1 #include <../src/mat/impls/aij/mpi/mpiaij.h> /*I "petscmat.h" I*/ 2 #include <../src/mat/impls/sell/mpi/mpisell.h> /*I "petscmat.h" I*/ 3 #include <petsc/private/vecimpl.h> 4 #include <petsc/private/isimpl.h> 5 #include <petscblaslapack.h> 6 #include <petscsf.h> 7 8 /*MC 9 MATSELL - MATSELL = "sell" - A matrix type to be used for sparse matrices. 10 11 This matrix type is identical to `MATSEQSELL` when constructed with a single process communicator, 12 and `MATMPISELL` otherwise. As a result, for single process communicators, 13 `MatSeqSELLSetPreallocation()` is supported, and similarly `MatMPISELLSetPreallocation()` is supported 14 for communicators controlling multiple processes. It is recommended that you call both of 15 the above preallocation routines for simplicity. 16 17 Options Database Keys: 18 . -mat_type sell - sets the matrix type to `MATSELL` during a call to `MatSetFromOptions()` 19 20 Level: beginner 21 22 .seealso: `Mat`, `MATAIJ`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSELL()`, `MatCreateSeqSELL()`, `MATSEQSELL`, `MATMPISELL` 23 M*/ 24 25 static PetscErrorCode MatDiagonalSet_MPISELL(Mat Y, Vec D, InsertMode is) 26 { 27 Mat_MPISELL *sell = (Mat_MPISELL *)Y->data; 28 29 PetscFunctionBegin; 30 if (Y->assembled && Y->rmap->rstart == Y->cmap->rstart && Y->rmap->rend == Y->cmap->rend) { 31 PetscCall(MatDiagonalSet(sell->A, D, is)); 32 } else { 33 PetscCall(MatDiagonalSet_Default(Y, D, is)); 34 } 35 PetscFunctionReturn(PETSC_SUCCESS); 36 } 37 38 /* 39 Local utility routine that creates a mapping from the global column 40 number to the local number in the off-diagonal part of the local 41 storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at 42 a slightly higher hash table cost; without it it is not scalable (each processor 43 has an order N integer array but is fast to access. 44 */ 45 PetscErrorCode MatCreateColmap_MPISELL_Private(Mat mat) 46 { 47 Mat_MPISELL *sell = (Mat_MPISELL *)mat->data; 48 PetscInt n = sell->B->cmap->n, i; 49 50 PetscFunctionBegin; 51 PetscCheck(sell->garray, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MPISELL Matrix was assembled but is missing garray"); 52 #if defined(PETSC_USE_CTABLE) 53 PetscCall(PetscHMapICreateWithSize(n, &sell->colmap)); 54 for (i = 0; i < n; i++) PetscCall(PetscHMapISet(sell->colmap, sell->garray[i] + 1, i + 1)); 55 #else 56 PetscCall(PetscCalloc1(mat->cmap->N + 1, &sell->colmap)); 57 for (i = 0; i < n; i++) sell->colmap[sell->garray[i]] = i + 1; 58 #endif 59 PetscFunctionReturn(PETSC_SUCCESS); 60 } 61 62 #define MatSetValues_SeqSELL_A_Private(row, col, value, addv, orow, ocol) \ 63 { \ 64 if (col <= lastcol1) low1 = 0; \ 65 else high1 = nrow1; \ 66 lastcol1 = col; \ 67 while (high1 - low1 > 5) { \ 68 t = (low1 + high1) / 2; \ 69 if (cp1[sliceheight * t] > col) high1 = t; \ 70 else low1 = t; \ 71 } \ 72 for (_i = low1; _i < high1; _i++) { \ 73 if (cp1[sliceheight * _i] > col) break; \ 74 if (cp1[sliceheight * _i] == col) { \ 75 if (addv == ADD_VALUES) vp1[sliceheight * _i] += value; \ 76 else vp1[sliceheight * _i] = value; \ 77 inserted = PETSC_TRUE; \ 78 goto a_noinsert; \ 79 } \ 80 } \ 81 if (value == 0.0 && ignorezeroentries) { \ 82 low1 = 0; \ 83 high1 = nrow1; \ 84 goto a_noinsert; \ 85 } \ 86 if (nonew == 1) { \ 87 low1 = 0; \ 88 high1 = nrow1; \ 89 goto a_noinsert; \ 90 } \ 91 PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \ 92 MatSeqXSELLReallocateSELL(A, am, 1, nrow1, a->sliidx, a->sliceheight, row / sliceheight, row, col, a->colidx, a->val, cp1, vp1, nonew, MatScalar); \ 93 /* shift up all the later entries in this row */ \ 94 for (ii = nrow1 - 1; ii >= _i; ii--) { \ 95 cp1[sliceheight * (ii + 1)] = cp1[sliceheight * ii]; \ 96 vp1[sliceheight * (ii + 1)] = vp1[sliceheight * ii]; \ 97 } \ 98 cp1[sliceheight * _i] = col; \ 99 vp1[sliceheight * _i] = value; \ 100 a->nz++; \ 101 nrow1++; \ 102 a_noinsert:; \ 103 a->rlen[row] = nrow1; \ 104 } 105 106 #define MatSetValues_SeqSELL_B_Private(row, col, value, addv, orow, ocol) \ 107 { \ 108 if (col <= lastcol2) low2 = 0; \ 109 else high2 = nrow2; \ 110 lastcol2 = col; \ 111 while (high2 - low2 > 5) { \ 112 t = (low2 + high2) / 2; \ 113 if (cp2[sliceheight * t] > col) high2 = t; \ 114 else low2 = t; \ 115 } \ 116 for (_i = low2; _i < high2; _i++) { \ 117 if (cp2[sliceheight * _i] > col) break; \ 118 if (cp2[sliceheight * _i] == col) { \ 119 if (addv == ADD_VALUES) vp2[sliceheight * _i] += value; \ 120 else vp2[sliceheight * _i] = value; \ 121 inserted = PETSC_TRUE; \ 122 goto b_noinsert; \ 123 } \ 124 } \ 125 if (value == 0.0 && ignorezeroentries) { \ 126 low2 = 0; \ 127 high2 = nrow2; \ 128 goto b_noinsert; \ 129 } \ 130 if (nonew == 1) { \ 131 low2 = 0; \ 132 high2 = nrow2; \ 133 goto b_noinsert; \ 134 } \ 135 PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \ 136 MatSeqXSELLReallocateSELL(B, bm, 1, nrow2, b->sliidx, b->sliceheight, row / sliceheight, row, col, b->colidx, b->val, cp2, vp2, nonew, MatScalar); \ 137 /* shift up all the later entries in this row */ \ 138 for (ii = nrow2 - 1; ii >= _i; ii--) { \ 139 cp2[sliceheight * (ii + 1)] = cp2[sliceheight * ii]; \ 140 vp2[sliceheight * (ii + 1)] = vp2[sliceheight * ii]; \ 141 } \ 142 cp2[sliceheight * _i] = col; \ 143 vp2[sliceheight * _i] = value; \ 144 b->nz++; \ 145 nrow2++; \ 146 b_noinsert:; \ 147 b->rlen[row] = nrow2; \ 148 } 149 150 static PetscErrorCode MatSetValues_MPISELL(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv) 151 { 152 Mat_MPISELL *sell = (Mat_MPISELL *)mat->data; 153 PetscScalar value; 154 PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend, shift1, shift2; 155 PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col; 156 PetscBool roworiented = sell->roworiented; 157 158 /* Some Variables required in the macro */ 159 Mat A = sell->A; 160 Mat_SeqSELL *a = (Mat_SeqSELL *)A->data; 161 PetscBool ignorezeroentries = a->ignorezeroentries, found; 162 Mat B = sell->B; 163 Mat_SeqSELL *b = (Mat_SeqSELL *)B->data; 164 PetscInt *cp1, *cp2, ii, _i, nrow1, nrow2, low1, high1, low2, high2, t, lastcol1, lastcol2, sliceheight = a->sliceheight; 165 MatScalar *vp1, *vp2; 166 167 PetscFunctionBegin; 168 for (i = 0; i < m; i++) { 169 if (im[i] < 0) continue; 170 PetscCheck(im[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], mat->rmap->N - 1); 171 if (im[i] >= rstart && im[i] < rend) { 172 row = im[i] - rstart; 173 lastcol1 = -1; 174 shift1 = a->sliidx[row / sliceheight] + (row % sliceheight); /* starting index of the row */ 175 cp1 = PetscSafePointerPlusOffset(a->colidx, shift1); 176 vp1 = PetscSafePointerPlusOffset(a->val, shift1); 177 nrow1 = a->rlen[row]; 178 low1 = 0; 179 high1 = nrow1; 180 lastcol2 = -1; 181 shift2 = b->sliidx[row / sliceheight] + (row % sliceheight); /* starting index of the row */ 182 cp2 = PetscSafePointerPlusOffset(b->colidx, shift2); 183 vp2 = PetscSafePointerPlusOffset(b->val, shift2); 184 nrow2 = b->rlen[row]; 185 low2 = 0; 186 high2 = nrow2; 187 188 for (j = 0; j < n; j++) { 189 if (roworiented) value = v[i * n + j]; 190 else value = v[i + j * m]; 191 if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES)) continue; 192 if (in[j] >= cstart && in[j] < cend) { 193 col = in[j] - cstart; 194 MatSetValue_SeqSELL_Private(A, row, col, value, addv, im[i], in[j], cp1, vp1, lastcol1, low1, high1); /* set one value */ 195 #if defined(PETSC_HAVE_CUDA) 196 if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && found) A->offloadmask = PETSC_OFFLOAD_CPU; 197 #endif 198 } else if (in[j] < 0) { 199 continue; 200 } else { 201 PetscCheck(in[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1); 202 if (mat->was_assembled) { 203 if (!sell->colmap) PetscCall(MatCreateColmap_MPISELL_Private(mat)); 204 #if defined(PETSC_USE_CTABLE) 205 PetscCall(PetscHMapIGetWithDefault(sell->colmap, in[j] + 1, 0, &col)); 206 col--; 207 #else 208 col = sell->colmap[in[j]] - 1; 209 #endif 210 if (col < 0 && !