1 2 /* 3 Provides an interface to the MUMPS sparse solver 4 */ 5 #include <petscpkg_version.h> 6 #include <../src/mat/impls/aij/mpi/mpiaij.h> /*I "petscmat.h" I*/ 7 #include <../src/mat/impls/sbaij/mpi/mpisbaij.h> 8 #include <../src/mat/impls/sell/mpi/mpisell.h> 9 10 EXTERN_C_BEGIN 11 #if defined(PETSC_USE_COMPLEX) 12 #if defined(PETSC_USE_REAL_SINGLE) 13 #include <cmumps_c.h> 14 #else 15 #include <zmumps_c.h> 16 #endif 17 #else 18 #if defined(PETSC_USE_REAL_SINGLE) 19 #include <smumps_c.h> 20 #else 21 #include <dmumps_c.h> 22 #endif 23 #endif 24 EXTERN_C_END 25 #define JOB_INIT -1 26 #define JOB_FACTSYMBOLIC 1 27 #define JOB_FACTNUMERIC 2 28 #define JOB_SOLVE 3 29 #define JOB_END -2 30 31 /* calls to MUMPS */ 32 #if defined(PETSC_USE_COMPLEX) 33 #if defined(PETSC_USE_REAL_SINGLE) 34 #define MUMPS_c cmumps_c 35 #else 36 #define MUMPS_c zmumps_c 37 #endif 38 #else 39 #if defined(PETSC_USE_REAL_SINGLE) 40 #define MUMPS_c smumps_c 41 #else 42 #define MUMPS_c dmumps_c 43 #endif 44 #endif 45 46 /* MUMPS uses MUMPS_INT for nonzero indices such as irn/jcn, irn_loc/jcn_loc and uses int64_t for 47 number of nonzeros such as nnz, nnz_loc. We typedef MUMPS_INT to PetscMUMPSInt to follow the 48 naming convention in PetscMPIInt, PetscBLASInt etc. 49 */ 50 typedef MUMPS_INT PetscMUMPSInt; 51 52 #if PETSC_PKG_MUMPS_VERSION_GE(5,3,0) 53 #if defined(MUMPS_INTSIZE64) /* MUMPS_INTSIZE64 is in MUMPS headers if it is built in full 64-bit mode, therefore the macro is more reliable */ 54 #error "Petsc has not been tested with full 64-bit MUMPS and we choose to error out" 55 #endif 56 #else 57 #if defined(INTSIZE64) /* INTSIZE64 is a command line macro one used to build MUMPS in full 64-bit mode */ 58 #error "Petsc has not been tested with full 64-bit MUMPS and we choose to error out" 59 #endif 60 #endif 61 62 #define MPIU_MUMPSINT MPI_INT 63 #define PETSC_MUMPS_INT_MAX 2147483647 64 #define PETSC_MUMPS_INT_MIN -2147483648 65 66 /* Cast PetscInt to PetscMUMPSInt. Usually there is no overflow since <a> is row/col indices or some small integers*/ 67 static inline PetscErrorCode PetscMUMPSIntCast(PetscInt a,PetscMUMPSInt *b) 68 { 69 PetscFunctionBegin; 70 #if PetscDefined(USE_64BIT_INDICES) 71 PetscAssert(a <= PETSC_MUMPS_INT_MAX && a >= PETSC_MUMPS_INT_MIN,PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"PetscInt too long for PetscMUMPSInt"); 72 #endif 73 *b = (PetscMUMPSInt)(a); 74 PetscFunctionReturn(0); 75 } 76 77 /* Put these utility routines here since they are only used in this file */ 78 static inline PetscErrorCode PetscOptionsMUMPSInt_Private(PetscOptionItems *PetscOptionsObject,const char opt[],const char text[],const char man[],PetscMUMPSInt currentvalue,PetscMUMPSInt *value,PetscBool *set,PetscMUMPSInt lb,PetscMUMPSInt ub) 79 { 80 PetscInt myval; 81 PetscBool myset; 82 PetscFunctionBegin; 83 /* PetscInt's size should be always >= PetscMUMPSInt's. It is safe to call PetscOptionsInt_Private to read a PetscMUMPSInt */ 84 PetscCall(PetscOptionsInt_Private(PetscOptionsObject,opt,text,man,(PetscInt)currentvalue,&myval,&myset,lb,ub)); 85 if (myset) PetscCall(PetscMUMPSIntCast(myval,value)); 86 if (set) *set = myset; 87 PetscFunctionReturn(0); 88 } 89 #define PetscOptionsMUMPSInt(a,b,c,d,e,f) PetscOptionsMUMPSInt_Private(PetscOptionsObject,a,b,c,d,e,f,PETSC_MUMPS_INT_MIN,PETSC_MUMPS_INT_MAX) 90 91 /* if using PETSc OpenMP support, we only call MUMPS on master ranks. Before/after the call, we change/restore CPUs the master ranks can run on */ 92 #if defined(PETSC_HAVE_OPENMP_SUPPORT) 93 #define PetscMUMPS_c(mumps) \ 94 do { \ 95 if (mumps->use_petsc_omp_support) { \ 96 if (mumps->is_omp_master) { \ 97 PetscCall(PetscOmpCtrlOmpRegionOnMasterBegin(mumps->omp_ctrl)); \ 98 MUMPS_c(&mumps->id); \ 99 PetscCall(PetscOmpCtrlOmpRegionOnMasterEnd(mumps->omp_ctrl)); \ 100 } \ 101 PetscCall(PetscOmpCtrlBarrier(mumps->omp_ctrl)); \ 102 /* Global info is same on all processes so we Bcast it within omp_comm. Local info is specific \ 103 to processes, so we only Bcast info[1], an error code and leave others (since they do not have \ 104 an easy translation between omp_comm and petsc_comm). See MUMPS-5.1.2 manual p82. \ 105 omp_comm is a small shared memory communicator, hence doing multiple Bcast as shown below is OK. \ 106 */ \ 107 PetscCallMPI(MPI_Bcast(mumps->id.infog, 40,MPIU_MUMPSINT, 0,mumps->omp_comm));\ 108 PetscCallMPI(MPI_Bcast(mumps->id.rinfog,20,MPIU_REAL, 0,mumps->omp_comm));\ 109 PetscCallMPI(MPI_Bcast(mumps->id.info, 1, MPIU_MUMPSINT, 0,mumps->omp_comm));\ 110 } else { \ 111 MUMPS_c(&mumps->id); \ 112 } \ 113 } while (0) 114 #else 115 #define PetscMUMPS_c(mumps) \ 116 do { MUMPS_c(&mumps->id); } while (0) 117 #endif 118 119 /* declare MumpsScalar */ 120 #if defined(PETSC_USE_COMPLEX) 121 #if defined(PETSC_USE_REAL_SINGLE) 122 #define MumpsScalar mumps_complex 123 #else 124 #define MumpsScalar mumps_double_complex 125 #endif 126 #else 127 #define MumpsScalar PetscScalar 128 #endif 129 130 /* macros s.t. indices match MUMPS documentation */ 131 #define ICNTL(I) icntl[(I)-1] 132 #define CNTL(I) cntl[(I)-1] 133 #define INFOG(I) infog[(I)-1] 134 #define INFO(I) info[(I)-1] 135 #define RINFOG(I) rinfog[(I)-1] 136 #define RINFO(I) rinfo[(I)-1] 137 138 typedef struct Mat_MUMPS Mat_MUMPS; 139 struct Mat_MUMPS { 140 #if defined(PETSC_USE_COMPLEX) 141 #if defined(PETSC_USE_REAL_SINGLE) 142 CMUMPS_STRUC_C id; 143 #else 144 ZMUMPS_STRUC_C id; 145 #endif 146 #else 147 #if defined(PETSC_USE_REAL_SINGLE) 148 SMUMPS_STRUC_C id; 149 #else 150 DMUMPS_STRUC_C id; 151 #endif 152 #endif 153 154 MatStructure matstruc; 155 PetscMPIInt myid,petsc_size; 156 PetscMUMPSInt *irn,*jcn; /* the (i,j,v) triplets passed to mumps. */ 157 PetscScalar *val,*val_alloc; /* For some matrices, we can directly access their data array without a buffer. For others, we need a buffer. So comes val_alloc. */ 158 PetscInt64 nnz; /* number of nonzeros. The type is called selective 64-bit in mumps */ 159 PetscMUMPSInt sym; 160 MPI_Comm mumps_comm; 161 PetscMUMPSInt ICNTL9_pre; /* check if ICNTL(9) is changed from previous MatSolve */ 162 VecScatter scat_rhs, scat_sol; /* used by MatSolve() */ 163 PetscMUMPSInt ICNTL20; /* use centralized (0) or distributed (10) dense RHS */ 164 PetscMUMPSInt lrhs_loc,nloc_rhs,*irhs_loc; 165 #if defined(PETSC_HAVE_OPENMP_SUPPORT) 166 PetscInt *rhs_nrow,max_nrhs; 167 PetscMPIInt *rhs_recvcounts,*rhs_disps; 168 PetscScalar *rhs_loc,*rhs_recvbuf; 169 #endif 170 Vec b_seq,x_seq; 171 PetscInt ninfo,*info; /* which INFO to display */ 172 PetscInt sizeredrhs; 173 PetscScalar *schur_sol; 174 PetscInt schur_sizesol; 175 PetscMUMPSInt *ia_alloc,*ja_alloc; /* work arrays used for the CSR struct for sparse rhs */ 176 PetscInt64 cur_ilen,cur_jlen; /* current len of ia_alloc[], ja_alloc[] */ 177 PetscErrorCode (*ConvertToTriples)(Mat,PetscInt,MatReuse,Mat_MUMPS*); 178 179 /* stuff used by petsc/mumps OpenMP support*/ 180 PetscBool use_petsc_omp_support; 181 PetscOmpCtrl omp_ctrl; /* an OpenMP controler that blocked processes will release their CPU (MPI_Barrier does not have this guarantee) */ 182 MPI_Comm petsc_comm,omp_comm; /* petsc_comm is petsc matrix's comm */ 183 PetscInt64 *recvcount; /* a collection of nnz on omp_master */ 184 PetscMPIInt tag,omp_comm_size; 185 PetscBool is_omp_master; /* is this rank the master of omp_comm */ 186 MPI_Request *reqs; 187 }; 188 189 /* Cast a 1-based CSR represented by (nrow, ia, ja) of type PetscInt to a CSR of type PetscMUMPSInt. 190 Here, nrow is number of rows, ia[] is row pointer and ja[] is column indices. 191 */ 192 static PetscErrorCode PetscMUMPSIntCSRCast(Mat_MUMPS *mumps,PetscInt nrow,PetscInt *ia,PetscInt *ja,PetscMUMPSInt **ia_mumps,PetscMUMPSInt **ja_mumps,PetscMUMPSInt *nnz_mumps) 193 { 194 PetscInt nnz=ia[nrow]-1; /* mumps uses 1-based indices. Uses PetscInt instead of PetscInt64 since mumps only uses PetscMUMPSInt for rhs */ 195 196 PetscFunctionBegin; 197 #if defined(PETSC_USE_64BIT_INDICES) 198 { 199 PetscInt i; 200 if (nrow+1 > mumps->cur_ilen) { /* realloc ia_alloc/ja_alloc to fit ia/ja */ 201 PetscCall(PetscFree(mumps->ia_alloc)); 202 PetscCall(PetscMalloc1(nrow+1,&mumps->ia_alloc)); 203 mumps->cur_ilen = nrow+1; 204 } 205 if (nnz > mumps->cur_jlen) { 206 PetscCall(PetscFree(mumps->ja_alloc)); 207 PetscCall(PetscMalloc1(nnz,&mumps->ja_alloc)); 208 mumps->cur_jlen = nnz; 209 } 210 for (i=0; i<nrow+1; i++) PetscCall(PetscMUMPSIntCast(ia[i],&(mumps->ia_alloc[i]))); 211 for (i=0; i<nnz; i++) PetscCall(PetscMUMPSIntCast(ja[i],&(mumps->ja_alloc[i]))); 212 *ia_mumps = mumps->ia_alloc; 213 *ja_mumps = mumps->ja_alloc; 214 } 215 #else 216 *ia_mumps = ia; 217 *ja_mumps = ja; 218 #endif 219 PetscCall(PetscMUMPSIntCast(nnz,nnz_mumps)); 220 PetscFunctionReturn(0); 221 } 222 223 static PetscErrorCode MatMumpsResetSchur_Private(Mat_MUMPS* mumps) 224 { 225 PetscFunctionBegin; 226 PetscCall(PetscFree(mumps->id.listvar_schur)); 227 PetscCall(PetscFree(mumps->id.redrhs)); 228 PetscCall(PetscFree(mumps->schur_sol)); 229 mumps->id.size_schur = 0; 230 mumps->id.schur_lld = 0; 231 mumps->id.ICNTL(19) = 0; 232 PetscFunctionReturn(0); 233 } 234 235 /* solve with rhs in mumps->id.redrhs and return in the same location */ 236 static PetscErrorCode MatMumpsSolveSchur_Private(Mat F) 237 { 238 Mat_MUMPS *mumps=(Mat_MUMPS*)F->data; 239 Mat S,B,X; 240 MatFactorSchurStatus schurstatus; 241 PetscInt sizesol; 242 243 PetscFunctionBegin; 244 PetscCall(MatFactorFactorizeSchurComplement(F)); 245 PetscCall(MatFactorGetSchurComplement(F,&S,&schurstatus)); 246 PetscCall(MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.nrhs,(PetscScalar*)mumps->id.redrhs,&B)); 247 PetscCall(MatSetType(B,((PetscObject)S)->type_name)); 248 #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) 249 PetscCall(MatBindToCPU(B,S->boundtocpu)); 250 #endif 251 switch (schurstatus) { 252 case MAT_FACTOR_SCHUR_FACTORED: 253 PetscCall(MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.nrhs,(PetscScalar*)mumps->id.redrhs,&X)); 254 PetscCall(MatSetType(X,((PetscObject)S)->type_name)); 255 #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) 256 PetscCall(MatBindToCPU(X,S->boundtocpu)); 257 #endif 258 if (!mumps->id.ICNTL(9)) { /* transpose solve */ 259 PetscCall(MatMatSolveTranspose(S,B,X)); 260 } else { 261 PetscCall(MatMatSolve(S,B,X)); 262 } 263 break; 264 case MAT_FACTOR_SCHUR_INVERTED: 265 sizesol = mumps->id.nrhs*mumps->id.size_schur; 266 if (!mumps->schur_sol || sizesol > mumps->schur_sizesol) { 267 PetscCall(PetscFree(mumps->schur_sol)); 268 PetscCall(PetscMalloc1(sizesol,&mumps->schur_sol)); 269 mumps->schur_sizesol = sizesol; 270 } 271 PetscCall(MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.nrhs,mumps->schur_sol,&X)); 272 PetscCall(MatSetType(X,((PetscObject)S)->type_name)); 273 #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) 274 PetscCall(MatBindToCPU(X,S->boundtocpu)); 275 #endif 276 PetscCall(MatProductCreateWithMat(S,B,NULL,X)); 277 if (!mumps->id.ICNTL(9)) { /* transpose solve */ 278 PetscCall(MatProductSetType(X,MATPRODUCT_AtB)); 279 } else { 280 PetscCall(MatProductSetType(X,MATPRODUCT_AB)); 281 } 282 PetscCall(MatProductSetFromOptions(X)); 283 PetscCall(MatProductSymbolic(X)); 284 PetscCall(MatProductNumeric(X)); 285 286 PetscCall(MatCopy(X,B,SAME_NONZERO_PATTERN)); 287 break; 288 default: 289 SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Unhandled MatFactorSchurStatus %d",F->schur_status); 290 } 291 PetscCall(MatFactorRestoreSchurComplement(F,&S,schurstatus)); 292 PetscCall(MatDestroy(&B)); 293 PetscCall(MatDestroy(&X)); 294 PetscFunctionReturn(0); 295 } 296 297 static PetscErrorCode MatMumpsHandleSchur_Private(Mat F, PetscBool expansion) 298 { 299 Mat_MUMPS *mumps=(Mat_MUMPS*)F->data; 300 301 PetscFunctionBegin; 302 if (!mumps->id.ICNTL(19)) { /* do nothing when Schur complement has not been computed */ 303 PetscFunctionReturn(0); 304 } 305 if (!expansion) { /* prepare for the condensation step */ 306 PetscInt sizeredrhs = mumps->id.nrhs*mumps->id.size_schur; 307 /* allocate MUMPS internal array to store reduced right-hand sides */ 308 if (!mumps->id.redrhs || sizeredrhs > mumps->sizeredrhs) { 309 PetscCall(PetscFree(mumps->id.redrhs)); 310 mumps->id.lredrhs = mumps->id.size_schur; 311 PetscCall(PetscMalloc1(mumps->id.nrhs*mumps->id.lredrhs,&mumps->id.redrhs)); 312 mumps->sizeredrhs = mumps->id.nrhs*mumps->id.lredrhs; 313 } 314 mumps->id.ICNTL(26) = 1; /* condensation phase */ 315 } else { /* prepare for the expansion step */ 316 /* solve Schur complement (this has to be done by the MUMPS user, so basically us) */ 317 PetscCall(MatMumpsSolveSchur_Private(F)); 318 mumps->id.ICNTL(26) = 2; /* expansion phase */ 319 PetscMUMPS_c(mumps); 320 PetscCheck(mumps->id.INFOG(1) >= 0,PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d",mumps->id.INFOG(1)); 321 /* restore defaults */ 322 mumps->id.ICNTL(26) = -1; 323 /* free MUMPS internal array for redrhs if we have solved for multiple rhs in order to save memory space */ 324 if (mumps->id.nrhs > 1) { 325 PetscCall(PetscFree(mumps->id.redrhs)); 326 mumps->id.lredrhs = 0; 327 mumps->sizeredrhs = 0; 328 } 329 } 330 PetscFunctionReturn(0); 331 } 332 333 /* 334 MatConvertToTriples_A_B - convert Petsc matrix to triples: row[nz], col[nz], val[nz] 335 336 input: 337 A - matrix in aij,baij or sbaij format 338 shift - 0: C style output triple; 1: Fortran style output triple. 339 reuse - MAT_INITIAL_MATRIX: spaces are allocated and values are set for the triple 340 MAT_REUSE_MATRIX: only the values in v array are updated 341 output: 342 nnz - dim of r, c, and v (number of local nonzero entries of A) 343 r, c, v - row and col index, matrix values (matrix triples) 344 345 The returned values r, c, and sometimes v are obtained in a single PetscMalloc(). Then in MatDestroy_MUMPS() it is 346 freed with PetscFree(mumps->irn); This is not ideal code, the fact that v is ONLY sometimes part of mumps->irn means 347 that the PetscMalloc() cannot easily be replaced with a PetscMalloc3(). 348 349 */ 350 351 PetscErrorCode MatConvertToTriples_seqaij_seqaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps) 352 { 353 const PetscScalar *av; 354 const PetscInt *ai,*aj,*ajj,M=A->rmap->n; 355 PetscInt64 nz,rnz,i,j,k; 356 PetscMUMPSInt *row,*col; 357 Mat_SeqAIJ *aa=(Mat_SeqAIJ*)A->data; 358 359 PetscFunctionBegin; 360 PetscCall(MatSeqAIJGetArrayRead(A,&av)); 361 mumps->val = (PetscScalar*)av; 362 if (reuse == MAT_INITIAL_MATRIX) { 363 nz = aa->nz; 364 ai = aa->i; 365 aj = aa->j; 366 PetscCall(PetscMalloc2(nz,&row,nz,&col)); 367 for (i=k=0; i<M; i++) { 368 rnz = ai[i+1] - ai[i]; 369 ajj = aj + ai[i]; 370 for (j=0; j<rnz; j++) { 371 PetscCall(PetscMUMPSIntCast(i+shift,&row[k])); 372 PetscCall(PetscMUMPSIntCast(ajj[j] + shift,&col[k])); 373 k++; 374 } 375 } 376 mumps->irn = row; 377 mumps->jcn = col; 378 mumps->nnz = nz; 379 } 380 PetscCall(MatSeqAIJRestoreArrayRead(A,&av)); 381 PetscFunctionReturn(0); 382 } 383 384 PetscErrorCode MatConvertToTriples_seqsell_seqaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps) 385 { 386 PetscInt64 nz,i,j,k,r; 387 Mat_SeqSELL *a=(Mat_SeqSELL*)A->data; 388 PetscMUMPSInt *row,*col; 389 390 PetscFunctionBegin; 391 mumps->val = a->val; 392 if (reuse == MAT_INITIAL_MATRIX) { 393 nz = a->sliidx[a->totalslices]; 394 PetscCall(PetscMalloc2(nz,&row,nz,&col)); 395 for (i=k=0; i<a->totalslices; i++) { 396 for (j=a->sliidx[i],r=0; j<a->sliidx[i+1]; j++,r=((r+1)&0x07)) { 397 PetscCall(PetscMUMPSIntCast(8*i+r+shift,&row[k++])); 398 } 399 } 400 for (i=0;i<nz;i++) PetscCall(PetscMUMPSIntCast(a->colidx[i]+shift,&col[i])); 401 mumps->irn = row; 402 mumps->jcn = col; 403 mumps->nnz = nz; 404 } 405 PetscFunctionReturn(0); 406 } 407 408 PetscErrorCode MatConvertToTriples_seqbaij_seqaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps) 409 { 410 Mat_SeqBAIJ *aa=(Mat_SeqBAIJ*)A->data; 411 const PetscInt *ai,*aj,*ajj,bs2 = aa->bs2; 412 PetscInt64 M,nz,idx=0,rnz,i,j,k,m; 413 PetscInt bs; 414 PetscMUMPSInt *row,*col; 415 416 PetscFunctionBegin; 417 PetscCall(MatGetBlockSize(A,&bs)); 418 M = A->rmap->N/bs; 419 mumps->val = aa->a; 420 if (reuse == MAT_INITIAL_MATRIX) { 421 ai = aa->i; aj = aa->j; 422 nz = bs2*aa->nz; 423 PetscCall(PetscMalloc2(nz,&row,nz,&col)); 424 for (i=0; i<M; i++) { 425 ajj = aj + ai[i]; 426 rnz = ai[i+1] - ai[i]; 427 for (k=0; k<rnz; k++) { 428 for (j=0; j<bs; j++) { 429 for (m=0; m<bs; m++) { 430 PetscCall(PetscMUMPSIntCast(i*bs + m + shift,&row[idx])); 431 PetscCall(PetscMUMPSIntCast(bs*ajj[k] + j + shift,&col[idx])); 432 idx++; 433 } 434 } 435 } 436 } 437 mumps->irn = row; 438 mumps->jcn = col; 439 mumps->nnz = nz; 440 } 441 PetscFunctionReturn(0); 442 } 443 444 PetscErrorCode MatConvertToTriples_seqsbaij_seqsbaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps) 445 { 446 const PetscInt *ai, *aj,*ajj; 447 PetscInt bs; 448 PetscInt64 nz,rnz,i,j,k,m; 449 PetscMUMPSInt *row,*col; 450 PetscScalar *val; 451 Mat_SeqSBAIJ *aa=(Mat_SeqSBAIJ*)A->data; 452 const PetscInt bs2=aa->bs2,mbs=aa->mbs; 453 #if defined(PETSC_USE_COMPLEX) 454 PetscBool hermitian; 455 #endif 456 457 PetscFunctionBegin; 458 #if defined(PETSC_USE_COMPLEX) 459 PetscCall(MatGetOption(A,MAT_HERMITIAN,&hermitian)); 460 PetscCheck(!hermitian,PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MUMPS does not support Hermitian symmetric matrices for Choleksy"); 461 #endif 462 ai = aa->i; 463 aj = aa->j; 464 PetscCall(MatGetBlockSize(A,&bs)); 465 if (reuse == MAT_INITIAL_MATRIX) { 466 nz = aa->nz; 467 PetscCall(PetscMalloc2(bs2*nz,&row,bs2*nz,&col)); 468 if (bs>1) { 469 PetscCall(PetscMalloc1(bs2*nz,&mumps->val_alloc)); 470 mumps->val = mumps->val_alloc; 471 } else { 472 mumps->val = aa->a; 473 } 474 mumps->irn = row; 475 mumps->jcn = col; 476 } else { 477 if (bs == 1) mumps->val = aa->a; 478 row = mumps->irn; 479 col = mumps->jcn; 480 } 481 val = mumps->val; 482 483 nz = 0; 484 if (bs>1) { 485 for (i=0; i<mbs; i++) { 486 rnz = ai[i+1] - ai[i]; 487 ajj = aj + ai[i]; 488 for (j=0; j<rnz; j++) { 489 for (k=0; k<bs; k++) { 490 for (m=0; m<bs; m++) { 491 if (ajj[j]>i || k>=m) { 492 if (reuse == MAT_INITIAL_MATRIX) { 493 PetscCall(PetscMUMPSIntCast(i*bs + m + shift,&row[nz])); 494 PetscCall(PetscMUMPSIntCast(ajj[j]*bs + k + shift,&col[nz])); 495 } 496 val[nz++] = aa->a[(ai[i]+j)*bs2 + m + k*bs]; 497 } 498 } 499 } 500 } 501 } 502 } else if (reuse == MAT_INITIAL_MATRIX) { 503 for (i=0; i<mbs; i++) { 504 rnz = ai[i+1] - ai[i]; 505 ajj = aj + ai[i]; 506 for (j=0; j<rnz; j++) { 507 PetscCall(PetscMUMPSIntCast(i+shift,&row[nz])); 508 PetscCall(PetscMUMPSIntCast(ajj[j] + shift,&col[nz])); 509 nz++; 510 } 511 } 512 PetscCheck(nz == aa->nz,PETSC_COMM_SELF,PETSC_ERR_PLIB,"Different numbers of nonzeros %" PetscInt64_FMT " != %" PetscInt_FMT,nz,aa->nz); 513 } 514 if (reuse == MAT_INITIAL_MATRIX) mumps->nnz = nz; 515 PetscFunctionReturn(0); 516 } 517 518 PetscErrorCode MatConvertToTriples_seqaij_seqsbaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps) 519 { 520 const PetscInt *ai,*aj,*ajj,*adiag,M=A->rmap->n; 521 PetscInt64 nz,rnz,i,j; 522 const PetscScalar *av,*v1; 523 PetscScalar *val; 524 PetscMUMPSInt *row,*col; 525 Mat_SeqAIJ *aa=(Mat_SeqAIJ*)A->data; 526 PetscBool missing; 527 #if defined(PETSC_USE_COMPLEX) 528 PetscBool hermitian; 529 #endif 530 531 PetscFunctionBegin; 532 #if defined(PETSC_USE_COMPLEX) 533 PetscCall(MatGetOption(A,MAT_HERMITIAN,&hermitian)); 534 PetscCheck(!hermitian,PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MUMPS does not support Hermitian symmetric matrices for Choleksy"); 535 #endif 536 PetscCall(MatSeqAIJGetArrayRead(A,&av)); 537 ai = aa->i; aj = aa->j; 538 adiag = aa->diag; 539 PetscCall(MatMissingDiagonal_SeqAIJ(A,&missing,NULL)); 540 if (reuse == MAT_INITIAL_MATRIX) { 541 /* count nz in the upper triangular part of A */ 542 nz = 0; 543 if (missing) { 544 for (i=0; i<M; i++) { 545 if (PetscUnlikely(adiag[i] >= ai[i+1])) { 546 for (j=ai[i];j<ai[i+1];j++) { 547 if (aj[j] < i) continue; 548 nz++; 549 } 550 } else { 551 nz += ai[i+1] - adiag[i]; 552 } 553 } 554 } else { 555 for (i=0; i<M; i++) nz += ai[i+1] - adiag[i]; 556 } 557 PetscCall(PetscMalloc2(nz,&row,nz,&col)); 558 PetscCall(PetscMalloc1(nz,&val)); 559 mumps->nnz = nz; 560 mumps->irn = row; 561 mumps->jcn = col; 562 mumps->val = mumps->val_alloc = val; 563 564 nz = 0; 565 if (missing) { 566 for (i=0; i<M; i++) { 567 if (PetscUnlikely(adiag[i] >= ai[i+1])) { 568 for (j=ai[i];j<ai[i+1];j++) { 569 if (aj[j] < i) continue; 570 PetscCall(PetscMUMPSIntCast(i+shift,&row[nz])); 571 PetscCall(PetscMUMPSIntCast(aj[j]+shift,&col[nz])); 572 val[nz] = av[j]; 573 nz++; 574 } 575 } else { 576 rnz = ai[i+1] - adiag[i]; 577 ajj = aj + adiag[i]; 578 v1 = av + adiag[i]; 579 for (j=0; j<rnz; j++) { 580 PetscCall(PetscMUMPSIntCast(i+shift,&row[nz])); 581 PetscCall(PetscMUMPSIntCast(ajj[j] + shift,&col[nz])); 582 val[nz++] = v1[j]; 583 } 584 } 585 } 586 } else { 587 for (i=0; i<M; i++) { 588 rnz = ai[i+1] - adiag[i]; 589 ajj = aj + adiag[i]; 590 v1 = av + adiag[i]; 591 for (j=0; j<rnz; j++) { 592 PetscCall(PetscMUMPSIntCast(i+shift,&row[nz])); 593 PetscCall(PetscMUMPSIntCast(ajj[j] + shift,&col[nz])); 594 val[nz++] = v1[j]; 595 } 596 } 597 } 598 } else { 599 nz = 0; 600 val = mumps->val; 601 if (missing) { 602 for (i=0; i <M; i++) { 603 if (PetscUnlikely(adiag[i] >= ai[i+1])) { 604 for (j=ai[i];j<ai[i+1];j++) { 605 if (aj[j] < i) continue; 606 val[nz++] = av[j]; 607 } 608 } else { 609 rnz = ai[i+1] - adiag[i]; 610 v1 = av + adiag[i]; 611 for (j=0; j<rnz; j++) { 612 val[nz++] = v1[j]; 613 } 614 } 615 } 616 } else { 617 for (i=0; i <M; i++) { 618 rnz = ai[i+1] - adiag[i]; 619 v1 = av + adiag[i]; 620 for (j=0; j<rnz; j++) { 621 val[nz++] = v1[j]; 622 } 623 } 624 } 625 } 626 PetscCall(MatSeqAIJRestoreArrayRead(A,&av)); 627 PetscFunctionReturn(0); 628 } 629 630 PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps) 631 { 632 const PetscInt *ai,*aj,*bi,*bj,*garray,*ajj,*bjj; 633 PetscInt bs; 634 PetscInt64 rstart,nz,i,j,k,m,jj,irow,countA,countB; 635 PetscMUMPSInt *row,*col; 636 const PetscScalar *av,*bv,*v1,*v2; 637 PetscScalar *val; 638 Mat_MPISBAIJ *mat = (Mat_MPISBAIJ*)A->data; 639 Mat_SeqSBAIJ *aa = (Mat_SeqSBAIJ*)(mat->A)->data; 640 Mat_SeqBAIJ *bb = (Mat_SeqBAIJ*)(mat->B)->data; 641 const PetscInt bs2=aa->bs2,mbs=aa->mbs; 642 #if defined(PETSC_USE_COMPLEX) 643 PetscBool hermitian; 644 #endif 645 646 PetscFunctionBegin; 647 #if defined(PETSC_USE_COMPLEX) 648 PetscCall(MatGetOption(A,MAT_HERMITIAN,&hermitian)); 649 PetscCheck(!hermitian,PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MUMPS does not support Hermitian symmetric matrices for Choleksy"); 650 #endif 651 PetscCall(MatGetBlockSize(A,&bs)); 652 rstart = A->rmap->rstart; 653 ai = aa->i; 654 aj = aa->j; 655 bi = bb->i; 656 bj = bb->j; 657 av = aa->a; 658 bv = bb->a; 659 660 garray = mat->garray; 661 662 if (reuse == MAT_INITIAL_MATRIX) { 663 nz = (aa->nz+bb->nz)*bs2; /* just a conservative estimate */ 664 PetscCall(PetscMalloc2(nz,&row,nz,&col)); 665 PetscCall(PetscMalloc1(nz,&val)); 666 /* can not decide the exact mumps->nnz now because of the SBAIJ */ 667 mumps->irn = row; 668 mumps->jcn = col; 669 mumps->val = mumps->val_alloc = val; 670 } else { 671 val = mumps->val; 672 } 673 674 jj = 0; irow = rstart; 675 for (i=0; i<mbs; i++) { 676 ajj = aj + ai[i]; /* ptr to the beginning of this row */ 677 countA = ai[i+1] - ai[i]; 678 countB = bi[i+1] - bi[i]; 679 bjj = bj + bi[i]; 680 v1 = av + ai[i]*bs2; 681 v2 = bv + bi[i]*bs2; 682 683 if (bs>1) { 684 /* A-part */ 685 for (j=0; j<countA; j++) { 686 for (k=0; k<bs; k++) { 687 for (m=0; m<bs; m++) { 688 if (rstart + ajj[j]*bs>irow || k>=m) { 689 if (reuse == MAT_INITIAL_MATRIX) { 690 PetscCall(PetscMUMPSIntCast(irow + m + shift,&row[jj])); 691 PetscCall(PetscMUMPSIntCast(rstart + ajj[j]*bs + k + shift,&col[jj])); 692 } 693 val[jj++] = v1[j*bs2 + m + k*bs]; 694 } 695 } 696 } 697 } 698 699 /* B-part */ 700 for (j=0; j < countB; j++) { 701 for (k=0; k<bs; k++) { 702 for (m=0; m<bs; m++) { 703 if (reuse == MAT_INITIAL_MATRIX) { 704 PetscCall(PetscMUMPSIntCast(irow + m + shift,&row[jj])); 705 PetscCall(PetscMUMPSIntCast(garray[bjj[j]]*bs + k + shift,&col[jj])); 706 } 707 val[jj++] = v2[j*bs2 + m + k*bs]; 708 } 709 } 710 } 711 } else { 712 /* A-part */ 713 for (j=0; j<countA; j++) { 714 if (reuse == MAT_INITIAL_MATRIX) { 715 PetscCall(PetscMUMPSIntCast(irow + shift,&row[jj])); 716 PetscCall(PetscMUMPSIntCast(rstart + ajj[j] + shift,&col[jj])); 717 } 718 val[jj++] = v1[j]; 719 } 720 721 /* B-part */ 722 for (j=0; j < countB; j++) { 723 if (reuse == MAT_INITIAL_MATRIX) { 724 PetscCall(PetscMUMPSIntCast(irow + shift,&row[jj])); 725 PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift,&col[jj])); 726 } 727 val[jj++] = v2[j]; 728 } 729 } 730 irow+=bs; 731 } 732 mumps->nnz = jj; 733 PetscFunctionReturn(0); 734 } 735 736 PetscErrorCode MatConvertToTriples_mpiaij_mpiaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps) 737 { 738 const PetscInt *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj; 739 PetscInt64 rstart,nz,i,j,jj,irow,countA,countB; 740 PetscMUMPSInt *row,*col; 741 const PetscScalar *av, *bv,*v1,*v2; 742 PetscScalar *val; 743 Mat Ad,Ao; 744 Mat_SeqAIJ *aa; 745 Mat_SeqAIJ *bb; 746 747 PetscFunctionBegin; 748 PetscCall(MatMPIAIJGetSeqAIJ(A,&Ad,&Ao,&garray)); 749 PetscCall(MatSeqAIJGetArrayRead(Ad,&av)); 750 PetscCall(MatSeqAIJGetArrayRead(Ao,&bv)); 751 752 aa = (Mat_SeqAIJ*)(Ad)->data; 753 bb = (Mat_SeqAIJ*)(Ao)->data; 754 ai = aa->i; 755 aj = aa->j; 756 bi = bb->i; 757 bj = bb->j; 758 759 rstart = A->rmap->rstart; 760 761 if (reuse == MAT_INITIAL_MATRIX) { 762 nz = (PetscInt64)aa->nz + bb->nz; /* make sure the sum won't overflow PetscInt */ 763 PetscCall(PetscMalloc2(nz,&row,nz,&col)); 764 PetscCall(PetscMalloc1(nz,&val)); 765 mumps->nnz = nz; 766 mumps->irn = row; 767 mumps->jcn = col; 768 mumps->val = mumps->val_alloc = val; 769 } else { 770 val = mumps->val; 771 } 772 773 jj = 0; irow = rstart; 774 for (i=0; i<m; i++) { 775 ajj = aj + ai[i]; /* ptr to the beginning of this row */ 776 countA = ai[i+1] - ai[i]; 777 countB = bi[i+1] - bi[i]; 778 bjj = bj + bi[i]; 779 v1 = av + ai[i]; 780 v2 = bv + bi[i]; 781 782 /* A-part */ 783 for (j=0; j<countA; j++) { 784 if (reuse == MAT_INITIAL_MATRIX) { 785 PetscCall(PetscMUMPSIntCast(irow + shift,&row[jj])); 786 PetscCall(PetscMUMPSIntCast(rstart + ajj[j] + shift,&col[jj])); 787 } 788 val[jj++] = v1[j]; 789 } 790 791 /* B-part */ 792 for (j=0; j < countB; j++) { 793 if (reuse == MAT_INITIAL_MATRIX) { 794 PetscCall(PetscMUMPSIntCast(irow + shift,&row[jj])); 795 PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift,&col[jj])); 796 } 797 val[jj++] = v2[j]; 798 } 799 irow++; 800 } 801 PetscCall(MatSeqAIJRestoreArrayRead(Ad,&av)); 802 PetscCall(MatSeqAIJRestoreArrayRead(Ao,&bv)); 803 PetscFunctionReturn(0); 804 } 805 806 PetscErrorCode MatConvertToTriples_mpibaij_mpiaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps) 807 { 808 Mat_MPIBAIJ *mat = (Mat_MPIBAIJ*)A->data; 809 Mat_SeqBAIJ *aa = (Mat_SeqBAIJ*)(mat->A)->data; 810 Mat_SeqBAIJ *bb = (Mat_SeqBAIJ*)(mat->B)->data; 811 const PetscInt *ai = aa->i, *bi = bb->i, *aj = aa->j, *bj = bb->j,*ajj, *bjj; 812 const PetscInt *garray = mat->garray,mbs=mat->mbs,rstart=A->rmap->rstart; 813 const PetscInt bs2=mat->bs2; 814 PetscInt bs; 815 PetscInt64 nz,i,j,k,n,jj,irow,countA,countB,idx; 816 PetscMUMPSInt *row,*col; 817 const PetscScalar *av=aa->a, *bv=bb->a,*v1,*v2; 818 PetscScalar *val; 819 820 PetscFunctionBegin; 821 PetscCall(MatGetBlockSize(A,&bs)); 822 if (reuse == MAT_INITIAL_MATRIX) { 823 nz = bs2*(aa->nz + bb->nz); 824 PetscCall(PetscMalloc2(nz,&row,nz,&col)); 825 PetscCall(PetscMalloc1(nz,&val)); 826 mumps->nnz = nz; 827 mumps->irn = row; 828 mumps->jcn = col; 829 mumps->val = mumps->val_alloc = val; 830 } else { 831 val = mumps->val; 832 } 833 834 jj = 0; irow = rstart; 835 for (i=0; i<mbs; i++) { 836 countA = ai[i+1] - ai[i]; 837 countB = bi[i+1] - bi[i]; 838 ajj = aj + ai[i]; 839 bjj = bj + bi[i]; 840 v1 = av + bs2*ai[i]; 841 v2 = bv + bs2*bi[i]; 842 843 idx = 0; 844 /* A-part */ 845 for (k=0; k<countA; k++) { 846 for (j=0; j<bs; j++) { 847 for (n=0; n<bs; n++) { 848 if (reuse == MAT_INITIAL_MATRIX) { 849 PetscCall(PetscMUMPSIntCast(irow + n + shift,&row[jj])); 850 PetscCall(PetscMUMPSIntCast(rstart + bs*ajj[k] + j + shift,&col[jj])); 851 } 852 val[jj++] = v1[idx++]; 853 } 854 } 855 } 856 857 idx = 0; 858 /* B-part */ 859 for (k=0; k<countB; k++) { 860 for (j=0; j<bs; j++) { 861 for (n=0; n<bs; n++) { 862 if (reuse == MAT_INITIAL_MATRIX) { 863 PetscCall(PetscMUMPSIntCast(irow + n + shift,&row[jj])); 864 PetscCall(PetscMUMPSIntCast(bs*garray[bjj[k]] + j + shift,&col[jj])); 865 } 866 val[jj++] = v2[idx++]; 867 } 868 } 869 } 870 irow += bs; 871 } 872 PetscFunctionReturn(0); 873 } 874 875 PetscErrorCode MatConvertToTriples_mpiaij_mpisbaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps) 876 { 877 const PetscInt *ai, *aj,*adiag, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj; 878 PetscInt64 rstart,nz,nza,nzb,i,j,jj,irow,countA,countB; 879 PetscMUMPSInt *row,*col; 880 const PetscScalar *av, *bv,*v1,*v2; 881 PetscScalar *val; 882 Mat Ad,Ao; 883 Mat_SeqAIJ *aa; 884 Mat_SeqAIJ *bb; 885 #if defined(PETSC_USE_COMPLEX) 886 PetscBool hermitian; 887 #endif 888 889 PetscFunctionBegin; 890 #if defined(PETSC_USE_COMPLEX) 891 PetscCall(MatGetOption(A,MAT_HERMITIAN,&hermitian)); 892 PetscCheck(!hermitian,PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MUMPS does not support Hermitian symmetric matrices for Choleksy"); 893 #endif 894 PetscCall(MatMPIAIJGetSeqAIJ(A,&Ad,&Ao,&garray)); 895 PetscCall(MatSeqAIJGetArrayRead(Ad,&av)); 896 PetscCall(MatSeqAIJGetArrayRead(Ao,&bv)); 897 898 aa = (Mat_SeqAIJ*)(Ad)->data; 899 bb = (Mat_SeqAIJ*)(Ao)->data; 900 ai = aa->i; 901 aj = aa->j; 902 adiag = aa->diag; 903 bi = bb->i; 904 bj = bb->j; 905 906 rstart = A->rmap->rstart; 907 908 if (reuse == MAT_INITIAL_MATRIX) { 909 nza = 0; /* num of upper triangular entries in mat->A, including diagonals */ 910 nzb = 0; /* num of upper triangular entries in mat->B */ 911 for (i=0; i<m; i++) { 912 nza += (ai[i+1] - adiag[i]); 913 countB = bi[i+1] - bi[i]; 914 bjj = bj + bi[i]; 915 for (j=0; j<countB; j++) { 916 if (garray[bjj[j]] > rstart) nzb++; 917 } 918 } 919 920 nz = nza + nzb; /* total nz of upper triangular part of mat */ 921 PetscCall(PetscMalloc2(nz,&row,nz,&col)); 922 PetscCall(PetscMalloc1(nz,&val)); 923 mumps->nnz = nz; 924 mumps->irn = row; 925 mumps->jcn = col; 926 mumps->val = mumps->val_alloc = val; 927 } else { 928 val = mumps->val; 929 } 930 931 jj = 0; irow = rstart; 932 for (i=0; i<m; i++) { 933 ajj = aj + adiag[i]; /* ptr to the beginning of the diagonal of this row */ 934 v1 = av + adiag[i]; 935 countA = ai[i+1] - adiag[i]; 936 countB = bi[i+1] - bi[i]; 937 bjj = bj + bi[i]; 938 v2 = bv + bi[i]; 939 940 /* A-part */ 941 for (j=0; j<countA; j++) { 942 if (reuse == MAT_INITIAL_MATRIX) { 943 PetscCall(PetscMUMPSIntCast(irow + shift,&row[jj])); 944 PetscCall(PetscMUMPSIntCast(rstart + ajj[j] + shift,&col[jj])); 945 } 946 val[jj++] = v1[j]; 947 } 948 949 /* B-part */ 950 for (j=0; j < countB; j++) { 951 if (garray[bjj[j]] > rstart) { 952 if (reuse == MAT_INITIAL_MATRIX) { 953 PetscCall(PetscMUMPSIntCast(irow + shift,&row[jj])); 954 PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift,&col[jj])); 955 } 956 val[jj++] = v2[j]; 957 } 958 } 959 irow++; 960 } 961 PetscCall(MatSeqAIJRestoreArrayRead(Ad,&av)); 962 PetscCall(MatSeqAIJRestoreArrayRead(Ao,&bv)); 963 PetscFunctionReturn(0); 964 } 965 966 PetscErrorCode MatDestroy_MUMPS(Mat A) 967 { 968 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 969 970 PetscFunctionBegin; 971 PetscCall(PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc)); 972 PetscCall(VecScatterDestroy(&mumps->scat_rhs)); 973 PetscCall(VecScatterDestroy(&mumps->scat_sol)); 974 PetscCall(VecDestroy(&mumps->b_seq)); 975 PetscCall(VecDestroy(&mumps->x_seq)); 976 PetscCall(PetscFree(mumps->id.perm_in)); 977 PetscCall(PetscFree2(mumps->irn,mumps->jcn)); 978 PetscCall(PetscFree(mumps->val_alloc)); 979 PetscCall(PetscFree(mumps->info)); 980 PetscCall(MatMumpsResetSchur_Private(mumps)); 981 mumps->id.job = JOB_END; 982 PetscMUMPS_c(mumps); 983 PetscCheck(mumps->id.INFOG(1) >= 0,PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in MatDestroy_MUMPS: INFOG(1)=%d",mumps->id.INFOG(1)); 984 #if defined(PETSC_HAVE_OPENMP_SUPPORT) 985 if (mumps->use_petsc_omp_support) { 986 PetscCall(PetscOmpCtrlDestroy(&mumps->omp_ctrl)); 987 PetscCall(PetscFree2(mumps->rhs_loc,mumps->rhs_recvbuf)); 988 PetscCall(PetscFree3(mumps->rhs_nrow,mumps->rhs_recvcounts,mumps->rhs_disps)); 989 } 990 #endif 991 PetscCall(PetscFree(mumps->ia_alloc)); 992 PetscCall(PetscFree(mumps->ja_alloc)); 993 PetscCall(PetscFree(mumps->recvcount)); 994 PetscCall(PetscFree(mumps->reqs)); 995 PetscCall(PetscFree(mumps->irhs_loc)); 996 if (mumps->mumps_comm != MPI_COMM_NULL) PetscCall(PetscCommRestoreComm(PetscObjectComm((PetscObject)A),&mumps->mumps_comm)); 997 PetscCall(PetscFree(A->data)); 998 999 /* clear composed functions */ 1000 PetscCall(PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverType_C",NULL)); 1001 PetscCall(PetscObjectComposeFunction((PetscObject)A,"MatFactorSetSchurIS_C",NULL)); 1002 PetscCall(PetscObjectComposeFunction((PetscObject)A,"MatFactorCreateSchurComplement_C",NULL)); 1003 PetscCall(PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetIcntl_C",NULL)); 1004 PetscCall(PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetIcntl_C",NULL)); 1005 PetscCall(PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetCntl_C",NULL)); 1006 PetscCall(PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetCntl_C",NULL)); 1007 PetscCall(PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfo_C",NULL)); 1008 PetscCall(PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfog_C",NULL)); 1009 PetscCall(PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfo_C",NULL)); 1010 PetscCall(PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfog_C",NULL)); 1011 PetscCall(PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInverse_C",NULL)); 1012 PetscCall(PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInverseTranspose_C",NULL)); 1013 PetscFunctionReturn(0); 1014 } 1015 1016 /* Set up the distributed RHS info for MUMPS. <nrhs> is the number of RHS. <array> points to start of RHS on the local processor. */ 1017 static PetscErrorCode MatMumpsSetUpDistRHSInfo(Mat A,PetscInt nrhs,const PetscScalar *array) 1018 { 1019 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 1020 const PetscMPIInt ompsize=mumps->omp_comm_size; 1021 PetscInt i,m,M,rstart; 1022 1023 PetscFunctionBegin; 1024 PetscCall(MatGetSize(A,&M,NULL)); 1025 PetscCall(MatGetLocalSize(A,&m,NULL)); 1026 PetscCheck(M <= PETSC_MUMPS_INT_MAX,PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"PetscInt too long for PetscMUMPSInt"); 1027 if (ompsize == 1) { 1028 if (!mumps->irhs_loc) { 1029 mumps->nloc_rhs = m; 1030 PetscCall(PetscMalloc1(m,&mumps->irhs_loc)); 1031 PetscCall(MatGetOwnershipRange(A,&rstart,NULL)); 1032 for (i=0; i<m; i++) mumps->irhs_loc[i] = rstart+i+1; /* use 1-based indices */ 1033 } 1034 mumps->id.rhs_loc = (MumpsScalar*)array; 1035 } else { 1036 #if defined(PETSC_HAVE_OPENMP_SUPPORT) 1037 const PetscInt *ranges; 1038 PetscMPIInt j,k,sendcount,*petsc_ranks,*omp_ranks; 1039 MPI_Group petsc_group,omp_group; 1040 PetscScalar *recvbuf=NULL; 1041 1042 if (mumps->is_omp_master) { 1043 /* Lazily initialize the omp stuff for distributed rhs */ 1044 if (!mumps->irhs_loc) { 1045 PetscCall(PetscMalloc2(ompsize,&omp_ranks,ompsize,&petsc_ranks)); 1046 PetscCall(PetscMalloc3(ompsize,&mumps->rhs_nrow,ompsize,&mumps->rhs_recvcounts,ompsize,&mumps->rhs_disps)); 