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: PetscErrorCode MatDestroy_MPIAIJ(Mat mat)
10: {
11: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
13: PetscFunctionBegin;
14: PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
15: PetscCall(MatStashDestroy_Private(&mat->stash));
16: PetscCall(VecDestroy(&aij->diag));
17: PetscCall(MatDestroy(&aij->A));
18: PetscCall(MatDestroy(&aij->B));
19: #if defined(PETSC_USE_CTABLE)
20: PetscCall(PetscHMapIDestroy(&aij->colmap));
21: #else
22: PetscCall(PetscFree(aij->colmap));
23: #endif
24: PetscCall(PetscFree(aij->garray));
25: PetscCall(VecDestroy(&aij->lvec));
26: PetscCall(VecScatterDestroy(&aij->Mvctx));
27: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
28: PetscCall(PetscFree(aij->ld));
30: PetscCall(PetscFree(mat->data));
32: /* may be created by MatCreateMPIAIJSumSeqAIJSymbolic */
33: PetscCall(PetscObjectCompose((PetscObject)mat, "MatMergeSeqsToMPI", NULL));
35: PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
36: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
37: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
38: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatIsTranspose_C", NULL));
39: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocation_C", NULL));
40: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatResetPreallocation_C", NULL));
41: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocationCSR_C", NULL));
42: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
43: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpibaij_C", NULL));
44: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisbaij_C", NULL));
45: #if defined(PETSC_HAVE_CUDA)
46: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcusparse_C", NULL));
47: #endif
48: #if defined(PETSC_HAVE_HIP)
49: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijhipsparse_C", NULL));
50: #endif
51: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
52: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijkokkos_C", NULL));
53: #endif
54: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpidense_C", NULL));
55: #if defined(PETSC_HAVE_ELEMENTAL)
56: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_elemental_C", NULL));
57: #endif
58: #if defined(PETSC_HAVE_SCALAPACK)
59: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_scalapack_C", NULL));
60: #endif
61: #if defined(PETSC_HAVE_HYPRE)
62: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_hypre_C", NULL));
63: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", NULL));
64: #endif
65: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
66: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_is_mpiaij_C", NULL));
67: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_mpiaij_mpiaij_C", NULL));
68: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetUseScalableIncreaseOverlap_C", NULL));
69: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijperm_C", NULL));
70: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijsell_C", NULL));
71: #if defined(PETSC_HAVE_MKL_SPARSE)
72: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijmkl_C", NULL));
73: #endif
74: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcrl_C", NULL));
75: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
76: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisell_C", NULL));
77: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetPreallocationCOO_C", NULL));
78: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetValuesCOO_C", NULL));
79: PetscFunctionReturn(PETSC_SUCCESS);
80: }
82: /* defines MatSetValues_MPI_Hash(), MatAssemblyBegin_MPI_Hash(), and MatAssemblyEnd_MPI_Hash() */
83: #define TYPE AIJ
84: #define TYPE_AIJ
85: #include "../src/mat/impls/aij/mpi/mpihashmat.h"
86: #undef TYPE
87: #undef TYPE_AIJ
89: static PetscErrorCode MatGetRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
90: {
91: Mat B;
93: PetscFunctionBegin;
94: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, &B));
95: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject)B));
96: PetscCall(MatGetRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
97: PetscCall(MatDestroy(&B));
98: PetscFunctionReturn(PETSC_SUCCESS);
99: }
101: static PetscErrorCode MatRestoreRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
102: {
103: Mat B;
105: PetscFunctionBegin;
106: PetscCall(PetscObjectQuery((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject *)&B));
107: PetscCall(MatRestoreRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
108: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", NULL));
109: PetscFunctionReturn(PETSC_SUCCESS);
110: }
112: /*MC
113: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
115: This matrix type is identical to` MATSEQAIJ` when constructed with a single process communicator,
116: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
117: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
118: for communicators controlling multiple processes. It is recommended that you call both of
119: the above preallocation routines for simplicity.
121: Options Database Key:
122: . -mat_type aij - sets the matrix type to `MATAIJ` during a call to `MatSetFromOptions()`
124: Developer Note:
125: Level: beginner
127: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, `MATAIJKOKKOS`,and also automatically switches over to use inodes when
128: enough exist.
130: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`
131: M*/
133: /*MC
134: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
136: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
137: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
138: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
139: for communicators controlling multiple processes. It is recommended that you call both of
140: the above preallocation routines for simplicity.
142: Options Database Key:
143: . -mat_type aijcrl - sets the matrix type to `MATMPIAIJCRL` during a call to `MatSetFromOptions()`
145: Level: beginner
147: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
148: M*/
150: static PetscErrorCode MatBindToCPU_MPIAIJ(Mat A, PetscBool flg)
151: {
152: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
154: PetscFunctionBegin;
155: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP) || defined(PETSC_HAVE_VIENNACL)
156: A->boundtocpu = flg;
157: #endif
158: if (a->A) PetscCall(MatBindToCPU(a->A, flg));
159: if (a->B) PetscCall(MatBindToCPU(a->B, flg));
161: /* In addition to binding the diagonal and off-diagonal matrices, bind the local vectors used for matrix-vector products.
162: * This maybe seems a little odd for a MatBindToCPU() call to do, but it makes no sense for the binding of these vectors
163: * to differ from the parent matrix. */
164: if (a->lvec) PetscCall(VecBindToCPU(a->lvec, flg));
165: if (a->diag) PetscCall(VecBindToCPU(a->diag, flg));
167: PetscFunctionReturn(PETSC_SUCCESS);
168: }
170: static PetscErrorCode MatSetBlockSizes_MPIAIJ(Mat M, PetscInt rbs, PetscInt cbs)
171: {
172: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
174: PetscFunctionBegin;
175: if (mat->A) {
176: PetscCall(MatSetBlockSizes(mat->A, rbs, cbs));
177: PetscCall(MatSetBlockSizes(mat->B, rbs, 1));
178: }
179: PetscFunctionReturn(PETSC_SUCCESS);
180: }
182: static PetscErrorCode MatFindNonzeroRows_MPIAIJ(Mat M, IS *keptrows)
183: {
184: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
185: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data;
186: Mat_SeqAIJ *b = (Mat_SeqAIJ *)mat->B->data;
187: const PetscInt *ia, *ib;
188: const MatScalar *aa, *bb, *aav, *bav;
189: PetscInt na, nb, i, j, *rows, cnt = 0, n0rows;
190: PetscInt m = M->rmap->n, rstart = M->rmap->rstart;
192: PetscFunctionBegin;
193: *keptrows = NULL;
195: ia = a->i;
196: ib = b->i;
197: PetscCall(MatSeqAIJGetArrayRead(mat->A, &aav));
198: PetscCall(MatSeqAIJGetArrayRead(mat->B, &bav));
199: for (i = 0; i < m; i++) {
200: na = ia[i + 1] - ia[i];
201: nb = ib[i + 1] - ib[i];
202: if (!na && !nb) {
203: cnt++;
204: goto ok1;
205: }
206: aa = aav + ia[i];
207: for (j = 0; j < na; j++) {
208: if (aa[j] != 0.0) goto ok1;
209: }
210: bb = bav ? bav + ib[i] : NULL;
211: for (j = 0; j < nb; j++) {
212: if (bb[j] != 0.0) goto ok1;
213: }
214: cnt++;
215: ok1:;
216: }
217: PetscCall(MPIU_Allreduce(&cnt, &n0rows, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)M)));
218: if (!n0rows) {
219: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
220: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
221: PetscFunctionReturn(PETSC_SUCCESS);
222: }
223: PetscCall(PetscMalloc1(M->rmap->n - cnt, &rows));
224: cnt = 0;
225: for (i = 0; i < m; i++) {
226: na = ia[i + 1] - ia[i];
227: nb = ib[i + 1] - ib[i];
228: if (!na && !nb) continue;
229: aa = aav + ia[i];
230: for (j = 0; j < na; j++) {
231: if (aa[j] != 0.0) {
232: rows[cnt++] = rstart + i;
233: goto ok2;
234: }
235: }
236: bb = bav ? bav + ib[i] : NULL;
237: for (j = 0; j < nb; j++) {
238: if (bb[j] != 0.0) {
239: rows[cnt++] = rstart + i;
240: goto ok2;
241: }
242: }
243: ok2:;
244: }
245: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), cnt, rows, PETSC_OWN_POINTER, keptrows));
246: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
247: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
248: PetscFunctionReturn(PETSC_SUCCESS);
249: }
251: static PetscErrorCode MatDiagonalSet_MPIAIJ(Mat Y, Vec D, InsertMode is)
252: {
253: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Y->data;
254: PetscBool cong;
256: PetscFunctionBegin;
257: PetscCall(MatHasCongruentLayouts(Y, &cong));
258: if (Y->assembled && cong) {
259: PetscCall(MatDiagonalSet(aij->A, D, is));
260: } else {
261: PetscCall(MatDiagonalSet_Default(Y, D, is));
262: }
263: PetscFunctionReturn(PETSC_SUCCESS);
264: }
266: static PetscErrorCode MatFindZeroDiagonals_MPIAIJ(Mat M, IS *zrows)
267: {
268: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)M->data;
269: PetscInt i, rstart, nrows, *rows;
271: PetscFunctionBegin;
272: *zrows = NULL;
273: PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(aij->A, &nrows, &rows));
274: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
275: for (i = 0; i < nrows; i++) rows[i] += rstart;
276: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), nrows, rows, PETSC_OWN_POINTER, zrows));
277: PetscFunctionReturn(PETSC_SUCCESS);
278: }
280: static PetscErrorCode MatGetColumnReductions_MPIAIJ(Mat A, PetscInt type, PetscReal *reductions)
281: {
282: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)A->data;
283: PetscInt i, m, n, *garray = aij->garray;
284: Mat_SeqAIJ *a_aij = (Mat_SeqAIJ *)aij->A->data;
285: Mat_SeqAIJ *b_aij = (Mat_SeqAIJ *)aij->B->data;
286: PetscReal *work;
287: const PetscScalar *dummy;
289: PetscFunctionBegin;
290: PetscCall(MatGetSize(A, &m, &n));
291: PetscCall(PetscCalloc1(n, &work));
292: PetscCall(MatSeqAIJGetArrayRead(aij->A, &dummy));
293: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &dummy));
294: PetscCall(MatSeqAIJGetArrayRead(aij->B, &dummy));
295: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &dummy));
296: if (type == NORM_2) {
297: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i] * a_aij->a[i]);
298: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i] * b_aij->a[i]);
299: } else if (type == NORM_1) {
300: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i]);
301: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]);
302: } else if (type == NORM_INFINITY) {
303: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] = PetscMax(PetscAbsScalar(a_aij->a[i]), work[A->cmap->rstart + a_aij->j[i]]);
304: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] = PetscMax(PetscAbsScalar(b_aij->a[i]), work[garray[b_aij->j[i]]]);
305: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
306: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscRealPart(a_aij->a[i]);
307: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscRealPart(b_aij->a[i]);
308: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
309: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscImaginaryPart(a_aij->a[i]);
310: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscImaginaryPart(b_aij->a[i]);
311: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
312: if (type == NORM_INFINITY) {
313: PetscCall(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
314: } else {
315: PetscCall(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
316: }
317: PetscCall(PetscFree(work));
318: if (type == NORM_2) {
319: for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
320: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
321: for (i = 0; i < n; i++) reductions[i] /= m;
322: }
323: PetscFunctionReturn(PETSC_SUCCESS);
324: }
326: static PetscErrorCode MatFindOffBlockDiagonalEntries_MPIAIJ(Mat A, IS *is)
327: {
328: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
329: IS sis, gis;
330: const PetscInt *isis, *igis;
331: PetscInt n, *iis, nsis, ngis, rstart, i;
333: PetscFunctionBegin;
334: PetscCall(MatFindOffBlockDiagonalEntries(a->A, &sis));
335: PetscCall(MatFindNonzeroRows(a->B, &gis));
336: PetscCall(ISGetSize(gis, &ngis));
337: PetscCall(ISGetSize(sis, &nsis));
338: PetscCall(ISGetIndices(sis, &isis));
339: PetscCall(ISGetIndices(gis, &igis));
341: PetscCall(PetscMalloc1(ngis + nsis, &iis));
342: PetscCall(PetscArraycpy(iis, igis, ngis));
343: PetscCall(PetscArraycpy(iis + ngis, isis, nsis));
344: n = ngis + nsis;
345: PetscCall(PetscSortRemoveDupsInt(&n, iis));
346: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
347: for (i = 0; i < n; i++) iis[i] += rstart;
348: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), n, iis, PETSC_OWN_POINTER, is));
350: PetscCall(ISRestoreIndices(sis, &isis));
351: PetscCall(ISRestoreIndices(gis, &igis));
352: PetscCall(ISDestroy(&sis));
353: PetscCall(ISDestroy(&gis));
354: PetscFunctionReturn(PETSC_SUCCESS);
355: }
357: /*
358: Local utility routine that creates a mapping from the global column
359: number to the local number in the off-diagonal part of the local
360: storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at
361: a slightly higher hash table cost; without it it is not scalable (each processor
362: has an order N integer array but is fast to access.
363: */
364: PetscErrorCode MatCreateColmap_MPIAIJ_Private(Mat mat)
365: {
366: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
367: PetscInt n = aij->B->cmap->n, i;
369: PetscFunctionBegin;
370: PetscCheck(!n || aij->garray, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MPIAIJ Matrix was assembled but is missing garray");
371: #if defined(PETSC_USE_CTABLE)
372: PetscCall(PetscHMapICreateWithSize(n, &aij->colmap));
373: for (i = 0; i < n; i++) PetscCall(PetscHMapISet(aij->colmap, aij->garray[i] + 1, i + 1));
374: #else
375: PetscCall(PetscCalloc1(mat->cmap->N + 1, &aij->colmap));
376: for (i = 0; i < n; i++) aij->colmap[aij->garray[i]] = i + 1;
377: #endif
378: PetscFunctionReturn(PETSC_SUCCESS);
379: }
381: #define MatSetValues_SeqAIJ_A_Private(row, col, value, addv, orow, ocol) \
382: do { \
383: if (col <= lastcol1) low1 = 0; \
384: else high1 = nrow1; \
385: lastcol1 = col; \
386: while (high1 - low1 > 5) { \
387: t = (low1 + high1) / 2; \
388: if (rp1[t] > col) high1 = t; \
389: else low1 = t; \
390: } \
391: for (_i = low1; _i < high1; _i++) { \
392: if (rp1[_i] > col) break; \
393: if (rp1[_i] == col) { \
394: if (addv == ADD_VALUES) { \
395: ap1[_i] += value; \
396: /* Not sure LogFlops will slow dow the code or not */ \
397: (void)PetscLogFlops(1.0); \
398: } else ap1[_i] = value; \
399: goto a_noinsert; \
400: } \
401: } \
402: if (value == 0.0 && ignorezeroentries && row != col) { \
403: low1 = 0; \
404: high1 = nrow1; \
405: goto a_noinsert; \
406: } \
407: if (nonew == 1) { \
408: low1 = 0; \
409: high1 = nrow1; \
410: goto a_noinsert; \
411: } \
412: 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); \
413: MatSeqXAIJReallocateAIJ(A, am, 1, nrow1, row, col, rmax1, aa, ai, aj, rp1, ap1, aimax, nonew, MatScalar); \
414: N = nrow1++ - 1; \
415: a->nz++; \
416: high1++; \
417: /* shift up all the later entries in this row */ \
418: PetscCall(PetscArraymove(rp1 + _i + 1, rp1 + _i, N - _i + 1)); \
419: PetscCall(PetscArraymove(ap1 + _i + 1, ap1 + _i, N - _i + 1)); \
420: rp1[_i] = col; \
421: ap1[_i] = value; \
422: A->nonzerostate++; \
423: a_noinsert:; \
424: ailen[row] = nrow1; \
425: } while (0)
427: #define MatSetValues_SeqAIJ_B_Private(row, col, value, addv, orow, ocol) \
428: do { \
429: if (col <= lastcol2) low2 = 0; \
430: else high2 = nrow2; \
431: lastcol2 = col; \
432: while (high2 - low2 > 5) { \
433: t = (low2 + high2) / 2; \
434: if (rp2[t] > col) high2 = t; \
435: else low2 = t; \
436: } \
437: for (_i = low2; _i < high2; _i++) { \
438: if (rp2[_i] > col) break; \
439: if (rp2[_i] == col) { \
440: if (addv == ADD_VALUES) { \
441: ap2[_i] += value; \
442: (void)PetscLogFlops(1.0); \
443: } else ap2[_i] = value; \
444: goto b_noinsert; \
445: } \
446: } \
447: if (value == 0.0 && ignorezeroentries) { \
448: low2 = 0; \
449: high2 = nrow2; \
450: goto b_noinsert; \
451: } \
452: if (nonew == 1) { \
453: low2 = 0; \
454: high2 = nrow2; \
455: goto b_noinsert; \
456: } \
457: 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); \
458: MatSeqXAIJReallocateAIJ(B, bm, 1, nrow2, row, col, rmax2, ba, bi, bj, rp2, ap2, bimax, nonew, MatScalar); \
459: N = nrow2++ - 1; \
460: b->nz++; \
461: high2++; \
462: /* shift up all the later entries in this row */ \
463: PetscCall(PetscArraymove(rp2 + _i + 1, rp2 + _i, N - _i + 1)); \
464: PetscCall(PetscArraymove(ap2 + _i + 1, ap2 + _i, N - _i + 1)); \
465: rp2[_i] = col; \
466: ap2[_i] = value; \
467: B->nonzerostate++; \
468: b_noinsert:; \
469: bilen[row] = nrow2; \
470: } while (0)
472: static PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A, PetscInt row, const PetscScalar v[])
473: {
474: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
475: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data, *b = (Mat_SeqAIJ *)mat->B->data;
476: PetscInt l, *garray = mat->garray, diag;
477: PetscScalar *aa, *ba;
479: PetscFunctionBegin;
480: /* code only works for square matrices A */
482: /* find size of row to the left of the diagonal part */
483: PetscCall(MatGetOwnershipRange(A, &diag, NULL));
484: row = row - diag;
485: for (l = 0; l < b->i[row + 1] - b->i[row]; l++) {
486: if (garray[b->j[b->i[row] + l]] > diag) break;
487: }
488: if (l) {
489: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
490: PetscCall(PetscArraycpy(ba + b->i[row], v, l));
491: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
492: }
494: /* diagonal part */
495: if (a->i[row + 1] - a->i[row]) {
496: PetscCall(MatSeqAIJGetArray(mat->A, &aa));
497: PetscCall(PetscArraycpy(aa + a->i[row], v + l, (a->i[row + 1] - a->i[row])));
498: PetscCall(MatSeqAIJRestoreArray(mat->A, &aa));
499: }
501: /* right of diagonal part */
502: if (b->i[row + 1] - b->i[row] - l) {
503: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
504: PetscCall(PetscArraycpy(ba + b->i[row] + l, v + l + a->i[row + 1] - a->i[row], b->i[row + 1] - b->i[row] - l));
505: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
506: }
507: PetscFunctionReturn(PETSC_SUCCESS);
508: }
510: PetscErrorCode MatSetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
511: {
512: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
513: PetscScalar value = 0.0;
514: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
515: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
516: PetscBool roworiented = aij->roworiented;
518: /* Some Variables required in the macro */
519: Mat A = aij->A;
520: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
521: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
522: PetscBool ignorezeroentries = a->ignorezeroentries;
523: Mat B = aij->B;
524: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
525: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
526: MatScalar *aa, *ba;
527: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
528: PetscInt nonew;
529: MatScalar *ap1, *ap2;
531: PetscFunctionBegin;
532: PetscCall(MatSeqAIJGetArray(A, &aa));
533: PetscCall(MatSeqAIJGetArray(B, &ba));
534: for (i = 0; i < m; i++) {
535: if (im[i] < 0) continue;
536: 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);
537: if (im[i] >= rstart && im[i] < rend) {
538: row = im[i] - rstart;
539: lastcol1 = -1;
540: rp1 = aj ? aj + ai[row] : NULL;
541: ap1 = aa ? aa + ai[row] : NULL;
542: rmax1 = aimax[row];
543: nrow1 = ailen[row];
544: low1 = 0;
545: high1 = nrow1;
546: lastcol2 = -1;
547: rp2 = bj ? bj + bi[row] : NULL;
548: ap2 = ba ? ba + bi[row] : NULL;
549: rmax2 = bimax[row];
550: nrow2 = bilen[row];
551: low2 = 0;
552: high2 = nrow2;
554: for (j = 0; j < n; j++) {
555: if (v) value = roworiented ? v[i * n + j] : v[i + j * m];
556: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
557: if (in[j] >= cstart && in[j] < cend) {
558: col = in[j] - cstart;
559: nonew = a->nonew;
560: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
561: } else if (in[j] < 0) {
562: continue;
563: } else {
564: 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);
565: if (mat->was_assembled) {
566: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
567: #if defined(PETSC_USE_CTABLE)
568: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col)); /* map global col ids to local ones */
569: col--;
570: #else
571: col = aij->colmap[in[j]] - 1;
572: #endif
573: if (col < 0 && !((Mat_SeqAIJ *)(aij->B->data))->nonew) { /* col < 0 means in[j] is a new col for B */
574: PetscCall(MatDisAssemble_MPIAIJ(mat)); /* Change aij->B from reduced/local format to expanded/global format */
575: col = in[j];
576: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
577: B = aij->B;
578: b = (Mat_SeqAIJ *)B->data;
579: bimax = b->imax;
580: bi = b->i;
581: bilen = b->ilen;
582: bj = b->j;
583: ba = b->a;
584: rp2 = bj + bi[row];
585: ap2 = ba + bi[row];
586: rmax2 = bimax[row];
587: nrow2 = bilen[row];
588: low2 = 0;
589: high2 = nrow2;
590: bm = aij->B->rmap->n;
591: ba = b->a;
592: } else if (col < 0 && !(ignorezeroentries && value == 0.0)) {
593: if (1 == ((Mat_SeqAIJ *)(aij->B->data))->nonew) {
594: 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]));
595: } else SETERRQ(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]);
596: }
597: } else col = in[j];
598: nonew = b->nonew;
599: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
600: }
601: }
602: } else {
603: 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]);
604: if (!aij->donotstash) {
605: mat->assembled = PETSC_FALSE;
606: if (roworiented) {
607: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v ? v + i * n : NULL, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
608: } else {
609: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v ? v + i : NULL, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
610: }
611: }
612: }
613: }
614: PetscCall(MatSeqAIJRestoreArray(A, &aa)); /* aa, bb might have been free'd due to reallocation above. But we don't access them here */
615: PetscCall(MatSeqAIJRestoreArray(B, &ba));
616: PetscFunctionReturn(PETSC_SUCCESS);
617: }
619: /*
620: This function sets the j and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
621: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
622: No off-processor parts off the matrix are allowed here and mat->was_assembled has to be PETSC_FALSE.
623: */
624: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[])
625: {
626: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
627: Mat A = aij->A; /* diagonal part of the matrix */
628: Mat B = aij->B; /* off-diagonal part of the matrix */
629: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
630: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
631: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, col;
632: PetscInt *ailen = a->ilen, *aj = a->j;
633: PetscInt *bilen = b->ilen, *bj = b->j;
634: PetscInt am = aij->A->rmap->n, j;
635: PetscInt diag_so_far = 0, dnz;
636: PetscInt offd_so_far = 0, onz;
638: PetscFunctionBegin;
639: /* Iterate over all rows of the matrix */
640: for (j = 0; j < am; j++) {
641: dnz = onz = 0;
642: /* Iterate over all non-zero columns of the current row */
643: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
644: /* If column is in the diagonal */
645: if (mat_j[col] >= cstart && mat_j[col] < cend) {
646: aj[diag_so_far++] = mat_j[col] - cstart;
647: dnz++;
648: } else { /* off-diagonal entries */
649: bj[offd_so_far++] = mat_j[col];
650: onz++;
651: }
652: }
653: ailen[j] = dnz;
654: bilen[j] = onz;
655: }
656: PetscFunctionReturn(PETSC_SUCCESS);
657: }
659: /*
660: This function sets the local j, a and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
661: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
662: No off-processor parts off the matrix are allowed here, they are set at a later point by MatSetValues_MPIAIJ.
663: Also, mat->was_assembled has to be false, otherwise the statement aj[rowstart_diag+dnz_row] = mat_j[col] - cstart;
664: would not be true and the more complex MatSetValues_MPIAIJ has to be used.
