Actual source code: mumps.c


  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>
  7: #include <../src/mat/impls/sbaij/mpi/mpisbaij.h>
  8: #include <../src/mat/impls/sell/mpi/mpisell.h>

 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_NULL         0
 27: #define JOB_FACTSYMBOLIC 1
 28: #define JOB_FACTNUMERIC  2
 29: #define JOB_SOLVE        3
 30: #define JOB_END          -2

 32: /* calls to MUMPS */
 33: #if defined(PETSC_USE_COMPLEX)
 34:   #if defined(PETSC_USE_REAL_SINGLE)
 35:     #define MUMPS_c cmumps_c
 36:   #else
 37:     #define MUMPS_c zmumps_c
 38:   #endif
 39: #else
 40:   #if defined(PETSC_USE_REAL_SINGLE)
 41:     #define MUMPS_c smumps_c
 42:   #else
 43:     #define MUMPS_c dmumps_c
 44:   #endif
 45: #endif

 47: /* MUMPS uses MUMPS_INT for nonzero indices such as irn/jcn, irn_loc/jcn_loc and uses int64_t for
 48:    number of nonzeros such as nnz, nnz_loc. We typedef MUMPS_INT to PetscMUMPSInt to follow the
 49:    naming convention in PetscMPIInt, PetscBLASInt etc.
 50: */
 51: typedef MUMPS_INT PetscMUMPSInt;

 53: #if PETSC_PKG_MUMPS_VERSION_GE(5, 3, 0)
 54:   #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 */
 55:     #error "Petsc has not been tested with full 64-bit MUMPS and we choose to error out"
 56:   #endif
 57: #else
 58:   #if defined(INTSIZE64) /* INTSIZE64 is a command line macro one used to build MUMPS in full 64-bit mode */
 59:     #error "Petsc has not been tested with full 64-bit MUMPS and we choose to error out"
 60:   #endif
 61: #endif

 63: #define MPIU_MUMPSINT       MPI_INT
 64: #define PETSC_MUMPS_INT_MAX 2147483647
 65: #define PETSC_MUMPS_INT_MIN -2147483648

 67: /* Cast PetscInt to PetscMUMPSInt. Usually there is no overflow since <a> is row/col indices or some small integers*/
 68: static inline PetscErrorCode PetscMUMPSIntCast(PetscInt a, PetscMUMPSInt *b)
 69: {
 70:   PetscFunctionBegin;
 71: #if PetscDefined(USE_64BIT_INDICES)
 72:   PetscAssert(a <= PETSC_MUMPS_INT_MAX && a >= PETSC_MUMPS_INT_MIN, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscInt too long for PetscMUMPSInt");
 73: #endif
 74:   *b = (PetscMUMPSInt)(a);
 75:   PetscFunctionReturn(PETSC_SUCCESS);
 76: }

 78: /* Put these utility routines here since they are only used in this file */
 79: 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)
 80: {
 81:   PetscInt  myval;
 82:   PetscBool myset;
 83:   PetscFunctionBegin;
 84:   /* PetscInt's size should be always >= PetscMUMPSInt's. It is safe to call PetscOptionsInt_Private to read a PetscMUMPSInt */
 85:   PetscCall(PetscOptionsInt_Private(PetscOptionsObject, opt, text, man, (PetscInt)currentvalue, &myval, &myset, lb, ub));
 86:   if (myset) PetscCall(PetscMUMPSIntCast(myval, value));
 87:   if (set) *set = myset;
 88:   PetscFunctionReturn(PETSC_SUCCESS);
 89: }
 90: #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)

 92: /* 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 */
 93: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
 94:   #define PetscMUMPS_c(mumps) \
 95:     do { \
 96:       if (mumps->use_petsc_omp_support) { \
 97:         if (mumps->is_omp_master) { \
 98:           PetscCall(PetscOmpCtrlOmpRegionOnMasterBegin(mumps->omp_ctrl)); \
 99:           PetscCall(PetscFPTrapPush(PETSC_FP_TRAP_OFF)); \
100:           PetscStackCallExternalVoid(PetscStringize(MUMPS_c), MUMPS_c(&mumps->id)); \
101:           PetscCall(PetscFPTrapPop()); \
102:           PetscCall(PetscOmpCtrlOmpRegionOnMasterEnd(mumps->omp_ctrl)); \
103:         } \
104:         PetscCall(PetscOmpCtrlBarrier(mumps->omp_ctrl)); \
105:         /* Global info is same on all processes so we Bcast it within omp_comm. Local info is specific      \
106:          to processes, so we only Bcast info[1], an error code and leave others (since they do not have   \
107:          an easy translation between omp_comm and petsc_comm). See MUMPS-5.1.2 manual p82.                   \
108:          omp_comm is a small shared memory communicator, hence doing multiple Bcast as shown below is OK. \
109:       */ \
110:         PetscCallMPI(MPI_Bcast(mumps->id.infog, PETSC_STATIC_ARRAY_LENGTH(mumps->id.infog), MPIU_MUMPSINT, 0, mumps->omp_comm)); \
111:         PetscCallMPI(MPI_Bcast(mumps->id.rinfog, PETSC_STATIC_ARRAY_LENGTH(mumps->id.rinfog), MPIU_REAL, 0, mumps->omp_comm)); \
112:         PetscCallMPI(MPI_Bcast(mumps->id.info, PETSC_STATIC_ARRAY_LENGTH(mumps->id.info), MPIU_MUMPSINT, 0, mumps->omp_comm)); \
113:         PetscCallMPI(MPI_Bcast(mumps->id.rinfo, PETSC_STATIC_ARRAY_LENGTH(mumps->id.rinfo), MPIU_REAL, 0, mumps->omp_comm)); \
114:       } else { \
115:         PetscCall(PetscFPTrapPush(PETSC_FP_TRAP_OFF)); \
116:         PetscStackCallExternalVoid(PetscStringize(MUMPS_c), MUMPS_c(&mumps->id)); \
117:         PetscCall(PetscFPTrapPop()); \
118:       } \
119:     } while (0)
120: #else
121:   #define PetscMUMPS_c(mumps) \
122:     do { \
123:       PetscCall(PetscFPTrapPush(PETSC_FP_TRAP_OFF)); \
124:       PetscStackCallExternalVoid(PetscStringize(MUMPS_c), MUMPS_c(&mumps->id)); \
125:       PetscCall(PetscFPTrapPop()); \
126:     } while (0)
127: #endif

129: /* declare MumpsScalar */
130: #if defined(PETSC_USE_COMPLEX)
131:   #if defined(PETSC_USE_REAL_SINGLE)
132:     #define MumpsScalar mumps_complex
133:   #else
134:     #define MumpsScalar mumps_double_complex
135:   #endif
136: #else
137:   #define MumpsScalar PetscScalar
138: #endif

140: /* macros s.t. indices match MUMPS documentation */
141: #define ICNTL(I)  icntl[(I)-1]
142: #define CNTL(I)   cntl[(I)-1]
143: #define INFOG(I)  infog[(I)-1]
144: #define INFO(I)   info[(I)-1]
145: #define RINFOG(I) rinfog[(I)-1]
146: #define RINFO(I)  rinfo[(I)-1]

148: typedef struct Mat_MUMPS Mat_MUMPS;
149: struct Mat_MUMPS {
150: #if defined(PETSC_USE_COMPLEX)
151:   #if defined(PETSC_USE_REAL_SINGLE)
152:   CMUMPS_STRUC_C id;
153:   #else
154:   ZMUMPS_STRUC_C id;
155:   #endif
156: #else
157:   #if defined(PETSC_USE_REAL_SINGLE)
158:   SMUMPS_STRUC_C id;
159:   #else
160:   DMUMPS_STRUC_C id;
161:   #endif
162: #endif

164:   MatStructure   matstruc;
165:   PetscMPIInt    myid, petsc_size;
166:   PetscMUMPSInt *irn, *jcn;       /* the (i,j,v) triplets passed to mumps. */
167:   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. */
168:   PetscInt64     nnz;             /* number of nonzeros. The type is called selective 64-bit in mumps */
169:   PetscMUMPSInt  sym;
170:   MPI_Comm       mumps_comm;
171:   PetscMUMPSInt *ICNTL_pre;
172:   PetscReal     *CNTL_pre;
173:   PetscMUMPSInt  ICNTL9_pre;         /* check if ICNTL(9) is changed from previous MatSolve */
174:   VecScatter     scat_rhs, scat_sol; /* used by MatSolve() */
175:   PetscMUMPSInt  ICNTL20;            /* use centralized (0) or distributed (10) dense RHS */
176:   PetscMUMPSInt  lrhs_loc, nloc_rhs, *irhs_loc;
177: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
178:   PetscInt    *rhs_nrow, max_nrhs;
179:   PetscMPIInt *rhs_recvcounts, *rhs_disps;
180:   PetscScalar *rhs_loc, *rhs_recvbuf;
181: #endif
182:   Vec            b_seq, x_seq;
183:   PetscInt       ninfo, *info; /* which INFO to display */
184:   PetscInt       sizeredrhs;
185:   PetscScalar   *schur_sol;
186:   PetscInt       schur_sizesol;
187:   PetscMUMPSInt *ia_alloc, *ja_alloc; /* work arrays used for the CSR struct for sparse rhs */
188:   PetscInt64     cur_ilen, cur_jlen;  /* current len of ia_alloc[], ja_alloc[] */
189:   PetscErrorCode (*ConvertToTriples)(Mat, PetscInt, MatReuse, Mat_MUMPS *);

191:   /* stuff used by petsc/mumps OpenMP support*/
192:   PetscBool    use_petsc_omp_support;
193:   PetscOmpCtrl omp_ctrl;             /* an OpenMP controller that blocked processes will release their CPU (MPI_Barrier does not have this guarantee) */
194:   MPI_Comm     petsc_comm, omp_comm; /* petsc_comm is petsc matrix's comm */
195:   PetscInt64  *recvcount;            /* a collection of nnz on omp_master */
196:   PetscMPIInt  tag, omp_comm_size;
197:   PetscBool    is_omp_master; /* is this rank the master of omp_comm */
198:   MPI_Request *reqs;
199: };

201: /* Cast a 1-based CSR represented by (nrow, ia, ja) of type PetscInt to a CSR of type PetscMUMPSInt.
202:    Here, nrow is number of rows, ia[] is row pointer and ja[] is column indices.
203:  */
204: static PetscErrorCode PetscMUMPSIntCSRCast(Mat_MUMPS *mumps, PetscInt nrow, PetscInt *ia, PetscInt *ja, PetscMUMPSInt **ia_mumps, PetscMUMPSInt **ja_mumps, PetscMUMPSInt *nnz_mumps)
205: {
206:   PetscInt nnz = ia[nrow] - 1; /* mumps uses 1-based indices. Uses PetscInt instead of PetscInt64 since mumps only uses PetscMUMPSInt for rhs */

208:   PetscFunctionBegin;
209: #if defined(PETSC_USE_64BIT_INDICES)
210:   {
211:     PetscInt i;
212:     if (nrow + 1 > mumps->cur_ilen) { /* realloc ia_alloc/ja_alloc to fit ia/ja */
213:       PetscCall(PetscFree(mumps->ia_alloc));
214:       PetscCall(PetscMalloc1(nrow + 1, &mumps->ia_alloc));
215:       mumps->cur_ilen = nrow + 1;
216:     }
217:     if (nnz > mumps->cur_jlen) {
218:       PetscCall(PetscFree(mumps->ja_alloc));
219:       PetscCall(PetscMalloc1(nnz, &mumps->ja_alloc));
220:       mumps->cur_jlen = nnz;
221:     }
222:     for (i = 0; i < nrow + 1; i++) PetscCall(PetscMUMPSIntCast(ia[i], &(mumps->ia_alloc[i])));
223:     for (i = 0; i < nnz; i++) PetscCall(PetscMUMPSIntCast(ja[i], &(mumps->ja_alloc[i])));
224:     *ia_mumps = mumps->ia_alloc;
225:     *ja_mumps = mumps->ja_alloc;
226:   }
227: #else
228:   *ia_mumps          = ia;
229:   *ja_mumps          = ja;
230: #endif
231:   PetscCall(PetscMUMPSIntCast(nnz, nnz_mumps));
232:   PetscFunctionReturn(PETSC_SUCCESS);
233: }

235: static PetscErrorCode MatMumpsResetSchur_Private(Mat_MUMPS *mumps)
236: {
237:   PetscFunctionBegin;
238:   PetscCall(PetscFree(mumps->id.listvar_schur));
239:   PetscCall(PetscFree(mumps->id.redrhs));
240:   PetscCall(PetscFree(mumps->schur_sol));
241:   mumps->id.size_schur = 0;
242:   mumps->id.schur_lld  = 0;
243:   mumps->id.ICNTL(19)  = 0;
244:   PetscFunctionReturn(PETSC_SUCCESS);
245: }

247: /* solve with rhs in mumps->id.redrhs and return in the same location */
248: static PetscErrorCode MatMumpsSolveSchur_Private(Mat F)
249: {
250:   Mat_MUMPS           *mumps = (Mat_MUMPS *)F->data;
251:   Mat                  S, B, X;
252:   MatFactorSchurStatus schurstatus;
253:   PetscInt             sizesol;

255:   PetscFunctionBegin;
256:   PetscCall(MatFactorFactorizeSchurComplement(F));
257:   PetscCall(MatFactorGetSchurComplement(F, &S, &schurstatus));
258:   PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, mumps->id.size_schur, mumps->id.nrhs, (PetscScalar *)mumps->id.redrhs, &B));
259:   PetscCall(MatSetType(B, ((PetscObject)S)->type_name));
260: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
261:   PetscCall(MatBindToCPU(B, S->boundtocpu));
262: #endif
263:   switch (schurstatus) {
264:   case MAT_FACTOR_SCHUR_FACTORED:
265:     PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, mumps->id.size_schur, mumps->id.nrhs, (PetscScalar *)mumps->id.redrhs, &X));
266:     PetscCall(MatSetType(X, ((PetscObject)S)->type_name));
267: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
268:     PetscCall(MatBindToCPU(X, S->boundtocpu));
269: #endif
270:     if (!mumps->id.ICNTL(9)) { /* transpose solve */
271:       PetscCall(MatMatSolveTranspose(S, B, X));
272:     } else {
273:       PetscCall(MatMatSolve(S, B, X));
274:     }
275:     break;
276:   case MAT_FACTOR_SCHUR_INVERTED:
277:     sizesol = mumps->id.nrhs * mumps->id.size_schur;
278:     if (!mumps->schur_sol || sizesol > mumps->schur_sizesol) {
279:       PetscCall(PetscFree(mumps->schur_sol));
280:       PetscCall(PetscMalloc1(sizesol, &mumps->schur_sol));
281:       mumps->schur_sizesol = sizesol;
282:     }
283:     PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, mumps->id.size_schur, mumps->id.nrhs, mumps->schur_sol, &X));
284:     PetscCall(MatSetType(X, ((PetscObject)S)->type_name));
285: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
286:     PetscCall(MatBindToCPU(X, S->boundtocpu));
287: #endif
288:     PetscCall(MatProductCreateWithMat(S, B, NULL, X));
289:     if (!mumps->id.ICNTL(9)) { /* transpose solve */
290:       PetscCall(MatProductSetType(X, MATPRODUCT_AtB));
291:     } else {
292:       PetscCall(MatProductSetType(X, MATPRODUCT_AB));
293:     }
294:     PetscCall(MatProductSetFromOptions(X));
295:     PetscCall(MatProductSymbolic(X));
296:     PetscCall(MatProductNumeric(X));

298:     PetscCall(MatCopy(X, B, SAME_NONZERO_PATTERN));
299:     break;
300:   default:
301:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
302:   }
303:   PetscCall(MatFactorRestoreSchurComplement(F, &S, schurstatus));
304:   PetscCall(MatDestroy(&B));
305:   PetscCall(MatDestroy(&X));
306:   PetscFunctionReturn(PETSC_SUCCESS);
307: }

309: static PetscErrorCode MatMumpsHandleSchur_Private(Mat F, PetscBool expansion)
310: {
311:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

313:   PetscFunctionBegin;
314:   if (!mumps->id.ICNTL(19)) { /* do nothing when Schur complement has not been computed */
315:     PetscFunctionReturn(PETSC_SUCCESS);
316:   }
317:   if (!expansion) { /* prepare for the condensation step */
318:     PetscInt sizeredrhs = mumps->id.nrhs * mumps->id.size_schur;
319:     /* allocate MUMPS internal array to store reduced right-hand sides */
320:     if (!mumps->id.redrhs || sizeredrhs > mumps->sizeredrhs) {
321:       PetscCall(PetscFree(mumps->id.redrhs));
322:       mumps->id.lredrhs = mumps->id.size_schur;
323:       PetscCall(PetscMalloc1(mumps->id.nrhs * mumps->id.lredrhs, &mumps->id.redrhs));
324:       mumps->sizeredrhs = mumps->id.nrhs * mumps->id.lredrhs;
325:     }
326:     mumps->id.ICNTL(26) = 1; /* condensation phase */
327:   } else {                   /* prepare for the expansion step */
328:     /* solve Schur complement (this has to be done by the MUMPS user, so basically us) */
329:     PetscCall(MatMumpsSolveSchur_Private(F));
330:     mumps->id.ICNTL(26) = 2; /* expansion phase */
331:     PetscMUMPS_c(mumps);
332:     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));
333:     /* restore defaults */
334:     mumps->id.ICNTL(26) = -1;
335:     /* free MUMPS internal array for redrhs if we have solved for multiple rhs in order to save memory space */
336:     if (mumps->id.nrhs > 1) {
337:       PetscCall(PetscFree(mumps->id.redrhs));
338:       mumps->id.lredrhs = 0;
339:       mumps->sizeredrhs = 0;
340:     }
341:   }
342:   PetscFunctionReturn(PETSC_SUCCESS);
343: }

345: /*
346:   MatConvertToTriples_A_B - convert Petsc matrix to triples: row[nz], col[nz], val[nz]

348:   input:
349:     A       - matrix in aij,baij or sbaij format
350:     shift   - 0: C style output triple; 1: Fortran style output triple.
351:     reuse   - MAT_INITIAL_MATRIX: spaces are allocated and values are set for the triple
352:               MAT_REUSE_MATRIX:   only the values in v array are updated
353:   output:
354:     nnz     - dim of r, c, and v (number of local nonzero entries of A)
355:     r, c, v - row and col index, matrix values (matrix triples)

357:   The returned values r, c, and sometimes v are obtained in a single PetscMalloc(). Then in MatDestroy_MUMPS() it is
358:   freed with PetscFree(mumps->irn);  This is not ideal code, the fact that v is ONLY sometimes part of mumps->irn means
359:   that the PetscMalloc() cannot easily be replaced with a PetscMalloc3().

