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, "MatMumpsGetNullPivots_C", NULL));
1025: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInverse_C", NULL));
1026: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMumpsGetInverseTranspose_C", NULL));
1027: PetscFunctionReturn(PETSC_SUCCESS);
1028: }
1030: /* Set up the distributed RHS info for MUMPS. <nrhs> is the number of RHS. <array> points to start of RHS on the local processor. */
1031: static PetscErrorCode MatMumpsSetUpDistRHSInfo(Mat A, PetscInt nrhs, const PetscScalar *array)
1032: {
1033: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1034: const PetscMPIInt ompsize = mumps->omp_comm_size;
1035: PetscInt i, m, M, rstart;
1037: PetscFunctionBegin;
1038: PetscCall(MatGetSize(A, &M, NULL));
1039: PetscCall(MatGetLocalSize(A, &m, NULL));
1040: PetscCheck(M <= PETSC_MUMPS_INT_MAX, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscInt too long for PetscMUMPSInt");
1041: if (ompsize == 1) {
1042: if (!mumps->irhs_loc) {
1043: mumps->nloc_rhs = m;
1044: PetscCall(PetscMalloc1(m, &mumps->irhs_loc));
1045: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
1046: for (i = 0; i < m; i++) mumps->irhs_loc[i] = rstart + i + 1; /* use 1-based indices */
1047: }
1048: mumps->id.rhs_loc = (MumpsScalar *)array;
1049: } else {
1050: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
1051: const PetscInt *ranges;
1052: PetscMPIInt j, k, sendcount, *petsc_ranks, *omp_ranks;
1053: MPI_Group petsc_group, omp_group;
1054: PetscScalar *recvbuf = NULL;
1056: if (mumps->is_omp_master) {
1057: /* Lazily initialize the omp stuff for distributed rhs */
1058: if (!mumps->irhs_loc) {
1059: PetscCall(PetscMalloc2(ompsize, &omp_ranks, ompsize, &petsc_ranks));
1060: PetscCall(PetscMalloc3(ompsize, &mumps->rhs_nrow, ompsize, &mumps->rhs_recvcounts, ompsize, &mumps->rhs_disps));
1061: PetscCallMPI(MPI_Comm_group(mumps->petsc_comm, &petsc_group));
1062: PetscCallMPI(MPI_Comm_group(mumps->omp_comm, &omp_group));
1063: for (j = 0; j < ompsize; j++) omp_ranks[j] = j;
1064: PetscCallMPI(MPI_Group_translate_ranks(omp_group, ompsize, omp_ranks, petsc_group, petsc_ranks));
1066: /* Populate mumps->irhs_loc[], rhs_nrow[] */
1067: mumps->nloc_rhs = 0;
1068: PetscCall(MatGetOwnershipRanges(A, &ranges));
1069: for (j = 0; j < ompsize; j++) {
1070: mumps->rhs_nrow[j] = ranges[petsc_ranks[j] + 1] - ranges[petsc_ranks[j]];
1071: mumps->nloc_rhs += mumps->rhs_nrow[j];
1072: }
1073: PetscCall(PetscMalloc1(mumps->nloc_rhs, &mumps->irhs_loc));
1074: for (j = k = 0; j < ompsize; j++) {
1075: for (i = ranges[petsc_ranks[j]]; i < ranges[petsc_ranks[j] + 1]; i++, k++) mumps->irhs_loc[k] = i + 1; /* uses 1-based indices */
1076: }
1078: PetscCall(PetscFree2(omp_ranks, petsc_ranks));
1079: PetscCallMPI(MPI_Group_free(&petsc_group));
1080: PetscCallMPI(MPI_Group_free(&omp_group));
1081: }
1083: /* Realloc buffers when current nrhs is bigger than what we have met */
1084: if (nrhs > mumps->max_nrhs) {
1085: PetscCall(PetscFree2(mumps->rhs_loc, mumps->rhs_recvbuf));
1086: PetscCall(PetscMalloc2(mumps->nloc_rhs * nrhs, &mumps->rhs_loc, mumps->nloc_rhs * nrhs, &mumps->rhs_recvbuf));
1087: mumps->max_nrhs = nrhs;
1088: }
1090: /* Setup recvcounts[], disps[], recvbuf on omp rank 0 for the upcoming MPI_Gatherv */
1091: for (j = 0; j < ompsize; j++) PetscCall(PetscMPIIntCast(mumps->rhs_nrow[j] * nrhs, &mumps->rhs_recvcounts[j]));
1092: mumps->rhs_disps[0] = 0;
1093: for (j = 1; j < ompsize; j++) {
1094: mumps->rhs_disps[j] = mumps->rhs_disps[j - 1] + mumps->rhs_recvcounts[j - 1];
1095: PetscCheck(mumps->rhs_disps[j] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "PetscMPIInt overflow!");
1096: }
1097: recvbuf = (nrhs == 1) ? mumps->rhs_loc : mumps->rhs_recvbuf; /* Directly use rhs_loc[] as recvbuf. Single rhs is common in Ax=b */
1098: }
1100: PetscCall(PetscMPIIntCast(m * nrhs, &sendcount));
1101: PetscCallMPI(MPI_Gatherv(array, sendcount, MPIU_SCALAR, recvbuf, mumps->rhs_recvcounts, mumps->rhs_disps, MPIU_SCALAR, 0, mumps->omp_comm));
1103: if (mumps->is_omp_master) {
1104: if (nrhs > 1) { /* Copy & re-arrange data from rhs_recvbuf[] to mumps->rhs_loc[] only when there are multiple rhs */
1105: PetscScalar *dst, *dstbase = mumps->rhs_loc;
1106: for (j = 0; j < ompsize; j++) {
1107: const PetscScalar *src = mumps->rhs_recvbuf + mumps->rhs_disps[j];
1108: dst = dstbase;
1109: for (i = 0; i < nrhs; i++) {
1110: PetscCall(PetscArraycpy(dst, src, mumps->rhs_nrow[j]));
1111: src += mumps->rhs_nrow[j];
1112: dst += mumps->nloc_rhs;
1113: }
1114: dstbase += mumps->rhs_nrow[j];
1115: }
1116: }
1117: mumps->id.rhs_loc = (MumpsScalar *)mumps->rhs_loc;
1118: }
1119: #endif /* PETSC_HAVE_OPENMP_SUPPORT */
1120: }
1121: mumps->id.nrhs = nrhs;
1122: mumps->id.nloc_rhs = mumps->nloc_rhs;
1123: mumps->id.lrhs_loc = mumps->nloc_rhs;
1124: mumps->id.irhs_loc = mumps->irhs_loc;
1125: PetscFunctionReturn(PETSC_SUCCESS);
1126: }
1128: PetscErrorCode MatSolve_MUMPS(Mat A, Vec b, Vec x)
1129: {
1130: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1131: const PetscScalar *rarray = NULL;
1132: PetscScalar *array;
1133: IS is_iden, is_petsc;
1134: PetscInt i;
1135: PetscBool second_solve = PETSC_FALSE;
1136: static PetscBool cite1 = PETSC_FALSE, cite2 = PETSC_FALSE;
1138: PetscFunctionBegin;
1139: 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 "
1140: "Journal on Matrix Analysis and Applications},\n volume = {23},\n number = {1},\n pages = {15--41},\n year = {2001}\n}\n",
1141: &cite1));
1142: 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 "
1143: "Computing},\n volume = {32},\n number = {2},\n pages = {136--156},\n year = {2006}\n}\n",
1144: &cite2));
1146: if (A->factorerrortype) {
1147: 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)));
1148: PetscCall(VecSetInf(x));
1149: PetscFunctionReturn(PETSC_SUCCESS);
1150: }
1152: mumps->id.nrhs = 1;
1153: if (mumps->petsc_size > 1) {
1154: if (mumps->ICNTL20 == 10) {
1155: mumps->id.ICNTL(20) = 10; /* dense distributed RHS */
1156: PetscCall(VecGetArrayRead(b, &rarray));
1157: PetscCall(MatMumpsSetUpDistRHSInfo(A, 1, rarray));
1158: } else {
1159: mumps->id.ICNTL(20) = 0; /* dense centralized RHS; Scatter b into a sequential rhs vector*/
1160: PetscCall(VecScatterBegin(mumps->scat_rhs, b, mumps->b_seq, INSERT_VALUES, SCATTER_FORWARD));
1161: PetscCall(VecScatterEnd(mumps->scat_rhs, b, mumps->b_seq, INSERT_VALUES, SCATTER_FORWARD));
1162: if (!mumps->myid) {
1163: PetscCall(VecGetArray(mumps->b_seq, &array));
1164: mumps->id.rhs = (MumpsScalar *)array;
1165: }
1166: }
1167: } else { /* petsc_size == 1 */
1168: mumps->id.ICNTL(20) = 0; /* dense centralized RHS */
1169: PetscCall(VecCopy(b, x));
1170: PetscCall(VecGetArray(x, &array));
1171: mumps->id.rhs = (MumpsScalar *)array;
1172: }
1174: /*
1175: handle condensation step of Schur complement (if any)
1176: We set by default ICNTL(26) == -1 when Schur indices have been provided by the user.
1177: According to MUMPS (5.0.0) manual, any value should be harmful during the factorization phase
1178: Unless the user provides a valid value for ICNTL(26), MatSolve and MatMatSolve routines solve the full system.
1179: This requires an extra call to PetscMUMPS_c and the computation of the factors for S
1180: */
1181: if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) {
1182: PetscCheck(mumps->petsc_size <= 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Parallel Schur complements not yet supported from PETSc");
1183: second_solve = PETSC_TRUE;
1184: PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1185: }
1186: /* solve phase */
1187: mumps->id.job = JOB_SOLVE;
1188: PetscMUMPS_c(mumps);
1189: 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));
1191: /* handle expansion step of Schur complement (if any) */
1192: if (second_solve) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_TRUE));
1194: if (mumps->petsc_size > 1) { /* convert mumps distributed solution to petsc mpi x */
1195: if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) {
1196: /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */
1197: PetscCall(VecScatterDestroy(&mumps->scat_sol));
1198: }
1199: if (!mumps->scat_sol) { /* create scatter scat_sol */
1200: PetscInt *isol2_loc = NULL;
1201: PetscCall(ISCreateStride(PETSC_COMM_SELF, mumps->id.lsol_loc, 0, 1, &is_iden)); /* from */
1202: PetscCall(PetscMalloc1(mumps->id.lsol_loc, &isol2_loc));
1203: for (i = 0; i < mumps->id.lsol_loc; i++) isol2_loc[i] = mumps->id.isol_loc[i] - 1; /* change Fortran style to C style */
1204: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, mumps->id.lsol_loc, isol2_loc, PETSC_OWN_POINTER, &is_petsc)); /* to */
1205: PetscCall(VecScatterCreate(mumps->x_seq, is_iden, x, is_petsc, &mumps->scat_sol));
1206: PetscCall(ISDestroy(&is_iden));
1207: PetscCall(ISDestroy(&is_petsc));
1208: mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */
1209: }
1211: PetscCall(VecScatterBegin(mumps->scat_sol, mumps->x_seq, x, INSERT_VALUES, SCATTER_FORWARD));
1212: PetscCall(VecScatterEnd(mumps->scat_sol, mumps->x_seq, x, INSERT_VALUES, SCATTER_FORWARD));
1213: }
1215: if (mumps->petsc_size > 1) {
1216: if (mumps->ICNTL20 == 10) {
1217: PetscCall(VecRestoreArrayRead(b, &rarray));
1218: } else if (!mumps->myid) {
1219: PetscCall(VecRestoreArray(mumps->b_seq, &array));
1220: }
1221: } else PetscCall(VecRestoreArray(x, &array));
1223: PetscCall(PetscLogFlops(2.0 * mumps->id.RINFO(3)));
1224: PetscFunctionReturn(PETSC_SUCCESS);
1225: }
1227: PetscErrorCode MatSolveTranspose_MUMPS(Mat A, Vec b, Vec x)
1228: {
1229: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1231: PetscFunctionBegin;
1232: mumps->id.ICNTL(9) = 0;
1233: PetscCall(MatSolve_MUMPS(A, b, x));
1234: mumps->id.ICNTL(9) = 1;
1235: PetscFunctionReturn(PETSC_SUCCESS);
1236: }
1238: PetscErrorCode MatMatSolve_MUMPS(Mat A, Mat B, Mat X)
1239: {
1240: Mat Bt = NULL;
1241: PetscBool denseX, denseB, flg, flgT;
1242: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1243: PetscInt i, nrhs, M;
1244: PetscScalar *array;
1245: const PetscScalar *rbray;
1246: PetscInt lsol_loc, nlsol_loc, *idxx, iidx = 0;
1247: PetscMUMPSInt *isol_loc, *isol_loc_save;
1248: PetscScalar *bray, *sol_loc, *sol_loc_save;
1249: IS is_to, is_from;
1250: PetscInt k, proc, j, m, myrstart;
1251: const PetscInt *rstart;
1252: Vec v_mpi, msol_loc;
1253: VecScatter scat_sol;
1254: Vec b_seq;
1255: VecScatter scat_rhs;
1256: PetscScalar *aa;
1257: PetscInt spnr, *ia, *ja;
1258: Mat_MPIAIJ *b = NULL;
1260: PetscFunctionBegin;
1261: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &denseX, MATSEQDENSE, MATMPIDENSE, NULL));
1262: PetscCheck(denseX, PetscObjectComm((PetscObject)X), PETSC_ERR_ARG_WRONG, "Matrix X must be MATDENSE matrix");
1264: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &denseB, MATSEQDENSE, MATMPIDENSE, NULL));
1265: if (denseB) {
1266: PetscCheck(B->rmap->n == X->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Matrix B and X must have same row distribution");
1267: mumps->id.ICNTL(20) = 0; /* dense RHS */
1268: } else { /* sparse B */
1269: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
1270: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATTRANSPOSEVIRTUAL, &flgT));
1271: if (flgT) { /* input B is transpose of actual RHS matrix,
1272: because mumps requires sparse compressed COLUMN storage! See MatMatTransposeSolve_MUMPS() */
1273: PetscCall(MatTransposeGetMat(B, &Bt));
1274: } else SETERRQ(PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Matrix B must be MATTRANSPOSEVIRTUAL matrix");
1275: mumps->id.ICNTL(20) = 1; /* sparse RHS */
1276: }
1278: PetscCall(MatGetSize(B, &M, &nrhs));
1279: mumps->id.nrhs = nrhs;
1280: mumps->id.lrhs = M;
1281: mumps->id.rhs = NULL;
1283: if (mumps->petsc_size == 1) {
1284: PetscScalar *aa;
1285: PetscInt spnr, *ia, *ja;
