Actual source code: mumps.c
2: /*
3: Provides an interface to the MUMPS sparse solver
4: */
5: #include <petscpkg_version.h>
6: #include <../src/mat/impls/aij/mpi/mpiaij.h>
7: #include <../src/mat/impls/sbaij/mpi/mpisbaij.h>
8: #include <../src/mat/impls/sell/mpi/mpisell.h>
10: EXTERN_C_BEGIN
11: #if defined(PETSC_USE_COMPLEX)
12: #if defined(PETSC_USE_REAL_SINGLE)
13: #include <cmumps_c.h>
14: #else
15: #include <zmumps_c.h>
16: #endif
17: #else
18: #if defined(PETSC_USE_REAL_SINGLE)
19: #include <smumps_c.h>
20: #else
21: #include <dmumps_c.h>
22: #endif
23: #endif
24: EXTERN_C_END
25: #define JOB_INIT -1
26: #define JOB_NULL 0
27: #define JOB_FACTSYMBOLIC 1
28: #define JOB_FACTNUMERIC 2
29: #define JOB_SOLVE 3
30: #define JOB_END -2
32: /* calls to MUMPS */
33: #if defined(PETSC_USE_COMPLEX)
34: #if defined(PETSC_USE_REAL_SINGLE)
35: #define MUMPS_c cmumps_c
36: #else
37: #define MUMPS_c zmumps_c
38: #endif
39: #else
40: #if defined(PETSC_USE_REAL_SINGLE)
41: #define MUMPS_c smumps_c
42: #else
43: #define MUMPS_c dmumps_c
44: #endif
45: #endif
47: /* MUMPS uses MUMPS_INT for nonzero indices such as irn/jcn, irn_loc/jcn_loc and uses int64_t for
48: number of nonzeros such as nnz, nnz_loc. We typedef MUMPS_INT to PetscMUMPSInt to follow the
49: naming convention in PetscMPIInt, PetscBLASInt etc.
50: */
51: typedef MUMPS_INT PetscMUMPSInt;
53: #if PETSC_PKG_MUMPS_VERSION_GE(5, 3, 0)
54: #if defined(MUMPS_INTSIZE64) /* MUMPS_INTSIZE64 is in MUMPS headers if it is built in full 64-bit mode, therefore the macro is more reliable */
55: #error "Petsc has not been tested with full 64-bit MUMPS and we choose to error out"
56: #endif
57: #else
58: #if defined(INTSIZE64) /* INTSIZE64 is a command line macro one used to build MUMPS in full 64-bit mode */
59: #error "Petsc has not been tested with full 64-bit MUMPS and we choose to error out"
60: #endif
61: #endif
63: #define MPIU_MUMPSINT MPI_INT
64: #define PETSC_MUMPS_INT_MAX 2147483647
65: #define PETSC_MUMPS_INT_MIN -2147483648
67: /* Cast PetscInt to PetscMUMPSInt. Usually there is no overflow since <a> is row/col indices or some small integers*/
68: static inline PetscErrorCode PetscMUMPSIntCast(PetscInt a, PetscMUMPSInt *b)
69: {
70: PetscFunctionBegin;
71: #if PetscDefined(USE_64BIT_INDICES)
72: PetscAssert(a <= PETSC_MUMPS_INT_MAX && a >= PETSC_MUMPS_INT_MIN, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscInt too long for PetscMUMPSInt");
73: #endif
74: *b = (PetscMUMPSInt)(a);
75: PetscFunctionReturn(PETSC_SUCCESS);
76: }
78: /* Put these utility routines here since they are only used in this file */
79: static inline PetscErrorCode PetscOptionsMUMPSInt_Private(PetscOptionItems *PetscOptionsObject, const char opt[], const char text[], const char man[], PetscMUMPSInt currentvalue, PetscMUMPSInt *value, PetscBool *set, PetscMUMPSInt lb, PetscMUMPSInt ub)
80: {
81: PetscInt myval;
82: PetscBool myset;
83: PetscFunctionBegin;
84: /* PetscInt's size should be always >= PetscMUMPSInt's. It is safe to call PetscOptionsInt_Private to read a PetscMUMPSInt */
85: PetscCall(PetscOptionsInt_Private(PetscOptionsObject, opt, text, man, (PetscInt)currentvalue, &myval, &myset, lb, ub));
86: if (myset) PetscCall(PetscMUMPSIntCast(myval, value));
87: if (set) *set = myset;
88: PetscFunctionReturn(PETSC_SUCCESS);
89: }
90: #define PetscOptionsMUMPSInt(a, b, c, d, e, f) PetscOptionsMUMPSInt_Private(PetscOptionsObject, a, b, c, d, e, f, PETSC_MUMPS_INT_MIN, PETSC_MUMPS_INT_MAX)
92: /* if using PETSc OpenMP support, we only call MUMPS on master ranks. Before/after the call, we change/restore CPUs the master ranks can run on */
93: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
94: #define PetscMUMPS_c(mumps) \
95: do { \
96: if (mumps->use_petsc_omp_support) { \
97: if (mumps->is_omp_master) { \
98: PetscCall(PetscOmpCtrlOmpRegionOnMasterBegin(mumps->omp_ctrl)); \
99: MUMPS_c(&mumps->id); \
100: PetscCall(PetscOmpCtrlOmpRegionOnMasterEnd(mumps->omp_ctrl)); \
101: } \
102: PetscCall(PetscOmpCtrlBarrier(mumps->omp_ctrl)); \
103: /* Global info is same on all processes so we Bcast it within omp_comm. Local info is specific \
104: to processes, so we only Bcast info[1], an error code and leave others (since they do not have \
105: an easy translation between omp_comm and petsc_comm). See MUMPS-5.1.2 manual p82. \
106: omp_comm is a small shared memory communicator, hence doing multiple Bcast as shown below is OK. \
107: */ \
108: PetscCallMPI(MPI_Bcast(mumps->id.infog, 40, MPIU_MUMPSINT, 0, mumps->omp_comm)); \
109: PetscCallMPI(MPI_Bcast(mumps->id.rinfog, 20, MPIU_REAL, 0, mumps->omp_comm)); \
110: PetscCallMPI(MPI_Bcast(mumps->id.info, 1, MPIU_MUMPSINT, 0, mumps->omp_comm)); \
111: } else { \
112: MUMPS_c(&mumps->id); \
113: } \
114: } while (0)
115: #else
116: #define PetscMUMPS_c(mumps) \
117: do { \
118: MUMPS_c(&mumps->id); \
119: } while (0)
120: #endif
122: /* declare MumpsScalar */
123: #if defined(PETSC_USE_COMPLEX)
124: #if defined(PETSC_USE_REAL_SINGLE)
125: #define MumpsScalar mumps_complex
126: #else
127: #define MumpsScalar mumps_double_complex
128: #endif
129: #else
130: #define MumpsScalar PetscScalar
131: #endif
133: /* macros s.t. indices match MUMPS documentation */
134: #define ICNTL(I) icntl[(I)-1]
135: #define CNTL(I) cntl[(I)-1]
136: #define INFOG(I) infog[(I)-1]
137: #define INFO(I) info[(I)-1]
138: #define RINFOG(I) rinfog[(I)-1]
139: #define RINFO(I) rinfo[(I)-1]
141: typedef struct Mat_MUMPS Mat_MUMPS;
142: struct Mat_MUMPS {
143: #if defined(PETSC_USE_COMPLEX)
144: #if defined(PETSC_USE_REAL_SINGLE)
145: CMUMPS_STRUC_C id;
146: #else
147: ZMUMPS_STRUC_C id;
148: #endif
149: #else
150: #if defined(PETSC_USE_REAL_SINGLE)
151: SMUMPS_STRUC_C id;
152: #else
153: DMUMPS_STRUC_C id;
154: #endif
155: #endif
157: MatStructure matstruc;
158: PetscMPIInt myid, petsc_size;
159: PetscMUMPSInt *irn, *jcn; /* the (i,j,v) triplets passed to mumps. */
160: PetscScalar *val, *val_alloc; /* For some matrices, we can directly access their data array without a buffer. For others, we need a buffer. So comes val_alloc. */
161: PetscInt64 nnz; /* number of nonzeros. The type is called selective 64-bit in mumps */
162: PetscMUMPSInt sym;
163: MPI_Comm mumps_comm;
164: PetscMUMPSInt *ICNTL_pre;
165: PetscReal *CNTL_pre;
166: PetscMUMPSInt ICNTL9_pre; /* check if ICNTL(9) is changed from previous MatSolve */
167: VecScatter scat_rhs, scat_sol; /* used by MatSolve() */
168: PetscMUMPSInt ICNTL20; /* use centralized (0) or distributed (10) dense RHS */
169: PetscMUMPSInt lrhs_loc, nloc_rhs, *irhs_loc;
170: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
171: PetscInt *rhs_nrow, max_nrhs;
172: PetscMPIInt *rhs_recvcounts, *rhs_disps;
173: PetscScalar *rhs_loc, *rhs_recvbuf;
174: #endif
175: Vec b_seq, x_seq;
176: PetscInt ninfo, *info; /* which INFO to display */
177: PetscInt sizeredrhs;
178: PetscScalar *schur_sol;
179: PetscInt schur_sizesol;
180: PetscMUMPSInt *ia_alloc, *ja_alloc; /* work arrays used for the CSR struct for sparse rhs */
181: PetscInt64 cur_ilen, cur_jlen; /* current len of ia_alloc[], ja_alloc[] */
182: PetscErrorCode (*ConvertToTriples)(Mat, PetscInt, MatReuse, Mat_MUMPS *);
184: /* stuff used by petsc/mumps OpenMP support*/
185: PetscBool use_petsc_omp_support;
186: PetscOmpCtrl omp_ctrl; /* an OpenMP controller that blocked processes will release their CPU (MPI_Barrier does not have this guarantee) */
187: MPI_Comm petsc_comm, omp_comm; /* petsc_comm is petsc matrix's comm */
188: PetscInt64 *recvcount; /* a collection of nnz on omp_master */
189: PetscMPIInt tag, omp_comm_size;
190: PetscBool is_omp_master; /* is this rank the master of omp_comm */
191: MPI_Request *reqs;
192: };
194: /* Cast a 1-based CSR represented by (nrow, ia, ja) of type PetscInt to a CSR of type PetscMUMPSInt.
195: Here, nrow is number of rows, ia[] is row pointer and ja[] is column indices.
196: */
197: static PetscErrorCode PetscMUMPSIntCSRCast(Mat_MUMPS *mumps, PetscInt nrow, PetscInt *ia, PetscInt *ja, PetscMUMPSInt **ia_mumps, PetscMUMPSInt **ja_mumps, PetscMUMPSInt *nnz_mumps)
198: {
199: PetscInt nnz = ia[nrow] - 1; /* mumps uses 1-based indices. Uses PetscInt instead of PetscInt64 since mumps only uses PetscMUMPSInt for rhs */
201: PetscFunctionBegin;
202: #if defined(PETSC_USE_64BIT_INDICES)
203: {
204: PetscInt i;
205: if (nrow + 1 > mumps->cur_ilen) { /* realloc ia_alloc/ja_alloc to fit ia/ja */
206: PetscCall(PetscFree(mumps->ia_alloc));
207: PetscCall(PetscMalloc1(nrow + 1, &mumps->ia_alloc));
208: mumps->cur_ilen = nrow + 1;
209: }
210: if (nnz > mumps->cur_jlen) {
211: PetscCall(PetscFree(mumps->ja_alloc));
212: PetscCall(PetscMalloc1(nnz, &mumps->ja_alloc));
213: mumps->cur_jlen = nnz;
214: }
215: for (i = 0; i < nrow + 1; i++) PetscCall(PetscMUMPSIntCast(ia[i], &(mumps->ia_alloc[i])));
216: for (i = 0; i < nnz; i++) PetscCall(PetscMUMPSIntCast(ja[i], &(mumps->ja_alloc[i])));
217: *ia_mumps = mumps->ia_alloc;
218: *ja_mumps = mumps->ja_alloc;
219: }
220: #else
221: *ia_mumps = ia;
222: *ja_mumps = ja;
223: #endif
224: PetscCall(PetscMUMPSIntCast(nnz, nnz_mumps));
225: PetscFunctionReturn(PETSC_SUCCESS);
226: }
228: static PetscErrorCode MatMumpsResetSchur_Private(Mat_MUMPS *mumps)
229: {
230: PetscFunctionBegin;
231: PetscCall(PetscFree(mumps->id.listvar_schur));
232: PetscCall(PetscFree(mumps->id.redrhs));
233: PetscCall(PetscFree(mumps->schur_sol));
234: mumps->id.size_schur = 0;
235: mumps->id.schur_lld = 0;
236: mumps->id.ICNTL(19) = 0;
237: PetscFunctionReturn(PETSC_SUCCESS);
238: }
240: /* solve with rhs in mumps->id.redrhs and return in the same location */
241: static PetscErrorCode MatMumpsSolveSchur_Private(Mat F)
242: {
243: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
244: Mat S, B, X;
245: MatFactorSchurStatus schurstatus;
246: PetscInt sizesol;
248: PetscFunctionBegin;
249: PetscCall(MatFactorFactorizeSchurComplement(F));
250: PetscCall(MatFactorGetSchurComplement(F, &S, &schurstatus));
251: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, mumps->id.size_schur, mumps->id.nrhs, (PetscScalar *)mumps->id.redrhs, &B));
252: PetscCall(MatSetType(B, ((PetscObject)S)->type_name));
253: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
254: PetscCall(MatBindToCPU(B, S->boundtocpu));
255: #endif
256: switch (schurstatus) {
257: case MAT_FACTOR_SCHUR_FACTORED:
258: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, mumps->id.size_schur, mumps->id.nrhs, (PetscScalar *)mumps->id.redrhs, &X));
259: PetscCall(MatSetType(X, ((PetscObject)S)->type_name));
260: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
261: PetscCall(MatBindToCPU(X, S->boundtocpu));
262: #endif
263: if (!mumps->id.ICNTL(9)) { /* transpose solve */
264: PetscCall(MatMatSolveTranspose(S, B, X));
265: } else {
266: PetscCall(MatMatSolve(S, B, X));
267: }
268: break;
269: case MAT_FACTOR_SCHUR_INVERTED:
270: sizesol = mumps->id.nrhs * mumps->id.size_schur;
271: if (!mumps->schur_sol || sizesol > mumps->schur_sizesol) {
272: PetscCall(PetscFree(mumps->schur_sol));
273: PetscCall(PetscMalloc1(sizesol, &mumps->schur_sol));
274: mumps->schur_sizesol = sizesol;
275: }
276: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, mumps->id.size_schur, mumps->id.nrhs, mumps->schur_sol, &X));
277: PetscCall(MatSetType(X, ((PetscObject)S)->type_name));
278: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
279: PetscCall(MatBindToCPU(X, S->boundtocpu));
280: #endif
281: PetscCall(MatProductCreateWithMat(S, B, NULL, X));
282: if (!mumps->id.ICNTL(9)) { /* transpose solve */
283: PetscCall(MatProductSetType(X, MATPRODUCT_AtB));
284: } else {
285: PetscCall(MatProductSetType(X, MATPRODUCT_AB));
286: }
287: PetscCall(MatProductSetFromOptions(X));
288: PetscCall(MatProductSymbolic(X));
289: PetscCall(MatProductNumeric(X));
291: PetscCall(MatCopy(X, B, SAME_NONZERO_PATTERN));
292: break;
293: default:
294: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
295: }
296: PetscCall(MatFactorRestoreSchurComplement(F, &S, schurstatus));
297: PetscCall(MatDestroy(&B));
298: PetscCall(MatDestroy(&X));
299: PetscFunctionReturn(PETSC_SUCCESS);
300: }
302: static PetscErrorCode MatMumpsHandleSchur_Private(Mat F, PetscBool expansion)
303: {
304: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
306: PetscFunctionBegin;
307: if (!mumps->id.ICNTL(19)) { /* do nothing when Schur complement has not been computed */
308: PetscFunctionReturn(PETSC_SUCCESS);
309: }
310: if (!expansion) { /* prepare for the condensation step */
311: PetscInt sizeredrhs = mumps->id.nrhs * mumps->id.size_schur;
312: /* allocate MUMPS internal array to store reduced right-hand sides */
313: if (!mumps->id.redrhs || sizeredrhs > mumps->sizeredrhs) {
314: PetscCall(PetscFree(mumps->id.redrhs));
315: mumps->id.lredrhs = mumps->id.size_schur;
316: PetscCall(PetscMalloc1(mumps->id.nrhs * mumps->id.lredrhs, &mumps->id.redrhs));
317: mumps->sizeredrhs = mumps->id.nrhs * mumps->id.lredrhs;
318: }
319: mumps->id.ICNTL(26) = 1; /* condensation phase */
320: } else { /* prepare for the expansion step */
321: /* solve Schur complement (this has to be done by the MUMPS user, so basically us) */
322: PetscCall(MatMumpsSolveSchur_Private(F));
323: mumps->id.ICNTL(26) = 2; /* expansion phase */
324: PetscMUMPS_c(mumps);
325: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MUMPS in solve phase: INFOG(1)=%d", mumps->id.INFOG(1));
326: /* restore defaults */
327: mumps->id.ICNTL(26) = -1;
328: /* free MUMPS internal array for redrhs if we have solved for multiple rhs in order to save memory space */
329: if (mumps->id.nrhs > 1) {
330: PetscCall(PetscFree(mumps->id.redrhs));
331: mumps->id.lredrhs = 0;
332: mumps->sizeredrhs = 0;
333: }
334: }
335: PetscFunctionReturn(PETSC_SUCCESS);
336: }
338: /*
339: MatConvertToTriples_A_B - convert Petsc matrix to triples: row[nz], col[nz], val[nz]
341: input:
342: A - matrix in aij,baij or sbaij format
343: shift - 0: C style output triple; 1: Fortran style output triple.
344: reuse - MAT_INITIAL_MATRIX: spaces are allocated and values are set for the triple
345: MAT_REUSE_MATRIX: only the values in v array are updated
346: output:
347: nnz - dim of r, c, and v (number of local nonzero entries of A)
348: r, c, v - row and col index, matrix values (matrix triples)
350: The returned values r, c, and sometimes v are obtained in a single PetscMalloc(). Then in MatDestroy_MUMPS() it is
351: freed with PetscFree(mumps->irn); This is not ideal code, the fact that v is ONLY sometimes part of mumps->irn means
352: that the PetscMalloc() cannot easily be replaced with a PetscMalloc3().
