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(PetscInt 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: PetscInt64 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: PetscInt64 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: PetscInt64 *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: PetscInt64 *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 PetscInt64 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: PetscInt64 nz, rnz, i, j, 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 (i = k = 0; i < M; i++) {
386: rnz = ai[i + 1] - ai[i];
387: ajj = aj + ai[i];
388: for (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: PetscInt64 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: PetscInt64 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: PetscInt64 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 PetscInt64 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 %" PetscInt64_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: PetscInt64 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: PetscInt64 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: PetscInt64 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 = (PetscInt64)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: PetscInt64 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: PetscInt64 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: PetscInt64 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: PetscInt64 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: PetscInt64 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 += (PetscInt64)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;
1160: mumps->nest_vals_start[r * nc + c] = cumnnz;
1161: if (!mumps->nest_convert_to_triples[r * nc + c]) continue;
1163: /* Extract inner blocks if needed */
1164: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATTRANSPOSEVIRTUAL, &isTrans));
1165: if (isTrans) PetscCall(MatTransposeGetMat(sub, &sub));
1166: else {
1167: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATHERMITIANTRANSPOSEVIRTUAL, &isHTrans));
1168: if (isHTrans) PetscCall(MatHermitianTransposeGetMat(sub, &sub));
1169: }
1170: swap = (PetscBool)(isTrans || isHTrans);
1172: /* Get column layout to map off-process columns */
1173: PetscCall(MatGetLayouts(sub, NULL, &cmap));
1175: /* Get row start to map on-process rows */
1176: PetscCall(MatGetOwnershipRange(sub, &rst, NULL));
1178: /* Directly use the mumps datastructure and use C ordering for now */
1179: PetscCall((*mumps->nest_convert_to_triples[r * nc + c])(sub, 0, MAT_INITIAL_MATRIX, mumps));
1181: /* Swap the role of rows and columns indices for transposed blocks
1182: since we need values with global final ordering */
1183: if (swap) {
1184: cidx = rows_idx[r];
1185: ridx = cols_idx[c];
1186: }
1188: /* Communicate column indices
1189: This could have been done with a single SF but it would have complicated the code a lot.
1190: But since we do it only once, we pay the price of setting up an SF for each block */
1191: if (PetscDefined(USE_64BIT_INDICES)) {
1192: for (PetscInt k = 0; k < mumps->nnz; k++) pjcns_w[k] = mumps->jcn[k];
1193: } else pjcns_w = (PetscInt *)mumps->jcn; /* This cast is needed only to silence warnings for 64bit integers builds */
1194: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &csf));
1195: PetscCall(PetscSFSetGraphLayout(csf, cmap, mumps->nnz, NULL, PETSC_OWN_POINTER, pjcns_w));
1196: PetscCall(PetscSFBcastBegin(csf, MPIU_INT, cidx, pjcns_w, MPI_REPLACE));
1197: PetscCall(PetscSFBcastEnd(csf, MPIU_INT, cidx, pjcns_w, MPI_REPLACE));
1198: PetscCall(PetscSFDestroy(&csf));
1200: /* Import indices: use direct map for rows and mapped indices for columns */
1201: if (swap) {
1202: for (PetscInt k = 0; k < mumps->nnz; k++) {
1203: PetscCall(PetscMUMPSIntCast(ridx[mumps->irn[k] - rst] + shift, &jcns[cumnnz + k]));
1204: PetscCall(PetscMUMPSIntCast(pjcns_w[k] + shift, &irns[cumnnz + k]));
1205: }
1206: } else {
1207: for (PetscInt k = 0; k < mumps->nnz; k++) {
1208: PetscCall(PetscMUMPSIntCast(ridx[mumps->irn[k] - rst] + shift, &irns[cumnnz + k]));
1209: PetscCall(PetscMUMPSIntCast(pjcns_w[k] + shift, &jcns[cumnnz + k]));
1210: }
1211: }
1213: /* Import values to full COO */
1214: PetscCall(PetscArraycpy(vals + cumnnz, mumps->val, mumps->nnz));
1215: if (isHTrans) { /* conjugate the entries */
1216: PetscScalar *v = vals + cumnnz;
1217: for (PetscInt k = 0; k < mumps->nnz; k++) v[k] = PetscConj(v[k]);
1218: }
1220: /* Shift new starting point and sanity check */
1221: cumnnz += mumps->nnz;
1222: PetscCheck(cumnnz <= totnnz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unexpected number of nonzeros %" PetscInt64_FMT " != %" PetscInt64_FMT, cumnnz, totnnz);
1224: /* Free scratch memory */
1225: PetscCall(PetscFree2(mumps->irn, mumps->jcn));
1226: PetscCall(PetscFree(mumps->val_alloc));
1227: mumps->val = NULL;
1228: mumps->nnz = 0;
1229: }
1230: }
1231: if (PetscDefined(USE_64BIT_INDICES)) PetscCall(PetscFree(pjcns_w));
1232: for (PetscInt r = 0; r < nr; r++) PetscCall(ISRestoreIndices(rows[r], (const PetscInt **)&rows_idx[r]));
1233: for (PetscInt c = 0; c < nc; c++) PetscCall(ISRestoreIndices(cols[c], (const PetscInt **)&cols_idx[c]));
1234: PetscCall(PetscFree4(rows, cols, rows_idx, cols_idx));
1235: if (!chol) PetscCheck(cumnnz == totnnz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Different number of nonzeros %" PetscInt64_FMT " != %" PetscInt64_FMT, cumnnz, totnnz);
1236: mumps->nest_vals_start[nr * nc] = cumnnz;
1238: /* Set pointers for final MUMPS data structure */
1239: mumps->nest_vals = vals;
1240: mumps->val_alloc = NULL; /* do not use val_alloc since it may be reallocated with the OMP callpath */
1241: mumps->val = vals;
1242: mumps->irn = irns;
1243: mumps->jcn = jcns;
1244: mumps->nnz = cumnnz;
1245: } else {
1246: PetscScalar *oval = mumps->nest_vals;
1247: for (PetscInt r = 0; r < nr; r++) {
1248: for (PetscInt c = 0; c < nc; c++) {
1249: PetscBool isTrans, isHTrans = PETSC_FALSE;
1250: Mat sub = mats[r][c];
1251: PetscInt midx = r * nc + c;
1253: if (!mumps->nest_convert_to_triples[midx]) continue;
1254: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATTRANSPOSEVIRTUAL, &isTrans));
1255: if (isTrans) PetscCall(MatTransposeGetMat(sub, &sub));
1256: else {
1257: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATHERMITIANTRANSPOSEVIRTUAL, &isHTrans));
1258: if (isHTrans) PetscCall(MatHermitianTransposeGetMat(sub, &sub));
1259: }
1260: mumps->val = oval + mumps->nest_vals_start[midx];
1261: PetscCall((*mumps->nest_convert_to_triples[midx])(sub, shift, MAT_REUSE_MATRIX, mumps));
1262: if (isHTrans) {
1263: PetscInt nnz = mumps->nest_vals_start[midx + 1] - mumps->nest_vals_start[midx];
1264: for (PetscInt k = 0; k < nnz; k++) mumps->val[k] = PetscConj(mumps->val[k]);
1265: }
1266: }
1267: }
1268: mumps->val = oval;
1269: }
1270: PetscFunctionReturn(PETSC_SUCCESS);
1271: }
1273: static PetscErrorCode MatDestroy_MUMPS(Mat A)
1274: {
1275: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1277: PetscFunctionBegin;
1278: PetscCall(PetscFree2(mumps->id.sol_loc, mumps->id.isol_loc));
1279: PetscCall(VecScatterDestroy(&mumps->scat_rhs));
1280: PetscCall(VecScatterDestroy(&mumps->scat_sol));
1281: PetscCall(VecDestroy(&mumps->b_seq));
1282: PetscCall(VecDestroy(&mumps->x_seq));
1283: PetscCall(PetscFree(mumps->id.perm_in));
1284: PetscCall(PetscFree2(mumps->irn, mumps->jcn));
1285: PetscCall(PetscFree(mumps->val_alloc));
1286: PetscCall(PetscFree(mumps->info));
1287: PetscCall(PetscFree(mumps->ICNTL_pre));
1288: PetscCall(PetscFree(mumps->CNTL_pre));
1289: PetscCall(MatMumpsResetSchur_Private(mumps));
1290: if (mumps->id.job != JOB_NULL) { /* cannot call PetscMUMPS_c() if JOB_INIT has never been called for this instance */
1291: mumps->id.job = JOB_END;
1292: PetscMUMPS_c(mumps);
1293: 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));
1294: if (mumps->mumps_comm != MPI_COMM_NULL) {
1295: if (PetscDefined(HAVE_OPENMP_SUPPORT) && mumps->use_petsc_omp_support) PetscCallMPI(MPI_Comm_free(&mumps->mumps_comm));
1296: else PetscCall(PetscCommRestoreComm(PetscObjectComm((PetscObject)A), &mumps->mumps_comm));
1297: }
1298: }
1299: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
1300: if (mumps->use_petsc_omp_support) {
1301: PetscCall(PetscOmpCtrlDestroy(&mumps->omp_ctrl));
1302: PetscCall(PetscFree2(mumps->rhs_loc, mumps->rhs_recvbuf));
1303: PetscCall(PetscFree3(mumps->rhs_nrow, mumps->rhs_recvcounts, mumps->rhs_disps));
1304: }
1305: #endif
1306: PetscCall(PetscFree(mumps->ia_alloc));
1307: PetscCall(PetscFree(mumps->ja_alloc));
1308: PetscCall(PetscFree(mumps->recvcount));
1309: PetscCall(PetscFree(mumps->reqs));
1310: PetscCall(PetscFree(mumps->irhs_loc));
1311: PetscCall(PetscFree2(mumps->nest_vals_start, mumps->nest_convert_to_triples));
1312: PetscCall(PetscFree(mumps->nest_vals));
1313: PetscCall(PetscFree(A->data));
1315: /* clear composed functions */
1316: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1317: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorSetSchurIS_C", NULL));
1318: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorCreateSchurComplement_C", NULL));
1319: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsSetIcntl_C", NULL));
1320: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetIcntl_C", NULL));
1321: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsSetCntl_C", NULL));
1322: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetCntl_C", NULL));
1323: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInfo_C", NULL));
1324: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInfog_C", NULL));
1325: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetRinfo_C", NULL));
1326: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetRinfog_C", NULL));
1327: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetNullPivots_C", NULL));
1328: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInverse_C", NULL));
1329: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInverseTranspose_C", NULL));
1330: PetscFunctionReturn(PETSC_SUCCESS);
1331: }
1333: /* Set up the distributed RHS info for MUMPS. <nrhs> is the number of RHS. <array> points to start of RHS on the local processor. */
1334: static PetscErrorCode MatMumpsSetUpDistRHSInfo(Mat A, PetscInt nrhs, const PetscScalar *array)
1335: {
1336: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1337: const PetscMPIInt ompsize = mumps->omp_comm_size;
1338: PetscInt i, m, M, rstart;
1340: PetscFunctionBegin;
1341: PetscCall(MatGetSize(A, &M, NULL));
1342: PetscCall(MatGetLocalSize(A, &m, NULL));
1343: PetscCheck(M <= PETSC_MUMPS_INT_MAX, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscInt too long for PetscMUMPSInt");
1344: if (ompsize == 1) {
1345: if (!mumps->irhs_loc) {
1346: mumps->nloc_rhs = m;
1347: PetscCall(PetscMalloc1(m, &mumps->irhs_loc));
1348: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
1349: for (i = 0; i < m; i++) mumps->irhs_loc[i] = rstart + i + 1; /* use 1-based indices */
1350: }
1351: mumps->id.rhs_loc = (MumpsScalar *)array;
1352: } else {
1353: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
1354: const PetscInt *ranges;
1355: PetscMPIInt j, k, sendcount, *petsc_ranks, *omp_ranks;
1356: MPI_Group petsc_group, omp_group;
1357: PetscScalar *recvbuf = NULL;
1359: if (mumps->is_omp_master) {
1360: /* Lazily initialize the omp stuff for distributed rhs */
1361: if (!mumps->irhs_loc) {
1362: PetscCall(PetscMalloc2(ompsize, &omp_ranks, ompsize, &petsc_ranks));
1363: PetscCall(PetscMalloc3(ompsize, &mumps->rhs_nrow, ompsize, &mumps->rhs_recvcounts, ompsize, &mumps->rhs_disps));
1364: PetscCallMPI(MPI_Comm_group(mumps->petsc_comm, &petsc_group));
1365: PetscCallMPI(MPI_Comm_group(mumps->omp_comm, &omp_group));
1366: for (j = 0; j < ompsize; j++) omp_ranks[j] = j;
1367: PetscCallMPI(MPI_Group_translate_ranks(omp_group, ompsize, omp_ranks, petsc_group, petsc_ranks));
1369: /* Populate mumps->irhs_loc[], rhs_nrow[] */
1370: mumps->nloc_rhs = 0;
1371: PetscCall(MatGetOwnershipRanges(A, &ranges));
1372: for (j = 0; j < ompsize; j++) {
1373: mumps->rhs_nrow[j] = ranges[petsc_ranks[j] + 1] - ranges[petsc_ranks[j]];
1374: mumps->nloc_rhs += mumps->rhs_nrow[j];
1375: }
1376: PetscCall(PetscMalloc1(mumps->nloc_rhs, &mumps->irhs_loc));
1377: for (j = k = 0; j < ompsize; j++) {
1378: for (i = ranges[petsc_ranks[j]]; i < ranges[petsc_ranks[j] + 1]; i++, k++) mumps->irhs_loc[k] = i + 1; /* uses 1-based indices */
1379: }
1381: PetscCall(PetscFree2(omp_ranks, petsc_ranks));
1382: PetscCallMPI(MPI_Group_free(&petsc_group));
1383: PetscCallMPI(MPI_Group_free(&omp_group));
1384: }
1386: /* Realloc buffers when current nrhs is bigger than what we have met */
1387: if (nrhs > mumps->max_nrhs) {
1388: PetscCall(PetscFree2(mumps->rhs_loc, mumps->rhs_recvbuf));
1389: PetscCall(PetscMalloc2(mumps->nloc_rhs * nrhs, &mumps->rhs_loc, mumps->nloc_rhs * nrhs, &mumps->rhs_recvbuf));
1390: mumps->max_nrhs = nrhs;
1391: }
1393: /* Setup recvcounts[], disps[], recvbuf on omp rank 0 for the upcoming MPI_Gatherv */
1394: for (j = 0; j < ompsize; j++) PetscCall(PetscMPIIntCast(mumps->rhs_nrow[j] * nrhs, &mumps->rhs_recvcounts[j]));
1395: mumps->rhs_disps[0] = 0;
1396: for (j = 1; j < ompsize; j++) {
1397: mumps->rhs_disps[j] = mumps->rhs_disps[j - 1] + mumps->rhs_recvcounts[j - 1];
1398: PetscCheck(mumps->rhs_disps[j] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscMPIInt overflow!");
1399: }
1400: recvbuf = (nrhs == 1) ? mumps->rhs_loc : mumps->rhs_recvbuf; /* Directly use rhs_loc[] as recvbuf. Single rhs is common in Ax=b */
1401: }
1403: PetscCall(PetscMPIIntCast(m * nrhs, &sendcount));
1404: PetscCallMPI(MPI_Gatherv(array, sendcount, MPIU_SCALAR, recvbuf, mumps->rhs_recvcounts, mumps->rhs_disps, MPIU_SCALAR, 0, mumps->omp_comm));
1406: if (mumps->is_omp_master) {
1407: if (nrhs > 1) { /* Copy & re-arrange data from rhs_recvbuf[] to mumps->rhs_loc[] only when there are multiple rhs */
1408: PetscScalar *dst, *dstbase = mumps->rhs_loc;
1409: for (j = 0; j < ompsize; j++) {
1410: const PetscScalar *src = mumps->rhs_recvbuf + mumps->rhs_disps[j];
1411: dst = dstbase;
1412: for (i = 0; i < nrhs; i++) {
1413: PetscCall(PetscArraycpy(dst, src, mumps->rhs_nrow[j]));
1414: src += mumps->rhs_nrow[j];
1415: dst += mumps->nloc_rhs;
1416: }
1417: dstbase += mumps->rhs_nrow[j];
1418: }
1419: }
1420: mumps->id.rhs_loc = (MumpsScalar *)mumps->rhs_loc;
1421: }
1422: #endif /* PETSC_HAVE_OPENMP_SUPPORT */
1423: }
1424: mumps->id.nrhs = nrhs;
1425: mumps->id.nloc_rhs = mumps->nloc_rhs;
1426: mumps->id.lrhs_loc = mumps->nloc_rhs;
1427: mumps->id.irhs_loc = mumps->irhs_loc;
1428: PetscFunctionReturn(PETSC_SUCCESS);
1429: }
1431: static PetscErrorCode MatSolve_MUMPS(Mat A, Vec b, Vec x)
1432: {
1433: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1434: const PetscScalar *rarray = NULL;
1435: PetscScalar *array;
1436: IS is_iden, is_petsc;
1437: PetscInt i;
1438: PetscBool second_solve = PETSC_FALSE;
1439: static PetscBool cite1 = PETSC_FALSE, cite2 = PETSC_FALSE;
1441: PetscFunctionBegin;
1442: 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 "
1443: "Journal on Matrix Analysis and Applications},\n volume = {23},\n number = {1},\n pages = {15--41},\n year = {2001}\n}\n",
1444: &cite1));
1445: 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 "
1446: "Computing},\n volume = {32},\n number = {2},\n pages = {136--156},\n year = {2006}\n}\n",
1447: &cite2));
1449: if (A->factorerrortype) {
1450: 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)));
1451: PetscCall(VecSetInf(x));
1452: PetscFunctionReturn(PETSC_SUCCESS);
1453: }
1455: mumps->id.nrhs = 1;
1456: if (mumps->petsc_size > 1) {
1457: if (mumps->ICNTL20 == 10) {
1458: mumps->id.ICNTL(20) = 10; /* dense distributed RHS */
1459: PetscCall(VecGetArrayRead(b, &rarray));
1460: PetscCall(MatMumpsSetUpDistRHSInfo(A, 1, rarray));
1461: } else {
1462: mumps->id.ICNTL(20) = 0; /* dense centralized RHS; Scatter b into a sequential rhs vector*/
1463: PetscCall(VecScatterBegin(mumps->scat_rhs, b, mumps->b_seq, INSERT_VALUES, SCATTER_FORWARD));
1464: PetscCall(VecScatterEnd(mumps->scat_rhs, b, mumps->b_seq, INSERT_VALUES, SCATTER_FORWARD));
1465: if (!mumps->myid) {
1466: PetscCall(VecGetArray(mumps->b_seq, &array));
1467: mumps->id.rhs = (MumpsScalar *)array;
1468: }
1469: }
1470: } else { /* petsc_size == 1 */
1471: mumps->id.ICNTL(20) = 0; /* dense centralized RHS */
1472: PetscCall(VecCopy(b, x));
1473: PetscCall(VecGetArray(x, &array));
1474: mumps->id.rhs = (MumpsScalar *)array;
1475: }
1477: /*
1478: handle condensation step of Schur complement (if any)
1479: We set by default ICNTL(26) == -1 when Schur indices have been provided by the user.
