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;
85: PetscFunctionBegin;
86: /* PetscInt's size should be always >= PetscMUMPSInt's. It is safe to call PetscOptionsInt_Private to read a PetscMUMPSInt */
87: PetscCall(PetscOptionsInt_Private(PetscOptionsObject, opt, text, man, (PetscInt)currentvalue, &myval, &myset, lb, ub));
88: if (myset) PetscCall(PetscMUMPSIntCast(myval, value));
89: if (set) *set = myset;
90: PetscFunctionReturn(PETSC_SUCCESS);
91: }
92: #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)
94: /* 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 */
95: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
96: #define PetscMUMPS_c(mumps) \
97: do { \
98: if (mumps->use_petsc_omp_support) { \
99: if (mumps->is_omp_master) { \
100: PetscCall(PetscOmpCtrlOmpRegionOnMasterBegin(mumps->omp_ctrl)); \
101: PetscCall(PetscFPTrapPush(PETSC_FP_TRAP_OFF)); \
102: PetscStackCallExternalVoid(PetscStringize(MUMPS_c), MUMPS_c(&mumps->id)); \
103: PetscCall(PetscFPTrapPop()); \
104: PetscCall(PetscOmpCtrlOmpRegionOnMasterEnd(mumps->omp_ctrl)); \
105: } \
106: PetscCall(PetscOmpCtrlBarrier(mumps->omp_ctrl)); \
107: /* Global info is same on all processes so we Bcast it within omp_comm. Local info is specific \
108: to processes, so we only Bcast info[1], an error code and leave others (since they do not have \
109: an easy translation between omp_comm and petsc_comm). See MUMPS-5.1.2 manual p82. \
110: omp_comm is a small shared memory communicator, hence doing multiple Bcast as shown below is OK. \
111: */ \
112: PetscCallMPI(MPI_Bcast(mumps->id.infog, PETSC_STATIC_ARRAY_LENGTH(mumps->id.infog), MPIU_MUMPSINT, 0, mumps->omp_comm)); \
113: PetscCallMPI(MPI_Bcast(mumps->id.rinfog, PETSC_STATIC_ARRAY_LENGTH(mumps->id.rinfog), MPIU_REAL, 0, mumps->omp_comm)); \
114: PetscCallMPI(MPI_Bcast(mumps->id.info, PETSC_STATIC_ARRAY_LENGTH(mumps->id.info), MPIU_MUMPSINT, 0, mumps->omp_comm)); \
115: PetscCallMPI(MPI_Bcast(mumps->id.rinfo, PETSC_STATIC_ARRAY_LENGTH(mumps->id.rinfo), MPIU_REAL, 0, mumps->omp_comm)); \
116: } else { \
117: PetscCall(PetscFPTrapPush(PETSC_FP_TRAP_OFF)); \
118: PetscStackCallExternalVoid(PetscStringize(MUMPS_c), MUMPS_c(&mumps->id)); \
119: PetscCall(PetscFPTrapPop()); \
120: } \
121: } while (0)
122: #else
123: #define PetscMUMPS_c(mumps) \
124: do { \
125: PetscCall(PetscFPTrapPush(PETSC_FP_TRAP_OFF)); \
126: PetscStackCallExternalVoid(PetscStringize(MUMPS_c), MUMPS_c(&mumps->id)); \
127: PetscCall(PetscFPTrapPop()); \
128: } while (0)
129: #endif
131: /* declare MumpsScalar */
132: #if defined(PETSC_USE_COMPLEX)
133: #if defined(PETSC_USE_REAL_SINGLE)
134: #define MumpsScalar mumps_complex
135: #else
136: #define MumpsScalar mumps_double_complex
137: #endif
138: #else
139: #define MumpsScalar PetscScalar
140: #endif
142: /* macros s.t. indices match MUMPS documentation */
143: #define ICNTL(I) icntl[(I)-1]
144: #define CNTL(I) cntl[(I)-1]
145: #define INFOG(I) infog[(I)-1]
146: #define INFO(I) info[(I)-1]
147: #define RINFOG(I) rinfog[(I)-1]
148: #define RINFO(I) rinfo[(I)-1]
150: typedef struct Mat_MUMPS Mat_MUMPS;
151: struct Mat_MUMPS {
152: #if defined(PETSC_USE_COMPLEX)
153: #if defined(PETSC_USE_REAL_SINGLE)
154: CMUMPS_STRUC_C id;
155: #else
156: ZMUMPS_STRUC_C id;
157: #endif
158: #else
159: #if defined(PETSC_USE_REAL_SINGLE)
160: SMUMPS_STRUC_C id;
161: #else
162: DMUMPS_STRUC_C id;
163: #endif
164: #endif
166: MatStructure matstruc;
167: PetscMPIInt myid, petsc_size;
168: PetscMUMPSInt *irn, *jcn; /* the (i,j,v) triplets passed to mumps. */
169: 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. */
170: PetscInt64 nnz; /* number of nonzeros. The type is called selective 64-bit in mumps */
171: PetscMUMPSInt sym;
172: MPI_Comm mumps_comm;
173: PetscMUMPSInt *ICNTL_pre;
174: PetscReal *CNTL_pre;
175: PetscMUMPSInt ICNTL9_pre; /* check if ICNTL(9) is changed from previous MatSolve */
176: VecScatter scat_rhs, scat_sol; /* used by MatSolve() */
177: PetscMUMPSInt ICNTL20; /* use centralized (0) or distributed (10) dense RHS */
178: PetscMUMPSInt lrhs_loc, nloc_rhs, *irhs_loc;
179: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
180: PetscInt *rhs_nrow, max_nrhs;
181: PetscMPIInt *rhs_recvcounts, *rhs_disps;
182: PetscScalar *rhs_loc, *rhs_recvbuf;
183: #endif
184: Vec b_seq, x_seq;
185: PetscInt ninfo, *info; /* which INFO to display */
186: PetscInt sizeredrhs;
187: PetscScalar *schur_sol;
188: PetscInt schur_sizesol;
189: PetscMUMPSInt *ia_alloc, *ja_alloc; /* work arrays used for the CSR struct for sparse rhs */
190: PetscInt64 cur_ilen, cur_jlen; /* current len of ia_alloc[], ja_alloc[] */
191: PetscErrorCode (*ConvertToTriples)(Mat, PetscInt, MatReuse, Mat_MUMPS *);
193: /* Support for MATNEST */
194: PetscErrorCode (**nest_convert_to_triples)(Mat, PetscInt, MatReuse, Mat_MUMPS *);
195: PetscInt64 *nest_vals_start;
196: PetscScalar *nest_vals;
198: /* stuff used by petsc/mumps OpenMP support*/
199: PetscBool use_petsc_omp_support;
200: PetscOmpCtrl omp_ctrl; /* an OpenMP controller that blocked processes will release their CPU (MPI_Barrier does not have this guarantee) */
201: MPI_Comm petsc_comm, omp_comm; /* petsc_comm is petsc matrix's comm */
202: PetscInt64 *recvcount; /* a collection of nnz on omp_master */
203: PetscMPIInt tag, omp_comm_size;
204: PetscBool is_omp_master; /* is this rank the master of omp_comm */
205: MPI_Request *reqs;
206: };
208: /* Cast a 1-based CSR represented by (nrow, ia, ja) of type PetscInt to a CSR of type PetscMUMPSInt.
209: Here, nrow is number of rows, ia[] is row pointer and ja[] is column indices.
210: */
211: static PetscErrorCode PetscMUMPSIntCSRCast(Mat_MUMPS *mumps, PetscInt nrow, PetscInt *ia, PetscInt *ja, PetscMUMPSInt **ia_mumps, PetscMUMPSInt **ja_mumps, PetscMUMPSInt *nnz_mumps)
212: {
213: PetscInt nnz = ia[nrow] - 1; /* mumps uses 1-based indices. Uses PetscInt instead of PetscInt64 since mumps only uses PetscMUMPSInt for rhs */
215: PetscFunctionBegin;
216: #if defined(PETSC_USE_64BIT_INDICES)
217: {
218: PetscInt i;
219: if (nrow + 1 > mumps->cur_ilen) { /* realloc ia_alloc/ja_alloc to fit ia/ja */
220: PetscCall(PetscFree(mumps->ia_alloc));
221: PetscCall(PetscMalloc1(nrow + 1, &mumps->ia_alloc));
222: mumps->cur_ilen = nrow + 1;
223: }
224: if (nnz > mumps->cur_jlen) {
225: PetscCall(PetscFree(mumps->ja_alloc));
226: PetscCall(PetscMalloc1(nnz, &mumps->ja_alloc));
227: mumps->cur_jlen = nnz;
228: }
229: for (i = 0; i < nrow + 1; i++) PetscCall(PetscMUMPSIntCast(ia[i], &(mumps->ia_alloc[i])));
230: for (i = 0; i < nnz; i++) PetscCall(PetscMUMPSIntCast(ja[i], &(mumps->ja_alloc[i])));
231: *ia_mumps = mumps->ia_alloc;
232: *ja_mumps = mumps->ja_alloc;
233: }
234: #else
235: *ia_mumps = ia;
236: *ja_mumps = ja;
237: #endif
238: PetscCall(PetscMUMPSIntCast(nnz, nnz_mumps));
239: PetscFunctionReturn(PETSC_SUCCESS);
240: }
242: static PetscErrorCode MatMumpsResetSchur_Private(Mat_MUMPS *mumps)
243: {
244: PetscFunctionBegin;
245: PetscCall(PetscFree(mumps->id.listvar_schur));
246: PetscCall(PetscFree(mumps->id.redrhs));
247: PetscCall(PetscFree(mumps->schur_sol));
248: mumps->id.size_schur = 0;
249: mumps->id.schur_lld = 0;
250: mumps->id.ICNTL(19) = 0;
251: PetscFunctionReturn(PETSC_SUCCESS);
252: }
254: /* solve with rhs in mumps->id.redrhs and return in the same location */
255: static PetscErrorCode MatMumpsSolveSchur_Private(Mat F)
256: {
257: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
258: Mat S, B, X;
259: MatFactorSchurStatus schurstatus;
260: PetscInt sizesol;
262: PetscFunctionBegin;
263: PetscCall(MatFactorFactorizeSchurComplement(F));
264: PetscCall(MatFactorGetSchurComplement(F, &S, &schurstatus));
265: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, mumps->id.size_schur, mumps->id.nrhs, (PetscScalar *)mumps->id.redrhs, &B));
266: PetscCall(MatSetType(B, ((PetscObject)S)->type_name));
267: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
268: PetscCall(MatBindToCPU(B, S->boundtocpu));
269: #endif
270: switch (schurstatus) {
271: case MAT_FACTOR_SCHUR_FACTORED:
272: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, mumps->id.size_schur, mumps->id.nrhs, (PetscScalar *)mumps->id.redrhs, &X));
273: PetscCall(MatSetType(X, ((PetscObject)S)->type_name));
274: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
275: PetscCall(MatBindToCPU(X, S->boundtocpu));
276: #endif
277: if (!mumps->id.ICNTL(9)) { /* transpose solve */
278: PetscCall(MatMatSolveTranspose(S, B, X));
279: } else {
280: PetscCall(MatMatSolve(S, B, X));
281: }
282: break;
283: case MAT_FACTOR_SCHUR_INVERTED:
284: sizesol = mumps->id.nrhs * mumps->id.size_schur;
285: if (!mumps->schur_sol || sizesol > mumps->schur_sizesol) {
286: PetscCall(PetscFree(mumps->schur_sol));
287: PetscCall(PetscMalloc1(sizesol, &mumps->schur_sol));
288: mumps->schur_sizesol = sizesol;
289: }
290: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, mumps->id.size_schur, mumps->id.nrhs, mumps->schur_sol, &X));
291: PetscCall(MatSetType(X, ((PetscObject)S)->type_name));
292: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
293: PetscCall(MatBindToCPU(X, S->boundtocpu));
294: #endif
295: PetscCall(MatProductCreateWithMat(S, B, NULL, X));
296: if (!mumps->id.ICNTL(9)) { /* transpose solve */
297: PetscCall(MatProductSetType(X, MATPRODUCT_AtB));
298: } else {
299: PetscCall(MatProductSetType(X, MATPRODUCT_AB));
300: }
301: PetscCall(MatProductSetFromOptions(X));
302: PetscCall(MatProductSymbolic(X));
303: PetscCall(MatProductNumeric(X));
305: PetscCall(MatCopy(X, B, SAME_NONZERO_PATTERN));
306: break;
307: default:
308: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
309: }
310: PetscCall(MatFactorRestoreSchurComplement(F, &S, schurstatus));
311: PetscCall(MatDestroy(&B));
312: PetscCall(MatDestroy(&X));
313: PetscFunctionReturn(PETSC_SUCCESS);
314: }
316: static PetscErrorCode MatMumpsHandleSchur_Private(Mat F, PetscBool expansion)
317: {
318: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
320: PetscFunctionBegin;
321: if (!mumps->id.ICNTL(19)) { /* do nothing when Schur complement has not been computed */
322: PetscFunctionReturn(PETSC_SUCCESS);
323: }
324: if (!expansion) { /* prepare for the condensation step */
325: PetscInt sizeredrhs = mumps->id.nrhs * mumps->id.size_schur;
326: /* allocate MUMPS internal array to store reduced right-hand sides */
327: if (!mumps->id.redrhs || sizeredrhs > mumps->sizeredrhs) {
328: PetscCall(PetscFree(mumps->id.redrhs));
329: mumps->id.lredrhs = mumps->id.size_schur;
330: PetscCall(PetscMalloc1(mumps->id.nrhs * mumps->id.lredrhs, &mumps->id.redrhs));
331: mumps->sizeredrhs = mumps->id.nrhs * mumps->id.lredrhs;
332: }
333: } else { /* prepare for the expansion step */
334: /* solve Schur complement (this has to be done by the MUMPS user, so basically us) */
335: PetscCall(MatMumpsSolveSchur_Private(F));
336: mumps->id.ICNTL(26) = 2; /* expansion phase */
337: PetscMUMPS_c(mumps);
338: 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));
339: /* restore defaults */
340: mumps->id.ICNTL(26) = -1;
341: /* free MUMPS internal array for redrhs if we have solved for multiple rhs in order to save memory space */
342: if (mumps->id.nrhs > 1) {
343: PetscCall(PetscFree(mumps->id.redrhs));
344: mumps->id.lredrhs = 0;
345: mumps->sizeredrhs = 0;
346: }
347: }
348: PetscFunctionReturn(PETSC_SUCCESS);
349: }
351: /*
352: MatConvertToTriples_A_B - convert Petsc matrix to triples: row[nz], col[nz], val[nz]
354: input:
355: A - matrix in aij,baij or sbaij format
356: shift - 0: C style output triple; 1: Fortran style output triple.
357: reuse - MAT_INITIAL_MATRIX: spaces are allocated and values are set for the triple
358: MAT_REUSE_MATRIX: only the values in v array are updated
359: output:
360: nnz - dim of r, c, and v (number of local nonzero entries of A)
361: r, c, v - row and col index, matrix values (matrix triples)
363: The returned values r, c, and sometimes v are obtained in a single PetscMalloc(). Then in MatDestroy_MUMPS() it is
364: freed with PetscFree(mumps->irn); This is not ideal code, the fact that v is ONLY sometimes part of mumps->irn means
365: that the PetscMalloc() cannot easily be replaced with a PetscMalloc3().
