1 2 /* 3 Provides an interface to the MUMPS sparse solver 4 */ 5 6 #include <../src/mat/impls/aij/mpi/mpiaij.h> /*I "petscmat.h" I*/ 7 #include <../src/mat/impls/sbaij/mpi/mpisbaij.h> 8 #include <../src/mat/impls/sell/mpi/mpisell.h> 9 10 EXTERN_C_BEGIN 11 #if defined(PETSC_USE_COMPLEX) 12 #if defined(PETSC_USE_REAL_SINGLE) 13 #include <cmumps_c.h> 14 #else 15 #include <zmumps_c.h> 16 #endif 17 #else 18 #if defined(PETSC_USE_REAL_SINGLE) 19 #include <smumps_c.h> 20 #else 21 #include <dmumps_c.h> 22 #endif 23 #endif 24 EXTERN_C_END 25 #define JOB_INIT -1 26 #define JOB_FACTSYMBOLIC 1 27 #define JOB_FACTNUMERIC 2 28 #define JOB_SOLVE 3 29 #define JOB_END -2 30 31 /* calls to MUMPS */ 32 #if defined(PETSC_USE_COMPLEX) 33 #if defined(PETSC_USE_REAL_SINGLE) 34 #define PetscMUMPS_c cmumps_c 35 #else 36 #define PetscMUMPS_c zmumps_c 37 #endif 38 #else 39 #if defined(PETSC_USE_REAL_SINGLE) 40 #define PetscMUMPS_c smumps_c 41 #else 42 #define PetscMUMPS_c dmumps_c 43 #endif 44 #endif 45 46 /* declare MumpsScalar */ 47 #if defined(PETSC_USE_COMPLEX) 48 #if defined(PETSC_USE_REAL_SINGLE) 49 #define MumpsScalar mumps_complex 50 #else 51 #define MumpsScalar mumps_double_complex 52 #endif 53 #else 54 #define MumpsScalar PetscScalar 55 #endif 56 57 /* macros s.t. indices match MUMPS documentation */ 58 #define ICNTL(I) icntl[(I)-1] 59 #define CNTL(I) cntl[(I)-1] 60 #define INFOG(I) infog[(I)-1] 61 #define INFO(I) info[(I)-1] 62 #define RINFOG(I) rinfog[(I)-1] 63 #define RINFO(I) rinfo[(I)-1] 64 65 typedef struct { 66 #if defined(PETSC_USE_COMPLEX) 67 #if defined(PETSC_USE_REAL_SINGLE) 68 CMUMPS_STRUC_C id; 69 #else 70 ZMUMPS_STRUC_C id; 71 #endif 72 #else 73 #if defined(PETSC_USE_REAL_SINGLE) 74 SMUMPS_STRUC_C id; 75 #else 76 DMUMPS_STRUC_C id; 77 #endif 78 #endif 79 80 MatStructure matstruc; 81 PetscMPIInt myid,size; 82 PetscInt *irn,*jcn,nz,sym; 83 PetscScalar *val; 84 MPI_Comm comm_mumps; 85 PetscInt ICNTL9_pre; /* check if ICNTL(9) is changed from previous MatSolve */ 86 VecScatter scat_rhs, scat_sol; /* used by MatSolve() */ 87 Vec b_seq,x_seq; 88 PetscInt ninfo,*info; /* display INFO */ 89 PetscInt sizeredrhs; 90 PetscScalar *schur_sol; 91 PetscInt schur_sizesol; 92 93 PetscErrorCode (*ConvertToTriples)(Mat, int, MatReuse, int*, int**, int**, PetscScalar**); 94 } Mat_MUMPS; 95 96 extern PetscErrorCode MatDuplicate_MUMPS(Mat,MatDuplicateOption,Mat*); 97 98 static PetscErrorCode MatMumpsResetSchur_Private(Mat_MUMPS* mumps) 99 { 100 PetscErrorCode ierr; 101 102 PetscFunctionBegin; 103 ierr = PetscFree2(mumps->id.listvar_schur,mumps->id.schur);CHKERRQ(ierr); 104 ierr = PetscFree(mumps->id.redrhs);CHKERRQ(ierr); 105 ierr = PetscFree(mumps->schur_sol);CHKERRQ(ierr); 106 mumps->id.size_schur = 0; 107 mumps->id.schur_lld = 0; 108 mumps->id.ICNTL(19) = 0; 109 PetscFunctionReturn(0); 110 } 111 112 /* solve with rhs in mumps->id.redrhs and return in the same location */ 113 static PetscErrorCode MatMumpsSolveSchur_Private(Mat F) 114 { 115 Mat_MUMPS *mumps=(Mat_MUMPS*)F->data; 116 Mat S,B,X; 117 MatFactorSchurStatus schurstatus; 118 PetscInt sizesol; 119 PetscErrorCode ierr; 120 121 PetscFunctionBegin; 122 ierr = MatFactorFactorizeSchurComplement(F);CHKERRQ(ierr); 123 ierr = MatFactorGetSchurComplement(F,&S,&schurstatus);CHKERRQ(ierr); 124 ierr = MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.nrhs,(PetscScalar*)mumps->id.redrhs,&B);CHKERRQ(ierr); 125 switch (schurstatus) { 126 case MAT_FACTOR_SCHUR_FACTORED: 127 ierr = MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.nrhs,(PetscScalar*)mumps->id.redrhs,&X);CHKERRQ(ierr); 128 if (!mumps->id.ICNTL(9)) { /* transpose solve */ 129 ierr = MatMatSolveTranspose(S,B,X);CHKERRQ(ierr); 130 } else { 131 ierr = MatMatSolve(S,B,X);CHKERRQ(ierr); 132 } 133 break; 134 case MAT_FACTOR_SCHUR_INVERTED: 135 sizesol = mumps->id.nrhs*mumps->id.size_schur; 136 if (!mumps->schur_sol || sizesol > mumps->schur_sizesol) { 137 ierr = PetscFree(mumps->schur_sol);CHKERRQ(ierr); 138 ierr = PetscMalloc1(sizesol,&mumps->schur_sol);CHKERRQ(ierr); 139 mumps->schur_sizesol = sizesol; 140 } 141 ierr = MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.nrhs,mumps->schur_sol,&X);CHKERRQ(ierr); 142 if (!mumps->id.ICNTL(9)) { /* transpose solve */ 143 ierr = MatTransposeMatMult(S,B,MAT_REUSE_MATRIX,PETSC_DEFAULT,&X);CHKERRQ(ierr); 144 } else { 145 ierr = MatMatMult(S,B,MAT_REUSE_MATRIX,PETSC_DEFAULT,&X);CHKERRQ(ierr); 146 } 147 ierr = MatCopy(X,B,SAME_NONZERO_PATTERN);CHKERRQ(ierr); 148 break; 149 default: 150 SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Unhandled MatFactorSchurStatus %D",F->schur_status); 151 break; 152 } 153 ierr = MatFactorRestoreSchurComplement(F,&S,schurstatus);CHKERRQ(ierr); 154 ierr = MatDestroy(&B);CHKERRQ(ierr); 155 ierr = MatDestroy(&X);CHKERRQ(ierr); 156 PetscFunctionReturn(0); 157 } 158 159 static PetscErrorCode MatMumpsHandleSchur_Private(Mat F, PetscBool expansion) 160 { 161 Mat_MUMPS *mumps=(Mat_MUMPS*)F->data; 162 PetscErrorCode ierr; 163 164 PetscFunctionBegin; 165 if (!mumps->id.ICNTL(19)) { /* do nothing when Schur complement has not been computed */ 166 PetscFunctionReturn(0); 167 } 168 if (!expansion) { /* prepare for the condensation step */ 169 PetscInt sizeredrhs = mumps->id.nrhs*mumps->id.size_schur; 170 /* allocate MUMPS internal array to store reduced right-hand sides */ 171 if (!mumps->id.redrhs || sizeredrhs > mumps->sizeredrhs) { 172 ierr = PetscFree(mumps->id.redrhs);CHKERRQ(ierr); 173 mumps->id.lredrhs = mumps->id.size_schur; 174 ierr = PetscMalloc1(mumps->id.nrhs*mumps->id.lredrhs,&mumps->id.redrhs);CHKERRQ(ierr); 175 mumps->sizeredrhs = mumps->id.nrhs*mumps->id.lredrhs; 176 } 177 mumps->id.ICNTL(26) = 1; /* condensation phase */ 178 } else { /* prepare for the expansion step */ 179 /* solve Schur complement (this has to be done by the MUMPS user, so basically us) */ 180 ierr = MatMumpsSolveSchur_Private(F);CHKERRQ(ierr); 181 mumps->id.ICNTL(26) = 2; /* expansion phase */ 182 PetscMUMPS_c(&mumps->id); 183 if (mumps->id.INFOG(1) < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d\n",mumps->id.INFOG(1)); 184 /* restore defaults */ 185 mumps->id.ICNTL(26) = -1; 186 /* free MUMPS internal array for redrhs if we have solved for multiple rhs in order to save memory space */ 187 if (mumps->id.nrhs > 1) { 188 ierr = PetscFree(mumps->id.redrhs);CHKERRQ(ierr); 189 mumps->id.lredrhs = 0; 190 mumps->sizeredrhs = 0; 191 } 192 } 193 PetscFunctionReturn(0); 194 } 195 196 /* 197 MatConvertToTriples_A_B - convert Petsc matrix to triples: row[nz], col[nz], val[nz] 198 199 input: 200 A - matrix in aij,baij or sbaij (bs=1) format 201 shift - 0: C style output triple; 1: Fortran style output triple. 202 reuse - MAT_INITIAL_MATRIX: spaces are allocated and values are set for the triple 203 MAT_REUSE_MATRIX: only the values in v array are updated 204 output: 205 nnz - dim of r, c, and v (number of local nonzero entries of A) 206 r, c, v - row and col index, matrix values (matrix triples) 207 208 The returned values r, c, and sometimes v are obtained in a single PetscMalloc(). Then in MatDestroy_MUMPS() it is 209 freed with PetscFree(mumps->irn); This is not ideal code, the fact that v is ONLY sometimes part of mumps->irn means 210 that the PetscMalloc() cannot easily be replaced with a PetscMalloc3(). 211 212 */ 213 214 PetscErrorCode MatConvertToTriples_seqaij_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 215 { 216 const PetscInt *ai,*aj,*ajj,M=A->rmap->n; 217 PetscInt nz,rnz,i,j; 218 PetscErrorCode ierr; 219 PetscInt *row,*col; 220 Mat_SeqAIJ *aa=(Mat_SeqAIJ*)A->data; 221 222 PetscFunctionBegin; 223 *v=aa->a; 224 if (reuse == MAT_INITIAL_MATRIX) { 225 nz = aa->nz; 226 ai = aa->i; 227 aj = aa->j; 228 *nnz = nz; 229 ierr = PetscMalloc1(2*nz, &row);CHKERRQ(ierr); 230 col = row + nz; 231 232 nz = 0; 233 for (i=0; i<M; i++) { 234 rnz = ai[i+1] - ai[i]; 235 ajj = aj + ai[i]; 236 for (j=0; j<rnz; j++) { 237 row[nz] = i+shift; col[nz++] = ajj[j] + shift; 238 } 239 } 240 *r = row; *c = col; 241 } 242 PetscFunctionReturn(0); 243 } 244 245 PetscErrorCode MatConvertToTriples_seqsell_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 246 { 247 Mat_SeqSELL *a=(Mat_SeqSELL*)A->data; 248 PetscInt *ptr; 249 250 PetscFunctionBegin; 251 *v = a->val; 252 if (reuse == MAT_INITIAL_MATRIX) { 253 PetscInt nz,i,j,row; 254 PetscErrorCode ierr; 255 256 nz = a->sliidx[a->totalslices]; 257 *nnz = nz; 258 ierr = PetscMalloc1(2*nz, &ptr);CHKERRQ(ierr); 259 *r = ptr; 260 *c = ptr + nz; 261 262 for (i=0; i<a->totalslices; i++) { 263 for (j=a->sliidx[i],row=0; j<a->sliidx[i+1]; j++,row=((row+1)&0x07)) { 264 *ptr++ = 8*i + row + shift; 265 } 266 } 267 for (i=0;i<nz;i++) *ptr++ = a->colidx[i] + shift; 268 } 269 PetscFunctionReturn(0); 270 } 271 272 PetscErrorCode MatConvertToTriples_seqbaij_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 273 { 274 Mat_SeqBAIJ *aa=(Mat_SeqBAIJ*)A->data; 275 const PetscInt *ai,*aj,*ajj,bs2 = aa->bs2; 276 PetscInt bs,M,nz,idx=0,rnz,i,j,k,m; 277 PetscErrorCode ierr; 278 PetscInt *row,*col; 279 280 PetscFunctionBegin; 281 ierr = MatGetBlockSize(A,&bs);CHKERRQ(ierr); 282 M = A->rmap->N/bs; 283 *v = aa->a; 284 if (reuse == MAT_INITIAL_MATRIX) { 285 ai = aa->i; aj = aa->j; 286 nz = bs2*aa->nz; 287 *nnz = nz; 288 ierr = PetscMalloc1(2*nz, &row);CHKERRQ(ierr); 289 col = row + nz; 290 291 for (i=0; i<M; i++) { 292 ajj = aj + ai[i]; 293 rnz = ai[i+1] - ai[i]; 294 for (k=0; k<rnz; k++) { 295 for (j=0; j<bs; j++) { 296 for (m=0; m<bs; m++) { 297 row[idx] = i*bs + m + shift; 298 col[idx++] = bs*(ajj[k]) + j + shift; 299 } 300 } 301 } 302 } 303 *r = row; *c = col; 304 } 305 PetscFunctionReturn(0); 306 } 307 308 PetscErrorCode MatConvertToTriples_seqsbaij_seqsbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 309 { 310 const PetscInt *ai, *aj,*ajj,M=A->rmap->n; 311 PetscInt nz,rnz,i,j; 312 PetscErrorCode ierr; 313 PetscInt *row,*col; 314 Mat_SeqSBAIJ *aa=(Mat_SeqSBAIJ*)A->data; 315 316 PetscFunctionBegin; 317 *v = aa->a; 318 if (reuse == MAT_INITIAL_MATRIX) { 319 nz = aa->nz; 320 ai = aa->i; 321 aj = aa->j; 322 *v = aa->a; 323 *nnz = nz; 324 ierr = PetscMalloc1(2*nz, &row);CHKERRQ(ierr); 325 col = row + nz; 326 327 nz = 0; 328 for (i=0; i<M; i++) { 329 rnz = ai[i+1] - ai[i]; 330 ajj = aj + ai[i]; 331 for (j=0; j<rnz; j++) { 332 row[nz] = i+shift; col[nz++] = ajj[j] + shift; 333 } 334 } 335 *r = row; *c = col; 336 } 337 PetscFunctionReturn(0); 338 } 339 340 PetscErrorCode MatConvertToTriples_seqaij_seqsbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 341 { 342 const PetscInt *ai,*aj,*ajj,*adiag,M=A->rmap->n; 343 PetscInt nz,rnz,i,j; 344 const PetscScalar *av,*v1; 345 PetscScalar *val; 346 PetscErrorCode ierr; 347 PetscInt *row,*col; 348 Mat_SeqAIJ *aa=(Mat_SeqAIJ*)A->data; 349 PetscBool missing; 350 351 PetscFunctionBegin; 352 ai = aa->i; aj = aa->j; av = aa->a; 353 adiag = aa->diag; 354 ierr = MatMissingDiagonal_SeqAIJ(A,&missing,&i);CHKERRQ(ierr); 355 if (reuse == MAT_INITIAL_MATRIX) { 356 /* count nz in the upper triangular part of A */ 357 nz = 0; 358 if (missing) { 359 for (i=0; i<M; i++) { 360 if (PetscUnlikely(adiag[i] >= ai[i+1])) { 361 for (j=ai[i];j<ai[i+1];j++) { 362 if (aj[j] < i) continue; 363 nz++; 364 } 365 } else { 366 nz += ai[i+1] - adiag[i]; 367 } 368 } 369 } else { 370 for (i=0; i<M; i++) nz += ai[i+1] - adiag[i]; 371 } 372 *nnz = nz; 373 374 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 375 col = row + nz; 376 val = (PetscScalar*)(col + nz); 377 378 nz = 0; 379 if (missing) { 380 for (i=0; i<M; i++) { 381 if (PetscUnlikely(adiag[i] >= ai[i+1])) { 382 for (j=ai[i];j<ai[i+1];j++) { 383 if (aj[j] < i) continue; 384 row[nz] = i+shift; 385 col[nz] = aj[j]+shift; 386 val[nz] = av[j]; 387 nz++; 388 } 389 } else { 390 rnz = ai[i+1] - adiag[i]; 391 ajj = aj + adiag[i]; 392 v1 = av + adiag[i]; 393 for (j=0; j<rnz; j++) { 394 row[nz] = i+shift; col[nz] = ajj[j] + shift; val[nz++] = v1[j]; 395 } 396 } 397 } 398 } else { 399 for (i=0; i<M; i++) { 400 rnz = ai[i+1] - adiag[i]; 401 ajj = aj + adiag[i]; 402 v1 = av + adiag[i]; 403 for (j=0; j<rnz; j++) { 404 row[nz] = i+shift; col[nz] = ajj[j] + shift; val[nz++] = v1[j]; 405 } 406 } 407 } 408 *r = row; *c = col; *v = val; 409 } else { 410 nz = 0; val = *v; 411 if (missing) { 412 for (i=0; i <M; i++) { 413 if (PetscUnlikely(adiag[i] >= ai[i+1])) { 414 for (j=ai[i];j<ai[i+1];j++) { 415 if (aj[j] < i) continue; 416 val[nz++] = av[j]; 417 } 418 } else { 419 rnz = ai[i+1] - adiag[i]; 420 v1 = av + adiag[i]; 421 for (j=0; j<rnz; j++) { 422 val[nz++] = v1[j]; 423 } 424 } 425 } 426 } else { 427 for (i=0; i <M; i++) { 428 rnz = ai[i+1] - adiag[i]; 429 v1 = av + adiag[i]; 430 for (j=0; j<rnz; j++) { 431 val[nz++] = v1[j]; 432 } 433 } 434 } 435 } 436 PetscFunctionReturn(0); 437 } 438 439 PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 440 { 441 const PetscInt *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj; 442 PetscErrorCode ierr; 