354: */
356: PetscErrorCode MatConvertToTriples_seqaij_seqaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
357: {
358: const PetscScalar *av;
359: const PetscInt *ai, *aj, *ajj, M = A->rmap->n;
360: PetscInt64 nz, rnz, i, j, k;
361: PetscMUMPSInt *row, *col;
362: Mat_SeqAIJ *aa = (Mat_SeqAIJ *)A->data;
364: PetscFunctionBegin;
365: PetscCall(MatSeqAIJGetArrayRead(A, &av));
366: mumps->val = (PetscScalar *)av;
367: if (reuse == MAT_INITIAL_MATRIX) {
368: nz = aa->nz;
369: ai = aa->i;
370: aj = aa->j;
371: PetscCall(PetscMalloc2(nz, &row, nz, &col));
372: for (i = k = 0; i < M; i++) {
373: rnz = ai[i + 1] - ai[i];
374: ajj = aj + ai[i];
375: for (j = 0; j < rnz; j++) {
376: PetscCall(PetscMUMPSIntCast(i + shift, &row[k]));
377: PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[k]));
378: k++;
379: }
380: }
381: mumps->irn = row;
382: mumps->jcn = col;
383: mumps->nnz = nz;
384: }
385: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
386: PetscFunctionReturn(PETSC_SUCCESS);
387: }
389: PetscErrorCode MatConvertToTriples_seqsell_seqaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
390: {
391: PetscInt64 nz, i, j, k, r;
392: Mat_SeqSELL *a = (Mat_SeqSELL *)A->data;
393: PetscMUMPSInt *row, *col;
395: PetscFunctionBegin;
396: mumps->val = a->val;
397: if (reuse == MAT_INITIAL_MATRIX) {
398: nz = a->sliidx[a->totalslices];
399: PetscCall(PetscMalloc2(nz, &row, nz, &col));
400: for (i = k = 0; i < a->totalslices; i++) {
401: for (j = a->sliidx[i], r = 0; j < a->sliidx[i + 1]; j++, r = ((r + 1) & 0x07)) PetscCall(PetscMUMPSIntCast(8 * i + r + shift, &row[k++]));
402: }
403: for (i = 0; i < nz; i++) PetscCall(PetscMUMPSIntCast(a->colidx[i] + shift, &col[i]));
404: mumps->irn = row;
405: mumps->jcn = col;
406: mumps->nnz = nz;
407: }
408: PetscFunctionReturn(PETSC_SUCCESS);
409: }
411: PetscErrorCode MatConvertToTriples_seqbaij_seqaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
412: {
413: Mat_SeqBAIJ *aa = (Mat_SeqBAIJ *)A->data;
414: const PetscInt *ai, *aj, *ajj, bs2 = aa->bs2;
415: PetscInt64 M, nz, idx = 0, rnz, i, j, k, m;
416: PetscInt bs;
417: PetscMUMPSInt *row, *col;
419: PetscFunctionBegin;
420: PetscCall(MatGetBlockSize(A, &bs));
421: M = A->rmap->N / bs;
422: mumps->val = aa->a;
423: if (reuse == MAT_INITIAL_MATRIX) {
424: ai = aa->i;
425: aj = aa->j;
426: nz = bs2 * aa->nz;
427: PetscCall(PetscMalloc2(nz, &row, nz, &col));
428: for (i = 0; i < M; i++) {
429: ajj = aj + ai[i];
430: rnz = ai[i + 1] - ai[i];
431: for (k = 0; k < rnz; k++) {
432: for (j = 0; j < bs; j++) {
433: for (m = 0; m < bs; m++) {
434: PetscCall(PetscMUMPSIntCast(i * bs + m + shift, &row[idx]));
435: PetscCall(PetscMUMPSIntCast(bs * ajj[k] + j + shift, &col[idx]));
436: idx++;
437: }
438: }
439: }
440: }
441: mumps->irn = row;
442: mumps->jcn = col;
443: mumps->nnz = nz;
444: }
445: PetscFunctionReturn(PETSC_SUCCESS);
446: }
448: PetscErrorCode MatConvertToTriples_seqsbaij_seqsbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
449: {
450: const PetscInt *ai, *aj, *ajj;
451: PetscInt bs;
452: PetscInt64 nz, rnz, i, j, k, m;
453: PetscMUMPSInt *row, *col;
454: PetscScalar *val;
455: Mat_SeqSBAIJ *aa = (Mat_SeqSBAIJ *)A->data;
456: const PetscInt bs2 = aa->bs2, mbs = aa->mbs;
457: #if defined(PETSC_USE_COMPLEX)
458: PetscBool isset, hermitian;
459: #endif
461: PetscFunctionBegin;
462: #if defined(PETSC_USE_COMPLEX)
463: PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
464: PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
465: #endif
466: ai = aa->i;
467: aj = aa->j;
468: PetscCall(MatGetBlockSize(A, &bs));
469: if (reuse == MAT_INITIAL_MATRIX) {
470: const PetscInt64 alloc_size = aa->nz * bs2;
472: PetscCall(PetscMalloc2(alloc_size, &row, alloc_size, &col));
473: if (bs > 1) {
474: PetscCall(PetscMalloc1(alloc_size, &mumps->val_alloc));
475: mumps->val = mumps->val_alloc;
476: } else {
477: mumps->val = aa->a;
478: }
479: mumps->irn = row;
480: mumps->jcn = col;
481: } else {
482: if (bs == 1) mumps->val = aa->a;
483: row = mumps->irn;
484: col = mumps->jcn;
485: }
486: val = mumps->val;
488: nz = 0;
489: if (bs > 1) {
490: for (i = 0; i < mbs; i++) {
491: rnz = ai[i + 1] - ai[i];
492: ajj = aj + ai[i];
493: for (j = 0; j < rnz; j++) {
494: for (k = 0; k < bs; k++) {
495: for (m = 0; m < bs; m++) {
496: if (ajj[j] > i || k >= m) {
497: if (reuse == MAT_INITIAL_MATRIX) {
498: PetscCall(PetscMUMPSIntCast(i * bs + m + shift, &row[nz]));
499: PetscCall(PetscMUMPSIntCast(ajj[j] * bs + k + shift, &col[nz]));
500: }
501: val[nz++] = aa->a[(ai[i] + j) * bs2 + m + k * bs];
502: }
503: }
504: }
505: }
506: }
507: } else if (reuse == MAT_INITIAL_MATRIX) {
508: for (i = 0; i < mbs; i++) {
509: rnz = ai[i + 1] - ai[i];
510: ajj = aj + ai[i];
511: for (j = 0; j < rnz; j++) {
512: PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
513: PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[nz]));
514: nz++;
515: }
516: }
517: PetscCheck(nz == aa->nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Different numbers of nonzeros %" PetscInt64_FMT " != %" PetscInt_FMT, nz, aa->nz);
518: }
519: if (reuse == MAT_INITIAL_MATRIX) mumps->nnz = nz;
520: PetscFunctionReturn(PETSC_SUCCESS);
521: }
523: PetscErrorCode MatConvertToTriples_seqaij_seqsbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
524: {
525: const PetscInt *ai, *aj, *ajj, *adiag, M = A->rmap->n;
526: PetscInt64 nz, rnz, i, j;
527: const PetscScalar *av, *v1;
528: PetscScalar *val;
529: PetscMUMPSInt *row, *col;
530: Mat_SeqAIJ *aa = (Mat_SeqAIJ *)A->data;
531: PetscBool missing;
532: #if defined(PETSC_USE_COMPLEX)
533: PetscBool hermitian, isset;
534: #endif
536: PetscFunctionBegin;
537: #if defined(PETSC_USE_COMPLEX)
538: PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
539: PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
540: #endif
541: PetscCall(MatSeqAIJGetArrayRead(A, &av));
542: ai = aa->i;
543: aj = aa->j;
544: adiag = aa->diag;
545: PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, NULL));
546: if (reuse == MAT_INITIAL_MATRIX) {
547: /* count nz in the upper triangular part of A */
548: nz = 0;
549: if (missing) {
550: for (i = 0; i < M; i++) {
551: if (PetscUnlikely(adiag[i] >= ai[i + 1])) {
552: for (j = ai[i]; j < ai[i + 1]; j++) {
553: if (aj[j] < i) continue;
554: nz++;
555: }
556: } else {
557: nz += ai[i + 1] - adiag[i];
558: }
559: }
560: } else {
561: for (i = 0; i < M; i++) nz += ai[i + 1] - adiag[i];
562: }
563: PetscCall(PetscMalloc2(nz, &row, nz, &col));
564: PetscCall(PetscMalloc1(nz, &val));
565: mumps->nnz = nz;
566: mumps->irn = row;
567: mumps->jcn = col;
568: mumps->val = mumps->val_alloc = val;
570: nz = 0;
571: if (missing) {
572: for (i = 0; i < M; i++) {
573: if (PetscUnlikely(adiag[i] >= ai[i + 1])) {
574: for (j = ai[i]; j < ai[i + 1]; j++) {
575: if (aj[j] < i) continue;
576: PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
577: PetscCall(PetscMUMPSIntCast(aj[j] + shift, &col[nz]));
578: val[nz] = av[j];
579: nz++;
580: }
581: } else {
582: rnz = ai[i + 1] - adiag[i];
583: ajj = aj + adiag[i];
584: v1 = av + adiag[i];
585: for (j = 0; j < rnz; j++) {
586: PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
587: PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[nz]));
588: val[nz++] = v1[j];
589: }
590: }
591: }
592: } else {
593: for (i = 0; i < M; i++) {
594: rnz = ai[i + 1] - adiag[i];
595: ajj = aj + adiag[i];
596: v1 = av + adiag[i];
597: for (j = 0; j < rnz; j++) {
598: PetscCall(PetscMUMPSIntCast(i + shift, &row[nz]));
599: PetscCall(PetscMUMPSIntCast(ajj[j] + shift, &col[nz]));
600: val[nz++] = v1[j];
601: }
602: }
603: }
604: } else {
605: nz = 0;
606: val = mumps->val;
607: if (missing) {
608: for (i = 0; i < M; i++) {
609: if (PetscUnlikely(adiag[i] >= ai[i + 1])) {
610: for (j = ai[i]; j < ai[i + 1]; j++) {
611: if (aj[j] < i) continue;
612: val[nz++] = av[j];
613: }
614: } else {
615: rnz = ai[i + 1] - adiag[i];
616: v1 = av + adiag[i];
617: for (j = 0; j < rnz; j++) val[nz++] = v1[j];
618: }
619: }
620: } else {
621: for (i = 0; i < M; i++) {
622: rnz = ai[i + 1] - adiag[i];
623: v1 = av + adiag[i];
624: for (j = 0; j < rnz; j++) val[nz++] = v1[j];
625: }
626: }
627: }
628: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
629: PetscFunctionReturn(PETSC_SUCCESS);
630: }
632: PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
633: {
634: const PetscInt *ai, *aj, *bi, *bj, *garray, *ajj, *bjj;
635: PetscInt bs;
636: PetscInt64 rstart, nz, i, j, k, m, jj, irow, countA, countB;
637: PetscMUMPSInt *row, *col;
638: const PetscScalar *av, *bv, *v1, *v2;
639: PetscScalar *val;
640: Mat_MPISBAIJ *mat = (Mat_MPISBAIJ *)A->data;
641: Mat_SeqSBAIJ *aa = (Mat_SeqSBAIJ *)(mat->A)->data;
642: Mat_SeqBAIJ *bb = (Mat_SeqBAIJ *)(mat->B)->data;
643: const PetscInt bs2 = aa->bs2, mbs = aa->mbs;
644: #if defined(PETSC_USE_COMPLEX)
645: PetscBool hermitian, isset;
646: #endif
648: PetscFunctionBegin;
649: #if defined(PETSC_USE_COMPLEX)
650: PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
651: PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
652: #endif
653: PetscCall(MatGetBlockSize(A, &bs));
654: rstart = A->rmap->rstart;
655: ai = aa->i;
656: aj = aa->j;
657: bi = bb->i;
658: bj = bb->j;
659: av = aa->a;
660: bv = bb->a;
662: garray = mat->garray;
664: if (reuse == MAT_INITIAL_MATRIX) {
665: nz = (aa->nz + bb->nz) * bs2; /* just a conservative estimate */
666: PetscCall(PetscMalloc2(nz, &row, nz, &col));
667: PetscCall(PetscMalloc1(nz, &val));
668: /* can not decide the exact mumps->nnz now because of the SBAIJ */
669: mumps->irn = row;
670: mumps->jcn = col;
671: mumps->val = mumps->val_alloc = val;
672: } else {
673: val = mumps->val;
674: }
676: jj = 0;
677: irow = rstart;
678: for (i = 0; i < mbs; i++) {
679: ajj = aj + ai[i]; /* ptr to the beginning of this row */
680: countA = ai[i + 1] - ai[i];
681: countB = bi[i + 1] - bi[i];
682: bjj = bj + bi[i];
683: v1 = av + ai[i] * bs2;
684: v2 = bv + bi[i] * bs2;
686: if (bs > 1) {
687: /* A-part */
688: for (j = 0; j < countA; j++) {
689: for (k = 0; k < bs; k++) {
690: for (m = 0; m < bs; m++) {
691: if (rstart + ajj[j] * bs > irow || k >= m) {
692: if (reuse == MAT_INITIAL_MATRIX) {
693: PetscCall(PetscMUMPSIntCast(irow + m + shift, &row[jj]));
694: PetscCall(PetscMUMPSIntCast(rstart + ajj[j] * bs + k + shift, &col[jj]));
695: }
696: val[jj++] = v1[j * bs2 + m + k * bs];
697: }
698: }
699: }
700: }
702: /* B-part */
703: for (j = 0; j < countB; j++) {
704: for (k = 0; k < bs; k++) {
705: for (m = 0; m < bs; m++) {
706: if (reuse == MAT_INITIAL_MATRIX) {
707: PetscCall(PetscMUMPSIntCast(irow + m + shift, &row[jj]));
708: PetscCall(PetscMUMPSIntCast(garray[bjj[j]] * bs + k + shift, &col[jj]));
709: }
710: val[jj++] = v2[j * bs2 + m + k * bs];
711: }
712: }
713: }
714: } else {
715: /* A-part */
716: for (j = 0; j < countA; j++) {
717: if (reuse == MAT_INITIAL_MATRIX) {
718: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
719: PetscCall(PetscMUMPSIntCast(rstart + ajj[j] + shift, &col[jj]));
720: }
721: val[jj++] = v1[j];
722: }
724: /* B-part */
725: for (j = 0; j < countB; j++) {
726: if (reuse == MAT_INITIAL_MATRIX) {
727: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
728: PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift, &col[jj]));
729: }
730: val[jj++] = v2[j];
731: }
732: }
733: irow += bs;
734: }
735: mumps->nnz = jj;
736: PetscFunctionReturn(PETSC_SUCCESS);
737: }
739: PetscErrorCode MatConvertToTriples_mpiaij_mpiaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
740: {
741: const PetscInt *ai, *aj, *bi, *bj, *garray, m = A->rmap->n, *ajj, *bjj;
742: PetscInt64 rstart, nz, i, j, jj, irow, countA, countB;
743: PetscMUMPSInt *row, *col;
744: const PetscScalar *av, *bv, *v1, *v2;
745: PetscScalar *val;
746: Mat Ad, Ao;
747: Mat_SeqAIJ *aa;
748: Mat_SeqAIJ *bb;
750: PetscFunctionBegin;
751: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &garray));
752: PetscCall(MatSeqAIJGetArrayRead(Ad, &av));
753: PetscCall(MatSeqAIJGetArrayRead(Ao, &bv));
755: aa = (Mat_SeqAIJ *)(Ad)->data;
756: bb = (Mat_SeqAIJ *)(Ao)->data;
757: ai = aa->i;
758: aj = aa->j;
759: bi = bb->i;
760: bj = bb->j;
762: rstart = A->rmap->rstart;
764: if (reuse == MAT_INITIAL_MATRIX) {
765: nz = (PetscInt64)aa->nz + bb->nz; /* make sure the sum won't overflow PetscInt */
766: PetscCall(PetscMalloc2(nz, &row, nz, &col));
767: PetscCall(PetscMalloc1(nz, &val));
768: mumps->nnz = nz;
769: mumps->irn = row;
770: mumps->jcn = col;
771: mumps->val = mumps->val_alloc = val;
772: } else {
773: val = mumps->val;
774: }
776: jj = 0;
777: irow = rstart;
778: for (i = 0; i < m; i++) {
779: ajj = aj + ai[i]; /* ptr to the beginning of this row */
780: countA = ai[i + 1] - ai[i];
781: countB = bi[i + 1] - bi[i];
782: bjj = bj + bi[i];
783: v1 = av + ai[i];
784: v2 = bv + bi[i];
786: /* A-part */
787: for (j = 0; j < countA; j++) {
788: if (reuse == MAT_INITIAL_MATRIX) {
789: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
790: PetscCall(PetscMUMPSIntCast(rstart + ajj[j] + shift, &col[jj]));
791: }
792: val[jj++] = v1[j];
793: }
795: /* B-part */
796: for (j = 0; j < countB; j++) {
797: if (reuse == MAT_INITIAL_MATRIX) {
798: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
799: PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift, &col[jj]));
800: }
801: val[jj++] = v2[j];
802: }
803: irow++;
804: }
805: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &av));
806: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &bv));
807: PetscFunctionReturn(PETSC_SUCCESS);
808: }
810: PetscErrorCode MatConvertToTriples_mpibaij_mpiaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
811: {
812: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)A->data;
813: Mat_SeqBAIJ *aa = (Mat_SeqBAIJ *)(mat->A)->data;
814: Mat_SeqBAIJ *bb = (Mat_SeqBAIJ *)(mat->B)->data;
815: const PetscInt *ai = aa->i, *bi = bb->i, *aj = aa->j, *bj = bb->j, *ajj, *bjj;
816: const PetscInt *garray = mat->garray, mbs = mat->mbs, rstart = A->rmap->rstart;
817: const PetscInt bs2 = mat->bs2;
818: PetscInt bs;
819: PetscInt64 nz, i, j, k, n, jj, irow, countA, countB, idx;
820: PetscMUMPSInt *row, *col;
821: const PetscScalar *av = aa->a, *bv = bb->a, *v1, *v2;
822: PetscScalar *val;
824: PetscFunctionBegin;
825: PetscCall(MatGetBlockSize(A, &bs));
826: if (reuse == MAT_INITIAL_MATRIX) {
827: nz = bs2 * (aa->nz + bb->nz);
828: PetscCall(PetscMalloc2(nz, &row, nz, &col));
829: PetscCall(PetscMalloc1(nz, &val));
830: mumps->nnz = nz;
831: mumps->irn = row;
832: mumps->jcn = col;
833: mumps->val = mumps->val_alloc = val;
834: } else {
835: val = mumps->val;
836: }
838: jj = 0;
839: irow = rstart;
840: for (i = 0; i < mbs; i++) {
841: countA = ai[i + 1] - ai[i];
842: countB = bi[i + 1] - bi[i];
843: ajj = aj + ai[i];
844: bjj = bj + bi[i];
845: v1 = av + bs2 * ai[i];
846: v2 = bv + bs2 * bi[i];
848: idx = 0;
849: /* A-part */
850: for (k = 0; k < countA; k++) {
851: for (j = 0; j < bs; j++) {
852: for (n = 0; n < bs; n++) {
853: if (reuse == MAT_INITIAL_MATRIX) {
854: PetscCall(PetscMUMPSIntCast(irow + n + shift, &row[jj]));
855: PetscCall(PetscMUMPSIntCast(rstart + bs * ajj[k] + j + shift, &col[jj]));
856: }
857: val[jj++] = v1[idx++];
858: }
859: }
860: }
862: idx = 0;
863: /* B-part */
864: for (k = 0; k < countB; k++) {
865: for (j = 0; j < bs; j++) {
866: for (n = 0; n < bs; n++) {
867: if (reuse == MAT_INITIAL_MATRIX) {
868: PetscCall(PetscMUMPSIntCast(irow + n + shift, &row[jj]));
869: PetscCall(PetscMUMPSIntCast(bs * garray[bjj[k]] + j + shift, &col[jj]));
870: }
871: val[jj++] = v2[idx++];
872: }
873: }
874: }
875: irow += bs;
876: }
877: PetscFunctionReturn(PETSC_SUCCESS);
878: }
880: PetscErrorCode MatConvertToTriples_mpiaij_mpisbaij(Mat A, PetscInt shift, MatReuse reuse, Mat_MUMPS *mumps)
881: {
882: const PetscInt *ai, *aj, *adiag, *bi, *bj, *garray, m = A->rmap->n, *ajj, *bjj;
883: PetscInt64 rstart, nz, nza, nzb, i, j, jj, irow, countA, countB;
884: PetscMUMPSInt *row, *col;
885: const PetscScalar *av, *bv, *v1, *v2;
886: PetscScalar *val;
887: Mat Ad, Ao;
888: Mat_SeqAIJ *aa;
889: Mat_SeqAIJ *bb;
890: #if defined(PETSC_USE_COMPLEX)
891: PetscBool hermitian, isset;
892: #endif
894: PetscFunctionBegin;
895: #if defined(PETSC_USE_COMPLEX)
896: PetscCall(MatIsHermitianKnown(A, &isset, &hermitian));
897: PetscCheck(!isset || !hermitian, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MUMPS does not support Hermitian symmetric matrices for Choleksy");
898: #endif
899: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &garray));
900: PetscCall(MatSeqAIJGetArrayRead(Ad, &av));
901: PetscCall(MatSeqAIJGetArrayRead(Ao, &bv));
903: aa = (Mat_SeqAIJ *)(Ad)->data;
904: bb = (Mat_SeqAIJ *)(Ao)->data;
905: ai = aa->i;
906: aj = aa->j;
907: adiag = aa->diag;
908: bi = bb->i;
909: bj = bb->j;
911: rstart = A->rmap->rstart;
913: if (reuse == MAT_INITIAL_MATRIX) {
914: nza = 0; /* num of upper triangular entries in mat->A, including diagonals */
915: nzb = 0; /* num of upper triangular entries in mat->B */
916: for (i = 0; i < m; i++) {
917: nza += (ai[i + 1] - adiag[i]);
918: countB = bi[i + 1] - bi[i];
919: bjj = bj + bi[i];
920: for (j = 0; j < countB; j++) {
921: if (garray[bjj[j]] > rstart) nzb++;
922: }
923: }
925: nz = nza + nzb; /* total nz of upper triangular part of mat */
926: PetscCall(PetscMalloc2(nz, &row, nz, &col));
927: PetscCall(PetscMalloc1(nz, &val));
928: mumps->nnz = nz;
929: mumps->irn = row;
930: mumps->jcn = col;
931: mumps->val = mumps->val_alloc = val;
932: } else {
933: val = mumps->val;
934: }
936: jj = 0;
937: irow = rstart;
938: for (i = 0; i < m; i++) {
939: ajj = aj + adiag[i]; /* ptr to the beginning of the diagonal of this row */
940: v1 = av + adiag[i];
941: countA = ai[i + 1] - adiag[i];
942: countB = bi[i + 1] - bi[i];
943: bjj = bj + bi[i];
944: v2 = bv + bi[i];
946: /* A-part */
947: for (j = 0; j < countA; j++) {
948: if (reuse == MAT_INITIAL_MATRIX) {
949: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
950: PetscCall(PetscMUMPSIntCast(rstart + ajj[j] + shift, &col[jj]));
951: }
952: val[jj++] = v1[j];
953: }
955: /* B-part */
956: for (j = 0; j < countB; j++) {
957: if (garray[bjj[j]] > rstart) {
958: if (reuse == MAT_INITIAL_MATRIX) {
959: PetscCall(PetscMUMPSIntCast(irow + shift, &row[jj]));
960: PetscCall(PetscMUMPSIntCast(garray[bjj[j]] + shift, &col[jj]));
961: }
962: val[jj++] = v2[j];
963: }
964: }
965: irow++;
966: }
