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 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInverseTranspose_C",NULL);CHKERRQ(ierr); 733 PetscFunctionReturn(0); 734 } 735 736 PetscErrorCode MatSolve_MUMPS(Mat A,Vec b,Vec x) 737 { 738 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 739 PetscScalar *array; 740 Vec b_seq; 741 IS is_iden,is_petsc; 742 PetscErrorCode ierr; 743 PetscInt i; 744 PetscBool second_solve = PETSC_FALSE; 745 static PetscBool cite1 = PETSC_FALSE,cite2 = PETSC_FALSE; 746 747 PetscFunctionBegin; 748 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); 749 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); 750 751 if (A->factorerrortype) { 752 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); 753 ierr = VecSetInf(x);CHKERRQ(ierr); 754 PetscFunctionReturn(0); 755 } 756 757 mumps->id.ICNTL(20)= 0; /* dense RHS */ 758 mumps->id.nrhs = 1; 759 b_seq = mumps->b_seq; 760 if (mumps->size > 1) { 761 /* MUMPS only supports centralized rhs. Scatter b into a seqential rhs vector */ 762 ierr = VecScatterBegin(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 763 ierr = VecScatterEnd(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 764 if (!mumps->myid) {ierr = VecGetArray(b_seq,&array);CHKERRQ(ierr);} 765 } else { /* size == 1 */ 766 ierr = VecCopy(b,x);CHKERRQ(ierr); 767 ierr = VecGetArray(x,&array);CHKERRQ(ierr); 768 } 769 if (!mumps->myid) { /* define rhs on the host */ 770 mumps->id.nrhs = 1; 771 mumps->id.rhs = (MumpsScalar*)array; 772 } 773 774 /* 775 handle condensation step of Schur complement (if any) 776 We set by default ICNTL(26) == -1 when Schur indices have been provided by the user. 777 According to MUMPS (5.0.0) manual, any value should be harmful during the factorization phase 778 Unless the user provides a valid value for ICNTL(26), MatSolve and MatMatSolve routines solve the full system. 779 This requires an extra call to PetscMUMPS_c and the computation of the factors for S 780 */ 781 if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) { 782 if (mumps->size > 1) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Parallel Schur complements not yet supported from PETSc\n"); 783 second_solve = PETSC_TRUE; 784 ierr = MatMumpsHandleSchur_Private(A,PETSC_FALSE);CHKERRQ(ierr); 785 } 786 /* solve phase */ 787 /*-------------*/ 788 mumps->id.job = JOB_SOLVE; 789 PetscMUMPS_c(&mumps->id); 790 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)); 791 792 /* handle expansion step of Schur complement (if any) */ 793 if (second_solve) { 794 ierr = MatMumpsHandleSchur_Private(A,PETSC_TRUE);CHKERRQ(ierr); 795 } 796 797 if (mumps->size > 1) { /* convert mumps distributed solution to petsc mpi x */ 798 if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) { 799 /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */ 800 ierr = VecScatterDestroy(&mumps->scat_sol);CHKERRQ(ierr); 801 } 802 if (!mumps->scat_sol) { /* create scatter scat_sol */ 803 ierr = ISCreateStride(PETSC_COMM_SELF,mumps->id.lsol_loc,0,1,&is_iden);CHKERRQ(ierr); /* from */ 804 for (i=0; i<mumps->id.lsol_loc; i++) { 805 mumps->id.isol_loc[i] -= 1; /* change Fortran style to C style */ 806 } 807 ierr = ISCreateGeneral(PETSC_COMM_SELF,mumps->id.lsol_loc,mumps->id.isol_loc,PETSC_COPY_VALUES,&is_petsc);CHKERRQ(ierr); /* to */ 808 ierr = VecScatterCreate(mumps->x_seq,is_iden,x,is_petsc,&mumps->scat_sol);CHKERRQ(ierr); 809 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 810 ierr = ISDestroy(&is_petsc);CHKERRQ(ierr); 811 812 mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */ 813 } 814 815 ierr = VecScatterBegin(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 816 ierr = VecScatterEnd(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 817 } 818 ierr = PetscLogFlops(2.0*mumps->id.RINFO(3));CHKERRQ(ierr); 819 PetscFunctionReturn(0); 820 } 821 822 PetscErrorCode MatSolveTranspose_MUMPS(Mat A,Vec b,Vec x) 823 { 824 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 825 PetscErrorCode ierr; 826 827 PetscFunctionBegin; 828 mumps->id.ICNTL(9) = 0; 829 ierr = MatSolve_MUMPS(A,b,x);CHKERRQ(ierr); 830 mumps->id.ICNTL(9) = 1; 831 PetscFunctionReturn(0); 832 } 833 834 PetscErrorCode MatMatSolve_MUMPS(Mat A,Mat B,Mat X) 835 { 836 PetscErrorCode ierr; 837 Mat Bt = NULL; 838 PetscBool flg, flgT; 839 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 840 PetscInt i,nrhs,M; 841 PetscScalar *array,*bray; 842 PetscInt lsol_loc,nlsol_loc,*isol_loc,*idx,*iidx,*idxx,*isol_loc_save; 843 MumpsScalar *sol_loc,*sol_loc_save; 844 IS is_to,is_from; 845 PetscInt k,proc,j,m; 846 const PetscInt *rstart; 847 Vec v_mpi,b_seq,x_seq; 848 VecScatter scat_rhs,scat_sol; 849 PetscScalar *aa; 850 PetscInt spnr,*ia,*ja; 851 Mat_MPIAIJ *b = NULL; 852 853 PetscFunctionBegin; 854 ierr = PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr); 855 if (!flg) SETERRQ(PetscObjectComm((PetscObject)X),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix"); 856 857 ierr = PetscObjectTypeCompareAny((PetscObject)B,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr); 858 if (flg) { /* dense B */ 859 if (B->rmap->n != X->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Matrix B and X must have same row distribution"); 860 mumps->id.ICNTL(20)= 0; /* dense RHS */ 861 } else { /* sparse B */ 862 ierr = PetscObjectTypeCompare((PetscObject)B,MATTRANSPOSEMAT,&flgT);CHKERRQ(ierr); 863 if (flgT) { /* input B is transpose of actural RHS matrix, 864 because mumps requires sparse compressed COLUMN storage! See MatMatTransposeSolve_MUMPS() */ 865 ierr = MatTransposeGetMat(B,&Bt);CHKERRQ(ierr); 866 } else SETERRQ(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix"); 867 mumps->id.ICNTL(20)= 1; /* sparse RHS */ 868 } 869 870 ierr = MatGetSize(B,&M,&nrhs);CHKERRQ(ierr); 871 mumps->id.nrhs = nrhs; 872 mumps->id.lrhs = M; 873 mumps->id.rhs = NULL; 874 875 if (mumps->size == 1) { 876 PetscScalar *aa; 877 PetscInt spnr,*ia,*ja; 878 PetscBool second_solve = PETSC_FALSE; 879 880 ierr = MatDenseGetArray(X,&array);CHKERRQ(ierr); 881 mumps->id.rhs = (MumpsScalar*)array; 882 883 if (!Bt) { /* dense B */ 884 /* copy B to X */ 885 ierr = MatDenseGetArray(B,&bray);CHKERRQ(ierr); 886 ierr = PetscMemcpy(array,bray,M*nrhs*sizeof(PetscScalar));CHKERRQ(ierr); 887 ierr = MatDenseRestoreArray(B,&bray);CHKERRQ(ierr); 888 } else { /* sparse B */ 889 ierr = MatSeqAIJGetArray(Bt,&aa);CHKERRQ(ierr); 890 ierr = MatGetRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 891 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 892 /* mumps requires ia and ja start at 1! */ 893 mumps->id.irhs_ptr = ia; 894 mumps->id.irhs_sparse = ja; 895 mumps->id.nz_rhs = ia[spnr] - 1; 896 mumps->id.rhs_sparse = (MumpsScalar*)aa; 897 } 898 /* handle condensation step of Schur complement (if any) */ 899 if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) { 900 second_solve = PETSC_TRUE; 901 ierr = MatMumpsHandleSchur_Private(A,PETSC_FALSE);CHKERRQ(ierr); 902 } 903 /* solve phase */ 904 /*-------------*/ 905 mumps->id.job = JOB_SOLVE; 906 PetscMUMPS_c(&mumps->id); 907 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)); 908 909 /* handle expansion step of Schur complement (if any) */ 910 if (second_solve) { 911 ierr = MatMumpsHandleSchur_Private(A,PETSC_TRUE);CHKERRQ(ierr); 912 } 913 if (Bt) { /* sparse B */ 914 ierr = MatSeqAIJRestoreArray(Bt,&aa);CHKERRQ(ierr); 915 ierr = MatRestoreRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 916 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot restore IJ structure"); 917 } 918 ierr = MatDenseRestoreArray(X,&array);CHKERRQ(ierr); 919 PetscFunctionReturn(0); 920 } 921 922 /*--------- parallel case: MUMPS requires rhs B to be centralized on the host! --------*/ 923 if (mumps->size > 1 && mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Parallel Schur complements not yet supported from PETSc\n"); 924 925 /* create x_seq to hold mumps local solution */ 926 isol_loc_save = mumps->id.isol_loc; /* save it for MatSovle() */ 927 sol_loc_save = mumps->id.sol_loc; 928 929 lsol_loc = mumps->id.INFO(23); 930 nlsol_loc = nrhs*lsol_loc; /* length of sol_loc */ 931 ierr = PetscMalloc2(nlsol_loc,&sol_loc,nlsol_loc,&isol_loc);CHKERRQ(ierr); 932 mumps->id.sol_loc = (MumpsScalar*)sol_loc; 933 mumps->id.isol_loc = isol_loc; 934 935 ierr = VecCreateSeqWithArray(PETSC_COMM_SELF,1,nlsol_loc,(PetscScalar*)sol_loc,&x_seq);CHKERRQ(ierr); 936 937 /* scatter v_mpi to b_seq because MUMPS only supports centralized rhs */ 938 /* idx: maps from k-th index of v_mpi to (i,j)-th global entry of B; 939 iidx: inverse of idx, will be used by scattering mumps x_seq -> petsc X */ 940 ierr = PetscMalloc1(nrhs*M,&idx);CHKERRQ(ierr); 941 942 if (!Bt) { /* dense B */ 943 /* wrap dense rhs matrix B into a vector v_mpi */ 944 ierr = MatGetLocalSize(B,&m,NULL);CHKERRQ(ierr); 945 ierr = MatDenseGetArray(B,&bray);CHKERRQ(ierr); 946 ierr = VecCreateMPIWithArray(PetscObjectComm((PetscObject)B),1,nrhs*m,nrhs*M,(const PetscScalar*)bray,&v_mpi);CHKERRQ(ierr); 947 ierr = MatDenseRestoreArray(B,&bray);CHKERRQ(ierr); 948 949 /* scatter v_mpi to b_seq in proc[0]. MUMPS requires rhs to be centralized on the host! */ 950 if (!mumps->myid) { 951 ierr = MatGetOwnershipRanges(B,&rstart);CHKERRQ(ierr); 952 k = 0; 953 for (proc=0; proc<mumps->size; proc++){ 954 for (j=0; j<nrhs; j++){ 955 for (i=rstart[proc]; i<rstart[proc+1]; i++){ 956 idx[k++] = j*M + i; 957 } 958 } 959 } 960 961 ierr = VecCreateSeq(PETSC_COMM_SELF,nrhs*M,&b_seq);CHKERRQ(ierr); 962 ierr = ISCreateGeneral(PETSC_COMM_SELF,nrhs*M,idx,PETSC_COPY_VALUES,&is_to);CHKERRQ(ierr); 963 ierr = ISCreateStride(PETSC_COMM_SELF,nrhs*M,0,1,&is_from);CHKERRQ(ierr); 964 } else { 965 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&b_seq);CHKERRQ(ierr); 966 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_to);CHKERRQ(ierr); 967 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_from);CHKERRQ(ierr); 968 } 969 ierr = VecScatterCreate(v_mpi,is_from,b_seq,is_to,&scat_rhs);CHKERRQ(ierr); 970 ierr = VecScatterBegin(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 971 ierr = ISDestroy(&is_to);CHKERRQ(ierr); 972 ierr = ISDestroy(&is_from);CHKERRQ(ierr); 973 ierr = VecScatterEnd(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 974 975 if (!mumps->myid) { /* define rhs on the host */ 976 ierr = VecGetArray(b_seq,&bray);CHKERRQ(ierr); 977 mumps->id.rhs = (MumpsScalar*)bray; 978 ierr = VecRestoreArray(b_seq,&bray);CHKERRQ(ierr); 979 } 980 981 } else { /* sparse B */ 982 b = (Mat_MPIAIJ*)Bt->data; 983 984 /* wrap dense X into a vector v_mpi */ 985 ierr = MatGetLocalSize(X,&m,NULL);CHKERRQ(ierr); 986 ierr = MatDenseGetArray(X,&bray);CHKERRQ(ierr); 987 ierr = VecCreateMPIWithArray(PetscObjectComm((PetscObject)X),1,nrhs*m,nrhs*M,(const PetscScalar*)bray,&v_mpi);CHKERRQ(ierr); 988 ierr = MatDenseRestoreArray(X,&bray);CHKERRQ(ierr); 989 990 if (!mumps->myid) { 991 ierr = MatSeqAIJGetArray(b->A,&aa);CHKERRQ(ierr); 992 ierr = MatGetRowIJ(b->A,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 993 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 994 /* mumps requires ia and ja start at 1! */ 995 mumps->id.irhs_ptr = ia; 996 mumps->id.irhs_sparse = ja; 997 mumps->id.nz_rhs = ia[spnr] - 1; 998 mumps->id.rhs_sparse = (MumpsScalar*)aa; 999 } else { 1000 mumps->id.irhs_ptr = NULL; 1001 mumps->id.irhs_sparse = NULL; 1002 mumps->id.nz_rhs = 0; 1003 mumps->id.rhs_sparse = NULL; 1004 } 1005 } 1006 1007 /* solve phase */ 1008 /*-------------*/ 1009 mumps->id.job = JOB_SOLVE; 1010 PetscMUMPS_c(&mumps->id); 1011 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)); 1012 1013 /* scatter mumps distributed solution to petsc vector v_mpi, which shares local arrays with solution matrix X */ 1014 ierr = MatDenseGetArray(X,&array);CHKERRQ(ierr); 1015 ierr = VecPlaceArray(v_mpi,array);CHKERRQ(ierr); 1016 1017 /* create scatter scat_sol */ 1018 ierr = MatGetOwnershipRanges(X,&rstart);CHKERRQ(ierr); 1019 /* iidx: inverse of idx computed above, used for scattering mumps x_seq to petsc X */ 1020 iidx = idx; 1021 k = 0; 1022 for (proc=0; proc<mumps->size; proc++){ 1023 for (j=0; j<nrhs; j++){ 1024 for (i=rstart[proc]; i<rstart[proc+1]; i++) iidx[j*M + i] = k++; 1025 } 1026 } 1027 1028 ierr = PetscMalloc1(nlsol_loc,&idxx);CHKERRQ(ierr); 1029 ierr = ISCreateStride(PETSC_COMM_SELF,nlsol_loc,0,1,&is_from);CHKERRQ(ierr); 1030 for (i=0; i<lsol_loc; i++) { 1031 isol_loc[i] -= 1; /* change Fortran style to C style */ 1032 idxx[i] = iidx[isol_loc[i]]; 1033 for (j=1; j<nrhs; j++){ 1034 idxx[j*lsol_loc+i] = iidx[isol_loc[i]+j*M]; 1035 } 1036 } 1037 ierr = ISCreateGeneral(PETSC_COMM_SELF,nlsol_loc,idxx,PETSC_COPY_VALUES,&is_to);CHKERRQ(ierr); 1038 ierr = VecScatterCreate(x_seq,is_from,v_mpi,is_to,&scat_sol);CHKERRQ(ierr); 1039 ierr = VecScatterBegin(scat_sol,x_seq,v_mpi,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1040 ierr = ISDestroy(&is_from);CHKERRQ(ierr); 1041 ierr = ISDestroy(&is_to);CHKERRQ(ierr); 1042 ierr = VecScatterEnd(scat_sol,x_seq,v_mpi,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1043 ierr = MatDenseRestoreArray(X,&array);CHKERRQ(ierr); 1044 1045 /* free spaces */ 1046 mumps->id.sol_loc = sol_loc_save; 1047 mumps->id.isol_loc = isol_loc_save; 1048 1049 ierr = PetscFree2(sol_loc,isol_loc);CHKERRQ(ierr); 1050 ierr = PetscFree(idx);CHKERRQ(ierr); 1051 ierr = PetscFree(idxx);CHKERRQ(ierr); 1052 ierr = VecDestroy(&x_seq);CHKERRQ(ierr); 1053 ierr = VecDestroy(&v_mpi);CHKERRQ(ierr); 1054 if (Bt) { 1055 if (!mumps->myid) { 1056 b = (Mat_MPIAIJ*)Bt->data; 1057 ierr = MatSeqAIJRestoreArray(b->A,&aa);CHKERRQ(ierr); 1058 ierr = MatRestoreRowIJ(b->A,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 1059 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot restore IJ structure"); 1060 } 1061 } else { 1062 ierr = VecDestroy(&b_seq);CHKERRQ(ierr); 1063 ierr = VecScatterDestroy(&scat_rhs);CHKERRQ(ierr); 1064 } 1065 ierr = VecScatterDestroy(&scat_sol);CHKERRQ(ierr); 1066 ierr = PetscLogFlops(2.0*nrhs*mumps->id.RINFO(3));CHKERRQ(ierr); 1067 PetscFunctionReturn(0); 1068 } 1069 1070 PetscErrorCode MatMatTransposeSolve_MUMPS(Mat A,Mat Bt,Mat X) 1071 { 1072 PetscErrorCode ierr; 1073 PetscBool flg; 1074 Mat B; 1075 1076 PetscFunctionBegin; 1077 ierr = PetscObjectTypeCompareAny((PetscObject)Bt,&flg,MATSEQAIJ,MATMPIAIJ,NULL);CHKERRQ(ierr); 1078 if (!flg) SETERRQ(PetscObjectComm((PetscObject)Bt),PETSC_ERR_ARG_WRONG,"Matrix Bt must be MATAIJ matrix"); 1079 1080 /* Create B=Bt^T that uses Bt's data structure */ 1081 ierr = MatCreateTranspose(Bt,&B);CHKERRQ(ierr); 1082 1083 ierr = MatMatSolve_MUMPS(A,B,X);CHKERRQ(ierr); 1084 ierr = MatDestroy(&B);CHKERRQ(ierr); 1085 PetscFunctionReturn(0); 1086 } 1087 1088 #if !defined(PETSC_USE_COMPLEX) 1089 /* 1090 input: 1091 F: numeric factor 1092 output: 1093 nneg: total number of negative pivots 1094 nzero: total number of zero pivots 1095 npos: (global dimension of F) - nneg - nzero 1096 */ 1097 PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F,int *nneg,int *nzero,int *npos) 1098 { 1099 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1100 PetscErrorCode ierr; 1101 PetscMPIInt size; 1102 1103 PetscFunctionBegin; 1104 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)F),&size);CHKERRQ(ierr); 1105 /* 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 */ 1106 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)); 1107 1108 if (nneg) *nneg = mumps->id.INFOG(12); 1109 if (nzero || npos) { 1110 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"); 1111 if (nzero) *nzero = mumps->id.INFOG(28); 1112 if (npos) *npos = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28)); 1113 } 1114 PetscFunctionReturn(0); 1115 } 1116 #endif 1117 1118 PetscErrorCode MatFactorNumeric_MUMPS(Mat F,Mat A,const MatFactorInfo *info) 1119 { 1120 Mat_MUMPS *mumps =(Mat_MUMPS*)(F)->data; 1121 PetscErrorCode ierr; 1122 PetscBool isMPIAIJ; 1123 1124 PetscFunctionBegin; 1125 if (mumps->id.INFOG(1) < 0) { 1126 if (mumps->id.INFOG(1) == -6) { 1127 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); 1128 } 1129 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); 1130 PetscFunctionReturn(0); 1131 } 1132 1133 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1134 1135 /* numerical factorization phase */ 1136 /*-------------------------------*/ 1137 mumps->id.job = JOB_FACTNUMERIC; 1138 if (!mumps->id.ICNTL(18)) { /* A is centralized */ 1139 if (!mumps->myid) { 1140 mumps->id.a = (MumpsScalar*)mumps->val; 1141 } 1142 } else { 1143 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1144 } 1145 PetscMUMPS_c(&mumps->id); 1146 if (mumps->id.INFOG(1) < 0) { 1147 if (A->erroriffailure) { 1148 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)); 1149 } else { 1150 if (mumps->id.INFOG(1) == -10) { /* numerically singular matrix */ 1151 ierr = PetscInfo2(F,"matrix is numerically singular, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1152 F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1153 } else if (mumps->id.INFOG(1) == -13) { 1154 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); 1155 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1156 } else if (mumps->id.INFOG(1) == -8 || mumps->id.INFOG(1) == -9 || (-16 < mumps->id.INFOG(1) && mumps->id.INFOG(1) < -10) ) { 1157 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); 1158 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1159 } else { 1160 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); 1161 F->factorerrortype = MAT_FACTOR_OTHER; 1162 } 1163 } 1164 } 1165 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)); 1166 1167 F->assembled = PETSC_TRUE; 1168 mumps->matstruc = SAME_NONZERO_PATTERN; 1169 if (F->schur) { /* reset Schur status to unfactored */ 1170 if (mumps->id.ICNTL(19) == 1) { /* stored by rows */ 1171 mumps->id.ICNTL(19) = 2; 1172 ierr = MatTranspose(F->schur,MAT_INPLACE_MATRIX,&F->schur);CHKERRQ(ierr); 1173 } 1174 ierr = MatFactorRestoreSchurComplement(F,NULL,MAT_FACTOR_SCHUR_UNFACTORED);CHKERRQ(ierr); 1175 } 1176 1177 /* just to be sure that ICNTL(19) value returned by a call from MatMumpsGetIcntl is always consistent */ 1178 if (!mumps->sym && mumps->id.ICNTL(19) && mumps->id.ICNTL(19) != 1) mumps->id.ICNTL(19) = 3; 1179 1180 if (mumps->size > 1) { 1181 PetscInt lsol_loc; 1182 PetscScalar *sol_loc; 1183 1184 ierr = PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&isMPIAIJ);CHKERRQ(ierr); 1185 1186 /* distributed solution; Create x_seq=sol_loc for repeated use */ 1187 if (mumps->x_seq) { 1188 ierr = VecScatterDestroy(&mumps->scat_sol);CHKERRQ(ierr); 1189 ierr = PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc);CHKERRQ(ierr); 1190 ierr = VecDestroy(&mumps->x_seq);CHKERRQ(ierr); 1191 } 1192 lsol_loc = mumps->id.INFO(23); /* length of sol_loc */ 1193 ierr = PetscMalloc2(lsol_loc,&sol_loc,lsol_loc,&mumps->id.isol_loc);CHKERRQ(ierr); 1194 mumps->id.lsol_loc = lsol_loc; 1195 mumps->id.sol_loc = (MumpsScalar*)sol_loc; 1196 ierr = VecCreateSeqWithArray(PETSC_COMM_SELF,1,lsol_loc,sol_loc,&mumps->x_seq);CHKERRQ(ierr); 1197 } 1198 ierr = PetscLogFlops(mumps->id.RINFO(2));CHKERRQ(ierr); 1199 PetscFunctionReturn(0); 1200 } 1201 1202 /* Sets MUMPS options from the options database */ 1203 PetscErrorCode PetscSetMUMPSFromOptions(Mat F, Mat A) 1204 { 1205 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1206 PetscErrorCode ierr; 1207 PetscInt icntl,info[40],i,ninfo=40; 1208 PetscBool flg; 1209 1210 PetscFunctionBegin; 1211 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"MUMPS Options","Mat");CHKERRQ(ierr); 1212 ierr = PetscOptionsInt("-mat_mumps_icntl_1","ICNTL(1): output stream for error messages","None",mumps->id.ICNTL(1),&icntl,&flg);CHKERRQ(ierr); 1213 if (flg) mumps->id.ICNTL(1) = icntl; 1214 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); 1215 if (flg) mumps->id.ICNTL(2) = icntl; 1216 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); 