443 PetscInt rstart,nz,i,j,jj,irow,countA,countB; 444 PetscInt *row,*col; 445 const PetscScalar *av, *bv,*v1,*v2; 446 PetscScalar *val; 447 Mat_MPISBAIJ *mat = (Mat_MPISBAIJ*)A->data; 448 Mat_SeqSBAIJ *aa = (Mat_SeqSBAIJ*)(mat->A)->data; 449 Mat_SeqBAIJ *bb = (Mat_SeqBAIJ*)(mat->B)->data; 450 451 PetscFunctionBegin; 452 ai=aa->i; aj=aa->j; bi=bb->i; bj=bb->j; rstart= A->rmap->rstart; 453 av=aa->a; bv=bb->a; 454 455 garray = mat->garray; 456 457 if (reuse == MAT_INITIAL_MATRIX) { 458 nz = aa->nz + bb->nz; 459 *nnz = nz; 460 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 461 col = row + nz; 462 val = (PetscScalar*)(col + nz); 463 464 *r = row; *c = col; *v = val; 465 } else { 466 row = *r; col = *c; val = *v; 467 } 468 469 jj = 0; irow = rstart; 470 for (i=0; i<m; i++) { 471 ajj = aj + ai[i]; /* ptr to the beginning of this row */ 472 countA = ai[i+1] - ai[i]; 473 countB = bi[i+1] - bi[i]; 474 bjj = bj + bi[i]; 475 v1 = av + ai[i]; 476 v2 = bv + bi[i]; 477 478 /* A-part */ 479 for (j=0; j<countA; j++) { 480 if (reuse == MAT_INITIAL_MATRIX) { 481 row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift; 482 } 483 val[jj++] = v1[j]; 484 } 485 486 /* B-part */ 487 for (j=0; j < countB; j++) { 488 if (reuse == MAT_INITIAL_MATRIX) { 489 row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift; 490 } 491 val[jj++] = v2[j]; 492 } 493 irow++; 494 } 495 PetscFunctionReturn(0); 496 } 497 498 PetscErrorCode MatConvertToTriples_mpiaij_mpiaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 499 { 500 const PetscInt *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj; 501 PetscErrorCode ierr; 502 PetscInt rstart,nz,i,j,jj,irow,countA,countB; 503 PetscInt *row,*col; 504 const PetscScalar *av, *bv,*v1,*v2; 505 PetscScalar *val; 506 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data; 507 Mat_SeqAIJ *aa = (Mat_SeqAIJ*)(mat->A)->data; 508 Mat_SeqAIJ *bb = (Mat_SeqAIJ*)(mat->B)->data; 509 510 PetscFunctionBegin; 511 ai=aa->i; aj=aa->j; bi=bb->i; bj=bb->j; rstart= A->rmap->rstart; 512 av=aa->a; bv=bb->a; 513 514 garray = mat->garray; 515 516 if (reuse == MAT_INITIAL_MATRIX) { 517 nz = aa->nz + bb->nz; 518 *nnz = nz; 519 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 520 col = row + nz; 521 val = (PetscScalar*)(col + nz); 522 523 *r = row; *c = col; *v = val; 524 } else { 525 row = *r; col = *c; val = *v; 526 } 527 528 jj = 0; irow = rstart; 529 for (i=0; i<m; i++) { 530 ajj = aj + ai[i]; /* ptr to the beginning of this row */ 531 countA = ai[i+1] - ai[i]; 532 countB = bi[i+1] - bi[i]; 533 bjj = bj + bi[i]; 534 v1 = av + ai[i]; 535 v2 = bv + bi[i]; 536 537 /* A-part */ 538 for (j=0; j<countA; j++) { 539 if (reuse == MAT_INITIAL_MATRIX) { 540 row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift; 541 } 542 val[jj++] = v1[j]; 543 } 544 545 /* B-part */ 546 for (j=0; j < countB; j++) { 547 if (reuse == MAT_INITIAL_MATRIX) { 548 row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift; 549 } 550 val[jj++] = v2[j]; 551 } 552 irow++; 553 } 554 PetscFunctionReturn(0); 555 } 556 557 PetscErrorCode MatConvertToTriples_mpibaij_mpiaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 558 { 559 Mat_MPIBAIJ *mat = (Mat_MPIBAIJ*)A->data; 560 Mat_SeqBAIJ *aa = (Mat_SeqBAIJ*)(mat->A)->data; 561 Mat_SeqBAIJ *bb = (Mat_SeqBAIJ*)(mat->B)->data; 562 const PetscInt *ai = aa->i, *bi = bb->i, *aj = aa->j, *bj = bb->j,*ajj, *bjj; 563 const PetscInt *garray = mat->garray,mbs=mat->mbs,rstart=A->rmap->rstart; 564 const PetscInt bs2=mat->bs2; 565 PetscErrorCode ierr; 566 PetscInt bs,nz,i,j,k,n,jj,irow,countA,countB,idx; 567 PetscInt *row,*col; 568 const PetscScalar *av=aa->a, *bv=bb->a,*v1,*v2; 569 PetscScalar *val; 570 571 PetscFunctionBegin; 572 ierr = MatGetBlockSize(A,&bs);CHKERRQ(ierr); 573 if (reuse == MAT_INITIAL_MATRIX) { 574 nz = bs2*(aa->nz + bb->nz); 575 *nnz = nz; 576 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 577 col = row + nz; 578 val = (PetscScalar*)(col + nz); 579 580 *r = row; *c = col; *v = val; 581 } else { 582 row = *r; col = *c; val = *v; 583 } 584 585 jj = 0; irow = rstart; 586 for (i=0; i<mbs; i++) { 587 countA = ai[i+1] - ai[i]; 588 countB = bi[i+1] - bi[i]; 589 ajj = aj + ai[i]; 590 bjj = bj + bi[i]; 591 v1 = av + bs2*ai[i]; 592 v2 = bv + bs2*bi[i]; 593 594 idx = 0; 595 /* A-part */ 596 for (k=0; k<countA; k++) { 597 for (j=0; j<bs; j++) { 598 for (n=0; n<bs; n++) { 599 if (reuse == MAT_INITIAL_MATRIX) { 600 row[jj] = irow + n + shift; 601 col[jj] = rstart + bs*ajj[k] + j + shift; 602 } 603 val[jj++] = v1[idx++]; 604 } 605 } 606 } 607 608 idx = 0; 609 /* B-part */ 610 for (k=0; k<countB; k++) { 611 for (j=0; j<bs; j++) { 612 for (n=0; n<bs; n++) { 613 if (reuse == MAT_INITIAL_MATRIX) { 614 row[jj] = irow + n + shift; 615 col[jj] = bs*garray[bjj[k]] + j + shift; 616 } 617 val[jj++] = v2[idx++]; 618 } 619 } 620 } 621 irow += bs; 622 } 623 PetscFunctionReturn(0); 624 } 625 626 PetscErrorCode MatConvertToTriples_mpiaij_mpisbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 627 { 628 const PetscInt *ai, *aj,*adiag, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj; 629 PetscErrorCode ierr; 630 PetscInt rstart,nz,nza,nzb,i,j,jj,irow,countA,countB; 631 PetscInt *row,*col; 632 const PetscScalar *av, *bv,*v1,*v2; 633 PetscScalar *val; 634 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data; 635 Mat_SeqAIJ *aa =(Mat_SeqAIJ*)(mat->A)->data; 636 Mat_SeqAIJ *bb =(Mat_SeqAIJ*)(mat->B)->data; 637 638 PetscFunctionBegin; 639 ai=aa->i; aj=aa->j; adiag=aa->diag; 640 bi=bb->i; bj=bb->j; garray = mat->garray; 641 av=aa->a; bv=bb->a; 642 643 rstart = A->rmap->rstart; 644 645 if (reuse == MAT_INITIAL_MATRIX) { 646 nza = 0; /* num of upper triangular entries in mat->A, including diagonals */ 647 nzb = 0; /* num of upper triangular entries in mat->B */ 648 for (i=0; i<m; i++) { 649 nza += (ai[i+1] - adiag[i]); 650 countB = bi[i+1] - bi[i]; 651 bjj = bj + bi[i]; 652 for (j=0; j<countB; j++) { 653 if (garray[bjj[j]] > rstart) nzb++; 654 } 655 } 656 657 nz = nza + nzb; /* total nz of upper triangular part of mat */ 658 *nnz = nz; 659 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 660 col = row + nz; 661 val = (PetscScalar*)(col + nz); 662 663 *r = row; *c = col; *v = val; 664 } else { 665 row = *r; col = *c; val = *v; 666 } 667 668 jj = 0; irow = rstart; 669 for (i=0; i<m; i++) { 670 ajj = aj + adiag[i]; /* ptr to the beginning of the diagonal of this row */ 671 v1 = av + adiag[i]; 672 countA = ai[i+1] - adiag[i]; 673 countB = bi[i+1] - bi[i]; 674 bjj = bj + bi[i]; 675 v2 = bv + bi[i]; 676 677 /* A-part */ 678 for (j=0; j<countA; j++) { 679 if (reuse == MAT_INITIAL_MATRIX) { 680 row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift; 681 } 682 val[jj++] = v1[j]; 683 } 684 685 /* B-part */ 686 for (j=0; j < countB; j++) { 687 if (garray[bjj[j]] > rstart) { 688 if (reuse == MAT_INITIAL_MATRIX) { 689 row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift; 690 } 691 val[jj++] = v2[j]; 692 } 693 } 694 irow++; 695 } 696 PetscFunctionReturn(0); 697 } 698 699 PetscErrorCode MatDestroy_MUMPS(Mat A) 700 { 701 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 702 PetscErrorCode ierr; 703 704 PetscFunctionBegin; 705 ierr = PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc);CHKERRQ(ierr); 706 ierr = VecScatterDestroy(&mumps->scat_rhs);CHKERRQ(ierr); 707 ierr = VecScatterDestroy(&mumps->scat_sol);CHKERRQ(ierr); 708 ierr = VecDestroy(&mumps->b_seq);CHKERRQ(ierr); 709 ierr = VecDestroy(&mumps->x_seq);CHKERRQ(ierr); 710 ierr = PetscFree(mumps->id.perm_in);CHKERRQ(ierr); 711 ierr = PetscFree(mumps->irn);CHKERRQ(ierr); 712 ierr = PetscFree(mumps->info);CHKERRQ(ierr); 713 ierr = MatMumpsResetSchur_Private(mumps);CHKERRQ(ierr); 714 mumps->id.job = JOB_END; 715 PetscMUMPS_c(&mumps->id); 716 ierr = MPI_Comm_free(&mumps->comm_mumps);CHKERRQ(ierr); 717 ierr = PetscFree(A->data);CHKERRQ(ierr); 718 719 /* clear composed functions */ 720 ierr = PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverType_C",NULL);CHKERRQ(ierr); 721 ierr = PetscObjectComposeFunction((PetscObject)A,"MatFactorSetSchurIS_C",NULL);CHKERRQ(ierr); 722 ierr = PetscObjectComposeFunction((PetscObject)A,"MatFactorCreateSchurComplement_C",NULL);CHKERRQ(ierr); 723 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetIcntl_C",NULL);CHKERRQ(ierr); 724 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetIcntl_C",NULL);CHKERRQ(ierr); 725 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetCntl_C",NULL);CHKERRQ(ierr); 726 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetCntl_C",NULL);CHKERRQ(ierr); 727 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfo_C",NULL);CHKERRQ(ierr); 728 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfog_C",NULL);CHKERRQ(ierr); 729 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfo_C",NULL);CHKERRQ(ierr); 730 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfog_C",NULL);CHKERRQ(ierr); 731 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInverse_C",NULL);CHKERRQ(ierr); 732 PetscFunctionReturn(0); 733 } 734 735 PetscErrorCode MatSolve_MUMPS(Mat A,Vec b,Vec x) 736 { 737 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 738 PetscScalar *array; 739 Vec b_seq; 740 IS is_iden,is_petsc; 741 PetscErrorCode ierr; 742 PetscInt i; 743 PetscBool second_solve = PETSC_FALSE; 744 static PetscBool cite1 = PETSC_FALSE,cite2 = PETSC_FALSE; 745 746 PetscFunctionBegin; 747 ierr = PetscCitationsRegister("@article{MUMPS01,\n author = {P.~R. Amestoy and I.~S. Duff and J.-Y. L'Excellent and J. Koster},\n title = {A fully asynchronous multifrontal solver using distributed dynamic scheduling},\n journal = {SIAM Journal on Matrix Analysis and Applications},\n volume = {23},\n number = {1},\n pages = {15--41},\n year = {2001}\n}\n",&cite1);CHKERRQ(ierr); 748 ierr = PetscCitationsRegister("@article{MUMPS02,\n author = {P.~R. Amestoy and A. Guermouche and J.-Y. L'Excellent and S. Pralet},\n title = {Hybrid scheduling for the parallel solution of linear systems},\n journal = {Parallel Computing},\n volume = {32},\n number = {2},\n pages = {136--156},\n year = {2006}\n}\n",&cite2);CHKERRQ(ierr); 749 750 if (A->factorerrortype) { 751 ierr = PetscInfo2(A,"MatSolve is called with singular matrix factor, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 752 ierr = VecSetInf(x);CHKERRQ(ierr); 753 PetscFunctionReturn(0); 754 } 755 756 mumps->id.nrhs = 1; 757 b_seq = mumps->b_seq; 758 if (mumps->size > 1) { 759 /* MUMPS only supports centralized rhs. Scatter b into a seqential rhs vector */ 760 ierr = VecScatterBegin(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 761 ierr = VecScatterEnd(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 762 if (!mumps->myid) {ierr = VecGetArray(b_seq,&array);CHKERRQ(ierr);} 763 } else { /* size == 1 */ 764 ierr = VecCopy(b,x);CHKERRQ(ierr); 765 ierr = VecGetArray(x,&array);CHKERRQ(ierr); 766 } 767 if (!mumps->myid) { /* define rhs on the host */ 768 mumps->id.nrhs = 1; 769 mumps->id.rhs = (MumpsScalar*)array; 770 } 771 772 /* 773 handle condensation step of Schur complement (if any) 774 We set by default ICNTL(26) == -1 when Schur indices have been provided by the user. 775 According to MUMPS (5.0.0) manual, any value should be harmful during the factorization phase 776 Unless the user provides a valid value for ICNTL(26), MatSolve and MatMatSolve routines solve the full system. 