1480: According to MUMPS (5.0.0) manual, any value should be harmful during the factorization phase
1481: Unless the user provides a valid value for ICNTL(26), MatSolve and MatMatSolve routines solve the full system.
1482: This requires an extra call to PetscMUMPS_c and the computation of the factors for S
1483: */
1484: if (mumps->id.size_schur > 0) {
1485: PetscCheck(mumps->petsc_size <= 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Parallel Schur complements not yet supported from PETSc");
1486: if (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2) {
1487: second_solve = PETSC_TRUE;
1488: PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1489: mumps->id.ICNTL(26) = 1; /* condensation phase */
1490: } else if (mumps->id.ICNTL(26) == 1) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1491: }
1492: /* solve phase */
1493: mumps->id.job = JOB_SOLVE;
1494: PetscMUMPS_c(mumps);
1495: 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));
1497: /* handle expansion step of Schur complement (if any) */
1498: if (second_solve) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_TRUE));
1499: else if (mumps->id.ICNTL(26) == 1) {
1500: PetscCall(MatMumpsSolveSchur_Private(A));
1501: for (i = 0; i < mumps->id.size_schur; ++i) {
1502: #if !defined(PETSC_USE_COMPLEX)
1503: PetscScalar val = mumps->id.redrhs[i];
1504: #else
1505: PetscScalar val = mumps->id.redrhs[i].r + PETSC_i * mumps->id.redrhs[i].i;
1506: #endif
1507: array[mumps->id.listvar_schur[i] - 1] = val;
1508: }
1509: }
1511: if (mumps->petsc_size > 1) { /* convert mumps distributed solution to petsc mpi x */
1512: if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) {
1513: /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */
1514: PetscCall(VecScatterDestroy(&mumps->scat_sol));
1515: }
1516: if (!mumps->scat_sol) { /* create scatter scat_sol */
1517: PetscInt *isol2_loc = NULL;
1518: PetscCall(ISCreateStride(PETSC_COMM_SELF, mumps->id.lsol_loc, 0, 1, &is_iden)); /* from */
1519: PetscCall(PetscMalloc1(mumps->id.lsol_loc, &isol2_loc));
1520: for (i = 0; i < mumps->id.lsol_loc; i++) isol2_loc[i] = mumps->id.isol_loc[i] - 1; /* change Fortran style to C style */
1521: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, mumps->id.lsol_loc, isol2_loc, PETSC_OWN_POINTER, &is_petsc)); /* to */
1522: PetscCall(VecScatterCreate(mumps->x_seq, is_iden, x, is_petsc, &mumps->scat_sol));
1523: PetscCall(ISDestroy(&is_iden));
1524: PetscCall(ISDestroy(&is_petsc));
1525: mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */
1526: }
1528: PetscCall(VecScatterBegin(mumps->scat_sol, mumps->x_seq, x, INSERT_VALUES, SCATTER_FORWARD));
1529: PetscCall(VecScatterEnd(mumps->scat_sol, mumps->x_seq, x, INSERT_VALUES, SCATTER_FORWARD));
1530: }
1532: if (mumps->petsc_size > 1) {
1533: if (mumps->ICNTL20 == 10) {
1534: PetscCall(VecRestoreArrayRead(b, &rarray));
1535: } else if (!mumps->myid) {
1536: PetscCall(VecRestoreArray(mumps->b_seq, &array));
1537: }
1538: } else PetscCall(VecRestoreArray(x, &array));
1540: PetscCall(PetscLogFlops(2.0 * PetscMax(0, (mumps->id.INFO(28) >= 0 ? mumps->id.INFO(28) : -1000000 * mumps->id.INFO(28)) - A->cmap->n)));
1541: PetscFunctionReturn(PETSC_SUCCESS);
1542: }
1544: static PetscErrorCode MatSolveTranspose_MUMPS(Mat A, Vec b, Vec x)
1545: {
1546: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1547: const PetscMUMPSInt value = mumps->id.ICNTL(9);
1549: PetscFunctionBegin;
1550: mumps->id.ICNTL(9) = 0;
1551: PetscCall(MatSolve_MUMPS(A, b, x));
1552: mumps->id.ICNTL(9) = value;
1553: PetscFunctionReturn(PETSC_SUCCESS);
1554: }
1556: static PetscErrorCode MatMatSolve_MUMPS(Mat A, Mat B, Mat X)
1557: {
1558: Mat Bt = NULL;
1559: PetscBool denseX, denseB, flg, flgT;
1560: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1561: PetscInt i, nrhs, M;
1562: PetscScalar *array;
1563: const PetscScalar *rbray;
1564: PetscInt lsol_loc, nlsol_loc, *idxx, iidx = 0;
1565: PetscMUMPSInt *isol_loc, *isol_loc_save;
1566: PetscScalar *bray, *sol_loc, *sol_loc_save;
1567: IS is_to, is_from;
1568: PetscInt k, proc, j, m, myrstart;
1569: const PetscInt *rstart;
1570: Vec v_mpi, msol_loc;
1571: VecScatter scat_sol;
1572: Vec b_seq;
1573: VecScatter scat_rhs;
1574: PetscScalar *aa;
1575: PetscInt spnr, *ia, *ja;
1576: Mat_MPIAIJ *b = NULL;
1578: PetscFunctionBegin;
1579: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &denseX, MATSEQDENSE, MATMPIDENSE, NULL));
1580: PetscCheck(denseX, PetscObjectComm((PetscObject)X), PETSC_ERR_ARG_WRONG, "Matrix X must be MATDENSE matrix");
1582: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &denseB, MATSEQDENSE, MATMPIDENSE, NULL));
1583: if (denseB) {
1584: PetscCheck(B->rmap->n == X->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Matrix B and X must have same row distribution");
1585: mumps->id.ICNTL(20) = 0; /* dense RHS */
1586: } else { /* sparse B */
1587: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
1588: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATTRANSPOSEVIRTUAL, &flgT));
1589: if (flgT) { /* input B is transpose of actual RHS matrix,
1590: because mumps requires sparse compressed COLUMN storage! See MatMatTransposeSolve_MUMPS() */
1591: PetscCall(MatTransposeGetMat(B, &Bt));
1592: } else SETERRQ(PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Matrix B must be MATTRANSPOSEVIRTUAL matrix");
1593: mumps->id.ICNTL(20) = 1; /* sparse RHS */
1594: }
1596: PetscCall(MatGetSize(B, &M, &nrhs));
1597: mumps->id.nrhs = nrhs;
1598: mumps->id.lrhs = M;
1599: mumps->id.rhs = NULL;
1601: if (mumps->petsc_size == 1) {
1602: PetscScalar *aa;
1603: PetscInt spnr, *ia, *ja;
1604: PetscBool second_solve = PETSC_FALSE;
1606: PetscCall(MatDenseGetArray(X, &array));
1607: mumps->id.rhs = (MumpsScalar *)array;
1609: if (denseB) {
1610: /* copy B to X */
1611: PetscCall(MatDenseGetArrayRead(B, &rbray));
1612: PetscCall(PetscArraycpy(array, rbray, M * nrhs));
1613: PetscCall(MatDenseRestoreArrayRead(B, &rbray));
1614: } else { /* sparse B */
1615: PetscCall(MatSeqAIJGetArray(Bt, &aa));
1616: PetscCall(MatGetRowIJ(Bt, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1617: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
1618: PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
1619: mumps->id.rhs_sparse = (MumpsScalar *)aa;
1620: }
1621: /* handle condensation step of Schur complement (if any) */
1622: if (mumps->id.size_schur > 0) {
1623: if (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2) {
1624: second_solve = PETSC_TRUE;
1625: PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1626: mumps->id.ICNTL(26) = 1; /* condensation phase */
1627: } else if (mumps->id.ICNTL(26) == 1) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1628: }
1629: /* solve phase */
1630: mumps->id.job = JOB_SOLVE;
1631: PetscMUMPS_c(mumps);
1632: 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));
1634: /* handle expansion step of Schur complement (if any) */
1635: if (second_solve) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_TRUE));
1636: else if (mumps->id.ICNTL(26) == 1) {
1637: PetscCall(MatMumpsSolveSchur_Private(A));
1638: for (j = 0; j < nrhs; ++j)
1639: for (i = 0; i < mumps->id.size_schur; ++i) {
1640: #if !defined(PETSC_USE_COMPLEX)
1641: PetscScalar val = mumps->id.redrhs[i + j * mumps->id.lredrhs];
1642: #else
1643: PetscScalar val = mumps->id.redrhs[i + j * mumps->id.lredrhs].r + PETSC_i * mumps->id.redrhs[i + j * mumps->id.lredrhs].i;
1644: #endif
1645: array[mumps->id.listvar_schur[i] - 1 + j * M] = val;
1646: }
1647: }
1648: if (!denseB) { /* sparse B */
1649: PetscCall(MatSeqAIJRestoreArray(Bt, &aa));
1650: PetscCall(MatRestoreRowIJ(Bt, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1651: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot restore IJ structure");
1652: }
1653: PetscCall(MatDenseRestoreArray(X, &array));
1654: PetscFunctionReturn(PETSC_SUCCESS);
1655: }
1657: /* parallel case: MUMPS requires rhs B to be centralized on the host! */
1658: PetscCheck(!mumps->id.ICNTL(19), PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Parallel Schur complements not yet supported from PETSc");
1660: /* create msol_loc to hold mumps local solution */
1661: isol_loc_save = mumps->id.isol_loc; /* save it for MatSolve() */
1662: sol_loc_save = (PetscScalar *)mumps->id.sol_loc;
1664: lsol_loc = mumps->id.lsol_loc;
1665: nlsol_loc = nrhs * lsol_loc; /* length of sol_loc */
1666: PetscCall(PetscMalloc2(nlsol_loc, &sol_loc, lsol_loc, &isol_loc));
1667: mumps->id.sol_loc = (MumpsScalar *)sol_loc;
1668: mumps->id.isol_loc = isol_loc;
1670: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, nlsol_loc, (PetscScalar *)sol_loc, &msol_loc));
1672: if (denseB) {
1673: if (mumps->ICNTL20 == 10) {
1674: mumps->id.ICNTL(20) = 10; /* dense distributed RHS */
1675: PetscCall(MatDenseGetArrayRead(B, &rbray));
1676: PetscCall(MatMumpsSetUpDistRHSInfo(A, nrhs, rbray));
1677: PetscCall(MatDenseRestoreArrayRead(B, &rbray));
1678: PetscCall(MatGetLocalSize(B, &m, NULL));
1679: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B), 1, nrhs * m, nrhs * M, NULL, &v_mpi));
1680: } else {
1681: mumps->id.ICNTL(20) = 0; /* dense centralized RHS */
1682: /* TODO: Because of non-contiguous indices, the created vecscatter scat_rhs is not done in MPI_Gather, resulting in
1683: very inefficient communication. An optimization is to use VecScatterCreateToZero to gather B to rank 0. Then on rank
1684: 0, re-arrange B into desired order, which is a local operation.