((Mat_SeqSELL *)sell->B->data)->nonew) { 211 PetscCall(MatDisAssemble_MPISELL(mat)); 212 col = in[j]; 213 /* Reinitialize the variables required by MatSetValues_SeqSELL_B_Private() */ 214 B = sell->B; 215 b = (Mat_SeqSELL *)B->data; 216 shift2 = b->sliidx[row / sliceheight] + (row % sliceheight); /* starting index of the row */ 217 cp2 = b->colidx + shift2; 218 vp2 = b->val + shift2; 219 nrow2 = b->rlen[row]; 220 low2 = 0; 221 high2 = nrow2; 222 found = PETSC_FALSE; 223 } else { 224 PetscCheck(col >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]); 225 } 226 } else col = in[j]; 227 MatSetValue_SeqSELL_Private(B, row, col, value, addv, im[i], in[j], cp2, vp2, lastcol2, low2, high2); /* set one value */ 228 #if defined(PETSC_HAVE_CUDA) 229 if (B->offloadmask != PETSC_OFFLOAD_UNALLOCATED && found) B->offloadmask = PETSC_OFFLOAD_CPU; 230 #endif 231 } 232 } 233 } else { 234 PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Setting off process row %" PetscInt_FMT " even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set", im[i]); 235 if (!sell->donotstash) { 236 mat->assembled = PETSC_FALSE; 237 if (roworiented) { 238 PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES)))); 239 } else { 240 PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES)))); 241 } 242 } 243 } 244 } 245 PetscFunctionReturn(PETSC_SUCCESS); 246 } 247 248 static PetscErrorCode MatGetValues_MPISELL(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[]) 249 { 250 Mat_MPISELL *sell = (Mat_MPISELL *)mat->data; 251 PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend; 252 PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col; 253 254 PetscFunctionBegin; 255 for (i = 0; i < m; i++) { 256 if (idxm[i] < 0) continue; /* negative row */ 257 PetscCheck(idxm[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, idxm[i], mat->rmap->N - 1); 258 if (idxm[i] >= rstart && idxm[i] < rend) { 259 row = idxm[i] - rstart; 260 for (j = 0; j < n; j++) { 261 if (idxn[j] < 0) continue; /* negative column */ 262 PetscCheck(idxn[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, idxn[j], mat->cmap->N - 1); 263 if (idxn[j] >= cstart && idxn[j] < cend) { 264 col = idxn[j] - cstart; 265 PetscCall(MatGetValues(sell->A, 1, &row, 1, &col, v + i * n + j)); 266 } else { 267 if (!sell->colmap) PetscCall(MatCreateColmap_MPISELL_Private(mat)); 268 #if defined(PETSC_USE_CTABLE) 269 PetscCall(PetscHMapIGetWithDefault(sell->colmap, idxn[j] + 1, 0, &col)); 270 col--; 271 #else 272 col = sell->colmap[idxn[j]] - 1; 273 #endif 274 if ((col < 0) || (sell->garray[col] != idxn[j])) *(v + i * n + j) = 0.0; 275 else PetscCall(MatGetValues(sell->B, 1, &row, 1, &col, v + i * n + j)); 276 } 277 } 278 } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local values currently supported"); 279 } 280 PetscFunctionReturn(PETSC_SUCCESS); 281 } 282 283 static PetscErrorCode MatAssemblyBegin_MPISELL(Mat mat, MatAssemblyType mode) 284 { 285 Mat_MPISELL *sell = (Mat_MPISELL *)mat->data; 286 PetscInt nstash, reallocs; 287 288 PetscFunctionBegin; 289 if (sell->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS); 290 291 PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range)); 292 PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs)); 293 PetscCall(PetscInfo(sell->A, "Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs)); 294 PetscFunctionReturn(PETSC_SUCCESS); 295 } 296 297 PetscErrorCode MatAssemblyEnd_MPISELL(Mat mat, MatAssemblyType mode) 298 { 299 Mat_MPISELL *sell = (Mat_MPISELL *)mat->data; 300 PetscMPIInt n; 301 PetscInt i, flg; 302 PetscInt *row, *col; 303 PetscScalar *val; 304 PetscBool other_disassembled; 305 /* do not use 'b = (Mat_SeqSELL*)sell->B->data' as B can be reset in disassembly */ 306 PetscFunctionBegin; 307 if (!sell->donotstash && !mat->nooffprocentries) { 308 while (1) { 309 PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg)); 310 if (!flg) break; 311 312 for (i = 0; i < n; i++) { /* assemble one by one */ 313 PetscCall(MatSetValues_MPISELL(mat, 1, row + i, 1, col + i, val + i, mat->insertmode)); 314 } 315 } 316 PetscCall(MatStashScatterEnd_Private(&mat->stash)); 317 } 318 #if defined(PETSC_HAVE_CUDA) 319 if (mat->offloadmask == PETSC_OFFLOAD_CPU) sell->A->offloadmask = PETSC_OFFLOAD_CPU; 320 #endif 321 PetscCall(MatAssemblyBegin(sell->A, mode)); 322 PetscCall(MatAssemblyEnd(sell->A, mode)); 323 324 /* 325 determine if any processor has disassembled, if so we must 326 also disassemble ourselves, in order that we may reassemble. 327 */ 328 /* 329 if nonzero structure of submatrix B cannot change then we know that 330 no processor disassembled thus we can skip this stuff 331 */ 332 if (!((Mat_SeqSELL *)sell->B->data)->nonew) { 333 PetscCall(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat))); 334 if (mat->was_assembled && !other_disassembled) PetscCall(MatDisAssemble_MPISELL(mat)); 335 } 336 if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPISELL(mat)); 337 #if defined(PETSC_HAVE_CUDA) 338 if (mat->offloadmask == PETSC_OFFLOAD_CPU && sell->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) sell->B->offloadmask = PETSC_OFFLOAD_CPU; 339 #endif 340 PetscCall(MatAssemblyBegin(sell->B, mode)); 341 PetscCall(MatAssemblyEnd(sell->B, mode)); 342 PetscCall(PetscFree2(sell->rowvalues, sell->rowindices)); 343 sell->rowvalues = NULL; 344 PetscCall(VecDestroy(&sell->diag)); 345 346 /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */ 347 if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqSELL *)sell->A->data)->nonew) { 348 PetscObjectState state = sell->A->nonzerostate + sell->B->nonzerostate; 349 PetscCall(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat))); 350 } 351 #if defined(PETSC_HAVE_CUDA) 352 mat->offloadmask = PETSC_OFFLOAD_BOTH; 353 #endif 354 PetscFunctionReturn(PETSC_SUCCESS); 355 } 356 357 static PetscErrorCode MatZeroEntries_MPISELL(Mat A) 358 { 359 Mat_MPISELL *l = (Mat_MPISELL *)A->data; 360 361 PetscFunctionBegin; 362 PetscCall(MatZeroEntries(l->A)); 363 PetscCall(MatZeroEntries(l->B)); 364 PetscFunctionReturn(PETSC_SUCCESS); 365 } 366 367 static PetscErrorCode MatMult_MPISELL(Mat A, Vec xx, Vec yy) 368 { 369 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 370 PetscInt nt; 371 372 PetscFunctionBegin; 373 PetscCall(VecGetLocalSize(xx, &nt)); 374 PetscCheck(nt == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A (%" PetscInt_FMT ") and xx (%" PetscInt_FMT ")", A->cmap->n, nt); 375 PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD)); 376 PetscCall((*a->A->ops->mult)(a->A, xx, yy)); 377 PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD)); 378 PetscCall((*a->B->ops->multadd)(a->B, a->lvec, yy, yy)); 379 PetscFunctionReturn(PETSC_SUCCESS); 380 } 381 382 static PetscErrorCode MatMultDiagonalBlock_MPISELL(Mat A, Vec bb, Vec xx) 383 { 384 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 385 386 PetscFunctionBegin; 387 PetscCall(MatMultDiagonalBlock(a->A, bb, xx)); 388 PetscFunctionReturn(PETSC_SUCCESS); 389 } 390 391 static PetscErrorCode MatMultAdd_MPISELL(Mat A, Vec xx, Vec yy, Vec zz) 392 { 393 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 394 395 PetscFunctionBegin; 396 PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD)); 397 PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz)); 398 PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD)); 399 PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz)); 400 PetscFunctionReturn(PETSC_SUCCESS); 401 } 402 403 static PetscErrorCode MatMultTranspose_MPISELL(Mat A, Vec xx, Vec yy) 404 { 405 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 406 407 PetscFunctionBegin; 408 /* do nondiagonal part */ 409 PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec)); 410 /* do local part */ 411 PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy)); 412 /* add partial results together */ 413 PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE)); 414 PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE)); 415 PetscFunctionReturn(PETSC_SUCCESS); 416 } 417 418 static PetscErrorCode MatIsTranspose_MPISELL(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f) 419 { 420 MPI_Comm comm; 421 Mat_MPISELL *Asell = (Mat_MPISELL *)Amat->data, *Bsell; 422 Mat Adia = Asell->A, Bdia, Aoff, Boff, *Aoffs, *Boffs; 423 IS Me, Notme; 424 PetscInt M, N, first, last, *notme, i; 425 PetscMPIInt size; 426 427 PetscFunctionBegin; 428 /* Easy test: symmetric diagonal block */ 429 Bsell = (Mat_MPISELL *)Bmat->data; 430 Bdia = Bsell->A; 431 PetscCall(MatIsTranspose(Adia, Bdia, tol, f)); 432 if (!