, 3217 0, 3218 MatGetMultiProcBlock_SeqAIJ, 3219 /*124*/ MatFindNonzeroRows_SeqAIJ, 3220 MatGetColumnNorms_SeqAIJ, 3221 MatInvertBlockDiagonal_SeqAIJ, 3222 0, 3223 0, 3224 /*129*/ 0, 3225 MatTransposeMatMult_SeqAIJ_SeqAIJ, 3226 MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ, 3227 MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ, 3228 MatTransposeColoringCreate_SeqAIJ, 3229 /*134*/ MatTransColoringApplySpToDen_SeqAIJ, 3230 MatTransColoringApplyDenToSp_SeqAIJ, 3231 MatRARt_SeqAIJ_SeqAIJ, 3232 MatRARtSymbolic_SeqAIJ_SeqAIJ, 3233 MatRARtNumeric_SeqAIJ_SeqAIJ, 3234 /*139*/0, 3235 0, 3236 0, 3237 MatFDColoringSetUp_SeqXAIJ, 3238 MatFindOffBlockDiagonalEntries_SeqAIJ, 3239 /*144*/MatCreateMPIMatConcatenateSeqMat_SeqAIJ 3240 }; 3241 3242 #undef __FUNCT__ 3243 #define __FUNCT__ "MatSeqAIJSetColumnIndices_SeqAIJ" 3244 PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices) 3245 { 3246 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data; 3247 PetscInt i,nz,n; 3248 3249 PetscFunctionBegin; 3250 nz = aij->maxnz; 3251 n = mat->rmap->n; 3252 for (i=0; i<nz; i++) { 3253 aij->j[i] = indices[i]; 3254 } 3255 aij->nz = nz; 3256 for (i=0; i<n; i++) { 3257 aij->ilen[i] = aij->imax[i]; 3258 } 3259 PetscFunctionReturn(0); 3260 } 3261 3262 #undef __FUNCT__ 3263 #define __FUNCT__ "MatSeqAIJSetColumnIndices" 3264 /*@ 3265 MatSeqAIJSetColumnIndices - Set the column indices for all the rows 3266 in the matrix. 3267 3268 Input Parameters: 3269 + mat - the SeqAIJ matrix 3270 - indices - the column indices 3271 3272 Level: advanced 3273 3274 Notes: 3275 This can be called if you have precomputed the nonzero structure of the 3276 matrix and want to provide it to the matrix object to improve the performance 3277 of the MatSetValues() operation. 3278 3279 You MUST have set the correct numbers of nonzeros per row in the call to 3280 MatCreateSeqAIJ(), and the columns indices MUST be sorted. 3281 3282 MUST be called before any calls to MatSetValues(); 3283 3284 The indices should start with zero, not one. 3285 3286 @*/ 3287 PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices) 3288 { 3289 PetscErrorCode ierr; 3290 3291 PetscFunctionBegin; 3292 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3293 PetscValidPointer(indices,2); 3294 ierr = PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));CHKERRQ(ierr); 3295 PetscFunctionReturn(0); 3296 } 3297 3298 /* ----------------------------------------------------------------------------------------*/ 3299 3300 #undef __FUNCT__ 3301 #define __FUNCT__ "MatStoreValues_SeqAIJ" 3302 PetscErrorCode MatStoreValues_SeqAIJ(Mat mat) 3303 { 3304 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data; 3305 PetscErrorCode ierr; 3306 size_t nz = aij->i[mat->rmap->n]; 3307 3308 PetscFunctionBegin; 3309 if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first"); 3310 3311 /* allocate space for values if not already there */ 3312 if (!aij->saved_values) { 3313 ierr = PetscMalloc1(nz+1,&aij->saved_values);CHKERRQ(ierr); 3314 ierr = PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));CHKERRQ(ierr); 3315 } 3316 3317 /* copy values over */ 3318 ierr = PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));CHKERRQ(ierr); 3319 PetscFunctionReturn(0); 3320 } 3321 3322 #undef __FUNCT__ 3323 #define __FUNCT__ "MatStoreValues" 3324 /*@ 3325 MatStoreValues - Stashes a copy of the matrix values; this allows, for 3326 example, reuse of the linear part of a Jacobian, while recomputing the 3327 nonlinear portion. 3328 3329 Collect on Mat 3330 3331 Input Parameters: 3332 . mat - the matrix (currently only AIJ matrices support this option) 3333 3334 Level: advanced 3335 3336 Common Usage, with SNESSolve(): 3337 $ Create Jacobian matrix 3338 $ Set linear terms into matrix 3339 $ Apply boundary conditions to matrix, at this time matrix must have 3340 $ final nonzero structure (i.e. setting the nonlinear terms and applying 3341 $ boundary conditions again will not change the nonzero structure 3342 $ ierr = MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); 3343 $ ierr = MatStoreValues(mat); 3344 $ Call SNESSetJacobian() with matrix 3345 $ In your Jacobian routine 3346 $ ierr = MatRetrieveValues(mat); 3347 $ Set nonlinear terms in matrix 3348 3349 Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself: 3350 $ // build linear portion of Jacobian 3351 $ ierr = MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); 3352 $ ierr = MatStoreValues(mat); 3353 $ loop over nonlinear iterations 3354 $ ierr = MatRetrieveValues(mat); 3355 $ // call MatSetValues(mat,...) to set nonliner portion of Jacobian 3356 $ // call MatAssemblyBegin/End() on matrix 3357 $ Solve linear system with Jacobian 3358 $ endloop 3359 3360 Notes: 3361 Matrix must already be assemblied before calling this routine 3362 Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before 3363 calling this routine. 3364 3365 When this is called multiple times it overwrites the previous set of stored values 3366 and does not allocated additional space. 3367 3368 .seealso: MatRetrieveValues() 3369 3370 @*/ 3371 PetscErrorCode MatStoreValues(Mat mat) 3372 { 3373 PetscErrorCode ierr; 3374 3375 PetscFunctionBegin; 3376 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3377 if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3378 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3379 ierr = PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));CHKERRQ(ierr); 3380 PetscFunctionReturn(0); 3381 } 3382 3383 #undef __FUNCT__ 3384 #define __FUNCT__ "MatRetrieveValues_SeqAIJ" 3385 PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat) 3386 { 3387 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data; 3388 PetscErrorCode ierr; 3389 PetscInt nz = aij->i[mat->rmap->n]; 3390 3391 PetscFunctionBegin; 3392 if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first"); 3393 if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first"); 3394 /* copy values over */ 3395 ierr = PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));CHKERRQ(ierr); 3396 PetscFunctionReturn(0); 3397 } 3398 3399 #undef __FUNCT__ 3400 #define __FUNCT__ "MatRetrieveValues" 3401 /*@ 3402 MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 3403 example, reuse of the linear part of a Jacobian, while recomputing the 3404 nonlinear portion. 