361:  */

363: PetscErrorCode MatConvertToTriples_seqaij_seqaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
364: {
365:   const PetscScalar *av;
366:   const PetscInt    *ai, *aj, *ajj, M = A->rmap->n;
367:   PetscInt64         nz, rnz, i, j, k;
368:   PetscMUMPSInt     *row, *col;
369:   Mat_SeqAIJ        *aa = (Mat_SeqAIJ *)A->data;

371:   PetscFunctionBegin;
372:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
373:   mumps->val = (PetscScalar *)av;
374:   if (reuse == MAT_INITIAL_MATRIX) {
375:     nz = aa->nz;
376:     ai = aa->i;
377:     aj = aa->j;
378:     PetscCall(PetscMalloc2(nz, &row, nz, &col));
379:     for (i = k = 0; i < M; i++) {
380:       rnz = ai[i + 1] - ai[i];
381:       ajj = aj + ai[i];
382:       for (j = 0; j < rnz; j++) {
383:         PetscCall(PetscMUMPSIntCast(i + shift, &row[k]));
384:         PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[k]));
385:         k++;
386:       }
387:     }
388:     mumps->irn = row;
389:     mumps->jcn = col;
390:     mumps->nnz = nz;
391:   }
392:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
393:   PetscFunctionReturn(PETSC_SUCCESS);
394: }

396: PetscErrorCode MatConvertToTriples_seqsell_seqaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
397: {
398:   PetscInt64     nz, i, j, k, r;
399:   Mat_SeqSELL   *a = (Mat_SeqSELL *)A->data;
400:   PetscMUMPSInt *row, *col;

402:   PetscFunctionBegin;
403:   mumps->val = a->val;
404:   if (reuse == MAT_INITIAL_MATRIX) {
405:     nz = a->sliidx[a->totalslices];
406:     PetscCall(PetscMalloc2(nz, &row, nz, &col));
407:     for (i = k = 0; i < a->totalslices; i++) {
408:       for (j = a->sliidx[i], r = 0; j < a->sliidx[i + 1]; j++, r = ((r + 1) & 0x07)) PetscCall(PetscMUMPSIntCast(8 * i + r + shift, &row[k++]));
409:     }
410:     for (i = 0; i < nz; i++) PetscCall(PetscMUMPSIntCast(a->colidx[i] + shift, &col[i]));
411:     mumps->irn = row;
412:     mumps->jcn = col;
413:     mumps->nnz = nz;
414:   }
415:   PetscFunctionReturn(PETSC_SUCCESS);
416: }

418: PetscErrorCode MatConvertToTriples_seqbaij_seqaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
419: {
420:   Mat_SeqBAIJ    *aa = (Mat_SeqBAIJ *)A->data;
421:   const PetscInt *ai, *aj, *ajj, bs2 = aa->bs2;
422:   PetscInt64      M, nz, idx = 0, rnz, i, j, k, m;
423:   PetscInt        bs;
424:   PetscMUMPSInt  *row, *col;

426:   PetscFunctionBegin;
427:   PetscCall(MatGetBlockSize(A, &bs));
428:   M          = A->rmap->N / bs;
429:   mumps->val = aa->a;
430:   if (reuse == MAT_INITIAL_MATRIX) {
431:     ai = aa->i;
432:     aj = aa->j;
433:     nz = bs2 * aa->nz;
434:     PetscCall(PetscMalloc2(nz, &row, nz, &col));
435:     for (i = 0; i < M; i++) {
436:       ajj = aj + ai[i];
437:       rnz = ai[i + 1] - ai[i];
438:       for (k = 0; k < rnz; k++) {
439:         for (j = 0; j < bs; j++) {
440:           for (m = 0; m < bs; m++) {
441:             PetscCall(PetscMUMPSIntCast(i * bs + m + shift, &row[idx]));
442:             PetscCall(PetscMUMPSIntCast(bs * ajj[k] + j + shift, &col[idx]));
443:             idx++;
444:           }
445:         }
446:       }
447:     }
448:     mumps->irn = row;
449:     mumps->jcn = col;
450:     mumps->nnz = nz;
451:   }
452:   PetscFunctionReturn(PETSC_SUCCESS);
453: }

455: PetscErrorCode MatConvertToTriples_seqsbaij_seqsbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
456: {
457:   const PetscInt *ai, *aj, *ajj;
458:   PetscInt        bs;
459:   PetscInt64      nz, rnz, i, j, k, m;
460:   PetscMUMPSInt  *row, *col;
461:   PetscScalar    *val;
462:   Mat_SeqSBAIJ   *aa  = (Mat_SeqSBAIJ *)A->data;
463:   const PetscInt  bs2 = aa->bs2, mbs = aa->mbs;
464: #if defined(PETSC_USE_COMPLEX)
465:   PetscBool isset, hermitian;
466: #endif

468:   PetscFunctionBegin;
469: #if defined(PETSC_USE_COMPLEX)
470:   PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
471:   PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
472: #endif
473:   ai = aa->i;
474:   aj = aa->j;
475:   PetscCall(MatGetBlockSize(A, &bs));
476:   if (reuse == MAT_INITIAL_MATRIX) {
477:     const PetscInt64 alloc_size = aa->nz * bs2;

479:     PetscCall(PetscMalloc2(alloc_size, &row, alloc_size, &col));
480:     if (bs > 1) {
481:       PetscCall(PetscMalloc1(alloc_size, &mumps->val_alloc));
482:       mumps->val = mumps->val_alloc;
483:     } else {
484:       mumps->val = aa->a;
485:     }
486:     mumps->irn = row;
487:     mumps->jcn = col;
488:   } else {
489:     if (bs == 1) mumps->val = aa->a;
490:     row = mumps->irn;
491:     col = mumps->jcn;
492:   }
493:   val = mumps->val;

495:   nz = 0;
496:   if (bs > 1) {
497:     for (i = 0; i < mbs; i++) {
498:       rnz = ai[i + 1] - ai[i];
499:       ajj = aj + ai[i];
500:       for (j = 0; j < rnz; j++) {
501:         for (k = 0; k < bs; k++) {
502:           for (m = 0; m < bs; m++) {
503:             if (ajj[j] > i || k >= m) {
504:               if (reuse == MAT_INITIAL_MATRIX) {
505:                 PetscCall(PetscMUMPSIntCast(i * bs + m + shift, &row[nz]));
506:                 PetscCall(PetscMUMPSIntCast(ajj[j] * bs + k + shift, &col[nz]));
507:               }
508:               val[nz++] = aa->a[(ai[i] + j) * bs2 + m + k * bs];
509:             }
510:           }
511:         }
512:       }
513:     }
514:   } else if (reuse == MAT_INITIAL_MATRIX) {
515:     for (i = 0; i < mbs; i++) {
516:       rnz = ai[i + 1] - ai[i];
517:       ajj = aj + ai[i];
518:       for (j = 0; j < rnz; j++) {
519:         PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
520:         PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[nz]));
521:         nz++;
522:       }
523:     }
524:     PetscCheck(nz == aa->nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Different numbers of nonzeros %" PetscInt64_FMT " != %" PetscInt_FMT, nz, aa->nz);
525:   }
526:   if (reuse == MAT_INITIAL_MATRIX) mumps->nnz = nz;
527:   PetscFunctionReturn(PETSC_SUCCESS);
528: }

530: PetscErrorCode MatConvertToTriples_seqaij_seqsbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
531: {
532:   const PetscInt    *ai, *aj, *ajj, *adiag, M = A->rmap->n;
533:   PetscInt64         nz, rnz, i, j;
534:   const PetscScalar *av, *v1;
535:   PetscScalar       *val;
536:   PetscMUMPSInt     *row, *col;
537:   Mat_SeqAIJ        *aa = (Mat_SeqAIJ *)A->data;
538:   PetscBool          missing;
539: #if defined(PETSC_USE_COMPLEX)
540:   PetscBool hermitian, isset;
541: #endif

543:   PetscFunctionBegin;
544: #if defined(PETSC_USE_COMPLEX)
545:   PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
546:   PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
547: #endif
548:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
549:   ai    = aa->i;
550:   aj    = aa->j;
551:   adiag = aa->diag;
552:   PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, NULL));
553:   if (reuse == MAT_INITIAL_MATRIX) {
554:     /* count nz in the upper triangular part of A */
555:     nz = 0;
556:     if (missing) {
557:       for (i = 0; i < M; i++) {
558:         if (PetscUnlikely(adiag[i] >= ai[i + 1])) {
559:           for (j = ai[i]; j < ai[i + 1]; j++) {
560:             if (aj[j] < i) continue;
561:             nz++;
562:           }
563:         } else {
564:           nz += ai[i + 1] - adiag[i];
565:         }
566:       }
567:     } else {
568:       for (i = 0; i < M; i++) nz += ai[i + 1] - adiag[i];
569:     }
570:     PetscCall(PetscMalloc2(nz, &row, nz, &col));
571:     PetscCall(PetscMalloc1(nz, &val));
572:     mumps->nnz = nz;
573:     mumps->irn = row;
574:     mumps->jcn = col;
575:     mumps->val = mumps->val_alloc = val;

577:     nz = 0;
578:     if (missing) {
579:       for (i = 0; i < M; i++) {
580:         if (PetscUnlikely(adiag[i] >= ai[i + 1])) {
581:           for (j = ai[i]; j < ai[i + 1]; j++) {
582:             if (aj[j] < i) continue;
583:             PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
584:             PetscCall(PetscMUMPSIntCast(aj[j] + shift, &col[nz]));
585:             val[nz] = av[j];
586:             nz++;
587:           }
588:         } else {
589:           rnz = ai[i + 1] - adiag[i];
590:           ajj = aj + adiag[i];
591:           v1  = av + adiag[i];
592:           for (j = 0; j < rnz; j++) {
593:             PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
594:             PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[nz]));
595:             val[nz++] = v1[j];
596:           }
597:         }
598:       }
599:     } else {
600:       for (i = 0; i < M; i++) {
601:         rnz = ai[i + 1] - adiag[i];
602:         ajj = aj + adiag[i];
603:         v1  = av + adiag[i];
604:         for (j = 0; j < rnz; j++) {
605:           PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
606:           PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[nz]));
607:           val[nz++] = v1[j];
608:         }
609:       }
610:     }
611:   } else {
612:     nz  = 0;
613:     val = mumps->val;
614:     if (missing) {
615:       for (i = 0; i < M; i++) {
616:         if (PetscUnlikely(adiag[i] >= ai[i + 1])) {
617:           for (j = ai[i]; j < ai[i + 1]; j++) {
618:             if (aj[j] < i) continue;
619:             val[nz++] = av[j];
620:           }
621:         } else {
622:           rnz = ai[i + 1] - adiag[i];
623:           v1  = av + adiag[i];
624:           for (j = 0; j < rnz; j++) val[nz++] = v1[j];
625:         }
626:       }
627:     } else {
628:       for (i = 0; i < M; i++) {
629:         rnz = ai[i + 1] - adiag[i];
630:         v1  = av + adiag[i];
631:         for (j = 0; j < rnz; j++) val[nz++] = v1[j];
632:       }
633:     }
634:   }
635:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
636:   PetscFunctionReturn(PETSC_SUCCESS);
637: }

639: PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
640: {
641:   const PetscInt    *ai, *aj, *bi, *bj, *garray, *ajj, *bjj;
642:   PetscInt           bs;
643:   PetscInt64         rstart, nz, i, j, k, m, jj, irow, countA, countB;
644:   PetscMUMPSInt     *row, *col;
645:   const PetscScalar *av, *bv, *v1, *v2;
646:   PetscScalar       *val;
647:   Mat_MPISBAIJ      *mat = (Mat_MPISBAIJ *)A->data;
648:   Mat_SeqSBAIJ      *aa  = (Mat_SeqSBAIJ *)(mat->A)->data;
649:   Mat_SeqBAIJ       *bb  = (Mat_SeqBAIJ *)(mat->B)->data;
650:   const PetscInt     bs2 = aa->bs2, mbs = aa->mbs;
651: #if defined(PETSC_USE_COMPLEX)
652:   PetscBool hermitian, isset;
653: #endif

655:   PetscFunctionBegin;
656: #if defined(PETSC_USE_COMPLEX)
657:   PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
658:   PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
659: #endif
660:   PetscCall(MatGetBlockSize(A, &bs));
661:   rstart = A->rmap->rstart;
662:   ai     = aa->i;
663:   aj     = aa->j;
664:   bi     = bb->i;
665:   bj     = bb->j;
666:   av     = aa->a;
667:   bv     = bb->a;

669:   garray = mat->garray;

671:   if (reuse == MAT_INITIAL_MATRIX) {
672:     nz = (aa->nz + bb->nz) * bs2; /* just a conservative estimate */
673:     PetscCall(PetscMalloc2(nz, &row, nz, &col));
674:     PetscCall(PetscMalloc1(nz, &val));
675:     /* can not decide the exact mumps->nnz now because of the SBAIJ */
676:     mumps->irn = row;
677:     mumps->jcn = col;
678:     mumps->val = mumps->val_alloc = val;
679:   } else {
680:     val = mumps->val;
681:   }

683:   jj   = 0;
684:   irow = rstart;
685:   for (i = 0; i < mbs; i++) {
686:     ajj    = aj + ai[i]; /* ptr to the beginning of this row */
687:     countA = ai[i + 1] - ai[i];
688:     countB = bi[i + 1] - bi[i];
689:     bjj    = bj + bi[i];
690:     v1     = av + ai[i] * bs2;
691:     v2     = bv + bi[i] * bs2;

693:     if (bs > 1) {
694:       /* A-part */
695:       for (j = 0; j < countA; j++) {
696:         for (k = 0; k < bs; k++) {
697:           for (m = 0; m < bs; m++) {
698:             if (rstart + ajj[j] * bs > irow || k >= m) {
699:               if (reuse == MAT_INITIAL_MATRIX) {
700:                 PetscCall(PetscMUMPSIntCast(irow + m + shift, &row[jj]));
701:                 PetscCall(PetscMUMPSIntCast(rstart + ajj[j] * bs + k + shift, &col[jj]));
702:               }
703:               val[jj++] = v1[j * bs2 + m + k * bs];
704:             }
705:           }
706:         }
707:       }

709:       /* B-part */
710:       for (j = 0; j < countB; j++) {
711:         for (k = 0; k < bs; k++) {
712:           for (m = 0; m < bs; m++) {
713:             if (reuse == MAT_INITIAL_MATRIX) {
714:               PetscCall(PetscMUMPSIntCast(irow + m + shift, &row[jj]));
715:               PetscCall(PetscMUMPSIntCast(garray[bjj[j]] * bs + k + shift, &col[jj]));
716:             }
717:             val[jj++] = v2[j * bs2 + m + k * bs];
718:           }
719:         }
720:       }
721:     } else {
722:       /* A-part */
723:       for (j = 0; j < countA; j++) {
724:         if (reuse == MAT_INITIAL_MATRIX) {
725:           PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
726:           PetscCall(PetscMUMPSIntCast(rstart + ajj[j] + shift, &col[jj]));
727:         }
728:         val[jj++] = v1[j];
729:       }

731:       /* B-part */
732:       for (j = 0; j < countB; j++) {
733:         if (reuse == MAT_INITIAL_MATRIX) {
734:           PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
735:           PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift, &col[jj]));
736:         }
737:         val[jj++] = v2[j];
738:       }
739:     }
740:     irow += bs;
741:   }
742:   mumps->nnz = jj;
743:   PetscFunctionReturn(PETSC_SUCCESS);
744: }

746: PetscErrorCode MatConvertToTriples_mpiaij_mpiaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
747: {
748:   const PetscInt    *ai, *aj, *bi, *bj, *garray, m = A->rmap->n, *ajj, *bjj;
749:   PetscInt64         rstart, nz, i, j, jj, irow, countA, countB;
750:   PetscMUMPSInt     *row, *col;
751:   const PetscScalar *av, *bv, *v1, *v2;
752:   PetscScalar       *val;
753:   Mat                Ad, Ao;
754:   Mat_SeqAIJ        *aa;
755:   Mat_SeqAIJ        *bb;

757:   PetscFunctionBegin;
758:   PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &garray));
759:   PetscCall(MatSeqAIJGetArrayRead(Ad, &av));
760:   PetscCall(MatSeqAIJGetArrayRead(Ao, &bv));

762:   aa = (Mat_SeqAIJ *)(Ad)->data;
763:   bb = (Mat_SeqAIJ *)(Ao)->data;
764:   ai = aa->i;
765:   aj = aa->j;
766:   bi = bb->i;
767:   bj = bb->j;

769:   rstart = A->rmap->rstart;

771:   if (reuse == MAT_INITIAL_MATRIX) {
772:     nz = (PetscInt64)aa->nz + bb->nz; /* make sure the sum won't overflow PetscInt */
773:     PetscCall(PetscMalloc2(nz, &row, nz, &col));
774:     PetscCall(PetscMalloc1(nz, &val));
775:     mumps->nnz = nz;
776:     mumps->irn = row;
777:     mumps->jcn = col;
778:     mumps->val = mumps->val_alloc = val;
779:   } else {
780:     val = mumps->val;
781:   }

783:   jj   = 0;
784:   irow = rstart;
785:   for (i = 0; i < m; i++) {
786:     ajj    = aj + ai[i]; /* ptr to the beginning of this row */
787:     countA = ai[i + 1] - ai[i];
788:     countB = bi[i + 1] - bi[i];
789:     bjj    = bj + bi[i];
790:     v1     = av + ai[i];
791:     v2     = bv + bi[i];

793:     /* A-part */
794:     for (j = 0; j < countA; j++) {
795:       if (reuse == MAT_INITIAL_MATRIX) {
796:         PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
797:         PetscCall(PetscMUMPSIntCast(rstart + ajj[j] + shift, &col[jj]));
798:       }
799:       val[jj++] = v1[j];
800:     }

802:     /* B-part */
803:     for (j = 0; j < countB; j++) {
804:       if (reuse == MAT_INITIAL_MATRIX) {
805:         PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
806:         PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift, &col[jj]));
807:       }
808:       val[jj++] = v2[j];
809:     }
810:     irow++;
811:   }
812:   PetscCall(MatSeqAIJRestoreArrayRead(Ad, &av));
813:   PetscCall(MatSeqAIJRestoreArrayRead(Ao, &bv));
814:   PetscFunctionReturn(PETSC_SUCCESS);
815: }

817: PetscErrorCode MatConvertToTriples_mpibaij_mpiaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
818: {
819:   Mat_MPIBAIJ       *mat = (Mat_MPIBAIJ *)A->data;
820:   Mat_SeqBAIJ       *aa  = (Mat_SeqBAIJ *)(mat->A)->data;
821:   Mat_SeqBAIJ       *bb  = (Mat_SeqBAIJ *)(mat->B)->data;
822:   const PetscInt    *ai = aa->i, *bi = bb->i, *aj = aa->j, *bj = bb->j, *ajj, *bjj;
823:   const PetscInt    *garray = mat->garray, mbs = mat->mbs, rstart = A->rmap->rstart;
824:   const PetscInt     bs2 = mat->bs2;
825:   PetscInt           bs;
826:   PetscInt64         nz, i, j, k, n, jj, irow, countA, countB, idx;
827:   PetscMUMPSInt     *row, *col;
828:   const PetscScalar *av = aa->a, *bv = bb->a, *v1, *v2;
829:   PetscScalar       *val;

831:   PetscFunctionBegin;
832:   PetscCall(MatGetBlockSize(A, &bs));
833:   if (reuse == MAT_INITIAL_MATRIX) {
834:     nz = bs2 * (aa->nz + bb->nz);
835:     PetscCall(PetscMalloc2(nz, &row, nz, &col));
836:     PetscCall(PetscMalloc1(nz, &val));
837:     mumps->nnz = nz;
838:     mumps->irn = row;
839:     mumps->jcn = col;
840:     mumps->val = mumps->val_alloc = val;
841:   } else {
842:     val = mumps->val;
843:   }

845:   jj   = 0;
846:   irow = rstart;
847:   for (i = 0; i < mbs; i++) {
848:     countA = ai[i + 1] - ai[i];
849:     countB = bi[i + 1] - bi[i];
850:     ajj    = aj + ai[i];
851:     bjj    = bj + bi[i];
852:     v1     = av + bs2 * ai[i];
853:     v2     = bv + bs2 * bi[i];

855:     idx = 0;
856:     /* A-part */
857:     for (k = 0; k < countA; k++) {
858:       for (j = 0; j < bs; j++) {
859:         for (n = 0; n < bs; n++) {
860:           if (reuse == MAT_INITIAL_MATRIX) {
861:             PetscCall(PetscMUMPSIntCast(irow + n + shift, &row[jj]));
862:             PetscCall(PetscMUMPSIntCast(rstart + bs * ajj[k] + j + shift, &col[jj]));
863:           }
864:           val[jj++] = v1[idx++];
865:         }
866:       }
867:     }

869:     idx = 0;
870:     /* B-part */
871:     for (k = 0; k < countB; k++) {
872:       for (j = 0; j < bs; j++) {
873:         for (n = 0; n < bs; n++) {
874:           if (reuse == MAT_INITIAL_MATRIX) {
875:             PetscCall(PetscMUMPSIntCast(irow + n + shift, &row[jj]));
876:             PetscCall(PetscMUMPSIntCast(bs * garray[bjj[k]] + j + shift, &col[jj]));
877:           }
878:           val[jj++] = v2[idx++];
879:         }
880:       }
881:     }
882:     irow += bs;
883:   }
884:   PetscFunctionReturn(PETSC_SUCCESS);
885: }