665: */
666: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[], const PetscScalar mat_a[])
667: {
668: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
669: Mat A = aij->A; /* diagonal part of the matrix */
670: Mat B = aij->B; /* off-diagonal part of the matrix */
671: Mat_SeqAIJ *aijd = (Mat_SeqAIJ *)(aij->A)->data, *aijo = (Mat_SeqAIJ *)(aij->B)->data;
672: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
673: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
674: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend;
675: PetscInt *ailen = a->ilen, *aj = a->j;
676: PetscInt *bilen = b->ilen, *bj = b->j;
677: PetscInt am = aij->A->rmap->n, j;
678: 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. */
679: PetscInt col, dnz_row, onz_row, rowstart_diag, rowstart_offd;
680: PetscScalar *aa = a->a, *ba = b->a;
682: PetscFunctionBegin;
683: /* Iterate over all rows of the matrix */
684: for (j = 0; j < am; j++) {
685: dnz_row = onz_row = 0;
686: rowstart_offd = full_offd_i[j];
687: rowstart_diag = full_diag_i[j];
688: /* Iterate over all non-zero columns of the current row */
689: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
690: /* If column is in the diagonal */
691: if (mat_j[col] >= cstart && mat_j[col] < cend) {
692: aj[rowstart_diag + dnz_row] = mat_j[col] - cstart;
693: aa[rowstart_diag + dnz_row] = mat_a[col];
694: dnz_row++;
695: } else { /* off-diagonal entries */
696: bj[rowstart_offd + onz_row] = mat_j[col];
697: ba[rowstart_offd + onz_row] = mat_a[col];
698: onz_row++;
699: }
700: }
701: ailen[j] = dnz_row;
702: bilen[j] = onz_row;
703: }
704: PetscFunctionReturn(PETSC_SUCCESS);
705: }
707: static PetscErrorCode MatGetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
708: {
709: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
710: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
711: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
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: if (idxn[j] >= cstart && idxn[j] < cend) {
723: col = idxn[j] - cstart;
724: PetscCall(MatGetValues(aij->A, 1, &row, 1, &col, v + i * n + j));
725: } else {
726: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
727: #if defined(PETSC_USE_CTABLE)
728: PetscCall(PetscHMapIGetWithDefault(aij->colmap, idxn[j] + 1, 0, &col));
729: col--;
730: #else
731: col = aij->colmap[idxn[j]] - 1;
732: #endif
733: if ((col < 0) || (aij->garray[col] != idxn[j])) *(v + i * n + j) = 0.0;
734: else PetscCall(MatGetValues(aij->B, 1, &row, 1, &col, v + i * n + j));
735: }
736: }
737: }
738: PetscFunctionReturn(PETSC_SUCCESS);
739: }
741: static PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat, MatAssemblyType mode)
742: {
743: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
744: PetscInt nstash, reallocs;
746: PetscFunctionBegin;
747: if (aij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
749: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
750: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
751: PetscCall(PetscInfo(aij->A, "Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
752: PetscFunctionReturn(PETSC_SUCCESS);
753: }
755: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat, MatAssemblyType mode)
756: {
757: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
758: PetscMPIInt n;
759: PetscInt i, j, rstart, ncols, flg;
760: PetscInt *row, *col;
761: PetscBool other_disassembled;
762: PetscScalar *val;
764: /* do not use 'b = (Mat_SeqAIJ*)aij->B->data' as B can be reset in disassembly */
766: PetscFunctionBegin;
767: if (!aij->donotstash && !mat->nooffprocentries) {
768: while (1) {
769: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
770: if (!flg) break;
772: for (i = 0; i < n;) {
773: /* Now identify the consecutive vals belonging to the same row */
774: for (j = i, rstart = row[j]; j < n; j++) {
775: if (row[j] != rstart) break;
776: }
777: if (j < n) ncols = j - i;
778: else ncols = n - i;
779: /* Now assemble all these values with a single function call */
780: PetscCall(MatSetValues_MPIAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
781: i = j;
782: }
783: }
784: PetscCall(MatStashScatterEnd_Private(&mat->stash));
785: }
786: #if defined(PETSC_HAVE_DEVICE)
787: if (mat->offloadmask == PETSC_OFFLOAD_CPU) aij->A->offloadmask = PETSC_OFFLOAD_CPU;
788: /* We call MatBindToCPU() on aij->A and aij->B here, because if MatBindToCPU_MPIAIJ() is called before assembly, it cannot bind these. */
789: if (mat->boundtocpu) {
790: PetscCall(MatBindToCPU(aij->A, PETSC_TRUE));
791: PetscCall(MatBindToCPU(aij->B, PETSC_TRUE));
792: }
793: #endif
794: PetscCall(MatAssemblyBegin(aij->A, mode));
795: PetscCall(MatAssemblyEnd(aij->A, mode));
797: /* determine if any processor has disassembled, if so we must
798: also disassemble ourself, in order that we may reassemble. */
799: /*
800: if nonzero structure of submatrix B cannot change then we know that
801: no processor disassembled thus we can skip this stuff
802: */
803: if (!((Mat_SeqAIJ *)aij->B->data)->nonew) {
804: PetscCall(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
805: if (mat->was_assembled && !other_disassembled) { /* mat on this rank has reduced off-diag B with local col ids, but globally it does not */
806: PetscCall(MatDisAssemble_MPIAIJ(mat));
807: }
808: }
809: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIAIJ(mat));
810: PetscCall(MatSetOption(aij->B, MAT_USE_INODES, PETSC_FALSE));
811: #if defined(PETSC_HAVE_DEVICE)
812: if (mat->offloadmask == PETSC_OFFLOAD_CPU && aij->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) aij->B->offloadmask = PETSC_OFFLOAD_CPU;
813: #endif
814: PetscCall(MatAssemblyBegin(aij->B, mode));
815: PetscCall(MatAssemblyEnd(aij->B, mode));
817: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
819: aij->rowvalues = NULL;
821: PetscCall(VecDestroy(&aij->diag));
823: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
824: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqAIJ *)(aij->A->data))->nonew) {
825: PetscObjectState state = aij->A->nonzerostate + aij->B->nonzerostate;
826: PetscCall(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
827: }
828: #if defined(PETSC_HAVE_DEVICE)
829: mat->offloadmask = PETSC_OFFLOAD_BOTH;
830: #endif
831: PetscFunctionReturn(PETSC_SUCCESS);
832: }
834: static PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
835: {
836: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
838: PetscFunctionBegin;
839: PetscCall(MatZeroEntries(l->A));
840: PetscCall(MatZeroEntries(l->B));
841: PetscFunctionReturn(PETSC_SUCCESS);
842: }
844: static PetscErrorCode MatZeroRows_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
845: {
846: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
847: PetscObjectState sA, sB;
848: PetscInt *lrows;
849: PetscInt r, len;
850: PetscBool cong, lch, gch;
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: sA = mat->A->nonzerostate;
870: sB = mat->B->nonzerostate;
872: if (diag != 0.0 && cong) {
873: PetscCall(MatZeroRows(mat->A, len, lrows, diag, NULL, NULL));
874: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
875: } else if (diag != 0.0) { /* non-square or non congruent layouts -> if keepnonzeropattern is false, we allow for new insertion */
876: Mat_SeqAIJ *aijA = (Mat_SeqAIJ *)mat->A->data;
877: Mat_SeqAIJ *aijB = (Mat_SeqAIJ *)mat->B->data;
878: PetscInt nnwA, nnwB;
879: PetscBool nnzA, nnzB;
881: nnwA = aijA->nonew;
882: nnwB = aijB->nonew;
883: nnzA = aijA->keepnonzeropattern;
884: nnzB = aijB->keepnonzeropattern;
885: if (!nnzA) {
886: PetscCall(PetscInfo(mat->A, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on diagonal block.\n"));
887: aijA->nonew = 0;
888: }
889: if (!nnzB) {
890: PetscCall(PetscInfo(mat->B, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on off-diagonal block.\n"));
891: aijB->nonew = 0;
892: }
893: /* Must zero here before the next loop */
894: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
895: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
896: for (r = 0; r < len; ++r) {
897: const PetscInt row = lrows[r] + A->rmap->rstart;
898: if (row >= A->cmap->N) continue;
899: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
900: }
901: aijA->nonew = nnwA;
902: aijB->nonew = nnwB;
903: } else {
904: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
905: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
906: }
907: PetscCall(PetscFree(lrows));
908: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
909: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
911: /* reduce nonzerostate */
912: lch = (PetscBool)(sA != mat->A->nonzerostate || sB != mat->B->nonzerostate);
913: PetscCall(MPIU_Allreduce(&lch, &gch, 1, MPIU_BOOL, MPI_LOR, PetscObjectComm((PetscObject)A)));
914: if (gch) A->nonzerostate++;
915: PetscFunctionReturn(PETSC_SUCCESS);
916: }
918: static PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
919: {
920: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
921: PetscMPIInt n = A->rmap->n;
922: PetscInt i, j, r, m, len = 0;
923: PetscInt *lrows, *owners = A->rmap->range;
924: PetscMPIInt p = 0;
925: PetscSFNode *rrows;
926: PetscSF sf;
927: const PetscScalar *xx;
928: PetscScalar *bb, *mask, *aij_a;
929: Vec xmask, lmask;
930: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)l->B->data;
931: const PetscInt *aj, *ii, *ridx;
932: PetscScalar *aa;
934: PetscFunctionBegin;
935: /* Create SF where leaves are input rows and roots are owned rows */
936: PetscCall(PetscMalloc1(n, &lrows));
937: for (r = 0; r < n; ++r) lrows[r] = -1;
938: PetscCall(PetscMalloc1(N, &rrows));
939: for (r = 0; r < N; ++r) {
940: const PetscInt idx = rows[r];
941: 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);
942: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
943: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
944: }
945: rrows[r].rank = p;
946: rrows[r].index = rows[r] - owners[p];
947: }
948: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
949: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
950: /* Collect flags for rows to be zeroed */
951: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
952: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
953: PetscCall(PetscSFDestroy(&sf));
954: /* Compress and put in row numbers */
955: for (r = 0; r < n; ++r)
956: if (lrows[r] >= 0) lrows[len++] = r;
957: /* zero diagonal part of matrix */
958: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
959: /* handle off-diagonal part of matrix */
960: PetscCall(MatCreateVecs(A, &xmask, NULL));
961: PetscCall(VecDuplicate(l->lvec, &lmask));
962: PetscCall(VecGetArray(xmask, &bb));
963: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
964: PetscCall(VecRestoreArray(xmask, &bb));
965: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
966: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
967: PetscCall(VecDestroy(&xmask));
968: if (x && b) { /* this code is buggy when the row and column layout don't match */
969: PetscBool cong;
971: PetscCall(MatHasCongruentLayouts(A, &cong));
972: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
973: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
974: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
975: PetscCall(VecGetArrayRead(l->lvec, &xx));
976: PetscCall(VecGetArray(b, &bb));
977: }
978: PetscCall(VecGetArray(lmask, &mask));
979: /* remove zeroed rows of off-diagonal matrix */
980: PetscCall(MatSeqAIJGetArray(l->B, &aij_a));
981: ii = aij->i;
982: for (i = 0; i < len; i++) PetscCall(PetscArrayzero(aij_a + ii[lrows[i]], ii[lrows[i] + 1] - ii[lrows[i]]));
983: /* loop over all elements of off process part of matrix zeroing removed columns*/
984: if (aij->compressedrow.use) {
985: m = aij->compressedrow.nrows;
986: ii = aij->compressedrow.i;
987: ridx = aij->compressedrow.rindex;
988: for (i = 0; i < m; i++) {
989: n = ii[i + 1] - ii[i];
990: aj = aij->j + ii[i];
991: aa = aij_a + ii[i];
993: for (j = 0; j < n; j++) {
994: if (PetscAbsScalar(mask[*aj])) {
995: if (b) bb[*ridx] -= *aa * xx[*aj];
996: *aa = 0.0;
997: }
998: aa++;
999: aj++;
1000: }
1001: ridx++;
1002: }
1003: } else { /* do not use compressed row format */
1004: m = l->B->rmap->n;
1005: for (i = 0; i < m; i++) {
1006: n = ii[i + 1] - ii[i];
1007: aj = aij->j + ii[i];
1008: aa = aij_a + ii[i];
1009: for (j = 0; j < n; j++) {
1010: if (PetscAbsScalar(mask[*aj])) {
1011: if (b) bb[i] -= *aa * xx[*aj];
1012: *aa = 0.0;
1013: }
1014: aa++;
1015: aj++;
1016: }
1017: }
1018: }
1019: if (x && b) {
1020: PetscCall(VecRestoreArray(b, &bb));
1021: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1022: }
1023: PetscCall(MatSeqAIJRestoreArray(l->B, &aij_a));
1024: PetscCall(VecRestoreArray(lmask, &mask));
1025: PetscCall(VecDestroy(&lmask));
1026: PetscCall(PetscFree(lrows));
1028: /* only change matrix nonzero state if pattern was allowed to be changed */
1029: if (!((Mat_SeqAIJ *)(l->A->data))->keepnonzeropattern) {
1030: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1031: PetscCall(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1032: }
1033: PetscFunctionReturn(PETSC_SUCCESS);
1034: }
1036: static PetscErrorCode MatMult_MPIAIJ(Mat A, Vec xx, Vec yy)
1037: {
1038: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1039: PetscInt nt;
1040: VecScatter Mvctx = a->Mvctx;
1042: PetscFunctionBegin;
1043: PetscCall(VecGetLocalSize(xx, &nt));
1044: 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);
1045: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1046: PetscUseTypeMethod(a->A, mult, xx, yy);
1047: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1048: PetscUseTypeMethod(a->B, multadd, a->lvec, yy, yy);
1049: PetscFunctionReturn(PETSC_SUCCESS);
1050: }
1052: static PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A, Vec bb, Vec xx)
1053: {
1054: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1056: PetscFunctionBegin;
1057: PetscCall(MatMultDiagonalBlock(a->A, bb, xx));
1058: PetscFunctionReturn(PETSC_SUCCESS);
1059: }
1061: static PetscErrorCode MatMultAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1062: {
1063: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1064: VecScatter Mvctx = a->Mvctx;
1066: PetscFunctionBegin;
1067: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1068: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1069: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1070: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1071: PetscFunctionReturn(PETSC_SUCCESS);
1072: }
1074: static PetscErrorCode MatMultTranspose_MPIAIJ(Mat A, Vec xx, Vec yy)
1075: {
1076: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1078: PetscFunctionBegin;
1079: /* do nondiagonal part */
1080: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1081: /* do local part */
1082: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1083: /* add partial results together */
1084: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1085: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1086: PetscFunctionReturn(PETSC_SUCCESS);
1087: }
1089: static PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f)
1090: {
1091: MPI_Comm comm;
1092: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)Amat->data, *Bij = (Mat_MPIAIJ *)Bmat->data;
1093: Mat Adia = Aij->A, Bdia = Bij->A, Aoff, Boff, *Aoffs, *Boffs;
1094: IS Me, Notme;
1095: PetscInt M, N, first, last, *notme, i;
1096: PetscBool lf;
1097: PetscMPIInt size;
1099: PetscFunctionBegin;
1100: /* Easy test: symmetric diagonal block */
1101: PetscCall(MatIsTranspose(Adia, Bdia, tol, &lf));
1102: PetscCall(MPIU_Allreduce(&lf, f, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)Amat)));
1103: if (!*f) PetscFunctionReturn(PETSC_SUCCESS);
1104: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1105: PetscCallMPI(MPI_Comm_size(comm, &size));
1106: if (size == 1) PetscFunctionReturn(PETSC_SUCCESS);
1108: /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */
1109: PetscCall(MatGetSize(Amat, &M, &N));
1110: PetscCall(MatGetOwnershipRange(Amat, &first, &last));
1111: PetscCall(PetscMalloc1(N - last + first, ¬me));
1112: for (i = 0; i < first; i++) notme[i] = i;
1113: for (i = last; i < M; i++) notme[i - last + first] = i;
1114: PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme));
1115: PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me));
1116: PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs));
1117: Aoff = Aoffs[0];
1118: PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs));
1119: Boff = Boffs[0];
1120: PetscCall(MatIsTranspose(Aoff, Boff, tol, f));
1121: PetscCall(MatDestroyMatrices(1, &Aoffs));
1122: PetscCall(MatDestroyMatrices(1, &Boffs));
1123: PetscCall(ISDestroy(&Me));
1124: PetscCall(ISDestroy(&Notme));
1125: PetscCall(PetscFree(notme));
1126: PetscFunctionReturn(PETSC_SUCCESS);
1127: }
1129: static PetscErrorCode MatIsSymmetric_MPIAIJ(Mat A, PetscReal tol, PetscBool *f)
1130: {
1131: PetscFunctionBegin;
1132: PetscCall(MatIsTranspose_MPIAIJ(A, A, tol, f));
1133: PetscFunctionReturn(PETSC_SUCCESS);
1134: }
1136: static PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1137: {
1138: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1140: PetscFunctionBegin;
1141: /* do nondiagonal part */
1142: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1143: /* do local part */
1144: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1145: /* add partial results together */
1146: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1147: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1148: PetscFunctionReturn(PETSC_SUCCESS);
1149: }
1151: /*
1152: This only works correctly for square matrices where the subblock A->A is the
1153: diagonal block
1154: */
1155: static PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A, Vec v)
1156: {
1157: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1159: PetscFunctionBegin;
1160: PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1161: 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");
1162: PetscCall(MatGetDiagonal(a->A, v));
1163: PetscFunctionReturn(PETSC_SUCCESS);
1164: }
1166: static PetscErrorCode MatScale_MPIAIJ(Mat A, PetscScalar aa)
1167: {
1168: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1170: PetscFunctionBegin;
1171: PetscCall(MatScale(a->A, aa));
1172: PetscCall(MatScale(a->B, aa));
1173: PetscFunctionReturn(PETSC_SUCCESS);
1174: }
1176: static PetscErrorCode MatView_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
1177: {
1178: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1179: Mat_SeqAIJ *A = (Mat_SeqAIJ *)aij->A->data;
1180: Mat_SeqAIJ *B = (Mat_SeqAIJ *)aij->B->data;
1181: const PetscInt *garray = aij->garray;
1182: const PetscScalar *aa, *ba;
1183: PetscInt header[4], M, N, m, rs, cs, cnt, i, ja, jb;
1184: PetscInt64 nz, hnz;
1185: PetscInt *rowlens;
1186: PetscInt *colidxs;
1187: PetscScalar *matvals;
1188: PetscMPIInt rank;
1190: PetscFunctionBegin;
1191: PetscCall(PetscViewerSetUp(viewer));
1193: M = mat->rmap->N;
1194: N = mat->cmap->N;
1195: m = mat->rmap->n;
1196: rs = mat->rmap->rstart;
1197: cs = mat->cmap->rstart;
1198: nz = A->nz + B->nz;
1200: /* write matrix header */
1201: header[0] = MAT_FILE_CLASSID;
1202: header[1] = M;
1203: header[2] = N;
1204: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1205: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1206: if (rank == 0) {
1207: if (hnz > PETSC_MAX_INT) header[3] = PETSC_MAX_INT;
1208: else header[3] = (PetscInt)hnz;
1209: }
1210: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1212: /* fill in and store row lengths */
1213: PetscCall(PetscMalloc1(m, &rowlens));
1214: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i];
1215: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1216: PetscCall(PetscFree(rowlens));
1218: /* fill in and store column indices */
1219: PetscCall(PetscMalloc1(nz, &colidxs));
1220: for (cnt = 0, i = 0; i < m; i++) {
1221: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1222: if (garray[B->j[jb]] > cs) break;
1223: colidxs[cnt++] = garray[B->j[jb]];
1224: }
1225: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) colidxs[cnt++] = A->j[ja] + cs;
1226: for (; jb < B->i[i + 1]; jb++) colidxs[cnt++] = garray[B->j[jb]];
1227: }
1228: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1229: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
1230: PetscCall(PetscFree(colidxs));
1232: /* fill in and store nonzero values */
1233: PetscCall(MatSeqAIJGetArrayRead(aij->A, &aa));
1234: PetscCall(MatSeqAIJGetArrayRead(aij->B, &ba));
1235: PetscCall(PetscMalloc1(nz, &matvals));
1236: for (cnt = 0, i = 0; i < m; i++) {
1237: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1238: if (garray[B->j[jb]] > cs) break;
1239: matvals[cnt++] = ba[jb];
1240: }
1241: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) matvals[cnt++] = aa[ja];
1242: for (; jb < B->i[i + 1]; jb++) matvals[cnt++] = ba[jb];
1243: }
1244: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &aa));
1245: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &ba));
1246: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1247: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
1248: PetscCall(PetscFree(matvals));
1250: /* write block size option to the viewer's .info file */
1251: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1252: PetscFunctionReturn(PETSC_SUCCESS);
1253: }
1255: #include <petscdraw.h>
1256: static PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1257: {
1258: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1259: PetscMPIInt rank = aij->rank, size = aij->size;
1260: PetscBool isdraw, iascii, isbinary;
1261: PetscViewer sviewer;
1262: PetscViewerFormat format;
1264: PetscFunctionBegin;
1265: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1266: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1267: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1268: if (iascii) {
1269: PetscCall(PetscViewerGetFormat(viewer, &format));
1270: if (format == PETSC_VIEWER_LOAD_BALANCE) {
1271: PetscInt i, nmax = 0, nmin = PETSC_MAX_INT, navg = 0, *nz, nzlocal = ((Mat_SeqAIJ *)(aij->A->data))->nz + ((Mat_SeqAIJ *)(aij->B->data))->nz;
1272: PetscCall(PetscMalloc1(size, &nz));
1273: PetscCallMPI(MPI_Allgather(&nzlocal, 1, MPIU_INT, nz, 1, MPIU_INT, PetscObjectComm((PetscObject)mat)));
1274: for (i = 0; i < (PetscInt)size; i++) {
1275: nmax = PetscMax(nmax, nz[i]);
1276: nmin = PetscMin(nmin, nz[i]);
1277: navg += nz[i];
1278: }
1279: PetscCall(PetscFree(nz));
1280: navg = navg / size;
1281: PetscCall(PetscViewerASCIIPrintf(viewer, "Load Balance - Nonzeros: Min %" PetscInt_FMT " avg %" PetscInt_FMT " max %" PetscInt_FMT "\n", nmin, navg, nmax));
1282: PetscFunctionReturn(PETSC_SUCCESS);
1283: }
1284: PetscCall(PetscViewerGetFormat(viewer, &format));
1285: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1286: MatInfo info;
1287: PetscInt *inodes = NULL;
1289: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1290: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1291: PetscCall(MatInodeGetInodeSizes(aij->A, NULL, &inodes, NULL));
1292: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1293: if (!inodes) {
1294: 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,
1295: (double)info.memory));
1296: } else {
1297: PetscCall(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,
1298: (double)info.memory));
1299: }
1300: PetscCall(MatGetInfo(aij->A, MAT_LOCAL, &info));
1301: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1302: PetscCall(MatGetInfo(aij->B, MAT_LOCAL, &info));
1303: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1304: PetscCall(PetscViewerFlush(viewer));
1305: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1306: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1307: PetscCall(VecScatterView(aij->Mvctx, viewer));
1308: PetscFunctionReturn(PETSC_SUCCESS);
1309: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1310: PetscInt inodecount, inodelimit, *inodes;
1311: PetscCall(MatInodeGetInodeSizes(aij->A, &inodecount, &inodes, &inodelimit));
1312: if (inodes) {
1313: PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
1314: } else {
1315: PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
1316: }
1317: PetscFunctionReturn(PETSC_SUCCESS);
1318: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1319: PetscFunctionReturn(PETSC_SUCCESS);
1320: }
1321: } else if (isbinary) {
1322: if (size == 1) {
1323: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1324: PetscCall(MatView(aij->A, viewer));
1325: } else {
1326: PetscCall(MatView_MPIAIJ_Binary(mat, viewer));
1327: }
1328: PetscFunctionReturn(PETSC_SUCCESS);
1329: } else if (iascii && size == 1) {
1330: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1331: PetscCall(MatView(aij->A, viewer));
1332: PetscFunctionReturn(PETSC_SUCCESS);
1333: } else if (isdraw) {
1334: PetscDraw draw;
1335: PetscBool isnull;
1336: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1337: PetscCall(PetscDrawIsNull(draw, &isnull));
1338: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1339: }
1341: { /* assemble the entire matrix onto first processor */
1342: Mat A = NULL, Av;
1343: IS isrow, iscol;
1345: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->rmap->N : 0, 0, 1, &isrow));
1346: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->cmap->N : 0, 0, 1, &iscol));
1347: PetscCall(MatCreateSubMatrix(mat, isrow, iscol, MAT_INITIAL_MATRIX, &A));
1348: PetscCall(MatMPIAIJGetSeqAIJ(A, &Av, NULL, NULL));
1349: /* The commented code uses MatCreateSubMatrices instead */
1350: /*
1351: Mat *AA, A = NULL, Av;
1352: IS isrow,iscol;
1354: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->rmap->N : 0,0,1,&isrow));
1355: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->cmap->N : 0,0,1,&iscol));
1356: PetscCall(MatCreateSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&AA));
1357: if (rank == 0) {
1358: PetscCall(PetscObjectReference((PetscObject)AA[0]));
1359: A = AA[0];
1360: Av = AA[0];
1361: }
1362: PetscCall(MatDestroySubMatrices(1,&AA));
1363: */
1364: PetscCall(ISDestroy(&iscol));
1365: PetscCall(ISDestroy(&isrow));
1366: /*
1367: Everyone has to call to draw the matrix since the graphics waits are
1368: synchronized across all processors that share the PetscDraw object
1369: */
1370: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1371: if (rank == 0) {
1372: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)Av, ((PetscObject)mat)->name));
1373: PetscCall(MatView_SeqAIJ(Av, sviewer));
1374: }
1375: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1376: PetscCall(PetscViewerFlush(viewer));
1377: PetscCall(MatDestroy(&A));
1378: }
1379: PetscFunctionReturn(PETSC_SUCCESS);
1380: }
1382: PetscErrorCode MatView_MPIAIJ(Mat mat, PetscViewer viewer)
1383: {
1384: PetscBool iascii, isdraw, issocket, isbinary;
1386: PetscFunctionBegin;
1387: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1388: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1389: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1390: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1391: if (iascii || isdraw || isbinary || issocket) PetscCall(MatView_MPIAIJ_ASCIIorDraworSocket(mat, viewer));
1392: PetscFunctionReturn(PETSC_SUCCESS);
1393: }
1395: static PetscErrorCode MatSOR_MPIAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1396: {
1397: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1398: Vec bb1 = NULL;
1399: PetscBool hasop;
1401: PetscFunctionBegin;
1402: if (flag == SOR_APPLY_UPPER) {
1403: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1404: PetscFunctionReturn(PETSC_SUCCESS);
1405: }
1407: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1));
1409: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
1410: if (flag & SOR_ZERO_INITIAL_GUESS) {
1411: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1412: its--;
1413: }
1415: while (its--) {
1416: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1417: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1419: /* update rhs: bb1 = bb - B*x */
1420: PetscCall(VecScale(mat->lvec, -1.0));
1421: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1423: /* local sweep */
1424: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
1425: }
1426: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1427: if (flag & SOR_ZERO_INITIAL_GUESS) {
1428: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1429: its--;
1430: }
1431: while (its--) {
1432: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1433: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1435: /* update rhs: bb1 = bb - B*x */
1436: PetscCall(VecScale(mat->lvec, -1.0));
1437: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1439: /* local sweep */
1440: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
1441: }
1442: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1443: if (flag & SOR_ZERO_INITIAL_GUESS) {
1444: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1445: its--;
1446: }
1447: while (its--) {
1448: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1449: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1451: /* update rhs: bb1 = bb - B*x */
1452: PetscCall(VecScale(mat->lvec, -1.0));
1453: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1455: /* local sweep */
1456: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
1457: }
1458: } else if (flag & SOR_EISENSTAT) {
1459: Vec xx1;
1461: PetscCall(VecDuplicate(bb, &xx1));
1462: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP), fshift, lits, 1, xx));
1464: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1465: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1466: if (!mat->diag) {
1467: PetscCall(MatCreateVecs(matin, &mat->diag, NULL));
1468: PetscCall(MatGetDiagonal(matin, mat->diag));
1469: }
1470: PetscCall(MatHasOperation(matin, MATOP_MULT_DIAGONAL_BLOCK, &hasop));
1471: if (hasop) {
1472: PetscCall(MatMultDiagonalBlock(matin, xx, bb1));
1473: } else {
1474: PetscCall(VecPointwiseMult(bb1, mat->diag, xx));
1475: }
1476: PetscCall(VecAYPX(bb1, (omega - 2.0) / omega, bb));
1478: PetscCall(MatMultAdd(mat->B, mat->lvec, bb1, bb1));
1480: /* local sweep */
1481: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP), fshift, lits, 1, xx1));
1482: PetscCall(VecAXPY(xx, 1.0, xx1));
1483: PetscCall(VecDestroy(&xx1));
1484: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");
1486: PetscCall(VecDestroy(&bb1));
1488: matin->factorerrortype = mat->A->factorerrortype;
1489: PetscFunctionReturn(PETSC_SUCCESS);
1490: }
1492: static PetscErrorCode MatPermute_MPIAIJ(Mat A, IS rowp, IS colp, Mat *B)
1493: {
1494: Mat aA, aB, Aperm;
1495: const PetscInt *rwant, *cwant, *gcols, *ai, *bi, *aj, *bj;
1496: PetscScalar *aa, *ba;
1497: PetscInt i, j, m, n, ng, anz, bnz, *dnnz, *onnz, *tdnnz, *tonnz, *rdest, *cdest, *work, *gcdest;
1498: PetscSF rowsf, sf;
1499: IS parcolp = NULL;
1500: PetscBool done;
1502: PetscFunctionBegin;
1503: PetscCall(MatGetLocalSize(A, &m, &n));
1504: PetscCall(ISGetIndices(rowp, &rwant));
1505: PetscCall(ISGetIndices(colp, &cwant));
1506: PetscCall(PetscMalloc3(PetscMax(m, n), &work, m, &rdest, n, &cdest));
1508: /* Invert row permutation to find out where my rows should go */
1509: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &rowsf));
1510: PetscCall(PetscSFSetGraphLayout(rowsf, A->rmap, A->rmap->n, NULL, PETSC_OWN_POINTER, rwant));
1511: PetscCall(PetscSFSetFromOptions(rowsf));
1512: for (i = 0; i < m; i++) work[i] = A->rmap->rstart + i;
1513: PetscCall(PetscSFReduceBegin(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1514: PetscCall(PetscSFReduceEnd(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1516: /* Invert column permutation to find out where my columns should go */
1517: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1518: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, A->cmap->n, NULL, PETSC_OWN_POINTER, cwant));
1519: PetscCall(PetscSFSetFromOptions(sf));
1520: for (i = 0; i < n; i++) work[i] = A->cmap->rstart + i;
1521: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1522: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1523: PetscCall(PetscSFDestroy(&sf));
1525: PetscCall(ISRestoreIndices(rowp, &rwant));
1526: PetscCall(ISRestoreIndices(colp, &cwant));
1527: PetscCall(MatMPIAIJGetSeqAIJ(A, &aA, &aB, &gcols));
1529: /* Find out where my gcols should go */
1530: PetscCall(MatGetSize(aB, NULL, &ng));
1531: PetscCall(PetscMalloc1(ng, &gcdest));
1532: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1533: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, ng, NULL, PETSC_OWN_POINTER, gcols));
1534: PetscCall(PetscSFSetFromOptions(sf));
1535: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1536: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1537: PetscCall(PetscSFDestroy(&sf));
1539: PetscCall(PetscCalloc4(m, &dnnz, m, &onnz, m, &tdnnz, m, &tonnz));
1540: PetscCall(MatGetRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1541: PetscCall(MatGetRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1542: for (i = 0; i < m; i++) {
1543: PetscInt row = rdest[i];
1544: PetscMPIInt rowner;
1545: PetscCall(PetscLayoutFindOwner(A->rmap, row, &rowner));
1546: for (j = ai[i]; j < ai[i + 1]; j++) {
1547: PetscInt col = cdest[aj[j]];
1548: PetscMPIInt cowner;
1549: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner)); /* Could build an index for the columns to eliminate this search */
1550: if (rowner == cowner) dnnz[i]++;
1551: else onnz[i]++;
1552: }
1553: for (j = bi[i]; j < bi[i + 1]; j++) {
1554: PetscInt col = gcdest[bj[j]];
1555: PetscMPIInt cowner;
1556: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner));
1557: if (rowner == cowner) dnnz[i]++;
1558: else onnz[i]++;
1559: }
1560: }
1561: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1562: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1563: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1564: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1565: PetscCall(PetscSFDestroy(&rowsf));
1567: PetscCall(MatCreateAIJ(PetscObjectComm((PetscObject)A), A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N, 0, tdnnz, 0, tonnz, &Aperm));
1568: PetscCall(MatSeqAIJGetArray(aA, &aa));
1569: PetscCall(MatSeqAIJGetArray(aB, &ba));
1570: for (i = 0; i < m; i++) {
1571: PetscInt *acols = dnnz, *bcols = onnz; /* Repurpose now-unneeded arrays */
1572: PetscInt j0, rowlen;
1573: rowlen = ai[i + 1] - ai[i];
1574: for (j0 = j = 0; j < rowlen; j0 = j) { /* rowlen could be larger than number of rows m, so sum in batches */
1575: for (; j < PetscMin(rowlen, j0 + m); j++) acols[j - j0] = cdest[aj[ai[i] + j]];
1576: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, acols, aa + ai[i] + j0, INSERT_VALUES));
1577: }
1578: rowlen = bi[i + 1] - bi[i];
1579: for (j0 = j = 0; j < rowlen; j0 = j) {
1580: for (; j < PetscMin(rowlen, j0 + m); j++) bcols[j - j0] = gcdest[bj[bi[i] + j]];
1581: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, bcols, ba + bi[i] + j0, INSERT_VALUES));
1582: }
1583: }
1584: PetscCall(MatAssemblyBegin(Aperm, MAT_FINAL_ASSEMBLY));
1585: PetscCall(MatAssemblyEnd(Aperm, MAT_FINAL_ASSEMBLY));
1586: PetscCall(MatRestoreRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1587: PetscCall(MatRestoreRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1588: PetscCall(MatSeqAIJRestoreArray(aA, &aa));
1589: PetscCall(MatSeqAIJRestoreArray(aB, &ba));
1590: PetscCall(PetscFree4(dnnz, onnz, tdnnz, tonnz));
1591: PetscCall(PetscFree3(work, rdest, cdest));
1592: PetscCall(PetscFree(gcdest));
1593: if (parcolp) PetscCall(ISDestroy(&colp));
1594: *B = Aperm;
1595: PetscFunctionReturn(PETSC_SUCCESS);
1596: }
1598: static PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
1599: {
1600: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1602: PetscFunctionBegin;
1603: PetscCall(MatGetSize(aij->B, NULL, nghosts));
1604: if (ghosts) *ghosts = aij->garray;
1605: PetscFunctionReturn(PETSC_SUCCESS);
1606: }
1608: static PetscErrorCode MatGetInfo_MPIAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1609: {
1610: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1611: Mat A = mat->A, B = mat->B;
1612: PetscLogDouble isend[5], irecv[5];
1614: PetscFunctionBegin;
1615: info->block_size = 1.0;
1616: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1618: isend[0] = info->nz_used;
1619: isend[1] = info->nz_allocated;
1620: isend[2] = info->nz_unneeded;
1621: isend[3] = info->memory;
1622: isend[4] = info->mallocs;
1624: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1626: isend[0] += info->nz_used;
1627: isend[1] += info->nz_allocated;
1628: isend[2] += info->nz_unneeded;
1629: isend[3] += info->memory;
1630: isend[4] += info->mallocs;
1631: if (flag == MAT_LOCAL) {
1632: info->nz_used = isend[0];
1633: info->nz_allocated = isend[1];
1634: info->nz_unneeded = isend[2];
1635: info->memory = isend[3];
1636: info->mallocs = isend[4];
1637: } else if (flag == MAT_GLOBAL_MAX) {
1638: PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1640: info->nz_used = irecv[0];
1641: info->nz_allocated = irecv[1];
1642: info->nz_unneeded = irecv[2];
1643: info->memory = irecv[3];
1644: info->mallocs = irecv[4];
1645: } else if (flag == MAT_GLOBAL_SUM) {
1646: PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1648: info->nz_used = irecv[0];
1649: info->nz_allocated = irecv[1];
1650: info->nz_unneeded = irecv[2];
1651: info->memory = irecv[3];
1652: info->mallocs = irecv[4];
1653: }
1654: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1655: info->fill_ratio_needed = 0;
1656: info->factor_mallocs = 0;
1657: PetscFunctionReturn(PETSC_SUCCESS);
1658: }
1660: PetscErrorCode MatSetOption_MPIAIJ(Mat A, MatOption op, PetscBool flg)
1661: {
1662: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1664: PetscFunctionBegin;
1665: switch (op) {
1666: case MAT_NEW_NONZERO_LOCATIONS:
1667: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1668: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1669: case MAT_KEEP_NONZERO_PATTERN:
1670: case MAT_NEW_NONZERO_LOCATION_ERR:
1671: case MAT_USE_INODES:
1672: case MAT_IGNORE_ZERO_ENTRIES:
1673: case MAT_FORM_EXPLICIT_TRANSPOSE:
1674: MatCheckPreallocated(A, 1);
1675: PetscCall(MatSetOption(a->A, op, flg));
1676: PetscCall(MatSetOption(a->B, op, flg));
1677: break;
1678: case MAT_ROW_ORIENTED:
1679: MatCheckPreallocated(A, 1);
1680: a->roworiented = flg;
1682: PetscCall(MatSetOption(a->A, op, flg));
1683: PetscCall(MatSetOption(a->B, op, flg));
1684: break;
1685: case MAT_FORCE_DIAGONAL_ENTRIES:
1686: case MAT_SORTED_FULL:
1687: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1688: break;
1689: case MAT_IGNORE_OFF_PROC_ENTRIES:
1690: a->donotstash = flg;
1691: break;
1692: /* Symmetry flags are handled directly by MatSetOption() and they don't affect preallocation */
1693: case MAT_SPD:
1694: case MAT_SYMMETRIC:
1695: case MAT_STRUCTURALLY_SYMMETRIC:
1696: case MAT_HERMITIAN:
1697: case MAT_SYMMETRY_ETERNAL:
1698: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1699: case MAT_SPD_ETERNAL:
1700: /* if the diagonal matrix is square it inherits some of the properties above */
1701: break;
1702: case MAT_SUBMAT_SINGLEIS:
1703: A->submat_singleis = flg;
1704: break;
1705: case MAT_STRUCTURE_ONLY:
1706: /* The option is handled directly by MatSetOption() */
1707: break;
1708: default:
1709: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1710: }
1711: PetscFunctionReturn(PETSC_SUCCESS);
1712: }
1714: PetscErrorCode MatGetRow_MPIAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1715: {
1716: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1717: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1718: PetscInt i, *cworkA, *cworkB, **pcA, **pcB, cstart = matin->cmap->rstart;
1719: PetscInt nztot, nzA, nzB, lrow, rstart = matin->rmap->rstart, rend = matin->rmap->rend;
1720: PetscInt *cmap, *idx_p;
1722: PetscFunctionBegin;
1723: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1724: mat->getrowactive = PETSC_TRUE;
1726: if (!mat->rowvalues && (idx || v)) {
1727: /*
1728: allocate enough space to hold information from the longest row.