777 This requires an extra call to PetscMUMPS_c and the computation of the factors for S 778 */ 779 if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) { 780 if (mumps->size > 1) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Parallel Schur complements not yet supported from PETSc\n"); 781 second_solve = PETSC_TRUE; 782 ierr = MatMumpsHandleSchur_Private(A,PETSC_FALSE);CHKERRQ(ierr); 783 } 784 /* solve phase */ 785 /*-------------*/ 786 mumps->id.job = JOB_SOLVE; 787 PetscMUMPS_c(&mumps->id); 788 if (mumps->id.INFOG(1) < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d\n",mumps->id.INFOG(1)); 789 790 /* handle expansion step of Schur complement (if any) */ 791 if (second_solve) { 792 ierr = MatMumpsHandleSchur_Private(A,PETSC_TRUE);CHKERRQ(ierr); 793 } 794 795 if (mumps->size > 1) { /* convert mumps distributed solution to petsc mpi x */ 796 if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) { 797 /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */ 798 ierr = VecScatterDestroy(&mumps->scat_sol);CHKERRQ(ierr); 799 } 800 if (!mumps->scat_sol) { /* create scatter scat_sol */ 801 ierr = ISCreateStride(PETSC_COMM_SELF,mumps->id.lsol_loc,0,1,&is_iden);CHKERRQ(ierr); /* from */ 802 for (i=0; i<mumps->id.lsol_loc; i++) { 803 mumps->id.isol_loc[i] -= 1; /* change Fortran style to C style */ 804 } 805 ierr = ISCreateGeneral(PETSC_COMM_SELF,mumps->id.lsol_loc,mumps->id.isol_loc,PETSC_COPY_VALUES,&is_petsc);CHKERRQ(ierr); /* to */ 806 ierr = VecScatterCreate(mumps->x_seq,is_iden,x,is_petsc,&mumps->scat_sol);CHKERRQ(ierr); 807 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 808 ierr = ISDestroy(&is_petsc);CHKERRQ(ierr); 809 810 mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */ 811 } 812 813 ierr = VecScatterBegin(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 814 ierr = VecScatterEnd(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 815 } 816 ierr = PetscLogFlops(2.0*mumps->id.RINFO(3));CHKERRQ(ierr); 817 PetscFunctionReturn(0); 818 } 819 820 PetscErrorCode MatSolveTranspose_MUMPS(Mat A,Vec b,Vec x) 821 { 822 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 823 PetscErrorCode ierr; 824 825 PetscFunctionBegin; 826 mumps->id.ICNTL(9) = 0; 827 ierr = MatSolve_MUMPS(A,b,x);CHKERRQ(ierr); 828 mumps->id.ICNTL(9) = 1; 829 PetscFunctionReturn(0); 830 } 831 832 PetscErrorCode MatMatSolve_MUMPS(Mat A,Mat B,Mat X) 833 { 834 PetscErrorCode ierr; 835 Mat Bt = NULL; 836 PetscBool flg, flgT; 837 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 838 PetscInt i,nrhs,M; 839 PetscScalar *array,*bray; 840 841 PetscFunctionBegin; 842 ierr = PetscObjectTypeCompareAny((PetscObject)B,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr); 843 ierr = PetscObjectTypeCompare((PetscObject)B,MATTRANSPOSEMAT,&flgT);CHKERRQ(ierr); 844 if (flgT) { 845 if (mumps->size > 1) SETERRQ(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix"); 846 ierr = MatTransposeGetMat(B,&Bt);CHKERRQ(ierr); 847 } else { 848 if (!flg) SETERRQ(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix"); 849 } 850 851 ierr = MatGetSize(B,&M,&nrhs);CHKERRQ(ierr); 852 mumps->id.nrhs = nrhs; 853 mumps->id.lrhs = M; 854 855 ierr = PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr); 856 if (!flg) SETERRQ(PetscObjectComm((PetscObject)X),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix"); 857 858 if (B->rmap->n != X->rmap->n) SETERRQ(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_WRONG,"Matrix B and X must have same row distribution"); 859 860 if (mumps->size == 1) { 861 PetscScalar *aa; 862 PetscInt spnr,*ia,*ja; 863 PetscBool second_solve = PETSC_FALSE; 864 865 /* copy B to X */ 866 ierr = MatDenseGetArray(X,&array);CHKERRQ(ierr); 867 mumps->id.rhs = (MumpsScalar*)array; 868 if (!Bt) { 869 ierr = MatDenseGetArray(B,&bray);CHKERRQ(ierr); 870 ierr = PetscMemcpy(array,bray,M*nrhs*sizeof(PetscScalar));CHKERRQ(ierr); 871 ierr = MatDenseRestoreArray(B,&bray);CHKERRQ(ierr); 872 } else { 873 PetscBool done; 874 875 ierr = MatSeqAIJGetArray(Bt,&aa);CHKERRQ(ierr); 876 ierr = MatGetRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&done);CHKERRQ(ierr); 877 if (!done) SETERRQ(PetscObjectComm((PetscObject)Bt),PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 878 mumps->id.irhs_ptr = ia; 879 mumps->id.irhs_sparse = ja; 880 mumps->id.nz_rhs = ia[spnr] - 1; 881 mumps->id.rhs_sparse = (MumpsScalar*)aa; 882 mumps->id.ICNTL(20) = 1; 883 } 884 /* handle condensation step of Schur complement (if any) */ 885 if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) { 886 second_solve = PETSC_TRUE; 887 ierr = MatMumpsHandleSchur_Private(A,PETSC_FALSE);CHKERRQ(ierr); 888 } 889 /* solve phase */ 890 /*-------------*/ 891 mumps->id.job = JOB_SOLVE; 892 PetscMUMPS_c(&mumps->id); 893 if (mumps->id.INFOG(1) < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d\n",mumps->id.INFOG(1)); 894 895 /* handle expansion step of Schur complement (if any) */ 896 if (second_solve) { 897 ierr = MatMumpsHandleSchur_Private(A,PETSC_TRUE);CHKERRQ(ierr); 898 } 899 if (Bt) { 900 PetscBool done; 901 902 ierr = MatSeqAIJRestoreArray(Bt,&aa);CHKERRQ(ierr); 903 ierr = MatRestoreRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&done);CHKERRQ(ierr); 904 if (!done) SETERRQ(PetscObjectComm((PetscObject)Bt),PETSC_ERR_ARG_WRONG,"Cannot restore IJ structure"); 905 mumps->id.ICNTL(20) = 0; 906 } 907 ierr = MatDenseRestoreArray(X,&array);CHKERRQ(ierr); 908 } else { /*--------- parallel case --------*/ 909 PetscInt lsol_loc,nlsol_loc,*isol_loc,*idx,*iidx,*idxx,*isol_loc_save; 910 MumpsScalar *sol_loc,*sol_loc_save; 911 IS is_to,is_from; 912 PetscInt k,proc,j,m; 913 const PetscInt *rstart; 914 Vec v_mpi,b_seq,x_seq; 915 VecScatter scat_rhs,scat_sol; 916 917 if (mumps->size > 1 && mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Parallel Schur complements not yet supported from PETSc\n"); 918 919 /* create x_seq to hold local solution */ 920 isol_loc_save = mumps->id.isol_loc; /* save it for MatSovle() */ 921 sol_loc_save = mumps->id.sol_loc; 922 923 lsol_loc = mumps->id.INFO(23); 924 nlsol_loc = nrhs*lsol_loc; /* length of sol_loc */ 925 ierr = PetscMalloc2(nlsol_loc,&sol_loc,nlsol_loc,&isol_loc);CHKERRQ(ierr); 926 mumps->id.sol_loc = (MumpsScalar*)sol_loc; 927 mumps->id.isol_loc = isol_loc; 928 929 ierr = VecCreateSeqWithArray(PETSC_COMM_SELF,1,nlsol_loc,(PetscScalar*)sol_loc,&x_seq);CHKERRQ(ierr); 930 931 /* copy rhs matrix B into vector v_mpi */ 932 ierr = MatGetLocalSize(B,&m,NULL);CHKERRQ(ierr); 933 ierr = MatDenseGetArray(B,&bray);CHKERRQ(ierr); 934 ierr = VecCreateMPIWithArray(PetscObjectComm((PetscObject)B),1,nrhs*m,nrhs*M,(const PetscScalar*)bray,&v_mpi);CHKERRQ(ierr); 935 ierr = MatDenseRestoreArray(B,&bray);CHKERRQ(ierr); 936 937 /* scatter v_mpi to b_seq because MUMPS only supports centralized rhs */ 938 /* idx: maps from k-th index of v_mpi to (i,j)-th global entry of B; 939 iidx: inverse of idx, will be used by scattering xx_seq -> X */ 940 ierr = PetscMalloc2(nrhs*M,&idx,nrhs*M,&iidx);CHKERRQ(ierr); 941 ierr = MatGetOwnershipRanges(B,&rstart);CHKERRQ(ierr); 942 k = 0; 943 for (proc=0; proc<mumps->size; proc++){ 944 for (j=0; j<nrhs; j++){ 945 for (i=rstart[proc]; i<rstart[proc+1]; i++){ 946 iidx[j*M + i] = k; 947 idx[k++] = j*M + i; 948 } 949 } 950 } 951 952 if (!mumps->myid) { 953 ierr = VecCreateSeq(PETSC_COMM_SELF,nrhs*M,&b_seq);CHKERRQ(ierr); 954 ierr = ISCreateGeneral(PETSC_COMM_SELF,nrhs*M,idx,PETSC_COPY_VALUES,&is_to);CHKERRQ(ierr); 955 ierr = ISCreateStride(PETSC_COMM_SELF,nrhs*M,0,1,&is_from);CHKERRQ(ierr); 956 } else { 957 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&b_seq);CHKERRQ(ierr); 958 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_to);CHKERRQ(ierr); 959 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_from);CHKERRQ(ierr); 960 } 961 ierr = VecScatterCreate(v_mpi,is_from,b_seq,is_to,&scat_rhs);CHKERRQ(ierr); 962 ierr = VecScatterBegin(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 963 ierr = ISDestroy(&is_to);CHKERRQ(ierr); 964 ierr = ISDestroy(&is_from);CHKERRQ(ierr); 965 ierr = VecScatterEnd(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 966 967 if (!mumps->myid) { /* define rhs on the host */ 968 ierr = VecGetArray(b_seq,&bray);CHKERRQ(ierr); 969 mumps->id.rhs = (MumpsScalar*)bray; 970 ierr = VecRestoreArray(b_seq,&bray);CHKERRQ(ierr); 971 } 972 973 /* solve phase */ 974 /*-------------*/ 975 mumps->id.job = JOB_SOLVE; 976 PetscMUMPS_c(&mumps->id); 977 if (mumps->id.INFOG(1) < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d\n",mumps->id.INFOG(1)); 978 979 /* scatter mumps distributed solution to petsc vector v_mpi, which shares local arrays with solution matrix X */ 980 ierr = MatDenseGetArray(X,&array);CHKERRQ(ierr); 981 ierr = VecPlaceArray(v_mpi,array);CHKERRQ(ierr); 982 983 /* create scatter scat_sol */ 984 ierr = PetscMalloc1(nlsol_loc,&idxx);CHKERRQ(ierr); 985 ierr = ISCreateStride(PETSC_COMM_SELF,nlsol_loc,0,1,&is_from);CHKERRQ(ierr); 986 for (i=0; i<lsol_loc; i++) { 987 isol_loc[i] -= 1; /* change Fortran style to C style */ 988 idxx[i] = iidx[isol_loc[i]]; 989 for (j=1; j<nrhs; j++){ 990 idxx[j*lsol_loc+i] = iidx[isol_loc[i]+j*M]; 991 } 992 } 993 ierr = ISCreateGeneral(PETSC_COMM_SELF,nlsol_loc,idxx,PETSC_COPY_VALUES,&is_to);CHKERRQ(ierr); 994 ierr = VecScatterCreate(x_seq,is_from,v_mpi,is_to,&scat_sol);CHKERRQ(ierr); 995 ierr = VecScatterBegin(scat_sol,x_seq,v_mpi,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 996 ierr = ISDestroy(&is_from);CHKERRQ(ierr); 997 ierr = ISDestroy(&is_to);CHKERRQ(ierr); 998 ierr = VecScatterEnd(scat_sol,x_seq,v_mpi,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 999 ierr = MatDenseRestoreArray(X,&array);CHKERRQ(ierr); 1000 1001 /* free spaces */ 1002 mumps->id.sol_loc = sol_loc_save; 1003 mumps->id.isol_loc = isol_loc_save; 1004 1005 ierr = PetscFree2(sol_loc,isol_loc);CHKERRQ(ierr); 1006 ierr = PetscFree2(idx,iidx);CHKERRQ(ierr); 1007 ierr = PetscFree(idxx);CHKERRQ(ierr); 1008 ierr = VecDestroy(&x_seq);CHKERRQ(ierr); 1009 ierr = VecDestroy(&v_mpi);CHKERRQ(ierr); 1010 ierr = VecDestroy(&b_seq);CHKERRQ(ierr); 1011 ierr = VecScatterDestroy(&scat_rhs);CHKERRQ(ierr); 1012 ierr = VecScatterDestroy(&scat_sol);CHKERRQ(ierr); 1013 } 1014 ierr = PetscLogFlops(2.0*nrhs*mumps->id.RINFO(3));CHKERRQ(ierr); 1015 PetscFunctionReturn(0); 1016 } 1017 1018 #if !defined(PETSC_USE_COMPLEX) 1019 /* 1020 input: 1021 F: numeric factor 1022 output: 1023 nneg: total number of negative pivots 1024 nzero: total number of zero pivots 1025 npos: (global dimension of F) - nneg - nzero 1026 */ 1027 PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F,int *nneg,int *nzero,int *npos) 1028 { 1029 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1030 PetscErrorCode ierr; 1031 PetscMPIInt size; 1032 1033 PetscFunctionBegin; 1034 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)F),&size);CHKERRQ(ierr); 1035 /* MUMPS 4.3.1 calls ScaLAPACK when ICNTL(13)=0 (default), which does not offer the possibility to compute the inertia of a dense matrix. Set ICNTL(13)=1 to skip ScaLAPACK */ 1036 if (size > 1 && mumps->id.ICNTL(13) != 1) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"ICNTL(13)=%d. -mat_mumps_icntl_13 must be set as 1 for correct global matrix inertia\n",mumps->id.INFOG(13)); 1037 1038 if (nneg) *nneg = mumps->id.INFOG(12); 1039 if (nzero || npos) { 1040 if (mumps->id.ICNTL(24) != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"-mat_mumps_icntl_24 must be set as 1 for null pivot row detection"); 1041 if (nzero) *nzero = mumps->id.INFOG(28); 1042 if (npos) *npos = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28)); 1043 } 1044 PetscFunctionReturn(0); 1045 } 1046 #endif 1047 1048 PetscErrorCode MatFactorNumeric_MUMPS(Mat F,Mat A,const MatFactorInfo *info) 1049 { 1050 Mat_MUMPS *mumps =(Mat_MUMPS*)(F)->data; 1051 PetscErrorCode ierr; 1052 PetscBool isMPIAIJ; 1053 1054 PetscFunctionBegin; 1055 if (mumps->id.INFOG(1) < 0) { 1056 if (mumps->id.INFOG(1) == -6) { 1057 ierr = PetscInfo2(A,"MatFactorNumeric is called with singular matrix structure, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1058 } 1059 ierr = PetscInfo2(A,"MatFactorNumeric is called after analysis phase fails, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1060 PetscFunctionReturn(0); 1061 } 1062 1063 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1064 1065 /* numerical factorization phase */ 1066 /*-------------------------------*/ 1067 mumps->id.job = JOB_FACTNUMERIC; 1068 if (!mumps->id.ICNTL(18)) { /* A is centralized */ 1069 if (!mumps->myid) { 1070 mumps->id.a = (MumpsScalar*)mumps->val; 1071 } 1072 } else { 1073 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1074 } 1075 PetscMUMPS_c(&mumps->id); 1076 if (mumps->id.INFOG(1) < 0) { 1077 if (A->erroriffailure) { 1078 SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2)); 1079 } else { 1080 if (mumps->id.INFOG(1) == -10) { /* numerically singular matrix */ 1081 ierr = PetscInfo2(F,"matrix is numerically singular, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1082 F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1083 } else if (mumps->id.INFOG(1) == -13) { 1084 ierr = PetscInfo2(F,"MUMPS in numerical factorization phase: INFOG(1)=%d, cannot allocate required memory %d megabytes\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1085 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1086 } else if (mumps->id.INFOG(1) == -8 || mumps->id.INFOG(1) == -9 || (-16 < mumps->id.INFOG(1) && mumps->id.INFOG(1) < -10) ) { 1087 ierr = PetscInfo2(F,"MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d, problem with workarray \n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1088 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1089 } else { 1090 ierr = PetscInfo2(F,"MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1091 F->factorerrortype = MAT_FACTOR_OTHER; 1092 } 1093 } 1094 } 1095 if (!mumps->myid && mumps->id.ICNTL(16) > 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB," mumps->id.ICNTL(16):=%d\n",mumps->id.INFOG(16)); 1096 1097 F->assembled = PETSC_TRUE; 1098 mumps->matstruc = SAME_NONZERO_PATTERN; 1099 if (F->schur) { /* reset Schur status to unfactored */ 1100 if (mumps->id.ICNTL(19) == 1) { /* stored by rows */ 1101 mumps->id.ICNTL(19) = 2; 1102 ierr = MatTranspose(F->schur,MAT_INPLACE_MATRIX,&F->schur);CHKERRQ(ierr); 1103 } 1104 ierr = MatFactorRestoreSchurComplement(F,NULL,MAT_FACTOR_SCHUR_UNFACTORED);CHKERRQ(ierr); 