1 /* 2 Defines the basic matrix operations for the AIJ (compressed row) 3 matrix storage format. 4 */ 5 6 #include <../src/mat/impls/aij/seq/aij.h> /*I "petscmat.h" I*/ 7 #include <petscblaslapack.h> 8 #include <petscbt.h> 9 #include <petsc/private/kernels/blocktranspose.h> 10 11 PetscErrorCode MatSeqAIJSetTypeFromOptions(Mat A) 12 { 13 PetscBool flg; 14 char type[256]; 15 16 PetscFunctionBegin; 17 PetscObjectOptionsBegin((PetscObject)A); 18 PetscCall(PetscOptionsFList("-mat_seqaij_type", "Matrix SeqAIJ type", "MatSeqAIJSetType", MatSeqAIJList, "seqaij", type, 256, &flg)); 19 if (flg) PetscCall(MatSeqAIJSetType(A, type)); 20 PetscOptionsEnd(); 21 PetscFunctionReturn(0); 22 } 23 24 PetscErrorCode MatGetColumnReductions_SeqAIJ(Mat A, PetscInt type, PetscReal *reductions) 25 { 26 PetscInt i, m, n; 27 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 28 29 PetscFunctionBegin; 30 PetscCall(MatGetSize(A, &m, &n)); 31 PetscCall(PetscArrayzero(reductions, n)); 32 if (type == NORM_2) { 33 for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i] * aij->a[i]); 34 } else if (type == NORM_1) { 35 for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i]); 36 } else if (type == NORM_INFINITY) { 37 for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]), reductions[aij->j[i]]); 38 } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) { 39 for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscRealPart(aij->a[i]); 40 } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) { 41 for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscImaginaryPart(aij->a[i]); 42 } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Unknown reduction type"); 43 44 if (type == NORM_2) { 45 for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]); 46 } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) { 47 for (i = 0; i < n; i++) reductions[i] /= m; 48 } 49 PetscFunctionReturn(0); 50 } 51 52 PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A, IS *is) 53 { 54 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 55 PetscInt i, m = A->rmap->n, cnt = 0, bs = A->rmap->bs; 56 const PetscInt *jj = a->j, *ii = a->i; 57 PetscInt *rows; 58 59 PetscFunctionBegin; 60 for (i = 0; i < m; i++) { 61 if ((ii[i] != ii[i + 1]) && ((jj[ii[i]] < bs * (i / bs)) || (jj[ii[i + 1] - 1] > bs * ((i + bs) / bs) - 1))) cnt++; 62 } 63 PetscCall(PetscMalloc1(cnt, &rows)); 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 PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, is)); 72 PetscFunctionReturn(0); 73 } 74 75 PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A, PetscInt *nrows, PetscInt **zrows) 76 { 77 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 78 const MatScalar *aa; 79 PetscInt i, m = A->rmap->n, cnt = 0; 80 const PetscInt *ii = a->i, *jj = a->j, *diag; 81 PetscInt *rows; 82 83 PetscFunctionBegin; 84 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 85 PetscCall(MatMarkDiagonal_SeqAIJ(A)); 86 diag = a->diag; 87 for (i = 0; i < m; i++) { 88 if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) cnt++; 89 } 90 PetscCall(PetscMalloc1(cnt, &rows)); 91 cnt = 0; 92 for (i = 0; i < m; i++) { 93 if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) rows[cnt++] = i; 94 } 95 *nrows = cnt; 96 *zrows = rows; 97 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 98 PetscFunctionReturn(0); 99 } 100 101 PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A, IS *zrows) 102 { 103 PetscInt nrows, *rows; 104 105 PetscFunctionBegin; 106 *zrows = NULL; 107 PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(A, &nrows, &rows)); 108 PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), nrows, rows, PETSC_OWN_POINTER, zrows)); 109 PetscFunctionReturn(0); 110 } 111 112 PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A, IS *keptrows) 113 { 114 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 115 const MatScalar *aa; 116 PetscInt m = A->rmap->n, cnt = 0; 117 const PetscInt *ii; 118 PetscInt n, i, j, *rows; 119 120 PetscFunctionBegin; 121 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 122 *keptrows = NULL; 123 ii = a->i; 124 for (i = 0; i < m; i++) { 125 n = ii[i + 1] - ii[i]; 126 if (!n) { 127 cnt++; 128 goto ok1; 129 } 130 for (j = ii[i]; j < ii[i + 1]; j++) { 131 if (aa[j] != 0.0) goto ok1; 132 } 133 cnt++; 134 ok1:; 135 } 136 if (!cnt) { 137 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 138 PetscFunctionReturn(0); 139 } 140 PetscCall(PetscMalloc1(A->rmap->n - cnt, &rows)); 141 cnt = 0; 142 for (i = 0; i < m; i++) { 143 n = ii[i + 1] - ii[i]; 144 if (!n) continue; 145 for (j = ii[i]; j < ii[i + 1]; j++) { 146 if (aa[j] != 0.0) { 147 rows[cnt++] = i; 148 break; 149 } 150 } 151 } 152 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 153 PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, keptrows)); 154 PetscFunctionReturn(0); 155 } 156 157 PetscErrorCode MatDiagonalSet_SeqAIJ(Mat Y, Vec D, InsertMode is) 158 { 159 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)Y->data; 160 PetscInt i, m = Y->rmap->n; 161 const PetscInt *diag; 162 MatScalar *aa; 163 const PetscScalar *v; 164 PetscBool missing; 165 166 PetscFunctionBegin; 167 if (Y->assembled) { 168 PetscCall(MatMissingDiagonal_SeqAIJ(Y, &missing, NULL)); 169 if (!missing) { 170 diag = aij->diag; 171 PetscCall(VecGetArrayRead(D, &v)); 172 PetscCall(MatSeqAIJGetArray(Y, &aa)); 173 if (is == INSERT_VALUES) { 174 for (i = 0; i < m; i++) aa[diag[i]] = v[i]; 175 } else { 176 for (i = 0; i < m; i++) aa[diag[i]] += v[i]; 177 } 178 PetscCall(MatSeqAIJRestoreArray(Y, &aa)); 179 PetscCall(VecRestoreArrayRead(D, &v)); 180 PetscFunctionReturn(0); 181 } 182 PetscCall(MatSeqAIJInvalidateDiagonal(Y)); 183 } 184 PetscCall(MatDiagonalSet_Default(Y, D, is)); 185 PetscFunctionReturn(0); 186 } 187 188 PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done) 189 { 190 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 191 PetscInt i, ishift; 192 193 PetscFunctionBegin; 194 if (m) *m = A->rmap->n; 195 if (!ia) PetscFunctionReturn(0); 196 ishift = 0; 197 if (symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) { 198 PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, ishift, oshift, (PetscInt **)ia, (PetscInt **)ja)); 199 } else if (oshift == 1) { 200 PetscInt *tia; 201 PetscInt nz = a->i[A->rmap->n]; 202 /* malloc space and add 1 to i and j indices */ 203 PetscCall(PetscMalloc1(A->rmap->n + 1, &tia)); 204 for (i = 0; i < A->rmap->n + 1; i++) tia[i] = a->i[i] + 1; 205 *ia = tia; 206 if (ja) { 207 PetscInt *tja; 208 PetscCall(PetscMalloc1(nz + 1, &tja)); 209 for (i = 0; i < nz; i++) tja[i] = a->j[i] + 1; 210 *ja = tja; 211 } 212 } else { 213 *ia = a->i; 214 if (ja) *ja = a->j; 215 } 216 PetscFunctionReturn(0); 217 } 218 219 PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done) 220 { 221 PetscFunctionBegin; 222 if (!ia) PetscFunctionReturn(0); 223 if ((symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) || oshift == 1) { 224 PetscCall(PetscFree(*ia)); 225 if (ja) PetscCall(PetscFree(*ja)); 226 } 227 PetscFunctionReturn(0); 228 } 229 230 PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done) 231 { 232 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 233 PetscInt i, *collengths, *cia, *cja, n = A->cmap->n, m = A->rmap->n; 234 PetscInt nz = a->i[m], row, *jj, mr, col; 235 236 PetscFunctionBegin; 237 *nn = n; 238 if (!ia) PetscFunctionReturn(0); 239 if (symmetric) { 240 PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, 0, oshift, (PetscInt **)ia, (PetscInt **)ja)); 241 } else { 242 PetscCall(PetscCalloc1(n, &collengths)); 243 PetscCall(PetscMalloc1(n + 1, &cia)); 244 PetscCall(PetscMalloc1(nz, &cja)); 245 jj = a->j; 246 for (i = 0; i < nz; i++) collengths[jj[i]]++; 247 cia[0] = oshift; 248 for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i]; 249 PetscCall(PetscArrayzero(collengths, n)); 250 jj = a->j; 251 for (row = 0; row < m; row++) { 252 mr = a->i[row + 1] - a->i[row]; 253 for (i = 0; i < mr; i++) { 254 col = *jj++; 255 256 cja[cia[col] + collengths[col]++ - oshift] = row + oshift; 257 } 258 } 259 PetscCall(PetscFree(collengths)); 260 *ia = cia; 261 *ja = cja; 262 } 263 PetscFunctionReturn(0); 264 } 265 266 PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done) 267 { 268 PetscFunctionBegin; 269 if (!ia) PetscFunctionReturn(0); 270 271 PetscCall(PetscFree(*ia)); 272 PetscCall(PetscFree(*ja)); 273 PetscFunctionReturn(0); 274 } 275 276 /* 277 MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from 278 MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output 279 spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ() 280 */ 281 PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done) 282 { 283 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 284 PetscInt i, *collengths, *cia, *cja, n = A->cmap->n, m = A->rmap->n; 285 PetscInt nz = a->i[m], row, mr, col, tmp; 286 PetscInt *cspidx; 287 const PetscInt *jj; 288 289 PetscFunctionBegin; 290 *nn = n; 291 if (!ia) PetscFunctionReturn(0); 292 293 PetscCall(PetscCalloc1(n, &collengths)); 294 PetscCall(PetscMalloc1(n + 1, &cia)); 295 PetscCall(PetscMalloc1(nz, &cja)); 296 PetscCall(PetscMalloc1(nz, &cspidx)); 297 jj = a->j; 298 for (i = 0; i < nz; i++) collengths[jj[i]]++; 299 cia[0] = oshift; 300 for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i]; 301 PetscCall(PetscArrayzero(collengths, n)); 302 jj = a->j; 303 for (row = 0; row < m; row++) { 304 mr = a->i[row + 1] - a->i[row]; 305 for (i = 0; i < mr; i++) { 306 col = *jj++; 307 tmp = cia[col] + collengths[col]++ - oshift; 308 cspidx[tmp] = a->i[row] + i; /* index of a->j */ 309 cja[tmp] = row + oshift; 310 } 311 } 312 PetscCall(PetscFree(collengths)); 313 *ia = cia; 314 *ja = cja; 315 *spidx = cspidx; 316 PetscFunctionReturn(0); 317 } 318 319 PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done) 320 { 321 PetscFunctionBegin; 322 PetscCall(MatRestoreColumnIJ_SeqAIJ(A, oshift, symmetric, inodecompressed, n, ia, ja, done)); 323 PetscCall(PetscFree(*spidx)); 324 PetscFunctionReturn(0); 325 } 326 327 PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A, PetscInt row, const PetscScalar v[]) 328 { 329 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 330 PetscInt *ai = a->i; 331 PetscScalar *aa; 332 333 PetscFunctionBegin; 334 PetscCall(MatSeqAIJGetArray(A, &aa)); 335 PetscCall(PetscArraycpy(aa + ai[row], v, ai[row + 1] - ai[row])); 336 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 337 PetscFunctionReturn(0); 338 } 339 340 /* 341 MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions 342 343 - a single row of values is set with each call 344 - no row or column indices are negative or (in error) larger than the number of rows or columns 345 - the values are always added to the matrix, not set 346 - no new locations are introduced in the nonzero structure of the matrix 347 348 This does NOT assume the global column indices are sorted 349 350 */ 351 352 #include <petsc/private/isimpl.h> 353 PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is) 354 { 355 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 356 PetscInt low, high, t, row, nrow, i, col, l; 357 const PetscInt *rp, *ai = a->i, *ailen = a->ilen, *aj = a->j; 358 PetscInt lastcol = -1; 359 MatScalar *ap, value, *aa; 360 const PetscInt *ridx = A->rmap->mapping->indices, *cidx = A->cmap->mapping->indices; 361 362 PetscFunctionBegin; 363 PetscCall(MatSeqAIJGetArray(A, &aa)); 364 row = ridx[im[0]]; 365 rp = aj + ai[row]; 366 ap = aa + ai[row]; 367 nrow = ailen[row]; 368 low = 0; 369 high = nrow; 370 for (l = 0; l < n; l++) { /* loop over added columns */ 371 col = cidx[in[l]]; 372 value = v[l]; 373 374 if (col <= lastcol) low = 0; 375 else high = nrow; 376 lastcol = col; 377 while (high - low > 5) { 378 t = (low + high) / 2; 379 if (rp[t] > col) high = t; 380 else low = t; 381 } 382 for (i = low; i < high; i++) { 383 if (rp[i] == col) { 384 ap[i] += value; 385 low = i + 1; 386 break; 387 } 388 } 389 } 390 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 391 return 0; 392 } 393 394 PetscErrorCode MatSetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is) 395 { 396 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 397 PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N; 398 PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen; 399 PetscInt *aj = a->j, nonew = a->nonew, lastcol = -1; 400 MatScalar *ap = NULL, value = 0.0, *aa; 401 PetscBool ignorezeroentries = a->ignorezeroentries; 402 PetscBool roworiented = a->roworiented; 403 404 PetscFunctionBegin; 405 PetscCall(MatSeqAIJGetArray(A, &aa)); 406 for (k = 0; k < m; k++) { /* loop over added rows */ 407 row = im[k]; 408 if (row < 0) continue; 409 PetscCheck(row < A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->n - 1); 410 rp = aj + ai[row]; 411 if (!A->structure_only) ap = aa + ai[row]; 412 rmax = imax[row]; 413 nrow = ailen[row]; 414 low = 0; 415 high = nrow; 416 for (l = 0; l < n; l++) { /* loop over added columns */ 417 if (in[l] < 0) continue; 418 PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1); 419 col = in[l]; 420 if (v && !A->structure_only) value = roworiented ? v[l + k * n] : v[k + l * m]; 421 if (!A->structure_only && value == 0.0 && ignorezeroentries && is == ADD_VALUES && row != col) continue; 422 423 if (col <= lastcol) low = 0; 424 else high = nrow; 425 lastcol = col; 426 while (high - low > 5) { 427 t = (low + high) / 2; 428 if (rp[t] > col) high = t; 429 else low = t; 430 } 431 for (i = low; i < high; i++) { 432 if (rp[i] > col) break; 433 if (rp[i] == col) { 434 if (!A->structure_only) { 435 if (is == ADD_VALUES) { 436 ap[i] += value; 437 (void)PetscLogFlops(1.0); 438 } else ap[i] = value; 439 } 440 low = i + 1; 441 goto noinsert; 442 } 443 } 444 if (value == 0.0 && ignorezeroentries && row != col) goto noinsert; 445 if (nonew == 1) goto noinsert; 446 PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at (%" PetscInt_FMT ",%" PetscInt_FMT ") in the matrix", row, col); 447 if (A->structure_only) { 448 MatSeqXAIJReallocateAIJ_structure_only(A, A->rmap->n, 1, nrow, row, col, rmax, ai, aj, rp, imax, nonew, MatScalar); 449 } else { 450 MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar); 451 } 452 N = nrow++ - 1; 453 a->nz++; 454 high++; 455 /* shift up all the later entries in this row */ 456 PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1)); 457 rp[i] = col; 458 if (!A->structure_only) { 459 PetscCall(PetscArraymove(ap + i + 1, ap + i, N - i + 1)); 460 ap[i] = value; 461 } 462 low = i + 1; 463 A->nonzerostate++; 464 noinsert:; 465 } 466 ailen[row] = nrow; 467 } 468 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 469 PetscFunctionReturn(0); 470 } 471 472 PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is) 473 { 474 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 475 PetscInt *rp, k, row; 476 PetscInt *ai = a->i; 477 PetscInt *aj = a->j; 478 MatScalar *aa, *ap; 479 480 PetscFunctionBegin; 481 PetscCheck(!A->was_assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot call on assembled matrix."); 482 PetscCheck(m * n + a->nz <= a->maxnz, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of entries in matrix will be larger than maximum nonzeros allocated for %" PetscInt_FMT " in MatSeqAIJSetTotalPreallocation()", a->maxnz); 483 484 PetscCall(MatSeqAIJGetArray(A, &aa)); 485 for (k = 0; k < m; k++) { /* loop over added rows */ 486 row = im[k]; 487 rp = aj + ai[row]; 488 ap = aa + ai[row]; 489 490 PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt))); 491 if (!A->structure_only) { 492 if (v) { 493 PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar))); 494 v += n; 495 } else { 496 PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar))); 497 } 498 } 499 a->ilen[row] = n; 500 a->imax[row] = n; 501 a->i[row + 1] = a->i[row] + n; 502 a->nz += n; 503 } 504 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 505 PetscFunctionReturn(0); 506 } 507 508 /*@ 509 MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix. 510 511 Input Parameters: 512 + A - the `MATSEQAIJ` matrix 513 - nztotal - bound on the number of nonzeros 514 515 Level: advanced 516 517 Notes: 518 This can be called if you will be provided the matrix row by row (from row zero) with sorted column indices for each row. 519 Simply call `MatSetValues()` after this call to provide the matrix entries in the usual manner. This matrix may be used 520 as always with multiple matrix assemblies. 521 522 .seealso: `MatSetOption()`, `MAT_SORTED_FULL`, `MatSetValues()`, `MatSeqAIJSetPreallocation()` 523 @*/ 524 525 PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A, PetscInt nztotal) 526 { 527 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 528 529 PetscFunctionBegin; 530 PetscCall(PetscLayoutSetUp(A->rmap)); 531 PetscCall(PetscLayoutSetUp(A->cmap)); 532 a->maxnz = nztotal; 533 if (!a->imax) { PetscCall(PetscMalloc1(A->rmap->n, &a->imax)); } 534 if (!a->ilen) { 535 PetscCall(PetscMalloc1(A->rmap->n, &a->ilen)); 536 } else { 537 PetscCall(PetscMemzero(a->ilen, A->rmap->n * sizeof(PetscInt))); 538 } 539 540 /* allocate the matrix space */ 541 if (A->structure_only) { 542 PetscCall(PetscMalloc1(nztotal, &a->j)); 543 PetscCall(PetscMalloc1(A->rmap->n + 1, &a->i)); 544 } else { 545 PetscCall(PetscMalloc3(nztotal, &a->a, nztotal, &a->j, A->rmap->n + 1, &a->i)); 546 } 547 a->i[0] = 0; 548 if (A->structure_only) { 549 a->singlemalloc = PETSC_FALSE; 550 a->free_a = PETSC_FALSE; 551 } else { 552 a->singlemalloc = PETSC_TRUE; 553 a->free_a = PETSC_TRUE; 554 } 555 a->free_ij = PETSC_TRUE; 556 A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation; 557 A->preallocated = PETSC_TRUE; 558 PetscFunctionReturn(0); 559 } 560 561 PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is) 562 { 563 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 564 PetscInt *rp, k, row; 565 PetscInt *ai = a->i, *ailen = a->ilen; 566 PetscInt *aj = a->j; 567 MatScalar *aa, *ap; 568 569 PetscFunctionBegin; 570 PetscCall(MatSeqAIJGetArray(A, &aa)); 571 for (k = 0; k < m; k++) { /* loop over added rows */ 572 row = im[k]; 573 PetscCheck(n <= a->imax[row], PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Preallocation for row %" PetscInt_FMT " does not match number of columns provided", n); 574 rp = aj + ai[row]; 575 ap = aa + ai[row]; 576 if (!A->was_assembled) PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt))); 577 if (!A->structure_only) { 578 if (v) { 579 PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar))); 580 v += n; 581 } else { 582 PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar))); 583 } 584 } 585 ailen[row] = n; 586 a->nz += n; 587 } 588 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 589 PetscFunctionReturn(0); 590 } 591 592 PetscErrorCode MatGetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[]) 593 { 594 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 595 PetscInt *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j; 596 PetscInt *ai = a->i, *ailen = a->ilen; 597 const MatScalar *ap, *aa; 598 599 PetscFunctionBegin; 600 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 601 for (k = 0; k < m; k++) { /* loop over rows */ 602 row = im[k]; 603 if (row < 0) { 604 v += n; 605 continue; 606 } /* negative row */ 607 PetscCheck(row < A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->n - 1); 608 rp = aj + ai[row]; 609 ap = aa + ai[row]; 610 nrow = ailen[row]; 611 for (l = 0; l < n; l++) { /* loop over columns */ 612 if (in[l] < 0) { 613 v++; 614 continue; 615 } /* negative column */ 616 PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1); 617 col = in[l]; 618 high = nrow; 619 low = 0; /* assume unsorted */ 620 while (high - low > 5) { 621 t = (low + high) / 2; 622 if (rp[t] > col) high = t; 623 else low = t; 624 } 625 for (i = low; i < high; i++) { 626 if (rp[i] > col) break; 627 if (rp[i] == col) { 628 *v++ = ap[i]; 629 goto finished; 630 } 631 } 632 *v++ = 0.0; 633 finished:; 634 } 635 } 636 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 637 PetscFunctionReturn(0); 638 } 639 640 PetscErrorCode MatView_SeqAIJ_Binary(Mat mat, PetscViewer viewer) 641 { 642 Mat_SeqAIJ *A = (Mat_SeqAIJ *)mat->data; 643 const PetscScalar *av; 644 PetscInt header[4], M, N, m, nz, i; 645 PetscInt *rowlens; 646 647 PetscFunctionBegin; 648 PetscCall(PetscViewerSetUp(viewer)); 649 650 M = mat->rmap->N; 651 N = mat->cmap->N; 652 m = mat->rmap->n; 653 nz = A->nz; 654 655 /* write matrix header */ 656 header[0] = MAT_FILE_CLASSID; 657 header[1] = M; 658 header[2] = N; 659 header[3] = nz; 660 PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT)); 661 662 /* fill in and store row lengths */ 663 PetscCall(PetscMalloc1(m, &rowlens)); 664 for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i]; 665 PetscCall(PetscViewerBinaryWrite(viewer, rowlens, m, PETSC_INT)); 666 PetscCall(PetscFree(rowlens)); 667 /* store column indices */ 668 PetscCall(PetscViewerBinaryWrite(viewer, A->j, nz, PETSC_INT)); 669 /* store nonzero values */ 670 PetscCall(MatSeqAIJGetArrayRead(mat, &av)); 671 PetscCall(PetscViewerBinaryWrite(viewer, av, nz, PETSC_SCALAR)); 672 PetscCall(MatSeqAIJRestoreArrayRead(mat, &av)); 673 674 /* write block size option to the viewer's .info file */ 675 PetscCall(MatView_Binary_BlockSizes(mat, viewer)); 676 PetscFunctionReturn(0); 677 } 678 679 static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A, PetscViewer viewer) 680 { 681 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 682 PetscInt i, k, m = A->rmap->N; 683 684 PetscFunctionBegin; 685 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 686 for (i = 0; i < m; i++) { 687 PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i)); 688 for (k = a->i[i]; k < a->i[i + 1]; k++) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ") ", a->j[k])); 689 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 690 } 691 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 692 PetscFunctionReturn(0); 693 } 694 695 extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat, PetscViewer); 696 697 PetscErrorCode MatView_SeqAIJ_ASCII(Mat A, PetscViewer viewer) 698 { 699 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 700 const PetscScalar *av; 701 PetscInt i, j, m = A->rmap->n; 702 const char *name; 703 PetscViewerFormat format; 704 705 PetscFunctionBegin; 706 if (A->structure_only) { 707 PetscCall(MatView_SeqAIJ_ASCII_structonly(A, viewer)); 708 PetscFunctionReturn(0); 709 } 710 711 PetscCall(PetscViewerGetFormat(viewer, &format)); 712 if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscFunctionReturn(0); 713 714 /* trigger copy to CPU if needed */ 715 PetscCall(MatSeqAIJGetArrayRead(A, &av)); 716 PetscCall(MatSeqAIJRestoreArrayRead(A, &av)); 717 if (format == PETSC_VIEWER_ASCII_MATLAB) { 718 PetscInt nofinalvalue = 0; 719 if (m && ((a->i[m] == a->i[m - 1]) || (a->j[a->nz - 1] != A->cmap->n - 1))) { 720 /* Need a dummy value to ensure the dimension of the matrix. */ 721 nofinalvalue = 1; 722 } 723 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 724 PetscCall(PetscViewerASCIIPrintf(viewer, "%% Size = %" PetscInt_FMT " %" PetscInt_FMT " \n", m, A->cmap->n)); 725 PetscCall(PetscViewerASCIIPrintf(viewer, "%% Nonzeros = %" PetscInt_FMT " \n", a->nz)); 726 #if defined(PETSC_USE_COMPLEX) 727 PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",4);\n", a->nz + nofinalvalue)); 728 #else 729 PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",3);\n", a->nz + nofinalvalue)); 730 #endif 731 PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = [\n")); 732 733 for (i = 0; i < m; i++) { 734 for (j = a->i[i]; j < a->i[i + 1]; j++) { 735 #if defined(PETSC_USE_COMPLEX) 736 PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e %18.16e\n", i + 1, a->j[j] + 1, (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j]))); 737 #else 738 PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", i + 1, a->j[j] + 1, (double)a->a[j])); 739 #endif 740 } 741 } 742 if (nofinalvalue) { 743 #if defined(PETSC_USE_COMPLEX) 744 PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e %18.16e\n", m, A->cmap->n, 0., 0.)); 745 #else 746 PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", m, A->cmap->n, 0.0)); 747 #endif 748 } 749 PetscCall(PetscObjectGetName((PetscObject)A, &name)); 750 PetscCall(PetscViewerASCIIPrintf(viewer, "];\n %s = spconvert(zzz);\n", name)); 751 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 752 } else if (format == PETSC_VIEWER_ASCII_COMMON) { 753 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 754 for (i = 0; i < m; i++) { 755 PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i)); 756 for (j = a->i[i]; j < a->i[i + 1]; j++) { 757 #if defined(PETSC_USE_COMPLEX) 758 if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) { 759 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j]))); 760 } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) { 761 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j]))); 762 } else if (PetscRealPart(a->a[j]) != 0.0) { 763 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j]))); 764 } 765 #else 766 if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j])); 767 #endif 768 } 769 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 770 } 771 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 772 } else if (format == PETSC_VIEWER_ASCII_SYMMODU) { 773 PetscInt nzd = 0, fshift = 1, *sptr; 774 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 775 PetscCall(PetscMalloc1(m + 1, &sptr)); 776 for (i = 0; i < m; i++) { 777 sptr[i] = nzd + 1; 778 for (j = a->i[i]; j < a->i[i + 1]; j++) { 779 if (a->j[j] >= i) { 780 #if defined(PETSC_USE_COMPLEX) 781 if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++; 782 #else 783 if (a->a[j] != 0.0) nzd++; 784 #endif 785 } 786 } 787 } 788 sptr[m] = nzd + 1; 789 PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n\n", m, nzd)); 790 for (i = 0; i < m + 1; i += 6) { 791 if (i + 4 < m) { 792 PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3], sptr[i + 4], sptr[i + 5])); 793 } else if (i + 3 < m) { 794 PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3], sptr[i + 4])); 795 } else if (i + 2 < m) { 796 PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3])); 797 } else if (i + 1 < m) { 798 PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2])); 799 } else if (i < m) { 800 PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1])); 801 } else { 802 PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT "\n", sptr[i])); 803 } 804 } 805 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 806 PetscCall(PetscFree(sptr)); 807 for (i = 0; i < m; i++) { 808 for (j = a->i[i]; j < a->i[i + 1]; j++) { 809 if (a->j[j] >= i) PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " ", a->j[j] + fshift)); 810 } 811 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 812 } 813 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 814 for (i = 0; i < m; i++) { 815 for (j = a->i[i]; j < a->i[i + 1]; j++) { 816 if (a->j[j] >= i) { 817 #if defined(PETSC_USE_COMPLEX) 818 if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " %18.16e %18.16e ", (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j]))); 819 #else 820 if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " %18.16e ", (double)a->a[j])); 821 #endif 822 } 823 } 824 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 825 } 826 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 827 } else if (format == PETSC_VIEWER_ASCII_DENSE) { 828 PetscInt cnt = 0, jcnt; 829 PetscScalar value; 830 #if defined(PETSC_USE_COMPLEX) 831 PetscBool realonly = PETSC_TRUE; 832 833 for (i = 0; i < a->i[m]; i++) { 834 if (PetscImaginaryPart(a->a[i]) != 0.0) { 835 realonly = PETSC_FALSE; 836 break; 837 } 838 } 839 #endif 840 841 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 842 for (i = 0; i < m; i++) { 843 jcnt = 0; 844 for (j = 0; j < A->cmap->n; j++) { 845 if (jcnt < a->i[i + 1] - a->i[i] && j == a->j[cnt]) { 846 value = a->a[cnt++]; 847 jcnt++; 848 } else { 849 value = 0.0; 850 } 851 #if defined(PETSC_USE_COMPLEX) 852 if (realonly) { 853 PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)PetscRealPart(value))); 854 } else { 855 PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e+%7.5e i ", (double)PetscRealPart(value), (double)PetscImaginaryPart(value))); 856 } 857 #else 858 PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)value)); 859 #endif 860 } 861 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 862 } 863 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 864 } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) { 865 PetscInt fshift = 1; 866 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 867 #if defined(PETSC_USE_COMPLEX) 868 PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate complex general\n")); 869 #else 870 PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate real general\n")); 871 #endif 872 PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", m, A->cmap->n, a->nz)); 873 for (i = 0; i < m; i++) { 874 for (j = a->i[i]; j < a->i[i + 1]; j++) { 875 #if defined(PETSC_USE_COMPLEX) 876 PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %g %g\n", i + fshift, a->j[j] + fshift, (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j]))); 877 #else 878 PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %g\n", i + fshift, a->j[j] + fshift, (double)a->a[j])); 879 #endif 880 } 881 } 882 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 883 } else { 884 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 885 if (A->factortype) { 886 for (i = 0; i < m; i++) { 887 PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i)); 888 /* L part */ 889 for (j = a->i[i]; j < a->i[i + 1]; j++) { 890 #if defined(PETSC_USE_COMPLEX) 891 if (PetscImaginaryPart(a->a[j]) > 0.0) { 892 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j]))); 893 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 894 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j])))); 895 } else { 896 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j]))); 897 } 898 #else 899 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j])); 900 #endif 901 } 902 /* diagonal */ 903 j = a->diag[i]; 904 #if defined(PETSC_USE_COMPLEX) 905 if (PetscImaginaryPart(a->a[j]) > 0.0) { 906 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(1.0 / a->a[j]), (double)PetscImaginaryPart(1.0 / a->a[j]))); 907 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 908 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(1.0 / a->a[j]), (double)(-PetscImaginaryPart(1.0 / a->a[j])))); 909 } else { 910 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(1.0 / a->a[j]))); 911 } 912 #else 913 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)(1.0 / a->a[j]))); 914 #endif 915 916 /* U part */ 917 for (j = a->diag[i + 1] + 1; j < a->diag[i]; j++) { 918 #if defined(PETSC_USE_COMPLEX) 919 if (PetscImaginaryPart(a->a[j]) > 0.0) { 920 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j]))); 921 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 922 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j])))); 923 } else { 924 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j]))); 925 } 926 #else 927 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j])); 928 #endif 929 } 930 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 931 } 932 } else { 933 for (i = 0; i < m; i++) { 934 PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i)); 935 for (j = a->i[i]; j < a->i[i + 1]; j++) { 936 #if defined(PETSC_USE_COMPLEX) 937 if (PetscImaginaryPart(a->a[j]) > 0.0) { 938 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j]))); 939 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 940 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j]))); 941 } else { 942 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j]))); 943 } 944 #else 945 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j])); 946 #endif 947 } 948 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 949 } 950 } 951 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 952 } 953 PetscCall(PetscViewerFlush(viewer)); 954 PetscFunctionReturn(0); 955 } 956 957 #include <petscdraw.h> 958 PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw, void *Aa) 959 { 960 Mat A = (Mat)Aa; 961 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 962 PetscInt i, j, m = A->rmap->n; 963 int color; 964 PetscReal xl, yl, xr, yr, x_l, x_r, y_l, y_r; 965 PetscViewer viewer; 966 PetscViewerFormat format; 967 const PetscScalar *aa; 968 969 PetscFunctionBegin; 970 PetscCall(PetscObjectQuery((PetscObject)A, "Zoomviewer", (PetscObject *)&viewer)); 971 PetscCall(PetscViewerGetFormat(viewer, &format)); 972 PetscCall(PetscDrawGetCoordinates(draw, &xl, &yl, &xr, &yr)); 973 974 /* loop over matrix elements drawing boxes */ 975 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 976 if (format != PETSC_VIEWER_DRAW_CONTOUR) { 977 PetscDrawCollectiveBegin(draw); 978 /* Blue for negative, Cyan for zero and Red for positive */ 979 color = PETSC_DRAW_BLUE; 980 for (i = 0; i < m; i++) { 981 y_l = m - i - 1.0; 982 y_r = y_l + 1.0; 983 for (j = a->i[i]; j < a->i[i + 1]; j++) { 984 x_l = a->j[j]; 985 x_r = x_l + 1.0; 986 if (PetscRealPart(aa[j]) >= 0.) continue; 987 PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color)); 988 } 989 } 990 color = PETSC_DRAW_CYAN; 991 for (i = 0; i < m; i++) { 992 y_l = m - i - 1.0; 993 y_r = y_l + 1.0; 994 for (j = a->i[i]; j < a->i[i + 1]; j++) { 995 x_l = a->j[j]; 996 x_r = x_l + 1.0; 997 if (aa[j] != 0.) continue; 998 PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color)); 999 } 1000 } 1001 color = PETSC_DRAW_RED; 1002 for (i = 0; i < m; i++) { 1003 y_l = m - i - 1.0; 1004 y_r = y_l + 1.0; 1005 for (j = a->i[i]; j < a->i[i + 1]; j++) { 1006 x_l = a->j[j]; 1007 x_r = x_l + 1.0; 1008 if (PetscRealPart(aa[j]) <= 0.) continue; 1009 PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color)); 1010 } 1011 } 1012 PetscDrawCollectiveEnd(draw); 1013 } else { 1014 /* use contour shading to indicate magnitude of values */ 1015 /* first determine max of all nonzero values */ 1016 PetscReal minv = 0.0, maxv = 0.0; 1017 PetscInt nz = a->nz, count = 0; 1018 PetscDraw popup; 1019 1020 for (i = 0; i < nz; i++) { 1021 if (PetscAbsScalar(aa[i]) > maxv) maxv = PetscAbsScalar(aa[i]); 1022 } 1023 if (minv >= maxv) maxv = minv + PETSC_SMALL; 1024 PetscCall(PetscDrawGetPopup(draw, &popup)); 1025 PetscCall(PetscDrawScalePopup(popup, minv, maxv)); 1026 1027 PetscDrawCollectiveBegin(draw); 1028 for (i = 0; i < m; i++) { 1029 y_l = m - i - 1.0; 1030 y_r = y_l + 1.0; 1031 for (j = a->i[i]; j < a->i[i + 1]; j++) { 1032 x_l = a->j[j]; 1033 x_r = x_l + 1.0; 1034 color = PetscDrawRealToColor(PetscAbsScalar(aa[count]), minv, maxv); 1035 PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color)); 1036 count++; 1037 } 1038 } 1039 PetscDrawCollectiveEnd(draw); 1040 } 1041 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 1042 PetscFunctionReturn(0); 1043 } 1044 1045 #include <petscdraw.h> 1046 PetscErrorCode MatView_SeqAIJ_Draw(Mat A, PetscViewer viewer) 1047 { 1048 PetscDraw draw; 1049 PetscReal xr, yr, xl, yl, h, w; 1050 PetscBool isnull; 1051 1052 PetscFunctionBegin; 1053 PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw)); 1054 PetscCall(PetscDrawIsNull(draw, &isnull)); 1055 if (isnull) PetscFunctionReturn(0); 1056 1057 xr = A->cmap->n; 1058 yr = A->rmap->n; 1059 h = yr / 10.0; 1060 w = xr / 10.0; 1061 xr += w; 1062 yr += h; 1063 xl = -w; 1064 yl = -h; 1065 PetscCall(PetscDrawSetCoordinates(draw, xl, yl, xr, yr)); 1066 PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", (PetscObject)viewer)); 1067 PetscCall(PetscDrawZoom(draw, MatView_SeqAIJ_Draw_Zoom, A)); 1068 PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", NULL)); 1069 PetscCall(PetscDrawSave(draw)); 1070 PetscFunctionReturn(0); 1071 } 1072 1073 PetscErrorCode MatView_SeqAIJ(Mat A, PetscViewer viewer) 1074 { 1075 PetscBool iascii, isbinary, isdraw; 1076 1077 PetscFunctionBegin; 1078 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii)); 1079 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary)); 1080 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw)); 1081 if (iascii) PetscCall(MatView_SeqAIJ_ASCII(A, viewer)); 1082 else if (isbinary) PetscCall(MatView_SeqAIJ_Binary(A, viewer)); 1083 else if (isdraw) PetscCall(MatView_SeqAIJ_Draw(A, viewer)); 1084 PetscCall(MatView_SeqAIJ_Inode(A, viewer)); 1085 PetscFunctionReturn(0); 1086 } 1087 1088 PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A, MatAssemblyType mode) 1089 { 1090 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1091 PetscInt fshift = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax; 1092 PetscInt m = A->rmap->n, *ip, N, *ailen = a->ilen, rmax = 0; 1093 MatScalar *aa = a->a, *ap; 1094 PetscReal ratio = 0.6; 1095 1096 PetscFunctionBegin; 1097 if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(0); 1098 PetscCall(MatSeqAIJInvalidateDiagonal(A)); 1099 if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) { 1100 /* we need to respect users asking to use or not the inodes routine in between matrix assemblies */ 1101 PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode)); 1102 PetscFunctionReturn(0); 1103 } 1104 1105 if (m) rmax = ailen[0]; /* determine row with most nonzeros */ 1106 for (i = 1; i < m; i++) { 1107 /* move each row back by the amount of empty slots (fshift) before it*/ 1108 fshift += imax[i - 1] - ailen[i - 1]; 1109 rmax = PetscMax(rmax, ailen[i]); 1110 if (fshift) { 1111 ip = aj + ai[i]; 1112 ap = aa + ai[i]; 1113 N = ailen[i]; 1114 PetscCall(PetscArraymove(ip - fshift, ip, N)); 1115 if (!A->structure_only) PetscCall(PetscArraymove(ap - fshift, ap, N)); 1116 } 1117 ai[i] = ai[i - 1] + ailen[i - 1]; 1118 } 1119 if (m) { 1120 fshift += imax[m - 1] - ailen[m - 1]; 1121 ai[m] = ai[m - 1] + ailen[m - 1]; 1122 } 1123 1124 /* reset ilen and imax for each row */ 1125 a->nonzerorowcnt = 0; 1126 if (A->structure_only) { 1127 PetscCall(PetscFree(a->imax)); 1128 PetscCall(PetscFree(a->ilen)); 1129 } else { /* !A->structure_only */ 1130 for (i = 0; i < m; i++) { 1131 ailen[i] = imax[i] = ai[i + 1] - ai[i]; 1132 a->nonzerorowcnt += ((ai[i + 1] - ai[i]) > 0); 1133 } 1134 } 1135 a->nz = ai[m]; 1136 PetscCheck(!fshift || a->nounused != -1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unused space detected in matrix: %" PetscInt_FMT " X %" PetscInt_FMT ", %" PetscInt_FMT " unneeded", m, A->cmap->n, fshift); 1137 1138 PetscCall(MatMarkDiagonal_SeqAIJ(A)); 1139 PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT "; storage space: %" PetscInt_FMT " unneeded,%" PetscInt_FMT " used\n", m, A->cmap->n, fshift, a->nz)); 1140 PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues() is %" PetscInt_FMT "\n", a->reallocs)); 1141 PetscCall(PetscInfo(A, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", rmax)); 1142 1143 A->info.mallocs += a->reallocs; 1144 a->reallocs = 0; 1145 A->info.nz_unneeded = (PetscReal)fshift; 1146 a->rmax = rmax; 1147 1148 if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, m, ratio)); 1149 PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode)); 1150 PetscFunctionReturn(0); 1151 } 1152 1153 PetscErrorCode MatRealPart_SeqAIJ(Mat A) 1154 { 1155 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1156 PetscInt i, nz = a->nz; 1157 MatScalar *aa; 1158 1159 PetscFunctionBegin; 1160 PetscCall(MatSeqAIJGetArray(A, &aa)); 1161 for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]); 1162 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 1163 PetscCall(MatSeqAIJInvalidateDiagonal(A)); 1164 PetscFunctionReturn(0); 1165 } 1166 1167 PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A) 1168 { 1169 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1170 PetscInt i, nz = a->nz; 1171 MatScalar *aa; 1172 1173 PetscFunctionBegin; 1174 PetscCall(MatSeqAIJGetArray(A, &aa)); 1175 for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]); 1176 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 1177 PetscCall(MatSeqAIJInvalidateDiagonal(A)); 1178 PetscFunctionReturn(0); 1179 } 1180 1181 PetscErrorCode MatZeroEntries_SeqAIJ(Mat A) 1182 { 1183 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1184 MatScalar *aa; 1185 1186 PetscFunctionBegin; 1187 PetscCall(MatSeqAIJGetArrayWrite(A, &aa)); 1188 PetscCall(PetscArrayzero(aa, a->i[A->rmap->n])); 1189 PetscCall(MatSeqAIJRestoreArrayWrite(A, &aa)); 1190 PetscCall(MatSeqAIJInvalidateDiagonal(A)); 1191 PetscFunctionReturn(0); 1192 } 1193 1194 PETSC_INTERN PetscErrorCode MatResetPreallocationCOO_SeqAIJ(Mat A) 1195 { 1196 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1197 1198 PetscFunctionBegin; 1199 PetscCall(PetscFree(a->perm)); 1200 PetscCall(PetscFree(a->jmap)); 1201 PetscFunctionReturn(0); 1202 } 1203 1204 PetscErrorCode MatDestroy_SeqAIJ(Mat A) 1205 { 1206 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1207 1208 PetscFunctionBegin; 1209 #if defined(PETSC_USE_LOG) 1210 PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->n, A->cmap->n, a->nz); 1211 #endif 1212 PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i)); 1213 PetscCall(MatResetPreallocationCOO_SeqAIJ(A)); 1214 PetscCall(ISDestroy(&a->row)); 1215 PetscCall(ISDestroy(&a->col)); 1216 PetscCall(PetscFree(a->diag)); 1217 PetscCall(PetscFree(a->ibdiag)); 1218 PetscCall(PetscFree(a->imax)); 1219 PetscCall(PetscFree(a->ilen)); 1220 PetscCall(PetscFree(a->ipre)); 1221 PetscCall(PetscFree3(a->idiag, a->mdiag, a->ssor_work)); 1222 PetscCall(PetscFree(a->solve_work)); 1223 PetscCall(ISDestroy(&a->icol)); 1224 PetscCall(PetscFree(a->saved_values)); 1225 PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex)); 1226 PetscCall(MatDestroy_SeqAIJ_Inode(A)); 1227 PetscCall(PetscFree(A->data)); 1228 1229 /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this. 1230 That function is so heavily used (sometimes in an hidden way through multnumeric function pointers) 1231 that is hard to properly add this data to the MatProduct data. We free it here to avoid 1232 users reusing the matrix object with different data to incur in obscure segmentation faults 1233 due to different matrix sizes */ 1234 PetscCall(PetscObjectCompose((PetscObject)A, "__PETSc__ab_dense", NULL)); 1235 1236 PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL)); 1237 PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEnginePut_C", NULL)); 1238 PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEngineGet_C", NULL)); 1239 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetColumnIndices_C", NULL)); 1240 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL)); 1241 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL)); 1242 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsbaij_C", NULL)); 1243 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqbaij_C", NULL)); 1244 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijperm_C", NULL)); 1245 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijsell_C", NULL)); 1246 #if defined(PETSC_HAVE_MKL_SPARSE) 1247 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijmkl_C", NULL)); 1248 #endif 1249 #if defined(PETSC_HAVE_CUDA) 1250 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcusparse_C", NULL)); 1251 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", NULL)); 1252 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", NULL)); 1253 #endif 1254 #if defined(PETSC_HAVE_KOKKOS_KERNELS) 1255 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijkokkos_C", NULL)); 1256 #endif 1257 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcrl_C", NULL)); 1258 #if defined(PETSC_HAVE_ELEMENTAL) 1259 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_elemental_C", NULL)); 1260 #endif 1261 #if defined(PETSC_HAVE_SCALAPACK) 1262 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_scalapack_C", NULL)); 1263 #endif 1264 #if defined(PETSC_HAVE_HYPRE) 1265 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_hypre_C", NULL)); 1266 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", NULL)); 1267 #endif 1268 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqdense_C", NULL)); 1269 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsell_C", NULL)); 1270 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_is_C", NULL)); 1271 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL)); 1272 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsHermitianTranspose_C", NULL)); 1273 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocation_C", NULL)); 1274 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetPreallocation_C", NULL)); 1275 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocationCSR_C", NULL)); 1276 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatReorderForNonzeroDiagonal_C", NULL)); 1277 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_is_seqaij_C", NULL)); 1278 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqdense_seqaij_C", NULL)); 1279 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaij_C", NULL)); 1280 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJKron_C", NULL)); 1281 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL)); 1282 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL)); 1283 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL)); 1284 /* these calls do not belong here: the subclasses Duplicate/Destroy are wrong */ 1285 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijsell_seqaij_C", NULL)); 1286 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijperm_seqaij_C", NULL)); 1287 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijviennacl_C", NULL)); 1288 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqdense_C", NULL)); 1289 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqaij_C", NULL)); 1290 PetscFunctionReturn(0); 1291 } 1292 1293 PetscErrorCode MatSetOption_SeqAIJ(Mat A, MatOption op, PetscBool flg) 1294 { 1295 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1296 1297 PetscFunctionBegin; 1298 switch (op) { 1299 case MAT_ROW_ORIENTED: 1300 a->roworiented = flg; 1301 break; 1302 case MAT_KEEP_NONZERO_PATTERN: 1303 a->keepnonzeropattern = flg; 1304 break; 1305 case MAT_NEW_NONZERO_LOCATIONS: 1306 a->nonew = (flg ? 