Actual source code: mumps.c
1: /*
2: Provides an interface to the MUMPS sparse solver
3: */
4: #include <petscpkg_version.h>
5: #include <petscsf.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: #define MUMPS_MANUALS "(see users manual https://mumps-solver.org/index.php?page=doc \"Error and warning diagnostics\")"
12: EXTERN_C_BEGIN
13: #if defined(PETSC_USE_COMPLEX)
14: #if defined(PETSC_USE_REAL_SINGLE)
15: #include <cmumps_c.h>
16: #else
17: #include <zmumps_c.h>
18: #endif
19: #else
20: #if defined(PETSC_USE_REAL_SINGLE)
21: #include <smumps_c.h>
22: #else
23: #include <dmumps_c.h>
24: #endif
25: #endif
26: EXTERN_C_END
27: #define JOB_INIT -1
28: #define JOB_NULL 0
29: #define JOB_FACTSYMBOLIC 1
30: #define JOB_FACTNUMERIC 2
31: #define JOB_SOLVE 3
32: #define JOB_END -2
34: /* calls to MUMPS */
35: #if defined(PETSC_USE_COMPLEX)
36: #if defined(PETSC_USE_REAL_SINGLE)
37: #define MUMPS_c cmumps_c
38: #else
39: #define MUMPS_c zmumps_c
40: #endif
41: #else
42: #if defined(PETSC_USE_REAL_SINGLE)
43: #define MUMPS_c smumps_c
44: #else
45: #define MUMPS_c dmumps_c
46: #endif
47: #endif
49: /* MUMPS uses MUMPS_INT for nonzero indices such as irn/jcn, irn_loc/jcn_loc and uses int64_t for
50: number of nonzeros such as nnz, nnz_loc. We typedef MUMPS_INT to PetscMUMPSInt to follow the
51: naming convention in PetscMPIInt, PetscBLASInt etc.
52: */
53: typedef MUMPS_INT PetscMUMPSInt;
55: #if PETSC_PKG_MUMPS_VERSION_GE(5, 3, 0)
56: #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 */
57: #error "Petsc has not been tested with full 64-bit MUMPS and we choose to error out"
58: #endif
59: #else
60: #if defined(INTSIZE64) /* INTSIZE64 is a command line macro one used to build MUMPS in full 64-bit mode */
61: #error "Petsc has not been tested with full 64-bit MUMPS and we choose to error out"
62: #endif
63: #endif
65: #define MPIU_MUMPSINT MPI_INT
66: #define PETSC_MUMPS_INT_MAX 2147483647
67: #define PETSC_MUMPS_INT_MIN -2147483648
69: /* Cast PetscInt to PetscMUMPSInt. Usually there is no overflow since <a> is row/col indices or some small integers*/
70: static inline PetscErrorCode PetscMUMPSIntCast(PetscCount a, PetscMUMPSInt *b)
71: {
72: PetscFunctionBegin;
73: #if PetscDefined(USE_64BIT_INDICES)
74: PetscAssert(a <= PETSC_MUMPS_INT_MAX && a >= PETSC_MUMPS_INT_MIN, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscInt too long for PetscMUMPSInt");
75: #endif
76: *b = (PetscMUMPSInt)a;
77: PetscFunctionReturn(PETSC_SUCCESS);
78: }
80: /* Put these utility routines here since they are only used in this file */
81: 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)
82: {
83: PetscInt myval;
84: PetscBool myset;
86: PetscFunctionBegin;
87: /* PetscInt's size should be always >= PetscMUMPSInt's. It is safe to call PetscOptionsInt_Private to read a PetscMUMPSInt */
88: PetscCall(PetscOptionsInt_Private(PetscOptionsObject, opt, text, man, (PetscInt)currentvalue, &myval, &myset, lb, ub));
89: if (myset) PetscCall(PetscMUMPSIntCast(myval, value));
90: if (set) *set = myset;
91: PetscFunctionReturn(PETSC_SUCCESS);
92: }
93: #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)
95: /* 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 */
96: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
97: #define PetscMUMPS_c(mumps) \
98: do { \
99: if (mumps->use_petsc_omp_support) { \
100: if (mumps->is_omp_master) { \
101: PetscCall(PetscOmpCtrlOmpRegionOnMasterBegin(mumps->omp_ctrl)); \
102: PetscCall(PetscFPTrapPush(PETSC_FP_TRAP_OFF)); \
103: PetscStackCallExternalVoid(PetscStringize(MUMPS_c), MUMPS_c(&mumps->id)); \
104: PetscCall(PetscFPTrapPop()); \
105: PetscCall(PetscOmpCtrlOmpRegionOnMasterEnd(mumps->omp_ctrl)); \
106: } \
107: PetscCall(PetscOmpCtrlBarrier(mumps->omp_ctrl)); \
108: /* Global info is same on all processes so we Bcast it within omp_comm. Local info is specific \
109: to processes, so we only Bcast info[1], an error code and leave others (since they do not have \
110: an easy translation between omp_comm and petsc_comm). See MUMPS-5.1.2 manual p82. \
111: omp_comm is a small shared memory communicator, hence doing multiple Bcast as shown below is OK. \
112: */ \
113: PetscCallMPI(MPI_Bcast(mumps->id.infog, PETSC_STATIC_ARRAY_LENGTH(mumps->id.infog), MPIU_MUMPSINT, 0, mumps->omp_comm)); \
114: PetscCallMPI(MPI_Bcast(mumps->id.rinfog, PETSC_STATIC_ARRAY_LENGTH(mumps->id.rinfog), MPIU_REAL, 0, mumps->omp_comm)); \
115: PetscCallMPI(MPI_Bcast(mumps->id.info, PETSC_STATIC_ARRAY_LENGTH(mumps->id.info), MPIU_MUMPSINT, 0, mumps->omp_comm)); \
116: PetscCallMPI(MPI_Bcast(mumps->id.rinfo, PETSC_STATIC_ARRAY_LENGTH(mumps->id.rinfo), MPIU_REAL, 0, mumps->omp_comm)); \
117: } else { \
118: PetscCall(PetscFPTrapPush(PETSC_FP_TRAP_OFF)); \
119: PetscStackCallExternalVoid(PetscStringize(MUMPS_c), MUMPS_c(&mumps->id)); \
120: PetscCall(PetscFPTrapPop()); \
121: } \
122: } while (0)
123: #else
124: #define PetscMUMPS_c(mumps) \
125: do { \
126: PetscCall(PetscFPTrapPush(PETSC_FP_TRAP_OFF)); \
127: PetscStackCallExternalVoid(PetscStringize(MUMPS_c), MUMPS_c(&mumps->id)); \
128: PetscCall(PetscFPTrapPop()); \
129: } while (0)
130: #endif
132: /* declare MumpsScalar */
133: #if defined(PETSC_USE_COMPLEX)
134: #if defined(PETSC_USE_REAL_SINGLE)
135: #define MumpsScalar mumps_complex
136: #else
137: #define MumpsScalar mumps_double_complex
138: #endif
139: #else
140: #define MumpsScalar PetscScalar
141: #endif
143: /* macros s.t. indices match MUMPS documentation */
144: #define ICNTL(I) icntl[(I) - 1]
145: #define CNTL(I) cntl[(I) - 1]
146: #define INFOG(I) infog[(I) - 1]
147: #define INFO(I) info[(I) - 1]
148: #define RINFOG(I) rinfog[(I) - 1]
149: #define RINFO(I) rinfo[(I) - 1]
151: typedef struct Mat_MUMPS Mat_MUMPS;
152: struct Mat_MUMPS {
153: #if defined(PETSC_USE_COMPLEX)
154: #if defined(PETSC_USE_REAL_SINGLE)
155: CMUMPS_STRUC_C id;
156: #else
157: ZMUMPS_STRUC_C id;
158: #endif
159: #else
160: #if defined(PETSC_USE_REAL_SINGLE)
161: SMUMPS_STRUC_C id;
162: #else
163: DMUMPS_STRUC_C id;
164: #endif
165: #endif
167: MatStructure matstruc;
168: PetscMPIInt myid, petsc_size;
169: PetscMUMPSInt *irn, *jcn; /* the (i,j,v) triplets passed to mumps. */
170: 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. */
171: PetscCount nnz; /* number of nonzeros. The type is called selective 64-bit in mumps */
172: PetscMUMPSInt sym;
173: MPI_Comm mumps_comm;
174: PetscMUMPSInt *ICNTL_pre;
175: PetscReal *CNTL_pre;
176: PetscMUMPSInt ICNTL9_pre; /* check if ICNTL(9) is changed from previous MatSolve */
177: VecScatter scat_rhs, scat_sol; /* used by MatSolve() */
178: PetscMUMPSInt ICNTL20; /* use centralized (0) or distributed (10) dense RHS */
179: PetscMUMPSInt lrhs_loc, nloc_rhs, *irhs_loc;
180: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
181: PetscInt *rhs_nrow, max_nrhs;
182: PetscMPIInt *rhs_recvcounts, *rhs_disps;
183: PetscScalar *rhs_loc, *rhs_recvbuf;
184: #endif
185: Vec b_seq, x_seq;
186: PetscInt ninfo, *info; /* which INFO to display */
187: PetscInt sizeredrhs;
188: PetscScalar *schur_sol;
189: PetscInt schur_sizesol;
190: PetscMUMPSInt *ia_alloc, *ja_alloc; /* work arrays used for the CSR struct for sparse rhs */
191: PetscCount cur_ilen, cur_jlen; /* current len of ia_alloc[], ja_alloc[] */
192: PetscErrorCode (*ConvertToTriples)(Mat, PetscInt, MatReuse, Mat_MUMPS *);
194: /* Support for MATNEST */
195: PetscErrorCode (**nest_convert_to_triples)(Mat, PetscInt, MatReuse, Mat_MUMPS *);
196: PetscCount *nest_vals_start;
197: PetscScalar *nest_vals;
199: /* stuff used by petsc/mumps OpenMP support*/
200: PetscBool use_petsc_omp_support;
201: PetscOmpCtrl omp_ctrl; /* an OpenMP controller that blocked processes will release their CPU (MPI_Barrier does not have this guarantee) */
202: MPI_Comm petsc_comm, omp_comm; /* petsc_comm is petsc matrix's comm */
203: PetscCount *recvcount; /* a collection of nnz on omp_master */
204: PetscMPIInt tag, omp_comm_size;
205: PetscBool is_omp_master; /* is this rank the master of omp_comm */
206: MPI_Request *reqs;
207: };
209: /* Cast a 1-based CSR represented by (nrow, ia, ja) of type PetscInt to a CSR of type PetscMUMPSInt.
210: Here, nrow is number of rows, ia[] is row pointer and ja[] is column indices.
211: */
212: static PetscErrorCode PetscMUMPSIntCSRCast(PETSC_UNUSED Mat_MUMPS *mumps, PetscInt nrow, PetscInt *ia, PetscInt *ja, PetscMUMPSInt **ia_mumps, PetscMUMPSInt **ja_mumps, PetscMUMPSInt *nnz_mumps)
213: {
214: PetscInt nnz = ia[nrow] - 1; /* mumps uses 1-based indices. Uses PetscInt instead of PetscCount since mumps only uses PetscMUMPSInt for rhs */
216: PetscFunctionBegin;
217: #if defined(PETSC_USE_64BIT_INDICES)
218: {
219: PetscInt i;
220: if (nrow + 1 > mumps->cur_ilen) { /* realloc ia_alloc/ja_alloc to fit ia/ja */
221: PetscCall(PetscFree(mumps->ia_alloc));
222: PetscCall(PetscMalloc1(nrow + 1, &mumps->ia_alloc));
223: mumps->cur_ilen = nrow + 1;
224: }
225: if (nnz > mumps->cur_jlen) {
226: PetscCall(PetscFree(mumps->ja_alloc));
227: PetscCall(PetscMalloc1(nnz, &mumps->ja_alloc));
228: mumps->cur_jlen = nnz;
229: }
230: for (i = 0; i < nrow + 1; i++) PetscCall(PetscMUMPSIntCast(ia[i], &mumps->ia_alloc[i]));
231: for (i = 0; i < nnz; i++) PetscCall(PetscMUMPSIntCast(ja[i], &mumps->ja_alloc[i]));
232: *ia_mumps = mumps->ia_alloc;
233: *ja_mumps = mumps->ja_alloc;
234: }
235: #else
236: *ia_mumps = ia;
237: *ja_mumps = ja;
238: #endif
239: PetscCall(PetscMUMPSIntCast(nnz, nnz_mumps));
240: PetscFunctionReturn(PETSC_SUCCESS);
241: }
243: static PetscErrorCode MatMumpsResetSchur_Private(Mat_MUMPS *mumps)
244: {
245: PetscFunctionBegin;
246: PetscCall(PetscFree(mumps->id.listvar_schur));
247: PetscCall(PetscFree(mumps->id.redrhs));
248: PetscCall(PetscFree(mumps->schur_sol));
249: mumps->id.size_schur = 0;
250: mumps->id.schur_lld = 0;
251: mumps->id.ICNTL(19) = 0;
252: PetscFunctionReturn(PETSC_SUCCESS);
253: }
255: /* solve with rhs in mumps->id.redrhs and return in the same location */
256: static PetscErrorCode MatMumpsSolveSchur_Private(Mat F)
257: {
258: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
259: Mat S, B, X;
260: MatFactorSchurStatus schurstatus;
261: PetscInt sizesol;
263: PetscFunctionBegin;
264: PetscCall(MatFactorFactorizeSchurComplement(F));
265: PetscCall(MatFactorGetSchurComplement(F, &S, &schurstatus));
266: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, mumps->id.size_schur, mumps->id.nrhs, (PetscScalar *)mumps->id.redrhs, &B));
267: PetscCall(MatSetType(B, ((PetscObject)S)->type_name));
268: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
269: PetscCall(MatBindToCPU(B, S->boundtocpu));
270: #endif
271: switch (schurstatus) {
272: case MAT_FACTOR_SCHUR_FACTORED:
273: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, mumps->id.size_schur, mumps->id.nrhs, (PetscScalar *)mumps->id.redrhs, &X));
274: PetscCall(MatSetType(X, ((PetscObject)S)->type_name));
275: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
276: PetscCall(MatBindToCPU(X, S->boundtocpu));
277: #endif
278: if (!mumps->id.ICNTL(9)) { /* transpose solve */
279: PetscCall(MatMatSolveTranspose(S, B, X));
280: } else {
281: PetscCall(MatMatSolve(S, B, X));
282: }
283: break;
284: case MAT_FACTOR_SCHUR_INVERTED:
285: sizesol = mumps->id.nrhs * mumps->id.size_schur;
286: if (!mumps->schur_sol || sizesol > mumps->schur_sizesol) {
287: PetscCall(PetscFree(mumps->schur_sol));
288: PetscCall(PetscMalloc1(sizesol, &mumps->schur_sol));
289: mumps->schur_sizesol = sizesol;
290: }
291: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, mumps->id.size_schur, mumps->id.nrhs, mumps->schur_sol, &X));
292: PetscCall(MatSetType(X, ((PetscObject)S)->type_name));
293: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
294: PetscCall(MatBindToCPU(X, S->boundtocpu));
295: #endif
296: PetscCall(MatProductCreateWithMat(S, B, NULL, X));
297: if (!mumps->id.ICNTL(9)) { /* transpose solve */
298: PetscCall(MatProductSetType(X, MATPRODUCT_AtB));
299: } else {
300: PetscCall(MatProductSetType(X, MATPRODUCT_AB));
301: }
302: PetscCall(MatProductSetFromOptions(X));
303: PetscCall(MatProductSymbolic(X));
304: PetscCall(MatProductNumeric(X));
306: PetscCall(MatCopy(X, B, SAME_NONZERO_PATTERN));
307: break;
308: default:
309: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
310: }
311: PetscCall(MatFactorRestoreSchurComplement(F, &S, schurstatus));
312: PetscCall(MatDestroy(&B));
313: PetscCall(MatDestroy(&X));
314: PetscFunctionReturn(PETSC_SUCCESS);
315: }
317: static PetscErrorCode MatMumpsHandleSchur_Private(Mat F, PetscBool expansion)
318: {
319: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
321: PetscFunctionBegin;
322: if (!mumps->id.ICNTL(19)) { /* do nothing when Schur complement has not been computed */
323: PetscFunctionReturn(PETSC_SUCCESS);
324: }
325: if (!expansion) { /* prepare for the condensation step */
326: PetscInt sizeredrhs = mumps->id.nrhs * mumps->id.size_schur;
327: /* allocate MUMPS internal array to store reduced right-hand sides */
328: if (!mumps->id.redrhs || sizeredrhs > mumps->sizeredrhs) {
329: PetscCall(PetscFree(mumps->id.redrhs));
330: mumps->id.lredrhs = mumps->id.size_schur;
331: PetscCall(PetscMalloc1(mumps->id.nrhs * mumps->id.lredrhs, &mumps->id.redrhs));
332: mumps->sizeredrhs = mumps->id.nrhs * mumps->id.lredrhs;
333: }
334: } else { /* prepare for the expansion step */
335: /* solve Schur complement (this has to be done by the MUMPS user, so basically us) */
336: PetscCall(MatMumpsSolveSchur_Private(F));
337: mumps->id.ICNTL(26) = 2; /* expansion phase */
338: PetscMUMPS_c(mumps);
339: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "MUMPS error in solve: INFOG(1)=%d " MUMPS_MANUALS, mumps->id.INFOG(1));
340: /* restore defaults */
341: mumps->id.ICNTL(26) = -1;
342: /* free MUMPS internal array for redrhs if we have solved for multiple rhs in order to save memory space */
343: if (mumps->id.nrhs > 1) {
344: PetscCall(PetscFree(mumps->id.redrhs));
345: mumps->id.lredrhs = 0;
346: mumps->sizeredrhs = 0;
347: }
348: }
349: PetscFunctionReturn(PETSC_SUCCESS);
350: }
352: /*
353: MatConvertToTriples_A_B - convert Petsc matrix to triples: row[nz], col[nz], val[nz]
355: input:
356: A - matrix in aij,baij or sbaij format
357: shift - 0: C style output triple; 1: Fortran style output triple.
358: reuse - MAT_INITIAL_MATRIX: spaces are allocated and values are set for the triple
359: MAT_REUSE_MATRIX: only the values in v array are updated
360: output:
361: nnz - dim of r, c, and v (number of local nonzero entries of A)
362: r, c, v - row and col index, matrix values (matrix triples)
364: The returned values r, c, and sometimes v are obtained in a single PetscMalloc(). Then in MatDestroy_MUMPS() it is
365: freed with PetscFree(mumps->irn); This is not ideal code, the fact that v is ONLY sometimes part of mumps->irn means
366: that the PetscMalloc() cannot easily be replaced with a PetscMalloc3().
