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 /* defines MatSetValues_Seq_Hash(), MatAssemblyEnd_Seq_Hash(), MatSetUp_Seq_Hash() */ 12 #define TYPE AIJ 13 #define TYPE_BS 14 #include "../src/mat/impls/aij/seq/seqhashmatsetvalues.h" 15 #include "../src/mat/impls/aij/seq/seqhashmat.h" 16 #undef TYPE 17 #undef TYPE_BS 18 19 static PetscErrorCode MatSeqAIJSetTypeFromOptions(Mat A) 20 { 21 PetscBool flg; 22 char type[256]; 23 24 PetscFunctionBegin; 25 PetscObjectOptionsBegin((PetscObject)A); 26 PetscCall(PetscOptionsFList("-mat_seqaij_type", "Matrix SeqAIJ type", "MatSeqAIJSetType", MatSeqAIJList, "seqaij", type, 256, &flg)); 27 if (flg) PetscCall(MatSeqAIJSetType(A, type)); 28 PetscOptionsEnd(); 29 PetscFunctionReturn(PETSC_SUCCESS); 30 } 31 32 static PetscErrorCode MatGetColumnReductions_SeqAIJ(Mat A, PetscInt type, PetscReal *reductions) 33 { 34 PetscInt i, m, n; 35 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 36 37 PetscFunctionBegin; 38 PetscCall(MatGetSize(A, &m, &n)); 39 PetscCall(PetscArrayzero(reductions, n)); 40 if (type == NORM_2) { 41 for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i] * aij->a[i]); 42 } else if (type == NORM_1) { 43 for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i]); 44 } else if (type == NORM_INFINITY) { 45 for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]), reductions[aij->j[i]]); 46 } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) { 47 for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscRealPart(aij->a[i]); 48 } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) { 49 for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscImaginaryPart(aij->a[i]); 50 } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type"); 51 52 if (type == NORM_2) { 53 for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]); 54 } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) { 55 for (i = 0; i < n; i++) reductions[i] /= m; 56 } 57 PetscFunctionReturn(PETSC_SUCCESS); 58 } 59 60 static PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A, IS *is) 61 { 62 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 63 PetscInt i, m = A->rmap->n, cnt = 0, bs = A->rmap->bs; 64 const PetscInt *jj = a->j, *ii = a->i; 65 PetscInt *rows; 66 67 PetscFunctionBegin; 68 for (i = 0; i < m; i++) { 69 if ((ii[i] != ii[i + 1]) && ((jj[ii[i]] < bs * (i / bs)) || (jj[ii[i + 1] - 1] > bs * ((i + bs) / bs) - 1))) cnt++; 70 } 71 PetscCall(PetscMalloc1(cnt, &rows)); 72 cnt = 0; 73 for (i = 0; i < m; i++) { 74 if ((ii[i] != ii[i + 1]) && ((jj[ii[i]] < bs * (i / bs)) || (jj[ii[i + 1] - 1] > bs * ((i + bs) / bs) - 1))) { 75 rows[cnt] = i; 76 cnt++; 77 } 78 } 79 PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, is)); 80 PetscFunctionReturn(PETSC_SUCCESS); 81 } 82 83 PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A, PetscInt *nrows, PetscInt **zrows) 84 { 85 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 86 const MatScalar *aa; 87 PetscInt i, m = A->rmap->n, cnt = 0; 88 const PetscInt *ii = a->i, *jj = a->j, *diag; 89 PetscInt *rows; 90 91 PetscFunctionBegin; 92 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 93 PetscCall(MatMarkDiagonal_SeqAIJ(A)); 94 diag = a->diag; 95 for (i = 0; i < m; i++) { 96 if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) cnt++; 97 } 98 PetscCall(PetscMalloc1(cnt, &rows)); 99 cnt = 0; 100 for (i = 0; i < m; i++) { 101 if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) rows[cnt++] = i; 102 } 103 *nrows = cnt; 104 *zrows = rows; 105 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 106 PetscFunctionReturn(PETSC_SUCCESS); 107 } 108 109 static PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A, IS *zrows) 110 { 111 PetscInt nrows, *rows; 112 113 PetscFunctionBegin; 114 *zrows = NULL; 115 PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(A, &nrows, &rows)); 116 PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), nrows, rows, PETSC_OWN_POINTER, zrows)); 117 PetscFunctionReturn(PETSC_SUCCESS); 118 } 119 120 static PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A, IS *keptrows) 121 { 122 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 123 const MatScalar *aa; 124 PetscInt m = A->rmap->n, cnt = 0; 125 const PetscInt *ii; 126 PetscInt n, i, j, *rows; 127 128 PetscFunctionBegin; 129 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 130 *keptrows = NULL; 131 ii = a->i; 132 for (i = 0; i < m; i++) { 133 n = ii[i + 1] - ii[i]; 134 if (!n) { 135 cnt++; 136 goto ok1; 137 } 138 for (j = ii[i]; j < ii[i + 1]; j++) { 139 if (aa[j] != 0.0) goto ok1; 140 } 141 cnt++; 142 ok1:; 143 } 144 if (!cnt) { 145 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 146 PetscFunctionReturn(PETSC_SUCCESS); 147 } 148 PetscCall(PetscMalloc1(A->rmap->n - cnt, &rows)); 149 cnt = 0; 150 for (i = 0; i < m; i++) { 151 n = ii[i + 1] - ii[i]; 152 if (!n) continue; 153 for (j = ii[i]; j < ii[i + 1]; j++) { 154 if (aa[j] != 0.0) { 155 rows[cnt++] = i; 156 break; 157 } 158 } 159 } 160 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 161 PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, keptrows)); 162 PetscFunctionReturn(PETSC_SUCCESS); 163 } 164 165 PetscErrorCode MatDiagonalSet_SeqAIJ(Mat Y, Vec D, InsertMode is) 166 { 167 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)Y->data; 168 PetscInt i, m = Y->rmap->n; 169 const PetscInt *diag; 170 MatScalar *aa; 171 const PetscScalar *v; 172 PetscBool missing; 173 174 PetscFunctionBegin; 175 if (Y->assembled) { 176 PetscCall(MatMissingDiagonal_SeqAIJ(Y, &missing, NULL)); 177 if (!missing) { 178 diag = aij->diag; 179 PetscCall(VecGetArrayRead(D, &v)); 180 PetscCall(MatSeqAIJGetArray(Y, &aa)); 181 if (is == INSERT_VALUES) { 182 for (i = 0; i < m; i++) aa[diag[i]] = v[i]; 183 } else { 184 for (i = 0; i < m; i++) aa[diag[i]] += v[i]; 185 } 186 PetscCall(MatSeqAIJRestoreArray(Y, &aa)); 187 PetscCall(VecRestoreArrayRead(D, &v)); 188 PetscFunctionReturn(PETSC_SUCCESS); 189 } 190 PetscCall(MatSeqAIJInvalidateDiagonal(Y)); 191 } 192 PetscCall(MatDiagonalSet_Default(Y, D, is)); 193 PetscFunctionReturn(PETSC_SUCCESS); 194 } 195 196 PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done) 197 { 198 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 199 PetscInt i, ishift; 200 201 PetscFunctionBegin; 202 if (m) *m = A->rmap->n; 203 if (!ia) PetscFunctionReturn(PETSC_SUCCESS); 204 ishift = 0; 205 if (symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) { 206 PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, ishift, oshift, (PetscInt **)ia, (PetscInt **)ja)); 207 } else if (oshift == 1) { 208 PetscInt *tia; 209 PetscInt nz = a->i[A->rmap->n]; 210 /* malloc space and add 1 to i and j indices */ 211 PetscCall(PetscMalloc1(A->rmap->n + 1, &tia)); 212 for (i = 0; i < A->rmap->n + 1; i++) tia[i] = a->i[i] + 1; 213 *ia = tia; 214 if (ja) { 215 PetscInt *tja; 216 PetscCall(PetscMalloc1(nz + 1, &tja)); 217 for (i = 0; i < nz; i++) tja[i] = a->j[i] + 1; 218 *ja = tja; 219 } 220 } else { 221 *ia = a->i; 222 if (ja) *ja = a->j; 223 } 224 PetscFunctionReturn(PETSC_SUCCESS); 225 } 226 227 PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done) 228 { 229 PetscFunctionBegin; 230 if (!ia) PetscFunctionReturn(PETSC_SUCCESS); 231 if ((symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) || oshift == 1) { 232 PetscCall(PetscFree(*ia)); 233 if (ja) PetscCall(PetscFree(*ja)); 234 } 235 PetscFunctionReturn(PETSC_SUCCESS); 236 } 237 238 PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done) 239 { 240 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 241 PetscInt i, *collengths, *cia, *cja, n = A->cmap->n, m = A->rmap->n; 242 PetscInt nz = a->i[m], row, *jj, mr, col; 243 244 PetscFunctionBegin; 245 *nn = n; 246 if (!ia) PetscFunctionReturn(PETSC_SUCCESS); 247 if (symmetric) { 248 PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, 0, oshift, (PetscInt **)ia, (PetscInt **)ja)); 249 } else { 250 PetscCall(PetscCalloc1(n, &collengths)); 251 PetscCall(PetscMalloc1(n + 1, &cia)); 252 PetscCall(PetscMalloc1(nz, &cja)); 253 jj = a->j; 254 for (i = 0; i < nz; i++) collengths[jj[i]]++; 255 cia[0] = oshift; 256 for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i]; 257 PetscCall(PetscArrayzero(collengths, n)); 258 jj = a->j; 259 for (row = 0; row < m; row++) { 260 mr = a->i[row + 1] - a->i[row]; 261 for (i = 0; i < mr; i++) { 262 col = *jj++; 263 264 cja[cia[col] + collengths[col]++ - oshift] = row + oshift; 265 } 266 } 267 PetscCall(PetscFree(collengths)); 268 *ia = cia; 269 *ja = cja; 270 } 271 PetscFunctionReturn(PETSC_SUCCESS); 272 } 273 274 PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done) 275 { 276 PetscFunctionBegin; 277 if (!ia) PetscFunctionReturn(PETSC_SUCCESS); 278 279 PetscCall(PetscFree(*ia)); 280 PetscCall(PetscFree(*ja)); 281 PetscFunctionReturn(PETSC_SUCCESS); 282 } 283 284 /* 285 MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from 286 MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output 287 spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ() 288 */ 289 PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done) 290 { 291 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 292 PetscInt i, *collengths, *cia, *cja, n = A->cmap->n, m = A->rmap->n; 293 PetscInt nz = a->i[m], row, mr, col, tmp; 294 PetscInt *cspidx; 295 const PetscInt *jj; 296 297 PetscFunctionBegin; 298 *nn = n; 299 if (!ia) PetscFunctionReturn(PETSC_SUCCESS); 300 301 PetscCall(PetscCalloc1(n, &collengths)); 302 PetscCall(PetscMalloc1(n + 1, &cia)); 303 PetscCall(PetscMalloc1(nz, &cja)); 304 PetscCall(PetscMalloc1(nz, &cspidx)); 305 jj = a->j; 306 for (i = 0; i < nz; i++) collengths[jj[i]]++; 307 cia[0] = oshift; 308 for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i]; 309 PetscCall(PetscArrayzero(collengths, n)); 310 jj = a->j; 311 for (row = 0; row < m; row++) { 312 mr = a->i[row + 1] - a->i[row]; 313 for (i = 0; i < mr; i++) { 314 col = *jj++; 315 tmp = cia[col] + collengths[col]++ - oshift; 316 cspidx[tmp] = a->i[row] + i; /* index of a->j */ 317 cja[tmp] = row + oshift; 318 } 319 } 320 PetscCall(PetscFree(collengths)); 321 *ia = cia; 322 *ja = cja; 323 *spidx = cspidx; 324 PetscFunctionReturn(PETSC_SUCCESS); 325 } 326 327 PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done) 328 { 329 PetscFunctionBegin; 330 PetscCall(MatRestoreColumnIJ_SeqAIJ(A, oshift, symmetric, inodecompressed, n, ia, ja, done)); 331 PetscCall(PetscFree(*spidx)); 332 PetscFunctionReturn(PETSC_SUCCESS); 333 } 334 335 static PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A, PetscInt row, const PetscScalar v[]) 336 { 337 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 338 PetscInt *ai = a->i; 339 PetscScalar *aa; 340 341 PetscFunctionBegin; 342 PetscCall(MatSeqAIJGetArray(A, &aa)); 343 PetscCall(PetscArraycpy(aa + ai[row], v, ai[row + 1] - ai[row])); 344 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 345 PetscFunctionReturn(PETSC_SUCCESS); 346 } 347 348 /* 349 MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions 350 351 - a single row of values is set with each call 352 - no row or column indices are negative or (in error) larger than the number of rows or columns 353 - the values are always added to the matrix, not set 354 - no new locations are introduced in the nonzero structure of the matrix 355 356 This does NOT assume the global column indices are sorted 357 358 */ 359 360 #include <petsc/private/isimpl.h> 361 PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is) 362 { 363 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 364 PetscInt low, high, t, row, nrow, i, col, l; 365 const PetscInt *rp, *ai = a->i, *ailen = a->ilen, *aj = a->j; 366 PetscInt lastcol = -1; 367 MatScalar *ap, value, *aa; 368 const PetscInt *ridx = A->rmap->mapping->indices, *cidx = A->cmap->mapping->indices; 369 370 PetscFunctionBegin; 371 PetscCall(MatSeqAIJGetArray(A, &aa)); 372 row = ridx[im[0]]; 373 rp = aj + ai[row]; 374 ap = aa + ai[row]; 375 nrow = ailen[row]; 376 low = 0; 377 high = nrow; 378 for (l = 0; l < n; l++) { /* loop over added columns */ 379 col = cidx[in[l]]; 380 value = v[l]; 381 382 if (col <= lastcol) low = 0; 383 else high = nrow; 384 lastcol = col; 385 while (high - low > 5) { 386 t = (low + high) / 2; 387 if (rp[t] > col) high = t; 388 else low = t; 389 } 390 for (i = low; i < high; i++) { 391 if (rp[i] == col) { 392 ap[i] += value; 393 low = i + 1; 394 break; 395 } 396 } 397 } 398 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 399 return PETSC_SUCCESS; 400 } 401 402 PetscErrorCode MatSetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is) 403 { 404 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 405 PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N; 406 PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen; 407 PetscInt *aj = a->j, nonew = a->nonew, lastcol = -1; 408 MatScalar *ap = NULL, value = 0.0, *aa; 409 PetscBool ignorezeroentries = a->ignorezeroentries; 410 PetscBool roworiented = a->roworiented; 411 412 PetscFunctionBegin; 413 PetscCall(MatSeqAIJGetArray(A, &aa)); 414 for (k = 0; k < m; k++) { /* loop over added rows */ 415 row = im[k]; 416 if (row < 0) continue; 417 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); 418 rp = PetscSafePointerPlusOffset(aj, ai[row]); 419 if (!A->structure_only) ap = PetscSafePointerPlusOffset(aa, ai[row]); 420 rmax = imax[row]; 421 nrow = ailen[row]; 422 low = 0; 423 high = nrow; 424 for (l = 0; l < n; l++) { /* loop over added columns */ 425 if (in[l] < 0) continue; 426 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); 427 col = in[l]; 428 if (v && !A->structure_only) value = roworiented ? v[l + k * n] : v[k + l * m]; 429 if (!A->structure_only && value == 0.0 && ignorezeroentries && is == ADD_VALUES && row != col) continue; 430 431 if (col <= lastcol) low = 0; 432 else high = nrow; 433 lastcol = col; 434 while (high - low > 5) { 435 t = (low + high) / 2; 436 if (rp[t] > col) high = t; 437 else low = t; 438 } 439 for (i = low; i < high; i++) { 440 if (rp[i] > col) break; 441 if (rp[i] == col) { 442 if (!A->structure_only) { 443 if (is == ADD_VALUES) { 444 ap[i] += value; 445 (void)PetscLogFlops(1.0); 446 } else ap[i] = value; 447 } 448 low = i + 1; 449 goto noinsert; 450 } 451 } 452 if (value == 0.0 && ignorezeroentries && row != col) goto noinsert; 453 if (nonew == 1) goto noinsert; 454 PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at (%" PetscInt_FMT ",%" PetscInt_FMT ") in the matrix", row, col); 455 if (A->structure_only) { 456 MatSeqXAIJReallocateAIJ_structure_only(A, A->rmap->n, 1, nrow, row, col, rmax, ai, aj, rp, imax, nonew, MatScalar); 457 } else { 458 MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar); 459 } 460 N = nrow++ - 1; 461 a->nz++; 462 high++; 463 /* shift up all the later entries in this row */ 464 PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1)); 465 rp[i] = col; 466 if (!A->structure_only) { 467 PetscCall(PetscArraymove(ap + i + 1, ap + i, N - i + 1)); 468 ap[i] = value; 469 } 470 low = i + 1; 471 A->nonzerostate++; 472 noinsert:; 473 } 474 ailen[row] = nrow; 475 } 476 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 477 PetscFunctionReturn(PETSC_SUCCESS); 478 } 479 480 static PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is) 481 { 482 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 483 PetscInt *rp, k, row; 484 PetscInt *ai = a->i; 485 PetscInt *aj = a->j; 486 MatScalar *aa, *ap; 487 488 PetscFunctionBegin; 489 PetscCheck(!A->was_assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot call on assembled matrix."); 490 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); 491 492 PetscCall(MatSeqAIJGetArray(A, &aa)); 493 for (k = 0; k < m; k++) { /* loop over added rows */ 494 row = im[k]; 495 rp = aj + ai[row]; 496 ap = PetscSafePointerPlusOffset(aa, ai[row]); 497 498 PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt))); 499 if (!A->structure_only) { 500 if (v) { 501 PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar))); 502 v += n; 503 } else { 504 PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar))); 505 } 506 } 507 a->ilen[row] = n; 508 a->imax[row] = n; 509 a->i[row + 1] = a->i[row] + n; 510 a->nz += n; 511 } 512 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 513 PetscFunctionReturn(PETSC_SUCCESS); 514 } 515 516 /*@ 517 MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix. 518 519 Input Parameters: 520 + A - the `MATSEQAIJ` matrix 521 - nztotal - bound on the number of nonzeros 522 523 Level: advanced 524 525 Notes: 526 This can be called if you will be provided the matrix row by row (from row zero) with sorted column indices for each row. 527 Simply call `MatSetValues()` after this call to provide the matrix entries in the usual manner. This matrix may be used 528 as always with multiple matrix assemblies. 529 530 .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MAT_SORTED_FULL`, `MatSetValues()`, `MatSeqAIJSetPreallocation()` 531 @*/ 532 PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A, PetscInt nztotal) 533 { 534 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 535 536 PetscFunctionBegin; 537 PetscCall(PetscLayoutSetUp(A->rmap)); 538 PetscCall(PetscLayoutSetUp(A->cmap)); 539 a->maxnz = nztotal; 540 if (!a->imax) { PetscCall(PetscMalloc1(A->rmap->n, &a->imax)); } 541 if (!a->ilen) { 542 PetscCall(PetscMalloc1(A->rmap->n, &a->ilen)); 543 } else { 544 PetscCall(PetscMemzero(a->ilen, A->rmap->n * sizeof(PetscInt))); 545 } 546 547 /* allocate the matrix space */ 548 if (A->structure_only) { 549 PetscCall(PetscMalloc1(nztotal, &a->j)); 550 PetscCall(PetscMalloc1(A->rmap->n + 1, &a->i)); 551 } else { 552 PetscCall(PetscMalloc3(nztotal, &a->a, nztotal, &a->j, A->rmap->n + 1, &a->i)); 553 } 554 a->i[0] = 0; 555 if (A->structure_only) { 556 a->singlemalloc = PETSC_FALSE; 557 a->free_a = PETSC_FALSE; 558 } else { 559 a->singlemalloc = PETSC_TRUE; 560 a->free_a = PETSC_TRUE; 561 } 562 a->free_ij = PETSC_TRUE; 563 A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation; 564 A->preallocated = PETSC_TRUE; 565 PetscFunctionReturn(PETSC_SUCCESS); 566 } 567 568 static PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is) 569 { 570 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 571 PetscInt *rp, k, row; 572 PetscInt *ai = a->i, *ailen = a->ilen; 573 PetscInt *aj = a->j; 574 MatScalar *aa, *ap; 575 576 PetscFunctionBegin; 577 PetscCall(MatSeqAIJGetArray(A, &aa)); 578 for (k = 0; k < m; k++) { /* loop over added rows */ 579 row = im[k]; 580 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); 581 rp = aj + ai[row]; 582 ap = aa + ai[row]; 583 if (!A->was_assembled) PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt))); 584 if (!A->structure_only) { 585 if (v) { 586 PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar))); 587 v += n; 588 } else { 589 PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar))); 590 } 591 } 592 ailen[row] = n; 593 a->nz += n; 594 } 595 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 596 PetscFunctionReturn(PETSC_SUCCESS); 597 } 598 599 static PetscErrorCode MatGetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[]) 600 { 601 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 602 PetscInt *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j; 603 PetscInt *ai = a->i, *ailen = a->ilen; 604 const MatScalar *ap, *aa; 605 606 PetscFunctionBegin; 607 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 608 for (k = 0; k < m; k++) { /* loop over rows */ 609 row = im[k]; 610 if (row < 0) { 611 v += n; 612 continue; 613 } /* negative row */ 614 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); 615 rp = PetscSafePointerPlusOffset(aj, ai[row]); 616 ap = PetscSafePointerPlusOffset(aa, ai[row]); 617 nrow = ailen[row]; 618 for (l = 0; l < n; l++) { /* loop over columns */ 619 if (in[l] < 0) { 620 v++; 621 continue; 622 } /* negative column */ 623 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); 624 col = in[l]; 625 high = nrow; 626 low = 0; /* assume unsorted */ 627 while (high - low > 5) { 628 t = (low + high) / 2; 629 if (rp[t] > col) high = t; 630 else low = t; 631 } 632 for (i = low; i < high; i++) { 633 if (rp[i] > col) break; 634 if (rp[i] == col) { 635 *v++ = ap[i]; 636 goto finished; 637 } 638 } 639 *v++ = 0.0; 640 finished:; 641 } 642 } 643 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 644 PetscFunctionReturn(PETSC_SUCCESS); 645 } 646 647 static PetscErrorCode MatView_SeqAIJ_Binary(Mat mat, PetscViewer viewer) 648 { 649 Mat_SeqAIJ *A = (Mat_SeqAIJ *)mat->data; 650 const PetscScalar *av; 651 PetscInt header[4], M, N, m, nz, i; 652 PetscInt *rowlens; 653 654 PetscFunctionBegin; 655 PetscCall(PetscViewerSetUp(viewer)); 656 657 M = mat->rmap->N; 658 N = mat->cmap->N; 659 m = mat->rmap->n; 660 nz = A->nz; 661 662 /* write matrix header */ 663 header[0] = MAT_FILE_CLASSID; 664 header[1] = M; 665 header[2] = N; 666 header[3] = nz; 667 PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT)); 668 669 /* fill in and store row lengths */ 670 PetscCall(PetscMalloc1(m, &rowlens)); 671 for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i]; 672 PetscCall(PetscViewerBinaryWrite(viewer, rowlens, m, PETSC_INT)); 673 PetscCall(PetscFree(rowlens)); 674 /* store column indices */ 675 PetscCall(PetscViewerBinaryWrite(viewer, A->j, nz, PETSC_INT)); 676 /* store nonzero values */ 677 PetscCall(MatSeqAIJGetArrayRead(mat, &av)); 678 PetscCall(PetscViewerBinaryWrite(viewer, av, nz, PETSC_SCALAR)); 679 PetscCall(MatSeqAIJRestoreArrayRead(mat, &av)); 680 681 /* write block size option to the viewer's .info file */ 682 PetscCall(MatView_Binary_BlockSizes(mat, viewer)); 683 PetscFunctionReturn(PETSC_SUCCESS); 684 } 685 686 static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A, PetscViewer viewer) 687 { 688 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 689 PetscInt i, k, m = A->rmap->N; 690 691 PetscFunctionBegin; 692 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 693 for (i = 0; i < m; i++) { 694 PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i)); 695 for (k = a->i[i]; k < a->i[i + 1]; k++) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ") ", a->j[k])); 696 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 697 } 698 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 699 PetscFunctionReturn(PETSC_SUCCESS); 700 } 701 702 extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat, PetscViewer); 703 704 static PetscErrorCode MatView_SeqAIJ_ASCII(Mat A, PetscViewer viewer) 705 { 706 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 707 const PetscScalar *av; 708 PetscInt i, j, m = A->rmap->n; 709 const char *name; 710 PetscViewerFormat format; 711 712 PetscFunctionBegin; 713 if (A->structure_only) { 714 PetscCall(MatView_SeqAIJ_ASCII_structonly(A, viewer)); 715 PetscFunctionReturn(PETSC_SUCCESS); 716 } 717 718 PetscCall(PetscViewerGetFormat(viewer, &format)); 719 if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscFunctionReturn(PETSC_SUCCESS); 720 721 /* trigger copy to CPU if needed */ 722 PetscCall(MatSeqAIJGetArrayRead(A, &av)); 723 PetscCall(MatSeqAIJRestoreArrayRead(A, &av)); 724 if (format == PETSC_VIEWER_ASCII_MATLAB) { 725 PetscInt nofinalvalue = 0; 726 if (m && ((a->i[m] == a->i[m - 1]) || (a->j[a->nz - 1] != A->cmap->n - 1))) { 727 /* Need a dummy value to ensure the dimension of the matrix. */ 728 nofinalvalue = 1; 729 } 730 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 731 PetscCall(PetscViewerASCIIPrintf(viewer, "%% Size = %" PetscInt_FMT " %" PetscInt_FMT " \n", m, A->cmap->n)); 732 PetscCall(PetscViewerASCIIPrintf(viewer, "%% Nonzeros = %" PetscInt_FMT " \n", a->nz)); 733 #if defined(PETSC_USE_COMPLEX) 734 PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",4);\n", a->nz + nofinalvalue)); 735 #else 736 PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",3);\n", a->nz + nofinalvalue)); 737 #endif 738 PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = [\n")); 739 740 for (i = 0; i < m; i++) { 741 for (j = a->i[i]; j < a->i[i + 1]; j++) { 742 #if defined(PETSC_USE_COMPLEX) 743 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]))); 744 #else 745 PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", i + 1, a->j[j] + 1, (double)a->a[j])); 746 #endif 747 } 748 } 749 if (nofinalvalue) { 750 #if defined(PETSC_USE_COMPLEX) 751 PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e %18.16e\n", m, A->cmap->n, 0., 0.)); 752 #else 753 PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", m, A->cmap->n, 0.0)); 754 #endif 755 } 756 PetscCall(PetscObjectGetName((PetscObject)A, &name)); 757 PetscCall(PetscViewerASCIIPrintf(viewer, "];\n %s = spconvert(zzz);\n", name)); 758 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 759 } else if (format == PETSC_VIEWER_ASCII_COMMON) { 760 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 761 for (i = 0; i < m; i++) { 762 PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i)); 763 for (j = a->i[i]; j < a->i[i + 1]; j++) { 764 #if defined(PETSC_USE_COMPLEX) 765 if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) { 766 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j]))); 767 } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) { 768 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j]))); 769 } else if (PetscRealPart(a->a[j]) != 0.0) { 770 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j]))); 771 } 772 #else 773 if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j])); 774 #endif 775 } 776 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 777 } 778 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 779 } else if (format == PETSC_VIEWER_ASCII_SYMMODU) { 780 PetscInt nzd = 0, fshift = 1, *sptr; 781 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 782 PetscCall(PetscMalloc1(m + 1, &sptr)); 783 for (i = 0; i < m; i++) { 784 sptr[i] = nzd + 1; 785 for (j = a->i[i]; j < a->i[i + 1]; j++) { 786 if (a->j[j] >= i) { 787 #if defined(PETSC_USE_COMPLEX) 788 if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++; 789 #else 790 if (a->a[j] != 0.0) nzd++; 791 #endif 792 } 793 } 794 } 795 sptr[m] = nzd + 1; 796 PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n\n", m, nzd)); 797 for (i = 0; i < m + 1; i += 6) { 798 if (i + 4 < m) { 799 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])); 800 } else if (i + 3 < m) { 801 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])); 802 } else if (i + 2 < m) { 803 PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3])); 804 } else if (i + 1 < m) { 805 PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2])); 806 } else if (i < m) { 807 PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1])); 808 } else { 809 PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT "\n", sptr[i])); 810 } 811 } 812 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 813 PetscCall(PetscFree(sptr)); 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) PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " ", a->j[j] + fshift)); 817 } 818 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 819 } 820 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 821 for (i = 0; i < m; i++) { 822 for (j = a->i[i]; j < a->i[i + 1]; j++) { 823 if (a->j[j] >= i) { 824 #if defined(PETSC_USE_COMPLEX) 825 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]))); 826 #else 827 if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " %18.16e ", (double)a->a[j])); 828 #endif 829 } 830 } 831 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 832 } 833 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 834 } else if (format == PETSC_VIEWER_ASCII_DENSE) { 835 PetscInt cnt = 0, jcnt; 836 PetscScalar value; 837 #if defined(PETSC_USE_COMPLEX) 838 PetscBool realonly = PETSC_TRUE; 839 840 for (i = 0; i < a->i[m]; i++) { 841 if (PetscImaginaryPart(a->a[i]) != 0.0) { 842 realonly = PETSC_FALSE; 843 break; 844 } 845 } 846 #endif 847 848 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 849 for (i = 0; i < m; i++) { 850 jcnt = 0; 851 for (j = 0; j < A->cmap->n; j++) { 852 if (jcnt < a->i[i + 1] - a->i[i] && j == a->j[cnt]) { 853 value = a->a[cnt++]; 854 jcnt++; 855 } else { 856 value = 0.0; 857 } 858 #if defined(PETSC_USE_COMPLEX) 859 if (realonly) { 860 PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)PetscRealPart(value))); 861 } else { 862 PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e+%7.5e i ", (double)PetscRealPart(value), (double)PetscImaginaryPart(value))); 863 } 864 #else 865 PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)value)); 866 #endif 867 } 868 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 869 } 870 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 871 } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) { 872 PetscInt fshift = 1; 873 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 874 #if defined(PETSC_USE_COMPLEX) 875 PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate complex general\n")); 876 #else 877 PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate real general\n")); 878 #endif 879 PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", m, A->cmap->n, a->nz)); 880 for (i = 0; i < m; i++) { 881 for (j = a->i[i]; j < a->i[i + 1]; j++) { 882 #if defined(PETSC_USE_COMPLEX) 883 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]))); 884 #else 885 PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %g\n", i + fshift, a->j[j] + fshift, (double)a->a[j])); 886 #endif 887 } 888 } 889 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 890 } else { 891 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 892 if (A->factortype) { 893 for (i = 0; i < m; i++) { 894 PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i)); 895 /* L part */ 896 for (j = a->i[i]; j < a->i[i + 1]; j++) { 897 #if defined(PETSC_USE_COMPLEX) 898 if (PetscImaginaryPart(a->a[j]) > 0.0) { 899 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j]))); 900 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 901 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j])))); 902 } else { 903 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j]))); 904 } 905 #else 906 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j])); 907 #endif 908 } 909 /* diagonal */ 910 j = a->diag[i]; 911 #if defined(PETSC_USE_COMPLEX) 912 if (PetscImaginaryPart(a->a[j]) > 0.0) { 913 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]))); 914 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 915 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])))); 916 } else { 917 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(1.0 / a->a[j]))); 918 } 919 #else 920 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)(1.0 / a->a[j]))); 921 #endif 922 923 /* U part */ 924 for (j = a->diag[i + 1] + 1; j < a->diag[i]; j++) { 925 #if defined(PETSC_USE_COMPLEX) 926 if (PetscImaginaryPart(a->a[j]) > 0.0) { 927 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j]))); 928 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 929 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j])))); 930 } else { 931 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j]))); 932 } 933 #else 934 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j])); 935 #endif 936 } 937 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 938 } 939 } else { 940 for (i = 0; i < m; i++) { 941 PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i)); 942 for (j = a->i[i]; j < a->i[i + 1]; j++) { 943 #if defined(PETSC_USE_COMPLEX) 944 if (PetscImaginaryPart(a->a[j]) > 0.0) { 945 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j]))); 946 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 947 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j]))); 948 } else { 949 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j]))); 950 } 951 #else 952 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j])); 953 #endif 954 } 955 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 956 } 957 } 958 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 959 } 960 PetscCall(PetscViewerFlush(viewer)); 961 PetscFunctionReturn(PETSC_SUCCESS); 962 } 963 964 #include <petscdraw.h> 965 static PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw, void *Aa) 966 { 967 Mat A = (Mat)Aa; 968 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 969 PetscInt i, j, m = A->rmap->n; 970 int color; 971 PetscReal xl, yl, xr, yr, x_l, x_r, y_l, y_r; 972 PetscViewer viewer; 973 PetscViewerFormat format; 974 const PetscScalar *aa; 975 976 PetscFunctionBegin; 977 PetscCall(PetscObjectQuery((PetscObject)A, "Zoomviewer", (PetscObject *)&viewer)); 978 PetscCall(PetscViewerGetFormat(viewer, &format)); 979 PetscCall(PetscDrawGetCoordinates(draw, &xl, &yl, &xr, &yr)); 980 981 /* loop over matrix elements drawing boxes */ 982 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 983 if (format != PETSC_VIEWER_DRAW_CONTOUR) { 984 PetscDrawCollectiveBegin(draw); 985 /* Blue for negative, Cyan for zero and Red for positive */ 986 color = PETSC_DRAW_BLUE; 987 for (i = 0; i < m; i++) { 988 y_l = m - i - 1.0; 989 y_r = y_l + 1.0; 990 for (j = a->i[i]; j < a->i[i + 1]; j++) { 991 x_l = a->j[j]; 992 x_r = x_l + 1.0; 993 if (PetscRealPart(aa[j]) >= 0.) continue; 994 PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color)); 995 } 996 } 997 color = PETSC_DRAW_CYAN; 998 for (i = 0; i < m; i++) { 999 y_l = m - i - 1.0; 1000 y_r = y_l + 1.0; 1001 for (j = a->i[i]; j < a->i[i + 1]; j++) { 1002 x_l = a->j[j]; 1003 x_r = x_l + 1.0; 1004 if (aa[j] != 0.) continue; 1005 PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color)); 1006 } 1007 } 1008 color = PETSC_DRAW_RED; 1009 for (i = 0; i < m; i++) { 1010 y_l = m - i - 1.0; 1011 y_r = y_l + 1.0; 1012 for (j = a->i[i]; j < a->i[i + 1]; j++) { 1013 x_l = a->j[j]; 1014 x_r = x_l + 1.0; 1015 if (PetscRealPart(aa[j]) <= 0.) continue; 1016 PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color)); 1017 } 1018 } 1019 PetscDrawCollectiveEnd(draw); 1020 } else { 1021 /* use contour shading to indicate magnitude of values */ 1022 /* first determine max of all nonzero values */ 1023 PetscReal minv = 0.0, maxv = 0.0; 1024 PetscInt nz = a->nz, count = 0; 1025 PetscDraw popup; 1026 1027 for (i = 0; i < nz; i++) { 1028 if (PetscAbsScalar(aa[i]) > maxv) maxv = PetscAbsScalar(aa[i]); 1029 } 1030 if (minv >= maxv) maxv = minv + PETSC_SMALL; 1031 PetscCall(PetscDrawGetPopup(draw, &popup)); 1032 PetscCall(PetscDrawScalePopup(popup, minv, maxv)); 1033 1034 PetscDrawCollectiveBegin(draw); 1035 for (i = 0; i < m; i++) { 1036 y_l = m - i - 1.0; 1037 y_r = y_l + 1.0; 1038 for (j = a->i[i]; j < a->i[i + 1]; j++) { 1039 x_l = a->j[j]; 1040 x_r = x_l + 1.0; 1041 color = PetscDrawRealToColor(PetscAbsScalar(aa[count]), minv, maxv); 1042 PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color)); 1043 count++; 1044 } 1045 } 1046 PetscDrawCollectiveEnd(draw); 1047 } 1048 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 1049 PetscFunctionReturn(PETSC_SUCCESS); 1050 } 1051 1052 #include <petscdraw.h> 1053 static PetscErrorCode MatView_SeqAIJ_Draw(Mat A, PetscViewer viewer) 1054 { 1055 PetscDraw draw; 1056 PetscReal xr, yr, xl, yl, h, w; 1057 PetscBool isnull; 1058 1059 PetscFunctionBegin; 1060 PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw)); 1061 PetscCall(PetscDrawIsNull(draw, &isnull)); 1062 if (isnull) PetscFunctionReturn(PETSC_SUCCESS); 1063 1064 xr = A->cmap->n; 1065 yr = A->rmap->n; 1066 h = yr / 10.0; 1067 w = xr / 10.0; 1068 xr += w; 1069 yr += h; 1070 xl = -w; 1071 yl = -h; 1072 PetscCall(PetscDrawSetCoordinates(draw, xl, yl, xr, yr)); 1073 PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", (PetscObject)viewer)); 1074 PetscCall(PetscDrawZoom(draw, MatView_SeqAIJ_Draw_Zoom, A)); 1075 PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", NULL)); 1076 PetscCall(PetscDrawSave(draw)); 1077 PetscFunctionReturn(PETSC_SUCCESS); 1078 } 1079 1080 PetscErrorCode MatView_SeqAIJ(Mat A, PetscViewer viewer) 1081 { 1082 PetscBool iascii, isbinary, isdraw; 1083 1084 PetscFunctionBegin; 1085 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii)); 1086 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary)); 1087 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw)); 1088 if (iascii) PetscCall(MatView_SeqAIJ_ASCII(A, viewer)); 1089 else if (isbinary) PetscCall(MatView_SeqAIJ_Binary(A, viewer)); 1090 else if (isdraw) PetscCall(MatView_SeqAIJ_Draw(A, viewer)); 1091 PetscCall(MatView_SeqAIJ_Inode(A, viewer)); 1092 PetscFunctionReturn(PETSC_SUCCESS); 1093 } 1094 1095 PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A, MatAssemblyType mode) 1096 { 1097 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1098 PetscInt fshift = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax; 1099 PetscInt m = A->rmap->n, *ip, N, *ailen = a->ilen, rmax = 0, n; 1100 MatScalar *aa = a->a, *ap; 1101 PetscReal ratio = 0.6; 1102 1103 PetscFunctionBegin; 1104 if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS); 1105 PetscCall(MatSeqAIJInvalidateDiagonal(A)); 1106 if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) { 1107 /* we need to respect users asking to use or not the inodes routine in between matrix assemblies */ 1108 PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode)); 1109 PetscFunctionReturn(PETSC_SUCCESS); 1110 } 1111 1112 if (m) rmax = ailen[0]; /* determine row with most nonzeros */ 1113 for (i = 1; i < m; i++) { 1114 /* move each row back by the amount of empty slots (fshift) before it*/ 1115 fshift += imax[i - 1] - ailen[i - 1]; 1116 rmax = PetscMax(rmax, ailen[i]); 1117 if (fshift) { 1118 ip = aj + ai[i]; 1119 ap = aa + ai[i]; 1120 N = ailen[i]; 1121 PetscCall(PetscArraymove(ip - fshift, ip, N)); 1122 if (!A->structure_only) PetscCall(PetscArraymove(ap - fshift, ap, N)); 1123 } 1124 ai[i] = ai[i - 1] + ailen[i - 1]; 1125 } 1126 if (m) { 1127 fshift += imax[m - 1] - ailen[m - 1]; 1128 ai[m] = ai[m - 1] + ailen[m - 1]; 1129 } 1130 /* reset ilen and imax for each row */ 1131 a->nonzerorowcnt = 0; 1132 if (A->structure_only) { 1133 PetscCall(PetscFree(a->imax)); 1134 PetscCall(PetscFree(a->ilen)); 1135 } else { /* !A->structure_only */ 1136 for (i = 0; i < m; i++) { 1137 ailen[i] = imax[i] = ai[i + 1] - ai[i]; 1138 a->nonzerorowcnt += ((ai[i + 1] - ai[i]) > 0); 1139 } 1140 } 1141 a->nz = ai[m]; 1142 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); 1143 PetscCall(MatMarkDiagonal_SeqAIJ(A)); // since diagonal info is used a lot, it is helpful to set them up at the end of assembly 1144 a->diagonaldense = PETSC_TRUE; 1145 n = PetscMin(A->rmap->n, A->cmap->n); 1146 for (i = 0; i < n; i++) { 1147 if (a->diag[i] >= ai[i + 1]) { 1148 a->diagonaldense = PETSC_FALSE; 1149 break; 1150 } 1151 } 1152 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)); 1153 PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues() is %" PetscInt_FMT "\n", a->reallocs)); 1154 PetscCall(PetscInfo(A, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", rmax)); 1155 1156 A->info.mallocs += a->reallocs; 1157 a->reallocs = 0; 1158 A->info.nz_unneeded = (PetscReal)fshift; 1159 a->rmax = rmax; 1160 1161 if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, m, ratio)); 1162 PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode)); 1163 PetscFunctionReturn(PETSC_SUCCESS); 1164 } 1165 1166 static PetscErrorCode MatRealPart_SeqAIJ(Mat A) 1167 { 1168 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1169 PetscInt i, nz = a->nz; 1170 MatScalar *aa; 1171 1172 PetscFunctionBegin; 1173 PetscCall(MatSeqAIJGetArray(A, &aa)); 1174 for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]); 1175 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 1176 PetscCall(MatSeqAIJInvalidateDiagonal(A)); 1177 PetscFunctionReturn(PETSC_SUCCESS); 1178 } 1179 1180 static PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A) 1181 { 1182 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1183 PetscInt i, nz = a->nz; 1184 MatScalar *aa; 1185 1186 PetscFunctionBegin; 1187 PetscCall(MatSeqAIJGetArray(A, &aa)); 1188 for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]); 1189 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 1190 PetscCall(MatSeqAIJInvalidateDiagonal(A)); 1191 PetscFunctionReturn(PETSC_SUCCESS); 1192 } 1193 1194 PetscErrorCode MatZeroEntries_SeqAIJ(Mat A) 1195 { 1196 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1197 MatScalar *aa; 1198 1199 PetscFunctionBegin; 1200 PetscCall(MatSeqAIJGetArrayWrite(A, &aa)); 1201 PetscCall(PetscArrayzero(aa, a->i[A->rmap->n])); 1202 PetscCall(MatSeqAIJRestoreArrayWrite(A, &aa)); 1203 PetscCall(MatSeqAIJInvalidateDiagonal(A)); 1204 PetscFunctionReturn(PETSC_SUCCESS); 1205 } 1206 1207 PetscErrorCode MatDestroy_SeqAIJ(Mat A) 1208 { 1209 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1210 1211 PetscFunctionBegin; 1212 if (A->hash_active) { 1213 A->ops[0] = a->cops; 1214 PetscCall(PetscHMapIJVDestroy(&a->ht)); 1215 PetscCall(PetscFree(a->dnz)); 1216 A->hash_active = PETSC_FALSE; 1217 } 1218 1219 PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->n, A->cmap->n, a->nz)); 1220 PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i)); 1221 PetscCall(ISDestroy(&a->row)); 1222 PetscCall(ISDestroy(&a->col)); 1223 PetscCall(PetscFree(a->diag)); 1224 PetscCall(PetscFree(a->ibdiag)); 1225 PetscCall(PetscFree(a->imax)); 1226 PetscCall(PetscFree(a->ilen)); 1227 PetscCall(PetscFree(a->ipre)); 1228 PetscCall(PetscFree3(a->idiag, a->mdiag, a->ssor_work)); 1229 PetscCall(PetscFree(a->solve_work)); 1230 PetscCall(ISDestroy(&a->icol)); 1231 PetscCall(PetscFree(a->saved_values)); 1232 PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex)); 1233 PetscCall(MatDestroy_SeqAIJ_Inode(A)); 1234 PetscCall(PetscFree(A->data)); 1235 1236 /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this. 1237 That function is so heavily used (sometimes in an hidden way through multnumeric function pointers) 1238 that is hard to properly add this data to the MatProduct data. We free it here to avoid 1239 users reusing the matrix object with different data to incur in obscure segmentation faults 1240 due to different matrix sizes */ 1241 PetscCall(PetscObjectCompose((PetscObject)A, "__PETSc__ab_dense", NULL)); 1242 1243 PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL)); 1244 PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEnginePut_C", NULL)); 1245 PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEngineGet_C", NULL)); 1246 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetColumnIndices_C", NULL)); 1247 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL)); 1248 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL)); 1249 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsbaij_C", NULL)); 1250 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqbaij_C", NULL)); 1251 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijperm_C", NULL)); 1252 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijsell_C", NULL)); 1253 #if defined(PETSC_HAVE_MKL_SPARSE) 1254 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijmkl_C", NULL)); 1255 #endif 1256 #if defined(PETSC_HAVE_CUDA) 1257 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcusparse_C", NULL)); 1258 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", NULL)); 1259 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", NULL)); 1260 #endif 1261 #if defined(PETSC_HAVE_HIP) 1262 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijhipsparse_C", NULL)); 1263 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", NULL)); 1264 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", NULL)); 1265 #endif 1266 #if defined(PETSC_HAVE_KOKKOS_KERNELS) 1267 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijkokkos_C", NULL)); 1268 #endif 1269 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcrl_C", NULL)); 1270 #if defined(PETSC_HAVE_ELEMENTAL) 1271 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_elemental_C", NULL)); 1272 #endif 1273 #if defined(PETSC_HAVE_SCALAPACK) 1274 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_scalapack_C", NULL)); 1275 #endif 1276 #if defined(PETSC_HAVE_HYPRE) 1277 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_hypre_C", NULL)); 1278 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", NULL)); 1279 #endif 1280 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqdense_C", NULL)); 1281 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsell_C", NULL)); 1282 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_is_C", NULL)); 1283 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL)); 1284 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsHermitianTranspose_C", NULL)); 1285 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocation_C", NULL)); 1286 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetPreallocation_C", NULL)); 1287 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocationCSR_C", NULL)); 1288 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatReorderForNonzeroDiagonal_C", NULL)); 1289 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_is_seqaij_C", NULL)); 1290 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqdense_seqaij_C", NULL)); 1291 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaij_C", NULL)); 1292 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJKron_C", NULL)); 1293 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL)); 1294 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL)); 1295 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL)); 1296 /* these calls do not belong here: the subclasses Duplicate/Destroy are wrong */ 1297 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijsell_seqaij_C", NULL)); 1298 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijperm_seqaij_C", NULL)); 1299 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijviennacl_C", NULL)); 1300 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqdense_C", NULL)); 1301 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqaij_C", NULL)); 1302 PetscFunctionReturn(PETSC_SUCCESS); 1303 } 1304 1305 PetscErrorCode MatSetOption_SeqAIJ(Mat A, MatOption op, PetscBool flg) 1306 { 1307 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1308 1309 PetscFunctionBegin; 1310 switch (op) { 1311 case MAT_ROW_ORIENTED: 1312 a->roworiented = flg; 1313 break; 1314 case MAT_KEEP_NONZERO_PATTERN: 1315 a->keepnonzeropattern = flg; 1316 break; 1317 case MAT_NEW_NONZERO_LOCATIONS: 1318 a->nonew = (flg ? 