1217 if (flg) mumps->id.ICNTL(3) = icntl; 1218 1219 ierr = PetscOptionsInt("-mat_mumps_icntl_4","ICNTL(4): level of printing (0 to 4)","None",mumps->id.ICNTL(4),&icntl,&flg);CHKERRQ(ierr); 1220 if (flg) mumps->id.ICNTL(4) = icntl; 1221 if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */ 1222 1223 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); 1224 if (flg) mumps->id.ICNTL(6) = icntl; 1225 1226 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); 1227 if (flg) { 1228 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"); 1229 else mumps->id.ICNTL(7) = icntl; 1230 } 1231 1232 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); 1233 /* 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() */ 1234 ierr = PetscOptionsInt("-mat_mumps_icntl_10","ICNTL(10): max num of refinements","None",mumps->id.ICNTL(10),&mumps->id.ICNTL(10),NULL);CHKERRQ(ierr); 1235 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); 1236 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); 1237 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); 1238 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); 1239 ierr = PetscOptionsInt("-mat_mumps_icntl_19","ICNTL(19): computes the Schur complement","None",mumps->id.ICNTL(19),&mumps->id.ICNTL(19),NULL);CHKERRQ(ierr); 1240 if (mumps->id.ICNTL(19) <= 0 || mumps->id.ICNTL(19) > 3) { /* reset any schur data (if any) */ 1241 ierr = MatDestroy(&F->schur);CHKERRQ(ierr); 1242 ierr = MatMumpsResetSchur_Private(mumps);CHKERRQ(ierr); 1243 } 1244 /* 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 */ 1245 /* 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 */ 1246 1247 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); 1248 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); 1249 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); 1250 if (mumps->id.ICNTL(24)) { 1251 mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */ 1252 } 1253 1254 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); 1255 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); 1256 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); 1257 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); 1258 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); 1259 /* 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 */ 1260 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); 1261 /* 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 */ 1262 ierr = PetscOptionsInt("-mat_mumps_icntl_33","ICNTL(33): compute determinant","None",mumps->id.ICNTL(33),&mumps->id.ICNTL(33),NULL);CHKERRQ(ierr); 1263 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); 1264 1265 ierr = PetscOptionsReal("-mat_mumps_cntl_1","CNTL(1): relative pivoting threshold","None",mumps->id.CNTL(1),&mumps->id.CNTL(1),NULL);CHKERRQ(ierr); 1266 ierr = PetscOptionsReal("-mat_mumps_cntl_2","CNTL(2): stopping criterion of refinement","None",mumps->id.CNTL(2),&mumps->id.CNTL(2),NULL);CHKERRQ(ierr); 1267 ierr = PetscOptionsReal("-mat_mumps_cntl_3","CNTL(3): absolute pivoting threshold","None",mumps->id.CNTL(3),&mumps->id.CNTL(3),NULL);CHKERRQ(ierr); 1268 ierr = PetscOptionsReal("-mat_mumps_cntl_4","CNTL(4): value for static pivoting","None",mumps->id.CNTL(4),&mumps->id.CNTL(4),NULL);CHKERRQ(ierr); 1269 ierr = PetscOptionsReal("-mat_mumps_cntl_5","CNTL(5): fixation for null pivots","None",mumps->id.CNTL(5),&mumps->id.CNTL(5),NULL);CHKERRQ(ierr); 1270 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); 1271 1272 ierr = PetscOptionsString("-mat_mumps_ooc_tmpdir", "out of core directory", "None", mumps->id.ooc_tmpdir, mumps->id.ooc_tmpdir, 256, NULL);CHKERRQ(ierr); 1273 1274 ierr = PetscOptionsIntArray("-mat_mumps_view_info","request INFO local to each processor","",info,&ninfo,NULL);CHKERRQ(ierr); 1275 if (ninfo) { 1276 if (ninfo > 40) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_USER,"number of INFO %d must <= 40\n",ninfo); 1277 ierr = PetscMalloc1(ninfo,&mumps->info);CHKERRQ(ierr); 1278 mumps->ninfo = ninfo; 1279 for (i=0; i<ninfo; i++) { 1280 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); 1281 else mumps->info[i] = info[i]; 1282 } 1283 } 1284 1285 ierr = PetscOptionsEnd();CHKERRQ(ierr); 1286 PetscFunctionReturn(0); 1287 } 1288 1289 PetscErrorCode PetscInitializeMUMPS(Mat A,Mat_MUMPS *mumps) 1290 { 1291 PetscErrorCode ierr; 1292 1293 PetscFunctionBegin; 1294 ierr = MPI_Comm_rank(PetscObjectComm((PetscObject)A), &mumps->myid);CHKERRQ(ierr); 1295 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)A),&mumps->size);CHKERRQ(ierr); 1296 ierr = MPI_Comm_dup(PetscObjectComm((PetscObject)A),&(mumps->comm_mumps));CHKERRQ(ierr); 1297 1298 mumps->id.comm_fortran = MPI_Comm_c2f(mumps->comm_mumps); 1299 1300 mumps->id.job = JOB_INIT; 1301 mumps->id.par = 1; /* host participates factorizaton and solve */ 1302 mumps->id.sym = mumps->sym; 1303 PetscMUMPS_c(&mumps->id); 1304 1305 mumps->scat_rhs = NULL; 1306 mumps->scat_sol = NULL; 1307 1308 /* set PETSc-MUMPS default options - override MUMPS default */ 1309 mumps->id.ICNTL(3) = 0; 1310 mumps->id.ICNTL(4) = 0; 1311 if (mumps->size == 1) { 1312 mumps->id.ICNTL(18) = 0; /* centralized assembled matrix input */ 1313 } else { 1314 mumps->id.ICNTL(18) = 3; /* distributed assembled matrix input */ 1315 mumps->id.ICNTL(20) = 0; /* rhs is in dense format */ 1316 mumps->id.ICNTL(21) = 1; /* distributed solution */ 1317 } 1318 1319 /* schur */ 1320 mumps->id.size_schur = 0; 1321 mumps->id.listvar_schur = NULL; 1322 mumps->id.schur = NULL; 1323 mumps->sizeredrhs = 0; 1324 mumps->schur_sol = NULL; 1325 mumps->schur_sizesol = 0; 1326 PetscFunctionReturn(0); 1327 } 1328 1329 PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F,Mat A,const MatFactorInfo *info,Mat_MUMPS *mumps) 1330 { 1331 PetscErrorCode ierr; 1332 1333 PetscFunctionBegin; 1334 if (mumps->id.INFOG(1) < 0) { 1335 if (A->erroriffailure) { 1336 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d\n",mumps->id.INFOG(1)); 1337 } else { 1338 if (mumps->id.INFOG(1) == -6) { 1339 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); 1340 F->factorerrortype = MAT_FACTOR_STRUCT_ZEROPIVOT; 1341 } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) { 1342 ierr = PetscInfo2(F,"problem of workspace, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1343 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1344 } else { 1345 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); 1346 F->factorerrortype = MAT_FACTOR_OTHER; 1347 } 1348 } 1349 } 1350 PetscFunctionReturn(0); 1351 } 1352 1353 /* Note Petsc r(=c) permutation is used when mumps->id.ICNTL(7)==1 with centralized assembled matrix input; otherwise r and c are ignored */ 1354 PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 1355 { 1356 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1357 PetscErrorCode ierr; 1358 Vec b; 1359 IS is_iden; 1360 const PetscInt M = A->rmap->N; 1361 1362 PetscFunctionBegin; 1363 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 1364 1365 /* Set MUMPS options from the options database */ 1366 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1367 1368 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1369 1370 /* analysis phase */ 1371 /*----------------*/ 1372 mumps->id.job = JOB_FACTSYMBOLIC; 1373 mumps->id.n = M; 1374 switch (mumps->id.ICNTL(18)) { 1375 case 0: /* centralized assembled matrix input */ 1376 if (!mumps->myid) { 1377 mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn; 1378 if (mumps->id.ICNTL(6)>1) { 1379 mumps->id.a = (MumpsScalar*)mumps->val; 1380 } 1381 if (mumps->id.ICNTL(7) == 1) { /* use user-provide matrix ordering - assuming r = c ordering */ 1382 /* 1383 PetscBool flag; 1384 ierr = ISEqual(r,c,&flag);CHKERRQ(ierr); 1385 if (!flag) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_USER,"row_perm != col_perm"); 1386 ierr = ISView(r,PETSC_VIEWER_STDOUT_SELF); 1387 */ 1388 if (!mumps->myid) { 1389 const PetscInt *idx; 1390 PetscInt i,*perm_in; 1391 1392 ierr = PetscMalloc1(M,&perm_in);CHKERRQ(ierr); 1393 ierr = ISGetIndices(r,&idx);CHKERRQ(ierr); 1394 1395 mumps->id.perm_in = perm_in; 1396 for (i=0; i<M; i++) perm_in[i] = idx[i]+1; /* perm_in[]: start from 1, not 0! */ 1397 ierr = ISRestoreIndices(r,&idx);CHKERRQ(ierr); 1398 } 1399 } 1400 } 1401 break; 1402 case 3: /* distributed assembled matrix input (size>1) */ 1403 mumps->id.nz_loc = mumps->nz; 1404 mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn; 1405 if (mumps->id.ICNTL(6)>1) { 1406 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1407 } 1408 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1409 if (!mumps->myid) { 1410 ierr = VecCreateSeq(PETSC_COMM_SELF,A->rmap->N,&mumps->b_seq);CHKERRQ(ierr); 1411 ierr = ISCreateStride(PETSC_COMM_SELF,A->rmap->N,0,1,&is_iden);CHKERRQ(ierr); 1412 } else { 1413 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);CHKERRQ(ierr); 1414 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1415 } 1416 ierr = MatCreateVecs(A,NULL,&b);CHKERRQ(ierr); 1417 ierr = VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);CHKERRQ(ierr); 1418 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1419 ierr = VecDestroy(&b);CHKERRQ(ierr); 1420 break; 1421 } 1422 PetscMUMPS_c(&mumps->id); 1423 ierr = MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);CHKERRQ(ierr); 1424 1425 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 1426 F->ops->solve = MatSolve_MUMPS; 1427 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 1428 F->ops->matsolve = MatMatSolve_MUMPS; 1429 F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS; 1430 PetscFunctionReturn(0); 1431 } 1432 1433 /* Note the Petsc r and c permutations are ignored */ 1434 PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 1435 { 1436 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1437 PetscErrorCode ierr; 1438 Vec b; 1439 IS is_iden; 1440 const PetscInt M = A->rmap->N; 1441 1442 PetscFunctionBegin; 1443 