368: */
370: static PetscErrorCode MatConvertToTriples_seqaij_seqaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
371: {
372: const PetscScalar *av;
373: const PetscInt *ai, *aj, *ajj, M = A->rmap->n;
374: PetscCount nz, rnz, k;
375: PetscMUMPSInt *row, *col;
376: Mat_SeqAIJ *aa = (Mat_SeqAIJ *)A->data;
378: PetscFunctionBegin;
379: PetscCall(MatSeqAIJGetArrayRead(A, &av));
380: if (reuse == MAT_INITIAL_MATRIX) {
381: nz = aa->nz;
382: ai = aa->i;
383: aj = aa->j;
384: PetscCall(PetscMalloc2(nz, &row, nz, &col));
385: for (PetscCount i = k = 0; i < M; i++) {
386: rnz = ai[i + 1] - ai[i];
387: ajj = aj + ai[i];
388: for (PetscCount j = 0; j < rnz; j++) {
389: PetscCall(PetscMUMPSIntCast(i + shift, &row[k]));
390: PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[k]));
391: k++;
392: }
393: }
394: mumps->val = (PetscScalar *)av;
395: mumps->irn = row;
396: mumps->jcn = col;
397: mumps->nnz = nz;
398: } else if (mumps->nest_vals) PetscCall(PetscArraycpy(mumps->val, av, aa->nz)); /* MatConvertToTriples_nest_xaij() allocates mumps->val outside of MatConvertToTriples_seqaij_seqaij(), so one needs to copy the memory */
399: else mumps->val = (PetscScalar *)av; /* in the default case, mumps->val is never allocated, one just needs to update the mumps->val pointer */
400: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
401: PetscFunctionReturn(PETSC_SUCCESS);
402: }
404: static PetscErrorCode MatConvertToTriples_seqsell_seqaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
405: {
406: PetscCount nz, i, j, k, r;
407: Mat_SeqSELL *a = (Mat_SeqSELL *)A->data;
408: PetscMUMPSInt *row, *col;
410: PetscFunctionBegin;
411: nz = a->sliidx[a->totalslices];
412: if (reuse == MAT_INITIAL_MATRIX) {
413: PetscCall(PetscMalloc2(nz, &row, nz, &col));
414: for (i = k = 0; i < a->totalslices; i++) {
415: 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++]));
416: }
417: for (i = 0; i < nz; i++) PetscCall(PetscMUMPSIntCast(a->colidx[i] + shift, &col[i]));
418: mumps->irn = row;
419: mumps->jcn = col;
420: mumps->nnz = nz;
421: mumps->val = a->val;
422: } else if (mumps->nest_vals) PetscCall(PetscArraycpy(mumps->val, a->val, nz)); /* MatConvertToTriples_nest_xaij() allocates mumps->val outside of MatConvertToTriples_seqsell_seqaij(), so one needs to copy the memory */
423: else mumps->val = a->val; /* in the default case, mumps->val is never allocated, one just needs to update the mumps->val pointer */
424: PetscFunctionReturn(PETSC_SUCCESS);
425: }
427: static PetscErrorCode MatConvertToTriples_seqbaij_seqaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
428: {
429: Mat_SeqBAIJ *aa = (Mat_SeqBAIJ *)A->data;
430: const PetscInt *ai, *aj, *ajj, bs2 = aa->bs2;
431: PetscCount M, nz = bs2 * aa->nz, idx = 0, rnz, i, j, k, m;
432: PetscInt bs;
433: PetscMUMPSInt *row, *col;
435: PetscFunctionBegin;
436: if (reuse == MAT_INITIAL_MATRIX) {
437: PetscCall(MatGetBlockSize(A, &bs));
438: M = A->rmap->N / bs;
439: ai = aa->i;
440: aj = aa->j;
441: PetscCall(PetscMalloc2(nz, &row, nz, &col));
442: for (i = 0; i < M; i++) {
443: ajj = aj + ai[i];
444: rnz = ai[i + 1] - ai[i];
445: for (k = 0; k < rnz; k++) {
446: for (j = 0; j < bs; j++) {
447: for (m = 0; m < bs; m++) {
448: PetscCall(PetscMUMPSIntCast(i * bs + m + shift, &row[idx]));
449: PetscCall(PetscMUMPSIntCast(bs * ajj[k] + j + shift, &col[idx]));
450: idx++;
451: }
452: }
453: }
454: }
455: mumps->irn = row;
456: mumps->jcn = col;
457: mumps->nnz = nz;
458: mumps->val = aa->a;
459: } else if (mumps->nest_vals) PetscCall(PetscArraycpy(mumps->val, aa->a, nz)); /* MatConvertToTriples_nest_xaij() allocates mumps->val outside of MatConvertToTriples_seqbaij_seqaij(), so one needs to copy the memory */
460: else mumps->val = aa->a; /* in the default case, mumps->val is never allocated, one just needs to update the mumps->val pointer */
461: PetscFunctionReturn(PETSC_SUCCESS);
462: }
464: static PetscErrorCode MatConvertToTriples_seqsbaij_seqsbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
465: {
466: const PetscInt *ai, *aj, *ajj;
467: PetscInt bs;
468: PetscCount nz, rnz, i, j, k, m;
469: PetscMUMPSInt *row, *col;
470: PetscScalar *val;
471: Mat_SeqSBAIJ *aa = (Mat_SeqSBAIJ *)A->data;
472: const PetscInt bs2 = aa->bs2, mbs = aa->mbs;
473: #if defined(PETSC_USE_COMPLEX)
474: PetscBool isset, hermitian;
475: #endif
477: PetscFunctionBegin;
478: #if defined(PETSC_USE_COMPLEX)
479: PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
480: PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
481: #endif
482: ai = aa->i;
483: aj = aa->j;
484: PetscCall(MatGetBlockSize(A, &bs));
485: if (reuse == MAT_INITIAL_MATRIX) {
486: const PetscCount alloc_size = aa->nz * bs2;
488: PetscCall(PetscMalloc2(alloc_size, &row, alloc_size, &col));
489: if (bs > 1) {
490: PetscCall(PetscMalloc1(alloc_size, &mumps->val_alloc));
491: mumps->val = mumps->val_alloc;
492: } else {
493: mumps->val = aa->a;
494: }
495: mumps->irn = row;
496: mumps->jcn = col;
497: } else {
498: row = mumps->irn;
499: col = mumps->jcn;
500: }
501: val = mumps->val;
503: nz = 0;
504: if (bs > 1) {
505: for (i = 0; i < mbs; i++) {
506: rnz = ai[i + 1] - ai[i];
507: ajj = aj + ai[i];
508: for (j = 0; j < rnz; j++) {
509: for (k = 0; k < bs; k++) {
510: for (m = 0; m < bs; m++) {
511: if (ajj[j] > i || k >= m) {
512: if (reuse == MAT_INITIAL_MATRIX) {
513: PetscCall(PetscMUMPSIntCast(i * bs + m + shift, &row[nz]));
514: PetscCall(PetscMUMPSIntCast(ajj[j] * bs + k + shift, &col[nz]));
515: }
516: val[nz++] = aa->a[(ai[i] + j) * bs2 + m + k * bs];
517: }
518: }
519: }
520: }
521: }
522: } else if (reuse == MAT_INITIAL_MATRIX) {
523: for (i = 0; i < mbs; i++) {
524: rnz = ai[i + 1] - ai[i];
525: ajj = aj + ai[i];
526: for (j = 0; j < rnz; j++) {
527: PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
528: PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[nz]));
529: nz++;
530: }
531: }
532: PetscCheck(nz == aa->nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Different numbers of nonzeros %" PetscCount_FMT " != %" PetscInt_FMT, nz, aa->nz);
533: } else if (mumps->nest_vals)
534: PetscCall(PetscArraycpy(mumps->val, aa->a, aa->nz)); /* bs == 1 and MAT_REUSE_MATRIX, MatConvertToTriples_nest_xaij() allocates mumps->val outside of MatConvertToTriples_seqsbaij_seqsbaij(), so one needs to copy the memory */
535: else mumps->val = aa->a; /* in the default case, mumps->val is never allocated, one just needs to update the mumps->val pointer */
536: if (reuse == MAT_INITIAL_MATRIX) mumps->nnz = nz;
537: PetscFunctionReturn(PETSC_SUCCESS);
538: }
540: static PetscErrorCode MatConvertToTriples_seqaij_seqsbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
541: {
542: const PetscInt *ai, *aj, *ajj, *adiag, M = A->rmap->n;
543: PetscCount nz, rnz, i, j;
544: const PetscScalar *av, *v1;
545: PetscScalar *val;
546: PetscMUMPSInt *row, *col;
547: Mat_SeqAIJ *aa = (Mat_SeqAIJ *)A->data;
548: PetscBool missing;
549: #if defined(PETSC_USE_COMPLEX)
550: PetscBool hermitian, isset;
551: #endif
553: PetscFunctionBegin;
554: #if defined(PETSC_USE_COMPLEX)
555: PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
556: PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
557: #endif
558: PetscCall(MatSeqAIJGetArrayRead(A, &av));
559: ai = aa->i;
560: aj = aa->j;
561: adiag = aa->diag;
562: PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, NULL));
563: if (reuse == MAT_INITIAL_MATRIX) {
564: /* count nz in the upper triangular part of A */
565: nz = 0;
566: if (missing) {
567: for (i = 0; i < M; i++) {
568: if (PetscUnlikely(adiag[i] >= ai[i + 1])) {
569: for (j = ai[i]; j < ai[i + 1]; j++) {
570: if (aj[j] < i) continue;
571: nz++;
572: }
573: } else {
574: nz += ai[i + 1] - adiag[i];
575: }
576: }
577: } else {
578: for (i = 0; i < M; i++) nz += ai[i + 1] - adiag[i];
579: }
580: PetscCall(PetscMalloc2(nz, &row, nz, &col));
581: PetscCall(PetscMalloc1(nz, &val));
582: mumps->nnz = nz;
583: mumps->irn = row;
584: mumps->jcn = col;
585: mumps->val = mumps->val_alloc = val;
587: nz = 0;
588: if (missing) {
589: for (i = 0; i < M; i++) {
590: if (PetscUnlikely(adiag[i] >= ai[i + 1])) {
591: for (j = ai[i]; j < ai[i + 1]; j++) {
592: if (aj[j] < i) continue;
593: PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
594: PetscCall(PetscMUMPSIntCast(aj[j] + shift, &col[nz]));
595: val[nz] = av[j];
596: nz++;
597: }
598: } else {
599: rnz = ai[i + 1] - adiag[i];
600: ajj = aj + adiag[i];
601: v1 = av + adiag[i];
602: for (j = 0; j < rnz; j++) {
603: PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
604: PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[nz]));
605: val[nz++] = v1[j];
606: }
607: }
608: }
609: } else {
610: for (i = 0; i < M; i++) {
611: rnz = ai[i + 1] - adiag[i];
612: ajj = aj + adiag[i];
613: v1 = av + adiag[i];
614: for (j = 0; j < rnz; j++) {
615: PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
616: PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[nz]));
617: val[nz++] = v1[j];
618: }
619: }
620: }
621: } else {
622: nz = 0;
623: val = mumps->val;
624: if (missing) {
625: for (i = 0; i < M; i++) {
626: if (PetscUnlikely(adiag[i] >= ai[i + 1])) {
627: for (j = ai[i]; j < ai[i + 1]; j++) {
628: if (aj[j] < i) continue;
629: val[nz++] = av[j];
630: }
631: } else {
632: rnz = ai[i + 1] - adiag[i];
633: v1 = av + adiag[i];
634: for (j = 0; j < rnz; j++) val[nz++] = v1[j];
635: }
636: }
637: } else {
638: for (i = 0; i < M; i++) {
639: rnz = ai[i + 1] - adiag[i];
640: v1 = av + adiag[i];
641: for (j = 0; j < rnz; j++) val[nz++] = v1[j];
642: }
643: }
644: }
645: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
646: PetscFunctionReturn(PETSC_SUCCESS);
647: }
649: static PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
650: {
651: const PetscInt *ai, *aj, *bi, *bj, *garray, *ajj, *bjj;
652: PetscInt bs;
653: PetscCount rstart, nz, i, j, k, m, jj, irow, countA, countB;
654: PetscMUMPSInt *row, *col;
655: const PetscScalar *av, *bv, *v1, *v2;
656: PetscScalar *val;
657: Mat_MPISBAIJ *mat = (Mat_MPISBAIJ *)A->data;
658: Mat_SeqSBAIJ *aa = (Mat_SeqSBAIJ *)mat->A->data;
659: Mat_SeqBAIJ *bb = (Mat_SeqBAIJ *)mat->B->data;
660: const PetscInt bs2 = aa->bs2, mbs = aa->mbs;
661: #if defined(PETSC_USE_COMPLEX)
662: PetscBool hermitian, isset;
663: #endif
665: PetscFunctionBegin;
666: #if defined(PETSC_USE_COMPLEX)
667: PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
668: PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
669: #endif
670: PetscCall(MatGetBlockSize(A, &bs));
671: rstart = A->rmap->rstart;
672: ai = aa->i;
673: aj = aa->j;
674: bi = bb->i;
675: bj = bb->j;
676: av = aa->a;
677: bv = bb->a;
679: garray = mat->garray;
681: if (reuse == MAT_INITIAL_MATRIX) {
682: nz = (aa->nz + bb->nz) * bs2; /* just a conservative estimate */
683: PetscCall(PetscMalloc2(nz, &row, nz, &col));
684: PetscCall(PetscMalloc1(nz, &val));
685: /* can not decide the exact mumps->nnz now because of the SBAIJ */
686: mumps->irn = row;
687: mumps->jcn = col;
688: mumps->val = mumps->val_alloc = val;
689: } else {
690: val = mumps->val;
691: }
693: jj = 0;
694: irow = rstart;
695: for (i = 0; i < mbs; i++) {
696: ajj = aj + ai[i]; /* ptr to the beginning of this row */
697: countA = ai[i + 1] - ai[i];
698: countB = bi[i + 1] - bi[i];
699: bjj = bj + bi[i];
700: v1 = av + ai[i] * bs2;
701: v2 = bv + bi[i] * bs2;
703: if (bs > 1) {
704: /* A-part */
705: for (j = 0; j < countA; j++) {
706: for (k = 0; k < bs; k++) {
707: for (m = 0; m < bs; m++) {
708: if (rstart + ajj[j] * bs > irow || k >= m) {
709: if (reuse == MAT_INITIAL_MATRIX) {
710: PetscCall(PetscMUMPSIntCast(irow + m + shift, &row[jj]));
711: PetscCall(PetscMUMPSIntCast(rstart + ajj[j] * bs + k + shift, &col[jj]));
712: }
713: val[jj++] = v1[j * bs2 + m + k * bs];
714: }
715: }
716: }
717: }
719: /* B-part */
720: for (j = 0; j < countB; j++) {
721: for (k = 0; k < bs; k++) {
722: for (m = 0; m < bs; m++) {
723: if (reuse == MAT_INITIAL_MATRIX) {
724: PetscCall(PetscMUMPSIntCast(irow + m + shift, &row[jj]));
725: PetscCall(PetscMUMPSIntCast(garray[bjj[j]] * bs + k + shift, &col[jj]));
726: }
727: val[jj++] = v2[j * bs2 + m + k * bs];
728: }
729: }
730: }
731: } else {
732: /* A-part */
733: for (j = 0; j < countA; j++) {
734: if (reuse == MAT_INITIAL_MATRIX) {
735: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
736: PetscCall(PetscMUMPSIntCast(rstart + ajj[j] + shift, &col[jj]));
737: }
738: val[jj++] = v1[j];
739: }
741: /* B-part */
742: for (j = 0; j < countB; j++) {
743: if (reuse == MAT_INITIAL_MATRIX) {
744: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
745: PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift, &col[jj]));
746: }
747: val[jj++] = v2[j];
748: }
749: }
750: irow += bs;
751: }
752: if (reuse == MAT_INITIAL_MATRIX) mumps->nnz = jj;
753: PetscFunctionReturn(PETSC_SUCCESS);
754: }
756: static PetscErrorCode MatConvertToTriples_mpiaij_mpiaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
757: {
758: const PetscInt *ai, *aj, *bi, *bj, *garray, m = A->rmap->n, *ajj, *bjj;
759: PetscCount rstart, cstart, nz, i, j, jj, irow, countA, countB;
760: PetscMUMPSInt *row, *col;
761: const PetscScalar *av, *bv, *v1, *v2;
762: PetscScalar *val;
763: Mat Ad, Ao;
764: Mat_SeqAIJ *aa;
765: Mat_SeqAIJ *bb;
767: PetscFunctionBegin;
768: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &garray));
769: PetscCall(MatSeqAIJGetArrayRead(Ad, &av));
770: PetscCall(MatSeqAIJGetArrayRead(Ao, &bv));
772: aa = (Mat_SeqAIJ *)Ad->data;
773: bb = (Mat_SeqAIJ *)Ao->data;
774: ai = aa->i;
775: aj = aa->j;
776: bi = bb->i;
777: bj = bb->j;
779: rstart = A->rmap->rstart;
780: cstart = A->cmap->rstart;
782: if (reuse == MAT_INITIAL_MATRIX) {
783: nz = (PetscCount)aa->nz + bb->nz; /* make sure the sum won't overflow PetscInt */
784: PetscCall(PetscMalloc2(nz, &row, nz, &col));
785: PetscCall(PetscMalloc1(nz, &val));
786: mumps->nnz = nz;
787: mumps->irn = row;
788: mumps->jcn = col;
789: mumps->val = mumps->val_alloc = val;
790: } else {
791: val = mumps->val;
792: }
794: jj = 0;
795: irow = rstart;
796: for (i = 0; i < m; i++) {
797: ajj = aj + ai[i]; /* ptr to the beginning of this row */
798: countA = ai[i + 1] - ai[i];
799: countB = bi[i + 1] - bi[i];
800: bjj = bj + bi[i];
801: v1 = av + ai[i];
802: v2 = bv + bi[i];
804: /* A-part */
805: for (j = 0; j < countA; j++) {
806: if (reuse == MAT_INITIAL_MATRIX) {
807: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
808: PetscCall(PetscMUMPSIntCast(cstart + ajj[j] + shift, &col[jj]));
809: }
810: val[jj++] = v1[j];
811: }
813: /* B-part */
814: for (j = 0; j < countB; j++) {
815: if (reuse == MAT_INITIAL_MATRIX) {
816: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
817: PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift, &col[jj]));
818: }
819: val[jj++] = v2[j];
820: }
821: irow++;
822: }
823: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &av));
824: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &bv));
825: PetscFunctionReturn(PETSC_SUCCESS);
826: }
828: static PetscErrorCode MatConvertToTriples_mpibaij_mpiaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
829: {
830: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)A->data;
831: Mat_SeqBAIJ *aa = (Mat_SeqBAIJ *)mat->A->data;
832: Mat_SeqBAIJ *bb = (Mat_SeqBAIJ *)mat->B->data;
833: const PetscInt *ai = aa->i, *bi = bb->i, *aj = aa->j, *bj = bb->j, *ajj, *bjj;
834: const PetscInt *garray = mat->garray, mbs = mat->mbs, rstart = A->rmap->rstart, cstart = A->cmap->rstart;
835: const PetscInt bs2 = mat->bs2;
836: PetscInt bs;
837: PetscCount nz, i, j, k, n, jj, irow, countA, countB, idx;
838: PetscMUMPSInt *row, *col;
839: const PetscScalar *av = aa->a, *bv = bb->a, *v1, *v2;
840: PetscScalar *val;
842: PetscFunctionBegin;
843: PetscCall(MatGetBlockSize(A, &bs));
844: if (reuse == MAT_INITIAL_MATRIX) {
845: nz = bs2 * (aa->nz + bb->nz);
846: PetscCall(PetscMalloc2(nz, &row, nz, &col));
847: PetscCall(PetscMalloc1(nz, &val));
848: mumps->nnz = nz;
849: mumps->irn = row;
850: mumps->jcn = col;
851: mumps->val = mumps->val_alloc = val;
852: } else {
853: val = mumps->val;
854: }
856: jj = 0;
857: irow = rstart;
858: for (i = 0; i < mbs; i++) {
859: countA = ai[i + 1] - ai[i];
860: countB = bi[i + 1] - bi[i];
861: ajj = aj + ai[i];
862: bjj = bj + bi[i];
863: v1 = av + bs2 * ai[i];
864: v2 = bv + bs2 * bi[i];
866: idx = 0;
867: /* A-part */
868: for (k = 0; k < countA; k++) {
869: for (j = 0; j < bs; j++) {
870: for (n = 0; n < bs; n++) {
871: if (reuse == MAT_INITIAL_MATRIX) {
872: PetscCall(PetscMUMPSIntCast(irow + n + shift, &row[jj]));
873: PetscCall(PetscMUMPSIntCast(cstart + bs * ajj[k] + j + shift, &col[jj]));
874: }
875: val[jj++] = v1[idx++];
876: }
877: }
878: }
880: idx = 0;
881: /* B-part */
882: for (k = 0; k < countB; k++) {
883: for (j = 0; j < bs; j++) {
884: for (n = 0; n < bs; n++) {
885: if (reuse == MAT_INITIAL_MATRIX) {
886: PetscCall(PetscMUMPSIntCast(irow + n + shift, &row[jj]));
887: PetscCall(PetscMUMPSIntCast(bs * garray[bjj[k]] + j + shift, &col[jj]));
888: }
889: val[jj++] = v2[idx++];
890: }
891: }
892: }
893: irow += bs;
894: }
895: PetscFunctionReturn(PETSC_SUCCESS);
896: }
898: static PetscErrorCode MatConvertToTriples_mpiaij_mpisbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
899: {
900: const PetscInt *ai, *aj, *adiag, *bi, *bj, *garray, m = A->rmap->n, *ajj, *bjj;
901: PetscCount rstart, nz, nza, nzb, i, j, jj, irow, countA, countB;
902: PetscMUMPSInt *row, *col;
903: const PetscScalar *av, *bv, *v1, *v2;
904: PetscScalar *val;
905: Mat Ad, Ao;
906: Mat_SeqAIJ *aa;
907: Mat_SeqAIJ *bb;
908: #if defined(PETSC_USE_COMPLEX)
909: PetscBool hermitian, isset;
910: #endif
912: PetscFunctionBegin;
913: #if defined(PETSC_USE_COMPLEX)
914: PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
915: PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
916: #endif
917: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &garray));
918: PetscCall(MatSeqAIJGetArrayRead(Ad, &av));
919: PetscCall(MatSeqAIJGetArrayRead(Ao, &bv));
921: aa = (Mat_SeqAIJ *)Ad->data;
922: bb = (Mat_SeqAIJ *)Ao->data;
923: ai = aa->i;
924: aj = aa->j;
925: adiag = aa->diag;
926: bi = bb->i;
927: bj = bb->j;
929: rstart = A->rmap->rstart;
931: if (reuse == MAT_INITIAL_MATRIX) {
932: nza = 0; /* num of upper triangular entries in mat->A, including diagonals */
933: nzb = 0; /* num of upper triangular entries in mat->B */
934: for (i = 0; i < m; i++) {
935: nza += (ai[i + 1] - adiag[i]);
936: countB = bi[i + 1] - bi[i];
937: bjj = bj + bi[i];
938: for (j = 0; j < countB; j++) {
939: if (garray[bjj[j]] > rstart) nzb++;
940: }
941: }
943: nz = nza + nzb; /* total nz of upper triangular part of mat */
944: PetscCall(PetscMalloc2(nz, &row, nz, &col));
945: PetscCall(PetscMalloc1(nz, &val));
946: mumps->nnz = nz;
947: mumps->irn = row;
948: mumps->jcn = col;
949: mumps->val = mumps->val_alloc = val;
950: } else {
951: val = mumps->val;
952: }
954: jj = 0;
955: irow = rstart;
956: for (i = 0; i < m; i++) {
957: ajj = aj + adiag[i]; /* ptr to the beginning of the diagonal of this row */
958: v1 = av + adiag[i];
959: countA = ai[i + 1] - adiag[i];
960: countB = bi[i + 1] - bi[i];
961: bjj = bj + bi[i];
962: v2 = bv + bi[i];
964: /* A-part */
965: for (j = 0; j < countA; j++) {
966: if (reuse == MAT_INITIAL_MATRIX) {
967: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
968: PetscCall(PetscMUMPSIntCast(rstart + ajj[j] + shift, &col[jj]));
969: }
970: val[jj++] = v1[j];
971: }
973: /* B-part */
974: for (j = 0; j < countB; j++) {
975: if (garray[bjj[j]] > rstart) {
976: if (reuse == MAT_INITIAL_MATRIX) {
977: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
978: PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift, &col[jj]));
979: }
980: val[jj++] = v2[j];
981: }
982: }
983: irow++;
984: }
985: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &av));
986: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &bv));
987: PetscFunctionReturn(PETSC_SUCCESS);
988: }
990: static PetscErrorCode MatConvertToTriples_diagonal_xaij(Mat A, PETSC_UNUSED PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
991: {
992: const PetscScalar *av;
993: const PetscInt M = A->rmap->n;
994: PetscCount i;
995: PetscMUMPSInt *row, *col;
996: Vec v;
998: PetscFunctionBegin;
999: PetscCall(MatDiagonalGetDiagonal(A, &v));
1000: PetscCall(VecGetArrayRead(v, &av));
1001: if (reuse == MAT_INITIAL_MATRIX) {
1002: PetscCall(PetscMalloc2(M, &row, M, &col));
1003: for (i = 0; i < M; i++) {
1004: PetscCall(PetscMUMPSIntCast(i + A->rmap->rstart, &row[i]));
1005: col[i] = row[i];
1006: }
1007: mumps->val = (PetscScalar *)av;
1008: mumps->irn = row;
1009: mumps->jcn = col;
1010: mumps->nnz = M;
1011: } else if (mumps->nest_vals) PetscCall(PetscArraycpy(mumps->val, av, M)); /* MatConvertToTriples_nest_xaij() allocates mumps->val outside of MatConvertToTriples_diagonal_xaij(), so one needs to copy the memory */
1012: else mumps->val = (PetscScalar *)av; /* in the default case, mumps->val is never allocated, one just needs to update the mumps->val pointer */
1013: PetscCall(VecRestoreArrayRead(v, &av));
1014: PetscFunctionReturn(PETSC_SUCCESS);
1015: }
1017: static PetscErrorCode MatConvertToTriples_dense_xaij(Mat A, PETSC_UNUSED PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
1018: {
1019: PetscScalar *v;
1020: const PetscInt m = A->rmap->n, N = A->cmap->N;
1021: PetscInt lda;
1022: PetscCount i, j;
1023: PetscMUMPSInt *row, *col;
1025: PetscFunctionBegin;
1026: PetscCall(MatDenseGetArray(A, &v));
1027: PetscCall(MatDenseGetLDA(A, &lda));