367: */
369: static PetscErrorCode MatConvertToTriples_seqaij_seqaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
370: {
371: const PetscScalar *av;
372: const PetscInt *ai, *aj, *ajj, M = A->rmap->n;
373: PetscInt64 nz, rnz, i, j, k;
374: PetscMUMPSInt *row, *col;
375: Mat_SeqAIJ *aa = (Mat_SeqAIJ *)A->data;
377: PetscFunctionBegin;
378: PetscCall(MatSeqAIJGetArrayRead(A, &av));
379: if (reuse == MAT_INITIAL_MATRIX) {
380: nz = aa->nz;
381: ai = aa->i;
382: aj = aa->j;
383: PetscCall(PetscMalloc2(nz, &row, nz, &col));
384: for (i = k = 0; i < M; i++) {
385: rnz = ai[i + 1] - ai[i];
386: ajj = aj + ai[i];
387: for (j = 0; j < rnz; j++) {
388: PetscCall(PetscMUMPSIntCast(i + shift, &row[k]));
389: PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[k]));
390: k++;
391: }
392: }
393: mumps->val = (PetscScalar *)av;
394: mumps->irn = row;
395: mumps->jcn = col;
396: mumps->nnz = nz;
397: } else PetscCall(PetscArraycpy(mumps->val, av, aa->nz));
398: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
399: PetscFunctionReturn(PETSC_SUCCESS);
400: }
402: static PetscErrorCode MatConvertToTriples_seqsell_seqaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
403: {
404: PetscInt64 nz, i, j, k, r;
405: Mat_SeqSELL *a = (Mat_SeqSELL *)A->data;
406: PetscMUMPSInt *row, *col;
408: PetscFunctionBegin;
409: nz = a->sliidx[a->totalslices];
410: if (reuse == MAT_INITIAL_MATRIX) {
411: PetscCall(PetscMalloc2(nz, &row, nz, &col));
412: for (i = k = 0; i < a->totalslices; i++) {
413: 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++]));
414: }
415: for (i = 0; i < nz; i++) PetscCall(PetscMUMPSIntCast(a->colidx[i] + shift, &col[i]));
416: mumps->irn = row;
417: mumps->jcn = col;
418: mumps->nnz = nz;
419: mumps->val = a->val;
420: } else PetscCall(PetscArraycpy(mumps->val, a->val, nz));
421: PetscFunctionReturn(PETSC_SUCCESS);
422: }
424: static PetscErrorCode MatConvertToTriples_seqbaij_seqaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
425: {
426: Mat_SeqBAIJ *aa = (Mat_SeqBAIJ *)A->data;
427: const PetscInt *ai, *aj, *ajj, bs2 = aa->bs2;
428: PetscInt64 M, nz = bs2 * aa->nz, idx = 0, rnz, i, j, k, m;
429: PetscInt bs;
430: PetscMUMPSInt *row, *col;
432: PetscFunctionBegin;
433: if (reuse == MAT_INITIAL_MATRIX) {
434: PetscCall(MatGetBlockSize(A, &bs));
435: M = A->rmap->N / bs;
436: ai = aa->i;
437: aj = aa->j;
438: PetscCall(PetscMalloc2(nz, &row, nz, &col));
439: for (i = 0; i < M; i++) {
440: ajj = aj + ai[i];
441: rnz = ai[i + 1] - ai[i];
442: for (k = 0; k < rnz; k++) {
443: for (j = 0; j < bs; j++) {
444: for (m = 0; m < bs; m++) {
445: PetscCall(PetscMUMPSIntCast(i * bs + m + shift, &row[idx]));
446: PetscCall(PetscMUMPSIntCast(bs * ajj[k] + j + shift, &col[idx]));
447: idx++;
448: }
449: }
450: }
451: }
452: mumps->irn = row;
453: mumps->jcn = col;
454: mumps->nnz = nz;
455: mumps->val = aa->a;
456: } else PetscCall(PetscArraycpy(mumps->val, aa->a, nz));
457: PetscFunctionReturn(PETSC_SUCCESS);
458: }
460: static PetscErrorCode MatConvertToTriples_seqsbaij_seqsbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
461: {
462: const PetscInt *ai, *aj, *ajj;
463: PetscInt bs;
464: PetscInt64 nz, rnz, i, j, k, m;
465: PetscMUMPSInt *row, *col;
466: PetscScalar *val;
467: Mat_SeqSBAIJ *aa = (Mat_SeqSBAIJ *)A->data;
468: const PetscInt bs2 = aa->bs2, mbs = aa->mbs;
469: #if defined(PETSC_USE_COMPLEX)
470: PetscBool isset, hermitian;
471: #endif
473: PetscFunctionBegin;
474: #if defined(PETSC_USE_COMPLEX)
475: PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
476: PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
477: #endif
478: ai = aa->i;
479: aj = aa->j;
480: PetscCall(MatGetBlockSize(A, &bs));
481: if (reuse == MAT_INITIAL_MATRIX) {
482: const PetscInt64 alloc_size = aa->nz * bs2;
484: PetscCall(PetscMalloc2(alloc_size, &row, alloc_size, &col));
485: if (bs > 1) {
486: PetscCall(PetscMalloc1(alloc_size, &mumps->val_alloc));
487: mumps->val = mumps->val_alloc;
488: } else {
489: mumps->val = aa->a;
490: }
491: mumps->irn = row;
492: mumps->jcn = col;
493: } else {
494: row = mumps->irn;
495: col = mumps->jcn;
496: }
497: val = mumps->val;
499: nz = 0;
500: if (bs > 1) {
501: for (i = 0; i < mbs; i++) {
502: rnz = ai[i + 1] - ai[i];
503: ajj = aj + ai[i];
504: for (j = 0; j < rnz; j++) {
505: for (k = 0; k < bs; k++) {
506: for (m = 0; m < bs; m++) {
507: if (ajj[j] > i || k >= m) {
508: if (reuse == MAT_INITIAL_MATRIX) {
509: PetscCall(PetscMUMPSIntCast(i * bs + m + shift, &row[nz]));
510: PetscCall(PetscMUMPSIntCast(ajj[j] * bs + k + shift, &col[nz]));
511: }
512: val[nz++] = aa->a[(ai[i] + j) * bs2 + m + k * bs];
513: }
514: }
515: }
516: }
517: }
518: } else if (reuse == MAT_INITIAL_MATRIX) {
519: for (i = 0; i < mbs; i++) {
520: rnz = ai[i + 1] - ai[i];
521: ajj = aj + ai[i];
522: for (j = 0; j < rnz; j++) {
523: PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
524: PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[nz]));
525: nz++;
526: }
527: }
528: PetscCheck(nz == aa->nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Different numbers of nonzeros %" PetscInt64_FMT " != %" PetscInt_FMT, nz, aa->nz);
529: } else PetscCall(PetscArraycpy(mumps->val, aa->a, aa->nz)); /* bs == 1 and MAT_REUSE_MATRIX */
530: if (reuse == MAT_INITIAL_MATRIX) mumps->nnz = nz;
531: PetscFunctionReturn(PETSC_SUCCESS);
532: }
534: static PetscErrorCode MatConvertToTriples_seqaij_seqsbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
535: {
536: const PetscInt *ai, *aj, *ajj, *adiag, M = A->rmap->n;
537: PetscInt64 nz, rnz, i, j;
538: const PetscScalar *av, *v1;
539: PetscScalar *val;
540: PetscMUMPSInt *row, *col;
541: Mat_SeqAIJ *aa = (Mat_SeqAIJ *)A->data;
542: PetscBool missing;
543: #if defined(PETSC_USE_COMPLEX)
544: PetscBool hermitian, isset;
545: #endif
547: PetscFunctionBegin;
548: #if defined(PETSC_USE_COMPLEX)
549: PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
550: PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
551: #endif
552: PetscCall(MatSeqAIJGetArrayRead(A, &av));
553: ai = aa->i;
554: aj = aa->j;
555: adiag = aa->diag;
556: PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, NULL));
557: if (reuse == MAT_INITIAL_MATRIX) {
558: /* count nz in the upper triangular part of A */
559: nz = 0;
560: if (missing) {
561: for (i = 0; i < M; i++) {
562: if (PetscUnlikely(adiag[i] >= ai[i + 1])) {
563: for (j = ai[i]; j < ai[i + 1]; j++) {
564: if (aj[j] < i) continue;
565: nz++;
566: }
567: } else {
568: nz += ai[i + 1] - adiag[i];
569: }
570: }
571: } else {
572: for (i = 0; i < M; i++) nz += ai[i + 1] - adiag[i];
573: }
574: PetscCall(PetscMalloc2(nz, &row, nz, &col));
575: PetscCall(PetscMalloc1(nz, &val));
576: mumps->nnz = nz;
577: mumps->irn = row;
578: mumps->jcn = col;
579: mumps->val = mumps->val_alloc = val;
581: nz = 0;
582: if (missing) {
583: for (i = 0; i < M; i++) {
584: if (PetscUnlikely(adiag[i] >= ai[i + 1])) {
585: for (j = ai[i]; j < ai[i + 1]; j++) {
586: if (aj[j] < i) continue;
587: PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
588: PetscCall(PetscMUMPSIntCast(aj[j] + shift, &col[nz]));
589: val[nz] = av[j];
590: nz++;
591: }
592: } else {
593: rnz = ai[i + 1] - adiag[i];
594: ajj = aj + adiag[i];
595: v1 = av + adiag[i];
596: for (j = 0; j < rnz; j++) {
597: PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
598: PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[nz]));
599: val[nz++] = v1[j];
600: }
601: }
602: }
603: } else {
604: for (i = 0; i < M; i++) {
605: rnz = ai[i + 1] - adiag[i];
606: ajj = aj + adiag[i];
607: v1 = av + adiag[i];
608: for (j = 0; j < rnz; j++) {
609: PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
610: PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[nz]));
611: val[nz++] = v1[j];
612: }
613: }
614: }
615: } else {
616: nz = 0;
617: val = mumps->val;
618: if (missing) {
619: for (i = 0; i < M; i++) {
620: if (PetscUnlikely(adiag[i] >= ai[i + 1])) {
621: for (j = ai[i]; j < ai[i + 1]; j++) {
622: if (aj[j] < i) continue;
623: val[nz++] = av[j];
624: }
625: } else {
626: rnz = ai[i + 1] - adiag[i];
627: v1 = av + adiag[i];
628: for (j = 0; j < rnz; j++) val[nz++] = v1[j];
629: }
630: }
631: } else {
632: for (i = 0; i < M; i++) {
633: rnz = ai[i + 1] - adiag[i];
634: v1 = av + adiag[i];
635: for (j = 0; j < rnz; j++) val[nz++] = v1[j];
636: }
637: }
638: }
639: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
640: PetscFunctionReturn(PETSC_SUCCESS);
641: }
643: static PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
644: {
645: const PetscInt *ai, *aj, *bi, *bj, *garray, *ajj, *bjj;
646: PetscInt bs;
647: PetscInt64 rstart, nz, i, j, k, m, jj, irow, countA, countB;
648: PetscMUMPSInt *row, *col;
649: const PetscScalar *av, *bv, *v1, *v2;
650: PetscScalar *val;
651: Mat_MPISBAIJ *mat = (Mat_MPISBAIJ *)A->data;
652: Mat_SeqSBAIJ *aa = (Mat_SeqSBAIJ *)(mat->A)->data;
653: Mat_SeqBAIJ *bb = (Mat_SeqBAIJ *)(mat->B)->data;
654: const PetscInt bs2 = aa->bs2, mbs = aa->mbs;
655: #if defined(PETSC_USE_COMPLEX)
656: PetscBool hermitian, isset;
657: #endif
659: PetscFunctionBegin;
660: #if defined(PETSC_USE_COMPLEX)
661: PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
662: PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
663: #endif
664: PetscCall(MatGetBlockSize(A, &bs));
665: rstart = A->rmap->rstart;
666: ai = aa->i;
667: aj = aa->j;
668: bi = bb->i;
669: bj = bb->j;
670: av = aa->a;
671: bv = bb->a;
673: garray = mat->garray;
675: if (reuse == MAT_INITIAL_MATRIX) {
676: nz = (aa->nz + bb->nz) * bs2; /* just a conservative estimate */
677: PetscCall(PetscMalloc2(nz, &row, nz, &col));
678: PetscCall(PetscMalloc1(nz, &val));
679: /* can not decide the exact mumps->nnz now because of the SBAIJ */
680: mumps->irn = row;
681: mumps->jcn = col;
682: mumps->val = mumps->val_alloc = val;
683: } else {
684: val = mumps->val;
685: }
687: jj = 0;
688: irow = rstart;
689: for (i = 0; i < mbs; i++) {
690: ajj = aj + ai[i]; /* ptr to the beginning of this row */
691: countA = ai[i + 1] - ai[i];
692: countB = bi[i + 1] - bi[i];
693: bjj = bj + bi[i];
694: v1 = av + ai[i] * bs2;
695: v2 = bv + bi[i] * bs2;
697: if (bs > 1) {
698: /* A-part */
699: for (j = 0; j < countA; j++) {
700: for (k = 0; k < bs; k++) {
701: for (m = 0; m < bs; m++) {
702: if (rstart + ajj[j] * bs > irow || k >= m) {
703: if (reuse == MAT_INITIAL_MATRIX) {
704: PetscCall(PetscMUMPSIntCast(irow + m + shift, &row[jj]));
705: PetscCall(PetscMUMPSIntCast(rstart + ajj[j] * bs + k + shift, &col[jj]));
706: }
707: val[jj++] = v1[j * bs2 + m + k * bs];
708: }
709: }
710: }
711: }
713: /* B-part */
714: for (j = 0; j < countB; j++) {
715: for (k = 0; k < bs; k++) {
716: for (m = 0; m < bs; m++) {
717: if (reuse == MAT_INITIAL_MATRIX) {
718: PetscCall(PetscMUMPSIntCast(irow + m + shift, &row[jj]));
719: PetscCall(PetscMUMPSIntCast(garray[bjj[j]] * bs + k + shift, &col[jj]));
720: }
721: val[jj++] = v2[j * bs2 + m + k * bs];
722: }
723: }
724: }
725: } else {
726: /* A-part */
727: for (j = 0; j < countA; j++) {
728: if (reuse == MAT_INITIAL_MATRIX) {
729: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
730: PetscCall(PetscMUMPSIntCast(rstart + ajj[j] + shift, &col[jj]));
731: }
732: val[jj++] = v1[j];
733: }
735: /* B-part */
736: for (j = 0; j < countB; j++) {
737: if (reuse == MAT_INITIAL_MATRIX) {
738: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
739: PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift, &col[jj]));
740: }
741: val[jj++] = v2[j];
742: }
743: }
744: irow += bs;
745: }
746: if (reuse == MAT_INITIAL_MATRIX) mumps->nnz = jj;
747: PetscFunctionReturn(PETSC_SUCCESS);
748: }
750: static PetscErrorCode MatConvertToTriples_mpiaij_mpiaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
751: {
752: const PetscInt *ai, *aj, *bi, *bj, *garray, m = A->rmap->n, *ajj, *bjj;
753: PetscInt64 rstart, cstart, nz, i, j, jj, irow, countA, countB;
754: PetscMUMPSInt *row, *col;
755: const PetscScalar *av, *bv, *v1, *v2;
756: PetscScalar *val;
757: Mat Ad, Ao;
758: Mat_SeqAIJ *aa;
759: Mat_SeqAIJ *bb;
761: PetscFunctionBegin;
762: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &garray));
763: PetscCall(MatSeqAIJGetArrayRead(Ad, &av));
764: PetscCall(MatSeqAIJGetArrayRead(Ao, &bv));
766: aa = (Mat_SeqAIJ *)(Ad)->data;
767: bb = (Mat_SeqAIJ *)(Ao)->data;
768: ai = aa->i;
769: aj = aa->j;
770: bi = bb->i;
771: bj = bb->j;
773: rstart = A->rmap->rstart;
774: cstart = A->cmap->rstart;
776: if (reuse == MAT_INITIAL_MATRIX) {
777: nz = (PetscInt64)aa->nz + bb->nz; /* make sure the sum won't overflow PetscInt */
778: PetscCall(PetscMalloc2(nz, &row, nz, &col));
779: PetscCall(PetscMalloc1(nz, &val));
780: mumps->nnz = nz;
781: mumps->irn = row;
782: mumps->jcn = col;
783: mumps->val = mumps->val_alloc = val;
784: } else {
785: val = mumps->val;
786: }
788: jj = 0;
789: irow = rstart;
790: for (i = 0; i < m; i++) {
791: ajj = aj + ai[i]; /* ptr to the beginning of this row */
792: countA = ai[i + 1] - ai[i];
793: countB = bi[i + 1] - bi[i];
794: bjj = bj + bi[i];
795: v1 = av + ai[i];
796: v2 = bv + bi[i];
798: /* A-part */
799: for (j = 0; j < countA; j++) {
800: if (reuse == MAT_INITIAL_MATRIX) {
801: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
802: PetscCall(PetscMUMPSIntCast(cstart + ajj[j] + shift, &col[jj]));
803: }
804: val[jj++] = v1[j];
805: }
807: /* B-part */
808: for (j = 0; j < countB; j++) {
809: if (reuse == MAT_INITIAL_MATRIX) {
810: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
811: PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift, &col[jj]));
812: }
813: val[jj++] = v2[j];
814: }
815: irow++;
816: }
817: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &av));
818: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &bv));
819: PetscFunctionReturn(PETSC_SUCCESS);
820: }
822: static PetscErrorCode MatConvertToTriples_mpibaij_mpiaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
823: {
824: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)A->data;
825: Mat_SeqBAIJ *aa = (Mat_SeqBAIJ *)(mat->A)->data;
826: Mat_SeqBAIJ *bb = (Mat_SeqBAIJ *)(mat->B)->data;
827: const PetscInt *ai = aa->i, *bi = bb->i, *aj = aa->j, *bj = bb->j, *ajj, *bjj;
828: const PetscInt *garray = mat->garray, mbs = mat->mbs, rstart = A->rmap->rstart, cstart = A->cmap->rstart;
829: const PetscInt bs2 = mat->bs2;
830: PetscInt bs;
831: PetscInt64 nz, i, j, k, n, jj, irow, countA, countB, idx;
832: PetscMUMPSInt *row, *col;
833: const PetscScalar *av = aa->a, *bv = bb->a, *v1, *v2;
834: PetscScalar *val;
836: PetscFunctionBegin;
837: PetscCall(MatGetBlockSize(A, &bs));
838: if (reuse == MAT_INITIAL_MATRIX) {
839: nz = bs2 * (aa->nz + bb->nz);
840: PetscCall(PetscMalloc2(nz, &row, nz, &col));
841: PetscCall(PetscMalloc1(nz, &val));
842: mumps->nnz = nz;
843: mumps->irn = row;
844: mumps->jcn = col;
845: mumps->val = mumps->val_alloc = val;
846: } else {
847: val = mumps->val;
848: }
850: jj = 0;
851: irow = rstart;
852: for (i = 0; i < mbs; i++) {
853: countA = ai[i + 1] - ai[i];
854: countB = bi[i + 1] - bi[i];
855: ajj = aj + ai[i];
856: bjj = bj + bi[i];
857: v1 = av + bs2 * ai[i];
858: v2 = bv + bs2 * bi[i];
860: idx = 0;
861: /* A-part */
862: for (k = 0; k < countA; k++) {
863: for (j = 0; j < bs; j++) {
864: for (n = 0; n < bs; n++) {
865: if (reuse == MAT_INITIAL_MATRIX) {
866: PetscCall(PetscMUMPSIntCast(irow + n + shift, &row[jj]));
867: PetscCall(PetscMUMPSIntCast(cstart + bs * ajj[k] + j + shift, &col[jj]));
868: }
869: val[jj++] = v1[idx++];
870: }
871: }
872: }
874: idx = 0;
875: /* B-part */
876: for (k = 0; k < countB; k++) {
877: for (j = 0; j < bs; j++) {
878: for (n = 0; n < bs; n++) {
879: if (reuse == MAT_INITIAL_MATRIX) {
880: PetscCall(PetscMUMPSIntCast(irow + n + shift, &row[jj]));
881: PetscCall(PetscMUMPSIntCast(bs * garray[bjj[k]] + j + shift, &col[jj]));
882: }
883: val[jj++] = v2[idx++];
884: }
885: }
886: }
887: irow += bs;
888: }
889: PetscFunctionReturn(PETSC_SUCCESS);
890: }
892: static PetscErrorCode MatConvertToTriples_mpiaij_mpisbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
893: {
894: const PetscInt *ai, *aj, *adiag, *bi, *bj, *garray, m = A->rmap->n, *ajj, *bjj;
895: PetscInt64 rstart, nz, nza, nzb, i, j, jj, irow, countA, countB;
896: PetscMUMPSInt *row, *col;
897: const PetscScalar *av, *bv, *v1, *v2;
898: PetscScalar *val;
899: Mat Ad, Ao;
900: Mat_SeqAIJ *aa;
901: Mat_SeqAIJ *bb;
902: #if defined(PETSC_USE_COMPLEX)
903: PetscBool hermitian, isset;
904: #endif
906: PetscFunctionBegin;
907: #if defined(PETSC_USE_COMPLEX)
908: PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
909: PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
910: #endif
911: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &garray));
912: PetscCall(MatSeqAIJGetArrayRead(Ad, &av));
913: PetscCall(MatSeqAIJGetArrayRead(Ao, &bv));
915: aa = (Mat_SeqAIJ *)(Ad)->data;
916: bb = (Mat_SeqAIJ *)(Ao)->data;
917: ai = aa->i;
918: aj = aa->j;
919: adiag = aa->diag;
920: bi = bb->i;
921: bj = bb->j;
923: rstart = A->rmap->rstart;
925: if (reuse == MAT_INITIAL_MATRIX) {
926: nza = 0; /* num of upper triangular entries in mat->A, including diagonals */
927: nzb = 0; /* num of upper triangular entries in mat->B */
928: for (i = 0; i < m; i++) {
929: nza += (ai[i + 1] - adiag[i]);
930: countB = bi[i + 1] - bi[i];
931: bjj = bj + bi[i];
932: for (j = 0; j < countB; j++) {
933: if (garray[bjj[j]] > rstart) nzb++;
934: }
935: }
937: nz = nza + nzb; /* total nz of upper triangular part of mat */
938: PetscCall(PetscMalloc2(nz, &row, nz, &col));
939: PetscCall(PetscMalloc1(nz, &val));
940: mumps->nnz = nz;
941: mumps->irn = row;
942: mumps->jcn = col;
943: mumps->val = mumps->val_alloc = val;
944: } else {
945: val = mumps->val;
946: }
948: jj = 0;
949: irow = rstart;
950: for (i = 0; i < m; i++) {
951: ajj = aj + adiag[i]; /* ptr to the beginning of the diagonal of this row */
952: v1 = av + adiag[i];
953: countA = ai[i + 1] - adiag[i];
954: countB = bi[i + 1] - bi[i];
955: bjj = bj + bi[i];
956: v2 = bv + bi[i];
958: /* A-part */
959: for (j = 0; j < countA; j++) {
960: if (reuse == MAT_INITIAL_MATRIX) {
961: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
962: PetscCall(PetscMUMPSIntCast(rstart + ajj[j] + shift, &col[jj]));
963: }
964: val[jj++] = v1[j];
965: }
967: /* B-part */
968: for (j = 0; j < countB; j++) {
969: if (garray[bjj[j]] > rstart) {
970: if (reuse == MAT_INITIAL_MATRIX) {
971: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
972: PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift, &col[jj]));
973: }
974: val[jj++] = v2[j];
975: }
976: }
977: irow++;
978: }
979: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &av));
980: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &bv));
981: PetscFunctionReturn(PETSC_SUCCESS);
982: }
984: static PetscErrorCode MatConvertToTriples_diagonal_xaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
985: {
986: const PetscScalar *av;
987: const PetscInt M = A->rmap->n;
988: PetscInt64 i;
989: PetscMUMPSInt *row, *col;
990: Vec v;
992: PetscFunctionBegin;
993: PetscCall(MatDiagonalGetDiagonal(A, &v));
994: PetscCall(VecGetArrayRead(v, &av));
995: if (reuse == MAT_INITIAL_MATRIX) {
996: PetscCall(PetscMalloc2(M, &row, M, &col));
997: for (i = 0; i < M; i++) {
998: PetscCall(PetscMUMPSIntCast(i + A->rmap->rstart, &row[i]));
999: col[i] = row[i];
1000: }
1001: mumps->val = (PetscScalar *)av;
1002: mumps->irn = row;
1003: mumps->jcn = col;
1004: mumps->nnz = M;
1005: } else PetscCall(PetscArraycpy(mumps->val, av, M));
1006: PetscCall(VecRestoreArrayRead(v, &av));
1007: PetscFunctionReturn(PETSC_SUCCESS);
1008: }
1010: static PetscErrorCode MatConvertToTriples_nest_xaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
1011: {
1012: Mat **mats;
1013: PetscInt nr, nc;
1014: PetscBool chol = mumps->sym ? PETSC_TRUE : PETSC_FALSE;
1016: PetscFunctionBegin;
1017: PetscCall(MatNestGetSubMats(A, &nr, &nc, &mats));
1018: if (reuse == MAT_INITIAL_MATRIX) {
1019: PetscMUMPSInt *irns, *jcns;
1020: PetscScalar *vals;
1021: PetscInt64 totnnz, cumnnz, maxnnz;
1022: PetscInt *pjcns_w;
1023: IS *rows, *cols;
1024: PetscInt **rows_idx, **cols_idx;
1026: cumnnz = 0;
1027: maxnnz = 0;
1028: PetscCall(PetscMalloc2(nr * nc + 1, &mumps->nest_vals_start, nr * nc, &mumps->nest_convert_to_triples));
1029: for (PetscInt r = 0; r < nr; r++) {
1030: for (PetscInt c = 0; c < nc; c++) {
1031: Mat sub = mats[r][c];
1033: mumps->nest_convert_to_triples[r * nc + c] = NULL;
1034: if (chol && c < r) continue; /* skip lower-triangular block for Cholesky */
1035: if (sub) {
1036: PetscErrorCode (*convert_to_triples)(Mat, PetscInt, MatReuse, Mat_MUMPS *) = NULL;
1037: PetscBool isSeqAIJ, isMPIAIJ, isSeqBAIJ, isMPIBAIJ, isSeqSBAIJ, isMPISBAIJ, isTrans, isHTrans = PETSC_FALSE, isDiag;
1038: MatInfo info;
1040: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATTRANSPOSEVIRTUAL, &isTrans));
1041: if (isTrans) PetscCall(MatTransposeGetMat(sub, &sub));
1042: else {
1043: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATHERMITIANTRANSPOSEVIRTUAL, &isHTrans));
1044: if (isHTrans) PetscCall(MatHermitianTransposeGetMat(sub, &sub));
1045: }
1046: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATSEQAIJ, &isSeqAIJ));
1047: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATMPIAIJ, &isMPIAIJ));
1048: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATSEQBAIJ, &isSeqBAIJ));
1049: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATMPIBAIJ, &isMPIBAIJ));
1050: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATSEQSBAIJ, &isSeqSBAIJ));
1051: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATMPISBAIJ, &isMPISBAIJ));
1052: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATDIAGONAL, &isDiag));
1054: if (chol) {
1055: if (r == c) {
1056: if (isSeqAIJ) convert_to_triples = MatConvertToTriples_seqaij_seqsbaij;
1057: else if (isMPIAIJ) convert_to_triples = MatConvertToTriples_mpiaij_mpisbaij;
1058: else if (isSeqSBAIJ) convert_to_triples = MatConvertToTriples_seqsbaij_seqsbaij;
1059: else if (isMPISBAIJ) convert_to_triples = MatConvertToTriples_mpisbaij_mpisbaij;
1060: else if (isDiag) convert_to_triples = MatConvertToTriples_diagonal_xaij;
1061: } else {
1062: if (isSeqAIJ) convert_to_triples = MatConvertToTriples_seqaij_seqaij;
1063: else if (isMPIAIJ) convert_to_triples = MatConvertToTriples_mpiaij_mpiaij;
1064: else if (isSeqBAIJ) convert_to_triples = MatConvertToTriples_seqbaij_seqaij;
1065: else if (isMPIBAIJ) convert_to_triples = MatConvertToTriples_mpibaij_mpiaij;
1066: else if (isDiag) convert_to_triples = MatConvertToTriples_diagonal_xaij;
1067: }
1068: } else {
1069: if (isSeqAIJ) convert_to_triples = MatConvertToTriples_seqaij_seqaij;
1070: else if (isMPIAIJ) convert_to_triples = MatConvertToTriples_mpiaij_mpiaij;
1071: else if (isSeqBAIJ) convert_to_triples = MatConvertToTriples_seqbaij_seqaij;
1072: else if (isMPIBAIJ) convert_to_triples = MatConvertToTriples_mpibaij_mpiaij;
1073: else if (isDiag) convert_to_triples = MatConvertToTriples_diagonal_xaij;
1074: }
1075: PetscCheck(convert_to_triples, PetscObjectComm((PetscObject)sub), PETSC_ERR_SUP, "Not for block of type %s", ((PetscObject)sub)->type_name);
1076: mumps->nest_convert_to_triples[r * nc + c] = convert_to_triples;
1077: PetscCall(MatGetInfo(sub, MAT_LOCAL, &info));
1078: cumnnz += (PetscInt64)info.nz_used; /* can be overestimated for Cholesky */
1079: maxnnz = PetscMax(maxnnz, info.nz_used);
1080: }
1081: }
1082: }
1084: /* Allocate total COO */
1085: totnnz = cumnnz;
1086: PetscCall(PetscMalloc2(totnnz, &irns, totnnz, &jcns));
1087: PetscCall(PetscMalloc1(totnnz, &vals));
1089: /* Handle rows and column maps
1090: We directly map rows and use an SF for the columns */
1091: PetscCall(PetscMalloc4(nr, &rows, nc, &cols, nr, &rows_idx, nc, &cols_idx));
1092: PetscCall(MatNestGetISs(A, rows, cols));
1093: for (PetscInt r = 0; r < nr; r++) PetscCall(ISGetIndices(rows[r], (const PetscInt **)&rows_idx[r]));
1094: for (PetscInt c = 0; c < nc; c++) PetscCall(ISGetIndices(cols[c], (const PetscInt **)&cols_idx[c]));
1095: if (PetscDefined(USE_64BIT_INDICES)) PetscCall(PetscMalloc1(maxnnz, &pjcns_w));
1096: else (void)maxnnz;
1098: cumnnz = 0;
1099: for (PetscInt r = 0; r < nr; r++) {
1100: for (PetscInt c = 0; c < nc; c++) {
1101: Mat sub = mats[r][c];
1102: const PetscInt *ridx = rows_idx[r];
1103: const PetscInt *cidx = cols_idx[c];
1104: PetscInt rst;
1105: PetscSF csf;
1106: PetscBool isTrans, isHTrans = PETSC_FALSE, swap;
1107: PetscLayout cmap;
1109: mumps->nest_vals_start[r * nc + c] = cumnnz;
1110: if (!mumps->nest_convert_to_triples[r * nc + c]) continue;
1112: /* Extract inner blocks if needed */
1113: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATTRANSPOSEVIRTUAL, &isTrans));
1114: if (isTrans) PetscCall(MatTransposeGetMat(sub, &sub));
1115: else {
1116: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATHERMITIANTRANSPOSEVIRTUAL, &isHTrans));
1117: if (isHTrans) PetscCall(MatHermitianTransposeGetMat(sub, &sub));
1118: }
1119: swap = (PetscBool)(isTrans || isHTrans);
1121: /* Get column layout to map off-process columns */
1122: PetscCall(MatGetLayouts(sub, NULL, &cmap));
1124: /* Get row start to map on-process rows */
1125: PetscCall(MatGetOwnershipRange(sub, &rst, NULL));
1127: /* Directly use the mumps datastructure and use C ordering for now */
1128: PetscCall((*mumps->nest_convert_to_triples[r * nc + c])(sub, 0, MAT_INITIAL_MATRIX, mumps));
1130: /* Swap the role of rows and columns indices for transposed blocks
1131: since we need values with global final ordering */
1132: if (swap) {
1133: cidx = rows_idx[r];
1134: ridx = cols_idx[c];
1135: }
1137: /* Communicate column indices
1138: This could have been done with a single SF but it would have complicated the code a lot.
