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 = PetscArraycpy(array,rbray,M*nrhs);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[80],i,ninfo=80; 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 Low Rank (BLR) based factorization","None",mumps->id.ICNTL(35),&mumps->id.ICNTL(35),NULL);CHKERRQ(ierr); 1349 ierr = PetscOptionsInt("-mat_mumps_icntl_36","ICNTL(36): choice of BLR factorization variant","None",mumps->id.ICNTL(36),&mumps->id.ICNTL(36),NULL);CHKERRQ(ierr); 1350 ierr = PetscOptionsInt("-mat_mumps_icntl_38","ICNTL(38): estimated compression rate of LU factors with BLR","None",mumps->id.ICNTL(38),&mumps->id.ICNTL(38),NULL);CHKERRQ(ierr); 1351 1352 ierr = PetscOptionsReal("-mat_mumps_cntl_1","CNTL(1): relative pivoting threshold","None",mumps->id.CNTL(1),&mumps->id.CNTL(1),NULL);CHKERRQ(ierr); 1353 ierr = PetscOptionsReal("-mat_mumps_cntl_2","CNTL(2): stopping criterion of refinement","None",mumps->id.CNTL(2),&mumps->id.CNTL(2),NULL);CHKERRQ(ierr); 1354 ierr = PetscOptionsReal("-mat_mumps_cntl_3","CNTL(3): absolute pivoting threshold","None",mumps->id.CNTL(3),&mumps->id.CNTL(3),NULL);CHKERRQ(ierr); 1355 ierr = PetscOptionsReal("-mat_mumps_cntl_4","CNTL(4): value for static pivoting","None",mumps->id.CNTL(4),&mumps->id.CNTL(4),NULL);CHKERRQ(ierr); 1356 ierr = PetscOptionsReal("-mat_mumps_cntl_5","CNTL(5): fixation for null pivots","None",mumps->id.CNTL(5),&mumps->id.CNTL(5),NULL);CHKERRQ(ierr); 1357 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); 1358 1359 ierr = PetscOptionsString("-mat_mumps_ooc_tmpdir", "out of core directory", "None", mumps->id.ooc_tmpdir, mumps->id.ooc_tmpdir, 256, NULL);CHKERRQ(ierr); 1360 1361 ierr = PetscOptionsIntArray("-mat_mumps_view_info","request INFO local to each processor","",info,&ninfo,NULL);CHKERRQ(ierr); 1362 if (ninfo) { 1363 if (ninfo > 80) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_USER,"number of INFO %d must <= 80\n",ninfo); 1364 ierr = PetscMalloc1(ninfo,&mumps->info);CHKERRQ(ierr); 1365 mumps->ninfo = ninfo; 1366 for (i=0; i<ninfo; i++) { 1367 if (info[i] < 0 || info[i]>80) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_USER,"index of INFO %d must between 1 and 80\n",ninfo); 1368 else mumps->info[i] = info[i]; 1369 } 1370 } 1371 1372 ierr = PetscOptionsEnd();CHKERRQ(ierr); 1373 PetscFunctionReturn(0); 1374 } 1375 1376 PetscErrorCode PetscInitializeMUMPS(Mat A,Mat_MUMPS *mumps) 1377 { 1378 PetscErrorCode ierr; 1379 PetscInt nthreads=0; 1380 1381 PetscFunctionBegin; 1382 mumps->petsc_comm = PetscObjectComm((PetscObject)A); 1383 ierr = MPI_Comm_size(mumps->petsc_comm,&mumps->petsc_size);CHKERRQ(ierr); 1384 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 */ 1385 1386 ierr = PetscOptionsHasName(NULL,NULL,"-mat_mumps_use_omp_threads",&mumps->use_petsc_omp_support);CHKERRQ(ierr); 1387 if (mumps->use_petsc_omp_support) nthreads = -1; /* -1 will let PetscOmpCtrlCreate() guess a proper value when user did not supply one */ 1388 ierr = PetscOptionsGetInt(NULL,NULL,"-mat_mumps_use_omp_threads",&nthreads,NULL);CHKERRQ(ierr); 1389 if (mumps->use_petsc_omp_support) { 1390 #if defined(PETSC_HAVE_OPENMP_SUPPORT) 1391 ierr = PetscOmpCtrlCreate(mumps->petsc_comm,nthreads,&mumps->omp_ctrl);CHKERRQ(ierr); 1392 ierr = PetscOmpCtrlGetOmpComms(mumps->omp_ctrl,&mumps->omp_comm,&mumps->mumps_comm,&mumps->is_omp_master);CHKERRQ(ierr); 1393 #else 1394 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"); 1395 #endif 1396 } else { 1397 mumps->omp_comm = PETSC_COMM_SELF; 1398 mumps->mumps_comm = mumps->petsc_comm; 1399 mumps->is_omp_master = PETSC_TRUE; 1400 } 1401 ierr = MPI_Comm_size(mumps->omp_comm,&mumps->omp_comm_size);CHKERRQ(ierr); 1402 1403 mumps->id.comm_fortran = MPI_Comm_c2f(mumps->mumps_comm); 1404 mumps->id.job = JOB_INIT; 1405 mumps->id.par = 1; /* host participates factorizaton and solve */ 1406 mumps->id.sym = mumps->sym; 1407 1408 PetscMUMPS_c(mumps); 1409 1410 /* copy MUMPS default control values from master to slaves. Although slaves do not call MUMPS, they may access these values in code. 1411 For example, ICNTL(9) is initialized to 1 by MUMPS and slaves check ICNTL(9) in MatSolve_MUMPS. 1412 */ 1413 ierr = MPI_Bcast(mumps->id.icntl,60,MPIU_INT, 0,mumps->omp_comm);CHKERRQ(ierr); /* see MUMPS-5.1.2 Manual Section 9 */ 1414 ierr = MPI_Bcast(mumps->id.cntl, 15,MPIU_REAL,0,mumps->omp_comm);CHKERRQ(ierr); 1415 1416 mumps->scat_rhs = NULL; 1417 mumps->scat_sol = NULL; 1418 1419 /* set PETSc-MUMPS default options - override MUMPS default */ 1420 mumps->id.ICNTL(3) = 0; 1421 mumps->id.ICNTL(4) = 0; 1422 if (mumps->petsc_size == 1) { 1423 mumps->id.ICNTL(18) = 0; /* centralized assembled matrix input */ 1424 } else { 1425 mumps->id.ICNTL(18) = 3; /* distributed assembled matrix input */ 1426 mumps->id.ICNTL(20) = 0; /* rhs is in dense format */ 1427 mumps->id.ICNTL(21) = 1; /* distributed solution */ 1428 } 1429 1430 /* schur */ 1431 mumps->id.size_schur = 0; 1432 mumps->id.listvar_schur = NULL; 1433 mumps->id.schur = NULL; 1434 mumps->sizeredrhs = 0; 1435 mumps->schur_sol = NULL; 1436 mumps->schur_sizesol = 0; 1437 PetscFunctionReturn(0); 1438 } 1439 1440 PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F,Mat A,const MatFactorInfo *info,Mat_MUMPS *mumps) 1441 { 1442 PetscErrorCode ierr; 1443 1444 PetscFunctionBegin; 1445 ierr = MPI_Bcast(mumps->id.infog, 80,MPIU_INT, 0,mumps->omp_comm);CHKERRQ(ierr); /* see MUMPS-5.1.2 manual p82 */ 1446 ierr = MPI_Bcast(mumps->id.rinfog,20,MPIU_REAL,0,mumps->omp_comm);CHKERRQ(ierr); 1447 if (mumps->id.INFOG(1) < 0) { 1448 if (A->erroriffailure) { 1449 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d\n",mumps->id.INFOG(1)); 1450 } else { 1451 if (mumps->id.INFOG(1) == -6) { 1452 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); 1453 F->factorerrortype = MAT_FACTOR_STRUCT_ZEROPIVOT; 1454 } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) { 1455 ierr = PetscInfo2(F,"problem of workspace, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));CHKERRQ(ierr); 1456 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1457 } else { 1458 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); 1459 F->factorerrortype = MAT_FACTOR_OTHER; 1460 } 1461 } 1462 } 1463 PetscFunctionReturn(0); 1464 } 1465 1466 /* Note Petsc r(=c) permutation is used when mumps->id.ICNTL(7)==1 with centralized assembled matrix input; otherwise r and c are ignored */ 1467 PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 1468 { 1469 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1470 PetscErrorCode ierr; 1471 Vec b; 1472 IS is_iden; 1473 const PetscInt M = A->rmap->N; 1474 1475 PetscFunctionBegin; 1476 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 1477 1478 /* Set MUMPS options from the options database */ 1479 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1480 1481 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1482 ierr = MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX,mumps);CHKERRQ(ierr); 1483 1484 /* analysis phase */ 1485 /*----------------*/ 1486 mumps->id.job = JOB_FACTSYMBOLIC; 1487 mumps->id.n = M; 1488 switch (mumps->id.ICNTL(18)) { 1489 case 0: /* centralized assembled matrix input */ 1490 if (!mumps->myid) { 1491 mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn; 1492 if (mumps->id.ICNTL(6)>1) { 1493 mumps->id.a = (MumpsScalar*)mumps->val; 1494 } 1495 if (mumps->id.ICNTL(7) == 1) { /* use user-provide matrix ordering - assuming r = c ordering */ 1496 /* 1497 PetscBool flag; 1498 ierr = ISEqual(r,c,&flag);CHKERRQ(ierr); 1499 if (!flag) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_USER,"row_perm != col_perm"); 1500 ierr = ISView(r,PETSC_VIEWER_STDOUT_SELF); 1501 */ 1502 if (!mumps->myid) { 1503 const PetscInt *idx; 1504 PetscInt i,*perm_in; 1505 1506 ierr = PetscMalloc1(M,&perm_in);CHKERRQ(ierr); 1507 ierr = ISGetIndices(r,&idx);CHKERRQ(ierr); 1508 1509 mumps->id.perm_in = perm_in; 1510 for (i=0; i<M; i++) perm_in[i] = idx[i]+1; /* perm_in[]: start from 1, not 0! */ 1511 ierr = ISRestoreIndices(r,&idx);CHKERRQ(ierr); 1512 } 1513 } 1514 } 1515 break; 1516 case 3: /* distributed