1028: if (reuse == MAT_INITIAL_MATRIX) {
1029: PetscCall(PetscMalloc2(m * N, &row, m * N, &col));
1030: for (i = 0; i < m; i++) {
1031: col[i] = 0;
1032: PetscCall(PetscMUMPSIntCast(i + A->rmap->rstart, &row[i]));
1033: }
1034: for (j = 1; j < N; j++) {
1035: for (i = 0; i < m; i++) PetscCall(PetscMUMPSIntCast(j, col + i + m * j));
1036: PetscCall(PetscArraycpy(row + m * j, row + m * (j - 1), m));
1037: }
1038: if (lda == m) mumps->val = v;
1039: else {
1040: PetscCall(PetscMalloc1(m * N, &mumps->val));
1041: mumps->val_alloc = mumps->val;
1042: for (j = 0; j < N; j++) PetscCall(PetscArraycpy(mumps->val + m * j, v + lda * j, m));
1043: }
1044: mumps->irn = row;
1045: mumps->jcn = col;
1046: mumps->nnz = m * N;
1047: } else {
1048: if (lda == m && !mumps->nest_vals) mumps->val = v;
1049: else {
1050: for (j = 0; j < N; j++) PetscCall(PetscArraycpy(mumps->val + m * j, v + lda * j, m));
1051: }
1052: }
1053: PetscCall(MatDenseRestoreArray(A, &v));
1054: PetscFunctionReturn(PETSC_SUCCESS);
1055: }
1057: static PetscErrorCode MatConvertToTriples_nest_xaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
1058: {
1059: Mat **mats;
1060: PetscInt nr, nc;
1061: PetscBool chol = mumps->sym ? PETSC_TRUE : PETSC_FALSE;
1063: PetscFunctionBegin;
1064: PetscCall(MatNestGetSubMats(A, &nr, &nc, &mats));
1065: if (reuse == MAT_INITIAL_MATRIX) {
1066: PetscMUMPSInt *irns, *jcns;
1067: PetscScalar *vals;
1068: PetscCount totnnz, cumnnz, maxnnz;
1069: PetscInt *pjcns_w;
1070: IS *rows, *cols;
1071: PetscInt **rows_idx, **cols_idx;
1073: cumnnz = 0;
1074: maxnnz = 0;
1075: PetscCall(PetscMalloc2(nr * nc + 1, &mumps->nest_vals_start, nr * nc, &mumps->nest_convert_to_triples));
1076: for (PetscInt r = 0; r < nr; r++) {
1077: for (PetscInt c = 0; c < nc; c++) {
1078: Mat sub = mats[r][c];
1080: mumps->nest_convert_to_triples[r * nc + c] = NULL;
1081: if (chol && c < r) continue; /* skip lower-triangular block for Cholesky */
1082: if (sub) {
1083: PetscErrorCode (*convert_to_triples)(Mat, PetscInt, MatReuse, Mat_MUMPS *) = NULL;
1084: PetscBool isSeqAIJ, isMPIAIJ, isSeqBAIJ, isMPIBAIJ, isSeqSBAIJ, isMPISBAIJ, isTrans, isHTrans = PETSC_FALSE, isDiag, isDense;
1085: MatInfo info;
1087: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATTRANSPOSEVIRTUAL, &isTrans));
1088: if (isTrans) PetscCall(MatTransposeGetMat(sub, &sub));
1089: else {
1090: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATHERMITIANTRANSPOSEVIRTUAL, &isHTrans));
1091: if (isHTrans) PetscCall(MatHermitianTransposeGetMat(sub, &sub));
1092: }
1093: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATSEQAIJ, &isSeqAIJ));
1094: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATMPIAIJ, &isMPIAIJ));
1095: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATSEQBAIJ, &isSeqBAIJ));
1096: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATMPIBAIJ, &isMPIBAIJ));
1097: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATSEQSBAIJ, &isSeqSBAIJ));
1098: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATMPISBAIJ, &isMPISBAIJ));
1099: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATDIAGONAL, &isDiag));
1100: PetscCall(PetscObjectTypeCompareAny((PetscObject)sub, &isDense, MATSEQDENSE, MATMPIDENSE, NULL));
1102: if (chol) {
1103: if (r == c) {
1104: if (isSeqAIJ) convert_to_triples = MatConvertToTriples_seqaij_seqsbaij;
1105: else if (isMPIAIJ) convert_to_triples = MatConvertToTriples_mpiaij_mpisbaij;
1106: else if (isSeqSBAIJ) convert_to_triples = MatConvertToTriples_seqsbaij_seqsbaij;
1107: else if (isMPISBAIJ) convert_to_triples = MatConvertToTriples_mpisbaij_mpisbaij;
1108: else if (isDiag) convert_to_triples = MatConvertToTriples_diagonal_xaij;
1109: else if (isDense) convert_to_triples = MatConvertToTriples_dense_xaij;
1110: } else {
1111: if (isSeqAIJ) convert_to_triples = MatConvertToTriples_seqaij_seqaij;
1112: else if (isMPIAIJ) convert_to_triples = MatConvertToTriples_mpiaij_mpiaij;
1113: else if (isSeqBAIJ) convert_to_triples = MatConvertToTriples_seqbaij_seqaij;
1114: else if (isMPIBAIJ) convert_to_triples = MatConvertToTriples_mpibaij_mpiaij;
1115: else if (isDiag) convert_to_triples = MatConvertToTriples_diagonal_xaij;
1116: else if (isDense) convert_to_triples = MatConvertToTriples_dense_xaij;
1117: }
1118: } else {
1119: if (isSeqAIJ) convert_to_triples = MatConvertToTriples_seqaij_seqaij;
1120: else if (isMPIAIJ) convert_to_triples = MatConvertToTriples_mpiaij_mpiaij;
1121: else if (isSeqBAIJ) convert_to_triples = MatConvertToTriples_seqbaij_seqaij;
1122: else if (isMPIBAIJ) convert_to_triples = MatConvertToTriples_mpibaij_mpiaij;
1123: else if (isDiag) convert_to_triples = MatConvertToTriples_diagonal_xaij;
1124: else if (isDense) convert_to_triples = MatConvertToTriples_dense_xaij;
1125: }
1126: PetscCheck(convert_to_triples, PetscObjectComm((PetscObject)sub), PETSC_ERR_SUP, "Not for block of type %s", ((PetscObject)sub)->type_name);
1127: mumps->nest_convert_to_triples[r * nc + c] = convert_to_triples;
1128: PetscCall(MatGetInfo(sub, MAT_LOCAL, &info));
1129: cumnnz += (PetscCount)info.nz_used; /* can be overestimated for Cholesky */
1130: maxnnz = PetscMax(maxnnz, info.nz_used);
1131: }
1132: }
1133: }
1135: /* Allocate total COO */
1136: totnnz = cumnnz;
1137: PetscCall(PetscMalloc2(totnnz, &irns, totnnz, &jcns));
1138: PetscCall(PetscMalloc1(totnnz, &vals));
1140: /* Handle rows and column maps
1141: We directly map rows and use an SF for the columns */
1142: PetscCall(PetscMalloc4(nr, &rows, nc, &cols, nr, &rows_idx, nc, &cols_idx));
1143: PetscCall(MatNestGetISs(A, rows, cols));
1144: for (PetscInt r = 0; r < nr; r++) PetscCall(ISGetIndices(rows[r], (const PetscInt **)&rows_idx[r]));
1145: for (PetscInt c = 0; c < nc; c++) PetscCall(ISGetIndices(cols[c], (const PetscInt **)&cols_idx[c]));
1146: if (PetscDefined(USE_64BIT_INDICES)) PetscCall(PetscMalloc1(maxnnz, &pjcns_w));
1147: else (void)maxnnz;
1149: cumnnz = 0;
1150: for (PetscInt r = 0; r < nr; r++) {
1151: for (PetscInt c = 0; c < nc; c++) {
1152: Mat sub = mats[r][c];
1153: const PetscInt *ridx = rows_idx[r];
1154: const PetscInt *cidx = cols_idx[c];
1155: PetscInt rst;
1156: PetscSF csf;
1157: PetscBool isTrans, isHTrans = PETSC_FALSE, swap;
1158: PetscLayout cmap;
1159: PetscInt innz;
1161: mumps->nest_vals_start[r * nc + c] = cumnnz;
1162: if (!mumps->nest_convert_to_triples[r * nc + c]) continue;
1164: /* Extract inner blocks if needed */
1165: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATTRANSPOSEVIRTUAL, &isTrans));
1166: if (isTrans) PetscCall(MatTransposeGetMat(sub, &sub));
1167: else {
1168: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATHERMITIANTRANSPOSEVIRTUAL, &isHTrans));
1169: if (isHTrans) PetscCall(MatHermitianTransposeGetMat(sub, &sub));
1170: }
1171: swap = (PetscBool)(isTrans || isHTrans);
1173: /* Get column layout to map off-process columns */
1174: PetscCall(MatGetLayouts(sub, NULL, &cmap));
1176: /* Get row start to map on-process rows */
1177: PetscCall(MatGetOwnershipRange(sub, &rst, NULL));
1179: /* Directly use the mumps datastructure and use C ordering for now */
1180: PetscCall((*mumps->nest_convert_to_triples[r * nc + c])(sub, 0, MAT_INITIAL_MATRIX, mumps));
1182: /* Swap the role of rows and columns indices for transposed blocks
1183: since we need values with global final ordering */
1184: if (swap) {
1185: cidx = rows_idx[r];
1186: ridx = cols_idx[c];
1187: }
1189: /* Communicate column indices
1190: This could have been done with a single SF but it would have complicated the code a lot.
1191: But since we do it only once, we pay the price of setting up an SF for each block */
1192: if (PetscDefined(USE_64BIT_INDICES)) {
1193: for (PetscInt k = 0; k < mumps->nnz; k++) pjcns_w[k] = mumps->jcn[k];
1194: } else pjcns_w = (PetscInt *)mumps->jcn; /* This cast is needed only to silence warnings for 64bit integers builds */
1195: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &csf));
1196: PetscCall(PetscIntCast(mumps->nnz, &innz));
1197: PetscCall(PetscSFSetGraphLayout(csf, cmap, innz, NULL, PETSC_OWN_POINTER, pjcns_w));
1198: PetscCall(PetscSFBcastBegin(csf, MPIU_INT, cidx, pjcns_w, MPI_REPLACE));
1199: PetscCall(PetscSFBcastEnd(csf, MPIU_INT, cidx, pjcns_w, MPI_REPLACE));
1200: PetscCall(PetscSFDestroy(&csf));
1202: /* Import indices: use direct map for rows and mapped indices for columns */
1203: if (swap) {
1204: for (PetscInt k = 0; k < mumps->nnz; k++) {
1205: PetscCall(PetscMUMPSIntCast(ridx[mumps->irn[k] - rst] + shift, &jcns[cumnnz + k]));
1206: PetscCall(PetscMUMPSIntCast(pjcns_w[k] + shift, &irns[cumnnz + k]));
1207: }
1208: } else {
1209: for (PetscInt k = 0; k < mumps->nnz; k++) {
1210: PetscCall(PetscMUMPSIntCast(ridx[mumps->irn[k] - rst] + shift, &irns[cumnnz + k]));
1211: PetscCall(PetscMUMPSIntCast(pjcns_w[k] + shift, &jcns[cumnnz + k]));
1212: }
1213: }
1215: /* Import values to full COO */
1216: PetscCall(PetscArraycpy(vals + cumnnz, mumps->val, mumps->nnz));
1217: if (isHTrans) { /* conjugate the entries */
1218: PetscScalar *v = vals + cumnnz;
1219: for (PetscInt k = 0; k < mumps->nnz; k++) v[k] = PetscConj(v[k]);
1220: }
1222: /* Shift new starting point and sanity check */
1223: cumnnz += mumps->nnz;
1224: PetscCheck(cumnnz <= totnnz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unexpected number of nonzeros %" PetscCount_FMT " != %" PetscCount_FMT, cumnnz, totnnz);
1226: /* Free scratch memory */
1227: PetscCall(PetscFree2(mumps->irn, mumps->jcn));
1228: PetscCall(PetscFree(mumps->val_alloc));
1229: mumps->val = NULL;
1230: mumps->nnz = 0;
1231: }
1232: }
1233: if (PetscDefined(USE_64BIT_INDICES)) PetscCall(PetscFree(pjcns_w));
1234: for (PetscInt r = 0; r < nr; r++) PetscCall(ISRestoreIndices(rows[r], (const PetscInt **)&rows_idx[r]));
1235: for (PetscInt c = 0; c < nc; c++) PetscCall(ISRestoreIndices(cols[c], (const PetscInt **)&cols_idx[c]));
1236: PetscCall(PetscFree4(rows, cols, rows_idx, cols_idx));
1237: if (!chol) PetscCheck(cumnnz == totnnz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Different number of nonzeros %" PetscCount_FMT " != %" PetscCount_FMT, cumnnz, totnnz);
1238: mumps->nest_vals_start[nr * nc] = cumnnz;
1240: /* Set pointers for final MUMPS data structure */
1241: mumps->nest_vals = vals;
1242: mumps->val_alloc = NULL; /* do not use val_alloc since it may be reallocated with the OMP callpath */
1243: mumps->val = vals;
1244: mumps->irn = irns;
1245: mumps->jcn = jcns;
1246: mumps->nnz = cumnnz;
1247: } else {
1248: PetscScalar *oval = mumps->nest_vals;
1249: for (PetscInt r = 0; r < nr; r++) {
1250: for (PetscInt c = 0; c < nc; c++) {
1251: PetscBool isTrans, isHTrans = PETSC_FALSE;
1252: Mat sub = mats[r][c];
1253: PetscInt midx = r * nc + c;
1255: if (!mumps->nest_convert_to_triples[midx]) continue;
1256: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATTRANSPOSEVIRTUAL, &isTrans));
1257: if (isTrans) PetscCall(MatTransposeGetMat(sub, &sub));
1258: else {
1259: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATHERMITIANTRANSPOSEVIRTUAL, &isHTrans));
1260: if (isHTrans) PetscCall(MatHermitianTransposeGetMat(sub, &sub));
1261: }
1262: mumps->val = oval + mumps->nest_vals_start[midx];
1263: PetscCall((*mumps->nest_convert_to_triples[midx])(sub, shift, MAT_REUSE_MATRIX, mumps));
1264: if (isHTrans) {
1265: PetscCount nnz = mumps->nest_vals_start[midx + 1] - mumps->nest_vals_start[midx];
1266: for (PetscCount k = 0; k < nnz; k++) mumps->val[k] = PetscConj(mumps->val[k]);
1267: }
1268: }
1269: }
1270: mumps->val = oval;
1271: }
1272: PetscFunctionReturn(PETSC_SUCCESS);
1273: }
1275: static PetscErrorCode MatDestroy_MUMPS(Mat A)
1276: {
1277: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1279: PetscFunctionBegin;
1280: PetscCall(PetscFree2(mumps->id.sol_loc, mumps->id.isol_loc));
1281: PetscCall(VecScatterDestroy(&mumps->scat_rhs));
1282: PetscCall(VecScatterDestroy(&mumps->scat_sol));
1283: PetscCall(VecDestroy(&mumps->b_seq));
1284: PetscCall(VecDestroy(&mumps->x_seq));
1285: PetscCall(PetscFree(mumps->id.perm_in));
1286: PetscCall(PetscFree2(mumps->irn, mumps->jcn));
1287: PetscCall(PetscFree(mumps->val_alloc));
1288: PetscCall(PetscFree(mumps->info));
1289: PetscCall(PetscFree(mumps->ICNTL_pre));
1290: PetscCall(PetscFree(mumps->CNTL_pre));
1291: PetscCall(MatMumpsResetSchur_Private(mumps));
1292: if (mumps->id.job != JOB_NULL) { /* cannot call PetscMUMPS_c() if JOB_INIT has never been called for this instance */
1293: mumps->id.job = JOB_END;
1294: PetscMUMPS_c(mumps);
1295: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "MUMPS error in termination: INFOG(1)=%d " MUMPS_MANUALS, mumps->id.INFOG(1));
1296: if (mumps->mumps_comm != MPI_COMM_NULL) {
1297: if (PetscDefined(HAVE_OPENMP_SUPPORT) && mumps->use_petsc_omp_support) PetscCallMPI(MPI_Comm_free(&mumps->mumps_comm));
1298: else PetscCall(PetscCommRestoreComm(PetscObjectComm((PetscObject)A), &mumps->mumps_comm));
1299: }
1300: }
1301: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
1302: if (mumps->use_petsc_omp_support) {
1303: PetscCall(PetscOmpCtrlDestroy(&mumps->omp_ctrl));
1304: PetscCall(PetscFree2(mumps->rhs_loc, mumps->rhs_recvbuf));
1305: PetscCall(PetscFree3(mumps->rhs_nrow, mumps->rhs_recvcounts, mumps->rhs_disps));
1306: }
1307: #endif
1308: PetscCall(PetscFree(mumps->ia_alloc));
1309: PetscCall(PetscFree(mumps->ja_alloc));
1310: PetscCall(PetscFree(mumps->recvcount));
1311: PetscCall(PetscFree(mumps->reqs));
1312: PetscCall(PetscFree(mumps->irhs_loc));
1313: PetscCall(PetscFree2(mumps->nest_vals_start, mumps->nest_convert_to_triples));
1314: PetscCall(PetscFree(mumps->nest_vals));
1315: PetscCall(PetscFree(A->data));
1317: /* clear composed functions */
1318: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1319: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorSetSchurIS_C", NULL));
1320: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorCreateSchurComplement_C", NULL));
1321: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsSetIcntl_C", NULL));
1322: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetIcntl_C", NULL));
1323: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsSetCntl_C", NULL));
1324: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetCntl_C", NULL));
1325: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInfo_C", NULL));
1326: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInfog_C", NULL));
1327: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetRinfo_C", NULL));
1328: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetRinfog_C", NULL));
1329: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetNullPivots_C", NULL));
1330: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInverse_C", NULL));
1331: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInverseTranspose_C", NULL));
1332: PetscFunctionReturn(PETSC_SUCCESS);
1333: }
1335: /* Set up the distributed RHS info for MUMPS. <nrhs> is the number of RHS. <array> points to start of RHS on the local processor. */
1336: static PetscErrorCode MatMumpsSetUpDistRHSInfo(Mat A, PetscInt nrhs, const PetscScalar *array)
1337: {
1338: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1339: const PetscMPIInt ompsize = mumps->omp_comm_size;
1340: PetscInt i, m, M, rstart;
1342: PetscFunctionBegin;
1343: PetscCall(MatGetSize(A, &M, NULL));
1344: PetscCall(MatGetLocalSize(A, &m, NULL));
1345: PetscCheck(M <= PETSC_MUMPS_INT_MAX, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscInt too long for PetscMUMPSInt");
1346: if (ompsize == 1) {
1347: if (!mumps->irhs_loc) {
1348: mumps->nloc_rhs = (PetscMUMPSInt)m;
1349: PetscCall(PetscMalloc1(m, &mumps->irhs_loc));
1350: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
1351: for (i = 0; i < m; i++) PetscCall(PetscMUMPSIntCast(rstart + i + 1, &mumps->irhs_loc[i])); /* use 1-based indices */
1352: }
1353: mumps->id.rhs_loc = (MumpsScalar *)array;
1354: } else {
1355: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
1356: const PetscInt *ranges;
1357: PetscMPIInt j, k, sendcount, *petsc_ranks, *omp_ranks;
1358: MPI_Group petsc_group, omp_group;
1359: PetscScalar *recvbuf = NULL;
1361: if (mumps->is_omp_master) {
1362: /* Lazily initialize the omp stuff for distributed rhs */
1363: if (!mumps->irhs_loc) {
1364: PetscCall(PetscMalloc2(ompsize, &omp_ranks, ompsize, &petsc_ranks));
1365: PetscCall(PetscMalloc3(ompsize, &mumps->rhs_nrow, ompsize, &mumps->rhs_recvcounts, ompsize, &mumps->rhs_disps));
1366: PetscCallMPI(MPI_Comm_group(mumps->petsc_comm, &petsc_group));
1367: PetscCallMPI(MPI_Comm_group(mumps->omp_comm, &omp_group));
1368: for (j = 0; j < ompsize; j++) omp_ranks[j] = j;
1369: PetscCallMPI(MPI_Group_translate_ranks(omp_group, ompsize, omp_ranks, petsc_group, petsc_ranks));
1371: /* Populate mumps->irhs_loc[], rhs_nrow[] */
1372: mumps->nloc_rhs = 0;
1373: PetscCall(MatGetOwnershipRanges(A, &ranges));
1374: for (j = 0; j < ompsize; j++) {
1375: mumps->rhs_nrow[j] = ranges[petsc_ranks[j] + 1] - ranges[petsc_ranks[j]];
1376: mumps->nloc_rhs += mumps->rhs_nrow[j];
1377: }
1378: PetscCall(PetscMalloc1(mumps->nloc_rhs, &mumps->irhs_loc));
1379: for (j = k = 0; j < ompsize; j++) {
1380: for (i = ranges[petsc_ranks[j]]; i < ranges[petsc_ranks[j] + 1]; i++, k++) mumps->irhs_loc[k] = i + 1; /* uses 1-based indices */
1381: }
1383: PetscCall(PetscFree2(omp_ranks, petsc_ranks));
1384: PetscCallMPI(MPI_Group_free(&petsc_group));
1385: PetscCallMPI(MPI_Group_free(&omp_group));
1386: }
1388: /* Realloc buffers when current nrhs is bigger than what we have met */
1389: if (nrhs > mumps->max_nrhs) {
1390: PetscCall(PetscFree2(mumps->rhs_loc, mumps->rhs_recvbuf));
1391: PetscCall(PetscMalloc2(mumps->nloc_rhs * nrhs, &mumps->rhs_loc, mumps->nloc_rhs * nrhs, &mumps->rhs_recvbuf));
1392: mumps->max_nrhs = nrhs;
1393: }
1395: /* Setup recvcounts[], disps[], recvbuf on omp rank 0 for the upcoming MPI_Gatherv */
1396: for (j = 0; j < ompsize; j++) PetscCall(PetscMPIIntCast(mumps->rhs_nrow[j] * nrhs, &mumps->rhs_recvcounts[j]));
1397: mumps->rhs_disps[0] = 0;
1398: for (j = 1; j < ompsize; j++) {
1399: mumps->rhs_disps[j] = mumps->rhs_disps[j - 1] + mumps->rhs_recvcounts[j - 1];
1400: PetscCheck(mumps->rhs_disps[j] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscMPIInt overflow!");
1401: }
1402: recvbuf = (nrhs == 1) ? mumps->rhs_loc : mumps->rhs_recvbuf; /* Directly use rhs_loc[] as recvbuf. Single rhs is common in Ax=b */
1403: }
1405: PetscCall(PetscMPIIntCast(m * nrhs, &sendcount));
1406: PetscCallMPI(MPI_Gatherv(array, sendcount, MPIU_SCALAR, recvbuf, mumps->rhs_recvcounts, mumps->rhs_disps, MPIU_SCALAR, 0, mumps->omp_comm));
1408: if (mumps->is_omp_master) {
1409: if (nrhs > 1) { /* Copy & re-arrange data from rhs_recvbuf[] to mumps->rhs_loc[] only when there are multiple rhs */
1410: PetscScalar *dst, *dstbase = mumps->rhs_loc;
1411: for (j = 0; j < ompsize; j++) {
1412: const PetscScalar *src = mumps->rhs_recvbuf + mumps->rhs_disps[j];
1413: dst = dstbase;
1414: for (i = 0; i < nrhs; i++) {
1415: PetscCall(PetscArraycpy(dst, src, mumps->rhs_nrow[j]));
1416: src += mumps->rhs_nrow[j];
1417: dst += mumps->nloc_rhs;
1418: }
1419: dstbase += mumps->rhs_nrow[j];
1420: }
1421: }
1422: mumps->id.rhs_loc = (MumpsScalar *)mumps->rhs_loc;
1423: }
1424: #endif /* PETSC_HAVE_OPENMP_SUPPORT */
1425: }
1426: mumps->id.nrhs = (PetscMUMPSInt)nrhs;
1427: mumps->id.nloc_rhs = (PetscMUMPSInt)mumps->nloc_rhs;
1428: mumps->id.lrhs_loc = mumps->nloc_rhs;
1429: mumps->id.irhs_loc = mumps->irhs_loc;
1430: PetscFunctionReturn(PETSC_SUCCESS);
1431: }
1433: static PetscErrorCode MatSolve_MUMPS(Mat A, Vec b, Vec x)
1434: {
1435: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1436: const PetscScalar *rarray = NULL;
1437: PetscScalar *array;
1438: IS is_iden, is_petsc;
1439: PetscInt i;
1440: PetscBool second_solve = PETSC_FALSE;
1441: static PetscBool cite1 = PETSC_FALSE, cite2 = PETSC_FALSE;
1443: PetscFunctionBegin;
1444: 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 "
1445: "Journal on Matrix Analysis and Applications},\n volume = {23},\n number = {1},\n pages = {15--41},\n year = {2001}\n}\n",
1446: &cite1));
1447: 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 "
1448: "Computing},\n volume = {32},\n number = {2},\n pages = {136--156},\n year = {2006}\n}\n",
1449: &cite2));
1451: PetscCall(VecFlag(x, A->factorerrortype));
1452: if (A->factorerrortype) {
1453: 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)));
1454: PetscFunctionReturn(PETSC_SUCCESS);
1455: }
1457: mumps->id.nrhs = 1;
1458: if (mumps->petsc_size > 1) {
1459: if (mumps->ICNTL20 == 10) {
1460: mumps->id.ICNTL(20) = 10; /* dense distributed RHS */
1461: PetscCall(VecGetArrayRead(b, &rarray));
1462: PetscCall(MatMumpsSetUpDistRHSInfo(A, 1, rarray));
1463: } else {
1464: mumps->id.ICNTL(20) = 0; /* dense centralized RHS; Scatter b into a sequential rhs vector*/
1465: PetscCall(VecScatterBegin(mumps->scat_rhs, b, mumps->b_seq, INSERT_VALUES, SCATTER_FORWARD));
1466: PetscCall(VecScatterEnd(mumps->scat_rhs, b, mumps->b_seq, INSERT_VALUES, SCATTER_FORWARD));
1467: if (!mumps->myid) {
1468: PetscCall(VecGetArray(mumps->b_seq, &array));
1469: mumps->id.rhs = (MumpsScalar *)array;
1470: }
1471: }
1472: } else { /* petsc_size == 1 */
1473: mumps->id.ICNTL(20) = 0; /* dense centralized RHS */
1474: PetscCall(VecCopy(b, x));
1475: PetscCall(VecGetArray(x, &array));
1476: mumps->id.rhs = (MumpsScalar *)array;
1477: }
1479: /*
1480: handle condensation step of Schur complement (if any)
1481: We set by default ICNTL(26) == -1 when Schur indices have been provided by the user.