0 : 1); 1319 break; 1320 case MAT_NEW_NONZERO_LOCATION_ERR: 1321 a->nonew = (flg ? -1 : 0); 1322 break; 1323 case MAT_NEW_NONZERO_ALLOCATION_ERR: 1324 a->nonew = (flg ? -2 : 0); 1325 break; 1326 case MAT_UNUSED_NONZERO_LOCATION_ERR: 1327 a->nounused = (flg ? -1 : 0); 1328 break; 1329 case MAT_IGNORE_ZERO_ENTRIES: 1330 a->ignorezeroentries = flg; 1331 break; 1332 case MAT_SPD: 1333 case MAT_SYMMETRIC: 1334 case MAT_STRUCTURALLY_SYMMETRIC: 1335 case MAT_HERMITIAN: 1336 case MAT_SYMMETRY_ETERNAL: 1337 case MAT_STRUCTURE_ONLY: 1338 case MAT_STRUCTURAL_SYMMETRY_ETERNAL: 1339 case MAT_SPD_ETERNAL: 1340 /* if the diagonal matrix is square it inherits some of the properties above */ 1341 break; 1342 case MAT_FORCE_DIAGONAL_ENTRIES: 1343 case MAT_IGNORE_OFF_PROC_ENTRIES: 1344 case MAT_USE_HASH_TABLE: 1345 PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op])); 1346 break; 1347 case MAT_USE_INODES: 1348 PetscCall(MatSetOption_SeqAIJ_Inode(A, MAT_USE_INODES, flg)); 1349 break; 1350 case MAT_SUBMAT_SINGLEIS: 1351 A->submat_singleis = flg; 1352 break; 1353 case MAT_SORTED_FULL: 1354 if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull; 1355 else A->ops->setvalues = MatSetValues_SeqAIJ; 1356 break; 1357 case MAT_FORM_EXPLICIT_TRANSPOSE: 1358 A->form_explicit_transpose = flg; 1359 break; 1360 default: 1361 SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op); 1362 } 1363 PetscFunctionReturn(PETSC_SUCCESS); 1364 } 1365 1366 static PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A, Vec v) 1367 { 1368 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1369 PetscInt i, j, n, *ai = a->i, *aj = a->j; 1370 PetscScalar *x; 1371 const PetscScalar *aa; 1372 1373 PetscFunctionBegin; 1374 PetscCall(VecGetLocalSize(v, &n)); 1375 PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector"); 1376 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 1377 if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) { 1378 PetscInt *diag = a->diag; 1379 PetscCall(VecGetArrayWrite(v, &x)); 1380 for (i = 0; i < n; i++) x[i] = 1.0 / aa[diag[i]]; 1381 PetscCall(VecRestoreArrayWrite(v, &x)); 1382 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 1383 PetscFunctionReturn(PETSC_SUCCESS); 1384 } 1385 1386 PetscCall(VecGetArrayWrite(v, &x)); 1387 for (i = 0; i < n; i++) { 1388 x[i] = 0.0; 1389 for (j = ai[i]; j < ai[i + 1]; j++) { 1390 if (aj[j] == i) { 1391 x[i] = aa[j]; 1392 break; 1393 } 1394 } 1395 } 1396 PetscCall(VecRestoreArrayWrite(v, &x)); 1397 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 1398 PetscFunctionReturn(PETSC_SUCCESS); 1399 } 1400 1401 #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h> 1402 PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A, Vec xx, Vec zz, Vec yy) 1403 { 1404 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1405 const MatScalar *aa; 1406 PetscScalar *y; 1407 const PetscScalar *x; 1408 PetscInt m = A->rmap->n; 1409 #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ) 1410 const MatScalar *v; 1411 PetscScalar alpha; 1412 PetscInt n, i, j; 1413 const PetscInt *idx, *ii, *ridx = NULL; 1414 Mat_CompressedRow cprow = a->compressedrow; 1415 PetscBool usecprow = cprow.use; 1416 #endif 1417 1418 PetscFunctionBegin; 1419 if (zz != yy) PetscCall(VecCopy(zz, yy)); 1420 PetscCall(VecGetArrayRead(xx, &x)); 1421 PetscCall(VecGetArray(yy, &y)); 1422 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 1423 1424 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ) 1425 fortranmulttransposeaddaij_(&m, x, a->i, a->j, aa, y); 1426 #else 1427 if (usecprow) { 1428 m = cprow.nrows; 1429 ii = cprow.i; 1430 ridx = cprow.rindex; 1431 } else { 1432 ii = a->i; 1433 } 1434 for (i = 0; i < m; i++) { 1435 idx = a->j + ii[i]; 1436 v = aa + ii[i]; 1437 n = ii[i + 1] - ii[i]; 1438 if (usecprow) { 1439 alpha = x[ridx[i]]; 1440 } else { 1441 alpha = x[i]; 1442 } 1443 for (j = 0; j < n; j++) y[idx[j]] += alpha * v[j]; 1444 } 1445 #endif 1446 PetscCall(PetscLogFlops(2.0 * a->nz)); 1447 PetscCall(VecRestoreArrayRead(xx, &x)); 1448 PetscCall(VecRestoreArray(yy, &y)); 1449 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 1450 PetscFunctionReturn(PETSC_SUCCESS); 1451 } 1452 1453 PetscErrorCode MatMultTranspose_SeqAIJ(Mat A, Vec xx, Vec yy) 1454 { 1455 PetscFunctionBegin; 1456 PetscCall(VecSet(yy, 0.0)); 1457 PetscCall(MatMultTransposeAdd_SeqAIJ(A, xx, yy, yy)); 1458 PetscFunctionReturn(PETSC_SUCCESS); 1459 } 1460 1461 #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h> 1462 1463 PetscErrorCode MatMult_SeqAIJ(Mat A, Vec xx, Vec yy) 1464 { 1465 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1466 PetscScalar *y; 1467 const PetscScalar *x; 1468 const MatScalar *aa, *a_a; 1469 PetscInt m = A->rmap->n; 1470 const PetscInt *aj, *ii, *ridx = NULL; 1471 PetscInt n, i; 1472 PetscScalar sum; 1473 PetscBool usecprow = a->compressedrow.use; 1474 1475 #if defined(PETSC_HAVE_PRAGMA_DISJOINT) 1476 #pragma disjoint(*x, *y, *aa) 1477 #endif 1478 1479 PetscFunctionBegin; 1480 if (a->inode.use && a->inode.checked) { 1481 PetscCall(MatMult_SeqAIJ_Inode(A, xx, yy)); 1482 PetscFunctionReturn(PETSC_SUCCESS); 1483 } 1484 PetscCall(MatSeqAIJGetArrayRead(A, &a_a)); 1485 PetscCall(VecGetArrayRead(xx, &x)); 1486 PetscCall(VecGetArray(yy, &y)); 1487 ii = a->i; 1488 if (usecprow) { /* use compressed row format */ 1489 PetscCall(PetscArrayzero(y, m)); 1490 m = a->compressedrow.nrows; 1491 ii = a->compressedrow.i; 1492 ridx = a->compressedrow.rindex; 1493 for (i = 0; i < m; i++) { 1494 n = ii[i + 1] - ii[i]; 1495 aj = a->j + ii[i]; 1496 aa = a_a + ii[i]; 1497 sum = 0.0; 1498 PetscSparseDensePlusDot(sum, x, aa, aj, n); 1499 /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */ 1500 y[*ridx++] = sum; 1501 } 1502 } else { /* do not use compressed row format */ 1503 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ) 1504 aj = a->j; 1505 aa = a_a; 1506 fortranmultaij_(&m, x, ii, aj, aa, y); 1507 #else 1508 for (i = 0; i < m; i++) { 1509 n = ii[i + 1] - ii[i]; 1510 aj = a->j + ii[i]; 1511 aa = a_a + ii[i]; 1512 sum = 0.0; 1513 PetscSparseDensePlusDot(sum, x, aa, aj, n); 1514 y[i] = sum; 1515 } 1516 #endif 1517 } 1518 PetscCall(PetscLogFlops(2.0 * a->nz - a->nonzerorowcnt)); 1519 PetscCall(VecRestoreArrayRead(xx, &x)); 1520 PetscCall(VecRestoreArray(yy, &y)); 1521 PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a)); 1522 PetscFunctionReturn(PETSC_SUCCESS); 1523 } 1524 1525 // HACK!!!!! Used by src/mat/tests/ex170.c 1526 PETSC_EXTERN PetscErrorCode MatMultMax_SeqAIJ(Mat A, Vec xx, Vec yy) 1527 { 1528 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1529 PetscScalar *y; 1530 const PetscScalar *x; 1531 const MatScalar *aa, *a_a; 1532 PetscInt m = A->rmap->n; 1533 const PetscInt *aj, *ii, *ridx = NULL; 1534 PetscInt n, i, nonzerorow = 0; 1535 PetscScalar sum; 1536 PetscBool usecprow = a->compressedrow.use; 1537 1538 #if defined(PETSC_HAVE_PRAGMA_DISJOINT) 1539 #pragma disjoint(*x, *y, *aa) 1540 #endif 1541 1542 PetscFunctionBegin; 1543 PetscCall(MatSeqAIJGetArrayRead(A, &a_a)); 1544 PetscCall(VecGetArrayRead(xx, &x)); 1545 PetscCall(VecGetArray(yy, &y)); 1546 if (usecprow) { /* use compressed row format */ 1547 m = a->compressedrow.nrows; 1548 ii = a->compressedrow.i; 1549 ridx = a->compressedrow.rindex; 1550 for (i = 0; i < m; i++) { 1551 n = ii[i + 1] - ii[i]; 1552 aj = a->j + ii[i]; 1553 aa = a_a + ii[i]; 1554 sum = 0.0; 1555 nonzerorow += (n > 0); 1556 PetscSparseDenseMaxDot(sum, x, aa, aj, n); 1557 /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */ 1558 y[*ridx++] = sum; 1559 } 1560 } else { /* do not use compressed row format */ 1561 ii = a->i; 1562 for (i = 0; i < m; i++) { 1563 n = ii[i + 1] - ii[i]; 1564 aj = a->j + ii[i]; 1565 aa = a_a + ii[i]; 1566 sum = 0.0; 1567 nonzerorow += (n > 0); 1568 PetscSparseDenseMaxDot(sum, x, aa, aj, n); 1569 y[i] = sum; 1570 } 1571 } 1572 PetscCall(PetscLogFlops(2.0 * a->nz - nonzerorow)); 1573 PetscCall(VecRestoreArrayRead(xx, &x)); 1574 PetscCall(VecRestoreArray(yy, &y)); 1575 PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a)); 1576 PetscFunctionReturn(PETSC_SUCCESS); 1577 } 1578 1579 // HACK!!!!! Used by src/mat/tests/ex170.c 1580 PETSC_EXTERN PetscErrorCode MatMultAddMax_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz) 1581 { 1582 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1583 PetscScalar *y, *z; 1584 const PetscScalar *x; 1585 const MatScalar *aa, *a_a; 1586 PetscInt m = A->rmap->n, *aj, *ii; 1587 PetscInt n, i, *ridx = NULL; 1588 PetscScalar sum; 1589 PetscBool usecprow = a->compressedrow.use; 1590 1591 PetscFunctionBegin; 1592 PetscCall(MatSeqAIJGetArrayRead(A, &a_a)); 1593 PetscCall(VecGetArrayRead(xx, &x)); 1594 PetscCall(VecGetArrayPair(yy, zz, &y, &z)); 1595 if (usecprow) { /* use compressed row format */ 1596 if (zz != yy) PetscCall(PetscArraycpy(z, y, m)); 1597 m = a->compressedrow.nrows; 1598 ii = a->compressedrow.i; 1599 ridx = a->compressedrow.rindex; 1600 for (i = 0; i < m; i++) { 1601 n = ii[i + 1] - ii[i]; 1602 aj = a->j + ii[i]; 1603 aa = a_a + ii[i]; 1604 sum = y[*ridx]; 1605 PetscSparseDenseMaxDot(sum, x, aa, aj, n); 1606 z[*ridx++] = sum; 1607 } 1608 } else { /* do not use compressed row format */ 1609 ii = a->i; 1610 for (i = 0; i < m; i++) { 1611 n = ii[i + 1] - ii[i]; 1612 aj = a->j + ii[i]; 1613 aa = a_a + ii[i]; 1614 sum = y[i]; 1615 PetscSparseDenseMaxDot(sum, x, aa, aj, n); 1616 z[i] = sum; 1617 } 1618 } 1619 PetscCall(PetscLogFlops(2.0 * a->nz)); 1620 PetscCall(VecRestoreArrayRead(xx, &x)); 1621 PetscCall(VecRestoreArrayPair(yy, zz, &y, &z)); 1622 PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a)); 1623 PetscFunctionReturn(PETSC_SUCCESS); 1624 } 1625 1626 #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h> 1627 PetscErrorCode MatMultAdd_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz) 1628 { 1629 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1630 PetscScalar *y, *z; 1631 const PetscScalar *x; 1632 const MatScalar *aa, *a_a; 1633 const PetscInt *aj, *ii, *ridx = NULL; 1634 PetscInt m = A->rmap->n, n, i; 1635 PetscScalar sum; 1636 PetscBool usecprow = a->compressedrow.use; 1637 1638 PetscFunctionBegin; 1639 if (a->inode.use && a->inode.checked) { 1640 PetscCall(MatMultAdd_SeqAIJ_Inode(A, xx, yy, zz)); 1641 PetscFunctionReturn(PETSC_SUCCESS); 1642 } 1643 PetscCall(MatSeqAIJGetArrayRead(A, &a_a)); 1644 PetscCall(VecGetArrayRead(xx, &x)); 1645 PetscCall(VecGetArrayPair(yy, zz, &y, &z)); 1646 if (usecprow) { /* use compressed row format */ 1647 if (zz != yy) PetscCall(PetscArraycpy(z, y, m)); 1648 m = a->compressedrow.nrows; 1649 ii = a->compressedrow.i; 1650 ridx = a->compressedrow.rindex; 1651 for (i = 0; i < m; i++) { 1652 n = ii[i + 1] - ii[i]; 1653 aj = a->j + ii[i]; 1654 aa = a_a + ii[i]; 1655 sum = y[*ridx]; 1656 PetscSparseDensePlusDot(sum, x, aa, aj, n); 1657 z[*ridx++] = sum; 1658 } 1659 } else { /* do not use compressed row format */ 1660 ii = a->i; 1661 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ) 1662 aj = a->j; 1663 aa = a_a; 1664 fortranmultaddaij_(&m, x, ii, aj, aa, y, z); 1665 #else 1666 for (i = 0; i < m; i++) { 1667 n = ii[i + 1] - ii[i]; 1668 aj = a->j + ii[i]; 1669 aa = a_a + ii[i]; 1670 sum = y[i]; 1671 PetscSparseDensePlusDot(sum, x, aa, aj, n); 1672 z[i] = sum; 1673 } 1674 #endif 1675 } 1676 PetscCall(PetscLogFlops(2.0 * a->nz)); 1677 PetscCall(VecRestoreArrayRead(xx, &x)); 1678 PetscCall(VecRestoreArrayPair(yy, zz, &y, &z)); 1679 PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a)); 1680 PetscFunctionReturn(PETSC_SUCCESS); 1681 } 1682 1683 /* 1684 Adds diagonal pointers to sparse matrix structure. 1685 */ 1686 PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A) 1687 { 1688 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1689 PetscInt i, j, m = A->rmap->n; 1690 PetscBool alreadySet = PETSC_TRUE; 1691 1692 PetscFunctionBegin; 1693 if (!a->diag) { 1694 PetscCall(PetscMalloc1(m, &a->diag)); 1695 alreadySet = PETSC_FALSE; 1696 } 1697 for (i = 0; i < A->rmap->n; i++) { 1698 /* If A's diagonal is already correctly set, this fast track enables cheap and repeated MatMarkDiagonal_SeqAIJ() calls */ 1699 if (alreadySet) { 1700 PetscInt pos = a->diag[i]; 1701 if (pos >= a->i[i] && pos < a->i[i + 1] && a->j[pos] == i) continue; 1702 } 1703 1704 a->diag[i] = a->i[i + 1]; 1705 for (j = a->i[i]; j < a->i[i + 1]; j++) { 1706 if (a->j[j] == i) { 1707 a->diag[i] = j; 1708 break; 1709 } 1710 } 1711 } 1712 PetscFunctionReturn(PETSC_SUCCESS); 1713 } 1714 1715 static PetscErrorCode MatShift_SeqAIJ(Mat A, PetscScalar v) 1716 { 1717 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1718 const PetscInt *diag = (const PetscInt *)a->diag; 1719 const PetscInt *ii = (const PetscInt *)a->i; 1720 PetscInt i, *mdiag = NULL; 1721 PetscInt cnt = 0; /* how many diagonals are missing */ 1722 1723 PetscFunctionBegin; 1724 if (!A->preallocated || !a->nz) { 1725 PetscCall(MatSeqAIJSetPreallocation(A, 1, NULL)); 1726 PetscCall(MatShift_Basic(A, v)); 1727 PetscFunctionReturn(PETSC_SUCCESS); 1728 } 1729 1730 if (a->diagonaldense) { 1731 cnt = 0; 1732 } else { 1733 PetscCall(PetscCalloc1(A->rmap->n, &mdiag)); 1734 for (i = 0; i < A->rmap->n; i++) { 1735 if (i < A->cmap->n && diag[i] >= ii[i + 1]) { /* 'out of range' rows never have diagonals */ 1736 cnt++; 1737 mdiag[i] = 1; 1738 } 1739 } 1740 } 1741 if (!cnt) { 1742 PetscCall(MatShift_Basic(A, v)); 1743 } else { 1744 PetscScalar *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */ 1745 PetscInt *oldj = a->j, *oldi = a->i; 1746 PetscBool singlemalloc = a->singlemalloc, free_a = a->free_a, free_ij = a->free_ij; 1747 const PetscScalar *Aa; 1748 1749 PetscCall(MatSeqAIJGetArrayRead(A, &Aa)); // sync the host 1750 PetscCall(MatSeqAIJRestoreArrayRead(A, &Aa)); 1751 1752 a->a = NULL; 1753 a->j = NULL; 1754 a->i = NULL; 1755 /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */ 1756 for (i = 0; i < PetscMin(A->rmap->n, A->cmap->n); i++) a->imax[i] += mdiag[i]; 1757 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, 0, a->imax)); 1758 1759 /* copy old values into new matrix data structure */ 1760 for (i = 0; i < A->rmap->n; i++) { 1761 PetscCall(MatSetValues(A, 1, &i, a->imax[i] - mdiag[i], &oldj[oldi[i]], &olda[oldi[i]], ADD_VALUES)); 1762 if (i < A->cmap->n) PetscCall(MatSetValue(A, i, i, v, ADD_VALUES)); 1763 } 1764 PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY)); 1765 PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY)); 1766 if (singlemalloc) { 1767 PetscCall(PetscFree3(olda, oldj, oldi)); 1768 } else { 1769 if (free_a) PetscCall(PetscFree(olda)); 1770 if (free_ij) PetscCall(PetscFree(oldj)); 1771 if (free_ij) PetscCall(PetscFree(oldi)); 1772 } 1773 } 1774 PetscCall(PetscFree(mdiag)); 1775 a->diagonaldense = PETSC_TRUE; 1776 PetscFunctionReturn(PETSC_SUCCESS); 1777 } 1778 1779 /* 1780 Checks for missing diagonals 1781 */ 1782 PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A, PetscBool *missing, PetscInt *d) 1783 { 1784 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1785 PetscInt *diag, *ii = a->i, i; 1786 1787 PetscFunctionBegin; 1788 *missing = PETSC_FALSE; 1789 if (A->rmap->n > 0 && !ii) { 1790 *missing = PETSC_TRUE; 1791 if (d) *d = 0; 1792 PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n")); 1793 } else { 1794 PetscInt n; 1795 n = PetscMin(A->rmap->n, A->cmap->n); 1796 diag = a->diag; 1797 for (i = 0; i < n; i++) { 1798 if (diag[i] >= ii[i + 1]) { 1799 *missing = PETSC_TRUE; 1800 if (d) *d = i; 1801 PetscCall(PetscInfo(A, "Matrix is missing diagonal number %" PetscInt_FMT "\n", i)); 1802 break; 1803 } 1804 } 1805 } 1806 PetscFunctionReturn(PETSC_SUCCESS); 1807 } 1808 1809 #include <petscblaslapack.h> 1810 #include <petsc/private/kernels/blockinvert.h> 1811 1812 /* 1813 Note that values is allocated externally by the PC and then passed into this routine 1814 */ 1815 static PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag) 1816 { 1817 PetscInt n = A->rmap->n, i, ncnt = 0, *indx, j, bsizemax = 0, *v_pivots; 1818 PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE; 1819 const PetscReal shift = 0.0; 1820 PetscInt ipvt[5]; 1821 PetscCount flops = 0; 1822 PetscScalar work[25], *v_work; 1823 1824 PetscFunctionBegin; 1825 allowzeropivot = PetscNot(A->erroriffailure); 1826 for (i = 0; i < nblocks; i++) ncnt += bsizes[i]; 1827 PetscCheck(ncnt == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Total blocksizes %" PetscInt_FMT " doesn't match number matrix rows %" PetscInt_FMT, ncnt, n); 1828 for (i = 0; i < nblocks; i++) bsizemax = PetscMax(bsizemax, bsizes[i]); 1829 PetscCall(PetscMalloc1(bsizemax, &indx)); 1830 if (bsizemax > 7) PetscCall(PetscMalloc2(bsizemax, &v_work, bsizemax, &v_pivots)); 1831 ncnt = 0; 1832 for (i = 0; i < nblocks; i++) { 1833 for (j = 0; j < bsizes[i]; j++) indx[j] = ncnt + j; 1834 PetscCall(MatGetValues(A, bsizes[i], indx, bsizes[i], indx, diag)); 1835 switch (bsizes[i]) { 1836 case 1: 1837 *diag = 1.0 / (*diag); 1838 break; 1839 case 2: 1840 PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected)); 1841 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1842 PetscCall(PetscKernel_A_gets_transpose_A_2(diag)); 1843 break; 1844 case 3: 1845 PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected)); 1846 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1847 PetscCall(PetscKernel_A_gets_transpose_A_3(diag)); 1848 break; 1849 case 4: 1850 PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected)); 1851 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1852 PetscCall(PetscKernel_A_gets_transpose_A_4(diag)); 1853 break; 1854 case 5: 1855 PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected)); 1856 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1857 PetscCall(PetscKernel_A_gets_transpose_A_5(diag)); 1858 break; 1859 case 6: 1860 PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected)); 1861 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1862 PetscCall(PetscKernel_A_gets_transpose_A_6(diag)); 1863 break; 1864 case 7: 1865 PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected)); 1866 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1867 PetscCall(PetscKernel_A_gets_transpose_A_7(diag)); 1868 break; 1869 default: 1870 PetscCall(PetscKernel_A_gets_inverse_A(bsizes[i], diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected)); 1871 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1872 PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bsizes[i])); 1873 } 1874 ncnt += bsizes[i]; 1875 diag += bsizes[i] * bsizes[i]; 1876 flops += 2 * PetscPowInt(bsizes[i], 3) / 3; 1877 } 1878 PetscCall(PetscLogFlops(flops)); 1879 if (bsizemax > 7) PetscCall(PetscFree2(v_work, v_pivots)); 1880 PetscCall(PetscFree(indx)); 1881 PetscFunctionReturn(PETSC_SUCCESS); 1882 } 1883 1884 /* 1885 Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways 1886 */ 1887 static PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A, PetscScalar omega, PetscScalar fshift) 1888 { 1889 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1890 PetscInt i, *diag, m = A->rmap->n; 1891 const MatScalar *v; 1892 PetscScalar *idiag, *mdiag; 1893 1894 PetscFunctionBegin; 1895 if (a->idiagvalid) PetscFunctionReturn(PETSC_SUCCESS); 1896 PetscCall(MatMarkDiagonal_SeqAIJ(A)); 1897 diag = a->diag; 1898 if (!a->idiag) { PetscCall(PetscMalloc3(m, &a->idiag, m, &a->mdiag, m, &a->ssor_work)); } 1899 1900 mdiag = a->mdiag; 1901 idiag = a->idiag; 1902 PetscCall(MatSeqAIJGetArrayRead(A, &v)); 1903 if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) { 1904 for (i = 0; i < m; i++) { 1905 mdiag[i] = v[diag[i]]; 1906 if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */ 1907 if (PetscRealPart(fshift)) { 1908 PetscCall(PetscInfo(A, "Zero diagonal on row %" PetscInt_FMT "\n", i)); 1909 A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1910 A->factorerror_zeropivot_value = 0.0; 1911 A->factorerror_zeropivot_row = i; 1912 } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Zero diagonal on row %" PetscInt_FMT, i); 1913 } 1914 idiag[i] = 1.0 / v[diag[i]]; 1915 } 1916 PetscCall(PetscLogFlops(m)); 1917 } else { 1918 for (i = 0; i < m; i++) { 1919 mdiag[i] = v[diag[i]]; 1920 idiag[i] = omega / (fshift + v[diag[i]]); 1921 } 1922 PetscCall(PetscLogFlops(2.0 * m)); 1923 } 1924 a->idiagvalid = PETSC_TRUE; 1925 PetscCall(MatSeqAIJRestoreArrayRead(A, &v)); 1926 PetscFunctionReturn(PETSC_SUCCESS); 1927 } 1928 1929 PetscErrorCode MatSOR_SeqAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx) 1930 { 1931 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 1932 PetscScalar *x, d, sum, *t, scale; 1933 const MatScalar *v, *idiag = NULL, *mdiag, *aa; 1934 const PetscScalar *b, *bs, *xb, *ts; 1935 PetscInt n, m = A->rmap->n, i; 1936 const PetscInt *idx, *diag; 1937 1938 PetscFunctionBegin; 1939 if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) { 1940 PetscCall(MatSOR_SeqAIJ_Inode(A, bb, omega, flag, fshift, its, lits, xx)); 1941 PetscFunctionReturn(PETSC_SUCCESS); 1942 } 1943 its = its * lits; 1944 1945 if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */ 1946 if (!a->idiagvalid) PetscCall(MatInvertDiagonal_SeqAIJ(A, omega, fshift)); 1947 a->fshift = fshift; 1948 a->omega = omega; 1949 1950 diag = a->diag; 1951 t = a->ssor_work; 1952 idiag = a->idiag; 1953 mdiag = a->mdiag; 1954 1955 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 1956 PetscCall(VecGetArray(xx, &x)); 1957 PetscCall(VecGetArrayRead(bb, &b)); 1958 /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */ 1959 if (flag == SOR_APPLY_UPPER) { 1960 /* apply (U + D/omega) to the vector */ 1961 bs = b; 1962 for (i = 0; i < m; i++) { 1963 d = fshift + mdiag[i]; 1964 n = a->i[i + 1] - diag[i] - 1; 1965 idx = a->j + diag[i] + 1; 1966 v = aa + diag[i] + 1; 1967 sum = b[i] * d / omega; 1968 PetscSparseDensePlusDot(sum, bs, v, idx, n); 1969 x[i] = sum; 1970 } 1971 PetscCall(VecRestoreArray(xx, &x)); 1972 PetscCall(VecRestoreArrayRead(bb, &b)); 1973 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 1974 PetscCall(PetscLogFlops(a->nz)); 1975 PetscFunctionReturn(PETSC_SUCCESS); 1976 } 1977 1978 PetscCheck(flag != SOR_APPLY_LOWER, PETSC_COMM_SELF, PETSC_ERR_SUP, "SOR_APPLY_LOWER is not implemented"); 1979 if (flag & SOR_EISENSTAT) { 1980 /* Let A = L + U + D; where L is lower triangular, 1981 U is upper triangular, E = D/omega; This routine applies 1982 1983 (L + E)^{-1} A (U + E)^{-1} 1984 1985 to a vector efficiently using Eisenstat's trick. 1986 */ 1987 scale = (2.0 / omega) - 1.0; 1988 1989 /* x = (E + U)^{-1} b */ 1990 for (i = m - 1; i >= 0; i--) { 1991 n = a->i[i + 1] - diag[i] - 1; 1992 idx = a->j + diag[i] + 1; 1993 v = aa + diag[i] + 1; 1994 sum = b[i]; 1995 PetscSparseDenseMinusDot(sum, x, v, idx, n); 1996 x[i] = sum * idiag[i]; 1997 } 1998 1999 /* t = b - (2*E - D)x */ 2000 v = aa; 2001 for (i = 0; i < m; i++) t[i] = b[i] - scale * (v[*diag++]) * x[i]; 2002 2003 /* t = (E + L)^{-1}t */ 2004 ts = t; 2005 diag = a->diag; 2006 for (i = 0; i < m; i++) { 2007 n = diag[i] - a->i[i]; 2008 idx = a->j + a->i[i]; 2009 v = aa + a->i[i]; 2010 sum = t[i]; 2011 PetscSparseDenseMinusDot(sum, ts, v, idx, n); 2012 t[i] = sum * idiag[i]; 2013 /* x = x + t */ 2014 x[i] += t[i]; 2015 } 2016 2017 PetscCall(PetscLogFlops(6.0 * m - 1 + 2.0 * a->nz)); 2018 PetscCall(VecRestoreArray(xx, &x)); 2019 PetscCall(VecRestoreArrayRead(bb, &b)); 2020 PetscFunctionReturn(PETSC_SUCCESS); 2021 } 2022 if (flag & SOR_ZERO_INITIAL_GUESS) { 2023 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) { 2024 for (i = 0; i < m; i++) { 2025 n = diag[i] - a->i[i]; 2026 idx = a->j + a->i[i]; 2027 v = aa + a->i[i]; 2028 sum = b[i]; 2029 PetscSparseDenseMinusDot(sum, x, v, idx, n); 2030 t[i] = sum; 2031 x[i] = sum * idiag[i]; 2032 } 2033 xb = t; 2034 PetscCall(PetscLogFlops(a->nz)); 2035 } else xb = b; 2036 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) { 2037 for (i = m - 1; i >= 0; i--) { 2038 n = a->i[i + 1] - diag[i] - 1; 2039 idx = a->j + diag[i] + 1; 2040 v = aa + diag[i] + 1; 2041 sum = xb[i]; 2042 PetscSparseDenseMinusDot(sum, x, v, idx, n); 2043 if (xb == b) { 2044 x[i] = sum * idiag[i]; 2045 } else { 2046 x[i] = (1 - omega) * x[i] + sum * idiag[i]; /* omega in idiag */ 2047 } 2048 } 2049 PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */ 2050 } 2051 its--; 2052 } 2053 while (its--) { 2054 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) { 2055 for (i = 0; i < m; i++) { 2056 /* lower */ 2057 n = diag[i] - a->i[i]; 2058 idx = a->j + a->i[i]; 2059 v = aa + a->i[i]; 2060 sum = b[i]; 2061 PetscSparseDenseMinusDot(sum, x, v, idx, n); 2062 t[i] = sum; /* save application of the lower-triangular part */ 2063 /* upper */ 2064 n = a->i[i + 1] - diag[i] - 1; 2065 idx = a->j + diag[i] + 1; 2066 v = aa + diag[i] + 1; 2067 PetscSparseDenseMinusDot(sum, x, v, idx, n); 2068 x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */ 2069 } 2070 xb = t; 2071 PetscCall(PetscLogFlops(2.0 * a->nz)); 2072 } else xb = b; 2073 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) { 2074 for (i = m - 1; i >= 0; i--) { 2075 sum = xb[i]; 2076 if (xb == b) { 2077 /* whole matrix (no checkpointing available) */ 2078 n = a->i[i + 1] - a->i[i]; 2079 idx = a->j + a->i[i]; 2080 v = aa + a->i[i]; 2081 PetscSparseDenseMinusDot(sum, x, v, idx, n); 2082 x[i] = (1. - omega) * x[i] + (sum + mdiag[i] * x[i]) * idiag[i]; 2083 } else { /* lower-triangular part has been saved, so only apply upper-triangular */ 2084 n = a->i[i + 1] - diag[i] - 1; 2085 idx = a->j + diag[i] + 1; 2086 v = aa + diag[i] + 1; 2087 PetscSparseDenseMinusDot(sum, x, v, idx, n); 2088 x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */ 2089 } 2090 } 2091 if (xb == b) { 2092 PetscCall(PetscLogFlops(2.0 * a->nz)); 2093 } else { 2094 PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */ 2095 } 2096 } 2097 } 2098 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 2099 PetscCall(VecRestoreArray(xx, &x)); 2100 PetscCall(VecRestoreArrayRead(bb, &b)); 2101 PetscFunctionReturn(PETSC_SUCCESS); 2102 } 2103 2104 static PetscErrorCode MatGetInfo_SeqAIJ(Mat A, MatInfoType flag, MatInfo *info) 2105 { 2106 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2107 2108 PetscFunctionBegin; 2109 info->block_size = 1.0; 2110 info->nz_allocated = a->maxnz; 2111 info->nz_used = a->nz; 2112 info->nz_unneeded = (a->maxnz - a->nz); 2113 info->assemblies = A->num_ass; 2114 info->mallocs = A->info.mallocs; 2115 info->memory = 0; /* REVIEW ME */ 2116 if (A->factortype) { 2117 info->fill_ratio_given = A->info.fill_ratio_given; 2118 info->fill_ratio_needed = A->info.fill_ratio_needed; 2119 info->factor_mallocs = A->info.factor_mallocs; 2120 } else { 2121 info->fill_ratio_given = 0; 2122 info->fill_ratio_needed = 0; 2123 info->factor_mallocs = 0; 2124 } 2125 PetscFunctionReturn(PETSC_SUCCESS); 2126 } 2127 2128 static PetscErrorCode MatZeroRows_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b) 2129 { 2130 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2131 PetscInt i, m = A->rmap->n - 1; 2132 const PetscScalar *xx; 2133 PetscScalar *bb, *aa; 2134 PetscInt d = 0; 2135 2136 PetscFunctionBegin; 2137 if (x && b) { 2138 PetscCall(VecGetArrayRead(x, &xx)); 2139 PetscCall(VecGetArray(b, &bb)); 2140 for (i = 0; i < N; i++) { 2141 PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]); 2142 if (rows[i] >= A->cmap->n) continue; 2143 bb[rows[i]] = diag * xx[rows[i]]; 2144 } 2145 PetscCall(VecRestoreArrayRead(x, &xx)); 2146 PetscCall(VecRestoreArray(b, &bb)); 2147 } 2148 2149 PetscCall(MatSeqAIJGetArray(A, &aa)); 2150 if (a->keepnonzeropattern) { 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 PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]])); 2154 } 2155 if (diag != 0.0) { 2156 for (i = 0; i < N; i++) { 2157 d = rows[i]; 2158 if (rows[i] >= A->cmap->n) continue; 2159 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); 2160 } 2161 for (i = 0; i < N; i++) { 2162 if (rows[i] >= A->cmap->n) continue; 2163 aa[a->diag[rows[i]]] = diag; 2164 } 2165 } 2166 } else { 2167 if (diag != 0.0) { 2168 for (i = 0; i < N; i++) { 2169 PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]); 2170 if (a->ilen[rows[i]] > 0) { 2171 if (rows[i] >= A->cmap->n) { 2172 a->ilen[rows[i]] = 0; 2173 } else { 2174 a->ilen[rows[i]] = 1; 2175 aa[a->i[rows[i]]] = diag; 2176 a->j[a->i[rows[i]]] = rows[i]; 2177 } 2178 } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */ 2179 PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES)); 2180 } 2181 } 2182 } else { 2183 for (i = 0; i < N; i++) { 2184 PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]); 2185 a->ilen[rows[i]] = 0; 2186 } 2187 } 2188 A->nonzerostate++; 2189 } 2190 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 2191 PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY); 2192 PetscFunctionReturn(PETSC_SUCCESS); 2193 } 2194 2195 static PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b) 2196 { 2197 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2198 PetscInt i, j, m = A->rmap->n - 1, d = 0; 2199 PetscBool missing, *zeroed, vecs = PETSC_FALSE; 2200 const PetscScalar *xx; 2201 PetscScalar *bb, *aa; 2202 2203 PetscFunctionBegin; 2204 if (!N) PetscFunctionReturn(PETSC_SUCCESS); 2205 PetscCall(MatSeqAIJGetArray(A, &aa)); 2206 if (x && b) { 2207 PetscCall(VecGetArrayRead(x, &xx)); 2208 PetscCall(VecGetArray(b, &bb)); 2209 vecs = PETSC_TRUE; 2210 } 2211 PetscCall(PetscCalloc1(A->rmap->n, &zeroed)); 2212 for (i = 0; i < N; i++) { 2213 PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]); 2214 PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aa, a->i[rows[i]]), a->ilen[rows[i]])); 2215 2216 zeroed[rows[i]] = PETSC_TRUE; 2217 } 2218 for (i = 0; i < A->rmap->n; i++) { 2219 if (!zeroed[i]) { 2220 for (j = a->i[i]; j < a->i[i + 1]; j++) { 2221 if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) { 2222 if (vecs) bb[i] -= aa[j] * xx[a->j[j]]; 2223 aa[j] = 0.0; 2224 } 2225 } 2226 } else if (vecs && i < A->cmap->N) bb[i] = diag * xx[i]; 2227 } 2228 if (x && b) { 2229 PetscCall(VecRestoreArrayRead(x, &xx)); 2230 PetscCall(VecRestoreArray(b, &bb)); 2231 } 2232 PetscCall(PetscFree(zeroed)); 2233 if (diag != 0.0) { 2234 PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, &d)); 2235 if (missing) { 2236 for (i = 0; i < N; i++) { 2237 if (rows[i] >= A->cmap->N) continue; 2238 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]); 2239 PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES)); 2240 } 2241 } else { 2242 for (i = 0; i < N; i++) aa[a->diag[rows[i]]] = diag; 2243 } 2244 } 2245 PetscCall(MatSeqAIJRestoreArray(A, &aa)); 2246 PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY); 2247 PetscFunctionReturn(PETSC_SUCCESS); 2248 } 2249 2250 PetscErrorCode MatGetRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v) 2251 { 2252 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2253 const PetscScalar *aa; 2254 2255 PetscFunctionBegin; 2256 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 2257 *nz = a->i[row + 1] - a->i[row]; 2258 if (v) *v = PetscSafePointerPlusOffset((PetscScalar *)aa, a->i[row]); 2259 if (idx) { 2260 if (*nz && a->j) *idx = a->j + a->i[row]; 2261 else *idx = NULL; 2262 } 2263 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 2264 PetscFunctionReturn(PETSC_SUCCESS); 2265 } 2266 2267 PetscErrorCode MatRestoreRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v) 2268 { 2269 PetscFunctionBegin; 2270 PetscFunctionReturn(PETSC_SUCCESS); 2271 } 2272 2273 static PetscErrorCode MatNorm_SeqAIJ(Mat A, NormType type, PetscReal *nrm) 2274 { 2275 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2276 const MatScalar *v; 2277 PetscReal sum = 0.0; 2278 PetscInt i, j; 2279 2280 PetscFunctionBegin; 2281 PetscCall(MatSeqAIJGetArrayRead(A, &v)); 2282 if (type == NORM_FROBENIUS) { 2283 #if defined(PETSC_USE_REAL___FP16) 2284 PetscBLASInt one = 1, nz = a->nz; 2285 PetscCallBLAS("BLASnrm2", *nrm = BLASnrm2_(&nz, v, &one)); 2286 #else 2287 for (i = 0; i < a->nz; i++) { 2288 sum += PetscRealPart(PetscConj(*v) * (*v)); 2289 v++; 2290 } 2291 *nrm = PetscSqrtReal(sum); 2292 #endif 2293 PetscCall(PetscLogFlops(2.0 * a->nz)); 2294 } else if (type == NORM_1) { 2295 PetscReal *tmp; 2296 PetscInt *jj = a->j; 2297 PetscCall(PetscCalloc1(A->cmap->n + 1, &tmp)); 2298 *nrm = 0.0; 2299 for (j = 0; j < a->nz; j++) { 2300 tmp[*jj++] += PetscAbsScalar(*v); 2301 v++; 2302 } 2303 for (j = 0; j < A->cmap->n; j++) { 2304 if (tmp[j] > *nrm) *nrm = tmp[j]; 2305 } 2306 PetscCall(PetscFree(tmp)); 2307 PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0))); 2308 } else if (type == NORM_INFINITY) { 2309 *nrm = 0.0; 2310 for (j = 0; j < A->rmap->n; j++) { 2311 const PetscScalar *v2 = PetscSafePointerPlusOffset(v, a->i[j]); 2312 sum = 0.0; 2313 for (i = 0; i < a->i[j + 1] - a->i[j]; i++) { 2314 sum += PetscAbsScalar(*v2); 2315 v2++; 2316 } 2317 if (sum > *nrm) *nrm = sum; 2318 } 2319 PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0))); 2320 } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for two norm"); 2321 PetscCall(MatSeqAIJRestoreArrayRead(A, &v)); 2322 PetscFunctionReturn(PETSC_SUCCESS); 2323 } 2324 2325 static PetscErrorCode MatIsTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f) 2326 { 2327 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data; 2328 PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr; 2329 const MatScalar *va, *vb; 2330 PetscInt ma, na, mb, nb, i; 2331 2332 PetscFunctionBegin; 2333 PetscCall(MatGetSize(A, &ma, &na)); 2334 PetscCall(MatGetSize(B, &mb, &nb)); 2335 if (ma != nb || na != mb) { 2336 *f = PETSC_FALSE; 2337 PetscFunctionReturn(PETSC_SUCCESS); 2338 } 2339 PetscCall(MatSeqAIJGetArrayRead(A, &va)); 2340 PetscCall(MatSeqAIJGetArrayRead(B, &vb)); 2341 aii = aij->i; 2342 bii = bij->i; 2343 adx = aij->j; 2344 bdx = bij->j; 2345 PetscCall(PetscMalloc1(ma, &aptr)); 2346 PetscCall(PetscMalloc1(mb, &bptr)); 2347 for (i = 0; i < ma; i++) aptr[i] = aii[i]; 2348 for (i = 0; i < mb; i++) bptr[i] = bii[i]; 2349 2350 *f = PETSC_TRUE; 2351 for (i = 0; i < ma; i++) { 2352 while (aptr[i] < aii[i + 1]) { 2353 PetscInt idc, idr; 2354 PetscScalar vc, vr; 2355 /* column/row index/value */ 2356 idc = adx[aptr[i]]; 2357 idr = bdx[bptr[idc]]; 2358 vc = va[aptr[i]]; 2359 vr = vb[bptr[idc]]; 2360 if (i != idr || PetscAbsScalar(vc - vr) > tol) { 2361 *f = PETSC_FALSE; 2362 goto done; 2363 } else { 2364 aptr[i]++; 2365 if (B || i != idc) bptr[idc]++; 2366 } 2367 } 2368 } 2369 done: 2370 PetscCall(PetscFree(aptr)); 2371 PetscCall(PetscFree(bptr)); 2372 PetscCall(MatSeqAIJRestoreArrayRead(A, &va)); 2373 PetscCall(MatSeqAIJRestoreArrayRead(B, &vb)); 2374 PetscFunctionReturn(PETSC_SUCCESS); 2375 } 2376 2377 static PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f) 2378 { 2379 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data; 2380 PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr; 2381 MatScalar *va, *vb; 2382 PetscInt ma, na, mb, nb, i; 2383 2384 PetscFunctionBegin; 2385 PetscCall(MatGetSize(A, &ma, &na)); 2386 PetscCall(MatGetSize(B, &mb, &nb)); 2387 if (ma != nb || na != mb) { 2388 *f = PETSC_FALSE; 2389 PetscFunctionReturn(PETSC_SUCCESS); 2390 } 2391 aii = aij->i; 2392 bii = bij->i; 2393 adx = aij->j; 2394 bdx = bij->j; 2395 va = aij->a; 2396 vb = bij->a; 2397 PetscCall(PetscMalloc1(ma, &aptr)); 2398 PetscCall(PetscMalloc1(mb, &bptr)); 2399 for (i = 0; i < ma; i++) aptr[i] = aii[i]; 2400 for (i = 0; i < mb; i++) bptr[i] = bii[i]; 2401 2402 *f = PETSC_TRUE; 2403 for (i = 0; i < ma; i++) { 2404 while (aptr[i] < aii[i + 1]) { 2405 PetscInt idc, idr; 2406 PetscScalar vc, vr; 2407 /* column/row index/value */ 2408 idc = adx[aptr[i]]; 2409 idr = bdx[bptr[idc]]; 2410 vc = va[aptr[i]]; 2411 vr = vb[bptr[idc]]; 2412 if (i != idr || PetscAbsScalar(vc - PetscConj(vr)) > tol) { 2413 *f = PETSC_FALSE; 2414 goto done; 2415 } else { 2416 aptr[i]++; 2417 if (B || i != idc) bptr[idc]++; 2418 } 2419 } 2420 } 2421 done: 2422 PetscCall(PetscFree(aptr)); 2423 PetscCall(PetscFree(bptr)); 2424 PetscFunctionReturn(PETSC_SUCCESS); 2425 } 2426 2427 static PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A, PetscReal tol, PetscBool *f) 2428 { 2429 PetscFunctionBegin; 2430 PetscCall(MatIsTranspose_SeqAIJ(A, A, tol, f)); 2431 PetscFunctionReturn(PETSC_SUCCESS); 2432 } 2433 2434 static PetscErrorCode MatIsHermitian_SeqAIJ(Mat A, PetscReal tol, PetscBool *f) 2435 { 2436 PetscFunctionBegin; 2437 PetscCall(MatIsHermitianTranspose_SeqAIJ(A, A, tol, f)); 2438 PetscFunctionReturn(PETSC_SUCCESS); 2439 } 2440 2441 PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A, Vec ll, Vec rr) 2442 { 2443 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2444 const PetscScalar *l, *r; 2445 PetscScalar x; 2446 MatScalar *v; 2447 PetscInt i, j, m = A->rmap->n, n = A->cmap->n, M, nz = a->nz; 2448 const PetscInt *jj; 2449 2450 PetscFunctionBegin; 2451 if (ll) { 2452 /* The local size is used so that VecMPI can be passed to this routine 2453 by MatDiagonalScale_MPIAIJ */ 2454 PetscCall(VecGetLocalSize(ll, &m)); 2455 PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length"); 2456 PetscCall(VecGetArrayRead(ll, &l)); 2457 PetscCall(MatSeqAIJGetArray(A, &v)); 2458 for (i = 0; i < m; i++) { 2459 x = l[i]; 2460 M = a->i[i + 1] - a->i[i]; 2461 for (j = 0; j < M; j++) (*v++) *= x; 2462 } 2463 PetscCall(VecRestoreArrayRead(ll, &l)); 2464 PetscCall(PetscLogFlops(nz)); 2465 PetscCall(MatSeqAIJRestoreArray(A, &v)); 2466 } 2467 if (rr) { 2468 PetscCall(VecGetLocalSize(rr, &n)); 2469 PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length"); 2470 PetscCall(VecGetArrayRead(rr, &r)); 2471 PetscCall(MatSeqAIJGetArray(A, &v)); 2472 jj = a->j; 2473 for (i = 0; i < nz; i++) (*v++) *= r[*jj++]; 2474 PetscCall(MatSeqAIJRestoreArray(A, &v)); 2475 PetscCall(VecRestoreArrayRead(rr, &r)); 2476 PetscCall(PetscLogFlops(nz)); 2477 } 2478 PetscCall(MatSeqAIJInvalidateDiagonal(A)); 2479 PetscFunctionReturn(PETSC_SUCCESS); 2480 } 2481 2482 PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A, IS isrow, IS iscol, PetscInt csize, MatReuse scall, Mat *B) 2483 { 2484 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *c; 2485 PetscInt *smap, i, k, kstart, kend, oldcols = A->cmap->n, *lens; 2486 PetscInt row, mat_i, *mat_j, tcol, first, step, *mat_ilen, sum, lensi; 2487 const PetscInt *irow, *icol; 2488 const PetscScalar *aa; 2489 PetscInt nrows, ncols; 2490 PetscInt *starts, *j_new, *i_new, *aj = a->j, *ai = a->i, ii, *ailen = a->ilen; 2491 MatScalar *a_new, *mat_a, *c_a; 2492 Mat C; 2493 PetscBool stride; 2494 2495 PetscFunctionBegin; 2496 PetscCall(ISGetIndices(isrow, &irow)); 2497 PetscCall(ISGetLocalSize(isrow, &nrows)); 2498 PetscCall(ISGetLocalSize(iscol, &ncols)); 2499 2500 PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &stride)); 2501 if (stride) { 2502 PetscCall(ISStrideGetInfo(iscol, &first, &step)); 2503 } else { 2504 first = 0; 2505 step = 0; 2506 } 2507 if (stride && step == 1) { 2508 /* special case of contiguous rows */ 2509 PetscCall(PetscMalloc2(nrows, &lens, nrows, &starts)); 2510 /* loop over new rows determining lens and starting points */ 2511 for (i = 0; i < nrows; i++) { 2512 kstart = ai[irow[i]]; 2513 kend = kstart + ailen[irow[i]]; 2514 starts[i] = kstart; 2515 for (k = kstart; k < kend; k++) { 2516 if (aj[k] >= first) { 2517 starts[i] = k; 2518 break; 2519 } 2520 } 2521 sum = 0; 2522 while (k < kend) { 2523 if (aj[k++] >= first + ncols) break; 2524 sum++; 2525 } 2526 lens[i] = sum; 2527 } 2528 /* create submatrix */ 2529 if (scall == MAT_REUSE_MATRIX) { 2530 PetscInt n_cols, n_rows; 2531 PetscCall(MatGetSize(*B, &n_rows, &n_cols)); 2532 PetscCheck(n_rows == nrows && n_cols == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Reused submatrix wrong size"); 2533 PetscCall(MatZeroEntries(*B)); 2534 C = *B; 2535 } else { 2536 PetscInt rbs, cbs; 2537 PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C)); 2538 PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE)); 2539 PetscCall(ISGetBlockSize(isrow, &rbs)); 2540 PetscCall(ISGetBlockSize(iscol, &cbs)); 2541 PetscCall(MatSetBlockSizes(C, rbs, cbs)); 2542 PetscCall(MatSetType(C, ((PetscObject)A)->type_name)); 2543 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens)); 2544 } 2545 c = (Mat_SeqAIJ *)C->data; 2546 2547 /* loop over rows inserting into submatrix */ 2548 PetscCall(MatSeqAIJGetArrayWrite(C, &a_new)); // Not 'a_new = c->a-new', since that raw usage ignores offload state of C 2549 j_new = c->j; 2550 i_new = c->i; 2551 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 2552 for (i = 0; i < nrows; i++) { 2553 ii = starts[i]; 2554 lensi = lens[i]; 2555 if (lensi) { 2556 for (k = 0; k < lensi; k++) *j_new++ = aj[ii + k] - first; 2557 PetscCall(PetscArraycpy(a_new, aa + starts[i], lensi)); 2558 a_new += lensi; 2559 } 2560 i_new[i + 1] = i_new[i] + lensi; 2561 c->ilen[i] = lensi; 2562 } 2563 PetscCall(MatSeqAIJRestoreArrayWrite(C, &a_new)); // Set C's offload state properly 2564 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 2565 PetscCall(PetscFree2(lens, starts)); 2566 } else { 2567 PetscCall(ISGetIndices(iscol, &icol)); 2568 PetscCall(PetscCalloc1(oldcols, &smap)); 2569 PetscCall(PetscMalloc1(1 + nrows, &lens)); 2570 for (i = 0; i < ncols; i++) { 2571 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); 2572 smap[icol[i]] = i + 1; 2573 } 2574 2575 /* determine lens of each row */ 2576 for (i = 0; i < nrows; i++) { 2577 kstart = ai[irow[i]]; 2578 kend = kstart + a->ilen[irow[i]]; 2579 lens[i] = 0; 2580 for (k = kstart; k < kend; k++) { 2581 if (smap[aj[k]]) lens[i]++; 2582 } 2583 } 2584 /* Create and fill new matrix */ 2585 if (scall == MAT_REUSE_MATRIX) { 2586 PetscBool equal; 2587 2588 c = (Mat_SeqAIJ *)((*B)->data); 2589 PetscCheck((*B)->rmap->n == nrows && (*B)->cmap->n == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong size"); 2590 PetscCall(PetscArraycmp(c->ilen, lens, (*B)->rmap->n, &equal)); 2591 PetscCheck(equal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong number of nonzeros"); 2592 PetscCall(PetscArrayzero(c->ilen, (*B)->rmap->n)); 2593 C = *B; 2594 } else { 2595 PetscInt rbs, cbs; 2596 PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C)); 2597 PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE)); 2598 PetscCall(ISGetBlockSize(isrow, &rbs)); 2599 PetscCall(ISGetBlockSize(iscol, &cbs)); 2600 if (rbs > 1 || cbs > 1) PetscCall(MatSetBlockSizes(C, rbs, cbs)); 2601 PetscCall(MatSetType(C, ((PetscObject)A)->type_name)); 2602 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens)); 2603 } 2604 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 2605 2606 c = (Mat_SeqAIJ *)(C->data); 2607 PetscCall(MatSeqAIJGetArrayWrite(C, &c_a)); // Not 'c->a', since that raw usage ignores offload state of C 2608 for (i = 0; i < nrows; i++) { 2609 row = irow[i]; 2610 kstart = ai[row]; 2611 kend = kstart + a->ilen[row]; 2612 mat_i = c->i[i]; 2613 mat_j = PetscSafePointerPlusOffset(c->j, mat_i); 2614 mat_a = PetscSafePointerPlusOffset(c_a, mat_i); 2615 mat_ilen = c->ilen + i; 2616 for (k = kstart; k < kend; k++) { 2617 if ((tcol = smap[a->j[k]])) { 2618 *mat_j++ = tcol - 1; 2619 *mat_a++ = aa[k]; 2620 (*mat_ilen)++; 2621 } 2622 } 2623 } 2624 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 2625 /* Free work space */ 2626 PetscCall(ISRestoreIndices(iscol, &icol)); 2627 PetscCall(PetscFree(smap)); 2628 PetscCall(PetscFree(lens)); 2629 /* sort */ 2630 for (i = 0; i < nrows; i++) { 2631 PetscInt ilen; 2632 2633 mat_i = c->i[i]; 2634 mat_j = PetscSafePointerPlusOffset(c->j, mat_i); 2635 mat_a = PetscSafePointerPlusOffset(c_a, mat_i); 2636 ilen = c->ilen[i]; 2637 PetscCall(PetscSortIntWithScalarArray(ilen, mat_j, mat_a)); 2638 } 2639 PetscCall(MatSeqAIJRestoreArrayWrite(C, &c_a)); 2640 } 2641 #if defined(PETSC_HAVE_DEVICE) 2642 PetscCall(MatBindToCPU(C, A->boundtocpu)); 2643 #endif 2644 PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY)); 2645 PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY)); 2646 2647 PetscCall(ISRestoreIndices(isrow, &irow)); 2648 *B = C; 2649 PetscFunctionReturn(PETSC_SUCCESS); 2650 } 2651 2652 static PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat) 2653 { 2654 Mat B; 2655 2656 PetscFunctionBegin; 2657 if (scall == MAT_INITIAL_MATRIX) { 2658 PetscCall(MatCreate(subComm, &B)); 2659 PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n)); 2660 PetscCall(MatSetBlockSizesFromMats(B, mat, mat)); 2661 PetscCall(MatSetType(B, MATSEQAIJ)); 2662 PetscCall(MatDuplicateNoCreate_SeqAIJ(B, mat, MAT_COPY_VALUES, PETSC_TRUE)); 2663 *subMat = B; 2664 } else { 2665 PetscCall(MatCopy_SeqAIJ(mat, *subMat, SAME_NONZERO_PATTERN)); 2666 } 2667 PetscFunctionReturn(PETSC_SUCCESS); 2668 } 2669 2670 static PetscErrorCode MatILUFactor_SeqAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info) 2671 { 2672 Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data; 2673 Mat outA; 2674 PetscBool row_identity, col_identity; 2675 2676 PetscFunctionBegin; 2677 PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels=0 supported for in-place ilu"); 2678 2679 PetscCall(ISIdentity(row, &row_identity)); 2680 PetscCall(ISIdentity(col, &col_identity)); 2681 2682 outA = inA; 2683 outA->factortype = MAT_FACTOR_LU; 2684 PetscCall(PetscFree(inA->solvertype)); 2685 PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype)); 2686 2687 PetscCall(PetscObjectReference((PetscObject)row)); 2688 PetscCall(ISDestroy(&a->row)); 2689 2690 a->row = row; 2691 2692 PetscCall(PetscObjectReference((PetscObject)col)); 2693 PetscCall(ISDestroy(&a->col)); 2694 2695 a->col = col; 2696 2697 /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */ 2698 PetscCall(ISDestroy(&a->icol)); 2699 PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol)); 2700 2701 if (!a->solve_work) { /* this matrix may have been factored before */ 2702 PetscCall(PetscMalloc1(inA->rmap->n + 1, &a->solve_work)); 2703 } 2704 2705 PetscCall(MatMarkDiagonal_SeqAIJ(inA)); 2706 if (row_identity && col_identity) { 2707 PetscCall(MatLUFactorNumeric_SeqAIJ_inplace(outA, inA, info)); 2708 } else { 2709 PetscCall(MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA, inA, info)); 2710 } 2711 PetscFunctionReturn(PETSC_SUCCESS); 2712 } 2713 2714 PetscErrorCode MatScale_SeqAIJ(Mat inA, PetscScalar alpha) 2715 { 2716 Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data; 2717 PetscScalar *v; 2718 PetscBLASInt one = 1, bnz; 2719 2720 PetscFunctionBegin; 2721 PetscCall(MatSeqAIJGetArray(inA, &v)); 2722 PetscCall(PetscBLASIntCast(a->nz, &bnz)); 2723 PetscCallBLAS("BLASscal", BLASscal_(&bnz, &alpha, v, &one)); 2724 PetscCall(PetscLogFlops(a->nz)); 2725 PetscCall(MatSeqAIJRestoreArray(inA, &v)); 2726 PetscCall(MatSeqAIJInvalidateDiagonal(inA)); 2727 PetscFunctionReturn(PETSC_SUCCESS); 2728 } 2729 2730 PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj) 2731 { 2732 PetscInt i; 2733 2734 PetscFunctionBegin; 2735 if (!submatj->id) { /* delete data that are linked only to submats[id=0] */ 2736 PetscCall(PetscFree4(submatj->sbuf1, submatj->ptr, submatj->tmp, submatj->ctr)); 2737 2738 for (i = 0; i < submatj->nrqr; ++i) PetscCall(PetscFree(submatj->sbuf2[i])); 2739 PetscCall(PetscFree3(submatj->sbuf2, submatj->req_size, submatj->req_source1)); 2740 2741 if (submatj->rbuf1) { 2742 PetscCall(PetscFree(submatj->rbuf1[0])); 2743 PetscCall(PetscFree(submatj->rbuf1)); 2744 } 2745 2746 for (i = 0; i < submatj->nrqs; ++i) PetscCall(PetscFree(submatj->rbuf3[i])); 2747 PetscCall(PetscFree3(submatj->req_source2, submatj->rbuf2, submatj->rbuf3)); 2748 PetscCall(PetscFree(submatj->pa)); 2749 } 2750 2751 #if defined(PETSC_USE_CTABLE) 2752 PetscCall(PetscHMapIDestroy(&submatj->rmap)); 2753 if (submatj->cmap_loc) PetscCall(PetscFree(submatj->cmap_loc)); 2754 PetscCall(PetscFree(submatj->rmap_loc)); 2755 #else 2756 PetscCall(PetscFree(submatj->rmap)); 2757 #endif 2758 2759 if (!submatj->allcolumns) { 2760 #if defined(PETSC_USE_CTABLE) 2761 PetscCall(PetscHMapIDestroy((PetscHMapI *)&submatj->cmap)); 2762 #else 2763 PetscCall(PetscFree(submatj->cmap)); 2764 #endif 2765 } 2766 PetscCall(PetscFree(submatj->row2proc)); 2767 2768 PetscCall(PetscFree(submatj)); 2769 PetscFunctionReturn(PETSC_SUCCESS); 2770 } 2771 2772 PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C) 2773 { 2774 Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data; 2775 Mat_SubSppt *submatj = c->submatis1; 2776 2777 PetscFunctionBegin; 2778 PetscCall((*submatj->destroy)(C)); 2779 PetscCall(MatDestroySubMatrix_Private(submatj)); 2780 PetscFunctionReturn(PETSC_SUCCESS); 2781 } 2782 2783 /* Note this has code duplication with MatDestroySubMatrices_SeqBAIJ() */ 2784 static PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n, Mat *mat[]) 2785 { 2786 PetscInt i; 2787 Mat C; 2788 Mat_SeqAIJ *c; 2789 Mat_SubSppt *submatj; 2790 2791 PetscFunctionBegin; 2792 for (i = 0; i < n; i++) { 2793 C = (*mat)[i]; 2794 c = (Mat_SeqAIJ *)C->data; 2795 submatj = c->submatis1; 2796 if (submatj) { 2797 if (--((PetscObject)C)->refct <= 0) { 2798 PetscCall(PetscFree(C->factorprefix)); 2799 PetscCall((*submatj->destroy)(C)); 2800 PetscCall(MatDestroySubMatrix_Private(submatj)); 2801 PetscCall(PetscFree(C->defaultvectype)); 2802 PetscCall(PetscFree(C->defaultrandtype)); 2803 PetscCall(PetscLayoutDestroy(&C->rmap)); 2804 PetscCall(PetscLayoutDestroy(&C->cmap)); 2805 PetscCall(PetscHeaderDestroy(&C)); 2806 } 2807 } else { 2808 PetscCall(MatDestroy(&C)); 2809 } 2810 } 2811 2812 /* Destroy Dummy submatrices created for reuse */ 2813 PetscCall(MatDestroySubMatrices_Dummy(n, mat)); 2814 2815 PetscCall(PetscFree(*mat)); 2816 PetscFunctionReturn(PETSC_SUCCESS); 2817 } 2818 2819 static PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *B[]) 2820 { 2821 PetscInt i; 2822 2823 PetscFunctionBegin; 2824 if (scall == MAT_INITIAL_MATRIX) PetscCall(PetscCalloc1(n + 1, B)); 2825 2826 for (i = 0; i < n; i++) PetscCall(MatCreateSubMatrix_SeqAIJ(A, irow[i], icol[i], PETSC_DECIDE, scall, &(*B)[i])); 2827 PetscFunctionReturn(PETSC_SUCCESS); 2828 } 2829 2830 static PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A, PetscInt is_max, IS is[], PetscInt ov) 2831 { 2832 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2833 PetscInt row, i, j, k, l, ll, m, n, *nidx, isz, val; 2834 const PetscInt *idx; 2835 PetscInt start, end, *ai, *aj, bs = (A->rmap->bs > 0 && A->rmap->bs == A->cmap->bs) ? A->rmap->bs : 1; 2836 PetscBT table; 2837 2838 PetscFunctionBegin; 2839 m = A->rmap->n / bs; 2840 ai = a->i; 2841 aj = a->j; 2842 2843 PetscCheck(ov >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "illegal negative overlap value used"); 2844 2845 PetscCall(PetscMalloc1(m + 1, &nidx)); 2846 PetscCall(PetscBTCreate(m, &table)); 2847 2848 for (i = 0; i < is_max; i++) { 2849 /* Initialize the two local arrays */ 2850 isz = 0; 2851 PetscCall(PetscBTMemzero(m, table)); 2852 2853 /* Extract the indices, assume there can be duplicate entries */ 2854 PetscCall(ISGetIndices(is[i], &idx)); 2855 PetscCall(ISGetLocalSize(is[i], &n)); 