assembled matrix input (size>1) */ 1517 mumps->id.nz_loc = mumps->nz; 1518 mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn; 1519 if (mumps->id.ICNTL(6)>1) { 1520 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1521 } 1522 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1523 if (!mumps->myid) { 1524 ierr = VecCreateSeq(PETSC_COMM_SELF,A->rmap->N,&mumps->b_seq);CHKERRQ(ierr); 1525 ierr = ISCreateStride(PETSC_COMM_SELF,A->rmap->N,0,1,&is_iden);CHKERRQ(ierr); 1526 } else { 1527 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);CHKERRQ(ierr); 1528 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1529 } 1530 ierr = MatCreateVecs(A,NULL,&b);CHKERRQ(ierr); 1531 ierr = VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);CHKERRQ(ierr); 1532 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1533 ierr = VecDestroy(&b);CHKERRQ(ierr); 1534 break; 1535 } 1536 PetscMUMPS_c(mumps); 1537 ierr = MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);CHKERRQ(ierr); 1538 1539 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 1540 F->ops->solve = MatSolve_MUMPS; 1541 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 1542 F->ops->matsolve = MatMatSolve_MUMPS; 1543 F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS; 1544 PetscFunctionReturn(0); 1545 } 1546 1547 /* Note the Petsc r and c permutations are ignored */ 1548 PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 1549 { 1550 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1551 PetscErrorCode ierr; 1552 Vec b; 1553 IS is_iden; 1554 const PetscInt M = A->rmap->N; 1555 1556 PetscFunctionBegin; 1557 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 1558 1559 /* Set MUMPS options from the options database */ 1560 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1561 1562 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1563 ierr = MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX,mumps);CHKERRQ(ierr); 1564 1565 /* analysis phase */ 1566 /*----------------*/ 1567 mumps->id.job = JOB_FACTSYMBOLIC; 1568 mumps->id.n = M; 1569 switch (mumps->id.ICNTL(18)) { 1570 case 0: /* centralized assembled matrix input */ 1571 if (!mumps->myid) { 1572 mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn; 1573 if (mumps->id.ICNTL(6)>1) { 1574 mumps->id.a = (MumpsScalar*)mumps->val; 1575 } 1576 } 1577 break; 1578 case 3: /* distributed assembled matrix input (size>1) */ 1579 mumps->id.nz_loc = mumps->nz; 1580 mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn; 1581 if (mumps->id.ICNTL(6)>1) { 1582 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1583 } 1584 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1585 if (!mumps->myid) { 1586 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);CHKERRQ(ierr); 1587 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 1588 } else { 1589 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);CHKERRQ(ierr); 1590 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1591 } 1592 ierr = MatCreateVecs(A,NULL,&b);CHKERRQ(ierr); 1593 ierr = VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);CHKERRQ(ierr); 1594 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1595 ierr = VecDestroy(&b);CHKERRQ(ierr); 1596 break; 1597 } 1598 PetscMUMPS_c(mumps); 1599 ierr = MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);CHKERRQ(ierr); 1600 1601 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 1602 F->ops->solve = MatSolve_MUMPS; 1603 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 1604 PetscFunctionReturn(0); 1605 } 1606 1607 /* Note the Petsc r permutation and factor info are ignored */ 1608 PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F,Mat A,IS r,const MatFactorInfo *info) 1609 { 1610 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1611 PetscErrorCode ierr; 1612 Vec b; 1613 IS is_iden; 1614 const PetscInt M = A->rmap->N; 1615 1616 PetscFunctionBegin; 1617 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 1618 1619 /* Set MUMPS options from the options database */ 1620 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1621 1622 ierr = (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);CHKERRQ(ierr); 1623 ierr = MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX,mumps);CHKERRQ(ierr); 1624 1625 /* analysis phase */ 1626 /*----------------*/ 1627 mumps->id.job = JOB_FACTSYMBOLIC; 1628 mumps->id.n = M; 1629 switch (mumps->id.ICNTL(18)) { 1630 case 0: /* centralized assembled matrix input */ 1631 if (!mumps->myid) { 1632 mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn; 1633 if (mumps->id.ICNTL(6)>1) { 1634 mumps->id.a = (MumpsScalar*)mumps->val; 1635 } 1636 } 1637 break; 1638 case 3: /* distributed assembled matrix input (size>1) */ 1639 mumps->id.nz_loc = mumps->nz; 1640 mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn; 1641 if (mumps->id.ICNTL(6)>1) { 1642 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1643 } 1644 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1645 if (!mumps->myid) { 1646 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);CHKERRQ(ierr); 1647 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 1648 } else { 1649 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);CHKERRQ(ierr); 1650 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1651 } 1652 ierr = MatCreateVecs(A,NULL,&b);CHKERRQ(ierr); 1653 ierr = VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);CHKERRQ(ierr); 1654 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1655 ierr = VecDestroy(&b);CHKERRQ(ierr); 1656 break; 1657 } 1658 PetscMUMPS_c(mumps); 1659 ierr = MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);CHKERRQ(ierr); 1660 1661 F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS; 1662 F->ops->solve = MatSolve_MUMPS; 1663 F->ops->solvetranspose = MatSolve_MUMPS; 1664 F->ops->matsolve = MatMatSolve_MUMPS; 1665 F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS; 1666 #if defined(PETSC_USE_COMPLEX) 1667 F->ops->getinertia = NULL; 1668 #else 1669 F->ops->getinertia = MatGetInertia_SBAIJMUMPS; 1670 #endif 1671 PetscFunctionReturn(0); 1672 } 1673 1674 PetscErrorCode MatView_MUMPS(Mat A,PetscViewer viewer) 1675 { 1676 PetscErrorCode ierr; 1677 PetscBool iascii; 1678 PetscViewerFormat format; 1679 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 1680 1681 PetscFunctionBegin; 1682 /* check if matrix is mumps type */ 1683 if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(0); 1684 1685 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 1686 if (iascii) { 1687 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 1688 if (format == PETSC_VIEWER_ASCII_INFO) { 1689 ierr = PetscViewerASCIIPrintf(viewer,"MUMPS run parameters:\n");CHKERRQ(ierr); 1690 ierr = PetscViewerASCIIPrintf(viewer," SYM (matrix type): %d \n",mumps->id.sym);CHKERRQ(ierr); 1691 ierr = PetscViewerASCIIPrintf(viewer," PAR (host participation): %d \n",mumps->id.par);CHKERRQ(ierr); 1692 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(1) (output for error): %d \n",mumps->id.ICNTL(1));CHKERRQ(ierr); 1693 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(2) (output of diagnostic msg): %d \n",mumps->id.ICNTL(2));CHKERRQ(ierr); 1694 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(3) (output for global info): %d \n",mumps->id.ICNTL(3));CHKERRQ(ierr); 1695 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(4) (level of printing): %d \n",mumps->id.ICNTL(4));CHKERRQ(ierr); 1696 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(5) (input mat struct): %d \n",mumps->id.ICNTL(5));CHKERRQ(ierr); 1697 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(6) (matrix prescaling): %d \n",mumps->id.ICNTL(6));CHKERRQ(ierr); 1698 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(7) (sequential matrix ordering):%d \n",mumps->id.ICNTL(7));CHKERRQ(ierr); 1699 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(8) (scaling strategy): %d \n",mumps->id.ICNTL(8));CHKERRQ(ierr); 1700 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(10) (max num of refinements): %d \n",mumps->id.ICNTL(10));CHKERRQ(ierr); 1701 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(11) (error