1047 PetscCallMPI(MPI_Comm_group(mumps->petsc_comm,&petsc_group)); 1048 PetscCallMPI(MPI_Comm_group(mumps->omp_comm,&omp_group)); 1049 for (j=0; j<ompsize; j++) omp_ranks[j] = j; 1050 PetscCallMPI(MPI_Group_translate_ranks(omp_group,ompsize,omp_ranks,petsc_group,petsc_ranks)); 1051 1052 /* Populate mumps->irhs_loc[], rhs_nrow[] */ 1053 mumps->nloc_rhs = 0; 1054 PetscCall(MatGetOwnershipRanges(A,&ranges)); 1055 for (j=0; j<ompsize; j++) { 1056 mumps->rhs_nrow[j] = ranges[petsc_ranks[j]+1] - ranges[petsc_ranks[j]]; 1057 mumps->nloc_rhs += mumps->rhs_nrow[j]; 1058 } 1059 PetscCall(PetscMalloc1(mumps->nloc_rhs,&mumps->irhs_loc)); 1060 for (j=k=0; j<ompsize; j++) { 1061 for (i=ranges[petsc_ranks[j]]; i<ranges[petsc_ranks[j]+1]; i++,k++) mumps->irhs_loc[k] = i+1; /* uses 1-based indices */ 1062 } 1063 1064 PetscCall(PetscFree2(omp_ranks,petsc_ranks)); 1065 PetscCallMPI(MPI_Group_free(&petsc_group)); 1066 PetscCallMPI(MPI_Group_free(&omp_group)); 1067 } 1068 1069 /* Realloc buffers when current nrhs is bigger than what we have met */ 1070 if (nrhs > mumps->max_nrhs) { 1071 PetscCall(PetscFree2(mumps->rhs_loc,mumps->rhs_recvbuf)); 1072 PetscCall(PetscMalloc2(mumps->nloc_rhs*nrhs,&mumps->rhs_loc,mumps->nloc_rhs*nrhs,&mumps->rhs_recvbuf)); 1073 mumps->max_nrhs = nrhs; 1074 } 1075 1076 /* Setup recvcounts[], disps[], recvbuf on omp rank 0 for the upcoming MPI_Gatherv */ 1077 for (j=0; j<ompsize; j++) PetscCall(PetscMPIIntCast(mumps->rhs_nrow[j]*nrhs,&mumps->rhs_recvcounts[j])); 1078 mumps->rhs_disps[0] = 0; 1079 for (j=1; j<ompsize; j++) { 1080 mumps->rhs_disps[j] = mumps->rhs_disps[j-1] + mumps->rhs_recvcounts[j-1]; 1081 PetscCheck(mumps->rhs_disps[j] >= 0,PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"PetscMPIInt overflow!"); 1082 } 1083 recvbuf = (nrhs == 1) ? mumps->rhs_loc : mumps->rhs_recvbuf; /* Directly use rhs_loc[] as recvbuf. Single rhs is common in Ax=b */ 1084 } 1085 1086 PetscCall(PetscMPIIntCast(m*nrhs,&sendcount)); 1087 PetscCallMPI(MPI_Gatherv(array,sendcount,MPIU_SCALAR,recvbuf,mumps->rhs_recvcounts,mumps->rhs_disps,MPIU_SCALAR,0,mumps->omp_comm)); 1088 1089 if (mumps->is_omp_master) { 1090 if (nrhs > 1) { /* Copy & re-arrange data from rhs_recvbuf[] to mumps->rhs_loc[] only when there are multiple rhs */ 1091 PetscScalar *dst,*dstbase = mumps->rhs_loc; 1092 for (j=0; j<ompsize; j++) { 1093 const PetscScalar *src = mumps->rhs_recvbuf + mumps->rhs_disps[j]; 1094 dst = dstbase; 1095 for (i=0; i<nrhs; i++) { 1096 PetscCall(PetscArraycpy(dst,src,mumps->rhs_nrow[j])); 1097 src += mumps->rhs_nrow[j]; 1098 dst += mumps->nloc_rhs; 1099 } 1100 dstbase += mumps->rhs_nrow[j]; 1101 } 1102 } 1103 mumps->id.rhs_loc = (MumpsScalar*)mumps->rhs_loc; 1104 } 1105 #endif /* PETSC_HAVE_OPENMP_SUPPORT */ 1106 } 1107 mumps->id.nrhs = nrhs; 1108 mumps->id.nloc_rhs = mumps->nloc_rhs; 1109 mumps->id.lrhs_loc = mumps->nloc_rhs; 1110 mumps->id.irhs_loc = mumps->irhs_loc; 1111 PetscFunctionReturn(0); 1112 } 1113 1114 PetscErrorCode MatSolve_MUMPS(Mat A,Vec b,Vec x) 1115 { 1116 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 1117 const PetscScalar *rarray = NULL; 1118 PetscScalar *array; 1119 IS is_iden,is_petsc; 1120 PetscInt i; 1121 PetscBool second_solve = PETSC_FALSE; 1122 static PetscBool cite1 = PETSC_FALSE,cite2 = PETSC_FALSE; 1123 1124 PetscFunctionBegin; 1125 PetscCall(PetscCitationsRegister("@article{MUMPS01,\n author = {P.~R. Amestoy and I.~S. Duff and J.-Y. L'Excellent and J. Koster},\n title = {A fully asynchronous multifrontal solver using distributed dynamic scheduling},\n journal = {SIAM Journal on Matrix Analysis and Applications},\n volume = {23},\n number = {1},\n pages = {15--41},\n year = {2001}\n}\n",&cite1)); 1126 PetscCall(PetscCitationsRegister("@article{MUMPS02,\n author = {P.~R. Amestoy and A. Guermouche and J.-Y. L'Excellent and S. Pralet},\n title = {Hybrid scheduling for the parallel solution of linear systems},\n journal = {Parallel Computing},\n volume = {32},\n number = {2},\n pages = {136--156},\n year = {2006}\n}\n",&cite2)); 1127 1128 if (A->factorerrortype) { 1129 PetscCall(PetscInfo(A,"MatSolve is called with singular matrix factor, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2))); 1130 PetscCall(VecSetInf(x)); 1131 PetscFunctionReturn(0); 1132 } 1133 1134 mumps->id.nrhs = 1; 1135 if (mumps->petsc_size > 1) { 1136 if (mumps->ICNTL20 == 10) { 1137 mumps->id.ICNTL(20) = 10; /* dense distributed RHS */ 1138 PetscCall(VecGetArrayRead(b,&rarray)); 1139 PetscCall(MatMumpsSetUpDistRHSInfo(A,1,rarray)); 1140 } else { 1141 mumps->id.ICNTL(20) = 0; /* dense centralized RHS; Scatter b into a sequential rhs vector*/ 1142 PetscCall(VecScatterBegin(mumps->scat_rhs,b,mumps->b_seq,INSERT_VALUES,SCATTER_FORWARD)); 1143 PetscCall(VecScatterEnd(mumps->scat_rhs,b,mumps->b_seq,INSERT_VALUES,SCATTER_FORWARD)); 1144 if (!mumps->myid) { 1145 PetscCall(VecGetArray(mumps->b_seq,&array)); 1146 mumps->id.rhs = (MumpsScalar*)array; 1147 } 1148 } 1149 } else { /* petsc_size == 1 */ 1150 mumps->id.ICNTL(20) = 0; /* dense centralized RHS */ 1151 PetscCall(VecCopy(b,x)); 1152 PetscCall(VecGetArray(x,&array)); 1153 mumps->id.rhs = (MumpsScalar*)array; 1154 } 1155 1156 /* 1157 handle condensation step of Schur complement (if any) 1158 We set by default ICNTL(26) == -1 when Schur indices have been provided by the user. 1159 According to MUMPS (5.0.0) manual, any value should be harmful during the factorization phase 1160 Unless the user provides a valid value for ICNTL(26), MatSolve and MatMatSolve routines solve the full system. 1161 This requires an extra call to PetscMUMPS_c and the computation of the factors for S 1162 */ 1163 if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) { 1164 PetscCheck(mumps->petsc_size <= 1,PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Parallel Schur complements not yet supported from PETSc"); 1165 second_solve = PETSC_TRUE; 1166 PetscCall(MatMumpsHandleSchur_Private(A,PETSC_FALSE)); 1167 } 1168 /* solve phase */ 1169 /*-------------*/ 1170 mumps->id.job = JOB_SOLVE; 1171 PetscMUMPS_c(mumps); 1172 PetscCheck(mumps->id.INFOG(1) >= 0,PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d",mumps->id.INFOG(1)); 1173 1174 /* handle expansion step of Schur complement (if any) */ 1175 if (second_solve) { 1176 PetscCall(MatMumpsHandleSchur_Private(A,PETSC_TRUE)); 1177 } 1178 1179 if (mumps->petsc_size > 1) { /* convert mumps distributed solution to petsc mpi x */ 1180 if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) { 1181 /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */ 1182 PetscCall(VecScatterDestroy(&mumps->scat_sol)); 1183 } 1184 if (!mumps->scat_sol) { /* create scatter scat_sol */ 1185 PetscInt *isol2_loc=NULL; 1186 PetscCall(ISCreateStride(PETSC_COMM_SELF,mumps->id.lsol_loc,0,1,&is_iden)); /* from */ 1187 PetscCall(PetscMalloc1(mumps->id.lsol_loc,&isol2_loc)); 1188 for (i=0; i<mumps->id.lsol_loc; i++) isol2_loc[i] = mumps->id.isol_loc[i]-1; /* change Fortran style to C style */ 1189 PetscCall(ISCreateGeneral(PETSC_COMM_SELF,mumps->id.lsol_loc,isol2_loc,PETSC_OWN_POINTER,&is_petsc)); /* to */ 1190 PetscCall(VecScatterCreate(mumps->x_seq,is_iden,x,is_petsc,&mumps->scat_sol)); 1191 PetscCall(ISDestroy(&is_iden)); 1192 PetscCall(ISDestroy(&is_petsc)); 1193 mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */ 1194 } 1195 1196 PetscCall(VecScatterBegin(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD)); 1197 PetscCall(VecScatterEnd(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD)); 1198 } 1199 1200 if (mumps->petsc_size > 1) { 1201 if (mumps->ICNTL20 == 10) { 1202 PetscCall(VecRestoreArrayRead(b,&rarray)); 1203 } else if (!mumps->myid) { 1204 PetscCall(VecRestoreArray(mumps->b_seq,&array)); 1205 } 1206 } else PetscCall(VecRestoreArray(x,&array)); 1207 1208 PetscCall(PetscLogFlops(2.0*mumps->id.RINFO(3))); 1209 PetscFunctionReturn(0); 1210 } 1211 1212 PetscErrorCode MatSolveTranspose_MUMPS(Mat A,Vec b,Vec x) 1213 { 1214 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 1215 1216 PetscFunctionBegin; 1217 mumps->id.ICNTL(9) = 0; 1218 PetscCall(MatSolve_MUMPS(A,b,x)); 1219 mumps->id.ICNTL(9) = 1; 1220 PetscFunctionReturn(0); 1221 } 1222 1223 PetscErrorCode MatMatSolve_MUMPS(Mat A,Mat B,Mat X) 1224 { 1225 Mat Bt = NULL; 1226 PetscBool denseX,denseB,flg,flgT; 1227 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 1228 PetscInt i,nrhs,M; 1229 PetscScalar *array; 1230 const PetscScalar *rbray; 1231 PetscInt lsol_loc,nlsol_loc,*idxx,iidx = 0; 1232 PetscMUMPSInt *isol_loc,*isol_loc_save; 1233 PetscScalar *bray,*sol_loc,*sol_loc_save; 1234 IS is_to,is_from; 1235 PetscInt k,proc,j,m,myrstart; 1236 const PetscInt *rstart; 1237 Vec v_mpi,msol_loc; 1238 VecScatter scat_sol; 1239 Vec b_seq; 1240 VecScatter scat_rhs; 1241 PetscScalar *aa; 1242 PetscInt spnr,*ia,*ja; 1243 Mat_MPIAIJ *b = NULL; 1244 1245 PetscFunctionBegin; 1246 PetscCall(PetscObjectTypeCompareAny((PetscObject)X,&denseX,MATSEQDENSE,MATMPIDENSE,NULL)); 1247 PetscCheck(denseX,PetscObjectComm((PetscObject)X),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix"); 1248 1249 PetscCall(PetscObjectTypeCompareAny((PetscObject)B,&denseB,MATSEQDENSE,MATMPIDENSE,NULL)); 1250 if (denseB) { 1251 PetscCheck(B->rmap->n == X->rmap->n,PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Matrix B and X must have same row distribution"); 1252 mumps->id.ICNTL(20)= 0; /* dense RHS */ 1253 } else { /* sparse B */ 1254 PetscCheck(X != B,PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_IDN,"X and B must be different matrices"); 1255 PetscCall(PetscObjectTypeCompare((PetscObject)B,MATTRANSPOSEMAT,&flgT)); 1256 if (flgT) { /* input B is transpose of actural RHS matrix, 1257 because mumps requires sparse compressed COLUMN storage! See MatMatTransposeSolve_MUMPS() */ 1258 PetscCall(MatTransposeGetMat(B,&Bt)); 1259 } else SETERRQ(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_WRONG,"Matrix B must be MATTRANSPOSEMAT matrix"); 1260 mumps->id.ICNTL(20)= 1; /* sparse RHS */ 1261 } 1262 1263 PetscCall(MatGetSize(B,&M,&nrhs)); 1264 mumps->id.nrhs = nrhs; 1265 mumps->id.lrhs = M; 1266 mumps->id.rhs = NULL; 1267 1268 if (mumps->petsc_size == 1) { 1269 PetscScalar *aa; 1270 PetscInt spnr,*ia,*ja; 1271 PetscBool second_solve = PETSC_FALSE; 1272 1273 PetscCall(MatDenseGetArray(X,&array)); 1274 mumps->id.rhs = (MumpsScalar*)array; 1275 1276 if (denseB) { 1277 /* copy B to X */ 1278 PetscCall(MatDenseGetArrayRead(B,&rbray)); 1279 PetscCall(PetscArraycpy(array,rbray,M*nrhs)); 1280 PetscCall(MatDenseRestoreArrayRead(B,&rbray)); 1281 } else { /* sparse B */ 1282 PetscCall(MatSeqAIJGetArray(Bt,&aa)); 1283 PetscCall(MatGetRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg)); 1284 PetscCheck(flg,PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 1285 PetscCall(PetscMUMPSIntCSRCast(mumps,spnr,ia,ja,&mumps->id.irhs_ptr,&mumps->id.irhs_sparse,&mumps->id.nz_rhs)); 1286 mumps->id.rhs_sparse = (MumpsScalar*)aa; 1287 } 1288 /* handle condensation step of Schur complement (if any) */ 1289 if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) { 1290 second_solve = PETSC_TRUE; 1291 PetscCall(MatMumpsHandleSchur_Private(A,PETSC_FALSE)); 1292 } 1293 /* solve phase */ 1294 /*-------------*/ 1295 mumps->id.job = JOB_SOLVE; 1296 PetscMUMPS_c(mumps); 1297 PetscCheck(mumps->id.INFOG(1) >= 0,PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d",mumps->id.INFOG(1)); 1298 1299 /* handle expansion step of Schur complement (if any) */ 1300 if (second_solve) { 1301 PetscCall(MatMumpsHandleSchur_Private(A,PETSC_TRUE)); 1302 } 1303 if (!denseB) { /* sparse B */ 1304 PetscCall(MatSeqAIJRestoreArray(Bt,&aa)); 1305 PetscCall(MatRestoreRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg)); 1306 PetscCheck(flg,PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot restore IJ structure"); 1307 } 1308 PetscCall(MatDenseRestoreArray(X,&array)); 1309 PetscFunctionReturn(0); 1310 } 1311 1312 /*--------- parallel case: MUMPS requires rhs B to be centralized on the host! --------*/ 1313 PetscCheck(mumps->petsc_size <= 1 || !mumps->id.ICNTL(19),PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Parallel Schur complements not yet supported from PETSc"); 1314 1315 /* create msol_loc to hold mumps local solution */ 1316 isol_loc_save = mumps->id.isol_loc; /* save it for MatSolve() */ 1317 sol_loc_save = (PetscScalar*)mumps->id.sol_loc; 1318 1319 lsol_loc = mumps->id.lsol_loc; 1320 nlsol_loc = nrhs*lsol_loc; /* length of sol_loc */ 1321 PetscCall(PetscMalloc2(nlsol_loc,&sol_loc,lsol_loc,&isol_loc)); 1322 mumps->id.sol_loc = (MumpsScalar*)sol_loc; 1323 mumps->id.isol_loc = isol_loc; 1324 1325 PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF,1,nlsol_loc,(PetscScalar*)sol_loc,&msol_loc)); 1326 1327 if (denseB) { 1328 if (mumps->ICNTL20 == 10) { 1329 mumps->id.ICNTL(20) = 10; /* dense distributed RHS */ 1330 PetscCall(MatDenseGetArrayRead(B,&rbray)); 1331 PetscCall(MatMumpsSetUpDistRHSInfo(A,nrhs,rbray)); 1332 PetscCall(MatDenseRestoreArrayRead(B,&rbray)); 1333 PetscCall(MatGetLocalSize(B,&m,NULL)); 1334 PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B),1,nrhs*m,nrhs*M,NULL,&v_mpi)); 1335 } else { 1336 mumps->id.ICNTL(20) = 0; /* dense centralized RHS */ 1337 /* TODO: Because of non-contiguous indices, the created vecscatter scat_rhs is not done in MPI_Gather, resulting in 1338 very inefficient communication. An optimization is to use VecScatterCreateToZero to gather B to rank 0. Then on rank 1339 0, re-arrange B into desired order, which is a local operation. 1340 */ 1341 1342 /* scatter v_mpi to b_seq because MUMPS before 5.3.0 only supports centralized rhs */ 1343 /* wrap dense rhs matrix B into a vector v_mpi */ 1344 PetscCall(MatGetLocalSize(B,&m,NULL)); 1345 PetscCall(MatDenseGetArray(B,&bray)); 1346 PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B),1,nrhs*m,nrhs*M,(const PetscScalar*)bray,&v_mpi)); 1347 PetscCall(MatDenseRestoreArray(B,&bray)); 1348 1349 /* scatter v_mpi to b_seq in proc[0]. MUMPS requires rhs to be centralized on the host! */ 1350 if (!mumps->myid) { 1351 PetscInt *idx; 1352 /* idx: maps from k-th index of v_mpi to (i,j)-th global entry of B */ 1353 PetscCall(PetscMalloc1(nrhs*M,&idx)); 1354 PetscCall(MatGetOwnershipRanges(B,&rstart)); 1355 k = 0; 1356 for (proc=0; proc<mumps->petsc_size; proc++) { 1357 for (j=0; j<nrhs; j++) { 1358 for (i=rstart[proc]; i<rstart[proc+1]; i++) idx[k++] = j*M + i; 1359 } 1360 } 1361 1362 PetscCall(VecCreateSeq(PETSC_COMM_SELF,nrhs*M,&b_seq)); 1363 PetscCall(ISCreateGeneral(PETSC_COMM_SELF,nrhs*M,idx,PETSC_OWN_POINTER,&is_to)); 1364 PetscCall(ISCreateStride(PETSC_COMM_SELF,nrhs*M,0,1,&is_from)); 1365 } else { 1366 PetscCall(VecCreateSeq(PETSC_COMM_SELF,0,&b_seq)); 1367 PetscCall(ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_to)); 1368 PetscCall(ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_from)); 1369 } 1370 PetscCall(VecScatterCreate(v_mpi,is_from,b_seq,is_to,&scat_rhs)); 1371 PetscCall(VecScatterBegin(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD)); 1372 PetscCall(ISDestroy(&is_to)); 1373 PetscCall(ISDestroy(&is_from)); 1374 PetscCall(VecScatterEnd(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD)); 1375 1376 if (!mumps->myid) { /* define rhs on the host */ 1377 PetscCall(VecGetArray(b_seq,&bray)); 1378 mumps->id.rhs = (MumpsScalar*)bray; 1379 PetscCall(VecRestoreArray(b_seq,&bray)); 1380 } 1381 } 1382 } else { /* sparse B */ 1383 b = (Mat_MPIAIJ*)Bt->data; 1384 1385 /* wrap dense X into a vector v_mpi */ 1386 PetscCall(MatGetLocalSize(X,&m,NULL)); 1387 PetscCall(MatDenseGetArray(X,&bray)); 1388 PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)X),1,nrhs*m,nrhs*M,(const PetscScalar*)bray,&v_mpi)); 1389 PetscCall(MatDenseRestoreArray(X,&bray)); 1390 1391 if (!mumps->myid) { 1392 PetscCall(MatSeqAIJGetArray(b->A,&aa)); 1393 PetscCall(MatGetRowIJ(b->A,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg)); 1394 PetscCheck(flg,PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 1395 PetscCall(PetscMUMPSIntCSRCast(mumps,spnr,ia,ja,&mumps->id.irhs_ptr,&mumps->id.irhs_sparse,&mumps->id.nz_rhs)); 1396 mumps->id.rhs_sparse = (MumpsScalar*)aa; 1397 } else { 1398 mumps->id.irhs_ptr = NULL; 1399 mumps->id.irhs_sparse = NULL; 1400 mumps->id.nz_rhs = 0; 1401 mumps->id.rhs_sparse = NULL; 1402 } 1403 } 1404 1405 /* solve phase */ 1406 /*-------------*/ 1407 mumps->id.job = JOB_SOLVE; 1408 PetscMUMPS_c(mumps); 1409 PetscCheck(mumps->id.INFOG(1) >= 0,PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d",mumps->id.INFOG(1)); 1410 1411 /* scatter mumps distributed solution to petsc vector v_mpi, which shares local arrays with solution matrix X */ 1412 PetscCall(MatDenseGetArray(X,&array)); 1413 PetscCall(VecPlaceArray(v_mpi,array)); 1414 1415 /* create scatter scat_sol */ 1416 PetscCall(MatGetOwnershipRanges(X,&rstart)); 1417 /* iidx: index for scatter mumps solution to petsc X */ 1418 1419 PetscCall(ISCreateStride(PETSC_COMM_SELF,nlsol_loc,0,1,&is_from)); 1420 PetscCall(PetscMalloc1(nlsol_loc,&idxx)); 1421 for (i=0; i<lsol_loc; i++) { 1422 isol_loc[i] -= 1; /* change Fortran style to C style. isol_loc[i+j*lsol_loc] contains x[isol_loc[i]] in j-th vector */ 1423 1424 for (proc=0; proc<mumps->petsc_size; proc++) { 1425 if (isol_loc[i] >= rstart[proc] && isol_loc[i] < rstart[proc+1]) { 1426 myrstart = rstart[proc]; 1427 k = isol_loc[i] - myrstart; /* local index on 1st column of petsc vector X */ 1428 iidx = k + myrstart*nrhs; /* maps mumps isol_loc[i] to petsc index in X */ 1429 m = rstart[proc+1] - rstart[proc]; /* rows of X for this proc */ 1430 break; 1431 } 1432 } 1433 1434 for (j=0; j<nrhs; j++) idxx[i+j*lsol_loc] = iidx + j*m; 1435 } 1436 PetscCall(ISCreateGeneral(PETSC_COMM_SELF,nlsol_loc,idxx,PETSC_COPY_VALUES,&is_to)); 1437 PetscCall(VecScatterCreate(msol_loc,is_from,v_mpi,is_to,&scat_sol)); 