2856 2857 if (bs > 1) { 2858 /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */ 2859 for (j = 0; j < n; ++j) { 2860 if (!PetscBTLookupSet(table, idx[j] / bs)) nidx[isz++] = idx[j] / bs; 2861 } 2862 PetscCall(ISRestoreIndices(is[i], &idx)); 2863 PetscCall(ISDestroy(&is[i])); 2864 2865 k = 0; 2866 for (j = 0; j < ov; j++) { /* for each overlap */ 2867 n = isz; 2868 for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */ 2869 for (ll = 0; ll < bs; ll++) { 2870 row = bs * nidx[k] + ll; 2871 start = ai[row]; 2872 end = ai[row + 1]; 2873 for (l = start; l < end; l++) { 2874 val = aj[l] / bs; 2875 if (!PetscBTLookupSet(table, val)) nidx[isz++] = val; 2876 } 2877 } 2878 } 2879 } 2880 PetscCall(ISCreateBlock(PETSC_COMM_SELF, bs, isz, nidx, PETSC_COPY_VALUES, (is + i))); 2881 } else { 2882 /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */ 2883 for (j = 0; j < n; ++j) { 2884 if (!PetscBTLookupSet(table, idx[j])) nidx[isz++] = idx[j]; 2885 } 2886 PetscCall(ISRestoreIndices(is[i], &idx)); 2887 PetscCall(ISDestroy(&is[i])); 2888 2889 k = 0; 2890 for (j = 0; j < ov; j++) { /* for each overlap */ 2891 n = isz; 2892 for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */ 2893 row = nidx[k]; 2894 start = ai[row]; 2895 end = ai[row + 1]; 2896 for (l = start; l < end; l++) { 2897 val = aj[l]; 2898 if (!PetscBTLookupSet(table, val)) nidx[isz++] = val; 2899 } 2900 } 2901 } 2902 PetscCall(ISCreateGeneral(PETSC_COMM_SELF, isz, nidx, PETSC_COPY_VALUES, (is + i))); 2903 } 2904 } 2905 PetscCall(PetscBTDestroy(&table)); 2906 PetscCall(PetscFree(nidx)); 2907 PetscFunctionReturn(PETSC_SUCCESS); 2908 } 2909 2910 static PetscErrorCode MatPermute_SeqAIJ(Mat A, IS rowp, IS colp, Mat *B) 2911 { 2912 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2913 PetscInt i, nz = 0, m = A->rmap->n, n = A->cmap->n; 2914 const PetscInt *row, *col; 2915 PetscInt *cnew, j, *lens; 2916 IS icolp, irowp; 2917 PetscInt *cwork = NULL; 2918 PetscScalar *vwork = NULL; 2919 2920 PetscFunctionBegin; 2921 PetscCall(ISInvertPermutation(rowp, PETSC_DECIDE, &irowp)); 2922 PetscCall(ISGetIndices(irowp, &row)); 2923 PetscCall(ISInvertPermutation(colp, PETSC_DECIDE, &icolp)); 2924 PetscCall(ISGetIndices(icolp, &col)); 2925 2926 /* determine lengths of permuted rows */ 2927 PetscCall(PetscMalloc1(m + 1, &lens)); 2928 for (i = 0; i < m; i++) lens[row[i]] = a->i[i + 1] - a->i[i]; 2929 PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B)); 2930 PetscCall(MatSetSizes(*B, m, n, m, n)); 2931 PetscCall(MatSetBlockSizesFromMats(*B, A, A)); 2932 PetscCall(MatSetType(*B, ((PetscObject)A)->type_name)); 2933 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*B, 0, lens)); 2934 PetscCall(PetscFree(lens)); 2935 2936 PetscCall(PetscMalloc1(n, &cnew)); 2937 for (i = 0; i < m; i++) { 2938 PetscCall(MatGetRow_SeqAIJ(A, i, &nz, &cwork, &vwork)); 2939 for (j = 0; j < nz; j++) cnew[j] = col[cwork[j]]; 2940 PetscCall(MatSetValues_SeqAIJ(*B, 1, &row[i], nz, cnew, vwork, INSERT_VALUES)); 2941 PetscCall(MatRestoreRow_SeqAIJ(A, i, &nz, &cwork, &vwork)); 2942 } 2943 PetscCall(PetscFree(cnew)); 2944 2945 (*B)->assembled = PETSC_FALSE; 2946 2947 #if defined(PETSC_HAVE_DEVICE) 2948 PetscCall(MatBindToCPU(*B, A->boundtocpu)); 2949 #endif 2950 PetscCall(MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY)); 2951 PetscCall(MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY)); 2952 PetscCall(ISRestoreIndices(irowp, &row)); 2953 PetscCall(ISRestoreIndices(icolp, &col)); 2954 PetscCall(ISDestroy(&irowp)); 2955 PetscCall(ISDestroy(&icolp)); 2956 if (rowp == colp) PetscCall(MatPropagateSymmetryOptions(A, *B)); 2957 PetscFunctionReturn(PETSC_SUCCESS); 2958 } 2959 2960 PetscErrorCode MatCopy_SeqAIJ(Mat A, Mat B, MatStructure str) 2961 { 2962 PetscFunctionBegin; 2963 /* If the two matrices have the same copy implementation, use fast copy. */ 2964 if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) { 2965 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2966 Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data; 2967 const PetscScalar *aa; 2968 2969 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 2970 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]); 2971 PetscCall(PetscArraycpy(b->a, aa, a->i[A->rmap->n])); 2972 PetscCall(PetscObjectStateIncrease((PetscObject)B)); 2973 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 2974 } else { 2975 PetscCall(MatCopy_Basic(A, B, str)); 2976 } 2977 PetscFunctionReturn(PETSC_SUCCESS); 2978 } 2979 2980 PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A, PetscScalar *array[]) 2981 { 2982 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 2983 2984 PetscFunctionBegin; 2985 *array = a->a; 2986 PetscFunctionReturn(PETSC_SUCCESS); 2987 } 2988 2989 PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A, PetscScalar *array[]) 2990 { 2991 PetscFunctionBegin; 2992 *array = NULL; 2993 PetscFunctionReturn(PETSC_SUCCESS); 2994 } 2995 2996 /* 2997 Computes the number of nonzeros per row needed for preallocation when X and Y 2998 have different nonzero structure. 2999 */ 3000 PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *yi, const PetscInt *yj, PetscInt *nnz) 3001 { 3002 PetscInt i, j, k, nzx, nzy; 3003 3004 PetscFunctionBegin; 3005 /* Set the number of nonzeros in the new matrix */ 3006 for (i = 0; i < m; i++) { 3007 const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]); 3008 nzx = xi[i + 1] - xi[i]; 3009 nzy = yi[i + 1] - yi[i]; 3010 nnz[i] = 0; 3011 for (j = 0, k = 0; j < nzx; j++) { /* Point in X */ 3012 for (; k < nzy && yjj[k] < xjj[j]; k++) nnz[i]++; /* Catch up to X */ 3013 if (k < nzy && yjj[k] == xjj[j]) k++; /* Skip duplicate */ 3014 nnz[i]++; 3015 } 3016 for (; k < nzy; k++) nnz[i]++; 3017 } 3018 PetscFunctionReturn(PETSC_SUCCESS); 3019 } 3020 3021 PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y, Mat X, PetscInt *nnz) 3022 { 3023 PetscInt m = Y->rmap->N; 3024 Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data; 3025 Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data; 3026 3027 PetscFunctionBegin; 3028 /* Set the number of nonzeros in the new matrix */ 3029 PetscCall(MatAXPYGetPreallocation_SeqX_private(m, x->i, x->j, y->i, y->j, nnz)); 3030 PetscFunctionReturn(PETSC_SUCCESS); 3031 } 3032 3033 PetscErrorCode MatAXPY_SeqAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str) 3034 { 3035 Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data; 3036 3037 PetscFunctionBegin; 3038 if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) { 3039 PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE; 3040 if (e) { 3041 PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e)); 3042 if (e) { 3043 PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e)); 3044 if (e) str = SAME_NONZERO_PATTERN; 3045 } 3046 } 3047 if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN"); 3048 } 3049 if (str == SAME_NONZERO_PATTERN) { 3050 const PetscScalar *xa; 3051 PetscScalar *ya, alpha = a; 3052 PetscBLASInt one = 1, bnz; 3053 3054 PetscCall(PetscBLASIntCast(x->nz, &bnz)); 3055 PetscCall(MatSeqAIJGetArray(Y, &ya)); 3056 PetscCall(MatSeqAIJGetArrayRead(X, &xa)); 3057 PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, xa, &one, ya, &one)); 3058 PetscCall(MatSeqAIJRestoreArrayRead(X, &xa)); 3059 PetscCall(MatSeqAIJRestoreArray(Y, &ya)); 3060 PetscCall(PetscLogFlops(2.0 * bnz)); 3061 PetscCall(MatSeqAIJInvalidateDiagonal(Y)); 3062 PetscCall(PetscObjectStateIncrease((PetscObject)Y)); 3063 } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */ 3064 PetscCall(MatAXPY_Basic(Y, a, X, str)); 3065 } else { 3066 Mat B; 3067 PetscInt *nnz; 3068 PetscCall(PetscMalloc1(Y->rmap->N, &nnz)); 3069 PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B)); 3070 PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name)); 3071 PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap)); 3072 PetscCall(MatSetType(B, ((PetscObject)Y)->type_name)); 3073 PetscCall(MatAXPYGetPreallocation_SeqAIJ(Y, X, nnz)); 3074 PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz)); 3075 PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str)); 3076 PetscCall(MatHeaderMerge(Y, &B)); 3077 PetscCall(MatSeqAIJCheckInode(Y)); 3078 PetscCall(PetscFree(nnz)); 3079 } 3080 PetscFunctionReturn(PETSC_SUCCESS); 3081 } 3082 3083 PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat) 3084 { 3085 #if defined(PETSC_USE_COMPLEX) 3086 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 3087 PetscInt i, nz; 3088 PetscScalar *a; 3089 3090 PetscFunctionBegin; 3091 nz = aij->nz; 3092 PetscCall(MatSeqAIJGetArray(mat, &a)); 3093 for (i = 0; i < nz; i++) a[i] = PetscConj(a[i]); 3094 PetscCall(MatSeqAIJRestoreArray(mat, &a)); 3095 #else 3096 PetscFunctionBegin; 3097 #endif 3098 PetscFunctionReturn(PETSC_SUCCESS); 3099 } 3100 3101 static PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[]) 3102 { 3103 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 3104 PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n; 3105 PetscReal atmp; 3106 PetscScalar *x; 3107 const MatScalar *aa, *av; 3108 3109 PetscFunctionBegin; 3110 PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 3111 PetscCall(MatSeqAIJGetArrayRead(A, &av)); 3112 aa = av; 3113 ai = a->i; 3114 aj = a->j; 3115 3116 PetscCall(VecSet(v, 0.0)); 3117 PetscCall(VecGetArrayWrite(v, &x)); 3118 PetscCall(VecGetLocalSize(v, &n)); 3119 PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector"); 3120 for (i = 0; i < m; i++) { 3121 ncols = ai[1] - ai[0]; 3122 ai++; 3123 for (j = 0; j < ncols; j++) { 3124 atmp = PetscAbsScalar(*aa); 3125 if (PetscAbsScalar(x[i]) < atmp) { 3126 x[i] = atmp; 3127 if (idx) idx[i] = *aj; 3128 } 3129 aa++; 3130 aj++; 3131 } 3132 } 3133 PetscCall(VecRestoreArrayWrite(v, &x)); 3134 PetscCall(MatSeqAIJRestoreArrayRead(A, &av)); 3135 PetscFunctionReturn(PETSC_SUCCESS); 3136 } 3137 3138 static PetscErrorCode MatGetRowMax_SeqAIJ(Mat A, Vec v, PetscInt idx[]) 3139 { 3140 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 3141 PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n; 3142 PetscScalar *x; 3143 const MatScalar *aa, *av; 3144 3145 PetscFunctionBegin; 3146 PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 3147 PetscCall(MatSeqAIJGetArrayRead(A, &av)); 3148 aa = av; 3149 ai = a->i; 3150 aj = a->j; 3151 3152 PetscCall(VecSet(v, 0.0)); 3153 PetscCall(VecGetArrayWrite(v, &x)); 3154 PetscCall(VecGetLocalSize(v, &n)); 3155 PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector"); 3156 for (i = 0; i < m; i++) { 3157 ncols = ai[1] - ai[0]; 3158 ai++; 3159 if (ncols == A->cmap->n) { /* row is dense */ 3160 x[i] = *aa; 3161 if (idx) idx[i] = 0; 3162 } else { /* row is sparse so already KNOW maximum is 0.0 or higher */ 3163 x[i] = 0.0; 3164 if (idx) { 3165 for (j = 0; j < ncols; j++) { /* find first implicit 0.0 in the row */ 3166 if (aj[j] > j) { 3167 idx[i] = j; 3168 break; 3169 } 3170 } 3171 /* in case first implicit 0.0 in the row occurs at ncols-th column */ 3172 if (j == ncols && j < A->cmap->n) idx[i] = j; 3173 } 3174 } 3175 for (j = 0; j < ncols; j++) { 3176 if (PetscRealPart(x[i]) < PetscRealPart(*aa)) { 3177 x[i] = *aa; 3178 if (idx) idx[i] = *aj; 3179 } 3180 aa++; 3181 aj++; 3182 } 3183 } 3184 PetscCall(VecRestoreArrayWrite(v, &x)); 3185 PetscCall(MatSeqAIJRestoreArrayRead(A, &av)); 3186 PetscFunctionReturn(PETSC_SUCCESS); 3187 } 3188 3189 static PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[]) 3190 { 3191 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 3192 PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n; 3193 PetscScalar *x; 3194 const MatScalar *aa, *av; 3195 3196 PetscFunctionBegin; 3197 PetscCall(MatSeqAIJGetArrayRead(A, &av)); 3198 aa = av; 3199 ai = a->i; 3200 aj = a->j; 3201 3202 PetscCall(VecSet(v, 0.0)); 3203 PetscCall(VecGetArrayWrite(v, &x)); 3204 PetscCall(VecGetLocalSize(v, &n)); 3205 PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector, %" PetscInt_FMT " vs. %" PetscInt_FMT " rows", m, n); 3206 for (i = 0; i < m; i++) { 3207 ncols = ai[1] - ai[0]; 3208 ai++; 3209 if (ncols == A->cmap->n) { /* row is dense */ 3210 x[i] = *aa; 3211 if (idx) idx[i] = 0; 3212 } else { /* row is sparse so already KNOW minimum is 0.0 or higher */ 3213 x[i] = 0.0; 3214 if (idx) { /* find first implicit 0.0 in the row */ 3215 for (j = 0; j < ncols; j++) { 3216 if (aj[j] > j) { 3217 idx[i] = j; 3218 break; 3219 } 3220 } 3221 /* in case first implicit 0.0 in the row occurs at ncols-th column */ 3222 if (j == ncols && j < A->cmap->n) idx[i] = j; 3223 } 3224 } 3225 for (j = 0; j < ncols; j++) { 3226 if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) { 3227 x[i] = *aa; 3228 if (idx) idx[i] = *aj; 3229 } 3230 aa++; 3231 aj++; 3232 } 3233 } 3234 PetscCall(VecRestoreArrayWrite(v, &x)); 3235 PetscCall(MatSeqAIJRestoreArrayRead(A, &av)); 3236 PetscFunctionReturn(PETSC_SUCCESS); 3237 } 3238 3239 static PetscErrorCode MatGetRowMin_SeqAIJ(Mat A, Vec v, PetscInt idx[]) 3240 { 3241 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 3242 PetscInt i, j, m = A->rmap->n, ncols, n; 3243 const PetscInt *ai, *aj; 3244 PetscScalar *x; 3245 const MatScalar *aa, *av; 3246 3247 PetscFunctionBegin; 3248 PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 3249 PetscCall(MatSeqAIJGetArrayRead(A, &av)); 3250 aa = av; 3251 ai = a->i; 3252 aj = a->j; 3253 3254 PetscCall(VecSet(v, 0.0)); 3255 PetscCall(VecGetArrayWrite(v, &x)); 3256 PetscCall(VecGetLocalSize(v, &n)); 3257 PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector"); 3258 for (i = 0; i < m; i++) { 3259 ncols = ai[1] - ai[0]; 3260 ai++; 3261 if (ncols == A->cmap->n) { /* row is dense */ 3262 x[i] = *aa; 3263 if (idx) idx[i] = 0; 3264 } else { /* row is sparse so already KNOW minimum is 0.0 or lower */ 3265 x[i] = 0.0; 3266 if (idx) { /* find first implicit 0.0 in the row */ 3267 for (j = 0; j < ncols; j++) { 3268 if (aj[j] > j) { 3269 idx[i] = j; 3270 break; 3271 } 3272 } 3273 /* in case first implicit 0.0 in the row occurs at ncols-th column */ 3274 if (j == ncols && j < A->cmap->n) idx[i] = j; 3275 } 3276 } 3277 for (j = 0; j < ncols; j++) { 3278 if (PetscRealPart(x[i]) > PetscRealPart(*aa)) { 3279 x[i] = *aa; 3280 if (idx) idx[i] = *aj; 3281 } 3282 aa++; 3283 aj++; 3284 } 3285 } 3286 PetscCall(VecRestoreArrayWrite(v, &x)); 3287 PetscCall(MatSeqAIJRestoreArrayRead(A, &av)); 3288 PetscFunctionReturn(PETSC_SUCCESS); 3289 } 3290 3291 static PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A, const PetscScalar **values) 3292 { 3293 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 3294 PetscInt i, bs = PetscAbs(A->rmap->bs), mbs = A->rmap->n / bs, ipvt[5], bs2 = bs * bs, *v_pivots, ij[7], *IJ, j; 3295 MatScalar *diag, work[25], *v_work; 3296 const PetscReal shift = 0.0; 3297 PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE; 3298 3299 PetscFunctionBegin; 3300 allowzeropivot = PetscNot(A->erroriffailure); 3301 if (a->ibdiagvalid) { 3302 if (values) *values = a->ibdiag; 3303 PetscFunctionReturn(PETSC_SUCCESS); 3304 } 3305 PetscCall(MatMarkDiagonal_SeqAIJ(A)); 3306 if (!a->ibdiag) { PetscCall(PetscMalloc1(bs2 * mbs, &a->ibdiag)); } 3307 diag = a->ibdiag; 3308 if (values) *values = a->ibdiag; 3309 /* factor and invert each block */ 3310 switch (bs) { 3311 case 1: 3312 for (i = 0; i < mbs; i++) { 3313 PetscCall(MatGetValues(A, 1, &i, 1, &i, diag + i)); 3314 if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) { 3315 if (allowzeropivot) { 3316 A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3317 A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]); 3318 A->factorerror_zeropivot_row = i; 3319 PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g\n", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON)); 3320 } 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); 3321 } 3322 diag[i] = (PetscScalar)1.0 / (diag[i] + shift); 3323 } 3324 break; 3325 case 2: 3326 for (i = 0; i < mbs; i++) { 3327 ij[0] = 2 * i; 3328 ij[1] = 2 * i + 1; 3329 PetscCall(MatGetValues(A, 2, ij, 2, ij, diag)); 3330 PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected)); 3331 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3332 PetscCall(PetscKernel_A_gets_transpose_A_2(diag)); 3333 diag += 4; 3334 } 3335 break; 3336 case 3: 3337 for (i = 0; i < mbs; i++) { 3338 ij[0] = 3 * i; 3339 ij[1] = 3 * i + 1; 3340 ij[2] = 3 * i + 2; 3341 PetscCall(MatGetValues(A, 3, ij, 3, ij, diag)); 3342 PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected)); 3343 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3344 PetscCall(PetscKernel_A_gets_transpose_A_3(diag)); 3345 diag += 9; 3346 } 3347 break; 3348 case 4: 3349 for (i = 0; i < mbs; i++) { 3350 ij[0] = 4 * i; 3351 ij[1] = 4 * i + 1; 3352 ij[2] = 4 * i + 2; 3353 ij[3] = 4 * i + 3; 3354 PetscCall(MatGetValues(A, 4, ij, 4, ij, diag)); 3355 PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected)); 3356 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3357 PetscCall(PetscKernel_A_gets_transpose_A_4(diag)); 3358 diag += 16; 3359 } 3360 break; 3361 case 5: 3362 for (i = 0; i < mbs; i++) { 3363 ij[0] = 5 * i; 3364 ij[1] = 5 * i + 1; 3365 ij[2] = 5 * i + 2; 3366 ij[3] = 5 * i + 3; 3367 ij[4] = 5 * i + 4; 3368 PetscCall(MatGetValues(A, 5, ij, 5, ij, diag)); 3369 PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected)); 3370 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3371 PetscCall(PetscKernel_A_gets_transpose_A_5(diag)); 3372 diag += 25; 3373 } 3374 break; 3375 case 6: 3376 for (i = 0; i < mbs; i++) { 3377 ij[0] = 6 * i; 3378 ij[1] = 6 * i + 1; 3379 ij[2] = 6 * i + 2; 3380 ij[3] = 6 * i + 3; 3381 ij[4] = 6 * i + 4; 3382 ij[5] = 6 * i + 5; 3383 PetscCall(MatGetValues(A, 6, ij, 6, ij, diag)); 3384 PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected)); 3385 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3386 PetscCall(PetscKernel_A_gets_transpose_A_6(diag)); 3387 diag += 36; 3388 } 3389 break; 3390 case 7: 3391 for (i = 0; i < mbs; i++) { 3392 ij[0] = 7 * i; 3393 ij[1] = 7 * i + 1; 3394 ij[2] = 7 * i + 2; 3395 ij[3] = 7 * i + 3; 3396 ij[4] = 7 * i + 4; 3397 ij[5] = 7 * i + 5; 3398 ij[6] = 7 * i + 6; 3399 PetscCall(MatGetValues(A, 7, ij, 7, ij, diag)); 3400 PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected)); 3401 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3402 PetscCall(PetscKernel_A_gets_transpose_A_7(diag)); 3403 diag += 49; 3404 } 3405 break; 3406 default: 3407 PetscCall(PetscMalloc3(bs, &v_work, bs, &v_pivots, bs, &IJ)); 3408 for (i = 0; i < mbs; i++) { 3409 for (j = 0; j < bs; j++) IJ[j] = bs * i + j; 3410 PetscCall(MatGetValues(A, bs, IJ, bs, IJ, diag)); 3411 PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected)); 3412 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 3413 PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bs)); 3414 diag += bs2; 3415 } 3416 PetscCall(PetscFree3(v_work, v_pivots, IJ)); 3417 } 3418 a->ibdiagvalid = PETSC_TRUE; 3419 PetscFunctionReturn(PETSC_SUCCESS); 3420 } 3421 3422 static PetscErrorCode MatSetRandom_SeqAIJ(Mat x, PetscRandom rctx) 3423 { 3424 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data; 3425 PetscScalar a, *aa; 3426 PetscInt m, n, i, j, col; 3427 3428 PetscFunctionBegin; 3429 if (!x->assembled) { 3430 PetscCall(MatGetSize(x, &m, &n)); 3431 for (i = 0; i < m; i++) { 3432 for (j = 0; j < aij->imax[i]; j++) { 3433 PetscCall(PetscRandomGetValue(rctx, &a)); 3434 col = (PetscInt)(n * PetscRealPart(a)); 3435 PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES)); 3436 } 3437 } 3438 } else { 3439 PetscCall(MatSeqAIJGetArrayWrite(x, &aa)); 3440 for (i = 0; i < aij->nz; i++) PetscCall(PetscRandomGetValue(rctx, aa + i)); 3441 PetscCall(MatSeqAIJRestoreArrayWrite(x, &aa)); 3442 } 3443 PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY)); 3444 PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY)); 3445 PetscFunctionReturn(PETSC_SUCCESS); 3446 } 3447 3448 /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */ 3449 PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x, PetscInt low, PetscInt high, PetscRandom rctx) 3450 { 3451 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data; 3452 PetscScalar a; 3453 PetscInt m, n, i, j, col, nskip; 3454 3455 PetscFunctionBegin; 3456 nskip = high - low; 3457 PetscCall(MatGetSize(x, &m, &n)); 3458 n -= nskip; /* shrink number of columns where nonzeros can be set */ 3459 for (i = 0; i < m; i++) { 3460 for (j = 0; j < aij->imax[i]; j++) { 3461 PetscCall(PetscRandomGetValue(rctx, &a)); 3462 col = (PetscInt)(n * PetscRealPart(a)); 3463 if (col >= low) col += nskip; /* shift col rightward to skip the hole */ 3464 PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES)); 3465 } 3466 } 3467 PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY)); 3468 PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY)); 3469 PetscFunctionReturn(PETSC_SUCCESS); 3470 } 3471 3472 static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ, 3473 MatGetRow_SeqAIJ, 3474 MatRestoreRow_SeqAIJ, 3475 MatMult_SeqAIJ, 3476 /* 4*/ MatMultAdd_SeqAIJ, 3477 MatMultTranspose_SeqAIJ, 3478 MatMultTransposeAdd_SeqAIJ, 3479 NULL, 3480 NULL, 3481 NULL, 3482 /* 10*/ NULL, 3483 MatLUFactor_SeqAIJ, 3484 NULL, 3485 MatSOR_SeqAIJ, 3486 MatTranspose_SeqAIJ, 3487 /*1 5*/ MatGetInfo_SeqAIJ, 3488 MatEqual_SeqAIJ, 3489 MatGetDiagonal_SeqAIJ, 3490 MatDiagonalScale_SeqAIJ, 3491 MatNorm_SeqAIJ, 3492 /* 20*/ NULL, 3493 MatAssemblyEnd_SeqAIJ, 3494 MatSetOption_SeqAIJ, 3495 MatZeroEntries_SeqAIJ, 3496 /* 24*/ MatZeroRows_SeqAIJ, 3497 NULL, 3498 NULL, 3499 NULL, 3500 NULL, 3501 /* 29*/ MatSetUp_Seq_Hash, 3502 NULL, 3503 NULL, 3504 NULL, 3505 NULL, 3506 /* 34*/ MatDuplicate_SeqAIJ, 3507 NULL, 3508 NULL, 3509 MatILUFactor_SeqAIJ, 3510 NULL, 3511 /* 39*/ MatAXPY_SeqAIJ, 3512 MatCreateSubMatrices_SeqAIJ, 3513 MatIncreaseOverlap_SeqAIJ, 3514 MatGetValues_SeqAIJ, 3515 MatCopy_SeqAIJ, 3516 /* 44*/ MatGetRowMax_SeqAIJ, 3517 MatScale_SeqAIJ, 3518 MatShift_SeqAIJ, 3519 MatDiagonalSet_SeqAIJ, 3520 MatZeroRowsColumns_SeqAIJ, 3521 /* 49*/ MatSetRandom_SeqAIJ, 3522 MatGetRowIJ_SeqAIJ, 3523 MatRestoreRowIJ_SeqAIJ, 3524 MatGetColumnIJ_SeqAIJ, 3525 MatRestoreColumnIJ_SeqAIJ, 3526 /* 54*/ MatFDColoringCreate_SeqXAIJ, 3527 NULL, 3528 NULL, 3529 MatPermute_SeqAIJ, 3530 NULL, 3531 /* 59*/ NULL, 3532 MatDestroy_SeqAIJ, 3533 MatView_SeqAIJ, 3534 NULL, 3535 NULL, 3536 /* 64*/ NULL, 3537 MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ, 3538 NULL, 3539 NULL, 3540 NULL, 3541 /* 69*/ MatGetRowMaxAbs_SeqAIJ, 3542 MatGetRowMinAbs_SeqAIJ, 3543 NULL, 3544 NULL, 3545 NULL, 3546 /* 74*/ NULL, 3547 MatFDColoringApply_AIJ, 3548 NULL, 3549 NULL, 3550 NULL, 3551 /* 79*/ MatFindZeroDiagonals_SeqAIJ, 3552 NULL, 3553 NULL, 3554 NULL, 3555 MatLoad_SeqAIJ, 3556 /* 84*/ MatIsSymmetric_SeqAIJ, 3557 MatIsHermitian_SeqAIJ, 3558 NULL, 3559 NULL, 3560 NULL, 3561 /* 89*/ NULL, 3562 NULL, 3563 MatMatMultNumeric_SeqAIJ_SeqAIJ, 3564 NULL, 3565 NULL, 3566 /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy, 3567 NULL, 3568 NULL, 3569 MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ, 3570 NULL, 3571 /* 99*/ MatProductSetFromOptions_SeqAIJ, 3572 NULL, 3573 NULL, 3574 MatConjugate_SeqAIJ, 3575 NULL, 3576 /*104*/ MatSetValuesRow_SeqAIJ, 3577 MatRealPart_SeqAIJ, 3578 MatImaginaryPart_SeqAIJ, 3579 NULL, 3580 NULL, 3581 /*109*/ MatMatSolve_SeqAIJ, 3582 NULL, 3583 MatGetRowMin_SeqAIJ, 3584 NULL, 3585 MatMissingDiagonal_SeqAIJ, 3586 /*114*/ NULL, 3587 NULL, 3588 NULL, 3589 NULL, 3590 NULL, 3591 /*119*/ NULL, 3592 NULL, 3593 NULL, 3594 NULL, 3595 MatGetMultiProcBlock_SeqAIJ, 3596 /*124*/ MatFindNonzeroRows_SeqAIJ, 3597 MatGetColumnReductions_SeqAIJ, 3598 MatInvertBlockDiagonal_SeqAIJ, 3599 MatInvertVariableBlockDiagonal_SeqAIJ, 3600 NULL, 3601 /*129*/ NULL, 3602 NULL, 3603 NULL, 3604 MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ, 3605 MatTransposeColoringCreate_SeqAIJ, 3606 /*134*/ MatTransColoringApplySpToDen_SeqAIJ, 3607 MatTransColoringApplyDenToSp_SeqAIJ, 3608 NULL, 3609 NULL, 3610 MatRARtNumeric_SeqAIJ_SeqAIJ, 3611 /*139*/ NULL, 3612 NULL, 3613 NULL, 3614 MatFDColoringSetUp_SeqXAIJ, 3615 MatFindOffBlockDiagonalEntries_SeqAIJ, 3616 MatCreateMPIMatConcatenateSeqMat_SeqAIJ, 3617 /*145*/ MatDestroySubMatrices_SeqAIJ, 3618 NULL, 3619 NULL, 3620 MatCreateGraph_Simple_AIJ, 3621 NULL, 3622 /*150*/ MatTransposeSymbolic_SeqAIJ, 3623 MatEliminateZeros_SeqAIJ}; 3624 3625 static PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat, PetscInt *indices) 3626 { 3627 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 3628 PetscInt i, nz, n; 3629 3630 PetscFunctionBegin; 3631 nz = aij->maxnz; 3632 n = mat->rmap->n; 3633 for (i = 0; i < nz; i++) aij->j[i] = indices[i]; 3634 aij->nz = nz; 3635 for (i = 0; i < n; i++) aij->ilen[i] = aij->imax[i]; 3636 PetscFunctionReturn(PETSC_SUCCESS); 3637 } 3638 3639 /* 3640 * Given a sparse matrix with global column indices, compact it by using a local column space. 