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 1444 1445 /* Set MUMPS options from the options database */ 1446 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1447 1448 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1449 1450 /* analysis phase */ 1451 /*----------------*/ 1452 mumps->id.job = JOB_FACTSYMBOLIC; 1453 mumps->id.n = M; 1454 switch (mumps->id.ICNTL(18)) { 1455 case 0: /* centralized assembled matrix input */ 1456 if (!mumps->myid) { 1457 mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn; 1458 if (mumps->id.ICNTL(6)>1) { 1459 mumps->id.a = (MumpsScalar*)mumps->val; 1460 } 1461 } 1462 break; 1463 case 3: /* distributed assembled matrix input (size>1) */ 1464 mumps->id.nz_loc = mumps->nz; 1465 mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn; 1466 if (mumps->id.ICNTL(6)>1) { 1467 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1468 } 1469 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1470 if (!mumps->myid) { 1471 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);CHKERRQ(ierr); 1472 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 1473 } else { 1474 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);CHKERRQ(ierr); 1475 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1476 } 1477 ierr = MatCreateVecs(A,NULL,&b);CHKERRQ(ierr); 1478 ierr = VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);CHKERRQ(ierr); 1479 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1480 ierr = VecDestroy(&b);CHKERRQ(ierr); 1481 break; 1482 } 1483 PetscMUMPS_c(&mumps->id); 1484 ierr = MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);CHKERRQ(ierr); 1485 1486 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 1487 F->ops->solve = MatSolve_MUMPS; 1488 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 1489 PetscFunctionReturn(0); 1490 } 1491 1492 /* Note the Petsc r permutation and factor info are ignored */ 1493 PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F,Mat A,IS r,const MatFactorInfo *info) 1494 { 1495 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1496 PetscErrorCode ierr; 1497 Vec b; 1498 IS is_iden; 1499 const PetscInt M = A->rmap->N; 1500 1501 PetscFunctionBegin; 1502 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 1503 1504 /* Set MUMPS options from the options database */ 1505 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1506 1507 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1508 1509 /* analysis phase */ 1510 /*----------------*/ 1511 mumps->id.job = JOB_FACTSYMBOLIC; 1512 mumps->id.n = M; 1513 switch (mumps->id.ICNTL(18)) { 1514 case 0: /* centralized assembled matrix input */ 1515 if (!mumps->myid) { 1516 mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn; 1517 if (mumps->id.ICNTL(6)>1) { 1518 mumps->id.a = (MumpsScalar*)mumps->val; 1519 } 1520 } 1521 break; 1522 case 3: /* distributed assembled matrix input (size>1) */ 1523 mumps->id.nz_loc = mumps->nz; 1524 mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn; 1525 if (mumps->id.ICNTL(6)>1) { 1526 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1527 } 1528 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1529 if (!mumps->myid) { 1530 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);CHKERRQ(ierr); 1531 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 1532 } else { 1533 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);CHKERRQ(ierr); 1534 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1535 } 1536 ierr = MatCreateVecs(A,NULL,&b);CHKERRQ(ierr); 1537 ierr = VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);CHKERRQ(ierr); 1538 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1539 ierr = VecDestroy(&b);CHKERRQ(ierr); 1540 break; 1541 } 1542 PetscMUMPS_c(&mumps->id); 1543 ierr = MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);CHKERRQ(ierr); 1544 1545 F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS; 1546 F->ops->solve = MatSolve_MUMPS; 1547 F->ops->solvetranspose = MatSolve_MUMPS; 1548 F->ops->matsolve = MatMatSolve_MUMPS; 1549 #if defined(PETSC_USE_COMPLEX) 1550 F->ops->getinertia = NULL; 1551 #else 1552 F->ops->getinertia = MatGetInertia_SBAIJMUMPS; 1553 #endif 1554 PetscFunctionReturn(0); 1555 } 1556 1557 PetscErrorCode MatView_MUMPS(Mat A,PetscViewer viewer) 1558 { 1559 PetscErrorCode ierr; 1560 PetscBool iascii; 1561 PetscViewerFormat format; 1562 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 1563 1564 PetscFunctionBegin; 1565 /* check if matrix is mumps type */ 1566 if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(0); 1567 1568 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 1569 if (iascii) { 1570 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 1571 if (format == PETSC_VIEWER_ASCII_INFO) { 1572 ierr = PetscViewerASCIIPrintf(viewer,"MUMPS run parameters:\n");CHKERRQ(ierr); 1573 ierr = PetscViewerASCIIPrintf(viewer," SYM (matrix type): %d \n",mumps->id.sym);CHKERRQ(ierr); 1574 ierr = PetscViewerASCIIPrintf(viewer," PAR (host participation): %d \n",mumps->id.par);CHKERRQ(ierr); 1575 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(1) (output for error): %d \n",mumps->id.ICNTL(1));CHKERRQ(ierr); 1576 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(2) (output of diagnostic msg): %d \n",mumps->id.ICNTL(2));CHKERRQ(ierr); 1577 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(3) (output for global info): %d \n",mumps->id.ICNTL(3));CHKERRQ(ierr); 1578 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(4) (level of printing): %d \n",mumps->id.ICNTL(4));CHKERRQ(ierr); 1579 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(5) (input mat struct): %d \n",mumps->id.ICNTL(5));CHKERRQ(ierr); 1580 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(6) (matrix prescaling): %d \n",mumps->id.ICNTL(6));CHKERRQ(ierr); 1581 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(7) (sequential matrix ordering):%d \n",mumps->id.ICNTL(7));CHKERRQ(ierr); 1582 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(8) (scaling strategy): %d \n",mumps->id.ICNTL(8));CHKERRQ(ierr); 1583 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(10) (max num of refinements): %d \n",mumps->id.ICNTL(10));CHKERRQ(ierr); 1584 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(11) (error analysis): %d \n",mumps->id.ICNTL(11));CHKERRQ(ierr); 1585 if (mumps->id.ICNTL(11)>0) { 1586 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(4) (inf norm of input mat): %g\n",mumps->id.RINFOG(4));CHKERRQ(ierr); 1587 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(5) (inf norm of solution): %g\n",mumps->id.RINFOG(5));CHKERRQ(ierr); 1588 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(6) (inf norm of residual): %g\n",mumps->id.RINFOG(6));CHKERRQ(ierr); 1589 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(7),RINFOG(8) (backward error est): %g, %g\n",mumps->id.RINFOG(7),mumps->id.RINFOG(8));CHKERRQ(ierr); 1590 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(9) (error estimate): %g \n",mumps->id.RINFOG(9));CHKERRQ(ierr); 1591 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n",mumps->id.RINFOG(10),mumps->id.RINFOG(11));CHKERRQ(ierr); 1592 } 1593 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(12) (efficiency control): %d \n",mumps->id.ICNTL(12));CHKERRQ(ierr); 1594 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(13) (efficiency control): %d \n",mumps->id.ICNTL(13));CHKERRQ(ierr); 1595 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(14) (percentage of estimated workspace increase): %d \n",mumps->id.ICNTL(14));CHKERRQ(ierr); 1596 /* ICNTL(15-17) not used */ 1597 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(18) (input mat struct): %d \n",mumps->id.ICNTL(18));CHKERRQ(ierr); 1598 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(19) (Schur complement info): %d \n",mumps->id.ICNTL(19));CHKERRQ(ierr); 1599 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(20) (rhs sparse pattern): %d \n",mumps->id.ICNTL(20));CHKERRQ(ierr); 1600 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(21) (solution struct): %d \n",mumps->id.ICNTL(21));CHKERRQ(ierr); 1601 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(22) (in-core/out-of-core facility): %d \n",mumps->id.ICNTL(22));CHKERRQ(ierr); 1602 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(23) (max size of memory can be allocated locally):%d \n",mumps->id.ICNTL(23));CHKERRQ(ierr); 1603 1604 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(24) (detection of null pivot rows): %d \n",mumps->id.ICNTL(24));CHKERRQ(ierr); 1605 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(25) (computation of a null space basis): %d \n",mumps->id.ICNTL(25));CHKERRQ(ierr); 1606 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(26) (Schur options for rhs or solution): %d \n",mumps->id.ICNTL(26));CHKERRQ(ierr); 1607 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(27) (experimental parameter): %d \n",mumps->id.ICNTL(27));CHKERRQ(ierr); 1608 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(28) (use parallel or sequential ordering): %d \n",mumps->id.ICNTL(28));CHKERRQ(ierr); 1609 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(29) (parallel ordering): %d \n",mumps->id.ICNTL(29));CHKERRQ(ierr); 1610 1611 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(30) (user-specified set of entries in inv(A)): %d \n",mumps->id.ICNTL(30));CHKERRQ(ierr); 1612 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(31) (factors is discarded in the solve phase): %d \n",mumps->id.ICNTL(31));CHKERRQ(ierr); 1613 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(33) (compute determinant): %d \n",mumps->id.ICNTL(33));CHKERRQ(ierr); 1614 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(35) (activate BLR based factorization): %d \n",mumps->id.ICNTL(35));CHKERRQ(ierr); 1615 1616 ierr = PetscViewerASCIIPrintf(viewer," CNTL(1) (relative pivoting threshold): %g \n",mumps->id.CNTL(1));CHKERRQ(ierr); 1617 ierr = PetscViewerASCIIPrintf(viewer," CNTL(2) (stopping criterion of refinement): %g \n",mumps->id.CNTL(2));CHKERRQ(ierr); 1618 ierr = PetscViewerASCIIPrintf(viewer," CNTL(3) (absolute pivoting threshold): %g \n",mumps->id.CNTL(3));CHKERRQ(ierr); 1619 ierr = PetscViewerASCIIPrintf(viewer," CNTL(4) (value of static pivoting): %g \n",mumps->id.CNTL(4));CHKERRQ(ierr); 1620 ierr = PetscViewerASCIIPrintf(viewer," CNTL(5) (fixation for null pivots): %g \n",mumps->id.CNTL(5));CHKERRQ(ierr); 1621 ierr = PetscViewerASCIIPrintf(viewer," CNTL(7) (dropping parameter for BLR): %g \n",mumps->id.CNTL(7));CHKERRQ(ierr); 1622 1623 /* infomation local to each processor */ 1624 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(1) (local estimated flops for the elimination after analysis): \n");CHKERRQ(ierr); 1625 ierr = PetscViewerASCIIPushSynchronized(viewer);CHKERRQ(ierr); 1626 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(1));CHKERRQ(ierr); 1627 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1628 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(2) (local estimated flops for the assembly after factorization): \n");CHKERRQ(ierr); 1629 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(2));CHKERRQ(ierr); 1630 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1631 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(3) (local estimated flops for the elimination after factorization): \n");CHKERRQ(ierr); 1632 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(3));CHKERRQ(ierr); 1633 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1634 1635 ierr = PetscViewerASCIIPrintf(viewer, " INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization): \n");CHKERRQ(ierr); 1636 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(15));CHKERRQ(ierr); 1637 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1638 1639 ierr = PetscViewerASCIIPrintf(viewer, " INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization): \n");CHKERRQ(ierr); 1640 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(16));CHKERRQ(ierr); 1641 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1642 1643 ierr = PetscViewerASCIIPrintf(viewer, " INFO(23) (num of pivots eliminated on this processor after factorization): \n");CHKERRQ(ierr); 1644 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(23));CHKERRQ(ierr); 1645 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1646 1647 if (mumps->ninfo && mumps->ninfo <= 40){ 1648 PetscInt i; 1649 for (i=0; i<mumps->ninfo; i++){ 1650 ierr = PetscViewerASCIIPrintf(viewer, " INFO(%d): \n",mumps->info[i]);CHKERRQ(ierr); 1651 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(mumps->info[i]));CHKERRQ(ierr); 1652 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1653 } 1654 } 1655 ierr = PetscViewerASCIIPopSynchronized(viewer);CHKERRQ(ierr); 1656 1657 if (!mumps->myid) { /* information from the host */ 1658 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(1) (global estimated flops for the elimination after analysis): %g \n",mumps->id.RINFOG(1));CHKERRQ(ierr); 1659 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(2) (global estimated flops for the assembly after factorization): %g \n",mumps->id.RINFOG(2));CHKERRQ(ierr); 1660 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(3) (global estimated flops for the elimination after factorization): %g \n",mumps->id.RINFOG(3));CHKERRQ(ierr); 1661 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); 1662 1663 ierr = PetscViewerASCIIPrintf(viewer," INFOG(3) (estimated real workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(3));CHKERRQ(ierr); 1664 ierr = PetscViewerASCIIPrintf(viewer," INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(4));CHKERRQ(ierr); 1665 ierr = PetscViewerASCIIPrintf(viewer," INFOG(5) (estimated maximum front size in the complete tree): %d \n",mumps->id.INFOG(5));CHKERRQ(ierr); 1666 ierr = PetscViewerASCIIPrintf(viewer," INFOG(6) (number of nodes in the complete tree): %d \n",mumps->id.INFOG(6));CHKERRQ(ierr); 1667 ierr = PetscViewerASCIIPrintf(viewer," INFOG(7) (ordering option effectively use after analysis): %d \n",mumps->id.INFOG(7));CHKERRQ(ierr); 1668 ierr = PetscViewerASCIIPrintf(viewer," INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d \n",mumps->id.INFOG(8));CHKERRQ(ierr); 1669 ierr = PetscViewerASCIIPrintf(viewer," INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d \n",mumps->id.INFOG(9));CHKERRQ(ierr); 1670 ierr = PetscViewerASCIIPrintf(viewer," INFOG(10) (total integer space store the matrix factors after factorization): %d \n",mumps->id.INFOG(10));CHKERRQ(ierr); 1671 ierr = PetscViewerASCIIPrintf(viewer," INFOG(11) (order of largest frontal matrix after factorization): %d \n",mumps->id.INFOG(11));CHKERRQ(ierr); 1672 ierr = PetscViewerASCIIPrintf(viewer," INFOG(12) (number of off-diagonal pivots): %d \n",mumps->id.INFOG(12));CHKERRQ(ierr); 1673 ierr = PetscViewerASCIIPrintf(viewer," INFOG(13) (number of delayed pivots after factorization): %d \n",mumps->id.INFOG(13));CHKERRQ(ierr); 1674 ierr = PetscViewerASCIIPrintf(viewer," INFOG(14) (number of memory compress after factorization): %d \n",mumps->id.INFOG(14));CHKERRQ(ierr); 1675 ierr = PetscViewerASCIIPrintf(viewer," INFOG(15) (number of steps of iterative refinement after solution): %d \n",mumps->id.INFOG(15));CHKERRQ(ierr); 1676 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); 1677 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); 1678 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); 1679 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); 1680 ierr = PetscViewerASCIIPrintf(viewer," INFOG(20) (estimated number of entries in the factors): %d \n",mumps->id.INFOG(20));CHKERRQ(ierr); 1681 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); 1682 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); 1683 ierr = PetscViewerASCIIPrintf(viewer," INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d \n",mumps->id.INFOG(23));CHKERRQ(ierr); 1684 ierr = PetscViewerASCIIPrintf(viewer," INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d \n",mumps->id.INFOG(24));CHKERRQ(ierr); 1685 ierr = PetscViewerASCIIPrintf(viewer," INFOG(25) (after factorization: number of pivots modified by static pivoting): %d \n",mumps->id.INFOG(25));CHKERRQ(ierr); 1686 ierr = PetscViewerASCIIPrintf(viewer," INFOG(28) (after factorization: number of null pivots encountered): %d\n",mumps->id.INFOG(28));CHKERRQ(ierr); 1687 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); 1688 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); 1689 ierr = PetscViewerASCIIPrintf(viewer," INFOG(32) (after analysis: type of analysis done): %d\n",mumps->id.INFOG(32));CHKERRQ(ierr); 1690 ierr = PetscViewerASCIIPrintf(viewer," INFOG(33) (value used for ICNTL(8)): %d\n",mumps->id.INFOG(33));CHKERRQ(ierr); 1691 ierr = PetscViewerASCIIPrintf(viewer," INFOG(34) (exponent of the determinant if determinant is requested): %d\n",mumps->id.INFOG(34));CHKERRQ(ierr); 1692 } 1693 } 1694 } 1695 PetscFunctionReturn(0); 1696 } 1697 1698 PetscErrorCode MatGetInfo_MUMPS(Mat A,MatInfoType flag,MatInfo *info) 1699 { 1700 Mat_MUMPS *mumps =(Mat_MUMPS*)A->data; 1701 1702 PetscFunctionBegin; 1703 info->block_size = 1.0; 1704 info->nz_allocated = mumps->id.INFOG(20); 1705 info->nz_used = mumps->id.INFOG(20); 1706 info->nz_unneeded = 0.0; 1707 info->assemblies = 0.0; 1708 info->mallocs = 0.0; 1709 info->memory = 0.0; 1710 info->fill_ratio_given = 0; 1711 info->fill_ratio_needed = 0; 1712 info->factor_mallocs = 0; 1713 PetscFunctionReturn(0); 1714 } 1715 1716 /* -------------------------------------------------------------------------------------------*/ 1717 PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is) 1718 { 1719 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1720 const PetscInt *idxs; 1721 PetscInt size,i; 1722 PetscErrorCode ierr; 1723 1724 PetscFunctionBegin; 1725 ierr = ISGetLocalSize(is,&size);CHKERRQ(ierr); 1726 if (mumps->size > 1) { 1727 PetscBool ls,gs; 1728 1729 ls = mumps->myid ? (size ? PETSC_FALSE : PETSC_TRUE) : PETSC_TRUE; 1730 ierr = MPI_Allreduce(&ls,&gs,1,MPIU_BOOL,MPI_LAND,mumps->comm_mumps);CHKERRQ(ierr); 1731 if (!gs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MUMPS distributed parallel Schur complements not yet supported from PETSc\n"); 1732 } 1733 if (mumps->id.size_schur != size) { 1734 ierr = PetscFree2(mumps->id.listvar_schur,mumps->id.schur);CHKERRQ(ierr); 1735 mumps->id.size_schur = size; 1736 mumps->id.schur_lld = size; 1737 ierr = PetscMalloc2(size,&mumps->id.listvar_schur,size*size,&mumps->id.schur);CHKERRQ(ierr); 1738 } 1739 1740 /* Schur complement matrix */ 1741 ierr = MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.size_schur,(PetscScalar*)mumps->id.schur,&F->schur);CHKERRQ(ierr); 1742 if (mumps->sym == 1) { 1743 ierr = MatSetOption(F->schur,MAT_SPD,PETSC_TRUE);CHKERRQ(ierr); 1744 } 1745 1746 /* MUMPS expects Fortran style indices */ 1747 ierr = ISGetIndices(is,&idxs);CHKERRQ(ierr); 1748 ierr = PetscMemcpy(mumps->id.listvar_schur,idxs,size*sizeof(PetscInt));CHKERRQ(ierr); 1749 for (i=0;i<size;i++) mumps->id.listvar_schur[i]++; 1750 ierr = ISRestoreIndices(is,&idxs);CHKERRQ(ierr); 1751 if (mumps->size > 1) { 1752 mumps->id.ICNTL(19) = 1; /* MUMPS returns Schur centralized on the host */ 1753 } else { 1754 if (F->factortype == MAT_FACTOR_LU) { 1755 mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */ 1756 } else { 1757 mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */ 1758 } 1759 } 1760 /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */ 1761 mumps->id.ICNTL(26) = -1; 1762 PetscFunctionReturn(0); 1763 } 1764 1765 /* -------------------------------------------------------------------------------------------*/ 1766 PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F,Mat* S) 1767 { 1768 Mat St; 1769 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1770 PetscScalar *array; 1771 #if defined(PETSC_USE_COMPLEX) 