1729: */
1730: Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)mat->A->data, *Ba = (Mat_SeqAIJ *)mat->B->data;
1731: PetscInt max = 1, tmp;
1732: for (i = 0; i < matin->rmap->n; i++) {
1733: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1734: if (max < tmp) max = tmp;
1735: }
1736: PetscCall(PetscMalloc2(max, &mat->rowvalues, max, &mat->rowindices));
1737: }
1739: PetscCheck(row >= rstart && row < rend, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Only local rows");
1740: lrow = row - rstart;
1742: pvA = &vworkA;
1743: pcA = &cworkA;
1744: pvB = &vworkB;
1745: pcB = &cworkB;
1746: if (!v) {
1747: pvA = NULL;
1748: pvB = NULL;
1749: }
1750: if (!idx) {
1751: pcA = NULL;
1752: if (!v) pcB = NULL;
1753: }
1754: PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1755: PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1756: nztot = nzA + nzB;
1758: cmap = mat->garray;
1759: if (v || idx) {
1760: if (nztot) {
1761: /* Sort by increasing column numbers, assuming A and B already sorted */
1762: PetscInt imark = -1;
1763: if (v) {
1764: *v = v_p = mat->rowvalues;
1765: for (i = 0; i < nzB; i++) {
1766: if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1767: else break;
1768: }
1769: imark = i;
1770: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1771: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1772: }
1773: if (idx) {
1774: *idx = idx_p = mat->rowindices;
1775: if (imark > -1) {
1776: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i]];
1777: } else {
1778: for (i = 0; i < nzB; i++) {
1779: if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1780: else break;
1781: }
1782: imark = i;
1783: }
1784: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart + cworkA[i];
1785: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i]];
1786: }
1787: } else {
1788: if (idx) *idx = NULL;
1789: if (v) *v = NULL;
1790: }
1791: }
1792: *nz = nztot;
1793: PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1794: PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1795: PetscFunctionReturn(PETSC_SUCCESS);
1796: }
1798: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1799: {
1800: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1802: PetscFunctionBegin;
1803: PetscCheck(aij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow() must be called first");
1804: aij->getrowactive = PETSC_FALSE;
1805: PetscFunctionReturn(PETSC_SUCCESS);
1806: }
1808: static PetscErrorCode MatNorm_MPIAIJ(Mat mat, NormType type, PetscReal *norm)
1809: {
1810: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1811: Mat_SeqAIJ *amat = (Mat_SeqAIJ *)aij->A->data, *bmat = (Mat_SeqAIJ *)aij->B->data;
1812: PetscInt i, j, cstart = mat->cmap->rstart;
1813: PetscReal sum = 0.0;
1814: const MatScalar *v, *amata, *bmata;
1816: PetscFunctionBegin;
1817: if (aij->size == 1) {
1818: PetscCall(MatNorm(aij->A, type, norm));
1819: } else {
1820: PetscCall(MatSeqAIJGetArrayRead(aij->A, &amata));
1821: PetscCall(MatSeqAIJGetArrayRead(aij->B, &bmata));
1822: if (type == NORM_FROBENIUS) {
1823: v = amata;
1824: for (i = 0; i < amat->nz; i++) {
1825: sum += PetscRealPart(PetscConj(*v) * (*v));
1826: v++;
1827: }
1828: v = bmata;
1829: for (i = 0; i < bmat->nz; i++) {
1830: sum += PetscRealPart(PetscConj(*v) * (*v));
1831: v++;
1832: }
1833: PetscCall(MPIU_Allreduce(&sum, norm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1834: *norm = PetscSqrtReal(*norm);
1835: PetscCall(PetscLogFlops(2.0 * amat->nz + 2.0 * bmat->nz));
1836: } else if (type == NORM_1) { /* max column norm */
1837: PetscReal *tmp, *tmp2;
1838: PetscInt *jj, *garray = aij->garray;
1839: PetscCall(PetscCalloc1(mat->cmap->N + 1, &tmp));
1840: PetscCall(PetscMalloc1(mat->cmap->N + 1, &tmp2));
1841: *norm = 0.0;
1842: v = amata;
1843: jj = amat->j;
1844: for (j = 0; j < amat->nz; j++) {
1845: tmp[cstart + *jj++] += PetscAbsScalar(*v);
1846: v++;
1847: }
1848: v = bmata;
1849: jj = bmat->j;
1850: for (j = 0; j < bmat->nz; j++) {
1851: tmp[garray[*jj++]] += PetscAbsScalar(*v);
1852: v++;
1853: }
1854: PetscCall(MPIU_Allreduce(tmp, tmp2, mat->cmap->N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1855: for (j = 0; j < mat->cmap->N; j++) {
1856: if (tmp2[j] > *norm) *norm = tmp2[j];
1857: }
1858: PetscCall(PetscFree(tmp));
1859: PetscCall(PetscFree(tmp2));
1860: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1861: } else if (type == NORM_INFINITY) { /* max row norm */
1862: PetscReal ntemp = 0.0;
1863: for (j = 0; j < aij->A->rmap->n; j++) {
1864: v = amata + amat->i[j];
1865: sum = 0.0;
1866: for (i = 0; i < amat->i[j + 1] - amat->i[j]; i++) {
1867: sum += PetscAbsScalar(*v);
1868: v++;
1869: }
1870: v = bmata + bmat->i[j];
1871: for (i = 0; i < bmat->i[j + 1] - bmat->i[j]; i++) {
1872: sum += PetscAbsScalar(*v);
1873: v++;
1874: }
1875: if (sum > ntemp) ntemp = sum;
1876: }
1877: PetscCall(MPIU_Allreduce(&ntemp, norm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
1878: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1879: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for two norm");
1880: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &amata));
1881: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &bmata));
1882: }
1883: PetscFunctionReturn(PETSC_SUCCESS);
1884: }
1886: static PetscErrorCode MatTranspose_MPIAIJ(Mat A, MatReuse reuse, Mat *matout)
1887: {
1888: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *b;
1889: Mat_SeqAIJ *Aloc = (Mat_SeqAIJ *)a->A->data, *Bloc = (Mat_SeqAIJ *)a->B->data, *sub_B_diag;
1890: 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;
1891: const PetscInt *ai, *aj, *bi, *bj, *B_diag_i;
1892: Mat B, A_diag, *B_diag;
1893: const MatScalar *pbv, *bv;
1895: PetscFunctionBegin;
1896: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1897: ma = A->rmap->n;
1898: na = A->cmap->n;
1899: mb = a->B->rmap->n;
1900: nb = a->B->cmap->n;
1901: ai = Aloc->i;
1902: aj = Aloc->j;
1903: bi = Bloc->i;
1904: bj = Bloc->j;
1905: if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1906: PetscInt *d_nnz, *g_nnz, *o_nnz;
1907: PetscSFNode *oloc;
1908: PETSC_UNUSED PetscSF sf;
1910: PetscCall(PetscMalloc4(na, &d_nnz, na, &o_nnz, nb, &g_nnz, nb, &oloc));
1911: /* compute d_nnz for preallocation */
1912: PetscCall(PetscArrayzero(d_nnz, na));
1913: for (i = 0; i < ai[ma]; i++) d_nnz[aj[i]]++;
1914: /* compute local off-diagonal contributions */
1915: PetscCall(PetscArrayzero(g_nnz, nb));
1916: for (i = 0; i < bi[ma]; i++) g_nnz[bj[i]]++;
1917: /* map those to global */
1918: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1919: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, nb, NULL, PETSC_USE_POINTER, a->garray));
1920: PetscCall(PetscSFSetFromOptions(sf));
1921: PetscCall(PetscArrayzero(o_nnz, na));
1922: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1923: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1924: PetscCall(PetscSFDestroy(&sf));
1926: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1927: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1928: PetscCall(MatSetBlockSizes(B, PetscAbs(A->cmap->bs), PetscAbs(A->rmap->bs)));
1929: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1930: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
1931: PetscCall(PetscFree4(d_nnz, o_nnz, g_nnz, oloc));
1932: } else {
1933: B = *matout;
1934: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
1935: }
1937: b = (Mat_MPIAIJ *)B->data;
1938: A_diag = a->A;
1939: B_diag = &b->A;
1940: sub_B_diag = (Mat_SeqAIJ *)(*B_diag)->data;
1941: A_diag_ncol = A_diag->cmap->N;
1942: B_diag_ilen = sub_B_diag->ilen;
1943: B_diag_i = sub_B_diag->i;
1945: /* Set ilen for diagonal of B */
1946: for (i = 0; i < A_diag_ncol; i++) B_diag_ilen[i] = B_diag_i[i + 1] - B_diag_i[i];
1948: /* Transpose the diagonal part of the matrix. In contrast to the off-diagonal part, this can be done
1949: very quickly (=without using MatSetValues), because all writes are local. */
1950: PetscCall(MatTransposeSetPrecursor(A_diag, *B_diag));
1951: PetscCall(MatTranspose(A_diag, MAT_REUSE_MATRIX, B_diag));
1953: /* copy over the B part */
1954: PetscCall(PetscMalloc1(bi[mb], &cols));
1955: PetscCall(MatSeqAIJGetArrayRead(a->B, &bv));
1956: pbv = bv;
1957: row = A->rmap->rstart;
1958: for (i = 0; i < bi[mb]; i++) cols[i] = a->garray[bj[i]];
1959: cols_tmp = cols;
1960: for (i = 0; i < mb; i++) {
1961: ncol = bi[i + 1] - bi[i];
1962: PetscCall(MatSetValues(B, ncol, cols_tmp, 1, &row, pbv, INSERT_VALUES));
1963: row++;
1964: if (pbv) pbv += ncol;
1965: if (cols_tmp) cols_tmp += ncol;
1966: }
1967: PetscCall(PetscFree(cols));
1968: PetscCall(MatSeqAIJRestoreArrayRead(a->B, &bv));
1970: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1971: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1972: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1973: *matout = B;
1974: } else {
1975: PetscCall(MatHeaderMerge(A, &B));
1976: }
1977: PetscFunctionReturn(PETSC_SUCCESS);
1978: }
1980: static PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat, Vec ll, Vec rr)
1981: {
1982: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1983: Mat a = aij->A, b = aij->B;
1984: PetscInt s1, s2, s3;
1986: PetscFunctionBegin;
1987: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1988: if (rr) {
1989: PetscCall(VecGetLocalSize(rr, &s1));
1990: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1991: /* Overlap communication with computation. */
1992: PetscCall(VecScatterBegin(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1993: }
1994: if (ll) {
1995: PetscCall(VecGetLocalSize(ll, &s1));
1996: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1997: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1998: }
1999: /* scale the diagonal block */
2000: PetscUseTypeMethod(a, diagonalscale, ll, rr);
2002: if (rr) {
2003: /* Do a scatter end and then right scale the off-diagonal block */
2004: PetscCall(VecScatterEnd(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
2005: PetscUseTypeMethod(b, diagonalscale, NULL, aij->lvec);
2006: }
2007: PetscFunctionReturn(PETSC_SUCCESS);
2008: }
2010: static PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2011: {
2012: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2014: PetscFunctionBegin;
2015: PetscCall(MatSetUnfactored(a->A));
2016: PetscFunctionReturn(PETSC_SUCCESS);
2017: }
2019: static PetscErrorCode MatEqual_MPIAIJ(Mat A, Mat B, PetscBool *flag)
2020: {
2021: Mat_MPIAIJ *matB = (Mat_MPIAIJ *)B->data, *matA = (Mat_MPIAIJ *)A->data;
2022: Mat a, b, c, d;
2023: PetscBool flg;
2025: PetscFunctionBegin;
2026: a = matA->A;
2027: b = matA->B;
2028: c = matB->A;
2029: d = matB->B;
2031: PetscCall(MatEqual(a, c, &flg));
2032: if (flg) PetscCall(MatEqual(b, d, &flg));
2033: PetscCall(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
2034: PetscFunctionReturn(PETSC_SUCCESS);
2035: }
2037: static PetscErrorCode MatCopy_MPIAIJ(Mat A, Mat B, MatStructure str)
2038: {
2039: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2040: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2042: PetscFunctionBegin;
2043: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2044: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2045: /* because of the column compression in the off-processor part of the matrix a->B,
2046: the number of columns in a->B and b->B may be different, hence we cannot call
2047: the MatCopy() directly on the two parts. If need be, we can provide a more
2048: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2049: then copying the submatrices */
2050: PetscCall(MatCopy_Basic(A, B, str));
2051: } else {
2052: PetscCall(MatCopy(a->A, b->A, str));
2053: PetscCall(MatCopy(a->B, b->B, str));
2054: }
2055: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2056: PetscFunctionReturn(PETSC_SUCCESS);
2057: }
2059: /*
2060: Computes the number of nonzeros per row needed for preallocation when X and Y
2061: have different nonzero structure.
2062: */
2063: 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)
2064: {
2065: PetscInt i, j, k, nzx, nzy;
2067: PetscFunctionBegin;
2068: /* Set the number of nonzeros in the new matrix */
2069: for (i = 0; i < m; i++) {
2070: const PetscInt *xjj = xj + xi[i], *yjj = yj + yi[i];
2071: nzx = xi[i + 1] - xi[i];
2072: nzy = yi[i + 1] - yi[i];
2073: nnz[i] = 0;
2074: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2075: for (; k < nzy && yltog[yjj[k]] < xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2076: if (k < nzy && yltog[yjj[k]] == xltog[xjj[j]]) k++; /* Skip duplicate */
2077: nnz[i]++;
2078: }
2079: for (; k < nzy; k++) nnz[i]++;
2080: }
2081: PetscFunctionReturn(PETSC_SUCCESS);
2082: }
2084: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2085: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
2086: {
2087: PetscInt m = Y->rmap->N;
2088: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2089: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
2091: PetscFunctionBegin;
2092: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
2093: PetscFunctionReturn(PETSC_SUCCESS);
2094: }
2096: static PetscErrorCode MatAXPY_MPIAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2097: {
2098: Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data, *yy = (Mat_MPIAIJ *)Y->data;
2100: PetscFunctionBegin;
2101: if (str == SAME_NONZERO_PATTERN) {
2102: PetscCall(MatAXPY(yy->A, a, xx->A, str));
2103: PetscCall(MatAXPY(yy->B, a, xx->B, str));
2104: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2105: PetscCall(MatAXPY_Basic(Y, a, X, str));
2106: } else {
2107: Mat B;
2108: PetscInt *nnz_d, *nnz_o;
2110: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
2111: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
2112: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2113: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2114: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2115: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2116: PetscCall(MatAXPYGetPreallocation_SeqAIJ(yy->A, xx->A, nnz_d));
2117: PetscCall(MatAXPYGetPreallocation_MPIAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
2118: PetscCall(MatMPIAIJSetPreallocation(B, 0, nnz_d, 0, nnz_o));
2119: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2120: PetscCall(MatHeaderMerge(Y, &B));
2121: PetscCall(PetscFree(nnz_d));
2122: PetscCall(PetscFree(nnz_o));
2123: }
2124: PetscFunctionReturn(PETSC_SUCCESS);
2125: }
2127: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);
2129: static PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2130: {
2131: PetscFunctionBegin;
2132: if (PetscDefined(USE_COMPLEX)) {
2133: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2135: PetscCall(MatConjugate_SeqAIJ(aij->A));
2136: PetscCall(MatConjugate_SeqAIJ(aij->B));
2137: }
2138: PetscFunctionReturn(PETSC_SUCCESS);
2139: }
2141: static PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2142: {
2143: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2145: PetscFunctionBegin;
2146: PetscCall(MatRealPart(a->A));
2147: PetscCall(MatRealPart(a->B));
2148: PetscFunctionReturn(PETSC_SUCCESS);
2149: }
2151: static PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
2152: {
2153: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2155: PetscFunctionBegin;
2156: PetscCall(MatImaginaryPart(a->A));
2157: PetscCall(MatImaginaryPart(a->B));
2158: PetscFunctionReturn(PETSC_SUCCESS);
2159: }
2161: static PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2162: {
2163: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2164: PetscInt i, *idxb = NULL, m = A->rmap->n;
2165: PetscScalar *va, *vv;
2166: Vec vB, vA;
2167: const PetscScalar *vb;
2169: PetscFunctionBegin;
2170: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vA));
2171: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
2173: PetscCall(VecGetArrayWrite(vA, &va));
2174: if (idx) {
2175: for (i = 0; i < m; i++) {
2176: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2177: }
2178: }
2180: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vB));
2181: PetscCall(PetscMalloc1(m, &idxb));
2182: PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));
2184: PetscCall(VecGetArrayWrite(v, &vv));
2185: PetscCall(VecGetArrayRead(vB, &vb));
2186: for (i = 0; i < m; i++) {
2187: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2188: vv[i] = vb[i];
2189: if (idx) idx[i] = a->garray[idxb[i]];
2190: } else {
2191: vv[i] = va[i];
2192: if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > a->garray[idxb[i]]) idx[i] = a->garray[idxb[i]];
2193: }
2194: }
2195: PetscCall(VecRestoreArrayWrite(vA, &vv));
2196: PetscCall(VecRestoreArrayWrite(vA, &va));
2197: PetscCall(VecRestoreArrayRead(vB, &vb));
2198: PetscCall(PetscFree(idxb));
2199: PetscCall(VecDestroy(&vA));
2200: PetscCall(VecDestroy(&vB));
2201: PetscFunctionReturn(PETSC_SUCCESS);
2202: }
2204: static PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2205: {
2206: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2207: PetscInt m = A->rmap->n, n = A->cmap->n;
2208: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2209: PetscInt *cmap = mat->garray;
2210: PetscInt *diagIdx, *offdiagIdx;
2211: Vec diagV, offdiagV;
2212: PetscScalar *a, *diagA, *offdiagA;
2213: const PetscScalar *ba, *bav;
2214: PetscInt r, j, col, ncols, *bi, *bj;
2215: Mat B = mat->B;
2216: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2218: PetscFunctionBegin;
2219: /* When a process holds entire A and other processes have no entry */
2220: if (A->cmap->N == n) {
2221: PetscCall(VecGetArrayWrite(v, &diagA));
2222: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2223: PetscCall(MatGetRowMinAbs(mat->A, diagV, idx));
2224: PetscCall(VecDestroy(&diagV));
2225: PetscCall(VecRestoreArrayWrite(v, &diagA));
2226: PetscFunctionReturn(PETSC_SUCCESS);
2227: } else if (n == 0) {
2228: if (m) {
2229: PetscCall(VecGetArrayWrite(v, &a));
2230: for (r = 0; r < m; r++) {
2231: a[r] = 0.0;
2232: if (idx) idx[r] = -1;
2233: }
2234: PetscCall(VecRestoreArrayWrite(v, &a));
2235: }
2236: PetscFunctionReturn(PETSC_SUCCESS);
2237: }
2239: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2240: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2241: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2242: PetscCall(MatGetRowMinAbs(mat->A, diagV, diagIdx));
2244: /* Get offdiagIdx[] for implicit 0.0 */
2245: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2246: ba = bav;
2247: bi = b->i;
2248: bj = b->j;
2249: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2250: for (r = 0; r < m; r++) {
2251: ncols = bi[r + 1] - bi[r];
2252: if (ncols == A->cmap->N - n) { /* Brow is dense */
2253: offdiagA[r] = *ba;
2254: offdiagIdx[r] = cmap[0];
2255: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2256: offdiagA[r] = 0.0;
2258: /* Find first hole in the cmap */
2259: for (j = 0; j < ncols; j++) {
2260: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2261: if (col > j && j < cstart) {
2262: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2263: break;
2264: } else if (col > j + n && j >= cstart) {
2265: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2266: break;
2267: }
2268: }
2269: if (j == ncols && ncols < A->cmap->N - n) {
2270: /* a hole is outside compressed Bcols */
2271: if (ncols == 0) {
2272: if (cstart) {
2273: offdiagIdx[r] = 0;
2274: } else offdiagIdx[r] = cend;
2275: } else { /* ncols > 0 */
2276: offdiagIdx[r] = cmap[ncols - 1] + 1;
2277: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2278: }
2279: }
2280: }
2282: for (j = 0; j < ncols; j++) {
2283: if (PetscAbsScalar(offdiagA[r]) > PetscAbsScalar(*ba)) {
2284: offdiagA[r] = *ba;
2285: offdiagIdx[r] = cmap[*bj];
2286: }
2287: ba++;
2288: bj++;
2289: }
2290: }
2292: PetscCall(VecGetArrayWrite(v, &a));
2293: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2294: for (r = 0; r < m; ++r) {
2295: if (PetscAbsScalar(diagA[r]) < PetscAbsScalar(offdiagA[r])) {
2296: a[r] = diagA[r];
2297: if (idx) idx[r] = cstart + diagIdx[r];
2298: } else if (PetscAbsScalar(diagA[r]) == PetscAbsScalar(offdiagA[r])) {
2299: a[r] = diagA[r];
2300: if (idx) {
2301: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2302: idx[r] = cstart + diagIdx[r];
2303: } else idx[r] = offdiagIdx[r];
2304: }
2305: } else {
2306: a[r] = offdiagA[r];
2307: if (idx) idx[r] = offdiagIdx[r];
2308: }
2309: }
2310: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2311: PetscCall(VecRestoreArrayWrite(v, &a));
2312: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2313: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2314: PetscCall(VecDestroy(&diagV));
2315: PetscCall(VecDestroy(&offdiagV));
2316: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2317: PetscFunctionReturn(PETSC_SUCCESS);
2318: }
2320: static PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2321: {
2322: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2323: PetscInt m = A->rmap->n, n = A->cmap->n;
2324: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2325: PetscInt *cmap = mat->garray;
2326: PetscInt *diagIdx, *offdiagIdx;
2327: Vec diagV, offdiagV;
2328: PetscScalar *a, *diagA, *offdiagA;
2329: const PetscScalar *ba, *bav;
2330: PetscInt r, j, col, ncols, *bi, *bj;
2331: Mat B = mat->B;
2332: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2334: PetscFunctionBegin;
2335: /* When a process holds entire A and other processes have no entry */
2336: if (A->cmap->N == n) {
2337: PetscCall(VecGetArrayWrite(v, &diagA));
2338: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2339: PetscCall(MatGetRowMin(mat->A, diagV, idx));
2340: PetscCall(VecDestroy(&diagV));
2341: PetscCall(VecRestoreArrayWrite(v, &diagA));
2342: PetscFunctionReturn(PETSC_SUCCESS);
2343: } else if (n == 0) {
2344: if (m) {
2345: PetscCall(VecGetArrayWrite(v, &a));
2346: for (r = 0; r < m; r++) {
2347: a[r] = PETSC_MAX_REAL;
2348: if (idx) idx[r] = -1;
2349: }
2350: PetscCall(VecRestoreArrayWrite(v, &a));
2351: }
2352: PetscFunctionReturn(PETSC_SUCCESS);
2353: }
2355: PetscCall(PetscCalloc2(m, &diagIdx, m, &offdiagIdx));
2356: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2357: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2358: PetscCall(MatGetRowMin(mat->A, diagV, diagIdx));
2360: /* Get offdiagIdx[] for implicit 0.0 */
2361: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2362: ba = bav;
2363: bi = b->i;
2364: bj = b->j;
2365: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2366: for (r = 0; r < m; r++) {
2367: ncols = bi[r + 1] - bi[r];
2368: if (ncols == A->cmap->N - n) { /* Brow is dense */
2369: offdiagA[r] = *ba;
2370: offdiagIdx[r] = cmap[0];
2371: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2372: offdiagA[r] = 0.0;
2374: /* Find first hole in the cmap */
2375: for (j = 0; j < ncols; j++) {
2376: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2377: if (col > j && j < cstart) {
2378: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2379: break;
2380: } else if (col > j + n && j >= cstart) {
2381: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2382: break;
2383: }
2384: }
2385: if (j == ncols && ncols < A->cmap->N - n) {
2386: /* a hole is outside compressed Bcols */
2387: if (ncols == 0) {
2388: if (cstart) {
2389: offdiagIdx[r] = 0;
2390: } else offdiagIdx[r] = cend;
2391: } else { /* ncols > 0 */
2392: offdiagIdx[r] = cmap[ncols - 1] + 1;
2393: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2394: }
2395: }
2396: }
2398: for (j = 0; j < ncols; j++) {
2399: if (PetscRealPart(offdiagA[r]) > PetscRealPart(*ba)) {
2400: offdiagA[r] = *ba;
2401: offdiagIdx[r] = cmap[*bj];
2402: }
2403: ba++;
2404: bj++;
2405: }
2406: }
2408: PetscCall(VecGetArrayWrite(v, &a));
2409: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2410: for (r = 0; r < m; ++r) {
2411: if (PetscRealPart(diagA[r]) < PetscRealPart(offdiagA[r])) {
2412: a[r] = diagA[r];
2413: if (idx) idx[r] = cstart + diagIdx[r];
2414: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2415: a[r] = diagA[r];
2416: if (idx) {
2417: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2418: idx[r] = cstart + diagIdx[r];
2419: } else idx[r] = offdiagIdx[r];
2420: }
2421: } else {
2422: a[r] = offdiagA[r];
2423: if (idx) idx[r] = offdiagIdx[r];
2424: }
2425: }
2426: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2427: PetscCall(VecRestoreArrayWrite(v, &a));
2428: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2429: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2430: PetscCall(VecDestroy(&diagV));
2431: PetscCall(VecDestroy(&offdiagV));
2432: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2433: PetscFunctionReturn(PETSC_SUCCESS);
2434: }
2436: static PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2437: {
2438: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2439: PetscInt m = A->rmap->n, n = A->cmap->n;
2440: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2441: PetscInt *cmap = mat->garray;
2442: PetscInt *diagIdx, *offdiagIdx;
2443: Vec diagV, offdiagV;
2444: PetscScalar *a, *diagA, *offdiagA;
2445: const PetscScalar *ba, *bav;
2446: PetscInt r, j, col, ncols, *bi, *bj;
2447: Mat B = mat->B;
2448: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2450: PetscFunctionBegin;
2451: /* When a process holds entire A and other processes have no entry */
2452: if (A->cmap->N == n) {
2453: PetscCall(VecGetArrayWrite(v, &diagA));
2454: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2455: PetscCall(MatGetRowMax(mat->A, diagV, idx));
2456: PetscCall(VecDestroy(&diagV));
2457: PetscCall(VecRestoreArrayWrite(v, &diagA));
2458: PetscFunctionReturn(PETSC_SUCCESS);
2459: } else if (n == 0) {
2460: if (m) {
2461: PetscCall(VecGetArrayWrite(v, &a));
2462: for (r = 0; r < m; r++) {
2463: a[r] = PETSC_MIN_REAL;
2464: if (idx) idx[r] = -1;
2465: }
2466: PetscCall(VecRestoreArrayWrite(v, &a));
2467: }
2468: PetscFunctionReturn(PETSC_SUCCESS);
2469: }
2471: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2472: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2473: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2474: PetscCall(MatGetRowMax(mat->A, diagV, diagIdx));
2476: /* Get offdiagIdx[] for implicit 0.0 */
2477: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2478: ba = bav;
2479: bi = b->i;
2480: bj = b->j;
2481: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2482: for (r = 0; r < m; r++) {
2483: ncols = bi[r + 1] - bi[r];
2484: if (ncols == A->cmap->N - n) { /* Brow is dense */
2485: offdiagA[r] = *ba;
2486: offdiagIdx[r] = cmap[0];
2487: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2488: offdiagA[r] = 0.0;
2490: /* Find first hole in the cmap */
2491: for (j = 0; j < ncols; j++) {
2492: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2493: if (col > j && j < cstart) {
2494: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2495: break;
2496: } else if (col > j + n && j >= cstart) {
2497: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2498: break;
2499: }
2500: }
2501: if (j == ncols && ncols < A->cmap->N - n) {
2502: /* a hole is outside compressed Bcols */
2503: if (ncols == 0) {
2504: if (cstart) {
2505: offdiagIdx[r] = 0;
2506: } else offdiagIdx[r] = cend;
2507: } else { /* ncols > 0 */
2508: offdiagIdx[r] = cmap[ncols - 1] + 1;
2509: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2510: }
2511: }
2512: }
2514: for (j = 0; j < ncols; j++) {
2515: if (PetscRealPart(offdiagA[r]) < PetscRealPart(*ba)) {
2516: offdiagA[r] = *ba;
2517: offdiagIdx[r] = cmap[*bj];
2518: }
2519: ba++;
2520: bj++;
2521: }
2522: }
2524: PetscCall(VecGetArrayWrite(v, &a));
2525: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2526: for (r = 0; r < m; ++r) {
2527: if (PetscRealPart(diagA[r]) > PetscRealPart(offdiagA[r])) {
2528: a[r] = diagA[r];
2529: if (idx) idx[r] = cstart + diagIdx[r];
2530: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2531: a[r] = diagA[r];
2532: if (idx) {
2533: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2534: idx[r] = cstart + diagIdx[r];
2535: } else idx[r] = offdiagIdx[r];
2536: }
2537: } else {
2538: a[r] = offdiagA[r];
2539: if (idx) idx[r] = offdiagIdx[r];
2540: }
2541: }
2542: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2543: PetscCall(VecRestoreArrayWrite(v, &a));
2544: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2545: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2546: PetscCall(VecDestroy(&diagV));
2547: PetscCall(VecDestroy(&offdiagV));
2548: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2549: PetscFunctionReturn(PETSC_SUCCESS);
2550: }
2552: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat, Mat *newmat)
2553: {
2554: Mat *dummy;
2556: PetscFunctionBegin;
2557: PetscCall(MatCreateSubMatrix_MPIAIJ_All(mat, MAT_DO_NOT_GET_VALUES, MAT_INITIAL_MATRIX, &dummy));
2558: *newmat = *dummy;
2559: PetscCall(PetscFree(dummy));
2560: PetscFunctionReturn(PETSC_SUCCESS);
2561: }
2563: static PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A, const PetscScalar **values)
2564: {
2565: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2567: PetscFunctionBegin;
2568: PetscCall(MatInvertBlockDiagonal(a->A, values));
2569: A->factorerrortype = a->A->factorerrortype;
2570: PetscFunctionReturn(PETSC_SUCCESS);
2571: }
2573: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x, PetscRandom rctx)
2574: {
2575: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)x->data;
2577: PetscFunctionBegin;
2578: PetscCheck(x->assembled || x->preallocated, PetscObjectComm((PetscObject)x), PETSC_ERR_ARG_WRONGSTATE, "MatSetRandom on an unassembled and unpreallocated MATMPIAIJ is not allowed");
2579: PetscCall(MatSetRandom(aij->A, rctx));
2580: if (x->assembled) {
2581: PetscCall(MatSetRandom(aij->B, rctx));
2582: } else {
2583: PetscCall(MatSetRandomSkipColumnRange_SeqAIJ_Private(aij->B, x->cmap->rstart, x->cmap->rend, rctx));
2584: }
2585: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
2586: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
2587: PetscFunctionReturn(PETSC_SUCCESS);
2588: }
2590: static PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ(Mat A, PetscBool sc)
2591: {
2592: PetscFunctionBegin;
2593: if (sc) A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ_Scalable;
2594: else A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ;
2595: PetscFunctionReturn(PETSC_SUCCESS);
2596: }
2598: /*@
2599: MatMPIAIJGetNumberNonzeros - gets the number of nonzeros in the matrix on this MPI rank
2601: Not Collective
2603: Input Parameter:
2604: . A - the matrix
2606: Output Parameter:
2607: . nz - the number of nonzeros
2609: Level: advanced
2611: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2612: @*/
2613: PetscErrorCode MatMPIAIJGetNumberNonzeros(Mat A, PetscCount *nz)
2614: {
2615: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)A->data;
2616: Mat_SeqAIJ *aaij = (Mat_SeqAIJ *)maij->A->data, *baij = (Mat_SeqAIJ *)maij->B->data;
2617: PetscBool isaij;
2619: PetscFunctionBegin;
2620: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATMPIAIJ, &isaij));
2621: PetscCheck(isaij, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for type %s", ((PetscObject)A)->type_name);
2622: *nz = aaij->i[A->rmap->n] + baij->i[A->rmap->n];
2623: PetscFunctionReturn(PETSC_SUCCESS);
2624: }
2626: /*@
2627: MatMPIAIJSetUseScalableIncreaseOverlap - Determine if the matrix uses a scalable algorithm to compute the overlap
2629: Collective
2631: Input Parameters:
2632: + A - the matrix
2633: - sc - `PETSC_TRUE` indicates use the scalable algorithm (default is not to use the scalable algorithm)
2635: Level: advanced
2637: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2638: @*/
2639: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap(Mat A, PetscBool sc)
2640: {
2641: PetscFunctionBegin;
2642: PetscTryMethod(A, "MatMPIAIJSetUseScalableIncreaseOverlap_C", (Mat, PetscBool), (A, sc));
2643: PetscFunctionReturn(PETSC_SUCCESS);
2644: }
2646: PetscErrorCode MatSetFromOptions_MPIAIJ(Mat A, PetscOptionItems *PetscOptionsObject)
2647: {
2648: PetscBool sc = PETSC_FALSE, flg;
2650: PetscFunctionBegin;
2651: PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJ options");
2652: if (A->ops->increaseoverlap == MatIncreaseOverlap_MPIAIJ_Scalable) sc = PETSC_TRUE;
2653: PetscCall(PetscOptionsBool("-mat_increase_overlap_scalable", "Use a scalable algorithm to compute the overlap", "MatIncreaseOverlap", sc, &sc, &flg));
2654: if (flg) PetscCall(MatMPIAIJSetUseScalableIncreaseOverlap(A, sc));
2655: PetscOptionsHeadEnd();
2656: PetscFunctionReturn(PETSC_SUCCESS);
2657: }
2659: static PetscErrorCode MatShift_MPIAIJ(Mat Y, PetscScalar a)
2660: {
2661: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)Y->data;
2662: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)maij->A->data;
2664: PetscFunctionBegin;
2665: if (!Y->preallocated) {
2666: PetscCall(MatMPIAIJSetPreallocation(Y, 1, NULL, 0, NULL));
2667: } else if (!aij->nz) { /* It does not matter if diagonals of Y only partially lie in maij->A. We just need an estimated preallocation. */
2668: PetscInt nonew = aij->nonew;
2669: PetscCall(MatSeqAIJSetPreallocation(maij->A, 1, NULL));
2670: aij->nonew = nonew;
2671: }
2672: PetscCall(MatShift_Basic(Y, a));
2673: PetscFunctionReturn(PETSC_SUCCESS);
2674: }
2676: static PetscErrorCode MatMissingDiagonal_MPIAIJ(Mat A, PetscBool *missing, PetscInt *d)
2677: {
2678: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2680: PetscFunctionBegin;
2681: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2682: PetscCall(MatMissingDiagonal(a->A, missing, d));
2683: if (d) {
2684: PetscInt rstart;
2685: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2686: *d += rstart;
2687: }
2688: PetscFunctionReturn(PETSC_SUCCESS);
2689: }
2691: static PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2692: {
2693: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2695: PetscFunctionBegin;
2696: PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2697: PetscFunctionReturn(PETSC_SUCCESS);
2698: }
2700: static PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A, PetscBool keep)
2701: {
2702: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2704: PetscFunctionBegin;
2705: PetscCall(MatEliminateZeros_SeqAIJ(a->A, keep)); // possibly keep zero diagonal coefficients
2706: PetscCall(MatEliminateZeros_SeqAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2707: PetscFunctionReturn(PETSC_SUCCESS);
2708: }
2710: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2711: MatGetRow_MPIAIJ,
2712: MatRestoreRow_MPIAIJ,
2713: MatMult_MPIAIJ,
2714: /* 4*/ MatMultAdd_MPIAIJ,
2715: MatMultTranspose_MPIAIJ,
2716: MatMultTransposeAdd_MPIAIJ,
2717: NULL,
2718: NULL,
2719: NULL,
2720: /*10*/ NULL,
2721: NULL,
2722: NULL,
2723: MatSOR_MPIAIJ,
2724: MatTranspose_MPIAIJ,
2725: /*15*/ MatGetInfo_MPIAIJ,
2726: MatEqual_MPIAIJ,
2727: MatGetDiagonal_MPIAIJ,
2728: MatDiagonalScale_MPIAIJ,
2729: MatNorm_MPIAIJ,
2730: /*20*/ MatAssemblyBegin_MPIAIJ,
2731: MatAssemblyEnd_MPIAIJ,
2732: MatSetOption_MPIAIJ,
2733: MatZeroEntries_MPIAIJ,
2734: /*24*/ MatZeroRows_MPIAIJ,
2735: NULL,
2736: NULL,