967: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &av));
968: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &bv));
969: PetscFunctionReturn(PETSC_SUCCESS);
970: }
972: PetscErrorCode MatDestroy_MUMPS(Mat A)
973: {
974: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
976: PetscFunctionBegin;
977: PetscCall(PetscFree2(mumps->id.sol_loc, mumps->id.isol_loc));
978: PetscCall(VecScatterDestroy(&mumps->scat_rhs));
979: PetscCall(VecScatterDestroy(&mumps->scat_sol));
980: PetscCall(VecDestroy(&mumps->b_seq));
981: PetscCall(VecDestroy(&mumps->x_seq));
982: PetscCall(PetscFree(mumps->id.perm_in));
983: PetscCall(PetscFree2(mumps->irn, mumps->jcn));
984: PetscCall(PetscFree(mumps->val_alloc));
985: PetscCall(PetscFree(mumps->info));
986: PetscCall(PetscFree(mumps->ICNTL_pre));
987: PetscCall(PetscFree(mumps->CNTL_pre));
988: PetscCall(MatMumpsResetSchur_Private(mumps));
989: if (mumps->id.job != JOB_NULL) { /* cannot call PetscMUMPS_c() if JOB_INIT has never been called for this instance */
990: mumps->id.job = JOB_END;
991: PetscMUMPS_c(mumps);
992: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MUMPS in MatDestroy_MUMPS: INFOG(1)=%d", mumps->id.INFOG(1));
993: if (mumps->mumps_comm != MPI_COMM_NULL) {
994: if (PetscDefined(HAVE_OPENMP_SUPPORT) && mumps->use_petsc_omp_support) PetscCallMPI(MPI_Comm_free(&mumps->mumps_comm));
995: else PetscCall(PetscCommRestoreComm(PetscObjectComm((PetscObject)A), &mumps->mumps_comm));
996: }
997: }
998: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
999: if (mumps->use_petsc_omp_support) {
1000: PetscCall(PetscOmpCtrlDestroy(&mumps->omp_ctrl));
1001: PetscCall(PetscFree2(mumps->rhs_loc, mumps->rhs_recvbuf));
1002: PetscCall(PetscFree3(mumps->rhs_nrow, mumps->rhs_recvcounts, mumps->rhs_disps));
1003: }
1004: #endif
1005: PetscCall(PetscFree(mumps->ia_alloc));
1006: PetscCall(PetscFree(mumps->ja_alloc));
1007: PetscCall(PetscFree(mumps->recvcount));
1008: PetscCall(PetscFree(mumps->reqs));
1009: PetscCall(PetscFree(mumps->irhs_loc));
1010: PetscCall(PetscFree(A->data));
1012: /* clear composed functions */
1013: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1014: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorSetSchurIS_C", NULL));
1015: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorCreateSchurComplement_C", NULL));
1016: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsSetIcntl_C", NULL));
1017: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetIcntl_C", NULL));
1018: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsSetCntl_C", NULL));
1019: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetCntl_C", NULL));
1020: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInfo_C", NULL));
1021: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInfog_C", NULL));
1022: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetRinfo_C", NULL));
1023: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetRinfog_C", NULL));
1024: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInverse_C", NULL));
1025: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInverseTranspose_C", NULL));
1026: PetscFunctionReturn(PETSC_SUCCESS);
1027: }
1029: /* Set up the distributed RHS info for MUMPS. <nrhs> is the number of RHS. <array> points to start of RHS on the local processor. */
1030: static PetscErrorCode MatMumpsSetUpDistRHSInfo(Mat A, PetscInt nrhs, const PetscScalar *array)
1031: {
1032: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1033: const PetscMPIInt ompsize = mumps->omp_comm_size;
1034: PetscInt i, m, M, rstart;
1036: PetscFunctionBegin;
1037: PetscCall(MatGetSize(A, &M, NULL));
1038: PetscCall(MatGetLocalSize(A, &m, NULL));
1039: PetscCheck(M <= PETSC_MUMPS_INT_MAX, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscInt too long for PetscMUMPSInt");
1040: if (ompsize == 1) {
1041: if (!mumps->irhs_loc) {
1042: mumps->nloc_rhs = m;
1043: PetscCall(PetscMalloc1(m, &mumps->irhs_loc));
1044: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
1045: for (i = 0; i < m; i++) mumps->irhs_loc[i] = rstart + i + 1; /* use 1-based indices */
1046: }
1047: mumps->id.rhs_loc = (MumpsScalar *)array;
1048: } else {
1049: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
1050: const PetscInt *ranges;
1051: PetscMPIInt j, k, sendcount, *petsc_ranks, *omp_ranks;
1052: MPI_Group petsc_group, omp_group;
1053: PetscScalar *recvbuf = NULL;
1055: if (mumps->is_omp_master) {
1056: /* Lazily initialize the omp stuff for distributed rhs */
1057: if (!mumps->irhs_loc) {
1058: PetscCall(PetscMalloc2(ompsize, &omp_ranks, ompsize, &petsc_ranks));
1059: PetscCall(PetscMalloc3(ompsize, &mumps->rhs_nrow, ompsize, &mumps->rhs_recvcounts, ompsize, &mumps->rhs_disps));
1060: PetscCallMPI(MPI_Comm_group(mumps->petsc_comm, &petsc_group));
1061: PetscCallMPI(MPI_Comm_group(mumps->omp_comm, &omp_group));
1062: for (j = 0; j < ompsize; j++) omp_ranks[j] = j;
1063: PetscCallMPI(MPI_Group_translate_ranks(omp_group, ompsize, omp_ranks, petsc_group, petsc_ranks));
1065: /* Populate mumps->irhs_loc[], rhs_nrow[] */
1066: mumps->nloc_rhs = 0;
1067: PetscCall(MatGetOwnershipRanges(A, &ranges));
1068: for (j = 0; j < ompsize; j++) {
1069: mumps->rhs_nrow[j] = ranges[petsc_ranks[j] + 1] - ranges[petsc_ranks[j]];
1070: mumps->nloc_rhs += mumps->rhs_nrow[j];
1071: }
1072: PetscCall(PetscMalloc1(mumps->nloc_rhs, &mumps->irhs_loc));
1073: for (j = k = 0; j < ompsize; j++) {
1074: for (i = ranges[petsc_ranks[j]]; i < ranges[petsc_ranks[j] + 1]; i++, k++) mumps->irhs_loc[k] = i + 1; /* uses 1-based indices */
1075: }
1077: PetscCall(PetscFree2(omp_ranks, petsc_ranks));
1078: PetscCallMPI(MPI_Group_free(&petsc_group));
1079: PetscCallMPI(MPI_Group_free(&omp_group));
1080: }
1082: /* Realloc buffers when current nrhs is bigger than what we have met */
1083: if (nrhs > mumps->max_nrhs) {
1084: PetscCall(PetscFree2(mumps->rhs_loc, mumps->rhs_recvbuf));
1085: PetscCall(PetscMalloc2(mumps->nloc_rhs * nrhs, &mumps->rhs_loc, mumps->nloc_rhs * nrhs, &mumps->rhs_recvbuf));
1086: mumps->max_nrhs = nrhs;
1087: }
1089: /* Setup recvcounts[], disps[], recvbuf on omp rank 0 for the upcoming MPI_Gatherv */
1090: for (j = 0; j < ompsize; j++) PetscCall(PetscMPIIntCast(mumps->rhs_nrow[j] * nrhs, &mumps->rhs_recvcounts[j]));
1091: mumps->rhs_disps[0] = 0;
1092: for (j = 1; j < ompsize; j++) {
1093: mumps->rhs_disps[j] = mumps->rhs_disps[j - 1] + mumps->rhs_recvcounts[j - 1];
1094: PetscCheck(mumps->rhs_disps[j] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscMPIInt overflow!");
1095: }
1096: recvbuf = (nrhs == 1) ? mumps->rhs_loc : mumps->rhs_recvbuf; /* Directly use rhs_loc[] as recvbuf. Single rhs is common in Ax=b */
1097: }
1099: PetscCall(PetscMPIIntCast(m * nrhs, &sendcount));
1100: PetscCallMPI(MPI_Gatherv(array, sendcount, MPIU_SCALAR, recvbuf, mumps->rhs_recvcounts, mumps->rhs_disps, MPIU_SCALAR, 0, mumps->omp_comm));
1102: if (mumps->is_omp_master) {
1103: if (nrhs > 1) { /* Copy & re-arrange data from rhs_recvbuf[] to mumps->rhs_loc[] only when there are multiple rhs */
1104: PetscScalar *dst, *dstbase = mumps->rhs_loc;
1105: for (j = 0; j < ompsize; j++) {
1106: const PetscScalar *src = mumps->rhs_recvbuf + mumps->rhs_disps[j];
1107: dst = dstbase;
1108: for (i = 0; i < nrhs; i++) {
1109: PetscCall(PetscArraycpy(dst, src, mumps->rhs_nrow[j]));
1110: src += mumps->rhs_nrow[j];
1111: dst += mumps->nloc_rhs;
1112: }
1113: dstbase += mumps->rhs_nrow[j];
1114: }
1115: }
1116: mumps->id.rhs_loc = (MumpsScalar *)mumps->rhs_loc;
1117: }
1118: #endif /* PETSC_HAVE_OPENMP_SUPPORT */
1119: }
1120: mumps->id.nrhs = nrhs;
1121: mumps->id.nloc_rhs = mumps->nloc_rhs;
1122: mumps->id.lrhs_loc = mumps->nloc_rhs;
1123: mumps->id.irhs_loc = mumps->irhs_loc;
1124: PetscFunctionReturn(PETSC_SUCCESS);
1125: }
1127: PetscErrorCode MatSolve_MUMPS(Mat A, Vec b, Vec x)
1128: {
1129: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1130: const PetscScalar *rarray = NULL;
1131: PetscScalar *array;
1132: IS is_iden, is_petsc;
1133: PetscInt i;
1134: PetscBool second_solve = PETSC_FALSE;
1135: static PetscBool cite1 = PETSC_FALSE, cite2 = PETSC_FALSE;
1137: PetscFunctionBegin;
1138: PetscCall(PetscCitationsRegister("@article{MUMPS01,\n author = {P.~R. Amestoy and I.~S. Duff and J.-Y. L'Excellent and J. Koster},\n title = {A fully asynchronous multifrontal solver using distributed dynamic scheduling},\n journal = {SIAM "
1139: "Journal on Matrix Analysis and Applications},\n volume = {23},\n number = {1},\n pages = {15--41},\n year = {2001}\n}\n",
1140: &cite1));
1141: PetscCall(PetscCitationsRegister("@article{MUMPS02,\n author = {P.~R. Amestoy and A. Guermouche and J.-Y. L'Excellent and S. Pralet},\n title = {Hybrid scheduling for the parallel solution of linear systems},\n journal = {Parallel "
1142: "Computing},\n volume = {32},\n number = {2},\n pages = {136--156},\n year = {2006}\n}\n",
1143: &cite2));
1145: if (A->factorerrortype) {
1146: PetscCall(PetscInfo(A, "MatSolve is called with singular matrix factor, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1147: PetscCall(VecSetInf(x));
1148: PetscFunctionReturn(PETSC_SUCCESS);
1149: }
1151: mumps->id.nrhs = 1;
1152: if (mumps->petsc_size > 1) {
1153: if (mumps->ICNTL20 == 10) {
1154: mumps->id.ICNTL(20) = 10; /* dense distributed RHS */
1155: PetscCall(VecGetArrayRead(b, &rarray));
1156: PetscCall(MatMumpsSetUpDistRHSInfo(A, 1, rarray));
1157: } else {
1158: mumps->id.ICNTL(20) = 0; /* dense centralized RHS; Scatter b into a sequential rhs vector*/
1159: PetscCall(VecScatterBegin(mumps->scat_rhs, b, mumps->b_seq, INSERT_VALUES, SCATTER_FORWARD));
1160: PetscCall(VecScatterEnd(mumps->scat_rhs, b, mumps->b_seq, INSERT_VALUES, SCATTER_FORWARD));
1161: if (!mumps->myid) {
1162: PetscCall(VecGetArray(mumps->b_seq, &array));
1163: mumps->id.rhs = (MumpsScalar *)array;
1164: }
1165: }
1166: } else { /* petsc_size == 1 */
1167: mumps->id.ICNTL(20) = 0; /* dense centralized RHS */
1168: PetscCall(VecCopy(b, x));
1169: PetscCall(VecGetArray(x, &array));
1170: mumps->id.rhs = (MumpsScalar *)array;
1171: }
1173: /*
1174: handle condensation step of Schur complement (if any)
1175: We set by default ICNTL(26) == -1 when Schur indices have been provided by the user.
1176: According to MUMPS (5.0.0) manual, any value should be harmful during the factorization phase
1177: Unless the user provides a valid value for ICNTL(26), MatSolve and MatMatSolve routines solve the full system.
1178: This requires an extra call to PetscMUMPS_c and the computation of the factors for S
1179: */
1180: if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) {
1181: PetscCheck(mumps->petsc_size <= 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Parallel Schur complements not yet supported from PETSc");
1182: second_solve = PETSC_TRUE;
1183: PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1184: }
1185: /* solve phase */
1186: mumps->id.job = JOB_SOLVE;
1187: PetscMUMPS_c(mumps);
1188: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MUMPS in solve phase: INFOG(1)=%d", mumps->id.INFOG(1));
1190: /* handle expansion step of Schur complement (if any) */
1191: if (second_solve) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_TRUE));
1193: if (mumps->petsc_size > 1) { /* convert mumps distributed solution to petsc mpi x */
1194: if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) {
1195: /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */
1196: PetscCall(VecScatterDestroy(&mumps->scat_sol));
1197: }
1198: if (!mumps->scat_sol) { /* create scatter scat_sol */
1199: PetscInt *isol2_loc = NULL;
1200: PetscCall(ISCreateStride(PETSC_COMM_SELF, mumps->id.lsol_loc, 0, 1, &is_iden)); /* from */
1201: PetscCall(PetscMalloc1(mumps->id.lsol_loc, &isol2_loc));
1202: for (i = 0; i < mumps->id.lsol_loc; i++) isol2_loc[i] = mumps->id.isol_loc[i] - 1; /* change Fortran style to C style */
1203: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, mumps->id.lsol_loc, isol2_loc, PETSC_OWN_POINTER, &is_petsc)); /* to */
1204: PetscCall(VecScatterCreate(mumps->x_seq, is_iden, x, is_petsc, &mumps->scat_sol));
1205: PetscCall(ISDestroy(&is_iden));
1206: PetscCall(ISDestroy(&is_petsc));
1207: mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */
1208: }
1210: PetscCall(VecScatterBegin(mumps->scat_sol, mumps->x_seq, x, INSERT_VALUES, SCATTER_FORWARD));
1211: PetscCall(VecScatterEnd(mumps->scat_sol, mumps->x_seq, x, INSERT_VALUES, SCATTER_FORWARD));
1212: }
1214: if (mumps->petsc_size > 1) {
1215: if (mumps->ICNTL20 == 10) {
1216: PetscCall(VecRestoreArrayRead(b, &rarray));
1217: } else if (!mumps->myid) {
1218: PetscCall(VecRestoreArray(mumps->b_seq, &array));
1219: }
1220: } else PetscCall(VecRestoreArray(x, &array));
1222: PetscCall(PetscLogFlops(2.0 * mumps->id.RINFO(3)));
1223: PetscFunctionReturn(PETSC_SUCCESS);
1224: }
1226: PetscErrorCode MatSolveTranspose_MUMPS(Mat A, Vec b, Vec x)
1227: {
1228: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1230: PetscFunctionBegin;
1231: mumps->id.ICNTL(9) = 0;
1232: PetscCall(MatSolve_MUMPS(A, b, x));
1233: mumps->id.ICNTL(9) = 1;
1234: PetscFunctionReturn(PETSC_SUCCESS);
1235: }
1237: PetscErrorCode MatMatSolve_MUMPS(Mat A, Mat B, Mat X)
1238: {
1239: Mat Bt = NULL;
1240: PetscBool denseX, denseB, flg, flgT;
1241: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1242: PetscInt i, nrhs, M;
1243: PetscScalar *array;
1244: const PetscScalar *rbray;
1245: PetscInt lsol_loc, nlsol_loc, *idxx, iidx = 0;
1246: PetscMUMPSInt *isol_loc, *isol_loc_save;
1247: PetscScalar *bray, *sol_loc, *sol_loc_save;
1248: IS is_to, is_from;
1249: PetscInt k, proc, j, m, myrstart;
1250: const PetscInt *rstart;
1251: Vec v_mpi, msol_loc;
1252: VecScatter scat_sol;
1253: Vec b_seq;
1254: VecScatter scat_rhs;
1255: PetscScalar *aa;
1256: PetscInt spnr, *ia, *ja;
1257: Mat_MPIAIJ *b = NULL;
1259: PetscFunctionBegin;
1260: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &denseX, MATSEQDENSE, MATMPIDENSE, NULL));
1261: PetscCheck(denseX, PetscObjectComm((PetscObject)X), PETSC_ERR_ARG_WRONG, "Matrix X must be MATDENSE matrix");
1263: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &denseB, MATSEQDENSE, MATMPIDENSE, NULL));
1264: if (denseB) {
1265: PetscCheck(B->rmap->n == X->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Matrix B and X must have same row distribution");
1266: mumps->id.ICNTL(20) = 0; /* dense RHS */
1267: } else { /* sparse B */
1268: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
1269: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATTRANSPOSEVIRTUAL, &flgT));
1270: if (flgT) { /* input B is transpose of actual RHS matrix,
1271: because mumps requires sparse compressed COLUMN storage! See MatMatTransposeSolve_MUMPS() */
1272: PetscCall(MatTransposeGetMat(B, &Bt));
1273: } else SETERRQ(PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Matrix B must be MATTRANSPOSEVIRTUAL matrix");
1274: mumps->id.ICNTL(20) = 1; /* sparse RHS */
1275: }
1277: PetscCall(MatGetSize(B, &M, &nrhs));
1278: mumps->id.nrhs = nrhs;
1279: mumps->id.lrhs = M;
1280: mumps->id.rhs = NULL;
1282: if (mumps->petsc_size == 1) {
1283: PetscScalar *aa;
1284: PetscInt spnr, *ia, *ja;
1285: PetscBool second_solve = PETSC_FALSE;
1287: PetscCall(MatDenseGetArray(X, &array));
1288: mumps->id.rhs = (MumpsScalar *)array;
1290: if (denseB) {
1291: /* copy B to X */
1292: PetscCall(MatDenseGetArrayRead(B, &rbray));
1293: PetscCall(PetscArraycpy(array, rbray, M * nrhs));
1294: PetscCall(MatDenseRestoreArrayRead(B, &rbray));
1295: } else { /* sparse B */
1296: PetscCall(MatSeqAIJGetArray(Bt, &aa));
1297: PetscCall(MatGetRowIJ(Bt, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1298: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
1299: PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
1300: mumps->id.rhs_sparse = (MumpsScalar *)aa;
1301: }
1302: /* handle condensation step of Schur complement (if any) */
1303: if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) {
1304: second_solve = PETSC_TRUE;
1305: PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1306: }
1307: /* solve phase */
1308: mumps->id.job = JOB_SOLVE;
1309: PetscMUMPS_c(mumps);
1310: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MUMPS in solve phase: INFOG(1)=%d", mumps->id.INFOG(1));
1312: /* handle expansion step of Schur complement (if any) */
1313: if (second_solve) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_TRUE));
1314: if (!denseB) { /* sparse B */
1315: PetscCall(MatSeqAIJRestoreArray(Bt, &aa));
1316: PetscCall(MatRestoreRowIJ(Bt, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1317: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot restore IJ structure");
1318: }
1319: PetscCall(MatDenseRestoreArray(X, &array));
1320: PetscFunctionReturn(PETSC_SUCCESS);
1321: }
1323: /* parallel case: MUMPS requires rhs B to be centralized on the host! */
1324: PetscCheck(mumps->petsc_size <= 1 || !mumps->id.ICNTL(19), PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Parallel Schur complements not yet supported from PETSc");
1326: /* create msol_loc to hold mumps local solution */
1327: isol_loc_save = mumps->id.isol_loc; /* save it for MatSolve() */
1328: sol_loc_save = (PetscScalar *)mumps->id.sol_loc;
1330: lsol_loc = mumps->id.lsol_loc;
1331: nlsol_loc = nrhs * lsol_loc; /* length of sol_loc */
1332: PetscCall(PetscMalloc2(nlsol_loc, &sol_loc, lsol_loc, &isol_loc));
1333: mumps->id.sol_loc = (MumpsScalar *)sol_loc;
1334: mumps->id.isol_loc = isol_loc;
1336: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, nlsol_loc, (PetscScalar *)sol_loc, &msol_loc));
1338: if (denseB) {
1339: if (mumps->ICNTL20 == 10) {
1340: mumps->id.ICNTL(20) = 10; /* dense distributed RHS */
1341: PetscCall(MatDenseGetArrayRead(B, &rbray));
1342: PetscCall(MatMumpsSetUpDistRHSInfo(A, nrhs, rbray));
1343: PetscCall(MatDenseRestoreArrayRead(B, &rbray));
1344: PetscCall(MatGetLocalSize(B, &m, NULL));
1345: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B), 1, nrhs * m, nrhs * M, NULL, &v_mpi));
1346: } else {
1347: mumps->id.ICNTL(20) = 0; /* dense centralized RHS */
1348: /* TODO: Because of non-contiguous indices, the created vecscatter scat_rhs is not done in MPI_Gather, resulting in
1349: very inefficient communication. An optimization is to use VecScatterCreateToZero to gather B to rank 0. Then on rank
1350: 0, re-arrange B into desired order, which is a local operation.
