1 #define PETSCMAT_DLL 2 3 /* 4 Provides an interface to the MUMPS sparse solver 5 */ 6 #include "../src/mat/impls/aij/seq/aij.h" /*I "petscmat.h" I*/ 7 #include "../src/mat/impls/aij/mpi/mpiaij.h" 8 #include "../src/mat/impls/sbaij/seq/sbaij.h" 9 #include "../src/mat/impls/sbaij/mpi/mpisbaij.h" 10 #include "../src/mat/impls/baij/seq/baij.h" 11 #include "../src/mat/impls/baij/mpi/mpibaij.h" 12 13 EXTERN_C_BEGIN 14 #if defined(PETSC_USE_COMPLEX) 15 #include "zmumps_c.h" 16 #else 17 #include "dmumps_c.h" 18 #endif 19 EXTERN_C_END 20 #define JOB_INIT -1 21 #define JOB_END -2 22 /* macros s.t. indices match MUMPS documentation */ 23 #define ICNTL(I) icntl[(I)-1] 24 #define CNTL(I) cntl[(I)-1] 25 #define INFOG(I) infog[(I)-1] 26 #define INFO(I) info[(I)-1] 27 #define RINFOG(I) rinfog[(I)-1] 28 #define RINFO(I) rinfo[(I)-1] 29 30 typedef struct { 31 #if defined(PETSC_USE_COMPLEX) 32 ZMUMPS_STRUC_C id; 33 #else 34 DMUMPS_STRUC_C id; 35 #endif 36 MatStructure matstruc; 37 PetscMPIInt myid,size; 38 PetscInt *irn,*jcn,nz,sym,nSolve; 39 PetscScalar *val; 40 MPI_Comm comm_mumps; 41 VecScatter scat_rhs, scat_sol; 42 PetscTruth isAIJ,CleanUpMUMPS,mumpsview; 43 Vec b_seq,x_seq; 44 PetscErrorCode (*MatDestroy)(Mat); 45 PetscErrorCode (*ConvertToTriples)(Mat, int, MatReuse, int*, int**, int**, PetscScalar**); 46 } Mat_MUMPS; 47 48 EXTERN PetscErrorCode MatDuplicate_MUMPS(Mat,MatDuplicateOption,Mat*); 49 50 51 /* MatConvertToTriples_A_B */ 52 /*convert Petsc matrix to triples: row[nz], col[nz], val[nz] */ 53 /* 54 input: 55 A - matrix in aij,baij or sbaij (bs=1) format 56 shift - 0: C style output triple; 1: Fortran style output triple. 57 reuse - MAT_INITIAL_MATRIX: spaces are allocated and values are set for the triple 58 MAT_REUSE_MATRIX: only the values in v array are updated 59 output: 60 nnz - dim of r, c, and v (number of local nonzero entries of A) 61 r, c, v - row and col index, matrix values (matrix triples) 62 */ 63 64 #undef __FUNCT__ 65 #define __FUNCT__ "MatConvertToTriples_seqaij_seqaij" 66 PetscErrorCode MatConvertToTriples_seqaij_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 67 { 68 const PetscInt *ai,*aj,*ajj,M=A->rmap->n; 69 PetscInt nz,rnz,i,j; 70 PetscErrorCode ierr; 71 PetscInt *row,*col; 72 Mat_SeqAIJ *aa=(Mat_SeqAIJ*)A->data; 73 74 PetscFunctionBegin; 75 *v=aa->a; 76 if (reuse == MAT_INITIAL_MATRIX){ 77 nz = aa->nz; ai = aa->i; aj = aa->j; 78 *nnz = nz; 79 ierr = PetscMalloc(2*nz*sizeof(PetscInt), &row);CHKERRQ(ierr); 80 col = row + nz; 81 82 nz = 0; 83 for(i=0; i<M; i++) { 84 rnz = ai[i+1] - ai[i]; 85 ajj = aj + ai[i]; 86 for(j=0; j<rnz; j++) { 87 row[nz] = i+shift; col[nz++] = ajj[j] + shift; 88 } 89 } 90 *r = row; *c = col; 91 } 92 PetscFunctionReturn(0); 93 } 94 95 #undef __FUNCT__ 96 #define __FUNCT__ "MatConvertToTriples_seqbaij_seqaij" 97 PetscErrorCode MatConvertToTriples_seqbaij_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v) 98 { 99 Mat_SeqBAIJ *aa=(Mat_SeqBAIJ*)A->data; 100 const PetscInt *ai,*aj,*ajj,bs=A->rmap->bs,bs2=aa->bs2,M=A->rmap->N/bs; 101 PetscInt nz,idx=0,rnz,i,j,k,m,ii; 102 PetscErrorCode ierr; 103 PetscInt *row,*col; 104 105 PetscFunctionBegin; 106 *v = aa->a; 107 if (reuse == MAT_INITIAL_MATRIX){ 108 ai = aa->i; aj = aa->j; 109 nz = bs2*aa->nz; 110 *nnz = nz; 111 ierr = PetscMalloc(2*nz*sizeof(PetscInt), &row);CHKERRQ(ierr); 112 col = row + nz; 113 114 for(i=0; i<M; i++) { 115 ii = 0; 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,rstartbs=mat->rstartbs; 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 = rstartbs; 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] = bs*irow + n + shift; 374 col[jj] = bs*(rstartbs + 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] = bs*irow + n + shift; 388 col[jj] = bs*(garray[bjj[k]]) + j + shift; 389 } 390 val[jj++] = bv[idx++]; 391 } 392 } 393 } 394 irow++; 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_low,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;nzb_low = 0; 421 for(i=0; i<m; i++){ 422 nza = nza + (ai[i+1] - adiag[i]); 423 countB = bi[i+1] - bi[i]; 424 bjj = bj + bi[i]; 425 426 j = 0; 427 while(garray[bjj[j]] < rstart) { 428 if(j == countB) break; 429 j++;nzb_low++; 430 } 431 } 432 /* Total nz = nz for the upper triangular A part + nz for the 2nd B part */ 433 nz = nza + (bb->nz - nzb_low); 434 *nnz = nz; 435 ierr = PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);CHKERRQ(ierr); 436 col = row + nz; 437 val = (PetscScalar*)(col + nz); 438 439 *r = row; *c = col; *v = val; 440 } else { 441 row = *r; col = *c; val = *v; 442 } 443 444 jj = 0; irow = rstart; 445 for ( i=0; i<m; i++ ) { 446 ajj = aj + adiag[i]; /* ptr to the beginning of the diagonal of this row */ 447 v1 = av + adiag[i]; 448 countA = ai[i+1] - adiag[i]; 449 countB = bi[i+1] - bi[i]; 450 bjj = bj + bi[i]; 451 v2 = bv + bi[i]; 452 453 /* A-part */ 454 for (j=0; j<countA; j++){ 455 if (reuse == MAT_INITIAL_MATRIX) { 456 row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift; 457 } 458 val[jj++] = v1[j]; 459 } 460 461 /* B-part */ 462 for(j=0; j < countB; j++){ 463 if (garray[bjj[j]] > rstart) { 464 if (reuse == MAT_INITIAL_MATRIX) { 465 row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift; 466 } 467 val[jj++] = v2[j]; 468 } 469 } 470 irow++; 471 } 472 PetscFunctionReturn(0); 