3405 3406 Collect on Mat 3407 3408 Input Parameters: 3409 . mat - the matrix (currently on AIJ matrices support this option) 3410 3411 Level: advanced 3412 3413 .seealso: MatStoreValues() 3414 3415 @*/ 3416 PetscErrorCode MatRetrieveValues(Mat mat) 3417 { 3418 PetscErrorCode ierr; 3419 3420 PetscFunctionBegin; 3421 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3422 if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3423 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3424 ierr = PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));CHKERRQ(ierr); 3425 PetscFunctionReturn(0); 3426 } 3427 3428 3429 /* --------------------------------------------------------------------------------*/ 3430 #undef __FUNCT__ 3431 #define __FUNCT__ "MatCreateSeqAIJ" 3432 /*@C 3433 MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format 3434 (the default parallel PETSc format). For good matrix assembly performance 3435 the user should preallocate the matrix storage by setting the parameter nz 3436 (or the array nnz). By setting these parameters accurately, performance 3437 during matrix assembly can be increased by more than a factor of 50. 3438 3439 Collective on MPI_Comm 3440 3441 Input Parameters: 3442 + comm - MPI communicator, set to PETSC_COMM_SELF 3443 . m - number of rows 3444 . n - number of columns 3445 . nz - number of nonzeros per row (same for all rows) 3446 - nnz - array containing the number of nonzeros in the various rows 3447 (possibly different for each row) or NULL 3448 3449 Output Parameter: 3450 . A - the matrix 3451 3452 It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(), 3453 MatXXXXSetPreallocation() paradgm instead of this routine directly. 3454 [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation] 3455 3456 Notes: 3457 If nnz is given then nz is ignored 3458 3459 The AIJ format (also called the Yale sparse matrix format or 3460 compressed row storage), is fully compatible with standard Fortran 77 3461 storage. That is, the stored row and column indices can begin at 3462 either one (as in Fortran) or zero. See the users' manual for details. 3463 3464 Specify the preallocated storage with either nz or nnz (not both). 3465 Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory 3466 allocation. For large problems you MUST preallocate memory or you 3467 will get TERRIBLE performance, see the users' manual chapter on matrices. 3468 3469 By default, this format uses inodes (identical nodes) when possible, to 3470 improve numerical efficiency of matrix-vector products and solves. We 3471 search for consecutive rows with the same nonzero structure, thereby 3472 reusing matrix information to achieve increased efficiency. 3473 3474 Options Database Keys: 3475 + -mat_no_inode - Do not use inodes 3476 - -mat_inode_limit <limit> - Sets inode limit (max limit=5) 3477 3478 Level: intermediate 3479 3480 .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays() 3481 3482 @*/ 3483 PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A) 3484 { 3485 PetscErrorCode ierr; 3486 3487 PetscFunctionBegin; 3488 ierr = MatCreate(comm,A);CHKERRQ(ierr); 3489 ierr = MatSetSizes(*A,m,n,m,n);CHKERRQ(ierr); 3490 ierr = MatSetType(*A,MATSEQAIJ);CHKERRQ(ierr); 3491 ierr = MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);CHKERRQ(ierr); 3492 PetscFunctionReturn(0); 3493 } 3494 3495 #undef __FUNCT__ 3496 #define __FUNCT__ "MatSeqAIJSetPreallocation" 3497 /*@C 3498 MatSeqAIJSetPreallocation - For good matrix assembly performance 3499 the user should preallocate the matrix storage by setting the parameter nz 3500 (or the array nnz). By setting these parameters accurately, performance 3501 during matrix assembly can be increased by more than a factor of 50. 3502 3503 Collective on MPI_Comm 3504 3505 Input Parameters: 3506 + B - The matrix 3507 . nz - number of nonzeros per row (same for all rows) 3508 - nnz - array containing the number of nonzeros in the various rows 3509 (possibly different for each row) or NULL 3510 3511 Notes: 3512 If nnz is given then nz is ignored 3513 3514 The AIJ format (also called the Yale sparse matrix format or 3515 compressed row storage), is fully compatible with standard Fortran 77 3516 storage. That is, the stored row and column indices can begin at 3517 either one (as in Fortran) or zero. See the users' manual for details. 3518 3519 Specify the preallocated storage with either nz or nnz (not both). 3520 Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory 3521 allocation. For large problems you MUST preallocate memory or you 3522 will get TERRIBLE performance, see the users' manual chapter on matrices. 3523 3524 You can call MatGetInfo() to get information on how effective the preallocation was; 3525 for example the fields mallocs,nz_allocated,nz_used,nz_unneeded; 3526 You can also run with the option -info and look for messages with the string 3527 malloc in them to see if additional memory allocation was needed. 3528 3529 Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix 3530 entries or columns indices 3531 3532 By default, this format uses inodes (identical nodes) when possible, to 3533 improve numerical efficiency of matrix-vector products and solves. We 3534 search for consecutive rows with the same nonzero structure, thereby 3535 reusing matrix information to achieve increased efficiency. 3536 3537 Options Database Keys: 3538 + -mat_no_inode - Do not use inodes 3539 . -mat_inode_limit <limit> - Sets inode limit (max limit=5) 3540 - -mat_aij_oneindex - Internally use indexing starting at 1 3541 rather than 0. Note that when calling MatSetValues(), 3542 the user still MUST index entries starting at 0! 3543 3544 Level: intermediate 3545 3546 .