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));
166: PetscFunctionReturn(PETSC_SUCCESS);
167: }
169: static PetscErrorCode MatSetBlockSizes_MPIAIJ(Mat M, PetscInt rbs, PetscInt cbs)
170: {
171: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
173: PetscFunctionBegin;
174: if (mat->A) {
175: PetscCall(MatSetBlockSizes(mat->A, rbs, cbs));
176: PetscCall(MatSetBlockSizes(mat->B, rbs, 1));
177: }
178: PetscFunctionReturn(PETSC_SUCCESS);
179: }
181: static PetscErrorCode MatFindNonzeroRows_MPIAIJ(Mat M, IS *keptrows)
182: {
183: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
184: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data;
185: Mat_SeqAIJ *b = (Mat_SeqAIJ *)mat->B->data;
186: const PetscInt *ia, *ib;
187: const MatScalar *aa, *bb, *aav, *bav;
188: PetscInt na, nb, i, j, *rows, cnt = 0, n0rows;
189: PetscInt m = M->rmap->n, rstart = M->rmap->rstart;
191: PetscFunctionBegin;
192: *keptrows = NULL;
194: ia = a->i;
195: ib = b->i;
196: PetscCall(MatSeqAIJGetArrayRead(mat->A, &aav));
197: PetscCall(MatSeqAIJGetArrayRead(mat->B, &bav));
198: for (i = 0; i < m; i++) {
199: na = ia[i + 1] - ia[i];
200: nb = ib[i + 1] - ib[i];
201: if (!na && !nb) {
202: cnt++;
203: goto ok1;
204: }
205: aa = aav + ia[i];
206: for (j = 0; j < na; j++) {
207: if (aa[j] != 0.0) goto ok1;
208: }
209: bb = PetscSafePointerPlusOffset(bav, ib[i]);
210: for (j = 0; j < nb; j++) {
211: if (bb[j] != 0.0) goto ok1;
212: }
213: cnt++;
214: ok1:;
215: }
216: PetscCallMPI(MPIU_Allreduce(&cnt, &n0rows, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)M)));
217: if (!n0rows) {
218: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
219: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
220: PetscFunctionReturn(PETSC_SUCCESS);
221: }
222: PetscCall(PetscMalloc1(M->rmap->n - cnt, &rows));
223: cnt = 0;
224: for (i = 0; i < m; i++) {
225: na = ia[i + 1] - ia[i];
226: nb = ib[i + 1] - ib[i];
227: if (!na && !nb) continue;
228: aa = aav + ia[i];
229: for (j = 0; j < na; j++) {
230: if (aa[j] != 0.0) {
231: rows[cnt++] = rstart + i;
232: goto ok2;
233: }
234: }
235: bb = PetscSafePointerPlusOffset(bav, ib[i]);
236: for (j = 0; j < nb; j++) {
237: if (bb[j] != 0.0) {
238: rows[cnt++] = rstart + i;
239: goto ok2;
240: }
241: }
242: ok2:;
243: }
244: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), cnt, rows, PETSC_OWN_POINTER, keptrows));
245: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
246: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
247: PetscFunctionReturn(PETSC_SUCCESS);
248: }
250: static PetscErrorCode MatDiagonalSet_MPIAIJ(Mat Y, Vec D, InsertMode is)
251: {
252: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Y->data;
253: PetscBool cong;
255: PetscFunctionBegin;
256: PetscCall(MatHasCongruentLayouts(Y, &cong));
257: if (Y->assembled && cong) {
258: PetscCall(MatDiagonalSet(aij->A, D, is));
259: } else {
260: PetscCall(MatDiagonalSet_Default(Y, D, is));
261: }
262: PetscFunctionReturn(PETSC_SUCCESS);
263: }
265: static PetscErrorCode MatFindZeroDiagonals_MPIAIJ(Mat M, IS *zrows)
266: {
267: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)M->data;
268: PetscInt i, rstart, nrows, *rows;
270: PetscFunctionBegin;
271: *zrows = NULL;
272: PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(aij->A, &nrows, &rows));
273: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
274: for (i = 0; i < nrows; i++) rows[i] += rstart;
275: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), nrows, rows, PETSC_OWN_POINTER, zrows));
276: PetscFunctionReturn(PETSC_SUCCESS);
277: }
279: static PetscErrorCode MatGetColumnReductions_MPIAIJ(Mat A, PetscInt type, PetscReal *reductions)
280: {
281: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)A->data;
282: PetscInt i, m, n, *garray = aij->garray;
283: Mat_SeqAIJ *a_aij = (Mat_SeqAIJ *)aij->A->data;
284: Mat_SeqAIJ *b_aij = (Mat_SeqAIJ *)aij->B->data;
285: PetscReal *work;
286: const PetscScalar *dummy;
287: PetscMPIInt in;
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: PetscCall(PetscMPIIntCast(n, &in));
313: if (type == NORM_INFINITY) {
314: PetscCallMPI(MPIU_Allreduce(work, reductions, in, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
315: } else {
316: PetscCallMPI(MPIU_Allreduce(work, reductions, in, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
317: }
318: PetscCall(PetscFree(work));
319: if (type == NORM_2) {
320: for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
321: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
322: for (i = 0; i < n; i++) reductions[i] /= m;
323: }
324: PetscFunctionReturn(PETSC_SUCCESS);
325: }
327: static PetscErrorCode MatFindOffBlockDiagonalEntries_MPIAIJ(Mat A, IS *is)
328: {
329: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
330: IS sis, gis;
331: const PetscInt *isis, *igis;
332: PetscInt n, *iis, nsis, ngis, rstart, i;
334: PetscFunctionBegin;
335: PetscCall(MatFindOffBlockDiagonalEntries(a->A, &sis));
336: PetscCall(MatFindNonzeroRows(a->B, &gis));
337: PetscCall(ISGetSize(gis, &ngis));
338: PetscCall(ISGetSize(sis, &nsis));
339: PetscCall(ISGetIndices(sis, &isis));
340: PetscCall(ISGetIndices(gis, &igis));
342: PetscCall(PetscMalloc1(ngis + nsis, &iis));
343: PetscCall(PetscArraycpy(iis, igis, ngis));
344: PetscCall(PetscArraycpy(iis + ngis, isis, nsis));
345: n = ngis + nsis;
346: PetscCall(PetscSortRemoveDupsInt(&n, iis));
347: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
348: for (i = 0; i < n; i++) iis[i] += rstart;
349: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), n, iis, PETSC_OWN_POINTER, is));
351: PetscCall(ISRestoreIndices(sis, &isis));
352: PetscCall(ISRestoreIndices(gis, &igis));
353: PetscCall(ISDestroy(&sis));
354: PetscCall(ISDestroy(&gis));
355: PetscFunctionReturn(PETSC_SUCCESS);
356: }
358: /*
359: Local utility routine that creates a mapping from the global column
360: number to the local number in the off-diagonal part of the local
361: storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at
362: a slightly higher hash table cost; without it it is not scalable (each processor
363: has an order N integer array but is fast to access.
364: */
365: PetscErrorCode MatCreateColmap_MPIAIJ_Private(Mat mat)
366: {
367: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
368: PetscInt n = aij->B->cmap->n, i;
370: PetscFunctionBegin;
371: PetscCheck(!n || aij->garray, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MPIAIJ Matrix was assembled but is missing garray");
372: #if defined(PETSC_USE_CTABLE)
373: PetscCall(PetscHMapICreateWithSize(n, &aij->colmap));
374: for (i = 0; i < n; i++) PetscCall(PetscHMapISet(aij->colmap, aij->garray[i] + 1, i + 1));
375: #else
376: PetscCall(PetscCalloc1(mat->cmap->N + 1, &aij->colmap));
377: for (i = 0; i < n; i++) aij->colmap[aij->garray[i]] = i + 1;
378: #endif
379: PetscFunctionReturn(PETSC_SUCCESS);
380: }
382: #define MatSetValues_SeqAIJ_A_Private(row, col, value, addv, orow, ocol) \
383: do { \
384: if (col <= lastcol1) low1 = 0; \
385: else high1 = nrow1; \
386: lastcol1 = col; \
387: while (high1 - low1 > 5) { \
388: t = (low1 + high1) / 2; \
389: if (rp1[t] > col) high1 = t; \
390: else low1 = t; \
391: } \
392: for (_i = low1; _i < high1; _i++) { \
393: if (rp1[_i] > col) break; \
394: if (rp1[_i] == col) { \
395: if (addv == ADD_VALUES) { \
396: ap1[_i] += value; \
397: /* Not sure LogFlops will slow dow the code or not */ \
398: (void)PetscLogFlops(1.0); \
399: } else ap1[_i] = value; \
400: goto a_noinsert; \
401: } \
402: } \
403: if (value == 0.0 && ignorezeroentries && row != col) { \
404: low1 = 0; \
405: high1 = nrow1; \
406: goto a_noinsert; \
407: } \
408: if (nonew == 1) { \
409: low1 = 0; \
410: high1 = nrow1; \
411: goto a_noinsert; \
412: } \
413: 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); \
414: MatSeqXAIJReallocateAIJ(A, am, 1, nrow1, row, col, rmax1, aa, ai, aj, rp1, ap1, aimax, nonew, MatScalar); \
415: N = nrow1++ - 1; \
416: a->nz++; \
417: high1++; \
418: /* shift up all the later entries in this row */ \
419: PetscCall(PetscArraymove(rp1 + _i + 1, rp1 + _i, N - _i + 1)); \
420: PetscCall(PetscArraymove(ap1 + _i + 1, ap1 + _i, N - _i + 1)); \
421: rp1[_i] = col; \
422: ap1[_i] = value; \
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_noinsert:; \
468: bilen[row] = nrow2; \
469: } while (0)
471: static PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A, PetscInt row, const PetscScalar v[])
472: {
473: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
474: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data, *b = (Mat_SeqAIJ *)mat->B->data;
475: PetscInt l, *garray = mat->garray, diag;
476: PetscScalar *aa, *ba;
478: PetscFunctionBegin;
479: /* code only works for square matrices A */
481: /* find size of row to the left of the diagonal part */
482: PetscCall(MatGetOwnershipRange(A, &diag, NULL));
483: row = row - diag;
484: for (l = 0; l < b->i[row + 1] - b->i[row]; l++) {
485: if (garray[b->j[b->i[row] + l]] > diag) break;
486: }
487: if (l) {
488: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
489: PetscCall(PetscArraycpy(ba + b->i[row], v, l));
490: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
491: }
493: /* diagonal part */
494: if (a->i[row + 1] - a->i[row]) {
495: PetscCall(MatSeqAIJGetArray(mat->A, &aa));
496: PetscCall(PetscArraycpy(aa + a->i[row], v + l, a->i[row + 1] - a->i[row]));
497: PetscCall(MatSeqAIJRestoreArray(mat->A, &aa));
498: }
500: /* right of diagonal part */
501: if (b->i[row + 1] - b->i[row] - l) {
502: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
503: PetscCall(PetscArraycpy(ba + b->i[row] + l, v + l + a->i[row + 1] - a->i[row], b->i[row + 1] - b->i[row] - l));
504: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
505: }
506: PetscFunctionReturn(PETSC_SUCCESS);
507: }
509: PetscErrorCode MatSetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
510: {
511: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
512: PetscScalar value = 0.0;
513: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
514: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
515: PetscBool roworiented = aij->roworiented;
517: /* Some Variables required in the macro */
518: Mat A = aij->A;
519: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
520: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
521: PetscBool ignorezeroentries = a->ignorezeroentries;
522: Mat B = aij->B;
523: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
524: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
525: MatScalar *aa, *ba;
526: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
527: PetscInt nonew;
528: MatScalar *ap1, *ap2;
530: PetscFunctionBegin;
531: PetscCall(MatSeqAIJGetArray(A, &aa));
532: PetscCall(MatSeqAIJGetArray(B, &ba));
533: for (i = 0; i < m; i++) {
534: if (im[i] < 0) continue;
535: PetscCheck(im[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], mat->rmap->N - 1);
536: if (im[i] >= rstart && im[i] < rend) {
537: row = im[i] - rstart;
538: lastcol1 = -1;
539: rp1 = PetscSafePointerPlusOffset(aj, ai[row]);
540: ap1 = PetscSafePointerPlusOffset(aa, ai[row]);
541: rmax1 = aimax[row];
542: nrow1 = ailen[row];
543: low1 = 0;
544: high1 = nrow1;
545: lastcol2 = -1;
546: rp2 = PetscSafePointerPlusOffset(bj, bi[row]);
547: ap2 = PetscSafePointerPlusOffset(ba, bi[row]);
548: rmax2 = bimax[row];
549: nrow2 = bilen[row];
550: low2 = 0;
551: high2 = nrow2;
553: for (j = 0; j < n; j++) {
554: if (v) value = roworiented ? v[i * n + j] : v[i + j * m];
555: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
556: if (in[j] >= cstart && in[j] < cend) {
557: col = in[j] - cstart;
558: nonew = a->nonew;
559: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
560: } else if (in[j] < 0) {
561: continue;
562: } else {
563: PetscCheck(in[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
564: if (mat->was_assembled) {
565: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
566: #if defined(PETSC_USE_CTABLE)
567: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col)); /* map global col ids to local ones */
568: col--;
569: #else
570: col = aij->colmap[in[j]] - 1;
571: #endif
572: if (col < 0 && !((Mat_SeqAIJ *)aij->B->data)->nonew) { /* col < 0 means in[j] is a new col for B */
573: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE)); /* Change aij->B from reduced/local format to expanded/global format */
574: col = in[j];
575: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
576: B = aij->B;
577: b = (Mat_SeqAIJ *)B->data;
578: bimax = b->imax;
579: bi = b->i;
580: bilen = b->ilen;
581: bj = b->j;
582: ba = b->a;
583: rp2 = PetscSafePointerPlusOffset(bj, bi[row]);
584: ap2 = PetscSafePointerPlusOffset(ba, bi[row]);
585: rmax2 = bimax[row];
586: nrow2 = bilen[row];
587: low2 = 0;
588: high2 = nrow2;
589: bm = aij->B->rmap->n;
590: ba = b->a;
591: } else if (col < 0 && !(ignorezeroentries && value == 0.0)) {
592: if (1 == ((Mat_SeqAIJ *)aij->B->data)->nonew) {
593: PetscCall(PetscInfo(mat, "Skipping of insertion of new nonzero location in off-diagonal portion of matrix %g(%" PetscInt_FMT ",%" PetscInt_FMT ")\n", (double)PetscRealPart(value), im[i], in[j]));
594: } 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]);
595: }
596: } else col = in[j];
597: nonew = b->nonew;
598: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
599: }
600: }
601: } else {
602: 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]);
603: if (!aij->donotstash) {
604: mat->assembled = PETSC_FALSE;
605: if (roworiented) {
606: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i * n), (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
607: } else {
608: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i), m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
609: }
610: }
611: }
612: }
613: PetscCall(MatSeqAIJRestoreArray(A, &aa)); /* aa, bb might have been free'd due to reallocation above. But we don't access them here */
614: PetscCall(MatSeqAIJRestoreArray(B, &ba));
615: PetscFunctionReturn(PETSC_SUCCESS);
616: }
618: /*
619: This function sets the j and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
620: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
621: No off-processor parts off the matrix are allowed here and mat->was_assembled has to be PETSC_FALSE.
622: */
623: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[])
624: {
625: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
626: Mat A = aij->A; /* diagonal part of the matrix */
627: Mat B = aij->B; /* off-diagonal part of the matrix */
628: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
629: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
630: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, col;
631: PetscInt *ailen = a->ilen, *aj = a->j;
632: PetscInt *bilen = b->ilen, *bj = b->j;
633: PetscInt am = aij->A->rmap->n, j;
634: PetscInt diag_so_far = 0, dnz;
635: PetscInt offd_so_far = 0, onz;
637: PetscFunctionBegin;
638: /* Iterate over all rows of the matrix */
639: for (j = 0; j < am; j++) {
640: dnz = onz = 0;
641: /* Iterate over all non-zero columns of the current row */
642: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
643: /* If column is in the diagonal */
644: if (mat_j[col] >= cstart && mat_j[col] < cend) {
645: aj[diag_so_far++] = mat_j[col] - cstart;
646: dnz++;
647: } else { /* off-diagonal entries */
648: bj[offd_so_far++] = mat_j[col];
649: onz++;
650: }
651: }
652: ailen[j] = dnz;
653: bilen[j] = onz;
654: }
655: PetscFunctionReturn(PETSC_SUCCESS);
656: }
658: /*
659: This function sets the local j, a and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
660: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
661: No off-processor parts off the matrix are allowed here, they are set at a later point by MatSetValues_MPIAIJ.