473 } 474 475 #undef __FUNCT__ 476 #define __FUNCT__ "MatDestroy_MUMPS" 477 PetscErrorCode MatDestroy_MUMPS(Mat A) 478 { 479 Mat_MUMPS *lu=(Mat_MUMPS*)A->spptr; 480 PetscErrorCode ierr; 481 PetscMPIInt size=lu->size; 482 483 PetscFunctionBegin; 484 if (lu->CleanUpMUMPS) { 485 /* Terminate instance, deallocate memories */ 486 if (size > 1){ 487 ierr = PetscFree2(lu->id.sol_loc,lu->id.isol_loc);CHKERRQ(ierr); 488 ierr = VecScatterDestroy(lu->scat_rhs);CHKERRQ(ierr); 489 ierr = VecDestroy(lu->b_seq);CHKERRQ(ierr); 490 if (lu->nSolve && lu->scat_sol){ierr = VecScatterDestroy(lu->scat_sol);CHKERRQ(ierr);} 491 if (lu->nSolve && lu->x_seq){ierr = VecDestroy(lu->x_seq);CHKERRQ(ierr);} 492 } 493 ierr = PetscFree(lu->irn);CHKERRQ(ierr); 494 lu->id.job=JOB_END; 495 #if defined(PETSC_USE_COMPLEX) 496 zmumps_c(&lu->id); 497 #else 498 dmumps_c(&lu->id); 499 #endif 500 ierr = MPI_Comm_free(&(lu->comm_mumps));CHKERRQ(ierr); 501 } 502 /* clear composed functions */ 503 ierr = PetscObjectComposeFunctionDynamic((PetscObject)A,"MatFactorGetSolverPackage_C","",PETSC_NULL);CHKERRQ(ierr); 504 ierr = PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSetMumpsIcntl_C","",PETSC_NULL);CHKERRQ(ierr); 505 ierr = (lu->MatDestroy)(A);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 = 3; 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,&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 #if !defined(PETSC_USE_COMPLEX) 570 /* 571 input: 572 F: numeric factor 573 output: 574 nneg: total number of negative pivots 575 nzero: 0 576 npos: (global dimension of F) - nneg 577 */ 578 579 #undef __FUNCT__ 580 #define __FUNCT__ "MatGetInertia_SBAIJMUMPS" 581 PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F,int *nneg,int *nzero,int *npos) 582 { 583 Mat_MUMPS *lu =(Mat_MUMPS*)F->spptr; 584 PetscErrorCode ierr; 585 PetscMPIInt size; 586 587 PetscFunctionBegin; 588 ierr = MPI_Comm_size(((PetscObject)F)->comm,&size);CHKERRQ(ierr); 589 /* 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 */ 590 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)); 591 if (nneg){ 592 if (!lu->myid){ 593 *nneg = lu->id.INFOG(12); 594 } 595 ierr = MPI_Bcast(nneg,1,MPI_INT,0,lu->comm_mumps);CHKERRQ(ierr); 596 } 597 if (nzero) *nzero = 0; 598 if (npos) *npos = F->rmap->N - (*nneg); 599 PetscFunctionReturn(0); 600 } 601 #endif /* !defined(PETSC_USE_COMPLEX) */ 602 603 #undef __FUNCT__ 604 #define __FUNCT__ "MatFactorNumeric_MUMPS" 605 PetscErrorCode MatFactorNumeric_MUMPS(Mat F,Mat A,const MatFactorInfo *info) 606 { 607 Mat_MUMPS *lu =(Mat_MUMPS*)(F)->spptr; 608 PetscErrorCode ierr; 609 MatReuse reuse; 610 Mat F_diag; 611 PetscTruth isMPIAIJ; 612 613 PetscFunctionBegin; 614 reuse = MAT_REUSE_MATRIX; 615 ierr = (*lu->ConvertToTriples)(A, 1, reuse, &lu->nz, &lu->irn, &lu->jcn, &lu->val);CHKERRQ(ierr); 616 617 /* numerical factorization phase */ 618 /*-------------------------------*/ 619 lu->id.job = 2; 620 if(!lu->id.ICNTL(18)) { 621 if (!lu->myid) { 622 #if defined(PETSC_USE_COMPLEX) 623 lu->id.a = (mumps_double_complex*)lu->val; 624 #else 625 lu->id.a = lu->val; 626 #endif 627 } 628 } else { 629 #if defined(PETSC_USE_COMPLEX) 630 lu->id.a_loc = (mumps_double_complex*)lu->val; 631 #else 632 lu->id.a_loc = lu->val; 633 #endif 634 } 635 #if defined(PETSC_USE_COMPLEX) 636 zmumps_c(&lu->id); 637 #else 638 dmumps_c(&lu->id); 639 #endif 640 if (lu->id.INFOG(1) < 0) { 641 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)); 642 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)); 643 } 644 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)); 645 646 if (lu->size > 1){ 647 ierr = PetscTypeCompare((PetscObject)A,MATMPIAIJ,&isMPIAIJ);CHKERRQ(ierr); 648 if(isMPIAIJ) { 649 F_diag = ((Mat_MPIAIJ *)(F)->data)->A; 650 } else { 651 F_diag = ((Mat_MPISBAIJ *)(F)->data)->A; 652 } 653 F_diag->assembled = PETSC_TRUE; 654 if (lu->nSolve){ 655 ierr = VecScatterDestroy(lu->scat_sol);CHKERRQ(ierr); 656 ierr = PetscFree2(lu->id.sol_loc,lu->id.isol_loc);CHKERRQ(ierr); 657 ierr = VecDestroy(lu->x_seq);CHKERRQ(ierr); 658 } 659 } 660 (F)->assembled = PETSC_TRUE; 661 lu->matstruc = SAME_NONZERO_PATTERN; 662 lu->CleanUpMUMPS = PETSC_TRUE; 663 lu->nSolve = 0; 664 665 if (lu->size > 1){ 666 /* distributed solution */ 667 lu->id.ICNTL(21) = 1; 668 if (!lu->nSolve){ 669 /* Create x_seq=sol_loc for repeated use */ 670 PetscInt lsol_loc; 671 PetscScalar *sol_loc; 672 lsol_loc = lu->id.INFO(23); /* length of sol_loc */ 673 ierr = PetscMalloc2(lsol_loc,PetscScalar,&sol_loc,lsol_loc,PetscInt,&lu->id.isol_loc);CHKERRQ(ierr); 674 lu->id.lsol_loc = lsol_loc; 675 #if defined(PETSC_USE_COMPLEX) 676 lu->id.sol_loc = (mumps_double_complex*)sol_loc; 677 #else 678 lu->id.sol_loc = sol_loc; 679 #endif 680 ierr = VecCreateSeqWithArray(PETSC_COMM_SELF,lsol_loc,sol_loc,&lu->x_seq);CHKERRQ(ierr); 681 } 682 } 683 PetscFunctionReturn(0); 684 } 685 686 #undef __FUNCT__ 687 #define __FUNCT__ "PetscSetMUMPSOptions" 688 PetscErrorCode PetscSetMUMPSOptions(Mat F, Mat A) 689 { 690 Mat_MUMPS *lu = (Mat_MUMPS*)F->spptr; 691 PetscErrorCode ierr; 692 PetscInt icntl; 693 PetscTruth flg; 694 695 PetscFunctionBegin; 696 ierr = PetscOptionsBegin(((PetscObject)A)->comm,((PetscObject)A)->prefix,"MUMPS Options","Mat");CHKERRQ(ierr); 697 ierr = PetscOptionsTruth("-mat_mumps_view","View