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 PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 2648 bs = A->rmap->bs; 2649 aa = a->a; 2650 ai = a->i; 2651 aj = a->j; 2652 mbs = a->mbs; 2653 2654 PetscCall(VecSet(v, zero)); 2655 PetscCall(VecGetArray(v, &x)); 2656 PetscCall(VecGetLocalSize(v, &n)); 2657 PetscCheck(n == A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector"); 2658 for (i = 0; i < mbs; i++) { 2659 ncols = ai[1] - ai[0]; 2660 ai++; 2661 brow = bs * i; 2662 for (j = 0; j < ncols; j++) { 2663 for (kcol = 0; kcol < bs; kcol++) { 2664 for (krow = 0; krow < bs; krow++) { 2665 atmp = PetscAbsScalar(*aa); 2666 aa++; 2667 row = brow + krow; /* row index */ 2668 if (PetscAbsScalar(x[row]) < atmp) { 2669 x[row] = atmp; 2670 if (idx) idx[row] = bs * (*aj) + kcol; 2671 } 2672 } 2673 } 2674 aj++; 2675 } 2676 } 2677 PetscCall(VecRestoreArray(v, &x)); 2678 PetscFunctionReturn(PETSC_SUCCESS); 2679 } 2680 2681 static PetscErrorCode MatGetRowSumAbs_SeqBAIJ(Mat A, Vec v) 2682 { 2683 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2684 PetscInt i, j, n, row, bs, *ai, mbs; 2685 PetscReal atmp; 2686 PetscScalar *x, zero = 0.0; 2687 MatScalar *aa; 2688 PetscInt ncols, brow, krow, kcol; 2689 2690 PetscFunctionBegin; 2691 PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 2692 bs = A->rmap->bs; 2693 aa = a->a; 2694 ai = a->i; 2695 mbs = a->mbs; 2696 2697 PetscCall(VecSet(v, zero)); 2698 PetscCall(VecGetArrayWrite(v, &x)); 2699 PetscCall(VecGetLocalSize(v, &n)); 2700 PetscCheck(n == A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector"); 2701 for (i = 0; i < mbs; i++) { 2702 ncols = ai[1] - ai[0]; 2703 ai++; 2704 brow = bs * i; 2705 for (j = 0; j < ncols; j++) { 2706 for (kcol = 0; kcol < bs; kcol++) { 2707 for (krow = 0; krow < bs; krow++) { 2708 atmp = PetscAbsScalar(*aa); 2709 aa++; 2710 row = brow + krow; /* row index */ 2711 x[row] += atmp; 2712 } 2713 } 2714 } 2715 } 2716 PetscCall(VecRestoreArrayWrite(v, &x)); 2717 PetscFunctionReturn(PETSC_SUCCESS); 2718 } 2719 2720 static PetscErrorCode MatCopy_SeqBAIJ(Mat A, Mat B, MatStructure str) 2721 { 2722 PetscFunctionBegin; 2723 /* If the two matrices have the same copy implementation, use fast copy. */ 2724 if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) { 2725 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2726 Mat_SeqBAIJ *b = (Mat_SeqBAIJ *)B->data; 2727 PetscInt ambs = a->mbs, bmbs = b->mbs, abs = A->rmap->bs, bbs = B->rmap->bs, bs2 = abs * abs; 2728 2729 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]); 2730 PetscCheck(abs == bbs, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Block size A %" PetscInt_FMT " and B %" PetscInt_FMT " are different", abs, bbs); 2731 PetscCall(PetscArraycpy(b->a, a->a, bs2 * a->i[ambs])); 2732 PetscCall(PetscObjectStateIncrease((PetscObject)B)); 2733 } else { 2734 PetscCall(MatCopy_Basic(A, B, str)); 2735 } 2736 PetscFunctionReturn(PETSC_SUCCESS); 2737 } 2738 2739 static PetscErrorCode MatSeqBAIJGetArray_SeqBAIJ(Mat A, PetscScalar *array[]) 2740 { 2741 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2742 2743 PetscFunctionBegin; 2744 *array = a->a; 2745 PetscFunctionReturn(PETSC_SUCCESS); 2746 } 2747 2748 static PetscErrorCode MatSeqBAIJRestoreArray_SeqBAIJ(Mat A, PetscScalar *array[]) 2749 { 2750 PetscFunctionBegin; 2751 *array = NULL; 2752 PetscFunctionReturn(PETSC_SUCCESS); 2753 } 2754 2755 PetscErrorCode MatAXPYGetPreallocation_SeqBAIJ(Mat Y, Mat X, PetscInt *nnz) 2756 { 2757 PetscInt bs = Y->rmap->bs, mbs = Y->rmap->N / bs; 2758 Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data; 2759 Mat_SeqBAIJ *y = (Mat_SeqBAIJ *)Y->data; 2760 2761 PetscFunctionBegin; 2762 /* Set the number of nonzeros in the new matrix */ 2763 PetscCall(MatAXPYGetPreallocation_SeqX_private(mbs, x->i, x->j, y->i, y->j, nnz)); 2764 PetscFunctionReturn(PETSC_SUCCESS); 2765 } 2766 2767 PetscErrorCode MatAXPY_SeqBAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str) 2768 { 2769 Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data, *y = (Mat_SeqBAIJ *)Y->data; 2770 PetscInt bs = Y->rmap->bs, bs2 = bs * bs; 2771 PetscBLASInt one = 1; 2772 2773 PetscFunctionBegin; 2774 if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) { 2775 PetscBool e = x->nz == y->nz && x->mbs == y->mbs && bs == X->rmap->bs ? PETSC_TRUE : PETSC_FALSE; 2776 if (e) { 2777 PetscCall(PetscArraycmp(x->i, y->i, x->mbs + 1, &e)); 2778 if (e) { 2779 PetscCall(PetscArraycmp(x->j, y->j, x->i[x->mbs], &e)); 2780 if (e) str = SAME_NONZERO_PATTERN; 2781 } 2782 } 2783 if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN"); 2784 } 2785 if (str == SAME_NONZERO_PATTERN) { 2786 PetscScalar alpha = a; 2787 PetscBLASInt bnz; 2788 PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz)); 2789 PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one)); 2790 PetscCall(PetscObjectStateIncrease((PetscObject)Y)); 2791 } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */ 2792 PetscCall(MatAXPY_Basic(Y, a, X, str)); 2793 } else { 2794 Mat B; 2795 PetscInt *nnz; 2796 PetscCheck(bs == X->rmap->bs, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrices must have same block size"); 2797 PetscCall(PetscMalloc1(Y->rmap->N, &nnz)); 2798 PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B)); 2799 PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name)); 2800 PetscCall(MatSetSizes(B, Y->rmap->n, Y->cmap->n, Y->rmap->N, Y->cmap->N)); 2801 PetscCall(MatSetBlockSizesFromMats(B, Y, Y)); 2802 PetscCall(MatSetType(B, (MatType)((PetscObject)Y)->type_name)); 2803 PetscCall(MatAXPYGetPreallocation_SeqBAIJ(Y, X, nnz)); 2804 PetscCall(MatSeqBAIJSetPreallocation(B, bs, 0, nnz)); 2805 PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str)); 2806 PetscCall(MatHeaderMerge(Y, &B)); 2807 PetscCall(PetscFree(nnz)); 2808 } 2809 PetscFunctionReturn(PETSC_SUCCESS); 2810 } 2811 2812 PETSC_INTERN PetscErrorCode MatConjugate_SeqBAIJ(Mat A) 2813 { 2814 #if PetscDefined(USE_COMPLEX) 2815 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2816 PetscInt i, nz = a->bs2 * a->i[a->mbs]; 2817 MatScalar *aa = a->a; 2818 2819 PetscFunctionBegin; 2820 for (i = 0; i < nz; i++) aa[i] = PetscConj(aa[i]); 2821 PetscFunctionReturn(PETSC_SUCCESS); 2822 #else 2823 (void)A; 2824 return PETSC_SUCCESS; 2825 #endif 2826 } 2827 2828 static PetscErrorCode MatRealPart_SeqBAIJ(Mat A) 2829 { 2830 #if PetscDefined(USE_COMPLEX) 2831 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2832 PetscInt i, nz = a->bs2 * a->i[a->mbs]; 2833 MatScalar *aa = a->a; 2834 2835 PetscFunctionBegin; 2836 for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]); 2837 PetscFunctionReturn(PETSC_SUCCESS); 2838 #else 2839 (void)A; 2840 return PETSC_SUCCESS; 2841 #endif 2842 } 2843 2844 static PetscErrorCode MatImaginaryPart_SeqBAIJ(Mat A) 2845 { 2846 #if PetscDefined(USE_COMPLEX) 2847 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2848 PetscInt i, nz = a->bs2 * a->i[a->mbs]; 2849 MatScalar *aa = a->a; 2850 2851 PetscFunctionBegin; 2852 for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]); 2853 PetscFunctionReturn(PETSC_SUCCESS); 2854 #else 2855 (void)A; 2856 return PETSC_SUCCESS; 2857 #endif 2858 } 2859 2860 /* 2861 Code almost identical to MatGetColumnIJ_SeqAIJ() should share common code 2862 */ 2863 static PetscErrorCode MatGetColumnIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done) 2864 { 2865 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2866 PetscInt bs = A->rmap->bs, i, *collengths, *cia, *cja, n = A->cmap->n / bs, m = A->rmap->n / bs; 2867 PetscInt nz = a->i[m], row, *jj, mr, col; 2868 2869 PetscFunctionBegin; 2870 *nn = n; 2871 if (!ia) PetscFunctionReturn(PETSC_SUCCESS); 2872 PetscCheck(!symmetric, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not for BAIJ matrices"); 2873 PetscCall(PetscCalloc1(n, &collengths)); 2874 PetscCall(PetscMalloc1(n + 1, &cia)); 2875 PetscCall(PetscMalloc1(nz, &cja)); 2876 jj = a->j; 2877 for (i = 0; i < nz; i++) collengths[jj[i]]++; 2878 cia[0] = oshift; 2879 for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i]; 2880 PetscCall(PetscArrayzero(collengths, n)); 2881 jj = a->j; 2882 for (row = 0; row < m; row++) { 2883 mr = a->i[row + 1] - a->i[row]; 2884 for (i = 0; i < mr; i++) { 2885 col = *jj++; 2886 2887 cja[cia[col] + collengths[col]++ - oshift] = row + oshift; 2888 } 2889 } 2890 PetscCall(PetscFree(collengths)); 2891 *ia = cia; 2892 *ja = cja; 2893 PetscFunctionReturn(PETSC_SUCCESS); 2894 } 2895 2896 static PetscErrorCode MatRestoreColumnIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done) 2897 { 2898 PetscFunctionBegin; 2899 if (!ia) PetscFunctionReturn(PETSC_SUCCESS); 2900 PetscCall(PetscFree(*ia)); 2901 PetscCall(PetscFree(*ja)); 2902 PetscFunctionReturn(PETSC_SUCCESS); 2903 } 2904 2905 /* 2906 MatGetColumnIJ_SeqBAIJ_Color() and MatRestoreColumnIJ_SeqBAIJ_Color() are customized from 2907 MatGetColumnIJ_SeqBAIJ() and MatRestoreColumnIJ_SeqBAIJ() by adding an output 2908 spidx[], index of a->a, to be used in MatTransposeColoringCreate() and MatFDColoringCreate() 2909 */ 2910 PetscErrorCode MatGetColumnIJ_SeqBAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done) 2911 { 2912 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2913 PetscInt i, *collengths, *cia, *cja, n = a->nbs, m = a->mbs; 2914 PetscInt nz = a->i[m], row, *jj, mr, col; 2915 PetscInt *cspidx; 2916 2917 PetscFunctionBegin; 2918 *nn = n; 2919 if (!ia) PetscFunctionReturn(PETSC_SUCCESS); 2920 2921 PetscCall(PetscCalloc1(n, &collengths)); 2922 PetscCall(PetscMalloc1(n + 1, &cia)); 2923 PetscCall(PetscMalloc1(nz, &cja)); 2924 PetscCall(PetscMalloc1(nz, &cspidx)); 2925 jj = a->j; 2926 for (i = 0; i < nz; i++) collengths[jj[i]]++; 2927 cia[0] = oshift; 2928 for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i]; 2929 PetscCall(PetscArrayzero(collengths, n)); 2930 jj = a->j; 2931 for (row = 0; row < m; row++) { 2932 mr = a->i[row + 1] - a->i[row]; 2933 for (i = 0; i < mr; i++) { 2934 col = *jj++; 2935 cspidx[cia[col] + collengths[col] - oshift] = a->i[row] + i; /* index of a->j */ 2936 cja[cia[col] + collengths[col]++ - oshift] = row + oshift; 2937 } 2938 } 2939 PetscCall(PetscFree(collengths)); 2940 *ia = cia; 2941 *ja = cja; 2942 *spidx = cspidx; 2943 PetscFunctionReturn(PETSC_SUCCESS); 2944 } 2945 2946 PetscErrorCode MatRestoreColumnIJ_SeqBAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done) 2947 { 2948 PetscFunctionBegin; 2949 PetscCall(MatRestoreColumnIJ_SeqBAIJ(A, oshift, symmetric, inodecompressed, n, ia, ja, done)); 2950 PetscCall(PetscFree(*spidx)); 2951 PetscFunctionReturn(PETSC_SUCCESS); 2952 } 2953 2954 static PetscErrorCode MatShift_SeqBAIJ(Mat Y, PetscScalar a) 2955 { 2956 Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)Y->data; 2957 2958 PetscFunctionBegin; 2959 if (!Y->preallocated || !aij->nz) PetscCall(MatSeqBAIJSetPreallocation(Y, Y->rmap->bs, 1, NULL)); 2960 PetscCall(MatShift_Basic(Y, a)); 2961 PetscFunctionReturn(PETSC_SUCCESS); 2962 } 2963 2964 PetscErrorCode MatEliminateZeros_SeqBAIJ(Mat A, PetscBool keep) 2965 { 2966 Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data; 2967 PetscInt fshift = 0, fshift_prev = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax, j, k; 2968 PetscInt m = A->rmap->N, *ailen = a->ilen; 2969 PetscInt mbs = a->mbs, bs2 = a->bs2, rmax = 0; 2970 MatScalar *aa = a->a, *ap; 2971 PetscBool zero; 2972 2973 PetscFunctionBegin; 2974 PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix"); 2975 if (m) rmax = ailen[0]; 2976 for (i = 1; i <= mbs; i++) { 2977 for (k = ai[i - 1]; k < ai[i]; k++) { 2978 zero = PETSC_TRUE; 2979 ap = aa + bs2 * k; 2980 for (j = 0; j < bs2 && zero; j++) { 2981 if (ap[j] != 0.0) zero = PETSC_FALSE; 2982 } 2983 if (zero && (aj[k] != i - 1 || !keep)) fshift++; 2984 else { 2985 if (zero && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal block at row %" PetscInt_FMT "\n", i - 1)); 2986 aj[k - fshift] = aj[k]; 2987 PetscCall(PetscArraymove(ap - bs2 * fshift, ap, bs2)); 2988 } 2989 } 2990 ai[i - 1] -= fshift_prev; 2991 fshift_prev = fshift; 2992 ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1]; 2993 a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0); 2994 rmax = PetscMax(rmax, ailen[i - 1]); 2995 } 2996 if (fshift) { 2997 if (mbs) { 2998 ai[mbs] -= fshift; 2999 a->nz = ai[mbs]; 3000 } 3001 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)); 3002 A->nonzerostate++; 3003 A->info.nz_unneeded += (PetscReal)fshift; 3004 a->rmax = rmax; 3005 PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY)); 3006 PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY)); 3007 } 3008 PetscFunctionReturn(PETSC_SUCCESS); 3009 } 3010 3011 static struct _MatOps MatOps_Values = {MatSetValues_SeqBAIJ, 3012 MatGetRow_SeqBAIJ, 3013 MatRestoreRow_SeqBAIJ, 3014 MatMult_SeqBAIJ_N, 3015 /* 4*/ MatMultAdd_SeqBAIJ_N, 3016 MatMultTranspose_SeqBAIJ, 3017 MatMultTransposeAdd_SeqBAIJ, 3018 NULL, 3019 NULL, 3020 NULL, 3021 /* 10*/ NULL, 3022 MatLUFactor_SeqBAIJ, 3023 NULL, 3024 NULL, 3025 MatTranspose_SeqBAIJ, 3026 /* 15*/ MatGetInfo_SeqBAIJ, 3027 MatEqual_SeqBAIJ, 3028 MatGetDiagonal_SeqBAIJ, 3029 MatDiagonalScale_SeqBAIJ, 3030 MatNorm_SeqBAIJ, 3031 /* 20*/ NULL, 3032 MatAssemblyEnd_SeqBAIJ, 3033 MatSetOption_SeqBAIJ, 3034 MatZeroEntries_SeqBAIJ, 3035 /* 24*/ MatZeroRows_SeqBAIJ, 3036 NULL, 3037 NULL, 3038 NULL, 3039 NULL, 3040 /* 29*/ MatSetUp_Seq_Hash, 3041 NULL, 3042 NULL, 3043 NULL, 3044 NULL, 3045 /* 34*/ MatDuplicate_SeqBAIJ, 3046 NULL, 3047 NULL, 3048 MatILUFactor_SeqBAIJ, 3049 NULL, 3050 /* 39*/ MatAXPY_SeqBAIJ, 3051 MatCreateSubMatrices_SeqBAIJ, 3052 MatIncreaseOverlap_SeqBAIJ, 3053 MatGetValues_SeqBAIJ, 3054 MatCopy_SeqBAIJ, 3055 /* 44*/ NULL, 3056 MatScale_SeqBAIJ, 3057 MatShift_SeqBAIJ, 3058 NULL, 3059 MatZeroRowsColumns_SeqBAIJ, 3060 /* 49*/ NULL, 3061 MatGetRowIJ_SeqBAIJ, 3062 MatRestoreRowIJ_SeqBAIJ, 3063 MatGetColumnIJ_SeqBAIJ, 3064 MatRestoreColumnIJ_SeqBAIJ, 3065 /* 54*/ MatFDColoringCreate_SeqXAIJ, 3066 NULL, 3067 NULL, 3068 NULL, 3069 MatSetValuesBlocked_SeqBAIJ, 3070 /* 59*/ MatCreateSubMatrix_SeqBAIJ, 3071 MatDestroy_SeqBAIJ, 3072 MatView_SeqBAIJ, 3073 NULL, 3074 NULL, 3075 /* 64*/ NULL, 3076 NULL, 3077 NULL, 3078 NULL, 3079 NULL, 3080 /* 69*/ MatGetRowMaxAbs_SeqBAIJ, 3081 NULL, 3082 MatConvert_Basic, 3083 NULL, 3084 NULL, 3085 /* 74*/ NULL, 3086 MatFDColoringApply_BAIJ, 3087 NULL, 3088 NULL, 3089 NULL, 3090 /* 79*/ NULL, 3091 NULL, 3092 NULL, 3093 NULL, 3094 MatLoad_SeqBAIJ, 3095 /* 84*/ NULL, 3096 NULL, 3097 NULL, 3098 NULL, 3099 NULL, 3100 /* 89*/ NULL, 3101 NULL, 3102 NULL, 3103 NULL, 3104 NULL, 3105 /* 94*/ NULL, 3106 NULL, 3107 NULL, 3108 NULL, 3109 NULL, 3110 /* 99*/ NULL, 3111 NULL, 3112 NULL, 3113 MatConjugate_SeqBAIJ, 3114 NULL, 3115 /*104*/ NULL, 3116 MatRealPart_SeqBAIJ, 3117 MatImaginaryPart_SeqBAIJ, 3118 NULL, 3119 NULL, 3120 /*109*/ NULL, 3121 NULL, 3122 NULL, 3123 NULL, 3124 MatMissingDiagonal_SeqBAIJ, 3125 /*114*/ NULL, 3126 NULL, 3127 NULL, 3128 NULL, 3129 NULL, 3130 /*119*/ NULL, 3131 NULL, 3132 MatMultHermitianTranspose_SeqBAIJ, 3133 MatMultHermitianTransposeAdd_SeqBAIJ, 3134 NULL, 3135 /*124*/ NULL, 3136 MatGetColumnReductions_SeqBAIJ, 3137 MatInvertBlockDiagonal_SeqBAIJ, 3138 NULL, 3139 NULL, 3140 /*129*/ NULL, 3141 NULL, 3142 NULL, 3143 NULL, 3144 NULL, 3145 /*134*/ NULL, 3146 NULL, 3147 NULL, 3148 NULL, 3149 NULL, 3150 /*139*/ MatSetBlockSizes_Default, 3151 NULL, 3152 NULL, 3153 MatFDColoringSetUp_SeqXAIJ, 3154 NULL, 3155 /*144*/ MatCreateMPIMatConcatenateSeqMat_SeqBAIJ, 3156 MatDestroySubMatrices_SeqBAIJ, 3157 NULL, 3158 NULL, 3159 NULL, 3160 NULL, 3161 /*150*/ NULL, 3162 MatEliminateZeros_SeqBAIJ, 3163 MatGetRowSumAbs_SeqBAIJ}; 3164 3165 static PetscErrorCode MatStoreValues_SeqBAIJ(Mat mat) 3166 { 3167 Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data; 3168 PetscInt nz = aij->i[aij->mbs] * aij->bs2; 3169 3170 PetscFunctionBegin; 3171 PetscCheck(aij->nonew == 1, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first"); 3172 3173 /* allocate space for values if not already there */ 3174 if (!aij->saved_values) PetscCall(PetscMalloc1(nz + 1, &aij->saved_values)); 3175 3176 /* copy values over */ 3177 PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz)); 3178 PetscFunctionReturn(PETSC_SUCCESS); 3179 } 3180 3181 static PetscErrorCode MatRetrieveValues_SeqBAIJ(Mat mat) 3182 { 3183 Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data; 3184 PetscInt nz = aij->i[aij->mbs] * aij->bs2; 3185 3186 PetscFunctionBegin; 3187 PetscCheck(aij->nonew == 1, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first"); 3188 PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first"); 3189 3190 /* copy values over */ 3191 PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz)); 3192 PetscFunctionReturn(PETSC_SUCCESS); 3193 } 3194 3195 PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat, MatType, MatReuse, Mat *); 3196 PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqSBAIJ(Mat, MatType, MatReuse, Mat *); 3197 3198 PetscErrorCode MatSeqBAIJSetPreallocation_SeqBAIJ(Mat B, PetscInt bs, PetscInt nz, const PetscInt nnz[]) 3199 { 3200 Mat_SeqBAIJ *b = (Mat_SeqBAIJ *)B->data; 3201 PetscInt i, mbs, nbs, bs2; 3202 PetscBool flg = PETSC_FALSE, skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE; 3203 3204 PetscFunctionBegin; 3205 if (B->hash_active) { 3206 PetscInt bs; 3207 B->ops[0] = b->cops; 3208 PetscCall(PetscHMapIJVDestroy(&b->ht)); 3209 PetscCall(MatGetBlockSize(B, &bs)); 3210 if (bs > 1) PetscCall(PetscHSetIJDestroy(&b->bht)); 3211 PetscCall(PetscFree(b->dnz)); 3212 PetscCall(PetscFree(b->bdnz)); 3213 B->hash_active = PETSC_FALSE; 3214 } 3215 if (nz >= 0 || nnz) realalloc = PETSC_TRUE; 3216 if (nz == MAT_SKIP_ALLOCATION) { 3217 skipallocation = PETSC_TRUE; 3218 nz = 0; 3219 } 3220 3221 PetscCall(MatSetBlockSize(B, PetscAbs(bs))); 3222 PetscCall(PetscLayoutSetUp(B->rmap)); 3223 PetscCall(PetscLayoutSetUp(B->cmap)); 3224 PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs)); 3225 3226 B->preallocated = PETSC_TRUE; 3227 3228 mbs = B->rmap->n / bs; 3229 nbs = B->cmap->n / bs; 3230 bs2 = bs * bs; 3231 3232 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); 