1105 } 1106 1107 /* just to be sure that ICNTL(19) value returned by a call from MatMumpsGetIcntl is always consistent */ 1108 if (!mumps->sym && mumps->id.ICNTL(19) && mumps->id.ICNTL(19) != 1) mumps->id.ICNTL(19) = 3; 1109 1110 if (mumps->size > 1) { 1111 PetscInt lsol_loc; 1112 PetscScalar *sol_loc; 1113 1114 ierr = PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&isMPIAIJ);CHKERRQ(ierr); 1115 1116 /* distributed solution; Create x_seq=sol_loc for repeated use */ 1117 if (mumps->x_seq) { 1118 ierr = VecScatterDestroy(&mumps->scat_sol);CHKERRQ(ierr); 1119 ierr = PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc);CHKERRQ(ierr); 1120 ierr = VecDestroy(&mumps->x_seq);CHKERRQ(ierr); 1121 } 1122 lsol_loc = mumps->id.INFO(23); /* length of sol_loc */ 1123 ierr = PetscMalloc2(lsol_loc,&sol_loc,lsol_loc,&mumps->id.isol_loc);CHKERRQ(ierr); 1124 mumps->id.lsol_loc = lsol_loc; 1125 mumps->id.sol_loc = (MumpsScalar*)sol_loc; 1126 ierr = VecCreateSeqWithArray(PETSC_COMM_SELF,1,lsol_loc,sol_loc,&mumps->x_seq);CHKERRQ(ierr); 1127 } 1128 ierr = PetscLogFlops(mumps->id.RINFO(2));CHKERRQ(ierr); 1129 PetscFunctionReturn(0); 1130 } 1131 1132 /* Sets MUMPS options from the options database */ 1133 PetscErrorCode PetscSetMUMPSFromOptions(Mat F, Mat A) 1134 { 1135 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1136 PetscErrorCode ierr; 1137 PetscInt icntl,info[40],i,ninfo=40; 1138 PetscBool flg; 1139 1140 PetscFunctionBegin; 1141 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"MUMPS Options","Mat");CHKERRQ(ierr); 1142 ierr = PetscOptionsInt("-mat_mumps_icntl_1","ICNTL(1): output stream for error messages","None",mumps->id.ICNTL(1),&icntl,&flg);CHKERRQ(ierr); 1143 if (flg) mumps->id.ICNTL(1) = icntl; 1144 ierr = PetscOptionsInt("-mat_mumps_icntl_2","ICNTL(2): output stream for diagnostic printing, statistics, and warning","None",mumps->id.ICNTL(2),&icntl,&flg);CHKERRQ(ierr); 1145 if (flg) mumps->id.ICNTL(2) = icntl; 1146 ierr = PetscOptionsInt("-mat_mumps_icntl_3","ICNTL(3): output stream for global information, collected on the host","None",mumps->id.ICNTL(3),&icntl,&flg);CHKERRQ(ierr); 1147 if (flg) mumps->id.ICNTL(3) = icntl; 1148 1149 ierr = PetscOptionsInt("-mat_mumps_icntl_4","ICNTL(4): level of printing (0 to 4)","None",mumps->id.ICNTL(4),&icntl,&flg);CHKERRQ(ierr); 1150 if (flg) mumps->id.ICNTL(4) = icntl; 1151 if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */ 1152 1153 ierr = PetscOptionsInt("-mat_mumps_icntl_6","ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7)","None",mumps->id.ICNTL(6),&icntl,&flg);CHKERRQ(ierr); 1154 if (flg) mumps->id.ICNTL(6) = icntl; 1155 1156 ierr = PetscOptionsInt("-mat_mumps_icntl_7","ICNTL(7): computes a symmetric permutation in sequential analysis (0 to 7). 3=Scotch, 4=PORD, 5=Metis","None",mumps->id.ICNTL(7),&icntl,&flg);CHKERRQ(ierr); 1157 if (flg) { 1158 if (icntl== 1 && mumps->size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"pivot order be set by the user in PERM_IN -- not supported by the PETSc/MUMPS interface\n"); 1159 else mumps->id.ICNTL(7) = icntl; 1160 } 1161 1162 ierr = PetscOptionsInt("-mat_mumps_icntl_8","ICNTL(8): scaling strategy (-2 to 8 or 77)","None",mumps->id.ICNTL(8),&mumps->id.ICNTL(8),NULL);CHKERRQ(ierr); 1163 /* ierr = PetscOptionsInt("-mat_mumps_icntl_9","ICNTL(9): computes the solution using A or A^T","None",mumps->id.ICNTL(9),&mumps->id.ICNTL(9),NULL);CHKERRQ(ierr); handled by MatSolveTranspose_MUMPS() */ 1164 ierr = PetscOptionsInt("-mat_mumps_icntl_10","ICNTL(10): max num of refinements","None",mumps->id.ICNTL(10),&mumps->id.ICNTL(10),NULL);CHKERRQ(ierr); 1165 ierr = PetscOptionsInt("-mat_mumps_icntl_11","ICNTL(11): statistics related to an error analysis (via -ksp_view)","None",mumps->id.ICNTL(11),&mumps->id.ICNTL(11),NULL);CHKERRQ(ierr); 1166 ierr = PetscOptionsInt("-mat_mumps_icntl_12","ICNTL(12): an ordering strategy for symmetric matrices (0 to 3)","None",mumps->id.ICNTL(12),&mumps->id.ICNTL(12),NULL);CHKERRQ(ierr); 1167 ierr = PetscOptionsInt("-mat_mumps_icntl_13","ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting","None",mumps->id.ICNTL(13),&mumps->id.ICNTL(13),NULL);CHKERRQ(ierr); 1168 ierr = PetscOptionsInt("-mat_mumps_icntl_14","ICNTL(14): percentage increase in the estimated working space","None",mumps->id.ICNTL(14),&mumps->id.ICNTL(14),NULL);CHKERRQ(ierr); 1169 ierr = PetscOptionsInt("-mat_mumps_icntl_19","ICNTL(19): computes the Schur complement","None",mumps->id.ICNTL(19),&mumps->id.ICNTL(19),NULL);CHKERRQ(ierr); 1170 if (mumps->id.ICNTL(19) <= 0 || mumps->id.ICNTL(19) > 3) { /* reset any schur data (if any) */ 1171 ierr = MatDestroy(&F->schur);CHKERRQ(ierr); 1172 ierr = MatMumpsResetSchur_Private(mumps);CHKERRQ(ierr); 1173 } 1174 /* ierr = PetscOptionsInt("-mat_mumps_icntl_20","ICNTL(20): the format (dense or sparse) of the right-hand sides","None",mumps->id.ICNTL(20),&mumps->id.ICNTL(20),NULL);CHKERRQ(ierr); -- sparse rhs is not supported in PETSc API */ 1175 /* ierr = PetscOptionsInt("-mat_mumps_icntl_21","ICNTL(21): the distribution (centralized or distributed) of the solution vectors","None",mumps->id.ICNTL(21),&mumps->id.ICNTL(21),NULL);CHKERRQ(ierr); we only use distributed solution vector */ 1176 1177 ierr = PetscOptionsInt("-mat_mumps_icntl_22","ICNTL(22): in-core/out-of-core factorization and solve (0 or 1)","None",mumps->id.ICNTL(22),&mumps->id.ICNTL(22),NULL);CHKERRQ(ierr); 1178 ierr = PetscOptionsInt("-mat_mumps_icntl_23","ICNTL(23): max size of the working memory (MB) that can allocate per processor","None",mumps->id.ICNTL(23),&mumps->id.ICNTL(23),NULL);CHKERRQ(ierr); 1179 ierr = PetscOptionsInt("-mat_mumps_icntl_24","ICNTL(24): detection of null pivot rows (0 or 1)","None",mumps->id.ICNTL(24),&mumps->id.ICNTL(24),NULL);CHKERRQ(ierr); 1180 if (mumps->id.ICNTL(24)) { 1181 mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */ 1182 } 1183 1184 ierr = PetscOptionsInt("-mat_mumps_icntl_25","ICNTL(25): computes a solution of a deficient matrix and a null space basis","None",mumps->id.ICNTL(25),&mumps->id.ICNTL(25),NULL);CHKERRQ(ierr); 1185 ierr = PetscOptionsInt("-mat_mumps_icntl_26","ICNTL(26): drives the solution phase if a Schur complement matrix","None",mumps->id.ICNTL(26),&mumps->id.ICNTL(26),NULL);CHKERRQ(ierr); 1186 ierr = PetscOptionsInt("-mat_mumps_icntl_27","ICNTL(27): the blocking size for multiple right-hand sides","None",mumps->id.ICNTL(27),&mumps->id.ICNTL(27),NULL);CHKERRQ(ierr); 1187 ierr = PetscOptionsInt("-mat_mumps_icntl_28","ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering","None",mumps->id.ICNTL(28),&mumps->id.ICNTL(28),NULL);CHKERRQ(ierr); 1188 ierr = PetscOptionsInt("-mat_mumps_icntl_29","ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis","None",mumps->id.ICNTL(29),&mumps->id.ICNTL(29),NULL);CHKERRQ(ierr); 1189 /* ierr = PetscOptionsInt("-mat_mumps_icntl_30","ICNTL(30): compute user-specified set of entries in inv(A)","None",mumps->id.ICNTL(30),&mumps->id.ICNTL(30),NULL);CHKERRQ(ierr); */ /* call MatMumpsGetInverse() directly */ 1190 ierr = PetscOptionsInt("-mat_mumps_icntl_31","ICNTL(31): indicates which factors may be discarded during factorization","None",mumps->id.ICNTL(31),&mumps->id.ICNTL(31),NULL);CHKERRQ(ierr); 1191 /* ierr = PetscOptionsInt("-mat_mumps_icntl_32","ICNTL(32): performs the forward elemination of the right-hand sides during factorization","None",mumps->id.ICNTL(32),&mumps->id.ICNTL(32),NULL);CHKERRQ(ierr); -- not supported by PETSc API */ 1192 ierr = PetscOptionsInt("-mat_mumps_icntl_33","ICNTL(33): compute determinant","None",mumps->id.ICNTL(33),&mumps->id.ICNTL(33),NULL);CHKERRQ(ierr); 1193 ierr = PetscOptionsInt("-mat_mumps_icntl_35","ICNTL(35): activates Block Lock Rank (BLR) based factorization","None",mumps->id.ICNTL(35),&mumps->id.ICNTL(35),NULL);CHKERRQ(ierr); 1194 1195 ierr = PetscOptionsReal("-mat_mumps_cntl_1","CNTL(1): relative pivoting threshold","None",mumps->id.CNTL(1),&mumps->id.CNTL(1),NULL);CHKERRQ(ierr); 1196 ierr = PetscOptionsReal("-mat_mumps_cntl_2","CNTL(2): stopping criterion of refinement","None",mumps->id.CNTL(2),&mumps->id.CNTL(2),NULL);CHKERRQ(ierr); 1197 ierr = PetscOptionsReal("-mat_mumps_cntl_3","CNTL(3): absolute pivoting threshold","None",mumps->id.CNTL(3),&mumps->id.CNTL(3),NULL);CHKERRQ(ierr); 1198 ierr = PetscOptionsReal("-mat_mumps_cntl_4","CNTL(4): value for static pivoting","None",mumps->id.CNTL(4),&mumps->id.CNTL(4),NULL);CHKERRQ(ierr); 1199 ierr = PetscOptionsReal("-mat_mumps_cntl_5","CNTL(5): fixation for null pivots","None",mumps->id.CNTL(5),&mumps->id.CNTL(5),NULL);CHKERRQ(ierr); 1200 ierr = PetscOptionsReal("-mat_mumps_cntl_7","CNTL(7): dropping parameter used during BLR","None",mumps->id.CNTL(7),&mumps->id.CNTL(7),NULL);CHKERRQ(ierr); 1201 1202 ierr = PetscOptionsString("-mat_mumps_ooc_tmpdir", "out of core directory", "None", mumps->id.ooc_tmpdir, mumps->id.ooc_tmpdir, 256, NULL);CHKERRQ(ierr); 1203 1204 ierr = PetscOptionsIntArray("-mat_mumps_view_info","request INFO local to each processor","",info,&ninfo,NULL);CHKERRQ(ierr); 1205 if (ninfo) { 1206 if (ninfo > 40) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_USER,"number of INFO %d must <= 40\n",ninfo); 1207 ierr = PetscMalloc1(ninfo,&mumps->info);CHKERRQ(ierr); 1208 mumps->ninfo = ninfo; 1209 for (i=0; i<ninfo; i++) { 1210 if (info[i] < 0 || info[i]>40) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_USER,"index of INFO %d must between 1 and 40\n",ninfo); 1211 else mumps->info[i] = info[i]; 1212 } 1213 } 1214 1215 ierr = PetscOptionsEnd();CHKERRQ(ierr); 1216 PetscFunctionReturn(0); 1217 } 1218 1219 PetscErrorCode PetscInitializeMUMPS(Mat A,Mat_MUMPS *mumps) 1220 { 1221 PetscErrorCode ierr; 1222 1223 PetscFunctionBegin; 1224 ierr = MPI_Comm_rank(PetscObjectComm((PetscObject)A), &mumps->myid);CHKERRQ(ierr); 1225 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)A),&mumps->size);CHKERRQ(ierr); 1226 ierr = MPI_Comm_dup(PetscObjectComm((PetscObject)A),&(mumps->comm_mumps));CHKERRQ(ierr); 1227 1228 mumps->id.comm_fortran = MPI_Comm_c2f(mumps->comm_mumps); 1229 1230 mumps->id.job = JOB_INIT; 1231 mumps->id.par = 1; /* host participates factorizaton and solve */ 1232 mumps->id.sym = mumps->sym; 1233 PetscMUMPS_c(&mumps->id); 1234 1235 mumps->scat_rhs = NULL; 1236 mumps->scat_sol = NULL; 1237 1238 /* set PETSc-MUMPS default options - override MUMPS default */ 1239 mumps->id.ICNTL(3) = 0; 1240 mumps->id.ICNTL(4) = 0; 1241 if (mumps->size == 1) { 1242 mumps->id.ICNTL(18) = 0; /* centralized assembled matrix input */ 1243 } else { 1244 mumps->id.ICNTL(18) = 3; /* distributed assembled matrix input */ 1245 mumps->id.ICNTL(20) = 0; /* rhs is in dense format */ 1246 mumps->id.ICNTL(21) = 1; /* distributed solution */ 1247 } 1248 1249 /* schur */ 1250 mumps->id.size_schur = 0; 1251 mumps->id.listvar_schur = NULL; 1252 mumps->id.schur = NULL; 1253 mumps->sizeredrhs = 0; 1254 mumps->schur_sol = NULL; 1255 mumps->schur_sizesol = 0; 1256 PetscFunctionReturn(0); 1257 } 1258 1259 PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F,Mat A,const MatFactorInfo *info,Mat_MUMPS *mumps) 1260 { 1261 PetscErrorCode ierr; 1262 1263 PetscFunctionBegin; 1264 if (mumps->id.INFOG(1) < 0) { 1265 if (A->erroriffailure) { 1266 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d\n",mumps->id.INFOG(1)); 1267 } else { 1268 if (mumps->id.INFOG(1) == -6) { 1269 ierr = PetscInfo2(F,"matrix is singular in structure, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1270 F->factorerrortype = MAT_FACTOR_STRUCT_ZEROPIVOT; 1271 } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) { 1272 ierr = PetscInfo2(F,"problem of workspace, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1273 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1274 } else { 1275 ierr = PetscInfo2(F,"Error reported by MUMPS in analysis phase: INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1276 F->factorerrortype = MAT_FACTOR_OTHER; 1277 } 1278 } 1279 } 1280 PetscFunctionReturn(0); 1281 } 1282 1283 /* Note Petsc r(=c) permutation is used when mumps->id.ICNTL(7)==1 with centralized assembled matrix input; otherwise r and c are ignored */ 1284 PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 1285 { 1286 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1287 PetscErrorCode ierr; 1288 Vec b; 1289 IS is_iden; 1290 const PetscInt M = A->rmap->N; 1291 1292 PetscFunctionBegin; 1293 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 1294 1295 /* Set MUMPS options from the options database */ 1296 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1297 1298 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1299 1300 /* analysis phase */ 1301 /*----------------*/ 1302 mumps->id.job = JOB_FACTSYMBOLIC; 1303 mumps->id.n = M; 1304 switch (mumps->id.ICNTL(18)) { 1305 case 0: /* centralized assembled matrix input */ 1306 if (!mumps->myid) { 1307 mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn; 