1 2 /* 3 Provides an interface to the MUMPS sparse solver 4 */ 5 6 #include <../src/mat/impls/aij/mpi/mpiaij.h> /*I "petscmat.h" I*/ 7 #include <../src/mat/impls/sbaij/mpi/mpisbaij.h> 8 9 EXTERN_C_BEGIN 10 #if defined(PETSC_USE_COMPLEX) 11 #include <zmumps_c.h> 12 #else 13 #include <dmumps_c.h> 14 #endif 15 EXTERN_C_END 16 #define JOB_INIT -1 17 #define JOB_FACTSYMBOLIC 1 18 #define JOB_FACTNUMERIC 2 19 #define JOB_SOLVE 3 20 #define JOB_END -2 21 22 23 /* macros s.t. indices match MUMPS documentation */ 24 #define ICNTL(I) icntl[(I)-1] 25 #define CNTL(I) cntl[(I)-1] 26 #define INFOG(I) infog[(I)-1] 27 #define INFO(I) info[(I)-1] 28 #define RINFOG(I) rinfog[(I)-1] 29 #define RINFO(I) rinfo[(I)-1] 30 31 typedef struct { 32 #if defined(PETSC_USE_COMPLEX) 33 ZMUMPS_STRUC_C id; 34 #else 35 DMUMPS_STRUC_C id; 36 #endif 37 MatStructure matstruc; 38 PetscMPIInt myid,size; 39 PetscInt *irn,*jcn,nz,sym,nSolve; 40 PetscScalar *val; 41 MPI_Comm comm_mumps; 42 VecScatter scat_rhs, scat_sol; 43 PetscBool isAIJ,CleanUpMUMPS; 44 Vec b_seq,x_seq; 45 PetscErrorCode (*Destroy)(Mat); 46 PetscErrorCode (*ConvertToTriples)(Mat, int, MatReuse, int*, int**, int**, PetscScalar**); 47 } Mat_MUMPS; 48 49 extern PetscErrorCode MatDuplicate_MUMPS(Mat,MatDuplicateOption,Mat*); 50 51 52 /* MatConvertToTriples_A_B */ 53 /*convert Petsc matrix to triples: row[nz], col[nz], val[nz] */ 54 /* 55 input: 56 A - matrix in aij,baij or sbaij (bs=1) format 57 shift - 0: C style output triple; 1: Fortran style output triple. 58 reuse - MAT_INITIAL_MATRIX: spaces are allocated and values are set for the triple 59 MAT_REUSE_MATRIX: only the values in v array are updated 60 output: 61 nnz - dim of r, c, and v (number of local nonzero entries of A) 62 r, c, v - row and col index, matrix values (matrix triples) 63 */ 64 65 #undef __FUNCT__ 66 #define __FUNCT__ "MatConvertToTriples_seqaij_seqaij" 67 PetscErrorCode MatConvertToTriples_seqaij_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 68 { 69 const PetscInt *ai,*aj,*ajj,M=A->rmap->n; 70 PetscInt nz,rnz,i,j; 71 PetscErrorCode ierr; 72 PetscInt *row,*col; 73 Mat_SeqAIJ *aa=(Mat_SeqAIJ*)A->data; 74 75 PetscFunctionBegin; 76 *v=aa->a; 77 if (reuse == MAT_INITIAL_MATRIX){ 78 nz = aa->nz; ai = aa->i; aj = aa->j; 79 *nnz = nz; 80 ierr = PetscMalloc(2*nz*sizeof(PetscInt), &row);CHKERRQ(ierr); 81 col = row + nz; 82 83 nz = 0; 84 for(i=0; i<M; i++) { 85 rnz = ai[i+1] - ai[i]; 86 ajj = aj + ai[i]; 87 for(j=0; j<rnz; j++) { 88 row[nz] = i+shift; col[nz++] = ajj[j] + shift; 89 } 90 } 91 *r = row; *c = col; 92 } 93 PetscFunctionReturn(0); 94 } 95 96 #undef __FUNCT__ 97 #define __FUNCT__ "MatConvertToTriples_seqbaij_seqaij" 98 PetscErrorCode MatConvertToTriples_seqbaij_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 99 { 100 Mat_SeqBAIJ *aa=(Mat_SeqBAIJ*)A->data; 101 const PetscInt *ai,*aj,*ajj,bs=A->rmap->bs,bs2=aa->bs2,M=A->rmap->N/bs; 102 PetscInt nz,idx=0,rnz,i,j,k,m; 103 PetscErrorCode ierr; 104 PetscInt *row,*col; 105 106 PetscFunctionBegin; 107 *v = aa->a; 108 if (reuse == MAT_INITIAL_MATRIX){ 109 ai = aa->i; aj = aa->j; 110 nz = bs2*aa->nz; 111 *nnz = nz; 112 ierr = PetscMalloc(2*nz*sizeof(PetscInt), &row);CHKERRQ(ierr); 113 col = row + nz; 114 115 for(i=0; i<M; i++) { 116 ajj = aj + ai[i]; 117 rnz = ai[i+1] - ai[i]; 118 for(k=0; k<rnz; k++) { 119 for(j=0; j<bs; j++) { 120 for(m=0; m<bs; m++) { 121 row[idx] = i*bs + m + shift; 122 col[idx++] = bs*(ajj[k]) + j + shift; 123 } 124 } 125 } 126 } 127 *r = row; *c = col; 128 } 129 PetscFunctionReturn(0); 130 } 131 132 #undef __FUNCT__ 133 #define __FUNCT__ "MatConvertToTriples_seqsbaij_seqsbaij" 134 PetscErrorCode MatConvertToTriples_seqsbaij_seqsbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 135 { 136 const PetscInt *ai, *aj,*ajj,M=A->rmap->n; 137 PetscInt nz,rnz,i,j; 138 PetscErrorCode ierr; 139 PetscInt *row,*col; 140 Mat_SeqSBAIJ *aa=(Mat_SeqSBAIJ*)A->data; 141 142 PetscFunctionBegin; 143 if (reuse == MAT_INITIAL_MATRIX){ 144 nz = aa->nz;ai=aa->i; aj=aa->j;*v=aa->a; 145 *nnz = nz; 146 ierr = PetscMalloc(2*nz*sizeof(PetscInt), &row);CHKERRQ(ierr); 147 col = row + nz; 148 149 nz = 0; 150 for(i=0; i<M; i++) { 151 rnz = ai[i+1] - ai[i]; 152 ajj = aj + ai[i]; 153 for(j=0; j<rnz; j++) { 154 row[nz] = i+shift; col[nz++] = ajj[j] + shift; 155 } 156 } 157 *r = row; *c = col; 158 } 159 PetscFunctionReturn(0); 160 } 161 162 #undef __FUNCT__ 163 #define __FUNCT__ "MatConvertToTriples_seqaij_seqsbaij" 164 PetscErrorCode MatConvertToTriples_seqaij_seqsbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 165 { 166 const PetscInt *ai,*aj,*ajj,*adiag,M=A->rmap->n; 167 PetscInt nz,rnz,i,j; 168 const PetscScalar *av,*v1; 169 PetscScalar *val; 170 PetscErrorCode ierr; 171 PetscInt *row,*col; 172 Mat_SeqSBAIJ *aa=(Mat_SeqSBAIJ*)A->data; 173 174 PetscFunctionBegin; 175 ai=aa->i; aj=aa->j;av=aa->a; 176 adiag=aa->diag; 177 if (reuse == MAT_INITIAL_MATRIX){ 178 nz = M + (aa->nz-M)/2; 179 *nnz = nz; 180 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 181 col = row + nz; 182 val = (PetscScalar*)(col + nz); 183 184 nz = 0; 185 for(i=0; i<M; i++) { 186 rnz = ai[i+1] - adiag[i]; 187 ajj = aj + adiag[i]; 188 v1 = av + adiag[i]; 189 for(j=0; j<rnz; j++) { 190 row[nz] = i+shift; col[nz] = ajj[j] + shift; val[nz++] = v1[j]; 191 } 192 } 193 *r = row; *c = col; *v = val; 194 } else { 195 nz = 0; val = *v; 196 for(i=0; i <M; i++) { 197 rnz = ai[i+1] - adiag[i]; 198 ajj = aj + adiag[i]; 199 v1 = av + adiag[i]; 200 for(j=0; j<rnz; j++) { 201 val[nz++] = v1[j]; 202 } 203 } 204 } 205 PetscFunctionReturn(0); 206 } 207 208 #undef __FUNCT__ 209 #define __FUNCT__ "MatConvertToTriples_mpisbaij_mpisbaij" 210 PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 211 { 212 const PetscInt *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj; 213 PetscErrorCode ierr; 214 PetscInt rstart,nz,i,j,jj,irow,countA,countB; 215 PetscInt *row,*col; 216 const PetscScalar *av, *bv,*v1,*v2; 217 PetscScalar *val; 218 Mat_MPISBAIJ *mat = (Mat_MPISBAIJ*)A->data; 219 Mat_SeqSBAIJ *aa=(Mat_SeqSBAIJ*)(mat->A)->data; 220 Mat_SeqBAIJ *bb=(Mat_SeqBAIJ*)(mat->B)->data; 221 222 PetscFunctionBegin; 223 ai=aa->i; aj=aa->j; bi=bb->i; bj=bb->j; rstart= A->rmap->rstart; 224 garray = mat->garray; 225 av=aa->a; bv=bb->a; 226 227 if (reuse == MAT_INITIAL_MATRIX){ 228 nz = aa->nz + bb->nz; 229 *nnz = nz; 230 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 231 col = row + nz; 232 val = (PetscScalar*)(col + nz); 233 234 *r = row; *c = col; *v = val; 235 } else { 236 row = *r; col = *c; val = *v; 237 } 238 239 jj = 0; irow = rstart; 240 for ( i=0; i<m; i++ ) { 241 ajj = aj + ai[i]; /* ptr to the beginning of this row */ 242 countA = ai[i+1] - ai[i]; 243 countB = bi[i+1] - bi[i]; 244 bjj = bj + bi[i]; 245 v1 = av + ai[i]; 246 v2 = bv + bi[i]; 247 248 /* A-part */ 249 for (j=0; j<countA; j++){ 250 if (reuse == MAT_INITIAL_MATRIX) { 251 row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift; 252 } 253 val[jj++] = v1[j]; 254 } 255 256 /* B-part */ 257 for(j=0; j < countB; j++){ 258 if (reuse == MAT_INITIAL_MATRIX) { 259 row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift; 260 } 261 val[jj++] = v2[j]; 262 } 263 irow++; 264 } 265 PetscFunctionReturn(0); 266 } 267 268 #undef __FUNCT__ 269 #define __FUNCT__ "MatConvertToTriples_mpiaij_mpiaij" 270 PetscErrorCode