3233 3234 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5; 3235 PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz); 3236 if (nnz) { 3237 for (i = 0; i < mbs; i++) { 3238 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]); 3239 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); 3240 } 3241 } 3242 3243 PetscOptionsBegin(PetscObjectComm((PetscObject)B), NULL, "Optimize options for SEQBAIJ matrix 2 ", "Mat"); 3244 PetscCall(PetscOptionsBool("-mat_no_unroll", "Do not optimize for block size (slow)", NULL, flg, &flg, NULL)); 3245 PetscOptionsEnd(); 3246 3247 if (!flg) { 3248 switch (bs) { 3249 case 1: 3250 B->ops->mult = MatMult_SeqBAIJ_1; 3251 B->ops->multadd = MatMultAdd_SeqBAIJ_1; 3252 break; 3253 case 2: 3254 B->ops->mult = MatMult_SeqBAIJ_2; 3255 B->ops->multadd = MatMultAdd_SeqBAIJ_2; 3256 break; 3257 case 3: 3258 B->ops->mult = MatMult_SeqBAIJ_3; 3259 B->ops->multadd = MatMultAdd_SeqBAIJ_3; 3260 break; 3261 case 4: 3262 B->ops->mult = MatMult_SeqBAIJ_4; 3263 B->ops->multadd = MatMultAdd_SeqBAIJ_4; 3264 break; 3265 case 5: 3266 B->ops->mult = MatMult_SeqBAIJ_5; 3267 B->ops->multadd = MatMultAdd_SeqBAIJ_5; 3268 break; 3269 case 6: 3270 B->ops->mult = MatMult_SeqBAIJ_6; 3271 B->ops->multadd = MatMultAdd_SeqBAIJ_6; 3272 break; 3273 case 7: 3274 B->ops->mult = MatMult_SeqBAIJ_7; 3275 B->ops->multadd = MatMultAdd_SeqBAIJ_7; 3276 break; 3277 case 9: { 3278 PetscInt version = 1; 3279 PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)B)->prefix, "-mat_baij_mult_version", &version, NULL)); 3280 switch (version) { 3281 #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES) 3282 case 1: 3283 B->ops->mult = MatMult_SeqBAIJ_9_AVX2; 3284 B->ops->multadd = MatMultAdd_SeqBAIJ_9_AVX2; 3285 PetscCall(PetscInfo((PetscObject)B, "Using AVX2 for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs)); 3286 break; 3287 #endif 3288 default: 3289 B->ops->mult = MatMult_SeqBAIJ_N; 3290 B->ops->multadd = MatMultAdd_SeqBAIJ_N; 3291 PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs)); 3292 break; 3293 } 3294 break; 3295 } 3296 case 11: 3297 B->ops->mult = MatMult_SeqBAIJ_11; 3298 B->ops->multadd = MatMultAdd_SeqBAIJ_11; 3299 break; 3300 case 12: { 3301 PetscInt version = 1; 3302 PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)B)->prefix, "-mat_baij_mult_version", &version, NULL)); 3303 switch (version) { 3304 case 1: 3305 B->ops->mult = MatMult_SeqBAIJ_12_ver1; 3306 B->ops->multadd = MatMultAdd_SeqBAIJ_12_ver1; 3307 PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs)); 3308 break; 3309 case 2: 3310 B->ops->mult = MatMult_SeqBAIJ_12_ver2; 3311 B->ops->multadd = MatMultAdd_SeqBAIJ_12_ver2; 3312 PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs)); 3313 break; 3314 #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES) 3315 case 3: 3316 B->ops->mult = MatMult_SeqBAIJ_12_AVX2; 3317 B->ops->multadd = MatMultAdd_SeqBAIJ_12_ver1; 3318 PetscCall(PetscInfo((PetscObject)B, "Using AVX2 for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs)); 3319 break; 3320 #endif 3321 default: 3322 B->ops->mult = MatMult_SeqBAIJ_N; 3323 B->ops->multadd = MatMultAdd_SeqBAIJ_N; 3324 PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs)); 3325 break; 3326 } 3327 break; 3328 } 3329 case 15: { 3330 PetscInt version = 1; 3331 PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)B)->prefix, "-mat_baij_mult_version", &version, NULL)); 3332 switch (version) { 3333 case 1: 3334 B->ops->mult = MatMult_SeqBAIJ_15_ver1; 3335 PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs)); 3336 break; 3337 case 2: 3338 B->ops->mult = MatMult_SeqBAIJ_15_ver2; 3339 PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs)); 3340 break; 3341 case 3: 3342 B->ops->mult = MatMult_SeqBAIJ_15_ver3; 3343 PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs)); 3344 break; 3345 case 4: 3346 B->ops->mult = MatMult_SeqBAIJ_15_ver4; 3347 PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs)); 3348 break; 3349 default: 3350 B->ops->mult = MatMult_SeqBAIJ_N; 3351 PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs)); 3352 break; 3353 } 3354 B->ops->multadd = MatMultAdd_SeqBAIJ_N; 3355 break; 3356 } 3357 default: 3358 B->ops->mult = MatMult_SeqBAIJ_N; 3359 B->ops->multadd = MatMultAdd_SeqBAIJ_N; 3360 PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs)); 3361 break; 3362 } 3363 } 3364 B->ops->sor = MatSOR_SeqBAIJ; 3365 b->mbs = mbs; 3366 b->nbs = nbs; 3367 if (!skipallocation) { 3368 if (!b->imax) { 3369 PetscCall(PetscMalloc2(mbs, &b->imax, mbs, &b->ilen)); 3370 3371 b->free_imax_ilen = PETSC_TRUE; 3372 } 3373 /* b->ilen will count nonzeros in each block row so far. */ 3374 for (i = 0; i < mbs; i++) b->ilen[i] = 0; 3375 if (!nnz) { 3376 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5; 3377 else if (nz < 0) nz = 1; 3378 nz = PetscMin(nz, nbs); 3379 for (i = 0; i < mbs; i++) b->imax[i] = nz; 3380 PetscCall(PetscIntMultError(nz, mbs, &nz)); 3381 } else { 3382 PetscInt64 nz64 = 0; 3383 for (i = 0; i < mbs; i++) { 3384 b->imax[i] = nnz[i]; 3385 nz64 += nnz[i]; 3386 } 3387 PetscCall(PetscIntCast(nz64, &nz)); 3388 } 3389 3390 /* allocate the matrix space */ 3391 PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i)); 3392 if (B->structure_only) { 3393 PetscCall(PetscMalloc1(nz, &b->j)); 3394 PetscCall(PetscMalloc1(B->rmap->N + 1, &b->i)); 3395 } else { 3396 PetscInt nzbs2 = 0; 3397 PetscCall(PetscIntMultError(nz, bs2, &nzbs2)); 3398 PetscCall(PetscMalloc3(nzbs2, &b->a, nz, &b->j, B->rmap->N + 1, &b->i)); 3399 PetscCall(PetscArrayzero(b->a, nz * bs2)); 3400 } 3401 PetscCall(PetscArrayzero(b->j, nz)); 3402 3403 if (B->structure_only) { 3404 b->singlemalloc = PETSC_FALSE; 3405 b->free_a = PETSC_FALSE; 3406 } else { 3407 b->singlemalloc = PETSC_TRUE; 3408 b->free_a = PETSC_TRUE; 3409 } 3410 b->free_ij = PETSC_TRUE; 3411 3412 b->i[0] = 0; 3413 for (i = 1; i < mbs + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1]; 3414 3415 } else { 3416 b->free_a = PETSC_FALSE; 3417 b->free_ij = PETSC_FALSE; 3418 } 3419 3420 