*f) PetscFunctionReturn(PETSC_SUCCESS); 433 PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm)); 434 PetscCallMPI(MPI_Comm_size(comm, &size)); 435 if (size == 1) PetscFunctionReturn(PETSC_SUCCESS); 436 437 /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */ 438 PetscCall(MatGetSize(Amat, &M, &N)); 439 PetscCall(MatGetOwnershipRange(Amat, &first, &last)); 440 PetscCall(PetscMalloc1(N - last + first, ¬me)); 441 for (i = 0; i < first; i++) notme[i] = i; 442 for (i = last; i < M; i++) notme[i - last + first] = i; 443 PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme)); 444 PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me)); 445 PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs)); 446 Aoff = Aoffs[0]; 447 PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs)); 448 Boff = Boffs[0]; 449 PetscCall(MatIsTranspose(Aoff, Boff, tol, f)); 450 PetscCall(MatDestroyMatrices(1, &Aoffs)); 451 PetscCall(MatDestroyMatrices(1, &Boffs)); 452 PetscCall(ISDestroy(&Me)); 453 PetscCall(ISDestroy(&Notme)); 454 PetscCall(PetscFree(notme)); 455 PetscFunctionReturn(PETSC_SUCCESS); 456 } 457 458 static PetscErrorCode MatMultTransposeAdd_MPISELL(Mat A, Vec xx, Vec yy, Vec zz) 459 { 460 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 461 462 PetscFunctionBegin; 463 /* do nondiagonal part */ 464 PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec)); 465 /* do local part */ 466 PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz)); 467 /* add partial results together */ 468 PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE)); 469 PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE)); 470 PetscFunctionReturn(PETSC_SUCCESS); 471 } 472 473 /* 474 This only works correctly for square matrices where the subblock A->A is the 475 diagonal block 476 */ 477 static PetscErrorCode MatGetDiagonal_MPISELL(Mat A, Vec v) 478 { 479 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 480 481 PetscFunctionBegin; 482 PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block"); 483 PetscCheck(A->rmap->rstart == A->cmap->rstart && A->rmap->rend == A->cmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "row partition must equal col partition"); 484 PetscCall(MatGetDiagonal(a->A, v)); 485 PetscFunctionReturn(PETSC_SUCCESS); 486 } 487 488 static PetscErrorCode MatScale_MPISELL(Mat A, PetscScalar aa) 489 { 490 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 491 492 PetscFunctionBegin; 493 PetscCall(MatScale(a->A, aa)); 494 PetscCall(MatScale(a->B, aa)); 495 PetscFunctionReturn(PETSC_SUCCESS); 496 } 497 498 PetscErrorCode MatDestroy_MPISELL(Mat mat) 499 { 500 Mat_MPISELL *sell = (Mat_MPISELL *)mat->data; 501 502 PetscFunctionBegin; 503 PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N)); 504 PetscCall(MatStashDestroy_Private(&mat->stash)); 505 PetscCall(VecDestroy(&sell->diag)); 506 PetscCall(MatDestroy(&sell->A)); 507 PetscCall(MatDestroy(&sell->B)); 508 #if defined(PETSC_USE_CTABLE) 509 PetscCall(PetscHMapIDestroy(&sell->colmap)); 510 #else 511 PetscCall(PetscFree(sell->colmap)); 512 #endif 513 PetscCall(PetscFree(sell->garray)); 514 PetscCall(VecDestroy(&sell->lvec)); 515 PetscCall(VecScatterDestroy(&sell->Mvctx)); 516 PetscCall(PetscFree2(sell->rowvalues, sell->rowindices)); 517 PetscCall(PetscFree(sell->ld)); 518 PetscCall(PetscFree(mat->data)); 519 520 PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL)); 521 PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL)); 522 PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL)); 523 PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatIsTranspose_C", NULL)); 524 PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPISELLSetPreallocation_C", NULL)); 525 PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpisell_mpiaij_C", NULL)); 526 #if defined(PETSC_HAVE_CUDA) 527 PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpisell_mpisellcuda_C", NULL)); 528 #endif 529 PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL)); 530 PetscFunctionReturn(PETSC_SUCCESS); 531 } 532 533 #include <petscdraw.h> 534 static PetscErrorCode MatView_MPISELL_ASCIIorDraworSocket(Mat mat, PetscViewer viewer) 535 { 536 Mat_MPISELL *sell = (Mat_MPISELL *)mat->data; 537 PetscMPIInt rank = sell->rank, size = sell->size; 538 PetscBool isdraw, iascii, isbinary; 539 PetscViewer sviewer; 540 PetscViewerFormat format; 541 542 PetscFunctionBegin; 543 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw)); 544 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii)); 545 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary)); 546 if (iascii) { 547 PetscCall(PetscViewerGetFormat(viewer, &format)); 548 if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) { 549 MatInfo info; 550 PetscInt *inodes; 551 552 PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank)); 553 PetscCall(MatGetInfo(mat, MAT_LOCAL, &info)); 554 PetscCall(MatInodeGetInodeSizes(sell->A, NULL, &inodes, NULL)); 555 PetscCall(PetscViewerASCIIPushSynchronized(viewer)); 556 if (!inodes) { 557 PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] Local rows %" PetscInt_FMT " nz %" PetscInt_FMT " nz alloced %" PetscInt_FMT " mem %" PetscInt_FMT ", not using I-node routines\n", rank, mat->rmap->n, (PetscInt)info.nz_used, 558 (PetscInt)info.nz_allocated, (PetscInt)info.memory)); 559 } else { 560 PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] Local rows %" PetscInt_FMT " nz %" PetscInt_FMT " nz alloced %" PetscInt_FMT " mem %" PetscInt_FMT ", using I-node routines\n", rank, mat->rmap->n, (PetscInt)info.nz_used, 561 (PetscInt)info.nz_allocated, (PetscInt)info.memory)); 562 } 563 PetscCall(MatGetInfo(sell->A, MAT_LOCAL, &info)); 564 PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used)); 565 PetscCall(MatGetInfo(sell->B, MAT_LOCAL, &info)); 566 PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used)); 567 PetscCall(PetscViewerFlush(viewer)); 568 PetscCall(PetscViewerASCIIPopSynchronized(viewer)); 569 PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n")); 570 PetscCall(VecScatterView(sell->Mvctx, viewer)); 571 PetscFunctionReturn(PETSC_SUCCESS); 572 } else if (format == PETSC_VIEWER_ASCII_INFO) { 573 PetscInt inodecount, inodelimit, *inodes; 574 PetscCall(MatInodeGetInodeSizes(sell->A, &inodecount, &inodes, &inodelimit)); 575 if (inodes) { 576 PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit)); 577 } else { 578 PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n")); 579 } 580 PetscFunctionReturn(PETSC_SUCCESS); 581 } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) { 582 PetscFunctionReturn(PETSC_SUCCESS); 583 } 584 } else if (isbinary) { 585 if (size == 1) { 586 PetscCall(PetscObjectSetName((PetscObject)sell->A, ((PetscObject)mat)->name)); 587 PetscCall(MatView(sell->A, viewer)); 588 } else { 589 /* PetscCall(MatView_MPISELL_Binary(mat,viewer)); */ 590 } 591 PetscFunctionReturn(PETSC_SUCCESS); 592 } else if (isdraw) { 593 PetscDraw draw; 594 PetscBool isnull; 595 PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw)); 596 PetscCall(PetscDrawIsNull(draw, &isnull)); 597 if (isnull) PetscFunctionReturn(PETSC_SUCCESS); 598 } 599 600 { 601 /* assemble the entire matrix onto first processor. */ 602 Mat A; 603 Mat_SeqSELL *Aloc; 604 PetscInt M = mat->rmap->N, N = mat->cmap->N, *acolidx, row, col, i, j; 605 MatScalar *aval; 606 PetscBool isnonzero; 607 608 PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &A)); 609 if (rank == 0) { 610 PetscCall(MatSetSizes(A, M, N, M, N)); 611 } else { 612 PetscCall(MatSetSizes(A, 0, 0, M, N)); 613 } 614 /* This is just a temporary matrix, so explicitly using MATMPISELL is probably best */ 615 PetscCall(MatSetType(A, MATMPISELL)); 616 PetscCall(MatMPISELLSetPreallocation(A, 0, NULL, 0, NULL)); 617 PetscCall(MatSetOption(A, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_FALSE)); 618 619 /* copy over the A part */ 620 Aloc = (Mat_SeqSELL *)sell->A->data; 621 acolidx = Aloc->colidx; 622 aval = Aloc->val; 623 for (i = 0; i < Aloc->totalslices; i++) { /* loop over slices */ 624 for (j = Aloc->sliidx[i]; j < Aloc->sliidx[i + 1]; j++) { 625 isnonzero = (PetscBool)((j - Aloc->sliidx[i]) / Aloc->sliceheight < Aloc->rlen[i * Aloc->sliceheight + j % Aloc->sliceheight]); 626 if (isnonzero) { /* check the mask bit */ 627 row = i * Aloc->sliceheight + j % Aloc->sliceheight + mat->rmap->rstart; 628 col = *acolidx + mat->rmap->rstart; 629 PetscCall(MatSetValues(A, 1, &row, 1, &col, aval, INSERT_VALUES)); 630 } 631 aval++; 632 acolidx++; 633 } 634 } 635 636 /* copy over the B part */ 637 Aloc = (Mat_SeqSELL *)sell->B->data; 638 acolidx = Aloc->colidx; 639 aval = Aloc->val; 640 for (i = 0; i < Aloc->totalslices; i++) { 641 for (j = Aloc->sliidx[i]; j < Aloc->sliidx[i + 1]; j++) { 642 isnonzero = (PetscBool)((j - Aloc->sliidx[i]) / Aloc->sliceheight < Aloc->rlen[i * Aloc->sliceheight + j % Aloc->sliceheight]); 643 if (isnonzero) { 644 row = i * Aloc->sliceheight + j % Aloc->sliceheight + mat->rmap->rstart; 645 col = sell->garray[*acolidx]; 646 PetscCall(MatSetValues(A, 1, &row, 1, &col, aval, INSERT_VALUES)); 647 } 648 aval++; 649 acolidx++; 650 } 651 } 652 653 PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY)); 654 PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY)); 655 /* 656 Everyone has to call to draw the matrix since the graphics waits are 657 synchronized across all processors that share the PetscDraw object 658 */ 659 PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer)); 660 if (rank == 0) { 661 PetscCall(PetscObjectSetName((PetscObject)((Mat_MPISELL *)A->data)->A, ((PetscObject)mat)->name)); 662 PetscCall(MatView_SeqSELL(((Mat_MPISELL *)A->data)->A, sviewer)); 663 } 664 PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer)); 665 PetscCall(MatDestroy(&A)); 666 } 667 PetscFunctionReturn(PETSC_SUCCESS); 668 } 669 670 static PetscErrorCode MatView_MPISELL(Mat mat, PetscViewer viewer) 671 { 672 PetscBool iascii, isdraw, issocket, isbinary; 673 674 PetscFunctionBegin; 675 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii)); 676 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw)); 677 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary)); 678 PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket)); 679 if (iascii || isdraw || isbinary || issocket) PetscCall(MatView_MPISELL_ASCIIorDraworSocket(mat, viewer)); 680 PetscFunctionReturn(PETSC_SUCCESS); 681 } 682 683 static PetscErrorCode MatGetGhosts_MPISELL(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[]) 684 { 685 Mat_MPISELL *sell = (Mat_MPISELL *)mat->data; 686 687 PetscFunctionBegin; 688 PetscCall(MatGetSize(sell->B, NULL, nghosts)); 689 if (ghosts) *ghosts = sell->garray; 690 PetscFunctionReturn(PETSC_SUCCESS); 691 } 692 693 static PetscErrorCode MatGetInfo_MPISELL(Mat matin, MatInfoType flag, MatInfo *info) 694 { 695 Mat_MPISELL *mat = (Mat_MPISELL *)matin->data; 696 Mat A = mat->A, B = mat->B; 697 PetscLogDouble isend[5], irecv[5]; 698 699 PetscFunctionBegin; 700 info->block_size = 1.0; 701 PetscCall(MatGetInfo(A, MAT_LOCAL, info)); 702 703 isend[0] = info->nz_used; 704 isend[1] = info->nz_allocated; 705 isend[2] = info->nz_unneeded; 706 isend[3] = info->memory; 707 isend[4] = info->mallocs; 708 709 PetscCall(MatGetInfo(B, MAT_LOCAL, info)); 710 711 isend[0] += info->nz_used; 712 isend[1] += info->nz_allocated; 713 isend[2] += info->nz_unneeded; 714 isend[3] += info->memory; 715 isend[4] += info->mallocs; 716 if (flag == MAT_LOCAL) { 717 info->nz_used = isend[0]; 718 info->nz_allocated = isend[1]; 719 info->nz_unneeded = isend[2]; 720 info->memory = isend[3]; 721 info->mallocs = isend[4]; 722 } else if (flag == MAT_GLOBAL_MAX) { 723 PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin))); 724 725 info->nz_used = irecv[0]; 726 info->nz_allocated = irecv[1]; 727 info->nz_unneeded = irecv[2]; 728 info->memory = irecv[3]; 729 info->mallocs = irecv[4]; 730 } else if (flag == MAT_GLOBAL_SUM) { 731 PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin))); 732 733 info->nz_used = irecv[0]; 734 info->nz_allocated = irecv[1]; 735 info->nz_unneeded = irecv[2]; 736 info->memory = irecv[3]; 737 info->mallocs = irecv[4]; 738 } 739 info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */ 740 info->fill_ratio_needed = 0; 741 info->factor_mallocs = 0; 742 PetscFunctionReturn(PETSC_SUCCESS); 743 } 744 745 static PetscErrorCode MatSetOption_MPISELL(Mat A, MatOption op, PetscBool flg) 746 { 747 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 748 749 PetscFunctionBegin; 750 switch (op) { 751 case MAT_NEW_NONZERO_LOCATIONS: 752 case MAT_NEW_NONZERO_ALLOCATION_ERR: 753 case MAT_UNUSED_NONZERO_LOCATION_ERR: 754 case MAT_KEEP_NONZERO_PATTERN: 755 case MAT_NEW_NONZERO_LOCATION_ERR: 756 case MAT_USE_INODES: 757 case MAT_IGNORE_ZERO_ENTRIES: 758 MatCheckPreallocated(A, 1); 759 PetscCall(MatSetOption(a->A, op, flg)); 760 PetscCall(MatSetOption(a->B, op, flg)); 761 break; 762 case MAT_ROW_ORIENTED: 763 MatCheckPreallocated(A, 1); 764 a->roworiented = flg; 765 766 PetscCall(MatSetOption(a->A, op, flg)); 767 PetscCall(MatSetOption(a->B, op, flg)); 768 break; 769 case MAT_FORCE_DIAGONAL_ENTRIES: 770 case MAT_SORTED_FULL: 771 PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op])); 772 break; 773 case MAT_IGNORE_OFF_PROC_ENTRIES: 774 a->donotstash = flg; 775 break; 776 case MAT_SPD: 777 case MAT_SPD_ETERNAL: 778 break; 779 case MAT_SYMMETRIC: 780 MatCheckPreallocated(A, 1); 781 PetscCall(MatSetOption(a->A, op, flg)); 782 break; 783 case MAT_STRUCTURALLY_SYMMETRIC: 784 MatCheckPreallocated(A, 1); 785 PetscCall(MatSetOption(a->A, op, flg)); 786 break; 787 case MAT_HERMITIAN: 788 MatCheckPreallocated(A, 1); 789 PetscCall(MatSetOption(a->A, op, flg)); 790 break; 791 case MAT_SYMMETRY_ETERNAL: 792 MatCheckPreallocated(A, 1); 793 PetscCall(MatSetOption(a->A, op, flg)); 794 break; 795 case MAT_STRUCTURAL_SYMMETRY_ETERNAL: 796 MatCheckPreallocated(A, 1); 797 PetscCall(MatSetOption(a->A, op, flg)); 798 break; 799 default: 800 SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op); 801 } 802 PetscFunctionReturn(PETSC_SUCCESS); 803 } 804 805 static PetscErrorCode MatDiagonalScale_MPISELL(Mat mat, Vec ll, Vec rr) 806 { 807 Mat_MPISELL *sell = (Mat_MPISELL *)mat->data; 808 Mat a = sell->A, b = sell->B; 809 PetscInt s1, s2, s3; 810 811 PetscFunctionBegin; 812 PetscCall(MatGetLocalSize(mat, &s2, &s3)); 813 if (rr) { 814 PetscCall(VecGetLocalSize(rr, &s1)); 815 PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size"); 816 /* Overlap communication with computation. */ 817 PetscCall(VecScatterBegin(sell->Mvctx, rr, sell->lvec, INSERT_VALUES, SCATTER_FORWARD)); 818 } 819 if (ll) { 820 PetscCall(VecGetLocalSize(ll, &s1)); 821 PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size"); 822 PetscUseTypeMethod(b, diagonalscale, ll, NULL); 823 } 824 /* scale the diagonal block */ 825 PetscUseTypeMethod(a, diagonalscale, ll, rr); 826 827 if (rr) { 828 /* Do a scatter end and then right scale the off-diagonal block */ 829 PetscCall(VecScatterEnd(sell->Mvctx, rr, sell->lvec, INSERT_VALUES, SCATTER_FORWARD)); 830 PetscUseTypeMethod(b, diagonalscale, NULL, sell->lvec); 831 } 832 PetscFunctionReturn(PETSC_SUCCESS); 833 } 834 835 static PetscErrorCode MatSetUnfactored_MPISELL(Mat A) 836 { 837 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 838 839 PetscFunctionBegin; 840 PetscCall(MatSetUnfactored(a->A)); 841 PetscFunctionReturn(PETSC_SUCCESS); 842 } 843 844 static PetscErrorCode MatEqual_MPISELL(Mat A, Mat B, PetscBool *flag) 845 { 846 Mat_MPISELL *matB = (Mat_MPISELL *)B->data, *matA = (Mat_MPISELL *)A->data; 847 Mat a, b, c, d; 848 PetscBool flg; 849 850 PetscFunctionBegin; 851 a = matA->A; 852 b = matA->B; 853 c = matB->A; 854 d = matB->B; 855 856 PetscCall(MatEqual(a, c, &flg)); 857 if (flg) PetscCall(MatEqual(b, d, &flg)); 858 PetscCall(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A))); 859 PetscFunctionReturn(PETSC_SUCCESS); 860 } 861 862 static PetscErrorCode MatCopy_MPISELL(Mat A, Mat B, MatStructure str) 863 { 864 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 865 Mat_MPISELL *b = (Mat_MPISELL *)B->data; 866 867 PetscFunctionBegin; 868 /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */ 869 if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) { 870 /* because of the column compression in the off-processor part of the matrix a->B, 871 the number of columns in a->B and b->B may be different, hence we cannot call 872 the MatCopy() directly on the two parts. If need be, we can provide a more 873 efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices 874 then copying the submatrices */ 875 PetscCall(MatCopy_Basic(A, B, str)); 876 } else { 877 PetscCall(MatCopy(a->A, b->A, str)); 878 PetscCall(MatCopy(a->B, b->B, str)); 879 } 880 PetscFunctionReturn(PETSC_SUCCESS); 881 } 882 883 static PetscErrorCode MatSetUp_MPISELL(Mat A) 884 { 885 PetscFunctionBegin; 886 PetscCall(MatMPISELLSetPreallocation(A, PETSC_DEFAULT, NULL, PETSC_DEFAULT, NULL)); 887 PetscFunctionReturn(PETSC_SUCCESS); 888 } 889 890 static PetscErrorCode MatConjugate_MPISELL(Mat mat) 891 { 892 PetscFunctionBegin; 893 if (PetscDefined(USE_COMPLEX)) { 894 Mat_MPISELL *sell = (Mat_MPISELL *)mat->data; 895 896 PetscCall(MatConjugate_SeqSELL(sell->A)); 897 PetscCall(MatConjugate_SeqSELL(sell->B)); 898 } 899 PetscFunctionReturn(PETSC_SUCCESS); 900 } 901 902 static PetscErrorCode MatRealPart_MPISELL(Mat A) 903 { 904 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 905 906 PetscFunctionBegin; 907 PetscCall(MatRealPart(a->A)); 908 PetscCall(MatRealPart(a->B)); 909 PetscFunctionReturn(PETSC_SUCCESS); 910 } 911 912 static PetscErrorCode MatImaginaryPart_MPISELL(Mat A) 913 { 914 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 915 916 PetscFunctionBegin; 917 PetscCall(MatImaginaryPart(a->A)); 918 PetscCall(MatImaginaryPart(a->B)); 919 PetscFunctionReturn(PETSC_SUCCESS); 920 } 921 922 static PetscErrorCode MatInvertBlockDiagonal_MPISELL(Mat A, const PetscScalar **values) 923 { 924 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 925 926 PetscFunctionBegin; 927 PetscCall(MatInvertBlockDiagonal(a->A, values)); 928 A->factorerrortype = a->A->factorerrortype; 929 PetscFunctionReturn(PETSC_SUCCESS); 930 } 931 932 static PetscErrorCode MatSetRandom_MPISELL(Mat x, PetscRandom rctx) 933 { 934 Mat_MPISELL *sell = (Mat_MPISELL *)x->data; 935 936 PetscFunctionBegin; 937 PetscCall(MatSetRandom(sell->A, rctx)); 