1139: But since we do it only once, we pay the price of setting up an SF for each block */
1140: if (PetscDefined(USE_64BIT_INDICES)) {
1141: for (PetscInt k = 0; k < mumps->nnz; k++) pjcns_w[k] = mumps->jcn[k];
1142: } else pjcns_w = (PetscInt *)(mumps->jcn); /* This cast is needed only to silence warnings for 64bit integers builds */
1143: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &csf));
1144: PetscCall(PetscSFSetGraphLayout(csf, cmap, mumps->nnz, NULL, PETSC_OWN_POINTER, pjcns_w));
1145: PetscCall(PetscSFBcastBegin(csf, MPIU_INT, cidx, pjcns_w, MPI_REPLACE));
1146: PetscCall(PetscSFBcastEnd(csf, MPIU_INT, cidx, pjcns_w, MPI_REPLACE));
1147: PetscCall(PetscSFDestroy(&csf));
1149: /* Import indices: use direct map for rows and mapped indices for columns */
1150: if (swap) {
1151: for (PetscInt k = 0; k < mumps->nnz; k++) {
1152: PetscCall(PetscMUMPSIntCast(ridx[mumps->irn[k] - rst] + shift, &jcns[cumnnz + k]));
1153: PetscCall(PetscMUMPSIntCast(pjcns_w[k] + shift, &irns[cumnnz + k]));
1154: }
1155: } else {
1156: for (PetscInt k = 0; k < mumps->nnz; k++) {
1157: PetscCall(PetscMUMPSIntCast(ridx[mumps->irn[k] - rst] + shift, &irns[cumnnz + k]));
1158: PetscCall(PetscMUMPSIntCast(pjcns_w[k] + shift, &jcns[cumnnz + k]));
1159: }
1160: }
1162: /* Import values to full COO */
1163: PetscCall(PetscArraycpy(vals + cumnnz, mumps->val, mumps->nnz));
1164: if (isHTrans) { /* conjugate the entries */
1165: PetscScalar *v = vals + cumnnz;
1166: for (PetscInt k = 0; k < mumps->nnz; k++) v[k] = PetscConj(v[k]);
1167: }
1169: /* Shift new starting point and sanity check */
1170: cumnnz += mumps->nnz;
1171: PetscCheck(cumnnz <= totnnz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unexpected number of nonzeros %" PetscInt64_FMT " != %" PetscInt64_FMT, cumnnz, totnnz);
1173: /* Free scratch memory */
1174: PetscCall(PetscFree2(mumps->irn, mumps->jcn));
1175: PetscCall(PetscFree(mumps->val_alloc));
1176: mumps->val = NULL;
1177: mumps->nnz = 0;
1178: }
1179: }
1180: if (PetscDefined(USE_64BIT_INDICES)) PetscCall(PetscFree(pjcns_w));
1181: for (PetscInt r = 0; r < nr; r++) PetscCall(ISRestoreIndices(rows[r], (const PetscInt **)&rows_idx[r]));
1182: for (PetscInt c = 0; c < nc; c++) PetscCall(ISRestoreIndices(cols[c], (const PetscInt **)&cols_idx[c]));
1183: PetscCall(PetscFree4(rows, cols, rows_idx, cols_idx));
1184: if (!chol) PetscCheck(cumnnz == totnnz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Different number of nonzeros %" PetscInt64_FMT " != %" PetscInt64_FMT, cumnnz, totnnz);
1185: mumps->nest_vals_start[nr * nc] = cumnnz;
1187: /* Set pointers for final MUMPS data structure */
1188: mumps->nest_vals = vals;
1189: mumps->val_alloc = NULL; /* do not use val_alloc since it may be reallocated with the OMP callpath */
1190: mumps->val = vals;
1191: mumps->irn = irns;
1192: mumps->jcn = jcns;
1193: mumps->nnz = cumnnz;
1194: } else {
1195: PetscScalar *oval = mumps->nest_vals;
1196: for (PetscInt r = 0; r < nr; r++) {
1197: for (PetscInt c = 0; c < nc; c++) {
1198: PetscBool isTrans, isHTrans = PETSC_FALSE;
1199: Mat sub = mats[r][c];
1200: PetscInt midx = r * nc + c;
1202: if (!mumps->nest_convert_to_triples[midx]) continue;
1203: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATTRANSPOSEVIRTUAL, &isTrans));
1204: if (isTrans) PetscCall(MatTransposeGetMat(sub, &sub));
1205: else {
1206: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATHERMITIANTRANSPOSEVIRTUAL, &isHTrans));
1207: if (isHTrans) PetscCall(MatHermitianTransposeGetMat(sub, &sub));
1208: }
1209: mumps->val = oval + mumps->nest_vals_start[midx];
1210: PetscCall((*mumps->nest_convert_to_triples[midx])(sub, shift, MAT_REUSE_MATRIX, mumps));
1211: if (isHTrans) {
1212: PetscInt nnz = mumps->nest_vals_start[midx + 1] - mumps->nest_vals_start[midx];
1213: for (PetscInt k = 0; k < nnz; k++) mumps->val[k] = PetscConj(mumps->val[k]);
1214: }
1215: }
1216: }
1217: mumps->val = oval;
1218: }
1219: PetscFunctionReturn(PETSC_SUCCESS);
1220: }
1222: static PetscErrorCode MatDestroy_MUMPS(Mat A)
1223: {
1224: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1226: PetscFunctionBegin;
1227: PetscCall(PetscFree2(mumps->id.sol_loc, mumps->id.isol_loc));
1228: PetscCall(VecScatterDestroy(&mumps->scat_rhs));
1229: PetscCall(VecScatterDestroy(&mumps->scat_sol));
1230: PetscCall(VecDestroy(&mumps->b_seq));
1231: PetscCall(VecDestroy(&mumps->x_seq));
1232: PetscCall(PetscFree(mumps->id.perm_in));
1233: PetscCall(PetscFree2(mumps->irn, mumps->jcn));
1234: PetscCall(PetscFree(mumps->val_alloc));
1235: PetscCall(PetscFree(mumps->info));
1236: PetscCall(PetscFree(mumps->ICNTL_pre));
1237: PetscCall(PetscFree(mumps->CNTL_pre));
1238: PetscCall(MatMumpsResetSchur_Private(mumps));
1239: if (mumps->id.job != JOB_NULL) { /* cannot call PetscMUMPS_c() if JOB_INIT has never been called for this instance */
1240: mumps->id.job = JOB_END;
1241: PetscMUMPS_c(mumps);
1242: 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));
1243: if (mumps->mumps_comm != MPI_COMM_NULL) {
1244: if (PetscDefined(HAVE_OPENMP_SUPPORT) && mumps->use_petsc_omp_support) PetscCallMPI(MPI_Comm_free(&mumps->mumps_comm));
1245: else PetscCall(PetscCommRestoreComm(PetscObjectComm((PetscObject)A), &mumps->mumps_comm));
1246: }
1247: }
1248: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
1249: if (mumps->use_petsc_omp_support) {
1250: PetscCall(PetscOmpCtrlDestroy(&mumps->omp_ctrl));
1251: PetscCall(PetscFree2(mumps->rhs_loc, mumps->rhs_recvbuf));
1252: PetscCall(PetscFree3(mumps->rhs_nrow, mumps->rhs_recvcounts, mumps->rhs_disps));
1253: }
1254: #endif
1255: PetscCall(PetscFree(mumps->ia_alloc));
1256: PetscCall(PetscFree(mumps->ja_alloc));
1257: PetscCall(PetscFree(mumps->recvcount));
1258: PetscCall(PetscFree(mumps->reqs));
1259: PetscCall(PetscFree(mumps->irhs_loc));
1260: PetscCall(PetscFree2(mumps->nest_vals_start, mumps->nest_convert_to_triples));
1261: PetscCall(PetscFree(mumps->nest_vals));
1262: PetscCall(PetscFree(A->data));
1264: /* clear composed functions */
1265: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1266: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorSetSchurIS_C", NULL));
1267: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorCreateSchurComplement_C", NULL));
1268: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsSetIcntl_C", NULL));
1269: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetIcntl_C", NULL));
1270: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsSetCntl_C", NULL));
1271: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetCntl_C", NULL));
1272: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInfo_C", NULL));
1273: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInfog_C", NULL));
1274: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetRinfo_C", NULL));
1275: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetRinfog_C", NULL));
1276: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetNullPivots_C", NULL));
1277: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInverse_C", NULL));
1278: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInverseTranspose_C", NULL));
1279: PetscFunctionReturn(PETSC_SUCCESS);
1280: }
1282: /* Set up the distributed RHS info for MUMPS. <nrhs> is the number of RHS. <array> points to start of RHS on the local processor. */
1283: static PetscErrorCode MatMumpsSetUpDistRHSInfo(Mat A, PetscInt nrhs, const PetscScalar *array)
1284: {
1285: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1286: const PetscMPIInt ompsize = mumps->omp_comm_size;
1287: PetscInt i, m, M, rstart;
1289: PetscFunctionBegin;
1290: PetscCall(MatGetSize(A, &M, NULL));
1291: PetscCall(MatGetLocalSize(A, &m, NULL));
1292: PetscCheck(M <= PETSC_MUMPS_INT_MAX, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscInt too long for PetscMUMPSInt");
1293: if (ompsize == 1) {
1294: if (!mumps->irhs_loc) {
1295: mumps->nloc_rhs = m;
1296: PetscCall(PetscMalloc1(m, &mumps->irhs_loc));
1297: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
1298: for (i = 0; i < m; i++) mumps->irhs_loc[i] = rstart + i + 1; /* use 1-based indices */
1299: }
1300: mumps->id.rhs_loc = (MumpsScalar *)array;
1301: } else {
1302: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
1303: const PetscInt *ranges;
1304: PetscMPIInt j, k, sendcount, *petsc_ranks, *omp_ranks;
1305: MPI_Group petsc_group, omp_group;
1306: PetscScalar *recvbuf = NULL;
1308: if (mumps->is_omp_master) {
1309: /* Lazily initialize the omp stuff for distributed rhs */
1310: if (!mumps->irhs_loc) {
1311: PetscCall(PetscMalloc2(ompsize, &omp_ranks, ompsize, &petsc_ranks));
1312: PetscCall(PetscMalloc3(ompsize, &mumps->rhs_nrow, ompsize, &mumps->rhs_recvcounts, ompsize, &mumps->rhs_disps));
1313: PetscCallMPI(MPI_Comm_group(mumps->petsc_comm, &petsc_group));
1314: PetscCallMPI(MPI_Comm_group(mumps->omp_comm, &omp_group));
1315: for (j = 0; j < ompsize; j++) omp_ranks[j] = j;
1316: PetscCallMPI(MPI_Group_translate_ranks(omp_group, ompsize, omp_ranks, petsc_group, petsc_ranks));
1318: /* Populate mumps->irhs_loc[], rhs_nrow[] */
1319: mumps->nloc_rhs = 0;
1320: PetscCall(MatGetOwnershipRanges(A, &ranges));
1321: for (j = 0; j < ompsize; j++) {
1322: mumps->rhs_nrow[j] = ranges[petsc_ranks[j] + 1] - ranges[petsc_ranks[j]];
1323: mumps->nloc_rhs += mumps->rhs_nrow[j];
1324: }
1325: PetscCall(PetscMalloc1(mumps->nloc_rhs, &mumps->irhs_loc));
1326: for (j = k = 0; j < ompsize; j++) {
1327: for (i = ranges[petsc_ranks[j]]; i < ranges[petsc_ranks[j] + 1]; i++, k++) mumps->irhs_loc[k] = i + 1; /* uses 1-based indices */
1328: }
1330: PetscCall(PetscFree2(omp_ranks, petsc_ranks));
1331: PetscCallMPI(MPI_Group_free(&petsc_group));
1332: PetscCallMPI(MPI_Group_free(&omp_group));
1333: }
1335: /* Realloc buffers when current nrhs is bigger than what we have met */
1336: if (nrhs > mumps->max_nrhs) {
1337: PetscCall(PetscFree2(mumps->rhs_loc, mumps->rhs_recvbuf));
1338: PetscCall(PetscMalloc2(mumps->nloc_rhs * nrhs, &mumps->rhs_loc, mumps->nloc_rhs * nrhs, &mumps->rhs_recvbuf));
1339: mumps->max_nrhs = nrhs;
1340: }
1342: /* Setup recvcounts[], disps[], recvbuf on omp rank 0 for the upcoming MPI_Gatherv */
1343: for (j = 0; j < ompsize; j++) PetscCall(PetscMPIIntCast(mumps->rhs_nrow[j] * nrhs, &mumps->rhs_recvcounts[j]));
1344: mumps->rhs_disps[0] = 0;
1345: for (j = 1; j < ompsize; j++) {
1346: mumps->rhs_disps[j] = mumps->rhs_disps[j - 1] + mumps->rhs_recvcounts[j - 1];
1347: PetscCheck(mumps->rhs_disps[j] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscMPIInt overflow!");
1348: }
1349: recvbuf = (nrhs == 1) ? mumps->rhs_loc : mumps->rhs_recvbuf; /* Directly use rhs_loc[] as recvbuf. Single rhs is common in Ax=b */
1350: }
1352: PetscCall(PetscMPIIntCast(m * nrhs, &sendcount));
1353: PetscCallMPI(MPI_Gatherv(array, sendcount, MPIU_SCALAR, recvbuf, mumps->rhs_recvcounts, mumps->rhs_disps, MPIU_SCALAR, 0, mumps->omp_comm));
1355: if (mumps->is_omp_master) {
1356: if (nrhs > 1) { /* Copy & re-arrange data from rhs_recvbuf[] to mumps->rhs_loc[] only when there are multiple rhs */
1357: PetscScalar *dst, *dstbase = mumps->rhs_loc;
1358: for (j = 0; j < ompsize; j++) {
1359: const PetscScalar *src = mumps->rhs_recvbuf + mumps->rhs_disps[j];
1360: dst = dstbase;
1361: for (i = 0; i < nrhs; i++) {
1362: PetscCall(PetscArraycpy(dst, src, mumps->rhs_nrow[j]));
1363: src += mumps->rhs_nrow[j];
1364: dst += mumps->nloc_rhs;
1365: }
1366: dstbase += mumps->rhs_nrow[j];
1367: }
1368: }
1369: mumps->id.rhs_loc = (MumpsScalar *)mumps->rhs_loc;
1370: }
1371: #endif /* PETSC_HAVE_OPENMP_SUPPORT */
1372: }
1373: mumps->id.nrhs = nrhs;
1374: mumps->id.nloc_rhs = mumps->nloc_rhs;
1375: mumps->id.lrhs_loc = mumps->nloc_rhs;
1376: mumps->id.irhs_loc = mumps->irhs_loc;
1377: PetscFunctionReturn(PETSC_SUCCESS);
1378: }
1380: static PetscErrorCode MatSolve_MUMPS(Mat A, Vec b, Vec x)
1381: {
1382: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1383: const PetscScalar *rarray = NULL;
1384: PetscScalar *array;
1385: IS is_iden, is_petsc;
1386: PetscInt i;
1387: PetscBool second_solve = PETSC_FALSE;
1388: static PetscBool cite1 = PETSC_FALSE, cite2 = PETSC_FALSE;
1390: PetscFunctionBegin;
1391: 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 "
1392: "Journal on Matrix Analysis and Applications},\n volume = {23},\n number = {1},\n pages = {15--41},\n year = {2001}\n}\n",
1393: &cite1));
1394: 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 "
1395: "Computing},\n volume = {32},\n number = {2},\n pages = {136--156},\n year = {2006}\n}\n",
1396: &cite2));
1398: if (A->factorerrortype) {
1399: 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)));
1400: PetscCall(VecSetInf(x));
1401: PetscFunctionReturn(PETSC_SUCCESS);
1402: }
1404: mumps->id.nrhs = 1;
1405: if (mumps->petsc_size > 1) {
1406: if (mumps->ICNTL20 == 10) {
1407: mumps->id.ICNTL(20) = 10; /* dense distributed RHS */
1408: PetscCall(VecGetArrayRead(b, &rarray));
1409: PetscCall(MatMumpsSetUpDistRHSInfo(A, 1, rarray));
1410: } else {
1411: mumps->id.ICNTL(20) = 0; /* dense centralized RHS; Scatter b into a sequential rhs vector*/
1412: PetscCall(VecScatterBegin(mumps->scat_rhs, b, mumps->b_seq, INSERT_VALUES, SCATTER_FORWARD));
1413: PetscCall(VecScatterEnd(mumps->scat_rhs, b, mumps->b_seq, INSERT_VALUES, SCATTER_FORWARD));
1414: if (!mumps->myid) {
1415: PetscCall(VecGetArray(mumps->b_seq, &array));
1416: mumps->id.rhs = (MumpsScalar *)array;
1417: }
1418: }
1419: } else { /* petsc_size == 1 */
1420: mumps->id.ICNTL(20) = 0; /* dense centralized RHS */
1421: PetscCall(VecCopy(b, x));
1422: PetscCall(VecGetArray(x, &array));
1423: mumps->id.rhs = (MumpsScalar *)array;
1424: }
1426: /*
1427: handle condensation step of Schur complement (if any)
1428: We set by default ICNTL(26) == -1 when Schur indices have been provided by the user.