b->bs2 = bs2; 3421 b->mbs = mbs; 3422 b->nz = 0; 3423 b->maxnz = nz; 3424 B->info.nz_unneeded = (PetscReal)b->maxnz * bs2; 3425 B->was_assembled = PETSC_FALSE; 3426 B->assembled = PETSC_FALSE; 3427 if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE)); 3428 PetscFunctionReturn(PETSC_SUCCESS); 3429 } 3430 3431 static PetscErrorCode MatSeqBAIJSetPreallocationCSR_SeqBAIJ(Mat B, PetscInt bs, const PetscInt ii[], const PetscInt jj[], const PetscScalar V[]) 3432 { 3433 PetscInt i, m, nz, nz_max = 0, *nnz; 3434 PetscScalar *values = NULL; 3435 PetscBool roworiented = ((Mat_SeqBAIJ *)B->data)->roworiented; 3436 3437 PetscFunctionBegin; 3438 PetscCheck(bs >= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Invalid block size specified, must be positive but it is %" PetscInt_FMT, bs); 3439 PetscCall(PetscLayoutSetBlockSize(B->rmap, bs)); 3440 PetscCall(PetscLayoutSetBlockSize(B->cmap, bs)); 3441 PetscCall(PetscLayoutSetUp(B->rmap)); 3442 PetscCall(PetscLayoutSetUp(B->cmap)); 3443 PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs)); 3444 m = B->rmap->n / bs; 3445 3446 PetscCheck(ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "ii[0] must be 0 but it is %" PetscInt_FMT, ii[0]); 3447 PetscCall(PetscMalloc1(m + 1, &nnz)); 3448 for (i = 0; i < m; i++) { 3449 nz = ii[i + 1] - ii[i]; 3450 PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz); 3451 nz_max = PetscMax(nz_max, nz); 3452 nnz[i] = nz; 3453 } 3454 PetscCall(MatSeqBAIJSetPreallocation(B, bs, 0, nnz)); 3455 PetscCall(PetscFree(nnz)); 3456 3457 values = (PetscScalar *)V; 3458 if (!values) PetscCall(PetscCalloc1(bs * bs * (nz_max + 1), &values)); 3459 for (i = 0; i < m; i++) { 3460 PetscInt ncols = ii[i + 1] - ii[i]; 3461 const PetscInt *icols = jj + ii[i]; 3462 if (bs == 1 || !roworiented) { 3463 const PetscScalar *svals = values + (V ? (bs * bs * ii[i]) : 0); 3464 PetscCall(MatSetValuesBlocked_SeqBAIJ(B, 1, &i, ncols, icols, svals, INSERT_VALUES)); 3465 } else { 3466 PetscInt j; 3467 for (j = 0; j < ncols; j++) { 3468 const PetscScalar *svals = values + (V ? (bs * bs * (ii[i] + j)) : 0); 3469 PetscCall(MatSetValuesBlocked_SeqBAIJ(B, 1, &i, 1, &icols[j], svals, INSERT_VALUES)); 3470 } 3471 } 3472 } 3473 if (!V) PetscCall(PetscFree(values)); 3474 PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY)); 3475 PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY)); 3476 PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE)); 3477 PetscFunctionReturn(PETSC_SUCCESS); 3478 } 3479 3480 /*@C 3481 MatSeqBAIJGetArray - gives read/write access to the array where the data for a `MATSEQBAIJ` matrix is stored 3482 3483 Not Collective 3484 3485 Input Parameter: 3486 . A - a `MATSEQBAIJ` matrix 3487 3488 Output Parameter: 3489 . array - pointer to the data 3490 3491 Level: intermediate 3492 3493 .seealso: [](ch_matrices), `Mat`, `MATSEQBAIJ`, `MatSeqBAIJRestoreArray()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()` 3494 @*/ 3495 PetscErrorCode MatSeqBAIJGetArray(Mat A, PetscScalar **array) 3496 { 3497 PetscFunctionBegin; 3498 PetscUseMethod(A, "MatSeqBAIJGetArray_C", (Mat, PetscScalar **), (A, array)); 3499 PetscFunctionReturn(PETSC_SUCCESS); 3500 } 3501 3502 /*@C 3503 MatSeqBAIJRestoreArray - returns access to the array where the data for a `MATSEQBAIJ` matrix is stored obtained by `MatSeqBAIJGetArray()` 3504 3505 Not Collective 3506 3507 Input Parameters: 3508 + A - a `MATSEQBAIJ` matrix 3509 - array - pointer to the data 3510 3511 Level: intermediate 3512 3513 .seealso: [](ch_matrices), `Mat`, `MatSeqBAIJGetArray()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()` 3514 @*/ 3515 PetscErrorCode MatSeqBAIJRestoreArray(Mat A, PetscScalar **array) 3516 { 3517 PetscFunctionBegin; 3518 PetscUseMethod(A, "MatSeqBAIJRestoreArray_C", (Mat, PetscScalar **), (A, array)); 3519 PetscFunctionReturn(PETSC_SUCCESS); 3520 } 3521 3522 /*MC 3523 MATSEQBAIJ - MATSEQBAIJ = "seqbaij" - A matrix type to be used for sequential block sparse matrices, based on 3524 block sparse compressed row format. 3525 3526 Options Database Keys: 3527 + -mat_type seqbaij - sets the matrix type to `MATSEQBAIJ` during a call to `MatSetFromOptions()` 3528 - -mat_baij_mult_version version - indicate the version of the matrix-vector product to use (0 often indicates using BLAS) 3529 3530 Level: beginner 3531 3532 Notes: 3533 `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no 3534 space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored 3535 3536 Run with `-info` to see what version of the matrix-vector product is being used 3537 3538 .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()` 3539 M*/ 3540 3541 PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBSTRM(Mat, MatType, MatReuse, Mat *); 3542 3543 PETSC_EXTERN PetscErrorCode MatCreate_SeqBAIJ(Mat B) 3544 { 3545 PetscMPIInt size; 3546 Mat_SeqBAIJ *b; 3547 3548 PetscFunctionBegin; 3549 PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size)); 3550 PetscCheck(size == 1, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Comm must be of size 1"); 3551 3552 PetscCall(PetscNew(&b)); 3553 B->data = (void *)b; 3554 B->ops[0] = MatOps_Values; 3555 3556 b->row = NULL; 3557 b->col = NULL; 3558 b->icol = NULL; 3559 b->reallocs = 0; 3560 b->saved_values = NULL; 3561 3562 b->roworiented = PETSC_TRUE; 3563 b->nonew = 0; 3564 b->diag = NULL; 3565 B->spptr = NULL; 3566 B->info.nz_unneeded = (PetscReal)b->maxnz * b->bs2; 3567 b->keepnonzeropattern = PETSC_FALSE; 3568 3569 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJGetArray_C", MatSeqBAIJGetArray_SeqBAIJ)); 3570 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJRestoreArray_C", MatSeqBAIJRestoreArray_SeqBAIJ)); 3571 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqBAIJ)); 3572 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqBAIJ)); 3573 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJSetColumnIndices_C", MatSeqBAIJSetColumnIndices_SeqBAIJ)); 3574 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_seqaij_C", MatConvert_SeqBAIJ_SeqAIJ)); 3575 