1351: */
1353: /* scatter v_mpi to b_seq because MUMPS before 5.3.0 only supports centralized rhs */
1354: /* wrap dense rhs matrix B into a vector v_mpi */
1355: PetscCall(MatGetLocalSize(B, &m, NULL));
1356: PetscCall(MatDenseGetArray(B, &bray));
1357: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B), 1, nrhs * m, nrhs * M, (const PetscScalar *)bray, &v_mpi));
1358: PetscCall(MatDenseRestoreArray(B, &bray));
1360: /* scatter v_mpi to b_seq in proc[0]. MUMPS requires rhs to be centralized on the host! */
1361: if (!mumps->myid) {
1362: PetscInt *idx;
1363: /* idx: maps from k-th index of v_mpi to (i,j)-th global entry of B */
1364: PetscCall(PetscMalloc1(nrhs * M, &idx));
1365: PetscCall(MatGetOwnershipRanges(B, &rstart));
1366: k = 0;
1367: for (proc = 0; proc < mumps->petsc_size; proc++) {
1368: for (j = 0; j < nrhs; j++) {
1369: for (i = rstart[proc]; i < rstart[proc + 1]; i++) idx[k++] = j * M + i;
1370: }
1371: }
1373: PetscCall(VecCreateSeq(PETSC_COMM_SELF, nrhs * M, &b_seq));
1374: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nrhs * M, idx, PETSC_OWN_POINTER, &is_to));
1375: PetscCall(ISCreateStride(PETSC_COMM_SELF, nrhs * M, 0, 1, &is_from));
1376: } else {
1377: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 0, &b_seq));
1378: PetscCall(ISCreateStride(PETSC_COMM_SELF, 0, 0, 1, &is_to));
1379: PetscCall(ISCreateStride(PETSC_COMM_SELF, 0, 0, 1, &is_from));
1380: }
1381: PetscCall(VecScatterCreate(v_mpi, is_from, b_seq, is_to, &scat_rhs));
1382: PetscCall(VecScatterBegin(scat_rhs, v_mpi, b_seq, INSERT_VALUES, SCATTER_FORWARD));
1383: PetscCall(ISDestroy(&is_to));
1384: PetscCall(ISDestroy(&is_from));
1385: PetscCall(VecScatterEnd(scat_rhs, v_mpi, b_seq, INSERT_VALUES, SCATTER_FORWARD));
1387: if (!mumps->myid) { /* define rhs on the host */
1388: PetscCall(VecGetArray(b_seq, &bray));
1389: mumps->id.rhs = (MumpsScalar *)bray;
1390: PetscCall(VecRestoreArray(b_seq, &bray));
1391: }
1392: }
1393: } else { /* sparse B */
1394: b = (Mat_MPIAIJ *)Bt->data;
1396: /* wrap dense X into a vector v_mpi */
1397: PetscCall(MatGetLocalSize(X, &m, NULL));
1398: PetscCall(MatDenseGetArray(X, &bray));
1399: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)X), 1, nrhs * m, nrhs * M, (const PetscScalar *)bray, &v_mpi));
1400: PetscCall(MatDenseRestoreArray(X, &bray));
1402: if (!mumps->myid) {
1403: PetscCall(MatSeqAIJGetArray(b->A, &aa));
1404: PetscCall(MatGetRowIJ(b->A, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1405: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
1406: PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
1407: mumps->id.rhs_sparse = (MumpsScalar *)aa;
1408: } else {
1409: mumps->id.irhs_ptr = NULL;
1410: mumps->id.irhs_sparse = NULL;
1411: mumps->id.nz_rhs = 0;
1412: mumps->id.rhs_sparse = NULL;
1413: }
1414: }
1416: /* solve phase */
1417: mumps->id.job = JOB_SOLVE;
1418: PetscMUMPS_c(mumps);
1419: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MUMPS in solve phase: INFOG(1)=%d", mumps->id.INFOG(1));
1421: /* scatter mumps distributed solution to petsc vector v_mpi, which shares local arrays with solution matrix X */
1422: PetscCall(MatDenseGetArray(X, &array));
1423: PetscCall(VecPlaceArray(v_mpi, array));
1425: /* create scatter scat_sol */
1426: PetscCall(MatGetOwnershipRanges(X, &rstart));
1427: /* iidx: index for scatter mumps solution to petsc X */
1429: PetscCall(ISCreateStride(PETSC_COMM_SELF, nlsol_loc, 0, 1, &is_from));
1430: PetscCall(PetscMalloc1(nlsol_loc, &idxx));
1431: for (i = 0; i < lsol_loc; i++) {
1432: isol_loc[i] -= 1; /* change Fortran style to C style. isol_loc[i+j*lsol_loc] contains x[isol_loc[i]] in j-th vector */
1434: for (proc = 0; proc < mumps->petsc_size; proc++) {
1435: if (isol_loc[i] >= rstart[proc] && isol_loc[i] < rstart[proc + 1]) {
1436: myrstart = rstart[proc];
1437: k = isol_loc[i] - myrstart; /* local index on 1st column of petsc vector X */
1438: iidx = k + myrstart * nrhs; /* maps mumps isol_loc[i] to petsc index in X */
1439: m = rstart[proc + 1] - rstart[proc]; /* rows of X for this proc */
1440: break;
1441: }
1442: }
1444: for (j = 0; j < nrhs; j++) idxx[i + j * lsol_loc] = iidx + j * m;
1445: }
1446: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nlsol_loc, idxx, PETSC_COPY_VALUES, &is_to));
1447: PetscCall(VecScatterCreate(msol_loc, is_from, v_mpi, is_to, &scat_sol));
1448: PetscCall(VecScatterBegin(scat_sol, msol_loc, v_mpi, INSERT_VALUES, SCATTER_FORWARD));
1449: PetscCall(ISDestroy(&is_from));
1450: PetscCall(ISDestroy(&is_to));
1451: PetscCall(VecScatterEnd(scat_sol, msol_loc, v_mpi, INSERT_VALUES, SCATTER_FORWARD));
1452: PetscCall(MatDenseRestoreArray(X, &array));
1454: /* free spaces */
1455: mumps->id.sol_loc = (MumpsScalar *)sol_loc_save;
1456: mumps->id.isol_loc = isol_loc_save;
1458: PetscCall(PetscFree2(sol_loc, isol_loc));
1459: PetscCall(PetscFree(idxx));
1460: PetscCall(VecDestroy(&msol_loc));
1461: PetscCall(VecDestroy(&v_mpi));
1462: if (!denseB) {
1463: if (!mumps->myid) {
1464: b = (Mat_MPIAIJ *)Bt->data;
1465: PetscCall(MatSeqAIJRestoreArray(b->A, &aa));
1466: PetscCall(MatRestoreRowIJ(b->A, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1467: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot restore IJ structure");
1468: }
1469: } else {
1470: if (mumps->ICNTL20 == 0) {
1471: PetscCall(VecDestroy(&b_seq));
1472: PetscCall(VecScatterDestroy(&scat_rhs));
1473: }
1474: }
1475: PetscCall(VecScatterDestroy(&scat_sol));
1476: PetscCall(PetscLogFlops(2.0 * nrhs * mumps->id.RINFO(3)));
1477: PetscFunctionReturn(PETSC_SUCCESS);
1478: }
1480: PetscErrorCode MatMatSolveTranspose_MUMPS(Mat A, Mat B, Mat X)
1481: {
1482: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1483: PetscMUMPSInt oldvalue = mumps->id.ICNTL(9);
1485: PetscFunctionBegin;
1486: mumps->id.ICNTL(9) = 0;
1487: PetscCall(MatMatSolve_MUMPS(A, B, X));
1488: mumps->id.ICNTL(9) = oldvalue;
1489: PetscFunctionReturn(PETSC_SUCCESS);
1490: }
1492: PetscErrorCode MatMatTransposeSolve_MUMPS(Mat A, Mat Bt, Mat X)
1493: {
1494: PetscBool flg;
1495: Mat B;
1497: PetscFunctionBegin;
1498: PetscCall(PetscObjectTypeCompareAny((PetscObject)Bt, &flg, MATSEQAIJ, MATMPIAIJ, NULL));
1499: PetscCheck(flg, PetscObjectComm((PetscObject)Bt), PETSC_ERR_ARG_WRONG, "Matrix Bt must be MATAIJ matrix");
1501: /* Create B=Bt^T that uses Bt's data structure */
1502: PetscCall(MatCreateTranspose(Bt, &B));
1504: PetscCall(MatMatSolve_MUMPS(A, B, X));
1505: PetscCall(MatDestroy(&B));
1506: PetscFunctionReturn(PETSC_SUCCESS);
1507: }
1509: #if !defined(PETSC_USE_COMPLEX)
1510: /*
1511: input:
1512: F: numeric factor
1513: output:
1514: nneg: total number of negative pivots
1515: nzero: total number of zero pivots
1516: npos: (global dimension of F) - nneg - nzero
1517: */
1518: PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
1519: {
1520: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
1521: PetscMPIInt size;
1523: PetscFunctionBegin;
1524: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)F), &size));
1525: /* MUMPS 4.3.1 calls ScaLAPACK when ICNTL(13)=0 (default), which does not offer the possibility to compute the inertia of a dense matrix. Set ICNTL(13)=1 to skip ScaLAPACK */
1526: PetscCheck(size <= 1 || mumps->id.ICNTL(13) == 1, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "ICNTL(13)=%d. -mat_mumps_icntl_13 must be set as 1 for correct global matrix inertia", mumps->id.INFOG(13));
1528: if (nneg) *nneg = mumps->id.INFOG(12);
1529: if (nzero || npos) {
1530: PetscCheck(mumps->id.ICNTL(24) == 1, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "-mat_mumps_icntl_24 must be set as 1 for null pivot row detection");
1531: if (nzero) *nzero = mumps->id.INFOG(28);
1532: if (npos) *npos = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28));
1533: }
1534: PetscFunctionReturn(PETSC_SUCCESS);
1535: }
1536: #endif
1538: PetscErrorCode MatMumpsGatherNonzerosOnMaster(MatReuse reuse, Mat_MUMPS *mumps)
1539: {
1540: PetscInt i, nreqs;
1541: PetscMUMPSInt *irn, *jcn;
1542: PetscMPIInt count;
1543: PetscInt64 totnnz, remain;
1544: const PetscInt osize = mumps->omp_comm_size;
1545: PetscScalar *val;
1547: PetscFunctionBegin;
1548: if (osize > 1) {
1549: if (reuse == MAT_INITIAL_MATRIX) {
1550: /* master first gathers counts of nonzeros to receive */
1551: if (mumps->is_omp_master) PetscCall(PetscMalloc1(osize, &mumps->recvcount));
1552: PetscCallMPI(MPI_Gather(&mumps->nnz, 1, MPIU_INT64, mumps->recvcount, 1, MPIU_INT64, 0 /*master*/, mumps->omp_comm));
1554: /* Then each computes number of send/recvs */
1555: if (mumps->is_omp_master) {
1556: /* Start from 1 since self communication is not done in MPI */
1557: nreqs = 0;
1558: for (i = 1; i < osize; i++) nreqs += (mumps->recvcount[i] + PETSC_MPI_INT_MAX - 1) / PETSC_MPI_INT_MAX;
1559: } else {
1560: nreqs = (mumps->nnz + PETSC_MPI_INT_MAX - 1) / PETSC_MPI_INT_MAX;
1561: }
1562: PetscCall(PetscMalloc1(nreqs * 3, &mumps->reqs)); /* Triple the requests since we send irn, jcn and val separately */
1564: /* The following code is doing a very simple thing: omp_master rank gathers irn/jcn/val from others.
1565: MPI_Gatherv would be enough if it supports big counts > 2^31-1. Since it does not, and mumps->nnz
1566: might be a prime number > 2^31-1, we have to slice the message. Note omp_comm_size
1567: is very small, the current approach should have no extra overhead compared to MPI_Gatherv.