1772 PetscScalar im = PetscSqrtScalar((PetscScalar)-1.0); 1773 #endif 1774 PetscErrorCode ierr; 1775 1776 PetscFunctionBegin; 1777 if (!mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it"); 1778 ierr = MatCreate(PETSC_COMM_SELF,&St);CHKERRQ(ierr); 1779 ierr = MatSetSizes(St,PETSC_DECIDE,PETSC_DECIDE,mumps->id.size_schur,mumps->id.size_schur);CHKERRQ(ierr); 1780 ierr = MatSetType(St,MATDENSE);CHKERRQ(ierr); 1781 ierr = MatSetUp(St);CHKERRQ(ierr); 1782 ierr = MatDenseGetArray(St,&array);CHKERRQ(ierr); 1783 if (!mumps->sym) { /* MUMPS always return a full matrix */ 1784 if (mumps->id.ICNTL(19) == 1) { /* stored by rows */ 1785 PetscInt i,j,N=mumps->id.size_schur; 1786 for (i=0;i<N;i++) { 1787 for (j=0;j<N;j++) { 1788 #if !defined(PETSC_USE_COMPLEX) 1789 PetscScalar val = mumps->id.schur[i*N+j]; 1790 #else 1791 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 1792 #endif 1793 array[j*N+i] = val; 1794 } 1795 } 1796 } else { /* stored by columns */ 1797 ierr = PetscMemcpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur*sizeof(PetscScalar));CHKERRQ(ierr); 1798 } 1799 } else { /* either full or lower-triangular (not packed) */ 1800 if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */ 1801 PetscInt i,j,N=mumps->id.size_schur; 1802 for (i=0;i<N;i++) { 1803 for (j=i;j<N;j++) { 1804 #if !defined(PETSC_USE_COMPLEX) 1805 PetscScalar val = mumps->id.schur[i*N+j]; 1806 #else 1807 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 1808 #endif 1809 array[i*N+j] = val; 1810 array[j*N+i] = val; 1811 } 1812 } 1813 } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */ 1814 ierr = PetscMemcpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur*sizeof(PetscScalar));CHKERRQ(ierr); 1815 } else { /* ICNTL(19) == 1 lower triangular stored by rows */ 1816 PetscInt i,j,N=mumps->id.size_schur; 1817 for (i=0;i<N;i++) { 1818 for (j=0;j<i+1;j++) { 1819 #if !defined(PETSC_USE_COMPLEX) 1820 PetscScalar val = mumps->id.schur[i*N+j]; 1821 #else 1822 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 1823 #endif 1824 array[i*N+j] = val; 1825 array[j*N+i] = val; 1826 } 1827 } 1828 } 1829 } 1830 ierr = MatDenseRestoreArray(St,&array);CHKERRQ(ierr); 1831 *S = St; 1832 PetscFunctionReturn(0); 1833 } 1834 1835 /* -------------------------------------------------------------------------------------------*/ 1836 PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt ival) 1837 { 1838 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1839 1840 PetscFunctionBegin; 1841 mumps->id.ICNTL(icntl) = ival; 1842 PetscFunctionReturn(0); 1843 } 1844 1845 PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt *ival) 1846 { 1847 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1848 1849 PetscFunctionBegin; 1850 *ival = mumps->id.ICNTL(icntl); 1851 PetscFunctionReturn(0); 1852 } 1853 1854 /*@ 1855 MatMumpsSetIcntl - Set MUMPS parameter ICNTL() 1856 1857 Logically Collective on Mat 1858 1859 Input Parameters: 1860 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1861 . icntl - index of MUMPS parameter array ICNTL() 1862 - ival - value of MUMPS ICNTL(icntl) 1863 1864 Options Database: 1865 . -mat_mumps_icntl_<icntl> <ival> 1866 1867 Level: beginner 1868 1869 References: 1870 . MUMPS Users' Guide 1871 1872 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 1873 @*/ 1874 PetscErrorCode MatMumpsSetIcntl(Mat F,PetscInt icntl,PetscInt ival) 1875 { 1876 PetscErrorCode ierr; 1877 1878 PetscFunctionBegin; 1879 PetscValidType(F,1); 1880 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 1881 PetscValidLogicalCollectiveInt(F,icntl,2); 1882 PetscValidLogicalCollectiveInt(F,ival,3); 1883 ierr = PetscTryMethod(F,"MatMumpsSetIcntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));CHKERRQ(ierr); 1884 PetscFunctionReturn(0); 1885 } 1886 1887 /*@ 1888 MatMumpsGetIcntl - Get MUMPS parameter ICNTL() 1889 1890 Logically Collective on Mat 1891 1892 Input Parameters: 1893 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1894 - icntl - index of MUMPS parameter array ICNTL() 1895 1896 Output Parameter: 1897 . ival - value of MUMPS ICNTL(icntl) 1898 1899 Level: beginner 1900 1901 References: 1902 . MUMPS Users' Guide 1903 1904 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 1905 @*/ 1906 PetscErrorCode MatMumpsGetIcntl(Mat F,PetscInt icntl,PetscInt *ival) 1907 { 1908 PetscErrorCode ierr; 1909 1910 PetscFunctionBegin; 1911 PetscValidType(F,1); 1912 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 1913 PetscValidLogicalCollectiveInt(F,icntl,2); 1914 PetscValidIntPointer(ival,3); 1915 ierr = PetscUseMethod(F,"MatMumpsGetIcntl_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));CHKERRQ(ierr); 1916 PetscFunctionReturn(0); 1917 } 1918 1919 /* -------------------------------------------------------------------------------------------*/ 1920 PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal val) 1921 { 1922 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1923 1924 PetscFunctionBegin; 1925 mumps->id.CNTL(icntl) = val; 1926 PetscFunctionReturn(0); 1927 } 1928 1929 PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal *val) 1930 { 1931 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1932 1933 PetscFunctionBegin; 1934 *val = mumps->id.CNTL(icntl); 1935 PetscFunctionReturn(0); 1936 } 1937 1938 /*@ 1939 MatMumpsSetCntl - Set MUMPS parameter CNTL() 1940 1941 Logically Collective on Mat 1942 1943 Input Parameters: 1944 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1945 . icntl - index of MUMPS parameter array CNTL() 1946 - val - value of MUMPS CNTL(icntl) 1947 1948 Options Database: 1949 . -mat_mumps_cntl_<icntl> <val> 1950 1951 Level: beginner 1952 1953 References: 1954 . MUMPS Users' Guide 1955 1956 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 1957 @*/ 1958 PetscErrorCode MatMumpsSetCntl(Mat F,PetscInt icntl,PetscReal val) 1959 { 1960 PetscErrorCode ierr; 1961 1962 PetscFunctionBegin; 1963 PetscValidType(F,1); 1964 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 1965 PetscValidLogicalCollectiveInt(F,icntl,2); 1966 PetscValidLogicalCollectiveReal(F,val,3); 1967 ierr = PetscTryMethod(F,"MatMumpsSetCntl_C",(Mat,PetscInt,PetscReal),(F,icntl,val));CHKERRQ(ierr); 1968 PetscFunctionReturn(0); 1969 } 1970 1971 /*@ 1972 MatMumpsGetCntl - Get MUMPS parameter CNTL() 1973 1974 Logically Collective on Mat 1975 1976 Input Parameters: 1977 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1978 - icntl - index of MUMPS parameter array CNTL() 1979 1980 Output Parameter: 1981 . val - value of MUMPS CNTL(icntl) 1982 1983 Level: beginner 1984 1985 References: 1986 . MUMPS Users' Guide 1987 1988 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 1989 @*/ 1990 PetscErrorCode MatMumpsGetCntl(Mat F,PetscInt icntl,PetscReal *val) 1991 { 1992 PetscErrorCode ierr; 1993 1994 PetscFunctionBegin; 1995 PetscValidType(F,1); 1996 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 1997 PetscValidLogicalCollectiveInt(F,icntl,2); 1998 PetscValidRealPointer(val,3); 1999 ierr = PetscUseMethod(F,"MatMumpsGetCntl_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));CHKERRQ(ierr); 2000 PetscFunctionReturn(0); 2001 } 2002 2003 PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F,PetscInt icntl,PetscInt *info) 2004 { 2005 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2006 2007 PetscFunctionBegin; 2008 *info = mumps->id.INFO(icntl); 2009 PetscFunctionReturn(0); 2010 } 2011 2012 PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F,PetscInt icntl,PetscInt *infog) 2013 { 2014 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2015 2016 PetscFunctionBegin; 2017 *infog = mumps->id.INFOG(icntl); 2018 PetscFunctionReturn(0); 2019 } 2020 2021 PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfo) 2022 { 2023 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2024 2025 PetscFunctionBegin; 2026 *rinfo = mumps->id.RINFO(icntl); 2027 PetscFunctionReturn(0); 2028 } 2029 2030 PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfog) 2031 { 2032 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2033 2034 PetscFunctionBegin; 2035 *rinfog = mumps->id.RINFOG(icntl); 2036 PetscFunctionReturn(0); 2037 } 2038 2039 PetscErrorCode MatMumpsGetInverse_MUMPS(Mat F,Mat spRHS) 2040 { 2041 PetscErrorCode ierr; 2042 Mat Bt = NULL,Btseq = NULL; 2043 PetscBool flg; 2044 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2045 PetscScalar *aa; 2046 PetscInt spnr,*ia,*ja; 2047 2048 PetscFunctionBegin; 2049 PetscValidIntPointer(spRHS,2); 2050 ierr = PetscObjectTypeCompare((PetscObject)spRHS,MATTRANSPOSEMAT,&flg);CHKERRQ(ierr); 2051 if (flg) { 2052 ierr = MatTransposeGetMat(spRHS,&Bt);CHKERRQ(ierr); 2053 } else SETERRQ(PetscObjectComm((PetscObject)spRHS),PETSC_ERR_ARG_WRONG,"Matrix spRHS must be type MATTRANSPOSEMAT matrix"); 2054 2055 ierr = MatMumpsSetIcntl(F,30,1);CHKERRQ(ierr); 2056 2057 if (mumps->size > 1) { 2058 Mat_MPIAIJ *b = (Mat_MPIAIJ*)Bt->data; 2059 Btseq = b->A; 2060 } else { 2061 Btseq = Bt; 2062 } 2063 2064 if (!mumps->myid) { 2065 ierr = MatSeqAIJGetArray(Btseq,&aa);CHKERRQ(ierr); 2066 ierr = MatGetRowIJ(Btseq,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 2067 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 2068 2069 mumps->id.irhs_ptr = ia; 2070 mumps->id.irhs_sparse = ja; 2071 mumps->id.nz_rhs = ia[spnr] - 1; 2072 mumps->id.rhs_sparse = (MumpsScalar*)aa; 2073 } else { 2074 mumps->id.irhs_ptr = NULL; 2075 mumps->id.irhs_sparse = NULL; 2076 mumps->id.nz_rhs = 0; 2077 mumps->id.rhs_sparse = NULL; 2078 } 2079 mumps->id.ICNTL(20) = 1; /* rhs is sparse */ 2080 mumps->id.ICNTL(21) = 0; /* solution is in assembled centralized format */ 2081 2082 /* solve phase */ 2083 /*-------------*/ 2084 mumps->id.job = JOB_SOLVE; 2085 PetscMUMPS_c(&mumps->id); 2086 if (mumps->id.INFOG(1) < 0) 2087 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)); 2088 2089 if (!mumps->myid) { 2090 ierr = MatSeqAIJRestoreArray(Btseq,&aa);CHKERRQ(ierr); 2091 