0 : 1); 1307 break; 1308 case MAT_NEW_NONZERO_LOCATION_ERR: 1309 a->nonew = (flg ? -1 : 0); 1310 break; 1311 case MAT_NEW_NONZERO_ALLOCATION_ERR: 1312 a->nonew = (flg ? -2 : 0); 1313 break; 1314 case MAT_UNUSED_NONZERO_LOCATION_ERR: 1315 a->nounused = (flg ? -1 : 0); 1316 break; 1317 case MAT_IGNORE_ZERO_ENTRIES: 1318 a->ignorezeroentries = flg; 1319 break; 1320 case MAT_SPD: 1321 case MAT_SYMMETRIC: 1322 case MAT_STRUCTURALLY_SYMMETRIC: 1323 case MAT_HERMITIAN: 1324 case MAT_SYMMETRY_ETERNAL: 1325 case MAT_STRUCTURE_ONLY: 1326 case MAT_STRUCTURAL_SYMMETRY_ETERNAL: 1327 case MAT_SPD_ETERNAL: 1328 /* if the diagonal matrix is square it inherits some of the properties above */ 1329 break; 1330 case MAT_FORCE_DIAGONAL_ENTRIES: 1331 case MAT_IGNORE_OFF_PROC_ENTRIES: 1332 case MAT_USE_HASH_TABLE: 1333 PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op])); 1334 break; 1335 case MAT_USE_INODES: 1336 PetscCall(MatSetOption_SeqAIJ_Inode(A, MAT_USE_INODES, flg)); 1337 break; 1338 case MAT_SUBMAT_SINGLEIS: 1339 A->submat_singleis = flg; 1340 break; 1341 case MAT_SORTED_FULL: 1342 if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull; 1343 else A->ops->setvalues = MatSetValues_SeqAIJ; 1344 break; 1345 case MAT_FORM_EXPLICIT_TRANSPOSE: 1346 A->form_explicit_transpose = flg; 1347 break; 1348 default: 1349 SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op); 1350 } 1351 PetscFunctionReturn(0); 1352 } 1353 1354 PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A, Vec v) 1355 { 1356 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1357 PetscInt i, j, n, *ai = a->i, *aj = a->j; 1358 PetscScalar *x; 1359 const PetscScalar *aa; 1360 1361 PetscFunctionBegin; 1362 PetscCall(VecGetLocalSize(v, &n)); 1363 PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector"); 1364 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 1365 if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) { 1366 PetscInt *diag = a->diag; 1367 PetscCall(VecGetArrayWrite(v, &x)); 1368 for (i = 0; i < n; i++) x[i] = 1.0 / aa[diag[i]]; 1369 PetscCall(VecRestoreArrayWrite(v, &x)); 1370 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 1371 PetscFunctionReturn(0); 1372 } 1373 1374 PetscCall(VecGetArrayWrite(v, &x)); 1375 for (i = 0; i < n; i++) { 1376 x[i] = 0.0; 1377 for (j = ai[i]; j < ai[i + 1]; j++) { 1378 if (aj[j] == i) { 1379 x[i] = aa[j]; 1380 break; 1381 } 1382 } 1383 } 1384 PetscCall(VecRestoreArrayWrite(v, &x)); 1385 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 1386 PetscFunctionReturn(0); 1387 } 1388 1389 #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h> 1390 PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A, Vec xx, Vec zz, Vec yy) 1391 { 1392 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1393 const MatScalar *aa; 1394 PetscScalar *y; 1395 const PetscScalar *x; 1396 PetscInt m = A->rmap->n; 1397 #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ) 1398 const MatScalar *v; 1399 PetscScalar alpha; 1400 PetscInt n, i, j; 1401 const PetscInt *idx, *ii, *ridx = NULL; 1402 Mat_CompressedRow cprow = a->compressedrow; 1403 PetscBool usecprow = cprow.use; 1404 #endif 1405 1406 PetscFunctionBegin; 1407 if (zz != yy) PetscCall(VecCopy(zz, yy)); 1408 PetscCall(VecGetArrayRead(xx, &x)); 1409 PetscCall(VecGetArray(yy, &y)); 1410 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 1411 1412 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ) 1413 fortranmulttransposeaddaij_(&m, x, a->i, a->j, aa, y); 1414 #else 1415 if (usecprow) { 1416 m = cprow.nrows; 1417 ii = cprow.i; 1418 ridx = cprow.rindex; 1419 } else { 1420 ii = a->i; 1421 } 1422 for (i = 0; i < m; i++) { 1423 idx = a->j + ii[i]; 1424 v = aa + ii[i]; 1425 n = ii[i + 1] - ii[i]; 1426 if (usecprow) { 1427 alpha = x[ridx[i]]; 1428 } else { 1429 alpha = x[i]; 1430 } 1431 for (j = 0; j < n; j++) y[idx[j]] += alpha * v[j]; 1432 } 1433 #endif 1434 PetscCall(PetscLogFlops(2.0 * a->nz)); 1435 PetscCall(VecRestoreArrayRead(xx, &x)); 1436 PetscCall(VecRestoreArray(yy, &y)); 1437 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 1438 PetscFunctionReturn(0); 1439 } 1440 1441 PetscErrorCode MatMultTranspose_SeqAIJ(Mat A, Vec xx, Vec yy) 1442 { 1443 PetscFunctionBegin; 1444 PetscCall(VecSet(yy, 0.0)); 1445 PetscCall(MatMultTransposeAdd_SeqAIJ(A, xx, yy, yy)); 1446 PetscFunctionReturn(0); 1447 } 1448 1449 #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h> 1450 1451 PetscErrorCode MatMult_SeqAIJ(Mat A, Vec xx, Vec yy) 1452 { 1453 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1454 PetscScalar *y; 1455 const PetscScalar *x; 1456 const MatScalar *aa, *a_a; 1457 PetscInt m = A->rmap->n; 1458 const PetscInt *aj, *ii, *ridx = NULL; 1459 PetscInt n, i; 1460 PetscScalar sum; 1461 PetscBool usecprow = a->compressedrow.use; 1462 1463 #if defined(PETSC_HAVE_PRAGMA_DISJOINT) 1464 #pragma disjoint(*x, *y, *aa) 1465 #endif 1466 1467 PetscFunctionBegin; 1468 if (a->inode.use && a->inode.checked) { 1469 PetscCall(MatMult_SeqAIJ_Inode(A, xx, yy)); 1470 PetscFunctionReturn(0); 1471 } 1472 PetscCall(MatSeqAIJGetArrayRead(A, &a_a)); 1473 PetscCall(VecGetArrayRead(xx, &x)); 1474 PetscCall(VecGetArray(yy, &y)); 1475 ii = a->i; 1476 if (usecprow) { /* use compressed row format */ 1477 PetscCall(PetscArrayzero(y, m)); 1478 m = a->compressedrow.nrows; 1479 ii = a->compressedrow.i; 1480 ridx = a->compressedrow.rindex; 1481 for (i = 0; i < m; i++) { 1482 n = ii[i + 1] - ii[i]; 1483 aj = a->j + ii[i]; 1484 aa = a_a + ii[i]; 1485 sum = 0.0; 1486 PetscSparseDensePlusDot(sum, x, aa, aj, n); 1487 /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */ 1488 y[*ridx++] = sum; 1489 } 1490 } else { /* do not use compressed row format */ 1491 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ) 1492 aj = a->j; 1493 aa = a_a; 1494 fortranmultaij_(&m, x, ii, aj, aa, y); 1495 #else 1496 for (i = 0; i < m; i++) { 1497 n = ii[i + 1] - ii[i]; 1498 aj = a->j + ii[i]; 1499 aa = a_a + ii[i]; 1500 sum = 0.0; 1501 PetscSparseDensePlusDot(sum, x, aa, aj, n); 1502 y[i] = sum; 1503 } 1504 #endif 1505 } 1506 PetscCall(PetscLogFlops(2.0 * a->nz - a->nonzerorowcnt)); 1507 PetscCall(VecRestoreArrayRead(xx, &x)); 1508 PetscCall(VecRestoreArray(yy, &y)); 1509 PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a)); 1510 PetscFunctionReturn(0); 1511 } 1512 1513 PetscErrorCode MatMultMax_SeqAIJ(Mat A, Vec xx, Vec yy) 1514 { 1515 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1516 PetscScalar *y; 1517 const PetscScalar *x; 1518 const MatScalar *aa, *a_a; 1519 PetscInt m = A->rmap->n; 1520 const PetscInt *aj, *ii, *ridx = NULL; 1521 PetscInt n, i, nonzerorow = 0; 1522 PetscScalar sum; 1523 PetscBool usecprow = a->compressedrow.use; 1524 1525 #if defined(PETSC_HAVE_PRAGMA_DISJOINT) 1526 #pragma disjoint(*x, *y, *aa) 1527 #endif 1528 1529 PetscFunctionBegin; 1530 PetscCall(MatSeqAIJGetArrayRead(A, &a_a)); 1531 PetscCall(VecGetArrayRead(xx, &x)); 1532 PetscCall(VecGetArray(yy, &y)); 1533 if (usecprow) { /* use compressed row format */ 1534 m = a->compressedrow.nrows; 1535 ii = a->compressedrow.i; 1536 ridx = a->compressedrow.rindex; 1537 for (i = 0; i < m; i++) { 1538 n = ii[i + 1] - ii[i]; 1539 aj = a->j + ii[i]; 1540 aa = a_a + ii[i]; 1541 sum = 0.0; 1542 nonzerorow += (n > 0); 1543 PetscSparseDenseMaxDot(sum, x, aa, aj, n); 1544 /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */ 1545 y[*ridx++] = sum; 1546 } 1547 } else { /* do not use compressed row format */ 1548 ii = a->i; 1549 for (i = 0; i < m; i++) { 1550 n = ii[i + 1] - ii[i]; 1551 aj = a->j + ii[i]; 1552 aa = a_a + ii[i]; 1553 sum = 0.0; 1554 nonzerorow += (n > 0); 1555 PetscSparseDenseMaxDot(sum, x, aa, aj, n); 1556 y[i] = sum; 1557 } 1558 } 1559 PetscCall(PetscLogFlops(2.0 * a->nz - nonzerorow)); 1560 PetscCall(VecRestoreArrayRead(xx, &x)); 1561 PetscCall(VecRestoreArray(yy, &y)); 1562 PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a)); 1563 PetscFunctionReturn(0); 1564 } 1565 1566 PetscErrorCode MatMultAddMax_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz) 1567 { 1568 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1569 PetscScalar *y, *z; 1570 const PetscScalar *x; 1571 const MatScalar *aa, *a_a; 1572 PetscInt m = A->rmap->n, *aj, *ii; 1573 PetscInt n, i, *ridx = NULL; 1574 PetscScalar sum; 1575 PetscBool usecprow = a->compressedrow.use; 1576 1577 PetscFunctionBegin; 1578 PetscCall(MatSeqAIJGetArrayRead(A, &a_a)); 1579 PetscCall(VecGetArrayRead(xx, &x)); 1580 PetscCall(VecGetArrayPair(yy, zz, &y, &z)); 1581 if (usecprow) { /* use compressed row format */ 1582 if (zz != yy) PetscCall(PetscArraycpy(z, y, m)); 1583 m = a->compressedrow.nrows; 1584 ii = a->compressedrow.i; 1585 ridx = a->compressedrow.rindex; 1586 for (i = 0; i < m; i++) { 1587 n = ii[i + 1] - ii[i]; 1588 aj = a->j + ii[i]; 1589 aa = a_a + ii[i]; 1590 sum = y[*ridx]; 1591 PetscSparseDenseMaxDot(sum, x, aa, aj, n); 1592 z[*ridx++] = sum; 1593 } 1594 } else { /* do not use compressed row format */ 1595 ii = a->i; 1596 for (i = 0; i < m; i++) { 1597 n = ii[i + 1] - ii[i]; 1598 aj = a->j + ii[i]; 1599 aa = a_a + ii[i]; 1600 sum = y[i]; 1601 PetscSparseDenseMaxDot(sum, x, aa, aj, n); 1602 z[i] = sum; 1603 } 1604 } 1605 PetscCall(PetscLogFlops(2.0 * a->nz)); 1606 PetscCall(VecRestoreArrayRead(xx, &x)); 1607 PetscCall(VecRestoreArrayPair(yy, zz, &y, &z)); 1608 PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a)); 1609 PetscFunctionReturn(0); 1610 } 1611 1612 #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h> 1613 PetscErrorCode MatMultAdd_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz) 1614 { 1615 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1616 PetscScalar *y, *z; 1617 const PetscScalar *x; 1618 const MatScalar *aa, *a_a; 1619 const PetscInt *aj, *ii, *ridx = NULL; 1620 PetscInt m = A->rmap->n, n, i; 1621 PetscScalar sum; 1622 PetscBool usecprow = a->compressedrow.use; 1623 1624 PetscFunctionBegin; 1625 if (a->inode.use && a->inode.checked) { 1626 PetscCall(MatMultAdd_SeqAIJ_Inode(A, xx, yy, zz)); 1627 PetscFunctionReturn(0); 1628 } 1629 PetscCall(MatSeqAIJGetArrayRead(A, &a_a)); 1630 PetscCall(VecGetArrayRead(xx, &x)); 1631 PetscCall(VecGetArrayPair(yy, zz, &y, &z)); 1632 if (usecprow) { /* use compressed row format */ 1633 if (zz != yy) PetscCall(PetscArraycpy(z, y, m)); 1634 m = a->compressedrow.nrows; 1635 ii = a->compressedrow.i; 1636 ridx = a->compressedrow.rindex; 1637 for (i = 0; i < m; i++) { 1638 n = ii[i + 1] - ii[i]; 1639 aj = a->j + ii[i]; 1640 aa = a_a + ii[i]; 1641 sum = y[*ridx]; 1642 PetscSparseDensePlusDot(sum, x, aa, aj, n); 1643 z[*ridx++] = sum; 1644 } 1645 } else { /* do not use compressed row format */ 1646 ii = a->i; 1647 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ) 1648 aj = a->j; 1649 aa = a_a; 1650 fortranmultaddaij_(&m, x, ii, aj, aa, y, z); 1651 #else 1652 for (i = 0; i < m; i++) { 1653 n = ii[i + 1] - ii[i]; 1654 aj = a->j + ii[i]; 1655 aa = a_a + ii[i]; 1656 sum = y[i]; 1657 PetscSparseDensePlusDot(sum, x, aa, aj, n); 1658 z[i] = sum; 1659 } 1660 #endif 1661 } 1662 PetscCall(PetscLogFlops(2.0 * a->nz)); 1663 PetscCall(VecRestoreArrayRead(xx, &x)); 1664 PetscCall(VecRestoreArrayPair(yy, zz, &y, &z)); 1665 PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a)); 1666 PetscFunctionReturn(0); 1667 } 1668 1669 /* 1670 Adds diagonal pointers to sparse matrix structure. 1671 */ 1672 PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A) 1673 { 1674 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1675 PetscInt i, j, m = A->rmap->n; 1676 PetscBool alreadySet = PETSC_TRUE; 1677 1678 PetscFunctionBegin; 1679 if (!a->diag) { 1680 PetscCall(PetscMalloc1(m, &a->diag)); 1681 alreadySet = PETSC_FALSE; 1682 } 1683 for (i = 0; i < A->rmap->n; i++) { 1684 /* If A's diagonal is already correctly set, this fast track enables cheap and repeated MatMarkDiagonal_SeqAIJ() calls */ 1685 if (alreadySet) { 1686 PetscInt pos = a->diag[i]; 1687 if (pos >= a->i[i] && pos < a->i[i + 1] && a->j[pos] == i) continue; 1688 } 1689 1690 a->diag[i] = a->i[i + 1]; 1691 for (j = a->i[i]; j < a->i[i + 1]; j++) { 1692 if (a->j[j] == i) { 1693 a->diag[i] = j; 1694 break; 1695 } 1696 } 1697 } 1698 PetscFunctionReturn(0); 1699 } 1700 1701 PetscErrorCode MatShift_SeqAIJ(Mat A, PetscScalar v) 1702 { 1703 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1704 const PetscInt *diag = (const PetscInt *)a->diag; 1705 const PetscInt *ii = (const PetscInt *)a->i; 1706 PetscInt i, *mdiag = NULL; 1707 PetscInt cnt = 0; /* how many diagonals are missing */ 1708 1709 PetscFunctionBegin; 1710 if (!A->preallocated || !a->nz) { 1711 PetscCall(MatSeqAIJSetPreallocation(A, 1, NULL)); 1712 PetscCall(MatShift_Basic(A, v)); 1713 PetscFunctionReturn(0); 1714 } 1715 1716 if (a->diagonaldense) { 1717 cnt = 0; 1718 } else { 1719 PetscCall(PetscCalloc1(A->rmap->n, &mdiag)); 1720 for (i = 0; i < A->rmap->n; i++) { 1721 if (i < A->cmap->n && diag[i] >= ii[i + 1]) { /* 'out of range' rows never have diagonals */ 1722 cnt++; 1723 mdiag[i] = 1; 1724 } 1725 } 1726 } 1727 if (!cnt) { 1728 PetscCall(MatShift_Basic(A, v)); 1729 } else { 1730 PetscScalar *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */ 1731 PetscInt *oldj = a->j, *oldi = a->i; 1732 PetscBool singlemalloc = a->singlemalloc, free_a = a->free_a, free_ij = a->free_ij; 1733 1734 a->a = NULL; 1735 a->j = NULL; 1736 a->i = NULL; 1737 /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */ 1738 for (i = 0; i < PetscMin(A->rmap->n, A->cmap->n); i++) a->imax[i] += mdiag[i]; 1739 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, 0, a->imax)); 1740 1741 /* copy old values into new matrix data structure */ 1742 for (i = 0; i < A->rmap->n; i++) { 1743 PetscCall(MatSetValues(A, 1, &i, a->imax[i] - mdiag[i], &oldj[oldi[i]], &olda[oldi[i]], ADD_VALUES)); 1744 if (i < A->cmap->n) PetscCall(MatSetValue(A, i, i, v, ADD_VALUES)); 1745 } 1746 PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY)); 1747 PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY)); 1748 if (singlemalloc) { 1749 PetscCall(PetscFree3(olda, oldj, oldi)); 1750 } else { 1751 if (free_a) PetscCall(PetscFree(olda)); 1752 if (free_ij) PetscCall(PetscFree(oldj)); 1753 if (free_ij) PetscCall(PetscFree(oldi)); 1754 } 1755 } 1756 PetscCall(PetscFree(mdiag)); 1757 a->diagonaldense = PETSC_TRUE; 1758 PetscFunctionReturn(0); 1759 } 1760 1761 /* 1762 Checks for missing diagonals 1763 */ 1764 PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A, PetscBool *missing, PetscInt *d) 1765 { 1766 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1767 PetscInt *diag, *ii = a->i, i; 1768 1769 PetscFunctionBegin; 1770 *missing = PETSC_FALSE; 1771 if (A->rmap->n > 0 && !ii) { 1772 *missing = PETSC_TRUE; 1773 if (d) *d = 0; 1774 PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n")); 1775 } else { 1776 PetscInt n; 1777 n = PetscMin(A->rmap->n, A->cmap->n); 1778 diag = a->diag; 1779 for (i = 0; i < n; i++) { 1780 if (diag[i] >= ii[i + 1]) { 1781 *missing = PETSC_TRUE; 1782 if (d) *d = i; 1783 PetscCall(PetscInfo(A, "Matrix is missing diagonal number %" PetscInt_FMT "\n", i)); 1784 break; 1785 } 1786 } 1787 } 1788 PetscFunctionReturn(0); 1789 } 1790 1791 #include <petscblaslapack.h> 1792 #include <petsc/private/kernels/blockinvert.h> 1793 1794 /* 1795 Note that values is allocated externally by the PC and then passed into this routine 1796 */ 1797 PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag) 1798 { 1799 PetscInt n = A->rmap->n, i, ncnt = 0, *indx, j, bsizemax = 0, *v_pivots; 1800 PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE; 1801 const PetscReal shift = 0.0; 1802 PetscInt ipvt[5]; 1803 PetscCount flops = 0; 1804 PetscScalar work[25], *v_work; 1805 1806 PetscFunctionBegin; 1807 allowzeropivot = PetscNot(A->erroriffailure); 1808 for (i = 0; i < nblocks; i++) ncnt += bsizes[i]; 1809 PetscCheck(ncnt == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Total blocksizes %" PetscInt_FMT " doesn't match number matrix rows %" PetscInt_FMT, ncnt, n); 1810 for (i = 0; i < nblocks; i++) bsizemax = PetscMax(bsizemax, bsizes[i]); 1811 PetscCall(PetscMalloc1(bsizemax, &indx)); 1812 if (bsizemax > 7) PetscCall(PetscMalloc2(bsizemax, &v_work, bsizemax, &v_pivots)); 1813 ncnt = 0; 1814 for (i = 0; i < nblocks; i++) { 1815 for (j = 0; j < bsizes[i]; j++) indx[j] = ncnt + j; 1816 PetscCall(MatGetValues(A, bsizes[i], indx, bsizes[i], indx, diag)); 1817 switch (bsizes[i]) { 1818 case 1: 1819 *diag = 1.0 / (*diag); 1820 break; 1821 case 2: 1822 PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected)); 1823 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1824 PetscCall(PetscKernel_A_gets_transpose_A_2(diag)); 1825 break; 1826 case 3: 1827 PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected)); 1828 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1829 PetscCall(PetscKernel_A_gets_transpose_A_3(diag)); 1830 break; 1831 case 4: 1832 PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected)); 1833 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1834 PetscCall(PetscKernel_A_gets_transpose_A_4(diag)); 1835 break; 1836 case 5: 1837 PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected)); 1838 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1839 PetscCall(PetscKernel_A_gets_transpose_A_5(diag)); 1840 break; 1841 case 6: 1842 PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected)); 1843 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1844 PetscCall(PetscKernel_A_gets_transpose_A_6(diag)); 1845 break; 1846 case 7: 1847 PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected)); 1848 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1849 PetscCall(PetscKernel_A_gets_transpose_A_7(diag)); 1850 break; 1851 default: 1852 PetscCall(PetscKernel_A_gets_inverse_A(bsizes[i], diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected)); 1853 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1854 PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bsizes[i])); 1855 } 1856 ncnt += bsizes[i]; 1857 diag += bsizes[i] * bsizes[i]; 1858 flops += 2 * PetscPowInt(bsizes[i], 3) / 3; 1859 } 1860 PetscLogFlops(flops); 1861 if (bsizemax > 7) PetscCall(PetscFree2(v_work, v_pivots)); 1862 PetscCall(PetscFree(indx)); 1863 PetscFunctionReturn(0); 1864 } 1865 1866 /* 1867 Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways 1868 */ 1869 PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A, PetscScalar omega, PetscScalar fshift) 1870 { 1871 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1872 PetscInt i, *diag, m = A->rmap->n; 1873 const MatScalar *v; 1874 PetscScalar *idiag, *mdiag; 1875 1876 PetscFunctionBegin; 1877 if (a->idiagvalid) PetscFunctionReturn(0); 1878 PetscCall(MatMarkDiagonal_SeqAIJ(A)); 1879 diag = a->diag; 1880 if (!a->idiag) { PetscCall(PetscMalloc3(m, &a->idiag, m, &a->mdiag, m, &a->ssor_work)); } 1881 1882 mdiag = a->mdiag; 1883 idiag = a->idiag; 1884 PetscCall(MatSeqAIJGetArrayRead(A, &v)); 1885 if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) { 1886 for (i = 0; i < m; i++) { 1887 mdiag[i] = v[diag[i]]; 1888 if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */ 1889 if (PetscRealPart(fshift)) { 1890 PetscCall(PetscInfo(A, "Zero diagonal on row %" PetscInt_FMT "\n", i)); 1891 A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1892 A->factorerror_zeropivot_value = 0.0; 1893 A->factorerror_zeropivot_row = i; 1894 } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Zero diagonal on row %" PetscInt_FMT, i); 1895 } 1896 idiag[i] = 1.0 / v[diag[i]]; 1897 } 1898 PetscCall(PetscLogFlops(m)); 1899 } else { 1900 for (i = 0; i < m; i++) { 1901 mdiag[i] = v[diag[i]]; 1902 idiag[i] = omega / (fshift + v[diag[i]]); 1903 } 1904 PetscCall(PetscLogFlops(2.0 * m)); 1905 } 1906 a->idiagvalid = PETSC_TRUE; 1907 PetscCall(MatSeqAIJRestoreArrayRead(A, &v)); 1908 PetscFunctionReturn(0); 1909 } 1910 1911 #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h> 1912 PetscErrorCode MatSOR_SeqAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx) 1913 { 1914 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1915 PetscScalar *x, d, sum, *t, scale; 1916 const MatScalar *v, *idiag = NULL, *mdiag, *aa; 1917 const PetscScalar *b, *bs, *xb, *ts; 1918 PetscInt n, m = A->rmap->n, i; 1919 const PetscInt *idx, *diag; 1920 1921 PetscFunctionBegin; 1922 if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) { 1923 PetscCall(MatSOR_SeqAIJ_Inode(A, bb, omega, flag, fshift, its, lits, xx)); 1924 PetscFunctionReturn(0); 1925 } 1926 its = its * lits; 1927 1928 if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */ 1929 if (!a->idiagvalid) PetscCall(MatInvertDiagonal_SeqAIJ(A, omega, fshift)); 1930 a->fshift = fshift; 1931 a->omega = omega; 1932 1933 diag = a->diag; 1934 t = a->ssor_work; 1935 idiag = a->idiag; 1936 mdiag = a->mdiag; 1937 1938 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 1939 PetscCall(VecGetArray(xx, &x)); 1940 PetscCall(VecGetArrayRead(bb, &b)); 1941 /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */ 1942 if (flag == SOR_APPLY_UPPER) { 1943 /* apply (U + D/omega) to the vector */ 1944 bs = b; 1945 for (i = 0; i < m; i++) { 1946 d = fshift + mdiag[i]; 1947 n = a->i[i + 1] - diag[i] - 1; 1948 idx = a->j + diag[i] + 1; 1949 v = aa + diag[i] + 1; 1950 sum = b[i] * d / omega; 1951 PetscSparseDensePlusDot(sum, bs, v, idx, n); 1952 x[i] = sum; 1953 } 1954 PetscCall(VecRestoreArray(xx, &x)); 1955 PetscCall(VecRestoreArrayRead(bb, &b)); 1956 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 1957 PetscCall(PetscLogFlops(a->nz)); 1958 PetscFunctionReturn(0); 1959 } 1960 1961 PetscCheck(flag != SOR_APPLY_LOWER, PETSC_COMM_SELF, PETSC_ERR_SUP, "SOR_APPLY_LOWER is not implemented"); 1962 if (flag & SOR_EISENSTAT) { 1963 /* Let A = L + U + D; where L is lower triangular, 1964 U is upper triangular, E = D/omega; This routine applies 1965 1966 (L + E)^{-1} A (U + E)^{-1} 1967 1968 to a vector efficiently using Eisenstat's trick. 