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo() 3547 3548 @*/ 3549 PetscErrorCode MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[]) 3550 { 3551 PetscErrorCode ierr; 3552 3553 PetscFunctionBegin; 3554 PetscValidHeaderSpecific(B,MAT_CLASSID,1); 3555 PetscValidType(B,1); 3556 ierr = PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));CHKERRQ(ierr); 3557 PetscFunctionReturn(0); 3558 } 3559 3560 #undef __FUNCT__ 3561 #define __FUNCT__ "MatSeqAIJSetPreallocation_SeqAIJ" 3562 PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz) 3563 { 3564 Mat_SeqAIJ *b; 3565 PetscBool skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE; 3566 PetscErrorCode ierr; 3567 PetscInt i; 3568 3569 PetscFunctionBegin; 3570 if (nz >= 0 || nnz) realalloc = PETSC_TRUE; 3571 if (nz == MAT_SKIP_ALLOCATION) { 3572 skipallocation = PETSC_TRUE; 3573 nz = 0; 3574 } 3575 3576 ierr = PetscLayoutSetUp(B->rmap);CHKERRQ(ierr); 3577 ierr = PetscLayoutSetUp(B->cmap);CHKERRQ(ierr); 3578 3579 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5; 3580 if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz); 3581 if (nnz) { 3582 for (i=0; i<B->rmap->n; i++) { 3583 if (nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %D value %D",i,nnz[i]); 3584 if (nnz[i] > B->cmap->n) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %D value %d rowlength %D",i,nnz[i],B->cmap->n); 3585 } 3586 } 3587 3588 B->preallocated = PETSC_TRUE; 3589 3590 b = (Mat_SeqAIJ*)B->data; 3591 3592 if (!skipallocation) { 3593 if (!b->imax) { 3594 ierr = PetscMalloc2(B->rmap->n,&b->imax,B->rmap->n,&b->ilen);CHKERRQ(ierr); 3595 ierr = PetscLogObjectMemory((PetscObject)B,2*B->rmap->n*sizeof(PetscInt));CHKERRQ(ierr); 3596 } 3597 if (!nnz) { 3598 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10; 3599 else if (nz < 0) nz = 1; 3600 for (i=0; i<B->rmap->n; i++) b->imax[i] = nz; 3601 nz = nz*B->rmap->n; 3602 } else { 3603 nz = 0; 3604 for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];} 3605 } 3606 /* b->ilen will count nonzeros in each row so far. */ 3607 for (i=0; i<B->rmap->n; i++) b->ilen[i] = 0; 3608 3609 /* allocate the matrix space */ 3610 /* FIXME: should B's old memory be unlogged? */ 3611 ierr = MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);CHKERRQ(ierr); 3612 ierr = PetscMalloc3(nz,&b->a,nz,&b->j,B->rmap->n+1,&b->i);CHKERRQ(ierr); 3613 ierr = PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));CHKERRQ(ierr); 3614 b->i[0] = 0; 3615 for (i=1; i<B->rmap->n+1; i++) { 3616 b->i[i] = b->i[i-1] + b->imax[i-1]; 3617 } 3618 b->singlemalloc = PETSC_TRUE; 3619 b->free_a = PETSC_TRUE; 3620 b->free_ij = PETSC_TRUE; 3621 } else { 3622 b->free_a = PETSC_FALSE; 3623 b->free_ij = PETSC_FALSE; 3624 } 3625 3626 b->nz = 0; 3627 b->maxnz = nz; 3628 B->info.nz_unneeded = (double)b->maxnz; 3629 if (realalloc) { 3630 ierr = MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);CHKERRQ(ierr); 3631 } 3632 PetscFunctionReturn(0); 3633 } 3634 3635 #undef __FUNCT__ 3636 #define __FUNCT__ "MatSeqAIJSetPreallocationCSR" 3637 /*@ 3638 MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format. 3639 3640 Input Parameters: 3641 + B - the matrix 3642 . i - the indices into j for the start of each row (starts with zero) 3643 . j - the column indices for each row (starts with zero) these must be sorted for each row 3644 - v - optional values in the matrix 3645 3646 Level: developer 3647 3648 The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays() 3649 3650 .keywords: matrix, aij, compressed row, sparse, sequential 3651 3652 .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), SeqAIJ 3653 @*/ 3654 PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[]) 3655 { 3656 PetscErrorCode ierr; 3657 3658 PetscFunctionBegin; 3659 PetscValidHeaderSpecific(B,MAT_CLASSID,1); 3660 PetscValidType(B,1); 3661 ierr = PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));CHKERRQ(ierr); 3662 PetscFunctionReturn(0); 3663 } 3664 3665 #undef __FUNCT__ 3666 #define __FUNCT__ "MatSeqAIJSetPreallocationCSR_SeqAIJ" 3667 PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[]) 3668 { 3669 PetscInt i; 3670 PetscInt m,n; 3671 PetscInt nz; 3672 PetscInt *nnz, nz_max = 0; 3673 PetscScalar *values; 3674 PetscErrorCode ierr; 3675 3676 PetscFunctionBegin; 3677 if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]); 3678 3679 ierr = PetscLayoutSetUp(B->rmap);CHKERRQ(ierr); 3680 ierr = PetscLayoutSetUp(B->cmap);CHKERRQ(ierr); 3681 3682 ierr = MatGetSize(B, &m, &n);CHKERRQ(ierr); 3683 ierr = PetscMalloc1(m+1, &nnz);CHKERRQ(ierr); 3684 for (i = 0; i < m; i++) { 3685 nz = Ii[i+1]- Ii[i]; 3686 nz_max = PetscMax(nz_max, nz); 3687 if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz); 3688 nnz[i] = nz; 3689 } 3690 ierr = MatSeqAIJSetPreallocation(B, 0, nnz);CHKERRQ(ierr); 3691 ierr = PetscFree(nnz);CHKERRQ(ierr); 3692 3693 if (v) { 3694 values = (PetscScalar*) v; 3695 } else { 3696 ierr = PetscCalloc1(nz_max, &values);CHKERRQ(ierr); 3697 } 3698 3699 for (i = 0; i < m; i++) { 3700 nz = Ii[i+1] - Ii[i]; 3701 ierr = MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);CHKERRQ(ierr); 3702 } 3703 3704 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 3705 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 3706 3707 if (!v) { 3708 ierr = PetscFree(values);CHKERRQ(ierr); 3709 } 3710 ierr = MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);CHKERRQ(ierr); 3711 PetscFunctionReturn(0); 3712 } 3713 3714 #include <../src/mat/impls/dense/seq/dense.h> 3715 #include <petsc/private/kernels/petscaxpy.h> 3716 3717 #undef __FUNCT__ 3718 #define __FUNCT__ "MatMatMultNumeric_SeqDense_SeqAIJ" 3719 /* 3720 Computes (B'*A')' since computing B*A directly is untenable 3721 3722 n p p 3723 ( ) ( ) ( ) 3724 m ( A ) * n ( B ) = m ( C ) 3725 ( ) ( ) ( ) 3726 3727 */ 3728 PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C) 3729 { 3730 PetscErrorCode ierr; 3731 Mat_SeqDense *sub_a = (Mat_SeqDense*)A->data; 3732 Mat_SeqAIJ *sub_b = (Mat_SeqAIJ*)B->data; 3733 Mat_SeqDense *sub_c = (Mat_SeqDense*)C->data; 3734 PetscInt i,n,m,q,p; 3735 const PetscInt *ii,*idx; 3736 const PetscScalar *b,*a,*a_q; 3737 PetscScalar *c,*c_q; 3738 3739 PetscFunctionBegin; 3740 m = A->rmap->n; 3741 n = A->cmap->n; 3742 p = B->cmap->n; 3743 a = sub_a->v; 3744 b = sub_b->a; 3745 c = sub_c->v; 3746 ierr = PetscMemzero(c,m*p*sizeof(PetscScalar));CHKERRQ(ierr); 3747 3748 ii = sub_b->i; 3749 idx = sub_b->j; 3750 for (i=0; i<n; i++) { 3751 q = ii[i+1] - ii[i]; 3752 while (q-->0) { 3753 c_q = c + m*(*idx); 3754 a_q = a + m*i; 3755 PetscKernelAXPY(c_q,*b,a_q,m); 3756 idx++; 3757 b++; 3758 } 3759 } 3760 PetscFunctionReturn(0); 3761 } 3762 3763 #undef __FUNCT__ 3764 #define __FUNCT__ "MatMatMultSymbolic_SeqDense_SeqAIJ" 3765 PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C) 3766 { 3767 PetscErrorCode ierr; 3768 PetscInt m=A->rmap->n,n=B->cmap->n; 3769 Mat Cmat; 3770 3771 PetscFunctionBegin; 3772 if (A->cmap->n != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"A->cmap->n %D != B->rmap->n %D\n",A->cmap->n,B->rmap->n); 3773 ierr = MatCreate(PetscObjectComm((PetscObject)A),&Cmat);CHKERRQ(ierr); 3774 ierr = MatSetSizes(Cmat,m,n,m,n);CHKERRQ(ierr); 3775 ierr = MatSetBlockSizesFromMats(Cmat,A,B);CHKERRQ(ierr); 3776 ierr = MatSetType(Cmat,MATSEQDENSE);CHKERRQ(ierr); 3777 ierr = MatSeqDenseSetPreallocation(Cmat,NULL);CHKERRQ(ierr); 3778 3779 Cmat->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ; 3780 3781 *C = Cmat; 3782 PetscFunctionReturn(0); 3783 } 3784 3785 /* ----------------------------------------------------------------*/ 3786 #undef __FUNCT__ 3787 #define __FUNCT__ "MatMatMult_SeqDense_SeqAIJ" 3788 PetscErrorCode MatMatMult_SeqDense_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 3789 { 3790 PetscErrorCode ierr; 3791 3792 PetscFunctionBegin; 3793 if (scall == MAT_INITIAL_MATRIX) { 3794 ierr = PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 3795 ierr = MatMatMultSymbolic_SeqDense_SeqAIJ(A,B,fill,C);CHKERRQ(ierr); 3796 ierr = PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 3797 } 3798 ierr = PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 3799 ierr = MatMatMultNumeric_SeqDense_SeqAIJ(A,B,*C);CHKERRQ(ierr); 3800 ierr = PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 3801 PetscFunctionReturn(0); 3802 } 3803 3804 3805 /*MC 3806 MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 3807 based on compressed sparse row format. 3808 3809 Options Database Keys: 3810 . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions() 3811 3812 Level: beginner 3813 3814 .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType 3815 M*/ 3816 3817 /*MC 3818 MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices. 3819 3820 This matrix type is identical to MATSEQAIJ when constructed with a single process communicator, 3821 and MATMPIAIJ otherwise. As a result, for single process communicators, 3822 MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported 3823 for communicators controlling multiple processes. It is recommended that you call both of 3824 the above preallocation routines for simplicity. 3825 3826 Options Database Keys: 3827 . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions() 3828 3829 Developer Notes: Subclasses include MATAIJCUSP, MATAIJPERM, MATAIJCRL, and also automatically switches over to use inodes when 3830 enough exist. 3831 3832 Level: beginner 3833 3834 .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ 3835 M*/ 3836 3837 /*MC 3838 MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices. 3839 3840 This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator, 3841 and MATMPIAIJCRL otherwise. As a result, for single process communicators, 3842 MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported 3843 for communicators controlling multiple processes. It is recommended that you call both of 3844 the above preallocation routines for simplicity. 3845 3846 Options Database Keys: 3847 . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions() 3848 3849 Level: beginner 3850 3851 .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL 3852 M*/ 3853 3854 PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*); 3855 #if defined(PETSC_HAVE_ELEMENTAL) 3856 PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat,MatType,MatReuse,Mat*); 3857 #endif 3858 PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqDense(Mat,MatType,MatReuse,Mat*); 3859 3860 #if defined(PETSC_HAVE_MATLAB_ENGINE) 3861 PETSC_EXTERN PetscErrorCode MatlabEnginePut_SeqAIJ(PetscObject,void*); 3862 PETSC_EXTERN PetscErrorCode MatlabEngineGet_SeqAIJ(PetscObject,void*); 3863 #endif 3864 3865 3866 #undef __FUNCT__ 3867 #define __FUNCT__ "MatSeqAIJGetArray" 3868 /*@C 3869 MatSeqAIJGetArray - gives access to the array where the data for a SeqSeqAIJ matrix is stored 3870 3871 Not Collective 3872 3873 Input Parameter: 3874 . mat - a MATSEQAIJ matrix 3875 3876 Output Parameter: 3877 . array - pointer to the data 3878 3879 Level: intermediate 3880 3881 .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90() 3882 @*/ 3883 PetscErrorCode MatSeqAIJGetArray(Mat A,PetscScalar **array) 3884 { 3885 PetscErrorCode ierr; 3886 3887 PetscFunctionBegin; 3888 ierr = PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));CHKERRQ(ierr); 3889 PetscFunctionReturn(0); 3890 } 3891 3892 #undef __FUNCT__ 3893 #define __FUNCT__ "MatSeqAIJGetMaxRowNonzeros" 3894 /*@C 3895 MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row 3896 3897 Not Collective 3898 3899 Input Parameter: 3900 . mat - a MATSEQAIJ matrix 3901 3902 Output Parameter: 3903 . nz - the maximum number of nonzeros in any row 3904 3905 Level: intermediate 3906 3907 .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90() 3908 @*/ 3909 PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A,PetscInt *nz) 3910 { 3911 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)A->data; 3912 3913 PetscFunctionBegin; 3914 *nz = aij->rmax; 3915 PetscFunctionReturn(0); 3916 } 3917 3918 #undef __FUNCT__ 3919 #define __FUNCT__ "MatSeqAIJRestoreArray" 3920 /*@C 3921 MatSeqAIJRestoreArray - returns access to the array where the data for a MATSEQAIJ matrix is stored obtained by MatSeqAIJGetArray() 3922 3923 Not Collective 3924 3925 Input Parameters: 3926 . mat - a MATSEQAIJ matrix 3927 . array - pointer to the data 3928 3929 Level: intermediate 3930 3931 .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90() 3932 @*/ 3933 PetscErrorCode MatSeqAIJRestoreArray(Mat A,PetscScalar **array) 3934 { 3935 PetscErrorCode ierr; 3936 3937 PetscFunctionBegin; 3938 ierr = PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));CHKERRQ(ierr); 3939 PetscFunctionReturn(0); 3940 } 3941 3942 #undef __FUNCT__ 3943 #define __FUNCT__ "MatCreate_SeqAIJ" 3944 PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B) 3945 { 3946 Mat_SeqAIJ *b; 3947 PetscErrorCode ierr; 3948 PetscMPIInt size; 3949 3950 PetscFunctionBegin; 3951 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);CHKERRQ(ierr); 3952 if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1"); 3953 3954 ierr = PetscNewLog(B,&b);CHKERRQ(ierr); 3955 3956 B->data = (void*)b; 3957 3958 ierr = PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 3959 3960 b->row = 0; 3961 b->col = 0; 3962 b->icol = 0; 3963 b->reallocs = 0; 3964 b->ignorezeroentries = PETSC_FALSE; 3965 b->roworiented = PETSC_TRUE; 