1685: */
1687: /* scatter v_mpi to b_seq because MUMPS before 5.3.0 only supports centralized rhs */
1688: /* wrap dense rhs matrix B into a vector v_mpi */
1689: PetscCall(MatGetLocalSize(B, &m, NULL));
1690: PetscCall(MatDenseGetArray(B, &bray));
1691: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B), 1, nrhs * m, nrhs * M, (const PetscScalar *)bray, &v_mpi));
1692: PetscCall(MatDenseRestoreArray(B, &bray));
1694: /* scatter v_mpi to b_seq in proc[0]. MUMPS requires rhs to be centralized on the host! */
1695: if (!mumps->myid) {
1696: PetscInt *idx;
1697: /* idx: maps from k-th index of v_mpi to (i,j)-th global entry of B */
1698: PetscCall(PetscMalloc1(nrhs * M, &idx));
1699: PetscCall(MatGetOwnershipRanges(B, &rstart));
1700: k = 0;
1701: for (proc = 0; proc < mumps->petsc_size; proc++) {
1702: for (j = 0; j < nrhs; j++) {
1703: for (i = rstart[proc]; i < rstart[proc + 1]; i++) idx[k++] = j * M + i;
1704: }
1705: }
1707: PetscCall(VecCreateSeq(PETSC_COMM_SELF, nrhs * M, &b_seq));
1708: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nrhs * M, idx, PETSC_OWN_POINTER, &is_to));
1709: PetscCall(ISCreateStride(PETSC_COMM_SELF, nrhs * M, 0, 1, &is_from));
1710: } else {
1711: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 0, &b_seq));
1712: PetscCall(ISCreateStride(PETSC_COMM_SELF, 0, 0, 1, &is_to));
1713: PetscCall(ISCreateStride(PETSC_COMM_SELF, 0, 0, 1, &is_from));
1714: }
1715: PetscCall(VecScatterCreate(v_mpi, is_from, b_seq, is_to, &scat_rhs));
1716: PetscCall(VecScatterBegin(scat_rhs, v_mpi, b_seq, INSERT_VALUES, SCATTER_FORWARD));
1717: PetscCall(ISDestroy(&is_to));
1718: PetscCall(ISDestroy(&is_from));
1719: PetscCall(VecScatterEnd(scat_rhs, v_mpi, b_seq, INSERT_VALUES, SCATTER_FORWARD));
1721: if (!mumps->myid) { /* define rhs on the host */
1722: PetscCall(VecGetArray(b_seq, &bray));
1723: mumps->id.rhs = (MumpsScalar *)bray;
1724: PetscCall(VecRestoreArray(b_seq, &bray));
1725: }
1726: }
1727: } else { /* sparse B */
1728: b = (Mat_MPIAIJ *)Bt->data;
1730: /* wrap dense X into a vector v_mpi */
1731: PetscCall(MatGetLocalSize(X, &m, NULL));
1732: PetscCall(MatDenseGetArray(X, &bray));
1733: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)X), 1, nrhs * m, nrhs * M, (const PetscScalar *)bray, &v_mpi));
1734: PetscCall(MatDenseRestoreArray(X, &bray));
1736: if (!mumps->myid) {
1737: PetscCall(MatSeqAIJGetArray(b->A, &aa));
1738: PetscCall(MatGetRowIJ(b->A, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1739: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
1740: PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
1741: mumps->id.rhs_sparse = (MumpsScalar *)aa;
1742: } else {
1743: mumps->id.irhs_ptr = NULL;
1744: mumps->id.irhs_sparse = NULL;
1745: mumps->id.nz_rhs = 0;
1746: mumps->id.rhs_sparse = NULL;
1747: }
1748: }
1750: /* solve phase */
1751: mumps->id.job = JOB_SOLVE;
1752: PetscMUMPS_c(mumps);
1753: 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));
1755: /* scatter mumps distributed solution to petsc vector v_mpi, which shares local arrays with solution matrix X */
1756: PetscCall(MatDenseGetArray(X, &array));
1757: PetscCall(VecPlaceArray(v_mpi, array));
1759: /* create scatter scat_sol */
1760: PetscCall(MatGetOwnershipRanges(X, &rstart));
1761: /* iidx: index for scatter mumps solution to petsc X */
1763: PetscCall(ISCreateStride(PETSC_COMM_SELF, nlsol_loc, 0, 1, &is_from));
1764: PetscCall(PetscMalloc1(nlsol_loc, &idxx));
1765: for (i = 0; i < lsol_loc; i++) {
1766: 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 */
1768: for (proc = 0; proc < mumps->petsc_size; proc++) {
1769: if (isol_loc[i] >= rstart[proc] && isol_loc[i] < rstart[proc + 1]) {
1770: myrstart = rstart[proc];
1771: k = isol_loc[i] - myrstart; /* local index on 1st column of petsc vector X */
1772: iidx = k + myrstart * nrhs; /* maps mumps isol_loc[i] to petsc index in X */
1773: m = rstart[proc + 1] - rstart[proc]; /* rows of X for this proc */
1774: break;
1775: }
1776: }
1778: for (j = 0; j < nrhs; j++) idxx[i + j * lsol_loc] = iidx + j * m;
1779: }
1780: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nlsol_loc, idxx, PETSC_COPY_VALUES, &is_to));
1781: PetscCall(VecScatterCreate(msol_loc, is_from, v_mpi, is_to, &scat_sol));
1782: PetscCall(VecScatterBegin(scat_sol, msol_loc, v_mpi, INSERT_VALUES, SCATTER_FORWARD));
1783: PetscCall(ISDestroy(&is_from));
1784: PetscCall(ISDestroy(&is_to));
1785: PetscCall(VecScatterEnd(scat_sol, msol_loc, v_mpi, INSERT_VALUES, SCATTER_FORWARD));
1786: PetscCall(MatDenseRestoreArray(X, &array));
1788: /* free spaces */
1789: mumps->id.sol_loc = (MumpsScalar *)sol_loc_save;
1790: mumps->id.isol_loc = isol_loc_save;
1792: PetscCall(PetscFree2(sol_loc, isol_loc));
1793: PetscCall(PetscFree(idxx));
1794: PetscCall(VecDestroy(&msol_loc));
1795: PetscCall(VecDestroy(&v_mpi));
1796: if (!denseB) {
1797: if (!mumps->myid) {
1798: b = (Mat_MPIAIJ *)Bt->data;
1799: PetscCall(MatSeqAIJRestoreArray(b->A, &aa));
1800: PetscCall(MatRestoreRowIJ(b->A, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1801: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot restore IJ structure");
1802: }
1803: } else {
1804: if (mumps->ICNTL20 == 0) {
1805: PetscCall(VecDestroy(&b_seq));
1806: PetscCall(VecScatterDestroy(&scat_rhs));
1807: }
1808: }
1809: PetscCall(VecScatterDestroy(&scat_sol));
1810: PetscCall(PetscLogFlops(nrhs * PetscMax(0, (2.0 * (mumps->id.INFO(28) >= 0 ? mumps->id.INFO(28) : -1000000 * mumps->id.INFO(28)) - A->cmap->n))));
1811: PetscFunctionReturn(PETSC_SUCCESS);
1812: }
1814: static PetscErrorCode MatMatSolveTranspose_MUMPS(Mat A, Mat B, Mat X)
1815: {
1816: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1817: const PetscMUMPSInt value = mumps->id.ICNTL(9);
1819: PetscFunctionBegin;
1820: mumps->id.ICNTL(9) = 0;
1821: PetscCall(MatMatSolve_MUMPS(A, B, X));
1822: mumps->id.ICNTL(9) = value;
1823: PetscFunctionReturn(PETSC_SUCCESS);
1824: }
1826: static PetscErrorCode MatMatTransposeSolve_MUMPS(Mat A, Mat Bt, Mat X)
1827: {
1828: PetscBool flg;
1829: Mat B;
1831: PetscFunctionBegin;
1832: PetscCall(PetscObjectTypeCompareAny((PetscObject)Bt, &flg, MATSEQAIJ, MATMPIAIJ, NULL));
1833: PetscCheck(flg, PetscObjectComm((PetscObject)Bt), PETSC_ERR_ARG_WRONG, "Matrix Bt must be MATAIJ matrix");
1835: /* Create B=Bt^T that uses Bt's data structure */
1836: PetscCall(MatCreateTranspose(Bt, &B));
1838: PetscCall(MatMatSolve_MUMPS(A, B, X));
1839: PetscCall(MatDestroy(&B));
1840: PetscFunctionReturn(PETSC_SUCCESS);
1841: }
1843: #if !defined(PETSC_USE_COMPLEX)
1844: /*
1845: input:
1846: F: numeric factor
1847: output:
1848: nneg: total number of negative pivots
1849: nzero: total number of zero pivots
1850: npos: (global dimension of F) - nneg - nzero
1851: */
1852: static PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
1853: {
1854: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
1855: PetscMPIInt size;
1857: PetscFunctionBegin;
1858: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)F), &size));
1859: /* 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 */
1860: 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));
1862: if (nneg) *nneg = mumps->id.INFOG(12);
1863: if (nzero || npos) {
1864: 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");
1865: if (nzero) *nzero = mumps->id.INFOG(28);
1866: if (npos) *npos = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28));
1867: }
1868: PetscFunctionReturn(PETSC_SUCCESS);
1869: }
1870: #endif
1872: static PetscErrorCode MatMumpsGatherNonzerosOnMaster(MatReuse reuse, Mat_MUMPS *mumps)
1873: {
1874: PetscInt i, nreqs;
1875: PetscMUMPSInt *irn, *jcn;
1876: PetscMPIInt count;
1877: PetscInt64 totnnz, remain;
1878: const PetscInt osize = mumps->omp_comm_size;
1879: PetscScalar *val;
1881: PetscFunctionBegin;
1882: if (osize > 1) {
1883: if (reuse == MAT_INITIAL_MATRIX) {
1884: /* master first gathers counts of nonzeros to receive */
1885: if (mumps->is_omp_master) PetscCall(PetscMalloc1(osize, &mumps->recvcount));
1886: PetscCallMPI(MPI_Gather(&mumps->nnz, 1, MPIU_INT64, mumps->recvcount, 1, MPIU_INT64, 0 /*master*/, mumps->omp_comm));
1888: /* Then each computes number of send/recvs */
1889: if (mumps->is_omp_master) {
1890: /* Start from 1 since self communication is not done in MPI */
1891: nreqs = 0;
1892: for (i = 1; i < osize; i++) nreqs += (mumps->recvcount[i] + PETSC_MPI_INT_MAX - 1) / PETSC_MPI_INT_MAX;
1893: } else {
1894: nreqs = (mumps->nnz + PETSC_MPI_INT_MAX - 1) / PETSC_MPI_INT_MAX;
1895: }
1896: PetscCall(PetscMalloc1(nreqs * 3, &mumps->reqs)); /* Triple the requests since we send irn, jcn and val separately */
1898: /* The following code is doing a very simple thing: omp_master rank gathers irn/jcn/val from others.
1899: MPI_Gatherv would be enough if it supports big counts > 2^31-1. Since it does not, and mumps->nnz
1900: might be a prime number > 2^31-1, we have to slice the message. Note omp_comm_size
1901: is very small, the current approach should have no extra overhead compared to MPI_Gatherv.
1902: */
1903: nreqs = 0; /* counter for actual send/recvs */
1904: if (mumps->is_omp_master) {
1905: for (i = 0, totnnz = 0; i < osize; i++) totnnz += mumps->recvcount[i]; /* totnnz = sum of nnz over omp_comm */
1906: PetscCall(PetscMalloc2(totnnz, &irn, totnnz, &jcn));
1907: PetscCall(PetscMalloc1(totnnz, &val));
1909: /* Self communication */
1910: PetscCall(PetscArraycpy(irn, mumps->irn, mumps->nnz));
1911: PetscCall(PetscArraycpy(jcn, mumps->jcn, mumps->nnz));
1912: PetscCall(PetscArraycpy(val, mumps->val, mumps->nnz));
1914: /* Replace mumps->irn/jcn etc on master with the newly allocated bigger arrays */
1915: PetscCall(PetscFree2(mumps->irn, mumps->jcn));
1916: PetscCall(PetscFree(mumps->val_alloc));
1917: mumps->nnz = totnnz;
1918: mumps->irn = irn;
1919: mumps->jcn = jcn;
1920: mumps->val = mumps->val_alloc = val;
1922: irn += mumps->recvcount[0]; /* recvcount[0] is old mumps->nnz on omp rank 0 */
1923: jcn += mumps->recvcount[0];
1924: val += mumps->recvcount[0];
1926: /* Remote communication */
1927: for (i = 1; i < osize; i++) {
1928: count = PetscMin(mumps->recvcount[i], (PetscMPIInt)PETSC_MPI_INT_MAX);
1929: remain = mumps->recvcount[i] - count;
1930: while (count > 0) {
1931: PetscCallMPI(MPI_Irecv(irn, count, MPIU_MUMPSINT, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1932: PetscCallMPI(MPI_Irecv(jcn, count, MPIU_MUMPSINT, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1933: PetscCallMPI(MPI_Irecv(val, count, MPIU_SCALAR, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1934: irn += count;
1935: jcn += count;
1936: val += count;
1937: count = PetscMin(remain, (PetscMPIInt)PETSC_MPI_INT_MAX);
1938: remain -= count;
1939: }
1940: }
1941: } else {
1942: irn = mumps->irn;
1943: jcn = mumps->jcn;
1944: val = mumps->val;
1945: count = PetscMin(mumps->nnz, (PetscMPIInt)PETSC_MPI_INT_MAX);
1946: remain = mumps->nnz - count;
1947: while (count > 0) {
1948: PetscCallMPI(MPI_Isend(irn, count, MPIU_MUMPSINT, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1949: PetscCallMPI(MPI_Isend(jcn, count, MPIU_MUMPSINT, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1950: PetscCallMPI(MPI_Isend(val, count, MPIU_SCALAR, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1951: irn += count;
1952: jcn += count;
1953: val += count;
1954: count = PetscMin(remain, (PetscMPIInt)PETSC_MPI_INT_MAX);
1955: remain -= count;
1956: }
1957: }
1958: } else {
1959: nreqs = 0;
1960: if (mumps->is_omp_master) {
1961: val = mumps->val + mumps->recvcount[0];
1962: for (i = 1; i < osize; i++) { /* Remote communication only since self data is already in place */
1963: count = PetscMin(mumps->recvcount[i], (PetscMPIInt)PETSC_MPI_INT_MAX);
1964: remain = mumps->recvcount[i] - count;
1965: while (count > 0) {
1966: PetscCallMPI(MPI_Irecv(val, count, MPIU_SCALAR, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1967: val += count;
1968: count = PetscMin(remain, (PetscMPIInt)PETSC_MPI_INT_MAX);
1969: remain -= count;
1970: }
1971: }
1972: } else {
1973: val = mumps->val;
1974: count = PetscMin(mumps->nnz, (PetscMPIInt)PETSC_MPI_INT_MAX);
1975: remain = mumps->nnz - count;
1976: while (count > 0) {
1977: PetscCallMPI(MPI_Isend(val, count, MPIU_SCALAR, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1978: val += count;
1979: count = PetscMin(remain, (PetscMPIInt)PETSC_MPI_INT_MAX);
1980: remain -= count;
1981: }
1982: }
1983: }
1984: PetscCallMPI(MPI_Waitall(nreqs, mumps->reqs, MPI_STATUSES_IGNORE));
1985: mumps->tag++; /* It is totally fine for above send/recvs to share one mpi tag */
1986: }
1987: PetscFunctionReturn(PETSC_SUCCESS);
1988: }
1990: static PetscErrorCode MatFactorNumeric_MUMPS(Mat F, Mat A, PETSC_UNUSED const MatFactorInfo *info)
1991: {
1992: Mat_MUMPS *mumps = (Mat_MUMPS *)(F)->data;
1993: PetscBool isMPIAIJ;
1995: PetscFunctionBegin;
1996: if (mumps->id.INFOG(1) < 0 && !(mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0)) {
1997: 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)));
1998: 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)));
1999: PetscFunctionReturn(PETSC_SUCCESS);
2000: }
2002: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, mumps));
2003: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_REUSE_MATRIX, mumps));
2005: /* numerical factorization phase */
2006: mumps->id.job = JOB_FACTNUMERIC;
2007: if (!mumps->id.ICNTL(18)) { /* A is centralized */
2008: if (!mumps->myid) mumps->id.a = (MumpsScalar *)mumps->val;
2009: } else {
2010: mumps->id.a_loc = (MumpsScalar *)mumps->val;
2011: }
2012: PetscMUMPS_c(mumps);
2013: if (mumps->id.INFOG(1) < 0) {
2014: 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));
2015: if (mumps->id.INFOG(1) == -10) {
2016: 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)));
2017: F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
2018: } else if (mumps->id.INFOG(1) == -13) {
2019: 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)));
2020: F->factorerrortype = MAT_FACTOR_OUTMEMORY;
2021: } else if (mumps->id.INFOG(1) == -8 || mumps->id.INFOG(1) == -9 || (-16 < mumps->id.INFOG(1) && mumps->id.INFOG(1) < -10)) {
2022: 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)));
2023: F->factorerrortype = MAT_FACTOR_OUTMEMORY;
2024: } else {
2025: PetscCall(PetscInfo(F, "MUMPS error in numerical factorization: INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
2026: F->factorerrortype = MAT_FACTOR_OTHER;
2027: }
2028: }
2029: 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));
2031: F->assembled = PETSC_TRUE;
2033: if (F->schur) { /* reset Schur status to unfactored */
2034: #if defined(PETSC_HAVE_CUDA)
2035: F->schur->offloadmask = PETSC_OFFLOAD_CPU;
2036: #endif
2037: if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
2038: mumps->id.ICNTL(19) = 2;
2039: PetscCall(MatTranspose(F->schur, MAT_INPLACE_MATRIX, &F->schur));
2040: }
2041: PetscCall(MatFactorRestoreSchurComplement(F, NULL, MAT_FACTOR_SCHUR_UNFACTORED));
2042: }
2044: /* just to be sure that ICNTL(19) value returned by a call from MatMumpsGetIcntl is always consistent */
2045: if (!mumps->sym && mumps->id.ICNTL(19) && mumps->id.ICNTL(19) != 1) mumps->id.ICNTL(19) = 3;
2047: if (!mumps->is_omp_master) mumps->id.INFO(23) = 0;
2048: if (mumps->petsc_size > 1) {
2049: PetscInt lsol_loc;
2050: PetscScalar *sol_loc;
2052: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &isMPIAIJ));
2054: /* distributed solution; Create x_seq=sol_loc for repeated use */
2055: if (mumps->x_seq) {
2056: PetscCall(VecScatterDestroy(&mumps->scat_sol));
2057: PetscCall(PetscFree2(mumps->id.sol_loc, mumps->id.isol_loc));
2058: PetscCall(VecDestroy(&mumps->x_seq));
2059: }
2060: lsol_loc = mumps->id.INFO(23); /* length of sol_loc */