MatConvertToTriples_mpiaij_mpiaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 271 { 272 const PetscInt *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj; 273 PetscErrorCode ierr; 274 PetscInt rstart,nz,i,j,jj,irow,countA,countB; 275 PetscInt *row,*col; 276 const PetscScalar *av, *bv,*v1,*v2; 277 PetscScalar *val; 278 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data; 279 Mat_SeqAIJ *aa=(Mat_SeqAIJ*)(mat->A)->data; 280 Mat_SeqAIJ *bb=(Mat_SeqAIJ*)(mat->B)->data; 281 282 PetscFunctionBegin; 283 ai=aa->i; aj=aa->j; bi=bb->i; bj=bb->j; rstart= A->rmap->rstart; 284 garray = mat->garray; 285 av=aa->a; bv=bb->a; 286 287 if (reuse == MAT_INITIAL_MATRIX){ 288 nz = aa->nz + bb->nz; 289 *nnz = nz; 290 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 291 col = row + nz; 292 val = (PetscScalar*)(col + nz); 293 294 *r = row; *c = col; *v = val; 295 } else { 296 row = *r; col = *c; val = *v; 297 } 298 299 jj = 0; irow = rstart; 300 for ( i=0; i<m; i++ ) { 301 ajj = aj + ai[i]; /* ptr to the beginning of this row */ 302 countA = ai[i+1] - ai[i]; 303 countB = bi[i+1] - bi[i]; 304 bjj = bj + bi[i]; 305 v1 = av + ai[i]; 306 v2 = bv + bi[i]; 307 308 /* A-part */ 309 for (j=0; j<countA; j++){ 310 if (reuse == MAT_INITIAL_MATRIX){ 311 row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift; 312 } 313 val[jj++] = v1[j]; 314 } 315 316 /* B-part */ 317 for(j=0; j < countB; j++){ 318 if (reuse == MAT_INITIAL_MATRIX){ 319 row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift; 320 } 321 val[jj++] = v2[j]; 322 } 323 irow++; 324 } 325 PetscFunctionReturn(0); 326 } 327 328 #undef __FUNCT__ 329 #define __FUNCT__ "MatConvertToTriples_mpibaij_mpiaij" 330 PetscErrorCode MatConvertToTriples_mpibaij_mpiaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 331 { 332 Mat_MPIBAIJ *mat = (Mat_MPIBAIJ*)A->data; 333 Mat_SeqBAIJ *aa=(Mat_SeqBAIJ*)(mat->A)->data; 334 Mat_SeqBAIJ *bb=(Mat_SeqBAIJ*)(mat->B)->data; 335 const PetscInt *ai = aa->i, *bi = bb->i, *aj = aa->j, *bj = bb->j,*ajj, *bjj; 336 const PetscInt *garray = mat->garray,mbs=mat->mbs,rstart=A->rmap->rstart; 337 const PetscInt bs = A->rmap->bs,bs2=mat->bs2; 338 PetscErrorCode ierr; 339 PetscInt nz,i,j,k,n,jj,irow,countA,countB,idx; 340 PetscInt *row,*col; 341 const PetscScalar *av=aa->a, *bv=bb->a,*v1,*v2; 342 PetscScalar *val; 343 344 PetscFunctionBegin; 345 346 if (reuse == MAT_INITIAL_MATRIX) { 347 nz = bs2*(aa->nz + bb->nz); 348 *nnz = nz; 349 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 350 col = row + nz; 351 val = (PetscScalar*)(col + nz); 352 353 *r = row; *c = col; *v = val; 354 } else { 355 row = *r; col = *c; val = *v; 356 } 357 358 jj = 0; irow = rstart; 359 for ( i=0; i<mbs; i++ ) { 360 countA = ai[i+1] - ai[i]; 361 countB = bi[i+1] - bi[i]; 362 ajj = aj + ai[i]; 363 bjj = bj + bi[i]; 364 v1 = av + bs2*ai[i]; 365 v2 = bv + bs2*bi[i]; 366 367 idx = 0; 368 /* A-part */ 369 for (k=0; k<countA; k++){ 370 for (j=0; j<bs; j++) { 371 for (n=0; n<bs; n++) { 372 if (reuse == MAT_INITIAL_MATRIX){ 373 row[jj] = irow + n + shift; 374 col[jj] = rstart + bs*ajj[k] + j + shift; 375 } 376 val[jj++] = v1[idx++]; 377 } 378 } 379 } 380 381 idx = 0; 382 /* B-part */ 383 for(k=0; k<countB; k++){ 384 for (j=0; j<bs; j++) { 385 for (n=0; n<bs; n++) { 386 if (reuse == MAT_INITIAL_MATRIX){ 387 row[jj] = irow + n + shift; 388 col[jj] = bs*garray[bjj[k]] + j + shift; 389 } 390 val[jj++] = v2[idx++]; 391 } 392 } 393 } 394 irow += bs; 395 } 396 PetscFunctionReturn(0); 397 } 398 399 #undef __FUNCT__ 400 #define __FUNCT__ "MatConvertToTriples_mpiaij_mpisbaij" 401 PetscErrorCode MatConvertToTriples_mpiaij_mpisbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 402 { 403 const PetscInt *ai, *aj,*adiag, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj; 404 PetscErrorCode ierr; 405 PetscInt rstart,nz,nza,nzb,i,j,jj,irow,countA,countB; 406 PetscInt *row,*col; 407 const PetscScalar *av, *bv,*v1,*v2; 408 PetscScalar *val; 409 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data; 410 Mat_SeqAIJ *aa=(Mat_SeqAIJ*)(mat->A)->data; 411 Mat_SeqAIJ *bb=(Mat_SeqAIJ*)(mat->B)->data; 412 413 PetscFunctionBegin; 414 ai=aa->i; aj=aa->j; adiag=aa->diag; 415 bi=bb->i; bj=bb->j; garray = mat->garray; 416 av=aa->a; bv=bb->a; 417 rstart = A->rmap->rstart; 418 419 if (reuse == MAT_INITIAL_MATRIX) { 420 nza = 0; /* num of upper triangular entries in mat->A, including diagonals */ 421 nzb = 0; /* num of upper triangular entries in mat->B */ 422 for(i=0; i<m; i++){ 423 nza += (ai[i+1] - adiag[i]); 424 countB = bi[i+1] - bi[i]; 425 bjj = bj + bi[i]; 426 for (j=0; j<countB; j++){ 427 if (garray[bjj[j]] > rstart) nzb++; 428 } 429 } 430 431 nz = nza + nzb; /* total nz of upper triangular part of mat */ 432 *nnz = nz; 433 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 434 col = row + nz; 435 val = (PetscScalar*)(col + nz); 436 437 *r = row; *c = col; *v = val; 438 } else { 439 row = *r; col = *c; val = *v; 440 } 441 442 jj = 0; irow = rstart; 443 for ( i=0; i<m; i++ ) { 444 ajj = aj + adiag[i]; /* ptr to the beginning of the diagonal of this row */ 445 v1 = av + adiag[i]; 446 countA = ai[i+1] - adiag[i]; 447 countB = bi[i+1] - bi[i]; 448 bjj = bj + bi[i]; 449 v2 = bv + bi[i]; 450 451 /* A-part */ 452 for (j=0; j<countA; j++){ 453 if (reuse == MAT_INITIAL_MATRIX) { 454 row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift; 455 } 456 val[jj++] = v1[j]; 457 } 458 459 /* B-part */ 460 for(j=0; j < countB; j++){ 461 if (garray[bjj[j]] > rstart) { 462 if (reuse == MAT_INITIAL_MATRIX) { 463 row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift; 464 } 465 val[jj++] = v2[j]; 466 } 467 } 468 irow++; 469 } 470 PetscFunctionReturn(0); 471 } 472 473 #undef __FUNCT__ 474 #define __FUNCT__ "MatDestroy_MUMPS" 475 PetscErrorCode MatDestroy_MUMPS(Mat A) 476 { 477 Mat_MUMPS *lu=(Mat_MUMPS*)A->spptr; 478 PetscErrorCode ierr; 479 480 PetscFunctionBegin; 481 if (lu && lu->CleanUpMUMPS) { 482 /* Terminate instance, deallocate memories */ 483 ierr = PetscFree2(lu->id.sol_loc,lu->id.isol_loc);CHKERRQ(ierr); 484 ierr = VecScatterDestroy(&lu->scat_rhs);CHKERRQ(ierr); 485 ierr = VecDestroy(&lu->b_seq);CHKERRQ(ierr); 486 ierr = VecScatterDestroy(&lu->scat_sol);CHKERRQ(ierr); 487 ierr = VecDestroy(&lu->x_seq);CHKERRQ(ierr); 488 ierr=PetscFree(lu->id.perm_in);CHKERRQ(ierr); 489 ierr = PetscFree(lu->irn);CHKERRQ(ierr); 490 lu->id.job=JOB_END; 491 #if defined(PETSC_USE_COMPLEX) 492 zmumps_c(&lu->id); 493 #else 494 dmumps_c(&lu->id); 495 #endif 496 ierr = MPI_Comm_free(&(lu->comm_mumps));CHKERRQ(ierr); 497 } 498 if (lu && lu->Destroy) { 499 ierr = (lu->Destroy)(A);CHKERRQ(ierr); 500 } 501 ierr = PetscFree(A->spptr);CHKERRQ(ierr); 502 503 /* clear composed functions */ 504 ierr = PetscObjectComposeFunctionDynamic((PetscObject)A,"MatFactorGetSolverPackage_C","",PETSC_NULL);CHKERRQ(ierr); 505 ierr = PetscObjectComposeFunctionDynamic((PetscObject)A,"MatMumpsSetIcntl_C","",PETSC_NULL);CHKERRQ(ierr); 506 PetscFunctionReturn(0); 507 } 508 509 #undef __FUNCT__ 510 #define __FUNCT__ "MatSolve_MUMPS" 511 PetscErrorCode MatSolve_MUMPS(Mat A,Vec b,Vec x) 512 { 513 Mat_MUMPS *lu=(Mat_MUMPS*)A->spptr; 514 PetscScalar *array; 515 Vec b_seq; 516 IS is_iden,is_petsc; 517 PetscErrorCode ierr; 518 PetscInt i; 519 520 PetscFunctionBegin; 521 lu->id.nrhs = 1; 522 b_seq = lu->b_seq; 523 if (lu->size > 1){ 524 /* MUMPS only supports centralized rhs. Scatter b into a seqential rhs vector */ 525 ierr = VecScatterBegin(lu->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 526 ierr = VecScatterEnd(lu->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 527 if (!lu->myid) {ierr = VecGetArray(b_seq,&array);CHKERRQ(ierr);} 528 } else { /* size == 1 */ 529 ierr = VecCopy(b,x);CHKERRQ(ierr); 530 ierr = VecGetArray(x,&array);CHKERRQ(ierr); 531 } 532 if (!lu->myid) { /* define rhs on the host */ 533 lu->id.nrhs = 1; 534 #if defined(PETSC_USE_COMPLEX) 535 lu->id.rhs = (mumps_double_complex*)array; 536 #else 537 lu->id.rhs = array; 538 #endif 539 } 540 541 /* solve phase */ 542 /*-------------*/ 543 lu->id.job = JOB_SOLVE; 544 #if defined(PETSC_USE_COMPLEX) 545 zmumps_c(&lu->id); 546 #else 547 dmumps_c(&lu->id); 548 #endif 549 if (lu->id.INFOG(1) < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d\n",lu->id.INFOG(1)); 550 551 if (lu->size > 1) { /* convert mumps distributed solution to petsc mpi x */ 552 if (!lu->nSolve){ /* create scatter scat_sol */ 553 ierr = ISCreateStride(PETSC_COMM_SELF,lu->id.lsol_loc,0,1,&is_iden);CHKERRQ(ierr); /* from */ 554 for (i=0; i<lu->id.lsol_loc; i++){ 555 lu->id.isol_loc[i] -= 1; /* change Fortran style to C style */ 556 } 557 ierr = ISCreateGeneral(PETSC_COMM_SELF,lu->id.lsol_loc,lu->id.isol_loc,PETSC_COPY_VALUES,&is_petsc);CHKERRQ(ierr); /* to */ 558 ierr = VecScatterCreate(lu->x_seq,is_iden,x,is_petsc,&lu->scat_sol);CHKERRQ(ierr); 559 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 560 ierr = ISDestroy(&is_petsc);CHKERRQ(ierr); 561 } 562 ierr = VecScatterBegin(lu->scat_sol,lu->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 563 ierr = VecScatterEnd(lu->scat_sol,lu->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 564 } 565 lu->nSolve++; 566 PetscFunctionReturn(0); 567 } 568 569 #undef __FUNCT__ 570 #define __FUNCT__ "MatSolveTranspose_MUMPS" 571 PetscErrorCode MatSolveTranspose_MUMPS(Mat A,Vec b,Vec x) 572 { 573 Mat_MUMPS *lu=(Mat_MUMPS*)A->spptr; 574 PetscErrorCode ierr; 575 576 PetscFunctionBegin; 577 lu->id.ICNTL(9) = 0; 578 ierr = MatSolve_MUMPS(A,b,x);CHKERRQ(ierr); 579 lu->id.ICNTL(9) = 1; 580 PetscFunctionReturn(0); 581 } 582 583 #undef __FUNCT__ 584 #define __FUNCT__ "MatMatSolve_MUMPS" 585 PetscErrorCode MatMatSolve_MUMPS(Mat A,Mat B,Mat X) 586 { 587 PetscErrorCode ierr; 588 PetscBool flg; 589 590 PetscFunctionBegin; 591 ierr = PetscTypeCompareAny((PetscObject)B,&flg,MATSEQDENSE,MATMPIDENSE,PETSC_NULL);CHKERRQ(ierr); 592 if (!flg) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix"); 593 ierr = PetscTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,PETSC_NULL);CHKERRQ(ierr); 594 if (!flg) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix"); SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatMatSolve_MUMPS() is not implemented yet"); 595 PetscFunctionReturn(0); 596 } 597 598 #if !defined(PETSC_USE_COMPLEX) 599 /* 600 input: 601 F: numeric factor 602 output: 603 nneg: total number of negative pivots 604 nzero: 0 605 npos: (global dimension of F) - nneg 606 */ 607 608 #undef __FUNCT__ 609 #define __FUNCT__ "MatGetInertia_SBAIJMUMPS" 610 PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F,int *nneg,int *nzero,int *npos) 611 { 612 Mat_MUMPS *lu =(Mat_MUMPS*)F->spptr; 613 PetscErrorCode ierr; 614 PetscMPIInt size; 615 616 PetscFunctionBegin; 617 ierr = MPI_Comm_size(((PetscObject)F)->comm,&size);CHKERRQ(ierr); 618 /* 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 */ 619 if (size > 1 && lu->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",lu->id.INFOG(13)); 620 if (nneg){ 621 if (!lu->myid){ 622 *nneg = lu->id.INFOG(12); 623 } 624 ierr = MPI_Bcast(nneg,1,MPI_INT,0,lu->comm_mumps);CHKERRQ(ierr); 625 } 626 if (nzero) *nzero = 0; 627 if (npos) *npos = F->rmap->N - (*nneg); 628 PetscFunctionReturn(0); 629 } 630 #endif /* !defined(PETSC_USE_COMPLEX) */ 631 632 #undef __FUNCT__ 633 #define __FUNCT__ "MatFactorNumeric_MUMPS" 634 PetscErrorCode MatFactorNumeric_MUMPS(Mat F,Mat A,const MatFactorInfo *info) 635 { 636 Mat_MUMPS *lu =(Mat_MUMPS*)(F)->spptr; 637 PetscErrorCode ierr; 638 MatReuse reuse; 639 Mat F_diag; 640 PetscBool isMPIAIJ; 641 642 PetscFunctionBegin; 643 reuse = MAT_REUSE_MATRIX; 644 ierr = (*lu->ConvertToTriples)(A, 1, reuse, &lu->nz, &lu->irn, &lu->jcn, &lu->val);CHKERRQ(ierr); 645 646 /* numerical factorization phase */ 647 /*-------------------------------*/ 648 lu->id.job = JOB_FACTNUMERIC; 649 if(!lu->id.ICNTL(18)) { 650 if (!lu->myid) { 651 #if defined(PETSC_USE_COMPLEX) 652 lu->id.a = (mumps_double_complex*)lu->val; 653 #else 654 lu->id.a = lu->val; 655 #endif 656 } 657 } else { 658 #if defined(PETSC_USE_COMPLEX) 659 lu->id.a_loc = (mumps_double_complex*)lu->val; 660 #else 661 lu->id.a_loc = lu->val; 662 #endif 663 } 664 #if defined(PETSC_USE_COMPLEX) 665 zmumps_c(&lu->id); 666 #else 667 dmumps_c(&lu->id); 668 #endif 669 if (lu->id.INFOG(1) < 0) { 670 if (lu->id.INFO(1) == -13) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in numerical factorization phase: Cannot allocate required memory %d megabytes\n",lu->id.INFO(2)); 671 else SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in numerical factorization phase: INFO(1)=%d, INFO(2)=%d\n",lu->id.INFO(1),lu->id.INFO(2)); 672 } 673 if (!lu->myid && lu->id.ICNTL(16) > 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB," lu->id.ICNTL(16):=%d\n",lu->id.INFOG(16)); 674 675 if (lu->size > 1){ 676 ierr = PetscTypeCompare((PetscObject)A,MATMPIAIJ,&isMPIAIJ);CHKERRQ(ierr); 677 if(isMPIAIJ) { 678 F_diag = ((Mat_MPIAIJ *)(F)->data)->A; 679 } else { 680 F_diag = ((Mat_MPISBAIJ *)(F)->data)->A; 681 } 682 F_diag->assembled = PETSC_TRUE; 683 if (lu->nSolve){ 684 ierr = VecScatterDestroy(&lu->scat_sol);CHKERRQ(ierr); 685 ierr = PetscFree2(lu->id.sol_loc,lu->id.isol_loc);CHKERRQ(ierr); 686 ierr = VecDestroy(&lu->x_seq);CHKERRQ(ierr); 687 } 688 } 689 (F)->assembled = PETSC_TRUE; 690 lu->matstruc = SAME_NONZERO_PATTERN; 691 lu->CleanUpMUMPS = PETSC_TRUE; 692 lu->nSolve = 0; 693 694 if (lu->size > 1){ 695 /* distributed solution */ 696 lu->id.ICNTL(21) = 1; 697 if (!lu->nSolve){ 698 /* Create x_seq=sol_loc for repeated use */ 699 PetscInt lsol_loc; 700 PetscScalar *sol_loc; 701 lsol_loc = lu->id.INFO(23); /* length of sol_loc */ 702 ierr = PetscMalloc2(lsol_loc,PetscScalar,&sol_loc,lsol_loc,PetscInt,&lu->id.isol_loc);CHKERRQ(ierr); 703 lu->id.lsol_loc = lsol_loc; 704 #if defined(PETSC_USE_COMPLEX) 705 lu->id.sol_loc = (mumps_double_complex*)sol_loc; 706 #else 707 lu->id.sol_loc = sol_loc; 708 #endif 709 ierr = VecCreateSeqWithArray(PETSC_COMM_SELF,lsol_loc,sol_loc,&lu->x_seq);CHKERRQ(ierr); 710 } 711 } 712 PetscFunctionReturn(0); 713 } 714 715 /* Sets MUMPS options from the options database */ 716 #undef __FUNCT__ 717 #define __FUNCT__ "PetscSetMUMPSFromOptions" 718 PetscErrorCode PetscSetMUMPSFromOptions(Mat F, Mat A) 719 { 720 Mat_MUMPS *mumps = (Mat_MUMPS*)F->spptr; 721 PetscErrorCode ierr; 722 PetscInt icntl; 723 PetscBool flg; 724 725 PetscFunctionBegin; 726 ierr = PetscOptionsBegin(((PetscObject)A)->comm,((PetscObject)A)->prefix,"MUMPS Options","Mat");CHKERRQ(ierr); 727 ierr = PetscOptionsInt("-mat_mumps_icntl_1","ICNTL(1): output stream for error messages","None",mumps->id.ICNTL(1),&icntl,&flg);CHKERRQ(ierr); 728 if (flg) mumps->id.ICNTL(1) = icntl; 729 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); 730 if (flg) mumps->id.ICNTL(2) = icntl; 731 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); 732 if (flg) mumps->id.ICNTL(3) = icntl; 733 734 ierr = PetscOptionsInt("-mat_mumps_icntl_4","ICNTL(4): level of printing (0 to 4)","None",mumps->id.ICNTL(4),&icntl,&flg);CHKERRQ(ierr); 735 if (flg) mumps->id.ICNTL(4) = icntl; 736 if (mumps->id.ICNTL(4) || PetscLogPrintInfo ) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */ 737 738 ierr = PetscOptionsInt("-mat_mumps_icntl_6","ICNTL(6): permuting and/or scaling the matrix (0 to 7)","None",mumps->id.ICNTL(6),&icntl,&flg);CHKERRQ(ierr); 739 if (flg) mumps->id.ICNTL(6) = icntl; 740 741 ierr = PetscOptionsInt("-mat_mumps_icntl_7","ICNTL(7): matrix ordering (0 to 7). 