1 2 /* 3 Provides an interface to the MUMPS sparse solver 4 */ 5 6 #include <../src/mat/impls/aij/mpi/mpiaij.h> /*I "petscmat.h" I*/ 7 #include <../src/mat/impls/sbaij/mpi/mpisbaij.h> 8 #include <../src/mat/impls/sell/mpi/mpisell.h> 9 10 EXTERN_C_BEGIN 11 #if defined(PETSC_USE_COMPLEX) 12 #if defined(PETSC_USE_REAL_SINGLE) 13 #include <cmumps_c.h> 14 #else 15 #include <zmumps_c.h> 16 #endif 17 #else 18 #if defined(PETSC_USE_REAL_SINGLE) 19 #include <smumps_c.h> 20 #else 21 #include <dmumps_c.h> 22 #endif 23 #endif 24 EXTERN_C_END 25 #define JOB_INIT -1 26 #define JOB_FACTSYMBOLIC 1 27 #define JOB_FACTNUMERIC 2 28 #define JOB_SOLVE 3 29 #define JOB_END -2 30 31 /* calls to MUMPS */ 32 #if defined(PETSC_USE_COMPLEX) 33 #if defined(PETSC_USE_REAL_SINGLE) 34 #define PetscMUMPS_c cmumps_c 35 #else 36 #define PetscMUMPS_c zmumps_c 37 #endif 38 #else 39 #if defined(PETSC_USE_REAL_SINGLE) 40 #define PetscMUMPS_c smumps_c 41 #else 42 #define PetscMUMPS_c dmumps_c 43 #endif 44 #endif 45 46 /* declare MumpsScalar */ 47 #if defined(PETSC_USE_COMPLEX) 48 #if defined(PETSC_USE_REAL_SINGLE) 49 #define MumpsScalar mumps_complex 50 #else 51 #define MumpsScalar mumps_double_complex 52 #endif 53 #else 54 #define MumpsScalar PetscScalar 55 #endif 56 57 /* macros s.t. indices match MUMPS documentation */ 58 #define ICNTL(I) icntl[(I)-1] 59 #define CNTL(I) cntl[(I)-1] 60 #define INFOG(I) infog[(I)-1] 61 #define INFO(I) info[(I)-1] 62 #define RINFOG(I) rinfog[(I)-1] 63 #define RINFO(I) rinfo[(I)-1] 64 65 typedef struct { 66 #if defined(PETSC_USE_COMPLEX) 67 #if defined(PETSC_USE_REAL_SINGLE) 68 CMUMPS_STRUC_C id; 69 #else 70 ZMUMPS_STRUC_C id; 71 #endif 72 #else 73 #if defined(PETSC_USE_REAL_SINGLE) 74 SMUMPS_STRUC_C id; 75 #else 76 DMUMPS_STRUC_C id; 77 #endif 78 #endif 79 80 MatStructure matstruc; 81 PetscMPIInt myid,size; 82 PetscInt *irn,*jcn,nz,sym; 83 PetscScalar *val; 84 MPI_Comm comm_mumps; 85 PetscInt ICNTL9_pre; /* check if ICNTL(9) is changed from previous MatSolve */ 86 VecScatter scat_rhs, scat_sol; /* used by MatSolve() */ 87 Vec b_seq,x_seq; 88 PetscInt ninfo,*info; /* display INFO */ 89 PetscInt sizeredrhs; 90 PetscScalar *schur_sol; 91 PetscInt schur_sizesol; 92 93 PetscErrorCode (*ConvertToTriples)(Mat, int, MatReuse, int*, int**, int**, PetscScalar**); 94 } Mat_MUMPS; 95 96 extern PetscErrorCode MatDuplicate_MUMPS(Mat,MatDuplicateOption,Mat*); 97 98 static PetscErrorCode MatMumpsResetSchur_Private(Mat_MUMPS* mumps) 99 { 100 PetscErrorCode ierr; 101 102 PetscFunctionBegin; 103 ierr = PetscFree2(mumps->id.listvar_schur,mumps->id.schur);CHKERRQ(ierr); 104 ierr = PetscFree(mumps->id.redrhs);CHKERRQ(ierr); 105 ierr = PetscFree(mumps->schur_sol);CHKERRQ(ierr); 106 mumps->id.size_schur = 0; 107 mumps->id.schur_lld = 0; 108 mumps->id.ICNTL(19) = 0; 109 PetscFunctionReturn(0); 110 } 111 112 /* solve with rhs in mumps->id.redrhs and return in the same location */ 113 static PetscErrorCode MatMumpsSolveSchur_Private(Mat F) 114 { 115 Mat_MUMPS *mumps=(Mat_MUMPS*)F->data; 116 Mat S,B,X; 117 MatFactorSchurStatus schurstatus; 118 PetscInt sizesol; 119 PetscErrorCode ierr; 120 121 PetscFunctionBegin; 122 ierr = MatFactorFactorizeSchurComplement(F);CHKERRQ(ierr); 123 ierr = MatFactorGetSchurComplement(F,&S,&schurstatus);CHKERRQ(ierr); 124 ierr = MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.nrhs,(PetscScalar*)mumps->id.redrhs,&B);CHKERRQ(ierr); 125 switch (schurstatus) { 126 case MAT_FACTOR_SCHUR_FACTORED: 127 ierr = MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.nrhs,(PetscScalar*)mumps->id.redrhs,&X);CHKERRQ(ierr); 128 if (!mumps->id.ICNTL(9)) { /* transpose solve */ 129 ierr = MatMatSolveTranspose(S,B,X);CHKERRQ(ierr); 130 } else { 131 ierr = MatMatSolve(S,B,X);CHKERRQ(ierr); 132 } 133 break; 134 case MAT_FACTOR_SCHUR_INVERTED: 135 sizesol = mumps->id.nrhs*mumps->id.size_schur; 136 if (!mumps->schur_sol || sizesol > mumps->schur_sizesol) { 137 ierr = PetscFree(mumps->schur_sol);CHKERRQ(ierr); 138 ierr = PetscMalloc1(sizesol,&mumps->schur_sol);CHKERRQ(ierr); 139 mumps->schur_sizesol = sizesol; 140 } 141 ierr = MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.nrhs,mumps->schur_sol,&X);CHKERRQ(ierr); 142 if (!mumps->id.ICNTL(9)) { /* transpose solve */ 143 ierr = MatTransposeMatMult(S,B,MAT_REUSE_MATRIX,PETSC_DEFAULT,&X);CHKERRQ(ierr); 144 } else { 145 ierr = MatMatMult(S,B,MAT_REUSE_MATRIX,PETSC_DEFAULT,&X);CHKERRQ(ierr); 146 } 147 ierr = MatCopy(X,B,SAME_NONZERO_PATTERN);CHKERRQ(ierr); 148 break; 149 default: 150 SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Unhandled MatFactorSchurStatus %D",F->schur_status); 151 break; 152 } 153 ierr = MatFactorRestoreSchurComplement(F,&S,schurstatus);CHKERRQ(ierr); 154 ierr = MatDestroy(&B);CHKERRQ(ierr); 155 ierr = MatDestroy(&X);CHKERRQ(ierr); 156 PetscFunctionReturn(0); 157 } 158 159 static PetscErrorCode MatMumpsHandleSchur_Private(Mat F, PetscBool expansion) 160 { 161 Mat_MUMPS *mumps=(Mat_MUMPS*)F->data; 162 PetscErrorCode ierr; 163 164 PetscFunctionBegin; 165 if (!mumps->id.ICNTL(19)) { /* do nothing when Schur complement has not been computed */ 166 PetscFunctionReturn(0); 167 } 168 if (!expansion) { /* prepare for the condensation step */ 169 PetscInt sizeredrhs = mumps->id.nrhs*mumps->id.size_schur; 170 /* allocate MUMPS internal array to store reduced right-hand sides */ 171 if (!mumps->id.redrhs || sizeredrhs > mumps->sizeredrhs) { 172 ierr = PetscFree(mumps->id.redrhs);CHKERRQ(ierr); 173 mumps->id.lredrhs = mumps->id.size_schur; 174 ierr = PetscMalloc1(mumps->id.nrhs*mumps->id.lredrhs,&mumps->id.redrhs);CHKERRQ(ierr); 175 mumps->sizeredrhs = mumps->id.nrhs*mumps->id.lredrhs; 176 } 177 mumps->id.ICNTL(26) = 1; /* condensation phase */ 178 } else { /* prepare for the expansion step */ 179 /* solve Schur complement (this has to be done by the MUMPS user, so basically us) */ 180 ierr = MatMumpsSolveSchur_Private(F);CHKERRQ(ierr); 181 mumps->id.ICNTL(26) = 2; /* expansion phase */ 182 PetscMUMPS_c(&mumps->id); 183 if (mumps->id.INFOG(1) < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d\n",mumps->id.INFOG(1)); 184 /* restore defaults */ 185 mumps->id.ICNTL(26) = -1; 186 /* free MUMPS internal array for redrhs if we have solved for multiple rhs in order to save memory space */ 187 if (mumps->id.nrhs > 1) { 188 ierr = PetscFree(mumps->id.redrhs);CHKERRQ(ierr); 189 mumps->id.lredrhs = 0; 190 mumps->sizeredrhs = 0; 191 } 192 } 193 PetscFunctionReturn(0); 194 } 195 196 /* 197 MatConvertToTriples_A_B - convert Petsc matrix to triples: row[nz], col[nz], val[nz] 198 199 input: 200 A - matrix in aij,baij or sbaij (bs=1) format 201 shift - 0: C style output triple; 1: Fortran style output triple. 202 reuse - MAT_INITIAL_MATRIX: spaces are allocated and values are set for the triple 203 MAT_REUSE_MATRIX: only the values in v array are updated 204 output: 205 nnz - dim of r, c, and v (number of local nonzero entries of A) 206 r, c, v - row and col index, matrix values (matrix triples) 207 208 The returned values r, c, and sometimes v are obtained in a single PetscMalloc(). Then in MatDestroy_MUMPS() it is 209 freed with PetscFree(mumps->irn); This is not ideal code, the fact that v is ONLY sometimes part of mumps->irn means 210 that the PetscMalloc() cannot easily be replaced with a PetscMalloc3(). 211 212 */ 213 214 PetscErrorCode MatConvertToTriples_seqaij_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 215 { 216 const PetscInt *ai,*aj,*ajj,M=A->rmap->n; 217 PetscInt nz,rnz,i,j; 218 PetscErrorCode ierr; 219 PetscInt *row,*col; 220 Mat_SeqAIJ *aa=(Mat_SeqAIJ*)A->data; 221 222 PetscFunctionBegin; 223 *v=aa->a; 224 if (reuse == MAT_INITIAL_MATRIX) { 225 nz = aa->nz; 226 ai = aa->i; 227 aj = aa->j; 228 *nnz = nz; 229 ierr = PetscMalloc1(2*nz, &row);CHKERRQ(ierr); 230 col = row + nz; 231 232 nz = 0; 233 for (i=0; i<M; i++) { 234 rnz = ai[i+1] - ai[i]; 235 ajj = aj + ai[i]; 236 for (j=0; j<rnz; j++) { 237 row[nz] = i+shift; col[nz++] = ajj[j] + shift; 238 } 239 } 240 *r = row; *c = col; 241 } 242 PetscFunctionReturn(0); 243 } 244 245 PetscErrorCode MatConvertToTriples_seqsell_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 246 { 247 Mat_SeqSELL *a=(Mat_SeqSELL*)A->data; 248 PetscInt *ptr; 249 250 PetscFunctionBegin; 251 *v = a->val; 252 if (reuse == MAT_INITIAL_MATRIX) { 253 PetscInt nz,i,j,row; 254 PetscErrorCode ierr; 255 256 nz = a->sliidx[a->totalslices]; 257 *nnz = nz; 258 ierr = PetscMalloc1(2*nz, &ptr);CHKERRQ(ierr); 259 *r = ptr; 260 *c = ptr + nz; 261 262 for (i=0; i<a->totalslices; i++) { 263 for (j=a->sliidx[i],row=0; j<a->sliidx[i+1]; j++,row=((row+1)&0x07)) { 264 *ptr++ = 8*i + row + shift; 265 } 266 } 267 for (i=0;i<nz;i++) *ptr++ = a->colidx[i] + shift; 268 } 269 PetscFunctionReturn(0); 270 } 271 272 PetscErrorCode MatConvertToTriples_seqbaij_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 273 { 274 Mat_SeqBAIJ *aa=(Mat_SeqBAIJ*)A->data; 275 const PetscInt *ai,*aj,*ajj,bs2 = aa->bs2; 276 PetscInt bs,M,nz,idx=0,rnz,i,j,k,m; 277 PetscErrorCode ierr; 278 PetscInt *row,*col; 279 280 PetscFunctionBegin; 281 ierr = MatGetBlockSize(A,&bs);CHKERRQ(ierr); 282 M = A->rmap->N/bs; 283 *v = aa->a; 284 if (reuse == MAT_INITIAL_MATRIX) { 285 ai = aa->i; aj = aa->j; 286 nz = bs2*aa->nz; 287 *nnz = nz; 288 ierr = PetscMalloc1(2*nz, &row);CHKERRQ(ierr); 289 col = row + nz; 290 291 for (i=0; i<M; i++) { 292 ajj = aj + ai[i]; 293 rnz = ai[i+1] - ai[i]; 294 for (k=0; k<rnz; k++) { 295 for (j=0; j<bs; j++) { 296 for (m=0; m<bs; m++) { 297 row[idx] = i*bs + m + shift; 298 col[idx++] = bs*(ajj[k]) + j + shift; 299 } 300 } 301 } 302 } 303 *r = row; *c = col; 304 } 305 PetscFunctionReturn(0); 306 } 307 308 PetscErrorCode MatConvertToTriples_seqsbaij_seqsbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 309 { 310 const PetscInt *ai, *aj,*ajj,M=A->rmap->n; 311 PetscInt nz,rnz,i,j; 312 PetscErrorCode ierr; 313 PetscInt *row,*col; 314 Mat_SeqSBAIJ *aa=(Mat_SeqSBAIJ*)A->data; 315 316 PetscFunctionBegin; 317 *v = aa->a; 318 if (reuse == MAT_INITIAL_MATRIX) { 319 nz = aa->nz; 320 ai = aa->i; 321 aj = aa->j; 322 *v = aa->a; 323 *nnz = nz; 324 ierr = PetscMalloc1(2*nz, &row);CHKERRQ(ierr); 325 col = row + nz; 326 327 nz = 0; 328 for (i=0; i<M; i++) { 329 rnz = ai[i+1] - ai[i]; 330 ajj = aj + ai[i]; 331 for (j=0; j<rnz; j++) { 332 row[nz] = i+shift; col[nz++] = ajj[j] + shift; 333 } 334 } 335 *r = row; *c = col; 336 } 337 PetscFunctionReturn(0); 338 } 339 340 PetscErrorCode MatConvertToTriples_seqaij_seqsbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 341 { 342 const PetscInt *ai,*aj,*ajj,*adiag,M=A->rmap->n; 343 PetscInt nz,rnz,i,j; 344 const PetscScalar *av,*v1; 345 PetscScalar *val; 346 PetscErrorCode ierr; 347 PetscInt *row,*col; 348 Mat_SeqAIJ *aa=(Mat_SeqAIJ*)A->data; 349 PetscBool missing; 350 351 PetscFunctionBegin; 352 ai = aa->i; aj = aa->j; av = aa->a; 353 adiag = aa->diag; 354 ierr = MatMissingDiagonal_SeqAIJ(A,&missing,&i);CHKERRQ(ierr); 355 if (reuse == MAT_INITIAL_MATRIX) { 356 /* count nz in the upper triangular part of A */ 357 nz = 0; 358 if (missing) { 359 for (i=0; i<M; i++) { 360 if (PetscUnlikely(adiag[i] >= ai[i+1])) { 361 for (j=ai[i];j<ai[i+1];j++) { 362 if (aj[j] < i) continue; 363 nz++; 364 } 365 } else { 366 nz += ai[i+1] - adiag[i]; 367 } 368 } 369 } else { 370 for (i=0; i<M; i++) nz += ai[i+1] - adiag[i]; 371 } 372 *nnz = nz; 373 374 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 375 col = row + nz; 376 val = (PetscScalar*)(col + nz); 377 378 nz = 0; 379 if (missing) { 380 for (i=0; i<M; i++) { 381 if (PetscUnlikely(adiag[i] >= ai[i+1])) { 382 for (j=ai[i];j<ai[i+1];j++) { 383 if (aj[j] < i) continue; 384 row[nz] = i+shift; 385 col[nz] = aj[j]+shift; 386 val[nz] = av[j]; 387 nz++; 388 } 389 } else { 390 rnz = ai[i+1] - adiag[i]; 391 ajj = aj + adiag[i]; 392 v1 = av + adiag[i]; 393 for (j=0; j<rnz; j++) { 394 row[nz] = i+shift; col[nz] = ajj[j] + shift; val[nz++] = v1[j]; 395 } 396 } 397 } 398 } else { 399 for (i=0; i<M; i++) { 400 rnz = ai[i+1] - adiag[i]; 401 ajj = aj + adiag[i]; 402 v1 = av + adiag[i]; 403 for (j=0; j<rnz; j++) { 404 row[nz] = i+shift; col[nz] = ajj[j] + shift; val[nz++] = v1[j]; 405 } 406 } 407 } 408 *r = row; *c = col; *v = val; 409 } else { 410 nz = 0; val = *v; 411 if (missing) { 412 for (i=0; i <M; i++) { 413 if (PetscUnlikely(adiag[i] >= ai[i+1])) { 414 for (j=ai[i];j<ai[i+1];j++) { 415 if (aj[j] < i) continue; 416 val[nz++] = av[j]; 417 } 418 } else { 419 rnz = ai[i+1] - adiag[i]; 420 v1 = av + adiag[i]; 421 for (j=0; j<rnz; j++) { 422 val[nz++] = v1[j]; 423 } 424 } 425 } 426 } else { 427 for (i=0; i <M; i++) { 428 rnz = ai[i+1] - adiag[i]; 429 v1 = av + adiag[i]; 430 for (j=0; j<rnz; j++) { 431 val[nz++] = v1[j]; 432 } 433 } 434 } 435 } 436 PetscFunctionReturn(0); 437 } 438 439 PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 440 { 441 const PetscInt *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj; 442 PetscErrorCode ierr; 443 PetscInt rstart,nz,i,j,jj,irow,countA,countB; 444 PetscInt *row,*col; 445 const PetscScalar *av, *bv,*v1,*v2; 446 PetscScalar *val; 447 Mat_MPISBAIJ *mat = (Mat_MPISBAIJ*)A->data; 448 Mat_SeqSBAIJ *aa = (Mat_SeqSBAIJ*)(mat->A)->data; 449 Mat_SeqBAIJ *bb = (Mat_SeqBAIJ*)(mat->B)->data; 450 451 PetscFunctionBegin; 452 ai=aa->i; aj=aa->j; bi=bb->i; bj=bb->j; rstart= A->rmap->rstart; 453 av=aa->a; bv=bb->a; 454 455 garray = mat->garray; 456 457 if (reuse == MAT_INITIAL_MATRIX) { 458 nz = aa->nz + bb->nz; 459 *nnz = nz; 460 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 461 col = row + nz; 462 val = (PetscScalar*)(col + nz); 463 464 *r = row; *c = col; *v = val; 465 } else { 466 row = *r; col = *c; val = *v; 467 } 468 469 jj = 0; irow = rstart; 470 for (i=0; i<m; i++) { 471 ajj = aj + ai[i]; /* ptr to the beginning of this row */ 472 countA = ai[i+1] - ai[i]; 473 countB = bi[i+1] - bi[i]; 474 bjj = bj + bi[i]; 475 v1 = av + ai[i]; 476 v2 = bv + bi[i]; 477 478 /* A-part */ 479 for (j=0; j<countA; j++) { 480 if (reuse == MAT_INITIAL_MATRIX) { 481 row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift; 482 } 483 val[jj++] = v1[j]; 484 } 485 486 /* B-part */ 487 for (j=0; j < countB; j++) { 488 if (reuse == MAT_INITIAL_MATRIX) { 489 row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift; 490 } 491 val[jj++] = v2[j]; 492 } 493 irow++; 494 } 495 PetscFunctionReturn(0); 496 } 497 498 PetscErrorCode MatConvertToTriples_mpiaij_mpiaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 499 { 500 const PetscInt *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj; 501 PetscErrorCode ierr; 502 PetscInt rstart,nz,i,j,jj,irow,countA,countB; 503 PetscInt *row,*col; 504 const PetscScalar *av, *bv,*v1,*v2; 505 PetscScalar *val; 506 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data; 507 Mat_SeqAIJ *aa = (Mat_SeqAIJ*)(mat->A)->data; 508 Mat_SeqAIJ *bb = (Mat_SeqAIJ*)(mat->B)->data; 509 510 PetscFunctionBegin; 511 ai=aa->i; aj=aa->j; bi=bb->i; bj=bb->j; rstart= A->rmap->rstart; 512 av=aa->a; bv=bb->a; 513 514 garray = mat->garray; 515 516 if (reuse == MAT_INITIAL_MATRIX) { 517 nz = aa->nz + bb->nz; 518 *nnz = nz; 519 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 520 col = row + nz; 521 val = (PetscScalar*)(col + nz); 522 523 *r = row; *c = col; *v = val; 524 } else { 525 row = *r; col = *c; val = *v; 526 } 527 528 jj = 0; irow = rstart; 529 for (i=0; i<m; i++) { 530 ajj = aj + ai[i]; /* ptr to the beginning of this row */ 531 countA = ai[i+1] - ai[i]; 532 countB = bi[i+1] - bi[i]; 533 bjj = bj + bi[i]; 534 v1 = av + ai[i]; 535 v2 = bv + bi[i]; 536 537 /* A-part */ 538 for (j=0; j<countA; j++) { 539 if (reuse == MAT_INITIAL_MATRIX) { 540 row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift; 541 } 542 val[jj++] = v1[j]; 543 } 544 545 /* B-part */ 546 for (j=0; j < countB; j++) { 547 if (reuse == MAT_INITIAL_MATRIX) { 548 row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift; 549 } 550 val[jj++] = v2[j]; 551 } 552 irow++; 553 } 554 PetscFunctionReturn(0); 555 } 556 557 PetscErrorCode MatConvertToTriples_mpibaij_mpiaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 558 { 559 Mat_MPIBAIJ *mat = (Mat_MPIBAIJ*)A->data; 560 Mat_SeqBAIJ *aa = (Mat_SeqBAIJ*)(mat->A)->data; 561 Mat_SeqBAIJ *bb = (Mat_SeqBAIJ*)(mat->B)->data; 562 const PetscInt *ai = aa->i, *bi = bb->i, *aj = aa->j, *bj = bb->j,*ajj, *bjj; 563 const PetscInt *garray = mat->garray,mbs=mat->mbs,rstart=A->rmap->rstart; 564 const PetscInt bs2=mat->bs2; 565 PetscErrorCode ierr; 566 PetscInt bs,nz,i,j,k,n,jj,irow,countA,countB,idx; 567 PetscInt *row,*col; 568 const PetscScalar *av=aa->a, *bv=bb->a,*v1,*v2; 569 PetscScalar *val; 570 571 PetscFunctionBegin; 572 ierr = MatGetBlockSize(A,&bs);CHKERRQ(ierr); 573 if (reuse == MAT_INITIAL_MATRIX) { 574 nz = bs2*(aa->nz + bb->nz); 575 *nnz = nz; 576 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 577 col = row + nz; 578 val = (PetscScalar*)(col + nz); 579 580 *r = row; *c = col; *v = val; 581 } else { 582 row = *r; col = *c; val = *v; 583 } 584 585 jj = 0; irow = rstart; 586 for (i=0; i<mbs; i++) { 587 countA = ai[i+1] - ai[i]; 588 countB = bi[i+1] - bi[i]; 589 ajj = aj + ai[i]; 590 bjj = bj + bi[i]; 591 v1 = av + bs2*ai[i]; 592 v2 = bv + bs2*bi[i]; 593 594 idx = 0; 595 /* A-part */ 596 for (k=0; k<countA; k++) { 597 for (j=0; j<bs; j++) { 598 for (n=0; n<bs; n++) { 599 if (reuse == MAT_INITIAL_MATRIX) { 600 row[jj] = irow + n + shift; 601 col[jj] = rstart + bs*ajj[k] + j + shift; 602 } 603 val[jj++] = v1[idx++]; 604 } 605 } 606 } 607 608 idx = 0; 609 /* B-part */ 610 for (k=0; k<countB; k++) { 611 for (j=0; j<bs; j++) { 612 for (n=0; n<bs; n++) { 613 if (reuse == MAT_INITIAL_MATRIX) { 614 row[jj] = irow + n + shift; 615 col[jj] = bs*garray[bjj[k]] + j + shift; 616 } 617 val[jj++] = v2[idx++]; 618 } 619 } 620 } 621 irow += bs; 622 } 623 PetscFunctionReturn(0); 624 } 625 626 PetscErrorCode MatConvertToTriples_mpiaij_mpisbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 627 { 628 const PetscInt *ai, *aj,*adiag, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj; 629 PetscErrorCode ierr; 630 PetscInt rstart,nz,nza,nzb,i,j,jj,irow,countA,countB; 631 PetscInt *row,*col; 632 const PetscScalar *av, *bv,*v1,*v2; 633 PetscScalar *val; 634 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data; 635 Mat_SeqAIJ *aa =(Mat_SeqAIJ*)(mat->A)->data; 636 Mat_SeqAIJ *bb =(Mat_SeqAIJ*)(mat->B)->data; 637 638 PetscFunctionBegin; 639 ai=aa->i; aj=aa->j; adiag=aa->diag; 640 bi=bb->i; bj=bb->j; garray = mat->garray; 641 av=aa->a; bv=bb->a; 642 643 rstart = A->rmap->rstart; 644 645 if (reuse == MAT_INITIAL_MATRIX) { 646 nza = 0; /* num of upper triangular entries in mat->A, including diagonals */ 647 nzb = 0; /* num of upper triangular entries in mat->B */ 648 for (i=0; i<m; i++) { 649 nza += (ai[i+1] - adiag[i]); 650 countB = bi[i+1] - bi[i]; 651 bjj = bj + bi[i]; 652 for (j=0; j<countB; j++) { 653 if (garray[bjj[j]] > rstart) nzb++; 654 } 655 } 656 657 nz = nza + nzb; /* total nz of upper triangular part of mat */ 658 *nnz = nz; 659 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 660 col = row + nz; 661 val = (PetscScalar*)(col + nz); 662 663 *r = row; *c = col; *v = val; 664 } else { 665 row = *r; col = *c; val = *v; 666 } 667 668 jj = 0; irow = rstart; 669 for (i=0; i<m; i++) { 670 ajj = aj + adiag[i]; /* ptr to the beginning of the diagonal of this row */ 671 v1 = av + adiag[i]; 672 countA = ai[i+1] - adiag[i]; 673 countB = bi[i+1] - bi[i]; 674 bjj = bj + bi[i]; 675 v2 = bv + bi[i]; 676 677 /* A-part */ 678 for (j=0; j<countA; j++) { 679 if (reuse == MAT_INITIAL_MATRIX) { 680 row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift; 681 } 682 val[jj++] = v1[j]; 683 } 684 685 /* B-part */ 686 for (j=0; j < countB; j++) { 687 if (garray[bjj[j]] > rstart) { 688 if (reuse == MAT_INITIAL_MATRIX) { 689 row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift; 690 } 691 val[jj++] = v2[j]; 692 } 693 } 694 irow++; 695 } 696 PetscFunctionReturn(0); 697 } 698 699 PetscErrorCode MatDestroy_MUMPS(Mat A) 700 { 701 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 702 PetscErrorCode ierr; 703 704 PetscFunctionBegin; 705 ierr = PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc);CHKERRQ(ierr); 706 ierr = VecScatterDestroy(&mumps->scat_rhs);CHKERRQ(ierr); 707 ierr = VecScatterDestroy(&mumps->scat_sol);CHKERRQ(ierr); 708 ierr = VecDestroy(&mumps->b_seq);CHKERRQ(ierr); 709 ierr = VecDestroy(&mumps->x_seq);CHKERRQ(ierr); 710 ierr = PetscFree(mumps->id.perm_in);CHKERRQ(ierr); 711 ierr = PetscFree(mumps->irn);CHKERRQ(ierr); 712 ierr = PetscFree(mumps->info);CHKERRQ(ierr); 713 ierr = MatMumpsResetSchur_Private(mumps);CHKERRQ(ierr); 714 mumps->id.job = JOB_END; 715 PetscMUMPS_c(&mumps->id); 716 ierr = MPI_Comm_free(&mumps->comm_mumps);CHKERRQ(ierr); 717 ierr = PetscFree(A->data);CHKERRQ(ierr); 718 719 /* clear composed functions */ 720 ierr = PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverType_C",NULL);CHKERRQ(ierr); 721 ierr = PetscObjectComposeFunction((PetscObject)A,"MatFactorSetSchurIS_C",NULL);CHKERRQ(ierr); 722 ierr = PetscObjectComposeFunction((PetscObject)A,"MatFactorCreateSchurComplement_C",NULL);CHKERRQ(ierr); 723 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetIcntl_C",NULL);CHKERRQ(ierr); 724 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetIcntl_C",NULL);CHKERRQ(ierr); 725 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetCntl_C",NULL);CHKERRQ(ierr); 726 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetCntl_C",NULL);CHKERRQ(ierr); 727 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfo_C",NULL);CHKERRQ(ierr); 728 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfog_C",NULL);CHKERRQ(ierr); 729 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfo_C",NULL);CHKERRQ(ierr); 730 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfog_C",NULL);CHKERRQ(ierr); 731 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInverse_C",NULL);CHKERRQ(ierr); 732 PetscFunctionReturn(0); 733 } 734 735 PetscErrorCode MatSolve_MUMPS(Mat A,Vec b,Vec x) 736 { 737 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 738 PetscScalar *array; 739 Vec b_seq; 740 IS is_iden,is_petsc; 741 PetscErrorCode ierr; 742 PetscInt i; 743 PetscBool second_solve = PETSC_FALSE; 744 static PetscBool cite1 = PETSC_FALSE,cite2 = PETSC_FALSE; 745 746 PetscFunctionBegin; 747 ierr = PetscCitationsRegister("@article{MUMPS01,\n author = {P.~R. Amestoy and I.~S. Duff and J.-Y. L'Excellent and J. Koster},\n title = {A fully asynchronous multifrontal solver using distributed dynamic scheduling},\n journal = {SIAM Journal on Matrix Analysis and Applications},\n volume = {23},\n number = {1},\n pages = {15--41},\n year = {2001}\n}\n",&cite1);CHKERRQ(ierr); 748 ierr = PetscCitationsRegister("@article{MUMPS02,\n author = {P.~R. Amestoy and A. Guermouche and J.-Y. L'Excellent and S. Pralet},\n title = {Hybrid scheduling for the parallel solution of linear systems},\n journal = {Parallel Computing},\n volume = {32},\n number = {2},\n pages = {136--156},\n year = {2006}\n}\n",&cite2);CHKERRQ(ierr); 749 750 if (A->factorerrortype) { 751 ierr = PetscInfo2(A,"MatSolve is called with singular matrix factor, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 752 ierr = VecSetInf(x);CHKERRQ(ierr); 753 PetscFunctionReturn(0); 754 } 755 756 mumps->id.ICNTL(20)= 0; /* dense RHS */ 757 mumps->id.nrhs = 1; 758 b_seq = mumps->b_seq; 759 if (mumps->size > 1) { 760 /* MUMPS only supports centralized rhs. Scatter b into a seqential rhs vector */ 761 ierr = VecScatterBegin(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 762 ierr = VecScatterEnd(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 763 if (!mumps->myid) {ierr = VecGetArray(b_seq,&array);CHKERRQ(ierr);} 764 } else { /* size == 1 */ 765 ierr = VecCopy(b,x);CHKERRQ(ierr); 766 ierr = VecGetArray(x,&array);CHKERRQ(ierr); 767 } 768 if (!mumps->myid) { /* define rhs on the host */ 769 mumps->id.nrhs = 1; 770 mumps->id.rhs = (MumpsScalar*)array; 771 } 772 773 /* 774 handle condensation step of Schur complement (if any) 775 We set by default ICNTL(26) == -1 when Schur indices have been provided by the user. 776 According to MUMPS (5.0.0) manual, any value should be harmful during the factorization phase 777 Unless the user provides a valid value for ICNTL(26), MatSolve and MatMatSolve routines solve the full system. 