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 #undef __FUNCT__ 716 #define __FUNCT__ "PetscSetMUMPSOptions" 717 PetscErrorCode PetscSetMUMPSOptions(Mat F, Mat A) 718 { 719 Mat_MUMPS *lu = (Mat_MUMPS*)F->spptr; 720 PetscErrorCode ierr; 721 PetscInt icntl; 722 PetscBool flg; 723 724 PetscFunctionBegin; 725 ierr = PetscOptionsBegin(((PetscObject)A)->comm,((PetscObject)A)->prefix,"MUMPS Options","Mat");CHKERRQ(ierr); 726 if (lu->size == 1){ 727 lu->id.ICNTL(18) = 0; /* centralized assembled matrix input */ 728 } else { 729 lu->id.ICNTL(18) = 3; /* distributed assembled matrix input */ 730 } 731 732 icntl=-1; 733 lu->id.ICNTL(4) = 0; /* level of printing; overwrite mumps default ICNTL(4)=2 */ 734 ierr = PetscOptionsInt("-mat_mumps_icntl_4","ICNTL(4): level of printing (0 to 4)","None",lu->id.ICNTL(4),&icntl,&flg);CHKERRQ(ierr); 735 if ((flg && icntl > 0) || PetscLogPrintInfo) { 736 lu->id.ICNTL(4)=icntl; /* and use mumps default icntl(i), i=1,2,3 */ 737 } else { /* no output */ 738 lu->id.ICNTL(1) = 0; /* error message, default= 6 */ 739 lu->id.ICNTL(2) = 0; /* output stream for diagnostic printing, statistics, and warning. default=0 */ 740 lu->id.ICNTL(3) = 0; /* output stream for global information, default=6 */ 741 } 742 ierr = PetscOptionsInt("-mat_mumps_icntl_6","ICNTL(6): column permutation and/or scaling to get a zero-free diagonal (0 to 7)","None",lu->id.ICNTL(6),&lu->id.ICNTL(6),PETSC_NULL);CHKERRQ(ierr); 743 icntl=-1; 744 ierr = PetscOptionsInt("-mat_mumps_icntl_7","ICNTL(7): sequential matrix ordering (0 to 7) 3 = Scotch, 5 = Metis","None",lu->id.ICNTL(7),&icntl,&flg);CHKERRQ(ierr); 745 if (flg) { 746 if (icntl== 1 && lu->size > 1){ 747 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"); 748 } else { 749 lu->id.ICNTL(7) = icntl; 750 } 751 } 752 753 ierr = PetscOptionsInt("-mat_mumps_icntl_8","ICNTL(8): scaling strategy (-2 to 7 or 77)","None",lu->id.ICNTL(8),&lu->id.ICNTL(8),PETSC_NULL);CHKERRQ(ierr); 754 ierr = PetscOptionsInt("-mat_mumps_icntl_10","ICNTL(10): max num of refinements","None",lu->id.ICNTL(10),&lu->id.ICNTL(10),PETSC_NULL);CHKERRQ(ierr); 755 ierr = PetscOptionsInt("-mat_mumps_icntl_11","ICNTL(11): statistics related to the linear system solved (via -ksp_view)","None",lu->id.ICNTL(11),&lu->id.ICNTL(11),PETSC_NULL);CHKERRQ(ierr); 756 ierr = PetscOptionsInt("-mat_mumps_icntl_12","ICNTL(12): efficiency control: defines the ordering strategy with scaling constraints (0 to 3","None",lu->id.ICNTL(12),&lu->id.ICNTL(12),PETSC_NULL);CHKERRQ(ierr); 757 ierr = PetscOptionsInt("-mat_mumps_icntl_13","ICNTL(13): efficiency control: with or without ScaLAPACK","None",lu->id.ICNTL(13),&lu->id.ICNTL(13),PETSC_NULL);CHKERRQ(ierr); 758 ierr = PetscOptionsInt("-mat_mumps_icntl_14","ICNTL(14): percentage of estimated workspace increase","None",lu->id.ICNTL(14),&lu->id.ICNTL(14),PETSC_NULL);CHKERRQ(ierr); 759 ierr = PetscOptionsInt("-mat_mumps_icntl_19","ICNTL(19): Schur complement","None",lu->id.ICNTL(19),&lu->id.ICNTL(19),PETSC_NULL);CHKERRQ(ierr); 760 761 ierr = PetscOptionsInt("-mat_mumps_icntl_22","ICNTL(22): in-core/out-of-core facility (0 or 1)","None",lu->id.ICNTL(22),&lu->id.ICNTL(22),PETSC_NULL);CHKERRQ(ierr); 762 ierr = PetscOptionsInt("-mat_mumps_icntl_23","ICNTL(23): max size of the working memory (MB) that can allocate per processor","None",lu->id.ICNTL(23),&lu->id.ICNTL(23),PETSC_NULL);CHKERRQ(ierr); 763 ierr = PetscOptionsInt("-mat_mumps_icntl_24","ICNTL(24): detection of null pivot rows (0 or 1)","None",lu->id.ICNTL(24),&lu->id.ICNTL(24),PETSC_NULL);CHKERRQ(ierr); 764 if (lu->id.ICNTL(24)){ 765 lu->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */ 766 } 767 768 ierr = PetscOptionsInt("-mat_mumps_icntl_25","ICNTL(25): computation of a null space basis","None",lu->id.ICNTL(25),&lu->id.ICNTL(25),PETSC_NULL);CHKERRQ(ierr); 769 ierr = PetscOptionsInt("-mat_mumps_icntl_26","ICNTL(26): Schur options for right-hand side or solution vector","None",lu->id.ICNTL(26),&lu->id.ICNTL(26),PETSC_NULL);CHKERRQ(ierr); 770 ierr = PetscOptionsInt("-mat_mumps_icntl_27","ICNTL(27): experimental parameter","None",lu->id.ICNTL(27),&lu->id.ICNTL(27),PETSC_NULL);CHKERRQ(ierr); 771 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",lu->id.ICNTL(28),&lu->id.ICNTL(28),PETSC_NULL);CHKERRQ(ierr); 772 ierr = PetscOptionsInt("-mat_mumps_icntl_29","ICNTL(29): parallel ordering 1 = ptscotch 2 = parmetis","None",lu->id.ICNTL(29),&lu->id.ICNTL(29),PETSC_NULL);CHKERRQ(ierr); 773 ierr = PetscOptionsInt("-mat_mumps_icntl_30","ICNTL(30): compute