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_seqsbaij_C", MatConvert_SeqBAIJ_SeqSBAIJ)); 3576 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJSetPreallocation_C", MatSeqBAIJSetPreallocation_SeqBAIJ)); 3577 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJSetPreallocationCSR_C", MatSeqBAIJSetPreallocationCSR_SeqBAIJ)); 3578 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqBAIJ)); 3579 #if defined(PETSC_HAVE_HYPRE) 3580 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_hypre_C", MatConvert_AIJ_HYPRE)); 3581 #endif 3582 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_is_C", MatConvert_XAIJ_IS)); 3583 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQBAIJ)); 3584 PetscFunctionReturn(PETSC_SUCCESS); 3585 } 3586 3587 PetscErrorCode MatDuplicateNoCreate_SeqBAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace) 3588 { 3589 Mat_SeqBAIJ *c = (Mat_SeqBAIJ *)C->data, *a = (Mat_SeqBAIJ *)A->data; 3590 PetscInt i, mbs = a->mbs, nz = a->nz, bs2 = a->bs2; 3591 3592 PetscFunctionBegin; 3593 PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix"); 3594 PetscCheck(a->i[mbs] == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Corrupt matrix"); 3595 3596 if (cpvalues == MAT_SHARE_NONZERO_PATTERN) { 3597 c->imax = a->imax; 3598 c->ilen = a->ilen; 3599 c->free_imax_ilen = PETSC_FALSE; 3600 } else { 3601 PetscCall(PetscMalloc2(mbs, &c->imax, mbs, &c->ilen)); 3602 for (i = 0; i < mbs; i++) { 3603 c->imax[i] = a->imax[i]; 3604 c->ilen[i] = a->ilen[i]; 3605 } 3606 c->free_imax_ilen = PETSC_TRUE; 3607 } 3608 3609 /* allocate the matrix space */ 3610 if (mallocmatspace) { 3611 if (cpvalues == MAT_SHARE_NONZERO_PATTERN) { 3612 PetscCall(PetscCalloc1(bs2 * nz, &c->a)); 3613 3614 c->i = a->i; 3615 c->j = a->j; 3616 c->singlemalloc = PETSC_FALSE; 3617 c->free_a = PETSC_TRUE; 3618 c->free_ij = PETSC_FALSE; 3619 c->parent = A; 3620 C->preallocated = PETSC_TRUE; 3621 C->assembled = PETSC_TRUE; 3622 3623 PetscCall(PetscObjectReference((PetscObject)A)); 3624 PetscCall(MatSetOption(A, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE)); 3625 PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE)); 3626 } else { 3627 PetscCall(PetscMalloc3(bs2 * nz, &c->a, nz, &c->j, mbs + 1, &c->i)); 3628 3629 c->singlemalloc = PETSC_TRUE; 3630 c->free_a = PETSC_TRUE; 3631 c->free_ij = PETSC_TRUE; 3632 3633 PetscCall(PetscArraycpy(c->i, a->i, mbs + 1)); 3634 if (mbs > 0) { 3635 PetscCall(PetscArraycpy(c->j, a->j, nz)); 3636 if (cpvalues == MAT_COPY_VALUES) { 3637 PetscCall(PetscArraycpy(c->a, a->a, bs2 * nz)); 3638 } else { 3639 PetscCall(PetscArrayzero(c->a, bs2 * nz)); 3640 } 3641 } 3642 C->preallocated = PETSC_TRUE; 3643 C->assembled = PETSC_TRUE; 3644 } 3645 } 3646 3647 c->roworiented = a->roworiented; 3648 c->nonew = a->nonew; 3649 3650 PetscCall(PetscLayoutReference(A->rmap, &C->rmap)); 3651 PetscCall(PetscLayoutReference(A->cmap, &C->cmap)); 3652 3653 c->bs2 = a->bs2; 3654 c->mbs = a->mbs; 3655 c->nbs = a->nbs; 3656 3657 if (a->diag) { 3658 if (cpvalues == MAT_SHARE_NONZERO_PATTERN) { 3659 c->diag = a->diag; 3660 c->free_diag = PETSC_FALSE; 3661 } else { 3662 PetscCall(PetscMalloc1(mbs + 1, &c->diag)); 3663 for (i = 0; i < mbs; i++) c->diag[i] = a->diag[i]; 3664 c->free_diag = PETSC_TRUE; 3665 } 3666 } else c->diag = NULL; 3667 3668 c->nz = a->nz; 3669 c->maxnz = a->nz; /* Since we allocate exactly the right amount */ 3670 c->solve_work = NULL; 3671 c->mult_work = NULL; 3672 c->sor_workt = NULL; 3673 c->sor_work = NULL; 3674 3675 c->compressedrow.use = a->compressedrow.use; 3676 c->compressedrow.nrows = a->compressedrow.nrows; 3677 if (a->compressedrow.use) { 3678 i = a->compressedrow.nrows; 3679 PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i + 1, &c->compressedrow.rindex)); 3680 PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1)); 3681 PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i)); 3682 } else { 3683 c->compressedrow.use = PETSC_FALSE; 3684 c->compressedrow.i = NULL; 3685 c->compressedrow.rindex = NULL; 3686 } 3687 c->nonzerorowcnt = a->nonzerorowcnt; 3688 C->nonzerostate = A->nonzerostate; 3689 3690 PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist)); 3691 PetscFunctionReturn(PETSC_SUCCESS); 3692 } 3693 3694 PetscErrorCode MatDuplicate_SeqBAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B) 3695 { 3696 PetscFunctionBegin; 3697 PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B)); 3698 PetscCall(MatSetSizes(*B, A->rmap->N, A->cmap->n, A->rmap->N, A->cmap->n)); 3699 PetscCall(MatSetType(*B, MATSEQBAIJ)); 3700 PetscCall(MatDuplicateNoCreate_SeqBAIJ(*B, A, cpvalues, PETSC_TRUE)); 3701 PetscFunctionReturn(PETSC_SUCCESS); 3702 } 3703 3704 /* Used for both SeqBAIJ and SeqSBAIJ matrices */ 3705 PetscErrorCode MatLoad_SeqBAIJ_Binary(Mat mat, PetscViewer viewer) 3706 { 3707 PetscInt header[4], M, N, nz, bs, m, n, mbs, nbs, rows, cols, sum, i, j, k; 3708 PetscInt *rowidxs, *colidxs; 3709 PetscScalar *matvals; 3710 3711 PetscFunctionBegin; 3712 PetscCall(PetscViewerSetUp(viewer)); 3713 3714 /* read matrix header */ 3715 PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT)); 3716 PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file"); 3717 M = header[1]; 3718 N = header[2]; 3719 nz = header[3]; 3720 PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M); 3721 PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N); 3722 PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqBAIJ"); 3723 3724 /* set block sizes from the viewer's .info file */ 3725 PetscCall(MatLoad_Binary_BlockSizes(mat, viewer)); 3726 /* set local and global sizes if not set already */ 3727 if (mat->rmap->n < 0) mat->rmap->n = M; 3728 if (mat->cmap->n < 0) mat->cmap->n = N; 3729 if (mat->rmap->N < 0) mat->rmap->N = M; 3730 if (mat->cmap->N < 0) mat->cmap->N = N; 3731 PetscCall(PetscLayoutSetUp(mat->rmap)); 3732 PetscCall(PetscLayoutSetUp(mat->cmap)); 3733 3734 /* check if the matrix sizes are correct */ 3735 PetscCall(MatGetSize(mat, &rows, &cols)); 3736 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); 