MUMPS parameters","None",lu->mumpsview,&lu->mumpsview,PETSC_NULL);CHKERRQ(ierr); 698 if (lu->size == 1){ 699 lu->id.ICNTL(18) = 0; /* centralized assembled matrix input */ 700 } else { 701 lu->id.ICNTL(18) = 3; /* distributed assembled matrix input */ 702 } 703 704 icntl=-1; 705 lu->id.ICNTL(4) = 0; /* level of printing; overwrite mumps default ICNTL(4)=2 */ 706 ierr = PetscOptionsInt("-mat_mumps_icntl_4","ICNTL(4): level of printing (0 to 4)","None",lu->id.ICNTL(4),&icntl,&flg);CHKERRQ(ierr); 707 if ((flg && icntl > 0) || PetscLogPrintInfo) { 708 lu->id.ICNTL(4)=icntl; /* and use mumps default icntl(i), i=1,2,3 */ 709 } else { /* no output */ 710 lu->id.ICNTL(1) = 0; /* error message, default= 6 */ 711 lu->id.ICNTL(2) = 0; /* output stream for diagnostic printing, statistics, and warning. default=0 */ 712 lu->id.ICNTL(3) = 0; /* output stream for global information, default=6 */ 713 } 714 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); 715 icntl=-1; 716 ierr = PetscOptionsInt("-mat_mumps_icntl_7","ICNTL(7): matrix ordering (0 to 7)","None",lu->id.ICNTL(7),&icntl,&flg);CHKERRQ(ierr); 717 if (flg) { 718 if (icntl== 1){ 719 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"); 720 } else { 721 lu->id.ICNTL(7) = icntl; 722 } 723 } 724 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); 725 ierr = PetscOptionsInt("-mat_mumps_icntl_9","ICNTL(9): A or A^T x=b to be solved. 1: A; otherwise: A^T","None",lu->id.ICNTL(9),&lu->id.ICNTL(9),PETSC_NULL);CHKERRQ(ierr); 726 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); 727 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); 728 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); 729 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); 730 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); 731 ierr = PetscOptionsInt("-mat_mumps_icntl_19","ICNTL(19): Schur complement","None",lu->id.ICNTL(19),&lu->id.ICNTL(19),PETSC_NULL);CHKERRQ(ierr); 732 733 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); 734 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); 735 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); 736 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); 737 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); 738 ierr = PetscOptionsInt("-mat_mumps_icntl_27","ICNTL(27): experimental parameter","None",lu->id.ICNTL(27),&lu->id.ICNTL(27),PETSC_NULL);CHKERRQ(ierr); 739 740 ierr = PetscOptionsReal("-mat_mumps_cntl_1","CNTL(1): relative pivoting threshold","None",lu->id.CNTL(1),&lu->id.CNTL(1),PETSC_NULL);CHKERRQ(ierr); 741 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); 742 ierr = PetscOptionsReal("-mat_mumps_cntl_3","CNTL(3): absolute pivoting threshold","None",lu->id.CNTL(3),&lu->id.CNTL(3),PETSC_NULL);CHKERRQ(ierr); 743 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); 744 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); 745 PetscOptionsEnd(); 746 PetscFunctionReturn(0); 747 } 748 749 #undef __FUNCT__ 750 #define __FUNCT__ "PetscInitializeMUMPS" 751 PetscErrorCode PetscInitializeMUMPS(Mat A,Mat_MUMPS* mumps) 752 { 753 PetscErrorCode ierr; 754 755 PetscFunctionBegin; 756 ierr = MPI_Comm_rank(((PetscObject)A)->comm, &mumps->myid); 757 ierr = MPI_Comm_size(((PetscObject)A)->comm,&mumps->size);CHKERRQ(ierr); 758 ierr = MPI_Comm_dup(((PetscObject)A)->comm,&(mumps->comm_mumps));CHKERRQ(ierr); 759 mumps->id.comm_fortran = MPI_Comm_c2f(mumps->comm_mumps); 760 761 mumps->id.job = JOB_INIT; 762 mumps->id.par = 1; /* host participates factorizaton and solve */ 763 mumps->id.sym = mumps->sym; 764 #if defined(PETSC_USE_COMPLEX) 765 zmumps_c(&mumps->id); 766 #else 767 dmumps_c(&mumps->id); 768 #endif 769 770 mumps->CleanUpMUMPS = PETSC_FALSE; 771 mumps->scat_rhs = PETSC_NULL; 772 mumps->scat_sol = PETSC_NULL; 773 mumps->nSolve = 0; 774 PetscFunctionReturn(0); 775 } 776 777 /* Note the Petsc r and c permutations are ignored */ 778 #undef __FUNCT__ 779 #define __FUNCT__ "MatLUFactorSymbolic_AIJMUMPS" 780 PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 781 { 782 Mat_MUMPS *lu = (Mat_MUMPS*)F->spptr; 783 PetscErrorCode ierr; 784 MatReuse reuse; 785 Vec b; 786 IS is_iden; 787 const PetscInt M = A->rmap->N; 788 789 PetscFunctionBegin; 790 lu->matstruc = DIFFERENT_NONZERO_PATTERN; 791 792 /* Set MUMPS options */ 793 ierr = PetscSetMUMPSOptions(F,A);CHKERRQ(ierr); 794 795 reuse = MAT_INITIAL_MATRIX; 796 ierr = (*lu->ConvertToTriples)(A, 1, reuse, &lu->nz, &lu->irn, &lu->jcn, &lu->val);CHKERRQ(ierr); 797 798 /* analysis phase */ 799 /*----------------*/ 800 lu->id.job = 1; 801 lu->id.n = M; 802 switch (lu->id.ICNTL(18)){ 803 case 0: /* centralized assembled matrix input */ 804 if (!lu->myid) { 805 lu->id.nz =lu->nz; lu->id.irn=lu->irn; lu->id.jcn=lu->jcn; 806 if (lu->id.ICNTL(6)>1){ 807 #if defined(PETSC_USE_COMPLEX) 808 lu->id.a = (mumps_double_complex*)lu->val; 809 #else 810 lu->id.a = lu->val; 811 #endif 812 } 813 } 814 break; 815 case 3: /* distributed assembled matrix input (size>1) */ 816 lu->id.nz_loc = lu->nz; 817 lu->id.irn_loc=lu->irn; lu->id.jcn_loc=lu->jcn; 818 if (lu->id.ICNTL(6)>1) { 819 #if defined(PETSC_USE_COMPLEX) 820 lu->id.a_loc = (mumps_double_complex*)lu->val; 821 #else 822 lu->id.a_loc = lu->val; 823 #endif 824 } 825 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 