1969 */ 1970 scale = (2.0 / omega) - 1.0; 1971 1972 /* x = (E + U)^{-1} b */ 1973 for (i = m - 1; i >= 0; i--) { 1974 n = a->i[i + 1] - diag[i] - 1; 1975 idx = a->j + diag[i] + 1; 1976 v = aa + diag[i] + 1; 1977 sum = b[i]; 1978 PetscSparseDenseMinusDot(sum, x, v, idx, n); 1979 x[i] = sum * idiag[i]; 1980 } 1981 1982 /* t = b - (2*E - D)x */ 1983 v = aa; 1984 for (i = 0; i < m; i++) t[i] = b[i] - scale * (v[*diag++]) * x[i]; 1985 1986 /* t = (E + L)^{-1}t */ 1987 ts = t; 1988 diag = a->diag; 1989 for (i = 0; i < m; i++) { 1990 n = diag[i] - a->i[i]; 1991 idx = a->j + a->i[i]; 1992 v = aa + a->i[i]; 1993 sum = t[i]; 1994 PetscSparseDenseMinusDot(sum, ts, v, idx, n); 1995 t[i] = sum * idiag[i]; 1996 /* x = x + t */ 1997 x[i] += t[i]; 1998 } 1999 2000 PetscCall(PetscLogFlops(6.0 * m - 1 + 2.0 * a->nz)); 2001 PetscCall(VecRestoreArray(xx, &x)); 2002 PetscCall(VecRestoreArrayRead(bb, &b)); 2003 PetscFunctionReturn(0); 2004 } 2005 if (flag & SOR_ZERO_INITIAL_GUESS) { 2006 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) { 2007 for (i = 0; i < m; i++) { 2008 n = diag[i] - a->i[i]; 2009 idx = a->j + a->i[i]; 2010 v = aa + a->i[i]; 2011 sum = b[i]; 2012 PetscSparseDenseMinusDot(sum, x, v, idx, n); 2013 t[i] = sum; 2014 x[i] = sum * idiag[i]; 2015 } 2016 xb = t; 2017 PetscCall(PetscLogFlops(a->nz)); 2018 } else xb = b; 2019 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) { 2020 for (i = m - 1; i >= 0; i--) { 2021 n = a->i[i + 1] - diag[i] - 1; 2022 idx = a->j + diag[i] + 1; 2023 v = aa + diag[i] + 1; 2024 sum = xb[i]; 2025 PetscSparseDenseMinusDot(sum, x, v, idx, n); 2026 if (xb == b) { 2027 x[i] = sum * idiag[i]; 2028 } else { 2029 x[i] = (1 - omega) * x[i] + sum * idiag[i]; /* omega in idiag */ 2030 } 2031 } 2032 PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */ 2033 } 2034 its--; 2035 } 2036 while (its--) { 2037 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) { 2038 for (i = 0; i < m; i++) { 2039 /* lower */ 2040 n = diag[i] - a->i[i]; 2041 idx = a->j + a->i[i]; 2042 v = aa + a->i[i]; 2043 sum = b[i]; 2044 PetscSparseDenseMinusDot(sum, x, v, idx, n); 2045 t[i] = sum; /* save application of the lower-triangular part */ 2046 /* upper */ 2047 n = a->i[i + 1] - diag[i] - 1; 2048 idx = a->j + diag[i] + 1; 2049 v = aa + diag[i] + 1; 2050 PetscSparseDenseMinusDot(sum, x, v, idx, n); 2051 x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */ 2052 } 2053 xb = t; 2054 PetscCall(PetscLogFlops(2.0 * a->nz)); 2055 } else xb = b; 2056 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) { 2057 for (i = m - 1; i >= 0; i--) { 2058 sum = xb[i]; 2059 if (xb == b) { 2060 /* whole matrix (no checkpointing available) */ 2061 n = a->i[i + 1] - a->i[i]; 2062 idx = a->j + a->i[i]; 2063 v = aa + a->i[i]; 2064 PetscSparseDenseMinusDot(sum, x, v, idx, n); 2065 x[i] = (1. - omega) * x[i] + (sum + mdiag[i] * x[i]) * idiag[i]; 2066 } else { /* lower-triangular part has been saved, so only apply upper-triangular */ 2067 n = a->i[i + 1] - diag[i] - 1; 2068 idx = a->j + diag[i] + 1; 2069 v = aa + diag[i] + 1; 2070 PetscSparseDenseMinusDot(sum, x, v, idx, n); 2071 x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */ 2072 } 2073 } 2074 if (xb == b) { 2075 PetscCall(PetscLogFlops(2.0 * a->nz)); 2076 } else { 2077 PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */ 2078 } 2079 } 2080 } 2081 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 2082 PetscCall(VecRestoreArray(xx, &x)); 2083 PetscCall(VecRestoreArrayRead(bb, &b)); 2084 PetscFunctionReturn(0); 2085 } 2086 2087 PetscErrorCode MatGetInfo_SeqAIJ(Mat A, MatInfoType flag, MatInfo *info) 2088 { 2089 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2090 2091 PetscFunctionBegin; 2092 info->block_size = 1.0; 2093 info->nz_allocated = a->maxnz; 2094 info->nz_used = a->nz; 2095 info->nz_unneeded = (a->maxnz - a->nz); 2096 info->assemblies = A->num_ass; 2097 info->mallocs = A->info.mallocs; 2098 info->memory = 0; /* REVIEW ME */ 2099 if (A->factortype) { 2100 info->fill_ratio_given = A->info.fill_ratio_given; 2101 info->fill_ratio_needed = A->info.fill_ratio_needed; 2102 info->factor_mallocs = A->info.factor_mallocs; 2103 } else { 2104 info->fill_ratio_given = 0; 2105 info->fill_ratio_needed = 0; 2106 info->factor_mallocs = 0; 2107 } 2108 PetscFunctionReturn(0); 2109 } 2110 2111 PetscErrorCode MatZeroRows_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b) 2112 { 2113 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2114 PetscInt i, m = A->rmap->n - 1; 2115 const PetscScalar *xx; 2116 PetscScalar *bb, *aa; 2117 PetscInt d = 0; 2118 2119 PetscFunctionBegin; 2120 if (x && b) { 2121 PetscCall(VecGetArrayRead(x, &xx)); 2122 PetscCall(VecGetArray(b, &bb)); 2123 for (i = 0; i < N; i++) { 2124 PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]); 2125 if (rows[i] >= A->cmap->n) continue; 2126 bb[rows[i]] = diag * xx[rows[i]]; 2127 } 2128 PetscCall(VecRestoreArrayRead(x, &xx)); 2129 PetscCall(VecRestoreArray(b, &bb)); 2130 } 2131 2132 PetscCall(MatSeqAIJGetArray(A, &aa)); 2133 if (a->keepnonzeropattern) { 2134 for (i = 0; i < N; i++) { 2135 PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]); 2136 PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]])); 2137 } 2138 if (diag != 0.0) { 2139 for (i = 0; i < N; i++) { 2140 d = rows[i]; 2141 if (rows[i] >= A->cmap->n) continue; 2142 PetscCheck(a->diag[d] < a->i[d + 1], PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry in the zeroed row %" PetscInt_FMT, d); 2143 } 2144 for (i = 0; i < N; i++) { 2145 if (rows[i] >= A->cmap->n) continue; 2146 aa[a->diag[rows[i]]] = diag; 2147 } 2148 } 2149 } else { 2150 if (diag != 0.0) { 2151 for (i = 0; i < N; i++) { 2152 PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]); 2153 if (a->ilen[rows[i]] > 0) { 2154 if (rows[i] >= A->cmap->n) { 2155 a->ilen[rows[i]] = 0; 2156 } else { 2157 a->ilen[rows[i]] = 1; 2158 aa[a->i[rows[i]]] = diag; 2159 a->j[a->i[rows[i]]] = rows[i]; 2160 } 2161 } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */ 2162 PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES)); 2163 } 2164 } 2165 } else { 2166 for (i = 0; i < N; i++) { 2167 PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]); 2168 a->ilen[rows[i]] = 0; 2169 } 2170 } 2171 A->nonzerostate++; 2172 } 2173 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 2174 PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY); 2175 PetscFunctionReturn(0); 2176 } 2177 2178 PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b) 2179 { 2180 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2181 PetscInt i, j, m = A->rmap->n - 1, d = 0; 2182 PetscBool missing, *zeroed, vecs = PETSC_FALSE; 2183 const PetscScalar *xx; 2184 PetscScalar *bb, *aa; 2185 2186 PetscFunctionBegin; 2187 if (!N) PetscFunctionReturn(0); 2188 PetscCall(MatSeqAIJGetArray(A, &aa)); 2189 if (x && b) { 2190 PetscCall(VecGetArrayRead(x, &xx)); 2191 PetscCall(VecGetArray(b, &bb)); 2192 vecs = PETSC_TRUE; 2193 } 2194 PetscCall(PetscCalloc1(A->rmap->n, &zeroed)); 2195 for (i = 0; i < N; i++) { 2196 PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]); 2197 PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]])); 2198 2199 zeroed[rows[i]] = PETSC_TRUE; 2200 } 2201 for (i = 0; i < A->rmap->n; i++) { 2202 if (!zeroed[i]) { 2203 for (j = a->i[i]; j < a->i[i + 1]; j++) { 2204 if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) { 2205 if (vecs) bb[i] -= aa[j] * xx[a->j[j]]; 2206 aa[j] = 0.0; 2207 } 2208 } 2209 } else if (vecs && i < A->cmap->N) bb[i] = diag * xx[i]; 2210 } 2211 if (x && b) { 2212 PetscCall(VecRestoreArrayRead(x, &xx)); 2213 PetscCall(VecRestoreArray(b, &bb)); 2214 } 2215 PetscCall(PetscFree(zeroed)); 2216 if (diag != 0.0) { 2217 PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, &d)); 2218 if (missing) { 2219 for (i = 0; i < N; i++) { 2220 if (rows[i] >= A->cmap->N) continue; 2221 PetscCheck(!a->nonew || rows[i] < d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry in row %" PetscInt_FMT " (%" PetscInt_FMT ")", d, rows[i]); 2222 PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES)); 2223 } 2224 } else { 2225 for (i = 0; i < N; i++) aa[a->diag[rows[i]]] = diag; 2226 } 2227 } 2228 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 2229 PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY); 2230 PetscFunctionReturn(0); 2231 } 2232 2233 PetscErrorCode MatGetRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v) 2234 { 2235 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2236 const PetscScalar *aa; 2237 PetscInt *itmp; 2238 2239 PetscFunctionBegin; 2240 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 2241 *nz = a->i[row + 1] - a->i[row]; 2242 if (v) *v = (PetscScalar *)(aa + a->i[row]); 2243 if (idx) { 2244 itmp = a->j + a->i[row]; 2245 if (*nz) *idx = itmp; 2246 else *idx = NULL; 2247 } 2248 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 2249 PetscFunctionReturn(0); 2250 } 2251 2252 PetscErrorCode MatRestoreRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v) 2253 { 2254 PetscFunctionBegin; 2255 if (nz) *nz = 0; 2256 if (idx) *idx = NULL; 2257 if (v) *v = NULL; 2258 PetscFunctionReturn(0); 2259 } 2260 2261 PetscErrorCode MatNorm_SeqAIJ(Mat A, NormType type, PetscReal *nrm) 2262 { 2263 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2264 const MatScalar *v; 2265 PetscReal sum = 0.0; 2266 PetscInt i, j; 2267 2268 PetscFunctionBegin; 2269 PetscCall(MatSeqAIJGetArrayRead(A, &v)); 2270 if (type == NORM_FROBENIUS) { 2271 #if defined(PETSC_USE_REAL___FP16) 2272 PetscBLASInt one = 1, nz = a->nz; 2273 PetscCallBLAS("BLASnrm2", *nrm = BLASnrm2_(&nz, v, &one)); 2274 #else 2275 for (i = 0; i < a->nz; i++) { 2276 sum += PetscRealPart(PetscConj(*v) * (*v)); 2277 v++; 2278 } 2279 *nrm = PetscSqrtReal(sum); 2280 #endif 2281 PetscCall(PetscLogFlops(2.0 * a->nz)); 2282 } else if (type == NORM_1) { 2283 PetscReal *tmp; 2284 PetscInt *jj = a->j; 2285 PetscCall(PetscCalloc1(A->cmap->n + 1, &tmp)); 2286 *nrm = 0.0; 2287 for (j = 0; j < a->nz; j++) { 2288 tmp[*jj++] += PetscAbsScalar(*v); 2289 v++; 2290 } 2291 for (j = 0; j < A->cmap->n; j++) { 2292 if (tmp[j] > *nrm) *nrm = tmp[j]; 2293 } 2294 PetscCall(PetscFree(tmp)); 2295 PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0))); 2296 } else if (type == NORM_INFINITY) { 2297 *nrm = 0.0; 2298 for (j = 0; j < A->rmap->n; j++) { 2299 const PetscScalar *v2 = v + a->i[j]; 2300 sum = 0.0; 2301 for (i = 0; i < a->i[j + 1] - a->i[j]; i++) { 2302 sum += PetscAbsScalar(*v2); 2303 v2++; 2304 } 2305 if (sum > *nrm) *nrm = sum; 2306 } 2307 PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0))); 2308 } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for two norm"); 2309 PetscCall(MatSeqAIJRestoreArrayRead(A, &v)); 2310 PetscFunctionReturn(0); 2311 } 2312 2313 PetscErrorCode MatIsTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f) 2314 { 2315 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data; 2316 PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr; 2317 const MatScalar *va, *vb; 2318 PetscInt ma, na, mb, nb, i; 2319 2320 PetscFunctionBegin; 2321 PetscCall(MatGetSize(A, &ma, &na)); 2322 PetscCall(MatGetSize(B, &mb, &nb)); 2323 if (ma != nb || na != mb) { 2324 *f = PETSC_FALSE; 2325 PetscFunctionReturn(0); 2326 } 2327 PetscCall(MatSeqAIJGetArrayRead(A, &va)); 2328 PetscCall(MatSeqAIJGetArrayRead(B, &vb)); 2329 aii = aij->i; 2330 bii = bij->i; 2331 adx = aij->j; 2332 bdx = bij->j; 2333 PetscCall(PetscMalloc1(ma, &aptr)); 2334 PetscCall(PetscMalloc1(mb, &bptr)); 2335 for (i = 0; i < ma; i++) aptr[i] = aii[i]; 2336 for (i = 0; i < mb; i++) bptr[i] = bii[i]; 2337 2338 *f = PETSC_TRUE; 2339 for (i = 0; i < ma; i++) { 2340 while (aptr[i] < aii[i + 1]) { 2341 PetscInt idc, idr; 2342 PetscScalar vc, vr; 2343 /* column/row index/value */ 2344 idc = adx[aptr[i]]; 2345 idr = bdx[bptr[idc]]; 2346 vc = va[aptr[i]]; 2347 vr = vb[bptr[idc]]; 2348 if (i != idr || PetscAbsScalar(vc - vr) > tol) { 2349 *f = PETSC_FALSE; 2350 goto done; 2351 } else { 2352 aptr[i]++; 2353 if (B || i != idc) bptr[idc]++; 2354 } 2355 } 2356 } 2357 done: 2358 PetscCall(PetscFree(aptr)); 2359 PetscCall(PetscFree(bptr)); 2360 PetscCall(MatSeqAIJRestoreArrayRead(A, &va)); 2361 PetscCall(MatSeqAIJRestoreArrayRead(B, &vb)); 2362 PetscFunctionReturn(0); 2363 } 2364 2365 PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f) 2366 { 2367 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data; 2368 PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr; 2369 MatScalar *va, *vb; 2370 PetscInt ma, na, mb, nb, i; 2371 2372 PetscFunctionBegin; 2373 PetscCall(MatGetSize(A, &ma, &na)); 2374 PetscCall(MatGetSize(B, &mb, &nb)); 2375 if (ma != nb || na != mb) { 2376 *f = PETSC_FALSE; 2377 PetscFunctionReturn(0); 2378 } 2379 aii = aij->i; 2380 bii = bij->i; 2381 adx = aij->j; 2382 bdx = bij->j; 2383 va = aij->a; 2384 vb = bij->a; 2385 PetscCall(PetscMalloc1(ma, &aptr)); 2386 PetscCall(PetscMalloc1(mb, &bptr)); 2387 for (i = 0; i < ma; i++) aptr[i] = aii[i]; 2388 for (i = 0; i < mb; i++) bptr[i] = bii[i]; 2389 2390 *f = PETSC_TRUE; 2391 for (i = 0; i < ma; i++) { 2392 while (aptr[i] < aii[i + 1]) { 2393 PetscInt idc, idr; 2394 PetscScalar vc, vr; 2395 /* column/row index/value */ 2396 idc = adx[aptr[i]]; 2397 idr = bdx[bptr[idc]]; 2398 vc = va[aptr[i]]; 2399 vr = vb[bptr[idc]]; 2400 if (i != idr || PetscAbsScalar(vc - PetscConj(vr)) > tol) { 2401 *f = PETSC_FALSE; 2402 goto done; 2403 } else { 2404 aptr[i]++; 2405 if (B || i != idc) bptr[idc]++; 2406 } 2407 } 2408 } 2409 done: 2410 PetscCall(PetscFree(aptr)); 2411 PetscCall(PetscFree(bptr)); 2412 PetscFunctionReturn(0); 2413 } 2414 2415 PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A, PetscReal tol, PetscBool *f) 2416 { 2417 PetscFunctionBegin; 2418 PetscCall(MatIsTranspose_SeqAIJ(A, A, tol, f)); 2419 PetscFunctionReturn(0); 2420 } 2421 2422 PetscErrorCode MatIsHermitian_SeqAIJ(Mat A, PetscReal tol, PetscBool *f) 2423 { 2424 PetscFunctionBegin; 2425 PetscCall(MatIsHermitianTranspose_SeqAIJ(A, A, tol, f)); 2426 PetscFunctionReturn(0); 2427 } 2428 2429 PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A, Vec ll, Vec rr) 2430 { 2431 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2432 const PetscScalar *l, *r; 2433 PetscScalar x; 2434 MatScalar *v; 2435 PetscInt i, j, m = A->rmap->n, n = A->cmap->n, M, nz = a->nz; 2436 const PetscInt *jj; 2437 2438 PetscFunctionBegin; 2439 if (ll) { 2440 /* The local size is used so that VecMPI can be passed to this routine 2441 by MatDiagonalScale_MPIAIJ */ 2442 PetscCall(VecGetLocalSize(ll, &m)); 2443 PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length"); 2444 PetscCall(VecGetArrayRead(ll, &l)); 2445 PetscCall(MatSeqAIJGetArray(A, &v)); 2446 for (i = 0; i < m; i++) { 2447 x = l[i]; 2448 M = a->i[i + 1] - a->i[i]; 2449 for (j = 0; j < M; j++) (*v++) *= x; 2450 } 2451 PetscCall(VecRestoreArrayRead(ll, &l)); 2452 PetscCall(PetscLogFlops(nz)); 2453 PetscCall(MatSeqAIJRestoreArray(A, &v)); 2454 } 2455 if (rr) { 2456 PetscCall(VecGetLocalSize(rr, &n)); 2457 PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length"); 2458 PetscCall(VecGetArrayRead(rr, &r)); 2459 PetscCall(MatSeqAIJGetArray(A, &v)); 2460 jj = a->j; 2461 for (i = 0; i < nz; i++) (*v++) *= r[*jj++]; 2462 PetscCall(MatSeqAIJRestoreArray(A, &v)); 2463 PetscCall(VecRestoreArrayRead(rr, &r)); 2464 PetscCall(PetscLogFlops(nz)); 2465 } 2466 PetscCall(MatSeqAIJInvalidateDiagonal(A)); 2467 PetscFunctionReturn(0); 2468 } 2469 2470 PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A, IS isrow, IS iscol, PetscInt csize, MatReuse scall, Mat *B) 2471 { 2472 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *c; 2473 PetscInt *smap, i, k, kstart, kend, oldcols = A->cmap->n, *lens; 2474 PetscInt row, mat_i, *mat_j, tcol, first, step, *mat_ilen, sum, lensi; 2475 const PetscInt *irow, *icol; 2476 const PetscScalar *aa; 2477 PetscInt nrows, ncols; 2478 PetscInt *starts, *j_new, *i_new, *aj = a->j, *ai = a->i, ii, *ailen = a->ilen; 2479 MatScalar *a_new, *mat_a; 2480 Mat C; 2481 PetscBool stride; 2482 2483 PetscFunctionBegin; 2484 PetscCall(ISGetIndices(isrow, &irow)); 2485 PetscCall(ISGetLocalSize(isrow, &nrows)); 2486 PetscCall(ISGetLocalSize(iscol, &ncols)); 2487 2488 PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &stride)); 2489 if (stride) { 2490 PetscCall(ISStrideGetInfo(iscol, &first, &step)); 2491 } else { 2492 first = 0; 2493 step = 0; 2494 } 2495 if (stride && step == 1) { 2496 /* special case of contiguous rows */ 2497 PetscCall(PetscMalloc2(nrows, &lens, nrows, &starts)); 2498 /* loop over new rows determining lens and starting points */ 2499 for (i = 0; i < nrows; i++) { 2500 kstart = ai[irow[i]]; 2501 kend = kstart + ailen[irow[i]]; 2502 starts[i] = kstart; 2503 for (k = kstart; k < kend; k++) { 2504 if (aj[k] >= first) { 2505 starts[i] = k; 2506 break; 2507 } 2508 } 2509 sum = 0; 2510 while (k < kend) { 2511 if (aj[k++] >= first + ncols) break; 2512 sum++; 2513 } 2514 lens[i] = sum; 2515 } 2516 /* create submatrix */ 2517 if (scall == MAT_REUSE_MATRIX) { 2518 PetscInt n_cols, n_rows; 2519 PetscCall(MatGetSize(*B, &n_rows, &n_cols)); 2520 PetscCheck(n_rows == nrows && n_cols == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Reused submatrix wrong size"); 2521 PetscCall(MatZeroEntries(*B)); 2522 C = *B; 2523 } else { 2524 PetscInt rbs, cbs; 2525 PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C)); 2526 PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE)); 2527 PetscCall(ISGetBlockSize(isrow, &rbs)); 2528 PetscCall(ISGetBlockSize(iscol, &cbs)); 2529 PetscCall(MatSetBlockSizes(C, rbs, cbs)); 2530 PetscCall(MatSetType(C, ((PetscObject)A)->type_name)); 2531 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens)); 2532 } 2533 c = (Mat_SeqAIJ *)C->data; 2534 2535 /* loop over rows inserting into submatrix */ 2536 a_new = c->a; 2537 j_new = c->j; 2538 i_new = c->i; 2539 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 2540 for (i = 0; i < nrows; i++) { 2541 ii = starts[i]; 2542 lensi = lens[i]; 2543 for (k = 0; k < lensi; k++) *j_new++ = aj[ii + k] - first; 2544 PetscCall(PetscArraycpy(a_new, aa + starts[i], lensi)); 2545 a_new += lensi; 2546 i_new[i + 1] = i_new[i] + lensi; 2547 c->ilen[i] = lensi; 2548 } 2549 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 2550 PetscCall(PetscFree2(lens, starts)); 2551 } else { 2552 PetscCall(ISGetIndices(iscol, &icol)); 2553 PetscCall(PetscCalloc1(oldcols, &smap)); 2554 PetscCall(PetscMalloc1(1 + nrows, &lens)); 2555 for (i = 0; i < ncols; i++) { 2556 PetscCheck(icol[i] < oldcols, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Requesting column beyond largest column icol[%" PetscInt_FMT "] %" PetscInt_FMT " >= A->cmap->n %" PetscInt_FMT, i, icol[i], oldcols); 2557 smap[icol[i]] = i + 1; 2558 } 2559 2560 /* determine lens of each row */ 2561 for (i = 0; i < nrows; i++) { 2562 kstart = ai[irow[i]]; 2563 kend = kstart + a->ilen[irow[i]]; 2564 lens[i] = 0; 2565 for (k = kstart; k < kend; k++) { 2566 if (smap[aj[k]]) lens[i]++; 2567 } 2568 } 2569 /* Create and fill new matrix */ 2570 if (scall == MAT_REUSE_MATRIX) { 2571 PetscBool equal; 2572 2573 c = (Mat_SeqAIJ *)((*B)->data); 2574 PetscCheck((*B)->rmap->n == nrows && (*B)->cmap->n == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong size"); 2575 PetscCall(PetscArraycmp(c->ilen, lens, (*B)->rmap->n, &equal)); 2576 PetscCheck(equal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong no of nonzeros"); 2577 PetscCall(PetscArrayzero(c->ilen, (*B)->rmap->n)); 2578 C = *B; 2579 } else { 2580 PetscInt rbs, cbs; 2581 PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C)); 2582 PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE)); 2583 PetscCall(ISGetBlockSize(isrow, &rbs)); 2584 PetscCall(ISGetBlockSize(iscol, &cbs)); 2585 PetscCall(MatSetBlockSizes(C, rbs, cbs)); 2586 PetscCall(MatSetType(C, ((PetscObject)A)->type_name)); 2587 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens)); 2588 } 2589 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 2590 c = (Mat_SeqAIJ *)(C->data); 2591 for (i = 0; i < nrows; i++) { 2592 row = irow[i]; 2593 kstart = ai[row]; 2594 kend = kstart + a->ilen[row]; 2595 mat_i = c->i[i]; 2596 mat_j = c->j + mat_i; 2597 mat_a = c->a + mat_i; 2598 mat_ilen = c->ilen + i; 2599 for (k = kstart; k < kend; k++) { 2600 if ((tcol = smap[a->j[k]])) { 2601 *mat_j++ = tcol - 1; 2602 *mat_a++ = aa[k]; 2603 (*mat_ilen)++; 2604 } 2605 } 2606 } 2607 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 2608 /* Free work space */ 2609 PetscCall(ISRestoreIndices(iscol, &icol)); 2610 PetscCall(PetscFree(smap)); 2611 PetscCall(PetscFree(lens)); 2612 /* sort */ 2613 for (i = 0; i < nrows; i++) { 2614 PetscInt ilen; 2615 2616 mat_i = c->i[i]; 2617 mat_j = c->j + mat_i; 2618 mat_a = c->a + mat_i; 2619 ilen = c->ilen[i]; 2620 PetscCall(PetscSortIntWithScalarArray(ilen, mat_j, mat_a)); 2621 } 2622 } 2623 #if defined(PETSC_HAVE_DEVICE) 2624 PetscCall(MatBindToCPU(C, A->boundtocpu)); 2625 #endif 2626 PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY)); 2627 PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY)); 2628 2629 PetscCall(ISRestoreIndices(isrow, &irow)); 2630 *B = C; 2631 PetscFunctionReturn(0); 2632 } 2633 2634 PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat) 2635 { 2636 Mat B; 2637 2638 PetscFunctionBegin; 2639 if (scall == MAT_INITIAL_MATRIX) { 2640 PetscCall(MatCreate(subComm, &B)); 2641 PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n)); 2642 PetscCall(MatSetBlockSizesFromMats(B, mat, mat)); 2643 PetscCall(MatSetType(B, MATSEQAIJ)); 2644 PetscCall(MatDuplicateNoCreate_SeqAIJ(B, mat, MAT_COPY_VALUES, PETSC_TRUE)); 2645 *subMat = B; 2646 } else { 2647 PetscCall(MatCopy_SeqAIJ(mat, *subMat, SAME_NONZERO_PATTERN)); 2648 } 2649 PetscFunctionReturn(0); 2650 } 2651 2652 PetscErrorCode MatILUFactor_SeqAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info) 2653 { 2654 Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data; 2655 Mat outA; 2656 PetscBool row_identity, col_identity; 2657 2658 PetscFunctionBegin; 2659 PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels=0 supported for in-place ilu"); 2660 2661 PetscCall(ISIdentity(row, &row_identity)); 2662 PetscCall(ISIdentity(col, &col_identity)); 2663 2664 outA = inA; 2665 outA->factortype = MAT_FACTOR_LU; 2666 PetscCall(PetscFree(inA->solvertype)); 2667 PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype)); 2668 2669 PetscCall(PetscObjectReference((PetscObject)row)); 2670 PetscCall(ISDestroy(&a->row)); 2671 2672 a->row = row; 2673 2674 PetscCall(PetscObjectReference((PetscObject)col)); 2675 PetscCall(ISDestroy(&a->col)); 2676 2677 a->col = col; 2678 2679 /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */ 2680 PetscCall(ISDestroy(&a->icol)); 2681 PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol)); 2682 2683 if (!a->solve_work) { /* this matrix may have been factored before */ 2684 PetscCall(PetscMalloc1(inA->rmap->n + 1, &a->solve_work)); 2685 } 2686 2687 PetscCall(MatMarkDiagonal_SeqAIJ(inA)); 2688 if (row_identity && col_identity) { 2689 PetscCall(MatLUFactorNumeric_SeqAIJ_inplace(outA, inA, info)); 2690 } else { 2691 PetscCall(MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA, inA, info)); 2692 } 2693 PetscFunctionReturn(0); 2694 } 2695 2696 PetscErrorCode MatScale_SeqAIJ(Mat inA, PetscScalar alpha) 2697 { 2698 Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data; 2699 PetscScalar *v; 2700 PetscBLASInt one = 1, bnz; 2701 2702 PetscFunctionBegin; 2703 PetscCall(MatSeqAIJGetArray(inA, &v)); 2704 PetscCall(PetscBLASIntCast(a->nz, &bnz)); 2705 PetscCallBLAS("BLASscal", BLASscal_(&bnz, &alpha, v, &one)); 2706 PetscCall(PetscLogFlops(a->nz)); 2707 PetscCall(MatSeqAIJRestoreArray(inA, &v)); 2708 PetscCall(MatSeqAIJInvalidateDiagonal(inA)); 2709 PetscFunctionReturn(0); 2710 } 2711 2712 PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj) 2713 { 2714 PetscInt i; 2715 2716 PetscFunctionBegin; 2717 if (!submatj->id) { /* delete data that are linked only to submats[id=0] */ 2718 PetscCall(PetscFree4(submatj->sbuf1, submatj->ptr, submatj->tmp, submatj->ctr)); 2719 2720 for (i = 0; i < submatj->nrqr; ++i) PetscCall(PetscFree(submatj->sbuf2[i])); 2721 PetscCall(PetscFree3(submatj->sbuf2, submatj->req_size, submatj->req_source1)); 2722 2723 if (submatj->rbuf1) { 2724 PetscCall(PetscFree(submatj->rbuf1[0])); 2725 PetscCall(PetscFree(submatj->rbuf1)); 2726 } 2727 2728 for (i = 0; i < submatj->nrqs; ++i) PetscCall(PetscFree(submatj->rbuf3[i])); 2729 PetscCall(PetscFree3(submatj->req_source2, submatj->rbuf2, submatj->rbuf3)); 2730 PetscCall(PetscFree(submatj->pa)); 2731 } 2732 2733 #if defined(PETSC_USE_CTABLE) 2734 PetscCall(PetscTableDestroy((PetscTable *)&submatj->rmap)); 2735 if (submatj->cmap_loc) PetscCall(PetscFree(submatj->cmap_loc)); 2736 PetscCall(PetscFree(submatj->rmap_loc)); 2737 #else 2738 PetscCall(PetscFree(submatj->rmap)); 2739 #endif 2740 2741 if (!submatj->allcolumns) { 2742 #if defined(PETSC_USE_CTABLE) 2743 PetscCall(PetscTableDestroy((PetscTable *)&submatj->cmap)); 2744 #else 2745 PetscCall(PetscFree(submatj->cmap)); 2746 #endif 2747 } 2748 PetscCall(PetscFree(submatj->row2proc)); 2749 2750 PetscCall(PetscFree(submatj)); 2751 PetscFunctionReturn(0); 2752 } 2753 2754 PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C) 2755 { 2756 Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data; 2757 Mat_SubSppt *submatj = c->submatis1; 2758 2759 PetscFunctionBegin; 2760 PetscCall((*submatj->destroy)(C)); 2761 PetscCall(MatDestroySubMatrix_Private(submatj)); 2762 PetscFunctionReturn(0); 2763 } 2764 2765 /* Note this has code duplication with MatDestroySubMatrices_SeqBAIJ() */ 2766 PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n, Mat *mat[]) 2767 { 2768 PetscInt i; 2769 Mat C; 2770 Mat_SeqAIJ *c; 2771 Mat_SubSppt *submatj; 2772 2773 PetscFunctionBegin; 2774 for (i = 0; i < n; i++) { 2775 C = (*mat)[i]; 2776 c = (Mat_SeqAIJ *)C->data; 2777 submatj = c->submatis1; 2778 if (submatj) { 2779 if (--((PetscObject)C)->refct <= 0) { 2780 PetscCall(PetscFree(C->factorprefix)); 2781 PetscCall((*submatj->destroy)(C)); 2782 PetscCall(MatDestroySubMatrix_Private(submatj)); 2783 PetscCall(PetscFree(C->defaultvectype)); 2784 PetscCall(PetscFree(C->defaultrandtype)); 2785 PetscCall(PetscLayoutDestroy(&C->rmap)); 2786 PetscCall(PetscLayoutDestroy(&C->cmap)); 2787 PetscCall(PetscHeaderDestroy(&C)); 2788 } 2789 } else { 2790 PetscCall(MatDestroy(&C)); 2791 } 2792 } 2793 2794 /* Destroy Dummy submatrices created for reuse */ 2795 PetscCall(MatDestroySubMatrices_Dummy(n, mat)); 2796 2797 PetscCall(PetscFree(*mat)); 2798 PetscFunctionReturn(0); 2799 } 2800 2801 PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *B[]) 2802 { 2803 PetscInt i; 2804 2805 PetscFunctionBegin; 2806 if (scall == MAT_INITIAL_MATRIX) PetscCall(PetscCalloc1(n + 1, B)); 2807 2808 for (i = 0; i < n; i++) PetscCall(MatCreateSubMatrix_SeqAIJ(A, irow[i], icol[i], PETSC_DECIDE, scall, &(*B)[i])); 2809 PetscFunctionReturn(0); 2810 } 2811 2812 PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A, PetscInt is_max, IS is[], PetscInt ov) 2813 { 2814 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2815 PetscInt row, i, j, k, l, ll, m, n, *nidx, isz, val; 2816 const PetscInt *idx; 2817 PetscInt start, end, *ai, *aj, bs = (A->rmap->bs > 0 && A->rmap->bs == A->cmap->bs) ? A->rmap->bs : 1; 2818 PetscBT table; 2819 2820 PetscFunctionBegin; 2821 m = A->rmap->n / bs; 2822 ai = a->i; 2823 aj = a->j; 2824 2825 PetscCheck(ov >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "illegal negative overlap value used"); 2826 2827 PetscCall(PetscMalloc1(m + 1, &nidx)); 2828 PetscCall(PetscBTCreate(m, &table)); 2829 2830 for (i = 0; i < is_max; i++) { 2831 /* Initialize the two local arrays */ 2832 isz = 0; 2833 PetscCall(PetscBTMemzero(m, table)); 2834 2835 /* Extract the indices, assume there can be duplicate entries */ 2836 PetscCall(ISGetIndices(is[i], &idx)); 2837 PetscCall(ISGetLocalSize(is[i], &n)); 2838 2839 if (bs > 1) { 2840 /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */ 2841 for (j = 0; j < n; ++j) { 2842 if (!PetscBTLookupSet(table, idx[j] / bs)) nidx[isz++] = idx[j] / bs; 2843 } 2844 PetscCall(ISRestoreIndices(is[i], &idx)); 2845 PetscCall(ISDestroy(&is[i])); 2846 2847 k = 0; 2848 for (j = 0; j < ov; j++) { /* for each overlap */ 2849 n = isz; 2850 for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */ 2851 for (ll = 0; ll < bs; ll++) { 2852 row = bs * nidx[k] + ll; 2853 start = ai[row]; 2854 end = ai[row + 1]; 2855 for (l = start; l < end; l++) { 2856 val = aj[l] / bs; 2857 if (!PetscBTLookupSet(table, val)) nidx[isz++] = val; 2858 } 2859 } 2860 } 2861 } 2862 PetscCall(ISCreateBlock(PETSC_COMM_SELF, bs, isz, nidx, PETSC_COPY_VALUES, (is + i))); 2863 } else { 2864 /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */ 2865 for (j = 0; j < n; ++j) { 2866 if (!PetscBTLookupSet(table, idx[j])) nidx[isz++] = idx[j]; 2867 } 2868 PetscCall(ISRestoreIndices(is[i], &idx)); 2869 PetscCall(ISDestroy(&is[i])); 2870 2871 k = 0; 2872 for (j = 0; j < ov; j++) { /* for each overlap */ 2873 n = isz; 2874 for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */ 2875 row = nidx[k]; 2876 start = ai[row]; 2877 end = ai[row + 1]; 2878 for (l = start; l < end; l++) { 2879 val = aj[l]; 2880 if (!PetscBTLookupSet(table, val)) nidx[isz++] = val; 2881 } 2882 } 2883 } 2884 PetscCall(ISCreateGeneral(PETSC_COMM_SELF, isz, nidx, PETSC_COPY_VALUES, (is + i))); 2885 } 2886 } 2887 PetscCall(PetscBTDestroy(&table)); 2888 PetscCall(PetscFree(nidx)); 2889 PetscFunctionReturn(0); 2890 } 2891 2892 /* -------------------------------------------------------------- */ 2893 PetscErrorCode MatPermute_SeqAIJ(Mat A, IS rowp, IS colp, Mat *B) 2894 { 2895 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2896 PetscInt i, nz = 0, m = A->rmap->n, n = A->cmap->n; 2897 const PetscInt *row, *col; 2898 PetscInt *cnew, j, *lens; 2899 IS icolp, irowp; 2900 PetscInt *cwork = NULL; 2901 PetscScalar *vwork = NULL; 2902 2903 PetscFunctionBegin; 2904 PetscCall(ISInvertPermutation(rowp, PETSC_DECIDE, &irowp)); 2905 PetscCall(ISGetIndices(irowp, &row)); 2906 PetscCall(ISInvertPermutation(colp, PETSC_DECIDE, &icolp)); 2907 PetscCall(ISGetIndices(icolp, &col)); 2908 2909 /* determine lengths of permuted rows */ 2910 PetscCall(PetscMalloc1(m + 1, &lens)); 2911 for (i = 0; i < m; i++) lens[row[i]] = a->i[i + 1] - a->i[i]; 2912 PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B)); 2913 PetscCall(MatSetSizes(*B, m, n, m, n)); 2914 PetscCall(MatSetBlockSizesFromMats(*B, A, A)); 2915 PetscCall(MatSetType(*B, ((PetscObject)A)->type_name)); 2916 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*B, 0, lens)); 2917 PetscCall(PetscFree(lens)); 2918 2919 PetscCall(PetscMalloc1(n, &cnew)); 2920 for (i = 0; i < m; i++) { 2921 PetscCall(MatGetRow_SeqAIJ(A, i, &nz, &cwork, &vwork)); 2922 for (j = 0; j < nz; j++) cnew[j] = col[cwork[j]]; 2923 PetscCall(MatSetValues_SeqAIJ(*B, 1, &row[i], nz, cnew, vwork, INSERT_VALUES)); 2924 PetscCall(MatRestoreRow_SeqAIJ(A, i, &nz, &cwork, &vwork)); 2925 } 2926 PetscCall(PetscFree(cnew)); 2927 2928 (*B)->assembled = PETSC_FALSE; 2929 2930 #if defined(PETSC_HAVE_DEVICE) 2931 PetscCall(MatBindToCPU(*B, A->boundtocpu)); 2932 #endif 2933 PetscCall(MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY)); 2934 PetscCall(MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY)); 2935 PetscCall(ISRestoreIndices(irowp, &row)); 2936 PetscCall(ISRestoreIndices(icolp, &col)); 2937 PetscCall(ISDestroy(&irowp)); 2938 PetscCall(ISDestroy(&icolp)); 2939 if (rowp == colp) PetscCall(MatPropagateSymmetryOptions(A, *B)); 2940 PetscFunctionReturn(0); 2941 } 2942 2943 PetscErrorCode MatCopy_SeqAIJ(Mat A, Mat B, MatStructure str) 2944 { 2945 PetscFunctionBegin; 2946 /* If the two matrices have the same copy implementation, use fast copy. */ 2947 if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) { 2948 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2949 Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data; 2950 const PetscScalar *aa; 2951 2952 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 2953 PetscCheck(a->i[A->rmap->n] == b->i[B->rmap->n], PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Number of nonzeros in two matrices are different %" PetscInt_FMT " != %" PetscInt_FMT, a->i[A->rmap->n], b->i[B->rmap->n]); 2954 PetscCall(PetscArraycpy(b->a, aa, a->i[A->rmap->n])); 2955 PetscCall(PetscObjectStateIncrease((PetscObject)B)); 2956 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 2957 } else { 2958 PetscCall(MatCopy_Basic(A, B, str)); 2959 } 2960 PetscFunctionReturn(0); 2961 } 2962 2963 PetscErrorCode MatSetUp_SeqAIJ(Mat A) 2964 { 2965 PetscFunctionBegin; 2966 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, PETSC_DEFAULT, NULL)); 2967 PetscFunctionReturn(0); 2968 } 2969 2970 PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A, PetscScalar *array[]) 2971 { 2972 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2973 2974 PetscFunctionBegin; 2975 *array = a->a; 2976 PetscFunctionReturn(0); 2977 } 2978 2979 PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A, PetscScalar *array[]) 2980 { 2981 PetscFunctionBegin; 2982 *array = NULL; 2983 PetscFunctionReturn(0); 2984 } 2985 2986 /* 2987 Computes the number of nonzeros per row needed for preallocation when X and Y 2988 have different nonzero structure. 2989 */ 2990 PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *yi, const PetscInt *yj, PetscInt *nnz) 2991 { 2992 PetscInt i, j, k, nzx, nzy; 2993 2994 PetscFunctionBegin; 2995 /* Set the number of nonzeros in the new matrix */ 2996 for (i = 0; i < m; i++) { 2997 const PetscInt *xjj = xj + xi[i], *yjj = yj + yi[i]; 2998 nzx = xi[i + 1] - xi[i]; 2999 nzy = yi[i + 1] - yi[i]; 3000 nnz[i] = 0; 3001 for (j = 0, k = 0; j < nzx; j++) { /* Point in X */ 3002 for (; k < nzy && yjj[k] < xjj[j]; k++) nnz[i]++; /* Catch up to X */ 3003 if (k < nzy && yjj[k] == xjj[j]) k++; /* Skip duplicate */ 3004 nnz[i]++; 3005 } 3006 for (; k < nzy; k++) nnz[i]++; 3007 } 3008 PetscFunctionReturn(0); 3009 } 3010 3011 PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y, Mat X, PetscInt *nnz) 3012 { 3013 PetscInt m = Y->rmap->N; 3014 Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data; 3015 Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data; 3016 3017 PetscFunctionBegin; 3018 /* Set the number of nonzeros in the new matrix */ 3019 PetscCall(MatAXPYGetPreallocation_SeqX_private(m, x->i, x->j, y->i, y->j, nnz)); 3020 PetscFunctionReturn(0); 3021 } 3022 3023 PetscErrorCode MatAXPY_SeqAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str) 3024 { 3025 Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data; 3026 3027 PetscFunctionBegin; 3028 if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) { 3029 PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE; 3030 if (e) { 3031 PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e)); 3032 if (e) { 3033 PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e)); 3034 if (e) str = SAME_NONZERO_PATTERN; 3035 } 3036 } 3037 if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN"); 3038 } 3039 if (str == SAME_NONZERO_PATTERN) { 3040 const PetscScalar *xa; 3041 PetscScalar *ya, alpha = a; 3042 PetscBLASInt one = 1, bnz; 3043 3044 PetscCall(PetscBLASIntCast(x->nz, &bnz)); 3045 PetscCall(MatSeqAIJGetArray(Y, &ya)); 3046 PetscCall(MatSeqAIJGetArrayRead(X, &xa)); 3047 PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, xa, &one, ya, &one)); 3048 PetscCall(MatSeqAIJRestoreArrayRead(X, &xa)); 3049 PetscCall(MatSeqAIJRestoreArray(Y, &ya)); 3050 PetscCall(PetscLogFlops(2.0 * bnz)); 3051 PetscCall(MatSeqAIJInvalidateDiagonal(Y)); 3052 PetscCall(PetscObjectStateIncrease((PetscObject)Y)); 3053 } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */ 3054 PetscCall(MatAXPY_Basic(Y, a, X, str)); 3055 } else { 3056 Mat B; 3057 PetscInt *nnz; 3058 PetscCall(PetscMalloc1(Y->rmap->N, &nnz)); 3059 PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B)); 3060 PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name)); 3061 PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap)); 3062 PetscCall(MatSetType(B, ((PetscObject)Y)->type_name)); 3063 PetscCall(MatAXPYGetPreallocation_SeqAIJ(Y, X, nnz)); 3064 PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz)); 3065 PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str)); 3066 PetscCall(MatHeaderMerge(Y, &B)); 3067 PetscCall(MatSeqAIJCheckInode(Y)); 3068 PetscCall(PetscFree(nnz)); 3069 } 3070 PetscFunctionReturn(0); 3071 } 3072 3073 PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat) 3074 { 3075 #if defined(PETSC_USE_COMPLEX) 3076 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 3077 PetscInt i, nz; 3078 PetscScalar *a; 3079 3080 PetscFunctionBegin; 3081 nz = aij->nz; 3082 PetscCall(MatSeqAIJGetArray(mat, &a)); 3083 for (i = 0; i < nz; i++) a[i] = PetscConj(a[i]); 3084 PetscCall(MatSeqAIJRestoreArray(mat, &a)); 3085 #else 3086 PetscFunctionBegin; 3087 #endif 3088 PetscFunctionReturn(0); 3089 } 3090 3091 PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[]) 3092 { 3093 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 3094 PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n; 3095 PetscReal atmp; 3096 PetscScalar *x; 3097 const MatScalar *aa, *av; 3098 3099 PetscFunctionBegin; 3100 PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 3101 PetscCall(MatSeqAIJGetArrayRead(A, &av)); 3102 aa = av; 3103 ai = a->i; 3104 aj = a->j; 3105 3106 PetscCall(VecSet(v, 0.0)); 3107 PetscCall(VecGetArrayWrite(v, &x)); 3108 PetscCall(VecGetLocalSize(v, &n)); 3109 PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector"); 3110 for (i = 0; i < m; i++) { 3111 ncols = ai[1] - ai[0]; 3112 ai++; 3113 for (j = 0; j < ncols; j++) { 3114 atmp = PetscAbsScalar(*aa); 3115 if (PetscAbsScalar(x[i]) < atmp) { 3116 x[i] = atmp; 3117 if (idx) idx[i] = *aj; 3118 } 3119 aa++; 3120 aj++; 3121 } 3122 } 3123 PetscCall(VecRestoreArrayWrite(v, &x)); 3124 PetscCall(MatSeqAIJRestoreArrayRead(A, &av)); 3125 PetscFunctionReturn(0); 3126 } 3127 3128 PetscErrorCode MatGetRowMax_SeqAIJ(Mat A, Vec v, PetscInt idx[]) 3129 { 3130 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 3131 PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n; 3132 PetscScalar *x; 3133 const MatScalar *aa, *av; 3134 3135 PetscFunctionBegin; 3136 PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 3137 PetscCall(MatSeqAIJGetArrayRead(A, &av)); 3138 aa = av; 3139 ai = a->i; 3140 aj = a->j; 3141 3142 PetscCall(VecSet(v, 0.0)); 3143 PetscCall(VecGetArrayWrite(v, &x)); 3144 PetscCall(VecGetLocalSize(v, &n)); 3145 PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector"); 3146 for (i = 0; i < m; i++) { 3147 ncols = ai[1] - ai[0]; 3148 ai++; 3149 if (ncols == A->cmap->n) { /* row is dense */ 3150 x[i] = *aa; 3151 if (idx) idx[i] = 0; 3152 } else { /* row is sparse so already KNOW maximum is 0.0 or higher */ 3153 x[i] = 0.0; 3154 if (idx) { 3155 for (j = 0; j < ncols; j++) { /* find first implicit 0.0 in the row */ 3156 if (aj[j] > j) { 3157 idx[i] = j; 3158 break; 3159 } 3160 } 3161 /* in case first implicit 0.0 in the row occurs at ncols-th column */ 3162 if (j == ncols && j < A->cmap->n) idx[i] = j; 3163 } 3164 } 3165 for (j = 0; j < ncols; j++) { 3166 if (PetscRealPart(x[i]) < PetscRealPart(*aa)) { 3167 x[i] = *aa; 3168 if (idx) idx[i] = *aj; 3169 } 3170 aa++; 3171 aj++; 3172 } 3173 } 3174 PetscCall(VecRestoreArrayWrite(v, &x)); 3175 PetscCall(MatSeqAIJRestoreArrayRead(A, &av)); 3176 PetscFunctionReturn(0); 3177 } 3178 3179 PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[]) 3180 { 3181 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 3182 PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n; 3183 PetscScalar *x; 3184 const MatScalar *aa, *av; 3185 3186 PetscFunctionBegin; 3187 PetscCall(MatSeqAIJGetArrayRead(A, &av)); 3188 aa = av; 3189 ai = a->i; 3190 aj = a->j; 3191 3192 PetscCall(VecSet(v, 0.0)); 3193 PetscCall(VecGetArrayWrite(v, &x)); 3194 PetscCall(VecGetLocalSize(v, &n)); 3195 PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector, %" PetscInt_FMT " vs. %" PetscInt_FMT " rows", m, n); 3196 for (i = 0; i < m; i++) { 3197 ncols = ai[1] - ai[0]; 3198 ai++; 3199 if (ncols == A->cmap->n) { /* row is dense */ 3200 x[i] = *aa; 3201 if (idx) idx[i] = 0; 3202 } else { /* row is sparse so already KNOW minimum is 0.0 or higher */ 3203 x[i] = 0.0; 3204 if (idx) { /* find first implicit 0.0 in the row */ 3205 for (j = 0; j < ncols; j++) { 3206 if (aj[j] > j) { 3207 idx[i] = j; 3208 break; 3209 } 3210 } 3211 /* in case first implicit 0.0 in the row occurs at ncols-th column */ 3212 if (j == ncols && j < A->cmap->n) idx[i] = j; 3213 } 3214 } 3215 for (j = 0; j < ncols; j++) { 3216 if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) { 3217 x[i] = *aa; 3218 if (idx) idx[i] = *aj; 3219 } 3220 aa++; 3221 aj++; 3222 } 3223 } 3224 PetscCall(VecRestoreArrayWrite(v, &x)); 3225 PetscCall(MatSeqAIJRestoreArrayRead(A, &av)); 3226 PetscFunctionReturn(0); 3227 } 3228 3229 PetscErrorCode MatGetRowMin_SeqAIJ(Mat A, Vec v, PetscInt idx[]) 3230 { 3231 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 3232 PetscInt i, j, m = A->rmap->n, ncols, n; 3233 const PetscInt *ai, *aj; 3234 PetscScalar *x; 3235 const MatScalar *aa, *av; 3236 3237 PetscFunctionBegin; 3238 PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 3239 PetscCall(MatSeqAIJGetArrayRead(A, &av)); 3240 aa = av; 3241 ai = a->i; 3242 aj = a->j; 3243 3244 PetscCall(VecSet(v, 0.0)); 3245 PetscCall(VecGetArrayWrite(v, &x)); 3246 PetscCall(VecGetLocalSize(v, &n)); 3247 PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector"); 3248 for (i = 0; i < m; i++) { 3249 ncols = ai[1] - ai[0]; 3250 ai++; 3251 if (ncols == A->cmap->n) { /* row is dense */ 3252 x[i] = *aa; 3253 if (idx) idx[i] = 0; 3254 } else { /* row is sparse so already KNOW minimum is 0.0 or lower */ 3255 x[i] = 0.0; 3256 if (idx) { /* find first implicit 0.0 in the row */ 3257 for (j = 0; j < ncols; j++) { 3258 if (aj[j] > j) { 3259 idx[i] = j; 3260 break; 3261 } 3262 } 3263 /* in case first implicit 0.0 in the row occurs at ncols-th column */ 3264 if (j == ncols && j < A->cmap->n) idx[i] = j; 3265 } 3266 } 3267 for (j = 0; j < ncols; j++) { 3268 if (PetscRealPart(x[i]) > PetscRealPart(*aa)) { 3269 x[i] = *aa; 3270 if (idx) idx[i] = *aj; 3271 } 3272 aa++; 3273 aj++; 3274 } 3275 } 3276 PetscCall(VecRestoreArrayWrite(v, &x)); 3277 PetscCall(MatSeqAIJRestoreArrayRead(A, &av)); 3278 PetscFunctionReturn(0); 3279 } 3280 3281 PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A, const PetscScalar **values) 3282 { 3283 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 3284 PetscInt i, bs = PetscAbs(A->rmap->bs), mbs = A->rmap->n / bs, ipvt[5], bs2 = bs * bs, *v_pivots, ij[7], *IJ, j; 3285 MatScalar *diag, work[25], *v_work; 3286 const PetscReal shift = 0.0; 3287 PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE; 3288 3289 PetscFunctionBegin; 3290 allowzeropivot = PetscNot(A->erroriffailure); 3291 if (a->ibdiagvalid) { 3292 if (values) *values = a->ibdiag; 3293 PetscFunctionReturn(0); 3294 } 3295 PetscCall(MatMarkDiagonal_SeqAIJ(A)); 3296 if (!a->ibdiag) { PetscCall(PetscMalloc1(bs2 * mbs, &a->ibdiag)); } 3297 diag = a->ibdiag; 3298 if (values) *values = a->ibdiag; 3299 /* factor and invert each block */ 3300 switch (bs) { 3301 case 1: 3302 for (i = 0; i < mbs; i++) { 3303 PetscCall(MatGetValues(A, 1, &i, 1, &i, diag + i)); 3304 if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) { 3305 if (allowzeropivot) { 3306 A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3307 A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]); 3308 A->factorerror_zeropivot_row = i; 3309 PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g\n", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON)); 3310 } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_MAT_LU_ZRPVT, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON); 3311 } 3312 diag[i] = (PetscScalar)1.0 / (diag[i] + shift); 3313 } 3314 break; 3315 case 2: 3316 for (i = 0; i < mbs; i++) { 3317 ij[0] = 2 * i; 3318 ij[1] = 2 * i + 1; 3319 PetscCall(MatGetValues(A, 2, ij, 2, ij, diag)); 3320 PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected)); 3321 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3322 PetscCall(PetscKernel_A_gets_transpose_A_2(diag)); 3323 diag += 4; 3324 } 3325 break; 3326 case 3: 3327 for (i = 0; i < mbs; i++) { 3328 ij[0] = 3 * i; 3329 ij[1] = 3 * i + 1; 3330 ij[2] = 3 * i + 2; 3331 PetscCall(MatGetValues(A, 3, ij, 3, ij, diag)); 3332 PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected)); 3333 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3334 PetscCall(PetscKernel_A_gets_transpose_A_3(diag)); 3335 diag += 9; 3336 } 3337 break; 3338 case 4: 3339 for (i = 0; i < mbs; i++) { 3340 ij[0] = 4 * i; 3341 ij[1] = 4 * i + 1; 3342 ij[2] = 4 * i + 2; 3343 ij[3] = 4 * i + 3; 3344 PetscCall(MatGetValues(A, 4, ij, 4, ij, diag)); 3345 PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected)); 3346 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3347 PetscCall(PetscKernel_A_gets_transpose_A_4(diag)); 3348 diag += 16; 3349 } 3350 break; 3351 case 5: 3352 for (i = 0; i < mbs; i++) { 3353 ij[0] = 5 * i; 3354 ij[1] = 5 * i + 1; 3355 ij[2] = 5 * i + 2; 3356 ij[3] = 5 * i + 3; 3357 ij[4] = 5 * i + 4; 3358 PetscCall(MatGetValues(A, 5, ij, 5, ij, diag)); 3359 PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected)); 3360 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3361 PetscCall(PetscKernel_A_gets_transpose_A_5(diag)); 3362 diag += 25; 3363 } 3364 break; 3365 case 6: 3366 for (i = 0; i < mbs; i++) { 3367 ij[0] = 6 * i; 3368 ij[1] = 6 * i + 1; 3369 ij[2] = 6 * i + 2; 3370 ij[3] = 6 * i + 3; 3371 ij[4] = 6 * i + 4; 3372 ij[5] = 6 * i + 5; 3373 PetscCall(MatGetValues(A, 6, ij, 6, ij, diag)); 3374 PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected)); 3375 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3376 PetscCall(PetscKernel_A_gets_transpose_A_6(diag)); 3377 diag += 36; 3378 } 3379 break; 3380 case 7: 3381 for (i = 0; i < mbs; i++) { 3382 ij[0] = 7 * i; 3383 ij[1] = 7 * i + 1; 3384 ij[2] = 7 * i + 2; 3385 ij[3] = 7 * i + 3; 3386 ij[4] = 7 * i + 4; 3387 ij[5] = 7 * i + 5; 3388 ij[6] = 7 * i + 6; 3389 PetscCall(MatGetValues(A, 7, ij, 7, ij, diag)); 3390 PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected)); 3391 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3392 PetscCall(PetscKernel_A_gets_transpose_A_7(diag)); 3393 diag += 49; 3394 } 3395 break; 3396 default: 3397 PetscCall(PetscMalloc3(bs, &v_work, bs, &v_pivots, bs, &IJ)); 3398 for (i = 0; i < mbs; i++) { 3399 for (j = 0; j < bs; j++) IJ[j] = bs * i + j; 3400 PetscCall(MatGetValues(A, bs, IJ, bs, IJ, diag)); 3401 PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected)); 3402 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3403 PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bs)); 3404 diag += bs2; 3405 } 3406 PetscCall(PetscFree3(v_work, v_pivots, IJ)); 3407 } 3408 a->ibdiagvalid = PETSC_TRUE; 3409 PetscFunctionReturn(0); 3410 } 3411 3412 static PetscErrorCode MatSetRandom_SeqAIJ(Mat x, PetscRandom rctx) 3413 { 3414 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data; 3415 PetscScalar a, *aa; 3416 PetscInt m, n, i, j, col; 3417 3418 PetscFunctionBegin; 3419 if (!x->assembled) { 3420 PetscCall(MatGetSize(x, &m, &n)); 3421 for (i = 0; i < m; i++) { 3422 for (j = 0; j < aij->imax[i]; j++) { 3423 PetscCall(PetscRandomGetValue(rctx, &a)); 3424 col = (PetscInt)(n * PetscRealPart(a)); 3425 PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES)); 3426 } 3427 } 3428 } else { 3429 PetscCall(MatSeqAIJGetArrayWrite(x, &aa)); 3430 for (i = 0; i < aij->nz; i++) PetscCall(PetscRandomGetValue(rctx, aa + i)); 3431 PetscCall(MatSeqAIJRestoreArrayWrite(x, &aa)); 3432 } 3433 PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY)); 3434 PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY)); 3435 PetscFunctionReturn(0); 3436 } 3437 3438 /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */ 3439 PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x, PetscInt low, PetscInt high, PetscRandom rctx) 3440 { 3441 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data; 3442 PetscScalar a; 3443 PetscInt m, n, i, j, col, nskip; 3444 3445 PetscFunctionBegin; 3446 nskip = high - low; 3447 PetscCall(MatGetSize(x, &m, &n)); 3448 n -= nskip; /* shrink number of columns where nonzeros can be set */ 3449 for (i = 0; i < m; i++) { 3450 for (j = 0; j < aij->imax[i]; j++) { 3451 PetscCall(PetscRandomGetValue(rctx, &a)); 3452 col = (PetscInt)(n * PetscRealPart(a)); 3453 if (col >= low) col += nskip; /* shift col rightward to skip the hole */ 3454 PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES)); 3455 } 3456 } 3457 PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY)); 3458 PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY)); 3459 PetscFunctionReturn(0); 3460 } 3461 3462 /* -------------------------------------------------------------------*/ 3463 static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ, 3464 MatGetRow_SeqAIJ, 3465 MatRestoreRow_SeqAIJ, 3466 MatMult_SeqAIJ, 3467 /* 4*/ MatMultAdd_SeqAIJ, 3468 MatMultTranspose_SeqAIJ, 3469 MatMultTransposeAdd_SeqAIJ, 3470 NULL, 3471 NULL, 3472 NULL, 3473 /* 10*/ NULL, 3474 MatLUFactor_SeqAIJ, 3475 NULL, 3476 MatSOR_SeqAIJ, 3477 MatTranspose_SeqAIJ, 3478 /*1 5*/ MatGetInfo_SeqAIJ, 3479 MatEqual_SeqAIJ, 3480 MatGetDiagonal_SeqAIJ, 3481 MatDiagonalScale_SeqAIJ, 3482 MatNorm_SeqAIJ, 3483 /* 20*/ NULL, 3484 MatAssemblyEnd_SeqAIJ, 3485 MatSetOption_SeqAIJ, 3486 MatZeroEntries_SeqAIJ, 3487 /* 24*/ MatZeroRows_SeqAIJ, 3488 NULL, 3489 NULL, 3490 NULL, 3491 NULL, 3492 /* 29*/ MatSetUp_SeqAIJ, 3493 NULL, 3494 NULL, 3495 NULL, 3496 NULL, 3497 /* 34*/ MatDuplicate_SeqAIJ, 3498 NULL, 3499 NULL, 3500 MatILUFactor_SeqAIJ, 3501 NULL, 3502 /* 39*/ MatAXPY_SeqAIJ, 3503 MatCreateSubMatrices_SeqAIJ, 3504 MatIncreaseOverlap_SeqAIJ, 3505 MatGetValues_SeqAIJ, 3506 MatCopy_SeqAIJ, 3507 /* 44*/ MatGetRowMax_SeqAIJ, 3508 MatScale_SeqAIJ, 3509 MatShift_SeqAIJ, 3510 MatDiagonalSet_SeqAIJ, 3511 MatZeroRowsColumns_SeqAIJ, 3512 /* 49*/ MatSetRandom_SeqAIJ, 3513 MatGetRowIJ_SeqAIJ, 3514 MatRestoreRowIJ_SeqAIJ, 3515 MatGetColumnIJ_SeqAIJ, 3516 MatRestoreColumnIJ_SeqAIJ, 3517 /* 54*/ MatFDColoringCreate_SeqXAIJ, 3518 NULL, 3519 NULL, 3520 MatPermute_SeqAIJ, 3521 NULL, 3522 /* 59*/ NULL, 3523 MatDestroy_SeqAIJ, 3524 MatView_SeqAIJ, 3525 NULL, 3526 NULL, 3527 /* 64*/ NULL, 3528 MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ, 3529 NULL, 3530 NULL, 3531 NULL, 3532 /* 69*/ MatGetRowMaxAbs_SeqAIJ, 3533 MatGetRowMinAbs_SeqAIJ, 3534 NULL, 3535 NULL, 3536 NULL, 3537 /* 74*/ NULL, 3538 MatFDColoringApply_AIJ, 3539 NULL, 3540 NULL, 3541 NULL, 3542 /* 79*/ MatFindZeroDiagonals_SeqAIJ, 3543 NULL, 3544 NULL, 3545 NULL, 3546 MatLoad_SeqAIJ, 3547 /* 84*/ MatIsSymmetric_SeqAIJ, 3548 MatIsHermitian_SeqAIJ, 3549 NULL, 3550 NULL, 3551 NULL, 3552 /* 89*/ NULL, 3553 NULL, 3554 MatMatMultNumeric_SeqAIJ_SeqAIJ, 3555 NULL, 3556 NULL, 3557 /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy, 3558 NULL, 3559 NULL, 3560 MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ, 3561 NULL, 3562 /* 99*/ MatProductSetFromOptions_SeqAIJ, 3563 NULL, 3564 NULL, 3565 MatConjugate_SeqAIJ, 3566 NULL, 3567 /*104*/ MatSetValuesRow_SeqAIJ, 3568 MatRealPart_SeqAIJ, 3569 MatImaginaryPart_SeqAIJ, 3570 NULL, 3571 NULL, 3572 /*109*/ MatMatSolve_SeqAIJ, 3573 NULL, 3574 MatGetRowMin_SeqAIJ, 3575 NULL, 3576 MatMissingDiagonal_SeqAIJ, 3577 /*114*/ NULL, 3578 NULL, 3579 NULL, 3580 NULL, 3581 NULL, 3582 /*119*/ NULL, 3583 NULL, 3584 NULL, 3585 NULL, 3586 MatGetMultiProcBlock_SeqAIJ, 3587 /*124*/ MatFindNonzeroRows_SeqAIJ, 3588 MatGetColumnReductions_SeqAIJ, 3589 MatInvertBlockDiagonal_SeqAIJ, 3590 MatInvertVariableBlockDiagonal_SeqAIJ, 3591 NULL, 3592 /*129*/ NULL, 3593 NULL, 3594 NULL, 3595 MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ, 3596 MatTransposeColoringCreate_SeqAIJ, 3597 /*134*/ MatTransColoringApplySpToDen_SeqAIJ, 3598 MatTransColoringApplyDenToSp_SeqAIJ, 3599 NULL, 3600 NULL, 3601 MatRARtNumeric_SeqAIJ_SeqAIJ, 3602 /*139*/ NULL, 3603 NULL, 3604 NULL, 3605 MatFDColoringSetUp_SeqXAIJ, 3606 MatFindOffBlockDiagonalEntries_SeqAIJ, 3607 MatCreateMPIMatConcatenateSeqMat_SeqAIJ, 3608 /*145*/ MatDestroySubMatrices_SeqAIJ, 3609 NULL, 3610 NULL, 3611 MatCreateGraph_Simple_AIJ, 3612 NULL, 3613 /*150*/ MatTransposeSymbolic_SeqAIJ}; 3614 3615 PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat, PetscInt *indices) 3616 { 3617 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 3618 PetscInt i, nz, n; 3619 3620 PetscFunctionBegin; 3621 nz = aij->maxnz; 3622 n = mat->rmap->n; 3623 for (i = 0; i < nz; i++) aij->j[i] = indices[i]; 3624 aij->nz = nz; 3625 for (i = 0; i < n; i++) aij->ilen[i] = aij->imax[i]; 3626 PetscFunctionReturn(0); 3627 } 3628 3629 /* 3630 * Given a sparse matrix with global column indices, compact it by using a local column space. 