3641 * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable() 3642 */ 3643 PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping) 3644 { 3645 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 3646 PetscHMapI gid1_lid1; 3647 PetscHashIter tpos; 3648 PetscInt gid, lid, i, ec, nz = aij->nz; 3649 PetscInt *garray, *jj = aij->j; 3650 3651 PetscFunctionBegin; 3652 PetscValidHeaderSpecific(mat, MAT_CLASSID, 1); 3653 PetscAssertPointer(mapping, 2); 3654 /* use a table */ 3655 PetscCall(PetscHMapICreateWithSize(mat->rmap->n, &gid1_lid1)); 3656 ec = 0; 3657 for (i = 0; i < nz; i++) { 3658 PetscInt data, gid1 = jj[i] + 1; 3659 PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &data)); 3660 if (!data) { 3661 /* one based table */ 3662 PetscCall(PetscHMapISet(gid1_lid1, gid1, ++ec)); 3663 } 3664 } 3665 /* form array of columns we need */ 3666 PetscCall(PetscMalloc1(ec, &garray)); 3667 PetscHashIterBegin(gid1_lid1, tpos); 3668 while (!PetscHashIterAtEnd(gid1_lid1, tpos)) { 3669 PetscHashIterGetKey(gid1_lid1, tpos, gid); 3670 PetscHashIterGetVal(gid1_lid1, tpos, lid); 3671 PetscHashIterNext(gid1_lid1, tpos); 3672 gid--; 3673 lid--; 3674 garray[lid] = gid; 3675 } 3676 PetscCall(PetscSortInt(ec, garray)); /* sort, and rebuild */ 3677 PetscCall(PetscHMapIClear(gid1_lid1)); 3678 for (i = 0; i < ec; i++) PetscCall(PetscHMapISet(gid1_lid1, garray[i] + 1, i + 1)); 3679 /* compact out the extra columns in B */ 3680 for (i = 0; i < nz; i++) { 3681 PetscInt gid1 = jj[i] + 1; 3682 PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &lid)); 3683 lid--; 3684 jj[i] = lid; 3685 } 3686 PetscCall(PetscLayoutDestroy(&mat->cmap)); 3687 PetscCall(PetscHMapIDestroy(&gid1_lid1)); 3688 PetscCall(PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat), ec, ec, 1, &mat->cmap)); 3689 PetscCall(ISLocalToGlobalMappingCreate(PETSC_COMM_SELF, mat->cmap->bs, mat->cmap->n, garray, PETSC_OWN_POINTER, mapping)); 3690 PetscCall(ISLocalToGlobalMappingSetType(*mapping, ISLOCALTOGLOBALMAPPINGHASH)); 3691 PetscFunctionReturn(PETSC_SUCCESS); 3692 } 3693 3694 /*@ 3695 MatSeqAIJSetColumnIndices - Set the column indices for all the rows 3696 in the matrix. 3697 3698 Input Parameters: 3699 + mat - the `MATSEQAIJ` matrix 3700 - indices - the column indices 3701 3702 Level: advanced 3703 3704 Notes: 3705 This can be called if you have precomputed the nonzero structure of the 3706 matrix and want to provide it to the matrix object to improve the performance 3707 of the `MatSetValues()` operation. 3708 3709 You MUST have set the correct numbers of nonzeros per row in the call to 3710 `MatCreateSeqAIJ()`, and the columns indices MUST be sorted. 3711 3712 MUST be called before any calls to `MatSetValues()` 3713 3714 The indices should start with zero, not one. 3715 3716 .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ` 3717 @*/ 3718 PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat, PetscInt *indices) 3719 { 3720 PetscFunctionBegin; 3721 PetscValidHeaderSpecific(mat, MAT_CLASSID, 1); 3722 PetscAssertPointer(indices, 2); 3723 PetscUseMethod(mat, "MatSeqAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices)); 3724 PetscFunctionReturn(PETSC_SUCCESS); 3725 } 3726 3727 static PetscErrorCode MatStoreValues_SeqAIJ(Mat mat) 3728 { 3729 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 3730 size_t nz = aij->i[mat->rmap->n]; 3731 3732 PetscFunctionBegin; 3733 PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first"); 3734 3735 /* allocate space for values if not already there */ 3736 if (!aij->saved_values) { PetscCall(PetscMalloc1(nz + 1, &aij->saved_values)); } 3737 3738 /* copy values over */ 3739 PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz)); 3740 PetscFunctionReturn(PETSC_SUCCESS); 3741 } 3742 3743 /*@ 3744 MatStoreValues - Stashes a copy of the matrix values; this allows reusing of the linear part of a Jacobian, while recomputing only the 3745 nonlinear portion. 3746 3747 Logically Collect 3748 3749 Input Parameter: 3750 . mat - the matrix (currently only `MATAIJ` matrices support this option) 3751 3752 Level: advanced 3753 3754 Example Usage: 3755 .vb 3756 Using SNES 3757 Create Jacobian matrix 3758 Set linear terms into matrix 3759 Apply boundary conditions to matrix, at this time matrix must have 3760 final nonzero structure (i.e. setting the nonlinear terms and applying 3761 boundary conditions again will not change the nonzero structure 3762 MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE); 3763 MatStoreValues(mat); 3764 Call SNESSetJacobian() with matrix 3765 In your Jacobian routine 3766 MatRetrieveValues(mat); 3767 Set nonlinear terms in matrix 3768 3769 Without `SNESSolve()`, i.e. when you handle nonlinear solve yourself: 3770 // build linear portion of Jacobian 3771 MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE); 3772 MatStoreValues(mat); 3773 loop over nonlinear iterations 3774 MatRetrieveValues(mat); 3775 // call MatSetValues(mat,...) to set nonliner portion of Jacobian 3776 // call MatAssemblyBegin/End() on matrix 3777 Solve linear system with Jacobian 3778 endloop 3779 .ve 3780 3781 Notes: 3782 Matrix must already be assembled before calling this routine 3783 Must set the matrix option `MatSetOption`(mat,`MAT_NEW_NONZERO_LOCATIONS`,`PETSC_FALSE`); before 3784 calling this routine. 3785 3786 When this is called multiple times it overwrites the previous set of stored values 3787 and does not allocated additional space. 3788 3789 .seealso: [](ch_matrices), `Mat`, `MatRetrieveValues()` 3790 @*/ 3791 PetscErrorCode MatStoreValues(Mat mat) 3792 { 3793 PetscFunctionBegin; 3794 PetscValidHeaderSpecific(mat, MAT_CLASSID, 1); 3795 PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix"); 3796 PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 3797 PetscUseMethod(mat, "MatStoreValues_C", (Mat), (mat)); 3798 PetscFunctionReturn(PETSC_SUCCESS); 3799 } 3800 3801 static PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat) 3802 { 3803 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 3804 PetscInt nz = aij->i[mat->rmap->n]; 3805 3806 PetscFunctionBegin; 3807 PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first"); 3808 PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first"); 3809 /* copy values over */ 3810 PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz)); 3811 PetscFunctionReturn(PETSC_SUCCESS); 3812 } 3813 3814 /*@ 3815 MatRetrieveValues - Retrieves the copy of the matrix values that was stored with `MatStoreValues()` 3816 3817 Logically Collect 3818 3819 Input Parameter: 3820 . mat - the matrix (currently only `MATAIJ` matrices support this option) 3821 3822 Level: advanced 3823 3824 .seealso: [](ch_matrices), `Mat`, `MatStoreValues()` 3825 @*/ 3826 PetscErrorCode MatRetrieveValues(Mat mat) 3827 { 3828 PetscFunctionBegin; 3829 PetscValidHeaderSpecific(mat, MAT_CLASSID, 1); 3830 PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix"); 3831 PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 3832 PetscUseMethod(mat, "MatRetrieveValues_C", (Mat), (mat)); 3833 PetscFunctionReturn(PETSC_SUCCESS); 3834 } 3835 3836 /*@C 3837 MatCreateSeqAIJ - Creates a sparse matrix in `MATSEQAIJ` (compressed row) format 3838 (the default parallel PETSc format). For good matrix assembly performance 3839 the user should preallocate the matrix storage by setting the parameter `nz` 3840 (or the array `nnz`). 3841 3842 Collective 3843 3844 Input Parameters: 3845 + comm - MPI communicator, set to `PETSC_COMM_SELF` 3846 . m - number of rows 3847 . n - number of columns 3848 . nz - number of nonzeros per row (same for all rows) 3849 - nnz - array containing the number of nonzeros in the various rows 3850 (possibly different for each row) or NULL 3851 3852 Output Parameter: 3853 . A - the matrix 3854 3855 Options Database Keys: 3856 + -mat_no_inode - Do not use inodes 3857 - -mat_inode_limit <limit> - Sets inode limit (max limit=5) 3858 3859 Level: intermediate 3860 3861 Notes: 3862 It is recommend to use `MatCreateFromOptions()` instead of this routine 3863 3864 If `nnz` is given then `nz` is ignored 3865 3866 The `MATSEQAIJ` format, also called 3867 compressed row storage, is fully compatible with standard Fortran 3868 storage. That is, the stored row and column indices can begin at 3869 either one (as in Fortran) or zero. 3870 3871 Specify the preallocated storage with either `nz` or `nnz` (not both). 3872 Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory 3873 allocation. 3874 3875 By default, this format uses inodes (identical nodes) when possible, to 3876 improve numerical efficiency of matrix-vector products and solves. We 3877 search for consecutive rows with the same nonzero structure, thereby 3878 reusing matrix information to achieve increased efficiency. 3879 3880 .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()` 3881 @*/ 3882 PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A) 3883 { 3884 PetscFunctionBegin; 3885 PetscCall(MatCreate(comm, A)); 3886 PetscCall(MatSetSizes(*A, m, n, m, n)); 3887 PetscCall(MatSetType(*A, MATSEQAIJ)); 3888 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz)); 3889 PetscFunctionReturn(PETSC_SUCCESS); 3890 } 3891 3892 /*@C 3893 MatSeqAIJSetPreallocation - For good matrix assembly performance 3894 the user should preallocate the matrix storage by setting the parameter nz 3895 (or the array nnz). By setting these parameters accurately, performance 3896 during matrix assembly can be increased by more than a factor of 50. 3897 3898 Collective 3899 3900 Input Parameters: 3901 + B - The matrix 3902 . nz - number of nonzeros per row (same for all rows) 3903 - nnz - array containing the number of nonzeros in the various rows 3904 (possibly different for each row) or NULL 3905 3906 Options Database Keys: 3907 + -mat_no_inode - Do not use inodes 3908 - -mat_inode_limit <limit> - Sets inode limit (max limit=5) 3909 3910 Level: intermediate 3911 3912 Notes: 3913 If `nnz` is given then `nz` is ignored 3914 3915 The `MATSEQAIJ` format also called 3916 compressed row storage, is fully compatible with standard Fortran 3917 storage. That is, the stored row and column indices can begin at 3918 either one (as in Fortran) or zero. See the users' manual for details. 3919 3920 Specify the preallocated storage with either `nz` or `nnz` (not both). 3921 Set nz = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory 3922 allocation. 3923 3924 You can call `MatGetInfo()` to get information on how effective the preallocation was; 3925 for example the fields mallocs,nz_allocated,nz_used,nz_unneeded; 3926 You can also run with the option -info and look for messages with the string 3927 malloc in them to see if additional memory allocation was needed. 3928 3929 Developer Notes: 3930 Use nz of `MAT_SKIP_ALLOCATION` to not allocate any space for the matrix 3931 entries or columns indices 3932 3933 By default, this format uses inodes (identical nodes) when possible, to 3934 improve numerical efficiency of matrix-vector products and solves. We 3935 search for consecutive rows with the same nonzero structure, thereby 3936 reusing matrix information to achieve increased efficiency. 3937 3938 .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatGetInfo()`, 3939 `MatSeqAIJSetTotalPreallocation()` 3940 @*/ 3941 PetscErrorCode MatSeqAIJSetPreallocation(Mat B, PetscInt nz, const PetscInt nnz[]) 3942 { 3943 PetscFunctionBegin; 3944 PetscValidHeaderSpecific(B, MAT_CLASSID, 1); 3945 PetscValidType(B, 1); 3946 PetscTryMethod(B, "MatSeqAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[]), (B, nz, nnz)); 3947 PetscFunctionReturn(PETSC_SUCCESS); 3948 } 3949 3950 PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B, PetscInt nz, const PetscInt *nnz) 3951 { 3952 Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data; 3953 PetscBool skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE; 3954 PetscInt i; 3955 3956 PetscFunctionBegin; 3957 if (B->hash_active) { 3958 B->ops[0] = b->cops; 3959 PetscCall(PetscHMapIJVDestroy(&b->ht)); 3960 PetscCall(PetscFree(b->dnz)); 3961 B->hash_active = PETSC_FALSE; 3962 } 3963 if (nz >= 0 || nnz) realalloc = PETSC_TRUE; 3964 if (nz == MAT_SKIP_ALLOCATION) { 3965 skipallocation = PETSC_TRUE; 3966 nz = 0; 3967 } 3968 PetscCall(PetscLayoutSetUp(B->rmap)); 3969 PetscCall(PetscLayoutSetUp(B->cmap)); 3970 3971 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5; 3972 PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz); 3973 if (PetscUnlikelyDebug(nnz)) { 3974 for (i = 0; i < B->rmap->n; i++) { 3975 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]); 3976 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); 3977 } 3978 } 3979 3980 B->preallocated = PETSC_TRUE; 3981 if (!skipallocation) { 3982 if (!b->imax) { PetscCall(PetscMalloc1(B->rmap->n, &b->imax)); } 3983 if (!b->ilen) { 3984 /* b->ilen will count nonzeros in each row so far. */ 3985 PetscCall(PetscCalloc1(B->rmap->n, &b->ilen)); 3986 } else { 3987 PetscCall(PetscMemzero(b->ilen, B->rmap->n * sizeof(PetscInt))); 3988 } 3989 if (!b->ipre) PetscCall(PetscMalloc1(B->rmap->n, &b->ipre)); 3990 if (!nnz) { 3991 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10; 3992 else if (nz < 0) nz = 1; 3993 nz = PetscMin(nz, B->cmap->n); 3994 for (i = 0; i < B->rmap->n; i++) b->imax[i] = nz; 3995 PetscCall(PetscIntMultError(nz, B->rmap->n, &nz)); 3996 } else { 3997 PetscInt64 nz64 = 0; 3998 for (i = 0; i < B->rmap->n; i++) { 3999 b->imax[i] = nnz[i]; 4000 nz64 += nnz[i]; 4001 } 4002 PetscCall(PetscIntCast(nz64, &nz)); 4003 } 4004 4005 /* allocate the matrix space */ 4006 /* FIXME: should B's old memory be unlogged? */ 4007 PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i)); 4008 if (B->structure_only) { 4009 PetscCall(PetscMalloc1(nz, &b->j)); 4010 PetscCall(PetscMalloc1(B->rmap->n + 1, &b->i)); 4011 } else { 4012 PetscCall(PetscMalloc3(nz, &b->a, nz, &b->j, B->rmap->n + 1, &b->i)); 4013 } 4014 b->i[0] = 0; 4015 for (i = 1; i < B->rmap->n + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1]; 4016 if (B->structure_only) { 4017 b->singlemalloc = PETSC_FALSE; 4018 b->free_a = PETSC_FALSE; 4019 } else { 4020 b->singlemalloc = PETSC_TRUE; 4021 b->free_a = PETSC_TRUE; 4022 } 4023 b->free_ij = PETSC_TRUE; 4024 } else { 4025 b->free_a = PETSC_FALSE; 4026 b->free_ij = PETSC_FALSE; 4027 } 4028 4029 if (b->ipre && nnz != b->ipre && b->imax) { 4030 /* reserve user-requested sparsity */ 4031 PetscCall(PetscArraycpy(b->ipre, b->imax, B->rmap->n)); 4032 } 4033 4034 b->nz = 0; 4035 b->maxnz = nz; 4036 B->info.nz_unneeded = (double)b->maxnz; 4037 if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE)); 4038 B->was_assembled = PETSC_FALSE; 4039 B->assembled = PETSC_FALSE; 4040 /* We simply deem preallocation has changed nonzero state. Updating the state 4041 will give clients (like AIJKokkos) a chance to know something has happened. 4042 */ 4043 B->nonzerostate++; 4044 PetscFunctionReturn(PETSC_SUCCESS); 4045 } 4046 4047 static PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A) 4048 { 4049 Mat_SeqAIJ *a; 4050 PetscInt i; 4051 PetscBool skipreset; 4052 4053 PetscFunctionBegin; 4054 PetscValidHeaderSpecific(A, MAT_CLASSID, 1); 4055 4056 /* Check local size. If zero, then return */ 4057 if (!A->rmap->n) PetscFunctionReturn(PETSC_SUCCESS); 4058 4059 a = (Mat_SeqAIJ *)A->data; 4060 /* if no saved info, we error out */ 4061 PetscCheck(a->ipre, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "No saved preallocation info "); 4062 4063 PetscCheck(a->i && a->imax && a->ilen, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "Memory info is incomplete, and can not reset preallocation "); 4064 4065 PetscCall(PetscArraycmp(a->ipre, a->ilen, A->rmap->n, &skipreset)); 4066 if (!skipreset) { 4067 PetscCall(PetscArraycpy(a->imax, a->ipre, A->rmap->n)); 4068 PetscCall(PetscArrayzero(a->ilen, A->rmap->n)); 4069 a->i[0] = 0; 4070 for (i = 1; i < A->rmap->n + 1; i++) a->i[i] = a->i[i - 1] + a->imax[i - 1]; 4071 A->preallocated = PETSC_TRUE; 4072 a->nz = 0; 4073 a->maxnz = a->i[A->rmap->n]; 4074 A->info.nz_unneeded = (double)a->maxnz; 4075 A->was_assembled = PETSC_FALSE; 4076 A->assembled = PETSC_FALSE; 4077 } 4078 PetscFunctionReturn(PETSC_SUCCESS); 4079 } 4080 4081 /*@ 4082 MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in `MATSEQAIJ` format. 4083 4084 Input Parameters: 4085 + B - the matrix 4086 . i - the indices into j for the start of each row (starts with zero) 4087 . j - the column indices for each row (starts with zero) these must be sorted for each row 4088 - v - optional values in the matrix 4089 4090 Level: developer 4091 4092 Notes: 4093 The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqAIJWithArrays()` 4094 4095 This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero 4096 structure will be the union of all the previous nonzero structures. 4097 4098 Developer Notes: 4099 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 4100 then just copies the `v` values directly with `PetscMemcpy()`. 4101 4102 This routine could also take a `PetscCopyMode` argument to allow sharing the values instead of always copying them. 4103 4104 .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`, `MATSEQAIJ`, `MatResetPreallocation()` 4105 @*/ 4106 PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[]) 4107 { 4108 PetscFunctionBegin; 4109 PetscValidHeaderSpecific(B, MAT_CLASSID, 1); 4110 PetscValidType(B, 1); 4111 PetscTryMethod(B, "MatSeqAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v)); 4112 PetscFunctionReturn(PETSC_SUCCESS); 4113 } 4114 4115 static PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[]) 4116 { 4117 PetscInt i; 4118 PetscInt m, n; 4119 PetscInt nz; 4120 PetscInt *nnz; 4121 4122 PetscFunctionBegin; 4123 PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]); 4124 4125 PetscCall(PetscLayoutSetUp(B->rmap)); 4126 PetscCall(PetscLayoutSetUp(B->cmap)); 4127 4128 PetscCall(MatGetSize(B, &m, &n)); 4129 PetscCall(PetscMalloc1(m + 1, &nnz)); 4130 for (i = 0; i < m; i++) { 4131 nz = Ii[i + 1] - Ii[i]; 4132 PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz); 4133 nnz[i] = nz; 4134 } 4135 PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz)); 4136 PetscCall(PetscFree(nnz)); 4137 4138 for (i = 0; i < m; i++) PetscCall(MatSetValues_SeqAIJ(B, 1, &i, Ii[i + 1] - Ii[i], J + Ii[i], PetscSafePointerPlusOffset(v, Ii[i]), INSERT_VALUES)); 4139 4140 PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY)); 4141 PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY)); 4142 4143 PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE)); 4144 PetscFunctionReturn(PETSC_SUCCESS); 4145 } 4146 4147 /*@ 4148 MatSeqAIJKron - Computes `C`, the Kronecker product of `A` and `B`. 4149 4150 Input Parameters: 4151 + A - left-hand side matrix 4152 . B - right-hand side matrix 4153 - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX` 4154 4155 Output Parameter: 4156 . C - Kronecker product of `A` and `B` 4157 4158 Level: intermediate 4159 4160 Note: 4161 `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the product matrix has not changed from that last call to `MatSeqAIJKron()`. 4162 4163 .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATKAIJ`, `MatReuse` 4164 @*/ 4165 PetscErrorCode MatSeqAIJKron(Mat A, Mat B, MatReuse reuse, Mat *C) 4166 { 4167 PetscFunctionBegin; 4168 PetscValidHeaderSpecific(A, MAT_CLASSID, 1); 4169 PetscValidType(A, 1); 4170 PetscValidHeaderSpecific(B, MAT_CLASSID, 2); 4171 PetscValidType(B, 2); 4172 PetscAssertPointer(C, 4); 4173 if (reuse == MAT_REUSE_MATRIX) { 4174 PetscValidHeaderSpecific(*C, MAT_CLASSID, 4); 4175 PetscValidType(*C, 4); 4176 } 4177 PetscTryMethod(A, "MatSeqAIJKron_C", (Mat, Mat, MatReuse, Mat *), (A, B, reuse, C)); 4178 PetscFunctionReturn(PETSC_SUCCESS); 4179 } 4180 4181 static PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A, Mat B, MatReuse reuse, Mat *C) 4182 { 4183 Mat newmat; 4184 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 4185 Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data; 4186 PetscScalar *v; 4187 const PetscScalar *aa, *ba; 4188 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; 4189 PetscBool flg; 4190 4191 PetscFunctionBegin; 4192 PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 4193 PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix"); 4194 PetscCheck(!B->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 4195 PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix"); 4196 PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJ, &flg)); 4197 PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatType %s", ((PetscObject)B)->type_name); 4198 PetscCheck(reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatReuse %d", (int)reuse); 4199 if (reuse == MAT_INITIAL_MATRIX) { 4200 PetscCall(PetscMalloc2(am * bm + 1, &i, a->i[am] * b->i[bm], &j)); 4201 PetscCall(MatCreate(PETSC_COMM_SELF, &newmat)); 4202 PetscCall(MatSetSizes(newmat, am * bm, an * bn, am * bm, an * bn)); 4203 PetscCall(MatSetType(newmat, MATAIJ)); 4204 i[0] = 0; 4205 for (m = 0; m < am; ++m) { 4206 for (p = 0; p < bm; ++p) { 4207 i[m * bm + p + 1] = i[m * bm + p] + (a->i[m + 1] - a->i[m]) * (b->i[p + 1] - b->i[p]); 4208 for (n = a->i[m]; n < a->i[m + 1]; ++n) { 4209 for (q = b->i[p]; q < b->i[p + 1]; ++q) j[nnz++] = a->j[n] * bn + b->j[q]; 4210 } 4211 } 4212 } 4213 PetscCall(MatSeqAIJSetPreallocationCSR(newmat, i, j, NULL)); 4214 *C = newmat; 4215 PetscCall(PetscFree2(i, j)); 4216 nnz = 0; 4217 } 4218 PetscCall(MatSeqAIJGetArray(*C, &v)); 4219 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 4220 PetscCall(MatSeqAIJGetArrayRead(B, &ba)); 4221 for (m = 0; m < am; ++m) { 4222 for (p = 0; p < bm; ++p) { 4223 for (n = a->i[m]; n < a->i[m + 1]; ++n) { 4224 for (q = b->i[p]; q < b->i[p + 1]; ++q) v[nnz++] = aa[n] * ba[q]; 4225 } 4226 } 4227 } 4228 PetscCall(MatSeqAIJRestoreArray(*C, &v)); 4229 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 4230 PetscCall(MatSeqAIJRestoreArrayRead(B, &ba)); 4231 PetscFunctionReturn(PETSC_SUCCESS); 4232 } 4233 4234 #include <../src/mat/impls/dense/seq/dense.h> 4235 #include <petsc/private/kernels/petscaxpy.h> 4236 4237 /* 4238 Computes (B'*A')' since computing B*A directly is untenable 4239 4240 n p p 4241 [ ] [ ] [ ] 4242 m [ A ] * n [ B ] = m [ C ] 4243 [ ] [ ] [ ] 4244 4245 */ 4246 PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A, Mat B, Mat C) 4247 { 4248 Mat_SeqDense *sub_a = (Mat_SeqDense *)A->data; 4249 Mat_SeqAIJ *sub_b = (Mat_SeqAIJ *)B->data; 4250 Mat_SeqDense *sub_c = (Mat_SeqDense *)C->data; 4251 PetscInt i, j, n, m, q, p; 4252 const PetscInt *ii, *idx; 4253 const PetscScalar *b, *a, *a_q; 4254 PetscScalar *c, *c_q; 4255 PetscInt clda = sub_c->lda; 4256 PetscInt alda = sub_a->lda; 4257 4258 PetscFunctionBegin; 4259 m = A->rmap->n; 4260 n = A->cmap->n; 4261 p = B->cmap->n; 4262 a = sub_a->v; 4263 b = sub_b->a; 4264 c = sub_c->v; 4265 if (clda == m) { 4266 PetscCall(PetscArrayzero(c, m * p)); 4267 } else { 4268 for (j = 0; j < p; j++) 4269 for (i = 0; i < m; i++) c[j * clda + i] = 0.0; 4270 } 4271 ii = sub_b->i; 4272 idx = sub_b->j; 4273 for (i = 0; i < n; i++) { 4274 q = ii[i + 1] - ii[i]; 4275 while (q-- > 0) { 4276 c_q = c + clda * (*idx); 4277 a_q = a + alda * i; 4278 PetscKernelAXPY(c_q, *b, a_q, m); 4279 idx++; 4280 b++; 4281 } 4282 } 4283 PetscFunctionReturn(PETSC_SUCCESS); 4284 } 4285 4286 PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A, Mat B, PetscReal fill, Mat C) 4287 { 4288 PetscInt m = A->rmap->n, n = B->cmap->n; 4289 PetscBool cisdense; 4290 4291 PetscFunctionBegin; 4292 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); 4293 PetscCall(MatSetSizes(C, m, n, m, n)); 4294 PetscCall(MatSetBlockSizesFromMats(C, A, B)); 4295 PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATSEQDENSE, MATSEQDENSECUDA, MATSEQDENSEHIP, "")); 4296 if (!cisdense) PetscCall(MatSetType(C, MATDENSE)); 4297 PetscCall(MatSetUp(C)); 4298 4299 C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ; 4300 PetscFunctionReturn(PETSC_SUCCESS); 4301 } 4302 4303 /*MC 4304 MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 4305 based on compressed sparse row format. 