826 if (!lu->myid){ 827 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&lu->b_seq);CHKERRQ(ierr); 828 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 829 } else { 830 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&lu->b_seq);CHKERRQ(ierr); 831 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 832 } 833 ierr = VecCreate(((PetscObject)A)->comm,&b);CHKERRQ(ierr); 834 ierr = VecSetSizes(b,A->rmap->n,PETSC_DECIDE);CHKERRQ(ierr); 835 ierr = VecSetFromOptions(b);CHKERRQ(ierr); 836 837 ierr = VecScatterCreate(b,is_iden,lu->b_seq,is_iden,&lu->scat_rhs);CHKERRQ(ierr); 838 ierr = ISDestroy(is_iden);CHKERRQ(ierr); 839 ierr = VecDestroy(b);CHKERRQ(ierr); 840 break; 841 } 842 #if defined(PETSC_USE_COMPLEX) 843 zmumps_c(&lu->id); 844 #else 845 dmumps_c(&lu->id); 846 #endif 847 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)); 848 849 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 850 F->ops->solve = MatSolve_MUMPS; 851 PetscFunctionReturn(0); 852 } 853 854 /* Note the Petsc r and c permutations are ignored */ 855 #undef __FUNCT__ 856 #define __FUNCT__ "MatLUFactorSymbolic_BAIJMUMPS" 857 PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 858 { 859 860 Mat_MUMPS *lu = (Mat_MUMPS*)F->spptr; 861 PetscErrorCode ierr; 862 MatReuse reuse; 863 Vec b; 864 IS is_iden; 865 const PetscInt M = A->rmap->N; 866 867 PetscFunctionBegin; 868 lu->matstruc = DIFFERENT_NONZERO_PATTERN; 869 870 /* Set MUMPS options */ 871 ierr = PetscSetMUMPSOptions(F,A);CHKERRQ(ierr); 872 873 reuse = MAT_INITIAL_MATRIX; 874 ierr = (*lu->ConvertToTriples)(A, 1, reuse, &lu->nz, &lu->irn, &lu->jcn, &lu->val);CHKERRQ(ierr); 875 876 /* analysis phase */ 877 /*----------------*/ 878 lu->id.job = 1; 879 lu->id.n = M; 880 switch (lu->id.ICNTL(18)){ 881 case 0: /* centralized assembled matrix input */ 882 if (!lu->myid) { 883 lu->id.nz =lu->nz; lu->id.irn=lu->irn; lu->id.jcn=lu->jcn; 884 if (lu->id.ICNTL(6)>1){ 885 #if defined(PETSC_USE_COMPLEX) 886 lu->id.a = (mumps_double_complex*)lu->val; 887 #else 888 lu->id.a = lu->val; 889 #endif 890 } 891 } 892 break; 893 case 3: /* distributed assembled matrix input (size>1) */ 894 lu->id.nz_loc = lu->nz; 895 lu->id.irn_loc=lu->irn; lu->id.jcn_loc=lu->jcn; 896 if (lu->id.ICNTL(6)>1) { 897 #if defined(PETSC_USE_COMPLEX) 898 lu->id.a_loc = (mumps_double_complex*)lu->val; 899 #else 900 lu->id.a_loc = lu->val; 901 #endif 902 } 903 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 904 if (!lu->myid){ 905 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&lu->b_seq);CHKERRQ(ierr); 906 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 907 } else { 908 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&lu->b_seq);CHKERRQ(ierr); 909 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 910 } 911 ierr = VecCreate(((PetscObject)A)->comm,&b);CHKERRQ(ierr); 912 ierr = VecSetSizes(b,A->rmap->n,PETSC_DECIDE);CHKERRQ(ierr); 913 ierr = VecSetFromOptions(b);CHKERRQ(ierr); 914 915 ierr = VecScatterCreate(b,is_iden,lu->b_seq,is_iden,&lu->scat_rhs);CHKERRQ(ierr); 916 ierr = ISDestroy(is_iden);CHKERRQ(ierr); 917 ierr = VecDestroy(b);CHKERRQ(ierr); 918 break; 919 } 920 #if defined(PETSC_USE_COMPLEX) 921 zmumps_c(&lu->id); 922 #else 923 dmumps_c(&lu->id); 924 #endif 925 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)); 926 927 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 928 F->ops->solve = MatSolve_MUMPS; 929 PetscFunctionReturn(0); 930 } 931 932 /* Note the Petsc r permutation is ignored */ 933 #undef __FUNCT__ 934 #define __FUNCT__ "MatCholeskyFactorSymbolic_MUMPS" 935 PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F,Mat A,IS r,const MatFactorInfo *info) 936 { 937 Mat_MUMPS *lu = (Mat_MUMPS*)F->spptr; 938 PetscErrorCode ierr; 939 MatReuse reuse; 940 Vec b; 941 IS is_iden; 942 const PetscInt M = A->rmap->N; 943 944 PetscFunctionBegin; 945 lu->matstruc = DIFFERENT_NONZERO_PATTERN; 946 947 /* Set MUMPS options */ 948 ierr = PetscSetMUMPSOptions(F,A);CHKERRQ(ierr); 949 950 reuse = MAT_INITIAL_MATRIX; 951 ierr = (*lu->ConvertToTriples)(A, 1 , reuse, &lu->nz, &lu->irn, &lu->jcn, &lu->val);CHKERRQ(ierr); 952 953 /* analysis phase */ 954 /*----------------*/ 955 lu->id.job = 1; 956 lu->id.n = M; 957 switch (lu->id.ICNTL(18)){ 958 case 0: /* centralized assembled matrix input */ 959 if (!lu->myid) { 960 lu->id.nz =lu->nz; lu->id.irn=lu->irn; lu->id.jcn=lu->jcn; 961 if (lu->id.ICNTL(6)>1){ 962 #if defined(PETSC_USE_COMPLEX) 963 lu->id.a = (mumps_double_complex*)lu->val; 964 #else 965 lu->id.a = lu->val; 966 #endif 967 } 968 } 969 break; 970 case 3: /* distributed assembled matrix input (size>1) */ 971 lu->id.nz_loc = lu->nz; 972 lu->id.irn_loc=lu->irn; lu->id.jcn_loc=lu->jcn; 973 if (lu->id.ICNTL(6)>1) { 974 #if defined(PETSC_USE_COMPLEX) 975 lu->id.a_loc = (mumps_double_complex*)lu->val; 976 #else 977 lu->id.a_loc = lu->val; 978 #endif 979 } 980 /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 981 if (!lu->myid){ 982 ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&lu->b_seq);CHKERRQ(ierr); 983 ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);CHKERRQ(ierr); 984 } else { 985 ierr = VecCreateSeq(PETSC_COMM_SELF,0,&lu->b_seq);CHKERRQ(ierr); 986 ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr); 987 } 988 ierr = VecCreate(((PetscObject)A)->comm,&b);CHKERRQ(ierr); 989 ierr = VecSetSizes(b,A->rmap->n,PETSC_DECIDE);CHKERRQ(ierr); 990 ierr = VecSetFromOptions(b);CHKERRQ(ierr); 991 992 ierr = VecScatterCreate(b,is_iden,lu->b_seq,is_iden,&lu->scat_rhs);CHKERRQ(ierr); 993 ierr = ISDestroy(is_iden);CHKERRQ(ierr); 994 ierr = VecDestroy(b);CHKERRQ(ierr); 995 break; 996 } 997 #if defined(PETSC_USE_COMPLEX) 998 zmumps_c(&lu->id); 999 #else 1000 dmumps_c(&lu->id); 1001 #endif 1002 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)); 1003 1004 F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS; 1005 F->ops->solve = MatSolve_MUMPS; 1006 #if !defined(PETSC_USE_COMPLEX) 1007 (F)->ops->getinertia = MatGetInertia_SBAIJMUMPS; 1008 #endif 1009 PetscFunctionReturn(0); 1010 } 1011 1012 #undef __FUNCT__ 1013 #define __FUNCT__ "MatFactorInfo_MUMPS" 1014 PetscErrorCode MatFactorInfo_MUMPS(Mat A,PetscViewer viewer) 1015 { 1016 Mat_MUMPS *lu=(Mat_MUMPS*)A->spptr; 1017 PetscErrorCode ierr; 1018 1019 PetscFunctionBegin; 1020 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(1) (output for error): %d \n",lu->id.ICNTL(1));CHKERRQ(ierr); 1021 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(2) (output of diagnostic msg):%d \n",lu->id.ICNTL(2));CHKERRQ(ierr); 1022 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(3) (output for global info): %d \n",lu->id.ICNTL(3));CHKERRQ(ierr); 1023 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(4) (level of printing): %d \n",lu->id.ICNTL(4));CHKERRQ(ierr); 1024 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(5) (input mat struct): %d \n",lu->id.ICNTL(5));CHKERRQ(ierr); 1025 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(6) (matrix prescaling): %d \n",lu->id.ICNTL(6));CHKERRQ(ierr); 1026 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(7) (matrix ordering): %d \n",lu->id.ICNTL(7));CHKERRQ(ierr); 1027 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(8) (scalling strategy): %d \n",lu->id.ICNTL(8));CHKERRQ(ierr); 1028 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(9) (A/A^T x=b is solved): %d \n",lu->id.ICNTL(9));CHKERRQ(ierr); 1029 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(10) (max num of refinements): %d \n",lu->id.ICNTL(10));CHKERRQ(ierr); 1030 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(11) (error analysis): %d \n",lu->id.ICNTL(11));CHKERRQ(ierr); 1031 if (lu->id.ICNTL(11)>0) { 1032 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(4) (inf norm of input mat): %g\n",lu->id.RINFOG(4));CHKERRQ(ierr); 1033 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(5) (inf norm of solution): %g\n",lu->id.RINFOG(5));CHKERRQ(ierr); 1034 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(6) (inf norm of residual): %g\n",lu->id.RINFOG(6));CHKERRQ(ierr); 1035 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(7),RINFOG(8) (backward error est): %g, %g\n",lu->id.RINFOG(7),lu->id.RINFOG(8));CHKERRQ(ierr); 1036 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(9) (error estimate): %g \n",lu->id.RINFOG(9));CHKERRQ(ierr); 1037 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n",lu->id.RINFOG(10),lu->id.RINFOG(11));CHKERRQ(ierr); 1038 1039 } 1040 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(12) (efficiency control): %d \n",lu->id.ICNTL(12));CHKERRQ(ierr); 1041 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(13) (efficiency control): %d \n",lu->id.ICNTL(13));CHKERRQ(ierr); 1042 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(14) (percentage of estimated workspace increase): %d \n",lu->id.ICNTL(14));CHKERRQ(ierr); 1043 /* ICNTL(15-17) not used */ 1044 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(18) (input mat struct): %d \n",lu->id.ICNTL(18));CHKERRQ(ierr); 1045 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(19) (Shur complement info): %d \n",lu->id.ICNTL(19));CHKERRQ(ierr); 1046 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(20) (rhs sparse pattern): %d \n",lu->id.ICNTL(20));CHKERRQ(ierr); 1047 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(21) (solution struct): %d \n",lu->id.ICNTL(21));CHKERRQ(ierr); 1048 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(22) (in-core/out-of-core facility): %d \n",lu->id.ICNTL(22));CHKERRQ(ierr); 1049 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(23) (max size of memory can be allocated locally):%d \n",lu->id.ICNTL(23));CHKERRQ(ierr); 1050 1051 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(24) (detection of null pivot rows): %d \n",lu->id.ICNTL(24));CHKERRQ(ierr); 1052 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(25) (computation of a null space basis): %d \n",lu->id.ICNTL(25));CHKERRQ(ierr); 1053 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(26) (Schur options for rhs or solution): %d \n",lu->id.ICNTL(26));CHKERRQ(ierr); 1054 ierr = PetscViewerASCIIPrintf(viewer," ICNTL(27) (experimental parameter): %d \n",lu->id.ICNTL(27));CHKERRQ(ierr); 1055 1056 ierr = PetscViewerASCIIPrintf(viewer," CNTL(1) (relative pivoting threshold): %g \n",lu->id.CNTL(1));CHKERRQ(ierr); 1057 ierr = PetscViewerASCIIPrintf(viewer," CNTL(2) (stopping criterion of refinement): %g \n",lu->id.CNTL(2));CHKERRQ(ierr); 1058 ierr = PetscViewerASCIIPrintf(viewer," CNTL(3) (absolute pivoting threshold): %g \n",lu->id.CNTL(3));CHKERRQ(ierr); 