1286: PetscBool second_solve = PETSC_FALSE;
1288: PetscCall(MatDenseGetArray(X, &array));
1289: mumps->id.rhs = (MumpsScalar *)array;
1291: if (denseB) {
1292: /* copy B to X */
1293: PetscCall(MatDenseGetArrayRead(B, &rbray));
1294: PetscCall(PetscArraycpy(array, rbray, M * nrhs));
1295: PetscCall(MatDenseRestoreArrayRead(B, &rbray));
1296: } else { /* sparse B */
1297: PetscCall(MatSeqAIJGetArray(Bt, &aa));
1298: PetscCall(MatGetRowIJ(Bt, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1299: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
1300: PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
1301: mumps->id.rhs_sparse = (MumpsScalar *)aa;
1302: }
1303: /* handle condensation step of Schur complement (if any) */
1304: if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) {
1305: second_solve = PETSC_TRUE;
1306: PetscCall(MatMumpsHandleSchur_Private(A, PETSC_FALSE));
1307: }
1308: /* solve phase */
1309: mumps->id.job = JOB_SOLVE;
1310: PetscMUMPS_c(mumps);
1311: 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));
1313: /* handle expansion step of Schur complement (if any) */
1314: if (second_solve) PetscCall(MatMumpsHandleSchur_Private(A, PETSC_TRUE));
1315: if (!denseB) { /* sparse B */
1316: PetscCall(MatSeqAIJRestoreArray(Bt, &aa));
1317: PetscCall(MatRestoreRowIJ(Bt, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1318: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot restore IJ structure");
1319: }
1320: PetscCall(MatDenseRestoreArray(X, &array));
1321: PetscFunctionReturn(PETSC_SUCCESS);
1322: }
1324: /* parallel case: MUMPS requires rhs B to be centralized on the host! */
1325: PetscCheck(mumps->petsc_size <= 1 || !mumps->id.ICNTL(19), PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Parallel Schur complements not yet supported from PETSc");
1327: /* create msol_loc to hold mumps local solution */
1328: isol_loc_save = mumps->id.isol_loc; /* save it for MatSolve() */
1329: sol_loc_save = (PetscScalar *)mumps->id.sol_loc;
1331: lsol_loc = mumps->id.lsol_loc;
1332: nlsol_loc = nrhs * lsol_loc; /* length of sol_loc */
1333: PetscCall(PetscMalloc2(nlsol_loc, &sol_loc, lsol_loc, &isol_loc));
1334: mumps->id.sol_loc = (MumpsScalar *)sol_loc;
1335: mumps->id.isol_loc = isol_loc;
1337: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, nlsol_loc, (PetscScalar *)sol_loc, &msol_loc));
1339: if (denseB) {
1340: if (mumps->ICNTL20 == 10) {
1341: mumps->id.ICNTL(20) = 10; /* dense distributed RHS */
1342: PetscCall(MatDenseGetArrayRead(B, &rbray));
1343: PetscCall(MatMumpsSetUpDistRHSInfo(A, nrhs, rbray));
1344: PetscCall(MatDenseRestoreArrayRead(B, &rbray));
1345: PetscCall(MatGetLocalSize(B, &m, NULL));
1346: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B), 1, nrhs * m, nrhs * M, NULL, &v_mpi));
1347: } else {
1348: mumps->id.ICNTL(20) = 0; /* dense centralized RHS */
1349: /* TODO: Because of non-contiguous indices, the created vecscatter scat_rhs is not done in MPI_Gather, resulting in
1350: very inefficient communication. An optimization is to use VecScatterCreateToZero to gather B to rank 0. Then on rank
1351: 0, re-arrange B into desired order, which is a local operation.
1352: */
1354: /* scatter v_mpi to b_seq because MUMPS before 5.3.0 only supports centralized rhs */
1355: /* wrap dense rhs matrix B into a vector v_mpi */
1356: PetscCall(MatGetLocalSize(B, &m, NULL));
1357: PetscCall(MatDenseGetArray(B, &bray));
1358: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)B), 1, nrhs * m, nrhs * M, (const PetscScalar *)bray, &v_mpi));
1359: PetscCall(MatDenseRestoreArray(B, &bray));
1361: /* scatter v_mpi to b_seq in proc[0]. MUMPS requires rhs to be centralized on the host! */
1362: if (!mumps->myid) {
1363: PetscInt *idx;
1364: /* idx: maps from k-th index of v_mpi to (i,j)-th global entry of B */
1365: PetscCall(PetscMalloc1(nrhs * M, &idx));
1366: PetscCall(MatGetOwnershipRanges(B, &rstart));
1367: k = 0;
1368: for (proc = 0; proc < mumps->petsc_size; proc++) {
1369: for (j = 0; j < nrhs; j++) {
1370: for (i = rstart[proc]; i < rstart[proc + 1]; i++) idx[k++] = j * M + i;
1371: }
1372: }
1374: PetscCall(VecCreateSeq(PETSC_COMM_SELF, nrhs * M, &b_seq));
1375: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nrhs * M, idx, PETSC_OWN_POINTER, &is_to));
1376: PetscCall(ISCreateStride(PETSC_COMM_SELF, nrhs * M, 0, 1, &is_from));
1377: } else {
1378: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 0, &b_seq));
1379: PetscCall(ISCreateStride(PETSC_COMM_SELF, 0, 0, 1, &is_to));
1380: PetscCall(ISCreateStride(PETSC_COMM_SELF, 0, 0, 1, &is_from));
1381: }
1382: PetscCall(VecScatterCreate(v_mpi, is_from, b_seq, is_to, &scat_rhs));
1383: PetscCall(VecScatterBegin(scat_rhs, v_mpi, b_seq, INSERT_VALUES, SCATTER_FORWARD));
1384: PetscCall(ISDestroy(&is_to));
1385: PetscCall(ISDestroy(&is_from));
1386: PetscCall(VecScatterEnd(scat_rhs, v_mpi, b_seq, INSERT_VALUES, SCATTER_FORWARD));
1388: if (!mumps->myid) { /* define rhs on the host */
1389: PetscCall(VecGetArray(b_seq, &bray));
1390: mumps->id.rhs = (MumpsScalar *)bray;
1391: PetscCall(VecRestoreArray(b_seq, &bray));
1392: }
1393: }
1394: } else { /* sparse B */
1395: b = (Mat_MPIAIJ *)Bt->data;
1397: /* wrap dense X into a vector v_mpi */
1398: PetscCall(MatGetLocalSize(X, &m, NULL));
1399: PetscCall(MatDenseGetArray(X, &bray));
1400: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)X), 1, nrhs * m, nrhs * M, (const PetscScalar *)bray, &v_mpi));
1401: PetscCall(MatDenseRestoreArray(X, &bray));
1403: if (!mumps->myid) {
1404: PetscCall(MatSeqAIJGetArray(b->A, &aa));
1405: PetscCall(MatGetRowIJ(b->A, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1406: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
1407: PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
1408: mumps->id.rhs_sparse = (MumpsScalar *)aa;
1409: } else {
1410: mumps->id.irhs_ptr = NULL;
1411: mumps->id.irhs_sparse = NULL;
1412: mumps->id.nz_rhs = 0;
1413: mumps->id.rhs_sparse = NULL;
1414: }
1415: }
1417: /* solve phase */
1418: mumps->id.job = JOB_SOLVE;
1419: PetscMUMPS_c(mumps);
1420: 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));
1422: /* scatter mumps distributed solution to petsc vector v_mpi, which shares local arrays with solution matrix X */
1423: PetscCall(MatDenseGetArray(X, &array));
1424: PetscCall(VecPlaceArray(v_mpi, array));
1426: /* create scatter scat_sol */
1427: PetscCall(MatGetOwnershipRanges(X, &rstart));
1428: /* iidx: index for scatter mumps solution to petsc X */
1430: PetscCall(ISCreateStride(PETSC_COMM_SELF, nlsol_loc, 0, 1, &is_from));
1431: PetscCall(PetscMalloc1(nlsol_loc, &idxx));
1432: for (i = 0; i < lsol_loc; i++) {
1433: 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 */
1435: for (proc = 0; proc < mumps->petsc_size; proc++) {
1436: if (isol_loc[i] >= rstart[proc] && isol_loc[i] < rstart[proc + 1]) {
1437: myrstart = rstart[proc];
1438: k = isol_loc[i] - myrstart; /* local index on 1st column of petsc vector X */
1439: iidx = k + myrstart * nrhs; /* maps mumps isol_loc[i] to petsc index in X */
1440: m = rstart[proc + 1] - rstart[proc]; /* rows of X for this proc */
1441: break;
1442: }
1443: }
1445: for (j = 0; j < nrhs; j++) idxx[i + j * lsol_loc] = iidx + j * m;
1446: }
1447: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, nlsol_loc, idxx, PETSC_COPY_VALUES, &is_to));
1448: PetscCall(VecScatterCreate(msol_loc, is_from, v_mpi, is_to, &scat_sol));
1449: PetscCall(VecScatterBegin(scat_sol, msol_loc, v_mpi, INSERT_VALUES, SCATTER_FORWARD));
1450: PetscCall(ISDestroy(&is_from));
1451: PetscCall(ISDestroy(&is_to));
1452: PetscCall(VecScatterEnd(scat_sol, msol_loc, v_mpi, INSERT_VALUES, SCATTER_FORWARD));
1453: PetscCall(MatDenseRestoreArray(X, &array));
1455: /* free spaces */
1456: mumps->id.sol_loc = (MumpsScalar *)sol_loc_save;
1457: mumps->id.isol_loc = isol_loc_save;
1459: PetscCall(PetscFree2(sol_loc, isol_loc));
1460: PetscCall(PetscFree(idxx));
1461: PetscCall(VecDestroy(&msol_loc));
1462: PetscCall(VecDestroy(&v_mpi));
1463: if (!denseB) {
1464: if (!mumps->myid) {
1465: b = (Mat_MPIAIJ *)Bt->data;
1466: PetscCall(MatSeqAIJRestoreArray(b->A, &aa));
1467: PetscCall(MatRestoreRowIJ(b->A, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
1468: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot restore IJ structure");
1469: }
1470: } else {
1471: if (mumps->ICNTL20 == 0) {
1472: PetscCall(VecDestroy(&b_seq));
1473: PetscCall(VecScatterDestroy(&scat_rhs));
1474: }
1475: }
1476: PetscCall(VecScatterDestroy(&scat_sol));
1477: PetscCall(PetscLogFlops(2.0 * nrhs * mumps->id.RINFO(3)));
1478: PetscFunctionReturn(PETSC_SUCCESS);
1479: }
1481: PetscErrorCode MatMatSolveTranspose_MUMPS(Mat A, Mat B, Mat X)
1482: {
1483: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
1484: PetscMUMPSInt oldvalue = mumps->id.ICNTL(9);
1486: PetscFunctionBegin;
1487: mumps->id.ICNTL(9) = 0;
1488: PetscCall(MatMatSolve_MUMPS(A, B, X));
1489: mumps->id.ICNTL(9) = oldvalue;
1490: PetscFunctionReturn(PETSC_SUCCESS);
1491: }
1493: PetscErrorCode MatMatTransposeSolve_MUMPS(Mat A, Mat Bt, Mat X)
1494: {
1495: PetscBool flg;
1496: Mat B;
1498: PetscFunctionBegin;
1499: PetscCall(PetscObjectTypeCompareAny((PetscObject)Bt, &flg, MATSEQAIJ, MATMPIAIJ, NULL));
1500: PetscCheck(flg, PetscObjectComm((PetscObject)Bt), PETSC_ERR_ARG_WRONG, "Matrix Bt must be MATAIJ matrix");
1502: /* Create B=Bt^T that uses Bt's data structure */
1503: PetscCall(MatCreateTranspose(Bt, &B));
1505: PetscCall(MatMatSolve_MUMPS(A, B, X));
1506: PetscCall(MatDestroy(&B));
1507: PetscFunctionReturn(PETSC_SUCCESS);
1508: }
1510: #if !defined(PETSC_USE_COMPLEX)
1511: /*
1512: input:
1513: F: numeric factor
1514: output:
1515: nneg: total number of negative pivots
1516: nzero: total number of zero pivots
1517: npos: (global dimension of F) - nneg - nzero
1518: */
1519: PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
1520: {
1521: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
1522: PetscMPIInt size;
1524: PetscFunctionBegin;
1525: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)F), &size));
1526: /* 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 */
1527: 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));
1529: if (nneg) *nneg = mumps->id.INFOG(12);
1530: if (nzero || npos) {
1531: 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");
1532: if (nzero) *nzero = mumps->id.INFOG(28);
1533: if (npos) *npos = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28));
1534: }
1535: PetscFunctionReturn(PETSC_SUCCESS);
1536: }
1537: #endif
1539: PetscErrorCode MatMumpsGatherNonzerosOnMaster(MatReuse reuse, Mat_MUMPS *mumps)
1540: {
1541: PetscInt i, nreqs;
1542: PetscMUMPSInt *irn, *jcn;
1543: PetscMPIInt count;
1544: PetscInt64 totnnz, remain;
1545: const PetscInt osize = mumps->omp_comm_size;
1546: PetscScalar *val;
1548: PetscFunctionBegin;
1549: if (osize > 1) {
1550: if (reuse == MAT_INITIAL_MATRIX) {
1551: /* master first gathers counts of nonzeros to receive */
1552: if (mumps->is_omp_master) PetscCall(PetscMalloc1(osize, &mumps->recvcount));
1553: PetscCallMPI(MPI_Gather(&mumps->nnz, 1, MPIU_INT64, mumps->recvcount, 1, MPIU_INT64, 0 /*master*/, mumps->omp_comm));
1555: /* Then each computes number of send/recvs */
1556: if (mumps->is_omp_master) {
1557: /* Start from 1 since self communication is not done in MPI */
1558: nreqs = 0;
1559: for (i = 1; i < osize; i++) nreqs += (mumps->recvcount[i] + PETSC_MPI_INT_MAX - 1) / PETSC_MPI_INT_MAX;
1560: } else {
1561: nreqs = (mumps->nnz + PETSC_MPI_INT_MAX - 1) / PETSC_MPI_INT_MAX;
1562: }
1563: PetscCall(PetscMalloc1(nreqs * 3, &mumps->reqs)); /* Triple the requests since we send irn, jcn and val separately */
1565: /* The following code is doing a very simple thing: omp_master rank gathers irn/jcn/val from others.