887: PetscErrorCode MatConvertToTriples_mpiaij_mpisbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
888: {
889:   const PetscInt    *ai, *aj, *adiag, *bi, *bj, *garray, m = A->rmap->n, *ajj, *bjj;
890:   PetscInt64         rstart, nz, nza, nzb, i, j, jj, irow, countA, countB;
891:   PetscMUMPSInt     *row, *col;
892:   const PetscScalar *av, *bv, *v1, *v2;
893:   PetscScalar       *val;
894:   Mat                Ad, Ao;
895:   Mat_SeqAIJ        *aa;
896:   Mat_SeqAIJ        *bb;
897: #if defined(PETSC_USE_COMPLEX)
898:   PetscBool hermitian, isset;
899: #endif

901:   PetscFunctionBegin;
902: #if defined(PETSC_USE_COMPLEX)
903:   PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
904:   PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
905: #endif
906:   PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &garray));
907:   PetscCall(MatSeqAIJGetArrayRead(Ad, &av));
908:   PetscCall(MatSeqAIJGetArrayRead(Ao, &bv));

910:   aa    = (Mat_SeqAIJ *)(Ad)->data;
911:   bb    = (Mat_SeqAIJ *)(Ao)->data;
912:   ai    = aa->i;
913:   aj    = aa->j;
914:   adiag = aa->diag;
915:   bi    = bb->i;
916:   bj    = bb->j;

918:   rstart = A->rmap->rstart;

920:   if (reuse == MAT_INITIAL_MATRIX) {
921:     nza = 0; /* num of upper triangular entries in mat->A, including diagonals */
922:     nzb = 0; /* num of upper triangular entries in mat->B */
923:     for (i = 0; i < m; i++) {
924:       nza += (ai[i + 1] - adiag[i]);
925:       countB = bi[i + 1] - bi[i];
926:       bjj    = bj + bi[i];
927:       for (j = 0; j < countB; j++) {
928:         if (garray[bjj[j]] > rstart) nzb++;
929:       }
930:     }

932:     nz = nza + nzb; /* total nz of upper triangular part of mat */
933:     PetscCall(PetscMalloc2(nz, &row, nz, &col));
934:     PetscCall(PetscMalloc1(nz, &val));
935:     mumps->nnz = nz;
936:     mumps->irn = row;
937:     mumps->jcn = col;
938:     mumps->val = mumps->val_alloc = val;
939:   } else {
940:     val = mumps->val;
941:   }

943:   jj   = 0;
944:   irow = rstart;
945:   for (i = 0; i < m; i++) {
946:     ajj    = aj + adiag[i]; /* ptr to the beginning of the diagonal of this row */
947:     v1     = av + adiag[i];
948:     countA = ai[i + 1] - adiag[i];
949:     countB = bi[i + 1] - bi[i];
950:     bjj    = bj + bi[i];
951:     v2     = bv + bi[i];

953:     /* A-part */
954:     for (j = 0; j < countA; j++) {
955:       if (reuse == MAT_INITIAL_MATRIX) {
956:         PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
957:         PetscCall(PetscMUMPSIntCast(rstart + ajj[j] + shift, &col[jj]));
958:       }
959:       val[jj++] = v1[j];
960:     }

962:     /* B-part */
963:     for (j = 0; j < countB; j++) {
964:       if (garray[bjj[j]] > rstart) {
965:         if (reuse == MAT_INITIAL_MATRIX) {
966:           PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
967:           PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift, &col[jj]));
968:         }
969:         val[jj++] = v2[j];
970:       }
971:     }
972:     irow++;
973:   }
974:   PetscCall(MatSeqAIJRestoreArrayRead(Ad, &av));
975:   PetscCall(MatSeqAIJRestoreArrayRead(Ao, &bv));
976:   PetscFunctionReturn(PETSC_SUCCESS);
977: }

979: PetscErrorCode MatDestroy_MUMPS(Mat A)
980: {
981:   Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;

983:   PetscFunctionBegin;
984:   PetscCall(PetscFree2(mumps->id.sol_loc, mumps->id.isol_loc));
985:   PetscCall(VecScatterDestroy(&mumps->scat_rhs));
986:   PetscCall(VecScatterDestroy(&mumps->scat_sol));
987:   PetscCall(VecDestroy(&mumps->b_seq));
988:   PetscCall(VecDestroy(&mumps->x_seq));
989:   PetscCall(PetscFree(mumps->id.perm_in));
990:   PetscCall(PetscFree2(mumps->irn, mumps->jcn));
991:   PetscCall(PetscFree(mumps->val_alloc));
992:   PetscCall(PetscFree(mumps->info));
993:   PetscCall(PetscFree(mumps->ICNTL_pre));
994:   PetscCall(PetscFree(mumps->CNTL_pre));
995:   PetscCall(MatMumpsResetSchur_Private(mumps));
996:   if (mumps->id.job != JOB_NULL) { /* cannot call PetscMUMPS_c() if JOB_INIT has never been called for this instance */
997:     mumps->id.job = JOB_END;
998:     PetscMUMPS_c(mumps);
999:     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));
1000:     if (mumps->mumps_comm != MPI_COMM_NULL) {
1001:       if (PetscDefined(HAVE_OPENMP_SUPPORT) && mumps->use_petsc_omp_support) PetscCallMPI(MPI_Comm_free(&mumps->mumps_comm));
1002:       else PetscCall(PetscCommRestoreComm(PetscObjectComm((PetscObject)A), &mumps->mumps_comm));
1003:     }
1004:   }
1005: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
1006:   if (mumps->use_petsc_omp_support) {
1007:     PetscCall(PetscOmpCtrlDestroy(&mumps->omp_ctrl));
1008:     PetscCall(PetscFree2(mumps->rhs_loc, mumps->rhs_recvbuf));
1009:     PetscCall(PetscFree3(mumps->rhs_nrow, mumps->rhs_recvcounts, mumps->rhs_disps));
1010:   }
1011: #endif
1012:   PetscCall(PetscFree(mumps->ia_alloc));
1013:   PetscCall(PetscFree(mumps->ja_alloc));
1014:   PetscCall(PetscFree(mumps->recvcount));
1015:   PetscCall(PetscFree(mumps->reqs));
1016:   PetscCall(PetscFree(mumps->irhs_loc));
1017:   PetscCall(PetscFree(A->data));

1019:   /* clear composed functions */
1020:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1021:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorSetSchurIS_C", NULL));
1022:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorCreateSchurComplement_C", NULL));
1023:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsSetIcntl_C", NULL));
1024:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetIcntl_C", NULL));
1025:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsSetCntl_C", NULL));
1026:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetCntl_C", NULL));
1027:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInfo_C", NULL));
1028:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInfog_C", NULL));
1029:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetRinfo_C", NULL));
1030:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetRinfog_C", NULL));
1031:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetNullPivots_C", NULL));
1032:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInverse_C", NULL));
1033:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInverseTranspose_C", NULL));
1034:   PetscFunctionReturn(PETSC_SUCCESS);
1035: }

1037: /* Set up the distributed RHS info for MUMPS. <nrhs> is the number of RHS. <array> points to start of RHS on the local processor. */
1038: static PetscErrorCode MatMumpsSetUpDistRHSInfo(Mat A, PetscInt nrhs, const PetscScalar *array)
1039: {
1040:   Mat_MUMPS        *mumps   = (Mat_MUMPS *)A->data;
1041:   const PetscMPIInt ompsize = mumps->omp_comm_size;
1042:   PetscInt          i, m, M, rstart;

1044:   PetscFunctionBegin;
1045:   PetscCall(MatGetSize(A, &M, NULL));
1046:   PetscCall(MatGetLocalSize(A, &m, NULL));
1047:   PetscCheck(M <= PETSC_MUMPS_INT_MAX, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscInt too long for PetscMUMPSInt");
1048:   if (ompsize == 1) {
1049:     if (!mumps->irhs_loc) {
1050:       mumps->nloc_rhs = m;
1051:       PetscCall(PetscMalloc1(m, &mumps->irhs_loc));
1052:       PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
1053:       for (i = 0; i < m; i++) mumps->irhs_loc[i] = rstart + i + 1; /* use 1-based indices */
1054:     }
1055:     mumps->id.rhs_loc = (MumpsScalar *)array;
1056:   } else {
1057: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
1058:     const PetscInt *ranges;
1059:     PetscMPIInt     j, k, sendcount, *petsc_ranks, *omp_ranks;
1060:     MPI_Group       petsc_group, omp_group;
1061:     PetscScalar    *recvbuf = NULL;

1063:     if (mumps->is_omp_master) {
1064:       /* Lazily initialize the omp stuff for distributed rhs */
1065:       if (!mumps->irhs_loc) {
1066:         PetscCall(PetscMalloc2(ompsize, &omp_ranks, ompsize, &petsc_ranks));
1067:         PetscCall(PetscMalloc3(ompsize, &mumps->rhs_nrow, ompsize, &mumps->rhs_recvcounts, ompsize, &mumps->rhs_disps));
1068:         PetscCallMPI(MPI_Comm_group(mumps->petsc_comm, &petsc_group));
1069:         PetscCallMPI(MPI_Comm_group(mumps->omp_comm, &omp_group));
1070:         for (j = 0; j < ompsize; j++) omp_ranks[j] = j;
1071:         PetscCallMPI(MPI_Group_translate_ranks(omp_group, ompsize, omp_ranks, petsc_group, petsc_ranks));

1073:         /* Populate mumps->irhs_loc[], rhs_nrow[] */
1074:         mumps->nloc_rhs = 0;
1075:         PetscCall(MatGetOwnershipRanges(A, &ranges));
1076:         for (j = 0; j < ompsize; j++) {
1077:           mumps->rhs_nrow[j] = ranges[petsc_ranks[j] + 1] - ranges[petsc_ranks[j]];
1078:           mumps->nloc_rhs += mumps->rhs_nrow[j];
1079:         }
1080:         PetscCall(PetscMalloc1(mumps->nloc_rhs, &mumps->irhs_loc));
1081:         for (j = k = 0; j < ompsize; j++) {
1082:           for (i = ranges[petsc_ranks[j]]; i < ranges[petsc_ranks[j] + 1]; i++, k++) mumps->irhs_loc[k] = i + 1; /* uses 1-based indices */
1083:         }

1085:         PetscCall(PetscFree2(omp_ranks, petsc_ranks));
1086:         PetscCallMPI(MPI_Group_free(&petsc_group));
1087:         PetscCallMPI(MPI_Group_free(&omp_group));
1088:       }

1090:       /* Realloc buffers when current nrhs is bigger than what we have met */
1091:       if (nrhs > mumps->max_nrhs) {
1092:         PetscCall(PetscFree2(mumps->rhs_loc, mumps->rhs_recvbuf));
1093:         PetscCall(PetscMalloc2(mumps->nloc_rhs * nrhs, &mumps->rhs_loc, mumps->nloc_rhs * nrhs, &mumps->rhs_recvbuf));
1094:         mumps->max_nrhs = nrhs;
1095:       }

1097:       /* Setup recvcounts[], disps[], recvbuf on omp rank 0 for the upcoming MPI_Gatherv */
1098:       for (j = 0; j < ompsize; j++) PetscCall(PetscMPIIntCast(mumps->rhs_nrow[j] * nrhs, &mumps->rhs_recvcounts[j]));
1099:       mumps->rhs_disps[0] = 0;
1100:       for (j = 1; j < ompsize; j++) {
1101:         mumps->rhs_disps[j] = mumps->rhs_disps[j - 1] + mumps->rhs_recvcounts[j - 1];
1102:         PetscCheck(mumps->rhs_disps[j] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscMPIInt overflow!");
1103:       }
1104:       recvbuf = (nrhs == 1) ? mumps->rhs_loc : mumps->rhs_recvbuf; /* Directly use rhs_loc[] as recvbuf. Single rhs is common in Ax=b */
1105:     }

1107:     PetscCall(PetscMPIIntCast(m * nrhs, &sendcount));
1108:     PetscCallMPI(MPI_Gatherv(array, sendcount, MPIU_SCALAR, recvbuf, mumps->rhs_recvcounts, mumps->rhs_disps, MPIU_SCALAR, 0, mumps->omp_comm));

1110:     if (mumps->is_omp_master) {
1111:       if (nrhs > 1) { /* Copy & re-arrange data from rhs_recvbuf[] to mumps->rhs_loc[] only when there are multiple rhs */
1112:         PetscScalar *dst, *dstbase = mumps->rhs_loc;
1113:         for (j = 0; j < ompsize; j++) {
1114:           const PetscScalar *src = mumps->rhs_recvbuf + mumps->rhs_disps[j];
1115:           dst                    = dstbase;
1116:           for (i = 0; i < nrhs; i++) {
1117:             PetscCall(PetscArraycpy(dst, src, mumps->rhs_nrow[j]));
1118:             src += mumps->rhs_nrow[j];
1119:             dst += mumps->nloc_rhs;
1120:           }
1121:           dstbase += mumps->rhs_nrow[j];
1122:         }
1123:       }
1124:       mumps->id.rhs_loc = (MumpsScalar *)mumps->rhs_loc;
1125:     }
1126: #endif /* PETSC_HAVE_OPENMP_SUPPORT */
1127:   }
1128:   mumps->id.nrhs     = nrhs;
1129:   mumps->id.nloc_rhs = mumps->nloc_rhs;
1130:   mumps->id.lrhs_loc = mumps->nloc_rhs;
1131:   mumps->id.irhs_loc = mumps->irhs_loc;
1132:   PetscFunctionReturn(PETSC_SUCCESS);
1133: }

1135: PetscErrorCode MatSolve_MUMPS(Mat A, Vec b, Vec x)
1136: {
1137:   Mat_MUMPS         *mumps  = (Mat_MUMPS *)A->data;
1138:   const PetscScalar *rarray = NULL;
1139:   PetscScalar       *array;
1140:   IS                 is_iden, is_petsc;
1141:   PetscInt           i;
1142:   PetscBool          second_solve = PETSC_FALSE;
1143:   static PetscBool   cite1 = PETSC_FALSE, cite2 = PETSC_FALSE;

1145:   PetscFunctionBegin;
1146:   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 "
1147:                                    "Journal on Matrix Analysis and Applications},\n  volume = {23},\n  number = {1},\n  pages = {15--41},\n  year = {2001}\n}\n",
1148:                                    &cite1));
1149:   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 "
1150:                                    "Computing},\n  volume = {32},\n  number = {2},\n  pages = {136--156},\n  year = {2006}\n}\n",
1151:                                    &cite2));

1153:   if (A->factorerrortype) {
1154:     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)));
1155:     PetscCall(VecSetInf(x));
1156:     PetscFunctionReturn(PETSC_SUCCESS);
1157:   }

1159:   mumps->id.nrhs = 1;
1160:   if (mumps->petsc_size > 1) {
1161:     if (mumps->ICNTL20 == 10) {
1162:       mumps->id.ICNTL(20) = 10; /* dense distributed RHS */
1163:       PetscCall(VecGetArrayRead(b, &rarray));
1164:       PetscCall(MatMumpsSetUpDistRHSInfo(A, 1, rarray));
1165:     } else {
1166:       mumps->id.ICNTL(20) = 0; /* dense centralized RHS; Scatter b into a sequential rhs vector*/
1167:       PetscCall(VecScatterBegin(mumps->scat_rhs, b, mumps->b_seq, INSERT_VALUES, SCATTER_FORWARD));
1168:       PetscCall(VecScatterEnd(mumps->scat_rhs, b, mumps->b_seq, INSERT_VALUES, SCATTER_FORWARD));
1169:       if (!mumps->myid) {
1170:         PetscCall(VecGetArray(mumps->b_seq, &array));
1171:         mumps->id.rhs = (MumpsScalar *)array;
1172:       }
1173:     }
1174:   } else {                   /* petsc_size == 1 */
1175:     mumps->id.ICNTL(20) = 0; /* dense centralized RHS */
1176:     PetscCall(VecCopy(b, x));
1177:     PetscCall(VecGetArray(x, &array));
1178:     mumps->id.rhs = (MumpsScalar *)array;
1179:   }

1181:   /*
1182:      handle condensation step of Schur complement (if any)
1183:      We set by default ICNTL(26) == -1 when Schur indices have been provided by the user.
1184:      According to MUMPS (5.0.0) manual, any value should be harmful during the factorization phase
1185:      Unless the user provides a valid value for ICNTL(26), MatSolve and MatMatSolve routines solve the full system.
1186:      This requires an extra call to PetscMUMPS_c and the computation of the factors for S
1187:   */
1188:   if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) {
1189:     PetscCheck(mumps->petsc_size <= 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Parallel Schur complements not yet supported from PETSc");
1190:     second_solve = PETSC_TRUE;
1191:     PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1192:   }
1193:   /* solve phase */
1194:   mumps->id.job = JOB_SOLVE;
1195:   PetscMUMPS_c(mumps);
1196:   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));

1198:   /* handle expansion step of Schur complement (if any) */
1199:   if (second_solve) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_TRUE));

1201:   if (mumps->petsc_size > 1) { /* convert mumps distributed solution to petsc mpi x */
1202:     if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) {
1203:       /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */
1204:       PetscCall(VecScatterDestroy(&mumps->scat_sol));
1205:     }
1206:     if (!mumps->scat_sol) { /* create scatter scat_sol */
1207:       PetscInt *isol2_loc = NULL;
1208:       PetscCall(ISCreateStride(PETSC_COMM_SELF, mumps->id.lsol_loc, 0, 1, &is_iden)); /* from */
1209:       PetscCall(PetscMalloc1(mumps->id.lsol_loc, &isol2_loc));
1210:       for (i = 0; i < mumps->id.lsol_loc; i++) isol2_loc[i] = mumps->id.isol_loc[i] - 1;                        /* change Fortran style to C style */
1211:       PetscCall(ISCreateGeneral(PETSC_COMM_SELF, mumps->id.lsol_loc, isol2_loc, PETSC_OWN_POINTER, &is_petsc)); /* to */
1212:       PetscCall(VecScatterCreate(mumps->x_seq, is_iden, x, is_petsc, &mumps->scat_sol));
1213:       PetscCall(ISDestroy(&is_iden));
1214:       PetscCall(ISDestroy(&is_petsc));
1215:       mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */
1216:     }

1218:     PetscCall(VecScatterBegin(mumps->scat_sol, mumps->x_seq, x, INSERT_VALUES, SCATTER_FORWARD));
1219:     PetscCall(VecScatterEnd(mumps->scat_sol, mumps->x_seq, x, INSERT_VALUES, SCATTER_FORWARD));
1220:   }

1222:   if (mumps->petsc_size > 1) {
1223:     if (mumps->ICNTL20 == 10) {
1224:       PetscCall(VecRestoreArrayRead(b, &rarray));
1225:     } else if (!mumps->myid) {
1226:       PetscCall(VecRestoreArray(mumps->b_seq, &array));
1227:     }
1228:   } else PetscCall(VecRestoreArray(x, &array));

1230:   PetscCall(PetscLogFlops(2.0 * PetscMax(0, (mumps->id.INFO(28) >= 0 ? mumps->id.INFO(28) : -1000000 * mumps->id.INFO(28)) - A->cmap->n)));
1231:   PetscFunctionReturn(PETSC_SUCCESS);
1232: }

1234: PetscErrorCode MatSolveTranspose_MUMPS(Mat A, Vec b, Vec x)
1235: {
1236:   Mat_MUMPS          *mumps = (Mat_MUMPS *)A->data;
1237:   const PetscMUMPSInt value = mumps->id.ICNTL(9);

1239:   PetscFunctionBegin;
1240:   mumps->id.ICNTL(9) = 0;
1241:   PetscCall(MatSolve_MUMPS(A, b, x));
1242:   mumps->id.ICNTL(9) = value;
1243:   PetscFunctionReturn(PETSC_SUCCESS);
1244: }

1246: PetscErrorCode MatMatSolve_MUMPS(Mat A, Mat B, Mat X)
1247: {
1248:   Mat                Bt = NULL;
1249:   PetscBool          denseX, denseB, flg, flgT;
1250:   Mat_MUMPS         *mumps = (Mat_MUMPS *)A->data;
1251:   PetscInt           i, nrhs, M;
1252:   PetscScalar       *array;
1253:   const PetscScalar *rbray;
1254:   PetscInt           lsol_loc, nlsol_loc, *idxx, iidx = 0;
1255:   PetscMUMPSInt     *isol_loc, *isol_loc_save;
1256:   PetscScalar       *bray, *sol_loc, *sol_loc_save;
1257:   IS                 is_to, is_from;
1258:   PetscInt           k, proc, j, m, myrstart;
1259:   const PetscInt    *rstart;
1260:   Vec                v_mpi, msol_loc;
1261:   VecScatter         scat_sol;
1262:   Vec                b_seq;
1263:   VecScatter         scat_rhs;
1264:   PetscScalar       *aa;
1265:   PetscInt           spnr, *ia, *ja;
1266:   Mat_MPIAIJ        *b = NULL;

