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:           MUMPS_c(&mumps->id); \
100:           PetscCall(PetscOmpCtrlOmpRegionOnMasterEnd(mumps->omp_ctrl)); \
101:         } \
102:         PetscCall(PetscOmpCtrlBarrier(mumps->omp_ctrl)); \
103:         /* Global info is same on all processes so we Bcast it within omp_comm. Local info is specific      \
104:          to processes, so we only Bcast info[1], an error code and leave others (since they do not have   \
105:          an easy translation between omp_comm and petsc_comm). See MUMPS-5.1.2 manual p82.                   \
106:          omp_comm is a small shared memory communicator, hence doing multiple Bcast as shown below is OK. \
107:       */ \
108:         PetscCallMPI(MPI_Bcast(mumps->id.infog, 40, MPIU_MUMPSINT, 0, mumps->omp_comm)); \
109:         PetscCallMPI(MPI_Bcast(mumps->id.rinfog, 20, MPIU_REAL, 0, mumps->omp_comm)); \
110:         PetscCallMPI(MPI_Bcast(mumps->id.info, 1, MPIU_MUMPSINT, 0, mumps->omp_comm)); \
111:       } else { \
112:         MUMPS_c(&mumps->id); \
113:       } \
114:     } while (0)
115: #else
116:   #define PetscMUMPS_c(mumps) \
117:     do { \
118:       MUMPS_c(&mumps->id); \
119:     } while (0)
120: #endif

122: /* declare MumpsScalar */
123: #if defined(PETSC_USE_COMPLEX)
124:   #if defined(PETSC_USE_REAL_SINGLE)
125:     #define MumpsScalar mumps_complex
126:   #else
127:     #define MumpsScalar mumps_double_complex
128:   #endif
129: #else
130:   #define MumpsScalar PetscScalar
131: #endif

133: /* macros s.t. indices match MUMPS documentation */
134: #define ICNTL(I)  icntl[(I)-1]
135: #define CNTL(I)   cntl[(I)-1]
136: #define INFOG(I)  infog[(I)-1]
137: #define INFO(I)   info[(I)-1]
138: #define RINFOG(I) rinfog[(I)-1]
139: #define RINFO(I)  rinfo[(I)-1]

141: typedef struct Mat_MUMPS Mat_MUMPS;
142: struct Mat_MUMPS {
143: #if defined(PETSC_USE_COMPLEX)
144:   #if defined(PETSC_USE_REAL_SINGLE)
145:   CMUMPS_STRUC_C id;
146:   #else
147:   ZMUMPS_STRUC_C id;
148:   #endif
149: #else
150:   #if defined(PETSC_USE_REAL_SINGLE)
151:   SMUMPS_STRUC_C id;
152:   #else
153:   DMUMPS_STRUC_C id;
154:   #endif
155: #endif

157:   MatStructure   matstruc;
158:   PetscMPIInt    myid, petsc_size;
159:   PetscMUMPSInt *irn, *jcn;       /* the (i,j,v) triplets passed to mumps. */
160:   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. */
161:   PetscInt64     nnz;             /* number of nonzeros. The type is called selective 64-bit in mumps */
162:   PetscMUMPSInt  sym;
163:   MPI_Comm       mumps_comm;
164:   PetscMUMPSInt *ICNTL_pre;
165:   PetscReal     *CNTL_pre;
166:   PetscMUMPSInt  ICNTL9_pre;         /* check if ICNTL(9) is changed from previous MatSolve */
167:   VecScatter     scat_rhs, scat_sol; /* used by MatSolve() */
168:   PetscMUMPSInt  ICNTL20;            /* use centralized (0) or distributed (10) dense RHS */
169:   PetscMUMPSInt  lrhs_loc, nloc_rhs, *irhs_loc;
170: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
171:   PetscInt    *rhs_nrow, max_nrhs;
172:   PetscMPIInt *rhs_recvcounts, *rhs_disps;
173:   PetscScalar *rhs_loc, *rhs_recvbuf;
174: #endif
175:   Vec            b_seq, x_seq;
176:   PetscInt       ninfo, *info; /* which INFO to display */
177:   PetscInt       sizeredrhs;
178:   PetscScalar   *schur_sol;
179:   PetscInt       schur_sizesol;
180:   PetscMUMPSInt *ia_alloc, *ja_alloc; /* work arrays used for the CSR struct for sparse rhs */
181:   PetscInt64     cur_ilen, cur_jlen;  /* current len of ia_alloc[], ja_alloc[] */
182:   PetscErrorCode (*ConvertToTriples)(Mat, PetscInt, MatReuse, Mat_MUMPS *);

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

194: /* Cast a 1-based CSR represented by (nrow, ia, ja) of type PetscInt to a CSR of type PetscMUMPSInt.
195:    Here, nrow is number of rows, ia[] is row pointer and ja[] is column indices.
196:  */
197: static PetscErrorCode PetscMUMPSIntCSRCast(Mat_MUMPS *mumps, PetscInt nrow, PetscInt *ia, PetscInt *ja, PetscMUMPSInt **ia_mumps, PetscMUMPSInt **ja_mumps, PetscMUMPSInt *nnz_mumps)
198: {
199:   PetscInt nnz = ia[nrow] - 1; /* mumps uses 1-based indices. Uses PetscInt instead of PetscInt64 since mumps only uses PetscMUMPSInt for rhs */

201:   PetscFunctionBegin;
202: #if defined(PETSC_USE_64BIT_INDICES)
203:   {
204:     PetscInt i;
205:     if (nrow + 1 > mumps->cur_ilen) { /* realloc ia_alloc/ja_alloc to fit ia/ja */
206:       PetscCall(PetscFree(mumps->ia_alloc));
207:       PetscCall(PetscMalloc1(nrow + 1, &mumps->ia_alloc));
208:       mumps->cur_ilen = nrow + 1;
209:     }
210:     if (nnz > mumps->cur_jlen) {
211:       PetscCall(PetscFree(mumps->ja_alloc));
212:       PetscCall(PetscMalloc1(nnz, &mumps->ja_alloc));
213:       mumps->cur_jlen = nnz;
214:     }
215:     for (i = 0; i < nrow + 1; i++) PetscCall(PetscMUMPSIntCast(ia[i], &(mumps->ia_alloc[i])));
216:     for (i = 0; i < nnz; i++) PetscCall(PetscMUMPSIntCast(ja[i], &(mumps->ja_alloc[i])));
217:     *ia_mumps = mumps->ia_alloc;
218:     *ja_mumps = mumps->ja_alloc;
219:   }
220: #else
221:   *ia_mumps          = ia;
222:   *ja_mumps          = ja;
223: #endif
224:   PetscCall(PetscMUMPSIntCast(nnz, nnz_mumps));
225:   PetscFunctionReturn(PETSC_SUCCESS);
226: }

228: static PetscErrorCode MatMumpsResetSchur_Private(Mat_MUMPS *mumps)
229: {
230:   PetscFunctionBegin;
231:   PetscCall(PetscFree(mumps->id.listvar_schur));
232:   PetscCall(PetscFree(mumps->id.redrhs));
233:   PetscCall(PetscFree(mumps->schur_sol));
234:   mumps->id.size_schur = 0;
235:   mumps->id.schur_lld  = 0;
236:   mumps->id.ICNTL(19)  = 0;
237:   PetscFunctionReturn(PETSC_SUCCESS);
238: }

240: /* solve with rhs in mumps->id.redrhs and return in the same location */
241: static PetscErrorCode MatMumpsSolveSchur_Private(Mat F)
242: {
243:   Mat_MUMPS           *mumps = (Mat_MUMPS *)F->data;
244:   Mat                  S, B, X;
245:   MatFactorSchurStatus schurstatus;
246:   PetscInt             sizesol;

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

291:     PetscCall(MatCopy(X, B, SAME_NONZERO_PATTERN));
292:     break;
293:   default:
294:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
295:   }
296:   PetscCall(MatFactorRestoreSchurComplement(F, &S, schurstatus));
297:   PetscCall(MatDestroy(&B));
298:   PetscCall(MatDestroy(&X));
299:   PetscFunctionReturn(PETSC_SUCCESS);
300: }

302: static PetscErrorCode MatMumpsHandleSchur_Private(Mat F, PetscBool expansion)
303: {
304:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

306:   PetscFunctionBegin;
307:   if (!mumps->id.ICNTL(19)) { /* do nothing when Schur complement has not been computed */
308:     PetscFunctionReturn(PETSC_SUCCESS);
309:   }
310:   if (!expansion) { /* prepare for the condensation step */
311:     PetscInt sizeredrhs = mumps->id.nrhs * mumps->id.size_schur;
312:     /* allocate MUMPS internal array to store reduced right-hand sides */
313:     if (!mumps->id.redrhs || sizeredrhs > mumps->sizeredrhs) {
314:       PetscCall(PetscFree(mumps->id.redrhs));
315:       mumps->id.lredrhs = mumps->id.size_schur;
316:       PetscCall(PetscMalloc1(mumps->id.nrhs * mumps->id.lredrhs, &mumps->id.redrhs));
317:       mumps->sizeredrhs = mumps->id.nrhs * mumps->id.lredrhs;
318:     }
319:     mumps->id.ICNTL(26) = 1; /* condensation phase */
320:   } else {                   /* prepare for the expansion step */
321:     /* solve Schur complement (this has to be done by the MUMPS user, so basically us) */
322:     PetscCall(MatMumpsSolveSchur_Private(F));
323:     mumps->id.ICNTL(26) = 2; /* expansion phase */
324:     PetscMUMPS_c(mumps);
325:     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));
326:     /* restore defaults */
327:     mumps->id.ICNTL(26) = -1;
328:     /* free MUMPS internal array for redrhs if we have solved for multiple rhs in order to save memory space */
329:     if (mumps->id.nrhs > 1) {
330:       PetscCall(PetscFree(mumps->id.redrhs));
331:       mumps->id.lredrhs = 0;
332:       mumps->sizeredrhs = 0;
333:     }
334:   }
335:   PetscFunctionReturn(PETSC_SUCCESS);
336: }

338: /*
339:   MatConvertToTriples_A_B - convert Petsc matrix to triples: row[nz], col[nz], val[nz]

341:   input:
342:     A       - matrix in aij,baij or sbaij format
343:     shift   - 0: C style output triple; 1: Fortran style output triple.
344:     reuse   - MAT_INITIAL_MATRIX: spaces are allocated and values are set for the triple
345:               MAT_REUSE_MATRIX:   only the values in v array are updated
346:   output:
347:     nnz     - dim of r, c, and v (number of local nonzero entries of A)
348:     r, c, v - row and col index, matrix values (matrix triples)

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

354:  */

356: PetscErrorCode MatConvertToTriples_seqaij_seqaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
357: {
358:   const PetscScalar *av;
359:   const PetscInt    *ai, *aj, *ajj, M = A->rmap->n;
360:   PetscInt64         nz, rnz, i, j, k;
361:   PetscMUMPSInt     *row, *col;
362:   Mat_SeqAIJ        *aa = (Mat_SeqAIJ *)A->data;

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

389: PetscErrorCode MatConvertToTriples_seqsell_seqaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
390: {
391:   PetscInt64     nz, i, j, k, r;
392:   Mat_SeqSELL   *a = (Mat_SeqSELL *)A->data;
393:   PetscMUMPSInt *row, *col;

395:   PetscFunctionBegin;
396:   mumps->val = a->val;
397:   if (reuse == MAT_INITIAL_MATRIX) {
398:     nz = a->sliidx[a->totalslices];
399:     PetscCall(PetscMalloc2(nz, &row, nz, &col));
400:     for (i = k = 0; i < a->totalslices; i++) {
401:       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++]));
402:     }
403:     for (i = 0; i < nz; i++) PetscCall(PetscMUMPSIntCast(a->colidx[i] + shift, &col[i]));
404:     mumps->irn = row;
405:     mumps->jcn = col;
406:     mumps->nnz = nz;
407:   }
408:   PetscFunctionReturn(PETSC_SUCCESS);
409: }

411: PetscErrorCode MatConvertToTriples_seqbaij_seqaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
412: {
413:   Mat_SeqBAIJ    *aa = (Mat_SeqBAIJ *)A->data;
414:   const PetscInt *ai, *aj, *ajj, bs2 = aa->bs2;
415:   PetscInt64      M, nz, idx = 0, rnz, i, j, k, m;
416:   PetscInt        bs;
417:   PetscMUMPSInt  *row, *col;

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

448: PetscErrorCode MatConvertToTriples_seqsbaij_seqsbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
449: {
450:   const PetscInt *ai, *aj, *ajj;
451:   PetscInt        bs;
452:   PetscInt64      nz, rnz, i, j, k, m;
453:   PetscMUMPSInt  *row, *col;
454:   PetscScalar    *val;
455:   Mat_SeqSBAIJ   *aa  = (Mat_SeqSBAIJ *)A->data;
456:   const PetscInt  bs2 = aa->bs2, mbs = aa->mbs;
457: #if defined(PETSC_USE_COMPLEX)
458:   PetscBool isset, hermitian;
459: #endif

461:   PetscFunctionBegin;
462: #if defined(PETSC_USE_COMPLEX)
463:   PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
464:   PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
465: #endif
466:   ai = aa->i;
467:   aj = aa->j;
468:   PetscCall(MatGetBlockSize(A, &bs));
469:   if (reuse == MAT_INITIAL_MATRIX) {
470:     const PetscInt64 alloc_size = aa->nz * bs2;

472:     PetscCall(PetscMalloc2(alloc_size, &row, alloc_size, &col));
473:     if (bs > 1) {
474:       PetscCall(PetscMalloc1(alloc_size, &mumps->val_alloc));
475:       mumps->val = mumps->val_alloc;
476:     } else {
477:       mumps->val = aa->a;
478:     }
479:     mumps->irn = row;
480:     mumps->jcn = col;
481:   } else {
482:     if (bs == 1) mumps->val = aa->a;
483:     row = mumps->irn;
484:     col = mumps->jcn;
485:   }
486:   val = mumps->val;

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

523: PetscErrorCode MatConvertToTriples_seqaij_seqsbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
524: {
525:   const PetscInt    *ai, *aj, *ajj, *adiag, M = A->rmap->n;
526:   PetscInt64         nz, rnz, i, j;
527:   const PetscScalar *av, *v1;
528:   PetscScalar       *val;
529:   PetscMUMPSInt     *row, *col;
530:   Mat_SeqAIJ        *aa = (Mat_SeqAIJ *)A->data;
531:   PetscBool          missing;
532: #if defined(PETSC_USE_COMPLEX)
533:   PetscBool hermitian, isset;
534: #endif

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

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

632: PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
633: {
634:   const PetscInt    *ai, *aj, *bi, *bj, *garray, *ajj, *bjj;
635:   PetscInt           bs;
636:   PetscInt64         rstart, nz, i, j, k, m, jj, irow, countA, countB;
637:   PetscMUMPSInt     *row, *col;
638:   const PetscScalar *av, *bv, *v1, *v2;
639:   PetscScalar       *val;
640:   Mat_MPISBAIJ      *mat = (Mat_MPISBAIJ *)A->data;
641:   Mat_SeqSBAIJ      *aa  = (Mat_SeqSBAIJ *)(mat->A)->data;
642:   Mat_SeqBAIJ       *bb  = (Mat_SeqBAIJ *)(mat->B)->data;
643:   const PetscInt     bs2 = aa->bs2, mbs = aa->mbs;
644: #if defined(PETSC_USE_COMPLEX)
645:   PetscBool hermitian, isset;
646: #endif

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

662:   garray = mat->garray;

664:   if (reuse == MAT_INITIAL_MATRIX) {
665:     nz = (aa->nz + bb->nz) * bs2; /* just a conservative estimate */
666:     PetscCall(PetscMalloc2(nz, &row, nz, &col));
667:     PetscCall(PetscMalloc1(nz, &val));
668:     /* can not decide the exact mumps->nnz now because of the SBAIJ */
669:     mumps->irn = row;
670:     mumps->jcn = col;
671:     mumps->val = mumps->val_alloc = val;
672:   } else {
673:     val = mumps->val;
674:   }

676:   jj   = 0;
677:   irow = rstart;
678:   for (i = 0; i < mbs; i++) {
679:     ajj    = aj + ai[i]; /* ptr to the beginning of this row */
680:     countA = ai[i + 1] - ai[i];
681:     countB = bi[i + 1] - bi[i];
682:     bjj    = bj + bi[i];
683:     v1     = av + ai[i] * bs2;
684:     v2     = bv + bi[i] * bs2;

686:     if (bs > 1) {
687:       /* A-part */
688:       for (j = 0; j < countA; j++) {
689:         for (k = 0; k < bs; k++) {
690:           for (m = 0; m < bs; m++) {
691:             if (rstart + ajj[j] * bs > irow || k >= m) {
692:               if (reuse == MAT_INITIAL_MATRIX) {
693:                 PetscCall(PetscMUMPSIntCast(irow + m + shift, &row[jj]));
694:                 PetscCall(PetscMUMPSIntCast(rstart + ajj[j] * bs + k + shift, &col[jj]));
695:               }
696:               val[jj++] = v1[j * bs2 + m + k * bs];
697:             }
698:           }
699:         }
700:       }

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

724:       /* B-part */
725:       for (j = 0; j < countB; j++) {
726:         if (reuse == MAT_INITIAL_MATRIX) {
727:           PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
728:           PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift, &col[jj]));
729:         }
730:         val[jj++] = v2[j];
731:       }
732:     }
733:     irow += bs;
734:   }
735:   mumps->nnz = jj;
736:   PetscFunctionReturn(PETSC_SUCCESS);
737: }