user-specified set of entries in inv(A)","None",lu->id.ICNTL(30),&lu->id.ICNTL(30),PETSC_NULL);CHKERRQ(ierr); 774 ierr = PetscOptionsInt("-mat_mumps_icntl_31","ICNTL(31): factors can be discarded in the solve phase","None",lu->id.ICNTL(31),&lu->id.ICNTL(31),PETSC_NULL);CHKERRQ(ierr); 775 ierr = PetscOptionsInt("-mat_mumps_icntl_33","ICNTL(33): compute determinant","None",lu->id.ICNTL(33),&lu->id.ICNTL(33),PETSC_NULL);CHKERRQ(ierr); 776 777 ierr = PetscOptionsReal("-mat_mumps_cntl_1","CNTL(1): relative pivoting threshold","None",lu->id.CNTL(1),&lu->id.CNTL(1),PETSC_NULL);CHKERRQ(ierr); 778 ierr = PetscOptionsReal("-mat_mumps_cntl_2","CNTL(2): stopping criterion of refinement","None",lu->id.CNTL(2),&lu->id.CNTL(2),PETSC_NULL);CHKERRQ(ierr); 779 ierr = PetscOptionsReal("-mat_mumps_cntl_3","CNTL(3): absolute pivoting threshold","None",lu->id.CNTL(3),&lu->id.CNTL(3),PETSC_NULL);CHKERRQ(ierr); 780 ierr = PetscOptionsReal("-mat_mumps_cntl_4","CNTL(4): value for static pivoting","None",lu->id.CNTL(4),&lu->id.CNTL(4),PETSC_NULL);CHKERRQ(ierr); 781 ierr = PetscOptionsReal("-mat_mumps_cntl_5","CNTL(5): fixation for null pivots","None",lu->id.CNTL(5),&lu->id.CNTL(5),PETSC_NULL);CHKERRQ(ierr); 782 PetscOptionsEnd(); 783 PetscFunctionReturn(0); 784 } 785 786 #undef __FUNCT__ 787 #define __FUNCT__ "PetscInitializeMUMPS" 788 PetscErrorCode PetscInitializeMUMPS(Mat A,Mat_MUMPS* mumps) 789 { 790 PetscErrorCode ierr; 791 792 PetscFunctionBegin; 793 ierr = MPI_Comm_rank(((PetscObject)A)->comm, &mumps->myid); 794 ierr = MPI_Comm_size(((PetscObject)A)->comm,&mumps->size);CHKERRQ(ierr); 795 ierr = MPI_Comm_dup(((PetscObject)A)->comm,&(mumps->comm_mumps));CHKERRQ(ierr); 796 mumps->id.comm_fortran = MPI_Comm_c2f(mumps->comm_mumps); 797 798 mumps->id.job = JOB_INIT; 799 mumps->id.par = 1; /* host participates factorizaton and solve */ 800 mumps->id.sym = mumps->sym; 801 #if defined(PETSC_USE_COMPLEX) 802 zmumps_c(&mumps->id); 803 #else 804 dmumps_c(&mumps->id); 805 #endif 806 807 mumps->CleanUpMUMPS = PETSC_FALSE; 808 mumps->scat_rhs = PETSC_NULL; 809 mumps->scat_sol = PETSC_NULL; 810 mumps->nSolve = 0; 811 PetscFunctionReturn(0); 812 } 813 814 /* Note the Petsc r and c permutations are ignored */ 815 #undef __FUNCT__ 816 #define __FUNCT__ "MatLUFactorSymbolic_AIJMUMPS" 817 PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 818 { 819 Mat_MUMPS *lu = (Mat_MUMPS*)F->spptr; 820 PetscErrorCode ierr; 821 MatReuse reuse; 822 Vec b; 823 IS is_iden; 824 const PetscInt M = A->rmap->N; 825 826 PetscFunctionBegin; 827 lu->matstruc = DIFFERENT_NONZERO_PATTERN; 828 829 /* Set MUMPS options */ 830 ierr = PetscSetMUMPSOptions(F,A);CHKERRQ(ierr); 831 832 reuse = MAT_INITIAL_MATRIX; 833 ierr = (*lu->ConvertToTriples)(A, 1, reuse, &lu->nz, &lu->irn, &lu->jcn, &lu->val);CHKERRQ(ierr); 834 835 /* analysis phase */ 836 /*----------------*/ 837 lu->id.job = JOB_FACTSYMBOLIC; 838 lu->id.n = M; 839 switch (lu->id.ICNTL(18)){ 840 case 0: /* centralized assembled matrix input */ 841 if (!lu->myid) { 842 lu->id.nz =lu->nz; lu->id.irn=lu->irn; lu->id.jcn=lu->jcn; 843 if (lu->id.ICNTL(6)>1){ 844 #if defined(PETSC_USE_COMPLEX) 845 lu->id.a = (mumps_double_complex*)lu->val; 846 #else 847 lu->id.a = lu->val; 848 #endif 849 } 850 if (lu->id.ICNTL(7) == 1){ /* use user-provide matrix ordering */ 851 if (!lu->myid) { 852 const PetscInt *idx; 853 PetscInt i,*perm_in; 854 ierr = PetscMalloc(M*sizeof(PetscInt),&perm_in);CHKERRQ(ierr); 855 ierr = ISGetIndices(r,&idx);CHKERRQ(ierr); 856 lu->id.perm_in = perm_in; 857 for (i=0; i<M; i++) perm_in[i] = idx[i]+1; /* perm_in[]: start from 1, not 0! */ 858 ierr = ISRestoreIndices(r,&idx);CHKERRQ(ierr); 859 } 860 } 861 } 862 break; 863 case 3: /* distributed assembled matrix input (size>1) */ 864 lu->id.nz_loc = lu->nz; 865 lu->id.irn_loc=lu->irn; lu->id.jcn_loc=lu->jcn; 866 if (lu->id.ICNTL(6)>1) { 867 #if defined(PETSC_USE_COMPLEX) 868 lu->id.a_loc = (mumps_double_complex*)lu->val; 869 #else 870 lu->id.a_loc = lu->val; 871 #endif 872 } 873 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 874 if (!lu->myid){ 875 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&lu->b_seq);CHKERRQ(ierr); 876 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 877 } else { 878 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&lu->b_seq);CHKERRQ(ierr); 879 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 880 } 881 ierr = VecCreate(((PetscObject)A)->comm,&b);CHKERRQ(ierr); 882 ierr = VecSetSizes(b,A->rmap->n,PETSC_DECIDE);CHKERRQ(ierr); 