1059 ierr = PetscViewerASCIIPrintf(viewer," CNTL(4) (value of static pivoting): %g \n",lu->id.CNTL(4));CHKERRQ(ierr); 1060 ierr = PetscViewerASCIIPrintf(viewer," CNTL(5) (fixation for null pivots): %g \n",lu->id.CNTL(5));CHKERRQ(ierr); 1061 1062 /* infomation local to each processor */ 1063 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(1) (local estimated flops for the elimination after analysis): \n");CHKERRQ(ierr); 1064 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",lu->myid,lu->id.RINFO(1));CHKERRQ(ierr); 1065 ierr = PetscViewerFlush(viewer); 1066 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(2) (local estimated flops for the assembly after factorization): \n");CHKERRQ(ierr); 1067 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",lu->myid,lu->id.RINFO(2));CHKERRQ(ierr); 1068 ierr = PetscViewerFlush(viewer); 1069 ierr = PetscViewerASCIIPrintf(viewer, " RINFO(3) (local estimated flops for the elimination after factorization): \n");CHKERRQ(ierr); 1070 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",lu->myid,lu->id.RINFO(3));CHKERRQ(ierr); 1071 ierr = PetscViewerFlush(viewer); 1072 1073 ierr = PetscViewerASCIIPrintf(viewer, " INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization): \n");CHKERRQ(ierr); 1074 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",lu->myid,lu->id.INFO(15));CHKERRQ(ierr); 1075 ierr = PetscViewerFlush(viewer); 1076 1077 ierr = PetscViewerASCIIPrintf(viewer, " INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization): \n");CHKERRQ(ierr); 1078 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",lu->myid,lu->id.INFO(16));CHKERRQ(ierr); 1079 ierr = PetscViewerFlush(viewer); 1080 1081 ierr = PetscViewerASCIIPrintf(viewer, " INFO(23) (num of pivots eliminated on this processor after factorization): \n");CHKERRQ(ierr); 1082 ierr = PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d \n",lu->myid,lu->id.INFO(23));CHKERRQ(ierr); 1083 ierr = PetscViewerFlush(viewer); 1084 1085 if (!lu->myid){ /* information from the host */ 1086 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(1) (global estimated flops for the elimination after analysis): %g \n",lu->id.RINFOG(1));CHKERRQ(ierr); 1087 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(2) (global estimated flops for the assembly after factorization): %g \n",lu->id.RINFOG(2));CHKERRQ(ierr); 1088 ierr = PetscViewerASCIIPrintf(viewer," RINFOG(3) (global estimated flops for the elimination after factorization): %g \n",lu->id.RINFOG(3));CHKERRQ(ierr); 1089 1090 ierr = PetscViewerASCIIPrintf(viewer," INFOG(3) (estimated real workspace for factors on all processors after analysis): %d \n",lu->id.INFOG(3));CHKERRQ(ierr); 1091 ierr = PetscViewerASCIIPrintf(viewer," INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d \n",lu->id.INFOG(4));CHKERRQ(ierr); 1092 ierr = PetscViewerASCIIPrintf(viewer," INFOG(5) (estimated maximum front size in the complete tree): %d \n",lu->id.INFOG(5));CHKERRQ(ierr); 1093 ierr = PetscViewerASCIIPrintf(viewer," INFOG(6) (number of nodes in the complete tree): %d \n",lu->id.INFOG(6));CHKERRQ(ierr); 1094 ierr = PetscViewerASCIIPrintf(viewer," INFOG(7) (ordering option effectively uese after analysis): %d \n",lu->id.INFOG(7));CHKERRQ(ierr); 1095 ierr = PetscViewerASCIIPrintf(viewer," INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d \n",lu->id.INFOG(8));CHKERRQ(ierr); 1096 ierr = PetscViewerASCIIPrintf(viewer," INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d \n",lu->id.INFOG(9));CHKERRQ(ierr); 1097 ierr = PetscViewerASCIIPrintf(viewer," INFOG(10) (total integer space store the matrix factors after factorization): %d \n",lu->id.INFOG(10));CHKERRQ(ierr); 1098 ierr = PetscViewerASCIIPrintf(viewer," INFOG(11) (order of largest frontal matrix after factorization): %d \n",lu->id.INFOG(11));CHKERRQ(ierr); 1099 ierr = PetscViewerASCIIPrintf(viewer," INFOG(12) (number of off-diagonal pivots): %d \n",lu->id.INFOG(12));CHKERRQ(ierr); 1100 ierr = PetscViewerASCIIPrintf(viewer," INFOG(13) (number of delayed pivots after factorization): %d \n",lu->id.INFOG(13));CHKERRQ(ierr); 1101 ierr = PetscViewerASCIIPrintf(viewer," INFOG(14) (number of memory compress after factorization): %d \n",lu->id.INFOG(14));CHKERRQ(ierr); 1102 ierr = PetscViewerASCIIPrintf(viewer," INFOG(15) (number of steps of iterative refinement after solution): %d \n",lu->id.INFOG(15));CHKERRQ(ierr); 1103 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); 1104 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); 1105 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); 1106 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); 1107 ierr = PetscViewerASCIIPrintf(viewer," INFOG(20) (estimated number of entries in the factors): %d \n",lu->id.INFOG(20));CHKERRQ(ierr); 1108 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); 1109 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); 1110 ierr = PetscViewerASCIIPrintf(viewer," INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d \n",lu->id.INFOG(23));CHKERRQ(ierr); 1111 ierr = PetscViewerASCIIPrintf(viewer," INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d \n",lu->id.INFOG(24));CHKERRQ(ierr); 1112 ierr = PetscViewerASCIIPrintf(viewer," INFOG(25) (after factorization: number of