1566: MPI_Gatherv would be enough if it supports big counts > 2^31-1. Since it does not, and mumps->nnz
1567: might be a prime number > 2^31-1, we have to slice the message. Note omp_comm_size
1568: is very small, the current approach should have no extra overhead compared to MPI_Gatherv.
1569: */
1570: nreqs = 0; /* counter for actual send/recvs */
1571: if (mumps->is_omp_master) {
1572: for (i = 0, totnnz = 0; i < osize; i++) totnnz += mumps->recvcount[i]; /* totnnz = sum of nnz over omp_comm */
1573: PetscCall(PetscMalloc2(totnnz, &irn, totnnz, &jcn));
1574: PetscCall(PetscMalloc1(totnnz, &val));
1576: /* Self communication */
1577: PetscCall(PetscArraycpy(irn, mumps->irn, mumps->nnz));
1578: PetscCall(PetscArraycpy(jcn, mumps->jcn, mumps->nnz));
1579: PetscCall(PetscArraycpy(val, mumps->val, mumps->nnz));
1581: /* Replace mumps->irn/jcn etc on master with the newly allocated bigger arrays */
1582: PetscCall(PetscFree2(mumps->irn, mumps->jcn));
1583: PetscCall(PetscFree(mumps->val_alloc));
1584: mumps->nnz = totnnz;
1585: mumps->irn = irn;
1586: mumps->jcn = jcn;
1587: mumps->val = mumps->val_alloc = val;
1589: irn += mumps->recvcount[0]; /* recvcount[0] is old mumps->nnz on omp rank 0 */
1590: jcn += mumps->recvcount[0];
1591: val += mumps->recvcount[0];
1593: /* Remote communication */
1594: for (i = 1; i < osize; i++) {
1595: count = PetscMin(mumps->recvcount[i], PETSC_MPI_INT_MAX);
1596: remain = mumps->recvcount[i] - count;
1597: while (count > 0) {
1598: PetscCallMPI(MPI_Irecv(irn, count, MPIU_MUMPSINT, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1599: PetscCallMPI(MPI_Irecv(jcn, count, MPIU_MUMPSINT, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1600: PetscCallMPI(MPI_Irecv(val, count, MPIU_SCALAR, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1601: irn += count;
1602: jcn += count;
1603: val += count;
1604: count = PetscMin(remain, PETSC_MPI_INT_MAX);
1605: remain -= count;
1606: }
1607: }
1608: } else {
1609: irn = mumps->irn;
1610: jcn = mumps->jcn;
1611: val = mumps->val;
1612: count = PetscMin(mumps->nnz, PETSC_MPI_INT_MAX);
1613: remain = mumps->nnz - count;
1614: while (count > 0) {
1615: PetscCallMPI(MPI_Isend(irn, count, MPIU_MUMPSINT, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1616: PetscCallMPI(MPI_Isend(jcn, count, MPIU_MUMPSINT, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1617: PetscCallMPI(MPI_Isend(val, count, MPIU_SCALAR, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1618: irn += count;
1619: jcn += count;
1620: val += count;
1621: count = PetscMin(remain, PETSC_MPI_INT_MAX);
1622: remain -= count;
1623: }
1624: }
1625: } else {
1626: nreqs = 0;
1627: if (mumps->is_omp_master) {
1628: val = mumps->val + mumps->recvcount[0];
1629: for (i = 1; i < osize; i++) { /* Remote communication only since self data is already in place */
1630: count = PetscMin(mumps->recvcount[i], PETSC_MPI_INT_MAX);
1631: remain = mumps->recvcount[i] - count;
1632: while (count > 0) {
1633: PetscCallMPI(MPI_Irecv(val, count, MPIU_SCALAR, i, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1634: val += count;
1635: count = PetscMin(remain, PETSC_MPI_INT_MAX);
1636: remain -= count;
1637: }
1638: }
1639: } else {
1640: val = mumps->val;
1641: count = PetscMin(mumps->nnz, PETSC_MPI_INT_MAX);
1642: remain = mumps->nnz - count;
1643: while (count > 0) {
1644: PetscCallMPI(MPI_Isend(val, count, MPIU_SCALAR, 0, mumps->tag, mumps->omp_comm, &mumps->reqs[nreqs++]));
1645: val += count;
1646: count = PetscMin(remain, PETSC_MPI_INT_MAX);
1647: remain -= count;
1648: }
1649: }
1650: }
1651: PetscCallMPI(MPI_Waitall(nreqs, mumps->reqs, MPI_STATUSES_IGNORE));
1652: mumps->tag++; /* It is totally fine for above send/recvs to share one mpi tag */
1653: }
1654: PetscFunctionReturn(PETSC_SUCCESS);
1655: }
1657: PetscErrorCode MatFactorNumeric_MUMPS(Mat F, Mat A, const MatFactorInfo *info)
1658: {
1659: Mat_MUMPS *mumps = (Mat_MUMPS *)(F)->data;
1660: PetscBool isMPIAIJ;
1662: PetscFunctionBegin;
1663: if (mumps->id.INFOG(1) < 0 && !(mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0)) {
1664: 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)));
1665: 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)));
1666: PetscFunctionReturn(PETSC_SUCCESS);
1667: }
1669: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, mumps));
1670: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_REUSE_MATRIX, mumps));
1672: /* numerical factorization phase */
1673: mumps->id.job = JOB_FACTNUMERIC;
1674: if (!mumps->id.ICNTL(18)) { /* A is centralized */
1675: if (!mumps->myid) mumps->id.a = (MumpsScalar *)mumps->val;
1676: } else {
1677: mumps->id.a_loc = (MumpsScalar *)mumps->val;
1678: }
1679: PetscMUMPS_c(mumps);
1680: if (mumps->id.INFOG(1) < 0) {
1681: 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));
1682: if (mumps->id.INFOG(1) == -10) { /* numerically singular matrix */
1683: PetscCall(PetscInfo(F, "matrix is numerically singular, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1684: F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1685: } else if (mumps->id.INFOG(1) == -13) {
1686: 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)));
1687: F->factorerrortype = MAT_FACTOR_OUTMEMORY;
1688: } else if (mumps->id.INFOG(1) == -8 || mumps->id.INFOG(1) == -9 || (-16 < mumps->id.INFOG(1) && mumps->id.INFOG(1) < -10)) {
1689: 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)));
1690: F->factorerrortype = MAT_FACTOR_OUTMEMORY;
1691: } else {
1692: PetscCall(PetscInfo(F, "MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1693: F->factorerrortype = MAT_FACTOR_OTHER;
1694: }
1695: }
1696: PetscCheck(mumps->myid || mumps->id.ICNTL(16) <= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, " mumps->id.ICNTL(16):=%d", mumps->id.INFOG(16));
1698: F->assembled = PETSC_TRUE;
1700: if (F->schur) { /* reset Schur status to unfactored */
1701: #if defined(PETSC_HAVE_CUDA)
1702: F->schur->offloadmask = PETSC_OFFLOAD_CPU;
1703: #endif
1704: if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
1705: mumps->id.ICNTL(19) = 2;
1706: PetscCall(MatTranspose(F->schur, MAT_INPLACE_MATRIX, &F->schur));
1707: }
1708: PetscCall(MatFactorRestoreSchurComplement(F, NULL, MAT_FACTOR_SCHUR_UNFACTORED));
1709: }
1711: /* just to be sure that ICNTL(19) value returned by a call from MatMumpsGetIcntl is always consistent */
1712: if (!mumps->sym && mumps->id.ICNTL(19) && mumps->id.ICNTL(19) != 1) mumps->id.ICNTL(19) = 3;
1714: if (!mumps->is_omp_master) mumps->id.INFO(23) = 0;
1715: if (mumps->petsc_size > 1) {
1716: PetscInt lsol_loc;
1717: PetscScalar *sol_loc;
1719: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &isMPIAIJ));
1721: /* distributed solution; Create x_seq=sol_loc for repeated use */
1722: if (mumps->x_seq) {
1723: PetscCall(VecScatterDestroy(&mumps->scat_sol));
1724: PetscCall(PetscFree2(mumps->id.sol_loc, mumps->id.isol_loc));
1725: PetscCall(VecDestroy(&mumps->x_seq));
1726: }
1727: lsol_loc = mumps->id.INFO(23); /* length of sol_loc */
1728: PetscCall(PetscMalloc2(lsol_loc, &sol_loc, lsol_loc, &mumps->id.isol_loc));
1729: mumps->id.lsol_loc = lsol_loc;
1730: mumps->id.sol_loc = (MumpsScalar *)sol_loc;
1731: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, lsol_loc, sol_loc, &mumps->x_seq));
1732: }
1733: PetscCall(PetscLogFlops(mumps->id.RINFO(2)));
1734: PetscFunctionReturn(PETSC_SUCCESS);
1735: }
1737: /* Sets MUMPS options from the options database */
1738: PetscErrorCode MatSetFromOptions_MUMPS(Mat F, Mat A)
1739: {
1740: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
1741: PetscMUMPSInt icntl = 0, size, *listvar_schur;
1742: PetscInt info[80], i, ninfo = 80, rbs, cbs;
1743: PetscBool flg = PETSC_FALSE, schur = (PetscBool)(mumps->id.ICNTL(26) == -1);
1744: MumpsScalar *arr;
1746: PetscFunctionBegin;
1747: PetscOptionsBegin(PetscObjectComm((PetscObject)F), ((PetscObject)F)->prefix, "MUMPS Options", "Mat");
1748: if (mumps->id.job == JOB_NULL) { /* MatSetFromOptions_MUMPS() has never been called before */
1749: PetscInt nthreads = 0;
1750: PetscInt nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
1751: PetscInt nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0;
1753: mumps->petsc_comm = PetscObjectComm((PetscObject)A);
1754: PetscCallMPI(MPI_Comm_size(mumps->petsc_comm, &mumps->petsc_size));
1755: PetscCallMPI(MPI_Comm_rank(mumps->petsc_comm, &mumps->myid)); /* "if (!myid)" still works even if mumps_comm is different */
1757: PetscCall(PetscOptionsName("-mat_mumps_use_omp_threads", "Convert MPI processes into OpenMP threads", "None", &mumps->use_petsc_omp_support));
1758: if (mumps->use_petsc_omp_support) nthreads = -1; /* -1 will let PetscOmpCtrlCreate() guess a proper value when user did not supply one */
1759: /* do not use PetscOptionsInt() so that the option -mat_mumps_use_omp_threads is not displayed twice in the help */
1760: PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)F)->prefix, "-mat_mumps_use_omp_threads", &nthreads, NULL));
1761: if (mumps->use_petsc_omp_support) {
1762: 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",
1763: ((PetscObject)F)->prefix ? ((PetscObject)F)->prefix : "");
1764: 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 : "");
1765: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
1766: PetscCall(PetscOmpCtrlCreate(mumps->petsc_comm, nthreads, &mumps->omp_ctrl));
1767: PetscCall(PetscOmpCtrlGetOmpComms(mumps->omp_ctrl, &mumps->omp_comm, &mumps->mumps_comm, &mumps->is_omp_master));
1768: #endif
1769: } else {
1770: mumps->omp_comm = PETSC_COMM_SELF;
1771: mumps->mumps_comm = mumps->petsc_comm;
1772: mumps->is_omp_master = PETSC_TRUE;
1773: }
1774: PetscCallMPI(MPI_Comm_size(mumps->omp_comm, &mumps->omp_comm_size));
1775: mumps->reqs = NULL;
1776: mumps->tag = 0;
1778: if (mumps->mumps_comm != MPI_COMM_NULL) {
1779: if (PetscDefined(HAVE_OPENMP_SUPPORT) && mumps->use_petsc_omp_support) {
1780: /* It looks like MUMPS does not dup the input comm. Dup a new comm for MUMPS to avoid any tag mismatches. */
1781: MPI_Comm comm;
1782: PetscCallMPI(MPI_Comm_dup(mumps->mumps_comm, &comm));
1783: mumps->mumps_comm = comm;
1784: } else PetscCall(PetscCommGetComm(mumps->petsc_comm, &mumps->mumps_comm));
1785: }
1787: mumps->id.comm_fortran = MPI_Comm_c2f(mumps->mumps_comm);
1788: mumps->id.job = JOB_INIT;
1789: mumps->id.par = 1; /* host participates factorizaton and solve */
1790: mumps->id.sym = mumps->sym;
1792: size = mumps->id.size_schur;
1793: arr = mumps->id.schur;
1794: listvar_schur = mumps->id.listvar_schur;
1795: PetscMUMPS_c(mumps);
1796: PetscCheck(mumps->id.INFOG(1) >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MUMPS: INFOG(1)=%d", mumps->id.INFOG(1));
1797: /* restore cached ICNTL and CNTL values */
1798: for (icntl = 0; icntl < nICNTL_pre; ++icntl) mumps->id.ICNTL(mumps->ICNTL_pre[1 + 2 * icntl]) = mumps->ICNTL_pre[2 + 2 * icntl];
1799: for (icntl = 0; icntl < nCNTL_pre; ++icntl) mumps->id.CNTL((PetscInt)mumps->CNTL_pre[1 + 2 * icntl]) = mumps->CNTL_pre[2 + 2 * icntl];
1800: PetscCall(PetscFree(mumps->ICNTL_pre));
1801: PetscCall(PetscFree(mumps->CNTL_pre));
1803: if (schur) {
1804: mumps->id.size_schur = size;
1805: mumps->id.schur_lld = size;
1806: mumps->id.schur = arr;
1807: mumps->id.listvar_schur = listvar_schur;
1808: if (mumps->petsc_size > 1) {
1809: PetscBool gs; /* gs is false if any rank other than root has non-empty IS */
1811: mumps->id.ICNTL(19) = 1; /* MUMPS returns Schur centralized on the host */
1812: gs = mumps->myid ? (mumps->id.size_schur ? PETSC_FALSE : PETSC_TRUE) : PETSC_TRUE; /* always true on root; false on others if their size != 0 */
1813: PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &gs, 1, MPIU_BOOL, MPI_LAND, mumps->petsc_comm));
1814: PetscCheck(gs, PETSC_COMM_SELF, PETSC_ERR_SUP, "MUMPS distributed parallel Schur complements not yet supported from PETSc");
1815: } else {
1816: if (F->factortype == MAT_FACTOR_LU) {
1817: mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */
1818: } else {
1819: mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */
1820: }
1821: }
1822: mumps->id.ICNTL(26) = -1;
1823: }
1825: /* copy MUMPS default control values from master to slaves. Although slaves do not call MUMPS, they may access these values in code.