1429: According to MUMPS (5.0.0) manual, any value should be harmful during the factorization phase
1430: Unless the user provides a valid value for ICNTL(26), MatSolve and MatMatSolve routines solve the full system.
1431: This requires an extra call to PetscMUMPS_c and the computation of the factors for S
1432: */
1433: if (mumps->id.size_schur > 0) {
1434: PetscCheck(mumps->petsc_size <= 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Parallel Schur complements not yet supported from PETSc");
1435: if (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2) {
1436: second_solve = PETSC_TRUE;
1437: PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1438: mumps->id.ICNTL(26) = 1; /* condensation phase */
1439: } else if (mumps->id.ICNTL(26) == 1) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1440: }
1441: /* solve phase */
1442: mumps->id.job = JOB_SOLVE;
1443: PetscMUMPS_c(mumps);
1444: 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));
1446: /* handle expansion step of Schur complement (if any) */
1447: if (second_solve) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_TRUE));
1448: else if (mumps->id.ICNTL(26) == 1) {
1449: PetscCall(MatMumpsSolveSchur_Private(A));
1450: for (i = 0; i < mumps->id.size_schur; ++i) {
1451: #if !defined(PETSC_USE_COMPLEX)
1452: PetscScalar val = mumps->id.redrhs[i];
1453: #else
1454: PetscScalar val = mumps->id.redrhs[i].r + PETSC_i * mumps->id.redrhs[i].i;
1455: #endif
1456: array[mumps->id.listvar_schur[i] - 1] = val;
1457: }
1458: }
1460: if (mumps->petsc_size > 1) { /* convert mumps distributed solution to petsc mpi x */
1461: if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) {
1462: /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */
1463: PetscCall(VecScatterDestroy(&mumps->scat_sol));
1464: }
1465: if (!mumps->scat_sol) { /* create scatter scat_sol */
1466: PetscInt *isol2_loc = NULL;
1467: PetscCall(ISCreateStride(PETSC_COMM_SELF, mumps->id.lsol_loc, 0, 1, &is_iden)); /* from */
1468: PetscCall(PetscMalloc1(mumps->id.lsol_loc, &isol2_loc));
1469: for (i = 0; i < mumps->id.lsol_loc; i++) isol2_loc[i] = mumps->id.isol_loc[i] - 1; /* change Fortran style to C style */
1470: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, mumps->id.lsol_loc, isol2_loc, PETSC_OWN_POINTER, &is_petsc)); /* to */
1471: PetscCall(VecScatterCreate(mumps->x_seq, is_iden, x, is_petsc, &mumps->scat_sol));
1472: PetscCall(ISDestroy(&is_iden));
1473: PetscCall(ISDestroy(&is_petsc));
1474: mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */
1475: }
1477: PetscCall(VecScatterBegin(mumps->scat_sol, mumps->x_seq, x, INSERT_VALUES, SCATTER_FORWARD));
1478: PetscCall(VecScatterEnd(mumps->scat_sol, mumps->x_seq, x, INSERT_VALUES, SCATTER_FORWARD));
1479: }
1481: if (mumps->petsc_size > 1) {
1482: if (mumps->ICNTL20 == 10) {
1483: PetscCall(VecRestoreArrayRead(b, &rarray));
1484: } else if (!mumps->myid) {
1485: PetscCall(VecRestoreArray(mumps->b_seq, &array));
1486: }
1487: } else PetscCall(VecRestoreArray(x, &array));
1489: PetscCall(PetscLogFlops(2.0 * PetscMax(0, (mumps->id.INFO(28) >= 0 ? mumps->id.INFO(28) : -1000000 * mumps->id.INFO(28)) - A->cmap->n)));
1490: PetscFunctionReturn(PETSC_SUCCESS);
1491: }
1493: static PetscErrorCode MatSolveTranspose_MUMPS(Mat A, Vec b, Vec x)
1494: {
1495: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1496: const PetscMUMPSInt value = mumps->id.ICNTL(9);
1498: PetscFunctionBegin;
1499: mumps->id.ICNTL(9) = 0;
1500: PetscCall(MatSolve_MUMPS(A, b, x));
1501: mumps->id.ICNTL(9) = value;
1502: PetscFunctionReturn(PETSC_SUCCESS);
1503: }
1505: static PetscErrorCode MatMatSolve_MUMPS(Mat A, Mat B, Mat X)
1506: {
1507: Mat Bt = NULL;
1508: PetscBool denseX, denseB, flg, flgT;
1509: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1510: PetscInt i, nrhs, M;
1511: PetscScalar *array;
1512: const PetscScalar *rbray;
1513: PetscInt lsol_loc, nlsol_loc, *idxx, iidx = 0;
1514: PetscMUMPSInt *isol_loc, *isol_loc_save;
1515: PetscScalar *bray, *sol_loc, *sol_loc_save;
1516: IS is_to, is_from;
1517: PetscInt k, proc, j, m, myrstart;
1518: const PetscInt *rstart;
1519: Vec v_mpi, msol_loc;
1520: VecScatter scat_sol;
1521: Vec b_seq;
1522: VecScatter scat_rhs;
1523: PetscScalar *aa;
1524: PetscInt spnr, *ia, *ja;
1525: Mat_MPIAIJ *b = NULL;
1527: PetscFunctionBegin;
1528: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &denseX, MATSEQDENSE, MATMPIDENSE, NULL));
1529: PetscCheck(denseX, PetscObjectComm((PetscObject)X), PETSC_ERR_ARG_WRONG, "Matrix X must be MATDENSE matrix");
1531: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &denseB, MATSEQDENSE, MATMPIDENSE, NULL));
1532: if (denseB) {
1533: PetscCheck(B->rmap->n == X->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Matrix B and X must have same row distribution");
1534: mumps->id.ICNTL(20) = 0; /* dense RHS */
1535: } else { /* sparse B */
1536: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
1537: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATTRANSPOSEVIRTUAL, &flgT));
1538: if (flgT) { /* input B is transpose of actual RHS matrix,
1539: because mumps requires sparse compressed COLUMN storage! See MatMatTransposeSolve_MUMPS() */
1540: PetscCall(MatTransposeGetMat(B, &Bt));
1541: } else SETERRQ(PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Matrix B must be MATTRANSPOSEVIRTUAL matrix");
1542: mumps->id.ICNTL(20) = 1; /* sparse RHS */
1543: }
1545: PetscCall(MatGetSize(B, &M, &nrhs));
1546: mumps->id.nrhs = nrhs;
1547: mumps->id.lrhs = M;
1548: mumps->id.rhs = NULL;
1550: if (mumps->petsc_size == 1) {
1551: PetscScalar *aa;
1552: PetscInt spnr, *ia, *ja;
1553: PetscBool second_solve = PETSC_FALSE;
1555: PetscCall(MatDenseGetArray(X, &array));
1556: mumps->id.rhs = (MumpsScalar *)array;
1558: if (denseB) {
1559: /* copy B to X */
1560: PetscCall(MatDenseGetArrayRead(B, &rbray));
1561: PetscCall(PetscArraycpy(array, rbray, M * nrhs));
1562: PetscCall(MatDenseRestoreArrayRead(B, &rbray));
1563: } else { /* sparse B */
1564: PetscCall(MatSeqAIJGetArray(Bt, &aa));
1565: PetscCall(MatGetRowIJ(Bt, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1566: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
1567: PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
1568: mumps->id.rhs_sparse = (MumpsScalar *)aa;
1569: }
1570: /* handle condensation step of Schur complement (if any) */
1571: if (mumps->id.size_schur > 0) {
1572: PetscCheck(mumps->petsc_size <= 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Parallel Schur complements not yet supported from PETSc");
1573: if (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2) {
1574: second_solve = PETSC_TRUE;
1575: PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1576: mumps->id.ICNTL(26) = 1; /* condensation phase */
1577: } else if (mumps->id.ICNTL(26) == 1) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1578: }
1579: /* solve phase */
1580: mumps->id.job = JOB_SOLVE;
1581: PetscMUMPS_c(mumps);
1582: 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));
1584: /* handle expansion step of Schur complement (if any) */
1585: if (second_solve) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_TRUE));
1586: else if (mumps->id.ICNTL(26) == 1) {
1587: PetscCall(MatMumpsSolveSchur_Private(A));
1588: for (j = 0; j < nrhs; ++j)
1589: for (i = 0; i < mumps->id.size_schur; ++i) {
1590: #if !defined(PETSC_USE_COMPLEX)
1591: PetscScalar val = mumps->id.redrhs[i + j * mumps->id.lredrhs];
1592: #else
1593: PetscScalar val = mumps->id.redrhs[i + j * mumps->id.lredrhs].r + PETSC_i * mumps->id.redrhs[i + j * mumps->id.lredrhs].i;
1594: #endif
1595: array[mumps->id.listvar_schur[i] - 1 + j * M] = val;
1596: }
1597: }
1598: if (!denseB) { /* sparse B */
1599: PetscCall(MatSeqAIJRestoreArray(Bt, &aa));
1600: PetscCall(MatRestoreRowIJ(Bt, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1601: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot restore IJ structure");
1602: }
1603: PetscCall(MatDenseRestoreArray(X, &array));
1604: PetscFunctionReturn(PETSC_SUCCESS);
1605: }
1607: /* parallel case: MUMPS requires rhs B to be centralized on the host! */
1608: PetscCheck(mumps->petsc_size <= 1 || !mumps->id.ICNTL(19), PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Parallel Schur complements not yet supported from PETSc");
1610: /* create msol_loc to hold mumps local solution */
1611: isol_loc_save = mumps->id.isol_loc; /* save it for MatSolve() */
1612: sol_loc_save = (PetscScalar *)mumps->id.sol_loc;
1614: lsol_loc = mumps->id.lsol_loc;
1615: nlsol_loc = nrhs * lsol_loc; /* length of sol_loc */
1616: PetscCall(PetscMalloc2(nlsol_loc, &sol_loc, lsol_loc, &isol_loc));
1617: mumps->id.sol_loc = (MumpsScalar *)sol_loc;
1618: mumps->id.isol_loc = isol_loc;
1620: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, nlsol_loc, (PetscScalar *)sol_loc, &msol_loc));
1622: if (denseB) {
1623: if (mumps->ICNTL20 == 10) {
1624: mumps->id.ICNTL(20) = 10; /* dense distributed RHS */
1625: PetscCall(MatDenseGetArrayRead(B, &rbray));
1626: PetscCall(MatMumpsSetUpDistRHSInfo(A, nrhs, rbray));
1627: PetscCall(MatDenseRestoreArrayRead(B, &rbray));
1628: PetscCall(MatGetLocalSize(B, &m, NULL));
1629: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B), 1, nrhs * m, nrhs * M, NULL, &v_mpi));
1630: } else {
1631: mumps->id.ICNTL(20) = 0; /* dense centralized RHS */
1632: /* TODO: Because of non-contiguous indices, the created vecscatter scat_rhs is not done in MPI_Gather, resulting in
1633: very inefficient communication. An optimization is to use VecScatterCreateToZero to gather B to rank 0. Then on rank
1634: 0, re-arrange B into desired order, which is a local operation.
1635: */
1637: /* scatter v_mpi to b_seq because MUMPS before 5.3.0 only supports centralized rhs */
1638: /* wrap dense rhs matrix B into a vector v_mpi */
1639: PetscCall(MatGetLocalSize(B, &m, NULL));
1640: PetscCall(MatDenseGetArray(B, &bray));
1641: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B), 1, nrhs * m, nrhs * M, (const PetscScalar *)bray, &v_mpi));
1642: PetscCall(MatDenseRestoreArray(B, &bray));
1644: /* scatter v_mpi to b_seq in proc[0]. MUMPS requires rhs to be centralized on the host! */
1645: if (!mumps->myid) {
1646: PetscInt *idx;
1647: /* idx: maps from k-th index of v_mpi to (i,j)-th global entry of B */
1648: PetscCall(PetscMalloc1(nrhs * M, &idx));
1649: PetscCall(MatGetOwnershipRanges(B, &rstart));
1650: k = 0;
1651: for (proc = 0; proc < mumps->petsc_size; proc++) {
1652: for (j = 0; j < nrhs; j++) {
1653: for (i = rstart[proc]; i < rstart[proc + 1]; i++) idx[k++] = j * M + i;
1654: }
1655: }
1657: PetscCall(VecCreateSeq(PETSC_COMM_SELF, nrhs * M, &b_seq));
1658: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nrhs * M, idx, PETSC_OWN_POINTER, &is_to));
1659: PetscCall(ISCreateStride(PETSC_COMM_SELF, nrhs * M, 0, 1, &is_from));
1660: } else {
1661: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 0, &b_seq));
1662: PetscCall(ISCreateStride(PETSC_COMM_SELF, 0, 0, 1, &is_to));
1663: PetscCall(ISCreateStride(PETSC_COMM_SELF, 0, 0, 1, &is_from));
1664: }
1665: PetscCall(VecScatterCreate(v_mpi, is_from, b_seq, is_to, &scat_rhs));
1666: PetscCall(VecScatterBegin(scat_rhs, v_mpi, b_seq, INSERT_VALUES, SCATTER_FORWARD));
1667: PetscCall(ISDestroy(&is_to));
1668: PetscCall(ISDestroy(&is_from));
1669: PetscCall(VecScatterEnd(scat_rhs, v_mpi, b_seq, INSERT_VALUES, SCATTER_FORWARD));
1671: if (!mumps->myid) { /* define rhs on the host */
1672: PetscCall(VecGetArray(b_seq, &bray));
1673: mumps->id.rhs = (MumpsScalar *)bray;
1674: PetscCall(VecRestoreArray(b_seq, &bray));
1675: }
1676: }
1677: } else { /* sparse B */
1678: b = (Mat_MPIAIJ *)Bt->data;
1680: /* wrap dense X into a vector v_mpi */
1681: PetscCall(MatGetLocalSize(X, &m, NULL));
1682: PetscCall(MatDenseGetArray(X, &bray));
1683: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)X), 1, nrhs * m, nrhs * M, (const PetscScalar *)bray, &v_mpi));
1684: PetscCall(MatDenseRestoreArray(X, &bray));
1686: if (!mumps->myid) {
1687: PetscCall(MatSeqAIJGetArray(b->A, &aa));
1688: PetscCall(MatGetRowIJ(b->A, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1689: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
1690: PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
1691: mumps->id.rhs_sparse = (MumpsScalar *)aa;
1692: } else {
1693: mumps->id.irhs_ptr = NULL;
1694: mumps->id.irhs_sparse = NULL;
1695: mumps->id.nz_rhs = 0;
1696: mumps->id.rhs_sparse = NULL;
1697: }
1698: }
1700: /* solve phase */
1701: mumps->id.job = JOB_SOLVE;
1702: PetscMUMPS_c(mumps);
1703: 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));
1705: /* scatter mumps distributed solution to petsc vector v_mpi, which shares local arrays with solution matrix X */
1706: PetscCall(MatDenseGetArray(X, &array));
1707: PetscCall(VecPlaceArray(v_mpi, array));
1709: /* create scatter scat_sol */
1710: PetscCall(MatGetOwnershipRanges(X, &rstart));
1711: /* iidx: index for scatter mumps solution to petsc X */
1713: PetscCall(ISCreateStride(PETSC_COMM_SELF, nlsol_loc, 0, 1, &is_from));
1714: PetscCall(PetscMalloc1(nlsol_loc, &idxx));
1715: for (i = 0; i < lsol_loc; i++) {
1716: 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 */
1718: for (proc = 0; proc < mumps->petsc_size; proc++) {
1719: if (isol_loc[i] >= rstart[proc] && isol_loc[i] < rstart[proc + 1]) {
1720: myrstart = rstart[proc];
1721: k = isol_loc[i] - myrstart; /* local index on 1st column of petsc vector X */
1722: iidx = k + myrstart * nrhs; /* maps mumps isol_loc[i] to petsc index in X */
1723: m = rstart[proc + 1] - rstart[proc]; /* rows of X for this proc */
1724: break;
1725: }
1726: }
1728: for (j = 0; j < nrhs; j++) idxx[i + j * lsol_loc] = iidx + j * m;
1729: }
1730: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nlsol_loc, idxx, PETSC_COPY_VALUES, &is_to));
1731: PetscCall(VecScatterCreate(msol_loc, is_from, v_mpi, is_to, &scat_sol));
1732: PetscCall(VecScatterBegin(scat_sol, msol_loc, v_mpi, INSERT_VALUES, SCATTER_FORWARD));
1733: PetscCall(ISDestroy(&is_from));
1734: PetscCall(ISDestroy(&is_to));
1735: PetscCall(VecScatterEnd(scat_sol, msol_loc, v_mpi, INSERT_VALUES, SCATTER_FORWARD));
1736: PetscCall(MatDenseRestoreArray(X, &array));
1738: /* free spaces */
1739: mumps->id.sol_loc = (MumpsScalar *)sol_loc_save;
1740: mumps->id.isol_loc = isol_loc_save;
1742: PetscCall(PetscFree2(sol_loc, isol_loc));
1743: PetscCall(PetscFree(idxx));
1744: PetscCall(VecDestroy(&msol_loc));
1745: PetscCall(VecDestroy(&v_mpi));
1746: if (!denseB) {
1747: if (!mumps->myid) {
1748: b = (Mat_MPIAIJ *)Bt->data;
1749: PetscCall(MatSeqAIJRestoreArray(b->A, &aa));
1750: PetscCall(MatRestoreRowIJ(b->A, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1751: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot restore IJ structure");
1752: }
1753: } else {
1754: if (mumps->ICNTL20 == 0) {
1755: PetscCall(VecDestroy(&b_seq));
1756: PetscCall(VecScatterDestroy(&scat_rhs));
1757: }
1758: }
1759: PetscCall(VecScatterDestroy(&scat_sol));
1760: PetscCall(PetscLogFlops(nrhs * PetscMax(0, (2.0 * (mumps->id.INFO(28) >= 0 ? mumps->id.INFO(28) : -1000000 * mumps->id.INFO(28)) - A->cmap->n))));
1761: PetscFunctionReturn(PETSC_SUCCESS);
1762: }
1764: static PetscErrorCode MatMatSolveTranspose_MUMPS(Mat A, Mat B, Mat X)
1765: {
1766: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1767: const PetscMUMPSInt value = mumps->id.ICNTL(9);
1769: PetscFunctionBegin;
1770: mumps->id.ICNTL(9) = 0;
1771: PetscCall(MatMatSolve_MUMPS(A, B, X));
1772: mumps->id.ICNTL(9) = value;
1773: PetscFunctionReturn(PETSC_SUCCESS);
1774: }
1776: static PetscErrorCode MatMatTransposeSolve_MUMPS(Mat A, Mat Bt, Mat X)
1777: {
1778: PetscBool flg;
1779: Mat B;
1781: PetscFunctionBegin;
1782: PetscCall(PetscObjectTypeCompareAny((PetscObject)Bt, &flg, MATSEQAIJ, MATMPIAIJ, NULL));
1783: PetscCheck(flg, PetscObjectComm((PetscObject)Bt), PETSC_ERR_ARG_WRONG, "Matrix Bt must be MATAIJ matrix");
1785: /* Create B=Bt^T that uses Bt's data structure */
1786: PetscCall(MatCreateTranspose(Bt, &B));
1788: PetscCall(MatMatSolve_MUMPS(A, B, X));
1789: PetscCall(MatDestroy(&B));
1790: PetscFunctionReturn(PETSC_SUCCESS);
1791: }
1793: #if !defined(PETSC_USE_COMPLEX)
1794: /*
1795: input:
1796: F: numeric factor
1797: output:
1798: nneg: total number of negative pivots
1799: nzero: total number of zero pivots
1800: npos: (global dimension of F) - nneg - nzero
1801: */
1802: static PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
1803: {
1804: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
1805: PetscMPIInt size;
1807: PetscFunctionBegin;
1808: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)F), &size));
1809: /* 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 */
1810: 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));
1812: if (nneg) *nneg = mumps->id.INFOG(12);
1813: if (nzero || npos) {
1814: 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");
1815: if (nzero) *nzero = mumps->id.INFOG(28);
1816: if (npos) *npos = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28));
1817: }
1818: PetscFunctionReturn(PETSC_SUCCESS);
1819: }
1820: #endif
1822: static PetscErrorCode MatMumpsGatherNonzerosOnMaster(MatReuse reuse, Mat_MUMPS *mumps)
1823: {
1824: PetscInt i, nreqs;
1825: PetscMUMPSInt *irn, *jcn;
1826: PetscMPIInt count;
1827: PetscInt64 totnnz, remain;
1828: const PetscInt osize = mumps->omp_comm_size;
1829: PetscScalar *val;
1831: PetscFunctionBegin;
1832: if (osize > 1) {
1833: if (reuse == MAT_INITIAL_MATRIX) {
1834: /* master first gathers counts of nonzeros to receive */
1835: if (mumps->is_omp_master) PetscCall(PetscMalloc1(osize, &mumps->recvcount));
1836: PetscCallMPI(MPI_Gather(&mumps->nnz, 1, MPIU_INT64, mumps->recvcount, 1, MPIU_INT64, 0 /*master*/, mumps->omp_comm));
1838: /* Then each computes number of send/recvs */
1839: if (mumps->is_omp_master) {
1840: /* Start from 1 since self communication is not done in MPI */
1841: nreqs = 0;
1842: for (i = 1; i < osize; i++) nreqs += (mumps->recvcount[i] + PETSC_MPI_INT_MAX - 1) / PETSC_MPI_INT_MAX;
1843: } else {
1844: nreqs = (mumps->nnz + PETSC_MPI_INT_MAX - 1) / PETSC_MPI_INT_MAX;
1845: }
1846: PetscCall(PetscMalloc1(nreqs * 3, &mumps->reqs)); /* Triple the requests since we send irn, jcn and val separately */
1848: /* The following code is doing a very simple thing: omp_master rank gathers irn/jcn/val from others.
