Actual source code: mpiaij.c
1: #include <../src/mat/impls/aij/mpi/mpiaij.h>
2: #include <petsc/private/vecimpl.h>
3: #include <petsc/private/sfimpl.h>
4: #include <petsc/private/isimpl.h>
5: #include <petscblaslapack.h>
6: #include <petscsf.h>
7: #include <petsc/private/hashmapi.h>
9: /* defines MatSetValues_MPI_Hash(), MatAssemblyBegin_MPI_Hash(), and MatAssemblyEnd_MPI_Hash() */
10: #define TYPE AIJ
11: #define TYPE_AIJ
12: #include "../src/mat/impls/aij/mpi/mpihashmat.h"
13: #undef TYPE
14: #undef TYPE_AIJ
16: static PetscErrorCode MatReset_MPIAIJ(Mat mat)
17: {
18: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
20: PetscFunctionBegin;
21: PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
22: PetscCall(MatStashDestroy_Private(&mat->stash));
23: PetscCall(VecDestroy(&aij->diag));
24: PetscCall(MatDestroy(&aij->A));
25: PetscCall(MatDestroy(&aij->B));
26: #if PetscDefined(USE_CTABLE)
27: PetscCall(PetscHMapIDestroy(&aij->colmap));
28: #else
29: PetscCall(PetscFree(aij->colmap));
30: #endif
31: PetscCall(PetscFree(aij->garray));
32: PetscCall(VecDestroy(&aij->lvec));
33: PetscCall(VecScatterDestroy(&aij->Mvctx));
34: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
35: PetscCall(PetscFree(aij->ld));
36: PetscFunctionReturn(PETSC_SUCCESS);
37: }
39: static PetscErrorCode MatResetHash_MPIAIJ(Mat mat)
40: {
41: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
42: /* Save the nonzero states of the component matrices because those are what are used to determine
43: the nonzero state of mat */
44: PetscObjectState Astate = aij->A->nonzerostate, Bstate = aij->B->nonzerostate;
46: PetscFunctionBegin;
47: PetscCall(MatReset_MPIAIJ(mat));
48: PetscCall(MatSetUp_MPI_Hash(mat));
49: aij->A->nonzerostate = ++Astate, aij->B->nonzerostate = ++Bstate;
50: PetscFunctionReturn(PETSC_SUCCESS);
51: }
53: PetscErrorCode MatDestroy_MPIAIJ(Mat mat)
54: {
55: PetscFunctionBegin;
56: PetscCall(MatReset_MPIAIJ(mat));
58: PetscCall(PetscFree(mat->data));
60: /* may be created by MatCreateMPIAIJSumSeqAIJSymbolic */
61: PetscCall(PetscObjectCompose((PetscObject)mat, "MatMergeSeqsToMPI", NULL));
63: PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
64: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
65: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
66: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatGetMultPetscSF_C", NULL));
67: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatIsTranspose_C", NULL));
68: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocation_C", NULL));
69: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatResetPreallocation_C", NULL));
70: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatResetHash_C", NULL));
71: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocationCSR_C", NULL));
72: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
73: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpibaij_C", NULL));
74: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisbaij_C", NULL));
75: #if PetscDefined(HAVE_CUDA)
76: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcusparse_C", NULL));
77: #endif
78: #if PetscDefined(HAVE_HIP)
79: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijhipsparse_C", NULL));
80: #endif
81: #if PetscDefined(HAVE_KOKKOS_KERNELS)
82: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijkokkos_C", NULL));
83: #endif
84: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpidense_C", NULL));
85: #if PetscDefined(HAVE_ELEMENTAL)
86: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_elemental_C", NULL));
87: #endif
88: #if PetscDefined(HAVE_SCALAPACK) && (PetscDefined(USE_REAL_SINGLE) || PetscDefined(USE_REAL_DOUBLE))
89: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_scalapack_C", NULL));
90: #endif
91: #if PetscDefined(HAVE_HYPRE)
92: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_hypre_C", NULL));
93: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", NULL));
94: #endif
95: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
96: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_is_mpiaij_C", NULL));
97: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_mpiaij_mpiaij_C", NULL));
98: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetUseScalableIncreaseOverlap_C", NULL));
99: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijperm_C", NULL));
100: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijsell_C", NULL));
101: #if PetscDefined(HAVE_MKL_SPARSE)
102: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijmkl_C", NULL));
103: #endif
104: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcrl_C", NULL));
105: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
106: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisell_C", NULL));
107: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetPreallocationCOO_C", NULL));
108: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetValuesCOO_C", NULL));
109: PetscFunctionReturn(PETSC_SUCCESS);
110: }
112: static PetscErrorCode MatGetRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
113: {
114: Mat B;
116: PetscFunctionBegin;
117: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, &B));
118: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject)B));
119: PetscCall(MatGetRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
120: PetscCall(MatDestroy(&B));
121: PetscFunctionReturn(PETSC_SUCCESS);
122: }
124: static PetscErrorCode MatRestoreRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
125: {
126: Mat B;
128: PetscFunctionBegin;
129: PetscCall(PetscObjectQuery((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject *)&B));
130: PetscCall(MatRestoreRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
131: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", NULL));
132: PetscFunctionReturn(PETSC_SUCCESS);
133: }
135: /*MC
136: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
138: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
139: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
140: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
141: for communicators controlling multiple processes. It is recommended that you call both of
142: the above preallocation routines for simplicity.
144: Options Database Key:
145: . -mat_type aijcrl - sets the matrix type to `MATMPIAIJCRL` during a call to `MatSetFromOptions()`
147: Level: beginner
149: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJ`, `MATMPIAIJ`, `MATAIJ`
150: M*/
152: static PetscErrorCode MatBindToCPU_MPIAIJ(Mat A, PetscBool flg)
153: {
154: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
156: PetscFunctionBegin;
157: #if PetscDefined(HAVE_CUDA) || PetscDefined(HAVE_HIP) || PetscDefined(HAVE_VIENNACL)
158: A->boundtocpu = flg;
159: #endif
160: if (a->A) PetscCall(MatBindToCPU(a->A, flg));
161: if (a->B) PetscCall(MatBindToCPU(a->B, flg));
163: /* In addition to binding the diagonal and off-diagonal matrices, bind the local vectors used for matrix-vector products.
164: * This maybe seems a little odd for a MatBindToCPU() call to do, but it makes no sense for the binding of these vectors
165: * to differ from the parent matrix. */
166: if (a->lvec) PetscCall(VecBindToCPU(a->lvec, flg));
167: if (a->diag) PetscCall(VecBindToCPU(a->diag, flg));
168: PetscFunctionReturn(PETSC_SUCCESS);
169: }
171: static PetscErrorCode MatSetBlockSizes_MPIAIJ(Mat M, PetscInt rbs, PetscInt cbs)
172: {
173: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
175: PetscFunctionBegin;
176: if (mat->A) {
177: PetscCall(MatSetBlockSizes(mat->A, rbs, cbs));
178: PetscCall(MatSetBlockSizes(mat->B, rbs, 1));
179: }
180: PetscFunctionReturn(PETSC_SUCCESS);
181: }
183: static PetscErrorCode MatFindNonzeroRows_MPIAIJ(Mat M, IS *keptrows)
184: {
185: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
186: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data;
187: Mat_SeqAIJ *b = (Mat_SeqAIJ *)mat->B->data;
188: const PetscInt *ia, *ib;
189: const MatScalar *aa, *bb, *aav, *bav;
190: PetscInt na, nb, i, j, *rows, cnt = 0, n0rows;
191: PetscInt m = M->rmap->n, rstart = M->rmap->rstart;
193: PetscFunctionBegin;
194: *keptrows = NULL;
196: ia = a->i;
197: ib = b->i;
198: PetscCall(MatSeqAIJGetArrayRead(mat->A, &aav));
199: PetscCall(MatSeqAIJGetArrayRead(mat->B, &bav));
200: for (i = 0; i < m; i++) {
201: na = ia[i + 1] - ia[i];
202: nb = ib[i + 1] - ib[i];
203: if (!na && !nb) {
204: cnt++;
205: goto ok1;
206: }
207: aa = aav + ia[i];
208: for (j = 0; j < na; j++) {
209: if (aa[j] != 0.0) goto ok1;
210: }
211: bb = PetscSafePointerPlusOffset(bav, ib[i]);
212: for (j = 0; j < nb; j++) {
213: if (bb[j] != 0.0) goto ok1;
214: }
215: cnt++;
216: ok1:;
217: }
218: PetscCallMPI(MPIU_Allreduce(&cnt, &n0rows, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)M)));
219: if (!n0rows) {
220: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
221: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
222: PetscFunctionReturn(PETSC_SUCCESS);
223: }
224: PetscCall(PetscMalloc1(M->rmap->n - cnt, &rows));
225: cnt = 0;
226: for (i = 0; i < m; i++) {
227: na = ia[i + 1] - ia[i];
228: nb = ib[i + 1] - ib[i];
229: if (!na && !nb) continue;
230: aa = aav + ia[i];
231: for (j = 0; j < na; j++) {
232: if (aa[j] != 0.0) {
233: rows[cnt++] = rstart + i;
234: goto ok2;
235: }
236: }
237: bb = PetscSafePointerPlusOffset(bav, ib[i]);
238: for (j = 0; j < nb; j++) {
239: if (bb[j] != 0.0) {
240: rows[cnt++] = rstart + i;
241: goto ok2;
242: }
243: }
244: ok2:;
245: }
246: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), cnt, rows, PETSC_OWN_POINTER, keptrows));
247: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
248: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
249: PetscFunctionReturn(PETSC_SUCCESS);
250: }
252: static PetscErrorCode MatDiagonalSet_MPIAIJ(Mat Y, Vec D, InsertMode is)
253: {
254: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Y->data;
255: PetscBool cong;
257: PetscFunctionBegin;
258: PetscCall(MatHasCongruentLayouts(Y, &cong));
259: if (Y->assembled && cong) PetscCall(MatDiagonalSet(aij->A, D, is));
260: else PetscCall(MatDiagonalSet_Default(Y, D, is));
261: PetscFunctionReturn(PETSC_SUCCESS);
262: }
264: static PetscErrorCode MatFindZeroDiagonals_MPIAIJ(Mat M, IS *zrows)
265: {
266: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)M->data;
267: PetscInt i, rstart, nrows, *rows;
269: PetscFunctionBegin;
270: *zrows = NULL;
271: PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(aij->A, &nrows, &rows));
272: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
273: for (i = 0; i < nrows; i++) rows[i] += rstart;
274: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), nrows, rows, PETSC_OWN_POINTER, zrows));
275: PetscFunctionReturn(PETSC_SUCCESS);
276: }
278: static PetscErrorCode MatGetColumnReductions_MPIAIJ(Mat A, PetscInt type, PetscReal *reductions)
279: {
280: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)A->data;
281: PetscInt i, m, n, *garray = aij->garray;
282: Mat_SeqAIJ *a_aij = (Mat_SeqAIJ *)aij->A->data;
283: Mat_SeqAIJ *b_aij = (Mat_SeqAIJ *)aij->B->data;
284: const PetscScalar *dummy;
286: PetscFunctionBegin;
287: PetscCall(MatGetSize(A, &m, &n));
288: PetscCall(PetscArrayzero(reductions, n));
289: PetscCall(MatSeqAIJGetArrayRead(aij->A, &dummy));
290: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &dummy));
291: PetscCall(MatSeqAIJGetArrayRead(aij->B, &dummy));
292: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &dummy));
293: if (type == NORM_2) {
294: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) reductions[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i] * a_aij->a[i]);
295: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) reductions[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i] * b_aij->a[i]);
296: } else if (type == NORM_1) {
297: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) reductions[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i]);
298: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) reductions[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]);
299: } else if (type == NORM_INFINITY) {
300: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) reductions[A->cmap->rstart + a_aij->j[i]] = PetscMax(PetscAbsScalar(a_aij->a[i]), reductions[A->cmap->rstart + a_aij->j[i]]);
301: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) reductions[garray[b_aij->j[i]]] = PetscMax(PetscAbsScalar(b_aij->a[i]), reductions[garray[b_aij->j[i]]]);
302: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
303: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) reductions[A->cmap->rstart + a_aij->j[i]] += PetscRealPart(a_aij->a[i]);
304: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) reductions[garray[b_aij->j[i]]] += PetscRealPart(b_aij->a[i]);
305: } else {
306: PetscCheck(type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
307: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) reductions[A->cmap->rstart + a_aij->j[i]] += PetscImaginaryPart(a_aij->a[i]);
308: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) reductions[garray[b_aij->j[i]]] += PetscImaginaryPart(b_aij->a[i]);
309: }
310: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, reductions, n, MPIU_REAL, type == NORM_INFINITY ? MPIU_MAX : MPIU_SUM, PetscObjectComm((PetscObject)A)));
311: if (type == NORM_2) {
312: for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
313: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
314: for (i = 0; i < n; i++) reductions[i] /= m;
315: }
316: PetscFunctionReturn(PETSC_SUCCESS);
317: }
319: static PetscErrorCode MatFindOffBlockDiagonalEntries_MPIAIJ(Mat A, IS *is)
320: {
321: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
322: IS sis, gis;
323: const PetscInt *isis, *igis;
324: PetscInt n, *iis, nsis, ngis, rstart, i;
326: PetscFunctionBegin;
327: PetscCall(MatFindOffBlockDiagonalEntries(a->A, &sis));
328: PetscCall(MatFindNonzeroRows(a->B, &gis));
329: PetscCall(ISGetSize(gis, &ngis));
330: PetscCall(ISGetSize(sis, &nsis));
331: PetscCall(ISGetIndices(sis, &isis));
332: PetscCall(ISGetIndices(gis, &igis));
334: PetscCall(PetscMalloc1(ngis + nsis, &iis));
335: PetscCall(PetscArraycpy(iis, igis, ngis));
336: PetscCall(PetscArraycpy(iis + ngis, isis, nsis));
337: n = ngis + nsis;
338: PetscCall(PetscSortRemoveDupsInt(&n, iis));
339: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
340: for (i = 0; i < n; i++) iis[i] += rstart;
341: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), n, iis, PETSC_OWN_POINTER, is));
343: PetscCall(ISRestoreIndices(sis, &isis));
344: PetscCall(ISRestoreIndices(gis, &igis));
345: PetscCall(ISDestroy(&sis));
346: PetscCall(ISDestroy(&gis));
347: PetscFunctionReturn(PETSC_SUCCESS);
348: }
350: /*
351: Local utility routine that creates a mapping from the global column
352: number to the local number in the off-diagonal part of the local
353: storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at
354: a slightly higher hash table cost; without it it is not scalable (each processor
355: has an order N integer array but is fast to access.
356: */
357: PetscErrorCode MatCreateColmap_MPIAIJ_Private(Mat mat)
358: {
359: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
360: PetscInt n = aij->B->cmap->n, i;
362: PetscFunctionBegin;
363: PetscCheck(!n || aij->garray, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MPIAIJ Matrix was assembled but is missing garray");
364: #if PetscDefined(USE_CTABLE)
365: PetscCall(PetscHMapICreateWithSize(n, &aij->colmap));
366: for (i = 0; i < n; i++) PetscCall(PetscHMapISet(aij->colmap, aij->garray[i] + 1, i + 1));
367: #else
368: PetscCall(PetscCalloc1(mat->cmap->N + 1, &aij->colmap));
369: for (i = 0; i < n; i++) aij->colmap[aij->garray[i]] = i + 1;
370: #endif
371: PetscFunctionReturn(PETSC_SUCCESS);
372: }
374: #define MatSetValues_SeqAIJ_A_Private(row, col, value, addv, orow, ocol) \
375: do { \
376: if ((col) <= lastcol1) low1 = 0; \
377: else high1 = nrow1; \
378: lastcol1 = col; \
379: while (high1 - low1 > 5) { \
380: t = (low1 + high1) / 2; \
381: if (rp1[t] > (col)) high1 = t; \
382: else low1 = t; \
383: } \
384: for (_i = low1; _i < high1; _i++) { \
385: if (rp1[_i] > (col)) break; \
386: if (rp1[_i] == (col)) { \
387: if (A->structure_only) goto a_noinsert; \
388: if ((addv) == ADD_VALUES) { \
389: ap1[_i] += value; \
390: /* Not sure LogFlops will slow down the code or not */ \
391: (void)PetscLogFlops(1.0); \
392: } else ap1[_i] = value; \
393: goto a_noinsert; \
394: } \
395: } \
396: if (!A->structure_only && (value) == 0.0 && ignorezeroentries && (orow) != (ocol)) { \
397: low1 = 0; \
398: high1 = nrow1; \
399: goto a_noinsert; \
400: } \
401: if (nonew == 1) { \
402: low1 = 0; \
403: high1 = nrow1; \
404: goto a_noinsert; \
405: } \
406: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
407: if (A->structure_only) MatSeqXAIJReallocateAIJ_structure_only(A, am, 1, nrow1, row, col, rmax1, ai, aj, rp1, aimax, nonew, MatScalar); \
408: else MatSeqXAIJReallocateAIJ(A, am, 1, nrow1, row, col, rmax1, aa, ai, aj, rp1, ap1, aimax, nonew, MatScalar); \
409: N = nrow1++ - 1; \
410: a->nz++; \
411: high1++; \
412: /* shift up all the later entries in this row */ \
413: PetscCall(PetscArraymove(rp1 + _i + 1, rp1 + _i, N - _i + 1)); \
414: rp1[_i] = col; \
415: if (!A->structure_only) { \
416: PetscCall(PetscArraymove(ap1 + _i + 1, ap1 + _i, N - _i + 1)); \
417: ap1[_i] = value; \
418: } \
419: a_noinsert:; \
420: ailen[row] = nrow1; \
421: } while (0)
423: #define MatSetValues_SeqAIJ_B_Private(row, col, value, addv, orow, ocol) \
424: do { \
425: if ((col) <= lastcol2) low2 = 0; \
426: else high2 = nrow2; \
427: lastcol2 = col; \
428: while (high2 - low2 > 5) { \
429: t = (low2 + high2) / 2; \
430: if (rp2[t] > (col)) high2 = t; \
431: else low2 = t; \
432: } \
433: for (_i = low2; _i < high2; _i++) { \
434: if (rp2[_i] > (col)) break; \
435: if (rp2[_i] == (col)) { \
436: if (B->structure_only) goto b_noinsert; \
437: if ((addv) == ADD_VALUES) { \
438: ap2[_i] += value; \
439: (void)PetscLogFlops(1.0); \
440: } else ap2[_i] = value; \
441: goto b_noinsert; \
442: } \
443: } \
444: if (!B->structure_only && (value) == 0.0 && ignorezeroentries) { \
445: low2 = 0; \
446: high2 = nrow2; \
447: goto b_noinsert; \
448: } \
449: if (nonew == 1) { \
450: low2 = 0; \
451: high2 = nrow2; \
452: goto b_noinsert; \
453: } \
454: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
455: if (B->structure_only) MatSeqXAIJReallocateAIJ_structure_only(B, bm, 1, nrow2, row, col, rmax2, bi, bj, rp2, bimax, nonew, MatScalar); \
456: else MatSeqXAIJReallocateAIJ(B, bm, 1, nrow2, row, col, rmax2, ba, bi, bj, rp2, ap2, bimax, nonew, MatScalar); \
457: N = nrow2++ - 1; \
458: b->nz++; \
459: high2++; \
460: /* shift up all the later entries in this row */ \
461: PetscCall(PetscArraymove(rp2 + _i + 1, rp2 + _i, N - _i + 1)); \
462: rp2[_i] = col; \
463: if (!B->structure_only) { \
464: PetscCall(PetscArraymove(ap2 + _i + 1, ap2 + _i, N - _i + 1)); \
465: ap2[_i] = value; \
466: } \
467: b_noinsert:; \
468: bilen[row] = nrow2; \
469: } while (0)
471: static PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A, PetscInt row, const PetscScalar v[])
472: {
473: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
474: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data, *b = (Mat_SeqAIJ *)mat->B->data;
475: PetscInt l, *garray = mat->garray, diag;
476: PetscScalar *aa, *ba;
478: PetscFunctionBegin;
479: /* code only works for square matrices A */
481: /* find size of row to the left of the diagonal part */
482: PetscCall(MatGetOwnershipRange(A, &diag, NULL));
483: row = row - diag;
484: for (l = 0; l < b->i[row + 1] - b->i[row]; l++) {
485: if (garray[b->j[b->i[row] + l]] > diag) break;
486: }
487: if (l) {
488: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
489: PetscCall(PetscArraycpy(ba + b->i[row], v, l));
490: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
491: }
493: /* diagonal part */
494: if (a->i[row + 1] - a->i[row]) {
495: PetscCall(MatSeqAIJGetArray(mat->A, &aa));
496: PetscCall(PetscArraycpy(aa + a->i[row], v + l, a->i[row + 1] - a->i[row]));
497: PetscCall(MatSeqAIJRestoreArray(mat->A, &aa));
498: }
500: /* right of diagonal part */
501: if (b->i[row + 1] - b->i[row] - l) {
502: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
503: PetscCall(PetscArraycpy(ba + b->i[row] + l, v + l + a->i[row + 1] - a->i[row], b->i[row + 1] - b->i[row] - l));
504: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
505: }
506: PetscFunctionReturn(PETSC_SUCCESS);
507: }
509: PetscErrorCode MatSetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
510: {
511: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
512: PetscScalar value = 0.0;
513: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
514: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
515: PetscBool roworiented = aij->roworiented;
517: /* Some Variables required in the macro */
518: Mat A = aij->A;
519: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
520: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
521: const PetscBool ignorezeroentries = (PetscBool)(!mat->structure_only && a->ignorezeroentries);
522: Mat B = aij->B;
523: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
524: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
525: MatScalar *aa, *ba;
526: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
527: PetscInt nonew;
528: MatScalar *ap1, *ap2;
530: PetscFunctionBegin;
531: PetscCall(MatSeqAIJGetArray(A, &aa));
532: PetscCall(MatSeqAIJGetArray(B, &ba));
533: for (i = 0; i < m; i++) {
534: if (im[i] < 0) continue;
535: PetscCheck(im[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], mat->rmap->N - 1);
536: if (im[i] >= rstart && im[i] < rend) {
537: row = im[i] - rstart;
538: lastcol1 = -1;
539: rp1 = PetscSafePointerPlusOffset(aj, ai[row]);
540: ap1 = PetscSafePointerPlusOffset(aa, ai[row]);
541: rmax1 = aimax[row];
542: nrow1 = ailen[row];
543: low1 = 0;
544: high1 = nrow1;
545: lastcol2 = -1;
546: rp2 = PetscSafePointerPlusOffset(bj, bi[row]);
547: ap2 = PetscSafePointerPlusOffset(ba, bi[row]);
548: rmax2 = bimax[row];
549: nrow2 = bilen[row];
550: low2 = 0;
551: high2 = nrow2;
553: for (j = 0; j < n; j++) {
554: if (v && !mat->structure_only) value = roworiented ? v[i * n + j] : v[i + j * m];
555: if (!mat->structure_only && ignorezeroentries && value == 0.0 && addv == ADD_VALUES && im[i] != in[j]) continue;
556: if (in[j] >= cstart && in[j] < cend) {
557: col = in[j] - cstart;
558: nonew = a->nonew;
559: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
560: } else if (in[j] < 0) {
561: continue;
562: } else {
563: PetscCheck(in[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
564: if (mat->was_assembled) {
565: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
566: #if PetscDefined(USE_CTABLE)
567: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col)); /* map global col ids to local ones */
568: col--;
569: #else
570: col = aij->colmap[in[j]] - 1;
571: #endif
572: if (col < 0 && !((Mat_SeqAIJ *)aij->B->data)->nonew) { /* col < 0 means in[j] is a new col for B */
573: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE)); /* Change aij->B from reduced/local format to expanded/global format */
574: col = in[j];
575: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
576: B = aij->B;
577: b = (Mat_SeqAIJ *)B->data;
578: bimax = b->imax;
579: bi = b->i;
580: bilen = b->ilen;
581: bj = b->j;
582: ba = b->a;
583: rp2 = PetscSafePointerPlusOffset(bj, bi[row]);
584: ap2 = PetscSafePointerPlusOffset(ba, bi[row]);
585: rmax2 = bimax[row];
586: nrow2 = bilen[row];
587: low2 = 0;
588: high2 = nrow2;
589: bm = aij->B->rmap->n;
590: ba = b->a;
591: } else if (col < 0 && !(ignorezeroentries && value == 0.0)) {
592: PetscCheck(1 == ((Mat_SeqAIJ *)aij->B->data)->nonew, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]);
593: PetscCall(PetscInfo(mat, "Skipping of insertion of new nonzero location in off-diagonal portion of matrix %g(%" PetscInt_FMT ",%" PetscInt_FMT ")\n", (double)PetscRealPart(value), im[i], in[j]));
594: }
595: } else col = in[j];
596: nonew = b->nonew;
597: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
598: }
599: }
600: } else {
601: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Setting off process row %" PetscInt_FMT " even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set", im[i]);
602: if (!aij->donotstash) {
603: mat->assembled = PETSC_FALSE;
604: if (roworiented) {
605: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i * n), (PetscBool)(ignorezeroentries && addv == ADD_VALUES)));
606: } else {
607: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i), m, (PetscBool)(ignorezeroentries && addv == ADD_VALUES)));
608: }
609: }
610: }
611: }
612: PetscCall(MatSeqAIJRestoreArray(A, &aa)); /* aa, bb might have been free'd due to reallocation above. But we don't access them here */
613: PetscCall(MatSeqAIJRestoreArray(B, &ba));
614: PetscFunctionReturn(PETSC_SUCCESS);
615: }
617: /*
618: This function sets the j and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
619: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
620: No off-processor parts off the matrix are allowed here and mat->was_assembled has to be PETSC_FALSE.
621: */
622: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[])
623: {
624: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
625: Mat A = aij->A; /* diagonal part of the matrix */
626: Mat B = aij->B; /* off-diagonal part of the matrix */
627: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
628: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
629: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, col;
630: PetscInt *ailen = a->ilen, *aj = a->j;
631: PetscInt *bilen = b->ilen, *bj = b->j;
632: PetscInt am = aij->A->rmap->n, j;
633: PetscInt diag_so_far = 0, dnz;
634: PetscInt offd_so_far = 0, onz;
636: PetscFunctionBegin;
637: /* Iterate over all rows of the matrix */
638: for (j = 0; j < am; j++) {
639: dnz = onz = 0;
640: /* Iterate over all non-zero columns of the current row */
641: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
642: /* If column is in the diagonal */
643: if (mat_j[col] >= cstart && mat_j[col] < cend) {
644: aj[diag_so_far++] = mat_j[col] - cstart;
645: dnz++;
646: } else { /* off-diagonal entries */
647: bj[offd_so_far++] = mat_j[col];
648: onz++;
649: }
650: }
651: ailen[j] = dnz;
652: bilen[j] = onz;
653: }
654: PetscFunctionReturn(PETSC_SUCCESS);
655: }
657: /*
658: This function sets the local j, a and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
659: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
660: No off-processor parts off the matrix are allowed here, they are set at a later point by MatSetValues_MPIAIJ.
661: Also, mat->was_assembled has to be false, otherwise the statement aj[rowstart_diag+dnz_row] = mat_j[col] - cstart;
662: would not be true and the more complex MatSetValues_MPIAIJ has to be used.