1482: According to MUMPS (5.0.0) manual, any value should be harmful during the factorization phase
1483: Unless the user provides a valid value for ICNTL(26), MatSolve and MatMatSolve routines solve the full system.
1484: This requires an extra call to PetscMUMPS_c and the computation of the factors for S
1485: */
1486: if (mumps->id.size_schur > 0) {
1487: PetscCheck(mumps->petsc_size <= 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Parallel Schur complements not yet supported from PETSc");
1488: if (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2) {
1489: second_solve = PETSC_TRUE;
1490: PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1491: mumps->id.ICNTL(26) = 1; /* condensation phase */
1492: } else if (mumps->id.ICNTL(26) == 1) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1493: }
1494: /* solve phase */
1495: mumps->id.job = JOB_SOLVE;
1496: PetscMUMPS_c(mumps);
1497: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "MUMPS error in solve: INFOG(1)=%d " MUMPS_MANUALS, mumps->id.INFOG(1));
1499: /* handle expansion step of Schur complement (if any) */
1500: if (second_solve) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_TRUE));
1501: else if (mumps->id.ICNTL(26) == 1) {
1502: PetscCall(MatMumpsSolveSchur_Private(A));
1503: for (i = 0; i < mumps->id.size_schur; ++i) {
1504: #if !defined(PETSC_USE_COMPLEX)
1505: PetscScalar val = mumps->id.redrhs[i];
1506: #else
1507: PetscScalar val = mumps->id.redrhs[i].r + PETSC_i * mumps->id.redrhs[i].i;
1508: #endif
1509: array[mumps->id.listvar_schur[i] - 1] = val;
1510: }
1511: }
1513: if (mumps->petsc_size > 1) { /* convert mumps distributed solution to petsc mpi x */
1514: if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) {
1515: /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */
1516: PetscCall(VecScatterDestroy(&mumps->scat_sol));
1517: }
1518: if (!mumps->scat_sol) { /* create scatter scat_sol */
1519: PetscInt *isol2_loc = NULL;
1520: PetscCall(ISCreateStride(PETSC_COMM_SELF, mumps->id.lsol_loc, 0, 1, &is_iden)); /* from */
1521: PetscCall(PetscMalloc1(mumps->id.lsol_loc, &isol2_loc));
1522: for (i = 0; i < mumps->id.lsol_loc; i++) isol2_loc[i] = mumps->id.isol_loc[i] - 1; /* change Fortran style to C style */
1523: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, mumps->id.lsol_loc, isol2_loc, PETSC_OWN_POINTER, &is_petsc)); /* to */
1524: PetscCall(VecScatterCreate(mumps->x_seq, is_iden, x, is_petsc, &mumps->scat_sol));
1525: PetscCall(ISDestroy(&is_iden));
1526: PetscCall(ISDestroy(&is_petsc));
1527: mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */
1528: }
1530: PetscCall(VecScatterBegin(mumps->scat_sol, mumps->x_seq, x, INSERT_VALUES, SCATTER_FORWARD));
1531: PetscCall(VecScatterEnd(mumps->scat_sol, mumps->x_seq, x, INSERT_VALUES, SCATTER_FORWARD));
1532: }
1534: if (mumps->petsc_size > 1) {
1535: if (mumps->ICNTL20 == 10) {
1536: PetscCall(VecRestoreArrayRead(b, &rarray));
1537: } else if (!mumps->myid) {
1538: PetscCall(VecRestoreArray(mumps->b_seq, &array));
1539: }
1540: } else PetscCall(VecRestoreArray(x, &array));
1542: PetscCall(PetscLogFlops(2.0 * PetscMax(0, (mumps->id.INFO(28) >= 0 ? mumps->id.INFO(28) : -1000000 * mumps->id.INFO(28)) - A->cmap->n)));
1543: PetscFunctionReturn(PETSC_SUCCESS);
1544: }
1546: static PetscErrorCode MatSolveTranspose_MUMPS(Mat A, Vec b, Vec x)
1547: {
1548: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1549: const PetscMUMPSInt value = mumps->id.ICNTL(9);
1551: PetscFunctionBegin;
1552: mumps->id.ICNTL(9) = 0;
1553: PetscCall(MatSolve_MUMPS(A, b, x));
1554: mumps->id.ICNTL(9) = value;
1555: PetscFunctionReturn(PETSC_SUCCESS);
1556: }
1558: static PetscErrorCode MatMatSolve_MUMPS(Mat A, Mat B, Mat X)
1559: {
1560: Mat Bt = NULL;
1561: PetscBool denseX, denseB, flg, flgT;
1562: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1563: PetscInt i, nrhs, M;
1564: PetscScalar *array;
1565: const PetscScalar *rbray;
1566: PetscInt lsol_loc, nlsol_loc, *idxx, iidx = 0;
1567: PetscMUMPSInt *isol_loc, *isol_loc_save;
1568: PetscScalar *bray, *sol_loc, *sol_loc_save;
1569: IS is_to, is_from;
1570: PetscInt k, proc, j, m, myrstart;
1571: const PetscInt *rstart;
1572: Vec v_mpi, msol_loc;
1573: VecScatter scat_sol;
1574: Vec b_seq;
1575: VecScatter scat_rhs;
1576: PetscScalar *aa;
1577: PetscInt spnr, *ia, *ja;
1578: Mat_MPIAIJ *b = NULL;
1580: PetscFunctionBegin;
1581: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &denseX, MATSEQDENSE, MATMPIDENSE, NULL));
1582: PetscCheck(denseX, PetscObjectComm((PetscObject)X), PETSC_ERR_ARG_WRONG, "Matrix X must be MATDENSE matrix");
1584: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &denseB, MATSEQDENSE, MATMPIDENSE, NULL));
1585: if (denseB) {
1586: PetscCheck(B->rmap->n == X->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Matrix B and X must have same row distribution");
1587: mumps->id.ICNTL(20) = 0; /* dense RHS */
1588: } else { /* sparse B */
1589: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
1590: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATTRANSPOSEVIRTUAL, &flgT));
1591: if (flgT) { /* input B is transpose of actual RHS matrix,
1592: because mumps requires sparse compressed COLUMN storage! See MatMatTransposeSolve_MUMPS() */
1593: PetscCall(MatTransposeGetMat(B, &Bt));
1594: } else SETERRQ(PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Matrix B must be MATTRANSPOSEVIRTUAL matrix");
1595: mumps->id.ICNTL(20) = 1; /* sparse RHS */
1596: }
1598: PetscCall(MatGetSize(B, &M, &nrhs));
1599: mumps->id.nrhs = (PetscMUMPSInt)nrhs;
1600: mumps->id.lrhs = (PetscMUMPSInt)M;
1601: mumps->id.rhs = NULL;
1603: if (mumps->petsc_size == 1) {
1604: PetscScalar *aa;
1605: PetscInt spnr, *ia, *ja;
1606: PetscBool second_solve = PETSC_FALSE;
1608: PetscCall(MatDenseGetArray(X, &array));
1609: mumps->id.rhs = (MumpsScalar *)array;
1611: if (denseB) {
1612: /* copy B to X */
1613: PetscCall(MatDenseGetArrayRead(B, &rbray));
1614: PetscCall(PetscArraycpy(array, rbray, M * nrhs));
1615: PetscCall(MatDenseRestoreArrayRead(B, &rbray));
1616: } else { /* sparse B */
1617: PetscCall(MatSeqAIJGetArray(Bt, &aa));
1618: PetscCall(MatGetRowIJ(Bt, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1619: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
1620: PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
1621: mumps->id.rhs_sparse = (MumpsScalar *)aa;
1622: }
1623: /* handle condensation step of Schur complement (if any) */
1624: if (mumps->id.size_schur > 0) {
1625: if (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2) {
1626: second_solve = PETSC_TRUE;
1627: PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1628: mumps->id.ICNTL(26) = 1; /* condensation phase */
1629: } else if (mumps->id.ICNTL(26) == 1) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1630: }
1631: /* solve phase */
1632: mumps->id.job = JOB_SOLVE;
1633: PetscMUMPS_c(mumps);
1634: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "MUMPS error in solve: INFOG(1)=%d " MUMPS_MANUALS, mumps->id.INFOG(1));
1636: /* handle expansion step of Schur complement (if any) */
1637: if (second_solve) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_TRUE));
1638: else if (mumps->id.ICNTL(26) == 1) {
1639: PetscCall(MatMumpsSolveSchur_Private(A));
1640: for (j = 0; j < nrhs; ++j)
1641: for (i = 0; i < mumps->id.size_schur; ++i) {
1642: #if !defined(PETSC_USE_COMPLEX)
1643: PetscScalar val = mumps->id.redrhs[i + j * mumps->id.lredrhs];
1644: #else
1645: PetscScalar val = mumps->id.redrhs[i + j * mumps->id.lredrhs].r + PETSC_i * mumps->id.redrhs[i + j * mumps->id.lredrhs].i;
1646: #endif
1647: array[mumps->id.listvar_schur[i] - 1 + j * M] = val;
1648: }
1649: }
1650: if (!denseB) { /* sparse B */
1651: PetscCall(MatSeqAIJRestoreArray(Bt, &aa));
1652: PetscCall(MatRestoreRowIJ(Bt, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1653: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot restore IJ structure");
1654: }
1655: PetscCall(MatDenseRestoreArray(X, &array));
1656: PetscFunctionReturn(PETSC_SUCCESS);
1657: }
1659: /* parallel case: MUMPS requires rhs B to be centralized on the host! */
1660: PetscCheck(!mumps->id.ICNTL(19), PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Parallel Schur complements not yet supported from PETSc");
1662: /* create msol_loc to hold mumps local solution */
1663: isol_loc_save = mumps->id.isol_loc; /* save it for MatSolve() */
1664: sol_loc_save = (PetscScalar *)mumps->id.sol_loc;
1666: lsol_loc = mumps->id.lsol_loc;
1667: nlsol_loc = nrhs * lsol_loc; /* length of sol_loc */
1668: PetscCall(PetscMalloc2(nlsol_loc, &sol_loc, lsol_loc, &isol_loc));
1669: mumps->id.sol_loc = (MumpsScalar *)sol_loc;
1670: mumps->id.isol_loc = isol_loc;
1672: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, nlsol_loc, (PetscScalar *)sol_loc, &msol_loc));
1674: if (denseB) {
1675: if (mumps->ICNTL20 == 10) {
1676: mumps->id.ICNTL(20) = 10; /* dense distributed RHS */
1677: PetscCall(MatDenseGetArrayRead(B, &rbray));
1678: PetscCall(MatMumpsSetUpDistRHSInfo(A, nrhs, rbray));
1679: PetscCall(MatDenseRestoreArrayRead(B, &rbray));
1680: PetscCall(MatGetLocalSize(B, &m, NULL));
1681: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B), 1, nrhs * m, nrhs * M, NULL, &v_mpi));
1682: } else {
1683: mumps->id.ICNTL(20) = 0; /* dense centralized RHS */
1684: /* TODO: Because of non-contiguous indices, the created vecscatter scat_rhs is not done in MPI_Gather, resulting in
1685: very inefficient communication. An optimization is to use VecScatterCreateToZero to gather B to rank 0. Then on rank
1686: 0, re-arrange B into desired order, which is a local operation.
1687: */
1689: /* scatter v_mpi to b_seq because MUMPS before 5.3.0 only supports centralized rhs */
1690: /* wrap dense rhs matrix B into a vector v_mpi */
1691: PetscCall(MatGetLocalSize(B, &m, NULL));
1692: PetscCall(MatDenseGetArray(B, &bray));
1693: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B), 1, nrhs * m, nrhs * M, (const PetscScalar *)bray, &v_mpi));
1694: PetscCall(MatDenseRestoreArray(B, &bray));
1696: /* scatter v_mpi to b_seq in proc[0]. MUMPS requires rhs to be centralized on the host! */
1697: if (!mumps->myid) {
1698: PetscInt *idx;
1699: /* idx: maps from k-th index of v_mpi to (i,j)-th global entry of B */
1700: PetscCall(PetscMalloc1(nrhs * M, &idx));
1701: PetscCall(MatGetOwnershipRanges(B, &rstart));
1702: k = 0;
1703: for (proc = 0; proc < mumps->petsc_size; proc++) {
1704: for (j = 0; j < nrhs; j++) {
1705: for (i = rstart[proc]; i < rstart[proc + 1]; i++) idx[k++] = j * M + i;
1706: }
1707: }
1709: PetscCall(VecCreateSeq(PETSC_COMM_SELF, nrhs * M, &b_seq));
1710: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nrhs * M, idx, PETSC_OWN_POINTER, &is_to));
1711: PetscCall(ISCreateStride(PETSC_COMM_SELF, nrhs * M, 0, 1, &is_from));
1712: } else {
1713: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 0, &b_seq));
1714: PetscCall(ISCreateStride(PETSC_COMM_SELF, 0, 0, 1, &is_to));
1715: PetscCall(ISCreateStride(PETSC_COMM_SELF, 0, 0, 1, &is_from));
1716: }
1717: PetscCall(VecScatterCreate(v_mpi, is_from, b_seq, is_to, &scat_rhs));
1718: PetscCall(VecScatterBegin(scat_rhs, v_mpi, b_seq, INSERT_VALUES, SCATTER_FORWARD));
1719: PetscCall(ISDestroy(&is_to));
1720: PetscCall(ISDestroy(&is_from));
1721: PetscCall(VecScatterEnd(scat_rhs, v_mpi, b_seq, INSERT_VALUES, SCATTER_FORWARD));
1723: if (!mumps->myid) { /* define rhs on the host */
1724: PetscCall(VecGetArray(b_seq, &bray));
1725: mumps->id.rhs = (MumpsScalar *)bray;
1726: PetscCall(VecRestoreArray(b_seq, &bray));
1727: }
1728: }
1729: } else { /* sparse B */
1730: b = (Mat_MPIAIJ *)Bt->data;
1732: /* wrap dense X into a vector v_mpi */
1733: PetscCall(MatGetLocalSize(X, &m, NULL));
1734: PetscCall(MatDenseGetArray(X, &bray));
1735: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)X), 1, nrhs * m, nrhs * M, (const PetscScalar *)bray, &v_mpi));
1736: PetscCall(MatDenseRestoreArray(X, &bray));
1738: if (!mumps->myid) {
1739: PetscCall(MatSeqAIJGetArray(b->A, &aa));
1740: PetscCall(MatGetRowIJ(b->A, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1741: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
1742: PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
1743: mumps->id.rhs_sparse = (MumpsScalar *)aa;
1744: } else {
1745: mumps->id.irhs_ptr = NULL;
1746: mumps->id.irhs_sparse = NULL;
1747: mumps->id.nz_rhs = 0;
1748: mumps->id.rhs_sparse = NULL;
1749: }
1750: }
1752: /* solve phase */
1753: mumps->id.job = JOB_SOLVE;
1754: PetscMUMPS_c(mumps);
1755: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "MUMPS error in solve: INFOG(1)=%d " MUMPS_MANUALS, mumps->id.INFOG(1));
1757: /* scatter mumps distributed solution to petsc vector v_mpi, which shares local arrays with solution matrix X */
1758: PetscCall(MatDenseGetArray(X, &array));
1759: PetscCall(VecPlaceArray(v_mpi, array));
1761: /* create scatter scat_sol */
1762: PetscCall(MatGetOwnershipRanges(X, &rstart));
1763: /* iidx: index for scatter mumps solution to petsc X */
1765: PetscCall(ISCreateStride(PETSC_COMM_SELF, nlsol_loc, 0, 1, &is_from));
1766: PetscCall(PetscMalloc1(nlsol_loc, &idxx));
1767: for (i = 0; i < lsol_loc; i++) {
1768: 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 */
1770: for (proc = 0; proc < mumps->petsc_size; proc++) {
1771: if (isol_loc[i] >= rstart[proc] && isol_loc[i] < rstart[proc + 1]) {
1772: myrstart = rstart[proc];
1773: k = isol_loc[i] - myrstart; /* local index on 1st column of petsc vector X */
1774: iidx = k + myrstart * nrhs; /* maps mumps isol_loc[i] to petsc index in X */
1775: m = rstart[proc + 1] - rstart[proc]; /* rows of X for this proc */
1776: break;
1777: }
1778: }
1780: for (j = 0; j < nrhs; j++) idxx[i + j * lsol_loc] = iidx + j * m;
1781: }
1782: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nlsol_loc, idxx, PETSC_COPY_VALUES, &is_to));
1783: PetscCall(VecScatterCreate(msol_loc, is_from, v_mpi, is_to, &scat_sol));
1784: PetscCall(VecScatterBegin(scat_sol, msol_loc, v_mpi, INSERT_VALUES, SCATTER_FORWARD));
1785: PetscCall(ISDestroy(&is_from));
1786: PetscCall(ISDestroy(&is_to));
1787: PetscCall(VecScatterEnd(scat_sol, msol_loc, v_mpi, INSERT_VALUES, SCATTER_FORWARD));
1788: PetscCall(MatDenseRestoreArray(X, &array));
1790: /* free spaces */
1791: mumps->id.sol_loc = (MumpsScalar *)sol_loc_save;
1792: mumps->id.isol_loc = isol_loc_save;
1794: PetscCall(PetscFree2(sol_loc, isol_loc));
1795: PetscCall(PetscFree(idxx));
1796: PetscCall(VecDestroy(&msol_loc));
1797: PetscCall(VecDestroy(&v_mpi));
1798: if (!denseB) {
1799: if (!mumps->myid) {
1800: b = (Mat_MPIAIJ *)Bt->data;
1801: PetscCall(MatSeqAIJRestoreArray(b->A, &aa));
1802: PetscCall(MatRestoreRowIJ(b->A, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1803: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot restore IJ structure");
1804: }
1805: } else {
1806: if (mumps->ICNTL20 == 0) {
1807: PetscCall(VecDestroy(&b_seq));
1808: PetscCall(VecScatterDestroy(&scat_rhs));
1809: }
1810: }
1811: PetscCall(VecScatterDestroy(&scat_sol));
1812: PetscCall(PetscLogFlops(nrhs * PetscMax(0, 2.0 * (mumps->id.INFO(28) >= 0 ? mumps->id.INFO(28) : -1000000 * mumps->id.INFO(28)) - A->cmap->n)));
1813: PetscFunctionReturn(PETSC_SUCCESS);
1814: }
1816: static PetscErrorCode MatMatSolveTranspose_MUMPS(Mat A, Mat B, Mat X)
1817: {
1818: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1819: const PetscMUMPSInt value = mumps->id.ICNTL(9);
1821: PetscFunctionBegin;
1822: mumps->id.ICNTL(9) = 0;
1823: PetscCall(MatMatSolve_MUMPS(A, B, X));
1824: mumps->id.ICNTL(9) = value;
1825: PetscFunctionReturn(PETSC_SUCCESS);
1826: }
1828: static PetscErrorCode MatMatTransposeSolve_MUMPS(Mat A, Mat Bt, Mat X)
1829: {
1830: PetscBool flg;
1831: Mat B;
1833: PetscFunctionBegin;
1834: PetscCall(PetscObjectTypeCompareAny((PetscObject)Bt, &flg, MATSEQAIJ, MATMPIAIJ, NULL));
1835: PetscCheck(flg, PetscObjectComm((PetscObject)Bt), PETSC_ERR_ARG_WRONG, "Matrix Bt must be MATAIJ matrix");
1837: /* Create B=Bt^T that uses Bt's data structure */
1838: PetscCall(MatCreateTranspose(Bt, &B));
1840: PetscCall(MatMatSolve_MUMPS(A, B, X));
1841: PetscCall(MatDestroy(&B));
1842: PetscFunctionReturn(PETSC_SUCCESS);
1843: }
1845: #if !defined(PETSC_USE_COMPLEX)
1846: /*
1847: input:
1848: F: numeric factor
1849: output:
1850: nneg: total number of negative pivots
1851: nzero: total number of zero pivots
1852: npos: (global dimension of F) - nneg - nzero
1853: */
1854: static PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
1855: {
1856: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
1857: PetscMPIInt size;
1859: PetscFunctionBegin;
1860: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)F), &size));
1861: /* 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 */
1862: 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));
1864: if (nneg) *nneg = mumps->id.INFOG(12);
1865: if (nzero || npos) {
1866: 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");
1867: if (nzero) *nzero = mumps->id.INFOG(28);
1868: if (npos) *npos = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28));
1869: }
1870: PetscFunctionReturn(PETSC_SUCCESS);
1871: }
1872: #endif
1874: static PetscErrorCode MatMumpsGatherNonzerosOnMaster(MatReuse reuse, Mat_MUMPS *mumps)
1875: {
1876: PetscMPIInt nreqs;
1877: PetscMUMPSInt *irn, *jcn;
1878: PetscMPIInt count;
1879: PetscCount totnnz, remain;
1880: const PetscInt osize = mumps->omp_comm_size;
1881: PetscScalar *val;
1883: PetscFunctionBegin;
1884: if (osize > 1) {
1885: if (reuse == MAT_INITIAL_MATRIX) {
1886: /* master first gathers counts of nonzeros to receive */
1887: if (mumps->is_omp_master) PetscCall(PetscMalloc1(osize, &mumps->recvcount));
1888: PetscCallMPI(MPI_Gather(&mumps->nnz, 1, MPIU_INT64, mumps->recvcount, 1, MPIU_INT64, 0 /*master*/, mumps->omp_comm));
1890: /* Then each computes number of send/recvs */
1891: if (mumps->is_omp_master) {
1892: /* Start from 1 since self communication is not done in MPI */
1893: nreqs = 0;
1894: for (PetscMPIInt i = 1; i < osize; i++) nreqs += (mumps->recvcount[i] + PETSC_MPI_INT_MAX - 1) / PETSC_MPI_INT_MAX;
1895: } else {
1896: nreqs = (PetscMPIInt)(((mumps->nnz + PETSC_MPI_INT_MAX - 1) / PETSC_MPI_INT_MAX));
1897: }
1898: PetscCall(PetscMalloc1(nreqs * 3, &mumps->reqs)); /* Triple the requests since we send irn, jcn and val separately */
1900: /* The following code is doing a very simple thing: omp_master rank gathers irn/jcn/val from others.