1826: For example, ICNTL(9) is initialized to 1 by MUMPS and slaves check ICNTL(9) in MatSolve_MUMPS.
1827: */
1828: PetscCallMPI(MPI_Bcast(mumps->id.icntl, 40, MPI_INT, 0, mumps->omp_comm));
1829: PetscCallMPI(MPI_Bcast(mumps->id.cntl, 15, MPIU_REAL, 0, mumps->omp_comm));
1831: mumps->scat_rhs = NULL;
1832: mumps->scat_sol = NULL;
1834: /* set PETSc-MUMPS default options - override MUMPS default */
1835: mumps->id.ICNTL(3) = 0;
1836: mumps->id.ICNTL(4) = 0;
1837: if (mumps->petsc_size == 1) {
1838: mumps->id.ICNTL(18) = 0; /* centralized assembled matrix input */
1839: mumps->id.ICNTL(7) = 7; /* automatic choice of ordering done by the package */
1840: } else {
1841: mumps->id.ICNTL(18) = 3; /* distributed assembled matrix input */
1842: mumps->id.ICNTL(21) = 1; /* distributed solution */
1843: }
1844: }
1845: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_1", "ICNTL(1): output stream for error messages", "None", mumps->id.ICNTL(1), &icntl, &flg));
1846: if (flg) mumps->id.ICNTL(1) = icntl;
1847: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_2", "ICNTL(2): output stream for diagnostic printing, statistics, and warning", "None", mumps->id.ICNTL(2), &icntl, &flg));
1848: if (flg) mumps->id.ICNTL(2) = icntl;
1849: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_3", "ICNTL(3): output stream for global information, collected on the host", "None", mumps->id.ICNTL(3), &icntl, &flg));
1850: if (flg) mumps->id.ICNTL(3) = icntl;
1852: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_4", "ICNTL(4): level of printing (0 to 4)", "None", mumps->id.ICNTL(4), &icntl, &flg));
1853: if (flg) mumps->id.ICNTL(4) = icntl;
1854: if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */
1856: 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));
1857: if (flg) mumps->id.ICNTL(6) = icntl;
1859: 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));
1860: if (flg) {
1861: 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");
1862: mumps->id.ICNTL(7) = icntl;
1863: }
1865: 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));
1866: /* 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() */
1867: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_10", "ICNTL(10): max num of refinements", "None", mumps->id.ICNTL(10), &mumps->id.ICNTL(10), NULL));
1868: 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));
1869: 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));
1870: 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));
1871: 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));
1872: PetscCall(MatGetBlockSizes(A, &rbs, &cbs));
1873: if (rbs == cbs && rbs > 1) mumps->id.ICNTL(15) = -rbs;
1874: 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));
1875: if (flg) {
1876: PetscCheck(mumps->id.ICNTL(15) <= 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Positive -mat_mumps_icntl_15 not handled");
1877: 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");
1878: }
1879: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_19", "ICNTL(19): computes the Schur complement", "None", mumps->id.ICNTL(19), &mumps->id.ICNTL(19), NULL));
1880: if (mumps->id.ICNTL(19) <= 0 || mumps->id.ICNTL(19) > 3) { /* reset any schur data (if any) */
1881: PetscCall(MatDestroy(&F->schur));
1882: PetscCall(MatMumpsResetSchur_Private(mumps));
1883: }
1885: /* Two MPICH Fortran MPI_IN_PLACE binding bugs prevented the use of 'mpich + mumps'. One happened with "mpi4py + mpich + mumps",
1886: and was reported by Firedrake. See https://bitbucket.org/mpi4py/mpi4py/issues/162/mpi4py-initialization-breaks-fortran
1887: and a petsc-maint mailing list thread with subject 'MUMPS segfaults in parallel because of ...'
1888: This bug was fixed by https://github.com/pmodels/mpich/pull/4149. But the fix brought a new bug,
1889: see https://github.com/pmodels/mpich/issues/5589. This bug was fixed by https://github.com/pmodels/mpich/pull/5590.
1890: In short, we could not use distributed RHS with MPICH until v4.0b1.
1891: */
1892: #if PETSC_PKG_MUMPS_VERSION_LT(5, 3, 0) || (defined(PETSC_HAVE_MPICH_NUMVERSION) && (PETSC_HAVE_MPICH_NUMVERSION < 40000101))
1893: mumps->ICNTL20 = 0; /* Centralized dense RHS*/
1894: #else
1895: mumps->ICNTL20 = 10; /* Distributed dense RHS*/
1896: #endif
1897: 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));
1898: 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);
1899: #if PETSC_PKG_MUMPS_VERSION_LT(5, 3, 0)
1900: PetscCheck(!flg || mumps->ICNTL20 != 10, PETSC_COMM_SELF, PETSC_ERR_SUP, "ICNTL(20)=10 is not supported before MUMPS-5.3.0");
1901: #endif
1902: /* 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 */
1904: 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));
1905: 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));
1906: 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));
1907: if (mumps->id.ICNTL(24)) { mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */ }
1909: 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));
1910: 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));
1911: 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));
1912: 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));
1913: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_29", "ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis", "None", mumps->id.ICNTL(29), &mumps->id.ICNTL(29), NULL));
1914: /* 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 */
1915: 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));
1916: /* 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 */
1917: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_33", "ICNTL(33): compute determinant", "None", mumps->id.ICNTL(33), &mumps->id.ICNTL(33), NULL));
1918: 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));
1919: PetscCall(PetscOptionsMUMPSInt("-mat_mumps_icntl_36", "ICNTL(36): choice of BLR factorization variant", "None", mumps->id.ICNTL(36), &mumps->id.ICNTL(36), NULL));
1920: 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));
1922: PetscCall(PetscOptionsReal("-mat_mumps_cntl_1", "CNTL(1): relative pivoting threshold", "None", mumps->id.CNTL(1), &mumps->id.CNTL(1), NULL));
1923: PetscCall(PetscOptionsReal("-mat_mumps_cntl_2", "CNTL(2): stopping criterion of refinement", "None", mumps->id.CNTL(2), &mumps->id.CNTL(2), NULL));
1924: PetscCall(PetscOptionsReal("-mat_mumps_cntl_3", "CNTL(3): absolute pivoting threshold", "None", mumps->id.CNTL(3), &mumps->id.CNTL(3), NULL));
1925: PetscCall(PetscOptionsReal("-mat_mumps_cntl_4", "CNTL(4): value for static pivoting", "None", mumps->id.CNTL(4), &mumps->id.CNTL(4), NULL));
1926: PetscCall(PetscOptionsReal("-mat_mumps_cntl_5", "CNTL(5): fixation for null pivots", "None", mumps->id.CNTL(5), &mumps->id.CNTL(5), NULL));
1927: PetscCall(PetscOptionsReal("-mat_mumps_cntl_7", "CNTL(7): dropping parameter used during BLR", "None", mumps->id.CNTL(7), &mumps->id.CNTL(7), NULL));
1929: 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));
1931: PetscCall(PetscOptionsIntArray("-mat_mumps_view_info", "request INFO local to each processor", "", info, &ninfo, NULL));
1932: if (ninfo) {
1933: PetscCheck(ninfo <= 80, PETSC_COMM_SELF, PETSC_ERR_USER, "number of INFO %" PetscInt_FMT " must <= 80", ninfo);
1934: PetscCall(PetscMalloc1(ninfo, &mumps->info));
1935: mumps->ninfo = ninfo;
1936: for (i = 0; i < ninfo; i++) {
1937: PetscCheck(info[i] >= 0 && info[i] <= 80, PETSC_COMM_SELF, PETSC_ERR_USER, "index of INFO %" PetscInt_FMT " must between 1 and 80", ninfo);
1938: mumps->info[i] = info[i];
1939: }
1940: }
1941: PetscOptionsEnd();
1942: PetscFunctionReturn(PETSC_SUCCESS);
1943: }
1945: PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F, Mat A, const MatFactorInfo *info, Mat_MUMPS *mumps)
1946: {
1947: PetscFunctionBegin;
1948: if (mumps->id.INFOG(1) < 0) {
1949: PetscCheck(!A->erroriffailure, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error reported by MUMPS in analysis phase: INFOG(1)=%d", mumps->id.INFOG(1));
1950: if (mumps->id.INFOG(1) == -6) {
1951: PetscCall(PetscInfo(F, "matrix is singular in structure, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1952: F->factorerrortype = MAT_FACTOR_STRUCT_ZEROPIVOT;
1953: } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) {
1954: PetscCall(PetscInfo(F, "problem of workspace, INFOG(1)=%d, INFO(2)=%d\n", mumps->id.INFOG(1), mumps->id.INFO(2)));
1955: F->factorerrortype = MAT_FACTOR_OUTMEMORY;
1956: } else if (mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0) {
1957: PetscCall(PetscInfo(F, "Empty matrix\n"));
1958: } else {
1959: 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)));
1960: F->factorerrortype = MAT_FACTOR_OTHER;
1961: }
1962: }
1963: PetscFunctionReturn(PETSC_SUCCESS);
1964: }
1966: PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
1967: {
1968: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
1969: Vec b;
1970: const PetscInt M = A->rmap->N;
1972: PetscFunctionBegin;
1973: if (mumps->matstruc == SAME_NONZERO_PATTERN) {
1974: /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */
1975: PetscFunctionReturn(PETSC_SUCCESS);
1976: }
1978: /* Set MUMPS options from the options database */
1979: PetscCall(MatSetFromOptions_MUMPS(F, A));
1981: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
1982: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));
1984: /* analysis phase */
1985: mumps->id.job = JOB_FACTSYMBOLIC;
1986: mumps->id.n = M;
1987: switch (mumps->id.ICNTL(18)) {
1988: case 0: /* centralized assembled matrix input */
1989: if (!mumps->myid) {
1990: mumps->id.nnz = mumps->nnz;
1991: mumps->id.irn = mumps->irn;
1992: mumps->id.jcn = mumps->jcn;
1993: if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
1994: if (r) {
1995: mumps->id.ICNTL(7) = 1;
1996: if (!mumps->myid) {
1997: const PetscInt *idx;
1998: PetscInt i;
2000: PetscCall(PetscMalloc1(M, &mumps->id.perm_in));
2001: PetscCall(ISGetIndices(r, &idx));
2002: for (i = 0; i < M; i++) PetscCall(PetscMUMPSIntCast(idx[i] + 1, &(mumps->id.perm_in[i]))); /* perm_in[]: start from 1, not 0! */
2003: PetscCall(ISRestoreIndices(r, &idx));
2004: }
2005: }
2006: }
2007: break;
2008: case 3: /* distributed assembled matrix input (size>1) */
2009: mumps->id.nnz_loc = mumps->nnz;
2010: mumps->id.irn_loc = mumps->irn;
2011: mumps->id.jcn_loc = mumps->jcn;
2012: if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2013: if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2014: PetscCall(MatCreateVecs(A, NULL, &b));
2015: PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2016: PetscCall(VecDestroy(&b));
2017: }
2018: break;
2019: }
2020: PetscMUMPS_c(mumps);
2021: PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));
2023: F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
2024: F->ops->solve = MatSolve_MUMPS;
2025: F->ops->solvetranspose = MatSolveTranspose_MUMPS;
2026: F->ops->matsolve = MatMatSolve_MUMPS;
2027: F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS;
2028: F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;
2030: mumps->matstruc = SAME_NONZERO_PATTERN;
2031: PetscFunctionReturn(PETSC_SUCCESS);
2032: }
2034: /* Note the Petsc r and c permutations are ignored */
2035: PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
2036: {
2037: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2038: Vec b;
2039: const PetscInt M = A->rmap->N;
2041: PetscFunctionBegin;
2042: if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2043: /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */
2044: PetscFunctionReturn(PETSC_SUCCESS);
2045: }
2047: /* Set MUMPS options from the options database */
2048: PetscCall(MatSetFromOptions_MUMPS(F, A));
2050: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2051: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));
2053: /* analysis phase */
2054: mumps->id.job = JOB_FACTSYMBOLIC;
2055: mumps->id.n = M;
2056: switch (mumps->id.ICNTL(18)) {
2057: case 0: /* centralized assembled matrix input */
2058: if (!mumps->myid) {
2059: mumps->id.nnz = mumps->nnz;
2060: mumps->id.irn = mumps->irn;
2061: mumps->id.jcn = mumps->jcn;
2062: if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2063: }
2064: break;
2065: case 3: /* distributed assembled matrix input (size>1) */
2066: mumps->id.nnz_loc = mumps->nnz;
2067: mumps->id.irn_loc = mumps->irn;
2068: mumps->id.jcn_loc = mumps->jcn;
2069: if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2070: if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2071: PetscCall(MatCreateVecs(A, NULL, &b));
2072: PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2073: PetscCall(VecDestroy(&b));
2074: }
2075: break;
2076: }
2077: PetscMUMPS_c(mumps);
2078: PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));
2080: F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
2081: F->ops->solve = MatSolve_MUMPS;
2082: F->ops->solvetranspose = MatSolveTranspose_MUMPS;
2083: F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;
2085: mumps->matstruc = SAME_NONZERO_PATTERN;
2086: PetscFunctionReturn(PETSC_SUCCESS);
2087: }
2089: /* Note the Petsc r permutation and factor info are ignored */
2090: PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F, Mat A, IS r, const MatFactorInfo *info)
2091: {