663: */
664: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[], const PetscScalar mat_a[])
665: {
666: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
667: Mat A = aij->A; /* diagonal part of the matrix */
668: Mat B = aij->B; /* off-diagonal part of the matrix */
669: Mat_SeqAIJ *aijd = (Mat_SeqAIJ *)aij->A->data, *aijo = (Mat_SeqAIJ *)aij->B->data;
670: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
671: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
672: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend;
673: PetscInt *ailen = a->ilen, *aj = a->j;
674: PetscInt *bilen = b->ilen, *bj = b->j;
675: PetscInt am = aij->A->rmap->n, j;
676: PetscInt *full_diag_i = aijd->i, *full_offd_i = aijo->i; /* These variables can also include non-local elements, which are set at a later point. */
677: PetscInt col, dnz_row, onz_row, rowstart_diag, rowstart_offd;
678: PetscScalar *aa = a->a, *ba = b->a;
680: PetscFunctionBegin;
681: /* Iterate over all rows of the matrix */
682: for (j = 0; j < am; j++) {
683: dnz_row = onz_row = 0;
684: rowstart_offd = full_offd_i[j];
685: rowstart_diag = full_diag_i[j];
686: /* Iterate over all non-zero columns of the current row */
687: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
688: /* If column is in the diagonal */
689: if (mat_j[col] >= cstart && mat_j[col] < cend) {
690: aj[rowstart_diag + dnz_row] = mat_j[col] - cstart;
691: aa[rowstart_diag + dnz_row] = mat_a[col];
692: dnz_row++;
693: } else { /* off-diagonal entries */
694: bj[rowstart_offd + onz_row] = mat_j[col];
695: ba[rowstart_offd + onz_row] = mat_a[col];
696: onz_row++;
697: }
698: }
699: ailen[j] = dnz_row;
700: bilen[j] = onz_row;
701: }
702: PetscFunctionReturn(PETSC_SUCCESS);
703: }
705: static PetscErrorCode MatGetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
706: {
707: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
708: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
709: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
710: PetscBool roworiented = aij->roworiented;
711: PetscScalar *value;
713: PetscFunctionBegin;
714: for (i = 0; i < m; i++) {
715: if (idxm[i] < 0) continue; /* negative row */
716: PetscCheck(idxm[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, idxm[i], mat->rmap->N - 1);
717: PetscCheck(idxm[i] >= rstart && idxm[i] < rend, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local values currently supported, row requested %" PetscInt_FMT " range [%" PetscInt_FMT " %" PetscInt_FMT ")", idxm[i], rstart, rend);
718: row = idxm[i] - rstart;
719: for (j = 0; j < n; j++) {
720: if (idxn[j] < 0) continue; /* negative column */
721: PetscCheck(idxn[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, idxn[j], mat->cmap->N - 1);
722: value = roworiented ? &v[j + i * n] : &v[i + j * m];
723: if (idxn[j] >= cstart && idxn[j] < cend) {
724: col = idxn[j] - cstart;
725: PetscCall(MatGetValues(aij->A, 1, &row, 1, &col, value));
726: } else {
727: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
728: #if PetscDefined(USE_CTABLE)
729: PetscCall(PetscHMapIGetWithDefault(aij->colmap, idxn[j] + 1, 0, &col));
730: col--;
731: #else
732: col = aij->colmap[idxn[j]] - 1;
733: #endif
734: if (col < 0 || aij->garray[col] != idxn[j]) *value = 0.0;
735: else PetscCall(MatGetValues(aij->B, 1, &row, 1, &col, value));
736: }
737: }
738: }
739: PetscFunctionReturn(PETSC_SUCCESS);
740: }
742: static PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat, MatAssemblyType mode)
743: {
744: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
745: PetscInt nstash, reallocs;
747: PetscFunctionBegin;
748: if (aij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
750: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
751: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
752: PetscCall(PetscInfo(mat, "Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
753: PetscFunctionReturn(PETSC_SUCCESS);
754: }
756: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat, MatAssemblyType mode)
757: {
758: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
759: PetscMPIInt n;
760: PetscInt i, j, rstart, ncols, flg;
761: PetscInt *row, *col;
762: PetscBool all_assembled;
763: PetscScalar *val;
765: /* do not use 'b = (Mat_SeqAIJ*)aij->B->data' as B can be reset in disassembly */
767: PetscFunctionBegin;
768: if (!aij->donotstash && !mat->nooffprocentries) {
769: while (1) {
770: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
771: if (!flg) break;
773: for (i = 0; i < n;) {
774: /* Now identify the consecutive vals belonging to the same row */
775: for (j = i, rstart = row[j]; j < n; j++) {
776: if (row[j] != rstart) break;
777: }
778: if (j < n) ncols = j - i;
779: else ncols = n - i;
780: /* Now assemble all these values with a single function call */
781: PetscCall(MatSetValues_MPIAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
782: i = j;
783: }
784: }
785: PetscCall(MatStashScatterEnd_Private(&mat->stash));
786: }
787: #if PetscDefined(HAVE_DEVICE)
788: if (mat->offloadmask == PETSC_OFFLOAD_CPU) aij->A->offloadmask = PETSC_OFFLOAD_CPU;
789: /* We call MatBindToCPU() on aij->A and aij->B here, because if MatBindToCPU_MPIAIJ() is called before assembly, it cannot bind these. */
790: if (mat->boundtocpu) {
791: PetscCall(MatBindToCPU(aij->A, PETSC_TRUE));
792: PetscCall(MatBindToCPU(aij->B, PETSC_TRUE));
793: }
794: #endif
795: PetscCall(MatAssemblyBegin(aij->A, mode));
796: PetscCall(MatAssemblyEnd(aij->A, mode));
798: /* determine if any process has disassembled, if so we must
799: also disassemble ourself, in order that we may reassemble. */
800: /*
801: if nonzero structure of submatrix B cannot change then we know that
802: no process disassembled thus we can skip this stuff
803: */
804: if (!((Mat_SeqAIJ *)aij->B->data)->nonew) {
805: PetscCallMPI(MPIU_Allreduce(&mat->was_assembled, &all_assembled, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
806: if (mat->was_assembled && !all_assembled) { /* mat on this rank has reduced off-diag B with local col ids, but globally it does not */
807: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));
808: }
809: }
810: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIAIJ(mat));
811: PetscCall(MatSetOption(aij->B, MAT_USE_INODES, PETSC_FALSE));
812: #if PetscDefined(HAVE_DEVICE)
813: if (mat->offloadmask == PETSC_OFFLOAD_CPU && aij->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) aij->B->offloadmask = PETSC_OFFLOAD_CPU;
814: #endif
815: PetscCall(MatAssemblyBegin(aij->B, mode));
816: PetscCall(MatAssemblyEnd(aij->B, mode));
818: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
820: aij->rowvalues = NULL;
822: PetscCall(VecDestroy(&aij->diag));
824: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
825: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqAIJ *)aij->A->data)->nonew) {
826: mat->nonzerostate = aij->A->nonzerostate + aij->B->nonzerostate;
827: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
828: }
829: #if PetscDefined(HAVE_DEVICE)
830: mat->offloadmask = PETSC_OFFLOAD_BOTH;
831: #endif
832: PetscFunctionReturn(PETSC_SUCCESS);
833: }
835: static PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
836: {
837: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
839: PetscFunctionBegin;
840: PetscCall(MatZeroEntries(l->A));
841: PetscCall(MatZeroEntries(l->B));
842: PetscFunctionReturn(PETSC_SUCCESS);
843: }
845: static PetscErrorCode MatZeroRows_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
846: {
847: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
848: PetscInt *lrows;
849: PetscInt r, len;
850: PetscBool cong;
852: PetscFunctionBegin;
853: /* get locally owned rows */
854: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
855: PetscCall(MatHasCongruentLayouts(A, &cong));
856: /* fix right-hand side if needed */
857: if (x && b) {
858: const PetscScalar *xx;
859: PetscScalar *bb;
861: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
862: PetscCall(VecGetArrayRead(x, &xx));
863: PetscCall(VecGetArray(b, &bb));
864: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
865: PetscCall(VecRestoreArrayRead(x, &xx));
866: PetscCall(VecRestoreArray(b, &bb));
867: }
869: if (diag != 0.0 && cong) {
870: PetscCall(MatZeroRows(mat->A, len, lrows, diag, NULL, NULL));
871: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
872: } else if (diag != 0.0) { /* non-square or non congruent layouts -> if keepnonzeropattern is false, we allow for new insertion */
873: Mat_SeqAIJ *aijA = (Mat_SeqAIJ *)mat->A->data;
874: Mat_SeqAIJ *aijB = (Mat_SeqAIJ *)mat->B->data;
875: PetscInt nnwA, nnwB;
876: PetscBool nnzA, nnzB;
878: nnwA = aijA->nonew;
879: nnwB = aijB->nonew;
880: nnzA = aijA->keepnonzeropattern;
881: nnzB = aijB->keepnonzeropattern;
882: if (!nnzA) {
883: PetscCall(PetscInfo(mat->A, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on diagonal block.\n"));
884: aijA->nonew = 0;
885: }
886: if (!nnzB) {
887: PetscCall(PetscInfo(mat->B, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on off-diagonal block.\n"));
888: aijB->nonew = 0;
889: }
890: /* Must zero here before the next loop */
891: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
892: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
893: for (r = 0; r < len; ++r) {
894: const PetscInt row = lrows[r] + A->rmap->rstart;
895: if (row >= A->cmap->N) continue;
896: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
897: }
898: aijA->nonew = nnwA;
899: aijB->nonew = nnwB;
900: } else {
901: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
902: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
903: }
904: PetscCall(PetscFree(lrows));
905: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
906: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
908: /* only change matrix nonzero state if pattern was allowed to be changed */
909: if (!((Mat_SeqAIJ *)mat->A->data)->keepnonzeropattern || !((Mat_SeqAIJ *)mat->A->data)->nonew) {
910: A->nonzerostate = mat->A->nonzerostate + mat->B->nonzerostate;
911: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
912: }
913: PetscFunctionReturn(PETSC_SUCCESS);
914: }
916: static PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
917: {
918: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
919: PetscInt n = A->rmap->n;
920: PetscInt i, j, r, m, len = 0;
921: PetscInt *lrows, *owners = A->rmap->range;
922: PetscMPIInt p = 0;
923: PetscSFNode *rrows;
924: PetscSF sf;
925: const PetscScalar *xx;
926: PetscScalar *bb, *mask, *aij_a;
927: Vec xmask, lmask;
928: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)l->B->data;
929: const PetscInt *aj, *ii, *ridx;
930: PetscScalar *aa;
932: PetscFunctionBegin;
933: /* Create SF where leaves are input rows and roots are owned rows */
934: PetscCall(PetscMalloc1(n, &lrows));
935: for (r = 0; r < n; ++r) lrows[r] = -1;
936: PetscCall(PetscMalloc1(N, &rrows));
937: for (r = 0; r < N; ++r) {
938: const PetscInt idx = rows[r];
939: PetscCheck(idx >= 0 && A->rmap->N > idx, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row %" PetscInt_FMT " out of range [0,%" PetscInt_FMT ")", idx, A->rmap->N);
940: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
941: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
942: }
943: rrows[r].rank = p;
944: rrows[r].index = rows[r] - owners[p];
945: }
946: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
947: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
948: /* Collect flags for rows to be zeroed */
949: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
950: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
951: PetscCall(PetscSFDestroy(&sf));
952: /* Compress and put in row numbers */
953: for (r = 0; r < n; ++r)
954: if (lrows[r] >= 0) lrows[len++] = r;
955: /* zero diagonal part of matrix */
956: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
957: /* handle off-diagonal part of matrix */
958: PetscCall(MatCreateVecs(A, &xmask, NULL));
959: PetscCall(VecDuplicate(l->lvec, &lmask));
960: PetscCall(VecGetArray(xmask, &bb));
961: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
962: PetscCall(VecRestoreArray(xmask, &bb));
963: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
964: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
965: PetscCall(VecDestroy(&xmask));
966: if (x && b) { /* this code is buggy when the row and column layout don't match */
967: PetscBool cong;
969: PetscCall(MatHasCongruentLayouts(A, &cong));
970: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
971: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
972: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
973: PetscCall(VecGetArrayRead(l->lvec, &xx));
974: PetscCall(VecGetArray(b, &bb));
975: }
976: PetscCall(VecGetArray(lmask, &mask));
977: /* remove zeroed rows of off-diagonal matrix */
978: PetscCall(MatSeqAIJGetArray(l->B, &aij_a));
979: ii = aij->i;
980: for (i = 0; i < len; i++) PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aij_a, ii[lrows[i]]), ii[lrows[i] + 1] - ii[lrows[i]]));
981: /* loop over all elements of off process part of matrix zeroing removed columns*/
982: if (aij->compressedrow.use) {
983: m = aij->compressedrow.nrows;
984: ii = aij->compressedrow.i;
985: ridx = aij->compressedrow.rindex;
986: for (i = 0; i < m; i++) {
987: n = ii[i + 1] - ii[i];
988: aj = aij->j + ii[i];
989: aa = aij_a + ii[i];
991: for (j = 0; j < n; j++) {
992: if (PetscAbsScalar(mask[*aj])) {
993: if (b) bb[*ridx] -= *aa * xx[*aj];
994: *aa = 0.0;
995: }
996: aa++;
997: aj++;
998: }
999: ridx++;
1000: }
1001: } else { /* do not use compressed row format */
1002: m = l->B->rmap->n;
1003: for (i = 0; i < m; i++) {
1004: n = ii[i + 1] - ii[i];
1005: aj = aij->j + ii[i];
1006: aa = aij_a + ii[i];
1007: for (j = 0; j < n; j++) {
1008: if (PetscAbsScalar(mask[*aj])) {
1009: if (b) bb[i] -= *aa * xx[*aj];
1010: *aa = 0.0;
1011: }
1012: aa++;
1013: aj++;
1014: }
1015: }
1016: }
1017: if (x && b) {
1018: PetscCall(VecRestoreArray(b, &bb));
1019: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1020: }
1021: PetscCall(MatSeqAIJRestoreArray(l->B, &aij_a));
1022: PetscCall(VecRestoreArray(lmask, &mask));
1023: PetscCall(VecDestroy(&lmask));
1024: PetscCall(PetscFree(lrows));
1026: /* only change matrix nonzero state if pattern was allowed to be changed */
1027: if (!((Mat_SeqAIJ *)l->A->data)->nonew) {
1028: A->nonzerostate = l->A->nonzerostate + l->B->nonzerostate;
1029: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1030: }
1031: PetscFunctionReturn(PETSC_SUCCESS);
1032: }
1034: static PetscErrorCode MatMult_MPIAIJ(Mat A, Vec xx, Vec yy)
1035: {
1036: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1037: PetscInt nt;
1038: VecScatter Mvctx = a->Mvctx;
1040: PetscFunctionBegin;
1041: PetscCall(VecGetLocalSize(xx, &nt));
1042: PetscCheck(nt == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A (%" PetscInt_FMT ") and xx (%" PetscInt_FMT ")", A->cmap->n, nt);
1043: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1044: PetscUseTypeMethod(a->A, mult, xx, yy);
1045: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1046: PetscUseTypeMethod(a->B, multadd, a->lvec, yy, yy);
1047: PetscFunctionReturn(PETSC_SUCCESS);
1048: }
1050: static PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A, Vec bb, Vec xx)
1051: {
1052: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1054: PetscFunctionBegin;
1055: PetscCall(MatMultDiagonalBlock(a->A, bb, xx));
1056: PetscFunctionReturn(PETSC_SUCCESS);
1057: }
1059: static PetscErrorCode MatMultAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1060: {
1061: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1062: VecScatter Mvctx = a->Mvctx;
1064: PetscFunctionBegin;
1065: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1066: PetscUseTypeMethod(a->A, multadd, xx, yy, zz);
1067: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1068: PetscUseTypeMethod(a->B, multadd, a->lvec, zz, zz);
1069: PetscFunctionReturn(PETSC_SUCCESS);
1070: }
1072: static PetscErrorCode MatMultTranspose_MPIAIJ(Mat A, Vec xx, Vec yy)
1073: {
1074: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1076: PetscFunctionBegin;
1077: /* do nondiagonal part */
1078: PetscUseTypeMethod(a->B, multtranspose, xx, a->lvec);
1079: /* do local part */
1080: PetscUseTypeMethod(a->A, multtranspose, xx, yy);
1081: /* add partial results together */
1082: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1083: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1084: PetscFunctionReturn(PETSC_SUCCESS);
1085: }
1087: static PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f)
1088: {
1089: MPI_Comm comm;
1090: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)Amat->data, *Bij = (Mat_MPIAIJ *)Bmat->data;
1091: Mat Adia = Aij->A, Bdia = Bij->A, Aoff, Boff, *Aoffs, *Boffs;
1092: IS Me, Notme;
1093: PetscInt M, N, first, last, *notme, i;
1094: PetscMPIInt size;
1096: PetscFunctionBegin;
1097: /* Easy test: symmetric diagonal block */
1098: PetscCall(MatIsTranspose(Adia, Bdia, tol, f));
1099: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, f, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)Amat)));
1100: if (!*f) PetscFunctionReturn(PETSC_SUCCESS);
1101: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1102: PetscCallMPI(MPI_Comm_size(comm, &size));
1103: if (size == 1) PetscFunctionReturn(PETSC_SUCCESS);
1105: /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */
1106: PetscCall(MatGetSize(Amat, &M, &N));
1107: PetscCall(MatGetOwnershipRange(Amat, &first, &last));
1108: PetscCall(PetscMalloc1(N - last + first, ¬me));
1109: for (i = 0; i < first; i++) notme[i] = i;
1110: for (i = last; i < M; i++) notme[i - last + first] = i;
1111: PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme));
1112: PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me));
1113: PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs));
1114: Aoff = Aoffs[0];
1115: PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs));
1116: Boff = Boffs[0];
1117: PetscCall(MatIsTranspose(Aoff, Boff, tol, f));
1118: PetscCall(MatDestroyMatrices(1, &Aoffs));
1119: PetscCall(MatDestroyMatrices(1, &Boffs));
1120: PetscCall(ISDestroy(&Me));
1121: PetscCall(ISDestroy(&Notme));
1122: PetscCall(PetscFree(notme));
1123: PetscFunctionReturn(PETSC_SUCCESS);
1124: }
1126: static PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1127: {
1128: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1130: PetscFunctionBegin;
1131: /* do nondiagonal part */
1132: PetscUseTypeMethod(a->B, multtranspose, xx, a->lvec);
1133: /* do local part */
1134: PetscUseTypeMethod(a->A, multtransposeadd, xx, yy, zz);
1135: /* add partial results together */
1136: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1137: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1138: PetscFunctionReturn(PETSC_SUCCESS);
1139: }
1141: /*
1142: This only works correctly for square matrices where the subblock A->A is the
1143: diagonal block
1144: */
1145: static PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A, Vec v)
1146: {
1147: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1149: PetscFunctionBegin;
1150: PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1151: PetscCheck(A->rmap->rstart == A->cmap->rstart && A->rmap->rend == A->cmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "row partition must equal col partition");
1152: PetscCall(MatGetDiagonal(a->A, v));
1153: PetscFunctionReturn(PETSC_SUCCESS);
1154: }
1156: static PetscErrorCode MatScale_MPIAIJ(Mat A, PetscScalar aa)
1157: {
1158: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1160: PetscFunctionBegin;
1161: PetscCall(MatScale(a->A, aa));
1162: PetscCall(MatScale(a->B, aa));
1163: PetscFunctionReturn(PETSC_SUCCESS);
1164: }
1166: static PetscErrorCode MatView_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
1167: {
1168: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1169: Mat_SeqAIJ *A = (Mat_SeqAIJ *)aij->A->data;
1170: Mat_SeqAIJ *B = (Mat_SeqAIJ *)aij->B->data;
1171: const PetscInt *garray = aij->garray;
1172: const PetscScalar *aa, *ba;
1173: PetscInt header[4], M, N, m, rs, cs, cnt, i, ja, jb;
1174: PetscInt64 nz, hnz;
1175: PetscInt *rowlens;
1176: PetscInt *colidxs;
1177: PetscScalar *matvals;
1178: PetscMPIInt rank;
1180: PetscFunctionBegin;
1181: PetscCall(PetscViewerSetUp(viewer));
1183: M = mat->rmap->N;
1184: N = mat->cmap->N;
1185: m = mat->rmap->n;
1186: rs = mat->rmap->rstart;
1187: cs = mat->cmap->rstart;
1188: nz = A->nz + B->nz;
1190: /* write matrix header */
1191: header[0] = MAT_FILE_CLASSID;
1192: header[1] = M;
1193: header[2] = N;
1194: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1195: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1196: if (rank == 0) PetscCall(PetscIntCast(hnz, &header[3]));
1197: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1199: /* fill in and store row lengths */
1200: PetscCall(PetscMalloc1(m, &rowlens));
1201: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i];
1202: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1203: PetscCall(PetscFree(rowlens));
1205: /* fill in and store column indices */
1206: PetscCall(PetscMalloc1(nz, &colidxs));
1207: for (cnt = 0, i = 0; i < m; i++) {
1208: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1209: if (garray[B->j[jb]] > cs) break;
1210: colidxs[cnt++] = garray[B->j[jb]];
1211: }
1212: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) colidxs[cnt++] = A->j[ja] + cs;
1213: for (; jb < B->i[i + 1]; jb++) colidxs[cnt++] = garray[B->j[jb]];
1214: }
1215: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1216: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
1217: PetscCall(PetscFree(colidxs));
1219: /* fill in and store nonzero values */
1220: PetscCall(MatSeqAIJGetArrayRead(aij->A, &aa));
1221: PetscCall(MatSeqAIJGetArrayRead(aij->B, &ba));
1222: PetscCall(PetscMalloc1(nz, &matvals));
1223: for (cnt = 0, i = 0; i < m; i++) {
1224: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1225: if (garray[B->j[jb]] > cs) break;
1226: matvals[cnt++] = ba[jb];
1227: }
1228: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) matvals[cnt++] = aa[ja];
1229: for (; jb < B->i[i + 1]; jb++) matvals[cnt++] = ba[jb];
1230: }
1231: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &aa));
1232: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &ba));
1233: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1234: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
1235: PetscCall(PetscFree(matvals));
1237: /* write block size option to the viewer's .info file */
1238: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1239: PetscFunctionReturn(PETSC_SUCCESS);
1240: }
1242: #include <petscdraw.h>
1243: static PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1244: {
1245: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1246: PetscMPIInt rank = aij->rank, size = aij->size;
1247: PetscBool isdraw, isascii, isbinary;
1248: PetscViewer sviewer;
1249: PetscViewerFormat format;
1251: PetscFunctionBegin;
1252: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1253: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1254: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1255: if (isascii) {
1256: PetscCall(PetscViewerGetFormat(viewer, &format));
1257: if (format == PETSC_VIEWER_LOAD_BALANCE) {
1258: PetscInt i, nmax = 0, nmin = PETSC_INT_MAX, navg = 0, *nz, nzlocal = ((Mat_SeqAIJ *)aij->A->data)->nz + ((Mat_SeqAIJ *)aij->B->data)->nz;
1259: PetscCall(PetscMalloc1(size, &nz));
1260: PetscCallMPI(MPI_Allgather(&nzlocal, 1, MPIU_INT, nz, 1, MPIU_INT, PetscObjectComm((PetscObject)mat)));
1261: for (i = 0; i < size; i++) {
1262: nmax = PetscMax(nmax, nz[i]);
1263: nmin = PetscMin(nmin, nz[i]);
1264: navg += nz[i];
1265: }
1266: PetscCall(PetscFree(nz));
1267: navg = navg / size;
1268: PetscCall(PetscViewerASCIIPrintf(viewer, "Load Balance - Nonzeros: Min %" PetscInt_FMT " avg %" PetscInt_FMT " max %" PetscInt_FMT "\n", nmin, navg, nmax));
1269: PetscFunctionReturn(PETSC_SUCCESS);
1270: }
1271: PetscCall(PetscViewerGetFormat(viewer, &format));
1272: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1273: MatInfo info;
1274: PetscInt *inodes = NULL;
1276: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1277: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1278: PetscCall(MatInodeGetInodeSizes(aij->A, NULL, &inodes, NULL));
1279: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1280: if (!inodes) {
1281: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] Local rows %" PetscInt_FMT " nz %" PetscInt_FMT " nz alloced %" PetscInt_FMT " mem %g, not using I-node routines\n", rank, mat->rmap->n, (PetscInt)info.nz_used, (PetscInt)info.nz_allocated,
1282: info.memory));
1283: } else {
1284: PetscCall(
1285: PetscViewerASCIISynchronizedPrintf(viewer, "[%d] Local rows %" PetscInt_FMT " nz %" PetscInt_FMT " nz alloced %" PetscInt_FMT " mem %g, using I-node routines\n", rank, mat->rmap->n, (PetscInt)info.nz_used, (PetscInt)info.nz_allocated, info.memory));
1286: }
1287: PetscCall(MatGetInfo(aij->A, MAT_LOCAL, &info));
1288: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1289: PetscCall(MatGetInfo(aij->B, MAT_LOCAL, &info));
1290: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1291: PetscCall(PetscViewerFlush(viewer));
1292: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1293: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1294: PetscCall(VecScatterView(aij->Mvctx, viewer));
1295: PetscFunctionReturn(PETSC_SUCCESS);
1296: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1297: PetscInt inodecount, inodelimit, *inodes;
1298: PetscCall(MatInodeGetInodeSizes(aij->A, &inodecount, &inodes, &inodelimit));
1299: if (inodes) {
1300: PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
1301: } else {
1302: PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
1303: }
1304: PetscFunctionReturn(PETSC_SUCCESS);
1305: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1306: PetscFunctionReturn(PETSC_SUCCESS);
1307: }
1308: } else if (isbinary) {
1309: if (size == 1) {
1310: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1311: PetscCall(MatView(aij->A, viewer));
1312: } else {
1313: PetscCall(MatView_MPIAIJ_Binary(mat, viewer));
1314: }
1315: PetscFunctionReturn(PETSC_SUCCESS);
1316: } else if (isascii && size == 1) {
1317: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1318: PetscCall(MatView(aij->A, viewer));
1319: PetscFunctionReturn(PETSC_SUCCESS);
1320: } else if (isdraw) {
1321: PetscDraw draw;
1322: PetscBool isnull;
1323: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1324: PetscCall(PetscDrawIsNull(draw, &isnull));
1325: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1326: }
1328: { /* assemble the entire matrix onto first process */
1329: Mat A, Av;
1330: IS isrow, iscol;
1332: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->rmap->N : 0, 0, 1, &isrow));
1333: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->cmap->N : 0, 0, 1, &iscol));
1334: PetscCall(MatCreateSubMatrix(mat, isrow, iscol, MAT_INITIAL_MATRIX, &A));
1335: PetscCall(MatMPIAIJGetSeqAIJ(A, &Av, NULL, NULL));
1336: PetscCall(ISDestroy(&iscol));
1337: PetscCall(ISDestroy(&isrow));
1338: /*
1339: Everyone has to call to draw the matrix since the graphics waits are
1340: synchronized across all processors that share the PetscDraw object
1341: */
1342: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1343: if (rank == 0) {
1344: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)Av, ((PetscObject)mat)->name));
1345: PetscCall(MatView_SeqAIJ(Av, sviewer));
1346: }
1347: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1348: PetscCall(MatDestroy(&A));
1349: }
1350: PetscFunctionReturn(PETSC_SUCCESS);
1351: }
1353: PetscErrorCode MatView_MPIAIJ(Mat mat, PetscViewer viewer)
1354: {
1355: PetscBool isascii, isdraw, issocket, isbinary;
1357: PetscFunctionBegin;
1358: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1359: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1360: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1361: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1362: if (isascii || isdraw || isbinary || issocket) PetscCall(MatView_MPIAIJ_ASCIIorDraworSocket(mat, viewer));
1363: PetscFunctionReturn(PETSC_SUCCESS);
1364: }
1366: static PetscErrorCode MatSOR_MPIAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1367: {
1368: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1369: Vec bb1 = NULL;
1370: PetscBool hasop;
1372: PetscFunctionBegin;
1373: if (flag == SOR_APPLY_UPPER) {
1374: PetscUseTypeMethod(mat->A, sor, bb, omega, flag, fshift, lits, 1, xx);
1375: PetscFunctionReturn(PETSC_SUCCESS);
1376: }
1378: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1));
1380: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
1381: if (flag & SOR_ZERO_INITIAL_GUESS) {
1382: PetscUseTypeMethod(mat->A, sor, bb, omega, flag, fshift, lits, 1, xx);
1383: its--;
1384: }
1386: while (its--) {
1387: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1388: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1390: /* update rhs: bb1 = bb - B*x */
1391: PetscCall(VecScale(mat->lvec, -1.0));
1392: PetscUseTypeMethod(mat->B, multadd, mat->lvec, bb, bb1);
1394: /* local sweep */
1395: PetscUseTypeMethod(mat->A, sor, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx);
1396: }
1397: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1398: if (flag & SOR_ZERO_INITIAL_GUESS) {
1399: PetscUseTypeMethod(mat->A, sor, bb, omega, flag, fshift, lits, 1, xx);
1400: its--;
1401: }
1402: while (its--) {
1403: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1404: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1406: /* update rhs: bb1 = bb - B*x */
1407: PetscCall(VecScale(mat->lvec, -1.0));
1408: PetscUseTypeMethod(mat->B, multadd, mat->lvec, bb, bb1);
1410: /* local sweep */
1411: PetscUseTypeMethod(mat->A, sor, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx);
1412: }
1413: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1414: if (flag & SOR_ZERO_INITIAL_GUESS) {
1415: PetscUseTypeMethod(mat->A, sor, bb, omega, flag, fshift, lits, 1, xx);
1416: its--;
1417: }
1418: while (its--) {
1419: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1420: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1422: /* update rhs: bb1 = bb - B*x */
1423: PetscCall(VecScale(mat->lvec, -1.0));
1424: PetscUseTypeMethod(mat->B, multadd, mat->lvec, bb, bb1);
1426: /* local sweep */
1427: PetscUseTypeMethod(mat->A, sor, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx);
1428: }
1429: } else if (flag & SOR_EISENSTAT) {
1430: Vec xx1;
1432: PetscCall(VecDuplicate(bb, &xx1));
1433: PetscUseTypeMethod(mat->A, sor, bb, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP), fshift, lits, 1, xx);
1435: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1436: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1437: if (!mat->diag) {
1438: PetscCall(MatCreateVecs(matin, &mat->diag, NULL));
1439: PetscCall(MatGetDiagonal(matin, mat->diag));
1440: }
1441: PetscCall(MatHasOperation(matin, MATOP_MULT_DIAGONAL_BLOCK, &hasop));
1442: if (hasop) PetscCall(MatMultDiagonalBlock(matin, xx, bb1));
1443: else PetscCall(VecPointwiseMult(bb1, mat->diag, xx));
1444: PetscCall(VecAYPX(bb1, (omega - 2.0) / omega, bb));
1446: PetscCall(MatMultAdd(mat->B, mat->lvec, bb1, bb1));
1448: /* local sweep */
1449: PetscUseTypeMethod(mat->A, sor, bb1, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP), fshift, lits, 1, xx1);
1450: PetscCall(VecAXPY(xx, 1.0, xx1));
1451: PetscCall(VecDestroy(&xx1));
1452: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");
1454: PetscCall(VecDestroy(&bb1));
1456: matin->factorerrortype = mat->A->factorerrortype;
1457: PetscFunctionReturn(PETSC_SUCCESS);
1458: }
1460: static PetscErrorCode MatPermute_MPIAIJ(Mat A, IS rowp, IS colp, Mat *B)
1461: {
1462: Mat aA, aB, Aperm;
1463: const PetscInt *rwant, *cwant, *gcols, *ai, *bi, *aj, *bj;
1464: PetscScalar *aa, *ba;
1465: PetscInt i, j, m, n, ng, anz, bnz, *dnnz, *onnz, *tdnnz, *tonnz, *rdest, *cdest, *work, *gcdest;
1466: PetscSF rowsf, sf;
1467: IS parcolp = NULL;
1468: PetscBool done;
1470: PetscFunctionBegin;
1471: PetscCall(MatGetLocalSize(A, &m, &n));
1472: PetscCall(ISGetIndices(rowp, &rwant));
1473: PetscCall(ISGetIndices(colp, &cwant));
1474: PetscCall(PetscMalloc3(PetscMax(m, n), &work, m, &rdest, n, &cdest));
1476: /* Invert row permutation to find out where my rows should go */
1477: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &rowsf));
1478: PetscCall(PetscSFSetGraphLayout(rowsf, A->rmap, A->rmap->n, NULL, PETSC_OWN_POINTER, rwant));
1479: PetscCall(PetscSFSetFromOptions(rowsf));
1480: for (i = 0; i < m; i++) work[i] = A->rmap->rstart + i;
1481: PetscCall(PetscSFReduceBegin(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1482: PetscCall(PetscSFReduceEnd(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1484: /* Invert column permutation to find out where my columns should go */
1485: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1486: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, A->cmap->n, NULL, PETSC_OWN_POINTER, cwant));
1487: PetscCall(PetscSFSetFromOptions(sf));
1488: for (i = 0; i < n; i++) work[i] = A->cmap->rstart + i;
1489: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1490: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1491: PetscCall(PetscSFDestroy(&sf));
1493: PetscCall(ISRestoreIndices(rowp, &rwant));
1494: PetscCall(ISRestoreIndices(colp, &cwant));
1495: PetscCall(MatMPIAIJGetSeqAIJ(A, &aA, &aB, &gcols));
1497: /* Find out where my gcols should go */
1498: PetscCall(MatGetSize(aB, NULL, &ng));
1499: PetscCall(PetscMalloc1(ng, &gcdest));
1500: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1501: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, ng, NULL, PETSC_OWN_POINTER, gcols));
1502: PetscCall(PetscSFSetFromOptions(sf));
1503: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1504: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1505: PetscCall(PetscSFDestroy(&sf));
1507: PetscCall(PetscCalloc4(m, &dnnz, m, &onnz, m, &tdnnz, m, &tonnz));
1508: PetscCall(MatGetRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1509: PetscCall(MatGetRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1510: for (i = 0; i < m; i++) {
1511: PetscInt row = rdest[i];
1512: PetscMPIInt rowner;
1513: PetscCall(PetscLayoutFindOwner(A->rmap, row, &rowner));
1514: for (j = ai[i]; j < ai[i + 1]; j++) {
1515: PetscInt col = cdest[aj[j]];
1516: PetscMPIInt cowner;
1517: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner)); /* Could build an index for the columns to eliminate this search */
1518: if (rowner == cowner) dnnz[i]++;
1519: else onnz[i]++;
1520: }
1521: for (j = bi[i]; j < bi[i + 1]; j++) {
1522: PetscInt col = gcdest[bj[j]];
1523: PetscMPIInt cowner;
1524: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner));
1525: if (rowner == cowner) dnnz[i]++;
1526: else onnz[i]++;
1527: }
1528: }
1529: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1530: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1531: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1532: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1533: PetscCall(PetscSFDestroy(&rowsf));
1535: PetscCall(MatCreateAIJ(PetscObjectComm((PetscObject)A), A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N, 0, tdnnz, 0, tonnz, &Aperm));
1536: PetscCall(MatSeqAIJGetArray(aA, &aa));
1537: PetscCall(MatSeqAIJGetArray(aB, &ba));
1538: for (i = 0; i < m; i++) {
1539: PetscInt *acols = dnnz, *bcols = onnz; /* Repurpose now-unneeded arrays */
1540: PetscInt rowlen;
1541: rowlen = ai[i + 1] - ai[i];
1542: for (PetscInt j0 = j = 0; j < rowlen; j0 = j) { /* rowlen could be larger than number of rows m, so sum in batches */
1543: for (; j < PetscMin(rowlen, j0 + m); j++) acols[j - j0] = cdest[aj[ai[i] + j]];
1544: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, acols, aa + ai[i] + j0, INSERT_VALUES));
1545: }
1546: rowlen = bi[i + 1] - bi[i];
1547: for (PetscInt j0 = j = 0; j < rowlen; j0 = j) {
1548: for (; j < PetscMin(rowlen, j0 + m); j++) bcols[j - j0] = gcdest[bj[bi[i] + j]];
1549: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, bcols, ba + bi[i] + j0, INSERT_VALUES));
1550: }
1551: }
1552: PetscCall(MatAssemblyBegin(Aperm, MAT_FINAL_ASSEMBLY));
1553: PetscCall(MatAssemblyEnd(Aperm, MAT_FINAL_ASSEMBLY));
1554: PetscCall(MatRestoreRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1555: PetscCall(MatRestoreRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1556: PetscCall(MatSeqAIJRestoreArray(aA, &aa));
1557: PetscCall(MatSeqAIJRestoreArray(aB, &ba));
1558: PetscCall(PetscFree4(dnnz, onnz, tdnnz, tonnz));
1559: PetscCall(PetscFree3(work, rdest, cdest));
1560: PetscCall(PetscFree(gcdest));
1561: if (parcolp) PetscCall(ISDestroy(&colp));
1562: *B = Aperm;
1563: PetscFunctionReturn(PETSC_SUCCESS);
1564: }
1566: static PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
1567: {
1568: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1570: PetscFunctionBegin;
1571: PetscCall(MatGetSize(aij->B, NULL, nghosts));
1572: if (ghosts) *ghosts = aij->garray;
1573: PetscFunctionReturn(PETSC_SUCCESS);
1574: }
1576: static PetscErrorCode MatGetInfo_MPIAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1577: {
1578: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1579: Mat A = mat->A, B = mat->B;
1580: PetscLogDouble irecv[5];
1582: PetscFunctionBegin;
1583: info->block_size = 1.0;
1584: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1586: irecv[0] = info->nz_used;
1587: irecv[1] = info->nz_allocated;
1588: irecv[2] = info->nz_unneeded;
1589: irecv[3] = info->memory;
1590: irecv[4] = info->mallocs;
1592: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1594: irecv[0] += info->nz_used;
1595: irecv[1] += info->nz_allocated;
1596: irecv[2] += info->nz_unneeded;
1597: irecv[3] += info->memory;
1598: irecv[4] += info->mallocs;
1599: if (flag == MAT_LOCAL) {
1600: info->nz_used = irecv[0];
1601: info->nz_allocated = irecv[1];
1602: info->nz_unneeded = irecv[2];
1603: info->memory = irecv[3];
1604: info->mallocs = irecv[4];
1605: } else if (flag == MAT_GLOBAL_MAX) {
1606: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1608: info->nz_used = irecv[0];
1609: info->nz_allocated = irecv[1];
1610: info->nz_unneeded = irecv[2];
1611: info->memory = irecv[3];
1612: info->mallocs = irecv[4];
1613: } else if (flag == MAT_GLOBAL_SUM) {
1614: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1616: info->nz_used = irecv[0];
1617: info->nz_allocated = irecv[1];
1618: info->nz_unneeded = irecv[2];
1619: info->memory = irecv[3];
1620: info->mallocs = irecv[4];
1621: }
1622: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1623: info->fill_ratio_needed = 0;
1624: info->factor_mallocs = 0;
1625: PetscFunctionReturn(PETSC_SUCCESS);
1626: }
1628: PetscErrorCode MatSetOption_MPIAIJ(Mat A, MatOption op, PetscBool flg)
1629: {
1630: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1632: PetscFunctionBegin;
1633: switch (op) {
1634: case MAT_NEW_NONZERO_LOCATIONS:
1635: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1636: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1637: case MAT_KEEP_NONZERO_PATTERN:
1638: case MAT_NEW_NONZERO_LOCATION_ERR:
1639: case MAT_USE_INODES:
1640: case MAT_IGNORE_ZERO_ENTRIES:
1641: case MAT_FORM_EXPLICIT_TRANSPOSE:
1642: case MAT_ROW_ORIENTED:
1643: MatCheckPreallocated(A, 1);
1644: if (op == MAT_ROW_ORIENTED) a->roworiented = flg;
1645: PetscCall(MatSetOption(a->A, op, flg));
1646: PetscCall(MatSetOption(a->B, op, flg));
1647: break;
1648: case MAT_STRUCTURE_ONLY:
1649: if (a->A) PetscCall(MatSetOption(a->A, op, flg));
1650: if (a->B) PetscCall(MatSetOption(a->B, op, flg));
1651: break;
1652: case MAT_IGNORE_OFF_PROC_ENTRIES:
1653: a->donotstash = flg;
1654: break;
1655: /* Symmetry flags are handled directly by MatSetOption() and they don't affect preallocation */
1656: case MAT_SPD:
1657: case MAT_SYMMETRIC:
1658: case MAT_STRUCTURALLY_SYMMETRIC:
1659: case MAT_HERMITIAN:
1660: case MAT_SYMMETRY_ETERNAL:
1661: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1662: case MAT_SPD_ETERNAL:
1663: /* if the diagonal matrix is square it inherits some of the properties above */
1664: if (a->A && A->rmap->n == A->cmap->n) PetscCall(MatSetOption(a->A, op, flg));
1665: break;
1666: case MAT_SUBMAT_SINGLEIS:
1667: A->submat_singleis = flg;
1668: break;
1669: default:
1670: break;
1671: }
1672: PetscFunctionReturn(PETSC_SUCCESS);
1673: }
1675: PetscErrorCode MatGetRow_MPIAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1676: {
1677: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1678: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1679: PetscInt i, *cworkA, *cworkB, **pcA, **pcB, cstart = matin->cmap->rstart;
1680: PetscInt nztot, nzA, nzB, lrow, rstart = matin->rmap->rstart, rend = matin->rmap->rend;
1681: PetscInt *cmap, *idx_p;
1683: PetscFunctionBegin;
1684: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1685: mat->getrowactive = PETSC_TRUE;
1687: if ((!mat->rowindices && (idx || v)) || (!mat->rowvalues && v)) {
1688: /*
1689: allocate enough space to hold information from the longest row.