4306 4307 Options Database Key: 4308 . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions() 4309 4310 Level: beginner 4311 4312 Notes: 4313 `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values, 4314 in this case the values associated with the rows and columns one passes in are set to zero 4315 in the matrix 4316 4317 `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no 4318 space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored 4319 4320 Developer Note: 4321 It would be nice if all matrix formats supported passing `NULL` in for the numerical values 4322 4323 .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MatSetFromOptions()`, `MatSetType()`, `MatCreate()`, `MatType`, `MATSELL`, `MATSEQSELL`, `MATMPISELL` 4324 M*/ 4325 4326 /*MC 4327 MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices. 4328 4329 This matrix type is identical to `MATSEQAIJ` when constructed with a single process communicator, 4330 and `MATMPIAIJ` otherwise. As a result, for single process communicators, 4331 `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported 4332 for communicators controlling multiple processes. It is recommended that you call both of 4333 the above preallocation routines for simplicity. 4334 4335 Options Database Key: 4336 . -mat_type aij - sets the matrix type to "aij" during a call to `MatSetFromOptions()` 4337 4338 Level: beginner 4339 4340 Note: 4341 Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, and also automatically switches over to use inodes when 4342 enough exist. 4343 4344 .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSELL`, `MATSEQSELL`, `MATMPISELL` 4345 M*/ 4346 4347 /*MC 4348 MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices. 4349 4350 Options Database Key: 4351 . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to `MatSetFromOptions()` 4352 4353 Level: beginner 4354 4355 Note: 4356 This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator, 4357 and `MATMPIAIJCRL` otherwise. As a result, for single process communicators, 4358 `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported 4359 for communicators controlling multiple processes. It is recommended that you call both of 4360 the above preallocation routines for simplicity. 4361 4362 .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL` 4363 M*/ 4364 4365 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *); 4366 #if defined(PETSC_HAVE_ELEMENTAL) 4367 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *); 4368 #endif 4369 #if defined(PETSC_HAVE_SCALAPACK) 4370 PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *); 4371 #endif 4372 #if defined(PETSC_HAVE_HYPRE) 4373 PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A, MatType, MatReuse, Mat *); 4374 #endif 4375 4376 PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat, MatType, MatReuse, Mat *); 4377 PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *); 4378 PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat); 4379 4380 /*@C 4381 MatSeqAIJGetArray - gives read/write access to the array where the data for a `MATSEQAIJ` matrix is stored 4382 4383 Not Collective 4384 4385 Input Parameter: 4386 . A - a `MATSEQAIJ` matrix 4387 4388 Output Parameter: 4389 . array - pointer to the data 4390 4391 Level: intermediate 4392 4393 Fortran Notes: 4394 `MatSeqAIJGetArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJGetArrayF90()` 4395 4396 .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()` 4397 @*/ 4398 PetscErrorCode MatSeqAIJGetArray(Mat A, PetscScalar **array) 4399 { 4400 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 4401 4402 PetscFunctionBegin; 4403 if (aij->ops->getarray) { 4404 PetscCall((*aij->ops->getarray)(A, array)); 4405 } else { 4406 *array = aij->a; 4407 } 4408 PetscFunctionReturn(PETSC_SUCCESS); 4409 } 4410 4411 /*@C 4412 MatSeqAIJRestoreArray - returns access to the array where the data for a `MATSEQAIJ` matrix is stored obtained by `MatSeqAIJGetArray()` 4413 4414 Not Collective 4415 4416 Input Parameters: 4417 + A - a `MATSEQAIJ` matrix 4418 - array - pointer to the data 4419 4420 Level: intermediate 4421 4422 Fortran Notes: 4423 `MatSeqAIJRestoreArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJRestoreArrayF90()` 4424 4425 .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayF90()` 4426 @*/ 4427 PetscErrorCode MatSeqAIJRestoreArray(Mat A, PetscScalar **array) 4428 { 4429 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 4430 4431 PetscFunctionBegin; 4432 if (aij->ops->restorearray) { 4433 PetscCall((*aij->ops->restorearray)(A, array)); 4434 } else { 4435 *array = NULL; 4436 } 4437 PetscCall(MatSeqAIJInvalidateDiagonal(A)); 4438 PetscCall(PetscObjectStateIncrease((PetscObject)A)); 4439 PetscFunctionReturn(PETSC_SUCCESS); 4440 } 4441 4442 /*@C 4443 MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a `MATSEQAIJ` matrix is stored 4444 4445 Not Collective; No Fortran Support 4446 4447 Input Parameter: 4448 . A - a `MATSEQAIJ` matrix 4449 4450 Output Parameter: 4451 . array - pointer to the data 4452 4453 Level: intermediate 4454 4455 .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()` 4456 @*/ 4457 PetscErrorCode MatSeqAIJGetArrayRead(Mat A, const PetscScalar **array) 4458 { 4459 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 4460 4461 PetscFunctionBegin; 4462 if (aij->ops->getarrayread) { 4463 PetscCall((*aij->ops->getarrayread)(A, array)); 4464 } else { 4465 *array = aij->a; 4466 } 4467 PetscFunctionReturn(PETSC_SUCCESS); 4468 } 4469 4470 /*@C 4471 MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJGetArrayRead()` 4472 4473 Not Collective; No Fortran Support 4474 4475 Input Parameter: 4476 . A - a `MATSEQAIJ` matrix 4477 4478 Output Parameter: 4479 . array - pointer to the data 4480 4481 Level: intermediate 4482 4483 .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()` 4484 @*/ 4485 PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A, const PetscScalar **array) 4486 { 4487 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 4488 4489 PetscFunctionBegin; 4490 if (aij->ops->restorearrayread) { 4491 PetscCall((*aij->ops->restorearrayread)(A, array)); 4492 } else { 4493 *array = NULL; 4494 } 4495 PetscFunctionReturn(PETSC_SUCCESS); 4496 } 4497 4498 /*@C 4499 MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a `MATSEQAIJ` matrix is stored 4500 4501 Not Collective; No Fortran Support 4502 4503 Input Parameter: 4504 . A - a `MATSEQAIJ` matrix 4505 4506 Output Parameter: 4507 . array - pointer to the data 4508 4509 Level: intermediate 4510 4511 .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()` 4512 @*/ 4513 PetscErrorCode MatSeqAIJGetArrayWrite(Mat A, PetscScalar **array) 4514 { 4515 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 4516 4517 PetscFunctionBegin; 4518 if (aij->ops->getarraywrite) { 4519 PetscCall((*aij->ops->getarraywrite)(A, array)); 4520 } else { 4521 *array = aij->a; 4522 } 4523 PetscCall(MatSeqAIJInvalidateDiagonal(A)); 4524 PetscCall(PetscObjectStateIncrease((PetscObject)A)); 4525 PetscFunctionReturn(PETSC_SUCCESS); 4526 } 4527 4528 /*@C 4529 MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead 4530 4531 Not Collective; No Fortran Support 4532 4533 Input Parameter: 4534 . A - a MATSEQAIJ matrix 4535 4536 Output Parameter: 4537 . array - pointer to the data 4538 4539 Level: intermediate 4540 4541 .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()` 4542 @*/ 4543 PetscErrorCode MatSeqAIJRestoreArrayWrite(Mat A, PetscScalar **array) 4544 { 4545 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 4546 4547 PetscFunctionBegin; 4548 if (aij->ops->restorearraywrite) { 4549 PetscCall((*aij->ops->restorearraywrite)(A, array)); 4550 } else { 4551 *array = NULL; 4552 } 4553 PetscFunctionReturn(PETSC_SUCCESS); 4554 } 4555 4556 /*@C 4557 MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the `MATSEQAIJ` matrix 4558 4559 Not Collective; No Fortran Support 4560 4561 Input Parameter: 4562 . mat - a matrix of type `MATSEQAIJ` or its subclasses 4563 4564 Output Parameters: 4565 + i - row map array of the matrix 4566 . j - column index array of the matrix 4567 . a - data array of the matrix 4568 - mtype - memory type of the arrays 4569 4570 Level: developer 4571 4572 Notes: 4573 Any of the output parameters can be `NULL`, in which case the corresponding value is not returned. 4574 If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host. 4575 4576 One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix. 4577 If the matrix is assembled, the data array `a` is guaranteed to have the latest values of the matrix. 4578 4579 .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()` 4580 @*/ 4581 PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat, const PetscInt **i, const PetscInt **j, PetscScalar **a, PetscMemType *mtype) 4582 { 4583 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 4584 4585 PetscFunctionBegin; 4586 PetscCheck(mat->preallocated, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "matrix is not preallocated"); 4587 if (aij->ops->getcsrandmemtype) { 4588 PetscCall((*aij->ops->getcsrandmemtype)(mat, i, j, a, mtype)); 4589 } else { 4590 if (i) *i = aij->i; 4591 if (j) *j = aij->j; 4592 if (a) *a = aij->a; 4593 if (mtype) *mtype = PETSC_MEMTYPE_HOST; 4594 } 4595 PetscFunctionReturn(PETSC_SUCCESS); 4596 } 4597 4598 /*@C 4599 MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row 4600 4601 Not Collective 4602 4603 Input Parameter: 4604 . A - a `MATSEQAIJ` matrix 4605 4606 Output Parameter: 4607 . nz - the maximum number of nonzeros in any row 4608 4609 Level: intermediate 4610 4611 .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()` 4612 @*/ 4613 PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A, PetscInt *nz) 4614 { 4615 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data; 4616 4617 PetscFunctionBegin; 4618 *nz = aij->rmax; 4619 PetscFunctionReturn(PETSC_SUCCESS); 4620 } 4621 4622 static PetscErrorCode MatCOOStructDestroy_SeqAIJ(void *data) 4623 { 4624 MatCOOStruct_SeqAIJ *coo = (MatCOOStruct_SeqAIJ *)data; 4625 PetscFunctionBegin; 4626 PetscCall(PetscFree(coo->perm)); 4627 PetscCall(PetscFree(coo->jmap)); 4628 PetscCall(PetscFree(coo)); 4629 PetscFunctionReturn(PETSC_SUCCESS); 4630 } 4631 4632 PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[]) 4633 { 4634 MPI_Comm comm; 4635 PetscInt *i, *j; 4636 PetscInt M, N, row; 4637 PetscCount k, p, q, nneg, nnz, start, end; /* Index the coo array, so use PetscCount as their type */ 4638 PetscInt *Ai; /* Change to PetscCount once we use it for row pointers */ 4639 PetscInt *Aj; 4640 PetscScalar *Aa; 4641 Mat_SeqAIJ *seqaij = (Mat_SeqAIJ *)(mat->data); 4642 MatType rtype; 4643 PetscCount *perm, *jmap; 4644 PetscContainer container; 4645 MatCOOStruct_SeqAIJ *coo; 4646 4647 PetscFunctionBegin; 4648 PetscCall(PetscObjectGetComm((PetscObject)mat, &comm)); 4649 PetscCall(MatGetSize(mat, &M, &N)); 4650 i = coo_i; 4651 j = coo_j; 4652 PetscCall(PetscMalloc1(coo_n, &perm)); 4653 for (k = 0; k < coo_n; k++) { /* Ignore entries with negative row or col indices */ 4654 if (j[k] < 0) i[k] = -1; 4655 perm[k] = k; 4656 } 4657 4658 /* Sort by row */ 4659 PetscCall(PetscSortIntWithIntCountArrayPair(coo_n, i, j, perm)); 4660 4661 /* Advance k to the first row with a non-negative index */ 4662 for (k = 0; k < coo_n; k++) 4663 if (i[k] >= 0) break; 4664 nneg = k; 4665 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 */ 4666 nnz = 0; /* Total number of unique nonzeros to be counted */ 4667 jmap++; /* Inc jmap by 1 for convenience */ 4668 4669 PetscCall(PetscCalloc1(M + 1, &Ai)); /* CSR of A */ 4670 PetscCall(PetscMalloc1(coo_n - nneg, &Aj)); /* We have at most coo_n-nneg unique nonzeros */ 4671 4672 /* Support for HYPRE */ 4673 PetscBool hypre; 4674 const char *name; 4675 PetscCall(PetscObjectGetName((PetscObject)mat, &name)); 4676 PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", name, &hypre)); 4677 4678 /* In each row, sort by column, then unique column indices to get row length */ 4679 Ai++; /* Inc by 1 for convenience */ 4680 q = 0; /* q-th unique nonzero, with q starting from 0 */ 4681 while (k < coo_n) { 4682 row = i[k]; 4683 start = k; /* [start,end) indices for this row */ 4684 while (k < coo_n && i[k] == row) k++; 4685 end = k; 4686 /* hack for HYPRE: swap min column to diag so that diagonal values will go first */ 4687 if (hypre) { 4688 PetscInt minj = PETSC_MAX_INT; 4689 PetscBool hasdiag = PETSC_FALSE; 4690 for (p = start; p < end; p++) { 4691 hasdiag = (PetscBool)(hasdiag || (j[p] == row)); 4692 minj = PetscMin(minj, j[p]); 4693 } 4694 if (hasdiag) { 4695 for (p = start; p < end; p++) { 4696 if (j[p] == minj) j[p] = row; 4697 else if (j[p] == row) j[p] = minj; 4698 } 4699 } 4700 } 4701 PetscCall(PetscSortIntWithCountArray(end - start, j + start, perm + start)); 4702 4703 /* Find number of unique col entries in this row */ 4704 Aj[q] = j[start]; /* Log the first nonzero in this row */ 4705 jmap[q] = 1; /* Number of repeats of this nonzero entry */ 4706 Ai[row] = 1; 4707 nnz++; 4708 4709 for (p = start + 1; p < end; p++) { /* Scan remaining nonzero in this row */ 4710 if (j[p] != j[p - 1]) { /* Meet a new nonzero */ 4711 q++; 4712 jmap[q] = 1; 4713 Aj[q] = j[p]; 4714 Ai[row]++; 4715 nnz++; 4716 } else { 4717 jmap[q]++; 4718 } 4719 } 4720 q++; /* Move to next row and thus next unique nonzero */ 4721 } 4722 Ai--; /* Back to the beginning of Ai[] */ 4723 for (k = 0; k < M; k++) Ai[k + 1] += Ai[k]; 4724 jmap--; /* Back to the beginning of jmap[] */ 4725 jmap[0] = 0; 4726 for (k = 0; k < nnz; k++) jmap[k + 1] += jmap[k]; 4727 if (nnz < coo_n - nneg) { /* Realloc with actual number of unique nonzeros */ 4728 PetscCount *jmap_new; 4729 PetscInt *Aj_new; 4730 4731 PetscCall(PetscMalloc1(nnz + 1, &jmap_new)); 4732 PetscCall(PetscArraycpy(jmap_new, jmap, nnz + 1)); 4733 PetscCall(PetscFree(jmap)); 4734 jmap = jmap_new; 4735 4736 PetscCall(PetscMalloc1(nnz, &Aj_new)); 4737 PetscCall(PetscArraycpy(Aj_new, Aj, nnz)); 4738 PetscCall(PetscFree(Aj)); 4739 Aj = Aj_new; 4740 } 4741 4742 if (nneg) { /* Discard heading entries with negative indices in perm[], as we'll access it from index 0 in MatSetValuesCOO */ 4743 PetscCount *perm_new; 4744 4745 PetscCall(PetscMalloc1(coo_n - nneg, &perm_new)); 4746 PetscCall(PetscArraycpy(perm_new, perm + nneg, coo_n - nneg)); 4747 PetscCall(PetscFree(perm)); 4748 perm = perm_new; 4749 } 4750 4751 PetscCall(MatGetRootType_Private(mat, &rtype)); 4752 PetscCall(PetscCalloc1(nnz, &Aa)); /* Zero the matrix */ 4753 PetscCall(MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF, M, N, Ai, Aj, Aa, rtype, mat)); 4754 4755 seqaij->singlemalloc = PETSC_FALSE; /* Ai, Aj and Aa are not allocated in one big malloc */ 4756 seqaij->free_a = seqaij->free_ij = PETSC_TRUE; /* Let newmat own Ai, Aj and Aa */ 4757 4758 // Put the COO struct in a container and then attach that to the matrix 4759 PetscCall(PetscMalloc1(1, &coo)); 4760 coo->nz = nnz; 4761 coo->n = coo_n; 4762 coo->Atot = coo_n - nneg; // Annz is seqaij->nz, so no need to record that again 4763 coo->jmap = jmap; // of length nnz+1 4764 coo->perm = perm; 4765 PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container)); 4766 PetscCall(PetscContainerSetPointer(container, coo)); 4767 PetscCall(PetscContainerSetUserDestroy(container, MatCOOStructDestroy_SeqAIJ)); 4768 PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container)); 4769 PetscCall(PetscContainerDestroy(&container)); 4770 PetscFunctionReturn(PETSC_SUCCESS); 4771 } 4772 4773 static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A, const PetscScalar v[], InsertMode imode) 4774 { 4775 Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)A->data; 4776 PetscCount i, j, Annz = aseq->nz; 4777 PetscCount *perm, *jmap; 4778 PetscScalar *Aa; 4779 PetscContainer container; 4780 MatCOOStruct_SeqAIJ *coo; 4781 4782 PetscFunctionBegin; 4783 PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container)); 4784 PetscCheck(container, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix"); 4785 PetscCall(PetscContainerGetPointer(container, (void **)&coo)); 4786 perm = coo->perm; 4787 jmap = coo->jmap; 4788 PetscCall(MatSeqAIJGetArray(A, &Aa)); 4789 for (i = 0; i < Annz; i++) { 4790 PetscScalar sum = 0.0; 4791 for (j = jmap[i]; j < jmap[i + 1]; j++) sum += v[perm[j]]; 4792 Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum; 4793 } 4794 PetscCall(MatSeqAIJRestoreArray(A, &Aa)); 4795 PetscFunctionReturn(PETSC_SUCCESS); 4796 } 4797 4798 #if defined(PETSC_HAVE_CUDA) 4799 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *); 4800 #endif 4801 #if defined(PETSC_HAVE_HIP) 4802 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *); 4803 #endif 4804 #if defined(PETSC_HAVE_KOKKOS_KERNELS) 4805 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat, MatType, MatReuse, Mat *); 4806 #endif 4807 4808 PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B) 4809 { 4810 Mat_SeqAIJ *b; 4811 PetscMPIInt size; 4812 4813 PetscFunctionBegin; 4814 PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size)); 4815 PetscCheck(size <= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Comm must be of size 1"); 4816 4817 PetscCall(PetscNew(&b)); 4818 4819 B->data = (void *)b; 4820 B->ops[0] = MatOps_Values; 4821 if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull; 4822 4823 b->row = NULL; 4824 b->col = NULL; 4825 b->icol = NULL; 4826 b->reallocs = 0; 4827 b->ignorezeroentries = PETSC_FALSE; 4828 b->roworiented = PETSC_TRUE; 4829 b->nonew = 0; 4830 b->diag = NULL; 4831 b->solve_work = NULL; 4832 B->spptr = NULL; 4833 b->saved_values = NULL; 4834 b->idiag = NULL; 4835 b->mdiag = NULL; 4836 b->ssor_work = NULL; 4837 b->omega = 1.0; 4838 b->fshift = 0.0; 4839 b->idiagvalid = PETSC_FALSE; 4840 b->ibdiagvalid = PETSC_FALSE; 4841 b->keepnonzeropattern = PETSC_FALSE; 4842 4843 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ)); 4844 #if defined(PETSC_HAVE_MATLAB) 4845 PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEnginePut_C", MatlabEnginePut_SeqAIJ)); 4846 PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEngineGet_C", MatlabEngineGet_SeqAIJ)); 4847 #endif 4848 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetColumnIndices_C", MatSeqAIJSetColumnIndices_SeqAIJ)); 4849 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqAIJ)); 4850 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqAIJ)); 4851 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsbaij_C", MatConvert_SeqAIJ_SeqSBAIJ)); 4852 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqbaij_C", MatConvert_SeqAIJ_SeqBAIJ)); 4853 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijperm_C", MatConvert_SeqAIJ_SeqAIJPERM)); 4854 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijsell_C", MatConvert_SeqAIJ_SeqAIJSELL)); 4855 #if defined(PETSC_HAVE_MKL_SPARSE) 4856 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijmkl_C", MatConvert_SeqAIJ_SeqAIJMKL)); 4857 #endif 4858 #if defined(PETSC_HAVE_CUDA) 4859 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcusparse_C", MatConvert_SeqAIJ_SeqAIJCUSPARSE)); 4860 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", MatProductSetFromOptions_SeqAIJ)); 4861 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJ)); 4862 #endif 4863 #if defined(PETSC_HAVE_HIP) 4864 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijhipsparse_C", MatConvert_SeqAIJ_SeqAIJHIPSPARSE)); 4865 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", MatProductSetFromOptions_SeqAIJ)); 4866 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", MatProductSetFromOptions_SeqAIJ)); 4867 #endif 4868 #if defined(PETSC_HAVE_KOKKOS_KERNELS) 4869 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijkokkos_C", MatConvert_SeqAIJ_SeqAIJKokkos)); 4870 #endif 4871 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcrl_C", MatConvert_SeqAIJ_SeqAIJCRL)); 4872 #if defined(PETSC_HAVE_ELEMENTAL) 4873 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_elemental_C", MatConvert_SeqAIJ_Elemental)); 4874 #endif 4875 #if defined(PETSC_HAVE_SCALAPACK) 4876 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_scalapack_C", MatConvert_AIJ_ScaLAPACK)); 4877 #endif 4878 #if defined(PETSC_HAVE_HYPRE) 4879 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_hypre_C", MatConvert_AIJ_HYPRE)); 4880 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ)); 4881 #endif 4882 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqdense_C", MatConvert_SeqAIJ_SeqDense)); 4883 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsell_C", MatConvert_SeqAIJ_SeqSELL)); 4884 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_is_C", MatConvert_XAIJ_IS)); 4885 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqAIJ)); 4886 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsHermitianTranspose_C", MatIsHermitianTranspose_SeqAIJ)); 4887 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocation_C", MatSeqAIJSetPreallocation_SeqAIJ)); 4888 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_SeqAIJ)); 4889 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocationCSR_C", MatSeqAIJSetPreallocationCSR_SeqAIJ)); 4890 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatReorderForNonzeroDiagonal_C", MatReorderForNonzeroDiagonal_SeqAIJ)); 4891 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_seqaij_C", MatProductSetFromOptions_IS_XAIJ)); 4892 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqdense_seqaij_C", MatProductSetFromOptions_SeqDense_SeqAIJ)); 4893 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaij_C", MatProductSetFromOptions_SeqAIJ)); 4894 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJKron_C", MatSeqAIJKron_SeqAIJ)); 4895 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJ)); 4896 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJ)); 4897 PetscCall(MatCreate_SeqAIJ_Inode(B)); 4898 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ)); 4899 PetscCall(MatSeqAIJSetTypeFromOptions(B)); /* this allows changing the matrix subtype to say MATSEQAIJPERM */ 4900 PetscFunctionReturn(PETSC_SUCCESS); 4901 } 4902 4903 /* 4904 Given a matrix generated with MatGetFactor() duplicates all the information in A into C 4905 */ 4906 PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace) 4907 { 4908 Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data, *a = (Mat_SeqAIJ *)A->data; 4909 PetscInt m = A->rmap->n, i; 4910 4911 PetscFunctionBegin; 4912 PetscCheck(A->assembled || cpvalues == MAT_DO_NOT_COPY_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix"); 4913 4914 C->factortype = A->factortype; 4915 c->row = NULL; 4916 c->col = NULL; 4917 c->icol = NULL; 4918 c->reallocs = 0; 4919 c->diagonaldense = a->diagonaldense; 4920 4921 C->assembled = A->assembled; 4922 4923 if (A->preallocated) { 4924 PetscCall(PetscLayoutReference(A->rmap, &C->rmap)); 4925 PetscCall(PetscLayoutReference(A->cmap, &C->cmap)); 4926 4927 if (!A->hash_active) { 4928 PetscCall(PetscMalloc1(m, &c->imax)); 4929 PetscCall(PetscMemcpy(c->imax, a->imax, m * sizeof(PetscInt))); 4930 PetscCall(PetscMalloc1(m, &c->ilen)); 4931 PetscCall(PetscMemcpy(c->ilen, a->ilen, m * sizeof(PetscInt))); 4932 4933 /* allocate the matrix space */ 4934 if (mallocmatspace) { 4935 PetscCall(PetscMalloc3(a->i[m], &c->a, a->i[m], &c->j, m + 1, &c->i)); 4936 4937 c->singlemalloc = PETSC_TRUE; 4938 4939 PetscCall(PetscArraycpy(c->i, a->i, m + 1)); 4940 if (m > 0) { 4941 PetscCall(PetscArraycpy(c->j, a->j, a->i[m])); 4942 if (cpvalues == MAT_COPY_VALUES) { 4943 const PetscScalar *aa; 4944 4945 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 4946 PetscCall(PetscArraycpy(c->a, aa, a->i[m])); 4947 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 4948 } else { 4949 PetscCall(PetscArrayzero(c->a, a->i[m])); 4950 } 4951 } 4952 } 4953 C->preallocated = PETSC_TRUE; 4954 } else { 4955 PetscCheck(mallocmatspace, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot malloc matrix memory from a non-preallocated matrix"); 4956 PetscCall(MatSetUp(C)); 4957 } 4958 4959 