739: PetscErrorCode MatConvertToTriples_mpiaij_mpiaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
740: {
741:   const PetscInt    *ai, *aj, *bi, *bj, *garray, m = A->rmap->n, *ajj, *bjj;
742:   PetscInt64         rstart, nz, i, j, jj, irow, countA, countB;
743:   PetscMUMPSInt     *row, *col;
744:   const PetscScalar *av, *bv, *v1, *v2;
745:   PetscScalar       *val;
746:   Mat                Ad, Ao;
747:   Mat_SeqAIJ        *aa;
748:   Mat_SeqAIJ        *bb;

750:   PetscFunctionBegin;
751:   PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &garray));
752:   PetscCall(MatSeqAIJGetArrayRead(Ad, &av));
753:   PetscCall(MatSeqAIJGetArrayRead(Ao, &bv));

755:   aa = (Mat_SeqAIJ *)(Ad)->data;
756:   bb = (Mat_SeqAIJ *)(Ao)->data;
757:   ai = aa->i;
758:   aj = aa->j;
759:   bi = bb->i;
760:   bj = bb->j;

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

764:   if (reuse == MAT_INITIAL_MATRIX) {
765:     nz = (PetscInt64)aa->nz + bb->nz; /* make sure the sum won't overflow PetscInt */
766:     PetscCall(PetscMalloc2(nz, &row, nz, &col));
767:     PetscCall(PetscMalloc1(nz, &val));
768:     mumps->nnz = nz;
769:     mumps->irn = row;
770:     mumps->jcn = col;
771:     mumps->val = mumps->val_alloc = val;
772:   } else {
773:     val = mumps->val;
774:   }

776:   jj   = 0;
777:   irow = rstart;
778:   for (i = 0; i < m; i++) {
779:     ajj    = aj + ai[i]; /* ptr to the beginning of this row */
780:     countA = ai[i + 1] - ai[i];
781:     countB = bi[i + 1] - bi[i];
782:     bjj    = bj + bi[i];
783:     v1     = av + ai[i];
784:     v2     = bv + bi[i];

786:     /* A-part */
787:     for (j = 0; j < countA; j++) {
788:       if (reuse == MAT_INITIAL_MATRIX) {
789:         PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
790:         PetscCall(PetscMUMPSIntCast(rstart + ajj[j] + shift, &col[jj]));
791:       }
792:       val[jj++] = v1[j];
793:     }

795:     /* B-part */
796:     for (j = 0; j < countB; j++) {
797:       if (reuse == MAT_INITIAL_MATRIX) {
798:         PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
799:         PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift, &col[jj]));
800:       }
801:       val[jj++] = v2[j];
802:     }
803:     irow++;
804:   }
805:   PetscCall(MatSeqAIJRestoreArrayRead(Ad, &av));
806:   PetscCall(MatSeqAIJRestoreArrayRead(Ao, &bv));
807:   PetscFunctionReturn(PETSC_SUCCESS);
808: }

810: PetscErrorCode MatConvertToTriples_mpibaij_mpiaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
811: {
812:   Mat_MPIBAIJ       *mat = (Mat_MPIBAIJ *)A->data;
813:   Mat_SeqBAIJ       *aa  = (Mat_SeqBAIJ *)(mat->A)->data;
814:   Mat_SeqBAIJ       *bb  = (Mat_SeqBAIJ *)(mat->B)->data;
815:   const PetscInt    *ai = aa->i, *bi = bb->i, *aj = aa->j, *bj = bb->j, *ajj, *bjj;
816:   const PetscInt    *garray = mat->garray, mbs = mat->mbs, rstart = A->rmap->rstart;
817:   const PetscInt     bs2 = mat->bs2;
818:   PetscInt           bs;
819:   PetscInt64         nz, i, j, k, n, jj, irow, countA, countB, idx;
820:   PetscMUMPSInt     *row, *col;
821:   const PetscScalar *av = aa->a, *bv = bb->a, *v1, *v2;
822:   PetscScalar       *val;

824:   PetscFunctionBegin;
825:   PetscCall(MatGetBlockSize(A, &bs));
826:   if (reuse == MAT_INITIAL_MATRIX) {
827:     nz = bs2 * (aa->nz + bb->nz);
828:     PetscCall(PetscMalloc2(nz, &row, nz, &col));
829:     PetscCall(PetscMalloc1(nz, &val));
830:     mumps->nnz = nz;
831:     mumps->irn = row;
832:     mumps->jcn = col;
833:     mumps->val = mumps->val_alloc = val;
834:   } else {
835:     val = mumps->val;
836:   }

838:   jj   = 0;
839:   irow = rstart;
840:   for (i = 0; i < mbs; i++) {
841:     countA = ai[i + 1] - ai[i];
842:     countB = bi[i + 1] - bi[i];
843:     ajj    = aj + ai[i];
844:     bjj    = bj + bi[i];
845:     v1     = av + bs2 * ai[i];
846:     v2     = bv + bs2 * bi[i];

848:     idx = 0;
849:     /* A-part */
850:     for (k = 0; k < countA; k++) {
851:       for (j = 0; j < bs; j++) {
852:         for (n = 0; n < bs; n++) {
853:           if (reuse == MAT_INITIAL_MATRIX) {
854:             PetscCall(PetscMUMPSIntCast(irow + n + shift, &row[jj]));
855:             PetscCall(PetscMUMPSIntCast(rstart + bs * ajj[k] + j + shift, &col[jj]));
856:           }
857:           val[jj++] = v1[idx++];
858:         }
859:       }
860:     }

862:     idx = 0;
863:     /* B-part */
864:     for (k = 0; k < countB; k++) {
865:       for (j = 0; j < bs; j++) {
866:         for (n = 0; n < bs; n++) {
867:           if (reuse == MAT_INITIAL_MATRIX) {
868:             PetscCall(PetscMUMPSIntCast(irow + n + shift, &row[jj]));
869:             PetscCall(PetscMUMPSIntCast(bs * garray[bjj[k]] + j + shift, &col[jj]));
870:           }
871:           val[jj++] = v2[idx++];
872:         }
873:       }
874:     }
875:     irow += bs;
876:   }
877:   PetscFunctionReturn(PETSC_SUCCESS);
878: }

880: PetscErrorCode MatConvertToTriples_mpiaij_mpisbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
881: {
882:   const PetscInt    *ai, *aj, *adiag, *bi, *bj, *garray, m = A->rmap->n, *ajj, *bjj;
883:   PetscInt64         rstart, nz, nza, nzb, i, j, jj, irow, countA, countB;
884:   PetscMUMPSInt     *row, *col;
885:   const PetscScalar *av, *bv, *v1, *v2;
886:   PetscScalar       *val;
887:   Mat                Ad, Ao;
888:   Mat_SeqAIJ        *aa;
889:   Mat_SeqAIJ        *bb;
890: #if defined(PETSC_USE_COMPLEX)
891:   PetscBool hermitian, isset;
892: #endif

894:   PetscFunctionBegin;
895: #if defined(PETSC_USE_COMPLEX)
896:   PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
897:   PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
898: #endif
899:   PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &garray));
900:   PetscCall(MatSeqAIJGetArrayRead(Ad, &av));
901:   PetscCall(MatSeqAIJGetArrayRead(Ao, &bv));

903:   aa    = (Mat_SeqAIJ *)(Ad)->data;
904:   bb    = (Mat_SeqAIJ *)(Ao)->data;
905:   ai    = aa->i;
906:   aj    = aa->j;
907:   adiag = aa->diag;
908:   bi    = bb->i;
909:   bj    = bb->j;

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

913:   if (reuse == MAT_INITIAL_MATRIX) {
914:     nza = 0; /* num of upper triangular entries in mat->A, including diagonals */
915:     nzb = 0; /* num of upper triangular entries in mat->B */
916:     for (i = 0; i < m; i++) {
917:       nza += (ai[i + 1] - adiag[i]);
918:       countB = bi[i + 1] - bi[i];
919:       bjj    = bj + bi[i];
920:       for (j = 0; j < countB; j++) {
921:         if (garray[bjj[j]] > rstart) nzb++;
922:       }
923:     }

925:     nz = nza + nzb; /* total nz of upper triangular part of mat */
926:     PetscCall(PetscMalloc2(nz, &row, nz, &col));
927:     PetscCall(PetscMalloc1(nz, &val));
928:     mumps->nnz = nz;
929:     mumps->irn = row;
930:     mumps->jcn = col;
931:     mumps->val = mumps->val_alloc = val;
932:   } else {
933:     val = mumps->val;
934:   }

936:   jj   = 0;
937:   irow = rstart;
938:   for (i = 0; i < m; i++) {
939:     ajj    = aj + adiag[i]; /* ptr to the beginning of the diagonal of this row */
940:     v1     = av + adiag[i];
941:     countA = ai[i + 1] - adiag[i];
942:     countB = bi[i + 1] - bi[i];
943:     bjj    = bj + bi[i];
944:     v2     = bv + bi[i];

946:     /* A-part */
947:     for (j = 0; j < countA; j++) {
948:       if (reuse == MAT_INITIAL_MATRIX) {
949:         PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
950:         PetscCall(PetscMUMPSIntCast(rstart + ajj[j] + shift, &col[jj]));
951:       }
952:       val[jj++] = v1[j];
953:     }

955:     /* B-part */
956:     for (j = 0; j < countB; j++) {
957:       if (garray[bjj[j]] > rstart) {
958:         if (reuse == MAT_INITIAL_MATRIX) {
959:           PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
960:           PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift, &col[jj]));
961:         }
962:         val[jj++] = v2[j];
963:       }
964:     }
965:     irow++;
966:   }
967:   PetscCall(MatSeqAIJRestoreArrayRead(Ad, &av));
968:   PetscCall(MatSeqAIJRestoreArrayRead(Ao, &bv));
969:   PetscFunctionReturn(PETSC_SUCCESS);
970: }

972: PetscErrorCode MatDestroy_MUMPS(Mat A)
973: {
974:   Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;

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

1012:   /* clear composed functions */
1013:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1014:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorSetSchurIS_C", NULL));
1015:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorCreateSchurComplement_C", NULL));
1016:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsSetIcntl_C", NULL));
1017:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetIcntl_C", NULL));
1018:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsSetCntl_C", NULL));
1019:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetCntl_C", NULL));
1020:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInfo_C", NULL));
1021:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInfog_C", NULL));
1022:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetRinfo_C", NULL));
1023:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetRinfog_C", NULL));
1024:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInverse_C", NULL));
1025:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInverseTranspose_C", NULL));
1026:   PetscFunctionReturn(PETSC_SUCCESS);
1027: }

1029: /* Set up the distributed RHS info for MUMPS. <nrhs> is the number of RHS. <array> points to start of RHS on the local processor. */
1030: static PetscErrorCode MatMumpsSetUpDistRHSInfo(Mat A, PetscInt nrhs, const PetscScalar *array)
1031: {
1032:   Mat_MUMPS        *mumps   = (Mat_MUMPS *)A->data;
1033:   const PetscMPIInt ompsize = mumps->omp_comm_size;
1034:   PetscInt          i, m, M, rstart;

1036:   PetscFunctionBegin;
1037:   PetscCall(MatGetSize(A, &M, NULL));
1038:   PetscCall(MatGetLocalSize(A, &m, NULL));
1039:   PetscCheck(M <= PETSC_MUMPS_INT_MAX, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscInt too long for PetscMUMPSInt");
1040:   if (ompsize == 1) {
1041:     if (!mumps->irhs_loc) {
1042:       mumps->nloc_rhs = m;
1043:       PetscCall(PetscMalloc1(m, &mumps->irhs_loc));
1044:       PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
1045:       for (i = 0; i < m; i++) mumps->irhs_loc[i] = rstart + i + 1; /* use 1-based indices */
1046:     }
1047:     mumps->id.rhs_loc = (MumpsScalar *)array;
1048:   } else {
1049: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
1050:     const PetscInt *ranges;
1051:     PetscMPIInt     j, k, sendcount, *petsc_ranks, *omp_ranks;
1052:     MPI_Group       petsc_group, omp_group;
1053:     PetscScalar    *recvbuf = NULL;

1055:     if (mumps->is_omp_master) {
1056:       /* Lazily initialize the omp stuff for distributed rhs */
1057:       if (!mumps->irhs_loc) {
1058:         PetscCall(PetscMalloc2(ompsize, &omp_ranks, ompsize, &petsc_ranks));
1059:         PetscCall(PetscMalloc3(ompsize, &mumps->rhs_nrow, ompsize, &mumps->rhs_recvcounts, ompsize, &mumps->rhs_disps));
1060:         PetscCallMPI(MPI_Comm_group(mumps->petsc_comm, &petsc_group));
1061:         PetscCallMPI(MPI_Comm_group(mumps->omp_comm, &omp_group));
1062:         for (j = 0; j < ompsize; j++) omp_ranks[j] = j;
1063:         PetscCallMPI(MPI_Group_translate_ranks(omp_group, ompsize, omp_ranks, petsc_group, petsc_ranks));

1065:         /* Populate mumps->irhs_loc[], rhs_nrow[] */
1066:         mumps->nloc_rhs = 0;
1067:         PetscCall(MatGetOwnershipRanges(A, &ranges));
1068:         for (j = 0; j < ompsize; j++) {
1069:           mumps->rhs_nrow[j] = ranges[petsc_ranks[j] + 1] - ranges[petsc_ranks[j]];
1070:           mumps->nloc_rhs += mumps->rhs_nrow[j];
1071:         }
1072:         PetscCall(PetscMalloc1(mumps->nloc_rhs, &mumps->irhs_loc));
1073:         for (j = k = 0; j < ompsize; j++) {
1074:           for (i = ranges[petsc_ranks[j]]; i < ranges[petsc_ranks[j] + 1]; i++, k++) mumps->irhs_loc[k] = i + 1; /* uses 1-based indices */
1075:         }

1077:         PetscCall(PetscFree2(omp_ranks, petsc_ranks));
1078:         PetscCallMPI(MPI_Group_free(&petsc_group));
1079:         PetscCallMPI(MPI_Group_free(&omp_group));
1080:       }

1082:       /* Realloc buffers when current nrhs is bigger than what we have met */
1083:       if (nrhs > mumps->max_nrhs) {
1084:         PetscCall(PetscFree2(mumps->rhs_loc, mumps->rhs_recvbuf));
1085:         PetscCall(PetscMalloc2(mumps->nloc_rhs * nrhs, &mumps->rhs_loc, mumps->nloc_rhs * nrhs, &mumps->rhs_recvbuf));
1086:         mumps->max_nrhs = nrhs;
1087:       }

1089:       /* Setup recvcounts[], disps[], recvbuf on omp rank 0 for the upcoming MPI_Gatherv */
1090:       for (j = 0; j < ompsize; j++) PetscCall(PetscMPIIntCast(mumps->rhs_nrow[j] * nrhs, &mumps->rhs_recvcounts[j]));
1091:       mumps->rhs_disps[0] = 0;
1092:       for (j = 1; j < ompsize; j++) {
1093:         mumps->rhs_disps[j] = mumps->rhs_disps[j - 1] + mumps->rhs_recvcounts[j - 1];
1094:         PetscCheck(mumps->rhs_disps[j] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscMPIInt overflow!");
1095:       }
1096:       recvbuf = (nrhs == 1) ? mumps->rhs_loc : mumps->rhs_recvbuf; /* Directly use rhs_loc[] as recvbuf. Single rhs is common in Ax=b */
1097:     }

1099:     PetscCall(PetscMPIIntCast(m * nrhs, &sendcount));
1100:     PetscCallMPI(MPI_Gatherv(array, sendcount, MPIU_SCALAR, recvbuf, mumps->rhs_recvcounts, mumps->rhs_disps, MPIU_SCALAR, 0, mumps->omp_comm));

1102:     if (mumps->is_omp_master) {
1103:       if (nrhs > 1) { /* Copy & re-arrange data from rhs_recvbuf[] to mumps->rhs_loc[] only when there are multiple rhs */
1104:         PetscScalar *dst, *dstbase = mumps->rhs_loc;
1105:         for (j = 0; j < ompsize; j++) {
1106:           const PetscScalar *src = mumps->rhs_recvbuf + mumps->rhs_disps[j];
1107:           dst                    = dstbase;
1108:           for (i = 0; i < nrhs; i++) {
1109:             PetscCall(PetscArraycpy(dst, src, mumps->rhs_nrow[j]));
1110:             src += mumps->rhs_nrow[j];
1111:             dst += mumps->nloc_rhs;
1112:           }
1113:           dstbase += mumps->rhs_nrow[j];
1114:         }
1115:       }
1116:       mumps->id.rhs_loc = (MumpsScalar *)mumps->rhs_loc;
1117:     }
1118: #endif /* PETSC_HAVE_OPENMP_SUPPORT */
1119:   }
1120:   mumps->id.nrhs     = nrhs;
1121:   mumps->id.nloc_rhs = mumps->nloc_rhs;
1122:   mumps->id.lrhs_loc = mumps->nloc_rhs;
1123:   mumps->id.irhs_loc = mumps->irhs_loc;
1124:   PetscFunctionReturn(PETSC_SUCCESS);
1125: }

1127: PetscErrorCode MatSolve_MUMPS(Mat A, Vec b, Vec x)
1128: {
1129:   Mat_MUMPS         *mumps  = (Mat_MUMPS *)A->data;
1130:   const PetscScalar *rarray = NULL;
1131:   PetscScalar       *array;
1132:   IS                 is_iden, is_petsc;
1133:   PetscInt           i;
1134:   PetscBool          second_solve = PETSC_FALSE;
1135:   static PetscBool   cite1 = PETSC_FALSE, cite2 = PETSC_FALSE;