2737: NULL,
2738: NULL,
2739: /*29*/ MatSetUp_MPI_Hash,
2740: NULL,
2741: NULL,
2742: MatGetDiagonalBlock_MPIAIJ,
2743: NULL,
2744: /*34*/ MatDuplicate_MPIAIJ,
2745: NULL,
2746: NULL,
2747: NULL,
2748: NULL,
2749: /*39*/ MatAXPY_MPIAIJ,
2750: MatCreateSubMatrices_MPIAIJ,
2751: MatIncreaseOverlap_MPIAIJ,
2752: MatGetValues_MPIAIJ,
2753: MatCopy_MPIAIJ,
2754: /*44*/ MatGetRowMax_MPIAIJ,
2755: MatScale_MPIAIJ,
2756: MatShift_MPIAIJ,
2757: MatDiagonalSet_MPIAIJ,
2758: MatZeroRowsColumns_MPIAIJ,
2759: /*49*/ MatSetRandom_MPIAIJ,
2760: MatGetRowIJ_MPIAIJ,
2761: MatRestoreRowIJ_MPIAIJ,
2762: NULL,
2763: NULL,
2764: /*54*/ MatFDColoringCreate_MPIXAIJ,
2765: NULL,
2766: MatSetUnfactored_MPIAIJ,
2767: MatPermute_MPIAIJ,
2768: NULL,
2769: /*59*/ MatCreateSubMatrix_MPIAIJ,
2770: MatDestroy_MPIAIJ,
2771: MatView_MPIAIJ,
2772: NULL,
2773: NULL,
2774: /*64*/ NULL,
2775: MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2776: NULL,
2777: NULL,
2778: NULL,
2779: /*69*/ MatGetRowMaxAbs_MPIAIJ,
2780: MatGetRowMinAbs_MPIAIJ,
2781: NULL,
2782: NULL,
2783: NULL,
2784: NULL,
2785: /*75*/ MatFDColoringApply_AIJ,
2786: MatSetFromOptions_MPIAIJ,
2787: NULL,
2788: NULL,
2789: MatFindZeroDiagonals_MPIAIJ,
2790: /*80*/ NULL,
2791: NULL,
2792: NULL,
2793: /*83*/ MatLoad_MPIAIJ,
2794: MatIsSymmetric_MPIAIJ,
2795: NULL,
2796: NULL,
2797: NULL,
2798: NULL,
2799: /*89*/ NULL,
2800: NULL,
2801: MatMatMultNumeric_MPIAIJ_MPIAIJ,
2802: NULL,
2803: NULL,
2804: /*94*/ MatPtAPNumeric_MPIAIJ_MPIAIJ,
2805: NULL,
2806: NULL,
2807: NULL,
2808: MatBindToCPU_MPIAIJ,
2809: /*99*/ MatProductSetFromOptions_MPIAIJ,
2810: NULL,
2811: NULL,
2812: MatConjugate_MPIAIJ,
2813: NULL,
2814: /*104*/ MatSetValuesRow_MPIAIJ,
2815: MatRealPart_MPIAIJ,
2816: MatImaginaryPart_MPIAIJ,
2817: NULL,
2818: NULL,
2819: /*109*/ NULL,
2820: NULL,
2821: MatGetRowMin_MPIAIJ,
2822: NULL,
2823: MatMissingDiagonal_MPIAIJ,
2824: /*114*/ MatGetSeqNonzeroStructure_MPIAIJ,
2825: NULL,
2826: MatGetGhosts_MPIAIJ,
2827: NULL,
2828: NULL,
2829: /*119*/ MatMultDiagonalBlock_MPIAIJ,
2830: NULL,
2831: NULL,
2832: NULL,
2833: MatGetMultiProcBlock_MPIAIJ,
2834: /*124*/ MatFindNonzeroRows_MPIAIJ,
2835: MatGetColumnReductions_MPIAIJ,
2836: MatInvertBlockDiagonal_MPIAIJ,
2837: MatInvertVariableBlockDiagonal_MPIAIJ,
2838: MatCreateSubMatricesMPI_MPIAIJ,
2839: /*129*/ NULL,
2840: NULL,
2841: NULL,
2842: MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2843: NULL,
2844: /*134*/ NULL,
2845: NULL,
2846: NULL,
2847: NULL,
2848: NULL,
2849: /*139*/ MatSetBlockSizes_MPIAIJ,
2850: NULL,
2851: NULL,
2852: MatFDColoringSetUp_MPIXAIJ,
2853: MatFindOffBlockDiagonalEntries_MPIAIJ,
2854: MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2855: /*145*/ NULL,
2856: NULL,
2857: NULL,
2858: MatCreateGraph_Simple_AIJ,
2859: NULL,
2860: /*150*/ NULL,
2861: MatEliminateZeros_MPIAIJ};
2863: static PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2864: {
2865: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2867: PetscFunctionBegin;
2868: PetscCall(MatStoreValues(aij->A));
2869: PetscCall(MatStoreValues(aij->B));
2870: PetscFunctionReturn(PETSC_SUCCESS);
2871: }
2873: static PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2874: {
2875: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2877: PetscFunctionBegin;
2878: PetscCall(MatRetrieveValues(aij->A));
2879: PetscCall(MatRetrieveValues(aij->B));
2880: PetscFunctionReturn(PETSC_SUCCESS);
2881: }
2883: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2884: {
2885: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2886: PetscMPIInt size;
2888: PetscFunctionBegin;
2889: if (B->hash_active) {
2890: B->ops[0] = b->cops;
2891: B->hash_active = PETSC_FALSE;
2892: }
2893: PetscCall(PetscLayoutSetUp(B->rmap));
2894: PetscCall(PetscLayoutSetUp(B->cmap));
2896: #if defined(PETSC_USE_CTABLE)
2897: PetscCall(PetscHMapIDestroy(&b->colmap));
2898: #else
2899: PetscCall(PetscFree(b->colmap));
2900: #endif
2901: PetscCall(PetscFree(b->garray));
2902: PetscCall(VecDestroy(&b->lvec));
2903: PetscCall(VecScatterDestroy(&b->Mvctx));
2905: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2906: PetscCall(MatDestroy(&b->B));
2907: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2908: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2909: PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2910: PetscCall(MatSetType(b->B, MATSEQAIJ));
2912: PetscCall(MatDestroy(&b->A));
2913: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2914: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2915: PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2916: PetscCall(MatSetType(b->A, MATSEQAIJ));
2918: PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2919: PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2920: B->preallocated = PETSC_TRUE;
2921: B->was_assembled = PETSC_FALSE;
2922: B->assembled = PETSC_FALSE;
2923: PetscFunctionReturn(PETSC_SUCCESS);
2924: }
2926: static PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2927: {
2928: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2930: PetscFunctionBegin;
2932: PetscCall(PetscLayoutSetUp(B->rmap));
2933: PetscCall(PetscLayoutSetUp(B->cmap));
2935: #if defined(PETSC_USE_CTABLE)
2936: PetscCall(PetscHMapIDestroy(&b->colmap));
2937: #else
2938: PetscCall(PetscFree(b->colmap));
2939: #endif
2940: PetscCall(PetscFree(b->garray));
2941: PetscCall(VecDestroy(&b->lvec));
2942: PetscCall(VecScatterDestroy(&b->Mvctx));
2944: PetscCall(MatResetPreallocation(b->A));
2945: PetscCall(MatResetPreallocation(b->B));
2946: B->preallocated = PETSC_TRUE;
2947: B->was_assembled = PETSC_FALSE;
2948: B->assembled = PETSC_FALSE;
2949: PetscFunctionReturn(PETSC_SUCCESS);
2950: }
2952: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2953: {
2954: Mat mat;
2955: Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;
2957: PetscFunctionBegin;
2958: *newmat = NULL;
2959: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2960: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2961: PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2962: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2963: a = (Mat_MPIAIJ *)mat->data;
2965: mat->factortype = matin->factortype;
2966: mat->assembled = matin->assembled;
2967: mat->insertmode = NOT_SET_VALUES;
2969: a->size = oldmat->size;
2970: a->rank = oldmat->rank;
2971: a->donotstash = oldmat->donotstash;
2972: a->roworiented = oldmat->roworiented;
2973: a->rowindices = NULL;
2974: a->rowvalues = NULL;
2975: a->getrowactive = PETSC_FALSE;
2977: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
2978: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
2979: if (matin->hash_active) {
2980: PetscCall(MatSetUp(mat));
2981: } else {
2982: mat->preallocated = matin->preallocated;
2983: if (oldmat->colmap) {
2984: #if defined(PETSC_USE_CTABLE)
2985: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
2986: #else
2987: PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
2988: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
2989: #endif
2990: } else a->colmap = NULL;
2991: if (oldmat->garray) {
2992: PetscInt len;
2993: len = oldmat->B->cmap->n;
2994: PetscCall(PetscMalloc1(len + 1, &a->garray));
2995: if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
2996: } else a->garray = NULL;
2998: /* It may happen MatDuplicate is called with a non-assembled matrix
2999: In fact, MatDuplicate only requires the matrix to be preallocated
3000: This may happen inside a DMCreateMatrix_Shell */
3001: if (oldmat->lvec) PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3002: if (oldmat->Mvctx) PetscCall(VecScatterCopy(oldmat->Mvctx, &a->Mvctx));
3003: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3004: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3005: }
3006: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3007: *newmat = mat;
3008: PetscFunctionReturn(PETSC_SUCCESS);
3009: }
3011: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3012: {
3013: PetscBool isbinary, ishdf5;
3015: PetscFunctionBegin;
3018: /* force binary viewer to load .info file if it has not yet done so */
3019: PetscCall(PetscViewerSetUp(viewer));
3020: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3021: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3022: if (isbinary) {
3023: PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3024: } else if (ishdf5) {
3025: #if defined(PETSC_HAVE_HDF5)
3026: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3027: #else
3028: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3029: #endif
3030: } else {
3031: 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);
3032: }
3033: PetscFunctionReturn(PETSC_SUCCESS);
3034: }
3036: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3037: {
3038: PetscInt header[4], M, N, m, nz, rows, cols, sum, i;
3039: PetscInt *rowidxs, *colidxs;
3040: PetscScalar *matvals;
3042: PetscFunctionBegin;
3043: PetscCall(PetscViewerSetUp(viewer));
3045: /* read in matrix header */
3046: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3047: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3048: M = header[1];
3049: N = header[2];
3050: nz = header[3];
3051: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3052: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3053: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");
3055: /* set block sizes from the viewer's .info file */
3056: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3057: /* set global sizes if not set already */
3058: if (mat->rmap->N < 0) mat->rmap->N = M;
3059: if (mat->cmap->N < 0) mat->cmap->N = N;
3060: PetscCall(PetscLayoutSetUp(mat->rmap));
3061: PetscCall(PetscLayoutSetUp(mat->cmap));
3063: /* check if the matrix sizes are correct */
3064: PetscCall(MatGetSize(mat, &rows, &cols));
3065: 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);
3067: /* read in row lengths and build row indices */
3068: PetscCall(MatGetLocalSize(mat, &m, NULL));
3069: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3070: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3071: rowidxs[0] = 0;
3072: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3073: if (nz != PETSC_MAX_INT) {
3074: PetscCall(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3075: 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);
3076: }
3078: /* read in column indices and matrix values */
3079: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3080: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3081: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3082: /* store matrix indices and values */
3083: PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3084: PetscCall(PetscFree(rowidxs));
3085: PetscCall(PetscFree2(colidxs, matvals));
3086: PetscFunctionReturn(PETSC_SUCCESS);
3087: }
3089: /* Not scalable because of ISAllGather() unless getting all columns. */
3090: static PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3091: {
3092: IS iscol_local;
3093: PetscBool isstride;
3094: PetscMPIInt lisstride = 0, gisstride;
3096: PetscFunctionBegin;
3097: /* check if we are grabbing all columns*/
3098: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));
3100: if (isstride) {
3101: PetscInt start, len, mstart, mlen;
3102: PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3103: PetscCall(ISGetLocalSize(iscol, &len));
3104: PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3105: if (mstart == start && mlen - mstart == len) lisstride = 1;
3106: }
3108: PetscCall(MPIU_Allreduce(&lisstride, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3109: if (gisstride) {
3110: PetscInt N;
3111: PetscCall(MatGetSize(mat, NULL, &N));
3112: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3113: PetscCall(ISSetIdentity(iscol_local));
3114: PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3115: } else {
3116: PetscInt cbs;
3117: PetscCall(ISGetBlockSize(iscol, &cbs));
3118: PetscCall(ISAllGather(iscol, &iscol_local));
3119: PetscCall(ISSetBlockSize(iscol_local, cbs));
3120: }
3122: *isseq = iscol_local;
3123: PetscFunctionReturn(PETSC_SUCCESS);
3124: }
3126: /*
3127: Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3128: (see MatCreateSubMatrix_MPIAIJ_nonscalable)
3130: Input Parameters:
3131: + mat - matrix
3132: . isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3133: i.e., mat->rstart <= isrow[i] < mat->rend
3134: - iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3135: i.e., mat->cstart <= iscol[i] < mat->cend
3137: Output Parameters:
3138: + isrow_d - sequential row index set for retrieving mat->A
3139: . iscol_d - sequential column index set for retrieving mat->A
3140: . iscol_o - sequential column index set for retrieving mat->B
3141: - garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3142: */
3143: static PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, const PetscInt *garray[])
3144: {
3145: Vec x, cmap;
3146: const PetscInt *is_idx;
3147: PetscScalar *xarray, *cmaparray;
3148: PetscInt ncols, isstart, *idx, m, rstart, *cmap1, count;
3149: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3150: Mat B = a->B;
3151: Vec lvec = a->lvec, lcmap;
3152: PetscInt i, cstart, cend, Bn = B->cmap->N;
3153: MPI_Comm comm;
3154: VecScatter Mvctx = a->Mvctx;
3156: PetscFunctionBegin;
3157: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3158: PetscCall(ISGetLocalSize(iscol, &ncols));
3160: /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3161: PetscCall(MatCreateVecs(mat, &x, NULL));
3162: PetscCall(VecSet(x, -1.0));
3163: PetscCall(VecDuplicate(x, &cmap));
3164: PetscCall(VecSet(cmap, -1.0));
3166: /* Get start indices */
3167: PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3168: isstart -= ncols;
3169: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
3171: PetscCall(ISGetIndices(iscol, &is_idx));
3172: PetscCall(VecGetArray(x, &xarray));
3173: PetscCall(VecGetArray(cmap, &cmaparray));
3174: PetscCall(PetscMalloc1(ncols, &idx));
3175: for (i = 0; i < ncols; i++) {
3176: xarray[is_idx[i] - cstart] = (PetscScalar)is_idx[i];
3177: cmaparray[is_idx[i] - cstart] = i + isstart; /* global index of iscol[i] */
3178: idx[i] = is_idx[i] - cstart; /* local index of iscol[i] */
3179: }
3180: PetscCall(VecRestoreArray(x, &xarray));
3181: PetscCall(VecRestoreArray(cmap, &cmaparray));
3182: PetscCall(ISRestoreIndices(iscol, &is_idx));
3184: /* Get iscol_d */
3185: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3186: PetscCall(ISGetBlockSize(iscol, &i));
3187: PetscCall(ISSetBlockSize(*iscol_d, i));
3189: /* Get isrow_d */
3190: PetscCall(ISGetLocalSize(isrow, &m));
3191: rstart = mat->rmap->rstart;
3192: PetscCall(PetscMalloc1(m, &idx));
3193: PetscCall(ISGetIndices(isrow, &is_idx));
3194: for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3195: PetscCall(ISRestoreIndices(isrow, &is_idx));
3197: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3198: PetscCall(ISGetBlockSize(isrow, &i));
3199: PetscCall(ISSetBlockSize(*isrow_d, i));
3201: /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3202: PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3203: PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3205: PetscCall(VecDuplicate(lvec, &lcmap));
3207: PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3208: PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3210: /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3211: /* off-process column indices */
3212: count = 0;
3213: PetscCall(PetscMalloc1(Bn, &idx));
3214: PetscCall(PetscMalloc1(Bn, &cmap1));
3216: PetscCall(VecGetArray(lvec, &xarray));
3217: PetscCall(VecGetArray(lcmap, &cmaparray));
3218: for (i = 0; i < Bn; i++) {
3219: if (PetscRealPart(xarray[i]) > -1.0) {
3220: idx[count] = i; /* local column index in off-diagonal part B */
3221: cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3222: count++;
3223: }
3224: }
3225: PetscCall(VecRestoreArray(lvec, &xarray));
3226: PetscCall(VecRestoreArray(lcmap, &cmaparray));
3228: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3229: /* cannot ensure iscol_o has same blocksize as iscol! */
3231: PetscCall(PetscFree(idx));
3232: *garray = cmap1;
3234: PetscCall(VecDestroy(&x));
3235: PetscCall(VecDestroy(&cmap));
3236: PetscCall(VecDestroy(&lcmap));
3237: PetscFunctionReturn(PETSC_SUCCESS);
3238: }
3240: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3241: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3242: {
3243: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3244: Mat M = NULL;
3245: MPI_Comm comm;
3246: IS iscol_d, isrow_d, iscol_o;
3247: Mat Asub = NULL, Bsub = NULL;
3248: PetscInt n;
3250: PetscFunctionBegin;
3251: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3253: if (call == MAT_REUSE_MATRIX) {
3254: /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3255: PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3256: PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");
3258: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3259: PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");
3261: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3262: PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");
3264: /* Update diagonal and off-diagonal portions of submat */
3265: asub = (Mat_MPIAIJ *)(*submat)->data;
3266: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3267: PetscCall(ISGetLocalSize(iscol_o, &n));
3268: if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3269: PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3270: PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));
3272: } else { /* call == MAT_INITIAL_MATRIX) */
3273: const PetscInt *garray;
3274: PetscInt BsubN;
3276: /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3277: PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));
3279: /* Create local submatrices Asub and Bsub */
3280: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3281: PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));
3283: /* Create submatrix M */
3284: PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, Asub, Bsub, garray, &M));
3286: /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3287: asub = (Mat_MPIAIJ *)M->data;
3289: PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3290: n = asub->B->cmap->N;
3291: if (BsubN > n) {
3292: /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3293: const PetscInt *idx;
3294: PetscInt i, j, *idx_new, *subgarray = asub->garray;
3295: PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));
3297: PetscCall(PetscMalloc1(n, &idx_new));
3298: j = 0;
3299: PetscCall(ISGetIndices(iscol_o, &idx));
3300: for (i = 0; i < n; i++) {
3301: if (j >= BsubN) break;
3302: while (subgarray[i] > garray[j]) j++;
3304: if (subgarray[i] == garray[j]) {
3305: idx_new[i] = idx[j++];
3306: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "subgarray[%" PetscInt_FMT "]=%" PetscInt_FMT " cannot < garray[%" PetscInt_FMT "]=%" PetscInt_FMT, i, subgarray[i], j, garray[j]);
3307: }
3308: PetscCall(ISRestoreIndices(iscol_o, &idx));
3310: PetscCall(ISDestroy(&iscol_o));
3311: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));
3313: } else if (BsubN < n) {
3314: SETERRQ(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);
3315: }
3317: PetscCall(PetscFree(garray));
3318: *submat = M;
3320: /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3321: PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3322: PetscCall(ISDestroy(&isrow_d));
3324: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3325: PetscCall(ISDestroy(&iscol_d));
3327: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3328: PetscCall(ISDestroy(&iscol_o));
3329: }
3330: PetscFunctionReturn(PETSC_SUCCESS);
3331: }
3333: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3334: {
3335: IS iscol_local = NULL, isrow_d;
3336: PetscInt csize;
3337: PetscInt n, i, j, start, end;
3338: PetscBool sameRowDist = PETSC_FALSE, sameDist[2], tsameDist[2];
3339: MPI_Comm comm;
3341: PetscFunctionBegin;
3342: /* If isrow has same processor distribution as mat,
3343: call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3344: if (call == MAT_REUSE_MATRIX) {
3345: PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3346: if (isrow_d) {
3347: sameRowDist = PETSC_TRUE;
3348: tsameDist[1] = PETSC_TRUE; /* sameColDist */
3349: } else {
3350: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3351: if (iscol_local) {
3352: sameRowDist = PETSC_TRUE;
3353: tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3354: }
3355: }
3356: } else {
3357: /* Check if isrow has same processor distribution as mat */
3358: sameDist[0] = PETSC_FALSE;
3359: PetscCall(ISGetLocalSize(isrow, &n));
3360: if (!n) {
3361: sameDist[0] = PETSC_TRUE;
3362: } else {
3363: PetscCall(ISGetMinMax(isrow, &i, &j));
3364: PetscCall(MatGetOwnershipRange(mat, &start, &end));
3365: if (i >= start && j < end) sameDist[0] = PETSC_TRUE;
3366: }
3368: /* Check if iscol has same processor distribution as mat */
3369: sameDist[1] = PETSC_FALSE;
3370: PetscCall(ISGetLocalSize(iscol, &n));
3371: if (!n) {
3372: sameDist[1] = PETSC_TRUE;
3373: } else {
3374: PetscCall(ISGetMinMax(iscol, &i, &j));
3375: PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3376: if (i >= start && j < end) sameDist[1] = PETSC_TRUE;
3377: }
3379: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3380: PetscCall(MPIU_Allreduce(&sameDist, &tsameDist, 2, MPIU_BOOL, MPI_LAND, comm));
3381: sameRowDist = tsameDist[0];
3382: }
3384: if (sameRowDist) {
3385: if (tsameDist[1]) { /* sameRowDist & sameColDist */
3386: /* isrow and iscol have same processor distribution as mat */
3387: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3388: PetscFunctionReturn(PETSC_SUCCESS);
3389: } else { /* sameRowDist */
3390: /* isrow has same processor distribution as mat */
3391: if (call == MAT_INITIAL_MATRIX) {
3392: PetscBool sorted;
3393: PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3394: PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3395: PetscCall(ISGetSize(iscol, &i));
3396: PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);
3398: PetscCall(ISSorted(iscol_local, &sorted));
3399: if (sorted) {
3400: /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3401: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3402: PetscFunctionReturn(PETSC_SUCCESS);
3403: }
3404: } else { /* call == MAT_REUSE_MATRIX */
3405: IS iscol_sub;
3406: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3407: if (iscol_sub) {
3408: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3409: PetscFunctionReturn(PETSC_SUCCESS);
3410: }
3411: }
3412: }
3413: }
3415: /* General case: iscol -> iscol_local which has global size of iscol */
3416: if (call == MAT_REUSE_MATRIX) {
3417: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3418: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3419: } else {
3420: if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3421: }
3423: PetscCall(ISGetLocalSize(iscol, &csize));
3424: PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));
3426: if (call == MAT_INITIAL_MATRIX) {
3427: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3428: PetscCall(ISDestroy(&iscol_local));
3429: }
3430: PetscFunctionReturn(PETSC_SUCCESS);
3431: }
3433: /*@C
3434: MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3435: and "off-diagonal" part of the matrix in CSR format.
3437: Collective
3439: Input Parameters:
3440: + comm - MPI communicator
3441: . A - "diagonal" portion of matrix
3442: . B - "off-diagonal" portion of matrix, may have empty columns, will be destroyed by this routine
3443: - garray - global index of `B` columns
3445: Output Parameter:
3446: . mat - the matrix, with input `A` as its local diagonal matrix
3448: Level: advanced
3450: Notes:
3451: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.
3453: `A` becomes part of output mat, `B` is destroyed by this routine. The user cannot use `A` and `B` anymore.
3455: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3456: @*/
3457: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, Mat A, Mat B, const PetscInt garray[], Mat *mat)
3458: {
3459: Mat_MPIAIJ *maij;
3460: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data, *bnew;
3461: PetscInt *oi = b->i, *oj = b->j, i, nz, col;
3462: const PetscScalar *oa;
3463: Mat Bnew;
3464: PetscInt m, n, N;
3465: MatType mpi_mat_type;
3467: PetscFunctionBegin;
3468: PetscCall(MatCreate(comm, mat));
3469: PetscCall(MatGetSize(A, &m, &n));
3470: PetscCheck(m == B->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Am %" PetscInt_FMT " != Bm %" PetscInt_FMT, m, B->rmap->N);
3471: PetscCheck(PetscAbs(A->rmap->bs) == PetscAbs(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);
3472: /* remove check below; When B is created using iscol_o from ISGetSeqIS_SameColDist_Private(), its bs may not be same as A */
3473: /* PetscCheck(A->cmap->bs == B->cmap->bs,PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"A column bs %" PetscInt_FMT " != B column bs %" PetscInt_FMT,A->cmap->bs,B->cmap->bs); */
3475: /* Get global columns of mat */
3476: PetscCall(MPIU_Allreduce(&n, &N, 1, MPIU_INT, MPI_SUM, comm));
3478: PetscCall(MatSetSizes(*mat, m, n, PETSC_DECIDE, N));
3479: /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3480: PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3481: PetscCall(MatSetType(*mat, mpi_mat_type));
3483: if (A->rmap->bs > 1 || A->cmap->bs > 1) PetscCall(MatSetBlockSizes(*mat, A->rmap->bs, A->cmap->bs));
3484: maij = (Mat_MPIAIJ *)(*mat)->data;
3486: (*mat)->preallocated = PETSC_TRUE;
3488: PetscCall(PetscLayoutSetUp((*mat)->rmap));
3489: PetscCall(PetscLayoutSetUp((*mat)->cmap));
3491: /* Set A as diagonal portion of *mat */
3492: maij->A = A;
3494: nz = oi[m];
3495: for (i = 0; i < nz; i++) {
3496: col = oj[i];
3497: oj[i] = garray[col];
3498: }
3500: /* Set Bnew as off-diagonal portion of *mat */
3501: PetscCall(MatSeqAIJGetArrayRead(B, &oa));
3502: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, N, oi, oj, (PetscScalar *)oa, &Bnew));
3503: PetscCall(MatSeqAIJRestoreArrayRead(B, &oa));
3504: bnew = (Mat_SeqAIJ *)Bnew->data;
3505: bnew->maxnz = b->maxnz; /* allocated nonzeros of B */
3506: maij->B = Bnew;
3508: PetscCheck(B->rmap->N == Bnew->rmap->N, PETSC_COMM_SELF, PETSC_ERR_PLIB, "BN %" PetscInt_FMT " != BnewN %" PetscInt_FMT, B->rmap->N, Bnew->rmap->N);
3510: b->singlemalloc = PETSC_FALSE; /* B arrays are shared by Bnew */
3511: b->free_a = PETSC_FALSE;
3512: b->free_ij = PETSC_FALSE;
3513: PetscCall(MatDestroy(&B));
3515: bnew->singlemalloc = PETSC_TRUE; /* arrays will be freed by MatDestroy(&Bnew) */
3516: bnew->free_a = PETSC_TRUE;
3517: bnew->free_ij = PETSC_TRUE;
3519: /* condense columns of maij->B */
3520: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3521: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
3522: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
3523: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
3524: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3525: PetscFunctionReturn(PETSC_SUCCESS);
3526: }
3528: extern PetscErrorCode MatCreateSubMatrices_MPIAIJ_SingleIS_Local(Mat, PetscInt, const IS[], const IS[], MatReuse, PetscBool, Mat *);
3530: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowDist(Mat mat, IS isrow, IS iscol, IS iscol_local, MatReuse call, Mat *newmat)
3531: {
3532: PetscInt i, m, n, rstart, row, rend, nz, j, bs, cbs;
3533: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3534: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3535: Mat M, Msub, B = a->B;
3536: MatScalar *aa;
3537: Mat_SeqAIJ *aij;
3538: PetscInt *garray = a->garray, *colsub, Ncols;
3539: PetscInt count, Bn = B->cmap->N, cstart = mat->cmap->rstart, cend = mat->cmap->rend;
3540: IS iscol_sub, iscmap;
3541: const PetscInt *is_idx, *cmap;
3542: PetscBool allcolumns = PETSC_FALSE;
3543: MPI_Comm comm;
3545: PetscFunctionBegin;
3546: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3547: if (call == MAT_REUSE_MATRIX) {
3548: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3549: PetscCheck(iscol_sub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "SubIScol passed in was not used before, cannot reuse");
3550: PetscCall(ISGetLocalSize(iscol_sub, &count));
3552: PetscCall(PetscObjectQuery((PetscObject)*newmat, "Subcmap", (PetscObject *)&iscmap));
3553: PetscCheck(iscmap, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Subcmap passed in was not used before, cannot reuse");
3555: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Msub));
3556: PetscCheck(Msub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3558: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_REUSE_MATRIX, PETSC_FALSE, &Msub));
3560: } else { /* call == MAT_INITIAL_MATRIX) */
3561: PetscBool flg;
3563: PetscCall(ISGetLocalSize(iscol, &n));
3564: PetscCall(ISGetSize(iscol, &Ncols));
3566: /* (1) iscol -> nonscalable iscol_local */
3567: /* Check for special case: each processor gets entire matrix columns */
3568: PetscCall(ISIdentity(iscol_local, &flg));
3569: if (flg && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3570: PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3571: if (allcolumns) {
3572: iscol_sub = iscol_local;
3573: PetscCall(PetscObjectReference((PetscObject)iscol_local));
3574: PetscCall(ISCreateStride(PETSC_COMM_SELF, n, 0, 1, &iscmap));
3576: } else {
3577: /* (2) iscol_local -> iscol_sub and iscmap. Implementation below requires iscol_local be sorted, it can have duplicate indices */
3578: PetscInt *idx, *cmap1, k;
3579: PetscCall(PetscMalloc1(Ncols, &idx));
3580: PetscCall(PetscMalloc1(Ncols, &cmap1));
3581: PetscCall(ISGetIndices(iscol_local, &is_idx));
3582: count = 0;
3583: k = 0;
3584: for (i = 0; i < Ncols; i++) {
3585: j = is_idx[i];
3586: if (j >= cstart && j < cend) {
3587: /* diagonal part of mat */
3588: idx[count] = j;
3589: cmap1[count++] = i; /* column index in submat */
3590: } else if (Bn) {
3591: /* off-diagonal part of mat */
3592: if (j == garray[k]) {
3593: idx[count] = j;
3594: cmap1[count++] = i; /* column index in submat */
3595: } else if (j > garray[k]) {
3596: while (j > garray[k] && k < Bn - 1) k++;
3597: if (j == garray[k]) {
3598: idx[count] = j;
3599: cmap1[count++] = i; /* column index in submat */
3600: }
3601: }
3602: }
3603: }
3604: PetscCall(ISRestoreIndices(iscol_local, &is_idx));
3606: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_OWN_POINTER, &iscol_sub));
3607: PetscCall(ISGetBlockSize(iscol, &cbs));
3608: PetscCall(ISSetBlockSize(iscol_sub, cbs));
3610: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)iscol_local), count, cmap1, PETSC_OWN_POINTER, &iscmap));
3611: }
3613: /* (3) Create sequential Msub */
3614: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_INITIAL_MATRIX, allcolumns, &Msub));
3615: }
3617: PetscCall(ISGetLocalSize(iscol_sub, &count));
3618: aij = (Mat_SeqAIJ *)(Msub)->data;
3619: ii = aij->i;
3620: PetscCall(ISGetIndices(iscmap, &cmap));
3622: /*
3623: m - number of local rows
3624: Ncols - number of columns (same on all processors)
3625: rstart - first row in new global matrix generated
3626: */
3627: PetscCall(MatGetSize(Msub, &m, NULL));
3629: if (call == MAT_INITIAL_MATRIX) {
3630: /* (4) Create parallel newmat */
3631: PetscMPIInt rank, size;
3632: PetscInt csize;
3634: PetscCallMPI(MPI_Comm_size(comm, &size));
3635: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3637: /*
3638: Determine the number of non-zeros in the diagonal and off-diagonal
3639: portions of the matrix in order to do correct preallocation
3640: */
3642: /* first get start and end of "diagonal" columns */
3643: PetscCall(ISGetLocalSize(iscol, &csize));
3644: if (csize == PETSC_DECIDE) {
3645: PetscCall(ISGetSize(isrow, &mglobal));
3646: if (mglobal == Ncols) { /* square matrix */
3647: nlocal = m;
3648: } else {
3649: nlocal = Ncols / size + ((Ncols % size) > rank);
3650: }
3651: } else {
3652: nlocal = csize;
3653: }
3654: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3655: rstart = rend - nlocal;
3656: 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);
3658: /* next, compute all the lengths */
3659: jj = aij->j;
3660: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3661: olens = dlens + m;
3662: for (i = 0; i < m; i++) {
3663: jend = ii[i + 1] - ii[i];
3664: olen = 0;
3665: dlen = 0;
3666: for (j = 0; j < jend; j++) {
3667: if (cmap[*jj] < rstart || cmap[*jj] >= rend) olen++;
3668: else dlen++;
3669: jj++;
3670: }
3671: olens[i] = olen;
3672: dlens[i] = dlen;
3673: }
3675: PetscCall(ISGetBlockSize(isrow, &bs));
3676: PetscCall(ISGetBlockSize(iscol, &cbs));
3678: PetscCall(MatCreate(comm, &M));
3679: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, Ncols));
3680: PetscCall(MatSetBlockSizes(M, bs, cbs));
3681: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3682: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3683: PetscCall(PetscFree(dlens));
3685: } else { /* call == MAT_REUSE_MATRIX */
3686: M = *newmat;
3687: PetscCall(MatGetLocalSize(M, &i, NULL));
3688: PetscCheck(i == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3689: PetscCall(MatZeroEntries(M));
3690: /*
3691: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3692: rather than the slower MatSetValues().
3693: */
3694: M->was_assembled = PETSC_TRUE;
3695: M->assembled = PETSC_FALSE;
3696: }
3698: /* (5) Set values of Msub to *newmat */
3699: PetscCall(PetscMalloc1(count, &colsub));
3700: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
3702: jj = aij->j;
3703: PetscCall(MatSeqAIJGetArrayRead(Msub, (const PetscScalar **)&aa));
3704: for (i = 0; i < m; i++) {
3705: row = rstart + i;
3706: nz = ii[i + 1] - ii[i];
3707: for (j = 0; j < nz; j++) colsub[j] = cmap[jj[j]];
3708: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, colsub, aa, INSERT_VALUES));
3709: jj += nz;
3710: aa += nz;
3711: }
3712: PetscCall(MatSeqAIJRestoreArrayRead(Msub, (const PetscScalar **)&aa));
3713: PetscCall(ISRestoreIndices(iscmap, &cmap));
3715: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3716: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3718: PetscCall(PetscFree(colsub));
3720: /* save Msub, iscol_sub and iscmap used in processor for next request */
3721: if (call == MAT_INITIAL_MATRIX) {
3722: *newmat = M;
3723: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "SubMatrix", (PetscObject)Msub));
3724: PetscCall(MatDestroy(&Msub));
3726: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "SubIScol", (PetscObject)iscol_sub));
3727: PetscCall(ISDestroy(&iscol_sub));
3729: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "Subcmap", (PetscObject)iscmap));
3730: PetscCall(ISDestroy(&iscmap));
3732: if (iscol_local) {
3733: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "ISAllGather", (PetscObject)iscol_local));
3734: PetscCall(ISDestroy(&iscol_local));
3735: }
3736: }
3737: PetscFunctionReturn(PETSC_SUCCESS);
3738: }
3740: /*
3741: Not great since it makes two copies of the submatrix, first an SeqAIJ
3742: in local and then by concatenating the local matrices the end result.