2061: PetscCall(PetscMalloc2(lsol_loc, &sol_loc, lsol_loc, &mumps->id.isol_loc));
2062: mumps->id.lsol_loc = lsol_loc;
2063: mumps->id.sol_loc = (MumpsScalar *)sol_loc;
2064: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, lsol_loc, sol_loc, &mumps->x_seq));
2065: }
2066: PetscCall(PetscLogFlops((double)mumps->id.RINFO(2)));
2067: PetscFunctionReturn(PETSC_SUCCESS);
2068: }
2070: /* Sets MUMPS options from the options database */
2071: static PetscErrorCode MatSetFromOptions_MUMPS(Mat F, Mat A)
2072: {
2073: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2074: PetscMUMPSInt icntl = 0, size, *listvar_schur;
2075: PetscInt info[80], i, ninfo = 80, rbs, cbs;
2076: PetscBool flg = PETSC_FALSE, schur = (PetscBool)(mumps->id.ICNTL(26) == -1);
2077: MumpsScalar *arr;
2079: PetscFunctionBegin;
2080: PetscOptionsBegin(PetscObjectComm((PetscObject)F), ((PetscObject)F)->prefix, "MUMPS Options", "Mat");
2081: if (mumps->id.job == JOB_NULL) { /* MatSetFromOptions_MUMPS() has never been called before */
2082: PetscInt nthreads = 0;
2083: PetscInt nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
2084: PetscInt nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0;
2086: mumps->petsc_comm = PetscObjectComm((PetscObject)A);
2087: PetscCallMPI(MPI_Comm_size(mumps->petsc_comm, &mumps->petsc_size));
2088: PetscCallMPI(MPI_Comm_rank(mumps->petsc_comm, &mumps->myid)); /* "if (!myid)" still works even if mumps_comm is different */
2090: PetscCall(PetscOptionsName("-mat_mumps_use_omp_threads", "Convert MPI processes into OpenMP threads", "None", &mumps->use_petsc_omp_support));
2091: if (mumps->use_petsc_omp_support) nthreads = -1; /* -1 will let PetscOmpCtrlCreate() guess a proper value when user did not supply one */
2092: /* do not use PetscOptionsInt() so that the option -mat_mumps_use_omp_threads is not displayed twice in the help */
2093: PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)F)->prefix, "-mat_mumps_use_omp_threads", &nthreads, NULL));
2094: if (mumps->use_petsc_omp_support) {
2095: 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",
2096: ((PetscObject)F)->prefix ? ((PetscObject)F)->prefix : "");
2097: 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 : "");
2098: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
2099: PetscCall(PetscOmpCtrlCreate(mumps->petsc_comm, nthreads, &mumps->omp_ctrl));
2100: PetscCall(PetscOmpCtrlGetOmpComms(mumps->omp_ctrl, &mumps->omp_comm, &mumps->mumps_comm, &mumps->is_omp_master));
2101: #endif
2102: } else {
2103: mumps->omp_comm = PETSC_COMM_SELF;
2104: mumps->mumps_comm = mumps->petsc_comm;
2105: mumps->is_omp_master = PETSC_TRUE;
2106: }
2107: PetscCallMPI(MPI_Comm_size(mumps->omp_comm, &mumps->omp_comm_size));
2108: mumps->reqs = NULL;
2109: mumps->tag = 0;
2111: if (mumps->mumps_comm != MPI_COMM_NULL) {
2112: if (PetscDefined(HAVE_OPENMP_SUPPORT) && mumps->use_petsc_omp_support) {
2113: /* It looks like MUMPS does not dup the input comm. Dup a new comm for MUMPS to avoid any tag mismatches. */
2114: MPI_Comm comm;
2115: PetscCallMPI(MPI_Comm_dup(mumps->mumps_comm, &comm));
2116: mumps->mumps_comm = comm;
2117: } else PetscCall(PetscCommGetComm(mumps->petsc_comm, &mumps->mumps_comm));
2118: }
2120: mumps->id.comm_fortran = MPI_Comm_c2f(mumps->mumps_comm);
2121: mumps->id.job = JOB_INIT;
2122: mumps->id.par = 1; /* host participates factorizaton and solve */
2123: mumps->id.sym = mumps->sym;
2125: size = mumps->id.size_schur;
2126: arr = mumps->id.schur;
2127: listvar_schur = mumps->id.listvar_schur;
2128: PetscMUMPS_c(mumps);
2129: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "MUMPS error: INFOG(1)=%d " MUMPS_MANUALS, mumps->id.INFOG(1));
2131: /* set PETSc-MUMPS default options - override MUMPS default */
2132: mumps->id.ICNTL(3) = 0;
2133: mumps->id.ICNTL(4) = 0;
2134: if (mumps->petsc_size == 1) {
2135: mumps->id.ICNTL(18) = 0; /* centralized assembled matrix input */
2136: mumps->id.ICNTL(7) = 7; /* automatic choice of ordering done by the package */
2137: } else {
2138: mumps->id.ICNTL(18) = 3; /* distributed assembled matrix input */
2139: mumps->id.ICNTL(21) = 1; /* distributed solution */
2140: }
2142: /* restore cached ICNTL and CNTL values */
2143: for (icntl = 0; icntl < nICNTL_pre; ++icntl) mumps->id.ICNTL(mumps->ICNTL_pre[1 + 2 * icntl]) = mumps->ICNTL_pre[2 + 2 * icntl];
2144: for (icntl = 0; icntl < nCNTL_pre; ++icntl) mumps->id.CNTL((PetscInt)mumps->CNTL_pre[1 + 2 * icntl]) = mumps->CNTL_pre[2 + 2 * icntl];
2145: PetscCall(PetscFree(mumps->ICNTL_pre));
2146: PetscCall(PetscFree(mumps->CNTL_pre));
2148: if (schur) {
2149: mumps->id.size_schur = size;
2150: mumps->id.schur_lld = size;
2151: mumps->id.schur = arr;
2152: mumps->id.listvar_schur = listvar_schur;
2153: if (mumps->petsc_size > 1) {
2154: PetscBool gs; /* gs is false if any rank other than root has non-empty IS */
2156: mumps->id.ICNTL(19) = 1; /* MUMPS returns Schur centralized on the host */
2157: gs = mumps->myid ? (mumps->id.size_schur ? PETSC_FALSE : PETSC_TRUE) : PETSC_TRUE; /* always true on root; false on others if their size != 0 */
2158: PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &gs, 1, MPIU_BOOL, MPI_LAND, mumps->petsc_comm));
2159: PetscCheck(gs, PETSC_COMM_SELF, PETSC_ERR_SUP, "MUMPS distributed parallel Schur complements not yet supported from PETSc");
2160: } else {
2161: if (F->factortype == MAT_FACTOR_LU) {
2162: mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */
2163: } else {
2164: mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */
2165: }
2166: }
2167: mumps->id.ICNTL(26) = -1;
2168: }
2170: /* copy MUMPS default control values from master to slaves. Although slaves do not call MUMPS, they may access these values in code.
2171: For example, ICNTL(9) is initialized to 1 by MUMPS and slaves check ICNTL(9) in MatSolve_MUMPS.
2172: */
2173: PetscCallMPI(MPI_Bcast(mumps->id.icntl, 40, MPI_INT, 0, mumps->omp_comm));
2174: PetscCallMPI(MPI_Bcast(mumps->id.cntl, 15, MPIU_REAL, 0, mumps->omp_comm));
2176: mumps->scat_rhs = NULL;
2177: mumps->scat_sol = NULL;
2178: }
2179: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_1", "ICNTL(1): output stream for error messages", "None", mumps->id.ICNTL(1), &icntl, &flg));
2180: if (flg) mumps->id.ICNTL(1) = icntl;
2181: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_2", "ICNTL(2): output stream for diagnostic printing, statistics, and warning", "None", mumps->id.ICNTL(2), &icntl, &flg));
2182: if (flg) mumps->id.ICNTL(2) = icntl;
2183: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_3", "ICNTL(3): output stream for global information, collected on the host", "None", mumps->id.ICNTL(3), &icntl, &flg));
2184: if (flg) mumps->id.ICNTL(3) = icntl;
2186: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_4", "ICNTL(4): level of printing (0 to 4)", "None", mumps->id.ICNTL(4), &icntl, &flg));
2187: if (flg) mumps->id.ICNTL(4) = icntl;
2188: if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */
2190: 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));
2191: if (flg) mumps->id.ICNTL(6) = icntl;
2193: 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));
2194: if (flg) {
2195: 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");
2196: mumps->id.ICNTL(7) = icntl;
2197: }
2199: 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));
2200: /* 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() */
2201: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_10", "ICNTL(10): max num of refinements", "None", mumps->id.ICNTL(10), &mumps->id.ICNTL(10), NULL));
2202: 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));
2203: 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));
2204: 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));
2205: 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));
2206: PetscCall(MatGetBlockSizes(A, &rbs, &cbs));
2207: if (rbs == cbs && rbs > 1) mumps->id.ICNTL(15) = -rbs;
2208: 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));
2209: if (flg) {
2210: PetscCheck(mumps->id.ICNTL(15) <= 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Positive -mat_mumps_icntl_15 not handled");
2211: 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");
2212: }
2213: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_19", "ICNTL(19): computes the Schur complement", "None", mumps->id.ICNTL(19), &mumps->id.ICNTL(19), NULL));
2214: if (mumps->id.ICNTL(19) <= 0 || mumps->id.ICNTL(19) > 3) { /* reset any schur data (if any) */
2215: PetscCall(MatDestroy(&F->schur));
2216: PetscCall(MatMumpsResetSchur_Private(mumps));
2217: }
2219: /* Two MPICH Fortran MPI_IN_PLACE binding bugs prevented the use of 'mpich + mumps'. One happened with "mpi4py + mpich + mumps",
2220: and was reported by Firedrake. See https://bitbucket.org/mpi4py/mpi4py/issues/162/mpi4py-initialization-breaks-fortran
2221: and a petsc-maint mailing list thread with subject 'MUMPS segfaults in parallel because of ...'
2222: This bug was fixed by https://github.com/pmodels/mpich/pull/4149. But the fix brought a new bug,
2223: see https://github.com/pmodels/mpich/issues/5589. This bug was fixed by https://github.com/pmodels/mpich/pull/5590.
2224: In short, we could not use distributed RHS until with MPICH v4.0b1 or we enabled a workaround in mumps-5.6.2+
2225: */
2226: #if PETSC_PKG_MUMPS_VERSION_GE(5, 6, 2) && defined(PETSC_HAVE_MUMPS_AVOID_MPI_IN_PLACE)
2227: mumps->ICNTL20 = 10;
2228: #elif PETSC_PKG_MUMPS_VERSION_LT(5, 3, 0) || (defined(PETSC_HAVE_MPICH_NUMVERSION) && (PETSC_HAVE_MPICH_NUMVERSION < 40000101))
2229: mumps->ICNTL20 = 0; /* Centralized dense RHS*/
2230: #else
2231: mumps->ICNTL20 = 10; /* Distributed dense RHS*/
2232: #endif
2233: 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));
2234: 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);
2235: #if PETSC_PKG_MUMPS_VERSION_LT(5, 3, 0)
2236: PetscCheck(!flg || mumps->ICNTL20 != 10, PETSC_COMM_SELF, PETSC_ERR_SUP, "ICNTL(20)=10 is not supported before MUMPS-5.3.0");
2237: #endif
2238: /* 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 */
2240: 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));
2241: 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));
2242: 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));
2243: if (mumps->id.ICNTL(24)) { mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */ }
2245: 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));
2246: 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));
2247: 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));
2248: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_28", "ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering", "None", mumps->id.ICNTL(28), &mumps->id.ICNTL(28), NULL));
2249: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_29", "ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis", "None", mumps->id.ICNTL(29), &mumps->id.ICNTL(29), NULL));
2250: /* 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 */
2251: 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));
2252: /* 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 */
2253: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_33", "ICNTL(33): compute determinant", "None", mumps->id.ICNTL(33), &mumps->id.ICNTL(33), NULL));
2254: 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));
2255: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_36", "ICNTL(36): choice of BLR factorization variant", "None", mumps->id.ICNTL(36), &mumps->id.ICNTL(36), NULL));
2256: 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));
2257: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_58", "ICNTL(58): defines options for symbolic factorization", "None", mumps->id.ICNTL(58), &mumps->id.ICNTL(58), NULL));
2259: PetscCall(PetscOptionsReal("-mat_mumps_cntl_1", "CNTL(1): relative pivoting threshold", "None", mumps->id.CNTL(1), &mumps->id.CNTL(1), NULL));
2260: PetscCall(PetscOptionsReal("-mat_mumps_cntl_2", "CNTL(2): stopping criterion of refinement", "None", mumps->id.CNTL(2), &mumps->id.CNTL(2), NULL));
2261: PetscCall(PetscOptionsReal("-mat_mumps_cntl_3", "CNTL(3): absolute pivoting threshold", "None", mumps->id.CNTL(3), &mumps->id.CNTL(3), NULL));
2262: PetscCall(PetscOptionsReal("-mat_mumps_cntl_4", "CNTL(4): value for static pivoting", "None", mumps->id.CNTL(4), &mumps->id.CNTL(4), NULL));
2263: PetscCall(PetscOptionsReal("-mat_mumps_cntl_5", "CNTL(5): fixation for null pivots", "None", mumps->id.CNTL(5), &mumps->id.CNTL(5), NULL));
2264: PetscCall(PetscOptionsReal("-mat_mumps_cntl_7", "CNTL(7): dropping parameter used during BLR", "None", mumps->id.CNTL(7), &mumps->id.CNTL(7), NULL));
2266: 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));
2268: PetscCall(PetscOptionsIntArray("-mat_mumps_view_info", "request INFO local to each processor", "", info, &ninfo, NULL));
2269: if (ninfo) {
2270: PetscCheck(ninfo <= 80, PETSC_COMM_SELF, PETSC_ERR_USER, "number of INFO %" PetscInt_FMT " must <= 80", ninfo);
2271: PetscCall(PetscMalloc1(ninfo, &mumps->info));
2272: mumps->ninfo = ninfo;
2273: for (i = 0; i < ninfo; i++) {
2274: PetscCheck(info[i] >= 0 && info[i] <= 80, PETSC_COMM_SELF, PETSC_ERR_USER, "index of INFO %" PetscInt_FMT " must between 1 and 80", ninfo);
2275: mumps->info[i] = info[i];
2276: }
2277: }
2278: PetscOptionsEnd();
2279: PetscFunctionReturn(PETSC_SUCCESS);
2280: }
2282: static PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F, Mat A, PETSC_UNUSED const MatFactorInfo *info, Mat_MUMPS *mumps)
2283: {
2284: PetscFunctionBegin;
2285: if (mumps->id.INFOG(1) < 0) {
2286: PetscCheck(!A->erroriffailure, PETSC_COMM_SELF, PETSC_ERR_LIB, "MUMPS error in analysis: INFOG(1)=%d " MUMPS_MANUALS, mumps->id.INFOG(1));
2287: if (mumps->id.INFOG(1) == -6) {
2288: 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)));
2289: F->factorerrortype = MAT_FACTOR_STRUCT_ZEROPIVOT;
2290: } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) {
2291: 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)));
2292: F->factorerrortype = MAT_FACTOR_OUTMEMORY;
2293: } else if (mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0) {
2294: PetscCall(PetscInfo(F, "MUMPS error in analysis: empty matrix\n"));
2295: } else {
2296: PetscCall(PetscInfo(F, "MUMPS error in analysis: INFOG(1)=%d, INFO(2)=%d " MUMPS_MANUALS "\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
2297: F->factorerrortype = MAT_FACTOR_OTHER;
2298: }
2299: }
2300: PetscFunctionReturn(PETSC_SUCCESS);
2301: }
2303: static PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F, Mat A, IS r, PETSC_UNUSED IS c, const MatFactorInfo *info)
2304: {
2305: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2306: Vec b;
2307: const PetscInt M = A->rmap->N;
2309: PetscFunctionBegin;
2310: if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2311: /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */
2312: PetscFunctionReturn(PETSC_SUCCESS);
2313: }
2315: /* Set MUMPS options from the options database */
2316: PetscCall(MatSetFromOptions_MUMPS(F, A));
2318: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2319: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));
2321: /* analysis phase */
2322: mumps->id.job = JOB_FACTSYMBOLIC;
2323: mumps->id.n = M;
2324: switch (mumps->id.ICNTL(18)) {
2325: case 0: /* centralized assembled matrix input */
2326: if (!mumps->myid) {
2327: mumps->id.nnz = mumps->nnz;
2328: mumps->id.irn = mumps->irn;
2329: mumps->id.jcn = mumps->jcn;
2330: if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2331: if (r && mumps->id.ICNTL(7) == 7) {
2332: mumps->id.ICNTL(7) = 1;
2333: if (!mumps->myid) {
2334: const PetscInt *idx;
2335: PetscInt i;
2337: PetscCall(PetscMalloc1(M, &mumps->id.perm_in));
2338: PetscCall(ISGetIndices(r, &idx));
