1 /* 2 Defines the basic matrix operations for the BAIJ (compressed row) 3 matrix storage format. 4 */ 5 #include <../src/mat/impls/baij/seq/baij.h> /*I "petscmat.h" I*/ 6 #include <petscblaslapack.h> 7 #include <petsc/private/kernels/blockinvert.h> 8 #include <petsc/private/kernels/blockmatmult.h> 9 10 /* defines MatSetValues_Seq_Hash(), MatAssemblyEnd_Seq_Hash(), MatSetUp_Seq_Hash() */ 11 #define TYPE BAIJ 12 #define TYPE_BS 13 #include "../src/mat/impls/aij/seq/seqhashmatsetvalues.h" 14 #undef TYPE_BS 15 #define TYPE_BS _BS 16 #define TYPE_BS_ON 17 #include "../src/mat/impls/aij/seq/seqhashmatsetvalues.h" 18 #undef TYPE_BS 19 #include "../src/mat/impls/aij/seq/seqhashmat.h" 20 #undef TYPE 21 #undef TYPE_BS_ON 22 23 #if defined(PETSC_HAVE_HYPRE) 24 PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *); 25 #endif 26 27 #if defined(PETSC_HAVE_MKL_SPARSE_OPTIMIZE) 28 PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBAIJMKL(Mat, MatType, MatReuse, Mat *); 29 #endif 30 PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *); 31 32 static PetscErrorCode MatGetColumnReductions_SeqBAIJ(Mat A, PetscInt type, PetscReal *reductions) 33 { 34 Mat_SeqBAIJ *a_aij = (Mat_SeqBAIJ *)A->data; 35 PetscInt m, n, ib, jb, bs = A->rmap->bs; 36 MatScalar *a_val = a_aij->a; 37 38 PetscFunctionBegin; 39 PetscCall(MatGetSize(A, &m, &n)); 40 PetscCall(PetscArrayzero(reductions, n)); 41 if (type == NORM_2) { 42 for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) { 43 for (jb = 0; jb < bs; jb++) { 44 for (ib = 0; ib < bs; ib++) { 45 reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val * *a_val); 46 a_val++; 47 } 48 } 49 } 50 } else if (type == NORM_1) { 51 for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) { 52 for (jb = 0; jb < bs; jb++) { 53 for (ib = 0; ib < bs; ib++) { 54 reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val); 55 a_val++; 56 } 57 } 58 } 59 } else if (type == NORM_INFINITY) { 60 for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) { 61 for (jb = 0; jb < bs; jb++) { 62 for (ib = 0; ib < bs; ib++) { 63 int col = A->cmap->rstart + a_aij->j[i] * bs + jb; 64 reductions[col] = PetscMax(PetscAbsScalar(*a_val), reductions[col]); 65 a_val++; 66 } 67 } 68 } 69 } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) { 70 for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) { 71 for (jb = 0; jb < bs; jb++) { 72 for (ib = 0; ib < bs; ib++) { 73 reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscRealPart(*a_val); 74 a_val++; 75 } 76 } 77 } 78 } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) { 79 for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) { 80 for (jb = 0; jb < bs; jb++) { 81 for (ib = 0; ib < bs; ib++) { 82 reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscImaginaryPart(*a_val); 83 a_val++; 84 } 85 } 86 } 87 } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type"); 88 if (type == NORM_2) { 89 for (PetscInt i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]); 90 } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) { 91 for (PetscInt i = 0; i < n; i++) reductions[i] /= m; 92 } 93 PetscFunctionReturn(PETSC_SUCCESS); 94 } 95 96 static PetscErrorCode MatInvertBlockDiagonal_SeqBAIJ(Mat A, const PetscScalar **values) 97 { 98 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 99 PetscInt *diag_offset, i, bs = A->rmap->bs, mbs = a->mbs, ipvt[5], bs2 = bs * bs, *v_pivots; 100 MatScalar *v = a->a, *odiag, *diag, work[25], *v_work; 101 PetscReal shift = 0.0; 102 PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE; 103 104 PetscFunctionBegin; 105 allowzeropivot = PetscNot(A->erroriffailure); 106 107 if (a->idiagvalid) { 108 if (values) *values = a->idiag; 109 PetscFunctionReturn(PETSC_SUCCESS); 110 } 111 PetscCall(MatMarkDiagonal_SeqBAIJ(A)); 112 diag_offset = a->diag; 113 if (!a->idiag) { PetscCall(PetscMalloc1(bs2 * mbs, &a->idiag)); } 114 diag = a->idiag; 115 if (values) *values = a->idiag; 116 /* factor and invert each block */ 117 switch (bs) { 118 case 1: 119 for (i = 0; i < mbs; i++) { 120 odiag = v + 1 * diag_offset[i]; 121 diag[0] = odiag[0]; 122 123 if (PetscAbsScalar(diag[0] + shift) < PETSC_MACHINE_EPSILON) { 124 if (allowzeropivot) { 125 A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 126 A->factorerror_zeropivot_value = PetscAbsScalar(diag[0]); 127 A->factorerror_zeropivot_row = i; 128 PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT "\n", i)); 129 } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_MAT_LU_ZRPVT, "Zero pivot, row %" PetscInt_FMT " pivot value %g tolerance %g", i, (double)PetscAbsScalar(diag[0]), (double)PETSC_MACHINE_EPSILON); 130 } 131 132 diag[0] = (PetscScalar)1.0 / (diag[0] + shift); 133 diag += 1; 134 } 135 break; 136 case 2: 137 for (i = 0; i < mbs; i++) { 138 odiag = v + 4 * diag_offset[i]; 139 diag[0] = odiag[0]; 140 diag[1] = odiag[1]; 141 diag[2] = odiag[2]; 142 diag[3] = odiag[3]; 143 PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected)); 144 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 145 diag += 4; 146 } 147 break; 148 case 3: 149 for (i = 0; i < mbs; i++) { 150 odiag = v + 9 * diag_offset[i]; 151 diag[0] = odiag[0]; 152 diag[1] = odiag[1]; 153 diag[2] = odiag[2]; 154 diag[3] = odiag[3]; 155 diag[4] = odiag[4]; 156 diag[5] = odiag[5]; 157 diag[6] = odiag[6]; 158 diag[7] = odiag[7]; 159 diag[8] = odiag[8]; 160 PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected)); 161 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 162 diag += 9; 163 } 164 break; 165 case 4: 166 for (i = 0; i < mbs; i++) { 167 odiag = v + 16 * diag_offset[i]; 168 PetscCall(PetscArraycpy(diag, odiag, 16)); 169 PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected)); 170 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 171 diag += 16; 172 } 173 break; 174 case 5: 175 for (i = 0; i < mbs; i++) { 176 odiag = v + 25 * diag_offset[i]; 177 PetscCall(PetscArraycpy(diag, odiag, 25)); 178 PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected)); 179 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 180 diag += 25; 181 } 182 break; 183 case 6: 184 for (i = 0; i < mbs; i++) { 185 odiag = v + 36 * diag_offset[i]; 186 PetscCall(PetscArraycpy(diag, odiag, 36)); 187 PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected)); 188 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 189 diag += 36; 190 } 191 break; 192 case 7: 193 for (i = 0; i < mbs; i++) { 194 odiag = v + 49 * diag_offset[i]; 195 PetscCall(PetscArraycpy(diag, odiag, 49)); 196 PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected)); 197 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 198 diag += 49; 199 } 200 break; 201 default: 202 PetscCall(PetscMalloc2(bs, &v_work, bs, &v_pivots)); 203 for (i = 0; i < mbs; i++) { 204 odiag = v + bs2 * diag_offset[i]; 205 PetscCall(PetscArraycpy(diag, odiag, bs2)); 206 PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected)); 207 if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 208 diag += bs2; 209 } 210 PetscCall(PetscFree2(v_work, v_pivots)); 211 } 212 a->idiagvalid = PETSC_TRUE; 213 PetscFunctionReturn(PETSC_SUCCESS); 214 } 215 216 static PetscErrorCode MatSOR_SeqBAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx) 217 { 218 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 219 PetscScalar *x, *work, *w, *workt, *t; 220 const MatScalar *v, *aa = a->a, *idiag; 221 const PetscScalar *b, *xb; 222 PetscScalar s[7], xw[7] = {0}; /* avoid some compilers thinking xw is uninitialized */ 223 PetscInt m = a->mbs, i, i2, nz, bs = A->rmap->bs, bs2 = bs * bs, k, j, idx, it; 224 const PetscInt *diag, *ai = a->i, *aj = a->j, *vi; 225 226 PetscFunctionBegin; 227 its = its * lits; 228 PetscCheck(!(flag & SOR_EISENSTAT), PETSC_COMM_SELF, PETSC_ERR_SUP, "No support yet for Eisenstat"); 229 PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " and local its %" PetscInt_FMT " both positive", its, lits); 230 PetscCheck(!fshift, PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for diagonal shift"); 231 PetscCheck(omega == 1.0, PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for non-trivial relaxation factor"); 232 PetscCheck(!(flag & SOR_APPLY_UPPER) && !(flag & SOR_APPLY_LOWER), PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for applying upper or lower triangular parts"); 233 234 if (!a->idiagvalid) PetscCall(MatInvertBlockDiagonal(A, NULL)); 235 236 if (!m) PetscFunctionReturn(PETSC_SUCCESS); 237 diag = a->diag; 238 idiag = a->idiag; 239 k = PetscMax(A->rmap->n, A->cmap->n); 240 if (!a->mult_work) PetscCall(PetscMalloc1(k + 1, &a->mult_work)); 241 if (!a->sor_workt) PetscCall(PetscMalloc1(k, &a->sor_workt)); 242 if (!a->sor_work) PetscCall(PetscMalloc1(bs, &a->sor_work)); 243 work = a->mult_work; 244 t = a->sor_workt; 245 w = a->sor_work; 246 247 PetscCall(VecGetArray(xx, &x)); 248 PetscCall(VecGetArrayRead(bb, &b)); 249 250 if (flag & SOR_ZERO_INITIAL_GUESS) { 251 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) { 252 switch (bs) { 253 case 1: 254 PetscKernel_v_gets_A_times_w_1(x, idiag, b); 255 t[0] = b[0]; 256 i2 = 1; 257 idiag += 1; 258 for (i = 1; i < m; i++) { 259 v = aa + ai[i]; 260 vi = aj + ai[i]; 261 nz = diag[i] - ai[i]; 262 s[0] = b[i2]; 263 for (j = 0; j < nz; j++) { 264 xw[0] = x[vi[j]]; 265 PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw); 266 } 267 t[i2] = s[0]; 268 PetscKernel_v_gets_A_times_w_1(xw, idiag, s); 269 x[i2] = xw[0]; 270 idiag += 1; 271 i2 += 1; 272 } 273 break; 274 case 2: 275 PetscKernel_v_gets_A_times_w_2(x, idiag, b); 276 t[0] = b[0]; 277 t[1] = b[1]; 278 i2 = 2; 279 idiag += 4; 280 for (i = 1; i < m; i++) { 281 v = aa + 4 * ai[i]; 282 vi = aj + ai[i]; 283 nz = diag[i] - ai[i]; 284 s[0] = b[i2]; 285 s[1] = b[i2 + 1]; 286 for (j = 0; j < nz; j++) { 287 idx = 2 * vi[j]; 288 it = 4 * j; 289 xw[0] = x[idx]; 290 xw[1] = x[1 + idx]; 291 PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw); 292 } 293 t[i2] = s[0]; 294 t[i2 + 1] = s[1]; 295 PetscKernel_v_gets_A_times_w_2(xw, idiag, s); 296 x[i2] = xw[0]; 297 x[i2 + 1] = xw[1]; 298 idiag += 4; 299 i2 += 2; 300 } 301 break; 302 case 3: 303 PetscKernel_v_gets_A_times_w_3(x, idiag, b); 304 t[0] = b[0]; 305 t[1] = b[1]; 306 t[2] = b[2]; 307 i2 = 3; 308 idiag += 9; 309 for (i = 1; i < m; i++) { 310 v = aa + 9 * ai[i]; 311 vi = aj + ai[i]; 312 nz = diag[i] - ai[i]; 313 s[0] = b[i2]; 314 s[1] = b[i2 + 1]; 315 s[2] = b[i2 + 2]; 316 while (nz--) { 317 idx = 3 * (*vi++); 318 xw[0] = x[idx]; 319 xw[1] = x[1 + idx]; 320 xw[2] = x[2 + idx]; 321 PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw); 322 v += 9; 323 } 324 t[i2] = s[0]; 325 t[i2 + 1] = s[1]; 326 t[i2 + 2] = s[2]; 327 PetscKernel_v_gets_A_times_w_3(xw, idiag, s); 328 x[i2] = xw[0]; 329 x[i2 + 1] = xw[1]; 330 x[i2 + 2] = xw[2]; 331 idiag += 9; 332 i2 += 3; 333 } 334 break; 335 case 4: 336 PetscKernel_v_gets_A_times_w_4(x, idiag, b); 337 t[0] = b[0]; 338 t[1] = b[1]; 339 t[2] = b[2]; 340 t[3] = b[3]; 341 i2 = 4; 342 idiag += 16; 343 for (i = 1; i < m; i++) { 344 v = aa + 16 * ai[i]; 345 vi = aj + ai[i]; 346 nz = diag[i] - ai[i]; 347 s[0] = b[i2]; 348 s[1] = b[i2 + 1]; 349 s[2] = b[i2 + 2]; 350 s[3] = b[i2 + 3]; 351 while (nz--) { 352 idx = 4 * (*vi++); 353 xw[0] = x[idx]; 354 xw[1] = x[1 + idx]; 355 xw[2] = x[2 + idx]; 356 xw[3] = x[3 + idx]; 357 PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw); 358 v += 16; 359 } 360 t[i2] = s[0]; 361 t[i2 + 1] = s[1]; 362 t[i2 + 2] = s[2]; 363 t[i2 + 3] = s[3]; 364 PetscKernel_v_gets_A_times_w_4(xw, idiag, s); 365 x[i2] = xw[0]; 366 x[i2 + 1] = xw[1]; 367 x[i2 + 2] = xw[2]; 368 x[i2 + 3] = xw[3]; 369 idiag += 16; 370 i2 += 4; 371 } 372 break; 373 case 5: 374 PetscKernel_v_gets_A_times_w_5(x, idiag, b); 375 t[0] = b[0]; 376 t[1] = b[1]; 377 t[2] = b[2]; 378 t[3] = b[3]; 379 t[4] = b[4]; 380 i2 = 5; 381 idiag += 25; 382 for (i = 1; i < m; i++) { 383 v = aa + 25 * ai[i]; 384 vi = aj + ai[i]; 385 nz = diag[i] - ai[i]; 386 s[0] = b[i2]; 387 s[1] = b[i2 + 1]; 388 s[2] = b[i2 + 2]; 389 s[3] = b[i2 + 3]; 390 s[4] = b[i2 + 4]; 391 while (nz--) { 392 idx = 5 * (*vi++); 393 xw[0] = x[idx]; 394 xw[1] = x[1 + idx]; 395 xw[2] = x[2 + idx]; 396 xw[3] = x[3 + idx]; 397 xw[4] = x[4 + idx]; 398 PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw); 399 v += 25; 400 } 401 t[i2] = s[0]; 402 t[i2 + 1] = s[1]; 403 t[i2 + 2] = s[2]; 404 t[i2 + 3] = s[3]; 405 t[i2 + 4] = s[4]; 406 PetscKernel_v_gets_A_times_w_5(xw, idiag, s); 407 x[i2] = xw[0]; 408 x[i2 + 1] = xw[1]; 409 x[i2 + 2] = xw[2]; 410 x[i2 + 3] = xw[3]; 411 x[i2 + 4] = xw[4]; 412 idiag += 25; 413 i2 += 5; 414 } 415 break; 416 case 6: 417 PetscKernel_v_gets_A_times_w_6(x, idiag, b); 418 t[0] = b[0]; 419 t[1] = b[1]; 420 t[2] = b[2]; 421 t[3] = b[3]; 422 t[4] = b[4]; 423 t[5] = b[5]; 424 i2 = 6; 425 idiag += 36; 426 for (i = 1; i < m; i++) { 427 v = aa + 36 * ai[i]; 428 vi = aj + ai[i]; 429 nz = diag[i] - ai[i]; 430 s[0] = b[i2]; 431 s[1] = b[i2 + 1]; 432 s[2] = b[i2 + 2]; 433 s[3] = b[i2 + 3]; 434 s[4] = b[i2 + 4]; 435 s[5] = b[i2 + 5]; 436 while (nz--) { 437 idx = 6 * (*vi++); 438 xw[0] = x[idx]; 439 xw[1] = x[1 + idx]; 440 xw[2] = x[2 + idx]; 441 xw[3] = x[3 + idx]; 442 xw[4] = x[4 + idx]; 443 xw[5] = x[5 + idx]; 444 PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw); 445 v += 36; 446 } 447 t[i2] = s[0]; 448 t[i2 + 1] = s[1]; 449 t[i2 + 2] = s[2]; 450 t[i2 + 3] = s[3]; 451 t[i2 + 4] = s[4]; 452 t[i2 + 5] = s[5]; 453 PetscKernel_v_gets_A_times_w_6(xw, idiag, s); 454 x[i2] = xw[0]; 455 x[i2 + 1] = xw[1]; 456 x[i2 + 2] = xw[2]; 457 x[i2 + 3] = xw[3]; 458 x[i2 + 4] = xw[4]; 459 x[i2 + 5] = xw[5]; 460 idiag += 36; 461 i2 += 6; 462 } 463 break; 464 case 7: 465 PetscKernel_v_gets_A_times_w_7(x, idiag, b); 466 t[0] = b[0]; 467 t[1] = b[1]; 468 t[2] = b[2]; 469 t[3] = b[3]; 470 t[4] = b[4]; 471 t[5] = b[5]; 472 t[6] = b[6]; 473 i2 = 7; 474 idiag += 49; 475 for (i = 1; i < m; i++) { 476 v = aa + 49 * ai[i]; 477 vi = aj + ai[i]; 478 nz = diag[i] - ai[i]; 479 s[0] = b[i2]; 480 s[1] = b[i2 + 1]; 481 s[2] = b[i2 + 2]; 482 s[3] = b[i2 + 3]; 483 s[4] = b[i2 + 4]; 484 s[5] = b[i2 + 5]; 485 s[6] = b[i2 + 6]; 486 while (nz--) { 487 idx = 7 * (*vi++); 488 xw[0] = x[idx]; 489 xw[1] = x[1 + idx]; 490 xw[2] = x[2 + idx]; 491 xw[3] = x[3 + idx]; 492 xw[4] = x[4 + idx]; 493 xw[5] = x[5 + idx]; 494 xw[6] = x[6 + idx]; 495 PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw); 496 v += 49; 497 } 498 t[i2] = s[0]; 499 t[i2 + 1] = s[1]; 500 t[i2 + 2] = s[2]; 501 t[i2 + 3] = s[3]; 502 t[i2 + 4] = s[4]; 503 t[i2 + 5] = s[5]; 504 t[i2 + 6] = s[6]; 505 PetscKernel_v_gets_A_times_w_7(xw, idiag, s); 506 x[i2] = xw[0]; 507 x[i2 + 1] = xw[1]; 508 x[i2 + 2] = xw[2]; 509 x[i2 + 3] = xw[3]; 510 x[i2 + 4] = xw[4]; 511 x[i2 + 5] = xw[5]; 512 x[i2 + 6] = xw[6]; 513 idiag += 49; 514 i2 += 7; 515 } 516 break; 517 default: 518 PetscKernel_w_gets_Ar_times_v(bs, bs, b, idiag, x); 519 PetscCall(PetscArraycpy(t, b, bs)); 520 i2 = bs; 521 idiag += bs2; 522 for (i = 1; i < m; i++) { 523 v = aa + bs2 * ai[i]; 524 vi = aj + ai[i]; 525 nz = diag[i] - ai[i]; 526 527 PetscCall(PetscArraycpy(w, b + i2, bs)); 528 /* copy all rows of x that are needed into contiguous space */ 529 workt = work; 530 for (j = 0; j < nz; j++) { 531 PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs)); 532 workt += bs; 533 } 534 PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work); 535 PetscCall(PetscArraycpy(t + i2, w, bs)); 536 PetscKernel_w_gets_Ar_times_v(bs, bs, w, idiag, x + i2); 537 538 idiag += bs2; 539 i2 += bs; 540 } 541 break; 542 } 543 /* for logging purposes assume number of nonzero in lower half is 1/2 of total */ 544 PetscCall(PetscLogFlops(1.0 * bs2 * a->nz)); 545 xb = t; 546 } else xb = b; 547 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) { 548 idiag = a->idiag + bs2 * (a->mbs - 1); 549 i2 = bs * (m - 1); 550 switch (bs) { 551 case 1: 552 s[0] = xb[i2]; 553 PetscKernel_v_gets_A_times_w_1(xw, idiag, s); 554 x[i2] = xw[0]; 555 i2 -= 1; 556 for (i = m - 2; i >= 0; i--) { 557 v = aa + (diag[i] + 1); 558 vi = aj + diag[i] + 1; 559 nz = ai[i + 1] - diag[i] - 1; 560 s[0] = xb[i2]; 561 for (j = 0; j < nz; j++) { 562 xw[0] = x[vi[j]]; 563 PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw); 564 } 565 PetscKernel_v_gets_A_times_w_1(xw, idiag, s); 566 x[i2] = xw[0]; 567 idiag -= 1; 568 i2 -= 1; 569 } 570 break; 571 case 2: 572 s[0] = xb[i2]; 573 s[1] = xb[i2 + 1]; 574 PetscKernel_v_gets_A_times_w_2(xw, idiag, s); 575 x[i2] = xw[0]; 576 x[i2 + 1] = xw[1]; 577 i2 -= 2; 578 idiag -= 4; 579 for (i = m - 2; i >= 0; i--) { 580 v = aa + 4 * (diag[i] + 1); 581 vi = aj + diag[i] + 1; 582 nz = ai[i + 1] - diag[i] - 1; 583 s[0] = xb[i2]; 584 s[1] = xb[i2 + 1]; 585 for (j = 0; j < nz; j++) { 586 idx = 2 * vi[j]; 587 it = 4 * j; 588 xw[0] = x[idx]; 589 xw[1] = x[1 + idx]; 590 PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw); 591 } 592 PetscKernel_v_gets_A_times_w_2(xw, idiag, s); 593 x[i2] = xw[0]; 594 x[i2 + 1] = xw[1]; 595 idiag -= 4; 596 i2 -= 2; 597 } 598 break; 599 case 3: 600 s[0] = xb[i2]; 601 s[1] = xb[i2 + 1]; 602 s[2] = xb[i2 + 2]; 603 PetscKernel_v_gets_A_times_w_3(xw, idiag, s); 604 x[i2] = xw[0]; 605 x[i2 + 1] = xw[1]; 606 x[i2 + 2] = xw[2]; 607 i2 -= 3; 608 idiag -= 9; 609 for (i = m - 2; i >= 0; i--) { 610 v = aa + 9 * (diag[i] + 1); 611 vi = aj + diag[i] + 1; 612 nz = ai[i + 1] - diag[i] - 1; 613 s[0] = xb[i2]; 614 s[1] = xb[i2 + 1]; 615 s[2] = xb[i2 + 2]; 616 while (nz--) { 617 idx = 3 * (*vi++); 618 xw[0] = x[idx]; 619 xw[1] = x[1 + idx]; 620 xw[2] = x[2 + idx]; 621 PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw); 622 v += 9; 623 } 624 PetscKernel_v_gets_A_times_w_3(xw, idiag, s); 625 x[i2] = xw[0]; 626 x[i2 + 1] = xw[1]; 627 x[i2 + 2] = xw[2]; 628 idiag -= 9; 629 i2 -= 3; 630 } 631 break; 632 case 4: 633 s[0] = xb[i2]; 634 s[1] = xb[i2 + 1]; 635 s[2] = xb[i2 + 2]; 636 s[3] = xb[i2 + 3]; 637 PetscKernel_v_gets_A_times_w_4(xw, idiag, s); 638 x[i2] = xw[0]; 639 x[i2 + 1] = xw[1]; 640 x[i2 + 2] = xw[2]; 641 x[i2 + 3] = xw[3]; 642 i2 -= 4; 643 idiag -= 16; 644 for (i = m - 2; i >= 0; i--) { 645 v = aa + 16 * (diag[i] + 1); 646 vi = aj + diag[i] + 1; 647 nz = ai[i + 1] - diag[i] - 1; 648 s[0] = xb[i2]; 649 s[1] = xb[i2 + 1]; 650 s[2] = xb[i2 + 2]; 651 s[3] = xb[i2 + 3]; 652 while (nz--) { 653 idx = 4 * (*vi++); 654 xw[0] = x[idx]; 655 xw[1] = x[1 + idx]; 656 xw[2] = x[2 + idx]; 657 xw[3] = x[3 + idx]; 658 PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw); 659 v += 16; 660 } 661 PetscKernel_v_gets_A_times_w_4(xw, idiag, s); 662 x[i2] = xw[0]; 663 x[i2 + 1] = xw[1]; 664 x[i2 + 2] = xw[2]; 665 x[i2 + 3] = xw[3]; 666 idiag -= 16; 667 i2 -= 4; 668 } 669 break; 670 case 5: 671 s[0] = xb[i2]; 672 s[1] = xb[i2 + 1]; 673 s[2] = xb[i2 + 2]; 674 s[3] = xb[i2 + 3]; 675 s[4] = xb[i2 + 4]; 676 PetscKernel_v_gets_A_times_w_5(xw, idiag, s); 677 x[i2] = xw[0]; 678 x[i2 + 1] = xw[1]; 679 x[i2 + 2] = xw[2]; 680 x[i2 + 3] = xw[3]; 681 x[i2 + 4] = xw[4]; 682 i2 -= 5; 683 idiag -= 25; 684 for (i = m - 2; i >= 0; i--) { 685 v = aa + 25 * (diag[i] + 1); 686 vi = aj + diag[i] + 1; 687 nz = ai[i + 1] - diag[i] - 1; 688 s[0] = xb[i2]; 689 s[1] = xb[i2 + 1]; 690 s[2] = xb[i2 + 2]; 691 s[3] = xb[i2 + 3]; 692 s[4] = xb[i2 + 4]; 693 while (nz--) { 694 idx = 5 * (*vi++); 695 xw[0] = x[idx]; 696 xw[1] = x[1 + idx]; 697 xw[2] = x[2 + idx]; 698 xw[3] = x[3 + idx]; 699 xw[4] = x[4 + idx]; 700 PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw); 701 v += 25; 702 } 703 PetscKernel_v_gets_A_times_w_5(xw, idiag, s); 704 x[i2] = xw[0]; 705 x[i2 + 1] = xw[1]; 706 x[i2 + 2] = xw[2]; 707 x[i2 + 3] = xw[3]; 708 x[i2 + 4] = xw[4]; 709 idiag -= 25; 710 i2 -= 5; 711 } 712 break; 713 case 6: 714 s[0] = xb[i2]; 715 s[1] = xb[i2 + 1]; 716 s[2] = xb[i2 + 2]; 717 s[3] = xb[i2 + 3]; 718 s[4] = xb[i2 + 4]; 719 s[5] = xb[i2 + 5]; 720 PetscKernel_v_gets_A_times_w_6(xw, idiag, s); 721 x[i2] = xw[0]; 722 x[i2 + 1] = xw[1]; 723 x[i2 + 2] = xw[2]; 724 x[i2 + 3] = xw[3]; 725 x[i2 + 4] = xw[4]; 726 x[i2 + 5] = xw[5]; 727 i2 -= 6; 728 idiag -= 36; 729 for (i = m - 2; i >= 0; i--) { 730 v = aa + 36 * (diag[i] + 1); 731 vi = aj + diag[i] + 1; 732 nz = ai[i + 1] - diag[i] - 1; 733 s[0] = xb[i2]; 734 s[1] = xb[i2 + 1]; 735 s[2] = xb[i2 + 2]; 736 s[3] = xb[i2 + 3]; 737 s[4] = xb[i2 + 4]; 738 s[5] = xb[i2 + 5]; 739 while (nz--) { 740 idx = 6 * (*vi++); 741 xw[0] = x[idx]; 742 xw[1] = x[1 + idx]; 743 xw[2] = x[2 + idx]; 744 xw[3] = x[3 + idx]; 745 xw[4] = x[4 + idx]; 746 xw[5] = x[5 + idx]; 747 PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw); 748 v += 36; 749 } 750 PetscKernel_v_gets_A_times_w_6(xw, idiag, s); 751 x[i2] = xw[0]; 752 x[i2 + 1] = xw[1]; 753 x[i2 + 2] = xw[2]; 754 x[i2 + 3] = xw[3]; 755 x[i2 + 4] = xw[4]; 756 x[i2 + 5] = xw[5]; 757 idiag -= 36; 758 i2 -= 6; 759 } 760 break; 761 case 7: 762 s[0] = xb[i2]; 763 s[1] = xb[i2 + 1]; 764 s[2] = xb[i2 + 2]; 765 s[3] = xb[i2 + 3]; 766 s[4] = xb[i2 + 4]; 767 s[5] = xb[i2 + 5]; 768 s[6] = xb[i2 + 6]; 769 PetscKernel_v_gets_A_times_w_7(x, idiag, b); 770 x[i2] = xw[0]; 771 x[i2 + 1] = xw[1]; 772 x[i2 + 2] = xw[2]; 773 x[i2 + 3] = xw[3]; 774 x[i2 + 4] = xw[4]; 775 x[i2 + 5] = xw[5]; 776 x[i2 + 6] = xw[6]; 777 i2 -= 7; 778 idiag -= 49; 779 for (i = m - 2; i >= 0; i--) { 780 v = aa + 49 * (diag[i] + 1); 781 vi = aj + diag[i] + 1; 782 nz = ai[i + 1] - diag[i] - 1; 783 s[0] = xb[i2]; 784 s[1] = xb[i2 + 1]; 785 s[2] = xb[i2 + 2]; 786 s[3] = xb[i2 + 3]; 787 s[4] = xb[i2 + 4]; 788 s[5] = xb[i2 + 5]; 789 s[6] = xb[i2 + 6]; 790 while (nz--) { 791 idx = 7 * (*vi++); 792 xw[0] = x[idx]; 793 xw[1] = x[1 + idx]; 794 xw[2] = x[2 + idx]; 795 xw[3] = x[3 + idx]; 796 xw[4] = x[4 + idx]; 797 xw[5] = x[5 + idx]; 798 xw[6] = x[6 + idx]; 799 PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw); 800 v += 49; 801 } 802 PetscKernel_v_gets_A_times_w_7(xw, idiag, s); 803 x[i2] = xw[0]; 804 x[i2 + 1] = xw[1]; 805 x[i2 + 2] = xw[2]; 806 x[i2 + 3] = xw[3]; 807 x[i2 + 4] = xw[4]; 808 x[i2 + 5] = xw[5]; 809 x[i2 + 6] = xw[6]; 810 idiag -= 49; 811 i2 -= 7; 812 } 813 break; 814 default: 815 PetscCall(PetscArraycpy(w, xb + i2, bs)); 816 PetscKernel_w_gets_Ar_times_v(bs, bs, w, idiag, x + i2); 817 i2 -= bs; 818 idiag -= bs2; 819 for (i = m - 2; i >= 0; i--) { 820 v = aa + bs2 * (diag[i] + 1); 821 vi = aj + diag[i] + 1; 822 nz = ai[i + 1] - diag[i] - 1; 823 824 PetscCall(PetscArraycpy(w, xb + i2, bs)); 825 /* copy all rows of x that are needed into contiguous space */ 826 workt = work; 827 for (j = 0; j < nz; j++) { 828 PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs)); 829 workt += bs; 830 } 831 PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work); 832 PetscKernel_w_gets_Ar_times_v(bs, bs, w, idiag, x + i2); 833 834 idiag -= bs2; 835 i2 -= bs; 836 } 837 break; 838 } 839 PetscCall(PetscLogFlops(1.0 * bs2 * (a->nz))); 840 } 841 its--; 842 } 843 while (its--) { 844 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) { 845 idiag = a->idiag; 846 i2 = 0; 847 switch (bs) { 848 case 1: 849 for (i = 0; i < m; i++) { 850 v = aa + ai[i]; 851 vi = aj + ai[i]; 852 nz = ai[i + 1] - ai[i]; 853 s[0] = b[i2]; 854 for (j = 0; j < nz; j++) { 855 xw[0] = x[vi[j]]; 856 PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw); 857 } 858 PetscKernel_v_gets_A_times_w_1(xw, idiag, s); 859 x[i2] += xw[0]; 860 idiag += 1; 861 i2 += 1; 862 } 863 break; 864 case 2: 865 for (i = 0; i < m; i++) { 866 v = aa + 4 * ai[i]; 867 vi = aj + ai[i]; 868 nz = ai[i + 1] - ai[i]; 869 s[0] = b[i2]; 870 s[1] = b[i2 + 1]; 871 for (j = 0; j < nz; j++) { 872 idx = 2 * vi[j]; 873 it = 4 * j; 874 xw[0] = x[idx]; 875 xw[1] = x[1 + idx]; 876 PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw); 877 } 878 PetscKernel_v_gets_A_times_w_2(xw, idiag, s); 879 x[i2] += xw[0]; 880 x[i2 + 1] += xw[1]; 881 idiag += 4; 882 i2 += 2; 883 } 884 break; 885 case 3: 886 for (i = 0; i < m; i++) { 887 v = aa + 9 * ai[i]; 888 vi = aj + ai[i]; 889 nz = ai[i + 1] - ai[i]; 890 s[0] = b[i2]; 891 s[1] = b[i2 + 1]; 892 s[2] = b[i2 + 2]; 893 while (nz--) { 894 idx = 3 * (*vi++); 895 xw[0] = x[idx]; 896 xw[1] = x[1 + idx]; 897 xw[2] = x[2 + idx]; 898 PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw); 899 v += 9; 900 } 901 PetscKernel_v_gets_A_times_w_3(xw, idiag, s); 902 x[i2] += xw[0]; 903 x[i2 + 1] += xw[1]; 904 x[i2 + 2] += xw[2]; 905 idiag += 9; 906 i2 += 3; 907 } 908 break; 909 case 4: 910 for (i = 0; i < m; i++) { 911 v = aa + 16 * ai[i]; 912 vi = aj + ai[i]; 913 nz = ai[i + 1] - ai[i]; 914 s[0] = b[i2]; 915 s[1] = b[i2 + 1]; 916 s[2] = b[i2 + 2]; 917 s[3] = b[i2 + 3]; 918 while (nz--) { 919 idx = 4 * (*vi++); 920 xw[0] = x[idx]; 921 xw[1] = x[1 + idx]; 922 xw[2] = x[2 + idx]; 923 xw[3] = x[3 + idx]; 924 PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw); 925 v += 16; 926 } 927 PetscKernel_v_gets_A_times_w_4(xw, idiag, s); 928 x[i2] += xw[0]; 929 x[i2 + 1] += xw[1]; 930 x[i2 + 2] += xw[2]; 931 x[i2 + 3] += xw[3]; 932 idiag += 16; 933 i2 += 4; 934 } 935 break; 936 case 5: 937 for (i = 0; i < m; i++) { 938 v = aa + 25 * ai[i]; 939 vi = aj + ai[i]; 940 nz = ai[i + 1] - ai[i]; 941 s[0] = b[i2]; 942 s[1] = b[i2 + 1]; 943 s[2] = b[i2 + 2]; 944 s[3] = b[i2 + 3]; 945 s[4] = b[i2 + 4]; 946 while (nz--) { 947 idx = 5 * (*vi++); 948 xw[0] = x[idx]; 949 xw[1] = x[1 + idx]; 950 xw[2] = x[2 + idx]; 951 xw[3] = x[3 + idx]; 952 xw[4] = x[4 + idx]; 953 PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw); 954 v += 25; 955 } 956 PetscKernel_v_gets_A_times_w_5(xw, idiag, s); 957 x[i2] += xw[0]; 958 x[i2 + 1] += xw[1]; 959 x[i2 + 2] += xw[2]; 960 x[i2 + 3] += xw[3]; 961 x[i2 + 4] += xw[4]; 962 idiag += 25; 963 i2 += 5; 964 } 965 break; 966 case 6: 967 for (i = 0; i < m; i++) { 968 v = aa + 36 * ai[i]; 969 vi = aj + ai[i]; 970 nz = ai[i + 1] - ai[i]; 971 s[0] = b[i2]; 972 s[1] = b[i2 + 1]; 973 s[2] = b[i2 + 2]; 974 s[3] = b[i2 + 3]; 975 s[4] = b[i2 + 4]; 976 s[5] = b[i2 + 5]; 977 while (nz--) { 978 idx = 6 * (*vi++); 979 xw[0] = x[idx]; 980 xw[1] = x[1 + idx]; 981 xw[2] = x[2 + idx]; 982 xw[3] = x[3 + idx]; 983 xw[4] = x[4 + idx]; 984 xw[5] = x[5 + idx]; 985 PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw); 986 v += 36; 987 } 988 PetscKernel_v_gets_A_times_w_6(xw, idiag, s); 989 x[i2] += xw[0]; 990 x[i2 + 1] += xw[1]; 991 x[i2 + 2] += xw[2]; 992 x[i2 + 3] += xw[3]; 993 x[i2 + 4] += xw[4]; 994 x[i2 + 5] += xw[5]; 995 idiag += 36; 996 i2 += 6; 997 } 998 break; 999 case 7: 1000 for (i = 0; i < m; i++) { 1001 v = aa + 49 * ai[i]; 1002 vi = aj + ai[i]; 1003 nz = ai[i + 1] - ai[i]; 1004 s[0] = b[i2]; 1005 s[1] = b[i2 + 1]; 1006 s[2] = b[i2 + 2]; 1007 s[3] = b[i2 + 3]; 1008 s[4] = b[i2 + 4]; 1009 s[5] = b[i2 + 5]; 1010 s[6] = b[i2 + 6]; 1011 while (nz--) { 1012 idx = 7 * (*vi++); 1013 xw[0] = x[idx]; 1014 xw[1] = x[1 + idx]; 1015 xw[2] = x[2 + idx]; 1016 xw[3] = x[3 + idx]; 1017 xw[4] = x[4 + idx]; 1018 xw[5] = x[5 + idx]; 1019 xw[6] = x[6 + idx]; 1020 PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw); 1021 v += 49; 1022 } 1023 PetscKernel_v_gets_A_times_w_7(xw, idiag, s); 1024 x[i2] += xw[0]; 1025 x[i2 + 1] += xw[1]; 1026 x[i2 + 2] += xw[2]; 1027 x[i2 + 3] += xw[3]; 1028 x[i2 + 4] += xw[4]; 1029 x[i2 + 5] += xw[5]; 1030 x[i2 + 6] += xw[6]; 1031 idiag += 49; 1032 i2 += 7; 1033 } 1034 break; 1035 default: 1036 for (i = 0; i < m; i++) { 1037 v = aa + bs2 * ai[i]; 1038 vi = aj + ai[i]; 1039 nz = ai[i + 1] - ai[i]; 1040 1041 PetscCall(PetscArraycpy(w, b + i2, bs)); 1042 /* copy all rows of x that are needed into contiguous space */ 1043 workt = work; 1044 for (j = 0; j < nz; j++) { 1045 PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs)); 1046 workt += bs; 1047 } 1048 PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work); 1049 PetscKernel_w_gets_w_plus_Ar_times_v(bs, bs, w, idiag, x + i2); 1050 1051 idiag += bs2; 1052 i2 += bs; 1053 } 1054 break; 1055 } 1056 PetscCall(PetscLogFlops(2.0 * bs2 * a->nz)); 1057 } 1058 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) { 1059 idiag = a->idiag + bs2 * (a->mbs - 1); 1060 i2 = bs * (m - 1); 1061 switch (bs) { 1062 case 1: 1063 for (i = m - 1; i >= 0; i--) { 1064 v = aa + ai[i]; 1065 vi = aj + ai[i]; 1066 nz = ai[i + 1] - ai[i]; 1067 s[0] = b[i2]; 1068 for (j = 0; j < nz; j++) { 1069 xw[0] = x[vi[j]]; 1070 PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw); 1071 } 1072 PetscKernel_v_gets_A_times_w_1(xw, idiag, s); 1073 x[i2] += xw[0]; 1074 idiag -= 1; 1075 i2 -= 1; 1076 } 1077 break; 1078 case 2: 1079 for (i = m - 1; i >= 0; i--) { 1080 v = aa + 4 * ai[i]; 1081 vi = aj + ai[i]; 1082 nz = ai[i + 1] - ai[i]; 1083 s[0] = b[i2]; 1084 s[1] = b[i2 + 1]; 1085 for (j = 0; j < nz; j++) { 1086 idx = 2 * vi[j]; 1087 it = 4 * j; 1088 xw[0] = x[idx]; 1089 xw[1] = x[1 + idx]; 1090 PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw); 1091 } 1092 PetscKernel_v_gets_A_times_w_2(xw, idiag, s); 1093 x[i2] += xw[0]; 1094 x[i2 + 1] += xw[1]; 1095 idiag -= 4; 1096 i2 -= 2; 1097 } 1098 break; 1099 case 3: 1100 for (i = m - 1; i >= 0; i--) { 1101 v = aa + 9 * ai[i]; 1102 vi = aj + ai[i]; 1103 nz = ai[i + 1] - ai[i]; 1104 s[0] = b[i2]; 1105 s[1] = b[i2 + 1]; 1106 s[2] = b[i2 + 2]; 1107 while (nz--) { 1108 idx = 3 * (*vi++); 1109 xw[0] = x[idx]; 1110 xw[1] = x[1 + idx]; 1111 xw[2] = x[2 + idx]; 1112 PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw); 1113 v += 9; 1114 } 1115 PetscKernel_v_gets_A_times_w_3(xw, idiag, s); 1116 x[i2] += xw[0]; 1117 x[i2 + 1] += xw[1]; 1118 x[i2 + 2] += xw[2]; 1119 idiag -= 9; 1120 i2 -= 3; 1121 } 1122 break; 1123 case 4: 1124 for (i = m - 1; i >= 0; i--) { 1125 v = aa + 16 * ai[i]; 1126 vi = aj + ai[i]; 1127 nz = ai[i + 1] - ai[i]; 1128 s[0] = b[i2]; 1129 s[1] = b[i2 + 1]; 1130 s[2] = b[i2 + 2]; 1131 s[3] = b[i2 + 3]; 1132 while (nz--) { 1133 idx = 4 * (*vi++); 1134 xw[0] = x[idx]; 1135 xw[1] = x[1 + idx]; 1136 xw[2] = x[2 + idx]; 1137 xw[3] = x[3 + idx]; 1138 PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw); 1139 v += 16; 1140 } 1141 PetscKernel_v_gets_A_times_w_4(xw, idiag, s); 1142 x[i2] += xw[0]; 1143 x[i2 + 1] += xw[1]; 1144 x[i2 + 2] += xw[2]; 1145 x[i2 + 3] += xw[3]; 1146 idiag -= 16; 1147 i2 -= 4; 1148 } 1149 break; 1150 case 5: 1151 for (i = m - 1; i >= 0; i--) { 1152 v = aa + 25 * ai[i]; 1153 vi = aj + ai[i]; 1154 nz = ai[i + 1] - ai[i]; 1155 s[0] = b[i2]; 1156 s[1] = b[i2 + 1]; 1157 s[2] = b[i2 + 2]; 1158 s[3] = b[i2 + 3]; 1159 s[4] = b[i2 + 4]; 1160 while (nz--) { 1161 idx = 5 * (*vi++); 1162 xw[0] = x[idx]; 1163 xw[1] = x[1 + idx]; 1164 xw[2] = x[2 + idx]; 1165 xw[3] = x[3 + idx]; 1166 xw[4] = x[4 + idx]; 1167 PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw); 1168 v += 25; 1169 } 1170 PetscKernel_v_gets_A_times_w_5(xw, idiag, s); 1171 x[i2] += xw[0]; 1172 x[i2 + 1] += xw[1]; 1173 x[i2 + 2] += xw[2]; 1174 x[i2 + 3] += xw[3]; 1175 x[i2 + 4] += xw[4]; 1176 idiag -= 25; 1177 i2 -= 5; 1178 } 1179 break; 1180 case 6: 1181 for (i = m - 1; i >= 0; i--) { 1182 v = aa + 36 * ai[i]; 1183 vi = aj + ai[i]; 1184 nz = ai[i + 1] - ai[i]; 1185 s[0] = b[i2]; 1186 s[1] = b[i2 + 1]; 1187 s[2] = b[i2 + 2]; 1188 s[3] = b[i2 + 3]; 1189 s[4] = b[i2 + 4]; 1190 s[5] = b[i2 + 5]; 1191 while (nz--) { 1192 idx = 6 * (*vi++); 1193 xw[0] = x[idx]; 1194 xw[1] = x[1 + idx]; 1195 xw[2] = x[2 + idx]; 1196 xw[3] = x[3 + idx]; 1197 xw[4] = x[4 + idx]; 1198 xw[5] = x[5 + idx]; 1199 PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw); 1200 v += 36; 1201 } 1202 PetscKernel_v_gets_A_times_w_6(xw, idiag, s); 1203 x[i2] += xw[0]; 1204 x[i2 + 1] += xw[1]; 1205 x[i2 + 2] += xw[2]; 1206 x[i2 + 3] += xw[3]; 1207 x[i2 + 4] += xw[4]; 1208 x[i2 + 5] += xw[5]; 1209 idiag -= 36; 1210 i2 -= 6; 1211 } 1212 break; 1213 case 7: 1214 for (i = m - 1; i >= 0; i--) { 1215 v = aa + 49 * ai[i]; 1216 vi = aj + ai[i]; 1217 nz = ai[i + 1] - ai[i]; 1218 s[0] = b[i2]; 1219 s[1] = b[i2 + 1]; 1220 s[2] = b[i2 + 2]; 1221 s[3] = b[i2 + 3]; 1222 s[4] = b[i2 + 4]; 1223 s[5] = b[i2 + 5]; 1224 s[6] = b[i2 + 6]; 1225 while (nz--) { 1226 idx = 7 * (*vi++); 1227 xw[0] = x[idx]; 1228 xw[1] = x[1 + idx]; 1229 xw[2] = x[2 + idx]; 1230 xw[3] = x[3 + idx]; 1231 xw[4] = x[4 + idx]; 1232 xw[5] = x[5 + idx]; 1233 xw[6] = x[6 + idx]; 1234 PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw); 1235 v += 49; 1236 } 1237 PetscKernel_v_gets_A_times_w_7(xw, idiag, s); 1238 x[i2] += xw[0]; 1239 x[i2 + 1] += xw[1]; 1240 x[i2 + 2] += xw[2]; 1241 x[i2 + 3] += xw[3]; 1242 x[i2 + 4] += xw[4]; 1243 x[i2 + 5] += xw[5]; 1244 x[i2 + 6] += xw[6]; 1245 idiag -= 49; 1246 i2 -= 7; 1247 } 1248 break; 1249 default: 1250 for (i = m - 1; i >= 0; i--) { 1251 v = aa + bs2 * ai[i]; 1252 vi = aj + ai[i]; 1253 nz = ai[i + 1] - ai[i]; 1254 1255 PetscCall(PetscArraycpy(w, b + i2, bs)); 1256 /* copy all rows of x that are needed into contiguous space */ 1257 workt = work; 1258 for (j = 0; j < nz; j++) { 1259 PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs)); 1260 workt += bs; 1261 } 1262 PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work); 1263 