3966 b->nonew = 0; 3967 b->diag = 0; 3968 b->solve_work = 0; 3969 B->spptr = 0; 3970 b->saved_values = 0; 3971 b->idiag = 0; 3972 b->mdiag = 0; 3973 b->ssor_work = 0; 3974 b->omega = 1.0; 3975 b->fshift = 0.0; 3976 b->idiagvalid = PETSC_FALSE; 3977 b->ibdiagvalid = PETSC_FALSE; 3978 b->keepnonzeropattern = PETSC_FALSE; 3979 3980 ierr = PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);CHKERRQ(ierr); 3981 ierr = PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);CHKERRQ(ierr); 3982 ierr = PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);CHKERRQ(ierr); 3983 3984 #if defined(PETSC_HAVE_MATLAB_ENGINE) 3985 ierr = PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);CHKERRQ(ierr); 3986 ierr = PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);CHKERRQ(ierr); 3987 #endif 3988 3989 ierr = PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);CHKERRQ(ierr); 3990 ierr = PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);CHKERRQ(ierr); 3991 ierr = PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);CHKERRQ(ierr); 3992 ierr = PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);CHKERRQ(ierr); 3993 ierr = PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);CHKERRQ(ierr); 3994 ierr = PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);CHKERRQ(ierr); 3995 ierr = PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);CHKERRQ(ierr); 3996 #if defined(PETSC_HAVE_ELEMENTAL) 3997 ierr = PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_elemental_C",MatConvert_SeqAIJ_Elemental);CHKERRQ(ierr); 3998 #endif 3999 ierr = PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqdense_C",MatConvert_SeqAIJ_SeqDense);CHKERRQ(ierr); 4000 ierr = PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);CHKERRQ(ierr); 4001 ierr = PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);CHKERRQ(ierr); 4002 ierr = PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);CHKERRQ(ierr); 4003 ierr = PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);CHKERRQ(ierr); 4004 ierr = PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);CHKERRQ(ierr); 4005 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMatMult_seqdense_seqaij_C",MatMatMult_SeqDense_SeqAIJ);CHKERRQ(ierr); 4006 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMatMultSymbolic_seqdense_seqaij_C",MatMatMultSymbolic_SeqDense_SeqAIJ);CHKERRQ(ierr); 4007 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMatMultNumeric_seqdense_seqaij_C",MatMatMultNumeric_SeqDense_SeqAIJ);CHKERRQ(ierr); 4008 ierr = MatCreate_SeqAIJ_Inode(B);CHKERRQ(ierr); 4009 ierr = PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);CHKERRQ(ierr); 4010 PetscFunctionReturn(0); 4011 } 4012 4013 #undef __FUNCT__ 4014 #define __FUNCT__ "MatDuplicateNoCreate_SeqAIJ" 4015 /* 4016 Given a matrix generated with MatGetFactor() duplicates all the information in A into B 4017 */ 4018 PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace) 4019 { 4020 Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data; 4021 PetscErrorCode ierr; 4022 PetscInt i,m = A->rmap->n; 4023 4024 PetscFunctionBegin; 4025 c = (Mat_SeqAIJ*)C->data; 4026 4027 C->factortype = A->factortype; 4028 c->row = 0; 4029 c->col = 0; 4030 c->icol = 0; 4031 c->reallocs = 0; 4032 4033 C->assembled = PETSC_TRUE; 4034 4035 ierr = PetscLayoutReference(A->rmap,&C->rmap);CHKERRQ(ierr); 4036 ierr = PetscLayoutReference(A->cmap,&C->cmap);CHKERRQ(ierr); 4037 4038 ierr = PetscMalloc2(m,&c->imax,m,&c->ilen);CHKERRQ(ierr); 4039 ierr = PetscLogObjectMemory((PetscObject)C, 2*m*sizeof(PetscInt));CHKERRQ(ierr); 4040 for (i=0; i<m; i++) { 4041 c->imax[i] = a->imax[i]; 4042 c->ilen[i] = a->ilen[i]; 4043 } 4044 4045 /* allocate the matrix space */ 4046 if (mallocmatspace) { 4047 ierr = PetscMalloc3(a->i[m],&c->a,a->i[m],&c->j,m+1,&c->i);CHKERRQ(ierr); 4048 ierr = PetscLogObjectMemory((PetscObject)C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));CHKERRQ(ierr); 4049 4050 c->singlemalloc = PETSC_TRUE; 4051 4052 ierr = PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));CHKERRQ(ierr); 4053 if (m > 0) { 4054 ierr = PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));CHKERRQ(ierr); 4055 if (cpvalues == MAT_COPY_VALUES) { 4056 ierr = PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));CHKERRQ(ierr); 4057 } else { 4058 ierr = PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));CHKERRQ(ierr); 4059 } 4060 } 4061 } 4062 4063 c->ignorezeroentries = a->ignorezeroentries; 4064 c->roworiented = a->roworiented; 4065 c->nonew = a->nonew; 4066 if (a->diag) { 4067 ierr = PetscMalloc1(m+1,&c->diag);CHKERRQ(ierr); 4068 ierr = PetscLogObjectMemory((PetscObject)C,(m+1)*sizeof(PetscInt));CHKERRQ(ierr); 4069 for (i=0; i<m; i++) { 4070 c->diag[i] = a->diag[i]; 4071 } 4072 } else c->diag = 0; 4073 4074 c->solve_work = 0; 4075 c->saved_values = 0; 4076 c->idiag = 0; 4077 c->ssor_work = 0; 4078 c->keepnonzeropattern = a->keepnonzeropattern; 4079 c->free_a = PETSC_TRUE; 4080 c->free_ij = PETSC_TRUE; 4081 4082 c->rmax = a->rmax; 4083 c->nz = a->nz; 4084 c->maxnz = a->nz; /* Since we allocate exactly the right amount */ 4085 C->preallocated = PETSC_TRUE; 4086 4087 c->compressedrow.use = a->compressedrow.use; 4088 c->compressedrow.nrows = a->compressedrow.nrows; 4089 if (a->compressedrow.use) { 4090 i = a->compressedrow.nrows; 4091 ierr = PetscMalloc2(i+1,&c->compressedrow.i,i,&c->compressedrow.rindex);CHKERRQ(ierr); 4092 ierr = PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));CHKERRQ(ierr); 4093 ierr = PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));CHKERRQ(ierr); 4094 } else { 4095 c->compressedrow.use = PETSC_FALSE; 4096 c->compressedrow.i = NULL; 4097 c->compressedrow.rindex = NULL; 4098 } 4099 c->nonzerorowcnt = a->nonzerorowcnt; 4100 C->nonzerostate = A->nonzerostate; 4101 4102 ierr = MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);CHKERRQ(ierr); 4103 ierr = PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);CHKERRQ(ierr); 4104 PetscFunctionReturn(0); 4105 } 4106 4107 #undef __FUNCT__ 4108 #define __FUNCT__ "MatDuplicate_SeqAIJ" 4109 PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B) 4110 { 4111 PetscErrorCode ierr; 4112 4113 PetscFunctionBegin; 4114 ierr = MatCreate(PetscObjectComm((PetscObject)A),B);CHKERRQ(ierr); 4115 ierr = MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);CHKERRQ(ierr); 