c->ignorezeroentries = a->ignorezeroentries; 4960 c->roworiented = a->roworiented; 4961 c->nonew = a->nonew; 4962 if (a->diag) { 4963 PetscCall(PetscMalloc1(m + 1, &c->diag)); 4964 PetscCall(PetscMemcpy(c->diag, a->diag, m * sizeof(PetscInt))); 4965 } else c->diag = NULL; 4966 4967 c->solve_work = NULL; 4968 c->saved_values = NULL; 4969 c->idiag = NULL; 4970 c->ssor_work = NULL; 4971 c->keepnonzeropattern = a->keepnonzeropattern; 4972 c->free_a = PETSC_TRUE; 4973 c->free_ij = PETSC_TRUE; 4974 4975 c->rmax = a->rmax; 4976 c->nz = a->nz; 4977 c->maxnz = a->nz; /* Since we allocate exactly the right amount */ 4978 4979 c->compressedrow.use = a->compressedrow.use; 4980 c->compressedrow.nrows = a->compressedrow.nrows; 4981 if (a->compressedrow.use) { 4982 i = a->compressedrow.nrows; 4983 PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i, &c->compressedrow.rindex)); 4984 PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1)); 4985 PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i)); 4986 } else { 4987 c->compressedrow.use = PETSC_FALSE; 4988 c->compressedrow.i = NULL; 4989 c->compressedrow.rindex = NULL; 4990 } 4991 c->nonzerorowcnt = a->nonzerorowcnt; 4992 C->nonzerostate = A->nonzerostate; 4993 4994 PetscCall(MatDuplicate_SeqAIJ_Inode(A, cpvalues, &C)); 4995 } 4996 PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist)); 4997 PetscFunctionReturn(PETSC_SUCCESS); 4998 } 4999 5000 PetscErrorCode MatDuplicate_SeqAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B) 5001 { 5002 PetscFunctionBegin; 5003 PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B)); 5004 PetscCall(MatSetSizes(*B, A->rmap->n, A->cmap->n, A->rmap->n, A->cmap->n)); 5005 if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) PetscCall(MatSetBlockSizesFromMats(*B, A, A)); 5006 PetscCall(MatSetType(*B, ((PetscObject)A)->type_name)); 5007 PetscCall(MatDuplicateNoCreate_SeqAIJ(*B, A, cpvalues, PETSC_TRUE)); 5008 PetscFunctionReturn(PETSC_SUCCESS); 5009 } 5010 5011 PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer) 5012 { 5013 PetscBool isbinary, ishdf5; 5014 5015 PetscFunctionBegin; 5016 PetscValidHeaderSpecific(newMat, MAT_CLASSID, 1); 5017 PetscValidHeaderSpecific(viewer, PETSC_VIEWER_CLASSID, 2); 5018 /* force binary viewer to load .info file if it has not yet done so */ 5019 PetscCall(PetscViewerSetUp(viewer)); 5020 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary)); 5021 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5)); 5022 if (isbinary) { 5023 PetscCall(MatLoad_SeqAIJ_Binary(newMat, viewer)); 5024 } else if (ishdf5) { 5025 #if defined(PETSC_HAVE_HDF5) 5026 PetscCall(MatLoad_AIJ_HDF5(newMat, viewer)); 5027 #else 5028 SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5"); 5029 #endif 5030 } else { 5031 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); 5032 } 5033 PetscFunctionReturn(PETSC_SUCCESS); 5034 } 5035 5036 PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer) 5037 { 5038 Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->data; 5039 PetscInt header[4], *rowlens, M, N, nz, sum, rows, cols, i; 5040 5041 PetscFunctionBegin; 5042 PetscCall(PetscViewerSetUp(viewer)); 5043 5044 /* read in matrix header */ 5045 PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT)); 5046 PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file"); 5047 M = header[1]; 5048 N = header[2]; 5049 nz = header[3]; 5050 PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M); 5051 PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N); 5052 PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqAIJ"); 5053 5054 /* set block sizes from the viewer's .info file */ 5055 PetscCall(MatLoad_Binary_BlockSizes(mat, viewer)); 5056 /* set local and global sizes if not set already */ 5057 if (mat->rmap->n < 0) mat->rmap->n = M; 5058 if (mat->cmap->n < 0) mat->cmap->n = N; 5059 if (mat->rmap->N < 0) mat->rmap->N = M; 5060 if (mat->cmap->N < 0) mat->cmap->N = N; 5061 PetscCall(PetscLayoutSetUp(mat->rmap)); 5062 PetscCall(PetscLayoutSetUp(mat->cmap)); 5063 5064 /* check if the matrix sizes are correct */ 5065 PetscCall(MatGetSize(mat, &rows, &cols)); 5066 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); 5067 5068 /* read in row lengths */ 5069 PetscCall(PetscMalloc1(M, &rowlens)); 5070 PetscCall(PetscViewerBinaryRead(viewer, rowlens, M, NULL, PETSC_INT)); 5071 /* check if sum(rowlens) is same as nz */ 5072 sum = 0; 5073 for (i = 0; i < M; i++) sum += rowlens[i]; 5074 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); 5075 /* preallocate and check sizes */ 5076 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(mat, 0, rowlens)); 5077 PetscCall(MatGetSize(mat, &rows, &cols)); 5078 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); 5079 /* store row lengths */ 5080 PetscCall(PetscArraycpy(a->ilen, rowlens, M)); 5081 PetscCall(PetscFree(rowlens)); 5082 5083 /* fill in "i" row pointers */ 5084 a->i[0] = 0; 5085 for (i = 0; i < M; i++) a->i[i + 1] = a->i[i] + a->ilen[i]; 5086 /* read in "j" column indices */ 5087 PetscCall(PetscViewerBinaryRead(viewer, a->j, nz, NULL, PETSC_INT)); 5088 /* read in "a" nonzero values */ 5089 PetscCall(PetscViewerBinaryRead(viewer, a->a, nz, NULL, PETSC_SCALAR)); 5090 5091 PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY)); 5092 PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY)); 5093 PetscFunctionReturn(PETSC_SUCCESS); 5094 } 5095 5096 PetscErrorCode MatEqual_SeqAIJ(Mat A, Mat B, PetscBool *flg) 5097 { 5098 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data; 5099 const PetscScalar *aa, *ba; 5100 #if defined(PETSC_USE_COMPLEX) 5101 PetscInt k; 5102 #endif 5103 5104 PetscFunctionBegin; 5105 /* If the matrix dimensions are not equal,or no of nonzeros */ 5106 if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) || (a->nz != b->nz)) { 5107 *flg = PETSC_FALSE; 5108 PetscFunctionReturn(PETSC_SUCCESS); 5109 } 5110 5111 /* if the a->i are the same */ 5112 PetscCall(PetscArraycmp(a->i, b->i, A->rmap->n + 1, flg)); 5113 if (!*flg) PetscFunctionReturn(PETSC_SUCCESS); 5114 5115 /* if a->j are the same */ 5116 PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg)); 5117 if (!*flg) PetscFunctionReturn(PETSC_SUCCESS); 5118 5119 PetscCall(MatSeqAIJGetArrayRead(A, &aa)); 5120 PetscCall(MatSeqAIJGetArrayRead(B, &ba)); 5121 /* if a->a are the same */ 5122 #if defined(PETSC_USE_COMPLEX) 5123 for (k = 0; k < a->nz; k++) { 5124 if (PetscRealPart(aa[k]) != PetscRealPart(ba[k]) || PetscImaginaryPart(aa[k]) != PetscImaginaryPart(ba[k])) { 5125 *flg = PETSC_FALSE; 5126 PetscFunctionReturn(PETSC_SUCCESS); 5127 } 5128 } 5129 #else 5130 PetscCall(PetscArraycmp(aa, ba, a->nz, flg)); 5131 #endif 5132 PetscCall(MatSeqAIJRestoreArrayRead(A, &aa)); 5133 PetscCall(MatSeqAIJRestoreArrayRead(B, &ba)); 5134 PetscFunctionReturn(PETSC_SUCCESS); 5135 } 5136 5137 /*@ 5138 MatCreateSeqAIJWithArrays - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in CSR format) 5139 provided by the user. 5140 5141 Collective 5142 5143 Input Parameters: 5144 + comm - must be an MPI communicator of size 1 5145 . m - number of rows 5146 . n - number of columns 5147 . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix 5148 . j - column indices 5149 - a - matrix values 5150 5151 Output Parameter: 5152 . mat - the matrix 5153 5154 Level: intermediate 5155 5156 Notes: 5157 The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays 5158 once the matrix is destroyed and not before 5159 5160 You cannot set new nonzero locations into this matrix, that will generate an error. 5161 5162 The `i` and `j` indices are 0 based 5163 5164 The format which is used for the sparse matrix input, is equivalent to a 5165 row-major ordering.. i.e for the following matrix, the input data expected is 5166 as shown 5167 .vb 5168 1 0 0 5169 2 0 3 5170 4 5 6 5171 5172 i = {0,1,3,6} [size = nrow+1 = 3+1] 5173 j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row 5174 v = {1,2,3,4,5,6} [size = 6] 5175 .ve 5176 5177 .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateMPIAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()` 5178 @*/ 5179 PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat) 5180 { 5181 PetscInt ii; 5182 Mat_SeqAIJ *aij; 5183 PetscInt jj; 5184 5185 PetscFunctionBegin; 5186 PetscCheck(m <= 0 || i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0"); 5187 PetscCall(MatCreate(comm, mat)); 5188 PetscCall(MatSetSizes(*mat, m, n, m, n)); 5189 /* PetscCall(MatSetBlockSizes(*mat,,)); */ 5190 PetscCall(MatSetType(*mat, MATSEQAIJ)); 5191 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, MAT_SKIP_ALLOCATION, NULL)); 5192 aij = (Mat_SeqAIJ *)(*mat)->data; 5193 PetscCall(PetscMalloc1(m, &aij->imax)); 5194 PetscCall(PetscMalloc1(m, &aij->ilen)); 5195 5196 aij->i = i; 5197 aij->j = j; 5198 aij->a = a; 5199 aij->singlemalloc = PETSC_FALSE; 5200 aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/ 5201 aij->free_a = PETSC_FALSE; 5202 aij->free_ij = PETSC_FALSE; 5203 5204 for (ii = 0, aij->nonzerorowcnt = 0, aij->rmax = 0; ii < m; ii++) { 5205 aij->ilen[ii] = aij->imax[ii] = i[ii + 1] - i[ii]; 5206 if (PetscDefined(USE_DEBUG)) { 5207 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]); 5208 for (jj = i[ii] + 1; jj < i[ii + 1]; jj++) { 5209 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); 5210 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); 5211 } 5212 } 5213 } 5214 if (PetscDefined(USE_DEBUG)) { 5215 for (ii = 0; ii < aij->i[m]; ii++) { 5216 PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]); 5217 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]); 5218 } 5219 } 5220 5221 PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY)); 5222 PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY)); 5223 PetscFunctionReturn(PETSC_SUCCESS); 5224 } 5225 5226 /*@ 5227 MatCreateSeqAIJFromTriple - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in COO format) 5228 provided by the user. 5229 5230 Collective 5231 5232 Input Parameters: 5233 + comm - must be an MPI communicator of size 1 5234 . m - number of rows 5235 . n - number of columns 5236 . i - row indices 5237 . j - column indices 5238 . a - matrix values 5239 . nz - number of nonzeros 5240 - idx - if the `i` and `j` indices start with 1 use `PETSC_TRUE` otherwise use `PETSC_FALSE` 5241 5242 Output Parameter: 5243 . mat - the matrix 5244 5245 Level: intermediate 5246 5247 Example: 5248 For the following matrix, the input data expected is as shown (using 0 based indexing) 5249 .vb 5250 1 0 0 5251 2 0 3 5252 4 5 6 5253 5254 i = {0,1,1,2,2,2} 5255 j = {0,0,2,0,1,2} 5256 v = {1,2,3,4,5,6} 5257 .ve 5258 5259 Note: 5260 Instead of using this function, users should also consider `MatSetPreallocationCOO()` and `MatSetValuesCOO()`, which allow repeated or remote entries, 5261 and are particularly useful in iterative applications. 5262 5263 .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateSeqAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`, `MatSetValuesCOO()`, `MatSetPreallocationCOO()` 5264 @*/ 5265 PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat, PetscInt nz, PetscBool idx) 5266 { 5267 PetscInt ii, *nnz, one = 1, row, col; 5268 5269 PetscFunctionBegin; 5270 PetscCall(PetscCalloc1(m, &nnz)); 5271 for (ii = 0; ii < nz; ii++) nnz[i[ii] - !!idx] += 1; 5272 PetscCall(MatCreate(comm, mat)); 5273 PetscCall(MatSetSizes(*mat, m, n, m, n)); 5274 PetscCall(MatSetType(*mat, MATSEQAIJ)); 5275 PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, 0, nnz)); 5276 for (ii = 0; ii < nz; ii++) { 5277 if (idx) { 5278 row = i[ii] - 1; 5279 col = j[ii] - 1; 5280 } else { 5281 row = i[ii]; 5282 col = j[ii]; 5283 } 5284 PetscCall(MatSetValues(*mat, one, &row, one, &col, &a[ii], ADD_VALUES)); 5285 } 5286 PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY)); 5287 PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY)); 5288 PetscCall(PetscFree(nnz)); 5289 PetscFunctionReturn(PETSC_SUCCESS); 5290 } 5291 5292 PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A) 5293 { 5294 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 5295 5296 PetscFunctionBegin; 5297 a->idiagvalid = PETSC_FALSE; 5298 a->ibdiagvalid = PETSC_FALSE; 5299 5300 PetscCall(MatSeqAIJInvalidateDiagonal_Inode(A)); 5301 PetscFunctionReturn(PETSC_SUCCESS); 5302 } 5303 5304 PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat) 5305 { 5306 PetscFunctionBegin; 5307 PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm, inmat, n, scall, outmat)); 5308 PetscFunctionReturn(PETSC_SUCCESS); 5309 } 5310 5311 /* 5312 Permute A into C's *local* index space using rowemb,colemb. 5313 The embedding are supposed to be injections and the above implies that the range of rowemb is a subset 5314 of [0,m), colemb is in [0,n). 5315 If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A. 5316 */ 5317 PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C, IS rowemb, IS colemb, MatStructure pattern, Mat B) 5318 { 5319 /* If making this function public, change the error returned in this function away from _PLIB. */ 5320 Mat_SeqAIJ *Baij; 5321 PetscBool seqaij; 5322 PetscInt m, n, *nz, i, j, count; 5323 PetscScalar v; 5324 const PetscInt *rowindices, *colindices; 5325 5326 PetscFunctionBegin; 5327 if (!B) PetscFunctionReturn(PETSC_SUCCESS); 5328 /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */ 5329 PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQAIJ, &seqaij)); 5330 PetscCheck(seqaij, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is of wrong type"); 5331 if (rowemb) { 5332 PetscCall(ISGetLocalSize(rowemb, &m)); 5333 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); 5334 } else { 5335 PetscCheck(C->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is row-incompatible with the target matrix"); 5336 } 5337 if (colemb) { 5338 PetscCall(ISGetLocalSize(colemb, &n)); 5339 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); 5340 } else { 5341 PetscCheck(C->cmap->n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is col-incompatible with the target matrix"); 5342 } 5343 5344 Baij = (Mat_SeqAIJ *)(B->data); 5345 if (pattern == DIFFERENT_NONZERO_PATTERN) { 5346 PetscCall(PetscMalloc1(B->rmap->n, &nz)); 5347 for (i = 0; i < B->rmap->n; i++) nz[i] = Baij->i[i + 1] - Baij->i[i]; 5348 PetscCall(MatSeqAIJSetPreallocation(C, 0, nz)); 5349 PetscCall(PetscFree(nz)); 5350 } 5351 if (pattern == SUBSET_NONZERO_PATTERN) PetscCall(MatZeroEntries(C)); 5352 count = 0; 5353 rowindices = NULL; 5354 colindices = NULL; 5355 if (rowemb) PetscCall(ISGetIndices(rowemb, &rowindices)); 5356 if (colemb) PetscCall(ISGetIndices(colemb, &colindices)); 5357 for (i = 0; i < B->rmap->n; i++) { 5358 PetscInt row; 5359 row = i; 5360 if (rowindices) row = rowindices[i]; 5361 for (j = Baij->i[i]; j < Baij->i[i + 1]; j++) { 5362 PetscInt col; 5363 col = Baij->j[count]; 5364 if (colindices) col = colindices[col]; 5365 v = Baij->a[count]; 5366 PetscCall(MatSetValues(C, 1, &row, 1, &col, &v, INSERT_VALUES)); 5367 ++count; 5368 } 5369 } 5370 /* FIXME: set C's nonzerostate correctly. */ 5371 /* Assembly for C is necessary. */ 5372 C->preallocated = PETSC_TRUE; 5373 C->assembled = PETSC_TRUE; 5374 C->was_assembled = PETSC_FALSE; 5375 PetscFunctionReturn(PETSC_SUCCESS); 5376 } 5377 5378 PetscErrorCode MatEliminateZeros_SeqAIJ(Mat A, PetscBool keep) 5379 { 5380 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 5381 MatScalar *aa = a->a; 5382 PetscInt m = A->rmap->n, fshift = 0, fshift_prev = 0, i, k; 5383 PetscInt *ailen = a->ilen, *imax = a->imax, *ai = a->i, *aj = a->j, rmax = 0; 5384 5385 PetscFunctionBegin; 5386 PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix"); 5387 if (m) rmax = ailen[0]; /* determine row with most nonzeros */ 5388 for (i = 1; i <= m; i++) { 5389 /* move each nonzero entry back by the amount of zero slots (fshift) before it*/ 5390 for (k = ai[i - 1]; k < ai[i]; k++) { 5391 if (aa[k] == 0 && (aj[k] != i - 1 || !keep)) fshift++; 5392 else { 5393 if (aa[k] == 0 && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal zero at row %" PetscInt_FMT "\n", i - 1)); 5394 aa[k - fshift] = aa[k]; 5395 aj[k - fshift] = aj[k]; 5396 } 5397 } 5398 ai[i - 1] -= fshift_prev; // safe to update ai[i-1] now since it will not be used in the next iteration 5399 fshift_prev = fshift; 5400 /* reset ilen and imax for each row */ 5401 ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1]; 5402 a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0); 5403 rmax = PetscMax(rmax, ailen[i - 1]); 5404 } 5405 if (fshift) { 5406 if (m) { 5407 ai[m] -= fshift; 5408 a->nz = ai[m]; 5409 } 5410 PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT "; zeros eliminated: %" PetscInt_FMT "; nonzeros left: %" PetscInt_FMT "\n", m, A->cmap->n, fshift, a->nz)); 5411 A->nonzerostate++; 5412 A->info.nz_unneeded += (PetscReal)fshift; 5413 a->rmax = rmax; 5414 if (a->inode.use && a->inode.checked) PetscCall(MatSeqAIJCheckInode(A)); 5415 PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY)); 5416 PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY)); 5417 } 5418 PetscFunctionReturn(PETSC_SUCCESS); 5419 } 5420 5421 PetscFunctionList MatSeqAIJList = NULL; 5422 5423 /*@C 5424 MatSeqAIJSetType - Converts a `MATSEQAIJ` matrix to a subtype 5425 5426 Collective 5427 5428 Input Parameters: 5429 + mat - the matrix object 5430 - matype - matrix type 5431 5432 Options Database Key: 5433 . -mat_seqaij_type <method> - for example seqaijcrl 5434 5435 Level: intermediate 5436 5437 .seealso: [](ch_matrices), `Mat`, `PCSetType()`, `VecSetType()`, `MatCreate()`, `MatType` 5438 @*/ 5439 PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype) 5440 { 5441 PetscBool sametype; 5442 PetscErrorCode (*r)(Mat, MatType, MatReuse, Mat *); 5443 5444 PetscFunctionBegin; 5445 PetscValidHeaderSpecific(mat, MAT_CLASSID, 1); 5446 PetscCall(PetscObjectTypeCompare((PetscObject)mat, matype, &sametype)); 5447 if (sametype) PetscFunctionReturn(PETSC_SUCCESS); 5448 5449 PetscCall(PetscFunctionListFind(MatSeqAIJList, matype, &r)); 5450 PetscCheck(r, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown Mat type given: %s", matype); 5451 PetscCall((*r)(mat, matype, MAT_INPLACE_MATRIX, &mat)); 5452 PetscFunctionReturn(PETSC_SUCCESS); 5453 } 5454 5455 /*@C 5456 MatSeqAIJRegister - - Adds a new sub-matrix type for sequential `MATSEQAIJ` matrices 5457 5458 Not Collective 5459 5460 Input Parameters: 5461 + sname - name of a new user-defined matrix type, for example `MATSEQAIJCRL` 5462 - function - routine to convert to subtype 5463 5464 Level: advanced 5465 5466 Notes: 5467 `MatSeqAIJRegister()` may be called multiple times to add several user-defined solvers. 5468 5469 Then, your matrix can be chosen with the procedural interface at runtime via the option 5470 $ -mat_seqaij_type my_mat 5471 5472 .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRegisterAll()` 5473 @*/ 5474 PetscErrorCode MatSeqAIJRegister(const char sname[], PetscErrorCode (*function)(Mat, MatType, MatReuse, Mat *)) 5475 { 5476 PetscFunctionBegin; 5477 PetscCall(MatInitializePackage()); 5478 PetscCall(PetscFunctionListAdd(&MatSeqAIJList, sname, function)); 5479 PetscFunctionReturn(PETSC_SUCCESS); 5480 } 5481 5482 PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE; 5483 5484 /*@C 5485 MatSeqAIJRegisterAll - Registers all of the matrix subtypes of `MATSSEQAIJ` 5486 5487 Not Collective 5488 5489 Level: advanced 5490 5491 Note: 5492 This registers the versions of `MATSEQAIJ` for GPUs 5493 5494 .seealso: [](ch_matrices), `Mat`, `MatRegisterAll()`, `MatSeqAIJRegister()` 5495 @*/ 5496 PetscErrorCode MatSeqAIJRegisterAll(void) 5497 { 5498 PetscFunctionBegin; 5499 if (MatSeqAIJRegisterAllCalled) PetscFunctionReturn(PETSC_SUCCESS); 5500 MatSeqAIJRegisterAllCalled = PETSC_TRUE; 5501 5502 PetscCall(MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL)); 5503 PetscCall(MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM)); 5504 PetscCall(MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL)); 5505 #if defined(PETSC_HAVE_MKL_SPARSE) 5506 PetscCall(MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL)); 5507 #endif 5508 #if defined(PETSC_HAVE_CUDA) 5509 PetscCall(MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE)); 5510 #endif 5511 #if defined(PETSC_HAVE_HIP) 5512 PetscCall(MatSeqAIJRegister(MATSEQAIJHIPSPARSE, MatConvert_SeqAIJ_SeqAIJHIPSPARSE)); 5513 #endif 5514 #if defined(PETSC_HAVE_KOKKOS_KERNELS) 5515 PetscCall(MatSeqAIJRegister(MATSEQAIJKOKKOS, MatConvert_SeqAIJ_SeqAIJKokkos)); 5516 #endif 5517 #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA) 5518 PetscCall(MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL)); 5519 #endif 5520 PetscFunctionReturn(PETSC_SUCCESS); 5521 } 5522 5523 /* 5524 Special version for direct calls from Fortran 5525 */ 5526 #include <petsc/private/fortranimpl.h> 5527 #if defined(PETSC_HAVE_FORTRAN_CAPS) 5528 #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ 5529 #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE) 5530 #define matsetvaluesseqaij_ matsetvaluesseqaij 5531 #endif 5532 5533 /* Change these macros so can be used in void function */ 5534 5535 /* Change these macros so can be used in void function */ 5536 /* Identical to PetscCallVoid, except it assigns to *_ierr */ 5537 #undef PetscCall 5538 #define PetscCall(...) \ 5539 do { \ 5540 PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \ 5541 if (PetscUnlikely(ierr_msv_mpiaij)) { \ 5542 *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \ 5543 return; \ 5544 } \ 5545 } while (0) 5546 5547 #undef SETERRQ 5548 #define SETERRQ(comm, ierr, ...) \ 5549 do { \ 5550 *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \ 5551 return; \ 5552 } while (0) 5553 5554 PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[], InsertMode *isis, PetscErrorCode *_ierr) 5555 { 5556 Mat A = *AA; 5557 PetscInt m = *mm, n = *nn; 5558 InsertMode is = *isis; 5559 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data; 5560 PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N; 5561 PetscInt *imax, *ai, *ailen; 5562 PetscInt *aj, nonew = a->nonew, lastcol = -1; 5563 MatScalar *ap, value, *aa; 5564 PetscBool ignorezeroentries = a->ignorezeroentries; 5565 PetscBool roworiented = a->roworiented; 5566 5567 PetscFunctionBegin; 5568 MatCheckPreallocated(A, 1); 5569 imax = a->imax; 5570 ai = a->i; 5571 ailen = a->ilen; 5572 aj = a->j; 5573 aa = a->a; 5574 5575 for (k = 0; k < m; k++) { /* loop over added rows */ 5576 row = im[k]; 5577 if (row < 0) continue; 5578 PetscCheck(row < A->rmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Row too large"); 5579 rp = aj + ai[row]; 5580 ap = aa + ai[row]; 5581 rmax = imax[row]; 5582 nrow = ailen[row]; 5583 low = 0; 5584 high = nrow; 5585 for (l = 0; l < n; l++) { /* loop over added columns */ 5586 if (in[l] < 0) continue; 5587 PetscCheck(in[l] < A->cmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Column too large"); 5588 col = in[l]; 5589 if (roworiented) value = v[l + k * n]; 5590 else value = v[k + l * m]; 5591 5592 if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue; 5593 5594 if (col <= lastcol) low = 0; 5595 else high = nrow; 5596 lastcol = col; 5597 while (high - low > 5) { 5598 t = (low + high) / 2; 5599 if (rp[t] > col) high = t; 5600 else low = t; 5601 } 5602 for (i = low; i < high; i++) { 5603 if (rp[i] > col) break; 5604 if (rp[i] == col) { 5605 if (is == ADD_VALUES) ap[i] += value; 5606 else ap[i] = value; 5607 goto noinsert; 5608 } 5609 } 5610 if (value == 0.0 && ignorezeroentries) goto noinsert; 5611 if (nonew == 1) goto noinsert; 5612 PetscCheck(nonew != -1, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero in the matrix"); 5613 MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar); 5614 N = nrow++ - 1; 5615 a->nz++; 5616 high++; 5617 /* shift up all the later entries in this row */ 5618 for (ii = N; ii >= i; ii--) { 5619 rp[ii + 1] = rp[ii]; 5620 ap[ii + 1] = ap[ii]; 5621 } 5622 rp[i] = col; 5623 ap[i] = value; 5624 A->nonzerostate++; 5625 noinsert:; 5626 low = i + 1; 5627 } 5628 ailen[row] = nrow; 5629 } 5630 PetscFunctionReturnVoid(); 5631 } 5632 /* Undefining these here since they were redefined from their original definition above! No 5633 * other PETSc functions should be defined past this point, as it is impossible to recover the 5634 * original definitions */ 5635 #undef PetscCall 5636 #undef SETERRQ 5637