1268:   PetscFunctionBegin;
1269:   PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &denseX, MATSEQDENSE, MATMPIDENSE, NULL));
1270:   PetscCheck(denseX, PetscObjectComm((PetscObject)X), PETSC_ERR_ARG_WRONG, "Matrix X must be MATDENSE matrix");

1272:   PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &denseB, MATSEQDENSE, MATMPIDENSE, NULL));
1273:   if (denseB) {
1274:     PetscCheck(B->rmap->n == X->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Matrix B and X must have same row distribution");
1275:     mumps->id.ICNTL(20) = 0; /* dense RHS */
1276:   } else {                   /* sparse B */
1277:     PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
1278:     PetscCall(PetscObjectTypeCompare((PetscObject)B, MATTRANSPOSEVIRTUAL, &flgT));
1279:     if (flgT) { /* input B is transpose of actual RHS matrix,
1280:                  because mumps requires sparse compressed COLUMN storage! See MatMatTransposeSolve_MUMPS() */
1281:       PetscCall(MatTransposeGetMat(B, &Bt));
1282:     } else SETERRQ(PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Matrix B must be MATTRANSPOSEVIRTUAL matrix");
1283:     mumps->id.ICNTL(20) = 1; /* sparse RHS */
1284:   }

1286:   PetscCall(MatGetSize(B, &M, &nrhs));
1287:   mumps->id.nrhs = nrhs;
1288:   mumps->id.lrhs = M;
1289:   mumps->id.rhs  = NULL;

1291:   if (mumps->petsc_size == 1) {
1292:     PetscScalar *aa;
1293:     PetscInt     spnr, *ia, *ja;
1294:     PetscBool    second_solve = PETSC_FALSE;

1296:     PetscCall(MatDenseGetArray(X, &array));
1297:     mumps->id.rhs = (MumpsScalar *)array;

1299:     if (denseB) {
1300:       /* copy B to X */
1301:       PetscCall(MatDenseGetArrayRead(B, &rbray));
1302:       PetscCall(PetscArraycpy(array, rbray, M * nrhs));
1303:       PetscCall(MatDenseRestoreArrayRead(B, &rbray));
1304:     } else { /* sparse B */
1305:       PetscCall(MatSeqAIJGetArray(Bt, &aa));
1306:       PetscCall(MatGetRowIJ(Bt, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1307:       PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
1308:       PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
1309:       mumps->id.rhs_sparse = (MumpsScalar *)aa;
1310:     }
1311:     /* handle condensation step of Schur complement (if any) */
1312:     if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) {
1313:       second_solve = PETSC_TRUE;
1314:       PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1315:     }
1316:     /* solve phase */
1317:     mumps->id.job = JOB_SOLVE;
1318:     PetscMUMPS_c(mumps);
1319:     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));

1321:     /* handle expansion step of Schur complement (if any) */
1322:     if (second_solve) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_TRUE));
1323:     if (!denseB) { /* sparse B */
1324:       PetscCall(MatSeqAIJRestoreArray(Bt, &aa));
1325:       PetscCall(MatRestoreRowIJ(Bt, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1326:       PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot restore IJ structure");
1327:     }
1328:     PetscCall(MatDenseRestoreArray(X, &array));
1329:     PetscFunctionReturn(PETSC_SUCCESS);
1330:   }

1332:   /* parallel case: MUMPS requires rhs B to be centralized on the host! */
1333:   PetscCheck(mumps->petsc_size <= 1 || !mumps->id.ICNTL(19), PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Parallel Schur complements not yet supported from PETSc");

1335:   /* create msol_loc to hold mumps local solution */
1336:   isol_loc_save = mumps->id.isol_loc; /* save it for MatSolve() */
1337:   sol_loc_save  = (PetscScalar *)mumps->id.sol_loc;

1339:   lsol_loc  = mumps->id.lsol_loc;
1340:   nlsol_loc = nrhs * lsol_loc; /* length of sol_loc */
1341:   PetscCall(PetscMalloc2(nlsol_loc, &sol_loc, lsol_loc, &isol_loc));
1342:   mumps->id.sol_loc  = (MumpsScalar *)sol_loc;
1343:   mumps->id.isol_loc = isol_loc;

1345:   PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, nlsol_loc, (PetscScalar *)sol_loc, &msol_loc));

1347:   if (denseB) {
1348:     if (mumps->ICNTL20 == 10) {
1349:       mumps->id.ICNTL(20) = 10; /* dense distributed RHS */
1350:       PetscCall(MatDenseGetArrayRead(B, &rbray));
1351:       PetscCall(MatMumpsSetUpDistRHSInfo(A, nrhs, rbray));
1352:       PetscCall(MatDenseRestoreArrayRead(B, &rbray));
1353:       PetscCall(MatGetLocalSize(B, &m, NULL));
1354:       PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B), 1, nrhs * m, nrhs * M, NULL, &v_mpi));
1355:     } else {
1356:       mumps->id.ICNTL(20) = 0; /* dense centralized RHS */
1357:       /* TODO: Because of non-contiguous indices, the created vecscatter scat_rhs is not done in MPI_Gather, resulting in
1358:         very inefficient communication. An optimization is to use VecScatterCreateToZero to gather B to rank 0. Then on rank
1359:         0, re-arrange B into desired order, which is a local operation.
1360:       */

1362:       /* scatter v_mpi to b_seq because MUMPS before 5.3.0 only supports centralized rhs */
1363:       /* wrap dense rhs matrix B into a vector v_mpi */
1364:       PetscCall(MatGetLocalSize(B, &m, NULL));
1365:       PetscCall(MatDenseGetArray(B, &bray));
1366:       PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B), 1, nrhs * m, nrhs * M, (const PetscScalar *)bray, &v_mpi));
1367:       PetscCall(MatDenseRestoreArray(B, &bray));

1369:       /* scatter v_mpi to b_seq in proc[0]. MUMPS requires rhs to be centralized on the host! */
1370:       if (!mumps->myid) {
1371:         PetscInt *idx;
1372:         /* idx: maps from k-th index of v_mpi to (i,j)-th global entry of B */
1373:         PetscCall(PetscMalloc1(nrhs * M, &idx));
1374:         PetscCall(MatGetOwnershipRanges(B, &rstart));
1375:         k = 0;
1376:         for (proc = 0; proc < mumps->petsc_size; proc++) {
1377:           for (j = 0; j < nrhs; j++) {
1378:             for (i = rstart[proc]; i < rstart[proc + 1]; i++) idx[k++] = j * M + i;
1379:           }
1380:         }

1382:         PetscCall(VecCreateSeq(PETSC_COMM_SELF, nrhs * M, &b_seq));
1383:         PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nrhs * M, idx, PETSC_OWN_POINTER, &is_to));
1384:         PetscCall(ISCreateStride(PETSC_COMM_SELF, nrhs * M, 0, 1, &is_from));
1385:       } else {
1386:         PetscCall(VecCreateSeq(PETSC_COMM_SELF, 0, &b_seq));
1387:         PetscCall(ISCreateStride(PETSC_COMM_SELF, 0, 0, 1, &is_to));
1388:         PetscCall(ISCreateStride(PETSC_COMM_SELF, 0, 0, 1, &is_from));
1389:       }
1390:       PetscCall(VecScatterCreate(v_mpi, is_from, b_seq, is_to, &scat_rhs));
1391:       PetscCall(VecScatterBegin(scat_rhs, v_mpi, b_seq, INSERT_VALUES, SCATTER_FORWARD));
1392:       PetscCall(ISDestroy(&is_to));
1393:       PetscCall(ISDestroy(&is_from));
1394:       PetscCall(VecScatterEnd(scat_rhs, v_mpi, b_seq, INSERT_VALUES, SCATTER_FORWARD));

1396:       if (!mumps->myid) { /* define rhs on the host */
1397:         PetscCall(VecGetArray(b_seq, &bray));
1398:         mumps->id.rhs = (MumpsScalar *)bray;
1399:         PetscCall(VecRestoreArray(b_seq, &bray));
1400:       }
1401:     }
1402:   } else { /* sparse B */
1403:     b = (Mat_MPIAIJ *)Bt->data;

1405:     /* wrap dense X into a vector v_mpi */
1406:     PetscCall(MatGetLocalSize(X, &m, NULL));
1407:     PetscCall(MatDenseGetArray(X, &bray));
1408:     PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)X), 1, nrhs * m, nrhs * M, (const PetscScalar *)bray, &v_mpi));
1409:     PetscCall(MatDenseRestoreArray(X, &bray));

1411:     if (!mumps->myid) {
1412:       PetscCall(MatSeqAIJGetArray(b->A, &aa));
1413:       PetscCall(MatGetRowIJ(b->A, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1414:       PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
1415:       PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
1416:       mumps->id.rhs_sparse = (MumpsScalar *)aa;
1417:     } else {
1418:       mumps->id.irhs_ptr    = NULL;
1419:       mumps->id.irhs_sparse = NULL;
1420:       mumps->id.nz_rhs      = 0;
1421:       mumps->id.rhs_sparse  = NULL;
1422:     }
1423:   }

1425:   /* solve phase */
1426:   mumps->id.job = JOB_SOLVE;
1427:   PetscMUMPS_c(mumps);
1428:   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));

1430:   /* scatter mumps distributed solution to petsc vector v_mpi, which shares local arrays with solution matrix X */
1431:   PetscCall(MatDenseGetArray(X, &array));
1432:   PetscCall(VecPlaceArray(v_mpi, array));

1434:   /* create scatter scat_sol */
1435:   PetscCall(MatGetOwnershipRanges(X, &rstart));
1436:   /* iidx: index for scatter mumps solution to petsc X */

1438:   PetscCall(ISCreateStride(PETSC_COMM_SELF, nlsol_loc, 0, 1, &is_from));
1439:   PetscCall(PetscMalloc1(nlsol_loc, &idxx));
1440:   for (i = 0; i < lsol_loc; i++) {
1441:     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 */

1443:     for (proc = 0; proc < mumps->petsc_size; proc++) {
1444:       if (isol_loc[i] >= rstart[proc] && isol_loc[i] < rstart[proc + 1]) {
1445:         myrstart = rstart[proc];
1446:         k        = isol_loc[i] - myrstart;          /* local index on 1st column of petsc vector X */
1447:         iidx     = k + myrstart * nrhs;             /* maps mumps isol_loc[i] to petsc index in X */
1448:         m        = rstart[proc + 1] - rstart[proc]; /* rows of X for this proc */
1449:         break;
1450:       }
1451:     }

1453:     for (j = 0; j < nrhs; j++) idxx[i + j * lsol_loc] = iidx + j * m;
1454:   }
1455:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nlsol_loc, idxx, PETSC_COPY_VALUES, &is_to));
1456:   PetscCall(VecScatterCreate(msol_loc, is_from, v_mpi, is_to, &scat_sol));
1457:   PetscCall(VecScatterBegin(scat_sol, msol_loc, v_mpi, INSERT_VALUES, SCATTER_FORWARD));
1458:   PetscCall(ISDestroy(&is_from));
1459:   PetscCall(ISDestroy(&is_to));
1460:   PetscCall(VecScatterEnd(scat_sol, msol_loc, v_mpi, INSERT_VALUES, SCATTER_FORWARD));
1461:   PetscCall(MatDenseRestoreArray(X, &array));

1463:   /* free spaces */
1464:   mumps->id.sol_loc  = (MumpsScalar *)sol_loc_save;
1465:   mumps->id.isol_loc = isol_loc_save;

1467:   PetscCall(PetscFree2(sol_loc, isol_loc));
1468:   PetscCall(PetscFree(idxx));
1469:   PetscCall(VecDestroy(&msol_loc));
1470:   PetscCall(VecDestroy(&v_mpi));
1471:   if (!denseB) {
1472:     if (!mumps->myid) {
1473:       b = (Mat_MPIAIJ *)Bt->data;
1474:       PetscCall(MatSeqAIJRestoreArray(b->A, &aa));
1475:       PetscCall(MatRestoreRowIJ(b->A, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1476:       PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot restore IJ structure");
1477:     }
1478:   } else {
1479:     if (mumps->ICNTL20 == 0) {
1480:       PetscCall(VecDestroy(&b_seq));
1481:       PetscCall(VecScatterDestroy(&scat_rhs));
1482:     }
1483:   }
1484:   PetscCall(VecScatterDestroy(&scat_sol));
1485:   PetscCall(PetscLogFlops(nrhs * PetscMax(0, (2.0 * (mumps->id.INFO(28) >= 0 ? mumps->id.INFO(28) : -1000000 * mumps->id.INFO(28)) - A->cmap->n))));
1486:   PetscFunctionReturn(PETSC_SUCCESS);
1487: }

1489: PetscErrorCode MatMatSolveTranspose_MUMPS(Mat A, Mat B, Mat X)
1490: {
1491:   Mat_MUMPS          *mumps = (Mat_MUMPS *)A->data;
1492:   const PetscMUMPSInt value = mumps->id.ICNTL(9);

1494:   PetscFunctionBegin;
1495:   mumps->id.ICNTL(9) = 0;
1496:   PetscCall(MatMatSolve_MUMPS(A, B, X));
1497:   mumps->id.ICNTL(9) = value;
1498:   PetscFunctionReturn(PETSC_SUCCESS);
1499: }

1501: PetscErrorCode MatMatTransposeSolve_MUMPS(Mat A, Mat Bt, Mat X)
1502: {
1503:   PetscBool flg;
1504:   Mat       B;

1506:   PetscFunctionBegin;
1507:   PetscCall(PetscObjectTypeCompareAny((PetscObject)Bt, &flg, MATSEQAIJ, MATMPIAIJ, NULL));
1508:   PetscCheck(flg, PetscObjectComm((PetscObject)Bt), PETSC_ERR_ARG_WRONG, "Matrix Bt must be MATAIJ matrix");

1510:   /* Create B=Bt^T that uses Bt's data structure */
1511:   PetscCall(MatCreateTranspose(Bt, &B));

1513:   PetscCall(MatMatSolve_MUMPS(A, B, X));
1514:   PetscCall(MatDestroy(&B));
1515:   PetscFunctionReturn(PETSC_SUCCESS);
1516: }

1518: #if !defined(PETSC_USE_COMPLEX)
1519: /*
1520:   input:
1521:    F:        numeric factor
1522:   output:
1523:    nneg:     total number of negative pivots
1524:    nzero:    total number of zero pivots
1525:    npos:     (global dimension of F) - nneg - nzero
1526: */
1527: PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
1528: {
1529:   Mat_MUMPS  *mumps = (Mat_MUMPS *)F->data;
1530:   PetscMPIInt size;

1532:   PetscFunctionBegin;
1533:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)F), &size));
1534:   /* 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 */
1535:   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));

1537:   if (nneg) *nneg = mumps->id.INFOG(12);
1538:   if (nzero || npos) {
1539:     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");
1540:     if (nzero) *nzero = mumps->id.INFOG(28);
1541:     if (npos) *npos = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28));
1542:   }
1543:   PetscFunctionReturn(PETSC_SUCCESS);
1544: }
1545: #endif

1547: PetscErrorCode MatMumpsGatherNonzerosOnMaster(MatReuse reuse, Mat_MUMPS *mumps)
1548: {
1549:   PetscInt       i, nreqs;
1550:   PetscMUMPSInt *irn, *jcn;
1551:   PetscMPIInt    count;
1552:   PetscInt64     totnnz, remain;
1553:   const PetscInt osize = mumps->omp_comm_size;
1554:   PetscScalar   *val;

1556:   PetscFunctionBegin;
1557:   if (osize > 1) {
1558:     if (reuse == MAT_INITIAL_MATRIX) {
1559:       /* master first gathers counts of nonzeros to receive */
1560:       if (mumps->is_omp_master) PetscCall(PetscMalloc1(osize, &mumps->recvcount));
1561:       PetscCallMPI(MPI_Gather(&mumps->nnz, 1, MPIU_INT64, mumps->recvcount, 1, MPIU_INT64, 0 /*master*/, mumps->omp_comm));

1563:       /* Then each computes number of send/recvs */
1564:       if (mumps->is_omp_master) {
1565:         /* Start from 1 since self communication is not done in MPI */
1566:         nreqs = 0;
1567:         for (i = 1; i < osize; i++) nreqs += (mumps->recvcount[i] + PETSC_MPI_INT_MAX - 1) / PETSC_MPI_INT_MAX;
1568:       } else {
1569:         nreqs = (mumps->nnz + PETSC_MPI_INT_MAX - 1) / PETSC_MPI_INT_MAX;
1570:       }
1571:       PetscCall(PetscMalloc1(nreqs * 3, &mumps->reqs)); /* Triple the requests since we send irn, jcn and val separately */

1573:       /* The following code is doing a very simple thing: omp_master rank gathers irn/jcn/val from others.
1574:          MPI_Gatherv would be enough if it supports big counts > 2^31-1. Since it does not, and mumps->nnz
1575:          might be a prime number > 2^31-1, we have to slice the message. Note omp_comm_size
1576:          is very small, the current approach should have no extra overhead compared to MPI_Gatherv.
1577:        */
1578:       nreqs = 0; /* counter for actual send/recvs */
1579:       if (mumps->is_omp_master) {
1580:         for (i = 0, totnnz = 0; i < osize; i++) totnnz += mumps->recvcount[i]; /* totnnz = sum of nnz over omp_comm */
1581:         PetscCall(PetscMalloc2(totnnz, &irn, totnnz, &jcn));
1582:         PetscCall(PetscMalloc1(totnnz, &val));

1584:         /* Self communication */
1585:         PetscCall(PetscArraycpy(irn, mumps->irn, mumps->nnz));
1586:         PetscCall(PetscArraycpy(jcn, mumps->jcn, mumps->nnz));
1587:         PetscCall(PetscArraycpy(val, mumps->val, mumps->nnz));

1589:         /* Replace mumps->irn/jcn etc on master with the newly allocated bigger arrays */
1590:         PetscCall(PetscFree2(mumps->irn, mumps->jcn));
1591:         PetscCall(PetscFree(mumps->val_alloc));
1592:         mumps->nnz = totnnz;
1593:         mumps->irn = irn;
1594:         mumps->jcn = jcn;
1595:         mumps->val = mumps->val_alloc = val;

1597:         irn += mumps->recvcount[0]; /* recvcount[0] is old mumps->nnz on omp rank 0 */
1598:         jcn += mumps->recvcount[0];
1599:         val += mumps->recvcount[0];