662: Also, mat->was_assembled has to be false, otherwise the statement aj[rowstart_diag+dnz_row] = mat_j[col] - cstart;
663: would not be true and the more complex MatSetValues_MPIAIJ has to be used.
664: */
665: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[], const PetscScalar mat_a[])
666: {
667: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
668: Mat A = aij->A; /* diagonal part of the matrix */
669: Mat B = aij->B; /* off-diagonal part of the matrix */
670: Mat_SeqAIJ *aijd = (Mat_SeqAIJ *)aij->A->data, *aijo = (Mat_SeqAIJ *)aij->B->data;
671: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
672: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
673: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend;
674: PetscInt *ailen = a->ilen, *aj = a->j;
675: PetscInt *bilen = b->ilen, *bj = b->j;
676: PetscInt am = aij->A->rmap->n, j;
677: 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. */
678: PetscInt col, dnz_row, onz_row, rowstart_diag, rowstart_offd;
679: PetscScalar *aa = a->a, *ba = b->a;
681: PetscFunctionBegin;
682: /* Iterate over all rows of the matrix */
683: for (j = 0; j < am; j++) {
684: dnz_row = onz_row = 0;
685: rowstart_offd = full_offd_i[j];
686: rowstart_diag = full_diag_i[j];
687: /* Iterate over all non-zero columns of the current row */
688: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
689: /* If column is in the diagonal */
690: if (mat_j[col] >= cstart && mat_j[col] < cend) {
691: aj[rowstart_diag + dnz_row] = mat_j[col] - cstart;
692: aa[rowstart_diag + dnz_row] = mat_a[col];
693: dnz_row++;
694: } else { /* off-diagonal entries */
695: bj[rowstart_offd + onz_row] = mat_j[col];
696: ba[rowstart_offd + onz_row] = mat_a[col];
697: onz_row++;
698: }
699: }
700: ailen[j] = dnz_row;
701: bilen[j] = onz_row;
702: }
703: PetscFunctionReturn(PETSC_SUCCESS);
704: }
706: static PetscErrorCode MatGetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
707: {
708: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
709: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
710: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
712: PetscFunctionBegin;
713: for (i = 0; i < m; i++) {
714: if (idxm[i] < 0) continue; /* negative row */
715: 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);
716: 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);
717: row = idxm[i] - rstart;
718: for (j = 0; j < n; j++) {
719: if (idxn[j] < 0) continue; /* negative column */
720: 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);
721: if (idxn[j] >= cstart && idxn[j] < cend) {
722: col = idxn[j] - cstart;
723: PetscCall(MatGetValues(aij->A, 1, &row, 1, &col, v + i * n + j));
724: } else {
725: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
726: #if defined(PETSC_USE_CTABLE)
727: PetscCall(PetscHMapIGetWithDefault(aij->colmap, idxn[j] + 1, 0, &col));
728: col--;
729: #else
730: col = aij->colmap[idxn[j]] - 1;
731: #endif
732: if ((col < 0) || (aij->garray[col] != idxn[j])) *(v + i * n + j) = 0.0;
733: else PetscCall(MatGetValues(aij->B, 1, &row, 1, &col, v + i * n + j));
734: }
735: }
736: }
737: PetscFunctionReturn(PETSC_SUCCESS);
738: }
740: static PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat, MatAssemblyType mode)
741: {
742: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
743: PetscInt nstash, reallocs;
745: PetscFunctionBegin;
746: if (aij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
748: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
749: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
750: PetscCall(PetscInfo(aij->A, "Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
751: PetscFunctionReturn(PETSC_SUCCESS);
752: }
754: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat, MatAssemblyType mode)
755: {
756: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
757: PetscMPIInt n;
758: PetscInt i, j, rstart, ncols, flg;
759: PetscInt *row, *col;
760: PetscBool other_disassembled;
761: PetscScalar *val;
763: /* do not use 'b = (Mat_SeqAIJ*)aij->B->data' as B can be reset in disassembly */
765: PetscFunctionBegin;
766: if (!aij->donotstash && !mat->nooffprocentries) {
767: while (1) {
768: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
769: if (!flg) break;
771: for (i = 0; i < n;) {
772: /* Now identify the consecutive vals belonging to the same row */
773: for (j = i, rstart = row[j]; j < n; j++) {
774: if (row[j] != rstart) break;
775: }
776: if (j < n) ncols = j - i;
777: else ncols = n - i;
778: /* Now assemble all these values with a single function call */
779: PetscCall(MatSetValues_MPIAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
780: i = j;
781: }
782: }
783: PetscCall(MatStashScatterEnd_Private(&mat->stash));
784: }
785: #if defined(PETSC_HAVE_DEVICE)
786: if (mat->offloadmask == PETSC_OFFLOAD_CPU) aij->A->offloadmask = PETSC_OFFLOAD_CPU;
787: /* We call MatBindToCPU() on aij->A and aij->B here, because if MatBindToCPU_MPIAIJ() is called before assembly, it cannot bind these. */
788: if (mat->boundtocpu) {
789: PetscCall(MatBindToCPU(aij->A, PETSC_TRUE));
790: PetscCall(MatBindToCPU(aij->B, PETSC_TRUE));
791: }
792: #endif
793: PetscCall(MatAssemblyBegin(aij->A, mode));
794: PetscCall(MatAssemblyEnd(aij->A, mode));
796: /* determine if any processor has disassembled, if so we must
797: also disassemble ourself, in order that we may reassemble. */
798: /*
799: if nonzero structure of submatrix B cannot change then we know that
800: no processor disassembled thus we can skip this stuff
801: */
802: if (!((Mat_SeqAIJ *)aij->B->data)->nonew) {
803: PetscCallMPI(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
804: if (mat->was_assembled && !other_disassembled) { /* mat on this rank has reduced off-diag B with local col ids, but globally it does not */
805: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));
806: }
807: }
808: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIAIJ(mat));
809: PetscCall(MatSetOption(aij->B, MAT_USE_INODES, PETSC_FALSE));
810: #if defined(PETSC_HAVE_DEVICE)
811: if (mat->offloadmask == PETSC_OFFLOAD_CPU && aij->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) aij->B->offloadmask = PETSC_OFFLOAD_CPU;
812: #endif
813: PetscCall(MatAssemblyBegin(aij->B, mode));
814: PetscCall(MatAssemblyEnd(aij->B, mode));
816: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
818: aij->rowvalues = NULL;
820: PetscCall(VecDestroy(&aij->diag));
822: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
823: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqAIJ *)aij->A->data)->nonew) {
824: PetscObjectState state = aij->A->nonzerostate + aij->B->nonzerostate;
825: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
826: }
827: #if defined(PETSC_HAVE_DEVICE)
828: mat->offloadmask = PETSC_OFFLOAD_BOTH;
829: #endif
830: PetscFunctionReturn(PETSC_SUCCESS);
831: }
833: static PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
834: {
835: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
837: PetscFunctionBegin;
838: PetscCall(MatZeroEntries(l->A));
839: PetscCall(MatZeroEntries(l->B));
840: PetscFunctionReturn(PETSC_SUCCESS);
841: }
843: static PetscErrorCode MatZeroRows_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
844: {
845: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
846: PetscInt *lrows;
847: PetscInt r, len;
848: PetscBool cong;
850: PetscFunctionBegin;
851: /* get locally owned rows */
852: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
853: PetscCall(MatHasCongruentLayouts(A, &cong));
854: /* fix right-hand side if needed */
855: if (x && b) {
856: const PetscScalar *xx;
857: PetscScalar *bb;
859: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
860: PetscCall(VecGetArrayRead(x, &xx));
861: PetscCall(VecGetArray(b, &bb));
862: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
863: PetscCall(VecRestoreArrayRead(x, &xx));
864: PetscCall(VecRestoreArray(b, &bb));
865: }
867: if (diag != 0.0 && cong) {
868: PetscCall(MatZeroRows(mat->A, len, lrows, diag, NULL, NULL));
869: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
870: } else if (diag != 0.0) { /* non-square or non congruent layouts -> if keepnonzeropattern is false, we allow for new insertion */
871: Mat_SeqAIJ *aijA = (Mat_SeqAIJ *)mat->A->data;
872: Mat_SeqAIJ *aijB = (Mat_SeqAIJ *)mat->B->data;
873: PetscInt nnwA, nnwB;
874: PetscBool nnzA, nnzB;
876: nnwA = aijA->nonew;
877: nnwB = aijB->nonew;
878: nnzA = aijA->keepnonzeropattern;
879: nnzB = aijB->keepnonzeropattern;
880: if (!nnzA) {
881: PetscCall(PetscInfo(mat->A, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on diagonal block.\n"));
882: aijA->nonew = 0;
883: }
884: if (!nnzB) {
885: PetscCall(PetscInfo(mat->B, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on off-diagonal block.\n"));
886: aijB->nonew = 0;
887: }
888: /* Must zero here before the next loop */
889: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
890: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
891: for (r = 0; r < len; ++r) {
892: const PetscInt row = lrows[r] + A->rmap->rstart;
893: if (row >= A->cmap->N) continue;
894: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
895: }
896: aijA->nonew = nnwA;
897: aijB->nonew = nnwB;
898: } else {
899: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
900: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
901: }
902: PetscCall(PetscFree(lrows));
903: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
904: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
906: /* only change matrix nonzero state if pattern was allowed to be changed */
907: if (!((Mat_SeqAIJ *)mat->A->data)->keepnonzeropattern || !((Mat_SeqAIJ *)mat->A->data)->nonew) {
908: PetscObjectState state = mat->A->nonzerostate + mat->B->nonzerostate;
909: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
910: }
911: PetscFunctionReturn(PETSC_SUCCESS);
912: }
914: static PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
915: {
916: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
917: PetscInt n = A->rmap->n;
918: PetscInt i, j, r, m, len = 0;
919: PetscInt *lrows, *owners = A->rmap->range;
920: PetscMPIInt p = 0;
921: PetscSFNode *rrows;
922: PetscSF sf;
923: const PetscScalar *xx;
924: PetscScalar *bb, *mask, *aij_a;
925: Vec xmask, lmask;
926: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)l->B->data;
927: const PetscInt *aj, *ii, *ridx;
928: PetscScalar *aa;
930: PetscFunctionBegin;
931: /* Create SF where leaves are input rows and roots are owned rows */
932: PetscCall(PetscMalloc1(n, &lrows));
933: for (r = 0; r < n; ++r) lrows[r] = -1;
934: PetscCall(PetscMalloc1(N, &rrows));
935: for (r = 0; r < N; ++r) {
936: const PetscInt idx = rows[r];
937: 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);
938: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
939: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
940: }
941: rrows[r].rank = p;
942: rrows[r].index = rows[r] - owners[p];
943: }
944: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
945: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
946: /* Collect flags for rows to be zeroed */
947: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
948: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
949: PetscCall(PetscSFDestroy(&sf));
950: /* Compress and put in row numbers */
951: for (r = 0; r < n; ++r)
952: if (lrows[r] >= 0) lrows[len++] = r;
953: /* zero diagonal part of matrix */
954: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
955: /* handle off-diagonal part of matrix */
956: PetscCall(MatCreateVecs(A, &xmask, NULL));
957: PetscCall(VecDuplicate(l->lvec, &lmask));
958: PetscCall(VecGetArray(xmask, &bb));
959: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
960: PetscCall(VecRestoreArray(xmask, &bb));
961: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
962: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
963: PetscCall(VecDestroy(&xmask));
964: if (x && b) { /* this code is buggy when the row and column layout don't match */
965: PetscBool cong;
967: PetscCall(MatHasCongruentLayouts(A, &cong));
968: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
969: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
970: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
971: PetscCall(VecGetArrayRead(l->lvec, &xx));
972: PetscCall(VecGetArray(b, &bb));
973: }
974: PetscCall(VecGetArray(lmask, &mask));
975: /* remove zeroed rows of off-diagonal matrix */
976: PetscCall(MatSeqAIJGetArray(l->B, &aij_a));
977: ii = aij->i;
978: for (i = 0; i < len; i++) PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aij_a, ii[lrows[i]]), ii[lrows[i] + 1] - ii[lrows[i]]));
979: /* loop over all elements of off process part of matrix zeroing removed columns*/
980: if (aij->compressedrow.use) {
981: m = aij->compressedrow.nrows;
982: ii = aij->compressedrow.i;
983: ridx = aij->compressedrow.rindex;
984: for (i = 0; i < m; i++) {
985: n = ii[i + 1] - ii[i];
986: aj = aij->j + ii[i];
987: aa = aij_a + ii[i];
989: for (j = 0; j < n; j++) {
990: if (PetscAbsScalar(mask[*aj])) {
991: if (b) bb[*ridx] -= *aa * xx[*aj];
992: *aa = 0.0;
993: }
994: aa++;
995: aj++;
996: }
997: ridx++;
998: }
999: } else { /* do not use compressed row format */
1000: m = l->B->rmap->n;
1001: for (i = 0; i < m; i++) {
1002: n = ii[i + 1] - ii[i];
1003: aj = aij->j + ii[i];
1004: aa = aij_a + ii[i];
1005: for (j = 0; j < n; j++) {
1006: if (PetscAbsScalar(mask[*aj])) {
1007: if (b) bb[i] -= *aa * xx[*aj];
1008: *aa = 0.0;
1009: }
1010: aa++;
1011: aj++;
1012: }
1013: }
1014: }
1015: if (x && b) {
1016: PetscCall(VecRestoreArray(b, &bb));
1017: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1018: }
1019: PetscCall(MatSeqAIJRestoreArray(l->B, &aij_a));
1020: PetscCall(VecRestoreArray(lmask, &mask));
1021: PetscCall(VecDestroy(&lmask));
1022: PetscCall(PetscFree(lrows));
1024: /* only change matrix nonzero state if pattern was allowed to be changed */
1025: if (!((Mat_SeqAIJ *)l->A->data)->nonew) {
1026: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1027: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1028: }
1029: PetscFunctionReturn(PETSC_SUCCESS);
1030: }
1032: static PetscErrorCode MatMult_MPIAIJ(Mat A, Vec xx, Vec yy)
1033: {
1034: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1035: PetscInt nt;
1036: VecScatter Mvctx = a->Mvctx;
1038: PetscFunctionBegin;
1039: PetscCall(VecGetLocalSize(xx, &nt));
1040: 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);
1041: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1042: PetscUseTypeMethod(a->A, mult, xx, yy);
1043: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1044: PetscUseTypeMethod(a->B, multadd, a->lvec, yy, yy);
1045: PetscFunctionReturn(PETSC_SUCCESS);
1046: }
1048: static PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A, Vec bb, Vec xx)
1049: {
1050: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1052: PetscFunctionBegin;
1053: PetscCall(MatMultDiagonalBlock(a->A, bb, xx));
1054: PetscFunctionReturn(PETSC_SUCCESS);
1055: }
1057: static PetscErrorCode MatMultAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1058: {
1059: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1060: VecScatter Mvctx = a->Mvctx;
1062: PetscFunctionBegin;
1063: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1064: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1065: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1066: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1067: PetscFunctionReturn(PETSC_SUCCESS);
1068: }
1070: static PetscErrorCode MatMultTranspose_MPIAIJ(Mat A, Vec xx, Vec yy)
1071: {
1072: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1074: PetscFunctionBegin;
1075: /* do nondiagonal part */
1076: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1077: /* do local part */