analysis): %d \n",mumps->id.ICNTL(11));CHKERRQ(ierr); 1702 if (mumps->id.ICNTL(11)>0) { 1703 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(4) (inf norm of input mat): %g\n",mumps->id.RINFOG(4));CHKERRQ(ierr); 1704 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(5) (inf norm of solution): %g\n",mumps->id.RINFOG(5));CHKERRQ(ierr); 1705 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(6) (inf norm of residual): %g\n",mumps->id.RINFOG(6));CHKERRQ(ierr); 1706 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(7),RINFOG(8) (backward error est): %g, %g\n",mumps->id.RINFOG(7),mumps->id.RINFOG(8));CHKERRQ(ierr); 1707 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(9) (error estimate): %g \n",mumps->id.RINFOG(9));CHKERRQ(ierr); 1708 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n",mumps->id.RINFOG(10),mumps->id.RINFOG(11));CHKERRQ(ierr); 1709 } 1710 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(12) (efficiency control): %d \n",mumps->id.ICNTL(12));CHKERRQ(ierr); 1711 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(13) (efficiency control): %d \n",mumps->id.ICNTL(13));CHKERRQ(ierr); 1712 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(14) (percentage of estimated workspace increase): %d \n",mumps->id.ICNTL(14));CHKERRQ(ierr); 1713 /* ICNTL(15-17) not used */ 1714 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(18) (input mat struct): %d \n",mumps->id.ICNTL(18));CHKERRQ(ierr); 1715 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(19) (Schur complement info): %d \n",mumps->id.ICNTL(19));CHKERRQ(ierr); 1716 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(20) (rhs sparse pattern): %d \n",mumps->id.ICNTL(20));CHKERRQ(ierr); 1717 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(21) (solution struct): %d \n",mumps->id.ICNTL(21));CHKERRQ(ierr); 1718 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(22) (in-core/out-of-core facility): %d \n",mumps->id.ICNTL(22));CHKERRQ(ierr); 1719 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(23) (max size of memory can be allocated locally):%d \n",mumps->id.ICNTL(23));CHKERRQ(ierr); 1720 1721 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(24) (detection of null pivot rows): %d \n",mumps->id.ICNTL(24));CHKERRQ(ierr); 1722 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(25) (computation of a null space basis): %d \n",mumps->id.ICNTL(25));CHKERRQ(ierr); 1723 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(26) (Schur options for rhs or solution): %d \n",mumps->id.ICNTL(26));CHKERRQ(ierr); 1724 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(27) (experimental parameter): %d \n",mumps->id.ICNTL(27));CHKERRQ(ierr); 1725 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(28) (use parallel or sequential ordering): %d \n",mumps->id.ICNTL(28));CHKERRQ(ierr); 1726 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(29) (parallel ordering): %d \n",mumps->id.ICNTL(29));CHKERRQ(ierr); 1727 1728 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(30) (user-specified set of entries in inv(A)): %d \n",mumps->id.ICNTL(30));CHKERRQ(ierr); 1729 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(31) (factors is discarded in the solve phase): %d \n",mumps->id.ICNTL(31));CHKERRQ(ierr); 1730 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(33) (compute determinant): %d \n",mumps->id.ICNTL(33));CHKERRQ(ierr); 1731 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(35) (activate BLR based factorization): %d \n",mumps->id.ICNTL(35));CHKERRQ(ierr); 1732 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(36) (choice of BLR factorization variant): %d \n",mumps->id.ICNTL(36));CHKERRQ(ierr); 1733 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(38) (estimated compression rate of LU factors): %d \n",mumps->id.ICNTL(38));CHKERRQ(ierr); 1734 1735 ierr = PetscViewerASCIIPrintf(viewer," CNTL(1) (relative pivoting threshold): %g \n",mumps->id.CNTL(1));CHKERRQ(ierr); 1736 ierr = PetscViewerASCIIPrintf(viewer," CNTL(2) (stopping criterion of refinement): %g \n",mumps->id.CNTL(2));CHKERRQ(ierr); 1737 ierr = PetscViewerASCIIPrintf(viewer," CNTL(3) (absolute pivoting threshold): %g \n",mumps->id.CNTL(3));CHKERRQ(ierr); 1738 ierr = PetscViewerASCIIPrintf(viewer," CNTL(4) (value of static pivoting): %g \n",mumps->id.CNTL(4));CHKERRQ(ierr); 1739 ierr = PetscViewerASCIIPrintf(viewer," CNTL(5) (fixation for null pivots): %g \n",mumps->id.CNTL(5));CHKERRQ(ierr); 1740 ierr = PetscViewerASCIIPrintf(viewer," CNTL(7) (dropping parameter for BLR): %g \n",mumps->id.CNTL(7));CHKERRQ(ierr); 1741 1742 /* infomation local to each processor */ 1743 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(1) (local estimated flops for the elimination after analysis): \n");CHKERRQ(ierr); 1744 ierr = PetscViewerASCIIPushSynchronized(viewer);CHKERRQ(ierr); 1745 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(1));CHKERRQ(ierr); 1746 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1747 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(2) (local estimated flops for the assembly after factorization): \n");CHKERRQ(ierr); 1748 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(2));CHKERRQ(ierr); 1749 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1750 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(3) (local estimated flops for the elimination after factorization): \n");CHKERRQ(ierr); 1751 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(3));CHKERRQ(ierr); 1752 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1753 1754 ierr = PetscViewerASCIIPrintf(viewer, " INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization): \n");CHKERRQ(ierr); 1755 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(15));CHKERRQ(ierr); 1756 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1757 1758 ierr = PetscViewerASCIIPrintf(viewer, " INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization): \n");CHKERRQ(ierr); 1759 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(16));CHKERRQ(ierr); 1760 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1761 1762 ierr = PetscViewerASCIIPrintf(viewer, " INFO(23) (num of pivots eliminated on this processor after factorization): \n");CHKERRQ(ierr); 1763 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(23));CHKERRQ(ierr); 1764 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1765 1766 if (mumps->ninfo && mumps->ninfo <= 80){ 1767 PetscInt i; 1768 for (i=0; i<mumps->ninfo; i++){ 1769 ierr = PetscViewerASCIIPrintf(viewer, " INFO(%d): \n",mumps->info[i]);CHKERRQ(ierr); 1770 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",mumps->myid,mumps->id.INFO(mumps->info[i]));CHKERRQ(ierr); 1771 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 1772 } 1773 } 1774 ierr = PetscViewerASCIIPopSynchronized(viewer);CHKERRQ(ierr); 1775 1776 if (!mumps->myid) { /* information from the host */ 1777 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(1) (global estimated flops for the elimination after analysis): %g \n",mumps->id.RINFOG(1));CHKERRQ(ierr); 1778 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(2) (global estimated flops for the assembly after factorization): %g \n",mumps->id.RINFOG(2));CHKERRQ(ierr); 1779 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(3) (global estimated flops for the elimination after factorization): %g \n",mumps->id.RINFOG(3));CHKERRQ(ierr); 1780 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); 1781 1782 ierr = PetscViewerASCIIPrintf(viewer," INFOG(3) (estimated real workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(3));CHKERRQ(ierr); 1783 ierr = PetscViewerASCIIPrintf(viewer," INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(4));CHKERRQ(ierr); 1784 ierr = PetscViewerASCIIPrintf(viewer," INFOG(5) (estimated maximum front size in the complete tree): %d \n",mumps->id.INFOG(5));CHKERRQ(ierr); 1785 ierr = PetscViewerASCIIPrintf(viewer," INFOG(6) (number of nodes in the complete tree): %d \n",mumps->id.INFOG(6));CHKERRQ(ierr); 1786 ierr = PetscViewerASCIIPrintf(viewer," INFOG(7) (ordering option effectively use