4116 if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) { 4117 ierr = MatSetBlockSizesFromMats(*B,A,A);CHKERRQ(ierr); 4118 } 4119 ierr = MatSetType(*B,((PetscObject)A)->type_name);CHKERRQ(ierr); 4120 ierr = MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);CHKERRQ(ierr); 4121 PetscFunctionReturn(0); 4122 } 4123 4124 #undef __FUNCT__ 4125 #define __FUNCT__ "MatLoad_SeqAIJ" 4126 PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer) 4127 { 4128 Mat_SeqAIJ *a; 4129 PetscErrorCode ierr; 4130 PetscInt i,sum,nz,header[4],*rowlengths = 0,M,N,rows,cols; 4131 int fd; 4132 PetscMPIInt size; 4133 MPI_Comm comm; 4134 PetscInt bs = newMat->rmap->bs; 4135 4136 PetscFunctionBegin; 4137 /* force binary viewer to load .info file if it has not yet done so */ 4138 ierr = PetscViewerSetUp(viewer);CHKERRQ(ierr); 4139 ierr = PetscObjectGetComm((PetscObject)viewer,&comm);CHKERRQ(ierr); 4140 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 4141 if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"view must have one processor"); 4142 4143 ierr = PetscOptionsBegin(comm,NULL,"Options for loading SEQAIJ matrix","Mat");CHKERRQ(ierr); 4144 ierr = PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);CHKERRQ(ierr); 4145 ierr = PetscOptionsEnd();CHKERRQ(ierr); 4146 if (bs < 0) bs = 1; 4147 ierr = MatSetBlockSize(newMat,bs);CHKERRQ(ierr); 4148 4149 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 4150 ierr = PetscBinaryRead(fd,header,4,PETSC_INT);CHKERRQ(ierr); 4151 if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file"); 4152 M = header[1]; N = header[2]; nz = header[3]; 4153 4154 if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ"); 4155 4156 /* read in row lengths */ 4157 ierr = PetscMalloc1(M,&rowlengths);CHKERRQ(ierr); 4158 ierr = PetscBinaryRead(fd,rowlengths,M,PETSC_INT);CHKERRQ(ierr); 4159 4160 /* check if sum of rowlengths is same as nz */ 4161 for (i=0,sum=0; i< M; i++) sum +=rowlengths[i]; 4162 if (sum != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_READ,"Inconsistant matrix data in file. no-nonzeros = %dD, sum-row-lengths = %D\n",nz,sum); 4163 4164 /* set global size if not set already*/ 4165 if (newMat->rmap->n < 0 && newMat->rmap->N < 0 && newMat->cmap->n < 0 && newMat->cmap->N < 0) { 4166 ierr = MatSetSizes(newMat,PETSC_DECIDE,PETSC_DECIDE,M,N);CHKERRQ(ierr); 4167 } else { 4168 /* if sizes and type are already set, check if the matrix global sizes are correct */ 4169 ierr = MatGetSize(newMat,&rows,&cols);CHKERRQ(ierr); 4170 if (rows < 0 && cols < 0) { /* user might provide local size instead of global size */ 4171 ierr = MatGetLocalSize(newMat,&rows,&cols);CHKERRQ(ierr); 4172 } 4173 if (M != rows || N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%D, %D) than the input matrix (%D, %D)",M,N,rows,cols); 4174 } 4175 ierr = MatSeqAIJSetPreallocation_SeqAIJ(newMat,0,rowlengths);CHKERRQ(ierr); 4176 a = (Mat_SeqAIJ*)newMat->data; 4177 4178 ierr = PetscBinaryRead(fd,a->j,nz,PETSC_INT);CHKERRQ(ierr); 4179 4180 /* read in nonzero values */ 4181 ierr = PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);CHKERRQ(ierr); 4182 4183 /* set matrix "i" values */ 4184 a->i[0] = 0; 4185 for (i=1; i<= M; i++) { 4186 a->i[i] = a->i[i-1] + rowlengths[i-1]; 4187 a->ilen[i-1] = rowlengths[i-1]; 4188 } 4189 ierr = PetscFree(rowlengths);CHKERRQ(ierr); 4190 4191 ierr = MatAssemblyBegin(newMat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 4192 ierr = MatAssemblyEnd(newMat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 4193 PetscFunctionReturn(0); 4194 } 4195 4196 #undef __FUNCT__ 4197 #define __FUNCT__ "MatEqual_SeqAIJ" 4198 PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg) 4199 { 4200 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data; 4201 PetscErrorCode ierr; 4202 #if defined(PETSC_USE_COMPLEX) 4203 PetscInt k; 4204 #endif 4205 4206 PetscFunctionBegin; 4207 /* If the matrix dimensions are not equal,or no of nonzeros */ 4208 if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) { 4209 *flg = PETSC_FALSE; 4210 PetscFunctionReturn(0); 4211 } 4212 4213 /* if the a->i are the same */ 4214 ierr = PetscMemcmp(a->i,b->i,(A->rmap->n+1)*sizeof(PetscInt),flg);CHKERRQ(ierr); 4215 if (!*flg) PetscFunctionReturn(0); 4216 4217 /* if a->j are the same */ 4218 ierr = PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);CHKERRQ(ierr); 4219 if (!*flg) PetscFunctionReturn(0); 4220 4221 /* if a->a are the same */ 4222 #if defined(PETSC_USE_COMPLEX) 4223 for (k=0; k<a->nz; k++) { 4224 if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) { 4225 *flg = PETSC_FALSE; 4226 PetscFunctionReturn(0); 4227 } 4228 } 4229 #else 4230 ierr = PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);CHKERRQ(ierr); 4231 #endif 4232 PetscFunctionReturn(0); 4233 } 4234 4235 #undef __FUNCT__ 4236 #define __FUNCT__ "MatCreateSeqAIJWithArrays" 4237 /*@ 4238 MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format) 4239 provided by the user. 4240 4241 Collective on MPI_Comm 4242 4243 Input Parameters: 4244 + comm - must be an MPI communicator of size 1 4245 . m - number of rows 4246 . n - number of columns 4247 . i - row indices 4248 . j - column indices 4249 - a - matrix values 4250 4251 Output Parameter: 4252 . mat - the matrix 4253 4254 Level: intermediate 4255 4256 Notes: 4257 The i, j, and a arrays are not copied by this routine, the user must free these arrays 4258 once the matrix is destroyed and not before 4259 4260 You cannot set new nonzero locations into this matrix, that will generate an error. 4261 4262 The i and j indices are 0 based 4263 4264 The format which is used for the sparse matrix input, is equivalent to a 4265 row-major ordering.. i.e for the following matrix, the input data expected is 4266 as shown 4267 4268 $ 1 0 0 4269 $ 2 0 3 4270 $ 4 5 6 4271 $ 4272 $ i = {0,1,3,6} [size = nrow+1 = 3+1] 4273 $ j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row 4274 $ v = {1,2,3,4,5,6} [size = 6] 4275 4276 4277 .