2339: for (i = 0; i < M; i++) PetscCall(PetscMUMPSIntCast(idx[i] + 1, &mumps->id.perm_in[i])); /* perm_in[]: start from 1, not 0! */
2340: PetscCall(ISRestoreIndices(r, &idx));
2341: }
2342: }
2343: }
2344: break;
2345: case 3: /* distributed assembled matrix input (size>1) */
2346: mumps->id.nnz_loc = mumps->nnz;
2347: mumps->id.irn_loc = mumps->irn;
2348: mumps->id.jcn_loc = mumps->jcn;
2349: if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2350: if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2351: PetscCall(MatCreateVecs(A, NULL, &b));
2352: PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2353: PetscCall(VecDestroy(&b));
2354: }
2355: break;
2356: }
2357: PetscMUMPS_c(mumps);
2358: PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));
2360: F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
2361: F->ops->solve = MatSolve_MUMPS;
2362: F->ops->solvetranspose = MatSolveTranspose_MUMPS;
2363: F->ops->matsolve = MatMatSolve_MUMPS;
2364: F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS;
2365: F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;
2367: mumps->matstruc = SAME_NONZERO_PATTERN;
2368: PetscFunctionReturn(PETSC_SUCCESS);
2369: }
2371: /* Note the Petsc r and c permutations are ignored */
2372: static PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F, Mat A, PETSC_UNUSED IS r, PETSC_UNUSED IS c, const MatFactorInfo *info)
2373: {
2374: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2375: Vec b;
2376: const PetscInt M = A->rmap->N;
2378: PetscFunctionBegin;
2379: if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2380: /* F is assembled by a previous call of MatLUFactorSymbolic_BAIJMUMPS() */
2381: PetscFunctionReturn(PETSC_SUCCESS);
2382: }
2384: /* Set MUMPS options from the options database */
2385: PetscCall(MatSetFromOptions_MUMPS(F, A));
2387: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2388: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));
2390: /* analysis phase */
2391: mumps->id.job = JOB_FACTSYMBOLIC;
2392: mumps->id.n = M;
2393: switch (mumps->id.ICNTL(18)) {
2394: case 0: /* centralized assembled matrix input */
2395: if (!mumps->myid) {
2396: mumps->id.nnz = mumps->nnz;
2397: mumps->id.irn = mumps->irn;
2398: mumps->id.jcn = mumps->jcn;
2399: if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2400: }
2401: break;
2402: case 3: /* distributed assembled matrix input (size>1) */
2403: mumps->id.nnz_loc = mumps->nnz;
2404: mumps->id.irn_loc = mumps->irn;
2405: mumps->id.jcn_loc = mumps->jcn;
2406: if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2407: if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2408: PetscCall(MatCreateVecs(A, NULL, &b));
2409: PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2410: PetscCall(VecDestroy(&b));
2411: }
2412: break;
2413: }
2414: PetscMUMPS_c(mumps);
2415: PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));
2417: F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
2418: F->ops->solve = MatSolve_MUMPS;
2419: F->ops->solvetranspose = MatSolveTranspose_MUMPS;
2420: F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;
2422: mumps->matstruc = SAME_NONZERO_PATTERN;
2423: PetscFunctionReturn(PETSC_SUCCESS);
2424: }
2426: /* Note the Petsc r permutation and factor info are ignored */
2427: static PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F, Mat A, PETSC_UNUSED IS r, const MatFactorInfo *info)
2428: {
2429: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2430: Vec b;
2431: const PetscInt M = A->rmap->N;
2433: PetscFunctionBegin;
2434: if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2435: /* F is assembled by a previous call of MatCholeskyFactorSymbolic_MUMPS() */
2436: PetscFunctionReturn(PETSC_SUCCESS);
2437: }
2439: /* Set MUMPS options from the options database */
2440: PetscCall(MatSetFromOptions_MUMPS(F, A));
2442: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2443: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));
2445: /* analysis phase */
2446: mumps->id.job = JOB_FACTSYMBOLIC;
2447: mumps->id.n = M;
2448: switch (mumps->id.ICNTL(18)) {
2449: case 0: /* centralized assembled matrix input */
2450: if (!mumps->myid) {
2451: mumps->id.nnz = mumps->nnz;
2452: mumps->id.irn = mumps->irn;
2453: mumps->id.jcn = mumps->jcn;
2454: if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2455: }
2456: break;
2457: case 3: /* distributed assembled matrix input (size>1) */
2458: mumps->id.nnz_loc = mumps->nnz;
2459: mumps->id.irn_loc = mumps->irn;
2460: mumps->id.jcn_loc = mumps->jcn;
2461: if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2462: if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2463: PetscCall(MatCreateVecs(A, NULL, &b));
2464: PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2465: PetscCall(VecDestroy(&b));
2466: }
2467: break;
2468: }
2469: PetscMUMPS_c(mumps);
2470: PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));
2472: F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS;
2473: F->ops->solve = MatSolve_MUMPS;
2474: F->ops->solvetranspose = MatSolve_MUMPS;
2475: F->ops->matsolve = MatMatSolve_MUMPS;
2476: F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS;
2477: F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;
2478: #if defined(PETSC_USE_COMPLEX)
2479: F->ops->getinertia = NULL;
2480: #else
2481: F->ops->getinertia = MatGetInertia_SBAIJMUMPS;
2482: #endif
2484: mumps->matstruc = SAME_NONZERO_PATTERN;
2485: PetscFunctionReturn(PETSC_SUCCESS);
2486: }
2488: static PetscErrorCode MatView_MUMPS(Mat A, PetscViewer viewer)
2489: {
2490: PetscBool iascii;
2491: PetscViewerFormat format;
2492: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
2494: PetscFunctionBegin;
2495: /* check if matrix is mumps type */
2496: if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(PETSC_SUCCESS);
2498: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
2499: if (iascii) {
2500: PetscCall(PetscViewerGetFormat(viewer, &format));
2501: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
2502: PetscCall(PetscViewerASCIIPrintf(viewer, "MUMPS run parameters:\n"));
2503: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
2504: PetscCall(PetscViewerASCIIPrintf(viewer, " SYM (matrix type): %d\n", mumps->id.sym));
2505: PetscCall(PetscViewerASCIIPrintf(viewer, " PAR (host participation): %d\n", mumps->id.par));
2506: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(1) (output for error): %d\n", mumps->id.ICNTL(1)));
2507: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(2) (output of diagnostic msg): %d\n", mumps->id.ICNTL(2)));
2508: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(3) (output for global info): %d\n", mumps->id.ICNTL(3)));
2509: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(4) (level of printing): %d\n", mumps->id.ICNTL(4)));
2510: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(5) (input mat struct): %d\n", mumps->id.ICNTL(5)));
2511: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(6) (matrix prescaling): %d\n", mumps->id.ICNTL(6)));
2512: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(7) (sequential matrix ordering):%d\n", mumps->id.ICNTL(7)));
2513: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(8) (scaling strategy): %d\n", mumps->id.ICNTL(8)));
2514: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(10) (max num of refinements): %d\n", mumps->id.ICNTL(10)));
2515: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(11) (error analysis): %d\n", mumps->id.ICNTL(11)));
2516: if (mumps->id.ICNTL(11) > 0) {
2517: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(4) (inf norm of input mat): %g\n", (double)mumps->id.RINFOG(4)));
2518: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(5) (inf norm of solution): %g\n", (double)mumps->id.RINFOG(5)));
2519: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(6) (inf norm of residual): %g\n", (double)mumps->id.RINFOG(6)));
2520: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(7),RINFOG(8) (backward error est): %g, %g\n", (double)mumps->id.RINFOG(7), (double)mumps->id.RINFOG(8)));
2521: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(9) (error estimate): %g\n", (double)mumps->id.RINFOG(9)));
2522: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n", (double)mumps->id.RINFOG(10), (double)mumps->id.RINFOG(11)));
2523: }
2524: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(12) (efficiency control): %d\n", mumps->id.ICNTL(12)));
2525: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(13) (sequential factorization of the root node): %d\n", mumps->id.ICNTL(13)));
2526: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(14) (percentage of estimated workspace increase): %d\n", mumps->id.ICNTL(14)));
2527: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(15) (compression of the input matrix): %d\n", mumps->id.ICNTL(15)));
2528: /* ICNTL(15-17) not used */
2529: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(18) (input mat struct): %d\n", mumps->id.ICNTL(18)));
2530: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(19) (Schur complement info): %d\n", mumps->id.ICNTL(19)));
2531: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(20) (RHS sparse pattern): %d\n", mumps->id.ICNTL(20)));
2532: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(21) (solution struct): %d\n", mumps->id.ICNTL(21)));
2533: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(22) (in-core/out-of-core facility): %d\n", mumps->id.ICNTL(22)));
2534: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(23) (max size of memory can be allocated locally):%d\n", mumps->id.ICNTL(23)));
2536: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(24) (detection of null pivot rows): %d\n", mumps->id.ICNTL(24)));
2537: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(25) (computation of a null space basis): %d\n", mumps->id.ICNTL(25)));
2538: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(26) (Schur options for RHS or solution): %d\n", mumps->id.ICNTL(26)));
2539: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(27) (blocking size for multiple RHS): %d\n", mumps->id.ICNTL(27)));
2540: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(28) (use parallel or sequential ordering): %d\n", mumps->id.ICNTL(28)));
2541: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(29) (parallel ordering): %d\n", mumps->id.ICNTL(29)));
2543: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(30) (user-specified set of entries in inv(A)): %d\n", mumps->id.ICNTL(30)));
2544: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(31) (factors is discarded in the solve phase): %d\n", mumps->id.ICNTL(31)));
2545: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(33) (compute determinant): %d\n", mumps->id.ICNTL(33)));
2546: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(35) (activate BLR based factorization): %d\n", mumps->id.ICNTL(35)));
2547: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(36) (choice of BLR factorization variant): %d\n", mumps->id.ICNTL(36)));
2548: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(38) (estimated compression rate of LU factors): %d\n", mumps->id.ICNTL(38)));
2549: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(58) (options for symbolic factorization): %d\n", mumps->id.ICNTL(58)));
2551: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(1) (relative pivoting threshold): %g\n", (double)mumps->id.CNTL(1)));
2552: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(2) (stopping criterion of refinement): %g\n", (double)mumps->id.CNTL(2)));
2553: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(3) (absolute pivoting threshold): %g\n", (double)mumps->id.CNTL(3)));
2554: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(4) (value of static pivoting): %g\n", (double)mumps->id.CNTL(4)));
2555: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(5) (fixation for null pivots): %g\n", (double)mumps->id.CNTL(5)));
2556: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(7) (dropping parameter for BLR): %g\n", (double)mumps->id.CNTL(7)));
2558: /* information local to each processor */
2559: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(1) (local estimated flops for the elimination after analysis):\n"));
2560: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
2561: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %g\n", mumps->myid, (double)mumps->id.RINFO(1)));
2562: PetscCall(PetscViewerFlush(viewer));
2563: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(2) (local estimated flops for the assembly after factorization):\n"));
2564: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %g\n", mumps->myid, (double)mumps->id.RINFO(2)));
2565: PetscCall(PetscViewerFlush(viewer));
2566: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(3) (local estimated flops for the elimination after factorization):\n"));
2567: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %g\n", mumps->myid, (double)mumps->id.RINFO(3)));
2568: PetscCall(PetscViewerFlush(viewer));
2570: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization):\n"));
2571: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(15)));
2572: PetscCall(PetscViewerFlush(viewer));
2574: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization):\n"));
2575: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(16)));
2576: PetscCall(PetscViewerFlush(viewer));
2578: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(23) (num of pivots eliminated on this processor after factorization):\n"));
2579: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(23)));
2580: PetscCall(PetscViewerFlush(viewer));
2582: if (mumps->ninfo && mumps->ninfo <= 80) {
2583: PetscInt i;
2584: for (i = 0; i < mumps->ninfo; i++) {
2585: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(%" PetscInt_FMT "):\n", mumps->info[i]));
2586: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(mumps->info[i])));
2587: PetscCall(PetscViewerFlush(viewer));
2588: }
2589: }
2590: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
2591: } else PetscCall(PetscViewerASCIIPrintf(viewer, " Use -%sksp_view ::ascii_info_detail to display information for all processes\n", ((PetscObject)A)->prefix ? ((PetscObject)A)->prefix : ""));
2593: if (mumps->myid == 0) { /* information from the host */
2594: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(1) (global estimated flops for the elimination after analysis): %g\n", (double)mumps->id.RINFOG(1)));
2595: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(2) (global estimated flops for the assembly after factorization): %g\n", (double)mumps->id.RINFOG(2)));
2596: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(3) (global estimated flops for the elimination after factorization): %g\n", (double)mumps->id.RINFOG(3)));
2597: 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)));
2599: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(3) (estimated real workspace for factors on all processors after analysis): %d\n", mumps->id.INFOG(3)));
2600: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d\n", mumps->id.INFOG(4)));
2601: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(5) (estimated maximum front size in the complete tree): %d\n", mumps->id.INFOG(5)));
2602: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(6) (number of nodes in the complete tree): %d\n", mumps->id.INFOG(6)));
2603: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(7) (ordering option effectively used after analysis): %d\n", mumps->id.INFOG(7)));
2604: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d\n", mumps->id.INFOG(8)));
2605: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d\n", mumps->id.INFOG(9)));