pivots modified by static pivoting): %d \n",lu->id.INFOG(25));CHKERRQ(ierr); 1113 } 1114 PetscFunctionReturn(0); 1115 } 1116 1117 #undef __FUNCT__ 1118 #define __FUNCT__ "MatView_MUMPS" 1119 PetscErrorCode MatView_MUMPS(Mat A,PetscViewer viewer) 1120 { 1121 PetscErrorCode ierr; 1122 PetscTruth iascii; 1123 PetscViewerFormat format; 1124 Mat_MUMPS *mumps=(Mat_MUMPS*)A->spptr; 1125 1126 PetscFunctionBegin; 1127 /* check if matrix is mumps type */ 1128 if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(0); 1129 1130 ierr = PetscTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 1131 if (iascii) { 1132 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 1133 if (format == PETSC_VIEWER_ASCII_INFO || mumps->mumpsview){ 1134 ierr = PetscViewerASCIIPrintf(viewer,"MUMPS run parameters:\n");CHKERRQ(ierr); 1135 ierr = PetscViewerASCIIPrintf(viewer," SYM (matrix type): %d \n",mumps->id.sym);CHKERRQ(ierr); 1136 ierr = PetscViewerASCIIPrintf(viewer," PAR (host participation): %d \n",mumps->id.par);CHKERRQ(ierr); 1137 if (mumps->mumpsview){ /* View all MUMPS parameters */ 1138 ierr = MatFactorInfo_MUMPS(A,viewer);CHKERRQ(ierr); 1139 } 1140 } 1141 } 1142 PetscFunctionReturn(0); 1143 } 1144 1145 #undef __FUNCT__ 1146 #define __FUNCT__ "MatGetInfo_MUMPS" 1147 PetscErrorCode MatGetInfo_MUMPS(Mat A,MatInfoType flag,MatInfo *info) 1148 { 1149 Mat_MUMPS *mumps =(Mat_MUMPS*)A->spptr; 1150 1151 PetscFunctionBegin; 1152 info->block_size = 1.0; 1153 info->nz_allocated = mumps->id.INFOG(20); 1154 info->nz_used = mumps->id.INFOG(20); 1155 info->nz_unneeded = 0.0; 1156 info->assemblies = 0.0; 1157 info->mallocs = 0.0; 1158 info->memory = 0.0; 1159 info->fill_ratio_given = 0; 1160 info->fill_ratio_needed = 0; 1161 info->factor_mallocs = 0; 1162 PetscFunctionReturn(0); 1163 } 1164 1165 /*MC 1166 MATSOLVERMUMPS - A matrix type providing direct solvers (LU and Cholesky) for 1167 distributed and sequential matrices via the external package MUMPS. 1168 1169 Works with MATAIJ and MATSBAIJ matrices 1170 1171 Options Database Keys: 1172 + -mat_mumps_sym <0,1,2> - 0 the matrix is unsymmetric, 1 symmetric positive definite, 2 symmetric 1173 . -mat_mumps_icntl_4 <0,...,4> - print level 1174 . -mat_mumps_icntl_6 <0,...,7> - matrix prescaling options (see MUMPS User's Guide) 1175 . -mat_mumps_icntl_7 <0,...,7> - matrix orderings (see MUMPS User's Guide) 1176 . -mat_mumps_icntl_9 <1,2> - A or A^T x=b to be solved: 1 denotes A, 2 denotes A^T 1177 . -mat_mumps_icntl_10 <n> - maximum number of iterative refinements 1178 . -mat_mumps_icntl_11 <n> - error analysis, a positive value returns statistics during -ksp_view 1179 . -mat_mumps_icntl_12 <n> - efficiency control (see MUMPS User's Guide) 1180 . -mat_mumps_icntl_13 <n> - efficiency control (see MUMPS User's Guide) 1181 . -mat_mumps_icntl_14 <n> - efficiency control (see MUMPS User's Guide) 1182 . -mat_mumps_icntl_15 <n> - efficiency control (see MUMPS User's Guide) 1183 . -mat_mumps_cntl_1 <delta> - relative pivoting threshold 1184 . -mat_mumps_cntl_2 <tol> - stopping criterion for refinement 1185 - -mat_mumps_cntl_3 <adelta> - absolute pivoting threshold 1186 1187 Level: beginner 1188 1189 .seealso: PCFactorSetMatSolverPackage(), MatSolverPackage 1190 1191 M*/ 1192 1193 EXTERN_C_BEGIN 1194 #undef __FUNCT__ 1195 #define __FUNCT__ "MatFactorGetSolverPackage_mumps" 1196 PetscErrorCode MatFactorGetSolverPackage_mumps(Mat A,const MatSolverPackage *type) 1197 { 1198 PetscFunctionBegin; 1199 *type = MATSOLVERMUMPS; 1200 PetscFunctionReturn(0); 1201 } 1202 EXTERN_C_END 1203 1204 EXTERN_C_BEGIN 1205 /* MatGetFactor for Seq and MPI AIJ matrices */ 1206 #undef __FUNCT__ 1207 #define __FUNCT__ "MatGetFactor_aij_mumps" 1208 PetscErrorCode MatGetFactor_aij_mumps(Mat A,MatFactorType ftype,Mat *F) 1209 { 1210 Mat B; 1211 PetscErrorCode ierr; 1212 Mat_MUMPS *mumps; 1213 PetscTruth isSeqAIJ; 1214 1215 PetscFunctionBegin; 1216 /* Create the factorization matrix */ 1217 ierr = PetscTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);CHKERRQ(ierr); 1218 ierr = MatCreate(((PetscObject)A)->comm,&B);CHKERRQ(ierr); 1219 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 1220 ierr = MatSetType(B,((PetscObject)A)->type_name);CHKERRQ(ierr); 1221 if (isSeqAIJ) { 1222 ierr = MatSeqAIJSetPreallocation(B,0,PETSC_NULL);CHKERRQ(ierr); 1223 } else { 1224 ierr = MatMPIAIJSetPreallocation(B,0,PETSC_NULL,0,PETSC_NULL);CHKERRQ(ierr); 1225 } 1226 1227 ierr = PetscNewLog(B,Mat_MUMPS,&mumps);CHKERRQ(ierr); 1228 B->ops->view = MatView_MUMPS; 1229 B->ops->getinfo = MatGetInfo_MUMPS; 1230 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatFactorGetSolverPackage_C","MatFactorGetSolverPackage_mumps",MatFactorGetSolverPackage_mumps);CHKERRQ(ierr); 1231 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSetMumpsIcntl_C","MatSetMumpsIcntl",MatSetMumpsIcntl);CHKERRQ(ierr); 1232 if (ftype == MAT_FACTOR_LU) { 1233 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS; 1234 B->factortype = MAT_FACTOR_LU; 1235 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij; 1236 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij; 1237 mumps->sym = 0; 1238 } else { 1239 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 1240 B->factortype = MAT_FACTOR_CHOLESKY; 1241 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij; 