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 MUMPS_c cmumps_c 35 #else 36 #define MUMPS_c zmumps_c 37 #endif 38 #else 39 #if defined(PETSC_USE_REAL_SINGLE) 40 #define MUMPS_c smumps_c 41 #else 42 #define MUMPS_c dmumps_c 43 #endif 44 #endif 45 46 /* if using PETSc OpenMP support, we only call MUMPS on master ranks. Before/after the call, we change/restore CPUs the master ranks can run on */ 47 #if defined(PETSC_HAVE_OPENMP_SUPPORT) 48 #define PetscMUMPS_c(mumps) \ 49 do { \ 50 if (mumps->use_petsc_omp_support) { \ 51 if (mumps->is_omp_master) { \ 52 ierr = PetscOmpCtrlOmpRegionOnMasterBegin(mumps->omp_ctrl);CHKERRQ(ierr); \ 53 MUMPS_c(&mumps->id); \ 54 ierr = PetscOmpCtrlOmpRegionOnMasterEnd(mumps->omp_ctrl);CHKERRQ(ierr); \ 55 } \ 56 ierr = PetscOmpCtrlBarrier(mumps->omp_ctrl);CHKERRQ(ierr); \ 57 } else { \ 58 MUMPS_c(&mumps->id); \ 59 } \ 60 } while(0) 61 #else 62 #define PetscMUMPS_c(mumps) \ 63 do { MUMPS_c(&mumps->id); } while (0) 64 #endif 65 66 /* declare MumpsScalar */ 67 #if defined(PETSC_USE_COMPLEX) 68 #if defined(PETSC_USE_REAL_SINGLE) 69 #define MumpsScalar mumps_complex 70 #else 71 #define MumpsScalar mumps_double_complex 72 #endif 73 #else 74 #define MumpsScalar PetscScalar 75 #endif 76 77 /* macros s.t. indices match MUMPS documentation */ 78 #define ICNTL(I) icntl[(I)-1] 79 #define CNTL(I) cntl[(I)-1] 80 #define INFOG(I) infog[(I)-1] 81 #define INFO(I) info[(I)-1] 82 #define RINFOG(I) rinfog[(I)-1] 83 #define RINFO(I) rinfo[(I)-1] 84 85 typedef struct { 86 #if defined(PETSC_USE_COMPLEX) 87 #if defined(PETSC_USE_REAL_SINGLE) 88 CMUMPS_STRUC_C id; 89 #else 90 ZMUMPS_STRUC_C id; 91 #endif 92 #else 93 #if defined(PETSC_USE_REAL_SINGLE) 94 SMUMPS_STRUC_C id; 95 #else 96 DMUMPS_STRUC_C id; 97 #endif 98 #endif 99 100 MatStructure matstruc; 101 PetscMPIInt myid,petsc_size; 102 PetscInt *irn,*jcn,nz,sym; 103 PetscScalar *val; 104 MPI_Comm mumps_comm; 105 PetscInt ICNTL9_pre; /* check if ICNTL(9) is changed from previous MatSolve */ 106 VecScatter scat_rhs, scat_sol; /* used by MatSolve() */ 107 Vec b_seq,x_seq; 108 PetscInt ninfo,*info; /* display INFO */ 109 PetscInt sizeredrhs; 110 PetscScalar *schur_sol; 111 PetscInt schur_sizesol; 112 113 PetscBool use_petsc_omp_support; 114 PetscOmpCtrl omp_ctrl; /* an OpenMP controler that blocked processes will release their CPU (MPI_Barrier does not have this guarantee) */ 115 MPI_Comm petsc_comm,omp_comm; /* petsc_comm is petsc matrix's comm */ 116 PetscMPIInt mpinz; /* on master rank, nz = sum(mpinz) over omp_comm; on other ranks, mpinz = nz*/ 117 PetscMPIInt omp_comm_size; 118 PetscBool is_omp_master; /* is this rank the master of omp_comm */ 119 PetscMPIInt *recvcount,*displs; 120 121 PetscErrorCode (*ConvertToTriples)(Mat, int, MatReuse, int*, int**, int**, PetscScalar**); 122 } Mat_MUMPS; 123 124 extern PetscErrorCode MatDuplicate_MUMPS(Mat,MatDuplicateOption,Mat*); 125 126 static PetscErrorCode MatMumpsResetSchur_Private(Mat_MUMPS* mumps) 127 { 128 PetscErrorCode ierr; 129 130 PetscFunctionBegin; 131 ierr = PetscFree2(mumps->id.listvar_schur,mumps->id.schur);CHKERRQ(ierr); 132 ierr = PetscFree(mumps->id.redrhs);CHKERRQ(ierr); 133 ierr = PetscFree(mumps->schur_sol);CHKERRQ(ierr); 134 mumps->id.size_schur = 0; 135 mumps->id.schur_lld = 0; 136 mumps->id.ICNTL(19) = 0; 137 PetscFunctionReturn(0); 138 } 139 140 /* solve with rhs in mumps->id.redrhs and return in the same location */ 141 static PetscErrorCode MatMumpsSolveSchur_Private(Mat F) 142 { 143 Mat_MUMPS *mumps=(Mat_MUMPS*)F->data; 144 Mat S,B,X; 145 MatFactorSchurStatus schurstatus; 146 PetscInt sizesol; 147 PetscErrorCode ierr; 148 149 PetscFunctionBegin; 150 ierr = MatFactorFactorizeSchurComplement(F);CHKERRQ(ierr); 151 ierr = MatFactorGetSchurComplement(F,&S,&schurstatus);CHKERRQ(ierr); 152 ierr = MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.nrhs,(PetscScalar*)mumps->id.redrhs,&B);CHKERRQ(ierr); 153 switch (schurstatus) { 154 case MAT_FACTOR_SCHUR_FACTORED: 155 ierr = MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.nrhs,(PetscScalar*)mumps->id.redrhs,&X);CHKERRQ(ierr); 156 if (!mumps->id.ICNTL(9)) { /* transpose solve */ 157 ierr = MatMatSolveTranspose(S,B,X);CHKERRQ(ierr); 158 } else { 159 ierr = MatMatSolve(S,B,X);CHKERRQ(ierr); 160 } 161 break; 162 case MAT_FACTOR_SCHUR_INVERTED: 163 sizesol = mumps->id.nrhs*mumps->id.size_schur; 164 if (!mumps->schur_sol || sizesol > mumps->schur_sizesol) { 165 ierr = PetscFree(mumps->schur_sol);CHKERRQ(ierr); 166 ierr = PetscMalloc1(sizesol,&mumps->schur_sol);CHKERRQ(ierr); 167 mumps->schur_sizesol = sizesol; 168 } 169 ierr = MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.nrhs,mumps->schur_sol,&X);CHKERRQ(ierr); 170 if (!mumps->id.ICNTL(9)) { /* transpose solve */ 171 ierr = MatTransposeMatMult(S,B,MAT_REUSE_MATRIX,PETSC_DEFAULT,&X);CHKERRQ(ierr); 172 } else { 173 ierr = MatMatMult(S,B,MAT_REUSE_MATRIX,PETSC_DEFAULT,&X);CHKERRQ(ierr); 174 } 175 ierr = MatCopy(X,B,SAME_NONZERO_PATTERN);CHKERRQ(ierr); 176 break; 177 default: 178 SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Unhandled MatFactorSchurStatus %D",F->schur_status); 179 break; 180 } 181 ierr = MatFactorRestoreSchurComplement(F,&S,schurstatus);CHKERRQ(ierr); 182 ierr = MatDestroy(&B);CHKERRQ(ierr); 183 ierr = MatDestroy(&X);CHKERRQ(ierr); 184 PetscFunctionReturn(0); 185 } 186 187 static PetscErrorCode MatMumpsHandleSchur_Private(Mat F, PetscBool expansion) 188 { 189 Mat_MUMPS *mumps=(Mat_MUMPS*)F->data; 190 PetscErrorCode ierr; 191 192 PetscFunctionBegin; 193 if (!mumps->id.ICNTL(19)) { /* do nothing when Schur complement has not been computed */ 194 PetscFunctionReturn(0); 195 } 196 if (!expansion) { /* prepare for the condensation step */ 197 PetscInt sizeredrhs = mumps->id.nrhs*mumps->id.size_schur; 198 /* allocate MUMPS internal array to store reduced right-hand sides */ 199 if (!mumps->id.redrhs || sizeredrhs > mumps->sizeredrhs) { 200 ierr = PetscFree(mumps->id.redrhs);CHKERRQ(ierr); 201 mumps->id.lredrhs = mumps->id.size_schur; 202 ierr = PetscMalloc1(mumps->id.nrhs*mumps->id.lredrhs,&mumps->id.redrhs);CHKERRQ(ierr); 203 mumps->sizeredrhs = mumps->id.nrhs*mumps->id.lredrhs; 204 } 205 mumps->id.ICNTL(26) = 1; /* condensation phase */ 206 } else { /* prepare for the expansion step */ 207 /* solve Schur complement (this has to be done by the MUMPS user, so basically us) */ 208 ierr = MatMumpsSolveSchur_Private(F);CHKERRQ(ierr); 209 mumps->id.ICNTL(26) = 2; /* expansion phase */ 210 PetscMUMPS_c(mumps); 211 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)); 212 /* restore defaults */ 213 mumps->id.ICNTL(26) = -1; 214 /* free MUMPS internal array for redrhs if we have solved for multiple rhs in order to save memory space */ 215 if (mumps->id.nrhs > 1) { 216 ierr = PetscFree(mumps->id.redrhs);CHKERRQ(ierr); 217 mumps->id.lredrhs = 0; 218 mumps->sizeredrhs = 0; 219 } 220 } 221 PetscFunctionReturn(0); 222 } 223 224 /* 225 MatConvertToTriples_A_B - convert Petsc matrix to triples: row[nz], col[nz], val[nz] 226 227 input: 228 A - matrix in aij,baij or sbaij (bs=1) format 229 shift - 0: C style output triple; 1: Fortran style output triple. 230 reuse - MAT_INITIAL_MATRIX: spaces are allocated and values are set for the triple 231 MAT_REUSE_MATRIX: only the values in v array are updated 232 output: 233 nnz - dim of r, c, and v (number of local nonzero entries of A) 234 r, c, v - row and col index, matrix values (matrix triples) 235 236 The returned values r, c, and sometimes v are obtained in a single PetscMalloc(). Then in MatDestroy_MUMPS() it is 237 freed with PetscFree(mumps->irn); This is not ideal code, the fact that v is ONLY sometimes part of mumps->irn means 238 that the PetscMalloc() cannot easily be replaced with a PetscMalloc3(). 239 240 */ 241 242 PetscErrorCode MatConvertToTriples_seqaij_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 243 { 244 const PetscInt *ai,*aj,*ajj,M=A->rmap->n; 245 PetscInt nz,rnz,i,j; 246 PetscErrorCode ierr; 247 PetscInt *row,*col; 248 Mat_SeqAIJ *aa=(Mat_SeqAIJ*)A->data; 249 250 PetscFunctionBegin; 251 *v=aa->a; 252 if (reuse == MAT_INITIAL_MATRIX) { 253 nz = aa->nz; 254 ai = aa->i; 255 aj = aa->j; 256 *nnz = nz; 257 ierr = PetscMalloc1(2*nz, &row);CHKERRQ(ierr); 258 col = row + nz; 259 260 nz = 0; 261 for (i=0; i<M; i++) { 262 rnz = ai[i+1] - ai[i]; 263 ajj = aj + ai[i]; 264 for (j=0; j<rnz; j++) { 265 row[nz] = i+shift; col[nz++] = ajj[j] + shift; 266 } 267 } 268 *r = row; *c = col; 269 } 270 PetscFunctionReturn(0); 271 } 272 273 PetscErrorCode MatConvertToTriples_seqsell_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 274 { 275 Mat_SeqSELL *a=(Mat_SeqSELL*)A->data; 276 PetscInt *ptr; 277 278 PetscFunctionBegin; 279 *v = a->val; 280 if (reuse == MAT_INITIAL_MATRIX) { 281 PetscInt nz,i,j,row; 282 PetscErrorCode ierr; 283 284 nz = a->sliidx[a->totalslices]; 285 *nnz = nz; 286 ierr = PetscMalloc1(2*nz, &ptr);CHKERRQ(ierr); 287 *r = ptr; 288 *c = ptr + nz; 289 290 for (i=0; i<a->totalslices; i++) { 291 for (j=a->sliidx[i],row=0; j<a->sliidx[i+1]; j++,row=((row+1)&0x07)) { 292 *ptr++ = 8*i + row + shift; 293 } 294 } 295 for (i=0;i<nz;i++) *ptr++ = a->colidx[i] + shift; 296 } 297 PetscFunctionReturn(0); 298 } 299 300 PetscErrorCode MatConvertToTriples_seqbaij_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 301 { 302 Mat_SeqBAIJ *aa=(Mat_SeqBAIJ*)A->data; 303 const PetscInt *ai,*aj,*ajj,bs2 = aa->bs2; 304 PetscInt bs,M,nz,idx=0,rnz,i,j,k,m; 305 PetscErrorCode ierr; 306 PetscInt *row,*col; 307 308 PetscFunctionBegin; 309 ierr = MatGetBlockSize(A,&bs);CHKERRQ(ierr); 310 M = A->rmap->N/bs; 311 *v = aa->a; 312 if (reuse == MAT_INITIAL_MATRIX) { 313 ai = aa->i; aj = aa->j; 314 nz = bs2*aa->nz; 315 *nnz = nz; 316 ierr = PetscMalloc1(2*nz, &row);CHKERRQ(ierr); 317 col = row + nz; 318 319 for (i=0; i<M; i++) { 320 ajj = aj + ai[i]; 321 rnz = ai[i+1] - ai[i]; 322 for (k=0; k<rnz; k++) { 323 for (j=0; j<bs; j++) { 324 for (m=0; m<bs; m++) { 325 row[idx] = i*bs + m + shift; 326 col[idx++] = bs*(ajj[k]) + j + shift; 327 } 328 } 329 } 330 } 331 *r = row; *c = col; 332 } 333 PetscFunctionReturn(0); 334 } 335 336 PetscErrorCode MatConvertToTriples_seqsbaij_seqsbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 337 { 338 const PetscInt *ai, *aj,*ajj,M=A->rmap->n; 339 PetscInt nz,rnz,i,j; 340 PetscErrorCode ierr; 341 PetscInt *row,*col; 342 Mat_SeqSBAIJ *aa=(Mat_SeqSBAIJ*)A->data; 343 344 PetscFunctionBegin; 345 *v = aa->a; 346 if (reuse == MAT_INITIAL_MATRIX) { 347 nz = aa->nz; 348 ai = aa->i; 349 aj = aa->j; 350 *v = aa->a; 351 *nnz = nz; 352 ierr = PetscMalloc1(2*nz, &row);CHKERRQ(ierr); 353 col = row + nz; 354 355 nz = 0; 356 for (i=0; i<M; i++) { 357 rnz = ai[i+1] - ai[i]; 358 ajj = aj + ai[i]; 359 for (j=0; j<rnz; j++) { 360 row[nz] = i+shift; col[nz++] = ajj[j] + shift; 361 } 362 } 363 *r = row; *c = col; 364 } 365 PetscFunctionReturn(0); 366 } 367 368 PetscErrorCode MatConvertToTriples_seqaij_seqsbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 369 { 370 const PetscInt *ai,*aj,*ajj,*adiag,M=A->rmap->n; 371 PetscInt nz,rnz,i,j; 372 const PetscScalar *av,*v1; 373 PetscScalar *val; 374 PetscErrorCode ierr; 375 PetscInt *row,*col; 376 Mat_SeqAIJ *aa=(Mat_SeqAIJ*)A->data; 377 PetscBool missing; 378 379 PetscFunctionBegin; 380 ai = aa->i; aj = aa->j; av = aa->a; 381 adiag = aa->diag; 382 ierr = MatMissingDiagonal_SeqAIJ(A,&missing,&i);CHKERRQ(ierr); 383 if (reuse == MAT_INITIAL_MATRIX) { 384 /* count nz in the upper triangular part of A */ 385 nz = 0; 386 if (missing) { 387 for (i=0; i<M; i++) { 388 if (PetscUnlikely(adiag[i] >= ai[i+1])) { 389 for (j=ai[i];j<ai[i+1];j++) { 390 if (aj[j] < i) continue; 391 nz++; 392 } 393 } else { 394 nz += ai[i+1] - adiag[i]; 395 } 396 } 397 } else { 398 for (i=0; i<M; i++) nz += ai[i+1] - adiag[i]; 399 } 400 *nnz = nz; 401 402 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 403 col = row + nz; 404 val = (PetscScalar*)(col + nz); 405 406 nz = 0; 407 if (missing) { 408 for (i=0; i<M; i++) { 409 if (PetscUnlikely(adiag[i] >= ai[i+1])) { 410 for (j=ai[i];j<ai[i+1];j++) { 411 if (aj[j] < i) continue; 412 row[nz] = i+shift; 413 col[nz] = aj[j]+shift; 414 val[nz] = av[j]; 415 nz++; 416 } 417 } else { 418 rnz = ai[i+1] - adiag[i]; 419 ajj = aj + adiag[i]; 420 v1 = av + adiag[i]; 421 for (j=0; j<rnz; j++) { 422 row[nz] = i+shift; col[nz] = ajj[j] + shift; val[nz++] = v1[j]; 423 } 424 } 425 } 426 } else { 427 for (i=0; i<M; i++) { 428 rnz = ai[i+1] - adiag[i]; 429 ajj = aj + adiag[i]; 430 v1 = av + adiag[i]; 431 for (j=0; j<rnz; j++) { 432 row[nz] = i+shift; col[nz] = ajj[j] + shift; val[nz++] = v1[j]; 433 } 434 } 435 } 436 *r = row; *c = col; *v = val; 437 } else { 438 nz = 0; val = *v; 439 if (missing) { 440 for (i=0; i <M; i++) { 441 if (PetscUnlikely(adiag[i] >= ai[i+1])) { 442 for (j=ai[i];j<ai[i+1];j++) { 443 if (aj[j] < i) continue; 444 val[nz++] = av[j]; 445 } 446 } else { 447 rnz = ai[i+1] - adiag[i]; 448 v1 = av + adiag[i]; 449 for (j=0; j<rnz; j++) { 450 val[nz++] = v1[j]; 451 } 452 } 453 } 454 } else { 455 for (i=0; i <M; i++) { 456 rnz = ai[i+1] - adiag[i]; 457 v1 = av + adiag[i]; 458 for (j=0; j<rnz; j++) { 459 val[nz++] = v1[j]; 460 } 461 } 462 } 463 } 464 PetscFunctionReturn(0); 465 } 466 467 PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 468 { 469 const PetscInt *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj; 470 PetscErrorCode ierr; 471 PetscInt rstart,nz,i,j,jj,irow,countA,countB; 472 PetscInt *row,*col; 473 const PetscScalar *av, *bv,*v1,*v2; 474 PetscScalar *val; 475 Mat_MPISBAIJ *mat = (Mat_MPISBAIJ*)A->data; 476 Mat_SeqSBAIJ *aa = (Mat_SeqSBAIJ*)(mat->A)->data; 477 Mat_SeqBAIJ *bb = (Mat_SeqBAIJ*)(mat->B)->data; 478 479 PetscFunctionBegin; 480 ai=aa->i; aj=aa->j; bi=bb->i; bj=bb->j; rstart= A->rmap->rstart; 481 av=aa->a; bv=bb->a; 482 483 garray = mat->garray; 484 485 if (reuse == MAT_INITIAL_MATRIX) { 486 nz = aa->nz + bb->nz; 487 *nnz = nz; 488 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 489 col = row + nz; 490 val = (PetscScalar*)(col + nz); 491 492 *r = row; *c = col; *v = val; 493 } else { 494 row = *r; col = *c; val = *v; 495 } 496 497 jj = 0; irow = rstart; 498 for (i=0; i<m; i++) { 499 ajj = aj + ai[i]; /* ptr to the beginning of this row */ 500 countA = ai[i+1] - ai[i]; 501 countB = bi[i+1] - bi[i]; 502 bjj = bj + bi[i]; 503 v1 = av + ai[i]; 504 v2 = bv + bi[i]; 505 506 /* A-part */ 507 for (j=0; j<countA; j++) { 508 if (reuse == MAT_INITIAL_MATRIX) { 509 row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift; 510 } 511 val[jj++] = v1[j]; 512 } 513 514 /* B-part */ 515 for (j=0; j < countB; j++) { 516 if (reuse == MAT_INITIAL_MATRIX) { 517 row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift; 518 } 519 val[jj++] = v2[j]; 520 } 521 irow++; 522 } 523 PetscFunctionReturn(0); 524 } 525 526 PetscErrorCode MatConvertToTriples_mpiaij_mpiaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 527 { 528 const PetscInt *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj; 529 PetscErrorCode ierr; 530 PetscInt rstart,nz,i,j,jj,irow,countA,countB; 531 PetscInt *row,*col; 532 const PetscScalar *av, *bv,*v1,*v2; 533 PetscScalar *val; 534 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data; 535 Mat_SeqAIJ *aa = (Mat_SeqAIJ*)(mat->A)->data; 536 Mat_SeqAIJ *bb = (Mat_SeqAIJ*)(mat->B)->data; 537 538 PetscFunctionBegin; 539 ai=aa->i; aj=aa->j; bi=bb->i; bj=bb->j; rstart= A->rmap->rstart; 540 av=aa->a; bv=bb->a; 541 542 garray = mat->garray; 543 544 if (reuse == MAT_INITIAL_MATRIX) { 545 nz = aa->nz + bb->nz; 546 *nnz = nz; 547 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 548 col = row + nz; 549 val = (PetscScalar*)(col + nz); 550 551 *r = row; *c = col; *v = val; 552 } else { 553 row = *r; col = *c; val = *v; 554 } 555 556 jj = 0; irow = rstart; 557 for (i=0; i<m; i++) { 558 ajj = aj + ai[i]; /* ptr to the beginning of this row */ 559 countA = ai[i+1] - ai[i]; 560 countB = bi[i+1] - bi[i]; 561 bjj = bj + bi[i]; 562 v1 = av + ai[i]; 563 v2 = bv + bi[i]; 564 565 /* A-part */ 566 for (j=0; j<countA; j++) { 567 if (reuse == MAT_INITIAL_MATRIX) { 568 row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift; 569 } 570 val[jj++] = v1[j]; 571 } 572 573 /* B-part */ 574 for (j=0; j < countB; j++) { 575 if (reuse == MAT_INITIAL_MATRIX) { 576 row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift; 577 } 578 val[jj++] = v2[j]; 579 } 580 irow++; 581 } 582 PetscFunctionReturn(0); 583 } 584 585 PetscErrorCode MatConvertToTriples_mpibaij_mpiaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 586 { 587 Mat_MPIBAIJ *mat = (Mat_MPIBAIJ*)A->data; 588 Mat_SeqBAIJ *aa = (Mat_SeqBAIJ*)(mat->A)->data; 589 Mat_SeqBAIJ *bb = (Mat_SeqBAIJ*)(mat->B)->data; 590 const PetscInt *ai = aa->i, *bi = bb->i, *aj = aa->j, *bj = bb->j,*ajj, *bjj; 591 const PetscInt *garray = mat->garray,mbs=mat->mbs,rstart=A->rmap->rstart; 592 const PetscInt bs2=mat->bs2; 593 PetscErrorCode ierr; 594 PetscInt bs,nz,i,j,k,n,jj,irow,countA,countB,idx; 595 PetscInt *row,*col; 596 const PetscScalar *av=aa->a, *bv=bb->a,*v1,*v2; 597 PetscScalar *val; 598 599 PetscFunctionBegin; 600 ierr = MatGetBlockSize(A,&bs);CHKERRQ(ierr); 601 if (reuse == MAT_INITIAL_MATRIX) { 602 nz = bs2*(aa->nz + bb->nz); 603 *nnz = nz; 604 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 605 col = row + nz; 606 val = (PetscScalar*)(col + nz); 607 608 *r = row; *c = col; *v = val; 609 } else { 610 row = *r; col = *c; val = *v; 611 } 612 613 jj = 0; irow = rstart; 614 for (i=0; i<mbs; i++) { 615 countA = ai[i+1] - ai[i]; 616 countB = bi[i+1] - bi[i]; 617 ajj = aj + ai[i]; 618 bjj = bj + bi[i]; 619 v1 = av + bs2*ai[i]; 620 v2 = bv + bs2*bi[i]; 621 622 idx = 0; 623 /* A-part */ 624 for (k=0; k<countA; k++) { 625 for (j=0; j<bs; j++) { 626 for (n=0; n<bs; n++) { 627 if (reuse == MAT_INITIAL_MATRIX) { 628 row[jj] = irow + n + shift; 629 col[jj] = rstart + bs*ajj[k] + j + shift; 630 } 631 val[jj++] = v1[idx++]; 632 } 633 } 634 } 635 636 idx = 0; 637 /* B-part */ 638 for (k=0; k<countB; k++) { 639 for (j=0; j<bs; j++) { 640 for (n=0; n<bs; n++) { 641 if (reuse == MAT_INITIAL_MATRIX) { 642 row[jj] = irow + n + shift; 643 col[jj] = bs*garray[bjj[k]] + j + shift; 644 } 645 val[jj++] = v2[idx++]; 646 } 647 } 648 } 649 irow += bs; 650 } 651 PetscFunctionReturn(0); 652 } 653 654 PetscErrorCode MatConvertToTriples_mpiaij_mpisbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 655 { 656 const PetscInt *ai, *aj,*adiag, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj; 657 PetscErrorCode ierr; 658 PetscInt rstart,nz,nza,nzb,i,j,jj,irow,countA,countB; 659 PetscInt *row,*col; 660 const PetscScalar *av, *bv,*v1,*v2; 661 PetscScalar *val; 662 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data; 663 Mat_SeqAIJ *aa =(Mat_SeqAIJ*)(mat->A)->data; 664 Mat_SeqAIJ *bb =(Mat_SeqAIJ*)(mat->B)->data; 665 666 PetscFunctionBegin; 667 ai=aa->i; aj=aa->j; adiag=aa->diag; 668 bi=bb->i; bj=bb->j; garray = mat->garray; 669 av=aa->a; bv=bb->a; 670 671 rstart = A->rmap->rstart; 672 673 if (reuse == MAT_INITIAL_MATRIX) { 674 nza = 0; /* num of upper triangular entries in mat->A, including diagonals */ 675 nzb = 0; /* num of upper triangular entries in mat->B */ 676 for (i=0; i<m; i++) { 677 nza += (ai[i+1] - adiag[i]); 678 countB = bi[i+1] - bi[i]; 679 bjj = bj + bi[i]; 680 for (j=0; j<countB; j++) { 681 if (garray[bjj[j]] > rstart) nzb++; 682 } 683 } 684 685 nz = nza + nzb; /* total nz of upper triangular part of mat */ 686 *nnz = nz; 687 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 688 col = row + nz; 689 val = (PetscScalar*)(col + nz); 690 691 *r = row; *c = col; *v = val; 692 } else { 693 row = *r; col = *c; val = *v; 694 } 695 696 jj = 0; irow = rstart; 697 for (i=0; i<m; i++) { 698 ajj = aj + adiag[i]; /* ptr to the beginning of the diagonal of this row */ 699 v1 = av + adiag[i]; 700 countA = ai[i+1] - adiag[i]; 701 countB = bi[i+1] - bi[i]; 702 bjj = bj + bi[i]; 703 v2 = bv + bi[i]; 704 705 /* A-part */ 706 for (j=0; j<countA; j++) { 707 if (reuse == MAT_INITIAL_MATRIX) { 708 row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift; 709 } 710 val[jj++] = v1[j]; 711 } 712 713 /* B-part */ 714 for (j=0; j < countB; j++) { 715 if (garray[bjj[j]] > rstart) { 716 if (reuse == MAT_INITIAL_MATRIX) { 717 row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift; 718 } 719 val[jj++] = v2[j]; 720 } 721 } 722 irow++; 723 } 724 PetscFunctionReturn(0); 725 } 726 727 PetscErrorCode MatDestroy_MUMPS(Mat A) 728 { 729 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 730 PetscErrorCode ierr; 731 732 PetscFunctionBegin; 733 ierr = PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc);CHKERRQ(ierr); 734 ierr = VecScatterDestroy(&mumps->scat_rhs);CHKERRQ(ierr); 735 ierr = VecScatterDestroy(&mumps->scat_sol);CHKERRQ(ierr); 736 ierr = VecDestroy(&mumps->b_seq);CHKERRQ(ierr); 737 ierr = VecDestroy(&mumps->x_seq);CHKERRQ(ierr); 738 ierr = PetscFree(mumps->id.perm_in);CHKERRQ(ierr); 739 ierr = PetscFree(mumps->irn);CHKERRQ(ierr); 740 ierr = PetscFree(mumps->info);CHKERRQ(ierr); 741 ierr = MatMumpsResetSchur_Private(mumps);CHKERRQ(ierr); 742 mumps->id.job = JOB_END; 743 PetscMUMPS_c(mumps); 744 #if defined(PETSC_HAVE_OPENMP_SUPPORT) 745 if (mumps->use_petsc_omp_support) { ierr = PetscOmpCtrlDestroy(&mumps->omp_ctrl);CHKERRQ(ierr); } 746 #endif 747 ierr = PetscFree2(mumps->recvcount,mumps->displs);CHKERRQ(ierr); 748 ierr = PetscFree(A->data);CHKERRQ(ierr); 749 750 /* clear composed functions */ 751 ierr = PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverType_C",NULL);CHKERRQ(ierr); 752 ierr = PetscObjectComposeFunction((PetscObject)A,"MatFactorSetSchurIS_C",NULL);CHKERRQ(ierr); 753 ierr = PetscObjectComposeFunction((PetscObject)A,"MatFactorCreateSchurComplement_C",NULL);CHKERRQ(ierr); 754 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetIcntl_C",NULL);CHKERRQ(ierr); 755 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetIcntl_C",NULL);CHKERRQ(ierr); 756 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetCntl_C",NULL);CHKERRQ(ierr); 757 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetCntl_C",NULL);CHKERRQ(ierr); 758 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfo_C",NULL);CHKERRQ(ierr); 759 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfog_C",NULL);CHKERRQ(ierr); 760 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfo_C",NULL);CHKERRQ(ierr); 761 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfog_C",NULL);CHKERRQ(ierr); 762 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInverse_C",NULL);CHKERRQ(ierr); 763 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInverseTranspose_C",NULL);CHKERRQ(ierr); 764 PetscFunctionReturn(0); 765 } 766 767 PetscErrorCode MatSolve_MUMPS(Mat A,Vec b,Vec x) 768 { 769 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 770 PetscScalar *array; 771 Vec b_seq; 772 IS is_iden,is_petsc; 773 PetscErrorCode ierr; 774 PetscInt i; 775 PetscBool second_solve = PETSC_FALSE; 776 static PetscBool cite1 = PETSC_FALSE,cite2 = PETSC_FALSE; 777 778 PetscFunctionBegin; 779 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); 780 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); 781 782 if (A->factorerrortype) { 783 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); 784 ierr = VecSetInf(x);CHKERRQ(ierr); 785 PetscFunctionReturn(0); 786 } 787 788 mumps->id.ICNTL(20)= 0; /* dense RHS */ 789 mumps->id.nrhs = 1; 790 b_seq = mumps->b_seq; 791 if (mumps->petsc_size > 1) { 792 /* MUMPS only supports centralized rhs. Scatter b into a seqential rhs vector */ 793 ierr = VecScatterBegin(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 794 ierr = VecScatterEnd(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 795 if (!mumps->myid) {ierr = VecGetArray(b_seq,&array);CHKERRQ(ierr);} 796 } else { /* petsc_size == 1 */ 797 ierr = VecCopy(b,x);CHKERRQ(ierr); 798 ierr = VecGetArray(x,&array);CHKERRQ(ierr); 799 } 800 if (!mumps->myid) { /* define rhs on the host */ 801 mumps->id.nrhs = 1; 802 mumps->id.rhs = (MumpsScalar*)array; 803 } 804 805 /* 806 handle condensation step of Schur complement (if any) 807 We set by default ICNTL(26) == -1 when Schur indices have been provided by the user. 808 According to MUMPS (5.0.0) manual, any value should be harmful during the factorization phase 809 Unless the user provides a valid value for ICNTL(26), MatSolve and MatMatSolve routines solve the full system. 810 This requires an extra call to PetscMUMPS_c and the computation of the factors for S 811 */ 812 if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) { 813 if (mumps->petsc_size > 1) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Parallel Schur complements not yet supported from PETSc\n"); 814 second_solve = PETSC_TRUE; 815 ierr = MatMumpsHandleSchur_Private(A,PETSC_FALSE);CHKERRQ(ierr); 816 } 817 /* solve phase */ 818 /*-------------*/ 819 mumps->id.job = JOB_SOLVE; 820 PetscMUMPS_c(mumps); 821 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)); 822 823 /* handle expansion step of Schur complement (if any) */ 824 if (second_solve) { 825 ierr = MatMumpsHandleSchur_Private(A,PETSC_TRUE);CHKERRQ(ierr); 826 } 827 828 if (mumps->petsc_size > 1) { /* convert mumps distributed solution to petsc mpi x */ 829 if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) { 830 /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */ 831 ierr = VecScatterDestroy(&mumps->scat_sol);CHKERRQ(ierr); 832 } 833 if (!mumps->scat_sol) { /* create scatter scat_sol */ 834 ierr = ISCreateStride(PETSC_COMM_SELF,mumps->id.lsol_loc,0,1,&is_iden);CHKERRQ(ierr); /* from */ 835 for (i=0; i<mumps->id.lsol_loc; i++) { 836 mumps->id.isol_loc[i] -= 1; /* change Fortran style to C style */ 837 } 838 ierr = ISCreateGeneral(PETSC_COMM_SELF,mumps->id.lsol_loc,mumps->id.isol_loc,PETSC_COPY_VALUES,&is_petsc);CHKERRQ(ierr); /* to */ 839 ierr = VecScatterCreate(mumps->x_seq,is_iden,x,is_petsc,&mumps->scat_sol);CHKERRQ(ierr); 840 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 841 ierr = ISDestroy(&is_petsc);CHKERRQ(ierr); 842 843 mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */ 844 } 845 846 ierr = VecScatterBegin(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 847 ierr = VecScatterEnd(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 848 } 849 850 if (mumps->petsc_size > 1) {if (!mumps->myid) {ierr = VecRestoreArray(b_seq,&array);CHKERRQ(ierr);}} 851 else {ierr = VecRestoreArray(x,&array);CHKERRQ(ierr);} 852 853 ierr = PetscLogFlops(2.0*mumps->id.RINFO(3));CHKERRQ(ierr); 854 PetscFunctionReturn(0); 855 } 856 857 PetscErrorCode MatSolveTranspose_MUMPS(Mat A,Vec b,Vec x) 858 { 859 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 860 PetscErrorCode ierr; 861 862 PetscFunctionBegin; 863 mumps->id.ICNTL(9) = 0; 864 ierr = MatSolve_MUMPS(A,b,x);CHKERRQ(ierr); 865 mumps->id.ICNTL(9) = 1; 866 PetscFunctionReturn(0); 867 } 868 869 PetscErrorCode MatMatSolve_MUMPS(Mat A,Mat B,Mat X) 870 { 871 PetscErrorCode ierr; 872 Mat Bt = NULL; 873 PetscBool flg, flgT; 874 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 875 PetscInt i,nrhs,M; 876 PetscScalar *array; 877 const PetscScalar *rbray; 878 PetscInt lsol_loc,nlsol_loc,*isol_loc,*idxx,*isol_loc_save,iidx = 0; 879 PetscScalar *bray,*sol_loc,*sol_loc_save; 880 IS is_to,is_from; 881 PetscInt k,proc,j,m,myrstart; 882 const PetscInt *rstart; 883 Vec v_mpi,b_seq,msol_loc; 884 VecScatter scat_rhs,scat_sol; 885 PetscScalar *aa; 886 PetscInt spnr,*ia,*ja; 887 Mat_MPIAIJ *b = NULL; 888 889 PetscFunctionBegin; 890 ierr = PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr); 891 if (!flg) SETERRQ(PetscObjectComm((PetscObject)X),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix"); 892 893 ierr = PetscObjectTypeCompareAny((PetscObject)B,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr); 894 if (flg) { /* dense B */ 895 if (B->rmap->n != X->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Matrix B and X must have same row distribution"); 896 mumps->id.ICNTL(20)= 0; /* dense RHS */ 897 } else { /* sparse B */ 898 ierr = PetscObjectTypeCompare((PetscObject)B,MATTRANSPOSEMAT,&flgT);CHKERRQ(ierr); 