after analysis): %d \n",mumps->id.INFOG(7));CHKERRQ(ierr); 1787 ierr = PetscViewerASCIIPrintf(viewer," INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d \n",mumps->id.INFOG(8));CHKERRQ(ierr); 1788 ierr = PetscViewerASCIIPrintf(viewer," INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d \n",mumps->id.INFOG(9));CHKERRQ(ierr); 1789 ierr = PetscViewerASCIIPrintf(viewer," INFOG(10) (total integer space store the matrix factors after factorization): %d \n",mumps->id.INFOG(10));CHKERRQ(ierr); 1790 ierr = PetscViewerASCIIPrintf(viewer," INFOG(11) (order of largest frontal matrix after factorization): %d \n",mumps->id.INFOG(11));CHKERRQ(ierr); 1791 ierr = PetscViewerASCIIPrintf(viewer," INFOG(12) (number of off-diagonal pivots): %d \n",mumps->id.INFOG(12));CHKERRQ(ierr); 1792 ierr = PetscViewerASCIIPrintf(viewer," INFOG(13) (number of delayed pivots after factorization): %d \n",mumps->id.INFOG(13));CHKERRQ(ierr); 1793 ierr = PetscViewerASCIIPrintf(viewer," INFOG(14) (number of memory compress after factorization): %d \n",mumps->id.INFOG(14));CHKERRQ(ierr); 1794 ierr = PetscViewerASCIIPrintf(viewer," INFOG(15) (number of steps of iterative refinement after solution): %d \n",mumps->id.INFOG(15));CHKERRQ(ierr); 1795 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); 1796 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); 1797 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); 1798 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); 1799 ierr = PetscViewerASCIIPrintf(viewer," INFOG(20) (estimated number of entries in the factors): %d \n",mumps->id.INFOG(20));CHKERRQ(ierr); 1800 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); 1801 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); 1802 ierr = PetscViewerASCIIPrintf(viewer," INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d \n",mumps->id.INFOG(23));CHKERRQ(ierr); 1803 ierr = PetscViewerASCIIPrintf(viewer," INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d \n",mumps->id.INFOG(24));CHKERRQ(ierr); 1804 ierr = PetscViewerASCIIPrintf(viewer," INFOG(25) (after factorization: number of pivots modified by static pivoting): %d \n",mumps->id.INFOG(25));CHKERRQ(ierr); 1805 ierr = PetscViewerASCIIPrintf(viewer," INFOG(28) (after factorization: number of null pivots encountered): %d\n",mumps->id.INFOG(28));CHKERRQ(ierr); 1806 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); 1807 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); 1808 ierr = PetscViewerASCIIPrintf(viewer," INFOG(32) (after analysis: type of analysis done): %d\n",mumps->id.INFOG(32));CHKERRQ(ierr); 1809 ierr = PetscViewerASCIIPrintf(viewer," INFOG(33) (value used for ICNTL(8)): %d\n",mumps->id.INFOG(33));CHKERRQ(ierr); 1810 ierr = PetscViewerASCIIPrintf(viewer," INFOG(34) (exponent of the determinant if determinant is requested): %d\n",mumps->id.INFOG(34));CHKERRQ(ierr); 1811 ierr = PetscViewerASCIIPrintf(viewer," INFOG(35) (after factorization: number of entries taking into account BLR factor compression - sum over all processors): %d\n",mumps->id.INFOG(35));CHKERRQ(ierr); 1812 ierr = PetscViewerASCIIPrintf(viewer," INFOG(36) (after analysis: estimated size of all MUMPS internal data for running BLR in-core - value on the most memory consuming processor): %d \n",mumps->id.INFOG(36));CHKERRQ(ierr); 1813 ierr = PetscViewerASCIIPrintf(viewer," INFOG(37) (after analysis: estimated size of all MUMPS internal data for running BLR in-core - sum over all processors): %d \n",mumps->id.INFOG(37));CHKERRQ(ierr); 1814 ierr = PetscViewerASCIIPrintf(viewer," INFOG(38) (after analysis: estimated size of all MUMPS internal data for running BLR out-of-core - value on the most memory consuming processor): %d \n",mumps->id.INFOG(38));CHKERRQ(ierr); 1815 ierr = PetscViewerASCIIPrintf(viewer," INFOG(39) (after analysis: estimated size of all MUMPS internal data for running BLR out-of-core - sum over all processors): %d \n",mumps->id.INFOG(39));CHKERRQ(ierr); 1816 } 1817 } 1818 } 1819 PetscFunctionReturn(0); 1820 } 1821 1822 PetscErrorCode MatGetInfo_MUMPS(Mat A,MatInfoType flag,MatInfo *info) 1823 { 1824 Mat_MUMPS *mumps =(Mat_MUMPS*)A->data; 1825 1826 PetscFunctionBegin; 1827 info->block_size = 1.0; 1828 info->nz_allocated = mumps->id.INFOG(20); 1829 info->nz_used = mumps->id.INFOG(20); 1830 info->nz_unneeded = 0.0; 1831 info->assemblies = 0.0; 1832 info->mallocs = 0.0; 1833 info->memory = 0.0; 1834 info->fill_ratio_given = 0; 1835 info->fill_ratio_needed = 0; 1836 info->factor_mallocs = 0; 1837 PetscFunctionReturn(0); 1838 } 1839 1840 /* -------------------------------------------------------------------------------------------*/ 1841 PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is) 1842 { 1843 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1844 const PetscInt *idxs; 1845 PetscInt size,i; 1846 PetscErrorCode ierr; 1847 1848 PetscFunctionBegin; 1849 ierr = ISGetLocalSize(is,&size);CHKERRQ(ierr); 1850 if (mumps->petsc_size > 1) { 1851 PetscBool ls,gs; /* gs is false if any rank other than root has non-empty IS */ 1852 1853 ls = mumps->myid ? (size ? PETSC_FALSE : PETSC_TRUE) : PETSC_TRUE; /* always true on root; false on others if their size != 0 */ 1854 ierr = MPI_Allreduce(&ls,&gs,1,MPIU_BOOL,MPI_LAND,mumps->petsc_comm);CHKERRQ(ierr); 1855 if (!gs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MUMPS distributed parallel Schur complements not yet supported from PETSc\n"); 1856 } 1857 if (mumps->id.size_schur != size) { 1858 ierr = PetscFree2(mumps->id.listvar_schur,mumps->id.schur);CHKERRQ(ierr); 1859 mumps->id.size_schur = size; 1860 mumps->id.schur_lld = size; 1861 ierr = PetscMalloc2(size,&mumps->id.listvar_schur,size*size,&mumps->id.schur);CHKERRQ(ierr); 1862 } 1863 1864 /* Schur complement matrix */ 1865 ierr = MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.size_schur,(PetscScalar*)mumps->id.schur,&F->schur);CHKERRQ(ierr); 1866 if (mumps->sym == 1) { 1867 ierr = MatSetOption(F->schur,MAT_SPD,PETSC_TRUE);CHKERRQ(ierr); 1868 } 1869 1870 /* MUMPS expects Fortran style indices */ 1871 ierr = ISGetIndices(is,&idxs);CHKERRQ(ierr); 1872 ierr = PetscArraycpy(mumps->id.listvar_schur,idxs,size);CHKERRQ(ierr); 1873 for (i=0;i<size;i++) mumps->id.listvar_schur[i]++; 1874 ierr = ISRestoreIndices(is,&idxs);CHKERRQ(ierr); 1875 if (mumps->petsc_size > 1) { 1876 mumps->id.ICNTL(19) = 1; /* MUMPS returns Schur centralized on the host */ 1877 } else { 1878 if (F->factortype == MAT_FACTOR_LU) { 1879 mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */ 1880 } else { 1881 mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */ 1882 } 1883 } 1884 /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */ 1885 mumps->id.ICNTL(26) = -1; 1886 PetscFunctionReturn(0); 1887 } 1888 1889 /* -------------------------------------------------------------------------------------------*/ 1890 PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F,Mat* S) 1891 { 1892 Mat St; 1893 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1894 PetscScalar *array; 1895 #if defined(PETSC_USE_COMPLEX) 1896 PetscScalar im = PetscSqrtScalar((PetscScalar)-1.0); 1897 #endif 1898 PetscErrorCode ierr; 1899 1900 PetscFunctionBegin; 1901 if (!mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it"); 1902 ierr = MatCreate(PETSC_COMM_SELF,&St);CHKERRQ(ierr); 1903 ierr = MatSetSizes(St,PETSC_DECIDE,PETSC_DECIDE,mumps->id.size_schur,mumps->id.size_schur);CHKERRQ(ierr); 1904 ierr = MatSetType(St,MATDENSE);CHKERRQ(ierr); 1905 ierr = MatSetUp(St);CHKERRQ(ierr); 1906 ierr = MatDenseGetArray(St,&array);CHKERRQ(ierr); 1907 if (!mumps->sym) { /* MUMPS always return a full matrix */ 1908 if (mumps->id.ICNTL(19) == 1) { /* stored by rows */ 1909 PetscInt i,j,N=mumps->id.size_schur; 1910 for (i=0;i<N;i++) { 1911 for (j=0;j<N;j++) { 1912 #if !defined(PETSC_USE_COMPLEX) 1913 PetscScalar val = mumps->id.schur[i*N+j]; 1914 #else 1915 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 1916 #endif 1917 array[j*N+i] = val; 1918 } 1919 } 1920 } else { /* stored by columns */ 1921 ierr = PetscArraycpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur);CHKERRQ(ierr); 