1438 PetscCall(VecScatterBegin(scat_sol,msol_loc,v_mpi,INSERT_VALUES,SCATTER_FORWARD)); 1439 PetscCall(ISDestroy(&is_from)); 1440 PetscCall(ISDestroy(&is_to)); 1441 PetscCall(VecScatterEnd(scat_sol,msol_loc,v_mpi,INSERT_VALUES,SCATTER_FORWARD)); 1442 PetscCall(MatDenseRestoreArray(X,&array)); 1443 1444 /* free spaces */ 1445 mumps->id.sol_loc = (MumpsScalar*)sol_loc_save; 1446 mumps->id.isol_loc = isol_loc_save; 1447 1448 PetscCall(PetscFree2(sol_loc,isol_loc)); 1449 PetscCall(PetscFree(idxx)); 1450 PetscCall(VecDestroy(&msol_loc)); 1451 PetscCall(VecDestroy(&v_mpi)); 1452 if (!denseB) { 1453 if (!mumps->myid) { 1454 b = (Mat_MPIAIJ*)Bt->data; 1455 PetscCall(MatSeqAIJRestoreArray(b->A,&aa)); 1456 PetscCall(MatRestoreRowIJ(b->A,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg)); 1457 PetscCheck(flg,PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot restore IJ structure"); 1458 } 1459 } else { 1460 if (mumps->ICNTL20 == 0) { 1461 PetscCall(VecDestroy(&b_seq)); 1462 PetscCall(VecScatterDestroy(&scat_rhs)); 1463 } 1464 } 1465 PetscCall(VecScatterDestroy(&scat_sol)); 1466 PetscCall(PetscLogFlops(2.0*nrhs*mumps->id.RINFO(3))); 1467 PetscFunctionReturn(0); 1468 } 1469 1470 PetscErrorCode MatMatTransposeSolve_MUMPS(Mat A,Mat Bt,Mat X) 1471 { 1472 PetscBool flg; 1473 Mat B; 1474 1475 PetscFunctionBegin; 1476 PetscCall(PetscObjectTypeCompareAny((PetscObject)Bt,&flg,MATSEQAIJ,MATMPIAIJ,NULL)); 1477 PetscCheck(flg,PetscObjectComm((PetscObject)Bt),PETSC_ERR_ARG_WRONG,"Matrix Bt must be MATAIJ matrix"); 1478 1479 /* Create B=Bt^T that uses Bt's data structure */ 1480 PetscCall(MatCreateTranspose(Bt,&B)); 1481 1482 PetscCall(MatMatSolve_MUMPS(A,B,X)); 1483 PetscCall(MatDestroy(&B)); 1484 PetscFunctionReturn(0); 1485 } 1486 1487 #if !defined(PETSC_USE_COMPLEX) 1488 /* 1489 input: 1490 F: numeric factor 1491 output: 1492 nneg: total number of negative pivots 1493 nzero: total number of zero pivots 1494 npos: (global dimension of F) - nneg - nzero 1495 */ 1496 PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F,PetscInt *nneg,PetscInt *nzero,PetscInt *npos) 1497 { 1498 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 1499 PetscMPIInt size; 1500 1501 PetscFunctionBegin; 1502 PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)F),&size)); 1503 /* 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 */ 1504 PetscCheck(size <= 1 || mumps->id.ICNTL(13) == 1,PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"ICNTL(13)=%d. -mat_mumps_icntl_13 must be set as 1 for correct global matrix inertia",mumps->id.INFOG(13)); 1505 1506 if (nneg) *nneg = mumps->id.INFOG(12); 1507 if (nzero || npos) { 1508 PetscCheck(mumps->id.ICNTL(24) == 1,PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"-mat_mumps_icntl_24 must be set as 1 for null pivot row detection"); 1509 if (nzero) *nzero = mumps->id.INFOG(28); 1510 if (npos) *npos = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28)); 1511 } 1512 PetscFunctionReturn(0); 1513 } 1514 #endif 1515 1516 PetscErrorCode MatMumpsGatherNonzerosOnMaster(MatReuse reuse,Mat_MUMPS *mumps) 1517 { 1518 PetscInt i,nreqs; 1519 PetscMUMPSInt *irn,*jcn; 1520 PetscMPIInt count; 1521 PetscInt64 totnnz,remain; 1522 const PetscInt osize=mumps->omp_comm_size; 1523 PetscScalar *val; 1524 1525 PetscFunctionBegin; 1526 if (osize > 1) { 1527 if (reuse == MAT_INITIAL_MATRIX) { 1528 /* master first gathers counts of nonzeros to receive */ 1529 if (mumps->is_omp_master) PetscCall(PetscMalloc1(osize,&mumps->recvcount)); 1530 PetscCallMPI(MPI_Gather(&mumps->nnz,1,MPIU_INT64,mumps->recvcount,1,MPIU_INT64,0/*master*/,mumps->omp_comm)); 1531 1532 /* Then each computes number of send/recvs */ 1533 if (mumps->is_omp_master) { 1534 /* Start from 1 since self communication is not done in MPI */ 1535 nreqs = 0; 1536 for (i=1; i<osize; i++) nreqs += (mumps->recvcount[i]+PETSC_MPI_INT_MAX-1)/PETSC_MPI_INT_MAX; 1537 } else { 1538 nreqs = (mumps->nnz+PETSC_MPI_INT_MAX-1)/PETSC_MPI_INT_MAX; 1539 } 1540 PetscCall(PetscMalloc1(nreqs*3,&mumps->reqs)); /* Triple the requests since we send irn, jcn and val seperately */ 1541 1542 /* The following code is doing a very simple thing: omp_master rank gathers irn/jcn/val from others. 1543 MPI_Gatherv would be enough if it supports big counts > 2^31-1. Since it does not, and mumps->nnz 1544 might be a prime number > 2^31-1, we have to slice the message. Note omp_comm_size 1545 is very small, the current approach should have no extra overhead compared to MPI_Gatherv. 1546 */ 1547 nreqs = 0; /* counter for actual send/recvs */ 1548 if (mumps->is_omp_master) { 1549 for (i=0,totnnz=0; i<osize; i++) totnnz += mumps->recvcount[i]; /* totnnz = sum of nnz over omp_comm */ 1550 PetscCall(PetscMalloc2(totnnz,&irn,totnnz,&jcn)); 1551 PetscCall(PetscMalloc1(totnnz,&val)); 1552 1553 /* Self communication */ 1554 PetscCall(PetscArraycpy(irn,mumps->irn,mumps->nnz)); 1555 PetscCall(PetscArraycpy(jcn,mumps->jcn,mumps->nnz)); 1556 PetscCall(PetscArraycpy(val,mumps->val,mumps->nnz)); 1557 1558 /* Replace mumps->irn/jcn etc on master with the newly allocated bigger arrays */ 1559 PetscCall(PetscFree2(mumps->irn,mumps->jcn)); 1560 PetscCall(PetscFree(mumps->val_alloc)); 1561 mumps->nnz = totnnz; 1562 mumps->irn = irn; 1563 mumps->jcn = jcn; 1564 mumps->val = mumps->val_alloc = val; 1565 1566 irn += mumps->recvcount[0]; /* recvcount[0] is old mumps->nnz on omp rank 0 */ 1567 jcn += mumps->recvcount[0]; 1568 val += mumps->recvcount[0]; 1569 1570 /* Remote communication */ 1571 for (i=1; i<osize; i++) { 1572 count = PetscMin(mumps->recvcount[i],PETSC_MPI_INT_MAX); 1573 remain = mumps->recvcount[i] - count; 1574 while (count>0) { 1575 PetscCallMPI(MPI_Irecv(irn,count,MPIU_MUMPSINT,i,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++])); 1576 PetscCallMPI(MPI_Irecv(jcn,count,MPIU_MUMPSINT,i,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++])); 1577 PetscCallMPI(MPI_Irecv(val,count,MPIU_SCALAR, i,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++])); 1578 irn += count; 1579 jcn += count; 1580 val += count; 1581 count = PetscMin(remain,PETSC_MPI_INT_MAX); 1582 remain -= count; 1583 } 1584 } 1585 } else { 1586 irn = mumps->irn; 1587 jcn = mumps->jcn; 1588 val = mumps->val; 1589 count = PetscMin(mumps->nnz,PETSC_MPI_INT_MAX); 1590 remain = mumps->nnz - count; 1591 while (count>0) { 1592 PetscCallMPI(MPI_Isend(irn,count,MPIU_MUMPSINT,0,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++])); 1593 PetscCallMPI(MPI_Isend(jcn,count,MPIU_MUMPSINT,0,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++])); 1594 PetscCallMPI(MPI_Isend(val,count,MPIU_SCALAR, 0,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++])); 1595 irn += count; 1596 jcn += count; 1597 val += count; 1598 count = PetscMin(remain,PETSC_MPI_INT_MAX); 1599 remain -= count; 1600 } 1601 } 1602 } else { 1603 nreqs = 0; 1604 if (mumps->is_omp_master) { 1605 val = mumps->val + mumps->recvcount[0]; 1606 for (i=1; i<osize; i++) { /* Remote communication only since self data is already in place */ 1607 count = PetscMin(mumps->recvcount[i],PETSC_MPI_INT_MAX); 1608 remain = mumps->recvcount[i] - count; 1609 while (count>0) { 1610 PetscCallMPI(MPI_Irecv(val,count,MPIU_SCALAR,i,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++])); 1611 val += count; 1612 count = PetscMin(remain,PETSC_MPI_INT_MAX); 1613 remain -= count; 1614 } 1615 } 1616 } else { 1617 val = mumps->val; 1618 count = PetscMin(mumps->nnz,PETSC_MPI_INT_MAX); 1619 remain = mumps->nnz - count; 1620 while (count>0) { 1621 PetscCallMPI(MPI_Isend(val,count,MPIU_SCALAR,0,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++])); 1622 val += count; 1623 count = PetscMin(remain,PETSC_MPI_INT_MAX); 1624 remain -= count; 1625 } 1626 } 1627 } 1628 PetscCallMPI(MPI_Waitall(nreqs,mumps->reqs,MPI_STATUSES_IGNORE)); 1629 mumps->tag++; /* It is totally fine for above send/recvs to share one mpi tag */ 1630 } 1631 PetscFunctionReturn(0); 1632 } 1633 1634 PetscErrorCode MatFactorNumeric_MUMPS(Mat F,Mat A,const MatFactorInfo *info) 1635 { 1636 Mat_MUMPS *mumps =(Mat_MUMPS*)(F)->data; 1637 PetscBool isMPIAIJ; 1638 1639 PetscFunctionBegin; 1640 if (mumps->id.INFOG(1) < 0 && !(mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0)) { 1641 if (mumps->id.INFOG(1) == -6) { 1642 PetscCall(PetscInfo(A,"MatFactorNumeric is called with singular matrix structure, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2))); 1643 } 1644 PetscCall(PetscInfo(A,"MatFactorNumeric is called after analysis phase fails, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2))); 1645 PetscFunctionReturn(0); 1646 } 1647 1648 PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, mumps)); 1649 PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_REUSE_MATRIX,mumps)); 1650 1651 /* numerical factorization phase */ 1652 /*-------------------------------*/ 1653 mumps->id.job = JOB_FACTNUMERIC; 1654 if (!mumps->id.ICNTL(18)) { /* A is centralized */ 1655 if (!mumps->myid) { 1656 mumps->id.a = (MumpsScalar*)mumps->val; 1657 } 1658 } else { 1659 mumps->id.a_loc = (MumpsScalar*)mumps->val; 1660 } 1661 PetscMUMPS_c(mumps); 1662 if (mumps->id.INFOG(1) < 0) { 1663 if (A->erroriffailure) { 1664 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d",mumps->id.INFOG(1),mumps->id.INFO(2)); 1665 } else { 1666 if (mumps->id.INFOG(1) == -10) { /* numerically singular matrix */ 1667 PetscCall(PetscInfo(F,"matrix is numerically singular, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2))); 1668 F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT; 1669 } else if (mumps->id.INFOG(1) == -13) { 1670 PetscCall(PetscInfo(F,"MUMPS in numerical factorization phase: INFOG(1)=%d, cannot allocate required memory %d megabytes\n",mumps->id.INFOG(1),mumps->id.INFO(2))); 1671 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1672 } else if (mumps->id.INFOG(1) == -8 || mumps->id.INFOG(1) == -9 || (-16 < mumps->id.INFOG(1) && mumps->id.INFOG(1) < -10)) { 1673 PetscCall(PetscInfo(F,"MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d, problem with workarray \n",mumps->id.INFOG(1),mumps->id.INFO(2))); 1674 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1675 } else { 1676 PetscCall(PetscInfo(F,"MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2))); 1677 F->factorerrortype = MAT_FACTOR_OTHER; 1678 } 1679 } 1680 } 1681 PetscCheck(mumps->myid || mumps->id.ICNTL(16) <= 0,PETSC_COMM_SELF,PETSC_ERR_LIB," mumps->id.ICNTL(16):=%d",mumps->id.INFOG(16)); 1682 1683 F->assembled = PETSC_TRUE; 1684 1685 if (F->schur) { /* reset Schur status to unfactored */ 1686 #if defined(PETSC_HAVE_CUDA) 1687 F->schur->offloadmask = PETSC_OFFLOAD_CPU; 1688 #endif 1689 if (mumps->id.ICNTL(19) == 1) { /* stored by rows */ 1690 mumps->id.ICNTL(19) = 2; 1691 PetscCall(MatTranspose(F->schur,MAT_INPLACE_MATRIX,&F->schur)); 1692 } 1693 PetscCall(MatFactorRestoreSchurComplement(F,NULL,MAT_FACTOR_SCHUR_UNFACTORED)); 1694 } 1695 1696 /* just to be sure that ICNTL(19) value returned by a call from MatMumpsGetIcntl is always consistent */ 1697 if (!mumps->sym && mumps->id.ICNTL(19) && mumps->id.ICNTL(19) != 1) mumps->id.ICNTL(19) = 3; 1698 1699 if (!mumps->is_omp_master) mumps->id.INFO(23) = 0; 1700 if (mumps->petsc_size > 1) { 1701 PetscInt lsol_loc; 1702 PetscScalar *sol_loc; 1703 1704 PetscCall(PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&isMPIAIJ)); 1705 1706 /* distributed solution; Create x_seq=sol_loc for repeated use */ 1707 if (mumps->x_seq) { 1708 PetscCall(VecScatterDestroy(&mumps->scat_sol)); 1709 PetscCall(PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc)); 1710 PetscCall(VecDestroy(&mumps->x_seq)); 1711 } 1712 lsol_loc = mumps->id.INFO(23); /* length of sol_loc */ 1713 PetscCall(PetscMalloc2(lsol_loc,&sol_loc,lsol_loc,&mumps->id.isol_loc)); 1714 mumps->id.lsol_loc = lsol_loc; 1715 mumps->id.sol_loc = (MumpsScalar*)sol_loc; 1716 PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF,1,lsol_loc,sol_loc,&mumps->x_seq)); 1717 } 1718 PetscCall(PetscLogFlops(mumps->id.RINFO(2))); 1719 PetscFunctionReturn(0); 1720 } 1721 1722 /* Sets MUMPS options from the options database */ 1723 PetscErrorCode MatSetFromOptions_MUMPS(Mat F, Mat A) 1724 { 1725 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1726 PetscMUMPSInt icntl=0; 1727 PetscInt info[80],i,ninfo=80,rbs,cbs; 1728 PetscBool flg=PETSC_FALSE; 1729 1730 PetscFunctionBegin; 1731 PetscOptionsBegin(PetscObjectComm((PetscObject)F),((PetscObject)F)->prefix,"MUMPS Options","Mat"); 1732 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_1","ICNTL(1): output stream for error messages","None",mumps->id.ICNTL(1),&icntl,&flg)); 1733 if (flg) mumps->id.ICNTL(1) = icntl; 1734 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_2","ICNTL(2): output stream for diagnostic printing, statistics, and warning","None",mumps->id.ICNTL(2),&icntl,&flg)); 1735 if (flg) mumps->id.ICNTL(2) = icntl; 1736 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_3","ICNTL(3): output stream for global information, collected on the host","None",mumps->id.ICNTL(3),&icntl,&flg)); 1737 if (flg) mumps->id.ICNTL(3) = icntl; 1738 1739 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_4","ICNTL(4): level of printing (0 to 4)","None",mumps->id.ICNTL(4),&icntl,&flg)); 1740 if (flg) mumps->id.ICNTL(4) = icntl; 1741 if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */ 1742 1743 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_6","ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7)","None",mumps->id.ICNTL(6),&icntl,&flg)); 1744 if (flg) mumps->id.ICNTL(6) = icntl; 1745 1746 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_7","ICNTL(7): computes a symmetric permutation in sequential analysis. 0=AMD, 2=AMF, 3=Scotch, 4=PORD, 5=Metis, 6=QAMD, and 7=auto(default)","None",mumps->id.ICNTL(7),&icntl,&flg)); 1747 if (flg) { 1748 PetscCheck(icntl != 1 && icntl >= 0 && icntl <= 7,PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Valid values are 0=AMD, 2=AMF, 3=Scotch, 4=PORD, 5=Metis, 6=QAMD, and 7=auto"); 1749 mumps->id.ICNTL(7) = icntl; 1750 } 1751 1752 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_8","ICNTL(8): scaling strategy (-2 to 8 or 77)","None",mumps->id.ICNTL(8),&mumps->id.ICNTL(8),NULL)); 1753 /* PetscCall(PetscOptionsInt("-mat_mumps_icntl_9","ICNTL(9): computes the solution using A or A^T","None",mumps->id.ICNTL(9),&mumps->id.ICNTL(9),NULL)); handled by MatSolveTranspose_MUMPS() */ 1754 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_10","ICNTL(10): max num of refinements","None",mumps->id.ICNTL(10),&mumps->id.ICNTL(10),NULL)); 1755 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_11","ICNTL(11): statistics related to an error analysis (via -ksp_view)","None",mumps->id.ICNTL(11),&mumps->id.ICNTL(11),NULL)); 1756 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_12","ICNTL(12): an ordering strategy for symmetric matrices (0 to 3)","None",mumps->id.ICNTL(12),&mumps->id.ICNTL(12),NULL)); 1757 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_13","ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting","None",mumps->id.ICNTL(13),&mumps->id.ICNTL(13),NULL)); 1758 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_14","ICNTL(14): percentage increase in the estimated working space","None",mumps->id.ICNTL(14),&mumps->id.ICNTL(14),NULL)); 1759 PetscCall(MatGetBlockSizes(A,&rbs,&cbs)); 1760 if (rbs == cbs && rbs > 1) mumps->id.ICNTL(15) = -rbs; 1761 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_15","ICNTL(15): compression of the input matrix resulting from a block format","None",mumps->id.ICNTL(15),&mumps->id.ICNTL(15),&flg)); 1762 if (flg) { 1763 PetscCheck(mumps->id.ICNTL(15) <= 0,PETSC_COMM_SELF,PETSC_ERR_SUP,"Positive -mat_mumps_icntl_15 not handled"); 1764 PetscCheck((-mumps->id.ICNTL(15) % cbs == 0) && (-mumps->id.ICNTL(15) % rbs == 0),PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"The opposite of -mat_mumps_icntl_15 must be a multiple of the column and row blocksizes"); 1765 } 1766 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_19","ICNTL(19): computes the Schur complement","None",mumps->id.ICNTL(19),&mumps->id.ICNTL(19),NULL)); 1767 if (mumps->id.ICNTL(19) <= 0 || mumps->id.ICNTL(19) > 3) { /* reset any schur data (if any) */ 1768 PetscCall(MatDestroy(&F->schur)); 1769 PetscCall(MatMumpsResetSchur_Private(mumps)); 1770 } 1771 1772 /* Two MPICH Fortran MPI_IN_PLACE binding bugs prevented the use of 'mpich + mumps'. One happened with "mpi4py + mpich + mumps", 1773 and was reported by Firedrake. See https://bitbucket.org/mpi4py/mpi4py/issues/162/mpi4py-initialization-breaks-fortran 1774 and a petsc-maint mailing list thread with subject 'MUMPS segfaults in parallel because of ...' 1775 This bug was fixed by https://github.com/pmodels/mpich/pull/4149. But the fix brought a new bug, 1776 see https://github.com/pmodels/mpich/issues/5589. This bug was fixed by https://github.com/pmodels/mpich/pull/5590. 1777 In short, we could not use distributed RHS with MPICH until v4.0b1. 