1078: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1079: /* add partial results together */
1080: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1081: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1082: PetscFunctionReturn(PETSC_SUCCESS);
1083: }
1085: static PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f)
1086: {
1087: MPI_Comm comm;
1088: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)Amat->data, *Bij = (Mat_MPIAIJ *)Bmat->data;
1089: Mat Adia = Aij->A, Bdia = Bij->A, Aoff, Boff, *Aoffs, *Boffs;
1090: IS Me, Notme;
1091: PetscInt M, N, first, last, *notme, i;
1092: PetscBool lf;
1093: PetscMPIInt size;
1095: PetscFunctionBegin;
1096: /* Easy test: symmetric diagonal block */
1097: PetscCall(MatIsTranspose(Adia, Bdia, tol, &lf));
1098: PetscCallMPI(MPIU_Allreduce(&lf, f, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)Amat)));
1099: if (!*f) PetscFunctionReturn(PETSC_SUCCESS);
1100: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1101: PetscCallMPI(MPI_Comm_size(comm, &size));
1102: if (size == 1) PetscFunctionReturn(PETSC_SUCCESS);
1104: /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */
1105: PetscCall(MatGetSize(Amat, &M, &N));
1106: PetscCall(MatGetOwnershipRange(Amat, &first, &last));
1107: PetscCall(PetscMalloc1(N - last + first, ¬me));
1108: for (i = 0; i < first; i++) notme[i] = i;
1109: for (i = last; i < M; i++) notme[i - last + first] = i;
1110: PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme));
1111: PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me));
1112: PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs));
1113: Aoff = Aoffs[0];
1114: PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs));
1115: Boff = Boffs[0];
1116: PetscCall(MatIsTranspose(Aoff, Boff, tol, f));
1117: PetscCall(MatDestroyMatrices(1, &Aoffs));
1118: PetscCall(MatDestroyMatrices(1, &Boffs));
1119: PetscCall(ISDestroy(&Me));
1120: PetscCall(ISDestroy(&Notme));
1121: PetscCall(PetscFree(notme));
1122: PetscFunctionReturn(PETSC_SUCCESS);
1123: }
1125: static PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1126: {
1127: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1129: PetscFunctionBegin;
1130: /* do nondiagonal part */
1131: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1132: /* do local part */
1133: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1134: /* add partial results together */
1135: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1136: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1137: PetscFunctionReturn(PETSC_SUCCESS);
1138: }
1140: /*
1141: This only works correctly for square matrices where the subblock A->A is the
1142: diagonal block
1143: */
1144: static PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A, Vec v)
1145: {
1146: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1148: PetscFunctionBegin;
1149: PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1150: 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");
1151: PetscCall(MatGetDiagonal(a->A, v));
1152: PetscFunctionReturn(PETSC_SUCCESS);
1153: }
1155: static PetscErrorCode MatScale_MPIAIJ(Mat A, PetscScalar aa)
1156: {
1157: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1159: PetscFunctionBegin;
1160: PetscCall(MatScale(a->A, aa));
1161: PetscCall(MatScale(a->B, aa));
1162: PetscFunctionReturn(PETSC_SUCCESS);
1163: }
1165: static PetscErrorCode MatView_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
1166: {
1167: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1168: Mat_SeqAIJ *A = (Mat_SeqAIJ *)aij->A->data;
1169: Mat_SeqAIJ *B = (Mat_SeqAIJ *)aij->B->data;
1170: const PetscInt *garray = aij->garray;
1171: const PetscScalar *aa, *ba;
1172: PetscInt header[4], M, N, m, rs, cs, cnt, i, ja, jb;
1173: PetscInt64 nz, hnz;
1174: PetscInt *rowlens;
1175: PetscInt *colidxs;
1176: PetscScalar *matvals;
1177: PetscMPIInt rank;
1179: PetscFunctionBegin;
1180: PetscCall(PetscViewerSetUp(viewer));
1182: M = mat->rmap->N;
1183: N = mat->cmap->N;
1184: m = mat->rmap->n;
1185: rs = mat->rmap->rstart;
1186: cs = mat->cmap->rstart;
1187: nz = A->nz + B->nz;
1189: /* write matrix header */
1190: header[0] = MAT_FILE_CLASSID;
1191: header[1] = M;
1192: header[2] = N;
1193: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1194: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1195: if (rank == 0) {
1196: if (hnz > PETSC_INT_MAX) header[3] = PETSC_INT_MAX;
1197: else header[3] = (PetscInt)hnz;
1198: }
1199: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1201: /* fill in and store row lengths */
1202: PetscCall(PetscMalloc1(m, &rowlens));
1203: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i];
1204: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1205: PetscCall(PetscFree(rowlens));
1207: /* fill in and store column indices */
1208: PetscCall(PetscMalloc1(nz, &colidxs));
1209: for (cnt = 0, i = 0; i < m; i++) {
1210: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1211: if (garray[B->j[jb]] > cs) break;
1212: colidxs[cnt++] = garray[B->j[jb]];
1213: }
1214: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) colidxs[cnt++] = A->j[ja] + cs;
1215: for (; jb < B->i[i + 1]; jb++) colidxs[cnt++] = garray[B->j[jb]];
1216: }
1217: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1218: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
1219: PetscCall(PetscFree(colidxs));
1221: /* fill in and store nonzero values */
1222: PetscCall(MatSeqAIJGetArrayRead(aij->A, &aa));
1223: PetscCall(MatSeqAIJGetArrayRead(aij->B, &ba));
1224: PetscCall(PetscMalloc1(nz, &matvals));
1225: for (cnt = 0, i = 0; i < m; i++) {
1226: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1227: if (garray[B->j[jb]] > cs) break;
1228: matvals[cnt++] = ba[jb];
1229: }
1230: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) matvals[cnt++] = aa[ja];
1231: for (; jb < B->i[i + 1]; jb++) matvals[cnt++] = ba[jb];
1232: }
1233: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &aa));
1234: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &ba));
1235: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1236: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
1237: PetscCall(PetscFree(matvals));
1239: /* write block size option to the viewer's .info file */
1240: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1241: PetscFunctionReturn(PETSC_SUCCESS);
1242: }
1244: #include <petscdraw.h>
1245: static PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1246: {
1247: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1248: PetscMPIInt rank = aij->rank, size = aij->size;
1249: PetscBool isdraw, iascii, isbinary;
1250: PetscViewer sviewer;
1251: PetscViewerFormat format;
1253: PetscFunctionBegin;
1254: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1255: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1256: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1257: if (iascii) {
1258: PetscCall(PetscViewerGetFormat(viewer, &format));
1259: if (format == PETSC_VIEWER_LOAD_BALANCE) {
1260: PetscInt i, nmax = 0, nmin = PETSC_INT_MAX, navg = 0, *nz, nzlocal = ((Mat_SeqAIJ *)aij->A->data)->nz + ((Mat_SeqAIJ *)aij->B->data)->nz;
1261: PetscCall(PetscMalloc1(size, &nz));
1262: PetscCallMPI(MPI_Allgather(&nzlocal, 1, MPIU_INT, nz, 1, MPIU_INT, PetscObjectComm((PetscObject)mat)));
1263: for (i = 0; i < (PetscInt)size; i++) {
1264: nmax = PetscMax(nmax, nz[i]);
1265: nmin = PetscMin(nmin, nz[i]);
1266: navg += nz[i];
1267: }
1268: PetscCall(PetscFree(nz));
1269: navg = navg / size;
1270: PetscCall(PetscViewerASCIIPrintf(viewer, "Load Balance - Nonzeros: Min %" PetscInt_FMT " avg %" PetscInt_FMT " max %" PetscInt_FMT "\n", nmin, navg, nmax));
1271: PetscFunctionReturn(PETSC_SUCCESS);
1272: }
1273: PetscCall(PetscViewerGetFormat(viewer, &format));
1274: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1275: MatInfo info;
1276: PetscInt *inodes = NULL;
1278: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1279: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1280: PetscCall(MatInodeGetInodeSizes(aij->A, NULL, &inodes, NULL));
1281: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1282: if (!inodes) {
1283: 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,
1284: (double)info.memory));
1285: } else {
1286: 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,
1287: (double)info.memory));
1288: }
1289: PetscCall(MatGetInfo(aij->A, MAT_LOCAL, &info));
1290: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1291: PetscCall(MatGetInfo(aij->B, MAT_LOCAL, &info));
1292: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1293: PetscCall(PetscViewerFlush(viewer));
1294: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1295: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1296: PetscCall(VecScatterView(aij->Mvctx, viewer));
1297: PetscFunctionReturn(PETSC_SUCCESS);
1298: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1299: PetscInt inodecount, inodelimit, *inodes;
1300: PetscCall(MatInodeGetInodeSizes(aij->A, &inodecount, &inodes, &inodelimit));
1301: if (inodes) {
1302: PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
1303: } else {
1304: PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
1305: }
1306: PetscFunctionReturn(PETSC_SUCCESS);
1307: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1308: PetscFunctionReturn(PETSC_SUCCESS);
1309: }
1310: } else if (isbinary) {
1311: if (size == 1) {
1312: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1313: PetscCall(MatView(aij->A, viewer));
1314: } else {
1315: PetscCall(MatView_MPIAIJ_Binary(mat, viewer));
1316: }
1317: PetscFunctionReturn(PETSC_SUCCESS);
1318: } else if (iascii && size == 1) {
1319: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1320: PetscCall(MatView(aij->A, viewer));
1321: PetscFunctionReturn(PETSC_SUCCESS);
1322: } else if (isdraw) {
1323: PetscDraw draw;
1324: PetscBool isnull;
1325: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1326: PetscCall(PetscDrawIsNull(draw, &isnull));
1327: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1328: }
1330: { /* assemble the entire matrix onto first processor */
1331: Mat A = NULL, Av;
1332: IS isrow, iscol;
1334: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->rmap->N : 0, 0, 1, &isrow));
1335: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->cmap->N : 0, 0, 1, &iscol));
1336: PetscCall(MatCreateSubMatrix(mat, isrow, iscol, MAT_INITIAL_MATRIX, &A));
1337: PetscCall(MatMPIAIJGetSeqAIJ(A, &Av, NULL, NULL));
1338: /* The commented code uses MatCreateSubMatrices instead */
1339: /*
1340: Mat *AA, A = NULL, Av;
1341: IS isrow,iscol;
1343: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->rmap->N : 0,0,1,&isrow));
1344: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->cmap->N : 0,0,1,&iscol));
1345: PetscCall(MatCreateSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&AA));
1346: if (rank == 0) {
1347: PetscCall(PetscObjectReference((PetscObject)AA[0]));
1348: A = AA[0];
1349: Av = AA[0];
1350: }
1351: PetscCall(MatDestroySubMatrices(1,&AA));
1352: */
1353: PetscCall(ISDestroy(&iscol));
1354: PetscCall(ISDestroy(&isrow));
1355: /*
1356: Everyone has to call to draw the matrix since the graphics waits are
1357: synchronized across all processors that share the PetscDraw object
1358: */
1359: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1360: if (rank == 0) {
1361: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)Av, ((PetscObject)mat)->name));
1362: PetscCall(MatView_SeqAIJ(Av, sviewer));
1363: }
1364: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1365: PetscCall(MatDestroy(&A));
1366: }
1367: PetscFunctionReturn(PETSC_SUCCESS);
1368: }
1370: PetscErrorCode MatView_MPIAIJ(Mat mat, PetscViewer viewer)
1371: {
1372: PetscBool iascii, isdraw, issocket, isbinary;
1374: PetscFunctionBegin;
1375: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1376: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1377: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1378: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1379: if (iascii || isdraw || isbinary || issocket) PetscCall(MatView_MPIAIJ_ASCIIorDraworSocket(mat, viewer));
1380: PetscFunctionReturn(PETSC_SUCCESS);
1381: }
1383: static PetscErrorCode MatSOR_MPIAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1384: {
1385: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1386: Vec bb1 = NULL;
1387: PetscBool hasop;
1389: PetscFunctionBegin;
1390: if (flag == SOR_APPLY_UPPER) {
1391: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1392: PetscFunctionReturn(PETSC_SUCCESS);
1393: }
1395: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1));
1397: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
1398: if (flag & SOR_ZERO_INITIAL_GUESS) {
1399: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1400: its--;
1401: }
1403: while (its--) {
1404: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1405: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1407: /* update rhs: bb1 = bb - B*x */
1408: PetscCall(VecScale(mat->lvec, -1.0));
1409: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1411: /* local sweep */
1412: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
1413: }
1414: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1415: if (flag & SOR_ZERO_INITIAL_GUESS) {
1416: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1417: its--;
1418: }
1419: while (its--) {
1420: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1421: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1423: /* update rhs: bb1 = bb - B*x */
1424: PetscCall(VecScale(mat->lvec, -1.0));
1425: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1427: /* local sweep */
1428: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
1429: }
1430: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1431: if (flag & SOR_ZERO_INITIAL_GUESS) {
1432: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1433: its--;
1434: }
1435: while (its--) {
1436: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1437: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1439: /* update rhs: bb1 = bb - B*x */
1440: PetscCall(VecScale(mat->lvec, -1.0));
1441: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1443: /* local sweep */
1444: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
1445: }
1446: } else if (flag & SOR_EISENSTAT) {
1447: Vec xx1;
1449: PetscCall(VecDuplicate(bb, &xx1));
1450: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP), fshift, lits, 1, xx));
1452: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1453: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1454: if (!mat->diag) {
1455: PetscCall(MatCreateVecs(matin, &mat->diag, NULL));
1456: PetscCall(MatGetDiagonal(matin, mat->diag));
1457: }
1458: PetscCall(MatHasOperation(matin, MATOP_MULT_DIAGONAL_BLOCK, &hasop));
1459: if (hasop) {
1460: PetscCall(MatMultDiagonalBlock(matin, xx, bb1));
1461: } else {
1462: PetscCall(VecPointwiseMult(bb1, mat->diag, xx));
1463: }
1464: PetscCall(VecAYPX(bb1, (omega - 2.0) / omega, bb));
1466: PetscCall(MatMultAdd(mat->B, mat->lvec, bb1, bb1));
1468: /* local sweep */
1469: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP), fshift, lits, 1, xx1));
1470: PetscCall(VecAXPY(xx, 1.0, xx1));
1471: PetscCall(VecDestroy(&xx1));
1472: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");
1474: PetscCall(VecDestroy(&bb1));
1476: matin->factorerrortype = mat->A->factorerrortype;
1477: PetscFunctionReturn(PETSC_SUCCESS);
1478: }
1480: static PetscErrorCode MatPermute_MPIAIJ(Mat A, IS rowp, IS colp, Mat *B)
1481: {
1482: Mat aA, aB, Aperm;
1483: const PetscInt *rwant, *cwant, *gcols, *ai, *bi, *aj, *bj;
1484: PetscScalar *aa, *ba;
1485: PetscInt i, j, m, n, ng, anz, bnz, *dnnz, *onnz, *tdnnz, *tonnz, *rdest, *cdest, *work, *gcdest;
1486: PetscSF rowsf, sf;
1487: IS parcolp = NULL;
1488: PetscBool done;
1490: PetscFunctionBegin;
1491: PetscCall(MatGetLocalSize(A, &m, &n));