1922 } 1923 } else { /* either full or lower-triangular (not packed) */ 1924 if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */ 1925 PetscInt i,j,N=mumps->id.size_schur; 1926 for (i=0;i<N;i++) { 1927 for (j=i;j<N;j++) { 1928 #if !defined(PETSC_USE_COMPLEX) 1929 PetscScalar val = mumps->id.schur[i*N+j]; 1930 #else 1931 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 1932 #endif 1933 array[i*N+j] = val; 1934 array[j*N+i] = val; 1935 } 1936 } 1937 } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */ 1938 ierr = PetscArraycpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur);CHKERRQ(ierr); 1939 } else { /* ICNTL(19) == 1 lower triangular stored by rows */ 1940 PetscInt i,j,N=mumps->id.size_schur; 1941 for (i=0;i<N;i++) { 1942 for (j=0;j<i+1;j++) { 1943 #if !defined(PETSC_USE_COMPLEX) 1944 PetscScalar val = mumps->id.schur[i*N+j]; 1945 #else 1946 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 1947 #endif 1948 array[i*N+j] = val; 1949 array[j*N+i] = val; 1950 } 1951 } 1952 } 1953 } 1954 ierr = MatDenseRestoreArray(St,&array);CHKERRQ(ierr); 1955 *S = St; 1956 PetscFunctionReturn(0); 1957 } 1958 1959 /* -------------------------------------------------------------------------------------------*/ 1960 PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt ival) 1961 { 1962 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1963 1964 PetscFunctionBegin; 1965 mumps->id.ICNTL(icntl) = ival; 1966 PetscFunctionReturn(0); 1967 } 1968 1969 PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt *ival) 1970 { 1971 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1972 1973 PetscFunctionBegin; 1974 *ival = mumps->id.ICNTL(icntl); 1975 PetscFunctionReturn(0); 1976 } 1977 1978 /*@ 1979 MatMumpsSetIcntl - Set MUMPS parameter ICNTL() 1980 1981 Logically Collective on Mat 1982 1983 Input Parameters: 1984 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1985 . icntl - index of MUMPS parameter array ICNTL() 1986 - ival - value of MUMPS ICNTL(icntl) 1987 1988 Options Database: 1989 . -mat_mumps_icntl_<icntl> <ival> 1990 1991 Level: beginner 1992 1993 References: 1994 . MUMPS Users' Guide 1995 1996 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 1997 @*/ 1998 PetscErrorCode MatMumpsSetIcntl(Mat F,PetscInt icntl,PetscInt ival) 1999 { 2000 PetscErrorCode ierr; 2001 2002 PetscFunctionBegin; 2003 PetscValidType(F,1); 2004 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2005 PetscValidLogicalCollectiveInt(F,icntl,2); 2006 PetscValidLogicalCollectiveInt(F,ival,3); 2007 ierr = PetscTryMethod(F,"MatMumpsSetIcntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));CHKERRQ(ierr); 2008 PetscFunctionReturn(0); 2009 } 2010 2011 /*@ 2012 MatMumpsGetIcntl - Get MUMPS parameter ICNTL() 2013 2014 Logically Collective on Mat 2015 2016 Input Parameters: 2017 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2018 - icntl - index of MUMPS parameter array ICNTL() 2019 2020 Output Parameter: 2021 . ival - value of MUMPS ICNTL(icntl) 2022 2023 Level: beginner 2024 2025 References: 2026 . MUMPS Users' Guide 2027 2028 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2029 @*/ 2030 PetscErrorCode MatMumpsGetIcntl(Mat F,PetscInt icntl,PetscInt *ival) 2031 { 2032 PetscErrorCode ierr; 2033 2034 PetscFunctionBegin; 2035 PetscValidType(F,1); 2036 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2037 PetscValidLogicalCollectiveInt(F,icntl,2); 2038 PetscValidIntPointer(ival,3); 2039 ierr = PetscUseMethod(F,"MatMumpsGetIcntl_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));CHKERRQ(ierr); 2040 PetscFunctionReturn(0); 2041 } 2042 2043 /* -------------------------------------------------------------------------------------------*/ 2044 PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal val) 2045 { 2046 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2047 2048 PetscFunctionBegin; 2049 mumps->id.CNTL(icntl) = val; 2050 PetscFunctionReturn(0); 2051 } 2052 2053 PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal *val) 2054 { 2055 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2056 2057 PetscFunctionBegin; 2058 *val = mumps->id.CNTL(icntl); 2059 PetscFunctionReturn(0); 2060 } 2061 2062 /*@ 2063 MatMumpsSetCntl - Set MUMPS parameter CNTL() 2064 2065 Logically Collective on Mat 2066 2067 Input Parameters: 2068 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2069 . icntl - index of MUMPS parameter array CNTL() 2070 - val - value of MUMPS CNTL(icntl) 2071 2072 Options Database: 2073 . -mat_mumps_cntl_<icntl> <val> 2074 2075 Level: beginner 2076 2077 References: 2078 . MUMPS Users' Guide 2079 2080 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2081 @*/ 2082 PetscErrorCode MatMumpsSetCntl(Mat F,PetscInt icntl,PetscReal val) 2083 { 2084 PetscErrorCode ierr; 2085 2086 PetscFunctionBegin; 2087 PetscValidType(F,1); 2088 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2089 PetscValidLogicalCollectiveInt(F,icntl,2); 2090 PetscValidLogicalCollectiveReal(F,val,3); 2091 ierr = PetscTryMethod(F,"MatMumpsSetCntl_C",(Mat,PetscInt,PetscReal),(F,icntl,val));CHKERRQ(ierr); 2092 PetscFunctionReturn(0); 2093 } 2094 2095 /*@ 2096 MatMumpsGetCntl - Get MUMPS parameter CNTL() 2097 2098 Logically Collective on Mat 2099 2100 Input Parameters: 2101 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2102 - icntl - index of MUMPS parameter array CNTL() 2103 2104 Output Parameter: 2105 . val - value of MUMPS CNTL(icntl) 2106 2107 Level: beginner 2108 2109 References: 2110 . MUMPS Users' Guide 2111 2112 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2113 @*/ 2114 PetscErrorCode MatMumpsGetCntl(Mat F,PetscInt icntl,PetscReal *val) 2115 { 2116 PetscErrorCode ierr; 2117 2118 PetscFunctionBegin; 2119 PetscValidType(F,1); 2120 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2121 PetscValidLogicalCollectiveInt(F,icntl,2); 2122 PetscValidRealPointer(val,3); 2123 ierr = PetscUseMethod(F,"MatMumpsGetCntl_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));CHKERRQ(ierr); 2124 PetscFunctionReturn(0); 2125 } 2126 2127 PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F,PetscInt icntl,PetscInt *info) 2128 { 2129 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2130 2131 PetscFunctionBegin; 2132 *info = mumps->id.INFO(icntl); 2133 PetscFunctionReturn(0); 2134 } 2135 2136 PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F,PetscInt icntl,PetscInt *infog) 2137 { 2138 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2139 2140 PetscFunctionBegin; 2141 *infog = mumps->id.INFOG(icntl); 2142 PetscFunctionReturn(0); 2143 } 2144 2145 PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfo) 2146 { 2147 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2148 2149 PetscFunctionBegin; 2150 *rinfo = mumps->id.RINFO(icntl); 2151 PetscFunctionReturn(0); 2152 } 2153 2154 PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfog) 2155 { 2156 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2157 2158 PetscFunctionBegin; 2159 *rinfog = mumps->id.RINFOG(icntl); 2160 PetscFunctionReturn(0); 2161 } 2162 2163 PetscErrorCode MatMumpsGetInverse_MUMPS(Mat F,Mat spRHS) 2164 { 2165 PetscErrorCode ierr; 2166 Mat Bt = NULL,Btseq = NULL; 2167 PetscBool flg; 2168 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2169 PetscScalar *aa; 2170 PetscInt spnr,*ia,*ja; 2171 2172 PetscFunctionBegin; 2173 PetscValidIntPointer(spRHS,2); 2174 ierr = PetscObjectTypeCompare((PetscObject)spRHS,MATTRANSPOSEMAT,&flg);CHKERRQ(ierr); 2175 if (flg) { 2176 ierr = MatTransposeGetMat(spRHS,&Bt);CHKERRQ(ierr); 2177 } else SETERRQ(PetscObjectComm((PetscObject)spRHS),PETSC_ERR_ARG_WRONG,"Matrix spRHS must be type MATTRANSPOSEMAT matrix"); 2178 2179 ierr = MatMumpsSetIcntl(F,30,1);CHKERRQ(ierr); 2180 2181 if (mumps->petsc_size > 1) { 2182 Mat_MPIAIJ *b = (Mat_MPIAIJ*)Bt->data; 2183 Btseq = b->A; 2184 } else { 2185 Btseq = Bt; 2186 } 2187 2188 if (!mumps->myid) { 2189 ierr = MatSeqAIJGetArray(Btseq,&aa);CHKERRQ(ierr); 2190 ierr = MatGetRowIJ(Btseq,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 2191 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 