1778 */ 1779 #if PETSC_PKG_MUMPS_VERSION_LT(5,3,0) || (defined(PETSC_HAVE_MPICH_NUMVERSION) && (PETSC_HAVE_MPICH_NUMVERSION < 40000101)) 1780 mumps->ICNTL20 = 0; /* Centralized dense RHS*/ 1781 #else 1782 mumps->ICNTL20 = 10; /* Distributed dense RHS*/ 1783 #endif 1784 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_20","ICNTL(20): give mumps centralized (0) or distributed (10) dense right-hand sides","None",mumps->ICNTL20,&mumps->ICNTL20,&flg)); 1785 PetscCheck(!flg || mumps->ICNTL20 == 10 || mumps->ICNTL20 == 0,PETSC_COMM_SELF,PETSC_ERR_SUP,"ICNTL(20)=%d is not supported by the PETSc/MUMPS interface. Allowed values are 0, 10",(int)mumps->ICNTL20); 1786 #if PETSC_PKG_MUMPS_VERSION_LT(5,3,0) 1787 PetscCheck(!flg || mumps->ICNTL20 != 10,PETSC_COMM_SELF,PETSC_ERR_SUP,"ICNTL(20)=10 is not supported before MUMPS-5.3.0"); 1788 #endif 1789 /* PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_21","ICNTL(21): the distribution (centralized or distributed) of the solution vectors","None",mumps->id.ICNTL(21),&mumps->id.ICNTL(21),NULL)); we only use distributed solution vector */ 1790 1791 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_22","ICNTL(22): in-core/out-of-core factorization and solve (0 or 1)","None",mumps->id.ICNTL(22),&mumps->id.ICNTL(22),NULL)); 1792 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_23","ICNTL(23): max size of the working memory (MB) that can allocate per processor","None",mumps->id.ICNTL(23),&mumps->id.ICNTL(23),NULL)); 1793 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_24","ICNTL(24): detection of null pivot rows (0 or 1)","None",mumps->id.ICNTL(24),&mumps->id.ICNTL(24),NULL)); 1794 if (mumps->id.ICNTL(24)) { 1795 mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */ 1796 } 1797 1798 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_25","ICNTL(25): computes a solution of a deficient matrix and a null space basis","None",mumps->id.ICNTL(25),&mumps->id.ICNTL(25),NULL)); 1799 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_26","ICNTL(26): drives the solution phase if a Schur complement matrix","None",mumps->id.ICNTL(26),&mumps->id.ICNTL(26),NULL)); 1800 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_27","ICNTL(27): controls the blocking size for multiple right-hand sides","None",mumps->id.ICNTL(27),&mumps->id.ICNTL(27),NULL)); 1801 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_28","ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering","None",mumps->id.ICNTL(28),&mumps->id.ICNTL(28),NULL)); 1802 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_29","ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis","None",mumps->id.ICNTL(29),&mumps->id.ICNTL(29),NULL)); 1803 /* PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_30","ICNTL(30): compute user-specified set of entries in inv(A)","None",mumps->id.ICNTL(30),&mumps->id.ICNTL(30),NULL)); */ /* call MatMumpsGetInverse() directly */ 1804 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_31","ICNTL(31): indicates which factors may be discarded during factorization","None",mumps->id.ICNTL(31),&mumps->id.ICNTL(31),NULL)); 1805 /* PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_32","ICNTL(32): performs the forward elemination of the right-hand sides during factorization","None",mumps->id.ICNTL(32),&mumps->id.ICNTL(32),NULL)); -- not supported by PETSc API */ 1806 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_33","ICNTL(33): compute determinant","None",mumps->id.ICNTL(33),&mumps->id.ICNTL(33),NULL)); 1807 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_35","ICNTL(35): activates Block Low Rank (BLR) based factorization","None",mumps->id.ICNTL(35),&mumps->id.ICNTL(35),NULL)); 1808 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_36","ICNTL(36): choice of BLR factorization variant","None",mumps->id.ICNTL(36),&mumps->id.ICNTL(36),NULL)); 1809 PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_38","ICNTL(38): estimated compression rate of LU factors with BLR","None",mumps->id.ICNTL(38),&mumps->id.ICNTL(38),NULL)); 1810 1811 PetscCall(PetscOptionsReal("-mat_mumps_cntl_1","CNTL(1): relative pivoting threshold","None",mumps->id.CNTL(1),&mumps->id.CNTL(1),NULL)); 1812 PetscCall(PetscOptionsReal("-mat_mumps_cntl_2","CNTL(2): stopping criterion of refinement","None",mumps->id.CNTL(2),&mumps->id.CNTL(2),NULL)); 1813 PetscCall(PetscOptionsReal("-mat_mumps_cntl_3","CNTL(3): absolute pivoting threshold","None",mumps->id.CNTL(3),&mumps->id.CNTL(3),NULL)); 1814 PetscCall(PetscOptionsReal("-mat_mumps_cntl_4","CNTL(4): value for static pivoting","None",mumps->id.CNTL(4),&mumps->id.CNTL(4),NULL)); 1815 PetscCall(PetscOptionsReal("-mat_mumps_cntl_5","CNTL(5): fixation for null pivots","None",mumps->id.CNTL(5),&mumps->id.CNTL(5),NULL)); 1816 PetscCall(PetscOptionsReal("-mat_mumps_cntl_7","CNTL(7): dropping parameter used during BLR","None",mumps->id.CNTL(7),&mumps->id.CNTL(7),NULL)); 1817 1818 PetscCall(PetscOptionsString("-mat_mumps_ooc_tmpdir", "out of core directory", "None", mumps->id.ooc_tmpdir, mumps->id.ooc_tmpdir, sizeof(mumps->id.ooc_tmpdir), NULL)); 1819 1820 PetscCall(PetscOptionsIntArray("-mat_mumps_view_info","request INFO local to each processor","",info,&ninfo,NULL)); 1821 if (ninfo) { 1822 PetscCheck(ninfo <= 80,PETSC_COMM_SELF,PETSC_ERR_USER,"number of INFO %" PetscInt_FMT " must <= 80",ninfo); 1823 PetscCall(PetscMalloc1(ninfo,&mumps->info)); 1824 mumps->ninfo = ninfo; 1825 for (i=0; i<ninfo; i++) { 1826 PetscCheck(info[i] >= 0 && info[i] <= 80,PETSC_COMM_SELF,PETSC_ERR_USER,"index of INFO %" PetscInt_FMT " must between 1 and 80",ninfo); 1827 else mumps->info[i] = info[i]; 1828 } 1829 } 1830 1831 PetscOptionsEnd(); 1832 PetscFunctionReturn(0); 1833 } 1834 1835 PetscErrorCode MatSetFromOptions_MUMPS_OpenMP(Mat F,Mat A) 1836 { 1837 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1838 PetscInt nthreads=0; 1839 1840 PetscFunctionBegin; 1841 mumps->petsc_comm = PetscObjectComm((PetscObject)A); 1842 PetscCallMPI(MPI_Comm_size(mumps->petsc_comm,&mumps->petsc_size)); 1843 PetscCallMPI(MPI_Comm_rank(mumps->petsc_comm,&mumps->myid));/* "if (!myid)" still works even if mumps_comm is different */ 1844 1845 PetscCall(PetscOptionsHasName(NULL,((PetscObject)F)->prefix,"-mat_mumps_use_omp_threads",&mumps->use_petsc_omp_support)); 1846 if (mumps->use_petsc_omp_support) nthreads = -1; /* -1 will let PetscOmpCtrlCreate() guess a proper value when user did not supply one */ 1847 PetscCall(PetscOptionsGetInt(NULL,((PetscObject)F)->prefix,"-mat_mumps_use_omp_threads",&nthreads,NULL)); 1848 if (mumps->use_petsc_omp_support) { 1849 #if defined(PETSC_HAVE_OPENMP_SUPPORT) 1850 PetscCall(PetscOmpCtrlCreate(mumps->petsc_comm,nthreads,&mumps->omp_ctrl)); 1851 PetscCall(PetscOmpCtrlGetOmpComms(mumps->omp_ctrl,&mumps->omp_comm,&mumps->mumps_comm,&mumps->is_omp_master)); 1852 #else 1853 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP_SYS,"the system does not have PETSc OpenMP support but you added the -%smat_mumps_use_omp_threads option. Configure PETSc with --with-openmp --download-hwloc (or --with-hwloc) to enable it, see more in MATSOLVERMUMPS manual",((PetscObject)A)->prefix?((PetscObject)A)->prefix:""); 1854 #endif 1855 } else { 1856 mumps->omp_comm = PETSC_COMM_SELF; 1857 mumps->mumps_comm = mumps->petsc_comm; 1858 mumps->is_omp_master = PETSC_TRUE; 1859 } 1860 PetscCallMPI(MPI_Comm_size(mumps->omp_comm,&mumps->omp_comm_size)); 1861 mumps->reqs = NULL; 1862 mumps->tag = 0; 1863 1864 /* It looks like MUMPS does not dup the input comm. Dup a new comm for MUMPS to avoid any tag mismatches. */ 1865 if (mumps->mumps_comm != MPI_COMM_NULL) { 1866 PetscCall(PetscCommGetComm(PetscObjectComm((PetscObject)A),&mumps->mumps_comm)); 1867 } 1868 1869 mumps->id.comm_fortran = MPI_Comm_c2f(mumps->mumps_comm); 1870 mumps->id.job = JOB_INIT; 1871 mumps->id.par = 1; /* host participates factorizaton and solve */ 1872 mumps->id.sym = mumps->sym; 1873 1874 PetscMUMPS_c(mumps); 1875 PetscCheck(mumps->id.INFOG(1) >= 0,PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS: INFOG(1)=%d",mumps->id.INFOG(1)); 1876 1877 /* copy MUMPS default control values from master to slaves. Although slaves do not call MUMPS, they may access these values in code. 1878 For example, ICNTL(9) is initialized to 1 by MUMPS and slaves check ICNTL(9) in MatSolve_MUMPS. 1879 */ 1880 PetscCallMPI(MPI_Bcast(mumps->id.icntl,40,MPI_INT, 0,mumps->omp_comm)); 1881 PetscCallMPI(MPI_Bcast(mumps->id.cntl, 15,MPIU_REAL,0,mumps->omp_comm)); 1882 1883 mumps->scat_rhs = NULL; 1884 mumps->scat_sol = NULL; 1885 1886 /* set PETSc-MUMPS default options - override MUMPS default */ 1887 mumps->id.ICNTL(3) = 0; 1888 mumps->id.ICNTL(4) = 0; 1889 if (mumps->petsc_size == 1) { 1890 mumps->id.ICNTL(18) = 0; /* centralized assembled matrix input */ 1891 mumps->id.ICNTL(7) = 7; /* automatic choice of ordering done by the package */ 1892 } else { 1893 mumps->id.ICNTL(18) = 3; /* distributed assembled matrix input */ 1894 mumps->id.ICNTL(21) = 1; /* distributed solution */ 1895 } 1896 1897 /* schur */ 1898 mumps->id.size_schur = 0; 1899 mumps->id.listvar_schur = NULL; 1900 mumps->id.schur = NULL; 1901 mumps->sizeredrhs = 0; 1902 mumps->schur_sol = NULL; 1903 mumps->schur_sizesol = 0; 1904 PetscFunctionReturn(0); 1905 } 1906 1907 PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F,Mat A,const MatFactorInfo *info,Mat_MUMPS *mumps) 1908 { 1909 PetscFunctionBegin; 1910 if (mumps->id.INFOG(1) < 0) { 1911 if (A->erroriffailure) { 1912 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d",mumps->id.INFOG(1)); 1913 } else { 1914 if (mumps->id.INFOG(1) == -6) { 1915 PetscCall(PetscInfo(F,"matrix is singular in structure, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2))); 1916 F->factorerrortype = MAT_FACTOR_STRUCT_ZEROPIVOT; 1917 } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) { 1918 PetscCall(PetscInfo(F,"problem of workspace, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2))); 1919 F->factorerrortype = MAT_FACTOR_OUTMEMORY; 1920 } else if (mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0) { 1921 PetscCall(PetscInfo(F,"Empty matrix\n")); 1922 } else { 1923 PetscCall(PetscInfo(F,"Error reported by MUMPS in analysis phase: INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2))); 1924 F->factorerrortype = MAT_FACTOR_OTHER; 1925 } 1926 } 1927 } 1928 PetscFunctionReturn(0); 1929 } 1930 1931 PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 1932 { 1933 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 1934 Vec b; 1935 const PetscInt M = A->rmap->N; 1936 1937 PetscFunctionBegin; 1938 if (mumps->matstruc == SAME_NONZERO_PATTERN) { 1939 /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */ 1940 PetscFunctionReturn(0); 1941 } 1942 1943 /* Set MUMPS options from the options database */ 1944 PetscCall(MatSetFromOptions_MUMPS(F,A)); 1945 1946 PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps)); 1947 PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX,mumps)); 1948 1949 /* analysis phase */ 1950 /*----------------*/ 1951 mumps->id.job = JOB_FACTSYMBOLIC; 1952 mumps->id.n = M; 1953 switch (mumps->id.ICNTL(18)) { 1954 case 0: /* centralized assembled matrix input */ 1955 if (!mumps->myid) { 1956 mumps->id.nnz = mumps->nnz; 1957 mumps->id.irn = mumps->irn; 1958 mumps->id.jcn = mumps->jcn; 1959 if (mumps->id.ICNTL(6)>1) mumps->id.a = (MumpsScalar*)mumps->val; 1960 if (r) { 1961 mumps->id.ICNTL(7) = 1; 1962 if (!mumps->myid) { 1963 const PetscInt *idx; 1964 PetscInt i; 1965 1966 PetscCall(PetscMalloc1(M,&mumps->id.perm_in)); 1967 PetscCall(ISGetIndices(r,&idx)); 1968 for (i=0; i<M; i++) PetscCall(PetscMUMPSIntCast(idx[i]+1,&(mumps->id.perm_in[i]))); /* perm_in[]: start from 1, not 0! */ 1969 PetscCall(ISRestoreIndices(r,&idx)); 1970 } 1971 } 1972 } 1973 break; 1974 case 3: /* distributed assembled matrix input (size>1) */ 1975 mumps->id.nnz_loc = mumps->nnz; 1976 mumps->id.irn_loc = mumps->irn; 1977 mumps->id.jcn_loc = mumps->jcn; 1978 if (mumps->id.ICNTL(6)>1) mumps->id.a_loc = (MumpsScalar*)mumps->val; 1979 if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 1980 PetscCall(MatCreateVecs(A,NULL,&b)); 1981 PetscCall(VecScatterCreateToZero(b,&mumps->scat_rhs,&mumps->b_seq)); 1982 PetscCall(VecDestroy(&b)); 1983 } 1984 break; 1985 } 1986 PetscMUMPS_c(mumps); 1987 PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps)); 1988 1989 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 1990 F->ops->solve = MatSolve_MUMPS; 1991 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 1992 F->ops->matsolve = MatMatSolve_MUMPS; 1993 F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS; 1994 1995 mumps->matstruc = SAME_NONZERO_PATTERN; 1996 PetscFunctionReturn(0); 1997 } 1998 1999 /* Note the Petsc r and c permutations are ignored */ 2000 PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 2001 { 2002 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 2003 Vec b; 2004 const PetscInt M = A->rmap->N; 2005 2006 PetscFunctionBegin; 2007 if (mumps->matstruc == SAME_NONZERO_PATTERN) { 2008 /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */ 2009 PetscFunctionReturn(0); 2010 } 2011 2012 /* Set MUMPS options from the options database */ 2013 PetscCall(MatSetFromOptions_MUMPS(F,A)); 2014 2015 PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps)); 2016 PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX,mumps)); 2017 2018 /* analysis phase */ 2019 /*----------------*/ 2020 mumps->id.job = JOB_FACTSYMBOLIC; 2021 mumps->id.n = M; 2022 switch (mumps->id.ICNTL(18)) { 2023 case 0: /* centralized assembled matrix input */ 2024 if (!mumps->myid) { 2025 mumps->id.nnz = mumps->nnz; 2026 mumps->id.irn = mumps->irn; 2027 mumps->id.jcn = mumps->jcn; 2028 if (mumps->id.ICNTL(6)>1) { 2029 mumps->id.a = (MumpsScalar*)mumps->val; 2030 } 2031 } 2032 break; 2033 case 3: /* distributed assembled matrix input (size>1) */ 2034 mumps->id.nnz_loc = mumps->nnz; 2035 mumps->id.irn_loc = mumps->irn; 2036 mumps->id.jcn_loc = mumps->jcn; 2037 if (mumps->id.ICNTL(6)>1) { 2038 mumps->id.a_loc = (MumpsScalar*)mumps->val; 2039 } 2040 if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 2041 PetscCall(MatCreateVecs(A,NULL,&b)); 2042 PetscCall(VecScatterCreateToZero(b,&mumps->scat_rhs,&mumps->b_seq)); 2043 PetscCall(VecDestroy(&b)); 2044 } 2045 break; 2046 } 2047 PetscMUMPS_c(mumps); 2048 PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps)); 2049 2050 F->ops->lufactornumeric = MatFactorNumeric_MUMPS; 2051 F->ops->solve = MatSolve_MUMPS; 2052 F->ops->solvetranspose = MatSolveTranspose_MUMPS; 2053 2054 mumps->matstruc = SAME_NONZERO_PATTERN; 2055 PetscFunctionReturn(0); 2056 } 2057 2058 /* Note the Petsc r permutation and factor info are ignored */ 2059 PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F,Mat A,IS r,const MatFactorInfo *info) 2060 { 2061 Mat_MUMPS *mumps = (Mat_MUMPS*)F->data; 2062 Vec b; 2063 const PetscInt M = A->rmap->N; 2064 2065 PetscFunctionBegin; 2066 if (mumps->matstruc == SAME_NONZERO_PATTERN) { 2067 /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */ 2068 PetscFunctionReturn(0); 2069 } 2070 2071 /* Set MUMPS options from the options database */ 2072 PetscCall(MatSetFromOptions_MUMPS(F,A)); 2073 2074 PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps)); 2075 PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX,mumps)); 2076 2077 /* analysis phase */ 2078 /*----------------*/ 2079 mumps->id.job = JOB_FACTSYMBOLIC; 2080 mumps->id.n = M; 2081 switch (mumps->id.ICNTL(18)) { 2082 case 0: /* centralized assembled matrix input */ 2083 if (!mumps->myid) { 2084 mumps->id.nnz = mumps->nnz; 2085 mumps->id.irn = mumps->irn; 2086 mumps->id.jcn = mumps->jcn; 2087 if (mumps->id.ICNTL(6)>1) { 2088 mumps->id.a = (MumpsScalar*)mumps->val; 2089 } 2090 } 2091 break; 2092 case 3: /* distributed assembled matrix input (size>1) */ 2093 mumps->id.nnz_loc = mumps->nnz; 2094 mumps->id.irn_loc = mumps->irn; 2095 mumps->id.jcn_loc = mumps->jcn; 2096 if (mumps->id.ICNTL(6)>1) { 2097 mumps->id.a_loc = (MumpsScalar*)mumps->val; 2098 } 2099 if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */ 2100 PetscCall(MatCreateVecs(A,NULL,&b)); 2101 PetscCall(VecScatterCreateToZero(b,&mumps->scat_rhs,&mumps->b_seq)); 2102 PetscCall(VecDestroy(&b)); 2103 } 2104 break; 2105 } 2106 PetscMUMPS_c(mumps); 2107 PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps)); 2108 2109 F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS; 2110 F->ops->solve = MatSolve_MUMPS; 2111 F->ops->solvetranspose = MatSolve_MUMPS; 2112 F->ops->matsolve = MatMatSolve_MUMPS; 2113 F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS; 2114 #if defined(PETSC_USE_COMPLEX) 2115 F->ops->getinertia = NULL; 2116 #else 2117 F->ops->getinertia = MatGetInertia_SBAIJMUMPS; 2118 #endif 2119 2120 mumps->matstruc = SAME_NONZERO_PATTERN; 2121 PetscFunctionReturn(0); 2122 } 2123 2124 PetscErrorCode MatView_MUMPS(Mat A,PetscViewer viewer) 2125 { 2126 PetscBool iascii; 2127 PetscViewerFormat format; 2128 Mat_MUMPS *mumps=(Mat_MUMPS*)A->data; 2129 2130 PetscFunctionBegin; 2131 /* check if matrix is mumps type */ 2132 if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(0); 2133 2134 PetscCall(PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii)); 2135 if (iascii) { 2136 PetscCall(PetscViewerGetFormat(viewer,&format)); 2137 if (format == PETSC_VIEWER_ASCII_INFO) { 2138 PetscCall(PetscViewerASCIIPrintf(viewer,"MUMPS run parameters:\n")); 2139 PetscCall(PetscViewerASCIIPrintf(viewer," SYM (matrix type): %d\n",mumps->id.sym)); 2140 PetscCall(PetscViewerASCIIPrintf(viewer," PAR (host participation): %d\n",mumps->id.par)); 2141 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(1) (output for error): %d\n",mumps->id.ICNTL(1))); 2142 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(2) (output of diagnostic msg): %d\n",mumps->id.ICNTL(2))); 2143 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(3) (output for global info): %d\n",mumps->id.ICNTL(3))); 2144 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(4) (level of printing): %d\n",mumps->id.ICNTL(4))); 2145 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(5) (input mat struct): %d\n",mumps->id.ICNTL(5))); 2146 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(6) (matrix prescaling): %d\n",mumps->id.ICNTL(6))); 2147 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(7) (sequential matrix ordering):%d\n",mumps->id.ICNTL(7))); 2148 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(8) (scaling strategy): %d\n",mumps->id.ICNTL(8))); 2149 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(10) (max num of refinements): %d\n",mumps->id.ICNTL(10))); 2150 