1137:   PetscFunctionBegin;
1138:   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 "
1139:                                    "Journal on Matrix Analysis and Applications},\n  volume = {23},\n  number = {1},\n  pages = {15--41},\n  year = {2001}\n}\n",
1140:                                    &cite1));
1141:   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 "
1142:                                    "Computing},\n  volume = {32},\n  number = {2},\n  pages = {136--156},\n  year = {2006}\n}\n",
1143:                                    &cite2));

1145:   if (A->factorerrortype) {
1146:     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)));
1147:     PetscCall(VecSetInf(x));
1148:     PetscFunctionReturn(PETSC_SUCCESS);
1149:   }

1151:   mumps->id.nrhs = 1;
1152:   if (mumps->petsc_size > 1) {
1153:     if (mumps->ICNTL20 == 10) {
1154:       mumps->id.ICNTL(20) = 10; /* dense distributed RHS */
1155:       PetscCall(VecGetArrayRead(b, &rarray));
1156:       PetscCall(MatMumpsSetUpDistRHSInfo(A, 1, rarray));
1157:     } else {
1158:       mumps->id.ICNTL(20) = 0; /* dense centralized RHS; Scatter b into a sequential rhs vector*/
1159:       PetscCall(VecScatterBegin(mumps->scat_rhs, b, mumps->b_seq, INSERT_VALUES, SCATTER_FORWARD));
1160:       PetscCall(VecScatterEnd(mumps->scat_rhs, b, mumps->b_seq, INSERT_VALUES, SCATTER_FORWARD));
1161:       if (!mumps->myid) {
1162:         PetscCall(VecGetArray(mumps->b_seq, &array));
1163:         mumps->id.rhs = (MumpsScalar *)array;
1164:       }
1165:     }
1166:   } else {                   /* petsc_size == 1 */
1167:     mumps->id.ICNTL(20) = 0; /* dense centralized RHS */
1168:     PetscCall(VecCopy(b, x));
1169:     PetscCall(VecGetArray(x, &array));
1170:     mumps->id.rhs = (MumpsScalar *)array;
1171:   }

1173:   /*
1174:      handle condensation step of Schur complement (if any)
1175:      We set by default ICNTL(26) == -1 when Schur indices have been provided by the user.
1176:      According to MUMPS (5.0.0) manual, any value should be harmful during the factorization phase
1177:      Unless the user provides a valid value for ICNTL(26), MatSolve and MatMatSolve routines solve the full system.
1178:      This requires an extra call to PetscMUMPS_c and the computation of the factors for S
1179:   */
1180:   if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) {
1181:     PetscCheck(mumps->petsc_size <= 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Parallel Schur complements not yet supported from PETSc");
1182:     second_solve = PETSC_TRUE;
1183:     PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1184:   }
1185:   /* solve phase */
1186:   mumps->id.job = JOB_SOLVE;
1187:   PetscMUMPS_c(mumps);
1188:   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));

1190:   /* handle expansion step of Schur complement (if any) */
1191:   if (second_solve) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_TRUE));

1193:   if (mumps->petsc_size > 1) { /* convert mumps distributed solution to petsc mpi x */
1194:     if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) {
1195:       /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */
1196:       PetscCall(VecScatterDestroy(&mumps->scat_sol));
1197:     }
1198:     if (!mumps->scat_sol) { /* create scatter scat_sol */
1199:       PetscInt *isol2_loc = NULL;
1200:       PetscCall(ISCreateStride(PETSC_COMM_SELF, mumps->id.lsol_loc, 0, 1, &is_iden)); /* from */
1201:       PetscCall(PetscMalloc1(mumps->id.lsol_loc, &isol2_loc));
1202:       for (i = 0; i < mumps->id.lsol_loc; i++) isol2_loc[i] = mumps->id.isol_loc[i] - 1;                        /* change Fortran style to C style */
1203:       PetscCall(ISCreateGeneral(PETSC_COMM_SELF, mumps->id.lsol_loc, isol2_loc, PETSC_OWN_POINTER, &is_petsc)); /* to */
1204:       PetscCall(VecScatterCreate(mumps->x_seq, is_iden, x, is_petsc, &mumps->scat_sol));
1205:       PetscCall(ISDestroy(&is_iden));
1206:       PetscCall(ISDestroy(&is_petsc));
1207:       mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */
1208:     }

1210:     PetscCall(VecScatterBegin(mumps->scat_sol, mumps->x_seq, x, INSERT_VALUES, SCATTER_FORWARD));
1211:     PetscCall(VecScatterEnd(mumps->scat_sol, mumps->x_seq, x, INSERT_VALUES, SCATTER_FORWARD));
1212:   }

1214:   if (mumps->petsc_size > 1) {
1215:     if (mumps->ICNTL20 == 10) {
1216:       PetscCall(VecRestoreArrayRead(b, &rarray));
1217:     } else if (!mumps->myid) {
1218:       PetscCall(VecRestoreArray(mumps->b_seq, &array));
1219:     }
1220:   } else PetscCall(VecRestoreArray(x, &array));

1222:   PetscCall(PetscLogFlops(2.0 * mumps->id.RINFO(3)));
1223:   PetscFunctionReturn(PETSC_SUCCESS);
1224: }

1226: PetscErrorCode MatSolveTranspose_MUMPS(Mat A, Vec b, Vec x)
1227: {
1228:   Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;

1230:   PetscFunctionBegin;
1231:   mumps->id.ICNTL(9) = 0;
1232:   PetscCall(MatSolve_MUMPS(A, b, x));
1233:   mumps->id.ICNTL(9) = 1;
1234:   PetscFunctionReturn(PETSC_SUCCESS);
1235: }

1237: PetscErrorCode MatMatSolve_MUMPS(Mat A, Mat B, Mat X)
1238: {
1239:   Mat                Bt = NULL;
1240:   PetscBool          denseX, denseB, flg, flgT;
1241:   Mat_MUMPS         *mumps = (Mat_MUMPS *)A->data;
1242:   PetscInt           i, nrhs, M;
1243:   PetscScalar       *array;
1244:   const PetscScalar *rbray;
1245:   PetscInt           lsol_loc, nlsol_loc, *idxx, iidx = 0;
1246:   PetscMUMPSInt     *isol_loc, *isol_loc_save;
1247:   PetscScalar       *bray, *sol_loc, *sol_loc_save;
1248:   IS                 is_to, is_from;
1249:   PetscInt           k, proc, j, m, myrstart;
1250:   const PetscInt    *rstart;
1251:   Vec                v_mpi, msol_loc;
1252:   VecScatter         scat_sol;
1253:   Vec                b_seq;
1254:   VecScatter         scat_rhs;
1255:   PetscScalar       *aa;
1256:   PetscInt           spnr, *ia, *ja;
1257:   Mat_MPIAIJ        *b = NULL;

1259:   PetscFunctionBegin;
1260:   PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &denseX, MATSEQDENSE, MATMPIDENSE, NULL));
1261:   PetscCheck(denseX, PetscObjectComm((PetscObject)X), PETSC_ERR_ARG_WRONG, "Matrix X must be MATDENSE matrix");

1263:   PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &denseB, MATSEQDENSE, MATMPIDENSE, NULL));
1264:   if (denseB) {
1265:     PetscCheck(B->rmap->n == X->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Matrix B and X must have same row distribution");
1266:     mumps->id.ICNTL(20) = 0; /* dense RHS */
1267:   } else {                   /* sparse B */
1268:     PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
1269:     PetscCall(PetscObjectTypeCompare((PetscObject)B, MATTRANSPOSEVIRTUAL, &flgT));
1270:     if (flgT) { /* input B is transpose of actual RHS matrix,
1271:                  because mumps requires sparse compressed COLUMN storage! See MatMatTransposeSolve_MUMPS() */
1272:       PetscCall(MatTransposeGetMat(B, &Bt));
1273:     } else SETERRQ(PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Matrix B must be MATTRANSPOSEVIRTUAL matrix");
1274:     mumps->id.ICNTL(20) = 1; /* sparse RHS */
1275:   }

1277:   PetscCall(MatGetSize(B, &M, &nrhs));
1278:   mumps->id.nrhs = nrhs;
1279:   mumps->id.lrhs = M;
1280:   mumps->id.rhs  = NULL;

1282:   if (mumps->petsc_size == 1) {
1283:     PetscScalar *aa;
1284:     PetscInt     spnr, *ia, *ja;
1285:     PetscBool    second_solve = PETSC_FALSE;

1287:     PetscCall(MatDenseGetArray(X, &array));
1288:     mumps->id.rhs = (MumpsScalar *)array;

1290:     if (denseB) {
1291:       /* copy B to X */
1292:       PetscCall(MatDenseGetArrayRead(B, &rbray));
1293:       PetscCall(PetscArraycpy(array, rbray, M * nrhs));
1294:       PetscCall(MatDenseRestoreArrayRead(B, &rbray));
1295:     } else { /* sparse B */
1296:       PetscCall(MatSeqAIJGetArray(Bt, &aa));
1297:       PetscCall(MatGetRowIJ(Bt, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1298:       PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
1299:       PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
1300:       mumps->id.rhs_sparse = (MumpsScalar *)aa;
1301:     }
1302:     /* handle condensation step of Schur complement (if any) */
1303:     if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) {
1304:       second_solve = PETSC_TRUE;
1305:       PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1306:     }
1307:     /* solve phase */
1308:     mumps->id.job = JOB_SOLVE;
1309:     PetscMUMPS_c(mumps);
1310:     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));

1312:     /* handle expansion step of Schur complement (if any) */
1313:     if (second_solve) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_TRUE));
1314:     if (!denseB) { /* sparse B */
1315:       PetscCall(MatSeqAIJRestoreArray(Bt, &aa));
1316:       PetscCall(MatRestoreRowIJ(Bt, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1317:       PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot restore IJ structure");
1318:     }
1319:     PetscCall(MatDenseRestoreArray(X, &array));
1320:     PetscFunctionReturn(PETSC_SUCCESS);
1321:   }

1323:   /* parallel case: MUMPS requires rhs B to be centralized on the host! */
1324:   PetscCheck(mumps->petsc_size <= 1 || !mumps->id.ICNTL(19), PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Parallel Schur complements not yet supported from PETSc");

1326:   /* create msol_loc to hold mumps local solution */
1327:   isol_loc_save = mumps->id.isol_loc; /* save it for MatSolve() */
1328:   sol_loc_save  = (PetscScalar *)mumps->id.sol_loc;

1330:   lsol_loc  = mumps->id.lsol_loc;
1331:   nlsol_loc = nrhs * lsol_loc; /* length of sol_loc */
1332:   PetscCall(PetscMalloc2(nlsol_loc, &sol_loc, lsol_loc, &isol_loc));
1333:   mumps->id.sol_loc  = (MumpsScalar *)sol_loc;
1334:   mumps->id.isol_loc = isol_loc;

1336:   PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, nlsol_loc, (PetscScalar *)sol_loc, &msol_loc));

1338:   if (denseB) {
1339:     if (mumps->ICNTL20 == 10) {
1340:       mumps->id.ICNTL(20) = 10; /* dense distributed RHS */
1341:       PetscCall(MatDenseGetArrayRead(B, &rbray));
1342:       PetscCall(MatMumpsSetUpDistRHSInfo(A, nrhs, rbray));
1343:       PetscCall(MatDenseRestoreArrayRead(B, &rbray));
1344:       PetscCall(MatGetLocalSize(B, &m, NULL));
1345:       PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B), 1, nrhs * m, nrhs * M, NULL, &v_mpi));
1346:     } else {
1347:       mumps->id.ICNTL(20) = 0; /* dense centralized RHS */
1348:       /* TODO: Because of non-contiguous indices, the created vecscatter scat_rhs is not done in MPI_Gather, resulting in
1349:         very inefficient communication. An optimization is to use VecScatterCreateToZero to gather B to rank 0. Then on rank
1350:         0, re-arrange B into desired order, which is a local operation.
1351:       */

1353:       /* scatter v_mpi to b_seq because MUMPS before 5.3.0 only supports centralized rhs */
1354:       /* wrap dense rhs matrix B into a vector v_mpi */
1355:       PetscCall(MatGetLocalSize(B, &m, NULL));
1356:       PetscCall(MatDenseGetArray(B, &bray));
1357:       PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B), 1, nrhs * m, nrhs * M, (const PetscScalar *)bray, &v_mpi));
1358:       PetscCall(MatDenseRestoreArray(B, &bray));

1360:       /* scatter v_mpi to b_seq in proc[0]. MUMPS requires rhs to be centralized on the host! */
1361:       if (!mumps->myid) {
1362:         PetscInt *idx;
1363:         /* idx: maps from k-th index of v_mpi to (i,j)-th global entry of B */
1364:         PetscCall(PetscMalloc1(nrhs * M, &idx));
1365:         PetscCall(MatGetOwnershipRanges(B, &rstart));
1366:         k = 0;
1367:         for (proc = 0; proc < mumps->petsc_size; proc++) {
1368:           for (j = 0; j < nrhs; j++) {
1369:             for (i = rstart[proc]; i < rstart[proc + 1]; i++) idx[k++] = j * M + i;
1370:           }
1371:         }

1373:         PetscCall(VecCreateSeq(PETSC_COMM_SELF, nrhs * M, &b_seq));
1374:         PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nrhs * M, idx, PETSC_OWN_POINTER, &is_to));
1375:         PetscCall(ISCreateStride(PETSC_COMM_SELF, nrhs * M, 0, 1, &is_from));
1376:       } else {
1377:         PetscCall(VecCreateSeq(PETSC_COMM_SELF, 0, &b_seq));
1378:         PetscCall(ISCreateStride(PETSC_COMM_SELF, 0, 0, 1, &is_to));
1379:         PetscCall(ISCreateStride(PETSC_COMM_SELF, 0, 0, 1, &is_from));
1380:       }
1381:       PetscCall(VecScatterCreate(v_mpi, is_from, b_seq, is_to, &scat_rhs));
1382:       PetscCall(VecScatterBegin(scat_rhs, v_mpi, b_seq, INSERT_VALUES, SCATTER_FORWARD));
1383:       PetscCall(ISDestroy(&is_to));
1384:       PetscCall(ISDestroy(&is_from));
1385:       PetscCall(VecScatterEnd(scat_rhs, v_mpi, b_seq, INSERT_VALUES, SCATTER_FORWARD));

1387:       if (!mumps->myid) { /* define rhs on the host */
1388:         PetscCall(VecGetArray(b_seq, &bray));
1389:         mumps->id.rhs = (MumpsScalar *)bray;
1390:         PetscCall(VecRestoreArray(b_seq, &bray));
1391:       }
1392:     }
1393:   } else { /* sparse B */
1394:     b = (Mat_MPIAIJ *)Bt->data;

1396:     /* wrap dense X into a vector v_mpi */
1397:     PetscCall(MatGetLocalSize(X, &m, NULL));
1398:     PetscCall(MatDenseGetArray(X, &bray));
1399:     PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)X), 1, nrhs * m, nrhs * M, (const PetscScalar *)bray, &v_mpi));
1400:     PetscCall(MatDenseRestoreArray(X, &bray));

1402:     if (!mumps->myid) {
1403:       PetscCall(MatSeqAIJGetArray(b->A, &aa));
1404:       PetscCall(MatGetRowIJ(b->A, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1405:       PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
1406:       PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
1407:       mumps->id.rhs_sparse = (MumpsScalar *)aa;
1408:     } else {
1409:       mumps->id.irhs_ptr    = NULL;
1410:       mumps->id.irhs_sparse = NULL;
1411:       mumps->id.nz_rhs      = 0;
1412:       mumps->id.rhs_sparse  = NULL;
1413:     }
1414:   }

1416:   /* solve phase */
1417:   mumps->id.job = JOB_SOLVE;
1418:   PetscMUMPS_c(mumps);
1419:   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));

1421:   /* scatter mumps distributed solution to petsc vector v_mpi, which shares local arrays with solution matrix X */
1422:   PetscCall(MatDenseGetArray(X, &array));
1423:   PetscCall(VecPlaceArray(v_mpi, array));

1425:   /* create scatter scat_sol */
1426:   PetscCall(MatGetOwnershipRanges(X, &rstart));
1427:   /* iidx: index for scatter mumps solution to petsc X */

1429:   PetscCall(ISCreateStride(PETSC_COMM_SELF, nlsol_loc, 0, 1, &is_from));
1430:   PetscCall(PetscMalloc1(nlsol_loc, &idxx));
1431:   for (i = 0; i < lsol_loc; i++) {
1432:     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 */

1434:     for (proc = 0; proc < mumps->petsc_size; proc++) {
1435:       if (isol_loc[i] >= rstart[proc] && isol_loc[i] < rstart[proc + 1]) {
1436:         myrstart = rstart[proc];
1437:         k        = isol_loc[i] - myrstart;          /* local index on 1st column of petsc vector X */
1438:         iidx     = k + myrstart * nrhs;             /* maps mumps isol_loc[i] to petsc index in X */
1439:         m        = rstart[proc + 1] - rstart[proc]; /* rows of X for this proc */
1440:         break;
1441:       }
1442:     }