2092: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2093: Vec b;
2094: const PetscInt M = A->rmap->N;
2096: PetscFunctionBegin;
2097: if (mumps->matstruc == SAME_NONZERO_PATTERN) {
2098: /* F is assembled by a previous call of MatLUFactorSymbolic_AIJMUMPS() */
2099: PetscFunctionReturn(PETSC_SUCCESS);
2100: }
2102: /* Set MUMPS options from the options database */
2103: PetscCall(MatSetFromOptions_MUMPS(F, A));
2105: PetscCall((*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps));
2106: PetscCall(MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX, mumps));
2108: /* analysis phase */
2109: mumps->id.job = JOB_FACTSYMBOLIC;
2110: mumps->id.n = M;
2111: switch (mumps->id.ICNTL(18)) {
2112: case 0: /* centralized assembled matrix input */
2113: if (!mumps->myid) {
2114: mumps->id.nnz = mumps->nnz;
2115: mumps->id.irn = mumps->irn;
2116: mumps->id.jcn = mumps->jcn;
2117: if (mumps->id.ICNTL(6) > 1) mumps->id.a = (MumpsScalar *)mumps->val;
2118: }
2119: break;
2120: case 3: /* distributed assembled matrix input (size>1) */
2121: mumps->id.nnz_loc = mumps->nnz;
2122: mumps->id.irn_loc = mumps->irn;
2123: mumps->id.jcn_loc = mumps->jcn;
2124: if (mumps->id.ICNTL(6) > 1) mumps->id.a_loc = (MumpsScalar *)mumps->val;
2125: if (mumps->ICNTL20 == 0) { /* Centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
2126: PetscCall(MatCreateVecs(A, NULL, &b));
2127: PetscCall(VecScatterCreateToZero(b, &mumps->scat_rhs, &mumps->b_seq));
2128: PetscCall(VecDestroy(&b));
2129: }
2130: break;
2131: }
2132: PetscMUMPS_c(mumps);
2133: PetscCall(MatFactorSymbolic_MUMPS_ReportIfError(F, A, info, mumps));
2135: F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS;
2136: F->ops->solve = MatSolve_MUMPS;
2137: F->ops->solvetranspose = MatSolve_MUMPS;
2138: F->ops->matsolve = MatMatSolve_MUMPS;
2139: F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS;
2140: F->ops->matsolvetranspose = MatMatSolveTranspose_MUMPS;
2141: #if defined(PETSC_USE_COMPLEX)
2142: F->ops->getinertia = NULL;
2143: #else
2144: F->ops->getinertia = MatGetInertia_SBAIJMUMPS;
2145: #endif
2147: mumps->matstruc = SAME_NONZERO_PATTERN;
2148: PetscFunctionReturn(PETSC_SUCCESS);
2149: }
2151: PetscErrorCode MatView_MUMPS(Mat A, PetscViewer viewer)
2152: {
2153: PetscBool iascii;
2154: PetscViewerFormat format;
2155: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
2157: PetscFunctionBegin;
2158: /* check if matrix is mumps type */
2159: if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(PETSC_SUCCESS);
2161: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
2162: if (iascii) {
2163: PetscCall(PetscViewerGetFormat(viewer, &format));
2164: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
2165: PetscCall(PetscViewerASCIIPrintf(viewer, "MUMPS run parameters:\n"));
2166: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
2167: PetscCall(PetscViewerASCIIPrintf(viewer, " SYM (matrix type): %d\n", mumps->id.sym));
2168: PetscCall(PetscViewerASCIIPrintf(viewer, " PAR (host participation): %d\n", mumps->id.par));
2169: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(1) (output for error): %d\n", mumps->id.ICNTL(1)));
2170: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(2) (output of diagnostic msg): %d\n", mumps->id.ICNTL(2)));
2171: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(3) (output for global info): %d\n", mumps->id.ICNTL(3)));
2172: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(4) (level of printing): %d\n", mumps->id.ICNTL(4)));
2173: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(5) (input mat struct): %d\n", mumps->id.ICNTL(5)));
2174: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(6) (matrix prescaling): %d\n", mumps->id.ICNTL(6)));
2175: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(7) (sequential matrix ordering):%d\n", mumps->id.ICNTL(7)));
2176: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(8) (scaling strategy): %d\n", mumps->id.ICNTL(8)));
2177: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(10) (max num of refinements): %d\n", mumps->id.ICNTL(10)));
2178: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(11) (error analysis): %d\n", mumps->id.ICNTL(11)));
2179: if (mumps->id.ICNTL(11) > 0) {
2180: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(4) (inf norm of input mat): %g\n", mumps->id.RINFOG(4)));
2181: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(5) (inf norm of solution): %g\n", mumps->id.RINFOG(5)));
2182: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(6) (inf norm of residual): %g\n", mumps->id.RINFOG(6)));
2183: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(7),RINFOG(8) (backward error est): %g, %g\n", mumps->id.RINFOG(7), mumps->id.RINFOG(8)));
2184: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(9) (error estimate): %g\n", mumps->id.RINFOG(9)));
2185: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n", mumps->id.RINFOG(10), mumps->id.RINFOG(11)));
2186: }
2187: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(12) (efficiency control): %d\n", mumps->id.ICNTL(12)));
2188: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(13) (sequential factorization of the root node): %d\n", mumps->id.ICNTL(13)));
2189: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(14) (percentage of estimated workspace increase): %d\n", mumps->id.ICNTL(14)));
2190: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(15) (compression of the input matrix): %d\n", mumps->id.ICNTL(15)));
2191: /* ICNTL(15-17) not used */
2192: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(18) (input mat struct): %d\n", mumps->id.ICNTL(18)));
2193: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(19) (Schur complement info): %d\n", mumps->id.ICNTL(19)));
2194: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(20) (RHS sparse pattern): %d\n", mumps->id.ICNTL(20)));
2195: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(21) (solution struct): %d\n", mumps->id.ICNTL(21)));
2196: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(22) (in-core/out-of-core facility): %d\n", mumps->id.ICNTL(22)));
2197: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(23) (max size of memory can be allocated locally):%d\n", mumps->id.ICNTL(23)));
2199: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(24) (detection of null pivot rows): %d\n", mumps->id.ICNTL(24)));
2200: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(25) (computation of a null space basis): %d\n", mumps->id.ICNTL(25)));
2201: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(26) (Schur options for RHS or solution): %d\n", mumps->id.ICNTL(26)));
2202: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(27) (blocking size for multiple RHS): %d\n", mumps->id.ICNTL(27)));
2203: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(28) (use parallel or sequential ordering): %d\n", mumps->id.ICNTL(28)));
2204: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(29) (parallel ordering): %d\n", mumps->id.ICNTL(29)));
2206: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(30) (user-specified set of entries in inv(A)): %d\n", mumps->id.ICNTL(30)));
2207: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(31) (factors is discarded in the solve phase): %d\n", mumps->id.ICNTL(31)));
2208: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(33) (compute determinant): %d\n", mumps->id.ICNTL(33)));
2209: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(35) (activate BLR based factorization): %d\n", mumps->id.ICNTL(35)));
2210: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(36) (choice of BLR factorization variant): %d\n", mumps->id.ICNTL(36)));
2211: PetscCall(PetscViewerASCIIPrintf(viewer, " ICNTL(38) (estimated compression rate of LU factors): %d\n", mumps->id.ICNTL(38)));
2213: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(1) (relative pivoting threshold): %g\n", mumps->id.CNTL(1)));
2214: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(2) (stopping criterion of refinement): %g\n", mumps->id.CNTL(2)));
2215: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(3) (absolute pivoting threshold): %g\n", mumps->id.CNTL(3)));
2216: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(4) (value of static pivoting): %g\n", mumps->id.CNTL(4)));
2217: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(5) (fixation for null pivots): %g\n", mumps->id.CNTL(5)));
2218: PetscCall(PetscViewerASCIIPrintf(viewer, " CNTL(7) (dropping parameter for BLR): %g\n", mumps->id.CNTL(7)));
2220: /* information local to each processor */
2221: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(1) (local estimated flops for the elimination after analysis):\n"));
2222: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
2223: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %g\n", mumps->myid, mumps->id.RINFO(1)));
2224: PetscCall(PetscViewerFlush(viewer));
2225: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(2) (local estimated flops for the assembly after factorization):\n"));
2226: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %g\n", mumps->myid, mumps->id.RINFO(2)));
2227: PetscCall(PetscViewerFlush(viewer));
2228: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFO(3) (local estimated flops for the elimination after factorization):\n"));
2229: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %g\n", mumps->myid, mumps->id.RINFO(3)));
2230: PetscCall(PetscViewerFlush(viewer));
2232: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization):\n"));
2233: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(15)));
2234: PetscCall(PetscViewerFlush(viewer));
2236: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization):\n"));
2237: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(16)));
2238: PetscCall(PetscViewerFlush(viewer));
2240: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(23) (num of pivots eliminated on this processor after factorization):\n"));
2241: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(23)));
2242: PetscCall(PetscViewerFlush(viewer));
2244: if (mumps->ninfo && mumps->ninfo <= 80) {
2245: PetscInt i;
2246: for (i = 0; i < mumps->ninfo; i++) {
2247: PetscCall(PetscViewerASCIIPrintf(viewer, " INFO(%" PetscInt_FMT "):\n", mumps->info[i]));
2248: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] %d\n", mumps->myid, mumps->id.INFO(mumps->info[i])));
2249: PetscCall(PetscViewerFlush(viewer));
2250: }
2251: }
2252: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
2253: } else PetscCall(PetscViewerASCIIPrintf(viewer, " Use -%sksp_view ::ascii_info_detail to display information for all processes\n", ((PetscObject)A)->prefix ? ((PetscObject)A)->prefix : ""));
2255: if (mumps->myid == 0) { /* information from the host */
2256: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(1) (global estimated flops for the elimination after analysis): %g\n", mumps->id.RINFOG(1)));
2257: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(2) (global estimated flops for the assembly after factorization): %g\n", mumps->id.RINFOG(2)));
2258: PetscCall(PetscViewerASCIIPrintf(viewer, " RINFOG(3) (global estimated flops for the elimination after factorization): %g\n", mumps->id.RINFOG(3)));
2259: 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)));
2261: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(3) (estimated real workspace for factors on all processors after analysis): %d\n", mumps->id.INFOG(3)));
2262: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d\n", mumps->id.INFOG(4)));
2263: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(5) (estimated maximum front size in the complete tree): %d\n", mumps->id.INFOG(5)));
2264: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(6) (number of nodes in the complete tree): %d\n", mumps->id.INFOG(6)));
2265: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(7) (ordering option effectively used after analysis): %d\n", mumps->id.INFOG(7)));
2266: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d\n", mumps->id.INFOG(8)));
2267: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d\n", mumps->id.INFOG(9)));
2268: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(10) (total integer space store the matrix factors after factorization): %d\n", mumps->id.INFOG(10)));
2269: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(11) (order of largest frontal matrix after factorization): %d\n", mumps->id.INFOG(11)));
2270: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(12) (number of off-diagonal pivots): %d\n", mumps->id.INFOG(12)));
2271: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(13) (number of delayed pivots after factorization): %d\n", mumps->id.INFOG(13)));
2272: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(14) (number of memory compress after factorization): %d\n", mumps->id.INFOG(14)));
2273: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(15) (number of steps of iterative refinement after solution): %d\n", mumps->id.INFOG(15)));
2274: 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)));
2275: 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)));
2276: 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)));
2277: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): %d\n", mumps->id.INFOG(19)));