883 ierr = VecSetFromOptions(b);CHKERRQ(ierr); 884 885 ierr = VecScatterCreate(b,is_iden,lu->b_seq,is_iden,&lu->scat_rhs);CHKERRQ(ierr); 886 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 887 ierr = VecDestroy(&b);CHKERRQ(ierr); 888 break; 889 } 890 #if defined(PETSC_USE_COMPLEX) 891 zmumps_c(&lu->id); 892 #else 893 dmumps_c(&lu->id); 894 #endif 895 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)); 896 897 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 898 F->ops->solve = MatSolve_MUMPS; 899 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 900 F->ops->matsolve = MatMatSolve_MUMPS; 901 PetscFunctionReturn(0); 902 } 903 904 /* Note the Petsc r and c permutations are ignored */ 905 #undef __FUNCT__ 906 #define __FUNCT__ "MatLUFactorSymbolic_BAIJMUMPS" 907 PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 908 { 909 910 Mat_MUMPS *lu = (Mat_MUMPS*)F->spptr; 911 PetscErrorCode ierr; 912 MatReuse reuse; 913 Vec b; 914 IS is_iden; 915 const PetscInt M = A->rmap->N; 916 917 PetscFunctionBegin; 918 lu->matstruc = DIFFERENT_NONZERO_PATTERN; 919 920 /* Set MUMPS options */ 921 ierr = PetscSetMUMPSOptions(F,A);CHKERRQ(ierr); 922 923 reuse = MAT_INITIAL_MATRIX; 924 ierr = (*lu->ConvertToTriples)(A, 1, reuse, &lu->nz, &lu->irn, &lu->jcn, &lu->val);CHKERRQ(ierr); 925 926 /* analysis phase */ 927 /*----------------*/ 928 lu->id.job = JOB_FACTSYMBOLIC; 929 lu->id.n = M; 930 switch (lu->id.ICNTL(18)){ 931 case 0: /* centralized assembled matrix input */ 932 if (!lu->myid) { 933 lu->id.nz =lu->nz; lu->id.irn=lu->irn; lu->id.jcn=lu->jcn; 934 if (lu->id.ICNTL(6)>1){ 935 #if defined(PETSC_USE_COMPLEX) 936 lu->id.a = (mumps_double_complex*)lu->val; 937 #else 938 lu->id.a = lu->val; 939 #endif 940 } 941 } 942 break; 943 case 3: /* distributed assembled matrix input (size>1) */ 944 lu->id.nz_loc = lu->nz; 945 lu->id.irn_loc=lu->irn; lu->id.jcn_loc=lu->jcn; 946 if (lu->id.ICNTL(6)>1) { 947 #if defined(PETSC_USE_COMPLEX) 948 lu->id.a_loc = (mumps_double_complex*)lu->val; 949 #else 950 lu->id.a_loc = lu->val; 951 #endif 952 } 953 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 954 if (!lu->myid){ 955 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&lu->b_seq);CHKERRQ(ierr); 956 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 957 } else { 958 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&lu->b_seq);CHKERRQ(ierr); 959 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 960 } 961 ierr = VecCreate(((PetscObject)A)->comm,&b);CHKERRQ(ierr); 962 ierr = VecSetSizes(b,A->rmap->n,PETSC_DECIDE);CHKERRQ(ierr); 963 ierr = VecSetFromOptions(b);CHKERRQ(ierr); 964 965 ierr = VecScatterCreate(b,is_iden,lu->b_seq,is_iden,&lu->scat_rhs);CHKERRQ(ierr); 966 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 967 ierr = VecDestroy(&b);CHKERRQ(ierr); 968 break; 969 } 970 #if defined(PETSC_USE_COMPLEX) 971 zmumps_c(&lu->id); 972 #else 973 dmumps_c(&lu->id); 974 #endif 975 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)); 976 977 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 978 F->ops->solve = MatSolve_MUMPS; 979 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 980 PetscFunctionReturn(0); 981 } 982 983 /* Note the Petsc r permutation and factor info are ignored */ 984 #undef __FUNCT__ 985 #define __FUNCT__ "MatCholeskyFactorSymbolic_MUMPS" 986 PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F,Mat A,IS r,const MatFactorInfo *info) 987 { 988 Mat_MUMPS *lu = (Mat_MUMPS*)F->spptr; 989 PetscErrorCode ierr; 990 MatReuse reuse; 991 Vec b; 992 IS is_iden; 993 const PetscInt M = A->rmap->N; 994 995 PetscFunctionBegin; 996 lu->matstruc = DIFFERENT_NONZERO_PATTERN; 997 998 /* Set MUMPS options */ 999 ierr = PetscSetMUMPSOptions(F,A);CHKERRQ(ierr); 1000 1001 reuse = MAT_INITIAL_MATRIX; 1002 ierr = (*lu->ConvertToTriples)(A, 1 , reuse, &lu->nz, &lu->irn, &lu->jcn, &lu->val);CHKERRQ(ierr); 1003 1004 /* analysis phase */ 1005 /*----------------*/ 1006 lu->id.job = JOB_FACTSYMBOLIC; 1007 lu->id.n = M; 1008 switch (lu->id.ICNTL(18)){ 1009 case 0: /* centralized assembled matrix input */ 1010 if (!lu->myid) { 1011 lu->id.nz =lu->nz; lu->id.irn=lu->irn; lu->id.jcn=lu->jcn; 1012 if (lu->id.ICNTL(6)>1){ 1013 #if defined(PETSC_USE_COMPLEX) 1014 lu->id.a = (mumps_double_complex*)lu->val; 1015 #else 1016 lu->id.a = lu->val; 1017 #endif 1018 } 1019 } 1020 break; 1021 case 3: /* distributed assembled matrix input (size>1) */ 1022 lu->id.nz_loc = lu->nz; 1023 lu->id.irn_loc=lu->irn; lu->id.jcn_loc=lu->jcn; 1024 