1492: PetscCall(ISGetIndices(rowp, &rwant));
1493: PetscCall(ISGetIndices(colp, &cwant));
1494: PetscCall(PetscMalloc3(PetscMax(m, n), &work, m, &rdest, n, &cdest));
1496: /* Invert row permutation to find out where my rows should go */
1497: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &rowsf));
1498: PetscCall(PetscSFSetGraphLayout(rowsf, A->rmap, A->rmap->n, NULL, PETSC_OWN_POINTER, rwant));
1499: PetscCall(PetscSFSetFromOptions(rowsf));
1500: for (i = 0; i < m; i++) work[i] = A->rmap->rstart + i;
1501: PetscCall(PetscSFReduceBegin(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1502: PetscCall(PetscSFReduceEnd(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1504: /* Invert column permutation to find out where my columns should go */
1505: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1506: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, A->cmap->n, NULL, PETSC_OWN_POINTER, cwant));
1507: PetscCall(PetscSFSetFromOptions(sf));
1508: for (i = 0; i < n; i++) work[i] = A->cmap->rstart + i;
1509: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1510: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1511: PetscCall(PetscSFDestroy(&sf));
1513: PetscCall(ISRestoreIndices(rowp, &rwant));
1514: PetscCall(ISRestoreIndices(colp, &cwant));
1515: PetscCall(MatMPIAIJGetSeqAIJ(A, &aA, &aB, &gcols));
1517: /* Find out where my gcols should go */
1518: PetscCall(MatGetSize(aB, NULL, &ng));
1519: PetscCall(PetscMalloc1(ng, &gcdest));
1520: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1521: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, ng, NULL, PETSC_OWN_POINTER, gcols));
1522: PetscCall(PetscSFSetFromOptions(sf));
1523: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1524: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1525: PetscCall(PetscSFDestroy(&sf));
1527: PetscCall(PetscCalloc4(m, &dnnz, m, &onnz, m, &tdnnz, m, &tonnz));
1528: PetscCall(MatGetRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1529: PetscCall(MatGetRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1530: for (i = 0; i < m; i++) {
1531: PetscInt row = rdest[i];
1532: PetscMPIInt rowner;
1533: PetscCall(PetscLayoutFindOwner(A->rmap, row, &rowner));
1534: for (j = ai[i]; j < ai[i + 1]; j++) {
1535: PetscInt col = cdest[aj[j]];
1536: PetscMPIInt cowner;
1537: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner)); /* Could build an index for the columns to eliminate this search */
1538: if (rowner == cowner) dnnz[i]++;
1539: else onnz[i]++;
1540: }
1541: for (j = bi[i]; j < bi[i + 1]; j++) {
1542: PetscInt col = gcdest[bj[j]];
1543: PetscMPIInt cowner;
1544: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner));
1545: if (rowner == cowner) dnnz[i]++;
1546: else onnz[i]++;
1547: }
1548: }
1549: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1550: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1551: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1552: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1553: PetscCall(PetscSFDestroy(&rowsf));
1555: PetscCall(MatCreateAIJ(PetscObjectComm((PetscObject)A), A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N, 0, tdnnz, 0, tonnz, &Aperm));
1556: PetscCall(MatSeqAIJGetArray(aA, &aa));
1557: PetscCall(MatSeqAIJGetArray(aB, &ba));
1558: for (i = 0; i < m; i++) {
1559: PetscInt *acols = dnnz, *bcols = onnz; /* Repurpose now-unneeded arrays */
1560: PetscInt j0, rowlen;
1561: rowlen = ai[i + 1] - ai[i];
1562: for (j0 = j = 0; j < rowlen; j0 = j) { /* rowlen could be larger than number of rows m, so sum in batches */
1563: for (; j < PetscMin(rowlen, j0 + m); j++) acols[j - j0] = cdest[aj[ai[i] + j]];
1564: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, acols, aa + ai[i] + j0, INSERT_VALUES));
1565: }
1566: rowlen = bi[i + 1] - bi[i];
1567: for (j0 = j = 0; j < rowlen; j0 = j) {
1568: for (; j < PetscMin(rowlen, j0 + m); j++) bcols[j - j0] = gcdest[bj[bi[i] + j]];
1569: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, bcols, ba + bi[i] + j0, INSERT_VALUES));
1570: }
1571: }
1572: PetscCall(MatAssemblyBegin(Aperm, MAT_FINAL_ASSEMBLY));
1573: PetscCall(MatAssemblyEnd(Aperm, MAT_FINAL_ASSEMBLY));
1574: PetscCall(MatRestoreRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1575: PetscCall(MatRestoreRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1576: PetscCall(MatSeqAIJRestoreArray(aA, &aa));
1577: PetscCall(MatSeqAIJRestoreArray(aB, &ba));
1578: PetscCall(PetscFree4(dnnz, onnz, tdnnz, tonnz));
1579: PetscCall(PetscFree3(work, rdest, cdest));
1580: PetscCall(PetscFree(gcdest));
1581: if (parcolp) PetscCall(ISDestroy(&colp));
1582: *B = Aperm;
1583: PetscFunctionReturn(PETSC_SUCCESS);
1584: }
1586: static PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
1587: {
1588: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1590: PetscFunctionBegin;
1591: PetscCall(MatGetSize(aij->B, NULL, nghosts));
1592: if (ghosts) *ghosts = aij->garray;
1593: PetscFunctionReturn(PETSC_SUCCESS);
1594: }
1596: static PetscErrorCode MatGetInfo_MPIAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1597: {
1598: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1599: Mat A = mat->A, B = mat->B;
1600: PetscLogDouble isend[5], irecv[5];
1602: PetscFunctionBegin;
1603: info->block_size = 1.0;
1604: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1606: isend[0] = info->nz_used;
1607: isend[1] = info->nz_allocated;
1608: isend[2] = info->nz_unneeded;
1609: isend[3] = info->memory;
1610: isend[4] = info->mallocs;
1612: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1614: isend[0] += info->nz_used;
1615: isend[1] += info->nz_allocated;
1616: isend[2] += info->nz_unneeded;
1617: isend[3] += info->memory;
1618: isend[4] += info->mallocs;
1619: if (flag == MAT_LOCAL) {
1620: info->nz_used = isend[0];
1621: info->nz_allocated = isend[1];
1622: info->nz_unneeded = isend[2];
1623: info->memory = isend[3];
1624: info->mallocs = isend[4];
1625: } else if (flag == MAT_GLOBAL_MAX) {
1626: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1628: info->nz_used = irecv[0];
1629: info->nz_allocated = irecv[1];
1630: info->nz_unneeded = irecv[2];
1631: info->memory = irecv[3];
1632: info->mallocs = irecv[4];
1633: } else if (flag == MAT_GLOBAL_SUM) {
1634: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1636: info->nz_used = irecv[0];
1637: info->nz_allocated = irecv[1];
1638: info->nz_unneeded = irecv[2];
1639: info->memory = irecv[3];
1640: info->mallocs = irecv[4];
1641: }
1642: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1643: info->fill_ratio_needed = 0;
1644: info->factor_mallocs = 0;
1645: PetscFunctionReturn(PETSC_SUCCESS);
1646: }
1648: PetscErrorCode MatSetOption_MPIAIJ(Mat A, MatOption op, PetscBool flg)
1649: {
1650: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1652: PetscFunctionBegin;
1653: switch (op) {
1654: case MAT_NEW_NONZERO_LOCATIONS:
1655: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1656: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1657: case MAT_KEEP_NONZERO_PATTERN:
1658: case MAT_NEW_NONZERO_LOCATION_ERR:
1659: case MAT_USE_INODES:
1660: case MAT_IGNORE_ZERO_ENTRIES:
1661: case MAT_FORM_EXPLICIT_TRANSPOSE:
1662: MatCheckPreallocated(A, 1);
1663: PetscCall(MatSetOption(a->A, op, flg));
1664: PetscCall(MatSetOption(a->B, op, flg));
1665: break;
1666: case MAT_ROW_ORIENTED:
1667: MatCheckPreallocated(A, 1);
1668: a->roworiented = flg;
1670: PetscCall(MatSetOption(a->A, op, flg));
1671: PetscCall(MatSetOption(a->B, op, flg));
1672: break;
1673: case MAT_FORCE_DIAGONAL_ENTRIES:
1674: case MAT_SORTED_FULL:
1675: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1676: break;
1677: case MAT_IGNORE_OFF_PROC_ENTRIES:
1678: a->donotstash = flg;
1679: break;
1680: /* Symmetry flags are handled directly by MatSetOption() and they don't affect preallocation */
1681: case MAT_SPD:
1682: case MAT_SYMMETRIC:
1683: case MAT_STRUCTURALLY_SYMMETRIC:
1684: case MAT_HERMITIAN:
1685: case MAT_SYMMETRY_ETERNAL:
1686: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1687: case MAT_SPD_ETERNAL:
1688: /* if the diagonal matrix is square it inherits some of the properties above */
1689: if (a->A && A->rmap->n == A->cmap->n) PetscCall(MatSetOption(a->A, op, flg));
1690: break;
1691: case MAT_SUBMAT_SINGLEIS:
1692: A->submat_singleis = flg;
1693: break;
1694: case MAT_STRUCTURE_ONLY:
1695: /* The option is handled directly by MatSetOption() */
1696: break;
1697: default:
1698: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1699: }
1700: PetscFunctionReturn(PETSC_SUCCESS);
1701: }
1703: PetscErrorCode MatGetRow_MPIAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1704: {
1705: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1706: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1707: PetscInt i, *cworkA, *cworkB, **pcA, **pcB, cstart = matin->cmap->rstart;
1708: PetscInt nztot, nzA, nzB, lrow, rstart = matin->rmap->rstart, rend = matin->rmap->rend;
1709: PetscInt *cmap, *idx_p;
1711: PetscFunctionBegin;
1712: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1713: mat->getrowactive = PETSC_TRUE;
1715: if (!mat->rowvalues && (idx || v)) {
1716: /*
1717: allocate enough space to hold information from the longest row.
1718: */
1719: Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)mat->A->data, *Ba = (Mat_SeqAIJ *)mat->B->data;
1720: PetscInt max = 1, tmp;
1721: for (i = 0; i < matin->rmap->n; i++) {
1722: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1723: if (max < tmp) max = tmp;
1724: }
1725: PetscCall(PetscMalloc2(max, &mat->rowvalues, max, &mat->rowindices));
1726: }
1728: PetscCheck(row >= rstart && row < rend, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Only local rows");
1729: lrow = row - rstart;
1731: pvA = &vworkA;
1732: pcA = &cworkA;
1733: pvB = &vworkB;
1734: pcB = &cworkB;
1735: if (!v) {
1736: pvA = NULL;
1737: pvB = NULL;
1738: }
1739: if (!idx) {
1740: pcA = NULL;
1741: if (!v) pcB = NULL;
1742: }
1743: PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1744: PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1745: nztot = nzA + nzB;
1747: cmap = mat->garray;
1748: if (v || idx) {
1749: if (nztot) {
1750: /* Sort by increasing column numbers, assuming A and B already sorted */
1751: PetscInt imark = -1;
1752: if (v) {
1753: *v = v_p = mat->rowvalues;
1754: for (i = 0; i < nzB; i++) {
1755: if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1756: else break;
1757: }
1758: imark = i;
1759: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1760: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1761: }
1762: if (idx) {
1763: *idx = idx_p = mat->rowindices;
1764: if (imark > -1) {
1765: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i]];
1766: } else {
1767: for (i = 0; i < nzB; i++) {
1768: if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1769: else break;
1770: }
1771: imark = i;
1772: }
1773: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart + cworkA[i];
1774: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i]];
1775: }
1776: } else {
1777: if (idx) *idx = NULL;
1778: if (v) *v = NULL;
1779: }
1780: }
1781: *nz = nztot;
1782: PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1783: PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1784: PetscFunctionReturn(PETSC_SUCCESS);
1785: }
1787: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1788: {
1789: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1791: PetscFunctionBegin;
1792: PetscCheck(aij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow() must be called first");
1793: aij->getrowactive = PETSC_FALSE;
1794: PetscFunctionReturn(PETSC_SUCCESS);
1795: }
1797: static PetscErrorCode MatNorm_MPIAIJ(Mat mat, NormType type, PetscReal *norm)
1798: {
1799: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1800: Mat_SeqAIJ *amat = (Mat_SeqAIJ *)aij->A->data, *bmat = (Mat_SeqAIJ *)aij->B->data;
1801: PetscInt i, j, cstart = mat->cmap->rstart;
1802: PetscReal sum = 0.0;
1803: const MatScalar *v, *amata, *bmata;
1804: PetscMPIInt iN;
1806: PetscFunctionBegin;
1807: if (aij->size == 1) {
1808: PetscCall(MatNorm(aij->A, type, norm));
1809: } else {
1810: PetscCall(MatSeqAIJGetArrayRead(aij->A, &amata));
1811: PetscCall(MatSeqAIJGetArrayRead(aij->B, &bmata));
1812: if (type == NORM_FROBENIUS) {
1813: v = amata;
1814: for (i = 0; i < amat->nz; i++) {
1815: sum += PetscRealPart(PetscConj(*v) * (*v));
1816: v++;
1817: }
1818: v = bmata;
1819: for (i = 0; i < bmat->nz; i++) {
1820: sum += PetscRealPart(PetscConj(*v) * (*v));
1821: v++;
1822: }
1823: PetscCallMPI(MPIU_Allreduce(&sum, norm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1824: *norm = PetscSqrtReal(*norm);
1825: PetscCall(PetscLogFlops(2.0 * amat->nz + 2.0 * bmat->nz));
1826: } else if (type == NORM_1) { /* max column norm */
1827: PetscReal *tmp, *tmp2;
1828: PetscInt *jj, *garray = aij->garray;
1829: PetscCall(PetscCalloc1(mat->cmap->N + 1, &tmp));
1830: PetscCall(PetscMalloc1(mat->cmap->N + 1, &tmp2));
1831: *norm = 0.0;
1832: v = amata;
1833: jj = amat->j;
1834: for (j = 0; j < amat->nz; j++) {
1835: tmp[cstart + *jj++] += PetscAbsScalar(*v);
1836: v++;
1837: }
1838: v = bmata;
1839: jj = bmat->j;
1840: for (j = 0; j < bmat->nz; j++) {
1841: tmp[garray[*jj++]] += PetscAbsScalar(*v);
1842: v++;
1843: }
1844: PetscCall(PetscMPIIntCast(mat->cmap->N, &iN));
1845: PetscCallMPI(MPIU_Allreduce(tmp, tmp2, iN, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1846: for (j = 0; j < mat->cmap->N; j++) {
1847: if (tmp2[j] > *norm) *norm = tmp2[j];
1848: }
1849: PetscCall(PetscFree(tmp));
1850: PetscCall(PetscFree(tmp2));
1851: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1852: } else if (type == NORM_INFINITY) { /* max row norm */
1853: PetscReal ntemp = 0.0;
1854: for (j = 0; j < aij->A->rmap->n; j++) {
1855: v = PetscSafePointerPlusOffset(amata, amat->i[j]);
1856: sum = 0.0;
1857: for (i = 0; i < amat->i[j + 1] - amat->i[j]; i++) {
1858: sum += PetscAbsScalar(*v);
1859: v++;
1860: }
1861: v = PetscSafePointerPlusOffset(bmata, bmat->i[j]);
1862: for (i = 0; i < bmat->i[j + 1] - bmat->i[j]; i++) {
1863: sum += PetscAbsScalar(*v);
1864: v++;
1865: }
1866: if (sum > ntemp) ntemp = sum;
1867: }
1868: PetscCallMPI(MPIU_Allreduce(&ntemp, norm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
1869: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1870: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for two norm");
1871: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &amata));
1872: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &bmata));
1873: }
1874: PetscFunctionReturn(PETSC_SUCCESS);
1875: }
1877: static PetscErrorCode MatTranspose_MPIAIJ(Mat A, MatReuse reuse, Mat *matout)
1878: {
1879: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *b;
1880: Mat_SeqAIJ *Aloc = (Mat_SeqAIJ *)a->A->data, *Bloc = (Mat_SeqAIJ *)a->B->data, *sub_B_diag;
1881: 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;
1882: const PetscInt *ai, *aj, *bi, *bj, *B_diag_i;
1883: Mat B, A_diag, *B_diag;
1884: const MatScalar *pbv, *bv;
1886: PetscFunctionBegin;
1887: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1888: ma = A->rmap->n;
1889: na = A->cmap->n;
1890: mb = a->B->rmap->n;
1891: nb = a->B->cmap->n;
1892: ai = Aloc->i;
1893: aj = Aloc->j;
1894: bi = Bloc->i;
1895: bj = Bloc->j;
1896: if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1897: PetscInt *d_nnz, *g_nnz, *o_nnz;
1898: PetscSFNode *oloc;
1899: PETSC_UNUSED PetscSF sf;
1901: PetscCall(PetscMalloc4(na, &d_nnz, na, &o_nnz, nb, &g_nnz, nb, &oloc));
1902: /* compute d_nnz for preallocation */
1903: PetscCall(PetscArrayzero(d_nnz, na));
1904: for (i = 0; i < ai[ma]; i++) d_nnz[aj[i]]++;
1905: /* compute local off-diagonal contributions */
1906: PetscCall(PetscArrayzero(g_nnz, nb));
1907: for (i = 0; i < bi[ma]; i++) g_nnz[bj[i]]++;