1901: MPI_Gatherv would be enough if it supports big counts > 2^31-1. Since it does not, and mumps->nnz
1902: might be a prime number > 2^31-1, we have to slice the message. Note omp_comm_size
1903: is very small, the current approach should have no extra overhead compared to MPI_Gatherv.
1904: */
1905: nreqs = 0; /* counter for actual send/recvs */
1906: if (mumps->is_omp_master) {
1907: totnnz = 0;
1909: for (PetscMPIInt i = 0; i < osize; i++) totnnz += mumps->recvcount[i]; /* totnnz = sum of nnz over omp_comm */
1910: PetscCall(PetscMalloc2(totnnz, &irn, totnnz, &jcn));
1911: PetscCall(PetscMalloc1(totnnz, &val));
1913: /* Self communication */
1914: PetscCall(PetscArraycpy(irn, mumps->irn, mumps->nnz));
1915: PetscCall(PetscArraycpy(jcn, mumps->jcn, mumps->nnz));
1916: PetscCall(PetscArraycpy(val, mumps->val, mumps->nnz));
1918: /* Replace mumps->irn/jcn etc on master with the newly allocated bigger arrays */
1919: PetscCall(PetscFree2(mumps->irn, mumps->jcn));
1920: PetscCall(PetscFree(mumps->val_alloc));
1921: mumps->nnz = totnnz;
1922: mumps->irn = irn;
1923: mumps->jcn = jcn;
1924: mumps->val = mumps->val_alloc = val;
1926: irn += mumps->recvcount[0]; /* recvcount[0] is old mumps->nnz on omp rank 0 */
1927: jcn += mumps->recvcount[0];
1928: val += mumps->recvcount[0];
1930: /* Remote communication */
1931: for (PetscMPIInt i = 1; i < osize; i++) {
1932: count = (PetscMPIInt)PetscMin(mumps->recvcount[i], (PetscMPIInt)PETSC_MPI_INT_MAX);
1933: remain = mumps->recvcount[i] - count;
1934: while (count > 0) {
1935: PetscCallMPI(MPIU_Irecv(irn, count, MPIU_MUMPSINT, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1936: PetscCallMPI(MPIU_Irecv(jcn, count, MPIU_MUMPSINT, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1937: PetscCallMPI(MPIU_Irecv(val, count, MPIU_SCALAR, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1938: irn += count;
1939: jcn += count;
1940: val += count;
1941: count = (PetscMPIInt)PetscMin(remain, (PetscMPIInt)PETSC_MPI_INT_MAX);
1942: remain -= count;
1943: }
1944: }
1945: } else {
1946: irn = mumps->irn;
1947: jcn = mumps->jcn;
1948: val = mumps->val;
1949: count = (PetscMPIInt)PetscMin(mumps->nnz, (PetscMPIInt)PETSC_MPI_INT_MAX);
1950: remain = mumps->nnz - count;
1951: while (count > 0) {
1952: PetscCallMPI(MPIU_Isend(irn, count, MPIU_MUMPSINT, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1953: PetscCallMPI(MPIU_Isend(jcn, count, MPIU_MUMPSINT, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1954: PetscCallMPI(MPIU_Isend(val, count, MPIU_SCALAR, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1955: irn += count;
1956: jcn += count;
1957: val += count;
1958: count = (PetscMPIInt)PetscMin(remain, (PetscMPIInt)PETSC_MPI_INT_MAX);
1959: remain -= count;
1960: }
1961: }
1962: } else {
1963: nreqs = 0;
1964: if (mumps->is_omp_master) {
1965: val = mumps->val + mumps->recvcount[0];
1966: for (PetscMPIInt i = 1; i < osize; i++) { /* Remote communication only since self data is already in place */
1967: count = (PetscMPIInt)PetscMin(mumps->recvcount[i], (PetscMPIInt)PETSC_MPI_INT_MAX);
1968: remain = mumps->recvcount[i] - count;
1969: while (count > 0) {
1970: PetscCallMPI(MPIU_Irecv(val, count, MPIU_SCALAR, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1971: val += count;
1972: count = (PetscMPIInt)PetscMin(remain, (PetscMPIInt)PETSC_MPI_INT_MAX);
1973: remain -= count;
1974: }
1975: }
1976: } else {
1977: val = mumps->val;
1978: count = (PetscMPIInt)PetscMin(mumps->nnz, (PetscMPIInt)PETSC_MPI_INT_MAX);
1979: remain = mumps->nnz - count;
1980: while (count > 0) {
1981: PetscCallMPI(MPIU_Isend(val, count, MPIU_SCALAR, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1982: val += count;
1983: count = (PetscMPIInt)PetscMin(remain, (PetscMPIInt)PETSC_MPI_INT_MAX);
1984: remain -= count;
1985: }
1986: }
1987: }
1988: PetscCallMPI(MPI_Waitall(nreqs, mumps->reqs, MPI_STATUSES_IGNORE));
1989: mumps->tag++; /* It is totally fine for above send/recvs to share one mpi tag */
1990: }
1991: PetscFunctionReturn(PETSC_SUCCESS);
1992: }
1994: static PetscErrorCode MatFactorNumeric_MUMPS(Mat F, Mat A, PETSC_UNUSED const MatFactorInfo *info)
1995: {
1996: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
1997: PetscBool isMPIAIJ;
1999: PetscFunctionBegin;
2000: if (mumps->id.INFOG(1) < 0 && !(mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0)) {
2001: 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)));
2002: 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)));
2003: PetscFunctionReturn(PETSC_SUCCESS);
2004: }
2006: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, mumps));
2007: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_REUSE_MATRIX, mumps));
2009: /* numerical factorization phase */
2010: mumps->id.job = JOB_FACTNUMERIC;
2011: if (!mumps->id.ICNTL(18)) { /* A is centralized */
2012: if (!mumps->myid) mumps->id.a = (MumpsScalar *)mumps->val;
2013: } else {
2014: mumps->id.a_loc = (MumpsScalar *)mumps->val;
2015: }
2016: PetscMUMPS_c(mumps);
2017: if (mumps->id.INFOG(1) < 0) {
2018: PetscCheck(!A->erroriffailure, PETSC_COMM_SELF, PETSC_ERR_LIB, "MUMPS error in numerical factorization: INFOG(1)=%d, INFO(2)=%d " MUMPS_MANUALS, mumps->id.INFOG(1), mumps->id.INFO(2));
2019: if (mumps->id.INFOG(1) == -10) {
2020: PetscCall(PetscInfo(F, "MUMPS error in numerical factorization: matrix is numerically singular, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
2021: F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
2022: } else if (mumps->id.INFOG(1) == -13) {
2023: PetscCall(PetscInfo(F, "MUMPS error in numerical factorization: INFOG(1)=%d, cannot allocate required memory %d megabytes\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
2024: F->factorerrortype = MAT_FACTOR_OUTMEMORY;
2025: } else if (mumps->id.INFOG(1) == -8 || mumps->id.INFOG(1) == -9 || (-16 < mumps->id.INFOG(1) && mumps->id.INFOG(1) < -10)) {
2026: PetscCall(PetscInfo(F, "MUMPS error in numerical factorizatione: INFOG(1)=%d, INFO(2)=%d, problem with work array\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
2027: F->factorerrortype = MAT_FACTOR_OUTMEMORY;
2028: } else {
2029: PetscCall(PetscInfo(F, "MUMPS error in numerical factorization: INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
2030: F->factorerrortype = MAT_FACTOR_OTHER;
2031: }
2032: }
2033: PetscCheck(mumps->myid || mumps->id.ICNTL(16) <= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "MUMPS error in numerical factorization: ICNTL(16)=%d " MUMPS_MANUALS, mumps->id.INFOG(16));
2035: F->assembled = PETSC_TRUE;
2037: if (F->schur) { /* reset Schur status to unfactored */
2038: #if defined(PETSC_HAVE_CUDA)
2039: F->schur->offloadmask = PETSC_OFFLOAD_CPU;
2040: #endif
2041: if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
2042: mumps->id.ICNTL(19) = 2;
2043: PetscCall(MatTranspose(F->schur, MAT_INPLACE_MATRIX, &F->schur));
2044: }
2045: PetscCall(MatFactorRestoreSchurComplement(F, NULL, MAT_FACTOR_SCHUR_UNFACTORED));
2046: }
2048: /* just to be sure that ICNTL(19) value returned by a call from MatMumpsGetIcntl is always consistent */
2049: if (!mumps->sym && mumps->id.ICNTL(19) && mumps->id.ICNTL(19) != 1) mumps->id.ICNTL(19) = 3;
2051: if (!mumps->is_omp_master) mumps->id.INFO(23) = 0;
2052: if (mumps->petsc_size > 1) {
2053: PetscInt lsol_loc;
2054: PetscScalar *sol_loc;
2056: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &isMPIAIJ));
2058: /* distributed solution; Create x_seq=sol_loc for repeated use */
2059: if (mumps->x_seq) {
2060: PetscCall(VecScatterDestroy(&mumps->scat_sol));
2061: PetscCall(PetscFree2(mumps->id.sol_loc, mumps->id.isol_loc));
2062: PetscCall(VecDestroy(&mumps->x_seq));
2063: }
2064: lsol_loc = mumps->id.INFO(23); /* length of sol_loc */
2065: PetscCall(PetscMalloc2(lsol_loc, &sol_loc, lsol_loc, &mumps->id.isol_loc));
2066: mumps->id.lsol_loc = (PetscMUMPSInt)lsol_loc;
2067: mumps->id.sol_loc = (MumpsScalar *)sol_loc;
2068: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, lsol_loc, sol_loc, &mumps->x_seq));
2069: }
2070: PetscCall(PetscLogFlops((double)mumps->id.RINFO(2)));
2071: PetscFunctionReturn(PETSC_SUCCESS);
2072: }
2074: /* Sets MUMPS options from the options database */
2075: static PetscErrorCode MatSetFromOptions_MUMPS(Mat F, Mat A)
2076: {
2077: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2078: PetscMUMPSInt icntl = 0, size, *listvar_schur;
2079: PetscInt info[80], i, ninfo = 80, rbs, cbs;
2080: PetscBool flg = PETSC_FALSE, schur = (PetscBool)(mumps->id.ICNTL(26) == -1);
2081: MumpsScalar *arr;
2083: PetscFunctionBegin;
2084: PetscOptionsBegin(PetscObjectComm((PetscObject)F), ((PetscObject)F)->prefix, "MUMPS Options", "Mat");
2085: if (mumps->id.job == JOB_NULL) { /* MatSetFromOptions_MUMPS() has never been called before */
2086: PetscInt nthreads = 0;
2087: PetscInt nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
2088: PetscInt nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0;
2090: mumps->petsc_comm = PetscObjectComm((PetscObject)A);
2091: PetscCallMPI(MPI_Comm_size(mumps->petsc_comm, &mumps->petsc_size));
2092: PetscCallMPI(MPI_Comm_rank(mumps->petsc_comm, &mumps->myid)); /* "if (!myid)" still works even if mumps_comm is different */
2094: PetscCall(PetscOptionsName("-mat_mumps_use_omp_threads", "Convert MPI processes into OpenMP threads", "None", &mumps->use_petsc_omp_support));
2095: if (mumps->use_petsc_omp_support) nthreads = -1; /* -1 will let PetscOmpCtrlCreate() guess a proper value when user did not supply one */
2096: /* do not use PetscOptionsInt() so that the option -mat_mumps_use_omp_threads is not displayed twice in the help */
2097: PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)F)->prefix, "-mat_mumps_use_omp_threads", &nthreads, NULL));
2098: if (mumps->use_petsc_omp_support) {
2099: 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",
2100: ((PetscObject)F)->prefix ? ((PetscObject)F)->prefix : "");
2101: 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 : "");
2102: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
2103: PetscCall(PetscOmpCtrlCreate(mumps->petsc_comm, nthreads, &mumps->omp_ctrl));
2104: PetscCall(PetscOmpCtrlGetOmpComms(mumps->omp_ctrl, &mumps->omp_comm, &mumps->mumps_comm, &mumps->is_omp_master));
2105: #endif
2106: } else {
2107: mumps->omp_comm = PETSC_COMM_SELF;
2108: mumps->mumps_comm = mumps->petsc_comm;
2109: mumps->is_omp_master = PETSC_TRUE;
2110: }
2111: PetscCallMPI(MPI_Comm_size(mumps->omp_comm, &mumps->omp_comm_size));
2112: mumps->reqs = NULL;
2113: mumps->tag = 0;
2115: if (mumps->mumps_comm != MPI_COMM_NULL) {
2116: if (PetscDefined(HAVE_OPENMP_SUPPORT) && mumps->use_petsc_omp_support) {
2117: /* It looks like MUMPS does not dup the input comm. Dup a new comm for MUMPS to avoid any tag mismatches. */
2118: MPI_Comm comm;
2119: PetscCallMPI(MPI_Comm_dup(mumps->mumps_comm, &comm));
2120: mumps->mumps_comm = comm;
2121: } else PetscCall(PetscCommGetComm(mumps->petsc_comm, &mumps->mumps_comm));
2122: }
2124: mumps->id.comm_fortran = MPI_Comm_c2f(mumps->mumps_comm);
2125: mumps->id.job = JOB_INIT;
2126: mumps->id.par = 1; /* host participates factorizaton and solve */
2127: mumps->id.sym = mumps->sym;
2129: size = mumps->id.size_schur;
2130: arr = mumps->id.schur;
2131: listvar_schur = mumps->id.listvar_schur;
2132: PetscMUMPS_c(mumps);
2133: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "MUMPS error: INFOG(1)=%d " MUMPS_MANUALS, mumps->id.INFOG(1));
2135: /* set PETSc-MUMPS default options - override MUMPS default */
2136: mumps->id.ICNTL(3) = 0;
2137: mumps->id.ICNTL(4) = 0;
2138: if (mumps->petsc_size == 1) {
2139: mumps->id.ICNTL(18) = 0; /* centralized assembled matrix input */
2140: mumps->id.ICNTL(7) = 7; /* automatic choice of ordering done by the package */
2141: } else {
2142: mumps->id.ICNTL(18) = 3; /* distributed assembled matrix input */
2143: mumps->id.ICNTL(21) = 1; /* distributed solution */
2144: }
2146: /* restore cached ICNTL and CNTL values */
2147: for (icntl = 0; icntl < nICNTL_pre; ++icntl) mumps->id.ICNTL(mumps->ICNTL_pre[1 + 2 * icntl]) = mumps->ICNTL_pre[2 + 2 * icntl];
2148: for (icntl = 0; icntl < nCNTL_pre; ++icntl) mumps->id.CNTL((PetscInt)mumps->CNTL_pre[1 + 2 * icntl]) = mumps->CNTL_pre[2 + 2 * icntl];
2149: PetscCall(PetscFree(mumps->ICNTL_pre));
2150: PetscCall(PetscFree(mumps->CNTL_pre));
2152: if (schur) {
2153: mumps->id.size_schur = size;
2154: mumps->id.schur_lld = size;
2155: mumps->id.schur = arr;
2156: mumps->id.listvar_schur = listvar_schur;
2157: if (mumps->petsc_size > 1) {
2158: PetscBool gs; /* gs is false if any rank other than root has non-empty IS */
2160: mumps->id.ICNTL(19) = 1; /* MUMPS returns Schur centralized on the host */
2161: gs = mumps->myid ? (mumps->id.size_schur ? PETSC_FALSE : PETSC_TRUE) : PETSC_TRUE; /* always true on root; false on others if their size != 0 */
2162: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &gs, 1, MPIU_BOOL, MPI_LAND, mumps->petsc_comm));
2163: PetscCheck(gs, PETSC_COMM_SELF, PETSC_ERR_SUP, "MUMPS distributed parallel Schur complements not yet supported from PETSc");
2164: } else {
2165: if (F->factortype == MAT_FACTOR_LU) {
2166: mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */
2167: } else {
2168: mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */
2169: }
2170: }
2171: mumps->id.ICNTL(26) = -1;
2172: }
2174: /* copy MUMPS default control values from master to slaves. Although slaves do not call MUMPS, they may access these values in code.
2175: For example, ICNTL(9) is initialized to 1 by MUMPS and slaves check ICNTL(9) in MatSolve_MUMPS.
2176: */
2177: PetscCallMPI(MPI_Bcast(mumps->id.icntl, 40, MPI_INT, 0, mumps->omp_comm));
2178: PetscCallMPI(MPI_Bcast(mumps->id.cntl, 15, MPIU_REAL, 0, mumps->omp_comm));
2180: mumps->scat_rhs = NULL;
2181: mumps->scat_sol = NULL;
2182: }
2183: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_1", "ICNTL(1): output stream for error messages", "None", mumps->id.ICNTL(1), &icntl, &flg));
2184: if (flg) mumps->id.ICNTL(1) = icntl;
2185: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_2", "ICNTL(2): output stream for diagnostic printing, statistics, and warning", "None", mumps->id.ICNTL(2), &icntl, &flg));
2186: if (flg) mumps->id.ICNTL(2) = icntl;
2187: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_3", "ICNTL(3): output stream for global information, collected on the host", "None", mumps->id.ICNTL(3), &icntl, &flg));
2188: if (flg) mumps->id.ICNTL(3) = icntl;
2190: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_4", "ICNTL(4): level of printing (0 to 4)", "None", mumps->id.ICNTL(4), &icntl, &flg));
2191: if (flg) mumps->id.ICNTL(4) = icntl;
2192: if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */
2194: 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));
2195: if (flg) mumps->id.ICNTL(6) = icntl;
2197: 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));
2198: if (flg) {
2199: 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");
2200: mumps->id.ICNTL(7) = icntl;
2201: }
2203: 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));
2204: /* 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() */
2205: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_10", "ICNTL(10): max num of refinements", "None", mumps->id.ICNTL(10), &mumps->id.ICNTL(10), NULL));
2206: 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));
2207: 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));
2208: 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));
2209: 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));
2210: PetscCall(MatGetBlockSizes(A, &rbs, &cbs));
2211: if (rbs == cbs && rbs > 1) mumps->id.ICNTL(15) = (PetscMUMPSInt)-rbs;
2212: 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));
2213: if (flg) {
2214: PetscCheck(mumps->id.ICNTL(15) <= 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Positive -mat_mumps_icntl_15 not handled");
2215: 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");
2216: }
2217: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_19", "ICNTL(19): computes the Schur complement", "None", mumps->id.ICNTL(19), &mumps->id.ICNTL(19), NULL));
2218: if (mumps->id.ICNTL(19) <= 0 || mumps->id.ICNTL(19) > 3) { /* reset any schur data (if any) */
2219: PetscCall(MatDestroy(&F->schur));
2220: PetscCall(MatMumpsResetSchur_Private(mumps));
2221: }
2223: /* Two MPICH Fortran MPI_IN_PLACE binding bugs prevented the use of 'mpich + mumps'. One happened with "mpi4py + mpich + mumps",
2224: and was reported by Firedrake. See https://bitbucket.org/mpi4py/mpi4py/issues/162/mpi4py-initialization-breaks-fortran
2225: and a petsc-maint mailing list thread with subject 'MUMPS segfaults in parallel because of ...'
2226: This bug was fixed by https://github.com/pmodels/mpich/pull/4149. But the fix brought a new bug,
2227: see https://github.com/pmodels/mpich/issues/5589. This bug was fixed by https://github.com/pmodels/mpich/pull/5590.