1308 if (mumps->id.ICNTL(6)>1) { 1309 mumps->id.a = (MumpsScalar*)mumps->val; 1310 } 1311 if (mumps->id.ICNTL(7) == 1) { /* use user-provide matrix ordering - assuming r = c ordering */ 1312 /* 1313 PetscBool flag; 1314 ierr = ISEqual(r,c,&flag);CHKERRQ(ierr); 1315 if (!flag) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_USER,"row_perm != col_perm"); 1316 ierr = ISView(r,PETSC_VIEWER_STDOUT_SELF); 1317 */ 1318 if (!mumps->myid) { 1319 const PetscInt *idx; 1320 PetscInt i,*perm_in; 1321 1322 ierr = PetscMalloc1(M,&perm_in);CHKERRQ(ierr); 1323 ierr = ISGetIndices(r,&idx);CHKERRQ(ierr); 1324 1325 mumps->id.perm_in = perm_in; 1326 for (i=0; i<M; i++) perm_in[i] = idx[i]+1; /* perm_in[]: start from 1, not 0! */ 1327 ierr = ISRestoreIndices(r,&idx);CHKERRQ(ierr); 1328 } 1329 } 1330 } 1331 break; 1332 case 3: /* distributed assembled matrix input (size>1) */ 1333 mumps->id.nz_loc = mumps->nz; 1334 mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn; 1335 if (mumps->id.ICNTL(6)>1) { 1336 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1337 } 1338 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1339 if (!mumps->myid) { 1340 ierr = VecCreateSeq(PETSC_COMM_SELF,A->rmap->N,&mumps->b_seq);CHKERRQ(ierr); 1341 ierr = ISCreateStride(PETSC_COMM_SELF,A->rmap->N,0,1,&is_iden);CHKERRQ(ierr); 1342 } else { 1343 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);CHKERRQ(ierr); 1344 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1345 } 1346 ierr = MatCreateVecs(A,NULL,&b);CHKERRQ(ierr); 1347 ierr = VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);CHKERRQ(ierr); 1348 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1349 ierr = VecDestroy(&b);CHKERRQ(ierr); 1350 break; 1351 } 1352 PetscMUMPS_c(&mumps->id); 1353 ierr = MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);CHKERRQ(ierr); 1354 1355 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 1356 F->ops->solve = MatSolve_MUMPS; 1357 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 1358 F->ops->matsolve = MatMatSolve_MUMPS; 1359 PetscFunctionReturn(0); 1360 } 1361 1362 /* Note the Petsc r and c permutations are ignored */ 1363 PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 1364 { 1365 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1366 PetscErrorCode ierr; 1367 Vec b; 1368 IS is_iden; 1369 const PetscInt M = A->rmap->N; 1370 1371 PetscFunctionBegin; 1372 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 1373 1374 /* Set MUMPS options from the options database */ 1375 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1376 1377 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1378 1379 /* analysis phase */ 1380 /*----------------*/ 1381 mumps->id.job = JOB_FACTSYMBOLIC; 1382 mumps->id.n = M; 1383 switch (mumps->id.ICNTL(18)) { 1384 case 0: /* centralized assembled matrix input */ 1385 if (!mumps->myid) { 1386 mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn; 1387 if (mumps->id.ICNTL(6)>1) { 1388 mumps->id.a = (MumpsScalar*)mumps->val; 1389 } 1390 } 1391 break; 1392 case 3: /* distributed assembled matrix input (size>1) */ 1393 mumps->id.nz_loc = mumps->nz; 1394 mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn; 1395 if (mumps->id.ICNTL(6)>1) { 1396 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1397 } 1398 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1399 if (!mumps->myid) { 1400 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);CHKERRQ(ierr); 1401 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 1402 } else { 1403 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);CHKERRQ(ierr); 1404 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1405 } 1406 ierr = MatCreateVecs(A,NULL,&b);CHKERRQ(ierr); 1407 ierr = VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);CHKERRQ(ierr); 1408 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1409 ierr = VecDestroy(&b);CHKERRQ(ierr); 1410 break; 1411 } 1412 PetscMUMPS_c(&mumps->id); 1413 ierr = MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);CHKERRQ(ierr); 1414 1415 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 1416 F->ops->solve = MatSolve_MUMPS; 1417 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 1418 PetscFunctionReturn(0); 1419 } 1420 1421 /* Note the Petsc r permutation and factor info are ignored */ 1422 PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F,Mat A,IS r,const MatFactorInfo *info) 1423 { 1424 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1425 PetscErrorCode ierr; 1426 Vec b; 1427 IS is_iden; 1428 const PetscInt M = A->rmap->N; 1429 1430 PetscFunctionBegin; 1431 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 1432 1433 /* Set MUMPS options from the options database */ 1434 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1435 1436 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1437 1438 /* analysis phase */ 1439 /*----------------*/ 1440 mumps->id.job = JOB_FACTSYMBOLIC; 1441 mumps->id.n = M; 1442 switch (mumps->id.ICNTL(18)) { 1443 case 0: /* centralized assembled matrix input */ 1444 if (!mumps->myid) { 1445 mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn; 1446 if (mumps->id.ICNTL(6)>1) { 1447 mumps->id.a = (MumpsScalar*)mumps->val; 1448 } 1449 } 1450 break; 1451 case 3: /* distributed assembled matrix input (size>1) */ 1452 mumps->id.nz_loc = mumps->nz; 1453 mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn; 1454 if (mumps->id.ICNTL(6)>1) { 1455 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1456 } 1457 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1458 if (!mumps->myid) { 1459 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);CHKERRQ(ierr); 1460 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 1461 } else { 1462 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);CHKERRQ(ierr); 1463 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1464 } 1465 ierr = MatCreateVecs(A,NULL,&b);CHKERRQ(ierr); 1466 ierr = VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);CHKERRQ(ierr); 1467 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1468 ierr = VecDestroy(&b);CHKERRQ(ierr); 1469 break; 1470 } 1471 PetscMUMPS_c(&mumps->id); 1472 ierr = MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);CHKERRQ(ierr); 1473 1474 F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS; 1475 F->ops->solve = MatSolve_MUMPS; 1476 F->ops->solvetranspose = MatSolve_MUMPS; 1477 F->ops->matsolve = MatMatSolve_MUMPS; 1478 #if defined(PETSC_USE_COMPLEX) 1479 F->ops->getinertia = NULL; 1480 #else 1481 F->ops->getinertia = MatGetInertia_SBAIJMUMPS; 1482 #endif 1483 PetscFunctionReturn(0); 1484 } 1485 1486 PetscErrorCode MatView_MUMPS(Mat A,PetscViewer viewer) 1487 { 1488 PetscErrorCode ierr; 1489 PetscBool iascii; 1490 PetscViewerFormat format; 1491 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 1492 1493 PetscFunctionBegin; 1494 /* check if matrix is mumps type */ 1495 if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(0); 1496 1497 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 1498 if (iascii) { 1499 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 1500 if (format == PETSC_VIEWER_ASCII_INFO) { 1501 ierr = PetscViewerASCIIPrintf(viewer,"MUMPS run parameters:\n");CHKERRQ(ierr); 1502 ierr = PetscViewerASCIIPrintf(viewer," SYM (matrix type): %d \n",mumps->id.sym);CHKERRQ(ierr); 1503 ierr = PetscViewerASCIIPrintf(viewer," PAR (host participation): %d \n",mumps->id.par);CHKERRQ(ierr); 1504 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(1) (output for error): %d \n",mumps->id.ICNTL(1));CHKERRQ(ierr); 1505 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(2) (output of diagnostic msg): %d \n",mumps->id.ICNTL(2));CHKERRQ(ierr); 1506 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(3) (output for global info): %d \n",mumps->id.ICNTL(3));CHKERRQ(ierr); 1507 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(4) (level of printing): %d \n",mumps->id.ICNTL(4));CHKERRQ(ierr); 1508 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(5) (input mat struct): %d \n",mumps->id.ICNTL(5));CHKERRQ(ierr); 1509 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(6) (matrix prescaling): %d \n",mumps->id.ICNTL(6));CHKERRQ(ierr); 1510 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(7) (sequential matrix ordering):%d \n",mumps->id.ICNTL(7));CHKERRQ(ierr); 1511 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(8) (scaling strategy): %d \n",mumps->id.ICNTL(8));CHKERRQ(ierr); 1512 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(10) (max num of refinements): %d \n",mumps->id.ICNTL(10));CHKERRQ(ierr); 1513 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(11) (error analysis): %d \n",mumps->id.ICNTL(11));CHKERRQ(ierr); 1514 if (mumps->id.ICNTL(11)>0) { 1515 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(4) (inf norm of input mat): %g\n",mumps->id.RINFOG(4));CHKERRQ(ierr); 1516 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(5) (inf norm of solution): %g\n",mumps->id.RINFOG(5));CHKERRQ(ierr); 1517 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(6) (inf norm of residual): %g\n",mumps->id.RINFOG(6));CHKERRQ(ierr); 1518 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(7),RINFOG(8) (backward error est): %g, %g\n",mumps->id.RINFOG(7),mumps->id.RINFOG(8));CHKERRQ(ierr); 1519 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(9) (error estimate): %g \n",mumps->id.RINFOG(9));CHKERRQ(ierr); 1520 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n",mumps->id.RINFOG(10),mumps->id.RINFOG(11));CHKERRQ(ierr); 1521 } 1522 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(12) (efficiency control): %d \n",mumps->id.ICNTL(12));CHKERRQ(ierr); 1523 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(13) (efficiency control): %d \n",mumps->id.ICNTL(13));CHKERRQ(ierr); 1524 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(14) (percentage of estimated workspace increase): %d \n",mumps->id.ICNTL(14));CHKERRQ(ierr); 1525 /* ICNTL(15-17) not used */ 1526 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(18) (input mat struct): %d \n",mumps->id.ICNTL(18));CHKERRQ(ierr); 1527 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(19) (Schur complement info): %d \n",mumps->id.ICNTL(19));CHKERRQ(ierr); 1528 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(20) (rhs sparse pattern): %d \n",mumps->id.ICNTL(20));CHKERRQ(ierr); 1529 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(21) (solution struct): %d \n",mumps->id.ICNTL(21));CHKERRQ(ierr); 1530 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(22) (in-core/out-of-core facility): %d \n",mumps->id.ICNTL(22));CHKERRQ(ierr); 1531 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(23) (max size of memory can be allocated locally):%d \n",mumps->id.ICNTL(23));CHKERRQ(ierr); 1532 1533 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(24) (detection of null pivot rows): %d \n",mumps->id.ICNTL(24));CHKERRQ(ierr); 1534 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(25) (computation of a null space basis): %d \n",mumps->id.ICNTL(25));CHKERRQ(ierr); 1535 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(26) (Schur options for rhs or solution): %d \n",mumps->id.ICNTL(26));CHKERRQ(ierr); 1536 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(27) (experimental parameter): %d \n",mumps->id.ICNTL(27));CHKERRQ(ierr); 1537 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(28) (use parallel or sequential ordering): %d \n",mumps->id.ICNTL(28));CHKERRQ(ierr); 1538 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(29) (parallel ordering): %d \n",mumps->id.ICNTL(29));CHKERRQ(ierr); 1539 1540 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(30) (user-specified set of entries in inv(A)): %d \n",mumps->id.ICNTL(30));CHKERRQ(ierr); 1541 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(31) (factors is discarded in the solve phase): %d \n",mumps->id.ICNTL(31));CHKERRQ(ierr); 1542 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(33) (compute determinant): %d \n",mumps->id.ICNTL(33));CHKERRQ(ierr); 1543 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(35) (activate BLR based factorization): %d \n",mumps->id.ICNTL(35));CHKERRQ(ierr); 1544 1545 ierr = PetscViewerASCIIPrintf(viewer," CNTL(1) (relative pivoting threshold): %g \n",mumps->id.CNTL(1));CHKERRQ(ierr); 1546 ierr = PetscViewerASCIIPrintf(viewer," CNTL(2) (stopping criterion of refinement): %g \n",mumps->id.CNTL(2));CHKERRQ(ierr); 1547 ierr = PetscViewerASCIIPrintf(viewer," CNTL(3) (absolute pivoting threshold): %g \n",mumps->id.CNTL(3));CHKERRQ(ierr); 1548 ierr = PetscViewerASCIIPrintf(viewer," CNTL(4) (value of static pivoting): %g \n",mumps->id.CNTL(4));CHKERRQ(ierr); 1549 ierr = PetscViewerASCIIPrintf(viewer," CNTL(5) (fixation for null pivots): %g \n",mumps->id.CNTL(5));CHKERRQ(ierr); 1550 ierr = PetscViewerASCIIPrintf(viewer," CNTL(7) (dropping parameter for BLR): %g \n",mumps->id.CNTL(7));CHKERRQ(ierr); 