2606: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(10) (total integer space store the matrix factors after factorization): %d\n", mumps->id.INFOG(10)));
2607: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(11) (order of largest frontal matrix after factorization): %d\n", mumps->id.INFOG(11)));
2608: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(12) (number of off-diagonal pivots): %d\n", mumps->id.INFOG(12)));
2609: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(13) (number of delayed pivots after factorization): %d\n", mumps->id.INFOG(13)));
2610: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(14) (number of memory compress after factorization): %d\n", mumps->id.INFOG(14)));
2611: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(15) (number of steps of iterative refinement after solution): %d\n", mumps->id.INFOG(15)));
2612: 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)));
2613: 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)));
2614: 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)));
2615: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): %d\n", mumps->id.INFOG(19)));
2616: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(20) (estimated number of entries in the factors): %d\n", mumps->id.INFOG(20)));
2617: 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)));
2618: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): %d\n", mumps->id.INFOG(22)));
2619: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d\n", mumps->id.INFOG(23)));
2620: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d\n", mumps->id.INFOG(24)));
2621: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(25) (after factorization: number of pivots modified by static pivoting): %d\n", mumps->id.INFOG(25)));
2622: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(28) (after factorization: number of null pivots encountered): %d\n", mumps->id.INFOG(28)));
2623: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(29) (after factorization: effective number of entries in the factors (sum over all processors)): %d\n", mumps->id.INFOG(29)));
2624: 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)));
2625: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(32) (after analysis: type of analysis done): %d\n", mumps->id.INFOG(32)));
2626: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(33) (value used for ICNTL(8)): %d\n", mumps->id.INFOG(33)));
2627: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(34) (exponent of the determinant if determinant is requested): %d\n", mumps->id.INFOG(34)));
2628: 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)));
2629: 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)));
2630: 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)));
2631: 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)));
2632: 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)));
2633: }
2634: }
2635: }
2636: PetscFunctionReturn(PETSC_SUCCESS);
2637: }
2639: static PetscErrorCode MatGetInfo_MUMPS(Mat A, PETSC_UNUSED MatInfoType flag, MatInfo *info)
2640: {
2641: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
2643: PetscFunctionBegin;
2644: info->block_size = 1.0;
2645: info->nz_allocated = mumps->id.INFOG(20) >= 0 ? mumps->id.INFOG(20) : -1000000 * mumps->id.INFOG(20);
2646: info->nz_used = mumps->id.INFOG(20) >= 0 ? mumps->id.INFOG(20) : -1000000 * mumps->id.INFOG(20);
2647: info->nz_unneeded = 0.0;
2648: info->assemblies = 0.0;
2649: info->mallocs = 0.0;
2650: info->memory = 0.0;
2651: info->fill_ratio_given = 0;
2652: info->fill_ratio_needed = 0;
2653: info->factor_mallocs = 0;
2654: PetscFunctionReturn(PETSC_SUCCESS);
2655: }
2657: static PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is)
2658: {
2659: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2660: const PetscScalar *arr;
2661: const PetscInt *idxs;
2662: PetscInt size, i;
2664: PetscFunctionBegin;
2665: PetscCall(ISGetLocalSize(is, &size));
2666: /* Schur complement matrix */
2667: PetscCall(MatDestroy(&F->schur));
2668: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, size, size, NULL, &F->schur));
2669: PetscCall(MatDenseGetArrayRead(F->schur, &arr));
2670: mumps->id.schur = (MumpsScalar *)arr;
2671: mumps->id.size_schur = size;
2672: mumps->id.schur_lld = size;
2673: PetscCall(MatDenseRestoreArrayRead(F->schur, &arr));
2674: if (mumps->sym == 1) PetscCall(MatSetOption(F->schur, MAT_SPD, PETSC_TRUE));
2676: /* MUMPS expects Fortran style indices */
2677: PetscCall(PetscFree(mumps->id.listvar_schur));
2678: PetscCall(PetscMalloc1(size, &mumps->id.listvar_schur));
2679: PetscCall(ISGetIndices(is, &idxs));
2680: for (i = 0; i < size; i++) PetscCall(PetscMUMPSIntCast(idxs[i] + 1, &mumps->id.listvar_schur[i]));
2681: PetscCall(ISRestoreIndices(is, &idxs));
2682: /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */
2683: mumps->id.ICNTL(26) = -1;
2684: PetscFunctionReturn(PETSC_SUCCESS);
2685: }
2687: static PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F, Mat *S)
2688: {
2689: Mat St;
2690: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2691: PetscScalar *array;
2693: PetscFunctionBegin;
2694: PetscCheck(mumps->id.ICNTL(19), PetscObjectComm((PetscObject)F), PETSC_ERR_ORDER, "Schur complement mode not selected! Call MatFactorSetSchurIS() to enable it");
2695: PetscCall(MatCreate(PETSC_COMM_SELF, &St));
2696: PetscCall(MatSetSizes(St, PETSC_DECIDE, PETSC_DECIDE, mumps->id.size_schur, mumps->id.size_schur));
2697: PetscCall(MatSetType(St, MATDENSE));
2698: PetscCall(MatSetUp(St));
2699: PetscCall(MatDenseGetArray(St, &array));
2700: if (!mumps->sym) { /* MUMPS always return a full matrix */
2701: if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
2702: PetscInt i, j, N = mumps->id.size_schur;
2703: for (i = 0; i < N; i++) {
2704: for (j = 0; j < N; j++) {
2705: #if !defined(PETSC_USE_COMPLEX)
2706: PetscScalar val = mumps->id.schur[i * N + j];
2707: #else
2708: PetscScalar val = mumps->id.schur[i * N + j].r + PETSC_i * mumps->id.schur[i * N + j].i;
2709: #endif
2710: array[j * N + i] = val;
2711: }
2712: }
2713: } else { /* stored by columns */
2714: PetscCall(PetscArraycpy(array, mumps->id.schur, mumps->id.size_schur * mumps->id.size_schur));
2715: }
2716: } else { /* either full or lower-triangular (not packed) */
2717: if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */
2718: PetscInt i, j, N = mumps->id.size_schur;
2719: for (i = 0; i < N; i++) {
2720: for (j = i; j < N; j++) {
2721: #if !defined(PETSC_USE_COMPLEX)
2722: PetscScalar val = mumps->id.schur[i * N + j];
2723: #else
2724: PetscScalar val = mumps->id.schur[i * N + j].r + PETSC_i * mumps->id.schur[i * N + j].i;
2725: #endif
2726: array[i * N + j] = array[j * N + i] = val;
2727: }
2728: }
2729: } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */
2730: PetscCall(PetscArraycpy(array, mumps->id.schur, mumps->id.size_schur * mumps->id.size_schur));
2731: } else { /* ICNTL(19) == 1 lower triangular stored by rows */
2732: PetscInt i, j, N = mumps->id.size_schur;
2733: for (i = 0; i < N; i++) {
2734: for (j = 0; j < i + 1; j++) {
2735: #if !defined(PETSC_USE_COMPLEX)
2736: PetscScalar val = mumps->id.schur[i * N + j];
2737: #else
2738: PetscScalar val = mumps->id.schur[i * N + j].r + PETSC_i * mumps->id.schur[i * N + j].i;
2739: #endif
2740: array[i * N + j] = array[j * N + i] = val;
2741: }
2742: }
2743: }
2744: }
2745: PetscCall(MatDenseRestoreArray(St, &array));
2746: *S = St;
2747: PetscFunctionReturn(PETSC_SUCCESS);
2748: }
2750: static PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F, PetscInt icntl, PetscInt ival)
2751: {
2752: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2754: PetscFunctionBegin;
2755: if (mumps->id.job == JOB_NULL) { /* need to cache icntl and ival since PetscMUMPS_c() has never been called */
2756: PetscInt i, nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0; /* number of already cached ICNTL */
2757: for (i = 0; i < nICNTL_pre; ++i)
2758: if (mumps->ICNTL_pre[1 + 2 * i] == icntl) break; /* is this ICNTL already cached? */
2759: if (i == nICNTL_pre) { /* not already cached */
2760: if (i > 0) PetscCall(PetscRealloc(sizeof(PetscMUMPSInt) * (2 * nICNTL_pre + 3), &mumps->ICNTL_pre));
2761: else PetscCall(PetscCalloc(sizeof(PetscMUMPSInt) * 3, &mumps->ICNTL_pre));
2762: mumps->ICNTL_pre[0]++;
2763: }
2764: mumps->ICNTL_pre[1 + 2 * i] = icntl;
2765: PetscCall(PetscMUMPSIntCast(ival, mumps->ICNTL_pre + 2 + 2 * i));
2766: } else PetscCall(PetscMUMPSIntCast(ival, &mumps->id.ICNTL(icntl)));
2767: PetscFunctionReturn(PETSC_SUCCESS);
2768: }
2770: static PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F, PetscInt icntl, PetscInt *ival)
2771: {
2772: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2774: PetscFunctionBegin;
2775: if (mumps->id.job == JOB_NULL) {
2776: PetscInt i, nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0;
2777: *ival = 0;
2778: for (i = 0; i < nICNTL_pre; ++i) {
2779: if (mumps->ICNTL_pre[1 + 2 * i] == icntl) *ival = mumps->ICNTL_pre[2 + 2 * i];
2780: }
2781: } else *ival = mumps->id.ICNTL(icntl);
2782: PetscFunctionReturn(PETSC_SUCCESS);
2783: }
2785: /*@
2786: MatMumpsSetIcntl - Set MUMPS parameter ICNTL() <https://mumps-solver.org/index.php?page=doc>
2788: Logically Collective
2790: Input Parameters:
2791: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2792: . icntl - index of MUMPS parameter array ICNTL()
2793: - ival - value of MUMPS ICNTL(icntl)
2795: Options Database Key:
2796: . -mat_mumps_icntl_<icntl> <ival> - change the option numbered icntl to ival
2798: Level: beginner
2800: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2801: @*/
2802: PetscErrorCode MatMumpsSetIcntl(Mat F, PetscInt icntl, PetscInt ival)
2803: {
2804: PetscFunctionBegin;
2806: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2809: PetscCheck((icntl >= 1 && icntl <= 38) || icntl == 58, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported ICNTL value %" PetscInt_FMT, icntl);
2810: PetscTryMethod(F, "MatMumpsSetIcntl_C", (Mat, PetscInt, PetscInt), (F, icntl, ival));
2811: PetscFunctionReturn(PETSC_SUCCESS);
2812: }
2814: /*@
2815: MatMumpsGetIcntl - Get MUMPS parameter ICNTL() <https://mumps-solver.org/index.php?page=doc>
2817: Logically Collective
2819: Input Parameters:
2820: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2821: - icntl - index of MUMPS parameter array ICNTL()
2823: Output Parameter:
2824: . ival - value of MUMPS ICNTL(icntl)
2826: Level: beginner
2828: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2829: @*/
2830: PetscErrorCode MatMumpsGetIcntl(Mat F, PetscInt icntl, PetscInt *ival)
2831: {
2832: PetscFunctionBegin;
2834: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2836: PetscAssertPointer(ival, 3);
2837: PetscCheck((icntl >= 1 && icntl <= 38) || icntl == 58, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported ICNTL value %" PetscInt_FMT, icntl);
2838: PetscUseMethod(F, "MatMumpsGetIcntl_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
2839: PetscFunctionReturn(PETSC_SUCCESS);
2840: }
2842: static PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F, PetscInt icntl, PetscReal val)
2843: {
2844: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2846: PetscFunctionBegin;
2847: if (mumps->id.job == JOB_NULL) {
2848: PetscInt i, nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
2849: for (i = 0; i < nCNTL_pre; ++i)
2850: if (mumps->CNTL_pre[1 + 2 * i] == icntl) break;
2851: if (i == nCNTL_pre) {
2852: if (i > 0) PetscCall(PetscRealloc(sizeof(PetscReal) * (2 * nCNTL_pre + 3), &mumps->CNTL_pre));
2853: else PetscCall(PetscCalloc(sizeof(PetscReal) * 3, &mumps->CNTL_pre));
2854: mumps->CNTL_pre[0]++;
2855: }
2856: mumps->CNTL_pre[1 + 2 * i] = icntl;
2857: mumps->CNTL_pre[2 + 2 * i] = val;
2858: } else mumps->id.CNTL(icntl) = val;
2859: PetscFunctionReturn(PETSC_SUCCESS);
2860: }
2862: static PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F, PetscInt icntl, PetscReal *val)
2863: {
2864: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2866: PetscFunctionBegin;
2867: if (mumps->id.job == JOB_NULL) {
2868: PetscInt i, nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
2869: *val = 0.0;
2870: for (i = 0; i < nCNTL_pre; ++i) {
2871: if (mumps->CNTL_pre[1 + 2 * i] == icntl) *val = mumps->CNTL_pre[2 + 2 * i];
2872: }
2873: } else *val = mumps->id.CNTL(icntl);
2874: PetscFunctionReturn(PETSC_SUCCESS);
2875: }
2877: /*@
2878: MatMumpsSetCntl - Set MUMPS parameter CNTL() <https://mumps-solver.org/index.php?page=doc>
2880: Logically Collective
2882: Input Parameters:
2883: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2884: . icntl - index of MUMPS parameter array CNTL()
2885: - val - value of MUMPS CNTL(icntl)
2887: Options Database Key:
2888: . -mat_mumps_cntl_<icntl> <val> - change the option numbered icntl to ival
2890: Level: beginner
2892: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2893: @*/
2894: PetscErrorCode MatMumpsSetCntl(Mat F, PetscInt icntl, PetscReal val)
2895: {
2896: PetscFunctionBegin;
2898: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2901: PetscCheck(icntl >= 1 && icntl <= 7, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported CNTL value %" PetscInt_FMT, icntl);
2902: PetscTryMethod(F, "MatMumpsSetCntl_C", (Mat, PetscInt, PetscReal), (F, icntl, val));
2903: PetscFunctionReturn(PETSC_SUCCESS);
2904: }
2906: /*@
2907: MatMumpsGetCntl - Get MUMPS parameter CNTL() <https://mumps-solver.org/index.php?page=doc>
2909: Logically Collective
2911: Input Parameters:
2912: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2913: - icntl - index of MUMPS parameter array CNTL()
2915: Output Parameter:
2916: . val - value of MUMPS CNTL(icntl)
2918: Level: beginner
2920: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2921: @*/
2922: PetscErrorCode MatMumpsGetCntl(Mat F, PetscInt icntl, PetscReal *val)
2923: {
2924: PetscFunctionBegin;
2926: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2928: PetscAssertPointer(val, 3);
2929: PetscCheck(icntl >= 1 && icntl <= 7, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported CNTL value %" PetscInt_FMT, icntl);
2930: PetscUseMethod(F, "MatMumpsGetCntl_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
2931: PetscFunctionReturn(PETSC_SUCCESS);
2932: }
2934: static PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F, PetscInt icntl, PetscInt *info)
2935: {
2936: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2938: PetscFunctionBegin;
2939: *info = mumps->id.INFO(icntl);
2940: PetscFunctionReturn(PETSC_SUCCESS);
2941: }
2943: static PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F, PetscInt icntl, PetscInt *infog)
2944: {
2945: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2947: PetscFunctionBegin;
2948: *infog = mumps->id.INFOG(icntl);
2949: PetscFunctionReturn(PETSC_SUCCESS);
2950: }
2952: static PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F, PetscInt icntl, PetscReal *rinfo)
2953: {
2954: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2956: PetscFunctionBegin;
2957: *rinfo = mumps->id.RINFO(icntl);
2958: PetscFunctionReturn(PETSC_SUCCESS);
2959: }
2961: static PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F, PetscInt icntl, PetscReal *rinfog)
2962: {
2963: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2965: PetscFunctionBegin;
2966: *rinfog = mumps->id.RINFOG(icntl);
2967: PetscFunctionReturn(PETSC_SUCCESS);
2968: }
2970: static PetscErrorCode MatMumpsGetNullPivots_MUMPS(Mat F, PetscInt *size, PetscInt **array)
2971: {
2972: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2974: PetscFunctionBegin;
2975: 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");
2976: *size = 0;
2977: *array = NULL;
2978: if (!mumps->myid) {
2979: *size = mumps->id.INFOG(28);