2228: In short, we could not use distributed RHS until with MPICH v4.0b1 or we enabled a workaround in mumps-5.6.2+
2229: */
2230: #if PETSC_PKG_MUMPS_VERSION_GE(5, 6, 2) && defined(PETSC_HAVE_MUMPS_AVOID_MPI_IN_PLACE)
2231: mumps->ICNTL20 = 10;
2232: #elif PETSC_PKG_MUMPS_VERSION_LT(5, 3, 0) || (defined(PETSC_HAVE_MPICH) && (MPICH_NUMVERSION < 40000101))
2233: mumps->ICNTL20 = 0; /* Centralized dense RHS*/
2234: #else
2235: mumps->ICNTL20 = 10; /* Distributed dense RHS*/
2236: #endif
2237: 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));
2238: 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);
2239: #if PETSC_PKG_MUMPS_VERSION_LT(5, 3, 0)
2240: PetscCheck(!flg || mumps->ICNTL20 != 10, PETSC_COMM_SELF, PETSC_ERR_SUP, "ICNTL(20)=10 is not supported before MUMPS-5.3.0");
2241: #endif
2242: /* 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 */
2244: 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));
2245: 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));
2246: 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));
2247: if (mumps->id.ICNTL(24)) { mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */ }
2249: 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));
2250: 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));
2251: 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));
2252: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_28", "ICNTL(28): use 1 for sequential analysis and ICNTL(7) ordering, or 2 for parallel analysis and ICNTL(29) ordering", "None", mumps->id.ICNTL(28), &mumps->id.ICNTL(28), NULL));
2253: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_29", "ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis", "None", mumps->id.ICNTL(29), &mumps->id.ICNTL(29), NULL));
2254: /* 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 */
2255: 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));
2256: /* PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_32","ICNTL(32): performs the forward elimination of the right-hand sides during factorization","None",mumps->id.ICNTL(32),&mumps->id.ICNTL(32),NULL)); -- not supported by PETSc API */
2257: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_33", "ICNTL(33): compute determinant", "None", mumps->id.ICNTL(33), &mumps->id.ICNTL(33), NULL));
2258: 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));
2259: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_36", "ICNTL(36): choice of BLR factorization variant", "None", mumps->id.ICNTL(36), &mumps->id.ICNTL(36), NULL));
2260: 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));
2261: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_48", "ICNTL(48): multithreading with tree parallelism", "None", mumps->id.ICNTL(48), &mumps->id.ICNTL(48), NULL));
2262: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_58", "ICNTL(58): defines options for symbolic factorization", "None", mumps->id.ICNTL(58), &mumps->id.ICNTL(58), NULL));
2264: PetscCall(PetscOptionsReal("-mat_mumps_cntl_1", "CNTL(1): relative pivoting threshold", "None", mumps->id.CNTL(1), &mumps->id.CNTL(1), NULL));
2265: PetscCall(PetscOptionsReal("-mat_mumps_cntl_2", "CNTL(2): stopping criterion of refinement", "None", mumps->id.CNTL(2), &mumps->id.CNTL(2), NULL));
2266: PetscCall(PetscOptionsReal("-mat_mumps_cntl_3", "CNTL(3): absolute pivoting threshold", "None", mumps->id.CNTL(3), &mumps->id.CNTL(3), NULL));
2267: PetscCall(PetscOptionsReal("-mat_mumps_cntl_4", "CNTL(4): value for static pivoting", "None", mumps->id.CNTL(4), &mumps->id.CNTL(4), NULL));
2268: PetscCall(PetscOptionsReal("-mat_mumps_cntl_5", "CNTL(5): fixation for null pivots", "None", mumps->id.CNTL(5), &mumps->id.CNTL(5), NULL));
2269: PetscCall(PetscOptionsReal("-mat_mumps_cntl_7", "CNTL(7): dropping parameter used during BLR", "None", mumps->id.CNTL(7), &mumps->id.CNTL(7), NULL));
2271: 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));
2273: PetscCall(PetscOptionsIntArray("-mat_mumps_view_info", "request INFO local to each processor", "", info, &ninfo, NULL));
2274: if (ninfo) {
2275: PetscCheck(ninfo <= 80, PETSC_COMM_SELF, PETSC_ERR_USER, "number of INFO %" PetscInt_FMT " must <= 80", ninfo);
2276: PetscCall(PetscMalloc1(ninfo, &mumps->info));
2277: mumps->ninfo = ninfo;
2278: for (i = 0; i < ninfo; i++) {
2279: PetscCheck(info[i] >= 0 && info[i] <= 80, PETSC_COMM_SELF, PETSC_ERR_USER, "index of INFO %" PetscInt_FMT " must between 1 and 80", ninfo);
2280: mumps->info[i] = info[i];
2281: }
2282: }
2283: PetscOptionsEnd();
2284: PetscFunctionReturn(PETSC_SUCCESS);
2285: }
2287: static PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F, Mat A, PETSC_UNUSED const MatFactorInfo *info, Mat_MUMPS *mumps)
2288: {
2289: PetscFunctionBegin;
2290: if (mumps->id.INFOG(1) < 0) {
2291: PetscCheck(!A->erroriffailure, PETSC_COMM_SELF, PETSC_ERR_LIB, "MUMPS error in analysis: INFOG(1)=%d " MUMPS_MANUALS, mumps->id.INFOG(1));
2292: if (mumps->id.INFOG(1) == -6) {
2293: PetscCall(PetscInfo(F, "MUMPS error in analysis: matrix is singular, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
2294: F->factorerrortype = MAT_FACTOR_STRUCT_ZEROPIVOT;
2295: } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) {
2296: PetscCall(PetscInfo(F, "MUMPS error in analysis: problem with work array, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
2297: F->factorerrortype = MAT_FACTOR_OUTMEMORY;
2298: } else if (mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0) {
2299: PetscCall(PetscInfo(F, "MUMPS error in analysis: empty matrix\n"));
2300: } else {
2301: PetscCall(PetscInfo(F, "MUMPS error in analysis: INFOG(1)=%d, INFO(2)=%d " MUMPS_MANUALS "\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
2302: F->factorerrortype = MAT_FACTOR_OTHER;
2303: }
2304: }
2305: if (!mumps->id.n) F->factorerrortype = MAT_FACTOR_NOERROR;
2306: PetscFunctionReturn(PETSC_SUCCESS);
2307: }
2309: static PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F, Mat A, IS r, PETSC_UNUSED IS c, const MatFactorInfo *info)
2310: {
2311: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2312: Vec b;
2313: const PetscInt M = A->rmap->N;
2315: PetscFunctionBegin;
2316: if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2317: /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */
2318: PetscFunctionReturn(PETSC_SUCCESS);
2319: }
2321: /* Set MUMPS options from the options database */
2322: PetscCall(MatSetFromOptions_MUMPS(F, A));
2324: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2325: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));
2327: /* analysis phase */
2328: mumps->id.job = JOB_FACTSYMBOLIC;
2329: PetscCall(PetscMUMPSIntCast(M, &mumps->id.n));
2330: switch (mumps->id.ICNTL(18)) {
2331: case 0: /* centralized assembled matrix input */
2332: if (!mumps->myid) {
2333: mumps->id.nnz = mumps->nnz;
2334: mumps->id.irn = mumps->irn;
2335: mumps->id.jcn = mumps->jcn;
2336: if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2337: if (r && mumps->id.ICNTL(7) == 7) {
2338: mumps->id.ICNTL(7) = 1;
2339: if (!mumps->myid) {
2340: const PetscInt *idx;
2341: PetscInt i;
2343: PetscCall(PetscMalloc1(M, &mumps->id.perm_in));
2344: PetscCall(ISGetIndices(r, &idx));
2345: for (i = 0; i < M; i++) PetscCall(PetscMUMPSIntCast(idx[i] + 1, &mumps->id.perm_in[i])); /* perm_in[]: start from 1, not 0! */
2346: PetscCall(ISRestoreIndices(r, &idx));
2347: }
2348: }
2349: }
2350: break;
2351: case 3: /* distributed assembled matrix input (size>1) */
2352: mumps->id.nnz_loc = mumps->nnz;
2353: mumps->id.irn_loc = mumps->irn;
2354: mumps->id.jcn_loc = mumps->jcn;
2355: if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2356: if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2357: PetscCall(MatCreateVecs(A, NULL, &b));
2358: PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2359: PetscCall(VecDestroy(&b));
2360: }
2361: break;
2362: }
2363: PetscMUMPS_c(mumps);
2364: PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));
2366: F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
2367: F->ops->solve = MatSolve_MUMPS;
2368: F->ops->solvetranspose = MatSolveTranspose_MUMPS;
2369: F->ops->matsolve = MatMatSolve_MUMPS;
2370: F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS;
2371: F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;
2373: mumps->matstruc = SAME_NONZERO_PATTERN;
2374: PetscFunctionReturn(PETSC_SUCCESS);
2375: }
2377: /* Note the Petsc r and c permutations are ignored */
2378: static PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F, Mat A, PETSC_UNUSED IS r, PETSC_UNUSED IS c, const MatFactorInfo *info)
2379: {
2380: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2381: Vec b;
2382: const PetscInt M = A->rmap->N;
2384: PetscFunctionBegin;
2385: if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2386: /* F is assembled by a previous call of MatLUFactorSymbolic_BAIJMUMPS() */
2387: PetscFunctionReturn(PETSC_SUCCESS);
2388: }
2390: /* Set MUMPS options from the options database */
2391: PetscCall(MatSetFromOptions_MUMPS(F, A));
2393: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2394: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));
2396: /* analysis phase */
2397: mumps->id.job = JOB_FACTSYMBOLIC;
2398: PetscCall(PetscMUMPSIntCast(M, &mumps->id.n));
2399: switch (mumps->id.ICNTL(18)) {
2400: case 0: /* centralized assembled matrix input */
2401: if (!mumps->myid) {
2402: mumps->id.nnz = mumps->nnz;
2403: mumps->id.irn = mumps->irn;
2404: mumps->id.jcn = mumps->jcn;
2405: if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2406: }
2407: break;
2408: case 3: /* distributed assembled matrix input (size>1) */
2409: mumps->id.nnz_loc = mumps->nnz;
2410: mumps->id.irn_loc = mumps->irn;
2411: mumps->id.jcn_loc = mumps->jcn;
2412: if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2413: if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2414: PetscCall(MatCreateVecs(A, NULL, &b));
2415: PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2416: PetscCall(VecDestroy(&b));
2417: }
2418: break;
2419: }
2420: PetscMUMPS_c(mumps);
2421: PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));
2423: F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
2424: F->ops->solve = MatSolve_MUMPS;
2425: F->ops->solvetranspose = MatSolveTranspose_MUMPS;
2426: F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;
2428: mumps->matstruc = SAME_NONZERO_PATTERN;
2429: PetscFunctionReturn(PETSC_SUCCESS);
2430: }
2432: /* Note the Petsc r permutation and factor info are ignored */
2433: static PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F, Mat A, PETSC_UNUSED IS r, const MatFactorInfo *info)
2434: {
2435: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2436: Vec b;
2437: const PetscInt M = A->rmap->N;
2439: PetscFunctionBegin;
2440: if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2441: /* F is assembled by a previous call of MatCholeskyFactorSymbolic_MUMPS() */
2442: PetscFunctionReturn(PETSC_SUCCESS);
2443: }
2445: /* Set MUMPS options from the options database */
2446: PetscCall(MatSetFromOptions_MUMPS(F, A));
2448: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2449: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));
2451: /* analysis phase */
2452: mumps->id.job = JOB_FACTSYMBOLIC;
2453: PetscCall(PetscMUMPSIntCast(M, &mumps->id.n));
2454: switch (mumps->id.ICNTL(18)) {
2455: case 0: /* centralized assembled matrix input */
2456: if (!mumps->myid) {
2457: mumps->id.nnz = mumps->nnz;
2458: mumps->id.irn = mumps->irn;
2459: mumps->id.jcn = mumps->jcn;
2460: if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2461: }
2462: break;
2463: case 3: /* distributed assembled matrix input (size>1) */
2464: mumps->id.nnz_loc = mumps->nnz;
2465: mumps->id.irn_loc = mumps->irn;
2466: mumps->id.jcn_loc = mumps->jcn;
2467: if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2468: if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2469: PetscCall(MatCreateVecs(A, NULL, &b));
2470: PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2471: PetscCall(VecDestroy(&b));
2472: }
2473: break;
2474: }
2475: PetscMUMPS_c(mumps);
2476: PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));
2478: F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS;
2479: F->ops->solve = MatSolve_MUMPS;
2480: F->ops->solvetranspose = MatSolve_MUMPS;
2481: F->ops->matsolve = MatMatSolve_MUMPS;
2482: F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS;
2483: F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;
2484: #if defined(PETSC_USE_COMPLEX)
2485: F->ops->getinertia = NULL;
2486: #else
2487: F->ops->getinertia = MatGetInertia_SBAIJMUMPS;
2488: #endif
2490: mumps->matstruc = SAME_NONZERO_PATTERN;
2491: PetscFunctionReturn(PETSC_SUCCESS);
2492: }
2494: static PetscErrorCode MatView_MUMPS(Mat A, PetscViewer viewer)
2495: {
2496: PetscBool iascii;
2497: PetscViewerFormat format;
2498: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
2500: PetscFunctionBegin;
2501: /* check if matrix is mumps type */
2502: if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(PETSC_SUCCESS);
2504: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
2505: if (iascii) {
2506: PetscCall(PetscViewerGetFormat(viewer, &format));
2507: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
2508: PetscCall(PetscViewerASCIIPrintf(viewer, "MUMPS run parameters:\n"));
2509: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
2510: PetscCall(PetscViewerASCIIPrintf(viewer, " SYM (matrix type): %d\n", mumps->id.sym));
2511: PetscCall(PetscViewerASCIIPrintf(viewer, " PAR (host participation): %d\n", mumps->id.par));
2512: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(1) (output for error): %d\n", mumps->id.ICNTL(1)));
2513: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(2) (output of diagnostic msg): %d\n", mumps->id.ICNTL(2)));
2514: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(3) (output for global info): %d\n", mumps->id.ICNTL(3)));
2515: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(4) (level of printing): %d\n", mumps->id.ICNTL(4)));
2516: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(5) (input mat struct): %d\n", mumps->id.ICNTL(5)));
2517: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(6) (matrix prescaling): %d\n", mumps->id.ICNTL(6)));
2518: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(7) (sequential matrix ordering):%d\n", mumps->id.ICNTL(7)));
2519: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(8) (scaling strategy): %d\n", mumps->id.ICNTL(8)));
2520: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(10) (max num of refinements): %d\n", mumps->id.ICNTL(10)));
2521: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(11) (error analysis): %d\n", mumps->id.ICNTL(11)));
2522: if (mumps->id.ICNTL(11) > 0) {
2523: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(4) (inf norm of input mat): %g\n", (double)mumps->id.RINFOG(4)));
2524: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(5) (inf norm of solution): %g\n", (double)mumps->id.RINFOG(5)));
2525: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(6) (inf norm of residual): %g\n", (double)mumps->id.RINFOG(6)));
2526: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(7),RINFOG(8) (backward error est): %g, %g\n", (double)mumps->id.RINFOG(7), (double)mumps->id.RINFOG(8)));
2527: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(9) (error estimate): %g\n", (double)mumps->id.RINFOG(9)));
2528: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n", (double)mumps->id.RINFOG(10), (double)mumps->id.RINFOG(11)));
2529: }
2530: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(12) (efficiency control): %d\n", mumps->id.ICNTL(12)));
2531: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(13) (sequential factorization of the root node): %d\n", mumps->id.ICNTL(13)));
2532: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(14) (percentage of estimated workspace increase): %d\n", mumps->id.ICNTL(14)));
2533: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(15) (compression of the input matrix): %d\n", mumps->id.ICNTL(15)));
2534: /* ICNTL(15-17) not used */
2535: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(18) (input mat struct): %d\n", mumps->id.ICNTL(18)));
2536: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(19) (Schur complement info): %d\n", mumps->id.ICNTL(19)));
2537: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(20) (RHS sparse pattern): %d\n", mumps->id.ICNTL(20)));
2538: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(21) (solution struct): %d\n", mumps->id.ICNTL(21)));
2539: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(22) (in-core/out-of-core facility): %d\n", mumps->id.ICNTL(22)));
2540: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(23) (max size of memory can be allocated locally):%d\n", mumps->id.ICNTL(23)));
2542: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(24) (detection of null pivot rows): %d\n", mumps->id.ICNTL(24)));
2543: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(25) (computation of a null space basis): %d\n", mumps->id.ICNTL(25)));
2544: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(26) (Schur options for RHS or solution): %d\n", mumps->id.ICNTL(26)));
2545: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(27) (blocking size for multiple RHS): %d\n", mumps->id.ICNTL(27)));
2546: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(28) (use parallel or sequential ordering): %d\n", mumps->id.ICNTL(28)));
2547: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(29) (parallel ordering): %d\n", mumps->id.ICNTL(29)));
2549: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(30) (user-specified set of entries in inv(A)): %d\n", mumps->id.ICNTL(30)));
2550: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(31) (factors is discarded in the solve phase): %d\n", mumps->id.ICNTL(31)));
2551: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(33) (compute determinant): %d\n", mumps->id.ICNTL(33)));
2552: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(35) (activate BLR based factorization): %d\n", mumps->id.ICNTL(35)));
2553: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(36) (choice of BLR factorization variant): %d\n", mumps->id.ICNTL(36)));
2554: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(38) (estimated compression rate of LU factors): %d\n", mumps->id.ICNTL(38)));
2555: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(48) (multithreading with tree parallelism): %d\n", mumps->id.ICNTL(48)));
2556: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(58) (options for symbolic factorization): %d\n", mumps->id.ICNTL(58)));
2558: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(1) (relative pivoting threshold): %g\n", (double)mumps->id.CNTL(1)));
2559: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(2) (stopping criterion of refinement): %g\n", (double)mumps->id.CNTL(2)));
2560: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(3) (absolute pivoting threshold): %g\n", (double)mumps->id.CNTL(3)));
2561: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(4) (value of static pivoting): %g\n", (double)mumps->id.CNTL(4)));
2562: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(5) (fixation for null pivots): %g\n", (double)mumps->id.CNTL(5)));
2563: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(7) (dropping parameter for BLR): %g\n", (double)mumps->id.CNTL(7)));
2565: /* information local to each processor */
2566: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(1) (local estimated flops for the elimination after analysis):\n"));
2567: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
2568: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %g\n", mumps->myid, (double)mumps->id.RINFO(1)));
2569: PetscCall(PetscViewerFlush(viewer));
2570: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(2) (local estimated flops for the assembly after factorization):\n"));
2571: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %g\n", mumps->myid, (double)mumps->id.RINFO(2)));
2572: PetscCall(PetscViewerFlush(viewer));
2573: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(3) (local estimated flops for the elimination after factorization):\n"));
2574: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %g\n", mumps->myid, (double)mumps->id.RINFO(3)));
2575: PetscCall(PetscViewerFlush(viewer));
2577: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization):\n"));
2578: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(15)));
2579: PetscCall(PetscViewerFlush(viewer));
2581: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization):\n"));
2582: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(16)));
2583: PetscCall(PetscViewerFlush(viewer));
2585: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(23) (num of pivots eliminated on this processor after factorization):\n"));
2586: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(23)));
2587: PetscCall(PetscViewerFlush(viewer));
2589: if (mumps->ninfo && mumps->ninfo <= 80) {
2590: PetscInt i;
2591: for (i = 0; i < mumps->ninfo; i++) {
2592: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(%" PetscInt_FMT "):\n", mumps->info[i]));
2593: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(mumps->info[i])));
2594: PetscCall(PetscViewerFlush(viewer));
2595: }
2596: }
2597: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
2598: } else PetscCall(PetscViewerASCIIPrintf(viewer, " Use -%sksp_view ::ascii_info_detail to display information for all processes\n", ((PetscObject)A)->prefix ? ((PetscObject)A)->prefix : ""));
2600: if (mumps->myid == 0) { /* information from the host */
2601: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(1) (global estimated flops for the elimination after analysis): %g\n", (double)mumps->id.RINFOG(1)));
2602: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(2) (global estimated flops for the assembly after factorization): %g\n", (double)mumps->id.RINFOG(2)));
2603: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(3) (global estimated flops for the elimination after factorization): %g\n", (double)mumps->id.RINFOG(3)));
2604: PetscCall(PetscViewerASCIIPrintf(viewer, " (RINFOG(12) RINFOG(13))*2^INFOG(34) (determinant): (%g,%g)*(2^%d)\n", (double)mumps->id.RINFOG(12), (double)mumps->id.RINFOG(13), mumps->id.INFOG(34)));
2606: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(3) (estimated real workspace for factors on all processors after analysis): %d\n", mumps->id.INFOG(3)));
2607: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d\n", mumps->id.INFOG(4)));
2608: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(5) (estimated maximum front size in the complete tree): %d\n", mumps->id.INFOG(5)));
2609: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(6) (number of nodes in the complete tree): %d\n", mumps->id.INFOG(6)));
2610: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(7) (ordering option effectively used after analysis): %d\n", mumps->id.INFOG(7)));
2611: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d\n", mumps->id.INFOG(8)));
2612: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d\n", mumps->id.INFOG(9)));
2613: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(10) (total integer space store the matrix factors after factorization): %d\n", mumps->id.INFOG(10)));
2614: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(11) (order of largest frontal matrix after factorization): %d\n", mumps->id.INFOG(11)));
2615: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(12) (number of off-diagonal pivots): %d\n", mumps->id.INFOG(12)));
2616: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(13) (number of delayed pivots after factorization): %d\n", mumps->id.INFOG(13)));
2617: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(14) (number of memory compress after factorization): %d\n", mumps->id.INFOG(14)));
2618: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(15) (number of steps of iterative refinement after solution): %d\n", mumps->id.INFOG(15)));
2619: 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)));