3631 * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable() 3632 */ 3633 PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping) 3634 { 3635 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 3636 PetscTable gid1_lid1; 3637 PetscTablePosition tpos; 3638 PetscInt gid, lid, i, ec, nz = aij->nz; 3639 PetscInt *garray, *jj = aij->j; 3640 3641 PetscFunctionBegin; 3642 PetscValidHeaderSpecific(mat, MAT_CLASSID, 1); 3643 PetscValidPointer(mapping, 2); 3644 /* use a table */ 3645 PetscCall(PetscTableCreate(mat->rmap->n, mat->cmap->N + 1, &gid1_lid1)); 3646 ec = 0; 3647 for (i = 0; i < nz; i++) { 3648 PetscInt data, gid1 = jj[i] + 1; 3649 PetscCall(PetscTableFind(gid1_lid1, gid1, &data)); 3650 if (!data) { 3651 /* one based table */ 3652 PetscCall(PetscTableAdd(gid1_lid1, gid1, ++ec, INSERT_VALUES)); 3653 } 3654 } 3655 /* form array of columns we need */ 3656 PetscCall(PetscMalloc1(ec, &garray)); 3657 PetscCall(PetscTableGetHeadPosition(gid1_lid1, &tpos)); 3658 while (tpos) { 3659 PetscCall(PetscTableGetNext(gid1_lid1, &tpos, &gid, &lid)); 3660 gid--; 3661 lid--; 3662 garray[lid] = gid; 3663 } 3664 PetscCall(PetscSortInt(ec, garray)); /* sort, and rebuild */ 3665 PetscCall(PetscTableRemoveAll(gid1_lid1)); 3666 for (i = 0; i < ec; i++) PetscCall(PetscTableAdd(gid1_lid1, garray[i] + 1, i + 1, INSERT_VALUES)); 3667 /* compact out the extra columns in B */ 3668 for (i = 0; i < nz; i++) { 3669 PetscInt gid1 = jj[i] + 1; 3670 PetscCall(PetscTableFind(gid1_lid1, gid1, &lid)); 3671 lid--; 3672 jj[i] = lid; 3673 } 3674 PetscCall(PetscLayoutDestroy(&mat->cmap)); 3675 PetscCall(PetscTableDestroy(&gid1_lid1)); 3676 PetscCall(PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat), ec, ec, 1, &mat->cmap)); 3677 PetscCall(ISLocalToGlobalMappingCreate(PETSC_COMM_SELF, mat->cmap->bs, mat->cmap->n, garray, PETSC_OWN_POINTER, mapping)); 3678 PetscCall(ISLocalToGlobalMappingSetType(*mapping, ISLOCALTOGLOBALMAPPINGHASH)); 3679 PetscFunctionReturn(0); 3680 } 3681 3682 /*@ 3683 MatSeqAIJSetColumnIndices - Set the column indices for all the rows 3684 in the matrix. 3685 3686 Input Parameters: 3687 + mat - the `MATSEQAIJ` matrix 3688 - indices - the column indices 3689 3690 Level: advanced 3691 3692 Notes: 3693 This can be called if you have precomputed the nonzero structure of the 3694 matrix and want to provide it to the matrix object to improve the performance 3695 of the `MatSetValues()` operation. 3696 3697 You MUST have set the correct numbers of nonzeros per row in the call to 3698 `MatCreateSeqAIJ()`, and the columns indices MUST be sorted. 3699 3700 MUST be called before any calls to `MatSetValues()` 3701 3702 The indices should start with zero, not one. 3703 3704 @*/ 3705 PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat, PetscInt *indices) 3706 { 3707 PetscFunctionBegin; 3708 PetscValidHeaderSpecific(mat, MAT_CLASSID, 1); 3709 PetscValidIntPointer(indices, 2); 3710 PetscUseMethod(mat, "MatSeqAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices)); 3711 PetscFunctionReturn(0); 3712 } 3713 3714 /* ----------------------------------------------------------------------------------------*/ 3715 3716 PetscErrorCode MatStoreValues_SeqAIJ(Mat mat) 3717 { 3718 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 3719 size_t nz = aij->i[mat->rmap->n]; 3720 3721 PetscFunctionBegin; 3722 PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first"); 3723 3724 /* allocate space for values if not already there */ 3725 if (!aij->saved_values) { PetscCall(PetscMalloc1(nz + 1, &aij->saved_values)); } 3726 3727 /* copy values over */ 3728 PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz)); 3729 PetscFunctionReturn(0); 3730 } 3731 3732 /*@ 3733 MatStoreValues - Stashes a copy of the matrix values; this allows, for 3734 example, reuse of the linear part of a Jacobian, while recomputing the 3735 nonlinear portion. 3736 3737 Logically Collect 3738 3739 Input Parameters: 3740 . mat - the matrix (currently only `MATAIJ` matrices support this option) 3741 3742 Level: advanced 3743 3744 Common Usage, with `SNESSolve()`: 3745 $ Create Jacobian matrix 3746 $ Set linear terms into matrix 3747 $ Apply boundary conditions to matrix, at this time matrix must have 3748 $ final nonzero structure (i.e. setting the nonlinear terms and applying 3749 $ boundary conditions again will not change the nonzero structure 3750 $ ierr = MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); 3751 $ ierr = MatStoreValues(mat); 3752 $ Call SNESSetJacobian() with matrix 3753 $ In your Jacobian routine 3754 $ ierr = MatRetrieveValues(mat); 3755 $ Set nonlinear terms in matrix 3756 3757 Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself: 3758 $ // build linear portion of Jacobian 3759 $ ierr = MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); 3760 $ ierr = MatStoreValues(mat); 3761 $ loop over nonlinear iterations 3762 $ ierr = MatRetrieveValues(mat); 3763 $ // call MatSetValues(mat,...) to set nonliner portion of Jacobian 3764 $ // call MatAssemblyBegin/End() on matrix 3765 $ Solve linear system with Jacobian 3766 $ endloop 3767 3768 Notes: 3769 Matrix must already be assemblied before calling this routine 3770 Must set the matrix option `MatSetOption`(mat,`MAT_NEW_NONZERO_LOCATIONS`,`PETSC_FALSE`); before 3771 calling this routine. 3772 3773 When this is called multiple times it overwrites the previous set of stored values 3774 and does not allocated additional space. 3775 3776 .seealso: `MatRetrieveValues()` 3777 @*/ 3778 PetscErrorCode MatStoreValues(Mat mat) 3779 { 3780 PetscFunctionBegin; 3781 PetscValidHeaderSpecific(mat, MAT_CLASSID, 1); 3782 PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix"); 3783 PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 3784 PetscUseMethod(mat, "MatStoreValues_C", (Mat), (mat)); 3785 PetscFunctionReturn(0); 3786 } 3787 3788 PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat) 3789 { 3790 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 3791 PetscInt nz = aij->i[mat->rmap->n]; 3792 3793 PetscFunctionBegin; 3794 PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first"); 3795 PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first"); 3796 /* copy values over */ 3797 PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz)); 3798 PetscFunctionReturn(0); 3799 } 3800 3801 /*@ 3802 MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 3803 example, reuse of the linear part of a Jacobian, while recomputing the 3804 nonlinear portion. 3805 3806 Logically Collect 3807 3808 Input Parameters: 3809 . mat - the matrix (currently only `MATAIJ` matrices support this option) 3810 3811 Level: advanced 3812 3813 .seealso: `MatStoreValues()` 3814 @*/ 3815 PetscErrorCode MatRetrieveValues(Mat mat) 3816 { 3817 PetscFunctionBegin; 3818 PetscValidHeaderSpecific(mat, MAT_CLASSID, 1); 3819 PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix"); 3820 PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 3821 PetscUseMethod(mat, "MatRetrieveValues_C", (Mat), (mat)); 3822 PetscFunctionReturn(0); 3823 } 3824 3825 /* --------------------------------------------------------------------------------*/ 3826 /*@C 3827 MatCreateSeqAIJ - Creates a sparse matrix in `MATSEQAIJ` (compressed row) format 3828 (the default parallel PETSc format). For good matrix assembly performance 3829 the user should preallocate the matrix storage by setting the parameter nz 3830 (or the array nnz). By setting these parameters accurately, performance 3831 during matrix assembly can be increased by more than a factor of 50. 3832 3833 Collective 3834 3835 Input Parameters: 3836 + comm - MPI communicator, set to `PETSC_COMM_SELF` 3837 . m - number of rows 3838 . n - number of columns 3839 . nz - number of nonzeros per row (same for all rows) 3840 - nnz - array containing the number of nonzeros in the various rows 3841 (possibly different for each row) or NULL 3842 3843 Output Parameter: 3844 . A - the matrix 3845 3846 It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`, 3847 MatXXXXSetPreallocation() paradigm instead of this routine directly. 3848 [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`] 3849 3850 Notes: 3851 If nnz is given then nz is ignored 3852 3853 The AIJ format, also called 3854 compressed row storage, is fully compatible with standard Fortran 77 3855 storage. That is, the stored row and column indices can begin at 3856 either one (as in Fortran) or zero. See the users' manual for details. 3857 3858 Specify the preallocated storage with either nz or nnz (not both). 3859 Set nz = `PETSC_DEFAULT` and nnz = NULL for PETSc to control dynamic memory 3860 allocation. For large problems you MUST preallocate memory or you 3861 will get TERRIBLE performance, see the users' manual chapter on matrices. 3862 3863 By default, this format uses inodes (identical nodes) when possible, to 3864 improve numerical efficiency of matrix-vector products and solves. We 3865 search for consecutive rows with the same nonzero structure, thereby 3866 reusing matrix information to achieve increased efficiency. 3867 3868 Options Database Keys: 3869 + -mat_no_inode - Do not use inodes 3870 - -mat_inode_limit <limit> - Sets inode limit (max limit=5) 3871 3872 Level: intermediate 3873 3874 .seealso: [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()` 3875 @*/ 3876 PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A) 3877 { 3878 PetscFunctionBegin; 3879 PetscCall(MatCreate(comm, A)); 3880 PetscCall(MatSetSizes(*A, m, n, m, n)); 3881 PetscCall(MatSetType(*A, MATSEQAIJ)); 3882 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz)); 3883 PetscFunctionReturn(0); 3884 } 3885 3886 /*@C 3887 MatSeqAIJSetPreallocation - For good matrix assembly performance 3888 the user should preallocate the matrix storage by setting the parameter nz 3889 (or the array nnz). By setting these parameters accurately, performance 3890 during matrix assembly can be increased by more than a factor of 50. 3891 3892 Collective 3893 3894 Input Parameters: 3895 + B - The matrix 3896 . nz - number of nonzeros per row (same for all rows) 3897 - nnz - array containing the number of nonzeros in the various rows 3898 (possibly different for each row) or NULL 3899 3900 Notes: 3901 If nnz is given then nz is ignored 3902 3903 The `MATSEQAIJ` format also called 3904 compressed row storage, is fully compatible with standard Fortran 77 3905 storage. That is, the stored row and column indices can begin at 3906 either one (as in Fortran) or zero. See the users' manual for details. 3907 3908 Specify the preallocated storage with either nz or nnz (not both). 3909 Set nz = `PETSC_DEFAULT` and nnz = NULL for PETSc to control dynamic memory 3910 allocation. For large problems you MUST preallocate memory or you 3911 will get TERRIBLE performance, see the users' manual chapter on matrices. 3912 3913 You can call `MatGetInfo()` to get information on how effective the preallocation was; 3914 for example the fields mallocs,nz_allocated,nz_used,nz_unneeded; 3915 You can also run with the option -info and look for messages with the string 3916 malloc in them to see if additional memory allocation was needed. 3917 3918 Developer Notes: 3919 Use nz of `MAT_SKIP_ALLOCATION` to not allocate any space for the matrix 3920 entries or columns indices 3921 3922 By default, this format uses inodes (identical nodes) when possible, to 3923 improve numerical efficiency of matrix-vector products and solves. We 3924 search for consecutive rows with the same nonzero structure, thereby 3925 reusing matrix information to achieve increased efficiency. 3926 3927 Options Database Keys: 3928 + -mat_no_inode - Do not use inodes 3929 - -mat_inode_limit <limit> - Sets inode limit (max limit=5) 3930 3931 Level: intermediate 3932 3933 .seealso: `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatGetInfo()`, 3934 `MatSeqAIJSetTotalPreallocation()` 3935 @*/ 3936 PetscErrorCode MatSeqAIJSetPreallocation(Mat B, PetscInt nz, const PetscInt nnz[]) 3937 { 3938 PetscFunctionBegin; 3939 PetscValidHeaderSpecific(B, MAT_CLASSID, 1); 3940 PetscValidType(B, 1); 3941 PetscTryMethod(B, "MatSeqAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[]), (B, nz, nnz)); 3942 PetscFunctionReturn(0); 3943 } 3944 3945 PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B, PetscInt nz, const PetscInt *nnz) 3946 { 3947 Mat_SeqAIJ *b; 3948 PetscBool skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE; 3949 PetscInt i; 3950 3951 PetscFunctionBegin; 3952 if (nz >= 0 || nnz) realalloc = PETSC_TRUE; 3953 if (nz == MAT_SKIP_ALLOCATION) { 3954 skipallocation = PETSC_TRUE; 3955 nz = 0; 3956 } 3957 PetscCall(PetscLayoutSetUp(B->rmap)); 3958 PetscCall(PetscLayoutSetUp(B->cmap)); 3959 3960 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5; 3961 PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz); 3962 if (PetscUnlikelyDebug(nnz)) { 3963 for (i = 0; i < B->rmap->n; i++) { 3964 PetscCheck(nnz[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be less than 0: local row %" PetscInt_FMT " value %" PetscInt_FMT, i, nnz[i]); 3965 PetscCheck(nnz[i] <= B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be greater than row length: local row %" PetscInt_FMT " value %" PetscInt_FMT " rowlength %" PetscInt_FMT, i, nnz[i], B->cmap->n); 3966 } 3967 } 3968 3969 B->preallocated = PETSC_TRUE; 3970 3971 b = (Mat_SeqAIJ *)B->data; 3972 3973 if (!skipallocation) { 3974 if (!b->imax) { PetscCall(PetscMalloc1(B->rmap->n, &b->imax)); } 3975 if (!b->ilen) { 3976 /* b->ilen will count nonzeros in each row so far. */ 3977 PetscCall(PetscCalloc1(B->rmap->n, &b->ilen)); 3978 } else { 3979 PetscCall(PetscMemzero(b->ilen, B->rmap->n * sizeof(PetscInt))); 3980 } 3981 if (!b->ipre) { PetscCall(PetscMalloc1(B->rmap->n, &b->ipre)); } 3982 if (!nnz) { 3983 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10; 3984 else if (nz < 0) nz = 1; 3985 nz = PetscMin(nz, B->cmap->n); 3986 for (i = 0; i < B->rmap->n; i++) b->imax[i] = nz; 3987 nz = nz * B->rmap->n; 3988 } else { 3989 PetscInt64 nz64 = 0; 3990 for (i = 0; i < B->rmap->n; i++) { 3991 b->imax[i] = nnz[i]; 3992 nz64 += nnz[i]; 3993 } 3994 PetscCall(PetscIntCast(nz64, &nz)); 3995 } 3996 3997 /* allocate the matrix space */ 3998 /* FIXME: should B's old memory be unlogged? */ 3999 PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i)); 4000 if (B->structure_only) { 4001 PetscCall(PetscMalloc1(nz, &b->j)); 4002 PetscCall(PetscMalloc1(B->rmap->n + 1, &b->i)); 4003 } else { 4004 PetscCall(PetscMalloc3(nz, &b->a, nz, &b->j, B->rmap->n + 1, &b->i)); 4005 } 4006 b->i[0] = 0; 4007 for (i = 1; i < B->rmap->n + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1]; 4008 if (B->structure_only) { 4009 b->singlemalloc = PETSC_FALSE; 4010 b->free_a = PETSC_FALSE; 4011 } else { 4012 b->singlemalloc = PETSC_TRUE; 4013 b->free_a = PETSC_TRUE; 4014 } 4015 b->free_ij = PETSC_TRUE; 4016 } else { 4017 b->free_a = PETSC_FALSE; 4018 b->free_ij = PETSC_FALSE; 4019 } 4020 4021 if (b->ipre && nnz != b->ipre && b->imax) { 4022 /* reserve user-requested sparsity */ 4023 PetscCall(PetscArraycpy(b->ipre, b->imax, B->rmap->n)); 4024 } 4025 4026 b->nz = 0; 4027 b->maxnz = nz; 4028 B->info.nz_unneeded = (double)b->maxnz; 4029 if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE)); 4030 B->was_assembled = PETSC_FALSE; 4031 B->assembled = PETSC_FALSE; 4032 /* We simply deem preallocation has changed nonzero state. Updating the state 4033 will give clients (like AIJKokkos) a chance to know something has happened. 4034 */ 4035 B->nonzerostate++; 4036 PetscFunctionReturn(0); 4037 } 4038 4039 PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A) 4040 { 4041 Mat_SeqAIJ *a; 4042 PetscInt i; 4043 4044 PetscFunctionBegin; 4045 PetscValidHeaderSpecific(A, MAT_CLASSID, 1); 4046 4047 /* Check local size. If zero, then return */ 4048 if (!A->rmap->n) PetscFunctionReturn(0); 4049 4050 a = (Mat_SeqAIJ *)A->data; 4051 /* if no saved info, we error out */ 4052 PetscCheck(a->ipre, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "No saved preallocation info "); 4053 4054 PetscCheck(a->i && a->j && a->a && a->imax && a->ilen, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "Memory info is incomplete, and can not reset preallocation "); 4055 4056 PetscCall(PetscArraycpy(a->imax, a->ipre, A->rmap->n)); 4057 PetscCall(PetscArrayzero(a->ilen, A->rmap->n)); 4058 a->i[0] = 0; 4059 for (i = 1; i < A->rmap->n + 1; i++) a->i[i] = a->i[i - 1] + a->imax[i - 1]; 4060 A->preallocated = PETSC_TRUE; 4061 a->nz = 0; 4062 a->maxnz = a->i[A->rmap->n]; 4063 A->info.nz_unneeded = (double)a->maxnz; 4064 A->was_assembled = PETSC_FALSE; 4065 A->assembled = PETSC_FALSE; 4066 PetscFunctionReturn(0); 4067 } 4068 4069 /*@ 4070 MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in `MATSEQAIJ` format. 4071 4072 Input Parameters: 4073 + B - the matrix 4074 . i - the indices into j for the start of each row (starts with zero) 4075 . j - the column indices for each row (starts with zero) these must be sorted for each row 4076 - v - optional values in the matrix 4077 4078 Level: developer 4079 4080 Notes: 4081 The i,j,v values are COPIED with this routine; to avoid the copy use `MatCreateSeqAIJWithArrays()` 4082 4083 This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero 4084 structure will be the union of all the previous nonzero structures. 4085 4086 Developer Notes: 4087 An optimization could be added to the implementation where it checks if the i, and j are identical to the current i and j and 4088 then just copies the v values directly with `PetscMemcpy()`. 4089 4090 This routine could also take a `PetscCopyMode` argument to allow sharing the values instead of always copying them. 4091 4092 .seealso: `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MatResetPreallocation()` 4093 @*/ 4094 PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[]) 4095 { 4096 PetscFunctionBegin; 4097 PetscValidHeaderSpecific(B, MAT_CLASSID, 1); 4098 PetscValidType(B, 1); 4099 PetscTryMethod(B, "MatSeqAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v)); 4100 PetscFunctionReturn(0); 4101 } 4102 4103 PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[]) 4104 { 4105 PetscInt i; 4106 PetscInt m, n; 4107 PetscInt nz; 4108 PetscInt *nnz; 4109 4110 PetscFunctionBegin; 4111 PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]); 4112 4113 PetscCall(PetscLayoutSetUp(B->rmap)); 4114 PetscCall(PetscLayoutSetUp(B->cmap)); 4115 4116 PetscCall(MatGetSize(B, &m, &n)); 4117 PetscCall(PetscMalloc1(m + 1, &nnz)); 4118 for (i = 0; i < m; i++) { 4119 nz = Ii[i + 1] - Ii[i]; 4120 PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz); 4121 nnz[i] = nz; 4122 } 4123 PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz)); 4124 PetscCall(PetscFree(nnz)); 4125 4126 for (i = 0; i < m; i++) PetscCall(MatSetValues_SeqAIJ(B, 1, &i, Ii[i + 1] - Ii[i], J + Ii[i], v ? v + Ii[i] : NULL, INSERT_VALUES)); 4127 4128 PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY)); 4129 PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY)); 4130 4131 PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE)); 4132 PetscFunctionReturn(0); 4133 } 4134 4135 /*@ 4136 MatSeqAIJKron - Computes C, the Kronecker product of A and B. 4137 4138 Input Parameters: 4139 + A - left-hand side matrix 4140 . B - right-hand side matrix 4141 - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX` 4142 4143 Output Parameter: 4144 . C - Kronecker product of A and B 4145 4146 Level: intermediate 4147 4148 Note: 4149 `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the product matrix has not changed from that last call to `MatSeqAIJKron()`. 