1568: */
1569: nreqs = 0; /* counter for actual send/recvs */
1570: if (mumps->is_omp_master) {
1571: for (i = 0, totnnz = 0; i < osize; i++) totnnz += mumps->recvcount[i]; /* totnnz = sum of nnz over omp_comm */
1572: PetscCall(PetscMalloc2(totnnz, &irn, totnnz, &jcn));
1573: PetscCall(PetscMalloc1(totnnz, &val));
1575: /* Self communication */
1576: PetscCall(PetscArraycpy(irn, mumps->irn, mumps->nnz));
1577: PetscCall(PetscArraycpy(jcn, mumps->jcn, mumps->nnz));
1578: PetscCall(PetscArraycpy(val, mumps->val, mumps->nnz));
1580: /* Replace mumps->irn/jcn etc on master with the newly allocated bigger arrays */
1581: PetscCall(PetscFree2(mumps->irn, mumps->jcn));
1582: PetscCall(PetscFree(mumps->val_alloc));
1583: mumps->nnz = totnnz;
1584: mumps->irn = irn;
1585: mumps->jcn = jcn;
1586: mumps->val = mumps->val_alloc = val;
1588: irn += mumps->recvcount[0]; /* recvcount[0] is old mumps->nnz on omp rank 0 */
1589: jcn += mumps->recvcount[0];
1590: val += mumps->recvcount[0];
1592: /* Remote communication */
1593: for (i = 1; i < osize; i++) {
1594: count = PetscMin(mumps->recvcount[i], PETSC_MPI_INT_MAX);
1595: remain = mumps->recvcount[i] - count;
1596: while (count > 0) {
1597: PetscCallMPI(MPI_Irecv(irn, count, MPIU_MUMPSINT, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1598: PetscCallMPI(MPI_Irecv(jcn, count, MPIU_MUMPSINT, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1599: PetscCallMPI(MPI_Irecv(val, count, MPIU_SCALAR, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1600: irn += count;
1601: jcn += count;
1602: val += count;
1603: count = PetscMin(remain, PETSC_MPI_INT_MAX);
1604: remain -= count;
1605: }
1606: }
1607: } else {
1608: irn = mumps->irn;
1609: jcn = mumps->jcn;
1610: val = mumps->val;
1611: count = PetscMin(mumps->nnz, PETSC_MPI_INT_MAX);
1612: remain = mumps->nnz - count;
1613: while (count > 0) {
1614: PetscCallMPI(MPI_Isend(irn, count, MPIU_MUMPSINT, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1615: PetscCallMPI(MPI_Isend(jcn, count, MPIU_MUMPSINT, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1616: PetscCallMPI(MPI_Isend(val, count, MPIU_SCALAR, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1617: irn += count;
1618: jcn += count;
1619: val += count;
1620: count = PetscMin(remain, PETSC_MPI_INT_MAX);
1621: remain -= count;
1622: }
1623: }
1624: } else {
1625: nreqs = 0;
1626: if (mumps->is_omp_master) {
1627: val = mumps->val + mumps->recvcount[0];
1628: for (i = 1; i < osize; i++) { /* Remote communication only since self data is already in place */
1629: count = PetscMin(mumps->recvcount[i], PETSC_MPI_INT_MAX);
1630: remain = mumps->recvcount[i] - count;
1631: while (count > 0) {
1632: PetscCallMPI(MPI_Irecv(val, count, MPIU_SCALAR, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1633: val += count;
1634: count = PetscMin(remain, PETSC_MPI_INT_MAX);
1635: remain -= count;
1636: }
1637: }
1638: } else {
1639: val = mumps->val;
1640: count = PetscMin(mumps->nnz, PETSC_MPI_INT_MAX);
1641: remain = mumps->nnz - count;
1642: while (count > 0) {
1643: PetscCallMPI(MPI_Isend(val, count, MPIU_SCALAR, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1644: val += count;
1645: count = PetscMin(remain, PETSC_MPI_INT_MAX);
1646: remain -= count;
1647: }
1648: }
1649: }
1650: PetscCallMPI(MPI_Waitall(nreqs, mumps->reqs, MPI_STATUSES_IGNORE));
1651: mumps->tag++; /* It is totally fine for above send/recvs to share one mpi tag */
1652: }
1653: PetscFunctionReturn(PETSC_SUCCESS);
1654: }
1656: PetscErrorCode MatFactorNumeric_MUMPS(Mat F, Mat A, const MatFactorInfo *info)
1657: {
1658: Mat_MUMPS *mumps = (Mat_MUMPS *)(F)->data;
1659: PetscBool isMPIAIJ;
1661: PetscFunctionBegin;
1662: if (mumps->id.INFOG(1) < 0 && !(mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0)) {
1663: if (mumps->id.INFOG(1) == -6) PetscCall(PetscInfo(A, "MatFactorNumeric is called with singular matrix structure, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1664: PetscCall(PetscInfo(A, "MatFactorNumeric is called after analysis phase fails, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1665: PetscFunctionReturn(PETSC_SUCCESS);
1666: }
1668: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, mumps));
1669: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_REUSE_MATRIX, mumps));
1671: /* numerical factorization phase */
1672: mumps->id.job = JOB_FACTNUMERIC;
1673: if (!mumps->id.ICNTL(18)) { /* A is centralized */
1674: if (!mumps->myid) mumps->id.a = (MumpsScalar *)mumps->val;
1675: } else {
1676: mumps->id.a_loc = (MumpsScalar *)mumps->val;
1677: }
1678: PetscMUMPS_c(mumps);
1679: if (mumps->id.INFOG(1) < 0) {
1680: PetscCheck(!A->erroriffailure, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d", mumps->id.INFOG(1), mumps->id.INFO(2));
1681: if (mumps->id.INFOG(1) == -10) { /* numerically singular matrix */
1682: PetscCall(PetscInfo(F, "matrix is numerically singular, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1683: F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1684: } else if (mumps->id.INFOG(1) == -13) {
1685: PetscCall(PetscInfo(F, "MUMPS in numerical factorization phase: INFOG(1)=%d, cannot allocate required memory %d megabytes\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1686: F->factorerrortype = MAT_FACTOR_OUTMEMORY;
1687: } else if (mumps->id.INFOG(1) == -8 || mumps->id.INFOG(1) == -9 || (-16 < mumps->id.INFOG(1) && mumps->id.INFOG(1) < -10)) {
1688: PetscCall(PetscInfo(F, "MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d, problem with workarray\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1689: F->factorerrortype = MAT_FACTOR_OUTMEMORY;
1690: } else {
1691: PetscCall(PetscInfo(F, "MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1692: F->factorerrortype = MAT_FACTOR_OTHER;
1693: }
1694: }
1695: PetscCheck(mumps->myid || mumps->id.ICNTL(16) <= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, " mumps->id.ICNTL(16):=%d", mumps->id.INFOG(16));
1697: F->assembled = PETSC_TRUE;
1699: if (F->schur) { /* reset Schur status to unfactored */
1700: #if defined(PETSC_HAVE_CUDA)
1701: F->schur->offloadmask = PETSC_OFFLOAD_CPU;
1702: #endif
1703: if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
1704: mumps->id.ICNTL(19) = 2;
1705: PetscCall(MatTranspose(F->schur, MAT_INPLACE_MATRIX, &F->schur));
1706: }
1707: PetscCall(MatFactorRestoreSchurComplement(F, NULL, MAT_FACTOR_SCHUR_UNFACTORED));
1708: }
1710: /* just to be sure that ICNTL(19) value returned by a call from MatMumpsGetIcntl is always consistent */
1711: if (!mumps->sym && mumps->id.ICNTL(19) && mumps->id.ICNTL(19) != 1) mumps->id.ICNTL(19) = 3;
1713: if (!mumps->is_omp_master) mumps->id.INFO(23) = 0;
1714: if (mumps->petsc_size > 1) {
1715: PetscInt lsol_loc;
1716: PetscScalar *sol_loc;
1718: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &isMPIAIJ));
1720: /* distributed solution; Create x_seq=sol_loc for repeated use */
1721: if (mumps->x_seq) {
1722: PetscCall(VecScatterDestroy(&mumps->scat_sol));
1723: PetscCall(PetscFree2(mumps->id.sol_loc, mumps->id.isol_loc));
1724: PetscCall(VecDestroy(&mumps->x_seq));
1725: }
1726: lsol_loc = mumps->id.INFO(23); /* length of sol_loc */
1727: PetscCall(PetscMalloc2(lsol_loc, &sol_loc, lsol_loc, &mumps->id.isol_loc));
1728: mumps->id.lsol_loc = lsol_loc;
1729: mumps->id.sol_loc = (MumpsScalar *)sol_loc;
1730: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, lsol_loc, sol_loc, &mumps->x_seq));
1731: }
1732: PetscCall(PetscLogFlops(mumps->id.RINFO(2)));
1733: PetscFunctionReturn(PETSC_SUCCESS);
1734: }
1736: /* Sets MUMPS options from the options database */
1737: PetscErrorCode MatSetFromOptions_MUMPS(Mat F, Mat A)
1738: {
1739: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
1740: PetscMUMPSInt icntl = 0, size, *listvar_schur;
1741: PetscInt info[80], i, ninfo = 80, rbs, cbs;
1742: PetscBool flg = PETSC_FALSE, schur = (PetscBool)(mumps->id.ICNTL(26) == -1);
1743: MumpsScalar *arr;
1745: PetscFunctionBegin;
1746: PetscOptionsBegin(PetscObjectComm((PetscObject)F), ((PetscObject)F)->prefix, "MUMPS Options", "Mat");
1747: if (mumps->id.job == JOB_NULL) { /* MatSetFromOptions_MUMPS() has never been called before */
1748: PetscInt nthreads = 0;
1749: PetscInt nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
1750: PetscInt nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0;
1752: mumps->petsc_comm = PetscObjectComm((PetscObject)A);
1753: PetscCallMPI(MPI_Comm_size(mumps->petsc_comm, &mumps->petsc_size));
1754: PetscCallMPI(MPI_Comm_rank(mumps->petsc_comm, &mumps->myid)); /* "if (!myid)" still works even if mumps_comm is different */
1756: PetscCall(PetscOptionsName("-mat_mumps_use_omp_threads", "Convert MPI processes into OpenMP threads", "None", &mumps->use_petsc_omp_support));
1757: if (mumps->use_petsc_omp_support) nthreads = -1; /* -1 will let PetscOmpCtrlCreate() guess a proper value when user did not supply one */
1758: /* do not use PetscOptionsInt() so that the option -mat_mumps_use_omp_threads is not displayed twice in the help */
1759: PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)F)->prefix, "-mat_mumps_use_omp_threads", &nthreads, NULL));
1760: if (mumps->use_petsc_omp_support) {
1761: PetscCheck(PetscDefined(HAVE_OPENMP_SUPPORT), PETSC_COMM_SELF, PETSC_ERR_SUP_SYS, "The system does not have PETSc OpenMP support but you added the -%smat_mumps_use_omp_threads option. Configure PETSc with --with-openmp --download-hwloc (or --with-hwloc) to enable it, see more in MATSOLVERMUMPS manual",
1762: ((PetscObject)F)->prefix ? ((PetscObject)F)->prefix : "");
1763: PetscCheck(!schur, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot use -%smat_mumps_use_omp_threads with the Schur complement feature", ((PetscObject)F)->prefix ? ((PetscObject)F)->prefix : "");
1764: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
1765: PetscCall(PetscOmpCtrlCreate(mumps->petsc_comm, nthreads, &mumps->omp_ctrl));
1766: PetscCall(PetscOmpCtrlGetOmpComms(mumps->omp_ctrl, &mumps->omp_comm, &mumps->mumps_comm, &mumps->is_omp_master));
1767: #endif
1768: } else {
1769: mumps->omp_comm = PETSC_COMM_SELF;
1770: mumps->mumps_comm = mumps->petsc_comm;
1771: mumps->is_omp_master = PETSC_TRUE;
1772: }
1773: PetscCallMPI(MPI_Comm_size(mumps->omp_comm, &mumps->omp_comm_size));
1774: mumps->reqs = NULL;
1775: mumps->tag = 0;
1777: if (mumps->mumps_comm != MPI_COMM_NULL) {
1778: if (PetscDefined(HAVE_OPENMP_SUPPORT) && mumps->use_petsc_omp_support) {
1779: /* It looks like MUMPS does not dup the input comm. Dup a new comm for MUMPS to avoid any tag mismatches. */
1780: MPI_Comm comm;
1781: PetscCallMPI(MPI_Comm_dup(mumps->mumps_comm, &comm));
1782: mumps->mumps_comm = comm;
1783: } else PetscCall(PetscCommGetComm(mumps->petsc_comm, &mumps->mumps_comm));
1784: }
1786: mumps->id.comm_fortran = MPI_Comm_c2f(mumps->mumps_comm);
1787: mumps->id.job = JOB_INIT;
1788: mumps->id.par = 1; /* host participates factorizaton and solve */
1789: mumps->id.sym = mumps->sym;
1791: size = mumps->id.size_schur;
1792: arr = mumps->id.schur;
1793: listvar_schur = mumps->id.listvar_schur;
1794: PetscMUMPS_c(mumps);
1795: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MUMPS: INFOG(1)=%d", mumps->id.INFOG(1));
1796: /* restore cached ICNTL and CNTL values */
1797: for (icntl = 0; icntl < nICNTL_pre; ++icntl) mumps->id.ICNTL(mumps->ICNTL_pre[1 + 2 * icntl]) = mumps->ICNTL_pre[2 + 2 * icntl];
1798: for (icntl = 0; icntl < nCNTL_pre; ++icntl) mumps->id.CNTL((PetscInt)mumps->CNTL_pre[1 + 2 * icntl]) = mumps->CNTL_pre[2 + 2 * icntl];
1799: PetscCall(PetscFree(mumps->ICNTL_pre));
1800: PetscCall(PetscFree(mumps->CNTL_pre));
1802: if (schur) {
1803: mumps->id.size_schur = size;
1804: mumps->id.schur_lld = size;
1805: mumps->id.schur = arr;
1806: mumps->id.listvar_schur = listvar_schur;
1807: if (mumps->petsc_size > 1) {
1808: PetscBool gs; /* gs is false if any rank other than root has non-empty IS */
1810: mumps->id.ICNTL(19) = 1; /* MUMPS returns Schur centralized on the host */
1811: gs = mumps->myid ? (mumps->id.size_schur ? PETSC_FALSE : PETSC_TRUE) : PETSC_TRUE; /* always true on root; false on others if their size != 0 */
1812: PetscCallMPI(MPI_Allreduce(MPI_IN_PLACE, &gs, 1, MPIU_BOOL, MPI_LAND, mumps->petsc_comm));
1813: PetscCheck(gs, PETSC_COMM_SELF, PETSC_ERR_SUP, "MUMPS distributed parallel Schur complements not yet supported from PETSc");
1814: } else {
1815: if (F->factortype == MAT_FACTOR_LU) {
1816: mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */
1817: } else {
1818: mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */
1819: }
1820: }
1821: mumps->id.ICNTL(26) = -1;
1822: }
1824: /* copy MUMPS default control values from master to slaves. Although slaves do not call MUMPS, they may access these values in code.
1825: For example, ICNTL(9) is initialized to 1 by MUMPS and slaves check ICNTL(9) in MatSolve_MUMPS.
1826: */
1827: PetscCallMPI(MPI_Bcast(mumps->id.icntl, 40, MPI_INT, 0, mumps->omp_comm));
1828: PetscCallMPI(MPI_Bcast(mumps->id.cntl, 15, MPIU_REAL, 0, mumps->omp_comm));
1830: mumps->scat_rhs = NULL;
1831: mumps->scat_sol = NULL;
1833: /* set PETSc-MUMPS default options - override MUMPS default */
1834: mumps->id.ICNTL(3) = 0;
1835: mumps->id.ICNTL(4) = 0;
1836: if (mumps->petsc_size == 1) {
1837: mumps->id.ICNTL(18) = 0; /* centralized assembled matrix input */
1838: mumps->id.ICNTL(7) = 7; /* automatic choice of ordering done by the package */
1839: } else {
1840: mumps->id.ICNTL(18) = 3; /* distributed assembled matrix input */
1841: mumps->id.ICNTL(21) = 1; /* distributed solution */
1842: }
1843: }
1844: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_1", "ICNTL(1): output stream for error messages", "None", mumps->id.ICNTL(1), &icntl, &flg));
1845: if (flg) mumps->id.ICNTL(1) = icntl;
1846: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_2", "ICNTL(2): output stream for diagnostic printing, statistics, and warning", "None", mumps->id.ICNTL(2), &icntl, &flg));
1847: if (flg) mumps->id.ICNTL(2) = icntl;
1848: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_3", "ICNTL(3): output stream for global information, collected on the host", "None", mumps->id.ICNTL(3), &icntl, &flg));
1849: if (flg) mumps->id.ICNTL(3) = icntl;
1851: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_4", "ICNTL(4): level of printing (0 to 4)", "None", mumps->id.ICNTL(4), &icntl, &flg));
1852: if (flg) mumps->id.ICNTL(4) = icntl;
1853: if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */
1855: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_6", "ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7)", "None", mumps->id.ICNTL(6), &icntl, &flg));
1856: if (flg) mumps->id.ICNTL(6) = icntl;
1858: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_7", "ICNTL(7): computes a symmetric permutation in sequential analysis. 0=AMD, 2=AMF, 3=Scotch, 4=PORD, 5=Metis, 6=QAMD, and 7=auto(default)", "None", mumps->id.ICNTL(7), &icntl, &flg));
1859: if (flg) {
1860: PetscCheck(icntl != 1 && icntl >= 0 && icntl <= 7, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Valid values are 0=AMD, 2=AMF, 3=Scotch, 4=PORD, 5=Metis, 6=QAMD, and 7=auto");
1861: mumps->id.ICNTL(7) = icntl;
1862: }
1864: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_8", "ICNTL(8): scaling strategy (-2 to 8 or 77)", "None", mumps->id.ICNTL(8), &mumps->id.ICNTL(8), NULL));
1865: /* PetscCall(PetscOptionsInt("-mat_mumps_icntl_9","ICNTL(9): computes the solution using A or A^T","None",mumps->id.ICNTL(9),&mumps->id.ICNTL(9),NULL)); handled by MatSolveTranspose_MUMPS() */
1866: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_10", "ICNTL(10): max num of refinements", "None", mumps->id.ICNTL(10), &mumps->id.ICNTL(10), NULL));
1867: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_11", "ICNTL(11): statistics related to an error analysis (via -ksp_view)", "None", mumps->id.ICNTL(11), &mumps->id.ICNTL(11), NULL));
1868: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_12", "ICNTL(12): an ordering strategy for symmetric matrices (0 to 3)", "None", mumps->id.ICNTL(12), &mumps->id.ICNTL(12), NULL));
1869: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_13", "ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting", "None", mumps->id.ICNTL(13), &mumps->id.ICNTL(13), NULL));
1870: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_14", "ICNTL(14): percentage increase in the estimated working space", "None", mumps->id.ICNTL(14), &mumps->id.ICNTL(14), NULL));
1871: PetscCall(MatGetBlockSizes(A, &rbs, &cbs));
1872: if (rbs == cbs && rbs > 1) mumps->id.ICNTL(15) = -rbs;
1873: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_15", "ICNTL(15): compression of the input matrix resulting from a block format", "None", mumps->id.ICNTL(15), &mumps->id.ICNTL(15), &flg));
1874: if (flg) {
1875: PetscCheck(mumps->id.ICNTL(15) <= 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Positive -mat_mumps_icntl_15 not handled");
1876: PetscCheck((-mumps->id.ICNTL(15) % cbs == 0) && (-mumps->id.ICNTL(15) % rbs == 0), PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "The opposite of -mat_mumps_icntl_15 must be a multiple of the column and row blocksizes");
1877: }
1878: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_19", "ICNTL(19): computes the Schur complement", "None", mumps->id.ICNTL(19), &mumps->id.ICNTL(19), NULL));
1879: if (mumps->id.ICNTL(19) <= 0 || mumps->id.ICNTL(19) > 3) { /* reset any schur data (if any) */
1880: PetscCall(MatDestroy(&F->schur));
1881: PetscCall(MatMumpsResetSchur_Private(mumps));
1882: }
1884: /* Two MPICH Fortran MPI_IN_PLACE binding bugs prevented the use of 'mpich + mumps'. One happened with "mpi4py + mpich + mumps",
1885: and was reported by Firedrake. See https://bitbucket.org/mpi4py/mpi4py/issues/162/mpi4py-initialization-breaks-fortran
1886: and a petsc-maint mailing list thread with subject 'MUMPS segfaults in parallel because of ...'
1887: This bug was fixed by https://github.com/pmodels/mpich/pull/4149. But the fix brought a new bug,
1888: see https://github.com/pmodels/mpich/issues/5589. This bug was fixed by https://github.com/pmodels/mpich/pull/5590.
1889: In short, we could not use distributed RHS with MPICH until v4.0b1.