1908: /* map those to global */
1909: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1910: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, nb, NULL, PETSC_USE_POINTER, a->garray));
1911: PetscCall(PetscSFSetFromOptions(sf));
1912: PetscCall(PetscArrayzero(o_nnz, na));
1913: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1914: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1915: PetscCall(PetscSFDestroy(&sf));
1917: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1918: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1919: PetscCall(MatSetBlockSizes(B, PetscAbs(A->cmap->bs), PetscAbs(A->rmap->bs)));
1920: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1921: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
1922: PetscCall(PetscFree4(d_nnz, o_nnz, g_nnz, oloc));
1923: } else {
1924: B = *matout;
1925: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
1926: }
1928: b = (Mat_MPIAIJ *)B->data;
1929: A_diag = a->A;
1930: B_diag = &b->A;
1931: sub_B_diag = (Mat_SeqAIJ *)(*B_diag)->data;
1932: A_diag_ncol = A_diag->cmap->N;
1933: B_diag_ilen = sub_B_diag->ilen;
1934: B_diag_i = sub_B_diag->i;
1936: /* Set ilen for diagonal of B */
1937: for (i = 0; i < A_diag_ncol; i++) B_diag_ilen[i] = B_diag_i[i + 1] - B_diag_i[i];
1939: /* Transpose the diagonal part of the matrix. In contrast to the off-diagonal part, this can be done
1940: very quickly (=without using MatSetValues), because all writes are local. */
1941: PetscCall(MatTransposeSetPrecursor(A_diag, *B_diag));
1942: PetscCall(MatTranspose(A_diag, MAT_REUSE_MATRIX, B_diag));
1944: /* copy over the B part */
1945: PetscCall(PetscMalloc1(bi[mb], &cols));
1946: PetscCall(MatSeqAIJGetArrayRead(a->B, &bv));
1947: pbv = bv;
1948: row = A->rmap->rstart;
1949: for (i = 0; i < bi[mb]; i++) cols[i] = a->garray[bj[i]];
1950: cols_tmp = cols;
1951: for (i = 0; i < mb; i++) {
1952: ncol = bi[i + 1] - bi[i];
1953: PetscCall(MatSetValues(B, ncol, cols_tmp, 1, &row, pbv, INSERT_VALUES));
1954: row++;
1955: if (pbv) pbv += ncol;
1956: if (cols_tmp) cols_tmp += ncol;
1957: }
1958: PetscCall(PetscFree(cols));
1959: PetscCall(MatSeqAIJRestoreArrayRead(a->B, &bv));
1961: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1962: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1963: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1964: *matout = B;
1965: } else {
1966: PetscCall(MatHeaderMerge(A, &B));
1967: }
1968: PetscFunctionReturn(PETSC_SUCCESS);
1969: }
1971: static PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat, Vec ll, Vec rr)
1972: {
1973: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1974: Mat a = aij->A, b = aij->B;
1975: PetscInt s1, s2, s3;
1977: PetscFunctionBegin;
1978: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1979: if (rr) {
1980: PetscCall(VecGetLocalSize(rr, &s1));
1981: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1982: /* Overlap communication with computation. */
1983: PetscCall(VecScatterBegin(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1984: }
1985: if (ll) {
1986: PetscCall(VecGetLocalSize(ll, &s1));
1987: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1988: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1989: }
1990: /* scale the diagonal block */
1991: PetscUseTypeMethod(a, diagonalscale, ll, rr);
1993: if (rr) {
1994: /* Do a scatter end and then right scale the off-diagonal block */
1995: PetscCall(VecScatterEnd(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1996: PetscUseTypeMethod(b, diagonalscale, NULL, aij->lvec);
1997: }
1998: PetscFunctionReturn(PETSC_SUCCESS);
1999: }
2001: static PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2002: {
2003: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2005: PetscFunctionBegin;
2006: PetscCall(MatSetUnfactored(a->A));
2007: PetscFunctionReturn(PETSC_SUCCESS);
2008: }
2010: static PetscErrorCode MatEqual_MPIAIJ(Mat A, Mat B, PetscBool *flag)
2011: {
2012: Mat_MPIAIJ *matB = (Mat_MPIAIJ *)B->data, *matA = (Mat_MPIAIJ *)A->data;
2013: Mat a, b, c, d;
2014: PetscBool flg;
2016: PetscFunctionBegin;
2017: a = matA->A;
2018: b = matA->B;
2019: c = matB->A;
2020: d = matB->B;
2022: PetscCall(MatEqual(a, c, &flg));
2023: if (flg) PetscCall(MatEqual(b, d, &flg));
2024: PetscCallMPI(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
2025: PetscFunctionReturn(PETSC_SUCCESS);
2026: }
2028: static PetscErrorCode MatCopy_MPIAIJ(Mat A, Mat B, MatStructure str)
2029: {
2030: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2031: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2033: PetscFunctionBegin;
2034: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2035: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2036: /* because of the column compression in the off-processor part of the matrix a->B,
2037: the number of columns in a->B and b->B may be different, hence we cannot call
2038: the MatCopy() directly on the two parts. If need be, we can provide a more
2039: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2040: then copying the submatrices */
2041: PetscCall(MatCopy_Basic(A, B, str));
2042: } else {
2043: PetscCall(MatCopy(a->A, b->A, str));
2044: PetscCall(MatCopy(a->B, b->B, str));
2045: }
2046: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2047: PetscFunctionReturn(PETSC_SUCCESS);
2048: }
2050: /*
2051: Computes the number of nonzeros per row needed for preallocation when X and Y
2052: have different nonzero structure.
2053: */
2054: 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)
2055: {
2056: PetscInt i, j, k, nzx, nzy;
2058: PetscFunctionBegin;
2059: /* Set the number of nonzeros in the new matrix */
2060: for (i = 0; i < m; i++) {
2061: const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2062: nzx = xi[i + 1] - xi[i];
2063: nzy = yi[i + 1] - yi[i];
2064: nnz[i] = 0;
2065: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2066: for (; k < nzy && yltog[yjj[k]] < xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2067: if (k < nzy && yltog[yjj[k]] == xltog[xjj[j]]) k++; /* Skip duplicate */
2068: nnz[i]++;
2069: }
2070: for (; k < nzy; k++) nnz[i]++;
2071: }
2072: PetscFunctionReturn(PETSC_SUCCESS);
2073: }
2075: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2076: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
2077: {
2078: PetscInt m = Y->rmap->N;
2079: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2080: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
2082: PetscFunctionBegin;
2083: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
2084: PetscFunctionReturn(PETSC_SUCCESS);
2085: }
2087: static PetscErrorCode MatAXPY_MPIAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2088: {
2089: Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data, *yy = (Mat_MPIAIJ *)Y->data;
2091: PetscFunctionBegin;
2092: if (str == SAME_NONZERO_PATTERN) {
2093: PetscCall(MatAXPY(yy->A, a, xx->A, str));
2094: PetscCall(MatAXPY(yy->B, a, xx->B, str));
2095: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2096: PetscCall(MatAXPY_Basic(Y, a, X, str));
2097: } else {
2098: Mat B;
2099: PetscInt *nnz_d, *nnz_o;
2101: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
2102: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
2103: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2104: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2105: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2106: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2107: PetscCall(MatAXPYGetPreallocation_SeqAIJ(yy->A, xx->A, nnz_d));
2108: PetscCall(MatAXPYGetPreallocation_MPIAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
2109: PetscCall(MatMPIAIJSetPreallocation(B, 0, nnz_d, 0, nnz_o));
2110: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2111: PetscCall(MatHeaderMerge(Y, &B));
2112: PetscCall(PetscFree(nnz_d));
2113: PetscCall(PetscFree(nnz_o));
2114: }
2115: PetscFunctionReturn(PETSC_SUCCESS);
2116: }
2118: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);
2120: static PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2121: {
2122: PetscFunctionBegin;
2123: if (PetscDefined(USE_COMPLEX)) {
2124: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2126: PetscCall(MatConjugate_SeqAIJ(aij->A));
2127: PetscCall(MatConjugate_SeqAIJ(aij->B));
2128: }
2129: PetscFunctionReturn(PETSC_SUCCESS);
2130: }
2132: static PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2133: {
2134: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2136: PetscFunctionBegin;
2137: PetscCall(MatRealPart(a->A));
2138: PetscCall(MatRealPart(a->B));
2139: PetscFunctionReturn(PETSC_SUCCESS);
2140: }
2142: static PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
2143: {
2144: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2146: PetscFunctionBegin;
2147: PetscCall(MatImaginaryPart(a->A));
2148: PetscCall(MatImaginaryPart(a->B));
2149: PetscFunctionReturn(PETSC_SUCCESS);
2150: }
2152: static PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2153: {
2154: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2155: PetscInt i, *idxb = NULL, m = A->rmap->n;
2156: PetscScalar *vv;
2157: Vec vB, vA;
2158: const PetscScalar *va, *vb;
2160: PetscFunctionBegin;
2161: PetscCall(MatCreateVecs(a->A, NULL, &vA));
2162: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
2164: PetscCall(VecGetArrayRead(vA, &va));
2165: if (idx) {
2166: for (i = 0; i < m; i++) {
2167: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2168: }
2169: }
2171: PetscCall(MatCreateVecs(a->B, NULL, &vB));
2172: PetscCall(PetscMalloc1(m, &idxb));
2173: PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));
2175: PetscCall(VecGetArrayWrite(v, &vv));
2176: PetscCall(VecGetArrayRead(vB, &vb));
2177: for (i = 0; i < m; i++) {
2178: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2179: vv[i] = vb[i];
2180: if (idx) idx[i] = a->garray[idxb[i]];
2181: } else {
2182: vv[i] = va[i];
2183: if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > a->garray[idxb[i]]) idx[i] = a->garray[idxb[i]];
2184: }
2185: }
2186: PetscCall(VecRestoreArrayWrite(v, &vv));
2187: PetscCall(VecRestoreArrayRead(vA, &va));
2188: PetscCall(VecRestoreArrayRead(vB, &vb));
2189: PetscCall(PetscFree(idxb));
2190: PetscCall(VecDestroy(&vA));
2191: PetscCall(VecDestroy(&vB));
2192: PetscFunctionReturn(PETSC_SUCCESS);
2193: }
2195: static PetscErrorCode MatGetRowSumAbs_MPIAIJ(Mat A, Vec v)
2196: {
2197: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2198: Vec vB, vA;
2200: PetscFunctionBegin;
2201: PetscCall(MatCreateVecs(a->A, NULL, &vA));
2202: PetscCall(MatGetRowSumAbs(a->A, vA));
2203: PetscCall(MatCreateVecs(a->B, NULL, &vB));
2204: PetscCall(MatGetRowSumAbs(a->B, vB));
2205: PetscCall(VecAXPY(vA, 1.0, vB));
2206: PetscCall(VecDestroy(&vB));
2207: PetscCall(VecCopy(vA, v));
2208: PetscCall(VecDestroy(&vA));
2209: PetscFunctionReturn(PETSC_SUCCESS);
2210: }
2212: static PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2213: {
2214: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2215: PetscInt m = A->rmap->n, n = A->cmap->n;
2216: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2217: PetscInt *cmap = mat->garray;
2218: PetscInt *diagIdx, *offdiagIdx;
2219: Vec diagV, offdiagV;
2220: PetscScalar *a, *diagA, *offdiagA;
2221: const PetscScalar *ba, *bav;
2222: PetscInt r, j, col, ncols, *bi, *bj;
2223: Mat B = mat->B;
2224: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2226: PetscFunctionBegin;
2227: /* When a process holds entire A and other processes have no entry */
2228: if (A->cmap->N == n) {
2229: PetscCall(VecGetArrayWrite(v, &diagA));
2230: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2231: PetscCall(MatGetRowMinAbs(mat->A, diagV, idx));
2232: PetscCall(VecDestroy(&diagV));
2233: PetscCall(VecRestoreArrayWrite(v, &diagA));
2234: PetscFunctionReturn(PETSC_SUCCESS);
2235: } else if (n == 0) {
2236: if (m) {
2237: PetscCall(VecGetArrayWrite(v, &a));
2238: for (r = 0; r < m; r++) {
2239: a[r] = 0.0;
2240: if (idx) idx[r] = -1;
2241: }
2242: PetscCall(VecRestoreArrayWrite(v, &a));
2243: }
2244: PetscFunctionReturn(PETSC_SUCCESS);
2245: }
2247: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2248: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2249: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2250: PetscCall(MatGetRowMinAbs(mat->A, diagV, diagIdx));
2252: /* Get offdiagIdx[] for implicit 0.0 */
2253: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2254: ba = bav;
2255: bi = b->i;
2256: bj = b->j;
2257: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2258: for (r = 0; r < m; r++) {
2259: ncols = bi[r + 1] - bi[r];
2260: if (ncols == A->cmap->N - n) { /* Brow is dense */
2261: offdiagA[r] = *ba;
2262: offdiagIdx[r] = cmap[0];
2263: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2264: offdiagA[r] = 0.0;
2266: /* Find first hole in the cmap */
2267: for (j = 0; j < ncols; j++) {
2268: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2269: if (col > j && j < cstart) {
2270: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2271: break;
2272: } else if (col > j + n && j >= cstart) {
2273: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2274: break;
2275: }
2276: }
2277: if (j == ncols && ncols < A->cmap->N - n) {
2278: /* a hole is outside compressed Bcols */
2279: if (ncols == 0) {
2280: if (cstart) {
2281: offdiagIdx[r] = 0;
2282: } else offdiagIdx[r] = cend;
2283: } else { /* ncols > 0 */
2284: offdiagIdx[r] = cmap[ncols - 1] + 1;
2285: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2286: }
2287: }
2288: }
2290: for (j = 0; j < ncols; j++) {
2291: if (PetscAbsScalar(offdiagA[r]) > PetscAbsScalar(*ba)) {
2292: offdiagA[r] = *ba;
2293: offdiagIdx[r] = cmap[*bj];
2294: }
2295: ba++;
2296: bj++;
2297: }
2298: }
2300: PetscCall(VecGetArrayWrite(v, &a));
2301: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2302: for (r = 0; r < m; ++r) {
2303: if (PetscAbsScalar(diagA[r]) < PetscAbsScalar(offdiagA[r])) {
2304: a[r] = diagA[r];
2305: if (idx) idx[r] = cstart + diagIdx[r];
2306: } else if (PetscAbsScalar(diagA[r]) == PetscAbsScalar(offdiagA[r])) {
2307: a[r] = diagA[r];
2308: if (idx) {
2309: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2310: idx[r] = cstart + diagIdx[r];
2311: } else idx[r] = offdiagIdx[r];
2312: }
2313: } else {
2314: a[r] = offdiagA[r];
2315: if (idx) idx[r] = offdiagIdx[r];
2316: }
2317: }
2318: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2319: PetscCall(VecRestoreArrayWrite(v, &a));
2320: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2321: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2322: PetscCall(VecDestroy(&diagV));
2323: PetscCall(VecDestroy(&offdiagV));
2324: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2325: PetscFunctionReturn(PETSC_SUCCESS);
2326: }
2328: static PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2329: {
2330: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2331: PetscInt m = A->rmap->n, n = A->cmap->n;
2332: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2333: PetscInt *cmap = mat->garray;
2334: PetscInt *diagIdx, *offdiagIdx;
2335: Vec diagV, offdiagV;
2336: PetscScalar *a, *diagA, *offdiagA;
2337: const PetscScalar *ba, *bav;
2338: PetscInt r, j, col, ncols, *bi, *bj;
2339: Mat B = mat->B;
2340: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2342: PetscFunctionBegin;
2343: /* When a process holds entire A and other processes have no entry */
2344: if (A->cmap->N == n) {
2345: PetscCall(VecGetArrayWrite(v, &diagA));
2346: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2347: PetscCall(MatGetRowMin(mat->A, diagV, idx));
2348: PetscCall(VecDestroy(&diagV));
2349: PetscCall(VecRestoreArrayWrite(v, &diagA));
2350: PetscFunctionReturn(PETSC_SUCCESS);
2351: } else if (n == 0) {
2352: if (m) {
2353: PetscCall(VecGetArrayWrite(v, &a));
2354: for (r = 0; r < m; r++) {