1551 1552 /* infomation local to each processor */ 1553 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(1) (local estimated flops for the elimination after analysis): \n");CHKERRQ(ierr); 1554 ierr = PetscViewerASCIIPushSynchronized(viewer);CHKERRQ(ierr); 1555 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(1));CHKERRQ(ierr); 1556 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1557 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(2) (local estimated flops for the assembly after factorization): \n");CHKERRQ(ierr); 1558 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(2));CHKERRQ(ierr); 1559 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1560 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(3) (local estimated flops for the elimination after factorization): \n");CHKERRQ(ierr); 1561 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(3));CHKERRQ(ierr); 1562 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1563 1564 ierr = PetscViewerASCIIPrintf(viewer, " INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization): \n");CHKERRQ(ierr); 1565 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(15));CHKERRQ(ierr); 1566 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1567 1568 ierr = PetscViewerASCIIPrintf(viewer, " INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization): \n");CHKERRQ(ierr); 1569 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(16));CHKERRQ(ierr); 1570 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1571 1572 ierr = PetscViewerASCIIPrintf(viewer, " INFO(23) (num of pivots eliminated on this processor after factorization): \n");CHKERRQ(ierr); 1573 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(23));CHKERRQ(ierr); 1574 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1575 1576 if (mumps->ninfo && mumps->ninfo <= 40){ 1577 PetscInt i; 1578 for (i=0; i<mumps->ninfo; i++){ 1579 ierr = PetscViewerASCIIPrintf(viewer, " INFO(%d): \n",mumps->info[i]);CHKERRQ(ierr); 1580 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(mumps->info[i]));CHKERRQ(ierr); 1581 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1582 } 1583 } 1584 ierr = PetscViewerASCIIPopSynchronized(viewer);CHKERRQ(ierr); 1585 1586 if (!mumps->myid) { /* information from the host */ 1587 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(1) (global estimated flops for the elimination after analysis): %g \n",mumps->id.RINFOG(1));CHKERRQ(ierr); 1588 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(2) (global estimated flops for the assembly after factorization): %g \n",mumps->id.RINFOG(2));CHKERRQ(ierr); 1589 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(3) (global estimated flops for the elimination after factorization): %g \n",mumps->id.RINFOG(3));CHKERRQ(ierr); 1590 ierr = PetscViewerASCIIPrintf(viewer," (RINFOG(12) RINFOG(13))*2^INFOG(34) (determinant): (%g,%g)*(2^%d)\n",mumps->id.RINFOG(12),mumps->id.RINFOG(13),mumps->id.INFOG(34));CHKERRQ(ierr); 1591 1592 ierr = PetscViewerASCIIPrintf(viewer," INFOG(3) (estimated real workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(3));CHKERRQ(ierr); 1593 ierr = PetscViewerASCIIPrintf(viewer," INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(4));CHKERRQ(ierr); 1594 ierr = PetscViewerASCIIPrintf(viewer," INFOG(5) (estimated maximum front size in the complete tree): %d \n",mumps->id.INFOG(5));CHKERRQ(ierr); 1595 ierr = PetscViewerASCIIPrintf(viewer," INFOG(6) (number of nodes in the complete tree): %d \n",mumps->id.INFOG(6));CHKERRQ(ierr); 1596 ierr = PetscViewerASCIIPrintf(viewer," INFOG(7) (ordering option effectively use after analysis): %d \n",mumps->id.INFOG(7));CHKERRQ(ierr); 1597 ierr = PetscViewerASCIIPrintf(viewer," INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d \n",mumps->id.INFOG(8));CHKERRQ(ierr); 1598 ierr = PetscViewerASCIIPrintf(viewer," INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d \n",mumps->id.INFOG(9));CHKERRQ(ierr); 1599 ierr = PetscViewerASCIIPrintf(viewer," INFOG(10) (total integer space store the matrix factors after factorization): %d \n",mumps->id.INFOG(10));CHKERRQ(ierr); 1600 ierr = PetscViewerASCIIPrintf(viewer," INFOG(11) (order of largest frontal matrix after factorization): %d \n",mumps->id.INFOG(11));CHKERRQ(ierr); 1601 ierr = PetscViewerASCIIPrintf(viewer," INFOG(12) (number of off-diagonal pivots): %d \n",mumps->id.INFOG(12));CHKERRQ(ierr); 1602 ierr = PetscViewerASCIIPrintf(viewer," INFOG(13) (number of delayed pivots after factorization): %d \n",mumps->id.INFOG(13));CHKERRQ(ierr); 1603 ierr = PetscViewerASCIIPrintf(viewer," INFOG(14) (number of memory compress after factorization): %d \n",mumps->id.INFOG(14));CHKERRQ(ierr); 1604 ierr = PetscViewerASCIIPrintf(viewer," INFOG(15) (number of steps of iterative refinement after solution): %d \n",mumps->id.INFOG(15));CHKERRQ(ierr); 1605 ierr = PetscViewerASCIIPrintf(viewer," INFOG(16) (estimated size (in MB) of all MUMPS internal data for factorization after analysis: value on the most memory consuming processor): %d \n",mumps->id.INFOG(16));CHKERRQ(ierr); 1606 ierr = PetscViewerASCIIPrintf(viewer," INFOG(17) (estimated size of all MUMPS internal data for factorization after analysis: sum over all processors): %d \n",mumps->id.INFOG(17));CHKERRQ(ierr); 1607 ierr = PetscViewerASCIIPrintf(viewer," INFOG(18) (size of all MUMPS internal data allocated during factorization: value on the most memory consuming processor): %d \n",mumps->id.INFOG(18));CHKERRQ(ierr); 1608 ierr = PetscViewerASCIIPrintf(viewer," INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): %d \n",mumps->id.INFOG(19));CHKERRQ(ierr); 1609 ierr = PetscViewerASCIIPrintf(viewer," INFOG(20) (estimated number of entries in the factors): %d \n",mumps->id.INFOG(20));CHKERRQ(ierr); 1610 ierr = PetscViewerASCIIPrintf(viewer," INFOG(21) (size in MB of memory effectively used during factorization - value on the most memory consuming processor): %d \n",mumps->id.INFOG(21));CHKERRQ(ierr); 1611 ierr = PetscViewerASCIIPrintf(viewer," INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): %d \n",mumps->id.INFOG(22));CHKERRQ(ierr); 1612 ierr = PetscViewerASCIIPrintf(viewer," INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d \n",mumps->id.INFOG(23));CHKERRQ(ierr); 1613 ierr = PetscViewerASCIIPrintf(viewer," INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d \n",mumps->id.INFOG(24));CHKERRQ(ierr); 1614 ierr = PetscViewerASCIIPrintf(viewer," INFOG(25) (after factorization: number of pivots modified by static pivoting): %d \n",mumps->id.INFOG(25));CHKERRQ(ierr); 1615 ierr = PetscViewerASCIIPrintf(viewer," INFOG(28) (after factorization: number of null pivots encountered): %d\n",mumps->id.INFOG(28));CHKERRQ(ierr); 1616 ierr = PetscViewerASCIIPrintf(viewer," INFOG(29) (after factorization: effective number of entries in the factors (sum over all processors)): %d\n",mumps->id.INFOG(29));CHKERRQ(ierr); 1617 ierr = PetscViewerASCIIPrintf(viewer," INFOG(30, 31) (after solution: size in Mbytes of memory used during solution phase): %d, %d\n",mumps->id.INFOG(30),mumps->id.INFOG(31));CHKERRQ(ierr); 1618 ierr = PetscViewerASCIIPrintf(viewer," INFOG(32) (after analysis: type of analysis done): %d\n",mumps->id.INFOG(32));CHKERRQ(ierr); 1619 ierr = PetscViewerASCIIPrintf(viewer," INFOG(33) (value used for ICNTL(8)): %d\n",mumps->id.INFOG(33));CHKERRQ(ierr); 1620 ierr = PetscViewerASCIIPrintf(viewer," INFOG(34) (exponent of the determinant if determinant is requested): %d\n",mumps->id.INFOG(34));CHKERRQ(ierr); 1621 } 1622 } 1623 } 1624 PetscFunctionReturn(0); 1625 } 1626 1627 PetscErrorCode MatGetInfo_MUMPS(Mat A,MatInfoType flag,MatInfo *info) 1628 { 1629 Mat_MUMPS *mumps =(Mat_MUMPS*)A->data; 1630 1631 PetscFunctionBegin; 1632 info->block_size = 1.0; 1633 info->nz_allocated = mumps->id.INFOG(20); 1634 info->nz_used = mumps->id.INFOG(20); 1635 info->nz_unneeded = 0.0; 1636 info->assemblies = 0.0; 1637 info->mallocs = 0.0; 1638 info->memory = 0.0; 1639 info->fill_ratio_given = 0; 1640 info->fill_ratio_needed = 0; 1641 info->factor_mallocs = 0; 1642 PetscFunctionReturn(0); 1643 } 1644 1645 /* -------------------------------------------------------------------------------------------*/ 1646 PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is) 1647 { 1648 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1649 const PetscInt *idxs; 1650 PetscInt size,i; 1651 PetscErrorCode ierr; 1652 1653 PetscFunctionBegin; 1654 ierr = ISGetLocalSize(is,&size);CHKERRQ(ierr); 1655 if (mumps->size > 1) { 1656 PetscBool ls,gs; 1657 1658 ls = mumps->myid ? (size ? PETSC_FALSE : PETSC_TRUE) : PETSC_TRUE; 1659 ierr = MPI_Allreduce(&ls,&gs,1,MPIU_BOOL,MPI_LAND,mumps->comm_mumps);CHKERRQ(ierr); 1660 if (!gs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MUMPS distributed parallel Schur complements not yet supported from PETSc\n"); 1661 } 1662 if (mumps->id.size_schur != size) { 1663 ierr = PetscFree2(mumps->id.listvar_schur,mumps->id.schur);CHKERRQ(ierr); 1664 mumps->id.size_schur = size; 1665 mumps->id.schur_lld = size; 1666 ierr = PetscMalloc2(size,&mumps->id.listvar_schur,size*size,&mumps->id.schur);CHKERRQ(ierr); 1667 } 1668 1669 /* Schur complement matrix */ 1670 ierr = MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.size_schur,(PetscScalar*)mumps->id.schur,&F->schur);CHKERRQ(ierr); 1671 if (mumps->sym == 1) { 1672 ierr = MatSetOption(F->schur,MAT_SPD,PETSC_TRUE);CHKERRQ(ierr); 1673 } 1674 1675 /* MUMPS expects Fortran style indices */ 1676 ierr = ISGetIndices(is,&idxs);CHKERRQ(ierr); 1677 ierr = PetscMemcpy(mumps->id.listvar_schur,idxs,size*sizeof(PetscInt));CHKERRQ(ierr); 1678 for (i=0;i<size;i++) mumps->id.listvar_schur[i]++; 1679 ierr = ISRestoreIndices(is,&idxs);CHKERRQ(ierr); 1680 if (mumps->size > 1) { 1681 mumps->id.ICNTL(19) = 1; /* MUMPS returns Schur centralized on the host */ 1682 } else { 1683 if (F->factortype == MAT_FACTOR_LU) { 1684 mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */ 1685 } else { 1686 mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */ 1687 } 1688 } 1689 /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */ 1690 mumps->id.ICNTL(26) = -1; 1691 PetscFunctionReturn(0); 1692 } 1693 1694 /* -------------------------------------------------------------------------------------------*/ 1695 PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F,Mat* S) 1696 { 1697 Mat St; 1698 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1699 PetscScalar *array; 1700 #if defined(PETSC_USE_COMPLEX) 1701 PetscScalar im = PetscSqrtScalar((PetscScalar)-1.0); 1702 #endif 1703 PetscErrorCode ierr; 1704 1705 PetscFunctionBegin; 1706 if (!mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it"); 1707 ierr = MatCreate(PETSC_COMM_SELF,&St);CHKERRQ(ierr); 1708 ierr = MatSetSizes(St,PETSC_DECIDE,PETSC_DECIDE,mumps->id.size_schur,mumps->id.size_schur);CHKERRQ(ierr); 1709 ierr = MatSetType(St,MATDENSE);CHKERRQ(ierr); 1710 ierr = MatSetUp(St);CHKERRQ(ierr); 1711 ierr = MatDenseGetArray(St,&array);CHKERRQ(ierr); 1712 if (!mumps->sym) { /* MUMPS always return a full matrix */ 1713 if (mumps->id.ICNTL(19) == 1) { /* stored by rows */ 1714 PetscInt i,j,N=mumps->id.size_schur; 1715 for (i=0;i<N;i++) { 1716 for (j=0;j<N;j++) { 1717 #if !defined(PETSC_USE_COMPLEX) 1718 PetscScalar val = mumps->id.schur[i*N+j]; 1719 #else 1720 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 1721 #endif 1722 array[j*N+i] = val; 1723 } 1724 } 1725 } else { /* stored by columns */ 1726 ierr = PetscMemcpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur*sizeof(PetscScalar));CHKERRQ(ierr); 1727 } 1728 } else { /* either full or lower-triangular (not packed) */ 1729 if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */ 1730 PetscInt i,j,N=mumps->id.size_schur; 1731 for (i=0;i<N;i++) { 1732 for (j=i;j<N;j++) { 1733 #if !defined(PETSC_USE_COMPLEX) 1734 PetscScalar val = mumps->id.schur[i*N+j]; 1735 #else 1736 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 1737 #endif 1738 array[i*N+j] = val; 1739 array[j*N+i] = val; 1740 } 1741 } 1742 } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */ 1743 ierr = PetscMemcpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur*sizeof(PetscScalar));CHKERRQ(ierr); 1744 } else { /* ICNTL(19) == 1 lower triangular stored by rows */ 1745 PetscInt i,j,N=mumps->id.size_schur; 1746 for (i=0;i<N;i++) { 1747 for (j=0;j<i+1;j++) { 1748 #if !defined(PETSC_USE_COMPLEX) 1749 PetscScalar val = mumps->id.schur[i*N+j]; 1750 #else 1751 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 1752 #endif 1753 array[i*N+j] = val; 1754 array[j*N+i] = val; 1755 } 1756 } 1757 } 1758 } 1759 ierr = MatDenseRestoreArray(St,&array);CHKERRQ(ierr); 1760 *S = St; 1761 PetscFunctionReturn(0); 1762 } 1763 1764 /* -------------------------------------------------------------------------------------------*/ 1765 PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt ival) 