ierr = MatRestoreRowIJ(Btseq,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 2092 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 2093 } 2094 PetscFunctionReturn(0); 2095 } 2096 2097 /*@ 2098 MatMumpsGetInverse - Get user-specified set of entries in inverse of A 2099 2100 Logically Collective on Mat 2101 2102 Input Parameters: 2103 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2104 - spRHS - sequential sparse matrix in MATTRANSPOSEMAT format holding specified indices in processor[0] 2105 2106 Output Parameter: 2107 . spRHS - requested entries of inverse of A 2108 2109 Level: beginner 2110 2111 References: 2112 . MUMPS Users' Guide 2113 2114 .seealso: MatGetFactor(), MatCreateTranspose() 2115 @*/ 2116 PetscErrorCode MatMumpsGetInverse(Mat F,Mat spRHS) 2117 { 2118 PetscErrorCode ierr; 2119 2120 PetscFunctionBegin; 2121 PetscValidType(F,1); 2122 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2123 ierr = PetscUseMethod(F,"MatMumpsGetInverse_C",(Mat,Mat),(F,spRHS));CHKERRQ(ierr); 2124 PetscFunctionReturn(0); 2125 } 2126 2127 PetscErrorCode MatMumpsGetInverseTranspose_MUMPS(Mat F,Mat spRHST) 2128 { 2129 PetscErrorCode ierr; 2130 Mat spRHS; 2131 2132 PetscFunctionBegin; 2133 ierr = MatCreateTranspose(spRHST,&spRHS);CHKERRQ(ierr); 2134 ierr = MatMumpsGetInverse_MUMPS(F,spRHS);CHKERRQ(ierr); 2135 ierr = MatDestroy(&spRHS);CHKERRQ(ierr); 2136 PetscFunctionReturn(0); 2137 } 2138 2139 /*@ 2140 MatMumpsGetInverseTranspose - Get user-specified set of entries in inverse of matrix A^T 2141 2142 Logically Collective on Mat 2143 2144 Input Parameters: 2145 + F - the factored matrix of A obtained by calling MatGetFactor() from PETSc-MUMPS interface 2146 - spRHST - sequential sparse matrix in MATAIJ format holding specified indices of A^T in processor[0] 2147 2148 Output Parameter: 2149 . spRHST - requested entries of inverse of A^T 2150 2151 Level: beginner 2152 2153 References: 2154 . MUMPS Users' Guide 2155 2156 .seealso: MatGetFactor(), MatCreateTranspose(), MatMumpsGetInverse() 2157 @*/ 2158 PetscErrorCode MatMumpsGetInverseTranspose(Mat F,Mat spRHST) 2159 { 2160 PetscErrorCode ierr; 2161 PetscBool flg; 2162 2163 PetscFunctionBegin; 2164 PetscValidType(F,1); 2165 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2166 ierr = PetscObjectTypeCompareAny((PetscObject)spRHST,&flg,MATSEQAIJ,MATMPIAIJ,NULL);CHKERRQ(ierr); 2167 if (!flg) SETERRQ(PetscObjectComm((PetscObject)spRHST),PETSC_ERR_ARG_WRONG,"Matrix spRHST must be MATAIJ matrix"); 2168 2169 ierr = PetscUseMethod(F,"MatMumpsGetInverseTranspose_C",(Mat,Mat),(F,spRHST));CHKERRQ(ierr); 2170 PetscFunctionReturn(0); 2171 } 2172 2173 /*@ 2174 MatMumpsGetInfo - Get MUMPS parameter INFO() 2175 2176 Logically Collective on Mat 2177 2178 Input Parameters: 2179 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2180 - icntl - index of MUMPS parameter array INFO() 2181 2182 Output Parameter: 2183 . ival - value of MUMPS INFO(icntl) 2184 2185 Level: beginner 2186 2187 References: 2188 . MUMPS Users' Guide 2189 2190 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2191 @*/ 2192 PetscErrorCode MatMumpsGetInfo(Mat F,PetscInt icntl,PetscInt *ival) 2193 { 2194 PetscErrorCode ierr; 2195 2196 PetscFunctionBegin; 2197 PetscValidType(F,1); 2198 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2199 PetscValidIntPointer(ival,3); 2200 ierr = PetscUseMethod(F,"MatMumpsGetInfo_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));CHKERRQ(ierr); 2201 PetscFunctionReturn(0); 2202 } 2203 2204 /*@ 2205 MatMumpsGetInfog - Get MUMPS parameter INFOG() 2206 2207 Logically Collective on Mat 2208 2209 Input Parameters: 2210 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2211 - icntl - index of MUMPS parameter array INFOG() 2212 2213 Output Parameter: 2214 . ival - value of MUMPS INFOG(icntl) 2215 2216 Level: beginner 2217 2218 References: 2219 . MUMPS Users' Guide 2220 2221 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2222 @*/ 2223 PetscErrorCode MatMumpsGetInfog(Mat F,PetscInt icntl,PetscInt *ival) 2224 { 2225 PetscErrorCode ierr; 2226 2227 PetscFunctionBegin; 2228 PetscValidType(F,1); 2229 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2230 PetscValidIntPointer(ival,3); 2231 ierr = PetscUseMethod(F,"MatMumpsGetInfog_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));CHKERRQ(ierr); 2232 PetscFunctionReturn(0); 2233 } 2234 2235 /*@ 2236 MatMumpsGetRinfo - Get MUMPS parameter RINFO() 2237 2238 Logically Collective on Mat 2239 2240 Input Parameters: 2241 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2242 - icntl - index of MUMPS parameter array RINFO() 2243 2244 Output Parameter: 2245 . val - value of MUMPS RINFO(icntl) 2246 2247 Level: beginner 2248 2249 References: 2250 . MUMPS Users' Guide 2251 2252 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2253 @*/ 2254 PetscErrorCode MatMumpsGetRinfo(Mat F,PetscInt icntl,PetscReal *val) 2255 { 2256 PetscErrorCode ierr; 2257 2258 PetscFunctionBegin; 2259 PetscValidType(F,1); 2260 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2261 PetscValidRealPointer(val,3); 2262 ierr = PetscUseMethod(F,"MatMumpsGetRinfo_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));CHKERRQ(ierr); 2263 PetscFunctionReturn(0); 2264 } 2265 2266 /*@ 2267 MatMumpsGetRinfog - Get MUMPS parameter RINFOG() 2268 2269 Logically Collective on Mat 2270 2271 Input Parameters: 2272 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2273 - icntl - index of MUMPS parameter array RINFOG() 2274 2275 Output Parameter: 2276 . val - value of MUMPS RINFOG(icntl) 2277 2278 Level: beginner 2279 2280 References: 2281 . MUMPS Users' Guide 2282 2283 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2284 @*/ 2285 PetscErrorCode MatMumpsGetRinfog(Mat F,PetscInt icntl,PetscReal *val) 2286 { 2287 PetscErrorCode ierr; 2288 2289 PetscFunctionBegin; 2290 PetscValidType(F,1); 2291 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2292 PetscValidRealPointer(val,3); 2293 ierr = PetscUseMethod(F,"MatMumpsGetRinfog_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));CHKERRQ(ierr); 2294 PetscFunctionReturn(0); 2295 } 2296 2297 /*MC 2298 MATSOLVERMUMPS - A matrix type providing direct solvers (LU and Cholesky) for 2299 distributed and sequential matrices via the external package MUMPS. 2300 2301 Works with MATAIJ and MATSBAIJ matrices 2302 2303 Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch to have PETSc installed with MUMPS 2304 2305 Use -pc_type cholesky or lu -pc_factor_mat_solver_type mumps to use this direct solver 2306 2307 Options Database Keys: 2308 + -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages 2309 . -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning 2310 . -mat_mumps_icntl_3 - ICNTL(3): output stream for global information, collected on the host 2311 . -mat_mumps_icntl_4 - ICNTL(4): level of printing (0 to 4) 2312 . -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7) 2313 . -mat_mumps_icntl_7 - ICNTL(7): computes a symmetric permutation in sequential analysis (0 to 7). 3=Scotch, 4=PORD, 5=Metis 2314 . -mat_mumps_icntl_8 - ICNTL(8): scaling strategy (-2 to 8 or 77) 2315 . -mat_mumps_icntl_10 - ICNTL(10): max num of refinements 2316 . -mat_mumps_icntl_11 - ICNTL(11): statistics related to an error analysis (via -ksp_view) 2317 . -mat_mumps_icntl_12 - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3) 2318 . -mat_mumps_icntl_13 - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting 2319 . -mat_mumps_icntl_14 - ICNTL(14): percentage increase in the estimated working space 2320 . -mat_mumps_icntl_19 - ICNTL(19): computes the Schur complement 2321 . -mat_mumps_icntl_22 - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1) 2322 . -mat_mumps_icntl_23 - ICNTL(23): max size of the working memory (MB) that can allocate per processor 2323 . -mat_mumps_icntl_24 - ICNTL(24): detection of null pivot rows (0 or 1) 2324 . -mat_mumps_icntl_25 - ICNTL(25): compute a solution of a deficient matrix and a null space basis 2325 . -mat_mumps_icntl_26 - ICNTL(26): drives the solution phase if a Schur complement matrix 2326 . -mat_mumps_icntl_28 - ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering 2327 . -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis 2328 . -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A) 2329 . -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization 2330 . -mat_mumps_icntl_33 - ICNTL(33): compute determinant 2331 . -mat_mumps_cntl_1 - CNTL(1): relative pivoting threshold 2332 . -mat_mumps_cntl_2 - CNTL(2): stopping criterion of refinement 2333 . -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold 2334 . -mat_mumps_cntl_4 - CNTL(4): value for static pivoting 2335 - -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots 2336 2337 Level: beginner 2338 2339 Notes: 2340 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 2341 $ KSPGetPC(ksp,&pc); 2342 $ PCFactorGetMatrix(pc,&mat); 2343 $ MatMumpsGetInfo(mat,....); 2344 $ MatMumpsGetInfog(mat,....); etc. 2345 Or you can run with -ksp_error_if_not_converged and the program will be stopped and the information printed in the error message. 