899 if (flgT) { /* input B is transpose of actural RHS matrix, 900 because mumps requires sparse compressed COLUMN storage! See MatMatTransposeSolve_MUMPS() */ 901 ierr = MatTransposeGetMat(B,&Bt);CHKERRQ(ierr); 902 } else SETERRQ(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_WRONG,"Matrix B must be MATTRANSPOSEMAT matrix"); 903 mumps->id.ICNTL(20)= 1; /* sparse RHS */ 904 } 905 906 ierr = MatGetSize(B,&M,&nrhs);CHKERRQ(ierr); 907 mumps->id.nrhs = nrhs; 908 mumps->id.lrhs = M; 909 mumps->id.rhs = NULL; 910 911 if (mumps->petsc_size == 1) { 912 PetscScalar *aa; 913 PetscInt spnr,*ia,*ja; 914 PetscBool second_solve = PETSC_FALSE; 915 916 ierr = MatDenseGetArray(X,&array);CHKERRQ(ierr); 917 mumps->id.rhs = (MumpsScalar*)array; 918 919 if (!Bt) { /* dense B */ 920 /* copy B to X */ 921 ierr = MatDenseGetArrayRead(B,&rbray);CHKERRQ(ierr); 922 ierr = PetscMemcpy(array,rbray,M*nrhs*sizeof(PetscScalar));CHKERRQ(ierr); 923 ierr = MatDenseRestoreArrayRead(B,&rbray);CHKERRQ(ierr); 924 } else { /* sparse B */ 925 ierr = MatSeqAIJGetArray(Bt,&aa);CHKERRQ(ierr); 926 ierr = MatGetRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 927 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 928 /* mumps requires ia and ja start at 1! */ 929 mumps->id.irhs_ptr = ia; 930 mumps->id.irhs_sparse = ja; 931 mumps->id.nz_rhs = ia[spnr] - 1; 932 mumps->id.rhs_sparse = (MumpsScalar*)aa; 933 } 934 /* handle condensation step of Schur complement (if any) */ 935 if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) { 936 second_solve = PETSC_TRUE; 937 ierr = MatMumpsHandleSchur_Private(A,PETSC_FALSE);CHKERRQ(ierr); 938 } 939 /* solve phase */ 940 /*-------------*/ 941 mumps->id.job = JOB_SOLVE; 942 PetscMUMPS_c(mumps); 943 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)); 944 945 /* handle expansion step of Schur complement (if any) */ 946 if (second_solve) { 947 ierr = MatMumpsHandleSchur_Private(A,PETSC_TRUE);CHKERRQ(ierr); 948 } 949 if (Bt) { /* sparse B */ 950 ierr = MatSeqAIJRestoreArray(Bt,&aa);CHKERRQ(ierr); 951 ierr = MatRestoreRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 952 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot restore IJ structure"); 953 } 954 ierr = MatDenseRestoreArray(X,&array);CHKERRQ(ierr); 955 PetscFunctionReturn(0); 956 } 957 958 /*--------- parallel case: MUMPS requires rhs B to be centralized on the host! --------*/ 959 if (mumps->petsc_size > 1 && mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Parallel Schur complements not yet supported from PETSc\n"); 960 961 /* create msol_loc to hold mumps local solution */ 962 isol_loc_save = mumps->id.isol_loc; /* save it for MatSolve() */ 963 sol_loc_save = (PetscScalar*)mumps->id.sol_loc; 964 965 lsol_loc = mumps->id.lsol_loc; 966 nlsol_loc = nrhs*lsol_loc; /* length of sol_loc */ 967 ierr = PetscMalloc2(nlsol_loc,&sol_loc,lsol_loc,&isol_loc);CHKERRQ(ierr); 968 mumps->id.sol_loc = (MumpsScalar*)sol_loc; 969 mumps->id.isol_loc = isol_loc; 970 971 ierr = VecCreateSeqWithArray(PETSC_COMM_SELF,1,nlsol_loc,(PetscScalar*)sol_loc,&msol_loc);CHKERRQ(ierr); 972 973 if (!Bt) { /* dense B */ 974 /* scatter v_mpi to b_seq because MUMPS only supports centralized rhs */ 975 /* wrap dense rhs matrix B into a vector v_mpi */ 976 ierr = MatGetLocalSize(B,&m,NULL);CHKERRQ(ierr); 977 ierr = MatDenseGetArray(B,&bray);CHKERRQ(ierr); 978 ierr = VecCreateMPIWithArray(PetscObjectComm((PetscObject)B),1,nrhs*m,nrhs*M,(const PetscScalar*)bray,&v_mpi);CHKERRQ(ierr); 979 ierr = MatDenseRestoreArray(B,&bray);CHKERRQ(ierr); 980 981 /* scatter v_mpi to b_seq in proc[0]. MUMPS requires rhs to be centralized on the host! */ 982 if (!mumps->myid) { 983 PetscInt *idx; 984 /* idx: maps from k-th index of v_mpi to (i,j)-th global entry of B */ 985 ierr = PetscMalloc1(nrhs*M,&idx);CHKERRQ(ierr); 986 ierr = MatGetOwnershipRanges(B,&rstart);CHKERRQ(ierr); 987 k = 0; 988 for (proc=0; proc<mumps->petsc_size; proc++){ 989 for (j=0; j<nrhs; j++){ 990 for (i=rstart[proc]; i<rstart[proc+1]; i++) idx[k++] = j*M + i; 991 } 992 } 993 994 ierr = VecCreateSeq(PETSC_COMM_SELF,nrhs*M,&b_seq);CHKERRQ(ierr); 995 ierr = ISCreateGeneral(PETSC_COMM_SELF,nrhs*M,idx,PETSC_OWN_POINTER,&is_to);CHKERRQ(ierr); 996 ierr = ISCreateStride(PETSC_COMM_SELF,nrhs*M,0,1,&is_from);CHKERRQ(ierr); 997 } else { 998 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&b_seq);CHKERRQ(ierr); 999 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_to);CHKERRQ(ierr); 1000 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_from);CHKERRQ(ierr); 1001 } 1002 ierr = VecScatterCreate(v_mpi,is_from,b_seq,is_to,&scat_rhs);CHKERRQ(ierr); 1003 ierr = VecScatterBegin(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1004 ierr = ISDestroy(&is_to);CHKERRQ(ierr); 1005 ierr = ISDestroy(&is_from);CHKERRQ(ierr); 1006 ierr = VecScatterEnd(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1007 1008 if (!mumps->myid) { /* define rhs on the host */ 1009 ierr = VecGetArray(b_seq,&bray);CHKERRQ(ierr); 1010 mumps->id.rhs = (MumpsScalar*)bray; 1011 ierr = VecRestoreArray(b_seq,&bray);CHKERRQ(ierr); 1012 } 1013 1014 } else { /* sparse B */ 1015 b = (Mat_MPIAIJ*)Bt->data; 1016 1017 /* wrap dense X into a vector v_mpi */ 1018 ierr = MatGetLocalSize(X,&m,NULL);CHKERRQ(ierr); 1019 ierr = MatDenseGetArray(X,&bray);CHKERRQ(ierr); 1020 ierr = VecCreateMPIWithArray(PetscObjectComm((PetscObject)X),1,nrhs*m,nrhs*M,(const PetscScalar*)bray,&v_mpi);CHKERRQ(ierr); 1021 ierr = MatDenseRestoreArray(X,&bray);CHKERRQ(ierr); 1022 1023 if (!mumps->myid) { 1024 ierr = MatSeqAIJGetArray(b->A,&aa);CHKERRQ(ierr); 1025 ierr = MatGetRowIJ(b->A,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 1026 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 1027 /* mumps requires ia and ja start at 1! */ 1028 mumps->id.irhs_ptr = ia; 1029 mumps->id.irhs_sparse = ja; 1030 mumps->id.nz_rhs = ia[spnr] - 1; 1031 mumps->id.rhs_sparse = (MumpsScalar*)aa; 1032 } else { 1033 mumps->id.irhs_ptr = NULL; 1034 mumps->id.irhs_sparse = NULL; 1035 mumps->id.nz_rhs = 0; 1036 mumps->id.rhs_sparse = NULL; 1037 } 1038 } 1039 1040 /* solve phase */ 1041 /*-------------*/ 1042 mumps->id.job = JOB_SOLVE; 1043 PetscMUMPS_c(mumps); 1044 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)); 1045 1046 /* scatter mumps distributed solution to petsc vector v_mpi, which shares local arrays with solution matrix X */ 1047 ierr = MatDenseGetArray(X,&array);CHKERRQ(ierr); 1048 ierr = VecPlaceArray(v_mpi,array);CHKERRQ(ierr); 1049 1050 /* create scatter scat_sol */ 1051 ierr = MatGetOwnershipRanges(X,&rstart);CHKERRQ(ierr); 1052 /* iidx: index for scatter mumps solution to petsc X */ 1053 1054 ierr = ISCreateStride(PETSC_COMM_SELF,nlsol_loc,0,1,&is_from);CHKERRQ(ierr); 1055 ierr = PetscMalloc1(nlsol_loc,&idxx);CHKERRQ(ierr); 1056 for (i=0; i<lsol_loc; i++) { 1057 isol_loc[i] -= 1; /* change Fortran style to C style. isol_loc[i+j*lsol_loc] contains x[isol_loc[i]] in j-th vector */ 1058 1059 for (proc=0; proc<mumps->petsc_size; proc++){ 1060 if (isol_loc[i] >= rstart[proc] && isol_loc[i] < rstart[proc+1]) { 1061 myrstart = rstart[proc]; 1062 k = isol_loc[i] - myrstart; /* local index on 1st column of petsc vector X */ 1063 iidx = k + myrstart*nrhs; /* maps mumps isol_loc[i] to petsc index in X */ 1064 m = rstart[proc+1] - rstart[proc]; /* rows of X for this proc */ 1065 break; 1066 } 1067 } 1068 1069 for (j=0; j<nrhs; j++) idxx[i+j*lsol_loc] = iidx + j*m; 1070 } 1071 ierr = ISCreateGeneral(PETSC_COMM_SELF,nlsol_loc,idxx,PETSC_COPY_VALUES,&is_to);CHKERRQ(ierr); 1072 ierr = VecScatterCreate(msol_loc,is_from,v_mpi,is_to,&scat_sol);CHKERRQ(ierr); 1073 ierr = VecScatterBegin(scat_sol,msol_loc,v_mpi,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1074 ierr = ISDestroy(&is_from);CHKERRQ(ierr); 1075 ierr = ISDestroy(&is_to);CHKERRQ(ierr); 1076 ierr = VecScatterEnd(scat_sol,msol_loc,v_mpi,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1077 ierr = MatDenseRestoreArray(X,&array);CHKERRQ(ierr); 1078 1079 /* free spaces */ 1080 mumps->id.sol_loc = (MumpsScalar*)sol_loc_save; 1081 mumps->id.isol_loc = isol_loc_save; 1082 1083 ierr = PetscFree2(sol_loc,isol_loc);CHKERRQ(ierr); 1084 ierr = PetscFree(idxx);CHKERRQ(ierr); 1085 ierr = VecDestroy(&msol_loc);CHKERRQ(ierr); 1086 ierr = VecDestroy(&v_mpi);CHKERRQ(ierr); 1087 if (Bt) { 1088 if (!mumps->myid) { 1089 b = (Mat_MPIAIJ*)Bt->data; 1090 ierr = MatSeqAIJRestoreArray(b->A,&aa);CHKERRQ(ierr); 1091 ierr = MatRestoreRowIJ(b->A,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 1092 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot restore IJ structure"); 1093 } 1094 } else { 1095 ierr = VecDestroy(&b_seq);CHKERRQ(ierr); 1096 ierr = VecScatterDestroy(&scat_rhs);CHKERRQ(ierr); 1097 } 1098 ierr = VecScatterDestroy(&scat_sol);CHKERRQ(ierr); 1099 ierr = PetscLogFlops(2.0*nrhs*mumps->id.RINFO(3));CHKERRQ(ierr); 1100 PetscFunctionReturn(0); 1101 } 1102 1103 PetscErrorCode MatMatTransposeSolve_MUMPS(Mat A,Mat Bt,Mat X) 1104 { 1105 PetscErrorCode ierr; 1106 PetscBool flg; 1107 Mat B; 1108 1109 PetscFunctionBegin; 1110 ierr = PetscObjectTypeCompareAny((PetscObject)Bt,&flg,MATSEQAIJ,MATMPIAIJ,NULL);CHKERRQ(ierr); 1111 if (!flg) SETERRQ(PetscObjectComm((PetscObject)Bt),PETSC_ERR_ARG_WRONG,"Matrix Bt must be MATAIJ matrix"); 1112 1113 /* Create B=Bt^T that uses Bt's data structure */ 1114 ierr = MatCreateTranspose(Bt,&B);CHKERRQ(ierr); 1115 1116 ierr = MatMatSolve_MUMPS(A,B,X);CHKERRQ(ierr); 1117 ierr = MatDestroy(&B);CHKERRQ(ierr); 1118 PetscFunctionReturn(0); 1119 } 1120 1121 #if !defined(PETSC_USE_COMPLEX) 1122 /* 1123 input: 1124 F: numeric factor 1125 output: 1126 nneg: total number of negative pivots 1127 nzero: total number of zero pivots 1128 npos: (global dimension of F) - nneg - nzero 1129 */ 1130 PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F,int *nneg,int *nzero,int *npos) 1131 { 1132 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1133 PetscErrorCode ierr; 1134 PetscMPIInt size; 1135 1136 PetscFunctionBegin; 1137 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)F),&size);CHKERRQ(ierr); 1138 /* 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 */ 1139 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)); 1140 1141 if (nneg) *nneg = mumps->id.INFOG(12); 1142 if (nzero || npos) { 1143 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"); 1144 if (nzero) *nzero = mumps->id.INFOG(28); 1145 if (npos) *npos = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28)); 1146 } 1147 PetscFunctionReturn(0); 1148 } 1149 #endif 1150 1151 PetscErrorCode MatMumpsGatherNonzerosOnMaster(MatReuse reuse,Mat_MUMPS *mumps) 1152 { 1153 PetscErrorCode ierr; 1154 PetscInt i,nz=0,*irn,*jcn=0; 1155 PetscScalar *val=0; 1156 PetscMPIInt mpinz,*recvcount=NULL,*displs=NULL; 1157 1158 PetscFunctionBegin; 1159 if (mumps->omp_comm_size > 1) { 1160 if (reuse == MAT_INITIAL_MATRIX) { 1161 /* master first gathers counts of nonzeros to receive */ 1162 if (mumps->is_omp_master) { ierr = PetscMalloc2(mumps->omp_comm_size,&recvcount,mumps->omp_comm_size,&displs);CHKERRQ(ierr); } 1163 ierr = PetscMPIIntCast(mumps->nz,&mpinz);CHKERRQ(ierr); 1164 ierr = MPI_Gather(&mpinz,1,MPI_INT,recvcount,1,MPI_INT,0/*root*/,mumps->omp_comm);CHKERRQ(ierr); 1165 1166 /* master allocates memory to receive nonzeros */ 1167 if (mumps->is_omp_master) { 1168 displs[0] = 0; 1169 for (i=1; i<mumps->omp_comm_size; i++) displs[i] = displs[i-1] + recvcount[i-1]; 1170 nz = displs[mumps->omp_comm_size-1] + recvcount[mumps->omp_comm_size-1]; 1171 ierr = PetscMalloc(2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar),&irn);CHKERRQ(ierr); 1172 jcn = irn + nz; 1173 val = (PetscScalar*)(jcn + nz); 1174 } 1175 1176 /* save the gatherv plan */ 1177 mumps->mpinz = mpinz; /* used as send count */ 1178 mumps->recvcount = recvcount; 1179 mumps->displs = displs; 1180 1181 /* master gathers nonzeros */ 1182 ierr = MPI_Gatherv(mumps->irn,mpinz,MPIU_INT,irn,mumps->recvcount,mumps->displs,MPIU_INT,0/*root*/,mumps->omp_comm);CHKERRQ(ierr); 1183 ierr = MPI_Gatherv(mumps->jcn,mpinz,MPIU_INT,jcn,mumps->recvcount,mumps->displs,MPIU_INT,0/*root*/,mumps->omp_comm);CHKERRQ(ierr); 1184 ierr = MPI_Gatherv(mumps->val,mpinz,MPIU_SCALAR,val,mumps->recvcount,mumps->displs,MPIU_SCALAR,0/*root*/,mumps->omp_comm);CHKERRQ(ierr); 1185 1186 /* master frees its row/col/val and replaces them with bigger arrays */ 1187 if (mumps->is_omp_master) { 1188 ierr = PetscFree(mumps->irn);CHKERRQ(ierr); /* irn/jcn/val are allocated together so free only irn */ 1189 mumps->nz = nz; /* it is a sum of mpinz over omp_comm */ 1190 mumps->irn = irn; 1191 mumps->jcn = jcn; 1192 mumps->val = val; 1193 } 1194 } else { 1195 ierr = MPI_Gatherv((mumps->is_omp_master?MPI_IN_PLACE:mumps->val),mumps->mpinz,MPIU_SCALAR,mumps->val,mumps->recvcount,mumps->displs,MPIU_SCALAR,0/*root*/,mumps->omp_comm);CHKERRQ(ierr); 1196 } 1197 } 1198 PetscFunctionReturn(0); 1199 } 1200 1201 PetscErrorCode MatFactorNumeric_MUMPS(Mat F,Mat A,const MatFactorInfo *info) 1202 { 1203 Mat_MUMPS *mumps =(Mat_MUMPS*)(F)->data; 1204 PetscErrorCode ierr; 1205 PetscBool isMPIAIJ; 1206 1207 PetscFunctionBegin; 1208 if (mumps->id.INFOG(1) < 0) { 1209 if (mumps->id.INFOG(1) == -6) { 1210 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); 1211 } 1212 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); 1213 PetscFunctionReturn(0); 1214 } 1215 1216 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1217 ierr = MatMumpsGatherNonzerosOnMaster(MAT_REUSE_MATRIX,mumps);CHKERRQ(ierr); 1218 1219 /* numerical factorization phase */ 1220 /*-------------------------------*/ 1221 mumps->id.job = JOB_FACTNUMERIC; 1222 if (!mumps->id.ICNTL(18)) { /* A is centralized */ 1223 if (!mumps->myid) { 1224 mumps->id.a = (MumpsScalar*)mumps->val; 1225 } 1226 } else { 1227 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1228 } 1229 PetscMUMPS_c(mumps); 1230 if (mumps->id.INFOG(1) < 0) { 1231 if (A->erroriffailure) { 1232 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)); 1233 } else { 1234 if (mumps->id.INFOG(1) == -10) { /* numerically singular matrix */ 1235 ierr = PetscInfo2(F,"matrix is numerically singular, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1236 F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1237 } else if (mumps->id.INFOG(1) == -13) { 1238 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); 1239 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1240 } else if (mumps->id.INFOG(1) == -8 || mumps->id.INFOG(1) == -9 || (-16 < mumps->id.INFOG(1) && mumps->id.INFOG(1) < -10) ) { 1241 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); 1242 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1243 } else { 1244 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); 1245 F->factorerrortype = MAT_FACTOR_OTHER; 1246 } 1247 } 1248 } 1249 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)); 1250 1251 F->assembled = PETSC_TRUE; 1252 mumps->matstruc = SAME_NONZERO_PATTERN; 1253 if (F->schur) { /* reset Schur status to unfactored */ 1254 if (mumps->id.ICNTL(19) == 1) { /* stored by rows */ 1255 mumps->id.ICNTL(19) = 2; 1256 ierr = MatTranspose(F->schur,MAT_INPLACE_MATRIX,&F->schur);CHKERRQ(ierr); 1257 } 1258 ierr = MatFactorRestoreSchurComplement(F,NULL,MAT_FACTOR_SCHUR_UNFACTORED);CHKERRQ(ierr); 1259 } 1260 1261 /* just to be sure that ICNTL(19) value returned by a call from MatMumpsGetIcntl is always consistent */ 1262 if (!mumps->sym && mumps->id.ICNTL(19) && mumps->id.ICNTL(19) != 1) mumps->id.ICNTL(19) = 3; 1263 1264 if (!mumps->is_omp_master) mumps->id.INFO(23) = 0; 1265 if (mumps->petsc_size > 1) { 1266 PetscInt lsol_loc; 1267 PetscScalar *sol_loc; 1268 1269 ierr = PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&isMPIAIJ);CHKERRQ(ierr); 1270 1271 /* distributed solution; Create x_seq=sol_loc for repeated use */ 1272 if (mumps->x_seq) { 1273 ierr = VecScatterDestroy(&mumps->scat_sol);CHKERRQ(ierr); 1274 ierr = PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc);CHKERRQ(ierr); 1275 ierr = VecDestroy(&mumps->x_seq);CHKERRQ(ierr); 1276 } 1277 lsol_loc = mumps->id.INFO(23); /* length of sol_loc */ 1278 ierr = PetscMalloc2(lsol_loc,&sol_loc,lsol_loc,&mumps->id.isol_loc);CHKERRQ(ierr); 1279 mumps->id.lsol_loc = lsol_loc; 1280 mumps->id.sol_loc = (MumpsScalar*)sol_loc; 1281 ierr = VecCreateSeqWithArray(PETSC_COMM_SELF,1,lsol_loc,sol_loc,&mumps->x_seq);CHKERRQ(ierr); 1282 } 1283 ierr = PetscLogFlops(mumps->id.RINFO(2));CHKERRQ(ierr); 1284 PetscFunctionReturn(0); 1285 } 1286 1287 /* Sets MUMPS options from the options database */ 1288 PetscErrorCode PetscSetMUMPSFromOptions(Mat F, Mat A) 1289 { 1290 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1291 PetscErrorCode ierr; 1292 PetscInt icntl,info[40],i,ninfo=40; 1293 PetscBool flg; 1294 1295 PetscFunctionBegin; 1296 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"MUMPS Options","Mat");CHKERRQ(ierr); 1297 ierr = PetscOptionsInt("-mat_mumps_icntl_1","ICNTL(1): output stream for error messages","None",mumps->id.ICNTL(1),&icntl,&flg);CHKERRQ(ierr); 1298 if (flg) mumps->id.ICNTL(1) = icntl; 1299 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); 1300 if (flg) mumps->id.ICNTL(2) = icntl; 1301 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); 1302 if (flg) mumps->id.ICNTL(3) = icntl; 1303 1304 ierr = PetscOptionsInt("-mat_mumps_icntl_4","ICNTL(4): level of printing (0 to 4)","None",mumps->id.ICNTL(4),&icntl,&flg);CHKERRQ(ierr); 1305 if (flg) mumps->id.ICNTL(4) = icntl; 1306 if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */ 1307 1308 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); 1309 if (flg) mumps->id.ICNTL(6) = icntl; 1310 1311 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); 1312 if (flg) { 1313 if (icntl== 1 && mumps->petsc_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"); 1314 else mumps->id.ICNTL(7) = icntl; 1315 } 1316 1317 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); 1318 /* 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() */ 1319 ierr = PetscOptionsInt("-mat_mumps_icntl_10","ICNTL(10): max num of refinements","None",mumps->id.ICNTL(10),&mumps->id.ICNTL(10),NULL);CHKERRQ(ierr); 1320 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); 1321 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); 1322 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); 1323 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); 1324 ierr = PetscOptionsInt("-mat_mumps_icntl_19","ICNTL(19): computes the Schur complement","None",mumps->id.ICNTL(19),&mumps->id.ICNTL(19),NULL);CHKERRQ(ierr); 1325 if (mumps->id.ICNTL(19) <= 0 || mumps->id.ICNTL(19) > 3) { /* reset any schur data (if any) */ 1326 ierr = MatDestroy(&F->schur);CHKERRQ(ierr); 1327 ierr = MatMumpsResetSchur_Private(mumps);CHKERRQ(ierr); 1328 } 1329 /* 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 */ 1330 /* 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 */ 1331 1332 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); 1333 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); 1334 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); 1335 if (mumps->id.ICNTL(24)) { 1336 mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */ 1337 } 1338 1339 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); 1340 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); 1341 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); 1342 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); 1343 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); 1344 /* 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 */ 1345 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); 1346 /* 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 */ 1347 ierr = PetscOptionsInt("-mat_mumps_icntl_33","ICNTL(33): compute determinant","None",mumps->id.ICNTL(33),&mumps->id.ICNTL(33),NULL);CHKERRQ(ierr); 1348 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); 1349 1350 ierr = PetscOptionsReal("-mat_mumps_cntl_1","CNTL(1): relative pivoting threshold","None",mumps->id.CNTL(1),&mumps->id.CNTL(1),NULL);CHKERRQ(ierr); 1351 ierr = PetscOptionsReal("-mat_mumps_cntl_2","CNTL(2): stopping criterion of refinement","None",mumps->id.CNTL(2),&mumps->id.CNTL(2),NULL);CHKERRQ(ierr); 1352 ierr = PetscOptionsReal("-mat_mumps_cntl_3","CNTL(3): absolute pivoting threshold","None",mumps->id.CNTL(3),&mumps->id.CNTL(3),NULL);CHKERRQ(ierr); 1353 ierr = PetscOptionsReal("-mat_mumps_cntl_4","CNTL(4): value for static pivoting","None",mumps->id.CNTL(4),&mumps->id.CNTL(4),NULL);CHKERRQ(ierr); 1354 ierr = PetscOptionsReal("-mat_mumps_cntl_5","CNTL(5): fixation for null pivots","None",mumps->id.CNTL(5),&mumps->id.CNTL(5),NULL);CHKERRQ(ierr); 1355 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); 1356 1357 ierr = PetscOptionsString("-mat_mumps_ooc_tmpdir", "out of core directory", "None", mumps->id.ooc_tmpdir, mumps->id.ooc_tmpdir, 256, NULL);CHKERRQ(ierr); 1358 1359 ierr = PetscOptionsIntArray("-mat_mumps_view_info","request INFO local to each processor","",info,&ninfo,NULL);CHKERRQ(ierr); 1360 if (ninfo) { 1361 if (ninfo > 40) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_USER,"number of INFO %d must <= 40\n",ninfo); 1362 ierr = PetscMalloc1(ninfo,&mumps->info);CHKERRQ(ierr); 1363 mumps->ninfo = ninfo; 1364 for (i=0; i<ninfo; i++) { 1365 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); 1366 else mumps->info[i] = info[i]; 1367 } 1368 } 1369 1370 ierr = PetscOptionsEnd();CHKERRQ(ierr); 1371 PetscFunctionReturn(0); 1372 } 1373 1374 PetscErrorCode PetscInitializeMUMPS(Mat A,Mat_MUMPS *mumps) 1375 { 1376 PetscErrorCode ierr; 1377 PetscInt nthreads=0; 1378 1379 PetscFunctionBegin; 1380 mumps->petsc_comm = PetscObjectComm((PetscObject)A); 1381 ierr = MPI_Comm_size(mumps->petsc_comm,&mumps->petsc_size);CHKERRQ(ierr); 1382 ierr = MPI_Comm_rank(mumps->petsc_comm,&mumps->myid);CHKERRQ(ierr); /* so that code like "if (!myid)" still works even if mumps_comm is different */ 1383 1384 ierr = PetscOptionsHasName(NULL,NULL,"-mat_mumps_use_omp_threads",&mumps->use_petsc_omp_support);CHKERRQ(ierr); 1385 if (mumps->use_petsc_omp_support) nthreads = -1; /* -1 will let PetscOmpCtrlCreate() guess a proper value when user did not supply one */ 1386 ierr = PetscOptionsGetInt(NULL,NULL,"-mat_mumps_use_omp_threads",&nthreads,NULL);CHKERRQ(ierr); 1387 if (mumps->use_petsc_omp_support) { 1388 #if defined(PETSC_HAVE_OPENMP_SUPPORT) 1389 ierr = PetscOmpCtrlCreate(mumps->petsc_comm,nthreads,&mumps->omp_ctrl);CHKERRQ(ierr); 1390 ierr = PetscOmpCtrlGetOmpComms(mumps->omp_ctrl,&mumps->omp_comm,&mumps->mumps_comm,&mumps->is_omp_master);CHKERRQ(ierr); 1391 #else 1392 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP_SYS,"the system does not have PETSc OpenMP support but you added the -mat_mumps_use_omp_threads option. Configure PETSc with --with-openmp --download-hwloc (or --with-hwloc) to enable it, see more in MATSOLVERMUMPS manual\n"); 1393 #endif 1394 } else { 1395 mumps->omp_comm = PETSC_COMM_SELF; 1396 mumps->mumps_comm = mumps->petsc_comm; 1397 mumps->is_omp_master = PETSC_TRUE; 1398 } 1399 ierr = MPI_Comm_size(mumps->omp_comm,&mumps->omp_comm_size);CHKERRQ(ierr); 1400 1401 mumps->id.comm_fortran = MPI_Comm_c2f(mumps->mumps_comm); 1402 mumps->id.job = JOB_INIT; 1403 mumps->id.par = 1; /* host participates factorizaton and solve */ 1404 mumps->id.sym = mumps->sym; 1405 1406 PetscMUMPS_c(mumps); 1407 1408 /* copy MUMPS default control values from master to slaves. Although slaves do not call MUMPS, they may access these values in code. 1409 For example, ICNTL(9) is initialized to 1 by MUMPS and slaves check ICNTL(9) in MatSolve_MUMPS. 1410 */ 1411 ierr = MPI_Bcast(mumps->id.icntl,40,MPIU_INT, 0,mumps->omp_comm);CHKERRQ(ierr); /* see MUMPS-5.1.2 Manual Section 9 */ 1412 ierr = MPI_Bcast(mumps->id.cntl, 15,MPIU_REAL,0,mumps->omp_comm);CHKERRQ(ierr); 1413 1414 mumps->scat_rhs = NULL; 1415 mumps->scat_sol = NULL; 1416 1417 /* set PETSc-MUMPS default options - override MUMPS default */ 1418 mumps->id.ICNTL(3) = 0; 1419 mumps->id.ICNTL(4) = 0; 1420 if (mumps->petsc_size == 1) { 1421 mumps->id.ICNTL(18) = 0; /* centralized assembled matrix input */ 1422 } else { 1423 mumps->id.ICNTL(18) = 3; /* distributed assembled matrix input */ 1424 mumps->id.ICNTL(20) = 0; /* rhs is in dense format */ 1425 mumps->id.ICNTL(21) = 1; /* distributed solution */ 1426 } 1427 1428 /* schur */ 1429 mumps->id.size_schur = 0; 1430 mumps->id.listvar_schur = NULL; 1431 mumps->id.schur = NULL; 1432 mumps->sizeredrhs = 0; 1433 mumps->schur_sol = NULL; 1434 mumps->schur_sizesol = 0; 1435 PetscFunctionReturn(0); 1436 } 1437 1438 PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F,Mat A,const MatFactorInfo *info,Mat_MUMPS *mumps) 1439 { 1440 PetscErrorCode ierr; 1441 1442 PetscFunctionBegin; 1443 ierr = MPI_Bcast(mumps->id.infog, 40,MPIU_INT, 0,mumps->omp_comm);CHKERRQ(ierr); /* see MUMPS-5.1.2 manual p82 */ 1444 ierr = MPI_Bcast(mumps->id.rinfog,20,MPIU_REAL,0,mumps->omp_comm);CHKERRQ(ierr); 1445 if (mumps->id.INFOG(1) < 0) { 1446 if (A->erroriffailure) { 1447 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d\n",mumps->id.INFOG(1)); 1448 } else { 1449 if (mumps->id.INFOG(1) == -6) { 1450 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); 1451 F->factorerrortype = MAT_FACTOR_STRUCT_ZEROPIVOT; 1452 } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) { 1453 ierr = PetscInfo2(F,"problem of workspace, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1454 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1455 } else { 1456 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); 1457 F->factorerrortype = MAT_FACTOR_OTHER; 1458 } 1459 } 1460 } 1461 PetscFunctionReturn(0); 1462 } 1463 1464 /* Note Petsc r(=c) permutation is used when mumps->id.ICNTL(7)==1 with centralized assembled matrix input; otherwise r and c are ignored */ 1465 PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 1466 { 1467 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1468 PetscErrorCode ierr; 1469 Vec b; 1470 IS is_iden; 1471 const PetscInt M = A->rmap->N; 1472 1473 PetscFunctionBegin; 1474 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 1475 1476 /* Set MUMPS options from the options database */ 1477 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1478 1479 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1480 ierr = MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX,mumps);CHKERRQ(ierr); 1481 1482 /* analysis phase */ 1483 /*----------------*/ 1484 mumps->id.job = JOB_FACTSYMBOLIC; 1485 mumps->id.n = M; 1486 switch (mumps->id.ICNTL(18)) { 1487 case 0: /* centralized assembled matrix input */ 1488 if (!mumps->myid) { 1489 mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn; 1490 if (mumps->id.ICNTL(6)>1) { 1491 mumps->id.a = (MumpsScalar*)mumps->val; 1492 } 1493 if (mumps->id.ICNTL(7) == 1) { /* use user-provide matrix ordering - assuming r = c ordering */ 1494 /* 1495 PetscBool flag; 1496 ierr = ISEqual(r,c,&flag);CHKERRQ(ierr); 1497 if (!flag) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_USER,"row_perm != col_perm"); 1498 ierr = ISView(r,PETSC_VIEWER_STDOUT_SELF); 1499 */ 1500 if (!mumps->myid) { 1501 const PetscInt *idx; 1502 PetscInt i,*perm_in; 1503 1504 ierr = PetscMalloc1(M,&perm_in);CHKERRQ(ierr); 1505 ierr = ISGetIndices(r,&idx);CHKERRQ(ierr); 1506 1507 mumps->id.perm_in = perm_in; 1508 for (i=0; i<M; i++) perm_in[i] = idx[i]+1; /* perm_in[]: start from 1, not 0! */ 1509 ierr = ISRestoreIndices(r,&idx);CHKERRQ(ierr); 1510 } 1511 } 1512 } 1513 break; 1514 case 3: /* distributed assembled matrix input (size>1) */ 1515 mumps->id.nz_loc = mumps->nz; 1516 mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn; 1517 if (mumps->id.ICNTL(6)>1) { 1518 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1519 } 1520 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1521 if (!mumps->myid) { 1522 ierr = VecCreateSeq(PETSC_COMM_SELF,A->rmap->N,&mumps->b_seq);CHKERRQ(ierr); 1523 ierr = ISCreateStride(PETSC_COMM_SELF,A->rmap->N,0,1,&is_iden);CHKERRQ(ierr); 1524 } else { 1525 