4150 4151 .seealso: `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATKAIJ`, `MatReuse` 4152 @*/ 4153 PetscErrorCode MatSeqAIJKron(Mat A, Mat B, MatReuse reuse, Mat *C) 4154 { 4155 PetscFunctionBegin; 4156 PetscValidHeaderSpecific(A, MAT_CLASSID, 1); 4157 PetscValidType(A, 1); 4158 PetscValidHeaderSpecific(B, MAT_CLASSID, 2); 4159 PetscValidType(B, 2); 4160 PetscValidPointer(C, 4); 4161 if (reuse == MAT_REUSE_MATRIX) { 4162 PetscValidHeaderSpecific(*C, MAT_CLASSID, 4); 4163 PetscValidType(*C, 4); 4164 } 4165 PetscTryMethod(A, "MatSeqAIJKron_C", (Mat, Mat, MatReuse, Mat *), (A, B, reuse, C)); 4166 PetscFunctionReturn(0); 4167 } 4168 4169 PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A, Mat B, MatReuse reuse, Mat *C) 4170 { 4171 Mat newmat; 4172 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 4173 Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data; 4174 PetscScalar *v; 4175 const PetscScalar *aa, *ba; 4176 PetscInt *i, *j, m, n, p, q, nnz = 0, am = A->rmap->n, bm = B->rmap->n, an = A->cmap->n, bn = B->cmap->n; 4177 PetscBool flg; 4178 4179 PetscFunctionBegin; 4180 PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 4181 PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix"); 4182 PetscCheck(!B->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 4183 PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix"); 4184 PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJ, &flg)); 4185 PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatType %s", ((PetscObject)B)->type_name); 4186 PetscCheck(reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatReuse %d", (int)reuse); 4187 if (reuse == MAT_INITIAL_MATRIX) { 4188 PetscCall(PetscMalloc2(am * bm + 1, &i, a->i[am] * b->i[bm], &j)); 4189 PetscCall(MatCreate(PETSC_COMM_SELF, &newmat)); 4190 PetscCall(MatSetSizes(newmat, am * bm, an * bn, am * bm, an * bn)); 4191 PetscCall(MatSetType(newmat, MATAIJ)); 4192 i[0] = 0; 4193 for (m = 0; m < am; ++m) { 4194 for (p = 0; p < bm; ++p) { 4195 i[m * bm + p + 1] = i[m * bm + p] + (a->i[m + 1] - a->i[m]) * (b->i[p + 1] - b->i[p]); 4196 for (n = a->i[m]; n < a->i[m + 1]; ++n) { 4197 for (q = b->i[p]; q < b->i[p + 1]; ++q) j[nnz++] = a->j[n] * bn + b->j[q]; 4198 } 4199 } 4200 } 4201 PetscCall(MatSeqAIJSetPreallocationCSR(newmat, i, j, NULL)); 4202 *C = newmat; 4203 PetscCall(PetscFree2(i, j)); 4204 nnz = 0; 4205 } 4206 PetscCall(MatSeqAIJGetArray(*C, &v)); 4207 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 4208 PetscCall(MatSeqAIJGetArrayRead(B, &ba)); 4209 for (m = 0; m < am; ++m) { 4210 for (p = 0; p < bm; ++p) { 4211 for (n = a->i[m]; n < a->i[m + 1]; ++n) { 4212 for (q = b->i[p]; q < b->i[p + 1]; ++q) v[nnz++] = aa[n] * ba[q]; 4213 } 4214 } 4215 } 4216 PetscCall(MatSeqAIJRestoreArray(*C, &v)); 4217 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 4218 PetscCall(MatSeqAIJRestoreArrayRead(B, &ba)); 4219 PetscFunctionReturn(0); 4220 } 4221 4222 #include <../src/mat/impls/dense/seq/dense.h> 4223 #include <petsc/private/kernels/petscaxpy.h> 4224 4225 /* 4226 Computes (B'*A')' since computing B*A directly is untenable 4227 4228 n p p 4229 [ ] [ ] [ ] 4230 m [ A ] * n [ B ] = m [ C ] 4231 [ ] [ ] [ ] 4232 4233 */ 4234 PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A, Mat B, Mat C) 4235 { 4236 Mat_SeqDense *sub_a = (Mat_SeqDense *)A->data; 4237 Mat_SeqAIJ *sub_b = (Mat_SeqAIJ *)B->data; 4238 Mat_SeqDense *sub_c = (Mat_SeqDense *)C->data; 4239 PetscInt i, j, n, m, q, p; 4240 const PetscInt *ii, *idx; 4241 const PetscScalar *b, *a, *a_q; 4242 PetscScalar *c, *c_q; 4243 PetscInt clda = sub_c->lda; 4244 PetscInt alda = sub_a->lda; 4245 4246 PetscFunctionBegin; 4247 m = A->rmap->n; 4248 n = A->cmap->n; 4249 p = B->cmap->n; 4250 a = sub_a->v; 4251 b = sub_b->a; 4252 c = sub_c->v; 4253 if (clda == m) { 4254 PetscCall(PetscArrayzero(c, m * p)); 4255 } else { 4256 for (j = 0; j < p; j++) 4257 for (i = 0; i < m; i++) c[j * clda + i] = 0.0; 4258 } 4259 ii = sub_b->i; 4260 idx = sub_b->j; 4261 for (i = 0; i < n; i++) { 4262 q = ii[i + 1] - ii[i]; 4263 while (q-- > 0) { 4264 c_q = c + clda * (*idx); 4265 a_q = a + alda * i; 4266 PetscKernelAXPY(c_q, *b, a_q, m); 4267 idx++; 4268 b++; 4269 } 4270 } 4271 PetscFunctionReturn(0); 4272 } 4273 4274 PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A, Mat B, PetscReal fill, Mat C) 4275 { 4276 PetscInt m = A->rmap->n, n = B->cmap->n; 4277 PetscBool cisdense; 4278 4279 PetscFunctionBegin; 4280 PetscCheck(A->cmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "A->cmap->n %" PetscInt_FMT " != B->rmap->n %" PetscInt_FMT, A->cmap->n, B->rmap->n); 4281 PetscCall(MatSetSizes(C, m, n, m, n)); 4282 PetscCall(MatSetBlockSizesFromMats(C, A, B)); 4283 PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATSEQDENSE, MATSEQDENSECUDA, "")); 4284 if (!cisdense) PetscCall(MatSetType(C, MATDENSE)); 4285 PetscCall(MatSetUp(C)); 4286 4287 C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ; 4288 PetscFunctionReturn(0); 4289 } 4290 4291 /* ----------------------------------------------------------------*/ 4292 /*MC 4293 MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 4294 based on compressed sparse row format. 4295 4296 Options Database Keys: 4297 . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions() 4298 4299 Level: beginner 4300 4301 Notes: 4302 `MatSetValues()` may be called for this matrix type with a NULL argument for the numerical values, 4303 in this case the values associated with the rows and columns one passes in are set to zero 4304 in the matrix 4305 4306 `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no 4307 space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored 4308 4309 Developer Note: 4310 It would be nice if all matrix formats supported passing NULL in for the numerical values 4311 4312 .seealso: `MatCreateSeqAIJ()`, `MatSetFromOptions()`, `MatSetType()`, `MatCreate()`, `MatType`, `MATSELL`, `MATSEQSELL`, `MATMPISELL` 4313 M*/ 4314 4315 /*MC 4316 MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices. 4317 4318 This matrix type is identical to `MATSEQAIJ` when constructed with a single process communicator, 4319 and `MATMPIAIJ` otherwise. As a result, for single process communicators, 4320 `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported 4321 for communicators controlling multiple processes. It is recommended that you call both of 4322 the above preallocation routines for simplicity. 4323 4324 Options Database Keys: 4325 . -mat_type aij - sets the matrix type to "aij" during a call to `MatSetFromOptions()` 4326 4327 Note: 4328 Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, and also automatically switches over to use inodes when 4329 enough exist. 4330 4331 Level: beginner 4332 4333 .seealso: `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSELL`, `MATSEQSELL`, `MATMPISELL` 4334 M*/ 4335 4336 /*MC 4337 MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices. 4338 4339 This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator, 4340 and `MATMPIAIJCRL` otherwise. As a result, for single process communicators, 4341 `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported 4342 for communicators controlling multiple processes. It is recommended that you call both of 4343 the above preallocation routines for simplicity. 4344 4345 Options Database Keys: 4346 . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to `MatSetFromOptions()` 4347 4348 Level: beginner 4349 4350 .seealso: `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL` 4351 M*/ 4352 4353 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *); 4354 #if defined(PETSC_HAVE_ELEMENTAL) 4355 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *); 4356 #endif 4357 #if defined(PETSC_HAVE_SCALAPACK) 4358 PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *); 4359 #endif 4360 #if defined(PETSC_HAVE_HYPRE) 4361 PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A, MatType, MatReuse, Mat *); 4362 #endif 4363 4364 PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat, MatType, MatReuse, Mat *); 4365 PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *); 4366 PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat); 4367 4368 /*@C 4369 MatSeqAIJGetArray - gives read/write access to the array where the data for a `MATSEQAIJ` matrix is stored 4370 4371 Not Collective 4372 4373 Input Parameter: 4374 . mat - a `MATSEQAIJ` matrix 4375 4376 Output Parameter: 4377 . array - pointer to the data 4378 4379 Level: intermediate 4380 4381 .seealso: `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()` 4382 @*/ 4383 PetscErrorCode MatSeqAIJGetArray(Mat A, PetscScalar **array) 4384 { 4385 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 4386 4387 PetscFunctionBegin; 4388 if (aij->ops->getarray) { 4389 PetscCall((*aij->ops->getarray)(A, array)); 4390 } else { 4391 *array = aij->a; 4392 } 4393 PetscFunctionReturn(0); 4394 } 4395 4396 /*@C 4397 MatSeqAIJRestoreArray - returns access to the array where the data for a `MATSEQAIJ` matrix is stored obtained by `MatSeqAIJGetArray()` 4398 4399 Not Collective 4400 4401 Input Parameters: 4402 + mat - a `MATSEQAIJ` matrix 4403 - array - pointer to the data 4404 4405 Level: intermediate 4406 4407 .seealso: `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayF90()` 4408 @*/ 4409 PetscErrorCode MatSeqAIJRestoreArray(Mat A, PetscScalar **array) 4410 { 4411 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 4412 4413 PetscFunctionBegin; 4414 if (aij->ops->restorearray) { 4415 PetscCall((*aij->ops->restorearray)(A, array)); 4416 } else { 4417 *array = NULL; 4418 } 4419 PetscCall(MatSeqAIJInvalidateDiagonal(A)); 4420 PetscCall(PetscObjectStateIncrease((PetscObject)A)); 4421 PetscFunctionReturn(0); 4422 } 4423 4424 /*@C 4425 MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a `MATSEQAIJ` matrix is stored 4426 4427 Not Collective 4428 4429 Input Parameter: 4430 . mat - a `MATSEQAIJ` matrix 4431 4432 Output Parameter: 4433 . array - pointer to the data 4434 4435 Level: intermediate 4436 4437 .seealso: `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()` 4438 @*/ 4439 PetscErrorCode MatSeqAIJGetArrayRead(Mat A, const PetscScalar **array) 4440 { 4441 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 4442 4443 PetscFunctionBegin; 4444 if (aij->ops->getarrayread) { 4445 PetscCall((*aij->ops->getarrayread)(A, array)); 4446 } else { 4447 *array = aij->a; 4448 } 4449 PetscFunctionReturn(0); 4450 } 4451 4452 /*@C 4453 MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJGetArrayRead()` 4454 4455 Not Collective 4456 4457 Input Parameter: 4458 . mat - a `MATSEQAIJ` matrix 4459 4460 Output Parameter: 4461 . array - pointer to the data 4462 4463 Level: intermediate 4464 4465 .seealso: `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()` 4466 @*/ 4467 PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A, const PetscScalar **array) 4468 { 4469 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 4470 4471 PetscFunctionBegin; 4472 if (aij->ops->restorearrayread) { 4473 PetscCall((*aij->ops->restorearrayread)(A, array)); 4474 } else { 4475 *array = NULL; 4476 } 4477 PetscFunctionReturn(0); 4478 } 4479 4480 /*@C 4481 MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a `MATSEQAIJ` matrix is stored 4482 4483 Not Collective 4484 4485 Input Parameter: 4486 . mat - a `MATSEQAIJ` matrix 4487 4488 Output Parameter: 4489 . array - pointer to the data 4490 4491 Level: intermediate 4492 4493 .seealso: `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()` 4494 @*/ 4495 PetscErrorCode MatSeqAIJGetArrayWrite(Mat A, PetscScalar **array) 4496 { 4497 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 4498 4499 PetscFunctionBegin; 4500 if (aij->ops->getarraywrite) { 4501 PetscCall((*aij->ops->getarraywrite)(A, array)); 4502 } else { 4503 *array = aij->a; 4504 } 4505 PetscCall(MatSeqAIJInvalidateDiagonal(A)); 4506 PetscCall(PetscObjectStateIncrease((PetscObject)A)); 4507 PetscFunctionReturn(0); 4508 } 4509 4510 /*@C 4511 MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead 4512 4513 Not Collective 4514 4515 Input Parameter: 4516 . mat - a MATSEQAIJ matrix 4517 4518 Output Parameter: 4519 . array - pointer to the data 4520 4521 Level: intermediate 4522 4523 .seealso: `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()` 4524 @*/ 4525 PetscErrorCode MatSeqAIJRestoreArrayWrite(Mat A, PetscScalar **array) 4526 { 4527 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 4528 4529 PetscFunctionBegin; 4530 if (aij->ops->restorearraywrite) { 4531 PetscCall((*aij->ops->restorearraywrite)(A, array)); 4532 } else { 4533 *array = NULL; 4534 } 4535 PetscFunctionReturn(0); 4536 } 4537 4538 /*@C 4539 MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the `MATSEQAIJ` matrix 4540 4541 Not Collective 4542 4543 Input Parameter: 4544 . mat - a matrix of type `MATSEQAIJ` or its subclasses 4545 4546 Output Parameters: 4547 + i - row map array of the matrix 4548 . j - column index array of the matrix 4549 . a - data array of the matrix 4550 - memtype - memory type of the arrays 4551 4552 Notes: 4553 Any of the output parameters can be NULL, in which case the corresponding value is not returned. 4554 If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host. 4555 4556 One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix. 4557 If the matrix is assembled, the data array 'a' is guaranteed to have the latest values of the matrix. 4558 4559 Level: Developer 4560 4561 .seealso: `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()` 4562 @*/ 4563 PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat, const PetscInt **i, const PetscInt **j, PetscScalar **a, PetscMemType *mtype) 4564 { 4565 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 4566 4567 PetscFunctionBegin; 4568 PetscCheck(mat->preallocated, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "matrix is not preallocated"); 4569 if (aij->ops->getcsrandmemtype) { 4570 PetscCall((*aij->ops->getcsrandmemtype)(mat, i, j, a, mtype)); 4571 } else { 4572 if (i) *i = aij->i; 4573 if (j) *j = aij->j; 4574 if (a) *a = aij->a; 4575 if (mtype) *mtype = PETSC_MEMTYPE_HOST; 4576 } 4577 PetscFunctionReturn(0); 4578 } 4579 4580 /*@C 4581 MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row 4582 4583 Not Collective 4584 4585 Input Parameter: 4586 . mat - a `MATSEQAIJ` matrix 4587 4588 Output Parameter: 4589 . nz - the maximum number of nonzeros in any row 4590 4591 Level: intermediate 4592 4593 .seealso: `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()` 4594 @*/ 4595 PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A, PetscInt *nz) 4596 { 4597 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 4598 4599 PetscFunctionBegin; 4600 *nz = aij->rmax; 4601 PetscFunctionReturn(0); 4602 } 4603 4604 PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[]) 4605 { 4606 MPI_Comm comm; 4607 PetscInt *i, *j; 4608 PetscInt M, N, row; 4609 PetscCount k, p, q, nneg, nnz, start, end; /* Index the coo array, so use PetscCount as their type */ 4610 PetscInt *Ai; /* Change to PetscCount once we use it for row pointers */ 4611 PetscInt *Aj; 4612 PetscScalar *Aa; 4613 Mat_SeqAIJ *seqaij = (Mat_SeqAIJ *)(mat->data); 4614 MatType rtype; 4615 PetscCount *perm, *jmap; 4616 4617 PetscFunctionBegin; 4618 PetscCall(MatResetPreallocationCOO_SeqAIJ(mat)); 4619 PetscCall(PetscObjectGetComm((PetscObject)mat, &comm)); 4620 PetscCall(MatGetSize(mat, &M, &N)); 4621 i = coo_i; 4622 j = coo_j; 4623 PetscCall(PetscMalloc1(coo_n, &perm)); 4624 for (k = 0; k < coo_n; k++) { /* Ignore entries with negative row or col indices */ 4625 if (j[k] < 0) i[k] = -1; 4626 perm[k] = k; 4627 } 4628 4629 /* Sort by row */ 4630 PetscCall(PetscSortIntWithIntCountArrayPair(coo_n, i, j, perm)); 4631 for (k = 0; k < coo_n; k++) { 4632 if (i[k] >= 0) break; 4633 } /* Advance k to the first row with a non-negative index */ 4634 nneg = k; 4635 PetscCall(PetscMalloc1(coo_n - nneg + 1, &jmap)); /* +1 to make a CSR-like data structure. jmap[i] originally is the number of repeats for i-th nonzero */ 4636 nnz = 0; /* Total number of unique nonzeros to be counted */ 4637 jmap++; /* Inc jmap by 1 for convenience */ 4638 4639 PetscCall(PetscCalloc1(M + 1, &Ai)); /* CSR of A */ 4640 PetscCall(PetscMalloc1(coo_n - nneg, &Aj)); /* We have at most coo_n-nneg unique nonzeros */ 4641 4642 /* In each row, sort by column, then unique column indices to get row length */ 4643 Ai++; /* Inc by 1 for convenience */ 4644 q = 0; /* q-th unique nonzero, with q starting from 0 */ 4645 while (k < coo_n) { 4646 row = i[k]; 4647 start = k; /* [start,end) indices for this row */ 4648 while (k < coo_n && i[k] == row) k++; 4649 end = k; 4650 PetscCall(PetscSortIntWithCountArray(end - start, j + start, perm + start)); 4651 /* Find number of unique col entries in this row */ 4652 Aj[q] = j[start]; /* Log the first nonzero in this row */ 4653 jmap[q] = 1; /* Number of repeats of this nozero entry */ 4654 Ai[row] = 1; 4655 nnz++; 4656 4657 for (p = start + 1; p < end; p++) { /* Scan remaining nonzero in this row */ 4658 if (j[p] != j[p - 1]) { /* Meet a new nonzero */ 4659 q++; 4660 jmap[q] = 1; 4661 Aj[q] = j[p]; 4662 Ai[row]++; 4663 nnz++; 4664 } else { 4665 jmap[q]++; 4666 } 4667 } 4668 q++; /* Move to next row and thus next unique nonzero */ 4669 } 4670 4671 Ai--; /* Back to the beginning of Ai[] */ 4672 for (k = 0; k < M; k++) Ai[k + 1] += Ai[k]; 4673 jmap--; /* Back to the beginning of jmap[] */ 4674 jmap[0] = 0; 4675 for (k = 0; k < nnz; k++) jmap[k + 1] += jmap[k]; 4676 if (nnz < coo_n - nneg) { /* Realloc with actual number of unique nonzeros */ 4677 PetscCount *jmap_new; 4678 PetscInt *Aj_new; 4679 4680 PetscCall(PetscMalloc1(nnz + 1, &jmap_new)); 4681 PetscCall(PetscArraycpy(jmap_new, jmap, nnz + 1)); 4682 PetscCall(PetscFree(jmap)); 4683 jmap = jmap_new; 4684 4685 PetscCall(PetscMalloc1(nnz, &Aj_new)); 4686 PetscCall(PetscArraycpy(Aj_new, Aj, nnz)); 4687 PetscCall(PetscFree(Aj)); 4688 Aj = Aj_new; 4689 } 4690 4691 if (nneg) { /* Discard heading entries with negative indices in perm[], as we'll access it from index 0 in MatSetValuesCOO */ 4692 PetscCount *perm_new; 4693 4694 PetscCall(PetscMalloc1(coo_n - nneg, &perm_new)); 4695 PetscCall(PetscArraycpy(perm_new, perm + nneg, coo_n - nneg)); 4696 PetscCall(PetscFree(perm)); 4697 perm = perm_new; 4698 } 4699 4700 PetscCall(MatGetRootType_Private(mat, &rtype)); 4701 PetscCall(PetscCalloc1(nnz, &Aa)); /* Zero the matrix */ 4702 PetscCall(MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF, M, N, Ai, Aj, Aa, rtype, mat)); 4703 4704 seqaij->singlemalloc = PETSC_FALSE; /* Ai, Aj and Aa are not allocated in one big malloc */ 4705 seqaij->free_a = seqaij->free_ij = PETSC_TRUE; /* Let newmat own Ai, Aj and Aa */ 4706 /* Record COO fields */ 4707 seqaij->coo_n = coo_n; 4708 seqaij->Atot = coo_n - nneg; /* Annz is seqaij->nz, so no need to record that again */ 4709 seqaij->jmap = jmap; /* of length nnz+1 */ 4710 seqaij->perm = perm; 4711 PetscFunctionReturn(0); 4712 } 4713 4714 static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A, const PetscScalar v[], InsertMode imode) 4715 { 4716 Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)A->data; 4717 PetscCount i, j, Annz = aseq->nz; 4718 PetscCount *perm = aseq->perm, *jmap = aseq->jmap; 4719 PetscScalar *Aa; 4720 4721 PetscFunctionBegin; 4722 PetscCall(MatSeqAIJGetArray(A, &Aa)); 4723 for (i = 0; i < Annz; i++) { 4724 PetscScalar sum = 0.0; 4725 for (j = jmap[i]; j < jmap[i + 1]; j++) sum += v[perm[j]]; 4726 Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum; 4727 } 4728 PetscCall(MatSeqAIJRestoreArray(A, &Aa)); 4729 PetscFunctionReturn(0); 4730 } 4731 4732 #if defined(PETSC_HAVE_CUDA) 4733 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *); 4734 #endif 4735 #if defined(PETSC_HAVE_KOKKOS_KERNELS) 4736 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat, MatType, MatReuse, Mat *); 4737 #endif 4738 4739 PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B) 4740 { 4741 Mat_SeqAIJ *b; 4742 PetscMPIInt size; 4743 4744 PetscFunctionBegin; 4745 PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size)); 4746 PetscCheck(size <= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Comm must be of size 1"); 4747 4748 PetscCall(PetscNew(&b)); 4749 4750 B->data = (void *)b; 4751 4752 PetscCall(PetscMemcpy(B->ops, &MatOps_Values, sizeof(struct _MatOps))); 4753 if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull; 4754 4755 b->row = NULL; 4756 b->col = NULL; 4757 b->icol = NULL; 4758 b->reallocs = 0; 4759 b->ignorezeroentries = PETSC_FALSE; 4760 b->roworiented = PETSC_TRUE; 4761 b->nonew = 0; 4762 b->diag = NULL; 4763 b->solve_work = NULL; 4764 B->spptr = NULL; 4765 b->saved_values = NULL; 4766 b->idiag = NULL; 4767 b->mdiag = NULL; 4768 b->ssor_work = NULL; 4769 b->omega = 1.0; 4770 b->fshift = 0.0; 4771 b->idiagvalid = PETSC_FALSE; 4772 b->ibdiagvalid = PETSC_FALSE; 4773 b->keepnonzeropattern = PETSC_FALSE; 4774 4775 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ)); 4776 #if defined(PETSC_HAVE_MATLAB) 4777 PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEnginePut_C", MatlabEnginePut_SeqAIJ)); 4778 PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEngineGet_C", MatlabEngineGet_SeqAIJ)); 4779 #endif 4780 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetColumnIndices_C", MatSeqAIJSetColumnIndices_SeqAIJ)); 4781 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqAIJ)); 4782 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqAIJ)); 4783 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsbaij_C", MatConvert_SeqAIJ_SeqSBAIJ)); 4784 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqbaij_C", MatConvert_SeqAIJ_SeqBAIJ)); 4785 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijperm_C", MatConvert_SeqAIJ_SeqAIJPERM)); 4786 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijsell_C", MatConvert_SeqAIJ_SeqAIJSELL)); 4787 #if defined(PETSC_HAVE_MKL_SPARSE) 4788 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijmkl_C", MatConvert_SeqAIJ_SeqAIJMKL)); 4789 #endif 4790 #if defined(PETSC_HAVE_CUDA) 4791 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcusparse_C", MatConvert_SeqAIJ_SeqAIJCUSPARSE)); 4792 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", MatProductSetFromOptions_SeqAIJ)); 4793 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJ)); 4794 #endif 4795 #if defined(PETSC_HAVE_KOKKOS_KERNELS) 4796 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijkokkos_C", MatConvert_SeqAIJ_SeqAIJKokkos)); 4797 #endif 4798 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcrl_C", MatConvert_SeqAIJ_SeqAIJCRL)); 4799 #if defined(PETSC_HAVE_ELEMENTAL) 4800 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_elemental_C", MatConvert_SeqAIJ_Elemental)); 4801 #endif 4802 #if defined(PETSC_HAVE_SCALAPACK) 4803 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_scalapack_C", MatConvert_AIJ_ScaLAPACK)); 4804 #endif 4805 #if defined(PETSC_HAVE_HYPRE) 4806 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_hypre_C", MatConvert_AIJ_HYPRE)); 4807 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ)); 4808 #endif 4809 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqdense_C", MatConvert_SeqAIJ_SeqDense)); 4810 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsell_C", MatConvert_SeqAIJ_SeqSELL)); 4811 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_is_C", MatConvert_XAIJ_IS)); 4812 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqAIJ)); 4813 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsHermitianTranspose_C", MatIsTranspose_SeqAIJ)); 4814 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocation_C", MatSeqAIJSetPreallocation_SeqAIJ)); 4815 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_SeqAIJ)); 4816 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocationCSR_C", MatSeqAIJSetPreallocationCSR_SeqAIJ)); 4817 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatReorderForNonzeroDiagonal_C", MatReorderForNonzeroDiagonal_SeqAIJ)); 4818 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_seqaij_C", MatProductSetFromOptions_IS_XAIJ)); 4819 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqdense_seqaij_C", MatProductSetFromOptions_SeqDense_SeqAIJ)); 4820 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaij_C", MatProductSetFromOptions_SeqAIJ)); 4821 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJKron_C", MatSeqAIJKron_SeqAIJ)); 4822 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJ)); 4823 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJ)); 4824 PetscCall(MatCreate_SeqAIJ_Inode(B)); 4825 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ)); 4826 PetscCall(MatSeqAIJSetTypeFromOptions(B)); /* this allows changing the matrix subtype to say MATSEQAIJPERM */ 4827 PetscFunctionReturn(0); 4828 } 4829 4830 /* 4831 Given a matrix generated with MatGetFactor() duplicates all the information in A into C 4832 */ 4833 PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace) 4834 { 4835 Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data, *a = (Mat_SeqAIJ *)A->data; 4836 PetscInt m = A->rmap->n, i; 4837 4838 PetscFunctionBegin; 4839 PetscCheck(A->assembled || cpvalues == MAT_DO_NOT_COPY_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix"); 4840 4841 C->factortype = A->factortype; 4842 c->row = NULL; 4843 c->col = NULL; 4844 c->icol = NULL; 4845 c->reallocs = 0; 4846 4847 C->assembled = A->assembled; 4848 C->preallocated = A->preallocated; 4849 4850 if (A->preallocated) { 4851 PetscCall(PetscLayoutReference(A->rmap, &C->rmap)); 4852 PetscCall(PetscLayoutReference(A->cmap, &C->cmap)); 4853 4854 