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR() 4278 4279 @*/ 4280 PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt *i,PetscInt *j,PetscScalar *a,Mat *mat) 4281 { 4282 PetscErrorCode ierr; 4283 PetscInt ii; 4284 Mat_SeqAIJ *aij; 4285 #if defined(PETSC_USE_DEBUG) 4286 PetscInt jj; 4287 #endif 4288 4289 PetscFunctionBegin; 4290 if (i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0"); 4291 ierr = MatCreate(comm,mat);CHKERRQ(ierr); 4292 ierr = MatSetSizes(*mat,m,n,m,n);CHKERRQ(ierr); 4293 /* ierr = MatSetBlockSizes(*mat,,);CHKERRQ(ierr); */ 4294 ierr = MatSetType(*mat,MATSEQAIJ);CHKERRQ(ierr); 4295 ierr = MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);CHKERRQ(ierr); 4296 aij = (Mat_SeqAIJ*)(*mat)->data; 4297 ierr = PetscMalloc2(m,&aij->imax,m,&aij->ilen);CHKERRQ(ierr); 4298 4299 aij->i = i; 4300 aij->j = j; 4301 aij->a = a; 4302 aij->singlemalloc = PETSC_FALSE; 4303 aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/ 4304 aij->free_a = PETSC_FALSE; 4305 aij->free_ij = PETSC_FALSE; 4306 4307 for (ii=0; ii<m; ii++) { 4308 aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii]; 4309 #if defined(PETSC_USE_DEBUG) 4310 if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %D length = %D",ii,i[ii+1] - i[ii]); 4311 for (jj=i[ii]+1; jj<i[ii+1]; jj++) { 4312 if (j[jj] < j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is not sorted",jj-i[ii],j[jj],ii); 4313 if (j[jj] == j[jj]-1) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is identical to previous entry",jj-i[ii],j[jj],ii); 4314 } 4315 #endif 4316 } 4317 #if defined(PETSC_USE_DEBUG) 4318 for (ii=0; ii<aij->i[m]; ii++) { 4319 if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %D index = %D",ii,j[ii]); 4320 if (j[ii] > n - 1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %D index = %D",ii,j[ii]); 4321 } 4322 #endif 4323 4324 ierr = MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 4325 ierr = MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 4326 PetscFunctionReturn(0); 4327 } 4328 #undef __FUNCT__ 4329 #define __FUNCT__ "MatCreateSeqAIJFromTriple" 4330 /*@C 4331 MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format) 4332 provided by the user. 4333 4334 Collective on MPI_Comm 4335 4336 Input Parameters: 4337 + comm - must be an MPI communicator of size 1 4338 . m - number of rows 4339 . n - number of columns 4340 . i - row indices 4341 . j - column indices 4342 . a - matrix values 4343 . nz - number of nonzeros 4344 - idx - 0 or 1 based 4345 4346 Output Parameter: 4347 . mat - the matrix 4348 4349 Level: intermediate 4350 4351 Notes: 4352 The i and j indices are 0 based 4353 4354 The format which is used for the sparse matrix input, is equivalent to a 4355 row-major ordering.. i.e for the following matrix, the input data expected is 4356 as shown: 4357 4358 1 0 0 4359 2 0 3 4360 4 5 6 4361 4362 i = {0,1,1,2,2,2} 4363 j = {0,0,2,0,1,2} 4364 v = {1,2,3,4,5,6} 4365 4366 4367 .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR() 4368 4369 @*/ 4370 PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt *i,PetscInt *j,PetscScalar *a,Mat *mat,PetscInt nz,PetscBool idx) 4371 { 4372 PetscErrorCode ierr; 4373 PetscInt ii, *nnz, one = 1,row,col; 4374 4375 4376 PetscFunctionBegin; 4377 ierr = PetscCalloc1(m,&nnz);CHKERRQ(ierr); 4378 for (ii = 0; ii < nz; ii++) { 4379 nnz[i[ii] - !!idx] += 1; 4380 } 4381 ierr = MatCreate(comm,mat);CHKERRQ(ierr); 4382 ierr = MatSetSizes(*mat,m,n,m,n);CHKERRQ(ierr); 4383 ierr = MatSetType(*mat,MATSEQAIJ);CHKERRQ(ierr); 4384 ierr = MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);CHKERRQ(ierr); 4385 for (ii = 0; ii < nz; ii++) { 4386 if (idx) { 4387 row = i[ii] - 1; 4388 col = j[ii] - 1; 4389 } else { 4390 row = i[ii]; 4391 col = j[ii]; 4392 } 4393 ierr = MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);CHKERRQ(ierr); 4394 } 4395 ierr = MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 4396 ierr = MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 4397 ierr = PetscFree(nnz);CHKERRQ(ierr); 4398 PetscFunctionReturn(0); 4399 } 4400 4401 #undef __FUNCT__ 4402 #define __FUNCT__ "MatSetColoring_SeqAIJ" 4403 PetscErrorCode MatSetColoring_SeqAIJ(Mat A,ISColoring coloring) 4404 { 4405 PetscErrorCode ierr; 4406 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 4407 4408 PetscFunctionBegin; 4409 if (coloring->ctype == IS_COLORING_GLOBAL) { 4410 ierr = ISColoringReference(coloring);CHKERRQ(ierr); 4411 a->coloring = coloring; 4412 } else if (coloring->ctype == IS_COLORING_GHOSTED) { 4413 PetscInt i,*larray; 4414 ISColoring ocoloring; 4415 ISColoringValue *colors; 4416 4417 /* set coloring for diagonal portion */ 4418 ierr = PetscMalloc1(A->cmap->n,&larray);CHKERRQ(ierr); 4419 for (i=0; i<A->cmap->n; i++) larray[i] = i; 4420 ierr = ISGlobalToLocalMappingApply(A->cmap->mapping,IS_GTOLM_MASK,A->cmap->n,larray,NULL,larray);CHKERRQ(ierr); 4421 ierr = PetscMalloc1(A->cmap->n,&colors);CHKERRQ(ierr); 4422 for (i=0; i<A->cmap->n; i++) colors[i] = coloring->colors[larray[i]]; 4423 ierr = PetscFree(larray);CHKERRQ(ierr); 4424 ierr = ISColoringCreate(PETSC_COMM_SELF,coloring->n,A->cmap->n,colors,PETSC_OWN_POINTER,&ocoloring);CHKERRQ(ierr); 4425 a->coloring = ocoloring; 4426 } 4427 PetscFunctionReturn(0); 4428 } 4429 4430 #undef __FUNCT__ 4431 #define __FUNCT__ "MatSetValuesAdifor_SeqAIJ" 4432 PetscErrorCode MatSetValuesAdifor_SeqAIJ(Mat A,PetscInt nl,void *advalues) 4433 { 4434 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 4435 PetscInt m = A->rmap->n,*ii = a->i,*jj = a->j,nz,i,j; 4436 MatScalar *v = a->a; 4437 PetscScalar *values = (PetscScalar*)advalues; 4438 ISColoringValue *color; 4439 4440 PetscFunctionBegin; 4441 if (!a->coloring) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix"); 4442 color = a->coloring->colors; 4443 /* loop over rows */ 4444 for (i=0; i<m; i++) { 4445 nz = ii[i+1] - ii[i]; 4446 /* loop over columns putting computed value into matrix */ 4447 for (j=0; j<nz; j++) *v++ = values[color[*jj++]]; 4448 values += nl; /* jump to next row of derivatives */ 4449 } 4450 PetscFunctionReturn(0); 4451 } 4452 4453 #undef __FUNCT__ 4454 #define __FUNCT__ "MatSeqAIJInvalidateDiagonal" 4455 PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A) 4456 { 4457 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data; 4458 PetscErrorCode ierr; 4459 4460 PetscFunctionBegin; 4461 a->idiagvalid = PETSC_FALSE; 4462 a->ibdiagvalid = PETSC_FALSE; 4463 4464 ierr = MatSeqAIJInvalidateDiagonal_Inode(A);CHKERRQ(ierr); 4465 PetscFunctionReturn(0); 4466 } 4467 4468 #undef __FUNCT__ 4469 #define __FUNCT__ "MatCreateMPIMatConcatenateSeqMat_SeqAIJ" 4470 PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat) 4471 { 4472 PetscErrorCode ierr; 4473 4474 PetscFunctionBegin; 4475 ierr = MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm,inmat,n,scall,outmat);CHKERRQ(ierr); 4476 PetscFunctionReturn(0); 4477 } 4478 4479 /* 4480 Permute A into C's *local* index space using rowemb,colemb. 