1690: */
1691: Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)mat->A->data, *Ba = (Mat_SeqAIJ *)mat->B->data;
1692: PetscInt max = 1, tmp;
1693: for (i = 0; i < matin->rmap->n; i++) {
1694: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1695: if (max < tmp) max = tmp;
1696: }
1697: PetscCall(PetscFree2(mat->rowvalues, mat->rowindices));
1698: PetscCall(PetscMalloc2(v ? max : 0, &mat->rowvalues, max, &mat->rowindices));
1699: }
1701: PetscCheck(row >= rstart && row < rend, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Only local rows");
1702: lrow = row - rstart;
1704: pvA = &vworkA;
1705: pcA = &cworkA;
1706: pvB = &vworkB;
1707: pcB = &cworkB;
1708: if (!v) {
1709: pvA = NULL;
1710: pvB = NULL;
1711: }
1712: if (!idx) {
1713: pcA = NULL;
1714: if (!v) pcB = NULL;
1715: }
1716: PetscUseTypeMethod(mat->A, getrow, lrow, &nzA, pcA, pvA);
1717: PetscUseTypeMethod(mat->B, getrow, lrow, &nzB, pcB, pvB);
1718: nztot = nzA + nzB;
1720: cmap = mat->garray;
1721: if (v || idx) {
1722: if (nztot) {
1723: /* Sort by increasing column numbers, assuming A and B already sorted */
1724: PetscInt imark = -1;
1725: if (v) {
1726: *v = v_p = mat->rowvalues;
1727: for (i = 0; i < nzB; i++) {
1728: if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1729: else break;
1730: }
1731: imark = i;
1732: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1733: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1734: }
1735: if (idx) {
1736: *idx = idx_p = mat->rowindices;
1737: if (imark > -1) {
1738: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i]];
1739: } else {
1740: for (i = 0; i < nzB; i++) {
1741: if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1742: else break;
1743: }
1744: imark = i;
1745: }
1746: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart + cworkA[i];
1747: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i]];
1748: }
1749: } else {
1750: if (idx) *idx = NULL;
1751: if (v) *v = NULL;
1752: }
1753: }
1754: *nz = nztot;
1755: PetscUseTypeMethod(mat->A, restorerow, lrow, &nzA, pcA, pvA);
1756: PetscUseTypeMethod(mat->B, restorerow, lrow, &nzB, pcB, pvB);
1757: PetscFunctionReturn(PETSC_SUCCESS);
1758: }
1760: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1761: {
1762: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1764: PetscFunctionBegin;
1765: PetscCheck(aij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow() must be called first");
1766: aij->getrowactive = PETSC_FALSE;
1767: PetscFunctionReturn(PETSC_SUCCESS);
1768: }
1770: static PetscErrorCode MatNorm_MPIAIJ(Mat mat, NormType type, PetscReal *norm)
1771: {
1772: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1773: Mat_SeqAIJ *amat = (Mat_SeqAIJ *)aij->A->data, *bmat = (Mat_SeqAIJ *)aij->B->data;
1774: PetscInt i, j;
1775: PetscReal sum = 0.0;
1776: const MatScalar *v, *amata, *bmata;
1778: PetscFunctionBegin;
1779: if (aij->size == 1) {
1780: PetscCall(MatNorm(aij->A, type, norm));
1781: } else {
1782: PetscCall(MatSeqAIJGetArrayRead(aij->A, &amata));
1783: PetscCall(MatSeqAIJGetArrayRead(aij->B, &bmata));
1784: if (type == NORM_FROBENIUS) {
1785: v = amata;
1786: for (i = 0; i < amat->nz; i++) {
1787: sum += PetscRealPart(PetscConj(*v) * (*v));
1788: v++;
1789: }
1790: v = bmata;
1791: for (i = 0; i < bmat->nz; i++) {
1792: sum += PetscRealPart(PetscConj(*v) * (*v));
1793: v++;
1794: }
1795: PetscCallMPI(MPIU_Allreduce(&sum, norm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1796: *norm = PetscSqrtReal(*norm);
1797: PetscCall(PetscLogFlops(2.0 * amat->nz + 2.0 * bmat->nz));
1798: } else if (type == NORM_1) { /* max column norm */
1799: Vec col, bcol;
1800: PetscScalar *array;
1801: PetscInt *jj, *garray = aij->garray;
1803: PetscCall(MatCreateVecs(mat, &col, NULL));
1804: PetscCall(VecGetArrayWrite(col, &array));
1805: v = amata;
1806: jj = amat->j;
1807: for (j = 0; j < amat->nz; j++) array[*jj++] += PetscAbsScalar(*v++);
1808: PetscCall(VecRestoreArrayWrite(col, &array));
1809: PetscCall(MatCreateVecs(aij->B, &bcol, NULL));
1810: PetscCall(VecGetArrayWrite(bcol, &array));
1811: v = bmata;
1812: jj = bmat->j;
1813: for (j = 0; j < bmat->nz; j++) array[*jj++] += PetscAbsScalar(*v++);
1814: PetscCall(VecSetValues(col, aij->B->cmap->n, garray, array, ADD_VALUES));
1815: PetscCall(VecRestoreArrayWrite(bcol, &array));
1816: PetscCall(VecDestroy(&bcol));
1817: PetscCall(VecAssemblyBegin(col));
1818: PetscCall(VecAssemblyEnd(col));
1819: PetscCall(VecNorm(col, NORM_INFINITY, norm));
1820: PetscCall(VecDestroy(&col));
1821: } else if (type == NORM_INFINITY) { /* max row norm */
1822: *norm = 0.0;
1823: for (j = 0; j < aij->A->rmap->n; j++) {
1824: v = PetscSafePointerPlusOffset(amata, amat->i[j]);
1825: sum = 0.0;
1826: for (i = 0; i < amat->i[j + 1] - amat->i[j]; i++) {
1827: sum += PetscAbsScalar(*v);
1828: v++;
1829: }
1830: v = PetscSafePointerPlusOffset(bmata, bmat->i[j]);
1831: for (i = 0; i < bmat->i[j + 1] - bmat->i[j]; i++) {
1832: sum += PetscAbsScalar(*v);
1833: v++;
1834: }
1835: if (sum > *norm) *norm = sum;
1836: }
1837: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, norm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
1838: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1839: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for two norm");
1840: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &amata));
1841: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &bmata));
1842: }
1843: PetscFunctionReturn(PETSC_SUCCESS);
1844: }
1846: static PetscErrorCode MatTranspose_MPIAIJ(Mat A, MatReuse reuse, Mat *matout)
1847: {
1848: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *b;
1849: Mat_SeqAIJ *Aloc = (Mat_SeqAIJ *)a->A->data, *Bloc = (Mat_SeqAIJ *)a->B->data, *sub_B_diag;
1850: PetscInt M = A->rmap->N, N = A->cmap->N, ma, na, mb, nb, row, *cols, *cols_tmp, *B_diag_ilen, i, ncol, A_diag_ncol;
1851: const PetscInt *ai, *aj, *bi, *bj, *B_diag_i;
1852: Mat B, A_diag, *B_diag;
1853: const MatScalar *pbv, *bv;
1855: PetscFunctionBegin;
1856: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1857: ma = A->rmap->n;
1858: na = A->cmap->n;
1859: mb = a->B->rmap->n;
1860: nb = a->B->cmap->n;
1861: ai = Aloc->i;
1862: aj = Aloc->j;
1863: bi = Bloc->i;
1864: bj = Bloc->j;
1865: if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1866: PetscInt *d_nnz, *g_nnz, *o_nnz;
1867: PetscSFNode *oloc;
1868: PETSC_UNUSED PetscSF sf;
1870: PetscCall(PetscMalloc4(na, &d_nnz, na, &o_nnz, nb, &g_nnz, nb, &oloc));
1871: /* compute d_nnz for preallocation */
1872: PetscCall(PetscArrayzero(d_nnz, na));
1873: for (i = 0; i < ai[ma]; i++) d_nnz[aj[i]]++;
1874: /* compute local off-diagonal contributions */
1875: PetscCall(PetscArrayzero(g_nnz, nb));
1876: for (i = 0; i < bi[ma]; i++) g_nnz[bj[i]]++;
1877: /* map those to global */
1878: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1879: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, nb, NULL, PETSC_USE_POINTER, a->garray));
1880: PetscCall(PetscSFSetFromOptions(sf));
1881: PetscCall(PetscArrayzero(o_nnz, na));
1882: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1883: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1884: PetscCall(PetscSFDestroy(&sf));
1886: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1887: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1888: PetscCall(MatSetBlockSizes(B, A->cmap->bs, A->rmap->bs));
1889: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1890: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
1891: PetscCall(PetscFree4(d_nnz, o_nnz, g_nnz, oloc));
1892: } else {
1893: B = *matout;
1894: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
1895: }
1897: b = (Mat_MPIAIJ *)B->data;
1898: A_diag = a->A;
1899: B_diag = &b->A;
1900: sub_B_diag = (Mat_SeqAIJ *)(*B_diag)->data;
1901: A_diag_ncol = A_diag->cmap->N;
1902: B_diag_ilen = sub_B_diag->ilen;
1903: B_diag_i = sub_B_diag->i;
1905: /* Set ilen for diagonal of B */
1906: for (i = 0; i < A_diag_ncol; i++) B_diag_ilen[i] = B_diag_i[i + 1] - B_diag_i[i];
1908: /* Transpose the diagonal part of the matrix. In contrast to the off-diagonal part, this can be done
1909: very quickly (=without using MatSetValues), because all writes are local. */
1910: PetscCall(MatTransposeSetPrecursor(A_diag, *B_diag));
1911: PetscCall(MatTranspose(A_diag, MAT_REUSE_MATRIX, B_diag));
1913: /* copy over the B part */
1914: PetscCall(PetscMalloc1(bi[mb], &cols));
1915: PetscCall(MatSeqAIJGetArrayRead(a->B, &bv));
1916: pbv = bv;
1917: row = A->rmap->rstart;
1918: for (i = 0; i < bi[mb]; i++) cols[i] = a->garray[bj[i]];
1919: cols_tmp = cols;
1920: for (i = 0; i < mb; i++) {
1921: ncol = bi[i + 1] - bi[i];
1922: PetscCall(MatSetValues(B, ncol, cols_tmp, 1, &row, pbv, INSERT_VALUES));
1923: row++;
1924: if (pbv) pbv += ncol;
1925: if (cols_tmp) cols_tmp += ncol;
1926: }
1927: PetscCall(PetscFree(cols));
1928: PetscCall(MatSeqAIJRestoreArrayRead(a->B, &bv));
1930: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1931: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1932: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1933: *matout = B;
1934: } else {
1935: PetscCall(MatHeaderMerge(A, &B));
1936: }
1937: PetscFunctionReturn(PETSC_SUCCESS);
1938: }
1940: static PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat, Vec ll, Vec rr)
1941: {
1942: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1943: Mat a = aij->A, b = aij->B;
1944: PetscInt s1, s2, s3;
1946: PetscFunctionBegin;
1947: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1948: if (rr) {
1949: PetscCall(VecGetLocalSize(rr, &s1));
1950: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1951: /* Overlap communication with computation. */
1952: PetscCall(VecScatterBegin(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1953: }
1954: if (ll) {
1955: PetscCall(VecGetLocalSize(ll, &s1));
1956: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1957: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1958: }
1959: /* scale the diagonal block */
1960: PetscUseTypeMethod(a, diagonalscale, ll, rr);
1962: if (rr) {
1963: /* Do a scatter end and then right scale the off-diagonal block */
1964: PetscCall(VecScatterEnd(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1965: PetscUseTypeMethod(b, diagonalscale, NULL, aij->lvec);
1966: }
1967: PetscFunctionReturn(PETSC_SUCCESS);
1968: }
1970: static PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
1971: {
1972: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1974: PetscFunctionBegin;
1975: PetscCall(MatSetUnfactored(a->A));
1976: PetscFunctionReturn(PETSC_SUCCESS);
1977: }
1979: static PetscErrorCode MatEqual_MPIAIJ(Mat A, Mat B, PetscBool *flag)
1980: {
1981: Mat_MPIAIJ *matB = (Mat_MPIAIJ *)B->data, *matA = (Mat_MPIAIJ *)A->data;
1982: Mat a, b, c, d;
1984: PetscFunctionBegin;
1985: a = matA->A;
1986: b = matA->B;
1987: c = matB->A;
1988: d = matB->B;
1990: PetscCall(MatEqual(a, c, flag));
1991: if (*flag) PetscCall(MatEqual(b, d, flag));
1992: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, flag, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
1993: PetscFunctionReturn(PETSC_SUCCESS);
1994: }
1996: static PetscErrorCode MatCopy_MPIAIJ(Mat A, Mat B, MatStructure str)
1997: {
1998: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1999: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2001: PetscFunctionBegin;
2002: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2003: if (str != SAME_NONZERO_PATTERN || A->ops->copy != B->ops->copy) {
2004: /* because of the column compression in the off-processor part of the matrix a->B,
2005: the number of columns in a->B and b->B may be different, hence we cannot call
2006: the MatCopy() directly on the two parts. If need be, we can provide a more
2007: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2008: then copying the submatrices */
2009: PetscCall(MatCopy_Basic(A, B, str));
2010: } else {
2011: PetscCall(MatCopy(a->A, b->A, str));
2012: PetscCall(MatCopy(a->B, b->B, str));
2013: }
2014: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2015: PetscFunctionReturn(PETSC_SUCCESS);
2016: }
2018: /*
2019: Computes the number of nonzeros per row needed for preallocation when X and Y
2020: have different nonzero structure.
2021: */
2022: PetscErrorCode MatAXPYGetPreallocation_MPIX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *xltog, const PetscInt *yi, const PetscInt *yj, const PetscInt *yltog, PetscInt *nnz)
2023: {
2024: PetscInt i, j, k, nzx, nzy;
2026: PetscFunctionBegin;
2027: /* Set the number of nonzeros in the new matrix */
2028: for (i = 0; i < m; i++) {
2029: const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2030: nzx = xi[i + 1] - xi[i];
2031: nzy = yi[i + 1] - yi[i];
2032: nnz[i] = 0;
2033: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2034: for (; k < nzy && yltog[yjj[k]] < xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2035: if (k < nzy && yltog[yjj[k]] == xltog[xjj[j]]) k++; /* Skip duplicate */
2036: nnz[i]++;
2037: }
2038: for (; k < nzy; k++) nnz[i]++;
2039: }
2040: PetscFunctionReturn(PETSC_SUCCESS);
2041: }
2043: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2044: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
2045: {
2046: PetscInt m = Y->rmap->N;
2047: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2048: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
2050: PetscFunctionBegin;
2051: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
2052: PetscFunctionReturn(PETSC_SUCCESS);
2053: }
2055: static PetscErrorCode MatAXPY_MPIAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2056: {
2057: Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data, *yy = (Mat_MPIAIJ *)Y->data;
2059: PetscFunctionBegin;
2060: if (str == SAME_NONZERO_PATTERN) {
2061: PetscCall(MatAXPY(yy->A, a, xx->A, str));
2062: PetscCall(MatAXPY(yy->B, a, xx->B, str));
2063: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2064: PetscCall(MatAXPY_Basic(Y, a, X, str));
2065: } else {
2066: Mat B;
2067: PetscInt *nnz_d, *nnz_o;
2069: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
2070: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
2071: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2072: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2073: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2074: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2075: PetscCall(MatAXPYGetPreallocation_SeqAIJ(yy->A, xx->A, nnz_d));
2076: PetscCall(MatAXPYGetPreallocation_MPIAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
2077: PetscCall(MatMPIAIJSetPreallocation(B, 0, nnz_d, 0, nnz_o));
2078: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2079: PetscCall(MatHeaderMerge(Y, &B));
2080: PetscCall(PetscFree(nnz_d));
2081: PetscCall(PetscFree(nnz_o));
2082: }
2083: PetscFunctionReturn(PETSC_SUCCESS);
2084: }
2086: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);
2088: static PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2089: {
2090: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2092: PetscFunctionBegin;
2093: PetscCall(MatConjugate_SeqAIJ(aij->A));
2094: PetscCall(MatConjugate_SeqAIJ(aij->B));
2095: PetscFunctionReturn(PETSC_SUCCESS);
2096: }
2098: static PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2099: {
2100: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2102: PetscFunctionBegin;
2103: PetscCall(MatRealPart(a->A));
2104: PetscCall(MatRealPart(a->B));
2105: PetscFunctionReturn(PETSC_SUCCESS);
2106: }
2108: static PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
2109: {
2110: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2112: PetscFunctionBegin;
2113: PetscCall(MatImaginaryPart(a->A));
2114: PetscCall(MatImaginaryPart(a->B));
2115: PetscFunctionReturn(PETSC_SUCCESS);
2116: }
2118: static PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2119: {
2120: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2121: PetscInt i, *idxb = NULL, m = A->rmap->n;
2122: PetscScalar *vv;
2123: Vec vB, vA;
2124: const PetscScalar *va, *vb;
2126: PetscFunctionBegin;
2127: PetscCall(MatCreateVecs(a->A, NULL, &vA));
2128: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
2130: PetscCall(VecGetArrayRead(vA, &va));
2131: if (idx) {
2132: for (i = 0; i < m; i++) {
2133: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2134: }
2135: }
2137: PetscCall(MatCreateVecs(a->B, NULL, &vB));
2138: PetscCall(PetscMalloc1(m, &idxb));
2139: PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));
2141: PetscCall(VecGetArrayWrite(v, &vv));
2142: PetscCall(VecGetArrayRead(vB, &vb));
2143: for (i = 0; i < m; i++) {
2144: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2145: vv[i] = vb[i];
2146: if (idx) idx[i] = a->garray[idxb[i]];
2147: } else {
2148: vv[i] = va[i];
2149: if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > a->garray[idxb[i]]) idx[i] = a->garray[idxb[i]];
2150: }
2151: }
2152: PetscCall(VecRestoreArrayWrite(v, &vv));
2153: PetscCall(VecRestoreArrayRead(vA, &va));
2154: PetscCall(VecRestoreArrayRead(vB, &vb));
2155: PetscCall(PetscFree(idxb));
2156: PetscCall(VecDestroy(&vA));
2157: PetscCall(VecDestroy(&vB));
2158: PetscFunctionReturn(PETSC_SUCCESS);
2159: }
2161: static PetscErrorCode MatGetRowSumAbs_MPIAIJ(Mat A, Vec v)
2162: {
2163: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2164: Vec vB, vA;
2166: PetscFunctionBegin;
2167: PetscCall(MatCreateVecs(a->A, NULL, &vA));
2168: PetscCall(MatGetRowSumAbs(a->A, vA));
2169: PetscCall(MatCreateVecs(a->B, NULL, &vB));
2170: PetscCall(MatGetRowSumAbs(a->B, vB));
2171: PetscCall(VecAXPY(vA, 1.0, vB));
2172: PetscCall(VecDestroy(&vB));
2173: PetscCall(VecCopy(vA, v));
2174: PetscCall(VecDestroy(&vA));
2175: PetscFunctionReturn(PETSC_SUCCESS);
2176: }
2178: static PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2179: {
2180: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2181: PetscInt m = A->rmap->n, n = A->cmap->n;
2182: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2183: PetscInt *cmap = mat->garray;
2184: PetscInt *diagIdx, *offdiagIdx;
2185: Vec diagV, offdiagV;
2186: PetscScalar *a, *diagA, *offdiagA;
2187: const PetscScalar *ba, *bav;
2188: PetscInt r, j, col, ncols, *bi, *bj;
2189: Mat B = mat->B;
2190: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2192: PetscFunctionBegin;
2193: /* When a process holds entire A and other processes have no entry */
2194: if (A->cmap->N == n) {
2195: PetscCall(VecGetArrayWrite(v, &diagA));
2196: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2197: PetscCall(MatGetRowMinAbs(mat->A, diagV, idx));
2198: PetscCall(VecDestroy(&diagV));
2199: PetscCall(VecRestoreArrayWrite(v, &diagA));
2200: PetscFunctionReturn(PETSC_SUCCESS);
2201: } else if (n == 0) {
2202: if (m) {
2203: PetscCall(VecGetArrayWrite(v, &a));
2204: for (r = 0; r < m; r++) {
2205: a[r] = 0.0;
2206: if (idx) idx[r] = -1;
2207: }
2208: PetscCall(VecRestoreArrayWrite(v, &a));
2209: }
2210: PetscFunctionReturn(PETSC_SUCCESS);
2211: }
2213: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2214: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2215: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2216: PetscCall(MatGetRowMinAbs(mat->A, diagV, diagIdx));
2218: /* Get offdiagIdx[] for implicit 0.0 */
2219: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2220: ba = bav;
2221: bi = b->i;
2222: bj = b->j;
2223: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2224: for (r = 0; r < m; r++) {
2225: ncols = bi[r + 1] - bi[r];
2226: if (ncols == A->cmap->N - n) { /* Brow is dense */
2227: offdiagA[r] = *ba;
2228: offdiagIdx[r] = cmap[0];
2229: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2230: offdiagA[r] = 0.0;
2232: /* Find first hole in the cmap */
2233: for (j = 0; j < ncols; j++) {
2234: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2235: if (col > j && j < cstart) {
2236: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2237: break;
2238: } else if (col > j + n && j >= cstart) {
2239: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2240: break;
2241: }
2242: }
2243: if (j == ncols && ncols < A->cmap->N - n) {
2244: /* a hole is outside compressed Bcols */
2245: if (ncols == 0) {
2246: if (cstart) {
2247: offdiagIdx[r] = 0;
2248: } else offdiagIdx[r] = cend;
2249: } else { /* ncols > 0 */
2250: offdiagIdx[r] = cmap[ncols - 1] + 1;
2251: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2252: }
2253: }
2254: }
2256: for (j = 0; j < ncols; j++) {
2257: if (PetscAbsScalar(offdiagA[r]) > PetscAbsScalar(*ba)) {
2258: offdiagA[r] = *ba;
2259: offdiagIdx[r] = cmap[*bj];
2260: }
2261: ba++;
2262: bj++;
2263: }
2264: }
2266: PetscCall(VecGetArrayWrite(v, &a));
2267: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2268: for (r = 0; r < m; ++r) {
2269: if (PetscAbsScalar(diagA[r]) < PetscAbsScalar(offdiagA[r])) {
2270: a[r] = diagA[r];
2271: if (idx) idx[r] = cstart + diagIdx[r];
2272: } else if (PetscAbsScalar(diagA[r]) == PetscAbsScalar(offdiagA[r])) {
2273: a[r] = diagA[r];
2274: if (idx) {
2275: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2276: idx[r] = cstart + diagIdx[r];
2277: } else idx[r] = offdiagIdx[r];
2278: }
2279: } else {
2280: a[r] = offdiagA[r];
2281: if (idx) idx[r] = offdiagIdx[r];
2282: }
2283: }
2284: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2285: PetscCall(VecRestoreArrayWrite(v, &a));
2286: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2287: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2288: PetscCall(VecDestroy(&diagV));
2289: PetscCall(VecDestroy(&offdiagV));
2290: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2291: PetscFunctionReturn(PETSC_SUCCESS);
2292: }
2294: static PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2295: {
2296: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2297: PetscInt m = A->rmap->n, n = A->cmap->n;
2298: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2299: PetscInt *cmap = mat->garray;
2300: PetscInt *diagIdx, *offdiagIdx;
2301: Vec diagV, offdiagV;
2302: PetscScalar *a, *diagA, *offdiagA;
2303: const PetscScalar *ba, *bav;
2304: PetscInt r, j, col, ncols, *bi, *bj;
2305: Mat B = mat->B;
2306: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2308: PetscFunctionBegin;
2309: /* When a process holds entire A and other processes have no entry */
2310: if (A->cmap->N == n) {
2311: PetscCall(VecGetArrayWrite(v, &diagA));
2312: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2313: PetscCall(MatGetRowMin(mat->A, diagV, idx));
2314: PetscCall(VecDestroy(&diagV));
2315: PetscCall(VecRestoreArrayWrite(v, &diagA));
2316: PetscFunctionReturn(PETSC_SUCCESS);
2317: } else if (n == 0) {
2318: if (m) {
2319: PetscCall(VecGetArrayWrite(v, &a));
2320: for (r = 0; r < m; r++) {
2321: a[r] = PETSC_MAX_REAL;
2322: if (idx) idx[r] = -1;
2323: }
2324: PetscCall(VecRestoreArrayWrite(v, &a));
2325: }
2326: PetscFunctionReturn(PETSC_SUCCESS);
2327: }
2329: PetscCall(PetscCalloc2(m, &diagIdx, m, &offdiagIdx));
2330: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2331: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2332: PetscCall(MatGetRowMin(mat->A, diagV, diagIdx));
2334: /* Get offdiagIdx[] for implicit 0.0 */
2335: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2336: ba = bav;
2337: bi = b->i;
2338: bj = b->j;
2339: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2340: for (r = 0; r < m; r++) {
2341: ncols = bi[r + 1] - bi[r];
2342: if (ncols == A->cmap->N - n) { /* Brow is dense */
2343: offdiagA[r] = *ba;
2344: offdiagIdx[r] = cmap[0];
2345: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2346: offdiagA[r] = 0.0;
2348: /* Find first hole in the cmap */
2349: for (j = 0; j < ncols; j++) {
2350: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2351: if (col > j && j < cstart) {
2352: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2353: break;
2354: } else if (col > j + n && j >= cstart) {
2355: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2356: break;
2357: }
2358: }
2359: if (j == ncols && ncols < A->cmap->N - n) {
2360: /* a hole is outside compressed Bcols */
2361: if (ncols == 0) {
2362: if (cstart) {
2363: offdiagIdx[r] = 0;
2364: } else offdiagIdx[r] = cend;
2365: } else { /* ncols > 0 */
2366: offdiagIdx[r] = cmap[ncols - 1] + 1;
2367: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2368: }
2369: }
2370: }
2372: for (j = 0; j < ncols; j++) {
2373: if (PetscRealPart(offdiagA[r]) > PetscRealPart(*ba)) {
2374: offdiagA[r] = *ba;
2375: offdiagIdx[r] = cmap[*bj];
2376: }
2377: ba++;
2378: bj++;
2379: }
2380: }
2382: PetscCall(VecGetArrayWrite(v, &a));
2383: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2384: for (r = 0; r < m; ++r) {
2385: if (PetscRealPart(diagA[r]) < PetscRealPart(offdiagA[r])) {
2386: a[r] = diagA[r];
2387: if (idx) idx[r] = cstart + diagIdx[r];
2388: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2389: a[r] = diagA[r];
2390: if (idx) {
2391: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2392: idx[r] = cstart + diagIdx[r];
2393: } else idx[r] = offdiagIdx[r];
2394: }
2395: } else {
2396: a[r] = offdiagA[r];
2397: if (idx) idx[r] = offdiagIdx[r];
2398: }
2399: }
2400: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2401: PetscCall(VecRestoreArrayWrite(v, &a));
2402: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2403: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2404: PetscCall(VecDestroy(&diagV));
2405: PetscCall(VecDestroy(&offdiagV));
2406: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2407: PetscFunctionReturn(PETSC_SUCCESS);
2408: }
2410: static PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2411: {
2412: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2413: PetscInt m = A->rmap->n, n = A->cmap->n;
2414: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2415: PetscInt *cmap = mat->garray;
2416: PetscInt *diagIdx, *offdiagIdx;
2417: Vec diagV, offdiagV;
2418: PetscScalar *a, *diagA, *offdiagA;
2419: const PetscScalar *ba, *bav;
2420: PetscInt r, j, col, ncols, *bi, *bj;
2421: Mat B = mat->B;
2422: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2424: PetscFunctionBegin;
2425: /* When a process holds entire A and other processes have no entry */
2426: if (A->cmap->N == n) {
2427: PetscCall(VecGetArrayWrite(v, &diagA));
2428: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2429: PetscCall(MatGetRowMax(mat->A, diagV, idx));
2430: PetscCall(VecDestroy(&diagV));
2431: PetscCall(VecRestoreArrayWrite(v, &diagA));
2432: PetscFunctionReturn(PETSC_SUCCESS);
2433: } else if (n == 0) {
2434: if (m) {
2435: PetscCall(VecGetArrayWrite(v, &a));
2436: for (r = 0; r < m; r++) {
2437: a[r] = PETSC_MIN_REAL;
2438: if (idx) idx[r] = -1;
2439: }
2440: PetscCall(VecRestoreArrayWrite(v, &a));
2441: }
2442: PetscFunctionReturn(PETSC_SUCCESS);
2443: }
2445: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2446: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2447: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2448: PetscCall(MatGetRowMax(mat->A, diagV, diagIdx));
2450: /* Get offdiagIdx[] for implicit 0.0 */
2451: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2452: ba = bav;
2453: bi = b->i;
2454: bj = b->j;
2455: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2456: for (r = 0; r < m; r++) {
2457: ncols = bi[r + 1] - bi[r];
2458: if (ncols == A->cmap->N - n) { /* Brow is dense */
2459: offdiagA[r] = *ba;
2460: offdiagIdx[r] = cmap[0];
2461: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2462: offdiagA[r] = 0.0;
2464: /* Find first hole in the cmap */
2465: for (j = 0; j < ncols; j++) {
2466: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2467: if (col > j && j < cstart) {
2468: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2469: break;
2470: } else if (col > j + n && j >= cstart) {
2471: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2472: break;
2473: }
2474: }
2475: if (j == ncols && ncols < A->cmap->N - n) {
2476: /* a hole is outside compressed Bcols */
2477: if (ncols == 0) {
2478: if (cstart) {
2479: offdiagIdx[r] = 0;
2480: } else offdiagIdx[r] = cend;
2481: } else { /* ncols > 0 */
2482: offdiagIdx[r] = cmap[ncols - 1] + 1;
2483: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2484: }
2485: }
2486: }
2488: for (j = 0; j < ncols; j++) {
2489: if (PetscRealPart(offdiagA[r]) < PetscRealPart(*ba)) {
2490: offdiagA[r] = *ba;
2491: offdiagIdx[r] = cmap[*bj];
2492: }
2493: ba++;
2494: bj++;
2495: }
2496: }
2498: PetscCall(VecGetArrayWrite(v, &a));
2499: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2500: for (r = 0; r < m; ++r) {
2501: if (PetscRealPart(diagA[r]) > PetscRealPart(offdiagA[r])) {
2502: a[r] = diagA[r];
2503: if (idx) idx[r] = cstart + diagIdx[r];
2504: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2505: a[r] = diagA[r];
2506: if (idx) {
2507: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2508: idx[r] = cstart + diagIdx[r];
2509: } else idx[r] = offdiagIdx[r];
2510: }
2511: } else {
2512: a[r] = offdiagA[r];
2513: if (idx) idx[r] = offdiagIdx[r];
2514: }
2515: }
2516: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2517: PetscCall(VecRestoreArrayWrite(v, &a));
2518: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2519: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2520: PetscCall(VecDestroy(&diagV));
2521: PetscCall(VecDestroy(&offdiagV));
2522: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2523: PetscFunctionReturn(PETSC_SUCCESS);
2524: }
2526: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat, Mat *newmat)
2527: {
2528: Mat *dummy;
2530: PetscFunctionBegin;
2531: PetscCall(MatCreateSubMatrix_MPIAIJ_All(mat, MAT_DO_NOT_GET_VALUES, MAT_INITIAL_MATRIX, &dummy));
2532: *newmat = *dummy;
2533: PetscCall(PetscFree(dummy));
2534: PetscFunctionReturn(PETSC_SUCCESS);
2535: }
2537: static PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A, const PetscScalar **values)
2538: {
2539: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2541: PetscFunctionBegin;
2542: PetscCall(MatInvertBlockDiagonal(a->A, values));
2543: A->factorerrortype = a->A->factorerrortype;
2544: PetscFunctionReturn(PETSC_SUCCESS);
2545: }
2547: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x, PetscRandom rctx)
2548: {
2549: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)x->data;
2551: PetscFunctionBegin;
2552: PetscCheck(x->assembled || x->preallocated, PetscObjectComm((PetscObject)x), PETSC_ERR_ARG_WRONGSTATE, "MatSetRandom on an unassembled and unpreallocated MATMPIAIJ is not allowed");
2553: PetscCall(MatSetRandom(aij->A, rctx));
2554: if (x->assembled) {
2555: PetscCall(MatSetRandom(aij->B, rctx));
2556: } else {
2557: PetscCall(MatSetRandomSkipColumnRange_SeqAIJ_Private(aij->B, x->cmap->rstart, x->cmap->rend, rctx));
2558: }
2559: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
2560: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
2561: PetscFunctionReturn(PETSC_SUCCESS);
2562: }
2564: static PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ(Mat A, PetscBool sc)
2565: {
2566: PetscFunctionBegin;
2567: if (sc) A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ_Scalable;
2568: else A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ;
2569: PetscFunctionReturn(PETSC_SUCCESS);
2570: }
2572: /*@
2573: MatMPIAIJGetNumberNonzeros - gets the number of nonzeros in the matrix on this MPI rank
2575: Not Collective
2577: Input Parameter:
2578: . A - the matrix
2580: Output Parameter:
2581: . nz - the number of nonzeros
2583: Level: advanced
2585: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2586: @*/
2587: PetscErrorCode MatMPIAIJGetNumberNonzeros(Mat A, PetscCount *nz)
2588: {
2589: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)A->data;
2590: Mat_SeqAIJ *aaij = (Mat_SeqAIJ *)maij->A->data, *baij = (Mat_SeqAIJ *)maij->B->data;
2591: PetscBool isaij;
2593: PetscFunctionBegin;
2594: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATMPIAIJ, &isaij));
2595: PetscCheck(isaij, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for type %s", ((PetscObject)A)->type_name);
2596: *nz = aaij->i[A->rmap->n] + baij->i[A->rmap->n];
2597: PetscFunctionReturn(PETSC_SUCCESS);
2598: }
2600: /*@
2601: MatMPIAIJSetUseScalableIncreaseOverlap - Determine if the matrix uses a scalable algorithm to compute the overlap
2603: Collective
2605: Input Parameters:
2606: + A - the matrix
2607: - sc - `PETSC_TRUE` indicates use the scalable algorithm (default is not to use the scalable algorithm)
2609: Level: advanced
2611: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2612: @*/
2613: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap(Mat A, PetscBool sc)
2614: {
2615: PetscFunctionBegin;
2616: PetscTryMethod(A, "MatMPIAIJSetUseScalableIncreaseOverlap_C", (Mat, PetscBool), (A, sc));
2617: PetscFunctionReturn(PETSC_SUCCESS);
2618: }
2620: PetscErrorCode MatSetFromOptions_MPIAIJ(Mat A, PetscOptionItems PetscOptionsObject)
2621: {
2622: PetscBool sc = PETSC_FALSE, flg;
2624: PetscFunctionBegin;
2625: PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJ options");
2626: if (A->ops->increaseoverlap == MatIncreaseOverlap_MPIAIJ_Scalable) sc = PETSC_TRUE;
2627: PetscCall(PetscOptionsBool("-mat_increase_overlap_scalable", "Use a scalable algorithm to compute the overlap", "MatIncreaseOverlap", sc, &sc, &flg));
2628: if (flg) PetscCall(MatMPIAIJSetUseScalableIncreaseOverlap(A, sc));
2629: PetscOptionsHeadEnd();
2630: PetscFunctionReturn(PETSC_SUCCESS);
2631: }
2633: static PetscErrorCode MatShift_MPIAIJ(Mat Y, PetscScalar a)
2634: {
2635: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)Y->data;
2636: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)maij->A->data;
2638: PetscFunctionBegin;
2639: if (!Y->preallocated) {
2640: PetscCall(MatMPIAIJSetPreallocation(Y, 1, NULL, 0, NULL));
2641: } else if (!aij->nz) { /* It does not matter if diagonals of Y only partially lie in maij->A. We just need an estimated preallocation. */
2642: PetscInt nonew = aij->nonew;
2643: PetscCall(MatSeqAIJSetPreallocation(maij->A, 1, NULL));
2644: aij->nonew = nonew;
2645: }
2646: PetscCall(MatShift_Basic(Y, a));
2647: PetscFunctionReturn(PETSC_SUCCESS);
2648: }
2650: static PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2651: {
2652: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2654: PetscFunctionBegin;
2655: PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2656: PetscFunctionReturn(PETSC_SUCCESS);
2657: }
2659: static PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A, PetscBool keep)
2660: {
2661: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2663: PetscFunctionBegin;
2664: PetscCall(MatEliminateZeros_SeqAIJ(a->A, keep)); // possibly keep zero diagonal coefficients
2665: PetscCall(MatEliminateZeros_SeqAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2666: PetscFunctionReturn(PETSC_SUCCESS);
2667: }
2669: static PetscErrorCode MatGetOrdering_MPIAIJ(Mat A, MatOrderingType type, IS *rperm, IS *cperm)
2670: {
2671: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2672: IS lrowperm, lcolperm;
2673: PetscInt i, rstart, rend, *idx;
2674: const PetscInt *lidx;
2676: PetscFunctionBegin;
2677: PetscCall(MatGetOrdering(a->A, type, &lrowperm, &lcolperm));
2678: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
2679: /* Remap row index set to global space */
2680: PetscCall(ISGetIndices(lrowperm, &lidx));
2681: PetscCall(PetscMalloc1(rend - rstart, &idx));
2682: for (i = 0; i + rstart < rend; i++) idx[i] = rstart + lidx[i];
2683: PetscCall(ISRestoreIndices(lrowperm, &lidx));
2684: PetscCall(ISDestroy(&lrowperm));
2685: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), rend - rstart, idx, PETSC_OWN_POINTER, rperm));
2686: PetscCall(ISSetPermutation(*rperm));
2687: /* Remap column index set to global space */
2688: PetscCall(ISGetIndices(lcolperm, &lidx));
2689: PetscCall(PetscMalloc1(rend - rstart, &idx));
2690: for (i = 0; i + rstart < rend; i++) idx[i] = rstart + lidx[i];
2691: PetscCall(ISRestoreIndices(lcolperm, &lidx));
2692: PetscCall(ISDestroy(&lcolperm));
2693: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), rend - rstart, idx, PETSC_OWN_POINTER, cperm));
2694: PetscCall(ISSetPermutation(*cperm));
2695: PetscFunctionReturn(PETSC_SUCCESS);
2696: }
2698: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2699: MatGetRow_MPIAIJ,
2700: MatRestoreRow_MPIAIJ,
2701: MatMult_MPIAIJ,
2702: /* 4*/ MatMultAdd_MPIAIJ,
2703: MatMultTranspose_MPIAIJ,
2704: MatMultTransposeAdd_MPIAIJ,
2705: NULL,
2706: NULL,
2707: NULL,
2708: /*10*/ NULL,
2709: NULL,
2710: NULL,
2711: MatSOR_MPIAIJ,
2712: MatTranspose_MPIAIJ,
2713: /*15*/ MatGetInfo_MPIAIJ,
2714: MatEqual_MPIAIJ,
2715: MatGetDiagonal_MPIAIJ,
2716: MatDiagonalScale_MPIAIJ,
2717: MatNorm_MPIAIJ,
2718: /*20*/ MatAssemblyBegin_MPIAIJ,
2719: MatAssemblyEnd_MPIAIJ,
2720: MatSetOption_MPIAIJ,
2721: MatZeroEntries_MPIAIJ,
2722: /*24*/ MatZeroRows_MPIAIJ,
2723: NULL,
2724: NULL,
2725: NULL,
2726: NULL,
2727: /*29*/ MatSetUp_MPI_Hash,
2728: NULL,
2729: NULL,
2730: MatGetDiagonalBlock_MPIAIJ,
2731: NULL,
2732: /*34*/ MatDuplicate_MPIAIJ,
2733: NULL,
2734: NULL,
2735: NULL,
2736: NULL,
2737: /*39*/ MatAXPY_MPIAIJ,
2738: MatCreateSubMatrices_MPIAIJ,
2739: MatIncreaseOverlap_MPIAIJ,
2740: MatGetValues_MPIAIJ,
2741: MatCopy_MPIAIJ,
2742: /*44*/ MatGetRowMax_MPIAIJ,
2743: MatScale_MPIAIJ,
2744: MatShift_MPIAIJ,
2745: MatDiagonalSet_MPIAIJ,
2746: MatZeroRowsColumns_MPIAIJ,
2747: /*49*/ MatSetRandom_MPIAIJ,
2748: MatGetRowIJ_MPIAIJ,
2749: MatRestoreRowIJ_MPIAIJ,
2750: NULL,
2751: NULL,
2752: /*54*/ MatFDColoringCreate_MPIXAIJ,
2753: NULL,
2754: MatSetUnfactored_MPIAIJ,
2755: MatPermute_MPIAIJ,
2756: NULL,
2757: /*59*/ MatCreateSubMatrix_MPIAIJ,
2758: MatDestroy_MPIAIJ,
2759: MatView_MPIAIJ,
2760: NULL,
2761: NULL,
2762: /*64*/ MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2763: NULL,
2764: NULL,
2765: NULL,
2766: MatGetRowMaxAbs_MPIAIJ,
2767: /*69*/ MatGetRowMinAbs_MPIAIJ,
2768: NULL,
2769: NULL,
2770: MatFDColoringApply_AIJ,
2771: MatSetFromOptions_MPIAIJ,
2772: MatFindZeroDiagonals_MPIAIJ,
2773: /*75*/ NULL,
2774: NULL,
2775: NULL,
2776: MatLoad_MPIAIJ,
2777: NULL,
2778: /*80*/ NULL,
2779: NULL,
2780: NULL,
2781: /*83*/ NULL,
2782: NULL,
2783: MatMatMultNumeric_MPIAIJ_MPIAIJ,
2784: MatPtAPNumeric_MPIAIJ_MPIAIJ,
2785: NULL,
2786: NULL,
2787: /*89*/ MatBindToCPU_MPIAIJ,
2788: MatProductSetFromOptions_MPIAIJ,
2789: NULL,
2790: NULL,
2791: MatConjugate_MPIAIJ,
2792: /*94*/ NULL,
2793: MatSetValuesRow_MPIAIJ,
2794: MatRealPart_MPIAIJ,
2795: MatImaginaryPart_MPIAIJ,
2796: NULL,
2797: /*99*/ NULL,
2798: NULL,
2799: NULL,
2800: MatGetRowMin_MPIAIJ,
2801: NULL,
2802: /*104*/ MatGetSeqNonzeroStructure_MPIAIJ,
2803: NULL,
2804: MatGetGhosts_MPIAIJ,
2805: NULL,
2806: NULL,
2807: /*109*/ MatMultDiagonalBlock_MPIAIJ,
2808: NULL,
2809: NULL,
2810: NULL,
2811: MatGetMultiProcBlock_MPIAIJ,
2812: /*114*/ MatFindNonzeroRows_MPIAIJ,
2813: MatGetColumnReductions_MPIAIJ,
2814: MatInvertBlockDiagonal_MPIAIJ,
2815: MatInvertVariableBlockDiagonal_MPIAIJ,
2816: MatCreateSubMatricesMPI_MPIAIJ,
2817: /*119*/ NULL,