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(11) (error analysis): %d\n",mumps->id.ICNTL(11))); 2151 if (mumps->id.ICNTL(11)>0) { 2152 PetscCall(PetscViewerASCIIPrintf(viewer," RINFOG(4) (inf norm of input mat): %g\n",mumps->id.RINFOG(4))); 2153 PetscCall(PetscViewerASCIIPrintf(viewer," RINFOG(5) (inf norm of solution): %g\n",mumps->id.RINFOG(5))); 2154 PetscCall(PetscViewerASCIIPrintf(viewer," RINFOG(6) (inf norm of residual): %g\n",mumps->id.RINFOG(6))); 2155 PetscCall(PetscViewerASCIIPrintf(viewer," RINFOG(7),RINFOG(8) (backward error est): %g, %g\n",mumps->id.RINFOG(7),mumps->id.RINFOG(8))); 2156 PetscCall(PetscViewerASCIIPrintf(viewer," RINFOG(9) (error estimate): %g \n",mumps->id.RINFOG(9))); 2157 PetscCall(PetscViewerASCIIPrintf(viewer," RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n",mumps->id.RINFOG(10),mumps->id.RINFOG(11))); 2158 } 2159 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(12) (efficiency control): %d\n",mumps->id.ICNTL(12))); 2160 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(13) (sequential factorization of the root node): %d\n",mumps->id.ICNTL(13))); 2161 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(14) (percentage of estimated workspace increase): %d\n",mumps->id.ICNTL(14))); 2162 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(15) (compression of the input matrix): %d\n",mumps->id.ICNTL(15))); 2163 /* ICNTL(15-17) not used */ 2164 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(18) (input mat struct): %d\n",mumps->id.ICNTL(18))); 2165 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(19) (Schur complement info): %d\n",mumps->id.ICNTL(19))); 2166 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(20) (RHS sparse pattern): %d\n",mumps->id.ICNTL(20))); 2167 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(21) (solution struct): %d\n",mumps->id.ICNTL(21))); 2168 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(22) (in-core/out-of-core facility): %d\n",mumps->id.ICNTL(22))); 2169 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(23) (max size of memory can be allocated locally):%d\n",mumps->id.ICNTL(23))); 2170 2171 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(24) (detection of null pivot rows): %d\n",mumps->id.ICNTL(24))); 2172 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(25) (computation of a null space basis): %d\n",mumps->id.ICNTL(25))); 2173 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(26) (Schur options for RHS or solution): %d\n",mumps->id.ICNTL(26))); 2174 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(27) (blocking size for multiple RHS): %d\n",mumps->id.ICNTL(27))); 2175 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(28) (use parallel or sequential ordering): %d\n",mumps->id.ICNTL(28))); 2176 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(29) (parallel ordering): %d\n",mumps->id.ICNTL(29))); 2177 2178 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(30) (user-specified set of entries in inv(A)): %d\n",mumps->id.ICNTL(30))); 2179 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(31) (factors is discarded in the solve phase): %d\n",mumps->id.ICNTL(31))); 2180 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(33) (compute determinant): %d\n",mumps->id.ICNTL(33))); 2181 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(35) (activate BLR based factorization): %d\n",mumps->id.ICNTL(35))); 2182 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(36) (choice of BLR factorization variant): %d\n",mumps->id.ICNTL(36))); 2183 PetscCall(PetscViewerASCIIPrintf(viewer," ICNTL(38) (estimated compression rate of LU factors): %d\n",mumps->id.ICNTL(38))); 2184 2185 PetscCall(PetscViewerASCIIPrintf(viewer," CNTL(1) (relative pivoting threshold): %g \n",mumps->id.CNTL(1))); 2186 PetscCall(PetscViewerASCIIPrintf(viewer," CNTL(2) (stopping criterion of refinement): %g \n",mumps->id.CNTL(2))); 2187 PetscCall(PetscViewerASCIIPrintf(viewer," CNTL(3) (absolute pivoting threshold): %g \n",mumps->id.CNTL(3))); 2188 PetscCall(PetscViewerASCIIPrintf(viewer," CNTL(4) (value of static pivoting): %g \n",mumps->id.CNTL(4))); 2189 PetscCall(PetscViewerASCIIPrintf(viewer," CNTL(5) (fixation for null pivots): %g \n",mumps->id.CNTL(5))); 2190 PetscCall(PetscViewerASCIIPrintf(viewer," CNTL(7) (dropping parameter for BLR): %g \n",mumps->id.CNTL(7))); 2191 2192 /* information local to each processor */ 2193 PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(1) (local estimated flops for the elimination after analysis): \n")); 2194 PetscCall(PetscViewerASCIIPushSynchronized(viewer)); 2195 PetscCall(PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(1))); 2196 PetscCall(PetscViewerFlush(viewer)); 2197 PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(2) (local estimated flops for the assembly after factorization): \n")); 2198 PetscCall(PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(2))); 2199 PetscCall(PetscViewerFlush(viewer)); 2200 PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(3) (local estimated flops for the elimination after factorization): \n")); 2201 PetscCall(PetscViewerASCIISynchronizedPrintf(viewer," [%d] %g \n",mumps->myid,mumps->id.RINFO(3))); 2202 PetscCall(PetscViewerFlush(viewer)); 2203 2204 PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization): \n")); 2205 PetscCall(PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d\n",mumps->myid,mumps->id.INFO(15))); 2206 PetscCall(PetscViewerFlush(viewer)); 2207 2208 PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization): \n")); 2209 PetscCall(PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d\n",mumps->myid,mumps->id.INFO(16))); 2210 PetscCall(PetscViewerFlush(viewer)); 2211 2212 PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(23) (num of pivots eliminated on this processor after factorization): \n")); 2213 PetscCall(PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d\n",mumps->myid,mumps->id.INFO(23))); 2214 PetscCall(PetscViewerFlush(viewer)); 2215 2216 if (mumps->ninfo && mumps->ninfo <= 80) { 2217 PetscInt i; 2218 for (i=0; i<mumps->ninfo; i++) { 2219 PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(%" PetscInt_FMT "): \n",mumps->info[i])); 2220 PetscCall(PetscViewerASCIISynchronizedPrintf(viewer," [%d] %d\n",mumps->myid,mumps->id.INFO(mumps->info[i]))); 2221 PetscCall(PetscViewerFlush(viewer)); 2222 } 2223 } 2224 PetscCall(PetscViewerASCIIPopSynchronized(viewer)); 2225 2226 if (!mumps->myid) { /* information from the host */ 2227 PetscCall(PetscViewerASCIIPrintf(viewer," RINFOG(1) (global estimated flops for the elimination after analysis): %g \n",mumps->id.RINFOG(1))); 2228 PetscCall(PetscViewerASCIIPrintf(viewer," RINFOG(2) (global estimated flops for the assembly after factorization): %g \n",mumps->id.RINFOG(2))); 2229 PetscCall(PetscViewerASCIIPrintf(viewer," RINFOG(3) (global estimated flops for the elimination after factorization): %g \n",mumps->id.RINFOG(3))); 2230 PetscCall(PetscViewerASCIIPrintf(viewer," (RINFOG(12) RINFOG(13))*2^INFOG(34) (determinant): (%g,%g)*(2^%d)\n",mumps->id.RINFOG(12),mumps->id.RINFOG(13),mumps->id.INFOG(34))); 2231 2232 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(3) (estimated real workspace for factors on all processors after analysis): %d\n",mumps->id.INFOG(3))); 2233 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d\n",mumps->id.INFOG(4))); 2234 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(5) (estimated maximum front size in the complete tree): %d\n",mumps->id.INFOG(5))); 2235 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(6) (number of nodes in the complete tree): %d\n",mumps->id.INFOG(6))); 2236 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(7) (ordering option effectively used after analysis): %d\n",mumps->id.INFOG(7))); 2237 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d\n",mumps->id.INFOG(8))); 2238 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d\n",mumps->id.INFOG(9))); 2239 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(10) (total integer space store the matrix factors after factorization): %d\n",mumps->id.INFOG(10))); 2240 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(11) (order of largest frontal matrix after factorization): %d\n",mumps->id.INFOG(11))); 2241 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(12) (number of off-diagonal pivots): %d\n",mumps->id.INFOG(12))); 2242 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(13) (number of delayed pivots after factorization): %d\n",mumps->id.INFOG(13))); 2243 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(14) (number of memory compress after factorization): %d\n",mumps->id.INFOG(14))); 2244 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(15) (number of steps of iterative refinement after solution): %d\n",mumps->id.INFOG(15))); 2245 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(16) (estimated size (in MB) of all MUMPS internal data for factorization after analysis: value on the most memory consuming processor): %d\n",mumps->id.INFOG(16))); 2246 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(17) (estimated size of all MUMPS internal data for factorization after analysis: sum over all processors): %d\n",mumps->id.INFOG(17))); 2247 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(18) (size of all MUMPS internal data allocated during factorization: value on the most memory consuming processor): %d\n",mumps->id.INFOG(18))); 2248 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): %d\n",mumps->id.INFOG(19))); 2249 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(20) (estimated number of entries in the factors): %d\n",mumps->id.INFOG(20))); 2250 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(21) (size in MB of memory effectively used during factorization - value on the most memory consuming processor): %d\n",mumps->id.INFOG(21))); 2251 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): %d\n",mumps->id.INFOG(22))); 2252 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d\n",mumps->id.INFOG(23))); 2253 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d\n",mumps->id.INFOG(24))); 2254 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(25) (after factorization: number of pivots modified by static pivoting): %d\n",mumps->id.INFOG(25))); 2255 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(28) (after factorization: number of null pivots encountered): %d\n",mumps->id.INFOG(28))); 2256 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(29) (after factorization: effective number of entries in the factors (sum over all processors)): %d\n",mumps->id.INFOG(29))); 2257 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(30, 31) (after solution: size in Mbytes of memory used during solution phase): %d, %d\n",mumps->id.INFOG(30),mumps->id.INFOG(31))); 2258 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(32) (after analysis: type of analysis done): %d\n",mumps->id.INFOG(32))); 2259 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(33) (value used for ICNTL(8)): %d\n",mumps->id.INFOG(33))); 2260 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(34) (exponent of the determinant if determinant is requested): %d\n",mumps->id.INFOG(34))); 2261 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(35) (after factorization: number of entries taking into account BLR factor compression - sum over all processors): %d\n",mumps->id.INFOG(35))); 2262 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(36) (after analysis: estimated size of all MUMPS internal data for running BLR in-core - value on the most memory consuming processor): %d\n",mumps->id.INFOG(36))); 2263 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(37) (after analysis: estimated size of all MUMPS internal data for running BLR in-core - sum over all processors): %d\n",mumps->id.INFOG(37))); 2264 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(38) (after analysis: estimated size of all MUMPS internal data for running BLR out-of-core - value on the most memory consuming processor): %d\n",mumps->id.INFOG(38))); 2265 PetscCall(PetscViewerASCIIPrintf(viewer," INFOG(39) (after analysis: estimated size of all MUMPS internal data for running BLR out-of-core - sum over all processors): %d\n",mumps->id.INFOG(39))); 2266 } 2267 } 2268 } 2269 PetscFunctionReturn(0); 2270 } 2271 2272 PetscErrorCode MatGetInfo_MUMPS(Mat A,MatInfoType flag,MatInfo *info) 2273 { 2274 Mat_MUMPS *mumps =(Mat_MUMPS*)A->data; 2275 2276 PetscFunctionBegin; 2277 info->block_size = 1.0; 2278 info->nz_allocated = mumps->id.INFOG(20); 2279 info->nz_used = mumps->id.INFOG(20); 2280 info->nz_unneeded = 0.0; 2281 info->assemblies = 0.0; 2282 info->mallocs = 0.0; 2283 info->memory = 0.0; 2284 info->fill_ratio_given = 0; 2285 info->fill_ratio_needed = 0; 2286 info->factor_mallocs = 0; 2287 PetscFunctionReturn(0); 2288 } 2289 2290 /* -------------------------------------------------------------------------------------------*/ 2291 PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is) 2292 { 2293 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2294 const PetscScalar *arr; 2295 const PetscInt *idxs; 2296 PetscInt size,i; 2297 2298 PetscFunctionBegin; 2299 PetscCall(ISGetLocalSize(is,&size)); 2300 if (mumps->petsc_size > 1) { 2301 PetscBool ls,gs; /* gs is false if any rank other than root has non-empty IS */ 2302 2303 ls = mumps->myid ? (size ? PETSC_FALSE : PETSC_TRUE) : PETSC_TRUE; /* always true on root; false on others if their size != 0 */ 2304 PetscCallMPI(MPI_Allreduce(&ls,&gs,1,MPIU_BOOL,MPI_LAND,mumps->petsc_comm)); 2305 PetscCheck(gs,PETSC_COMM_SELF,PETSC_ERR_SUP,"MUMPS distributed parallel Schur complements not yet supported from PETSc"); 2306 } 2307 2308 /* Schur complement matrix */ 2309 PetscCall(MatDestroy(&F->schur)); 2310 PetscCall(MatCreateSeqDense(PETSC_COMM_SELF,size,size,NULL,&F->schur)); 2311 PetscCall(MatDenseGetArrayRead(F->schur,&arr)); 2312 mumps->id.schur = (MumpsScalar*)arr; 2313 mumps->id.size_schur = size; 2314 mumps->id.schur_lld = size; 2315 PetscCall(MatDenseRestoreArrayRead(F->schur,&arr)); 2316 if (mumps->sym == 1) { 2317 PetscCall(MatSetOption(F->schur,MAT_SPD,PETSC_TRUE)); 2318 } 2319 2320 /* MUMPS expects Fortran style indices */ 2321 PetscCall(PetscFree(mumps->id.listvar_schur)); 2322 PetscCall(PetscMalloc1(size,&mumps->id.listvar_schur)); 2323 PetscCall(ISGetIndices(is,&idxs)); 2324 for (i=0; i<size; i++) PetscCall(PetscMUMPSIntCast(idxs[i]+1,&(mumps->id.listvar_schur[i]))); 2325 PetscCall(ISRestoreIndices(is,&idxs)); 2326 if (mumps->petsc_size > 1) { 2327 mumps->id.ICNTL(19) = 1; /* MUMPS returns Schur centralized on the host */ 2328 } else { 2329 if (F->factortype == MAT_FACTOR_LU) { 2330 mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */ 2331 } else { 2332 mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */ 2333 } 2334 } 2335 /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */ 2336 mumps->id.ICNTL(26) = -1; 2337 PetscFunctionReturn(0); 2338 } 2339 2340 /* -------------------------------------------------------------------------------------------*/ 2341 PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F,Mat* S) 2342 { 2343 Mat St; 2344 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2345 PetscScalar *array; 2346 #if defined(PETSC_USE_COMPLEX) 2347 PetscScalar im = PetscSqrtScalar((PetscScalar)-1.0); 2348 #endif 2349 2350 PetscFunctionBegin; 2351 PetscCheck(mumps->id.ICNTL(19),PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it"); 2352 PetscCall(MatCreate(PETSC_COMM_SELF,&St)); 2353 PetscCall(MatSetSizes(St,PETSC_DECIDE,PETSC_DECIDE,mumps->id.size_schur,mumps->id.size_schur)); 2354 PetscCall(MatSetType(St,MATDENSE)); 2355 PetscCall(MatSetUp(St)); 2356 PetscCall(MatDenseGetArray(St,&array)); 2357 if (!mumps->sym) { /* MUMPS always return a full matrix */ 2358 if (mumps->id.ICNTL(19) == 1) { /* stored by rows */ 2359 PetscInt i,j,N=mumps->id.size_schur; 2360 for (i=0;i<N;i++) { 2361 for (j=0;j<N;j++) { 2362 #if !defined(PETSC_USE_COMPLEX) 2363 PetscScalar val = mumps->id.schur[i*N+j]; 2364 #else 2365 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 2366 #endif 2367 array[j*N+i] = val; 2368 } 2369 } 2370 } else { /* stored by columns */ 2371 PetscCall(PetscArraycpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur)); 2372 } 2373 } else { /* either full or lower-triangular (not packed) */ 2374 if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */ 2375 PetscInt i,j,N=mumps->id.size_schur; 2376 for (i=0;i<N;i++) { 2377 for (j=i;j<N;j++) { 2378 #if !defined(PETSC_USE_COMPLEX) 2379 PetscScalar val = mumps->id.schur[i*N+j]; 2380 #else 2381 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 2382 #endif 2383 array[i*N+j] = val; 2384 array[j*N+i] = val; 2385 } 2386 } 2387 } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */ 2388 PetscCall(PetscArraycpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur)); 2389 } else { /* ICNTL(19) == 1 lower triangular stored by rows */ 2390 PetscInt i,j,N=mumps->id.size_schur; 2391 for (i=0;i<N;i++) { 2392 for (j=0;j<i+1;j++) { 2393 #if !defined(PETSC_USE_COMPLEX) 2394 PetscScalar val = mumps->id.schur[i*N+j]; 2395 #else 2396 PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i; 2397 #endif 2398 array[i*N+j] = val; 2399 array[j*N+i] = val; 2400 } 2401 } 2402 } 2403 } 2404 PetscCall(MatDenseRestoreArray(St,&array)); 2405 *S = St; 2406 PetscFunctionReturn(0); 2407 } 2408 2409 /* -------------------------------------------------------------------------------------------*/ 2410 PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt ival) 2411 { 2412 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2413 2414 PetscFunctionBegin; 2415 PetscCall(PetscMUMPSIntCast(ival,&mumps->id.ICNTL(icntl))); 2416 PetscFunctionReturn(0); 2417 } 2418 2419 PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt *ival) 2420 { 2421 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2422 2423 PetscFunctionBegin; 2424 *ival = mumps->id.ICNTL(icntl); 2425 PetscFunctionReturn(0); 2426 } 2427 2428 /*@ 2429 MatMumpsSetIcntl - Set MUMPS parameter ICNTL() 2430 2431 Logically Collective on Mat 2432 2433 Input Parameters: 2434 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2435 . icntl - index of MUMPS parameter array ICNTL() 2436 - ival - value of MUMPS ICNTL(icntl) 2437 2438 Options Database: 2439 . -mat_mumps_icntl_<icntl> <ival> - change the option numbered icntl to ival 2440 2441 Level: beginner 2442 2443 References: 2444 . * - MUMPS Users' Guide 2445 2446 .seealso: `MatGetFactor()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()` 2447 @*/ 2448 PetscErrorCode MatMumpsSetIcntl(Mat F,PetscInt icntl,PetscInt ival) 2449 { 2450 PetscFunctionBegin; 2451 PetscValidType(F,1); 2452 PetscCheck(F->factortype,PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2453 PetscValidLogicalCollectiveInt(F,icntl,2); 2454 PetscValidLogicalCollectiveInt(F,ival,3); 2455 PetscTryMethod(F,"MatMumpsSetIcntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival)); 2456 PetscFunctionReturn(0); 2457 } 2458 2459 /*@ 2460 MatMumpsGetIcntl - Get MUMPS parameter ICNTL() 2461 2462 Logically Collective on Mat 2463 2464 Input Parameters: 2465 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2466 - icntl - index of MUMPS parameter array ICNTL() 2467 2468 Output Parameter: 2469 . ival - value of MUMPS ICNTL(icntl) 2470 2471 Level: beginner 2472 2473 References: 2474 . * - MUMPS Users' Guide 2475 2476 .seealso: `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()` 2477 @*/ 2478 PetscErrorCode MatMumpsGetIcntl(Mat F,PetscInt icntl,PetscInt *ival) 2479 { 2480 PetscFunctionBegin; 2481 PetscValidType(F,1); 2482 PetscCheck(F->factortype,PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2483 PetscValidLogicalCollectiveInt(F,icntl,2); 2484 PetscValidIntPointer(ival,3); 2485 PetscUseMethod(F,"MatMumpsGetIcntl_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival)); 2486 PetscFunctionReturn(0); 2487 } 2488 2489 /* -------------------------------------------------------------------------------------------*/ 2490 PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal val) 2491 { 2492 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2493 2494 PetscFunctionBegin; 2495 mumps->id.CNTL(icntl) = val; 2496 PetscFunctionReturn(0); 2497 } 2498 2499 PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal *val) 2500 { 2501 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2502 2503 PetscFunctionBegin; 2504 *val = mumps->id.CNTL(icntl); 2505 PetscFunctionReturn(0); 2506 } 2507 2508 /*@ 2509 MatMumpsSetCntl - Set MUMPS parameter CNTL() 2510 2511 Logically Collective on Mat 2512 2513 Input Parameters: 2514 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2515 . icntl - index of MUMPS parameter array CNTL() 2516 - val - value of MUMPS CNTL(icntl) 2517 2518 Options Database: 2519 . -mat_mumps_cntl_<icntl> <val> - change the option numbered icntl to ival 2520 2521 Level: beginner 2522 2523 References: 2524 . * - MUMPS Users' Guide 2525 2526 .seealso: `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()` 2527 @*/ 2528 PetscErrorCode MatMumpsSetCntl(Mat F,PetscInt icntl,PetscReal val) 2529 { 2530 PetscFunctionBegin; 2531 PetscValidType(F,1); 2532 PetscCheck(F->factortype,PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2533 PetscValidLogicalCollectiveInt(F,icntl,2); 2534 PetscValidLogicalCollectiveReal(F,val,3); 2535 PetscTryMethod(F,"MatMumpsSetCntl_C",(Mat,PetscInt,PetscReal),(F,icntl,val)); 2536 PetscFunctionReturn(0); 2537 } 2538 2539 /*@ 2540 MatMumpsGetCntl - Get MUMPS parameter CNTL() 2541 2542 Logically Collective on Mat 2543 2544 Input Parameters: 2545 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2546 - icntl - index of MUMPS parameter array CNTL() 2547 2548 Output Parameter: 2549 . val - value of MUMPS CNTL(icntl) 2550 2551 Level: beginner 2552 2553 References: 2554 . * - MUMPS Users' Guide 2555 2556 .seealso: `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()` 2557 @*/ 2558 PetscErrorCode MatMumpsGetCntl(Mat F,PetscInt icntl,PetscReal *val) 2559 { 2560 PetscFunctionBegin; 2561 PetscValidType(F,1); 2562 PetscCheck(F->factortype,PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2563 PetscValidLogicalCollectiveInt(F,icntl,2); 2564 PetscValidRealPointer(val,3); 2565 PetscUseMethod(F,"MatMumpsGetCntl_C",(Mat,PetscInt,PetscReal*),(F,icntl,val)); 2566 PetscFunctionReturn(0); 2567 } 2568 2569 PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F,PetscInt icntl,PetscInt *info) 2570 { 2571 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2572 2573 PetscFunctionBegin; 2574 *info = mumps->id.INFO(icntl); 2575 PetscFunctionReturn(0); 2576 } 2577 2578 PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F,PetscInt icntl,PetscInt *infog) 2579 { 2580 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2581 2582 PetscFunctionBegin; 2583 *infog = mumps->id.INFOG(icntl); 2584 PetscFunctionReturn(0); 2585 } 2586 2587 PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfo) 2588 { 2589 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2590 2591 PetscFunctionBegin; 2592 *rinfo = mumps->id.RINFO(icntl); 2593 PetscFunctionReturn(0); 2594 } 2595 2596 PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfog) 2597 { 2598 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2599 2600 PetscFunctionBegin; 2601 *rinfog = mumps->id.RINFOG(icntl); 2602 PetscFunctionReturn(0); 2603 } 2604 2605 PetscErrorCode MatMumpsGetInverse_MUMPS(Mat F,Mat spRHS) 2606 { 2607 Mat Bt = NULL,Btseq = NULL; 2608 PetscBool flg; 2609 Mat_MUMPS *mumps =(Mat_MUMPS*)F->data; 2610 PetscScalar *aa; 2611 PetscInt spnr,*ia,*ja,M,nrhs; 2612 2613 PetscFunctionBegin; 2614 PetscValidPointer(spRHS,2); 2615 PetscCall(PetscObjectTypeCompare((PetscObject)spRHS,MATTRANSPOSEMAT,&flg)); 2616 if (flg) { 2617 PetscCall(MatTransposeGetMat(spRHS,&Bt)); 2618 } else SETERRQ(PetscObjectComm((PetscObject)spRHS),PETSC_ERR_ARG_WRONG,"Matrix spRHS must be type MATTRANSPOSEMAT matrix"); 2619 2620 PetscCall(MatMumpsSetIcntl(F,30,1)); 2621 2622 if (mumps->petsc_size > 1) { 2623 Mat_MPIAIJ *b = (Mat_MPIAIJ*)Bt->data; 2624 Btseq = b->A; 2625 } else { 2626 Btseq = Bt; 2627 } 2628 2629 PetscCall(MatGetSize(spRHS,&M,&nrhs)); 2630 mumps->id.nrhs = nrhs; 2631 mumps->id.lrhs = M; 2632 mumps->id.rhs = NULL; 2633 2634 if (!mumps->myid) { 2635 PetscCall(MatSeqAIJGetArray(Btseq,&aa)); 2636 PetscCall(MatGetRowIJ(Btseq,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg)); 2637 PetscCheck(flg,PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 2638 PetscCall(PetscMUMPSIntCSRCast(mumps,spnr,ia,ja,&mumps->id.irhs_ptr,&mumps->id.irhs_sparse,&mumps->id.nz_rhs)); 2639 mumps->id.rhs_sparse = (MumpsScalar*)aa; 2640 } else { 2641 mumps->id.irhs_ptr = NULL; 2642 mumps->id.irhs_sparse = NULL; 2643 mumps->id.nz_rhs = 0; 2644 mumps->id.rhs_sparse = NULL; 2645 } 2646 mumps->id.ICNTL(20) = 1; /* rhs is sparse */ 2647 mumps->id.ICNTL(21) = 0; /* solution is in assembled centralized format */ 2648 2649 /* solve phase */ 2650 /*-------------*/ 2651 mumps->id.job = JOB_SOLVE; 2652 PetscMUMPS_c(mumps); 2653 if (mumps->id.INFOG(1) < 0) 2654 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d INFO(2)=%d",mumps->id.INFOG(1),mumps->id.INFO(2)); 2655 2656 if (!mumps->myid) { 2657 PetscCall(MatSeqAIJRestoreArray(Btseq,&aa)); 2658 PetscCall(MatRestoreRowIJ(Btseq,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg)); 2659 PetscCheck(flg,PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure"); 2660 } 2661 PetscFunctionReturn(0); 2662 } 2663 2664 /*@ 2665 MatMumpsGetInverse - Get user-specified set of entries in inverse of A 2666 2667 Logically Collective on Mat 2668 2669 Input Parameters: 2670 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2671 - spRHS - sequential sparse matrix in MATTRANSPOSEMAT format holding specified indices in processor[0] 2672 2673 Output Parameter: 2674 . spRHS - requested entries of inverse of A 2675 2676 Level: beginner 2677 2678 References: 2679 . * - MUMPS Users' Guide 2680 2681 .seealso: `MatGetFactor()`, `MatCreateTranspose()` 2682 @*/ 2683 PetscErrorCode MatMumpsGetInverse(Mat F,Mat spRHS) 2684 { 2685 PetscFunctionBegin; 2686 PetscValidType(F,1); 2687 PetscCheck(F->factortype,PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2688 PetscUseMethod(F,"MatMumpsGetInverse_C",(Mat,Mat),(F,spRHS)); 2689 PetscFunctionReturn(0); 2690 } 2691 2692 PetscErrorCode MatMumpsGetInverseTranspose_MUMPS(Mat F,Mat spRHST) 2693 { 2694 Mat spRHS; 2695 2696 PetscFunctionBegin; 2697 PetscCall(MatCreateTranspose(spRHST,&spRHS)); 2698 PetscCall(MatMumpsGetInverse_MUMPS(F,spRHS)); 2699 PetscCall(MatDestroy(&spRHS)); 2700 PetscFunctionReturn(0); 2701 } 2702 2703 /*@ 2704 MatMumpsGetInverseTranspose - Get user-specified set of entries in inverse of matrix A^T 2705 2706 Logically Collective on Mat 2707 2708 Input Parameters: 2709 + F - the factored matrix of A obtained by calling MatGetFactor() from PETSc-MUMPS interface 2710 - spRHST - sequential sparse matrix in MATAIJ format holding specified indices of A^T in processor[0] 2711 2712 Output Parameter: 2713 . spRHST - requested entries of inverse of A^T 2714 2715 Level: beginner 2716 2717 References: 2718 . * - MUMPS Users' Guide 2719 2720 .seealso: `MatGetFactor()`, `MatCreateTranspose()`, `MatMumpsGetInverse()` 2721 @*/ 2722 PetscErrorCode MatMumpsGetInverseTranspose(Mat F,Mat spRHST) 2723 { 2724 PetscBool flg; 2725 2726 PetscFunctionBegin; 2727 PetscValidType(F,1); 2728 PetscCheck(F->factortype,PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2729 PetscCall(PetscObjectTypeCompareAny((PetscObject)spRHST,&flg,MATSEQAIJ,MATMPIAIJ,NULL)); 2730 PetscCheck(flg,PetscObjectComm((PetscObject)spRHST),PETSC_ERR_ARG_WRONG,"Matrix spRHST must be MATAIJ matrix"); 2731 2732 PetscUseMethod(F,"MatMumpsGetInverseTranspose_C",(Mat,Mat),(F,spRHST)); 2733 PetscFunctionReturn(0); 2734 } 2735 2736 /*@ 2737 MatMumpsGetInfo - Get MUMPS parameter INFO() 2738 2739 Logically Collective on Mat 2740 2741 Input Parameters: 2742 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2743 - icntl - index of MUMPS parameter array INFO() 2744 2745 Output Parameter: 2746 . ival - value of MUMPS INFO(icntl) 2747 2748 Level: beginner 2749 2750 References: 2751 . * - MUMPS Users' Guide 2752 2753 .seealso: `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()` 2754 @*/ 2755 PetscErrorCode MatMumpsGetInfo(Mat F,PetscInt icntl,PetscInt *ival) 2756 { 2757 PetscFunctionBegin; 2758 PetscValidType(F,1); 2759 PetscCheck(F->factortype,PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2760 PetscValidIntPointer(ival,3); 2761 PetscUseMethod(F,"MatMumpsGetInfo_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival)); 2762 PetscFunctionReturn(0); 2763 } 2764 2765 /*@ 2766 MatMumpsGetInfog - Get MUMPS parameter INFOG() 2767 2768 Logically Collective on Mat 2769 2770 Input Parameters: 2771 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2772 - icntl - index of MUMPS parameter array INFOG() 2773 2774 Output Parameter: 2775 . ival - value of MUMPS INFOG(icntl) 2776 2777 Level: beginner 2778 2779 References: 2780 . * - MUMPS Users' Guide 2781 2782 .seealso: `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()` 2783 @*/ 2784 PetscErrorCode MatMumpsGetInfog(Mat F,PetscInt icntl,PetscInt *ival) 2785 { 2786 PetscFunctionBegin; 2787 PetscValidType(F,1); 2788 PetscCheck(F->factortype,PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2789 PetscValidIntPointer(ival,3); 2790 PetscUseMethod(F,"MatMumpsGetInfog_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival)); 2791 PetscFunctionReturn(0); 2792 } 2793 2794 /*@ 2795 MatMumpsGetRinfo - Get MUMPS parameter RINFO() 2796 2797 Logically Collective on Mat 2798 2799 Input Parameters: 2800 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2801 - icntl - index of MUMPS parameter array RINFO() 2802 2803 Output Parameter: 2804 . val - value of MUMPS RINFO(icntl) 2805 2806 Level: beginner 2807 2808 References: 2809 . * - MUMPS Users' Guide 2810 2811 .seealso: `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfog()` 2812 @*/ 2813 PetscErrorCode MatMumpsGetRinfo(Mat F,PetscInt icntl,PetscReal *val) 2814 { 2815 PetscFunctionBegin; 2816 PetscValidType(F,1); 2817 PetscCheck(F->factortype,PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2818 PetscValidRealPointer(val,3); 2819 PetscUseMethod(F,"MatMumpsGetRinfo_C",(Mat,PetscInt,PetscReal*),(F,icntl,val)); 2820 PetscFunctionReturn(0); 2821 } 2822 2823 /*@ 2824 MatMumpsGetRinfog - Get MUMPS parameter RINFOG() 2825 2826 Logically Collective on Mat 2827 2828 Input Parameters: 2829 + F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface 2830 - icntl - index of MUMPS parameter array RINFOG() 2831 2832 Output Parameter: 2833 . val - value of MUMPS RINFOG(icntl) 2834 2835 Level: beginner 2836 2837 References: 2838 . * - MUMPS Users' Guide 2839 2840 .seealso: `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()` 2841 @*/ 2842 PetscErrorCode MatMumpsGetRinfog(Mat F,PetscInt icntl,PetscReal *val) 2843 { 2844 PetscFunctionBegin; 2845 PetscValidType(F,1); 2846 PetscCheck(F->factortype,PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 2847 PetscValidRealPointer(val,3); 2848 PetscUseMethod(F,"MatMumpsGetRinfog_C",(Mat,PetscInt,PetscReal*),(F,icntl,val)); 2849 PetscFunctionReturn(0); 2850 } 2851 2852 /*MC 2853 MATSOLVERMUMPS - A matrix type providing direct solvers (LU and Cholesky) for 2854 distributed and sequential matrices via the external package MUMPS. 2855 2856 Works with MATAIJ and MATSBAIJ matrices 2857 2858 Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch to have PETSc installed with MUMPS 2859 2860 Use ./configure --with-openmp --download-hwloc (or --with-hwloc) to enable running MUMPS in MPI+OpenMP hybrid mode and non-MUMPS in flat-MPI mode. See details below. 2861 2862 Use -pc_type cholesky or lu -pc_factor_mat_solver_type mumps to use this direct solver 2863 2864 Options Database Keys: 2865 + -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages 2866 . -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning 2867 . -mat_mumps_icntl_3 - ICNTL(3): output stream for global information, collected on the host 2868 . -mat_mumps_icntl_4 - ICNTL(4): level of printing (0 to 4) 2869 . -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7) 2870 . -mat_mumps_icntl_7 - ICNTL(7): computes a symmetric permutation in sequential analysis, 0=AMD, 2=AMF, 3=Scotch, 4=PORD, 5=Metis, 6=QAMD, and 7=auto 2871 Use -pc_factor_mat_ordering_type <type> to have PETSc perform the ordering (sequential only) 2872 . -mat_mumps_icntl_8 - ICNTL(8): scaling strategy (-2 to 8 or 77) 2873 . -mat_mumps_icntl_10 - ICNTL(10): max num of refinements 2874 . -mat_mumps_icntl_11 - ICNTL(11): statistics related to an error analysis (via -ksp_view) 2875 . -mat_mumps_icntl_12 - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3) 2876 . -mat_mumps_icntl_13 - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting 2877 . -mat_mumps_icntl_14 - ICNTL(14): percentage increase in the estimated working space 2878 . -mat_mumps_icntl_15 - ICNTL(15): compression of the input matrix resulting from a block format 2879 . -mat_mumps_icntl_19 - ICNTL(19): computes the Schur complement 2880 . -mat_mumps_icntl_20 - ICNTL(20): give MUMPS centralized (0) or distributed (10) dense RHS 2881 . -mat_mumps_icntl_22 - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1) 2882 . -mat_mumps_icntl_23 - ICNTL(23): max size of the working memory (MB) that can allocate per processor 2883 . -mat_mumps_icntl_24 - ICNTL(24): detection of null pivot rows (0 or 1) 2884 . -mat_mumps_icntl_25 - ICNTL(25): compute a solution of a deficient matrix and a null space basis 2885 . -mat_mumps_icntl_26 - ICNTL(26): drives the solution phase if a Schur complement matrix 2886 . -mat_mumps_icntl_28 - ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering 2887 . -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis 2888 . -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A) 2889 . -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization 2890 . -mat_mumps_icntl_33 - ICNTL(33): compute determinant 2891 . -mat_mumps_icntl_35 - ICNTL(35): level of activation of BLR (Block Low-Rank) feature 2892 . -mat_mumps_icntl_36 - ICNTL(36): controls the choice of BLR factorization variant 2893 . -mat_mumps_icntl_38 - ICNTL(38): sets the estimated compression rate of LU factors with BLR 2894 . -mat_mumps_cntl_1 - CNTL(1): relative pivoting threshold 2895 . -mat_mumps_cntl_2 - CNTL(2): stopping criterion of refinement 2896 . -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold 2897 . -mat_mumps_cntl_4 - CNTL(4): value for static pivoting 2898 . -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots 2899 . -mat_mumps_cntl_7 - CNTL(7): precision of the dropping parameter used during BLR factorization 2900 - -mat_mumps_use_omp_threads [m] - run MUMPS in MPI+OpenMP hybrid mode as if omp_set_num_threads(m) is called before calling MUMPS. 2901 Default might be the number of cores per CPU package (socket) as reported by hwloc and suggested by the MUMPS manual. 2902 2903 Level: beginner 2904 2905 Notes: 2906 MUMPS Cholesky does not handle (complex) Hermitian matrices http://mumps.enseeiht.fr/doc/userguide_5.2.1.pdf so using it will error if the matrix is Hermitian. 2907 2908 When used within a `KSP`/`PC` solve the options are prefixed with that of the `PC`. Otherwise one can set the options prefix by calling 2909 `MatSetOptionsPrefixFactor()` on the matrix from which the factor was obtained or `MatSetOptionsPrefix()` on the factor matrix. 