1601:         /* Remote communication */
1602:         for (i = 1; i < osize; i++) {
1603:           count  = PetscMin(mumps->recvcount[i], PETSC_MPI_INT_MAX);
1604:           remain = mumps->recvcount[i] - count;
1605:           while (count > 0) {
1606:             PetscCallMPI(MPI_Irecv(irn, count, MPIU_MUMPSINT, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1607:             PetscCallMPI(MPI_Irecv(jcn, count, MPIU_MUMPSINT, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1608:             PetscCallMPI(MPI_Irecv(val, count, MPIU_SCALAR, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1609:             irn += count;
1610:             jcn += count;
1611:             val += count;
1612:             count = PetscMin(remain, PETSC_MPI_INT_MAX);
1613:             remain -= count;
1614:           }
1615:         }
1616:       } else {
1617:         irn    = mumps->irn;
1618:         jcn    = mumps->jcn;
1619:         val    = mumps->val;
1620:         count  = PetscMin(mumps->nnz, PETSC_MPI_INT_MAX);
1621:         remain = mumps->nnz - count;
1622:         while (count > 0) {
1623:           PetscCallMPI(MPI_Isend(irn, count, MPIU_MUMPSINT, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1624:           PetscCallMPI(MPI_Isend(jcn, count, MPIU_MUMPSINT, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1625:           PetscCallMPI(MPI_Isend(val, count, MPIU_SCALAR, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1626:           irn += count;
1627:           jcn += count;
1628:           val += count;
1629:           count = PetscMin(remain, PETSC_MPI_INT_MAX);
1630:           remain -= count;
1631:         }
1632:       }
1633:     } else {
1634:       nreqs = 0;
1635:       if (mumps->is_omp_master) {
1636:         val = mumps->val + mumps->recvcount[0];
1637:         for (i = 1; i < osize; i++) { /* Remote communication only since self data is already in place */
1638:           count  = PetscMin(mumps->recvcount[i], PETSC_MPI_INT_MAX);
1639:           remain = mumps->recvcount[i] - count;
1640:           while (count > 0) {
1641:             PetscCallMPI(MPI_Irecv(val, count, MPIU_SCALAR, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1642:             val += count;
1643:             count = PetscMin(remain, PETSC_MPI_INT_MAX);
1644:             remain -= count;
1645:           }
1646:         }
1647:       } else {
1648:         val    = mumps->val;
1649:         count  = PetscMin(mumps->nnz, PETSC_MPI_INT_MAX);
1650:         remain = mumps->nnz - count;
1651:         while (count > 0) {
1652:           PetscCallMPI(MPI_Isend(val, count, MPIU_SCALAR, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1653:           val += count;
1654:           count = PetscMin(remain, PETSC_MPI_INT_MAX);
1655:           remain -= count;
1656:         }
1657:       }
1658:     }
1659:     PetscCallMPI(MPI_Waitall(nreqs, mumps->reqs, MPI_STATUSES_IGNORE));
1660:     mumps->tag++; /* It is totally fine for above send/recvs to share one mpi tag */
1661:   }
1662:   PetscFunctionReturn(PETSC_SUCCESS);
1663: }

1665: PetscErrorCode MatFactorNumeric_MUMPS(Mat F, Mat A, const MatFactorInfo *info)
1666: {
1667:   Mat_MUMPS *mumps = (Mat_MUMPS *)(F)->data;
1668:   PetscBool  isMPIAIJ;

1670:   PetscFunctionBegin;
1671:   if (mumps->id.INFOG(1) < 0 && !(mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0)) {
1672:     if (mumps->id.INFOG(1) == -6) 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)));
1673:     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)));
1674:     PetscFunctionReturn(PETSC_SUCCESS);
1675:   }

1677:   PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, mumps));
1678:   PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_REUSE_MATRIX, mumps));

1680:   /* numerical factorization phase */
1681:   mumps->id.job = JOB_FACTNUMERIC;
1682:   if (!mumps->id.ICNTL(18)) { /* A is centralized */
1683:     if (!mumps->myid) mumps->id.a = (MumpsScalar *)mumps->val;
1684:   } else {
1685:     mumps->id.a_loc = (MumpsScalar *)mumps->val;
1686:   }
1687:   PetscMUMPS_c(mumps);
1688:   if (mumps->id.INFOG(1) < 0) {
1689:     PetscCheck(!A->erroriffailure, 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));
1690:     if (mumps->id.INFOG(1) == -10) { /* numerically singular matrix */
1691:       PetscCall(PetscInfo(F, "matrix is numerically singular, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1692:       F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1693:     } else if (mumps->id.INFOG(1) == -13) {
1694:       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)));
1695:       F->factorerrortype = MAT_FACTOR_OUTMEMORY;
1696:     } else if (mumps->id.INFOG(1) == -8 || mumps->id.INFOG(1) == -9 || (-16 < mumps->id.INFOG(1) && mumps->id.INFOG(1) < -10)) {
1697:       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)));
1698:       F->factorerrortype = MAT_FACTOR_OUTMEMORY;
1699:     } else {
1700:       PetscCall(PetscInfo(F, "MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1701:       F->factorerrortype = MAT_FACTOR_OTHER;
1702:     }
1703:   }
1704:   PetscCheck(mumps->myid || mumps->id.ICNTL(16) <= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "  mumps->id.ICNTL(16):=%d", mumps->id.INFOG(16));

1706:   F->assembled = PETSC_TRUE;

1708:   if (F->schur) { /* reset Schur status to unfactored */
1709: #if defined(PETSC_HAVE_CUDA)
1710:     F->schur->offloadmask = PETSC_OFFLOAD_CPU;
1711: #endif
1712:     if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
1713:       mumps->id.ICNTL(19) = 2;
1714:       PetscCall(MatTranspose(F->schur, MAT_INPLACE_MATRIX, &F->schur));
1715:     }
1716:     PetscCall(MatFactorRestoreSchurComplement(F, NULL, MAT_FACTOR_SCHUR_UNFACTORED));
1717:   }

1719:   /* just to be sure that ICNTL(19) value returned by a call from MatMumpsGetIcntl is always consistent */
1720:   if (!mumps->sym && mumps->id.ICNTL(19) && mumps->id.ICNTL(19) != 1) mumps->id.ICNTL(19) = 3;

1722:   if (!mumps->is_omp_master) mumps->id.INFO(23) = 0;
1723:   if (mumps->petsc_size > 1) {
1724:     PetscInt     lsol_loc;
1725:     PetscScalar *sol_loc;

1727:     PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &isMPIAIJ));

1729:     /* distributed solution; Create x_seq=sol_loc for repeated use */
1730:     if (mumps->x_seq) {
1731:       PetscCall(VecScatterDestroy(&mumps->scat_sol));
1732:       PetscCall(PetscFree2(mumps->id.sol_loc, mumps->id.isol_loc));
1733:       PetscCall(VecDestroy(&mumps->x_seq));
1734:     }
1735:     lsol_loc = mumps->id.INFO(23); /* length of sol_loc */
1736:     PetscCall(PetscMalloc2(lsol_loc, &sol_loc, lsol_loc, &mumps->id.isol_loc));
1737:     mumps->id.lsol_loc = lsol_loc;
1738:     mumps->id.sol_loc  = (MumpsScalar *)sol_loc;
1739:     PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, lsol_loc, sol_loc, &mumps->x_seq));
1740:   }
1741:   PetscCall(PetscLogFlops(mumps->id.RINFO(2)));
1742:   PetscFunctionReturn(PETSC_SUCCESS);
1743: }

1745: /* Sets MUMPS options from the options database */
1746: PetscErrorCode MatSetFromOptions_MUMPS(Mat F, Mat A)
1747: {
1748:   Mat_MUMPS    *mumps = (Mat_MUMPS *)F->data;
1749:   PetscMUMPSInt icntl = 0, size, *listvar_schur;
1750:   PetscInt      info[80], i, ninfo = 80, rbs, cbs;
1751:   PetscBool     flg = PETSC_FALSE, schur = (PetscBool)(mumps->id.ICNTL(26) == -1);
1752:   MumpsScalar  *arr;

1754:   PetscFunctionBegin;
1755:   PetscOptionsBegin(PetscObjectComm((PetscObject)F), ((PetscObject)F)->prefix, "MUMPS Options", "Mat");
1756:   if (mumps->id.job == JOB_NULL) { /* MatSetFromOptions_MUMPS() has never been called before */
1757:     PetscInt nthreads   = 0;
1758:     PetscInt nCNTL_pre  = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
1759:     PetscInt nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0;

1761:     mumps->petsc_comm = PetscObjectComm((PetscObject)A);
1762:     PetscCallMPI(MPI_Comm_size(mumps->petsc_comm, &mumps->petsc_size));
1763:     PetscCallMPI(MPI_Comm_rank(mumps->petsc_comm, &mumps->myid)); /* "if (!myid)" still works even if mumps_comm is different */

1765:     PetscCall(PetscOptionsName("-mat_mumps_use_omp_threads", "Convert MPI processes into OpenMP threads", "None", &mumps->use_petsc_omp_support));
1766:     if (mumps->use_petsc_omp_support) nthreads = -1; /* -1 will let PetscOmpCtrlCreate() guess a proper value when user did not supply one */
1767:     /* do not use PetscOptionsInt() so that the option -mat_mumps_use_omp_threads is not displayed twice in the help */
1768:     PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)F)->prefix, "-mat_mumps_use_omp_threads", &nthreads, NULL));
1769:     if (mumps->use_petsc_omp_support) {
1770:       PetscCheck(PetscDefined(HAVE_OPENMP_SUPPORT), 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",
1771:                  ((PetscObject)F)->prefix ? ((PetscObject)F)->prefix : "");
1772:       PetscCheck(!schur, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot use -%smat_mumps_use_omp_threads with the Schur complement feature", ((PetscObject)F)->prefix ? ((PetscObject)F)->prefix : "");
1773: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
1774:       PetscCall(PetscOmpCtrlCreate(mumps->petsc_comm, nthreads, &mumps->omp_ctrl));
1775:       PetscCall(PetscOmpCtrlGetOmpComms(mumps->omp_ctrl, &mumps->omp_comm, &mumps->mumps_comm, &mumps->is_omp_master));
1776: #endif
1777:     } else {
1778:       mumps->omp_comm      = PETSC_COMM_SELF;
1779:       mumps->mumps_comm    = mumps->petsc_comm;
1780:       mumps->is_omp_master = PETSC_TRUE;
1781:     }
1782:     PetscCallMPI(MPI_Comm_size(mumps->omp_comm, &mumps->omp_comm_size));
1783:     mumps->reqs = NULL;
1784:     mumps->tag  = 0;

1786:     if (mumps->mumps_comm != MPI_COMM_NULL) {
1787:       if (PetscDefined(HAVE_OPENMP_SUPPORT) && mumps->use_petsc_omp_support) {
1788:         /* It looks like MUMPS does not dup the input comm. Dup a new comm for MUMPS to avoid any tag mismatches. */
1789:         MPI_Comm comm;
1790:         PetscCallMPI(MPI_Comm_dup(mumps->mumps_comm, &comm));
1791:         mumps->mumps_comm = comm;
1792:       } else PetscCall(PetscCommGetComm(mumps->petsc_comm, &mumps->mumps_comm));
1793:     }

1795:     mumps->id.comm_fortran = MPI_Comm_c2f(mumps->mumps_comm);
1796:     mumps->id.job          = JOB_INIT;
1797:     mumps->id.par          = 1; /* host participates factorizaton and solve */
1798:     mumps->id.sym          = mumps->sym;

1800:     size          = mumps->id.size_schur;
1801:     arr           = mumps->id.schur;
1802:     listvar_schur = mumps->id.listvar_schur;
1803:     PetscMUMPS_c(mumps);
1804:     PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MUMPS: INFOG(1)=%d", mumps->id.INFOG(1));
1805:     /* restore cached ICNTL and CNTL values */
1806:     for (icntl = 0; icntl < nICNTL_pre; ++icntl) mumps->id.ICNTL(mumps->ICNTL_pre[1 + 2 * icntl]) = mumps->ICNTL_pre[2 + 2 * icntl];
1807:     for (icntl = 0; icntl < nCNTL_pre; ++icntl) mumps->id.CNTL((PetscInt)mumps->CNTL_pre[1 + 2 * icntl]) = mumps->CNTL_pre[2 + 2 * icntl];
1808:     PetscCall(PetscFree(mumps->ICNTL_pre));
1809:     PetscCall(PetscFree(mumps->CNTL_pre));

1811:     if (schur) {
1812:       mumps->id.size_schur    = size;
1813:       mumps->id.schur_lld     = size;
1814:       mumps->id.schur         = arr;
1815:       mumps->id.listvar_schur = listvar_schur;
1816:       if (mumps->petsc_size > 1) {
1817:         PetscBool gs; /* gs is false if any rank other than root has non-empty IS */

1819:         mumps->id.ICNTL(19) = 1;                                                                            /* MUMPS returns Schur centralized on the host */
1820:         gs                  = mumps->myid ? (mumps->id.size_schur ? PETSC_FALSE : PETSC_TRUE) : PETSC_TRUE; /* always true on root; false on others if their size != 0 */
1821:         PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &gs, 1, MPIU_BOOL, MPI_LAND, mumps->petsc_comm));
1822:         PetscCheck(gs, PETSC_COMM_SELF, PETSC_ERR_SUP, "MUMPS distributed parallel Schur complements not yet supported from PETSc");
1823:       } else {
1824:         if (F->factortype == MAT_FACTOR_LU) {
1825:           mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */
1826:         } else {
1827:           mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */
1828:         }
1829:       }
1830:       mumps->id.ICNTL(26) = -1;
1831:     }

1833:     /* copy MUMPS default control values from master to slaves. Although slaves do not call MUMPS, they may access these values in code.
1834:        For example, ICNTL(9) is initialized to 1 by MUMPS and slaves check ICNTL(9) in MatSolve_MUMPS.
1835:      */
1836:     PetscCallMPI(MPI_Bcast(mumps->id.icntl, 40, MPI_INT, 0, mumps->omp_comm));
1837:     PetscCallMPI(MPI_Bcast(mumps->id.cntl, 15, MPIU_REAL, 0, mumps->omp_comm));

1839:     mumps->scat_rhs = NULL;
1840:     mumps->scat_sol = NULL;

1842:     /* set PETSc-MUMPS default options - override MUMPS default */
1843:     mumps->id.ICNTL(3) = 0;
1844:     mumps->id.ICNTL(4) = 0;
1845:     if (mumps->petsc_size == 1) {
1846:       mumps->id.ICNTL(18) = 0; /* centralized assembled matrix input */
1847:       mumps->id.ICNTL(7)  = 7; /* automatic choice of ordering done by the package */
1848:     } else {
1849:       mumps->id.ICNTL(18) = 3; /* distributed assembled matrix input */
1850:       mumps->id.ICNTL(21) = 1; /* distributed solution */
1851:     }
1852:   }
1853:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_1", "ICNTL(1): output stream for error messages", "None", mumps->id.ICNTL(1), &icntl, &flg));
1854:   if (flg) mumps->id.ICNTL(1) = icntl;
1855:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_2", "ICNTL(2): output stream for diagnostic printing, statistics, and warning", "None", mumps->id.ICNTL(2), &icntl, &flg));
1856:   if (flg) mumps->id.ICNTL(2) = icntl;
1857:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_3", "ICNTL(3): output stream for global information, collected on the host", "None", mumps->id.ICNTL(3), &icntl, &flg));
1858:   if (flg) mumps->id.ICNTL(3) = icntl;

1860:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_4", "ICNTL(4): level of printing (0 to 4)", "None", mumps->id.ICNTL(4), &icntl, &flg));
1861:   if (flg) mumps->id.ICNTL(4) = icntl;
1862:   if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */

1864:   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));
1865:   if (flg) mumps->id.ICNTL(6) = icntl;

1867:   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));
1868:   if (flg) {
1869:     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");
1870:     mumps->id.ICNTL(7) = icntl;
1871:   }

1873:   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));
1874:   /* 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() */
1875:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_10", "ICNTL(10): max num of refinements", "None", mumps->id.ICNTL(10), &mumps->id.ICNTL(10), NULL));
1876:   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));
1877:   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));
1878:   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));
1879:   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));
1880:   PetscCall(MatGetBlockSizes(A, &rbs, &cbs));
1881:   if (rbs == cbs && rbs > 1) mumps->id.ICNTL(15) = -rbs;
1882:   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));
1883:   if (flg) {
1884:     PetscCheck(mumps->id.ICNTL(15) <= 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Positive -mat_mumps_icntl_15 not handled");
1885:     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");
1886:   }
1887:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_19", "ICNTL(19): computes the Schur complement", "None", mumps->id.ICNTL(19), &mumps->id.ICNTL(19), NULL));
1888:   if (mumps->id.ICNTL(19) <= 0 || mumps->id.ICNTL(19) > 3) { /* reset any schur data (if any) */
1889:     PetscCall(MatDestroy(&F->schur));
1890:     PetscCall(MatMumpsResetSchur_Private(mumps));
1891:   }

1893:   /* Two MPICH Fortran MPI_IN_PLACE binding bugs prevented the use of 'mpich + mumps'. One happened with "mpi4py + mpich + mumps",
1894:      and was reported by Firedrake. See https://bitbucket.org/mpi4py/mpi4py/issues/162/mpi4py-initialization-breaks-fortran
1895:      and a petsc-maint mailing list thread with subject 'MUMPS segfaults in parallel because of ...'
1896:      This bug was fixed by https://github.com/pmodels/mpich/pull/4149. But the fix brought a new bug,
1897:      see https://github.com/pmodels/mpich/issues/5589. This bug was fixed by https://github.com/pmodels/mpich/pull/5590.
1898:      In short, we could not use distributed RHS with MPICH until v4.0b1.
1899:    */
1900: #if PETSC_PKG_MUMPS_VERSION_LT(5, 3, 0) || (defined(PETSC_HAVE_MPICH_NUMVERSION) && (PETSC_HAVE_MPICH_NUMVERSION < 40000101))
1901:   mumps->ICNTL20 = 0; /* Centralized dense RHS*/
1902: #else
1903:   mumps->ICNTL20     = 10; /* Distributed dense RHS*/
1904: #endif
1905:   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));
1906:   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);
1907: #if PETSC_PKG_MUMPS_VERSION_LT(5, 3, 0)
1908:   PetscCheck(!flg || mumps->ICNTL20 != 10, PETSC_COMM_SELF, PETSC_ERR_SUP, "ICNTL(20)=10 is not supported before MUMPS-5.3.0");
1909: #endif
1910:   /* 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 */

1912:   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));
1913:   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));
1914:   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));
1915:   if (mumps->id.ICNTL(24)) { mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */ }

1917:   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));
1918:   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));
1919:   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));
1920:   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));
1921:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_29", "ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis", "None", mumps->id.ICNTL(29), &mumps->id.ICNTL(29), NULL));
1922:   /* 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 */
1923:   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));
1924:   /* 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 */
1925:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_33", "ICNTL(33): compute determinant", "None", mumps->id.ICNTL(33), &mumps->id.ICNTL(33), NULL));
1926:   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));
1927:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_36", "ICNTL(36): choice of BLR factorization variant", "None", mumps->id.ICNTL(36), &mumps->id.ICNTL(36), NULL));
1928:   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));

1930:   PetscCall(PetscOptionsReal("-mat_mumps_cntl_1", "CNTL(1): relative pivoting threshold", "None", mumps->id.CNTL(1), &mumps->id.CNTL(1), NULL));
1931:   PetscCall(PetscOptionsReal("-mat_mumps_cntl_2", "CNTL(2): stopping criterion of refinement", "None", mumps->id.CNTL(2), &mumps->id.CNTL(2), NULL));
1932:   PetscCall(PetscOptionsReal("-mat_mumps_cntl_3", "CNTL(3): absolute pivoting threshold", "None", mumps->id.CNTL(3), &mumps->id.CNTL(3), NULL));
1933:   PetscCall(PetscOptionsReal("-mat_mumps_cntl_4", "CNTL(4): value for static pivoting", "None", mumps->id.CNTL(4), &mumps->id.CNTL(4), NULL));
1934:   PetscCall(PetscOptionsReal("-mat_mumps_cntl_5", "CNTL(5): fixation for null pivots", "None", mumps->id.CNTL(5), &mumps->id.CNTL(5), NULL));
1935:   PetscCall(PetscOptionsReal("-mat_mumps_cntl_7", "CNTL(7): dropping parameter used during BLR", "None", mumps->id.CNTL(7), &mumps->id.CNTL(7), NULL));

1937:   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));

1939:   PetscCall(PetscOptionsIntArray("-mat_mumps_view_info", "request INFO local to each processor", "", info, &ninfo, NULL));
1940:   if (ninfo) {
1941:     PetscCheck(ninfo <= 80, PETSC_COMM_SELF, PETSC_ERR_USER, "number of INFO %" PetscInt_FMT " must <= 80", ninfo);
1942:     PetscCall(PetscMalloc1(ninfo, &mumps->info));
1943:     mumps->ninfo = ninfo;
1944:     for (i = 0; i < ninfo; i++) {
1945:       PetscCheck(info[i] >= 0 && info[i] <= 80, PETSC_COMM_SELF, PETSC_ERR_USER, "index of INFO %" PetscInt_FMT " must between 1 and 80", ninfo);
1946:       mumps->info[i] = info[i];
1947:     }
1948:   }
1949:   PetscOptionsEnd();
1950:   PetscFunctionReturn(PETSC_SUCCESS);
1951: }