2818: MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2819: NULL,
2820: NULL,
2821: NULL,
2822: /*124*/ NULL,
2823: MatSetBlockSizes_MPIAIJ,
2824: NULL,
2825: MatFDColoringSetUp_MPIXAIJ,
2826: MatFindOffBlockDiagonalEntries_MPIAIJ,
2827: /*129*/ MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2828: NULL,
2829: NULL,
2830: NULL,
2831: MatCreateGraph_Simple_AIJ,
2832: /*134*/ NULL,
2833: MatEliminateZeros_MPIAIJ,
2834: MatGetRowSumAbs_MPIAIJ,
2835: NULL,
2836: NULL,
2837: /*139*/ NULL,
2838: MatCopyHashToXAIJ_MPI_Hash,
2839: MatGetCurrentMemType_MPIAIJ,
2840: NULL,
2841: NULL,
2842: /*144*/ NULL,
2843: NULL,
2844: NULL,
2845: MatGetOrdering_MPIAIJ};
2847: static PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2848: {
2849: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2851: PetscFunctionBegin;
2852: PetscCall(MatStoreValues(aij->A));
2853: PetscCall(MatStoreValues(aij->B));
2854: PetscFunctionReturn(PETSC_SUCCESS);
2855: }
2857: static PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2858: {
2859: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2861: PetscFunctionBegin;
2862: PetscCall(MatRetrieveValues(aij->A));
2863: PetscCall(MatRetrieveValues(aij->B));
2864: PetscFunctionReturn(PETSC_SUCCESS);
2865: }
2867: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2868: {
2869: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2870: PetscMPIInt size;
2872: PetscFunctionBegin;
2873: if (B->hash_active) {
2874: B->ops[0] = b->cops;
2875: B->hash_active = PETSC_FALSE;
2876: }
2877: PetscCall(PetscLayoutSetUp(B->rmap));
2878: PetscCall(PetscLayoutSetUp(B->cmap));
2880: #if PetscDefined(USE_CTABLE)
2881: PetscCall(PetscHMapIDestroy(&b->colmap));
2882: #else
2883: PetscCall(PetscFree(b->colmap));
2884: #endif
2885: PetscCall(PetscFree(b->garray));
2886: PetscCall(VecDestroy(&b->lvec));
2887: PetscCall(VecScatterDestroy(&b->Mvctx));
2889: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2891: MatSeqXAIJGetOptions_Private(b->B);
2892: PetscCall(MatDestroy(&b->B));
2893: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2894: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2895: PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2896: PetscCall(MatSetType(b->B, MATSEQAIJ));
2897: MatSeqXAIJRestoreOptions_Private(b->B);
2898: PetscCall(MatSetOption(b->B, MAT_STRUCTURE_ONLY, B->structure_only));
2900: MatSeqXAIJGetOptions_Private(b->A);
2901: PetscCall(MatDestroy(&b->A));
2902: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2903: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2904: PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2905: PetscCall(MatSetType(b->A, MATSEQAIJ));
2906: MatSeqXAIJRestoreOptions_Private(b->A);
2907: PetscCall(MatSetOption(b->A, MAT_STRUCTURE_ONLY, B->structure_only));
2909: PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2910: PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2911: B->preallocated = PETSC_TRUE;
2912: B->was_assembled = PETSC_FALSE;
2913: B->assembled = PETSC_FALSE;
2914: PetscFunctionReturn(PETSC_SUCCESS);
2915: }
2917: static PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2918: {
2919: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2920: PetscBool ondiagreset, offdiagreset, memoryreset;
2922: PetscFunctionBegin;
2924: PetscCheck(B->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset preallocation after setting some values but not yet calling MatAssemblyBegin()/MatAssemblyEnd()");
2925: if (B->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
2927: PetscCall(MatResetPreallocation_SeqAIJ_Private(b->A, &ondiagreset));
2928: PetscCall(MatResetPreallocation_SeqAIJ_Private(b->B, &offdiagreset));
2929: memoryreset = (PetscBool)(ondiagreset || offdiagreset);
2930: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &memoryreset, 1, MPI_C_BOOL, MPI_LOR, PetscObjectComm((PetscObject)B)));
2931: if (!memoryreset) PetscFunctionReturn(PETSC_SUCCESS);
2933: PetscCall(PetscLayoutSetUp(B->rmap));
2934: PetscCall(PetscLayoutSetUp(B->cmap));
2935: PetscCheck(B->assembled || B->was_assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Should not need to reset preallocation if the matrix was never assembled");
2936: PetscCall(MatDisAssemble_MPIAIJ(B, PETSC_TRUE));
2937: PetscCall(VecScatterDestroy(&b->Mvctx));
2939: B->preallocated = PETSC_TRUE;
2940: B->was_assembled = PETSC_FALSE;
2941: B->assembled = PETSC_FALSE;
2942: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
2943: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2944: PetscFunctionReturn(PETSC_SUCCESS);
2945: }
2947: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2948: {
2949: Mat mat;
2950: Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;
2952: PetscFunctionBegin;
2953: *newmat = NULL;
2954: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2955: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2956: PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2957: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2958: PetscCall(MatSetOption(mat, MAT_STRUCTURE_ONLY, matin->structure_only));
2959: a = (Mat_MPIAIJ *)mat->data;
2961: mat->factortype = matin->factortype;
2962: mat->assembled = matin->assembled;
2963: mat->insertmode = NOT_SET_VALUES;
2965: a->size = oldmat->size;
2966: a->rank = oldmat->rank;
2967: a->donotstash = oldmat->donotstash;
2968: a->roworiented = oldmat->roworiented;
2969: a->rowindices = NULL;
2970: a->rowvalues = NULL;
2971: a->getrowactive = PETSC_FALSE;
2973: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
2974: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
2975: if (matin->hash_active) PetscCall(MatSetUp(mat));
2976: else {
2977: mat->preallocated = matin->preallocated;
2978: if (oldmat->colmap) {
2979: #if PetscDefined(USE_CTABLE)
2980: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
2981: #else
2982: PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
2983: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
2984: #endif
2985: } else a->colmap = NULL;
2986: if (oldmat->garray) {
2987: PetscInt len;
2988: len = oldmat->B->cmap->n;
2989: PetscCall(PetscMalloc1(len, &a->garray));
2990: if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
2991: } else a->garray = NULL;
2993: /* It may happen MatDuplicate is called with a non-assembled matrix
2994: In fact, MatDuplicate only requires the matrix to be preallocated
2995: This may happen inside a DMCreateMatrix_Shell */
2996: if (oldmat->lvec) PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
2997: if (oldmat->Mvctx) {
2998: a->Mvctx = oldmat->Mvctx;
2999: PetscCall(PetscObjectReference((PetscObject)oldmat->Mvctx));
3000: }
3001: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3002: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3003: }
3004: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3005: *newmat = mat;
3006: PetscFunctionReturn(PETSC_SUCCESS);
3007: }
3009: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3010: {
3011: PetscBool isbinary, ishdf5;
3013: PetscFunctionBegin;
3016: /* force binary viewer to load .info file if it has not yet done so */
3017: PetscCall(PetscViewerSetUp(viewer));
3018: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3019: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3020: if (isbinary) {
3021: PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3022: } else if (ishdf5) {
3023: #if PetscDefined(HAVE_HDF5)
3024: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3025: #else
3026: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3027: #endif
3028: } else {
3029: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "Viewer type %s not yet supported for reading %s matrices", ((PetscObject)viewer)->type_name, ((PetscObject)newMat)->type_name);
3030: }
3031: PetscFunctionReturn(PETSC_SUCCESS);
3032: }
3034: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3035: {
3036: PetscInt header[4], M, N, m, nz, rows, cols, sum, i;
3037: PetscInt *rowidxs, *colidxs;
3038: PetscScalar *matvals;
3040: PetscFunctionBegin;
3041: PetscCall(PetscViewerSetUp(viewer));
3043: /* read in matrix header */
3044: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3045: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3046: M = header[1];
3047: N = header[2];
3048: nz = header[3];
3049: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3050: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3051: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");
3053: /* set block sizes from the viewer's .info file */
3054: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3055: /* set global sizes if not set already */
3056: if (mat->rmap->N < 0) mat->rmap->N = M;
3057: if (mat->cmap->N < 0) mat->cmap->N = N;
3058: PetscCall(PetscLayoutSetUp(mat->rmap));
3059: PetscCall(PetscLayoutSetUp(mat->cmap));
3061: /* check if the matrix sizes are correct */
3062: PetscCall(MatGetSize(mat, &rows, &cols));
3063: PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different sizes (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols);
3065: /* read in row lengths and build row indices */
3066: PetscCall(MatGetLocalSize(mat, &m, NULL));
3067: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3068: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3069: rowidxs[0] = 0;
3070: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3071: if (nz != PETSC_INT_MAX) {
3072: PetscCallMPI(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3073: PetscCheck(sum == nz, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Inconsistent matrix data in file: nonzeros = %" PetscInt_FMT ", sum-row-lengths = %" PetscInt_FMT, nz, sum);
3074: }
3076: /* read in column indices and matrix values */
3077: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3078: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3079: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3080: /* store matrix indices and values */
3081: PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3082: PetscCall(PetscFree(rowidxs));
3083: PetscCall(PetscFree2(colidxs, matvals));
3084: PetscFunctionReturn(PETSC_SUCCESS);
3085: }
3087: /* Not scalable because of ISAllGather() unless getting all columns. */
3088: static PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3089: {
3090: IS iscol_local;
3091: PetscBool isstride;
3092: PetscMPIInt gisstride = 0;
3094: PetscFunctionBegin;
3095: /* check if we are grabbing all columns*/
3096: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));
3098: if (isstride) {
3099: PetscInt start, len, mstart, mlen;
3100: PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3101: PetscCall(ISGetLocalSize(iscol, &len));
3102: PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3103: if (mstart == start && mlen - mstart == len) gisstride = 1;
3104: }
3106: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3107: if (gisstride) {
3108: PetscInt N;
3109: PetscCall(MatGetSize(mat, NULL, &N));
3110: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3111: PetscCall(ISSetIdentity(iscol_local));
3112: PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3113: } else {
3114: PetscInt cbs;
3115: PetscCall(ISGetBlockSize(iscol, &cbs));
3116: PetscCall(ISAllGather(iscol, &iscol_local));
3117: PetscCall(ISSetBlockSize(iscol_local, cbs));
3118: }
3120: *isseq = iscol_local;
3121: PetscFunctionReturn(PETSC_SUCCESS);
3122: }
3124: /*
3125: Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3126: (see MatCreateSubMatrix_MPIAIJ_nonscalable)
3128: Input Parameters:
3129: + mat - matrix
3130: . isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3131: i.e., mat->rstart <= isrow[i] < mat->rend
3132: - iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3133: i.e., mat->cstart <= iscol[i] < mat->cend
3135: Output Parameters:
3136: + isrow_d - sequential row index set for retrieving mat->A
3137: . iscol_d - sequential column index set for retrieving mat->A
3138: . iscol_o - sequential column index set for retrieving mat->B
3139: - garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3140: */
3141: static PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, PetscInt *garray[])
3142: {
3143: Vec x, cmap;
3144: const PetscInt *is_idx;
3145: PetscScalar *xarray, *cmaparray;
3146: PetscInt ncols, isstart, *idx, m, rstart, *cmap1, count;
3147: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3148: Mat B = a->B;
3149: Vec lvec = a->lvec, lcmap;
3150: PetscInt i, cstart, cend, Bn = B->cmap->N;
3151: MPI_Comm comm;
3152: VecScatter Mvctx = a->Mvctx;
3154: PetscFunctionBegin;
3155: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3156: PetscCall(ISGetLocalSize(iscol, &ncols));
3158: /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3159: PetscCall(MatCreateVecs(mat, &x, NULL));
3160: PetscCall(VecSet(x, -1.0));
3161: PetscCall(VecDuplicate(x, &cmap));
3162: PetscCall(VecSet(cmap, -1.0));
3164: /* Get start indices */
3165: PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3166: isstart -= ncols;
3167: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
3169: PetscCall(ISGetIndices(iscol, &is_idx));
3170: PetscCall(VecGetArray(x, &xarray));
3171: PetscCall(VecGetArray(cmap, &cmaparray));
3172: PetscCall(PetscMalloc1(ncols, &idx));
3173: for (i = 0; i < ncols; i++) {
3174: xarray[is_idx[i] - cstart] = (PetscScalar)is_idx[i];
3175: cmaparray[is_idx[i] - cstart] = i + isstart; /* global index of iscol[i] */
3176: idx[i] = is_idx[i] - cstart; /* local index of iscol[i] */
3177: }
3178: PetscCall(VecRestoreArray(x, &xarray));
3179: PetscCall(VecRestoreArray(cmap, &cmaparray));
3180: PetscCall(ISRestoreIndices(iscol, &is_idx));
3182: /* Get iscol_d */
3183: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3184: PetscCall(ISGetBlockSize(iscol, &i));
3185: PetscCall(ISSetBlockSize(*iscol_d, i));
3187: /* Get isrow_d */
3188: PetscCall(ISGetLocalSize(isrow, &m));
3189: rstart = mat->rmap->rstart;
3190: PetscCall(PetscMalloc1(m, &idx));
3191: PetscCall(ISGetIndices(isrow, &is_idx));
3192: for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3193: PetscCall(ISRestoreIndices(isrow, &is_idx));
3195: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3196: PetscCall(ISGetBlockSize(isrow, &i));
3197: PetscCall(ISSetBlockSize(*isrow_d, i));
3199: /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3200: PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3201: PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3203: PetscCall(VecDuplicate(lvec, &lcmap));
3205: PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3206: PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3208: /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3209: /* off-process column indices */
3210: count = 0;
3211: PetscCall(PetscMalloc1(Bn, &idx));
3212: PetscCall(PetscMalloc1(Bn, &cmap1));
3214: PetscCall(VecGetArray(lvec, &xarray));
3215: PetscCall(VecGetArray(lcmap, &cmaparray));
3216: for (i = 0; i < Bn; i++) {
3217: if (PetscRealPart(xarray[i]) > -1.0) {
3218: idx[count] = i; /* local column index in off-diagonal part B */
3219: cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3220: count++;
3221: }
3222: }
3223: PetscCall(VecRestoreArray(lvec, &xarray));
3224: PetscCall(VecRestoreArray(lcmap, &cmaparray));
3226: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3227: /* cannot ensure iscol_o has same blocksize as iscol! */
3229: PetscCall(PetscFree(idx));
3230: *garray = cmap1;
3232: PetscCall(VecDestroy(&x));
3233: PetscCall(VecDestroy(&cmap));
3234: PetscCall(VecDestroy(&lcmap));
3235: PetscFunctionReturn(PETSC_SUCCESS);
3236: }
3238: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3239: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3240: {
3241: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3242: Mat M = NULL;
3243: MPI_Comm comm;
3244: IS iscol_d, isrow_d, iscol_o;
3245: Mat Asub = NULL, Bsub = NULL;
3246: PetscInt n, count, M_size, N_size;
3248: PetscFunctionBegin;
3249: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3251: if (call == MAT_REUSE_MATRIX) {
3252: /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3253: PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3254: PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");
3256: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3257: PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");
3259: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3260: PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");
3262: /* Update diagonal and off-diagonal portions of submat */
3263: asub = (Mat_MPIAIJ *)(*submat)->data;
3264: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3265: PetscCall(ISGetLocalSize(iscol_o, &n));
3266: if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3267: PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3268: PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));
3270: } else { /* call == MAT_INITIAL_MATRIX) */
3271: PetscInt *garray, *garray_compact;
3272: PetscInt BsubN;
3274: /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3275: PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));
3277: /* Create local submatrices Asub and Bsub */
3278: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3279: PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));
3281: // Compact garray so its not of size Bn
3282: PetscCall(ISGetSize(iscol_o, &count));
3283: PetscCall(PetscMalloc1(count, &garray_compact));
3284: PetscCall(PetscArraycpy(garray_compact, garray, count));
3286: /* Create submatrix M */
3287: PetscCall(ISGetSize(isrow, &M_size));
3288: PetscCall(ISGetSize(iscol, &N_size));
3289: PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, M_size, N_size, Asub, Bsub, garray_compact, &M));
3291: /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3292: asub = (Mat_MPIAIJ *)M->data;
3294: PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3295: n = asub->B->cmap->N;
3296: if (BsubN > n) {
3297: /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3298: const PetscInt *idx;
3299: PetscInt i, j, *idx_new, *subgarray = asub->garray;
3300: PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));
3302: PetscCall(PetscMalloc1(n, &idx_new));
3303: j = 0;
3304: PetscCall(ISGetIndices(iscol_o, &idx));
3305: for (i = 0; i < n; i++) {
3306: if (j >= BsubN) break;
3307: while (subgarray[i] > garray[j]) j++;
3309: PetscCheck(subgarray[i] == garray[j], PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "subgarray[%" PetscInt_FMT "]=%" PetscInt_FMT " cannot < garray[%" PetscInt_FMT "]=%" PetscInt_FMT, i, subgarray[i], j, garray[j]);
3310: idx_new[i] = idx[j++];
3311: }
3312: PetscCall(ISRestoreIndices(iscol_o, &idx));
3314: PetscCall(ISDestroy(&iscol_o));
3315: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));
3317: } else PetscCheck(BsubN >= n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Columns of Bsub (%" PetscInt_FMT ") cannot be smaller than B's (%" PetscInt_FMT ")", BsubN, asub->B->cmap->N);
3319: PetscCall(PetscFree(garray));
3320: *submat = M;
3322: /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3323: PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3324: PetscCall(ISDestroy(&isrow_d));
3326: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3327: PetscCall(ISDestroy(&iscol_d));
3329: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3330: PetscCall(ISDestroy(&iscol_o));
3331: }
3332: PetscFunctionReturn(PETSC_SUCCESS);
3333: }
3335: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3336: {
3337: IS iscol_local = NULL, isrow_d;
3338: PetscInt csize;
3339: PetscInt n, i, j, start, end;
3340: PetscBool sameRowDist = PETSC_FALSE, tsameDist[2];
3341: MPI_Comm comm;
3343: PetscFunctionBegin;
3344: /* If isrow has same processor distribution as mat,
3345: call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3346: if (call == MAT_REUSE_MATRIX) {
3347: PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3348: if (isrow_d) {
3349: sameRowDist = PETSC_TRUE;
3350: tsameDist[1] = PETSC_TRUE; /* sameColDist */
3351: } else {
3352: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3353: if (iscol_local) {
3354: sameRowDist = PETSC_TRUE;
3355: tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3356: }
3357: }
3358: } else {
3359: /* Check if isrow has same processor distribution as mat */
3360: tsameDist[0] = PETSC_FALSE;
3361: PetscCall(ISGetLocalSize(isrow, &n));
3362: if (!n) {
3363: tsameDist[0] = PETSC_TRUE;
3364: } else {
3365: PetscCall(ISGetMinMax(isrow, &i, &j));
3366: PetscCall(MatGetOwnershipRange(mat, &start, &end));
3367: if (i >= start && j < end) tsameDist[0] = PETSC_TRUE;
3368: }
3370: /* Check if iscol has same processor distribution as mat */
3371: tsameDist[1] = PETSC_FALSE;
3372: PetscCall(ISGetLocalSize(iscol, &n));
3373: if (!n) {
3374: tsameDist[1] = PETSC_TRUE;
3375: } else {
3376: PetscCall(ISGetMinMax(iscol, &i, &j));
3377: PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3378: if (i >= start && j < end) tsameDist[1] = PETSC_TRUE;
3379: }
3381: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3382: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, tsameDist, 2, MPI_C_BOOL, MPI_LAND, comm));
3383: sameRowDist = tsameDist[0];
3384: }
3386: if (sameRowDist) {
3387: if (tsameDist[1]) { /* sameRowDist & sameColDist */
3388: /* isrow and iscol have same processor distribution as mat */
3389: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3390: PetscFunctionReturn(PETSC_SUCCESS);
3391: } else { /* sameRowDist */
3392: /* isrow has same processor distribution as mat */
3393: if (call == MAT_INITIAL_MATRIX) {
3394: PetscBool sorted;
3395: PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3396: PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3397: PetscCall(ISGetSize(iscol, &i));
3398: PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);
3400: PetscCall(ISSorted(iscol_local, &sorted));
3401: if (sorted) {
3402: /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3403: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3404: PetscFunctionReturn(PETSC_SUCCESS);
3405: }
3406: } else { /* call == MAT_REUSE_MATRIX */
3407: IS iscol_sub;
3408: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3409: if (iscol_sub) {
3410: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3411: PetscFunctionReturn(PETSC_SUCCESS);
3412: }
3413: }
3414: }
3415: }
3417: /* General case: iscol -> iscol_local which has global size of iscol */
3418: if (call == MAT_REUSE_MATRIX) {
3419: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3420: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3421: } else {
3422: if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3423: }
3425: PetscCall(ISGetLocalSize(iscol, &csize));
3426: PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));
3428: if (call == MAT_INITIAL_MATRIX) {
3429: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3430: PetscCall(ISDestroy(&iscol_local));
3431: }
3432: PetscFunctionReturn(PETSC_SUCCESS);
3433: }
3435: /*@
3436: MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3437: and "off-diagonal" part of the matrix in CSR format.
3439: Collective
3441: Input Parameters:
3442: + comm - MPI communicator
3443: . M - the global row size
3444: . N - the global column size
3445: . A - "diagonal" portion of matrix
3446: . B - if garray is `NULL`, B should be the offdiag matrix using global col ids and of size N - if garray is not `NULL`, B should be the offdiag matrix using local col ids and of size garray
3447: - garray - either `NULL` or the global index of `B` columns. If not `NULL`, it should be allocated by `PetscMalloc1()` and will be owned by `mat` thereafter.
3449: Output Parameter:
3450: . mat - the matrix, with input `A` as its local diagonal matrix
3452: Level: advanced
3454: Notes:
3455: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.
3457: `A` and `B` becomes part of output mat. The user cannot use `A` and `B` anymore.
3459: If `garray` is `NULL`, `B` will be compacted to use local indices. In this sense, `B`'s sparsity pattern (nonzerostate) will be changed. If `B` is a device matrix, we need to somehow also update
3460: `B`'s copy on device. We do so by increasing `B`'s nonzerostate. In use of `B` on device, device matrix types should detect this change (ref. internal routines `MatSeqAIJCUSPARSECopyToGPU()` or
3461: `MatAssemblyEnd_SeqAIJKokkos()`) and will just destroy and then recreate the device copy of `B`. It is not optimal, but is easy to implement and less hacky. To avoid this overhead, try to compute `garray`
3462: yourself, see algorithms in the private function `MatSetUpMultiply_MPIAIJ()`.
3464: The `NULL`-ness of `garray` doesn't need to be collective, in other words, `garray` can be `NULL` on some processes while not on others.
3466: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3467: @*/
3468: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, PetscInt M, PetscInt N, Mat A, Mat B, PetscInt *garray, Mat *mat)
3469: {
3470: PetscInt m, n;
3471: MatType mpi_mat_type;
3472: Mat_MPIAIJ *mpiaij;
3473: Mat C;
3475: PetscFunctionBegin;
3476: PetscCall(MatCreate(comm, &C));
3477: PetscCall(MatGetSize(A, &m, &n));
3478: PetscCheck(m == B->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Am %" PetscInt_FMT " != Bm %" PetscInt_FMT, m, B->rmap->N);
3479: PetscCheck(A->rmap->bs == B->rmap->bs, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "A row bs %" PetscInt_FMT " != B row bs %" PetscInt_FMT, A->rmap->bs, B->rmap->bs);
3481: PetscCheck(A->structure_only == B->structure_only, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Diagonal and off-diagonal matrices must have the same MAT_STRUCTURE_ONLY option value");
3482: PetscCall(MatSetSizes(C, m, n, M, N));
3483: /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3484: PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3485: PetscCall(MatSetType(C, mpi_mat_type));
3486: if (!garray) {
3487: const PetscScalar *ba;
3489: B->nonzerostate++;
3490: PetscCall(MatSeqAIJGetArrayRead(B, &ba)); /* Since we will destroy B's device copy, we need to make sure the host copy is up to date */
3491: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
3492: }
3494: PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
3495: PetscCall(PetscLayoutSetUp(C->rmap));
3496: PetscCall(PetscLayoutSetUp(C->cmap));
3498: mpiaij = (Mat_MPIAIJ *)C->data;
3499: mpiaij->A = A;
3500: mpiaij->B = B;
3501: mpiaij->garray = garray;
3502: C->preallocated = PETSC_TRUE;
3503: C->nooffprocentries = PETSC_TRUE; /* See MatAssemblyBegin_MPIAIJ. In effect, making MatAssemblyBegin a nop */
3505: PetscCall(MatSetOption(C, MAT_STRUCTURE_ONLY, A->structure_only));
3506: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3507: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
3508: /* MatAssemblyEnd is critical here. It sets mat->offloadmask according to A and B's, and
3509: also gets mpiaij->B compacted (if garray is NULL), with its col ids and size reduced
3510: */
3511: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
3512: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
3513: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3514: *mat = C;
3515: PetscFunctionReturn(PETSC_SUCCESS);
3516: }
3518: extern PetscErrorCode MatCreateSubMatrices_MPIAIJ_SingleIS_Local(Mat, PetscInt, const IS[], const IS[], MatReuse, PetscBool, Mat *);
3520: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowDist(Mat mat, IS isrow, IS iscol, IS iscol_local, MatReuse call, Mat *newmat)
3521: {
3522: PetscInt i, m, n, rstart, row, rend, nz, j, bs, cbs;
3523: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3524: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3525: Mat M, Msub, B = a->B;
3526: MatScalar *aa;
3527: Mat_SeqAIJ *aij;
3528: PetscInt *garray = a->garray, *colsub, Ncols;
3529: PetscInt count, Bn = B->cmap->N, cstart = mat->cmap->rstart, cend = mat->cmap->rend;
3530: IS iscol_sub, iscmap;
3531: const PetscInt *is_idx, *cmap;
3532: PetscBool allcolumns = PETSC_FALSE;
3533: MPI_Comm comm;
3535: PetscFunctionBegin;
3536: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3537: if (call == MAT_REUSE_MATRIX) {
3538: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3539: PetscCheck(iscol_sub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "SubIScol passed in was not used before, cannot reuse");
3540: PetscCall(ISGetLocalSize(iscol_sub, &count));
3542: PetscCall(PetscObjectQuery((PetscObject)*newmat, "Subcmap", (PetscObject *)&iscmap));
3543: PetscCheck(iscmap, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Subcmap passed in was not used before, cannot reuse");
3545: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Msub));
3546: PetscCheck(Msub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3548: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_REUSE_MATRIX, PETSC_FALSE, &Msub));
3550: } else { /* call == MAT_INITIAL_MATRIX) */
3551: PetscBool flg;
3553: PetscCall(ISGetLocalSize(iscol, &n));
3554: PetscCall(ISGetSize(iscol, &Ncols));
3556: /* (1) iscol -> nonscalable iscol_local */
3557: /* Check for special case: each processor gets entire matrix columns */
3558: PetscCall(ISIdentity(iscol_local, &flg));
3559: if (flg && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3560: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3561: if (allcolumns) {
3562: iscol_sub = iscol_local;
3563: PetscCall(PetscObjectReference((PetscObject)iscol_local));
3564: PetscCall(ISCreateStride(PETSC_COMM_SELF, n, 0, 1, &iscmap));
3566: } else {
3567: /* (2) iscol_local -> iscol_sub and iscmap. Implementation below requires iscol_local be sorted, it can have duplicate indices */
3568: PetscInt *idx, *cmap1, k;
3569: PetscCall(PetscMalloc1(Ncols, &idx));
3570: PetscCall(PetscMalloc1(Ncols, &cmap1));
3571: PetscCall(ISGetIndices(iscol_local, &is_idx));
3572: count = 0;
3573: k = 0;
3574: for (i = 0; i < Ncols; i++) {
3575: j = is_idx[i];
3576: if (j >= cstart && j < cend) {
3577: /* diagonal part of mat */
3578: idx[count] = j;
3579: cmap1[count++] = i; /* column index in submat */
3580: } else if (Bn) {
3581: /* off-diagonal part of mat */
3582: if (j == garray[k]) {
3583: idx[count] = j;
3584: cmap1[count++] = i; /* column index in submat */
3585: } else if (j > garray[k]) {
3586: while (j > garray[k] && k < Bn - 1) k++;
3587: if (j == garray[k]) {
3588: idx[count] = j;
3589: cmap1[count++] = i; /* column index in submat */
3590: }
3591: }
3592: }
3593: }
3594: PetscCall(ISRestoreIndices(iscol_local, &is_idx));
3596: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_OWN_POINTER, &iscol_sub));
3597: PetscCall(ISGetBlockSize(iscol, &cbs));
3598: PetscCall(ISSetBlockSize(iscol_sub, cbs));
3600: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)iscol_local), count, cmap1, PETSC_OWN_POINTER, &iscmap));
3601: }
3603: /* (3) Create sequential Msub */
3604: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_INITIAL_MATRIX, allcolumns, &Msub));
3605: }
3607: PetscCall(ISGetLocalSize(iscol_sub, &count));
3608: aij = (Mat_SeqAIJ *)Msub->data;
3609: ii = aij->i;
3610: PetscCall(ISGetIndices(iscmap, &cmap));
3612: /*
3613: m - number of local rows
3614: Ncols - number of columns (same on all processors)
3615: rstart - first row in new global matrix generated
3616: */
3617: PetscCall(MatGetSize(Msub, &m, NULL));
3619: if (call == MAT_INITIAL_MATRIX) {
3620: /* (4) Create parallel newmat */
3621: PetscMPIInt rank, size;
3622: PetscInt csize;
3624: PetscCallMPI(MPI_Comm_size(comm, &size));
3625: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3627: /*
3628: Determine the number of non-zeros in the diagonal and off-diagonal
3629: portions of the matrix in order to do correct preallocation
3630: */
3632: /* first get start and end of "diagonal" columns */
3633: PetscCall(ISGetLocalSize(iscol, &csize));
3634: if (csize == PETSC_DECIDE) {
3635: PetscCall(ISGetSize(isrow, &mglobal));
3636: if (mglobal == Ncols) { /* square matrix */
3637: nlocal = m;
3638: } else {
3639: nlocal = Ncols / size + ((Ncols % size) > rank);
3640: }
3641: } else {
3642: nlocal = csize;
3643: }
3644: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3645: rstart = rend - nlocal;
3646: PetscCheck(rank != size - 1 || rend == Ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Local column sizes %" PetscInt_FMT " do not add up to total number of columns %" PetscInt_FMT, rend, Ncols);
3648: /* next, compute all the lengths */
3649: jj = aij->j;
3650: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3651: olens = dlens + m;
3652: for (i = 0; i < m; i++) {
3653: jend = ii[i + 1] - ii[i];
3654: olen = 0;
3655: dlen = 0;
3656: for (j = 0; j < jend; j++) {
3657: if (cmap[*jj] < rstart || cmap[*jj] >= rend) olen++;
3658: else dlen++;
3659: jj++;
3660: }
3661: olens[i] = olen;
3662: dlens[i] = dlen;
3663: }
3665: PetscCall(ISGetBlockSize(isrow, &bs));
3666: PetscCall(ISGetBlockSize(iscol, &cbs));
3668: PetscCall(MatCreate(comm, &M));
3669: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, Ncols));
3670: PetscCall(MatSetBlockSizes(M, bs, cbs));
3671: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3672: PetscCall(MatSetOption(M, MAT_STRUCTURE_ONLY, mat->structure_only));
3673: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3674: PetscCall(PetscFree(dlens));
3676: } else { /* call == MAT_REUSE_MATRIX */
3677: M = *newmat;
3678: PetscCall(MatGetLocalSize(M, &i, NULL));
3679: PetscCheck(i == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3680: PetscCall(MatZeroEntries(M));
3681: /*
3682: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3683: rather than the slower MatSetValues().
3684: */
3685: M->was_assembled = PETSC_TRUE;
3686: M->assembled = PETSC_FALSE;
3687: }
3689: /* (5) Set values of Msub to *newmat */
3690: PetscCall(PetscMalloc1(count, &colsub));
3691: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
3693: jj = aij->j;
3694: PetscCall(MatSeqAIJGetArrayRead(Msub, (const PetscScalar **)&aa));
3695: for (i = 0; i < m; i++) {
3696: row = rstart + i;
3697: nz = ii[i + 1] - ii[i];
3698: for (j = 0; j < nz; j++) colsub[j] = cmap[jj[j]];
3699: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, colsub, aa, INSERT_VALUES));
3700: jj += nz;
3701: if (!mat->structure_only) aa += nz;
3702: }
3703: PetscCall(MatSeqAIJRestoreArrayRead(Msub, (const PetscScalar **)&aa));
3704: PetscCall(ISRestoreIndices(iscmap, &cmap));
3706: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3707: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3709: PetscCall(PetscFree(colsub));
3711: /* save Msub, iscol_sub and iscmap used in processor for next request */
3712: if (call == MAT_INITIAL_MATRIX) {
3713: *newmat = M;
3714: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubMatrix", (PetscObject)Msub));
3715: PetscCall(MatDestroy(&Msub));
3717: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubIScol", (PetscObject)iscol_sub));
3718: PetscCall(ISDestroy(&iscol_sub));
3720: PetscCall(PetscObjectCompose((PetscObject)*newmat, "Subcmap", (PetscObject)iscmap));
3721: PetscCall(ISDestroy(&iscmap));
3723: if (iscol_local) {
3724: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3725: PetscCall(ISDestroy(&iscol_local));
3726: }
3727: }
3728: PetscFunctionReturn(PETSC_SUCCESS);
3729: }
3731: /*
3732: Not great since it makes two copies of the submatrix, first an SeqAIJ
3733: in local and then by concatenating the local matrices the end result.
3734: Writing it directly would be much like MatCreateSubMatrices_MPIAIJ()
3736: This requires a sequential iscol with all indices.
3737: */
3738: PetscErrorCode MatCreateSubMatrix_MPIAIJ_nonscalable(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
3739: {
3740: PetscMPIInt rank, size;
3741: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs, cbs;
3742: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3743: Mat M, Mreuse;
3744: MatScalar *aa, *vwork;
3745: MPI_Comm comm;
3746: Mat_SeqAIJ *aij;
3747: PetscBool colflag, allcolumns = PETSC_FALSE;
3749: PetscFunctionBegin;
3750: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3751: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3752: PetscCallMPI(MPI_Comm_size(comm, &size));
3754: /* Check for special case: each processor gets entire matrix columns */
3755: PetscCall(ISIdentity(iscol, &colflag));
3756: PetscCall(ISGetLocalSize(iscol, &n));
3757: if (colflag && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3758: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3760: if (call == MAT_REUSE_MATRIX) {
3761: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
3762: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3763: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_REUSE_MATRIX, allcolumns, &Mreuse));
3764: } else {
3765: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_INITIAL_MATRIX, allcolumns, &Mreuse));
3766: }
3768: /*
3769: m - number of local rows
3770: n - number of columns (same on all processors)
3771: rstart - first row in new global matrix generated
3772: */
3773: PetscCall(MatGetSize(Mreuse, &m, &n));
3774: PetscCall(MatGetBlockSizes(Mreuse, &bs, &cbs));
3775: if (call == MAT_INITIAL_MATRIX) {
3776: aij = (Mat_SeqAIJ *)Mreuse->data;
3777: ii = aij->i;
3778: jj = aij->j;
3780: /*
3781: Determine the number of non-zeros in the diagonal and off-diagonal
3782: portions of the matrix in order to do correct preallocation
3783: */
3785: /* first get start and end of "diagonal" columns */
3786: if (csize == PETSC_DECIDE) {
3787: PetscCall(ISGetSize(isrow, &mglobal));
3788: if (mglobal == n) { /* square matrix */
3789: nlocal = m;
3790: } else {
3791: nlocal = n / size + ((n % size) > rank);
3792: }
3793: } else {
3794: nlocal = csize;
3795: }
3796: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3797: rstart = rend - nlocal;
3798: PetscCheck(rank != size - 1 || rend == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Local column sizes %" PetscInt_FMT " do not add up to total number of columns %" PetscInt_FMT, rend, n);
3800: /* next, compute all the lengths */
3801: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3802: olens = dlens + m;
3803: for (i = 0; i < m; i++) {
3804: jend = ii[i + 1] - ii[i];
3805: olen = 0;
3806: dlen = 0;
3807: for (j = 0; j < jend; j++) {
3808: if (*jj < rstart || *jj >= rend) olen++;
3809: else dlen++;
3810: jj++;
3811: }
3812: olens[i] = olen;
3813: dlens[i] = dlen;
3814: }
3815: PetscCall(MatCreate(comm, &M));
3816: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, n));
3817: PetscCall(MatSetBlockSizes(M, bs, cbs));
3818: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3819: PetscCall(MatSetOption(M, MAT_STRUCTURE_ONLY, mat->structure_only));
3820: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3821: PetscCall(PetscFree(dlens));
3822: } else {
3823: PetscInt ml, nl;
3825: M = *newmat;
3826: PetscCall(MatGetLocalSize(M, &ml, &nl));
3827: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3828: PetscCall(MatZeroEntries(M));
3829: /*
3830: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3831: rather than the slower MatSetValues().