2980: PetscCall(PetscMalloc1(*size, array));
2981: for (int i = 0; i < *size; i++) (*array)[i] = mumps->id.pivnul_list[i] - 1;
2982: }
2983: PetscFunctionReturn(PETSC_SUCCESS);
2984: }
2986: static PetscErrorCode MatMumpsGetInverse_MUMPS(Mat F, Mat spRHS)
2987: {
2988: Mat Bt = NULL, Btseq = NULL;
2989: PetscBool flg;
2990: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2991: PetscScalar *aa;
2992: PetscInt spnr, *ia, *ja, M, nrhs;
2994: PetscFunctionBegin;
2995: PetscAssertPointer(spRHS, 2);
2996: PetscCall(PetscObjectTypeCompare((PetscObject)spRHS, MATTRANSPOSEVIRTUAL, &flg));
2997: if (flg) {
2998: PetscCall(MatTransposeGetMat(spRHS, &Bt));
2999: } else SETERRQ(PetscObjectComm((PetscObject)spRHS), PETSC_ERR_ARG_WRONG, "Matrix spRHS must be type MATTRANSPOSEVIRTUAL matrix");
3001: PetscCall(MatMumpsSetIcntl(F, 30, 1));
3003: if (mumps->petsc_size > 1) {
3004: Mat_MPIAIJ *b = (Mat_MPIAIJ *)Bt->data;
3005: Btseq = b->A;
3006: } else {
3007: Btseq = Bt;
3008: }
3010: PetscCall(MatGetSize(spRHS, &M, &nrhs));
3011: mumps->id.nrhs = nrhs;
3012: mumps->id.lrhs = M;
3013: mumps->id.rhs = NULL;
3015: if (!mumps->myid) {
3016: PetscCall(MatSeqAIJGetArray(Btseq, &aa));
3017: PetscCall(MatGetRowIJ(Btseq, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
3018: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
3019: PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
3020: mumps->id.rhs_sparse = (MumpsScalar *)aa;
3021: } else {
3022: mumps->id.irhs_ptr = NULL;
3023: mumps->id.irhs_sparse = NULL;
3024: mumps->id.nz_rhs = 0;
3025: mumps->id.rhs_sparse = NULL;
3026: }
3027: mumps->id.ICNTL(20) = 1; /* rhs is sparse */
3028: mumps->id.ICNTL(21) = 0; /* solution is in assembled centralized format */
3030: /* solve phase */
3031: mumps->id.job = JOB_SOLVE;
3032: PetscMUMPS_c(mumps);
3033: 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));
3035: if (!mumps->myid) {
3036: PetscCall(MatSeqAIJRestoreArray(Btseq, &aa));
3037: PetscCall(MatRestoreRowIJ(Btseq, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
3038: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
3039: }
3040: PetscFunctionReturn(PETSC_SUCCESS);
3041: }
3043: /*@
3044: MatMumpsGetInverse - Get user-specified set of entries in inverse of `A` <https://mumps-solver.org/index.php?page=doc>
3046: Logically Collective
3048: Input Parameter:
3049: . F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3051: Output Parameter:
3052: . spRHS - sequential sparse matrix in `MATTRANSPOSEVIRTUAL` format with requested entries of inverse of `A`
3054: Level: beginner
3056: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCreateTranspose()`
3057: @*/
3058: PetscErrorCode MatMumpsGetInverse(Mat F, Mat spRHS)
3059: {
3060: PetscFunctionBegin;
3062: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3063: PetscUseMethod(F, "MatMumpsGetInverse_C", (Mat, Mat), (F, spRHS));
3064: PetscFunctionReturn(PETSC_SUCCESS);
3065: }
3067: static PetscErrorCode MatMumpsGetInverseTranspose_MUMPS(Mat F, Mat spRHST)
3068: {
3069: Mat spRHS;
3071: PetscFunctionBegin;
3072: PetscCall(MatCreateTranspose(spRHST, &spRHS));
3073: PetscCall(MatMumpsGetInverse_MUMPS(F, spRHS));
3074: PetscCall(MatDestroy(&spRHS));
3075: PetscFunctionReturn(PETSC_SUCCESS);
3076: }
3078: /*@
3079: MatMumpsGetInverseTranspose - Get user-specified set of entries in inverse of matrix $A^T $ <https://mumps-solver.org/index.php?page=doc>
3081: Logically Collective
3083: Input Parameter:
3084: . F - the factored matrix of A obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3086: Output Parameter:
3087: . spRHST - sequential sparse matrix in `MATAIJ` format containing the requested entries of inverse of `A`^T
3089: Level: beginner
3091: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCreateTranspose()`, `MatMumpsGetInverse()`
3092: @*/
3093: PetscErrorCode MatMumpsGetInverseTranspose(Mat F, Mat spRHST)
3094: {
3095: PetscBool flg;
3097: PetscFunctionBegin;
3099: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3100: PetscCall(PetscObjectTypeCompareAny((PetscObject)spRHST, &flg, MATSEQAIJ, MATMPIAIJ, NULL));
3101: PetscCheck(flg, PetscObjectComm((PetscObject)spRHST), PETSC_ERR_ARG_WRONG, "Matrix spRHST must be MATAIJ matrix");
3103: PetscUseMethod(F, "MatMumpsGetInverseTranspose_C", (Mat, Mat), (F, spRHST));
3104: PetscFunctionReturn(PETSC_SUCCESS);
3105: }
3107: /*@
3108: MatMumpsGetInfo - Get MUMPS parameter INFO() <https://mumps-solver.org/index.php?page=doc>
3110: Logically Collective
3112: Input Parameters:
3113: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3114: - icntl - index of MUMPS parameter array INFO()
3116: Output Parameter:
3117: . ival - value of MUMPS INFO(icntl)
3119: Level: beginner
3121: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
3122: @*/
3123: PetscErrorCode MatMumpsGetInfo(Mat F, PetscInt icntl, PetscInt *ival)
3124: {
3125: PetscFunctionBegin;
3127: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3128: PetscAssertPointer(ival, 3);
3129: PetscUseMethod(F, "MatMumpsGetInfo_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
3130: PetscFunctionReturn(PETSC_SUCCESS);
3131: }
3133: /*@
3134: MatMumpsGetInfog - Get MUMPS parameter INFOG() <https://mumps-solver.org/index.php?page=doc>
3136: Logically Collective
3138: Input Parameters:
3139: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3140: - icntl - index of MUMPS parameter array INFOG()
3142: Output Parameter:
3143: . ival - value of MUMPS INFOG(icntl)
3145: Level: beginner
3147: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
3148: @*/
3149: PetscErrorCode MatMumpsGetInfog(Mat F, PetscInt icntl, PetscInt *ival)
3150: {
3151: PetscFunctionBegin;
3153: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3154: PetscAssertPointer(ival, 3);
3155: PetscUseMethod(F, "MatMumpsGetInfog_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
3156: PetscFunctionReturn(PETSC_SUCCESS);
3157: }
3159: /*@
3160: MatMumpsGetRinfo - Get MUMPS parameter RINFO() <https://mumps-solver.org/index.php?page=doc>
3162: Logically Collective
3164: Input Parameters:
3165: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3166: - icntl - index of MUMPS parameter array RINFO()
3168: Output Parameter:
3169: . val - value of MUMPS RINFO(icntl)
3171: Level: beginner
3173: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfog()`
3174: @*/
3175: PetscErrorCode MatMumpsGetRinfo(Mat F, PetscInt icntl, PetscReal *val)
3176: {
3177: PetscFunctionBegin;
3179: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3180: PetscAssertPointer(val, 3);
3181: PetscUseMethod(F, "MatMumpsGetRinfo_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
3182: PetscFunctionReturn(PETSC_SUCCESS);
3183: }
3185: /*@
3186: MatMumpsGetRinfog - Get MUMPS parameter RINFOG() <https://mumps-solver.org/index.php?page=doc>
3188: Logically Collective
3190: Input Parameters:
3191: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3192: - icntl - index of MUMPS parameter array RINFOG()
3194: Output Parameter:
3195: . val - value of MUMPS RINFOG(icntl)
3197: Level: beginner
3199: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`
3200: @*/
3201: PetscErrorCode MatMumpsGetRinfog(Mat F, PetscInt icntl, PetscReal *val)
3202: {
3203: PetscFunctionBegin;
3205: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3206: PetscAssertPointer(val, 3);
3207: PetscUseMethod(F, "MatMumpsGetRinfog_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
3208: PetscFunctionReturn(PETSC_SUCCESS);
3209: }
3211: /*@
3212: MatMumpsGetNullPivots - Get MUMPS parameter PIVNUL_LIST() <https://mumps-solver.org/index.php?page=doc>
3214: Logically Collective
3216: Input Parameter:
3217: . F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3219: Output Parameters:
3220: + size - local size of the array. The size of the array is non-zero only on the host.
3221: - 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
3222: for freeing this array.
3224: Level: beginner
3226: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`
3227: @*/
3228: PetscErrorCode MatMumpsGetNullPivots(Mat F, PetscInt *size, PetscInt **array)
3229: {
3230: PetscFunctionBegin;
3232: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3233: PetscAssertPointer(size, 2);
3234: PetscAssertPointer(array, 3);
3235: PetscUseMethod(F, "MatMumpsGetNullPivots_C", (Mat, PetscInt *, PetscInt **), (F, size, array));
3236: PetscFunctionReturn(PETSC_SUCCESS);
3237: }
3239: /*MC
3240: MATSOLVERMUMPS - A matrix type providing direct solvers (LU and Cholesky) for
3241: distributed and sequential matrices via the external package MUMPS <https://mumps-solver.org/index.php?page=doc>
3243: Works with `MATAIJ` and `MATSBAIJ` matrices
3245: Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch to have PETSc installed with MUMPS
3247: 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.
3248: See details below.
3250: Use `-pc_type cholesky` or `lu` `-pc_factor_mat_solver_type mumps` to use this direct solver
3252: Options Database Keys:
3253: + -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages
3254: . -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning
3255: . -mat_mumps_icntl_3 - ICNTL(3): output stream for global information, collected on the host
3256: . -mat_mumps_icntl_4 - ICNTL(4): level of printing (0 to 4)
3257: . -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7)
3258: . -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
3259: Use -pc_factor_mat_ordering_type <type> to have PETSc perform the ordering (sequential only)
3260: . -mat_mumps_icntl_8 - ICNTL(8): scaling strategy (-2 to 8 or 77)
3261: . -mat_mumps_icntl_10 - ICNTL(10): max num of refinements
3262: . -mat_mumps_icntl_11 - ICNTL(11): statistics related to an error analysis (via -ksp_view)
3263: . -mat_mumps_icntl_12 - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3)
3264: . -mat_mumps_icntl_13 - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting
3265: . -mat_mumps_icntl_14 - ICNTL(14): percentage increase in the estimated working space
3266: . -mat_mumps_icntl_15 - ICNTL(15): compression of the input matrix resulting from a block format
3267: . -mat_mumps_icntl_19 - ICNTL(19): computes the Schur complement
3268: . -mat_mumps_icntl_20 - ICNTL(20): give MUMPS centralized (0) or distributed (10) dense RHS
3269: . -mat_mumps_icntl_22 - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1)
3270: . -mat_mumps_icntl_23 - ICNTL(23): max size of the working memory (MB) that can allocate per processor
3271: . -mat_mumps_icntl_24 - ICNTL(24): detection of null pivot rows (0 or 1)
3272: . -mat_mumps_icntl_25 - ICNTL(25): compute a solution of a deficient matrix and a null space basis
3273: . -mat_mumps_icntl_26 - ICNTL(26): drives the solution phase if a Schur complement matrix
3274: . -mat_mumps_icntl_28 - ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering
3275: . -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis
3276: . -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A)
3277: . -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization
3278: . -mat_mumps_icntl_33 - ICNTL(33): compute determinant
3279: . -mat_mumps_icntl_35 - ICNTL(35): level of activation of BLR (Block Low-Rank) feature
3280: . -mat_mumps_icntl_36 - ICNTL(36): controls the choice of BLR factorization variant
3281: . -mat_mumps_icntl_38 - ICNTL(38): sets the estimated compression rate of LU factors with BLR
3282: . -mat_mumps_icntl_58 - ICNTL(58): options for symbolic factorization
3283: . -mat_mumps_cntl_1 - CNTL(1): relative pivoting threshold
3284: . -mat_mumps_cntl_2 - CNTL(2): stopping criterion of refinement
3285: . -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold
3286: . -mat_mumps_cntl_4 - CNTL(4): value for static pivoting
3287: . -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots
3288: . -mat_mumps_cntl_7 - CNTL(7): precision of the dropping parameter used during BLR factorization
3289: - -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.
3290: Default might be the number of cores per CPU package (socket) as reported by hwloc and suggested by the MUMPS manual.
3292: Level: beginner
3294: Notes:
3295: 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
3296: error if the matrix is Hermitian.
3298: 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
3299: `MatSetOptionsPrefixFactor()` on the matrix from which the factor was obtained or `MatSetOptionsPrefix()` on the factor matrix.
3301: When a MUMPS factorization fails inside a KSP solve, for example with a `KSP_DIVERGED_PC_FAILED`, one can find the MUMPS information about
3302: the failure with
3303: .vb
3304: KSPGetPC(ksp,&pc);
3305: PCFactorGetMatrix(pc,&mat);
3306: MatMumpsGetInfo(mat,....);
3307: MatMumpsGetInfog(mat,....); etc.
3308: .ve
3309: Or run with `-ksp_error_if_not_converged` and the program will be stopped and the information printed in the error message.
3311: MUMPS provides 64-bit integer support in two build modes:
3312: full 64-bit: here MUMPS is built with C preprocessing flag -DINTSIZE64 and Fortran compiler option -i8, -fdefault-integer-8 or equivalent, and
3313: requires all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS built the same way with 64-bit integers (for example ILP64 Intel MKL and MPI).
3315: selective 64-bit: with the default MUMPS build, 64-bit integers have been introduced where needed. In compressed sparse row (CSR) storage of matrices,
3316: 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
3317: 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
3318: integer) build of all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS.
3320: 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.
3322: Two modes to run MUMPS/PETSc with OpenMP
3323: .vb
3324: Set OMP_NUM_THREADS and run with fewer MPI ranks than cores. For example, if you want to have 16 OpenMP
3325: threads per rank, then you may use "export OMP_NUM_THREADS=16 && mpirun -n 4 ./test".
3326: .ve
3328: .vb
3329: -mat_mumps_use_omp_threads [m] and run your code with as many MPI ranks as the number of cores. For example,
3330: 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"
3331: .ve
3333: To run MUMPS in MPI+OpenMP hybrid mode (i.e., enable multithreading in MUMPS), but still run the non-MUMPS part
3334: (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`
3335: (or `--with-hwloc`), and have an MPI that supports MPI-3.0's process shared memory (which is usually available). Since MUMPS calls BLAS
3336: libraries, to really get performance, you should have multithreaded BLAS libraries such as Intel MKL, AMD ACML, Cray libSci or OpenBLAS
3337: (PETSc will automatically try to utilized a threaded BLAS if --with-openmp is provided).
3339: 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
3340: processes on each compute node. Listing the processes in rank ascending order, we split processes on a node into consecutive groups of
3341: 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
3342: 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
3343: 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.
3344: 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,
3345: 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
3346: 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
3347: cores in socket 1, that definitely hurts locality. On the other hand, if you map MPI ranks consecutively on the two sockets, then the
3348: 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.
3349: 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
3350: examine the mapping result.
3352: 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,
3353: 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
3354: calls `omp_set_num_threads`(m) internally before calling MUMPS.