938 PetscCall(MatSetRandom(sell->B, rctx)); 939 PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY)); 940 PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY)); 941 PetscFunctionReturn(PETSC_SUCCESS); 942 } 943 944 static PetscErrorCode MatSetFromOptions_MPISELL(Mat A, PetscOptionItems *PetscOptionsObject) 945 { 946 PetscFunctionBegin; 947 PetscOptionsHeadBegin(PetscOptionsObject, "MPISELL options"); 948 PetscOptionsHeadEnd(); 949 PetscFunctionReturn(PETSC_SUCCESS); 950 } 951 952 static PetscErrorCode MatShift_MPISELL(Mat Y, PetscScalar a) 953 { 954 Mat_MPISELL *msell = (Mat_MPISELL *)Y->data; 955 Mat_SeqSELL *sell = (Mat_SeqSELL *)msell->A->data; 956 957 PetscFunctionBegin; 958 if (!Y->preallocated) { 959 PetscCall(MatMPISELLSetPreallocation(Y, 1, NULL, 0, NULL)); 960 } else if (!sell->nz) { 961 PetscInt nonew = sell->nonew; 962 PetscCall(MatSeqSELLSetPreallocation(msell->A, 1, NULL)); 963 sell->nonew = nonew; 964 } 965 PetscCall(MatShift_Basic(Y, a)); 966 PetscFunctionReturn(PETSC_SUCCESS); 967 } 968 969 static PetscErrorCode MatMissingDiagonal_MPISELL(Mat A, PetscBool *missing, PetscInt *d) 970 { 971 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 972 973 PetscFunctionBegin; 974 PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices"); 975 PetscCall(MatMissingDiagonal(a->A, missing, d)); 976 if (d) { 977 PetscInt rstart; 978 PetscCall(MatGetOwnershipRange(A, &rstart, NULL)); 979 *d += rstart; 980 } 981 PetscFunctionReturn(PETSC_SUCCESS); 982 } 983 984 static PetscErrorCode MatGetDiagonalBlock_MPISELL(Mat A, Mat *a) 985 { 986 PetscFunctionBegin; 987 *a = ((Mat_MPISELL *)A->data)->A; 988 PetscFunctionReturn(PETSC_SUCCESS); 989 } 990 991 static PetscErrorCode MatStoreValues_MPISELL(Mat mat) 992 { 993 Mat_MPISELL *sell = (Mat_MPISELL *)mat->data; 994 995 PetscFunctionBegin; 996 PetscCall(MatStoreValues(sell->A)); 997 PetscCall(MatStoreValues(sell->B)); 998 PetscFunctionReturn(PETSC_SUCCESS); 999 } 1000 1001 static PetscErrorCode MatRetrieveValues_MPISELL(Mat mat) 1002 { 1003 Mat_MPISELL *sell = (Mat_MPISELL *)mat->data; 1004 1005 PetscFunctionBegin; 1006 PetscCall(MatRetrieveValues(sell->A)); 1007 PetscCall(MatRetrieveValues(sell->B)); 1008 PetscFunctionReturn(PETSC_SUCCESS); 1009 } 1010 1011 static PetscErrorCode MatMPISELLSetPreallocation_MPISELL(Mat B, PetscInt d_rlenmax, const PetscInt d_rlen[], PetscInt o_rlenmax, const PetscInt o_rlen[]) 1012 { 1013 Mat_MPISELL *b; 1014 1015 PetscFunctionBegin; 1016 PetscCall(PetscLayoutSetUp(B->rmap)); 1017 PetscCall(PetscLayoutSetUp(B->cmap)); 1018 b = (Mat_MPISELL *)B->data; 1019 1020 if (!B->preallocated) { 1021 /* Explicitly create 2 MATSEQSELL matrices. */ 1022 PetscCall(MatCreate(PETSC_COMM_SELF, &b->A)); 1023 PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n)); 1024 PetscCall(MatSetBlockSizesFromMats(b->A, B, B)); 1025 PetscCall(MatSetType(b->A, MATSEQSELL)); 1026 PetscCall(MatCreate(PETSC_COMM_SELF, &b->B)); 1027 PetscCall(MatSetSizes(b->B, B->rmap->n, B->cmap->N, B->rmap->n, B->cmap->N)); 1028 PetscCall(MatSetBlockSizesFromMats(b->B, B, B)); 1029 PetscCall(MatSetType(b->B, MATSEQSELL)); 1030 } 1031 1032 PetscCall(MatSeqSELLSetPreallocation(b->A, d_rlenmax, d_rlen)); 1033 PetscCall(MatSeqSELLSetPreallocation(b->B, o_rlenmax, o_rlen)); 1034 B->preallocated = PETSC_TRUE; 1035 B->was_assembled = PETSC_FALSE; 1036 /* 1037 critical for MatAssemblyEnd to work. 1038 MatAssemblyBegin checks it to set up was_assembled 1039 and MatAssemblyEnd checks was_assembled to determine whether to build garray 1040 */ 1041 B->assembled = PETSC_FALSE; 1042 PetscFunctionReturn(PETSC_SUCCESS); 1043 } 1044 1045 static PetscErrorCode MatDuplicate_MPISELL(Mat matin, MatDuplicateOption cpvalues, Mat *newmat) 1046 { 1047 Mat mat; 1048 Mat_MPISELL *a, *oldmat = (Mat_MPISELL *)matin->data; 1049 1050 PetscFunctionBegin; 1051 *newmat = NULL; 1052 PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat)); 1053 PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N)); 1054 PetscCall(MatSetBlockSizesFromMats(mat, matin, matin)); 1055 PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name)); 1056 a = (Mat_MPISELL *)mat->data; 1057 1058 mat->factortype = matin->factortype; 1059 mat->assembled = PETSC_TRUE; 1060 mat->insertmode = NOT_SET_VALUES; 1061 mat->preallocated = PETSC_TRUE; 1062 1063 a->size = oldmat->size; 1064 a->rank = oldmat->rank; 1065 a->donotstash = oldmat->donotstash; 1066 a->roworiented = oldmat->roworiented; 1067 a->rowindices = NULL; 1068 a->rowvalues = NULL; 1069 a->getrowactive = PETSC_FALSE; 1070 1071 PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap)); 1072 PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap)); 1073 1074 if (oldmat->colmap) { 1075 #if defined(PETSC_USE_CTABLE) 1076 PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap)); 1077 #else 1078 PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap)); 1079 PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N)); 1080 #endif 1081 } else a->colmap = NULL; 1082 if (oldmat->garray) { 1083 PetscInt len; 1084 len = oldmat->B->cmap->n; 1085 PetscCall(PetscMalloc1(len + 1, &a->garray)); 1086 if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len)); 1087 } else a->garray = NULL; 1088 1089 PetscCall(VecDuplicate(oldmat->lvec, &a->lvec)); 1090 PetscCall(VecScatterCopy(oldmat->Mvctx, &a->Mvctx)); 1091 PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A)); 1092 PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B)); 1093 PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist)); 1094 *newmat = mat; 1095 PetscFunctionReturn(PETSC_SUCCESS); 1096 } 1097 1098 static const struct _MatOps MatOps_Values = {MatSetValues_MPISELL, 1099 NULL, 1100 NULL, 1101 MatMult_MPISELL, 1102 /* 4*/ MatMultAdd_MPISELL, 1103 MatMultTranspose_MPISELL, 1104 MatMultTransposeAdd_MPISELL, 1105 NULL, 1106 NULL, 1107 NULL, 1108 /*10*/ NULL, 1109 NULL, 1110 NULL, 1111 MatSOR_MPISELL, 1112 NULL, 1113 /*15*/ MatGetInfo_MPISELL, 1114 MatEqual_MPISELL, 1115 MatGetDiagonal_MPISELL, 1116 MatDiagonalScale_MPISELL, 1117 NULL, 1118 /*20*/ MatAssemblyBegin_MPISELL, 1119 MatAssemblyEnd_MPISELL, 1120 MatSetOption_MPISELL, 1121 MatZeroEntries_MPISELL, 1122 /*24*/ NULL, 1123 NULL, 1124 NULL, 1125 NULL, 1126 NULL, 1127 /*29*/ MatSetUp_MPISELL, 1128 NULL, 1129 NULL, 1130 MatGetDiagonalBlock_MPISELL, 1131 NULL, 1132 /*34*/ MatDuplicate_MPISELL, 1133 NULL, 1134 NULL, 1135 NULL, 1136 NULL, 1137 /*39*/ NULL, 1138 NULL, 1139 NULL, 1140 MatGetValues_MPISELL, 1141 MatCopy_MPISELL, 1142 /*44*/ NULL, 1143 MatScale_MPISELL, 1144 MatShift_MPISELL, 1145 MatDiagonalSet_MPISELL, 1146 NULL, 1147 /*49*/ MatSetRandom_MPISELL, 1148 NULL, 1149 NULL, 1150 NULL, 1151 NULL, 1152 /*54*/ MatFDColoringCreate_MPIXAIJ, 1153 NULL, 1154 MatSetUnfactored_MPISELL, 1155 NULL, 1156 NULL, 1157 /*59*/ NULL, 1158 MatDestroy_MPISELL, 1159 MatView_MPISELL, 1160 NULL, 1161 NULL, 1162 /*64*/ NULL, 1163 NULL, 1164 NULL, 1165 NULL, 1166 NULL, 1167 /*69*/ NULL, 1168 NULL, 1169 NULL, 1170 NULL, 1171 NULL, 1172 NULL, 1173 /*75*/ MatFDColoringApply_AIJ, /* reuse AIJ function */ 1174 MatSetFromOptions_MPISELL, 1175 NULL, 1176 NULL, 1177 NULL, 1178 /*80*/ NULL, 1179 NULL, 1180 NULL, 1181 /*83*/ NULL, 1182 NULL, 1183 NULL, 1184 NULL, 1185 NULL, 1186 NULL, 1187 /*89*/ NULL, 1188 NULL, 1189 NULL, 1190 NULL, 1191 NULL, 1192 /*94*/ NULL, 1193 NULL, 1194 NULL, 1195 NULL, 1196 NULL, 1197 /*99*/ NULL, 1198 NULL, 1199 NULL, 1200 MatConjugate_MPISELL, 1201 NULL, 1202 /*104*/ NULL, 1203 MatRealPart_MPISELL, 1204 MatImaginaryPart_MPISELL, 1205 NULL, 1206 NULL, 1207 /*109*/ NULL, 1208 NULL, 1209 NULL, 1210 NULL, 1211 MatMissingDiagonal_MPISELL, 1212 /*114*/ NULL, 1213 NULL, 1214 MatGetGhosts_MPISELL, 1215 NULL, 1216 NULL, 1217 /*119*/ MatMultDiagonalBlock_MPISELL, 1218 NULL, 1219 NULL, 1220 NULL, 1221 NULL, 1222 /*124*/ NULL, 1223 NULL, 1224 MatInvertBlockDiagonal_MPISELL, 1225 NULL, 1226 NULL, 1227 /*129*/ NULL, 1228 NULL, 1229 NULL, 1230 NULL, 1231 NULL, 1232 /*134*/ NULL, 1233 NULL, 1234 NULL, 1235 NULL, 1236 NULL, 1237 /*139*/ NULL, 1238 NULL, 1239 NULL, 1240 MatFDColoringSetUp_MPIXAIJ, 1241 NULL, 1242 /*144*/ NULL, 1243 NULL, 1244 NULL, 1245 NULL, 1246 NULL, 1247 NULL, 1248 /*150*/ NULL, 1249 NULL, 1250 NULL, 1251 NULL}; 1252 1253 /*@C 1254 MatMPISELLSetPreallocation - Preallocates memory for a `MATMPISELL` sparse parallel matrix in sell format. 1255 For good matrix assembly performance the user should preallocate the matrix storage by 1256 setting the parameters `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`). 1257 1258 Collective 1259 1260 Input Parameters: 1261 + B - the matrix 1262 . