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 #if defined(PETSC_THREADCOMM_ACTIVE) 13 #include <petscthreadcomm.h> 14 #endif 15 16 #undef __FUNCT__ 17 #define __FUNCT__ "MatGetColumnNorms_SeqAIJ" 18 PetscErrorCode MatGetColumnNorms_SeqAIJ(Mat A,NormType type,PetscReal *norms) 19 { 20 PetscErrorCode ierr; 21 PetscInt i,m,n; 22 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)A->data; 23 24 PetscFunctionBegin; 25 ierr = MatGetSize(A,&m,&n);CHKERRQ(ierr); 26 ierr = PetscMemzero(norms,n*sizeof(PetscReal));CHKERRQ(ierr); 27 if (type == NORM_2) { 28 for (i=0; i<aij->i[m]; i++) { 29 norms[aij->j[i]] += PetscAbsScalar(aij->a[i]*aij->a[i]); 30 } 31 } else if (type == NORM_1) { 32 for (i=0; i<aij->i[m]; i++) { 33 norms[aij->j[i]] += PetscAbsScalar(aij->a[i]); 34 } 35 } else if (type == NORM_INFINITY) { 36 for (i=0; i<aij->i[m]; i++) { 37 norms[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]),norms[aij->j[i]]); 38 } 39 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown NormType"); 40 41 if (type == NORM_2) { 42 for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]); 43 } 44 PetscFunctionReturn(0); 45 } 46 47 #undef __FUNCT__ 48 #define __FUNCT__ "MatFindOffBlockDiagonalEntries_SeqAIJ" 49 PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A,IS *is) 50 { 51 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 52 PetscInt i,m=A->rmap->n,cnt = 0, bs = A->rmap->bs; 53 const PetscInt *jj = a->j,*ii = a->i; 54 PetscInt *rows; 55 PetscErrorCode ierr; 56 57 PetscFunctionBegin; 58 for (i=0; i<m; i++) { 59 if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) { 60 cnt++; 61 } 62 } 63 ierr = PetscMalloc1(cnt,&rows);CHKERRQ(ierr); 64 cnt = 0; 65 for (i=0; i<m; i++) { 66 if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) { 67 rows[cnt] = i; 68 cnt++; 69 } 70 } 71 ierr = ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,is);CHKERRQ(ierr); 72 PetscFunctionReturn(0); 73 } 74 75 #undef __FUNCT__ 76 #define __FUNCT__ "MatFindZeroDiagonals_SeqAIJ_Private" 77 PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A,PetscInt *nrows,PetscInt **zrows) 78 { 79 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 80 const MatScalar *aa = a->a; 81 PetscInt i,m=A->rmap->n,cnt = 0; 82 const PetscInt *jj = a->j,*diag; 83 PetscInt *rows; 84 PetscErrorCode ierr; 85 86 PetscFunctionBegin; 87 ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr); 88 diag = a->diag; 89 for (i=0; i<m; i++) { 90 if ((jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) { 91 cnt++; 92 } 93 } 94 ierr = PetscMalloc1(cnt,&rows);CHKERRQ(ierr); 95 cnt = 0; 96 for (i=0; i<m; i++) { 97 if ((jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) { 98 rows[cnt++] = i; 99 } 100 } 101 *nrows = cnt; 102 *zrows = rows; 103 PetscFunctionReturn(0); 104 } 105 106 #undef __FUNCT__ 107 #define __FUNCT__ "MatFindZeroDiagonals_SeqAIJ" 108 PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows) 109 { 110 PetscInt nrows,*rows; 111 PetscErrorCode ierr; 112 113 PetscFunctionBegin; 114 *zrows = NULL; 115 ierr = MatFindZeroDiagonals_SeqAIJ_Private(A,&nrows,&rows);CHKERRQ(ierr); 116 ierr = ISCreateGeneral(PetscObjectComm((PetscObject)A),nrows,rows,PETSC_OWN_POINTER,zrows);CHKERRQ(ierr); 117 PetscFunctionReturn(0); 118 } 119 120 #undef __FUNCT__ 121 #define __FUNCT__ "MatFindNonzeroRows_SeqAIJ" 122 PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A,IS *keptrows) 123 { 124 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 125 const MatScalar *aa; 126 PetscInt m=A->rmap->n,cnt = 0; 127 const PetscInt *ii; 128 PetscInt n,i,j,*rows; 129 PetscErrorCode ierr; 130 131 PetscFunctionBegin; 132 *keptrows = 0; 133 ii = a->i; 134 for (i=0; i<m; i++) { 135 n = ii[i+1] - ii[i]; 136 if (!n) { 137 cnt++; 138 goto ok1; 139 } 140 aa = a->a + ii[i]; 141 for (j=0; j<n; j++) { 142 if (aa[j] != 0.0) goto ok1; 143 } 144 cnt++; 145 ok1:; 146 } 147 if (!cnt) PetscFunctionReturn(0); 148 ierr = PetscMalloc1((A->rmap->n-cnt),&rows);CHKERRQ(ierr); 149 cnt = 0; 150 for (i=0; i<m; i++) { 151 n = ii[i+1] - ii[i]; 152 if (!n) continue; 153 aa = a->a + ii[i]; 154 for (j=0; j<n; j++) { 155 if (aa[j] != 0.0) { 156 rows[cnt++] = i; 157 break; 158 } 159 } 160 } 161 ierr = ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,keptrows);CHKERRQ(ierr); 162 PetscFunctionReturn(0); 163 } 164 165 #undef __FUNCT__ 166 #define __FUNCT__ "MatDiagonalSet_SeqAIJ" 167 PetscErrorCode MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is) 168 { 169 PetscErrorCode ierr; 170 Mat_SeqAIJ *aij = (Mat_SeqAIJ*) Y->data; 171 PetscInt i,*diag, m = Y->rmap->n; 172 MatScalar *aa = aij->a; 173 PetscScalar *v; 174 PetscBool missing; 175 176 PetscFunctionBegin; 177 if (Y->assembled) { 178 ierr = MatMissingDiagonal_SeqAIJ(Y,&missing,NULL);CHKERRQ(ierr); 179 if (!missing) { 180 diag = aij->diag; 181 ierr = VecGetArray(D,&v);CHKERRQ(ierr); 182 if (is == INSERT_VALUES) { 183 for (i=0; i<m; i++) { 184 aa[diag[i]] = v[i]; 185 } 186 } else { 187 for (i=0; i<m; i++) { 188 aa[diag[i]] += v[i]; 189 } 190 } 191 ierr = VecRestoreArray(D,&v);CHKERRQ(ierr); 192 PetscFunctionReturn(0); 193 } 194 ierr = MatSeqAIJInvalidateDiagonal(Y);CHKERRQ(ierr); 195 } 196 ierr = MatDiagonalSet_Default(Y,D,is);CHKERRQ(ierr); 197 PetscFunctionReturn(0); 198 } 199 200 #undef __FUNCT__ 201 #define __FUNCT__ "MatGetRowIJ_SeqAIJ" 202 PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *m,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) 203 { 204 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 205 PetscErrorCode ierr; 206 PetscInt i,ishift; 207 208 PetscFunctionBegin; 209 *m = A->rmap->n; 210 if (!ia) PetscFunctionReturn(0); 211 ishift = 0; 212 if (symmetric && !A->structurally_symmetric) { 213 ierr = MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,ishift,oshift,(PetscInt**)ia,(PetscInt**)ja);CHKERRQ(ierr); 214 } else if (oshift == 1) { 215 PetscInt *tia; 216 PetscInt nz = a->i[A->rmap->n]; 217 /* malloc space and add 1 to i and j indices */ 218 ierr = PetscMalloc1((A->rmap->n+1),&tia);CHKERRQ(ierr); 219 for (i=0; i<A->rmap->n+1; i++) tia[i] = a->i[i] + 1; 220 *ia = tia; 221 if (ja) { 222 PetscInt *tja; 223 ierr = PetscMalloc1((nz+1),&tja);CHKERRQ(ierr); 224 for (i=0; i<nz; i++) tja[i] = a->j[i] + 1; 225 *ja = tja; 226 } 227 } else { 228 *ia = a->i; 229 if (ja) *ja = a->j; 230 } 231 PetscFunctionReturn(0); 232 } 233 234 #undef __FUNCT__ 235 #define __FUNCT__ "MatRestoreRowIJ_SeqAIJ" 236 PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) 237 { 238 PetscErrorCode ierr; 239 240 PetscFunctionBegin; 241 if (!ia) PetscFunctionReturn(0); 242 if ((symmetric && !A->structurally_symmetric) || oshift == 1) { 243 ierr = PetscFree(*ia);CHKERRQ(ierr); 244 if (ja) {ierr = PetscFree(*ja);CHKERRQ(ierr);} 245 } 246 PetscFunctionReturn(0); 247 } 248 249 #undef __FUNCT__ 250 #define __FUNCT__ "MatGetColumnIJ_SeqAIJ" 251 PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) 252 { 253 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 254 PetscErrorCode ierr; 255 PetscInt i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n; 256 PetscInt nz = a->i[m],row,*jj,mr,col; 257 258 PetscFunctionBegin; 259 *nn = n; 260 if (!ia) PetscFunctionReturn(0); 261 if (symmetric) { 262 ierr = MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,0,oshift,(PetscInt**)ia,(PetscInt**)ja);CHKERRQ(ierr); 263 } else { 264 ierr = PetscCalloc1(n+1,&collengths);CHKERRQ(ierr); 265 ierr = PetscMalloc1((n+1),&cia);CHKERRQ(ierr); 266 ierr = PetscMalloc1((nz+1),&cja);CHKERRQ(ierr); 267 jj = a->j; 268 for (i=0; i<nz; i++) { 269 collengths[jj[i]]++; 270 } 271 cia[0] = oshift; 272 for (i=0; i<n; i++) { 273 cia[i+1] = cia[i] + collengths[i]; 274 } 275 ierr = PetscMemzero(collengths,n*sizeof(PetscInt));CHKERRQ(ierr); 276 jj = a->j; 277 for (row=0; row<m; row++) { 278 mr = a->i[row+1] - a->i[row]; 279 for (i=0; i<mr; i++) { 280 col = *jj++; 281 282 cja[cia[col] + collengths[col]++ - oshift] = row + oshift; 283 } 284 } 285 ierr = PetscFree(collengths);CHKERRQ(ierr); 286 *ia = cia; *ja = cja; 287 } 288 PetscFunctionReturn(0); 289 } 290 291 #undef __FUNCT__ 292 #define __FUNCT__ "MatRestoreColumnIJ_SeqAIJ" 293 PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) 294 { 295 PetscErrorCode ierr; 296 297 PetscFunctionBegin; 298 if (!ia) PetscFunctionReturn(0); 299 300 ierr = PetscFree(*ia);CHKERRQ(ierr); 301 ierr = PetscFree(*ja);CHKERRQ(ierr); 302 PetscFunctionReturn(0); 303 } 304 305 /* 306 MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from 307 MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output 308 spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ() 309 */ 310 #undef __FUNCT__ 311 #define __FUNCT__ "MatGetColumnIJ_SeqAIJ_Color" 312 PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool *done) 313 { 314 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 315 PetscErrorCode ierr; 316 PetscInt i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n; 317 PetscInt nz = a->i[m],row,*jj,mr,col; 318 PetscInt *cspidx; 319 320 PetscFunctionBegin; 321 *nn = n; 322 if (!ia) PetscFunctionReturn(0); 323 324 ierr = PetscCalloc1(n+1,&collengths);CHKERRQ(ierr); 325 ierr = PetscMalloc1((n+1),&cia);CHKERRQ(ierr); 326 ierr = PetscMalloc1((nz+1),&cja);CHKERRQ(ierr); 327 ierr = PetscMalloc1((nz+1),&cspidx);CHKERRQ(ierr); 328 jj = a->j; 329 for (i=0; i<nz; i++) { 330 collengths[jj[i]]++; 331 } 332 cia[0] = oshift; 333 for (i=0; i<n; i++) { 334 cia[i+1] = cia[i] + collengths[i]; 335 } 336 ierr = PetscMemzero(collengths,n*sizeof(PetscInt));CHKERRQ(ierr); 337 jj = a->j; 338 for (row=0; row<m; row++) { 339 mr = a->i[row+1] - a->i[row]; 340 for (i=0; i<mr; i++) { 341 col = *jj++; 342 cspidx[cia[col] + collengths[col] - oshift] = a->i[row] + i; /* index of a->j */ 343 cja[cia[col] + collengths[col]++ - oshift] = row + oshift; 344 } 345 } 346 ierr = PetscFree(collengths);CHKERRQ(ierr); 347 *ia = cia; *ja = cja; 348 *spidx = cspidx; 349 PetscFunctionReturn(0); 350 } 351 352 #undef __FUNCT__ 353 #define __FUNCT__ "MatRestoreColumnIJ_SeqAIJ_Color" 354 PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool *done) 355 { 356 PetscErrorCode ierr; 357 358 PetscFunctionBegin; 359 ierr = MatRestoreColumnIJ_SeqAIJ(A,oshift,symmetric,inodecompressed,n,ia,ja,done);CHKERRQ(ierr); 360 ierr = PetscFree(*spidx);CHKERRQ(ierr); 361 PetscFunctionReturn(0); 362 } 363 364 #undef __FUNCT__ 365 #define __FUNCT__ "MatSetValuesRow_SeqAIJ" 366 PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[]) 367 { 368 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 369 PetscInt *ai = a->i; 370 PetscErrorCode ierr; 371 372 PetscFunctionBegin; 373 ierr = PetscMemcpy(a->a+ai[row],v,(ai[row+1]-ai[row])*sizeof(PetscScalar));CHKERRQ(ierr); 374 PetscFunctionReturn(0); 375 } 376 377 /* 378 MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions 379 380 - a single row of values is set with each call 381 - no row or column indices are negative or (in error) larger than the number of rows or columns 382 - the values are always added to the matrix, not set 383 - no new locations are introduced in the nonzero structure of the matrix 384 385 This does NOT assume the global column indices are sorted 386 387 */ 388 389 #include <petsc-private/isimpl.h> 390 #undef __FUNCT__ 391 #define __FUNCT__ "MatSeqAIJSetValuesLocalFast" 392 PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is) 393 { 394 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 395 PetscInt low,high,t,row,nrow,i,col,l; 396 const PetscInt *rp,*ai = a->i,*ailen = a->ilen,*aj = a->j; 397 PetscInt lastcol = -1; 398 MatScalar *ap,value,*aa = a->a; 399 const PetscInt *ridx = A->rmap->mapping->indices,*cidx = A->cmap->mapping->indices; 400 401 row = ridx[im[0]]; 402 rp = aj + ai[row]; 403 ap = aa + ai[row]; 404 nrow = ailen[row]; 405 low = 0; 406 high = nrow; 407 for (l=0; l<n; l++) { /* loop over added columns */ 408 col = cidx[in[l]]; 409 value = v[l]; 410 411 if (col <= lastcol) low = 0; 412 else high = nrow; 413 lastcol = col; 414 while (high-low > 5) { 415 t = (low+high)/2; 416 if (rp[t] > col) high = t; 417 else low = t; 418 } 419 for (i=low; i<high; i++) { 420 if (rp[i] == col) { 421 ap[i] += value; 422 low = i + 1; 423 break; 424 } 425 } 426 } 427 return 0; 428 } 429 430 #undef __FUNCT__ 431 #define __FUNCT__ "MatSetValues_SeqAIJ" 432 PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is) 433 { 434 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 435 PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N; 436 PetscInt *imax = a->imax,*ai = a->i,*ailen = a->ilen; 437 PetscErrorCode ierr; 438 PetscInt *aj = a->j,nonew = a->nonew,lastcol = -1; 439 MatScalar *ap,value,*aa = a->a; 440 PetscBool ignorezeroentries = a->ignorezeroentries; 441 PetscBool roworiented = a->roworiented; 442 443 PetscFunctionBegin; 444 for (k=0; k<m; k++) { /* loop over added rows */ 445 row = im[k]; 446 if (row < 0) continue; 447 #if defined(PETSC_USE_DEBUG) 448 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); 449 #endif 450 rp = aj + ai[row]; ap = aa + ai[row]; 451 rmax = imax[row]; nrow = ailen[row]; 452 low = 0; 453 high = nrow; 454 for (l=0; l<n; l++) { /* loop over added columns */ 455 if (in[l] < 0) continue; 456 #if defined(PETSC_USE_DEBUG) 457 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); 458 #endif 459 col = in[l]; 460 if (roworiented) { 461 value = v[l + k*n]; 462 } else { 463 value = v[k + l*m]; 464 } 465 if ((value == 0.0 && ignorezeroentries) && (is == ADD_VALUES)) continue; 466 467 if (col <= lastcol) low = 0; 468 else high = nrow; 469 lastcol = col; 470 while (high-low > 5) { 471 t = (low+high)/2; 472 if (rp[t] > col) high = t; 473 else low = t; 474 } 475 for (i=low; i<high; i++) { 476 if (rp[i] > col) break; 477 if (rp[i] == col) { 478 if (is == ADD_VALUES) ap[i] += value; 479 else ap[i] = value; 480 low = i + 1; 481 goto noinsert; 482 } 483 } 484 if (value == 0.0 && ignorezeroentries) goto noinsert; 485 if (nonew == 1) goto noinsert; 486 if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col); 487 MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar); 488 N = nrow++ - 1; a->nz++; high++; 489 /* shift up all the later entries in this row */ 490 for (ii=N; ii>=i; ii--) { 491 rp[ii+1] = rp[ii]; 492 ap[ii+1] = ap[ii]; 493 } 494 rp[i] = col; 495 ap[i] = value; 496 low = i + 1; 497 A->nonzerostate++; 498 noinsert:; 499 } 500 ailen[row] = nrow; 501 } 502 PetscFunctionReturn(0); 503 } 504 505 506 #undef __FUNCT__ 507 #define __FUNCT__ "MatGetValues_SeqAIJ" 508 PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[]) 509 { 510 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 511 PetscInt *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j; 512 PetscInt *ai = a->i,*ailen = a->ilen; 513 MatScalar *ap,*aa = a->a; 514 515 PetscFunctionBegin; 516 for (k=0; k<m; k++) { /* loop over rows */ 517 row = im[k]; 518 if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */ 519 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); 520 rp = aj + ai[row]; ap = aa + ai[row]; 521 nrow = ailen[row]; 522 for (l=0; l<n; l++) { /* loop over columns */ 523 if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */ 524 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); 525 col = in[l]; 526 high = nrow; low = 0; /* assume unsorted */ 527 while (high-low > 5) { 528 t = (low+high)/2; 529 if (rp[t] > col) high = t; 530 else low = t; 531 } 532 for (i=low; i<high; i++) { 533 if (rp[i] > col) break; 534 if (rp[i] == col) { 535 *v++ = ap[i]; 536 goto finished; 537 } 538 } 539 *v++ = 0.0; 540 finished:; 541 } 542 } 543 PetscFunctionReturn(0); 544 } 545 546 547 #undef __FUNCT__ 548 #define __FUNCT__ "MatView_SeqAIJ_Binary" 549 PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer) 550 { 551 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 552 PetscErrorCode ierr; 553 PetscInt i,*col_lens; 554 int fd; 555 FILE *file; 556 557 PetscFunctionBegin; 558 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 559 ierr = PetscMalloc1((4+A->rmap->n),&col_lens);CHKERRQ(ierr); 560 561 col_lens[0] = MAT_FILE_CLASSID; 562 col_lens[1] = A->rmap->n; 563 col_lens[2] = A->cmap->n; 564 col_lens[3] = a->nz; 565 566 /* store lengths of each row and write (including header) to file */ 567 for (i=0; i<A->rmap->n; i++) { 568 col_lens[4+i] = a->i[i+1] - a->i[i]; 569 } 570 ierr = PetscBinaryWrite(fd,col_lens,4+A->rmap->n,PETSC_INT,PETSC_TRUE);CHKERRQ(ierr); 571 ierr = PetscFree(col_lens);CHKERRQ(ierr); 572 573 /* store column indices (zero start index) */ 574 ierr = PetscBinaryWrite(fd,a->j,a->nz,PETSC_INT,PETSC_FALSE);CHKERRQ(ierr); 575 576 /* store nonzero values */ 577 ierr = PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);CHKERRQ(ierr); 578 579 ierr = PetscViewerBinaryGetInfoPointer(viewer,&file);CHKERRQ(ierr); 580 if (file) { 581 fprintf(file,"-matload_block_size %d\n",(int)PetscAbs(A->rmap->bs)); 582 } 583 PetscFunctionReturn(0); 584 } 585 586 extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer); 587 588 #undef __FUNCT__ 589 #define __FUNCT__ "MatView_SeqAIJ_ASCII" 590 PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer) 591 { 592 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 593 PetscErrorCode ierr; 594 PetscInt i,j,m = A->rmap->n; 595 const char *name; 596 PetscViewerFormat format; 597 598 PetscFunctionBegin; 599 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 600 if (format == PETSC_VIEWER_ASCII_MATLAB) { 601 PetscInt nofinalvalue = 0; 602 if (m && ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-1))) { 603 /* Need a dummy value to ensure the dimension of the matrix. */ 604 nofinalvalue = 1; 605 } 606 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);CHKERRQ(ierr); 607 ierr = PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);CHKERRQ(ierr); 608 ierr = PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);CHKERRQ(ierr); 609 #if defined(PETSC_USE_COMPLEX) 610 ierr = PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,4);\n",a->nz+nofinalvalue);CHKERRQ(ierr); 611 #else 612 ierr = PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);CHKERRQ(ierr); 613 #endif 614 ierr = PetscViewerASCIIPrintf(viewer,"zzz = [\n");CHKERRQ(ierr); 615 616 for (i=0; i<m; i++) { 617 for (j=a->i[i]; j<a->i[i+1]; j++) { 618 #if defined(PETSC_USE_COMPLEX) 619 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); 620 #else 621 ierr = PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",i+1,a->j[j]+1,(double)a->a[j]);CHKERRQ(ierr); 622 #endif 623 } 624 } 625 if (nofinalvalue) { 626 #if defined(PETSC_USE_COMPLEX) 627 ierr = PetscViewerASCIIPrintf(viewer,"%D %D %18.16e %18.16e\n",m,A->cmap->n,0.,0.);CHKERRQ(ierr); 628 #else 629 ierr = PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",m,A->cmap->n,0.0);CHKERRQ(ierr); 630 #endif 631 } 632 ierr = PetscObjectGetName((PetscObject)A,&name);CHKERRQ(ierr); 633 ierr = PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);CHKERRQ(ierr); 634 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);CHKERRQ(ierr); 635 } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) { 636 PetscFunctionReturn(0); 637 } else if (format == PETSC_VIEWER_ASCII_COMMON) { 638 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);CHKERRQ(ierr); 639 for (i=0; i<m; i++) { 640 ierr = PetscViewerASCIIPrintf(viewer,"row %D:",i);CHKERRQ(ierr); 641 for (j=a->i[i]; j<a->i[i+1]; j++) { 642 #if defined(PETSC_USE_COMPLEX) 643 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 (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) { 646 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 647 } else if (PetscRealPart(a->a[j]) != 0.0) { 648 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));CHKERRQ(ierr); 649 } 650 #else 651 if (a->a[j] != 0.0) {ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);CHKERRQ(ierr);} 652 #endif 653 } 654 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 655 } 656 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);CHKERRQ(ierr); 657 } else if (format == PETSC_VIEWER_ASCII_SYMMODU) { 658 PetscInt nzd=0,fshift=1,*sptr; 659 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);CHKERRQ(ierr); 660 ierr = PetscMalloc1((m+1),&sptr);CHKERRQ(ierr); 661 for (i=0; i<m; i++) { 662 sptr[i] = nzd+1; 663 for (j=a->i[i]; j<a->i[i+1]; j++) { 664 if (a->j[j] >= i) { 665 #if defined(PETSC_USE_COMPLEX) 666 if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++; 667 #else 668 if (a->a[j] != 0.0) nzd++; 669 #endif 670 } 671 } 672 } 673 sptr[m] = nzd+1; 674 ierr = PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);CHKERRQ(ierr); 675 for (i=0; i<m+1; i+=6) { 676 if (i+4<m) { 677 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); 678 } else if (i+3<m) { 679 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); 680 } else if (i+2<m) { 681 ierr = PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);CHKERRQ(ierr); 682 } else if (i+1<m) { 683 ierr = PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);CHKERRQ(ierr); 684 } else if (i<m) { 685 ierr = PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);CHKERRQ(ierr); 686 } else { 687 ierr = PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);CHKERRQ(ierr); 688 } 689 } 690 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 691 ierr = PetscFree(sptr);CHKERRQ(ierr); 692 for (i=0; i<m; i++) { 693 for (j=a->i[i]; j<a->i[i+1]; j++) { 694 if (a->j[j] >= i) {ierr = PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);CHKERRQ(ierr);} 695 } 696 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 697 } 698 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 699 for (i=0; i<m; i++) { 700 for (j=a->i[i]; j<a->i[i+1]; j++) { 701 if (a->j[j] >= i) { 702 #if defined(PETSC_USE_COMPLEX) 703 if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) { 704 ierr = PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 705 } 706 #else 707 if (a->a[j] != 0.0) {ierr = PetscViewerASCIIPrintf(viewer," %18.16e ",(double)a->a[j]);CHKERRQ(ierr);} 708 #endif 709 } 710 } 711 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 712 } 713 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);CHKERRQ(ierr); 714 } else if (format == PETSC_VIEWER_ASCII_DENSE) { 715 PetscInt cnt = 0,jcnt; 716 PetscScalar value; 717 #if defined(PETSC_USE_COMPLEX) 718 PetscBool realonly = PETSC_TRUE; 719 720 for (i=0; i<a->i[m]; i++) { 721 if (PetscImaginaryPart(a->a[i]) != 0.0) { 722 realonly = PETSC_FALSE; 723 break; 724 } 725 } 726 #endif 727 728 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);CHKERRQ(ierr); 729 for (i=0; i<m; i++) { 730 jcnt = 0; 731 for (j=0; j<A->cmap->n; j++) { 732 if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) { 733 value = a->a[cnt++]; 734 jcnt++; 735 } else { 736 value = 0.0; 737 } 738 #if defined(PETSC_USE_COMPLEX) 739 if (realonly) { 740 ierr = PetscViewerASCIIPrintf(viewer," %7.5e ",(double)PetscRealPart(value));CHKERRQ(ierr); 741 } else { 742 ierr = PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",(double)PetscRealPart(value),(double)PetscImaginaryPart(value));CHKERRQ(ierr); 743 } 744 #else 745 ierr = PetscViewerASCIIPrintf(viewer," %7.5e ",(double)value);CHKERRQ(ierr); 746 #endif 747 } 748 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 749 } 750 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);CHKERRQ(ierr); 751 } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) { 752 PetscInt fshift=1; 753 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);CHKERRQ(ierr); 754 #if defined(PETSC_USE_COMPLEX) 755 ierr = PetscViewerASCIIPrintf(viewer,"%%matrix complex general\n");CHKERRQ(ierr); 756 #else 757 ierr = PetscViewerASCIIPrintf(viewer,"%%matrix real general\n");CHKERRQ(ierr); 758 #endif 759 ierr = PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);CHKERRQ(ierr); 760 for (i=0; i<m; i++) { 761 for (j=a->i[i]; j<a->i[i+1]; j++) { 762 #if defined(PETSC_USE_COMPLEX) 763 if (PetscImaginaryPart(a->a[j]) > 0.0) { 764 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); 765 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 766 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); 767 } else { 768 ierr = PetscViewerASCIIPrintf(viewer,"%D %D, %g\n", i+fshift,a->j[j]+fshift,(double)PetscRealPart(a->a[j]));CHKERRQ(ierr); 769 } 770 #else 771 ierr = PetscViewerASCIIPrintf(viewer,"%D %D %g\n", i+fshift, a->j[j]+fshift, (double)a->a[j]);CHKERRQ(ierr); 772 #endif 773 } 774 } 775 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);CHKERRQ(ierr); 776 } else { 777 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);CHKERRQ(ierr); 778 if (A->factortype) { 779 for (i=0; i<m; i++) { 780 ierr = PetscViewerASCIIPrintf(viewer,"row %D:",i);CHKERRQ(ierr); 781 /* L part */ 782 for (j=a->i[i]; j<a->i[i+1]; j++) { 783 #if defined(PETSC_USE_COMPLEX) 784 if (PetscImaginaryPart(a->a[j]) > 0.0) { 785 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 786 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 787 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));CHKERRQ(ierr); 788 } else { 789 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));CHKERRQ(ierr); 790 } 791 #else 792 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);CHKERRQ(ierr); 793 #endif 794 } 795 /* diagonal */ 796 j = a->diag[i]; 797 #if defined(PETSC_USE_COMPLEX) 798 if (PetscImaginaryPart(a->a[j]) > 0.0) { 799 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); 800 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 801 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); 802 } else { 803 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(1.0/a->a[j]));CHKERRQ(ierr); 804 } 805 #else 806 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)(1.0/a->a[j]));CHKERRQ(ierr); 807 #endif 808 809 /* U part */ 810 for (j=a->diag[i+1]+1; j<a->diag[i]; j++) { 811 #if defined(PETSC_USE_COMPLEX) 812 if (PetscImaginaryPart(a->a[j]) > 0.0) { 813 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 814 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 815 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));CHKERRQ(ierr); 816 } else { 817 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));CHKERRQ(ierr); 818 } 819 #else 820 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);CHKERRQ(ierr); 821 #endif 822 } 823 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 824 } 825 } else { 826 for (i=0; i<m; i++) { 827 ierr = PetscViewerASCIIPrintf(viewer,"row %D:",i);CHKERRQ(ierr); 828 for (j=a->i[i]; j<a->i[i+1]; j++) { 829 #if defined(PETSC_USE_COMPLEX) 830 if (PetscImaginaryPart(a->a[j]) > 0.0) { 831 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 832 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 833 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 834 } else { 835 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));CHKERRQ(ierr); 836 } 837 #else 838 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);CHKERRQ(ierr); 839 #endif 840 } 841 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 842 } 843 } 844 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);CHKERRQ(ierr); 845 } 846 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 847 PetscFunctionReturn(0); 848 } 849 850 #include <petscdraw.h> 851 #undef __FUNCT__ 852 #define __FUNCT__ "MatView_SeqAIJ_Draw_Zoom" 853 PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa) 854 { 855 Mat A = (Mat) Aa; 856 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 857 PetscErrorCode ierr; 858 PetscInt i,j,m = A->rmap->n,color; 859 PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0; 860 PetscViewer viewer; 861 PetscViewerFormat format; 862 863 PetscFunctionBegin; 864 ierr = PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);CHKERRQ(ierr); 865 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 866 867 ierr = PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);CHKERRQ(ierr); 868 /* loop over matrix elements drawing boxes */ 869 870 if (format != PETSC_VIEWER_DRAW_CONTOUR) { 871 /* Blue for negative, Cyan for zero and Red for positive */ 872 color = PETSC_DRAW_BLUE; 873 for (i=0; i<m; i++) { 874 y_l = m - i - 1.0; y_r = y_l + 1.0; 875 for (j=a->i[i]; j<a->i[i+1]; j++) { 876 x_l = a->j[j]; x_r = x_l + 1.0; 877 if (PetscRealPart(a->a[j]) >= 0.) continue; 878 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 879 } 880 } 881 color = PETSC_DRAW_CYAN; 882 for (i=0; i<m; i++) { 883 y_l = m - i - 1.0; y_r = y_l + 1.0; 884 for (j=a->i[i]; j<a->i[i+1]; j++) { 885 x_l = a->j[j]; x_r = x_l + 1.0; 886 if (a->a[j] != 0.) continue; 887 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 888 } 889 } 890 color = PETSC_DRAW_RED; 891 for (i=0; i<m; i++) { 892 y_l = m - i - 1.0; y_r = y_l + 1.0; 893 for (j=a->i[i]; j<a->i[i+1]; j++) { 894 x_l = a->j[j]; x_r = x_l + 1.0; 895 if (PetscRealPart(a->a[j]) <= 0.) continue; 896 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 897 } 898 } 899 } else { 900 /* use contour shading to indicate magnitude of values */ 901 /* first determine max of all nonzero values */ 902 PetscInt nz = a->nz,count; 903 PetscDraw popup; 904 PetscReal scale; 905 906 for (i=0; i<nz; i++) { 907 if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]); 908 } 909 scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv; 910 ierr = PetscDrawGetPopup(draw,&popup);CHKERRQ(ierr); 911 if (popup) { 912 ierr = PetscDrawScalePopup(popup,0.0,maxv);CHKERRQ(ierr); 913 } 914 count = 0; 915 for (i=0; i<m; i++) { 916 y_l = m - i - 1.0; y_r = y_l + 1.0; 917 for (j=a->i[i]; j<a->i[i+1]; j++) { 918 x_l = a->j[j]; x_r = x_l + 1.0; 919 color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(a->a[count])); 920 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 921 count++; 922 } 923 } 924 } 925 PetscFunctionReturn(0); 926 } 927 928 #include <petscdraw.h> 929 #undef __FUNCT__ 930 #define __FUNCT__ "MatView_SeqAIJ_Draw" 931 PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer) 932 { 933 PetscErrorCode ierr; 934 PetscDraw draw; 935 PetscReal xr,yr,xl,yl,h,w; 936 PetscBool isnull; 937 938 PetscFunctionBegin; 939 ierr = PetscViewerDrawGetDraw(viewer,0,&draw);CHKERRQ(ierr); 940 ierr = PetscDrawIsNull(draw,&isnull);CHKERRQ(ierr); 941 if (isnull) PetscFunctionReturn(0); 942 943 ierr = PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);CHKERRQ(ierr); 944 xr = A->cmap->n; yr = A->rmap->n; h = yr/10.0; w = xr/10.0; 945 xr += w; yr += h; xl = -w; yl = -h; 946 ierr = PetscDrawSetCoordinates(draw,xl,yl,xr,yr);CHKERRQ(ierr); 947 ierr = PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);CHKERRQ(ierr); 948 ierr = PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);CHKERRQ(ierr); 949 PetscFunctionReturn(0); 950 } 951 952 #undef __FUNCT__ 953 #define __FUNCT__ "MatView_SeqAIJ" 954 PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer) 955 { 956 PetscErrorCode ierr; 957 PetscBool iascii,isbinary,isdraw; 958 959 PetscFunctionBegin; 960 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 961 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);CHKERRQ(ierr); 962 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);CHKERRQ(ierr); 963 if (iascii) { 964 ierr = MatView_SeqAIJ_ASCII(A,viewer);CHKERRQ(ierr); 965 } else if (isbinary) { 966 ierr = MatView_SeqAIJ_Binary(A,viewer);CHKERRQ(ierr); 967 } else if (isdraw) { 968 ierr = MatView_SeqAIJ_Draw(A,viewer);CHKERRQ(ierr); 969 } 970 ierr = MatView_SeqAIJ_Inode(A,viewer);CHKERRQ(ierr); 971 PetscFunctionReturn(0); 972 } 973 974 #undef __FUNCT__ 975 #define __FUNCT__ "MatAssemblyEnd_SeqAIJ" 976 PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode) 977 { 978 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 979 PetscErrorCode ierr; 980 PetscInt fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax; 981 PetscInt m = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0; 982 MatScalar *aa = a->a,*ap; 983 PetscReal ratio = 0.6; 984 985 PetscFunctionBegin; 986 if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(0); 987 988 if (m) rmax = ailen[0]; /* determine row with most nonzeros */ 989 for (i=1; i<m; i++) { 990 /* move each row back by the amount of empty slots (fshift) before it*/ 991 fshift += imax[i-1] - ailen[i-1]; 992 rmax = PetscMax(rmax,ailen[i]); 993 if (fshift) { 994 ip = aj + ai[i]; 995 ap = aa + ai[i]; 996 N = ailen[i]; 997 for (j=0; j<N; j++) { 998 ip[j-fshift] = ip[j]; 999 ap[j-fshift] = ap[j]; 1000 } 1001 } 1002 ai[i] = ai[i-1] + ailen[i-1]; 1003 } 1004 if (m) { 1005 fshift += imax[m-1] - ailen[m-1]; 1006 ai[m] = ai[m-1] + ailen[m-1]; 1007 } 1008 1009 /* reset ilen and imax for each row */ 1010 a->nonzerorowcnt = 0; 1011 for (i=0; i<m; i++) { 1012 ailen[i] = imax[i] = ai[i+1] - ai[i]; 1013 a->nonzerorowcnt += ((ai[i+1] - ai[i]) > 0); 1014 } 1015 a->nz = ai[m]; 1016 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); 1017 1018 ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr); 1019 ierr = PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap->n,fshift,a->nz);CHKERRQ(ierr); 1020 ierr = PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);CHKERRQ(ierr); 1021 ierr = PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);CHKERRQ(ierr); 1022 1023 A->info.mallocs += a->reallocs; 1024 a->reallocs = 0; 1025 A->info.nz_unneeded = (PetscReal)fshift; 1026 a->rmax = rmax; 1027 1028 ierr = MatCheckCompressedRow(A,a->nonzerorowcnt,&a->compressedrow,a->i,m,ratio);CHKERRQ(ierr); 1029 ierr = MatAssemblyEnd_SeqAIJ_Inode(A,mode);CHKERRQ(ierr); 1030 ierr = MatSeqAIJInvalidateDiagonal(A);CHKERRQ(ierr); 1031 PetscFunctionReturn(0); 1032 } 1033 1034 #undef __FUNCT__ 1035 #define __FUNCT__ "MatRealPart_SeqAIJ" 1036 PetscErrorCode MatRealPart_SeqAIJ(Mat A) 1037 { 1038 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1039 PetscInt i,nz = a->nz; 1040 MatScalar *aa = a->a; 1041 PetscErrorCode ierr; 1042 1043 PetscFunctionBegin; 1044 for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]); 1045 ierr = MatSeqAIJInvalidateDiagonal(A);CHKERRQ(ierr); 1046 PetscFunctionReturn(0); 1047 } 1048 1049 #undef __FUNCT__ 1050 #define __FUNCT__ "MatImaginaryPart_SeqAIJ" 1051 PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A) 1052 { 1053 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1054 PetscInt i,nz = a->nz; 1055 MatScalar *aa = a->a; 1056 PetscErrorCode ierr; 1057 1058 PetscFunctionBegin; 1059 for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]); 1060 ierr = MatSeqAIJInvalidateDiagonal(A);CHKERRQ(ierr); 1061 PetscFunctionReturn(0); 1062 } 1063 1064 #if defined(PETSC_THREADCOMM_ACTIVE) 1065 PetscErrorCode MatZeroEntries_SeqAIJ_Kernel(PetscInt thread_id,Mat A) 1066 { 1067 PetscErrorCode ierr; 1068 PetscInt *trstarts=A->rmap->trstarts; 1069 PetscInt n,start,end; 1070 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1071 1072 start = trstarts[thread_id]; 1073 end = trstarts[thread_id+1]; 1074 n = a->i[end] - a->i[start]; 1075 ierr = PetscMemzero(a->a+a->i[start],n*sizeof(PetscScalar));CHKERRQ(ierr); 1076 return 0; 1077 } 1078 1079 #undef __FUNCT__ 1080 #define __FUNCT__ "MatZeroEntries_SeqAIJ" 1081 PetscErrorCode MatZeroEntries_SeqAIJ(Mat A) 1082 { 1083 PetscErrorCode ierr; 1084 1085 PetscFunctionBegin; 1086 ierr = PetscThreadCommRunKernel(PetscObjectComm((PetscObject)A),(PetscThreadKernel)MatZeroEntries_SeqAIJ_Kernel,1,A);CHKERRQ(ierr); 1087 ierr = MatSeqAIJInvalidateDiagonal(A);CHKERRQ(ierr); 1088 PetscFunctionReturn(0); 1089 } 1090 #else 1091 #undef __FUNCT__ 1092 #define __FUNCT__ "MatZeroEntries_SeqAIJ" 1093 PetscErrorCode MatZeroEntries_SeqAIJ(Mat A) 1094 { 1095 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1096 PetscErrorCode ierr; 1097 1098 PetscFunctionBegin; 1099 ierr = PetscMemzero(a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));CHKERRQ(ierr); 1100 ierr = MatSeqAIJInvalidateDiagonal(A);CHKERRQ(ierr); 1101 PetscFunctionReturn(0); 1102 } 1103 #endif 1104 1105 extern PetscErrorCode MatDestroy_Redundant(Mat_Redundant **); 1106 1107 #undef __FUNCT__ 1108 #define __FUNCT__ "MatDestroy_SeqAIJ" 1109 PetscErrorCode MatDestroy_SeqAIJ(Mat A) 1110 { 1111 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1112 PetscErrorCode ierr; 1113 1114 PetscFunctionBegin; 1115 #if defined(PETSC_USE_LOG) 1116 PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz); 1117 #endif 1118 ierr = MatDestroy_Redundant(&a->redundant);CHKERRQ(ierr); 1119 ierr = MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);CHKERRQ(ierr); 1120 ierr = ISDestroy(&a->row);CHKERRQ(ierr); 1121 ierr = ISDestroy(&a->col);CHKERRQ(ierr); 1122 ierr = PetscFree(a->diag);CHKERRQ(ierr); 1123 ierr = PetscFree(a->ibdiag);CHKERRQ(ierr); 1124 ierr = PetscFree2(a->imax,a->ilen);CHKERRQ(ierr); 1125 ierr = PetscFree3(a->idiag,a->mdiag,a->ssor_work);CHKERRQ(ierr); 1126 ierr = PetscFree(a->solve_work);CHKERRQ(ierr); 1127 ierr = ISDestroy(&a->icol);CHKERRQ(ierr); 1128 ierr = PetscFree(a->saved_values);CHKERRQ(ierr); 1129 ierr = ISColoringDestroy(&a->coloring);CHKERRQ(ierr); 1130 ierr = PetscFree(a->xtoy);CHKERRQ(ierr); 1131 ierr = MatDestroy(&a->XtoY);CHKERRQ(ierr); 1132 ierr = PetscFree2(a->compressedrow.i,a->compressedrow.rindex);CHKERRQ(ierr); 1133 ierr = PetscFree(a->matmult_abdense);CHKERRQ(ierr); 1134 1135 ierr = MatDestroy_SeqAIJ_Inode(A);CHKERRQ(ierr); 1136 ierr = PetscFree(A->data);CHKERRQ(ierr); 1137 1138 ierr = PetscObjectChangeTypeName((PetscObject)A,0);CHKERRQ(ierr); 1139 ierr = PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetColumnIndices_C",NULL);CHKERRQ(ierr); 1140 ierr = PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);CHKERRQ(ierr); 1141 ierr = PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);CHKERRQ(ierr); 1142 ierr = PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsbaij_C",NULL);CHKERRQ(ierr); 1143 ierr = PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqbaij_C",NULL);CHKERRQ(ierr); 1144 ierr = PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijperm_C",NULL);CHKERRQ(ierr); 1145 ierr = PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);CHKERRQ(ierr); 1146 ierr = PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);CHKERRQ(ierr); 1147 ierr = PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);CHKERRQ(ierr); 1148 ierr = PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);CHKERRQ(ierr); 1149 PetscFunctionReturn(0); 1150 } 1151 1152 #undef __FUNCT__ 1153 #define __FUNCT__ "MatSetOption_SeqAIJ" 1154 PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg) 1155 { 1156 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1157 PetscErrorCode ierr; 1158 1159 PetscFunctionBegin; 1160 switch (op) { 1161 case MAT_ROW_ORIENTED: 1162 a->roworiented = flg; 1163 break; 1164 case MAT_KEEP_NONZERO_PATTERN: 1165 a->keepnonzeropattern = flg; 1166 break; 1167 case MAT_NEW_NONZERO_LOCATIONS: 1168 a->nonew = (flg ? 