1766 { 1767 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1768 1769 PetscFunctionBegin; 1770 mumps->id.ICNTL(icntl) = ival; 1771 PetscFunctionReturn(0); 1772 } 1773 1774 PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt *ival) 1775 { 1776 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1777 1778 PetscFunctionBegin; 1779 *ival = mumps->id.ICNTL(icntl); 1780 PetscFunctionReturn(0); 1781 } 1782 1783 /*@ 1784 MatMumpsSetIcntl - Set MUMPS parameter ICNTL() 1785 1786 Logically Collective on Mat 1787 1788 Input Parameters: 1789 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1790 . icntl - index of MUMPS parameter array ICNTL() 1791 - ival - value of MUMPS ICNTL(icntl) 1792 1793 Options Database: 1794 . -mat_mumps_icntl_<icntl> <ival> 1795 1796 Level: beginner 1797 1798 References: 1799 . MUMPS Users' Guide 1800 1801 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 1802 @*/ 1803 PetscErrorCode MatMumpsSetIcntl(Mat F,PetscInt icntl,PetscInt ival) 1804 { 1805 PetscErrorCode ierr; 1806 1807 PetscFunctionBegin; 1808 PetscValidType(F,1); 1809 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 1810 PetscValidLogicalCollectiveInt(F,icntl,2); 1811 PetscValidLogicalCollectiveInt(F,ival,3); 1812 ierr = PetscTryMethod(F,"MatMumpsSetIcntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));CHKERRQ(ierr); 1813 PetscFunctionReturn(0); 1814 } 1815 1816 /*@ 1817 MatMumpsGetIcntl - Get MUMPS parameter ICNTL() 1818 1819 Logically Collective on Mat 1820 1821 Input Parameters: 1822 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1823 - icntl - index of MUMPS parameter array ICNTL() 1824 1825 Output Parameter: 1826 . ival - value of MUMPS ICNTL(icntl) 1827 1828 Level: beginner 1829 1830 References: 1831 . MUMPS Users' Guide 1832 1833 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 1834 @*/ 1835 PetscErrorCode MatMumpsGetIcntl(Mat F,PetscInt icntl,PetscInt *ival) 1836 { 1837 PetscErrorCode ierr; 1838 1839 PetscFunctionBegin; 1840 PetscValidType(F,1); 1841 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 1842 PetscValidLogicalCollectiveInt(F,icntl,2); 1843 PetscValidIntPointer(ival,3); 1844 ierr = PetscUseMethod(F,"MatMumpsGetIcntl_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));CHKERRQ(ierr); 1845 PetscFunctionReturn(0); 1846 } 1847 1848 /* -------------------------------------------------------------------------------------------*/ 1849 PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal val) 1850 { 1851 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1852 1853 PetscFunctionBegin; 1854 mumps->id.CNTL(icntl) = val; 1855 PetscFunctionReturn(0); 1856 } 1857 1858 PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal *val) 1859 { 1860 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1861 1862 PetscFunctionBegin; 1863 *val = mumps->id.CNTL(icntl); 1864 PetscFunctionReturn(0); 1865 } 1866 1867 /*@ 1868 MatMumpsSetCntl - Set MUMPS parameter CNTL() 1869 1870 Logically Collective on Mat 1871 1872 Input Parameters: 1873 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1874 . icntl - index of MUMPS parameter array CNTL() 1875 - val - value of MUMPS CNTL(icntl) 1876 1877 Options Database: 1878 . -mat_mumps_cntl_<icntl> <val> 1879 1880 Level: beginner 1881 1882 References: 1883 . MUMPS Users' Guide 1884 1885 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 1886 @*/ 1887 PetscErrorCode MatMumpsSetCntl(Mat F,PetscInt icntl,PetscReal val) 1888 { 1889 PetscErrorCode ierr; 1890 1891 PetscFunctionBegin; 1892 PetscValidType(F,1); 1893 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 1894 PetscValidLogicalCollectiveInt(F,icntl,2); 1895 PetscValidLogicalCollectiveReal(F,val,3); 1896 ierr = PetscTryMethod(F,"MatMumpsSetCntl_C",(Mat,PetscInt,PetscReal),(F,icntl,val));CHKERRQ(ierr); 1897 PetscFunctionReturn(0); 1898 } 1899 1900 /*@ 1901 MatMumpsGetCntl - Get MUMPS parameter CNTL() 1902 1903 Logically Collective on Mat 1904 1905 Input Parameters: 1906 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1907 - icntl - index of MUMPS parameter array CNTL() 1908 1909 Output Parameter: 1910 . val - value of MUMPS CNTL(icntl) 1911 1912 Level: beginner 1913 1914 References: 1915 . MUMPS Users' Guide 1916 1917 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 1918 @*/ 1919 PetscErrorCode MatMumpsGetCntl(Mat F,PetscInt icntl,PetscReal *val) 1920 { 1921 PetscErrorCode ierr; 1922 1923 PetscFunctionBegin; 1924 PetscValidType(F,1); 1925 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 1926 PetscValidLogicalCollectiveInt(F,icntl,2); 1927 PetscValidRealPointer(val,3); 1928 ierr = PetscUseMethod(F,"MatMumpsGetCntl_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));CHKERRQ(ierr); 1929 PetscFunctionReturn(0); 1930 } 1931 1932 PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F,PetscInt icntl,PetscInt *info) 1933 { 1934 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1935 1936 PetscFunctionBegin; 1937 *info = mumps->id.INFO(icntl); 1938 PetscFunctionReturn(0); 1939 } 1940 1941 PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F,PetscInt icntl,PetscInt *infog) 1942 { 1943 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1944 1945 PetscFunctionBegin; 1946 *infog = mumps->id.INFOG(icntl); 1947 PetscFunctionReturn(0); 1948 } 1949 1950 PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfo) 1951 { 1952 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1953 1954 PetscFunctionBegin; 1955 *rinfo = mumps->id.RINFO(icntl); 1956 PetscFunctionReturn(0); 1957 } 1958 1959 PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfog) 1960 { 1961 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1962 1963 PetscFunctionBegin; 1964 *rinfog = mumps->id.RINFOG(icntl); 1965 PetscFunctionReturn(0); 1966 } 1967 1968 PetscErrorCode MatMumpsGetInverse_MUMPS(Mat F,Mat spRHS) 1969 { 1970 PetscErrorCode ierr; 1971 Mat Bt = NULL; 1972 PetscBool flgT; 1973 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1974 PetscBool done; 1975 PetscScalar *aa; 1976 PetscInt spnr,*ia,*ja; 1977 1978 PetscFunctionBegin; 1979 if (!mumps->myid) { 1980 PetscValidIntPointer(spRHS,2); 1981 ierr = PetscObjectTypeCompare((PetscObject)spRHS,MATTRANSPOSEMAT,&flgT);CHKERRQ(ierr); 1982 if (flgT) { 1983 ierr = MatTransposeGetMat(spRHS,&Bt);CHKERRQ(ierr); 1984 } else { 1985 SETERRQ(PetscObjectComm((PetscObject)spRHS),PETSC_ERR_ARG_WRONG,"Matrix spRHS must be type MATTRANSPOSEMAT matrix"); 1986 } 1987 } 1988 1989 ierr = MatMumpsSetIcntl(F,30,1);CHKERRQ(ierr); 1990 1991 if (!mumps->myid) { 1992 ierr = MatSeqAIJGetArray(Bt,&aa);CHKERRQ(ierr); 1993 ierr = MatGetRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&done);CHKERRQ(ierr); 1994 if (!done) SETERRQ(PetscObjectComm((PetscObject)Bt),PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 1995 1996 mumps->id.irhs_ptr = ia; 1997 mumps->id.irhs_sparse = ja; 1998 mumps->id.nz_rhs = ia[spnr] - 1; 1999 mumps->id.rhs_sparse = (MumpsScalar*)aa; 2000 } else { 2001 mumps->id.irhs_ptr = NULL; 2002 mumps->id.irhs_sparse = NULL; 2003 mumps->id.nz_rhs = 0; 2004 mumps->id.rhs_sparse = NULL; 2005 } 2006 mumps->id.ICNTL(20) = 1; /* rhs is sparse */ 2007 mumps->id.ICNTL(21) = 0; /* solution is in assembled centralized format */ 2008 2009 /* solve phase */ 2010 /*-------------*/ 2011 mumps->id.job = JOB_SOLVE; 2012 PetscMUMPS_c(&mumps->id); 2013 if (mumps->id.INFOG(1) < 0) 2014 SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2)); 2015 2016 if (!mumps->myid) { 2017 ierr = MatSeqAIJRestoreArray(Bt,&aa);CHKERRQ(ierr); 2018 ierr = MatRestoreRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&done);CHKERRQ(ierr); 2019 } 2020 PetscFunctionReturn(0); 2021 } 2022 2023 /*@ 2024 MatMumpsGetInverse - Get user-specified set of entries in inverse of A 2025 2026 Logically Collective on Mat 2027 2028 Input Parameters: 2029 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2030 - spRHS - sequential sparse matrix in MATTRANSPOSEMAT format holding specified indices in processor[0] 2031 2032 Output Parameter: 2033 . spRHS - requested entries of inverse of A 2034 2035 Level: beginner 2036 2037 References: 2038 . MUMPS Users' Guide 2039 2040 .seealso: MatGetFactor(), MatCreateTranspose() 2041 @*/ 2042 PetscErrorCode MatMumpsGetInverse(Mat F,Mat spRHS) 2043 { 2044 PetscErrorCode ierr; 2045 2046 PetscFunctionBegin; 2047 PetscValidType(F,1); 2048 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2049 ierr = PetscUseMethod(F,"MatMumpsGetInverse_C",(Mat,Mat),(F,spRHS));CHKERRQ(ierr); 2050 PetscFunctionReturn(0); 2051 } 2052 2053 /*@ 2054 MatMumpsGetInfo - Get MUMPS parameter INFO() 2055 2056 Logically Collective on Mat 2057 2058 Input Parameters: 2059 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2060 - icntl - index of MUMPS parameter array INFO() 2061 2062 Output Parameter: 2063 . ival - value of MUMPS INFO(icntl) 2064 2065 Level: beginner 2066 2067 References: 2068 . MUMPS Users' Guide 2069 2070 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2071 @*/ 2072 PetscErrorCode MatMumpsGetInfo(Mat F,PetscInt icntl,PetscInt *ival) 2073 { 2074 PetscErrorCode ierr; 2075 2076 PetscFunctionBegin; 2077 PetscValidType(F,1); 2078 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2079 PetscValidIntPointer(ival,3); 2080 ierr = PetscUseMethod(F,"MatMumpsGetInfo_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));CHKERRQ(ierr); 2081 PetscFunctionReturn(0); 2082 } 2083 2084 /*@ 2085 MatMumpsGetInfog - Get MUMPS parameter INFOG() 2086 2087 Logically Collective on Mat 2088 2089 Input Parameters: 2090 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2091 - icntl - index of MUMPS parameter array INFOG() 2092 2093 Output Parameter: 2094 . ival - value of MUMPS INFOG(icntl) 2095 2096 Level: beginner 2097 2098 References: 2099 . MUMPS Users' Guide 2100 2101 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2102 @*/ 2103 PetscErrorCode MatMumpsGetInfog(Mat F,PetscInt icntl,PetscInt *ival) 2104 { 2105 PetscErrorCode ierr; 2106 2107 PetscFunctionBegin; 2108 PetscValidType(F,1); 2109 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2110 PetscValidIntPointer(ival,3); 2111 ierr = PetscUseMethod(F,"MatMumpsGetInfog_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));CHKERRQ(ierr); 2112 PetscFunctionReturn(0); 2113 } 2114 2115 /*@ 2116 MatMumpsGetRinfo - Get MUMPS parameter RINFO() 2117 2118 Logically Collective on Mat 2119 2120 Input Parameters: 2121 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2122 - icntl - index of MUMPS parameter array RINFO() 2123 2124 Output Parameter: 2125 . val - value of MUMPS RINFO(icntl) 2126 2127 Level: beginner 2128 2129 References: 2130 . MUMPS Users' Guide 2131 2132 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2133 @*/ 2134 PetscErrorCode MatMumpsGetRinfo(Mat F,PetscInt icntl,PetscReal *val) 2135 { 2136 PetscErrorCode ierr; 2137 2138 PetscFunctionBegin; 2139 PetscValidType(F,1); 2140 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2141 PetscValidRealPointer(val,3); 2142 ierr = PetscUseMethod(F,"MatMumpsGetRinfo_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));CHKERRQ(ierr); 2143 PetscFunctionReturn(0); 2144 } 2145 2146 /*@ 2147 MatMumpsGetRinfog - Get MUMPS parameter RINFOG() 2148 2149 Logically Collective on Mat 2150 2151 Input Parameters: 2152 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2153 - icntl - index of MUMPS parameter array RINFOG() 2154 2155 Output Parameter: 2156 . val - value of MUMPS RINFOG(icntl) 2157 2158 Level: beginner 2159 2160 References: 2161 . MUMPS Users' Guide 2162 2163 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2164 @*/ 2165 PetscErrorCode MatMumpsGetRinfog(Mat F,PetscInt icntl,PetscReal *val) 2166 { 2167 PetscErrorCode ierr; 2168 2169 PetscFunctionBegin; 2170 PetscValidType(F,1); 2171 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2172 PetscValidRealPointer(val,3); 2173 ierr = PetscUseMethod(F,"MatMumpsGetRinfog_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));CHKERRQ(ierr); 2174 PetscFunctionReturn(0); 2175 } 2176 2177 /*MC 2178 MATSOLVERMUMPS - A matrix type providing direct solvers (LU and Cholesky) for 2179 distributed and sequential matrices via the external package MUMPS. 2180 2181 Works with MATAIJ and MATSBAIJ matrices 2182 2183 Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch to have PETSc installed with MUMPS 2184 2185 Use -pc_type cholesky or lu -pc_factor_mat_solver_type mumps to use this direct solver 2186 2187 Options Database Keys: 2188 + -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages 2189 . -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning 2190 . -mat_mumps_icntl_3 - ICNTL(3): output stream for global information, collected on the host 2191 . -mat_mumps_icntl_4 - ICNTL(4): level of printing (0 to 4) 2192 . -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7) 2193 . -mat_mumps_icntl_7 - ICNTL(7): computes a symmetric permutation in sequential analysis (0 to 7). 