3832: */
3833: M->was_assembled = PETSC_TRUE;
3834: M->assembled = PETSC_FALSE;
3835: }
3836: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
3837: aij = (Mat_SeqAIJ *)Mreuse->data;
3838: ii = aij->i;
3839: jj = aij->j;
3841: /* trigger copy to CPU if needed */
3842: PetscCall(MatSeqAIJGetArrayRead(Mreuse, (const PetscScalar **)&aa));
3843: for (i = 0; i < m; i++) {
3844: row = rstart + i;
3845: nz = ii[i + 1] - ii[i];
3846: cwork = jj;
3847: jj = PetscSafePointerPlusOffset(jj, nz);
3848: vwork = aa;
3849: aa = PetscSafePointerPlusOffset(aa, nz);
3850: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
3851: }
3852: PetscCall(MatSeqAIJRestoreArrayRead(Mreuse, (const PetscScalar **)&aa));
3854: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3855: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3856: *newmat = M;
3858: /* save submatrix used in processor for next request */
3859: if (call == MAT_INITIAL_MATRIX) {
3860: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
3861: PetscCall(MatDestroy(&Mreuse));
3862: }
3863: PetscFunctionReturn(PETSC_SUCCESS);
3864: }
3866: static PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
3867: {
3868: PetscInt m, cstart, cend, j, nnz, i, d, *ld;
3869: PetscInt *d_nnz, *o_nnz, nnz_max = 0, rstart, ii, irstart;
3870: const PetscInt *JJ;
3871: PetscBool nooffprocentries;
3872: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)B->data;
3874: PetscFunctionBegin;
3875: PetscCall(PetscLayoutSetUp(B->rmap));
3876: PetscCall(PetscLayoutSetUp(B->cmap));
3877: m = B->rmap->n;
3878: cstart = B->cmap->rstart;
3879: cend = B->cmap->rend;
3880: rstart = B->rmap->rstart;
3881: irstart = Ii[0];
3883: PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));
3885: if (PetscDefined(USE_DEBUG)) {
3886: for (i = 0; i < m; i++) {
3887: nnz = Ii[i + 1] - Ii[i];
3888: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3889: PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3890: PetscCheck(!nnz || !(JJ[0] < 0), PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Row %" PetscInt_FMT " starts with negative column index %" PetscInt_FMT, i, JJ[0]);
3891: PetscCheck(!nnz || !(JJ[nnz - 1] >= B->cmap->N), PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Row %" PetscInt_FMT " ends with too large a column index %" PetscInt_FMT " (max allowed %" PetscInt_FMT ")", i, JJ[nnz - 1], B->cmap->N);
3892: }
3893: }
3895: for (i = 0; i < m; i++) {
3896: nnz = Ii[i + 1] - Ii[i];
3897: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3898: nnz_max = PetscMax(nnz_max, nnz);
3899: d = 0;
3900: for (j = 0; j < nnz; j++) {
3901: if (cstart <= JJ[j] && JJ[j] < cend) d++;
3902: }
3903: d_nnz[i] = d;
3904: o_nnz[i] = nnz - d;
3905: }
3906: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3907: PetscCall(PetscFree2(d_nnz, o_nnz));
3909: for (i = 0; i < m; i++) {
3910: ii = i + rstart;
3911: PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], PetscSafePointerPlusOffset(J, Ii[i] - irstart), PetscSafePointerPlusOffset(v, Ii[i] - irstart), INSERT_VALUES));
3912: }
3913: nooffprocentries = B->nooffprocentries;
3914: B->nooffprocentries = PETSC_TRUE;
3915: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3916: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3917: B->nooffprocentries = nooffprocentries;
3919: /* count number of entries below block diagonal */
3920: PetscCall(PetscFree(Aij->ld));
3921: PetscCall(PetscCalloc1(m, &ld));
3922: Aij->ld = ld;
3923: for (i = 0; i < m; i++) {
3924: nnz = Ii[i + 1] - Ii[i];
3925: j = 0;
3926: while (j < nnz && J[j] < cstart) j++;
3927: ld[i] = j;
3928: if (J) J += nnz;
3929: }
3931: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3932: PetscFunctionReturn(PETSC_SUCCESS);
3933: }
3935: /*@
3936: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
3937: (the default parallel PETSc format).
3939: Collective
3941: Input Parameters:
3942: + B - the matrix
3943: . i - the indices into `j` for the start of each local row (indices start with zero)
3944: . j - the column indices for each local row (indices start with zero)
3945: - v - optional values in the matrix
3947: Level: developer
3949: Notes:
3950: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
3951: thus you CANNOT change the matrix entries by changing the values of `v` after you have
3952: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3954: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
3956: A convenience routine for this functionality is `MatCreateMPIAIJWithArrays()`.
3958: You can update the matrix with new numerical values using `MatUpdateMPIAIJWithArrays()` after this call if the column indices in `j` are sorted.
3960: If you do **not** use `MatUpdateMPIAIJWithArrays()`, the column indices in `j` do not need to be sorted. If you will use
3961: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
3963: The format which is used for the sparse matrix input, is equivalent to a
3964: row-major ordering.. i.e for the following matrix, the input data expected is
3965: as shown
3966: .vb
3967: 1 0 0
3968: 2 0 3 P0
3969: -------
3970: 4 5 6 P1
3972: Process0 [P0] rows_owned=[0,1]
3973: i = {0,1,3} [size = nrow+1 = 2+1]
3974: j = {0,0,2} [size = 3]
3975: v = {1,2,3} [size = 3]
3977: Process1 [P1] rows_owned=[2]
3978: i = {0,3} [size = nrow+1 = 1+1]
3979: j = {0,1,2} [size = 3]
3980: v = {4,5,6} [size = 3]
3981: .ve
3983: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`,
3984: `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`, `MatCreateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
3985: @*/
3986: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
3987: {
3988: PetscFunctionBegin;
3989: PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
3990: PetscFunctionReturn(PETSC_SUCCESS);
3991: }
3993: /*@
3994: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
3995: (the default parallel PETSc format). For good matrix assembly performance
3996: the user should preallocate the matrix storage by setting the parameters
3997: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
3999: Collective
4001: Input Parameters:
4002: + B - the matrix
4003: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4004: (same value is used for all local rows)
4005: . d_nnz - array containing the number of nonzeros in the various rows of the
4006: DIAGONAL portion of the local submatrix (possibly different for each row)
4007: or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4008: The size of this array is equal to the number of local rows, i.e 'm'.
4009: For matrices that will be factored, you must leave room for (and set)
4010: the diagonal entry even if it is zero.
4011: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4012: submatrix (same value is used for all local rows).
4013: - o_nnz - array containing the number of nonzeros in the various rows of the
4014: OFF-DIAGONAL portion of the local submatrix (possibly different for
4015: each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4016: structure. The size of this array is equal to the number
4017: of local rows, i.e 'm'.
4019: Example Usage:
4020: Consider the following 8x8 matrix with 34 non-zero values, that is
4021: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4022: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4023: as follows
4025: .vb
4026: 1 2 0 | 0 3 0 | 0 4
4027: Proc0 0 5 6 | 7 0 0 | 8 0
4028: 9 0 10 | 11 0 0 | 12 0
4029: -------------------------------------
4030: 13 0 14 | 15 16 17 | 0 0
4031: Proc1 0 18 0 | 19 20 21 | 0 0
4032: 0 0 0 | 22 23 0 | 24 0
4033: -------------------------------------
4034: Proc2 25 26 27 | 0 0 28 | 29 0
4035: 30 0 0 | 31 32 33 | 0 34
4036: .ve
4038: This can be represented as a collection of submatrices as
4039: .vb
4040: A B C
4041: D E F
4042: G H I
4043: .ve
4045: Where the submatrices A,B,C are owned by proc0, D,E,F are
4046: owned by proc1, G,H,I are owned by proc2.
4048: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4049: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4050: The 'M','N' parameters are 8,8, and have the same values on all procs.
4052: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4053: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4054: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4055: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4056: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4057: matrix, and [DF] as another `MATSEQAIJ` matrix.
4059: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4060: allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4061: storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4062: One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4063: the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4064: In this case, the values of `d_nz`, `o_nz` are
4065: .vb
4066: proc0 dnz = 2, o_nz = 2
4067: proc1 dnz = 3, o_nz = 2
4068: proc2 dnz = 1, o_nz = 4
4069: .ve
4070: We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4071: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4072: for proc3. i.e we are using 12+15+10=37 storage locations to store
4073: 34 values.
4075: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4076: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4077: In the above case the values for `d_nnz`, `o_nnz` are
4078: .vb
4079: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4080: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4081: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4082: .ve
4083: Here the space allocated is sum of all the above values i.e 34, and
4084: hence pre-allocation is perfect.
4086: Level: intermediate
4088: Notes:
4089: If the *_nnz parameter is given then the *_nz parameter is ignored
4091: The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4092: storage. The stored row and column indices begin with zero.
4093: See [Sparse Matrices](sec_matsparse) for details.
4095: The parallel matrix is partitioned such that the first m0 rows belong to
4096: process 0, the next m1 rows belong to process 1, the next m2 rows belong
4097: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
4099: The DIAGONAL portion of the local submatrix of a processor can be defined
4100: as the submatrix which is obtained by extraction the part corresponding to
4101: the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4102: first row that belongs to the processor, r2 is the last row belonging to
4103: the this processor, and c1-c2 is range of indices of the local part of a
4104: vector suitable for applying the matrix to. This is an mxn matrix. In the
4105: common case of a square matrix, the row and column ranges are the same and
4106: the DIAGONAL part is also square. The remaining portion of the local
4107: submatrix (mxN) constitute the OFF-DIAGONAL portion.
4109: If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.
4111: You can call `MatGetInfo()` to get information on how effective the preallocation was;
4112: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4113: You can also run with the option `-info` and look for messages with the string
4114: malloc in them to see if additional memory allocation was needed.
4116: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4117: `MatGetInfo()`, `PetscSplitOwnership()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4118: @*/
4119: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4120: {
4121: PetscFunctionBegin;
4124: PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4125: PetscFunctionReturn(PETSC_SUCCESS);
4126: }
4128: /*@
4129: MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4130: CSR format for the local rows.
4132: Collective
4134: Input Parameters:
4135: + comm - MPI communicator
4136: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4137: . n - This value should be the same as the local size used in creating the
4138: x vector for the matrix-vector product $ y = Ax$. (or `PETSC_DECIDE` to have
4139: calculated if `N` is given) For square matrices n is almost always `m`.
4140: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
4141: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
4142: . i - row indices (of length m+1); that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
4143: . j - global column indices
4144: - a - optional matrix values
4146: Output Parameter:
4147: . mat - the matrix
4149: Level: intermediate
4151: Notes:
4152: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4153: thus you CANNOT change the matrix entries by changing the values of `a[]` after you have
4154: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
4156: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
4158: Once you have created the matrix you can update it with new numerical values using `MatUpdateMPIAIJWithArray()`
4160: If you do **not** use `MatUpdateMPIAIJWithArray()`, the column indices in `j` do not need to be sorted. If you will use
4161: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
4163: The format which is used for the sparse matrix input, is equivalent to a
4164: row-major ordering, i.e., for the following matrix, the input data expected is
4165: as shown
4166: .vb
4167: 1 0 0
4168: 2 0 3 P0
4169: -------
4170: 4 5 6 P1
4172: Process0 [P0] rows_owned=[0,1]
4173: i = {0,1,3} [size = nrow+1 = 2+1]
4174: j = {0,0,2} [size = 3]
4175: v = {1,2,3} [size = 3]
4177: Process1 [P1] rows_owned=[2]
4178: i = {0,3} [size = nrow+1 = 1+1]
4179: j = {0,1,2} [size = 3]
4180: v = {4,5,6} [size = 3]
4181: .ve
4183: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4184: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4185: @*/
4186: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
4187: {
4188: PetscFunctionBegin;
4189: PetscCheck(!i || !i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4190: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4191: PetscCall(MatCreate(comm, mat));
4192: PetscCall(MatSetSizes(*mat, m, n, M, N));
4193: /* PetscCall(MatSetBlockSizes(M,bs,cbs)); */
4194: PetscCall(MatSetType(*mat, MATMPIAIJ));
4195: PetscCall(MatMPIAIJSetPreallocationCSR(*mat, i, j, a));
4196: PetscFunctionReturn(PETSC_SUCCESS);
4197: }
4199: /*@
4200: MatUpdateMPIAIJWithArrays - updates a `MATMPIAIJ` matrix using arrays that contain in standard
4201: CSR format for the local rows. Only the numerical values are updated the other arrays must be identical to what was passed
4202: from `MatCreateMPIAIJWithArrays()`
4204: Deprecated: Use `MatUpdateMPIAIJWithArray()`
4206: Collective
4208: Input Parameters:
4209: + mat - the matrix
4210: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4211: . n - This value should be the same as the local size used in creating the
4212: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4213: calculated if N is given) For square matrices n is almost always m.
4214: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4215: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4216: . Ii - row indices; that is Ii[0] = 0, Ii[row] = Ii[row-1] + number of elements in that row of the matrix
4217: . J - column indices
4218: - v - matrix values
4220: Level: deprecated
4222: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4223: `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4224: @*/
4225: PetscErrorCode MatUpdateMPIAIJWithArrays(Mat mat, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4226: {
4227: PetscInt nnz, i;
4228: PetscBool nooffprocentries;
4229: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4230: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4231: PetscScalar *ad, *ao;
4232: PetscInt ldi, Iii, md;
4233: const PetscInt *Adi = Ad->i;
4234: PetscInt *ld = Aij->ld;
4236: PetscFunctionBegin;
4237: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4238: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4239: PetscCheck(m == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of rows cannot change from call to MatUpdateMPIAIJWithArrays()");
4240: PetscCheck(n == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of columns cannot change from call to MatUpdateMPIAIJWithArrays()");
4242: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4243: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4245: for (i = 0; i < m; i++) {
4246: if (PetscDefined(USE_DEBUG)) {
4247: for (PetscInt j = Ii[i] + 1; j < Ii[i + 1]; ++j) {
4248: PetscCheck(J[j] >= J[j - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is not sorted", j - Ii[i], J[j], i);
4249: PetscCheck(J[j] != J[j - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is identical to previous entry", j - Ii[i], J[j], i);
4250: }
4251: }
4252: nnz = Ii[i + 1] - Ii[i];
4253: Iii = Ii[i];
4254: ldi = ld[i];
4255: md = Adi[i + 1] - Adi[i];
4256: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4257: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4258: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4259: ad += md;
4260: ao += nnz - md;
4261: }
4262: nooffprocentries = mat->nooffprocentries;
4263: mat->nooffprocentries = PETSC_TRUE;
4264: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4265: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4266: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4267: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4268: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4269: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4270: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4271: mat->nooffprocentries = nooffprocentries;
4272: PetscFunctionReturn(PETSC_SUCCESS);
4273: }
4275: /*@
4276: MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values
4278: Collective
4280: Input Parameters:
4281: + mat - the matrix
4282: - v - matrix values, stored by row
4284: Level: intermediate
4286: Notes:
4287: The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`
4289: The column indices in the call to `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()` must have been sorted for this call to work correctly
4291: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4292: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4293: @*/
4294: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4295: {
4296: PetscInt nnz, i, m;
4297: PetscBool nooffprocentries;
4298: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4299: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4300: Mat_SeqAIJ *Ao = (Mat_SeqAIJ *)Aij->B->data;
4301: PetscScalar *ad, *ao;
4302: const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4303: PetscInt ldi, Iii, md;
4304: PetscInt *ld = Aij->ld;
4306: PetscFunctionBegin;
4307: m = mat->rmap->n;
4309: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4310: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4311: Iii = 0;
4312: for (i = 0; i < m; i++) {
4313: nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4314: ldi = ld[i];
4315: md = Adi[i + 1] - Adi[i];
4316: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4317: ad += md;
4318: if (ao) {
4319: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4320: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4321: ao += nnz - md;
4322: }
4323: Iii += nnz;
4324: }
4325: nooffprocentries = mat->nooffprocentries;
4326: mat->nooffprocentries = PETSC_TRUE;
4327: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4328: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4329: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4330: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4331: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4332: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4333: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4334: mat->nooffprocentries = nooffprocentries;
4335: PetscFunctionReturn(PETSC_SUCCESS);
4336: }
4338: /*@
4339: MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4340: (the default parallel PETSc format). For good matrix assembly performance
4341: the user should preallocate the matrix storage by setting the parameters
4342: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4344: Collective
4346: Input Parameters:
4347: + comm - MPI communicator
4348: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4349: This value should be the same as the local size used in creating the
4350: y vector for the matrix-vector product y = Ax.
4351: . n - This value should be the same as the local size used in creating the
4352: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4353: calculated if N is given) For square matrices n is almost always m.
4354: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4355: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4356: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4357: (same value is used for all local rows)
4358: . d_nnz - array containing the number of nonzeros in the various rows of the
4359: DIAGONAL portion of the local submatrix (possibly different for each row)
4360: or `NULL`, if `d_nz` is used to specify the nonzero structure.
4361: The size of this array is equal to the number of local rows, i.e 'm'.
4362: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4363: submatrix (same value is used for all local rows).
4364: - o_nnz - array containing the number of nonzeros in the various rows of the
4365: OFF-DIAGONAL portion of the local submatrix (possibly different for
4366: each row) or `NULL`, if `o_nz` is used to specify the nonzero
4367: structure. The size of this array is equal to the number
4368: of local rows, i.e 'm'.
4370: Output Parameter:
4371: . A - the matrix
4373: Options Database Keys:
4374: + -mat_no_inode - Do not use inodes
4375: . -mat_inode_limit limit - Sets inode limit (max limit=5)
4376: - -matmult_vecscatter_view viewer - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4377: See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the `VecScatter`
4378: to be viewed as a matrix. Entry (i,j) is the size of message (in bytes) rank i sends to rank j in one `MatMult()` call.
4380: Level: intermediate
4382: Notes:
4383: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4384: MatXXXXSetPreallocation() paradigm instead of this routine directly.
4385: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
4387: If the *_nnz parameter is given then the *_nz parameter is ignored
4389: The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4390: processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4391: storage requirements for this matrix.
4393: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one
4394: processor than it must be used on all processors that share the object for
4395: that argument.
4397: If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by
4398: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`.
4400: The user MUST specify either the local or global matrix dimensions
4401: (possibly both).
4403: The parallel matrix is partitioned across processors such that the
4404: first `m0` rows belong to process 0, the next `m1` rows belong to
4405: process 1, the next `m2` rows belong to process 2, etc., where
4406: `m0`, `m1`, `m2`... are the input parameter `m` on each MPI process. I.e., each MPI process stores
4407: values corresponding to [m x N] submatrix.
4409: The columns are logically partitioned with the n0 columns belonging
4410: to 0th partition, the next n1 columns belonging to the next
4411: partition etc.. where n0,n1,n2... are the input parameter 'n'.
4413: The DIAGONAL portion of the local submatrix on any given processor
4414: is the submatrix corresponding to the rows and columns m,n
4415: corresponding to the given processor. i.e diagonal matrix on
4416: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4417: etc. The remaining portion of the local submatrix [m x (N-n)]
4418: constitute the OFF-DIAGONAL portion. The example below better
4419: illustrates this concept. The two matrices, the DIAGONAL portion and
4420: the OFF-DIAGONAL portion are each stored as `MATSEQAIJ` matrices.
4422: For a square global matrix we define each processor's diagonal portion
4423: to be its local rows and the corresponding columns (a square submatrix);
4424: each processor's off-diagonal portion encompasses the remainder of the
4425: local matrix (a rectangular submatrix).
4427: If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.
4429: When calling this routine with a single process communicator, a matrix of
4430: type `MATSEQAIJ` is returned. If a matrix of type `MATMPIAIJ` is desired for this
4431: type of communicator, use the construction mechanism
4432: .vb
4433: MatCreate(..., &A);
4434: MatSetType(A, MATMPIAIJ);
4435: MatSetSizes(A, m, n, M, N);
4436: MatMPIAIJSetPreallocation(A, ...);
4437: .ve
4439: By default, this format uses inodes (identical nodes) when possible.
4440: We search for consecutive rows with the same nonzero structure, thereby
4441: reusing matrix information to achieve increased efficiency.
4443: Example Usage:
4444: Consider the following 8x8 matrix with 34 non-zero values, that is
4445: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4446: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4447: as follows
4449: .vb
4450: 1 2 0 | 0 3 0 | 0 4
4451: Proc0 0 5 6 | 7 0 0 | 8 0
4452: 9 0 10 | 11 0 0 | 12 0
4453: -------------------------------------
4454: 13 0 14 | 15 16 17 | 0 0
4455: Proc1 0 18 0 | 19 20 21 | 0 0
4456: 0 0 0 | 22 23 0 | 24 0
4457: -------------------------------------
4458: Proc2 25 26 27 | 0 0 28 | 29 0
4459: 30 0 0 | 31 32 33 | 0 34
4460: .ve
4462: This can be represented as a collection of submatrices as
4464: .vb
4465: A B C
4466: D E F
4467: G H I
4468: .ve
4470: Where the submatrices A,B,C are owned by proc0, D,E,F are
4471: owned by proc1, G,H,I are owned by proc2.
4473: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4474: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4475: The 'M','N' parameters are 8,8, and have the same values on all procs.
4477: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4478: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4479: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4480: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4481: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4482: matrix, and [DF] as another SeqAIJ matrix.
4484: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4485: allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4486: storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4487: One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4488: the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4489: In this case, the values of `d_nz`,`o_nz` are
4490: .vb
4491: proc0 dnz = 2, o_nz = 2
4492: proc1 dnz = 3, o_nz = 2
4493: proc2 dnz = 1, o_nz = 4
4494: .ve
4495: We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4496: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4497: for proc3. i.e we are using 12+15+10=37 storage locations to store
4498: 34 values.
4500: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4501: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4502: In the above case the values for d_nnz,o_nnz are
4503: .vb
4504: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4505: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4506: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4507: .ve
4508: Here the space allocated is sum of all the above values i.e 34, and
4509: hence pre-allocation is perfect.
4511: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4512: `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`,
4513: `MatGetOwnershipRangesColumn()`, `PetscLayout`
4514: @*/
4515: PetscErrorCode MatCreateAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[], Mat *A)
4516: {
4517: PetscMPIInt size;
4519: PetscFunctionBegin;
4520: PetscCall(MatCreate(comm, A));
4521: PetscCall(MatSetSizes(*A, m, n, M, N));
4522: PetscCallMPI(MPI_Comm_size(comm, &size));
4523: if (size > 1) {
4524: PetscCall(MatSetType(*A, MATMPIAIJ));
4525: PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4526: } else {
4527: PetscCall(MatSetType(*A, MATSEQAIJ));
4528: PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4529: }
4530: PetscFunctionReturn(PETSC_SUCCESS);
4531: }
4533: /*@
4534: MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix
4536: Not Collective
4538: Input Parameter:
4539: . A - The `MATMPIAIJ` matrix
4541: Output Parameters:
4542: + Ad - The local diagonal block as a `MATSEQAIJ` matrix
4543: . Ao - The local off-diagonal block as a `MATSEQAIJ` matrix
4544: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4546: Level: intermediate
4548: Note:
4549: The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4550: in `Ad` are in [0, Nc) where Nc is the number of local columns. The columns are `Ao` are in [0, Nco), where Nco is
4551: the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4552: local column numbers to global column numbers in the original matrix.
4554: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATSEQAIJ`
4555: @*/
4556: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4557: {
4558: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4559: PetscBool flg;
4561: PetscFunctionBegin;
4562: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4563: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4564: if (Ad) *Ad = a->A;
4565: if (Ao) *Ao = a->B;
4566: if (colmap) *colmap = a->garray;
4567: PetscFunctionReturn(PETSC_SUCCESS);
4568: }
4570: static PetscErrorCode MatGetMultPetscSF_MPIAIJ(Mat A, PetscSF *sf)
4571: {
4572: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4574: PetscFunctionBegin;
4575: *sf = a->Mvctx;
4576: PetscFunctionReturn(PETSC_SUCCESS);
4577: }
4579: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4580: {
4581: PetscInt m, N, i, rstart, nnz, Ii;
4582: PetscInt *indx;
4583: PetscScalar *values;
4584: MatType rootType;
4586: PetscFunctionBegin;
4587: PetscCall(MatGetSize(inmat, &m, &N));
4588: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4589: PetscInt *dnz, *onz, sum, bs, cbs;
4591: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4592: /* Check sum(n) = N */
4593: PetscCallMPI(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4594: PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);
4596: PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4597: rstart -= m;
4599: MatPreallocateBegin(comm, m, n, dnz, onz);
4600: for (i = 0; i < m; i++) {
4601: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4602: PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4603: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4604: }
4606: PetscCall(MatCreate(comm, outmat));
4607: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4608: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4609: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4610: PetscCall(MatGetRootType_Private(inmat, &rootType));
4611: PetscCall(MatSetType(*outmat, rootType));
4612: PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4613: PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4614: MatPreallocateEnd(dnz, onz);
4615: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4616: }
4618: /* numeric phase */
4619: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4620: for (i = 0; i < m; i++) {
4621: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4622: Ii = i + rstart;
4623: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4624: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4625: }
4626: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4627: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4628: PetscFunctionReturn(PETSC_SUCCESS);
4629: }
4631: static PetscErrorCode MatMergeSeqsToMPIDestroy(PetscCtxRt data)
4632: {
4633: MatMergeSeqsToMPI *merge = *(MatMergeSeqsToMPI **)data;
4635: PetscFunctionBegin;
4636: if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4637: PetscCall(PetscFree(merge->id_r));
4638: PetscCall(PetscFree(merge->len_s));
4639: PetscCall(PetscFree(merge->len_r));
4640: PetscCall(PetscFree(merge->bi));
4641: PetscCall(PetscFree(merge->bj));
4642: PetscCall(PetscFree(merge->buf_ri[0]));
4643: PetscCall(PetscFree(merge->buf_ri));
4644: PetscCall(PetscFree(merge->buf_rj[0]));
4645: PetscCall(PetscFree(merge->buf_rj));
4646: PetscCall(PetscFree(merge->coi));
4647: PetscCall(PetscFree(merge->coj));
4648: PetscCall(PetscFree(merge->owners_co));
4649: PetscCall(PetscLayoutDestroy(&merge->rowmap));
4650: PetscCall(PetscFree(merge));
4651: PetscFunctionReturn(PETSC_SUCCESS);
4652: }
4654: #include <../src/mat/utils/freespace.h>
4655: #include <petscbt.h>
4657: /*@
4658: MatCreateMPIAIJSumSeqAIJNumeric - Fill the numerical values of an `MATMPIAIJ` matrix previously created by
4659: `MatCreateMPIAIJSumSeqAIJSymbolic()` by summing the local `MATSEQAIJ` contributions from each process.
4661: Collective
4663: Input Parameters:
4664: + seqmat - the local `MATSEQAIJ` contribution from this process
4665: - mpimat - the target `MATMPIAIJ` matrix created by `MatCreateMPIAIJSumSeqAIJSymbolic()`
4667: Level: developer
4669: .seealso: `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJSumSeqAIJSymbolic()`, `MatCreateMPIAIJSumSeqAIJ()`
4670: @*/
4671: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4672: {
4673: MPI_Comm comm;
4674: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4675: PetscMPIInt size, rank, taga, *len_s;
4676: PetscInt N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj, m;
4677: PetscMPIInt proc, k;
4678: PetscInt **buf_ri, **buf_rj;
4679: PetscInt anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4680: PetscInt nrows, **buf_ri_k, **nextrow, **nextai;
4681: MPI_Request *s_waits, *r_waits;
4682: MPI_Status *status;
4683: const MatScalar *aa, *a_a;
4684: MatScalar **abuf_r, *ba_i;
4685: MatMergeSeqsToMPI *merge;
4686: PetscContainer container;
4688: PetscFunctionBegin;
4689: PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4690: PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));
4692: PetscCallMPI(MPI_Comm_size(comm, &size));
4693: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4695: PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4696: PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4697: PetscCall(PetscContainerGetPointer(container, &merge));
4698: PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4699: aa = a_a;
4701: bi = merge->bi;
4702: bj = merge->bj;
4703: buf_ri = merge->buf_ri;
4704: buf_rj = merge->buf_rj;
4706: PetscCall(PetscMalloc1(size, &status));
4707: owners = merge->rowmap->range;
4708: len_s = merge->len_s;
4710: /* send and recv matrix values */
4711: PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4712: PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));
4714: PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4715: for (proc = 0, k = 0; proc < size; proc++) {
4716: if (!len_s[proc]) continue;
4717: i = owners[proc];
4718: PetscCallMPI(MPIU_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4719: k++;
4720: }
4722: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4723: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4724: PetscCall(PetscFree(status));
4726: PetscCall(PetscFree(s_waits));
4727: PetscCall(PetscFree(r_waits));
4729: /* insert mat values of mpimat */
4730: PetscCall(PetscMalloc1(N, &ba_i));
4731: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4733: for (k = 0; k < merge->nrecv; k++) {
4734: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4735: nrows = *buf_ri_k[k];
4736: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4737: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4738: }
4740: /* set values of ba */
4741: m = merge->rowmap->n;
4742: for (i = 0; i < m; i++) {
4743: arow = owners[rank] + i;
4744: bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4745: bnzi = bi[i + 1] - bi[i];
4746: PetscCall(PetscArrayzero(ba_i, bnzi));
4748: /* add local non-zero vals of this proc's seqmat into ba */
4749: anzi = ai[arow + 1] - ai[arow];
4750: aj = a->j + ai[arow];
4751: aa = a_a + ai[arow];
4752: nextaj = 0;
4753: for (j = 0; nextaj < anzi; j++) {
4754: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4755: ba_i[j] += aa[nextaj++];
4756: }
4757: }
4759: /* add received vals into ba */
4760: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4761: /* i-th row */
4762: if (i == *nextrow[k]) {
4763: anzi = *(nextai[k] + 1) - *nextai[k];
4764: aj = buf_rj[k] + *nextai[k];
4765: aa = abuf_r[k] + *nextai[k];
4766: nextaj = 0;
4767: for (j = 0; nextaj < anzi; j++) {
4768: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4769: ba_i[j] += aa[nextaj++];
4770: }
4771: }
4772: nextrow[k]++;
4773: nextai[k]++;
4774: }
4775: }
4776: PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4777: }
4778: PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4779: PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4780: PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));
4782: PetscCall(PetscFree(abuf_r[0]));
4783: PetscCall(PetscFree(abuf_r));
4784: PetscCall(PetscFree(ba_i));
4785: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4786: PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4787: PetscFunctionReturn(PETSC_SUCCESS);
4788: }
4790: /*@
4791: MatCreateMPIAIJSumSeqAIJSymbolic - Create the symbolic (nonzero-pattern) portion of an `MATMPIAIJ` matrix
4792: obtained by summing local `MATSEQAIJ` contributions from each process.
4794: Collective
4796: Input Parameters:
4797: + comm - the communicator
4798: . seqmat - the local `MATSEQAIJ` contribution from this process
4799: . m - the number of local rows for the resulting `MATMPIAIJ` matrix, or `PETSC_DECIDE`
4800: - n - the number of local columns for the resulting `MATMPIAIJ` matrix, or `PETSC_DECIDE`
4802: Output Parameter:
4803: . mpimat - the newly created `MATMPIAIJ` matrix
4805: Level: developer
4807: Note:
4808: The numerical values are filled in by a subsequent call to `MatCreateMPIAIJSumSeqAIJNumeric()`.
4810: .seealso: `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJSumSeqAIJNumeric()`, `MatCreateMPIAIJSumSeqAIJ()`
4811: @*/
4812: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4813: {
4814: Mat B_mpi;
4815: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4816: PetscMPIInt size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4817: PetscInt **buf_rj, **buf_ri, **buf_ri_k;
4818: PetscInt M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4819: PetscInt len, *dnz, *onz, bs, cbs;
4820: PetscInt k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4821: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4822: MPI_Request *si_waits, *sj_waits, *ri_waits, *rj_waits;
4823: MPI_Status *status;
4824: PetscFreeSpaceList free_space = NULL, current_space = NULL;
4825: PetscBT lnkbt;
4826: MatMergeSeqsToMPI *merge;
4827: PetscContainer container;
4829: PetscFunctionBegin;
4830: PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));
4832: /* make sure it is a PETSc comm */
4833: PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4834: PetscCallMPI(MPI_Comm_size(comm, &size));
4835: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4837: PetscCall(PetscNew(&merge));
4838: PetscCall(PetscMalloc1(size, &status));
4840: /* determine row ownership */
4841: PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4842: PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4843: PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4844: PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4845: PetscCall(PetscLayoutSetUp(merge->rowmap));
4846: PetscCall(PetscMalloc1(size, &len_si));
4847: PetscCall(PetscMalloc1(size, &merge->len_s));
4849: m = merge->rowmap->n;
4850: owners = merge->rowmap->range;
4852: /* determine the number of messages to send, their lengths */
4853: len_s = merge->len_s;
4855: len = 0; /* length of buf_si[] */
4856: merge->nsend = 0;
4857: for (PetscMPIInt proc = 0; proc < size; proc++) {
4858: len_si[proc] = 0;
4859: if (proc == rank) {
4860: len_s[proc] = 0;
4861: } else {
4862: PetscCall(PetscMPIIntCast(owners[proc + 1] - owners[proc] + 1, &len_si[proc]));
4863: PetscCall(PetscMPIIntCast(ai[owners[proc + 1]] - ai[owners[proc]], &len_s[proc])); /* num of rows to be sent to [proc] */
4864: }
4865: if (len_s[proc]) {
4866: merge->nsend++;
4867: nrows = 0;
4868: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4869: if (ai[i + 1] > ai[i]) nrows++;
4870: }
4871: PetscCall(PetscMPIIntCast(2 * (nrows + 1), &len_si[proc]));
4872: len += len_si[proc];
4873: }
4874: }
4876: /* determine the number and length of messages to receive for ij-structure */
4877: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4878: PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));
4880: /* post the Irecv of j-structure */
4881: PetscCall(PetscCommGetNewTag(comm, &tagj));
4882: PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));
4884: /* post the Isend of j-structure */
4885: PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));
4887: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4888: if (!len_s[proc]) continue;
4889: i = owners[proc];
4890: PetscCallMPI(MPIU_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4891: k++;
4892: }
4894: /* receives and sends of j-structure are complete */
4895: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4896: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));
4898: /* send and recv i-structure */
4899: PetscCall(PetscCommGetNewTag(comm, &tagi));
4900: PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));
4902: PetscCall(PetscMalloc1(len + 1, &buf_s));
4903: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4904: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4905: if (!len_s[proc]) continue;
4906: /* form outgoing message for i-structure:
4907: buf_si[0]: nrows to be sent
4908: [1:nrows]: row index (global)
4909: [nrows+1:2*nrows+1]: i-structure index
4910: */
4911: nrows = len_si[proc] / 2 - 1;
4912: buf_si_i = buf_si + nrows + 1;
4913: buf_si[0] = nrows;
4914: buf_si_i[0] = 0;
4915: nrows = 0;
4916: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4917: anzi = ai[i + 1] - ai[i];
4918: if (anzi) {
4919: buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4920: buf_si[nrows + 1] = i - owners[proc]; /* local row index */
4921: nrows++;
4922: }
4923: }
4924: PetscCallMPI(MPIU_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4925: k++;
4926: buf_si += len_si[proc];
4927: }
4929: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4930: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));
4932: PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4933: for (i = 0; i < merge->nrecv; i++) PetscCall(PetscInfo(seqmat, "recv len_ri=%d, len_rj=%d from [%d]\n", len_ri[i], merge->len_r[i], merge->id_r[i]));
4935: PetscCall(PetscFree(len_si));
4936: PetscCall(PetscFree(len_ri));
4937: PetscCall(PetscFree(rj_waits));
4938: PetscCall(PetscFree2(si_waits, sj_waits));
4939: PetscCall(PetscFree(ri_waits));
4940: PetscCall(PetscFree(buf_s));
4941: PetscCall(PetscFree(status));
4943: /* compute a local seq matrix in each processor */
4944: /* allocate bi array and free space for accumulating nonzero column info */
4945: PetscCall(PetscMalloc1(m + 1, &bi));
4946: bi[0] = 0;
4948: /* create and initialize a linked list */
4949: nlnk = N + 1;
4950: PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));
4952: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4953: len = ai[owners[rank + 1]] - ai[owners[rank]];
4954: PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));
4956: current_space = free_space;
4958: /* determine symbolic info for each local row */
4959: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4961: for (k = 0; k < merge->nrecv; k++) {
4962: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4963: nrows = *buf_ri_k[k];
4964: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4965: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4966: }
4968: MatPreallocateBegin(comm, m, n, dnz, onz);
4969: len = 0;
4970: for (i = 0; i < m; i++) {
4971: bnzi = 0;
4972: /* add local non-zero cols of this proc's seqmat into lnk */
4973: arow = owners[rank] + i;
4974: anzi = ai[arow + 1] - ai[arow];
4975: aj = a->j + ai[arow];
4976: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
4977: bnzi += nlnk;
4978: /* add received col data into lnk */
4979: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4980: if (i == *nextrow[k]) { /* i-th row */
4981: anzi = *(nextai[k] + 1) - *nextai[k];
4982: aj = buf_rj[k] + *nextai[k];
4983: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
4984: bnzi += nlnk;
4985: nextrow[k]++;
4986: nextai[k]++;
4987: }
4988: }
4989: if (len < bnzi) len = bnzi; /* =max(bnzi) */
4991: /* if free space is not available, make more free space */
4992: if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), ¤t_space));
4993: /* copy data into free space, then initialize lnk */
4994: PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
4995: PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));
4997: current_space->array += bnzi;
4998: current_space->local_used += bnzi;
4999: current_space->local_remaining -= bnzi;
5001: bi[i + 1] = bi[i] + bnzi;
5002: }
5004: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
5006: PetscCall(PetscMalloc1(bi[m], &bj));
5007: PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
5008: PetscCall(PetscLLDestroy(lnk, lnkbt));
5010: /* create symbolic parallel matrix B_mpi */
5011: PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
5012: PetscCall(MatCreate(comm, &B_mpi));
5013: if (n == PETSC_DECIDE) PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
5014: else PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
5015: PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
5016: PetscCall(MatSetType(B_mpi, MATMPIAIJ));
5017: PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
5018: MatPreallocateEnd(dnz, onz);
5019: PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));
5021: /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
5022: B_mpi->assembled = PETSC_FALSE;
5023: merge->bi = bi;
5024: merge->bj = bj;
5025: merge->buf_ri = buf_ri;
5026: merge->buf_rj = buf_rj;
5027: merge->coi = NULL;
5028: merge->coj = NULL;
5029: merge->owners_co = NULL;
5031: PetscCall(PetscCommDestroy(&comm));
5033: /* attach the supporting struct to B_mpi for reuse */
5034: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5035: PetscCall(PetscContainerSetPointer(container, merge));
5036: PetscCall(PetscContainerSetCtxDestroy(container, MatMergeSeqsToMPIDestroy));
5037: PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5038: PetscCall(PetscContainerDestroy(&container));
5039: *mpimat = B_mpi;
5041: PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5042: PetscFunctionReturn(PETSC_SUCCESS);
5043: }
5045: /*@
5046: MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5047: matrices from each processor
5049: Collective
5051: Input Parameters:
5052: + comm - the communicators the parallel matrix will live on
5053: . seqmat - the input sequential matrices
5054: . m - number of local rows (or `PETSC_DECIDE`)
5055: . n - number of local columns (or `PETSC_DECIDE`)
5056: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5058: Output Parameter:
5059: . mpimat - the parallel matrix generated
5061: Level: advanced
5063: Note:
5064: The dimensions of the sequential matrix in each processor MUST be the same.