1953: PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F, Mat A, const MatFactorInfo *info, Mat_MUMPS *mumps)
1954: {
1955:   PetscFunctionBegin;
1956:   if (mumps->id.INFOG(1) < 0) {
1957:     PetscCheck(!A->erroriffailure, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MUMPS in analysis phase: INFOG(1)=%d", mumps->id.INFOG(1));
1958:     if (mumps->id.INFOG(1) == -6) {
1959:       PetscCall(PetscInfo(F, "matrix is singular in structure, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1960:       F->factorerrortype = MAT_FACTOR_STRUCT_ZEROPIVOT;
1961:     } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) {
1962:       PetscCall(PetscInfo(F, "problem of workspace, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1963:       F->factorerrortype = MAT_FACTOR_OUTMEMORY;
1964:     } else if (mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0) {
1965:       PetscCall(PetscInfo(F, "Empty matrix\n"));
1966:     } else {
1967:       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)));
1968:       F->factorerrortype = MAT_FACTOR_OTHER;
1969:     }
1970:   }
1971:   PetscFunctionReturn(PETSC_SUCCESS);
1972: }

1974: PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
1975: {
1976:   Mat_MUMPS     *mumps = (Mat_MUMPS *)F->data;
1977:   Vec            b;
1978:   const PetscInt M = A->rmap->N;

1980:   PetscFunctionBegin;
1981:   if (mumps->matstruc == SAME_NONZERO_PATTERN) {
1982:     /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */
1983:     PetscFunctionReturn(PETSC_SUCCESS);
1984:   }

1986:   /* Set MUMPS options from the options database */
1987:   PetscCall(MatSetFromOptions_MUMPS(F, A));

1989:   PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
1990:   PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));

1992:   /* analysis phase */
1993:   mumps->id.job = JOB_FACTSYMBOLIC;
1994:   mumps->id.n   = M;
1995:   switch (mumps->id.ICNTL(18)) {
1996:   case 0: /* centralized assembled matrix input */
1997:     if (!mumps->myid) {
1998:       mumps->id.nnz = mumps->nnz;
1999:       mumps->id.irn = mumps->irn;
2000:       mumps->id.jcn = mumps->jcn;
2001:       if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2002:       if (r) {
2003:         mumps->id.ICNTL(7) = 1;
2004:         if (!mumps->myid) {
2005:           const PetscInt *idx;
2006:           PetscInt        i;

2008:           PetscCall(PetscMalloc1(M, &mumps->id.perm_in));
2009:           PetscCall(ISGetIndices(r, &idx));
2010:           for (i = 0; i < M; i++) PetscCall(PetscMUMPSIntCast(idx[i] + 1, &(mumps->id.perm_in[i]))); /* perm_in[]: start from 1, not 0! */
2011:           PetscCall(ISRestoreIndices(r, &idx));
2012:         }
2013:       }
2014:     }
2015:     break;
2016:   case 3: /* distributed assembled matrix input (size>1) */
2017:     mumps->id.nnz_loc = mumps->nnz;
2018:     mumps->id.irn_loc = mumps->irn;
2019:     mumps->id.jcn_loc = mumps->jcn;
2020:     if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2021:     if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2022:       PetscCall(MatCreateVecs(A, NULL, &b));
2023:       PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2024:       PetscCall(VecDestroy(&b));
2025:     }
2026:     break;
2027:   }
2028:   PetscMUMPS_c(mumps);
2029:   PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));

2031:   F->ops->lufactornumeric   = MatFactorNumeric_MUMPS;
2032:   F->ops->solve             = MatSolve_MUMPS;
2033:   F->ops->solvetranspose    = MatSolveTranspose_MUMPS;
2034:   F->ops->matsolve          = MatMatSolve_MUMPS;
2035:   F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS;
2036:   F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;

2038:   mumps->matstruc = SAME_NONZERO_PATTERN;
2039:   PetscFunctionReturn(PETSC_SUCCESS);
2040: }

2042: /* Note the Petsc r and c permutations are ignored */
2043: PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
2044: {
2045:   Mat_MUMPS     *mumps = (Mat_MUMPS *)F->data;
2046:   Vec            b;
2047:   const PetscInt M = A->rmap->N;

2049:   PetscFunctionBegin;
2050:   if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2051:     /* F is assembled by a previous call of MatLUFactorSymbolic_BAIJMUMPS() */
2052:     PetscFunctionReturn(PETSC_SUCCESS);
2053:   }

2055:   /* Set MUMPS options from the options database */
2056:   PetscCall(MatSetFromOptions_MUMPS(F, A));

2058:   PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2059:   PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));

2061:   /* analysis phase */
2062:   mumps->id.job = JOB_FACTSYMBOLIC;
2063:   mumps->id.n   = M;
2064:   switch (mumps->id.ICNTL(18)) {
2065:   case 0: /* centralized assembled matrix input */
2066:     if (!mumps->myid) {
2067:       mumps->id.nnz = mumps->nnz;
2068:       mumps->id.irn = mumps->irn;
2069:       mumps->id.jcn = mumps->jcn;
2070:       if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2071:     }
2072:     break;
2073:   case 3: /* distributed assembled matrix input (size>1) */
2074:     mumps->id.nnz_loc = mumps->nnz;
2075:     mumps->id.irn_loc = mumps->irn;
2076:     mumps->id.jcn_loc = mumps->jcn;
2077:     if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2078:     if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2079:       PetscCall(MatCreateVecs(A, NULL, &b));
2080:       PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2081:       PetscCall(VecDestroy(&b));
2082:     }
2083:     break;
2084:   }
2085:   PetscMUMPS_c(mumps);
2086:   PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));

2088:   F->ops->lufactornumeric   = MatFactorNumeric_MUMPS;
2089:   F->ops->solve             = MatSolve_MUMPS;
2090:   F->ops->solvetranspose    = MatSolveTranspose_MUMPS;
2091:   F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;

2093:   mumps->matstruc = SAME_NONZERO_PATTERN;
2094:   PetscFunctionReturn(PETSC_SUCCESS);
2095: }

2097: /* Note the Petsc r permutation and factor info are ignored */
2098: PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F, Mat A, IS r, const MatFactorInfo *info)
2099: {
2100:   Mat_MUMPS     *mumps = (Mat_MUMPS *)F->data;
2101:   Vec            b;
2102:   const PetscInt M = A->rmap->N;

2104:   PetscFunctionBegin;
2105:   if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2106:     /* F is assembled by a previous call of MatCholeskyFactorSymbolic_MUMPS() */
2107:     PetscFunctionReturn(PETSC_SUCCESS);
2108:   }

2110:   /* Set MUMPS options from the options database */
2111:   PetscCall(MatSetFromOptions_MUMPS(F, A));

2113:   PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2114:   PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));

2116:   /* analysis phase */
2117:   mumps->id.job = JOB_FACTSYMBOLIC;
2118:   mumps->id.n   = M;
2119:   switch (mumps->id.ICNTL(18)) {
2120:   case 0: /* centralized assembled matrix input */
2121:     if (!mumps->myid) {
2122:       mumps->id.nnz = mumps->nnz;
2123:       mumps->id.irn = mumps->irn;
2124:       mumps->id.jcn = mumps->jcn;
2125:       if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2126:     }
2127:     break;
2128:   case 3: /* distributed assembled matrix input (size>1) */
2129:     mumps->id.nnz_loc = mumps->nnz;
2130:     mumps->id.irn_loc = mumps->irn;
2131:     mumps->id.jcn_loc = mumps->jcn;
2132:     if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2133:     if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2134:       PetscCall(MatCreateVecs(A, NULL, &b));
2135:       PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2136:       PetscCall(VecDestroy(&b));
2137:     }
2138:     break;
2139:   }
2140:   PetscMUMPS_c(mumps);
2141:   PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));

2143:   F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS;
2144:   F->ops->solve                 = MatSolve_MUMPS;
2145:   F->ops->solvetranspose        = MatSolve_MUMPS;
2146:   F->ops->matsolve              = MatMatSolve_MUMPS;
2147:   F->ops->mattransposesolve     = MatMatTransposeSolve_MUMPS;
2148:   F->ops->matsolvetranspose     = MatMatSolveTranspose_MUMPS;
2149: #if defined(PETSC_USE_COMPLEX)
2150:   F->ops->getinertia = NULL;
2151: #else
2152:   F->ops->getinertia = MatGetInertia_SBAIJMUMPS;
2153: #endif

2155:   mumps->matstruc = SAME_NONZERO_PATTERN;
2156:   PetscFunctionReturn(PETSC_SUCCESS);
2157: }

2159: PetscErrorCode MatView_MUMPS(Mat A, PetscViewer viewer)
2160: {
2161:   PetscBool         iascii;
2162:   PetscViewerFormat format;
2163:   Mat_MUMPS        *mumps = (Mat_MUMPS *)A->data;

2165:   PetscFunctionBegin;
2166:   /* check if matrix is mumps type */
2167:   if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(PETSC_SUCCESS);

2169:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
2170:   if (iascii) {
2171:     PetscCall(PetscViewerGetFormat(viewer, &format));
2172:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
2173:       PetscCall(PetscViewerASCIIPrintf(viewer, "MUMPS run parameters:\n"));
2174:       if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
2175:         PetscCall(PetscViewerASCIIPrintf(viewer, "  SYM (matrix type):                   %d\n", mumps->id.sym));
2176:         PetscCall(PetscViewerASCIIPrintf(viewer, "  PAR (host participation):            %d\n", mumps->id.par));
2177:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(1) (output for error):         %d\n", mumps->id.ICNTL(1)));
2178:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(2) (output of diagnostic msg): %d\n", mumps->id.ICNTL(2)));
2179:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(3) (output for global info):   %d\n", mumps->id.ICNTL(3)));
2180:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(4) (level of printing):        %d\n", mumps->id.ICNTL(4)));
2181:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(5) (input mat struct):         %d\n", mumps->id.ICNTL(5)));
2182:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(6) (matrix prescaling):        %d\n", mumps->id.ICNTL(6)));
2183:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(7) (sequential matrix ordering):%d\n", mumps->id.ICNTL(7)));
2184:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(8) (scaling strategy):         %d\n", mumps->id.ICNTL(8)));
2185:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(10) (max num of refinements):  %d\n", mumps->id.ICNTL(10)));
2186:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(11) (error analysis):          %d\n", mumps->id.ICNTL(11)));
2187:         if (mumps->id.ICNTL(11) > 0) {
2188:           PetscCall(PetscViewerASCIIPrintf(viewer, "    RINFOG(4) (inf norm of input mat):        %g\n", mumps->id.RINFOG(4)));
2189:           PetscCall(PetscViewerASCIIPrintf(viewer, "    RINFOG(5) (inf norm of solution):         %g\n", mumps->id.RINFOG(5)));
2190:           PetscCall(PetscViewerASCIIPrintf(viewer, "    RINFOG(6) (inf norm of residual):         %g\n", mumps->id.RINFOG(6)));
2191:           PetscCall(PetscViewerASCIIPrintf(viewer, "    RINFOG(7),RINFOG(8) (backward error est): %g, %g\n", mumps->id.RINFOG(7), mumps->id.RINFOG(8)));
2192:           PetscCall(PetscViewerASCIIPrintf(viewer, "    RINFOG(9) (error estimate):               %g\n", mumps->id.RINFOG(9)));
2193:           PetscCall(PetscViewerASCIIPrintf(viewer, "    RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n", mumps->id.RINFOG(10), mumps->id.RINFOG(11)));
2194:         }
2195:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(12) (efficiency control):                         %d\n", mumps->id.ICNTL(12)));
2196:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(13) (sequential factorization of the root node):  %d\n", mumps->id.ICNTL(13)));
2197:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(14) (percentage of estimated workspace increase): %d\n", mumps->id.ICNTL(14)));
2198:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(15) (compression of the input matrix):            %d\n", mumps->id.ICNTL(15)));
2199:         /* ICNTL(15-17) not used */
2200:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(18) (input mat struct):                           %d\n", mumps->id.ICNTL(18)));
2201:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(19) (Schur complement info):                      %d\n", mumps->id.ICNTL(19)));
2202:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(20) (RHS sparse pattern):                         %d\n", mumps->id.ICNTL(20)));
2203:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(21) (solution struct):                            %d\n", mumps->id.ICNTL(21)));
2204:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(22) (in-core/out-of-core facility):               %d\n", mumps->id.ICNTL(22)));
2205:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(23) (max size of memory can be allocated locally):%d\n", mumps->id.ICNTL(23)));

2207:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(24) (detection of null pivot rows):               %d\n", mumps->id.ICNTL(24)));
2208:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(25) (computation of a null space basis):          %d\n", mumps->id.ICNTL(25)));
2209:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(26) (Schur options for RHS or solution):          %d\n", mumps->id.ICNTL(26)));
2210:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(27) (blocking size for multiple RHS):             %d\n", mumps->id.ICNTL(27)));
2211:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(28) (use parallel or sequential ordering):        %d\n", mumps->id.ICNTL(28)));
2212:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(29) (parallel ordering):                          %d\n", mumps->id.ICNTL(29)));

2214:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(30) (user-specified set of entries in inv(A)):    %d\n", mumps->id.ICNTL(30)));
2215:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(31) (factors is discarded in the solve phase):    %d\n", mumps->id.ICNTL(31)));
2216:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(33) (compute determinant):                        %d\n", mumps->id.ICNTL(33)));
2217:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(35) (activate BLR based factorization):           %d\n", mumps->id.ICNTL(35)));
2218:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(36) (choice of BLR factorization variant):        %d\n", mumps->id.ICNTL(36)));
2219:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(38) (estimated compression rate of LU factors):   %d\n", mumps->id.ICNTL(38)));

2221:         PetscCall(PetscViewerASCIIPrintf(viewer, "  CNTL(1) (relative pivoting threshold):      %g\n", mumps->id.CNTL(1)));
2222:         PetscCall(PetscViewerASCIIPrintf(viewer, "  CNTL(2) (stopping criterion of refinement): %g\n", mumps->id.CNTL(2)));
2223:         PetscCall(PetscViewerASCIIPrintf(viewer, "  CNTL(3) (absolute pivoting threshold):      %g\n", mumps->id.CNTL(3)));
2224:         PetscCall(PetscViewerASCIIPrintf(viewer, "  CNTL(4) (value of static pivoting):         %g\n", mumps->id.CNTL(4)));
2225:         PetscCall(PetscViewerASCIIPrintf(viewer, "  CNTL(5) (fixation for null pivots):         %g\n", mumps->id.CNTL(5)));
2226:         PetscCall(PetscViewerASCIIPrintf(viewer, "  CNTL(7) (dropping parameter for BLR):       %g\n", mumps->id.CNTL(7)));

2228:         /* information local to each processor */
2229:         PetscCall(PetscViewerASCIIPrintf(viewer, "  RINFO(1) (local estimated flops for the elimination after analysis):\n"));
2230:         PetscCall(PetscViewerASCIIPushSynchronized(viewer));
2231:         PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "    [%d] %g\n", mumps->myid, mumps->id.RINFO(1)));
2232:         PetscCall(PetscViewerFlush(viewer));
2233:         PetscCall(PetscViewerASCIIPrintf(viewer, "  RINFO(2) (local estimated flops for the assembly after factorization):\n"));
2234:         PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "    [%d] %g\n", mumps->myid, mumps->id.RINFO(2)));
2235:         PetscCall(PetscViewerFlush(viewer));
2236:         PetscCall(PetscViewerASCIIPrintf(viewer, "  RINFO(3) (local estimated flops for the elimination after factorization):\n"));
2237:         PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "    [%d] %g\n", mumps->myid, mumps->id.RINFO(3)));
2238:         PetscCall(PetscViewerFlush(viewer));

2240:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization):\n"));
2241:         PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "    [%d] %d\n", mumps->myid, mumps->id.INFO(15)));
2242:         PetscCall(PetscViewerFlush(viewer));

2244:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization):\n"));
2245:         PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "    [%d] %d\n", mumps->myid, mumps->id.INFO(16)));
2246:         PetscCall(PetscViewerFlush(viewer));

2248:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFO(23) (num of pivots eliminated on this processor after factorization):\n"));
2249:         PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "    [%d] %d\n", mumps->myid, mumps->id.INFO(23)));
2250:         PetscCall(PetscViewerFlush(viewer));

2252:         if (mumps->ninfo && mumps->ninfo <= 80) {
2253:           PetscInt i;
2254:           for (i = 0; i < mumps->ninfo; i++) {
2255:             PetscCall(PetscViewerASCIIPrintf(viewer, "  INFO(%" PetscInt_FMT "):\n", mumps->info[i]));
2256:             PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "    [%d] %d\n", mumps->myid, mumps->id.INFO(mumps->info[i])));
2257:             PetscCall(PetscViewerFlush(viewer));
2258:           }
2259:         }
2260:         PetscCall(PetscViewerASCIIPopSynchronized(viewer));
2261:       } else PetscCall(PetscViewerASCIIPrintf(viewer, "  Use -%sksp_view ::ascii_info_detail to display information for all processes\n", ((PetscObject)A)->prefix ? ((PetscObject)A)->prefix : ""));

2263:       if (mumps->myid == 0) { /* information from the host */
2264:         PetscCall(PetscViewerASCIIPrintf(viewer, "  RINFOG(1) (global estimated flops for the elimination after analysis): %g\n", mumps->id.RINFOG(1)));
2265:         PetscCall(PetscViewerASCIIPrintf(viewer, "  RINFOG(2) (global estimated flops for the assembly after factorization): %g\n", mumps->id.RINFOG(2)));
2266:         PetscCall(PetscViewerASCIIPrintf(viewer, "  RINFOG(3) (global estimated flops for the elimination after factorization): %g\n", mumps->id.RINFOG(3)));
2267:         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)));

2269:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(3) (estimated real workspace for factors on all processors after analysis): %d\n", mumps->id.INFOG(3)));
2270:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d\n", mumps->id.INFOG(4)));
2271:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(5) (estimated maximum front size in the complete tree): %d\n", mumps->id.INFOG(5)));
2272:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(6) (number of nodes in the complete tree): %d\n", mumps->id.INFOG(6)));
2273:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(7) (ordering option effectively used after analysis): %d\n", mumps->id.INFOG(7)));
2274:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d\n", mumps->id.INFOG(8)));
2275:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d\n", mumps->id.INFOG(9)));
2276:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(10) (total integer space store the matrix factors after factorization): %d\n", mumps->id.INFOG(10)));
2277:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(11) (order of largest frontal matrix after factorization): %d\n", mumps->id.INFOG(11)));
2278:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(12) (number of off-diagonal pivots): %d\n", mumps->id.INFOG(12)));
2279:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(13) (number of delayed pivots after factorization): %d\n", mumps->id.INFOG(13)));
2280:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(14) (number of memory compress after factorization): %d\n", mumps->id.INFOG(14)));
2281:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(15) (number of steps of iterative refinement after solution): %d\n", mumps->id.INFOG(15)));
2282:         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)));
2283:         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)));
2284:         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)));
2285:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): %d\n", mumps->id.INFOG(19)));
2286:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(20) (estimated number of entries in the factors): %d\n", mumps->id.INFOG(20)));
2287:         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)));
2288:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): %d\n", mumps->id.INFOG(22)));
2289:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d\n", mumps->id.INFOG(23)));
2290:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d\n", mumps->id.INFOG(24)));
2291:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(25) (after factorization: number of pivots modified by static pivoting): %d\n", mumps->id.INFOG(25)));
2292:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(28) (after factorization: number of null pivots encountered): %d\n", mumps->id.INFOG(28)));
2293:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(29) (after factorization: effective number of entries in the factors (sum over all processors)): %d\n", mumps->id.INFOG(29)));
2294:         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)));
2295:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(32) (after analysis: type of analysis done): %d\n", mumps->id.INFOG(32)));
2296:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(33) (value used for ICNTL(8)): %d\n", mumps->id.INFOG(33)));
2297:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(34) (exponent of the determinant if determinant is requested): %d\n", mumps->id.INFOG(34)));
2298:         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)));
2299:         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)));
2300:         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)));
2301:         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)));
2302:         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)));
2303:       }
2304:     }
2305:   }
2306:   PetscFunctionReturn(PETSC_SUCCESS);
2307: }

2309: PetscErrorCode MatGetInfo_MUMPS(Mat A, MatInfoType flag, MatInfo *info)
2310: {
2311:   Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;