1849: MPI_Gatherv would be enough if it supports big counts > 2^31-1. Since it does not, and mumps->nnz
1850: might be a prime number > 2^31-1, we have to slice the message. Note omp_comm_size
1851: is very small, the current approach should have no extra overhead compared to MPI_Gatherv.
1852: */
1853: nreqs = 0; /* counter for actual send/recvs */
1854: if (mumps->is_omp_master) {
1855: for (i = 0, totnnz = 0; i < osize; i++) totnnz += mumps->recvcount[i]; /* totnnz = sum of nnz over omp_comm */
1856: PetscCall(PetscMalloc2(totnnz, &irn, totnnz, &jcn));
1857: PetscCall(PetscMalloc1(totnnz, &val));
1859: /* Self communication */
1860: PetscCall(PetscArraycpy(irn, mumps->irn, mumps->nnz));
1861: PetscCall(PetscArraycpy(jcn, mumps->jcn, mumps->nnz));
1862: PetscCall(PetscArraycpy(val, mumps->val, mumps->nnz));
1864: /* Replace mumps->irn/jcn etc on master with the newly allocated bigger arrays */
1865: PetscCall(PetscFree2(mumps->irn, mumps->jcn));
1866: PetscCall(PetscFree(mumps->val_alloc));
1867: mumps->nnz = totnnz;
1868: mumps->irn = irn;
1869: mumps->jcn = jcn;
1870: mumps->val = mumps->val_alloc = val;
1872: irn += mumps->recvcount[0]; /* recvcount[0] is old mumps->nnz on omp rank 0 */
1873: jcn += mumps->recvcount[0];
1874: val += mumps->recvcount[0];
1876: /* Remote communication */
1877: for (i = 1; i < osize; i++) {
1878: count = PetscMin(mumps->recvcount[i], PETSC_MPI_INT_MAX);
1879: remain = mumps->recvcount[i] - count;
1880: while (count > 0) {
1881: PetscCallMPI(MPI_Irecv(irn, count, MPIU_MUMPSINT, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1882: PetscCallMPI(MPI_Irecv(jcn, count, MPIU_MUMPSINT, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1883: PetscCallMPI(MPI_Irecv(val, count, MPIU_SCALAR, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1884: irn += count;
1885: jcn += count;
1886: val += count;
1887: count = PetscMin(remain, PETSC_MPI_INT_MAX);
1888: remain -= count;
1889: }
1890: }
1891: } else {
1892: irn = mumps->irn;
1893: jcn = mumps->jcn;
1894: val = mumps->val;
1895: count = PetscMin(mumps->nnz, PETSC_MPI_INT_MAX);
1896: remain = mumps->nnz - count;
1897: while (count > 0) {
1898: PetscCallMPI(MPI_Isend(irn, count, MPIU_MUMPSINT, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1899: PetscCallMPI(MPI_Isend(jcn, count, MPIU_MUMPSINT, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1900: PetscCallMPI(MPI_Isend(val, count, MPIU_SCALAR, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1901: irn += count;
1902: jcn += count;
1903: val += count;
1904: count = PetscMin(remain, PETSC_MPI_INT_MAX);
1905: remain -= count;
1906: }
1907: }
1908: } else {
1909: nreqs = 0;
1910: if (mumps->is_omp_master) {
1911: val = mumps->val + mumps->recvcount[0];
1912: for (i = 1; i < osize; i++) { /* Remote communication only since self data is already in place */
1913: count = PetscMin(mumps->recvcount[i], PETSC_MPI_INT_MAX);
1914: remain = mumps->recvcount[i] - count;
1915: while (count > 0) {
1916: PetscCallMPI(MPI_Irecv(val, count, MPIU_SCALAR, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1917: val += count;
1918: count = PetscMin(remain, PETSC_MPI_INT_MAX);
1919: remain -= count;
1920: }
1921: }
1922: } else {
1923: val = mumps->val;
1924: count = PetscMin(mumps->nnz, PETSC_MPI_INT_MAX);
1925: remain = mumps->nnz - count;
1926: while (count > 0) {
1927: PetscCallMPI(MPI_Isend(val, count, MPIU_SCALAR, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1928: val += count;
1929: count = PetscMin(remain, PETSC_MPI_INT_MAX);
1930: remain -= count;
1931: }
1932: }
1933: }
1934: PetscCallMPI(MPI_Waitall(nreqs, mumps->reqs, MPI_STATUSES_IGNORE));
1935: mumps->tag++; /* It is totally fine for above send/recvs to share one mpi tag */
1936: }
1937: PetscFunctionReturn(PETSC_SUCCESS);
1938: }
1940: static PetscErrorCode MatFactorNumeric_MUMPS(Mat F, Mat A, const MatFactorInfo *info)
1941: {
1942: Mat_MUMPS *mumps = (Mat_MUMPS *)(F)->data;
1943: PetscBool isMPIAIJ;
1945: PetscFunctionBegin;
1946: if (mumps->id.INFOG(1) < 0 && !(mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0)) {
1947: 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)));
1948: 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)));
1949: PetscFunctionReturn(PETSC_SUCCESS);
1950: }
1952: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, mumps));
1953: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_REUSE_MATRIX, mumps));
1955: /* numerical factorization phase */
1956: mumps->id.job = JOB_FACTNUMERIC;
1957: if (!mumps->id.ICNTL(18)) { /* A is centralized */
1958: if (!mumps->myid) mumps->id.a = (MumpsScalar *)mumps->val;
1959: } else {
1960: mumps->id.a_loc = (MumpsScalar *)mumps->val;
1961: }
1962: PetscMUMPS_c(mumps);
1963: if (mumps->id.INFOG(1) < 0) {
1964: 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));
1965: if (mumps->id.INFOG(1) == -10) {
1966: 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)));
1967: F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1968: } else if (mumps->id.INFOG(1) == -13) {
1969: 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)));
1970: F->factorerrortype = MAT_FACTOR_OUTMEMORY;
1971: } else if (mumps->id.INFOG(1) == -8 || mumps->id.INFOG(1) == -9 || (-16 < mumps->id.INFOG(1) && mumps->id.INFOG(1) < -10)) {
1972: 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)));
1973: F->factorerrortype = MAT_FACTOR_OUTMEMORY;
1974: } else {
1975: PetscCall(PetscInfo(F, "MUMPS error in numerical factorization: INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1976: F->factorerrortype = MAT_FACTOR_OTHER;
1977: }
1978: }
1979: 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));
1981: F->assembled = PETSC_TRUE;
1983: if (F->schur) { /* reset Schur status to unfactored */
1984: #if defined(PETSC_HAVE_CUDA)
1985: F->schur->offloadmask = PETSC_OFFLOAD_CPU;
1986: #endif
1987: if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
1988: mumps->id.ICNTL(19) = 2;
1989: PetscCall(MatTranspose(F->schur, MAT_INPLACE_MATRIX, &F->schur));
1990: }
1991: PetscCall(MatFactorRestoreSchurComplement(F, NULL, MAT_FACTOR_SCHUR_UNFACTORED));
1992: }
1994: /* just to be sure that ICNTL(19) value returned by a call from MatMumpsGetIcntl is always consistent */
1995: if (!mumps->sym && mumps->id.ICNTL(19) && mumps->id.ICNTL(19) != 1) mumps->id.ICNTL(19) = 3;
1997: if (!mumps->is_omp_master) mumps->id.INFO(23) = 0;
1998: if (mumps->petsc_size > 1) {
1999: PetscInt lsol_loc;
2000: PetscScalar *sol_loc;
2002: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &isMPIAIJ));
2004: /* distributed solution; Create x_seq=sol_loc for repeated use */
2005: if (mumps->x_seq) {
2006: PetscCall(VecScatterDestroy(&mumps->scat_sol));
2007: PetscCall(PetscFree2(mumps->id.sol_loc, mumps->id.isol_loc));
2008: PetscCall(VecDestroy(&mumps->x_seq));
2009: }
2010: lsol_loc = mumps->id.INFO(23); /* length of sol_loc */
2011: PetscCall(PetscMalloc2(lsol_loc, &sol_loc, lsol_loc, &mumps->id.isol_loc));
2012: mumps->id.lsol_loc = lsol_loc;
2013: mumps->id.sol_loc = (MumpsScalar *)sol_loc;
2014: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, lsol_loc, sol_loc, &mumps->x_seq));
2015: }
2016: PetscCall(PetscLogFlops(mumps->id.RINFO(2)));
2017: PetscFunctionReturn(PETSC_SUCCESS);
2018: }
2020: /* Sets MUMPS options from the options database */
2021: static PetscErrorCode MatSetFromOptions_MUMPS(Mat F, Mat A)
2022: {
2023: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2024: PetscMUMPSInt icntl = 0, size, *listvar_schur;
2025: PetscInt info[80], i, ninfo = 80, rbs, cbs;
2026: PetscBool flg = PETSC_FALSE, schur = (PetscBool)(mumps->id.ICNTL(26) == -1);
2027: MumpsScalar *arr;
2029: PetscFunctionBegin;
2030: PetscOptionsBegin(PetscObjectComm((PetscObject)F), ((PetscObject)F)->prefix, "MUMPS Options", "Mat");
2031: if (mumps->id.job == JOB_NULL) { /* MatSetFromOptions_MUMPS() has never been called before */
2032: PetscInt nthreads = 0;
2033: PetscInt nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
2034: PetscInt nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0;
2036: mumps->petsc_comm = PetscObjectComm((PetscObject)A);
2037: PetscCallMPI(MPI_Comm_size(mumps->petsc_comm, &mumps->petsc_size));
2038: PetscCallMPI(MPI_Comm_rank(mumps->petsc_comm, &mumps->myid)); /* "if (!myid)" still works even if mumps_comm is different */
2040: PetscCall(PetscOptionsName("-mat_mumps_use_omp_threads", "Convert MPI processes into OpenMP threads", "None", &mumps->use_petsc_omp_support));
2041: if (mumps->use_petsc_omp_support) nthreads = -1; /* -1 will let PetscOmpCtrlCreate() guess a proper value when user did not supply one */
2042: /* do not use PetscOptionsInt() so that the option -mat_mumps_use_omp_threads is not displayed twice in the help */
2043: PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)F)->prefix, "-mat_mumps_use_omp_threads", &nthreads, NULL));
2044: if (mumps->use_petsc_omp_support) {
2045: 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",
2046: ((PetscObject)F)->prefix ? ((PetscObject)F)->prefix : "");
2047: 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 : "");
2048: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
2049: PetscCall(PetscOmpCtrlCreate(mumps->petsc_comm, nthreads, &mumps->omp_ctrl));
2050: PetscCall(PetscOmpCtrlGetOmpComms(mumps->omp_ctrl, &mumps->omp_comm, &mumps->mumps_comm, &mumps->is_omp_master));
2051: #endif
2052: } else {
2053: mumps->omp_comm = PETSC_COMM_SELF;
2054: mumps->mumps_comm = mumps->petsc_comm;
2055: mumps->is_omp_master = PETSC_TRUE;
2056: }
2057: PetscCallMPI(MPI_Comm_size(mumps->omp_comm, &mumps->omp_comm_size));
2058: mumps->reqs = NULL;
2059: mumps->tag = 0;
2061: if (mumps->mumps_comm != MPI_COMM_NULL) {
2062: if (PetscDefined(HAVE_OPENMP_SUPPORT) && mumps->use_petsc_omp_support) {
2063: /* It looks like MUMPS does not dup the input comm. Dup a new comm for MUMPS to avoid any tag mismatches. */
2064: MPI_Comm comm;
2065: PetscCallMPI(MPI_Comm_dup(mumps->mumps_comm, &comm));
2066: mumps->mumps_comm = comm;
2067: } else PetscCall(PetscCommGetComm(mumps->petsc_comm, &mumps->mumps_comm));
2068: }
2070: mumps->id.comm_fortran = MPI_Comm_c2f(mumps->mumps_comm);
2071: mumps->id.job = JOB_INIT;
2072: mumps->id.par = 1; /* host participates factorizaton and solve */
2073: mumps->id.sym = mumps->sym;
2075: size = mumps->id.size_schur;
2076: arr = mumps->id.schur;
2077: listvar_schur = mumps->id.listvar_schur;
2078: PetscMUMPS_c(mumps);
2079: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "MUMPS error: INFOG(1)=%d " MUMPS_MANUALS, mumps->id.INFOG(1));
2080: /* restore cached ICNTL and CNTL values */
2081: for (icntl = 0; icntl < nICNTL_pre; ++icntl) mumps->id.ICNTL(mumps->ICNTL_pre[1 + 2 * icntl]) = mumps->ICNTL_pre[2 + 2 * icntl];
2082: for (icntl = 0; icntl < nCNTL_pre; ++icntl) mumps->id.CNTL((PetscInt)mumps->CNTL_pre[1 + 2 * icntl]) = mumps->CNTL_pre[2 + 2 * icntl];
2083: PetscCall(PetscFree(mumps->ICNTL_pre));
2084: PetscCall(PetscFree(mumps->CNTL_pre));
2086: if (schur) {
2087: mumps->id.size_schur = size;
2088: mumps->id.schur_lld = size;
2089: mumps->id.schur = arr;
2090: mumps->id.listvar_schur = listvar_schur;
2091: if (mumps->petsc_size > 1) {
2092: PetscBool gs; /* gs is false if any rank other than root has non-empty IS */
2094: mumps->id.ICNTL(19) = 1; /* MUMPS returns Schur centralized on the host */
2095: gs = mumps->myid ? (mumps->id.size_schur ? PETSC_FALSE : PETSC_TRUE) : PETSC_TRUE; /* always true on root; false on others if their size != 0 */
2096: PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &gs, 1, MPIU_BOOL, MPI_LAND, mumps->petsc_comm));
2097: PetscCheck(gs, PETSC_COMM_SELF, PETSC_ERR_SUP, "MUMPS distributed parallel Schur complements not yet supported from PETSc");
2098: } else {
2099: if (F->factortype == MAT_FACTOR_LU) {
2100: mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */
2101: } else {
2102: mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */
2103: }
2104: }
2105: mumps->id.ICNTL(26) = -1;
2106: }
2108: /* copy MUMPS default control values from master to slaves. Although slaves do not call MUMPS, they may access these values in code.
2109: For example, ICNTL(9) is initialized to 1 by MUMPS and slaves check ICNTL(9) in MatSolve_MUMPS.
2110: */
2111: PetscCallMPI(MPI_Bcast(mumps->id.icntl, 40, MPI_INT, 0, mumps->omp_comm));
2112: PetscCallMPI(MPI_Bcast(mumps->id.cntl, 15, MPIU_REAL, 0, mumps->omp_comm));
2114: mumps->scat_rhs = NULL;
2115: mumps->scat_sol = NULL;
2117: /* set PETSc-MUMPS default options - override MUMPS default */
2118: mumps->id.ICNTL(3) = 0;
2119: mumps->id.ICNTL(4) = 0;
2120: if (mumps->petsc_size == 1) {
2121: mumps->id.ICNTL(18) = 0; /* centralized assembled matrix input */
2122: mumps->id.ICNTL(7) = 7; /* automatic choice of ordering done by the package */
2123: } else {
2124: mumps->id.ICNTL(18) = 3; /* distributed assembled matrix input */
2125: mumps->id.ICNTL(21) = 1; /* distributed solution */
2126: }
2127: }
2128: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_1", "ICNTL(1): output stream for error messages", "None", mumps->id.ICNTL(1), &icntl, &flg));
2129: if (flg) mumps->id.ICNTL(1) = icntl;
2130: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_2", "ICNTL(2): output stream for diagnostic printing, statistics, and warning", "None", mumps->id.ICNTL(2), &icntl, &flg));
2131: if (flg) mumps->id.ICNTL(2) = icntl;
2132: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_3", "ICNTL(3): output stream for global information, collected on the host", "None", mumps->id.ICNTL(3), &icntl, &flg));
2133: if (flg) mumps->id.ICNTL(3) = icntl;
2135: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_4", "ICNTL(4): level of printing (0 to 4)", "None", mumps->id.ICNTL(4), &icntl, &flg));
2136: if (flg) mumps->id.ICNTL(4) = icntl;
2137: if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */
2139: 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));
2140: if (flg) mumps->id.ICNTL(6) = icntl;
2142: 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));
2143: if (flg) {
2144: 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");
2145: mumps->id.ICNTL(7) = icntl;
2146: }
2148: 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));
2149: /* 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() */
2150: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_10", "ICNTL(10): max num of refinements", "None", mumps->id.ICNTL(10), &mumps->id.ICNTL(10), NULL));
2151: 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));
2152: 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));
2153: 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));
2154: 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));
2155: PetscCall(MatGetBlockSizes(A, &rbs, &cbs));
2156: if (rbs == cbs && rbs > 1) mumps->id.ICNTL(15) = -rbs;
2157: 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));
2158: if (flg) {
2159: PetscCheck(mumps->id.ICNTL(15) <= 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Positive -mat_mumps_icntl_15 not handled");
2160: 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");
2161: }
2162: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_19", "ICNTL(19): computes the Schur complement", "None", mumps->id.ICNTL(19), &mumps->id.ICNTL(19), NULL));
2163: if (mumps->id.ICNTL(19) <= 0 || mumps->id.ICNTL(19) > 3) { /* reset any schur data (if any) */
2164: PetscCall(MatDestroy(&F->schur));
2165: PetscCall(MatMumpsResetSchur_Private(mumps));
2166: }
2168: /* Two MPICH Fortran MPI_IN_PLACE binding bugs prevented the use of 'mpich + mumps'. One happened with "mpi4py + mpich + mumps",
2169: and was reported by Firedrake. See https://bitbucket.org/mpi4py/mpi4py/issues/162/mpi4py-initialization-breaks-fortran
2170: and a petsc-maint mailing list thread with subject 'MUMPS segfaults in parallel because of ...'
2171: This bug was fixed by https://github.com/pmodels/mpich/pull/4149. But the fix brought a new bug,
2172: see https://github.com/pmodels/mpich/issues/5589. This bug was fixed by https://github.com/pmodels/mpich/pull/5590.
2173: In short, we could not use distributed RHS with MPICH until v4.0b1.