5065: The input seqmat is included into the container `MatMergeSeqsToMPIDestroy`, and will be
5066: destroyed when `mpimat` is destroyed. Call `PetscObjectQuery()` to access `seqmat`.
5068: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`
5069: @*/
5070: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5071: {
5072: PetscMPIInt size;
5074: PetscFunctionBegin;
5075: PetscCallMPI(MPI_Comm_size(comm, &size));
5076: if (size == 1) {
5077: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5078: if (scall == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5079: else PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5080: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5081: PetscFunctionReturn(PETSC_SUCCESS);
5082: }
5083: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5084: if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5085: PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5086: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5087: PetscFunctionReturn(PETSC_SUCCESS);
5088: }
5090: /*@
5091: MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix.
5093: Not Collective
5095: Input Parameter:
5096: . A - the matrix
5098: Output Parameter:
5099: . A_loc - the local sequential matrix generated
5101: Level: developer
5103: Notes:
5104: The matrix is created by taking `A`'s local rows and putting them into a sequential matrix
5105: with `mlocal` rows and `n` columns. Where `mlocal` is obtained with `MatGetLocalSize()` and
5106: `n` is the global column count obtained with `MatGetSize()`
5108: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5110: For parallel matrices this creates an entirely new matrix. If the matrix is sequential it merely increases the reference count.
5112: Destroy the matrix with `MatDestroy()`
5114: .seealso: [](ch_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5115: @*/
5116: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5117: {
5118: PetscBool mpi;
5120: PetscFunctionBegin;
5121: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5122: if (mpi) PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5123: else {
5124: *A_loc = A;
5125: PetscCall(PetscObjectReference((PetscObject)*A_loc));
5126: }
5127: PetscFunctionReturn(PETSC_SUCCESS);
5128: }
5130: /*@
5131: MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix.
5133: Not Collective
5135: Input Parameters:
5136: + A - the matrix
5137: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5139: Output Parameter:
5140: . A_loc - the local sequential matrix generated
5142: Level: developer
5144: Notes:
5145: The matrix is created by taking all `A`'s local rows and putting them into a sequential
5146: matrix with `mlocal` rows and `n` columns.`mlocal` is the row count obtained with
5147: `MatGetLocalSize()` and `n` is the global column count obtained with `MatGetSize()`.
5149: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5151: When `A` is sequential and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A` (which contains the entire matrix),
5152: with its reference count increased by one. Hence changing values of `A_loc` changes `A`. If `MAT_REUSE_MATRIX` is requested on a sequential matrix
5153: then `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called to fill `A_loc`. Thus one can preallocate the appropriate sequential matrix `A_loc`
5154: and then call this routine with `MAT_REUSE_MATRIX`. In this case, one can modify the values of `A_loc` without affecting the original sequential matrix.
5156: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5157: @*/
5158: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5159: {
5160: PetscFunctionBegin;
5161: PetscCall(MatMPIAIJGetLocalMat_Private(A, scall, PETSC_FALSE, A_loc));
5162: PetscFunctionReturn(PETSC_SUCCESS);
5163: }
5165: PetscErrorCode MatMPIAIJGetLocalMat_Private(Mat A, MatReuse scall, PetscBool structure_only, Mat *A_loc)
5166: {
5167: Mat_MPIAIJ *mpimat = (Mat_MPIAIJ *)A->data;
5168: Mat_SeqAIJ *mat, *a, *b;
5169: PetscInt *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5170: const PetscScalar *aa, *ba, *aav = NULL, *bav = NULL;
5171: PetscScalar *ca = NULL, *cam;
5172: PetscMPIInt size;
5173: PetscInt am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5174: PetscInt *ci, *cj, col, ncols_d, ncols_o, jo;
5175: PetscBool match;
5177: PetscFunctionBegin;
5178: PetscCheck(!structure_only || scall == MAT_INITIAL_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "Structure-only extraction requires MAT_INITIAL_MATRIX");
5179: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5180: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5181: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5182: if (size == 1 && !structure_only) {
5183: if (scall == MAT_INITIAL_MATRIX) {
5184: PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5185: *A_loc = mpimat->A;
5186: } else if (scall == MAT_REUSE_MATRIX) PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5187: PetscFunctionReturn(PETSC_SUCCESS);
5188: }
5190: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5191: a = (Mat_SeqAIJ *)mpimat->A->data;
5192: b = (Mat_SeqAIJ *)mpimat->B->data;
5193: ai = a->i;
5194: aj = a->j;
5195: bi = b->i;
5196: bj = b->j;
5197: if (!structure_only) {
5198: PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5199: PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5200: }
5201: aa = aav;
5202: ba = bav;
5203: if (scall == MAT_INITIAL_MATRIX) {
5204: PetscCall(PetscMalloc1(1 + am, &ci));
5205: ci[0] = 0;
5206: for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5207: PetscCall(PetscMalloc1(ci[am], &cj));
5208: if (!structure_only) PetscCall(PetscMalloc1(ci[am], &ca));
5209: k = 0;
5210: for (i = 0; i < am; i++) {
5211: ncols_o = bi[i + 1] - bi[i];
5212: ncols_d = ai[i + 1] - ai[i];
5213: /* off-diagonal portion of A */
5214: for (jo = 0; jo < ncols_o; jo++, bj++, k++) {
5215: col = cmap[*bj];
5216: if (col >= cstart) break;
5217: cj[k] = col;
5218: if (!structure_only) ca[k] = *ba++;
5219: }
5220: /* diagonal portion of A */
5221: for (j = 0; j < ncols_d; j++, k++) {
5222: cj[k] = cstart + *aj++;
5223: if (!structure_only) ca[k] = *aa++;
5224: }
5225: /* off-diagonal portion of A */
5226: for (j = jo; j < ncols_o; j++, k++) {
5227: cj[k] = cmap[*bj++];
5228: if (!structure_only) ca[k] = *ba++;
5229: }
5230: }
5231: /* put together the new matrix */
5232: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5233: PetscCall(MatSetOption(*A_loc, MAT_STRUCTURE_ONLY, structure_only));
5234: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5235: /* Since these are PETSc arrays, change flags to free them as necessary. */
5236: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5237: mat->free_a = PETSC_TRUE;
5238: mat->free_ij = PETSC_TRUE;
5239: mat->nonew = 0;
5240: } else if (scall == MAT_REUSE_MATRIX) {
5241: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5242: ci = mat->i;
5243: cj = mat->j;
5244: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5245: for (i = 0; i < am; i++) {
5246: /* off-diagonal portion of A */
5247: ncols_o = bi[i + 1] - bi[i];
5248: for (jo = 0; jo < ncols_o; jo++, bj++) {
5249: col = cmap[*bj];
5250: if (col >= cstart) break;
5251: *cam++ = *ba++;
5252: }
5253: /* diagonal portion of A */
5254: ncols_d = ai[i + 1] - ai[i];
5255: for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5256: /* off-diagonal portion of A */
5257: for (j = jo; j < ncols_o; j++, bj++) *cam++ = *ba++;
5258: }
5259: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5260: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5261: if (!structure_only) {
5262: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5263: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5264: }
5265: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5266: PetscFunctionReturn(PETSC_SUCCESS);
5267: }
5269: /*@
5270: MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5271: mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and off-diagonal part
5273: Not Collective
5275: Input Parameters:
5276: + A - the matrix
5277: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5279: Output Parameters:
5280: + glob - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5281: - A_loc - the local sequential matrix generated
5283: Level: developer
5285: Note:
5286: This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5287: part, then those associated with the off-diagonal part (in its local ordering)
5289: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5290: @*/
5291: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5292: {
5293: Mat Ao, Ad;
5294: const PetscInt *cmap;
5295: PetscMPIInt size;
5296: PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);
5298: PetscFunctionBegin;
5299: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5300: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5301: if (size == 1) {
5302: if (scall == MAT_INITIAL_MATRIX) {
5303: PetscCall(PetscObjectReference((PetscObject)Ad));
5304: *A_loc = Ad;
5305: } else if (scall == MAT_REUSE_MATRIX) {
5306: PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5307: }
5308: if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5309: PetscFunctionReturn(PETSC_SUCCESS);
5310: }
5311: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5312: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5313: if (f) PetscCall((*f)(A, scall, glob, A_loc));
5314: else {
5315: Mat_SeqAIJ *a = (Mat_SeqAIJ *)Ad->data;
5316: Mat_SeqAIJ *b = (Mat_SeqAIJ *)Ao->data;
5317: Mat_SeqAIJ *c;
5318: PetscInt *ai = a->i, *aj = a->j;
5319: PetscInt *bi = b->i, *bj = b->j;
5320: PetscInt *ci, *cj;
5321: const PetscScalar *aa, *ba;
5322: PetscScalar *ca;
5323: PetscInt i, j, am, dn, on;
5325: PetscCall(MatGetLocalSize(Ad, &am, &dn));
5326: PetscCall(MatGetLocalSize(Ao, NULL, &on));
5327: PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5328: PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5329: if (scall == MAT_INITIAL_MATRIX) {
5330: PetscInt k;
5331: PetscCall(PetscMalloc1(1 + am, &ci));
5332: PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5333: PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5334: ci[0] = 0;
5335: for (i = 0, k = 0; i < am; i++) {
5336: const PetscInt ncols_o = bi[i + 1] - bi[i];
5337: const PetscInt ncols_d = ai[i + 1] - ai[i];
5338: ci[i + 1] = ci[i] + ncols_o + ncols_d;
5339: /* diagonal portion of A */
5340: for (j = 0; j < ncols_d; j++, k++) {
5341: cj[k] = *aj++;
5342: ca[k] = *aa++;
5343: }
5344: /* off-diagonal portion of A */
5345: for (j = 0; j < ncols_o; j++, k++) {
5346: cj[k] = dn + *bj++;
5347: ca[k] = *ba++;
5348: }
5349: }
5350: /* put together the new matrix */
5351: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5352: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5353: /* Since these are PETSc arrays, change flags to free them as necessary. */
5354: c = (Mat_SeqAIJ *)(*A_loc)->data;
5355: c->free_a = PETSC_TRUE;
5356: c->free_ij = PETSC_TRUE;
5357: c->nonew = 0;
5358: PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5359: } else if (scall == MAT_REUSE_MATRIX) {
5360: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5361: for (i = 0; i < am; i++) {
5362: const PetscInt ncols_d = ai[i + 1] - ai[i];
5363: const PetscInt ncols_o = bi[i + 1] - bi[i];
5364: /* diagonal portion of A */
5365: for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5366: /* off-diagonal portion of A */
5367: for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5368: }
5369: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5370: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5371: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5372: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5373: if (glob) {
5374: PetscInt cst, *gidx;
5376: PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5377: PetscCall(PetscMalloc1(dn + on, &gidx));
5378: for (i = 0; i < dn; i++) gidx[i] = cst + i;
5379: for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5380: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5381: }
5382: }
5383: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5384: PetscFunctionReturn(PETSC_SUCCESS);
5385: }
5387: /*@
5388: MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns
5390: Not Collective
5392: Input Parameters:
5393: + A - the matrix
5394: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5395: . row - index set of rows to extract (or `NULL`)
5396: - col - index set of columns to extract (or `NULL`)
5398: Output Parameter:
5399: . A_loc - the local sequential matrix generated
5401: Level: developer
5403: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5404: @*/
5405: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5406: {
5407: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5408: PetscInt i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5409: IS isrowa, iscola;
5410: Mat *aloc;
5411: PetscBool match;
5413: PetscFunctionBegin;
5414: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5415: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5416: PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5417: if (!row) {
5418: start = A->rmap->rstart;
5419: end = A->rmap->rend;
5420: PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5421: } else {
5422: isrowa = *row;
5423: }
5424: if (!col) {
5425: start = A->cmap->rstart;
5426: cmap = a->garray;
5427: nzA = a->A->cmap->n;
5428: nzB = a->B->cmap->n;
5429: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5430: ncols = 0;
5431: for (i = 0; i < nzB; i++) {
5432: if (cmap[i] < start) idx[ncols++] = cmap[i];
5433: else break;
5434: }
5435: imark = i;
5436: for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5437: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5438: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5439: } else iscola = *col;
5440: if (scall != MAT_INITIAL_MATRIX) {
5441: PetscCall(PetscMalloc1(1, &aloc));
5442: aloc[0] = *A_loc;
5443: }
5444: PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5445: if (!col) { /* attach global id of condensed columns */
5446: PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5447: }
5448: *A_loc = aloc[0];
5449: PetscCall(PetscFree(aloc));
5450: if (!row) PetscCall(ISDestroy(&isrowa));
5451: if (!col) PetscCall(ISDestroy(&iscola));
5452: PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5453: PetscFunctionReturn(PETSC_SUCCESS);
5454: }
5456: /*
5457: * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5458: * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5459: * on a global size.
5460: * */
5461: static PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5462: {
5463: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
5464: Mat_SeqAIJ *pd = (Mat_SeqAIJ *)p->A->data, *po = (Mat_SeqAIJ *)p->B->data, *p_oth;
5465: PetscInt plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5466: PetscMPIInt owner;
5467: PetscSFNode *iremote, *oiremote;
5468: const PetscInt *lrowindices;
5469: PetscSF sf, osf;
5470: PetscInt pcstart, *roffsets, *loffsets, *pnnz, j;
5471: PetscInt ontotalcols, dntotalcols, ntotalcols, nout;
5472: MPI_Comm comm;
5473: ISLocalToGlobalMapping mapping;
5474: const PetscScalar *pd_a, *po_a;
5476: PetscFunctionBegin;
5477: PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5478: /* plocalsize is the number of roots
5479: * nrows is the number of leaves
5480: * */
5481: PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5482: PetscCall(ISGetLocalSize(rows, &nrows));
5483: PetscCall(PetscCalloc1(nrows, &iremote));
5484: PetscCall(ISGetIndices(rows, &lrowindices));
5485: for (i = 0; i < nrows; i++) {
5486: /* Find a remote index and an owner for a row
5487: * The row could be local or remote
5488: * */
5489: owner = 0;
5490: lidx = 0;
5491: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5492: iremote[i].index = lidx;
5493: iremote[i].rank = owner;
5494: }
5495: /* Create SF to communicate how many nonzero columns for each row */
5496: PetscCall(PetscSFCreate(comm, &sf));
5497: /* SF will figure out the number of nonzero columns for each row, and their
5498: * offsets
5499: * */
5500: PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5501: PetscCall(PetscSFSetFromOptions(sf));
5502: PetscCall(PetscSFSetUp(sf));
5504: PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5505: PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5506: PetscCall(PetscCalloc1(nrows, &pnnz));
5507: roffsets[0] = 0;
5508: roffsets[1] = 0;
5509: for (i = 0; i < plocalsize; i++) {
5510: /* diagonal */
5511: nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5512: /* off-diagonal */
5513: nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5514: /* compute offsets so that we relative location for each row */
5515: roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5516: roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5517: }
5518: PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5519: PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5520: /* 'r' means root, and 'l' means leaf */
5521: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5522: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5523: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5524: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5525: PetscCall(PetscSFDestroy(&sf));
5526: PetscCall(PetscFree(roffsets));
5527: PetscCall(PetscFree(nrcols));
5528: dntotalcols = 0;
5529: ontotalcols = 0;
5530: ncol = 0;
5531: for (i = 0; i < nrows; i++) {
5532: pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5533: ncol = PetscMax(pnnz[i], ncol);
5534: /* diagonal */
5535: dntotalcols += nlcols[i * 2 + 0];
5536: /* off-diagonal */
5537: ontotalcols += nlcols[i * 2 + 1];
5538: }
5539: /* We do not need to figure the right number of columns
5540: * since all the calculations will be done by going through the raw data
5541: * */
5542: PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5543: PetscCall(MatSetUp(*P_oth));
5544: PetscCall(PetscFree(pnnz));
5545: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5546: /* diagonal */
5547: PetscCall(PetscCalloc1(dntotalcols, &iremote));
5548: /* off-diagonal */
5549: PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5550: /* diagonal */
5551: PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5552: /* off-diagonal */
5553: PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5554: dntotalcols = 0;
5555: ontotalcols = 0;
5556: ntotalcols = 0;
5557: for (i = 0; i < nrows; i++) {
5558: owner = 0;
5559: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5560: /* Set iremote for diag matrix */
5561: for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5562: iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5563: iremote[dntotalcols].rank = owner;
5564: /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5565: ilocal[dntotalcols++] = ntotalcols++;
5566: }
5567: /* off-diagonal */
5568: for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5569: oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5570: oiremote[ontotalcols].rank = owner;
5571: oilocal[ontotalcols++] = ntotalcols++;
5572: }
5573: }
5574: PetscCall(ISRestoreIndices(rows, &lrowindices));
5575: PetscCall(PetscFree(loffsets));
5576: PetscCall(PetscFree(nlcols));
5577: PetscCall(PetscSFCreate(comm, &sf));
5578: /* P serves as roots and P_oth is leaves
5579: * Diag matrix
5580: * */
5581: PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5582: PetscCall(PetscSFSetFromOptions(sf));
5583: PetscCall(PetscSFSetUp(sf));
5585: PetscCall(PetscSFCreate(comm, &osf));
5586: /* off-diagonal */
5587: PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5588: PetscCall(PetscSFSetFromOptions(osf));
5589: PetscCall(PetscSFSetUp(osf));
5590: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5591: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5592: /* operate on the matrix internal data to save memory */
5593: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5594: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5595: PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5596: /* Convert to global indices for diag matrix */
5597: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5598: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5599: /* We want P_oth store global indices */
5600: PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5601: /* Use memory scalable approach */
5602: PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5603: PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5604: PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5605: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5606: /* Convert back to local indices */
5607: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5608: PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5609: nout = 0;
5610: PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5611: PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5612: PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5613: /* Exchange values */
5614: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5615: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5616: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5617: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5618: /* Stop PETSc from shrinking memory */
5619: for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5620: PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5621: PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5622: /* Attach PetscSF objects to P_oth so that we can reuse it later */
5623: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5624: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5625: PetscCall(PetscSFDestroy(&sf));
5626: PetscCall(PetscSFDestroy(&osf));
5627: PetscFunctionReturn(PETSC_SUCCESS);
5628: }
5630: /*
5631: * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5632: * This supports MPIAIJ and MAIJ
5633: * */
5634: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5635: {
5636: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5637: Mat_SeqAIJ *p_oth;
5638: IS rows, map;
5639: PetscHMapI hamp;
5640: PetscInt i, htsize, *rowindices, off, *mapping, key, count;
5641: MPI_Comm comm;
5642: PetscSF sf, osf;
5643: PetscBool has;
5645: PetscFunctionBegin;
5646: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5647: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5648: /* If it is the first time, create an index set of off-diag nonzero columns of A,
5649: * and then create a submatrix (that often is an overlapping matrix)
5650: * */
5651: if (reuse == MAT_INITIAL_MATRIX) {
5652: /* Use a hash table to figure out unique keys */
5653: PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5654: PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5655: count = 0;
5656: /* Assume that a->g is sorted, otherwise the following does not make sense */
5657: for (i = 0; i < a->B->cmap->n; i++) {
5658: key = a->garray[i] / dof;
5659: PetscCall(PetscHMapIHas(hamp, key, &has));
5660: if (!has) {
5661: mapping[i] = count;
5662: PetscCall(PetscHMapISet(hamp, key, count++));
5663: } else {
5664: /* Current 'i' has the same value the previous step */
5665: mapping[i] = count - 1;
5666: }
5667: }
5668: PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5669: PetscCall(PetscHMapIGetSize(hamp, &htsize));
5670: PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5671: PetscCall(PetscCalloc1(htsize, &rowindices));
5672: off = 0;
5673: PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5674: PetscCall(PetscHMapIDestroy(&hamp));
5675: PetscCall(PetscSortInt(htsize, rowindices));
5676: PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5677: /* In case, the matrix was already created but users want to recreate the matrix */
5678: PetscCall(MatDestroy(P_oth));
5679: PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5680: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5681: PetscCall(ISDestroy(&map));
5682: PetscCall(ISDestroy(&rows));
5683: } else if (reuse == MAT_REUSE_MATRIX) {
5684: /* If matrix was already created, we simply update values using SF objects
5685: * that as attached to the matrix earlier.
5686: */
5687: const PetscScalar *pd_a, *po_a;
5689: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5690: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5691: PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5692: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5693: /* Update values in place */
5694: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5695: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5696: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5697: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5698: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5699: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5700: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5701: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5702: } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5703: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5704: PetscFunctionReturn(PETSC_SUCCESS);
5705: }
5707: /*@
5708: MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`
5710: Collective
5712: Input Parameters:
5713: + A - the first matrix in `MATMPIAIJ` format
5714: . B - the second matrix in `MATMPIAIJ` format
5715: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5717: Output Parameters:
5718: + rowb - On input index sets of rows of B to extract (or `NULL`), modified on output
5719: . colb - On input index sets of columns of B to extract (or `NULL`), modified on output
5720: - B_seq - the sequential matrix generated
5722: Level: developer
5724: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5725: @*/
5726: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5727: {
5728: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5729: PetscInt *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5730: IS isrowb, iscolb;
5731: Mat *bseq = NULL;
5733: PetscFunctionBegin;
5734: PetscCheck(A->cmap->rstart == B->rmap->rstart && A->cmap->rend == B->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrix local dimensions are incompatible, (%" PetscInt_FMT ", %" PetscInt_FMT ") != (%" PetscInt_FMT ",%" PetscInt_FMT ")",
5735: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5736: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));
5738: if (scall == MAT_INITIAL_MATRIX) {
5739: start = A->cmap->rstart;
5740: cmap = a->garray;
5741: nzA = a->A->cmap->n;
5742: nzB = a->B->cmap->n;
5743: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5744: ncols = 0;
5745: for (i = 0; i < nzB; i++) { /* row < local row index */
5746: if (cmap[i] < start) idx[ncols++] = cmap[i];
5747: else break;
5748: }
5749: imark = i;
5750: for (i = 0; i < nzA; i++) idx[ncols++] = start + i; /* local rows */
5751: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5752: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5753: PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5754: } else {
5755: PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5756: isrowb = *rowb;
5757: iscolb = *colb;
5758: PetscCall(PetscMalloc1(1, &bseq));
5759: bseq[0] = *B_seq;
5760: }
5761: PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5762: *B_seq = bseq[0];
5763: PetscCall(PetscFree(bseq));
5764: if (!rowb) {
5765: PetscCall(ISDestroy(&isrowb));
5766: } else {
5767: *rowb = isrowb;
5768: }
5769: if (!colb) {
5770: PetscCall(ISDestroy(&iscolb));
5771: } else {
5772: *colb = iscolb;
5773: }
5774: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5775: PetscFunctionReturn(PETSC_SUCCESS);
5776: }
5778: // PetscClangLinter pragma disable: -fdoc-sowing-chars
5779: /*
5780: MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5781: of the OFF-DIAGONAL portion of local A
5783: Collective
5785: Input Parameters:
5786: + A - the first matrix in `MATMPIAIJ` format
5787: . B - the second matrix in `MATMPIAIJ` format
5788: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5790: Output Parameters:
5791: + startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5792: . startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5793: . bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5794: - B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N
5796: Level: developer
5798: Developer Note:
5799: This directly accesses information inside the VecScatter associated with the matrix-vector product
5800: for this matrix. This is not desirable.
5802: .seealso: [](ch_mat), `Mat`, `MATMPIAIJ`
5803: */
5804: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5805: {
5806: PetscFunctionBegin;
5807: PetscCall(MatGetBrowsOfAoCols_MPIAIJ_Private(A, B, scall, PETSC_FALSE, startsj_s, startsj_r, bufa_ptr, B_oth));
5808: PetscFunctionReturn(PETSC_SUCCESS);
5809: }
5811: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ_Private(Mat A, Mat B, MatReuse scall, PetscBool structure_only, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5812: {
5813: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5814: VecScatter ctx;
5815: MPI_Comm comm;
5816: const PetscMPIInt *rprocs, *sprocs;
5817: PetscMPIInt nrecvs, nsends;
5818: const PetscInt *srow, *rstarts, *sstarts;
5819: PetscInt *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5820: PetscInt i, j, k = 0, l, ll, nrows, *rstartsj = NULL, *sstartsj, len;
5821: PetscScalar *b_otha = NULL, *bufa = NULL, *bufA, *vals = NULL;
5822: MPI_Request *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5823: PetscMPIInt size, tag, rank, nreqs;
5825: PetscFunctionBegin;
5826: PetscCheck(!structure_only || scall == MAT_INITIAL_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "Structure-only extraction requires MAT_INITIAL_MATRIX");
5827: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5828: PetscCallMPI(MPI_Comm_size(comm, &size));
5830: PetscCheck(A->cmap->rstart == B->rmap->rstart && A->cmap->rend == B->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrix local dimensions are incompatible, (%" PetscInt_FMT ", %" PetscInt_FMT ") != (%" PetscInt_FMT ",%" PetscInt_FMT ")",
5831: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5832: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5833: PetscCallMPI(MPI_Comm_rank(comm, &rank));
5835: if (size == 1) {
5836: startsj_s = NULL;
5837: bufa_ptr = NULL;
5838: *B_oth = NULL;
5839: PetscFunctionReturn(PETSC_SUCCESS);
5840: }
5842: ctx = a->Mvctx;
5843: tag = ((PetscObject)ctx)->tag;
5845: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5846: /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5847: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5848: PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5849: PetscCall(PetscMalloc1(nreqs, &reqs));
5850: rwaits = reqs;
5851: swaits = PetscSafePointerPlusOffset(reqs, nrecvs);
5853: if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5854: if (scall == MAT_INITIAL_MATRIX) {
5855: /* i-array */
5856: /* post receives */
5857: if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5858: for (i = 0; i < nrecvs; i++) {
5859: rowlen = rvalues + rstarts[i] * rbs;
5860: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5861: PetscCallMPI(MPIU_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5862: }
5864: /* pack the outgoing message */
5865: PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));
5867: sstartsj[0] = 0;
5868: rstartsj[0] = 0;
5869: len = 0; /* total length of j or a array to be sent */
5870: if (nsends) {
5871: k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5872: PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5873: }
5874: for (i = 0; i < nsends; i++) {
5875: rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5876: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5877: for (j = 0; j < nrows; j++) {
5878: row = srow[k] + B->rmap->range[rank]; /* global row idx */
5879: for (l = 0; l < sbs; l++) {
5880: PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */
5882: rowlen[j * sbs + l] = ncols;
5884: len += ncols;
5885: PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5886: }
5887: k++;
5888: }
5889: PetscCallMPI(MPIU_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));
5891: sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5892: }
5893: /* recvs and sends of i-array are completed */
5894: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5895: PetscCall(PetscFree(svalues));
5897: /* allocate buffers for sending j and a arrays */
5898: PetscCall(PetscMalloc1(len, &bufj));
5899: if (!structure_only) PetscCall(PetscMalloc1(len, &bufa));
5901: /* create i-array of B_oth */
5902: PetscCall(PetscMalloc1(aBn + 1, &b_othi));
5904: b_othi[0] = 0;
5905: len = 0; /* total length of j or a array to be received */
5906: k = 0;
5907: for (i = 0; i < nrecvs; i++) {
5908: rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5909: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5910: for (j = 0; j < nrows; j++) {
5911: b_othi[k + 1] = b_othi[k] + rowlen[j];
5912: PetscCall(PetscIntSumError(rowlen[j], len, &len));
5913: k++;
5914: }
5915: rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5916: }
5917: PetscCall(PetscFree(rvalues));
5919: /* allocate space for j and a arrays of B_oth */
5920: PetscCall(PetscMalloc1(b_othi[aBn], &b_othj));
5921: if (!structure_only) PetscCall(PetscMalloc1(b_othi[aBn], &b_otha));
5923: /* j-array */
5924: /* post receives of j-array */
5925: for (i = 0; i < nrecvs; i++) {
5926: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5927: PetscCallMPI(MPIU_Irecv(PetscSafePointerPlusOffset(b_othj, rstartsj[i]), nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5928: }
5930: /* pack the outgoing message j-array */
5931: if (nsends) k = sstarts[0];
5932: for (i = 0; i < nsends; i++) {
5933: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5934: bufJ = PetscSafePointerPlusOffset(bufj, sstartsj[i]);
5935: for (j = 0; j < nrows; j++) {
5936: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5937: for (ll = 0; ll < sbs; ll++) {
5938: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5939: for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5940: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5941: }
5942: }
5943: PetscCallMPI(MPIU_Isend(PetscSafePointerPlusOffset(bufj, sstartsj[i]), sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5944: }
5946: /* recvs and sends of j-array are completed */
5947: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5948: } else if (scall == MAT_REUSE_MATRIX) {
5949: sstartsj = *startsj_s;
5950: rstartsj = *startsj_r;
5951: bufa = *bufa_ptr;
5952: PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5953: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");
5955: if (!structure_only) {
5956: /* a-array */
5957: /* post receives of a-array */
5958: for (i = 0; i < nrecvs; i++) {
5959: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5960: PetscCallMPI(MPIU_Irecv(PetscSafePointerPlusOffset(b_otha, rstartsj[i]), nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
5961: }
5963: /* pack the outgoing message a-array */
5964: if (nsends) k = sstarts[0];
5965: for (i = 0; i < nsends; i++) {
5966: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5967: bufA = PetscSafePointerPlusOffset(bufa, sstartsj[i]);
5968: for (j = 0; j < nrows; j++) {
5969: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5970: for (ll = 0; ll < sbs; ll++) {
5971: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5972: for (l = 0; l < ncols; l++) *bufA++ = vals[l];
5973: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5974: }
5975: }
5976: PetscCallMPI(MPIU_Isend(PetscSafePointerPlusOffset(bufa, sstartsj[i]), sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
5977: }
5978: /* recvs and sends of a-array are completed */
5979: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5980: }
5981: PetscCall(PetscFree(reqs));
5983: if (scall == MAT_INITIAL_MATRIX) {
5984: Mat_SeqAIJ *b_oth;
5986: /* put together the new matrix */
5987: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));
5988: PetscCall(MatSetOption(*B_oth, MAT_STRUCTURE_ONLY, structure_only));
5990: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5991: /* Since these are PETSc arrays, change flags to free them as necessary. */
5992: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
5993: b_oth->free_a = PETSC_TRUE;
5994: b_oth->free_ij = PETSC_TRUE;
5995: b_oth->nonew = 0;
5997: PetscCall(PetscFree(bufj));
5998: if (!startsj_s || !bufa_ptr) {
5999: PetscCall(PetscFree2(sstartsj, rstartsj));
6000: PetscCall(PetscFree(bufa_ptr));
6001: } else {
6002: *startsj_s = sstartsj;
6003: *startsj_r = rstartsj;
6004: *bufa_ptr = bufa;
6005: }
6006: } else if (scall == MAT_REUSE_MATRIX) {
6007: PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
6008: }
6010: PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
6011: PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
6012: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
6013: PetscFunctionReturn(PETSC_SUCCESS);
6014: }
6016: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
6017: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
6018: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
6019: #if PetscDefined(HAVE_MKL_SPARSE)
6020: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
6021: #endif
6022: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
6023: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
6024: #if PetscDefined(HAVE_ELEMENTAL)
6025: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
6026: #endif
6027: #if PetscDefined(HAVE_SCALAPACK) && (PetscDefined(USE_REAL_SINGLE) || PetscDefined(USE_REAL_DOUBLE))
6028: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
6029: #endif
6030: #if PetscDefined(HAVE_HYPRE)
6031: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
6032: #endif
6033: #if PetscDefined(HAVE_CUDA)
6034: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
6035: #endif
6036: #if PetscDefined(HAVE_HIP)
6037: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6038: #endif
6039: #if PetscDefined(HAVE_KOKKOS_KERNELS)
6040: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6041: #endif
6042: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6043: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6044: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
6046: /*
6047: Computes (B'*A')' since computing B*A directly is untenable
6049: n p p
6050: [ ] [ ] [ ]
6051: m [ A ] * n [ B ] = m [ C ]
6052: [ ] [ ] [ ]
6054: */
6055: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6056: {
6057: Mat At, Bt, Ct;
6059: PetscFunctionBegin;
6060: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6061: PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6062: PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_CURRENT, &Ct));
6063: PetscCall(MatDestroy(&At));
6064: PetscCall(MatDestroy(&Bt));
6065: PetscCall(MatTransposeSetPrecursor(Ct, C));
6066: PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6067: PetscCall(MatDestroy(&Ct));
6068: PetscFunctionReturn(PETSC_SUCCESS);
6069: }
6071: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6072: {
6073: PetscBool cisdense;
6075: PetscFunctionBegin;
6076: PetscCheck(A->cmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "A->cmap->n %" PetscInt_FMT " != B->rmap->n %" PetscInt_FMT, A->cmap->n, B->rmap->n);
6077: PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6078: PetscCall(MatSetBlockSizesFromMats(C, A, B));
6079: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6080: if (!cisdense) {
6081: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6082: PetscCall(MatSetVecType(C, A->defaultvectype));
6083: }
6084: PetscCall(MatSetUp(C));
6086: C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6087: PetscFunctionReturn(PETSC_SUCCESS);
6088: }
6090: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6091: {
6092: Mat_Product *product = C->product;
6093: Mat A = product->A, B = product->B;
6095: PetscFunctionBegin;
6096: PetscCheck(A->cmap->rstart == B->rmap->rstart && A->cmap->rend == B->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrix local dimensions are incompatible, (%" PetscInt_FMT ", %" PetscInt_FMT ") != (%" PetscInt_FMT ",%" PetscInt_FMT ")",
6097: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6098: C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6099: C->ops->productsymbolic = MatProductSymbolic_AB;
6100: PetscFunctionReturn(PETSC_SUCCESS);
6101: }
6103: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6104: {
6105: Mat_Product *product = C->product;
6107: PetscFunctionBegin;
6108: if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6109: PetscFunctionReturn(PETSC_SUCCESS);
6110: }
6112: /*
6113: Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix
6115: Input Parameters:
6117: j1,rowBegin1,rowEnd1,jmap1: describe the first set of nonzeros (Set1)
6118: j2,rowBegin2,rowEnd2,jmap2: describe the second set of nonzeros (Set2)
6120: mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat
6122: For Set1, j1[] contains column indices of the nonzeros.