2620: 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)));
2621: 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)));
2622: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): %d\n", mumps->id.INFOG(19)));
2623: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(20) (estimated number of entries in the factors): %d\n", mumps->id.INFOG(20)));
2624: 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)));
2625: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): %d\n", mumps->id.INFOG(22)));
2626: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d\n", mumps->id.INFOG(23)));
2627: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d\n", mumps->id.INFOG(24)));
2628: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(25) (after factorization: number of pivots modified by static pivoting): %d\n", mumps->id.INFOG(25)));
2629: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(28) (after factorization: number of null pivots encountered): %d\n", mumps->id.INFOG(28)));
2630: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(29) (after factorization: effective number of entries in the factors (sum over all processors)): %d\n", mumps->id.INFOG(29)));
2631: 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)));
2632: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(32) (after analysis: type of analysis done): %d\n", mumps->id.INFOG(32)));
2633: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(33) (value used for ICNTL(8)): %d\n", mumps->id.INFOG(33)));
2634: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(34) (exponent of the determinant if determinant is requested): %d\n", mumps->id.INFOG(34)));
2635: 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)));
2636: 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)));
2637: 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)));
2638: 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)));
2639: 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)));
2640: }
2641: }
2642: }
2643: PetscFunctionReturn(PETSC_SUCCESS);
2644: }
2646: static PetscErrorCode MatGetInfo_MUMPS(Mat A, PETSC_UNUSED MatInfoType flag, MatInfo *info)
2647: {
2648: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
2650: PetscFunctionBegin;
2651: info->block_size = 1.0;
2652: info->nz_allocated = mumps->id.INFOG(20) >= 0 ? mumps->id.INFOG(20) : -1000000 * mumps->id.INFOG(20);
2653: info->nz_used = mumps->id.INFOG(20) >= 0 ? mumps->id.INFOG(20) : -1000000 * mumps->id.INFOG(20);
2654: info->nz_unneeded = 0.0;
2655: info->assemblies = 0.0;
2656: info->mallocs = 0.0;
2657: info->memory = 0.0;
2658: info->fill_ratio_given = 0;
2659: info->fill_ratio_needed = 0;
2660: info->factor_mallocs = 0;
2661: PetscFunctionReturn(PETSC_SUCCESS);
2662: }
2664: static PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is)
2665: {
2666: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2667: const PetscScalar *arr;
2668: const PetscInt *idxs;
2669: PetscInt size, i;
2671: PetscFunctionBegin;
2672: PetscCall(ISGetLocalSize(is, &size));
2673: /* Schur complement matrix */
2674: PetscCall(MatDestroy(&F->schur));
2675: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, size, size, NULL, &F->schur));
2676: PetscCall(MatDenseGetArrayRead(F->schur, &arr));
2677: mumps->id.schur = (MumpsScalar *)arr;
2678: PetscCall(PetscMUMPSIntCast(size, &mumps->id.size_schur));
2679: PetscCall(PetscMUMPSIntCast(size, &mumps->id.schur_lld));
2680: PetscCall(MatDenseRestoreArrayRead(F->schur, &arr));
2681: if (mumps->sym == 1) PetscCall(MatSetOption(F->schur, MAT_SPD, PETSC_TRUE));
2683: /* MUMPS expects Fortran style indices */
2684: PetscCall(PetscFree(mumps->id.listvar_schur));
2685: PetscCall(PetscMalloc1(size, &mumps->id.listvar_schur));
2686: PetscCall(ISGetIndices(is, &idxs));
2687: for (i = 0; i < size; i++) PetscCall(PetscMUMPSIntCast(idxs[i] + 1, &mumps->id.listvar_schur[i]));
2688: PetscCall(ISRestoreIndices(is, &idxs));
2689: /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */
2690: mumps->id.ICNTL(26) = -1;
2691: PetscFunctionReturn(PETSC_SUCCESS);
2692: }
2694: static PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F, Mat *S)
2695: {
2696: Mat St;
2697: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2698: PetscScalar *array;
2700: PetscFunctionBegin;
2701: PetscCheck(mumps->id.ICNTL(19), PetscObjectComm((PetscObject)F), PETSC_ERR_ORDER, "Schur complement mode not selected! Call MatFactorSetSchurIS() to enable it");
2702: PetscCall(MatCreate(PETSC_COMM_SELF, &St));
2703: PetscCall(MatSetSizes(St, PETSC_DECIDE, PETSC_DECIDE, mumps->id.size_schur, mumps->id.size_schur));
2704: PetscCall(MatSetType(St, MATDENSE));
2705: PetscCall(MatSetUp(St));
2706: PetscCall(MatDenseGetArray(St, &array));
2707: if (!mumps->sym) { /* MUMPS always return a full matrix */
2708: if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
2709: PetscInt i, j, N = mumps->id.size_schur;
2710: for (i = 0; i < N; i++) {
2711: for (j = 0; j < N; j++) {
2712: #if !defined(PETSC_USE_COMPLEX)
2713: PetscScalar val = mumps->id.schur[i * N + j];
2714: #else
2715: PetscScalar val = mumps->id.schur[i * N + j].r + PETSC_i * mumps->id.schur[i * N + j].i;
2716: #endif
2717: array[j * N + i] = val;
2718: }
2719: }
2720: } else { /* stored by columns */
2721: PetscCall(PetscArraycpy(array, mumps->id.schur, mumps->id.size_schur * mumps->id.size_schur));
2722: }
2723: } else { /* either full or lower-triangular (not packed) */
2724: if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */
2725: PetscInt i, j, N = mumps->id.size_schur;
2726: for (i = 0; i < N; i++) {
2727: for (j = i; j < N; j++) {
2728: #if !defined(PETSC_USE_COMPLEX)
2729: PetscScalar val = mumps->id.schur[i * N + j];
2730: #else
2731: PetscScalar val = mumps->id.schur[i * N + j].r + PETSC_i * mumps->id.schur[i * N + j].i;
2732: #endif
2733: array[i * N + j] = array[j * N + i] = val;
2734: }
2735: }
2736: } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */
2737: PetscCall(PetscArraycpy(array, mumps->id.schur, mumps->id.size_schur * mumps->id.size_schur));
2738: } else { /* ICNTL(19) == 1 lower triangular stored by rows */
2739: PetscInt i, j, N = mumps->id.size_schur;
2740: for (i = 0; i < N; i++) {
2741: for (j = 0; j < i + 1; j++) {
2742: #if !defined(PETSC_USE_COMPLEX)
2743: PetscScalar val = mumps->id.schur[i * N + j];
2744: #else
2745: PetscScalar val = mumps->id.schur[i * N + j].r + PETSC_i * mumps->id.schur[i * N + j].i;
2746: #endif
2747: array[i * N + j] = array[j * N + i] = val;
2748: }
2749: }
2750: }
2751: }
2752: PetscCall(MatDenseRestoreArray(St, &array));
2753: *S = St;
2754: PetscFunctionReturn(PETSC_SUCCESS);
2755: }
2757: static PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F, PetscInt icntl, PetscInt ival)
2758: {
2759: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2761: PetscFunctionBegin;
2762: if (mumps->id.job == JOB_NULL) { /* need to cache icntl and ival since PetscMUMPS_c() has never been called */
2763: PetscMUMPSInt i, nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0; /* number of already cached ICNTL */
2764: for (i = 0; i < nICNTL_pre; ++i)
2765: if (mumps->ICNTL_pre[1 + 2 * i] == icntl) break; /* is this ICNTL already cached? */
2766: if (i == nICNTL_pre) { /* not already cached */
2767: if (i > 0) PetscCall(PetscRealloc(sizeof(PetscMUMPSInt) * (2 * nICNTL_pre + 3), &mumps->ICNTL_pre));
2768: else PetscCall(PetscCalloc(sizeof(PetscMUMPSInt) * 3, &mumps->ICNTL_pre));
2769: mumps->ICNTL_pre[0]++;
2770: }
2771: mumps->ICNTL_pre[1 + 2 * i] = (PetscMUMPSInt)icntl;
2772: PetscCall(PetscMUMPSIntCast(ival, mumps->ICNTL_pre + 2 + 2 * i));
2773: } else PetscCall(PetscMUMPSIntCast(ival, &mumps->id.ICNTL(icntl)));
2774: PetscFunctionReturn(PETSC_SUCCESS);
2775: }
2777: static PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F, PetscInt icntl, PetscInt *ival)
2778: {
2779: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2781: PetscFunctionBegin;
2782: if (mumps->id.job == JOB_NULL) {
2783: PetscInt i, nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0;
2784: *ival = 0;
2785: for (i = 0; i < nICNTL_pre; ++i) {
2786: if (mumps->ICNTL_pre[1 + 2 * i] == icntl) *ival = mumps->ICNTL_pre[2 + 2 * i];
2787: }
2788: } else *ival = mumps->id.ICNTL(icntl);
2789: PetscFunctionReturn(PETSC_SUCCESS);
2790: }
2792: /*@
2793: MatMumpsSetIcntl - Set MUMPS parameter ICNTL() <https://mumps-solver.org/index.php?page=doc>
2795: Logically Collective
2797: Input Parameters:
2798: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2799: . icntl - index of MUMPS parameter array ICNTL()
2800: - ival - value of MUMPS ICNTL(icntl)
2802: Options Database Key:
2803: . -mat_mumps_icntl_<icntl> <ival> - change the option numbered icntl to ival
2805: Level: beginner
2807: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2808: @*/
2809: PetscErrorCode MatMumpsSetIcntl(Mat F, PetscInt icntl, PetscInt ival)
2810: {
2811: PetscFunctionBegin;
2813: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2816: PetscCheck((icntl >= 1 && icntl <= 38) || icntl == 48 || icntl == 58, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported ICNTL value %" PetscInt_FMT, icntl);
2817: PetscTryMethod(F, "MatMumpsSetIcntl_C", (Mat, PetscInt, PetscInt), (F, icntl, ival));
2818: PetscFunctionReturn(PETSC_SUCCESS);
2819: }
2821: /*@
2822: MatMumpsGetIcntl - Get MUMPS parameter ICNTL() <https://mumps-solver.org/index.php?page=doc>
2824: Logically Collective
2826: Input Parameters:
2827: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2828: - icntl - index of MUMPS parameter array ICNTL()
2830: Output Parameter:
2831: . ival - value of MUMPS ICNTL(icntl)
2833: Level: beginner
2835: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2836: @*/
2837: PetscErrorCode MatMumpsGetIcntl(Mat F, PetscInt icntl, PetscInt *ival)
2838: {
2839: PetscFunctionBegin;
2841: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2843: PetscAssertPointer(ival, 3);
2844: PetscCheck((icntl >= 1 && icntl <= 38) || icntl == 48 || icntl == 58, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported ICNTL value %" PetscInt_FMT, icntl);
2845: PetscUseMethod(F, "MatMumpsGetIcntl_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
2846: PetscFunctionReturn(PETSC_SUCCESS);
2847: }
2849: static PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F, PetscInt icntl, PetscReal val)
2850: {
2851: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2853: PetscFunctionBegin;
2854: if (mumps->id.job == JOB_NULL) {
2855: PetscInt i, nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
2856: for (i = 0; i < nCNTL_pre; ++i)
2857: if (mumps->CNTL_pre[1 + 2 * i] == icntl) break;
2858: if (i == nCNTL_pre) {
2859: if (i > 0) PetscCall(PetscRealloc(sizeof(PetscReal) * (2 * nCNTL_pre + 3), &mumps->CNTL_pre));
2860: else PetscCall(PetscCalloc(sizeof(PetscReal) * 3, &mumps->CNTL_pre));
2861: mumps->CNTL_pre[0]++;
2862: }
2863: mumps->CNTL_pre[1 + 2 * i] = icntl;
2864: mumps->CNTL_pre[2 + 2 * i] = val;
2865: } else mumps->id.CNTL(icntl) = val;
2866: PetscFunctionReturn(PETSC_SUCCESS);
2867: }
2869: static PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F, PetscInt icntl, PetscReal *val)
2870: {
2871: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2873: PetscFunctionBegin;
2874: if (mumps->id.job == JOB_NULL) {
2875: PetscInt i, nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
2876: *val = 0.0;
2877: for (i = 0; i < nCNTL_pre; ++i) {
2878: if (mumps->CNTL_pre[1 + 2 * i] == icntl) *val = mumps->CNTL_pre[2 + 2 * i];
2879: }
2880: } else *val = mumps->id.CNTL(icntl);
2881: PetscFunctionReturn(PETSC_SUCCESS);
2882: }
2884: /*@
2885: MatMumpsSetCntl - Set MUMPS parameter CNTL() <https://mumps-solver.org/index.php?page=doc>
2887: Logically Collective
2889: Input Parameters:
2890: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2891: . icntl - index of MUMPS parameter array CNTL()
2892: - val - value of MUMPS CNTL(icntl)
2894: Options Database Key:
2895: . -mat_mumps_cntl_<icntl> <val> - change the option numbered icntl to ival
2897: Level: beginner
2899: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2900: @*/
2901: PetscErrorCode MatMumpsSetCntl(Mat F, PetscInt icntl, PetscReal val)
2902: {
2903: PetscFunctionBegin;
2905: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2908: PetscCheck(icntl >= 1 && icntl <= 7, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported CNTL value %" PetscInt_FMT, icntl);
2909: PetscTryMethod(F, "MatMumpsSetCntl_C", (Mat, PetscInt, PetscReal), (F, icntl, val));
2910: PetscFunctionReturn(PETSC_SUCCESS);
2911: }
2913: /*@
2914: MatMumpsGetCntl - Get MUMPS parameter CNTL() <https://mumps-solver.org/index.php?page=doc>
2916: Logically Collective
2918: Input Parameters:
2919: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2920: - icntl - index of MUMPS parameter array CNTL()
2922: Output Parameter:
2923: . val - value of MUMPS CNTL(icntl)
2925: Level: beginner
2927: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2928: @*/
2929: PetscErrorCode MatMumpsGetCntl(Mat F, PetscInt icntl, PetscReal *val)
2930: {
2931: PetscFunctionBegin;
2933: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2935: PetscAssertPointer(val, 3);
2936: PetscCheck(icntl >= 1 && icntl <= 7, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported CNTL value %" PetscInt_FMT, icntl);
2937: PetscUseMethod(F, "MatMumpsGetCntl_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
2938: PetscFunctionReturn(PETSC_SUCCESS);
2939: }
2941: static PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F, PetscInt icntl, PetscInt *info)
2942: {
2943: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2945: PetscFunctionBegin;
2946: *info = mumps->id.INFO(icntl);
2947: PetscFunctionReturn(PETSC_SUCCESS);
2948: }
2950: static PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F, PetscInt icntl, PetscInt *infog)
2951: {
2952: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2954: PetscFunctionBegin;
2955: *infog = mumps->id.INFOG(icntl);
2956: PetscFunctionReturn(PETSC_SUCCESS);
2957: }
2959: static PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F, PetscInt icntl, PetscReal *rinfo)
2960: {
2961: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2963: PetscFunctionBegin;
2964: *rinfo = mumps->id.RINFO(icntl);
2965: PetscFunctionReturn(PETSC_SUCCESS);
2966: }
2968: static PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F, PetscInt icntl, PetscReal *rinfog)
2969: {
2970: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2972: PetscFunctionBegin;
2973: *rinfog = mumps->id.RINFOG(icntl);
2974: PetscFunctionReturn(PETSC_SUCCESS);
2975: }
2977: static PetscErrorCode MatMumpsGetNullPivots_MUMPS(Mat F, PetscInt *size, PetscInt **array)
2978: {
2979: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2981: PetscFunctionBegin;
2982: 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");
2983: *size = 0;
2984: *array = NULL;
2985: if (!mumps->myid) {
2986: *size = mumps->id.INFOG(28);
2987: PetscCall(PetscMalloc1(*size, array));
2988: for (int i = 0; i < *size; i++) (*array)[i] = mumps->id.pivnul_list[i] - 1;
2989: }
2990: PetscFunctionReturn(PETSC_SUCCESS);
2991: }
2993: static PetscErrorCode MatMumpsGetInverse_MUMPS(Mat F, Mat spRHS)
2994: {
2995: Mat Bt = NULL, Btseq = NULL;
2996: PetscBool flg;
2997: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2998: PetscScalar *aa;
2999: PetscInt spnr, *ia, *ja, M, nrhs;
3001: PetscFunctionBegin;
3002: PetscAssertPointer(spRHS, 2);
3003: PetscCall(PetscObjectTypeCompare((PetscObject)spRHS, MATTRANSPOSEVIRTUAL, &flg));
3004: if (flg) {
3005: PetscCall(MatTransposeGetMat(spRHS, &Bt));
3006: } else SETERRQ(PetscObjectComm((PetscObject)spRHS), PETSC_ERR_ARG_WRONG, "Matrix spRHS must be type MATTRANSPOSEVIRTUAL matrix");
3008: PetscCall(MatMumpsSetIcntl(F, 30, 1));
3010: if (mumps->petsc_size > 1) {
3011: Mat_MPIAIJ *b = (Mat_MPIAIJ *)Bt->data;
3012: Btseq = b->A;
3013: } else {
3014: Btseq = Bt;
3015: }
3017: PetscCall(MatGetSize(spRHS, &M, &nrhs));
3018: mumps->id.nrhs = (PetscMUMPSInt)nrhs;
3019: PetscCall(PetscMUMPSIntCast(M, &mumps->id.lrhs));
3020: mumps->id.rhs = NULL;
3022: if (!mumps->myid) {
3023: PetscCall(MatSeqAIJGetArray(Btseq, &aa));
3024: PetscCall(MatGetRowIJ(Btseq, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
3025: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
3026: PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
3027: mumps->id.rhs_sparse = (MumpsScalar *)aa;
3028: } else {
3029: mumps->id.irhs_ptr = NULL;
3030: mumps->id.irhs_sparse = NULL;
3031: mumps->id.nz_rhs = 0;
3032: mumps->id.rhs_sparse = NULL;
3033: }
3034: mumps->id.ICNTL(20) = 1; /* rhs is sparse */
3035: mumps->id.ICNTL(21) = 0; /* solution is in assembled centralized format */
3037: /* solve phase */
3038: mumps->id.job = JOB_SOLVE;
3039: PetscMUMPS_c(mumps);
3040: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "MUMPS error in solve: INFOG(1)=%d INFO(2)=%d " MUMPS_MANUALS, mumps->id.INFOG(1), mumps->id.INFO(2));
3042: if (!mumps->myid) {
3043: PetscCall(MatSeqAIJRestoreArray(Btseq, &aa));
3044: PetscCall(MatRestoreRowIJ(Btseq, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
3045: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
3046: }
3047: PetscFunctionReturn(PETSC_SUCCESS);
3048: }
3050: /*@
3051: MatMumpsGetInverse - Get user-specified set of entries in inverse of `A` <https://mumps-solver.org/index.php?page=doc>
3053: Logically Collective
3055: Input Parameter:
3056: . F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3058: Output Parameter:
3059: . spRHS - sequential sparse matrix in `MATTRANSPOSEVIRTUAL` format with requested entries of inverse of `A`
3061: Level: beginner
3063: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCreateTranspose()`
3064: @*/
3065: PetscErrorCode MatMumpsGetInverse(Mat F, Mat spRHS)
3066: {
3067: PetscFunctionBegin;
3069: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3070: PetscUseMethod(F, "MatMumpsGetInverse_C", (Mat, Mat), (F, spRHS));
3071: PetscFunctionReturn(PETSC_SUCCESS);
3072: }
3074: static PetscErrorCode MatMumpsGetInverseTranspose_MUMPS(Mat F, Mat spRHST)
3075: {
3076: Mat spRHS;
3078: PetscFunctionBegin;
3079: PetscCall(MatCreateTranspose(spRHST, &spRHS));
3080: PetscCall(MatMumpsGetInverse_MUMPS(F, spRHS));
3081: PetscCall(MatDestroy(&spRHS));
3082: PetscFunctionReturn(PETSC_SUCCESS);
3083: }
3085: /*@
3086: MatMumpsGetInverseTranspose - Get user-specified set of entries in inverse of matrix $A^T $ <https://mumps-solver.org/index.php?page=doc>
3088: Logically Collective
3090: Input Parameter:
3091: . F - the factored matrix of A obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3093: Output Parameter:
3094: . spRHST - sequential sparse matrix in `MATAIJ` format containing the requested entries of inverse of `A`^T
3096: Level: beginner
3098: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCreateTranspose()`, `MatMumpsGetInverse()`
3099: @*/
3100: PetscErrorCode MatMumpsGetInverseTranspose(Mat F, Mat spRHST)
3101: {
3102: PetscBool flg;
3104: PetscFunctionBegin;
3106: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3107: PetscCall(PetscObjectTypeCompareAny((PetscObject)spRHST, &flg, MATSEQAIJ, MATMPIAIJ, NULL));
3108: PetscCheck(flg, PetscObjectComm((PetscObject)spRHST), PETSC_ERR_ARG_WRONG, "Matrix spRHST must be MATAIJ matrix");
3110: PetscUseMethod(F, "MatMumpsGetInverseTranspose_C", (Mat, Mat), (F, spRHST));
3111: PetscFunctionReturn(PETSC_SUCCESS);
3112: }
3114: /*@
3115: MatMumpsGetInfo - Get MUMPS parameter INFO() <https://mumps-solver.org/index.php?page=doc>
3117: Logically Collective
3119: Input Parameters:
3120: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3121: - icntl - index of MUMPS parameter array INFO()
3123: Output Parameter:
3124: . ival - value of MUMPS INFO(icntl)
3126: Level: beginner
3128: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
3129: @*/
3130: PetscErrorCode MatMumpsGetInfo(Mat F, PetscInt icntl, PetscInt *ival)
3131: {
3132: PetscFunctionBegin;
3134: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3135: PetscAssertPointer(ival, 3);
3136: PetscUseMethod(F, "MatMumpsGetInfo_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
3137: PetscFunctionReturn(PETSC_SUCCESS);
3138: }
3140: /*@
3141: MatMumpsGetInfog - Get MUMPS parameter INFOG() <https://mumps-solver.org/index.php?page=doc>
3143: Logically Collective
3145: Input Parameters:
3146: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3147: - icntl - index of MUMPS parameter array INFOG()
3149: Output Parameter:
3150: . ival - value of MUMPS INFOG(icntl)
3152: Level: beginner
3154: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
3155: @*/
3156: PetscErrorCode MatMumpsGetInfog(Mat F, PetscInt icntl, PetscInt *ival)
3157: {
3158: PetscFunctionBegin;
3160: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3161: PetscAssertPointer(ival, 3);
3162: PetscUseMethod(F, "MatMumpsGetInfog_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
3163: PetscFunctionReturn(PETSC_SUCCESS);
3164: }
3166: /*@
3167: MatMumpsGetRinfo - Get MUMPS parameter RINFO() <https://mumps-solver.org/index.php?page=doc>
3169: Logically Collective
3171: Input Parameters:
3172: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3173: - icntl - index of MUMPS parameter array RINFO()
3175: Output Parameter:
3176: . val - value of MUMPS RINFO(icntl)
3178: Level: beginner
3180: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfog()`
3181: @*/
3182: PetscErrorCode MatMumpsGetRinfo(Mat F, PetscInt icntl, PetscReal *val)
3183: {
3184: PetscFunctionBegin;
3186: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3187: PetscAssertPointer(val, 3);
3188: PetscUseMethod(F, "MatMumpsGetRinfo_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
3189: PetscFunctionReturn(PETSC_SUCCESS);
3190: }
3192: /*@
3193: MatMumpsGetRinfog - Get MUMPS parameter RINFOG() <https://mumps-solver.org/index.php?page=doc>
3195: Logically Collective
3197: Input Parameters:
3198: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3199: - icntl - index of MUMPS parameter array RINFOG()
3201: Output Parameter:
3202: . val - value of MUMPS RINFOG(icntl)
3204: Level: beginner
3206: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`
3207: @*/
3208: PetscErrorCode MatMumpsGetRinfog(Mat F, PetscInt icntl, PetscReal *val)
3209: {
3210: PetscFunctionBegin;
3212: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3213: PetscAssertPointer(val, 3);
3214: PetscUseMethod(F, "MatMumpsGetRinfog_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
3215: PetscFunctionReturn(PETSC_SUCCESS);
3216: }
3218: /*@
3219: MatMumpsGetNullPivots - Get MUMPS parameter PIVNUL_LIST() <https://mumps-solver.org/index.php?page=doc>
3221: Logically Collective
3223: Input Parameter:
3224: . F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3226: Output Parameters:
3227: + size - local size of the array. The size of the array is non-zero only on the host.
3228: - array - array of rows with null pivot, these rows follow 0-based indexing. The array gets allocated within the function and the user is responsible
3229: for freeing this array.
3231: Level: beginner
3233: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`
3234: @*/
3235: PetscErrorCode MatMumpsGetNullPivots(Mat F, PetscInt *size, PetscInt **array)
3236: {
3237: PetscFunctionBegin;
3239: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3240: PetscAssertPointer(size, 2);
3241: PetscAssertPointer(array, 3);
3242: PetscUseMethod(F, "MatMumpsGetNullPivots_C", (Mat, PetscInt *, PetscInt **), (F, size, array));
3243: PetscFunctionReturn(PETSC_SUCCESS);
3244: }
3246: /*MC
3247: MATSOLVERMUMPS - A matrix type providing direct solvers (LU and Cholesky) for
3248: distributed and sequential matrices via the external package MUMPS <https://mumps-solver.org/index.php?page=doc>
3250: Works with `MATAIJ` and `MATSBAIJ` matrices
3252: Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch to have PETSc installed with MUMPS
3254: 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.
3255: See details below.