1890: */
1891: #if PETSC_PKG_MUMPS_VERSION_LT(5, 3, 0) || (defined(PETSC_HAVE_MPICH_NUMVERSION) && (PETSC_HAVE_MPICH_NUMVERSION < 40000101))
1892: mumps->ICNTL20 = 0; /* Centralized dense RHS*/
1893: #else
1894: mumps->ICNTL20 = 10; /* Distributed dense RHS*/
1895: #endif
1896: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_20", "ICNTL(20): give mumps centralized (0) or distributed (10) dense right-hand sides", "None", mumps->ICNTL20, &mumps->ICNTL20, &flg));
1897: PetscCheck(!flg || mumps->ICNTL20 == 10 || mumps->ICNTL20 == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "ICNTL(20)=%d is not supported by the PETSc/MUMPS interface. Allowed values are 0, 10", (int)mumps->ICNTL20);
1898: #if PETSC_PKG_MUMPS_VERSION_LT(5, 3, 0)
1899: PetscCheck(!flg || mumps->ICNTL20 != 10, PETSC_COMM_SELF, PETSC_ERR_SUP, "ICNTL(20)=10 is not supported before MUMPS-5.3.0");
1900: #endif
1901: /* PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_21","ICNTL(21): the distribution (centralized or distributed) of the solution vectors","None",mumps->id.ICNTL(21),&mumps->id.ICNTL(21),NULL)); we only use distributed solution vector */
1903: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_22", "ICNTL(22): in-core/out-of-core factorization and solve (0 or 1)", "None", mumps->id.ICNTL(22), &mumps->id.ICNTL(22), NULL));
1904: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_23", "ICNTL(23): max size of the working memory (MB) that can allocate per processor", "None", mumps->id.ICNTL(23), &mumps->id.ICNTL(23), NULL));
1905: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_24", "ICNTL(24): detection of null pivot rows (0 or 1)", "None", mumps->id.ICNTL(24), &mumps->id.ICNTL(24), NULL));
1906: if (mumps->id.ICNTL(24)) { mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */ }
1908: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_25", "ICNTL(25): computes a solution of a deficient matrix and a null space basis", "None", mumps->id.ICNTL(25), &mumps->id.ICNTL(25), NULL));
1909: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_26", "ICNTL(26): drives the solution phase if a Schur complement matrix", "None", mumps->id.ICNTL(26), &mumps->id.ICNTL(26), NULL));
1910: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_27", "ICNTL(27): controls the blocking size for multiple right-hand sides", "None", mumps->id.ICNTL(27), &mumps->id.ICNTL(27), NULL));
1911: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_28", "ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering", "None", mumps->id.ICNTL(28), &mumps->id.ICNTL(28), NULL));
1912: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_29", "ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis", "None", mumps->id.ICNTL(29), &mumps->id.ICNTL(29), NULL));
1913: /* PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_30","ICNTL(30): compute user-specified set of entries in inv(A)","None",mumps->id.ICNTL(30),&mumps->id.ICNTL(30),NULL)); */ /* call MatMumpsGetInverse() directly */
1914: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_31", "ICNTL(31): indicates which factors may be discarded during factorization", "None", mumps->id.ICNTL(31), &mumps->id.ICNTL(31), NULL));
1915: /* PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_32","ICNTL(32): performs the forward elemination of the right-hand sides during factorization","None",mumps->id.ICNTL(32),&mumps->id.ICNTL(32),NULL)); -- not supported by PETSc API */
1916: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_33", "ICNTL(33): compute determinant", "None", mumps->id.ICNTL(33), &mumps->id.ICNTL(33), NULL));
1917: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_35", "ICNTL(35): activates Block Low Rank (BLR) based factorization", "None", mumps->id.ICNTL(35), &mumps->id.ICNTL(35), NULL));
1918: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_36", "ICNTL(36): choice of BLR factorization variant", "None", mumps->id.ICNTL(36), &mumps->id.ICNTL(36), NULL));
1919: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_38", "ICNTL(38): estimated compression rate of LU factors with BLR", "None", mumps->id.ICNTL(38), &mumps->id.ICNTL(38), NULL));
1921: PetscCall(PetscOptionsReal("-mat_mumps_cntl_1", "CNTL(1): relative pivoting threshold", "None", mumps->id.CNTL(1), &mumps->id.CNTL(1), NULL));
1922: PetscCall(PetscOptionsReal("-mat_mumps_cntl_2", "CNTL(2): stopping criterion of refinement", "None", mumps->id.CNTL(2), &mumps->id.CNTL(2), NULL));
1923: PetscCall(PetscOptionsReal("-mat_mumps_cntl_3", "CNTL(3): absolute pivoting threshold", "None", mumps->id.CNTL(3), &mumps->id.CNTL(3), NULL));
1924: PetscCall(PetscOptionsReal("-mat_mumps_cntl_4", "CNTL(4): value for static pivoting", "None", mumps->id.CNTL(4), &mumps->id.CNTL(4), NULL));
1925: PetscCall(PetscOptionsReal("-mat_mumps_cntl_5", "CNTL(5): fixation for null pivots", "None", mumps->id.CNTL(5), &mumps->id.CNTL(5), NULL));
1926: PetscCall(PetscOptionsReal("-mat_mumps_cntl_7", "CNTL(7): dropping parameter used during BLR", "None", mumps->id.CNTL(7), &mumps->id.CNTL(7), NULL));
1928: PetscCall(PetscOptionsString("-mat_mumps_ooc_tmpdir", "out of core directory", "None", mumps->id.ooc_tmpdir, mumps->id.ooc_tmpdir, sizeof(mumps->id.ooc_tmpdir), NULL));
1930: PetscCall(PetscOptionsIntArray("-mat_mumps_view_info", "request INFO local to each processor", "", info, &ninfo, NULL));
1931: if (ninfo) {
1932: PetscCheck(ninfo <= 80, PETSC_COMM_SELF, PETSC_ERR_USER, "number of INFO %" PetscInt_FMT " must <= 80", ninfo);
1933: PetscCall(PetscMalloc1(ninfo, &mumps->info));
1934: mumps->ninfo = ninfo;
1935: for (i = 0; i < ninfo; i++) {
1936: PetscCheck(info[i] >= 0 && info[i] <= 80, PETSC_COMM_SELF, PETSC_ERR_USER, "index of INFO %" PetscInt_FMT " must between 1 and 80", ninfo);
1937: mumps->info[i] = info[i];
1938: }
1939: }
1940: PetscOptionsEnd();
1941: PetscFunctionReturn(PETSC_SUCCESS);
1942: }
1944: PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F, Mat A, const MatFactorInfo *info, Mat_MUMPS *mumps)
1945: {
1946: PetscFunctionBegin;
1947: if (mumps->id.INFOG(1) < 0) {
1948: PetscCheck(!A->erroriffailure, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MUMPS in analysis phase: INFOG(1)=%d", mumps->id.INFOG(1));
1949: if (mumps->id.INFOG(1) == -6) {
1950: PetscCall(PetscInfo(F, "matrix is singular in structure, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1951: F->factorerrortype = MAT_FACTOR_STRUCT_ZEROPIVOT;
1952: } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) {
1953: PetscCall(PetscInfo(F, "problem of workspace, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1954: F->factorerrortype = MAT_FACTOR_OUTMEMORY;
1955: } else if (mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0) {
1956: PetscCall(PetscInfo(F, "Empty matrix\n"));
1957: } else {
1958: PetscCall(PetscInfo(F, "Error reported by MUMPS in analysis phase: INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1959: F->factorerrortype = MAT_FACTOR_OTHER;
1960: }
1961: }
1962: PetscFunctionReturn(PETSC_SUCCESS);
1963: }
1965: PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
1966: {
1967: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
1968: Vec b;
1969: const PetscInt M = A->rmap->N;
1971: PetscFunctionBegin;
1972: if (mumps->matstruc == SAME_NONZERO_PATTERN) {
1973: /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */
1974: PetscFunctionReturn(PETSC_SUCCESS);
1975: }
1977: /* Set MUMPS options from the options database */
1978: PetscCall(MatSetFromOptions_MUMPS(F, A));
1980: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
1981: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));
1983: /* analysis phase */
1984: mumps->id.job = JOB_FACTSYMBOLIC;
1985: mumps->id.n = M;
1986: switch (mumps->id.ICNTL(18)) {
1987: case 0: /* centralized assembled matrix input */
1988: if (!mumps->myid) {
1989: mumps->id.nnz = mumps->nnz;
1990: mumps->id.irn = mumps->irn;
1991: mumps->id.jcn = mumps->jcn;
1992: if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
1993: if (r) {
1994: mumps->id.ICNTL(7) = 1;
1995: if (!mumps->myid) {
1996: const PetscInt *idx;
1997: PetscInt i;
1999: PetscCall(PetscMalloc1(M, &mumps->id.perm_in));
2000: PetscCall(ISGetIndices(r, &idx));
2001: for (i = 0; i < M; i++) PetscCall(PetscMUMPSIntCast(idx[i] + 1, &(mumps->id.perm_in[i]))); /* perm_in[]: start from 1, not 0! */
2002: PetscCall(ISRestoreIndices(r, &idx));
2003: }
2004: }
2005: }
2006: break;
2007: case 3: /* distributed assembled matrix input (size>1) */
2008: mumps->id.nnz_loc = mumps->nnz;
2009: mumps->id.irn_loc = mumps->irn;
2010: mumps->id.jcn_loc = mumps->jcn;
2011: if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2012: if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2013: PetscCall(MatCreateVecs(A, NULL, &b));
2014: PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2015: PetscCall(VecDestroy(&b));
2016: }
2017: break;
2018: }
2019: PetscMUMPS_c(mumps);
2020: PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));
2022: F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
2023: F->ops->solve = MatSolve_MUMPS;
2024: F->ops->solvetranspose = MatSolveTranspose_MUMPS;
2025: F->ops->matsolve = MatMatSolve_MUMPS;
2026: F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS;
2027: F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;
2029: mumps->matstruc = SAME_NONZERO_PATTERN;
2030: PetscFunctionReturn(PETSC_SUCCESS);
2031: }
2033: /* Note the Petsc r and c permutations are ignored */
2034: PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
2035: {
2036: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2037: Vec b;
2038: const PetscInt M = A->rmap->N;
2040: PetscFunctionBegin;
2041: if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2042: /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */
2043: PetscFunctionReturn(PETSC_SUCCESS);
2044: }
2046: /* Set MUMPS options from the options database */
2047: PetscCall(MatSetFromOptions_MUMPS(F, A));
2049: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2050: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));
2052: /* analysis phase */
2053: mumps->id.job = JOB_FACTSYMBOLIC;
2054: mumps->id.n = M;
2055: switch (mumps->id.ICNTL(18)) {
2056: case 0: /* centralized assembled matrix input */
2057: if (!mumps->myid) {
2058: mumps->id.nnz = mumps->nnz;
2059: mumps->id.irn = mumps->irn;
2060: mumps->id.jcn = mumps->jcn;
2061: if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2062: }
2063: break;
2064: case 3: /* distributed assembled matrix input (size>1) */
2065: mumps->id.nnz_loc = mumps->nnz;
2066: mumps->id.irn_loc = mumps->irn;
2067: mumps->id.jcn_loc = mumps->jcn;
2068: if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2069: if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2070: PetscCall(MatCreateVecs(A, NULL, &b));
2071: PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2072: PetscCall(VecDestroy(&b));
2073: }
2074: break;
2075: }
2076: PetscMUMPS_c(mumps);
2077: PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));
2079: F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
2080: F->ops->solve = MatSolve_MUMPS;
2081: F->ops->solvetranspose = MatSolveTranspose_MUMPS;
2082: F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;
2084: mumps->matstruc = SAME_NONZERO_PATTERN;
2085: PetscFunctionReturn(PETSC_SUCCESS);
2086: }
2088: /* Note the Petsc r permutation and factor info are ignored */
2089: PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F, Mat A, IS r, const MatFactorInfo *info)
2090: {
2091: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2092: Vec b;
2093: const PetscInt M = A->rmap->N;
2095: PetscFunctionBegin;
2096: if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2097: /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */
2098: PetscFunctionReturn(PETSC_SUCCESS);
2099: }
2101: /* Set MUMPS options from the options database */
2102: PetscCall(MatSetFromOptions_MUMPS(F, A));
2104: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2105: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));
2107: /* analysis phase */
2108: mumps->id.job = JOB_FACTSYMBOLIC;
2109: mumps->id.n = M;
2110: switch (mumps->id.ICNTL(18)) {
2111: case 0: /* centralized assembled matrix input */
2112: if (!mumps->myid) {
2113: mumps->id.nnz = mumps->nnz;
2114: mumps->id.irn = mumps->irn;
2115: mumps->id.jcn = mumps->jcn;
2116: if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2117: }
2118: break;
2119: case 3: /* distributed assembled matrix input (size>1) */
2120: mumps->id.nnz_loc = mumps->nnz;
2121: mumps->id.irn_loc = mumps->irn;
2122: mumps->id.jcn_loc = mumps->jcn;
2123: if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2124: if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2125: PetscCall(MatCreateVecs(A, NULL, &b));
2126: PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2127: PetscCall(VecDestroy(&b));
2128: }
2129: break;
2130: }
2131: PetscMUMPS_c(mumps);
2132: PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));
2134: F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS;
2135: F->ops->solve = MatSolve_MUMPS;
2136: F->ops->solvetranspose = MatSolve_MUMPS;
2137: F->ops->matsolve = MatMatSolve_MUMPS;
2138: F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS;
2139: F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;
2140: #if defined(PETSC_USE_COMPLEX)
2141: F->ops->getinertia = NULL;
2142: #else
2143: F->ops->getinertia = MatGetInertia_SBAIJMUMPS;
2144: #endif
2146: mumps->matstruc = SAME_NONZERO_PATTERN;
2147: PetscFunctionReturn(PETSC_SUCCESS);
2148: }
2150: PetscErrorCode MatView_MUMPS(Mat A, PetscViewer viewer)
2151: {
2152: PetscBool iascii;
2153: PetscViewerFormat format;
2154: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
2156: PetscFunctionBegin;
2157: /* check if matrix is mumps type */
2158: if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(PETSC_SUCCESS);
2160: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
2161: if (iascii) {
2162: PetscCall(PetscViewerGetFormat(viewer, &format));
2163: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
2164: PetscCall(PetscViewerASCIIPrintf(viewer, "MUMPS run parameters:\n"));
2165: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
2166: PetscCall(PetscViewerASCIIPrintf(viewer, " SYM (matrix type): %d\n", mumps->id.sym));
2167: PetscCall(PetscViewerASCIIPrintf(viewer, " PAR (host participation): %d\n", mumps->id.par));
2168: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(1) (output for error): %d\n", mumps->id.ICNTL(1)));
2169: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(2) (output of diagnostic msg): %d\n", mumps->id.ICNTL(2)));
2170: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(3) (output for global info): %d\n", mumps->id.ICNTL(3)));
2171: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(4) (level of printing): %d\n", mumps->id.ICNTL(4)));
2172: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(5) (input mat struct): %d\n", mumps->id.ICNTL(5)));
2173: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(6) (matrix prescaling): %d\n", mumps->id.ICNTL(6)));
2174: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(7) (sequential matrix ordering):%d\n", mumps->id.ICNTL(7)));
2175: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(8) (scaling strategy): %d\n", mumps->id.ICNTL(8)));
2176: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(10) (max num of refinements): %d\n", mumps->id.ICNTL(10)));
2177: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(11) (error analysis): %d\n", mumps->id.ICNTL(11)));
2178: if (mumps->id.ICNTL(11) > 0) {
2179: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(4) (inf norm of input mat): %g\n", mumps->id.RINFOG(4)));
2180: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(5) (inf norm of solution): %g\n", mumps->id.RINFOG(5)));
2181: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(6) (inf norm of residual): %g\n", mumps->id.RINFOG(6)));
2182: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(7),RINFOG(8) (backward error est): %g, %g\n", mumps->id.RINFOG(7), mumps->id.RINFOG(8)));
2183: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(9) (error estimate): %g\n", mumps->id.RINFOG(9)));
2184: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n", mumps->id.RINFOG(10), mumps->id.RINFOG(11)));
2185: }
2186: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(12) (efficiency control): %d\n", mumps->id.ICNTL(12)));
2187: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(13) (sequential factorization of the root node): %d\n", mumps->id.ICNTL(13)));
2188: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(14) (percentage of estimated workspace increase): %d\n", mumps->id.ICNTL(14)));
2189: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(15) (compression of the input matrix): %d\n", mumps->id.ICNTL(15)));
2190: /* ICNTL(15-17) not used */
2191: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(18) (input mat struct): %d\n", mumps->id.ICNTL(18)));
2192: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(19) (Schur complement info): %d\n", mumps->id.ICNTL(19)));
2193: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(20) (RHS sparse pattern): %d\n", mumps->id.ICNTL(20)));
2194: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(21) (solution struct): %d\n", mumps->id.ICNTL(21)));
2195: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(22) (in-core/out-of-core facility): %d\n", mumps->id.ICNTL(22)));
2196: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(23) (max size of memory can be allocated locally):%d\n", mumps->id.ICNTL(23)));
2198: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(24) (detection of null pivot rows): %d\n", mumps->id.ICNTL(24)));
2199: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(25) (computation of a null space basis): %d\n", mumps->id.ICNTL(25)));
2200: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(26) (Schur options for RHS or solution): %d\n", mumps->id.ICNTL(26)));
2201: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(27) (blocking size for multiple RHS): %d\n", mumps->id.ICNTL(27)));
2202: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(28) (use parallel or sequential ordering): %d\n", mumps->id.ICNTL(28)));
2203: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(29) (parallel ordering): %d\n", mumps->id.ICNTL(29)));
2205: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(30) (user-specified set of entries in inv(A)): %d\n", mumps->id.ICNTL(30)));
2206: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(31) (factors is discarded in the solve phase): %d\n", mumps->id.ICNTL(31)));
2207: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(33) (compute determinant): %d\n", mumps->id.ICNTL(33)));
2208: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(35) (activate BLR based factorization): %d\n", mumps->id.ICNTL(35)));
2209: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(36) (choice of BLR factorization variant): %d\n", mumps->id.ICNTL(36)));
2210: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(38) (estimated compression rate of LU factors): %d\n", mumps->id.ICNTL(38)));
2212: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(1) (relative pivoting threshold): %g\n", mumps->id.CNTL(1)));
2213: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(2) (stopping criterion of refinement): %g\n", mumps->id.CNTL(2)));
2214: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(3) (absolute pivoting threshold): %g\n", mumps->id.CNTL(3)));
2215: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(4) (value of static pivoting): %g\n", mumps->id.CNTL(4)));
2216: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(5) (fixation for null pivots): %g\n", mumps->id.CNTL(5)));
2217: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(7) (dropping parameter for BLR): %g\n", mumps->id.CNTL(7)));
2219: /* information local to each processor */
2220: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(1) (local estimated flops for the elimination after analysis):\n"));
2221: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
2222: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %g\n", mumps->myid, mumps->id.RINFO(1)));
2223: PetscCall(PetscViewerFlush(viewer));
2224: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(2) (local estimated flops for the assembly after factorization):\n"));
2225: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %g\n", mumps->myid, mumps->id.RINFO(2)));
2226: PetscCall(PetscViewerFlush(viewer));
2227: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(3) (local estimated flops for the elimination after factorization):\n"));
2228: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %g\n", mumps->myid, mumps->id.RINFO(3)));
2229: PetscCall(PetscViewerFlush(viewer));
2231: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization):\n"));
2232: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(15)));
2233: PetscCall(PetscViewerFlush(viewer));
2235: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization):\n"));
2236: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(16)));
2237: PetscCall(PetscViewerFlush(viewer));
2239: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(23) (num of pivots eliminated on this processor after factorization):\n"));
2240: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(23)));
2241: PetscCall(PetscViewerFlush(viewer));
2243: if (mumps->ninfo && mumps->ninfo <= 80) {
2244: PetscInt i;
2245: for (i = 0; i < mumps->ninfo; i++) {
2246: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(%" PetscInt_FMT "):\n", mumps->info[i]));
2247: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(mumps->info[i])));
2248: PetscCall(PetscViewerFlush(viewer));
2249: }
2250: }
2251: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
2252: } else PetscCall(PetscViewerASCIIPrintf(viewer, " Use -%sksp_view ::ascii_info_detail to display information for all processes\n", ((PetscObject)A)->prefix ? ((PetscObject)A)->prefix : ""));
2254: if (mumps->myid == 0) { /* information from the host */
2255: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(1) (global estimated flops for the elimination after analysis): %g\n", mumps->id.RINFOG(1)));
2256: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(2) (global estimated flops for the assembly after factorization): %g\n", mumps->id.RINFOG(2)));
2257: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(3) (global estimated flops for the elimination after factorization): %g\n", mumps->id.RINFOG(3)));
2258: PetscCall(PetscViewerASCIIPrintf(viewer, " (RINFOG(12) RINFOG(13))*2^INFOG(34) (determinant): (%g,%g)*(2^%d)\n", mumps->id.RINFOG(12), mumps->id.RINFOG(13), mumps->id.INFOG(34)));
2260: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(3) (estimated real workspace for factors on all processors after analysis): %d\n", mumps->id.INFOG(3)));
2261: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d\n", mumps->id.INFOG(4)));
2262: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(5) (estimated maximum front size in the complete tree): %d\n", mumps->id.INFOG(5)));
2263: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(6) (number of nodes in the complete tree): %d\n", mumps->id.INFOG(6)));
2264: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(7) (ordering option effectively used after analysis): %d\n", mumps->id.INFOG(7)));
2265: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d\n", mumps->id.INFOG(8)));
2266: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d\n", mumps->id.INFOG(9)));