PetscKernel_w_gets_w_plus_Ar_times_v(bs, bs, w, idiag, x + i2); 1264 1265 idiag -= bs2; 1266 i2 -= bs; 1267 } 1268 break; 1269 } 1270 PetscCall(PetscLogFlops(2.0 * bs2 * (a->nz))); 1271 } 1272 } 1273 PetscCall(VecRestoreArray(xx, &x)); 1274 PetscCall(VecRestoreArrayRead(bb, &b)); 1275 PetscFunctionReturn(PETSC_SUCCESS); 1276 } 1277 1278 /* 1279 Special version for direct calls from Fortran (Used in PETSc-fun3d) 1280 */ 1281 #if defined(PETSC_HAVE_FORTRAN_CAPS) 1282 #define matsetvaluesblocked4_ MATSETVALUESBLOCKED4 1283 #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE) 1284 #define matsetvaluesblocked4_ matsetvaluesblocked4 1285 #endif 1286 1287 PETSC_EXTERN void matsetvaluesblocked4_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[]) 1288 { 1289 Mat A = *AA; 1290 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 1291 PetscInt *rp, k, low, high, t, ii, jj, row, nrow, i, col, l, N, m = *mm, n = *nn; 1292 PetscInt *ai = a->i, *ailen = a->ilen; 1293 PetscInt *aj = a->j, stepval, lastcol = -1; 1294 const PetscScalar *value = v; 1295 MatScalar *ap, *aa = a->a, *bap; 1296 1297 PetscFunctionBegin; 1298 if (A->rmap->bs != 4) SETERRABORT(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Can only be called with a block size of 4"); 1299 stepval = (n - 1) * 4; 1300 for (k = 0; k < m; k++) { /* loop over added rows */ 1301 row = im[k]; 1302 rp = aj + ai[row]; 1303 ap = aa + 16 * ai[row]; 1304 nrow = ailen[row]; 1305 low = 0; 1306 high = nrow; 1307 for (l = 0; l < n; l++) { /* loop over added columns */ 1308 col = in[l]; 1309 if (col <= lastcol) low = 0; 1310 else high = nrow; 1311 lastcol = col; 1312 value = v + k * (stepval + 4 + l) * 4; 1313 while (high - low > 7) { 1314 t = (low + high) / 2; 1315 if (rp[t] > col) high = t; 1316 else low = t; 1317 } 1318 for (i = low; i < high; i++) { 1319 if (rp[i] > col) break; 1320 if (rp[i] == col) { 1321 bap = ap + 16 * i; 1322 for (ii = 0; ii < 4; ii++, value += stepval) { 1323 for (jj = ii; jj < 16; jj += 4) bap[jj] += *value++; 1324 } 1325 goto noinsert2; 1326 } 1327 } 1328 N = nrow++ - 1; 1329 high++; /* added new column index thus must search to one higher than before */ 1330 /* shift up all the later entries in this row */ 1331 for (ii = N; ii >= i; ii--) { 1332 rp[ii + 1] = rp[ii]; 1333 PetscCallVoid(PetscArraycpy(ap + 16 * (ii + 1), ap + 16 * (ii), 16)); 1334 } 1335 if (N >= i) PetscCallVoid(PetscArrayzero(ap + 16 * i, 16)); 1336 rp[i] = col; 1337 bap = ap + 16 * i; 1338 for (ii = 0; ii < 4; ii++, value += stepval) { 1339 for (jj = ii; jj < 16; jj += 4) bap[jj] = *value++; 1340 } 1341 noinsert2:; 1342 low = i; 1343 } 1344 ailen[row] = nrow; 1345 } 1346 PetscFunctionReturnVoid(); 1347 } 1348 1349 #if defined(PETSC_HAVE_FORTRAN_CAPS) 1350 #define matsetvalues4_ MATSETVALUES4 1351 #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE) 1352 #define matsetvalues4_ matsetvalues4 1353 #endif 1354 1355 PETSC_EXTERN void matsetvalues4_(Mat *AA, PetscInt *mm, PetscInt *im, PetscInt *nn, PetscInt *in, PetscScalar *v) 1356 { 1357 Mat A = *AA; 1358 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 1359 PetscInt *rp, k, low, high, t, row, nrow, i, col, l, N, n = *nn, m = *mm; 1360 PetscInt *ai = a->i, *ailen = a->ilen; 1361 PetscInt *aj = a->j, brow, bcol; 1362 PetscInt ridx, cidx, lastcol = -1; 1363 MatScalar *ap, value, *aa = a->a, *bap; 1364 1365 PetscFunctionBegin; 1366 for (k = 0; k < m; k++) { /* loop over added rows */ 1367 row = im[k]; 1368 brow = row / 4; 1369 rp = aj + ai[brow]; 1370 ap = aa + 16 * ai[brow]; 1371 nrow = ailen[brow]; 1372 low = 0; 1373 high = nrow; 1374 for (l = 0; l < n; l++) { /* loop over added columns */ 1375 col = in[l]; 1376 bcol = col / 4; 1377 ridx = row % 4; 1378 cidx = col % 4; 1379 value = v[l + k * n]; 1380 if (col <= lastcol) low = 0; 1381 else high = nrow; 1382 lastcol = col; 1383 while (high - low > 7) { 1384 t = (low + high) / 2; 1385 if (rp[t] > bcol) high = t; 1386 else low = t; 1387 } 1388 for (i = low; i < high; i++) { 1389 if (rp[i] > bcol) break; 1390 if (rp[i] == bcol) { 1391 bap = ap + 16 * i + 4 * cidx + ridx; 1392 *bap += value; 1393 goto noinsert1; 1394 } 1395 } 1396 N = nrow++ - 1; 1397 high++; /* added new column thus must search to one higher than before */ 1398 /* shift up all the later entries in this row */ 1399 PetscCallVoid(PetscArraymove(rp + i + 1, rp + i, N - i + 1)); 1400 PetscCallVoid(PetscArraymove(ap + 16 * i + 16, ap + 16 * i, 16 * (N - i + 1))); 1401 PetscCallVoid(PetscArrayzero(ap + 16 * i, 16)); 1402 rp[i] = bcol; 1403 ap[16 * i + 4 * cidx + ridx] = value; 1404 noinsert1:; 1405 low = i; 1406 } 1407 ailen[brow] = nrow; 1408 } 1409 PetscFunctionReturnVoid(); 1410 } 1411 1412 /* 1413 Checks for missing diagonals 1414 */ 1415 PetscErrorCode MatMissingDiagonal_SeqBAIJ(Mat A, PetscBool *missing, PetscInt *d) 1416 { 1417 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 1418 PetscInt *diag, *ii = a->i, i; 1419 1420 PetscFunctionBegin; 1421 PetscCall(MatMarkDiagonal_SeqBAIJ(A)); 1422 *missing = PETSC_FALSE; 1423 if (A->rmap->n > 0 && !ii) { 1424 *missing = PETSC_TRUE; 1425 if (d) *d = 0; 1426 PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n")); 1427 } else { 1428 PetscInt n; 1429 n = PetscMin(a->mbs, a->nbs); 1430 diag = a->diag; 1431 for (i = 0; i < n; i++) { 1432 if (diag[i] >= ii[i + 1]) { 1433 *missing = PETSC_TRUE; 1434 if (d) *d = i; 1435 PetscCall(PetscInfo(A, "Matrix is missing block diagonal number %" PetscInt_FMT "\n", i)); 1436 break; 1437 } 1438 } 1439 } 1440 PetscFunctionReturn(PETSC_SUCCESS); 1441 } 1442 1443 PetscErrorCode MatMarkDiagonal_SeqBAIJ(Mat A) 1444 { 1445 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 1446 PetscInt i, j, m = a->mbs; 1447 1448 PetscFunctionBegin; 1449 if (!a->diag) { 1450 PetscCall(PetscMalloc1(m, &a->diag)); 1451 a->free_diag = PETSC_TRUE; 1452 } 1453 for (i = 0; i < m; i++) { 1454 a->diag[i] = a->i[i + 1]; 1455 for (j = a->i[i]; j < a->i[i + 1]; j++) { 1456 if (a->j[j] == i) { 1457 a->diag[i] = j; 1458 break; 1459 } 1460 } 1461 } 1462 PetscFunctionReturn(PETSC_SUCCESS); 1463 } 1464 1465 static PetscErrorCode MatGetRowIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool blockcompressed, PetscInt *nn, const PetscInt *inia[], const PetscInt *inja[], PetscBool *done) 1466 { 1467 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 1468 PetscInt i, j, n = a->mbs, nz = a->i[n], *tia, *tja, bs = A->rmap->bs, k, l, cnt; 1469 PetscInt **ia = (PetscInt **)inia, **ja = (PetscInt **)inja; 1470 1471 PetscFunctionBegin; 1472 *nn = n; 1473 if (!ia) PetscFunctionReturn(PETSC_SUCCESS); 1474 if (symmetric) { 1475 PetscCall(MatToSymmetricIJ_SeqAIJ(n, a->i, a->j, PETSC_TRUE, 0, 0, &tia, &tja)); 1476 nz = tia[n]; 1477 } else { 1478 tia = a->i; 1479 tja = a->j; 1480 } 1481 1482 if (!blockcompressed && bs > 1) { 1483 (*nn) *= bs; 1484 /* malloc & create the natural set of indices */ 1485 PetscCall(PetscMalloc1((n + 1) * bs, ia)); 1486 if (n) { 1487 (*ia)[0] = oshift; 1488 for (j = 1; j < bs; j++) (*ia)[j] = (tia[1] - tia[0]) * bs + (*ia)[j - 1]; 1489 } 1490 1491 for (i = 1; i < n; i++) { 1492 (*ia)[i * bs] = (tia[i] - tia[i - 1]) * bs + (*ia)[i * bs - 1]; 1493 for (j = 1; j < bs; j++) (*ia)[i * bs + j] = (tia[i + 1] - tia[i]) * bs + (*ia)[i * bs + j - 1]; 1494 } 1495 if (n) (*ia)[n * bs] = (tia[n] - tia[n - 1]) * bs + (*ia)[n * bs - 1]; 1496 1497 if (inja) { 1498 PetscCall(PetscMalloc1(nz * bs * bs, ja)); 1499 cnt = 0; 1500 for (i = 0; i < n; i++) { 1501 for (j = 0; j < bs; j++) { 1502 for (k = tia[i]; k < tia[i + 1]; k++) { 1503 for (l = 0; l < bs; l++) (*ja)[cnt++] = bs * tja[k] + l; 1504 } 1505 } 1506 } 1507 } 1508 1509 if (symmetric) { /* deallocate memory allocated in MatToSymmetricIJ_SeqAIJ() */ 1510 PetscCall(PetscFree(tia)); 1511 PetscCall(PetscFree(tja)); 1512 } 1513 } else if (oshift == 1) { 1514 if (symmetric) { 1515 nz = tia[A->rmap->n / bs]; 1516 /* add 1 to i and j indices */ 1517 for (i = 0; i < A->rmap->n / bs + 1; i++) tia[i] = tia[i] + 1; 1518 *ia = tia; 1519 if (ja) { 1520 for (i = 0; i < nz; i++) tja[i] = tja[i] + 1; 1521 *ja = tja; 1522 } 1523 } else { 1524 nz = a->i[A->rmap->n / bs]; 1525 /* malloc space and add 1 to i and j indices */ 1526 PetscCall(PetscMalloc1(A->rmap->n / bs + 1, ia)); 1527 for (i = 0; i < A->rmap->n / bs + 1; i++) (*ia)[i] = a->i[i] + 1; 1528 if (ja) { 1529 PetscCall(PetscMalloc1(nz, ja)); 1530 for (i = 0; i < nz; i++) (*ja)[i] = a->j[i] + 1; 1531 } 1532 } 1533 } else { 1534 *ia = tia; 1535 if (ja) *ja = tja; 1536 } 1537 PetscFunctionReturn(PETSC_SUCCESS); 1538 } 1539 1540 static PetscErrorCode MatRestoreRowIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool blockcompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done) 1541 { 1542 PetscFunctionBegin; 1543 if (!ia) PetscFunctionReturn(PETSC_SUCCESS); 1544 if ((!blockcompressed && A->rmap->bs > 1) || (symmetric || oshift == 1)) { 1545 PetscCall(PetscFree(*ia)); 1546 if (ja) PetscCall(PetscFree(*ja)); 1547 } 1548 PetscFunctionReturn(PETSC_SUCCESS); 1549 } 1550 1551 PetscErrorCode MatDestroy_SeqBAIJ(Mat A) 1552 { 1553 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 1554 1555 PetscFunctionBegin; 1556 if (A->hash_active) { 1557 PetscInt bs; 1558 A->ops[0] = a->cops; 1559 PetscCall(PetscHMapIJVDestroy(&a->ht)); 1560 PetscCall(MatGetBlockSize(A, &bs)); 1561 if (bs > 1) PetscCall(PetscHSetIJDestroy(&a->bht)); 1562 PetscCall(PetscFree(a->dnz)); 1563 PetscCall(PetscFree(a->bdnz)); 1564 A->hash_active = PETSC_FALSE; 1565 } 1566 PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->N, A->cmap->n, a->nz)); 1567 PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i)); 1568 PetscCall(ISDestroy(&a->row)); 1569 PetscCall(ISDestroy(&a->col)); 1570 if (a->free_diag) PetscCall(PetscFree(a->diag)); 1571 PetscCall(PetscFree(a->idiag)); 1572 if (a->free_imax_ilen) PetscCall(PetscFree2(a->imax, a->ilen)); 1573 PetscCall(PetscFree(a->solve_work)); 1574 PetscCall(PetscFree(a->mult_work)); 1575 PetscCall(PetscFree(a->sor_workt)); 1576 PetscCall(PetscFree(a->sor_work)); 1577 PetscCall(ISDestroy(&a->icol)); 1578 PetscCall(PetscFree(a->saved_values)); 1579 PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex)); 1580 1581 PetscCall(MatDestroy(&a->sbaijMat)); 1582 PetscCall(MatDestroy(&a->parent)); 1583 PetscCall(PetscFree(A->data)); 1584 1585 PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL)); 1586 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJGetArray_C", NULL)); 1587 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJRestoreArray_C", NULL)); 1588 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL)); 1589 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL)); 1590 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJSetColumnIndices_C", NULL)); 1591 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_seqaij_C", NULL)); 1592 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_seqsbaij_C", NULL)); 1593 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJSetPreallocation_C", NULL)); 1594 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJSetPreallocationCSR_C", NULL)); 1595 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_seqbstrm_C", NULL)); 1596 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL)); 1597 #if defined(PETSC_HAVE_HYPRE) 1598 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_hypre_C", NULL)); 1599 #endif 1600 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_is_C", NULL)); 1601 PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL)); 1602 PetscFunctionReturn(PETSC_SUCCESS); 1603 } 1604 1605 static PetscErrorCode MatSetOption_SeqBAIJ(Mat A, MatOption op, PetscBool flg) 1606 { 1607 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 1608 1609 PetscFunctionBegin; 1610 switch (op) { 1611 case MAT_ROW_ORIENTED: 1612 a->roworiented = flg; 1613 break; 1614 case MAT_KEEP_NONZERO_PATTERN: 1615 a->keepnonzeropattern = flg; 1616 break; 1617 case MAT_NEW_NONZERO_LOCATIONS: 1618 a->nonew = (flg ? 0 : 1); 1619 break; 1620 case MAT_NEW_NONZERO_LOCATION_ERR: 1621 a->nonew = (flg ? -1 : 0); 1622 break; 1623 case MAT_NEW_NONZERO_ALLOCATION_ERR: 1624 a->nonew = (flg ? -2 : 0); 1625 break; 1626 case MAT_UNUSED_NONZERO_LOCATION_ERR: 1627 a->nounused = (flg ? -1 : 0); 1628 break; 1629 case MAT_FORCE_DIAGONAL_ENTRIES: 1630 case MAT_IGNORE_OFF_PROC_ENTRIES: 1631 case MAT_USE_HASH_TABLE: 1632 case MAT_SORTED_FULL: 1633 PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op])); 1634 break; 1635 case MAT_SPD: 1636 case MAT_SYMMETRIC: 1637 case MAT_STRUCTURALLY_SYMMETRIC: 1638 case MAT_HERMITIAN: 1639 case MAT_SYMMETRY_ETERNAL: 1640 case MAT_STRUCTURAL_SYMMETRY_ETERNAL: 1641 case MAT_SUBMAT_SINGLEIS: 1642 case MAT_STRUCTURE_ONLY: 1643 case MAT_SPD_ETERNAL: 1644 /* if the diagonal matrix is square it inherits some of the properties above */ 1645 break; 1646 default: 1647 SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op); 1648 } 1649 PetscFunctionReturn(PETSC_SUCCESS); 1650 } 1651 1652 /* used for both SeqBAIJ and SeqSBAIJ matrices */ 1653 PetscErrorCode MatGetRow_SeqBAIJ_private(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v, PetscInt *ai, PetscInt *aj, PetscScalar *aa) 1654 { 1655 PetscInt itmp, i, j, k, M, bn, bp, *idx_i, bs, bs2; 1656 MatScalar *aa_i; 1657 PetscScalar *v_i; 1658 1659 PetscFunctionBegin; 1660 bs = A->rmap->bs; 1661 bs2 = bs * bs; 1662 PetscCheck(row >= 0 && row < A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row %" PetscInt_FMT " out of range", row); 1663 1664 bn = row / bs; /* Block number */ 1665 bp = row % bs; /* Block Position */ 1666 M = ai[bn + 1] - ai[bn]; 1667 *nz = bs * M; 1668 1669 if (v) { 1670 *v = NULL; 1671 if (*nz) { 1672 PetscCall(PetscMalloc1(*nz, v)); 1673 for (i = 0; i < M; i++) { /* for each block in the block row */ 1674 v_i = *v + i * bs; 1675 aa_i = aa + bs2 * (ai[bn] + i); 1676 for (j = bp, k = 0; j < bs2; j += bs, k++) v_i[k] = aa_i[j]; 1677 } 1678 } 1679 } 1680 1681 if (idx) { 1682 *idx = NULL; 1683 if (*nz) { 1684 PetscCall(PetscMalloc1(*nz, idx)); 1685 for (i = 0; i < M; i++) { /* for each block in the block row */ 1686 idx_i = *idx + i * bs; 1687 itmp = bs * aj[ai[bn] + i]; 1688 for (j = 0; j < bs; j++) idx_i[j] = itmp++; 1689 } 1690 } 1691 } 1692 PetscFunctionReturn(PETSC_SUCCESS); 1693 } 1694 1695 PetscErrorCode MatGetRow_SeqBAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v) 1696 { 1697 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 1698 1699 PetscFunctionBegin; 1700 PetscCall(MatGetRow_SeqBAIJ_private(A, row, nz, idx, v, a->i, a->j, a->a)); 1701 PetscFunctionReturn(PETSC_SUCCESS); 1702 } 1703 1704 PetscErrorCode MatRestoreRow_SeqBAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v) 1705 { 1706 PetscFunctionBegin; 1707 if (idx) PetscCall(PetscFree(*idx)); 1708 if (v) PetscCall(PetscFree(*v)); 1709 PetscFunctionReturn(PETSC_SUCCESS); 1710 } 1711 1712 static PetscErrorCode MatTranspose_SeqBAIJ(Mat A, MatReuse reuse, Mat *B) 1713 { 1714 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data, *at; 1715 Mat C; 1716 PetscInt i, j, k, *aj = a->j, *ai = a->i, bs = A->rmap->bs, mbs = a->mbs, nbs = a->nbs, *atfill; 1717 PetscInt bs2 = a->bs2, *ati, *atj, anzj, kr; 1718 MatScalar *ata, *aa = a->a; 1719 1720 PetscFunctionBegin; 1721 if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *B)); 1722 PetscCall(PetscCalloc1(1 + nbs, &atfill)); 1723 if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) { 1724 for (i = 0; i < ai[mbs]; i++) atfill[aj[i]] += 1; /* count num of non-zeros in row aj[i] */ 1725 1726 PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C)); 1727 PetscCall(MatSetSizes(C, A->cmap->n, A->rmap->N, A->cmap->n, A->rmap->N)); 1728 PetscCall(MatSetType(C, ((PetscObject)A)->type_name)); 1729 PetscCall(MatSeqBAIJSetPreallocation(C, bs, 0, atfill)); 1730 1731 at = (Mat_SeqBAIJ *)C->data; 1732 ati = at->i; 1733 for (i = 0; i < nbs; i++) at->ilen[i] = at->imax[i] = ati[i + 1] - ati[i]; 1734 } else { 1735 C = *B; 1736 at = (Mat_SeqBAIJ *)C->data; 1737 ati = at->i; 1738 } 1739 1740 atj = at->j; 1741 ata = at->a; 1742 1743 /* Copy ati into atfill so we have locations of the next free space in atj */ 1744 PetscCall(PetscArraycpy(atfill, ati, nbs)); 1745 1746 /* Walk through A row-wise and mark nonzero entries of A^T. */ 1747 for (i = 0; i < mbs; i++) { 1748 anzj = ai[i + 1] - ai[i]; 1749 for (j = 0; j < anzj; j++) { 1750 atj[atfill[*aj]] = i; 1751 for (kr = 0; kr < bs; kr++) { 1752 for (k = 0; k < bs; k++) ata[bs2 * atfill[*aj] + k * bs + kr] = *aa++; 1753 } 1754 atfill[*aj++] += 1; 1755 } 1756 } 1757 PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY)); 1758 PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY)); 1759 1760 /* Clean up temporary space and complete requests. */ 1761 PetscCall(PetscFree(atfill)); 1762 1763 if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) { 1764 PetscCall(MatSetBlockSizes(C, PetscAbs(A->cmap->bs), PetscAbs(A->rmap->bs))); 1765 *B = C; 1766 } else { 1767 PetscCall(MatHeaderMerge(A, &C)); 1768 } 1769 PetscFunctionReturn(PETSC_SUCCESS); 1770 } 1771 1772 static PetscErrorCode MatIsTranspose_SeqBAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f) 1773 { 1774 Mat Btrans; 1775 1776 PetscFunctionBegin; 1777 *f = PETSC_FALSE; 1778 PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &Btrans)); 1779 PetscCall(MatEqual_SeqBAIJ(B, Btrans, f)); 1780 PetscCall(MatDestroy(&Btrans)); 1781 PetscFunctionReturn(PETSC_SUCCESS); 1782 } 1783 1784 /* Used for both SeqBAIJ and SeqSBAIJ matrices */ 1785 PetscErrorCode MatView_SeqBAIJ_Binary(Mat mat, PetscViewer viewer) 1786 { 1787 Mat_SeqBAIJ *A = (Mat_SeqBAIJ *)mat->data; 1788 PetscInt header[4], M, N, m, bs, nz, cnt, i, j, k, l; 1789 PetscInt *rowlens, *colidxs; 1790 PetscScalar *matvals; 1791 1792 PetscFunctionBegin; 1793 PetscCall(PetscViewerSetUp(viewer)); 1794 1795 M = mat->rmap->N; 1796 N = mat->cmap->N; 1797 m = mat->rmap->n; 1798 bs = mat->rmap->bs; 1799 nz = bs * bs * A->nz; 1800 1801 /* write matrix header */ 1802 header[0] = MAT_FILE_CLASSID; 1803 header[1] = M; 1804 header[2] = N; 1805 header[3] = nz; 1806 PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT)); 1807 1808 /* store row lengths */ 1809 PetscCall(PetscMalloc1(m, &rowlens)); 1810 for (cnt = 0, i = 0; i < A->mbs; i++) 1811 for (j = 0; j < bs; j++) rowlens[cnt++] = bs * (A->i[i + 1] - A->i[i]); 1812 PetscCall(PetscViewerBinaryWrite(viewer, rowlens, m, PETSC_INT)); 1813 PetscCall(PetscFree(rowlens)); 1814 1815 /* store column indices */ 1816 PetscCall(PetscMalloc1(nz, &colidxs)); 1817 for (cnt = 0, i = 0; i < A->mbs; i++) 1818 for (k = 0; k < bs; k++) 1819 for (j = A->i[i]; j < A->i[i + 1]; j++) 1820 for (l = 0; l < bs; l++) colidxs[cnt++] = bs * A->j[j] + l; 1821 PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt_FMT, cnt, nz); 1822 PetscCall(PetscViewerBinaryWrite(viewer, colidxs, nz, PETSC_INT)); 1823 PetscCall(PetscFree(colidxs)); 1824 1825 /* store nonzero values */ 1826 PetscCall(PetscMalloc1(nz, &matvals)); 1827 for (cnt = 0, i = 0; i < A->mbs; i++) 1828 for (k = 0; k < bs; k++) 1829 for (j = A->i[i]; j < A->i[i + 1]; j++) 1830 for (l = 0; l < bs; l++) matvals[cnt++] = A->a[bs * (bs * j + l) + k]; 1831 PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt_FMT, cnt, nz); 1832 PetscCall(PetscViewerBinaryWrite(viewer, matvals, nz, PETSC_SCALAR)); 1833 PetscCall(PetscFree(matvals)); 1834 1835 /* write block size option to the viewer's .info file */ 1836 PetscCall(MatView_Binary_BlockSizes(mat, viewer)); 1837 PetscFunctionReturn(PETSC_SUCCESS); 1838 } 1839 1840 static PetscErrorCode MatView_SeqBAIJ_ASCII_structonly(Mat A, PetscViewer viewer) 1841 { 1842 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 1843 PetscInt i, bs = A->rmap->bs, k; 1844 1845 PetscFunctionBegin; 1846 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 1847 for (i = 0; i < a->mbs; i++) { 1848 PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT "-%" PetscInt_FMT ":", i * bs, i * bs + bs - 1)); 1849 for (k = a->i[i]; k < a->i[i + 1]; k++) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT "-%" PetscInt_FMT ") ", bs * a->j[k], bs * a->j[k] + bs - 1)); 1850 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 1851 } 1852 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 1853 PetscFunctionReturn(PETSC_SUCCESS); 1854 } 1855 1856 static PetscErrorCode MatView_SeqBAIJ_ASCII(Mat A, PetscViewer viewer) 1857 { 1858 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 1859 PetscInt i, j, bs = A->rmap->bs, k, l, bs2 = a->bs2; 1860 PetscViewerFormat format; 1861 1862 PetscFunctionBegin; 1863 if (A->structure_only) { 1864 PetscCall(MatView_SeqBAIJ_ASCII_structonly(A, viewer)); 1865 PetscFunctionReturn(PETSC_SUCCESS); 1866 } 1867 1868 PetscCall(PetscViewerGetFormat(viewer, &format)); 1869 if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) { 1870 PetscCall(PetscViewerASCIIPrintf(viewer, " block size is %" PetscInt_FMT "\n", bs)); 1871 } else if (format == PETSC_VIEWER_ASCII_MATLAB) { 1872 const char *matname; 1873 Mat aij; 1874 PetscCall(MatConvert(A, MATSEQAIJ, MAT_INITIAL_MATRIX, &aij)); 1875 PetscCall(PetscObjectGetName((PetscObject)A, &matname)); 1876 PetscCall(PetscObjectSetName((PetscObject)aij, matname)); 1877 PetscCall(MatView(aij, viewer)); 1878 PetscCall(MatDestroy(&aij)); 1879 } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) { 1880 PetscFunctionReturn(PETSC_SUCCESS); 1881 } else if (format == PETSC_VIEWER_ASCII_COMMON) { 1882 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 1883 for (i = 0; i < a->mbs; i++) { 1884 for (j = 0; j < bs; j++) { 1885 PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i * bs + j)); 1886 for (k = a->i[i]; k < a->i[i + 1]; k++) { 1887 for (l = 0; l < bs; l++) { 1888 #if defined(PETSC_USE_COMPLEX) 1889 if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) > 0.0 && PetscRealPart(a->a[bs2 * k + l * bs + j]) != 0.0) { 1890 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %gi) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), (double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j]))); 1891 } else if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) < 0.0 && PetscRealPart(a->a[bs2 * k + l * bs + j]) != 0.0) { 1892 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %gi) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), -(double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j]))); 1893 } else if (PetscRealPart(a->a[bs2 * k + l * bs + j]) != 0.0) { 1894 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]))); 1895 } 1896 #else 1897 if (a->a[bs2 * k + l * bs + j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)a->a[bs2 * k + l * bs + j])); 1898 #endif 1899 } 1900 } 1901 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 1902 } 1903 } 1904 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 1905 } else { 1906 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE)); 1907 for (i = 0; i < a->mbs; i++) { 1908 for (j = 0; j < bs; j++) { 1909 PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i * bs + j)); 1910 for (k = a->i[i]; k < a->i[i + 1]; k++) { 1911 for (l = 0; l < bs; l++) { 1912 #if defined(PETSC_USE_COMPLEX) 1913 if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) > 0.0) { 1914 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), (double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j]))); 1915 } else if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) < 0.0) { 1916 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), -(double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j]))); 1917 } else { 1918 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]))); 1919 } 1920 #else 1921 PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)a->a[bs2 * k + l * bs + j])); 1922 #endif 1923 } 1924 } 1925 PetscCall(PetscViewerASCIIPrintf(viewer, "\n")); 1926 } 1927 } 1928 PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE)); 1929 } 1930 PetscCall(PetscViewerFlush(viewer)); 1931 PetscFunctionReturn(PETSC_SUCCESS); 1932 } 1933 1934 #include <petscdraw.h> 1935 static PetscErrorCode MatView_SeqBAIJ_Draw_Zoom(PetscDraw draw, void *Aa) 1936 { 1937 Mat A = (Mat)Aa; 1938 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 1939 PetscInt row, i, j, k, l, mbs = a->mbs, color, bs = A->rmap->bs, bs2 = a->bs2; 1940 PetscReal xl, yl, xr, yr, x_l, x_r, y_l, y_r; 1941 MatScalar *aa; 1942 PetscViewer viewer; 1943 PetscViewerFormat format; 1944 1945 PetscFunctionBegin; 1946 PetscCall(PetscObjectQuery((PetscObject)A, "Zoomviewer", (PetscObject *)&viewer)); 1947 PetscCall(PetscViewerGetFormat(viewer, &format)); 1948 PetscCall(PetscDrawGetCoordinates(draw, &xl, &yl, &xr, &yr)); 1949 1950 /* loop over matrix elements drawing boxes */ 1951 1952 if (format != PETSC_VIEWER_DRAW_CONTOUR) { 1953 PetscDrawCollectiveBegin(draw); 1954 /* Blue for negative, Cyan for zero and Red for positive */ 1955 color = PETSC_DRAW_BLUE; 1956 for (i = 0, row = 0; i < mbs; i++, row += bs) { 1957 for (j = a->i[i]; j < a->i[i + 1]; j++) { 1958 y_l = A->rmap->N - row - 1.0; 1959 y_r = y_l + 1.0; 1960 x_l = a->j[j] * bs; 1961 x_r = x_l + 1.0; 1962 aa = a->a + j * bs2; 1963 for (k = 0; k < bs; k++) { 1964 for (l = 0; l < bs; l++) { 1965 if (PetscRealPart(*aa++) >= 0.) continue; 1966 PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color)); 1967 } 1968 } 1969 } 1970 } 1971 color = PETSC_DRAW_CYAN; 1972 for (i = 0, row = 0; i < mbs; i++, row += bs) { 1973 for (j = a->i[i]; j < a->i[i + 1]; j++) { 1974 y_l = A->rmap->N - row - 1.0; 1975 y_r = y_l + 1.0; 1976 x_l = a->j[j] * bs; 1977 x_r = x_l + 1.0; 1978 aa = a->a + j * bs2; 1979 for (k = 0; k < bs; k++) { 1980 for (l = 0; l < bs; l++) { 1981 if (PetscRealPart(*aa++) != 0.) continue; 1982 PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color)); 1983 } 1984 } 1985 } 1986 } 1987 color = PETSC_DRAW_RED; 1988 for (i = 0, row = 0; i < mbs; i++, row += bs) { 1989 for (j = a->i[i]; j < a->i[i + 1]; j++) { 1990 y_l = A->rmap->N - row - 1.0; 1991 y_r = y_l + 1.0; 1992 x_l = a->j[j] * bs; 1993 x_r = x_l + 1.0; 1994 aa = a->a + j * bs2; 1995 for (k = 0; k < bs; k++) { 1996 for (l = 0; l < bs; l++) { 1997 if (PetscRealPart(*aa++) <= 0.) continue; 1998 PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color)); 1999 } 2000 } 2001 } 2002 } 2003 PetscDrawCollectiveEnd(draw); 2004 } else { 2005 /* use contour shading to indicate magnitude of values */ 2006 /* first determine max of all nonzero values */ 2007 PetscReal minv = 0.0, maxv = 0.0; 2008 PetscDraw popup; 2009 2010 for (i = 0; i < a->nz * a->bs2; i++) { 2011 if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]); 2012 } 2013 if (minv >= maxv) maxv = minv + PETSC_SMALL; 2014 PetscCall(PetscDrawGetPopup(draw, &popup)); 2015 PetscCall(PetscDrawScalePopup(popup, 0.0, maxv)); 2016 2017 PetscDrawCollectiveBegin(draw); 2018 for (i = 0, row = 0; i < mbs; i++, row += bs) { 2019 for (j = a->i[i]; j < a->i[i + 1]; j++) { 2020 y_l = A->rmap->N - row - 1.0; 2021 y_r = y_l + 1.0; 2022 x_l = a->j[j] * bs; 2023 x_r = x_l + 1.0; 2024 aa = a->a + j * bs2; 2025 for (k = 0; k < bs; k++) { 2026 for (l = 0; l < bs; l++) { 2027 MatScalar v = *aa++; 2028 color = PetscDrawRealToColor(PetscAbsScalar(v), minv, maxv); 2029 PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color)); 2030 } 2031 } 2032 } 2033 } 2034 PetscDrawCollectiveEnd(draw); 2035 } 2036 PetscFunctionReturn(PETSC_SUCCESS); 2037 } 2038 2039 static PetscErrorCode MatView_SeqBAIJ_Draw(Mat A, PetscViewer viewer) 2040 { 2041 PetscReal xl, yl, xr, yr, w, h; 2042 PetscDraw draw; 2043 PetscBool isnull; 2044 2045 PetscFunctionBegin; 2046 PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw)); 2047 PetscCall(PetscDrawIsNull(draw, &isnull)); 2048 if (isnull) PetscFunctionReturn(PETSC_SUCCESS); 2049 2050 xr = A->cmap->n; 2051 yr = A->rmap->N; 2052 h = yr / 10.0; 2053 w = xr / 10.0; 2054 xr += w; 2055 yr += h; 2056 xl = -w; 2057 yl = -h; 2058 PetscCall(PetscDrawSetCoordinates(draw, xl, yl, xr, yr)); 2059 PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", (PetscObject)viewer)); 2060 PetscCall(PetscDrawZoom(draw, MatView_SeqBAIJ_Draw_Zoom, A)); 2061 PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", NULL)); 2062 PetscCall(PetscDrawSave(draw)); 2063 PetscFunctionReturn(PETSC_SUCCESS); 2064 } 2065 2066 PetscErrorCode MatView_SeqBAIJ(Mat A, PetscViewer viewer) 2067 { 2068 PetscBool iascii, isbinary, isdraw; 2069 2070 PetscFunctionBegin; 2071 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii)); 2072 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary)); 2073 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw)); 2074 if (iascii) { 2075 PetscCall(MatView_SeqBAIJ_ASCII(A, viewer)); 2076 } else if (isbinary) { 2077 PetscCall(MatView_SeqBAIJ_Binary(A, viewer)); 2078 } else if (isdraw) { 2079 PetscCall(MatView_SeqBAIJ_Draw(A, viewer)); 2080 } else { 2081 Mat B; 2082 PetscCall(MatConvert(A, MATSEQAIJ, MAT_INITIAL_MATRIX, &B)); 2083 PetscCall(MatView(B, viewer)); 2084 PetscCall(MatDestroy(&B)); 2085 } 2086 PetscFunctionReturn(PETSC_SUCCESS); 2087 } 2088 2089 PetscErrorCode MatGetValues_SeqBAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[]) 2090 { 2091 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2092 PetscInt *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j; 2093 PetscInt *ai = a->i, *ailen = a->ilen; 2094 PetscInt brow, bcol, ridx, cidx, bs = A->rmap->bs, bs2 = a->bs2; 2095 MatScalar *ap, *aa = a->a; 2096 2097 PetscFunctionBegin; 2098 for (k = 0; k < m; k++) { /* loop over rows */ 2099 row = im[k]; 2100 brow = row / bs; 2101 if (row < 0) { 2102 v += n; 2103 continue; 2104 } /* negative row */ 2105 PetscCheck(row < A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row %" PetscInt_FMT " too large", row); 2106 rp = PetscSafePointerPlusOffset(aj, ai[brow]); 2107 ap = PetscSafePointerPlusOffset(aa, bs2 * ai[brow]); 2108 nrow = ailen[brow]; 2109 for (l = 0; l < n; l++) { /* loop over columns */ 2110 if (in[l] < 0) { 2111 v++; 2112 continue; 2113 } /* negative column */ 2114 PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column %" PetscInt_FMT " too large", in[l]); 2115 col = in[l]; 2116 bcol = col / bs; 2117 cidx = col % bs; 2118 ridx = row % bs; 2119 high = nrow; 2120 low = 0; /* assume unsorted */ 2121 while (high - low > 5) { 2122 t = (low + high) / 2; 2123 if (rp[t] > bcol) high = t; 2124 else low = t; 2125 } 2126 for (i = low; i < high; i++) { 2127 if (rp[i] > bcol) break; 2128 if (rp[i] == bcol) { 2129 *v++ = ap[bs2 * i + bs * cidx + ridx]; 2130 goto finished; 2131 } 2132 } 2133 *v++ = 0.0; 2134 finished:; 2135 } 2136 } 2137 PetscFunctionReturn(PETSC_SUCCESS); 2138 } 2139 2140 PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is) 2141 { 2142 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2143 PetscInt *rp, k, low, high, t, ii, jj, row, nrow, i, col, l, rmax, N, lastcol = -1; 2144 PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen; 2145 PetscInt *aj = a->j, nonew = a->nonew, bs2 = a->bs2, bs = A->rmap->bs, stepval; 2146 PetscBool roworiented = a->roworiented; 2147 const PetscScalar *value = v; 2148 MatScalar *ap = NULL, *aa = a->a, *bap; 2149 2150 PetscFunctionBegin; 2151 if (roworiented) { 2152 stepval = (n - 1) * bs; 2153 } else { 2154 stepval = (m - 1) * bs; 2155 } 2156 for (k = 0; k < m; k++) { /* loop over added rows */ 2157 row = im[k]; 2158 if (row < 0) continue; 2159 PetscCheck(row < a->mbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Block row index too large %" PetscInt_FMT " max %" PetscInt_FMT, row, a->mbs - 1); 2160 rp = aj + ai[row]; 2161 if (!A->structure_only) ap = aa + bs2 * ai[row]; 2162 rmax = imax[row]; 2163 nrow = ailen[row]; 2164 low = 0; 2165 high = nrow; 2166 for (l = 0; l < n; l++) { /* loop over added columns */ 2167 if (in[l] < 0) continue; 2168 PetscCheck(in[l] < a->nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Block column index too large %" PetscInt_FMT " max %" PetscInt_FMT, in[l], a->nbs - 1); 2169 col = in[l]; 2170 if (!A->structure_only) { 2171 if (roworiented) { 2172 value = v + (k * (stepval + bs) + l) * bs; 2173 } else { 2174 value = v + (l * (stepval + bs) + k) * bs; 2175 } 2176 } 2177 if (col <= lastcol) low = 0; 2178 else high = nrow; 2179 lastcol = col; 2180 while (high - low > 7) { 2181 t = (low + high) / 2; 2182 if (rp[t] > col) high = t; 2183 else low = t; 2184 } 2185 for (i = low; i < high; i++) { 2186 if (rp[i] > col) break; 2187 if (rp[i] == col) { 2188 if (A->structure_only) goto noinsert2; 2189 bap = ap + bs2 * i; 2190 if (roworiented) { 2191 if (is == ADD_VALUES) { 2192 for (ii = 0; ii < bs; ii++, value += stepval) { 2193 for (jj = ii; jj < bs2; jj += bs) bap[jj] += *value++; 2194 } 2195 } else { 2196 for (ii = 0; ii < bs; ii++, value += stepval) { 2197 for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++; 2198 } 2199 } 2200 } else { 2201 if (is == ADD_VALUES) { 2202 for (ii = 0; ii < bs; ii++, value += bs + stepval) { 2203 for (jj = 0; jj < bs; jj++) bap[jj] += value[jj]; 2204 bap += bs; 2205 } 2206 } else { 2207 for (ii = 0; ii < bs; ii++, value += bs + stepval) { 2208 for (jj = 0; jj < bs; jj++) bap[jj] = value[jj]; 2209 bap += bs; 2210 } 2211 } 2212 } 2213 goto noinsert2; 2214 } 2215 } 2216 if (nonew == 1) goto noinsert2; 2217 PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new blocked index new nonzero block (%" PetscInt_FMT ", %" PetscInt_FMT ") in the matrix", row, col); 2218 if (A->structure_only) { 2219 MatSeqXAIJReallocateAIJ_structure_only(A, a->mbs, bs2, nrow, row, col, rmax, ai, aj, rp, imax, nonew, MatScalar); 2220 } else { 2221 MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar); 2222 } 2223 N = nrow++ - 1; 2224 high++; 2225 /* shift up all the later entries in this row */ 2226 PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1)); 2227 rp[i] = col; 2228 if (!A->structure_only) { 2229 PetscCall(PetscArraymove(ap + bs2 * (i + 1), ap + bs2 * i, bs2 * (N - i + 1))); 2230 bap = ap + bs2 * i; 2231 if (roworiented) { 2232 for (ii = 0; ii < bs; ii++, value += stepval) { 2233 for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++; 2234 } 2235 } else { 2236 for (ii = 0; ii < bs; ii++, value += stepval) { 2237 for (jj = 0; jj < bs; jj++) *bap++ = *value++; 2238 } 2239 } 2240 } 2241 noinsert2:; 2242 low = i; 2243 } 2244 ailen[row] = nrow; 2245 } 2246 PetscFunctionReturn(PETSC_SUCCESS); 2247 } 2248 2249 PetscErrorCode MatAssemblyEnd_SeqBAIJ(Mat A, MatAssemblyType mode) 2250 { 2251 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2252 PetscInt fshift = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax; 2253 PetscInt m = A->rmap->N, *ip, N, *ailen = a->ilen; 2254 PetscInt mbs = a->mbs, bs2 = a->bs2, rmax = 0; 2255 MatScalar *aa = a->a, *ap; 2256 PetscReal ratio = 0.6; 2257 2258 PetscFunctionBegin; 2259 if (mode == MAT_FLUSH_ASSEMBLY || (A->was_assembled && A->ass_nonzerostate == A->nonzerostate)) PetscFunctionReturn(PETSC_SUCCESS); 2260 2261 if (m) rmax = ailen[0]; 2262 for (i = 1; i < mbs; i++) { 2263 /* move each row back by the amount of empty slots (fshift) before it*/ 2264 fshift += imax[i - 1] - ailen[i - 1]; 2265 rmax = PetscMax(rmax, ailen[i]); 2266 if (fshift) { 2267 ip = aj + ai[i]; 2268 ap = aa + bs2 * ai[i]; 2269 N = ailen[i]; 2270 PetscCall(PetscArraymove(ip - fshift, ip, N)); 2271 if (!A->structure_only) PetscCall(PetscArraymove(ap - bs2 * fshift, ap, bs2 * N)); 2272 } 2273 ai[i] = ai[i - 1] + ailen[i - 1]; 2274 } 2275 if (mbs) { 2276 fshift += imax[mbs - 1] - ailen[mbs - 1]; 2277 ai[mbs] = ai[mbs - 1] + ailen[mbs - 1]; 2278 } 2279 2280 /* reset ilen and imax for each row */ 2281 a->nonzerorowcnt = 0; 2282 if (A->structure_only) { 2283 PetscCall(PetscFree2(a->imax, a->ilen)); 2284 } else { /* !A->structure_only */ 2285 for (i = 0; i < mbs; i++) { 2286 ailen[i] = imax[i] = ai[i + 1] - ai[i]; 2287 a->nonzerorowcnt += ((ai[i + 1] - ai[i]) > 0); 2288 } 2289 } 2290 a->nz = ai[mbs]; 2291 2292 /* diagonals may have moved, so kill the diagonal pointers */ 2293 a->idiagvalid = PETSC_FALSE; 2294 if (fshift && a->diag) PetscCall(PetscFree(a->diag)); 2295 if (fshift) PetscCheck(a->nounused != -1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unused space detected in matrix: %" PetscInt_FMT " X %" PetscInt_FMT " block size %" PetscInt_FMT ", %" PetscInt_FMT " unneeded", m, A->cmap->n, A->rmap->bs, fshift * bs2); 2296 PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT ", block size %" PetscInt_FMT "; storage space: %" PetscInt_FMT " unneeded, %" PetscInt_FMT " used\n", m, A->cmap->n, A->rmap->bs, fshift * bs2, a->nz * bs2)); 2297 PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues is %" PetscInt_FMT "\n", a->reallocs)); 2298 PetscCall(PetscInfo(A, "Most nonzeros blocks in any row is %" PetscInt_FMT "\n", rmax)); 2299 2300 A->info.mallocs += a->reallocs; 2301 a->reallocs = 0; 2302 A->info.nz_unneeded = (PetscReal)fshift * bs2; 2303 a->rmax = rmax; 2304 2305 if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, mbs, ratio)); 2306 PetscFunctionReturn(PETSC_SUCCESS); 2307 } 2308 2309 /* 2310 This function returns an array of flags which indicate the locations of contiguous 2311 blocks that should be zeroed. for eg: if bs = 3 and is = [0,1,2,3,5,6,7,8,9] 2312 then the resulting sizes = [3,1,1,3,1] corresponding to sets [(0,1,2),(3),(5),(6,7,8),(9)] 2313 Assume: sizes should be long enough to hold all the values. 2314 */ 2315 static PetscErrorCode MatZeroRows_SeqBAIJ_Check_Blocks(PetscInt idx[], PetscInt n, PetscInt bs, PetscInt sizes[], PetscInt *bs_max) 2316 { 2317 PetscInt j = 0; 2318 2319 PetscFunctionBegin; 2320 for (PetscInt i = 0; i < n; j++) { 2321 PetscInt row = idx[i]; 2322 if (row % bs != 0) { /* Not the beginning of a block */ 2323 sizes[j] = 1; 2324 i++; 2325 } else if (i + bs > n) { /* complete block doesn't exist (at idx end) */ 2326 sizes[j] = 1; /* Also makes sure at least 'bs' values exist for next else */ 2327 i++; 2328 } else { /* Beginning of the block, so check if the complete block exists */ 2329 PetscBool flg = PETSC_TRUE; 2330 for (PetscInt k = 1; k < bs; k++) { 2331 if (row + k != idx[i + k]) { /* break in the block */ 2332 flg = PETSC_FALSE; 2333 break; 2334 } 2335 } 2336 if (flg) { /* No break in the bs */ 2337 sizes[j] = bs; 2338 i += bs; 2339 } else { 2340 sizes[j] = 1; 2341 i++; 2342 } 2343 } 2344 } 2345 *bs_max = j; 2346 PetscFunctionReturn(PETSC_SUCCESS); 2347 } 2348 2349 PetscErrorCode MatZeroRows_SeqBAIJ(Mat A, PetscInt is_n, const PetscInt is_idx[], PetscScalar diag, Vec x, Vec b) 2350 { 2351 Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)A->data; 2352 PetscInt i, j, k, count, *rows; 2353 PetscInt bs = A->rmap->bs, bs2 = baij->bs2, *sizes, row, bs_max; 2354 PetscScalar zero = 0.0; 2355 MatScalar *aa; 2356 const PetscScalar *xx; 2357 PetscScalar *bb; 2358 2359 PetscFunctionBegin; 2360 /* fix right-hand side if needed */ 2361 if (x && b) { 2362 PetscCall(VecGetArrayRead(x, &xx)); 2363 PetscCall(VecGetArray(b, &bb)); 2364 for (i = 0; i < is_n; i++) bb[is_idx[i]] = diag * xx[is_idx[i]]; 2365 PetscCall(VecRestoreArrayRead(x, &xx)); 2366 PetscCall(VecRestoreArray(b, &bb)); 2367 } 2368 2369 /* Make a copy of the IS and sort it */ 2370 /* allocate memory for rows,sizes */ 2371 PetscCall(PetscMalloc2(is_n, &rows, 2 * is_n, &sizes)); 2372 2373 /* copy IS values to rows, and sort them */ 2374 for (i = 0; i < is_n; i++) rows[i] = is_idx[i]; 2375 PetscCall(PetscSortInt(is_n, rows)); 2376 2377 if (baij->keepnonzeropattern) { 2378 for (i = 0; i < is_n; i++) sizes[i] = 1; 2379 bs_max = is_n; 2380 } else { 2381 PetscCall(MatZeroRows_SeqBAIJ_Check_Blocks(rows, is_n, bs, sizes, &bs_max)); 2382 A->nonzerostate++; 2383 } 2384 2385 for (i = 0, j = 0; i < bs_max; j += sizes[i], i++) { 2386 row = rows[j]; 2387 PetscCheck(row >= 0 && row <= A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", row); 2388 count = (baij->i[row / bs + 1] - baij->i[row / bs]) * bs; 2389 aa = ((MatScalar *)baij->a) + baij->i[row / bs] * bs2 + (row % bs); 2390 if (sizes[i] == bs && !baij->keepnonzeropattern) { 2391 if (diag != (PetscScalar)0.0) { 2392 if (baij->ilen[row / bs] > 0) { 2393 baij->ilen[row / bs] = 1; 2394 baij->j[baij->i[row / bs]] = row / bs; 2395 2396 PetscCall(PetscArrayzero(aa, count * bs)); 2397 } 2398 /* Now insert all the diagonal values for this bs */ 2399 for (k = 0; k < bs; k++) PetscUseTypeMethod(A, setvalues, 1, rows + j + k, 1, rows + j + k, &diag, INSERT_VALUES); 2400 } else { /* (diag == 0.0) */ 2401 baij->ilen[row / bs] = 0; 2402 } /* end (diag == 0.0) */ 2403 } else { /* (sizes[i] != bs) */ 2404 PetscAssert(sizes[i] == 1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal Error. Value should be 1"); 2405 for (k = 0; k < count; k++) { 2406 aa[0] = zero; 2407 aa += bs; 2408 } 2409 if (diag != (PetscScalar)0.0) PetscUseTypeMethod(A, setvalues, 1, rows + j, 1, rows + j, &diag, INSERT_VALUES); 2410 } 2411 } 2412 2413 PetscCall(PetscFree2(rows, sizes)); 2414 PetscCall(MatAssemblyEnd_SeqBAIJ(A, MAT_FINAL_ASSEMBLY)); 2415 PetscFunctionReturn(PETSC_SUCCESS); 2416 } 2417 2418 static PetscErrorCode MatZeroRowsColumns_SeqBAIJ(Mat A, PetscInt is_n, const PetscInt is_idx[], PetscScalar diag, Vec x, Vec b) 2419 { 2420 Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)A->data; 2421 PetscInt i, j, k, count; 2422 PetscInt bs = A->rmap->bs, bs2 = baij->bs2, row, col; 2423 PetscScalar zero = 0.0; 2424 MatScalar *aa; 2425 const PetscScalar *xx; 2426 PetscScalar *bb; 2427 PetscBool *zeroed, vecs = PETSC_FALSE; 2428 2429 PetscFunctionBegin; 2430 /* fix right-hand side if needed */ 2431 if (x && b) { 2432 PetscCall(VecGetArrayRead(x, &xx)); 2433 PetscCall(VecGetArray(b, &bb)); 2434 vecs = PETSC_TRUE; 2435 } 2436 2437 /* zero the columns */ 2438 PetscCall(PetscCalloc1(A->rmap->n, &zeroed)); 2439 for (i = 0; i < is_n; i++) { 2440 PetscCheck(is_idx[i] >= 0 && is_idx[i] < A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", is_idx[i]); 2441 zeroed[is_idx[i]] = PETSC_TRUE; 2442 } 2443 for (i = 0; i < A->rmap->N; i++) { 2444 if (!zeroed[i]) { 2445 row = i / bs; 2446 for (j = baij->i[row]; j < baij->i[row + 1]; j++) { 2447 for (k = 0; k < bs; k++) { 2448 col = bs * baij->j[j] + k; 2449 if (zeroed[col]) { 2450 aa = ((MatScalar *)baij->a) + j * bs2 + (i % bs) + bs * k; 2451 if (vecs) bb[i] -= aa[0] * xx[col]; 2452 aa[0] = 0.0; 2453 } 2454 } 2455 } 2456 } else if (vecs) bb[i] = diag * xx[i]; 2457 } 2458 PetscCall(PetscFree(zeroed)); 2459 if (vecs) { 2460 PetscCall(VecRestoreArrayRead(x, &xx)); 2461 PetscCall(VecRestoreArray(b, &bb)); 2462 } 2463 2464 /* zero the rows */ 2465 for (i = 0; i < is_n; i++) { 2466 row = is_idx[i]; 2467 count = (baij->i[row / bs + 1] - baij->i[row / bs]) * bs; 2468 aa = ((MatScalar *)baij->a) + baij->i[row / bs] * bs2 + (row % bs); 2469 for (k = 0; k < count; k++) { 2470 aa[0] = zero; 2471 aa += bs; 2472 } 2473 if (diag != (PetscScalar)0.0) PetscUseTypeMethod(A, setvalues, 1, &row, 1, &row, &diag, INSERT_VALUES); 2474 } 2475 PetscCall(MatAssemblyEnd_SeqBAIJ(A, MAT_FINAL_ASSEMBLY)); 2476 PetscFunctionReturn(PETSC_SUCCESS); 2477 } 2478 2479 PetscErrorCode MatSetValues_SeqBAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is) 2480 { 2481 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2482 PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N, lastcol = -1; 2483 PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen; 2484 PetscInt *aj = a->j, nonew = a->nonew, bs = A->rmap->bs, brow, bcol; 2485 PetscInt ridx, cidx, bs2 = a->bs2; 2486 PetscBool roworiented = a->roworiented; 2487 MatScalar *ap = NULL, value = 0.0, *aa = a->a, *bap; 2488 2489 PetscFunctionBegin; 2490 for (k = 0; k < m; k++) { /* loop over added rows */ 2491 row = im[k]; 2492 brow = row / bs; 2493 if (row < 0) continue; 2494 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); 2495 rp = PetscSafePointerPlusOffset(aj, ai[brow]); 2496 if (!A->structure_only) ap = PetscSafePointerPlusOffset(aa, bs2 * ai[brow]); 2497 rmax = imax[brow]; 2498 nrow = ailen[brow]; 2499 low = 0; 2500 high = nrow; 2501 for (l = 0; l < n; l++) { /* loop over added columns */ 2502 if (in[l] < 0) continue; 2503 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); 2504 col = in[l]; 2505 bcol = col / bs; 2506 ridx = row % bs; 2507 cidx = col % bs; 2508 if (!A->structure_only) { 2509 if (roworiented) { 2510 value = v[l + k * n]; 2511 } else { 2512 value = v[k + l * m]; 2513 } 2514 } 2515 if (col <= lastcol) low = 0; 2516 else high = nrow; 2517 lastcol = col; 2518 while (high - low > 7) { 2519 t = (low + high) / 2; 2520 if (rp[t] > bcol) high = t; 2521 else low = t; 2522 } 2523 for (i = low; i < high; i++) { 2524 if (rp[i] > bcol) break; 2525 if (rp[i] == bcol) { 2526 bap = PetscSafePointerPlusOffset(ap, bs2 * i + bs * cidx + ridx); 2527 if (!A->structure_only) { 2528 if (is == ADD_VALUES) *bap += value; 2529 else *bap = value; 2530 } 2531 goto noinsert1; 2532 } 2533 } 2534 if (nonew == 1) goto noinsert1; 2535 PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero (%" PetscInt_FMT ", %" PetscInt_FMT ") in the matrix", row, col); 2536 if (A->structure_only) { 2537 MatSeqXAIJReallocateAIJ_structure_only(A, a->mbs, bs2, nrow, brow, bcol, rmax, ai, aj, rp, imax, nonew, MatScalar); 2538 } else { 2539 MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, brow, bcol, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar); 2540 } 2541 N = nrow++ - 1; 2542 high++; 2543 /* shift up all the later entries in this row */ 2544 PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1)); 2545 rp[i] = bcol; 2546 if (!A->structure_only) { 2547 PetscCall(PetscArraymove(ap + bs2 * (i + 1), ap + bs2 * i, bs2 * (N - i + 1))); 2548 PetscCall(PetscArrayzero(ap + bs2 * i, bs2)); 2549 ap[bs2 * i + bs * cidx + ridx] = value; 2550 } 2551 a->nz++; 2552 noinsert1:; 2553 low = i; 2554 } 2555 ailen[brow] = nrow; 2556 } 2557 PetscFunctionReturn(PETSC_SUCCESS); 2558 } 2559 2560 static PetscErrorCode MatILUFactor_SeqBAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info) 2561 { 2562 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)inA->data; 2563 Mat outA; 2564 PetscBool row_identity, col_identity; 2565 2566 PetscFunctionBegin; 2567 PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels = 0 supported for in-place ILU"); 2568 PetscCall(ISIdentity(row, &row_identity)); 2569 PetscCall(ISIdentity(col, &col_identity)); 2570 PetscCheck(row_identity && col_identity, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Row and column permutations must be identity for in-place ILU"); 2571 2572 outA = inA; 2573 inA->factortype = MAT_FACTOR_LU; 2574 PetscCall(PetscFree(inA->solvertype)); 2575 PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype)); 2576 2577 PetscCall(MatMarkDiagonal_SeqBAIJ(inA)); 2578 2579 PetscCall(PetscObjectReference((PetscObject)row)); 2580 PetscCall(ISDestroy(&a->row)); 2581 a->row = row; 2582 PetscCall(PetscObjectReference((PetscObject)col)); 2583 PetscCall(ISDestroy(&a->col)); 2584 a->col = col; 2585 2586 /* Create the invert permutation so that it can be used in MatLUFactorNumeric() */ 2587 PetscCall(ISDestroy(&a->icol)); 2588 PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol)); 2589 2590 PetscCall(MatSeqBAIJSetNumericFactorization_inplace(inA, (PetscBool)(row_identity && col_identity))); 2591 if (!a->solve_work) PetscCall(PetscMalloc1(inA->rmap->N + inA->rmap->bs, &a->solve_work)); 2592 PetscCall(MatLUFactorNumeric(outA, inA, info)); 2593 PetscFunctionReturn(PETSC_SUCCESS); 2594 } 2595 2596 static PetscErrorCode MatSeqBAIJSetColumnIndices_SeqBAIJ(Mat mat, const PetscInt *indices) 2597 { 2598 Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)mat->data; 2599 2600 PetscFunctionBegin; 2601 baij->nz = baij->maxnz; 2602 PetscCall(PetscArraycpy(baij->j, indices, baij->nz)); 2603 PetscCall(PetscArraycpy(baij->ilen, baij->imax, baij->mbs)); 2604 PetscFunctionReturn(PETSC_SUCCESS); 2605 } 2606 2607 /*@ 2608 MatSeqBAIJSetColumnIndices - Set the column indices for all the block rows in the matrix. 2609 2610 Input Parameters: 2611 + mat - the `MATSEQBAIJ` matrix 2612 - indices - the block column indices 2613 2614 Level: advanced 2615 2616 Notes: 2617 This can be called if you have precomputed the nonzero structure of the 2618 matrix and want to provide it to the matrix object to improve the performance 2619 of the `MatSetValues()` operation. 2620 2621 You MUST have set the correct numbers of nonzeros per row in the call to 2622 `MatCreateSeqBAIJ()`, and the columns indices MUST be sorted. 