3=Scotch, 4=PORD, 5=Metis","None",mumps->id.ICNTL(7),&icntl,&flg);CHKERRQ(ierr); 742 if (flg) { 743 if (icntl== 1 && mumps->size > 1){ 744 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"); 745 } else { 746 mumps->id.ICNTL(7) = icntl; 747 } 748 } 749 750 ierr = PetscOptionsInt("-mat_mumps_icntl_8","ICNTL(8): scaling strategy (-2 to 8 or 77)","None",mumps->id.ICNTL(8),&icntl,&flg);CHKERRQ(ierr); 751 if (flg) mumps->id.ICNTL(8) = icntl; 752 753 ierr = PetscOptionsInt("-mat_mumps_icntl_10","ICNTL(10): max num of refinements","None",mumps->id.ICNTL(10),&mumps->id.ICNTL(10),PETSC_NULL);CHKERRQ(ierr); 754 ierr = PetscOptionsInt("-mat_mumps_icntl_11","ICNTL(11): statistics related to the linear system solved (via -ksp_view)","None",mumps->id.ICNTL(11),&mumps->id.ICNTL(11),PETSC_NULL);CHKERRQ(ierr); 755 ierr = PetscOptionsInt("-mat_mumps_icntl_12","ICNTL(12): efficiency control: defines the ordering strategy with scaling constraints (0 to 3","None",mumps->id.ICNTL(12),&mumps->id.ICNTL(12),PETSC_NULL);CHKERRQ(ierr); 756 ierr = PetscOptionsInt("-mat_mumps_icntl_13","ICNTL(13): efficiency control: with or without ScaLAPACK","None",mumps->id.ICNTL(13),&mumps->id.ICNTL(13),PETSC_NULL);CHKERRQ(ierr); 757 ierr = PetscOptionsInt("-mat_mumps_icntl_14","ICNTL(14): percentage of estimated workspace increase","None",mumps->id.ICNTL(14),&mumps->id.ICNTL(14),PETSC_NULL);CHKERRQ(ierr); 758 ierr = PetscOptionsInt("-mat_mumps_icntl_19","ICNTL(19): Schur complement","None",mumps->id.ICNTL(19),&mumps->id.ICNTL(19),PETSC_NULL);CHKERRQ(ierr); 759 760 ierr = PetscOptionsInt("-mat_mumps_icntl_22","ICNTL(22): in-core/out-of-core facility (0 or 1)","None",mumps->id.ICNTL(22),&mumps->id.ICNTL(22),PETSC_NULL);CHKERRQ(ierr); 761 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),PETSC_NULL);CHKERRQ(ierr); 762 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),PETSC_NULL);CHKERRQ(ierr); 763 if (mumps->id.ICNTL(24)){ 764 mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */ 765 } 766 767 ierr = PetscOptionsInt("-mat_mumps_icntl_25","ICNTL(25): computation of a null space basis","None",mumps->id.ICNTL(25),&mumps->id.ICNTL(25),PETSC_NULL);CHKERRQ(ierr); 768 ierr = PetscOptionsInt("-mat_mumps_icntl_26","ICNTL(26): Schur options for right-hand side or solution vector","None",mumps->id.ICNTL(26),&mumps->id.ICNTL(26),PETSC_NULL);CHKERRQ(ierr); 769 ierr = PetscOptionsInt("-mat_mumps_icntl_27","ICNTL(27): experimental parameter","None",mumps->id.ICNTL(27),&mumps->id.ICNTL(27),PETSC_NULL);CHKERRQ(ierr); 770 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),PETSC_NULL);CHKERRQ(ierr); 771 ierr = PetscOptionsInt("-mat_mumps_icntl_29","ICNTL(29): parallel ordering 1 = ptscotch 2 = parmetis","None",mumps->id.ICNTL(29),&mumps->id.ICNTL(29),PETSC_NULL);CHKERRQ(ierr); 772 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),PETSC_NULL);CHKERRQ(ierr); 773 ierr = PetscOptionsInt("-mat_mumps_icntl_31","ICNTL(31): factors can be discarded in the solve phase","None",mumps->id.ICNTL(31),&mumps->id.ICNTL(31),PETSC_NULL);CHKERRQ(ierr); 774 ierr = PetscOptionsInt("-mat_mumps_icntl_33","ICNTL(33): compute determinant","None",mumps->id.ICNTL(33),&mumps->id.ICNTL(33),PETSC_NULL);CHKERRQ(ierr); 775 776 ierr = PetscOptionsReal("-mat_mumps_cntl_1","CNTL(1): relative pivoting threshold","None",mumps->id.CNTL(1),&mumps->id.CNTL(1),PETSC_NULL);CHKERRQ(ierr); 777 ierr = PetscOptionsReal("-mat_mumps_cntl_2","CNTL(2): stopping criterion of refinement","None",mumps->id.CNTL(2),&mumps->id.CNTL(2),PETSC_NULL);CHKERRQ(ierr); 778 ierr = PetscOptionsReal("-mat_mumps_cntl_3","CNTL(3): absolute pivoting threshold","None",mumps->id.CNTL(3),&mumps->id.CNTL(3),PETSC_NULL);CHKERRQ(ierr); 779 ierr = PetscOptionsReal("-mat_mumps_cntl_4","CNTL(4): value for static pivoting","None",mumps->id.CNTL(4),&mumps->id.CNTL(4),PETSC_NULL);CHKERRQ(ierr); 780 ierr = PetscOptionsReal("-mat_mumps_cntl_5","CNTL(5): fixation for null pivots","None",mumps->id.CNTL(5),&mumps->id.CNTL(5),PETSC_NULL);CHKERRQ(ierr); 781 PetscOptionsEnd(); 782 PetscFunctionReturn(0); 783 } 784 785 #undef __FUNCT__ 786 #define __FUNCT__ "PetscInitializeMUMPS" 787 PetscErrorCode PetscInitializeMUMPS(Mat A,Mat_MUMPS* mumps) 788 { 789 PetscErrorCode ierr; 790 791 PetscFunctionBegin; 792 ierr = MPI_Comm_rank(((PetscObject)A)->comm, &mumps->myid); 793 ierr = MPI_Comm_size(((PetscObject)A)->comm,&mumps->size);CHKERRQ(ierr); 794 ierr = MPI_Comm_dup(((PetscObject)A)->comm,&(mumps->comm_mumps));CHKERRQ(ierr); 795 mumps->id.comm_fortran = MPI_Comm_c2f(mumps->comm_mumps); 796 797 mumps->id.job = JOB_INIT; 798 mumps->id.par = 1; /* host participates factorizaton and solve */ 799 mumps->id.sym = mumps->sym; 800 #if defined(PETSC_USE_COMPLEX) 801 zmumps_c(&mumps->id); 802 #else 803 dmumps_c(&mumps->id); 804 #endif 805 806 mumps->CleanUpMUMPS = PETSC_FALSE; 807 mumps->scat_rhs = PETSC_NULL; 808 mumps->scat_sol = PETSC_NULL; 809 mumps->nSolve = 0; 810 811 /* set PETSc-MUMPS default options */ 812 mumps->id.ICNTL(3) = 0; 813 mumps->id.ICNTL(4) = 0; 814 815 if (mumps->size == 1){ 816 mumps->id.ICNTL(18) = 0; /* centralized assembled matrix input */ 817 } else { 818 mumps->id.ICNTL(18) = 3; /* distributed assembled matrix input */ 819 } 820 PetscFunctionReturn(0); 821 } 822 823 /* Note the Petsc r and c permutations are ignored */ 824 #undef __FUNCT__ 825 #define __FUNCT__ "MatLUFactorSymbolic_AIJMUMPS" 826 PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 827 { 828 Mat_MUMPS *lu = (Mat_MUMPS*)F->spptr; 829 PetscErrorCode ierr; 830 MatReuse reuse; 831 Vec b; 832 IS is_iden; 833 const PetscInt M = A->rmap->N; 834 835 PetscFunctionBegin; 836 lu->matstruc = DIFFERENT_NONZERO_PATTERN; 837 838 /* Set MUMPS options from the options database */ 839 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 840 841 reuse = MAT_INITIAL_MATRIX; 842 ierr = (*lu->ConvertToTriples)(A, 1, reuse, &lu->nz, &lu->irn, &lu->jcn, &lu->val);CHKERRQ(ierr); 843 844 /* analysis phase */ 845 /*----------------*/ 846 lu->id.job = JOB_FACTSYMBOLIC; 847 lu->id.n = M; 848 switch (lu->id.ICNTL(18)){ 849 case 0: /* centralized assembled matrix input */ 850 if (!lu->myid) { 851 lu->id.nz =lu->nz; lu->id.irn=lu->irn; lu->id.jcn=lu->jcn; 852 if (lu->id.ICNTL(6)>1){ 853 #if defined(PETSC_USE_COMPLEX) 854 lu->id.a = (mumps_double_complex*)lu->val; 855 #else 856 lu->id.a = lu->val; 857 #endif 858 } 859 if (lu->id.ICNTL(7) == 1){ /* use user-provide matrix ordering */ 860 if (!lu->myid) { 861 const PetscInt *idx; 862 PetscInt i,*perm_in; 