3737 PetscCall(MatGetBlockSize(mat, &bs)); 3738 PetscCall(MatGetLocalSize(mat, &m, &n)); 3739 mbs = m / bs; 3740 nbs = n / bs; 3741 3742 /* read in row lengths, column indices and nonzero values */ 3743 PetscCall(PetscMalloc1(m + 1, &rowidxs)); 3744 PetscCall(PetscViewerBinaryRead(viewer, rowidxs + 1, m, NULL, PETSC_INT)); 3745 rowidxs[0] = 0; 3746 for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i]; 3747 sum = rowidxs[m]; 3748 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); 3749 3750 /* read in column indices and nonzero values */ 3751 PetscCall(PetscMalloc2(rowidxs[m], &colidxs, nz, &matvals)); 3752 PetscCall(PetscViewerBinaryRead(viewer, colidxs, rowidxs[m], NULL, PETSC_INT)); 3753 PetscCall(PetscViewerBinaryRead(viewer, matvals, rowidxs[m], NULL, PETSC_SCALAR)); 3754 3755 { /* preallocate matrix storage */ 3756 PetscBT bt; /* helper bit set to count nonzeros */ 3757 PetscInt *nnz; 3758 PetscBool sbaij; 3759 3760 PetscCall(PetscBTCreate(nbs, &bt)); 3761 PetscCall(PetscCalloc1(mbs, &nnz)); 3762 PetscCall(PetscObjectTypeCompare((PetscObject)mat, MATSEQSBAIJ, &sbaij)); 3763 for (i = 0; i < mbs; i++) { 3764 PetscCall(PetscBTMemzero(nbs, bt)); 3765 for (k = 0; k < bs; k++) { 3766 PetscInt row = bs * i + k; 3767 for (j = rowidxs[row]; j < rowidxs[row + 1]; j++) { 3768 PetscInt col = colidxs[j]; 3769 if (!sbaij || col >= row) 3770 if (!PetscBTLookupSet(bt, col / bs)) nnz[i]++; 3771 } 3772 } 3773 } 3774 PetscCall(PetscBTDestroy(&bt)); 3775 PetscCall(MatSeqBAIJSetPreallocation(mat, bs, 0, nnz)); 3776 PetscCall(MatSeqSBAIJSetPreallocation(mat, bs, 0, nnz)); 3777 PetscCall(PetscFree(nnz)); 3778 } 3779 3780 /* store matrix values */ 3781 for (i = 0; i < m; i++) { 3782 PetscInt row = i, s = rowidxs[i], e = rowidxs[i + 1]; 3783 PetscUseTypeMethod(mat, setvalues, 1, &row, e - s, colidxs + s, matvals + s, INSERT_VALUES); 3784 } 3785 3786 PetscCall(PetscFree(rowidxs)); 3787 PetscCall(PetscFree2(colidxs, matvals)); 3788 PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY)); 3789 PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY)); 3790 PetscFunctionReturn(PETSC_SUCCESS); 3791 } 3792 3793 PetscErrorCode MatLoad_SeqBAIJ(Mat mat, PetscViewer viewer) 3794 { 3795 PetscBool isbinary; 3796 3797 PetscFunctionBegin; 3798 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary)); 3799 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); 3800 PetscCall(MatLoad_SeqBAIJ_Binary(mat, viewer)); 3801 PetscFunctionReturn(PETSC_SUCCESS); 3802 } 3803 3804 /*@C 3805 MatCreateSeqBAIJ - Creates a sparse matrix in `MATSEQAIJ` (block 3806 compressed row) format. For good matrix assembly performance the 3807 user should preallocate the matrix storage by setting the parameter `nz` 3808 (or the array `nnz`). 3809 3810 Collective 3811 3812 Input Parameters: 3813 + comm - MPI communicator, set to `PETSC_COMM_SELF` 3814 . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row 3815 blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()` 3816 . m - number of rows 3817 . n - number of columns 3818 . nz - number of nonzero blocks per block row (same for all rows) 3819 - nnz - array containing the number of nonzero blocks in the various block rows 3820 (possibly different for each block row) or `NULL` 3821 3822 Output Parameter: 3823 . A - the matrix 3824 3825 Options Database Keys: 3826 + -mat_no_unroll - uses code that does not unroll the loops in the block calculations (much slower) 3827 - -mat_block_size - size of the blocks to use 3828 3829 Level: intermediate 3830 3831 Notes: 3832 It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`, 3833 MatXXXXSetPreallocation() paradigm instead of this routine directly. 3834 [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`] 3835 3836 The number of rows and columns must be divisible by blocksize. 3837 3838 If the `nnz` parameter is given then the `nz` parameter is ignored 3839 3840 A nonzero block is any block that as 1 or more nonzeros in it 3841 3842 The `MATSEQBAIJ` format is fully compatible with standard Fortran 3843 storage. That is, the stored row and column indices can begin at 3844 either one (as in Fortran) or zero. 3845 3846 Specify the preallocated storage with either `nz` or `nnz` (not both). 3847 Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory 3848 allocation. See [Sparse Matrices](sec_matsparse) for details. 3849 matrices. 3850 3851 .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateBAIJ()` 3852 @*/ 3853 PetscErrorCode MatCreateSeqBAIJ(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A) 3854 { 3855 PetscFunctionBegin; 3856 PetscCall(MatCreate(comm, A)); 3857 PetscCall(MatSetSizes(*A, m, n, m, n)); 3858 PetscCall(MatSetType(*A, MATSEQBAIJ)); 3859 PetscCall(MatSeqBAIJSetPreallocation(*A, bs, nz, (PetscInt *)nnz)); 3860 PetscFunctionReturn(PETSC_SUCCESS); 3861 } 3862 3863 /*@C 3864 MatSeqBAIJSetPreallocation - Sets the block size and expected nonzeros 3865 per row in the matrix. For good matrix assembly performance the 3866 user should preallocate the matrix storage by setting the parameter `nz` 3867 (or the array `nnz`). 3868 3869 Collective 3870 3871 Input Parameters: 3872 + B - the matrix 3873 . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row 3874 blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()` 3875 . nz - number of block nonzeros per block row (same for all rows) 3876 - nnz - array containing the number of block nonzeros in the various block rows 3877 (possibly different for each block row) or `NULL` 3878 3879 Options Database Keys: 3880 + -mat_no_unroll - uses code that does not unroll the loops in the block calculations (much slower) 3881 - -mat_block_size - size of the blocks to use 3882 3883 Level: intermediate 3884 3885 Notes: 3886 If the `nnz` parameter is given then the `nz` parameter is ignored 3887 3888 You can call `MatGetInfo()` to get information on how effective the preallocation was; 3889 for example the fields mallocs,nz_allocated,nz_used,nz_unneeded; 3890 You can also run with the option `-info` and look for messages with the string 3891 malloc in them to see if additional memory allocation was needed. 