2623 2624 MUST be called before any calls to `MatSetValues()` 2625 2626 .seealso: [](ch_matrices), `Mat`, `MATSEQBAIJ`, `MatSetValues()` 2627 @*/ 2628 PetscErrorCode MatSeqBAIJSetColumnIndices(Mat mat, PetscInt *indices) 2629 { 2630 PetscFunctionBegin; 2631 PetscValidHeaderSpecific(mat, MAT_CLASSID, 1); 2632 PetscAssertPointer(indices, 2); 2633 PetscUseMethod(mat, "MatSeqBAIJSetColumnIndices_C", (Mat, const PetscInt *), (mat, (const PetscInt *)indices)); 2634 PetscFunctionReturn(PETSC_SUCCESS); 2635 } 2636 2637 static PetscErrorCode MatGetRowMaxAbs_SeqBAIJ(Mat A, Vec v, PetscInt idx[]) 2638 { 2639 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2640 PetscInt i, j, n, row, bs, *ai, *aj, mbs; 2641 PetscReal atmp; 2642 PetscScalar *x, zero = 0.0; 2643 MatScalar *aa; 2644 PetscInt ncols, brow, krow, kcol; 2645 2646 PetscFunctionBegin; 2647 /* why is this not a macro???????????????????????????????????????????????????????????????? */ 2648 PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 2649 bs = A->rmap->bs; 2650 aa = a->a; 2651 ai = a->i; 2652 aj = a->j; 2653 mbs = a->mbs; 2654 2655 PetscCall(VecSet(v, zero)); 2656 PetscCall(VecGetArray(v, &x)); 2657 PetscCall(VecGetLocalSize(v, &n)); 2658 PetscCheck(n == A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector"); 2659 for (i = 0; i < mbs; i++) { 2660 ncols = ai[1] - ai[0]; 2661 ai++; 2662 brow = bs * i; 2663 for (j = 0; j < ncols; j++) { 2664 for (kcol = 0; kcol < bs; kcol++) { 2665 for (krow = 0; krow < bs; krow++) { 2666 atmp = PetscAbsScalar(*aa); 2667 aa++; 2668 row = brow + krow; /* row index */ 2669 if (PetscAbsScalar(x[row]) < atmp) { 2670 x[row] = atmp; 2671 if (idx) idx[row] = bs * (*aj) + kcol; 2672 } 2673 } 2674 } 2675 aj++; 2676 } 2677 } 2678 PetscCall(VecRestoreArray(v, &x)); 2679 PetscFunctionReturn(PETSC_SUCCESS); 2680 } 2681 2682 static PetscErrorCode MatGetRowSumAbs_SeqBAIJ(Mat A, Vec v) 2683 { 2684 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2685 PetscInt i, j, n, row, bs, *ai, mbs; 2686 PetscReal atmp; 2687 PetscScalar *x, zero = 0.0; 2688 MatScalar *aa; 2689 PetscInt ncols, brow, krow, kcol; 2690 2691 PetscFunctionBegin; 2692 PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 2693 bs = A->rmap->bs; 2694 aa = a->a; 2695 ai = a->i; 2696 mbs = a->mbs; 2697 2698 PetscCall(VecSet(v, zero)); 2699 PetscCall(VecGetArrayWrite(v, &x)); 2700 PetscCall(VecGetLocalSize(v, &n)); 2701 PetscCheck(n == A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector"); 2702 for (i = 0; i < mbs; i++) { 2703 ncols = ai[1] - ai[0]; 2704 ai++; 2705 brow = bs * i; 2706 for (j = 0; j < ncols; j++) { 2707 for (kcol = 0; kcol < bs; kcol++) { 2708 for (krow = 0; krow < bs; krow++) { 2709 atmp = PetscAbsScalar(*aa); 2710 aa++; 2711 row = brow + krow; /* row index */ 2712 x[row] += atmp; 2713 } 2714 } 2715 } 2716 } 2717 PetscCall(VecRestoreArrayWrite(v, &x)); 2718 PetscFunctionReturn(PETSC_SUCCESS); 2719 } 2720 2721 static PetscErrorCode MatCopy_SeqBAIJ(Mat A, Mat B, MatStructure str) 2722 { 2723 PetscFunctionBegin; 2724 /* If the two matrices have the same copy implementation, use fast copy. */ 2725 if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) { 2726 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2727 Mat_SeqBAIJ *b = (Mat_SeqBAIJ *)B->data; 2728 PetscInt ambs = a->mbs, bmbs = b->mbs, abs = A->rmap->bs, bbs = B->rmap->bs, bs2 = abs * abs; 2729 2730 PetscCheck(a->i[ambs] == b->i[bmbs], PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Number of nonzero blocks in matrices A %" PetscInt_FMT " and B %" PetscInt_FMT " are different", a->i[ambs], b->i[bmbs]); 2731 PetscCheck(abs == bbs, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Block size A %" PetscInt_FMT " and B %" PetscInt_FMT " are different", abs, bbs); 2732 PetscCall(PetscArraycpy(b->a, a->a, bs2 * a->i[ambs])); 2733 PetscCall(PetscObjectStateIncrease((PetscObject)B)); 2734 } else { 2735 PetscCall(MatCopy_Basic(A, B, str)); 2736 } 2737 PetscFunctionReturn(PETSC_SUCCESS); 2738 } 2739 2740 static PetscErrorCode MatSeqBAIJGetArray_SeqBAIJ(Mat A, PetscScalar *array[]) 2741 { 2742 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2743 2744 PetscFunctionBegin; 2745 *array = a->a; 2746 PetscFunctionReturn(PETSC_SUCCESS); 2747 } 2748 2749 static PetscErrorCode MatSeqBAIJRestoreArray_SeqBAIJ(Mat A, PetscScalar *array[]) 2750 { 2751 PetscFunctionBegin; 2752 *array = NULL; 2753 PetscFunctionReturn(PETSC_SUCCESS); 2754 } 2755 2756 PetscErrorCode MatAXPYGetPreallocation_SeqBAIJ(Mat Y, Mat X, PetscInt *nnz) 2757 { 2758 PetscInt bs = Y->rmap->bs, mbs = Y->rmap->N / bs; 2759 Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data; 2760 Mat_SeqBAIJ *y = (Mat_SeqBAIJ *)Y->data; 2761 2762 PetscFunctionBegin; 2763 /* Set the number of nonzeros in the new matrix */ 2764 PetscCall(MatAXPYGetPreallocation_SeqX_private(mbs, x->i, x->j, y->i, y->j, nnz)); 2765 PetscFunctionReturn(PETSC_SUCCESS); 2766 } 2767 2768 PetscErrorCode MatAXPY_SeqBAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str) 2769 { 2770 Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data, *y = (Mat_SeqBAIJ *)Y->data; 2771 PetscInt bs = Y->rmap->bs, bs2 = bs * bs; 2772 PetscBLASInt one = 1; 2773 2774 PetscFunctionBegin; 2775 if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) { 2776 PetscBool e = x->nz == y->nz && x->mbs == y->mbs && bs == X->rmap->bs ? PETSC_TRUE : PETSC_FALSE; 2777 if (e) { 2778 PetscCall(PetscArraycmp(x->i, y->i, x->mbs + 1, &e)); 2779 if (e) { 2780 PetscCall(PetscArraycmp(x->j, y->j, x->i[x->mbs], &e)); 2781 if (e) str = SAME_NONZERO_PATTERN; 2782 } 2783 } 2784 if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN"); 2785 } 2786 if (str == SAME_NONZERO_PATTERN) { 2787 PetscScalar alpha = a; 2788 PetscBLASInt bnz; 2789 PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz)); 2790 PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one)); 2791 PetscCall(PetscObjectStateIncrease((PetscObject)Y)); 2792 } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */ 2793 PetscCall(MatAXPY_Basic(Y, a, X, str)); 2794 } else { 2795 Mat B; 2796 PetscInt *nnz; 2797 PetscCheck(bs == X->rmap->bs, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrices must have same block size"); 2798 PetscCall(PetscMalloc1(Y->rmap->N, &nnz)); 2799 PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B)); 2800 PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name)); 2801 PetscCall(MatSetSizes(B, Y->rmap->n, Y->cmap->n, Y->rmap->N, Y->cmap->N)); 2802 PetscCall(MatSetBlockSizesFromMats(B, Y, Y)); 2803 PetscCall(MatSetType(B, (MatType)((PetscObject)Y)->type_name)); 2804 PetscCall(MatAXPYGetPreallocation_SeqBAIJ(Y, X, nnz)); 2805 PetscCall(MatSeqBAIJSetPreallocation(B, bs, 0, nnz)); 2806 PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str)); 2807 PetscCall(MatHeaderMerge(Y, &B)); 2808 PetscCall(PetscFree(nnz)); 2809 } 2810 PetscFunctionReturn(PETSC_SUCCESS); 2811 } 2812 2813 PETSC_INTERN PetscErrorCode MatConjugate_SeqBAIJ(Mat A) 2814 { 2815 #if PetscDefined(USE_COMPLEX) 2816 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2817 PetscInt i, nz = a->bs2 * a->i[a->mbs]; 2818 MatScalar *aa = a->a; 2819 2820 PetscFunctionBegin; 2821 for (i = 0; i < nz; i++) aa[i] = PetscConj(aa[i]); 2822 PetscFunctionReturn(PETSC_SUCCESS); 2823 #else 2824 (void)A; 2825 return PETSC_SUCCESS; 2826 #endif 2827 } 2828 2829 static PetscErrorCode MatRealPart_SeqBAIJ(Mat A) 2830 { 2831 #if PetscDefined(USE_COMPLEX) 2832 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2833 PetscInt i, nz = a->bs2 * a->i[a->mbs]; 2834 MatScalar *aa = a->a; 2835 2836 PetscFunctionBegin; 2837 for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]); 2838 PetscFunctionReturn(PETSC_SUCCESS); 2839 #else 2840 (void)A; 2841 return PETSC_SUCCESS; 2842 #endif 2843 } 2844 2845 static PetscErrorCode MatImaginaryPart_SeqBAIJ(Mat A) 2846 { 2847 #if PetscDefined(USE_COMPLEX) 2848 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2849 PetscInt i, nz = a->bs2 * a->i[a->mbs]; 2850 MatScalar *aa = a->a; 2851 2852 PetscFunctionBegin; 2853 for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]); 2854 PetscFunctionReturn(PETSC_SUCCESS); 2855 #else 2856 (void)A; 2857 return PETSC_SUCCESS; 2858 #endif 2859 } 2860 2861 /* 2862 Code almost identical to MatGetColumnIJ_SeqAIJ() should share common code 2863 */ 2864 static PetscErrorCode MatGetColumnIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done) 2865 { 2866 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2867 PetscInt bs = A->rmap->bs, i, *collengths, *cia, *cja, n = A->cmap->n / bs, m = A->rmap->n / bs; 2868 PetscInt nz = a->i[m], row, *jj, mr, col; 2869 2870 PetscFunctionBegin; 2871 *nn = n; 2872 if (!ia) PetscFunctionReturn(PETSC_SUCCESS); 2873 PetscCheck(!symmetric, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not for BAIJ matrices"); 2874 PetscCall(PetscCalloc1(n, &collengths)); 2875 PetscCall(PetscMalloc1(n + 1, &cia)); 2876 PetscCall(PetscMalloc1(nz, &cja)); 2877 jj = a->j; 2878 for (i = 0; i < nz; i++) collengths[jj[i]]++; 2879 cia[0] = oshift; 2880 for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i]; 2881 PetscCall(PetscArrayzero(collengths, n)); 2882 jj = a->j; 2883 for (row = 0; row < m; row++) { 2884 mr = a->i[row + 1] - a->i[row]; 2885 for (i = 0; i < mr; i++) { 2886 col = *jj++; 2887 2888 cja[cia[col] + collengths[col]++ - oshift] = row + oshift; 2889 } 2890 } 2891 PetscCall(PetscFree(collengths)); 2892 *ia = cia; 2893 *ja = cja; 2894 PetscFunctionReturn(PETSC_SUCCESS); 2895 } 2896 2897 static PetscErrorCode MatRestoreColumnIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done) 2898 { 2899 PetscFunctionBegin; 2900 if (!ia) PetscFunctionReturn(PETSC_SUCCESS); 2901 PetscCall(PetscFree(*ia)); 2902 PetscCall(PetscFree(*ja)); 2903 PetscFunctionReturn(PETSC_SUCCESS); 2904 } 2905 2906 /* 2907 MatGetColumnIJ_SeqBAIJ_Color() and MatRestoreColumnIJ_SeqBAIJ_Color() are customized from 2908 MatGetColumnIJ_SeqBAIJ() and MatRestoreColumnIJ_SeqBAIJ() by adding an output 2909 spidx[], index of a->a, to be used in MatTransposeColoringCreate() and MatFDColoringCreate() 2910 */ 2911 PetscErrorCode MatGetColumnIJ_SeqBAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done) 2912 { 2913 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2914 PetscInt i, *collengths, *cia, *cja, n = a->nbs, m = a->mbs; 2915 PetscInt nz = a->i[m], row, *jj, mr, col; 2916 PetscInt *cspidx; 2917 2918 PetscFunctionBegin; 2919 *nn = n; 2920 if (!ia) PetscFunctionReturn(PETSC_SUCCESS); 2921 2922 PetscCall(PetscCalloc1(n, &collengths)); 2923 PetscCall(PetscMalloc1(n + 1, &cia)); 2924 PetscCall(PetscMalloc1(nz, &cja)); 2925 PetscCall(PetscMalloc1(nz, &cspidx)); 2926 jj = a->j; 2927 for (i = 0; i < nz; i++) collengths[jj[i]]++; 2928 cia[0] = oshift; 2929 for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i]; 2930 PetscCall(PetscArrayzero(collengths, n)); 2931 jj = a->j; 2932 for (row = 0; row < m; row++) { 2933 mr = a->i[row + 1] - a->i[row]; 2934 for (i = 0; i < mr; i++) { 2935 col = *jj++; 2936 cspidx[cia[col] + collengths[col] - oshift] = a->i[row] + i; /* index of a->j */ 2937 cja[cia[col] + collengths[col]++ - oshift] = row + oshift; 2938 } 2939 } 2940 PetscCall(PetscFree(collengths)); 2941 *ia = cia; 2942 *ja = cja; 2943 *spidx = cspidx; 2944 PetscFunctionReturn(PETSC_SUCCESS); 2945 } 2946 2947 PetscErrorCode MatRestoreColumnIJ_SeqBAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done) 2948 { 2949 PetscFunctionBegin; 2950 PetscCall(MatRestoreColumnIJ_SeqBAIJ(A, oshift, symmetric, inodecompressed, n, ia, ja, done)); 2951 PetscCall(PetscFree(*spidx)); 2952 PetscFunctionReturn(PETSC_SUCCESS); 2953 } 2954 2955 static PetscErrorCode MatShift_SeqBAIJ(Mat Y, PetscScalar a) 2956 { 2957 Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)Y->data; 2958 2959 PetscFunctionBegin; 2960 if (!Y->preallocated || !aij->nz) PetscCall(MatSeqBAIJSetPreallocation(Y, Y->rmap->bs, 1, NULL)); 2961 PetscCall(MatShift_Basic(Y, a)); 2962 PetscFunctionReturn(PETSC_SUCCESS); 2963 } 2964 2965 PetscErrorCode MatEliminateZeros_SeqBAIJ(Mat A, PetscBool keep) 2966 { 2967 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2968 PetscInt fshift = 0, fshift_prev = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax, j, k; 2969 PetscInt m = A->rmap->N, *ailen = a->ilen; 2970 PetscInt mbs = a->mbs, bs2 = a->bs2, rmax = 0; 2971 MatScalar *aa = a->a, *ap; 2972 PetscBool zero; 2973 2974 PetscFunctionBegin; 2975 PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix"); 2976 if (m) rmax = ailen[0]; 2977 for (i = 1; i <= mbs; i++) { 2978 for (k = ai[i - 1]; k < ai[i]; k++) { 2979 zero = PETSC_TRUE; 2980 ap = aa + bs2 * k; 2981 for (j = 0; j < bs2 && zero; j++) { 2982 if (ap[j] != 0.0) zero = PETSC_FALSE; 2983 } 2984 if (zero && (aj[k] != i - 1 || !keep)) fshift++; 2985 else { 2986 if (zero && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal block at row %" PetscInt_FMT "\n", i - 1)); 2987 aj[k - fshift] = aj[k]; 2988 PetscCall(PetscArraymove(ap - bs2 * fshift, ap, bs2)); 2989 } 2990 } 2991 ai[i - 1] -= fshift_prev; 2992 fshift_prev = fshift; 2993 ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1]; 2994 a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0); 2995 rmax = PetscMax(rmax, ailen[i - 1]); 2996 } 2997 if (fshift) { 2998 if (mbs) { 2999 ai[mbs] -= fshift; 3000 a->nz = ai[mbs]; 3001 } 3002 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)); 3003 A->nonzerostate++; 3004 A->info.nz_unneeded += (PetscReal)fshift; 3005 a->rmax = rmax; 3006 PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY)); 3007 PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY)); 3008 } 3009 PetscFunctionReturn(PETSC_SUCCESS); 3010 } 3011 3012 static struct _MatOps MatOps_Values = {MatSetValues_SeqBAIJ, 3013 MatGetRow_SeqBAIJ, 3014 MatRestoreRow_SeqBAIJ, 3015 MatMult_SeqBAIJ_N, 3016 /* 4*/ MatMultAdd_SeqBAIJ_N, 3017 MatMultTranspose_SeqBAIJ, 3018 MatMultTransposeAdd_SeqBAIJ, 3019 NULL, 3020 NULL, 3021 NULL, 3022 /* 10*/ NULL, 3023 MatLUFactor_SeqBAIJ, 3024 NULL, 3025 NULL, 3026 MatTranspose_SeqBAIJ, 3027 /* 15*/ MatGetInfo_SeqBAIJ, 3028 MatEqual_SeqBAIJ, 3029 MatGetDiagonal_SeqBAIJ, 3030 MatDiagonalScale_SeqBAIJ, 3031 MatNorm_SeqBAIJ, 3032 /* 20*/ NULL, 3033 MatAssemblyEnd_SeqBAIJ, 3034 MatSetOption_SeqBAIJ, 3035 MatZeroEntries_SeqBAIJ, 3036 /* 24*/ MatZeroRows_SeqBAIJ, 3037 NULL, 3038 NULL, 3039 NULL, 3040 NULL, 3041 /* 29*/ MatSetUp_Seq_Hash, 3042 NULL, 3043 NULL, 3044 NULL, 3045 NULL, 3046 /* 34*/ MatDuplicate_SeqBAIJ, 3047 NULL, 3048 NULL, 3049 MatILUFactor_SeqBAIJ, 3050 NULL, 3051 /* 39*/ MatAXPY_SeqBAIJ, 3052 MatCreateSubMatrices_SeqBAIJ, 3053 MatIncreaseOverlap_SeqBAIJ, 3054 MatGetValues_SeqBAIJ, 3055 MatCopy_SeqBAIJ, 3056 /* 44*/ NULL, 3057 MatScale_SeqBAIJ, 3058 MatShift_SeqBAIJ, 3059 NULL, 3060 MatZeroRowsColumns_SeqBAIJ, 3061 /* 49*/ NULL, 3062 MatGetRowIJ_SeqBAIJ, 3063 MatRestoreRowIJ_SeqBAIJ, 3064 MatGetColumnIJ_SeqBAIJ, 3065 MatRestoreColumnIJ_SeqBAIJ, 3066 /* 54*/ MatFDColoringCreate_SeqXAIJ, 3067 NULL, 3068 NULL, 3069 NULL, 3070 MatSetValuesBlocked_SeqBAIJ, 3071 /* 59*/ MatCreateSubMatrix_SeqBAIJ, 3072 MatDestroy_SeqBAIJ, 3073 MatView_SeqBAIJ, 3074 NULL, 3075 NULL, 3076 /* 64*/ NULL, 3077 NULL, 3078 NULL, 3079 NULL, 3080 NULL, 3081 /* 69*/ MatGetRowMaxAbs_SeqBAIJ, 3082 NULL, 3083 MatConvert_Basic, 3084 NULL, 3085 NULL, 3086 /* 74*/ NULL, 3087 MatFDColoringApply_BAIJ, 3088 NULL, 3089 NULL, 3090 NULL, 3091 /* 79*/ NULL, 3092 NULL, 3093 NULL, 3094 NULL, 3095 MatLoad_SeqBAIJ, 3096 /* 84*/ NULL, 3097 NULL, 3098 NULL, 3099 NULL, 3100 NULL, 3101 /* 89*/ NULL, 3102 NULL, 3103 NULL, 3104 NULL, 3105 NULL, 3106 /* 94*/ NULL, 3107 NULL, 3108 NULL, 3109 NULL, 3110 NULL, 3111 /* 99*/ NULL, 3112 NULL, 3113 NULL, 3114 MatConjugate_SeqBAIJ, 3115 NULL, 3116 /*104*/ NULL, 3117 MatRealPart_SeqBAIJ, 3118 MatImaginaryPart_SeqBAIJ, 3119 NULL, 3120 NULL, 3121 /*109*/ NULL, 3122 NULL, 3123 NULL, 3124 NULL, 3125 MatMissingDiagonal_SeqBAIJ, 3126 /*114*/ NULL, 3127 NULL, 3128 NULL, 3129 NULL, 3130 NULL, 3131 /*119*/ NULL, 3132 NULL, 3133 MatMultHermitianTranspose_SeqBAIJ, 3134 MatMultHermitianTransposeAdd_SeqBAIJ, 3135 NULL, 3136 /*124*/ NULL, 3137 MatGetColumnReductions_SeqBAIJ, 3138 MatInvertBlockDiagonal_SeqBAIJ, 3139 NULL, 3140 NULL, 3141 /*129*/ NULL, 3142 NULL, 3143 NULL, 3144 NULL, 3145 NULL, 3146 /*134*/ NULL, 3147 NULL, 3148 NULL, 3149 NULL, 3150 NULL, 3151 /*139*/ MatSetBlockSizes_Default, 3152 NULL, 3153 NULL, 3154 MatFDColoringSetUp_SeqXAIJ, 3155 NULL, 3156 /*144*/ MatCreateMPIMatConcatenateSeqMat_SeqBAIJ, 3157 MatDestroySubMatrices_SeqBAIJ, 3158 NULL, 3159 NULL, 3160 NULL, 3161 NULL, 3162 /*150*/ NULL, 3163 MatEliminateZeros_SeqBAIJ, 3164 MatGetRowSumAbs_SeqBAIJ}; 3165 3166 static PetscErrorCode MatStoreValues_SeqBAIJ(Mat mat) 3167 { 3168 Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data; 3169 PetscInt nz = aij->i[aij->mbs] * aij->bs2; 3170 3171 PetscFunctionBegin; 3172 PetscCheck(aij->nonew == 1, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first"); 3173 3174 /* allocate space for values if not already there */ 3175 if (!aij->saved_values) PetscCall(PetscMalloc1(nz + 1, &aij->saved_values)); 3176 3177 /* copy values over */ 3178 PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz)); 3179 PetscFunctionReturn(PETSC_SUCCESS); 3180 } 3181 3182 static PetscErrorCode MatRetrieveValues_SeqBAIJ(Mat