if (lu->id.ICNTL(6)>1) { 1025 #if defined(PETSC_USE_COMPLEX) 1026 lu->id.a_loc = (mumps_double_complex*)lu->val; 1027 #else 1028 lu->id.a_loc = lu->val; 1029 #endif 1030 } 1031 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1032 if (!lu->myid){ 1033 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&lu->b_seq);CHKERRQ(ierr); 1034 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 1035 } else { 1036 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&lu->b_seq);CHKERRQ(ierr); 1037 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 1038 } 1039 ierr = VecCreate(((PetscObject)A)->comm,&b);CHKERRQ(ierr); 1040 ierr = VecSetSizes(b,A->rmap->n,PETSC_DECIDE);CHKERRQ(ierr); 1041 ierr = VecSetFromOptions(b);CHKERRQ(ierr); 1042 1043 ierr = VecScatterCreate(b,is_iden,lu->b_seq,is_iden,&lu->scat_rhs);CHKERRQ(ierr); 1044 ierr = ISDestroy(&is_iden);CHKERRQ(ierr); 1045 ierr = VecDestroy(&b);CHKERRQ(ierr); 1046 break; 1047 } 1048 #if defined(PETSC_USE_COMPLEX) 1049 zmumps_c(&lu->id); 1050 #else 1051 dmumps_c(&lu->id); 1052 #endif 1053 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)); 1054 1055 F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS; 1056 F->ops->solve = MatSolve_MUMPS; 1057 F->ops->solvetranspose = MatSolve_MUMPS; 1058 #if !defined(PETSC_USE_COMPLEX) 1059 F->ops->getinertia = MatGetInertia_SBAIJMUMPS; 1060 #else 1061 F->ops->getinertia = PETSC_NULL; 1062 #endif 1063 PetscFunctionReturn(0); 1064 } 1065 1066 #undef __FUNCT__ 1067 #define __FUNCT__ "MatView_MUMPS" 1068 PetscErrorCode MatView_MUMPS(Mat A,PetscViewer viewer) 1069 { 1070 PetscErrorCode ierr; 1071 PetscBool iascii; 1072 PetscViewerFormat format; 1073 Mat_MUMPS *lu=(Mat_MUMPS*)A->spptr; 1074 1075 PetscFunctionBegin; 1076 /* check if matrix is mumps type */ 1077 if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(0); 1078 1079 ierr = PetscTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 1080 if (iascii) { 1081 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 1082 if (format == PETSC_VIEWER_ASCII_INFO){ 1083 ierr = PetscViewerASCIIPrintf(viewer,"MUMPS run parameters:\n");CHKERRQ(ierr); 1084 ierr = PetscViewerASCIIPrintf(viewer," SYM (matrix type): %d \n",lu->id.sym);CHKERRQ(ierr); 1085 ierr = PetscViewerASCIIPrintf(viewer," PAR (host participation): %d \n",lu->id.par);CHKERRQ(ierr); 1086 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(1) (output for error): %d \n",lu->id.ICNTL(1));CHKERRQ(ierr); 1087 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(2) (output of diagnostic msg): %d \n",lu->id.ICNTL(2));CHKERRQ(ierr); 1088 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(3) (output for global info): %d \n",lu->id.ICNTL(3));CHKERRQ(ierr); 1089 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(4) (level of printing): %d \n",lu->id.ICNTL(4));CHKERRQ(ierr); 1090 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(5) (input mat struct): %d \n",lu->id.ICNTL(5));CHKERRQ(ierr); 1091 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(6) (matrix prescaling): %d \n",lu->id.ICNTL(6));CHKERRQ(ierr); 1092 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(7) (sequentia matrix ordering):%d \n",lu->id.ICNTL(7));CHKERRQ(ierr); 1093 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(8) (scalling strategy): %d \n",lu->id.ICNTL(8));CHKERRQ(ierr); 1094 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(10) (max num of refinements): %d \n",lu->id.ICNTL(10));CHKERRQ(ierr); 1095 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(11) (error analysis): %d \n",lu->id.ICNTL(11));CHKERRQ(ierr); 1096 if (lu->id.ICNTL(11)>0) { 1097 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(4) (inf norm of input mat): %g\n",lu->id.RINFOG(4));CHKERRQ(ierr); 1098 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(5) (inf norm of solution): %g\n",lu->id.RINFOG(5));CHKERRQ(ierr); 1099 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(6) (inf norm of residual): %g\n",lu->id.RINFOG(6));CHKERRQ(ierr); 1100 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(7),RINFOG(8) (backward error est): %g, %g\n",lu->id.RINFOG(7),lu->id.RINFOG(8));CHKERRQ(ierr); 1101 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(9) (error estimate): %g \n",lu->id.RINFOG(9));CHKERRQ(ierr); 1102 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n",lu->id.RINFOG(10),lu->id.RINFOG(11));CHKERRQ(ierr); 1103 } 1104 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(12) (efficiency control): %d \n",lu->id.ICNTL(12));CHKERRQ(ierr); 1105 