2313:   PetscFunctionBegin;
2314:   info->block_size        = 1.0;
2315:   info->nz_allocated      = mumps->id.INFOG(20) >= 0 ? mumps->id.INFOG(20) : -1000000 * mumps->id.INFOG(20);
2316:   info->nz_used           = mumps->id.INFOG(20) >= 0 ? mumps->id.INFOG(20) : -1000000 * mumps->id.INFOG(20);
2317:   info->nz_unneeded       = 0.0;
2318:   info->assemblies        = 0.0;
2319:   info->mallocs           = 0.0;
2320:   info->memory            = 0.0;
2321:   info->fill_ratio_given  = 0;
2322:   info->fill_ratio_needed = 0;
2323:   info->factor_mallocs    = 0;
2324:   PetscFunctionReturn(PETSC_SUCCESS);
2325: }

2327: PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is)
2328: {
2329:   Mat_MUMPS         *mumps = (Mat_MUMPS *)F->data;
2330:   const PetscScalar *arr;
2331:   const PetscInt    *idxs;
2332:   PetscInt           size, i;

2334:   PetscFunctionBegin;
2335:   PetscCall(ISGetLocalSize(is, &size));
2336:   /* Schur complement matrix */
2337:   PetscCall(MatDestroy(&F->schur));
2338:   PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, size, size, NULL, &F->schur));
2339:   PetscCall(MatDenseGetArrayRead(F->schur, &arr));
2340:   mumps->id.schur      = (MumpsScalar *)arr;
2341:   mumps->id.size_schur = size;
2342:   mumps->id.schur_lld  = size;
2343:   PetscCall(MatDenseRestoreArrayRead(F->schur, &arr));
2344:   if (mumps->sym == 1) PetscCall(MatSetOption(F->schur, MAT_SPD, PETSC_TRUE));

2346:   /* MUMPS expects Fortran style indices */
2347:   PetscCall(PetscFree(mumps->id.listvar_schur));
2348:   PetscCall(PetscMalloc1(size, &mumps->id.listvar_schur));
2349:   PetscCall(ISGetIndices(is, &idxs));
2350:   for (i = 0; i < size; i++) PetscCall(PetscMUMPSIntCast(idxs[i] + 1, &(mumps->id.listvar_schur[i])));
2351:   PetscCall(ISRestoreIndices(is, &idxs));
2352:   /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */
2353:   mumps->id.ICNTL(26) = -1;
2354:   PetscFunctionReturn(PETSC_SUCCESS);
2355: }

2357: PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F, Mat *S)
2358: {
2359:   Mat          St;
2360:   Mat_MUMPS   *mumps = (Mat_MUMPS *)F->data;
2361:   PetscScalar *array;
2362: #if defined(PETSC_USE_COMPLEX)
2363:   PetscScalar im = PetscSqrtScalar((PetscScalar)-1.0);
2364: #endif

2366:   PetscFunctionBegin;
2367:   PetscCheck(mumps->id.ICNTL(19), PetscObjectComm((PetscObject)F), PETSC_ERR_ORDER, "Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it");
2368:   PetscCall(MatCreate(PETSC_COMM_SELF, &St));
2369:   PetscCall(MatSetSizes(St, PETSC_DECIDE, PETSC_DECIDE, mumps->id.size_schur, mumps->id.size_schur));
2370:   PetscCall(MatSetType(St, MATDENSE));
2371:   PetscCall(MatSetUp(St));
2372:   PetscCall(MatDenseGetArray(St, &array));
2373:   if (!mumps->sym) {                /* MUMPS always return a full matrix */
2374:     if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
2375:       PetscInt i, j, N = mumps->id.size_schur;
2376:       for (i = 0; i < N; i++) {
2377:         for (j = 0; 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[j * N + i] = val;
2384:         }
2385:       }
2386:     } else { /* stored by columns */
2387:       PetscCall(PetscArraycpy(array, mumps->id.schur, mumps->id.size_schur * mumps->id.size_schur));
2388:     }
2389:   } else {                          /* either full or lower-triangular (not packed) */
2390:     if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */
2391:       PetscInt i, j, N = mumps->id.size_schur;
2392:       for (i = 0; i < N; i++) {
2393:         for (j = i; j < N; j++) {
2394: #if !defined(PETSC_USE_COMPLEX)
2395:           PetscScalar val = mumps->id.schur[i * N + j];
2396: #else
2397:           PetscScalar val = mumps->id.schur[i * N + j].r + im * mumps->id.schur[i * N + j].i;
2398: #endif
2399:           array[i * N + j] = val;
2400:           array[j * N + i] = val;
2401:         }
2402:       }
2403:     } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */
2404:       PetscCall(PetscArraycpy(array, mumps->id.schur, mumps->id.size_schur * mumps->id.size_schur));
2405:     } else { /* ICNTL(19) == 1 lower triangular stored by rows */
2406:       PetscInt i, j, N = mumps->id.size_schur;
2407:       for (i = 0; i < N; i++) {
2408:         for (j = 0; j < i + 1; j++) {
2409: #if !defined(PETSC_USE_COMPLEX)
2410:           PetscScalar val = mumps->id.schur[i * N + j];
2411: #else
2412:           PetscScalar val = mumps->id.schur[i * N + j].r + im * mumps->id.schur[i * N + j].i;
2413: #endif
2414:           array[i * N + j] = val;
2415:           array[j * N + i] = val;
2416:         }
2417:       }
2418:     }
2419:   }
2420:   PetscCall(MatDenseRestoreArray(St, &array));
2421:   *S = St;
2422:   PetscFunctionReturn(PETSC_SUCCESS);
2423: }

2425: PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F, PetscInt icntl, PetscInt ival)
2426: {
2427:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2429:   PetscFunctionBegin;
2430:   if (mumps->id.job == JOB_NULL) {                                       /* need to cache icntl and ival since PetscMUMPS_c() has never been called */
2431:     PetscInt i, nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0; /* number of already cached ICNTL */
2432:     for (i = 0; i < nICNTL_pre; ++i)
2433:       if (mumps->ICNTL_pre[1 + 2 * i] == icntl) break; /* is this ICNTL already cached? */
2434:     if (i == nICNTL_pre) {                             /* not already cached */
2435:       if (i > 0) PetscCall(PetscRealloc(sizeof(PetscMUMPSInt) * (2 * nICNTL_pre + 3), &mumps->ICNTL_pre));
2436:       else PetscCall(PetscCalloc(sizeof(PetscMUMPSInt) * 3, &mumps->ICNTL_pre));
2437:       mumps->ICNTL_pre[0]++;
2438:     }
2439:     mumps->ICNTL_pre[1 + 2 * i] = icntl;
2440:     PetscCall(PetscMUMPSIntCast(ival, mumps->ICNTL_pre + 2 + 2 * i));
2441:   } else PetscCall(PetscMUMPSIntCast(ival, &mumps->id.ICNTL(icntl)));
2442:   PetscFunctionReturn(PETSC_SUCCESS);
2443: }

2445: PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F, PetscInt icntl, PetscInt *ival)
2446: {
2447:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2449:   PetscFunctionBegin;
2450:   if (mumps->id.job == JOB_NULL) {
2451:     PetscInt i, nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0;
2452:     *ival = 0;
2453:     for (i = 0; i < nICNTL_pre; ++i) {
2454:       if (mumps->ICNTL_pre[1 + 2 * i] == icntl) *ival = mumps->ICNTL_pre[2 + 2 * i];
2455:     }
2456:   } else *ival = mumps->id.ICNTL(icntl);
2457:   PetscFunctionReturn(PETSC_SUCCESS);
2458: }

2460: /*@
2461:   MatMumpsSetIcntl - Set MUMPS parameter ICNTL()

2463:    Logically Collective

2465:    Input Parameters:
2466: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2467: .  icntl - index of MUMPS parameter array ICNTL()
2468: -  ival - value of MUMPS ICNTL(icntl)

2470:   Options Database Key:
2471: .   -mat_mumps_icntl_<icntl> <ival> - change the option numbered icntl to ival

2473:    Level: beginner

2475:    References:
2476: .  * - MUMPS Users' Guide

2478: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2479: @*/
2480: PetscErrorCode MatMumpsSetIcntl(Mat F, PetscInt icntl, PetscInt ival)
2481: {
2482:   PetscFunctionBegin;
2484:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2487:   PetscCheck(icntl >= 1 && icntl <= 38, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported ICNTL value %" PetscInt_FMT, icntl);
2488:   PetscTryMethod(F, "MatMumpsSetIcntl_C", (Mat, PetscInt, PetscInt), (F, icntl, ival));
2489:   PetscFunctionReturn(PETSC_SUCCESS);
2490: }

2492: /*@
2493:   MatMumpsGetIcntl - Get MUMPS parameter ICNTL()

2495:    Logically Collective

2497:    Input Parameters:
2498: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2499: -  icntl - index of MUMPS parameter array ICNTL()

2501:   Output Parameter:
2502: .  ival - value of MUMPS ICNTL(icntl)

2504:    Level: beginner

2506:    References:
2507: .  * - MUMPS Users' Guide

2509: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2510: @*/
2511: PetscErrorCode MatMumpsGetIcntl(Mat F, PetscInt icntl, PetscInt *ival)
2512: {
2513:   PetscFunctionBegin;
2515:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2518:   PetscCheck(icntl >= 1 && icntl <= 38, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported ICNTL value %" PetscInt_FMT, icntl);
2519:   PetscUseMethod(F, "MatMumpsGetIcntl_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
2520:   PetscFunctionReturn(PETSC_SUCCESS);
2521: }

2523: PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F, PetscInt icntl, PetscReal val)
2524: {
2525:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2527:   PetscFunctionBegin;
2528:   if (mumps->id.job == JOB_NULL) {
2529:     PetscInt i, nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
2530:     for (i = 0; i < nCNTL_pre; ++i)
2531:       if (mumps->CNTL_pre[1 + 2 * i] == icntl) break;
2532:     if (i == nCNTL_pre) {
2533:       if (i > 0) PetscCall(PetscRealloc(sizeof(PetscReal) * (2 * nCNTL_pre + 3), &mumps->CNTL_pre));
2534:       else PetscCall(PetscCalloc(sizeof(PetscReal) * 3, &mumps->CNTL_pre));
2535:       mumps->CNTL_pre[0]++;
2536:     }
2537:     mumps->CNTL_pre[1 + 2 * i] = icntl;
2538:     mumps->CNTL_pre[2 + 2 * i] = val;
2539:   } else mumps->id.CNTL(icntl) = val;
2540:   PetscFunctionReturn(PETSC_SUCCESS);
2541: }

2543: PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F, PetscInt icntl, PetscReal *val)
2544: {
2545:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2547:   PetscFunctionBegin;
2548:   if (mumps->id.job == JOB_NULL) {
2549:     PetscInt i, nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
2550:     *val = 0.0;
2551:     for (i = 0; i < nCNTL_pre; ++i) {
2552:       if (mumps->CNTL_pre[1 + 2 * i] == icntl) *val = mumps->CNTL_pre[2 + 2 * i];
2553:     }
2554:   } else *val = mumps->id.CNTL(icntl);
2555:   PetscFunctionReturn(PETSC_SUCCESS);
2556: }

2558: /*@
2559:   MatMumpsSetCntl - Set MUMPS parameter CNTL()

2561:    Logically Collective

2563:    Input Parameters:
2564: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2565: .  icntl - index of MUMPS parameter array CNTL()
2566: -  val - value of MUMPS CNTL(icntl)

2568:   Options Database Key:
2569: .   -mat_mumps_cntl_<icntl> <val>  - change the option numbered icntl to ival

2571:    Level: beginner

2573:    References:
2574: .  * - MUMPS Users' Guide

2576: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2577: @*/
2578: PetscErrorCode MatMumpsSetCntl(Mat F, PetscInt icntl, PetscReal val)
2579: {
2580:   PetscFunctionBegin;
2582:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2585:   PetscCheck(icntl >= 1 && icntl <= 7, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported CNTL value %" PetscInt_FMT, icntl);
2586:   PetscTryMethod(F, "MatMumpsSetCntl_C", (Mat, PetscInt, PetscReal), (F, icntl, val));
2587:   PetscFunctionReturn(PETSC_SUCCESS);
2588: }

2590: /*@
2591:   MatMumpsGetCntl - Get MUMPS parameter CNTL()

2593:    Logically Collective

2595:    Input Parameters:
2596: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2597: -  icntl - index of MUMPS parameter array CNTL()

2599:   Output Parameter:
2600: .  val - value of MUMPS CNTL(icntl)

2602:    Level: beginner

2604:    References:
2605: .  * - MUMPS Users' Guide

2607: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2608: @*/
2609: PetscErrorCode MatMumpsGetCntl(Mat F, PetscInt icntl, PetscReal *val)
2610: {
2611:   PetscFunctionBegin;
2613:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2616:   PetscCheck(icntl >= 1 && icntl <= 7, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported CNTL value %" PetscInt_FMT, icntl);
2617:   PetscUseMethod(F, "MatMumpsGetCntl_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
2618:   PetscFunctionReturn(PETSC_SUCCESS);
2619: }

2621: PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F, PetscInt icntl, PetscInt *info)
2622: {
2623:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2625:   PetscFunctionBegin;
2626:   *info = mumps->id.INFO(icntl);
2627:   PetscFunctionReturn(PETSC_SUCCESS);
2628: }

2630: PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F, PetscInt icntl, PetscInt *infog)
2631: {
2632:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2634:   PetscFunctionBegin;
2635:   *infog = mumps->id.INFOG(icntl);
2636:   PetscFunctionReturn(PETSC_SUCCESS);
2637: }

2639: PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F, PetscInt icntl, PetscReal *rinfo)
2640: {
2641:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2643:   PetscFunctionBegin;
2644:   *rinfo = mumps->id.RINFO(icntl);
2645:   PetscFunctionReturn(PETSC_SUCCESS);
2646: }

2648: PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F, PetscInt icntl, PetscReal *rinfog)
2649: {
2650:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2652:   PetscFunctionBegin;
2653:   *rinfog = mumps->id.RINFOG(icntl);
2654:   PetscFunctionReturn(PETSC_SUCCESS);
2655: }

2657: PetscErrorCode MatMumpsGetNullPivots_MUMPS(Mat F, PetscInt *size, PetscInt **array)
2658: {
2659:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2661:   PetscFunctionBegin;
2662:   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");
2663:   *size  = 0;
2664:   *array = NULL;
2665:   if (!mumps->myid) {
2666:     *size = mumps->id.INFOG(28);
2667:     PetscCall(PetscMalloc1(*size, array));
2668:     for (int i = 0; i < *size; i++) (*array)[i] = mumps->id.pivnul_list[i] - 1;
2669:   }
2670:   PetscFunctionReturn(PETSC_SUCCESS);
2671: }

2673: PetscErrorCode MatMumpsGetInverse_MUMPS(Mat F, Mat spRHS)
2674: {
2675:   Mat          Bt = NULL, Btseq = NULL;
2676:   PetscBool    flg;
2677:   Mat_MUMPS   *mumps = (Mat_MUMPS *)F->data;
2678:   PetscScalar *aa;
2679:   PetscInt     spnr, *ia, *ja, M, nrhs;

2681:   PetscFunctionBegin;
2683:   PetscCall(PetscObjectTypeCompare((PetscObject)spRHS, MATTRANSPOSEVIRTUAL, &flg));
2684:   if (flg) {
2685:     PetscCall(MatTransposeGetMat(spRHS, &Bt));
2686:   } else SETERRQ(PetscObjectComm((PetscObject)spRHS), PETSC_ERR_ARG_WRONG, "Matrix spRHS must be type MATTRANSPOSEVIRTUAL matrix");

2688:   PetscCall(MatMumpsSetIcntl(F, 30, 1));

2690:   if (mumps->petsc_size > 1) {
2691:     Mat_MPIAIJ *b = (Mat_MPIAIJ *)Bt->data;
2692:     Btseq         = b->A;
2693:   } else {
2694:     Btseq = Bt;
2695:   }

2697:   PetscCall(MatGetSize(spRHS, &M, &nrhs));
2698:   mumps->id.nrhs = nrhs;
2699:   mumps->id.lrhs = M;
2700:   mumps->id.rhs  = NULL;

2702:   if (!mumps->myid) {
2703:     PetscCall(MatSeqAIJGetArray(Btseq, &aa));
2704:     PetscCall(MatGetRowIJ(Btseq, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
2705:     PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
2706:     PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
2707:     mumps->id.rhs_sparse = (MumpsScalar *)aa;
2708:   } else {
2709:     mumps->id.irhs_ptr    = NULL;
2710:     mumps->id.irhs_sparse = NULL;
2711:     mumps->id.nz_rhs      = 0;
2712:     mumps->id.rhs_sparse  = NULL;
2713:   }
2714:   mumps->id.ICNTL(20) = 1; /* rhs is sparse */
2715:   mumps->id.ICNTL(21) = 0; /* solution is in assembled centralized format */

2717:   /* solve phase */
2718:   mumps->id.job = JOB_SOLVE;
2719:   PetscMUMPS_c(mumps);
2720:   PetscCheck(mumps->id.INFOG(1) >= 0, 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));

2722:   if (!mumps->myid) {
2723:     PetscCall(MatSeqAIJRestoreArray(Btseq, &aa));
2724:     PetscCall(MatRestoreRowIJ(Btseq, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
2725:     PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
2726:   }
2727:   PetscFunctionReturn(PETSC_SUCCESS);
2728: }

2730: /*@
2731:   MatMumpsGetInverse - Get user-specified set of entries in inverse of `A`

2733:    Logically Collective

2735:    Input Parameter:
2736: .  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface

2738:   Output Parameter:
2739: . spRHS - sequential sparse matrix in `MATTRANSPOSEVIRTUAL` format with requested entries of inverse of `A`

2741:    Level: beginner

2743:    References:
2744: .  * - MUMPS Users' Guide

2746: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCreateTranspose()`
2747: @*/
2748: PetscErrorCode MatMumpsGetInverse(Mat F, Mat spRHS)
2749: {
2750:   PetscFunctionBegin;
2752:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2753:   PetscUseMethod(F, "MatMumpsGetInverse_C", (Mat, Mat), (F, spRHS));
2754:   PetscFunctionReturn(PETSC_SUCCESS);
2755: }

2757: PetscErrorCode MatMumpsGetInverseTranspose_MUMPS(Mat F, Mat spRHST)
2758: {
2759:   Mat spRHS;

2761:   PetscFunctionBegin;
2762:   PetscCall(MatCreateTranspose(spRHST, &spRHS));
2763:   PetscCall(MatMumpsGetInverse_MUMPS(F, spRHS));
2764:   PetscCall(MatDestroy(&spRHS));
2765:   PetscFunctionReturn(PETSC_SUCCESS);
2766: }

2768: /*@
2769:   MatMumpsGetInverseTranspose - Get user-specified set of entries in inverse of matrix `A`^T

2771:    Logically Collective

2773:    Input Parameter:
2774: .  F - the factored matrix of A obtained by calling `MatGetFactor()` from PETSc-MUMPS interface

2776:   Output Parameter:
2777: . spRHST - sequential sparse matrix in `MATAIJ` format containing the requested entries of inverse of `A`^T

2779:    Level: beginner

2781:    References:
2782: .  * - MUMPS Users' Guide

2784: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCreateTranspose()`, `MatMumpsGetInverse()`
2785: @*/
2786: PetscErrorCode MatMumpsGetInverseTranspose(Mat F, Mat spRHST)
2787: {
2788:   PetscBool flg;

2790:   PetscFunctionBegin;
2792:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2793:   PetscCall(PetscObjectTypeCompareAny((PetscObject)spRHST, &flg, MATSEQAIJ, MATMPIAIJ, NULL));
2794:   PetscCheck(flg, PetscObjectComm((PetscObject)spRHST), PETSC_ERR_ARG_WRONG, "Matrix spRHST must be MATAIJ matrix");

2796:   PetscUseMethod(F, "MatMumpsGetInverseTranspose_C", (Mat, Mat), (F, spRHST));
2797:   PetscFunctionReturn(PETSC_SUCCESS);
2798: }

2800: /*@
2801:   MatMumpsGetInfo - Get MUMPS parameter INFO()

2803:    Logically Collective

2805:    Input Parameters:
2806: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2807: -  icntl - index of MUMPS parameter array INFO()

2809:   Output Parameter:
2810: .  ival - value of MUMPS INFO(icntl)