0 : 1); 1169 break; 1170 case MAT_NEW_NONZERO_LOCATION_ERR: 1171 a->nonew = (flg ? -1 : 0); 1172 break; 1173 case MAT_NEW_NONZERO_ALLOCATION_ERR: 1174 a->nonew = (flg ? -2 : 0); 1175 break; 1176 case MAT_UNUSED_NONZERO_LOCATION_ERR: 1177 a->nounused = (flg ? -1 : 0); 1178 break; 1179 case MAT_IGNORE_ZERO_ENTRIES: 1180 a->ignorezeroentries = flg; 1181 break; 1182 case MAT_SPD: 1183 case MAT_SYMMETRIC: 1184 case MAT_STRUCTURALLY_SYMMETRIC: 1185 case MAT_HERMITIAN: 1186 case MAT_SYMMETRY_ETERNAL: 1187 /* These options are handled directly by MatSetOption() */ 1188 break; 1189 case MAT_NEW_DIAGONALS: 1190 case MAT_IGNORE_OFF_PROC_ENTRIES: 1191 case MAT_USE_HASH_TABLE: 1192 ierr = PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);CHKERRQ(ierr); 1193 break; 1194 case MAT_USE_INODES: 1195 /* Not an error because MatSetOption_SeqAIJ_Inode handles this one */ 1196 break; 1197 default: 1198 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op); 1199 } 1200 ierr = MatSetOption_SeqAIJ_Inode(A,op,flg);CHKERRQ(ierr); 1201 PetscFunctionReturn(0); 1202 } 1203 1204 #undef __FUNCT__ 1205 #define __FUNCT__ "MatGetDiagonal_SeqAIJ" 1206 PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v) 1207 { 1208 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1209 PetscErrorCode ierr; 1210 PetscInt i,j,n,*ai=a->i,*aj=a->j,nz; 1211 PetscScalar *aa=a->a,*x,zero=0.0; 1212 1213 PetscFunctionBegin; 1214 ierr = VecGetLocalSize(v,&n);CHKERRQ(ierr); 1215 if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector"); 1216 1217 if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) { 1218 PetscInt *diag=a->diag; 1219 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 1220 for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]]; 1221 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 1222 PetscFunctionReturn(0); 1223 } 1224 1225 ierr = VecSet(v,zero);CHKERRQ(ierr); 1226 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 1227 for (i=0; i<n; i++) { 1228 nz = ai[i+1] - ai[i]; 1229 if (!nz) x[i] = 0.0; 1230 for (j=ai[i]; j<ai[i+1]; j++) { 1231 if (aj[j] == i) { 1232 x[i] = aa[j]; 1233 break; 1234 } 1235 } 1236 } 1237 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 1238 PetscFunctionReturn(0); 1239 } 1240 1241 #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h> 1242 #undef __FUNCT__ 1243 #define __FUNCT__ "MatMultTransposeAdd_SeqAIJ" 1244 PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy) 1245 { 1246 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1247 PetscScalar *x,*y; 1248 PetscErrorCode ierr; 1249 PetscInt m = A->rmap->n; 1250 #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ) 1251 MatScalar *v; 1252 PetscScalar alpha; 1253 PetscInt n,i,j,*idx,*ii,*ridx=NULL; 1254 Mat_CompressedRow cprow = a->compressedrow; 1255 PetscBool usecprow = cprow.use; 1256 #endif 1257 1258 PetscFunctionBegin; 1259 if (zz != yy) {ierr = VecCopy(zz,yy);CHKERRQ(ierr);} 1260 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 1261 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 1262 1263 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ) 1264 fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y); 1265 #else 1266 if (usecprow) { 1267 m = cprow.nrows; 1268 ii = cprow.i; 1269 ridx = cprow.rindex; 1270 } else { 1271 ii = a->i; 1272 } 1273 for (i=0; i<m; i++) { 1274 idx = a->j + ii[i]; 1275 v = a->a + ii[i]; 1276 n = ii[i+1] - ii[i]; 1277 if (usecprow) { 1278 alpha = x[ridx[i]]; 1279 } else { 1280 alpha = x[i]; 1281 } 1282 for (j=0; j<n; j++) y[idx[j]] += alpha*v[j]; 1283 } 1284 #endif 1285 ierr = PetscLogFlops(2.0*a->nz);CHKERRQ(ierr); 1286 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1287 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 1288 PetscFunctionReturn(0); 1289 } 1290 1291 #undef __FUNCT__ 1292 #define __FUNCT__ "MatMultTranspose_SeqAIJ" 1293 PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy) 1294 { 1295 PetscErrorCode ierr; 1296 1297 PetscFunctionBegin; 1298 ierr = VecSet(yy,0.0);CHKERRQ(ierr); 1299 ierr = MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);CHKERRQ(ierr); 1300 PetscFunctionReturn(0); 1301 } 1302 1303 #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h> 1304 #if defined(PETSC_THREADCOMM_ACTIVE) 1305 PetscErrorCode MatMult_SeqAIJ_Kernel(PetscInt thread_id,Mat A,Vec xx,Vec yy) 1306 { 1307 PetscErrorCode ierr; 1308 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1309 PetscScalar *y; 1310 const PetscScalar *x; 1311 const MatScalar *aa; 1312 PetscInt *trstarts=A->rmap->trstarts; 1313 PetscInt n,start,end,i; 1314 const PetscInt *aj,*ai; 1315 PetscScalar sum; 1316 1317 ierr = VecGetArrayRead(xx,&x);CHKERRQ(ierr); 1318 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 1319 start = trstarts[thread_id]; 1320 end = trstarts[thread_id+1]; 1321 aj = a->j; 1322 aa = a->a; 1323 ai = a->i; 1324 for (i=start; i<end; i++) { 1325 n = ai[i+1] - ai[i]; 1326 aj = a->j + ai[i]; 1327 aa = a->a + ai[i]; 1328 sum = 0.0; 1329 PetscSparseDensePlusDot(sum,x,aa,aj,n); 1330 y[i] = sum; 1331 } 1332 ierr = VecRestoreArrayRead(xx,&x);CHKERRQ(ierr); 1333 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 1334 return 0; 1335 } 1336 1337 #undef __FUNCT__ 1338 #define __FUNCT__ "MatMult_SeqAIJ" 1339 PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy) 1340 { 1341 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1342 PetscScalar *y; 1343 const PetscScalar *x; 1344 const MatScalar *aa; 1345 PetscErrorCode ierr; 1346 PetscInt m=A->rmap->n; 1347 const PetscInt *aj,*ii,*ridx=NULL; 1348 PetscInt n,i; 1349 PetscScalar sum; 1350 PetscBool usecprow=a->compressedrow.use; 1351 1352 #if defined(PETSC_HAVE_PRAGMA_DISJOINT) 1353 #pragma disjoint(*x,*y,*aa) 1354 #endif 1355 1356 PetscFunctionBegin; 1357 aj = a->j; 1358 aa = a->a; 1359 ii = a->i; 1360 if (usecprow) { /* use compressed row format */ 1361 ierr = VecGetArrayRead(xx,&x);CHKERRQ(ierr); 1362 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 1363 ierr = PetscMemzero(y,m*sizeof(PetscScalar));CHKERRQ(ierr); 1364 m = a->compressedrow.nrows; 1365 ii = a->compressedrow.i; 1366 ridx = a->compressedrow.rindex; 1367 for (i=0; i<m; i++) { 1368 n = ii[i+1] - ii[i]; 1369 aj = a->j + ii[i]; 1370 aa = a->a + ii[i]; 1371 sum = 0.0; 1372 PetscSparseDensePlusDot(sum,x,aa,aj,n); 1373 /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */ 1374 y[*ridx++] = sum; 1375 } 1376 ierr = VecRestoreArrayRead(xx,&x);CHKERRQ(ierr); 1377 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 1378 } else { /* do not use compressed row format */ 1379 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ) 1380 fortranmultaij_(&m,x,ii,aj,aa,y); 1381 #else 1382 ierr = PetscThreadCommRunKernel(PetscObjectComm((PetscObject)A),(PetscThreadKernel)MatMult_SeqAIJ_Kernel,3,A,xx,yy);CHKERRQ(ierr); 1383 #endif 1384 } 1385 ierr = PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);CHKERRQ(ierr); 1386 PetscFunctionReturn(0); 1387 } 1388 #else 1389 #undef __FUNCT__ 1390 #define __FUNCT__ "MatMult_SeqAIJ" 1391 PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy) 1392 { 1393 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1394 PetscScalar *y; 1395 const PetscScalar *x; 1396 const MatScalar *aa; 1397 PetscErrorCode ierr; 1398 PetscInt m=A->rmap->n; 1399 const PetscInt *aj,*ii,*ridx=NULL; 1400 PetscInt n,i; 1401 PetscScalar sum; 1402 PetscBool usecprow=a->compressedrow.use; 1403 1404 #if defined(PETSC_HAVE_PRAGMA_DISJOINT) 1405 #pragma disjoint(*x,*y,*aa) 1406 #endif 1407 1408 PetscFunctionBegin; 1409 ierr = VecGetArrayRead(xx,&x);CHKERRQ(ierr); 1410 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 1411 aj = a->j; 1412 aa = a->a; 1413 ii = a->i; 1414 if (usecprow) { /* use compressed row format */ 1415 ierr = PetscMemzero(y,m*sizeof(PetscScalar));CHKERRQ(ierr); 1416 m = a->compressedrow.nrows; 1417 ii = a->compressedrow.i; 1418 ridx = a->compressedrow.rindex; 1419 for (i=0; i<m; i++) { 1420 n = ii[i+1] - ii[i]; 1421 aj = a->j + ii[i]; 1422 aa = a->a + ii[i]; 1423 sum = 0.0; 1424 PetscSparseDensePlusDot(sum,x,aa,aj,n); 1425 /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */ 1426 y[*ridx++] = sum; 1427 } 1428 } else { /* do not use compressed row format */ 1429 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ) 1430 fortranmultaij_(&m,x,ii,aj,aa,y); 1431 #else 1432 #if defined(PETSC_THREADCOMM_ACTIVE) 1433 ierr = PetscThreadCommRunKernel(PetscObjectComm((PetscObject)A),(PetscThreadKernel)MatMult_SeqAIJ_Kernel,3,A,xx,yy);CHKERRQ(ierr); 1434 #else 1435 for (i=0; i<m; i++) { 1436 n = ii[i+1] - ii[i]; 1437 aj = a->j + ii[i]; 1438 aa = a->a + ii[i]; 1439 sum = 0.0; 1440 PetscSparseDensePlusDot(sum,x,aa,aj,n); 1441 y[i] = sum; 1442 } 1443 #endif 1444 #endif 1445 } 1446 ierr = PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);CHKERRQ(ierr); 1447 ierr = VecRestoreArrayRead(xx,&x);CHKERRQ(ierr); 1448 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 1449 PetscFunctionReturn(0); 1450 } 1451 #endif 1452 1453 #undef __FUNCT__ 1454 #define __FUNCT__ "MatMultMax_SeqAIJ" 1455 PetscErrorCode MatMultMax_SeqAIJ(Mat A,Vec xx,Vec yy) 1456 { 1457 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1458 PetscScalar *y; 1459 const PetscScalar *x; 1460 const MatScalar *aa; 1461 PetscErrorCode ierr; 1462 PetscInt m=A->rmap->n; 1463 const PetscInt *aj,*ii,*ridx=NULL; 1464 PetscInt n,i,nonzerorow=0; 1465 PetscScalar sum; 1466 PetscBool usecprow=a->compressedrow.use; 1467 1468 #if defined(PETSC_HAVE_PRAGMA_DISJOINT) 1469 #pragma disjoint(*x,*y,*aa) 1470 #endif 1471 1472 PetscFunctionBegin; 1473 ierr = VecGetArrayRead(xx,&x);CHKERRQ(ierr); 1474 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 1475 aj = a->j; 1476 aa = a->a; 1477 ii = a->i; 1478 if (usecprow) { /* use compressed row format */ 1479 m = a->compressedrow.nrows; 1480 ii = a->compressedrow.i; 1481 ridx = a->compressedrow.rindex; 1482 for (i=0; i<m; i++) { 1483 n = ii[i+1] - ii[i]; 1484 aj = a->j + ii[i]; 1485 aa = a->a + ii[i]; 1486 sum = 0.0; 1487 nonzerorow += (n>0); 1488 PetscSparseDenseMaxDot(sum,x,aa,aj,n); 1489 /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */ 1490 y[*ridx++] = sum; 1491 } 1492 } else { /* do not use compressed row format */ 1493 for (i=0; i<m; i++) { 1494 n = ii[i+1] - ii[i]; 1495 aj = a->j + ii[i]; 1496 aa = a->a + ii[i]; 1497 sum = 0.0; 1498 nonzerorow += (n>0); 1499 PetscSparseDenseMaxDot(sum,x,aa,aj,n); 1500 y[i] = sum; 1501 } 1502 } 1503 ierr = PetscLogFlops(2.0*a->nz - nonzerorow);CHKERRQ(ierr); 1504 ierr = VecRestoreArrayRead(xx,&x);CHKERRQ(ierr); 1505 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 1506 PetscFunctionReturn(0); 1507 } 1508 1509 #undef __FUNCT__ 1510 #define __FUNCT__ "MatMultAddMax_SeqAIJ" 1511 PetscErrorCode MatMultAddMax_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz) 1512 { 1513 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1514 PetscScalar *y,*z; 1515 const PetscScalar *x; 1516 const MatScalar *aa; 1517 PetscErrorCode ierr; 1518 PetscInt m = A->rmap->n,*aj,*ii; 1519 PetscInt n,i,*ridx=NULL; 1520 PetscScalar sum; 1521 PetscBool usecprow=a->compressedrow.use; 1522 1523 PetscFunctionBegin; 1524 ierr = VecGetArrayRead(xx,&x);CHKERRQ(ierr); 1525 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 1526 if (zz != yy) { 1527 ierr = VecGetArray(zz,&z);CHKERRQ(ierr); 1528 } else { 1529 z = y; 1530 } 1531 1532 aj = a->j; 1533 aa = a->a; 1534 ii = a->i; 1535 if (usecprow) { /* use compressed row format */ 1536 if (zz != yy) { 1537 ierr = PetscMemcpy(z,y,m*sizeof(PetscScalar));CHKERRQ(ierr); 1538 } 1539 m = a->compressedrow.nrows; 1540 ii = a->compressedrow.i; 1541 ridx = a->compressedrow.rindex; 1542 for (i=0; i<m; i++) { 1543 n = ii[i+1] - ii[i]; 1544 aj = a->j + ii[i]; 1545 aa = a->a + ii[i]; 1546 sum = y[*ridx]; 1547 PetscSparseDenseMaxDot(sum,x,aa,aj,n); 1548 z[*ridx++] = sum; 1549 } 1550 } else { /* do not use compressed row format */ 1551 for (i=0; i<m; i++) { 1552 n = ii[i+1] - ii[i]; 1553 aj = a->j + ii[i]; 1554 aa = a->a + ii[i]; 1555 sum = y[i]; 1556 PetscSparseDenseMaxDot(sum,x,aa,aj,n); 1557 z[i] = sum; 1558 } 1559 } 1560 ierr = PetscLogFlops(2.0*a->nz);CHKERRQ(ierr); 1561 ierr = VecRestoreArrayRead(xx,&x);CHKERRQ(ierr); 1562 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 1563 if (zz != yy) { 1564 ierr = VecRestoreArray(zz,&z);CHKERRQ(ierr); 1565 } 1566 PetscFunctionReturn(0); 1567 } 1568 1569 #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h> 1570 #undef __FUNCT__ 1571 #define __FUNCT__ "MatMultAdd_SeqAIJ" 1572 PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz) 1573 { 1574 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1575 PetscScalar *y,*z; 1576 const PetscScalar *x; 1577 const MatScalar *aa; 1578 PetscErrorCode ierr; 1579 PetscInt m = A->rmap->n,*aj,*ii; 1580 PetscInt n,i,*ridx=NULL; 1581 PetscScalar sum; 1582 PetscBool usecprow=a->compressedrow.use; 1583 1584 PetscFunctionBegin; 1585 ierr = VecGetArrayRead(xx,&x);CHKERRQ(ierr); 1586 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 1587 if (zz != yy) { 1588 ierr = VecGetArray(zz,&z);CHKERRQ(ierr); 1589 } else { 1590 z = y; 1591 } 1592 1593 aj = a->j; 1594 aa = a->a; 1595 ii = a->i; 1596 if (usecprow) { /* use compressed row format */ 1597 if (zz != yy) { 1598 ierr = PetscMemcpy(z,y,m*sizeof(PetscScalar));CHKERRQ(ierr); 1599 } 1600 m = a->compressedrow.nrows; 1601 ii = a->compressedrow.i; 1602 ridx = a->compressedrow.rindex; 1603 for (i=0; i<m; i++) { 1604 n = ii[i+1] - ii[i]; 1605 aj = a->j + ii[i]; 1606 aa = a->a + ii[i]; 1607 sum = y[*ridx]; 1608 PetscSparseDensePlusDot(sum,x,aa,aj,n); 1609 z[*ridx++] = sum; 1610 } 1611 } else { /* do not use compressed row format */ 1612 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ) 1613 fortranmultaddaij_(&m,x,ii,aj,aa,y,z); 1614 #else 1615 for (i=0; i<m; i++) { 1616 n = ii[i+1] - ii[i]; 1617 aj = a->j + ii[i]; 1618 aa = a->a + ii[i]; 1619 sum = y[i]; 1620 PetscSparseDensePlusDot(sum,x,aa,aj,n); 1621 z[i] = sum; 1622 } 1623 #endif 1624 } 1625 ierr = PetscLogFlops(2.0*a->nz);CHKERRQ(ierr); 1626 ierr = VecRestoreArrayRead(xx,&x);CHKERRQ(ierr); 1627 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 1628 if (zz != yy) { 1629 ierr = VecRestoreArray(zz,&z);CHKERRQ(ierr); 1630 } 1631 #if defined(PETSC_HAVE_CUSP) 1632 /* 1633 ierr = VecView(xx,0);CHKERRQ(ierr); 1634 ierr = VecView(zz,0);CHKERRQ(ierr); 1635 ierr = MatView(A,0);CHKERRQ(ierr); 1636 */ 1637 #endif 1638 PetscFunctionReturn(0); 1639 } 1640 1641 /* 1642 Adds diagonal pointers to sparse matrix structure. 1643 */ 1644 #undef __FUNCT__ 1645 #define __FUNCT__ "MatMarkDiagonal_SeqAIJ" 1646 PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A) 1647 { 1648 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1649 PetscErrorCode ierr; 1650 PetscInt i,j,m = A->rmap->n; 1651 1652 PetscFunctionBegin; 1653 if (!a->diag) { 1654 ierr = PetscMalloc1(m,&a->diag);CHKERRQ(ierr); 1655 ierr = PetscLogObjectMemory((PetscObject)A, m*sizeof(PetscInt));CHKERRQ(ierr); 1656 } 1657 for (i=0; i<A->rmap->n; i++) { 1658 a->diag[i] = a->i[i+1]; 1659 for (j=a->i[i]; j<a->i[i+1]; j++) { 1660 if (a->j[j] == i) { 1661 a->diag[i] = j; 1662 break; 1663 } 1664 } 1665 } 1666 PetscFunctionReturn(0); 1667 } 1668 1669 /* 1670 Checks for missing diagonals 1671 */ 1672 #undef __FUNCT__ 1673 #define __FUNCT__ "MatMissingDiagonal_SeqAIJ" 1674 PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool *missing,PetscInt *d) 1675 { 1676 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1677 PetscInt *diag,*ii = a->i,i; 1678 1679 PetscFunctionBegin; 1680 *missing = PETSC_FALSE; 1681 if (A->rmap->n > 0 && !ii) { 1682 *missing = PETSC_TRUE; 1683 if (d) *d = 0; 1684 PetscInfo(A,"Matrix has no entries therefore is missing diagonal"); 1685 } else { 1686 diag = a->diag; 1687 for (i=0; i<A->rmap->n; i++) { 1688 if (diag[i] >= ii[i+1]) { 1689 *missing = PETSC_TRUE; 1690 if (d) *d = i; 1691 PetscInfo1(A,"Matrix is missing diagonal number %D",i); 1692 break; 1693 } 1694 } 1695 } 1696 PetscFunctionReturn(0); 1697 } 1698 1699 #undef __FUNCT__ 1700 #define __FUNCT__ "MatInvertDiagonal_SeqAIJ" 1701 PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift) 1702 { 1703 Mat_SeqAIJ *a = (Mat_SeqAIJ*) A->data; 1704 PetscErrorCode ierr; 1705 PetscInt i,*diag,m = A->rmap->n; 1706 MatScalar *v = a->a; 1707 PetscScalar *idiag,*mdiag; 1708 1709 PetscFunctionBegin; 1710 if (a->idiagvalid) PetscFunctionReturn(0); 1711 ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr); 1712 diag = a->diag; 1713 if (!a->idiag) { 1714 ierr = PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);CHKERRQ(ierr); 1715 ierr = PetscLogObjectMemory((PetscObject)A, 3*m*sizeof(PetscScalar));CHKERRQ(ierr); 1716 v = a->a; 1717 } 1718 mdiag = a->mdiag; 1719 idiag = a->idiag; 1720 1721 if (omega == 1.0 && !PetscAbsScalar(fshift)) { 1722 for (i=0; i<m; i++) { 1723 mdiag[i] = v[diag[i]]; 1724 if (!PetscAbsScalar(mdiag[i])) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i); 1725 idiag[i] = 1.0/v[diag[i]]; 1726 } 1727 ierr = PetscLogFlops(m);CHKERRQ(ierr); 1728 } else { 1729 for (i=0; i<m; i++) { 1730 mdiag[i] = v[diag[i]]; 1731 idiag[i] = omega/(fshift + v[diag[i]]); 1732 } 1733 ierr = PetscLogFlops(2.0*m);CHKERRQ(ierr); 1734 } 1735 a->idiagvalid = PETSC_TRUE; 1736 PetscFunctionReturn(0); 1737 } 1738 1739 #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h> 1740 #undef __FUNCT__ 1741 #define __FUNCT__ "MatSOR_SeqAIJ" 1742 PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx) 1743 { 1744 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1745 PetscScalar *x,d,sum,*t,scale; 1746 const MatScalar *v = a->a,*idiag=0,*mdiag; 1747 const PetscScalar *b, *bs,*xb, *ts; 1748 PetscErrorCode ierr; 1749 PetscInt n = A->cmap->n,m = A->rmap->n,i; 1750 const PetscInt *idx,*diag; 1751 1752 PetscFunctionBegin; 1753 its = its*lits; 1754 1755 if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */ 1756 if (!a->idiagvalid) {ierr = MatInvertDiagonal_SeqAIJ(A,omega,fshift);CHKERRQ(ierr);} 1757 a->fshift = fshift; 1758 a->omega = omega; 1759 1760 diag = a->diag; 1761 t = a->ssor_work; 1762 idiag = a->idiag; 1763 mdiag = a->mdiag; 1764 1765 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 1766 ierr = VecGetArrayRead(bb,&b);CHKERRQ(ierr); 1767 /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */ 1768 if (flag == SOR_APPLY_UPPER) { 1769 /* apply (U + D/omega) to the vector */ 1770 bs = b; 1771 for (i=0; i<m; i++) { 1772 d = fshift + mdiag[i]; 1773 n = a->i[i+1] - diag[i] - 1; 1774 idx = a->j + diag[i] + 1; 1775 v = a->a + diag[i] + 1; 1776 sum = b[i]*d/omega; 1777 PetscSparseDensePlusDot(sum,bs,v,idx,n); 1778 x[i] = sum; 1779 } 1780 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1781 ierr = VecRestoreArrayRead(bb,&b);CHKERRQ(ierr); 1782 ierr = PetscLogFlops(a->nz);CHKERRQ(ierr); 1783 PetscFunctionReturn(0); 1784 } 1785 1786 if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented"); 1787 else if (flag & SOR_EISENSTAT) { 1788 /* Let A = L + U + D; where L is lower trianglar, 1789 U is upper triangular, E = D/omega; This routine applies 1790 1791 (L + E)^{-1} A (U + E)^{-1} 1792 1793 to a vector efficiently using Eisenstat's trick. 