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(13) (efficiency control): %d \n",lu->id.ICNTL(13));CHKERRQ(ierr); 1106 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(14) (percentage of estimated workspace increase): %d \n",lu->id.ICNTL(14));CHKERRQ(ierr); 1107 /* ICNTL(15-17) not used */ 1108 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(18) (input mat struct): %d \n",lu->id.ICNTL(18));CHKERRQ(ierr); 1109 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(19) (Shur complement info): %d \n",lu->id.ICNTL(19));CHKERRQ(ierr); 1110 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(20) (rhs sparse pattern): %d \n",lu->id.ICNTL(20));CHKERRQ(ierr); 1111 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(21) (solution struct): %d \n",lu->id.ICNTL(21));CHKERRQ(ierr); 1112 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(22) (in-core/out-of-core facility): %d \n",lu->id.ICNTL(22));CHKERRQ(ierr); 1113 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(23) (max size of memory can be allocated locally):%d \n",lu->id.ICNTL(23));CHKERRQ(ierr); 1114 1115 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(24) (detection of null pivot rows): %d \n",lu->id.ICNTL(24));CHKERRQ(ierr); 1116 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(25) (computation of a null space basis): %d \n",lu->id.ICNTL(25));CHKERRQ(ierr); 1117 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(26) (Schur options for rhs or solution): %d \n",lu->id.ICNTL(26));CHKERRQ(ierr); 1118 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(27) (experimental parameter): %d \n",lu->id.ICNTL(27));CHKERRQ(ierr); 1119 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(28) (use parallel or sequential ordering): %d \n",lu->id.ICNTL(28));CHKERRQ(ierr); 1120 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(29) (parallel ordering): %d \n",lu->id.ICNTL(29));CHKERRQ(ierr); 1121 1122 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(30) (user-specified set of entries in inv(A)): %d \n",lu->id.ICNTL(30));CHKERRQ(ierr); 1123 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(31) (factors is discarded in the solve phase): %d \n",lu->id.ICNTL(31));CHKERRQ(ierr); 1124 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(33) (compute determinant): %d \n",lu->id.ICNTL(33));CHKERRQ(ierr); 1125 1126 ierr = PetscViewerASCIIPrintf(viewer," CNTL(1) (relative pivoting threshold): %g \n",lu->id.CNTL(1));CHKERRQ(ierr); 1127 ierr = PetscViewerASCIIPrintf(viewer," CNTL(2) (stopping criterion of refinement): %g \n",lu->id.CNTL(2));CHKERRQ(ierr); 1128 ierr = PetscViewerASCIIPrintf(viewer," CNTL(3) (absolute pivoting threshold): %g \n",lu->id.CNTL(3));CHKERRQ(ierr); 1129 ierr = PetscViewerASCIIPrintf(viewer," CNTL(4) (value of static pivoting): %g \n",lu->id.CNTL(4));CHKERRQ(ierr); 1130 ierr = PetscViewerASCIIPrintf(viewer," CNTL(5) (fixation for null pivots): %g \n",lu->id.CNTL(5));CHKERRQ(ierr); 1131 1132 /* infomation local to each processor */ 1133 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(1) (local estimated flops for the elimination after analysis): \n");CHKERRQ(ierr); 1134 ierr = PetscViewerASCIISynchronizedAllow(viewer,PETSC_TRUE);CHKERRQ(ierr); 1135 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",lu->myid,lu->id.RINFO(1));CHKERRQ(ierr); 1136 ierr = PetscViewerFlush(viewer); 1137 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(2) (local estimated flops for the assembly after factorization): \n");CHKERRQ(ierr); 1138 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",lu->myid,lu->id.RINFO(2));CHKERRQ(ierr); 1139 ierr = PetscViewerFlush(viewer); 1140 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(3) (local estimated flops for the elimination after factorization): \n");CHKERRQ(ierr); 1141 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",lu->myid,lu->id.RINFO(3));CHKERRQ(ierr); 1142 ierr = PetscViewerFlush(viewer); 1143 1144 ierr = PetscViewerASCIIPrintf(viewer, " INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization): \n");CHKERRQ(ierr); 1145 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",lu->myid,lu->id.INFO(15));CHKERRQ(ierr); 1146 ierr = PetscViewerFlush(viewer); 1147 1148 ierr = PetscViewerASCIIPrintf(viewer, " INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization): \n");CHKERRQ(ierr); 1149 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",lu->myid,lu->id.INFO(16));CHKERRQ(ierr); 1150 ierr = PetscViewerFlush(viewer); 1151 1152 ierr = PetscViewerASCIIPrintf(viewer, " INFO(23) (num of