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix 1263 (same value is used for all local rows) 1264 . d_nnz - array containing the number of nonzeros in the various rows of the 1265 DIAGONAL portion of the local submatrix (possibly different for each row) 1266 or NULL (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure. 1267 The size of this array is equal to the number of local rows, i.e 'm'. 1268 For matrices that will be factored, you must leave room for (and set) 1269 the diagonal entry even if it is zero. 1270 . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local 1271 submatrix (same value is used for all local rows). 1272 - o_nnz - array containing the number of nonzeros in the various rows of the 1273 OFF-DIAGONAL portion of the local submatrix (possibly different for 1274 each row) or NULL (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero 1275 structure. The size of this array is equal to the number 1276 of local rows, i.e 'm'. 1277 1278 Example usage: 1279 Consider the following 8x8 matrix with 34 non-zero values, that is 1280 assembled across 3 processors. Lets assume that proc0 owns 3 rows, 1281 proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown 1282 as follows 1283 1284 .vb 1285 1 2 0 | 0 3 0 | 0 4 1286 Proc0 0 5 6 | 7 0 0 | 8 0 1287 9 0 10 | 11 0 0 | 12 0 1288 ------------------------------------- 1289 13 0 14 | 15 16 17 | 0 0 1290 Proc1 0 18 0 | 19 20 21 | 0 0 1291 0 0 0 | 22 23 0 | 24 0 1292 ------------------------------------- 1293 Proc2 25 26 27 | 0 0 28 | 29 0 1294 30 0 0 | 31 32 33 | 0 34 1295 .ve 1296 1297 This can be represented as a collection of submatrices as 1298 1299 .vb 1300 A B C 1301 D E F 1302 G H I 1303 .ve 1304 1305 Where the submatrices A,B,C are owned by proc0, D,E,F are 1306 owned by proc1, G,H,I are owned by proc2. 1307 1308 The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 1309 The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 1310 The 'M','N' parameters are 8,8, and have the same values on all procs. 1311 1312 The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are 1313 submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices 1314 corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively. 1315 Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL 1316 part as `MATSEQSELL` matrices. For example, proc1 will store [E] as a `MATSEQSELL` 1317 matrix, ans [DF] as another SeqSELL matrix. 1318 1319 When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are 1320 allocated for every row of the local diagonal submatrix, and o_nz 1321 storage locations are allocated for every row of the OFF-DIAGONAL submat. 1322 One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local 1323 rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices. 1324 In this case, the values of d_nz,o_nz are 1325 .vb 1326 proc0 dnz = 2, o_nz = 2 1327 proc1 dnz = 3, o_nz = 2 1328 proc2 dnz = 1, o_nz = 4 1329 .ve 1330 We are allocating m*(d_nz+o_nz) storage locations for every proc. This 1331 translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10 1332 for proc3. i.e we are using 12+15+10=37 storage locations to store 1333 34 values. 1334 1335 When `d_nnz`, `o_nnz` parameters are specified, the storage is specified 1336 for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices. 1337 In the above case the values for d_nnz,o_nnz are 1338 .vb 1339 proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2] 1340 proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1] 1341 proc2 d_nnz = [1,1] and o_nnz = [4,4] 1342 .ve 1343 Here the space allocated is according to nz (or maximum values in the nnz 1344 if nnz is provided) for DIAGONAL and OFF-DIAGONAL submatrices, i.e (2+2+3+2)*3+(1+4)*2=37 1345 1346 Level: intermediate 1347 1348 Notes: 1349 If the *_nnz parameter is given then the *_nz parameter is ignored 1350 1351 The stored row and column indices begin with zero. 1352 1353 The parallel matrix is partitioned such that the first m0 rows belong to 1354 process 0, the next m1 rows belong to process 1, the next m2 rows belong 1355 to process 2 etc.. where m0,m1,m2... are the input parameter 'm'. 1356 1357 The DIAGONAL portion of the local submatrix of a processor can be defined 1358 as the submatrix which is obtained by extraction the part corresponding to 1359 the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the 1360 first row that belongs to the processor, r2 is the last row belonging to 1361 the this processor, and c1-c2 is range of indices of the local part of a 1362 vector suitable for applying the matrix to. This is an mxn matrix. In the 1363 common case of a square matrix, the row and column ranges are the same and 1364 the DIAGONAL part is also square. The remaining portion of the local 1365 submatrix (mxN) constitute the OFF-DIAGONAL portion. 1366 1367 If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored. 1368 1369 You can call `MatGetInfo()` to get information on how effective the preallocation was; 1370 for example the fields mallocs,nz_allocated,nz_used,nz_unneeded; 1371 You can also run with the option -info and look for messages with the string 1372 malloc in them to see if additional memory allocation was needed. 1373 1374 .seealso: `Mat`, `MatCreate()`, `MatCreateSeqSELL()`, `MatSetValues()`, `MatCreateSELL()`, 1375 `MATMPISELL`, `MatGetInfo()`, `PetscSplitOwnership()`, `MATSELL` 1376 @*/ 1377 PetscErrorCode MatMPISELLSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[]) 1378 { 1379 PetscFunctionBegin; 1380 PetscValidHeaderSpecific(B, MAT_CLASSID, 1); 1381 PetscValidType(B, 1); 1382 PetscTryMethod(B, "MatMPISELLSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz)); 1383 PetscFunctionReturn(PETSC_SUCCESS); 1384 } 1385 1386 /*MC 1387 MATMPISELL - MATMPISELL = "mpisell" - A matrix type to be used for MPI sparse matrices, 1388 based on the sliced Ellpack format 1389 1390 Options Database Key: 1391 . -mat_type sell - sets the matrix type to `MATSELL` during a call to `MatSetFromOptions()` 1392 1393 Level: beginner 1394 1395 .seealso: `Mat`, `MatCreateSELL()`, `MATSEQSELL`, `MATSELL`, `MATSEQAIJ`, `MATAIJ`, `MATMPIAIJ` 1396 M*/ 1397 1398 /*@C 1399 MatCreateSELL - Creates a sparse parallel matrix in `MATSELL` format. 1400 1401 Collective 1402 1403 Input Parameters: 1404 + comm - MPI communicator 1405 . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given) 1406 This value should be the same as the local size used in creating the 1407 y vector for the matrix-vector product y = Ax. 1408 . n - This value should be the same as the local size used in creating the 1409 x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have 1410 calculated if `N` is given) For square matrices n is almost always `m`. 1411 . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given) 1412 . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given) 1413 . d_rlenmax - max number of nonzeros per row in DIAGONAL portion of local submatrix 1414 (same value is used for all local rows) 1415 . d_rlen - array containing the number of nonzeros in the various rows of the 1416 DIAGONAL portion of the local submatrix (possibly different for each row) 1417 or `NULL`, if d_rlenmax is used to specify the nonzero structure. 1418 The size of this array is equal to the number of local rows, i.e `m`. 1419 . o_rlenmax - max number of nonzeros per row in the OFF-DIAGONAL portion of local 1420 submatrix (same value is used for all local rows). 1421 - o_rlen - array containing the number of nonzeros in the various rows of the 1422 OFF-DIAGONAL portion of the local submatrix (possibly different for 1423 each row) or `NULL`, if `o_rlenmax` is used to specify the nonzero 1424 structure. The size of this array is equal to the number 1425 of local rows, i.e `m`. 1426 1427 Output Parameter: 1428 . A - the matrix 1429 1430 Options Database Key: 1431 . -mat_sell_oneindex - Internally use indexing starting at 1 1432 rather than 0. When calling `MatSetValues()`, 1433 the user still MUST index entries starting at 0! 1434 1435 Example: 1436 Consider the following 8x8 matrix with 34 non-zero values, that is 1437 assembled across 3 processors. Lets assume that proc0 owns 3 rows, 1438 proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown 1439 as follows 1440 1441 .vb 1442 1 2 0 | 0 3 0 | 0 4 1443 Proc0 0 5 6 | 7 0 0 | 8 0 1444 9 0 10 | 11 0 0 | 12 0 1445 ------------------------------------- 1446 13 0 14 | 15 16 17 | 0 0 1447 Proc1 0 18 0 | 19 20 21 | 0 0 1448 0 0 0 | 22 23 0 | 24 0 1449 ------------------------------------- 1450 Proc2 25 26 27 | 0 0 28 | 29 0 1451 30 0 0 | 31 32 33 | 0 34 1452 .ve 1453 1454 This can be represented as a collection of submatrices as 1455 .vb 1456 A B C 1457 D E F 1458 G H I 1459 .ve 1460 1461 Where the submatrices A,B,C are owned by proc0, D,E,F are 1462 owned by proc1, G,H,I are owned by proc2. 