1242 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij; 1243 if (A->spd_set && A->spd) mumps->sym = 1; 1244 else mumps->sym = 2; 1245 } 1246 1247 mumps->isAIJ = PETSC_TRUE; 1248 mumps->MatDestroy = B->ops->destroy; 1249 B->ops->destroy = MatDestroy_MUMPS; 1250 B->spptr = (void*)mumps; 1251 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 1252 1253 *F = B; 1254 PetscFunctionReturn(0); 1255 } 1256 EXTERN_C_END 1257 1258 1259 EXTERN_C_BEGIN 1260 /* MatGetFactor for Seq and MPI SBAIJ matrices */ 1261 #undef __FUNCT__ 1262 #define __FUNCT__ "MatGetFactor_sbaij_mumps" 1263 PetscErrorCode MatGetFactor_sbaij_mumps(Mat A,MatFactorType ftype,Mat *F) 1264 { 1265 Mat B; 1266 PetscErrorCode ierr; 1267 Mat_MUMPS *mumps; 1268 PetscTruth isSeqSBAIJ; 1269 1270 PetscFunctionBegin; 1271 if (ftype != MAT_FACTOR_CHOLESKY) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_SUP,"Cannot use PETSc SBAIJ matrices with MUMPS LU, use AIJ matrix"); 1272 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"); 1273 ierr = PetscTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ);CHKERRQ(ierr); 1274 /* Create the factorization matrix */ 1275 ierr = MatCreate(((PetscObject)A)->comm,&B);CHKERRQ(ierr); 1276 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 1277 ierr = MatSetType(B,((PetscObject)A)->type_name);CHKERRQ(ierr); 1278 ierr = PetscNewLog(B,Mat_MUMPS,&mumps);CHKERRQ(ierr); 1279 if (isSeqSBAIJ) { 1280 ierr = MatSeqSBAIJSetPreallocation(B,1,0,PETSC_NULL);CHKERRQ(ierr); 1281 mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij; 1282 } else { 1283 ierr = MatMPISBAIJSetPreallocation(B,1,0,PETSC_NULL,0,PETSC_NULL);CHKERRQ(ierr); 1284 mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij; 1285 } 1286 1287 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 1288 B->ops->view = MatView_MUMPS; 1289 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatFactorGetSolverPackage_C","MatFactorGetSolverPackage_mumps",MatFactorGetSolverPackage_mumps);CHKERRQ(ierr); 1290 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSetMumpsIcntl_C","MatSetMumpsIcntl",MatSetMumpsIcntl);CHKERRQ(ierr); 1291 B->factortype = MAT_FACTOR_CHOLESKY; 1292 if (A->spd_set && A->spd) mumps->sym = 1; 1293 else mumps->sym = 2; 1294 1295 mumps->isAIJ = PETSC_FALSE; 1296 mumps->MatDestroy = B->ops->destroy; 1297 B->ops->destroy = MatDestroy_MUMPS; 1298 B->spptr = (void*)mumps; 1299 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 1300 1301 *F = B; 1302 PetscFunctionReturn(0); 1303 } 1304 EXTERN_C_END 1305 1306 EXTERN_C_BEGIN 1307 #undef __FUNCT__ 1308 #define __FUNCT__ "MatGetFactor_baij_mumps" 1309 PetscErrorCode MatGetFactor_baij_mumps(Mat A,MatFactorType ftype,Mat *F) 1310 { 1311 Mat B; 1312 PetscErrorCode ierr; 1313 Mat_MUMPS *mumps; 1314 PetscTruth isSeqBAIJ; 1315 1316 PetscFunctionBegin; 1317 /* Create the factorization matrix */ 1318 ierr = PetscTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ);CHKERRQ(ierr); 1319 ierr = MatCreate(((PetscObject)A)->comm,&B);CHKERRQ(ierr); 1320 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 1321 ierr = MatSetType(B,((PetscObject)A)->type_name);CHKERRQ(ierr); 1322 if (isSeqBAIJ) { 1323 ierr = MatSeqBAIJSetPreallocation(B,A->rmap->bs,0,PETSC_NULL);CHKERRQ(ierr); 1324 } else { 1325 ierr = MatMPIBAIJSetPreallocation(B,A->rmap->bs,0,PETSC_NULL,0,PETSC_NULL);CHKERRQ(ierr); 1326 } 1327 1328 ierr = PetscNewLog(B,Mat_MUMPS,&mumps);CHKERRQ(ierr); 1329 if (ftype == MAT_FACTOR_LU) { 1330 B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS; 1331 B->factortype = MAT_FACTOR_LU; 1332 if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij; 1333 else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij; 1334 mumps->sym = 0; 1335 } else { 1336 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead\n"); 1337 } 1338 1339 B->ops->view = MatView_MUMPS; 1340 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatFactorGetSolverPackage_C","MatFactorGetSolverPackage_mumps",MatFactorGetSolverPackage_mumps);CHKERRQ(ierr); 1341 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSetMumpsIcntl_C","MatSetMumpsIcntl",MatSetMumpsIcntl);CHKERRQ(ierr); 1342 1343 mumps->isAIJ = PETSC_TRUE; 1344 mumps->MatDestroy = B->ops->destroy; 1345 B->ops->destroy = MatDestroy_MUMPS; 1346 B->spptr = (void*)mumps; 1347 ierr = PetscInitializeMUMPS(A,mumps);CHKERRQ(ierr); 1348 1349 *F = B; 1350 PetscFunctionReturn(0); 1351 } 1352 EXTERN_C_END 1353 1354 /* -------------------------------------------------------------------------------------------*/ 1355 #undef __FUNCT__ 1356 #define __FUNCT__ "MatSetMumpsIcntl" 1357 /*@ 1358 MatSetMumpsIcntl - Set MUMPS parameter ICNTL() 1359 1360 Collective on Mat 1361 1362 Input Parameters: 1363 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 1364 . icntl - index of MUMPS parameter array ICNTL() 1365 - ival - value of MUMPS ICNTL(icntl) 1366 1367 Options Database: 1368 . -mat_mumps_icntl_<icntl> <ival> 1369 1370 Level: beginner 1371 1372 References: MUMPS Users' Guide 1373 1374 .seealso: MatGetFactor() 1375 @*/ 1376 PetscErrorCode MatSetMumpsIcntl(Mat F,PetscInt icntl,PetscInt ival) 1377 { 1378 Mat_MUMPS *lu =(Mat_MUMPS*)(F)->spptr; 1379 1380 PetscFunctionBegin; 1381 lu->id.ICNTL(icntl) = ival; 1382 PetscFunctionReturn(0); 1383 } 1384 1385