pivots eliminated on this processor after factorization): \n");CHKERRQ(ierr); 1153 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",lu->myid,lu->id.INFO(23));CHKERRQ(ierr); 1154 ierr = PetscViewerFlush(viewer); 1155 ierr = PetscViewerASCIISynchronizedAllow(viewer,PETSC_FALSE);CHKERRQ(ierr); 1156 1157 if (!lu->myid){ /* information from the host */ 1158 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(1) (global estimated flops for the elimination after analysis): %g \n",lu->id.RINFOG(1));CHKERRQ(ierr); 1159 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(2) (global estimated flops for the assembly after factorization): %g \n",lu->id.RINFOG(2));CHKERRQ(ierr); 1160 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(3) (global estimated flops for the elimination after factorization): %g \n",lu->id.RINFOG(3));CHKERRQ(ierr); 1161 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); 1162 1163 ierr = PetscViewerASCIIPrintf(viewer," INFOG(3) (estimated real workspace for factors on all processors after analysis): %d \n",lu->id.INFOG(3));CHKERRQ(ierr); 1164 ierr = PetscViewerASCIIPrintf(viewer," INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d \n",lu->id.INFOG(4));CHKERRQ(ierr); 1165 ierr = PetscViewerASCIIPrintf(viewer," INFOG(5) (estimated maximum front size in the complete tree): %d \n",lu->id.INFOG(5));CHKERRQ(ierr); 1166 ierr = PetscViewerASCIIPrintf(viewer," INFOG(6) (number of nodes in the complete tree): %d \n",lu->id.INFOG(6));CHKERRQ(ierr); 1167 ierr = PetscViewerASCIIPrintf(viewer," INFOG(7) (ordering option effectively use after analysis): %d \n",lu->id.INFOG(7));CHKERRQ(ierr); 1168 ierr = PetscViewerASCIIPrintf(viewer," INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d \n",lu->id.INFOG(8));CHKERRQ(ierr); 1169 ierr = PetscViewerASCIIPrintf(viewer," INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d \n",lu->id.INFOG(9));CHKERRQ(ierr); 1170 ierr = PetscViewerASCIIPrintf(viewer," INFOG(10) (total integer space store the matrix factors after factorization): %d \n",lu->id.INFOG(10));CHKERRQ(ierr); 1171 ierr = PetscViewerASCIIPrintf(viewer," INFOG(11) (order of largest frontal matrix after factorization): %d \n",lu->id.INFOG(11));CHKERRQ(ierr); 1172 ierr = PetscViewerASCIIPrintf(viewer," INFOG(12) (number of off-diagonal pivots): %d \n",lu->id.INFOG(12));CHKERRQ(ierr); 1173 ierr = PetscViewerASCIIPrintf(viewer," INFOG(13) (number of delayed pivots after factorization): %d \n",lu->id.INFOG(13));CHKERRQ(ierr); 1174 ierr = PetscViewerASCIIPrintf(viewer," INFOG(14) (number of memory compress after factorization): %d \n",lu->id.INFOG(14));CHKERRQ(ierr); 1175 ierr = PetscViewerASCIIPrintf(viewer," INFOG(15) (number of steps of iterative refinement after solution): %d \n",lu->id.INFOG(15));CHKERRQ(ierr); 1176 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); 1177 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); 1178 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); 1179 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); 1180 ierr = PetscViewerASCIIPrintf(viewer," INFOG(20) (estimated number of entries in the factors): %d \n",lu->id.INFOG(20));CHKERRQ(ierr); 1181 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); 1182 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); 1183 ierr = PetscViewerASCIIPrintf(viewer," INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d \n",lu->id.INFOG(23));CHKERRQ(ierr); 1184 ierr = PetscViewerASCIIPrintf(viewer," INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d \n",lu->id.INFOG(24));CHKERRQ(ierr); 1185 ierr = PetscViewerASCIIPrintf(viewer," INFOG(25) (after factorization: number of pivots modified by static pivoting): %d \n",lu->id.INFOG(25));CHKERRQ(ierr); 1186 } 1187 } 1188 } 1189 PetscFunctionReturn(0); 1190 } 1191 1192 #undef __FUNCT__ 1193 #define __FUNCT__ "MatGetInfo_MUMPS" 1194 PetscErrorCode MatGetInfo_MUMPS(Mat A,MatInfoType flag,MatInfo *info) 1195 { 1196 Mat_MUMPS *mumps =(Mat_MUMPS*)A->spptr; 1197 1198 PetscFunctionBegin; 1199 info->block_size = 1.0; 1200 info->nz_allocated = mumps->id.INFOG(20); 1201 info->nz_used = mumps->id.INFOG(20); 1202 info->nz_unneeded = 0.0; 1203 info->assemblies = 0.0; 1204 info->mallocs = 0.0; 1205 info->memory = 0.0; 1206 info->fill_ratio_given = 0; 1207 info->fill_ratio_needed = 0; 1208 info->factor_mallocs = 0; 1209 PetscFunctionReturn(0); 1210 } 1211 1212 /* -------------------------------------------------------------------------------------------*/ 1213 #undef __FUNCT__ 1214 #define __FUNCT__ "MatMumpsSetIcntl_MUMPS" 1215 PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt ival) 1216 { 1217 Mat_MUMPS *lu =(Mat_MUMPS*)F->spptr; 1218 1219 PetscFunctionBegin; 1220 lu->id.ICNTL(icntl) = ival; 1221 PetscFunctionReturn(0); 1222 } 1223 1224 #undef __FUNCT__ 1225 #define __FUNCT__ "MatMumpsSetIcntl" 1226 /*@ 1227 MatMumpsSetIcntl - Set MUMPS parameter ICNTL() 1228 1229 Logically Collective on Mat 1230 1231 Input Parameters: 1232 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1233 . icntl - index of MUMPS parameter array ICNTL() 1234 - ival - value of MUMPS ICNTL(icntl) 1235 1236 Options Database: 1237 . -mat_mumps_icntl_<icntl> <ival> 1238 1239 Level: beginner 1240 1241 References: MUMPS Users' Guide 1242 1243 .seealso: MatGetFactor() 1244 @*/ 1245 PetscErrorCode MatMumpsSetIcntl(Mat F,PetscInt icntl,PetscInt ival) 1246 { 1247 PetscErrorCode ierr; 1248 1249 PetscFunctionBegin; 1250 PetscValidLogicalCollectiveInt(F,icntl,2); 1251 PetscValidLogicalCollectiveInt(F,ival,3); 1252 ierr = PetscTryMethod(F,"MatMumpsSetIcntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));CHKERRQ(ierr); 1253 PetscFunctionReturn(0); 1254 } 1255 1256 /*MC 1257 MATSOLVERMUMPS - A matrix type providing direct solvers (LU and Cholesky) for 1258 distributed and sequential matrices via the external package MUMPS. 1259 1260 Works with MATAIJ and MATSBAIJ matrices 1261 1262 Options Database Keys: 1263 + -mat_mumps_icntl_4 <0,...,4> - print level 1264 . -mat_mumps_icntl_6 <0,...,7> - matrix prescaling options (see MUMPS User's Guide) 1265 . -mat_mumps_icntl_7 <0,...,7> - matrix orderings (see MUMPS User's Guidec) 1266 . -mat_mumps_icntl_9 <1,2> - A or A^T x=b to be solved: 1 denotes A, 2 denotes A^T 1267 . -mat_mumps_icntl_10 <n> - maximum number of iterative refinements 1268 . -mat_mumps_icntl_11 <n> - error analysis, a positive value returns statistics during -ksp_view 1269 . -mat_mumps_icntl_12 <n> - efficiency control (see MUMPS User's Guide) 1270 . -mat_mumps_icntl_13 <n> - efficiency control (see MUMPS User's Guide) 1271 . -mat_mumps_icntl_14 <n> - efficiency control (see MUMPS User's Guide) 1272 . -mat_mumps_icntl_15 <n> - efficiency control (see MUMPS User's Guide) 1273 . -mat_mumps_cntl_1 <delta> - relative pivoting threshold 1274 . -mat_mumps_cntl_2 <tol> - stopping criterion for refinement 1275 - -mat_mumps_cntl_3 <adelta> - absolute pivoting threshold 1276 1277 Level: beginner 1278 1279 .seealso: PCFactorSetMatSolverPackage(), MatSolverPackage 1280 1281 M*/ 1282 1283 EXTERN_C_BEGIN 1284 #undef __FUNCT__ 1285 #define __FUNCT__ "MatFactorGetSolverPackage_mumps" 1286 PetscErrorCode MatFactorGetSolverPackage_mumps(Mat A,const MatSolverPackage *type) 1287 { 1288 PetscFunctionBegin; 1289 *type = MATSOLVERMUMPS; 1290 PetscFunctionReturn(0); 1291 } 1292 EXTERN_C_END 1293 1294 EXTERN_C_BEGIN 1295 /* MatGetFactor for Seq and MPI AIJ matrices */ 1296 #undef __FUNCT__ 1297 #define __FUNCT__ "MatGetFactor_aij_mumps" 1298 PetscErrorCode MatGetFactor_aij_mumps(Mat A,MatFactorType ftype,Mat *F) 1299 { 1300 Mat B; 1301 PetscErrorCode ierr; 1302 Mat_MUMPS *mumps; 1303 PetscBool isSeqAIJ; 1304 1305 PetscFunctionBegin; 1306 /* Create the factorization matrix */ 1307 ierr = PetscTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);CHKERRQ(ierr); 1308 ierr = MatCreate(((PetscObject)A)->comm,&B);CHKERRQ(ierr); 1309 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 1310 ierr = MatSetType(B,((PetscObject)A)->type_name);CHKERRQ(ierr); 1311 if (isSeqAIJ) { 1312 ierr = MatSeqAIJSetPreallocation(B,0,PETSC_NULL);CHKERRQ(ierr); 1313 } else { 1314 ierr = MatMPIAIJSetPreallocation(B,0,PETSC_NULL,0,PETSC_NULL);CHKERRQ(ierr); 1315 } 1316 1317 ierr = PetscNewLog(B,Mat_MUMPS,&mumps);CHKERRQ(ierr); 1318 B->ops->view = MatView_MUMPS; 1319 B->ops->getinfo = MatGetInfo_MUMPS; 1320 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatFactorGetSolverPackage_C","MatFactorGetSolverPackage_mumps",MatFactorGetSolverPackage_mumps);CHKERRQ(ierr); 1321 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMumpsSetIcntl_C","MatMumpsSetIcntl_MUMPS",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 1322 if (ftype == MAT_FACTOR_LU) { 1323 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS; 1324 B->factortype = MAT_FACTOR_LU; 1325 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij; 1326 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij; 1327 mumps->sym = 0; 1328 } else { 1329 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 1330 