1444:     for (j = 0; j < nrhs; j++) idxx[i + j * lsol_loc] = iidx + j * m;
1445:   }
1446:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nlsol_loc, idxx, PETSC_COPY_VALUES, &is_to));
1447:   PetscCall(VecScatterCreate(msol_loc, is_from, v_mpi, is_to, &scat_sol));
1448:   PetscCall(VecScatterBegin(scat_sol, msol_loc, v_mpi, INSERT_VALUES, SCATTER_FORWARD));
1449:   PetscCall(ISDestroy(&is_from));
1450:   PetscCall(ISDestroy(&is_to));
1451:   PetscCall(VecScatterEnd(scat_sol, msol_loc, v_mpi, INSERT_VALUES, SCATTER_FORWARD));
1452:   PetscCall(MatDenseRestoreArray(X, &array));

1454:   /* free spaces */
1455:   mumps->id.sol_loc  = (MumpsScalar *)sol_loc_save;
1456:   mumps->id.isol_loc = isol_loc_save;

1458:   PetscCall(PetscFree2(sol_loc, isol_loc));
1459:   PetscCall(PetscFree(idxx));
1460:   PetscCall(VecDestroy(&msol_loc));
1461:   PetscCall(VecDestroy(&v_mpi));
1462:   if (!denseB) {
1463:     if (!mumps->myid) {
1464:       b = (Mat_MPIAIJ *)Bt->data;
1465:       PetscCall(MatSeqAIJRestoreArray(b->A, &aa));
1466:       PetscCall(MatRestoreRowIJ(b->A, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1467:       PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot restore IJ structure");
1468:     }
1469:   } else {
1470:     if (mumps->ICNTL20 == 0) {
1471:       PetscCall(VecDestroy(&b_seq));
1472:       PetscCall(VecScatterDestroy(&scat_rhs));
1473:     }
1474:   }
1475:   PetscCall(VecScatterDestroy(&scat_sol));
1476:   PetscCall(PetscLogFlops(2.0 * nrhs * mumps->id.RINFO(3)));
1477:   PetscFunctionReturn(PETSC_SUCCESS);
1478: }

1480: PetscErrorCode MatMatSolveTranspose_MUMPS(Mat A, Mat B, Mat X)
1481: {
1482:   Mat_MUMPS    *mumps    = (Mat_MUMPS *)A->data;
1483:   PetscMUMPSInt oldvalue = mumps->id.ICNTL(9);

1485:   PetscFunctionBegin;
1486:   mumps->id.ICNTL(9) = 0;
1487:   PetscCall(MatMatSolve_MUMPS(A, B, X));
1488:   mumps->id.ICNTL(9) = oldvalue;
1489:   PetscFunctionReturn(PETSC_SUCCESS);
1490: }

1492: PetscErrorCode MatMatTransposeSolve_MUMPS(Mat A, Mat Bt, Mat X)
1493: {
1494:   PetscBool flg;
1495:   Mat       B;

1497:   PetscFunctionBegin;
1498:   PetscCall(PetscObjectTypeCompareAny((PetscObject)Bt, &flg, MATSEQAIJ, MATMPIAIJ, NULL));
1499:   PetscCheck(flg, PetscObjectComm((PetscObject)Bt), PETSC_ERR_ARG_WRONG, "Matrix Bt must be MATAIJ matrix");

1501:   /* Create B=Bt^T that uses Bt's data structure */
1502:   PetscCall(MatCreateTranspose(Bt, &B));

1504:   PetscCall(MatMatSolve_MUMPS(A, B, X));
1505:   PetscCall(MatDestroy(&B));
1506:   PetscFunctionReturn(PETSC_SUCCESS);
1507: }

1509: #if !defined(PETSC_USE_COMPLEX)
1510: /*
1511:   input:
1512:    F:        numeric factor
1513:   output:
1514:    nneg:     total number of negative pivots
1515:    nzero:    total number of zero pivots
1516:    npos:     (global dimension of F) - nneg - nzero
1517: */
1518: PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
1519: {
1520:   Mat_MUMPS  *mumps = (Mat_MUMPS *)F->data;
1521:   PetscMPIInt size;

1523:   PetscFunctionBegin;
1524:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)F), &size));
1525:   /* 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 */
1526:   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));

1528:   if (nneg) *nneg = mumps->id.INFOG(12);
1529:   if (nzero || npos) {
1530:     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");
1531:     if (nzero) *nzero = mumps->id.INFOG(28);
1532:     if (npos) *npos = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28));
1533:   }
1534:   PetscFunctionReturn(PETSC_SUCCESS);
1535: }
1536: #endif

1538: PetscErrorCode MatMumpsGatherNonzerosOnMaster(MatReuse reuse, Mat_MUMPS *mumps)
1539: {
1540:   PetscInt       i, nreqs;
1541:   PetscMUMPSInt *irn, *jcn;
1542:   PetscMPIInt    count;
1543:   PetscInt64     totnnz, remain;
1544:   const PetscInt osize = mumps->omp_comm_size;
1545:   PetscScalar   *val;

1547:   PetscFunctionBegin;
1548:   if (osize > 1) {
1549:     if (reuse == MAT_INITIAL_MATRIX) {
1550:       /* master first gathers counts of nonzeros to receive */
1551:       if (mumps->is_omp_master) PetscCall(PetscMalloc1(osize, &mumps->recvcount));
1552:       PetscCallMPI(MPI_Gather(&mumps->nnz, 1, MPIU_INT64, mumps->recvcount, 1, MPIU_INT64, 0 /*master*/, mumps->omp_comm));

1554:       /* Then each computes number of send/recvs */
1555:       if (mumps->is_omp_master) {
1556:         /* Start from 1 since self communication is not done in MPI */
1557:         nreqs = 0;
1558:         for (i = 1; i < osize; i++) nreqs += (mumps->recvcount[i] + PETSC_MPI_INT_MAX - 1) / PETSC_MPI_INT_MAX;
1559:       } else {
1560:         nreqs = (mumps->nnz + PETSC_MPI_INT_MAX - 1) / PETSC_MPI_INT_MAX;
1561:       }
1562:       PetscCall(PetscMalloc1(nreqs * 3, &mumps->reqs)); /* Triple the requests since we send irn, jcn and val separately */

1564:       /* The following code is doing a very simple thing: omp_master rank gathers irn/jcn/val from others.
1565:          MPI_Gatherv would be enough if it supports big counts > 2^31-1. Since it does not, and mumps->nnz
1566:          might be a prime number > 2^31-1, we have to slice the message. Note omp_comm_size
1567:          is very small, the current approach should have no extra overhead compared to MPI_Gatherv.
1568:        */
1569:       nreqs = 0; /* counter for actual send/recvs */
1570:       if (mumps->is_omp_master) {
1571:         for (i = 0, totnnz = 0; i < osize; i++) totnnz += mumps->recvcount[i]; /* totnnz = sum of nnz over omp_comm */
1572:         PetscCall(PetscMalloc2(totnnz, &irn, totnnz, &jcn));
1573:         PetscCall(PetscMalloc1(totnnz, &val));

1575:         /* Self communication */
1576:         PetscCall(PetscArraycpy(irn, mumps->irn, mumps->nnz));
1577:         PetscCall(PetscArraycpy(jcn, mumps->jcn, mumps->nnz));
1578:         PetscCall(PetscArraycpy(val, mumps->val, mumps->nnz));

1580:         /* Replace mumps->irn/jcn etc on master with the newly allocated bigger arrays */
1581:         PetscCall(PetscFree2(mumps->irn, mumps->jcn));
1582:         PetscCall(PetscFree(mumps->val_alloc));
1583:         mumps->nnz = totnnz;
1584:         mumps->irn = irn;
1585:         mumps->jcn = jcn;
1586:         mumps->val = mumps->val_alloc = val;

1588:         irn += mumps->recvcount[0]; /* recvcount[0] is old mumps->nnz on omp rank 0 */
1589:         jcn += mumps->recvcount[0];
1590:         val += mumps->recvcount[0];

1592:         /* Remote communication */
1593:         for (i = 1; i < osize; i++) {
1594:           count  = PetscMin(mumps->recvcount[i], PETSC_MPI_INT_MAX);
1595:           remain = mumps->recvcount[i] - count;
1596:           while (count > 0) {
1597:             PetscCallMPI(MPI_Irecv(irn, count, MPIU_MUMPSINT, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1598:             PetscCallMPI(MPI_Irecv(jcn, count, MPIU_MUMPSINT, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1599:             PetscCallMPI(MPI_Irecv(val, count, MPIU_SCALAR, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1600:             irn += count;
1601:             jcn += count;
1602:             val += count;
1603:             count = PetscMin(remain, PETSC_MPI_INT_MAX);
1604:             remain -= count;
1605:           }
1606:         }
1607:       } else {
1608:         irn    = mumps->irn;
1609:         jcn    = mumps->jcn;
1610:         val    = mumps->val;
1611:         count  = PetscMin(mumps->nnz, PETSC_MPI_INT_MAX);
1612:         remain = mumps->nnz - count;
1613:         while (count > 0) {
1614:           PetscCallMPI(MPI_Isend(irn, count, MPIU_MUMPSINT, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1615:           PetscCallMPI(MPI_Isend(jcn, count, MPIU_MUMPSINT, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1616:           PetscCallMPI(MPI_Isend(val, count, MPIU_SCALAR, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1617:           irn += count;
1618:           jcn += count;
1619:           val += count;
1620:           count = PetscMin(remain, PETSC_MPI_INT_MAX);
1621:           remain -= count;
1622:         }
1623:       }
1624:     } else {
1625:       nreqs = 0;
1626:       if (mumps->is_omp_master) {
1627:         val = mumps->val + mumps->recvcount[0];
1628:         for (i = 1; i < osize; i++) { /* Remote communication only since self data is already in place */
1629:           count  = PetscMin(mumps->recvcount[i], PETSC_MPI_INT_MAX);
1630:           remain = mumps->recvcount[i] - count;
1631:           while (count > 0) {
1632:             PetscCallMPI(MPI_Irecv(val, count, MPIU_SCALAR, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1633:             val += count;
1634:             count = PetscMin(remain, PETSC_MPI_INT_MAX);
1635:             remain -= count;
1636:           }
1637:         }
1638:       } else {
1639:         val    = mumps->val;
1640:         count  = PetscMin(mumps->nnz, PETSC_MPI_INT_MAX);
1641:         remain = mumps->nnz - count;
1642:         while (count > 0) {
1643:           PetscCallMPI(MPI_Isend(val, count, MPIU_SCALAR, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1644:           val += count;
1645:           count = PetscMin(remain, PETSC_MPI_INT_MAX);
1646:           remain -= count;
1647:         }
1648:       }
1649:     }
1650:     PetscCallMPI(MPI_Waitall(nreqs, mumps->reqs, MPI_STATUSES_IGNORE));
1651:     mumps->tag++; /* It is totally fine for above send/recvs to share one mpi tag */
1652:   }
1653:   PetscFunctionReturn(PETSC_SUCCESS);
1654: }

1656: PetscErrorCode MatFactorNumeric_MUMPS(Mat F, Mat A, const MatFactorInfo *info)
1657: {
1658:   Mat_MUMPS *mumps = (Mat_MUMPS *)(F)->data;
1659:   PetscBool  isMPIAIJ;

1661:   PetscFunctionBegin;
1662:   if (mumps->id.INFOG(1) < 0 && !(mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0)) {
1663:     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)));
1664:     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)));
1665:     PetscFunctionReturn(PETSC_SUCCESS);
1666:   }

1668:   PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, mumps));
1669:   PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_REUSE_MATRIX, mumps));

1671:   /* numerical factorization phase */
1672:   mumps->id.job = JOB_FACTNUMERIC;
1673:   if (!mumps->id.ICNTL(18)) { /* A is centralized */
1674:     if (!mumps->myid) mumps->id.a = (MumpsScalar *)mumps->val;
1675:   } else {
1676:     mumps->id.a_loc = (MumpsScalar *)mumps->val;
1677:   }
1678:   PetscMUMPS_c(mumps);
1679:   if (mumps->id.INFOG(1) < 0) {
1680:     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));
1681:     if (mumps->id.INFOG(1) == -10) { /* numerically singular matrix */
1682:       PetscCall(PetscInfo(F, "matrix is numerically singular, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1683:       F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1684:     } else if (mumps->id.INFOG(1) == -13) {
1685:       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)));
1686:       F->factorerrortype = MAT_FACTOR_OUTMEMORY;
1687:     } else if (mumps->id.INFOG(1) == -8 || mumps->id.INFOG(1) == -9 || (-16 < mumps->id.INFOG(1) && mumps->id.INFOG(1) < -10)) {
1688:       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)));
1689:       F->factorerrortype = MAT_FACTOR_OUTMEMORY;
1690:     } else {
1691:       PetscCall(PetscInfo(F, "MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1692:       F->factorerrortype = MAT_FACTOR_OTHER;
1693:     }
1694:   }
1695:   PetscCheck(mumps->myid || mumps->id.ICNTL(16) <= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "  mumps->id.ICNTL(16):=%d", mumps->id.INFOG(16));

1697:   F->assembled = PETSC_TRUE;

1699:   if (F->schur) { /* reset Schur status to unfactored */
1700: #if defined(PETSC_HAVE_CUDA)
1701:     F->schur->offloadmask = PETSC_OFFLOAD_CPU;
1702: #endif
1703:     if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
1704:       mumps->id.ICNTL(19) = 2;
1705:       PetscCall(MatTranspose(F->schur, MAT_INPLACE_MATRIX, &F->schur));
1706:     }
1707:     PetscCall(MatFactorRestoreSchurComplement(F, NULL, MAT_FACTOR_SCHUR_UNFACTORED));
1708:   }

1710:   /* just to be sure that ICNTL(19) value returned by a call from MatMumpsGetIcntl is always consistent */
1711:   if (!mumps->sym && mumps->id.ICNTL(19) && mumps->id.ICNTL(19) != 1) mumps->id.ICNTL(19) = 3;

1713:   if (!mumps->is_omp_master) mumps->id.INFO(23) = 0;
1714:   if (mumps->petsc_size > 1) {
1715:     PetscInt     lsol_loc;
1716:     PetscScalar *sol_loc;

1718:     PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &isMPIAIJ));

1720:     /* distributed solution; Create x_seq=sol_loc for repeated use */
1721:     if (mumps->x_seq) {
1722:       PetscCall(VecScatterDestroy(&mumps->scat_sol));
1723:       PetscCall(PetscFree2(mumps->id.sol_loc, mumps->id.isol_loc));
1724:       PetscCall(VecDestroy(&mumps->x_seq));
1725:     }
1726:     lsol_loc = mumps->id.INFO(23); /* length of sol_loc */
1727:     PetscCall(PetscMalloc2(lsol_loc, &sol_loc, lsol_loc, &mumps->id.isol_loc));
1728:     mumps->id.lsol_loc = lsol_loc;
1729:     mumps->id.sol_loc  = (MumpsScalar *)sol_loc;
1730:     PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, lsol_loc, sol_loc, &mumps->x_seq));
1731:   }
1732:   PetscCall(PetscLogFlops(mumps->id.RINFO(2)));
1733:   PetscFunctionReturn(PETSC_SUCCESS);
1734: }

1736: /* Sets MUMPS options from the options database */
1737: PetscErrorCode MatSetFromOptions_MUMPS(Mat F, Mat A)
1738: {
1739:   Mat_MUMPS    *mumps = (Mat_MUMPS *)F->data;
1740:   PetscMUMPSInt icntl = 0, size, *listvar_schur;
1741:   PetscInt      info[80], i, ninfo = 80, rbs, cbs;
1742:   PetscBool     flg = PETSC_FALSE, schur = (PetscBool)(mumps->id.ICNTL(26) == -1);
1743:   MumpsScalar  *arr;

1745:   PetscFunctionBegin;
1746:   PetscOptionsBegin(PetscObjectComm((PetscObject)F), ((PetscObject)F)->prefix, "MUMPS Options", "Mat");
1747:   if (mumps->id.job == JOB_NULL) { /* MatSetFromOptions_MUMPS() has never been called before */
1748:     PetscInt nthreads   = 0;
1749:     PetscInt nCNTL_pre  = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
1750:     PetscInt nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0;

1752:     mumps->petsc_comm = PetscObjectComm((PetscObject)A);
1753:     PetscCallMPI(MPI_Comm_size(mumps->petsc_comm, &mumps->petsc_size));
1754:     PetscCallMPI(MPI_Comm_rank(mumps->petsc_comm, &mumps->myid)); /* "if (!myid)" still works even if mumps_comm is different */

1756:     PetscCall(PetscOptionsName("-mat_mumps_use_omp_threads", "Convert MPI processes into OpenMP threads", "None", &mumps->use_petsc_omp_support));
1757:     if (mumps->use_petsc_omp_support) nthreads = -1; /* -1 will let PetscOmpCtrlCreate() guess a proper value when user did not supply one */
1758:     /* do not use PetscOptionsInt() so that the option -mat_mumps_use_omp_threads is not displayed twice in the help */
1759:     PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)F)->prefix, "-mat_mumps_use_omp_threads", &nthreads, NULL));
1760:     if (mumps->use_petsc_omp_support) {
1761:       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",
1762:                  ((PetscObject)F)->prefix ? ((PetscObject)F)->prefix : "");
1763:       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 : "");
1764: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
1765:       PetscCall(PetscOmpCtrlCreate(mumps->petsc_comm, nthreads, &mumps->omp_ctrl));
1766:       PetscCall(PetscOmpCtrlGetOmpComms(mumps->omp_ctrl, &mumps->omp_comm, &mumps->mumps_comm, &mumps->is_omp_master));
1767: #endif
1768:     } else {
1769:       mumps->omp_comm      = PETSC_COMM_SELF;
1770:       mumps->mumps_comm    = mumps->petsc_comm;
1771:       mumps->is_omp_master = PETSC_TRUE;
1772:     }
1773:     PetscCallMPI(MPI_Comm_size(mumps->omp_comm, &mumps->omp_comm_size));
1774:     mumps->reqs = NULL;
1775:     mumps->tag  = 0;