1794 */ 1795 scale = (2.0/omega) - 1.0; 1796 1797 /* x = (E + U)^{-1} b */ 1798 for (i=m-1; i>=0; i--) { 1799 n = a->i[i+1] - diag[i] - 1; 1800 idx = a->j + diag[i] + 1; 1801 v = a->a + diag[i] + 1; 1802 sum = b[i]; 1803 PetscSparseDenseMinusDot(sum,x,v,idx,n); 1804 x[i] = sum*idiag[i]; 1805 } 1806 1807 /* t = b - (2*E - D)x */ 1808 v = a->a; 1809 for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i]; 1810 1811 /* t = (E + L)^{-1}t */ 1812 ts = t; 1813 diag = a->diag; 1814 for (i=0; i<m; i++) { 1815 n = diag[i] - a->i[i]; 1816 idx = a->j + a->i[i]; 1817 v = a->a + a->i[i]; 1818 sum = t[i]; 1819 PetscSparseDenseMinusDot(sum,ts,v,idx,n); 1820 t[i] = sum*idiag[i]; 1821 /* x = x + t */ 1822 x[i] += t[i]; 1823 } 1824 1825 ierr = PetscLogFlops(6.0*m-1 + 2.0*a->nz);CHKERRQ(ierr); 1826 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1827 ierr = VecRestoreArrayRead(bb,&b);CHKERRQ(ierr); 1828 PetscFunctionReturn(0); 1829 } 1830 if (flag & SOR_ZERO_INITIAL_GUESS) { 1831 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) { 1832 for (i=0; i<m; i++) { 1833 n = diag[i] - a->i[i]; 1834 idx = a->j + a->i[i]; 1835 v = a->a + a->i[i]; 1836 sum = b[i]; 1837 PetscSparseDenseMinusDot(sum,x,v,idx,n); 1838 t[i] = sum; 1839 x[i] = sum*idiag[i]; 1840 } 1841 xb = t; 1842 ierr = PetscLogFlops(a->nz);CHKERRQ(ierr); 1843 } else xb = b; 1844 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) { 1845 for (i=m-1; i>=0; i--) { 1846 n = a->i[i+1] - diag[i] - 1; 1847 idx = a->j + diag[i] + 1; 1848 v = a->a + diag[i] + 1; 1849 sum = xb[i]; 1850 PetscSparseDenseMinusDot(sum,x,v,idx,n); 1851 if (xb == b) { 1852 x[i] = sum*idiag[i]; 1853 } else { 1854 x[i] = (1-omega)*x[i] + sum*idiag[i]; /* omega in idiag */ 1855 } 1856 } 1857 ierr = PetscLogFlops(a->nz);CHKERRQ(ierr); /* assumes 1/2 in upper */ 1858 } 1859 its--; 1860 } 1861 while (its--) { 1862 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) { 1863 for (i=0; i<m; i++) { 1864 /* lower */ 1865 n = diag[i] - a->i[i]; 1866 idx = a->j + a->i[i]; 1867 v = a->a + a->i[i]; 1868 sum = b[i]; 1869 PetscSparseDenseMinusDot(sum,x,v,idx,n); 1870 t[i] = sum; /* save application of the lower-triangular part */ 1871 /* upper */ 1872 n = a->i[i+1] - diag[i] - 1; 1873 idx = a->j + diag[i] + 1; 1874 v = a->a + diag[i] + 1; 1875 PetscSparseDenseMinusDot(sum,x,v,idx,n); 1876 x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */ 1877 } 1878 xb = t; 1879 ierr = PetscLogFlops(2.0*a->nz);CHKERRQ(ierr); 1880 } else xb = b; 1881 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) { 1882 for (i=m-1; i>=0; i--) { 1883 sum = xb[i]; 1884 if (xb == b) { 1885 /* whole matrix (no checkpointing available) */ 1886 n = a->i[i+1] - a->i[i]; 1887 idx = a->j + a->i[i]; 1888 v = a->a + a->i[i]; 1889 PetscSparseDenseMinusDot(sum,x,v,idx,n); 1890 x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i]; 1891 } else { /* lower-triangular part has been saved, so only apply upper-triangular */ 1892 n = a->i[i+1] - diag[i] - 1; 1893 idx = a->j + diag[i] + 1; 1894 v = a->a + diag[i] + 1; 1895 PetscSparseDenseMinusDot(sum,x,v,idx,n); 1896 x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */ 1897 } 1898 } 1899 if (xb == b) { 1900 ierr = PetscLogFlops(2.0*a->nz);CHKERRQ(ierr); 1901 } else { 1902 ierr = PetscLogFlops(a->nz);CHKERRQ(ierr); /* assumes 1/2 in upper */ 1903 } 1904 } 1905 } 1906 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1907 ierr = VecRestoreArrayRead(bb,&b);CHKERRQ(ierr); 1908 PetscFunctionReturn(0); 1909 } 1910 1911 1912 #undef __FUNCT__ 1913 #define __FUNCT__ "MatGetInfo_SeqAIJ" 1914 PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info) 1915 { 1916 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1917 1918 PetscFunctionBegin; 1919 info->block_size = 1.0; 1920 info->nz_allocated = (double)a->maxnz; 1921 info->nz_used = (double)a->nz; 1922 info->nz_unneeded = (double)(a->maxnz - a->nz); 1923 info->assemblies = (double)A->num_ass; 1924 info->mallocs = (double)A->info.mallocs; 1925 info->memory = ((PetscObject)A)->mem; 1926 if (A->factortype) { 1927 info->fill_ratio_given = A->info.fill_ratio_given; 1928 info->fill_ratio_needed = A->info.fill_ratio_needed; 1929 info->factor_mallocs = A->info.factor_mallocs; 1930 } else { 1931 info->fill_ratio_given = 0; 1932 info->fill_ratio_needed = 0; 1933 info->factor_mallocs = 0; 1934 } 1935 PetscFunctionReturn(0); 1936 } 1937 1938 #undef __FUNCT__ 1939 #define __FUNCT__ "MatZeroRows_SeqAIJ" 1940 PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b) 1941 { 1942 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1943 PetscInt i,m = A->rmap->n - 1,d = 0; 1944 PetscErrorCode ierr; 1945 const PetscScalar *xx; 1946 PetscScalar *bb; 1947 PetscBool missing; 1948 1949 PetscFunctionBegin; 1950 if (x && b) { 1951 ierr = VecGetArrayRead(x,&xx);CHKERRQ(ierr); 1952 ierr = VecGetArray(b,&bb);CHKERRQ(ierr); 1953 for (i=0; i<N; i++) { 1954 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]); 1955 bb[rows[i]] = diag*xx[rows[i]]; 1956 } 1957 ierr = VecRestoreArrayRead(x,&xx);CHKERRQ(ierr); 1958 ierr = VecRestoreArray(b,&bb);CHKERRQ(ierr); 1959 } 1960 1961 if (a->keepnonzeropattern) { 1962 for (i=0; i<N; i++) { 1963 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]); 1964 ierr = PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));CHKERRQ(ierr); 1965 } 1966 if (diag != 0.0) { 1967 ierr = MatMissingDiagonal_SeqAIJ(A,&missing,&d);CHKERRQ(ierr); 1968 if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d); 1969 for (i=0; i<N; i++) { 1970 a->a[a->diag[rows[i]]] = diag; 1971 } 1972 } 1973 } else { 1974 if (diag != 0.0) { 1975 for (i=0; i<N; i++) { 1976 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]); 1977 if (a->ilen[rows[i]] > 0) { 1978 a->ilen[rows[i]] = 1; 1979 a->a[a->i[rows[i]]] = diag; 1980 a->j[a->i[rows[i]]] = rows[i]; 1981 } else { /* in case row was completely empty */ 1982 ierr = MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);CHKERRQ(ierr); 1983 } 1984 } 1985 } else { 1986 for (i=0; i<N; i++) { 1987 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]); 1988 a->ilen[rows[i]] = 0; 1989 } 1990 } 1991 A->nonzerostate++; 1992 } 1993 ierr = MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1994 PetscFunctionReturn(0); 1995 } 1996 1997 #undef __FUNCT__ 1998 #define __FUNCT__ "MatZeroRowsColumns_SeqAIJ" 1999 PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b) 2000 { 2001 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2002 PetscInt i,j,m = A->rmap->n - 1,d = 0; 2003 PetscErrorCode ierr; 2004 PetscBool missing,*zeroed,vecs = PETSC_FALSE; 2005 const PetscScalar *xx; 2006 PetscScalar *bb; 2007 2008 PetscFunctionBegin; 2009 if (x && b) { 2010 ierr = VecGetArrayRead(x,&xx);CHKERRQ(ierr); 2011 ierr = VecGetArray(b,&bb);CHKERRQ(ierr); 2012 vecs = PETSC_TRUE; 2013 } 2014 ierr = PetscCalloc1(A->rmap->n,&zeroed);CHKERRQ(ierr); 2015 for (i=0; i<N; i++) { 2016 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]); 2017 ierr = PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));CHKERRQ(ierr); 2018 2019 zeroed[rows[i]] = PETSC_TRUE; 2020 } 2021 for (i=0; i<A->rmap->n; i++) { 2022 if (!zeroed[i]) { 2023 for (j=a->i[i]; j<a->i[i+1]; j++) { 2024 if (zeroed[a->j[j]]) { 2025 if (vecs) bb[i] -= a->a[j]*xx[a->j[j]]; 2026 a->a[j] = 0.0; 2027 } 2028 } 2029 } else if (vecs) bb[i] = diag*xx[i]; 2030 } 2031 if (x && b) { 2032 ierr = VecRestoreArrayRead(x,&xx);CHKERRQ(ierr); 2033 ierr = VecRestoreArray(b,&bb);CHKERRQ(ierr); 2034 } 2035 ierr = PetscFree(zeroed);CHKERRQ(ierr); 2036 if (diag != 0.0) { 2037 ierr = MatMissingDiagonal_SeqAIJ(A,&missing,&d);CHKERRQ(ierr); 2038 if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d); 2039 for (i=0; i<N; i++) { 2040 a->a[a->diag[rows[i]]] = diag; 2041 } 2042 } 2043 ierr = MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2044 PetscFunctionReturn(0); 2045 } 2046 2047 #undef __FUNCT__ 2048 #define __FUNCT__ "MatGetRow_SeqAIJ" 2049 PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v) 2050 { 2051 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2052 PetscInt *itmp; 2053 2054 PetscFunctionBegin; 2055 if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row); 2056 2057 *nz = a->i[row+1] - a->i[row]; 2058 if (v) *v = a->a + a->i[row]; 2059 if (idx) { 2060 itmp = a->j + a->i[row]; 2061 if (*nz) *idx = itmp; 2062 else *idx = 0; 2063 } 2064 PetscFunctionReturn(0); 2065 } 2066 2067 /* remove this function? */ 2068 #undef __FUNCT__ 2069 #define __FUNCT__ "MatRestoreRow_SeqAIJ" 2070 PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v) 2071 { 2072 PetscFunctionBegin; 2073 PetscFunctionReturn(0); 2074 } 2075 2076 #undef __FUNCT__ 2077 #define __FUNCT__ "MatNorm_SeqAIJ" 2078 PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm) 2079 { 2080 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2081 MatScalar *v = a->a; 2082 PetscReal sum = 0.0; 2083 PetscErrorCode ierr; 2084 PetscInt i,j; 2085 2086 PetscFunctionBegin; 2087 if (type == NORM_FROBENIUS) { 2088 for (i=0; i<a->nz; i++) { 2089 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 2090 } 2091 *nrm = PetscSqrtReal(sum); 2092 } else if (type == NORM_1) { 2093 PetscReal *tmp; 2094 PetscInt *jj = a->j; 2095 ierr = PetscCalloc1(A->cmap->n+1,&tmp);CHKERRQ(ierr); 2096 *nrm = 0.0; 2097 for (j=0; j<a->nz; j++) { 2098 tmp[*jj++] += PetscAbsScalar(*v); v++; 2099 } 2100 for (j=0; j<A->cmap->n; j++) { 2101 if (tmp[j] > *nrm) *nrm = tmp[j]; 2102 } 2103 ierr = PetscFree(tmp);CHKERRQ(ierr); 2104 } else if (type == NORM_INFINITY) { 2105 *nrm = 0.0; 2106 for (j=0; j<A->rmap->n; j++) { 2107 v = a->a + a->i[j]; 2108 sum = 0.0; 2109 for (i=0; i<a->i[j+1]-a->i[j]; i++) { 2110 sum += PetscAbsScalar(*v); v++; 2111 } 2112 if (sum > *nrm) *nrm = sum; 2113 } 2114 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm"); 2115 PetscFunctionReturn(0); 2116 } 2117 2118 /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */ 2119 #undef __FUNCT__ 2120 #define __FUNCT__ "MatTransposeSymbolic_SeqAIJ" 2121 PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B) 2122 { 2123 PetscErrorCode ierr; 2124 PetscInt i,j,anzj; 2125 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data,*b; 2126 PetscInt an=A->cmap->N,am=A->rmap->N; 2127 PetscInt *ati,*atj,*atfill,*ai=a->i,*aj=a->j; 2128 2129 PetscFunctionBegin; 2130 /* Allocate space for symbolic transpose info and work array */ 2131 ierr = PetscCalloc1((an+1),&ati);CHKERRQ(ierr); 2132 ierr = PetscMalloc1(ai[am],&atj);CHKERRQ(ierr); 2133 ierr = PetscMalloc1(an,&atfill);CHKERRQ(ierr); 2134 2135 /* Walk through aj and count ## of non-zeros in each row of A^T. */ 2136 /* Note: offset by 1 for fast conversion into csr format. */ 2137 for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1; 2138 /* Form ati for csr format of A^T. */ 2139 for (i=0;i<an;i++) ati[i+1] += ati[i]; 2140 2141 /* Copy ati into atfill so we have locations of the next free space in atj */ 2142 ierr = PetscMemcpy(atfill,ati,an*sizeof(PetscInt));CHKERRQ(ierr); 2143 2144 /* Walk through A row-wise and mark nonzero entries of A^T. */ 2145 for (i=0;i<am;i++) { 2146 anzj = ai[i+1] - ai[i]; 2147 for (j=0;j<anzj;j++) { 2148 atj[atfill[*aj]] = i; 2149 atfill[*aj++] += 1; 2150 } 2151 } 2152 2153 /* Clean up temporary space and complete requests. */ 2154 ierr = PetscFree(atfill);CHKERRQ(ierr); 2155 ierr = MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);CHKERRQ(ierr); 2156 ierr = MatSetBlockSizes(*B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));CHKERRQ(ierr); 2157 2158 b = (Mat_SeqAIJ*)((*B)->data); 2159 b->free_a = PETSC_FALSE; 2160 b->free_ij = PETSC_TRUE; 2161 b->nonew = 0; 2162 PetscFunctionReturn(0); 2163 } 2164 2165 #undef __FUNCT__ 2166 #define __FUNCT__ "MatTranspose_SeqAIJ" 2167 PetscErrorCode MatTranspose_SeqAIJ(Mat A,MatReuse reuse,Mat *B) 2168 { 2169 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2170 Mat C; 2171 PetscErrorCode ierr; 2172 PetscInt i,*aj = a->j,*ai = a->i,m = A->rmap->n,len,*col; 2173 MatScalar *array = a->a; 2174 2175 PetscFunctionBegin; 2176 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"); 2177 2178 if (reuse == MAT_INITIAL_MATRIX || *B == A) { 2179 ierr = PetscCalloc1((1+A->cmap->n),&col);CHKERRQ(ierr); 2180 2181 for (i=0; i<ai[m]; i++) col[aj[i]] += 1; 2182 ierr = MatCreate(PetscObjectComm((PetscObject)A),&C);CHKERRQ(ierr); 2183 ierr = MatSetSizes(C,A->cmap->n,m,A->cmap->n,m);CHKERRQ(ierr); 2184 ierr = MatSetBlockSizes(C,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));CHKERRQ(ierr); 2185 ierr = MatSetType(C,((PetscObject)A)->type_name);CHKERRQ(ierr); 2186 ierr = MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);CHKERRQ(ierr); 2187 ierr = PetscFree(col);CHKERRQ(ierr); 2188 } else { 2189 C = *B; 2190 } 2191 2192 for (i=0; i<m; i++) { 2193 len = ai[i+1]-ai[i]; 2194 ierr = MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);CHKERRQ(ierr); 2195 array += len; 2196 aj += len; 2197 } 2198 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2199 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2200 2201 if (reuse == MAT_INITIAL_MATRIX || *B != A) { 2202 *B = C; 2203 } else { 2204 ierr = MatHeaderMerge(A,C);CHKERRQ(ierr); 2205 } 2206 PetscFunctionReturn(0); 2207 } 2208 2209 #undef __FUNCT__ 2210 #define __FUNCT__ "MatIsTranspose_SeqAIJ" 2211 PetscErrorCode MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f) 2212 { 2213 Mat_SeqAIJ *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) A->data; 2214 PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr; 2215 MatScalar *va,*vb; 2216 PetscErrorCode ierr; 2217 PetscInt ma,na,mb,nb, i; 2218 2219 PetscFunctionBegin; 2220 bij = (Mat_SeqAIJ*) B->data; 2221 2222 ierr = MatGetSize(A,&ma,&na);CHKERRQ(ierr); 2223 ierr = MatGetSize(B,&mb,&nb);CHKERRQ(ierr); 2224 if (ma!=nb || na!=mb) { 2225 *f = PETSC_FALSE; 2226 PetscFunctionReturn(0); 2227 } 2228 aii = aij->i; bii = bij->i; 2229 adx = aij->j; bdx = bij->j; 2230 va = aij->a; vb = bij->a; 2231 ierr = PetscMalloc1(ma,&aptr);CHKERRQ(ierr); 2232 ierr = PetscMalloc1(mb,&bptr);CHKERRQ(ierr); 2233 for (i=0; i<ma; i++) aptr[i] = aii[i]; 2234 for (i=0; i<mb; i++) bptr[i] = bii[i]; 2235 2236 *f = PETSC_TRUE; 2237 for (i=0; i<ma; i++) { 2238 while (aptr[i]<aii[i+1]) { 2239 PetscInt idc,idr; 2240 PetscScalar vc,vr; 2241 /* column/row index/value */ 2242 idc = adx[aptr[i]]; 2243 idr = bdx[bptr[idc]]; 2244 vc = va[aptr[i]]; 2245 vr = vb[bptr[idc]]; 2246 if (i!=idr || PetscAbsScalar(vc-vr) > tol) { 2247 *f = PETSC_FALSE; 2248 goto done; 2249 } else { 2250 aptr[i]++; 2251 if (B || i!=idc) bptr[idc]++; 2252 } 2253 } 2254 } 2255 done: 2256 ierr = PetscFree(aptr);CHKERRQ(ierr); 2257 ierr = PetscFree(bptr);CHKERRQ(ierr); 2258 PetscFunctionReturn(0); 2259 } 2260 2261 #undef __FUNCT__ 2262 #define __FUNCT__ "MatIsHermitianTranspose_SeqAIJ" 2263 PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f) 2264 { 2265 Mat_SeqAIJ *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) A->data; 2266 PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr; 2267 MatScalar *va,*vb; 2268 PetscErrorCode ierr; 2269 PetscInt ma,na,mb,nb, i; 2270 2271 PetscFunctionBegin; 2272 bij = (Mat_SeqAIJ*) B->data; 2273 2274 ierr = MatGetSize(A,&ma,&na);CHKERRQ(ierr); 2275 ierr = MatGetSize(B,&mb,&nb);CHKERRQ(ierr); 2276 if (ma!=nb || na!=mb) { 2277 *f = PETSC_FALSE; 2278 PetscFunctionReturn(0); 2279 } 2280 aii = aij->i; bii = bij->i; 2281 adx = aij->j; bdx = bij->j; 2282 va = aij->a; vb = bij->a; 2283 ierr = PetscMalloc1(ma,&aptr);CHKERRQ(ierr); 2284 ierr = PetscMalloc1(mb,&bptr);CHKERRQ(ierr); 2285 for (i=0; i<ma; i++) aptr[i] = aii[i]; 2286 for (i=0; i<mb; i++) bptr[i] = bii[i]; 2287 2288 *f = PETSC_TRUE; 2289 for (i=0; i<ma; i++) { 2290 while (aptr[i]<aii[i+1]) { 2291 PetscInt idc,idr; 2292 PetscScalar vc,vr; 2293 /* column/row index/value */ 2294 idc = adx[aptr[i]]; 2295 idr = bdx[bptr[idc]]; 2296 vc = va[aptr[i]]; 2297 vr = vb[bptr[idc]]; 2298 if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) { 2299 *f = PETSC_FALSE; 2300 goto done; 2301 } else { 2302 aptr[i]++; 2303 if (B || i!=idc) bptr[idc]++; 2304 } 2305 } 2306 } 2307 done: 2308 ierr = PetscFree(aptr);CHKERRQ(ierr); 2309 ierr = PetscFree(bptr);CHKERRQ(ierr); 2310 PetscFunctionReturn(0); 2311 } 2312 2313 #undef __FUNCT__ 2314 #define __FUNCT__ "MatIsSymmetric_SeqAIJ" 2315 PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool *f) 2316 { 2317 PetscErrorCode ierr; 2318 2319 PetscFunctionBegin; 2320 ierr = MatIsTranspose_SeqAIJ(A,A,tol,f);CHKERRQ(ierr); 2321 PetscFunctionReturn(0); 2322 } 2323 2324 #undef __FUNCT__ 2325 #define __FUNCT__ "MatIsHermitian_SeqAIJ" 2326 PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool *f) 2327 { 2328 PetscErrorCode ierr; 2329 2330 PetscFunctionBegin; 2331 ierr = MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);CHKERRQ(ierr); 2332 PetscFunctionReturn(0); 2333 } 2334 2335 #undef __FUNCT__ 2336 #define __FUNCT__ "MatDiagonalScale_SeqAIJ" 2337 PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr) 2338 { 2339 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2340 PetscScalar *l,*r,x; 2341 MatScalar *v; 2342 PetscErrorCode ierr; 2343 PetscInt i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz,*jj; 2344 2345 PetscFunctionBegin; 2346 if (ll) { 2347 /* The local size is used so that VecMPI can be passed to this routine 2348 by MatDiagonalScale_MPIAIJ */ 2349 ierr = VecGetLocalSize(ll,&m);CHKERRQ(ierr); 2350 if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length"); 2351 ierr = VecGetArray(ll,&l);CHKERRQ(ierr); 2352 v = a->a; 2353 for (i=0; i<m; i++) { 2354 x = l[i]; 2355 M = a->i[i+1] - a->i[i]; 2356 for (j=0; j<M; j++) (*v++) *= x; 2357 } 2358 ierr = VecRestoreArray(ll,&l);CHKERRQ(ierr); 2359 ierr = PetscLogFlops(nz);CHKERRQ(ierr); 2360 } 2361 if (rr) { 2362 ierr = VecGetLocalSize(rr,&n);CHKERRQ(ierr); 2363 if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length"); 2364 ierr = VecGetArray(rr,&r);CHKERRQ(ierr); 2365 v = a->a; jj = a->j; 2366 for (i=0; i<nz; i++) (*v++) *= r[*jj++]; 2367 ierr = VecRestoreArray(rr,&r);CHKERRQ(ierr); 2368 ierr = PetscLogFlops(nz);CHKERRQ(ierr); 2369 } 2370 ierr = MatSeqAIJInvalidateDiagonal(A);CHKERRQ(ierr); 2371 PetscFunctionReturn(0); 2372 } 2373 2374 #undef __FUNCT__ 2375 #define __FUNCT__ "MatGetSubMatrix_SeqAIJ" 2376 PetscErrorCode MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B) 2377 { 2378 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*c; 2379 PetscErrorCode ierr; 2380 PetscInt *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens; 2381 PetscInt row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi; 2382 const PetscInt *irow,*icol; 2383 PetscInt nrows,ncols; 2384 PetscInt *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen; 2385 MatScalar *a_new,*mat_a; 2386 Mat C; 2387 PetscBool stride,sorted; 2388 2389 PetscFunctionBegin; 2390 ierr = ISSorted(isrow,&sorted);CHKERRQ(ierr); 2391 if (!sorted) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted"); 2392 ierr = ISSorted(iscol,&sorted);CHKERRQ(ierr); 2393 if (!sorted) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted"); 2394 2395 ierr = ISGetIndices(isrow,&irow);CHKERRQ(ierr); 2396 ierr = ISGetLocalSize(isrow,&nrows);CHKERRQ(ierr); 2397 ierr = ISGetLocalSize(iscol,&ncols);CHKERRQ(ierr); 2398 2399 ierr = PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);CHKERRQ(ierr); 2400 if (stride) { 2401 ierr = ISStrideGetInfo(iscol,&first,&step);CHKERRQ(ierr); 2402 } else { 2403 first = 0; 2404 step = 0; 2405 } 2406 if (stride && step == 1) { 2407 /* special case of contiguous rows */ 2408 ierr = PetscMalloc2(nrows,&lens,nrows,&starts);CHKERRQ(ierr); 2409 /* loop over new rows determining lens and starting points */ 2410 for (i=0; i<nrows; i++) { 2411 kstart = ai[irow[i]]; 2412 kend = kstart + ailen[irow[i]]; 2413 starts[i] = kstart; 2414 for (k=kstart; k<kend; k++) { 2415 if (aj[k] >= first) { 2416 starts[i] = k; 2417 break; 2418 } 2419 } 2420 sum = 0; 2421 while (k < kend) { 2422 if (aj[k++] >= first+ncols) break; 2423 sum++; 2424 } 2425 lens[i] = sum; 2426 } 2427 /* create submatrix */ 2428 if (scall == MAT_REUSE_MATRIX) { 2429 PetscInt n_cols,n_rows; 2430 ierr = MatGetSize(*B,&n_rows,&n_cols);CHKERRQ(ierr); 2431 if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size"); 2432 ierr = MatZeroEntries(*B);CHKERRQ(ierr); 2433 C = *B; 2434 } else { 2435 PetscInt rbs,cbs; 2436 ierr = MatCreate(PetscObjectComm((PetscObject)A),&C);CHKERRQ(ierr); 2437 ierr = MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);CHKERRQ(ierr); 2438 ierr = ISGetBlockSize(isrow,&rbs);CHKERRQ(ierr); 2439 ierr = ISGetBlockSize(iscol,&cbs);CHKERRQ(ierr); 2440 ierr = MatSetBlockSizes(C,rbs,cbs);CHKERRQ(ierr); 2441 ierr = MatSetType(C,((PetscObject)A)->type_name);CHKERRQ(ierr); 2442 ierr = MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);CHKERRQ(ierr); 2443 } 2444 c = (Mat_SeqAIJ*)C->data; 2445 2446 /* loop over rows inserting into submatrix */ 2447 a_new = c->a; 2448 j_new = c->j; 2449 i_new = c->i; 2450 2451 for (i=0; i<nrows; i++) { 2452 ii = starts[i]; 2453 lensi = lens[i]; 2454 for (k=0; k<lensi; k++) { 2455 *j_new++ = aj[ii+k] - first; 2456 } 2457 ierr = PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));CHKERRQ(ierr); 2458 a_new += lensi; 2459 i_new[i+1] = i_new[i] + lensi; 2460 c->ilen[i] = lensi; 2461 } 2462 ierr = PetscFree2(lens,starts);CHKERRQ(ierr); 2463 } else { 2464 ierr = ISGetIndices(iscol,&icol);CHKERRQ(ierr); 2465 ierr = PetscCalloc1(oldcols,&smap);CHKERRQ(ierr); 2466 ierr = PetscMalloc1((1+nrows),&lens);CHKERRQ(ierr); 2467 for (i=0; i<ncols; i++) { 2468 #if defined(PETSC_USE_DEBUG) 2469 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); 2470 #endif 2471 smap[icol[i]] = i+1; 2472 } 2473 2474 /* determine lens of each row */ 2475 for (i=0; i<nrows; i++) { 2476 kstart = ai[irow[i]]; 2477 kend = kstart + a->ilen[irow[i]]; 2478 lens[i] = 0; 2479 for (k=kstart; k<kend; k++) { 2480 if (smap[aj[k]]) { 2481 lens[i]++; 2482 } 2483 } 2484 } 2485 /* Create and fill new matrix */ 2486 if (scall == MAT_REUSE_MATRIX) { 2487 PetscBool equal; 2488 2489 c = (Mat_SeqAIJ*)((*B)->data); 2490 if ((*B)->rmap->n != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size"); 2491 ierr = PetscMemcmp(c->ilen,lens,(*B)->rmap->n*sizeof(PetscInt),&equal);CHKERRQ(ierr); 2492 if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros"); 2493 ierr = PetscMemzero(c->ilen,(*B)->rmap->n*sizeof(PetscInt));CHKERRQ(ierr); 2494 C = *B; 2495 } else { 2496 PetscInt rbs,cbs; 2497 ierr = MatCreate(PetscObjectComm((PetscObject)A),&C);CHKERRQ(ierr); 2498 ierr = MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);CHKERRQ(ierr); 2499 ierr = ISGetBlockSize(isrow,&rbs);CHKERRQ(ierr); 2500 ierr = ISGetBlockSize(iscol,&cbs);CHKERRQ(ierr); 2501 ierr = MatSetBlockSizes(C,rbs,cbs);CHKERRQ(ierr); 2502 ierr = MatSetType(C,((PetscObject)A)->type_name);CHKERRQ(ierr); 2503 ierr = MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);CHKERRQ(ierr); 2504 } 2505 c = (Mat_SeqAIJ*)(C->data); 2506 for (i=0; i<nrows; i++) { 2507 row = irow[i]; 2508 kstart = ai[row]; 2509 kend = kstart + a->ilen[row]; 2510 mat_i = c->i[i]; 2511 mat_j = c->j + mat_i; 2512 mat_a = c->a + mat_i; 2513 mat_ilen = c->ilen + i; 2514 for (k=kstart; k<kend; k++) { 2515 if ((tcol=smap[a->j[k]])) { 2516 *mat_j++ = tcol - 1; 2517 *mat_a++ = a->a[k]; 2518 (*mat_ilen)++; 2519 2520 } 2521 } 2522 } 2523 /* Free work space */ 2524 ierr = ISRestoreIndices(iscol,&icol);CHKERRQ(ierr); 2525 ierr = PetscFree(smap);CHKERRQ(ierr); 2526 ierr = PetscFree(lens);CHKERRQ(ierr); 2527 } 2528 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2529 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2530 2531 ierr = ISRestoreIndices(isrow,&irow);CHKERRQ(ierr); 2532 *B = C; 2533 PetscFunctionReturn(0); 2534 } 2535 2536 #undef __FUNCT__ 2537 #define __FUNCT__ "MatGetMultiProcBlock_SeqAIJ" 2538 PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat) 2539 { 2540 PetscErrorCode ierr; 2541 Mat B; 2542 2543 PetscFunctionBegin; 2544 if (scall == MAT_INITIAL_MATRIX) { 2545 ierr = MatCreate(subComm,&B);CHKERRQ(ierr); 2546 ierr = MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);CHKERRQ(ierr); 2547 ierr = MatSetBlockSizesFromMats(B,mat,mat);CHKERRQ(ierr); 2548 ierr = MatSetType(B,MATSEQAIJ);CHKERRQ(ierr); 2549 ierr = MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);CHKERRQ(ierr); 2550 *subMat = B; 2551 } else { 2552 ierr = MatCopy_SeqAIJ(mat,*subMat,SAME_NONZERO_PATTERN);CHKERRQ(ierr); 2553 } 2554 PetscFunctionReturn(0); 2555 } 2556 2557 #undef __FUNCT__ 2558 #define __FUNCT__ "MatILUFactor_SeqAIJ" 2559 PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info) 2560 { 2561 Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data; 2562 PetscErrorCode ierr; 2563 Mat outA; 2564 PetscBool row_identity,col_identity; 2565 2566 PetscFunctionBegin; 2567 if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu"); 2568 2569 ierr = ISIdentity(row,&row_identity);CHKERRQ(ierr); 2570 ierr = ISIdentity(col,&col_identity);CHKERRQ(ierr); 2571 2572 outA = inA; 2573 outA->factortype = MAT_FACTOR_LU; 2574 2575 ierr = PetscObjectReference((PetscObject)row);CHKERRQ(ierr); 2576 ierr = ISDestroy(&a->row);CHKERRQ(ierr); 2577 2578 a->row = row; 2579 2580 ierr = PetscObjectReference((PetscObject)col);CHKERRQ(ierr); 2581 ierr = ISDestroy(&a->col);CHKERRQ(ierr); 2582 2583 a->col = col; 2584 2585 /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */ 2586 ierr = ISDestroy(&a->icol);CHKERRQ(ierr); 2587 ierr = ISInvertPermutation(col,PETSC_DECIDE,&a->icol);CHKERRQ(ierr); 2588 ierr = PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);CHKERRQ(ierr); 2589 2590 if (!a->solve_work) { /* this matrix may have been factored before */ 2591 ierr = PetscMalloc1((inA->rmap->n+1),&a->solve_work);CHKERRQ(ierr); 2592 ierr = PetscLogObjectMemory((PetscObject)inA, (inA->rmap->n+1)*sizeof(PetscScalar));CHKERRQ(ierr); 2593 } 2594 2595 ierr = MatMarkDiagonal_SeqAIJ(inA);CHKERRQ(ierr); 2596 if (row_identity && col_identity) { 2597 ierr = MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);CHKERRQ(ierr); 2598 } else { 2599 ierr = MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);CHKERRQ(ierr); 2600 } 2601 PetscFunctionReturn(0); 2602 } 2603 2604 #undef __FUNCT__ 2605 #define __FUNCT__ "MatScale_SeqAIJ" 2606 PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha) 2607 { 2608 Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data; 2609 PetscScalar oalpha = alpha; 2610 PetscErrorCode ierr; 2611 PetscBLASInt one = 1,bnz; 2612 2613 PetscFunctionBegin; 2614 ierr = PetscBLASIntCast(a->nz,&bnz);CHKERRQ(ierr); 2615 PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&oalpha,a->a,&one)); 2616 ierr = PetscLogFlops(a->nz);CHKERRQ(ierr); 2617 ierr = MatSeqAIJInvalidateDiagonal(inA);CHKERRQ(ierr); 2618 PetscFunctionReturn(0); 2619 } 2620 2621 #undef __FUNCT__ 2622 #define __FUNCT__ "MatGetSubMatrices_SeqAIJ" 2623 PetscErrorCode MatGetSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[]) 2624 { 2625 PetscErrorCode ierr; 2626 PetscInt i; 2627 2628 PetscFunctionBegin; 2629 if (scall == MAT_INITIAL_MATRIX) { 2630 ierr = PetscMalloc1((n+1),B);CHKERRQ(ierr); 2631 } 2632 2633 for (i=0; i<n; i++) { 2634 ierr = MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);CHKERRQ(ierr); 2635 } 2636 PetscFunctionReturn(0); 2637 } 2638 2639 #undef __FUNCT__ 2640 #define __FUNCT__ "MatIncreaseOverlap_SeqAIJ" 2641 PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov) 2642 { 2643 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2644 PetscErrorCode ierr; 2645 PetscInt row,i,j,k,l,m,n,*nidx,isz,val; 2646 const PetscInt *idx; 2647 PetscInt start,end,*ai,*aj; 2648 PetscBT table; 2649 2650 PetscFunctionBegin; 2651 m = A->rmap->n; 2652 ai = a->i; 2653 aj = a->j; 2654 2655 if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used"); 2656 2657 ierr = PetscMalloc1((m+1),&nidx);CHKERRQ(ierr); 2658 ierr = PetscBTCreate(m,&table);CHKERRQ(ierr); 2659 2660 for (i=0; i<is_max; i++) { 2661 /* Initialize the two local arrays */ 2662 isz = 0; 2663 ierr = PetscBTMemzero(m,table);CHKERRQ(ierr); 2664 2665 /* Extract the indices, assume there can be duplicate entries */ 2666 ierr = ISGetIndices(is[i],&idx);CHKERRQ(ierr); 2667 ierr = ISGetLocalSize(is[i],&n);CHKERRQ(ierr); 2668 2669 /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */ 2670 for (j=0; j<n; ++j) { 2671 if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j]; 2672 } 2673 ierr = ISRestoreIndices(is[i],&idx);CHKERRQ(ierr); 2674 ierr = ISDestroy(&is[i]);CHKERRQ(ierr); 2675 2676 k = 0; 2677 for (j=0; j<ov; j++) { /* for each overlap */ 2678 n = isz; 2679 for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */ 2680 row = nidx[k]; 2681 start = ai[row]; 2682 end = ai[row+1]; 2683 for (l = start; l<end; l++) { 2684 val = aj[l]; 2685 if (!PetscBTLookupSet(table,val)) nidx[isz++] = val; 2686 } 2687 } 2688 } 2689 ierr = ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));CHKERRQ(ierr); 2690 } 2691 ierr = PetscBTDestroy(&table);CHKERRQ(ierr); 2692 ierr = PetscFree(nidx);CHKERRQ(ierr); 2693 PetscFunctionReturn(0); 2694 } 2695 2696 /* -------------------------------------------------------------- */ 2697 #undef __FUNCT__ 2698 #define __FUNCT__ "MatPermute_SeqAIJ" 2699 PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B) 2700 { 2701 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2702 PetscErrorCode ierr; 2703 PetscInt i,nz = 0,m = A->rmap->n,n = A->cmap->n; 2704 const PetscInt *row,*col; 2705 PetscInt *cnew,j,*lens; 2706 IS icolp,irowp; 2707 PetscInt *cwork = NULL; 2708 PetscScalar *vwork = NULL; 2709 2710 PetscFunctionBegin; 2711 ierr = ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);CHKERRQ(ierr); 2712 ierr = ISGetIndices(irowp,&row);CHKERRQ(ierr); 2713 ierr = ISInvertPermutation(colp,PETSC_DECIDE,&icolp);CHKERRQ(ierr); 2714 ierr = ISGetIndices(icolp,&col);CHKERRQ(ierr); 2715 2716 /* determine lengths of permuted rows */ 2717 ierr = PetscMalloc1((m+1),&lens);CHKERRQ(ierr); 2718 for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i]; 2719 ierr = MatCreate(PetscObjectComm((PetscObject)A),B);CHKERRQ(ierr); 2720 ierr = MatSetSizes(*B,m,n,m,n);CHKERRQ(ierr); 2721 ierr = MatSetBlockSizesFromMats(*B,A,A);CHKERRQ(ierr); 2722 ierr = MatSetType(*B,((PetscObject)A)->type_name);CHKERRQ(ierr); 2723 ierr = MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);CHKERRQ(ierr); 2724 ierr = PetscFree(lens);CHKERRQ(ierr); 2725 2726 ierr = PetscMalloc1(n,&cnew);CHKERRQ(ierr); 2727 for (i=0; i<m; i++) { 2728 ierr = MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 2729 for (j=0; j<nz; j++) cnew[j] = col[cwork[j]]; 2730 ierr = MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);CHKERRQ(ierr); 2731 ierr = MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 2732 } 2733 ierr = PetscFree(cnew);CHKERRQ(ierr); 2734 2735 (*B)->assembled = PETSC_FALSE; 2736 2737 ierr = MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2738 ierr = MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2739 ierr = ISRestoreIndices(irowp,&row);CHKERRQ(ierr); 2740 ierr = ISRestoreIndices(icolp,&col);CHKERRQ(ierr); 2741 ierr = ISDestroy(&irowp);CHKERRQ(ierr); 2742 ierr = ISDestroy(&icolp);CHKERRQ(ierr); 2743 PetscFunctionReturn(0); 2744 } 2745 2746 #undef __FUNCT__ 2747 #define __FUNCT__ "MatCopy_SeqAIJ" 2748 PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str) 2749 { 2750 PetscErrorCode ierr; 2751 2752 PetscFunctionBegin; 2753 /* If the two matrices have the same copy implementation, use fast copy. */ 2754 if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) { 2755 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2756 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 2757 2758 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"); 2759 ierr = PetscMemcpy(b->a,a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));CHKERRQ(ierr); 2760 } else { 2761 ierr = MatCopy_Basic(A,B,str);CHKERRQ(ierr); 2762 } 2763 PetscFunctionReturn(0); 2764 } 2765 2766 #undef __FUNCT__ 2767 #define __FUNCT__ "MatSetUp_SeqAIJ" 2768 PetscErrorCode MatSetUp_SeqAIJ(Mat A) 2769 { 2770 PetscErrorCode ierr; 2771 2772 PetscFunctionBegin; 2773 ierr = MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);CHKERRQ(ierr); 2774 PetscFunctionReturn(0); 2775 } 2776 2777 #undef __FUNCT__ 2778 #define __FUNCT__ "MatSeqAIJGetArray_SeqAIJ" 2779 PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[]) 2780 { 2781 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2782 2783 PetscFunctionBegin; 2784 *array = a->a; 2785 PetscFunctionReturn(0); 2786 } 2787 2788 #undef __FUNCT__ 2789 #define __FUNCT__ "MatSeqAIJRestoreArray_SeqAIJ" 2790 PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[]) 2791 { 2792 PetscFunctionBegin; 2793 PetscFunctionReturn(0); 2794 } 2795 2796 /* 2797 Computes the number of nonzeros per row needed for preallocation when X and Y 2798 have different nonzero structure. 2799 */ 2800 #undef __FUNCT__ 2801 #define __FUNCT__ "MatAXPYGetPreallocation_SeqX_private" 2802 PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *yi,const PetscInt *yj,PetscInt *nnz) 2803 { 2804 PetscInt i,j,k,nzx,nzy; 2805 2806 PetscFunctionBegin; 2807 /* Set the number of nonzeros in the new matrix */ 2808 for (i=0; i<m; i++) { 2809 const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i]; 2810 nzx = xi[i+1] - xi[i]; 2811 nzy = yi[i+1] - yi[i]; 2812 nnz[i] = 0; 2813 for (j=0,k=0; j<nzx; j++) { /* Point in X */ 2814 for (; k<nzy && yjj[k]<xjj[j]; k++) nnz[i]++; /* Catch up to X */ 2815 if (k<nzy && yjj[k]==xjj[j]) k++; /* Skip duplicate */ 2816 nnz[i]++; 2817 } 2818 for (; k<nzy; k++) nnz[i]++; 2819 } 2820 PetscFunctionReturn(0); 2821 } 2822 2823 #undef __FUNCT__ 2824 #define __FUNCT__ "MatAXPYGetPreallocation_SeqAIJ" 2825 PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz) 2826 { 2827 PetscInt m = Y->rmap->N; 2828 Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data; 2829 Mat_SeqAIJ *y = (Mat_SeqAIJ*)Y->data; 2830 PetscErrorCode ierr; 2831 2832 PetscFunctionBegin; 2833 /* Set the number of nonzeros in the new matrix */ 2834 ierr = MatAXPYGetPreallocation_SeqX_private(m,x->i,x->j,y->i,y->j,nnz);CHKERRQ(ierr); 2835 PetscFunctionReturn(0); 2836 } 2837 2838 #undef __FUNCT__ 2839 #define __FUNCT__ "MatAXPY_SeqAIJ" 2840 PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str) 2841 { 2842 PetscErrorCode ierr; 2843 Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data; 2844 PetscBLASInt one=1,bnz; 2845 2846 PetscFunctionBegin; 2847 ierr = PetscBLASIntCast(x->nz,&bnz);CHKERRQ(ierr); 2848 if (str == SAME_NONZERO_PATTERN) { 2849 PetscScalar alpha = a; 2850 PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one)); 2851 ierr = MatSeqAIJInvalidateDiagonal(Y);CHKERRQ(ierr); 2852 ierr = PetscObjectStateIncrease((PetscObject)Y);CHKERRQ(ierr); 2853 } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */ 2854 ierr = MatAXPY_Basic(Y,a,X,str);CHKERRQ(ierr); 2855 } else { 2856 Mat B; 2857 PetscInt *nnz; 2858 ierr = PetscMalloc1(Y->rmap->N,&nnz);CHKERRQ(ierr); 2859 ierr = MatCreate(PetscObjectComm((PetscObject)Y),&B);CHKERRQ(ierr); 2860 ierr = PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);CHKERRQ(ierr); 2861 ierr = MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);CHKERRQ(ierr); 2862 ierr = MatSetBlockSizesFromMats(B,Y,Y);CHKERRQ(ierr); 2863 ierr = MatSetType(B,(MatType) ((PetscObject)Y)->type_name);CHKERRQ(ierr); 2864 ierr = MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);CHKERRQ(ierr); 2865 ierr = MatSeqAIJSetPreallocation(B,0,nnz);CHKERRQ(ierr); 2866 ierr = MatAXPY_BasicWithPreallocation(B,Y,a,X,str);CHKERRQ(ierr); 2867 ierr = MatHeaderReplace(Y,B);CHKERRQ(ierr); 2868 ierr = PetscFree(nnz);CHKERRQ(ierr); 2869 } 2870 PetscFunctionReturn(0); 2871 } 2872 2873 #undef __FUNCT__ 2874 #define __FUNCT__ "MatConjugate_SeqAIJ" 2875 PetscErrorCode MatConjugate_SeqAIJ(Mat mat) 2876 { 2877 #if defined(PETSC_USE_COMPLEX) 2878 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data; 2879 PetscInt i,nz; 2880 PetscScalar *a; 2881 2882 PetscFunctionBegin; 2883 nz = aij->nz; 2884 a = aij->a; 2885 for (i=0; i<nz; i++) a[i] = PetscConj(a[i]); 2886 #else 2887 PetscFunctionBegin; 2888 #endif 2889 PetscFunctionReturn(0); 2890 } 2891 2892 #undef __FUNCT__ 2893 #define __FUNCT__ "MatGetRowMaxAbs_SeqAIJ" 2894 PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[]) 2895 { 2896 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2897 PetscErrorCode ierr; 2898 PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n; 2899 PetscReal atmp; 2900 PetscScalar *x; 2901 MatScalar *aa; 2902 2903 PetscFunctionBegin; 2904 if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2905 aa = a->a; 2906 ai = a->i; 2907 aj = a->j; 2908 2909 ierr = VecSet(v,0.0);CHKERRQ(ierr); 2910 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 2911 ierr = VecGetLocalSize(v,&n);CHKERRQ(ierr); 2912 if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector"); 2913 for (i=0; i<m; i++) { 2914 ncols = ai[1] - ai[0]; ai++; 2915 x[i] = 0.0; 2916 for (j=0; j<ncols; j++) { 2917 atmp = PetscAbsScalar(*aa); 2918 if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;} 2919 aa++; aj++; 2920 } 2921 } 2922 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 2923 PetscFunctionReturn(0); 2924 } 2925 2926 #undef __FUNCT__ 2927 #define __FUNCT__ "MatGetRowMax_SeqAIJ" 2928 PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[]) 2929 { 2930 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2931 PetscErrorCode ierr; 2932 PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n; 2933 PetscScalar *x; 2934 MatScalar *aa; 2935 2936 PetscFunctionBegin; 2937 if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2938 aa = a->a; 2939 ai = a->i; 2940 aj = a->j; 2941 2942 ierr = VecSet(v,0.0);CHKERRQ(ierr); 2943 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 2944 ierr = VecGetLocalSize(v,&n);CHKERRQ(ierr); 2945 if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector"); 2946 for (i=0; i<m; i++) { 2947 ncols = ai[1] - ai[0]; ai++; 2948 if (ncols == A->cmap->n) { /* row is dense */ 2949 x[i] = *aa; if (idx) idx[i] = 0; 2950 } else { /* row is sparse so already KNOW maximum is 0.0 or higher */ 2951 x[i] = 0.0; 2952 if (idx) { 2953 idx[i] = 0; /* in case ncols is zero */ 2954 for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */ 2955 if (aj[j] > j) { 2956 idx[i] = j; 2957 break; 2958 } 2959 } 2960 } 2961 } 2962 for (j=0; j<ncols; j++) { 2963 if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;} 2964 aa++; aj++; 2965 } 2966 } 2967 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 2968 PetscFunctionReturn(0); 2969 } 2970 2971 #undef __FUNCT__ 2972 #define __FUNCT__ "MatGetRowMinAbs_SeqAIJ" 2973 PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[]) 2974 { 2975 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2976 PetscErrorCode ierr; 2977 PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n; 2978 PetscReal atmp; 2979 PetscScalar *x; 2980 MatScalar *aa; 2981 2982 PetscFunctionBegin; 2983 if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2984 aa = a->a; 2985 ai = a->i; 2986 aj = a->j; 2987 2988 ierr = VecSet(v,0.0);CHKERRQ(ierr); 2989 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 2990 ierr = VecGetLocalSize(v,&n);CHKERRQ(ierr); 2991 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); 2992 for (i=0; i<m; i++) { 2993 ncols = ai[1] - ai[0]; ai++; 2994 if (ncols) { 2995 /* Get first nonzero */ 2996 for (j = 0; j < ncols; j++) { 2997 atmp = PetscAbsScalar(aa[j]); 2998 if (atmp > 1.0e-12) { 2999 x[i] = atmp; 3000 if (idx) idx[i] = aj[j]; 3001 break; 3002 } 3003 } 3004 if (j == ncols) {x[i] = PetscAbsScalar(*aa); if (idx) idx[i] = *aj;} 3005 } else { 3006 x[i] = 0.0; if (idx) idx[i] = 0; 3007 } 3008 for (j = 0; j < ncols; j++) { 3009 atmp = PetscAbsScalar(*aa); 3010 if (atmp > 1.0e-12 && PetscAbsScalar(x[i]) > atmp) {x[i] = atmp; if (idx) idx[i] = *aj;} 3011 aa++; aj++; 3012 } 3013 } 3014 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 3015 PetscFunctionReturn(0); 3016 } 3017 3018 #undef __FUNCT__ 3019 #define __FUNCT__ "MatGetRowMin_SeqAIJ" 3020 PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[]) 3021 { 3022 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 3023 PetscErrorCode ierr; 3024 PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n; 3025 PetscScalar *x; 3026 MatScalar *aa; 3027 3028 PetscFunctionBegin; 3029 if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3030 aa = a->a; 3031 ai = a->i; 3032 aj = a->j; 3033 3034 ierr = VecSet(v,0.0);CHKERRQ(ierr); 3035 