2192 2193 mumps->id.irhs_ptr = ia; 2194 mumps->id.irhs_sparse = ja; 2195 mumps->id.nz_rhs = ia[spnr] - 1; 2196 mumps->id.rhs_sparse = (MumpsScalar*)aa; 2197 } else { 2198 mumps->id.irhs_ptr = NULL; 2199 mumps->id.irhs_sparse = NULL; 2200 mumps->id.nz_rhs = 0; 2201 mumps->id.rhs_sparse = NULL; 2202 } 2203 mumps->id.ICNTL(20) = 1; /* rhs is sparse */ 2204 mumps->id.ICNTL(21) = 0; /* solution is in assembled centralized format */ 2205 2206 /* solve phase */ 2207 /*-------------*/ 2208 mumps->id.job = JOB_SOLVE; 2209 PetscMUMPS_c(mumps); 2210 if (mumps->id.INFOG(1) < 0) 2211 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)); 2212 2213 if (!mumps->myid) { 2214 ierr = MatSeqAIJRestoreArray(Btseq,&aa);CHKERRQ(ierr); 2215 ierr = MatRestoreRowIJ(Btseq,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);CHKERRQ(ierr); 2216 if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 2217 } 2218 PetscFunctionReturn(0); 2219 } 2220 2221 /*@ 2222 MatMumpsGetInverse - Get user-specified set of entries in inverse of A 2223 2224 Logically Collective on Mat 2225 2226 Input Parameters: 2227 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2228 - spRHS - sequential sparse matrix in MATTRANSPOSEMAT format holding specified indices in processor[0] 2229 2230 Output Parameter: 2231 . spRHS - requested entries of inverse of A 2232 2233 Level: beginner 2234 2235 References: 2236 . MUMPS Users' Guide 2237 2238 .seealso: MatGetFactor(), MatCreateTranspose() 2239 @*/ 2240 PetscErrorCode MatMumpsGetInverse(Mat F,Mat spRHS) 2241 { 2242 PetscErrorCode ierr; 2243 2244 PetscFunctionBegin; 2245 PetscValidType(F,1); 2246 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2247 ierr = PetscUseMethod(F,"MatMumpsGetInverse_C",(Mat,Mat),(F,spRHS));CHKERRQ(ierr); 2248 PetscFunctionReturn(0); 2249 } 2250 2251 PetscErrorCode MatMumpsGetInverseTranspose_MUMPS(Mat F,Mat spRHST) 2252 { 2253 PetscErrorCode ierr; 2254 Mat spRHS; 2255 2256 PetscFunctionBegin; 2257 ierr = MatCreateTranspose(spRHST,&spRHS);CHKERRQ(ierr); 2258 ierr = MatMumpsGetInverse_MUMPS(F,spRHS);CHKERRQ(ierr); 2259 ierr = MatDestroy(&spRHS);CHKERRQ(ierr); 2260 PetscFunctionReturn(0); 2261 } 2262 2263 /*@ 2264 MatMumpsGetInverseTranspose - Get user-specified set of entries in inverse of matrix A^T 2265 2266 Logically Collective on Mat 2267 2268 Input Parameters: 2269 + F - the factored matrix of A obtained by calling MatGetFactor() from PETSc-MUMPS interface 2270 - spRHST - sequential sparse matrix in MATAIJ format holding specified indices of A^T in processor[0] 2271 2272 Output Parameter: 2273 . spRHST - requested entries of inverse of A^T 2274 2275 Level: beginner 2276 2277 References: 2278 . MUMPS Users' Guide 2279 2280 .seealso: MatGetFactor(), MatCreateTranspose(), MatMumpsGetInverse() 2281 @*/ 2282 PetscErrorCode MatMumpsGetInverseTranspose(Mat F,Mat spRHST) 2283 { 2284 PetscErrorCode ierr; 2285 PetscBool flg; 2286 2287 PetscFunctionBegin; 2288 PetscValidType(F,1); 2289 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2290 ierr = PetscObjectTypeCompareAny((PetscObject)spRHST,&flg,MATSEQAIJ,MATMPIAIJ,NULL);CHKERRQ(ierr); 2291 if (!flg) SETERRQ(PetscObjectComm((PetscObject)spRHST),PETSC_ERR_ARG_WRONG,"Matrix spRHST must be MATAIJ matrix"); 2292 2293 ierr = PetscUseMethod(F,"MatMumpsGetInverseTranspose_C",(Mat,Mat),(F,spRHST));CHKERRQ(ierr); 2294 PetscFunctionReturn(0); 2295 } 2296 2297 /*@ 2298 MatMumpsGetInfo - Get MUMPS parameter INFO() 2299 2300 Logically Collective on Mat 2301 2302 Input Parameters: 2303 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2304 - icntl - index of MUMPS parameter array INFO() 2305 2306 Output Parameter: 2307 . ival - value of MUMPS INFO(icntl) 2308 2309 Level: beginner 2310 2311 References: 2312 . MUMPS Users' Guide 2313 2314 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2315 @*/ 2316 PetscErrorCode MatMumpsGetInfo(Mat F,PetscInt icntl,PetscInt *ival) 2317 { 2318 PetscErrorCode ierr; 2319 2320 PetscFunctionBegin; 2321 PetscValidType(F,1); 2322 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2323 PetscValidIntPointer(ival,3); 2324 ierr = PetscUseMethod(F,"MatMumpsGetInfo_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));CHKERRQ(ierr); 2325 PetscFunctionReturn(0); 2326 } 2327 2328 /*@ 2329 MatMumpsGetInfog - Get MUMPS parameter INFOG() 2330 2331 Logically Collective on Mat 2332 2333 Input Parameters: 2334 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2335 - icntl - index of MUMPS parameter array INFOG() 2336 2337 Output Parameter: 2338 . ival - value of MUMPS INFOG(icntl) 2339 2340 Level: beginner 2341 2342 References: 2343 . MUMPS Users' Guide 2344 2345 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2346 @*/ 2347 PetscErrorCode MatMumpsGetInfog(Mat F,PetscInt icntl,PetscInt *ival) 2348 { 2349 PetscErrorCode ierr; 2350 2351 PetscFunctionBegin; 2352 PetscValidType(F,1); 2353 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2354 PetscValidIntPointer(ival,3); 2355 ierr = PetscUseMethod(F,"MatMumpsGetInfog_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));CHKERRQ(ierr); 2356 PetscFunctionReturn(0); 2357 } 2358 2359 /*@ 2360 MatMumpsGetRinfo - Get MUMPS parameter RINFO() 2361 2362 Logically Collective on Mat 2363 2364 Input Parameters: 2365 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2366 - icntl - index of MUMPS parameter array RINFO() 2367 2368 Output Parameter: 2369 . val - value of MUMPS RINFO(icntl) 2370 2371 Level: beginner 2372 2373 References: 2374 . MUMPS Users' Guide 2375 2376 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2377 @*/ 2378 PetscErrorCode MatMumpsGetRinfo(Mat F,PetscInt icntl,PetscReal *val) 2379 { 2380 PetscErrorCode ierr; 2381 2382 PetscFunctionBegin; 2383 PetscValidType(F,1); 2384 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2385 PetscValidRealPointer(val,3); 2386 ierr = PetscUseMethod(F,"MatMumpsGetRinfo_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));CHKERRQ(ierr); 2387 PetscFunctionReturn(0); 2388 } 2389 2390 /*@ 2391 MatMumpsGetRinfog - Get MUMPS parameter RINFOG() 2392 2393 Logically Collective on Mat 2394 2395 Input Parameters: 2396 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2397 - icntl - index of MUMPS parameter array RINFOG() 2398 2399 Output Parameter: 2400 . val - value of MUMPS RINFOG(icntl) 2401 2402 Level: beginner 2403 2404 References: 2405 . MUMPS Users' Guide 2406 2407 .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog() 2408 @*/ 2409 PetscErrorCode MatMumpsGetRinfog(Mat F,PetscInt icntl,PetscReal *val) 2410 { 2411 PetscErrorCode ierr; 2412 2413 PetscFunctionBegin; 2414 PetscValidType(F,1); 2415 if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2416 PetscValidRealPointer(val,3); 2417 ierr = PetscUseMethod(F,"MatMumpsGetRinfog_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));CHKERRQ(ierr); 2418 PetscFunctionReturn(0); 2419 } 2420 2421 /*MC 2422 MATSOLVERMUMPS - A matrix type providing direct solvers (LU and Cholesky) for 2423 distributed and sequential matrices via the external package MUMPS. 2424 2425 Works with MATAIJ and MATSBAIJ matrices 2426 2427 Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch to have PETSc installed with MUMPS 2428 2429 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. 2430 2431 Use -pc_type cholesky or lu -pc_factor_mat_solver_type mumps to use this direct solver 2432 2433 Options Database Keys: 2434 + -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages 2435 . -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning 2436 . -mat_mumps_icntl_3 - ICNTL(3): output stream for global information, collected on the host 2437 . -mat_mumps_icntl_4 - ICNTL(4): level of printing (0 to 4) 2438 . -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7) 2439 . -mat_mumps_icntl_7 - ICNTL(7): computes a symmetric permutation in sequential analysis (0 to 7). 