3356: See {cite}`heroux2011bi` and {cite}`gutierrez2017accommodating`
3358: .seealso: [](ch_matrices), `Mat`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`, `KSPGetPC()`, `PCFactorGetMatrix()`
3359: M*/
3361: static PetscErrorCode MatFactorGetSolverType_mumps(PETSC_UNUSED Mat A, MatSolverType *type)
3362: {
3363: PetscFunctionBegin;
3364: *type = MATSOLVERMUMPS;
3365: PetscFunctionReturn(PETSC_SUCCESS);
3366: }
3368: /* MatGetFactor for Seq and MPI AIJ matrices */
3369: static PetscErrorCode MatGetFactor_aij_mumps(Mat A, MatFactorType ftype, Mat *F)
3370: {
3371: Mat B;
3372: Mat_MUMPS *mumps;
3373: PetscBool isSeqAIJ, isDiag, isDense;
3374: PetscMPIInt size;
3376: PetscFunctionBegin;
3377: #if defined(PETSC_USE_COMPLEX)
3378: if (ftype == MAT_FACTOR_CHOLESKY && A->hermitian == PETSC_BOOL3_TRUE && A->symmetric != PETSC_BOOL3_TRUE) {
3379: PetscCall(PetscInfo(A, "Hermitian MAT_FACTOR_CHOLESKY is not supported. Use MAT_FACTOR_LU instead.\n"));
3380: *F = NULL;
3381: PetscFunctionReturn(PETSC_SUCCESS);
3382: }
3383: #endif
3384: /* Create the factorization matrix */
3385: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATSEQAIJ, &isSeqAIJ));
3386: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATDIAGONAL, &isDiag));
3387: PetscCall(PetscObjectTypeCompareAny((PetscObject)A, &isDense, MATSEQDENSE, MATMPIDENSE, NULL));
3388: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3389: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3390: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &((PetscObject)B)->type_name));
3391: PetscCall(MatSetUp(B));
3393: PetscCall(PetscNew(&mumps));
3395: B->ops->view = MatView_MUMPS;
3396: B->ops->getinfo = MatGetInfo_MUMPS;
3398: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3399: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3400: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3401: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3402: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3403: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3404: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3405: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3406: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3407: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3408: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3409: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3410: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3411: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3413: if (ftype == MAT_FACTOR_LU) {
3414: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3415: B->factortype = MAT_FACTOR_LU;
3416: if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij;
3417: else if (isDiag) mumps->ConvertToTriples = MatConvertToTriples_diagonal_xaij;
3418: else if (isDense) mumps->ConvertToTriples = MatConvertToTriples_dense_xaij;
3419: else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij;
3420: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3421: mumps->sym = 0;
3422: } else {
3423: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3424: B->factortype = MAT_FACTOR_CHOLESKY;
3425: if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij;
3426: else if (isDiag) mumps->ConvertToTriples = MatConvertToTriples_diagonal_xaij;
3427: else if (isDense) mumps->ConvertToTriples = MatConvertToTriples_dense_xaij;
3428: else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij;
3429: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_CHOLESKY]));
3430: #if defined(PETSC_USE_COMPLEX)
3431: mumps->sym = 2;
3432: #else
3433: if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3434: else mumps->sym = 2;
3435: #endif
3436: }
3438: /* set solvertype */
3439: PetscCall(PetscFree(B->solvertype));
3440: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3441: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3442: if (size == 1) {
3443: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3444: B->canuseordering = PETSC_TRUE;
3445: }
3446: B->ops->destroy = MatDestroy_MUMPS;
3447: B->data = (void *)mumps;
3449: *F = B;
3450: mumps->id.job = JOB_NULL;
3451: mumps->ICNTL_pre = NULL;
3452: mumps->CNTL_pre = NULL;
3453: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3454: PetscFunctionReturn(PETSC_SUCCESS);
3455: }
3457: /* MatGetFactor for Seq and MPI SBAIJ matrices */
3458: static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A, PETSC_UNUSED MatFactorType ftype, Mat *F)
3459: {
3460: Mat B;
3461: Mat_MUMPS *mumps;
3462: PetscBool isSeqSBAIJ;
3463: PetscMPIInt size;
3465: PetscFunctionBegin;
3466: #if defined(PETSC_USE_COMPLEX)
3467: if (ftype == MAT_FACTOR_CHOLESKY && A->hermitian == PETSC_BOOL3_TRUE && A->symmetric != PETSC_BOOL3_TRUE) {
3468: PetscCall(PetscInfo(A, "Hermitian MAT_FACTOR_CHOLESKY is not supported. Use MAT_FACTOR_LU instead.\n"));
3469: *F = NULL;
3470: PetscFunctionReturn(PETSC_SUCCESS);
3471: }
3472: #endif
3473: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3474: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3475: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &((PetscObject)B)->type_name));
3476: PetscCall(MatSetUp(B));
3478: PetscCall(PetscNew(&mumps));
3479: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQSBAIJ, &isSeqSBAIJ));
3480: if (isSeqSBAIJ) {
3481: mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij;
3482: } else {
3483: mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij;
3484: }
3486: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3487: B->ops->view = MatView_MUMPS;
3488: B->ops->getinfo = MatGetInfo_MUMPS;
3490: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3491: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3492: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3493: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3494: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3495: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3496: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3497: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3498: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3499: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3500: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3501: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3502: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3503: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3505: B->factortype = MAT_FACTOR_CHOLESKY;
3506: #if defined(PETSC_USE_COMPLEX)
3507: mumps->sym = 2;
3508: #else
3509: if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3510: else mumps->sym = 2;
3511: #endif
3513: /* set solvertype */
3514: PetscCall(PetscFree(B->solvertype));
3515: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3516: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3517: if (size == 1) {
3518: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3519: B->canuseordering = PETSC_TRUE;
3520: }
3521: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_CHOLESKY]));
3522: B->ops->destroy = MatDestroy_MUMPS;
3523: B->data = (void *)mumps;
3525: *F = B;
3526: mumps->id.job = JOB_NULL;
3527: mumps->ICNTL_pre = NULL;
3528: mumps->CNTL_pre = NULL;
3529: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3530: PetscFunctionReturn(PETSC_SUCCESS);
3531: }
3533: static PetscErrorCode MatGetFactor_baij_mumps(Mat A, MatFactorType ftype, Mat *F)
3534: {
3535: Mat B;
3536: Mat_MUMPS *mumps;
3537: PetscBool isSeqBAIJ;
3538: PetscMPIInt size;
3540: PetscFunctionBegin;
3541: /* Create the factorization matrix */
3542: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQBAIJ, &isSeqBAIJ));
3543: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3544: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3545: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &((PetscObject)B)->type_name));
3546: PetscCall(MatSetUp(B));
3548: PetscCall(PetscNew(&mumps));
3549: if (ftype == MAT_FACTOR_LU) {
3550: B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS;
3551: B->factortype = MAT_FACTOR_LU;
3552: if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij;
3553: else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij;
3554: mumps->sym = 0;
3555: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3556: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead");
3558: B->ops->view = MatView_MUMPS;
3559: B->ops->getinfo = MatGetInfo_MUMPS;
3561: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3562: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3563: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3564: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3565: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3566: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3567: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3568: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3569: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3570: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3571: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3572: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3573: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3574: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3576: /* set solvertype */
3577: PetscCall(PetscFree(B->solvertype));
3578: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3579: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3580: if (size == 1) {
3581: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3582: B->canuseordering = PETSC_TRUE;
3583: }
3584: B->ops->destroy = MatDestroy_MUMPS;
3585: B->data = (void *)mumps;
3587: *F = B;
3588: mumps->id.job = JOB_NULL;
3589: mumps->ICNTL_pre = NULL;
3590: mumps->CNTL_pre = NULL;
3591: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3592: PetscFunctionReturn(PETSC_SUCCESS);
3593: }
3595: /* MatGetFactor for Seq and MPI SELL matrices */
3596: static PetscErrorCode MatGetFactor_sell_mumps(Mat A, MatFactorType ftype, Mat *F)
3597: {
3598: Mat B;
3599: Mat_MUMPS *mumps;
3600: PetscBool isSeqSELL;
3601: PetscMPIInt size;
3603: PetscFunctionBegin;
3604: /* Create the factorization matrix */
3605: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQSELL, &isSeqSELL));
3606: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3607: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3608: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &((PetscObject)B)->type_name));
3609: PetscCall(MatSetUp(B));
3611: PetscCall(PetscNew(&mumps));
3613: B->ops->view = MatView_MUMPS;
3614: B->ops->getinfo = MatGetInfo_MUMPS;
3616: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3617: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3618: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3619: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3620: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3621: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3622: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3623: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3624: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3625: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3626: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3627: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3629: if (ftype == MAT_FACTOR_LU) {
3630: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3631: B->factortype = MAT_FACTOR_LU;
3632: if (isSeqSELL) mumps->ConvertToTriples = MatConvertToTriples_seqsell_seqaij;
3633: else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "To be implemented");
3634: mumps->sym = 0;
3635: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3636: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "To be implemented");
3638: /* set solvertype */
3639: PetscCall(PetscFree(B->solvertype));
3640: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3641: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3642: if (size == 1) {
3643: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3644: B->canuseordering = PETSC_TRUE;
3645: }
3646: B->ops->destroy = MatDestroy_MUMPS;
3647: B->data = (void *)mumps;
3649: *F = B;
3650: mumps->id.job = JOB_NULL;
3651: mumps->ICNTL_pre = NULL;
3652: mumps->CNTL_pre = NULL;
3653: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3654: PetscFunctionReturn(PETSC_SUCCESS);
3655: }
3657: /* MatGetFactor for MATNEST matrices */
3658: static PetscErrorCode MatGetFactor_nest_mumps(Mat A, MatFactorType ftype, Mat *F)
3659: {
3660: Mat B, **mats;
3661: Mat_MUMPS *mumps;
3662: PetscInt nr, nc;
3663: PetscMPIInt size;
3664: PetscBool flg = PETSC_TRUE;
3666: PetscFunctionBegin;
3667: #if defined(PETSC_USE_COMPLEX)
3668: if (ftype == MAT_FACTOR_CHOLESKY && A->hermitian == PETSC_BOOL3_TRUE && A->symmetric != PETSC_BOOL3_TRUE) {
3669: PetscCall(PetscInfo(A, "Hermitian MAT_FACTOR_CHOLESKY is not supported. Use MAT_FACTOR_LU instead.\n"));
3670: *F = NULL;
3671: PetscFunctionReturn(PETSC_SUCCESS);
3672: }
3673: #endif
3675: /* Return if some condition is not satisfied */
3676: *F = NULL;
3677: PetscCall(MatNestGetSubMats(A, &nr, &nc, &mats));
3678: if (ftype == MAT_FACTOR_CHOLESKY) {
3679: IS *rows, *cols;
3680: PetscInt *m, *M;
3682: 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);
3683: PetscCall(PetscMalloc2(nr, &rows, nc, &cols));
3684: PetscCall(MatNestGetISs(A, rows, cols));
3685: for (PetscInt r = 0; flg && r < nr; r++) PetscCall(ISEqualUnsorted(rows[r], cols[r], &flg));
3686: if (!flg) {
3687: PetscCall(PetscFree2(rows, cols));
3688: PetscCall(PetscInfo(A, "MAT_FACTOR_CHOLESKY not supported for unequal row and column maps. Use MAT_FACTOR_LU.\n"));
3689: PetscFunctionReturn(PETSC_SUCCESS);
3690: }
3691: PetscCall(PetscMalloc2(nr, &m, nr, &M));
3692: for (PetscInt r = 0; r < nr; r++) PetscCall(ISGetMinMax(rows[r], &m[r], &M[r]));
3693: for (PetscInt r = 0; flg && r < nr; r++)
3694: for (PetscInt k = r + 1; flg && k < nr; k++)
3695: if ((m[k] <= m[r] && m[r] <= M[k]) || (m[k] <= M[r] && M[r] <= M[k])) flg = PETSC_FALSE;
3696: PetscCall(PetscFree2(m, M));
3697: PetscCall(PetscFree2(rows, cols));
3698: if (!flg) {
3699: PetscCall(PetscInfo(A, "MAT_FACTOR_CHOLESKY not supported for intersecting row maps. Use MAT_FACTOR_LU.\n"));
3700: PetscFunctionReturn(PETSC_SUCCESS);
3701: }
3702: }
3704: for (PetscInt r = 0; r < nr; r++) {
3705: for (PetscInt c = 0; c < nc; c++) {
3706: Mat sub = mats[r][c];
3707: PetscBool isSeqAIJ, isMPIAIJ, isSeqBAIJ, isMPIBAIJ, isSeqSBAIJ, isMPISBAIJ, isTrans, isDiag, isDense;
3709: if (!sub || (ftype == MAT_FACTOR_CHOLESKY && c < r)) continue;
3710: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATTRANSPOSEVIRTUAL, &isTrans));
3711: if (isTrans) PetscCall(MatTransposeGetMat(sub, &sub));
3712: else {
3713: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATHERMITIANTRANSPOSEVIRTUAL, &isTrans));
3714: if (isTrans) PetscCall(MatHermitianTransposeGetMat(sub, &sub));
3715: }
3716: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATSEQAIJ, &isSeqAIJ));
3717: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATMPIAIJ, &isMPIAIJ));
3718: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATSEQBAIJ, &isSeqBAIJ));
3719: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATMPIBAIJ, &isMPIBAIJ));
3720: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATSEQSBAIJ, &isSeqSBAIJ));
3721: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATMPISBAIJ, &isMPISBAIJ));
3722: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATDIAGONAL, &isDiag));
3723: PetscCall(PetscObjectTypeCompareAny((PetscObject)sub, &isDense, MATSEQDENSE, MATMPIDENSE, NULL));
3724: if (ftype == MAT_FACTOR_CHOLESKY) {
3725: if (r == c) {
3726: if (!isSeqAIJ && !isMPIAIJ && !isSeqBAIJ && !isMPIBAIJ && !isSeqSBAIJ && !isMPISBAIJ && !isDiag && !isDense) {
3727: PetscCall(PetscInfo(sub, "MAT_FACTOR_CHOLESKY not supported for diagonal block of type %s.\n", ((PetscObject)sub)->type_name));
3728: flg = PETSC_FALSE;
3729: }
3730: } else if (!isSeqAIJ && !isMPIAIJ && !isSeqBAIJ && !isMPIBAIJ && !isDiag && !isDense) {
3731: PetscCall(PetscInfo(sub, "MAT_FACTOR_CHOLESKY not supported for off-diagonal block of type %s.\n", ((PetscObject)sub)->type_name));
3732: flg = PETSC_FALSE;
3733: }
3734: } else if (!isSeqAIJ && !isMPIAIJ && !isSeqBAIJ && !isMPIBAIJ && !isDiag && !isDense) {
3735: PetscCall(PetscInfo(sub, "MAT_LU_FACTOR not supported for block of type %s.\n", ((PetscObject)sub)->type_name));
3736: flg = PETSC_FALSE;
3737: }
3738: }
3739: }
3740: if (!flg) PetscFunctionReturn(PETSC_SUCCESS);
3742: /* Create the factorization matrix */
3743: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3744: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3745: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &((PetscObject)B)->type_name));
3746: PetscCall(MatSetUp(B));
3748: PetscCall(PetscNew(&mumps));
3750: B->ops->view = MatView_MUMPS;
3751: B->ops->getinfo = MatGetInfo_MUMPS;
3753: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3754: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3755: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3756: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3757: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3758: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3759: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3760: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3761: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3762: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3763: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3764: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3765: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3766: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3768: if (ftype == MAT_FACTOR_LU) {
3769: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3770: B->factortype = MAT_FACTOR_LU;
3771: mumps->sym = 0;
3772: } else {
3773: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3774: B->factortype = MAT_FACTOR_CHOLESKY;
3775: #if defined(PETSC_USE_COMPLEX)
3776: mumps->sym = 2;
3777: #else
3778: if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3779: else mumps->sym = 2;
3780: #endif
3781: }
3782: mumps->ConvertToTriples = MatConvertToTriples_nest_xaij;
3783: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[ftype]));
3785: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3786: if (size == 1) {
3787: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3788: B->canuseordering = PETSC_TRUE;
3789: }
3791: /* set solvertype */
3792: PetscCall(PetscFree(B->solvertype));
3793: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3794: B->ops->destroy = MatDestroy_MUMPS;
3795: B->data = (void *)mumps;
3797: *F = B;
3798: mumps->id.job = JOB_NULL;
3799: mumps->ICNTL_pre = NULL;
3800: mumps->CNTL_pre = NULL;
3801: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3802: PetscFunctionReturn(PETSC_SUCCESS);
3803: }
3805: PETSC_INTERN PetscErrorCode MatSolverTypeRegister_MUMPS(void)
3806: {
3807: PetscFunctionBegin;
3808: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3809: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3810: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIBAIJ, MAT_FACTOR_LU, MatGetFactor_baij_mumps));
3811: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_baij_mumps));
3812: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPISBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_sbaij_mumps));
3813: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3814: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3815: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQBAIJ, MAT_FACTOR_LU, MatGetFactor_baij_mumps));
3816: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_baij_mumps));
3817: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQSBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_sbaij_mumps));
3818: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQSELL, MAT_FACTOR_LU, MatGetFactor_sell_mumps));
3819: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATDIAGONAL, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3820: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATDIAGONAL, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3821: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQDENSE, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3822: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQDENSE, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3823: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIDENSE, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3824: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIDENSE, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3825: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATNEST, MAT_FACTOR_LU, MatGetFactor_nest_mumps));
3826: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATNEST, MAT_FACTOR_CHOLESKY, MatGetFactor_nest_mumps));
3827: PetscFunctionReturn(PETSC_SUCCESS);
3828: }