PetscCall(PetscMalloc1(m, &c->imax)); 4855 PetscCall(PetscMemcpy(c->imax, a->imax, m * sizeof(PetscInt))); 4856 PetscCall(PetscMalloc1(m, &c->ilen)); 4857 PetscCall(PetscMemcpy(c->ilen, a->ilen, m * sizeof(PetscInt))); 4858 4859 /* allocate the matrix space */ 4860 if (mallocmatspace) { 4861 PetscCall(PetscMalloc3(a->i[m], &c->a, a->i[m], &c->j, m + 1, &c->i)); 4862 4863 c->singlemalloc = PETSC_TRUE; 4864 4865 PetscCall(PetscArraycpy(c->i, a->i, m + 1)); 4866 if (m > 0) { 4867 PetscCall(PetscArraycpy(c->j, a->j, a->i[m])); 4868 if (cpvalues == MAT_COPY_VALUES) { 4869 const PetscScalar *aa; 4870 4871 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 4872 PetscCall(PetscArraycpy(c->a, aa, a->i[m])); 4873 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 4874 } else { 4875 PetscCall(PetscArrayzero(c->a, a->i[m])); 4876 } 4877 } 4878 } 4879 4880 c->ignorezeroentries = a->ignorezeroentries; 4881 c->roworiented = a->roworiented; 4882 c->nonew = a->nonew; 4883 if (a->diag) { 4884 PetscCall(PetscMalloc1(m + 1, &c->diag)); 4885 PetscCall(PetscMemcpy(c->diag, a->diag, m * sizeof(PetscInt))); 4886 } else c->diag = NULL; 4887 4888 c->solve_work = NULL; 4889 c->saved_values = NULL; 4890 c->idiag = NULL; 4891 c->ssor_work = NULL; 4892 c->keepnonzeropattern = a->keepnonzeropattern; 4893 c->free_a = PETSC_TRUE; 4894 c->free_ij = PETSC_TRUE; 4895 4896 c->rmax = a->rmax; 4897 c->nz = a->nz; 4898 c->maxnz = a->nz; /* Since we allocate exactly the right amount */ 4899 4900 c->compressedrow.use = a->compressedrow.use; 4901 c->compressedrow.nrows = a->compressedrow.nrows; 4902 if (a->compressedrow.use) { 4903 i = a->compressedrow.nrows; 4904 PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i, &c->compressedrow.rindex)); 4905 PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1)); 4906 PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i)); 4907 } else { 4908 c->compressedrow.use = PETSC_FALSE; 4909 c->compressedrow.i = NULL; 4910 c->compressedrow.rindex = NULL; 4911 } 4912 c->nonzerorowcnt = a->nonzerorowcnt; 4913 C->nonzerostate = A->nonzerostate; 4914 4915 PetscCall(MatDuplicate_SeqAIJ_Inode(A, cpvalues, &C)); 4916 } 4917 PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist)); 4918 PetscFunctionReturn(0); 4919 } 4920 4921 PetscErrorCode MatDuplicate_SeqAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B) 4922 { 4923 PetscFunctionBegin; 4924 PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B)); 4925 PetscCall(MatSetSizes(*B, A->rmap->n, A->cmap->n, A->rmap->n, A->cmap->n)); 4926 if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) PetscCall(MatSetBlockSizesFromMats(*B, A, A)); 4927 PetscCall(MatSetType(*B, ((PetscObject)A)->type_name)); 4928 PetscCall(MatDuplicateNoCreate_SeqAIJ(*B, A, cpvalues, PETSC_TRUE)); 4929 PetscFunctionReturn(0); 4930 } 4931 4932 PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer) 4933 { 4934 PetscBool isbinary, ishdf5; 4935 4936 PetscFunctionBegin; 4937 PetscValidHeaderSpecific(newMat, MAT_CLASSID, 1); 4938 PetscValidHeaderSpecific(viewer, PETSC_VIEWER_CLASSID, 2); 4939 /* force binary viewer to load .info file if it has not yet done so */ 4940 PetscCall(PetscViewerSetUp(viewer)); 4941 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary)); 4942 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5)); 4943 if (isbinary) { 4944 PetscCall(MatLoad_SeqAIJ_Binary(newMat, viewer)); 4945 } else if (ishdf5) { 4946 #if defined(PETSC_HAVE_HDF5) 4947 PetscCall(MatLoad_AIJ_HDF5(newMat, viewer)); 4948 #else 4949 SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5"); 4950 #endif 4951 } else { 4952 SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "Viewer type %s not yet supported for reading %s matrices", ((PetscObject)viewer)->type_name, ((PetscObject)newMat)->type_name); 4953 } 4954 PetscFunctionReturn(0); 4955 } 4956 4957 PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer) 4958 { 4959 Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->data; 4960 PetscInt header[4], *rowlens, M, N, nz, sum, rows, cols, i; 4961 4962 PetscFunctionBegin; 4963 PetscCall(PetscViewerSetUp(viewer)); 4964 4965 /* read in matrix header */ 4966 PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT)); 4967 PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file"); 4968 M = header[1]; 4969 N = header[2]; 4970 nz = header[3]; 4971 PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M); 4972 PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N); 4973 PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqAIJ"); 4974 4975 /* set block sizes from the viewer's .info file */ 4976 PetscCall(MatLoad_Binary_BlockSizes(mat, viewer)); 4977 /* set local and global sizes if not set already */ 4978 if (mat->rmap->n < 0) mat->rmap->n = M; 4979 if (mat->cmap->n < 0) mat->cmap->n = N; 4980 if (mat->rmap->N < 0) mat->rmap->N = M; 4981 if (mat->cmap->N < 0) mat->cmap->N = N; 4982 PetscCall(PetscLayoutSetUp(mat->rmap)); 4983 PetscCall(PetscLayoutSetUp(mat->cmap)); 4984 4985 /* check if the matrix sizes are correct */ 4986 PetscCall(MatGetSize(mat, &rows, &cols)); 4987 PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different sizes (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols); 4988 4989 /* read in row lengths */ 4990 PetscCall(PetscMalloc1(M, &rowlens)); 4991 PetscCall(PetscViewerBinaryRead(viewer, rowlens, M, NULL, PETSC_INT)); 4992 /* check if sum(rowlens) is same as nz */ 4993 sum = 0; 4994 for (i = 0; i < M; i++) sum += rowlens[i]; 4995 PetscCheck(sum == nz, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Inconsistent matrix data in file: nonzeros = %" PetscInt_FMT ", sum-row-lengths = %" PetscInt_FMT, nz, sum); 4996 /* preallocate and check sizes */ 4997 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(mat, 0, rowlens)); 4998 PetscCall(MatGetSize(mat, &rows, &cols)); 4999 PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols); 5000 /* store row lengths */ 5001 PetscCall(PetscArraycpy(a->ilen, rowlens, M)); 5002 PetscCall(PetscFree(rowlens)); 5003 5004 /* fill in "i" row pointers */ 5005 a->i[0] = 0; 5006 for (i = 0; i < M; i++) a->i[i + 1] = a->i[i] + a->ilen[i]; 5007 /* read in "j" column indices */ 5008 PetscCall(PetscViewerBinaryRead(viewer, a->j, nz, NULL, PETSC_INT)); 5009 /* read in "a" nonzero values */ 5010 PetscCall(PetscViewerBinaryRead(viewer, a->a, nz, NULL, PETSC_SCALAR)); 5011 5012 PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY)); 5013 PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY)); 5014 PetscFunctionReturn(0); 5015 } 5016 5017 PetscErrorCode MatEqual_SeqAIJ(Mat A, Mat B, PetscBool *flg) 5018 { 5019 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data; 5020 const PetscScalar *aa, *ba; 5021 #if defined(PETSC_USE_COMPLEX) 5022 PetscInt k; 5023 #endif 5024 5025 PetscFunctionBegin; 5026 /* If the matrix dimensions are not equal,or no of nonzeros */ 5027 if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) || (a->nz != b->nz)) { 5028 *flg = PETSC_FALSE; 5029 PetscFunctionReturn(0); 5030 } 5031 5032 /* if the a->i are the same */ 5033 PetscCall(PetscArraycmp(a->i, b->i, A->rmap->n + 1, flg)); 5034 if (!*flg) PetscFunctionReturn(0); 5035 5036 /* if a->j are the same */ 5037 PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg)); 5038 if (!*flg) PetscFunctionReturn(0); 5039 5040 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 5041 PetscCall(MatSeqAIJGetArrayRead(B, &ba)); 5042 /* if a->a are the same */ 5043 #if defined(PETSC_USE_COMPLEX) 5044 for (k = 0; k < a->nz; k++) { 5045 if (PetscRealPart(aa[k]) != PetscRealPart(ba[k]) || PetscImaginaryPart(aa[k]) != PetscImaginaryPart(ba[k])) { 5046 *flg = PETSC_FALSE; 5047 PetscFunctionReturn(0); 5048 } 5049 } 5050 #else 5051 PetscCall(PetscArraycmp(aa, ba, a->nz, flg)); 5052 #endif 5053 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 5054 PetscCall(MatSeqAIJRestoreArrayRead(B, &ba)); 5055 PetscFunctionReturn(0); 5056 } 5057 5058 /*@ 5059 MatCreateSeqAIJWithArrays - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in CSR format) 5060 provided by the user. 5061 5062 Collective 5063 5064 Input Parameters: 5065 + comm - must be an MPI communicator of size 1 5066 . m - number of rows 5067 . n - number of columns 5068 . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix 5069 . j - column indices 5070 - a - matrix values 5071 5072 Output Parameter: 5073 . mat - the matrix 5074 5075 Level: intermediate 5076 5077 Notes: 5078 The i, j, and a arrays are not copied by this routine, the user must free these arrays 5079 once the matrix is destroyed and not before 5080 5081 You cannot set new nonzero locations into this matrix, that will generate an error. 5082 5083 The i and j indices are 0 based 5084 5085 The format which is used for the sparse matrix input, is equivalent to a 5086 row-major ordering.. i.e for the following matrix, the input data expected is 5087 as shown 5088 5089 $ 1 0 0 5090 $ 2 0 3 5091 $ 4 5 6 5092 $ 5093 $ i = {0,1,3,6} [size = nrow+1 = 3+1] 5094 $ j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row 5095 $ v = {1,2,3,4,5,6} [size = 6] 5096 5097 .seealso: `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateMPIAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()` 5098 @*/ 5099 PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat) 5100 { 5101 PetscInt ii; 5102 Mat_SeqAIJ *aij; 5103 PetscInt jj; 5104 5105 PetscFunctionBegin; 5106 PetscCheck(m <= 0 || i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0"); 5107 PetscCall(MatCreate(comm, mat)); 5108 PetscCall(MatSetSizes(*mat, m, n, m, n)); 5109 /* PetscCall(MatSetBlockSizes(*mat,,)); */ 5110 PetscCall(MatSetType(*mat, MATSEQAIJ)); 5111 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, MAT_SKIP_ALLOCATION, NULL)); 5112 aij = (Mat_SeqAIJ *)(*mat)->data; 5113 PetscCall(PetscMalloc1(m, &aij->imax)); 5114 PetscCall(PetscMalloc1(m, &aij->ilen)); 5115 5116 aij->i = i; 5117 aij->j = j; 5118 aij->a = a; 5119 aij->singlemalloc = PETSC_FALSE; 5120 aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/ 5121 aij->free_a = PETSC_FALSE; 5122 aij->free_ij = PETSC_FALSE; 5123 5124 for (ii = 0, aij->nonzerorowcnt = 0, aij->rmax = 0; ii < m; ii++) { 5125 aij->ilen[ii] = aij->imax[ii] = i[ii + 1] - i[ii]; 5126 if (PetscDefined(USE_DEBUG)) { 5127 PetscCheck(i[ii + 1] - i[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row length in i (row indices) row = %" PetscInt_FMT " length = %" PetscInt_FMT, ii, i[ii + 1] - i[ii]); 5128 for (jj = i[ii] + 1; jj < i[ii + 1]; jj++) { 5129 PetscCheck(j[jj] >= j[jj - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is not sorted", jj - i[ii], j[jj], ii); 5130 PetscCheck(j[jj] != j[jj - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is identical to previous entry", jj - i[ii], j[jj], ii); 5131 } 5132 } 5133 } 5134 if (PetscDefined(USE_DEBUG)) { 5135 for (ii = 0; ii < aij->i[m]; ii++) { 5136 PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]); 5137 PetscCheck(j[ii] <= n - 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index to large at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]); 5138 } 5139 } 5140 5141 PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY)); 5142 PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY)); 5143 PetscFunctionReturn(0); 5144 } 5145 5146 /*@ 5147 MatCreateSeqAIJFromTriple - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in COO format) 5148 provided by the user. 5149 5150 Collective 5151 5152 Input Parameters: 5153 + comm - must be an MPI communicator of size 1 5154 . m - number of rows 5155 . n - number of columns 5156 . i - row indices 5157 . j - column indices 5158 . a - matrix values 5159 . nz - number of nonzeros 5160 - idx - if the i and j indices start with 1 use `PETSC_TRUE` otherwise use `PETSC_FALSE` 5161 5162 Output Parameter: 5163 . mat - the matrix 5164 5165 Level: intermediate 5166 5167 Example: 5168 For the following matrix, the input data expected is as shown (using 0 based indexing) 5169 .vb 5170 1 0 0 5171 2 0 3 5172 4 5 6 5173 5174 i = {0,1,1,2,2,2} 5175 j = {0,0,2,0,1,2} 5176 v = {1,2,3,4,5,6} 5177 .ve 5178 Notes: 5179 Instead of using this function, users should also consider `MatSetPreallocationCOO()` and `MatSetValuesCOO()`, which allow repeated or remote entries, 5180 and are particularly useful in iterative applications. 5181 5182 .seealso: `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateSeqAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`, `MatSetValuesCOO()`, `MatSetPreallocationCOO()` 5183 @*/ 5184 PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat, PetscInt nz, PetscBool idx) 5185 { 5186 PetscInt ii, *nnz, one = 1, row, col; 5187 5188 PetscFunctionBegin; 5189 PetscCall(PetscCalloc1(m, &nnz)); 5190 for (ii = 0; ii < nz; ii++) nnz[i[ii] - !!idx] += 1; 5191 PetscCall(MatCreate(comm, mat)); 5192 PetscCall(MatSetSizes(*mat, m, n, m, n)); 5193 PetscCall(MatSetType(*mat, MATSEQAIJ)); 5194 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, 0, nnz)); 5195 for (ii = 0; ii < nz; ii++) { 5196 if (idx) { 5197 row = i[ii] - 1; 5198 col = j[ii] - 1; 5199 } else { 5200 row = i[ii]; 5201 col = j[ii]; 5202 } 5203 PetscCall(MatSetValues(*mat, one, &row, one, &col, &a[ii], ADD_VALUES)); 5204 } 5205 PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY)); 5206 PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY)); 5207 PetscCall(PetscFree(nnz)); 5208 PetscFunctionReturn(0); 5209 } 5210 5211 PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A) 5212 { 5213 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 5214 5215 PetscFunctionBegin; 5216 a->idiagvalid = PETSC_FALSE; 5217 a->ibdiagvalid = PETSC_FALSE; 5218 5219 PetscCall(MatSeqAIJInvalidateDiagonal_Inode(A)); 5220 PetscFunctionReturn(0); 5221 } 5222 5223 PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat) 5224 { 5225 PetscFunctionBegin; 5226 PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm, inmat, n, scall, outmat)); 5227 PetscFunctionReturn(0); 5228 } 5229 5230 /* 5231 Permute A into C's *local* index space using rowemb,colemb. 5232 The embedding are supposed to be injections and the above implies that the range of rowemb is a subset 5233 of [0,m), colemb is in [0,n). 5234 If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A. 5235 */ 5236 PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C, IS rowemb, IS colemb, MatStructure pattern, Mat B) 5237 { 5238 /* If making this function public, change the error returned in this function away from _PLIB. */ 5239 Mat_SeqAIJ *Baij; 5240 PetscBool seqaij; 5241 PetscInt m, n, *nz, i, j, count; 5242 PetscScalar v; 5243 const PetscInt *rowindices, *colindices; 5244 5245 PetscFunctionBegin; 5246 if (!B) PetscFunctionReturn(0); 5247 /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */ 5248 PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQAIJ, &seqaij)); 5249 PetscCheck(seqaij, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is of wrong type"); 5250 if (rowemb) { 5251 PetscCall(ISGetLocalSize(rowemb, &m)); 5252 PetscCheck(m == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Row IS of size %" PetscInt_FMT " is incompatible with matrix row size %" PetscInt_FMT, m, B->rmap->n); 5253 } else { 5254 PetscCheck(C->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is row-incompatible with the target matrix"); 5255 } 5256 if (colemb) { 5257 PetscCall(ISGetLocalSize(colemb, &n)); 5258 PetscCheck(n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Diag col IS of size %" PetscInt_FMT " is incompatible with input matrix col size %" PetscInt_FMT, n, B->cmap->n); 5259 } else { 5260 PetscCheck(C->cmap->n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is col-incompatible with the target matrix"); 5261 } 5262 5263 Baij = (Mat_SeqAIJ *)(B->data); 5264 if (pattern == DIFFERENT_NONZERO_PATTERN) { 5265 PetscCall(PetscMalloc1(B->rmap->n, &nz)); 5266 for (i = 0; i < B->rmap->n; i++) nz[i] = Baij->i[i + 1] - Baij->i[i]; 5267 PetscCall(MatSeqAIJSetPreallocation(C, 0, nz)); 5268 PetscCall(PetscFree(nz)); 5269 } 5270 if (pattern == SUBSET_NONZERO_PATTERN) PetscCall(MatZeroEntries(C)); 5271 count = 0; 5272 rowindices = NULL; 5273 colindices = NULL; 5274 if (rowemb) PetscCall(ISGetIndices(rowemb, &rowindices)); 5275 if (colemb) PetscCall(ISGetIndices(colemb, &colindices)); 5276 for (i = 0; i < B->rmap->n; i++) { 5277 PetscInt row; 5278 row = i; 5279 if (rowindices) row = rowindices[i]; 5280 for (j = Baij->i[i]; j < Baij->i[i + 1]; j++) { 5281 PetscInt col; 5282 col = Baij->j[count]; 5283 if (colindices) col = colindices[col]; 5284 v = Baij->a[count]; 5285 PetscCall(MatSetValues(C, 1, &row, 1, &col, &v, INSERT_VALUES)); 5286 ++count; 5287 } 5288 } 5289 /* FIXME: set C's nonzerostate correctly. */ 5290 /* Assembly for C is necessary. */ 5291 C->preallocated = PETSC_TRUE; 5292 C->assembled = PETSC_TRUE; 5293 C->was_assembled = PETSC_FALSE; 5294 PetscFunctionReturn(0); 5295 } 5296 5297 PetscFunctionList MatSeqAIJList = NULL; 5298 5299 /*@C 5300 MatSeqAIJSetType - Converts a `MATSEQAIJ` matrix to a subtype 5301 5302 Collective 5303 5304 Input Parameters: 5305 + mat - the matrix object 5306 - matype - matrix type 5307 5308 Options Database Key: 5309 . -mat_seqai_type <method> - for example seqaijcrl 5310 5311 Level: intermediate 5312 5313 .seealso: `PCSetType()`, `VecSetType()`, `MatCreate()`, `MatType`, `Mat` 5314 @*/ 5315 PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype) 5316 { 5317 PetscBool sametype; 5318 PetscErrorCode (*r)(Mat, MatType, MatReuse, Mat *); 5319 5320 PetscFunctionBegin; 5321 PetscValidHeaderSpecific(mat, MAT_CLASSID, 1); 5322 PetscCall(PetscObjectTypeCompare((PetscObject)mat, matype, &sametype)); 5323 if (sametype) PetscFunctionReturn(0); 5324 5325 PetscCall(PetscFunctionListFind(MatSeqAIJList, matype, &r)); 5326 PetscCheck(r, PETSC_COMM_SELF, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown Mat type given: %s", matype); 5327 PetscCall((*r)(mat, matype, MAT_INPLACE_MATRIX, &mat)); 5328 PetscFunctionReturn(0); 5329 } 5330 5331 /*@C 5332 MatSeqAIJRegister - - Adds a new sub-matrix type for sequential `MATSEQAIJ` matrices 5333 5334 Not Collective 5335 5336 Input Parameters: 5337 + name - name of a new user-defined matrix type, for example `MATSEQAIJCRL` 5338 - function - routine to convert to subtype 5339 5340 Notes: 5341 `MatSeqAIJRegister()` may be called multiple times to add several user-defined solvers. 5342 5343 Then, your matrix can be chosen with the procedural interface at runtime via the option 5344 $ -mat_seqaij_type my_mat 5345 5346 Level: advanced 5347 5348 .seealso: `MatSeqAIJRegisterAll()` 5349 @*/ 5350 PetscErrorCode MatSeqAIJRegister(const char sname[], PetscErrorCode (*function)(Mat, MatType, MatReuse, Mat *)) 5351 { 5352 PetscFunctionBegin; 5353 PetscCall(MatInitializePackage()); 5354 PetscCall(PetscFunctionListAdd(&MatSeqAIJList, sname, function)); 5355 PetscFunctionReturn(0); 5356 } 5357 5358 PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE; 5359 5360 /*@C 5361 MatSeqAIJRegisterAll - Registers all of the matrix subtypes of `MATSSEQAIJ` 5362 5363 Not Collective 5364 5365 Level: advanced 5366 5367 .seealso: `MatRegisterAll()`, `MatSeqAIJRegister()` 5368 @*/ 5369 PetscErrorCode MatSeqAIJRegisterAll(void) 5370 { 5371 PetscFunctionBegin; 5372 if (MatSeqAIJRegisterAllCalled) PetscFunctionReturn(0); 5373 MatSeqAIJRegisterAllCalled = PETSC_TRUE; 5374 5375 PetscCall(MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL)); 5376 PetscCall(MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM)); 5377 PetscCall(MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL)); 5378 #if defined(PETSC_HAVE_MKL_SPARSE) 5379 PetscCall(MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL)); 5380 #endif 5381 #if defined(PETSC_HAVE_CUDA) 5382 PetscCall(MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE)); 5383 #endif 5384 #if defined(PETSC_HAVE_KOKKOS_KERNELS) 5385 PetscCall(MatSeqAIJRegister(MATSEQAIJKOKKOS, MatConvert_SeqAIJ_SeqAIJKokkos)); 5386 #endif 5387 #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA) 5388 PetscCall(MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL)); 5389 #endif 5390 PetscFunctionReturn(0); 5391 } 5392 5393 /* 5394 Special version for direct calls from Fortran 5395 */ 5396 #include <petsc/private/fortranimpl.h> 5397 #if defined(PETSC_HAVE_FORTRAN_CAPS) 5398 #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ 5399 #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE) 5400 #define matsetvaluesseqaij_ matsetvaluesseqaij 5401 #endif 5402 5403 /* Change these macros so can be used in void function */ 5404 5405 /* Change these macros so can be used in void function */ 5406 /* Identical to PetscCallVoid, except it assigns to *_ierr */ 5407 #undef PetscCall 5408 #define PetscCall(...) \ 5409 do { \ 5410 PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \ 5411 if (PetscUnlikely(ierr_msv_mpiaij)) { \ 5412 *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \ 5413 return; \ 5414 } \ 5415 } while (0) 5416 5417 #undef SETERRQ 5418 #define SETERRQ(comm, ierr, ...) \ 5419 do { \ 5420 *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \ 5421 return; \ 5422 } while (0) 5423 5424 PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[], InsertMode *isis, PetscErrorCode *_ierr) 5425 { 5426 Mat A = *AA; 5427 PetscInt m = *mm, n = *nn; 5428 InsertMode is = *isis; 5429 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 5430 PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N; 5431 PetscInt *imax, *ai, *ailen; 5432 PetscInt *aj, nonew = a->nonew, lastcol = -1; 5433 MatScalar *ap, value, *aa; 5434 PetscBool ignorezeroentries = a->ignorezeroentries; 5435 PetscBool roworiented = a->roworiented; 5436 5437 PetscFunctionBegin; 5438 MatCheckPreallocated(A, 1); 5439 imax = a->imax; 5440 ai = a->i; 5441 ailen = a->ilen; 5442 aj = a->j; 5443 aa = a->a; 5444 5445 for (k = 0; k < m; k++) { /* loop over added rows */ 5446 row = im[k]; 5447 if (row < 0) continue; 5448 PetscCheck(row < A->rmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Row too large"); 5449 rp = aj + ai[row]; 5450 ap = aa + ai[row]; 5451 rmax = imax[row]; 5452 nrow = ailen[row]; 5453 low = 0; 5454 high = nrow; 5455 for (l = 0; l < n; l++) { /* loop over added columns */ 5456 if (in[l] < 0) continue; 5457 PetscCheck(in[l] < A->cmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Column too large"); 5458 col = in[l]; 5459 if (roworiented) value = v[l + k * n]; 5460 else value = v[k + l * m]; 5461 5462 if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue; 5463 5464 if (col <= lastcol) low = 0; 5465 else high = nrow; 5466 lastcol = col; 5467 while (high - low > 5) { 5468 t = (low + high) / 2; 5469 if (rp[t] > col) high = t; 5470 else low = t; 5471 } 5472 for (i = low; i < high; i++) { 5473 if (rp[i] > col) break; 5474 if (rp[i] == col) { 5475 if (is == ADD_VALUES) ap[i] += value; 5476 else ap[i] = value; 5477 goto noinsert; 5478 } 5479 } 5480 if (value == 0.0 && ignorezeroentries) goto noinsert; 5481 if (nonew == 1) goto noinsert; 5482 PetscCheck(nonew != -1, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero in the matrix"); 5483 MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar); 5484 N = nrow++ - 1; 5485 a->nz++; 5486 high++; 5487 /* shift up all the later entries in this row */ 5488 for (ii = N; ii >= i; ii--) { 5489 rp[ii + 1] = rp[ii]; 5490 ap[ii + 1] = ap[ii]; 5491 } 5492 rp[i] = col; 5493 ap[i] = value; 5494 A->nonzerostate++; 5495 noinsert:; 5496 low = i + 1; 5497 } 5498 ailen[row] = nrow; 5499 } 5500 PetscFunctionReturnVoid(); 5501 } 5502 /* Undefining these here since they were redefined from their original definition above! No 5503 * other PETSc functions should be defined past this point, as it is impossible to recover the 5504 * original definitions */ 5505 #undef PetscCall 5506 #undef SETERRQ 5507