3892 3893 The `MATSEQBAIJ` format is fully compatible with standard Fortran 3894 storage. That is, the stored row and column indices can begin at 3895 either one (as in Fortran) or zero. 3896 3897 Specify the preallocated storage with either `nz` or `nnz` (not both). 3898 Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory 3899 allocation. See [Sparse Matrices](sec_matsparse) for details. 3900 3901 .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`, `MatGetInfo()` 3902 @*/ 3903 PetscErrorCode MatSeqBAIJSetPreallocation(Mat B, PetscInt bs, PetscInt nz, const PetscInt nnz[]) 3904 { 3905 PetscFunctionBegin; 3906 PetscValidHeaderSpecific(B, MAT_CLASSID, 1); 3907 PetscValidType(B, 1); 3908 PetscValidLogicalCollectiveInt(B, bs, 2); 3909 PetscTryMethod(B, "MatSeqBAIJSetPreallocation_C", (Mat, PetscInt, PetscInt, const PetscInt[]), (B, bs, nz, nnz)); 3910 PetscFunctionReturn(PETSC_SUCCESS); 3911 } 3912 3913 /*@C 3914 MatSeqBAIJSetPreallocationCSR - Creates a sparse sequential matrix in `MATSEQBAIJ` format using the given nonzero structure and (optional) numerical values 3915 3916 Collective 3917 3918 Input Parameters: 3919 + B - the matrix 3920 . bs - the blocksize 3921 . i - the indices into `j` for the start of each local row (indices start with zero) 3922 . j - the column indices for each local row (indices start with zero) these must be sorted for each row 3923 - v - optional values in the matrix, use `NULL` if not provided 3924 3925 Level: advanced 3926 3927 Notes: 3928 The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqBAIJWithArrays()` 3929 3930 The order of the entries in values is specified by the `MatOption` `MAT_ROW_ORIENTED`. For example, C programs 3931 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 3932 over rows within a block and the last index is over columns within a block row. Fortran programs will likely set 3933 `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 3934 block column and the second index is over columns within a block. 3935 3936 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 3937 3938 .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatSeqBAIJSetPreallocation()`, `MATSEQBAIJ` 3939 @*/ 3940 PetscErrorCode MatSeqBAIJSetPreallocationCSR(Mat B, PetscInt bs, const PetscInt i[], const PetscInt j[], const PetscScalar v[]) 3941 { 3942 PetscFunctionBegin; 3943 PetscValidHeaderSpecific(B, MAT_CLASSID, 1); 3944 PetscValidType(B, 1); 3945 PetscValidLogicalCollectiveInt(B, bs, 2); 3946 PetscTryMethod(B, "MatSeqBAIJSetPreallocationCSR_C", (Mat, PetscInt, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, bs, i, j, v)); 3947 PetscFunctionReturn(PETSC_SUCCESS); 3948 } 3949 3950 /*@ 3951 MatCreateSeqBAIJWithArrays - Creates a `MATSEQBAIJ` matrix using matrix elements provided by the user. 3952 3953 Collective 3954 3955 Input Parameters: 3956 + comm - must be an MPI communicator of size 1 3957 . bs - size of block 3958 . m - number of rows 3959 . n - number of columns 3960 . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row block row of the matrix 3961 . j - column indices 3962 - a - matrix values 3963 3964 Output Parameter: 3965 . mat - the matrix 3966 3967 Level: advanced 3968 3969 Notes: 3970 The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays 3971 once the matrix is destroyed 3972 3973 You cannot set new nonzero locations into this matrix, that will generate an error. 3974 3975 The `i` and `j` indices are 0 based 3976 3977 When block size is greater than 1 the matrix values must be stored using the `MATSEQBAIJ` storage format 3978 3979 The order of the entries in values is the same as the block compressed sparse row storage format; that is, it is 3980 the same as a three dimensional array in Fortran values(bs,bs,nnz) that contains the first column of the first 3981 block, followed by the second column of the first block etc etc. That is, the blocks are contiguous in memory 3982 with column-major ordering within blocks. 3983 3984 .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateBAIJ()`, `MatCreateSeqBAIJ()` 3985 @*/ 3986 PetscErrorCode MatCreateSeqBAIJWithArrays(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat) 3987 { 3988 Mat_SeqBAIJ *baij; 3989 3990 PetscFunctionBegin; 3991 PetscCheck(bs == 1, PETSC_COMM_SELF, PETSC_ERR_SUP, "block size %" PetscInt_FMT " > 1 is not supported yet", bs); 3992 if (m > 0) PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0"); 3993 3994 PetscCall(MatCreate(comm, mat)); 3995 PetscCall(MatSetSizes(*mat, m, n, m, n)); 3996 PetscCall(MatSetType(*mat, MATSEQBAIJ)); 3997 PetscCall(MatSeqBAIJSetPreallocation(*mat, bs, MAT_SKIP_ALLOCATION, NULL)); 3998 baij = (Mat_SeqBAIJ *)(*mat)->data; 3999 PetscCall(PetscMalloc2(m, &baij->imax, m, &baij->ilen)); 4000 4001 baij->i = i; 4002 baij->j = j; 4003 baij->a = a; 4004 4005 baij->singlemalloc = PETSC_FALSE; 4006 baij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/ 4007 baij->free_a = PETSC_FALSE; 4008 baij->free_ij = PETSC_FALSE; 4009 baij->free_imax_ilen = PETSC_TRUE; 4010 4011 for (PetscInt ii = 0; ii < m; ii++) { 4012 const PetscInt row_len = i[ii + 1] - i[ii]; 4013 4014 baij->ilen[ii] = baij->imax[ii] = row_len; 4015 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); 4016 } 4017 if (PetscDefined(USE_DEBUG)) { 4018 for (PetscInt ii = 0; ii < baij->i[m]; ii++) { 4019 PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]); 4020 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]); 4021 } 4022 } 4023 4024 PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY)); 4025 PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY)); 4026 PetscFunctionReturn(PETSC_SUCCESS); 4027 } 4028 4029 PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqBAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat) 4030 { 4031 PetscFunctionBegin; 4032 PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIBAIJ(comm, inmat, n, scall, outmat)); 4033 PetscFunctionReturn(PETSC_SUCCESS); 4034 } 4035