6123: For the k-th row (0<=k<m), [rowBegin1[k],rowEnd1[k]) index into j1[] and point to the begin/end nonzero in row k
6124: respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6125: but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.
6127: Similar for Set2.
6129: This routine merges the two sets of nonzeros row by row and removes repeats.
6131: Output Parameters: (memory is allocated by the caller)
6133: i[],j[]: the CSR of the merged matrix, which has m rows.
6134: imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6135: imap2[]: similar to imap1[], but for Set2.
6136: Note we order nonzeros row-by-row and from left to right.
6137: */
6138: static PetscErrorCode MatMergeEntries_Internal(Mat mat, const PetscInt j1[], const PetscInt j2[], const PetscCount rowBegin1[], const PetscCount rowEnd1[], const PetscCount rowBegin2[], const PetscCount rowEnd2[], const PetscCount jmap1[], const PetscCount jmap2[], PetscCount imap1[], PetscCount imap2[], PetscInt i[], PetscInt j[])
6139: {
6140: PetscInt r, m; /* Row index of mat */
6141: PetscCount t, t1, t2, b1, e1, b2, e2;
6143: PetscFunctionBegin;
6144: PetscCall(MatGetLocalSize(mat, &m, NULL));
6145: t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6146: i[0] = 0;
6147: for (r = 0; r < m; r++) { /* Do row by row merging */
6148: b1 = rowBegin1[r];
6149: e1 = rowEnd1[r];
6150: b2 = rowBegin2[r];
6151: e2 = rowEnd2[r];
6152: while (b1 < e1 && b2 < e2) {
6153: if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6154: j[t] = j1[b1];
6155: imap1[t1] = t;
6156: imap2[t2] = t;
6157: b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6158: b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6159: t1++;
6160: t2++;
6161: t++;
6162: } else if (j1[b1] < j2[b2]) {
6163: j[t] = j1[b1];
6164: imap1[t1] = t;
6165: b1 += jmap1[t1 + 1] - jmap1[t1];
6166: t1++;
6167: t++;
6168: } else {
6169: j[t] = j2[b2];
6170: imap2[t2] = t;
6171: b2 += jmap2[t2 + 1] - jmap2[t2];
6172: t2++;
6173: t++;
6174: }
6175: }
6176: /* Merge the remaining in either j1[] or j2[] */
6177: while (b1 < e1) {
6178: j[t] = j1[b1];
6179: imap1[t1] = t;
6180: b1 += jmap1[t1 + 1] - jmap1[t1];
6181: t1++;
6182: t++;
6183: }
6184: while (b2 < e2) {
6185: j[t] = j2[b2];
6186: imap2[t2] = t;
6187: b2 += jmap2[t2 + 1] - jmap2[t2];
6188: t2++;
6189: t++;
6190: }
6191: PetscCall(PetscIntCast(t, i + r + 1));
6192: }
6193: PetscFunctionReturn(PETSC_SUCCESS);
6194: }
6196: /*
6197: Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block
6199: Input Parameters:
6200: mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6201: n,i[],j[],perm[]: there are n input entries, belonging to m rows. Row/col indices of the entries are stored in i[] and j[]
6202: respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.
6204: i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6205: i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.
6207: Output Parameters:
6208: j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6209: rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6210: They contain indices pointing to j[]. For 0<=r<m, [rowBegin[r],rowMid[r]) point to begin/end entries of row r of the diagonal block,
6211: and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.
6213: Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6214: Atot: number of entries belonging to the diagonal block.
6215: Annz: number of unique nonzeros belonging to the diagonal block.
6216: Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6217: repeats (i.e., same 'i,j' pair).
6218: Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6219: is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.
6221: Atot: number of entries belonging to the diagonal block
6222: Annz: number of unique nonzeros belonging to the diagonal block.
6224: Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.
6226: Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6227: */
6228: static PetscErrorCode MatSplitEntries_Internal(Mat mat, PetscCount n, const PetscInt i[], PetscInt j[], PetscCount perm[], PetscCount rowBegin[], PetscCount rowMid[], PetscCount rowEnd[], PetscCount *Atot_, PetscCount **Aperm_, PetscCount *Annz_, PetscCount **Ajmap_, PetscCount *Btot_, PetscCount **Bperm_, PetscCount *Bnnz_, PetscCount **Bjmap_)
6229: {
6230: PetscInt cstart, cend, rstart, rend, row, col;
6231: PetscCount Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6232: PetscCount Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6233: PetscCount k, m, p, q, r, s, mid;
6234: PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;
6236: PetscFunctionBegin;
6237: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6238: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6239: m = rend - rstart;
6241: /* Skip negative rows */
6242: for (k = 0; k < n; k++)
6243: if (i[k] >= 0) break;
6245: /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6246: fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6247: */
6248: while (k < n) {
6249: row = i[k];
6250: /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6251: for (s = k; s < n; s++)
6252: if (i[s] != row) break;
6254: /* Shift diag columns to range of [-PETSC_INT_MAX, -1] */
6255: for (p = k; p < s; p++) {
6256: if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_INT_MAX;
6257: }
6258: PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6259: PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6260: rowBegin[row - rstart] = k;
6261: rowMid[row - rstart] = mid;
6262: rowEnd[row - rstart] = s;
6263: PetscCheck(k == s || j[s - 1] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index %" PetscInt_FMT " is >= matrix column size %" PetscInt_FMT, j[s - 1], mat->cmap->N);
6265: /* Count nonzeros of this diag/offdiag row, which might have repeats */
6266: Atot += mid - k;
6267: Btot += s - mid;
6269: /* Count unique nonzeros of this diag row */
6270: for (p = k; p < mid;) {
6271: col = j[p];
6272: do {
6273: j[p] += PETSC_INT_MAX; /* Revert the modified diagonal indices */
6274: p++;
6275: } while (p < mid && j[p] == col);
6276: Annz++;
6277: }
6279: /* Count unique nonzeros of this offdiag row */
6280: for (p = mid; p < s;) {
6281: col = j[p];
6282: do {
6283: p++;
6284: } while (p < s && j[p] == col);
6285: Bnnz++;
6286: }
6287: k = s;
6288: }
6290: /* Allocation according to Atot, Btot, Annz, Bnnz */
6291: PetscCall(PetscMalloc1(Atot, &Aperm));
6292: PetscCall(PetscMalloc1(Btot, &Bperm));
6293: PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6294: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));
6296: /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6297: Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6298: for (r = 0; r < m; r++) {
6299: k = rowBegin[r];
6300: mid = rowMid[r];
6301: s = rowEnd[r];
6302: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Aperm, Atot), PetscSafePointerPlusOffset(perm, k), mid - k));
6303: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Bperm, Btot), PetscSafePointerPlusOffset(perm, mid), s - mid));
6304: Atot += mid - k;
6305: Btot += s - mid;
6307: /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6308: for (p = k; p < mid;) {
6309: col = j[p];
6310: q = p;
6311: do {
6312: p++;
6313: } while (p < mid && j[p] == col);
6314: Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6315: Annz++;
6316: }
6318: for (p = mid; p < s;) {
6319: col = j[p];
6320: q = p;
6321: do {
6322: p++;
6323: } while (p < s && j[p] == col);
6324: Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6325: Bnnz++;
6326: }
6327: }
6328: /* Output */
6329: *Aperm_ = Aperm;
6330: *Annz_ = Annz;
6331: *Atot_ = Atot;
6332: *Ajmap_ = Ajmap;
6333: *Bperm_ = Bperm;
6334: *Bnnz_ = Bnnz;
6335: *Btot_ = Btot;
6336: *Bjmap_ = Bjmap;
6337: PetscFunctionReturn(PETSC_SUCCESS);
6338: }
6340: /*
6341: Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix
6343: Input Parameters:
6344: nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6345: nnz: number of unique nonzeros in the merged matrix
6346: imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6347: jmap[nnz1+1]: i-th nonzero in the set has jmap[i+1] - jmap[i] repeats in the set
6349: Output Parameter: (memory is allocated by the caller)
6350: jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set
6352: Example:
6353: nnz1 = 4
6354: nnz = 6
6355: imap = [1,3,4,5]
6356: jmap = [0,3,5,6,7]
6357: then,
6358: jmap_new = [0,0,3,3,5,6,7]
6359: */
6360: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6361: {
6362: PetscCount k, p;
6364: PetscFunctionBegin;
6365: jmap_new[0] = 0;
6366: p = nnz; /* p loops over jmap_new[] backwards */
6367: for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6368: for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6369: }
6370: for (; p >= 0; p--) jmap_new[p] = jmap[0];
6371: PetscFunctionReturn(PETSC_SUCCESS);
6372: }
6374: static PetscErrorCode MatCOOStructDestroy_MPIAIJ(PetscCtxRt data)
6375: {
6376: MatCOOStruct_MPIAIJ *coo = *(MatCOOStruct_MPIAIJ **)data;
6378: PetscFunctionBegin;
6379: PetscCall(PetscSFDestroy(&coo->sf));
6380: PetscCall(PetscFree(coo->Aperm1));
6381: PetscCall(PetscFree(coo->Bperm1));
6382: PetscCall(PetscFree(coo->Ajmap1));
6383: PetscCall(PetscFree(coo->Bjmap1));
6384: PetscCall(PetscFree(coo->Aimap2));
6385: PetscCall(PetscFree(coo->Bimap2));
6386: PetscCall(PetscFree(coo->Aperm2));
6387: PetscCall(PetscFree(coo->Bperm2));
6388: PetscCall(PetscFree(coo->Ajmap2));
6389: PetscCall(PetscFree(coo->Bjmap2));
6390: PetscCall(PetscFree(coo->Cperm1));
6391: PetscCall(PetscFree2(coo->sendbuf, coo->recvbuf));
6392: PetscCall(PetscFree(coo));
6393: PetscFunctionReturn(PETSC_SUCCESS);
6394: }
6396: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6397: {
6398: MPI_Comm comm;
6399: PetscMPIInt rank, size;
6400: PetscInt m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6401: PetscCount k, p, q, rem; /* Loop variables over coo arrays */
6402: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6403: PetscContainer container;
6404: MatCOOStruct_MPIAIJ *coo;
6406: PetscFunctionBegin;
6407: PetscCall(PetscFree(mpiaij->garray));
6408: PetscCall(VecDestroy(&mpiaij->lvec));
6409: #if PetscDefined(USE_CTABLE)
6410: PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6411: #else
6412: PetscCall(PetscFree(mpiaij->colmap));
6413: #endif
6414: PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6415: mat->assembled = PETSC_FALSE;
6416: mat->was_assembled = PETSC_FALSE;
6418: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6419: PetscCallMPI(MPI_Comm_size(comm, &size));
6420: PetscCallMPI(MPI_Comm_rank(comm, &rank));
6421: PetscCall(PetscLayoutSetUp(mat->rmap));
6422: PetscCall(PetscLayoutSetUp(mat->cmap));
6423: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6424: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6425: PetscCall(MatGetLocalSize(mat, &m, &n));
6426: PetscCall(MatGetSize(mat, &M, &N));
6428: /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6429: /* entries come first, then local rows, then remote rows. */
6430: PetscCount n1 = coo_n, *perm1;
6431: PetscInt *i1 = coo_i, *j1 = coo_j;
6433: PetscCall(PetscMalloc1(n1, &perm1));
6434: for (k = 0; k < n1; k++) perm1[k] = k;
6436: /* Manipulate indices so that entries with negative row or col indices will have smallest
6437: row indices, local entries will have greater but negative row indices, and remote entries
6438: will have positive row indices.
6439: */
6440: for (k = 0; k < n1; k++) {
6441: if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_INT_MIN; /* e.g., -2^31, minimal to move them ahead */
6442: else if (i1[k] >= rstart && i1[k] < rend) i1[k] -= PETSC_INT_MAX; /* e.g., minus 2^31-1 to shift local rows to range of [-PETSC_INT_MAX, -1] */
6443: else {
6444: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6445: if (mpiaij->donotstash) i1[k] = PETSC_INT_MIN; /* Ignore offproc entries as if they had negative indices */
6446: }
6447: }
6449: /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6450: PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));
6452: /* Advance k to the first entry we need to take care of */
6453: for (k = 0; k < n1; k++)
6454: if (i1[k] > PETSC_INT_MIN) break;
6455: PetscCount i1start = k;
6457: PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_INT_MAX, &rem)); /* rem is upper bound of the last local row */
6458: for (; k < rem; k++) i1[k] += PETSC_INT_MAX; /* Revert row indices of local rows*/
6460: PetscCheck(n1 == 0 || i1[n1 - 1] < M, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "COO row index %" PetscInt_FMT " is >= the matrix row size %" PetscInt_FMT, i1[n1 - 1], M);
6462: /* Send remote rows to their owner */
6463: /* Find which rows should be sent to which remote ranks*/
6464: PetscInt nsend = 0; /* Number of MPI ranks to send data to */
6465: PetscMPIInt *sendto; /* [nsend], storing remote ranks */
6466: PetscInt *nentries; /* [nsend], storing number of entries sent to remote ranks; Assume PetscInt is big enough for this count, and error if not */
6467: const PetscInt *ranges;
6468: PetscInt maxNsend = size >= 128 ? 128 : size; /* Assume max 128 neighbors; realloc when needed */
6470: PetscCall(PetscLayoutGetRanges(mat->rmap, &ranges));
6471: PetscCall(PetscMalloc2(maxNsend, &sendto, maxNsend, &nentries));
6472: for (k = rem; k < n1;) {
6473: PetscMPIInt owner;
6474: PetscInt firstRow, lastRow;
6476: /* Locate a row range */
6477: firstRow = i1[k]; /* first row of this owner */
6478: PetscCall(PetscLayoutFindOwner(mat->rmap, firstRow, &owner));
6479: lastRow = ranges[owner + 1] - 1; /* last row of this owner */
6481: /* Find the first index 'p' in [k,n) with i1[p] belonging to next owner */
6482: PetscCall(PetscSortedIntUpperBound(i1, k, n1, lastRow, &p));
6484: /* All entries in [k,p) belong to this remote owner */
6485: if (nsend >= maxNsend) { /* Double the remote ranks arrays if not long enough */
6486: PetscMPIInt *sendto2;
6487: PetscInt *nentries2;
6488: PetscInt maxNsend2 = (maxNsend <= size / 2) ? maxNsend * 2 : size;
6490: PetscCall(PetscMalloc2(maxNsend2, &sendto2, maxNsend2, &nentries2));
6491: PetscCall(PetscArraycpy(sendto2, sendto, maxNsend));
6492: PetscCall(PetscArraycpy(nentries2, nentries, maxNsend));
6493: PetscCall(PetscFree2(sendto, nentries));
6494: sendto = sendto2;
6495: nentries = nentries2;
6496: maxNsend = maxNsend2;
6497: }
6498: sendto[nsend] = owner;
6499: PetscCall(PetscIntCast(p - k, &nentries[nsend]));
6500: nsend++;
6501: k = p;
6502: }
6504: /* Build 1st SF to know offsets on remote to send data */
6505: PetscSF sf1;
6506: PetscInt nroots = 1, nroots2 = 0;
6507: PetscInt nleaves = nsend, nleaves2 = 0;
6508: PetscInt *offsets;
6509: PetscSFNode *iremote;
6511: PetscCall(PetscSFCreate(comm, &sf1));
6512: PetscCall(PetscMalloc1(nsend, &iremote));
6513: PetscCall(PetscMalloc1(nsend, &offsets));
6514: for (k = 0; k < nsend; k++) {
6515: iremote[k].rank = sendto[k];
6516: iremote[k].index = 0;
6517: nleaves2 += nentries[k];
6518: PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6519: }
6520: PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6521: PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6522: PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6523: PetscCall(PetscSFDestroy(&sf1));
6524: PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT, nleaves2, n1 - rem);
6526: /* Build 2nd SF to send remote COOs to their owner */
6527: PetscSF sf2;
6528: nroots = nroots2;
6529: nleaves = nleaves2;
6530: PetscCall(PetscSFCreate(comm, &sf2));
6531: PetscCall(PetscSFSetFromOptions(sf2));
6532: PetscCall(PetscMalloc1(nleaves, &iremote));
6533: p = 0;
6534: for (k = 0; k < nsend; k++) {
6535: PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6536: for (q = 0; q < nentries[k]; q++, p++) {
6537: iremote[p].rank = sendto[k];
6538: PetscCall(PetscIntCast(offsets[k] + q, &iremote[p].index));
6539: }
6540: }
6541: PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6543: /* Send the remote COOs to their owner */
6544: PetscInt n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6545: PetscCount *perm2; /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6546: PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6547: PetscAssert(rem == 0 || i1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6548: PetscAssert(rem == 0 || j1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6549: PetscInt *i1prem = PetscSafePointerPlusOffset(i1, rem);
6550: PetscInt *j1prem = PetscSafePointerPlusOffset(j1, rem);
6551: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1prem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6552: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1prem, i2, MPI_REPLACE));
6553: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1prem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6554: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1prem, j2, MPI_REPLACE));
6556: PetscCall(PetscFree(offsets));
6557: PetscCall(PetscFree2(sendto, nentries));
6559: /* Sort received COOs by row along with the permutation array */
6560: for (k = 0; k < n2; k++) perm2[k] = k;
6561: PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));
6563: /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6564: PetscCount *Cperm1;
6565: PetscAssert(rem == 0 || perm1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6566: PetscCount *perm1prem = PetscSafePointerPlusOffset(perm1, rem);
6567: PetscCall(PetscMalloc1(nleaves, &Cperm1));
6568: PetscCall(PetscArraycpy(Cperm1, perm1prem, nleaves));
6570: /* Support for HYPRE matrices, kind of a hack.
6571: Swap min column with diagonal so that diagonal values will go first */
6572: PetscBool hypre;
6573: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", ((PetscObject)mat)->name, &hypre));
6574: if (hypre) {
6575: PetscInt *minj;
6576: PetscBT hasdiag;
6578: PetscCall(PetscBTCreate(m, &hasdiag));
6579: PetscCall(PetscMalloc1(m, &minj));
6580: for (k = 0; k < m; k++) minj[k] = PETSC_INT_MAX;
6581: for (k = i1start; k < rem; k++) {
6582: if (j1[k] < cstart || j1[k] >= cend) continue;
6583: const PetscInt rindex = i1[k] - rstart;
6584: if ((j1[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6585: minj[rindex] = PetscMin(minj[rindex], j1[k]);
6586: }
6587: for (k = 0; k < n2; k++) {
6588: if (j2[k] < cstart || j2[k] >= cend) continue;
6589: const PetscInt rindex = i2[k] - rstart;
6590: if ((j2[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6591: minj[rindex] = PetscMin(minj[rindex], j2[k]);
6592: }
6593: for (k = i1start; k < rem; k++) {
6594: const PetscInt rindex = i1[k] - rstart;
6595: if (j1[k] < cstart || j1[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6596: if (j1[k] == minj[rindex]) j1[k] = i1[k] + (cstart - rstart);
6597: else if ((j1[k] - cstart) == rindex) j1[k] = minj[rindex];
6598: }
6599: for (k = 0; k < n2; k++) {
6600: const PetscInt rindex = i2[k] - rstart;
6601: if (j2[k] < cstart || j2[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6602: if (j2[k] == minj[rindex]) j2[k] = i2[k] + (cstart - rstart);
6603: else if ((j2[k] - cstart) == rindex) j2[k] = minj[rindex];
6604: }
6605: PetscCall(PetscBTDestroy(&hasdiag));
6606: PetscCall(PetscFree(minj));
6607: }
6609: /* Split local COOs and received COOs into diag/offdiag portions */
6610: PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6611: PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1;
6612: PetscCount Annz1, Bnnz1, Atot1, Btot1;
6613: PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6614: PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6615: PetscCount Annz2, Bnnz2, Atot2, Btot2;
6617: PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6618: PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6619: PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6620: PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));
6622: /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6623: PetscInt *Ai, *Bi;
6624: PetscInt *Aj, *Bj;
6626: PetscCall(PetscMalloc1(m + 1, &Ai));
6627: PetscCall(PetscMalloc1(m + 1, &Bi));
6628: PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6629: PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));
6631: PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6632: PetscCall(PetscMalloc1(Annz1, &Aimap1));
6633: PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6634: PetscCall(PetscMalloc1(Annz2, &Aimap2));
6635: PetscCall(PetscMalloc1(Bnnz2, &Bimap2));
6637: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6638: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));
6640: /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we */
6641: /* expect nonzeros in A/B most likely have local contributing entries */
6642: PetscInt Annz = Ai[m];
6643: PetscInt Bnnz = Bi[m];
6644: PetscCount *Ajmap1_new, *Bjmap1_new;
6646: PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6647: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));
6649: PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6650: PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));
6652: PetscCall(PetscFree(Aimap1));
6653: PetscCall(PetscFree(Ajmap1));
6654: PetscCall(PetscFree(Bimap1));
6655: PetscCall(PetscFree(Bjmap1));
6656: PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6657: PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6658: PetscCall(PetscFree(perm1));
6659: PetscCall(PetscFree3(i2, j2, perm2));
6661: Ajmap1 = Ajmap1_new;
6662: Bjmap1 = Bjmap1_new;
6664: /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6665: if (Annz < Annz1 + Annz2) {
6666: PetscInt *Aj_new;
6667: PetscCall(PetscMalloc1(Annz, &Aj_new));
6668: PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6669: PetscCall(PetscFree(Aj));
6670: Aj = Aj_new;
6671: }
6673: if (Bnnz < Bnnz1 + Bnnz2) {
6674: PetscInt *Bj_new;
6675: PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6676: PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6677: PetscCall(PetscFree(Bj));
6678: Bj = Bj_new;
6679: }
6681: /* Create new submatrices for on-process and off-process coupling */
6682: PetscScalar *Aa = NULL, *Ba = NULL;
6683: MatType rtype;
6684: Mat_SeqAIJ *a, *b;
6685: if (!mat->structure_only) {
6686: PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6687: PetscCall(PetscCalloc1(Bnnz, &Ba));
6688: }
6689: /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6690: if (cstart) {
6691: for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6692: }
6694: PetscCall(MatGetRootType_Private(mat, &rtype));
6696: MatSeqXAIJGetOptions_Private(mpiaij->A);
6697: PetscCall(MatDestroy(&mpiaij->A));
6698: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6699: PetscCall(MatSetBlockSizesFromMats(mpiaij->A, mat, mat));
6700: MatSeqXAIJRestoreOptions_Private(mpiaij->A);
6701: PetscCall(MatSetOption(mpiaij->A, MAT_STRUCTURE_ONLY, mat->structure_only));
6703: MatSeqXAIJGetOptions_Private(mpiaij->B);
6704: PetscCall(MatDestroy(&mpiaij->B));
6705: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6706: PetscCall(MatSetBlockSizesFromMats(mpiaij->B, mat, mat));
6707: MatSeqXAIJRestoreOptions_Private(mpiaij->B);
6708: PetscCall(MatSetOption(mpiaij->B, MAT_STRUCTURE_ONLY, mat->structure_only));
6710: PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6711: mat->was_assembled = PETSC_TRUE; // was_assembled in effect means the Mvctx is built; doing so avoids redundant MatSetUpMultiply_MPIAIJ
6712: mat->nonzerostate = mpiaij->A->nonzerostate + mpiaij->B->nonzerostate;
6713: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
6715: a = (Mat_SeqAIJ *)mpiaij->A->data;
6716: b = (Mat_SeqAIJ *)mpiaij->B->data;
6717: a->free_a = (PetscBool)!mat->structure_only;
6718: a->free_ij = PETSC_TRUE;
6719: b->free_a = (PetscBool)!mat->structure_only;
6720: b->free_ij = PETSC_TRUE;
6721: a->maxnz = a->nz;
6722: b->maxnz = b->nz;
6724: /* conversion must happen AFTER multiply setup */
6725: PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6726: PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6727: PetscCall(VecDestroy(&mpiaij->lvec));
6728: PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));
6730: // Put the COO struct in a container and then attach that to the matrix
6731: PetscCall(PetscMalloc1(1, &coo));
6732: coo->n = coo_n;
6733: coo->sf = sf2;
6734: coo->sendlen = nleaves;
6735: coo->recvlen = nroots;
6736: coo->Annz = Annz;
6737: coo->Bnnz = Bnnz;
6738: coo->Annz2 = Annz2;
6739: coo->Bnnz2 = Bnnz2;
6740: coo->Atot1 = Atot1;
6741: coo->Atot2 = Atot2;
6742: coo->Btot1 = Btot1;
6743: coo->Btot2 = Btot2;
6744: coo->Ajmap1 = Ajmap1;
6745: coo->Aperm1 = Aperm1;
6746: coo->Bjmap1 = Bjmap1;
6747: coo->Bperm1 = Bperm1;
6748: coo->Aimap2 = Aimap2;
6749: coo->Ajmap2 = Ajmap2;
6750: coo->Aperm2 = Aperm2;
6751: coo->Bimap2 = Bimap2;
6752: coo->Bjmap2 = Bjmap2;
6753: coo->Bperm2 = Bperm2;
6754: coo->Cperm1 = Cperm1;
6755: // Allocate in preallocation. If not used, it has zero cost on host
6756: if (!mat->structure_only) PetscCall(PetscMalloc2(coo->sendlen, &coo->sendbuf, coo->recvlen, &coo->recvbuf));
6757: else coo->sendbuf = coo->recvbuf = NULL;
6758: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
6759: PetscCall(PetscContainerSetPointer(container, coo));
6760: PetscCall(PetscContainerSetCtxDestroy(container, MatCOOStructDestroy_MPIAIJ));
6761: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
6762: PetscCall(PetscContainerDestroy(&container));
6763: PetscFunctionReturn(PETSC_SUCCESS);
6764: }
6766: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6767: {
6768: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6769: Mat A = mpiaij->A, B = mpiaij->B;
6770: PetscScalar *Aa, *Ba;
6771: PetscScalar *sendbuf, *recvbuf;
6772: const PetscCount *Ajmap1, *Ajmap2, *Aimap2;
6773: const PetscCount *Bjmap1, *Bjmap2, *Bimap2;
6774: const PetscCount *Aperm1, *Aperm2, *Bperm1, *Bperm2;
6775: const PetscCount *Cperm1;
6776: PetscContainer container;
6777: MatCOOStruct_MPIAIJ *coo;
6779: PetscFunctionBegin;
6780: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
6781: PetscCheck(container, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
6782: PetscCall(PetscContainerGetPointer(container, &coo));
6783: sendbuf = coo->sendbuf;
6784: recvbuf = coo->recvbuf;
6785: Ajmap1 = coo->Ajmap1;
6786: Ajmap2 = coo->Ajmap2;
6787: Aimap2 = coo->Aimap2;
6788: Bjmap1 = coo->Bjmap1;
6789: Bjmap2 = coo->Bjmap2;
6790: Bimap2 = coo->Bimap2;
6791: Aperm1 = coo->Aperm1;
6792: Aperm2 = coo->Aperm2;
6793: Bperm1 = coo->Bperm1;
6794: Bperm2 = coo->Bperm2;
6795: Cperm1 = coo->Cperm1;
6797: PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6798: PetscCall(MatSeqAIJGetArray(B, &Ba));
6800: /* Pack entries to be sent to remote */
6801: for (PetscCount i = 0; i < coo->sendlen; i++) sendbuf[i] = v[Cperm1[i]];
6803: /* Send remote entries to their owner and overlap the communication with local computation */
6804: PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6805: /* Add local entries to A and B */
6806: for (PetscCount i = 0; i < coo->Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6807: PetscScalar sum = 0.0; /* Do partial summation first to improve numerical stability */
6808: for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6809: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6810: }
6811: for (PetscCount i = 0; i < coo->Bnnz; i++) {
6812: PetscScalar sum = 0.0;
6813: for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6814: Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6815: }
6816: PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));
6818: /* Add received remote entries to A and B */
6819: for (PetscCount i = 0; i < coo->Annz2; i++) {
6820: for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6821: }
6822: for (PetscCount i = 0; i < coo->Bnnz2; i++) {
6823: for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6824: }
6825: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6826: PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6827: PetscFunctionReturn(PETSC_SUCCESS);
6828: }
6830: /*MC
6831: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
6833: Options Database Keys:
6834: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`
6836: Level: beginner
6838: Notes:
6839: `MatSetValues()` may be called with a `NULL` argument for the numerical values to insert zeros at the supplied row and column indices.
6841: Call `MatSetOption(A, MAT_STRUCTURE_ONLY, PETSC_TRUE)` before preallocation or `MatSetUp()` to store only the nonzero pattern.
6842: The assembled matrix has no numerical value array. Row and column indices supplied during insertion are retained, while numerical values are ignored.
6843: Such matrices can be used for structural operations, but not for numerical operations.
6845: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6846: M*/
6847: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6848: {
6849: Mat_MPIAIJ *b;
6850: PetscMPIInt size;
6852: PetscFunctionBegin;
6853: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
6855: PetscCall(PetscNew(&b));
6856: B->data = (void *)b;
6857: B->ops[0] = MatOps_Values;
6858: B->assembled = PETSC_FALSE;
6859: B->insertmode = NOT_SET_VALUES;
6860: b->size = size;
6862: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
6864: /* build cache for off array entries formed */
6865: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
6867: b->donotstash = PETSC_FALSE;
6868: b->colmap = NULL;
6869: b->garray = NULL;
6870: b->roworiented = PETSC_TRUE;
6872: /* stuff used for matrix vector multiply */
6873: b->lvec = NULL;
6874: b->Mvctx = NULL;
6876: /* stuff for MatGetRow() */
6877: b->rowindices = NULL;
6878: b->rowvalues = NULL;
6879: b->getrowactive = PETSC_FALSE;
6881: /* flexible pointer used in CUSPARSE classes */
6882: b->spptr = NULL;
6884: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6885: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6886: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6887: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6888: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6889: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6890: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetHash_C", MatResetHash_MPIAIJ));
6891: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6892: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6893: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6894: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6895: #if PetscDefined(HAVE_CUDA)
6896: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6897: #endif
6898: #if PetscDefined(HAVE_HIP)
6899: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6900: #endif
6901: #if PetscDefined(HAVE_KOKKOS_KERNELS)
6902: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6903: #endif
6904: #if PetscDefined(HAVE_MKL_SPARSE)
6905: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6906: #endif
6907: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6908: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6909: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6910: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6911: #if PetscDefined(HAVE_ELEMENTAL)
6912: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6913: #endif
6914: #if PetscDefined(HAVE_SCALAPACK) && (PetscDefined(USE_REAL_SINGLE) || PetscDefined(USE_REAL_DOUBLE))
6915: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6916: #endif
6917: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6918: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6919: #if PetscDefined(HAVE_HYPRE)
6920: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6921: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6922: #endif
6923: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6924: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6925: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6926: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6927: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatGetMultPetscSF_C", MatGetMultPetscSF_MPIAIJ));
6928: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6929: PetscFunctionReturn(PETSC_SUCCESS);
6930: }
6932: /*@
6933: MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6934: and "off-diagonal" part of the matrix in CSR format.
6936: Collective
6938: Input Parameters:
6939: + comm - MPI communicator
6940: . m - number of local rows (Cannot be `PETSC_DECIDE`)
6941: . n - This value should be the same as the local size used in creating the
6942: x vector for the matrix-vector product $y = Ax$. (or `PETSC_DECIDE` to have
6943: calculated if `N` is given) For square matrices `n` is almost always `m`.
6944: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6945: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6946: . i - row indices for "diagonal" portion of matrix; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
6947: . j - column indices, which must be local, i.e., based off the start column of the diagonal portion
6948: . a - matrix values
6949: . oi - row indices for "off-diagonal" portion of matrix; that is oi[0] = 0, oi[row] = oi[row-1] + number of elements in that row of the matrix
6950: . oj - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6951: - oa - matrix values
6953: Output Parameter:
6954: . mat - the matrix
6956: Level: advanced
6958: Notes:
6959: The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc (even in Fortran). The user
6960: must free the arrays once the matrix has been destroyed and not before.
6962: The `i` and `j` indices are 0 based
6964: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix
6966: This sets local rows and cannot be used to set off-processor values.
6968: Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6969: legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6970: not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
6971: the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
6972: keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
6973: communication if it is known that only local entries will be set.
6975: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
6976: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
6977: @*/
6978: PetscErrorCode MatCreateMPIAIJWithSplitArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, PetscInt i[], PetscInt j[], PetscScalar a[], PetscInt oi[], PetscInt oj[], PetscScalar oa[], Mat *mat)
6979: {
6980: Mat_MPIAIJ *maij;
6982: PetscFunctionBegin;
6983: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
6984: PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
6985: PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
6986: PetscCall(MatCreate(comm, mat));
6987: PetscCall(MatSetSizes(*mat, m, n, M, N));
6988: PetscCall(MatSetType(*mat, MATMPIAIJ));
6989: maij = (Mat_MPIAIJ *)(*mat)->data;
6991: (*mat)->preallocated = PETSC_TRUE;
6993: PetscCall(PetscLayoutSetUp((*mat)->rmap));
6994: PetscCall(PetscLayoutSetUp((*mat)->cmap));
6996: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
6997: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));
6999: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
7000: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
7001: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
7002: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
7003: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
7004: PetscFunctionReturn(PETSC_SUCCESS);
7005: }
7007: typedef struct {
7008: Mat *mp; /* intermediate products */
7009: PetscBool *mptmp; /* is the intermediate product temporary ? */
7010: PetscInt cp; /* number of intermediate products */
7012: /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
7013: PetscInt *startsj_s, *startsj_r;
7014: PetscScalar *bufa;
7015: Mat P_oth;
7017: /* may take advantage of merging product->B */
7018: Mat Bloc; /* B-local by merging diag and off-diag */
7020: /* cusparse does not have support to split between symbolic and numeric phases.
7021: When api_user is true, we don't need to update the numerical values
7022: of the temporary storage */
7023: PetscBool reusesym;
7025: /* support for COO values insertion */
7026: PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
7027: PetscInt **own; /* own[i] points to address of on-process COO indices for Mat mp[i] */
7028: PetscInt **off; /* off[i] points to address of off-process COO indices for Mat mp[i] */
7029: PetscBool hasoffproc; /* if true, have off-process values insertion (i.e. AtB or PtAP) */
7030: PetscSF sf; /* used for non-local values insertion and memory malloc */
7031: PetscMemType mtype;
7033: /* customization */
7034: PetscBool abmerge;
7035: PetscBool P_oth_bind;
7036: } MatMatMPIAIJBACKEND;
7038: static PetscErrorCode MatProductCtxDestroy_MatMatMPIAIJBACKEND(PetscCtxRt data)
7039: {
7040: MatMatMPIAIJBACKEND *mmdata = *(MatMatMPIAIJBACKEND **)data;
7041: PetscInt i;
7043: PetscFunctionBegin;
7044: PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
7045: PetscCall(PetscFree(mmdata->bufa));
7046: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
7047: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
7048: PetscCall(MatDestroy(&mmdata->P_oth));
7049: PetscCall(MatDestroy(&mmdata->Bloc));
7050: PetscCall(PetscSFDestroy(&mmdata->sf));
7051: for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
7052: PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
7053: PetscCall(PetscFree(mmdata->own[0]));
7054: PetscCall(PetscFree(mmdata->own));
7055: PetscCall(PetscFree(mmdata->off[0]));
7056: PetscCall(PetscFree(mmdata->off));
7057: PetscCall(PetscFree(mmdata));
7058: PetscFunctionReturn(PETSC_SUCCESS);
7059: }
7061: /* Copy selected n entries with indices in idx[] of A to v[].