3743: Writing it directly would be much like MatCreateSubMatrices_MPIAIJ()
3745: This requires a sequential iscol with all indices.
3746: */
3747: PetscErrorCode MatCreateSubMatrix_MPIAIJ_nonscalable(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
3748: {
3749: PetscMPIInt rank, size;
3750: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs, cbs;
3751: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3752: Mat M, Mreuse;
3753: MatScalar *aa, *vwork;
3754: MPI_Comm comm;
3755: Mat_SeqAIJ *aij;
3756: PetscBool colflag, allcolumns = PETSC_FALSE;
3758: PetscFunctionBegin;
3759: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3760: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3761: PetscCallMPI(MPI_Comm_size(comm, &size));
3763: /* Check for special case: each processor gets entire matrix columns */
3764: PetscCall(ISIdentity(iscol, &colflag));
3765: PetscCall(ISGetLocalSize(iscol, &n));
3766: if (colflag && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3767: PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3769: if (call == MAT_REUSE_MATRIX) {
3770: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
3771: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3772: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_REUSE_MATRIX, allcolumns, &Mreuse));
3773: } else {
3774: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_INITIAL_MATRIX, allcolumns, &Mreuse));
3775: }
3777: /*
3778: m - number of local rows
3779: n - number of columns (same on all processors)
3780: rstart - first row in new global matrix generated
3781: */
3782: PetscCall(MatGetSize(Mreuse, &m, &n));
3783: PetscCall(MatGetBlockSizes(Mreuse, &bs, &cbs));
3784: if (call == MAT_INITIAL_MATRIX) {
3785: aij = (Mat_SeqAIJ *)(Mreuse)->data;
3786: ii = aij->i;
3787: jj = aij->j;
3789: /*
3790: Determine the number of non-zeros in the diagonal and off-diagonal
3791: portions of the matrix in order to do correct preallocation
3792: */
3794: /* first get start and end of "diagonal" columns */
3795: if (csize == PETSC_DECIDE) {
3796: PetscCall(ISGetSize(isrow, &mglobal));
3797: if (mglobal == n) { /* square matrix */
3798: nlocal = m;
3799: } else {
3800: nlocal = n / size + ((n % size) > rank);
3801: }
3802: } else {
3803: nlocal = csize;
3804: }
3805: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3806: rstart = rend - nlocal;
3807: 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);
3809: /* next, compute all the lengths */
3810: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3811: olens = dlens + m;
3812: for (i = 0; i < m; i++) {
3813: jend = ii[i + 1] - ii[i];
3814: olen = 0;
3815: dlen = 0;
3816: for (j = 0; j < jend; j++) {
3817: if (*jj < rstart || *jj >= rend) olen++;
3818: else dlen++;
3819: jj++;
3820: }
3821: olens[i] = olen;
3822: dlens[i] = dlen;
3823: }
3824: PetscCall(MatCreate(comm, &M));
3825: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, n));
3826: PetscCall(MatSetBlockSizes(M, bs, cbs));
3827: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3828: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3829: PetscCall(PetscFree(dlens));
3830: } else {
3831: PetscInt ml, nl;
3833: M = *newmat;
3834: PetscCall(MatGetLocalSize(M, &ml, &nl));
3835: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3836: PetscCall(MatZeroEntries(M));
3837: /*
3838: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3839: rather than the slower MatSetValues().
3840: */
3841: M->was_assembled = PETSC_TRUE;
3842: M->assembled = PETSC_FALSE;
3843: }
3844: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
3845: aij = (Mat_SeqAIJ *)(Mreuse)->data;
3846: ii = aij->i;
3847: jj = aij->j;
3849: /* trigger copy to CPU if needed */
3850: PetscCall(MatSeqAIJGetArrayRead(Mreuse, (const PetscScalar **)&aa));
3851: for (i = 0; i < m; i++) {
3852: row = rstart + i;
3853: nz = ii[i + 1] - ii[i];
3854: cwork = jj;
3855: jj += nz;
3856: vwork = aa;
3857: aa += nz;
3858: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
3859: }
3860: PetscCall(MatSeqAIJRestoreArrayRead(Mreuse, (const PetscScalar **)&aa));
3862: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3863: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3864: *newmat = M;
3866: /* save submatrix used in processor for next request */
3867: if (call == MAT_INITIAL_MATRIX) {
3868: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
3869: PetscCall(MatDestroy(&Mreuse));
3870: }
3871: PetscFunctionReturn(PETSC_SUCCESS);
3872: }
3874: static PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
3875: {
3876: PetscInt m, cstart, cend, j, nnz, i, d, *ld;
3877: PetscInt *d_nnz, *o_nnz, nnz_max = 0, rstart, ii;
3878: const PetscInt *JJ;
3879: PetscBool nooffprocentries;
3880: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)B->data;
3882: PetscFunctionBegin;
3883: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);
3885: PetscCall(PetscLayoutSetUp(B->rmap));
3886: PetscCall(PetscLayoutSetUp(B->cmap));
3887: m = B->rmap->n;
3888: cstart = B->cmap->rstart;
3889: cend = B->cmap->rend;
3890: rstart = B->rmap->rstart;
3892: PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));
3894: if (PetscDefined(USE_DEBUG)) {
3895: for (i = 0; i < m; i++) {
3896: nnz = Ii[i + 1] - Ii[i];
3897: JJ = J ? J + Ii[i] : NULL;
3898: PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3899: 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]);
3900: 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);
3901: }
3902: }
3904: for (i = 0; i < m; i++) {
3905: nnz = Ii[i + 1] - Ii[i];
3906: JJ = J ? J + Ii[i] : NULL;
3907: nnz_max = PetscMax(nnz_max, nnz);
3908: d = 0;
3909: for (j = 0; j < nnz; j++) {
3910: if (cstart <= JJ[j] && JJ[j] < cend) d++;
3911: }
3912: d_nnz[i] = d;
3913: o_nnz[i] = nnz - d;
3914: }
3915: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3916: PetscCall(PetscFree2(d_nnz, o_nnz));
3918: for (i = 0; i < m; i++) {
3919: ii = i + rstart;
3920: PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], J ? J + Ii[i] : NULL, v ? v + Ii[i] : NULL, INSERT_VALUES));
3921: }
3922: nooffprocentries = B->nooffprocentries;
3923: B->nooffprocentries = PETSC_TRUE;
3924: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3925: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3926: B->nooffprocentries = nooffprocentries;
3928: /* count number of entries below block diagonal */
3929: PetscCall(PetscFree(Aij->ld));
3930: PetscCall(PetscCalloc1(m, &ld));
3931: Aij->ld = ld;
3932: for (i = 0; i < m; i++) {
3933: nnz = Ii[i + 1] - Ii[i];
3934: j = 0;
3935: while (j < nnz && J[j] < cstart) j++;
3936: ld[i] = j;
3937: if (J) J += nnz;
3938: }
3940: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3941: PetscFunctionReturn(PETSC_SUCCESS);
3942: }
3944: /*@
3945: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
3946: (the default parallel PETSc format).
3948: Collective
3950: Input Parameters:
3951: + B - the matrix
3952: . i - the indices into j for the start of each local row (starts with zero)
3953: . j - the column indices for each local row (starts with zero)
3954: - v - optional values in the matrix
3956: Level: developer
3958: Notes:
3959: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
3960: thus you CANNOT change the matrix entries by changing the values of `v` after you have
3961: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3963: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
3965: The format which is used for the sparse matrix input, is equivalent to a
3966: row-major ordering.. i.e for the following matrix, the input data expected is
3967: as shown
3969: .vb
3970: 1 0 0
3971: 2 0 3 P0
3972: -------
3973: 4 5 6 P1
3975: Process0 [P0] rows_owned=[0,1]
3976: i = {0,1,3} [size = nrow+1 = 2+1]
3977: j = {0,0,2} [size = 3]
3978: v = {1,2,3} [size = 3]
3980: Process1 [P1] rows_owned=[2]
3981: i = {0,3} [size = nrow+1 = 1+1]
3982: j = {0,1,2} [size = 3]
3983: v = {4,5,6} [size = 3]
3984: .ve
3986: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`,
3987: `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`
3988: @*/
3989: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
3990: {
3991: PetscFunctionBegin;
3992: PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
3993: PetscFunctionReturn(PETSC_SUCCESS);
3994: }
3996: /*@C
3997: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
3998: (the default parallel PETSc format). For good matrix assembly performance
3999: the user should preallocate the matrix storage by setting the parameters
4000: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4002: Collective
4004: Input Parameters:
4005: + B - the matrix
4006: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4007: (same value is used for all local rows)
4008: . d_nnz - array containing the number of nonzeros in the various rows of the
4009: DIAGONAL portion of the local submatrix (possibly different for each row)
4010: or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4011: The size of this array is equal to the number of local rows, i.e 'm'.
4012: For matrices that will be factored, you must leave room for (and set)
4013: the diagonal entry even if it is zero.
4014: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4015: submatrix (same value is used for all local rows).
4016: - o_nnz - array containing the number of nonzeros in the various rows of the
4017: OFF-DIAGONAL portion of the local submatrix (possibly different for
4018: each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4019: structure. The size of this array is equal to the number
4020: of local rows, i.e 'm'.
4022: Example Usage:
4023: Consider the following 8x8 matrix with 34 non-zero values, that is
4024: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4025: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4026: as follows
4028: .vb
4029: 1 2 0 | 0 3 0 | 0 4
4030: Proc0 0 5 6 | 7 0 0 | 8 0
4031: 9 0 10 | 11 0 0 | 12 0
4032: -------------------------------------
4033: 13 0 14 | 15 16 17 | 0 0
4034: Proc1 0 18 0 | 19 20 21 | 0 0
4035: 0 0 0 | 22 23 0 | 24 0
4036: -------------------------------------
4037: Proc2 25 26 27 | 0 0 28 | 29 0
4038: 30 0 0 | 31 32 33 | 0 34
4039: .ve
4041: This can be represented as a collection of submatrices as
4042: .vb
4043: A B C
4044: D E F
4045: G H I
4046: .ve
4048: Where the submatrices A,B,C are owned by proc0, D,E,F are
4049: owned by proc1, G,H,I are owned by proc2.
4051: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4052: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4053: The 'M','N' parameters are 8,8, and have the same values on all procs.
4055: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4056: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4057: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4058: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4059: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4060: matrix, ans [DF] as another `MATSEQAIJ` matrix.
4062: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4063: allocated for every row of the local diagonal submatrix, and `o_nz`
4064: storage locations are allocated for every row of the OFF-DIAGONAL submat.
4065: One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local
4066: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4067: In this case, the values of `d_nz`, `o_nz` are
4068: .vb
4069: proc0 dnz = 2, o_nz = 2
4070: proc1 dnz = 3, o_nz = 2
4071: proc2 dnz = 1, o_nz = 4
4072: .ve
4073: We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4074: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4075: for proc3. i.e we are using 12+15+10=37 storage locations to store
4076: 34 values.
4078: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4079: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4080: In the above case the values for `d_nnz`, `o_nnz` are
4081: .vb
4082: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4083: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4084: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4085: .ve
4086: Here the space allocated is sum of all the above values i.e 34, and
4087: hence pre-allocation is perfect.
4089: Level: intermediate
4091: Notes:
4092: If the *_nnz parameter is given then the *_nz parameter is ignored
4094: The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4095: storage. The stored row and column indices begin with zero.
4096: See [Sparse Matrices](sec_matsparse) for details.
4098: The parallel matrix is partitioned such that the first m0 rows belong to
4099: process 0, the next m1 rows belong to process 1, the next m2 rows belong
4100: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
4102: The DIAGONAL portion of the local submatrix of a processor can be defined
4103: as the submatrix which is obtained by extraction the part corresponding to
4104: the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4105: first row that belongs to the processor, r2 is the last row belonging to
4106: the this processor, and c1-c2 is range of indices of the local part of a
4107: vector suitable for applying the matrix to. This is an mxn matrix. In the
4108: common case of a square matrix, the row and column ranges are the same and
4109: the DIAGONAL part is also square. The remaining portion of the local
4110: submatrix (mxN) constitute the OFF-DIAGONAL portion.
4112: If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.
4114: You can call `MatGetInfo()` to get information on how effective the preallocation was;
4115: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4116: You can also run with the option `-info` and look for messages with the string
4117: malloc in them to see if additional memory allocation was needed.
4119: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4120: `MatGetInfo()`, `PetscSplitOwnership()`
4121: @*/
4122: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4123: {
4124: PetscFunctionBegin;
4127: PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4128: PetscFunctionReturn(PETSC_SUCCESS);
4129: }
4131: /*@
4132: MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4133: CSR format for the local rows.
4135: Collective
4137: Input Parameters:
4138: + comm - MPI communicator
4139: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4140: . n - This value should be the same as the local size used in creating the
4141: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4142: calculated if N is given) For square matrices n is almost always m.
4143: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4144: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4145: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
4146: . j - column indices
4147: - a - optional matrix values
4149: Output Parameter:
4150: . mat - the matrix
4152: Level: intermediate
4154: Notes:
4155: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4156: thus you CANNOT change the matrix entries by changing the values of a[] after you have
4157: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
4159: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
4161: The format which is used for the sparse matrix input, is equivalent to a
4162: row-major ordering.. i.e for the following matrix, the input data expected is
4163: as shown
4165: Once you have created the matrix you can update it with new numerical values using MatUpdateMPIAIJWithArrays
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`, `MATMPIAIK`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4184: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`
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()`
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: nnz = Ii[i + 1] - Ii[i];
4247: Iii = Ii[i];
4248: ldi = ld[i];
4249: md = Adi[i + 1] - Adi[i];
4250: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4251: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4252: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4253: ad += md;
4254: ao += nnz - md;
4255: }
4256: nooffprocentries = mat->nooffprocentries;
4257: mat->nooffprocentries = PETSC_TRUE;
4258: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4259: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4260: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4261: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4262: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4263: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4264: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4265: mat->nooffprocentries = nooffprocentries;
4266: PetscFunctionReturn(PETSC_SUCCESS);
4267: }
4269: /*@
4270: MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values
4272: Collective
4274: Input Parameters:
4275: + mat - the matrix
4276: - v - matrix values, stored by row
4278: Level: intermediate
4280: Note:
4281: The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`
4283: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4284: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`
4285: @*/
4286: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4287: {
4288: PetscInt nnz, i, m;
4289: PetscBool nooffprocentries;
4290: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4291: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4292: Mat_SeqAIJ *Ao = (Mat_SeqAIJ *)Aij->B->data;
4293: PetscScalar *ad, *ao;
4294: const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4295: PetscInt ldi, Iii, md;
4296: PetscInt *ld = Aij->ld;
4298: PetscFunctionBegin;
4299: m = mat->rmap->n;
4301: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4302: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4303: Iii = 0;
4304: for (i = 0; i < m; i++) {
4305: nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4306: ldi = ld[i];
4307: md = Adi[i + 1] - Adi[i];
4308: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4309: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4310: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4311: ad += md;
4312: ao += nnz - md;
4313: Iii += nnz;
4314: }
4315: nooffprocentries = mat->nooffprocentries;
4316: mat->nooffprocentries = PETSC_TRUE;
4317: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4318: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4319: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4320: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4321: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4322: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4323: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4324: mat->nooffprocentries = nooffprocentries;
4325: PetscFunctionReturn(PETSC_SUCCESS);
4326: }
4328: /*@C
4329: MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4330: (the default parallel PETSc format). For good matrix assembly performance
4331: the user should preallocate the matrix storage by setting the parameters
4332: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4334: Collective
4336: Input Parameters:
4337: + comm - MPI communicator
4338: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4339: This value should be the same as the local size used in creating the
4340: y vector for the matrix-vector product y = Ax.
4341: . n - This value should be the same as the local size used in creating the
4342: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4343: calculated if N is given) For square matrices n is almost always m.
4344: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4345: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4346: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4347: (same value is used for all local rows)
4348: . d_nnz - array containing the number of nonzeros in the various rows of the
4349: DIAGONAL portion of the local submatrix (possibly different for each row)
4350: or `NULL`, if `d_nz` is used to specify the nonzero structure.
4351: The size of this array is equal to the number of local rows, i.e 'm'.
4352: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4353: submatrix (same value is used for all local rows).
4354: - o_nnz - array containing the number of nonzeros in the various rows of the
4355: OFF-DIAGONAL portion of the local submatrix (possibly different for
4356: each row) or `NULL`, if `o_nz` is used to specify the nonzero
4357: structure. The size of this array is equal to the number
4358: of local rows, i.e 'm'.
4360: Output Parameter:
4361: . A - the matrix
4363: Options Database Keys:
4364: + -mat_no_inode - Do not use inodes
4365: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
4366: - -matmult_vecscatter_view <viewer> - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4367: See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the vecscatter be viewed as a matrix.
4368: Entry (i,j) is the size of message (in bytes) rank i sends to rank j in one `MatMult()` call.
4370: Level: intermediate
4372: Notes:
4373: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4374: MatXXXXSetPreallocation() paradigm instead of this routine directly.
4375: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
4377: If the *_nnz parameter is given then the *_nz parameter is ignored
4379: The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4380: processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4381: storage requirements for this matrix.
4383: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one
4384: processor than it must be used on all processors that share the object for
4385: that argument.
4387: The user MUST specify either the local or global matrix dimensions
4388: (possibly both).
4390: The parallel matrix is partitioned across processors such that the
4391: first m0 rows belong to process 0, the next m1 rows belong to
4392: process 1, the next m2 rows belong to process 2 etc.. where
4393: m0,m1,m2,.. are the input parameter 'm'. i.e each processor stores
4394: values corresponding to [m x N] submatrix.
4396: The columns are logically partitioned with the n0 columns belonging
4397: to 0th partition, the next n1 columns belonging to the next
4398: partition etc.. where n0,n1,n2... are the input parameter 'n'.
4400: The DIAGONAL portion of the local submatrix on any given processor
4401: is the submatrix corresponding to the rows and columns m,n
4402: corresponding to the given processor. i.e diagonal matrix on
4403: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4404: etc. The remaining portion of the local submatrix [m x (N-n)]
4405: constitute the OFF-DIAGONAL portion. The example below better
4406: illustrates this concept.
4408: For a square global matrix we define each processor's diagonal portion
4409: to be its local rows and the corresponding columns (a square submatrix);
4410: each processor's off-diagonal portion encompasses the remainder of the
4411: local matrix (a rectangular submatrix).
4413: If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.
4415: When calling this routine with a single process communicator, a matrix of
4416: type `MATSEQAIJ` is returned. If a matrix of type `MATMPIAIJ` is desired for this
4417: type of communicator, use the construction mechanism
4418: .vb
4419: MatCreate(..., &A);
4420: MatSetType(A, MATMPIAIJ);
4421: MatSetSizes(A, m, n, M, N);
4422: MatMPIAIJSetPreallocation(A, ...);
4423: .ve
4425: By default, this format uses inodes (identical nodes) when possible.
4426: We search for consecutive rows with the same nonzero structure, thereby
4427: reusing matrix information to achieve increased efficiency.
4429: Example Usage:
4430: Consider the following 8x8 matrix with 34 non-zero values, that is
4431: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4432: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4433: as follows
4435: .vb
4436: 1 2 0 | 0 3 0 | 0 4
4437: Proc0 0 5 6 | 7 0 0 | 8 0
4438: 9 0 10 | 11 0 0 | 12 0
4439: -------------------------------------
4440: 13 0 14 | 15 16 17 | 0 0
4441: Proc1 0 18 0 | 19 20 21 | 0 0
4442: 0 0 0 | 22 23 0 | 24 0
4443: -------------------------------------
4444: Proc2 25 26 27 | 0 0 28 | 29 0
4445: 30 0 0 | 31 32 33 | 0 34
4446: .ve
4448: This can be represented as a collection of submatrices as
4450: .vb
4451: A B C
4452: D E F
4453: G H I
4454: .ve
4456: Where the submatrices A,B,C are owned by proc0, D,E,F are
4457: owned by proc1, G,H,I are owned by proc2.
4459: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4460: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4461: The 'M','N' parameters are 8,8, and have the same values on all procs.
4463: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4464: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4465: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4466: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4467: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4468: matrix, ans [DF] as another SeqAIJ matrix.
4470: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4471: allocated for every row of the local diagonal submatrix, and `o_nz`
4472: storage locations are allocated for every row of the OFF-DIAGONAL submat.
4473: One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local
4474: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4475: In this case, the values of `d_nz`,`o_nz` are
4476: .vb
4477: proc0 dnz = 2, o_nz = 2
4478: proc1 dnz = 3, o_nz = 2
4479: proc2 dnz = 1, o_nz = 4
4480: .ve
4481: We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4482: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4483: for proc3. i.e we are using 12+15+10=37 storage locations to store
4484: 34 values.
4486: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4487: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4488: In the above case the values for d_nnz,o_nnz are
4489: .vb
4490: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4491: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4492: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4493: .ve
4494: Here the space allocated is sum of all the above values i.e 34, and
4495: hence pre-allocation is perfect.
4497: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4498: `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`
4499: @*/
4500: 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)
4501: {
4502: PetscMPIInt size;
4504: PetscFunctionBegin;
4505: PetscCall(MatCreate(comm, A));
4506: PetscCall(MatSetSizes(*A, m, n, M, N));
4507: PetscCallMPI(MPI_Comm_size(comm, &size));
4508: if (size > 1) {
4509: PetscCall(MatSetType(*A, MATMPIAIJ));
4510: PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4511: } else {
4512: PetscCall(MatSetType(*A, MATSEQAIJ));
4513: PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4514: }
4515: PetscFunctionReturn(PETSC_SUCCESS);
4516: }
4518: /*MC
4519: MatMPIAIJGetSeqAIJF90 - Returns the local pieces of this distributed matrix
4521: Synopsis:
4522: MatMPIAIJGetSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)
4524: Not Collective
4526: Input Parameter:
4527: . A - the `MATMPIAIJ` matrix
4529: Output Parameters:
4530: + Ad - the diagonal portion of the matrix
4531: . Ao - the off-diagonal portion of the matrix
4532: . colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4533: - ierr - error code
4535: Level: advanced
4537: Note:
4538: Use `MatMPIAIJRestoreSeqAIJF90()` when you no longer need access to the matrices and `colmap`
4540: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJRestoreSeqAIJF90()`
4541: M*/
4543: /*MC
4544: MatMPIAIJRestoreSeqAIJF90 - call after `MatMPIAIJGetSeqAIJF90()` when you no longer need access to the matrices and `colmap`
4546: Synopsis:
4547: MatMPIAIJRestoreSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)
4549: Not Collective
4551: Input Parameters:
4552: + A - the `MATMPIAIJ` matrix
4553: . Ad - the diagonal portion of the matrix
4554: . Ao - the off-diagonal portion of the matrix
4555: . colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4556: - ierr - error code
4558: Level: advanced
4560: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJGetSeqAIJF90()`
4561: M*/
4563: /*@C
4564: MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix
4566: Not Collective
4568: Input Parameter:
4569: . A - The `MATMPIAIJ` matrix
4571: Output Parameters:
4572: + Ad - The local diagonal block as a `MATSEQAIJ` matrix
4573: . Ao - The local off-diagonal block as a `MATSEQAIJ` matrix
4574: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4576: Level: intermediate
4578: Note:
4579: The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4580: 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
4581: the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4582: local column numbers to global column numbers in the original matrix.
4584: Fortran Notes:
4585: `MatMPIAIJGetSeqAIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatMPIAIJGetSeqAIJF90()`
4587: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJF90()`, `MatMPIAIJRestoreSeqAIJF90()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATSEQAIJ`
4588: @*/
4589: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4590: {
4591: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4592: PetscBool flg;
4594: PetscFunctionBegin;
4595: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4596: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4597: if (Ad) *Ad = a->A;
4598: if (Ao) *Ao = a->B;
4599: if (colmap) *colmap = a->garray;
4600: PetscFunctionReturn(PETSC_SUCCESS);
4601: }
4603: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4604: {
4605: PetscInt m, N, i, rstart, nnz, Ii;
4606: PetscInt *indx;
4607: PetscScalar *values;
4608: MatType rootType;
4610: PetscFunctionBegin;
4611: PetscCall(MatGetSize(inmat, &m, &N));
4612: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4613: PetscInt *dnz, *onz, sum, bs, cbs;
4615: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4616: /* Check sum(n) = N */
4617: PetscCall(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4618: PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);
4620: PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4621: rstart -= m;
4623: MatPreallocateBegin(comm, m, n, dnz, onz);
4624: for (i = 0; i < m; i++) {
4625: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4626: PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4627: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4628: }
4630: PetscCall(MatCreate(comm, outmat));
4631: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4632: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4633: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4634: PetscCall(MatGetRootType_Private(inmat, &rootType));
4635: PetscCall(MatSetType(*outmat, rootType));
4636: PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4637: PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4638: MatPreallocateEnd(dnz, onz);
4639: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4640: }
4642: /* numeric phase */
4643: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4644: for (i = 0; i < m; i++) {
4645: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4646: Ii = i + rstart;
4647: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4648: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4649: }
4650: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4651: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4652: PetscFunctionReturn(PETSC_SUCCESS);
4653: }
4655: static PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(void *data)
4656: {
4657: Mat_Merge_SeqsToMPI *merge = (Mat_Merge_SeqsToMPI *)data;
4659: PetscFunctionBegin;
4660: if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4661: PetscCall(PetscFree(merge->id_r));
4662: PetscCall(PetscFree(merge->len_s));
4663: PetscCall(PetscFree(merge->len_r));
4664: PetscCall(PetscFree(merge->bi));
4665: PetscCall(PetscFree(merge->bj));
4666: PetscCall(PetscFree(merge->buf_ri[0]));
4667: PetscCall(PetscFree(merge->buf_ri));
4668: PetscCall(PetscFree(merge->buf_rj[0]));
4669: PetscCall(PetscFree(merge->buf_rj));
4670: PetscCall(PetscFree(merge->coi));
4671: PetscCall(PetscFree(merge->coj));
4672: PetscCall(PetscFree(merge->owners_co));
4673: PetscCall(PetscLayoutDestroy(&merge->rowmap));
4674: PetscCall(PetscFree(merge));
4675: PetscFunctionReturn(PETSC_SUCCESS);
4676: }
4678: #include <../src/mat/utils/freespace.h>
4679: #include <petscbt.h>
4681: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4682: {
4683: MPI_Comm comm;
4684: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4685: PetscMPIInt size, rank, taga, *len_s;
4686: PetscInt N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj;
4687: PetscInt proc, m;
4688: PetscInt **buf_ri, **buf_rj;
4689: PetscInt k, anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4690: PetscInt nrows, **buf_ri_k, **nextrow, **nextai;
4691: MPI_Request *s_waits, *r_waits;
4692: MPI_Status *status;
4693: const MatScalar *aa, *a_a;
4694: MatScalar **abuf_r, *ba_i;
4695: Mat_Merge_SeqsToMPI *merge;
4696: PetscContainer container;
4698: PetscFunctionBegin;
4699: PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4700: PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));
4702: PetscCallMPI(MPI_Comm_size(comm, &size));
4703: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4705: PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4706: PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4707: PetscCall(PetscContainerGetPointer(container, (void **)&merge));
4708: PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4709: aa = a_a;
4711: bi = merge->bi;
4712: bj = merge->bj;
4713: buf_ri = merge->buf_ri;
4714: buf_rj = merge->buf_rj;
4716: PetscCall(PetscMalloc1(size, &status));
4717: owners = merge->rowmap->range;
4718: len_s = merge->len_s;
4720: /* send and recv matrix values */
4721: PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4722: PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));
4724: PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4725: for (proc = 0, k = 0; proc < size; proc++) {
4726: if (!len_s[proc]) continue;
4727: i = owners[proc];
4728: PetscCallMPI(MPI_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4729: k++;
4730: }
4732: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4733: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4734: PetscCall(PetscFree(status));
4736: PetscCall(PetscFree(s_waits));
4737: PetscCall(PetscFree(r_waits));
4739: /* insert mat values of mpimat */
4740: PetscCall(PetscMalloc1(N, &ba_i));
4741: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4743: for (k = 0; k < merge->nrecv; k++) {
4744: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4745: nrows = *(buf_ri_k[k]);
4746: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4747: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4748: }
4750: /* set values of ba */
4751: m = merge->rowmap->n;
4752: for (i = 0; i < m; i++) {
4753: arow = owners[rank] + i;
4754: bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4755: bnzi = bi[i + 1] - bi[i];
4756: PetscCall(PetscArrayzero(ba_i, bnzi));
4758: /* add local non-zero vals of this proc's seqmat into ba */
4759: anzi = ai[arow + 1] - ai[arow];
4760: aj = a->j + ai[arow];
4761: aa = a_a + ai[arow];
4762: nextaj = 0;
4763: for (j = 0; nextaj < anzi; j++) {
4764: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4765: ba_i[j] += aa[nextaj++];
4766: }
4767: }
4769: /* add received vals into ba */
4770: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4771: /* i-th row */
4772: if (i == *nextrow[k]) {
4773: anzi = *(nextai[k] + 1) - *nextai[k];
4774: aj = buf_rj[k] + *(nextai[k]);
4775: aa = abuf_r[k] + *(nextai[k]);
4776: nextaj = 0;
4777: for (j = 0; nextaj < anzi; j++) {
4778: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4779: ba_i[j] += aa[nextaj++];
4780: }
4781: }
4782: nextrow[k]++;
4783: nextai[k]++;
4784: }
4785: }
4786: PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4787: }
4788: PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4789: PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4790: PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));
4792: PetscCall(PetscFree(abuf_r[0]));
4793: PetscCall(PetscFree(abuf_r));
4794: PetscCall(PetscFree(ba_i));
4795: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4796: PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4797: PetscFunctionReturn(PETSC_SUCCESS);
4798: }
4800: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4801: {
4802: Mat B_mpi;
4803: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4804: PetscMPIInt size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4805: PetscInt **buf_rj, **buf_ri, **buf_ri_k;
4806: PetscInt M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4807: PetscInt len, proc, *dnz, *onz, bs, cbs;
4808: PetscInt k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4809: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4810: MPI_Request *si_waits, *sj_waits, *ri_waits, *rj_waits;
4811: MPI_Status *status;
4812: PetscFreeSpaceList free_space = NULL, current_space = NULL;
4813: PetscBT lnkbt;
4814: Mat_Merge_SeqsToMPI *merge;
4815: PetscContainer container;
4817: PetscFunctionBegin;
4818: PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));
4820: /* make sure it is a PETSc comm */
4821: PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4822: PetscCallMPI(MPI_Comm_size(comm, &size));
4823: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4825: PetscCall(PetscNew(&merge));
4826: PetscCall(PetscMalloc1(size, &status));
4828: /* determine row ownership */
4829: PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4830: PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4831: PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4832: PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4833: PetscCall(PetscLayoutSetUp(merge->rowmap));
4834: PetscCall(PetscMalloc1(size, &len_si));
4835: PetscCall(PetscMalloc1(size, &merge->len_s));
4837: m = merge->rowmap->n;
4838: owners = merge->rowmap->range;
4840: /* determine the number of messages to send, their lengths */
4841: len_s = merge->len_s;
4843: len = 0; /* length of buf_si[] */
4844: merge->nsend = 0;
4845: for (proc = 0; proc < size; proc++) {
4846: len_si[proc] = 0;
4847: if (proc == rank) {
4848: len_s[proc] = 0;
4849: } else {
4850: len_si[proc] = owners[proc + 1] - owners[proc] + 1;
4851: len_s[proc] = ai[owners[proc + 1]] - ai[owners[proc]]; /* num of rows to be sent to [proc] */
4852: }
4853: if (len_s[proc]) {
4854: merge->nsend++;
4855: nrows = 0;
4856: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4857: if (ai[i + 1] > ai[i]) nrows++;
4858: }
4859: len_si[proc] = 2 * (nrows + 1);
4860: len += len_si[proc];
4861: }
4862: }
4864: /* determine the number and length of messages to receive for ij-structure */
4865: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4866: PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));
4868: /* post the Irecv of j-structure */
4869: PetscCall(PetscCommGetNewTag(comm, &tagj));
4870: PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));
4872: /* post the Isend of j-structure */
4873: PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));
4875: for (proc = 0, k = 0; proc < size; proc++) {
4876: if (!len_s[proc]) continue;
4877: i = owners[proc];
4878: PetscCallMPI(MPI_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4879: k++;
4880: }
4882: /* receives and sends of j-structure are complete */
4883: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4884: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));
4886: /* send and recv i-structure */
4887: PetscCall(PetscCommGetNewTag(comm, &tagi));
4888: PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));
4890: PetscCall(PetscMalloc1(len + 1, &buf_s));
4891: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4892: for (proc = 0, k = 0; proc < size; proc++) {
4893: if (!len_s[proc]) continue;
4894: /* form outgoing message for i-structure:
4895: buf_si[0]: nrows to be sent
4896: [1:nrows]: row index (global)
4897: [nrows+1:2*nrows+1]: i-structure index
4898: */
4899: nrows = len_si[proc] / 2 - 1;
4900: buf_si_i = buf_si + nrows + 1;
4901: buf_si[0] = nrows;
4902: buf_si_i[0] = 0;
4903: nrows = 0;
4904: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4905: anzi = ai[i + 1] - ai[i];
4906: if (anzi) {
4907: buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4908: buf_si[nrows + 1] = i - owners[proc]; /* local row index */
4909: nrows++;
4910: }
4911: }
4912: PetscCallMPI(MPI_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4913: k++;
4914: buf_si += len_si[proc];
4915: }
4917: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4918: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));
4920: PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4921: 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]));
4923: PetscCall(PetscFree(len_si));
4924: PetscCall(PetscFree(len_ri));
4925: PetscCall(PetscFree(rj_waits));
4926: PetscCall(PetscFree2(si_waits, sj_waits));
4927: PetscCall(PetscFree(ri_waits));
4928: PetscCall(PetscFree(buf_s));
4929: PetscCall(PetscFree(status));
4931: /* compute a local seq matrix in each processor */
4932: /* allocate bi array and free space for accumulating nonzero column info */
4933: PetscCall(PetscMalloc1(m + 1, &bi));
4934: bi[0] = 0;
4936: /* create and initialize a linked list */
4937: nlnk = N + 1;
4938: PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));
4940: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4941: len = ai[owners[rank + 1]] - ai[owners[rank]];
4942: PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));
4944: current_space = free_space;
4946: /* determine symbolic info for each local row */
4947: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4949: for (k = 0; k < merge->nrecv; k++) {
4950: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4951: nrows = *buf_ri_k[k];
4952: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4953: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4954: }
4956: MatPreallocateBegin(comm, m, n, dnz, onz);
4957: len = 0;
4958: for (i = 0; i < m; i++) {
4959: bnzi = 0;
4960: /* add local non-zero cols of this proc's seqmat into lnk */
4961: arow = owners[rank] + i;
4962: anzi = ai[arow + 1] - ai[arow];
4963: aj = a->j + ai[arow];
4964: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
4965: bnzi += nlnk;
4966: /* add received col data into lnk */
4967: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4968: if (i == *nextrow[k]) { /* i-th row */
4969: anzi = *(nextai[k] + 1) - *nextai[k];
4970: aj = buf_rj[k] + *nextai[k];
4971: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
4972: bnzi += nlnk;
4973: nextrow[k]++;
4974: nextai[k]++;
4975: }
4976: }
4977: if (len < bnzi) len = bnzi; /* =max(bnzi) */
4979: /* if free space is not available, make more free space */
4980: if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), ¤t_space));
4981: /* copy data into free space, then initialize lnk */
4982: PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
4983: PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));
4985: current_space->array += bnzi;
4986: current_space->local_used += bnzi;
4987: current_space->local_remaining -= bnzi;
4989: bi[i + 1] = bi[i] + bnzi;
4990: }
4992: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4994: PetscCall(PetscMalloc1(bi[m] + 1, &bj));
4995: PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
4996: PetscCall(PetscLLDestroy(lnk, lnkbt));
4998: /* create symbolic parallel matrix B_mpi */
4999: PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
5000: PetscCall(MatCreate(comm, &B_mpi));
5001: if (n == PETSC_DECIDE) {
5002: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
5003: } else {
5004: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
5005: }
5006: PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
5007: PetscCall(MatSetType(B_mpi, MATMPIAIJ));
5008: PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
5009: MatPreallocateEnd(dnz, onz);
5010: PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));
5012: /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
5013: B_mpi->assembled = PETSC_FALSE;
5014: merge->bi = bi;
5015: merge->bj = bj;
5016: merge->buf_ri = buf_ri;
5017: merge->buf_rj = buf_rj;
5018: merge->coi = NULL;
5019: merge->coj = NULL;
5020: merge->owners_co = NULL;
5022: PetscCall(PetscCommDestroy(&comm));
5024: /* attach the supporting struct to B_mpi for reuse */
5025: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5026: PetscCall(PetscContainerSetPointer(container, merge));
5027: PetscCall(PetscContainerSetUserDestroy(container, MatDestroy_MPIAIJ_SeqsToMPI));
5028: PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5029: PetscCall(PetscContainerDestroy(&container));
5030: *mpimat = B_mpi;
5032: PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5033: PetscFunctionReturn(PETSC_SUCCESS);
5034: }
5036: /*@C
5037: MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5038: matrices from each processor
5040: Collective
5042: Input Parameters:
5043: + comm - the communicators the parallel matrix will live on
5044: . seqmat - the input sequential matrices
5045: . m - number of local rows (or `PETSC_DECIDE`)
5046: . n - number of local columns (or `PETSC_DECIDE`)
5047: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5049: Output Parameter:
5050: . mpimat - the parallel matrix generated
5052: Level: advanced
5054: Note:
5055: The dimensions of the sequential matrix in each processor MUST be the same.