778 This requires an extra call to PetscMUMPS_c and the computation of the factors for S 779 */ 780 if (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2) { 781 if (mumps->size > 1) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Parallel Schur complements not yet supported from PETSc\n"); 782 second_solve = PETSC_TRUE; 783 ierr = MatMumpsHandleSchur_Private(A,PETSC_FALSE);CHKERRQ(ierr); 784 } 785 /* solve phase */ 786 /*-------------*/ 787 mumps->id.job = JOB_SOLVE; 788 PetscMUMPS_c(&mumps->id); 789 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)); 790 791 /* handle expansion step of Schur complement (if any) */ 792 if (second_solve) { 793 ierr = MatMumpsHandleSchur_Private(A,PETSC_TRUE);CHKERRQ(ierr); 794 } 795 796 if (mumps->size > 1) { /* convert mumps distributed solution to petsc mpi x */ 797 if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) { 798 /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */ 799 ierr = VecScatterDestroy(&mumps->scat_sol);CHKERRQ(ierr); 800 } 801 if (!mumps->scat_sol) { /* create scatter scat_sol */ 802 ierr = ISCreateStride(PETSC_COMM_SELF,mumps->id.lsol_loc,0,1,&is_iden);CHKERRQ(ierr); /* from */ 803 for (i=0; i<mumps->id.lsol_loc; i++) { 804 mumps->id.isol_loc[i] -= 1; /* change Fortran style to C style */ 805 } 806 ierr = ISCreateGeneral(PETSC_COMM_SELF,mumps->id.lsol_loc,mumps->id.isol_loc,PETSC_COPY_VALUES,&is_petsc);CHKERRQ(ierr); /* to */ 807 ierr = VecScatterCreate(mumps->x_seq,is_iden,x,is_petsc,&mumps->scat_sol);CHKERRQ(ierr); 808 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 809 ierr = ISDestroy(&is_petsc);CHKERRQ(ierr); 810 811 mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */ 812 } 813 814 ierr = VecScatterBegin(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 815 ierr = VecScatterEnd(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 816 } 817 PetscFunctionReturn(0); 818 } 819 820 PetscErrorCode MatSolveTranspose_MUMPS(Mat A,Vec b,Vec x) 821 { 822 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 823 PetscErrorCode ierr; 824 825 PetscFunctionBegin; 826 mumps->id.ICNTL(9) = 0; 827 ierr = MatSolve_MUMPS(A,b,x);CHKERRQ(ierr); 828 mumps->id.ICNTL(9) = 1; 829 PetscFunctionReturn(0); 830 } 831 832 PetscErrorCode MatMatSolve_MUMPS(Mat A,Mat B,Mat X) 833 { 834 PetscErrorCode ierr; 835 Mat Bt = NULL; 836 PetscBool flg, flgT; 837 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 838 PetscInt i,nrhs,M; 839 PetscScalar *array,*bray; 840 PetscInt lsol_loc,nlsol_loc,*isol_loc,*idx,*iidx,*idxx,*isol_loc_save; 841 MumpsScalar *sol_loc,*sol_loc_save; 842 IS is_to,is_from; 843 PetscInt k,proc,j,m; 844 const PetscInt *rstart; 845 Vec v_mpi,b_seq,x_seq; 846 VecScatter scat_rhs,scat_sol; 847 PetscScalar *aa; 848 PetscInt spnr,*ia,*ja; 849 Mat_MPIAIJ *b; 850 851 PetscFunctionBegin; 852 ierr = PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr); 853 if (!flg) SETERRQ(PetscObjectComm((PetscObject)X),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix"); 854 855 ierr = PetscObjectTypeCompareAny((PetscObject)B,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr); 856 if (flg) { /* dense B */ 857 if (B->rmap->n != X->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Matrix B and X must have same row distribution"); 858 mumps->id.ICNTL(20)= 0; /* dense RHS */ 859 } else { 860 ierr = PetscObjectTypeCompare((PetscObject)B,MATTRANSPOSEMAT,&flgT);CHKERRQ(ierr); 861 if (flgT) { /* input B is transpose of actural RHS matrix, because mumps requires sparse compressed COLUMN storage! */ 862 ierr = MatTransposeGetMat(B,&Bt);CHKERRQ(ierr); 863 } else SETERRQ(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix"); 864 mumps->id.ICNTL(20)= 1; /* sparse RHS */ 865 } 866 867 ierr = MatGetSize(B,&M,&nrhs);CHKERRQ(ierr); 868 mumps->id.nrhs = nrhs; 869 mumps->id.lrhs = M; 870 mumps->id.rhs = NULL; 871 872 if (mumps->size == 1) { 873 PetscScalar *aa; 874 PetscInt spnr,*ia,*ja; 875 PetscBool second_solve = PETSC_FALSE; 876 877 ierr = MatDenseGetArray(X,&array);CHKERRQ(ierr); 878 mumps->id.rhs = (MumpsScalar*)array; 879 880 if (!Bt) { /* dense B */ 881 /* copy B to X */ 882 ierr = MatDenseGetArray(B,&bray);CHKERRQ(ierr); 883 ierr = PetscMemcpy(array,bray,M*nrhs*sizeof(PetscScalar));CHKERRQ(ierr); 884 ierr = MatDenseRestoreArray(B,&bray);CHKERRQ(ierr); 885 } else { /* sparse B */ 886 ierr = MatSeqAIJGetArray(Bt,&aa);CHKERRQ(ierr); 887 ierr = MatGetRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 888 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 889 /* mumps requires ia and ja start at 1! */ 890 mumps->id.irhs_ptr = ia; 891 mumps->id.irhs_sparse = ja; 892 mumps->id.nz_rhs = ia[spnr] - 1; 893 mumps->id.rhs_sparse = (MumpsScalar*)aa; 894 } 895 /* handle condensation step of Schur complement (if any) */ 896 if (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2) { 897 second_solve = PETSC_TRUE; 898 ierr = MatMumpsHandleSchur_Private(A,PETSC_FALSE);CHKERRQ(ierr); 899 } 900 /* solve phase */ 901 /*-------------*/ 902 mumps->id.job = JOB_SOLVE; 903 PetscMUMPS_c(&mumps->id); 904 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)); 905 906 /* handle expansion step of Schur complement (if any) */ 907 if (second_solve) { 908 ierr = MatMumpsHandleSchur_Private(A,PETSC_TRUE);CHKERRQ(ierr); 909 } 910 if (Bt) { /* sparse B */ 911 ierr = MatSeqAIJRestoreArray(Bt,&aa);CHKERRQ(ierr); 912 ierr = MatRestoreRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 913 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot restore IJ structure"); 914 } 915 ierr = MatDenseRestoreArray(X,&array);CHKERRQ(ierr); 916 PetscFunctionReturn(0); 917 } 918 919 /*--------- parallel case: MUMPS requires rhs B to be centralized on the host! --------*/ 920 if (mumps->size > 1 && mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Parallel Schur complements not yet supported from PETSc\n"); 921 922 /* create x_seq to hold mumps local solution */ 923 isol_loc_save = mumps->id.isol_loc; /* save it for MatSovle() */ 924 sol_loc_save = mumps->id.sol_loc; 925 926 lsol_loc = mumps->id.INFO(23); 927 nlsol_loc = nrhs*lsol_loc; /* length of sol_loc */ 928 ierr = PetscMalloc2(nlsol_loc,&sol_loc,nlsol_loc,&isol_loc);CHKERRQ(ierr); 929 mumps->id.sol_loc = (MumpsScalar*)sol_loc; 930 mumps->id.isol_loc = isol_loc; 931 932 ierr = VecCreateSeqWithArray(PETSC_COMM_SELF,1,nlsol_loc,(PetscScalar*)sol_loc,&x_seq);CHKERRQ(ierr); 933 934 /* scatter v_mpi to b_seq because MUMPS only supports centralized rhs */ 935 /* idx: maps from k-th index of v_mpi to (i,j)-th global entry of B; 936 iidx: inverse of idx, will be used by scattering mumps x_seq -> petsc X */ 937 ierr = PetscMalloc1(nrhs*M,&idx);CHKERRQ(ierr); 938 939 if (!Bt) { /* dense B */ 940 /* wrap dense rhs matrix B into a vector v_mpi */ 941 ierr = MatGetLocalSize(B,&m,NULL);CHKERRQ(ierr); 942 ierr = MatDenseGetArray(B,&bray);CHKERRQ(ierr); 943 ierr = VecCreateMPIWithArray(PetscObjectComm((PetscObject)B),1,nrhs*m,nrhs*M,(const PetscScalar*)bray,&v_mpi);CHKERRQ(ierr); 944 ierr = MatDenseRestoreArray(B,&bray);CHKERRQ(ierr); 945 946 /* scatter v_mpi to b_seq in proc[0]. MUMPS requires rhs to be centralized on the host! */ 947 if (!mumps->myid) { 948 ierr = MatGetOwnershipRanges(B,&rstart);CHKERRQ(ierr); 949 k = 0; 950 for (proc=0; proc<mumps->size; proc++){ 951 for (j=0; j<nrhs; j++){ 952 for (i=rstart[proc]; i<rstart[proc+1]; i++){ 953 idx[k++] = j*M + i; 954 } 955 } 956 } 957 958 ierr = VecCreateSeq(PETSC_COMM_SELF,nrhs*M,&b_seq);CHKERRQ(ierr); 959 ierr = ISCreateGeneral(PETSC_COMM_SELF,nrhs*M,idx,PETSC_COPY_VALUES,&is_to);CHKERRQ(ierr); 960 ierr = ISCreateStride(PETSC_COMM_SELF,nrhs*M,0,1,&is_from);CHKERRQ(ierr); 961 } else { 962 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&b_seq);CHKERRQ(ierr); 963 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_to);CHKERRQ(ierr); 964 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_from);CHKERRQ(ierr); 965 } 966 ierr = VecScatterCreate(v_mpi,is_from,b_seq,is_to,&scat_rhs);CHKERRQ(ierr); 967 ierr = VecScatterBegin(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 968 ierr = ISDestroy(&is_to);CHKERRQ(ierr); 969 ierr = ISDestroy(&is_from);CHKERRQ(ierr); 970 ierr = VecScatterEnd(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 971 972 if (!mumps->myid) { /* define rhs on the host */ 973 ierr = VecGetArray(b_seq,&bray);CHKERRQ(ierr); 974 mumps->id.rhs = (MumpsScalar*)bray; 975 ierr = VecRestoreArray(b_seq,&bray);CHKERRQ(ierr); 976 } 977 978 } else { /* sparse B */ 979 b = (Mat_MPIAIJ*)Bt->data; 980 981 /* wrap dense X into a vector v_mpi */ 982 ierr = MatGetLocalSize(X,&m,NULL);CHKERRQ(ierr); 983 ierr = MatDenseGetArray(X,&bray);CHKERRQ(ierr); 984 ierr = VecCreateMPIWithArray(PetscObjectComm((PetscObject)X),1,nrhs*m,nrhs*M,(const PetscScalar*)bray,&v_mpi);CHKERRQ(ierr); 985 ierr = MatDenseRestoreArray(X,&bray);CHKERRQ(ierr); 986 987 if (!mumps->myid) { 988 ierr = MatSeqAIJGetArray(b->A,&aa);CHKERRQ(ierr); 989 ierr = MatGetRowIJ(b->A,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 990 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 991 /* mumps requires ia and ja start at 1! */ 992 mumps->id.irhs_ptr = ia; 993 mumps->id.irhs_sparse = ja; 994 mumps->id.nz_rhs = ia[spnr] - 1; 995 mumps->id.rhs_sparse = (MumpsScalar*)aa; 996 } else { 997 mumps->id.irhs_ptr = NULL; 998 mumps->id.irhs_sparse = NULL; 999 mumps->id.nz_rhs = 0; 1000 mumps->id.rhs_sparse = NULL; 1001 } 1002 } 1003 1004 /* solve phase */ 1005 /*-------------*/ 1006 mumps->id.job = JOB_SOLVE; 1007 PetscMUMPS_c(&mumps->id); 1008 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)); 1009 1010 /* scatter mumps distributed solution to petsc vector v_mpi, which shares local arrays with solution matrix X */ 1011 ierr = MatDenseGetArray(X,&array);CHKERRQ(ierr); 1012 ierr = VecPlaceArray(v_mpi,array);CHKERRQ(ierr); 1013 1014 /* create scatter scat_sol */ 1015 ierr = MatGetOwnershipRanges(X,&rstart);CHKERRQ(ierr); 1016 /* iidx: inverse of idx computed above, used for scattering mumps x_seq to petsc X */ 1017 iidx = idx; 1018 k = 0; 1019 for (proc=0; proc<mumps->size; proc++){ 1020 for (j=0; j<nrhs; j++){ 1021 for (i=rstart[proc]; i<rstart[proc+1]; i++) iidx[j*M + i] = k++; 1022 } 1023 } 1024 1025 ierr = PetscMalloc1(nlsol_loc,&idxx);CHKERRQ(ierr); 1026 ierr = ISCreateStride(PETSC_COMM_SELF,nlsol_loc,0,1,&is_from);CHKERRQ(ierr); 1027 for (i=0; i<lsol_loc; i++) { 1028 isol_loc[i] -= 1; /* change Fortran style to C style */ 1029 idxx[i] = iidx[isol_loc[i]]; 1030 for (j=1; j<nrhs; j++){ 1031 idxx[j*lsol_loc+i] = iidx[isol_loc[i]+j*M]; 1032 } 1033 } 1034 ierr = ISCreateGeneral(PETSC_COMM_SELF,nlsol_loc,idxx,PETSC_COPY_VALUES,&is_to);CHKERRQ(ierr); 1035 ierr = VecScatterCreate(x_seq,is_from,v_mpi,is_to,&scat_sol);CHKERRQ(ierr); 1036 ierr = VecScatterBegin(scat_sol,x_seq,v_mpi,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1037 ierr = ISDestroy(&is_from);CHKERRQ(ierr); 1038 ierr = ISDestroy(&is_to);CHKERRQ(ierr); 1039 ierr = VecScatterEnd(scat_sol,x_seq,v_mpi,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1040 ierr = MatDenseRestoreArray(X,&array);CHKERRQ(ierr); 1041 1042 /* free spaces */ 1043 mumps->id.sol_loc = sol_loc_save; 1044 mumps->id.isol_loc = isol_loc_save; 1045 1046 ierr = PetscFree2(sol_loc,isol_loc);CHKERRQ(ierr); 1047 ierr = PetscFree(idx);CHKERRQ(ierr); 1048 ierr = PetscFree(idxx);CHKERRQ(ierr); 1049 ierr = VecDestroy(&x_seq);CHKERRQ(ierr); 1050 ierr = VecDestroy(&v_mpi);CHKERRQ(ierr); 1051 if (Bt) { 1052 if (!mumps->myid) { 1053 ierr = MatSeqAIJRestoreArray(b->A,&aa);CHKERRQ(ierr); 1054 ierr = MatRestoreRowIJ(b->A,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 1055 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot restore IJ structure"); 1056 } 1057 } else { 1058 ierr = VecDestroy(&b_seq);CHKERRQ(ierr); 1059 ierr = VecScatterDestroy(&scat_rhs);CHKERRQ(ierr); 1060 } 1061 ierr = VecScatterDestroy(&scat_sol);CHKERRQ(ierr); 1062 PetscFunctionReturn(0); 1063 } 1064 1065 PetscErrorCode MatMatTransposeSolve_MUMPS(Mat A,Mat Bt,Mat X) 1066 { 1067 PetscErrorCode ierr; 1068 PetscBool flg; 1069 Mat B; 1070 1071 PetscFunctionBegin; 1072 printf("MatMatTransposeSolve_MUMPS...