1777:     if (mumps->mumps_comm != MPI_COMM_NULL) {
1778:       if (PetscDefined(HAVE_OPENMP_SUPPORT) && mumps->use_petsc_omp_support) {
1779:         /* It looks like MUMPS does not dup the input comm. Dup a new comm for MUMPS to avoid any tag mismatches. */
1780:         MPI_Comm comm;
1781:         PetscCallMPI(MPI_Comm_dup(mumps->mumps_comm, &comm));
1782:         mumps->mumps_comm = comm;
1783:       } else PetscCall(PetscCommGetComm(mumps->petsc_comm, &mumps->mumps_comm));
1784:     }

1786:     mumps->id.comm_fortran = MPI_Comm_c2f(mumps->mumps_comm);
1787:     mumps->id.job          = JOB_INIT;
1788:     mumps->id.par          = 1; /* host participates factorizaton and solve */
1789:     mumps->id.sym          = mumps->sym;

1791:     size          = mumps->id.size_schur;
1792:     arr           = mumps->id.schur;
1793:     listvar_schur = mumps->id.listvar_schur;
1794:     PetscMUMPS_c(mumps);
1795:     PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MUMPS: INFOG(1)=%d", mumps->id.INFOG(1));
1796:     /* restore cached ICNTL and CNTL values */
1797:     for (icntl = 0; icntl < nICNTL_pre; ++icntl) mumps->id.ICNTL(mumps->ICNTL_pre[1 + 2 * icntl]) = mumps->ICNTL_pre[2 + 2 * icntl];
1798:     for (icntl = 0; icntl < nCNTL_pre; ++icntl) mumps->id.CNTL((PetscInt)mumps->CNTL_pre[1 + 2 * icntl]) = mumps->CNTL_pre[2 + 2 * icntl];
1799:     PetscCall(PetscFree(mumps->ICNTL_pre));
1800:     PetscCall(PetscFree(mumps->CNTL_pre));

1802:     if (schur) {
1803:       mumps->id.size_schur    = size;
1804:       mumps->id.schur_lld     = size;
1805:       mumps->id.schur         = arr;
1806:       mumps->id.listvar_schur = listvar_schur;
1807:       if (mumps->petsc_size > 1) {
1808:         PetscBool gs; /* gs is false if any rank other than root has non-empty IS */

1810:         mumps->id.ICNTL(19) = 1;                                                                            /* MUMPS returns Schur centralized on the host */
1811:         gs                  = mumps->myid ? (mumps->id.size_schur ? PETSC_FALSE : PETSC_TRUE) : PETSC_TRUE; /* always true on root; false on others if their size != 0 */
1812:         PetscCallMPI(MPI_Allreduce(MPI_IN_PLACE, &gs, 1, MPIU_BOOL, MPI_LAND, mumps->petsc_comm));
1813:         PetscCheck(gs, PETSC_COMM_SELF, PETSC_ERR_SUP, "MUMPS distributed parallel Schur complements not yet supported from PETSc");
1814:       } else {
1815:         if (F->factortype == MAT_FACTOR_LU) {
1816:           mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */
1817:         } else {
1818:           mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */
1819:         }
1820:       }
1821:       mumps->id.ICNTL(26) = -1;
1822:     }

1824:     /* copy MUMPS default control values from master to slaves. Although slaves do not call MUMPS, they may access these values in code.
1825:        For example, ICNTL(9) is initialized to 1 by MUMPS and slaves check ICNTL(9) in MatSolve_MUMPS.
1826:      */
1827:     PetscCallMPI(MPI_Bcast(mumps->id.icntl, 40, MPI_INT, 0, mumps->omp_comm));
1828:     PetscCallMPI(MPI_Bcast(mumps->id.cntl, 15, MPIU_REAL, 0, mumps->omp_comm));

1830:     mumps->scat_rhs = NULL;
1831:     mumps->scat_sol = NULL;

1833:     /* set PETSc-MUMPS default options - override MUMPS default */
1834:     mumps->id.ICNTL(3) = 0;
1835:     mumps->id.ICNTL(4) = 0;
1836:     if (mumps->petsc_size == 1) {
1837:       mumps->id.ICNTL(18) = 0; /* centralized assembled matrix input */
1838:       mumps->id.ICNTL(7)  = 7; /* automatic choice of ordering done by the package */
1839:     } else {
1840:       mumps->id.ICNTL(18) = 3; /* distributed assembled matrix input */
1841:       mumps->id.ICNTL(21) = 1; /* distributed solution */
1842:     }
1843:   }
1844:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_1", "ICNTL(1): output stream for error messages", "None", mumps->id.ICNTL(1), &icntl, &flg));
1845:   if (flg) mumps->id.ICNTL(1) = icntl;
1846:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_2", "ICNTL(2): output stream for diagnostic printing, statistics, and warning", "None", mumps->id.ICNTL(2), &icntl, &flg));
1847:   if (flg) mumps->id.ICNTL(2) = icntl;
1848:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_3", "ICNTL(3): output stream for global information, collected on the host", "None", mumps->id.ICNTL(3), &icntl, &flg));
1849:   if (flg) mumps->id.ICNTL(3) = icntl;

1851:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_4", "ICNTL(4): level of printing (0 to 4)", "None", mumps->id.ICNTL(4), &icntl, &flg));
1852:   if (flg) mumps->id.ICNTL(4) = icntl;
1853:   if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */

1855:   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));
1856:   if (flg) mumps->id.ICNTL(6) = icntl;

1858:   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));
1859:   if (flg) {
1860:     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");
1861:     mumps->id.ICNTL(7) = icntl;
1862:   }

1864:   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));
1865:   /* 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() */
1866:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_10", "ICNTL(10): max num of refinements", "None", mumps->id.ICNTL(10), &mumps->id.ICNTL(10), NULL));
1867:   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));
1868:   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));
1869:   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));
1870:   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));
1871:   PetscCall(MatGetBlockSizes(A, &rbs, &cbs));
1872:   if (rbs == cbs && rbs > 1) mumps->id.ICNTL(15) = -rbs;
1873:   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));
1874:   if (flg) {
1875:     PetscCheck(mumps->id.ICNTL(15) <= 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Positive -mat_mumps_icntl_15 not handled");
1876:     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");
1877:   }
1878:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_19", "ICNTL(19): computes the Schur complement", "None", mumps->id.ICNTL(19), &mumps->id.ICNTL(19), NULL));
1879:   if (mumps->id.ICNTL(19) <= 0 || mumps->id.ICNTL(19) > 3) { /* reset any schur data (if any) */
1880:     PetscCall(MatDestroy(&F->schur));
1881:     PetscCall(MatMumpsResetSchur_Private(mumps));
1882:   }

1884:   /* Two MPICH Fortran MPI_IN_PLACE binding bugs prevented the use of 'mpich + mumps'. One happened with "mpi4py + mpich + mumps",
1885:      and was reported by Firedrake. See https://bitbucket.org/mpi4py/mpi4py/issues/162/mpi4py-initialization-breaks-fortran
1886:      and a petsc-maint mailing list thread with subject 'MUMPS segfaults in parallel because of ...'
1887:      This bug was fixed by https://github.com/pmodels/mpich/pull/4149. But the fix brought a new bug,
1888:      see https://github.com/pmodels/mpich/issues/5589. This bug was fixed by https://github.com/pmodels/mpich/pull/5590.
1889:      In short, we could not use distributed RHS with MPICH until v4.0b1.
1890:    */
1891: #if PETSC_PKG_MUMPS_VERSION_LT(5, 3, 0) || (defined(PETSC_HAVE_MPICH_NUMVERSION) && (PETSC_HAVE_MPICH_NUMVERSION < 40000101))
1892:   mumps->ICNTL20 = 0; /* Centralized dense RHS*/
1893: #else
1894:   mumps->ICNTL20     = 10; /* Distributed dense RHS*/
1895: #endif
1896:   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));
1897:   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);
1898: #if PETSC_PKG_MUMPS_VERSION_LT(5, 3, 0)
1899:   PetscCheck(!flg || mumps->ICNTL20 != 10, PETSC_COMM_SELF, PETSC_ERR_SUP, "ICNTL(20)=10 is not supported before MUMPS-5.3.0");
1900: #endif
1901:   /* 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 */

1903:   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));
1904:   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));
1905:   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));
1906:   if (mumps->id.ICNTL(24)) { mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */ }

1908:   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));
1909:   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));
1910:   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));
1911:   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));
1912:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_29", "ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis", "None", mumps->id.ICNTL(29), &mumps->id.ICNTL(29), NULL));
1913:   /* 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 */
1914:   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));
1915:   /* 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 */
1916:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_33", "ICNTL(33): compute determinant", "None", mumps->id.ICNTL(33), &mumps->id.ICNTL(33), NULL));
1917:   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));
1918:   PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_36", "ICNTL(36): choice of BLR factorization variant", "None", mumps->id.ICNTL(36), &mumps->id.ICNTL(36), NULL));
1919:   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));

1921:   PetscCall(PetscOptionsReal("-mat_mumps_cntl_1", "CNTL(1): relative pivoting threshold", "None", mumps->id.CNTL(1), &mumps->id.CNTL(1), NULL));
1922:   PetscCall(PetscOptionsReal("-mat_mumps_cntl_2", "CNTL(2): stopping criterion of refinement", "None", mumps->id.CNTL(2), &mumps->id.CNTL(2), NULL));
1923:   PetscCall(PetscOptionsReal("-mat_mumps_cntl_3", "CNTL(3): absolute pivoting threshold", "None", mumps->id.CNTL(3), &mumps->id.CNTL(3), NULL));
1924:   PetscCall(PetscOptionsReal("-mat_mumps_cntl_4", "CNTL(4): value for static pivoting", "None", mumps->id.CNTL(4), &mumps->id.CNTL(4), NULL));
1925:   PetscCall(PetscOptionsReal("-mat_mumps_cntl_5", "CNTL(5): fixation for null pivots", "None", mumps->id.CNTL(5), &mumps->id.CNTL(5), NULL));
1926:   PetscCall(PetscOptionsReal("-mat_mumps_cntl_7", "CNTL(7): dropping parameter used during BLR", "None", mumps->id.CNTL(7), &mumps->id.CNTL(7), NULL));

1928:   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));

1930:   PetscCall(PetscOptionsIntArray("-mat_mumps_view_info", "request INFO local to each processor", "", info, &ninfo, NULL));
1931:   if (ninfo) {
1932:     PetscCheck(ninfo <= 80, PETSC_COMM_SELF, PETSC_ERR_USER, "number of INFO %" PetscInt_FMT " must <= 80", ninfo);
1933:     PetscCall(PetscMalloc1(ninfo, &mumps->info));
1934:     mumps->ninfo = ninfo;
1935:     for (i = 0; i < ninfo; i++) {
1936:       PetscCheck(info[i] >= 0 && info[i] <= 80, PETSC_COMM_SELF, PETSC_ERR_USER, "index of INFO %" PetscInt_FMT " must between 1 and 80", ninfo);
1937:       mumps->info[i] = info[i];
1938:     }
1939:   }
1940:   PetscOptionsEnd();
1941:   PetscFunctionReturn(PETSC_SUCCESS);
1942: }

1944: PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F, Mat A, const MatFactorInfo *info, Mat_MUMPS *mumps)
1945: {
1946:   PetscFunctionBegin;
1947:   if (mumps->id.INFOG(1) < 0) {
1948:     PetscCheck(!A->erroriffailure, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MUMPS in analysis phase: INFOG(1)=%d", mumps->id.INFOG(1));
1949:     if (mumps->id.INFOG(1) == -6) {
1950:       PetscCall(PetscInfo(F, "matrix is singular in structure, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1951:       F->factorerrortype = MAT_FACTOR_STRUCT_ZEROPIVOT;
1952:     } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) {
1953:       PetscCall(PetscInfo(F, "problem of workspace, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1954:       F->factorerrortype = MAT_FACTOR_OUTMEMORY;
1955:     } else if (mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0) {
1956:       PetscCall(PetscInfo(F, "Empty matrix\n"));
1957:     } else {
1958:       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)));
1959:       F->factorerrortype = MAT_FACTOR_OTHER;
1960:     }
1961:   }
1962:   PetscFunctionReturn(PETSC_SUCCESS);
1963: }

1965: PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
1966: {
1967:   Mat_MUMPS     *mumps = (Mat_MUMPS *)F->data;
1968:   Vec            b;
1969:   const PetscInt M = A->rmap->N;

1971:   PetscFunctionBegin;
1972:   if (mumps->matstruc == SAME_NONZERO_PATTERN) {
1973:     /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */
1974:     PetscFunctionReturn(PETSC_SUCCESS);
1975:   }

1977:   /* Set MUMPS options from the options database */
1978:   PetscCall(MatSetFromOptions_MUMPS(F, A));

1980:   PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
1981:   PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));

1983:   /* analysis phase */
1984:   mumps->id.job = JOB_FACTSYMBOLIC;
1985:   mumps->id.n   = M;
1986:   switch (mumps->id.ICNTL(18)) {
1987:   case 0: /* centralized assembled matrix input */
1988:     if (!mumps->myid) {
1989:       mumps->id.nnz = mumps->nnz;
1990:       mumps->id.irn = mumps->irn;
1991:       mumps->id.jcn = mumps->jcn;
1992:       if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
1993:       if (r) {
1994:         mumps->id.ICNTL(7) = 1;
1995:         if (!mumps->myid) {
1996:           const PetscInt *idx;
1997:           PetscInt        i;

1999:           PetscCall(PetscMalloc1(M, &mumps->id.perm_in));
2000:           PetscCall(ISGetIndices(r, &idx));
2001:           for (i = 0; i < M; i++) PetscCall(PetscMUMPSIntCast(idx[i] + 1, &(mumps->id.perm_in[i]))); /* perm_in[]: start from 1, not 0! */
2002:           PetscCall(ISRestoreIndices(r, &idx));
2003:         }
2004:       }
2005:     }
2006:     break;
2007:   case 3: /* distributed assembled matrix input (size>1) */
2008:     mumps->id.nnz_loc = mumps->nnz;
2009:     mumps->id.irn_loc = mumps->irn;
2010:     mumps->id.jcn_loc = mumps->jcn;
2011:     if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2012:     if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2013:       PetscCall(MatCreateVecs(A, NULL, &b));
2014:       PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2015:       PetscCall(VecDestroy(&b));
2016:     }
2017:     break;
2018:   }
2019:   PetscMUMPS_c(mumps);
2020:   PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));

2022:   F->ops->lufactornumeric   = MatFactorNumeric_MUMPS;
2023:   F->ops->solve             = MatSolve_MUMPS;
2024:   F->ops->solvetranspose    = MatSolveTranspose_MUMPS;
2025:   F->ops->matsolve          = MatMatSolve_MUMPS;
2026:   F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS;
2027:   F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;

2029:   mumps->matstruc = SAME_NONZERO_PATTERN;
2030:   PetscFunctionReturn(PETSC_SUCCESS);
2031: }

2033: /* Note the Petsc r and c permutations are ignored */
2034: PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
2035: {
2036:   Mat_MUMPS     *mumps = (Mat_MUMPS *)F->data;
2037:   Vec            b;
2038:   const PetscInt M = A->rmap->N;

2040:   PetscFunctionBegin;
2041:   if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2042:     /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */
2043:     PetscFunctionReturn(PETSC_SUCCESS);
2044:   }

2046:   /* Set MUMPS options from the options database */
2047:   PetscCall(MatSetFromOptions_MUMPS(F, A));

2049:   PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2050:   PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));

2052:   /* analysis phase */
2053:   mumps->id.job = JOB_FACTSYMBOLIC;
2054:   mumps->id.n   = M;
2055:   switch (mumps->id.ICNTL(18)) {
2056:   case 0: /* centralized assembled matrix input */
2057:     if (!mumps->myid) {
2058:       mumps->id.nnz = mumps->nnz;
2059:       mumps->id.irn = mumps->irn;
2060:       mumps->id.jcn = mumps->jcn;
2061:       if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2062:     }
2063:     break;
2064:   case 3: /* distributed assembled matrix input (size>1) */
2065:     mumps->id.nnz_loc = mumps->nnz;
2066:     mumps->id.irn_loc = mumps->irn;
2067:     mumps->id.jcn_loc = mumps->jcn;
2068:     if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2069:     if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2070:       PetscCall(MatCreateVecs(A, NULL, &b));
2071:       PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2072:       PetscCall(VecDestroy(&b));
2073:     }
2074:     break;
2075:   }
2076:   PetscMUMPS_c(mumps);
2077:   PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));

2079:   F->ops->lufactornumeric   = MatFactorNumeric_MUMPS;
2080:   F->ops->solve             = MatSolve_MUMPS;
2081:   F->ops->solvetranspose    = MatSolveTranspose_MUMPS;
2082:   F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;

2084:   mumps->matstruc = SAME_NONZERO_PATTERN;
2085:   PetscFunctionReturn(PETSC_SUCCESS);
2086: }

2088: /* Note the Petsc r permutation and factor info are ignored */
2089: PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F, Mat A, IS r, const MatFactorInfo *info)
2090: {
2091:   Mat_MUMPS     *mumps = (Mat_MUMPS *)F->data;
2092:   Vec            b;
2093:   const PetscInt M = A->rmap->N;