5056: The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
5057: destroyed when mpimat is destroyed. Call `PetscObjectQuery()` to access seqmat.
5059: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`
5060: @*/
5061: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5062: {
5063: PetscMPIInt size;
5065: PetscFunctionBegin;
5066: PetscCallMPI(MPI_Comm_size(comm, &size));
5067: if (size == 1) {
5068: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5069: if (scall == MAT_INITIAL_MATRIX) {
5070: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5071: } else {
5072: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5073: }
5074: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5075: PetscFunctionReturn(PETSC_SUCCESS);
5076: }
5077: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5078: if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5079: PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5080: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5081: PetscFunctionReturn(PETSC_SUCCESS);
5082: }
5084: /*@
5085: MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix.
5087: Not Collective
5089: Input Parameter:
5090: . A - the matrix
5092: Output Parameter:
5093: . A_loc - the local sequential matrix generated
5095: Level: developer
5097: Notes:
5098: The matrix is created by taking `A`'s local rows and putting them into a sequential matrix
5099: with `mlocal` rows and `n` columns. Where `mlocal` is obtained with `MatGetLocalSize()` and
5100: `n` is the global column count obtained with `MatGetSize()`
5102: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5104: For parallel matrices this creates an entirely new matrix. If the matrix is sequential it merely increases the reference count.
5106: Destroy the matrix with `MatDestroy()`
5108: .seealso: [](ch_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5109: @*/
5110: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5111: {
5112: PetscBool mpi;
5114: PetscFunctionBegin;
5115: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5116: if (mpi) {
5117: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5118: } else {
5119: *A_loc = A;
5120: PetscCall(PetscObjectReference((PetscObject)*A_loc));
5121: }
5122: PetscFunctionReturn(PETSC_SUCCESS);
5123: }
5125: /*@
5126: MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix.
5128: Not Collective
5130: Input Parameters:
5131: + A - the matrix
5132: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5134: Output Parameter:
5135: . A_loc - the local sequential matrix generated
5137: Level: developer
5139: Notes:
5140: The matrix is created by taking all `A`'s local rows and putting them into a sequential
5141: matrix with `mlocal` rows and `n` columns.`mlocal` is the row count obtained with
5142: `MatGetLocalSize()` and `n` is the global column count obtained with `MatGetSize()`.
5144: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5146: When `A` is sequential and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A` (which contains the entire matrix),
5147: 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
5148: then `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called to fill `A_loc`. Thus one can preallocate the appropriate sequential matrix `A_loc`
5149: 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.
5151: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5152: @*/
5153: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5154: {
5155: Mat_MPIAIJ *mpimat = (Mat_MPIAIJ *)A->data;
5156: Mat_SeqAIJ *mat, *a, *b;
5157: PetscInt *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5158: const PetscScalar *aa, *ba, *aav, *bav;
5159: PetscScalar *ca, *cam;
5160: PetscMPIInt size;
5161: PetscInt am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5162: PetscInt *ci, *cj, col, ncols_d, ncols_o, jo;
5163: PetscBool match;
5165: PetscFunctionBegin;
5166: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5167: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5168: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5169: if (size == 1) {
5170: if (scall == MAT_INITIAL_MATRIX) {
5171: PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5172: *A_loc = mpimat->A;
5173: } else if (scall == MAT_REUSE_MATRIX) {
5174: PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5175: }
5176: PetscFunctionReturn(PETSC_SUCCESS);
5177: }
5179: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5180: a = (Mat_SeqAIJ *)(mpimat->A)->data;
5181: b = (Mat_SeqAIJ *)(mpimat->B)->data;
5182: ai = a->i;
5183: aj = a->j;
5184: bi = b->i;
5185: bj = b->j;
5186: PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5187: PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5188: aa = aav;
5189: ba = bav;
5190: if (scall == MAT_INITIAL_MATRIX) {
5191: PetscCall(PetscMalloc1(1 + am, &ci));
5192: ci[0] = 0;
5193: for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5194: PetscCall(PetscMalloc1(1 + ci[am], &cj));
5195: PetscCall(PetscMalloc1(1 + ci[am], &ca));
5196: k = 0;
5197: for (i = 0; i < am; i++) {
5198: ncols_o = bi[i + 1] - bi[i];
5199: ncols_d = ai[i + 1] - ai[i];
5200: /* off-diagonal portion of A */
5201: for (jo = 0; jo < ncols_o; jo++) {
5202: col = cmap[*bj];
5203: if (col >= cstart) break;
5204: cj[k] = col;
5205: bj++;
5206: ca[k++] = *ba++;
5207: }
5208: /* diagonal portion of A */
5209: for (j = 0; j < ncols_d; j++) {
5210: cj[k] = cstart + *aj++;
5211: ca[k++] = *aa++;
5212: }
5213: /* off-diagonal portion of A */
5214: for (j = jo; j < ncols_o; j++) {
5215: cj[k] = cmap[*bj++];
5216: ca[k++] = *ba++;
5217: }
5218: }
5219: /* put together the new matrix */
5220: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5221: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5222: /* Since these are PETSc arrays, change flags to free them as necessary. */
5223: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5224: mat->free_a = PETSC_TRUE;
5225: mat->free_ij = PETSC_TRUE;
5226: mat->nonew = 0;
5227: } else if (scall == MAT_REUSE_MATRIX) {
5228: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5229: ci = mat->i;
5230: cj = mat->j;
5231: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5232: for (i = 0; i < am; i++) {
5233: /* off-diagonal portion of A */
5234: ncols_o = bi[i + 1] - bi[i];
5235: for (jo = 0; jo < ncols_o; jo++) {
5236: col = cmap[*bj];
5237: if (col >= cstart) break;
5238: *cam++ = *ba++;
5239: bj++;
5240: }
5241: /* diagonal portion of A */
5242: ncols_d = ai[i + 1] - ai[i];
5243: for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5244: /* off-diagonal portion of A */
5245: for (j = jo; j < ncols_o; j++) {
5246: *cam++ = *ba++;
5247: bj++;
5248: }
5249: }
5250: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5251: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5252: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5253: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5254: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5255: PetscFunctionReturn(PETSC_SUCCESS);
5256: }
5258: /*@
5259: MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5260: mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and off-diagonal part
5262: Not Collective
5264: Input Parameters:
5265: + A - the matrix
5266: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5268: Output Parameters:
5269: + glob - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5270: - A_loc - the local sequential matrix generated
5272: Level: developer
5274: Note:
5275: This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5276: part, then those associated with the off-diagonal part (in its local ordering)
5278: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5279: @*/
5280: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5281: {
5282: Mat Ao, Ad;
5283: const PetscInt *cmap;
5284: PetscMPIInt size;
5285: PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);
5287: PetscFunctionBegin;
5288: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5289: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5290: if (size == 1) {
5291: if (scall == MAT_INITIAL_MATRIX) {
5292: PetscCall(PetscObjectReference((PetscObject)Ad));
5293: *A_loc = Ad;
5294: } else if (scall == MAT_REUSE_MATRIX) {
5295: PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5296: }
5297: if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5298: PetscFunctionReturn(PETSC_SUCCESS);
5299: }
5300: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5301: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5302: if (f) {
5303: PetscCall((*f)(A, scall, glob, A_loc));
5304: } else {
5305: Mat_SeqAIJ *a = (Mat_SeqAIJ *)Ad->data;
5306: Mat_SeqAIJ *b = (Mat_SeqAIJ *)Ao->data;
5307: Mat_SeqAIJ *c;
5308: PetscInt *ai = a->i, *aj = a->j;
5309: PetscInt *bi = b->i, *bj = b->j;
5310: PetscInt *ci, *cj;
5311: const PetscScalar *aa, *ba;
5312: PetscScalar *ca;
5313: PetscInt i, j, am, dn, on;
5315: PetscCall(MatGetLocalSize(Ad, &am, &dn));
5316: PetscCall(MatGetLocalSize(Ao, NULL, &on));
5317: PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5318: PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5319: if (scall == MAT_INITIAL_MATRIX) {
5320: PetscInt k;
5321: PetscCall(PetscMalloc1(1 + am, &ci));
5322: PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5323: PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5324: ci[0] = 0;
5325: for (i = 0, k = 0; i < am; i++) {
5326: const PetscInt ncols_o = bi[i + 1] - bi[i];
5327: const PetscInt ncols_d = ai[i + 1] - ai[i];
5328: ci[i + 1] = ci[i] + ncols_o + ncols_d;
5329: /* diagonal portion of A */
5330: for (j = 0; j < ncols_d; j++, k++) {
5331: cj[k] = *aj++;
5332: ca[k] = *aa++;
5333: }
5334: /* off-diagonal portion of A */
5335: for (j = 0; j < ncols_o; j++, k++) {
5336: cj[k] = dn + *bj++;
5337: ca[k] = *ba++;
5338: }
5339: }
5340: /* put together the new matrix */
5341: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5342: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5343: /* Since these are PETSc arrays, change flags to free them as necessary. */
5344: c = (Mat_SeqAIJ *)(*A_loc)->data;
5345: c->free_a = PETSC_TRUE;
5346: c->free_ij = PETSC_TRUE;
5347: c->nonew = 0;
5348: PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5349: } else if (scall == MAT_REUSE_MATRIX) {
5350: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5351: for (i = 0; i < am; i++) {
5352: const PetscInt ncols_d = ai[i + 1] - ai[i];
5353: const PetscInt ncols_o = bi[i + 1] - bi[i];
5354: /* diagonal portion of A */
5355: for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5356: /* off-diagonal portion of A */
5357: for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5358: }
5359: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5360: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5361: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5362: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5363: if (glob) {
5364: PetscInt cst, *gidx;
5366: PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5367: PetscCall(PetscMalloc1(dn + on, &gidx));
5368: for (i = 0; i < dn; i++) gidx[i] = cst + i;
5369: for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5370: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5371: }
5372: }
5373: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5374: PetscFunctionReturn(PETSC_SUCCESS);
5375: }
5377: /*@C
5378: MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns
5380: Not Collective
5382: Input Parameters:
5383: + A - the matrix
5384: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5385: . row - index set of rows to extract (or `NULL`)
5386: - col - index set of columns to extract (or `NULL`)
5388: Output Parameter:
5389: . A_loc - the local sequential matrix generated
5391: Level: developer
5393: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5394: @*/
5395: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5396: {
5397: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5398: PetscInt i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5399: IS isrowa, iscola;
5400: Mat *aloc;
5401: PetscBool match;
5403: PetscFunctionBegin;
5404: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5405: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5406: PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5407: if (!row) {
5408: start = A->rmap->rstart;
5409: end = A->rmap->rend;
5410: PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5411: } else {
5412: isrowa = *row;
5413: }
5414: if (!col) {
5415: start = A->cmap->rstart;
5416: cmap = a->garray;
5417: nzA = a->A->cmap->n;
5418: nzB = a->B->cmap->n;
5419: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5420: ncols = 0;
5421: for (i = 0; i < nzB; i++) {
5422: if (cmap[i] < start) idx[ncols++] = cmap[i];
5423: else break;
5424: }
5425: imark = i;
5426: for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5427: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5428: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5429: } else {
5430: iscola = *col;
5431: }
5432: if (scall != MAT_INITIAL_MATRIX) {
5433: PetscCall(PetscMalloc1(1, &aloc));
5434: aloc[0] = *A_loc;
5435: }
5436: PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5437: if (!col) { /* attach global id of condensed columns */
5438: PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5439: }
5440: *A_loc = aloc[0];
5441: PetscCall(PetscFree(aloc));
5442: if (!row) PetscCall(ISDestroy(&isrowa));
5443: if (!col) PetscCall(ISDestroy(&iscola));
5444: PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5445: PetscFunctionReturn(PETSC_SUCCESS);
5446: }
5448: /*
5449: * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5450: * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5451: * on a global size.
5452: * */
5453: static PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5454: {
5455: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
5456: Mat_SeqAIJ *pd = (Mat_SeqAIJ *)(p->A)->data, *po = (Mat_SeqAIJ *)(p->B)->data, *p_oth;
5457: PetscInt plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5458: PetscMPIInt owner;
5459: PetscSFNode *iremote, *oiremote;
5460: const PetscInt *lrowindices;
5461: PetscSF sf, osf;
5462: PetscInt pcstart, *roffsets, *loffsets, *pnnz, j;
5463: PetscInt ontotalcols, dntotalcols, ntotalcols, nout;
5464: MPI_Comm comm;
5465: ISLocalToGlobalMapping mapping;
5466: const PetscScalar *pd_a, *po_a;
5468: PetscFunctionBegin;
5469: PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5470: /* plocalsize is the number of roots
5471: * nrows is the number of leaves
5472: * */
5473: PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5474: PetscCall(ISGetLocalSize(rows, &nrows));
5475: PetscCall(PetscCalloc1(nrows, &iremote));
5476: PetscCall(ISGetIndices(rows, &lrowindices));
5477: for (i = 0; i < nrows; i++) {
5478: /* Find a remote index and an owner for a row
5479: * The row could be local or remote
5480: * */
5481: owner = 0;
5482: lidx = 0;
5483: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5484: iremote[i].index = lidx;
5485: iremote[i].rank = owner;
5486: }
5487: /* Create SF to communicate how many nonzero columns for each row */
5488: PetscCall(PetscSFCreate(comm, &sf));
5489: /* SF will figure out the number of nonzero columns for each row, and their
5490: * offsets
5491: * */
5492: PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5493: PetscCall(PetscSFSetFromOptions(sf));
5494: PetscCall(PetscSFSetUp(sf));
5496: PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5497: PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5498: PetscCall(PetscCalloc1(nrows, &pnnz));
5499: roffsets[0] = 0;
5500: roffsets[1] = 0;
5501: for (i = 0; i < plocalsize; i++) {
5502: /* diagonal */
5503: nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5504: /* off-diagonal */
5505: nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5506: /* compute offsets so that we relative location for each row */
5507: roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5508: roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5509: }
5510: PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5511: PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5512: /* 'r' means root, and 'l' means leaf */
5513: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5514: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5515: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5516: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5517: PetscCall(PetscSFDestroy(&sf));
5518: PetscCall(PetscFree(roffsets));
5519: PetscCall(PetscFree(nrcols));
5520: dntotalcols = 0;
5521: ontotalcols = 0;
5522: ncol = 0;
5523: for (i = 0; i < nrows; i++) {
5524: pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5525: ncol = PetscMax(pnnz[i], ncol);
5526: /* diagonal */
5527: dntotalcols += nlcols[i * 2 + 0];
5528: /* off-diagonal */
5529: ontotalcols += nlcols[i * 2 + 1];
5530: }
5531: /* We do not need to figure the right number of columns
5532: * since all the calculations will be done by going through the raw data
5533: * */
5534: PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5535: PetscCall(MatSetUp(*P_oth));
5536: PetscCall(PetscFree(pnnz));
5537: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5538: /* diagonal */
5539: PetscCall(PetscCalloc1(dntotalcols, &iremote));
5540: /* off-diagonal */
5541: PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5542: /* diagonal */
5543: PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5544: /* off-diagonal */
5545: PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5546: dntotalcols = 0;
5547: ontotalcols = 0;
5548: ntotalcols = 0;
5549: for (i = 0; i < nrows; i++) {
5550: owner = 0;
5551: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5552: /* Set iremote for diag matrix */
5553: for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5554: iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5555: iremote[dntotalcols].rank = owner;
5556: /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5557: ilocal[dntotalcols++] = ntotalcols++;
5558: }
5559: /* off-diagonal */
5560: for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5561: oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5562: oiremote[ontotalcols].rank = owner;
5563: oilocal[ontotalcols++] = ntotalcols++;
5564: }
5565: }
5566: PetscCall(ISRestoreIndices(rows, &lrowindices));
5567: PetscCall(PetscFree(loffsets));
5568: PetscCall(PetscFree(nlcols));
5569: PetscCall(PetscSFCreate(comm, &sf));
5570: /* P serves as roots and P_oth is leaves
5571: * Diag matrix
5572: * */
5573: PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5574: PetscCall(PetscSFSetFromOptions(sf));
5575: PetscCall(PetscSFSetUp(sf));
5577: PetscCall(PetscSFCreate(comm, &osf));
5578: /* off-diagonal */
5579: PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5580: PetscCall(PetscSFSetFromOptions(osf));
5581: PetscCall(PetscSFSetUp(osf));
5582: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5583: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5584: /* operate on the matrix internal data to save memory */
5585: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5586: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5587: PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5588: /* Convert to global indices for diag matrix */
5589: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5590: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5591: /* We want P_oth store global indices */
5592: PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5593: /* Use memory scalable approach */
5594: PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5595: PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5596: PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5597: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5598: /* Convert back to local indices */
5599: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5600: PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5601: nout = 0;
5602: PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5603: PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5604: PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5605: /* Exchange values */
5606: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5607: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5608: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5609: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5610: /* Stop PETSc from shrinking memory */
5611: for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5612: PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5613: PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5614: /* Attach PetscSF objects to P_oth so that we can reuse it later */
5615: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5616: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5617: PetscCall(PetscSFDestroy(&sf));
5618: PetscCall(PetscSFDestroy(&osf));
5619: PetscFunctionReturn(PETSC_SUCCESS);
5620: }
5622: /*
5623: * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5624: * This supports MPIAIJ and MAIJ
5625: * */
5626: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5627: {
5628: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5629: Mat_SeqAIJ *p_oth;
5630: IS rows, map;
5631: PetscHMapI hamp;
5632: PetscInt i, htsize, *rowindices, off, *mapping, key, count;
5633: MPI_Comm comm;
5634: PetscSF sf, osf;
5635: PetscBool has;
5637: PetscFunctionBegin;
5638: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5639: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5640: /* If it is the first time, create an index set of off-diag nonzero columns of A,
5641: * and then create a submatrix (that often is an overlapping matrix)
5642: * */
5643: if (reuse == MAT_INITIAL_MATRIX) {
5644: /* Use a hash table to figure out unique keys */
5645: PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5646: PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5647: count = 0;
5648: /* Assume that a->g is sorted, otherwise the following does not make sense */
5649: for (i = 0; i < a->B->cmap->n; i++) {
5650: key = a->garray[i] / dof;
5651: PetscCall(PetscHMapIHas(hamp, key, &has));
5652: if (!has) {
5653: mapping[i] = count;
5654: PetscCall(PetscHMapISet(hamp, key, count++));
5655: } else {
5656: /* Current 'i' has the same value the previous step */
5657: mapping[i] = count - 1;
5658: }
5659: }
5660: PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5661: PetscCall(PetscHMapIGetSize(hamp, &htsize));
5662: PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5663: PetscCall(PetscCalloc1(htsize, &rowindices));
5664: off = 0;
5665: PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5666: PetscCall(PetscHMapIDestroy(&hamp));
5667: PetscCall(PetscSortInt(htsize, rowindices));
5668: PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5669: /* In case, the matrix was already created but users want to recreate the matrix */
5670: PetscCall(MatDestroy(P_oth));
5671: PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5672: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5673: PetscCall(ISDestroy(&map));
5674: PetscCall(ISDestroy(&rows));
5675: } else if (reuse == MAT_REUSE_MATRIX) {
5676: /* If matrix was already created, we simply update values using SF objects
5677: * that as attached to the matrix earlier.
5678: */
5679: const PetscScalar *pd_a, *po_a;
5681: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5682: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5683: PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5684: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5685: /* Update values in place */
5686: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5687: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5688: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5689: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5690: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5691: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5692: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5693: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5694: } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5695: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5696: PetscFunctionReturn(PETSC_SUCCESS);
5697: }
5699: /*@C
5700: MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`
5702: Collective
5704: Input Parameters:
5705: + A - the first matrix in `MATMPIAIJ` format
5706: . B - the second matrix in `MATMPIAIJ` format
5707: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5709: Output Parameters:
5710: + rowb - On input index sets of rows of B to extract (or `NULL`), modified on output
5711: . colb - On input index sets of columns of B to extract (or `NULL`), modified on output
5712: - B_seq - the sequential matrix generated
5714: Level: developer
5716: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5717: @*/
5718: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5719: {
5720: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5721: PetscInt *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5722: IS isrowb, iscolb;
5723: Mat *bseq = NULL;
5725: PetscFunctionBegin;
5726: 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 ")",
5727: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5728: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));
5730: if (scall == MAT_INITIAL_MATRIX) {
5731: start = A->cmap->rstart;
5732: cmap = a->garray;
5733: nzA = a->A->cmap->n;
5734: nzB = a->B->cmap->n;
5735: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5736: ncols = 0;
5737: for (i = 0; i < nzB; i++) { /* row < local row index */
5738: if (cmap[i] < start) idx[ncols++] = cmap[i];
5739: else break;
5740: }
5741: imark = i;
5742: for (i = 0; i < nzA; i++) idx[ncols++] = start + i; /* local rows */
5743: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5744: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5745: PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5746: } else {
5747: PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5748: isrowb = *rowb;
5749: iscolb = *colb;
5750: PetscCall(PetscMalloc1(1, &bseq));
5751: bseq[0] = *B_seq;
5752: }
5753: PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5754: *B_seq = bseq[0];
5755: PetscCall(PetscFree(bseq));
5756: if (!rowb) {
5757: PetscCall(ISDestroy(&isrowb));
5758: } else {
5759: *rowb = isrowb;
5760: }
5761: if (!colb) {
5762: PetscCall(ISDestroy(&iscolb));
5763: } else {
5764: *colb = iscolb;
5765: }
5766: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5767: PetscFunctionReturn(PETSC_SUCCESS);
5768: }
5770: /*
5771: MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5772: of the OFF-DIAGONAL portion of local A
5774: Collective
5776: Input Parameters:
5777: + A,B - the matrices in `MATMPIAIJ` format
5778: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5780: Output Parameter:
5781: + startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5782: . startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5783: . bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5784: - B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N
5786: Developer Note:
5787: This directly accesses information inside the VecScatter associated with the matrix-vector product
5788: for this matrix. This is not desirable..
5790: Level: developer
5792: */
5793: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5794: {
5795: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5796: Mat_SeqAIJ *b_oth;
5797: VecScatter ctx;
5798: MPI_Comm comm;
5799: const PetscMPIInt *rprocs, *sprocs;
5800: const PetscInt *srow, *rstarts, *sstarts;
5801: PetscInt *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5802: PetscInt i, j, k = 0, l, ll, nrecvs, nsends, nrows, *rstartsj = NULL, *sstartsj, len;
5803: PetscScalar *b_otha, *bufa, *bufA, *vals = NULL;
5804: MPI_Request *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5805: PetscMPIInt size, tag, rank, nreqs;
5807: PetscFunctionBegin;
5808: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5809: PetscCallMPI(MPI_Comm_size(comm, &size));
5811: 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 ")",
5812: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5813: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5814: PetscCallMPI(MPI_Comm_rank(comm, &rank));
5816: if (size == 1) {
5817: startsj_s = NULL;
5818: bufa_ptr = NULL;
5819: *B_oth = NULL;
5820: PetscFunctionReturn(PETSC_SUCCESS);
5821: }
5823: ctx = a->Mvctx;
5824: tag = ((PetscObject)ctx)->tag;
5826: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5827: /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5828: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5829: PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5830: PetscCall(PetscMalloc1(nreqs, &reqs));
5831: rwaits = reqs;
5832: swaits = reqs + nrecvs;
5834: if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5835: if (scall == MAT_INITIAL_MATRIX) {
5836: /* i-array */
5837: /* post receives */
5838: if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5839: for (i = 0; i < nrecvs; i++) {
5840: rowlen = rvalues + rstarts[i] * rbs;
5841: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5842: PetscCallMPI(MPI_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5843: }
5845: /* pack the outgoing message */
5846: PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));
5848: sstartsj[0] = 0;
5849: rstartsj[0] = 0;
5850: len = 0; /* total length of j or a array to be sent */
5851: if (nsends) {
5852: k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5853: PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5854: }
5855: for (i = 0; i < nsends; i++) {
5856: rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5857: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5858: for (j = 0; j < nrows; j++) {
5859: row = srow[k] + B->rmap->range[rank]; /* global row idx */
5860: for (l = 0; l < sbs; l++) {
5861: PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */
5863: rowlen[j * sbs + l] = ncols;
5865: len += ncols;
5866: PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5867: }
5868: k++;
5869: }
5870: PetscCallMPI(MPI_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));
5872: sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5873: }
5874: /* recvs and sends of i-array are completed */
5875: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5876: PetscCall(PetscFree(svalues));
5878: /* allocate buffers for sending j and a arrays */
5879: PetscCall(PetscMalloc1(len + 1, &bufj));
5880: PetscCall(PetscMalloc1(len + 1, &bufa));
5882: /* create i-array of B_oth */
5883: PetscCall(PetscMalloc1(aBn + 2, &b_othi));
5885: b_othi[0] = 0;
5886: len = 0; /* total length of j or a array to be received */
5887: k = 0;
5888: for (i = 0; i < nrecvs; i++) {
5889: rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5890: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5891: for (j = 0; j < nrows; j++) {
5892: b_othi[k + 1] = b_othi[k] + rowlen[j];
5893: PetscCall(PetscIntSumError(rowlen[j], len, &len));
5894: k++;
5895: }
5896: rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5897: }
5898: PetscCall(PetscFree(rvalues));
5900: /* allocate space for j and a arrays of B_oth */
5901: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_othj));
5902: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_otha));
5904: /* j-array */
5905: /* post receives of j-array */
5906: for (i = 0; i < nrecvs; i++) {
5907: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5908: PetscCallMPI(MPI_Irecv(b_othj + rstartsj[i], nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5909: }
5911: /* pack the outgoing message j-array */
5912: if (nsends) k = sstarts[0];
5913: for (i = 0; i < nsends; i++) {
5914: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5915: bufJ = bufj + sstartsj[i];
5916: for (j = 0; j < nrows; j++) {
5917: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5918: for (ll = 0; ll < sbs; ll++) {
5919: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5920: for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5921: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5922: }
5923: }
5924: PetscCallMPI(MPI_Isend(bufj + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5925: }
5927: /* recvs and sends of j-array are completed */
5928: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5929: } else if (scall == MAT_REUSE_MATRIX) {
5930: sstartsj = *startsj_s;
5931: rstartsj = *startsj_r;
5932: bufa = *bufa_ptr;
5933: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
5934: PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5935: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");
5937: /* a-array */
5938: /* post receives of a-array */
5939: for (i = 0; i < nrecvs; i++) {
5940: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5941: PetscCallMPI(MPI_Irecv(b_otha + rstartsj[i], nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
5942: }
5944: /* pack the outgoing message a-array */
5945: if (nsends) k = sstarts[0];
5946: for (i = 0; i < nsends; i++) {
5947: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5948: bufA = bufa + sstartsj[i];
5949: for (j = 0; j < nrows; j++) {
5950: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5951: for (ll = 0; ll < sbs; ll++) {
5952: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5953: for (l = 0; l < ncols; l++) *bufA++ = vals[l];
5954: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5955: }
5956: }
5957: PetscCallMPI(MPI_Isend(bufa + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
5958: }
5959: /* recvs and sends of a-array are completed */
5960: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5961: PetscCall(PetscFree(reqs));
5963: if (scall == MAT_INITIAL_MATRIX) {
5964: /* put together the new matrix */
5965: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));
5967: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5968: /* Since these are PETSc arrays, change flags to free them as necessary. */
5969: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
5970: b_oth->free_a = PETSC_TRUE;
5971: b_oth->free_ij = PETSC_TRUE;
5972: b_oth->nonew = 0;
5974: PetscCall(PetscFree(bufj));
5975: if (!startsj_s || !bufa_ptr) {
5976: PetscCall(PetscFree2(sstartsj, rstartsj));
5977: PetscCall(PetscFree(bufa_ptr));
5978: } else {
5979: *startsj_s = sstartsj;
5980: *startsj_r = rstartsj;
5981: *bufa_ptr = bufa;
5982: }
5983: } else if (scall == MAT_REUSE_MATRIX) {
5984: PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
5985: }
5987: PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
5988: PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
5989: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
5990: PetscFunctionReturn(PETSC_SUCCESS);
5991: }
5993: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
5994: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
5995: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
5996: #if defined(PETSC_HAVE_MKL_SPARSE)
5997: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
5998: #endif
5999: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
6000: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
6001: #if defined(PETSC_HAVE_ELEMENTAL)
6002: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
6003: #endif
6004: #if defined(PETSC_HAVE_SCALAPACK)
6005: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
6006: #endif
6007: #if defined(PETSC_HAVE_HYPRE)
6008: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
6009: #endif
6010: #if defined(PETSC_HAVE_CUDA)
6011: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
6012: #endif
6013: #if defined(PETSC_HAVE_HIP)
6014: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6015: #endif
6016: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6017: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6018: #endif
6019: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6020: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6021: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
6023: /*
6024: Computes (B'*A')' since computing B*A directly is untenable
6026: n p p
6027: [ ] [ ] [ ]
6028: m [ A ] * n [ B ] = m [ C ]
6029: [ ] [ ] [ ]
6031: */
6032: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6033: {
6034: Mat At, Bt, Ct;
6036: PetscFunctionBegin;
6037: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6038: PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6039: PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_DEFAULT, &Ct));
6040: PetscCall(MatDestroy(&At));
6041: PetscCall(MatDestroy(&Bt));
6042: PetscCall(MatTransposeSetPrecursor(Ct, C));
6043: PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6044: PetscCall(MatDestroy(&Ct));
6045: PetscFunctionReturn(PETSC_SUCCESS);
6046: }
6048: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6049: {
6050: PetscBool cisdense;
6052: PetscFunctionBegin;
6053: 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);
6054: PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6055: PetscCall(MatSetBlockSizesFromMats(C, A, B));
6056: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6057: if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6058: PetscCall(MatSetUp(C));
6060: C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6061: PetscFunctionReturn(PETSC_SUCCESS);
6062: }
6064: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6065: {
6066: Mat_Product *product = C->product;
6067: Mat A = product->A, B = product->B;
6069: PetscFunctionBegin;
6070: 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 ")",
6071: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6072: C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6073: C->ops->productsymbolic = MatProductSymbolic_AB;
6074: PetscFunctionReturn(PETSC_SUCCESS);
6075: }
6077: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6078: {
6079: Mat_Product *product = C->product;
6081: PetscFunctionBegin;
6082: if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6083: PetscFunctionReturn(PETSC_SUCCESS);
6084: }
6086: /*
6087: Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix
6089: Input Parameters:
6091: j1,rowBegin1,rowEnd1,jmap1: describe the first set of nonzeros (Set1)
6092: j2,rowBegin2,rowEnd2,jmap2: describe the second set of nonzeros (Set2)
6094: mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat
6096: For Set1, j1[] contains column indices of the nonzeros.