3257: Use `-pc_type cholesky` or `lu` `-pc_factor_mat_solver_type mumps` to use this direct solver
3259: Options Database Keys:
3260: + -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages
3261: . -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning
3262: . -mat_mumps_icntl_3 - ICNTL(3): output stream for global information, collected on the host
3263: . -mat_mumps_icntl_4 - ICNTL(4): level of printing (0 to 4)
3264: . -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7)
3265: . -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
3266: Use -pc_factor_mat_ordering_type <type> to have PETSc perform the ordering (sequential only)
3267: . -mat_mumps_icntl_8 - ICNTL(8): scaling strategy (-2 to 8 or 77)
3268: . -mat_mumps_icntl_10 - ICNTL(10): max num of refinements
3269: . -mat_mumps_icntl_11 - ICNTL(11): statistics related to an error analysis (via -ksp_view)
3270: . -mat_mumps_icntl_12 - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3)
3271: . -mat_mumps_icntl_13 - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting
3272: . -mat_mumps_icntl_14 - ICNTL(14): percentage increase in the estimated working space
3273: . -mat_mumps_icntl_15 - ICNTL(15): compression of the input matrix resulting from a block format
3274: . -mat_mumps_icntl_19 - ICNTL(19): computes the Schur complement
3275: . -mat_mumps_icntl_20 - ICNTL(20): give MUMPS centralized (0) or distributed (10) dense RHS
3276: . -mat_mumps_icntl_22 - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1)
3277: . -mat_mumps_icntl_23 - ICNTL(23): max size of the working memory (MB) that can allocate per processor
3278: . -mat_mumps_icntl_24 - ICNTL(24): detection of null pivot rows (0 or 1)
3279: . -mat_mumps_icntl_25 - ICNTL(25): compute a solution of a deficient matrix and a null space basis
3280: . -mat_mumps_icntl_26 - ICNTL(26): drives the solution phase if a Schur complement matrix
3281: . -mat_mumps_icntl_28 - ICNTL(28): use 1 for sequential analysis and ICNTL(7) ordering, or 2 for parallel analysis and ICNTL(29) ordering
3282: . -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis
3283: . -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A)
3284: . -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization
3285: . -mat_mumps_icntl_33 - ICNTL(33): compute determinant
3286: . -mat_mumps_icntl_35 - ICNTL(35): level of activation of BLR (Block Low-Rank) feature
3287: . -mat_mumps_icntl_36 - ICNTL(36): controls the choice of BLR factorization variant
3288: . -mat_mumps_icntl_38 - ICNTL(38): sets the estimated compression rate of LU factors with BLR
3289: . -mat_mumps_icntl_48 - ICNTL(48): multithreading with tree parallelism
3290: . -mat_mumps_icntl_58 - ICNTL(58): options for symbolic factorization
3291: . -mat_mumps_cntl_1 - CNTL(1): relative pivoting threshold
3292: . -mat_mumps_cntl_2 - CNTL(2): stopping criterion of refinement
3293: . -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold
3294: . -mat_mumps_cntl_4 - CNTL(4): value for static pivoting
3295: . -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots
3296: . -mat_mumps_cntl_7 - CNTL(7): precision of the dropping parameter used during BLR factorization
3297: - -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.
3298: Default might be the number of cores per CPU package (socket) as reported by hwloc and suggested by the MUMPS manual.
3300: Level: beginner
3302: Notes:
3303: 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
3304: error if the matrix is Hermitian.
3306: 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
3307: `MatSetOptionsPrefixFactor()` on the matrix from which the factor was obtained or `MatSetOptionsPrefix()` on the factor matrix.
3309: When a MUMPS factorization fails inside a KSP solve, for example with a `KSP_DIVERGED_PC_FAILED`, one can find the MUMPS information about
3310: the failure with
3311: .vb
3312: KSPGetPC(ksp,&pc);
3313: PCFactorGetMatrix(pc,&mat);
3314: MatMumpsGetInfo(mat,....);
3315: MatMumpsGetInfog(mat,....); etc.
3316: .ve
3317: Or run with `-ksp_error_if_not_converged` and the program will be stopped and the information printed in the error message.
3319: MUMPS provides 64-bit integer support in two build modes:
3320: full 64-bit: here MUMPS is built with C preprocessing flag -DINTSIZE64 and Fortran compiler option -i8, -fdefault-integer-8 or equivalent, and
3321: requires all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS built the same way with 64-bit integers (for example ILP64 Intel MKL and MPI).
3323: selective 64-bit: with the default MUMPS build, 64-bit integers have been introduced where needed. In compressed sparse row (CSR) storage of matrices,
3324: 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
3325: 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
3326: integer) build of all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS.
3328: 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.
3330: Two modes to run MUMPS/PETSc with OpenMP
3331: .vb
3332: Set OMP_NUM_THREADS and run with fewer MPI ranks than cores. For example, if you want to have 16 OpenMP
3333: threads per rank, then you may use "export OMP_NUM_THREADS=16 && mpirun -n 4 ./test".
3334: .ve
3336: .vb
3337: -mat_mumps_use_omp_threads [m] and run your code with as many MPI ranks as the number of cores. For example,
3338: 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"
3339: .ve
3341: To run MUMPS in MPI+OpenMP hybrid mode (i.e., enable multithreading in MUMPS), but still run the non-MUMPS part
3342: (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`
3343: (or `--with-hwloc`), and have an MPI that supports MPI-3.0's process shared memory (which is usually available). Since MUMPS calls BLAS
3344: libraries, to really get performance, you should have multithreaded BLAS libraries such as Intel MKL, AMD ACML, Cray libSci or OpenBLAS
3345: (PETSc will automatically try to utilized a threaded BLAS if --with-openmp is provided).
3347: 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
3348: processes on each compute node. Listing the processes in rank ascending order, we split processes on a node into consecutive groups of
3349: 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
3350: 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
3351: 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.
3352: 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,
3353: 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
3354: 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
3355: cores in socket 1, that definitely hurts locality. On the other hand, if you map MPI ranks consecutively on the two sockets, then the
3356: 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.
3357: 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
3358: examine the mapping result.
3360: 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,
3361: 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
3362: calls `omp_set_num_threads`(m) internally before calling MUMPS.
3364: See {cite}`heroux2011bi` and {cite}`gutierrez2017accommodating`
3366: .seealso: [](ch_matrices), `Mat`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`, `KSPGetPC()`, `PCFactorGetMatrix()`
3367: M*/
3369: static PetscErrorCode MatFactorGetSolverType_mumps(PETSC_UNUSED Mat A, MatSolverType *type)
3370: {
3371: PetscFunctionBegin;
3372: *type = MATSOLVERMUMPS;
3373: PetscFunctionReturn(PETSC_SUCCESS);
3374: }
3376: /* MatGetFactor for Seq and MPI AIJ matrices */
3377: static PetscErrorCode MatGetFactor_aij_mumps(Mat A, MatFactorType ftype, Mat *F)
3378: {
3379: Mat B;
3380: Mat_MUMPS *mumps;
3381: PetscBool isSeqAIJ, isDiag, isDense;
3382: PetscMPIInt size;
3384: PetscFunctionBegin;
3385: #if defined(PETSC_USE_COMPLEX)
3386: if (ftype == MAT_FACTOR_CHOLESKY && A->hermitian == PETSC_BOOL3_TRUE && A->symmetric != PETSC_BOOL3_TRUE) {
3387: PetscCall(PetscInfo(A, "Hermitian MAT_FACTOR_CHOLESKY is not supported. Use MAT_FACTOR_LU instead.\n"));
3388: *F = NULL;
3389: PetscFunctionReturn(PETSC_SUCCESS);
3390: }
3391: #endif
3392: /* Create the factorization matrix */
3393: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATSEQAIJ, &isSeqAIJ));
3394: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATDIAGONAL, &isDiag));
3395: PetscCall(PetscObjectTypeCompareAny((PetscObject)A, &isDense, MATSEQDENSE, MATMPIDENSE, NULL));
3396: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3397: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3398: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &((PetscObject)B)->type_name));
3399: PetscCall(MatSetUp(B));
3401: PetscCall(PetscNew(&mumps));
3403: B->ops->view = MatView_MUMPS;
3404: B->ops->getinfo = MatGetInfo_MUMPS;
3406: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3407: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3408: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3409: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3410: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3411: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3412: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3413: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3414: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3415: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3416: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3417: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3418: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3419: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3421: if (ftype == MAT_FACTOR_LU) {
3422: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3423: B->factortype = MAT_FACTOR_LU;
3424: if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij;
3425: else if (isDiag) mumps->ConvertToTriples = MatConvertToTriples_diagonal_xaij;
3426: else if (isDense) mumps->ConvertToTriples = MatConvertToTriples_dense_xaij;
3427: else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij;
3428: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3429: mumps->sym = 0;
3430: } else {
3431: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3432: B->factortype = MAT_FACTOR_CHOLESKY;
3433: if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij;
3434: else if (isDiag) mumps->ConvertToTriples = MatConvertToTriples_diagonal_xaij;
3435: else if (isDense) mumps->ConvertToTriples = MatConvertToTriples_dense_xaij;
3436: else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij;
3437: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_CHOLESKY]));
3438: #if defined(PETSC_USE_COMPLEX)
3439: mumps->sym = 2;
3440: #else
3441: if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3442: else mumps->sym = 2;
3443: #endif
3444: }
3446: /* set solvertype */
3447: PetscCall(PetscFree(B->solvertype));
3448: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3449: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3450: if (size == 1) {
3451: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3452: B->canuseordering = PETSC_TRUE;
3453: }
3454: B->ops->destroy = MatDestroy_MUMPS;
3455: B->data = (void *)mumps;
3457: *F = B;
3458: mumps->id.job = JOB_NULL;
3459: mumps->ICNTL_pre = NULL;
3460: mumps->CNTL_pre = NULL;
3461: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3462: PetscFunctionReturn(PETSC_SUCCESS);
3463: }
3465: /* MatGetFactor for Seq and MPI SBAIJ matrices */
3466: static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A, PETSC_UNUSED MatFactorType ftype, Mat *F)
3467: {
3468: Mat B;
3469: Mat_MUMPS *mumps;
3470: PetscBool isSeqSBAIJ;
3471: PetscMPIInt size;
3473: PetscFunctionBegin;
3474: #if defined(PETSC_USE_COMPLEX)
3475: if (ftype == MAT_FACTOR_CHOLESKY && A->hermitian == PETSC_BOOL3_TRUE && A->symmetric != PETSC_BOOL3_TRUE) {
3476: PetscCall(PetscInfo(A, "Hermitian MAT_FACTOR_CHOLESKY is not supported. Use MAT_FACTOR_LU instead.\n"));
3477: *F = NULL;
3478: PetscFunctionReturn(PETSC_SUCCESS);
3479: }
3480: #endif
3481: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3482: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3483: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &((PetscObject)B)->type_name));
3484: PetscCall(MatSetUp(B));
3486: PetscCall(PetscNew(&mumps));
3487: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQSBAIJ, &isSeqSBAIJ));
3488: if (isSeqSBAIJ) {
3489: mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij;
3490: } else {
3491: mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij;
3492: }
3494: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3495: B->ops->view = MatView_MUMPS;
3496: B->ops->getinfo = MatGetInfo_MUMPS;
3498: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3499: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3500: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3501: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3502: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3503: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3504: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3505: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3506: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3507: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3508: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3509: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3510: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3511: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3513: B->factortype = MAT_FACTOR_CHOLESKY;
3514: #if defined(PETSC_USE_COMPLEX)
3515: mumps->sym = 2;
3516: #else
3517: if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3518: else mumps->sym = 2;
3519: #endif
3521: /* set solvertype */
3522: PetscCall(PetscFree(B->solvertype));
3523: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3524: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3525: if (size == 1) {
3526: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3527: B->canuseordering = PETSC_TRUE;
3528: }
3529: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_CHOLESKY]));
3530: B->ops->destroy = MatDestroy_MUMPS;
3531: B->data = (void *)mumps;
3533: *F = B;
3534: mumps->id.job = JOB_NULL;
3535: mumps->ICNTL_pre = NULL;
3536: mumps->CNTL_pre = NULL;
3537: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3538: PetscFunctionReturn(PETSC_SUCCESS);
3539: }
3541: static PetscErrorCode MatGetFactor_baij_mumps(Mat A, MatFactorType ftype, Mat *F)
3542: {
3543: Mat B;
3544: Mat_MUMPS *mumps;
3545: PetscBool isSeqBAIJ;
3546: PetscMPIInt size;
3548: PetscFunctionBegin;
3549: /* Create the factorization matrix */
3550: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQBAIJ, &isSeqBAIJ));
3551: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3552: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3553: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &((PetscObject)B)->type_name));
3554: PetscCall(MatSetUp(B));
3556: PetscCall(PetscNew(&mumps));
3557: if (ftype == MAT_FACTOR_LU) {
3558: B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS;
3559: B->factortype = MAT_FACTOR_LU;
3560: if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij;
3561: else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij;
3562: mumps->sym = 0;
3563: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3564: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead");
3566: B->ops->view = MatView_MUMPS;
3567: B->ops->getinfo = MatGetInfo_MUMPS;
3569: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3570: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3571: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3572: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3573: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3574: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3575: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3576: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3577: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3578: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3579: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3580: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3581: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3582: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3584: /* set solvertype */
3585: PetscCall(PetscFree(B->solvertype));
3586: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3587: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3588: if (size == 1) {
3589: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3590: B->canuseordering = PETSC_TRUE;
3591: }
3592: B->ops->destroy = MatDestroy_MUMPS;
3593: B->data = (void *)mumps;
3595: *F = B;
3596: mumps->id.job = JOB_NULL;
3597: mumps->ICNTL_pre = NULL;
3598: mumps->CNTL_pre = NULL;
3599: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3600: PetscFunctionReturn(PETSC_SUCCESS);
3601: }
3603: /* MatGetFactor for Seq and MPI SELL matrices */
3604: static PetscErrorCode MatGetFactor_sell_mumps(Mat A, MatFactorType ftype, Mat *F)
3605: {
3606: Mat B;
3607: Mat_MUMPS *mumps;
3608: PetscBool isSeqSELL;
3609: PetscMPIInt size;
3611: PetscFunctionBegin;
3612: /* Create the factorization matrix */
3613: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQSELL, &isSeqSELL));
3614: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3615: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3616: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &((PetscObject)B)->type_name));
3617: PetscCall(MatSetUp(B));
3619: PetscCall(PetscNew(&mumps));
3621: B->ops->view = MatView_MUMPS;
3622: B->ops->getinfo = MatGetInfo_MUMPS;
3624: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3625: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3626: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3627: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3628: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3629: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3630: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3631: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3632: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3633: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3634: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3635: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3637: if (ftype == MAT_FACTOR_LU) {
3638: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3639: B->factortype = MAT_FACTOR_LU;
3640: if (isSeqSELL) mumps->ConvertToTriples = MatConvertToTriples_seqsell_seqaij;
3641: else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "To be implemented");
3642: mumps->sym = 0;
3643: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3644: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "To be implemented");
3646: /* set solvertype */
3647: PetscCall(PetscFree(B->solvertype));
3648: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3649: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3650: if (size == 1) {
3651: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3652: B->canuseordering = PETSC_TRUE;
3653: }
3654: B->ops->destroy = MatDestroy_MUMPS;
3655: B->data = (void *)mumps;
3657: *F = B;
3658: mumps->id.job = JOB_NULL;
3659: mumps->ICNTL_pre = NULL;
3660: mumps->CNTL_pre = NULL;
3661: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3662: PetscFunctionReturn(PETSC_SUCCESS);
3663: }
3665: /* MatGetFactor for MATNEST matrices */
3666: static PetscErrorCode MatGetFactor_nest_mumps(Mat A, MatFactorType ftype, Mat *F)
3667: {
3668: Mat B, **mats;
3669: Mat_MUMPS *mumps;
3670: PetscInt nr, nc;
3671: PetscMPIInt size;
3672: PetscBool flg = PETSC_TRUE;
3674: PetscFunctionBegin;
3675: #if defined(PETSC_USE_COMPLEX)
3676: if (ftype == MAT_FACTOR_CHOLESKY && A->hermitian == PETSC_BOOL3_TRUE && A->symmetric != PETSC_BOOL3_TRUE) {
3677: PetscCall(PetscInfo(A, "Hermitian MAT_FACTOR_CHOLESKY is not supported. Use MAT_FACTOR_LU instead.\n"));
3678: *F = NULL;
3679: PetscFunctionReturn(PETSC_SUCCESS);
3680: }
3681: #endif
3683: /* Return if some condition is not satisfied */
3684: *F = NULL;
3685: PetscCall(MatNestGetSubMats(A, &nr, &nc, &mats));
3686: if (ftype == MAT_FACTOR_CHOLESKY) {
3687: IS *rows, *cols;
3688: PetscInt *m, *M;
3690: PetscCheck(nr == nc, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_FACTOR_CHOLESKY not supported for nest sizes %" PetscInt_FMT " != %" PetscInt_FMT ". Use MAT_FACTOR_LU.", nr, nc);
3691: PetscCall(PetscMalloc2(nr, &rows, nc, &cols));
3692: PetscCall(MatNestGetISs(A, rows, cols));
3693: for (PetscInt r = 0; flg && r < nr; r++) PetscCall(ISEqualUnsorted(rows[r], cols[r], &flg));
3694: if (!flg) {
3695: PetscCall(PetscFree2(rows, cols));
3696: PetscCall(PetscInfo(A, "MAT_FACTOR_CHOLESKY not supported for unequal row and column maps. Use MAT_FACTOR_LU.\n"));
3697: PetscFunctionReturn(PETSC_SUCCESS);
3698: }
3699: PetscCall(PetscMalloc2(nr, &m, nr, &M));
3700: for (PetscInt r = 0; r < nr; r++) PetscCall(ISGetMinMax(rows[r], &m[r], &M[r]));
3701: for (PetscInt r = 0; flg && r < nr; r++)
3702: for (PetscInt k = r + 1; flg && k < nr; k++)
3703: if ((m[k] <= m[r] && m[r] <= M[k]) || (m[k] <= M[r] && M[r] <= M[k])) flg = PETSC_FALSE;
3704: PetscCall(PetscFree2(m, M));
3705: PetscCall(PetscFree2(rows, cols));
3706: if (!flg) {
3707: PetscCall(PetscInfo(A, "MAT_FACTOR_CHOLESKY not supported for intersecting row maps. Use MAT_FACTOR_LU.\n"));
3708: PetscFunctionReturn(PETSC_SUCCESS);
3709: }
3710: }
3712: for (PetscInt r = 0; r < nr; r++) {
3713: for (PetscInt c = 0; c < nc; c++) {
3714: Mat sub = mats[r][c];
3715: PetscBool isSeqAIJ, isMPIAIJ, isSeqBAIJ, isMPIBAIJ, isSeqSBAIJ, isMPISBAIJ, isTrans, isDiag, isDense;
3717: if (!sub || (ftype == MAT_FACTOR_CHOLESKY && c < r)) continue;
3718: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATTRANSPOSEVIRTUAL, &isTrans));
3719: if (isTrans) PetscCall(MatTransposeGetMat(sub, &sub));
3720: else {
3721: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATHERMITIANTRANSPOSEVIRTUAL, &isTrans));
3722: if (isTrans) PetscCall(MatHermitianTransposeGetMat(sub, &sub));
3723: }
3724: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATSEQAIJ, &isSeqAIJ));
3725: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATMPIAIJ, &isMPIAIJ));
3726: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATSEQBAIJ, &isSeqBAIJ));
3727: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATMPIBAIJ, &isMPIBAIJ));
3728: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATSEQSBAIJ, &isSeqSBAIJ));
3729: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATMPISBAIJ, &isMPISBAIJ));
3730: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATDIAGONAL, &isDiag));
3731: PetscCall(PetscObjectTypeCompareAny((PetscObject)sub, &isDense, MATSEQDENSE, MATMPIDENSE, NULL));
3732: if (ftype == MAT_FACTOR_CHOLESKY) {
3733: if (r == c) {
3734: if (!isSeqAIJ && !isMPIAIJ && !isSeqBAIJ && !isMPIBAIJ && !isSeqSBAIJ && !isMPISBAIJ && !isDiag && !isDense) {
3735: PetscCall(PetscInfo(sub, "MAT_FACTOR_CHOLESKY not supported for diagonal block of type %s.\n", ((PetscObject)sub)->type_name));
3736: flg = PETSC_FALSE;
3737: }
3738: } else if (!isSeqAIJ && !isMPIAIJ && !isSeqBAIJ && !isMPIBAIJ && !isDiag && !isDense) {
3739: PetscCall(PetscInfo(sub, "MAT_FACTOR_CHOLESKY not supported for off-diagonal block of type %s.\n", ((PetscObject)sub)->type_name));
3740: flg = PETSC_FALSE;
3741: }
3742: } else if (!isSeqAIJ && !isMPIAIJ && !isSeqBAIJ && !isMPIBAIJ && !isDiag && !isDense) {
3743: PetscCall(PetscInfo(sub, "MAT_FACTOR_LU not supported for block of type %s.\n", ((PetscObject)sub)->type_name));
3744: flg = PETSC_FALSE;
3745: }
3746: }
3747: }
3748: if (!flg) PetscFunctionReturn(PETSC_SUCCESS);
3750: /* Create the factorization matrix */
3751: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3752: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3753: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &((PetscObject)B)->type_name));
3754: PetscCall(MatSetUp(B));
3756: PetscCall(PetscNew(&mumps));
3758: B->ops->view = MatView_MUMPS;
3759: B->ops->getinfo = MatGetInfo_MUMPS;
3761: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3762: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3763: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3764: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3765: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3766: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3767: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3768: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3769: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3770: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3771: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3772: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3773: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3774: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3776: if (ftype == MAT_FACTOR_LU) {
3777: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3778: B->factortype = MAT_FACTOR_LU;
3779: mumps->sym = 0;
3780: } else {
3781: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3782: B->factortype = MAT_FACTOR_CHOLESKY;
3783: #if defined(PETSC_USE_COMPLEX)
3784: mumps->sym = 2;
3785: #else
3786: if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3787: else mumps->sym = 2;
3788: #endif
3789: }
3790: mumps->ConvertToTriples = MatConvertToTriples_nest_xaij;
3791: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[ftype]));
3793: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3794: if (size == 1) {
3795: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3796: B->canuseordering = PETSC_TRUE;
3797: }
3799: /* set solvertype */
3800: PetscCall(PetscFree(B->solvertype));
3801: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3802: B->ops->destroy = MatDestroy_MUMPS;
3803: B->data = (void *)mumps;
3805: *F = B;
3806: mumps->id.job = JOB_NULL;
3807: mumps->ICNTL_pre = NULL;
3808: mumps->CNTL_pre = NULL;
3809: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3810: PetscFunctionReturn(PETSC_SUCCESS);
3811: }
3813: PETSC_INTERN PetscErrorCode MatSolverTypeRegister_MUMPS(void)
3814: {
3815: PetscFunctionBegin;
3816: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3817: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3818: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIBAIJ, MAT_FACTOR_LU, MatGetFactor_baij_mumps));
3819: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_baij_mumps));
3820: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPISBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_sbaij_mumps));
3821: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3822: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3823: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQBAIJ, MAT_FACTOR_LU, MatGetFactor_baij_mumps));
3824: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_baij_mumps));
3825: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQSBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_sbaij_mumps));
3826: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQSELL, MAT_FACTOR_LU, MatGetFactor_sell_mumps));
3827: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATDIAGONAL, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3828: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATDIAGONAL, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3829: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQDENSE, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3830: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQDENSE, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3831: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIDENSE, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3832: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIDENSE, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3833: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATNEST, MAT_FACTOR_LU, MatGetFactor_nest_mumps));
3834: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATNEST, MAT_FACTOR_CHOLESKY, MatGetFactor_nest_mumps));
3835: PetscFunctionReturn(PETSC_SUCCESS);
3836: }