2346 2347 .seealso: PCFactorSetMatSolverType(), MatSolverType, MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog(), KSPGetPC(), PCGetFactor(), PCFactorGetMatrix() 2348 2349 M*/ 2350 2351 static PetscErrorCode MatFactorGetSolverType_mumps(Mat A,MatSolverType *type) 2352 { 2353 PetscFunctionBegin; 2354 *type = MATSOLVERMUMPS; 2355 PetscFunctionReturn(0); 2356 } 2357 2358 /* MatGetFactor for Seq and MPI AIJ matrices */ 2359 static PetscErrorCode MatGetFactor_aij_mumps(Mat A,MatFactorType ftype,Mat *F) 2360 { 2361 Mat B; 2362 PetscErrorCode ierr; 2363 Mat_MUMPS *mumps; 2364 PetscBool isSeqAIJ; 2365 2366 PetscFunctionBegin; 2367 /* Create the factorization matrix */ 2368 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);CHKERRQ(ierr); 2369 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2370 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2371 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2372 ierr = MatSetUp(B);CHKERRQ(ierr); 2373 2374 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2375 2376 B->ops->view = MatView_MUMPS; 2377 B->ops->getinfo = MatGetInfo_MUMPS; 2378 2379 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2380 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2381 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2382 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2383 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2384 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2385 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2386 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2387 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2388 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2389 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2390 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);CHKERRQ(ierr); 2391 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverseTranspose_C",MatMumpsGetInverseTranspose_MUMPS);CHKERRQ(ierr); 2392 2393 if (ftype == MAT_FACTOR_LU) { 2394 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS; 2395 B->factortype = MAT_FACTOR_LU; 2396 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij; 2397 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij; 2398 mumps->sym = 0; 2399 } else { 2400 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 2401 B->factortype = MAT_FACTOR_CHOLESKY; 2402 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij; 2403 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij; 2404 #if defined(PETSC_USE_COMPLEX) 2405 mumps->sym = 2; 2406 #else 2407 if (A->spd_set && A->spd) mumps->sym = 1; 2408 else mumps->sym = 2; 2409 #endif 2410 } 2411 2412 /* set solvertype */ 2413 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2414 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2415 2416 B->ops->destroy = MatDestroy_MUMPS; 2417 B->data = (void*)mumps; 2418 2419 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2420 2421 *F = B; 2422 PetscFunctionReturn(0); 2423 } 2424 2425 /* MatGetFactor for Seq and MPI SBAIJ matrices */ 2426 static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A,MatFactorType ftype,Mat *F) 2427 { 2428 Mat B; 2429 PetscErrorCode ierr; 2430 Mat_MUMPS *mumps; 2431 PetscBool isSeqSBAIJ; 2432 2433 PetscFunctionBegin; 2434 if (ftype != MAT_FACTOR_CHOLESKY) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Cannot use PETSc SBAIJ matrices with MUMPS LU, use AIJ matrix"); 2435 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"); 2436 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ);CHKERRQ(ierr); 2437 /* Create the factorization matrix */ 2438 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2439 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2440 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2441 ierr = MatSetUp(B);CHKERRQ(ierr); 2442 2443 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2444 if (isSeqSBAIJ) { 2445 mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij; 2446 } else { 2447 mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij; 2448 } 2449 2450 B->ops->getinfo = MatGetInfo_External; 2451 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 2452 B->ops->view = MatView_MUMPS; 2453 2454 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2455 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2456 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2457 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2458 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2459 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2460 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2461 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2462 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2463 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2464 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2465 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);CHKERRQ(ierr); 2466 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverseTranspose_C",MatMumpsGetInverseTranspose_MUMPS);CHKERRQ(ierr); 2467 2468 B->factortype = MAT_FACTOR_CHOLESKY; 2469 #if defined(PETSC_USE_COMPLEX) 2470 mumps->sym = 2; 2471 #else 2472 if (A->spd_set && A->spd) mumps->sym = 1; 2473 else mumps->sym = 2; 2474 #endif 2475 2476 /* set solvertype */ 2477 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2478 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2479 2480 B->ops->destroy = MatDestroy_MUMPS; 2481 B->data = (void*)mumps; 2482 2483 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2484 2485 *F = B; 2486 PetscFunctionReturn(0); 2487 } 2488 2489 static PetscErrorCode MatGetFactor_baij_mumps(Mat A,MatFactorType ftype,Mat *F) 2490 { 2491 Mat B; 2492 PetscErrorCode ierr; 2493 Mat_MUMPS *mumps; 2494 PetscBool isSeqBAIJ; 2495 2496 PetscFunctionBegin; 2497 /* Create the factorization matrix */ 2498 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ);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 if (ftype == MAT_FACTOR_LU) { 2506 B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS; 2507 B->factortype = MAT_FACTOR_LU; 2508 if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij; 2509 else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij; 2510 mumps->sym = 0; 2511 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead\n"); 2512 2513 B->ops->getinfo = MatGetInfo_External; 2514 B->ops->view = MatView_MUMPS; 2515 2516 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2517 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2518 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2519 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2520 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2521 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2522 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2523 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2524 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2525 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2526 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2527 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);CHKERRQ(ierr); 2528 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverseTranspose_C",MatMumpsGetInverseTranspose_MUMPS);CHKERRQ(ierr); 2529 2530 /* set solvertype */ 2531 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2532 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2533 2534 B->ops->destroy = MatDestroy_MUMPS; 2535 B->data = (void*)mumps; 2536 2537 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2538 2539 *F = B; 2540 PetscFunctionReturn(0); 2541 } 2542 2543 /* MatGetFactor for Seq and MPI SELL matrices */ 2544 static PetscErrorCode MatGetFactor_sell_mumps(Mat A,MatFactorType ftype,Mat *F) 2545 { 2546 Mat B; 2547 PetscErrorCode ierr; 2548 Mat_MUMPS *mumps; 2549 PetscBool isSeqSELL; 2550 2551 PetscFunctionBegin; 2552 /* Create the factorization matrix */ 2553 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQSELL,&isSeqSELL);CHKERRQ(ierr); 2554 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2555 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2556 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2557 ierr = MatSetUp(B);CHKERRQ(ierr); 2558 2559 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2560 2561 B->ops->view = MatView_MUMPS; 2562 B->ops->getinfo = MatGetInfo_MUMPS; 2563 2564 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2565 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2566 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2567 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2568 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2569 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2570 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2571 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2572 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2573 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2574 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2575 2576 if (ftype == MAT_FACTOR_LU) { 2577 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS; 2578 B->factortype = MAT_FACTOR_LU; 2579 if (isSeqSELL) mumps->ConvertToTriples = MatConvertToTriples_seqsell_seqaij; 2580 else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"To be implemented"); 2581 mumps->sym = 0; 2582 } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"To be implemented"); 2583 2584 /* set solvertype */ 2585 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2586 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2587 2588 B->ops->destroy = MatDestroy_MUMPS; 2589 B->data = (void*)mumps; 2590 2591 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2592 2593 *F = B; 2594 PetscFunctionReturn(0); 2595 } 2596 2597 PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MUMPS(void) 2598 { 2599 PetscErrorCode ierr; 2600 2601 PetscFunctionBegin; 2602 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2603 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2604 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2605 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2606 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPISBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);CHKERRQ(ierr); 2607 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2608 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2609 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2610 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2611 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQSBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);CHKERRQ(ierr); 2612 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQSELL,MAT_FACTOR_LU,MatGetFactor_sell_mumps);CHKERRQ(ierr); 2613 PetscFunctionReturn(0); 2614 } 2615 2616