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 3036 ierr = VecGetLocalSize(v,&n);CHKERRQ(ierr); 3037 if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector"); 3038 for (i=0; i<m; i++) { 3039 ncols = ai[1] - ai[0]; ai++; 3040 if (ncols == A->cmap->n) { /* row is dense */ 3041 x[i] = *aa; if (idx) idx[i] = 0; 3042 } else { /* row is sparse so already KNOW minimum is 0.0 or lower */ 3043 x[i] = 0.0; 3044 if (idx) { /* find first implicit 0.0 in the row */ 3045 idx[i] = 0; /* in case ncols is zero */ 3046 for (j=0; j<ncols; j++) { 3047 if (aj[j] > j) { 3048 idx[i] = j; 3049 break; 3050 } 3051 } 3052 } 3053 } 3054 for (j=0; j<ncols; j++) { 3055 if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;} 3056 aa++; aj++; 3057 } 3058 } 3059 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 3060 PetscFunctionReturn(0); 3061 } 3062 3063 #include <petscblaslapack.h> 3064 #include <petsc-private/kernels/blockinvert.h> 3065 3066 #undef __FUNCT__ 3067 #define __FUNCT__ "MatInvertBlockDiagonal_SeqAIJ" 3068 PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values) 3069 { 3070 Mat_SeqAIJ *a = (Mat_SeqAIJ*) A->data; 3071 PetscErrorCode ierr; 3072 PetscInt i,bs = PetscAbs(A->rmap->bs),mbs = A->rmap->n/bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j; 3073 MatScalar *diag,work[25],*v_work; 3074 PetscReal shift = 0.0; 3075 3076 PetscFunctionBegin; 3077 if (a->ibdiagvalid) { 3078 if (values) *values = a->ibdiag; 3079 PetscFunctionReturn(0); 3080 } 3081 ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr); 3082 if (!a->ibdiag) { 3083 ierr = PetscMalloc1(bs2*mbs,&a->ibdiag);CHKERRQ(ierr); 3084 ierr = PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));CHKERRQ(ierr); 3085 } 3086 diag = a->ibdiag; 3087 if (values) *values = a->ibdiag; 3088 /* factor and invert each block */ 3089 switch (bs) { 3090 case 1: 3091 for (i=0; i<mbs; i++) { 3092 ierr = MatGetValues(A,1,&i,1,&i,diag+i);CHKERRQ(ierr); 3093 diag[i] = (PetscScalar)1.0 / (diag[i] + shift); 3094 } 3095 break; 3096 case 2: 3097 for (i=0; i<mbs; i++) { 3098 ij[0] = 2*i; ij[1] = 2*i + 1; 3099 ierr = MatGetValues(A,2,ij,2,ij,diag);CHKERRQ(ierr); 3100 ierr = PetscKernel_A_gets_inverse_A_2(diag,shift);CHKERRQ(ierr); 3101 ierr = PetscKernel_A_gets_transpose_A_2(diag);CHKERRQ(ierr); 3102 diag += 4; 3103 } 3104 break; 3105 case 3: 3106 for (i=0; i<mbs; i++) { 3107 ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2; 3108 ierr = MatGetValues(A,3,ij,3,ij,diag);CHKERRQ(ierr); 3109 ierr = PetscKernel_A_gets_inverse_A_3(diag,shift);CHKERRQ(ierr); 3110 ierr = PetscKernel_A_gets_transpose_A_3(diag);CHKERRQ(ierr); 3111 diag += 9; 3112 } 3113 break; 3114 case 4: 3115 for (i=0; i<mbs; i++) { 3116 ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3; 3117 ierr = MatGetValues(A,4,ij,4,ij,diag);CHKERRQ(ierr); 3118 ierr = PetscKernel_A_gets_inverse_A_4(diag,shift);CHKERRQ(ierr); 3119 ierr = PetscKernel_A_gets_transpose_A_4(diag);CHKERRQ(ierr); 3120 diag += 16; 3121 } 3122 break; 3123 case 5: 3124 for (i=0; i<mbs; i++) { 3125 ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4; 3126 ierr = MatGetValues(A,5,ij,5,ij,diag);CHKERRQ(ierr); 3127 ierr = PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift);CHKERRQ(ierr); 3128 ierr = PetscKernel_A_gets_transpose_A_5(diag);CHKERRQ(ierr); 3129 diag += 25; 3130 } 3131 break; 3132 case 6: 3133 for (i=0; i<mbs; i++) { 3134 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; 3135 ierr = MatGetValues(A,6,ij,6,ij,diag);CHKERRQ(ierr); 3136 ierr = PetscKernel_A_gets_inverse_A_6(diag,shift);CHKERRQ(ierr); 3137 ierr = PetscKernel_A_gets_transpose_A_6(diag);CHKERRQ(ierr); 3138 diag += 36; 3139 } 3140 break; 3141 case 7: 3142 for (i=0; i<mbs; i++) { 3143 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; 3144 ierr = MatGetValues(A,7,ij,7,ij,diag);CHKERRQ(ierr); 3145 ierr = PetscKernel_A_gets_inverse_A_7(diag,shift);CHKERRQ(ierr); 3146 ierr = PetscKernel_A_gets_transpose_A_7(diag);CHKERRQ(ierr); 3147 diag += 49; 3148 } 3149 break; 3150 default: 3151 ierr = PetscMalloc3(bs,&v_work,bs,&v_pivots,bs,&IJ);CHKERRQ(ierr); 3152 for (i=0; i<mbs; i++) { 3153 for (j=0; j<bs; j++) { 3154 IJ[j] = bs*i + j; 3155 } 3156 ierr = MatGetValues(A,bs,IJ,bs,IJ,diag);CHKERRQ(ierr); 3157 ierr = PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work);CHKERRQ(ierr); 3158 ierr = PetscKernel_A_gets_transpose_A_N(diag,bs);CHKERRQ(ierr); 3159 diag += bs2; 3160 } 3161 ierr = PetscFree3(v_work,v_pivots,IJ);CHKERRQ(ierr); 3162 } 3163 a->ibdiagvalid = PETSC_TRUE; 3164 PetscFunctionReturn(0); 3165 } 3166 3167 #undef __FUNCT__ 3168 #define __FUNCT__ "MatSetRandom_SeqAIJ" 3169 static PetscErrorCode MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx) 3170 { 3171 PetscErrorCode ierr; 3172 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)x->data; 3173 PetscScalar a; 3174 PetscInt m,n,i,j,col; 3175 3176 PetscFunctionBegin; 3177 if (!x->assembled) { 3178 ierr = MatGetSize(x,&m,&n);CHKERRQ(ierr); 3179 for (i=0; i<m; i++) { 3180 for (j=0; j<aij->imax[i]; j++) { 3181 ierr = PetscRandomGetValue(rctx,&a);CHKERRQ(ierr); 3182 col = (PetscInt)(n*PetscRealPart(a)); 3183 ierr = MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);CHKERRQ(ierr); 3184 } 3185 } 3186 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not yet coded"); 3187 ierr = MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 3188 ierr = MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 3189 PetscFunctionReturn(0); 3190 } 3191 3192 /* -------------------------------------------------------------------*/ 3193 static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ, 3194 MatGetRow_SeqAIJ, 3195 MatRestoreRow_SeqAIJ, 3196 MatMult_SeqAIJ, 3197 /* 4*/ MatMultAdd_SeqAIJ, 3198 MatMultTranspose_SeqAIJ, 3199 MatMultTransposeAdd_SeqAIJ, 3200 0, 3201 0, 3202 0, 3203 /* 10*/ 0, 3204 MatLUFactor_SeqAIJ, 3205 0, 3206 MatSOR_SeqAIJ, 3207 MatTranspose_SeqAIJ, 3208 /*1 5*/ MatGetInfo_SeqAIJ, 3209 MatEqual_SeqAIJ, 3210 MatGetDiagonal_SeqAIJ, 3211 MatDiagonalScale_SeqAIJ, 3212 MatNorm_SeqAIJ, 3213 /* 20*/ 0, 3214 MatAssemblyEnd_SeqAIJ, 3215 MatSetOption_SeqAIJ, 3216 MatZeroEntries_SeqAIJ, 3217 /* 24*/ MatZeroRows_SeqAIJ, 3218 0, 3219 0, 3220 0, 3221 0, 3222 /* 29*/ MatSetUp_SeqAIJ, 3223 0, 3224 0, 3225 0, 3226 0, 3227 /* 34*/ MatDuplicate_SeqAIJ, 3228 0, 3229 0, 3230 MatILUFactor_SeqAIJ, 3231 0, 3232 /* 39*/ MatAXPY_SeqAIJ, 3233 MatGetSubMatrices_SeqAIJ, 3234 MatIncreaseOverlap_SeqAIJ, 3235 MatGetValues_SeqAIJ, 3236 MatCopy_SeqAIJ, 3237 /* 44*/ MatGetRowMax_SeqAIJ, 3238 MatScale_SeqAIJ, 3239 0, 3240 MatDiagonalSet_SeqAIJ, 3241 MatZeroRowsColumns_SeqAIJ, 3242 /* 49*/ MatSetRandom_SeqAIJ, 3243 MatGetRowIJ_SeqAIJ, 3244 MatRestoreRowIJ_SeqAIJ, 3245 MatGetColumnIJ_SeqAIJ, 3246 MatRestoreColumnIJ_SeqAIJ, 3247 /* 54*/ MatFDColoringCreate_SeqXAIJ, 3248 0, 3249 0, 3250 MatPermute_SeqAIJ, 3251 0, 3252 /* 59*/ 0, 3253 MatDestroy_SeqAIJ, 3254 MatView_SeqAIJ, 3255 0, 3256 MatMatMatMult_SeqAIJ_SeqAIJ_SeqAIJ, 3257 /* 64*/ MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ, 3258 MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ, 3259 0, 3260 0, 3261 0, 3262 /* 69*/ MatGetRowMaxAbs_SeqAIJ, 3263 MatGetRowMinAbs_SeqAIJ, 3264 0, 3265 MatSetColoring_SeqAIJ, 3266 0, 3267 /* 74*/ MatSetValuesAdifor_SeqAIJ, 3268 MatFDColoringApply_AIJ, 3269 0, 3270 0, 3271 0, 3272 /* 79*/ MatFindZeroDiagonals_SeqAIJ, 3273 0, 3274 0, 3275 0, 3276 MatLoad_SeqAIJ, 3277 /* 84*/ MatIsSymmetric_SeqAIJ, 3278 MatIsHermitian_SeqAIJ, 3279 0, 3280 0, 3281 0, 3282 /* 89*/ MatMatMult_SeqAIJ_SeqAIJ, 3283 MatMatMultSymbolic_SeqAIJ_SeqAIJ, 3284 MatMatMultNumeric_SeqAIJ_SeqAIJ, 3285 MatPtAP_SeqAIJ_SeqAIJ, 3286 MatPtAPSymbolic_SeqAIJ_SeqAIJ_DenseAxpy, 3287 /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ, 3288 MatMatTransposeMult_SeqAIJ_SeqAIJ, 3289 MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ, 3290 MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ, 3291 0, 3292 /* 99*/ 0, 3293 0, 3294 0, 3295 MatConjugate_SeqAIJ, 3296 0, 3297 /*104*/ MatSetValuesRow_SeqAIJ, 3298 MatRealPart_SeqAIJ, 3299 MatImaginaryPart_SeqAIJ, 3300 0, 3301 0, 3302 /*109*/ MatMatSolve_SeqAIJ, 3303 0, 3304 MatGetRowMin_SeqAIJ, 3305 0, 3306 MatMissingDiagonal_SeqAIJ, 3307 /*114*/ 0, 3308 0, 3309 0, 3310 0, 3311 0, 3312 /*119*/ 0, 3313 0, 3314 0, 3315 0, 3316 MatGetMultiProcBlock_SeqAIJ, 3317 /*124*/ MatFindNonzeroRows_SeqAIJ, 3318 MatGetColumnNorms_SeqAIJ, 3319 MatInvertBlockDiagonal_SeqAIJ, 3320 0, 3321 0, 3322 /*129*/ 0, 3323 MatTransposeMatMult_SeqAIJ_SeqAIJ, 3324 MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ, 3325 MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ, 3326 MatTransposeColoringCreate_SeqAIJ, 3327 /*134*/ MatTransColoringApplySpToDen_SeqAIJ, 3328 MatTransColoringApplyDenToSp_SeqAIJ, 3329 MatRARt_SeqAIJ_SeqAIJ, 3330 MatRARtSymbolic_SeqAIJ_SeqAIJ, 3331 MatRARtNumeric_SeqAIJ_SeqAIJ, 3332 /*139*/0, 3333 0, 3334 0, 3335 MatFDColoringSetUp_SeqXAIJ, 3336 MatFindOffBlockDiagonalEntries_SeqAIJ 3337 }; 3338 3339 #undef __FUNCT__ 3340 #define __FUNCT__ "MatSeqAIJSetColumnIndices_SeqAIJ" 3341 PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices) 3342 { 3343 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data; 3344 PetscInt i,nz,n; 3345 3346 PetscFunctionBegin; 3347 nz = aij->maxnz; 3348 n = mat->rmap->n; 3349 for (i=0; i<nz; i++) { 3350 aij->j[i] = indices[i]; 3351 } 3352 aij->nz = nz; 3353 for (i=0; i<n; i++) { 3354 aij->ilen[i] = aij->imax[i]; 3355 } 3356 PetscFunctionReturn(0); 3357 } 3358 3359 #undef __FUNCT__ 3360 #define __FUNCT__ "MatSeqAIJSetColumnIndices" 3361 /*@ 3362 MatSeqAIJSetColumnIndices - Set the column indices for all the rows 3363 in the matrix. 3364 3365 Input Parameters: 3366 + mat - the SeqAIJ matrix 3367 - indices - the column indices 3368 3369 Level: advanced 3370 3371 Notes: 3372 This can be called if you have precomputed the nonzero structure of the 3373 matrix and want to provide it to the matrix object to improve the performance 3374 of the MatSetValues() operation. 3375 3376 You MUST have set the correct numbers of nonzeros per row in the call to 3377 MatCreateSeqAIJ(), and the columns indices MUST be sorted. 3378 3379 MUST be called before any calls to MatSetValues(); 3380 3381 The indices should start with zero, not one. 3382 3383 @*/ 3384 PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices) 3385 { 3386 PetscErrorCode ierr; 3387 3388 PetscFunctionBegin; 3389 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3390 PetscValidPointer(indices,2); 3391 ierr = PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));CHKERRQ(ierr); 3392 PetscFunctionReturn(0); 3393 } 3394 3395 /* ----------------------------------------------------------------------------------------*/ 3396 3397 #undef __FUNCT__ 3398 #define __FUNCT__ "MatStoreValues_SeqAIJ" 3399 PetscErrorCode MatStoreValues_SeqAIJ(Mat mat) 3400 { 3401 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data; 3402 PetscErrorCode ierr; 3403 size_t nz = aij->i[mat->rmap->n]; 3404 3405 PetscFunctionBegin; 3406 if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first"); 3407 3408 /* allocate space for values if not already there */ 3409 if (!aij->saved_values) { 3410 ierr = PetscMalloc1((nz+1),&aij->saved_values);CHKERRQ(ierr); 3411 ierr = PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));CHKERRQ(ierr); 3412 } 3413 3414 /* copy values over */ 3415 ierr = PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));CHKERRQ(ierr); 3416 PetscFunctionReturn(0); 3417 } 3418 3419 #undef __FUNCT__ 3420 #define __FUNCT__ "MatStoreValues" 3421 /*@ 3422 MatStoreValues - Stashes a copy of the matrix values; this allows, for 3423 example, reuse of the linear part of a Jacobian, while recomputing the 3424 nonlinear portion. 3425 3426 Collect on Mat 3427 3428 Input Parameters: 3429 . mat - the matrix (currently only AIJ matrices support this option) 3430 3431 Level: advanced 3432 3433 Common Usage, with SNESSolve(): 3434 $ Create Jacobian matrix 3435 $ Set linear terms into matrix 3436 $ Apply boundary conditions to matrix, at this time matrix must have 3437 $ final nonzero structure (i.e. setting the nonlinear terms and applying 3438 $ boundary conditions again will not change the nonzero structure 3439 $ ierr = MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); 3440 $ ierr = MatStoreValues(mat); 3441 $ Call SNESSetJacobian() with matrix 3442 $ In your Jacobian routine 3443 $ ierr = MatRetrieveValues(mat); 3444 $ Set nonlinear terms in matrix 3445 3446 Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself: 3447 $ // build linear portion of Jacobian 3448 $ ierr = MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); 3449 $ ierr = MatStoreValues(mat); 3450 $ loop over nonlinear iterations 3451 $ ierr = MatRetrieveValues(mat); 3452 $ // call MatSetValues(mat,...) to set nonliner portion of Jacobian 3453 $ // call MatAssemblyBegin/End() on matrix 3454 $ Solve linear system with Jacobian 3455 $ endloop 3456 3457 Notes: 3458 Matrix must already be assemblied before calling this routine 3459 Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before 3460 calling this routine. 3461 3462 When this is called multiple times it overwrites the previous set of stored values 3463 and does not allocated additional space. 3464 3465 .seealso: MatRetrieveValues() 3466 3467 @*/ 3468 PetscErrorCode MatStoreValues(Mat mat) 3469 { 3470 PetscErrorCode ierr; 3471 3472 PetscFunctionBegin; 3473 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3474 if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3475 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3476 ierr = PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));CHKERRQ(ierr); 3477 PetscFunctionReturn(0); 3478 } 3479 3480 #undef __FUNCT__ 3481 #define __FUNCT__ "MatRetrieveValues_SeqAIJ" 3482 PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat) 3483 { 3484 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data; 3485 PetscErrorCode ierr; 3486 PetscInt nz = aij->i[mat->rmap->n]; 3487 3488 PetscFunctionBegin; 3489 if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first"); 3490 if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first"); 3491 /* copy values over */ 3492 ierr = PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));CHKERRQ(ierr); 3493 PetscFunctionReturn(0); 3494 } 3495 3496 #undef __FUNCT__ 3497 #define __FUNCT__ "MatRetrieveValues" 3498 /*@ 3499 MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 3500 example, reuse of the linear part of a Jacobian, while recomputing the 3501 nonlinear portion. 3502 3503 Collect on Mat 3504 3505 Input Parameters: 3506 . mat - the matrix (currently on AIJ matrices support this option) 3507 3508 Level: advanced 3509 3510 .seealso: MatStoreValues() 3511 3512 @*/ 3513 PetscErrorCode MatRetrieveValues(Mat mat) 3514 { 3515 PetscErrorCode ierr; 3516 3517 PetscFunctionBegin; 3518 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3519 if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3520 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3521 ierr = PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));CHKERRQ(ierr); 3522 PetscFunctionReturn(0); 3523 } 3524 3525 3526 /* --------------------------------------------------------------------------------*/ 3527 #undef __FUNCT__ 3528 #define __FUNCT__ "MatCreateSeqAIJ" 3529 /*@C 3530 MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format 3531 (the default parallel PETSc format). For good matrix assembly performance 3532 the user should preallocate the matrix storage by setting the parameter nz 3533 (or the array nnz). By setting these parameters accurately, performance 3534 during matrix assembly can be increased by more than a factor of 50. 3535 3536 Collective on MPI_Comm 3537 3538 Input Parameters: 3539 + comm - MPI communicator, set to PETSC_COMM_SELF 3540 . m - number of rows 3541 . n - number of columns 3542 . nz - number of nonzeros per row (same for all rows) 3543 - nnz - array containing the number of nonzeros in the various rows 3544 (possibly different for each row) or NULL 3545 3546 Output Parameter: 3547 . A - the matrix 3548 3549 It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(), 3550 MatXXXXSetPreallocation() paradgm instead of this routine directly. 3551 [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation] 3552 3553 Notes: 3554 If nnz is given then nz is ignored 3555 3556 The AIJ format (also called the Yale sparse matrix format or 3557 compressed row storage), is fully compatible with standard Fortran 77 3558 storage. That is, the stored row and column indices can begin at 3559 either one (as in Fortran) or zero. See the users' manual for details. 3560 3561 Specify the preallocated storage with either nz or nnz (not both). 3562 Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory 3563 allocation. For large problems you MUST preallocate memory or you 3564 will get TERRIBLE performance, see the users' manual chapter on matrices. 3565 3566 By default, this format uses inodes (identical nodes) when possible, to 3567 improve numerical efficiency of matrix-vector products and solves. We 3568 search for consecutive rows with the same nonzero structure, thereby 3569 reusing matrix information to achieve increased efficiency. 3570 3571 Options Database Keys: 3572 + -mat_no_inode - Do not use inodes 3573 - -mat_inode_limit <limit> - Sets inode limit (max limit=5) 3574 3575 Level: intermediate 3576 3577 .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays() 3578 3579 @*/ 3580 PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A) 3581 { 3582 PetscErrorCode ierr; 3583 3584 PetscFunctionBegin; 3585 ierr = MatCreate(comm,A);CHKERRQ(ierr); 3586 ierr = MatSetSizes(*A,m,n,m,n);CHKERRQ(ierr); 3587 ierr = MatSetType(*A,MATSEQAIJ);CHKERRQ(ierr); 3588 ierr = MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);CHKERRQ(ierr); 3589 PetscFunctionReturn(0); 3590 } 3591 3592 #undef __FUNCT__ 3593 #define __FUNCT__ "MatSeqAIJSetPreallocation" 3594 /*@C 3595 MatSeqAIJSetPreallocation - For good matrix assembly performance 3596 the user should preallocate the matrix storage by setting the parameter nz 3597 (or the array nnz). By setting these parameters accurately, performance 3598 during matrix assembly can be increased by more than a factor of 50. 3599 3600 Collective on MPI_Comm 3601 3602 Input Parameters: 3603 + B - The matrix 3604 . nz - number of nonzeros per row (same for all rows) 3605 - nnz - array containing the number of nonzeros in the various rows 3606 (possibly different for each row) or NULL 3607 3608 Notes: 3609 If nnz is given then nz is ignored 3610 3611 The AIJ format (also called the Yale sparse matrix format or 3612 compressed row storage), is fully compatible with standard Fortran 77 3613 storage. That is, the stored row and column indices can begin at 3614 either one (as in Fortran) or zero. See the users' manual for details. 3615 3616 Specify the preallocated storage with either nz or nnz (not both). 3617 Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory 3618 allocation. For large problems you MUST preallocate memory or you 3619 will get TERRIBLE performance, see the users' manual chapter on matrices. 3620 3621 You can call MatGetInfo() to get information on how effective the preallocation was; 3622 for example the fields mallocs,nz_allocated,nz_used,nz_unneeded; 3623 You can also run with the option -info and look for messages with the string 3624 malloc in them to see if additional memory allocation was needed. 