1463 1464 The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 1465 The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively. 1466 The 'M','N' parameters are 8,8, and have the same values on all procs. 1467 1468 The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are 1469 submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices 1470 corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively. 1471 Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL 1472 part as `MATSEQSELL` matrices. For example, proc1 will store [E] as a `MATSEQSELL` 1473 matrix, ans [DF] as another `MATSEQSELL` matrix. 1474 1475 When d_rlenmax, o_rlenmax parameters are specified, d_rlenmax storage elements are 1476 allocated for every row of the local diagonal submatrix, and o_rlenmax 1477 storage locations are allocated for every row of the OFF-DIAGONAL submat. 1478 One way to choose d_rlenmax and o_rlenmax is to use the max nonzerors per local 1479 rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices. 1480 In this case, the values of d_rlenmax,o_rlenmax are 1481 .vb 1482 proc0 - d_rlenmax = 2, o_rlenmax = 2 1483 proc1 - d_rlenmax = 3, o_rlenmax = 2 1484 proc2 - d_rlenmax = 1, o_rlenmax = 4 1485 .ve 1486 We are allocating m*(d_rlenmax+o_rlenmax) storage locations for every proc. This 1487 translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10 1488 for proc3. i.e we are using 12+15+10=37 storage locations to store 1489 34 values. 1490 1491 When `d_rlen`, `o_rlen` parameters are specified, the storage is specified 1492 for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices. 1493 In the above case the values for `d_nnz`, `o_nnz` are 1494 .vb 1495 proc0 - d_nnz = [2,2,2] and o_nnz = [2,2,2] 1496 proc1 - d_nnz = [3,3,2] and o_nnz = [2,1,1] 1497 proc2 - d_nnz = [1,1] and o_nnz = [4,4] 1498 .ve 1499 Here the space allocated is still 37 though there are 34 nonzeros because 1500 the allocation is always done according to rlenmax. 1501 1502 Level: intermediate 1503 1504 Notes: 1505 It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`, 1506 MatXXXXSetPreallocation() paradigm instead of this routine directly. 1507 [MatXXXXSetPreallocation() is, for example, `MatSeqSELLSetPreallocation()`] 1508 1509 If the *_rlen parameter is given then the *_rlenmax parameter is ignored 1510 1511 `m`, `n`, `M`, `N` parameters specify the size of the matrix, and its partitioning across 1512 processors, while `d_rlenmax`, `d_rlen`, `o_rlenmax` , `o_rlen` parameters specify the approximate 1513 storage requirements for this matrix. 1514 1515 If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one 1516 processor than it must be used on all processors that share the object for 1517 that argument. 1518 1519 The user MUST specify either the local or global matrix dimensions 1520 (possibly both). 1521 1522 The parallel matrix is partitioned across processors such that the 1523 first m0 rows belong to process 0, the next m1 rows belong to 1524 process 1, the next m2 rows belong to process 2 etc.. where 1525 m0,m1,m2,.. are the input parameter 'm'. i.e each processor stores 1526 values corresponding to [`m` x `N`] submatrix. 1527 1528 The columns are logically partitioned with the n0 columns belonging 1529 to 0th partition, the next n1 columns belonging to the next 1530 partition etc.. where n0,n1,n2... are the input parameter `n`. 1531 1532 The DIAGONAL portion of the local submatrix on any given processor 1533 is the submatrix corresponding to the rows and columns `m`, `n` 1534 corresponding to the given processor. i.e diagonal matrix on 1535 process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1] 1536 etc. The remaining portion of the local submatrix [m x (N-n)] 1537 constitute the OFF-DIAGONAL portion. The example below better 1538 illustrates this concept. 1539 1540 For a square global matrix we define each processor's diagonal portion 1541 to be its local rows and the corresponding columns (a square submatrix); 1542 each processor's off-diagonal portion encompasses the remainder of the 1543 local matrix (a rectangular submatrix). 1544 1545 If `o_rlen`, `d_rlen` are specified, then `o_rlenmax`, and `d_rlenmax` are ignored. 1546 1547 When calling this routine with a single process communicator, a matrix of 1548 type `MATSEQSELL` is returned. If a matrix of type `MATMPISELL` is desired for this 1549 type of communicator, use the construction mechanism 1550 .vb 1551 MatCreate(...,&A); 1552 MatSetType(A,MATMPISELL); 1553 MatSetSizes(A, m,n,M,N); 1554 MatMPISELLSetPreallocation(A,...); 1555 .ve 1556 1557 .seealso: `Mat`, `MATSELL`, `MatCreate()`, `MatCreateSeqSELL()`, `MatSetValues()`, `MatMPISELLSetPreallocation()`, `MATMPISELL` 1558 @*/ 1559 PetscErrorCode MatCreateSELL(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, PetscInt d_rlenmax, const PetscInt d_rlen[], PetscInt o_rlenmax, const PetscInt o_rlen[], Mat *A) 1560 { 1561 PetscMPIInt size; 1562 1563 PetscFunctionBegin; 1564 PetscCall(MatCreate(comm, A)); 1565 PetscCall(MatSetSizes(*A, m, n, M, N)); 1566 PetscCallMPI(MPI_Comm_size(comm, &size)); 1567 if (size > 1) { 1568 PetscCall(MatSetType(*A, MATMPISELL)); 1569 PetscCall(MatMPISELLSetPreallocation(*A, d_rlenmax, d_rlen, o_rlenmax, o_rlen)); 1570 } else { 1571 PetscCall(MatSetType(*A, MATSEQSELL)); 1572 PetscCall(MatSeqSELLSetPreallocation(*A, d_rlenmax, d_rlen)); 1573 } 1574 PetscFunctionReturn(PETSC_SUCCESS); 1575 } 1576 1577 /*@C 1578 MatMPISELLGetSeqSELL - Returns the local pieces of this distributed matrix 1579 1580 Not Collective 1581 1582 Input Parameter: 1583 . A - the `MATMPISELL` matrix 1584 1585 Output Parameters: 1586 + Ad - The diagonal portion of `A` 1587 . Ao - The off-diagonal portion of `A` 1588 - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix 1589 1590 Level: advanced 1591 1592 .seealso: `Mat`, `MATSEQSELL`, `MATMPISELL` 1593 @*/ 1594 PetscErrorCode MatMPISELLGetSeqSELL(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[]) 1595 { 1596 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 1597 PetscBool flg; 1598 1599 PetscFunctionBegin; 1600 PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPISELL, &flg)); 1601 PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPISELL matrix as input"); 1602 if (Ad) *Ad = a->A; 1603 if (Ao) *Ao = a->B; 1604 if (colmap) *colmap = a->garray; 1605 PetscFunctionReturn(PETSC_SUCCESS); 1606 } 1607 1608 /*@C 1609 MatMPISELLGetLocalMatCondensed - Creates a `MATSEQSELL` matrix from an `MATMPISELL` matrix by 1610 taking all its local rows and NON-ZERO columns 1611 1612 Not Collective 1613 1614 Input Parameters: 1615 + A - the matrix 1616 . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX` 1617 . row - index sets of rows to extract (or `NULL`) 1618 - col - index sets of columns to extract (or `NULL`) 1619 1620 Output Parameter: 1621 . A_loc - the local sequential matrix generated 1622 1623 Level: advanced 1624 1625 .seealso: `Mat`, `MATSEQSELL`, `MATMPISELL`, `MatGetOwnershipRange()`, `MatMPISELLGetLocalMat()` 1626 @*/ 1627 PetscErrorCode MatMPISELLGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc) 1628 { 1629 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 1630 PetscInt i, start, end, ncols, nzA, nzB, *cmap, imark, *idx; 1631 IS isrowa, iscola; 1632 Mat *aloc; 1633 PetscBool match; 1634 1635 PetscFunctionBegin; 1636 PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPISELL, &match)); 1637 PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPISELL matrix as input"); 1638 PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0)); 1639 if (!row) { 1640 start = A->rmap->rstart; 1641 end = A->rmap->rend; 1642 PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa)); 1643 } else { 1644 isrowa = *row; 1645 } 1646 if (!col) { 1647 start = A->cmap->rstart; 1648 cmap = a->garray; 1649 nzA = a->A->cmap->n; 1650 nzB = a->B->cmap->n; 1651 PetscCall(PetscMalloc1(nzA + nzB, &idx)); 1652 ncols = 0; 1653 for (i = 0; i < nzB; i++) { 1654 if (cmap[i] < start) idx[ncols++] = cmap[i]; 1655 else break; 1656 } 1657 imark = i; 1658 for (i = 0; i < nzA; i++) idx[ncols++] = start + i; 1659 for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; 1660 PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola)); 1661 } else { 1662 iscola = *col; 1663 } 1664 if (scall != MAT_INITIAL_MATRIX) { 1665 PetscCall(PetscMalloc1(1, &aloc)); 1666 aloc[0] = *A_loc; 1667 } 1668 PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc)); 1669 *A_loc = aloc[0]; 1670 PetscCall(PetscFree(aloc)); 1671 if (!row) PetscCall(ISDestroy(&isrowa)); 1672 if (!col) PetscCall(ISDestroy(&iscola)); 1673 PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0)); 1674 