3=Scotch, 4=PORD, 5=Metis 2440 . -mat_mumps_icntl_8 - ICNTL(8): scaling strategy (-2 to 8 or 77) 2441 . -mat_mumps_icntl_10 - ICNTL(10): max num of refinements 2442 . -mat_mumps_icntl_11 - ICNTL(11): statistics related to an error analysis (via -ksp_view) 2443 . -mat_mumps_icntl_12 - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3) 2444 . -mat_mumps_icntl_13 - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting 2445 . -mat_mumps_icntl_14 - ICNTL(14): percentage increase in the estimated working space 2446 . -mat_mumps_icntl_19 - ICNTL(19): computes the Schur complement 2447 . -mat_mumps_icntl_22 - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1) 2448 . -mat_mumps_icntl_23 - ICNTL(23): max size of the working memory (MB) that can allocate per processor 2449 . -mat_mumps_icntl_24 - ICNTL(24): detection of null pivot rows (0 or 1) 2450 . -mat_mumps_icntl_25 - ICNTL(25): compute a solution of a deficient matrix and a null space basis 2451 . -mat_mumps_icntl_26 - ICNTL(26): drives the solution phase if a Schur complement matrix 2452 . -mat_mumps_icntl_28 - ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering 2453 . -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis 2454 . -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A) 2455 . -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization 2456 . -mat_mumps_icntl_33 - ICNTL(33): compute determinant 2457 . -mat_mumps_icntl_35 - ICNTL(35): level of activation of BLR (Block Low-Rank) feature 2458 . -mat_mumps_icntl_36 - ICNTL(36): controls the choice of BLR factorization variant 2459 . -mat_mumps_icntl_38 - ICNTL(38): sets the estimated compression rate of LU factors with BLR 2460 . -mat_mumps_cntl_1 - CNTL(1): relative pivoting threshold 2461 . -mat_mumps_cntl_2 - CNTL(2): stopping criterion of refinement 2462 . -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold 2463 . -mat_mumps_cntl_4 - CNTL(4): value for static pivoting 2464 . -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots 2465 . -mat_mumps_cntl_7 - CNTL(7): precision of the dropping parameter used during BLR factorization 2466 - -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. 2467 Default might be the number of cores per CPU package (socket) as reported by hwloc and suggested by the MUMPS manual. 2468 2469 Level: beginner 2470 2471 Notes: 2472 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 2473 $ KSPGetPC(ksp,&pc); 2474 $ PCFactorGetMatrix(pc,&mat); 2475 $ MatMumpsGetInfo(mat,....); 2476 $ MatMumpsGetInfog(mat,....); etc. 2477 Or you can run with -ksp_error_if_not_converged and the program will be stopped and the information printed in the error message. 2478 2479 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 2480 (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 2481 (or --with-hwloc), and have an MPI that supports MPI-3.0's process shared memory (which is usually available). Since MUMPS calls BLAS 2482 libraries, to really get performance, you should have multithreaded BLAS libraries such as Intel MKL, AMD ACML, Cray libSci or open sourced 2483 OpenBLAS (PETSc has configure options to install/specify them). With these conditions met, you can run your program as before but with 2484 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 2485 per CPU socket (or package, in hwloc term), or number of PETSc MPI processes on a node, whichever is smaller. 2486 2487 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, 2488 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, 2489 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 2490 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 2491 part of your code is run with fewer cores and CPUs are wasted. "-mat_mumps_use_omp_threads [m]" provides an alternative such that 2492 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, 2493 you can use "mpirun -n 64 ./test -mat_mumps_use_omp_threads 16". 2494 2495 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 2496 processes on each compute node. Listing the processes in rank ascending order, we split processes on a node into consecutive groups of 2497 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 2498 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 2499 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. 2500 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, 2501 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 2502 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 2503 cores in socket 1, that definitely hurts locality. On the other hand, if you map MPI ranks consecutively on the two sockets, then the 2504 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. 2505 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 2506 examine the mapping result. 2507 2508 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, 2509 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 2510 calls omp_set_num_threads(m) internally before calling MUMPS. 2511 2512 References: 2513 + 1. - Heroux, Michael A., R. Brightwell, and Michael M. Wolf. "Bi-modal MPI and MPI+ threads computing on scalable multicore systems." IJHPCA (Submitted) (2011). 2514 - 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. 2515 2516 .seealso: PCFactorSetMatSolverType(), MatSolverType, MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog(), KSPGetPC(), PCGetFactor(), PCFactorGetMatrix() 2517 2518 M*/ 2519 2520 static PetscErrorCode MatFactorGetSolverType_mumps(Mat A,MatSolverType *type) 2521 { 2522 PetscFunctionBegin; 2523 *type = MATSOLVERMUMPS; 2524 PetscFunctionReturn(0); 2525 } 2526 2527 /* MatGetFactor for Seq and MPI AIJ matrices */ 2528 static PetscErrorCode MatGetFactor_aij_mumps(Mat A,MatFactorType ftype,Mat *F) 2529 { 2530 Mat B; 2531 PetscErrorCode ierr; 2532 Mat_MUMPS *mumps; 2533 PetscBool isSeqAIJ; 2534 2535 PetscFunctionBegin; 2536 /* Create the factorization matrix */ 2537 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);CHKERRQ(ierr); 2538 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2539 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2540 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2541 ierr = MatSetUp(B);CHKERRQ(ierr); 2542 2543 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2544 2545 B->ops->view = MatView_MUMPS; 2546 B->ops->getinfo = MatGetInfo_MUMPS; 2547 2548 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2549 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2550 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2551 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2552 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2553 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2554 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2555 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2556 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2557 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2558 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2559 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);CHKERRQ(ierr); 2560 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverseTranspose_C",MatMumpsGetInverseTranspose_MUMPS);CHKERRQ(ierr); 2561 2562 if (ftype == MAT_FACTOR_LU) { 2563 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS; 2564 B->factortype = MAT_FACTOR_LU; 2565 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij; 2566 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij; 2567 mumps->sym = 0; 2568 } else { 2569 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 2570 B->factortype = MAT_FACTOR_CHOLESKY; 2571 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij; 2572 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij; 2573 #if defined(PETSC_USE_COMPLEX) 2574 mumps->sym = 2; 2575 #else 2576 if (A->spd_set && A->spd) mumps->sym = 1; 2577 else mumps->sym = 2; 2578 #endif 2579 } 2580 2581 /* set solvertype */ 2582 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2583 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2584 2585 B->ops->destroy = MatDestroy_MUMPS; 2586 B->data = (void*)mumps; 2587 2588 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2589 2590 *F = B; 2591 PetscFunctionReturn(0); 2592 } 2593 2594 /* MatGetFactor for Seq and MPI SBAIJ matrices */ 2595 static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A,MatFactorType ftype,Mat *F) 