2278: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(20) (estimated number of entries in the factors): %d\n", mumps->id.INFOG(20)));
2279: 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)));
2280: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): %d\n", mumps->id.INFOG(22)));
2281: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d\n", mumps->id.INFOG(23)));
2282: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d\n", mumps->id.INFOG(24)));
2283: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(25) (after factorization: number of pivots modified by static pivoting): %d\n", mumps->id.INFOG(25)));
2284: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(28) (after factorization: number of null pivots encountered): %d\n", mumps->id.INFOG(28)));
2285: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(29) (after factorization: effective number of entries in the factors (sum over all processors)): %d\n", mumps->id.INFOG(29)));
2286: 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)));
2287: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(32) (after analysis: type of analysis done): %d\n", mumps->id.INFOG(32)));
2288: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(33) (value used for ICNTL(8)): %d\n", mumps->id.INFOG(33)));
2289: PetscCall(PetscViewerASCIIPrintf(viewer, " INFOG(34) (exponent of the determinant if determinant is requested): %d\n", mumps->id.INFOG(34)));
2290: 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)));
2291: 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)));
2292: 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)));
2293: 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)));
2294: 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)));
2295: }
2296: }
2297: }
2298: PetscFunctionReturn(PETSC_SUCCESS);
2299: }
2301: PetscErrorCode MatGetInfo_MUMPS(Mat A, MatInfoType flag, MatInfo *info)
2302: {
2303: Mat_MUMPS *mumps = (Mat_MUMPS *)A->data;
2305: PetscFunctionBegin;
2306: info->block_size = 1.0;
2307: info->nz_allocated = mumps->id.INFOG(20);
2308: info->nz_used = mumps->id.INFOG(20);
2309: info->nz_unneeded = 0.0;
2310: info->assemblies = 0.0;
2311: info->mallocs = 0.0;
2312: info->memory = 0.0;
2313: info->fill_ratio_given = 0;
2314: info->fill_ratio_needed = 0;
2315: info->factor_mallocs = 0;
2316: PetscFunctionReturn(PETSC_SUCCESS);
2317: }
2319: PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is)
2320: {
2321: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2322: const PetscScalar *arr;
2323: const PetscInt *idxs;
2324: PetscInt size, i;
2326: PetscFunctionBegin;
2327: PetscCall(ISGetLocalSize(is, &size));
2328: /* Schur complement matrix */
2329: PetscCall(MatDestroy(&F->schur));
2330: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, size, size, NULL, &F->schur));
2331: PetscCall(MatDenseGetArrayRead(F->schur, &arr));
2332: mumps->id.schur = (MumpsScalar *)arr;
2333: mumps->id.size_schur = size;
2334: mumps->id.schur_lld = size;
2335: PetscCall(MatDenseRestoreArrayRead(F->schur, &arr));
2336: if (mumps->sym == 1) PetscCall(MatSetOption(F->schur, MAT_SPD, PETSC_TRUE));
2338: /* MUMPS expects Fortran style indices */
2339: PetscCall(PetscFree(mumps->id.listvar_schur));
2340: PetscCall(PetscMalloc1(size, &mumps->id.listvar_schur));
2341: PetscCall(ISGetIndices(is, &idxs));
2342: for (i = 0; i < size; i++) PetscCall(PetscMUMPSIntCast(idxs[i] + 1, &(mumps->id.listvar_schur[i])));
2343: PetscCall(ISRestoreIndices(is, &idxs));
2344: /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */
2345: mumps->id.ICNTL(26) = -1;
2346: PetscFunctionReturn(PETSC_SUCCESS);
2347: }
2349: PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F, Mat *S)
2350: {
2351: Mat St;
2352: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2353: PetscScalar *array;
2354: #if defined(PETSC_USE_COMPLEX)
2355: PetscScalar im = PetscSqrtScalar((PetscScalar)-1.0);
2356: #endif
2358: PetscFunctionBegin;
2359: PetscCheck(mumps->id.ICNTL(19), PetscObjectComm((PetscObject)F), PETSC_ERR_ORDER, "Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it");
2360: PetscCall(MatCreate(PETSC_COMM_SELF, &St));
2361: PetscCall(MatSetSizes(St, PETSC_DECIDE, PETSC_DECIDE, mumps->id.size_schur, mumps->id.size_schur));
2362: PetscCall(MatSetType(St, MATDENSE));
2363: PetscCall(MatSetUp(St));
2364: PetscCall(MatDenseGetArray(St, &array));
2365: if (!mumps->sym) { /* MUMPS always return a full matrix */
2366: if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
2367: PetscInt i, j, N = mumps->id.size_schur;
2368: for (i = 0; i < N; i++) {
2369: for (j = 0; j < N; j++) {
2370: #if !defined(PETSC_USE_COMPLEX)
2371: PetscScalar val = mumps->id.schur[i * N + j];
2372: #else
2373: PetscScalar val = mumps->id.schur[i * N + j].r + im * mumps->id.schur[i * N + j].i;
2374: #endif
2375: array[j * N + i] = val;
2376: }
2377: }
2378: } else { /* stored by columns */
2379: PetscCall(PetscArraycpy(array, mumps->id.schur, mumps->id.size_schur * mumps->id.size_schur));
2380: }
2381: } else { /* either full or lower-triangular (not packed) */
2382: if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */
2383: PetscInt i, j, N = mumps->id.size_schur;
2384: for (i = 0; i < N; i++) {
2385: for (j = i; j < N; j++) {
2386: #if !defined(PETSC_USE_COMPLEX)
2387: PetscScalar val = mumps->id.schur[i * N + j];
2388: #else
2389: PetscScalar val = mumps->id.schur[i * N + j].r + im * mumps->id.schur[i * N + j].i;
2390: #endif
2391: array[i * N + j] = val;
2392: array[j * N + i] = val;
2393: }
2394: }
2395: } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */
2396: PetscCall(PetscArraycpy(array, mumps->id.schur, mumps->id.size_schur * mumps->id.size_schur));
2397: } else { /* ICNTL(19) == 1 lower triangular stored by rows */
2398: PetscInt i, j, N = mumps->id.size_schur;
2399: for (i = 0; i < N; i++) {
2400: for (j = 0; j < i + 1; j++) {
2401: #if !defined(PETSC_USE_COMPLEX)
2402: PetscScalar val = mumps->id.schur[i * N + j];
2403: #else
2404: PetscScalar val = mumps->id.schur[i * N + j].r + im * mumps->id.schur[i * N + j].i;
2405: #endif
2406: array[i * N + j] = val;
2407: array[j * N + i] = val;
2408: }
2409: }
2410: }
2411: }
2412: PetscCall(MatDenseRestoreArray(St, &array));
2413: *S = St;
2414: PetscFunctionReturn(PETSC_SUCCESS);
2415: }
2417: PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F, PetscInt icntl, PetscInt ival)
2418: {
2419: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2421: PetscFunctionBegin;
2422: if (mumps->id.job == JOB_NULL) { /* need to cache icntl and ival since PetscMUMPS_c() has never been called */
2423: PetscInt i, nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0; /* number of already cached ICNTL */
2424: for (i = 0; i < nICNTL_pre; ++i)
2425: if (mumps->ICNTL_pre[1 + 2 * i] == icntl) break; /* is this ICNTL already cached? */
2426: if (i == nICNTL_pre) { /* not already cached */
2427: if (i > 0) PetscCall(PetscRealloc(sizeof(PetscMUMPSInt) * (2 * nICNTL_pre + 3), &mumps->ICNTL_pre));
2428: else PetscCall(PetscCalloc(sizeof(PetscMUMPSInt) * 3, &mumps->ICNTL_pre));
2429: mumps->ICNTL_pre[0]++;
2430: }
2431: mumps->ICNTL_pre[1 + 2 * i] = icntl;
2432: PetscCall(PetscMUMPSIntCast(ival, mumps->ICNTL_pre + 2 + 2 * i));
2433: } else PetscCall(PetscMUMPSIntCast(ival, &mumps->id.ICNTL(icntl)));
2434: PetscFunctionReturn(PETSC_SUCCESS);
2435: }
2437: PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F, PetscInt icntl, PetscInt *ival)
2438: {
2439: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2441: PetscFunctionBegin;
2442: if (mumps->id.job == JOB_NULL) {
2443: PetscInt i, nICNTL_pre = mumps->ICNTL_pre ? mumps->ICNTL_pre[0] : 0;
2444: *ival = 0;
2445: for (i = 0; i < nICNTL_pre; ++i) {
2446: if (mumps->ICNTL_pre[1 + 2 * i] == icntl) *ival = mumps->ICNTL_pre[2 + 2 * i];
2447: }
2448: } else *ival = mumps->id.ICNTL(icntl);
2449: PetscFunctionReturn(PETSC_SUCCESS);
2450: }
2452: /*@
2453: MatMumpsSetIcntl - Set MUMPS parameter ICNTL()
2455: Logically Collective
2457: Input Parameters:
2458: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2459: . icntl - index of MUMPS parameter array ICNTL()
2460: - ival - value of MUMPS ICNTL(icntl)
2462: Options Database Key:
2463: . -mat_mumps_icntl_<icntl> <ival> - change the option numbered icntl to ival
2465: Level: beginner
2467: References:
2468: . * - MUMPS Users' Guide
2470: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2471: @*/
2472: PetscErrorCode MatMumpsSetIcntl(Mat F, PetscInt icntl, PetscInt ival)
2473: {
2474: PetscFunctionBegin;
2476: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2479: PetscCheck(icntl >= 1 && icntl <= 38, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported ICNTL value %" PetscInt_FMT, icntl);
2480: PetscTryMethod(F, "MatMumpsSetIcntl_C", (Mat, PetscInt, PetscInt), (F, icntl, ival));
2481: PetscFunctionReturn(PETSC_SUCCESS);
2482: }
2484: /*@
2485: MatMumpsGetIcntl - Get MUMPS parameter ICNTL()
2487: Logically Collective
2489: Input Parameters:
2490: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2491: - icntl - index of MUMPS parameter array ICNTL()
2493: Output Parameter:
2494: . ival - value of MUMPS ICNTL(icntl)
2496: Level: beginner
2498: References:
2499: . * - MUMPS Users' Guide
2501: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2502: @*/
2503: PetscErrorCode MatMumpsGetIcntl(Mat F, PetscInt icntl, PetscInt *ival)
2504: {
2505: PetscFunctionBegin;
2507: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2510: PetscCheck(icntl >= 1 && icntl <= 38, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported ICNTL value %" PetscInt_FMT, icntl);
2511: PetscUseMethod(F, "MatMumpsGetIcntl_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
2512: PetscFunctionReturn(PETSC_SUCCESS);
2513: }
2515: PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F, PetscInt icntl, PetscReal val)
2516: {
2517: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2519: PetscFunctionBegin;
2520: if (mumps->id.job == JOB_NULL) {
2521: PetscInt i, nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
2522: for (i = 0; i < nCNTL_pre; ++i)
2523: if (mumps->CNTL_pre[1 + 2 * i] == icntl) break;
2524: if (i == nCNTL_pre) {
2525: if (i > 0) PetscCall(PetscRealloc(sizeof(PetscReal) * (2 * nCNTL_pre + 3), &mumps->CNTL_pre));
2526: else PetscCall(PetscCalloc(sizeof(PetscReal) * 3, &mumps->CNTL_pre));
2527: mumps->CNTL_pre[0]++;
2528: }
2529: mumps->CNTL_pre[1 + 2 * i] = icntl;
2530: mumps->CNTL_pre[2 + 2 * i] = val;
2531: } else mumps->id.CNTL(icntl) = val;
2532: PetscFunctionReturn(PETSC_SUCCESS);
2533: }
2535: PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F, PetscInt icntl, PetscReal *val)
2536: {
2537: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2539: PetscFunctionBegin;
2540: if (mumps->id.job == JOB_NULL) {
2541: PetscInt i, nCNTL_pre = mumps->CNTL_pre ? mumps->CNTL_pre[0] : 0;
2542: *val = 0.0;
2543: for (i = 0; i < nCNTL_pre; ++i) {
2544: if (mumps->CNTL_pre[1 + 2 * i] == icntl) *val = mumps->CNTL_pre[2 + 2 * i];
2545: }
2546: } else *val = mumps->id.CNTL(icntl);
2547: PetscFunctionReturn(PETSC_SUCCESS);
2548: }
2550: /*@
2551: MatMumpsSetCntl - Set MUMPS parameter CNTL()
2553: Logically Collective
2555: Input Parameters:
2556: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2557: . icntl - index of MUMPS parameter array CNTL()
2558: - val - value of MUMPS CNTL(icntl)
2560: Options Database Key:
2561: . -mat_mumps_cntl_<icntl> <val> - change the option numbered icntl to ival
2563: Level: beginner
2565: References:
2566: . * - MUMPS Users' Guide
2568: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2569: @*/
2570: PetscErrorCode MatMumpsSetCntl(Mat F, PetscInt icntl, PetscReal val)
2571: {
2572: PetscFunctionBegin;
2574: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2577: PetscCheck(icntl >= 1 && icntl <= 7, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported CNTL value %" PetscInt_FMT, icntl);
2578: PetscTryMethod(F, "MatMumpsSetCntl_C", (Mat, PetscInt, PetscReal), (F, icntl, val));
2579: PetscFunctionReturn(PETSC_SUCCESS);
2580: }
2582: /*@
2583: MatMumpsGetCntl - Get MUMPS parameter CNTL()
2585: Logically Collective
2587: Input Parameters:
2588: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2589: - icntl - index of MUMPS parameter array CNTL()
2591: Output Parameter:
2592: . val - value of MUMPS CNTL(icntl)
2594: Level: beginner
2596: References:
2597: . * - MUMPS Users' Guide
2599: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2600: @*/
2601: PetscErrorCode MatMumpsGetCntl(Mat F, PetscInt icntl, PetscReal *val)
2602: {
2603: PetscFunctionBegin;
2605: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2608: PetscCheck(icntl >= 1 && icntl <= 7, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONG, "Unsupported CNTL value %" PetscInt_FMT, icntl);
2609: PetscUseMethod(F, "MatMumpsGetCntl_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
2610: PetscFunctionReturn(PETSC_SUCCESS);
2611: }
2613: PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F, PetscInt icntl, PetscInt *info)
2614: {
2615: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2617: PetscFunctionBegin;
2618: *info = mumps->id.INFO(icntl);