2267: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(10) (total integer space store the matrix factors after factorization): %d\n", mumps->id.INFOG(10)));
2268: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(11) (order of largest frontal matrix after factorization): %d\n", mumps->id.INFOG(11)));
2269: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(12) (number of off-diagonal pivots): %d\n", mumps->id.INFOG(12)));
2270: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(13) (number of delayed pivots after factorization): %d\n", mumps->id.INFOG(13)));
2271: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(14) (number of memory compress after factorization): %d\n", mumps->id.INFOG(14)));
2272: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(15) (number of steps of iterative refinement after solution): %d\n", mumps->id.INFOG(15)));
2273: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(16) (estimated size (in MB) of all MUMPS internal data for factorization after analysis: value on the most memory consuming processor): %d\n", mumps->id.INFOG(16)));
2274: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(17) (estimated size of all MUMPS internal data for factorization after analysis: sum over all processors): %d\n", mumps->id.INFOG(17)));
2275: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(18) (size of all MUMPS internal data allocated during factorization: value on the most memory consuming processor): %d\n", mumps->id.INFOG(18)));
2276: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): %d\n", mumps->id.INFOG(19)));
2277: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(20) (estimated number of entries in the factors): %d\n", mumps->id.INFOG(20)));
2278: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(21) (size in MB of memory effectively used during factorization - value on the most memory consuming processor): %d\n", mumps->id.INFOG(21)));
2279: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): %d\n", mumps->id.INFOG(22)));
2280: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d\n", mumps->id.INFOG(23)));
2281: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d\n", mumps->id.INFOG(24)));
2282: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(25) (after factorization: number of pivots modified by static pivoting): %d\n", mumps->id.INFOG(25)));
2283: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(28) (after factorization: number of null pivots encountered): %d\n", mumps->id.INFOG(28)));
2284: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(29) (after factorization: effective number of entries in the factors (sum over all processors)): %d\n", mumps->id.INFOG(29)));
2285: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(30, 31) (after solution: size in Mbytes of memory used during solution phase): %d, %d\n", mumps->id.INFOG(30), mumps->id.INFOG(31)));
2286: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(32) (after analysis: type of analysis done): %d\n", mumps->id.INFOG(32)));
2287: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(33) (value used for ICNTL(8)): %d\n", mumps->id.INFOG(33)));
2288: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(34) (exponent of the determinant if determinant is requested): %d\n", mumps->id.INFOG(34)));
2289: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(35) (after factorization: number of entries taking into account BLR factor compression - sum over all processors): %d\n", mumps->id.INFOG(35)));
2290: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(36) (after analysis: estimated size of all MUMPS internal data for running BLR in-core - value on the most memory consuming processor): %d\n", mumps->id.INFOG(36)));
2291: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(37) (after analysis: estimated size of all MUMPS internal data for running BLR in-core - sum over all processors): %d\n", mumps->id.INFOG(37)));
2292: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(38) (after analysis: estimated size of all MUMPS internal data for running BLR out-of-core - value on the most memory consuming processor): %d\n", mumps->id.INFOG(38)));
2293: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(39) (after analysis: estimated size of all MUMPS internal data for running BLR out-of-core - sum over all processors): %d\n", mumps->id.INFOG(39)));
2294: }
2295: }
2296: }
2297: PetscFunctionReturn(PETSC_SUCCESS);
2298: }
2300: PetscErrorCode MatGetInfo_MUMPS(Mat A, MatInfoType flag, MatInfo *info)
2301: {
2302: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
2304: PetscFunctionBegin;
2305: info->block_size = 1.0;
2306: info->nz_allocated = mumps->id.INFOG(20);
2307: info->nz_used = mumps->id.INFOG(20);
2308: info->nz_unneeded = 0.0;
2309: info->assemblies = 0.0;
2310: info->mallocs = 0.0;
2311: info->memory = 0.0;
2312: info->fill_ratio_given = 0;
2313: info->fill_ratio_needed = 0;
2314: info->factor_mallocs = 0;
2315: PetscFunctionReturn(PETSC_SUCCESS);
2316: }
2318: PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is)
2319: {
2320: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2321: const PetscScalar *arr;
2322: const PetscInt *idxs;
2323: PetscInt size, i;
2325: PetscFunctionBegin;
2326: PetscCall(ISGetLocalSize(is, &size));
2327: /* Schur complement matrix */
2328: PetscCall(MatDestroy(&F->schur));
2329: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, size, size, NULL, &F->schur));
2330: PetscCall(MatDenseGetArrayRead(F->schur, &arr));
2331: mumps->id.schur = (MumpsScalar *)arr;
2332: mumps->id.size_schur = size;
2333: mumps->id.schur_lld = size;
2334: PetscCall(MatDenseRestoreArrayRead(F->schur, &arr));
2335: if (mumps->sym == 1) PetscCall(MatSetOption(F->schur, MAT_SPD, PETSC_TRUE));
2337: /* MUMPS expects Fortran style indices */
2338: PetscCall(PetscFree(mumps->id.listvar_schur));
2339: PetscCall(PetscMalloc1(size, &mumps->id.listvar_schur));
2340: PetscCall(ISGetIndices(is, &idxs));
2341: for (i = 0; i < size; i++) PetscCall(PetscMUMPSIntCast(idxs[i] + 1, &(mumps->id.listvar_schur[i])));
2342: PetscCall(ISRestoreIndices(is, &idxs));
2343: /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */
2344: mumps->id.ICNTL(26) = -1;
2345: PetscFunctionReturn(PETSC_SUCCESS);
2346: }
2348: PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F, Mat *S)
2349: {
2350: Mat St;
2351: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2352: PetscScalar *array;
2353: #if defined(PETSC_USE_COMPLEX)
2354: PetscScalar im = PetscSqrtScalar((PetscScalar)-1.0);
2355: #endif
2357: PetscFunctionBegin;
2358: PetscCheck(mumps->id.ICNTL(19), PetscObjectComm((PetscObject)F), PETSC_ERR_ORDER, "Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it");
2359: PetscCall(MatCreate(PETSC_COMM_SELF, &St));
2360: PetscCall(MatSetSizes(St, PETSC_DECIDE, PETSC_DECIDE, mumps->id.size_schur, mumps->id.size_schur));
2361: PetscCall(MatSetType(St, MATDENSE));
2362: PetscCall(MatSetUp(St));
2363: PetscCall(MatDenseGetArray(St, &array));
2364: if (!mumps->sym) { /* MUMPS always return a full matrix */
2365: if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
2366: PetscInt i, j, N = mumps->id.size_schur;
2367: for (i = 0; i < N; i++) {
2368: for (j = 0; j < N; j++) {
2369: #if !defined(PETSC_USE_COMPLEX)
2370: PetscScalar val = mumps->id.schur[i * N + j];
2371: #else
2372: PetscScalar val = mumps->id.schur[i * N + j].r + im * mumps->id.schur[i * N + j].i;
2373: #endif
2374: array[j * N + i] = val;
2375: }
2376: }
2377: } else { /* stored by columns */
2378: PetscCall(PetscArraycpy(array, mumps->id.schur, mumps->id.size_schur * mumps->id.size_schur));
2379: }
2380: } else { /* either full or lower-triangular (not packed) */
2381: if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */
2382: PetscInt i, j, N = mumps->id.size_schur;
2383: for (i = 0; i < N; i++) {
2384: for (j = i; j < N; j++) {
2385: #if !defined(PETSC_USE_COMPLEX)
2386: PetscScalar val = mumps->id.schur[i * N + j];
2387: #else
2388: PetscScalar val = mumps->id.schur[i * N + j].r + im * mumps->id.schur[i * N + j].i;
2389: #endif
2390: array[i * N + j] = val;
2391: array[j * N + i] = val;
2392: }
2393: }
2394: } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */
2395: PetscCall(PetscArraycpy(array, mumps->id.schur, mumps->id.size_schur * mumps->id.size_schur));
2396: } else { /* ICNTL(19) == 1 lower triangular stored by rows */
2397: PetscInt i, j, N = mumps->id.size_schur;
2398: for (i = 0; i < N; i++) {
2399: for (j = 0; j < i + 1; j++) {
2400: #if !defined(PETSC_USE_COMPLEX)
2401: PetscScalar val = mumps->id.schur[i * N + j];
2402: #else
2403: PetscScalar val = mumps->id.schur[i * N + j].r + im * mumps->id.schur[i * N + j].i;
2404: #endif
2405: array[i * N + j] = val;
2406: array[j * N + i] = val;
2407: }
2408: }
2409: }
2410: }
2411: PetscCall(MatDenseRestoreArray(St, &array));
2412: *S = St;
2413: PetscFunctionReturn(PETSC_SUCCESS);
2414: }
2416: PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F, PetscInt icntl, PetscInt ival)
2417: {
2418: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2420: PetscFunctionBegin;
2421: if (mumps->id.job == JOB_NULL) { /* need to cache icntl and ival since PetscMUMPS_c() has never been called */
2422: PetscInt i, nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0; /* number of already cached ICNTL */
2423: for (i = 0; i < nICNTL_pre; ++i)
2424: if (mumps->ICNTL_pre[1 + 2 * i] == icntl) break; /* is this ICNTL already cached? */
2425: if (i == nICNTL_pre) { /* not already cached */
2426: if (i > 0) PetscCall(PetscRealloc(sizeof(PetscMUMPSInt) * (2 * nICNTL_pre + 3), &mumps->ICNTL_pre));
2427: else PetscCall(PetscCalloc(sizeof(PetscMUMPSInt) * 3, &mumps->ICNTL_pre));
2428: mumps->ICNTL_pre[0]++;
2429: }
2430: mumps->ICNTL_pre[1 + 2 * i] = icntl;
2431: PetscCall(PetscMUMPSIntCast(ival, mumps->ICNTL_pre + 2 + 2 * i));
2432: } else PetscCall(PetscMUMPSIntCast(ival, &mumps->id.ICNTL(icntl)));
2433: PetscFunctionReturn(PETSC_SUCCESS);
2434: }
2436: PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F, PetscInt icntl, PetscInt *ival)
2437: {
2438: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2440: PetscFunctionBegin;
2441: *ival = mumps->id.ICNTL(icntl);
2442: PetscFunctionReturn(PETSC_SUCCESS);
2443: }
2445: /*@
2446: MatMumpsSetIcntl - Set MUMPS parameter ICNTL()
2448: Logically Collective
2450: Input Parameters:
2451: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2452: . icntl - index of MUMPS parameter array ICNTL()
2453: - ival - value of MUMPS ICNTL(icntl)
2455: Options Database Key:
2456: . -mat_mumps_icntl_<icntl> <ival> - change the option numbered icntl to ival
2458: Level: beginner
2460: References:
2461: . * - MUMPS Users' Guide
2463: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2464: @*/
2465: PetscErrorCode MatMumpsSetIcntl(Mat F, PetscInt icntl, PetscInt ival)
2466: {
2467: PetscFunctionBegin;
2469: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2472: PetscCheck(icntl >= 1 && icntl <= 38, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported ICNTL value %" PetscInt_FMT, icntl);
2473: PetscTryMethod(F, "MatMumpsSetIcntl_C", (Mat, PetscInt, PetscInt), (F, icntl, ival));
2474: PetscFunctionReturn(PETSC_SUCCESS);
2475: }
2477: /*@
2478: MatMumpsGetIcntl - Get MUMPS parameter ICNTL()
2480: Logically Collective
2482: Input Parameters:
2483: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2484: - icntl - index of MUMPS parameter array ICNTL()
2486: Output Parameter:
2487: . ival - value of MUMPS ICNTL(icntl)
2489: Level: beginner
2491: References:
2492: . * - MUMPS Users' Guide
2494: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2495: @*/
2496: PetscErrorCode MatMumpsGetIcntl(Mat F, PetscInt icntl, PetscInt *ival)
2497: {
2498: PetscFunctionBegin;
2500: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2503: PetscCheck(icntl >= 1 && icntl <= 38, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported ICNTL value %" PetscInt_FMT, icntl);
2504: PetscUseMethod(F, "MatMumpsGetIcntl_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
2505: PetscFunctionReturn(PETSC_SUCCESS);
2506: }
2508: PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F, PetscInt icntl, PetscReal val)
2509: {
2510: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2512: PetscFunctionBegin;
2513: if (mumps->id.job == JOB_NULL) {
2514: PetscInt i, nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
2515: for (i = 0; i < nCNTL_pre; ++i)
2516: if (mumps->CNTL_pre[1 + 2 * i] == icntl) break;
2517: if (i == nCNTL_pre) {
2518: if (i > 0) PetscCall(PetscRealloc(sizeof(PetscReal) * (2 * nCNTL_pre + 3), &mumps->CNTL_pre));
2519: else PetscCall(PetscCalloc(sizeof(PetscReal) * 3, &mumps->CNTL_pre));
2520: mumps->CNTL_pre[0]++;
2521: }
2522: mumps->CNTL_pre[1 + 2 * i] = icntl;
2523: mumps->CNTL_pre[2 + 2 * i] = val;
2524: } else mumps->id.CNTL(icntl) = val;
2525: PetscFunctionReturn(PETSC_SUCCESS);
2526: }
2528: PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F, PetscInt icntl, PetscReal *val)
2529: {
2530: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2532: PetscFunctionBegin;
2533: *val = mumps->id.CNTL(icntl);
2534: PetscFunctionReturn(PETSC_SUCCESS);
2535: }
2537: /*@
2538: MatMumpsSetCntl - Set MUMPS parameter CNTL()
2540: Logically Collective
2542: Input Parameters:
2543: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2544: . icntl - index of MUMPS parameter array CNTL()
2545: - val - value of MUMPS CNTL(icntl)
2547: Options Database Key:
2548: . -mat_mumps_cntl_<icntl> <val> - change the option numbered icntl to ival
2550: Level: beginner
2552: References:
2553: . * - MUMPS Users' Guide
2555: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2556: @*/
2557: PetscErrorCode MatMumpsSetCntl(Mat F, PetscInt icntl, PetscReal val)
2558: {
2559: PetscFunctionBegin;
2561: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2564: PetscCheck(icntl >= 1 && icntl <= 7, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported CNTL value %" PetscInt_FMT, icntl);
2565: PetscTryMethod(F, "MatMumpsSetCntl_C", (Mat, PetscInt, PetscReal), (F, icntl, val));
2566: PetscFunctionReturn(PETSC_SUCCESS);
2567: }
2569: /*@
2570: MatMumpsGetCntl - Get MUMPS parameter CNTL()
2572: Logically Collective
2574: Input Parameters:
2575: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2576: - icntl - index of MUMPS parameter array CNTL()
2578: Output Parameter:
2579: . val - value of MUMPS CNTL(icntl)
2581: Level: beginner
2583: References:
2584: . * - MUMPS Users' Guide
2586: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2587: @*/
2588: PetscErrorCode MatMumpsGetCntl(Mat F, PetscInt icntl, PetscReal *val)
2589: {
2590: PetscFunctionBegin;
2592: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2595: PetscCheck(icntl >= 1 && icntl <= 7, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported CNTL value %" PetscInt_FMT, icntl);
2596: PetscUseMethod(F, "MatMumpsGetCntl_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
2597: PetscFunctionReturn(PETSC_SUCCESS);
2598: }
2600: PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F, PetscInt icntl, PetscInt *info)
2601: {
2602: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2604: PetscFunctionBegin;
2605: *info = mumps->id.INFO(icntl);
2606: PetscFunctionReturn(PETSC_SUCCESS);
2607: }
2609: PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F, PetscInt icntl, PetscInt *infog)
2610: {
2611: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2613: PetscFunctionBegin;
2614: *infog = mumps->id.INFOG(icntl);
2615: PetscFunctionReturn(PETSC_SUCCESS);
2616: }
2618: PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F, PetscInt icntl, PetscReal *rinfo)
2619: {
2620: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2622: PetscFunctionBegin;
2623: *rinfo = mumps->id.RINFO(icntl);
2624: PetscFunctionReturn(PETSC_SUCCESS);
2625: }
2627: PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F, PetscInt icntl, PetscReal *rinfog)
2628: {
2629: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2631: PetscFunctionBegin;
2632: *rinfog = mumps->id.RINFOG(icntl);
2633: PetscFunctionReturn(PETSC_SUCCESS);
2634: }
2636: PetscErrorCode MatMumpsGetInverse_MUMPS(Mat F, Mat spRHS)
2637: {
2638: Mat Bt = NULL, Btseq = NULL;
2639: PetscBool flg;
2640: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2641: PetscScalar *aa;
2642: PetscInt spnr, *ia, *ja, M, nrhs;
2644: PetscFunctionBegin;
2646: PetscCall(PetscObjectTypeCompare((PetscObject)spRHS, MATTRANSPOSEVIRTUAL, &flg));
2647: if (flg) {
2648: PetscCall(MatTransposeGetMat(spRHS, &Bt));
2649: } else SETERRQ(PetscObjectComm((PetscObject)spRHS), PETSC_ERR_ARG_WRONG, "Matrix spRHS must be type MATTRANSPOSEVIRTUAL matrix");
2651: PetscCall(MatMumpsSetIcntl(F, 30, 1));
2653: if (mumps->petsc_size > 1) {
2654: Mat_MPIAIJ *b = (Mat_MPIAIJ *)Bt->data;
2655: Btseq = b->A;
2656: } else {
2657: Btseq = Bt;
2658: }
2660: PetscCall(MatGetSize(spRHS, &M, &nrhs));
2661: mumps->id.nrhs = nrhs;
2662: mumps->id.lrhs = M;
2663: mumps->id.rhs = NULL;
2665: if (!mumps->myid) {
2666: PetscCall(MatSeqAIJGetArray(Btseq, &aa));
2667: PetscCall(MatGetRowIJ(Btseq, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
2668: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
2669: PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
2670: mumps->id.rhs_sparse = (MumpsScalar *)aa;
2671: } else {
2672: mumps->id.irhs_ptr = NULL;
2673: mumps->id.irhs_sparse = NULL;
2674: mumps->id.nz_rhs = 0;
2675: mumps->id.rhs_sparse = NULL;
2676: }
2677: mumps->id.ICNTL(20) = 1; /* rhs is sparse */
2678: mumps->id.ICNTL(21) = 0; /* solution is in assembled centralized format */
2680: /* solve phase */
2681: mumps->id.job = JOB_SOLVE;
2682: PetscMUMPS_c(mumps);
2683: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MUMPS in solve phase: INFOG(1)=%d INFO(2)=%d", mumps->id.INFOG(1), mumps->id.INFO(2));
2685: if (!mumps->myid) {
2686: PetscCall(MatSeqAIJRestoreArray(Btseq, &aa));
2687: PetscCall(MatRestoreRowIJ(Btseq, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
2688: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
2689: }
2690: PetscFunctionReturn(PETSC_SUCCESS);
2691: }
2693: /*@
2694: MatMumpsGetInverse - Get user-specified set of entries in inverse of `A`
2696: Logically Collective
2698: Input Parameters:
2699: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2700: - spRHS - sequential sparse matrix in `MATTRANSPOSEVIRTUAL` format holding specified indices in processor[0]
2702: Output Parameter:
2703: . spRHS - requested entries of inverse of `A`
2705: Level: beginner
2707: References:
2708: . * - MUMPS Users' Guide
2710: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatCreateTranspose()`
2711: @*/
2712: PetscErrorCode MatMumpsGetInverse(Mat F, Mat spRHS)
2713: {
2714: PetscFunctionBegin;
2716: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2717: PetscUseMethod(F, "MatMumpsGetInverse_C", (Mat, Mat), (F, spRHS));
2718: PetscFunctionReturn(PETSC_SUCCESS);
2719: }
2721: PetscErrorCode MatMumpsGetInverseTranspose_MUMPS(Mat F, Mat spRHST)
2722: {
2723: Mat spRHS;
2725: PetscFunctionBegin;
2726: PetscCall(MatCreateTranspose(spRHST, &spRHS));
2727: PetscCall(MatMumpsGetInverse_MUMPS(F, spRHS));
2728: PetscCall(MatDestroy(&spRHS));
2729: PetscFunctionReturn(PETSC_SUCCESS);
2730: }
2732: /*@
2733: MatMumpsGetInverseTranspose - Get user-specified set of entries in inverse of matrix `A`^T
2735: Logically Collective
2737: Input Parameters:
2738: + F - the factored matrix of A obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2739: - spRHST - sequential sparse matrix in `MATAIJ` format holding specified indices of `A`^T in processor[0]
2741: Output Parameter:
2742: . spRHST - requested entries of inverse of `A`^T
2744: Level: beginner
2746: References:
2747: . * - MUMPS Users' Guide
2749: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatCreateTranspose()`, `MatMumpsGetInverse()`
2750: @*/
2751: PetscErrorCode MatMumpsGetInverseTranspose(Mat F, Mat spRHST)
2752: {
2753: PetscBool flg;
2755: PetscFunctionBegin;
2757: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2758: PetscCall(PetscObjectTypeCompareAny((PetscObject)spRHST, &flg, MATSEQAIJ, MATMPIAIJ, NULL));
2759: PetscCheck(flg, PetscObjectComm((PetscObject)spRHST), PETSC_ERR_ARG_WRONG, "Matrix spRHST must be MATAIJ matrix");
2761: PetscUseMethod(F, "MatMumpsGetInverseTranspose_C", (Mat, Mat), (F, spRHST));
2762: PetscFunctionReturn(PETSC_SUCCESS);
2763: }
2765: /*@
2766: MatMumpsGetInfo - Get MUMPS parameter INFO()
2768: Logically Collective
2770: Input Parameters:
2771: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2772: - icntl - index of MUMPS parameter array INFO()
2774: Output Parameter:
2775: . ival - value of MUMPS INFO(icntl)
2777: Level: beginner
2779: References:
2780: . * - MUMPS Users' Guide
2782: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2783: @*/
2784: PetscErrorCode MatMumpsGetInfo(Mat F, PetscInt icntl, PetscInt *ival)
2785: {
2786: PetscFunctionBegin;
2788: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2790: PetscUseMethod(F, "MatMumpsGetInfo_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
2791: PetscFunctionReturn(PETSC_SUCCESS);
2792: }
2794: /*@
2795: MatMumpsGetInfog - Get MUMPS parameter INFOG()
2797: Logically Collective
2799: Input Parameters:
2800: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2801: - icntl - index of MUMPS parameter array INFOG()
2803: Output Parameter:
2804: . ival - value of MUMPS INFOG(icntl)
2806: Level: beginner
2808: References:
2809: . * - MUMPS Users' Guide
2811: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2812: @*/
2813: PetscErrorCode MatMumpsGetInfog(Mat F, PetscInt icntl, PetscInt *ival)
2814: {
2815: PetscFunctionBegin;
2817: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2819: PetscUseMethod(F, "MatMumpsGetInfog_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
2820: PetscFunctionReturn(PETSC_SUCCESS);
2821: }
2823: /*@
2824: MatMumpsGetRinfo - Get MUMPS parameter RINFO()
2826: Logically Collective
2828: Input Parameters:
2829: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2830: - icntl - index of MUMPS parameter array RINFO()
2832: Output Parameter:
2833: . val - value of MUMPS RINFO(icntl)
2835: Level: beginner
2837: References:
2838: . * - MUMPS Users' Guide
2840: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfog()`
2841: @*/
2842: PetscErrorCode MatMumpsGetRinfo(Mat F, PetscInt icntl, PetscReal *val)
2843: {
2844: PetscFunctionBegin;
2846: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2848: PetscUseMethod(F, "MatMumpsGetRinfo_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
2849: PetscFunctionReturn(PETSC_SUCCESS);
2850: }
2852: /*@
2853: MatMumpsGetRinfog - Get MUMPS parameter RINFOG()
2855: Logically Collective
2857: Input Parameters:
2858: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2859: - icntl - index of MUMPS parameter array RINFOG()
2861: Output Parameter:
2862: . val - value of MUMPS RINFOG(icntl)
2864: Level: beginner
2866: References:
2867: . * - MUMPS Users' Guide
2869: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`
2870: @*/
2871: PetscErrorCode MatMumpsGetRinfog(Mat F, PetscInt icntl, PetscReal *val)
2872: {
2873: PetscFunctionBegin;
2875: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2877: PetscUseMethod(F, "MatMumpsGetRinfog_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
2878: PetscFunctionReturn(PETSC_SUCCESS);
2879: }
2881: /*MC
2882: MATSOLVERMUMPS - A matrix type providing direct solvers (LU and Cholesky) for
2883: distributed and sequential matrices via the external package MUMPS.