7062: If idx is NULL, copy the whole data array of A to v[]
7063: */
7064: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
7065: {
7066: PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);
7068: PetscFunctionBegin;
7069: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
7070: if (f) PetscCall((*f)(A, n, idx, v));
7071: else {
7072: const PetscScalar *vv;
7074: PetscCall(MatSeqAIJGetArrayRead(A, &vv));
7075: if (n && idx) {
7076: PetscScalar *w = v;
7077: const PetscInt *oi = idx;
7079: for (PetscInt j = 0; j < n; j++) *w++ = vv[*oi++];
7080: } else {
7081: PetscCall(PetscArraycpy(v, vv, n));
7082: }
7083: PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7084: }
7085: PetscFunctionReturn(PETSC_SUCCESS);
7086: }
7088: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7089: {
7090: MatMatMPIAIJBACKEND *mmdata;
7091: PetscInt i, n_d, n_o;
7093: PetscFunctionBegin;
7094: MatCheckProduct(C, 1);
7095: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7096: mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7097: if (!mmdata->reusesym) { /* update temporary matrices */
7098: if (mmdata->P_oth) PetscCall(MatGetBrowsOfAoCols_MPIAIJ(C->product->A, C->product->B, MAT_REUSE_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7099: if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7100: }
7101: mmdata->reusesym = PETSC_FALSE;
7103: for (i = 0; i < mmdata->cp; i++) {
7104: PetscCheck(mmdata->mp[i]->ops->productnumeric, PetscObjectComm((PetscObject)mmdata->mp[i]), PETSC_ERR_PLIB, "Missing numeric op for %s", MatProductTypes[mmdata->mp[i]->product->type]);
7105: PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7106: }
7107: for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7108: PetscInt noff;
7110: PetscCall(PetscIntCast(mmdata->off[i + 1] - mmdata->off[i], &noff));
7111: if (mmdata->mptmp[i]) continue;
7112: if (noff) {
7113: PetscInt nown;
7115: PetscCall(PetscIntCast(mmdata->own[i + 1] - mmdata->own[i], &nown));
7116: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7117: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7118: n_o += noff;
7119: n_d += nown;
7120: } else {
7121: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;
7123: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7124: n_d += mm->nz;
7125: }
7126: }
7127: if (mmdata->hasoffproc) { /* offprocess insertion */
7128: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7129: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7130: }
7131: PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7132: PetscFunctionReturn(PETSC_SUCCESS);
7133: }
7135: /* Support for Pt * A, A * P, or Pt * A * P */
7136: #define MAX_NUMBER_INTERMEDIATE 4
7137: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7138: {
7139: Mat_Product *product = C->product;
7140: Mat A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7141: Mat_MPIAIJ *a, *p;
7142: MatMatMPIAIJBACKEND *mmdata;
7143: ISLocalToGlobalMapping P_oth_l2g = NULL;
7144: IS glob = NULL;
7145: const char *prefix;
7146: char pprefix[256];
7147: const PetscInt *globidx, *P_oth_idx;
7148: PetscInt i, j, cp, m, n, M, N, *coo_i, *coo_j;
7149: PetscCount ncoo, ncoo_d, ncoo_o, ncoo_oown;
7150: PetscInt cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7151: /* type-0: consecutive, start from 0; type-1: consecutive with */
7152: /* a base offset; type-2: sparse with a local to global map table */
7153: const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE]; /* col/row local to global map array (table) for type-2 map type */
7155: MatProductType ptype;
7156: PetscBool mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7157: PetscMPIInt size;
7159: PetscFunctionBegin;
7160: MatCheckProduct(C, 1);
7161: PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7162: ptype = product->type;
7163: if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7164: ptype = MATPRODUCT_AB;
7165: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7166: }
7167: switch (ptype) {
7168: case MATPRODUCT_AB:
7169: A = product->A;
7170: P = product->B;
7171: m = A->rmap->n;
7172: n = P->cmap->n;
7173: M = A->rmap->N;
7174: N = P->cmap->N;
7175: hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7176: break;
7177: case MATPRODUCT_AtB:
7178: P = product->A;
7179: A = product->B;
7180: m = P->cmap->n;
7181: n = A->cmap->n;
7182: M = P->cmap->N;
7183: N = A->cmap->N;
7184: hasoffproc = PETSC_TRUE;
7185: break;
7186: case MATPRODUCT_PtAP:
7187: A = product->A;
7188: P = product->B;
7189: m = P->cmap->n;
7190: n = P->cmap->n;
7191: M = P->cmap->N;
7192: N = P->cmap->N;
7193: hasoffproc = PETSC_TRUE;
7194: break;
7195: default:
7196: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7197: }
7198: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7199: if (size == 1) hasoffproc = PETSC_FALSE;
7201: /* defaults */
7202: for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7203: mp[i] = NULL;
7204: mptmp[i] = PETSC_FALSE;
7205: rmapt[i] = -1;
7206: cmapt[i] = -1;
7207: rmapa[i] = NULL;
7208: cmapa[i] = NULL;
7209: }
7211: /* customization */
7212: PetscCall(PetscNew(&mmdata));
7213: mmdata->reusesym = product->api_user;
7214: if (ptype == MATPRODUCT_AB) {
7215: if (product->api_user) {
7216: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7217: PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7218: PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7219: PetscOptionsEnd();
7220: } else {
7221: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7222: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7223: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7224: PetscOptionsEnd();
7225: }
7226: } else if (ptype == MATPRODUCT_PtAP) {
7227: if (product->api_user) {
7228: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7229: PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7230: PetscOptionsEnd();
7231: } else {
7232: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7233: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7234: PetscOptionsEnd();
7235: }
7236: }
7237: a = (Mat_MPIAIJ *)A->data;
7238: p = (Mat_MPIAIJ *)P->data;
7239: PetscCall(MatSetSizes(C, m, n, M, N));
7240: PetscCall(PetscLayoutSetUp(C->rmap));
7241: PetscCall(PetscLayoutSetUp(C->cmap));
7242: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7243: PetscCall(MatGetOptionsPrefix(C, &prefix));
7245: cp = 0;
7246: switch (ptype) {
7247: case MATPRODUCT_AB: /* A * P */
7248: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7250: /* A_diag * P_local (merged or not) */
7251: if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7252: /* P is product->B */
7253: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7254: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7255: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7256: PetscCall(MatProductSetFill(mp[cp], product->fill));
7257: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7258: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7259: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7260: mp[cp]->product->api_user = product->api_user;
7261: PetscCall(MatProductSetFromOptions(mp[cp]));
7262: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7263: PetscCall(ISGetIndices(glob, &globidx));
7264: rmapt[cp] = 1;
7265: cmapt[cp] = 2;
7266: cmapa[cp] = globidx;
7267: mptmp[cp] = PETSC_FALSE;
7268: cp++;
7269: } else { /* A_diag * P_diag and A_diag * P_off */
7270: PetscCall(MatProductCreate(a->A, p->A, NULL, &mp[cp]));
7271: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7272: PetscCall(MatProductSetFill(mp[cp], product->fill));
7273: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7274: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7275: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7276: mp[cp]->product->api_user = product->api_user;
7277: PetscCall(MatProductSetFromOptions(mp[cp]));
7278: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7279: rmapt[cp] = 1;
7280: cmapt[cp] = 1;
7281: mptmp[cp] = PETSC_FALSE;
7282: cp++;
7283: PetscCall(MatProductCreate(a->A, p->B, NULL, &mp[cp]));
7284: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7285: PetscCall(MatProductSetFill(mp[cp], product->fill));
7286: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7287: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7288: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7289: mp[cp]->product->api_user = product->api_user;
7290: PetscCall(MatProductSetFromOptions(mp[cp]));
7291: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7292: rmapt[cp] = 1;
7293: cmapt[cp] = 2;
7294: cmapa[cp] = p->garray;
7295: mptmp[cp] = PETSC_FALSE;
7296: cp++;
7297: }
7299: /* A_off * P_other */
7300: if (mmdata->P_oth) {
7301: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7302: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7303: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7304: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7305: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7306: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7307: PetscCall(MatProductSetFill(mp[cp], product->fill));
7308: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7309: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7310: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7311: mp[cp]->product->api_user = product->api_user;
7312: PetscCall(MatProductSetFromOptions(mp[cp]));
7313: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7314: rmapt[cp] = 1;
7315: cmapt[cp] = 2;
7316: cmapa[cp] = P_oth_idx;
7317: mptmp[cp] = PETSC_FALSE;
7318: cp++;
7319: }
7320: break;
7322: case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7323: /* A is product->B */
7324: PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7325: if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7326: PetscCall(MatProductCreate(mmdata->Bloc, mmdata->Bloc, NULL, &mp[cp]));
7327: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7328: PetscCall(MatProductSetFill(mp[cp], product->fill));
7329: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7330: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7331: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7332: mp[cp]->product->api_user = product->api_user;
7333: PetscCall(MatProductSetFromOptions(mp[cp]));
7334: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7335: PetscCall(ISGetIndices(glob, &globidx));
7336: rmapt[cp] = 2;
7337: rmapa[cp] = globidx;
7338: cmapt[cp] = 2;
7339: cmapa[cp] = globidx;
7340: mptmp[cp] = PETSC_FALSE;
7341: cp++;
7342: } else {
7343: PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7344: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7345: PetscCall(MatProductSetFill(mp[cp], product->fill));
7346: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7347: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7348: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7349: mp[cp]->product->api_user = product->api_user;
7350: PetscCall(MatProductSetFromOptions(mp[cp]));
7351: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7352: PetscCall(ISGetIndices(glob, &globidx));
7353: rmapt[cp] = 1;
7354: cmapt[cp] = 2;
7355: cmapa[cp] = globidx;
7356: mptmp[cp] = PETSC_FALSE;
7357: cp++;
7358: PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7359: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7360: PetscCall(MatProductSetFill(mp[cp], product->fill));
7361: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7362: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7363: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7364: mp[cp]->product->api_user = product->api_user;
7365: PetscCall(MatProductSetFromOptions(mp[cp]));
7366: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7367: rmapt[cp] = 2;
7368: rmapa[cp] = p->garray;
7369: cmapt[cp] = 2;
7370: cmapa[cp] = globidx;
7371: mptmp[cp] = PETSC_FALSE;
7372: cp++;
7373: }
7374: break;
7375: case MATPRODUCT_PtAP:
7376: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7377: /* P is product->B */
7378: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7379: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7380: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
7381: PetscCall(MatProductSetFill(mp[cp], product->fill));
7382: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7383: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7384: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7385: mp[cp]->product->api_user = product->api_user;
7386: PetscCall(MatProductSetFromOptions(mp[cp]));
7387: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7388: PetscCall(ISGetIndices(glob, &globidx));
7389: rmapt[cp] = 2;
7390: rmapa[cp] = globidx;
7391: cmapt[cp] = 2;
7392: cmapa[cp] = globidx;
7393: mptmp[cp] = PETSC_FALSE;
7394: cp++;
7395: if (mmdata->P_oth) {
7396: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7397: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7398: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7399: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7400: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7401: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7402: PetscCall(MatProductSetFill(mp[cp], product->fill));
7403: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7404: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7405: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7406: mp[cp]->product->api_user = product->api_user;
7407: PetscCall(MatProductSetFromOptions(mp[cp]));
7408: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7409: mptmp[cp] = PETSC_TRUE;
7410: cp++;
7411: PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7412: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7413: PetscCall(MatProductSetFill(mp[cp], product->fill));
7414: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7415: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7416: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7417: mp[cp]->product->api_user = product->api_user;
7418: PetscCall(MatProductSetFromOptions(mp[cp]));
7419: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7420: rmapt[cp] = 2;
7421: rmapa[cp] = globidx;
7422: cmapt[cp] = 2;
7423: cmapa[cp] = P_oth_idx;
7424: mptmp[cp] = PETSC_FALSE;
7425: cp++;
7426: }
7427: break;
7428: default:
7429: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7430: }
7431: /* sanity check */
7432: if (size > 1)
7433: for (i = 0; i < cp; i++) PetscCheck(rmapt[i] != 2 || hasoffproc, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unexpected offproc map type for product %" PetscInt_FMT, i);
7435: PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7436: for (i = 0; i < cp; i++) {
7437: mmdata->mp[i] = mp[i];
7438: mmdata->mptmp[i] = mptmp[i];
7439: }
7440: mmdata->cp = cp;
7441: C->product->data = mmdata;
7442: C->product->destroy = MatProductCtxDestroy_MatMatMPIAIJBACKEND;
7443: C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;
7445: /* memory type */
7446: mmdata->mtype = PETSC_MEMTYPE_HOST;
7447: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7448: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7449: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7450: if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7451: else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7452: else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;
7454: /* prepare coo coordinates for values insertion */
7456: /* count total nonzeros of those intermediate seqaij Mats
7457: ncoo_d: # of nonzeros of matrices that do not have offproc entries
7458: ncoo_o: # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7459: ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7460: */
7461: for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7462: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7463: if (mptmp[cp]) continue;
7464: if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7465: const PetscInt *rmap = rmapa[cp];
7466: const PetscInt mr = mp[cp]->rmap->n;
7467: const PetscInt rs = C->rmap->rstart;
7468: const PetscInt re = C->rmap->rend;
7469: const PetscInt *ii = mm->i;
7470: for (i = 0; i < mr; i++) {
7471: const PetscInt gr = rmap[i];
7472: const PetscInt nz = ii[i + 1] - ii[i];
7473: if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7474: else ncoo_oown += nz; /* this row is local */
7475: }
7476: } else ncoo_d += mm->nz;
7477: }
7479: /*
7480: ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc
7482: ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.
7484: off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].
7486: off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7487: own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7488: so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.
7490: coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7491: Ex. coo_i[]: the beginning part (of size ncoo_d + ncoo_oown) stores i of local nonzeros, and the remaining part stores i of nonzeros I will receive.
7492: */
7493: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7494: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));
7496: /* gather (i,j) of nonzeros inserted by remote procs */
7497: if (hasoffproc) {
7498: PetscSF msf;
7499: PetscInt ncoo2, *coo_i2, *coo_j2;
7501: PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7502: PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7503: PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */
7505: for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7506: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7507: PetscInt *idxoff = mmdata->off[cp];
7508: PetscInt *idxown = mmdata->own[cp];
7509: if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7510: const PetscInt *rmap = rmapa[cp];
7511: const PetscInt *cmap = cmapa[cp];
7512: const PetscInt *ii = mm->i;
7513: PetscInt *coi = coo_i + ncoo_o;
7514: PetscInt *coj = coo_j + ncoo_o;
7515: const PetscInt mr = mp[cp]->rmap->n;
7516: const PetscInt rs = C->rmap->rstart;
7517: const PetscInt re = C->rmap->rend;
7518: const PetscInt cs = C->cmap->rstart;
7519: for (i = 0; i < mr; i++) {
7520: const PetscInt *jj = mm->j + ii[i];
7521: const PetscInt gr = rmap[i];
7522: const PetscInt nz = ii[i + 1] - ii[i];
7523: if (gr < rs || gr >= re) { /* this is an offproc row */
7524: for (j = ii[i]; j < ii[i + 1]; j++) {
7525: *coi++ = gr;
7526: *idxoff++ = j;
7527: }
7528: if (!cmapt[cp]) { /* already global */
7529: for (j = 0; j < nz; j++) *coj++ = jj[j];
7530: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7531: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7532: } else { /* offdiag */
7533: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7534: }
7535: ncoo_o += nz;
7536: } else { /* this is a local row */
7537: for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7538: }
7539: }
7540: }
7541: mmdata->off[cp + 1] = idxoff;
7542: mmdata->own[cp + 1] = idxown;
7543: }
7545: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7546: PetscInt incoo_o;
7547: PetscCall(PetscIntCast(ncoo_o, &incoo_o));
7548: PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, incoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7549: PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7550: PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7551: ncoo = ncoo_d + ncoo_oown + ncoo2;
7552: PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7553: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7554: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7555: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7556: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7557: PetscCall(PetscFree2(coo_i, coo_j));
7558: /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7559: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7560: coo_i = coo_i2;
7561: coo_j = coo_j2;
7562: } else { /* no offproc values insertion */
7563: ncoo = ncoo_d;
7564: PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));
7566: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7567: PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7568: PetscCall(PetscSFSetUp(mmdata->sf));
7569: }
7570: mmdata->hasoffproc = hasoffproc;
7572: /* gather (i,j) of nonzeros inserted locally */
7573: for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7574: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7575: PetscInt *coi = coo_i + ncoo_d;
7576: PetscInt *coj = coo_j + ncoo_d;
7577: const PetscInt *jj = mm->j;
7578: const PetscInt *ii = mm->i;
7579: const PetscInt *cmap = cmapa[cp];
7580: const PetscInt *rmap = rmapa[cp];
7581: const PetscInt mr = mp[cp]->rmap->n;
7582: const PetscInt rs = C->rmap->rstart;
7583: const PetscInt re = C->rmap->rend;
7584: const PetscInt cs = C->cmap->rstart;
7586: if (mptmp[cp]) continue;
7587: if (rmapt[cp] == 1) { /* consecutive rows */
7588: /* fill coo_i */
7589: for (i = 0; i < mr; i++) {
7590: const PetscInt gr = i + rs;
7591: for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7592: }
7593: /* fill coo_j */
7594: if (!cmapt[cp]) { /* type-0, already global */
7595: PetscCall(PetscArraycpy(coj, jj, mm->nz));
7596: } else if (cmapt[cp] == 1) { /* type-1, local to global for consecutive columns of C */
7597: for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7598: } else { /* type-2, local to global for sparse columns */
7599: for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7600: }
7601: ncoo_d += mm->nz;
7602: } else if (rmapt[cp] == 2) { /* sparse rows */
7603: for (i = 0; i < mr; i++) {
7604: const PetscInt *jj = mm->j + ii[i];
7605: const PetscInt gr = rmap[i];
7606: const PetscInt nz = ii[i + 1] - ii[i];
7607: if (gr >= rs && gr < re) { /* local rows */
7608: for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7609: if (!cmapt[cp]) { /* type-0, already global */
7610: for (j = 0; j < nz; j++) *coj++ = jj[j];
7611: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7612: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7613: } else { /* type-2, local to global for sparse columns */
7614: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7615: }
7616: ncoo_d += nz;
7617: }
7618: }
7619: }
7620: }
7621: if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7622: PetscCall(ISDestroy(&glob));
7623: if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7624: PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7625: /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7626: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));
7628: /* set block sizes */
7629: A = product->A;
7630: P = product->B;
7631: switch (ptype) {
7632: case MATPRODUCT_PtAP:
7633: PetscCall(MatSetBlockSizes(C, P->cmap->bs, P->cmap->bs));
7634: break;
7635: case MATPRODUCT_RARt:
7636: PetscCall(MatSetBlockSizes(C, P->rmap->bs, P->rmap->bs));
7637: break;
7638: case MATPRODUCT_ABC:
7639: PetscCall(MatSetBlockSizesFromMats(C, A, product->C));
7640: break;
7641: case MATPRODUCT_AB:
7642: PetscCall(MatSetBlockSizesFromMats(C, A, P));
7643: break;
7644: case MATPRODUCT_AtB:
7645: PetscCall(MatSetBlockSizes(C, A->cmap->bs, P->cmap->bs));
7646: break;
7647: case MATPRODUCT_ABt:
7648: PetscCall(MatSetBlockSizes(C, A->rmap->bs, P->rmap->bs));
7649: break;
7650: default:
7651: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for ProductType %s", MatProductTypes[ptype]);
7652: }
7654: /* preallocate with COO data */
7655: PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7656: PetscCall(PetscFree2(coo_i, coo_j));
7657: PetscFunctionReturn(PETSC_SUCCESS);
7658: }
7660: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7661: {
7662: Mat_Product *product = mat->product;
7663: #if PetscDefined(HAVE_DEVICE)
7664: PetscBool match = PETSC_FALSE;
7665: PetscBool usecpu = PETSC_FALSE;
7666: #else
7667: PetscBool match = PETSC_TRUE;
7668: #endif
7670: PetscFunctionBegin;
7671: MatCheckProduct(mat, 1);
7672: #if PetscDefined(HAVE_DEVICE)
7673: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7674: if (match) { /* we can always fallback to the CPU if requested */
7675: switch (product->type) {
7676: case MATPRODUCT_AB:
7677: if (product->api_user) {
7678: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7679: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7680: PetscOptionsEnd();
7681: } else {
7682: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7683: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7684: PetscOptionsEnd();
7685: }
7686: break;
7687: case MATPRODUCT_AtB:
7688: if (product->api_user) {
7689: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7690: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7691: PetscOptionsEnd();
7692: } else {
7693: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7694: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7695: PetscOptionsEnd();
7696: }
7697: break;
7698: case MATPRODUCT_PtAP:
7699: if (product->api_user) {
7700: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7701: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7702: PetscOptionsEnd();
7703: } else {
7704: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7705: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7706: PetscOptionsEnd();
7707: }
7708: break;
7709: default:
7710: break;
7711: }
7712: match = (PetscBool)!usecpu;
7713: }
7714: #endif
7715: if (match) {
7716: switch (product->type) {
7717: case MATPRODUCT_AB:
7718: case MATPRODUCT_AtB:
7719: case MATPRODUCT_PtAP:
7720: mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7721: break;
7722: default:
7723: break;
7724: }
7725: }
7726: /* fallback to MPIAIJ ops */
7727: if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7728: PetscFunctionReturn(PETSC_SUCCESS);
7729: }
7731: /*
7732: Produces a set of block column indices of the matrix row, one for each block represented in the original row
7734: n - the number of block indices in cc[]
7735: cc - the block indices (must be large enough to contain the indices)
7736: */
7737: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7738: {
7739: PetscInt cnt = -1, nidx, j;
7740: const PetscInt *idx;
7742: PetscFunctionBegin;
7743: PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7744: if (nidx) {
7745: cnt = 0;
7746: cc[cnt] = idx[0] / bs;
7747: for (j = 1; j < nidx; j++) {
7748: if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7749: }
7750: }
7751: PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7752: *n = cnt + 1;
7753: PetscFunctionReturn(PETSC_SUCCESS);
7754: }
7756: /*
7757: Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows
7759: ncollapsed - the number of block indices
7760: collapsed - the block indices (must be large enough to contain the indices)
7761: */
7762: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7763: {
7764: PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;
7766: PetscFunctionBegin;
7767: PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7768: for (i = start + 1; i < start + bs; i++) {
7769: PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7770: PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7771: cprevtmp = cprev;
7772: cprev = merged;
7773: merged = cprevtmp;
7774: }
7775: *ncollapsed = nprev;
7776: if (collapsed) *collapsed = cprev;
7777: PetscFunctionReturn(PETSC_SUCCESS);
7778: }
7780: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, PetscInt index_size, PetscInt index[], Mat *a_Gmat)
7781: {
7782: PetscInt Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7783: MPI_Comm comm;
7784: Mat Gmat;
7785: PetscBool ismpiaij, isseqaij;
7786: Mat a, b, c;
7787: MatType jtype;
7789: PetscFunctionBegin;
7790: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7791: PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7792: PetscCall(MatGetSize(Amat, &MM, &NN));
7793: PetscCall(MatGetBlockSize(Amat, &bs));
7794: nloc = (Iend - Istart) / bs;
7796: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7797: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7798: PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");
7800: /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7801: /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7802: implementation */
7803: if (bs > 1) {
7804: PetscCall(MatGetType(Amat, &jtype));
7805: PetscCall(MatCreate(comm, &Gmat));
7806: PetscCall(MatSetType(Gmat, jtype));
7807: PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7808: PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7809: if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7810: PetscInt *d_nnz, *o_nnz;
7811: MatScalar *aa, val, *AA;
7812: PetscInt *aj, *ai, *AJ, nc, nmax = 0;
7814: if (isseqaij) {
7815: a = Amat;
7816: b = NULL;
7817: } else {
7818: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7819: a = d->A;
7820: b = d->B;
7821: }
7822: PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7823: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7824: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7825: PetscInt *nnz = (c == a) ? d_nnz : o_nnz;
7826: const PetscInt *cols1, *cols2;
7828: for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7829: PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7830: nnz[brow / bs] = nc2 / bs;
7831: if (nc2 % bs) ok = 0;
7832: if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7833: for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7834: PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7835: if (nc1 != nc2) ok = 0;
7836: else {
7837: for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7838: if (cols1[jj] != cols2[jj]) ok = 0;
7839: if (cols1[jj] % bs != jj % bs) ok = 0;
7840: }
7841: }
7842: PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7843: }
7844: PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7845: if (!ok) {
7846: PetscCall(PetscFree2(d_nnz, o_nnz));
7847: PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7848: goto old_bs;
7849: }
7850: }
7851: }
7852: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7853: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7854: PetscCall(PetscFree2(d_nnz, o_nnz));
7855: PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7856: // diag
7857: for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7858: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;
7860: ai = aseq->i;
7861: n = ai[brow + 1] - ai[brow];
7862: aj = aseq->j + ai[brow];
7863: for (PetscInt k = 0; k < n; k += bs) { // block columns
7864: AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7865: val = 0;
7866: if (index_size == 0) {
7867: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7868: aa = aseq->a + ai[brow + ii] + k;
7869: for (PetscInt jj = 0; jj < bs; jj++) { // columns in block
7870: val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7871: }
7872: }
7873: } else { // use (index,index) value if provided
7874: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7875: PetscInt ii = index[iii];
7876: aa = aseq->a + ai[brow + ii] + k;
7877: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // columns in block
7878: PetscInt jj = index[jjj];
7879: val += PetscAbs(PetscRealPart(aa[jj]));
7880: }
7881: }
7882: }
7883: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%" PetscInt_FMT ") >= nmax (%" PetscInt_FMT ")", k / bs, nmax);
7884: AA[k / bs] = val;
7885: }
7886: grow = Istart / bs + brow / bs;
7887: PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, ADD_VALUES));
7888: }
7889: // off-diag
7890: if (ismpiaij) {
7891: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Amat->data;
7892: const PetscScalar *vals;
7893: const PetscInt *cols, *garray = aij->garray;
7895: PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7896: for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7897: PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7898: for (PetscInt k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7899: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7900: AA[k / bs] = 0;
7901: AJ[cidx] = garray[cols[k]] / bs;
7902: }
7903: nc = ncols / bs;
7904: PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7905: if (index_size == 0) {
7906: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7907: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7908: for (PetscInt k = 0; k < ncols; k += bs) {
7909: for (PetscInt jj = 0; jj < bs; jj++) { // cols in block
7910: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%" PetscInt_FMT ") >= nmax (%" PetscInt_FMT ")", k / bs, nmax);
7911: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7912: }
7913: }
7914: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7915: }
7916: } else { // use (index,index) value if provided
7917: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7918: PetscInt ii = index[iii];
7919: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7920: for (PetscInt k = 0; k < ncols; k += bs) {
7921: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // cols in block
7922: PetscInt jj = index[jjj];
7923: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7924: }
7925: }
7926: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7927: }
7928: }
7929: grow = Istart / bs + brow / bs;
7930: PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, ADD_VALUES));
7931: }
7932: }
7933: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7934: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7935: PetscCall(PetscFree2(AA, AJ));
7936: } else {
7937: const PetscScalar *vals;
7938: const PetscInt *idx;
7939: PetscInt *d_nnz, *o_nnz, *w0, *w1, *w2;
7940: old_bs:
7941: /*
7942: Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7943: */
7944: PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7945: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7946: if (isseqaij) {
7947: PetscInt max_d_nnz;
7949: /*
7950: Determine exact preallocation count for (sequential) scalar matrix
7951: */
7952: PetscCall(MatSeqAIJGetMaxRowNonzeros(Amat, &max_d_nnz));
7953: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7954: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7955: for (Ii = 0, jj = 0; Ii < Iend; Ii += bs, jj++) PetscCall(MatCollapseRows(Amat, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7956: PetscCall(PetscFree3(w0, w1, w2));
7957: } else if (ismpiaij) {
7958: Mat Daij, Oaij;
7959: const PetscInt *garray;
7960: PetscInt max_d_nnz;
7962: PetscCall(MatMPIAIJGetSeqAIJ(Amat, &Daij, &Oaij, &garray));
7963: /*
7964: Determine exact preallocation count for diagonal block portion of scalar matrix
7965: */
7966: PetscCall(MatSeqAIJGetMaxRowNonzeros(Daij, &max_d_nnz));
7967: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7968: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7969: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) PetscCall(MatCollapseRows(Daij, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7970: PetscCall(PetscFree3(w0, w1, w2));
7971: /*
7972: Over estimate (usually grossly over), preallocation count for off-diagonal portion of scalar matrix
7973: */
7974: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) {
7975: o_nnz[jj] = 0;
7976: for (kk = 0; kk < bs; kk++) { /* rows that get collapsed to a single row */
7977: PetscCall(MatGetRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7978: o_nnz[jj] += ncols;
7979: PetscCall(MatRestoreRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7980: }
7981: if (o_nnz[jj] > (NN / bs - nloc)) o_nnz[jj] = NN / bs - nloc;
7982: }
7983: } else SETERRQ(comm, PETSC_ERR_USER, "Require AIJ matrix type");
7984: /* get scalar copy (norms) of matrix */
7985: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7986: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7987: PetscCall(PetscFree2(d_nnz, o_nnz));
7988: for (Ii = Istart; Ii < Iend; Ii++) {
7989: PetscInt dest_row = Ii / bs;
7991: PetscCall(MatGetRow(Amat, Ii, &ncols, &idx, &vals));
7992: for (jj = 0; jj < ncols; jj++) {
7993: PetscInt dest_col = idx[jj] / bs;
7994: PetscScalar sv = PetscAbs(PetscRealPart(vals[jj]));
7996: PetscCall(MatSetValues(Gmat, 1, &dest_row, 1, &dest_col, &sv, ADD_VALUES));
7997: }
7998: PetscCall(MatRestoreRow(Amat, Ii, &ncols, &idx, &vals));
7999: }
8000: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
8001: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
8002: }
8003: } else {
8004: if (symmetrize || filter >= 0 || scale) PetscCall(MatDuplicate(Amat, MAT_COPY_VALUES, &Gmat));
8005: else {
8006: Gmat = Amat;
8007: PetscCall(PetscObjectReference((PetscObject)Gmat));
8008: }
8009: if (isseqaij) {
8010: a = Gmat;
8011: b = NULL;
8012: } else {
8013: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
8014: a = d->A;
8015: b = d->B;
8016: }
8017: if (filter >= 0 || scale) {
8018: /* take absolute value of each entry */
8019: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
8020: MatInfo info;
8021: PetscScalar *avals;
8023: PetscCall(MatGetInfo(c, MAT_LOCAL, &info));
8024: PetscCall(MatSeqAIJGetArray(c, &avals));
8025: for (int jj = 0; jj < info.nz_used; jj++) avals[jj] = PetscAbsScalar(avals[jj]);
8026: PetscCall(MatSeqAIJRestoreArray(c, &avals));
8027: }
8028: }
8029: }
8030: if (symmetrize) {
8031: PetscBool isset, issym;
8033: PetscCall(MatIsSymmetricKnown(Amat, &isset, &issym));
8034: if (!isset || !issym) {
8035: Mat matTrans;
8037: PetscCall(MatTranspose(Gmat, MAT_INITIAL_MATRIX, &matTrans));
8038: PetscCall(MatAXPY(Gmat, 1.0, matTrans, Gmat->structurally_symmetric == PETSC_BOOL3_TRUE ? SAME_NONZERO_PATTERN : DIFFERENT_NONZERO_PATTERN));
8039: PetscCall(MatDestroy(&matTrans));
8040: }
8041: PetscCall(MatSetOption(Gmat, MAT_SYMMETRIC, PETSC_TRUE));
8042: } else if (Amat != Gmat) PetscCall(MatPropagateSymmetryOptions(Amat, Gmat));
8043: if (scale) {
8044: /* scale c for all diagonal values = 1 or -1 */
8045: Vec diag;
8047: PetscCall(MatCreateVecs(Gmat, &diag, NULL));
8048: PetscCall(MatGetDiagonal(Gmat, diag));
8049: PetscCall(VecReciprocal(diag));
8050: PetscCall(VecSqrtAbs(diag));
8051: PetscCall(MatDiagonalScale(Gmat, diag, diag));
8052: PetscCall(VecDestroy(&diag));
8053: }
8054: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_graph_view"));
8055: if (filter >= 0) {
8056: PetscCall(MatFilter(Gmat, filter, PETSC_TRUE, PETSC_TRUE));
8057: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_filter_graph_view"));
8058: }
8059: *a_Gmat = Gmat;
8060: PetscFunctionReturn(PETSC_SUCCESS);
8061: }
8063: PETSC_INTERN PetscErrorCode MatGetCurrentMemType_MPIAIJ(Mat A, PetscMemType *memtype)
8064: {
8065: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)A->data;
8066: PetscMemType mD = PETSC_MEMTYPE_HOST, mO = PETSC_MEMTYPE_HOST;
8068: PetscFunctionBegin;
8069: if (mpiaij->A) PetscCall(MatGetCurrentMemType(mpiaij->A, &mD));
8070: if (mpiaij->B) PetscCall(MatGetCurrentMemType(mpiaij->B, &mO));
8071: *memtype = (mD == mO) ? mD : PETSC_MEMTYPE_HOST;
8072: PetscFunctionReturn(PETSC_SUCCESS);
8073: }
8075: /*
8076: Special version for direct calls from Fortran
8077: */
8079: /* Change these macros so can be used in void function */
8080: /* Identical to PetscCallVoid, except it assigns to *_ierr */
8081: #undef PetscCall
8082: #define PetscCall(...) \
8083: do { \
8084: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
8085: if (PetscUnlikely(ierr_msv_mpiaij)) { \
8086: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
8087: return; \
8088: } \
8089: } while (0)
8091: #undef SETERRQ
8092: #define SETERRQ(comm, ierr, ...) \
8093: do { \
8094: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
8095: return; \
8096: } while (0)
8098: #if PetscDefined(HAVE_FORTRAN_CAPS)
8099: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
8100: #elif !PetscDefined(HAVE_FORTRAN_UNDERSCORE)
8101: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
8102: #else
8103: #endif
8104: PETSC_EXTERN void matsetvaluesmpiaij_(Mat *mmat, PetscInt *mm, const PetscInt im[], PetscInt *mn, const PetscInt in[], const PetscScalar v[], InsertMode *maddv, PetscErrorCode *_ierr)
8105: {
8106: Mat mat = *mmat;
8107: PetscInt m = *mm, n = *mn;
8108: InsertMode addv = *maddv;
8109: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
8110: PetscScalar value;
8112: MatCheckPreallocated(mat, 1);
8113: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
8114: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
8115: {
8116: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
8117: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
8118: PetscBool roworiented = aij->roworiented;
8120: /* Some Variables required in the macro */
8121: Mat A = aij->A;
8122: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
8123: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
8124: MatScalar *aa;
8125: PetscBool ignorezeroentries = (a->ignorezeroentries && addv == ADD_VALUES) ? PETSC_TRUE : PETSC_FALSE;
8126: Mat B = aij->B;
8127: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
8128: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
8129: MatScalar *ba;
8130: /* This variable below is only for the PETSC_HAVE_VIENNACL or PETSC_HAVE_CUDA cases, but we define it in all cases because we
8131: * cannot use "#if defined" inside a macro. */
8132: PETSC_UNUSED PetscBool inserted = PETSC_FALSE;
8134: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
8135: PetscInt nonew = a->nonew;
8136: MatScalar *ap1, *ap2;
8138: PetscFunctionBegin;
8139: PetscCall(MatSeqAIJGetArray(A, &aa));
8140: PetscCall(MatSeqAIJGetArray(B, &ba));
8141: for (i = 0; i < m; i++) {
8142: if (im[i] < 0) continue;
8143: PetscCheck(im[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], mat->rmap->N - 1);
8144: if (im[i] >= rstart && im[i] < rend) {
8145: row = im[i] - rstart;
8146: lastcol1 = -1;
8147: rp1 = aj + ai[row];
8148: ap1 = aa + ai[row];
8149: rmax1 = aimax[row];
8150: nrow1 = ailen[row];
8151: low1 = 0;
8152: high1 = nrow1;
8153: lastcol2 = -1;
8154: rp2 = bj + bi[row];
8155: ap2 = ba + bi[row];
8156: rmax2 = bimax[row];
8157: nrow2 = bilen[row];
8158: low2 = 0;
8159: high2 = nrow2;
8161: for (j = 0; j < n; j++) {
8162: if (roworiented) value = v[i * n + j];
8163: else value = v[i + j * m];
8164: if (ignorezeroentries && value == 0.0 && addv == ADD_VALUES && im[i] != in[j]) continue;
8165: if (in[j] >= cstart && in[j] < cend) {
8166: col = in[j] - cstart;
8167: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
8168: } else if (in[j] < 0) continue;
8169: else if (PetscUnlikelyDebug(in[j] >= mat->cmap->N)) {
8170: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
8171: } else {
8172: if (mat->was_assembled) {
8173: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
8174: #if PetscDefined(USE_CTABLE)
8175: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col));
8176: col--;
8177: #else
8178: col = aij->colmap[in[j]] - 1;
8179: #endif
8180: if (col < 0 && !((Mat_SeqAIJ *)aij->A->data)->nonew) {
8181: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));
8182: col = in[j];
8183: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
8184: B = aij->B;
8185: b = (Mat_SeqAIJ *)B->data;
8186: bimax = b->imax;
8187: bi = b->i;
8188: bilen = b->ilen;
8189: bj = b->j;
8190: rp2 = bj + bi[row];
8191: ap2 = ba + bi[row];
8192: rmax2 = bimax[row];
8193: nrow2 = bilen[row];
8194: low2 = 0;
8195: high2 = nrow2;
8196: bm = aij->B->rmap->n;
8197: ba = b->a;
8198: inserted = PETSC_FALSE;
8199: }
8200: } else col = in[j];
8201: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
8202: }
8203: }
8204: } else if (!aij->donotstash) {
8205: if (roworiented) {
8206: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && addv == ADD_VALUES)));
8207: } else {
8208: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && addv == ADD_VALUES)));
8209: }
8210: }
8211: }
8212: PetscCall(MatSeqAIJRestoreArray(A, &aa));
8213: PetscCall(MatSeqAIJRestoreArray(B, &ba));
8214: }
8215: PetscFunctionReturnVoid();
8216: }
8218: /* Undefining these here since they were redefined from their original definition above! No
8219: * other PETSc functions should be defined past this point, as it is impossible to recover the
8220: * original definitions */
8221: #undef PetscCall
8222: #undef SETERRQ