2174: */
2175: #if PETSC_PKG_MUMPS_VERSION_LT(5, 3, 0) || (defined(PETSC_HAVE_MPICH_NUMVERSION) && (PETSC_HAVE_MPICH_NUMVERSION < 40000101))
2176: mumps->ICNTL20 = 0; /* Centralized dense RHS*/
2177: #else
2178: mumps->ICNTL20 = 10; /* Distributed dense RHS*/
2179: #endif
2180: 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));
2181: 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);
2182: #if PETSC_PKG_MUMPS_VERSION_LT(5, 3, 0)
2183: PetscCheck(!flg || mumps->ICNTL20 != 10, PETSC_COMM_SELF, PETSC_ERR_SUP, "ICNTL(20)=10 is not supported before MUMPS-5.3.0");
2184: #endif
2185: /* 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 */
2187: 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));
2188: 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));
2189: 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));
2190: if (mumps->id.ICNTL(24)) { mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */ }
2192: 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));
2193: 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));
2194: 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));
2195: 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));
2196: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_29", "ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis", "None", mumps->id.ICNTL(29), &mumps->id.ICNTL(29), NULL));
2197: /* 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 */
2198: 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));
2199: /* 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 */
2200: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_33", "ICNTL(33): compute determinant", "None", mumps->id.ICNTL(33), &mumps->id.ICNTL(33), NULL));
2201: 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));
2202: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_36", "ICNTL(36): choice of BLR factorization variant", "None", mumps->id.ICNTL(36), &mumps->id.ICNTL(36), NULL));
2203: 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));
2204: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_58", "ICNTL(58): defines options for symbolic factorization", "None", mumps->id.ICNTL(58), &mumps->id.ICNTL(58), NULL));
2206: PetscCall(PetscOptionsReal("-mat_mumps_cntl_1", "CNTL(1): relative pivoting threshold", "None", mumps->id.CNTL(1), &mumps->id.CNTL(1), NULL));
2207: PetscCall(PetscOptionsReal("-mat_mumps_cntl_2", "CNTL(2): stopping criterion of refinement", "None", mumps->id.CNTL(2), &mumps->id.CNTL(2), NULL));
2208: PetscCall(PetscOptionsReal("-mat_mumps_cntl_3", "CNTL(3): absolute pivoting threshold", "None", mumps->id.CNTL(3), &mumps->id.CNTL(3), NULL));
2209: PetscCall(PetscOptionsReal("-mat_mumps_cntl_4", "CNTL(4): value for static pivoting", "None", mumps->id.CNTL(4), &mumps->id.CNTL(4), NULL));
2210: PetscCall(PetscOptionsReal("-mat_mumps_cntl_5", "CNTL(5): fixation for null pivots", "None", mumps->id.CNTL(5), &mumps->id.CNTL(5), NULL));
2211: PetscCall(PetscOptionsReal("-mat_mumps_cntl_7", "CNTL(7): dropping parameter used during BLR", "None", mumps->id.CNTL(7), &mumps->id.CNTL(7), NULL));
2213: 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));
2215: PetscCall(PetscOptionsIntArray("-mat_mumps_view_info", "request INFO local to each processor", "", info, &ninfo, NULL));
2216: if (ninfo) {
2217: PetscCheck(ninfo <= 80, PETSC_COMM_SELF, PETSC_ERR_USER, "number of INFO %" PetscInt_FMT " must <= 80", ninfo);
2218: PetscCall(PetscMalloc1(ninfo, &mumps->info));
2219: mumps->ninfo = ninfo;
2220: for (i = 0; i < ninfo; i++) {
2221: PetscCheck(info[i] >= 0 && info[i] <= 80, PETSC_COMM_SELF, PETSC_ERR_USER, "index of INFO %" PetscInt_FMT " must between 1 and 80", ninfo);
2222: mumps->info[i] = info[i];
2223: }
2224: }
2225: PetscOptionsEnd();
2226: PetscFunctionReturn(PETSC_SUCCESS);
2227: }
2229: static PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F, Mat A, const MatFactorInfo *info, Mat_MUMPS *mumps)
2230: {
2231: PetscFunctionBegin;
2232: if (mumps->id.INFOG(1) < 0) {
2233: PetscCheck(!A->erroriffailure, PETSC_COMM_SELF, PETSC_ERR_LIB, "MUMPS error in analysis: INFOG(1)=%d " MUMPS_MANUALS, mumps->id.INFOG(1));
2234: if (mumps->id.INFOG(1) == -6) {
2235: 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)));
2236: F->factorerrortype = MAT_FACTOR_STRUCT_ZEROPIVOT;
2237: } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) {
2238: 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)));
2239: F->factorerrortype = MAT_FACTOR_OUTMEMORY;
2240: } else if (mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0) {
2241: PetscCall(PetscInfo(F, "MUMPS error in analysis: empty matrix\n"));
2242: } else {
2243: PetscCall(PetscInfo(F, "MUMPS error in analysis: INFOG(1)=%d, INFO(2)=%d " MUMPS_MANUALS "\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
2244: F->factorerrortype = MAT_FACTOR_OTHER;
2245: }
2246: }
2247: PetscFunctionReturn(PETSC_SUCCESS);
2248: }
2250: static PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
2251: {
2252: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2253: Vec b;
2254: const PetscInt M = A->rmap->N;
2256: PetscFunctionBegin;
2257: if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2258: /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */
2259: PetscFunctionReturn(PETSC_SUCCESS);
2260: }
2262: /* Set MUMPS options from the options database */
2263: PetscCall(MatSetFromOptions_MUMPS(F, A));
2265: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2266: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));
2268: /* analysis phase */
2269: mumps->id.job = JOB_FACTSYMBOLIC;
2270: mumps->id.n = M;
2271: switch (mumps->id.ICNTL(18)) {
2272: case 0: /* centralized assembled matrix input */
2273: if (!mumps->myid) {
2274: mumps->id.nnz = mumps->nnz;
2275: mumps->id.irn = mumps->irn;
2276: mumps->id.jcn = mumps->jcn;
2277: if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2278: if (r) {
2279: mumps->id.ICNTL(7) = 1;
2280: if (!mumps->myid) {
2281: const PetscInt *idx;
2282: PetscInt i;
2284: PetscCall(PetscMalloc1(M, &mumps->id.perm_in));
2285: PetscCall(ISGetIndices(r, &idx));
2286: for (i = 0; i < M; i++) PetscCall(PetscMUMPSIntCast(idx[i] + 1, &(mumps->id.perm_in[i]))); /* perm_in[]: start from 1, not 0! */
2287: PetscCall(ISRestoreIndices(r, &idx));
2288: }
2289: }
2290: }
2291: break;
2292: case 3: /* distributed assembled matrix input (size>1) */
2293: mumps->id.nnz_loc = mumps->nnz;
2294: mumps->id.irn_loc = mumps->irn;
2295: mumps->id.jcn_loc = mumps->jcn;
2296: if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2297: if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2298: PetscCall(MatCreateVecs(A, NULL, &b));
2299: PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2300: PetscCall(VecDestroy(&b));
2301: }
2302: break;
2303: }
2304: PetscMUMPS_c(mumps);
2305: PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));
2307: F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
2308: F->ops->solve = MatSolve_MUMPS;
2309: F->ops->solvetranspose = MatSolveTranspose_MUMPS;
2310: F->ops->matsolve = MatMatSolve_MUMPS;
2311: F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS;
2312: F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;
2314: mumps->matstruc = SAME_NONZERO_PATTERN;
2315: PetscFunctionReturn(PETSC_SUCCESS);
2316: }
2318: /* Note the Petsc r and c permutations are ignored */
2319: static PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
2320: {
2321: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2322: Vec b;
2323: const PetscInt M = A->rmap->N;
2325: PetscFunctionBegin;
2326: if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2327: /* F is assembled by a previous call of MatLUFactorSymbolic_BAIJMUMPS() */
2328: PetscFunctionReturn(PETSC_SUCCESS);
2329: }
2331: /* Set MUMPS options from the options database */
2332: PetscCall(MatSetFromOptions_MUMPS(F, A));
2334: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2335: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));
2337: /* analysis phase */
2338: mumps->id.job = JOB_FACTSYMBOLIC;
2339: mumps->id.n = M;
2340: switch (mumps->id.ICNTL(18)) {
2341: case 0: /* centralized assembled matrix input */
2342: if (!mumps->myid) {
2343: mumps->id.nnz = mumps->nnz;
2344: mumps->id.irn = mumps->irn;
2345: mumps->id.jcn = mumps->jcn;
2346: if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2347: }
2348: break;
2349: case 3: /* distributed assembled matrix input (size>1) */
2350: mumps->id.nnz_loc = mumps->nnz;
2351: mumps->id.irn_loc = mumps->irn;
2352: mumps->id.jcn_loc = mumps->jcn;
2353: if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2354: if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2355: PetscCall(MatCreateVecs(A, NULL, &b));
2356: PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2357: PetscCall(VecDestroy(&b));
2358: }
2359: break;
2360: }
2361: PetscMUMPS_c(mumps);
2362: PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));
2364: F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
2365: F->ops->solve = MatSolve_MUMPS;
2366: F->ops->solvetranspose = MatSolveTranspose_MUMPS;
2367: F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;
2369: mumps->matstruc = SAME_NONZERO_PATTERN;
2370: PetscFunctionReturn(PETSC_SUCCESS);
2371: }
2373: /* Note the Petsc r permutation and factor info are ignored */
2374: static PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F, Mat A, IS r, const MatFactorInfo *info)
2375: {
2376: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2377: Vec b;
2378: const PetscInt M = A->rmap->N;
2380: PetscFunctionBegin;
2381: if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2382: /* F is assembled by a previous call of MatCholeskyFactorSymbolic_MUMPS() */
2383: PetscFunctionReturn(PETSC_SUCCESS);
2384: }
2386: /* Set MUMPS options from the options database */
2387: PetscCall(MatSetFromOptions_MUMPS(F, A));
2389: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2390: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));
2392: /* analysis phase */
2393: mumps->id.job = JOB_FACTSYMBOLIC;
2394: mumps->id.n = M;
2395: switch (mumps->id.ICNTL(18)) {
2396: case 0: /* centralized assembled matrix input */
2397: if (!mumps->myid) {
2398: mumps->id.nnz = mumps->nnz;
2399: mumps->id.irn = mumps->irn;
2400: mumps->id.jcn = mumps->jcn;
2401: if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2402: }
2403: break;
2404: case 3: /* distributed assembled matrix input (size>1) */
2405: mumps->id.nnz_loc = mumps->nnz;
2406: mumps->id.irn_loc = mumps->irn;
2407: mumps->id.jcn_loc = mumps->jcn;
2408: if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2409: if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2410: PetscCall(MatCreateVecs(A, NULL, &b));
2411: PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2412: PetscCall(VecDestroy(&b));
2413: }
2414: break;
2415: }
2416: PetscMUMPS_c(mumps);
2417: PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));
2419: F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS;
2420: F->ops->solve = MatSolve_MUMPS;
2421: F->ops->solvetranspose = MatSolve_MUMPS;
2422: F->ops->matsolve = MatMatSolve_MUMPS;
2423: F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS;
2424: F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;
2425: #if defined(PETSC_USE_COMPLEX)
2426: F->ops->getinertia = NULL;
2427: #else
2428: F->ops->getinertia = MatGetInertia_SBAIJMUMPS;
2429: #endif
2431: mumps->matstruc = SAME_NONZERO_PATTERN;
2432: PetscFunctionReturn(PETSC_SUCCESS);
2433: }
2435: static PetscErrorCode MatView_MUMPS(Mat A, PetscViewer viewer)
2436: {
2437: PetscBool iascii;
2438: PetscViewerFormat format;
2439: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
2441: PetscFunctionBegin;
2442: /* check if matrix is mumps type */
2443: if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(PETSC_SUCCESS);
2445: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
2446: if (iascii) {
2447: PetscCall(PetscViewerGetFormat(viewer, &format));
2448: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
2449: PetscCall(PetscViewerASCIIPrintf(viewer, "MUMPS run parameters:\n"));
2450: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
2451: PetscCall(PetscViewerASCIIPrintf(viewer, " SYM (matrix type): %d\n", mumps->id.sym));
2452: PetscCall(PetscViewerASCIIPrintf(viewer, " PAR (host participation): %d\n", mumps->id.par));
2453: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(1) (output for error): %d\n", mumps->id.ICNTL(1)));
2454: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(2) (output of diagnostic msg): %d\n", mumps->id.ICNTL(2)));
2455: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(3) (output for global info): %d\n", mumps->id.ICNTL(3)));
2456: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(4) (level of printing): %d\n", mumps->id.ICNTL(4)));
2457: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(5) (input mat struct): %d\n", mumps->id.ICNTL(5)));
2458: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(6) (matrix prescaling): %d\n", mumps->id.ICNTL(6)));
2459: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(7) (sequential matrix ordering):%d\n", mumps->id.ICNTL(7)));
2460: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(8) (scaling strategy): %d\n", mumps->id.ICNTL(8)));
2461: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(10) (max num of refinements): %d\n", mumps->id.ICNTL(10)));
2462: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(11) (error analysis): %d\n", mumps->id.ICNTL(11)));
2463: if (mumps->id.ICNTL(11) > 0) {
2464: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(4) (inf norm of input mat): %g\n", mumps->id.RINFOG(4)));
2465: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(5) (inf norm of solution): %g\n", mumps->id.RINFOG(5)));
2466: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(6) (inf norm of residual): %g\n", mumps->id.RINFOG(6)));
2467: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(7),RINFOG(8) (backward error est): %g, %g\n", mumps->id.RINFOG(7), mumps->id.RINFOG(8)));
2468: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(9) (error estimate): %g\n", mumps->id.RINFOG(9)));
2469: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n", mumps->id.RINFOG(10), mumps->id.RINFOG(11)));
2470: }
2471: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(12) (efficiency control): %d\n", mumps->id.ICNTL(12)));
2472: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(13) (sequential factorization of the root node): %d\n", mumps->id.ICNTL(13)));
2473: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(14) (percentage of estimated workspace increase): %d\n", mumps->id.ICNTL(14)));
2474: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(15) (compression of the input matrix): %d\n", mumps->id.ICNTL(15)));
2475: /* ICNTL(15-17) not used */
2476: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(18) (input mat struct): %d\n", mumps->id.ICNTL(18)));
2477: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(19) (Schur complement info): %d\n", mumps->id.ICNTL(19)));
2478: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(20) (RHS sparse pattern): %d\n", mumps->id.ICNTL(20)));
2479: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(21) (solution struct): %d\n", mumps->id.ICNTL(21)));
2480: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(22) (in-core/out-of-core facility): %d\n", mumps->id.ICNTL(22)));
2481: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(23) (max size of memory can be allocated locally):%d\n", mumps->id.ICNTL(23)));
2483: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(24) (detection of null pivot rows): %d\n", mumps->id.ICNTL(24)));
2484: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(25) (computation of a null space basis): %d\n", mumps->id.ICNTL(25)));
2485: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(26) (Schur options for RHS or solution): %d\n", mumps->id.ICNTL(26)));
2486: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(27) (blocking size for multiple RHS): %d\n", mumps->id.ICNTL(27)));
2487: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(28) (use parallel or sequential ordering): %d\n", mumps->id.ICNTL(28)));
2488: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(29) (parallel ordering): %d\n", mumps->id.ICNTL(29)));
2490: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(30) (user-specified set of entries in inv(A)): %d\n", mumps->id.ICNTL(30)));
2491: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(31) (factors is discarded in the solve phase): %d\n", mumps->id.ICNTL(31)));
2492: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(33) (compute determinant): %d\n", mumps->id.ICNTL(33)));
2493: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(35) (activate BLR based factorization): %d\n", mumps->id.ICNTL(35)));
2494: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(36) (choice of BLR factorization variant): %d\n", mumps->id.ICNTL(36)));
2495: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(38) (estimated compression rate of LU factors): %d\n", mumps->id.ICNTL(38)));
2496: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(58) (options for symbolic factorization): %d\n", mumps->id.ICNTL(58)));
2498: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(1) (relative pivoting threshold): %g\n", mumps->id.CNTL(1)));
2499: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(2) (stopping criterion of refinement): %g\n", mumps->id.CNTL(2)));
2500: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(3) (absolute pivoting threshold): %g\n", mumps->id.CNTL(3)));
2501: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(4) (value of static pivoting): %g\n", mumps->id.CNTL(4)));
2502: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(5) (fixation for null pivots): %g\n", mumps->id.CNTL(5)));
2503: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(7) (dropping parameter for BLR): %g\n", mumps->id.CNTL(7)));
2505: /* information local to each processor */
2506: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(1) (local estimated flops for the elimination after analysis):\n"));
2507: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
2508: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %g\n", mumps->myid, mumps->id.RINFO(1)));
2509: PetscCall(PetscViewerFlush(viewer));
2510: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(2) (local estimated flops for the assembly after factorization):\n"));
2511: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %g\n", mumps->myid, mumps->id.RINFO(2)));
2512: PetscCall(PetscViewerFlush(viewer));
2513: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(3) (local estimated flops for the elimination after factorization):\n"));
2514: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %g\n", mumps->myid, mumps->id.RINFO(3)));
2515: PetscCall(PetscViewerFlush(viewer));
2517: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization):\n"));
2518: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(15)));
2519: PetscCall(PetscViewerFlush(viewer));
2521: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization):\n"));
2522: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(16)));
2523: PetscCall(PetscViewerFlush(viewer));
2525: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(23) (num of pivots eliminated on this processor after factorization):\n"));
2526: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(23)));
2527: PetscCall(PetscViewerFlush(viewer));
2529: if (mumps->ninfo && mumps->ninfo <= 80) {
2530: PetscInt i;
2531: for (i = 0; i < mumps->ninfo; i++) {
2532: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(%" PetscInt_FMT "):\n", mumps->info[i]));
2533: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(mumps->info[i])));
2534: PetscCall(PetscViewerFlush(viewer));
2535: }
2536: }
2537: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
2538: } else PetscCall(PetscViewerASCIIPrintf(viewer, " Use -%sksp_view ::ascii_info_detail to display information for all processes\n", ((PetscObject)A)->prefix ? ((PetscObject)A)->prefix : ""));
2540: if (mumps->myid == 0) { /* information from the host */
2541: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(1) (global estimated flops for the elimination after analysis): %g\n", mumps->id.RINFOG(1)));
2542: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(2) (global estimated flops for the assembly after factorization): %g\n", mumps->id.RINFOG(2)));
2543: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(3) (global estimated flops for the elimination after factorization): %g\n", mumps->id.RINFOG(3)));
2544: PetscCall(PetscViewerASCIIPrintf(viewer, " (RINFOG(12) RINFOG(13))*2^INFOG(34) (determinant): (%g,%g)*(2^%d)\n", mumps->id.RINFOG(12), mumps->id.RINFOG(13), mumps->id.INFOG(34)));
2546: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(3) (estimated real workspace for factors on all processors after analysis): %d\n", mumps->id.INFOG(3)));
2547: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d\n", mumps->id.INFOG(4)));
2548: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(5) (estimated maximum front size in the complete tree): %d\n", mumps->id.INFOG(5)));
2549: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(6) (number of nodes in the complete tree): %d\n", mumps->id.INFOG(6)));
2550: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(7) (ordering option effectively used after analysis): %d\n", mumps->id.INFOG(7)));
2551: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d\n", mumps->id.INFOG(8)));
2552: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d\n", mumps->id.INFOG(9)));
2553: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(10) (total integer space store the matrix factors after factorization): %d\n", mumps->id.INFOG(10)));
2554: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(11) (order of largest frontal matrix after factorization): %d\n", mumps->id.INFOG(11)));
2555: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(12) (number of off-diagonal pivots): %d\n", mumps->id.INFOG(12)));
2556: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(13) (number of delayed pivots after factorization): %d\n", mumps->id.INFOG(13)));
2557: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(14) (number of memory compress after factorization): %d\n", mumps->id.INFOG(14)));
2558: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(15) (number of steps of iterative refinement after solution): %d\n", mumps->id.INFOG(15)));
2559: 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)));
2560: 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)));
2561: 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)));
2562: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): %d\n", mumps->id.INFOG(19)));
2563: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(20) (estimated number of entries in the factors): %d\n", mumps->id.INFOG(20)));
2564: 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)));
2565: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): %d\n", mumps->id.INFOG(22)));
2566: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d\n", mumps->id.INFOG(23)));
2567: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d\n", mumps->id.INFOG(24)));
2568: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(25) (after factorization: number of pivots modified by static pivoting): %d\n", mumps->id.INFOG(25)));
2569: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(28) (after factorization: number of null pivots encountered): %d\n", mumps->id.INFOG(28)));
2570: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(29) (after factorization: effective number of entries in the factors (sum over all processors)): %d\n", mumps->id.INFOG(29)));
2571: 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)));
2572: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(32) (after analysis: type of analysis done): %d\n", mumps->id.INFOG(32)));
2573: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(33) (value used for ICNTL(8)): %d\n", mumps->id.INFOG(33)));
2574: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(34) (exponent of the determinant if determinant is requested): %d\n", mumps->id.INFOG(34)));
2575: 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)));
2576: 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)));
2577: 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)));
2578: 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)));
2579: 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)));
2580: }
2581: }
2582: }
2583: PetscFunctionReturn(PETSC_SUCCESS);
2584: }