6097: 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
6098: respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6099: but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.
6101: Similar for Set2.
6103: This routine merges the two sets of nonzeros row by row and removes repeats.
6105: Output Parameters: (memory is allocated by the caller)
6107: i[],j[]: the CSR of the merged matrix, which has m rows.
6108: imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6109: imap2[]: similar to imap1[], but for Set2.
6110: Note we order nonzeros row-by-row and from left to right.
6111: */
6112: 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[])
6113: {
6114: PetscInt r, m; /* Row index of mat */
6115: PetscCount t, t1, t2, b1, e1, b2, e2;
6117: PetscFunctionBegin;
6118: PetscCall(MatGetLocalSize(mat, &m, NULL));
6119: t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6120: i[0] = 0;
6121: for (r = 0; r < m; r++) { /* Do row by row merging */
6122: b1 = rowBegin1[r];
6123: e1 = rowEnd1[r];
6124: b2 = rowBegin2[r];
6125: e2 = rowEnd2[r];
6126: while (b1 < e1 && b2 < e2) {
6127: if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6128: j[t] = j1[b1];
6129: imap1[t1] = t;
6130: imap2[t2] = t;
6131: b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6132: b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6133: t1++;
6134: t2++;
6135: t++;
6136: } else if (j1[b1] < j2[b2]) {
6137: j[t] = j1[b1];
6138: imap1[t1] = t;
6139: b1 += jmap1[t1 + 1] - jmap1[t1];
6140: t1++;
6141: t++;
6142: } else {
6143: j[t] = j2[b2];
6144: imap2[t2] = t;
6145: b2 += jmap2[t2 + 1] - jmap2[t2];
6146: t2++;
6147: t++;
6148: }
6149: }
6150: /* Merge the remaining in either j1[] or j2[] */
6151: while (b1 < e1) {
6152: j[t] = j1[b1];
6153: imap1[t1] = t;
6154: b1 += jmap1[t1 + 1] - jmap1[t1];
6155: t1++;
6156: t++;
6157: }
6158: while (b2 < e2) {
6159: j[t] = j2[b2];
6160: imap2[t2] = t;
6161: b2 += jmap2[t2 + 1] - jmap2[t2];
6162: t2++;
6163: t++;
6164: }
6165: i[r + 1] = t;
6166: }
6167: PetscFunctionReturn(PETSC_SUCCESS);
6168: }
6170: /*
6171: Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block
6173: Input Parameters:
6174: mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6175: 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[]
6176: respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.
6178: i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6179: i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.
6181: Output Parameters:
6182: j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6183: rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6184: 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,
6185: and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.
6187: Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6188: Atot: number of entries belonging to the diagonal block.
6189: Annz: number of unique nonzeros belonging to the diagonal block.
6190: Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6191: repeats (i.e., same 'i,j' pair).
6192: Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6193: is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.
6195: Atot: number of entries belonging to the diagonal block
6196: Annz: number of unique nonzeros belonging to the diagonal block.
6198: Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.
6200: Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6201: */
6202: 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_)
6203: {
6204: PetscInt cstart, cend, rstart, rend, row, col;
6205: PetscCount Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6206: PetscCount Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6207: PetscCount k, m, p, q, r, s, mid;
6208: PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;
6210: PetscFunctionBegin;
6211: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6212: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6213: m = rend - rstart;
6215: /* Skip negative rows */
6216: for (k = 0; k < n; k++)
6217: if (i[k] >= 0) break;
6219: /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6220: fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6221: */
6222: while (k < n) {
6223: row = i[k];
6224: /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6225: for (s = k; s < n; s++)
6226: if (i[s] != row) break;
6228: /* Shift diag columns to range of [-PETSC_MAX_INT, -1] */
6229: for (p = k; p < s; p++) {
6230: if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_MAX_INT;
6231: else PetscAssert((j[p] >= 0) && (j[p] <= mat->cmap->N), PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index %" PetscInt_FMT " is out of range", j[p]);
6232: }
6233: PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6234: PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6235: rowBegin[row - rstart] = k;
6236: rowMid[row - rstart] = mid;
6237: rowEnd[row - rstart] = s;
6239: /* Count nonzeros of this diag/offdiag row, which might have repeats */
6240: Atot += mid - k;
6241: Btot += s - mid;
6243: /* Count unique nonzeros of this diag row */
6244: for (p = k; p < mid;) {
6245: col = j[p];
6246: do {
6247: j[p] += PETSC_MAX_INT; /* Revert the modified diagonal indices */
6248: p++;
6249: } while (p < mid && j[p] == col);
6250: Annz++;
6251: }
6253: /* Count unique nonzeros of this offdiag row */
6254: for (p = mid; p < s;) {
6255: col = j[p];
6256: do {
6257: p++;
6258: } while (p < s && j[p] == col);
6259: Bnnz++;
6260: }
6261: k = s;
6262: }
6264: /* Allocation according to Atot, Btot, Annz, Bnnz */
6265: PetscCall(PetscMalloc1(Atot, &Aperm));
6266: PetscCall(PetscMalloc1(Btot, &Bperm));
6267: PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6268: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));
6270: /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6271: Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6272: for (r = 0; r < m; r++) {
6273: k = rowBegin[r];
6274: mid = rowMid[r];
6275: s = rowEnd[r];
6276: PetscCall(PetscArraycpy(Aperm + Atot, perm + k, mid - k));
6277: PetscCall(PetscArraycpy(Bperm + Btot, perm + mid, s - mid));
6278: Atot += mid - k;
6279: Btot += s - mid;
6281: /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6282: for (p = k; p < mid;) {
6283: col = j[p];
6284: q = p;
6285: do {
6286: p++;
6287: } while (p < mid && j[p] == col);
6288: Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6289: Annz++;
6290: }
6292: for (p = mid; p < s;) {
6293: col = j[p];
6294: q = p;
6295: do {
6296: p++;
6297: } while (p < s && j[p] == col);
6298: Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6299: Bnnz++;
6300: }
6301: }
6302: /* Output */
6303: *Aperm_ = Aperm;
6304: *Annz_ = Annz;
6305: *Atot_ = Atot;
6306: *Ajmap_ = Ajmap;
6307: *Bperm_ = Bperm;
6308: *Bnnz_ = Bnnz;
6309: *Btot_ = Btot;
6310: *Bjmap_ = Bjmap;
6311: PetscFunctionReturn(PETSC_SUCCESS);
6312: }
6314: /*
6315: Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix
6317: Input Parameters:
6318: nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6319: nnz: number of unique nonzeros in the merged matrix
6320: imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6321: jmap[nnz1+1]: i-th nonzero in the set has jmap[i+1] - jmap[i] repeats in the set
6323: Output Parameter: (memory is allocated by the caller)
6324: jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set
6326: Example:
6327: nnz1 = 4
6328: nnz = 6
6329: imap = [1,3,4,5]
6330: jmap = [0,3,5,6,7]
6331: then,
6332: jmap_new = [0,0,3,3,5,6,7]
6333: */
6334: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6335: {
6336: PetscCount k, p;
6338: PetscFunctionBegin;
6339: jmap_new[0] = 0;
6340: p = nnz; /* p loops over jmap_new[] backwards */
6341: for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6342: for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6343: }
6344: for (; p >= 0; p--) jmap_new[p] = jmap[0];
6345: PetscFunctionReturn(PETSC_SUCCESS);
6346: }
6348: static PetscErrorCode MatCOOStructDestroy_MPIAIJ(void *data)
6349: {
6350: MatCOOStruct_MPIAIJ *coo = (MatCOOStruct_MPIAIJ *)data;
6352: PetscFunctionBegin;
6353: PetscCall(PetscSFDestroy(&coo->sf));
6354: PetscCall(PetscFree(coo->Aperm1));
6355: PetscCall(PetscFree(coo->Bperm1));
6356: PetscCall(PetscFree(coo->Ajmap1));
6357: PetscCall(PetscFree(coo->Bjmap1));
6358: PetscCall(PetscFree(coo->Aimap2));
6359: PetscCall(PetscFree(coo->Bimap2));
6360: PetscCall(PetscFree(coo->Aperm2));
6361: PetscCall(PetscFree(coo->Bperm2));
6362: PetscCall(PetscFree(coo->Ajmap2));
6363: PetscCall(PetscFree(coo->Bjmap2));
6364: PetscCall(PetscFree(coo->Cperm1));
6365: PetscCall(PetscFree2(coo->sendbuf, coo->recvbuf));
6366: PetscCall(PetscFree(coo));
6367: PetscFunctionReturn(PETSC_SUCCESS);
6368: }
6370: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6371: {
6372: MPI_Comm comm;
6373: PetscMPIInt rank, size;
6374: PetscInt m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6375: PetscCount k, p, q, rem; /* Loop variables over coo arrays */
6376: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6377: PetscContainer container;
6378: MatCOOStruct_MPIAIJ *coo;
6380: PetscFunctionBegin;
6381: PetscCall(PetscFree(mpiaij->garray));
6382: PetscCall(VecDestroy(&mpiaij->lvec));
6383: #if defined(PETSC_USE_CTABLE)
6384: PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6385: #else
6386: PetscCall(PetscFree(mpiaij->colmap));
6387: #endif
6388: PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6389: mat->assembled = PETSC_FALSE;
6390: mat->was_assembled = PETSC_FALSE;
6392: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6393: PetscCallMPI(MPI_Comm_size(comm, &size));
6394: PetscCallMPI(MPI_Comm_rank(comm, &rank));
6395: PetscCall(PetscLayoutSetUp(mat->rmap));
6396: PetscCall(PetscLayoutSetUp(mat->cmap));
6397: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6398: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6399: PetscCall(MatGetLocalSize(mat, &m, &n));
6400: PetscCall(MatGetSize(mat, &M, &N));
6402: /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6403: /* entries come first, then local rows, then remote rows. */
6404: PetscCount n1 = coo_n, *perm1;
6405: PetscInt *i1 = coo_i, *j1 = coo_j;
6407: PetscCall(PetscMalloc1(n1, &perm1));
6408: for (k = 0; k < n1; k++) perm1[k] = k;
6410: /* Manipulate indices so that entries with negative row or col indices will have smallest
6411: row indices, local entries will have greater but negative row indices, and remote entries
6412: will have positive row indices.
6413: */
6414: for (k = 0; k < n1; k++) {
6415: if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_MIN_INT; /* e.g., -2^31, minimal to move them ahead */
6416: else if (i1[k] >= rstart && i1[k] < rend) i1[k] -= PETSC_MAX_INT; /* e.g., minus 2^31-1 to shift local rows to range of [-PETSC_MAX_INT, -1] */
6417: else {
6418: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6419: if (mpiaij->donotstash) i1[k] = PETSC_MIN_INT; /* Ignore offproc entries as if they had negative indices */
6420: }
6421: }
6423: /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6424: PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));
6426: /* Advance k to the first entry we need to take care of */
6427: for (k = 0; k < n1; k++)
6428: if (i1[k] > PETSC_MIN_INT) break;
6429: PetscInt i1start = k;
6431: PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_MAX_INT, &rem)); /* rem is upper bound of the last local row */
6432: for (; k < rem; k++) i1[k] += PETSC_MAX_INT; /* Revert row indices of local rows*/
6434: /* Send remote rows to their owner */
6435: /* Find which rows should be sent to which remote ranks*/
6436: PetscInt nsend = 0; /* Number of MPI ranks to send data to */
6437: PetscMPIInt *sendto; /* [nsend], storing remote ranks */
6438: PetscInt *nentries; /* [nsend], storing number of entries sent to remote ranks; Assume PetscInt is big enough for this count, and error if not */
6439: const PetscInt *ranges;
6440: PetscInt maxNsend = size >= 128 ? 128 : size; /* Assume max 128 neighbors; realloc when needed */
6442: PetscCall(PetscLayoutGetRanges(mat->rmap, &ranges));
6443: PetscCall(PetscMalloc2(maxNsend, &sendto, maxNsend, &nentries));
6444: for (k = rem; k < n1;) {
6445: PetscMPIInt owner;
6446: PetscInt firstRow, lastRow;
6448: /* Locate a row range */
6449: firstRow = i1[k]; /* first row of this owner */
6450: PetscCall(PetscLayoutFindOwner(mat->rmap, firstRow, &owner));
6451: lastRow = ranges[owner + 1] - 1; /* last row of this owner */
6453: /* Find the first index 'p' in [k,n) with i[p] belonging to next owner */
6454: PetscCall(PetscSortedIntUpperBound(i1, k, n1, lastRow, &p));
6456: /* All entries in [k,p) belong to this remote owner */
6457: if (nsend >= maxNsend) { /* Double the remote ranks arrays if not long enough */
6458: PetscMPIInt *sendto2;
6459: PetscInt *nentries2;
6460: PetscInt maxNsend2 = (maxNsend <= size / 2) ? maxNsend * 2 : size;
6462: PetscCall(PetscMalloc2(maxNsend2, &sendto2, maxNsend2, &nentries2));
6463: PetscCall(PetscArraycpy(sendto2, sendto, maxNsend));
6464: PetscCall(PetscArraycpy(nentries2, nentries2, maxNsend + 1));
6465: PetscCall(PetscFree2(sendto, nentries2));
6466: sendto = sendto2;
6467: nentries = nentries2;
6468: maxNsend = maxNsend2;
6469: }
6470: sendto[nsend] = owner;
6471: nentries[nsend] = p - k;
6472: PetscCall(PetscCountCast(p - k, &nentries[nsend]));
6473: nsend++;
6474: k = p;
6475: }
6477: /* Build 1st SF to know offsets on remote to send data */
6478: PetscSF sf1;
6479: PetscInt nroots = 1, nroots2 = 0;
6480: PetscInt nleaves = nsend, nleaves2 = 0;
6481: PetscInt *offsets;
6482: PetscSFNode *iremote;
6484: PetscCall(PetscSFCreate(comm, &sf1));
6485: PetscCall(PetscMalloc1(nsend, &iremote));
6486: PetscCall(PetscMalloc1(nsend, &offsets));
6487: for (k = 0; k < nsend; k++) {
6488: iremote[k].rank = sendto[k];
6489: iremote[k].index = 0;
6490: nleaves2 += nentries[k];
6491: PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6492: }
6493: PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6494: PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6495: PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6496: PetscCall(PetscSFDestroy(&sf1));
6497: PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT "", nleaves2, n1 - rem);
6499: /* Build 2nd SF to send remote COOs to their owner */
6500: PetscSF sf2;
6501: nroots = nroots2;
6502: nleaves = nleaves2;
6503: PetscCall(PetscSFCreate(comm, &sf2));
6504: PetscCall(PetscSFSetFromOptions(sf2));
6505: PetscCall(PetscMalloc1(nleaves, &iremote));
6506: p = 0;
6507: for (k = 0; k < nsend; k++) {
6508: PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6509: for (q = 0; q < nentries[k]; q++, p++) {
6510: iremote[p].rank = sendto[k];
6511: iremote[p].index = offsets[k] + q;
6512: }
6513: }
6514: PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6516: /* Send the remote COOs to their owner */
6517: PetscInt n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6518: PetscCount *perm2; /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6519: PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6520: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1 + rem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6521: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1 + rem, i2, MPI_REPLACE));
6522: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1 + rem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6523: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1 + rem, j2, MPI_REPLACE));
6525: PetscCall(PetscFree(offsets));
6526: PetscCall(PetscFree2(sendto, nentries));
6528: /* Sort received COOs by row along with the permutation array */
6529: for (k = 0; k < n2; k++) perm2[k] = k;
6530: PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));
6532: /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6533: PetscCount *Cperm1;
6534: PetscCall(PetscMalloc1(nleaves, &Cperm1));
6535: PetscCall(PetscArraycpy(Cperm1, perm1 + rem, nleaves));
6537: /* Support for HYPRE matrices, kind of a hack.
6538: Swap min column with diagonal so that diagonal values will go first */
6539: PetscBool hypre;
6540: const char *name;
6541: PetscCall(PetscObjectGetName((PetscObject)mat, &name));
6542: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", name, &hypre));
6543: if (hypre) {
6544: PetscInt *minj;
6545: PetscBT hasdiag;
6547: PetscCall(PetscBTCreate(m, &hasdiag));
6548: PetscCall(PetscMalloc1(m, &minj));
6549: for (k = 0; k < m; k++) minj[k] = PETSC_MAX_INT;
6550: for (k = i1start; k < rem; k++) {
6551: if (j1[k] < cstart || j1[k] >= cend) continue;
6552: const PetscInt rindex = i1[k] - rstart;
6553: if ((j1[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6554: minj[rindex] = PetscMin(minj[rindex], j1[k]);
6555: }
6556: for (k = 0; k < n2; k++) {
6557: if (j2[k] < cstart || j2[k] >= cend) continue;
6558: const PetscInt rindex = i2[k] - rstart;
6559: if ((j2[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6560: minj[rindex] = PetscMin(minj[rindex], j2[k]);
6561: }
6562: for (k = i1start; k < rem; k++) {
6563: const PetscInt rindex = i1[k] - rstart;
6564: if (j1[k] < cstart || j1[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6565: if (j1[k] == minj[rindex]) j1[k] = i1[k] + (cstart - rstart);
6566: else if ((j1[k] - cstart) == rindex) j1[k] = minj[rindex];
6567: }
6568: for (k = 0; k < n2; k++) {
6569: const PetscInt rindex = i2[k] - rstart;
6570: if (j2[k] < cstart || j2[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6571: if (j2[k] == minj[rindex]) j2[k] = i2[k] + (cstart - rstart);
6572: else if ((j2[k] - cstart) == rindex) j2[k] = minj[rindex];
6573: }
6574: PetscCall(PetscBTDestroy(&hasdiag));
6575: PetscCall(PetscFree(minj));
6576: }
6578: /* Split local COOs and received COOs into diag/offdiag portions */
6579: PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6580: PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1;
6581: PetscCount Annz1, Bnnz1, Atot1, Btot1;
6582: PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6583: PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6584: PetscCount Annz2, Bnnz2, Atot2, Btot2;
6586: PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6587: PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6588: PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6589: PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));
6591: /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6592: PetscInt *Ai, *Bi;
6593: PetscInt *Aj, *Bj;
6595: PetscCall(PetscMalloc1(m + 1, &Ai));
6596: PetscCall(PetscMalloc1(m + 1, &Bi));
6597: PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6598: PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));
6600: PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6601: PetscCall(PetscMalloc1(Annz1, &Aimap1));
6602: PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6603: PetscCall(PetscMalloc1(Annz2, &Aimap2));
6604: PetscCall(PetscMalloc1(Bnnz2, &Bimap2));
6606: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6607: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));
6609: /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we */
6610: /* expect nonzeros in A/B most likely have local contributing entries */
6611: PetscInt Annz = Ai[m];
6612: PetscInt Bnnz = Bi[m];
6613: PetscCount *Ajmap1_new, *Bjmap1_new;
6615: PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6616: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));
6618: PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6619: PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));
6621: PetscCall(PetscFree(Aimap1));
6622: PetscCall(PetscFree(Ajmap1));
6623: PetscCall(PetscFree(Bimap1));
6624: PetscCall(PetscFree(Bjmap1));
6625: PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6626: PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6627: PetscCall(PetscFree(perm1));
6628: PetscCall(PetscFree3(i2, j2, perm2));
6630: Ajmap1 = Ajmap1_new;
6631: Bjmap1 = Bjmap1_new;
6633: /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6634: if (Annz < Annz1 + Annz2) {
6635: PetscInt *Aj_new;
6636: PetscCall(PetscMalloc1(Annz, &Aj_new));
6637: PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6638: PetscCall(PetscFree(Aj));
6639: Aj = Aj_new;
6640: }
6642: if (Bnnz < Bnnz1 + Bnnz2) {
6643: PetscInt *Bj_new;
6644: PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6645: PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6646: PetscCall(PetscFree(Bj));
6647: Bj = Bj_new;
6648: }
6650: /* Create new submatrices for on-process and off-process coupling */
6651: PetscScalar *Aa, *Ba;
6652: MatType rtype;
6653: Mat_SeqAIJ *a, *b;
6654: PetscObjectState state;
6655: PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6656: PetscCall(PetscCalloc1(Bnnz, &Ba));
6657: /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6658: if (cstart) {
6659: for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6660: }
6661: PetscCall(MatDestroy(&mpiaij->A));
6662: PetscCall(MatDestroy(&mpiaij->B));
6663: PetscCall(MatGetRootType_Private(mat, &rtype));
6664: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6665: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6666: PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6667: mat->was_assembled = PETSC_TRUE; // was_assembled in effect means the Mvctx is built; doing so avoids redundant MatSetUpMultiply_MPIAIJ
6668: state = mpiaij->A->nonzerostate + mpiaij->B->nonzerostate;
6669: PetscCall(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
6671: a = (Mat_SeqAIJ *)mpiaij->A->data;
6672: b = (Mat_SeqAIJ *)mpiaij->B->data;
6673: a->singlemalloc = b->singlemalloc = PETSC_FALSE; /* Let newmat own Ai,Aj,Aa,Bi,Bj,Ba */
6674: a->free_a = b->free_a = PETSC_TRUE;
6675: a->free_ij = b->free_ij = PETSC_TRUE;
6677: /* conversion must happen AFTER multiply setup */
6678: PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6679: PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6680: PetscCall(VecDestroy(&mpiaij->lvec));
6681: PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));
6683: // Put the COO struct in a container and then attach that to the matrix
6684: PetscCall(PetscMalloc1(1, &coo));
6685: coo->n = coo_n;
6686: coo->sf = sf2;
6687: coo->sendlen = nleaves;
6688: coo->recvlen = nroots;
6689: coo->Annz = Annz;
6690: coo->Bnnz = Bnnz;
6691: coo->Annz2 = Annz2;
6692: coo->Bnnz2 = Bnnz2;
6693: coo->Atot1 = Atot1;
6694: coo->Atot2 = Atot2;
6695: coo->Btot1 = Btot1;
6696: coo->Btot2 = Btot2;
6697: coo->Ajmap1 = Ajmap1;
6698: coo->Aperm1 = Aperm1;
6699: coo->Bjmap1 = Bjmap1;
6700: coo->Bperm1 = Bperm1;
6701: coo->Aimap2 = Aimap2;
6702: coo->Ajmap2 = Ajmap2;
6703: coo->Aperm2 = Aperm2;
6704: coo->Bimap2 = Bimap2;
6705: coo->Bjmap2 = Bjmap2;
6706: coo->Bperm2 = Bperm2;
6707: coo->Cperm1 = Cperm1;
6708: // Allocate in preallocation. If not used, it has zero cost on host
6709: PetscCall(PetscMalloc2(coo->sendlen, &coo->sendbuf, coo->recvlen, &coo->recvbuf));
6710: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
6711: PetscCall(PetscContainerSetPointer(container, coo));
6712: PetscCall(PetscContainerSetUserDestroy(container, MatCOOStructDestroy_MPIAIJ));
6713: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
6714: PetscCall(PetscContainerDestroy(&container));
6715: PetscFunctionReturn(PETSC_SUCCESS);
6716: }
6718: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6719: {
6720: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6721: Mat A = mpiaij->A, B = mpiaij->B;
6722: PetscScalar *Aa, *Ba;
6723: PetscScalar *sendbuf, *recvbuf;
6724: const PetscCount *Ajmap1, *Ajmap2, *Aimap2;
6725: const PetscCount *Bjmap1, *Bjmap2, *Bimap2;
6726: const PetscCount *Aperm1, *Aperm2, *Bperm1, *Bperm2;
6727: const PetscCount *Cperm1;
6728: PetscContainer container;
6729: MatCOOStruct_MPIAIJ *coo;
6731: PetscFunctionBegin;
6732: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
6733: PetscCheck(container, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
6734: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
6735: sendbuf = coo->sendbuf;
6736: recvbuf = coo->recvbuf;
6737: Ajmap1 = coo->Ajmap1;
6738: Ajmap2 = coo->Ajmap2;
6739: Aimap2 = coo->Aimap2;
6740: Bjmap1 = coo->Bjmap1;
6741: Bjmap2 = coo->Bjmap2;
6742: Bimap2 = coo->Bimap2;
6743: Aperm1 = coo->Aperm1;
6744: Aperm2 = coo->Aperm2;
6745: Bperm1 = coo->Bperm1;
6746: Bperm2 = coo->Bperm2;
6747: Cperm1 = coo->Cperm1;
6749: PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6750: PetscCall(MatSeqAIJGetArray(B, &Ba));
6752: /* Pack entries to be sent to remote */
6753: for (PetscCount i = 0; i < coo->sendlen; i++) sendbuf[i] = v[Cperm1[i]];
6755: /* Send remote entries to their owner and overlap the communication with local computation */
6756: PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6757: /* Add local entries to A and B */
6758: for (PetscCount i = 0; i < coo->Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6759: PetscScalar sum = 0.0; /* Do partial summation first to improve numerical stability */
6760: for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6761: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6762: }
6763: for (PetscCount i = 0; i < coo->Bnnz; i++) {
6764: PetscScalar sum = 0.0;
6765: for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6766: Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6767: }
6768: PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));
6770: /* Add received remote entries to A and B */
6771: for (PetscCount i = 0; i < coo->Annz2; i++) {
6772: for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6773: }
6774: for (PetscCount i = 0; i < coo->Bnnz2; i++) {
6775: for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6776: }
6777: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6778: PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6779: PetscFunctionReturn(PETSC_SUCCESS);
6780: }
6782: /*MC
6783: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
6785: Options Database Keys:
6786: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`
6788: Level: beginner
6790: Notes:
6791: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
6792: in this case the values associated with the rows and columns one passes in are set to zero
6793: in the matrix
6795: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
6796: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
6798: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6799: M*/
6800: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6801: {
6802: Mat_MPIAIJ *b;
6803: PetscMPIInt size;
6805: PetscFunctionBegin;
6806: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
6808: PetscCall(PetscNew(&b));
6809: B->data = (void *)b;
6810: B->ops[0] = MatOps_Values;
6811: B->assembled = PETSC_FALSE;
6812: B->insertmode = NOT_SET_VALUES;
6813: b->size = size;
6815: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
6817: /* build cache for off array entries formed */
6818: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
6820: b->donotstash = PETSC_FALSE;
6821: b->colmap = NULL;
6822: b->garray = NULL;
6823: b->roworiented = PETSC_TRUE;
6825: /* stuff used for matrix vector multiply */
6826: b->lvec = NULL;
6827: b->Mvctx = NULL;
6829: /* stuff for MatGetRow() */
6830: b->rowindices = NULL;
6831: b->rowvalues = NULL;
6832: b->getrowactive = PETSC_FALSE;
6834: /* flexible pointer used in CUSPARSE classes */
6835: b->spptr = NULL;
6837: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6838: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6839: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6840: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6841: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6842: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6843: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6844: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6845: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6846: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6847: #if defined(PETSC_HAVE_CUDA)
6848: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6849: #endif
6850: #if defined(PETSC_HAVE_HIP)
6851: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6852: #endif
6853: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6854: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6855: #endif
6856: #if defined(PETSC_HAVE_MKL_SPARSE)
6857: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6858: #endif
6859: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6860: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6861: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6862: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6863: #if defined(PETSC_HAVE_ELEMENTAL)
6864: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6865: #endif
6866: #if defined(PETSC_HAVE_SCALAPACK)
6867: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6868: #endif
6869: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6870: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6871: #if defined(PETSC_HAVE_HYPRE)
6872: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6873: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6874: #endif
6875: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6876: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6877: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6878: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6879: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6880: PetscFunctionReturn(PETSC_SUCCESS);
6881: }
6883: /*@C
6884: MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6885: and "off-diagonal" part of the matrix in CSR format.
6887: Collective
6889: Input Parameters:
6890: + comm - MPI communicator
6891: . m - number of local rows (Cannot be `PETSC_DECIDE`)
6892: . n - This value should be the same as the local size used in creating the
6893: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
6894: calculated if `N` is given) For square matrices `n` is almost always `m`.
6895: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6896: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6897: . 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
6898: . j - column indices, which must be local, i.e., based off the start column of the diagonal portion
6899: . a - matrix values
6900: . 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
6901: . oj - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6902: - oa - matrix values
6904: Output Parameter:
6905: . mat - the matrix
6907: Level: advanced
6909: Notes:
6910: The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc. The user
6911: must free the arrays once the matrix has been destroyed and not before.
6913: The `i` and `j` indices are 0 based
6915: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix
6917: This sets local rows and cannot be used to set off-processor values.
6919: Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6920: legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6921: not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
6922: the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
6923: keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
6924: communication if it is known that only local entries will be set.
6926: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
6927: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
6928: @*/
6929: 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)
6930: {
6931: Mat_MPIAIJ *maij;
6933: PetscFunctionBegin;
6934: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
6935: PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
6936: PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
6937: PetscCall(MatCreate(comm, mat));
6938: PetscCall(MatSetSizes(*mat, m, n, M, N));
6939: PetscCall(MatSetType(*mat, MATMPIAIJ));
6940: maij = (Mat_MPIAIJ *)(*mat)->data;
6942: (*mat)->preallocated = PETSC_TRUE;
6944: PetscCall(PetscLayoutSetUp((*mat)->rmap));
6945: PetscCall(PetscLayoutSetUp((*mat)->cmap));
6947: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
6948: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));
6950: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
6951: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
6952: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
6953: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
6954: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
6955: PetscFunctionReturn(PETSC_SUCCESS);
6956: }
6958: typedef struct {
6959: Mat *mp; /* intermediate products */
6960: PetscBool *mptmp; /* is the intermediate product temporary ? */
6961: PetscInt cp; /* number of intermediate products */
6963: /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
6964: PetscInt *startsj_s, *startsj_r;
6965: PetscScalar *bufa;
6966: Mat P_oth;
6968: /* may take advantage of merging product->B */
6969: Mat Bloc; /* B-local by merging diag and off-diag */
6971: /* cusparse does not have support to split between symbolic and numeric phases.
6972: When api_user is true, we don't need to update the numerical values
6973: of the temporary storage */
6974: PetscBool reusesym;
6976: /* support for COO values insertion */
6977: PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
6978: PetscInt **own; /* own[i] points to address of on-process COO indices for Mat mp[i] */
6979: PetscInt **off; /* off[i] points to address of off-process COO indices for Mat mp[i] */
6980: PetscBool hasoffproc; /* if true, have off-process values insertion (i.e. AtB or PtAP) */
6981: PetscSF sf; /* used for non-local values insertion and memory malloc */
6982: PetscMemType mtype;
6984: /* customization */
6985: PetscBool abmerge;
6986: PetscBool P_oth_bind;
6987: } MatMatMPIAIJBACKEND;
6989: static PetscErrorCode MatDestroy_MatMatMPIAIJBACKEND(void *data)
6990: {
6991: MatMatMPIAIJBACKEND *mmdata = (MatMatMPIAIJBACKEND *)data;
6992: PetscInt i;
6994: PetscFunctionBegin;
6995: PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
6996: PetscCall(PetscFree(mmdata->bufa));
6997: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
6998: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
6999: PetscCall(MatDestroy(&mmdata->P_oth));
7000: PetscCall(MatDestroy(&mmdata->Bloc));
7001: PetscCall(PetscSFDestroy(&mmdata->sf));
7002: for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
7003: PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
7004: PetscCall(PetscFree(mmdata->own[0]));
7005: PetscCall(PetscFree(mmdata->own));
7006: PetscCall(PetscFree(mmdata->off[0]));
7007: PetscCall(PetscFree(mmdata->off));
7008: PetscCall(PetscFree(mmdata));
7009: PetscFunctionReturn(PETSC_SUCCESS);
7010: }
7012: /* Copy selected n entries with indices in idx[] of A to v[].