3=Scotch, 4=PORD, 5=Metis 2194 . -mat_mumps_icntl_8 - ICNTL(8): scaling strategy (-2 to 8 or 77) 2195 . -mat_mumps_icntl_10 - ICNTL(10): max num of refinements 2196 . -mat_mumps_icntl_11 - ICNTL(11): statistics related to an error analysis (via -ksp_view) 2197 . -mat_mumps_icntl_12 - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3) 2198 . -mat_mumps_icntl_13 - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting 2199 . -mat_mumps_icntl_14 - ICNTL(14): percentage increase in the estimated working space 2200 . -mat_mumps_icntl_19 - ICNTL(19): computes the Schur complement 2201 . -mat_mumps_icntl_22 - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1) 2202 . -mat_mumps_icntl_23 - ICNTL(23): max size of the working memory (MB) that can allocate per processor 2203 . -mat_mumps_icntl_24 - ICNTL(24): detection of null pivot rows (0 or 1) 2204 . -mat_mumps_icntl_25 - ICNTL(25): compute a solution of a deficient matrix and a null space basis 2205 . -mat_mumps_icntl_26 - ICNTL(26): drives the solution phase if a Schur complement matrix 2206 . -mat_mumps_icntl_28 - ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering 2207 . -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis 2208 . -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A) 2209 . -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization 2210 . -mat_mumps_icntl_33 - ICNTL(33): compute determinant 2211 . -mat_mumps_cntl_1 - CNTL(1): relative pivoting threshold 2212 . -mat_mumps_cntl_2 - CNTL(2): stopping criterion of refinement 2213 . -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold 2214 . -mat_mumps_cntl_4 - CNTL(4): value for static pivoting 2215 - -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots 2216 2217 Level: beginner 2218 2219 Notes: When a MUMPS factorization fails inside a KSP solve, for example with a KSP_DIVERGED_PCSETUP_FAILED, one can find the MUMPS information about the failure by calling 2220 $ KSPGetPC(ksp,&pc); 2221 $ PCFactorGetMatrix(pc,&mat); 2222 $ MatMumpsGetInfo(mat,....); 2223 $ MatMumpsGetInfog(mat,....); etc. 2224 Or you can run with -ksp_error_if_not_converged and the program will be stopped and the information printed in the error message. 2225 2226 .seealso: PCFactorSetMatSolverType(), MatSolverType, MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog(), KSPGetPC(), PCGetFactor(), PCFactorGetMatrix() 2227 2228 M*/ 2229 2230 static PetscErrorCode MatFactorGetSolverType_mumps(Mat A,MatSolverType *type) 2231 { 2232 PetscFunctionBegin; 2233 *type = MATSOLVERMUMPS; 2234 PetscFunctionReturn(0); 2235 } 2236 2237 /* MatGetFactor for Seq and MPI AIJ matrices */ 2238 static PetscErrorCode MatGetFactor_aij_mumps(Mat A,MatFactorType ftype,Mat *F) 2239 { 2240 Mat B; 2241 PetscErrorCode ierr; 2242 Mat_MUMPS *mumps; 2243 PetscBool isSeqAIJ; 2244 2245 PetscFunctionBegin; 2246 /* Create the factorization matrix */ 2247 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);CHKERRQ(ierr); 2248 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2249 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2250 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2251 ierr = MatSetUp(B);CHKERRQ(ierr); 2252 2253 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2254 2255 B->ops->view = MatView_MUMPS; 2256 B->ops->getinfo = MatGetInfo_MUMPS; 2257 2258 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2259 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2260 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2261 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2262 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2263 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2264 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2265 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2266 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2267 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2268 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2269 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);CHKERRQ(ierr); 2270 2271 if (ftype == MAT_FACTOR_LU) { 2272 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS; 2273 B->factortype = MAT_FACTOR_LU; 2274 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij; 2275 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij; 2276 mumps->sym = 0; 2277 } else { 2278 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 2279 B->factortype = MAT_FACTOR_CHOLESKY; 2280 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij; 2281 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij; 2282 #if defined(PETSC_USE_COMPLEX) 2283 mumps->sym = 2; 2284 #else 2285 if (A->spd_set && A->spd) mumps->sym = 1; 2286 else mumps->sym = 2; 2287 #endif 2288 } 2289 2290 /* set solvertype */ 2291 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2292 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2293 2294 B->ops->destroy = MatDestroy_MUMPS; 2295 B->data = (void*)mumps; 2296 2297 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2298 2299 *F = B; 2300 PetscFunctionReturn(0); 2301 } 2302 2303 /* MatGetFactor for Seq and MPI SBAIJ matrices */ 2304 static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A,MatFactorType ftype,Mat *F) 2305 { 2306 Mat B; 2307 PetscErrorCode ierr; 2308 Mat_MUMPS *mumps; 2309 PetscBool isSeqSBAIJ; 2310 2311 PetscFunctionBegin; 2312 if (ftype != MAT_FACTOR_CHOLESKY) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Cannot use PETSc SBAIJ matrices with MUMPS LU, use AIJ matrix"); 2313 if (A->rmap->bs > 1) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Cannot use PETSc SBAIJ matrices with block size > 1 with MUMPS Cholesky, use AIJ matrix instead"); 2314 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ);CHKERRQ(ierr); 2315 /* Create the factorization matrix */ 2316 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2317 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2318 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2319 ierr = MatSetUp(B);CHKERRQ(ierr); 2320 2321 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2322 if (isSeqSBAIJ) { 2323 mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij; 2324 } else { 2325 mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij; 2326 } 2327 2328 B->ops->getinfo = MatGetInfo_External; 2329 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 2330 B->ops->view = MatView_MUMPS; 2331 2332 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2333 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2334 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2335 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2336 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2337 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2338 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2339 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2340 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2341 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2342 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2343 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);CHKERRQ(ierr); 2344 2345 B->factortype = MAT_FACTOR_CHOLESKY; 2346 #if defined(PETSC_USE_COMPLEX) 2347 mumps->sym = 2; 2348 #else 2349 if (A->spd_set && A->spd) mumps->sym = 1; 2350 else mumps->sym = 2; 2351 #endif 2352 2353 /* set solvertype */ 2354 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2355 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2356 2357 B->ops->destroy = MatDestroy_MUMPS; 2358 B->data = (void*)mumps; 2359 2360 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2361 2362 *F = B; 2363 PetscFunctionReturn(0); 2364 } 2365 2366 static PetscErrorCode MatGetFactor_baij_mumps(Mat A,MatFactorType ftype,Mat *F) 2367 { 2368 Mat B; 2369 PetscErrorCode ierr; 2370 Mat_MUMPS *mumps; 2371 PetscBool isSeqBAIJ; 2372 2373 PetscFunctionBegin; 2374 /* Create the factorization matrix */ 2375 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ);CHKERRQ(ierr); 2376 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2377 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2378 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2379 ierr = MatSetUp(B);CHKERRQ(ierr); 2380 2381 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2382 if (ftype == MAT_FACTOR_LU) { 2383 B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS; 2384 B->factortype = MAT_FACTOR_LU; 2385 if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij; 2386 else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij; 2387 mumps->sym = 0; 2388 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead\n"); 2389 2390 B->ops->getinfo = MatGetInfo_External; 2391 B->ops->view = MatView_MUMPS; 2392 2393 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2394 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2395 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2396 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2397 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2398 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2399 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2400 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2401 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2402 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2403 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2404 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);CHKERRQ(ierr); 2405 2406 /* set solvertype */ 2407 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2408 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2409 2410 B->ops->destroy = MatDestroy_MUMPS; 2411 B->data = (void*)mumps; 2412 2413 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2414 2415 *F = B; 2416 PetscFunctionReturn(0); 2417 } 2418 2419 /* MatGetFactor for Seq and MPI SELL matrices */ 2420 static PetscErrorCode MatGetFactor_sell_mumps(Mat A,MatFactorType ftype,Mat *F) 2421 { 2422 Mat B; 2423 PetscErrorCode ierr; 2424 Mat_MUMPS *mumps; 2425 PetscBool isSeqSELL; 2426 2427 PetscFunctionBegin; 2428 /* Create the factorization matrix */ 2429 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQSELL,&isSeqSELL);CHKERRQ(ierr); 2430 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2431 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2432 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2433 ierr = MatSetUp(B);CHKERRQ(ierr); 2434 2435 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2436 2437 B->ops->view = MatView_MUMPS; 2438 B->ops->getinfo = MatGetInfo_MUMPS; 2439 2440 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2441 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2442 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2443 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2444 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2445 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2446 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2447 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2448 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2449 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2450 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2451 2452 if (ftype == MAT_FACTOR_LU) { 2453 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS; 2454 B->factortype = MAT_FACTOR_LU; 2455 if (isSeqSELL) mumps->ConvertToTriples = MatConvertToTriples_seqsell_seqaij; 2456 else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"To be implemented"); 2457 mumps->sym = 0; 2458 } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"To be implemented"); 2459 2460 /* set solvertype */ 2461 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2462 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2463 2464 B->ops->destroy = MatDestroy_MUMPS; 2465 B->data = (void*)mumps; 2466 2467 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2468 2469 *F = B; 2470 PetscFunctionReturn(0); 2471 } 2472 2473 PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MUMPS(void) 2474 { 2475 PetscErrorCode ierr; 2476 2477 PetscFunctionBegin; 2478 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2479 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2480 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2481 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2482 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPISBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);CHKERRQ(ierr); 2483 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2484 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2485 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2486 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2487 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQSBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);CHKERRQ(ierr); 2488 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQSELL,MAT_FACTOR_LU,MatGetFactor_sell_mumps);CHKERRQ(ierr); 2489 PetscFunctionReturn(0); 2490 } 2491 2492