\n"); 1073 ierr = PetscObjectTypeCompareAny((PetscObject)Bt,&flg,MATSEQAIJ,MATMPIAIJ,NULL);CHKERRQ(ierr); 1074 if (!flg) SETERRQ(PetscObjectComm((PetscObject)Bt),PETSC_ERR_ARG_WRONG,"Matrix Bt must be MATAIJ matrix"); 1075 1076 /* Create B=Bt^T that uses Bt's data structure */ 1077 ierr = MatCreateTranspose(Bt,&B);CHKERRQ(ierr); 1078 1079 ierr = MatDestroy(&B);CHKERRQ(ierr); 1080 PetscFunctionReturn(0); 1081 } 1082 1083 1084 #if !defined(PETSC_USE_COMPLEX) 1085 /* 1086 input: 1087 F: numeric factor 1088 output: 1089 nneg: total number of negative pivots 1090 nzero: total number of zero pivots 1091 npos: (global dimension of F) - nneg - nzero 1092 */ 1093 PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F,int *nneg,int *nzero,int *npos) 1094 { 1095 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1096 PetscErrorCode ierr; 1097 PetscMPIInt size; 1098 1099 PetscFunctionBegin; 1100 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)F),&size);CHKERRQ(ierr); 1101 /* 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 */ 1102 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)); 1103 1104 if (nneg) *nneg = mumps->id.INFOG(12); 1105 if (nzero || npos) { 1106 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"); 1107 if (nzero) *nzero = mumps->id.INFOG(28); 1108 if (npos) *npos = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28)); 1109 } 1110 PetscFunctionReturn(0); 1111 } 1112 #endif 1113 1114 PetscErrorCode MatFactorNumeric_MUMPS(Mat F,Mat A,const MatFactorInfo *info) 1115 { 1116 Mat_MUMPS *mumps =(Mat_MUMPS*)(F)->data; 1117 PetscErrorCode ierr; 1118 PetscBool isMPIAIJ; 1119 1120 PetscFunctionBegin; 1121 if (mumps->id.INFOG(1) < 0) { 1122 if (mumps->id.INFOG(1) == -6) { 1123 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); 1124 } 1125 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); 1126 PetscFunctionReturn(0); 1127 } 1128 1129 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1130 1131 /* numerical factorization phase */ 1132 /*-------------------------------*/ 1133 mumps->id.job = JOB_FACTNUMERIC; 1134 if (!mumps->id.ICNTL(18)) { /* A is centralized */ 1135 if (!mumps->myid) { 1136 mumps->id.a = (MumpsScalar*)mumps->val; 1137 } 1138 } else { 1139 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1140 } 1141 PetscMUMPS_c(&mumps->id); 1142 if (mumps->id.INFOG(1) < 0) { 1143 if (A->erroriffailure) { 1144 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)); 1145 } else { 1146 if (mumps->id.INFOG(1) == -10) { /* numerically singular matrix */ 1147 ierr = PetscInfo2(F,"matrix is numerically singular, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1148 F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1149 } else if (mumps->id.INFOG(1) == -13) { 1150 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); 1151 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1152 } else if (mumps->id.INFOG(1) == -8 || mumps->id.INFOG(1) == -9 || (-16 < mumps->id.INFOG(1) && mumps->id.INFOG(1) < -10) ) { 1153 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); 1154 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1155 } else { 1156 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); 1157 F->factorerrortype = MAT_FACTOR_OTHER; 1158 } 1159 } 1160 } 1161 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)); 1162 1163 F->assembled = PETSC_TRUE; 1164 mumps->matstruc = SAME_NONZERO_PATTERN; 1165 if (F->schur) { /* reset Schur status to unfactored */ 1166 if (mumps->id.ICNTL(19) == 1) { /* stored by rows */ 1167 mumps->id.ICNTL(19) = 2; 1168 ierr = MatTranspose(F->schur,MAT_INPLACE_MATRIX,&F->schur);CHKERRQ(ierr); 1169 } 1170 ierr = MatFactorRestoreSchurComplement(F,NULL,MAT_FACTOR_SCHUR_UNFACTORED);CHKERRQ(ierr); 1171 } 1172 1173 /* just to be sure that ICNTL(19) value returned by a call from MatMumpsGetIcntl is always consistent */ 1174 if (!mumps->sym && mumps->id.ICNTL(19) && mumps->id.ICNTL(19) != 1) mumps->id.ICNTL(19) = 3; 1175 1176 if (mumps->size > 1) { 1177 PetscInt lsol_loc; 1178 PetscScalar *sol_loc; 1179 1180 ierr = PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&isMPIAIJ);CHKERRQ(ierr); 1181 1182 /* distributed solution; Create x_seq=sol_loc for repeated use */ 1183 if (mumps->x_seq) { 1184 ierr = VecScatterDestroy(&mumps->scat_sol);CHKERRQ(ierr); 1185 ierr = PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc);CHKERRQ(ierr); 1186 ierr = VecDestroy(&mumps->x_seq);CHKERRQ(ierr); 1187 } 1188 lsol_loc = mumps->id.INFO(23); /* length of sol_loc */ 1189 ierr = PetscMalloc2(lsol_loc,&sol_loc,lsol_loc,&mumps->id.isol_loc);CHKERRQ(ierr); 1190 mumps->id.lsol_loc = lsol_loc; 1191 mumps->id.sol_loc = (MumpsScalar*)sol_loc; 1192 ierr = VecCreateSeqWithArray(PETSC_COMM_SELF,1,lsol_loc,sol_loc,&mumps->x_seq);CHKERRQ(ierr); 1193 } 1194 PetscFunctionReturn(0); 1195 } 1196 1197 /* Sets MUMPS options from the options database */ 1198 PetscErrorCode PetscSetMUMPSFromOptions(Mat F, Mat A) 1199 { 1200 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1201 PetscErrorCode ierr; 1202 PetscInt icntl,info[40],i,ninfo=40; 1203 PetscBool flg; 1204 1205 PetscFunctionBegin; 1206 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"MUMPS Options","Mat");CHKERRQ(ierr); 1207 ierr = PetscOptionsInt("-mat_mumps_icntl_1","ICNTL(1): output stream for error messages","None",mumps->id.ICNTL(1),&icntl,&flg);CHKERRQ(ierr); 1208 if (flg) mumps->id.ICNTL(1) = icntl; 1209 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); 1210 if (flg) mumps->id.ICNTL(2) = icntl; 1211 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); 1212 if (flg) mumps->id.ICNTL(3) = icntl; 1213 1214 ierr = PetscOptionsInt("-mat_mumps_icntl_4","ICNTL(4): level of printing (0 to 4)","None",mumps->id.ICNTL(4),&icntl,&flg);CHKERRQ(ierr); 1215 if (flg) mumps->id.ICNTL(4) = icntl; 1216 if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */ 1217 1218 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); 1219 if (flg) mumps->id.ICNTL(6) = icntl; 1220 1221 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); 1222 if (flg) { 1223 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"); 1224 else mumps->id.ICNTL(7) = icntl; 1225 } 1226 1227 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); 1228 /* 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() */ 1229 ierr = PetscOptionsInt("-mat_mumps_icntl_10","ICNTL(10): max num of refinements","None",mumps->id.ICNTL(10),&mumps->id.ICNTL(10),NULL);CHKERRQ(ierr); 1230 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); 1231 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); 1232 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); 1233 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); 1234 ierr = PetscOptionsInt("-mat_mumps_icntl_19","ICNTL(19): computes the Schur complement","None",mumps->id.ICNTL(19),&mumps->id.ICNTL(19),NULL);CHKERRQ(ierr); 1235 if (mumps->id.ICNTL(19) <= 0 || mumps->id.ICNTL(19) > 3) { /* reset any schur data (if any) */ 1236 ierr = MatDestroy(&F->schur);CHKERRQ(ierr); 1237 ierr = MatMumpsResetSchur_Private(mumps);CHKERRQ(ierr); 1238 } 1239 /* 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 */ 1240 /* 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 */ 1241 1242 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); 1243 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); 1244 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); 1245 if (mumps->id.ICNTL(24)) { 1246 mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */ 1247 } 1248 1249 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); 1250 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); 1251 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); 1252 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); 1253 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); 1254 /* ierr = PetscOptionsInt("-mat_mumps_icntl_30","ICNTL(30): compute user-specified set of entries in inv(A)","None",mumps->id.ICNTL(30),&mumps->id.ICNTL(30),NULL);CHKERRQ(ierr); */ /* call MatMumpsGetInverse() directly */ 1255 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); 1256 /* 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 */ 1257 ierr = PetscOptionsInt("-mat_mumps_icntl_33","ICNTL(33): compute determinant","None",mumps->id.ICNTL(33),&mumps->id.ICNTL(33),NULL);CHKERRQ(ierr); 1258 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); 1259 1260 ierr = PetscOptionsReal("-mat_mumps_cntl_1","CNTL(1): relative pivoting threshold","None",mumps->id.CNTL(1),&mumps->id.CNTL(1),NULL);CHKERRQ(ierr); 1261 ierr = PetscOptionsReal("-mat_mumps_cntl_2","CNTL(2): stopping criterion of refinement","None",mumps->id.CNTL(2),&mumps->id.CNTL(2),NULL);CHKERRQ(ierr); 1262 ierr = PetscOptionsReal("-mat_mumps_cntl_3","CNTL(3): absolute pivoting threshold","None",mumps->id.CNTL(3),&mumps->id.CNTL(3),NULL);CHKERRQ(ierr); 1263 ierr = PetscOptionsReal("-mat_mumps_cntl_4","CNTL(4): value for static pivoting","None",mumps->id.CNTL(4),&mumps->id.CNTL(4),NULL);CHKERRQ(ierr); 1264 ierr = PetscOptionsReal("-mat_mumps_cntl_5","CNTL(5): fixation for null pivots","None",mumps->id.CNTL(5),&mumps->id.CNTL(5),NULL);CHKERRQ(ierr); 1265 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); 1266 1267 ierr = PetscOptionsString("-mat_mumps_ooc_tmpdir", "out of core directory", "None", mumps->id.ooc_tmpdir, mumps->id.ooc_tmpdir, 256, NULL);CHKERRQ(ierr); 1268 1269 ierr = PetscOptionsIntArray("-mat_mumps_view_info","request INFO local to each processor","",info,&ninfo,NULL);CHKERRQ(ierr); 1270 if (ninfo) { 1271 if (ninfo > 40) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_USER,"number of INFO %d must <= 40\n",ninfo); 1272 ierr = PetscMalloc1(ninfo,&mumps->info);CHKERRQ(ierr); 1273 mumps->ninfo = ninfo; 1274 for (i=0; i<ninfo; i++) { 1275 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); 1276 else mumps->info[i] = info[i]; 1277 } 1278 } 1279 1280 ierr = PetscOptionsEnd();CHKERRQ(ierr); 1281 PetscFunctionReturn(0); 1282 } 1283 1284 PetscErrorCode PetscInitializeMUMPS(Mat A,Mat_MUMPS *mumps) 1285 { 1286 PetscErrorCode ierr; 1287 1288 PetscFunctionBegin; 1289 ierr = MPI_Comm_rank(PetscObjectComm((PetscObject)A), &mumps->myid);CHKERRQ(ierr); 1290 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)A),&mumps->size);CHKERRQ(ierr); 1291 ierr = MPI_Comm_dup(PetscObjectComm((PetscObject)A),&(mumps->comm_mumps));CHKERRQ(ierr); 1292 1293 mumps->id.comm_fortran = MPI_Comm_c2f(mumps->comm_mumps); 1294 1295 mumps->id.job = JOB_INIT; 1296 mumps->id.par = 1; /* host participates factorizaton and solve */ 1297 mumps->id.sym = mumps->sym; 1298 PetscMUMPS_c(&mumps->id); 1299 1300 mumps->scat_rhs = NULL; 1301 mumps->scat_sol = NULL; 1302 1303 /* set PETSc-MUMPS default options - override MUMPS default */ 1304 mumps->id.ICNTL(3) = 0; 1305 mumps->id.ICNTL(4) = 0; 1306 if (mumps->size == 1) { 1307 mumps->id.ICNTL(18) = 0; /* centralized assembled matrix input */ 1308 } else { 1309 mumps->id.ICNTL(18) = 3; /* distributed assembled matrix input */ 1310 mumps->id.ICNTL(20) = 0; /* rhs is in dense format */ 1311 mumps->id.ICNTL(21) = 1; /* distributed solution */ 1312 } 1313 1314 /* schur */ 1315 mumps->id.size_schur = 0; 1316 mumps->id.listvar_schur = NULL; 1317 mumps->id.schur = NULL; 1318 mumps->sizeredrhs = 0; 1319 mumps->schur_sol = NULL; 1320 mumps->schur_sizesol = 0; 1321 PetscFunctionReturn(0); 1322 } 1323 1324 PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F,Mat A,const MatFactorInfo *info,Mat_MUMPS *mumps) 1325 { 1326 PetscErrorCode ierr; 1327 1328 PetscFunctionBegin; 1329 if (mumps->id.INFOG(1) < 0) { 1330 if (A->erroriffailure) { 1331 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d\n",mumps->id.INFOG(1)); 1332 } else { 1333 if (mumps->id.INFOG(1) == -6) { 1334 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); 1335 F->factorerrortype = MAT_FACTOR_STRUCT_ZEROPIVOT; 1336 } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) { 1337 ierr = PetscInfo2(F,"problem of workspace, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1338 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1339 } else { 1340 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); 1341 F->factorerrortype = MAT_FACTOR_OTHER; 1342 } 1343 } 1344 } 1345 PetscFunctionReturn(0); 1346 } 1347 1348 /* Note Petsc r(=c) permutation is used when mumps->id.ICNTL(7)==1 with centralized assembled matrix input; otherwise r and c are ignored */ 1349 PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 1350 { 1351 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1352 PetscErrorCode ierr; 1353 Vec b; 1354 IS is_iden; 1355 const PetscInt M = A->rmap->N; 1356 1357 PetscFunctionBegin; 1358 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 1359 1360 /* Set MUMPS options from the options database */ 1361 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1362 1363 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1364 1365 /* analysis phase */ 1366 /*----------------*/ 1367 mumps->id.job = JOB_FACTSYMBOLIC; 1368 mumps->id.n = M; 1369 switch (mumps->id.ICNTL(18)) { 1370 case 0: /* centralized assembled matrix input */ 1371 if (!mumps->myid) { 1372 mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn; 1373 if (mumps->id.ICNTL(6)>1) { 1374 mumps->id.a = (MumpsScalar*)mumps->val; 1375 } 1376 if (mumps->id.ICNTL(7) == 1) { /* use user-provide matrix ordering - assuming r = c ordering */ 1377 /* 1378 PetscBool flag; 1379 ierr = ISEqual(r,c,&flag);CHKERRQ(ierr); 1380 if (!flag) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_USER,"row_perm != col_perm"); 1381 ierr = ISView(r,PETSC_VIEWER_STDOUT_SELF); 1382 */ 1383 if (!mumps->myid) { 1384 const PetscInt *idx; 1385 PetscInt i,*perm_in; 1386 1387 ierr = PetscMalloc1(M,&perm_in);CHKERRQ(ierr); 1388 ierr = ISGetIndices(r,&idx);CHKERRQ(ierr); 1389 1390 mumps->id.perm_in = perm_in; 1391 for (i=0; i<M; i++) perm_in[i] = idx[i]+1; /* perm_in[]: start from 1, not 0! */ 1392 ierr = ISRestoreIndices(r,&idx);CHKERRQ(ierr); 1393 } 1394 } 1395 } 1396 break; 1397 case 3: /* distributed assembled matrix input (size>1) */ 1398 mumps->id.nz_loc = mumps->nz; 1399 mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn; 1400 if (mumps->id.ICNTL(6)>1) { 1401 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1402 } 1403 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1404 if (!mumps->myid) { 1405 ierr = VecCreateSeq(PETSC_COMM_SELF,A->rmap->N,&mumps->b_seq);CHKERRQ(ierr); 1406 ierr = ISCreateStride(PETSC_COMM_SELF,A->rmap->N,0,1,&is_iden);CHKERRQ(ierr); 1407 } else { 1408 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);CHKERRQ(ierr); 1409 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1410 } 1411 ierr = MatCreateVecs(A,NULL,&b);CHKERRQ(ierr); 1412 ierr = VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);CHKERRQ(ierr); 1413 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1414 ierr = VecDestroy(&b);CHKERRQ(ierr); 1415 break; 1416 } 1417 PetscMUMPS_c(&mumps->id); 1418 ierr = MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);CHKERRQ(ierr); 1419 1420 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 1421 F->ops->solve = MatSolve_MUMPS; 1422 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 1423 F->ops->matsolve = MatMatSolve_MUMPS; 1424 F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS; 1425 PetscFunctionReturn(0); 1426 } 1427 1428 /* Note the Petsc r and c permutations are ignored */ 1429 PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 1430 { 1431 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1432 PetscErrorCode ierr; 1433 Vec b; 1434 IS is_iden; 1435 const PetscInt M = A->rmap->N; 1436 1437 PetscFunctionBegin; 1438 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 1439 1440 /* Set MUMPS options from the options database */ 1441 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1442 1443 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1444 1445 /* analysis phase */ 1446 /*----------------*/ 1447 mumps->id.job = JOB_FACTSYMBOLIC; 1448 mumps->id.n = M; 1449 switch (mumps->id.ICNTL(18)) { 1450 case 0: /* centralized assembled matrix input */ 1451 if (!mumps->myid) { 1452 mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn; 1453 if (mumps->id.ICNTL(6)>1) { 1454 mumps->id.a = (MumpsScalar*)mumps->val; 1455 } 1456 } 1457 break; 1458 case 3: /* distributed assembled matrix input (size>1) */ 1459 mumps->id.nz_loc = mumps->nz; 1460 mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn; 1461 if (mumps->id.ICNTL(6)>1) { 1462 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1463 } 1464 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1465 if (!mumps->myid) { 1466 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);CHKERRQ(ierr); 1467 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 1468 } else { 1469 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);CHKERRQ(ierr); 1470 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1471 } 1472 ierr = MatCreateVecs(A,NULL,&b);CHKERRQ(ierr); 1473 ierr = VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);CHKERRQ(ierr); 1474 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1475 ierr = VecDestroy(&b);CHKERRQ(ierr); 1476 break; 1477 } 1478 PetscMUMPS_c(&mumps->id); 1479 ierr = MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);CHKERRQ(ierr); 1480 1481 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 1482 F->ops->solve = MatSolve_MUMPS; 1483 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 1484 PetscFunctionReturn(0); 1485 } 1486 1487 /* Note the Petsc r permutation and factor info are ignored */ 1488 PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F,Mat A,IS r,const MatFactorInfo *info) 1489 { 1490 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1491 PetscErrorCode ierr; 1492 Vec b; 1493 IS is_iden; 1494 const PetscInt M = A->rmap->N; 1495 1496 PetscFunctionBegin; 1497 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 1498 1499 /* Set MUMPS options from the options database */ 1500 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1501 1502 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1503 1504 /* analysis phase */ 1505 /*----------------*/ 1506 mumps->id.job = JOB_FACTSYMBOLIC; 1507 mumps->id.n = M; 1508 switch (mumps->id.ICNTL(18)) { 1509 case 0: /* centralized assembled matrix input */ 1510 if (!mumps->myid) { 1511 mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn; 1512 if (mumps->id.ICNTL(6)>1) { 1513 mumps->id.a = (MumpsScalar*)mumps->val; 1514 } 1515 } 1516 break; 1517 case 3: /* distributed assembled matrix input (size>1) */ 1518 mumps->id.nz_loc = mumps->nz; 1519 mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn; 1520 if (mumps->id.ICNTL(6)>1) { 1521 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1522 } 1523 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1524 if (!mumps->myid) { 1525 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);CHKERRQ(ierr); 1526 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 1527 } else { 1528 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);CHKERRQ(ierr); 1529 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1530 } 1531 ierr = MatCreateVecs(A,NULL,&b);CHKERRQ(ierr); 1532 ierr = VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);CHKERRQ(ierr); 1533 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1534 ierr = VecDestroy(&b);CHKERRQ(ierr); 1535 break; 1536 } 1537 PetscMUMPS_c(&mumps->id); 1538 ierr = MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);CHKERRQ(ierr); 1539 1540 F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS; 1541 F->ops->solve = MatSolve_MUMPS; 1542 F->ops->solvetranspose = MatSolve_MUMPS; 1543 F->ops->matsolve = MatMatSolve_MUMPS; 1544 #if defined(PETSC_USE_COMPLEX) 1545 F->ops->getinertia = NULL; 1546 #else 1547 F->ops->getinertia = MatGetInertia_SBAIJMUMPS; 1548 #endif 1549 PetscFunctionReturn(0); 1550 } 1551 1552 PetscErrorCode MatView_MUMPS(Mat A,PetscViewer viewer) 1553 { 1554 PetscErrorCode ierr; 1555 PetscBool iascii; 1556 PetscViewerFormat format; 1557 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 1558 1559 PetscFunctionBegin; 1560 /* check if matrix is mumps type */ 1561 if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(0); 1562 1563 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 1564 if (iascii) { 1565 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 1566 if (format == PETSC_VIEWER_ASCII_INFO) { 1567 ierr = PetscViewerASCIIPrintf(viewer,"MUMPS run parameters:\n");CHKERRQ(ierr); 1568 ierr = PetscViewerASCIIPrintf(viewer," SYM (matrix type): %d \n",mumps->id.sym);CHKERRQ(ierr); 1569 ierr = PetscViewerASCIIPrintf(viewer," PAR (host participation): %d \n",mumps->id.par);CHKERRQ(ierr); 1570 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(1) (output for error): %d \n",mumps->id.ICNTL(1));CHKERRQ(ierr); 1571 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(2) (output of diagnostic msg): %d \n",mumps->id.ICNTL(2));CHKERRQ(ierr); 1572 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(3) (output for global info): %d \n",mumps->id.ICNTL(3));CHKERRQ(ierr); 1573 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(4) (level of printing): %d \n",mumps->id.ICNTL(4));CHKERRQ(ierr); 1574 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(5) (input mat struct): %d \n",mumps->id.ICNTL(5));CHKERRQ(ierr); 1575 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(6) (matrix prescaling): %d \n",mumps->id.ICNTL(6));CHKERRQ(ierr); 1576 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(7) (sequential matrix ordering):%d \n",mumps->id.ICNTL(7));CHKERRQ(ierr); 1577 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(8) (scaling strategy): %d \n",mumps->id.ICNTL(8));CHKERRQ(ierr); 1578 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(10) (max num of refinements): %d \n",mumps->id.ICNTL(10));CHKERRQ(ierr); 1579 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(11) (error analysis): %d \n",mumps->id.ICNTL(11));CHKERRQ(ierr); 1580 if (mumps->id.ICNTL(11)>0) { 1581 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(4) (inf norm of input mat): %g\n",mumps->id.RINFOG(4));CHKERRQ(ierr); 1582 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(5) (inf norm of solution): %g\n",mumps->id.RINFOG(5));CHKERRQ(ierr); 1583 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(6) (inf norm of residual): %g\n",mumps->id.RINFOG(6));CHKERRQ(ierr); 1584 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(7),RINFOG(8) (backward error est): %g, %g\n",mumps->id.RINFOG(7),mumps->id.RINFOG(8));CHKERRQ(ierr); 1585 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(9) (error estimate): %g \n",mumps->id.RINFOG(9));CHKERRQ(ierr); 1586 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n",mumps->id.RINFOG(10),mumps->id.RINFOG(11));CHKERRQ(ierr); 1587 } 1588 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(12) (efficiency control): %d \n",mumps->id.ICNTL(12));CHKERRQ(ierr); 1589 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(13) (efficiency control): %d \n",mumps->id.ICNTL(13));CHKERRQ(ierr); 1590 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(14) (percentage of estimated workspace increase): %d \n",mumps->id.ICNTL(14));CHKERRQ(ierr); 1591 /* ICNTL(15-17) not used */ 1592 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(18) (input mat struct): %d \n",mumps->id.ICNTL(18));CHKERRQ(ierr); 1593 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(19) (Schur complement info): %d \n",mumps->id.ICNTL(19));CHKERRQ(ierr); 1594 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(20) (rhs sparse pattern): %d \n",mumps->id.ICNTL(20));CHKERRQ(ierr); 1595 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(21) (solution struct): %d \n",mumps->id.ICNTL(21));CHKERRQ(ierr); 1596 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(22) (in-core/out-of-core facility): %d \n",mumps->id.ICNTL(22));CHKERRQ(ierr); 1597 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(23) (max size of memory can be allocated locally):%d \n",mumps->id.ICNTL(23));CHKERRQ(ierr); 1598 1599 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(24) (detection of null pivot rows): %d \n",mumps->id.ICNTL(24));CHKERRQ(ierr); 1600 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(25) (computation of a null space basis): %d \n",mumps->id.ICNTL(25));CHKERRQ(ierr); 1601 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(26) (Schur options for rhs or solution): %d \n",mumps->id.ICNTL(26));CHKERRQ(ierr); 1602 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(27) (experimental parameter): %d \n",mumps->id.ICNTL(27));CHKERRQ(ierr); 1603 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(28) (use parallel or sequential ordering): %d \n",mumps->id.ICNTL(28));CHKERRQ(ierr); 1604 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(29) (parallel ordering): %d \n",mumps->id.ICNTL(29));CHKERRQ(ierr); 1605 1606 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(30) (user-specified set of entries in inv(A)): %d \n",mumps->id.ICNTL(30));CHKERRQ(ierr); 1607 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(31) (factors is discarded in the solve phase): %d \n",mumps->id.ICNTL(31));CHKERRQ(ierr); 1608 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(33) (compute determinant): %d \n",mumps->id.ICNTL(33));CHKERRQ(ierr); 1609 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(35) (activate BLR based factorization): %d \n",mumps->id.ICNTL(35));CHKERRQ(ierr); 1610 1611 ierr = PetscViewerASCIIPrintf(viewer," CNTL(1) (relative pivoting threshold): %g \n",mumps->id.CNTL(1));CHKERRQ(ierr); 1612 ierr = PetscViewerASCIIPrintf(viewer," CNTL(2) (stopping criterion of refinement): %g \n",mumps->id.CNTL(2));CHKERRQ(ierr); 1613 ierr = PetscViewerASCIIPrintf(viewer," CNTL(3) (absolute pivoting threshold): %g \n",mumps->id.CNTL(3));CHKERRQ(ierr); 1614 ierr = PetscViewerASCIIPrintf(viewer," CNTL(4) (value of static pivoting): %g \n",mumps->id.CNTL(4));CHKERRQ(ierr); 1615 ierr = PetscViewerASCIIPrintf(viewer," CNTL(5) (fixation