3625 3626 Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix 3627 entries or columns indices 3628 3629 By default, this format uses inodes (identical nodes) when possible, to 3630 improve numerical efficiency of matrix-vector products and solves. We 3631 search for consecutive rows with the same nonzero structure, thereby 3632 reusing matrix information to achieve increased efficiency. 3633 3634 Options Database Keys: 3635 + -mat_no_inode - Do not use inodes 3636 . -mat_inode_limit <limit> - Sets inode limit (max limit=5) 3637 - -mat_aij_oneindex - Internally use indexing starting at 1 3638 rather than 0. Note that when calling MatSetValues(), 3639 the user still MUST index entries starting at 0! 3640 3641 Level: intermediate 3642 3643 .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo() 3644 3645 @*/ 3646 PetscErrorCode MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[]) 3647 { 3648 PetscErrorCode ierr; 3649 3650 PetscFunctionBegin; 3651 PetscValidHeaderSpecific(B,MAT_CLASSID,1); 3652 PetscValidType(B,1); 3653 ierr = PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));CHKERRQ(ierr); 3654 PetscFunctionReturn(0); 3655 } 3656 3657 #undef __FUNCT__ 3658 #define __FUNCT__ "MatSeqAIJSetPreallocation_SeqAIJ" 3659 PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz) 3660 { 3661 Mat_SeqAIJ *b; 3662 PetscBool skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE; 3663 PetscErrorCode ierr; 3664 PetscInt i; 3665 3666 PetscFunctionBegin; 3667 if (nz >= 0 || nnz) realalloc = PETSC_TRUE; 3668 if (nz == MAT_SKIP_ALLOCATION) { 3669 skipallocation = PETSC_TRUE; 3670 nz = 0; 3671 } 3672 3673 ierr = PetscLayoutSetUp(B->rmap);CHKERRQ(ierr); 3674 ierr = PetscLayoutSetUp(B->cmap);CHKERRQ(ierr); 3675 3676 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5; 3677 if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz); 3678 if (nnz) { 3679 for (i=0; i<B->rmap->n; i++) { 3680 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]); 3681 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); 3682 } 3683 } 3684 3685 B->preallocated = PETSC_TRUE; 3686 3687 b = (Mat_SeqAIJ*)B->data; 3688 3689 if (!skipallocation) { 3690 if (!b->imax) { 3691 ierr = PetscMalloc2(B->rmap->n,&b->imax,B->rmap->n,&b->ilen);CHKERRQ(ierr); 3692 ierr = PetscLogObjectMemory((PetscObject)B,2*B->rmap->n*sizeof(PetscInt));CHKERRQ(ierr); 3693 } 3694 if (!nnz) { 3695 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10; 3696 else if (nz < 0) nz = 1; 3697 for (i=0; i<B->rmap->n; i++) b->imax[i] = nz; 3698 nz = nz*B->rmap->n; 3699 } else { 3700 nz = 0; 3701 for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];} 3702 } 3703 /* b->ilen will count nonzeros in each row so far. */ 3704 for (i=0; i<B->rmap->n; i++) b->ilen[i] = 0; 3705 3706 /* allocate the matrix space */ 3707 ierr = MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);CHKERRQ(ierr); 3708 ierr = PetscMalloc3(nz,&b->a,nz,&b->j,B->rmap->n+1,&b->i);CHKERRQ(ierr); 3709 ierr = PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));CHKERRQ(ierr); 3710 b->i[0] = 0; 3711 for (i=1; i<B->rmap->n+1; i++) { 3712 b->i[i] = b->i[i-1] + b->imax[i-1]; 3713 } 3714 b->singlemalloc = PETSC_TRUE; 3715 b->free_a = PETSC_TRUE; 3716 b->free_ij = PETSC_TRUE; 3717 #if defined(PETSC_THREADCOMM_ACTIVE) 3718 ierr = MatZeroEntries_SeqAIJ(B);CHKERRQ(ierr); 3719 #endif 3720 } else { 3721 b->free_a = PETSC_FALSE; 3722 b->free_ij = PETSC_FALSE; 3723 } 3724 3725 b->nz = 0; 3726 b->maxnz = nz; 3727 B->info.nz_unneeded = (double)b->maxnz; 3728 if (realalloc) { 3729 ierr = MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);CHKERRQ(ierr); 3730 } 3731 PetscFunctionReturn(0); 3732 } 3733 3734 #undef __FUNCT__ 3735 #define __FUNCT__ "MatSeqAIJSetPreallocationCSR" 3736 /*@ 3737 MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format. 3738 3739 Input Parameters: 3740 + B - the matrix 3741 . i - the indices into j for the start of each row (starts with zero) 3742 . j - the column indices for each row (starts with zero) these must be sorted for each row 3743 - v - optional values in the matrix 3744 3745 Level: developer 3746 3747 The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays() 3748 3749 .keywords: matrix, aij, compressed row, sparse, sequential 3750 3751 .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), SeqAIJ 3752 @*/ 3753 PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[]) 3754 { 3755 PetscErrorCode ierr; 3756 3757 PetscFunctionBegin; 3758 PetscValidHeaderSpecific(B,MAT_CLASSID,1); 3759 PetscValidType(B,1); 3760 ierr = PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));CHKERRQ(ierr); 3761 PetscFunctionReturn(0); 3762 } 3763 3764 #undef __FUNCT__ 3765 #define __FUNCT__ "MatSeqAIJSetPreallocationCSR_SeqAIJ" 3766 PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[]) 3767 { 3768 PetscInt i; 3769 PetscInt m,n; 3770 PetscInt nz; 3771 PetscInt *nnz, nz_max = 0; 3772 PetscScalar *values; 3773 PetscErrorCode ierr; 3774 3775 PetscFunctionBegin; 3776 if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]); 3777 3778 ierr = PetscLayoutSetUp(B->rmap);CHKERRQ(ierr); 3779 ierr = PetscLayoutSetUp(B->cmap);CHKERRQ(ierr); 3780 3781 ierr = MatGetSize(B, &m, &n);CHKERRQ(ierr); 3782 ierr = PetscMalloc1((m+1), &nnz);CHKERRQ(ierr); 3783 for (i = 0; i < m; i++) { 3784 nz = Ii[i+1]- Ii[i]; 3785 nz_max = PetscMax(nz_max, nz); 3786 if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz); 3787 nnz[i] = nz; 3788 } 3789 ierr = MatSeqAIJSetPreallocation(B, 0, nnz);CHKERRQ(ierr); 3790 ierr = PetscFree(nnz);CHKERRQ(ierr); 3791 3792 if (v) { 3793 values = (PetscScalar*) v; 3794 } else { 3795 ierr = PetscCalloc1(nz_max, &values);CHKERRQ(ierr); 3796 } 3797 3798 for (i = 0; i < m; i++) { 3799 nz = Ii[i+1] - Ii[i]; 3800 ierr = MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);CHKERRQ(ierr); 3801 } 3802 3803 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 3804 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 3805 3806 if (!v) { 3807 ierr = PetscFree(values);CHKERRQ(ierr); 3808 } 3809 ierr = MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);CHKERRQ(ierr); 3810 PetscFunctionReturn(0); 3811 } 3812 3813 #include <../src/mat/impls/dense/seq/dense.h> 3814 #include <petsc-private/kernels/petscaxpy.h> 3815 3816 #undef __FUNCT__ 3817 #define __FUNCT__ "MatMatMultNumeric_SeqDense_SeqAIJ" 3818 /* 3819 Computes (B'*A')' since computing B*A directly is untenable 3820 3821 n p p 3822 ( ) ( ) ( ) 3823 m ( A ) * n ( B ) = m ( C ) 3824 ( ) ( ) ( ) 3825 3826 */ 3827 PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C) 3828 { 3829 PetscErrorCode ierr; 3830 Mat_SeqDense *sub_a = (Mat_SeqDense*)A->data; 3831 Mat_SeqAIJ *sub_b = (Mat_SeqAIJ*)B->data; 3832 Mat_SeqDense *sub_c = (Mat_SeqDense*)C->data; 3833 PetscInt i,n,m,q,p; 3834 const PetscInt *ii,*idx; 3835 const PetscScalar *b,*a,*a_q; 3836 PetscScalar *c,*c_q; 3837 3838 PetscFunctionBegin; 3839 m = A->rmap->n; 3840 n = A->cmap->n; 3841 p = B->cmap->n; 3842 a = sub_a->v; 3843 b = sub_b->a; 3844 c = sub_c->v; 3845 ierr = PetscMemzero(c,m*p*sizeof(PetscScalar));CHKERRQ(ierr); 3846 3847 ii = sub_b->i; 3848 idx = sub_b->j; 3849 for (i=0; i<n; i++) { 3850 q = ii[i+1] - ii[i]; 3851 while (q-->0) { 3852 c_q = c + m*(*idx); 3853 a_q = a + m*i; 3854 PetscKernelAXPY(c_q,*b,a_q,m); 3855 idx++; 3856 b++; 3857 } 3858 } 3859 PetscFunctionReturn(0); 3860 } 3861 3862 #undef __FUNCT__ 3863 #define __FUNCT__ "MatMatMultSymbolic_SeqDense_SeqAIJ" 3864 PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C) 3865 { 3866 PetscErrorCode ierr; 3867 PetscInt m=A->rmap->n,n=B->cmap->n; 3868 Mat Cmat; 3869 3870 PetscFunctionBegin; 3871 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); 3872 ierr = MatCreate(PetscObjectComm((PetscObject)A),&Cmat);CHKERRQ(ierr); 3873 ierr = MatSetSizes(Cmat,m,n,m,n);CHKERRQ(ierr); 3874 ierr = MatSetBlockSizesFromMats(Cmat,A,B);CHKERRQ(ierr); 3875 ierr = MatSetType(Cmat,MATSEQDENSE);CHKERRQ(ierr); 3876 ierr = MatSeqDenseSetPreallocation(Cmat,NULL);CHKERRQ(ierr); 3877 3878 Cmat->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ; 3879 3880 *C = Cmat; 3881 PetscFunctionReturn(0); 3882 } 3883 3884 /* ----------------------------------------------------------------*/ 3885 #undef __FUNCT__ 3886 #define __FUNCT__ "MatMatMult_SeqDense_SeqAIJ" 3887 PetscErrorCode MatMatMult_SeqDense_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 3888 { 3889 PetscErrorCode ierr; 3890 3891 PetscFunctionBegin; 3892 if (scall == MAT_INITIAL_MATRIX) { 3893 ierr = PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 3894 ierr = MatMatMultSymbolic_SeqDense_SeqAIJ(A,B,fill,C);CHKERRQ(ierr); 3895 ierr = PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 3896 } 3897 ierr = PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 3898 ierr = MatMatMultNumeric_SeqDense_SeqAIJ(A,B,*C);CHKERRQ(ierr); 3899 ierr = PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 3900 PetscFunctionReturn(0); 3901 } 3902 3903 3904 /*MC 3905 MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 3906 based on compressed sparse row format. 3907 3908 Options Database Keys: 3909 . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions() 3910 3911 Level: beginner 3912 3913 .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType 3914 M*/ 3915 3916 /*MC 3917 MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices. 3918 3919 This matrix type is identical to MATSEQAIJ when constructed with a single process communicator, 3920 and MATMPIAIJ otherwise. As a result, for single process communicators, 3921 MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported 3922 for communicators controlling multiple processes. It is recommended that you call both of 3923 the above preallocation routines for simplicity. 3924 3925 Options Database Keys: 3926 . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions() 3927 3928 Developer Notes: Subclasses include MATAIJCUSP, MATAIJPERM, MATAIJCRL, and also automatically switches over to use inodes when 3929 enough exist. 3930 3931 Level: beginner 3932 3933 .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ 3934 M*/ 3935 3936 /*MC 3937 MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices. 3938 3939 This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator, 3940 and MATMPIAIJCRL otherwise. As a result, for single process communicators, 3941 MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported 3942 for communicators controlling multiple processes. It is recommended that you call both of 3943 the above preallocation routines for simplicity. 3944 3945 Options Database Keys: 3946 . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions() 3947 3948 Level: beginner 3949 3950 .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL 3951 M*/ 3952 3953 #if defined(PETSC_HAVE_PASTIX) 3954 PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_pastix(Mat,MatFactorType,Mat*); 3955 #endif 3956 #if defined(PETSC_HAVE_ESSL) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_REAL_SINGLE) && !defined(PETSC_USE_REAL___FLOAT128) 3957 PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_essl(Mat,MatFactorType,Mat*); 3958 #endif 3959 PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*); 3960 PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_petsc(Mat,MatFactorType,Mat*); 3961 PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_bas(Mat,MatFactorType,Mat*); 3962 extern PetscErrorCode MatGetFactorAvailable_seqaij_petsc(Mat,MatFactorType,PetscBool*); 3963 #if defined(PETSC_HAVE_MUMPS) 3964 PETSC_EXTERN PetscErrorCode MatGetFactor_aij_mumps(Mat,MatFactorType,Mat*); 3965 #endif 3966 #if defined(PETSC_HAVE_SUPERLU) 3967 PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_superlu(Mat,MatFactorType,Mat*); 3968 #endif 3969 #if defined(PETSC_HAVE_MKL_PARDISO) 3970 PETSC_EXTERN PetscErrorCode MatGetFactor_aij_mkl_pardiso(Mat,MatFactorType,Mat*); 3971 #endif 3972 #if defined(PETSC_HAVE_SUPERLU_DIST) 3973 PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_superlu_dist(Mat,MatFactorType,Mat*); 3974 #endif 3975 #if defined(PETSC_HAVE_SUITESPARSE) 3976 PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_umfpack(Mat,MatFactorType,Mat*); 3977 #endif 3978 #if defined(PETSC_HAVE_SUITESPARSE) 3979 PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_cholmod(Mat,MatFactorType,Mat*); 3980 #endif 3981 #if defined(PETSC_HAVE_SUITESPARSE) 3982 PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_klu(Mat,MatFactorType,Mat*); 3983 #endif 3984 #if defined(PETSC_HAVE_LUSOL) 3985 PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_lusol(Mat,MatFactorType,Mat*); 3986 #endif 3987 #if defined(PETSC_HAVE_MATLAB_ENGINE) 3988 PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_matlab(Mat,MatFactorType,Mat*); 3989 extern PetscErrorCode MatlabEnginePut_SeqAIJ(PetscObject,void*); 3990 extern PetscErrorCode MatlabEngineGet_SeqAIJ(PetscObject,void*); 3991 #endif 3992 #if defined(PETSC_HAVE_CLIQUE) 3993 PETSC_EXTERN PetscErrorCode MatGetFactor_aij_clique(Mat,MatFactorType,Mat*); 3994 #endif 3995 3996 3997 #undef __FUNCT__ 3998 #define __FUNCT__ "MatSeqAIJGetArray" 3999 /*@C 4000 MatSeqAIJGetArray - gives access to the array where the data for a SeqSeqAIJ matrix is stored 4001 4002 Not Collective 4003 4004 Input Parameter: 4005 . mat - a MATSEQAIJ matrix 4006 4007 Output Parameter: 4008 . array - pointer to the data 4009 4010 Level: intermediate 4011 4012 .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90() 4013 @*/ 4014 PetscErrorCode MatSeqAIJGetArray(Mat A,PetscScalar **array) 4015 { 4016 PetscErrorCode ierr; 4017 4018 PetscFunctionBegin; 4019 ierr = PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));CHKERRQ(ierr); 4020 PetscFunctionReturn(0); 4021 } 4022 4023 #undef __FUNCT__ 4024 #define __FUNCT__ "MatSeqAIJGetMaxRowNonzeros" 4025 /*@C 4026 MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row 4027 4028 Not Collective 4029 4030 Input Parameter: 4031 . mat - a MATSEQAIJ matrix 4032 4033 Output Parameter: 4034 . nz - the maximum number of nonzeros in any row 4035 4036 Level: intermediate 4037 4038 .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90() 4039 @*/ 4040 PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A,PetscInt *nz) 4041 { 4042 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)A->data; 4043 4044 PetscFunctionBegin; 4045 *nz = aij->rmax; 4046 PetscFunctionReturn(0); 4047 } 4048 4049 #undef __FUNCT__ 4050 #define __FUNCT__ "MatSeqAIJRestoreArray" 4051 /*@C 4052 MatSeqAIJRestoreArray - returns access to the array where the data for a MATSEQAIJ matrix is stored obtained by MatSeqAIJGetArray() 4053 4054 Not Collective 4055 4056 Input Parameters: 4057 . mat - a MATSEQAIJ matrix 4058 . array - pointer to the data 4059 4060 Level: intermediate 4061 4062 .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90() 4063 @*/ 4064 PetscErrorCode MatSeqAIJRestoreArray(Mat A,PetscScalar **array) 4065 { 4066 PetscErrorCode ierr; 4067 4068 PetscFunctionBegin; 4069 ierr = PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));CHKERRQ(ierr); 4070 PetscFunctionReturn(0); 4071 } 4072 4073 #undef __FUNCT__ 4074 #define __FUNCT__ "MatCreate_SeqAIJ" 4075 PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B) 4076 { 4077 Mat_SeqAIJ *b; 4078 PetscErrorCode ierr; 4079 PetscMPIInt size; 4080 4081 PetscFunctionBegin; 4082 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);CHKERRQ(ierr); 4083 if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1"); 4084 4085 ierr = PetscNewLog(B,&b);CHKERRQ(ierr); 4086 4087 B->data = (void*)b; 4088 4089 ierr = PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 4090 4091 b->row = 0; 4092 b->col = 0; 4093 b->icol = 0; 4094 b->reallocs = 0; 4095 b->ignorezeroentries = PETSC_FALSE; 4096 b->roworiented = PETSC_TRUE; 4097 b->nonew = 0; 4098 b->diag = 0; 4099 b->solve_work = 0; 4100 B->spptr = 0; 4101 b->saved_values = 0; 4102 b->idiag = 0; 4103 b->mdiag = 0; 4104 b->ssor_work = 0; 4105 b->omega = 1.0; 4106 b->fshift = 0.0; 4107 b->idiagvalid = PETSC_FALSE; 4108 b->ibdiagvalid = PETSC_FALSE; 4109 b->keepnonzeropattern = PETSC_FALSE; 4110 b->xtoy = 0; 4111 b->XtoY = 0; 4112 4113 ierr = PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);CHKERRQ(ierr); 4114 ierr = PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);CHKERRQ(ierr); 4115 ierr = PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);CHKERRQ(ierr); 4116 4117 #if defined(PETSC_HAVE_MATLAB_ENGINE) 4118 ierr = PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_matlab_C",MatGetFactor_seqaij_matlab);CHKERRQ(ierr); 4119 ierr = PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);CHKERRQ(ierr); 4120 ierr = PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);CHKERRQ(ierr); 4121 #endif 4122 #if defined(PETSC_HAVE_PASTIX) 4123 ierr = PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_pastix_C",MatGetFactor_seqaij_pastix);CHKERRQ(ierr); 4124 #endif 4125 #if defined(PETSC_HAVE_ESSL) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_REAL_SINGLE) && !defined(PETSC_USE_REAL___FLOAT128) 4126 ierr = PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_essl_C",MatGetFactor_seqaij_essl);CHKERRQ(ierr); 4127 #endif 4128 #if defined(PETSC_HAVE_SUPERLU) 4129 ierr = PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_superlu_C",MatGetFactor_seqaij_superlu);CHKERRQ(ierr); 4130 #endif 4131 #if defined(PETSC_HAVE_MKL_PARDISO) 4132 ierr = PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_mkl_pardiso_C",MatGetFactor_aij_mkl_pardiso);CHKERRQ(ierr); 4133 #endif 4134 #if defined(PETSC_HAVE_SUPERLU_DIST) 4135 ierr = PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_superlu_dist_C",MatGetFactor_seqaij_superlu_dist);CHKERRQ(ierr); 4136 #endif 4137 #if defined(PETSC_HAVE_MUMPS) 4138 ierr = PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_mumps_C",MatGetFactor_aij_mumps);CHKERRQ(ierr); 4139 #endif 4140 #if defined(PETSC_HAVE_SUITESPARSE) 4141 ierr = PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_umfpack_C",MatGetFactor_seqaij_umfpack);CHKERRQ(ierr); 4142 #endif 4143 #if defined(PETSC_HAVE_SUITESPARSE) 4144 ierr = PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_cholmod_C",MatGetFactor_seqaij_cholmod);CHKERRQ(ierr); 4145 #endif 4146 #if defined(PETSC_HAVE_SUITESPARSE) 4147 ierr = PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_klu_C",MatGetFactor_seqaij_klu);CHKERRQ(ierr); 4148 #endif 4149 #if defined(PETSC_HAVE_LUSOL) 4150 ierr = PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_lusol_C",MatGetFactor_seqaij_lusol);CHKERRQ(ierr); 4151 #endif 4152 #if defined(PETSC_HAVE_CLIQUE) 4153 ierr = PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_clique_C",MatGetFactor_aij_clique);CHKERRQ(ierr); 4154 #endif 4155 4156 ierr = PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_petsc_C",MatGetFactor_seqaij_petsc);CHKERRQ(ierr); 4157 ierr = PetscObjectComposeFunction((PetscObject)B,"MatGetFactorAvailable_petsc_C",MatGetFactorAvailable_seqaij_petsc);CHKERRQ(ierr); 4158 ierr = PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_bas_C",MatGetFactor_seqaij_bas);CHKERRQ(ierr); 4159 ierr = PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);CHKERRQ(ierr); 4160 ierr = PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);CHKERRQ(ierr); 4161 ierr = PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);CHKERRQ(ierr); 4162 ierr = PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);CHKERRQ(ierr); 4163 ierr = PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);CHKERRQ(ierr); 4164 ierr = PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);CHKERRQ(ierr); 4165 ierr = PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);CHKERRQ(ierr); 4166 ierr = PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);CHKERRQ(ierr); 4167 ierr = PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);CHKERRQ(ierr); 4168 ierr = PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);CHKERRQ(ierr); 4169 ierr = PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);CHKERRQ(ierr); 4170 ierr = PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);CHKERRQ(ierr); 4171 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMatMult_seqdense_seqaij_C",MatMatMult_SeqDense_SeqAIJ);CHKERRQ(ierr); 4172 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMatMultSymbolic_seqdense_seqaij_C",MatMatMultSymbolic_SeqDense_SeqAIJ);CHKERRQ(ierr); 4173 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMatMultNumeric_seqdense_seqaij_C",MatMatMultNumeric_SeqDense_SeqAIJ);CHKERRQ(ierr); 4174 ierr = MatCreate_SeqAIJ_Inode(B);CHKERRQ(ierr); 4175 ierr = PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);CHKERRQ(ierr); 4176 PetscFunctionReturn(0); 4177 } 4178 4179 #undef __FUNCT__ 4180 #define __FUNCT__ "MatDuplicateNoCreate_SeqAIJ" 4181 /* 4182 Given a matrix generated with MatGetFactor() duplicates all the information in A into B 4183 */ 4184 PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace) 4185 { 4186 Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data; 4187 PetscErrorCode ierr; 4188 PetscInt i,m = A->rmap->n; 4189 4190 PetscFunctionBegin; 4191 c = (Mat_SeqAIJ*)C->data; 4192 4193 C->factortype = A->factortype; 4194 c->row = 0; 4195 c->col = 0; 4196 c->icol = 0; 4197 c->reallocs = 0; 4198 4199 C->assembled = PETSC_TRUE; 4200 4201 ierr = PetscLayoutReference(A->rmap,&C->rmap);CHKERRQ(ierr); 4202 ierr = PetscLayoutReference(A->cmap,&C->cmap);CHKERRQ(ierr); 4203 4204 ierr = PetscMalloc2(m,&c->imax,m,&c->ilen);CHKERRQ(ierr); 4205 ierr = PetscLogObjectMemory((PetscObject)C, 2*m*sizeof(PetscInt));CHKERRQ(ierr); 4206 for (i=0; i<m; i++) { 4207 c->imax[i] = a->imax[i]; 4208 c->ilen[i] = a->ilen[i]; 4209 } 4210 4211 /* allocate the matrix space */ 4212 if (mallocmatspace) { 4213 ierr = PetscMalloc3(a->i[m],&c->a,a->i[m],&c->j,m+1,&c->i);CHKERRQ(ierr); 4214 ierr = PetscLogObjectMemory((PetscObject)C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));CHKERRQ(ierr); 4215 4216 c->singlemalloc = PETSC_TRUE; 4217 4218 ierr = PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));CHKERRQ(ierr); 4219 if (m > 0) { 4220 ierr = PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));CHKERRQ(ierr); 4221 if (cpvalues == MAT_COPY_VALUES) { 4222 ierr = PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));CHKERRQ(ierr); 4223 } else { 4224 ierr = PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));CHKERRQ(ierr); 4225 } 4226 } 4227 } 4228 4229 c->ignorezeroentries = a->ignorezeroentries; 4230 c->roworiented = a->roworiented; 4231 c->nonew = a->nonew; 4232 if (a->diag) { 4233 ierr = PetscMalloc1((m+1),&c->diag);CHKERRQ(ierr); 4234 ierr = PetscLogObjectMemory((PetscObject)C,(m+1)*sizeof(PetscInt));CHKERRQ(ierr); 4235 for (i=0; i<m; i++) { 4236 c->diag[i] = a->diag[i]; 4237 } 4238 } else c->diag = 0; 4239 4240 c->solve_work = 0; 4241 c->saved_values = 0; 4242 c->idiag = 0; 4243 c->ssor_work = 0; 4244 c->keepnonzeropattern = a->keepnonzeropattern; 4245 c->free_a = PETSC_TRUE; 4246 c->free_ij = PETSC_TRUE; 4247 c->xtoy = 0; 4248 c->XtoY = 0; 4249 4250 c->rmax = a->rmax; 4251 c->nz = a->nz; 4252 c->maxnz = a->nz; /* Since we allocate exactly the right amount */ 4253 C->preallocated = PETSC_TRUE; 4254 4255 c->compressedrow.use = a->compressedrow.use; 4256 c->compressedrow.nrows = a->compressedrow.nrows; 4257 if (a->compressedrow.use) { 4258 i = a->compressedrow.nrows; 4259 ierr = PetscMalloc2(i+1,&c->compressedrow.i,i,&c->compressedrow.rindex);CHKERRQ(ierr); 4260 ierr = PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));CHKERRQ(ierr); 4261 ierr = PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));CHKERRQ(ierr); 4262 } else { 4263 c->compressedrow.use = PETSC_FALSE; 4264 c->compressedrow.i = NULL; 4265 c->compressedrow.rindex = NULL; 4266 } 4267 C->nonzerostate = A->nonzerostate; 4268 4269 ierr = MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);CHKERRQ(ierr); 4270 ierr = PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);CHKERRQ(ierr); 4271 PetscFunctionReturn(0); 4272 } 4273 4274 #undef __FUNCT__ 4275 #define __FUNCT__ "MatDuplicate_SeqAIJ" 4276 PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B) 4277 { 4278 PetscErrorCode ierr; 4279 4280 PetscFunctionBegin; 4281 ierr = MatCreate(PetscObjectComm((PetscObject)A),B);CHKERRQ(ierr); 4282 ierr = MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);CHKERRQ(ierr); 4283 if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) { 4284 ierr = MatSetBlockSizesFromMats(*B,A,A);CHKERRQ(ierr); 4285 } 4286 ierr = MatSetType(*B,((PetscObject)A)->type_name);CHKERRQ(ierr); 4287 ierr = MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);CHKERRQ(ierr); 4288 PetscFunctionReturn(0); 4289 } 4290 4291 #undef __FUNCT__ 4292 #define __FUNCT__ "MatLoad_SeqAIJ" 4293 PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer) 4294 { 4295 Mat_SeqAIJ *a; 4296 PetscErrorCode ierr; 4297 PetscInt i,sum,nz,header[4],*rowlengths = 0,M,N,rows,cols; 4298 int fd; 4299 PetscMPIInt size; 4300 MPI_Comm comm; 4301 PetscInt bs = newMat->rmap->bs; 4302 4303 PetscFunctionBegin; 4304 ierr = PetscObjectGetComm((PetscObject)viewer,&comm);CHKERRQ(ierr); 4305 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 4306 if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"view must have one processor"); 4307 4308 ierr = PetscOptionsBegin(comm,NULL,"Options for loading SEQAIJ matrix","Mat");CHKERRQ(ierr); 4309 ierr = PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);CHKERRQ(ierr); 4310 ierr = PetscOptionsEnd();CHKERRQ(ierr); 4311 if (bs < 0) bs = 1; 4312 ierr = MatSetBlockSize(newMat,bs);CHKERRQ(ierr); 4313 4314 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 4315 ierr = PetscBinaryRead(fd,header,4,PETSC_INT);CHKERRQ(ierr); 4316 if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file"); 4317 M = header[1]; N = header[2]; nz = header[3]; 4318 4319 if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ"); 4320 4321 /* read in row lengths */ 4322 ierr = PetscMalloc1(M,&rowlengths);CHKERRQ(ierr); 4323 ierr = PetscBinaryRead(fd,rowlengths,M,PETSC_INT);CHKERRQ(ierr); 4324 4325 /* check if sum of rowlengths is same as nz */ 4326 for (i=0,sum=0; i< M; i++) sum +=rowlengths[i]; 4327 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); 4328 4329 /* set global size if not set already*/ 4330 if (newMat->rmap->n < 0 && newMat->rmap->N < 0 && newMat->cmap->n < 0 && newMat->cmap->N < 0) { 4331 ierr = MatSetSizes(newMat,PETSC_DECIDE,PETSC_DECIDE,M,N);CHKERRQ(ierr); 4332 } else { 4333 /* if sizes and type are already set, check if the vector global sizes are correct */ 4334 ierr = MatGetSize(newMat,&rows,&cols);CHKERRQ(ierr); 4335 if (rows < 0 && cols < 0) { /* user might provide local size instead of global size */ 4336 ierr = MatGetLocalSize(newMat,&rows,&cols);CHKERRQ(ierr); 4337 } 4338 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); 4339 } 4340 ierr = MatSeqAIJSetPreallocation_SeqAIJ(newMat,0,rowlengths);CHKERRQ(ierr); 4341 a = (Mat_SeqAIJ*)newMat->data; 4342 4343 ierr = PetscBinaryRead(fd,a->j,nz,PETSC_INT);CHKERRQ(ierr); 4344 4345 /* read in nonzero values */ 4346 ierr = PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);CHKERRQ(ierr); 4347 4348 /* set matrix "i" values */ 4349 a->i[0] = 0; 4350 for (i=1; i<= M; i++) { 4351 a->i[i] = a->i[i-1] + rowlengths[i-1]; 4352 a->ilen[i-1] = rowlengths[i-1]; 4353 } 4354 ierr = PetscFree(rowlengths);CHKERRQ(ierr); 4355 4356 ierr = MatAssemblyBegin(newMat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 4357 ierr = MatAssemblyEnd(newMat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 4358 PetscFunctionReturn(0); 4359 } 4360 4361 #undef __FUNCT__ 4362 #define __FUNCT__ "MatEqual_SeqAIJ" 4363 PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg) 4364 { 4365 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data; 4366 PetscErrorCode ierr; 4367 #if defined(PETSC_USE_COMPLEX) 4368 PetscInt k; 4369 #endif 4370 4371 PetscFunctionBegin; 4372 /* If the matrix dimensions are not equal,or no of nonzeros */ 4373 if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) { 4374 *flg = PETSC_FALSE; 4375 PetscFunctionReturn(0); 4376 } 4377 4378 /* if the a->i are the same */ 4379 ierr = PetscMemcmp(a->i,b->i,(A->rmap->n+1)*sizeof(PetscInt),flg);CHKERRQ(ierr); 4380 if (!*flg) PetscFunctionReturn(0); 4381 4382 /* if a->j are the same */ 4383 ierr = PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);CHKERRQ(ierr); 4384 if (!*flg) PetscFunctionReturn(0); 4385 4386 /* if a->a are the same */ 4387 #if defined(PETSC_USE_COMPLEX) 4388 for (k=0; k<a->nz; k++) { 4389 if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) { 4390 *flg = PETSC_FALSE; 4391 PetscFunctionReturn(0); 4392 } 4393 } 4394 #else 4395 ierr = PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);CHKERRQ(ierr); 4396 #endif 4397 PetscFunctionReturn(0); 4398 } 4399 4400 #undef __FUNCT__ 4401 #define __FUNCT__ "MatCreateSeqAIJWithArrays" 4402 /*@ 4403 MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format) 4404 provided by the user. 4405 4406 Collective on MPI_Comm 4407 4408 Input Parameters: 4409 + comm - must be an MPI communicator of size 1 4410 . m - number of rows 4411 . n - number of columns 4412 . i - row indices 4413 . j - column indices 4414 - a - matrix values 4415 4416 Output Parameter: 4417 . mat - the matrix 4418 4419 Level: intermediate 4420 4421 Notes: 4422 The i, j, and a arrays are not copied by this routine, the user must free these arrays 4423 once the matrix is destroyed and not before 4424 4425 You cannot set new nonzero locations into this matrix, that will generate an error. 4426 4427 The i and j indices are 0 based 4428 4429 The format which is used for the sparse matrix input, is equivalent to a 4430 row-major ordering.. i.e for the following matrix, the input data expected is 4431 as shown: 4432 4433 1 0 0 4434 2 0 3 4435 4 5 6 4436 4437 i = {0,1,3,6} [size = nrow+1 = 3+1] 4438 j = {0,0,2,0,1,2} [size = nz = 6]; values must be sorted for each row 4439 v = {1,2,3,4,5,6} [size = nz = 6] 4440 4441 4442 .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR() 4443 4444 @*/ 4445 PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt *i,PetscInt *j,PetscScalar *a,Mat *mat) 4446 { 4447 PetscErrorCode ierr; 4448 PetscInt ii; 4449 Mat_SeqAIJ *aij; 4450 #if defined(PETSC_USE_DEBUG) 4451 PetscInt jj; 4452 #endif 4453 4454 PetscFunctionBegin; 4455 if (i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0"); 4456 ierr = MatCreate(comm,mat);CHKERRQ(ierr); 4457 ierr = MatSetSizes(*mat,m,n,m,n);CHKERRQ(ierr); 4458 /* ierr = MatSetBlockSizes(*mat,,);CHKERRQ(ierr); */ 4459 ierr = MatSetType(*mat,MATSEQAIJ);CHKERRQ(ierr); 4460 ierr = MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);CHKERRQ(ierr); 4461 aij = (Mat_SeqAIJ*)(*mat)->data; 4462 ierr = PetscMalloc2(m,&aij->imax,m,&aij->ilen);CHKERRQ(ierr); 4463 4464 aij->i = i; 4465 aij->j = j; 4466 aij->a = a; 4467 aij->singlemalloc = PETSC_FALSE; 4468 aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/ 4469 aij->free_a = PETSC_FALSE; 4470 aij->free_ij = PETSC_FALSE; 4471 4472 for (ii=0; ii<m; ii++) { 4473 aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii]; 4474 #if defined(PETSC_USE_DEBUG) 4475 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]); 4476 for (jj=i[ii]+1; jj<i[ii+1]; jj++) { 4477 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); 4478 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); 4479 } 4480 #endif 4481 } 4482 #if defined(PETSC_USE_DEBUG) 4483 for (ii=0; ii<aij->i[m]; ii++) { 4484 if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %D index = %D",ii,j[ii]); 4485 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]); 4486 } 4487 #endif 4488 4489 ierr = MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 4490 ierr = MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 4491 PetscFunctionReturn(0); 4492 } 4493 #undef __FUNCT__ 4494 #define __FUNCT__ "MatCreateSeqAIJFromTriple" 4495 /*@C 4496 MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format) 4497 provided by the user. 4498 4499 Collective on MPI_Comm 4500 4501 Input Parameters: 4502 + comm - must be an MPI communicator of size 1 4503 . m - number of rows 4504 . n - number of columns 4505 . i - row indices 4506 . j - column indices 4507 . a - matrix values 4508 . nz - number of nonzeros 4509 - idx - 0 or 1 based 4510 4511 Output Parameter: 4512 . mat - the matrix 4513 4514 Level: intermediate 4515 4516 Notes: 4517 The i and j indices are 0 based 4518 4519 The format which is used for the sparse matrix input, is equivalent to a 4520 row-major ordering.. i.e for the following matrix, the input data expected is 4521 as shown: 4522 4523 1 0 0 4524 2 0 3 4525 4 5 6 4526 4527 i = {0,1,1,2,2,2} 4528 j = {0,0,2,0,1,2} 4529 v = {1,2,3,4,5,6} 4530 4531 4532 .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR() 4533 4534 @*/ 4535 PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt *i,PetscInt *j,PetscScalar *a,Mat *mat,PetscInt nz,PetscBool idx) 4536 { 4537 PetscErrorCode ierr; 4538 PetscInt ii, *nnz, one = 1,row,col; 4539 4540 4541 PetscFunctionBegin; 4542 ierr = PetscCalloc1(m,&nnz);CHKERRQ(ierr); 4543 for (ii = 0; ii < nz; ii++) { 4544 nnz[i[ii] - !!idx] += 1; 4545 } 4546 ierr = MatCreate(comm,mat);CHKERRQ(ierr); 4547 ierr = MatSetSizes(*mat,m,n,m,n);CHKERRQ(ierr); 4548 ierr = MatSetType(*mat,MATSEQAIJ);CHKERRQ(ierr); 4549 ierr = MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);CHKERRQ(ierr); 4550 for (ii = 0; ii < nz; ii++) { 4551 if (idx) { 4552 row = i[ii] - 1; 4553 col = j[ii] - 1; 4554 } else { 4555 row = i[ii]; 4556 col = j[ii]; 4557 } 4558 ierr = MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);CHKERRQ(ierr); 4559 } 4560 ierr = MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 4561 ierr = MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 4562 ierr = PetscFree(nnz);CHKERRQ(ierr); 4563 PetscFunctionReturn(0); 4564 } 4565 4566 #undef __FUNCT__ 4567 #define __FUNCT__ "MatSetColoring_SeqAIJ" 4568 PetscErrorCode MatSetColoring_SeqAIJ(Mat A,ISColoring coloring) 4569 { 4570 PetscErrorCode ierr; 4571 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 4572 4573 PetscFunctionBegin; 4574 if (coloring->ctype == IS_COLORING_GLOBAL) { 4575 ierr = ISColoringReference(coloring);CHKERRQ(ierr); 4576 a->coloring = coloring; 4577 } else if (coloring->ctype == IS_COLORING_GHOSTED) { 4578 PetscInt i,*larray; 4579 ISColoring ocoloring; 4580 ISColoringValue *colors; 4581 4582 /* set coloring for diagonal portion */ 4583 ierr = PetscMalloc1(A->cmap->n,&larray);CHKERRQ(ierr); 4584 for (i=0; i<A->cmap->n; i++) larray[i] = i; 4585 ierr = ISGlobalToLocalMappingApply(A->cmap->mapping,IS_GTOLM_MASK,A->cmap->n,larray,NULL,larray);CHKERRQ(ierr); 4586 ierr = PetscMalloc1(A->cmap->n,&colors);CHKERRQ(ierr); 4587 for (i=0; i<A->cmap->n; i++) colors[i] = coloring->colors[larray[i]]; 4588 ierr = PetscFree(larray);CHKERRQ(ierr); 4589 ierr = ISColoringCreate(PETSC_COMM_SELF,coloring->n,A->cmap->n,colors,&ocoloring);CHKERRQ(ierr); 4590 a->coloring = ocoloring; 4591 } 4592 PetscFunctionReturn(0); 4593 } 4594 4595 #undef __FUNCT__ 4596 #define __FUNCT__ "MatSetValuesAdifor_SeqAIJ" 4597 PetscErrorCode MatSetValuesAdifor_SeqAIJ(Mat A,PetscInt nl,void *advalues) 4598 { 4599 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 4600 PetscInt m = A->rmap->n,*ii = a->i,*jj = a->j,nz,i,j; 4601 MatScalar *v = a->a; 4602 PetscScalar *values = (PetscScalar*)advalues; 4603 ISColoringValue *color; 4604 4605 PetscFunctionBegin; 4606 if (!a->coloring) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix"); 4607 color = a->coloring->colors; 4608 /* loop over rows */ 4609 for (i=0; i<m; i++) { 4610 nz = ii[i+1] - ii[i]; 4611 /* loop over columns putting computed value into matrix */ 4612 for (j=0; j<nz; j++) *v++ = values[color[*jj++]]; 4613 values += nl; /* jump to next row of derivatives */ 4614 } 4615 PetscFunctionReturn(0); 4616 } 4617 4618 #undef __FUNCT__ 4619 #define __FUNCT__ "MatSeqAIJInvalidateDiagonal" 4620 PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A) 4621 { 4622 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data; 4623 PetscErrorCode ierr; 4624 4625 PetscFunctionBegin; 4626 a->idiagvalid = PETSC_FALSE; 4627 a->ibdiagvalid = PETSC_FALSE; 4628 4629 ierr = MatSeqAIJInvalidateDiagonal_Inode(A);CHKERRQ(ierr); 4630 PetscFunctionReturn(0); 4631 } 4632 4633 /* 4634 Special version for direct calls from Fortran 4635 */ 4636 #include <petsc-private/fortranimpl.h> 4637 #if defined(PETSC_HAVE_FORTRAN_CAPS) 4638 #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ 4639 #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE) 4640 #define matsetvaluesseqaij_ matsetvaluesseqaij 4641 #endif 4642 4643 /* Change these macros so can be used in void function */ 4644 #undef CHKERRQ 4645 #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr) 4646 #undef SETERRQ2 4647 #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr) 4648 #undef SETERRQ3 4649 #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr) 4650 4651 #undef __FUNCT__ 4652 #define __FUNCT__ "matsetvaluesseqaij_" 4653 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) 4654 { 4655 Mat A = *AA; 4656 PetscInt m = *mm, n = *nn; 4657 InsertMode is = *isis; 4658 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 4659 PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N; 4660 PetscInt *imax,*ai,*ailen; 4661 PetscErrorCode ierr; 4662 PetscInt *aj,nonew = a->nonew,lastcol = -1; 4663 MatScalar *ap,value,*aa; 4664 PetscBool ignorezeroentries = a->ignorezeroentries; 4665 PetscBool roworiented = a->roworiented; 4666 4667 PetscFunctionBegin; 4668 MatCheckPreallocated(A,1); 4669 imax = a->imax; 4670 ai = a->i; 4671 ailen = a->ilen; 4672 aj = a->j; 4673 aa = a->a; 4674 4675 for (k=0; k<m; k++) { /* loop over added rows */ 4676 row = im[k]; 4677 if (row < 0) continue; 4678 #if defined(PETSC_USE_DEBUG) 4679 if (row >= A->rmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large"); 4680 #endif 4681 rp = aj + ai[row]; ap = aa + ai[row]; 4682 rmax = imax[row]; nrow = ailen[row]; 4683 low = 0; 4684 high = nrow; 4685 for (l=0; l<n; l++) { /* loop over added columns */ 4686 if (in[l] < 0) continue; 4687 #if defined(PETSC_USE_DEBUG) 4688 if (in[l] >= A->cmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large"); 4689 #endif 4690 col = in[l]; 4691 if (roworiented) value = v[l + k*n]; 4692 else value = v[k + l*m]; 4693 4694 if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue; 4695 4696 if (col <= lastcol) low = 0; 4697 else high = nrow; 4698 lastcol = col; 4699 while (high-low > 5) { 4700 t = (low+high)/2; 4701 if (rp[t] > col) high = t; 4702 else low = t; 4703 } 4704 for (i=low; i<high; i++) { 4705 if (rp[i] > col) break; 4706 if (rp[i] == col) { 4707 if (is == ADD_VALUES) ap[i] += value; 4708 else ap[i] = value; 4709 goto noinsert; 4710 } 4711 } 4712 if (value == 0.0 && ignorezeroentries) goto noinsert; 4713 if (nonew == 1) goto noinsert; 4714 if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix"); 4715 MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar); 4716 N = nrow++ - 1; a->nz++; high++; 4717 /* shift up all the later entries in this row */ 4718 for (ii=N; ii>=i; ii--) { 4719 rp[ii+1] = rp[ii]; 4720 ap[ii+1] = ap[ii]; 4721 } 4722 rp[i] = col; 4723 ap[i] = value; 4724 A->nonzerostate++; 4725 noinsert:; 4726 low = i + 1; 4727 } 4728 ailen[row] = nrow; 4729 } 4730 PetscFunctionReturnVoid(); 4731 } 4732 4733 4734