863 ierr = PetscMalloc(M*sizeof(PetscInt),&perm_in);CHKERRQ(ierr); 864 ierr = ISGetIndices(r,&idx);CHKERRQ(ierr); 865 lu->id.perm_in = perm_in; 866 for (i=0; i<M; i++) perm_in[i] = idx[i]+1; /* perm_in[]: start from 1, not 0! */ 867 ierr = ISRestoreIndices(r,&idx);CHKERRQ(ierr); 868 } 869 } 870 } 871 break; 872 case 3: /* distributed assembled matrix input (size>1) */ 873 lu->id.nz_loc = lu->nz; 874 lu->id.irn_loc=lu->irn; lu->id.jcn_loc=lu->jcn; 875 if (lu->id.ICNTL(6)>1) { 876 #if defined(PETSC_USE_COMPLEX) 877 lu->id.a_loc = (mumps_double_complex*)lu->val; 878 #else 879 lu->id.a_loc = lu->val; 880 #endif 881 } 882 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 883 if (!lu->myid){ 884 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&lu->b_seq);CHKERRQ(ierr); 885 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 886 } else { 887 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&lu->b_seq);CHKERRQ(ierr); 888 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 889 } 890 ierr = VecCreate(((PetscObject)A)->comm,&b);CHKERRQ(ierr); 891 ierr = VecSetSizes(b,A->rmap->n,PETSC_DECIDE);CHKERRQ(ierr); 892 ierr = VecSetFromOptions(b);CHKERRQ(ierr); 893 894 ierr = VecScatterCreate(b,is_iden,lu->b_seq,is_iden,&lu->scat_rhs);CHKERRQ(ierr); 895 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 896 ierr = VecDestroy(&b);CHKERRQ(ierr); 897 break; 898 } 899 #if defined(PETSC_USE_COMPLEX) 900 zmumps_c(&lu->id); 901 #else 902 dmumps_c(&lu->id); 903 #endif 904 if (lu->id.INFOG(1) < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d\n",lu->id.INFOG(1)); 905 906 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 907 F->ops->solve = MatSolve_MUMPS; 908 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 909 F->ops->matsolve = MatMatSolve_MUMPS; 910 PetscFunctionReturn(0); 911 } 912 913 /* Note the Petsc r and c permutations are ignored */ 914 #undef __FUNCT__ 915 #define __FUNCT__ "MatLUFactorSymbolic_BAIJMUMPS" 916 PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 917 { 918 919 Mat_MUMPS *lu = (Mat_MUMPS*)F->spptr; 920 PetscErrorCode ierr; 921 MatReuse reuse; 922 Vec b; 923 IS is_iden; 924 const PetscInt M = A->rmap->N; 925 926 PetscFunctionBegin; 927 lu->matstruc = DIFFERENT_NONZERO_PATTERN; 928 929 /* Set MUMPS options from the options database */ 930 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 931 932 reuse = MAT_INITIAL_MATRIX; 933 ierr = (*lu->ConvertToTriples)(A, 1, reuse, &lu->nz, &lu->irn, &lu->jcn, &lu->val);CHKERRQ(ierr); 934 935 /* analysis phase */ 936 /*----------------*/ 937 lu->id.job = JOB_FACTSYMBOLIC; 938 lu->id.n = M; 939 switch (lu->id.ICNTL(18)){ 940 case 0: /* centralized assembled matrix input */ 941 if (!lu->myid) { 942 lu->id.nz =lu->nz; lu->id.irn=lu->irn; lu->id.jcn=lu->jcn; 943 if (lu->id.ICNTL(6)>1){ 944 #if defined(PETSC_USE_COMPLEX) 945 lu->id.a = (mumps_double_complex*)lu->val; 946 #else 947 lu->id.a = lu->val; 948 #endif 949 } 950 } 951 break; 952 case 3: /* distributed assembled matrix input (size>1) */ 953 lu->id.nz_loc = lu->nz; 954 lu->id.irn_loc=lu->irn; lu->id.jcn_loc=lu->jcn; 955 if (lu->id.ICNTL(6)>1) { 956 #if defined(PETSC_USE_COMPLEX) 957 lu->id.a_loc = (mumps_double_complex*)lu->val; 958 #else 959 lu->id.a_loc = lu->val; 960 #endif 961 } 962 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 963 if (!lu->myid){ 964 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&lu->b_seq);CHKERRQ(ierr); 965 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 966 } else { 967 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&lu->b_seq);CHKERRQ(ierr); 968 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 969 } 970 ierr = VecCreate(((PetscObject)A)->comm,&b);CHKERRQ(ierr); 971 ierr = VecSetSizes(b,A->rmap->n,PETSC_DECIDE);CHKERRQ(ierr); 972 ierr = VecSetFromOptions(b);CHKERRQ(ierr); 973 974 ierr = VecScatterCreate(b,is_iden,lu->b_seq,is_iden,&lu->scat_rhs);CHKERRQ(ierr); 975 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 976 ierr = VecDestroy(&b);CHKERRQ(ierr); 977 break; 978 } 979 #if defined(PETSC_USE_COMPLEX) 980 zmumps_c(&lu->id); 981 #else 982 dmumps_c(&lu->id); 983 #endif 984 if (lu->id.INFOG(1) < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d\n",lu->id.INFOG(1)); 985 986 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 987 F->ops->solve = MatSolve_MUMPS; 988 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 989 PetscFunctionReturn(0); 990 } 991 992 /* Note the Petsc r permutation and factor info are ignored */ 993 #undef __FUNCT__ 994 #define __FUNCT__ "MatCholeskyFactorSymbolic_MUMPS" 995 PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F,Mat A,IS r,const MatFactorInfo *info) 996 { 997 Mat_MUMPS *lu = (Mat_MUMPS*)F->spptr; 998 PetscErrorCode ierr; 999 MatReuse reuse; 1000 Vec b; 1001 IS is_iden; 1002 const PetscInt M = A->rmap->N; 1003 1004 PetscFunctionBegin; 1005 lu->matstruc = DIFFERENT_NONZERO_PATTERN; 1006 1007 /* Set MUMPS options from the options database */ 1008 ierr = PetscSetMUMPSFromOptions(F,A);CHKERRQ(ierr); 1009 1010 reuse = MAT_INITIAL_MATRIX; 1011 ierr = (*lu->ConvertToTriples)(A, 1 , reuse, &lu->nz, &lu->irn, &lu->jcn, &lu->val);CHKERRQ(ierr); 1012 1013 /* analysis phase */ 1014 /*----------------*/ 1015 lu->id.job = JOB_FACTSYMBOLIC; 1016 lu->id.n = M; 1017 switch (lu->id.ICNTL(18)){ 1018 case 0: /* centralized assembled matrix input */ 1019 if (!lu->myid) { 1020 lu->id.nz =lu->nz; lu->id.irn=lu->irn; lu->id.jcn=lu->jcn; 1021 if (lu->id.ICNTL(6)>1){ 1022 #if defined(PETSC_USE_COMPLEX) 1023 lu->id.a = (mumps_double_complex*)lu->val; 1024 #else 1025 lu->id.a = lu->val; 1026 #endif 1027 } 1028 } 1029 break; 1030 case 3: /* distributed assembled matrix input (size>1) */ 1031 lu->id.nz_loc = lu->nz; 1032 lu->id.irn_loc=lu->irn; lu->id.jcn_loc=lu->jcn; 1033 if (lu->id.ICNTL(6)>1) { 1034 #if defined(PETSC_USE_COMPLEX) 1035 lu->id.a_loc = (mumps_double_complex*)lu->val; 1036 #else 1037 lu->id.a_loc = lu->val; 1038 #endif 1039 } 1040 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1041 if (!lu->myid){ 1042 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&lu->b_seq);CHKERRQ(ierr); 1043 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 1044 } else { 1045 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&lu->b_seq);CHKERRQ(ierr); 1046 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1047 } 1048 ierr = VecCreate(((PetscObject)A)->comm,&b);CHKERRQ(ierr); 1049 ierr = VecSetSizes(b,A->rmap->n,PETSC_DECIDE);CHKERRQ(ierr); 1050 ierr = VecSetFromOptions(b);CHKERRQ(ierr); 1051 1052 ierr = VecScatterCreate(b,is_iden,lu->b_seq,is_iden,&lu->scat_rhs);CHKERRQ(ierr); 1053 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1054 ierr = VecDestroy(&b);CHKERRQ(ierr); 1055 break; 1056 } 1057 #if defined(PETSC_USE_COMPLEX) 1058 zmumps_c(&lu->id); 1059 #else 1060 dmumps_c(&lu->id); 1061 #endif 1062 if (lu->id.INFOG(1) < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d\n",lu->id.INFOG(1)); 1063 1064 F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS; 1065 F->ops->solve = MatSolve_MUMPS; 1066 F->ops->solvetranspose = MatSolve_MUMPS; 1067 #if !defined(PETSC_USE_COMPLEX) 1068 F->ops->getinertia = MatGetInertia_SBAIJMUMPS; 1069 #else 1070 F->ops->getinertia = PETSC_NULL; 1071 #endif 1072 PetscFunctionReturn(0); 1073 } 1074 1075 #undef __FUNCT__ 1076 #define __FUNCT__ "MatView_MUMPS" 1077 PetscErrorCode MatView_MUMPS(Mat A,PetscViewer viewer) 1078 { 1079 PetscErrorCode ierr; 1080 PetscBool iascii; 1081 PetscViewerFormat format; 1082 Mat_MUMPS *lu=(Mat_MUMPS*)A->spptr; 1083 1084 PetscFunctionBegin; 1085 /* check if matrix is mumps type */ 1086 if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(0); 1087 1088 ierr = PetscTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 1089 if (iascii) { 1090 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 1091 if (format == PETSC_VIEWER_ASCII_INFO){ 1092 ierr = PetscViewerASCIIPrintf(viewer,"MUMPS run parameters:\n");CHKERRQ(ierr); 1093 ierr = PetscViewerASCIIPrintf(viewer," SYM (matrix type): %d \n",lu->id.sym);CHKERRQ(ierr); 1094 ierr = PetscViewerASCIIPrintf(viewer," PAR (host participation): %d \n",lu->id.par);CHKERRQ(ierr); 1095 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(1) (output for error): %d \n",lu->id.ICNTL(1));CHKERRQ(ierr); 1096 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(2) (output of diagnostic msg): %d \n",lu->id.ICNTL(2));CHKERRQ(ierr); 1097 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(3) (output for global info): %d \n",lu->id.ICNTL(3));CHKERRQ(ierr); 1098 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(4) (level of printing): %d \n",lu->id.ICNTL(4));CHKERRQ(ierr); 1099 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(5) (input mat struct): %d \n",lu->id.ICNTL(5));CHKERRQ(ierr); 1100 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(6) (matrix prescaling): %d \n",lu->id.ICNTL(6));CHKERRQ(ierr); 1101 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(7) (sequentia matrix ordering):%d \n",lu->id.ICNTL(7));CHKERRQ(ierr); 1102 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(8) (scalling strategy): %d \n",lu->id.ICNTL(8));CHKERRQ(ierr); 1103 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(10) (max num of refinements): %d \n",lu->id.ICNTL(10));CHKERRQ(ierr); 1104 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(11) (error analysis): %d \n",lu->id.ICNTL(11));CHKERRQ(ierr); 1105 if (lu->id.ICNTL(11)>0) { 1106 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(4) (inf norm of input mat): %g\n",lu->id.RINFOG(4));CHKERRQ(ierr); 1107 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(5) (inf norm of solution): %g\n",lu->id.RINFOG(5));CHKERRQ(ierr); 1108 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(6) (inf norm of residual): %g\n",lu->id.RINFOG(6));CHKERRQ(ierr); 1109 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(7),RINFOG(8) (backward error est): %g, %g\n",lu->id.RINFOG(7),lu->id.RINFOG(8));CHKERRQ(ierr); 1110 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(9) (error estimate): %g \n",lu->id.RINFOG(9));CHKERRQ(ierr); 1111 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n",lu->id.RINFOG(10),lu->id.RINFOG(11));CHKERRQ(ierr); 1112 } 1113 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(12) (efficiency control): %d \n",lu->id.ICNTL(12));CHKERRQ(ierr); 1114 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(13) (efficiency control): %d \n",lu->id.ICNTL(13));CHKERRQ(ierr); 1115 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(14) (percentage of estimated workspace increase): %d \n",lu->id.ICNTL(14));CHKERRQ(ierr); 1116 /* ICNTL(15-17) not used */ 1117 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(18) (input mat struct): %d \n",lu->id.ICNTL(18));CHKERRQ(ierr); 1118 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(19) (Shur complement info): %d \n",lu->id.ICNTL(19));CHKERRQ(ierr); 1119 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(20) (rhs sparse pattern): %d \n",lu->id.ICNTL(20));CHKERRQ(ierr); 1120 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(21) (solution struct): %d \n",lu->id.ICNTL(21));CHKERRQ(ierr); 1121 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(22) (in-core/out-of-core facility): %d \n",lu->id.ICNTL(22));CHKERRQ(ierr); 1122 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(23) (max size of memory can be allocated locally):%d \n",lu->id.ICNTL(23));CHKERRQ(ierr); 1123 1124 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(24) (detection of null pivot rows): %d \n",lu->id.ICNTL(24));CHKERRQ(ierr); 1125 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(25) (computation of a null space basis): %d \n",lu->id.ICNTL(25));CHKERRQ(ierr); 1126 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(26) (Schur options for rhs or solution): %d \n",lu->id.ICNTL(26));CHKERRQ(ierr); 1127 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(27) (experimental parameter): %d \n",lu->id.ICNTL(27));CHKERRQ(ierr); 1128 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(28) (use parallel or sequential ordering): %d \n",lu->id.ICNTL(28));CHKERRQ(ierr); 1129 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(29) (parallel ordering): %d \n",lu->id.ICNTL(29));CHKERRQ(ierr); 1130 1131 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(30) (user-specified set of entries in inv(A)): %d \n",lu->id.ICNTL(30));CHKERRQ(ierr); 1132 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(31) (factors is discarded in the solve phase): %d \n",lu->id.ICNTL(31));CHKERRQ(ierr); 1133 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(33) (compute determinant): %d \n",lu->id.ICNTL(33));CHKERRQ(ierr); 1134 1135 ierr = PetscViewerASCIIPrintf(viewer," CNTL(1) (relative pivoting threshold): %g \n",lu->id.CNTL(1));CHKERRQ(ierr); 1136 ierr = PetscViewerASCIIPrintf(viewer," CNTL(2) (stopping criterion of refinement): %g \n",lu->id.CNTL(2));CHKERRQ(ierr); 1137 ierr = PetscViewerASCIIPrintf(viewer," CNTL(3) (absolute pivoting threshold): %g \n",lu->id.CNTL(3));CHKERRQ(ierr); 1138 ierr = PetscViewerASCIIPrintf(viewer," CNTL(4) (value of static pivoting): %g \n",lu->id.CNTL(4));CHKERRQ(ierr); 1139 ierr = PetscViewerASCIIPrintf(viewer," CNTL(5) (fixation for null pivots): %g \n",lu->id.CNTL(5));CHKERRQ(ierr); 1140 1141 /* infomation local to each processor */ 1142 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(1) (local estimated flops for the elimination after analysis): \n");CHKERRQ(ierr); 1143 ierr = PetscViewerASCIISynchronizedAllow(viewer,PETSC_TRUE);CHKERRQ(ierr); 1144 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",lu->myid,lu->id.RINFO(1));CHKERRQ(ierr); 1145 ierr = PetscViewerFlush(viewer); 1146 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(2) (local estimated flops for the assembly after factorization): \n");CHKERRQ(ierr); 1147 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",lu->myid,lu->id.RINFO(2));CHKERRQ(ierr); 1148 ierr = PetscViewerFlush(viewer); 1149 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(3) (local estimated flops for the elimination after factorization): \n");CHKERRQ(ierr); 1150 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",lu->myid,lu->id.RINFO(3));CHKERRQ(ierr); 1151 ierr = PetscViewerFlush(viewer); 1152 1153 ierr = PetscViewerASCIIPrintf(viewer, " INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization): \n");CHKERRQ(ierr); 1154 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",lu->myid,lu->id.INFO(15));CHKERRQ(ierr); 1155 ierr = PetscViewerFlush(viewer); 1156 1157 ierr = PetscViewerASCIIPrintf(viewer, " INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization): \n");CHKERRQ(ierr); 