2355: a[r] = PETSC_MAX_REAL;
2356: if (idx) idx[r] = -1;
2357: }
2358: PetscCall(VecRestoreArrayWrite(v, &a));
2359: }
2360: PetscFunctionReturn(PETSC_SUCCESS);
2361: }
2363: PetscCall(PetscCalloc2(m, &diagIdx, m, &offdiagIdx));
2364: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2365: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2366: PetscCall(MatGetRowMin(mat->A, diagV, diagIdx));
2368: /* Get offdiagIdx[] for implicit 0.0 */
2369: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2370: ba = bav;
2371: bi = b->i;
2372: bj = b->j;
2373: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2374: for (r = 0; r < m; r++) {
2375: ncols = bi[r + 1] - bi[r];
2376: if (ncols == A->cmap->N - n) { /* Brow is dense */
2377: offdiagA[r] = *ba;
2378: offdiagIdx[r] = cmap[0];
2379: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2380: offdiagA[r] = 0.0;
2382: /* Find first hole in the cmap */
2383: for (j = 0; j < ncols; j++) {
2384: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2385: if (col > j && j < cstart) {
2386: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2387: break;
2388: } else if (col > j + n && j >= cstart) {
2389: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2390: break;
2391: }
2392: }
2393: if (j == ncols && ncols < A->cmap->N - n) {
2394: /* a hole is outside compressed Bcols */
2395: if (ncols == 0) {
2396: if (cstart) {
2397: offdiagIdx[r] = 0;
2398: } else offdiagIdx[r] = cend;
2399: } else { /* ncols > 0 */
2400: offdiagIdx[r] = cmap[ncols - 1] + 1;
2401: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2402: }
2403: }
2404: }
2406: for (j = 0; j < ncols; j++) {
2407: if (PetscRealPart(offdiagA[r]) > PetscRealPart(*ba)) {
2408: offdiagA[r] = *ba;
2409: offdiagIdx[r] = cmap[*bj];
2410: }
2411: ba++;
2412: bj++;
2413: }
2414: }
2416: PetscCall(VecGetArrayWrite(v, &a));
2417: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2418: for (r = 0; r < m; ++r) {
2419: if (PetscRealPart(diagA[r]) < PetscRealPart(offdiagA[r])) {
2420: a[r] = diagA[r];
2421: if (idx) idx[r] = cstart + diagIdx[r];
2422: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2423: a[r] = diagA[r];
2424: if (idx) {
2425: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2426: idx[r] = cstart + diagIdx[r];
2427: } else idx[r] = offdiagIdx[r];
2428: }
2429: } else {
2430: a[r] = offdiagA[r];
2431: if (idx) idx[r] = offdiagIdx[r];
2432: }
2433: }
2434: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2435: PetscCall(VecRestoreArrayWrite(v, &a));
2436: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2437: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2438: PetscCall(VecDestroy(&diagV));
2439: PetscCall(VecDestroy(&offdiagV));
2440: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2441: PetscFunctionReturn(PETSC_SUCCESS);
2442: }
2444: static PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2445: {
2446: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2447: PetscInt m = A->rmap->n, n = A->cmap->n;
2448: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2449: PetscInt *cmap = mat->garray;
2450: PetscInt *diagIdx, *offdiagIdx;
2451: Vec diagV, offdiagV;
2452: PetscScalar *a, *diagA, *offdiagA;
2453: const PetscScalar *ba, *bav;
2454: PetscInt r, j, col, ncols, *bi, *bj;
2455: Mat B = mat->B;
2456: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2458: PetscFunctionBegin;
2459: /* When a process holds entire A and other processes have no entry */
2460: if (A->cmap->N == n) {
2461: PetscCall(VecGetArrayWrite(v, &diagA));
2462: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2463: PetscCall(MatGetRowMax(mat->A, diagV, idx));
2464: PetscCall(VecDestroy(&diagV));
2465: PetscCall(VecRestoreArrayWrite(v, &diagA));
2466: PetscFunctionReturn(PETSC_SUCCESS);
2467: } else if (n == 0) {
2468: if (m) {
2469: PetscCall(VecGetArrayWrite(v, &a));
2470: for (r = 0; r < m; r++) {
2471: a[r] = PETSC_MIN_REAL;
2472: if (idx) idx[r] = -1;
2473: }
2474: PetscCall(VecRestoreArrayWrite(v, &a));
2475: }
2476: PetscFunctionReturn(PETSC_SUCCESS);
2477: }
2479: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2480: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2481: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2482: PetscCall(MatGetRowMax(mat->A, diagV, diagIdx));
2484: /* Get offdiagIdx[] for implicit 0.0 */
2485: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2486: ba = bav;
2487: bi = b->i;
2488: bj = b->j;
2489: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2490: for (r = 0; r < m; r++) {
2491: ncols = bi[r + 1] - bi[r];
2492: if (ncols == A->cmap->N - n) { /* Brow is dense */
2493: offdiagA[r] = *ba;
2494: offdiagIdx[r] = cmap[0];
2495: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2496: offdiagA[r] = 0.0;
2498: /* Find first hole in the cmap */
2499: for (j = 0; j < ncols; j++) {
2500: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2501: if (col > j && j < cstart) {
2502: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2503: break;
2504: } else if (col > j + n && j >= cstart) {
2505: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2506: break;
2507: }
2508: }
2509: if (j == ncols && ncols < A->cmap->N - n) {
2510: /* a hole is outside compressed Bcols */
2511: if (ncols == 0) {
2512: if (cstart) {
2513: offdiagIdx[r] = 0;
2514: } else offdiagIdx[r] = cend;
2515: } else { /* ncols > 0 */
2516: offdiagIdx[r] = cmap[ncols - 1] + 1;
2517: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2518: }
2519: }
2520: }
2522: for (j = 0; j < ncols; j++) {
2523: if (PetscRealPart(offdiagA[r]) < PetscRealPart(*ba)) {
2524: offdiagA[r] = *ba;
2525: offdiagIdx[r] = cmap[*bj];
2526: }
2527: ba++;
2528: bj++;
2529: }
2530: }
2532: PetscCall(VecGetArrayWrite(v, &a));
2533: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2534: for (r = 0; r < m; ++r) {
2535: if (PetscRealPart(diagA[r]) > PetscRealPart(offdiagA[r])) {
2536: a[r] = diagA[r];
2537: if (idx) idx[r] = cstart + diagIdx[r];
2538: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2539: a[r] = diagA[r];
2540: if (idx) {
2541: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2542: idx[r] = cstart + diagIdx[r];
2543: } else idx[r] = offdiagIdx[r];
2544: }
2545: } else {
2546: a[r] = offdiagA[r];
2547: if (idx) idx[r] = offdiagIdx[r];
2548: }
2549: }
2550: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2551: PetscCall(VecRestoreArrayWrite(v, &a));
2552: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2553: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2554: PetscCall(VecDestroy(&diagV));
2555: PetscCall(VecDestroy(&offdiagV));
2556: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2557: PetscFunctionReturn(PETSC_SUCCESS);
2558: }
2560: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat, Mat *newmat)
2561: {
2562: Mat *dummy;
2564: PetscFunctionBegin;
2565: PetscCall(MatCreateSubMatrix_MPIAIJ_All(mat, MAT_DO_NOT_GET_VALUES, MAT_INITIAL_MATRIX, &dummy));
2566: *newmat = *dummy;
2567: PetscCall(PetscFree(dummy));
2568: PetscFunctionReturn(PETSC_SUCCESS);
2569: }
2571: static PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A, const PetscScalar **values)
2572: {
2573: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2575: PetscFunctionBegin;
2576: PetscCall(MatInvertBlockDiagonal(a->A, values));
2577: A->factorerrortype = a->A->factorerrortype;
2578: PetscFunctionReturn(PETSC_SUCCESS);
2579: }
2581: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x, PetscRandom rctx)
2582: {
2583: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)x->data;
2585: PetscFunctionBegin;
2586: PetscCheck(x->assembled || x->preallocated, PetscObjectComm((PetscObject)x), PETSC_ERR_ARG_WRONGSTATE, "MatSetRandom on an unassembled and unpreallocated MATMPIAIJ is not allowed");
2587: PetscCall(MatSetRandom(aij->A, rctx));
2588: if (x->assembled) {
2589: PetscCall(MatSetRandom(aij->B, rctx));
2590: } else {
2591: PetscCall(MatSetRandomSkipColumnRange_SeqAIJ_Private(aij->B, x->cmap->rstart, x->cmap->rend, rctx));
2592: }
2593: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
2594: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
2595: PetscFunctionReturn(PETSC_SUCCESS);
2596: }
2598: static PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ(Mat A, PetscBool sc)
2599: {
2600: PetscFunctionBegin;
2601: if (sc) A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ_Scalable;
2602: else A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ;
2603: PetscFunctionReturn(PETSC_SUCCESS);
2604: }
2606: /*@
2607: MatMPIAIJGetNumberNonzeros - gets the number of nonzeros in the matrix on this MPI rank
2609: Not Collective
2611: Input Parameter:
2612: . A - the matrix
2614: Output Parameter:
2615: . nz - the number of nonzeros
2617: Level: advanced
2619: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2620: @*/
2621: PetscErrorCode MatMPIAIJGetNumberNonzeros(Mat A, PetscCount *nz)
2622: {
2623: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)A->data;
2624: Mat_SeqAIJ *aaij = (Mat_SeqAIJ *)maij->A->data, *baij = (Mat_SeqAIJ *)maij->B->data;
2625: PetscBool isaij;
2627: PetscFunctionBegin;
2628: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATMPIAIJ, &isaij));
2629: PetscCheck(isaij, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for type %s", ((PetscObject)A)->type_name);
2630: *nz = aaij->i[A->rmap->n] + baij->i[A->rmap->n];
2631: PetscFunctionReturn(PETSC_SUCCESS);
2632: }
2634: /*@
2635: MatMPIAIJSetUseScalableIncreaseOverlap - Determine if the matrix uses a scalable algorithm to compute the overlap
2637: Collective
2639: Input Parameters:
2640: + A - the matrix
2641: - sc - `PETSC_TRUE` indicates use the scalable algorithm (default is not to use the scalable algorithm)
2643: Level: advanced
2645: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2646: @*/
2647: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap(Mat A, PetscBool sc)
2648: {
2649: PetscFunctionBegin;
2650: PetscTryMethod(A, "MatMPIAIJSetUseScalableIncreaseOverlap_C", (Mat, PetscBool), (A, sc));
2651: PetscFunctionReturn(PETSC_SUCCESS);
2652: }
2654: PetscErrorCode MatSetFromOptions_MPIAIJ(Mat A, PetscOptionItems *PetscOptionsObject)
2655: {
2656: PetscBool sc = PETSC_FALSE, flg;
2658: PetscFunctionBegin;
2659: PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJ options");
2660: if (A->ops->increaseoverlap == MatIncreaseOverlap_MPIAIJ_Scalable) sc = PETSC_TRUE;
2661: PetscCall(PetscOptionsBool("-mat_increase_overlap_scalable", "Use a scalable algorithm to compute the overlap", "MatIncreaseOverlap", sc, &sc, &flg));
2662: if (flg) PetscCall(MatMPIAIJSetUseScalableIncreaseOverlap(A, sc));
2663: PetscOptionsHeadEnd();
2664: PetscFunctionReturn(PETSC_SUCCESS);
2665: }
2667: static PetscErrorCode MatShift_MPIAIJ(Mat Y, PetscScalar a)
2668: {
2669: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)Y->data;
2670: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)maij->A->data;
2672: PetscFunctionBegin;
2673: if (!Y->preallocated) {
2674: PetscCall(MatMPIAIJSetPreallocation(Y, 1, NULL, 0, NULL));
2675: } else if (!aij->nz) { /* It does not matter if diagonals of Y only partially lie in maij->A. We just need an estimated preallocation. */
2676: PetscInt nonew = aij->nonew;
2677: PetscCall(MatSeqAIJSetPreallocation(maij->A, 1, NULL));
2678: aij->nonew = nonew;
2679: }
2680: PetscCall(MatShift_Basic(Y, a));
2681: PetscFunctionReturn(PETSC_SUCCESS);
2682: }
2684: static PetscErrorCode MatMissingDiagonal_MPIAIJ(Mat A, PetscBool *missing, PetscInt *d)
2685: {
2686: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2688: PetscFunctionBegin;
2689: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2690: PetscCall(MatMissingDiagonal(a->A, missing, d));
2691: if (d) {
2692: PetscInt rstart;
2693: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2694: *d += rstart;
2695: }
2696: PetscFunctionReturn(PETSC_SUCCESS);
2697: }
2699: static PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2700: {
2701: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2703: PetscFunctionBegin;
2704: PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2705: PetscFunctionReturn(PETSC_SUCCESS);
2706: }
2708: static PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A, PetscBool keep)
2709: {
2710: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2712: PetscFunctionBegin;
2713: PetscCall(MatEliminateZeros_SeqAIJ(a->A, keep)); // possibly keep zero diagonal coefficients
2714: PetscCall(MatEliminateZeros_SeqAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2715: PetscFunctionReturn(PETSC_SUCCESS);
2716: }
2718: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2719: MatGetRow_MPIAIJ,
2720: MatRestoreRow_MPIAIJ,
2721: MatMult_MPIAIJ,
2722: /* 4*/ MatMultAdd_MPIAIJ,
2723: MatMultTranspose_MPIAIJ,
2724: MatMultTransposeAdd_MPIAIJ,
2725: NULL,
2726: NULL,
2727: NULL,
2728: /*10*/ NULL,
2729: NULL,
2730: NULL,
2731: MatSOR_MPIAIJ,
2732: MatTranspose_MPIAIJ,
2733: /*15*/ MatGetInfo_MPIAIJ,
2734: MatEqual_MPIAIJ,
2735: MatGetDiagonal_MPIAIJ,
2736: MatDiagonalScale_MPIAIJ,
2737: MatNorm_MPIAIJ,
2738: /*20*/ MatAssemblyBegin_MPIAIJ,
2739: MatAssemblyEnd_MPIAIJ,
2740: MatSetOption_MPIAIJ,
2741: MatZeroEntries_MPIAIJ,
2742: /*24*/ MatZeroRows_MPIAIJ,
2743: NULL,
2744: NULL,
2745: NULL,
2746: NULL,
2747: /*29*/ MatSetUp_MPI_Hash,
2748: NULL,
2749: NULL,
2750: MatGetDiagonalBlock_MPIAIJ,
2751: NULL,
2752: /*34*/ MatDuplicate_MPIAIJ,
2753: NULL,
2754: NULL,
2755: NULL,
2756: NULL,
2757: /*39*/ MatAXPY_MPIAIJ,
2758: MatCreateSubMatrices_MPIAIJ,
2759: MatIncreaseOverlap_MPIAIJ,
2760: MatGetValues_MPIAIJ,
2761: MatCopy_MPIAIJ,
2762: /*44*/ MatGetRowMax_MPIAIJ,
2763: MatScale_MPIAIJ,
2764: MatShift_MPIAIJ,
2765: MatDiagonalSet_MPIAIJ,
2766: MatZeroRowsColumns_MPIAIJ,
2767: /*49*/ MatSetRandom_MPIAIJ,
2768: MatGetRowIJ_MPIAIJ,
2769: MatRestoreRowIJ_MPIAIJ,
2770: NULL,
2771: NULL,
2772: /*54*/ MatFDColoringCreate_MPIXAIJ,
2773: NULL,
2774: MatSetUnfactored_MPIAIJ,
2775: MatPermute_MPIAIJ,
2776: NULL,
2777: /*59*/ MatCreateSubMatrix_MPIAIJ,
2778: MatDestroy_MPIAIJ,
2779: MatView_MPIAIJ,
2780: NULL,
2781: NULL,
2782: /*64*/ NULL,
2783: MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2784: NULL,
2785: NULL,
2786: NULL,
2787: /*69*/ MatGetRowMaxAbs_MPIAIJ,
2788: MatGetRowMinAbs_MPIAIJ,
2789: NULL,
2790: NULL,
2791: NULL,
2792: NULL,
2793: /*75*/ MatFDColoringApply_AIJ,
2794: MatSetFromOptions_MPIAIJ,
2795: NULL,
2796: NULL,
2797: MatFindZeroDiagonals_MPIAIJ,
2798: /*80*/ NULL,
2799: NULL,
2800: NULL,
2801: /*83*/ MatLoad_MPIAIJ,
2802: NULL,
2803: NULL,
2804: NULL,
2805: NULL,
2806: NULL,
2807: /*89*/ NULL,
2808: NULL,
2809: MatMatMultNumeric_MPIAIJ_MPIAIJ,
2810: NULL,
2811: NULL,
2812: /*94*/ MatPtAPNumeric_MPIAIJ_MPIAIJ,
2813: NULL,
2814: NULL,
2815: NULL,
2816: MatBindToCPU_MPIAIJ,
2817: /*99*/ MatProductSetFromOptions_MPIAIJ,
2818: NULL,
2819: NULL,
2820: MatConjugate_MPIAIJ,
2821: NULL,
2822: /*104*/ MatSetValuesRow_MPIAIJ,
2823: MatRealPart_MPIAIJ,
2824: MatImaginaryPart_MPIAIJ,
2825: NULL,
2826: NULL,
2827: /*109*/ NULL,
2828: NULL,
2829: MatGetRowMin_MPIAIJ,
2830: NULL,
2831: MatMissingDiagonal_MPIAIJ,
2832: /*114*/ MatGetSeqNonzeroStructure_MPIAIJ,
2833: NULL,
2834: MatGetGhosts_MPIAIJ,
2835: NULL,
2836: NULL,
2837: /*119*/ MatMultDiagonalBlock_MPIAIJ,
2838: NULL,
2839: NULL,
2840: NULL,
2841: MatGetMultiProcBlock_MPIAIJ,
2842: /*124*/ MatFindNonzeroRows_MPIAIJ,
2843: MatGetColumnReductions_MPIAIJ,
2844: MatInvertBlockDiagonal_MPIAIJ,
2845: MatInvertVariableBlockDiagonal_MPIAIJ,
2846: MatCreateSubMatricesMPI_MPIAIJ,
2847: /*129*/ NULL,
2848: NULL,
2849: NULL,
2850: MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2851: NULL,
2852: /*134*/ NULL,
2853: NULL,
2854: NULL,
2855: NULL,
2856: NULL,
2857: /*139*/ MatSetBlockSizes_MPIAIJ,
2858: NULL,
2859: NULL,
2860: MatFDColoringSetUp_MPIXAIJ,
2861: MatFindOffBlockDiagonalEntries_MPIAIJ,