2885: Works with `MATAIJ` and `MATSBAIJ` matrices
2887: Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch to have PETSc installed with MUMPS
2889: Use ./configure --with-openmp --download-hwloc (or --with-hwloc) to enable running MUMPS in MPI+OpenMP hybrid mode and non-MUMPS in flat-MPI mode.
2890: See details below.
2892: Use `-pc_type cholesky` or `lu` `-pc_factor_mat_solver_type mumps` to use this direct solver
2894: Options Database Keys:
2895: + -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages
2896: . -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning
2897: . -mat_mumps_icntl_3 - ICNTL(3): output stream for global information, collected on the host
2898: . -mat_mumps_icntl_4 - ICNTL(4): level of printing (0 to 4)
2899: . -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7)
2900: . -mat_mumps_icntl_7 - ICNTL(7): computes a symmetric permutation in sequential analysis, 0=AMD, 2=AMF, 3=Scotch, 4=PORD, 5=Metis, 6=QAMD, and 7=auto
2901: Use -pc_factor_mat_ordering_type <type> to have PETSc perform the ordering (sequential only)
2902: . -mat_mumps_icntl_8 - ICNTL(8): scaling strategy (-2 to 8 or 77)
2903: . -mat_mumps_icntl_10 - ICNTL(10): max num of refinements
2904: . -mat_mumps_icntl_11 - ICNTL(11): statistics related to an error analysis (via -ksp_view)
2905: . -mat_mumps_icntl_12 - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3)
2906: . -mat_mumps_icntl_13 - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting
2907: . -mat_mumps_icntl_14 - ICNTL(14): percentage increase in the estimated working space
2908: . -mat_mumps_icntl_15 - ICNTL(15): compression of the input matrix resulting from a block format
2909: . -mat_mumps_icntl_19 - ICNTL(19): computes the Schur complement
2910: . -mat_mumps_icntl_20 - ICNTL(20): give MUMPS centralized (0) or distributed (10) dense RHS
2911: . -mat_mumps_icntl_22 - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1)
2912: . -mat_mumps_icntl_23 - ICNTL(23): max size of the working memory (MB) that can allocate per processor
2913: . -mat_mumps_icntl_24 - ICNTL(24): detection of null pivot rows (0 or 1)
2914: . -mat_mumps_icntl_25 - ICNTL(25): compute a solution of a deficient matrix and a null space basis
2915: . -mat_mumps_icntl_26 - ICNTL(26): drives the solution phase if a Schur complement matrix
2916: . -mat_mumps_icntl_28 - ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering
2917: . -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis
2918: . -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A)
2919: . -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization
2920: . -mat_mumps_icntl_33 - ICNTL(33): compute determinant
2921: . -mat_mumps_icntl_35 - ICNTL(35): level of activation of BLR (Block Low-Rank) feature
2922: . -mat_mumps_icntl_36 - ICNTL(36): controls the choice of BLR factorization variant
2923: . -mat_mumps_icntl_38 - ICNTL(38): sets the estimated compression rate of LU factors with BLR
2924: . -mat_mumps_cntl_1 - CNTL(1): relative pivoting threshold
2925: . -mat_mumps_cntl_2 - CNTL(2): stopping criterion of refinement
2926: . -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold
2927: . -mat_mumps_cntl_4 - CNTL(4): value for static pivoting
2928: . -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots
2929: . -mat_mumps_cntl_7 - CNTL(7): precision of the dropping parameter used during BLR factorization
2930: - -mat_mumps_use_omp_threads [m] - run MUMPS in MPI+OpenMP hybrid mode as if omp_set_num_threads(m) is called before calling MUMPS.
2931: Default might be the number of cores per CPU package (socket) as reported by hwloc and suggested by the MUMPS manual.
2933: Level: beginner
2935: Notes:
2936: MUMPS Cholesky does not handle (complex) Hermitian matrices (see User's Guide at https://mumps-solver.org/index.php?page=doc) so using it will
2937: error if the matrix is Hermitian.
2939: When used within a `KSP`/`PC` solve the options are prefixed with that of the `PC`. Otherwise one can set the options prefix by calling
2940: `MatSetOptionsPrefixFactor()` on the matrix from which the factor was obtained or `MatSetOptionsPrefix()` on the factor matrix.
2942: When a MUMPS factorization fails inside a KSP solve, for example with a `KSP_DIVERGED_PC_FAILED`, one can find the MUMPS information about
2943: the failure with
2944: .vb
2945: KSPGetPC(ksp,&pc);
2946: PCFactorGetMatrix(pc,&mat);
2947: MatMumpsGetInfo(mat,....);
2948: MatMumpsGetInfog(mat,....); etc.
2949: .ve
2950: Or run with `-ksp_error_if_not_converged` and the program will be stopped and the information printed in the error message.
2952: MUMPS provides 64-bit integer support in two build modes:
2953: full 64-bit: here MUMPS is built with C preprocessing flag -DINTSIZE64 and Fortran compiler option -i8, -fdefault-integer-8 or equivalent, and
2954: requires all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS built the same way with 64-bit integers (for example ILP64 Intel MKL and MPI).
2956: selective 64-bit: with the default MUMPS build, 64-bit integers have been introduced where needed. In compressed sparse row (CSR) storage of matrices,
2957: MUMPS stores column indices in 32-bit, but row offsets in 64-bit, so you can have a huge number of non-zeros, but must have less than 2^31 rows and
2958: columns. This can lead to significant memory and performance gains with respect to a full 64-bit integer MUMPS version. This requires a regular (32-bit
2959: integer) build of all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS.
2961: With --download-mumps=1, PETSc always build MUMPS in selective 64-bit mode, which can be used by both --with-64-bit-indices=0/1 variants of PETSc.
2963: Two modes to run MUMPS/PETSc with OpenMP
2964: .vb
2965: Set OMP_NUM_THREADS and run with fewer MPI ranks than cores. For example, if you want to have 16 OpenMP
2966: threads per rank, then you may use "export OMP_NUM_THREADS=16 && mpirun -n 4 ./test".
2967: .ve
2969: .vb
2970: -mat_mumps_use_omp_threads [m] and run your code with as many MPI ranks as the number of cores. For example,
2971: if a compute node has 32 cores and you run on two nodes, you may use "mpirun -n 64 ./test -mat_mumps_use_omp_threads 16"
2972: .ve
2974: To run MUMPS in MPI+OpenMP hybrid mode (i.e., enable multithreading in MUMPS), but still run the non-MUMPS part
2975: (i.e., PETSc part) of your code in the so-called flat-MPI (aka pure-MPI) mode, you need to configure PETSc with `--with-openmp` `--download-hwloc`
2976: (or `--with-hwloc`), and have an MPI that supports MPI-3.0's process shared memory (which is usually available). Since MUMPS calls BLAS
2977: libraries, to really get performance, you should have multithreaded BLAS libraries such as Intel MKL, AMD ACML, Cray libSci or OpenBLAS
2978: (PETSc will automatically try to utilized a threaded BLAS if --with-openmp is provided).
2980: If you run your code through a job submission system, there are caveats in MPI rank mapping. We use MPI_Comm_split_type() to obtain MPI
2981: processes on each compute node. Listing the processes in rank ascending order, we split processes on a node into consecutive groups of
2982: size m and create a communicator called omp_comm for each group. Rank 0 in an omp_comm is called the master rank, and others in the omp_comm
2983: are called slave ranks (or slaves). Only master ranks are seen to MUMPS and slaves are not. We will free CPUs assigned to slaves (might be set
2984: by CPU binding policies in job scripts) and make the CPUs available to the master so that OMP threads spawned by MUMPS can run on the CPUs.
2985: In a multi-socket compute node, MPI rank mapping is an issue. Still use the above example and suppose your compute node has two sockets,
2986: if you interleave MPI ranks on the two sockets, in other words, even ranks are placed on socket 0, and odd ranks are on socket 1, and bind
2987: MPI ranks to cores, then with -mat_mumps_use_omp_threads 16, a master rank (and threads it spawns) will use half cores in socket 0, and half
2988: cores in socket 1, that definitely hurts locality. On the other hand, if you map MPI ranks consecutively on the two sockets, then the
2989: problem will not happen. Therefore, when you use -mat_mumps_use_omp_threads, you need to keep an eye on your MPI rank mapping and CPU binding.
2990: For example, with the Slurm job scheduler, one can use srun --cpu-bind=verbose -m block:block to map consecutive MPI ranks to sockets and
2991: examine the mapping result.
2993: PETSc does not control thread binding in MUMPS. So to get best performance, one still has to set `OMP_PROC_BIND` and `OMP_PLACES` in job scripts,
2994: for example, export `OMP_PLACES`=threads and export `OMP_PROC_BIND`=spread. One does not need to export `OMP_NUM_THREADS`=m in job scripts as PETSc
2995: calls `omp_set_num_threads`(m) internally before calling MUMPS.
2997: References:
2998: + * - Heroux, Michael A., R. Brightwell, and Michael M. Wolf. "Bi-modal MPI and MPI+ threads computing on scalable multicore systems." IJHPCA (Submitted) (2011).
2999: - * - Gutierrez, Samuel K., et al. "Accommodating Thread-Level Heterogeneity in Coupled Parallel Applications." Parallel and Distributed Processing Symposium (IPDPS), 2017 IEEE International. IEEE, 2017.
3001: .seealso: [](chapter_matrices), `Mat`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`, `KSPGetPC()`, `PCFactorGetMatrix()`
3002: M*/
3004: static PetscErrorCode MatFactorGetSolverType_mumps(Mat A, MatSolverType *type)
3005: {
3006: PetscFunctionBegin;
3007: *type = MATSOLVERMUMPS;
3008: PetscFunctionReturn(PETSC_SUCCESS);
3009: }
3011: /* MatGetFactor for Seq and MPI AIJ matrices */
3012: static PetscErrorCode MatGetFactor_aij_mumps(Mat A, MatFactorType ftype, Mat *F)
3013: {
3014: Mat B;
3015: Mat_MUMPS *mumps;
3016: PetscBool isSeqAIJ;
3017: PetscMPIInt size;
3019: PetscFunctionBegin;
3020: #if defined(PETSC_USE_COMPLEX)
3021: PetscCheck(A->hermitian != PETSC_BOOL3_TRUE || A->symmetric == PETSC_BOOL3_TRUE || ftype != MAT_FACTOR_CHOLESKY, PETSC_COMM_SELF, PETSC_ERR_SUP, "Hermitian CHOLESKY Factor is not supported");
3022: #endif
3023: /* Create the factorization matrix */
3024: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATSEQAIJ, &isSeqAIJ));
3025: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3026: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3027: PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3028: PetscCall(MatSetUp(B));
3030: PetscCall(PetscNew(&mumps));
3032: B->ops->view = MatView_MUMPS;
3033: B->ops->getinfo = MatGetInfo_MUMPS;
3035: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3036: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3037: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3038: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3039: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3040: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3041: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3042: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3043: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3044: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3045: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3046: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3047: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3049: if (ftype == MAT_FACTOR_LU) {
3050: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3051: B->factortype = MAT_FACTOR_LU;
3052: if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij;
3053: else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij;
3054: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3055: mumps->sym = 0;
3056: } else {
3057: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3058: B->factortype = MAT_FACTOR_CHOLESKY;
3059: if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij;
3060: else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij;
3061: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_CHOLESKY]));
3062: #if defined(PETSC_USE_COMPLEX)
3063: mumps->sym = 2;
3064: #else
3065: if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3066: else mumps->sym = 2;
3067: #endif
3068: }
3070: /* set solvertype */
3071: PetscCall(PetscFree(B->solvertype));
3072: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3073: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3074: if (size == 1) {
3075: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3076: B->canuseordering = PETSC_TRUE;
3077: }
3078: B->ops->destroy = MatDestroy_MUMPS;
3079: B->data = (void *)mumps;
3081: *F = B;
3082: mumps->id.job = JOB_NULL;
3083: mumps->ICNTL_pre = NULL;
3084: mumps->CNTL_pre = NULL;
3085: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3086: PetscFunctionReturn(PETSC_SUCCESS);
3087: }
3089: /* MatGetFactor for Seq and MPI SBAIJ matrices */
3090: static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A, MatFactorType ftype, Mat *F)
3091: {
3092: Mat B;
3093: Mat_MUMPS *mumps;
3094: PetscBool isSeqSBAIJ;
3095: PetscMPIInt size;
3097: PetscFunctionBegin;
3098: #if defined(PETSC_USE_COMPLEX)
3099: PetscCheck(A->hermitian != PETSC_BOOL3_TRUE || A->symmetric == PETSC_BOOL3_TRUE, PETSC_COMM_SELF, PETSC_ERR_SUP, "Hermitian CHOLESKY Factor is not supported");
3100: #endif
3101: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3102: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3103: PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3104: PetscCall(MatSetUp(B));
3106: PetscCall(PetscNew(&mumps));
3107: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQSBAIJ, &isSeqSBAIJ));
3108: if (isSeqSBAIJ) {
3109: mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij;
3110: } else {
3111: mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij;
3112: }
3114: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3115: B->ops->view = MatView_MUMPS;
3116: B->ops->getinfo = MatGetInfo_MUMPS;
3118: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3119: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3120: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3121: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3122: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3123: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3124: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3125: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3126: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3127: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3128: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3129: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3130: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3132: B->factortype = MAT_FACTOR_CHOLESKY;
3133: #if defined(PETSC_USE_COMPLEX)
3134: mumps->sym = 2;
3135: #else
3136: if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3137: else mumps->sym = 2;
3138: #endif
3140: /* set solvertype */
3141: PetscCall(PetscFree(B->solvertype));
3142: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3143: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3144: if (size == 1) {
3145: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3146: B->canuseordering = PETSC_TRUE;
3147: }
3148: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_CHOLESKY]));
3149: B->ops->destroy = MatDestroy_MUMPS;
3150: B->data = (void *)mumps;
3152: *F = B;
3153: mumps->id.job = JOB_NULL;
3154: mumps->ICNTL_pre = NULL;
3155: mumps->CNTL_pre = NULL;
3156: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3157: PetscFunctionReturn(PETSC_SUCCESS);
3158: }
3160: static PetscErrorCode MatGetFactor_baij_mumps(Mat A, MatFactorType ftype, Mat *F)
3161: {
3162: Mat B;
3163: Mat_MUMPS *mumps;
3164: PetscBool isSeqBAIJ;
3165: PetscMPIInt size;
3167: PetscFunctionBegin;
3168: /* Create the factorization matrix */
3169: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQBAIJ, &isSeqBAIJ));
3170: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3171: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3172: PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3173: PetscCall(MatSetUp(B));
3175: PetscCall(PetscNew(&mumps));
3176: if (ftype == MAT_FACTOR_LU) {
3177: B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS;
3178: B->factortype = MAT_FACTOR_LU;
3179: if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij;
3180: else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij;
3181: mumps->sym = 0;
3182: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3183: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead");
3185: B->ops->view = MatView_MUMPS;
3186: B->ops->getinfo = MatGetInfo_MUMPS;
3188: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3189: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3190: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3191: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3192: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3193: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3194: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3195: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3196: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3197: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3198: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3199: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3200: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3202: /* set solvertype */
3203: PetscCall(PetscFree(B->solvertype));
3204: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3205: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3206: if (size == 1) {
3207: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3208: B->canuseordering = PETSC_TRUE;
3209: }
3210: B->ops->destroy = MatDestroy_MUMPS;
3211: B->data = (void *)mumps;
3213: *F = B;
3214: mumps->id.job = JOB_NULL;
3215: mumps->ICNTL_pre = NULL;
3216: mumps->CNTL_pre = NULL;
3217: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3218: PetscFunctionReturn(PETSC_SUCCESS);
3219: }
3221: /* MatGetFactor for Seq and MPI SELL matrices */
3222: static PetscErrorCode MatGetFactor_sell_mumps(Mat A, MatFactorType ftype, Mat *F)
3223: {
3224: Mat B;
3225: Mat_MUMPS *mumps;
3226: PetscBool isSeqSELL;
3227: PetscMPIInt size;
3229: PetscFunctionBegin;
3230: /* Create the factorization matrix */
3231: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQSELL, &isSeqSELL));
3232: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3233: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3234: PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3235: PetscCall(MatSetUp(B));
3237: PetscCall(PetscNew(&mumps));
3239: B->ops->view = MatView_MUMPS;
3240: B->ops->getinfo = MatGetInfo_MUMPS;
3242: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3243: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3244: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3245: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3246: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3247: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3248: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3249: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3250: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3251: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3252: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3254: if (ftype == MAT_FACTOR_LU) {
3255: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3256: B->factortype = MAT_FACTOR_LU;
3257: if (isSeqSELL) mumps->ConvertToTriples = MatConvertToTriples_seqsell_seqaij;
3258: else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "To be implemented");
3259: mumps->sym = 0;
3260: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3261: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "To be implemented");
3263: /* set solvertype */
3264: PetscCall(PetscFree(B->solvertype));
3265: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3266: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3267: if (size == 1) {
3268: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3269: B->canuseordering = PETSC_TRUE;
3270: }
3271: B->ops->destroy = MatDestroy_MUMPS;
3272: B->data = (void *)mumps;
3274: *F = B;
3275: mumps->id.job = JOB_NULL;
3276: mumps->ICNTL_pre = NULL;
3277: mumps->CNTL_pre = NULL;
3278: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3279: PetscFunctionReturn(PETSC_SUCCESS);
3280: }
3282: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MUMPS(void)
3283: {
3284: PetscFunctionBegin;
3285: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3286: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3287: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIBAIJ, MAT_FACTOR_LU, MatGetFactor_baij_mumps));
3288: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_baij_mumps));
3289: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPISBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_sbaij_mumps));
3290: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3291: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3292: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQBAIJ, MAT_FACTOR_LU, MatGetFactor_baij_mumps));
3293: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_baij_mumps));
3294: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQSBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_sbaij_mumps));
3295: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQSELL, MAT_FACTOR_LU, MatGetFactor_sell_mumps));
3296: PetscFunctionReturn(PETSC_SUCCESS);
3297: }