1158 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",lu->myid,lu->id.INFO(16));CHKERRQ(ierr); 1159 ierr = PetscViewerFlush(viewer); 1160 1161 ierr = PetscViewerASCIIPrintf(viewer, " INFO(23) (num of pivots eliminated on this processor after factorization): \n");CHKERRQ(ierr); 1162 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",lu->myid,lu->id.INFO(23));CHKERRQ(ierr); 1163 ierr = PetscViewerFlush(viewer); 1164 ierr = PetscViewerASCIISynchronizedAllow(viewer,PETSC_FALSE);CHKERRQ(ierr); 1165 1166 if (!lu->myid){ /* information from the host */ 1167 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(1) (global estimated flops for the elimination after analysis): %g \n",lu->id.RINFOG(1));CHKERRQ(ierr); 1168 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(2) (global estimated flops for the assembly after factorization): %g \n",lu->id.RINFOG(2));CHKERRQ(ierr); 1169 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(3) (global estimated flops for the elimination after factorization): %g \n",lu->id.RINFOG(3));CHKERRQ(ierr); 1170 ierr = PetscViewerASCIIPrintf(viewer," (RINFOG(12) RINFOG(13))*2^INFOG(34) (determinant): (%g,%g)*(2^%d)\n",lu->id.RINFOG(12),lu->id.RINFOG(13),lu->id.INFOG(34));CHKERRQ(ierr); 1171 1172 ierr = PetscViewerASCIIPrintf(viewer," INFOG(3) (estimated real workspace for factors on all processors after analysis): %d \n",lu->id.INFOG(3));CHKERRQ(ierr); 1173 ierr = PetscViewerASCIIPrintf(viewer," INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d \n",lu->id.INFOG(4));CHKERRQ(ierr); 1174 ierr = PetscViewerASCIIPrintf(viewer," INFOG(5) (estimated maximum front size in the complete tree): %d \n",lu->id.INFOG(5));CHKERRQ(ierr); 1175 ierr = PetscViewerASCIIPrintf(viewer," INFOG(6) (number of nodes in the complete tree): %d \n",lu->id.INFOG(6));CHKERRQ(ierr); 1176 ierr = PetscViewerASCIIPrintf(viewer," INFOG(7) (ordering option effectively use after analysis): %d \n",lu->id.INFOG(7));CHKERRQ(ierr); 1177 ierr = PetscViewerASCIIPrintf(viewer," INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d \n",lu->id.INFOG(8));CHKERRQ(ierr); 1178 ierr = PetscViewerASCIIPrintf(viewer," INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d \n",lu->id.INFOG(9));CHKERRQ(ierr); 1179 ierr = PetscViewerASCIIPrintf(viewer," INFOG(10) (total integer space store the matrix factors after factorization): %d \n",lu->id.INFOG(10));CHKERRQ(ierr); 1180 ierr = PetscViewerASCIIPrintf(viewer," INFOG(11) (order of largest frontal matrix after factorization): %d \n",lu->id.INFOG(11));CHKERRQ(ierr); 1181 ierr = PetscViewerASCIIPrintf(viewer," INFOG(12) (number of off-diagonal pivots): %d \n",lu->id.INFOG(12));CHKERRQ(ierr); 1182 ierr = PetscViewerASCIIPrintf(viewer," INFOG(13) (number of delayed pivots after factorization): %d \n",lu->id.INFOG(13));CHKERRQ(ierr); 1183 ierr = PetscViewerASCIIPrintf(viewer," INFOG(14) (number of memory compress after factorization): %d \n",lu->id.INFOG(14));CHKERRQ(ierr); 1184 ierr = PetscViewerASCIIPrintf(viewer," INFOG(15) (number of steps of iterative refinement after solution): %d \n",lu->id.INFOG(15));CHKERRQ(ierr); 1185 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",lu->id.INFOG(16));CHKERRQ(ierr); 1186 ierr = PetscViewerASCIIPrintf(viewer," INFOG(17) (estimated size of all MUMPS internal data for factorization after analysis: sum over all processors): %d \n",lu->id.INFOG(17));CHKERRQ(ierr); 1187 ierr = PetscViewerASCIIPrintf(viewer," INFOG(18) (size of all MUMPS internal data allocated during factorization: value on the most memory consuming processor): %d \n",lu->id.INFOG(18));CHKERRQ(ierr); 1188 ierr = PetscViewerASCIIPrintf(viewer," INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): %d \n",lu->id.INFOG(19));CHKERRQ(ierr); 1189 ierr = PetscViewerASCIIPrintf(viewer," INFOG(20) (estimated number of entries in the factors): %d \n",lu->id.INFOG(20));CHKERRQ(ierr); 1190 ierr = PetscViewerASCIIPrintf(viewer," INFOG(21) (size in MB of memory effectively used during factorization - value on the most memory consuming processor): %d \n",lu->id.INFOG(21));CHKERRQ(ierr); 1191 ierr = PetscViewerASCIIPrintf(viewer," INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): %d \n",lu->id.INFOG(22));CHKERRQ(ierr); 1192 ierr = PetscViewerASCIIPrintf(viewer," INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d \n",lu->id.INFOG(23));CHKERRQ(ierr); 1193 ierr = PetscViewerASCIIPrintf(viewer," INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d \n",lu->id.INFOG(24));CHKERRQ(ierr); 1194 ierr = PetscViewerASCIIPrintf(viewer," INFOG(25) (after factorization: number of pivots modified by static pivoting): %d \n",lu->id.INFOG(25));CHKERRQ(ierr); 1195 } 1196 } 1197 } 1198 PetscFunctionReturn(0); 1199 } 1200 1201 #undef __FUNCT__ 1202 #define __FUNCT__ "MatGetInfo_MUMPS" 1203 PetscErrorCode MatGetInfo_MUMPS(Mat A,MatInfoType flag,MatInfo *info) 1204 { 1205 Mat_MUMPS *mumps =(Mat_MUMPS*)A->spptr; 1206 1207 PetscFunctionBegin; 1208 info->block_size = 1.0; 1209 info->nz_allocated = mumps->id.INFOG(20); 1210 info->nz_used = mumps->id.INFOG(20); 1211 info->nz_unneeded = 0.0; 1212 info->assemblies = 0.0; 1213 info->mallocs = 0.0; 1214 info->memory = 0.0; 1215 info->fill_ratio_given = 0; 1216 info->fill_ratio_needed = 0; 1217 info->factor_mallocs = 0; 1218 PetscFunctionReturn(0); 1219 } 1220 1221 /* -------------------------------------------------------------------------------------------*/ 1222 #undef __FUNCT__ 1223 #define __FUNCT__ "MatMumpsSetIcntl_MUMPS" 1224 PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt ival) 1225 { 1226 Mat_MUMPS *lu =(Mat_MUMPS*)F->spptr; 1227 1228 PetscFunctionBegin; 1229 lu->id.ICNTL(icntl) = ival; 1230 PetscFunctionReturn(0); 1231 } 1232 1233 #undef __FUNCT__ 1234 #define __FUNCT__ "MatMumpsSetIcntl" 1235 /*@ 1236 MatMumpsSetIcntl - Set MUMPS parameter ICNTL() 1237 1238 Logically Collective on Mat 1239 1240 Input Parameters: 1241 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1242 . icntl - index of MUMPS parameter array ICNTL() 1243 - ival - value of MUMPS ICNTL(icntl) 1244 1245 Options Database: 1246 . -mat_mumps_icntl_<icntl> <ival> 1247 1248 Level: beginner 1249 1250 References: MUMPS Users' Guide 1251 1252 .seealso: MatGetFactor() 1253 @*/ 1254 PetscErrorCode MatMumpsSetIcntl(Mat F,PetscInt icntl,PetscInt ival) 1255 { 1256 PetscErrorCode ierr; 1257 1258 PetscFunctionBegin; 1259 PetscValidLogicalCollectiveInt(F,icntl,2); 1260 PetscValidLogicalCollectiveInt(F,ival,3); 1261 ierr = PetscTryMethod(F,"MatMumpsSetIcntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));CHKERRQ(ierr); 1262 PetscFunctionReturn(0); 1263 } 1264 1265 /*MC 1266 MATSOLVERMUMPS - A matrix type providing direct solvers (LU and Cholesky) for 1267 distributed and sequential matrices via the external package MUMPS. 