2812:    Level: beginner

2814:    References:
2815: .  * - MUMPS Users' Guide

2817: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2818: @*/
2819: PetscErrorCode MatMumpsGetInfo(Mat F, PetscInt icntl, PetscInt *ival)
2820: {
2821:   PetscFunctionBegin;
2823:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2825:   PetscUseMethod(F, "MatMumpsGetInfo_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
2826:   PetscFunctionReturn(PETSC_SUCCESS);
2827: }

2829: /*@
2830:   MatMumpsGetInfog - Get MUMPS parameter INFOG()

2832:    Logically Collective

2834:    Input Parameters:
2835: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2836: -  icntl - index of MUMPS parameter array INFOG()

2838:   Output Parameter:
2839: .  ival - value of MUMPS INFOG(icntl)

2841:    Level: beginner

2843:    References:
2844: .  * - MUMPS Users' Guide

2846: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2847: @*/
2848: PetscErrorCode MatMumpsGetInfog(Mat F, PetscInt icntl, PetscInt *ival)
2849: {
2850:   PetscFunctionBegin;
2852:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2854:   PetscUseMethod(F, "MatMumpsGetInfog_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
2855:   PetscFunctionReturn(PETSC_SUCCESS);
2856: }

2858: /*@
2859:   MatMumpsGetRinfo - Get MUMPS parameter RINFO()

2861:    Logically Collective

2863:    Input Parameters:
2864: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2865: -  icntl - index of MUMPS parameter array RINFO()

2867:   Output Parameter:
2868: .  val - value of MUMPS RINFO(icntl)

2870:    Level: beginner

2872:    References:
2873: .  * - MUMPS Users' Guide

2875: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfog()`
2876: @*/
2877: PetscErrorCode MatMumpsGetRinfo(Mat F, PetscInt icntl, PetscReal *val)
2878: {
2879:   PetscFunctionBegin;
2881:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2883:   PetscUseMethod(F, "MatMumpsGetRinfo_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
2884:   PetscFunctionReturn(PETSC_SUCCESS);
2885: }

2887: /*@
2888:   MatMumpsGetRinfog - Get MUMPS parameter RINFOG()

2890:    Logically Collective

2892:    Input Parameters:
2893: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2894: -  icntl - index of MUMPS parameter array RINFOG()

2896:   Output Parameter:
2897: .  val - value of MUMPS RINFOG(icntl)

2899:    Level: beginner

2901:    References:
2902: .  * - MUMPS Users' Guide

2904: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`
2905: @*/
2906: PetscErrorCode MatMumpsGetRinfog(Mat F, PetscInt icntl, PetscReal *val)
2907: {
2908:   PetscFunctionBegin;
2910:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2912:   PetscUseMethod(F, "MatMumpsGetRinfog_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
2913:   PetscFunctionReturn(PETSC_SUCCESS);
2914: }

2916: /*@
2917:   MatMumpsGetNullPivots - Get MUMPS parameter PIVNUL_LIST()

2919:    Logically Collective

2921:    Input Parameter:
2922: .  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface

2924:   Output Parameters:
2925: +  size - local size of the array. The size of the array is non-zero only on the host.
2926: -  array - array of rows with null pivot, these rows follow 0-based indexing. The array gets allocated within the function and the user is responsible
2927:            for freeing this array.

2929:    Level: beginner

2931:    References:
2932: .  * - MUMPS Users' Guide

2934: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`
2935: @*/
2936: PetscErrorCode MatMumpsGetNullPivots(Mat F, PetscInt *size, PetscInt **array)
2937: {
2938:   PetscFunctionBegin;
2940:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2943:   PetscUseMethod(F, "MatMumpsGetNullPivots_C", (Mat, PetscInt *, PetscInt **), (F, size, array));
2944:   PetscFunctionReturn(PETSC_SUCCESS);
2945: }

2947: /*MC
2948:   MATSOLVERMUMPS -  A matrix type providing direct solvers (LU and Cholesky) for
2949:   distributed and sequential matrices via the external package MUMPS.

2951:   Works with `MATAIJ` and `MATSBAIJ` matrices

2953:   Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch to have PETSc installed with MUMPS

2955:   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.
2956:   See details below.

2958:   Use `-pc_type cholesky` or `lu` `-pc_factor_mat_solver_type mumps` to use this direct solver

2960:   Options Database Keys:
2961: +  -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages
2962: .  -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning
2963: .  -mat_mumps_icntl_3 -  ICNTL(3): output stream for global information, collected on the host
2964: .  -mat_mumps_icntl_4 -  ICNTL(4): level of printing (0 to 4)
2965: .  -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7)
2966: .  -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
2967:                         Use -pc_factor_mat_ordering_type <type> to have PETSc perform the ordering (sequential only)
2968: .  -mat_mumps_icntl_8  - ICNTL(8): scaling strategy (-2 to 8 or 77)
2969: .  -mat_mumps_icntl_10  - ICNTL(10): max num of refinements
2970: .  -mat_mumps_icntl_11  - ICNTL(11): statistics related to an error analysis (via -ksp_view)
2971: .  -mat_mumps_icntl_12  - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3)
2972: .  -mat_mumps_icntl_13  - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting
2973: .  -mat_mumps_icntl_14  - ICNTL(14): percentage increase in the estimated working space
2974: .  -mat_mumps_icntl_15  - ICNTL(15): compression of the input matrix resulting from a block format
2975: .  -mat_mumps_icntl_19  - ICNTL(19): computes the Schur complement
2976: .  -mat_mumps_icntl_20  - ICNTL(20): give MUMPS centralized (0) or distributed (10) dense RHS
2977: .  -mat_mumps_icntl_22  - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1)
2978: .  -mat_mumps_icntl_23  - ICNTL(23): max size of the working memory (MB) that can allocate per processor
2979: .  -mat_mumps_icntl_24  - ICNTL(24): detection of null pivot rows (0 or 1)
2980: .  -mat_mumps_icntl_25  - ICNTL(25): compute a solution of a deficient matrix and a null space basis
2981: .  -mat_mumps_icntl_26  - ICNTL(26): drives the solution phase if a Schur complement matrix
2982: .  -mat_mumps_icntl_28  - ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering
2983: .  -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis
2984: .  -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A)
2985: .  -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization
2986: .  -mat_mumps_icntl_33 - ICNTL(33): compute determinant
2987: .  -mat_mumps_icntl_35 - ICNTL(35): level of activation of BLR (Block Low-Rank) feature
2988: .  -mat_mumps_icntl_36 - ICNTL(36): controls the choice of BLR factorization variant
2989: .  -mat_mumps_icntl_38 - ICNTL(38): sets the estimated compression rate of LU factors with BLR
2990: .  -mat_mumps_cntl_1  - CNTL(1): relative pivoting threshold
2991: .  -mat_mumps_cntl_2  -  CNTL(2): stopping criterion of refinement
2992: .  -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold
2993: .  -mat_mumps_cntl_4 - CNTL(4): value for static pivoting
2994: .  -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots
2995: .  -mat_mumps_cntl_7 - CNTL(7): precision of the dropping parameter used during BLR factorization
2996: -  -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.
2997:                                    Default might be the number of cores per CPU package (socket) as reported by hwloc and suggested by the MUMPS manual.

2999:   Level: beginner

3001:     Notes:
3002:     MUMPS Cholesky does not handle (complex) Hermitian matrices (see User's Guide at https://mumps-solver.org/index.php?page=doc) so using it will
3003:     error if the matrix is Hermitian.

3005:     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
3006:     `MatSetOptionsPrefixFactor()` on the matrix from which the factor was obtained or `MatSetOptionsPrefix()` on the factor matrix.

3008:     When a MUMPS factorization fails inside a KSP solve, for example with a `KSP_DIVERGED_PC_FAILED`, one can find the MUMPS information about
3009:     the failure with
3010: .vb
3011:           KSPGetPC(ksp,&pc);
3012:           PCFactorGetMatrix(pc,&mat);
3013:           MatMumpsGetInfo(mat,....);
3014:           MatMumpsGetInfog(mat,....); etc.
3015: .ve
3016:     Or run with `-ksp_error_if_not_converged` and the program will be stopped and the information printed in the error message.

3018:     MUMPS provides 64-bit integer support in two build modes:
3019:       full 64-bit: here MUMPS is built with C preprocessing flag -DINTSIZE64 and Fortran compiler option -i8, -fdefault-integer-8 or equivalent, and
3020:       requires all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS built the same way with 64-bit integers (for example ILP64 Intel MKL and MPI).

3022:       selective 64-bit: with the default MUMPS build, 64-bit integers have been introduced where needed. In compressed sparse row (CSR) storage of matrices,
3023:       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
3024:       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
3025:       integer) build of all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS.

3027:     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.

3029:   Two modes to run MUMPS/PETSc with OpenMP
3030: .vb
3031:      Set OMP_NUM_THREADS and run with fewer MPI ranks than cores. For example, if you want to have 16 OpenMP
3032:      threads per rank, then you may use "export OMP_NUM_THREADS=16 && mpirun -n 4 ./test".
3033: .ve

3035: .vb
3036:      -mat_mumps_use_omp_threads [m] and run your code with as many MPI ranks as the number of cores. For example,
3037:     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"
3038: .ve

3040:    To run MUMPS in MPI+OpenMP hybrid mode (i.e., enable multithreading in MUMPS), but still run the non-MUMPS part
3041:    (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`
3042:    (or `--with-hwloc`), and have an MPI that supports MPI-3.0's process shared memory (which is usually available). Since MUMPS calls BLAS
3043:    libraries, to really get performance, you should have multithreaded BLAS libraries such as Intel MKL, AMD ACML, Cray libSci or OpenBLAS
3044:    (PETSc will automatically try to utilized a threaded BLAS if --with-openmp is provided).

3046:    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
3047:    processes on each compute node. Listing the processes in rank ascending order, we split processes on a node into consecutive groups of
3048:    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
3049:    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
3050:    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.
3051:    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,
3052:    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
3053:    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
3054:    cores in socket 1, that definitely hurts locality. On the other hand, if you map MPI ranks consecutively on the two sockets, then the
3055:    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.
3056:    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
3057:    examine the mapping result.

3059:    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,
3060:    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
3061:    calls `omp_set_num_threads`(m) internally before calling MUMPS.

3063:    References:
3064: +  * - Heroux, Michael A., R. Brightwell, and Michael M. Wolf. "Bi-modal MPI and MPI+ threads computing on scalable multicore systems." IJHPCA (Submitted) (2011).
3065: -  * - Gutierrez, Samuel K., et al. "Accommodating Thread-Level Heterogeneity in Coupled Parallel Applications." Parallel and Distributed Processing Symposium (IPDPS), 2017 IEEE International. IEEE, 2017.

3067: .seealso: [](ch_matrices), `Mat`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`, `KSPGetPC()`, `PCFactorGetMatrix()`
3068: M*/

3070: static PetscErrorCode MatFactorGetSolverType_mumps(Mat A, MatSolverType *type)
3071: {
3072:   PetscFunctionBegin;
3073:   *type = MATSOLVERMUMPS;
3074:   PetscFunctionReturn(PETSC_SUCCESS);
3075: }

3077: /* MatGetFactor for Seq and MPI AIJ matrices */
3078: static PetscErrorCode MatGetFactor_aij_mumps(Mat A, MatFactorType ftype, Mat *F)
3079: {
3080:   Mat         B;
3081:   Mat_MUMPS  *mumps;
3082:   PetscBool   isSeqAIJ;
3083:   PetscMPIInt size;

3085:   PetscFunctionBegin;
3086: #if defined(PETSC_USE_COMPLEX)
3087:   PetscCheck(A->hermitian != PETSC_BOOL3_TRUE || A->symmetric == PETSC_BOOL3_TRUE || ftype != MAT_FACTOR_CHOLESKY, PETSC_COMM_SELF, PETSC_ERR_SUP, "Hermitian CHOLESKY Factor is not supported");
3088: #endif
3089:   /* Create the factorization matrix */
3090:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATSEQAIJ, &isSeqAIJ));
3091:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3092:   PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3093:   PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3094:   PetscCall(MatSetUp(B));

3096:   PetscCall(PetscNew(&mumps));

3098:   B->ops->view    = MatView_MUMPS;
3099:   B->ops->getinfo = MatGetInfo_MUMPS;

3101:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3102:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3103:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3104:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3105:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3106:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3107:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3108:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3109:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3110:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3111:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3112:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3113:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3114:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));

3116:   if (ftype == MAT_FACTOR_LU) {
3117:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3118:     B->factortype            = MAT_FACTOR_LU;
3119:     if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij;
3120:     else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij;
3121:     PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3122:     mumps->sym = 0;
3123:   } else {
3124:     B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3125:     B->factortype                  = MAT_FACTOR_CHOLESKY;
3126:     if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij;
3127:     else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij;
3128:     PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_CHOLESKY]));
3129: #if defined(PETSC_USE_COMPLEX)
3130:     mumps->sym = 2;
3131: #else
3132:     if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3133:     else mumps->sym = 2;
3134: #endif
3135:   }

3137:   /* set solvertype */
3138:   PetscCall(PetscFree(B->solvertype));
3139:   PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3140:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3141:   if (size == 1) {
3142:     /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3143:     B->canuseordering = PETSC_TRUE;
3144:   }
3145:   B->ops->destroy = MatDestroy_MUMPS;
3146:   B->data         = (void *)mumps;

3148:   *F               = B;
3149:   mumps->id.job    = JOB_NULL;
3150:   mumps->ICNTL_pre = NULL;
3151:   mumps->CNTL_pre  = NULL;
3152:   mumps->matstruc  = DIFFERENT_NONZERO_PATTERN;
3153:   PetscFunctionReturn(PETSC_SUCCESS);
3154: }

3156: /* MatGetFactor for Seq and MPI SBAIJ matrices */
3157: static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A, MatFactorType ftype, Mat *F)
3158: {
3159:   Mat         B;
3160:   Mat_MUMPS  *mumps;
3161:   PetscBool   isSeqSBAIJ;
3162:   PetscMPIInt size;

3164:   PetscFunctionBegin;
3165: #if defined(PETSC_USE_COMPLEX)
3166:   PetscCheck(A->hermitian != PETSC_BOOL3_TRUE || A->symmetric == PETSC_BOOL3_TRUE, PETSC_COMM_SELF, PETSC_ERR_SUP, "Hermitian CHOLESKY Factor is not supported");
3167: #endif
3168:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3169:   PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3170:   PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3171:   PetscCall(MatSetUp(B));

3173:   PetscCall(PetscNew(&mumps));
3174:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQSBAIJ, &isSeqSBAIJ));
3175:   if (isSeqSBAIJ) {
3176:     mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij;
3177:   } else {
3178:     mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij;
3179:   }

3181:   B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3182:   B->ops->view                   = MatView_MUMPS;
3183:   B->ops->getinfo                = MatGetInfo_MUMPS;

3185:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3186:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3187:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3188:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3189:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3190:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3191:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3192:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3193:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3194:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3195:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3196:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3197:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3198:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));

3200:   B->factortype = MAT_FACTOR_CHOLESKY;
3201: #if defined(PETSC_USE_COMPLEX)
3202:   mumps->sym = 2;
3203: #else
3204:   if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3205:   else mumps->sym = 2;
3206: #endif

3208:   /* set solvertype */
3209:   PetscCall(PetscFree(B->solvertype));
3210:   PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3211:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3212:   if (size == 1) {
3213:     /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3214:     B->canuseordering = PETSC_TRUE;
3215:   }
3216:   PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_CHOLESKY]));
3217:   B->ops->destroy = MatDestroy_MUMPS;
3218:   B->data         = (void *)mumps;

3220:   *F               = B;
3221:   mumps->id.job    = JOB_NULL;
3222:   mumps->ICNTL_pre = NULL;
3223:   mumps->CNTL_pre  = NULL;
3224:   mumps->matstruc  = DIFFERENT_NONZERO_PATTERN;
3225:   PetscFunctionReturn(PETSC_SUCCESS);
3226: }

3228: static PetscErrorCode MatGetFactor_baij_mumps(Mat A, MatFactorType ftype, Mat *F)
3229: {
3230:   Mat         B;
3231:   Mat_MUMPS  *mumps;
3232:   PetscBool   isSeqBAIJ;
3233:   PetscMPIInt size;

3235:   PetscFunctionBegin;
3236:   /* Create the factorization matrix */
3237:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQBAIJ, &isSeqBAIJ));
3238:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3239:   PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3240:   PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3241:   PetscCall(MatSetUp(B));

3243:   PetscCall(PetscNew(&mumps));
3244:   if (ftype == MAT_FACTOR_LU) {
3245:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS;
3246:     B->factortype            = MAT_FACTOR_LU;
3247:     if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij;
3248:     else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij;
3249:     mumps->sym = 0;
3250:     PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3251:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead");

3253:   B->ops->view    = MatView_MUMPS;
3254:   B->ops->getinfo = MatGetInfo_MUMPS;

3256:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3257:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3258:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3259:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3260:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3261:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3262:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3263:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3264:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3265:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3266:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3267:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3268:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3269:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));

3271:   /* set solvertype */
3272:   PetscCall(PetscFree(B->solvertype));
3273:   PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3274:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3275:   if (size == 1) {
3276:     /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3277:     B->canuseordering = PETSC_TRUE;
3278:   }
3279:   B->ops->destroy = MatDestroy_MUMPS;
3280:   B->data         = (void *)mumps;

3282:   *F               = B;
3283:   mumps->id.job    = JOB_NULL;
3284:   mumps->ICNTL_pre = NULL;
3285:   mumps->CNTL_pre  = NULL;
3286:   mumps->matstruc  = DIFFERENT_NONZERO_PATTERN;
3287:   PetscFunctionReturn(PETSC_SUCCESS);
3288: }

3290: /* MatGetFactor for Seq and MPI SELL matrices */
3291: static PetscErrorCode MatGetFactor_sell_mumps(Mat A, MatFactorType ftype, Mat *F)
3292: {
3293:   Mat         B;
3294:   Mat_MUMPS  *mumps;
3295:   PetscBool   isSeqSELL;
3296:   PetscMPIInt size;

3298:   PetscFunctionBegin;
3299:   /* Create the factorization matrix */
3300:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQSELL, &isSeqSELL));
3301:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3302:   PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3303:   PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3304:   PetscCall(MatSetUp(B));

3306:   PetscCall(PetscNew(&mumps));

3308:   B->ops->view    = MatView_MUMPS;
3309:   B->ops->getinfo = MatGetInfo_MUMPS;

3311:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3312:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3313:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3314:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3315:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3316:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3317:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3318:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3319:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3320:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3321:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3322:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));

3324:   if (ftype == MAT_FACTOR_LU) {
3325:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3326:     B->factortype            = MAT_FACTOR_LU;
3327:     if (isSeqSELL) mumps->ConvertToTriples = MatConvertToTriples_seqsell_seqaij;
3328:     else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "To be implemented");
3329:     mumps->sym = 0;
3330:     PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3331:   } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "To be implemented");

3333:   /* set solvertype */
3334:   PetscCall(PetscFree(B->solvertype));
3335:   PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3336:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3337:   if (size == 1) {
3338:     /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization  */
3339:     B->canuseordering = PETSC_TRUE;
3340:   }
3341:   B->ops->destroy = MatDestroy_MUMPS;
3342:   B->data         = (void *)mumps;

3344:   *F               = B;
3345:   mumps->id.job    = JOB_NULL;
3346:   mumps->ICNTL_pre = NULL;
3347:   mumps->CNTL_pre  = NULL;
3348:   mumps->matstruc  = DIFFERENT_NONZERO_PATTERN;
3349:   PetscFunctionReturn(PETSC_SUCCESS);
3350: }

3352: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MUMPS(void)
3353: {
3354:   PetscFunctionBegin;
3355:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3356:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3357:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIBAIJ, MAT_FACTOR_LU, MatGetFactor_baij_mumps));
3358:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_baij_mumps));
3359:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPISBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_sbaij_mumps));
3360:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3361:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3362:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQBAIJ, MAT_FACTOR_LU, MatGetFactor_baij_mumps));
3363:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_baij_mumps));
3364:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQSBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_sbaij_mumps));
3365:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQSELL, MAT_FACTOR_LU, MatGetFactor_sell_mumps));
3366:   PetscFunctionReturn(PETSC_SUCCESS);
3367: }