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);CHKERRQ(ierr); 1526 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1527 } 1528 ierr = MatCreateVecs(A,NULL,&b);CHKERRQ(ierr); 1529 ierr = VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);CHKERRQ(ierr); 1530 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1531 ierr = VecDestroy(&b);CHKERRQ(ierr); 1532 break; 1533 } 1534 PetscMUMPS_c(mumps); 1535 ierr = MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);CHKERRQ(ierr); 1536 1537 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 1538 F->ops->solve = MatSolve_MUMPS; 1539 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 1540 F->ops->matsolve = MatMatSolve_MUMPS; 1541 F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS; 1542 PetscFunctionReturn(0); 1543 } 1544 1545 /* Note the Petsc r and c permutations are ignored */ 1546 PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 1547 { 1548 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1549 PetscErrorCode ierr; 1550 Vec b; 1551 IS is_iden; 1552 const PetscInt M = A->rmap->N; 1553 1554 PetscFunctionBegin; 1555 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 1556 1557 /* Set MUMPS options from the options database */ 1558 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1559 1560 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1561 ierr = MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX,mumps);CHKERRQ(ierr); 1562 1563 /* analysis phase */ 1564 /*----------------*/ 1565 mumps->id.job = JOB_FACTSYMBOLIC; 1566 mumps->id.n = M; 1567 switch (mumps->id.ICNTL(18)) { 1568 case 0: /* centralized assembled matrix input */ 1569 if (!mumps->myid) { 1570 mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn; 1571 if (mumps->id.ICNTL(6)>1) { 1572 mumps->id.a = (MumpsScalar*)mumps->val; 1573 } 1574 } 1575 break; 1576 case 3: /* distributed assembled matrix input (size>1) */ 1577 mumps->id.nz_loc = mumps->nz; 1578 mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn; 1579 if (mumps->id.ICNTL(6)>1) { 1580 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1581 } 1582 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1583 if (!mumps->myid) { 1584 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);CHKERRQ(ierr); 1585 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 1586 } else { 1587 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);CHKERRQ(ierr); 1588 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1589 } 1590 ierr = MatCreateVecs(A,NULL,&b);CHKERRQ(ierr); 1591 ierr = VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);CHKERRQ(ierr); 1592 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1593 ierr = VecDestroy(&b);CHKERRQ(ierr); 1594 break; 1595 } 1596 PetscMUMPS_c(mumps); 1597 ierr = MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);CHKERRQ(ierr); 1598 1599 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 1600 F->ops->solve = MatSolve_MUMPS; 1601 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 1602 PetscFunctionReturn(0); 1603 } 1604 1605 /* Note the Petsc r permutation and factor info are ignored */ 1606 PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F,Mat A,IS r,const MatFactorInfo *info) 1607 { 1608 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1609 PetscErrorCode ierr; 1610 Vec b; 1611 IS is_iden; 1612 const PetscInt M = A->rmap->N; 1613 1614 PetscFunctionBegin; 1615 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 1616 1617 /* Set MUMPS options from the options database */ 1618 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1619 1620 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1621 ierr = MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX,mumps);CHKERRQ(ierr); 1622 1623 /* analysis phase */ 1624 /*----------------*/ 1625 mumps->id.job = JOB_FACTSYMBOLIC; 1626 mumps->id.n = M; 1627 switch (mumps->id.ICNTL(18)) { 1628 case 0: /* centralized assembled matrix input */ 1629 if (!mumps->myid) { 1630 mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn; 1631 if (mumps->id.ICNTL(6)>1) { 1632 mumps->id.a = (MumpsScalar*)mumps->val; 1633 } 1634 } 1635 break; 1636 case 3: /* distributed assembled matrix input (size>1) */ 1637 mumps->id.nz_loc = mumps->nz; 1638 mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn; 1639 if (mumps->id.ICNTL(6)>1) { 1640 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1641 } 1642 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1643 if (!mumps->myid) { 1644 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);CHKERRQ(ierr); 1645 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 1646 } else { 1647 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);CHKERRQ(ierr); 1648 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1649 } 1650 ierr = MatCreateVecs(A,NULL,&b);CHKERRQ(ierr); 1651 ierr = VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);CHKERRQ(ierr); 1652 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1653 ierr = VecDestroy(&b);CHKERRQ(ierr); 1654 break; 1655 } 1656 PetscMUMPS_c(mumps); 1657 ierr = MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);CHKERRQ(ierr); 1658 1659 F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS; 1660 F->ops->solve = MatSolve_MUMPS; 1661 F->ops->solvetranspose = MatSolve_MUMPS; 1662 F->ops->matsolve = MatMatSolve_MUMPS; 1663 F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS; 1664 #if defined(PETSC_USE_COMPLEX) 1665 F->ops->getinertia = NULL; 1666 #else 1667 F->ops->getinertia = MatGetInertia_SBAIJMUMPS; 1668 #endif 1669 PetscFunctionReturn(0); 1670 } 1671 1672 PetscErrorCode MatView_MUMPS(Mat A,PetscViewer viewer) 1673 { 1674 PetscErrorCode ierr; 1675 PetscBool iascii; 1676 PetscViewerFormat format; 1677 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 1678 1679 PetscFunctionBegin; 1680 /* check if matrix is mumps type */ 1681 if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(0); 1682 1683 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 1684 if (iascii) { 1685 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 1686 if (format == PETSC_VIEWER_ASCII_INFO) { 1687 ierr = PetscViewerASCIIPrintf(viewer,"MUMPS run parameters:\n");CHKERRQ(ierr); 1688 ierr = PetscViewerASCIIPrintf(viewer," SYM (matrix type): %d \n",mumps->id.sym);CHKERRQ(ierr); 1689 ierr = PetscViewerASCIIPrintf(viewer," PAR (host participation): %d \n",mumps->id.par);CHKERRQ(ierr); 1690 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(1) (output for error): %d \n",mumps->id.ICNTL(1));CHKERRQ(ierr); 1691 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(2) (output of diagnostic msg): %d \n",mumps->id.ICNTL(2));CHKERRQ(ierr); 1692 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(3) (output for global info): %d \n",mumps->id.ICNTL(3));CHKERRQ(ierr); 1693 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(4) (level of printing): %d \n",mumps->id.ICNTL(4));CHKERRQ(ierr); 1694 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(5) (input mat struct): %d \n",mumps->id.ICNTL(5));CHKERRQ(ierr); 1695 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(6) (matrix prescaling): %d \n",mumps->id.ICNTL(6));CHKERRQ(ierr); 1696 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(7) (sequential matrix ordering):%d \n",mumps->id.ICNTL(7));CHKERRQ(ierr); 1697 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(8) (scaling strategy): %d \n",mumps->id.ICNTL(8));CHKERRQ(ierr); 1698 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(10) (max num of refinements): %d \n",mumps->id.ICNTL(10));CHKERRQ(ierr); 1699 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(11) (error analysis): %d \n",mumps->id.ICNTL(11));CHKERRQ(ierr); 1700 if (mumps->id.ICNTL(11)>0) { 1701 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(4) (inf norm of input mat): %g\n",mumps->id.RINFOG(4));CHKERRQ(ierr); 1702 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(5) (inf norm of solution): %g\n",mumps->id.RINFOG(5));CHKERRQ(ierr); 1703 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(6) (inf norm of residual): %g\n",mumps->id.RINFOG(6));CHKERRQ(ierr); 1704 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(7),RINFOG(8) (backward error est): %g, %g\n",mumps->id.RINFOG(7),mumps->id.RINFOG(8));CHKERRQ(ierr); 1705 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(9) (error estimate): %g \n",mumps->id.RINFOG(9));CHKERRQ(ierr); 1706 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n",mumps->id.RINFOG(10),mumps->id.RINFOG(11));CHKERRQ(ierr); 1707 } 1708 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(12) (efficiency control): %d \n",mumps->id.ICNTL(12));CHKERRQ(ierr); 1709 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(13) (efficiency control): %d \n",mumps->id.ICNTL(13));CHKERRQ(ierr); 1710 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(14) (percentage of estimated workspace increase): %d \n",mumps->id.ICNTL(14));CHKERRQ(ierr); 1711 /* ICNTL(15-17) not used */ 1712 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(18) (input mat struct): %d \n",mumps->id.ICNTL(18));CHKERRQ(ierr); 1713 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(19) (Schur complement info): %d \n",mumps->id.ICNTL(19));CHKERRQ(ierr); 1714 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(20) (rhs sparse pattern): %d \n",mumps->id.ICNTL(20));CHKERRQ(ierr); 1715 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(21) (solution struct): %d \n",mumps->id.ICNTL(21));CHKERRQ(ierr); 1716 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(22) (in-core/out-of-core facility): %d \n",mumps->id.ICNTL(22));CHKERRQ(ierr); 1717 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(23) (max size of memory can be allocated locally):%d \n",mumps->id.ICNTL(23));CHKERRQ(ierr); 1718 1719 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(24) (detection of null pivot rows): %d \n",mumps->id.ICNTL(24));CHKERRQ(ierr); 1720 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(25) (computation of a null space basis): %d \n",mumps->id.ICNTL(25));CHKERRQ(ierr); 1721 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(26) (Schur options for rhs or solution): %d \n",mumps->id.ICNTL(26));CHKERRQ(ierr); 1722 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(27) (experimental parameter): %d \n",mumps->id.ICNTL(27));CHKERRQ(ierr); 1723 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(28) (use parallel or sequential ordering): %d \n",mumps->id.ICNTL(28));CHKERRQ(ierr); 1724 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(29) (parallel ordering): %d \n",mumps->id.ICNTL(29));CHKERRQ(ierr); 1725 1726 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(30) (user-specified set of entries in inv(A)): %d \n",mumps->id.ICNTL(30));CHKERRQ(ierr); 1727 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(31) (factors is discarded in the solve phase): %d \n",mumps->id.ICNTL(31));CHKERRQ(ierr); 1728 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(33) (compute determinant): %d \n",mumps->id.ICNTL(33));CHKERRQ(ierr); 1729 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(35) (activate BLR based factorization): %d \n",mumps->id.ICNTL(35));CHKERRQ(ierr); 1730 1731 ierr = PetscViewerASCIIPrintf(viewer," CNTL(1) (relative pivoting threshold): %g \n",mumps->id.CNTL(1));CHKERRQ(ierr); 1732 ierr = PetscViewerASCIIPrintf(viewer," CNTL(2) (stopping criterion of refinement): %g \n",mumps->id.CNTL(2));CHKERRQ(ierr); 1733 ierr = PetscViewerASCIIPrintf(viewer," CNTL(3) (absolute pivoting threshold): %g \n",mumps->id.CNTL(3));CHKERRQ(ierr); 1734 ierr = PetscViewerASCIIPrintf(viewer," CNTL(4) (value of static pivoting): %g \n",mumps->id.CNTL(4));CHKERRQ(ierr); 1735 ierr = PetscViewerASCIIPrintf(viewer," CNTL(5) (fixation for null pivots): %g \n",mumps->id.CNTL(5));CHKERRQ(ierr); 1736 ierr = PetscViewerASCIIPrintf(viewer," CNTL(7) (dropping parameter for BLR): %g \n",mumps->id.CNTL(7));CHKERRQ(ierr); 1737 1738 /* infomation local to each processor */ 1739 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(1) (local estimated flops for the elimination after analysis): \n");CHKERRQ(ierr); 1740 ierr = PetscViewerASCIIPushSynchronized(viewer);CHKERRQ(ierr); 1741 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(1));CHKERRQ(ierr); 1742 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1743 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(2) (local estimated flops for the assembly after factorization): \n");CHKERRQ(ierr); 1744 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(2));CHKERRQ(ierr); 1745 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1746 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(3) (local estimated flops for the elimination after factorization): \n");CHKERRQ(ierr); 1747 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(3));CHKERRQ(ierr); 1748 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1749 1750 ierr = PetscViewerASCIIPrintf(viewer, " INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization): \n");CHKERRQ(ierr); 1751 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(15));CHKERRQ(ierr); 1752 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1753 1754 ierr = PetscViewerASCIIPrintf(viewer, " INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization): \n");CHKERRQ(ierr); 1755 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(16));CHKERRQ(ierr); 1756 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1757 1758 ierr = PetscViewerASCIIPrintf(viewer, " INFO(23) (num of pivots eliminated on this processor after factorization): \n");CHKERRQ(ierr); 1759 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(23));CHKERRQ(ierr); 1760 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1761 1762 if (mumps->ninfo && mumps->ninfo <= 40){ 1763 PetscInt i; 1764 for (i=0; i<mumps->ninfo; i++){ 1765 ierr = PetscViewerASCIIPrintf(viewer, " INFO(%d): \n",mumps->info[i]);CHKERRQ(ierr); 1766 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(mumps->info[i]));CHKERRQ(ierr); 1767 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1768 } 1769 } 1770 ierr = PetscViewerASCIIPopSynchronized(viewer);CHKERRQ(ierr); 1771 1772 if (!mumps->myid) { /* information from the host */ 1773 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(1) (global estimated flops for the elimination after analysis): %g \n",mumps->id.RINFOG(1));CHKERRQ(ierr); 1774 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(2) (global estimated flops for the assembly after factorization): %g \n",mumps->id.RINFOG(2));CHKERRQ(ierr); 1775 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(3) (global estimated flops for the elimination after factorization): %g \n",mumps->id.RINFOG(3));CHKERRQ(ierr); 1776 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); 1777 1778 ierr = PetscViewerASCIIPrintf(viewer," INFOG(3) (estimated real workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(3));CHKERRQ(ierr); 1779 ierr = PetscViewerASCIIPrintf(viewer," INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(4));CHKERRQ(ierr); 1780 ierr = PetscViewerASCIIPrintf(viewer," INFOG(5) (estimated maximum front size in the complete tree): %d \n",mumps->id.INFOG(5));CHKERRQ(ierr); 1781 ierr = PetscViewerASCIIPrintf(viewer," INFOG(6) (number of nodes in the complete tree): %d \n",mumps->id.INFOG(6));CHKERRQ(ierr); 1782 ierr = PetscViewerASCIIPrintf(viewer," INFOG(7) (ordering option effectively use after analysis): %d \n",mumps->id.INFOG(7));CHKERRQ(ierr); 1783 ierr = PetscViewerASCIIPrintf(viewer," INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d \n",mumps->id.INFOG(8));CHKERRQ(ierr); 1784 ierr = PetscViewerASCIIPrintf(viewer," INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d \n",mumps->id.INFOG(9));CHKERRQ(ierr); 1785 ierr = PetscViewerASCIIPrintf(viewer," INFOG(10) (total integer space store the matrix factors after factorization): %d \n",mumps->id.INFOG(10));CHKERRQ(ierr); 1786 ierr = PetscViewerASCIIPrintf(viewer," INFOG(11) (order of largest frontal matrix after factorization): %d \n",mumps->id.INFOG(11));CHKERRQ(ierr); 1787 ierr = PetscViewerASCIIPrintf(viewer," INFOG(12) (number of off-diagonal pivots): %d \n",mumps->id.INFOG(12));CHKERRQ(ierr); 1788 ierr = PetscViewerASCIIPrintf(viewer," INFOG(13) (number of delayed pivots after factorization): %d \n",mumps->id.INFOG(13));CHKERRQ(ierr); 1789 ierr = PetscViewerASCIIPrintf(viewer," INFOG(14) (number of memory compress after factorization): %d \n",mumps->id.INFOG(14));CHKERRQ(ierr); 1790 ierr = PetscViewerASCIIPrintf(viewer," INFOG(15) (number of steps of iterative refinement after solution): %d \n",mumps->id.INFOG(15));CHKERRQ(ierr); 1791 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); 1792 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); 1793 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); 1794 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); 1795 ierr = PetscViewerASCIIPrintf(viewer," INFOG(20) (estimated number of entries in the factors): %d \n",mumps->id.INFOG(20));CHKERRQ(ierr); 1796 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); 1797 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); 1798 ierr = PetscViewerASCIIPrintf(viewer," INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d \n",mumps->id.INFOG(23));CHKERRQ(ierr); 1799 ierr = PetscViewerASCIIPrintf(viewer," INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d \n",mumps->id.INFOG(24));CHKERRQ(ierr); 1800 ierr = PetscViewerASCIIPrintf(viewer," INFOG(25) (after factorization: number of pivots modified by static pivoting): %d \n",mumps->id.INFOG(25));CHKERRQ(ierr); 1801 ierr = PetscViewerASCIIPrintf(viewer," INFOG(28) (after factorization: number of null pivots encountered): %d\n",mumps->id.INFOG(28));CHKERRQ(ierr); 1802 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); 1803 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); 1804 ierr = PetscViewerASCIIPrintf(viewer," INFOG(32) (after analysis: type of analysis done): %d\n",mumps->id.INFOG(32));CHKERRQ(ierr); 1805 ierr = PetscViewerASCIIPrintf(viewer," INFOG(33) (value used for ICNTL(8)): %d\n",mumps->id.INFOG(33));CHKERRQ(ierr); 1806 ierr = PetscViewerASCIIPrintf(viewer," INFOG(34) (exponent of the determinant if determinant is requested): %d\n",mumps->id.INFOG(34));CHKERRQ(ierr); 1807 } 1808 } 1809 } 1810 PetscFunctionReturn(0); 1811 } 1812 1813 PetscErrorCode MatGetInfo_MUMPS(Mat A,MatInfoType flag,MatInfo *info) 1814 { 1815 Mat_MUMPS *mumps =(Mat_MUMPS*)A->data; 1816 1817 PetscFunctionBegin; 1818 info->block_size = 1.0; 1819 info->nz_allocated = mumps->id.INFOG(20); 1820 info->nz_used = mumps->id.INFOG(20); 1821 info->nz_unneeded = 0.0; 1822 info->assemblies = 0.0; 1823 info->mallocs = 0.0; 1824 info->memory = 0.0; 1825 info->fill_ratio_given = 0; 1826 info->fill_ratio_needed = 0; 1827 info->factor_mallocs = 0; 1828 PetscFunctionReturn(0); 1829 } 1830 1831 /* -------------------------------------------------------------------------------------------*/ 1832 PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is) 1833 { 1834 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1835 const PetscInt *idxs; 1836 PetscInt size,i; 1837 PetscErrorCode ierr; 1838 1839 PetscFunctionBegin; 1840 ierr = ISGetLocalSize(is,&size);CHKERRQ(ierr); 1841 if (mumps->petsc_size > 1) { 1842 PetscBool ls,gs; /* gs is false if any rank other than root has non-empty IS */ 1843 1844 ls = mumps->myid ? (size ? PETSC_FALSE : PETSC_TRUE) : PETSC_TRUE; /* always true on root; false on others if their size != 0 */ 1845 ierr = MPI_Allreduce(&ls,&gs,1,MPIU_BOOL,MPI_LAND,mumps->petsc_comm);CHKERRQ(ierr); 1846 if (!gs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MUMPS distributed parallel Schur complements not yet supported from PETSc\n"); 1847 } 1848 if (mumps->id.size_schur != size) { 1849 ierr = PetscFree2(mumps->id.listvar_schur,mumps->id.schur);CHKERRQ(ierr); 1850 mumps->id.size_schur = size; 1851 mumps->id.schur_lld = size; 1852 ierr = PetscMalloc2(size,&mumps->id.listvar_schur,size*size,&mumps->id.schur);CHKERRQ(ierr); 1853 } 1854 1855 /* Schur complement matrix */ 1856 ierr = MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.size_schur,(PetscScalar*)mumps->id.schur,&F->schur);CHKERRQ(ierr); 1857 if (mumps->sym == 1) { 1858 ierr = MatSetOption(F->schur,MAT_SPD,PETSC_TRUE);CHKERRQ(ierr); 1859 } 1860 1861 /* MUMPS expects Fortran style indices */ 1862 ierr = ISGetIndices(is,&idxs);CHKERRQ(ierr); 1863 ierr = PetscMemcpy(mumps->id.listvar_schur,idxs,size*sizeof(PetscInt));CHKERRQ(ierr); 1864 for (i=0;i<size;i++) mumps->id.listvar_schur[i]++; 1865 ierr = ISRestoreIndices(is,&idxs);CHKERRQ(ierr); 1866 if (mumps->petsc_size > 1) { 1867 mumps->id.ICNTL(19) = 1; /* MUMPS returns Schur centralized on the host */ 1868 } else { 1869 if (F->factortype == MAT_FACTOR_LU) { 1870 mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */ 1871 } else { 1872 mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */ 1873 } 1874 } 1875 /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */ 1876 mumps->id.ICNTL(26) = -1; 1877 PetscFunctionReturn(0); 1878 } 1879 1880 /* -------------------------------------------------------------------------------------------*/ 1881 PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F,Mat* S) 1882 { 1883 Mat St; 1884 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1885 PetscScalar *array; 1886 #if defined(PETSC_USE_COMPLEX) 1887 PetscScalar im = PetscSqrtScalar((PetscScalar)-1.0); 1888 #endif 1889 PetscErrorCode ierr; 1890 1891 PetscFunctionBegin; 1892 if (!mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it"); 1893 ierr = MatCreate(PETSC_COMM_SELF,&St);CHKERRQ(ierr); 1894 ierr = MatSetSizes(St,PETSC_DECIDE,PETSC_DECIDE,mumps->id.size_schur,mumps->id.size_schur);CHKERRQ(ierr); 1895 ierr = MatSetType(St,MATDENSE);CHKERRQ(ierr); 1896 ierr = MatSetUp(St);CHKERRQ(ierr); 1897 ierr = MatDenseGetArray(St,&array);CHKERRQ(ierr); 1898 if (!mumps->sym) { /* MUMPS always return a full matrix */ 1899 if (mumps->id.ICNTL(19) == 1) { /* stored by rows */ 1900 PetscInt i,j,N=mumps->id.size_schur; 1901 for (i=0;i<N;i++) { 1902 for (j=0;j<N;j++) { 1903 #if !defined(PETSC_USE_COMPLEX) 1904 PetscScalar val = mumps->id.schur[i*N+j]; 1905 #else 1906 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 1907 #endif 1908 array[j*N+i] = val; 1909 } 1910 } 1911 } else { /* stored by columns */ 1912 ierr = PetscMemcpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur*sizeof(PetscScalar));CHKERRQ(ierr); 1913 } 1914 } else { /* either full or lower-triangular (not packed) */ 1915 if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */ 1916 PetscInt i,j,N=mumps->id.size_schur; 1917 for (i=0;i<N;i++) { 1918 for (j=i;j<N;j++) { 1919 #if !defined(PETSC_USE_COMPLEX) 1920 PetscScalar val = mumps->id.schur[i*N+j]; 1921 #else 1922 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 1923 #endif 1924 array[i*N+j] = val; 1925 array[j*N+i] = val; 1926 } 1927 } 1928 } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */ 1929 ierr = PetscMemcpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur*sizeof(PetscScalar));CHKERRQ(ierr); 1930 } else { /* ICNTL(19) == 1 lower triangular stored by rows */ 1931 PetscInt i,j,N=mumps->id.size_schur; 1932 for (i=0;i<N;i++) { 1933 for (j=0;j<i+1;j++) { 1934 #if !defined(PETSC_USE_COMPLEX) 1935 PetscScalar val = mumps->id.schur[i*N+j]; 1936 #else 1937 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 1938 #endif 1939 array[i*N+j] = val; 1940 array[j*N+i] = val; 1941 } 1942 } 1943 } 1944 } 1945 ierr = MatDenseRestoreArray(St,&array);CHKERRQ(ierr); 1946 *S = St; 1947 PetscFunctionReturn(0); 1948 } 1949 1950 /* -------------------------------------------------------------------------------------------*/ 1951 PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt ival) 1952 { 1953 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1954 1955 PetscFunctionBegin; 1956 mumps->id.ICNTL(icntl) = ival; 1957 PetscFunctionReturn(0); 1958 } 1959 1960 PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt *ival) 1961 { 1962 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1963 1964 PetscFunctionBegin; 1965 *ival = mumps->id.ICNTL(icntl); 1966 PetscFunctionReturn(0); 1967 } 1968 1969 /*@ 1970 MatMumpsSetIcntl - Set MUMPS parameter ICNTL() 1971 1972 Logically Collective on Mat 1973 1974 Input Parameters: 1975 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1976 . icntl - index of MUMPS parameter array ICNTL() 1977 - ival - value of MUMPS ICNTL(icntl) 1978 1979 Options Database: 1980 . -mat_mumps_icntl_<icntl> <ival> 1981 1982 Level: beginner 1983 1984 References: 1985 . MUMPS Users' Guide 1986 1987 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 1988 @*/ 1989 PetscErrorCode MatMumpsSetIcntl(Mat F,PetscInt icntl,PetscInt ival) 1990 { 1991 PetscErrorCode ierr; 1992 1993 PetscFunctionBegin; 1994 PetscValidType(F,1); 1995 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 1996 PetscValidLogicalCollectiveInt(F,icntl,2); 1997 PetscValidLogicalCollectiveInt(F,ival,3); 1998 ierr = PetscTryMethod(F,"MatMumpsSetIcntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));CHKERRQ(ierr); 1999 PetscFunctionReturn(0); 2000 } 2001 2002 /*@ 2003 MatMumpsGetIcntl - Get MUMPS parameter ICNTL() 2004 2005 Logically Collective on Mat 2006 2007 Input Parameters: 2008 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2009 - icntl - index of MUMPS parameter array ICNTL() 2010 2011 Output Parameter: 2012 . ival - value of MUMPS ICNTL(icntl) 2013 2014 Level: beginner 2015 2016 References: 2017 . MUMPS Users' Guide 2018 2019 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2020 @*/ 2021 PetscErrorCode MatMumpsGetIcntl(Mat F,PetscInt icntl,PetscInt *ival) 2022 { 2023 PetscErrorCode ierr; 2024 2025 PetscFunctionBegin; 2026 PetscValidType(F,1); 2027 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2028 PetscValidLogicalCollectiveInt(F,icntl,2); 2029 PetscValidIntPointer(ival,3); 2030 ierr = PetscUseMethod(F,"MatMumpsGetIcntl_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));CHKERRQ(ierr); 2031 PetscFunctionReturn(0); 2032 } 2033 2034 /* -------------------------------------------------------------------------------------------*/ 2035 PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal val) 2036 { 2037 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2038 2039 PetscFunctionBegin; 2040 mumps->id.CNTL(icntl) = val; 2041 PetscFunctionReturn(0); 2042 } 2043 2044 PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal *val) 2045 { 2046 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2047 2048 PetscFunctionBegin; 2049 *val = mumps->id.CNTL(icntl); 2050 PetscFunctionReturn(0); 2051 } 2052 2053 /*@ 2054 MatMumpsSetCntl - Set MUMPS parameter CNTL() 2055 2056 Logically Collective on Mat 2057 2058 Input Parameters: 2059 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2060 . icntl - index of MUMPS parameter array CNTL() 2061 - val - value of MUMPS CNTL(icntl) 2062 2063 Options Database: 2064 . -mat_mumps_cntl_<icntl> <val> 2065 2066 Level: beginner 2067 2068 References: 2069 . MUMPS Users' Guide 2070 2071 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2072 @*/ 2073 PetscErrorCode MatMumpsSetCntl(Mat F,PetscInt icntl,PetscReal val) 2074 { 2075 PetscErrorCode ierr; 2076 2077 PetscFunctionBegin; 2078 PetscValidType(F,1); 2079 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2080 PetscValidLogicalCollectiveInt(F,icntl,2); 2081 PetscValidLogicalCollectiveReal(F,val,3); 2082 ierr = PetscTryMethod(F,"MatMumpsSetCntl_C",(Mat,PetscInt,PetscReal),(F,icntl,val));CHKERRQ(ierr); 2083 PetscFunctionReturn(0); 2084 } 2085 2086 /*@ 2087 MatMumpsGetCntl - Get MUMPS parameter CNTL() 2088 2089 Logically Collective on Mat 2090 2091 Input Parameters: 2092 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2093 - icntl - index of MUMPS parameter array CNTL() 2094 2095 Output Parameter: 2096 . val - value of MUMPS CNTL(icntl) 2097 2098 Level: beginner 2099 2100 References: 2101 . MUMPS Users' Guide 2102 2103 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2104 @*/ 2105 PetscErrorCode MatMumpsGetCntl(Mat F,PetscInt