2619: PetscFunctionReturn(PETSC_SUCCESS);
2620: }
2622: PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F, PetscInt icntl, PetscInt *infog)
2623: {
2624: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2626: PetscFunctionBegin;
2627: *infog = mumps->id.INFOG(icntl);
2628: PetscFunctionReturn(PETSC_SUCCESS);
2629: }
2631: PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F, PetscInt icntl, PetscReal *rinfo)
2632: {
2633: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2635: PetscFunctionBegin;
2636: *rinfo = mumps->id.RINFO(icntl);
2637: PetscFunctionReturn(PETSC_SUCCESS);
2638: }
2640: PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F, PetscInt icntl, PetscReal *rinfog)
2641: {
2642: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2644: PetscFunctionBegin;
2645: *rinfog = mumps->id.RINFOG(icntl);
2646: PetscFunctionReturn(PETSC_SUCCESS);
2647: }
2649: PetscErrorCode MatMumpsGetNullPivots_MUMPS(Mat F, PetscInt *size, PetscInt **array)
2650: {
2651: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2653: PetscFunctionBegin;
2654: 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");
2655: *size = 0;
2656: *array = NULL;
2657: if (!mumps->myid) {
2658: *size = mumps->id.INFOG(28);
2659: PetscCall(PetscMalloc1(*size, array));
2660: for (int i = 0; i < *size; i++) (*array)[i] = mumps->id.pivnul_list[i] - 1;
2661: }
2662: PetscFunctionReturn(PETSC_SUCCESS);
2663: }
2665: PetscErrorCode MatMumpsGetInverse_MUMPS(Mat F, Mat spRHS)
2666: {
2667: Mat Bt = NULL, Btseq = NULL;
2668: PetscBool flg;
2669: Mat_MUMPS *mumps = (Mat_MUMPS *)F->data;
2670: PetscScalar *aa;
2671: PetscInt spnr, *ia, *ja, M, nrhs;
2673: PetscFunctionBegin;
2675: PetscCall(PetscObjectTypeCompare((PetscObject)spRHS, MATTRANSPOSEVIRTUAL, &flg));
2676: if (flg) {
2677: PetscCall(MatTransposeGetMat(spRHS, &Bt));
2678: } else SETERRQ(PetscObjectComm((PetscObject)spRHS), PETSC_ERR_ARG_WRONG, "Matrix spRHS must be type MATTRANSPOSEVIRTUAL matrix");
2680: PetscCall(MatMumpsSetIcntl(F, 30, 1));
2682: if (mumps->petsc_size > 1) {
2683: Mat_MPIAIJ *b = (Mat_MPIAIJ *)Bt->data;
2684: Btseq = b->A;
2685: } else {
2686: Btseq = Bt;
2687: }
2689: PetscCall(MatGetSize(spRHS, &M, &nrhs));
2690: mumps->id.nrhs = nrhs;
2691: mumps->id.lrhs = M;
2692: mumps->id.rhs = NULL;
2694: if (!mumps->myid) {
2695: PetscCall(MatSeqAIJGetArray(Btseq, &aa));
2696: PetscCall(MatGetRowIJ(Btseq, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
2697: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
2698: PetscCall(PetscMUMPSIntCSRCast(mumps, spnr, ia, ja, &mumps->id.irhs_ptr, &mumps->id.irhs_sparse, &mumps->id.nz_rhs));
2699: mumps->id.rhs_sparse = (MumpsScalar *)aa;
2700: } else {
2701: mumps->id.irhs_ptr = NULL;
2702: mumps->id.irhs_sparse = NULL;
2703: mumps->id.nz_rhs = 0;
2704: mumps->id.rhs_sparse = NULL;
2705: }
2706: mumps->id.ICNTL(20) = 1; /* rhs is sparse */
2707: mumps->id.ICNTL(21) = 0; /* solution is in assembled centralized format */
2709: /* solve phase */
2710: mumps->id.job = JOB_SOLVE;
2711: PetscMUMPS_c(mumps);
2712: 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));
2714: if (!mumps->myid) {
2715: PetscCall(MatSeqAIJRestoreArray(Btseq, &aa));
2716: PetscCall(MatRestoreRowIJ(Btseq, 1, PETSC_FALSE, PETSC_FALSE, &spnr, (const PetscInt **)&ia, (const PetscInt **)&ja, &flg));
2717: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot get IJ structure");
2718: }
2719: PetscFunctionReturn(PETSC_SUCCESS);
2720: }
2722: /*@
2723: MatMumpsGetInverse - Get user-specified set of entries in inverse of `A`
2725: Logically Collective
2727: Input Parameter:
2728: . F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2730: Output Parameter:
2731: . spRHS - sequential sparse matrix in `MATTRANSPOSEVIRTUAL` format with requested entries of inverse of `A`
2733: Level: beginner
2735: References:
2736: . * - MUMPS Users' Guide
2738: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatCreateTranspose()`
2739: @*/
2740: PetscErrorCode MatMumpsGetInverse(Mat F, Mat spRHS)
2741: {
2742: PetscFunctionBegin;
2744: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2745: PetscUseMethod(F, "MatMumpsGetInverse_C", (Mat, Mat), (F, spRHS));
2746: PetscFunctionReturn(PETSC_SUCCESS);
2747: }
2749: PetscErrorCode MatMumpsGetInverseTranspose_MUMPS(Mat F, Mat spRHST)
2750: {
2751: Mat spRHS;
2753: PetscFunctionBegin;
2754: PetscCall(MatCreateTranspose(spRHST, &spRHS));
2755: PetscCall(MatMumpsGetInverse_MUMPS(F, spRHS));
2756: PetscCall(MatDestroy(&spRHS));
2757: PetscFunctionReturn(PETSC_SUCCESS);
2758: }
2760: /*@
2761: MatMumpsGetInverseTranspose - Get user-specified set of entries in inverse of matrix `A`^T
2763: Logically Collective
2765: Input Parameter:
2766: . F - the factored matrix of A obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2768: Output Parameter:
2769: . spRHST - sequential sparse matrix in `MATAIJ` format containing the requested entries of inverse of `A`^T
2771: Level: beginner
2773: References:
2774: . * - MUMPS Users' Guide
2776: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatCreateTranspose()`, `MatMumpsGetInverse()`
2777: @*/
2778: PetscErrorCode MatMumpsGetInverseTranspose(Mat F, Mat spRHST)
2779: {
2780: PetscBool flg;
2782: PetscFunctionBegin;
2784: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2785: PetscCall(PetscObjectTypeCompareAny((PetscObject)spRHST, &flg, MATSEQAIJ, MATMPIAIJ, NULL));
2786: PetscCheck(flg, PetscObjectComm((PetscObject)spRHST), PETSC_ERR_ARG_WRONG, "Matrix spRHST must be MATAIJ matrix");
2788: PetscUseMethod(F, "MatMumpsGetInverseTranspose_C", (Mat, Mat), (F, spRHST));
2789: PetscFunctionReturn(PETSC_SUCCESS);
2790: }
2792: /*@
2793: MatMumpsGetInfo - Get MUMPS parameter INFO()
2795: Logically Collective
2797: Input Parameters:
2798: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2799: - icntl - index of MUMPS parameter array INFO()
2801: Output Parameter:
2802: . ival - value of MUMPS INFO(icntl)
2804: Level: beginner
2806: References:
2807: . * - MUMPS Users' Guide
2809: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2810: @*/
2811: PetscErrorCode MatMumpsGetInfo(Mat F, PetscInt icntl, PetscInt *ival)
2812: {
2813: PetscFunctionBegin;
2815: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2817: PetscUseMethod(F, "MatMumpsGetInfo_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
2818: PetscFunctionReturn(PETSC_SUCCESS);
2819: }
2821: /*@
2822: MatMumpsGetInfog - Get MUMPS parameter INFOG()
2824: Logically Collective
2826: Input Parameters:
2827: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2828: - icntl - index of MUMPS parameter array INFOG()
2830: Output Parameter:
2831: . ival - value of MUMPS INFOG(icntl)
2833: Level: beginner
2835: References:
2836: . * - MUMPS Users' Guide
2838: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`
2839: @*/
2840: PetscErrorCode MatMumpsGetInfog(Mat F, PetscInt icntl, PetscInt *ival)
2841: {
2842: PetscFunctionBegin;
2844: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2846: PetscUseMethod(F, "MatMumpsGetInfog_C", (Mat, PetscInt, PetscInt *), (F, icntl, ival));
2847: PetscFunctionReturn(PETSC_SUCCESS);
2848: }
2850: /*@
2851: MatMumpsGetRinfo - Get MUMPS parameter RINFO()
2853: Logically Collective
2855: Input Parameters:
2856: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2857: - icntl - index of MUMPS parameter array RINFO()
2859: Output Parameter:
2860: . val - value of MUMPS RINFO(icntl)
2862: Level: beginner
2864: References:
2865: . * - MUMPS Users' Guide
2867: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfog()`
2868: @*/
2869: PetscErrorCode MatMumpsGetRinfo(Mat F, PetscInt icntl, PetscReal *val)
2870: {
2871: PetscFunctionBegin;
2873: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2875: PetscUseMethod(F, "MatMumpsGetRinfo_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
2876: PetscFunctionReturn(PETSC_SUCCESS);
2877: }
2879: /*@
2880: MatMumpsGetRinfog - Get MUMPS parameter RINFOG()
2882: Logically Collective
2884: Input Parameters:
2885: + F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2886: - icntl - index of MUMPS parameter array RINFOG()
2888: Output Parameter:
2889: . val - value of MUMPS RINFOG(icntl)
2891: Level: beginner
2893: References:
2894: . * - MUMPS Users' Guide
2896: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`
2897: @*/
2898: PetscErrorCode MatMumpsGetRinfog(Mat F, PetscInt icntl, PetscReal *val)
2899: {
2900: PetscFunctionBegin;
2902: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2904: PetscUseMethod(F, "MatMumpsGetRinfog_C", (Mat, PetscInt, PetscReal *), (F, icntl, val));
2905: PetscFunctionReturn(PETSC_SUCCESS);
2906: }
2908: /*@
2909: MatMumpsGetNullPivots - Get MUMPS parameter PIVNUL_LIST()
2911: Logically Collective
2913: Input Parameter:
2914: . F - the factored matrix obtained by calling `MatGetFactor()` from PETSc-MUMPS interface
2916: Output Parameters:
2917: + size - local size of the array. The size of the array is non-zero only on the host.
2918: - array - array of rows with null pivot, these rows follow 0-based indexing. The array gets allocated within the function and the user is responsible
2919: for freeing this array.
2921: Level: beginner
2923: References:
2924: . * - MUMPS Users' Guide
2926: .seealso: [](chapter_matrices), `Mat`, `MatGetFactor()`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`
2927: @*/
2928: PetscErrorCode MatMumpsGetNullPivots(Mat F, PetscInt *size, PetscInt **array)
2929: {
2930: PetscFunctionBegin;
2932: PetscCheck(F->factortype, PetscObjectComm((PetscObject)F), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
2935: PetscUseMethod(F, "MatMumpsGetNullPivots_C", (Mat, PetscInt *, PetscInt **), (F, size, array));
2936: PetscFunctionReturn(PETSC_SUCCESS);
2937: }
2939: /*MC
2940: MATSOLVERMUMPS - A matrix type providing direct solvers (LU and Cholesky) for
2941: distributed and sequential matrices via the external package MUMPS.
2943: Works with `MATAIJ` and `MATSBAIJ` matrices
2945: Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch to have PETSc installed with MUMPS
2947: 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.
2948: See details below.
2950: Use `-pc_type cholesky` or `lu` `-pc_factor_mat_solver_type mumps` to use this direct solver
2952: Options Database Keys:
2953: + -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages
2954: . -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning
2955: . -mat_mumps_icntl_3 - ICNTL(3): output stream for global information, collected on the host
2956: . -mat_mumps_icntl_4 - ICNTL(4): level of printing (0 to 4)
2957: . -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7)
2958: . -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
2959: Use -pc_factor_mat_ordering_type <type> to have PETSc perform the ordering (sequential only)
2960: . -mat_mumps_icntl_8 - ICNTL(8): scaling strategy (-2 to 8 or 77)
2961: . -mat_mumps_icntl_10 - ICNTL(10): max num of refinements
2962: . -mat_mumps_icntl_11 - ICNTL(11): statistics related to an error analysis (via -ksp_view)
2963: . -mat_mumps_icntl_12 - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3)
2964: . -mat_mumps_icntl_13 - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting
2965: . -mat_mumps_icntl_14 - ICNTL(14): percentage increase in the estimated working space
2966: . -mat_mumps_icntl_15 - ICNTL(15): compression of the input matrix resulting from a block format
2967: . -mat_mumps_icntl_19 - ICNTL(19): computes the Schur complement
2968: . -mat_mumps_icntl_20 - ICNTL(20): give MUMPS centralized (0) or distributed (10) dense RHS
2969: . -mat_mumps_icntl_22 - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1)
2970: . -mat_mumps_icntl_23 - ICNTL(23): max size of the working memory (MB) that can allocate per processor
2971: . -mat_mumps_icntl_24 - ICNTL(24): detection of null pivot rows (0 or 1)
2972: . -mat_mumps_icntl_25 - ICNTL(25): compute a solution of a deficient matrix and a null space basis
2973: . -mat_mumps_icntl_26 - ICNTL(26): drives the solution phase if a Schur complement matrix
2974: . -mat_mumps_icntl_28 - ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering
2975: . -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis
2976: . -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A)
2977: . -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization
2978: . -mat_mumps_icntl_33 - ICNTL(33): compute determinant
2979: . -mat_mumps_icntl_35 - ICNTL(35): level of activation of BLR (Block Low-Rank) feature
2980: . -mat_mumps_icntl_36 - ICNTL(36): controls the choice of BLR factorization variant
2981: . -mat_mumps_icntl_38 - ICNTL(38): sets the estimated compression rate of LU factors with BLR
2982: . -mat_mumps_cntl_1 - CNTL(1): relative pivoting threshold
2983: . -mat_mumps_cntl_2 - CNTL(2): stopping criterion of refinement
2984: . -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold
2985: . -mat_mumps_cntl_4 - CNTL(4): value for static pivoting
2986: . -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots
2987: . -mat_mumps_cntl_7 - CNTL(7): precision of the dropping parameter used during BLR factorization
2988: - -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.