2586: static PetscErrorCode MatGetInfo_MUMPS(Mat A, MatInfoType flag, MatInfo *info)
2587: {
2588: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
2590: PetscFunctionBegin;
2591: info->block_size = 1.0;
2592: info->nz_allocated = mumps->id.INFOG(20) >= 0 ? mumps->id.INFOG(20) : -1000000 * mumps->id.INFOG(20);
2593: info->nz_used = mumps->id.INFOG(20) >= 0 ? mumps->id.INFOG(20) : -1000000 * mumps->id.INFOG(20);
2594: info->nz_unneeded = 0.0;
2595: info->assemblies = 0.0;
2596: info->mallocs = 0.0;
2597: info->memory = 0.0;
2598: info->fill_ratio_given = 0;
2599: info->fill_ratio_needed = 0;
2600: info->factor_mallocs = 0;
2601: PetscFunctionReturn(PETSC_SUCCESS);
2602: }
2604: static PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is)
2605: {
2606: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2607: const PetscScalar *arr;
2608: const PetscInt *idxs;
2609: PetscInt size, i;
2611: PetscFunctionBegin;
2612: PetscCall(ISGetLocalSize(is, &size));
2613: /* Schur complement matrix */
2614: PetscCall(MatDestroy(&F->schur));
2615: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, size, size, NULL, &F->schur));
2616: PetscCall(MatDenseGetArrayRead(F->schur, &arr));
2617: mumps->id.schur = (MumpsScalar *)arr;
2618: mumps->id.size_schur = size;
2619: mumps->id.schur_lld = size;
2620: PetscCall(MatDenseRestoreArrayRead(F->schur, &arr));
2621: if (mumps->sym == 1) PetscCall(MatSetOption(F->schur, MAT_SPD, PETSC_TRUE));
2623: /* MUMPS expects Fortran style indices */
2624: PetscCall(PetscFree(mumps->id.listvar_schur));
2625: PetscCall(PetscMalloc1(size, &mumps->id.listvar_schur));
2626: PetscCall(ISGetIndices(is, &idxs));
2627: for (i = 0; i < size; i++) PetscCall(PetscMUMPSIntCast(idxs[i] + 1, &(mumps->id.listvar_schur[i])));
2628: PetscCall(ISRestoreIndices(is, &idxs));
2629: /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */
2630: mumps->id.ICNTL(26) = -1;
2631: PetscFunctionReturn(PETSC_SUCCESS);
2632: }
2634: static PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F, Mat *S)
2635: {
2636: Mat St;
2637: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2638: PetscScalar *array;
2640: PetscFunctionBegin;
2641: PetscCheck(mumps->id.ICNTL(19), PetscObjectComm((PetscObject)F), PETSC_ERR_ORDER, "Schur complement mode not selected! Call MatFactorSetSchurIS() to enable it");
2642: PetscCall(MatCreate(PETSC_COMM_SELF, &St));
2643: PetscCall(MatSetSizes(St, PETSC_DECIDE, PETSC_DECIDE, mumps->id.size_schur, mumps->id.size_schur));
2644: PetscCall(MatSetType(St, MATDENSE));
2645: PetscCall(MatSetUp(St));
2646: PetscCall(MatDenseGetArray(St, &array));
2647: if (!mumps->sym) { /* MUMPS always return a full matrix */
2648: if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
2649: PetscInt i, j, N = mumps->id.size_schur;
2650: for (i = 0; i < N; i++) {
2651: for (j = 0; j < N; j++) {
2652: #if !defined(PETSC_USE_COMPLEX)
2653: PetscScalar val = mumps->id.schur[i * N + j];
2654: #else
2655: PetscScalar val = mumps->id.schur[i * N + j].r + PETSC_i * mumps->id.schur[i * N + j].i;
2656: #endif
2657: array[j * N + i] = val;
2658: }
2659: }
2660: } else { /* stored by columns */
2661: PetscCall(PetscArraycpy(array, mumps->id.schur, mumps->id.size_schur * mumps->id.size_schur));
2662: }
2663: } else { /* either full or lower-triangular (not packed) */
2664: if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */
2665: PetscInt i, j, N = mumps->id.size_schur;
2666: for (i = 0; i < N; i++) {
2667: for (j = i; j < N; j++) {
2668: #if !defined(PETSC_USE_COMPLEX)
2669: PetscScalar val = mumps->id.schur[i * N + j];
2670: #else
2671: PetscScalar val = mumps->id.schur[i * N + j].r + PETSC_i * mumps->id.schur[i * N + j].i;
2672: #endif
2673: array[i * N + j] = array[j * N + i] = val;
2674: }
2675: }
2676: } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */
2677: PetscCall(PetscArraycpy(array, mumps->id.schur, mumps->id.size_schur * mumps->id.size_schur));
2678: } else { /* ICNTL(19) == 1 lower triangular stored by rows */
2679: PetscInt i, j, N = mumps->id.size_schur;
2680: for (i = 0; i < N; i++) {
2681: for (j = 0; j < i + 1; j++) {
2682: #if !defined(PETSC_USE_COMPLEX)
2683: PetscScalar val = mumps->id.schur[i * N + j];
2684: #else
2685: PetscScalar val = mumps->id.schur[i * N + j].r + PETSC_i * mumps->id.schur[i * N + j].i;
2686: #endif
2687: array[i * N + j] = array[j * N + i] = val;
2688: }
2689: }
2690: }
2691: }
2692: PetscCall(MatDenseRestoreArray(St, &array));
2693: *S = St;
2694: PetscFunctionReturn(PETSC_SUCCESS);
2695: }
2697: static PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F, PetscInt icntl, PetscInt ival)
2698: {
2699: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2701: PetscFunctionBegin;
2702: if (mumps->id.job == JOB_NULL) { /* need to cache icntl and ival since PetscMUMPS_c() has never been called */
2703: PetscInt i, nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0; /* number of already cached ICNTL */
2704: for (i = 0; i < nICNTL_pre; ++i)
2705: if (mumps->ICNTL_pre[1 + 2 * i] == icntl) break; /* is this ICNTL already cached? */
2706: if (i == nICNTL_pre) { /* not already cached */
2707: if (i > 0) PetscCall(PetscRealloc(sizeof(PetscMUMPSInt) * (2 * nICNTL_pre + 3), &mumps->ICNTL_pre));
2708: else PetscCall(PetscCalloc(sizeof(PetscMUMPSInt) * 3, &mumps->ICNTL_pre));
2709: mumps->ICNTL_pre[0]++;
2710: }
2711: mumps->ICNTL_pre[1 + 2 * i] = icntl;
2712: PetscCall(PetscMUMPSIntCast(ival, mumps->ICNTL_pre + 2 + 2 * i));
2713: } else PetscCall(PetscMUMPSIntCast(ival, &mumps->id.ICNTL(icntl)));
2714: PetscFunctionReturn(PETSC_SUCCESS);
2715: }
2717: static PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F, PetscInt icntl, PetscInt *ival)
2718: {
2719: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2721: PetscFunctionBegin;
2722: if (mumps->id.job == JOB_NULL) {
2723: PetscInt i, nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0;
2724: *ival = 0;
2725: for (i = 0; i < nICNTL_pre; ++i) {
2726: if (mumps->ICNTL_pre[1 + 2 * i] == icntl) *ival = mumps->ICNTL_pre[2 + 2 * i];
2727: }
2728: } else *ival = mumps->id.ICNTL(icntl);
2729: PetscFunctionReturn(PETSC_SUCCESS);
2730: }
2732: /*@
2733: MatMumpsSetIcntl - Set MUMPS parameter ICNTL()
2735: Logically Collective
2737: Input Parameters:
2738: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2739: . icntl - index of MUMPS parameter array ICNTL()
2740: - ival - value of MUMPS ICNTL(icntl)
2742: Options Database Key:
2743: . -mat_mumps_icntl_<icntl> <ival> - change the option numbered icntl to ival
2745: Level: beginner
2747: References:
2748: . * - MUMPS Users' Guide
2750: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2751: @*/
2752: PetscErrorCode MatMumpsSetIcntl(Mat F, PetscInt icntl, PetscInt ival)
2753: {
2754: PetscFunctionBegin;
2756: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2759: PetscCheck((icntl >= 1 && icntl <= 38) || icntl == 58, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported ICNTL value %" PetscInt_FMT, icntl);
2760: PetscTryMethod(F, "MatMumpsSetIcntl_C", (Mat, PetscInt, PetscInt), (F, icntl, ival));
2761: PetscFunctionReturn(PETSC_SUCCESS);
2762: }
2764: /*@
2765: MatMumpsGetIcntl - Get MUMPS parameter ICNTL()
2767: Logically Collective
2769: Input Parameters:
2770: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2771: - icntl - index of MUMPS parameter array ICNTL()
2773: Output Parameter:
2774: . ival - value of MUMPS ICNTL(icntl)
2776: Level: beginner
2778: References:
2779: . * - MUMPS Users' Guide
2781: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2782: @*/
2783: PetscErrorCode MatMumpsGetIcntl(Mat F, PetscInt icntl, PetscInt *ival)
2784: {
2785: PetscFunctionBegin;
2787: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2789: PetscAssertPointer(ival, 3);
2790: PetscCheck((icntl >= 1 && icntl <= 38) || icntl == 58, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported ICNTL value %" PetscInt_FMT, icntl);
2791: PetscUseMethod(F, "MatMumpsGetIcntl_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
2792: PetscFunctionReturn(PETSC_SUCCESS);
2793: }
2795: static PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F, PetscInt icntl, PetscReal val)
2796: {
2797: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2799: PetscFunctionBegin;
2800: if (mumps->id.job == JOB_NULL) {
2801: PetscInt i, nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
2802: for (i = 0; i < nCNTL_pre; ++i)
2803: if (mumps->CNTL_pre[1 + 2 * i] == icntl) break;
2804: if (i == nCNTL_pre) {
2805: if (i > 0) PetscCall(PetscRealloc(sizeof(PetscReal) * (2 * nCNTL_pre + 3), &mumps->CNTL_pre));
2806: else PetscCall(PetscCalloc(sizeof(PetscReal) * 3, &mumps->CNTL_pre));
2807: mumps->CNTL_pre[0]++;
2808: }
2809: mumps->CNTL_pre[1 + 2 * i] = icntl;
2810: mumps->CNTL_pre[2 + 2 * i] = val;
2811: } else mumps->id.CNTL(icntl) = val;
2812: PetscFunctionReturn(PETSC_SUCCESS);
2813: }
2815: static PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F, PetscInt icntl, PetscReal *val)
2816: {
2817: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2819: PetscFunctionBegin;
2820: if (mumps->id.job == JOB_NULL) {
2821: PetscInt i, nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
2822: *val = 0.0;
2823: for (i = 0; i < nCNTL_pre; ++i) {
2824: if (mumps->CNTL_pre[1 + 2 * i] == icntl) *val = mumps->CNTL_pre[2 + 2 * i];
2825: }
2826: } else *val = mumps->id.CNTL(icntl);
2827: PetscFunctionReturn(PETSC_SUCCESS);
2828: }
2830: /*@
2831: MatMumpsSetCntl - Set MUMPS parameter CNTL()
2833: Logically Collective
2835: Input Parameters:
2836: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2837: . icntl - index of MUMPS parameter array CNTL()
2838: - val - value of MUMPS CNTL(icntl)
2840: Options Database Key:
2841: . -mat_mumps_cntl_<icntl> <val> - change the option numbered icntl to ival
2843: Level: beginner
2845: References:
2846: . * - MUMPS Users' Guide
2848: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2849: @*/
2850: PetscErrorCode MatMumpsSetCntl(Mat F, PetscInt icntl, PetscReal val)
2851: {
2852: PetscFunctionBegin;
2854: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2857: PetscCheck(icntl >= 1 && icntl <= 7, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported CNTL value %" PetscInt_FMT, icntl);
2858: PetscTryMethod(F, "MatMumpsSetCntl_C", (Mat, PetscInt, PetscReal), (F, icntl, val));
2859: PetscFunctionReturn(PETSC_SUCCESS);
2860: }
2862: /*@
2863: MatMumpsGetCntl - Get MUMPS parameter CNTL()
2865: Logically Collective
2867: Input Parameters:
2868: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2869: - icntl - index of MUMPS parameter array CNTL()
2871: Output Parameter:
2872: . val - value of MUMPS CNTL(icntl)
2874: Level: beginner
2876: References:
2877: . * - MUMPS Users' Guide
2879: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2880: @*/
2881: PetscErrorCode MatMumpsGetCntl(Mat F, PetscInt icntl, PetscReal *val)
2882: {
2883: PetscFunctionBegin;
2885: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2887: PetscAssertPointer(val, 3);
2888: PetscCheck(icntl >= 1 && icntl <= 7, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported CNTL value %" PetscInt_FMT, icntl);
2889: PetscUseMethod(F, "MatMumpsGetCntl_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
2890: PetscFunctionReturn(PETSC_SUCCESS);
2891: }
2893: static PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F, PetscInt icntl, PetscInt *info)
2894: {
2895: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2897: PetscFunctionBegin;
2898: *info = mumps->id.INFO(icntl);
2899: PetscFunctionReturn(PETSC_SUCCESS);
2900: }
2902: static PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F, PetscInt icntl, PetscInt *infog)
2903: {
2904: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2906: PetscFunctionBegin;
2907: *infog = mumps->id.INFOG(icntl);
2908: PetscFunctionReturn(PETSC_SUCCESS);
2909: }
2911: static PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F, PetscInt icntl, PetscReal *rinfo)
2912: {
2913: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2915: PetscFunctionBegin;
2916: *rinfo = mumps->id.RINFO(icntl);
2917: PetscFunctionReturn(PETSC_SUCCESS);
2918: }
2920: static PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F, PetscInt icntl, PetscReal *rinfog)
2921: {
2922: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2924: PetscFunctionBegin;
2925: *rinfog = mumps->id.RINFOG(icntl);
2926: PetscFunctionReturn(PETSC_SUCCESS);
2927: }
2929: static PetscErrorCode MatMumpsGetNullPivots_MUMPS(Mat F, PetscInt *size, PetscInt **array)
2930: {
2931: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2933: PetscFunctionBegin;
2934: 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");
2935: *size = 0;
2936: *array = NULL;
2937: if (!mumps->myid) {
2938: *size = mumps->id.INFOG(28);
2939: PetscCall(PetscMalloc1(*size, array));
2940: for (int i = 0; i < *size; i++) (*array)[i] = mumps->id.pivnul_list[i] - 1;
2941: }
2942: PetscFunctionReturn(PETSC_SUCCESS);
2943: }
2945: static PetscErrorCode MatMumpsGetInverse_MUMPS(Mat F, Mat spRHS)
2946: {
2947: Mat Bt = NULL, Btseq = NULL;
2948: PetscBool flg;
2949: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2950: PetscScalar *aa;
2951: PetscInt spnr, *ia, *ja, M, nrhs;
2953: PetscFunctionBegin;
2954: PetscAssertPointer(spRHS, 2);
2955: PetscCall(PetscObjectTypeCompare((PetscObject)spRHS, MATTRANSPOSEVIRTUAL, &flg));
2956: if (flg) {
2957: PetscCall(MatTransposeGetMat(spRHS, &Bt));
2958: } else SETERRQ(PetscObjectComm((PetscObject)spRHS), PETSC_ERR_ARG_WRONG, "Matrix spRHS must be type MATTRANSPOSEVIRTUAL matrix");
2960: PetscCall(MatMumpsSetIcntl(F, 30, 1));
2962: if (mumps->petsc_size > 1) {
2963: Mat_MPIAIJ *b = (Mat_MPIAIJ *)Bt->data;
2964: Btseq = b->A;
2965: } else {
2966: Btseq = Bt;
2967: }
2969: PetscCall(MatGetSize(spRHS, &M, &nrhs));
2970: mumps->id.nrhs = nrhs;
2971: mumps->id.lrhs = M;
2972: mumps->id.rhs = NULL;
2974: if (!mumps->myid) {
2975: PetscCall(MatSeqAIJGetArray(Btseq, &aa));
2976: PetscCall(MatGetRowIJ(Btseq, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
2977: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
2978: PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
2979: mumps->id.rhs_sparse = (MumpsScalar *)aa;
2980: } else {
2981: mumps->id.irhs_ptr = NULL;
2982: mumps->id.irhs_sparse = NULL;
2983: mumps->id.nz_rhs = 0;
2984: mumps->id.rhs_sparse = NULL;
2985: }
2986: mumps->id.ICNTL(20) = 1; /* rhs is sparse */
2987: mumps->id.ICNTL(21) = 0; /* solution is in assembled centralized format */
2989: /* solve phase */
2990: mumps->id.job = JOB_SOLVE;
2991: PetscMUMPS_c(mumps);
2992: 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));
2994: if (!mumps->myid) {
2995: PetscCall(MatSeqAIJRestoreArray(Btseq, &aa));
2996: PetscCall(MatRestoreRowIJ(Btseq, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
2997: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
2998: }
2999: PetscFunctionReturn(PETSC_SUCCESS);
3000: }
3002: /*@
3003: MatMumpsGetInverse - Get user-specified set of entries in inverse of `A`
3005: Logically Collective
3007: Input Parameter:
3008: . F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3010: Output Parameter:
3011: . spRHS - sequential sparse matrix in `MATTRANSPOSEVIRTUAL` format with requested entries of inverse of `A`
3013: Level: beginner
3015: References:
3016: . * - MUMPS Users' Guide
3018: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCreateTranspose()`
3019: @*/
3020: PetscErrorCode MatMumpsGetInverse(Mat F, Mat spRHS)
3021: {
3022: PetscFunctionBegin;
3024: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3025: PetscUseMethod(F, "MatMumpsGetInverse_C", (Mat, Mat), (F, spRHS));
3026: PetscFunctionReturn(PETSC_SUCCESS);
3027: }
3029: static PetscErrorCode MatMumpsGetInverseTranspose_MUMPS(Mat F, Mat spRHST)
3030: {
3031: Mat spRHS;
3033: PetscFunctionBegin;
3034: PetscCall(MatCreateTranspose(spRHST, &spRHS));
3035: PetscCall(MatMumpsGetInverse_MUMPS(F, spRHS));
3036: PetscCall(MatDestroy(&spRHS));
3037: PetscFunctionReturn(PETSC_SUCCESS);
3038: }
3040: /*@
3041: MatMumpsGetInverseTranspose - Get user-specified set of entries in inverse of matrix `A`^T
3043: Logically Collective
3045: Input Parameter:
3046: . F - the factored matrix of A obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3048: Output Parameter:
3049: . spRHST - sequential sparse matrix in `MATAIJ` format containing the requested entries of inverse of `A`^T
3051: Level: beginner
3053: References:
3054: . * - MUMPS Users' Guide
3056: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCreateTranspose()`, `MatMumpsGetInverse()`
3057: @*/
3058: PetscErrorCode MatMumpsGetInverseTranspose(Mat F, Mat spRHST)
3059: {
3060: PetscBool flg;
3062: PetscFunctionBegin;
3064: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3065: PetscCall(PetscObjectTypeCompareAny((PetscObject)spRHST, &flg, MATSEQAIJ, MATMPIAIJ, NULL));
3066: PetscCheck(flg, PetscObjectComm((PetscObject)spRHST), PETSC_ERR_ARG_WRONG, "Matrix spRHST must be MATAIJ matrix");
3068: PetscUseMethod(F, "MatMumpsGetInverseTranspose_C", (Mat, Mat), (F, spRHST));
3069: PetscFunctionReturn(PETSC_SUCCESS);
3070: }
3072: /*@
3073: MatMumpsGetInfo - Get MUMPS parameter INFO()
3075: Logically Collective
3077: Input Parameters:
3078: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3079: - icntl - index of MUMPS parameter array INFO()
3081: Output Parameter:
3082: . ival - value of MUMPS INFO(icntl)
3084: Level: beginner
3086: References:
3087: . * - MUMPS Users' Guide
3089: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
3090: @*/
3091: PetscErrorCode MatMumpsGetInfo(Mat F, PetscInt icntl, PetscInt *ival)
3092: {
3093: PetscFunctionBegin;
3095: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3096: PetscAssertPointer(ival, 3);
3097: PetscUseMethod(F, "MatMumpsGetInfo_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
3098: PetscFunctionReturn(PETSC_SUCCESS);
3099: }
3101: /*@
3102: MatMumpsGetInfog - Get MUMPS parameter INFOG()
3104: Logically Collective
3106: Input Parameters:
3107: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3108: - icntl - index of MUMPS parameter array INFOG()
3110: Output Parameter:
3111: . ival - value of MUMPS INFOG(icntl)
3113: Level: beginner
3115: References:
3116: . * - MUMPS Users' Guide
3118: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
3119: @*/
3120: PetscErrorCode MatMumpsGetInfog(Mat F, PetscInt icntl, PetscInt *ival)
3121: {
3122: PetscFunctionBegin;
3124: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3125: PetscAssertPointer(ival, 3);
3126: PetscUseMethod(F, "MatMumpsGetInfog_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
3127: PetscFunctionReturn(PETSC_SUCCESS);
3128: }
3130: /*@
3131: MatMumpsGetRinfo - Get MUMPS parameter RINFO()
3133: Logically Collective
3135: Input Parameters:
3136: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3137: - icntl - index of MUMPS parameter array RINFO()
3139: Output Parameter:
3140: . val - value of MUMPS RINFO(icntl)
3142: Level: beginner
3144: References:
3145: . * - MUMPS Users' Guide
3147: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfog()`
3148: @*/
3149: PetscErrorCode MatMumpsGetRinfo(Mat F, PetscInt icntl, PetscReal *val)
3150: {
3151: PetscFunctionBegin;
3153: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3154: PetscAssertPointer(val, 3);
3155: PetscUseMethod(F, "MatMumpsGetRinfo_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
3156: PetscFunctionReturn(PETSC_SUCCESS);
3157: }
3159: /*@
3160: MatMumpsGetRinfog - Get MUMPS parameter RINFOG()
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 RINFOG()
3168: Output Parameter:
3169: . val - value of MUMPS RINFOG(icntl)
3171: Level: beginner
3173: References:
3174: . * - MUMPS Users' Guide
3176: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`
3177: @*/
3178: PetscErrorCode MatMumpsGetRinfog(Mat F, PetscInt icntl, PetscReal *val)
3179: {
3180: PetscFunctionBegin;
3182: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3183: PetscAssertPointer(val, 3);
3184: PetscUseMethod(F, "MatMumpsGetRinfog_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
3185: PetscFunctionReturn(PETSC_SUCCESS);
3186: }
3188: /*@
3189: MatMumpsGetNullPivots - Get MUMPS parameter PIVNUL_LIST()
3191: Logically Collective
3193: Input Parameter:
3194: . F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
3196: Output Parameters:
3197: + size - local size of the array. The size of the array is non-zero only on the host.
3198: - 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
3199: for freeing this array.
3201: Level: beginner
3203: References:
3204: . * - MUMPS Users' Guide
3206: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`
3207: @*/
3208: PetscErrorCode MatMumpsGetNullPivots(Mat F, PetscInt *size, PetscInt **array)
3209: {
3210: PetscFunctionBegin;
3212: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
3213: PetscAssertPointer(size, 2);
3214: PetscAssertPointer(array, 3);
3215: PetscUseMethod(F, "MatMumpsGetNullPivots_C", (Mat, PetscInt *, PetscInt **), (F, size, array));
3216: PetscFunctionReturn(PETSC_SUCCESS);
3217: }
3219: /*MC
3220: MATSOLVERMUMPS - A matrix type providing direct solvers (LU and Cholesky) for
3221: distributed and sequential matrices via the external package MUMPS.
3223: Works with `MATAIJ` and `MATSBAIJ` matrices
3225: Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch to have PETSc installed with MUMPS
3227: 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.
3228: See details below.
3230: Use `-pc_type cholesky` or `lu` `-pc_factor_mat_solver_type mumps` to use this direct solver
3232: Options Database Keys:
3233: + -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages
3234: . -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning
3235: . -mat_mumps_icntl_3 - ICNTL(3): output stream for global information, collected on the host
3236: . -mat_mumps_icntl_4 - ICNTL(4): level of printing (0 to 4)
3237: . -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7)
3238: . -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
3239: Use -pc_factor_mat_ordering_type <type> to have PETSc perform the ordering (sequential only)
3240: . -mat_mumps_icntl_8 - ICNTL(8): scaling strategy (-2 to 8 or 77)
3241: . -mat_mumps_icntl_10 - ICNTL(10): max num of refinements
3242: . -mat_mumps_icntl_11 - ICNTL(11): statistics related to an error analysis (via -ksp_view)
3243: . -mat_mumps_icntl_12 - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3)
3244: . -mat_mumps_icntl_13 - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting
3245: . -mat_mumps_icntl_14 - ICNTL(14): percentage increase in the estimated working space
3246: . -mat_mumps_icntl_15 - ICNTL(15): compression of the input matrix resulting from a block format
3247: . -mat_mumps_icntl_19 - ICNTL(19): computes the Schur complement
3248: . -mat_mumps_icntl_20 - ICNTL(20): give MUMPS centralized (0) or distributed (10) dense RHS
3249: . -mat_mumps_icntl_22 - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1)
3250: . -mat_mumps_icntl_23 - ICNTL(23): max size of the working memory (MB) that can allocate per processor
3251: . -mat_mumps_icntl_24 - ICNTL(24): detection of null pivot rows (0 or 1)
3252: . -mat_mumps_icntl_25 - ICNTL(25): compute a solution of a deficient matrix and a null space basis
3253: . -mat_mumps_icntl_26 - ICNTL(26): drives the solution phase if a Schur complement matrix
3254: . -mat_mumps_icntl_28 - ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering
3255: . -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis
3256: . -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A)
3257: . -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization
3258: . -mat_mumps_icntl_33 - ICNTL(33): compute determinant
3259: . -mat_mumps_icntl_35 - ICNTL(35): level of activation of BLR (Block Low-Rank) feature
3260: . -mat_mumps_icntl_36 - ICNTL(36): controls the choice of BLR factorization variant
3261: . -mat_mumps_icntl_38 - ICNTL(38): sets the estimated compression rate of LU factors with BLR
3262: . -mat_mumps_icntl_58 - ICNTL(58): options for symbolic factorization
3263: . -mat_mumps_cntl_1 - CNTL(1): relative pivoting threshold
3264: . -mat_mumps_cntl_2 - CNTL(2): stopping criterion of refinement
3265: . -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold
3266: . -mat_mumps_cntl_4 - CNTL(4): value for static pivoting
3267: . -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots
3268: . -mat_mumps_cntl_7 - CNTL(7): precision of the dropping parameter used during BLR factorization
3269: - -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.