B->factortype = MAT_FACTOR_CHOLESKY; 1331 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij; 1332 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij; 1333 if (A->spd_set && A->spd) mumps->sym = 1; 1334 else mumps->sym = 2; 1335 } 1336 1337 mumps->isAIJ = PETSC_TRUE; 1338 mumps->Destroy = B->ops->destroy; 1339 B->ops->destroy = MatDestroy_MUMPS; 1340 B->spptr = (void*)mumps; 1341 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 1342 1343 *F = B; 1344 PetscFunctionReturn(0); 1345 } 1346 EXTERN_C_END 1347 1348 1349 EXTERN_C_BEGIN 1350 /* MatGetFactor for Seq and MPI SBAIJ matrices */ 1351 #undef __FUNCT__ 1352 #define __FUNCT__ "MatGetFactor_sbaij_mumps" 1353 PetscErrorCode MatGetFactor_sbaij_mumps(Mat A,MatFactorType ftype,Mat *F) 1354 { 1355 Mat B; 1356 PetscErrorCode ierr; 1357 Mat_MUMPS *mumps; 1358 PetscBool isSeqSBAIJ; 1359 1360 PetscFunctionBegin; 1361 if (ftype != MAT_FACTOR_CHOLESKY) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_SUP,"Cannot use PETSc SBAIJ matrices with MUMPS LU, use AIJ matrix"); 1362 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"); 1363 ierr = PetscTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ);CHKERRQ(ierr); 1364 /* Create the factorization matrix */ 1365 ierr = MatCreate(((PetscObject)A)->comm,&B);CHKERRQ(ierr); 1366 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 1367 ierr = MatSetType(B,((PetscObject)A)->type_name);CHKERRQ(ierr); 1368 ierr = PetscNewLog(B,Mat_MUMPS,&mumps);CHKERRQ(ierr); 1369 if (isSeqSBAIJ) { 1370 ierr = MatSeqSBAIJSetPreallocation(B,1,0,PETSC_NULL);CHKERRQ(ierr); 1371 mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij; 1372 } else { 1373 ierr = MatMPISBAIJSetPreallocation(B,1,0,PETSC_NULL,0,PETSC_NULL);CHKERRQ(ierr); 1374 mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij; 1375 } 1376 1377 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 1378 B->ops->view = MatView_MUMPS; 1379 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatFactorGetSolverPackage_C","MatFactorGetSolverPackage_mumps",MatFactorGetSolverPackage_mumps);CHKERRQ(ierr); 1380 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMumpsSetIcntl_C","MatMumpsSetIcntl",MatMumpsSetIcntl);CHKERRQ(ierr); 1381 B->factortype = MAT_FACTOR_CHOLESKY; 1382 if (A->spd_set && A->spd) mumps->sym = 1; 1383 else mumps->sym = 2; 1384 1385 mumps->isAIJ = PETSC_FALSE; 1386 mumps->Destroy = B->ops->destroy; 1387 B->ops->destroy = MatDestroy_MUMPS; 1388 B->spptr = (void*)mumps; 1389 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 1390 1391 *F = B; 1392 PetscFunctionReturn(0); 1393 } 1394 EXTERN_C_END 1395 1396 EXTERN_C_BEGIN 1397 #undef __FUNCT__ 1398 #define __FUNCT__ "MatGetFactor_baij_mumps" 1399 PetscErrorCode MatGetFactor_baij_mumps(Mat A,MatFactorType ftype,Mat *F) 1400 { 1401 Mat B; 1402 PetscErrorCode ierr; 1403 Mat_MUMPS *mumps; 1404 PetscBool isSeqBAIJ; 1405 1406 PetscFunctionBegin; 1407 /* Create the factorization matrix */ 1408 ierr = PetscTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ);CHKERRQ(ierr); 1409 ierr = MatCreate(((PetscObject)A)->comm,&B);CHKERRQ(ierr); 1410 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 1411 ierr = MatSetType(B,((PetscObject)A)->type_name);CHKERRQ(ierr); 1412 if (isSeqBAIJ) { 1413 ierr = MatSeqBAIJSetPreallocation(B,A->rmap->bs,0,PETSC_NULL);CHKERRQ(ierr); 1414 } else { 1415 ierr = MatMPIBAIJSetPreallocation(B,A->rmap->bs,0,PETSC_NULL,0,PETSC_NULL);CHKERRQ(ierr); 1416 } 1417 1418 ierr = PetscNewLog(B,Mat_MUMPS,&mumps);CHKERRQ(ierr); 1419 if (ftype == MAT_FACTOR_LU) { 1420 B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS; 1421 B->factortype = MAT_FACTOR_LU; 1422 if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij; 1423 else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij; 1424 mumps->sym = 0; 1425 } else { 1426 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead\n"); 1427 } 1428 1429 B->ops->view = MatView_MUMPS; 1430 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatFactorGetSolverPackage_C","MatFactorGetSolverPackage_mumps",MatFactorGetSolverPackage_mumps);CHKERRQ(ierr); 1431 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMumpsSetIcntl_C","MatMumpsSetIcntl_MUMPS",MatMumpsSetIcntl_MUMPS);CHKERRQ(ierr); 1432 1433 mumps->isAIJ = PETSC_TRUE; 1434 mumps->Destroy = B->ops->destroy; 1435 B->ops->destroy = MatDestroy_MUMPS; 1436 B->spptr = (void*)mumps; 1437 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 1438 1439 *F = B; 1440 PetscFunctionReturn(0); 1441 } 1442 EXTERN_C_END 1443 1444