2095:   PetscFunctionBegin;
2096:   if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2097:     /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */
2098:     PetscFunctionReturn(PETSC_SUCCESS);
2099:   }

2101:   /* Set MUMPS options from the options database */
2102:   PetscCall(MatSetFromOptions_MUMPS(F, A));

2104:   PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2105:   PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));

2107:   /* analysis phase */
2108:   mumps->id.job = JOB_FACTSYMBOLIC;
2109:   mumps->id.n   = M;
2110:   switch (mumps->id.ICNTL(18)) {
2111:   case 0: /* centralized assembled matrix input */
2112:     if (!mumps->myid) {
2113:       mumps->id.nnz = mumps->nnz;
2114:       mumps->id.irn = mumps->irn;
2115:       mumps->id.jcn = mumps->jcn;
2116:       if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2117:     }
2118:     break;
2119:   case 3: /* distributed assembled matrix input (size>1) */
2120:     mumps->id.nnz_loc = mumps->nnz;
2121:     mumps->id.irn_loc = mumps->irn;
2122:     mumps->id.jcn_loc = mumps->jcn;
2123:     if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2124:     if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2125:       PetscCall(MatCreateVecs(A, NULL, &b));
2126:       PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2127:       PetscCall(VecDestroy(&b));
2128:     }
2129:     break;
2130:   }
2131:   PetscMUMPS_c(mumps);
2132:   PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));

2134:   F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS;
2135:   F->ops->solve                 = MatSolve_MUMPS;
2136:   F->ops->solvetranspose        = MatSolve_MUMPS;
2137:   F->ops->matsolve              = MatMatSolve_MUMPS;
2138:   F->ops->mattransposesolve     = MatMatTransposeSolve_MUMPS;
2139:   F->ops->matsolvetranspose     = MatMatSolveTranspose_MUMPS;
2140: #if defined(PETSC_USE_COMPLEX)
2141:   F->ops->getinertia = NULL;
2142: #else
2143:   F->ops->getinertia = MatGetInertia_SBAIJMUMPS;
2144: #endif

2146:   mumps->matstruc = SAME_NONZERO_PATTERN;
2147:   PetscFunctionReturn(PETSC_SUCCESS);
2148: }

2150: PetscErrorCode MatView_MUMPS(Mat A, PetscViewer viewer)
2151: {
2152:   PetscBool         iascii;
2153:   PetscViewerFormat format;
2154:   Mat_MUMPS        *mumps = (Mat_MUMPS *)A->data;

2156:   PetscFunctionBegin;
2157:   /* check if matrix is mumps type */
2158:   if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(PETSC_SUCCESS);

2160:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
2161:   if (iascii) {
2162:     PetscCall(PetscViewerGetFormat(viewer, &format));
2163:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
2164:       PetscCall(PetscViewerASCIIPrintf(viewer, "MUMPS run parameters:\n"));
2165:       if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
2166:         PetscCall(PetscViewerASCIIPrintf(viewer, "  SYM (matrix type):                   %d\n", mumps->id.sym));
2167:         PetscCall(PetscViewerASCIIPrintf(viewer, "  PAR (host participation):            %d\n", mumps->id.par));
2168:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(1) (output for error):         %d\n", mumps->id.ICNTL(1)));
2169:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(2) (output of diagnostic msg): %d\n", mumps->id.ICNTL(2)));
2170:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(3) (output for global info):   %d\n", mumps->id.ICNTL(3)));
2171:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(4) (level of printing):        %d\n", mumps->id.ICNTL(4)));
2172:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(5) (input mat struct):         %d\n", mumps->id.ICNTL(5)));
2173:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(6) (matrix prescaling):        %d\n", mumps->id.ICNTL(6)));
2174:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(7) (sequential matrix ordering):%d\n", mumps->id.ICNTL(7)));
2175:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(8) (scaling strategy):         %d\n", mumps->id.ICNTL(8)));
2176:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(10) (max num of refinements):  %d\n", mumps->id.ICNTL(10)));
2177:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(11) (error analysis):          %d\n", mumps->id.ICNTL(11)));
2178:         if (mumps->id.ICNTL(11) > 0) {
2179:           PetscCall(PetscViewerASCIIPrintf(viewer, "    RINFOG(4) (inf norm of input mat):        %g\n", mumps->id.RINFOG(4)));
2180:           PetscCall(PetscViewerASCIIPrintf(viewer, "    RINFOG(5) (inf norm of solution):         %g\n", mumps->id.RINFOG(5)));
2181:           PetscCall(PetscViewerASCIIPrintf(viewer, "    RINFOG(6) (inf norm of residual):         %g\n", mumps->id.RINFOG(6)));
2182:           PetscCall(PetscViewerASCIIPrintf(viewer, "    RINFOG(7),RINFOG(8) (backward error est): %g, %g\n", mumps->id.RINFOG(7), mumps->id.RINFOG(8)));
2183:           PetscCall(PetscViewerASCIIPrintf(viewer, "    RINFOG(9) (error estimate):               %g\n", mumps->id.RINFOG(9)));
2184:           PetscCall(PetscViewerASCIIPrintf(viewer, "    RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n", mumps->id.RINFOG(10), mumps->id.RINFOG(11)));
2185:         }
2186:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(12) (efficiency control):                         %d\n", mumps->id.ICNTL(12)));
2187:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(13) (sequential factorization of the root node):  %d\n", mumps->id.ICNTL(13)));
2188:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(14) (percentage of estimated workspace increase): %d\n", mumps->id.ICNTL(14)));
2189:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(15) (compression of the input matrix):            %d\n", mumps->id.ICNTL(15)));
2190:         /* ICNTL(15-17) not used */
2191:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(18) (input mat struct):                           %d\n", mumps->id.ICNTL(18)));
2192:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(19) (Schur complement info):                      %d\n", mumps->id.ICNTL(19)));
2193:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(20) (RHS sparse pattern):                         %d\n", mumps->id.ICNTL(20)));
2194:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(21) (solution struct):                            %d\n", mumps->id.ICNTL(21)));
2195:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(22) (in-core/out-of-core facility):               %d\n", mumps->id.ICNTL(22)));
2196:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(23) (max size of memory can be allocated locally):%d\n", mumps->id.ICNTL(23)));

2198:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(24) (detection of null pivot rows):               %d\n", mumps->id.ICNTL(24)));
2199:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(25) (computation of a null space basis):          %d\n", mumps->id.ICNTL(25)));
2200:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(26) (Schur options for RHS or solution):          %d\n", mumps->id.ICNTL(26)));
2201:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(27) (blocking size for multiple RHS):             %d\n", mumps->id.ICNTL(27)));
2202:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(28) (use parallel or sequential ordering):        %d\n", mumps->id.ICNTL(28)));
2203:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(29) (parallel ordering):                          %d\n", mumps->id.ICNTL(29)));

2205:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(30) (user-specified set of entries in inv(A)):    %d\n", mumps->id.ICNTL(30)));
2206:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(31) (factors is discarded in the solve phase):    %d\n", mumps->id.ICNTL(31)));
2207:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(33) (compute determinant):                        %d\n", mumps->id.ICNTL(33)));
2208:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(35) (activate BLR based factorization):           %d\n", mumps->id.ICNTL(35)));
2209:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(36) (choice of BLR factorization variant):        %d\n", mumps->id.ICNTL(36)));
2210:         PetscCall(PetscViewerASCIIPrintf(viewer, "  ICNTL(38) (estimated compression rate of LU factors):   %d\n", mumps->id.ICNTL(38)));

2212:         PetscCall(PetscViewerASCIIPrintf(viewer, "  CNTL(1) (relative pivoting threshold):      %g\n", mumps->id.CNTL(1)));
2213:         PetscCall(PetscViewerASCIIPrintf(viewer, "  CNTL(2) (stopping criterion of refinement): %g\n", mumps->id.CNTL(2)));
2214:         PetscCall(PetscViewerASCIIPrintf(viewer, "  CNTL(3) (absolute pivoting threshold):      %g\n", mumps->id.CNTL(3)));
2215:         PetscCall(PetscViewerASCIIPrintf(viewer, "  CNTL(4) (value of static pivoting):         %g\n", mumps->id.CNTL(4)));
2216:         PetscCall(PetscViewerASCIIPrintf(viewer, "  CNTL(5) (fixation for null pivots):         %g\n", mumps->id.CNTL(5)));
2217:         PetscCall(PetscViewerASCIIPrintf(viewer, "  CNTL(7) (dropping parameter for BLR):       %g\n", mumps->id.CNTL(7)));

2219:         /* information local to each processor */
2220:         PetscCall(PetscViewerASCIIPrintf(viewer, "  RINFO(1) (local estimated flops for the elimination after analysis):\n"));
2221:         PetscCall(PetscViewerASCIIPushSynchronized(viewer));
2222:         PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "    [%d] %g\n", mumps->myid, mumps->id.RINFO(1)));
2223:         PetscCall(PetscViewerFlush(viewer));
2224:         PetscCall(PetscViewerASCIIPrintf(viewer, "  RINFO(2) (local estimated flops for the assembly after factorization):\n"));
2225:         PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "    [%d] %g\n", mumps->myid, mumps->id.RINFO(2)));
2226:         PetscCall(PetscViewerFlush(viewer));
2227:         PetscCall(PetscViewerASCIIPrintf(viewer, "  RINFO(3) (local estimated flops for the elimination after factorization):\n"));
2228:         PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "    [%d] %g\n", mumps->myid, mumps->id.RINFO(3)));
2229:         PetscCall(PetscViewerFlush(viewer));

2231:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization):\n"));
2232:         PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "    [%d] %d\n", mumps->myid, mumps->id.INFO(15)));
2233:         PetscCall(PetscViewerFlush(viewer));

2235:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization):\n"));
2236:         PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "    [%d] %d\n", mumps->myid, mumps->id.INFO(16)));
2237:         PetscCall(PetscViewerFlush(viewer));

2239:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFO(23) (num of pivots eliminated on this processor after factorization):\n"));
2240:         PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "    [%d] %d\n", mumps->myid, mumps->id.INFO(23)));
2241:         PetscCall(PetscViewerFlush(viewer));

2243:         if (mumps->ninfo && mumps->ninfo <= 80) {
2244:           PetscInt i;
2245:           for (i = 0; i < mumps->ninfo; i++) {
2246:             PetscCall(PetscViewerASCIIPrintf(viewer, "  INFO(%" PetscInt_FMT "):\n", mumps->info[i]));
2247:             PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "    [%d] %d\n", mumps->myid, mumps->id.INFO(mumps->info[i])));
2248:             PetscCall(PetscViewerFlush(viewer));
2249:           }
2250:         }
2251:         PetscCall(PetscViewerASCIIPopSynchronized(viewer));
2252:       } else PetscCall(PetscViewerASCIIPrintf(viewer, "  Use -%sksp_view ::ascii_info_detail to display information for all processes\n", ((PetscObject)A)->prefix ? ((PetscObject)A)->prefix : ""));

2254:       if (mumps->myid == 0) { /* information from the host */
2255:         PetscCall(PetscViewerASCIIPrintf(viewer, "  RINFOG(1) (global estimated flops for the elimination after analysis): %g\n", mumps->id.RINFOG(1)));
2256:         PetscCall(PetscViewerASCIIPrintf(viewer, "  RINFOG(2) (global estimated flops for the assembly after factorization): %g\n", mumps->id.RINFOG(2)));
2257:         PetscCall(PetscViewerASCIIPrintf(viewer, "  RINFOG(3) (global estimated flops for the elimination after factorization): %g\n", mumps->id.RINFOG(3)));
2258:         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)));

2260:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(3) (estimated real workspace for factors on all processors after analysis): %d\n", mumps->id.INFOG(3)));
2261:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d\n", mumps->id.INFOG(4)));
2262:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(5) (estimated maximum front size in the complete tree): %d\n", mumps->id.INFOG(5)));
2263:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(6) (number of nodes in the complete tree): %d\n", mumps->id.INFOG(6)));
2264:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(7) (ordering option effectively used after analysis): %d\n", mumps->id.INFOG(7)));
2265:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d\n", mumps->id.INFOG(8)));
2266:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d\n", mumps->id.INFOG(9)));
2267:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(10) (total integer space store the matrix factors after factorization): %d\n", mumps->id.INFOG(10)));
2268:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(11) (order of largest frontal matrix after factorization): %d\n", mumps->id.INFOG(11)));
2269:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(12) (number of off-diagonal pivots): %d\n", mumps->id.INFOG(12)));
2270:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(13) (number of delayed pivots after factorization): %d\n", mumps->id.INFOG(13)));
2271:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(14) (number of memory compress after factorization): %d\n", mumps->id.INFOG(14)));
2272:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(15) (number of steps of iterative refinement after solution): %d\n", mumps->id.INFOG(15)));
2273:         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)));
2274:         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)));
2275:         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)));
2276:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): %d\n", mumps->id.INFOG(19)));
2277:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(20) (estimated number of entries in the factors): %d\n", mumps->id.INFOG(20)));
2278:         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)));
2279:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): %d\n", mumps->id.INFOG(22)));
2280:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d\n", mumps->id.INFOG(23)));
2281:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d\n", mumps->id.INFOG(24)));
2282:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(25) (after factorization: number of pivots modified by static pivoting): %d\n", mumps->id.INFOG(25)));
2283:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(28) (after factorization: number of null pivots encountered): %d\n", mumps->id.INFOG(28)));
2284:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(29) (after factorization: effective number of entries in the factors (sum over all processors)): %d\n", mumps->id.INFOG(29)));
2285:         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)));
2286:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(32) (after analysis: type of analysis done): %d\n", mumps->id.INFOG(32)));
2287:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(33) (value used for ICNTL(8)): %d\n", mumps->id.INFOG(33)));
2288:         PetscCall(PetscViewerASCIIPrintf(viewer, "  INFOG(34) (exponent of the determinant if determinant is requested): %d\n", mumps->id.INFOG(34)));
2289:         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)));
2290:         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)));
2291:         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)));
2292:         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)));
2293:         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)));
2294:       }
2295:     }
2296:   }
2297:   PetscFunctionReturn(PETSC_SUCCESS);
2298: }

2300: PetscErrorCode MatGetInfo_MUMPS(Mat A, MatInfoType flag, MatInfo *info)
2301: {
2302:   Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;

2304:   PetscFunctionBegin;
2305:   info->block_size        = 1.0;
2306:   info->nz_allocated      = mumps->id.INFOG(20);
2307:   info->nz_used           = mumps->id.INFOG(20);
2308:   info->nz_unneeded       = 0.0;
2309:   info->assemblies        = 0.0;
2310:   info->mallocs           = 0.0;
2311:   info->memory            = 0.0;
2312:   info->fill_ratio_given  = 0;
2313:   info->fill_ratio_needed = 0;
2314:   info->factor_mallocs    = 0;
2315:   PetscFunctionReturn(PETSC_SUCCESS);
2316: }

2318: PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is)
2319: {
2320:   Mat_MUMPS         *mumps = (Mat_MUMPS *)F->data;
2321:   const PetscScalar *arr;
2322:   const PetscInt    *idxs;
2323:   PetscInt           size, i;

2325:   PetscFunctionBegin;
2326:   PetscCall(ISGetLocalSize(is, &size));
2327:   /* Schur complement matrix */
2328:   PetscCall(MatDestroy(&F->schur));
2329:   PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, size, size, NULL, &F->schur));
2330:   PetscCall(MatDenseGetArrayRead(F->schur, &arr));
2331:   mumps->id.schur      = (MumpsScalar *)arr;
2332:   mumps->id.size_schur = size;
2333:   mumps->id.schur_lld  = size;
2334:   PetscCall(MatDenseRestoreArrayRead(F->schur, &arr));
2335:   if (mumps->sym == 1) PetscCall(MatSetOption(F->schur, MAT_SPD, PETSC_TRUE));

2337:   /* MUMPS expects Fortran style indices */
2338:   PetscCall(PetscFree(mumps->id.listvar_schur));
2339:   PetscCall(PetscMalloc1(size, &mumps->id.listvar_schur));
2340:   PetscCall(ISGetIndices(is, &idxs));
2341:   for (i = 0; i < size; i++) PetscCall(PetscMUMPSIntCast(idxs[i] + 1, &(mumps->id.listvar_schur[i])));
2342:   PetscCall(ISRestoreIndices(is, &idxs));
2343:   /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */
2344:   mumps->id.ICNTL(26) = -1;
2345:   PetscFunctionReturn(PETSC_SUCCESS);
2346: }

2348: PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F, Mat *S)
2349: {
2350:   Mat          St;
2351:   Mat_MUMPS   *mumps = (Mat_MUMPS *)F->data;
2352:   PetscScalar *array;
2353: #if defined(PETSC_USE_COMPLEX)
2354:   PetscScalar im = PetscSqrtScalar((PetscScalar)-1.0);
2355: #endif