1268 1269 Works with MATAIJ and MATSBAIJ matrices 1270 1271 Options Database Keys: 1272 + -mat_mumps_icntl_4 <0,...,4> - print level 1273 . -mat_mumps_icntl_6 <0,...,7> - matrix prescaling options (see MUMPS User's Guide) 1274 . -mat_mumps_icntl_7 <0,...,7> - matrix orderings (see MUMPS User's Guidec) 1275 . -mat_mumps_icntl_9 <1,2> - A or A^T x=b to be solved: 1 denotes A, 2 denotes A^T 1276 . -mat_mumps_icntl_10 <n> - maximum number of iterative refinements 1277 . -mat_mumps_icntl_11 <n> - error analysis, a positive value returns statistics during -ksp_view 1278 . -mat_mumps_icntl_12 <n> - efficiency control (see MUMPS User's Guide) 1279 . -mat_mumps_icntl_13 <n> - efficiency control (see MUMPS User's Guide) 1280 . -mat_mumps_icntl_14 <n> - efficiency control (see MUMPS User's Guide) 1281 . -mat_mumps_icntl_15 <n> - efficiency control (see MUMPS User's Guide) 1282 . -mat_mumps_cntl_1 <delta> - relative pivoting threshold 1283 . -mat_mumps_cntl_2 <tol> - stopping criterion for refinement 1284 - -mat_mumps_cntl_3 <adelta> - absolute pivoting threshold 1285 1286 Level: beginner 1287 1288 .seealso: PCFactorSetMatSolverPackage(), MatSolverPackage 1289 1290 M*/ 1291 1292 EXTERN_C_BEGIN 1293 #undef __FUNCT__ 1294 #define __FUNCT__ "MatFactorGetSolverPackage_mumps" 1295 PetscErrorCode MatFactorGetSolverPackage_mumps(Mat A,const MatSolverPackage *type) 1296 { 1297 PetscFunctionBegin; 1298 *type = MATSOLVERMUMPS; 1299 PetscFunctionReturn(0); 1300 } 1301 EXTERN_C_END 1302 1303 EXTERN_C_BEGIN 1304 /* MatGetFactor for Seq and MPI AIJ matrices */ 1305 #undef __FUNCT__ 1306 #define __FUNCT__ "MatGetFactor_aij_mumps" 1307 PetscErrorCode MatGetFactor_aij_mumps(Mat A,MatFactorType ftype,Mat *F) 1308 { 1309 Mat B; 1310 PetscErrorCode ierr; 1311 Mat_MUMPS *mumps; 1312 PetscBool isSeqAIJ; 1313 1314 PetscFunctionBegin; 1315 /* Create the factorization matrix */ 1316 ierr = PetscTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);CHKERRQ(ierr); 1317 ierr = MatCreate(((PetscObject)A)->comm,&B);CHKERRQ(ierr); 1318 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 1319 ierr = MatSetType(B,((PetscObject)A)->type_name);CHKERRQ(ierr); 1320 if (isSeqAIJ) { 1321 ierr = MatSeqAIJSetPreallocation(B,0,PETSC_NULL);CHKERRQ(ierr); 1322 } else { 1323 ierr = MatMPIAIJSetPreallocation(B,0,PETSC_NULL,0,PETSC_NULL);CHKERRQ(ierr); 1324 } 1325 1326 ierr = PetscNewLog(B,Mat_MUMPS,&mumps);CHKERRQ(ierr); 1327 B->ops->view = MatView_MUMPS; 1328 B->ops->getinfo = MatGetInfo_MUMPS; 1329 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatFactorGetSolverPackage_C","MatFactorGetSolverPackage_mumps",MatFactorGetSolverPackage_mumps);CHKERRQ(ierr); 1330 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMumpsSetIcntl_C","MatMumpsSetIcntl_MUMPS",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 1331 if (ftype == MAT_FACTOR_LU) { 1332 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS; 1333 B->factortype = MAT_FACTOR_LU; 1334 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij; 1335 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij; 1336 mumps->sym = 0; 1337 } else { 1338 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 1339 B->factortype = MAT_FACTOR_CHOLESKY; 1340 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij; 1341 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij; 1342 if (A->spd_set && A->spd) mumps->sym = 1; 1343 else mumps->sym = 2; 1344 } 1345 1346 mumps->isAIJ = PETSC_TRUE; 1347 mumps->Destroy = B->ops->destroy; 1348 B->ops->destroy = MatDestroy_MUMPS; 1349 B->spptr = (void*)mumps; 1350 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 1351 1352 *F = B; 1353 PetscFunctionReturn(0); 1354 } 1355 EXTERN_C_END 1356 1357 1358 EXTERN_C_BEGIN 1359 /* MatGetFactor for Seq and MPI SBAIJ matrices */ 1360 #undef __FUNCT__ 1361 #define __FUNCT__ "MatGetFactor_sbaij_mumps" 1362 PetscErrorCode MatGetFactor_sbaij_mumps(Mat A,MatFactorType ftype,Mat *F) 1363 { 1364 Mat B; 1365 PetscErrorCode ierr; 1366 Mat_MUMPS *mumps; 1367 PetscBool isSeqSBAIJ; 1368 1369 PetscFunctionBegin; 1370 if (ftype != MAT_FACTOR_CHOLESKY) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_SUP,"Cannot use PETSc SBAIJ matrices with MUMPS LU, use AIJ matrix"); 1371 if(A->rmap->bs > 1) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_SUP,"Cannot use PETSc SBAIJ matrices with block size > 1 with MUMPS Cholesky, use AIJ matrix instead"); 1372 ierr = PetscTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ);CHKERRQ(ierr); 1373 /* Create the factorization matrix */ 1374 ierr = MatCreate(((PetscObject)A)->comm,&B);CHKERRQ(ierr); 1375 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 1376 ierr = MatSetType(B,((PetscObject)A)->type_name);CHKERRQ(ierr); 1377 ierr = PetscNewLog(B,Mat_MUMPS,&mumps);CHKERRQ(ierr); 1378 if (isSeqSBAIJ) { 1379 ierr = MatSeqSBAIJSetPreallocation(B,1,0,PETSC_NULL);CHKERRQ(ierr); 1380 mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij; 1381 } else { 1382 ierr = MatMPISBAIJSetPreallocation(B,1,0,PETSC_NULL,0,PETSC_NULL);CHKERRQ(ierr); 1383 mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij; 1384 } 1385 1386 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 1387 B->ops->view = MatView_MUMPS; 1388 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatFactorGetSolverPackage_C","MatFactorGetSolverPackage_mumps",MatFactorGetSolverPackage_mumps);CHKERRQ(ierr); 1389 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMumpsSetIcntl_C","MatMumpsSetIcntl",MatMumpsSetIcntl);CHKERRQ(ierr); 1390 B->factortype = MAT_FACTOR_CHOLESKY; 1391 if (A->spd_set && A->spd) mumps->sym = 1; 1392 else mumps->sym = 2; 1393 1394 mumps->isAIJ = PETSC_FALSE; 1395 mumps->Destroy = B->ops->destroy; 1396 B->ops->destroy = MatDestroy_MUMPS; 1397 B->spptr = (void*)mumps; 1398 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 1399 1400 *F = B; 1401 PetscFunctionReturn(0); 1402 } 1403 EXTERN_C_END 1404 1405 EXTERN_C_BEGIN 1406 #undef __FUNCT__ 1407 #define __FUNCT__ "MatGetFactor_baij_mumps" 1408 PetscErrorCode MatGetFactor_baij_mumps(Mat A,MatFactorType ftype,Mat *F) 1409 { 1410 Mat B; 1411 PetscErrorCode ierr; 1412 Mat_MUMPS *mumps; 1413 PetscBool isSeqBAIJ; 1414 1415 PetscFunctionBegin; 1416 /* Create the factorization matrix */ 1417 ierr = PetscTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ);CHKERRQ(ierr); 1418 ierr = MatCreate(((PetscObject)A)->comm,&B);CHKERRQ(ierr); 1419 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 1420 ierr = MatSetType(B,((PetscObject)A)->type_name);CHKERRQ(ierr); 1421 if (isSeqBAIJ) { 1422 ierr = MatSeqBAIJSetPreallocation(B,A->rmap->bs,0,PETSC_NULL);CHKERRQ(ierr); 1423 } else { 1424 ierr = MatMPIBAIJSetPreallocation(B,A->rmap->bs,0,PETSC_NULL,0,PETSC_NULL);CHKERRQ(ierr); 1425 } 1426 1427 ierr = PetscNewLog(B,Mat_MUMPS,&mumps);CHKERRQ(ierr); 1428 if (ftype == MAT_FACTOR_LU) { 1429 B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS; 1430 B->factortype = MAT_FACTOR_LU; 1431 if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij; 1432 else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij; 1433 mumps->sym = 0; 1434 } else { 1435 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead\n"); 1436 } 1437 1438 B->ops->view = MatView_MUMPS; 1439 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatFactorGetSolverPackage_C","MatFactorGetSolverPackage_mumps",MatFactorGetSolverPackage_mumps);CHKERRQ(ierr); 1440 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMumpsSetIcntl_C","MatMumpsSetIcntl_MUMPS",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 1441 1442 mumps->isAIJ = PETSC_TRUE; 1443 mumps->Destroy = B->ops->destroy; 1444 B->ops->destroy = MatDestroy_MUMPS; 1445 B->spptr = (void*)mumps; 1446 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 1447 1448 *F = B; 1449 PetscFunctionReturn(0); 1450 } 1451 EXTERN_C_END 1452 1453