mat) 3183 { 3184 Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data; 3185 PetscInt nz = aij->i[aij->mbs] * aij->bs2; 3186 3187 PetscFunctionBegin; 3188 PetscCheck(aij->nonew == 1, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first"); 3189 PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first"); 3190 3191 /* copy values over */ 3192 PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz)); 3193 PetscFunctionReturn(PETSC_SUCCESS); 3194 } 3195 3196 PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat, MatType, MatReuse, Mat *); 3197 PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqSBAIJ(Mat, MatType, MatReuse, Mat *); 3198 3199 PetscErrorCode MatSeqBAIJSetPreallocation_SeqBAIJ(Mat B, PetscInt bs, PetscInt nz, const PetscInt nnz[]) 3200 { 3201 Mat_SeqBAIJ *b = (Mat_SeqBAIJ *)B->data; 3202 PetscInt i, mbs, nbs, bs2; 3203 PetscBool flg = PETSC_FALSE, skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE; 3204 3205 PetscFunctionBegin; 3206 if (B->hash_active) { 3207 PetscInt bs; 3208 B->ops[0] = b->cops; 3209 PetscCall(PetscHMapIJVDestroy(&b->ht)); 3210 PetscCall(MatGetBlockSize(B, &bs)); 3211 if (bs > 1) PetscCall(PetscHSetIJDestroy(&b->bht)); 3212 PetscCall(PetscFree(b->dnz)); 3213 PetscCall(PetscFree(b->bdnz)); 3214 B->hash_active = PETSC_FALSE; 3215 } 3216 if (nz >= 0 || nnz) realalloc = PETSC_TRUE; 3217 if (nz == MAT_SKIP_ALLOCATION) { 3218 skipallocation = PETSC_TRUE; 3219 nz = 0; 3220 } 3221 3222 PetscCall(MatSetBlockSize(B, PetscAbs(bs))); 3223 PetscCall(PetscLayoutSetUp(B->rmap)); 3224 PetscCall(PetscLayoutSetUp(B->cmap)); 3225 PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs)); 3226 3227 B->preallocated = PETSC_TRUE; 3228 3229 mbs = B->rmap->n / bs; 3230 nbs = B->cmap->n / bs; 3231 bs2 = bs * bs; 3232 3233 PetscCheck(mbs * bs == B->rmap->n && nbs * bs == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Number rows %" PetscInt_FMT ", cols %" PetscInt_FMT " must be divisible by blocksize %" PetscInt_FMT, B->rmap->N, B->cmap->n, bs); 3234 3235 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5; 3236 PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz); 3237 if (nnz) { 3238 for (i = 0; i < mbs; i++) { 3239 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]); 3240 PetscCheck(nnz[i] <= nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be greater than block row length: local row %" PetscInt_FMT " value %" PetscInt_FMT " rowlength %" PetscInt_FMT, i, nnz[i], nbs); 3241 } 3242 } 3243 3244 PetscOptionsBegin(PetscObjectComm((PetscObject)B), NULL, "Optimize options for SEQBAIJ matrix 2 ", "Mat"); 3245 PetscCall(PetscOptionsBool("-mat_no_unroll", "Do not optimize for block size (slow)", NULL, flg, &flg, NULL)); 3246 PetscOptionsEnd(); 3247 3248 if (!flg) { 3249 switch (bs) { 3250 case 1: 3251 B->ops->mult = MatMult_SeqBAIJ_1; 3252 B->ops->multadd = MatMultAdd_SeqBAIJ_1; 3253 break; 3254 case 2: 3255 B->ops->mult = MatMult_SeqBAIJ_2; 3256 B->ops->multadd = MatMultAdd_SeqBAIJ_2; 3257 break; 3258 case 3: 3259 B->ops->mult = MatMult_SeqBAIJ_3; 3260 B->ops->multadd = MatMultAdd_SeqBAIJ_3; 3261 break; 3262 case 4: 3263 B->ops->mult = MatMult_SeqBAIJ_4; 3264 B->ops->multadd = MatMultAdd_SeqBAIJ_4; 3265 break; 3266 case 5: 3267 B->ops->mult = MatMult_SeqBAIJ_5; 3268 B->ops->multadd = MatMultAdd_SeqBAIJ_5; 3269 break; 3270 case 6: 3271 B->ops->mult = MatMult_SeqBAIJ_6; 3272 B->ops->multadd = MatMultAdd_SeqBAIJ_6; 3273 break; 3274 case 7: 3275 B->ops->mult = MatMult_SeqBAIJ_7; 3276 B->ops->multadd = MatMultAdd_SeqBAIJ_7; 3277 break; 3278 case 9: { 3279 PetscInt version = 1; 3280 PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)B)->prefix, "-mat_baij_mult_version", &version, NULL)); 3281 switch (version) { 3282 #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES) 3283 case 1: 3284 B->ops->mult = MatMult_SeqBAIJ_9_AVX2; 3285 B->ops->multadd = MatMultAdd_SeqBAIJ_9_AVX2; 3286 PetscCall(PetscInfo((PetscObject)B, "Using AVX2 for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs)); 3287 break; 3288 #endif 3289 default: 3290 B->ops->mult = MatMult_SeqBAIJ_N; 3291 B->ops->multadd = MatMultAdd_SeqBAIJ_N; 3292 PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs)); 3293 break; 3294 } 3295 break; 3296 } 3297 case 11: 3298 B->ops->mult = MatMult_SeqBAIJ_11; 3299 B->ops->multadd = MatMultAdd_SeqBAIJ_11; 3300 break; 3301 case 12: { 3302 PetscInt version = 1; 3303 PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)B)->prefix, "-mat_baij_mult_version", &version, NULL)); 3304 switch (version) { 3305 case 1: 3306 B->ops->mult = MatMult_SeqBAIJ_12_ver1; 3307 B->ops->multadd = MatMultAdd_SeqBAIJ_12_ver1; 3308 PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs)); 3309 break; 3310 case 2: 3311 B->ops->mult = MatMult_SeqBAIJ_12_ver2; 3312 B->ops->multadd = MatMultAdd_SeqBAIJ_12_ver2; 3313 PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs)); 3314 break; 3315 #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES) 3316 case 3: 3317 B->ops->mult = MatMult_SeqBAIJ_12_AVX2; 3318 B->ops->multadd = MatMultAdd_SeqBAIJ_12_ver1; 3319 PetscCall(PetscInfo((PetscObject)B, "Using AVX2 for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs)); 3320 break; 3321 #endif 3322 default: 3323 B->ops->mult = MatMult_SeqBAIJ_N; 3324 B->ops->multadd = MatMultAdd_SeqBAIJ_N; 3325 PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs)); 3326 break; 3327 } 3328 break; 3329 } 3330 case 15: { 3331 PetscInt version = 1; 3332 PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)B)->prefix, "-mat_baij_mult_version", &version, NULL)); 3333 switch (version) { 3334 case 1: 3335 B->ops->mult = MatMult_SeqBAIJ_15_ver1; 3336 PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs)); 3337 break; 3338 case 2: 3339 B->ops->mult = MatMult_SeqBAIJ_15_ver2; 3340 PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs)); 3341 break; 3342 case 3: 3343 B->ops->mult = MatMult_SeqBAIJ_15_ver3; 3344 PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs)); 3345 break; 3346 case 4: 3347 B->ops->mult = MatMult_SeqBAIJ_15_ver4; 3348 PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs)); 3349 break; 3350 default: 3351 B->ops->mult = MatMult_SeqBAIJ_N; 3352 PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs)); 3353 break; 3354 } 3355 B->ops->multadd = MatMultAdd_SeqBAIJ_N; 3356 break; 3357 } 3358 default: 3359 B->ops->mult = MatMult_SeqBAIJ_N; 3360 B->ops->multadd = MatMultAdd_SeqBAIJ_N; 3361 PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs)); 3362 break; 3363 } 3364 } 3365 B->ops->sor = MatSOR_SeqBAIJ; 3366 b->mbs = mbs; 3367 b->nbs = nbs; 3368 if (!skipallocation) { 3369 if (!b->imax) { 3370 PetscCall(PetscMalloc2(mbs, &b->imax, mbs, &b->ilen)); 3371 3372 b->free_imax_ilen = PETSC_TRUE; 3373 } 3374 /* b->ilen will count nonzeros in each block row so far. */ 3375 for (i = 0; i < mbs; i++) b->ilen[i] = 0; 3376 if (!nnz) { 3377 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5; 3378 else if (nz < 0) nz = 1; 3379 nz = PetscMin(nz, nbs); 3380 for (i = 0; i < mbs; i++) b->imax[i] = nz; 3381 PetscCall(PetscIntMultError(nz, mbs, &nz)); 3382 } else { 3383 PetscInt64 nz64 = 0; 3384 for (i = 0; i < mbs; i++) { 3385 b->imax[i] = nnz[i]; 3386 nz64 += nnz[i]; 3387 } 3388 PetscCall(PetscIntCast(nz64, &nz)); 3389 } 3390 3391 /* allocate the matrix space */ 3392 PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i)); 3393 if (B->structure_only) { 3394 PetscCall(PetscMalloc1(nz, &b->j)); 3395 PetscCall(PetscMalloc1(B->rmap->N + 1, &b->i)); 3396 } else { 3397 PetscInt nzbs2 = 0; 3398 PetscCall(PetscIntMultError(nz, bs2, &nzbs2)); 3399 PetscCall(PetscMalloc3(nzbs2, &b->a, nz, &b->j, B->rmap->N + 1, &b->i)); 3400 PetscCall(PetscArrayzero(b->a, nz * bs2)); 3401 } 3402 PetscCall(PetscArrayzero(b->j, nz)); 3403 3404 if (B->structure_only) { 3405 b->singlemalloc = PETSC_FALSE; 3406 b->free_a = PETSC_FALSE; 3407 } else { 3408 b->singlemalloc = PETSC_TRUE; 3409 b->free_a = PETSC_TRUE; 3410 } 3411 b->free_ij = PETSC_TRUE; 3412 3413 b->i[0] = 0; 3414 for (i = 1; i < mbs + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1]; 3415 3416 } else { 3417 b->free_a = PETSC_FALSE; 3418 b->free_ij = PETSC_FALSE; 3419 } 3420 3421 b->bs2 = bs2; 3422 b->mbs = mbs; 3423 b->nz = 0; 3424 b->maxnz = nz; 3425 B->info.nz_unneeded = (PetscReal)b->maxnz * bs2; 3426 B->was_assembled = PETSC_FALSE; 3427 B->assembled = PETSC_FALSE; 3428 if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE)); 3429 PetscFunctionReturn(PETSC_SUCCESS); 3430 } 3431 3432 static PetscErrorCode MatSeqBAIJSetPreallocationCSR_SeqBAIJ(Mat B, PetscInt bs, const PetscInt ii[], const PetscInt jj[], const PetscScalar V[]) 3433 { 3434 PetscInt i, m, nz, nz_max = 0, *nnz; 3435 PetscScalar *values = NULL; 3436 PetscBool roworiented = ((Mat_SeqBAIJ *)B->data)->roworiented; 3437 3438 PetscFunctionBegin; 3439 PetscCheck(bs >= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Invalid block size specified, must be positive but it is %" PetscInt_FMT, bs); 3440 PetscCall(PetscLayoutSetBlockSize(B->rmap, bs)); 3441 PetscCall(PetscLayoutSetBlockSize(B->cmap, bs)); 3442 PetscCall(PetscLayoutSetUp(B->rmap)); 3443 PetscCall(PetscLayoutSetUp(B->cmap)); 3444 PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs)); 3445 m = B->rmap->n / bs; 3446 3447 PetscCheck(ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "ii[0] must be 0 but it is %" PetscInt_FMT, ii[0]); 3448 PetscCall(PetscMalloc1(m + 1, &nnz)); 3449 for (i = 0; i < m; i++) { 3450 nz = ii[i + 1] - ii[i]; 3451 PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz); 3452 nz_max = PetscMax(nz_max, nz); 3453 nnz[i] = nz; 3454 } 3455 PetscCall(MatSeqBAIJSetPreallocation(B, bs, 0, nnz)); 3456 PetscCall(PetscFree(nnz)); 3457 3458 values = (PetscScalar *)V; 3459 if (!values) PetscCall(PetscCalloc1(bs * bs * (nz_max + 1), &values)); 3460 for (i = 0; i < m; i++) { 3461 PetscInt ncols = ii[i + 1] - ii[i]; 3462 const PetscInt *icols = jj + ii[i]; 3463 if (bs == 1 || !roworiented) { 3464 const PetscScalar *svals = values + (V ? (bs * bs * ii[i]) : 0); 3465 PetscCall(MatSetValuesBlocked_SeqBAIJ(B, 1, &i, ncols, icols, svals, INSERT_VALUES)); 3466 } else { 3467 PetscInt j; 3468 for (j = 0; j < ncols; j++) { 3469 const PetscScalar *svals = values + (V ? (bs * bs * (ii[i] + j)) : 0); 3470 PetscCall(MatSetValuesBlocked_SeqBAIJ(B, 1, &i, 1, &icols[j], svals, INSERT_VALUES)); 3471 } 3472 } 3473 } 3474 if (!V) PetscCall(PetscFree(values)); 3475 PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY)); 3476 PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY)); 3477 PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE)); 3478 PetscFunctionReturn(PETSC_SUCCESS); 3479 } 3480 3481 /*@C 3482 MatSeqBAIJGetArray - gives read/write access to the array where the data for a `MATSEQBAIJ` matrix is stored 3483 3484 Not Collective 3485 3486 Input Parameter: 3487 . A - a `MATSEQBAIJ` matrix 3488 3489 Output Parameter: 3490 . array - pointer to the data 3491 3492 Level: intermediate 3493 3494 .seealso: [](ch_matrices), `Mat`, `MATSEQBAIJ`, `MatSeqBAIJRestoreArray()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()` 3495 @*/ 3496 PetscErrorCode MatSeqBAIJGetArray(Mat A, PetscScalar **array) 3497 { 3498 PetscFunctionBegin; 3499 PetscUseMethod(A, "MatSeqBAIJGetArray_C", (Mat, PetscScalar **), (A, array)); 3500 PetscFunctionReturn(PETSC_SUCCESS); 3501 } 3502 3503 /*@C 3504 MatSeqBAIJRestoreArray - returns access to the array where the data for a `MATSEQBAIJ` matrix is stored obtained by `MatSeqBAIJGetArray()` 3505 3506 Not Collective 3507 3508 Input Parameters: 3509 + A - a `MATSEQBAIJ` matrix 3510 - array - pointer to the data 3511 3512 Level: intermediate 3513 3514 .seealso: [](ch_matrices), `Mat`, `MatSeqBAIJGetArray()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()` 3515 @*/ 3516 PetscErrorCode MatSeqBAIJRestoreArray(Mat A, PetscScalar **array) 3517 { 3518 PetscFunctionBegin; 3519 PetscUseMethod(A, "MatSeqBAIJRestoreArray_C", (Mat, PetscScalar **), (A, array)); 3520 PetscFunctionReturn(PETSC_SUCCESS); 3521 } 3522 3523 /*MC 3524 MATSEQBAIJ - MATSEQBAIJ = "seqbaij" - A matrix type to be used for sequential block sparse matrices, based on 3525 block sparse compressed row format. 3526 3527 Options Database Keys: 3528 + -mat_type seqbaij - sets the matrix type to `MATSEQBAIJ` during a call to `MatSetFromOptions()` 3529 - -mat_baij_mult_version version - indicate the version of the matrix-vector product to use (0 often indicates using BLAS) 3530 3531 Level: beginner 3532 3533 Notes: 3534 `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no 3535 space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored 3536 3537 Run with `-info` to see what version of the matrix-vector product is being used 3538 3539 .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()` 3540 M*/ 3541 3542 PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBSTRM(Mat, MatType, MatReuse, Mat *); 3543 3544 PETSC_EXTERN PetscErrorCode MatCreate_SeqBAIJ(Mat B) 3545 { 3546 PetscMPIInt size; 3547 Mat_SeqBAIJ *b; 3548 3549 PetscFunctionBegin; 3550 PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size)); 3551 PetscCheck(size == 1, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Comm must be of size 1"); 3552 3553 PetscCall(PetscNew(&b)); 3554 B->data = (void *)b; 3555 B->ops[0] = MatOps_Values; 3556 3557 b->row = NULL; 3558 b->col = NULL; 3559 b->icol = NULL; 3560 b->reallocs = 0; 3561 b->saved_values = NULL; 3562 3563 b->roworiented = PETSC_TRUE; 3564 b->nonew = 0; 3565 b->diag = NULL; 3566 B->spptr = NULL; 3567 B->info.nz_unneeded = (PetscReal)b->maxnz * b->bs2; 3568 b->keepnonzeropattern = PETSC_FALSE; 3569 3570 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJGetArray_C", MatSeqBAIJGetArray_SeqBAIJ)); 3571 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJRestoreArray_C", MatSeqBAIJRestoreArray_SeqBAIJ)); 3572 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqBAIJ)); 3573 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqBAIJ)); 3574 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJSetColumnIndices_C", MatSeqBAIJSetColumnIndices_SeqBAIJ)); 3575 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_seqaij_C", MatConvert_SeqBAIJ_SeqAIJ)); 3576 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_seqsbaij_C", MatConvert_SeqBAIJ_SeqSBAIJ)); 3577 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJSetPreallocation_C", MatSeqBAIJSetPreallocation_SeqBAIJ)); 3578 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJSetPreallocationCSR_C", MatSeqBAIJSetPreallocationCSR_SeqBAIJ)); 3579 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqBAIJ)); 3580 #if defined(PETSC_HAVE_HYPRE) 3581 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_hypre_C", MatConvert_AIJ_HYPRE)); 3582 #endif 3583 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_is_C", MatConvert_XAIJ_IS)); 3584 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQBAIJ)); 3585 PetscFunctionReturn(PETSC_SUCCESS); 3586 } 3587 3588 PetscErrorCode MatDuplicateNoCreate_SeqBAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace) 3589 { 3590 Mat_SeqBAIJ *c = (Mat_SeqBAIJ *)C->data, *a = (Mat_SeqBAIJ *)A->data; 3591 PetscInt i, mbs = a->mbs, nz = a->nz, bs2 = a->bs2; 3592 3593 PetscFunctionBegin; 3594 PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix"); 3595 PetscCheck(a->i[mbs] == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Corrupt matrix"); 3596 3597 if (cpvalues == MAT_SHARE_NONZERO_PATTERN) { 3598 c->imax = a->imax; 3599 c->ilen = a->ilen; 3600 c->free_imax_ilen = PETSC_FALSE; 3601 } else { 3602 PetscCall(PetscMalloc2(mbs, &c->imax, mbs, &c->ilen)); 3603 for (i = 0; i < mbs; i++) { 3604 c->imax[i] = a->imax[i]; 3605 c->ilen[i] = a->ilen[i]; 3606 } 3607 c->free_imax_ilen = PETSC_TRUE; 3608 } 3609 3610 /* allocate the matrix space */ 3611 if (mallocmatspace) { 3612 if (cpvalues == MAT_SHARE_NONZERO_PATTERN) { 3613 PetscCall(PetscCalloc1(bs2 * nz, &c->a)); 3614 3615 c->i = a->i; 3616 c->j = a->j; 3617 c->singlemalloc = PETSC_FALSE; 3618 c->free_a = PETSC_TRUE; 3619 c->free_ij = PETSC_FALSE; 3620 c->parent = A; 3621 C->preallocated = PETSC_TRUE; 3622 C->assembled = PETSC_TRUE; 3623 3624 PetscCall(PetscObjectReference((PetscObject)A)); 3625 PetscCall(MatSetOption(A, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE)); 3626 PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE)); 3627 } else { 3628 PetscCall(PetscMalloc3(bs2 * nz, &c->a, nz, &c->j, mbs + 1, &c->i)); 3629 3630 c->singlemalloc = PETSC_TRUE; 3631 c->free_a = PETSC_TRUE; 3632 c->free_ij = PETSC_TRUE; 3633 3634 PetscCall(PetscArraycpy(c->i, a->i, mbs + 1)); 3635 if (mbs > 0) { 3636 PetscCall(PetscArraycpy(c->j, a->j, nz)); 3637 if (cpvalues == MAT_COPY_VALUES) { 3638 PetscCall(PetscArraycpy(c->a, a->a, bs2 * nz)); 3639 } else { 3640 PetscCall(PetscArrayzero(c->a, bs2 * nz)); 3641 } 3642 } 3643 C->preallocated = PETSC_TRUE; 3644 C->assembled = PETSC_TRUE; 3645 } 3646 } 3647 3648 c->roworiented = a->roworiented; 3649 c->nonew = a->nonew; 3650 3651 PetscCall(PetscLayoutReference(A->rmap, &C->rmap)); 3652 PetscCall(PetscLayoutReference(A->cmap, &C->cmap)); 3653 3654 c->bs2 = a->bs2; 3655 c->mbs = a->mbs; 3656 c->nbs = a->nbs; 3657 3658 if (a->diag) { 3659 if (cpvalues == MAT_SHARE_NONZERO_PATTERN) { 3660 c->diag = a->diag; 3661 c->free_diag = PETSC_FALSE; 3662 } else { 3663 PetscCall(PetscMalloc1(mbs + 1, &c->diag)); 3664 for (i = 0; i < mbs; i++) c->diag[i] = a->diag[i]; 3665 c->free_diag = PETSC_TRUE; 3666 } 3667 } else c->diag = NULL; 3668 3669 c->nz = a->nz; 3670 c->maxnz = a->nz; /* Since we allocate exactly the right amount */ 3671 c->solve_work = NULL; 3672 c->mult_work = NULL; 3673 c->sor_workt = NULL; 3674 c->sor_work = NULL; 3675 3676 c->compressedrow.use = a->compressedrow.use; 3677 c->compressedrow.nrows = a->compressedrow.nrows; 3678 if (a->compressedrow.use) { 3679 i = a->compressedrow.nrows; 3680 PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i + 1, &c->compressedrow.rindex)); 3681 PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1)); 3682 PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i)); 3683 } else { 3684 c->compressedrow.use = PETSC_FALSE; 3685 c->compressedrow.i = NULL; 3686 c->compressedrow.rindex = NULL; 3687 } 3688 c->nonzerorowcnt = a->nonzerorowcnt; 3689 C->nonzerostate = A->nonzerostate; 3690 3691 PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist)); 3692 PetscFunctionReturn(PETSC_SUCCESS); 3693 } 3694 3695 PetscErrorCode MatDuplicate_SeqBAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B) 3696 { 3697 PetscFunctionBegin; 3698 PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B)); 3699 PetscCall(MatSetSizes(*B, A->rmap->N, A->cmap->n, A->rmap->N, A->cmap->n)); 3700 PetscCall(MatSetType(*B, MATSEQBAIJ)); 3701 PetscCall(MatDuplicateNoCreate_SeqBAIJ(*B, A, cpvalues, PETSC_TRUE)); 3702 PetscFunctionReturn(PETSC_SUCCESS); 3703 } 3704 3705 /* Used for both SeqBAIJ and SeqSBAIJ matrices */ 3706 PetscErrorCode MatLoad_SeqBAIJ_Binary(Mat mat, PetscViewer viewer) 3707 { 3708 PetscInt header[4], M, N, nz, bs, m, n, mbs, nbs, rows, cols, sum, i, j, k; 3709 PetscInt *rowidxs, *colidxs; 3710 PetscScalar *matvals; 3711 3712 PetscFunctionBegin; 3713 PetscCall(PetscViewerSetUp(viewer)); 3714 3715 /* read matrix header */ 3716 PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT)); 3717 PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file"); 3718 M = header[1]; 3719 N = header[2]; 3720 nz = header[3]; 3721 PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M); 3722 PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N); 3723 PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqBAIJ"); 3724 3725 /* set block sizes from the viewer's .info file */ 3726 PetscCall(MatLoad_Binary_BlockSizes(mat, viewer)); 3727 /* set local and global sizes if not set already */ 3728 if (mat->rmap->n < 0) mat->rmap->n = M; 3729 if (mat->cmap->n < 0) mat->cmap->n = N; 3730 if (mat->rmap->N < 0) mat->rmap->N = M; 3731 if (mat->cmap->N < 0) mat->cmap->N = N; 3732 PetscCall(PetscLayoutSetUp(mat->rmap)); 3733 PetscCall(PetscLayoutSetUp(mat->cmap)); 3734 3735 /* check if the matrix sizes are correct */ 3736 PetscCall(MatGetSize(mat, &rows, &cols)); 3737 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); 3738 PetscCall(MatGetBlockSize(mat, &bs)); 3739 PetscCall(MatGetLocalSize(mat, &m, &n)); 3740 mbs = m / bs; 3741 nbs = n / bs; 3742 3743 /* read in row lengths, column indices and nonzero values */ 3744 PetscCall(PetscMalloc1(m + 1, &rowidxs)); 3745 PetscCall(PetscViewerBinaryRead(viewer, rowidxs + 1, m, NULL, PETSC_INT)); 3746 rowidxs[0] = 0; 3747 for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i]; 3748 sum = rowidxs[m]; 3749 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); 3750 3751 /* read in column indices and nonzero values */ 3752 PetscCall(PetscMalloc2(rowidxs[m], &colidxs, nz, &matvals)); 3753 PetscCall(PetscViewerBinaryRead(viewer, colidxs, rowidxs[m], NULL, PETSC_INT)); 3754 PetscCall(PetscViewerBinaryRead(viewer, matvals, rowidxs[m], NULL, PETSC_SCALAR)); 3755 3756 { /* preallocate matrix storage */ 3757 PetscBT bt; /* helper bit set to count nonzeros */ 3758 PetscInt *nnz; 3759 PetscBool sbaij; 3760 3761 PetscCall(PetscBTCreate(nbs, &bt)); 3762 PetscCall(PetscCalloc1(mbs, &nnz)); 3763 PetscCall(PetscObjectTypeCompare((PetscObject)mat, MATSEQSBAIJ, &sbaij)); 3764 for (i = 0; i < mbs; i++) { 3765 PetscCall(PetscBTMemzero(nbs, bt)); 3766 for (k = 0; k < bs; k++) { 3767 PetscInt row = bs * i + k; 3768 for (j = rowidxs[row]; j < rowidxs[row + 1]; j++) { 3769 PetscInt col = colidxs[j]; 3770 if (!sbaij || col >= row) 3771 if (!PetscBTLookupSet(bt, col / bs)) nnz[i]++; 3772 } 3773 } 3774 } 3775 PetscCall(PetscBTDestroy(&bt)); 3776 PetscCall(MatSeqBAIJSetPreallocation(mat, bs, 0, nnz)); 3777 PetscCall(MatSeqSBAIJSetPreallocation(mat, bs, 0, nnz)); 3778 PetscCall(PetscFree(nnz)); 3779 } 3780 3781 /* store matrix values */ 3782 for (i = 0; i < m; i++) { 3783 PetscInt row = i, s = rowidxs[i], e = rowidxs[i + 1]; 3784 PetscUseTypeMethod(mat, setvalues, 1, &row, e - s, colidxs + s, matvals + s, INSERT_VALUES); 3785 } 3786 3787 PetscCall(PetscFree(rowidxs)); 3788 PetscCall(PetscFree2(colidxs, matvals)); 3789 PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY)); 3790 PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY)); 3791 PetscFunctionReturn(PETSC_SUCCESS); 3792 } 3793 3794 PetscErrorCode MatLoad_SeqBAIJ(Mat mat, PetscViewer viewer) 3795 { 3796 PetscBool isbinary; 3797 3798 PetscFunctionBegin; 3799 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary)); 3800 PetscCheck(isbinary, PetscObjectComm((PetscObject)viewer), PETSC_ERR_SUP, "Viewer type %s not yet supported for reading %s matrices", ((PetscObject)viewer)->type_name, ((PetscObject)mat)->type_name); 3801 PetscCall(MatLoad_SeqBAIJ_Binary(mat, viewer)); 3802 PetscFunctionReturn(PETSC_SUCCESS); 3803 } 3804 3805 /*@C 3806 MatCreateSeqBAIJ - Creates a sparse matrix in `MATSEQAIJ` (block 3807 compressed row) format. For good matrix assembly performance the 3808 user should preallocate the matrix storage by setting the parameter `nz` 3809 (or the array `nnz`). 3810 3811 Collective 3812 3813 Input Parameters: 3814 + comm - MPI communicator, set to `PETSC_COMM_SELF` 3815 . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row 3816 blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()` 3817 . m - number of rows 3818 . n - number of columns 3819 . nz - number of nonzero blocks per block row (same for all rows) 3820 - nnz - array containing the number of nonzero blocks in the various block rows 3821 (possibly different for each block row) or `NULL` 3822 3823 Output Parameter: 3824 . A - the matrix 3825 3826 Options Database Keys: 3827 + -mat_no_unroll - uses code that does not unroll the loops in the block calculations (much slower) 3828 - -mat_block_size - size of the blocks to use 3829 3830 Level: intermediate 3831 3832 Notes: 3833 It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`, 3834 MatXXXXSetPreallocation() paradigm instead of this routine directly. 3835 [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`] 3836 3837 The number of rows and columns must be divisible by blocksize. 3838 3839 If the `nnz` parameter is given then the `nz` parameter is ignored 3840 3841 A nonzero block is any block that as 1 or more nonzeros in it 3842 3843 The `MATSEQBAIJ` format is fully compatible with standard Fortran 3844 storage. That is, the stored row and column indices can begin at 3845 either one (as in Fortran) or zero. 3846 3847 Specify the preallocated storage with either `nz` or `nnz` (not both). 3848 Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory 3849 allocation. See [Sparse Matrices](sec_matsparse) for details. 3850 matrices. 3851 3852 .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateBAIJ()` 3853 @*/ 3854 PetscErrorCode MatCreateSeqBAIJ(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A) 3855 { 3856 PetscFunctionBegin; 3857 PetscCall(MatCreate(comm, A)); 3858 PetscCall(MatSetSizes(*A, m, n, m, n)); 3859 PetscCall(MatSetType(*A, MATSEQBAIJ)); 3860 PetscCall(MatSeqBAIJSetPreallocation(*A, bs, nz, (PetscInt *)nnz)); 3861 PetscFunctionReturn(PETSC_SUCCESS); 3862 } 3863 3864 /*@C 3865 MatSeqBAIJSetPreallocation - Sets the block size and expected nonzeros 3866 per row in the matrix. For good matrix assembly performance the 3867 user should preallocate the matrix storage by setting the parameter `nz` 3868 (or the array `nnz`). 3869 3870 Collective 3871 3872 Input Parameters: 3873 + B - the matrix 3874 . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row 3875 blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()` 3876 . nz - number of block nonzeros per block row (same for all rows) 3877 - nnz - array containing the number of block nonzeros in the various block rows 3878 (possibly different for each block row) or `NULL` 3879 3880 Options Database Keys: 3881 + -mat_no_unroll - uses code that does not unroll the loops in the block calculations (much slower) 3882 - -mat_block_size - size of the blocks to use 3883 3884 Level: intermediate 3885 3886 Notes: 3887 If the `nnz` parameter is given then the `nz` parameter is ignored 3888 3889 You can call `MatGetInfo()` to get information on how effective the preallocation was; 3890 for example the fields mallocs,nz_allocated,nz_used,nz_unneeded; 3891 You can also run with the option `-info` and look for messages with the string 3892 malloc in them to see if additional memory allocation was needed. 3893 3894 The `MATSEQBAIJ` format is fully compatible with standard Fortran 3895 storage. That is, the stored row and column indices can begin at 3896 either one (as in Fortran) or zero. 3897 3898 Specify the preallocated storage with either `nz` or `nnz` (not both). 3899 Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory 3900 allocation. See [Sparse Matrices](sec_matsparse) for details. 3901 3902 .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`, `MatGetInfo()` 3903 @*/ 3904 PetscErrorCode MatSeqBAIJSetPreallocation(Mat B, PetscInt bs, PetscInt nz, const PetscInt nnz[]) 3905 { 3906 PetscFunctionBegin; 3907 PetscValidHeaderSpecific(B, MAT_CLASSID, 1); 3908 PetscValidType(B, 1); 3909 PetscValidLogicalCollectiveInt(B, bs, 2); 3910 PetscTryMethod(B, "MatSeqBAIJSetPreallocation_C", (Mat, PetscInt, PetscInt, const PetscInt[]), (B, bs, nz, nnz)); 3911 PetscFunctionReturn(PETSC_SUCCESS); 3912 } 3913 3914 /*@C 3915 MatSeqBAIJSetPreallocationCSR - Creates a sparse sequential matrix in `MATSEQBAIJ` format using the given nonzero structure and (optional) numerical values 3916 3917 Collective 3918 3919 Input Parameters: 3920 + B - the matrix 3921 . bs - the blocksize 3922 . i - the indices into `j` for the start of each local row (indices start with zero) 3923 . j - the column indices for each local row (indices start with zero) these must be sorted for each row 3924 - v - optional values in the matrix, use `NULL` if not provided 3925 3926 Level: advanced 3927 3928 Notes: 3929 The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqBAIJWithArrays()` 3930 3931 The order of the entries in values is specified by the `MatOption` `MAT_ROW_ORIENTED`. For example, C programs 3932 may want to use the default `MAT_ROW_ORIENTED` of `PETSC_TRUE` and use an array v[nnz][bs][bs] where the second index is 3933 over rows within a block and the last index is over columns within a block row. Fortran programs will likely set 3934 `MAT_ROW_ORIENTED` of `PETSC_FALSE` and use a Fortran array v(bs,bs,nnz) in which the first index is over rows within a 3935 block column and the second index is over columns within a block. 3936 3937 Though this routine has Preallocation() in the name it also sets the exact nonzero locations of the matrix entries and usually the numerical values as well 3938 3939 .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatSeqBAIJSetPreallocation()`, `MATSEQBAIJ` 3940 @*/ 3941 PetscErrorCode MatSeqBAIJSetPreallocationCSR(Mat B, PetscInt bs, const PetscInt i[], const PetscInt j[], const PetscScalar v[]) 3942 { 3943 PetscFunctionBegin; 3944 PetscValidHeaderSpecific(B, MAT_CLASSID, 1); 3945 PetscValidType(B, 1); 3946 PetscValidLogicalCollectiveInt(B, bs, 2); 3947 PetscTryMethod(B, "MatSeqBAIJSetPreallocationCSR_C", (Mat, PetscInt, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, bs, i, j, v)); 3948 PetscFunctionReturn(PETSC_SUCCESS); 3949 } 3950 3951 /*@ 3952 MatCreateSeqBAIJWithArrays - Creates a `MATSEQBAIJ` matrix using matrix elements provided by the user. 3953 3954 Collective 3955 3956 Input Parameters: 3957 + comm - must be an MPI communicator of size 1 3958 . bs - size of block 3959 . m - number of rows 3960 . n - number of columns 3961 . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row block row of the matrix 3962 . j - column indices 3963 - a - matrix values 3964 3965 Output Parameter: 3966 . mat - the matrix 3967 3968 Level: advanced 3969 3970 Notes: 3971 The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays 3972 once the matrix is destroyed 3973 3974 You cannot set new nonzero locations into this matrix, that will generate an error. 3975 3976 The `i` and `j` indices are 0 based 3977 3978 When block size is greater than 1 the matrix values must be stored using the `MATSEQBAIJ` storage format 3979 3980 The order of the entries in values is the same as the block compressed sparse row storage format; that is, it is 3981 the same as a three dimensional array in Fortran values(bs,bs,nnz) that contains the first column of the first 3982 block, followed by the second column of the first block etc etc. That is, the blocks are contiguous in memory 3983 with column-major ordering within blocks. 3984 3985 .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateBAIJ()`, `MatCreateSeqBAIJ()` 3986 @*/ 3987 PetscErrorCode MatCreateSeqBAIJWithArrays(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat) 3988 { 3989 Mat_SeqBAIJ *baij; 3990 3991 PetscFunctionBegin; 3992 PetscCheck(bs == 1, PETSC_COMM_SELF, PETSC_ERR_SUP, "block size %" PetscInt_FMT " > 1 is not supported yet", bs); 3993 if (m > 0) PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0"); 3994 3995 PetscCall(MatCreate(comm, mat)); 3996 PetscCall(MatSetSizes(*mat, m, n, m, n)); 3997 PetscCall(MatSetType(*mat, MATSEQBAIJ)); 3998 PetscCall(MatSeqBAIJSetPreallocation(*mat, bs, MAT_SKIP_ALLOCATION, NULL)); 3999 baij = (Mat_SeqBAIJ *)(*mat)->data; 4000 PetscCall(PetscMalloc2(m, &baij->imax, m, &baij->ilen)); 4001 4002 baij->i = i; 4003 baij->j = j; 4004 baij->a = a; 4005 4006 baij->singlemalloc = PETSC_FALSE; 4007 baij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/ 4008 baij->free_a = PETSC_FALSE; 4009 baij->free_ij = PETSC_FALSE; 4010 baij->free_imax_ilen = PETSC_TRUE; 4011 4012 for (PetscInt ii = 0; ii < m; ii++) { 4013 const PetscInt row_len = i[ii + 1] - i[ii]; 4014 4015 baij->ilen[ii] = baij->imax[ii] = row_len; 4016 PetscCheck(row_len >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row length in i (row indices) row = %" PetscInt_FMT " length = %" PetscInt_FMT, ii, row_len); 4017 } 4018 if (PetscDefined(USE_DEBUG)) { 4019 for (PetscInt ii = 0; ii < baij->i[m]; ii++) { 4020 PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]); 4021 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]); 4022 } 4023 } 4024 4025 PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY)); 4026 PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY)); 4027 PetscFunctionReturn(PETSC_SUCCESS); 4028 } 4029 4030 PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqBAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat) 4031 { 4032 PetscFunctionBegin; 4033 PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIBAIJ(comm, inmat, n, scall, outmat)); 4034 PetscFunctionReturn(PETSC_SUCCESS); 4035 } 4036