icntl,PetscReal *val) 2106 { 2107 PetscErrorCode ierr; 2108 2109 PetscFunctionBegin; 2110 PetscValidType(F,1); 2111 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2112 PetscValidLogicalCollectiveInt(F,icntl,2); 2113 PetscValidRealPointer(val,3); 2114 ierr = PetscUseMethod(F,"MatMumpsGetCntl_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));CHKERRQ(ierr); 2115 PetscFunctionReturn(0); 2116 } 2117 2118 PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F,PetscInt icntl,PetscInt *info) 2119 { 2120 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2121 2122 PetscFunctionBegin; 2123 *info = mumps->id.INFO(icntl); 2124 PetscFunctionReturn(0); 2125 } 2126 2127 PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F,PetscInt icntl,PetscInt *infog) 2128 { 2129 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2130 2131 PetscFunctionBegin; 2132 *infog = mumps->id.INFOG(icntl); 2133 PetscFunctionReturn(0); 2134 } 2135 2136 PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfo) 2137 { 2138 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2139 2140 PetscFunctionBegin; 2141 *rinfo = mumps->id.RINFO(icntl); 2142 PetscFunctionReturn(0); 2143 } 2144 2145 PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfog) 2146 { 2147 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2148 2149 PetscFunctionBegin; 2150 *rinfog = mumps->id.RINFOG(icntl); 2151 PetscFunctionReturn(0); 2152 } 2153 2154 PetscErrorCode MatMumpsGetInverse_MUMPS(Mat F,Mat spRHS) 2155 { 2156 PetscErrorCode ierr; 2157 Mat Bt = NULL,Btseq = NULL; 2158 PetscBool flg; 2159 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2160 PetscScalar *aa; 2161 PetscInt spnr,*ia,*ja; 2162 2163 PetscFunctionBegin; 2164 PetscValidIntPointer(spRHS,2); 2165 ierr = PetscObjectTypeCompare((PetscObject)spRHS,MATTRANSPOSEMAT,&flg);CHKERRQ(ierr); 2166 if (flg) { 2167 ierr = MatTransposeGetMat(spRHS,&Bt);CHKERRQ(ierr); 2168 } else SETERRQ(PetscObjectComm((PetscObject)spRHS),PETSC_ERR_ARG_WRONG,"Matrix spRHS must be type MATTRANSPOSEMAT matrix"); 2169 2170 ierr = MatMumpsSetIcntl(F,30,1);CHKERRQ(ierr); 2171 2172 if (mumps->petsc_size > 1) { 2173 Mat_MPIAIJ *b = (Mat_MPIAIJ*)Bt->data; 2174 Btseq = b->A; 2175 } else { 2176 Btseq = Bt; 2177 } 2178 2179 if (!mumps->myid) { 2180 ierr = MatSeqAIJGetArray(Btseq,&aa);CHKERRQ(ierr); 2181 ierr = MatGetRowIJ(Btseq,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 2182 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 2183 2184 mumps->id.irhs_ptr = ia; 2185 mumps->id.irhs_sparse = ja; 2186 mumps->id.nz_rhs = ia[spnr] - 1; 2187 mumps->id.rhs_sparse = (MumpsScalar*)aa; 2188 } else { 2189 mumps->id.irhs_ptr = NULL; 2190 mumps->id.irhs_sparse = NULL; 2191 mumps->id.nz_rhs = 0; 2192 mumps->id.rhs_sparse = NULL; 2193 } 2194 mumps->id.ICNTL(20) = 1; /* rhs is sparse */ 2195 mumps->id.ICNTL(21) = 0; /* solution is in assembled centralized format */ 2196 2197 /* solve phase */ 2198 /*-------------*/ 2199 mumps->id.job = JOB_SOLVE; 2200 PetscMUMPS_c(mumps); 2201 if (mumps->id.INFOG(1) < 0) 2202 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)); 2203 2204 if (!mumps->myid) { 2205 ierr = MatSeqAIJRestoreArray(Btseq,&aa);CHKERRQ(ierr); 2206 ierr = MatRestoreRowIJ(Btseq,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 2207 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 2208 } 2209 PetscFunctionReturn(0); 2210 } 2211 2212 /*@ 2213 MatMumpsGetInverse - Get user-specified set of entries in inverse of A 2214 2215 Logically Collective on Mat 2216 2217 Input Parameters: 2218 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2219 - spRHS - sequential sparse matrix in MATTRANSPOSEMAT format holding specified indices in processor[0] 2220 2221 Output Parameter: 2222 . spRHS - requested entries of inverse of A 2223 2224 Level: beginner 2225 2226 References: 2227 . MUMPS Users' Guide 2228 2229 .seealso: MatGetFactor(), MatCreateTranspose() 2230 @*/ 2231 PetscErrorCode MatMumpsGetInverse(Mat F,Mat spRHS) 2232 { 2233 PetscErrorCode ierr; 2234 2235 PetscFunctionBegin; 2236 PetscValidType(F,1); 2237 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2238 ierr = PetscUseMethod(F,"MatMumpsGetInverse_C",(Mat,Mat),(F,spRHS));CHKERRQ(ierr); 2239 PetscFunctionReturn(0); 2240 } 2241 2242 PetscErrorCode MatMumpsGetInverseTranspose_MUMPS(Mat F,Mat spRHST) 2243 { 2244 PetscErrorCode ierr; 2245 Mat spRHS; 2246 2247 PetscFunctionBegin; 2248 ierr = MatCreateTranspose(spRHST,&spRHS);CHKERRQ(ierr); 2249 ierr = MatMumpsGetInverse_MUMPS(F,spRHS);CHKERRQ(ierr); 2250 ierr = MatDestroy(&spRHS);CHKERRQ(ierr); 2251 PetscFunctionReturn(0); 2252 } 2253 2254 /*@ 2255 MatMumpsGetInverseTranspose - Get user-specified set of entries in inverse of matrix A^T 2256 2257 Logically Collective on Mat 2258 2259 Input Parameters: 2260 + F - the factored matrix of A obtained by calling MatGetFactor() from PETSc-MUMPS interface 2261 - spRHST - sequential sparse matrix in MATAIJ format holding specified indices of A^T in processor[0] 2262 2263 Output Parameter: 2264 . spRHST - requested entries of inverse of A^T 2265 2266 Level: beginner 2267 2268 References: 2269 . MUMPS Users' Guide 2270 2271 .seealso: MatGetFactor(), MatCreateTranspose(), MatMumpsGetInverse() 2272 @*/ 2273 PetscErrorCode MatMumpsGetInverseTranspose(Mat F,Mat spRHST) 2274 { 2275 PetscErrorCode ierr; 2276 PetscBool flg; 2277 2278 PetscFunctionBegin; 2279 PetscValidType(F,1); 2280 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2281 ierr = PetscObjectTypeCompareAny((PetscObject)spRHST,&flg,MATSEQAIJ,MATMPIAIJ,NULL);CHKERRQ(ierr); 2282 if (!flg) SETERRQ(PetscObjectComm((PetscObject)spRHST),PETSC_ERR_ARG_WRONG,"Matrix spRHST must be MATAIJ matrix"); 2283 2284 ierr = PetscUseMethod(F,"MatMumpsGetInverseTranspose_C",(Mat,Mat),(F,spRHST));CHKERRQ(ierr); 2285 PetscFunctionReturn(0); 2286 } 2287 2288 /*@ 2289 MatMumpsGetInfo - Get MUMPS parameter INFO() 2290 2291 Logically Collective on Mat 2292 2293 Input Parameters: 2294 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2295 - icntl - index of MUMPS parameter array INFO() 2296 2297 Output Parameter: 2298 . ival - value of MUMPS INFO(icntl) 2299 2300 Level: beginner 2301 2302 References: 2303 . MUMPS Users' Guide 2304 2305 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2306 @*/ 2307 PetscErrorCode MatMumpsGetInfo(Mat F,PetscInt icntl,PetscInt *ival) 2308 { 2309 PetscErrorCode ierr; 2310 2311 PetscFunctionBegin; 2312 PetscValidType(F,1); 2313 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2314 PetscValidIntPointer(ival,3); 2315 ierr = PetscUseMethod(F,"MatMumpsGetInfo_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));CHKERRQ(ierr); 2316 PetscFunctionReturn(0); 2317 } 2318 2319 /*@ 2320 MatMumpsGetInfog - Get MUMPS parameter INFOG() 2321 2322 Logically Collective on Mat 2323 2324 Input Parameters: 2325 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2326 - icntl - index of MUMPS parameter array INFOG() 2327 2328 Output Parameter: 2329 . ival - value of MUMPS INFOG(icntl) 2330 2331 Level: beginner 2332 2333 References: 2334 . MUMPS Users' Guide 2335 2336 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2337 @*/ 2338 PetscErrorCode MatMumpsGetInfog(Mat F,PetscInt icntl,PetscInt *ival) 2339 { 2340 PetscErrorCode ierr; 2341 2342 PetscFunctionBegin; 2343 PetscValidType(F,1); 2344 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2345 PetscValidIntPointer(ival,3); 2346 ierr = PetscUseMethod(F,"MatMumpsGetInfog_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));CHKERRQ(ierr); 2347 PetscFunctionReturn(0); 2348 } 2349 2350 /*@ 2351 MatMumpsGetRinfo - Get MUMPS parameter RINFO() 2352 2353 Logically Collective on Mat 2354 2355 Input Parameters: 2356 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2357 - icntl - index of MUMPS parameter array RINFO() 2358 2359 Output Parameter: 2360 . val - value of MUMPS RINFO(icntl) 2361 2362 Level: beginner 2363 2364 References: 2365 . MUMPS Users' Guide 2366 2367 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2368 @*/ 2369 PetscErrorCode MatMumpsGetRinfo(Mat F,PetscInt icntl,PetscReal *val) 2370 { 2371 PetscErrorCode ierr; 2372 2373 PetscFunctionBegin; 2374 PetscValidType(F,1); 2375 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2376 PetscValidRealPointer(val,3); 2377 ierr = PetscUseMethod(F,"MatMumpsGetRinfo_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));CHKERRQ(ierr); 2378 PetscFunctionReturn(0); 2379 } 2380 2381 /*@ 2382 MatMumpsGetRinfog - Get MUMPS parameter RINFOG() 2383 2384 Logically Collective on Mat 2385 2386 Input Parameters: 2387 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2388 - icntl - index of MUMPS parameter array RINFOG() 2389 2390 Output Parameter: 2391 . val - value of MUMPS RINFOG(icntl) 2392 2393 Level: beginner 2394 2395 References: 2396 . MUMPS Users' Guide 2397 2398 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2399 @*/ 2400 PetscErrorCode MatMumpsGetRinfog(Mat F,PetscInt icntl,PetscReal *val) 2401 { 2402 PetscErrorCode ierr; 2403 2404 PetscFunctionBegin; 2405 PetscValidType(F,1); 2406 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2407 PetscValidRealPointer(val,3); 2408 ierr = PetscUseMethod(F,"MatMumpsGetRinfog_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));CHKERRQ(ierr); 2409 PetscFunctionReturn(0); 2410 } 2411 2412 /*MC 2413 MATSOLVERMUMPS - A matrix type providing direct solvers (LU and Cholesky) for 2414 distributed and sequential matrices via the external package MUMPS. 2415 2416 Works with MATAIJ and MATSBAIJ matrices 2417 2418 Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch to have PETSc installed with MUMPS 2419 2420 Use ./configure --with-openmp --download-hwloc (or --with-hwloc) to enable running MUMPS in MPI+OpenMP hybrid mode and non-MUMPS in flat-MPI mode. See details below. 2421 2422 Use -pc_type cholesky or lu -pc_factor_mat_solver_type mumps to use this direct solver 2423 2424 Options Database Keys: 2425 + -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages 2426 . -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning 2427 . -mat_mumps_icntl_3 - ICNTL(3): output stream for global information, collected on the host 2428 . -mat_mumps_icntl_4 - ICNTL(4): level of printing (0 to 4) 2429 . -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7) 2430 . -mat_mumps_icntl_7 - ICNTL(7): computes a symmetric permutation in sequential analysis (0 to 7). 3=Scotch, 4=PORD, 5=Metis 2431 . -mat_mumps_icntl_8 - ICNTL(8): scaling strategy (-2 to 8 or 77) 2432 . -mat_mumps_icntl_10 - ICNTL(10): max num of refinements 2433 . -mat_mumps_icntl_11 - ICNTL(11): statistics related to an error analysis (via -ksp_view) 2434 . -mat_mumps_icntl_12 - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3) 2435 . -mat_mumps_icntl_13 - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting 2436 . -mat_mumps_icntl_14 - ICNTL(14): percentage increase in the estimated working space 2437 . -mat_mumps_icntl_19 - ICNTL(19): computes the Schur complement 2438 . -mat_mumps_icntl_22 - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1) 2439 . -mat_mumps_icntl_23 - ICNTL(23): max size of the working memory (MB) that can allocate per processor 2440 . -mat_mumps_icntl_24 - ICNTL(24): detection of null pivot rows (0 or 1) 2441 . -mat_mumps_icntl_25 - ICNTL(25): compute a solution of a deficient matrix and a null space basis 2442 . -mat_mumps_icntl_26 - ICNTL(26): drives the solution phase if a Schur complement matrix 2443 . -mat_mumps_icntl_28 - ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering 2444 . -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis 2445 . -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A) 2446 . -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization 2447 . -mat_mumps_icntl_33 - ICNTL(33): compute determinant 2448 . -mat_mumps_cntl_1 - CNTL(1): relative pivoting threshold 2449 . -mat_mumps_cntl_2 - CNTL(2): stopping criterion of refinement 2450 . -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold 2451 . -mat_mumps_cntl_4 - CNTL(4): value for static pivoting 2452 . -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots 2453 - -mat_mumps_use_omp_threads [m] - run MUMPS in MPI+OpenMP hybrid mode as if omp_set_num_threads(m) is called before calling MUMPS. 2454 Default might be the number of cores per CPU package (socket) as reported by hwloc and suggested by the MUMPS manual. 2455 2456 Level: beginner 2457 2458 Notes: 2459 When a MUMPS factorization fails inside a KSP solve, for example with a KSP_DIVERGED_PC_FAILED, one can find the MUMPS information about the failure by calling 2460 $ KSPGetPC(ksp,&pc); 2461 $ PCFactorGetMatrix(pc,&mat); 2462 $ MatMumpsGetInfo(mat,....); 2463 $ MatMumpsGetInfog(mat,....); etc. 2464 Or you can run with -ksp_error_if_not_converged and the program will be stopped and the information printed in the error message. 2465 2466 If you want to run MUMPS in MPI+OpenMP hybrid mode (i.e., enable multithreading in MUMPS), but still want to run the non-MUMPS part 2467 (i.e., PETSc part) of your code in the so-called flat-MPI (aka pure-MPI) mode, you need to configure PETSc with --with-openmp --download-hwloc 2468 (or --with-hwloc), and have an MPI that supports MPI-3.0's process shared memory (which is usually available). Since MUMPS calls BLAS 2469 libraries, to really get performance, you should have multithreaded BLAS libraries such as Intel MKL, AMD ACML, Cray libSci or open sourced 2470 OpenBLAS (PETSc has configure options to install/specify them). With these conditions met, you can run your program as before but with 2471 an extra option -mat_mumps_use_omp_threads [m]. It works as if we set OMP_NUM_THREADS=m to MUMPS, with m defaults to the number of cores 2472 per CPU socket (or package, in hwloc term), or number of PETSc MPI processes on a node, whichever is smaller. 