for null pivots): %g \n",mumps->id.CNTL(5));CHKERRQ(ierr); 1616 ierr = PetscViewerASCIIPrintf(viewer," CNTL(7) (dropping parameter for BLR): %g \n",mumps->id.CNTL(7));CHKERRQ(ierr); 1617 1618 /* infomation local to each processor */ 1619 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(1) (local estimated flops for the elimination after analysis): \n");CHKERRQ(ierr); 1620 ierr = PetscViewerASCIIPushSynchronized(viewer);CHKERRQ(ierr); 1621 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(1));CHKERRQ(ierr); 1622 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1623 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(2) (local estimated flops for the assembly after factorization): \n");CHKERRQ(ierr); 1624 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(2));CHKERRQ(ierr); 1625 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1626 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(3) (local estimated flops for the elimination after factorization): \n");CHKERRQ(ierr); 1627 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(3));CHKERRQ(ierr); 1628 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1629 1630 ierr = PetscViewerASCIIPrintf(viewer, " INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization): \n");CHKERRQ(ierr); 1631 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(15));CHKERRQ(ierr); 1632 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1633 1634 ierr = PetscViewerASCIIPrintf(viewer, " INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization): \n");CHKERRQ(ierr); 1635 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(16));CHKERRQ(ierr); 1636 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1637 1638 ierr = PetscViewerASCIIPrintf(viewer, " INFO(23) (num of pivots eliminated on this processor after factorization): \n");CHKERRQ(ierr); 1639 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(23));CHKERRQ(ierr); 1640 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1641 1642 if (mumps->ninfo && mumps->ninfo <= 40){ 1643 PetscInt i; 1644 for (i=0; i<mumps->ninfo; i++){ 1645 ierr = PetscViewerASCIIPrintf(viewer, " INFO(%d): \n",mumps->info[i]);CHKERRQ(ierr); 1646 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(mumps->info[i]));CHKERRQ(ierr); 1647 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1648 } 1649 } 1650 1651 1652 ierr = PetscViewerASCIIPopSynchronized(viewer);CHKERRQ(ierr); 1653 1654 if (!mumps->myid) { /* information from the host */ 1655 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(1) (global estimated flops for the elimination after analysis): %g \n",mumps->id.RINFOG(1));CHKERRQ(ierr); 1656 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(2) (global estimated flops for the assembly after factorization): %g \n",mumps->id.RINFOG(2));CHKERRQ(ierr); 1657 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(3) (global estimated flops for the elimination after factorization): %g \n",mumps->id.RINFOG(3));CHKERRQ(ierr); 1658 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); 1659 1660 ierr = PetscViewerASCIIPrintf(viewer," INFOG(3) (estimated real workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(3));CHKERRQ(ierr); 1661 ierr = PetscViewerASCIIPrintf(viewer," INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(4));CHKERRQ(ierr); 1662 ierr = PetscViewerASCIIPrintf(viewer," INFOG(5) (estimated maximum front size in the complete tree): %d \n",mumps->id.INFOG(5));CHKERRQ(ierr); 1663 ierr = PetscViewerASCIIPrintf(viewer," INFOG(6) (number of nodes in the complete tree): %d \n",mumps->id.INFOG(6));CHKERRQ(ierr); 1664 ierr = PetscViewerASCIIPrintf(viewer," INFOG(7) (ordering option effectively use after analysis): %d \n",mumps->id.INFOG(7));CHKERRQ(ierr); 1665 ierr = PetscViewerASCIIPrintf(viewer," INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d \n",mumps->id.INFOG(8));CHKERRQ(ierr); 1666 ierr = PetscViewerASCIIPrintf(viewer," INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d \n",mumps->id.INFOG(9));CHKERRQ(ierr); 1667 ierr = PetscViewerASCIIPrintf(viewer," INFOG(10) (total integer space store the matrix factors after factorization): %d \n",mumps->id.INFOG(10));CHKERRQ(ierr); 1668 ierr = PetscViewerASCIIPrintf(viewer," INFOG(11) (order of largest frontal matrix after factorization): %d \n",mumps->id.INFOG(11));CHKERRQ(ierr); 1669 ierr = PetscViewerASCIIPrintf(viewer," INFOG(12) (number of off-diagonal pivots): %d \n",mumps->id.INFOG(12));CHKERRQ(ierr); 1670 ierr = PetscViewerASCIIPrintf(viewer," INFOG(13) (number of delayed pivots after factorization): %d \n",mumps->id.INFOG(13));CHKERRQ(ierr); 1671 ierr = PetscViewerASCIIPrintf(viewer," INFOG(14) (number of memory compress after factorization): %d \n",mumps->id.INFOG(14));CHKERRQ(ierr); 1672 ierr = PetscViewerASCIIPrintf(viewer," INFOG(15) (number of steps of iterative refinement after solution): %d \n",mumps->id.INFOG(15));CHKERRQ(ierr); 1673 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); 1674 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); 1675 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); 1676 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); 1677 ierr = PetscViewerASCIIPrintf(viewer," INFOG(20) (estimated number of entries in the factors): %d \n",mumps->id.INFOG(20));CHKERRQ(ierr); 1678 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); 1679 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); 1680 ierr = PetscViewerASCIIPrintf(viewer," INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d \n",mumps->id.INFOG(23));CHKERRQ(ierr); 1681 ierr = PetscViewerASCIIPrintf(viewer," INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d \n",mumps->id.INFOG(24));CHKERRQ(ierr); 1682 ierr = PetscViewerASCIIPrintf(viewer," INFOG(25) (after factorization: number of pivots modified by static pivoting): %d \n",mumps->id.INFOG(25));CHKERRQ(ierr); 1683 ierr = PetscViewerASCIIPrintf(viewer," INFOG(28) (after factorization: number of null pivots encountered): %d\n",mumps->id.INFOG(28));CHKERRQ(ierr); 1684 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); 1685 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); 1686 ierr = PetscViewerASCIIPrintf(viewer," INFOG(32) (after analysis: type of analysis done): %d\n",mumps->id.INFOG(32));CHKERRQ(ierr); 1687 ierr = PetscViewerASCIIPrintf(viewer," INFOG(33) (value used for ICNTL(8)): %d\n",mumps->id.INFOG(33));CHKERRQ(ierr); 1688 ierr = PetscViewerASCIIPrintf(viewer," INFOG(34) (exponent of the determinant if determinant is requested): %d\n",mumps->id.INFOG(34));CHKERRQ(ierr); 1689 } 1690 } 1691 } 1692 PetscFunctionReturn(0); 1693 } 1694 1695 PetscErrorCode MatGetInfo_MUMPS(Mat A,MatInfoType flag,MatInfo *info) 1696 { 1697 Mat_MUMPS *mumps =(Mat_MUMPS*)A->data; 1698 1699 PetscFunctionBegin; 1700 info->block_size = 1.0; 1701 info->nz_allocated = mumps->id.INFOG(20); 1702 info->nz_used = mumps->id.INFOG(20); 1703 info->nz_unneeded = 0.0; 1704 info->assemblies = 0.0; 1705 info->mallocs = 0.0; 1706 info->memory = 0.0; 1707 info->fill_ratio_given = 0; 1708 info->fill_ratio_needed = 0; 1709 info->factor_mallocs = 0; 1710 PetscFunctionReturn(0); 1711 } 1712 1713 /* -------------------------------------------------------------------------------------------*/ 1714 PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is) 1715 { 1716 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1717 const PetscInt *idxs; 1718 PetscInt size,i; 1719 PetscErrorCode ierr; 1720 1721 PetscFunctionBegin; 1722 ierr = ISGetLocalSize(is,&size);CHKERRQ(ierr); 1723 if (mumps->size > 1) { 1724 PetscBool ls,gs; 1725 1726 ls = mumps->myid ? (size ? PETSC_FALSE : PETSC_TRUE) : PETSC_TRUE; 1727 ierr = MPI_Allreduce(&ls,&gs,1,MPIU_BOOL,MPI_LAND,mumps->comm_mumps);CHKERRQ(ierr); 1728 if (!gs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MUMPS distributed parallel Schur complements not yet supported from PETSc\n"); 1729 } 1730 if (mumps->id.size_schur != size) { 1731 ierr = PetscFree2(mumps->id.listvar_schur,mumps->id.schur);CHKERRQ(ierr); 1732 mumps->id.size_schur = size; 1733 mumps->id.schur_lld = size; 1734 ierr = PetscMalloc2(size,&mumps->id.listvar_schur,size*size,&mumps->id.schur);CHKERRQ(ierr); 1735 } 1736 1737 /* Schur complement matrix */ 1738 ierr = MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.size_schur,(PetscScalar*)mumps->id.schur,&F->schur);CHKERRQ(ierr); 1739 if (mumps->sym == 1) { 1740 ierr = MatSetOption(F->schur,MAT_SPD,PETSC_TRUE);CHKERRQ(ierr); 1741 } 1742 1743 /* MUMPS expects Fortran style indices */ 1744 ierr = ISGetIndices(is,&idxs);CHKERRQ(ierr); 1745 ierr = PetscMemcpy(mumps->id.listvar_schur,idxs,size*sizeof(PetscInt));CHKERRQ(ierr); 1746 for (i=0;i<size;i++) mumps->id.listvar_schur[i]++; 1747 ierr = ISRestoreIndices(is,&idxs);CHKERRQ(ierr); 1748 if (mumps->size > 1) { 1749 mumps->id.ICNTL(19) = 1; /* MUMPS returns Schur centralized on the host */ 1750 } else { 1751 if (F->factortype == MAT_FACTOR_LU) { 1752 mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */ 1753 } else { 1754 mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */ 1755 } 1756 } 1757 /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */ 1758 mumps->id.ICNTL(26) = -1; 1759 PetscFunctionReturn(0); 1760 } 1761 1762 /* -------------------------------------------------------------------------------------------*/ 1763 PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F,Mat* S) 1764 { 1765 Mat St; 1766 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1767 PetscScalar *array; 1768 #if defined(PETSC_USE_COMPLEX) 1769 PetscScalar im = PetscSqrtScalar((PetscScalar)-1.0); 1770 #endif 1771 PetscErrorCode ierr; 1772 1773 PetscFunctionBegin; 1774 if (!mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it"); 1775 ierr = MatCreate(PETSC_COMM_SELF,&St);CHKERRQ(ierr); 1776 ierr = MatSetSizes(St,PETSC_DECIDE,PETSC_DECIDE,mumps->id.size_schur,mumps->id.size_schur);CHKERRQ(ierr); 1777 ierr = MatSetType(St,MATDENSE);CHKERRQ(ierr); 1778 ierr = MatSetUp(St);CHKERRQ(ierr); 1779 ierr = MatDenseGetArray(St,&array);CHKERRQ(ierr); 1780 if (!mumps->sym) { /* MUMPS always return a full matrix */ 1781 if (mumps->id.ICNTL(19) == 1) { /* stored by rows */ 1782 PetscInt i,j,N=mumps->id.size_schur; 1783 for (i=0;i<N;i++) { 1784 for (j=0;j<N;j++) { 1785 #if !defined(PETSC_USE_COMPLEX) 1786 PetscScalar val = mumps->id.schur[i*N+j]; 1787 #else 1788 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 1789 #endif 1790 array[j*N+i] = val; 1791 } 1792 } 1793 } else { /* stored by columns */ 1794 ierr = PetscMemcpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur*sizeof(PetscScalar));CHKERRQ(ierr); 1795 } 1796 } else { /* either full or lower-triangular (not packed) */ 1797 if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */ 1798 PetscInt i,j,N=mumps->id.size_schur; 1799 for (i=0;i<N;i++) { 1800 for (j=i;j<N;j++) { 1801 #if !defined(PETSC_USE_COMPLEX) 1802 PetscScalar val = mumps->id.schur[i*N+j]; 1803 #else 1804 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 1805 #endif 1806 array[i*N+j] = val; 1807 array[j*N+i] = val; 1808 } 1809 } 1810 } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */ 1811 ierr = PetscMemcpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur*sizeof(PetscScalar));CHKERRQ(ierr); 1812 } else { /* ICNTL(19) == 1 lower triangular stored by rows */ 1813 PetscInt i,j,N=mumps->id.size_schur; 1814 for (i=0;i<N;i++) { 1815 for (j=0;j<i+1;j++) { 1816 #if !defined(PETSC_USE_COMPLEX) 1817 PetscScalar val = mumps->id.schur[i*N+j]; 1818 #else 1819 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 1820 #endif 1821 array[i*N+j] = val; 1822 array[j*N+i] = val; 1823 } 1824 } 1825 } 1826 } 1827 ierr = MatDenseRestoreArray(St,&array);CHKERRQ(ierr); 1828 *S = St; 1829 PetscFunctionReturn(0); 1830 } 1831 1832 /* -------------------------------------------------------------------------------------------*/ 1833 PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt ival) 1834 { 1835 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1836 1837 PetscFunctionBegin; 1838 mumps->id.ICNTL(icntl) = ival; 1839 PetscFunctionReturn(0); 1840 } 1841 1842 PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt *ival) 1843 { 1844 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1845 1846 PetscFunctionBegin; 1847 *ival = mumps->id.ICNTL(icntl); 1848 PetscFunctionReturn(0); 1849 } 1850 1851 /*@ 1852 MatMumpsSetIcntl - Set MUMPS parameter ICNTL() 1853 1854 Logically Collective on Mat 1855 1856 