2862: MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2863: /*145*/ NULL,
2864: NULL,
2865: NULL,
2866: MatCreateGraph_Simple_AIJ,
2867: NULL,
2868: /*150*/ NULL,
2869: MatEliminateZeros_MPIAIJ,
2870: MatGetRowSumAbs_MPIAIJ,
2871: NULL,
2872: NULL,
2873: NULL};
2875: static PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2876: {
2877: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2879: PetscFunctionBegin;
2880: PetscCall(MatStoreValues(aij->A));
2881: PetscCall(MatStoreValues(aij->B));
2882: PetscFunctionReturn(PETSC_SUCCESS);
2883: }
2885: static PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2886: {
2887: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2889: PetscFunctionBegin;
2890: PetscCall(MatRetrieveValues(aij->A));
2891: PetscCall(MatRetrieveValues(aij->B));
2892: PetscFunctionReturn(PETSC_SUCCESS);
2893: }
2895: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2896: {
2897: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2898: PetscMPIInt size;
2900: PetscFunctionBegin;
2901: if (B->hash_active) {
2902: B->ops[0] = b->cops;
2903: B->hash_active = PETSC_FALSE;
2904: }
2905: PetscCall(PetscLayoutSetUp(B->rmap));
2906: PetscCall(PetscLayoutSetUp(B->cmap));
2908: #if defined(PETSC_USE_CTABLE)
2909: PetscCall(PetscHMapIDestroy(&b->colmap));
2910: #else
2911: PetscCall(PetscFree(b->colmap));
2912: #endif
2913: PetscCall(PetscFree(b->garray));
2914: PetscCall(VecDestroy(&b->lvec));
2915: PetscCall(VecScatterDestroy(&b->Mvctx));
2917: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2919: MatSeqXAIJGetOptions_Private(b->B);
2920: PetscCall(MatDestroy(&b->B));
2921: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2922: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2923: PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2924: PetscCall(MatSetType(b->B, MATSEQAIJ));
2925: MatSeqXAIJRestoreOptions_Private(b->B);
2927: MatSeqXAIJGetOptions_Private(b->A);
2928: PetscCall(MatDestroy(&b->A));
2929: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2930: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2931: PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2932: PetscCall(MatSetType(b->A, MATSEQAIJ));
2933: MatSeqXAIJRestoreOptions_Private(b->A);
2935: PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2936: PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2937: B->preallocated = PETSC_TRUE;
2938: B->was_assembled = PETSC_FALSE;
2939: B->assembled = PETSC_FALSE;
2940: PetscFunctionReturn(PETSC_SUCCESS);
2941: }
2943: static PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2944: {
2945: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2946: /* Save the nonzero states of the component matrices because those are what are used to determine
2947: the nonzero state of mat */
2948: PetscObjectState diagstate = b->A->nonzerostate, offdiagstate = b->B->nonzerostate;
2950: PetscFunctionBegin;
2952: PetscCall(PetscLayoutSetUp(B->rmap));
2953: PetscCall(PetscLayoutSetUp(B->cmap));
2954: if (B->assembled || B->was_assembled) PetscCall(MatDisAssemble_MPIAIJ(B, PETSC_TRUE));
2955: else {
2956: #if defined(PETSC_USE_CTABLE)
2957: PetscCall(PetscHMapIDestroy(&b->colmap));
2958: #else
2959: PetscCall(PetscFree(b->colmap));
2960: #endif
2961: PetscCall(PetscFree(b->garray));
2962: PetscCall(VecDestroy(&b->lvec));
2963: }
2964: PetscCall(VecScatterDestroy(&b->Mvctx));
2966: PetscCall(MatResetPreallocation(b->A));
2967: PetscCall(MatResetPreallocation(b->B));
2968: B->preallocated = PETSC_TRUE;
2969: B->was_assembled = PETSC_FALSE;
2970: B->assembled = PETSC_FALSE;
2971: b->A->nonzerostate = ++diagstate, b->B->nonzerostate = ++offdiagstate;
2972: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
2973: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2974: PetscFunctionReturn(PETSC_SUCCESS);
2975: }
2977: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2978: {
2979: Mat mat;
2980: Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;
2982: PetscFunctionBegin;
2983: *newmat = NULL;
2984: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2985: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2986: PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2987: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2988: a = (Mat_MPIAIJ *)mat->data;
2990: mat->factortype = matin->factortype;
2991: mat->assembled = matin->assembled;
2992: mat->insertmode = NOT_SET_VALUES;
2994: a->size = oldmat->size;
2995: a->rank = oldmat->rank;
2996: a->donotstash = oldmat->donotstash;
2997: a->roworiented = oldmat->roworiented;
2998: a->rowindices = NULL;
2999: a->rowvalues = NULL;
3000: a->getrowactive = PETSC_FALSE;
3002: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
3003: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
3004: if (matin->hash_active) {
3005: PetscCall(MatSetUp(mat));
3006: } else {
3007: mat->preallocated = matin->preallocated;
3008: if (oldmat->colmap) {
3009: #if defined(PETSC_USE_CTABLE)
3010: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3011: #else
3012: PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
3013: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
3014: #endif
3015: } else a->colmap = NULL;
3016: if (oldmat->garray) {
3017: PetscInt len;
3018: len = oldmat->B->cmap->n;
3019: PetscCall(PetscMalloc1(len + 1, &a->garray));
3020: if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3021: } else a->garray = NULL;
3023: /* It may happen MatDuplicate is called with a non-assembled matrix
3024: In fact, MatDuplicate only requires the matrix to be preallocated
3025: This may happen inside a DMCreateMatrix_Shell */
3026: if (oldmat->lvec) PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3027: if (oldmat->Mvctx) {
3028: a->Mvctx = oldmat->Mvctx;
3029: PetscCall(PetscObjectReference((PetscObject)oldmat->Mvctx));
3030: }
3031: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3032: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3033: }
3034: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3035: *newmat = mat;
3036: PetscFunctionReturn(PETSC_SUCCESS);
3037: }
3039: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3040: {
3041: PetscBool isbinary, ishdf5;
3043: PetscFunctionBegin;
3046: /* force binary viewer to load .info file if it has not yet done so */
3047: PetscCall(PetscViewerSetUp(viewer));
3048: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3049: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3050: if (isbinary) {
3051: PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3052: } else if (ishdf5) {
3053: #if defined(PETSC_HAVE_HDF5)
3054: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3055: #else
3056: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3057: #endif
3058: } else {
3059: 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);
3060: }
3061: PetscFunctionReturn(PETSC_SUCCESS);
3062: }
3064: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3065: {
3066: PetscInt header[4], M, N, m, nz, rows, cols, sum, i;
3067: PetscInt *rowidxs, *colidxs;
3068: PetscScalar *matvals;
3070: PetscFunctionBegin;
3071: PetscCall(PetscViewerSetUp(viewer));
3073: /* read in matrix header */
3074: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3075: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3076: M = header[1];
3077: N = header[2];
3078: nz = header[3];
3079: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3080: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3081: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");
3083: /* set block sizes from the viewer's .info file */
3084: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3085: /* set global sizes if not set already */
3086: if (mat->rmap->N < 0) mat->rmap->N = M;
3087: if (mat->cmap->N < 0) mat->cmap->N = N;
3088: PetscCall(PetscLayoutSetUp(mat->rmap));
3089: PetscCall(PetscLayoutSetUp(mat->cmap));
3091: /* check if the matrix sizes are correct */
3092: PetscCall(MatGetSize(mat, &rows, &cols));
3093: 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);
3095: /* read in row lengths and build row indices */
3096: PetscCall(MatGetLocalSize(mat, &m, NULL));
3097: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3098: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3099: rowidxs[0] = 0;
3100: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3101: if (nz != PETSC_INT_MAX) {
3102: PetscCallMPI(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3103: 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);
3104: }
3106: /* read in column indices and matrix values */
3107: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3108: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3109: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3110: /* store matrix indices and values */
3111: PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3112: PetscCall(PetscFree(rowidxs));
3113: PetscCall(PetscFree2(colidxs, matvals));
3114: PetscFunctionReturn(PETSC_SUCCESS);
3115: }
3117: /* Not scalable because of ISAllGather() unless getting all columns. */
3118: static PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3119: {
3120: IS iscol_local;
3121: PetscBool isstride;
3122: PetscMPIInt gisstride = 0;
3124: PetscFunctionBegin;
3125: /* check if we are grabbing all columns*/
3126: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));
3128: if (isstride) {
3129: PetscInt start, len, mstart, mlen;
3130: PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3131: PetscCall(ISGetLocalSize(iscol, &len));
3132: PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3133: if (mstart == start && mlen - mstart == len) gisstride = 1;
3134: }
3136: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3137: if (gisstride) {
3138: PetscInt N;
3139: PetscCall(MatGetSize(mat, NULL, &N));
3140: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3141: PetscCall(ISSetIdentity(iscol_local));
3142: PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3143: } else {
3144: PetscInt cbs;
3145: PetscCall(ISGetBlockSize(iscol, &cbs));
3146: PetscCall(ISAllGather(iscol, &iscol_local));
3147: PetscCall(ISSetBlockSize(iscol_local, cbs));
3148: }
3150: *isseq = iscol_local;
3151: PetscFunctionReturn(PETSC_SUCCESS);
3152: }
3154: /*
3155: Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3156: (see MatCreateSubMatrix_MPIAIJ_nonscalable)
3158: Input Parameters:
3159: + mat - matrix
3160: . isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3161: i.e., mat->rstart <= isrow[i] < mat->rend
3162: - iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3163: i.e., mat->cstart <= iscol[i] < mat->cend
3165: Output Parameters:
3166: + isrow_d - sequential row index set for retrieving mat->A
3167: . iscol_d - sequential column index set for retrieving mat->A
3168: . iscol_o - sequential column index set for retrieving mat->B
3169: - garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3170: */
3171: static PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, const PetscInt *garray[])
3172: {
3173: Vec x, cmap;
3174: const PetscInt *is_idx;
3175: PetscScalar *xarray, *cmaparray;
3176: PetscInt ncols, isstart, *idx, m, rstart, *cmap1, count;
3177: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3178: Mat B = a->B;
3179: Vec lvec = a->lvec, lcmap;
3180: PetscInt i, cstart, cend, Bn = B->cmap->N;
3181: MPI_Comm comm;
3182: VecScatter Mvctx = a->Mvctx;
3184: PetscFunctionBegin;
3185: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3186: PetscCall(ISGetLocalSize(iscol, &ncols));
3188: /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3189: PetscCall(MatCreateVecs(mat, &x, NULL));
3190: PetscCall(VecSet(x, -1.0));
3191: PetscCall(VecDuplicate(x, &cmap));
3192: PetscCall(VecSet(cmap, -1.0));
3194: /* Get start indices */
3195: PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3196: isstart -= ncols;
3197: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
3199: PetscCall(ISGetIndices(iscol, &is_idx));
3200: PetscCall(VecGetArray(x, &xarray));
3201: PetscCall(VecGetArray(cmap, &cmaparray));
3202: PetscCall(PetscMalloc1(ncols, &idx));
3203: for (i = 0; i < ncols; i++) {
3204: xarray[is_idx[i] - cstart] = (PetscScalar)is_idx[i];
3205: cmaparray[is_idx[i] - cstart] = i + isstart; /* global index of iscol[i] */
3206: idx[i] = is_idx[i] - cstart; /* local index of iscol[i] */
3207: }
3208: PetscCall(VecRestoreArray(x, &xarray));
3209: PetscCall(VecRestoreArray(cmap, &cmaparray));
3210: PetscCall(ISRestoreIndices(iscol, &is_idx));
3212: /* Get iscol_d */
3213: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3214: PetscCall(ISGetBlockSize(iscol, &i));
3215: PetscCall(ISSetBlockSize(*iscol_d, i));
3217: /* Get isrow_d */
3218: PetscCall(ISGetLocalSize(isrow, &m));
3219: rstart = mat->rmap->rstart;
3220: PetscCall(PetscMalloc1(m, &idx));
3221: PetscCall(ISGetIndices(isrow, &is_idx));
3222: for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3223: PetscCall(ISRestoreIndices(isrow, &is_idx));
3225: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3226: PetscCall(ISGetBlockSize(isrow, &i));
3227: PetscCall(ISSetBlockSize(*isrow_d, i));
3229: /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3230: PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3231: PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3233: PetscCall(VecDuplicate(lvec, &lcmap));
3235: PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3236: PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3238: /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3239: /* off-process column indices */
3240: count = 0;
3241: PetscCall(PetscMalloc1(Bn, &idx));
3242: PetscCall(PetscMalloc1(Bn, &cmap1));
3244: PetscCall(VecGetArray(lvec, &xarray));
3245: PetscCall(VecGetArray(lcmap, &cmaparray));
3246: for (i = 0; i < Bn; i++) {
3247: if (PetscRealPart(xarray[i]) > -1.0) {
3248: idx[count] = i; /* local column index in off-diagonal part B */
3249: cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3250: count++;
3251: }
3252: }
3253: PetscCall(VecRestoreArray(lvec, &xarray));
3254: PetscCall(VecRestoreArray(lcmap, &cmaparray));
3256: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3257: /* cannot ensure iscol_o has same blocksize as iscol! */
3259: PetscCall(PetscFree(idx));
3260: *garray = cmap1;
3262: PetscCall(VecDestroy(&x));
3263: PetscCall(VecDestroy(&cmap));
3264: PetscCall(VecDestroy(&lcmap));
3265: PetscFunctionReturn(PETSC_SUCCESS);
3266: }
3268: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3269: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3270: {
3271: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3272: Mat M = NULL;
3273: MPI_Comm comm;
3274: IS iscol_d, isrow_d, iscol_o;
3275: Mat Asub = NULL, Bsub = NULL;
3276: PetscInt n;
3278: PetscFunctionBegin;
3279: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3281: if (call == MAT_REUSE_MATRIX) {
3282: /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3283: PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3284: PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");
3286: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3287: PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");
3289: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3290: PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");
3292: /* Update diagonal and off-diagonal portions of submat */
3293: asub = (Mat_MPIAIJ *)(*submat)->data;
3294: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3295: PetscCall(ISGetLocalSize(iscol_o, &n));
3296: if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3297: PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3298: PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));
3300: } else { /* call == MAT_INITIAL_MATRIX) */
3301: const PetscInt *garray;
3302: PetscInt BsubN;
3304: /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3305: PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));