2473 2474 By flat-MPI or pure-MPI mode, it means you run your code with as many MPI ranks as the number of cores. For example, 2475 if a compute node has 32 cores and you run on two nodes, you may use "mpirun -n 64 ./test". To run MPI+OpenMP hybrid MUMPS, 2476 the tranditional way is to set OMP_NUM_THREADS and run with fewer MPI ranks than cores. For example, if you want to have 16 OpenMP 2477 threads per rank, then you may use "export OMP_NUM_THREADS=16 && mpirun -n 4 ./test". The problem of this approach is that the non-MUMPS 2478 part of your code is run with fewer cores and CPUs are wasted. "-mat_mumps_use_omp_threads [m]" provides an alternative such that 2479 you can stil run your code with as many MPI ranks as the number of cores, but have MUMPS run in MPI+OpenMP hybrid mode. In our example, 2480 you can use "mpirun -n 64 ./test -mat_mumps_use_omp_threads 16". 2481 2482 If you run your code through a job submission system, there are caveats in MPI rank mapping. We use MPI_Comm_split_type to get MPI 2483 processes on each compute node. Listing the processes in rank ascending order, we split processes on a node into consecutive groups of 2484 size m and create a communicator called omp_comm for each group. Rank 0 in an omp_comm is called the master rank, and others in the omp_comm 2485 are called slave ranks (or slaves). Only master ranks are seen to MUMPS and slaves are not. We will free CPUs assigned to slaves (might be set 2486 by CPU binding policies in job scripts) and make the CPUs available to the master so that OMP threads spawned by MUMPS can run on the CPUs. 2487 In a multi-socket compute node, MPI rank mapping is an issue. Still use the above example and suppose your compute node has two sockets, 2488 if you interleave MPI ranks on the two sockets, in other words, even ranks are placed on socket 0, and odd ranks are on socket 1, and bind 2489 MPI ranks to cores, then with -mat_mumps_use_omp_threads 16, a master rank (and threads it spawns) will use half cores in socket 0, and half 2490 cores in socket 1, that definitely hurts locality. On the other hand, if you map MPI ranks consecutively on the two sockets, then the 2491 problem will not happen. Therefore, when you use -mat_mumps_use_omp_threads, you need to keep an eye on your MPI rank mapping and CPU binding. 2492 For example, with the Slurm job scheduler, one can use srun --cpu-bind=verbsoe -m block:block to map consecutive MPI ranks to sockets and 2493 examine the mapping result. 2494 2495 PETSc does not control thread binding in MUMPS. So to get best performance, one still has to set OMP_PROC_BIND and OMP_PLACES in job scripts, 2496 for example, export OMP_PLACES=threads and export OMP_PROC_BIND=spread. One does not need to export OMP_NUM_THREADS=m in job scripts as PETSc 2497 calls omp_set_num_threads(m) internally before calling MUMPS. 2498 2499 References: 2500 + 1. - Heroux, Michael A., R. Brightwell, and Michael M. Wolf. "Bi-modal MPI and MPI+ threads computing on scalable multicore systems." IJHPCA (Submitted) (2011). 2501 - 2. - Gutierrez, Samuel K., et al. "Accommodating Thread-Level Heterogeneity in Coupled Parallel Applications." Parallel and Distributed Processing Symposium (IPDPS), 2017 IEEE International. IEEE, 2017. 2502 2503 .seealso: PCFactorSetMatSolverType(), MatSolverType, MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog(), KSPGetPC(), PCGetFactor(), PCFactorGetMatrix() 2504 2505 M*/ 2506 2507 static PetscErrorCode MatFactorGetSolverType_mumps(Mat A,MatSolverType *type) 2508 { 2509 PetscFunctionBegin; 2510 *type = MATSOLVERMUMPS; 2511 PetscFunctionReturn(0); 2512 } 2513 2514 /* MatGetFactor for Seq and MPI AIJ matrices */ 2515 static PetscErrorCode MatGetFactor_aij_mumps(Mat A,MatFactorType ftype,Mat *F) 2516 { 2517 Mat B; 2518 PetscErrorCode ierr; 2519 Mat_MUMPS *mumps; 2520 PetscBool isSeqAIJ; 2521 2522 PetscFunctionBegin; 2523 /* Create the factorization matrix */ 2524 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);CHKERRQ(ierr); 2525 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2526 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2527 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2528 ierr = MatSetUp(B);CHKERRQ(ierr); 2529 2530 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2531 2532 B->ops->view = MatView_MUMPS; 2533 B->ops->getinfo = MatGetInfo_MUMPS; 2534 2535 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2536 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2537 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2538 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2539 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2540 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2541 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2542 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2543 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2544 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2545 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2546 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);CHKERRQ(ierr); 2547 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverseTranspose_C",MatMumpsGetInverseTranspose_MUMPS);CHKERRQ(ierr); 2548 2549 if (ftype == MAT_FACTOR_LU) { 2550 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS; 2551 B->factortype = MAT_FACTOR_LU; 2552 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij; 2553 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij; 2554 mumps->sym = 0; 2555 } else { 2556 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 2557 B->factortype = MAT_FACTOR_CHOLESKY; 2558 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij; 2559 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij; 2560 #if defined(PETSC_USE_COMPLEX) 2561 mumps->sym = 2; 2562 #else 2563 if (A->spd_set && A->spd) mumps->sym = 1; 2564 else mumps->sym = 2; 2565 #endif 2566 } 2567 2568 /* set solvertype */ 2569 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2570 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2571 2572 B->ops->destroy = MatDestroy_MUMPS; 2573 B->data = (void*)mumps; 2574 2575 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2576 2577 *F = B; 2578 PetscFunctionReturn(0); 2579 } 2580 2581 /* MatGetFactor for Seq and MPI SBAIJ matrices */ 2582 static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A,MatFactorType ftype,Mat *F) 2583 { 2584 Mat B; 2585 PetscErrorCode ierr; 2586 Mat_MUMPS *mumps; 2587 PetscBool isSeqSBAIJ; 2588 2589 PetscFunctionBegin; 2590 if (ftype != MAT_FACTOR_CHOLESKY) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Cannot use PETSc SBAIJ matrices with MUMPS LU, use AIJ matrix"); 2591 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"); 2592 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ);CHKERRQ(ierr); 2593 /* Create the factorization matrix */ 2594 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2595 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2596 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2597 ierr = MatSetUp(B);CHKERRQ(ierr); 2598 2599 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2600 if (isSeqSBAIJ) { 2601 mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij; 2602 } else { 2603 mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij; 2604 } 2605 2606 B->ops->getinfo = MatGetInfo_External; 2607 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 2608 B->ops->view = MatView_MUMPS; 2609 2610 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2611 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2612 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2613 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2614 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2615 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2616 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2617 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2618 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2619 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2620 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2621 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);CHKERRQ(ierr); 2622 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverseTranspose_C",MatMumpsGetInverseTranspose_MUMPS);CHKERRQ(ierr); 2623 2624 B->factortype = MAT_FACTOR_CHOLESKY; 2625 #if defined(PETSC_USE_COMPLEX) 2626 mumps->sym = 2; 2627 #else 2628 if (A->spd_set && A->spd) mumps->sym = 1; 2629 else mumps->sym = 2; 2630 #endif 2631 2632 /* set solvertype */ 2633 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2634 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2635 2636 B->ops->destroy = MatDestroy_MUMPS; 2637 B->data = (void*)mumps; 2638 2639 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2640 2641 *F = B; 2642 PetscFunctionReturn(0); 2643 } 2644 2645 static PetscErrorCode MatGetFactor_baij_mumps(Mat A,MatFactorType ftype,Mat *F) 2646 { 2647 Mat B; 2648 PetscErrorCode ierr; 2649 Mat_MUMPS *mumps; 2650 PetscBool isSeqBAIJ; 2651 2652 PetscFunctionBegin; 2653 /* Create the factorization matrix */ 2654 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ);CHKERRQ(ierr); 2655 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2656 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2657 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2658 ierr = MatSetUp(B);CHKERRQ(ierr); 2659 2660 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2661 if (ftype == MAT_FACTOR_LU) { 2662 B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS; 2663 B->factortype = MAT_FACTOR_LU; 2664 if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij; 2665 else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij; 2666 mumps->sym = 0; 2667 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead\n"); 2668 2669 B->ops->getinfo = MatGetInfo_External; 2670 B->ops->view = MatView_MUMPS; 2671 2672 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2673 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2674 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2675 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2676 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2677 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2678 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2679 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2680 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2681 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2682 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2683 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);CHKERRQ(ierr); 2684 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverseTranspose_C",MatMumpsGetInverseTranspose_MUMPS);CHKERRQ(ierr); 2685 2686 /* set solvertype */ 2687 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2688 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2689 2690 B->ops->destroy = MatDestroy_MUMPS; 2691 B->data = (void*)mumps; 2692 2693 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2694 2695 *F = B; 2696 PetscFunctionReturn(0); 2697 } 2698 2699 /* MatGetFactor for Seq and MPI SELL matrices */ 2700 static PetscErrorCode MatGetFactor_sell_mumps(Mat A,MatFactorType ftype,Mat *F) 2701 { 2702 Mat B; 2703 PetscErrorCode ierr; 2704 Mat_MUMPS *mumps; 2705 PetscBool isSeqSELL; 2706 2707 PetscFunctionBegin; 2708 /* Create the factorization matrix */ 2709 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQSELL,&isSeqSELL);CHKERRQ(ierr); 2710 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2711 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2712 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2713 ierr = MatSetUp(B);CHKERRQ(ierr); 2714 2715 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2716 2717 B->ops->view = MatView_MUMPS; 2718 B->ops->getinfo = MatGetInfo_MUMPS; 2719 2720 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2721 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2722 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2723 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2724 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2725 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2726 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2727 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2728 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2729 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2730 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2731 2732 if (ftype == MAT_FACTOR_LU) { 2733 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS; 2734 B->factortype = MAT_FACTOR_LU; 2735 if (isSeqSELL) mumps->ConvertToTriples = MatConvertToTriples_seqsell_seqaij; 2736 else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"To be implemented"); 2737 mumps->sym = 0; 2738 } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"To be implemented"); 2739 2740 /* set solvertype */ 2741 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2742 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2743 2744 B->ops->destroy = MatDestroy_MUMPS; 2745 B->data = (void*)mumps; 2746 2747 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2748 2749 *F = B; 2750 PetscFunctionReturn(0); 2751 } 2752 2753 PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MUMPS(void) 2754 { 2755 PetscErrorCode ierr; 2756 2757 PetscFunctionBegin; 2758 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2759 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2760 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2761 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2762 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPISBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);CHKERRQ(ierr); 2763 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2764 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2765 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2766 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2767 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQSBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);CHKERRQ(ierr); 2768 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQSELL,MAT_FACTOR_LU,MatGetFactor_sell_mumps);CHKERRQ(ierr); 2769 PetscFunctionReturn(0); 2770 } 2771 2772