2989: Default might be the number of cores per CPU package (socket) as reported by hwloc and suggested by the MUMPS manual.
2991: Level: beginner
2993: Notes:
2994: 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
2995: error if the matrix is Hermitian.
2997: 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
2998: `MatSetOptionsPrefixFactor()` on the matrix from which the factor was obtained or `MatSetOptionsPrefix()` on the factor matrix.
3000: When a MUMPS factorization fails inside a KSP solve, for example with a `KSP_DIVERGED_PC_FAILED`, one can find the MUMPS information about
3001: the failure with
3002: .vb
3003: KSPGetPC(ksp,&pc);
3004: PCFactorGetMatrix(pc,&mat);
3005: MatMumpsGetInfo(mat,....);
3006: MatMumpsGetInfog(mat,....); etc.
3007: .ve
3008: Or run with `-ksp_error_if_not_converged` and the program will be stopped and the information printed in the error message.
3010: MUMPS provides 64-bit integer support in two build modes:
3011: full 64-bit: here MUMPS is built with C preprocessing flag -DINTSIZE64 and Fortran compiler option -i8, -fdefault-integer-8 or equivalent, and
3012: requires all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS built the same way with 64-bit integers (for example ILP64 Intel MKL and MPI).
3014: selective 64-bit: with the default MUMPS build, 64-bit integers have been introduced where needed. In compressed sparse row (CSR) storage of matrices,
3015: 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
3016: 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
3017: integer) build of all dependent libraries MPI, ScaLAPACK, LAPACK and BLAS.
3019: 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.
3021: Two modes to run MUMPS/PETSc with OpenMP
3022: .vb
3023: Set OMP_NUM_THREADS and run with fewer MPI ranks than cores. For example, if you want to have 16 OpenMP
3024: threads per rank, then you may use "export OMP_NUM_THREADS=16 && mpirun -n 4 ./test".
3025: .ve
3027: .vb
3028: -mat_mumps_use_omp_threads [m] and run your code with as many MPI ranks as the number of cores. For example,
3029: 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"
3030: .ve
3032: To run MUMPS in MPI+OpenMP hybrid mode (i.e., enable multithreading in MUMPS), but still run the non-MUMPS part
3033: (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`
3034: (or `--with-hwloc`), and have an MPI that supports MPI-3.0's process shared memory (which is usually available). Since MUMPS calls BLAS
3035: libraries, to really get performance, you should have multithreaded BLAS libraries such as Intel MKL, AMD ACML, Cray libSci or OpenBLAS
3036: (PETSc will automatically try to utilized a threaded BLAS if --with-openmp is provided).
3038: 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
3039: processes on each compute node. Listing the processes in rank ascending order, we split processes on a node into consecutive groups of
3040: 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
3041: 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
3042: 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.
3043: 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,
3044: 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
3045: 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
3046: cores in socket 1, that definitely hurts locality. On the other hand, if you map MPI ranks consecutively on the two sockets, then the
3047: 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.
3048: 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
3049: examine the mapping result.
3051: 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,
3052: 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
3053: calls `omp_set_num_threads`(m) internally before calling MUMPS.
3055: References:
3056: + * - Heroux, Michael A., R. Brightwell, and Michael M. Wolf. "Bi-modal MPI and MPI+ threads computing on scalable multicore systems." IJHPCA (Submitted) (2011).
3057: - * - Gutierrez, Samuel K., et al. "Accommodating Thread-Level Heterogeneity in Coupled Parallel Applications." Parallel and Distributed Processing Symposium (IPDPS), 2017 IEEE International. IEEE, 2017.
3059: .seealso: [](chapter_matrices), `Mat`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatMumpsSetIcntl()`, `MatMumpsGetIcntl()`, `MatMumpsSetCntl()`, `MatMumpsGetCntl()`, `MatMumpsGetInfo()`, `MatMumpsGetInfog()`, `MatMumpsGetRinfo()`, `MatMumpsGetRinfog()`, `KSPGetPC()`, `PCFactorGetMatrix()`
3060: M*/
3062: static PetscErrorCode MatFactorGetSolverType_mumps(Mat A, MatSolverType *type)
3063: {
3064: PetscFunctionBegin;
3065: *type = MATSOLVERMUMPS;
3066: PetscFunctionReturn(PETSC_SUCCESS);
3067: }
3069: /* MatGetFactor for Seq and MPI AIJ matrices */
3070: static PetscErrorCode MatGetFactor_aij_mumps(Mat A, MatFactorType ftype, Mat *F)
3071: {
3072: Mat B;
3073: Mat_MUMPS *mumps;
3074: PetscBool isSeqAIJ;
3075: PetscMPIInt size;
3077: PetscFunctionBegin;
3078: #if defined(PETSC_USE_COMPLEX)
3079: 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");
3080: #endif
3081: /* Create the factorization matrix */
3082: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATSEQAIJ, &isSeqAIJ));
3083: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3084: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3085: PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3086: PetscCall(MatSetUp(B));
3088: PetscCall(PetscNew(&mumps));
3090: B->ops->view = MatView_MUMPS;
3091: B->ops->getinfo = MatGetInfo_MUMPS;
3093: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3094: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3095: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3096: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3097: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3098: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3099: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3100: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3101: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3102: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3103: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3104: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3105: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3106: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3108: if (ftype == MAT_FACTOR_LU) {
3109: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3110: B->factortype = MAT_FACTOR_LU;
3111: if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij;
3112: else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij;
3113: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3114: mumps->sym = 0;
3115: } else {
3116: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3117: B->factortype = MAT_FACTOR_CHOLESKY;
3118: if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij;
3119: else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij;
3120: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_CHOLESKY]));
3121: #if defined(PETSC_USE_COMPLEX)
3122: mumps->sym = 2;
3123: #else
3124: if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3125: else mumps->sym = 2;
3126: #endif
3127: }
3129: /* set solvertype */
3130: PetscCall(PetscFree(B->solvertype));
3131: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3132: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3133: if (size == 1) {
3134: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3135: B->canuseordering = PETSC_TRUE;
3136: }
3137: B->ops->destroy = MatDestroy_MUMPS;
3138: B->data = (void *)mumps;
3140: *F = B;
3141: mumps->id.job = JOB_NULL;
3142: mumps->ICNTL_pre = NULL;
3143: mumps->CNTL_pre = NULL;
3144: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3145: PetscFunctionReturn(PETSC_SUCCESS);
3146: }
3148: /* MatGetFactor for Seq and MPI SBAIJ matrices */
3149: static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A, MatFactorType ftype, Mat *F)
3150: {
3151: Mat B;
3152: Mat_MUMPS *mumps;
3153: PetscBool isSeqSBAIJ;
3154: PetscMPIInt size;
3156: PetscFunctionBegin;
3157: #if defined(PETSC_USE_COMPLEX)
3158: PetscCheck(A->hermitian != PETSC_BOOL3_TRUE || A->symmetric == PETSC_BOOL3_TRUE, PETSC_COMM_SELF, PETSC_ERR_SUP, "Hermitian CHOLESKY Factor is not supported");
3159: #endif
3160: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3161: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3162: PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3163: PetscCall(MatSetUp(B));
3165: PetscCall(PetscNew(&mumps));
3166: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQSBAIJ, &isSeqSBAIJ));
3167: if (isSeqSBAIJ) {
3168: mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij;
3169: } else {
3170: mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij;
3171: }
3173: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
3174: B->ops->view = MatView_MUMPS;
3175: B->ops->getinfo = MatGetInfo_MUMPS;
3177: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3178: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3179: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3180: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3181: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3182: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3183: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3184: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3185: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3186: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3187: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3188: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3189: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3190: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
3192: B->factortype = MAT_FACTOR_CHOLESKY;
3193: #if defined(PETSC_USE_COMPLEX)
3194: mumps->sym = 2;
3195: #else
3196: if (A->spd == PETSC_BOOL3_TRUE) mumps->sym = 1;
3197: else mumps->sym = 2;
3198: #endif
3200: /* set solvertype */
3201: PetscCall(PetscFree(B->solvertype));
3202: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3203: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3204: if (size == 1) {
3205: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3206: B->canuseordering = PETSC_TRUE;
3207: }
3208: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_CHOLESKY]));
3209: B->ops->destroy = MatDestroy_MUMPS;
3210: B->data = (void *)mumps;
3212: *F = B;
3213: mumps->id.job = JOB_NULL;
3214: mumps->ICNTL_pre = NULL;
3215: mumps->CNTL_pre = NULL;
3216: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3217: PetscFunctionReturn(PETSC_SUCCESS);
3218: }
3220: static PetscErrorCode MatGetFactor_baij_mumps(Mat A, MatFactorType ftype, Mat *F)
3221: {
3222: Mat B;
3223: Mat_MUMPS *mumps;
3224: PetscBool isSeqBAIJ;
3225: PetscMPIInt size;
3227: PetscFunctionBegin;
3228: /* Create the factorization matrix */
3229: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQBAIJ, &isSeqBAIJ));
3230: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3231: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3232: PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3233: PetscCall(MatSetUp(B));
3235: PetscCall(PetscNew(&mumps));
3236: if (ftype == MAT_FACTOR_LU) {
3237: B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS;
3238: B->factortype = MAT_FACTOR_LU;
3239: if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij;
3240: else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij;
3241: mumps->sym = 0;
3242: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3243: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead");
3245: B->ops->view = MatView_MUMPS;
3246: B->ops->getinfo = MatGetInfo_MUMPS;
3248: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3249: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3250: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3251: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3252: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3253: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3254: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3255: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3256: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3257: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3258: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3259: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3260: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverse_C", MatMumpsGetInverse_MUMPS));
3261: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInverseTranspose_C", MatMumpsGetInverseTranspose_MUMPS));
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: /* MatGetFactor for Seq and MPI SELL matrices */
3283: static PetscErrorCode MatGetFactor_sell_mumps(Mat A, MatFactorType ftype, Mat *F)
3284: {
3285: Mat B;
3286: Mat_MUMPS *mumps;
3287: PetscBool isSeqSELL;
3288: PetscMPIInt size;
3290: PetscFunctionBegin;
3291: /* Create the factorization matrix */
3292: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQSELL, &isSeqSELL));
3293: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
3294: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
3295: PetscCall(PetscStrallocpy("mumps", &((PetscObject)B)->type_name));
3296: PetscCall(MatSetUp(B));
3298: PetscCall(PetscNew(&mumps));
3300: B->ops->view = MatView_MUMPS;
3301: B->ops->getinfo = MatGetInfo_MUMPS;
3303: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_mumps));
3304: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorSetSchurIS_C", MatFactorSetSchurIS_MUMPS));
3305: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorCreateSchurComplement_C", MatFactorCreateSchurComplement_MUMPS));
3306: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetIcntl_C", MatMumpsSetIcntl_MUMPS));
3307: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetIcntl_C", MatMumpsGetIcntl_MUMPS));
3308: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsSetCntl_C", MatMumpsSetCntl_MUMPS));
3309: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetCntl_C", MatMumpsGetCntl_MUMPS));
3310: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfo_C", MatMumpsGetInfo_MUMPS));
3311: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetInfog_C", MatMumpsGetInfog_MUMPS));
3312: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfo_C", MatMumpsGetRinfo_MUMPS));
3313: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetRinfog_C", MatMumpsGetRinfog_MUMPS));
3314: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMumpsGetNullPivots_C", MatMumpsGetNullPivots_MUMPS));
3316: if (ftype == MAT_FACTOR_LU) {
3317: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
3318: B->factortype = MAT_FACTOR_LU;
3319: if (isSeqSELL) mumps->ConvertToTriples = MatConvertToTriples_seqsell_seqaij;
3320: else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "To be implemented");
3321: mumps->sym = 0;
3322: PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, (char **)&B->preferredordering[MAT_FACTOR_LU]));
3323: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "To be implemented");
3325: /* set solvertype */
3326: PetscCall(PetscFree(B->solvertype));
3327: PetscCall(PetscStrallocpy(MATSOLVERMUMPS, &B->solvertype));
3328: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
3329: if (size == 1) {
3330: /* MUMPS option -mat_mumps_icntl_7 1 is automatically set if PETSc ordering is passed into symbolic factorization */
3331: B->canuseordering = PETSC_TRUE;
3332: }
3333: B->ops->destroy = MatDestroy_MUMPS;
3334: B->data = (void *)mumps;
3336: *F = B;
3337: mumps->id.job = JOB_NULL;
3338: mumps->ICNTL_pre = NULL;
3339: mumps->CNTL_pre = NULL;
3340: mumps->matstruc = DIFFERENT_NONZERO_PATTERN;
3341: PetscFunctionReturn(PETSC_SUCCESS);
3342: }
3344: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MUMPS(void)
3345: {
3346: PetscFunctionBegin;
3347: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3348: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3349: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIBAIJ, MAT_FACTOR_LU, MatGetFactor_baij_mumps));
3350: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPIBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_baij_mumps));
3351: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATMPISBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_sbaij_mumps));
3352: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQAIJ, MAT_FACTOR_LU, MatGetFactor_aij_mumps));
3353: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_aij_mumps));
3354: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQBAIJ, MAT_FACTOR_LU, MatGetFactor_baij_mumps));
3355: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_baij_mumps));
3356: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQSBAIJ, MAT_FACTOR_CHOLESKY, MatGetFactor_sbaij_mumps));
3357: PetscCall(MatSolverTypeRegister(MATSOLVERMUMPS, MATSEQSELL, MAT_FACTOR_LU, MatGetFactor_sell_mumps));
3358: PetscFunctionReturn(PETSC_SUCCESS);
3359: }