2596 { 2597 Mat B; 2598 PetscErrorCode ierr; 2599 Mat_MUMPS *mumps; 2600 PetscBool isSeqSBAIJ; 2601 2602 PetscFunctionBegin; 2603 if (ftype != MAT_FACTOR_CHOLESKY) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Cannot use PETSc SBAIJ matrices with MUMPS LU, use AIJ matrix"); 2604 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"); 2605 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ);CHKERRQ(ierr); 2606 /* Create the factorization matrix */ 2607 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2608 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2609 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2610 ierr = MatSetUp(B);CHKERRQ(ierr); 2611 2612 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2613 if (isSeqSBAIJ) { 2614 mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij; 2615 } else { 2616 mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij; 2617 } 2618 2619 B->ops->getinfo = MatGetInfo_External; 2620 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 2621 B->ops->view = MatView_MUMPS; 2622 2623 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2624 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2625 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2626 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2627 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2628 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2629 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2630 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2631 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2632 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2633 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2634 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);CHKERRQ(ierr); 2635 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverseTranspose_C",MatMumpsGetInverseTranspose_MUMPS);CHKERRQ(ierr); 2636 2637 B->factortype = MAT_FACTOR_CHOLESKY; 2638 #if defined(PETSC_USE_COMPLEX) 2639 mumps->sym = 2; 2640 #else 2641 if (A->spd_set && A->spd) mumps->sym = 1; 2642 else mumps->sym = 2; 2643 #endif 2644 2645 /* set solvertype */ 2646 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2647 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2648 2649 B->ops->destroy = MatDestroy_MUMPS; 2650 B->data = (void*)mumps; 2651 2652 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2653 2654 *F = B; 2655 PetscFunctionReturn(0); 2656 } 2657 2658 static PetscErrorCode MatGetFactor_baij_mumps(Mat A,MatFactorType ftype,Mat *F) 2659 { 2660 Mat B; 2661 PetscErrorCode ierr; 2662 Mat_MUMPS *mumps; 2663 PetscBool isSeqBAIJ; 2664 2665 PetscFunctionBegin; 2666 /* Create the factorization matrix */ 2667 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ);CHKERRQ(ierr); 2668 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2669 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2670 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2671 ierr = MatSetUp(B);CHKERRQ(ierr); 2672 2673 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2674 if (ftype == MAT_FACTOR_LU) { 2675 B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS; 2676 B->factortype = MAT_FACTOR_LU; 2677 if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij; 2678 else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij; 2679 mumps->sym = 0; 2680 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead\n"); 2681 2682 B->ops->getinfo = MatGetInfo_External; 2683 B->ops->view = MatView_MUMPS; 2684 2685 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2686 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2687 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2688 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2689 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2690 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2691 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2692 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2693 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2694 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2695 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2696 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);CHKERRQ(ierr); 2697 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverseTranspose_C",MatMumpsGetInverseTranspose_MUMPS);CHKERRQ(ierr); 2698 2699 /* set solvertype */ 2700 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2701 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2702 2703 B->ops->destroy = MatDestroy_MUMPS; 2704 B->data = (void*)mumps; 2705 2706 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2707 2708 *F = B; 2709 PetscFunctionReturn(0); 2710 } 2711 2712 /* MatGetFactor for Seq and MPI SELL matrices */ 2713 static PetscErrorCode MatGetFactor_sell_mumps(Mat A,MatFactorType ftype,Mat *F) 2714 { 2715 Mat B; 2716 PetscErrorCode ierr; 2717 Mat_MUMPS *mumps; 2718 PetscBool isSeqSELL; 2719 2720 PetscFunctionBegin; 2721 /* Create the factorization matrix */ 2722 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQSELL,&isSeqSELL);CHKERRQ(ierr); 2723 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 2724 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 2725 ierr = PetscStrallocpy("mumps",&((PetscObject)B)->type_name);CHKERRQ(ierr); 2726 ierr = MatSetUp(B);CHKERRQ(ierr); 2727 2728 ierr = PetscNewLog(B,&mumps);CHKERRQ(ierr); 2729 2730 B->ops->view = MatView_MUMPS; 2731 B->ops->getinfo = MatGetInfo_MUMPS; 2732 2733 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);CHKERRQ(ierr); 2734 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);CHKERRQ(ierr); 2735 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);CHKERRQ(ierr); 2736 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 2737 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);CHKERRQ(ierr); 2738 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);CHKERRQ(ierr); 2739 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);CHKERRQ(ierr); 2740 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);CHKERRQ(ierr); 2741 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);CHKERRQ(ierr); 2742 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);CHKERRQ(ierr); 2743 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);CHKERRQ(ierr); 2744 2745 if (ftype == MAT_FACTOR_LU) { 2746 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS; 2747 B->factortype = MAT_FACTOR_LU; 2748 if (isSeqSELL) mumps->ConvertToTriples = MatConvertToTriples_seqsell_seqaij; 2749 else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"To be implemented"); 2750 mumps->sym = 0; 2751 } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"To be implemented"); 2752 2753 /* set solvertype */ 2754 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 2755 ierr = PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);CHKERRQ(ierr); 2756 2757 B->ops->destroy = MatDestroy_MUMPS; 2758 B->data = (void*)mumps; 2759 2760 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 2761 2762 *F = B; 2763 PetscFunctionReturn(0); 2764 } 2765 2766 PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MUMPS(void) 2767 { 2768 PetscErrorCode ierr; 2769 2770 PetscFunctionBegin; 2771 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2772 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2773 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2774 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2775 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATMPISBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);CHKERRQ(ierr); 2776 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2777 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);CHKERRQ(ierr); 2778 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2779 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);CHKERRQ(ierr); 2780 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQSBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);CHKERRQ(ierr); 2781 ierr = MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQSELL,MAT_FACTOR_LU,MatGetFactor_sell_mumps);CHKERRQ(ierr); 2782 PetscFunctionReturn(0); 2783 } 2784 2785