7013: If idx is NULL, copy the whole data array of A to v[]
7014: */
7015: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
7016: {
7017: PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);
7019: PetscFunctionBegin;
7020: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
7021: if (f) {
7022: PetscCall((*f)(A, n, idx, v));
7023: } else {
7024: const PetscScalar *vv;
7026: PetscCall(MatSeqAIJGetArrayRead(A, &vv));
7027: if (n && idx) {
7028: PetscScalar *w = v;
7029: const PetscInt *oi = idx;
7030: PetscInt j;
7032: for (j = 0; j < n; j++) *w++ = vv[*oi++];
7033: } else {
7034: PetscCall(PetscArraycpy(v, vv, n));
7035: }
7036: PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7037: }
7038: PetscFunctionReturn(PETSC_SUCCESS);
7039: }
7041: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7042: {
7043: MatMatMPIAIJBACKEND *mmdata;
7044: PetscInt i, n_d, n_o;
7046: PetscFunctionBegin;
7047: MatCheckProduct(C, 1);
7048: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7049: mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7050: if (!mmdata->reusesym) { /* update temporary matrices */
7051: 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));
7052: if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7053: }
7054: mmdata->reusesym = PETSC_FALSE;
7056: for (i = 0; i < mmdata->cp; i++) {
7057: 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]);
7058: PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7059: }
7060: for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7061: PetscInt noff = mmdata->off[i + 1] - mmdata->off[i];
7063: if (mmdata->mptmp[i]) continue;
7064: if (noff) {
7065: PetscInt nown = mmdata->own[i + 1] - mmdata->own[i];
7067: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7068: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7069: n_o += noff;
7070: n_d += nown;
7071: } else {
7072: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;
7074: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7075: n_d += mm->nz;
7076: }
7077: }
7078: if (mmdata->hasoffproc) { /* offprocess insertion */
7079: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7080: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7081: }
7082: PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7083: PetscFunctionReturn(PETSC_SUCCESS);
7084: }
7086: /* Support for Pt * A, A * P, or Pt * A * P */
7087: #define MAX_NUMBER_INTERMEDIATE 4
7088: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7089: {
7090: Mat_Product *product = C->product;
7091: Mat A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7092: Mat_MPIAIJ *a, *p;
7093: MatMatMPIAIJBACKEND *mmdata;
7094: ISLocalToGlobalMapping P_oth_l2g = NULL;
7095: IS glob = NULL;
7096: const char *prefix;
7097: char pprefix[256];
7098: const PetscInt *globidx, *P_oth_idx;
7099: PetscInt i, j, cp, m, n, M, N, *coo_i, *coo_j;
7100: PetscCount ncoo, ncoo_d, ncoo_o, ncoo_oown;
7101: PetscInt cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7102: /* type-0: consecutive, start from 0; type-1: consecutive with */
7103: /* a base offset; type-2: sparse with a local to global map table */
7104: const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE]; /* col/row local to global map array (table) for type-2 map type */
7106: MatProductType ptype;
7107: PetscBool mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7108: PetscMPIInt size;
7110: PetscFunctionBegin;
7111: MatCheckProduct(C, 1);
7112: PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7113: ptype = product->type;
7114: if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7115: ptype = MATPRODUCT_AB;
7116: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7117: }
7118: switch (ptype) {
7119: case MATPRODUCT_AB:
7120: A = product->A;
7121: P = product->B;
7122: m = A->rmap->n;
7123: n = P->cmap->n;
7124: M = A->rmap->N;
7125: N = P->cmap->N;
7126: hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7127: break;
7128: case MATPRODUCT_AtB:
7129: P = product->A;
7130: A = product->B;
7131: m = P->cmap->n;
7132: n = A->cmap->n;
7133: M = P->cmap->N;
7134: N = A->cmap->N;
7135: hasoffproc = PETSC_TRUE;
7136: break;
7137: case MATPRODUCT_PtAP:
7138: A = product->A;
7139: P = product->B;
7140: m = P->cmap->n;
7141: n = P->cmap->n;
7142: M = P->cmap->N;
7143: N = P->cmap->N;
7144: hasoffproc = PETSC_TRUE;
7145: break;
7146: default:
7147: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7148: }
7149: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7150: if (size == 1) hasoffproc = PETSC_FALSE;
7152: /* defaults */
7153: for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7154: mp[i] = NULL;
7155: mptmp[i] = PETSC_FALSE;
7156: rmapt[i] = -1;
7157: cmapt[i] = -1;
7158: rmapa[i] = NULL;
7159: cmapa[i] = NULL;
7160: }
7162: /* customization */
7163: PetscCall(PetscNew(&mmdata));
7164: mmdata->reusesym = product->api_user;
7165: if (ptype == MATPRODUCT_AB) {
7166: if (product->api_user) {
7167: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7168: PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7169: PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7170: PetscOptionsEnd();
7171: } else {
7172: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7173: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7174: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7175: PetscOptionsEnd();
7176: }
7177: } else if (ptype == MATPRODUCT_PtAP) {
7178: if (product->api_user) {
7179: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7180: PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7181: PetscOptionsEnd();
7182: } else {
7183: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7184: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7185: PetscOptionsEnd();
7186: }
7187: }
7188: a = (Mat_MPIAIJ *)A->data;
7189: p = (Mat_MPIAIJ *)P->data;
7190: PetscCall(MatSetSizes(C, m, n, M, N));
7191: PetscCall(PetscLayoutSetUp(C->rmap));
7192: PetscCall(PetscLayoutSetUp(C->cmap));
7193: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7194: PetscCall(MatGetOptionsPrefix(C, &prefix));
7196: cp = 0;
7197: switch (ptype) {
7198: case MATPRODUCT_AB: /* A * P */
7199: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7201: /* A_diag * P_local (merged or not) */
7202: if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7203: /* P is product->B */
7204: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7205: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7206: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7207: PetscCall(MatProductSetFill(mp[cp], product->fill));
7208: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7209: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7210: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7211: mp[cp]->product->api_user = product->api_user;
7212: PetscCall(MatProductSetFromOptions(mp[cp]));
7213: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7214: PetscCall(ISGetIndices(glob, &globidx));
7215: rmapt[cp] = 1;
7216: cmapt[cp] = 2;
7217: cmapa[cp] = globidx;
7218: mptmp[cp] = PETSC_FALSE;
7219: cp++;
7220: } else { /* A_diag * P_diag and A_diag * P_off */
7221: PetscCall(MatProductCreate(a->A, p->A, NULL, &mp[cp]));
7222: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7223: PetscCall(MatProductSetFill(mp[cp], product->fill));
7224: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7225: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7226: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7227: mp[cp]->product->api_user = product->api_user;
7228: PetscCall(MatProductSetFromOptions(mp[cp]));
7229: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7230: rmapt[cp] = 1;
7231: cmapt[cp] = 1;
7232: mptmp[cp] = PETSC_FALSE;
7233: cp++;
7234: PetscCall(MatProductCreate(a->A, p->B, NULL, &mp[cp]));
7235: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7236: PetscCall(MatProductSetFill(mp[cp], product->fill));
7237: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7238: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7239: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7240: mp[cp]->product->api_user = product->api_user;
7241: PetscCall(MatProductSetFromOptions(mp[cp]));
7242: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7243: rmapt[cp] = 1;
7244: cmapt[cp] = 2;
7245: cmapa[cp] = p->garray;
7246: mptmp[cp] = PETSC_FALSE;
7247: cp++;
7248: }
7250: /* A_off * P_other */
7251: if (mmdata->P_oth) {
7252: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7253: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7254: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)(a->B))->type_name));
7255: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7256: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7257: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7258: PetscCall(MatProductSetFill(mp[cp], product->fill));
7259: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7260: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7261: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7262: mp[cp]->product->api_user = product->api_user;
7263: PetscCall(MatProductSetFromOptions(mp[cp]));
7264: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7265: rmapt[cp] = 1;
7266: cmapt[cp] = 2;
7267: cmapa[cp] = P_oth_idx;
7268: mptmp[cp] = PETSC_FALSE;
7269: cp++;
7270: }
7271: break;
7273: case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7274: /* A is product->B */
7275: PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7276: if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7277: PetscCall(MatProductCreate(mmdata->Bloc, mmdata->Bloc, NULL, &mp[cp]));
7278: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7279: PetscCall(MatProductSetFill(mp[cp], product->fill));
7280: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7281: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7282: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7283: mp[cp]->product->api_user = product->api_user;
7284: PetscCall(MatProductSetFromOptions(mp[cp]));
7285: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7286: PetscCall(ISGetIndices(glob, &globidx));
7287: rmapt[cp] = 2;
7288: rmapa[cp] = globidx;
7289: cmapt[cp] = 2;
7290: cmapa[cp] = globidx;
7291: mptmp[cp] = PETSC_FALSE;
7292: cp++;
7293: } else {
7294: PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7295: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7296: PetscCall(MatProductSetFill(mp[cp], product->fill));
7297: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7298: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7299: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7300: mp[cp]->product->api_user = product->api_user;
7301: PetscCall(MatProductSetFromOptions(mp[cp]));
7302: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7303: PetscCall(ISGetIndices(glob, &globidx));
7304: rmapt[cp] = 1;
7305: cmapt[cp] = 2;
7306: cmapa[cp] = globidx;
7307: mptmp[cp] = PETSC_FALSE;
7308: cp++;
7309: PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7310: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7311: PetscCall(MatProductSetFill(mp[cp], product->fill));
7312: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7313: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7314: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7315: mp[cp]->product->api_user = product->api_user;
7316: PetscCall(MatProductSetFromOptions(mp[cp]));
7317: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7318: rmapt[cp] = 2;
7319: rmapa[cp] = p->garray;
7320: cmapt[cp] = 2;
7321: cmapa[cp] = globidx;
7322: mptmp[cp] = PETSC_FALSE;
7323: cp++;
7324: }
7325: break;
7326: case MATPRODUCT_PtAP:
7327: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7328: /* P is product->B */
7329: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7330: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7331: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
7332: PetscCall(MatProductSetFill(mp[cp], product->fill));
7333: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7334: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7335: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7336: mp[cp]->product->api_user = product->api_user;
7337: PetscCall(MatProductSetFromOptions(mp[cp]));
7338: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7339: PetscCall(ISGetIndices(glob, &globidx));
7340: rmapt[cp] = 2;
7341: rmapa[cp] = globidx;
7342: cmapt[cp] = 2;
7343: cmapa[cp] = globidx;
7344: mptmp[cp] = PETSC_FALSE;
7345: cp++;
7346: if (mmdata->P_oth) {
7347: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7348: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7349: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)(a->B))->type_name));
7350: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7351: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7352: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7353: PetscCall(MatProductSetFill(mp[cp], product->fill));
7354: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7355: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7356: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7357: mp[cp]->product->api_user = product->api_user;
7358: PetscCall(MatProductSetFromOptions(mp[cp]));
7359: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7360: mptmp[cp] = PETSC_TRUE;
7361: cp++;
7362: PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7363: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7364: PetscCall(MatProductSetFill(mp[cp], product->fill));
7365: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7366: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7367: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7368: mp[cp]->product->api_user = product->api_user;
7369: PetscCall(MatProductSetFromOptions(mp[cp]));
7370: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7371: rmapt[cp] = 2;
7372: rmapa[cp] = globidx;
7373: cmapt[cp] = 2;
7374: cmapa[cp] = P_oth_idx;
7375: mptmp[cp] = PETSC_FALSE;
7376: cp++;
7377: }
7378: break;
7379: default:
7380: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7381: }
7382: /* sanity check */
7383: if (size > 1)
7384: 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);
7386: PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7387: for (i = 0; i < cp; i++) {
7388: mmdata->mp[i] = mp[i];
7389: mmdata->mptmp[i] = mptmp[i];
7390: }
7391: mmdata->cp = cp;
7392: C->product->data = mmdata;
7393: C->product->destroy = MatDestroy_MatMatMPIAIJBACKEND;
7394: C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;
7396: /* memory type */
7397: mmdata->mtype = PETSC_MEMTYPE_HOST;
7398: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7399: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7400: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7401: if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7402: else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7403: else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;
7405: /* prepare coo coordinates for values insertion */
7407: /* count total nonzeros of those intermediate seqaij Mats
7408: ncoo_d: # of nonzeros of matrices that do not have offproc entries
7409: ncoo_o: # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7410: ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7411: */
7412: for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7413: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7414: if (mptmp[cp]) continue;
7415: if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7416: const PetscInt *rmap = rmapa[cp];
7417: const PetscInt mr = mp[cp]->rmap->n;
7418: const PetscInt rs = C->rmap->rstart;
7419: const PetscInt re = C->rmap->rend;
7420: const PetscInt *ii = mm->i;
7421: for (i = 0; i < mr; i++) {
7422: const PetscInt gr = rmap[i];
7423: const PetscInt nz = ii[i + 1] - ii[i];
7424: if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7425: else ncoo_oown += nz; /* this row is local */
7426: }
7427: } else ncoo_d += mm->nz;
7428: }
7430: /*
7431: ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc
7433: ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.
7435: off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].
7437: off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7438: own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7439: so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.
7441: coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7442: 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.
7443: */
7444: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7445: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));
7447: /* gather (i,j) of nonzeros inserted by remote procs */
7448: if (hasoffproc) {
7449: PetscSF msf;
7450: PetscInt ncoo2, *coo_i2, *coo_j2;
7452: PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7453: PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7454: PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */
7456: for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7457: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7458: PetscInt *idxoff = mmdata->off[cp];
7459: PetscInt *idxown = mmdata->own[cp];
7460: if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7461: const PetscInt *rmap = rmapa[cp];
7462: const PetscInt *cmap = cmapa[cp];
7463: const PetscInt *ii = mm->i;
7464: PetscInt *coi = coo_i + ncoo_o;
7465: PetscInt *coj = coo_j + ncoo_o;
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 cs = C->cmap->rstart;
7470: for (i = 0; i < mr; i++) {
7471: const PetscInt *jj = mm->j + ii[i];
7472: const PetscInt gr = rmap[i];
7473: const PetscInt nz = ii[i + 1] - ii[i];
7474: if (gr < rs || gr >= re) { /* this is an offproc row */
7475: for (j = ii[i]; j < ii[i + 1]; j++) {
7476: *coi++ = gr;
7477: *idxoff++ = j;
7478: }
7479: if (!cmapt[cp]) { /* already global */
7480: for (j = 0; j < nz; j++) *coj++ = jj[j];
7481: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7482: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7483: } else { /* offdiag */
7484: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7485: }
7486: ncoo_o += nz;
7487: } else { /* this is a local row */
7488: for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7489: }
7490: }
7491: }
7492: mmdata->off[cp + 1] = idxoff;
7493: mmdata->own[cp + 1] = idxown;
7494: }
7496: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7497: PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, ncoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7498: PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7499: PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7500: ncoo = ncoo_d + ncoo_oown + ncoo2;
7501: PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7502: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7503: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7504: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7505: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7506: PetscCall(PetscFree2(coo_i, coo_j));
7507: /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7508: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7509: coo_i = coo_i2;
7510: coo_j = coo_j2;
7511: } else { /* no offproc values insertion */
7512: ncoo = ncoo_d;
7513: PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));
7515: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7516: PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7517: PetscCall(PetscSFSetUp(mmdata->sf));
7518: }
7519: mmdata->hasoffproc = hasoffproc;
7521: /* gather (i,j) of nonzeros inserted locally */
7522: for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7523: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7524: PetscInt *coi = coo_i + ncoo_d;
7525: PetscInt *coj = coo_j + ncoo_d;
7526: const PetscInt *jj = mm->j;
7527: const PetscInt *ii = mm->i;
7528: const PetscInt *cmap = cmapa[cp];
7529: const PetscInt *rmap = rmapa[cp];
7530: const PetscInt mr = mp[cp]->rmap->n;
7531: const PetscInt rs = C->rmap->rstart;
7532: const PetscInt re = C->rmap->rend;
7533: const PetscInt cs = C->cmap->rstart;
7535: if (mptmp[cp]) continue;
7536: if (rmapt[cp] == 1) { /* consecutive rows */
7537: /* fill coo_i */
7538: for (i = 0; i < mr; i++) {
7539: const PetscInt gr = i + rs;
7540: for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7541: }
7542: /* fill coo_j */
7543: if (!cmapt[cp]) { /* type-0, already global */
7544: PetscCall(PetscArraycpy(coj, jj, mm->nz));
7545: } else if (cmapt[cp] == 1) { /* type-1, local to global for consecutive columns of C */
7546: for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7547: } else { /* type-2, local to global for sparse columns */
7548: for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7549: }
7550: ncoo_d += mm->nz;
7551: } else if (rmapt[cp] == 2) { /* sparse rows */
7552: for (i = 0; i < mr; i++) {
7553: const PetscInt *jj = mm->j + ii[i];
7554: const PetscInt gr = rmap[i];
7555: const PetscInt nz = ii[i + 1] - ii[i];
7556: if (gr >= rs && gr < re) { /* local rows */
7557: for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7558: if (!cmapt[cp]) { /* type-0, already global */
7559: for (j = 0; j < nz; j++) *coj++ = jj[j];
7560: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7561: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7562: } else { /* type-2, local to global for sparse columns */
7563: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7564: }
7565: ncoo_d += nz;
7566: }
7567: }
7568: }
7569: }
7570: if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7571: PetscCall(ISDestroy(&glob));
7572: if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7573: PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7574: /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7575: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));
7577: /* preallocate with COO data */
7578: PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7579: PetscCall(PetscFree2(coo_i, coo_j));
7580: PetscFunctionReturn(PETSC_SUCCESS);
7581: }
7583: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7584: {
7585: Mat_Product *product = mat->product;
7586: #if defined(PETSC_HAVE_DEVICE)
7587: PetscBool match = PETSC_FALSE;
7588: PetscBool usecpu = PETSC_FALSE;
7589: #else
7590: PetscBool match = PETSC_TRUE;
7591: #endif
7593: PetscFunctionBegin;
7594: MatCheckProduct(mat, 1);
7595: #if defined(PETSC_HAVE_DEVICE)
7596: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7597: if (match) { /* we can always fallback to the CPU if requested */
7598: switch (product->type) {
7599: case MATPRODUCT_AB:
7600: if (product->api_user) {
7601: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7602: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7603: PetscOptionsEnd();
7604: } else {
7605: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7606: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7607: PetscOptionsEnd();
7608: }
7609: break;
7610: case MATPRODUCT_AtB:
7611: if (product->api_user) {
7612: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7613: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7614: PetscOptionsEnd();
7615: } else {
7616: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7617: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7618: PetscOptionsEnd();
7619: }
7620: break;
7621: case MATPRODUCT_PtAP:
7622: if (product->api_user) {
7623: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7624: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7625: PetscOptionsEnd();
7626: } else {
7627: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7628: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7629: PetscOptionsEnd();
7630: }
7631: break;
7632: default:
7633: break;
7634: }
7635: match = (PetscBool)!usecpu;
7636: }
7637: #endif
7638: if (match) {
7639: switch (product->type) {
7640: case MATPRODUCT_AB:
7641: case MATPRODUCT_AtB:
7642: case MATPRODUCT_PtAP:
7643: mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7644: break;
7645: default:
7646: break;
7647: }
7648: }
7649: /* fallback to MPIAIJ ops */
7650: if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7651: PetscFunctionReturn(PETSC_SUCCESS);
7652: }
7654: /*
7655: Produces a set of block column indices of the matrix row, one for each block represented in the original row
7657: n - the number of block indices in cc[]
7658: cc - the block indices (must be large enough to contain the indices)
7659: */
7660: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7661: {
7662: PetscInt cnt = -1, nidx, j;
7663: const PetscInt *idx;
7665: PetscFunctionBegin;
7666: PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7667: if (nidx) {
7668: cnt = 0;
7669: cc[cnt] = idx[0] / bs;
7670: for (j = 1; j < nidx; j++) {
7671: if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7672: }
7673: }
7674: PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7675: *n = cnt + 1;
7676: PetscFunctionReturn(PETSC_SUCCESS);
7677: }
7679: /*
7680: Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows
7682: ncollapsed - the number of block indices
7683: collapsed - the block indices (must be large enough to contain the indices)
7684: */
7685: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7686: {
7687: PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;
7689: PetscFunctionBegin;
7690: PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7691: for (i = start + 1; i < start + bs; i++) {
7692: PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7693: PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7694: cprevtmp = cprev;
7695: cprev = merged;
7696: merged = cprevtmp;
7697: }
7698: *ncollapsed = nprev;
7699: if (collapsed) *collapsed = cprev;
7700: PetscFunctionReturn(PETSC_SUCCESS);
7701: }
7703: /*
7704: MatCreateGraph_Simple_AIJ - create simple scalar matrix (graph) from potentially blocked matrix
7706: Input Parameter:
7707: . Amat - matrix
7708: - symmetrize - make the result symmetric
7709: + scale - scale with diagonal
7711: Output Parameter:
7712: . a_Gmat - output scalar graph >= 0
7714: */
7715: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, Mat *a_Gmat)
7716: {
7717: PetscInt Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7718: MPI_Comm comm;
7719: Mat Gmat;
7720: PetscBool ismpiaij, isseqaij;
7721: Mat a, b, c;
7722: MatType jtype;
7724: PetscFunctionBegin;
7725: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7726: PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7727: PetscCall(MatGetSize(Amat, &MM, &NN));
7728: PetscCall(MatGetBlockSize(Amat, &bs));
7729: nloc = (Iend - Istart) / bs;
7731: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7732: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7733: PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");
7735: /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7736: /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7737: implementation */
7738: if (bs > 1) {
7739: PetscCall(MatGetType(Amat, &jtype));
7740: PetscCall(MatCreate(comm, &Gmat));
7741: PetscCall(MatSetType(Gmat, jtype));
7742: PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7743: PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7744: if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7745: PetscInt *d_nnz, *o_nnz;
7746: MatScalar *aa, val, *AA;
7747: PetscInt *aj, *ai, *AJ, nc, nmax = 0;
7748: if (isseqaij) {
7749: a = Amat;
7750: b = NULL;
7751: } else {
7752: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7753: a = d->A;
7754: b = d->B;
7755: }
7756: PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7757: PetscCall(PetscMalloc2(nloc, &d_nnz, isseqaij ? 0 : nloc, &o_nnz));
7758: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7759: PetscInt *nnz = (c == a) ? d_nnz : o_nnz;
7760: const PetscInt *cols1, *cols2;
7761: for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7762: PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7763: nnz[brow / bs] = nc2 / bs;
7764: if (nc2 % bs) ok = 0;
7765: if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7766: for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7767: PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7768: if (nc1 != nc2) ok = 0;
7769: else {
7770: for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7771: if (cols1[jj] != cols2[jj]) ok = 0;
7772: if (cols1[jj] % bs != jj % bs) ok = 0;
7773: }
7774: }
7775: PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7776: }
7777: PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7778: if (!ok) {
7779: PetscCall(PetscFree2(d_nnz, o_nnz));
7780: PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7781: goto old_bs;
7782: }
7783: }
7784: }
7785: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7786: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7787: PetscCall(PetscFree2(d_nnz, o_nnz));
7788: PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7789: // diag
7790: for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7791: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;
7792: ai = aseq->i;
7793: n = ai[brow + 1] - ai[brow];
7794: aj = aseq->j + ai[brow];
7795: for (int k = 0; k < n; k += bs) { // block columns
7796: AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7797: val = 0;
7798: for (int ii = 0; ii < bs; ii++) { // rows in block
7799: aa = aseq->a + ai[brow + ii] + k;
7800: for (int jj = 0; jj < bs; jj++) { // columns in block
7801: val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7802: }
7803: }
7804: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7805: AA[k / bs] = val;
7806: }
7807: grow = Istart / bs + brow / bs;
7808: PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, INSERT_VALUES));
7809: }
7810: // off-diag
7811: if (ismpiaij) {
7812: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Amat->data;
7813: const PetscScalar *vals;
7814: const PetscInt *cols, *garray = aij->garray;
7815: PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7816: for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7817: PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7818: for (int k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7819: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7820: AA[k / bs] = 0;
7821: AJ[cidx] = garray[cols[k]] / bs;
7822: }
7823: nc = ncols / bs;
7824: PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7825: for (int ii = 0; ii < bs; ii++) { // rows in block
7826: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7827: for (int k = 0; k < ncols; k += bs) {
7828: for (int jj = 0; jj < bs; jj++) { // cols in block
7829: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7830: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7831: }
7832: }
7833: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7834: }
7835: grow = Istart / bs + brow / bs;
7836: PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, INSERT_VALUES));
7837: }
7838: }
7839: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7840: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7841: PetscCall(PetscFree2(AA, AJ));
7842: } else {
7843: const PetscScalar *vals;
7844: const PetscInt *idx;
7845: PetscInt *d_nnz, *o_nnz, *w0, *w1, *w2;
7846: old_bs:
7847: /*
7848: Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7849: */
7850: PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7851: PetscCall(PetscMalloc2(nloc, &d_nnz, isseqaij ? 0 : nloc, &o_nnz));
7852: if (isseqaij) {
7853: PetscInt max_d_nnz;
7854: /*
7855: Determine exact preallocation count for (sequential) scalar matrix
7856: */
7857: PetscCall(MatSeqAIJGetMaxRowNonzeros(Amat, &max_d_nnz));
7858: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7859: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7860: for (Ii = 0, jj = 0; Ii < Iend; Ii += bs, jj++) PetscCall(MatCollapseRows(Amat, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7861: PetscCall(PetscFree3(w0, w1, w2));
7862: } else if (ismpiaij) {
7863: Mat Daij, Oaij;
7864: const PetscInt *garray;
7865: PetscInt max_d_nnz;
7866: PetscCall(MatMPIAIJGetSeqAIJ(Amat, &Daij, &Oaij, &garray));
7867: /*
7868: Determine exact preallocation count for diagonal block portion of scalar matrix
7869: */
7870: PetscCall(MatSeqAIJGetMaxRowNonzeros(Daij, &max_d_nnz));
7871: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7872: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7873: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) PetscCall(MatCollapseRows(Daij, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7874: PetscCall(PetscFree3(w0, w1, w2));
7875: /*
7876: Over estimate (usually grossly over), preallocation count for off-diagonal portion of scalar matrix
7877: */
7878: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) {
7879: o_nnz[jj] = 0;
7880: for (kk = 0; kk < bs; kk++) { /* rows that get collapsed to a single row */
7881: PetscCall(MatGetRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7882: o_nnz[jj] += ncols;
7883: PetscCall(MatRestoreRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7884: }
7885: if (o_nnz[jj] > (NN / bs - nloc)) o_nnz[jj] = NN / bs - nloc;
7886: }
7887: } else SETERRQ(comm, PETSC_ERR_USER, "Require AIJ matrix type");
7888: /* get scalar copy (norms) of matrix */
7889: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7890: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7891: PetscCall(PetscFree2(d_nnz, o_nnz));
7892: for (Ii = Istart; Ii < Iend; Ii++) {
7893: PetscInt dest_row = Ii / bs;
7894: PetscCall(MatGetRow(Amat, Ii, &ncols, &idx, &vals));
7895: for (jj = 0; jj < ncols; jj++) {
7896: PetscInt dest_col = idx[jj] / bs;
7897: PetscScalar sv = PetscAbs(PetscRealPart(vals[jj]));
7898: PetscCall(MatSetValues(Gmat, 1, &dest_row, 1, &dest_col, &sv, ADD_VALUES));
7899: }
7900: PetscCall(MatRestoreRow(Amat, Ii, &ncols, &idx, &vals));
7901: }
7902: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7903: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7904: }
7905: } else {
7906: if (symmetrize || filter >= 0 || scale) PetscCall(MatDuplicate(Amat, MAT_COPY_VALUES, &Gmat));
7907: else {
7908: Gmat = Amat;
7909: PetscCall(PetscObjectReference((PetscObject)Gmat));
7910: }
7911: if (isseqaij) {
7912: a = Gmat;
7913: b = NULL;
7914: } else {
7915: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
7916: a = d->A;
7917: b = d->B;
7918: }
7919: if (filter >= 0 || scale) {
7920: /* take absolute value of each entry */
7921: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7922: MatInfo info;
7923: PetscScalar *avals;
7924: PetscCall(MatGetInfo(c, MAT_LOCAL, &info));
7925: PetscCall(MatSeqAIJGetArray(c, &avals));
7926: for (int jj = 0; jj < info.nz_used; jj++) avals[jj] = PetscAbsScalar(avals[jj]);
7927: PetscCall(MatSeqAIJRestoreArray(c, &avals));
7928: }
7929: }
7930: }
7931: if (symmetrize) {
7932: PetscBool isset, issym;
7933: PetscCall(MatIsSymmetricKnown(Amat, &isset, &issym));
7934: if (!isset || !issym) {
7935: Mat matTrans;
7936: PetscCall(MatTranspose(Gmat, MAT_INITIAL_MATRIX, &matTrans));
7937: PetscCall(MatAXPY(Gmat, 1.0, matTrans, Gmat->structurally_symmetric == PETSC_BOOL3_TRUE ? SAME_NONZERO_PATTERN : DIFFERENT_NONZERO_PATTERN));
7938: PetscCall(MatDestroy(&matTrans));
7939: }
7940: PetscCall(MatSetOption(Gmat, MAT_SYMMETRIC, PETSC_TRUE));
7941: } else if (Amat != Gmat) PetscCall(MatPropagateSymmetryOptions(Amat, Gmat));
7942: if (scale) {
7943: /* scale c for all diagonal values = 1 or -1 */
7944: Vec diag;
7945: PetscCall(MatCreateVecs(Gmat, &diag, NULL));
7946: PetscCall(MatGetDiagonal(Gmat, diag));
7947: PetscCall(VecReciprocal(diag));
7948: PetscCall(VecSqrtAbs(diag));
7949: PetscCall(MatDiagonalScale(Gmat, diag, diag));
7950: PetscCall(VecDestroy(&diag));
7951: }
7952: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_graph_view"));
7954: if (filter >= 0) {
7955: PetscCall(MatFilter(Gmat, filter, PETSC_TRUE, PETSC_TRUE));
7956: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_filter_graph_view"));
7957: }
7958: *a_Gmat = Gmat;
7959: PetscFunctionReturn(PETSC_SUCCESS);
7960: }
7962: /*
7963: Special version for direct calls from Fortran
7964: */
7965: #include <petsc/private/fortranimpl.h>
7967: /* Change these macros so can be used in void function */
7968: /* Identical to PetscCallVoid, except it assigns to *_ierr */
7969: #undef PetscCall
7970: #define PetscCall(...) \
7971: do { \
7972: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
7973: if (PetscUnlikely(ierr_msv_mpiaij)) { \
7974: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
7975: return; \
7976: } \
7977: } while (0)
7979: #undef SETERRQ
7980: #define SETERRQ(comm, ierr, ...) \
7981: do { \
7982: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
7983: return; \
7984: } while (0)
7986: #if defined(PETSC_HAVE_FORTRAN_CAPS)
7987: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
7988: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
7989: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
7990: #else
7991: #endif
7992: PETSC_EXTERN void matsetvaluesmpiaij_(Mat *mmat, PetscInt *mm, const PetscInt im[], PetscInt *mn, const PetscInt in[], const PetscScalar v[], InsertMode *maddv, PetscErrorCode *_ierr)
7993: {
7994: Mat mat = *mmat;
7995: PetscInt m = *mm, n = *mn;
7996: InsertMode addv = *maddv;
7997: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
7998: PetscScalar value;
8000: MatCheckPreallocated(mat, 1);
8001: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
8002: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
8003: {
8004: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
8005: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
8006: PetscBool roworiented = aij->roworiented;
8008: /* Some Variables required in the macro */
8009: Mat A = aij->A;
8010: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
8011: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
8012: MatScalar *aa;
8013: PetscBool ignorezeroentries = (((a->ignorezeroentries) && (addv == ADD_VALUES)) ? PETSC_TRUE : PETSC_FALSE);
8014: Mat B = aij->B;
8015: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
8016: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
8017: MatScalar *ba;
8018: /* This variable below is only for the PETSC_HAVE_VIENNACL or PETSC_HAVE_CUDA cases, but we define it in all cases because we
8019: * cannot use "#if defined" inside a macro. */
8020: PETSC_UNUSED PetscBool inserted = PETSC_FALSE;
8022: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
8023: PetscInt nonew = a->nonew;
8024: MatScalar *ap1, *ap2;
8026: PetscFunctionBegin;
8027: PetscCall(MatSeqAIJGetArray(A, &aa));
8028: PetscCall(MatSeqAIJGetArray(B, &ba));
8029: for (i = 0; i < m; i++) {
8030: if (im[i] < 0) continue;
8031: 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);
8032: if (im[i] >= rstart && im[i] < rend) {
8033: row = im[i] - rstart;
8034: lastcol1 = -1;
8035: rp1 = aj + ai[row];
8036: ap1 = aa + ai[row];
8037: rmax1 = aimax[row];
8038: nrow1 = ailen[row];
8039: low1 = 0;
8040: high1 = nrow1;
8041: lastcol2 = -1;
8042: rp2 = bj + bi[row];
8043: ap2 = ba + bi[row];
8044: rmax2 = bimax[row];
8045: nrow2 = bilen[row];
8046: low2 = 0;
8047: high2 = nrow2;
8049: for (j = 0; j < n; j++) {
8050: if (roworiented) value = v[i * n + j];
8051: else value = v[i + j * m];
8052: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
8053: if (in[j] >= cstart && in[j] < cend) {
8054: col = in[j] - cstart;
8055: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
8056: } else if (in[j] < 0) continue;
8057: else if (PetscUnlikelyDebug(in[j] >= mat->cmap->N)) {
8058: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
8059: } else {
8060: if (mat->was_assembled) {
8061: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
8062: #if defined(PETSC_USE_CTABLE)
8063: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col));
8064: col--;
8065: #else
8066: col = aij->colmap[in[j]] - 1;
8067: #endif
8068: if (col < 0 && !((Mat_SeqAIJ *)(aij->A->data))->nonew) {
8069: PetscCall(MatDisAssemble_MPIAIJ(mat));
8070: col = in[j];
8071: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
8072: B = aij->B;
8073: b = (Mat_SeqAIJ *)B->data;
8074: bimax = b->imax;
8075: bi = b->i;
8076: bilen = b->ilen;
8077: bj = b->j;
8078: rp2 = bj + bi[row];
8079: ap2 = ba + bi[row];
8080: rmax2 = bimax[row];
8081: nrow2 = bilen[row];
8082: low2 = 0;
8083: high2 = nrow2;
8084: bm = aij->B->rmap->n;
8085: ba = b->a;
8086: inserted = PETSC_FALSE;
8087: }
8088: } else col = in[j];
8089: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
8090: }
8091: }
8092: } else if (!aij->donotstash) {
8093: if (roworiented) {
8094: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8095: } else {
8096: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8097: }
8098: }
8099: }
8100: PetscCall(MatSeqAIJRestoreArray(A, &aa));
8101: PetscCall(MatSeqAIJRestoreArray(B, &ba));
8102: }
8103: PetscFunctionReturnVoid();
8104: }
8106: /* Undefining these here since they were redefined from their original definition above! No
8107: * other PETSc functions should be defined past this point, as it is impossible to recover the
8108: * original definitions */
8109: #undef PetscCall
8110: #undef SETERRQ