2357:   PetscFunctionBegin;
2358:   PetscCheck(mumps->id.ICNTL(19), PetscObjectComm((PetscObject)F), PETSC_ERR_ORDER, "Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it");
2359:   PetscCall(MatCreate(PETSC_COMM_SELF, &St));
2360:   PetscCall(MatSetSizes(St, PETSC_DECIDE, PETSC_DECIDE, mumps->id.size_schur, mumps->id.size_schur));
2361:   PetscCall(MatSetType(St, MATDENSE));
2362:   PetscCall(MatSetUp(St));
2363:   PetscCall(MatDenseGetArray(St, &array));
2364:   if (!mumps->sym) {                /* MUMPS always return a full matrix */
2365:     if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
2366:       PetscInt i, j, N = mumps->id.size_schur;
2367:       for (i = 0; i < N; i++) {
2368:         for (j = 0; j < N; j++) {
2369: #if !defined(PETSC_USE_COMPLEX)
2370:           PetscScalar val = mumps->id.schur[i * N + j];
2371: #else
2372:           PetscScalar val = mumps->id.schur[i * N + j].r + im * mumps->id.schur[i * N + j].i;
2373: #endif
2374:           array[j * N + i] = val;
2375:         }
2376:       }
2377:     } else { /* stored by columns */
2378:       PetscCall(PetscArraycpy(array, mumps->id.schur, mumps->id.size_schur * mumps->id.size_schur));
2379:     }
2380:   } else {                          /* either full or lower-triangular (not packed) */
2381:     if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */
2382:       PetscInt i, j, N = mumps->id.size_schur;
2383:       for (i = 0; i < N; i++) {
2384:         for (j = i; j < N; j++) {
2385: #if !defined(PETSC_USE_COMPLEX)
2386:           PetscScalar val = mumps->id.schur[i * N + j];
2387: #else
2388:           PetscScalar val = mumps->id.schur[i * N + j].r + im * mumps->id.schur[i * N + j].i;
2389: #endif
2390:           array[i * N + j] = val;
2391:           array[j * N + i] = val;
2392:         }
2393:       }
2394:     } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */
2395:       PetscCall(PetscArraycpy(array, mumps->id.schur, mumps->id.size_schur * mumps->id.size_schur));
2396:     } else { /* ICNTL(19) == 1 lower triangular stored by rows */
2397:       PetscInt i, j, N = mumps->id.size_schur;
2398:       for (i = 0; i < N; i++) {
2399:         for (j = 0; j < i + 1; j++) {
2400: #if !defined(PETSC_USE_COMPLEX)
2401:           PetscScalar val = mumps->id.schur[i * N + j];
2402: #else
2403:           PetscScalar val = mumps->id.schur[i * N + j].r + im * mumps->id.schur[i * N + j].i;
2404: #endif
2405:           array[i * N + j] = val;
2406:           array[j * N + i] = val;
2407:         }
2408:       }
2409:     }
2410:   }
2411:   PetscCall(MatDenseRestoreArray(St, &array));
2412:   *S = St;
2413:   PetscFunctionReturn(PETSC_SUCCESS);
2414: }

2416: PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F, PetscInt icntl, PetscInt ival)
2417: {
2418:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2420:   PetscFunctionBegin;
2421:   if (mumps->id.job == JOB_NULL) {                                       /* need to cache icntl and ival since PetscMUMPS_c() has never been called */
2422:     PetscInt i, nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0; /* number of already cached ICNTL */
2423:     for (i = 0; i < nICNTL_pre; ++i)
2424:       if (mumps->ICNTL_pre[1 + 2 * i] == icntl) break; /* is this ICNTL already cached? */
2425:     if (i == nICNTL_pre) {                             /* not already cached */
2426:       if (i > 0) PetscCall(PetscRealloc(sizeof(PetscMUMPSInt) * (2 * nICNTL_pre + 3), &mumps->ICNTL_pre));
2427:       else PetscCall(PetscCalloc(sizeof(PetscMUMPSInt) * 3, &mumps->ICNTL_pre));
2428:       mumps->ICNTL_pre[0]++;
2429:     }
2430:     mumps->ICNTL_pre[1 + 2 * i] = icntl;
2431:     PetscCall(PetscMUMPSIntCast(ival, mumps->ICNTL_pre + 2 + 2 * i));
2432:   } else PetscCall(PetscMUMPSIntCast(ival, &mumps->id.ICNTL(icntl)));
2433:   PetscFunctionReturn(PETSC_SUCCESS);
2434: }

2436: PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F, PetscInt icntl, PetscInt *ival)
2437: {
2438:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2440:   PetscFunctionBegin;
2441:   *ival = mumps->id.ICNTL(icntl);
2442:   PetscFunctionReturn(PETSC_SUCCESS);
2443: }

2445: /*@
2446:   MatMumpsSetIcntl - Set MUMPS parameter ICNTL()

2448:    Logically Collective

2450:    Input Parameters:
2451: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2452: .  icntl - index of MUMPS parameter array ICNTL()
2453: -  ival - value of MUMPS ICNTL(icntl)

2455:   Options Database Key:
2456: .   -mat_mumps_icntl_<icntl> <ival> - change the option numbered icntl to ival

2458:    Level: beginner

2460:    References:
2461: .  * - MUMPS Users' Guide

2463: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2464: @*/
2465: PetscErrorCode MatMumpsSetIcntl(Mat F, PetscInt icntl, PetscInt ival)
2466: {
2467:   PetscFunctionBegin;
2469:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2472:   PetscCheck(icntl >= 1 && icntl <= 38, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported ICNTL value %" PetscInt_FMT, icntl);
2473:   PetscTryMethod(F, "MatMumpsSetIcntl_C", (Mat, PetscInt, PetscInt), (F, icntl, ival));
2474:   PetscFunctionReturn(PETSC_SUCCESS);
2475: }

2477: /*@
2478:   MatMumpsGetIcntl - Get MUMPS parameter ICNTL()

2480:    Logically Collective

2482:    Input Parameters:
2483: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2484: -  icntl - index of MUMPS parameter array ICNTL()

2486:   Output Parameter:
2487: .  ival - value of MUMPS ICNTL(icntl)

2489:    Level: beginner

2491:    References:
2492: .  * - MUMPS Users' Guide

2494: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2495: @*/
2496: PetscErrorCode MatMumpsGetIcntl(Mat F, PetscInt icntl, PetscInt *ival)
2497: {
2498:   PetscFunctionBegin;
2500:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2503:   PetscCheck(icntl >= 1 && icntl <= 38, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported ICNTL value %" PetscInt_FMT, icntl);
2504:   PetscUseMethod(F, "MatMumpsGetIcntl_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
2505:   PetscFunctionReturn(PETSC_SUCCESS);
2506: }

2508: PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F, PetscInt icntl, PetscReal val)
2509: {
2510:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2512:   PetscFunctionBegin;
2513:   if (mumps->id.job == JOB_NULL) {
2514:     PetscInt i, nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
2515:     for (i = 0; i < nCNTL_pre; ++i)
2516:       if (mumps->CNTL_pre[1 + 2 * i] == icntl) break;
2517:     if (i == nCNTL_pre) {
2518:       if (i > 0) PetscCall(PetscRealloc(sizeof(PetscReal) * (2 * nCNTL_pre + 3), &mumps->CNTL_pre));
2519:       else PetscCall(PetscCalloc(sizeof(PetscReal) * 3, &mumps->CNTL_pre));
2520:       mumps->CNTL_pre[0]++;
2521:     }
2522:     mumps->CNTL_pre[1 + 2 * i] = icntl;
2523:     mumps->CNTL_pre[2 + 2 * i] = val;
2524:   } else mumps->id.CNTL(icntl) = val;
2525:   PetscFunctionReturn(PETSC_SUCCESS);
2526: }

2528: PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F, PetscInt icntl, PetscReal *val)
2529: {
2530:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2532:   PetscFunctionBegin;
2533:   *val = mumps->id.CNTL(icntl);
2534:   PetscFunctionReturn(PETSC_SUCCESS);
2535: }

2537: /*@
2538:   MatMumpsSetCntl - Set MUMPS parameter CNTL()

2540:    Logically Collective

2542:    Input Parameters:
2543: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2544: .  icntl - index of MUMPS parameter array CNTL()
2545: -  val - value of MUMPS CNTL(icntl)

2547:   Options Database Key:
2548: .   -mat_mumps_cntl_<icntl> <val>  - change the option numbered icntl to ival

2550:    Level: beginner

2552:    References:
2553: .  * - MUMPS Users' Guide

2555: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2556: @*/
2557: PetscErrorCode MatMumpsSetCntl(Mat F, PetscInt icntl, PetscReal val)
2558: {
2559:   PetscFunctionBegin;
2561:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2564:   PetscCheck(icntl >= 1 && icntl <= 7, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported CNTL value %" PetscInt_FMT, icntl);
2565:   PetscTryMethod(F, "MatMumpsSetCntl_C", (Mat, PetscInt, PetscReal), (F, icntl, val));
2566:   PetscFunctionReturn(PETSC_SUCCESS);
2567: }

2569: /*@
2570:   MatMumpsGetCntl - Get MUMPS parameter CNTL()

2572:    Logically Collective

2574:    Input Parameters:
2575: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2576: -  icntl - index of MUMPS parameter array CNTL()

2578:   Output Parameter:
2579: .  val - value of MUMPS CNTL(icntl)

2581:    Level: beginner

2583:    References:
2584: .  * - MUMPS Users' Guide

2586: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2587: @*/
2588: PetscErrorCode MatMumpsGetCntl(Mat F, PetscInt icntl, PetscReal *val)
2589: {
2590:   PetscFunctionBegin;
2592:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2595:   PetscCheck(icntl >= 1 && icntl <= 7, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported CNTL value %" PetscInt_FMT, icntl);
2596:   PetscUseMethod(F, "MatMumpsGetCntl_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
2597:   PetscFunctionReturn(PETSC_SUCCESS);
2598: }

2600: PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F, PetscInt icntl, PetscInt *info)
2601: {
2602:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2604:   PetscFunctionBegin;
2605:   *info = mumps->id.INFO(icntl);
2606:   PetscFunctionReturn(PETSC_SUCCESS);
2607: }

2609: PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F, PetscInt icntl, PetscInt *infog)
2610: {
2611:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2613:   PetscFunctionBegin;
2614:   *infog = mumps->id.INFOG(icntl);
2615:   PetscFunctionReturn(PETSC_SUCCESS);
2616: }

2618: PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F, PetscInt icntl, PetscReal *rinfo)
2619: {
2620:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2622:   PetscFunctionBegin;
2623:   *rinfo = mumps->id.RINFO(icntl);
2624:   PetscFunctionReturn(PETSC_SUCCESS);
2625: }

2627: PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F, PetscInt icntl, PetscReal *rinfog)
2628: {
2629:   Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;

2631:   PetscFunctionBegin;
2632:   *rinfog = mumps->id.RINFOG(icntl);
2633:   PetscFunctionReturn(PETSC_SUCCESS);
2634: }

2636: PetscErrorCode MatMumpsGetInverse_MUMPS(Mat F, Mat spRHS)
2637: {
2638:   Mat          Bt = NULL, Btseq = NULL;
2639:   PetscBool    flg;
2640:   Mat_MUMPS   *mumps = (Mat_MUMPS *)F->data;
2641:   PetscScalar *aa;
2642:   PetscInt     spnr, *ia, *ja, M, nrhs;

2644:   PetscFunctionBegin;
2646:   PetscCall(PetscObjectTypeCompare((PetscObject)spRHS, MATTRANSPOSEVIRTUAL, &flg));
2647:   if (flg) {
2648:     PetscCall(MatTransposeGetMat(spRHS, &Bt));
2649:   } else SETERRQ(PetscObjectComm((PetscObject)spRHS), PETSC_ERR_ARG_WRONG, "Matrix spRHS must be type MATTRANSPOSEVIRTUAL matrix");

2651:   PetscCall(MatMumpsSetIcntl(F, 30, 1));

2653:   if (mumps->petsc_size > 1) {
2654:     Mat_MPIAIJ *b = (Mat_MPIAIJ *)Bt->data;
2655:     Btseq         = b->A;
2656:   } else {
2657:     Btseq = Bt;
2658:   }

2660:   PetscCall(MatGetSize(spRHS, &M, &nrhs));
2661:   mumps->id.nrhs = nrhs;
2662:   mumps->id.lrhs = M;
2663:   mumps->id.rhs  = NULL;

2665:   if (!mumps->myid) {
2666:     PetscCall(MatSeqAIJGetArray(Btseq, &aa));
2667:     PetscCall(MatGetRowIJ(Btseq, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
2668:     PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
2669:     PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
2670:     mumps->id.rhs_sparse = (MumpsScalar *)aa;
2671:   } else {
2672:     mumps->id.irhs_ptr    = NULL;
2673:     mumps->id.irhs_sparse = NULL;
2674:     mumps->id.nz_rhs      = 0;
2675:     mumps->id.rhs_sparse  = NULL;
2676:   }
2677:   mumps->id.ICNTL(20) = 1; /* rhs is sparse */
2678:   mumps->id.ICNTL(21) = 0; /* solution is in assembled centralized format */

2680:   /* solve phase */
2681:   mumps->id.job = JOB_SOLVE;
2682:   PetscMUMPS_c(mumps);
2683:   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));

2685:   if (!mumps->myid) {
2686:     PetscCall(MatSeqAIJRestoreArray(Btseq, &aa));
2687:     PetscCall(MatRestoreRowIJ(Btseq, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
2688:     PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
2689:   }
2690:   PetscFunctionReturn(PETSC_SUCCESS);
2691: }

2693: /*@
2694:   MatMumpsGetInverse - Get user-specified set of entries in inverse of `A`

2696:    Logically Collective

2698:    Input Parameters:
2699: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2700: -  spRHS - sequential sparse matrix in `MATTRANSPOSEVIRTUAL` format holding specified indices in processor[0]

2702:   Output Parameter:
2703: . spRHS - requested entries of inverse of `A`

2705:    Level: beginner

2707:    References:
2708: .  * - MUMPS Users' Guide

2710: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatCreateTranspose()`
2711: @*/
2712: PetscErrorCode MatMumpsGetInverse(Mat F, Mat spRHS)
2713: {
2714:   PetscFunctionBegin;
2716:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2717:   PetscUseMethod(F, "MatMumpsGetInverse_C", (Mat, Mat), (F, spRHS));
2718:   PetscFunctionReturn(PETSC_SUCCESS);
2719: }

2721: PetscErrorCode MatMumpsGetInverseTranspose_MUMPS(Mat F, Mat spRHST)
2722: {
2723:   Mat spRHS;

2725:   PetscFunctionBegin;
2726:   PetscCall(MatCreateTranspose(spRHST, &spRHS));
2727:   PetscCall(MatMumpsGetInverse_MUMPS(F, spRHS));
2728:   PetscCall(MatDestroy(&spRHS));
2729:   PetscFunctionReturn(PETSC_SUCCESS);
2730: }

2732: /*@
2733:   MatMumpsGetInverseTranspose - Get user-specified set of entries in inverse of matrix `A`^T

2735:    Logically Collective

2737:    Input Parameters:
2738: +  F - the factored matrix of A obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2739: -  spRHST - sequential sparse matrix in `MATAIJ` format holding specified indices of `A`^T in processor[0]

2741:   Output Parameter:
2742: . spRHST - requested entries of inverse of `A`^T

2744:    Level: beginner

2746:    References:
2747: .  * - MUMPS Users' Guide

2749: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatCreateTranspose()`, `MatMumpsGetInverse()`
2750: @*/
2751: PetscErrorCode MatMumpsGetInverseTranspose(Mat F, Mat spRHST)
2752: {
2753:   PetscBool flg;

2755:   PetscFunctionBegin;
2757:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2758:   PetscCall(PetscObjectTypeCompareAny((PetscObject)spRHST, &flg, MATSEQAIJ, MATMPIAIJ, NULL));
2759:   PetscCheck(flg, PetscObjectComm((PetscObject)spRHST), PETSC_ERR_ARG_WRONG, "Matrix spRHST must be MATAIJ matrix");

2761:   PetscUseMethod(F, "MatMumpsGetInverseTranspose_C", (Mat, Mat), (F, spRHST));
2762:   PetscFunctionReturn(PETSC_SUCCESS);
2763: }

2765: /*@
2766:   MatMumpsGetInfo - Get MUMPS parameter INFO()

2768:    Logically Collective

2770:    Input Parameters:
2771: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2772: -  icntl - index of MUMPS parameter array INFO()

2774:   Output Parameter:
2775: .  ival - value of MUMPS INFO(icntl)

2777:    Level: beginner

2779:    References:
2780: .  * - MUMPS Users' Guide

2782: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2783: @*/
2784: PetscErrorCode MatMumpsGetInfo(Mat F, PetscInt icntl, PetscInt *ival)
2785: {
2786:   PetscFunctionBegin;
2788:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2790:   PetscUseMethod(F, "MatMumpsGetInfo_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
2791:   PetscFunctionReturn(PETSC_SUCCESS);
2792: }

2794: /*@
2795:   MatMumpsGetInfog - Get MUMPS parameter INFOG()

2797:    Logically Collective

2799:    Input Parameters:
2800: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2801: -  icntl - index of MUMPS parameter array INFOG()

2803:   Output Parameter:
2804: .  ival - value of MUMPS INFOG(icntl)

2806:    Level: beginner

2808:    References:
2809: .  * - MUMPS Users' Guide

2811: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2812: @*/
2813: PetscErrorCode MatMumpsGetInfog(Mat F, PetscInt icntl, PetscInt *ival)
2814: {
2815:   PetscFunctionBegin;
2817:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2819:   PetscUseMethod(F, "MatMumpsGetInfog_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
2820:   PetscFunctionReturn(PETSC_SUCCESS);
2821: }

2823: /*@
2824:   MatMumpsGetRinfo - Get MUMPS parameter RINFO()

2826:    Logically Collective

2828:    Input Parameters:
2829: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2830: -  icntl - index of MUMPS parameter array RINFO()

2832:   Output Parameter:
2833: .  val - value of MUMPS RINFO(icntl)

2835:    Level: beginner

2837:    References:
2838: .  * - MUMPS Users' Guide

2840: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfog()`
2841: @*/
2842: PetscErrorCode MatMumpsGetRinfo(Mat F, PetscInt icntl, PetscReal *val)
2843: {
2844:   PetscFunctionBegin;
2846:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2848:   PetscUseMethod(F, "MatMumpsGetRinfo_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
2849:   PetscFunctionReturn(PETSC_SUCCESS);
2850: }

2852: /*@
2853:   MatMumpsGetRinfog - Get MUMPS parameter RINFOG()

2855:    Logically Collective

2857:    Input Parameters:
2858: +  F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2859: -  icntl - index of MUMPS parameter array RINFOG()

2861:   Output Parameter:
2862: .  val - value of MUMPS RINFOG(icntl)

2864:    Level: beginner

2866:    References:
2867: .  * - MUMPS Users' Guide

2869: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`
2870: @*/
2871: PetscErrorCode MatMumpsGetRinfog(Mat F, PetscInt icntl, PetscReal *val)
2872: {
2873:   PetscFunctionBegin;
2875:   PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2877:   PetscUseMethod(F, "MatMumpsGetRinfog_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
2878:   PetscFunctionReturn(PETSC_SUCCESS);
2879: }

2881: /*MC
2882:   MATSOLVERMUMPS -  A matrix type providing direct solvers (LU and Cholesky) for
2883:   distributed and sequential matrices via the external package MUMPS.