3270: Default might be the number of cores per CPU package (socket) as reported by hwloc and suggested by the MUMPS manual.
3272: Level: beginner
3274: Notes:
3275: 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
3276: error if the matrix is Hermitian.
3278: 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
3279: `MatSetOptionsPrefixFactor()` on the matrix from which the factor was obtained or `MatSetOptionsPrefix()` on the factor matrix.
3281: When a MUMPS factorization fails inside a KSP solve, for example with a `KSP_DIVERGED_PC_FAILED`, one can find the MUMPS information about
3282: the failure with
3283: .vb
3284: KSPGetPC(ksp,&pc);
3285: PCFactorGetMatrix(pc,&mat);
3286: MatMumpsGetInfo(mat,....);
3287: MatMumpsGetInfog(mat,....); etc.
3288: .ve
3289: Or run with `-ksp_error_if_not_converged` and the program will be stopped and the information printed in the error message.
3291: MUMPS provides 64-bit integer support in two build modes:
3292: full 64-bit: here MUMPS is built with C preprocessing flag -DINTSIZE64 and Fortran compiler option -i8, -fdefault-integer-8 or equivalent, and
3293: requires all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS built the same way with 64-bit integers (for example ILP64 Intel MKL and MPI).
3295: selective 64-bit: with the default MUMPS build, 64-bit integers have been introduced where needed. In compressed sparse row (CSR) storage of matrices,
3296: 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
3297: 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
3298: integer) build of all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS.
3300: 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.
3302: Two modes to run MUMPS/PETSc with OpenMP
3303: .vb
3304: Set OMP_NUM_THREADS and run with fewer MPI ranks than cores. For example, if you want to have 16 OpenMP
3305: threads per rank, then you may use "export OMP_NUM_THREADS=16 && mpirun -n 4 ./test".
3306: .ve
3308: .vb
3309: -mat_mumps_use_omp_threads [m] and run your code with as many MPI ranks as the number of cores. For example,
3310: 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"
3311: .ve
3313: To run MUMPS in MPI+OpenMP hybrid mode (i.e., enable multithreading in MUMPS), but still run the non-MUMPS part
3314: (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`
3315: (or `--with-hwloc`), and have an MPI that supports MPI-3.0's process shared memory (which is usually available). Since MUMPS calls BLAS
3316: libraries, to really get performance, you should have multithreaded BLAS libraries such as Intel MKL, AMD ACML, Cray libSci or OpenBLAS
3317: (PETSc will automatically try to utilized a threaded BLAS if --with-openmp is provided).
3319: 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
3320: processes on each compute node. Listing the processes in rank ascending order, we split processes on a node into consecutive groups of
3321: 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
3322: 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
3323: 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.
3324: 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,
3325: 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
3326: 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
3327: cores in socket 1, that definitely hurts locality. On the other hand, if you map MPI ranks consecutively on the two sockets, then the
3328: 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.
3329: 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
3330: examine the mapping result.
3332: 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,
3333: 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
3334: calls `omp_set_num_threads`(m) internally before calling MUMPS.
3336: References:
3337: + * - Heroux, Michael A., R. Brightwell, and Michael M. Wolf. "Bi-modal MPI and MPI+ threads computing on scalable multicore systems." IJHPCA (Submitted) (2011).
3338: - * - Gutierrez, Samuel K., et al. "Accommodating Thread-Level Heterogeneity in Coupled Parallel Applications." Parallel and Distributed Processing Symposium (IPDPS), 2017 IEEE International. IEEE, 2017.
3340: .seealso: [](ch_matrices), `Mat`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`, `KSPGetPC()`, `PCFactorGetMatrix()`
3341: M*/
3343: static PetscErrorCode MatFactorGetSolverType_mumps(Mat A, MatSolverType *type)
3344: {
3345: PetscFunctionBegin;
3346: *type = MATSOLVERMUMPS;
3347: PetscFunctionReturn(PETSC_SUCCESS);
3348: }
3350: /* MatGetFactor for Seq and MPI AIJ matrices */
3351: static PetscErrorCode MatGetFactor_aij_mumps(Mat A, MatFactorType ftype, Mat *F)
3352: {
3353: Mat B;
3354: Mat_MUMPS *mumps;
3355: PetscBool isSeqAIJ, isDiag;
3356: PetscMPIInt size;
3358: PetscFunctionBegin;
3359: #if defined(PETSC_USE_COMPLEX)
3360: if (ftype == MAT_FACTOR_CHOLESKY && A->hermitian == PETSC_BOOL3_TRUE && A->symmetric != PETSC_BOOL3_TRUE) {
3361: PetscCall(PetscInfo(A, "Hermitian MAT_FACTOR_CHOLESKY is not supported. Use MAT_FACTOR_LU instead.\n"));
3362: *F = NULL;
3363: PetscFunctionReturn(PETSC_SUCCESS);
3364: }
3365: #endif
3366: /* Create the factorization matrix */
3367: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATSEQAIJ, &isSeqAIJ));
3368: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATDIAGONAL, &isDiag));
3369: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3370: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3371: PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3372: PetscCall(MatSetUp(B));
3374: PetscCall(PetscNew(&mumps));
3376: B->ops->view = MatView_MUMPS;
3377: B->ops->getinfo = MatGetInfo_MUMPS;
3379: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3380: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3381: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3382: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3383: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3384: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3385: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3386: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3387: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3388: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3389: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3390: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3391: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3392: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3394: if (ftype == MAT_FACTOR_LU) {
3395: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3396: B->factortype = MAT_FACTOR_LU;
3397: if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij;
3398: else if (isDiag) mumps->ConvertToTriples = MatConvertToTriples_diagonal_xaij;
3399: else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij;
3400: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3401: mumps->sym = 0;
3402: } else {
3403: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3404: B->factortype = MAT_FACTOR_CHOLESKY;
3405: if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij;
3406: else if (isDiag) mumps->ConvertToTriples = MatConvertToTriples_diagonal_xaij;
3407: else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij;
3408: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_CHOLESKY]));
3409: #if defined(PETSC_USE_COMPLEX)
3410: mumps->sym = 2;
3411: #else
3412: if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3413: else mumps->sym = 2;
3414: #endif
3415: }
3417: /* set solvertype */
3418: PetscCall(PetscFree(B->solvertype));
3419: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3420: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3421: if (size == 1) {
3422: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3423: B->canuseordering = PETSC_TRUE;
3424: }
3425: B->ops->destroy = MatDestroy_MUMPS;
3426: B->data = (void *)mumps;
3428: *F = B;
3429: mumps->id.job = JOB_NULL;
3430: mumps->ICNTL_pre = NULL;
3431: mumps->CNTL_pre = NULL;
3432: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3433: PetscFunctionReturn(PETSC_SUCCESS);
3434: }
3436: /* MatGetFactor for Seq and MPI SBAIJ matrices */
3437: static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A, MatFactorType ftype, Mat *F)
3438: {
3439: Mat B;
3440: Mat_MUMPS *mumps;
3441: PetscBool isSeqSBAIJ;
3442: PetscMPIInt size;
3444: PetscFunctionBegin;
3445: #if defined(PETSC_USE_COMPLEX)
3446: if (ftype == MAT_FACTOR_CHOLESKY && A->hermitian == PETSC_BOOL3_TRUE && A->symmetric != PETSC_BOOL3_TRUE) {
3447: PetscCall(PetscInfo(A, "Hermitian MAT_FACTOR_CHOLESKY is not supported. Use MAT_FACTOR_LU instead.\n"));
3448: *F = NULL;
3449: PetscFunctionReturn(PETSC_SUCCESS);
3450: }
3451: #endif
3452: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3453: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3454: PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3455: PetscCall(MatSetUp(B));
3457: PetscCall(PetscNew(&mumps));
3458: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQSBAIJ, &isSeqSBAIJ));
3459: if (isSeqSBAIJ) {
3460: mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij;
3461: } else {
3462: mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij;
3463: }
3465: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3466: B->ops->view = MatView_MUMPS;
3467: B->ops->getinfo = MatGetInfo_MUMPS;
3469: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3470: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3471: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3472: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3473: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3474: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3475: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3476: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3477: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3478: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3479: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3480: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3481: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3482: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3484: B->factortype = MAT_FACTOR_CHOLESKY;
3485: #if defined(PETSC_USE_COMPLEX)
3486: mumps->sym = 2;
3487: #else
3488: if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3489: else mumps->sym = 2;
3490: #endif
3492: /* set solvertype */
3493: PetscCall(PetscFree(B->solvertype));
3494: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3495: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3496: if (size == 1) {
3497: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3498: B->canuseordering = PETSC_TRUE;
3499: }
3500: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_CHOLESKY]));
3501: B->ops->destroy = MatDestroy_MUMPS;
3502: B->data = (void *)mumps;
3504: *F = B;
3505: mumps->id.job = JOB_NULL;
3506: mumps->ICNTL_pre = NULL;
3507: mumps->CNTL_pre = NULL;
3508: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3509: PetscFunctionReturn(PETSC_SUCCESS);
3510: }
3512: static PetscErrorCode MatGetFactor_baij_mumps(Mat A, MatFactorType ftype, Mat *F)
3513: {
3514: Mat B;
3515: Mat_MUMPS *mumps;
3516: PetscBool isSeqBAIJ;
3517: PetscMPIInt size;
3519: PetscFunctionBegin;
3520: /* Create the factorization matrix */
3521: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQBAIJ, &isSeqBAIJ));
3522: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3523: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3524: PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3525: PetscCall(MatSetUp(B));
3527: PetscCall(PetscNew(&mumps));
3528: if (ftype == MAT_FACTOR_LU) {
3529: B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS;
3530: B->factortype = MAT_FACTOR_LU;
3531: if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij;
3532: else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij;
3533: mumps->sym = 0;
3534: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3535: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead");
3537: B->ops->view = MatView_MUMPS;
3538: B->ops->getinfo = MatGetInfo_MUMPS;
3540: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3541: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3542: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3543: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3544: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3545: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3546: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3547: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3548: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3549: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3550: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3551: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3552: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3553: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3555: /* set solvertype */
3556: PetscCall(PetscFree(B->solvertype));
3557: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3558: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3559: if (size == 1) {
3560: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3561: B->canuseordering = PETSC_TRUE;
3562: }
3563: B->ops->destroy = MatDestroy_MUMPS;
3564: B->data = (void *)mumps;
3566: *F = B;
3567: mumps->id.job = JOB_NULL;
3568: mumps->ICNTL_pre = NULL;
3569: mumps->CNTL_pre = NULL;
3570: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3571: PetscFunctionReturn(PETSC_SUCCESS);
3572: }
3574: /* MatGetFactor for Seq and MPI SELL matrices */
3575: static PetscErrorCode MatGetFactor_sell_mumps(Mat A, MatFactorType ftype, Mat *F)
3576: {
3577: Mat B;
3578: Mat_MUMPS *mumps;
3579: PetscBool isSeqSELL;
3580: PetscMPIInt size;
3582: PetscFunctionBegin;
3583: /* Create the factorization matrix */
3584: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQSELL, &isSeqSELL));
3585: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3586: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3587: PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3588: PetscCall(MatSetUp(B));
3590: PetscCall(PetscNew(&mumps));
3592: B->ops->view = MatView_MUMPS;
3593: B->ops->getinfo = MatGetInfo_MUMPS;
3595: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3596: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3597: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3598: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3599: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3600: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3601: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3602: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3603: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3604: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3605: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3606: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3608: if (ftype == MAT_FACTOR_LU) {
3609: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3610: B->factortype = MAT_FACTOR_LU;
3611: if (isSeqSELL) mumps->ConvertToTriples = MatConvertToTriples_seqsell_seqaij;
3612: else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "To be implemented");
3613: mumps->sym = 0;
3614: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3615: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "To be implemented");
3617: /* set solvertype */
3618: PetscCall(PetscFree(B->solvertype));
3619: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3620: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3621: if (size == 1) {
3622: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3623: B->canuseordering = PETSC_TRUE;
3624: }
3625: B->ops->destroy = MatDestroy_MUMPS;
3626: B->data = (void *)mumps;
3628: *F = B;
3629: mumps->id.job = JOB_NULL;
3630: mumps->ICNTL_pre = NULL;
3631: mumps->CNTL_pre = NULL;
3632: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3633: PetscFunctionReturn(PETSC_SUCCESS);
3634: }
3636: /* MatGetFactor for MATNEST matrices */
3637: static PetscErrorCode MatGetFactor_nest_mumps(Mat A, MatFactorType ftype, Mat *F)
3638: {
3639: Mat B, **mats;
3640: Mat_MUMPS *mumps;
3641: PetscInt nr, nc;
3642: PetscMPIInt size;
3643: PetscBool flg = PETSC_TRUE;
3645: PetscFunctionBegin;
3646: #if defined(PETSC_USE_COMPLEX)
3647: if (ftype == MAT_FACTOR_CHOLESKY && A->hermitian == PETSC_BOOL3_TRUE && A->symmetric != PETSC_BOOL3_TRUE) {
3648: PetscCall(PetscInfo(A, "Hermitian MAT_FACTOR_CHOLESKY is not supported. Use MAT_FACTOR_LU instead.\n"));
3649: *F = NULL;
3650: PetscFunctionReturn(PETSC_SUCCESS);
3651: }
3652: #endif
3654: /* Return if some condition is not satisfied */
3655: *F = NULL;
3656: PetscCall(MatNestGetSubMats(A, &nr, &nc, &mats));
3657: if (ftype == MAT_FACTOR_CHOLESKY) {
3658: IS *rows, *cols;
3659: PetscInt *m, *M;
3661: 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);
3662: PetscCall(PetscMalloc2(nr, &rows, nc, &cols));
3663: PetscCall(MatNestGetISs(A, rows, cols));
3664: for (PetscInt r = 0; flg && r < nr; r++) PetscCall(ISEqualUnsorted(rows[r], cols[r], &flg));
3665: if (!flg) {
3666: PetscCall(PetscFree2(rows, cols));
3667: PetscCall(PetscInfo(A, "MAT_FACTOR_CHOLESKY not supported for unequal row and column maps. Use MAT_FACTOR_LU.\n"));
3668: PetscFunctionReturn(PETSC_SUCCESS);
3669: }
3670: PetscCall(PetscMalloc2(nr, &m, nr, &M));
3671: for (PetscInt r = 0; r < nr; r++) PetscCall(ISGetMinMax(rows[r], &m[r], &M[r]));
3672: for (PetscInt r = 0; flg && r < nr; r++)
3673: for (PetscInt k = r + 1; flg && k < nr; k++)
3674: if ((m[k] <= m[r] && m[r] <= M[k]) || (m[k] <= M[r] && M[r] <= M[k])) flg = PETSC_FALSE;
3675: PetscCall(PetscFree2(m, M));
3676: PetscCall(PetscFree2(rows, cols));
3677: if (!flg) {
3678: PetscCall(PetscInfo(A, "MAT_FACTOR_CHOLESKY not supported for intersecting row maps. Use MAT_FACTOR_LU.\n"));
3679: PetscFunctionReturn(PETSC_SUCCESS);
3680: }
3681: }
3683: for (PetscInt r = 0; r < nr; r++) {
3684: for (PetscInt c = 0; c < nc; c++) {
3685: Mat sub = mats[r][c];
3686: PetscBool isSeqAIJ, isMPIAIJ, isSeqBAIJ, isMPIBAIJ, isSeqSBAIJ, isMPISBAIJ, isTrans, isDiag;
3688: if (!sub || (ftype == MAT_FACTOR_CHOLESKY && c < r)) continue;
3689: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATTRANSPOSEVIRTUAL, &isTrans));
3690: if (isTrans) PetscCall(MatTransposeGetMat(sub, &sub));
3691: else {
3692: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATHERMITIANTRANSPOSEVIRTUAL, &isTrans));
3693: if (isTrans) PetscCall(MatHermitianTransposeGetMat(sub, &sub));
3694: }
3695: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATSEQAIJ, &isSeqAIJ));
3696: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATMPIAIJ, &isMPIAIJ));
3697: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATSEQBAIJ, &isSeqBAIJ));
3698: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATMPIBAIJ, &isMPIBAIJ));
3699: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATSEQSBAIJ, &isSeqSBAIJ));
3700: PetscCall(PetscObjectBaseTypeCompare((PetscObject)sub, MATMPISBAIJ, &isMPISBAIJ));
3701: PetscCall(PetscObjectTypeCompare((PetscObject)sub, MATDIAGONAL, &isDiag));
3702: if (ftype == MAT_FACTOR_CHOLESKY) {
3703: if (r == c) {
3704: if (!isSeqAIJ && !isMPIAIJ && !isSeqBAIJ && !isMPIBAIJ && !isSeqSBAIJ && !isMPISBAIJ && !isDiag) {
3705: PetscCall(PetscInfo(sub, "MAT_CHOLESKY_FACTOR not supported for diagonal block of type %s.\n", ((PetscObject)sub)->type_name));
3706: flg = PETSC_FALSE;
3707: }
3708: } else if (!isSeqAIJ && !isMPIAIJ && !isSeqBAIJ && !isMPIBAIJ && !isDiag) {
3709: PetscCall(PetscInfo(sub, "MAT_CHOLESKY_FACTOR not supported for off-diagonal block of type %s.\n", ((PetscObject)sub)->type_name));
3710: flg = PETSC_FALSE;
3711: }
3712: } else if (!isSeqAIJ && !isMPIAIJ && !isSeqBAIJ && !isMPIBAIJ && !isDiag) {
3713: PetscCall(PetscInfo(sub, "MAT_LU_FACTOR not supported for block of type %s.\n", ((PetscObject)sub)->type_name));
3714: flg = PETSC_FALSE;
3715: }
3716: }
3717: }
3718: if (!flg) PetscFunctionReturn(PETSC_SUCCESS);
3720: /* Create the factorization matrix */
3721: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3722: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3723: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &((PetscObject)B)->type_name));
3724: PetscCall(MatSetUp(B));
3726: PetscCall(PetscNew(&mumps));
3728: B->ops->view = MatView_MUMPS;
3729: B->ops->getinfo = MatGetInfo_MUMPS;
3731: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3732: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3733: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3734: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3735: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3736: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3737: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3738: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3739: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3740: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3741: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3742: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3743: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3744: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3746: if (ftype == MAT_FACTOR_LU) {
3747: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3748: B->factortype = MAT_FACTOR_LU;
3749: mumps->sym = 0;
3750: } else {
3751: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3752: B->factortype = MAT_FACTOR_CHOLESKY;
3753: #if defined(PETSC_USE_COMPLEX)
3754: mumps->sym = 2;
3755: #else
3756: if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3757: else mumps->sym = 2;
3758: #endif
3759: }
3760: mumps->ConvertToTriples = MatConvertToTriples_nest_xaij;
3761: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[ftype]));
3763: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3764: if (size == 1) {
3765: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3766: B->canuseordering = PETSC_TRUE;
3767: }
3769: /* set solvertype */
3770: PetscCall(PetscFree(B->solvertype));
3771: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3772: B->ops->destroy = MatDestroy_MUMPS;
3773: B->data = (void *)mumps;
3775: *F = B;
3776: mumps->id.job = JOB_NULL;
3777: mumps->ICNTL_pre = NULL;
3778: mumps->CNTL_pre = NULL;
3779: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3780: PetscFunctionReturn(PETSC_SUCCESS);
3781: }
3783: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MUMPS(void)
3784: {
3785: PetscFunctionBegin;
3786: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3787: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3788: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIBAIJ, MAT_FACTOR_LU, MatGetFactor_baij_mumps));
3789: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_baij_mumps));
3790: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPISBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_sbaij_mumps));
3791: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3792: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3793: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQBAIJ, MAT_FACTOR_LU, MatGetFactor_baij_mumps));
3794: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_baij_mumps));
3795: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQSBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_sbaij_mumps));
3796: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQSELL, MAT_FACTOR_LU, MatGetFactor_sell_mumps));
3797: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATDIAGONAL, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3798: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATDIAGONAL, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3799: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATNEST, MAT_FACTOR_LU, MatGetFactor_nest_mumps));
3800: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATNEST, MAT_FACTOR_CHOLESKY, MatGetFactor_nest_mumps));
3801: PetscFunctionReturn(PETSC_SUCCESS);
3802: }