4481 The embedding are supposed to be injections and the above implies that the range of rowemb is a subset 4482 of [0,m), colemb is in [0,n). 4483 If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A. 4484 */ 4485 #undef __FUNCT__ 4486 #define __FUNCT__ "MatSetSeqMat_SeqAIJ" 4487 PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C,IS rowemb,IS colemb,MatStructure pattern,Mat B) 4488 { 4489 /* If making this function public, change the error returned in this function away from _PLIB. */ 4490 PetscErrorCode ierr; 4491 Mat_SeqAIJ *Baij; 4492 PetscBool seqaij; 4493 PetscInt m,n,*nz,i,j,count; 4494 PetscScalar v; 4495 const PetscInt *rowindices,*colindices; 4496 4497 PetscFunctionBegin; 4498 if (!B) PetscFunctionReturn(0); 4499 /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */ 4500 ierr = PetscObjectTypeCompare((PetscObject)B,MATSEQAIJ,&seqaij);CHKERRQ(ierr); 4501 if (!seqaij) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is of wrong type"); 4502 if (rowemb) { 4503 ierr = ISGetLocalSize(rowemb,&m);CHKERRQ(ierr); 4504 if (m != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Row IS of size %D is incompatible with matrix row size %D",m,B->rmap->n); 4505 } else { 4506 if (C->rmap->n != B->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is row-incompatible with the target matrix"); 4507 } 4508 if (colemb) { 4509 ierr = ISGetLocalSize(colemb,&n);CHKERRQ(ierr); 4510 if (n != B->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Diag col IS of size %D is incompatible with input matrix col size %D",n,B->cmap->n); 4511 } else { 4512 if (C->cmap->n != B->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is col-incompatible with the target matrix"); 4513 } 4514 4515 Baij = (Mat_SeqAIJ*)(B->data); 4516 if (pattern == DIFFERENT_NONZERO_PATTERN) { 4517 ierr = PetscMalloc1(B->rmap->n,&nz);CHKERRQ(ierr); 4518 for (i=0; i<B->rmap->n; i++) { 4519 nz[i] = Baij->i[i+1] - Baij->i[i]; 4520 } 4521 ierr = MatSeqAIJSetPreallocation(C,0,nz);CHKERRQ(ierr); 4522 ierr = PetscFree(nz);CHKERRQ(ierr); 4523 } 4524 if (pattern == SUBSET_NONZERO_PATTERN) { 4525 ierr = MatZeroEntries(C);CHKERRQ(ierr); 4526 } 4527 count = 0; 4528 rowindices = NULL; 4529 colindices = NULL; 4530 if (rowemb) { 4531 ierr = ISGetIndices(rowemb,&rowindices);CHKERRQ(ierr); 4532 } 4533 if (colemb) { 4534 ierr = ISGetIndices(colemb,&colindices);CHKERRQ(ierr); 4535 } 4536 for (i=0; i<B->rmap->n; i++) { 4537 PetscInt row; 4538 row = i; 4539 if (rowindices) row = rowindices[i]; 4540 for (j=Baij->i[i]; j<Baij->i[i+1]; j++) { 4541 PetscInt col; 4542 col = Baij->j[count]; 4543 if (colindices) col = colindices[col]; 4544 v = Baij->a[count]; 4545 ierr = MatSetValues(C,1,&row,1,&col,&v,INSERT_VALUES);CHKERRQ(ierr); 4546 ++count; 4547 } 4548 } 4549 /* FIXME: set C's nonzerostate correctly. */ 4550 /* Assembly for C is necessary. */ 4551 C->preallocated = PETSC_TRUE; 4552 C->assembled = PETSC_TRUE; 4553 C->was_assembled = PETSC_FALSE; 4554 PetscFunctionReturn(0); 4555 } 4556 4557 4558 /* 4559 Special version for direct calls from Fortran 4560 */ 4561 #include <petsc/private/fortranimpl.h> 4562 #if defined(PETSC_HAVE_FORTRAN_CAPS) 4563 #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ 4564 #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE) 4565 #define matsetvaluesseqaij_ matsetvaluesseqaij 4566 #endif 4567 4568 /* Change these macros so can be used in void function */ 4569 #undef CHKERRQ 4570 #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr) 4571 #undef SETERRQ2 4572 #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr) 4573 #undef SETERRQ3 4574 #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr) 4575 4576 #undef __FUNCT__ 4577 #define __FUNCT__ "matsetvaluesseqaij_" 4578 PETSC_EXTERN void PETSC_STDCALL matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr) 4579 { 4580 Mat A = *AA; 4581 PetscInt m = *mm, n = *nn; 4582 InsertMode is = *isis; 4583 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 4584 PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N; 4585 PetscInt *imax,*ai,*ailen; 4586 PetscErrorCode ierr; 4587 PetscInt *aj,nonew = a->nonew,lastcol = -1; 4588 MatScalar *ap,value,*aa; 4589 PetscBool ignorezeroentries = a->ignorezeroentries; 4590 PetscBool roworiented = a->roworiented; 4591 4592 PetscFunctionBegin; 4593 MatCheckPreallocated(A,1); 4594 imax = a->imax; 4595 ai = a->i; 4596 ailen = a->ilen; 4597 aj = a->j; 4598 aa = a->a; 4599 4600 for (k=0; k<m; k++) { /* loop over added rows */ 4601 row = im[k]; 4602 if (row < 0) continue; 4603 #if defined(PETSC_USE_DEBUG) 4604 if (row >= A->rmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large"); 4605 #endif 4606 rp = aj + ai[row]; ap = aa + ai[row]; 4607 rmax = imax[row]; nrow = ailen[row]; 4608 low = 0; 4609 high = nrow; 4610 for (l=0; l<n; l++) { /* loop over added columns */ 4611 if (in[l] < 0) continue; 4612 #if defined(PETSC_USE_DEBUG) 4613 if (in[l] >= A->cmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large"); 4614 #endif 4615 col = in[l]; 4616 if (roworiented) value = v[l + k*n]; 4617 else value = v[k + l*m]; 4618 4619 if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue; 4620 4621 if (col <= lastcol) low = 0; 4622 else high = nrow; 4623 lastcol = col; 4624 while (high-low > 5) { 4625 t = (low+high)/2; 4626 if (rp[t] > col) high = t; 4627 else low = t; 4628 } 4629 for (i=low; i<high; i++) { 4630 if (rp[i] > col) break; 4631 if (rp[i] == col) { 4632 if (is == ADD_VALUES) ap[i] += value; 4633 else ap[i] = value; 4634 goto noinsert; 4635 } 4636 } 4637 if (value == 0.0 && ignorezeroentries) goto noinsert; 4638 if (nonew == 1) goto noinsert; 4639 if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix"); 4640 MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar); 4641 N = nrow++ - 1; a->nz++; high++; 4642 /* shift up all the later entries in this row */ 4643 for (ii=N; ii>=i; ii--) { 4644 rp[ii+1] = rp[ii]; 4645 ap[ii+1] = ap[ii]; 4646 } 4647 rp[i] = col; 4648 ap[i] = value; 4649 A->nonzerostate++; 4650 noinsert:; 4651 low = i + 1; 4652 } 4653 ailen[row] = nrow; 4654 } 4655 PetscFunctionReturnVoid(); 4656 } 4657 4658