Input Parameters: 1857 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1858 . icntl - index of MUMPS parameter array ICNTL() 1859 - ival - value of MUMPS ICNTL(icntl) 1860 1861 Options Database: 1862 . -mat_mumps_icntl_<icntl> <ival> 1863 1864 Level: beginner 1865 1866 References: 1867 . MUMPS Users' Guide 1868 1869 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 1870 @*/ 1871 PetscErrorCode MatMumpsSetIcntl(Mat F,PetscInt icntl,PetscInt ival) 1872 { 1873 PetscErrorCode ierr; 1874 1875 PetscFunctionBegin; 1876 PetscValidType(F,1); 1877 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 1878 PetscValidLogicalCollectiveInt(F,icntl,2); 1879 PetscValidLogicalCollectiveInt(F,ival,3); 1880 ierr = PetscTryMethod(F,"MatMumpsSetIcntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));CHKERRQ(ierr); 1881 PetscFunctionReturn(0); 1882 } 1883 1884 /*@ 1885 MatMumpsGetIcntl - Get MUMPS parameter ICNTL() 1886 1887 Logically Collective on Mat 1888 1889 Input Parameters: 1890 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1891 - icntl - index of MUMPS parameter array ICNTL() 1892 1893 Output Parameter: 1894 . ival - value of MUMPS ICNTL(icntl) 1895 1896 Level: beginner 1897 1898 References: 1899 . MUMPS Users' Guide 1900 1901 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 1902 @*/ 1903 PetscErrorCode MatMumpsGetIcntl(Mat F,PetscInt icntl,PetscInt *ival) 1904 { 1905 PetscErrorCode ierr; 1906 1907 PetscFunctionBegin; 1908 PetscValidType(F,1); 1909 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 1910 PetscValidLogicalCollectiveInt(F,icntl,2); 1911 PetscValidIntPointer(ival,3); 1912 ierr = PetscUseMethod(F,"MatMumpsGetIcntl_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));CHKERRQ(ierr); 1913 PetscFunctionReturn(0); 1914 } 1915 1916 /* -------------------------------------------------------------------------------------------*/ 1917 PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal val) 1918 { 1919 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1920 1921 PetscFunctionBegin; 1922 mumps->id.CNTL(icntl) = val; 1923 PetscFunctionReturn(0); 1924 } 1925 1926 PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal *val) 1927 { 1928 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1929 1930 PetscFunctionBegin; 1931 *val = mumps->id.CNTL(icntl); 1932 PetscFunctionReturn(0); 1933 } 1934 1935 /*@ 1936 MatMumpsSetCntl - Set MUMPS parameter CNTL() 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 CNTL() 1943 - val - value of MUMPS CNTL(icntl) 1944 1945 Options Database: 1946 . -mat_mumps_cntl_<icntl> <val> 1947 1948 Level: beginner 1949 1950 References: 1951 . MUMPS Users' Guide 1952 1953 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 1954 @*/ 1955 PetscErrorCode MatMumpsSetCntl(Mat F,PetscInt icntl,PetscReal val) 1956 { 1957 PetscErrorCode ierr; 1958 1959 PetscFunctionBegin; 1960 PetscValidType(F,1); 1961 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 1962 PetscValidLogicalCollectiveInt(F,icntl,2); 1963 PetscValidLogicalCollectiveReal(F,val,3); 1964 ierr = PetscTryMethod(F,"MatMumpsSetCntl_C",(Mat,PetscInt,PetscReal),(F,icntl,val));CHKERRQ(ierr); 1965 PetscFunctionReturn(0); 1966 } 1967 1968 /*@ 1969 MatMumpsGetCntl - Get MUMPS parameter CNTL() 1970 1971 Logically Collective on Mat 1972 1973 Input Parameters: 1974 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1975 - icntl - index of MUMPS parameter array CNTL() 1976 1977 Output Parameter: 1978 . val - value of MUMPS CNTL(icntl) 1979 1980 Level: beginner 1981 1982 References: 1983 . MUMPS Users' Guide 1984 1985 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 1986 @*/ 1987 PetscErrorCode MatMumpsGetCntl(Mat F,PetscInt icntl,PetscReal *val) 1988 { 1989 PetscErrorCode ierr; 1990 1991 PetscFunctionBegin; 1992 PetscValidType(F,1); 1993 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 1994 PetscValidLogicalCollectiveInt(F,icntl,2); 1995 PetscValidRealPointer(val,3); 1996 ierr = PetscUseMethod(F,"MatMumpsGetCntl_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));CHKERRQ(ierr); 1997 PetscFunctionReturn(0); 1998 } 1999 2000 PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F,PetscInt icntl,PetscInt *info) 2001 { 2002 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2003 2004 PetscFunctionBegin; 2005 *info = mumps->id.INFO(icntl); 2006 PetscFunctionReturn(0); 2007 } 2008 2009 PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F,PetscInt icntl,PetscInt *infog) 2010 { 2011 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2012 2013 PetscFunctionBegin; 2014 *infog = mumps->id.INFOG(icntl); 2015 PetscFunctionReturn(0); 2016 } 2017 2018 PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfo) 2019 { 2020 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2021 2022 PetscFunctionBegin; 2023 *rinfo = mumps->id.RINFO(icntl); 2024 PetscFunctionReturn(0); 2025 } 2026 2027 PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfog) 2028 { 2029 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2030 2031 PetscFunctionBegin; 2032 *rinfog = mumps->id.RINFOG(icntl); 2033 PetscFunctionReturn(0); 2034 } 2035 2036 PetscErrorCode MatMumpsGetInverse_MUMPS(Mat F,Mat spRHS) 2037 { 2038 PetscErrorCode ierr; 2039 Mat Bt = NULL; 2040 PetscBool flgT; 2041 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2042 PetscBool done; 2043 PetscScalar *aa; 2044 PetscInt spnr,*ia,*ja; 2045 2046 PetscFunctionBegin; 2047 if (!mumps->myid) { 2048 PetscValidIntPointer(spRHS,2); 2049 ierr = PetscObjectTypeCompare((PetscObject)spRHS,MATTRANSPOSEMAT,&flgT);CHKERRQ(ierr); 2050 if (flgT) { 2051 ierr = MatTransposeGetMat(spRHS,&Bt);CHKERRQ(ierr); 2052 } else { 2053 SETERRQ(PetscObjectComm((PetscObject)spRHS),PETSC_ERR_ARG_WRONG,"Matrix spRHS must be type MATTRANSPOSEMAT matrix"); 2054 } 2055 } 2056 2057 ierr = MatMumpsSetIcntl(F,30,1);CHKERRQ(ierr); 2058 2059 if (!mumps->myid) { 2060 ierr = MatSeqAIJGetArray(Bt,&aa);CHKERRQ(ierr); 2061 ierr = MatGetRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&done);CHKERRQ(ierr); 2062 if (!done) SETERRQ(PetscObjectComm((PetscObject)Bt),PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 2063 2064 mumps->id.irhs_ptr = ia; 2065 mumps->id.irhs_sparse = ja; 2066 mumps->id.nz_rhs = ia[spnr] - 1; 2067 mumps->id.rhs_sparse = (MumpsScalar*)aa; 2068 } else { 2069 mumps->id.irhs_ptr = NULL; 2070 mumps->id.irhs_sparse = NULL; 2071 mumps->id.nz_rhs = 0; 2072 mumps->id.rhs_sparse = NULL; 2073 } 2074 mumps->id.ICNTL(20) = 1; /* rhs is sparse */ 2075 mumps->id.ICNTL(21) = 0; /* solution is in assembled centralized format */ 2076 2077 /* solve phase */ 2078 /*-------------*/ 2079 mumps->id.job = JOB_SOLVE; 2080 PetscMUMPS_c(&mumps->id); 2081 if (mumps->id.INFOG(1) < 0) 2082 SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2)); 2083 2084 if (!mumps->myid) { 2085 ierr = MatSeqAIJRestoreArray(Bt,&aa);CHKERRQ(ierr); 2086 ierr = MatRestoreRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&done);CHKERRQ(ierr); 2087 } 2088 PetscFunctionReturn(0); 2089 } 2090 2091 /*@ 2092 MatMumpsGetInverse - Get user-specified set of entries in inverse of A 2093 2094 Logically Collective on Mat 2095 2096 Input Parameters: 2097 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2098 - spRHS - sequential sparse matrix in MATTRANSPOSEMAT format holding specified indices in processor[0] 2099 2100 Output Parameter: 2101 . spRHS - requested entries of inverse of A 2102 2103 Level: beginner 2104 2105 References: 2106 . MUMPS Users' Guide 2107 2108 .seealso: MatGetFactor(), MatCreateTranspose() 2109 @*/ 2110 PetscErrorCode MatMumpsGetInverse(Mat F,Mat spRHS) 2111 { 2112 PetscErrorCode ierr; 2113 2114 PetscFunctionBegin; 2115 PetscValidType(F,1); 2116 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2117 ierr = PetscUseMethod(F,"MatMumpsGetInverse_C",(Mat,Mat),(F,spRHS));CHKERRQ(ierr); 2118 PetscFunctionReturn(0); 2119 } 2120 2121 /*@ 2122 MatMumpsGetInfo - Get MUMPS parameter INFO() 2123 2124 Logically Collective on Mat 2125 2126 Input Parameters: 2127 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2128 - icntl - index of MUMPS parameter array INFO() 2129 2130 Output Parameter: 2131 . ival - value of MUMPS INFO(icntl) 2132 2133 Level: beginner 2134 2135 References: 2136 . MUMPS Users' Guide 2137 2138 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2139 @*/ 2140 PetscErrorCode MatMumpsGetInfo(Mat F,PetscInt icntl,PetscInt *ival) 2141 { 2142 PetscErrorCode ierr; 2143 2144 PetscFunctionBegin; 2145 PetscValidType(F,1); 2146 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2147 PetscValidIntPointer(ival,3); 2148 ierr = PetscUseMethod(F,"MatMumpsGetInfo_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));CHKERRQ(ierr); 2149 PetscFunctionReturn(0); 2150 } 2151 2152 /*@ 2153 MatMumpsGetInfog - Get MUMPS parameter INFOG() 2154 2155 Logically Collective on Mat 2156 2157 Input Parameters: 2158 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2159 - icntl - index of MUMPS parameter array INFOG() 2160 2161 Output Parameter: 2162 . ival - value of MUMPS INFOG(icntl) 2163 2164 Level: beginner 2165 2166 References: 2167 . MUMPS Users' Guide 2168 2169 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2170 @*/ 2171 PetscErrorCode MatMumpsGetInfog(Mat F,PetscInt icntl,PetscInt *ival) 2172 { 2173 PetscErrorCode ierr; 2174 2175 PetscFunctionBegin; 2176 PetscValidType(F,1); 2177 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2178 PetscValidIntPointer(ival,3); 2179 ierr = PetscUseMethod(F,"MatMumpsGetInfog_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));CHKERRQ(ierr); 2180 PetscFunctionReturn(0); 2181 } 2182 2183 /*@ 2184 MatMumpsGetRinfo - Get MUMPS parameter RINFO() 2185 2186 Logically Collective on Mat 2187 2188 Input Parameters: 2189 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2190 - icntl - index of MUMPS parameter array RINFO() 2191 2192 Output Parameter: 2193 . val - value of MUMPS RINFO(icntl) 2194 2195 Level: beginner 2196 2197 References: 2198 . MUMPS Users' Guide 2199 2200 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2201 @*/ 2202 PetscErrorCode MatMumpsGetRinfo(Mat F,PetscInt icntl,PetscReal *val) 2203 { 2204 PetscErrorCode ierr; 2205 2206 PetscFunctionBegin; 2207 PetscValidType(F,1); 2208 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2209 PetscValidRealPointer(val,3); 2210 ierr = PetscUseMethod(F,"MatMumpsGetRinfo_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));CHKERRQ(ierr); 2211 PetscFunctionReturn(0); 2212 } 2213 2214 /*@ 2215 MatMumpsGetRinfog - Get MUMPS parameter RINFOG() 2216 2217 Logically Collective on Mat 2218 2219 Input Parameters: 2220 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2221 - icntl - index of MUMPS parameter array RINFOG() 2222 2223 Output Parameter: 2224 . val - value of MUMPS RINFOG(icntl) 2225 2226 Level: beginner 2227 2228 References: 2229 . MUMPS Users' Guide 2230 2231 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2232 @*/ 2233 PetscErrorCode MatMumpsGetRinfog(Mat F,PetscInt icntl,PetscReal *val) 2234 { 2235 PetscErrorCode ierr; 2236 2237 PetscFunctionBegin; 2238 PetscValidType(F,1); 2239 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2240 PetscValidRealPointer(val,3); 2241 ierr = PetscUseMethod(F,"MatMumpsGetRinfog_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));CHKERRQ(ierr); 2242 PetscFunctionReturn(0); 2243 } 2244 2245 /*MC 2246 MATSOLVERMUMPS - A matrix type providing direct solvers (LU and Cholesky) for 2247 distributed and sequential matrices via the external package MUMPS. 2248 2249 Works with MATAIJ and MATSBAIJ matrices 2250 2251 Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch to have PETSc installed with MUMPS 2252 2253 Use -pc_type cholesky or lu -pc_factor_mat_solver_type mumps to use this direct solver 2254 2255 Options Database Keys: 2256 + -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages 2257 . -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning 2258 . -mat_mumps_icntl_3 - ICNTL(3): output stream for global information, collected on the host 2259 . -mat_mumps_icntl_4 - ICNTL(4): level of printing (0 to 4) 2260 . -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7) 2261 . -mat_mumps_icntl_7 - ICNTL(7): computes a symmetric permutation in sequential analysis (0 to 7). 