2885:   Works with `MATAIJ` and `MATSBAIJ` matrices

2887:   Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch to have PETSc installed with MUMPS

2889:   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.
2890:   See details below.

2892:   Use `-pc_type cholesky` or `lu` `-pc_factor_mat_solver_type mumps` to use this direct solver

2894:   Options Database Keys:
2895: +  -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages
2896: .  -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning
2897: .  -mat_mumps_icntl_3 -  ICNTL(3): output stream for global information, collected on the host
2898: .  -mat_mumps_icntl_4 -  ICNTL(4): level of printing (0 to 4)
2899: .  -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7)
2900: .  -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
2901:                         Use -pc_factor_mat_ordering_type <type> to have PETSc perform the ordering (sequential only)
2902: .  -mat_mumps_icntl_8  - ICNTL(8): scaling strategy (-2 to 8 or 77)
2903: .  -mat_mumps_icntl_10  - ICNTL(10): max num of refinements
2904: .  -mat_mumps_icntl_11  - ICNTL(11): statistics related to an error analysis (via -ksp_view)
2905: .  -mat_mumps_icntl_12  - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3)
2906: .  -mat_mumps_icntl_13  - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting
2907: .  -mat_mumps_icntl_14  - ICNTL(14): percentage increase in the estimated working space
2908: .  -mat_mumps_icntl_15  - ICNTL(15): compression of the input matrix resulting from a block format
2909: .  -mat_mumps_icntl_19  - ICNTL(19): computes the Schur complement
2910: .  -mat_mumps_icntl_20  - ICNTL(20): give MUMPS centralized (0) or distributed (10) dense RHS
2911: .  -mat_mumps_icntl_22  - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1)
2912: .  -mat_mumps_icntl_23  - ICNTL(23): max size of the working memory (MB) that can allocate per processor
2913: .  -mat_mumps_icntl_24  - ICNTL(24): detection of null pivot rows (0 or 1)
2914: .  -mat_mumps_icntl_25  - ICNTL(25): compute a solution of a deficient matrix and a null space basis
2915: .  -mat_mumps_icntl_26  - ICNTL(26): drives the solution phase if a Schur complement matrix
2916: .  -mat_mumps_icntl_28  - ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering
2917: .  -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis
2918: .  -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A)
2919: .  -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization
2920: .  -mat_mumps_icntl_33 - ICNTL(33): compute determinant
2921: .  -mat_mumps_icntl_35 - ICNTL(35): level of activation of BLR (Block Low-Rank) feature
2922: .  -mat_mumps_icntl_36 - ICNTL(36): controls the choice of BLR factorization variant
2923: .  -mat_mumps_icntl_38 - ICNTL(38): sets the estimated compression rate of LU factors with BLR
2924: .  -mat_mumps_cntl_1  - CNTL(1): relative pivoting threshold
2925: .  -mat_mumps_cntl_2  -  CNTL(2): stopping criterion of refinement
2926: .  -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold
2927: .  -mat_mumps_cntl_4 - CNTL(4): value for static pivoting
2928: .  -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots
2929: .  -mat_mumps_cntl_7 - CNTL(7): precision of the dropping parameter used during BLR factorization
2930: -  -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.
2931:                                    Default might be the number of cores per CPU package (socket) as reported by hwloc and suggested by the MUMPS manual.

2933:   Level: beginner

2935:     Notes:
2936:     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
2937:     error if the matrix is Hermitian.

2939:     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
2940:     `MatSetOptionsPrefixFactor()` on the matrix from which the factor was obtained or `MatSetOptionsPrefix()` on the factor matrix.

2942:     When a MUMPS factorization fails inside a KSP solve, for example with a `KSP_DIVERGED_PC_FAILED`, one can find the MUMPS information about
2943:     the failure with
2944: .vb
2945:           KSPGetPC(ksp,&pc);
2946:           PCFactorGetMatrix(pc,&mat);
2947:           MatMumpsGetInfo(mat,....);
2948:           MatMumpsGetInfog(mat,....); etc.
2949: .ve
2950:     Or run with `-ksp_error_if_not_converged` and the program will be stopped and the information printed in the error message.

2952:     MUMPS provides 64-bit integer support in two build modes:
2953:       full 64-bit: here MUMPS is built with C preprocessing flag -DINTSIZE64 and Fortran compiler option -i8, -fdefault-integer-8 or equivalent, and
2954:       requires all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS built the same way with 64-bit integers (for example ILP64 Intel MKL and MPI).

2956:       selective 64-bit: with the default MUMPS build, 64-bit integers have been introduced where needed. In compressed sparse row (CSR) storage of matrices,
2957:       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
2958:       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
2959:       integer) build of all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS.

2961:     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.

2963:   Two modes to run MUMPS/PETSc with OpenMP
2964: .vb
2965:      Set OMP_NUM_THREADS and run with fewer MPI ranks than cores. For example, if you want to have 16 OpenMP
2966:      threads per rank, then you may use "export OMP_NUM_THREADS=16 && mpirun -n 4 ./test".
2967: .ve

2969: .vb
2970:      -mat_mumps_use_omp_threads [m] and run your code with as many MPI ranks as the number of cores. For example,
2971:     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"
2972: .ve

2974:    To run MUMPS in MPI+OpenMP hybrid mode (i.e., enable multithreading in MUMPS), but still run the non-MUMPS part
2975:    (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`
2976:    (or `--with-hwloc`), and have an MPI that supports MPI-3.0's process shared memory (which is usually available). Since MUMPS calls BLAS
2977:    libraries, to really get performance, you should have multithreaded BLAS libraries such as Intel MKL, AMD ACML, Cray libSci or OpenBLAS
2978:    (PETSc will automatically try to utilized a threaded BLAS if --with-openmp is provided).

2980:    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
2981:    processes on each compute node. Listing the processes in rank ascending order, we split processes on a node into consecutive groups of
2982:    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
2983:    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
2984:    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.
2985:    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,
2986:    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
2987:    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
2988:    cores in socket 1, that definitely hurts locality. On the other hand, if you map MPI ranks consecutively on the two sockets, then the
2989:    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.
2990:    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
2991:    examine the mapping result.

2993:    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,
2994:    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
2995:    calls `omp_set_num_threads`(m) internally before calling MUMPS.

2997:    References:
2998: +  * - Heroux, Michael A., R. Brightwell, and Michael M. Wolf. "Bi-modal MPI and MPI+ threads computing on scalable multicore systems." IJHPCA (Submitted) (2011).
2999: -  * - Gutierrez, Samuel K., et al. "Accommodating Thread-Level Heterogeneity in Coupled Parallel Applications." Parallel and Distributed Processing Symposium (IPDPS), 2017 IEEE International. IEEE, 2017.

3001: .seealso: [](chapter_matrices), `Mat`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`, `KSPGetPC()`, `PCFactorGetMatrix()`
3002: M*/

3004: static PetscErrorCode MatFactorGetSolverType_mumps(Mat A, MatSolverType *type)
3005: {
3006:   PetscFunctionBegin;
3007:   *type = MATSOLVERMUMPS;
3008:   PetscFunctionReturn(PETSC_SUCCESS);
3009: }

3011: /* MatGetFactor for Seq and MPI AIJ matrices */
3012: static PetscErrorCode MatGetFactor_aij_mumps(Mat A, MatFactorType ftype, Mat *F)
3013: {
3014:   Mat         B;
3015:   Mat_MUMPS  *mumps;
3016:   PetscBool   isSeqAIJ;
3017:   PetscMPIInt size;

3019:   PetscFunctionBegin;
3020: #if defined(PETSC_USE_COMPLEX)
3021:   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");
3022: #endif
3023:   /* Create the factorization matrix */
3024:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATSEQAIJ, &isSeqAIJ));
3025:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3026:   PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3027:   PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3028:   PetscCall(MatSetUp(B));

3030:   PetscCall(PetscNew(&mumps));

3032:   B->ops->view    = MatView_MUMPS;
3033:   B->ops->getinfo = MatGetInfo_MUMPS;

3035:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3036:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3037:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3038:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3039:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3040:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3041:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3042:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3043:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3044:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3045:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3046:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3047:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));

3049:   if (ftype == MAT_FACTOR_LU) {
3050:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3051:     B->factortype            = MAT_FACTOR_LU;
3052:     if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij;
3053:     else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij;
3054:     PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3055:     mumps->sym = 0;
3056:   } else {
3057:     B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3058:     B->factortype                  = MAT_FACTOR_CHOLESKY;
3059:     if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij;
3060:     else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij;
3061:     PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_CHOLESKY]));
3062: #if defined(PETSC_USE_COMPLEX)
3063:     mumps->sym = 2;
3064: #else
3065:     if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3066:     else mumps->sym = 2;
3067: #endif
3068:   }

3070:   /* set solvertype */
3071:   PetscCall(PetscFree(B->solvertype));
3072:   PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3073:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3074:   if (size == 1) {
3075:     /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3076:     B->canuseordering = PETSC_TRUE;
3077:   }
3078:   B->ops->destroy = MatDestroy_MUMPS;
3079:   B->data         = (void *)mumps;

3081:   *F               = B;
3082:   mumps->id.job    = JOB_NULL;
3083:   mumps->ICNTL_pre = NULL;
3084:   mumps->CNTL_pre  = NULL;
3085:   mumps->matstruc  = DIFFERENT_NONZERO_PATTERN;
3086:   PetscFunctionReturn(PETSC_SUCCESS);
3087: }

3089: /* MatGetFactor for Seq and MPI SBAIJ matrices */
3090: static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A, MatFactorType ftype, Mat *F)
3091: {
3092:   Mat         B;
3093:   Mat_MUMPS  *mumps;
3094:   PetscBool   isSeqSBAIJ;
3095:   PetscMPIInt size;

3097:   PetscFunctionBegin;
3098: #if defined(PETSC_USE_COMPLEX)
3099:   PetscCheck(A->hermitian != PETSC_BOOL3_TRUE || A->symmetric == PETSC_BOOL3_TRUE, PETSC_COMM_SELF, PETSC_ERR_SUP, "Hermitian CHOLESKY Factor is not supported");
3100: #endif
3101:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3102:   PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3103:   PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3104:   PetscCall(MatSetUp(B));

3106:   PetscCall(PetscNew(&mumps));
3107:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQSBAIJ, &isSeqSBAIJ));
3108:   if (isSeqSBAIJ) {
3109:     mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij;
3110:   } else {
3111:     mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij;
3112:   }

3114:   B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3115:   B->ops->view                   = MatView_MUMPS;
3116:   B->ops->getinfo                = MatGetInfo_MUMPS;

3118:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3119:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3120:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3121:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3122:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3123:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3124:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3125:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3126:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3127:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3128:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3129:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3130:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));

3132:   B->factortype = MAT_FACTOR_CHOLESKY;
3133: #if defined(PETSC_USE_COMPLEX)
3134:   mumps->sym = 2;
3135: #else
3136:   if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3137:   else mumps->sym = 2;
3138: #endif

3140:   /* set solvertype */
3141:   PetscCall(PetscFree(B->solvertype));
3142:   PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3143:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3144:   if (size == 1) {
3145:     /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3146:     B->canuseordering = PETSC_TRUE;
3147:   }
3148:   PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_CHOLESKY]));
3149:   B->ops->destroy = MatDestroy_MUMPS;
3150:   B->data         = (void *)mumps;

3152:   *F               = B;
3153:   mumps->id.job    = JOB_NULL;
3154:   mumps->ICNTL_pre = NULL;
3155:   mumps->CNTL_pre  = NULL;
3156:   mumps->matstruc  = DIFFERENT_NONZERO_PATTERN;
3157:   PetscFunctionReturn(PETSC_SUCCESS);
3158: }

3160: static PetscErrorCode MatGetFactor_baij_mumps(Mat A, MatFactorType ftype, Mat *F)
3161: {
3162:   Mat         B;
3163:   Mat_MUMPS  *mumps;
3164:   PetscBool   isSeqBAIJ;
3165:   PetscMPIInt size;

3167:   PetscFunctionBegin;
3168:   /* Create the factorization matrix */
3169:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQBAIJ, &isSeqBAIJ));
3170:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3171:   PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3172:   PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3173:   PetscCall(MatSetUp(B));

3175:   PetscCall(PetscNew(&mumps));
3176:   if (ftype == MAT_FACTOR_LU) {
3177:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS;
3178:     B->factortype            = MAT_FACTOR_LU;
3179:     if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij;
3180:     else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij;
3181:     mumps->sym = 0;
3182:     PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3183:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead");

3185:   B->ops->view    = MatView_MUMPS;
3186:   B->ops->getinfo = MatGetInfo_MUMPS;

3188:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3189:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3190:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3191:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3192:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3193:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3194:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3195:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3196:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3197:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3198:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3199:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3200:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));

3202:   /* set solvertype */
3203:   PetscCall(PetscFree(B->solvertype));
3204:   PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3205:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3206:   if (size == 1) {
3207:     /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3208:     B->canuseordering = PETSC_TRUE;
3209:   }
3210:   B->ops->destroy = MatDestroy_MUMPS;
3211:   B->data         = (void *)mumps;

3213:   *F               = B;
3214:   mumps->id.job    = JOB_NULL;
3215:   mumps->ICNTL_pre = NULL;
3216:   mumps->CNTL_pre  = NULL;
3217:   mumps->matstruc  = DIFFERENT_NONZERO_PATTERN;
3218:   PetscFunctionReturn(PETSC_SUCCESS);
3219: }

3221: /* MatGetFactor for Seq and MPI SELL matrices */
3222: static PetscErrorCode MatGetFactor_sell_mumps(Mat A, MatFactorType ftype, Mat *F)
3223: {
3224:   Mat         B;
3225:   Mat_MUMPS  *mumps;
3226:   PetscBool   isSeqSELL;
3227:   PetscMPIInt size;

3229:   PetscFunctionBegin;
3230:   /* Create the factorization matrix */
3231:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQSELL, &isSeqSELL));
3232:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3233:   PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3234:   PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3235:   PetscCall(MatSetUp(B));

3237:   PetscCall(PetscNew(&mumps));

3239:   B->ops->view    = MatView_MUMPS;
3240:   B->ops->getinfo = MatGetInfo_MUMPS;

3242:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3243:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3244:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3245:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3246:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3247:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3248:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3249:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3250:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3251:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3252:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));

3254:   if (ftype == MAT_FACTOR_LU) {
3255:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3256:     B->factortype            = MAT_FACTOR_LU;
3257:     if (isSeqSELL) mumps->ConvertToTriples = MatConvertToTriples_seqsell_seqaij;
3258:     else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "To be implemented");
3259:     mumps->sym = 0;
3260:     PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3261:   } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "To be implemented");

3263:   /* set solvertype */
3264:   PetscCall(PetscFree(B->solvertype));
3265:   PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3266:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3267:   if (size == 1) {
3268:     /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization  */
3269:     B->canuseordering = PETSC_TRUE;
3270:   }
3271:   B->ops->destroy = MatDestroy_MUMPS;
3272:   B->data         = (void *)mumps;

3274:   *F               = B;
3275:   mumps->id.job    = JOB_NULL;
3276:   mumps->ICNTL_pre = NULL;
3277:   mumps->CNTL_pre  = NULL;
3278:   mumps->matstruc  = DIFFERENT_NONZERO_PATTERN;
3279:   PetscFunctionReturn(PETSC_SUCCESS);
3280: }

3282: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MUMPS(void)
3283: {
3284:   PetscFunctionBegin;
3285:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3286:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3287:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIBAIJ, MAT_FACTOR_LU, MatGetFactor_baij_mumps));
3288:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_baij_mumps));
3289:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPISBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_sbaij_mumps));
3290:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3291:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3292:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQBAIJ, MAT_FACTOR_LU, MatGetFactor_baij_mumps));
3293:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_baij_mumps));
3294:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQSBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_sbaij_mumps));
3295:   PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQSELL, MAT_FACTOR_LU, MatGetFactor_sell_mumps));
3296:   PetscFunctionReturn(PETSC_SUCCESS);
3297: }