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