2910 2911 When a MUMPS factorization fails inside a KSP solve, for example with a KSP_DIVERGED_PC_FAILED, one can find the MUMPS information about the failure by calling 2912 $ KSPGetPC(ksp,&pc); 2913 $ PCFactorGetMatrix(pc,&mat); 2914 $ MatMumpsGetInfo(mat,....); 2915 $ MatMumpsGetInfog(mat,....); etc. 2916 Or you can run with -ksp_error_if_not_converged and the program will be stopped and the information printed in the error message. 2917 2918 Using MUMPS with 64-bit integers 2919 MUMPS provides 64-bit integer support in two build modes: 2920 full 64-bit: here MUMPS is built with C preprocessing flag -DINTSIZE64 and Fortran compiler option -i8, -fdefault-integer-8 or equivalent, and 2921 requires all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS built the same way with 64-bit integers (for example ILP64 Intel MKL and MPI). 2922 2923 selective 64-bit: with the default MUMPS build, 64-bit integers have been introduced where needed. In compressed sparse row (CSR) storage of matrices, 2924 MUMPS stores column indices in 32-bit, but row offsets in 64-bit, so you can have a huge number of non-zeros, but must have less than 2^31 rows and 2925 columns. This can lead to significant memory and performance gains with respect to a full 64-bit integer MUMPS version. This requires a regular (32-bit 2926 integer) build of all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS. 2927 2928 With --download-mumps=1, PETSc always build MUMPS in selective 64-bit mode, which can be used by both --with-64-bit-indices=0/1 variants of PETSc. 2929 2930 Two modes to run MUMPS/PETSc with OpenMP 2931 $ Set OMP_NUM_THREADS and run with fewer MPI ranks than cores. For example, if you want to have 16 OpenMP 2932 $ threads per rank, then you may use "export OMP_NUM_THREADS=16 && mpirun -n 4 ./test". 2933 2934 $ -mat_mumps_use_omp_threads [m] and run your code with as many MPI ranks as the number of cores. For example, 2935 $ if a compute node has 32 cores and you run on two nodes, you may use "mpirun -n 64 ./test -mat_mumps_use_omp_threads 16" 2936 2937 To run MUMPS in MPI+OpenMP hybrid mode (i.e., enable multithreading in MUMPS), but still run the non-MUMPS part 2938 (i.e., PETSc part) of your code in the so-called flat-MPI (aka pure-MPI) mode, you need to configure PETSc with --with-openmp --download-hwloc 2939 (or --with-hwloc), and have an MPI that supports MPI-3.0's process shared memory (which is usually available). Since MUMPS calls BLAS 2940 libraries, to really get performance, you should have multithreaded BLAS libraries such as Intel MKL, AMD ACML, Cray libSci or OpenBLAS 2941 (PETSc will automatically try to utilized a threaded BLAS if --with-openmp is provided). 2942 2943 If you run your code through a job submission system, there are caveats in MPI rank mapping. We use MPI_Comm_split_type() to obtain MPI 2944 processes on each compute node. Listing the processes in rank ascending order, we split processes on a node into consecutive groups of 2945 size m and create a communicator called omp_comm for each group. Rank 0 in an omp_comm is called the master rank, and others in the omp_comm 2946 are called slave ranks (or slaves). Only master ranks are seen to MUMPS and slaves are not. We will free CPUs assigned to slaves (might be set 2947 by CPU binding policies in job scripts) and make the CPUs available to the master so that OMP threads spawned by MUMPS can run on the CPUs. 2948 In a multi-socket compute node, MPI rank mapping is an issue. Still use the above example and suppose your compute node has two sockets, 2949 if you interleave MPI ranks on the two sockets, in other words, even ranks are placed on socket 0, and odd ranks are on socket 1, and bind 2950 MPI ranks to cores, then with -mat_mumps_use_omp_threads 16, a master rank (and threads it spawns) will use half cores in socket 0, and half 2951 cores in socket 1, that definitely hurts locality. On the other hand, if you map MPI ranks consecutively on the two sockets, then the 2952 problem will not happen. Therefore, when you use -mat_mumps_use_omp_threads, you need to keep an eye on your MPI rank mapping and CPU binding. 2953 For example, with the Slurm job scheduler, one can use srun --cpu-bind=verbose -m block:block to map consecutive MPI ranks to sockets and 2954 examine the mapping result. 2955 2956 PETSc does not control thread binding in MUMPS. So to get best performance, one still has to set OMP_PROC_BIND and OMP_PLACES in job scripts, 2957 for example, export OMP_PLACES=threads and export OMP_PROC_BIND=spread. One does not need to export OMP_NUM_THREADS=m in job scripts as PETSc 2958 calls omp_set_num_threads(m) internally before calling MUMPS. 2959 2960 References: 2961 + * - Heroux, Michael A., R. Brightwell, and Michael M. Wolf. "Bi-modal MPI and MPI+ threads computing on scalable multicore systems." IJHPCA (Submitted) (2011). 2962 - * - Gutierrez, Samuel K., et al. "Accommodating Thread-Level Heterogeneity in Coupled Parallel Applications." Parallel and Distributed Processing Symposium (IPDPS), 2017 IEEE International. IEEE, 2017. 2963 2964 .seealso: `PCFactorSetMatSolverType()`, `MatSolverType`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`, `KSPGetPC()`, `PCFactorGetMatrix()` 2965 2966 M*/ 2967 2968 static PetscErrorCode MatFactorGetSolverType_mumps(Mat A,MatSolverType *type) 2969 { 2970 PetscFunctionBegin; 2971 *type = MATSOLVERMUMPS; 2972 PetscFunctionReturn(0); 2973 } 2974 2975 /* MatGetFactor for Seq and MPI AIJ matrices */ 2976 static PetscErrorCode MatGetFactor_aij_mumps(Mat A,MatFactorType ftype,Mat *F) 2977 { 2978 Mat B; 2979 Mat_MUMPS *mumps; 2980 PetscBool isSeqAIJ; 2981 PetscMPIInt size; 2982 2983 PetscFunctionBegin; 2984 #if defined(PETSC_USE_COMPLEX) 2985 PetscCheck(!A->hermitian || A->symmetric || ftype != MAT_FACTOR_CHOLESKY,PETSC_COMM_SELF,PETSC_ERR_SUP,"Hermitian CHOLESKY Factor is not supported"); 2986 #endif 2987 /* Create the factorization matrix */ 2988 PetscCall(PetscObjectBaseTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ)); 2989 PetscCall(MatCreate(PetscObjectComm((PetscObject)A),&B)); 2990 PetscCall(MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N)); 2991 PetscCall(PetscStrallocpy("mumps",&((PetscObject)B)->type_name)); 2992 PetscCall(MatSetUp(B)); 2993 2994 PetscCall(PetscNewLog(B,&mumps)); 2995 2996 B->ops->view = MatView_MUMPS; 2997 B->ops->getinfo = MatGetInfo_MUMPS; 2998 2999 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps)); 3000 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS)); 3001 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS)); 3002 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS)); 3003 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS)); 3004 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS)); 3005 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS)); 3006 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS)); 3007 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS)); 3008 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS)); 3009 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS)); 3010 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS)); 3011 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverseTranspose_C",MatMumpsGetInverseTranspose_MUMPS)); 3012 3013 if (ftype == MAT_FACTOR_LU) { 3014 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS; 3015 B->factortype = MAT_FACTOR_LU; 3016 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij; 3017 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij; 3018 PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL,(char**)&B->preferredordering[MAT_FACTOR_LU])); 3019 mumps->sym = 0; 3020 } else { 3021 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 3022 B->factortype = MAT_FACTOR_CHOLESKY; 3023 if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij; 3024 else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij; 3025 PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL,(char**)&B->preferredordering[MAT_FACTOR_CHOLESKY])); 3026 #if defined(PETSC_USE_COMPLEX) 3027 mumps->sym = 2; 3028 #else 3029 if (A->spd_set && A->spd) mumps->sym = 1; 3030 else mumps->sym = 2; 3031 #endif 3032 } 3033 3034 /* set solvertype */ 3035 PetscCall(PetscFree(B->solvertype)); 3036 PetscCall(PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype)); 3037 PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A),&size)); 3038 if (size == 1) { 3039 /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */ 3040 B->canuseordering = PETSC_TRUE; 3041 } 3042 B->ops->destroy = MatDestroy_MUMPS; 3043 B->data = (void*)mumps; 3044 3045 PetscCall(MatSetFromOptions_MUMPS_OpenMP(B,A)); 3046 3047 *F = B; 3048 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 3049 PetscFunctionReturn(0); 3050 } 3051 3052 /* MatGetFactor for Seq and MPI SBAIJ matrices */ 3053 static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A,MatFactorType ftype,Mat *F) 3054 { 3055 Mat B; 3056 Mat_MUMPS *mumps; 3057 PetscBool isSeqSBAIJ; 3058 PetscMPIInt size; 3059 3060 PetscFunctionBegin; 3061 #if defined(PETSC_USE_COMPLEX) 3062 PetscCheck(!A->hermitian || A->symmetric,PETSC_COMM_SELF,PETSC_ERR_SUP,"Hermitian CHOLESKY Factor is not supported"); 3063 #endif 3064 PetscCall(MatCreate(PetscObjectComm((PetscObject)A),&B)); 3065 PetscCall(MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N)); 3066 PetscCall(PetscStrallocpy("mumps",&((PetscObject)B)->type_name)); 3067 PetscCall(MatSetUp(B)); 3068 3069 PetscCall(PetscNewLog(B,&mumps)); 3070 PetscCall(PetscObjectTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ)); 3071 if (isSeqSBAIJ) { 3072 mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij; 3073 } else { 3074 mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij; 3075 } 3076 3077 B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS; 3078 B->ops->view = MatView_MUMPS; 3079 B->ops->getinfo = MatGetInfo_MUMPS; 3080 3081 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps)); 3082 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS)); 3083 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS)); 3084 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS)); 3085 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS)); 3086 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS)); 3087 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS)); 3088 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS)); 3089 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS)); 3090 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS)); 3091 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS)); 3092 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS)); 3093 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverseTranspose_C",MatMumpsGetInverseTranspose_MUMPS)); 3094 3095 B->factortype = MAT_FACTOR_CHOLESKY; 3096 #if defined(PETSC_USE_COMPLEX) 3097 mumps->sym = 2; 3098 #else 3099 if (A->spd_set && A->spd) mumps->sym = 1; 3100 else mumps->sym = 2; 3101 #endif 3102 3103 /* set solvertype */ 3104 PetscCall(PetscFree(B->solvertype)); 3105 PetscCall(PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype)); 3106 PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A),&size)); 3107 if (size == 1) { 3108 /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */ 3109 B->canuseordering = PETSC_TRUE; 3110 } 3111 PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL,(char**)&B->preferredordering[MAT_FACTOR_CHOLESKY])); 3112 B->ops->destroy = MatDestroy_MUMPS; 3113 B->data = (void*)mumps; 3114 3115 PetscCall(MatSetFromOptions_MUMPS_OpenMP(B,A)); 3116 3117 *F = B; 3118 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 3119 PetscFunctionReturn(0); 3120 } 3121 3122 static PetscErrorCode MatGetFactor_baij_mumps(Mat A,MatFactorType ftype,Mat *F) 3123 { 3124 Mat B; 3125 Mat_MUMPS *mumps; 3126 PetscBool isSeqBAIJ; 3127 PetscMPIInt size; 3128 3129 PetscFunctionBegin; 3130 /* Create the factorization matrix */ 3131 PetscCall(PetscObjectTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ)); 3132 PetscCall(MatCreate(PetscObjectComm((PetscObject)A),&B)); 3133 PetscCall(MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N)); 3134 PetscCall(PetscStrallocpy("mumps",&((PetscObject)B)->type_name)); 3135 PetscCall(MatSetUp(B)); 3136 3137 PetscCall(PetscNewLog(B,&mumps)); 3138 if (ftype == MAT_FACTOR_LU) { 3139 B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS; 3140 B->factortype = MAT_FACTOR_LU; 3141 if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij; 3142 else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij; 3143 mumps->sym = 0; 3144 PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL,(char**)&B->preferredordering[MAT_FACTOR_LU])); 3145 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead"); 3146 3147 B->ops->view = MatView_MUMPS; 3148 B->ops->getinfo = MatGetInfo_MUMPS; 3149 3150 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps)); 3151 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS)); 3152 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS)); 3153 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS)); 3154 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS)); 3155 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS)); 3156 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS)); 3157 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS)); 3158 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS)); 3159 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS)); 3160 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS)); 3161 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS)); 3162 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverseTranspose_C",MatMumpsGetInverseTranspose_MUMPS)); 3163 3164 /* set solvertype */ 3165 PetscCall(PetscFree(B->solvertype)); 3166 PetscCall(PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype)); 3167 PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A),&size)); 3168 if (size == 1) { 3169 /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */ 3170 B->canuseordering = PETSC_TRUE; 3171 } 3172 B->ops->destroy = MatDestroy_MUMPS; 3173 B->data = (void*)mumps; 3174 3175 PetscCall(MatSetFromOptions_MUMPS_OpenMP(B,A)); 3176 3177 *F = B; 3178 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 3179 PetscFunctionReturn(0); 3180 } 3181 3182 /* MatGetFactor for Seq and MPI SELL matrices */ 3183 static PetscErrorCode MatGetFactor_sell_mumps(Mat A,MatFactorType ftype,Mat *F) 3184 { 3185 Mat B; 3186 Mat_MUMPS *mumps; 3187 PetscBool isSeqSELL; 3188 PetscMPIInt size; 3189 3190 PetscFunctionBegin; 3191 /* Create the factorization matrix */ 3192 PetscCall(PetscObjectTypeCompare((PetscObject)A,MATSEQSELL,&isSeqSELL)); 3193 PetscCall(MatCreate(PetscObjectComm((PetscObject)A),&B)); 3194 PetscCall(MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N)); 3195 PetscCall(PetscStrallocpy("mumps",&((PetscObject)B)->type_name)); 3196 PetscCall(MatSetUp(B)); 3197 3198 PetscCall(PetscNewLog(B,&mumps)); 3199 3200 B->ops->view = MatView_MUMPS; 3201 B->ops->getinfo = MatGetInfo_MUMPS; 3202 3203 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps)); 3204 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS)); 3205 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS)); 3206 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS)); 3207 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS)); 3208 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS)); 3209 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS)); 3210 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS)); 3211 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS)); 3212 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS)); 3213 PetscCall(PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS)); 3214 3215 if (ftype == MAT_FACTOR_LU) { 3216 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS; 3217 B->factortype = MAT_FACTOR_LU; 3218 if (isSeqSELL) mumps->ConvertToTriples = MatConvertToTriples_seqsell_seqaij; 3219 else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"To be implemented"); 3220 mumps->sym = 0; 3221 PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL,(char**)&B->preferredordering[MAT_FACTOR_LU])); 3222 } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"To be implemented"); 3223 3224 /* set solvertype */ 3225 PetscCall(PetscFree(B->solvertype)); 3226 PetscCall(PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype)); 3227 PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A),&size)); 3228 if (size == 1) { 3229 /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */ 3230 B->canuseordering = PETSC_TRUE; 3231 } 3232 B->ops->destroy = MatDestroy_MUMPS; 3233 B->data = (void*)mumps; 3234 3235 PetscCall(MatSetFromOptions_MUMPS_OpenMP(B,A)); 3236 3237 *F = B; 3238 mumps->matstruc = DIFFERENT_NONZERO_PATTERN; 3239 PetscFunctionReturn(0); 3240 } 3241 3242 PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MUMPS(void) 3243 { 3244 PetscFunctionBegin; 3245 PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps)); 3246 PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps)); 3247 PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps)); 3248 PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps)); 3249 PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS,MATMPISBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps)); 3250 PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps)); 3251 PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps)); 3252 PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps)); 3253 PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps)); 3254 PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQSBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps)); 3255 PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQSELL,MAT_FACTOR_LU,MatGetFactor_sell_mumps)); 3256 PetscFunctionReturn(0); 3257 } 3258