3=Scotch, 4=PORD, 5=Metis 2262 . -mat_mumps_icntl_8 - ICNTL(8): scaling strategy (-2 to 8 or 77) 2263 . -mat_mumps_icntl_10 - ICNTL(10): max num of refinements 2264 . -mat_mumps_icntl_11 - ICNTL(11): statistics related to an error analysis (via -ksp_view) 2265 . -mat_mumps_icntl_12 - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3) 2266 . -mat_mumps_icntl_13 - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting 2267 . -mat_mumps_icntl_14 - ICNTL(14): percentage increase in the estimated working space 2268 . -mat_mumps_icntl_19 - ICNTL(19): computes the Schur complement 2269 . -mat_mumps_icntl_22 - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1) 2270 . -mat_mumps_icntl_23 - ICNTL(23): max size of the working memory (MB) that can allocate per processor 2271 . -mat_mumps_icntl_24 - ICNTL(24): detection of null pivot rows (0 or 1) 2272 . -mat_mumps_icntl_25 - ICNTL(25): compute a solution of a deficient matrix and a null space basis 2273 . -mat_mumps_icntl_26 - ICNTL(26): drives the solution phase if a Schur complement matrix 2274 . -mat_mumps_icntl_28 - ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering 2275 . -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis 2276 . -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A) 2277 . -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization 2278 . -mat_mumps_icntl_33 - ICNTL(33): compute determinant 2279 . -mat_mumps_cntl_1 - CNTL(1): relative pivoting threshold 2280 . -mat_mumps_cntl_2 - CNTL(2): stopping criterion of refinement 2281 . -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold 2282 . -mat_mumps_cntl_4 - CNTL(4): value for static pivoting 2283 - -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots 2284 2285 Level: beginner 2286 2287 Notes: 2288 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 2289 $ KSPGetPC(ksp,&pc); 2290 $ PCFactorGetMatrix(pc,&mat); 2291 $ MatMumpsGetInfo(mat,....); 2292 $ MatMumpsGetInfog(mat,....); etc. 2293 Or you can run with -ksp_error_if_not_converged and the program will be stopped and the information printed in the error message. 2294 2295 .seealso: PCFactorSetMatSolverType(), MatSolverType, MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog(), KSPGetPC(), PCGetFactor(), PCFactorGetMatrix() 2296 2297 M*/ 2298 2299 static PetscErrorCode MatFactorGetSolverType_mumps(Mat A,MatSolverType *type) 2300 { 2301 PetscFunctionBegin; 2302 *type = MATSOLVERMUMPS; 2303 PetscFunctionReturn(0); 2304 } 2305 2306 /* MatGetFactor for Seq and MPI AIJ matrices */ 2307 static PetscErrorCode MatGetFactor_aij_mumps(Mat A,MatFactorType ftype,Mat *F) 2308 { 2309 Mat B; 2310 PetscErrorCode ierr; 2311 Mat_MUMPS *mumps; 2312 PetscBool isSeqAIJ; 2313 2314 PetscFunctionBegin; 2315 /* Create the factorization matrix */ 2316 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);CHKERRQ(ierr); 2317 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2318 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2319 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2320 ierr = MatSetUp(B);CHKERRQ(ierr); 2321 2322 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2323 2324 B->ops->view = MatView_MUMPS; 2325 B->ops->getinfo = MatGetInfo_MUMPS; 2326 2327 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2328 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2329 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2330 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2331 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2332 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2333 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2334 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2335 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2336 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2337 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2338 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);CHKERRQ(ierr); 2339 2340 if (ftype == MAT_FACTOR_LU) { 2341 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS; 2342 B->factortype = MAT_FACTOR_LU; 2343 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij; 2344 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij; 2345 mumps->sym = 0; 2346 } else { 2347 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 2348 B->factortype = MAT_FACTOR_CHOLESKY; 2349 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij; 2350 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij; 2351 #if defined(PETSC_USE_COMPLEX) 2352 mumps->sym = 2; 2353 #else 2354 if (A->spd_set && A->spd) mumps->sym = 1; 2355 else mumps->sym = 2; 2356 #endif 2357 } 2358 2359 /* set solvertype */ 2360 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2361 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2362 2363 B->ops->destroy = MatDestroy_MUMPS; 2364 B->data = (void*)mumps; 2365 2366 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2367 2368 *F = B; 2369 PetscFunctionReturn(0); 2370 } 2371 2372 /* MatGetFactor for Seq and MPI SBAIJ matrices */ 2373 static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A,MatFactorType ftype,Mat *F) 2374 { 2375 Mat B; 2376 PetscErrorCode ierr; 2377 Mat_MUMPS *mumps; 2378 PetscBool isSeqSBAIJ; 2379 2380 PetscFunctionBegin; 2381 if (ftype != MAT_FACTOR_CHOLESKY) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Cannot use PETSc SBAIJ matrices with MUMPS LU, use AIJ matrix"); 2382 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"); 2383 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ);CHKERRQ(ierr); 2384 /* Create the factorization matrix */ 2385 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2386 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2387 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2388 ierr = MatSetUp(B);CHKERRQ(ierr); 2389 2390 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2391 if (isSeqSBAIJ) { 2392 mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij; 2393 } else { 2394 mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij; 2395 } 2396 2397 B->ops->getinfo = MatGetInfo_External; 2398 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 2399 B->ops->view = MatView_MUMPS; 2400 2401 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2402 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2403 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2404 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2405 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2406 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2407 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2408 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2409 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2410 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2411 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2412 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);CHKERRQ(ierr); 2413 2414 B->factortype = MAT_FACTOR_CHOLESKY; 2415 #if defined(PETSC_USE_COMPLEX) 2416 mumps->sym = 2; 2417 #else 2418 if (A->spd_set && A->spd) mumps->sym = 1; 2419 else mumps->sym = 2; 2420 #endif 2421 2422 /* set solvertype */ 2423 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2424 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2425 2426 B->ops->destroy = MatDestroy_MUMPS; 2427 B->data = (void*)mumps; 2428 2429 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2430 2431 *F = B; 2432 PetscFunctionReturn(0); 2433 } 2434 2435 static PetscErrorCode MatGetFactor_baij_mumps(Mat A,MatFactorType ftype,Mat *F) 2436 { 2437 Mat B; 2438 PetscErrorCode ierr; 2439 Mat_MUMPS *mumps; 2440 PetscBool isSeqBAIJ; 2441 2442 PetscFunctionBegin; 2443 /* Create the factorization matrix */ 2444 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ);CHKERRQ(ierr); 2445 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2446 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2447 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2448 ierr = MatSetUp(B);CHKERRQ(ierr); 2449 2450 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2451 if (ftype == MAT_FACTOR_LU) { 2452 B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS; 2453 B->factortype = MAT_FACTOR_LU; 2454 if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij; 2455 else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij; 2456 mumps->sym = 0; 2457 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead\n"); 2458 2459 B->ops->getinfo = MatGetInfo_External; 2460 B->ops->view = MatView_MUMPS; 2461 2462 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2463 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2464 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2465 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2466 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2467 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2468 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2469 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2470 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2471 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2472 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2473 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);CHKERRQ(ierr); 2474 2475 /* set solvertype */ 2476 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2477 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2478 2479 B->ops->destroy = MatDestroy_MUMPS; 2480 B->data = (void*)mumps; 2481 2482 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2483 2484 *F = B; 2485 PetscFunctionReturn(0); 2486 } 2487 2488 /* MatGetFactor for Seq and MPI SELL matrices */ 2489 static PetscErrorCode MatGetFactor_sell_mumps(Mat A,MatFactorType ftype,Mat *F) 2490 { 2491 Mat B; 2492 PetscErrorCode ierr; 2493 Mat_MUMPS *mumps; 2494 PetscBool isSeqSELL; 2495 2496 PetscFunctionBegin; 2497 /* Create the factorization matrix */ 2498 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQSELL,&isSeqSELL);CHKERRQ(ierr); 2499 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2500 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2501 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2502 ierr = MatSetUp(B);CHKERRQ(ierr); 2503 2504 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2505 2506 B->ops->view = MatView_MUMPS; 2507 B->ops->getinfo = MatGetInfo_MUMPS; 2508 2509 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2510 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2511 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2512 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2513 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2514 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2515 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2516 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2517 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2518 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2519 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2520 2521 if (ftype == MAT_FACTOR_LU) { 2522 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS; 2523 B->factortype = MAT_FACTOR_LU; 2524 if (isSeqSELL) mumps->ConvertToTriples = MatConvertToTriples_seqsell_seqaij; 2525 else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"To be implemented"); 2526 mumps->sym = 0; 2527 } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"To be implemented"); 2528 2529 /* set solvertype */ 2530 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2531 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2532 2533 B->ops->destroy = MatDestroy_MUMPS; 2534 B->data = (void*)mumps; 2535 2536 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2537 2538 *F = B; 2539 PetscFunctionReturn(0); 2540 } 2541 2542 PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MUMPS(void) 2543 { 2544 PetscErrorCode ierr; 2545 2546 PetscFunctionBegin; 2547 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2548 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2549 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2550 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2551 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPISBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);CHKERRQ(ierr); 2552 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2553 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2554 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2555 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2556 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQSBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);CHKERRQ(ierr); 2557 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQSELL,MAT_FACTOR_LU,MatGetFactor_sell_mumps);CHKERRQ(ierr); 2558 PetscFunctionReturn(0); 2559 } 2560 2561