PetscFunctionReturn(PETSC_SUCCESS); 1675 } 1676 1677 #include <../src/mat/impls/aij/mpi/mpiaij.h> 1678 1679 PetscErrorCode MatConvert_MPISELL_MPIAIJ(Mat A, MatType newtype, MatReuse reuse, Mat *newmat) 1680 { 1681 Mat_MPISELL *a = (Mat_MPISELL *)A->data; 1682 Mat B; 1683 Mat_MPIAIJ *b; 1684 1685 PetscFunctionBegin; 1686 PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Matrix must be assembled"); 1687 1688 if (reuse == MAT_REUSE_MATRIX) { 1689 B = *newmat; 1690 } else { 1691 PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B)); 1692 PetscCall(MatSetType(B, MATMPIAIJ)); 1693 PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N)); 1694 PetscCall(MatSetBlockSizes(B, A->rmap->bs, A->cmap->bs)); 1695 PetscCall(MatSeqAIJSetPreallocation(B, 0, NULL)); 1696 PetscCall(MatMPIAIJSetPreallocation(B, 0, NULL, 0, NULL)); 1697 } 1698 b = (Mat_MPIAIJ *)B->data; 1699 1700 if (reuse == MAT_REUSE_MATRIX) { 1701 PetscCall(MatConvert_SeqSELL_SeqAIJ(a->A, MATSEQAIJ, MAT_REUSE_MATRIX, &b->A)); 1702 PetscCall(MatConvert_SeqSELL_SeqAIJ(a->B, MATSEQAIJ, MAT_REUSE_MATRIX, &b->B)); 1703 } else { 1704 PetscCall(MatDestroy(&b->A)); 1705 PetscCall(MatDestroy(&b->B)); 1706 PetscCall(MatDisAssemble_MPISELL(A)); 1707 PetscCall(MatConvert_SeqSELL_SeqAIJ(a->A, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->A)); 1708 PetscCall(MatConvert_SeqSELL_SeqAIJ(a->B, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->B)); 1709 PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY)); 1710 PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY)); 1711 PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY)); 1712 PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY)); 1713 } 1714 1715 if (reuse == MAT_INPLACE_MATRIX) { 1716 PetscCall(MatHeaderReplace(A, &B)); 1717 } else { 1718 *newmat = B; 1719 } 1720 PetscFunctionReturn(PETSC_SUCCESS); 1721 } 1722 1723 PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat A, MatType newtype, MatReuse reuse, Mat *newmat) 1724 { 1725 Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data; 1726 Mat B; 1727 Mat_MPISELL *b; 1728 1729 PetscFunctionBegin; 1730 PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Matrix must be assembled"); 1731 1732 if (reuse == MAT_REUSE_MATRIX) { 1733 B = *newmat; 1734 } else { 1735 Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)a->A->data, *Ba = (Mat_SeqAIJ *)a->B->data; 1736 PetscInt i, d_nz = 0, o_nz = 0, m = A->rmap->N, n = A->cmap->N, lm = A->rmap->n, ln = A->cmap->n; 1737 PetscInt *d_nnz, *o_nnz; 1738 PetscCall(PetscMalloc2(lm, &d_nnz, lm, &o_nnz)); 1739 for (i = 0; i < lm; i++) { 1740 d_nnz[i] = Aa->i[i + 1] - Aa->i[i]; 1741 o_nnz[i] = Ba->i[i + 1] - Ba->i[i]; 1742 if (d_nnz[i] > d_nz) d_nz = d_nnz[i]; 1743 if (o_nnz[i] > o_nz) o_nz = o_nnz[i]; 1744 } 1745 PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B)); 1746 PetscCall(MatSetType(B, MATMPISELL)); 1747 PetscCall(MatSetSizes(B, lm, ln, m, n)); 1748 PetscCall(MatSetBlockSizes(B, A->rmap->bs, A->cmap->bs)); 1749 PetscCall(MatSeqSELLSetPreallocation(B, d_nz, d_nnz)); 1750 PetscCall(MatMPISELLSetPreallocation(B, d_nz, d_nnz, o_nz, o_nnz)); 1751 PetscCall(PetscFree2(d_nnz, o_nnz)); 1752 } 1753 b = (Mat_MPISELL *)B->data; 1754 1755 if (reuse == MAT_REUSE_MATRIX) { 1756 PetscCall(MatConvert_SeqAIJ_SeqSELL(a->A, MATSEQSELL, MAT_REUSE_MATRIX, &b->A)); 1757 PetscCall(MatConvert_SeqAIJ_SeqSELL(a->B, MATSEQSELL, MAT_REUSE_MATRIX, &b->B)); 1758 } else { 1759 PetscCall(MatDestroy(&b->A)); 1760 PetscCall(MatDestroy(&b->B)); 1761 PetscCall(MatConvert_SeqAIJ_SeqSELL(a->A, MATSEQSELL, MAT_INITIAL_MATRIX, &b->A)); 1762 PetscCall(MatConvert_SeqAIJ_SeqSELL(a->B, MATSEQSELL, MAT_INITIAL_MATRIX, &b->B)); 1763 PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY)); 1764 PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY)); 1765 PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY)); 1766 PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY)); 1767 } 1768 1769 if (reuse == MAT_INPLACE_MATRIX) { 1770 PetscCall(MatHeaderReplace(A, &B)); 1771 } else { 1772 *newmat = B; 1773 } 1774 PetscFunctionReturn(PETSC_SUCCESS); 1775 } 1776 1777 PetscErrorCode MatSOR_MPISELL(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx) 1778 { 1779 Mat_MPISELL *mat = (Mat_MPISELL *)matin->data; 1780 Vec bb1 = NULL; 1781 1782 PetscFunctionBegin; 1783 if (flag == SOR_APPLY_UPPER) { 1784 PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx)); 1785 PetscFunctionReturn(PETSC_SUCCESS); 1786 } 1787 1788 if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1)); 1789 1790 if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) { 1791 if (flag & SOR_ZERO_INITIAL_GUESS) { 1792 PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx)); 1793 its--; 1794 } 1795 1796 while (its--) { 1797 PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD)); 1798 PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD)); 1799 1800 /* update rhs: bb1 = bb - B*x */ 1801 PetscCall(VecScale(mat->lvec, -1.0)); 1802 PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1)); 1803 1804 /* local sweep */ 1805 PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx)); 1806 } 1807 } else if (flag & SOR_LOCAL_FORWARD_SWEEP) { 1808 if (flag & SOR_ZERO_INITIAL_GUESS) { 1809 PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx)); 1810 its--; 1811 } 1812 while (its--) { 1813 PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD)); 1814 PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD)); 1815 1816 /* update rhs: bb1 = bb - B*x */ 1817 PetscCall(VecScale(mat->lvec, -1.0)); 1818 PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1)); 1819 1820 /* local sweep */ 1821 PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx)); 1822 } 1823 } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) { 1824 if (flag & SOR_ZERO_INITIAL_GUESS) { 1825 PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx)); 1826 its--; 1827 } 1828 while (its--) { 1829 PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD)); 1830 PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD)); 1831 1832 /* update rhs: bb1 = bb - B*x */ 1833 PetscCall(VecScale(mat->lvec, -1.0)); 1834 PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1)); 1835 1836 /* local sweep */ 1837 PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx)); 1838 } 1839 } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported"); 1840 1841 PetscCall(VecDestroy(&bb1)); 1842 1843 matin->factorerrortype = mat->A->factorerrortype; 1844 PetscFunctionReturn(PETSC_SUCCESS); 1845 } 1846 1847 #if defined(PETSC_HAVE_CUDA) 1848 PETSC_INTERN PetscErrorCode MatConvert_MPISELL_MPISELLCUDA(Mat, MatType, MatReuse, Mat *); 1849 #endif 1850 1851 /*MC 1852 MATMPISELL - MATMPISELL = "MPISELL" - A matrix type to be used for parallel sparse matrices. 1853 1854 Options Database Keys: 1855 . -mat_type mpisell - sets the matrix type to `MATMPISELL` during a call to `MatSetFromOptions()` 1856 1857 Level: beginner 1858 1859 .seealso: `Mat`, `MATSELL`, `MATSEQSELL` `MatCreateSELL()` 1860 M*/ 1861 PETSC_EXTERN PetscErrorCode MatCreate_MPISELL(Mat B) 1862 { 1863 Mat_MPISELL *b; 1864 PetscMPIInt size; 1865 1866 PetscFunctionBegin; 1867 PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size)); 1868 PetscCall(PetscNew(&b)); 1869 B->data = (void *)b; 1870 B->ops[0] = MatOps_Values; 1871 B->assembled = PETSC_FALSE; 1872 B->insertmode = NOT_SET_VALUES; 1873 b->size = size; 1874 PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank)); 1875 /* build cache for off array entries formed */ 1876 PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash)); 1877 1878 b->donotstash = PETSC_FALSE; 1879 b->colmap = NULL; 1880 b->garray = NULL; 1881 b->roworiented = PETSC_TRUE; 1882 1883 /* stuff used for matrix vector multiply */ 1884 b->lvec = NULL; 1885 b->Mvctx = NULL; 1886 1887 /* stuff for MatGetRow() */ 1888 b->rowindices = NULL; 1889 b->rowvalues = NULL; 1890 b->getrowactive = PETSC_FALSE; 1891 1892 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPISELL)); 1893 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPISELL)); 1894 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPISELL)); 1895 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPISELLSetPreallocation_C", MatMPISELLSetPreallocation_MPISELL)); 1896 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpisell_mpiaij_C", MatConvert_MPISELL_MPIAIJ)); 1897 #if defined(PETSC_HAVE_CUDA) 1898 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpisell_mpisellcuda_C", MatConvert_MPISELL_MPISELLCUDA)); 1899 #endif 1900 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPISELL)); 1901 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPISELL)); 1902 PetscFunctionReturn(PETSC_SUCCESS); 1903 } 1904