3307: /* Create local submatrices Asub and Bsub */
3308: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3309: PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));
3311: /* Create submatrix M */
3312: PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, Asub, Bsub, garray, &M));
3314: /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3315: asub = (Mat_MPIAIJ *)M->data;
3317: PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3318: n = asub->B->cmap->N;
3319: if (BsubN > n) {
3320: /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3321: const PetscInt *idx;
3322: PetscInt i, j, *idx_new, *subgarray = asub->garray;
3323: PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));
3325: PetscCall(PetscMalloc1(n, &idx_new));
3326: j = 0;
3327: PetscCall(ISGetIndices(iscol_o, &idx));
3328: for (i = 0; i < n; i++) {
3329: if (j >= BsubN) break;
3330: while (subgarray[i] > garray[j]) j++;
3332: if (subgarray[i] == garray[j]) {
3333: idx_new[i] = idx[j++];
3334: } 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]);
3335: }
3336: PetscCall(ISRestoreIndices(iscol_o, &idx));
3338: PetscCall(ISDestroy(&iscol_o));
3339: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));
3341: } else if (BsubN < n) {
3342: 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);
3343: }
3345: PetscCall(PetscFree(garray));
3346: *submat = M;
3348: /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3349: PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3350: PetscCall(ISDestroy(&isrow_d));
3352: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3353: PetscCall(ISDestroy(&iscol_d));
3355: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3356: PetscCall(ISDestroy(&iscol_o));
3357: }
3358: PetscFunctionReturn(PETSC_SUCCESS);
3359: }
3361: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3362: {
3363: IS iscol_local = NULL, isrow_d;
3364: PetscInt csize;
3365: PetscInt n, i, j, start, end;
3366: PetscBool sameRowDist = PETSC_FALSE, sameDist[2], tsameDist[2];
3367: MPI_Comm comm;
3369: PetscFunctionBegin;
3370: /* If isrow has same processor distribution as mat,
3371: call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3372: if (call == MAT_REUSE_MATRIX) {
3373: PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3374: if (isrow_d) {
3375: sameRowDist = PETSC_TRUE;
3376: tsameDist[1] = PETSC_TRUE; /* sameColDist */
3377: } else {
3378: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3379: if (iscol_local) {
3380: sameRowDist = PETSC_TRUE;
3381: tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3382: }
3383: }
3384: } else {
3385: /* Check if isrow has same processor distribution as mat */
3386: sameDist[0] = PETSC_FALSE;
3387: PetscCall(ISGetLocalSize(isrow, &n));
3388: if (!n) {
3389: sameDist[0] = PETSC_TRUE;
3390: } else {
3391: PetscCall(ISGetMinMax(isrow, &i, &j));
3392: PetscCall(MatGetOwnershipRange(mat, &start, &end));
3393: if (i >= start && j < end) sameDist[0] = PETSC_TRUE;
3394: }
3396: /* Check if iscol has same processor distribution as mat */
3397: sameDist[1] = PETSC_FALSE;
3398: PetscCall(ISGetLocalSize(iscol, &n));
3399: if (!n) {
3400: sameDist[1] = PETSC_TRUE;
3401: } else {
3402: PetscCall(ISGetMinMax(iscol, &i, &j));
3403: PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3404: if (i >= start && j < end) sameDist[1] = PETSC_TRUE;
3405: }
3407: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3408: PetscCallMPI(MPIU_Allreduce(&sameDist, &tsameDist, 2, MPIU_BOOL, MPI_LAND, comm));
3409: sameRowDist = tsameDist[0];
3410: }
3412: if (sameRowDist) {
3413: if (tsameDist[1]) { /* sameRowDist & sameColDist */
3414: /* isrow and iscol have same processor distribution as mat */
3415: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3416: PetscFunctionReturn(PETSC_SUCCESS);
3417: } else { /* sameRowDist */
3418: /* isrow has same processor distribution as mat */
3419: if (call == MAT_INITIAL_MATRIX) {
3420: PetscBool sorted;
3421: PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3422: PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3423: PetscCall(ISGetSize(iscol, &i));
3424: PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);
3426: PetscCall(ISSorted(iscol_local, &sorted));
3427: if (sorted) {
3428: /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3429: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3430: PetscFunctionReturn(PETSC_SUCCESS);
3431: }
3432: } else { /* call == MAT_REUSE_MATRIX */
3433: IS iscol_sub;
3434: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3435: if (iscol_sub) {
3436: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3437: PetscFunctionReturn(PETSC_SUCCESS);
3438: }
3439: }
3440: }
3441: }
3443: /* General case: iscol -> iscol_local which has global size of iscol */
3444: if (call == MAT_REUSE_MATRIX) {
3445: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3446: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3447: } else {
3448: if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3449: }
3451: PetscCall(ISGetLocalSize(iscol, &csize));
3452: PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));
3454: if (call == MAT_INITIAL_MATRIX) {
3455: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3456: PetscCall(ISDestroy(&iscol_local));
3457: }
3458: PetscFunctionReturn(PETSC_SUCCESS);
3459: }
3461: /*@C
3462: MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3463: and "off-diagonal" part of the matrix in CSR format.
3465: Collective
3467: Input Parameters:
3468: + comm - MPI communicator
3469: . A - "diagonal" portion of matrix
3470: . B - "off-diagonal" portion of matrix, may have empty columns, will be destroyed by this routine
3471: - garray - global index of `B` columns
3473: Output Parameter:
3474: . mat - the matrix, with input `A` as its local diagonal matrix
3476: Level: advanced
3478: Notes:
3479: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.
3481: `A` becomes part of output mat, `B` is destroyed by this routine. The user cannot use `A` and `B` anymore.
3483: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3484: @*/
3485: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, Mat A, Mat B, const PetscInt garray[], Mat *mat)
3486: {
3487: Mat_MPIAIJ *maij;
3488: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data, *bnew;
3489: PetscInt *oi = b->i, *oj = b->j, i, nz, col;
3490: const PetscScalar *oa;
3491: Mat Bnew;
3492: PetscInt m, n, N;
3493: MatType mpi_mat_type;
3495: PetscFunctionBegin;
3496: PetscCall(MatCreate(comm, mat));
3497: PetscCall(MatGetSize(A, &m, &n));
3498: PetscCheck(m == B->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Am %" PetscInt_FMT " != Bm %" PetscInt_FMT, m, B->rmap->N);
3499: 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);
3500: /* remove check below; When B is created using iscol_o from ISGetSeqIS_SameColDist_Private(), its bs may not be same as A */
3501: /* 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); */
3503: /* Get global columns of mat */
3504: PetscCallMPI(MPIU_Allreduce(&n, &N, 1, MPIU_INT, MPI_SUM, comm));
3506: PetscCall(MatSetSizes(*mat, m, n, PETSC_DECIDE, N));
3507: /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3508: PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3509: PetscCall(MatSetType(*mat, mpi_mat_type));
3511: if (A->rmap->bs > 1 || A->cmap->bs > 1) PetscCall(MatSetBlockSizes(*mat, A->rmap->bs, A->cmap->bs));
3512: maij = (Mat_MPIAIJ *)(*mat)->data;
3514: (*mat)->preallocated = PETSC_TRUE;
3516: PetscCall(PetscLayoutSetUp((*mat)->rmap));
3517: PetscCall(PetscLayoutSetUp((*mat)->cmap));
3519: /* Set A as diagonal portion of *mat */
3520: maij->A = A;
3522: nz = oi[m];
3523: for (i = 0; i < nz; i++) {
3524: col = oj[i];
3525: oj[i] = garray[col];
3526: }
3528: /* Set Bnew as off-diagonal portion of *mat */
3529: PetscCall(MatSeqAIJGetArrayRead(B, &oa));
3530: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, N, oi, oj, (PetscScalar *)oa, &Bnew));
3531: PetscCall(MatSeqAIJRestoreArrayRead(B, &oa));
3532: bnew = (Mat_SeqAIJ *)Bnew->data;
3533: bnew->maxnz = b->maxnz; /* allocated nonzeros of B */
3534: maij->B = Bnew;
3536: 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);
3538: b->free_a = PETSC_FALSE;
3539: b->free_ij = PETSC_FALSE;
3540: PetscCall(MatDestroy(&B));
3542: bnew->free_a = PETSC_TRUE;
3543: bnew->free_ij = PETSC_TRUE;
3545: /* condense columns of maij->B */
3546: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3547: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
3548: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
3549: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
3550: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3551: PetscFunctionReturn(PETSC_SUCCESS);
3552: }
3554: extern PetscErrorCode MatCreateSubMatrices_MPIAIJ_SingleIS_Local(Mat, PetscInt, const IS[], const IS[], MatReuse, PetscBool, Mat *);
3556: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowDist(Mat mat, IS isrow, IS iscol, IS iscol_local, MatReuse call, Mat *newmat)
3557: {
3558: PetscInt i, m, n, rstart, row, rend, nz, j, bs, cbs;
3559: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3560: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3561: Mat M, Msub, B = a->B;
3562: MatScalar *aa;
3563: Mat_SeqAIJ *aij;
3564: PetscInt *garray = a->garray, *colsub, Ncols;
3565: PetscInt count, Bn = B->cmap->N, cstart = mat->cmap->rstart, cend = mat->cmap->rend;
3566: IS iscol_sub, iscmap;
3567: const PetscInt *is_idx, *cmap;
3568: PetscBool allcolumns = PETSC_FALSE;
3569: MPI_Comm comm;
3571: PetscFunctionBegin;
3572: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3573: if (call == MAT_REUSE_MATRIX) {
3574: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3575: PetscCheck(iscol_sub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "SubIScol passed in was not used before, cannot reuse");
3576: PetscCall(ISGetLocalSize(iscol_sub, &count));
3578: PetscCall(PetscObjectQuery((PetscObject)*newmat, "Subcmap", (PetscObject *)&iscmap));
3579: PetscCheck(iscmap, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Subcmap passed in was not used before, cannot reuse");
3581: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Msub));
3582: PetscCheck(Msub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3584: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_REUSE_MATRIX, PETSC_FALSE, &Msub));
3586: } else { /* call == MAT_INITIAL_MATRIX) */
3587: PetscBool flg;
3589: PetscCall(ISGetLocalSize(iscol, &n));
3590: PetscCall(ISGetSize(iscol, &Ncols));
3592: /* (1) iscol -> nonscalable iscol_local */
3593: /* Check for special case: each processor gets entire matrix columns */
3594: PetscCall(ISIdentity(iscol_local, &flg));
3595: if (flg && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3596: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3597: if (allcolumns) {
3598: iscol_sub = iscol_local;
3599: PetscCall(PetscObjectReference((PetscObject)iscol_local));
3600: PetscCall(ISCreateStride(PETSC_COMM_SELF, n, 0, 1, &iscmap));
3602: } else {
3603: /* (2) iscol_local -> iscol_sub and iscmap. Implementation below requires iscol_local be sorted, it can have duplicate indices */
3604: PetscInt *idx, *cmap1, k;
3605: PetscCall(PetscMalloc1(Ncols, &idx));
3606: PetscCall(PetscMalloc1(Ncols, &cmap1));
3607: PetscCall(ISGetIndices(iscol_local, &is_idx));
3608: count = 0;
3609: k = 0;
3610: for (i = 0; i < Ncols; i++) {
3611: j = is_idx[i];
3612: if (j >= cstart && j < cend) {
3613: /* diagonal part of mat */
3614: idx[count] = j;
3615: cmap1[count++] = i; /* column index in submat */
3616: } else if (Bn) {
3617: /* off-diagonal part of mat */
3618: if (j == garray[k]) {
3619: idx[count] = j;
3620: cmap1[count++] = i; /* column index in submat */
3621: } else if (j > garray[k]) {
3622: while (j > garray[k] && k < Bn - 1) k++;
3623: if (j == garray[k]) {
3624: idx[count] = j;
3625: cmap1[count++] = i; /* column index in submat */
3626: }
3627: }
3628: }
3629: }
3630: PetscCall(ISRestoreIndices(iscol_local, &is_idx));
3632: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_OWN_POINTER, &iscol_sub));
3633: PetscCall(ISGetBlockSize(iscol, &cbs));
3634: PetscCall(ISSetBlockSize(iscol_sub, cbs));
3636: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)iscol_local), count, cmap1, PETSC_OWN_POINTER, &iscmap));
3637: }
3639: /* (3) Create sequential Msub */
3640: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_INITIAL_MATRIX, allcolumns, &Msub));
3641: }
3643: PetscCall(ISGetLocalSize(iscol_sub, &count));
3644: aij = (Mat_SeqAIJ *)Msub->data;
3645: ii = aij->i;
3646: PetscCall(ISGetIndices(iscmap, &cmap));
3648: /*
3649: m - number of local rows
3650: Ncols - number of columns (same on all processors)
3651: rstart - first row in new global matrix generated
3652: */
3653: PetscCall(MatGetSize(Msub, &m, NULL));
3655: if (call == MAT_INITIAL_MATRIX) {
3656: /* (4) Create parallel newmat */
3657: PetscMPIInt rank, size;
3658: PetscInt csize;
3660: PetscCallMPI(MPI_Comm_size(comm, &size));
3661: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3663: /*
3664: Determine the number of non-zeros in the diagonal and off-diagonal
3665: portions of the matrix in order to do correct preallocation
3666: */
3668: /* first get start and end of "diagonal" columns */
3669: PetscCall(ISGetLocalSize(iscol, &csize));
3670: if (csize == PETSC_DECIDE) {
3671: PetscCall(ISGetSize(isrow, &mglobal));
3672: if (mglobal == Ncols) { /* square matrix */
3673: nlocal = m;
3674: } else {
3675: nlocal = Ncols / size + ((Ncols % size) > rank);
3676: }
3677: } else {
3678: nlocal = csize;
3679: }
3680: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3681: rstart = rend - nlocal;
3682: 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);
3684: /* next, compute all the lengths */
3685: jj = aij->j;
3686: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3687: olens = dlens + m;
3688: for (i = 0; i < m; i++) {
3689: jend = ii[i + 1] - ii[i];
3690: olen = 0;
3691: dlen = 0;
3692: for (j = 0; j < jend; j++) {
3693: if (cmap[*jj] < rstart || cmap[*jj] >= rend) olen++;
3694: else dlen++;
3695: jj++;
3696: }
3697: olens[i] = olen;
3698: dlens[i] = dlen;
3699: }
3701: PetscCall(ISGetBlockSize(isrow, &bs));
3702: PetscCall(ISGetBlockSize(iscol, &cbs));
3704: PetscCall(MatCreate(comm, &M));
3705: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, Ncols));
3706: PetscCall(MatSetBlockSizes(M, bs, cbs));
3707: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3708: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3709: PetscCall(PetscFree(dlens));
3711: } else { /* call == MAT_REUSE_MATRIX */
3712: M = *newmat;
3713: PetscCall(MatGetLocalSize(M, &i, NULL));
3714: PetscCheck(i == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3715: PetscCall(MatZeroEntries(M));
3716: /*
3717: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3718: rather than the slower MatSetValues().
3719: */
3720: M->was_assembled = PETSC_TRUE;
3721: M->assembled = PETSC_FALSE;
3722: }
3724: /* (5) Set values of Msub to *newmat */
3725: PetscCall(PetscMalloc1(count, &colsub));
3726: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
3728: jj = aij->j;
3729: PetscCall(MatSeqAIJGetArrayRead(Msub, (const PetscScalar **)&aa));
3730: for (i = 0; i < m; i++) {
3731: row = rstart + i;
3732: nz = ii[i + 1] - ii[i];
3733: for (j = 0; j < nz; j++) colsub[j] = cmap[jj[j]];
3734: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, colsub, aa, INSERT_VALUES));
3735: jj += nz;
3736: aa += nz;
3737: }
3738: PetscCall(MatSeqAIJRestoreArrayRead(Msub, (const PetscScalar **)&aa));
3739: PetscCall(ISRestoreIndices(iscmap, &cmap));
3741: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3742: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3744: PetscCall(PetscFree(colsub));
3746: /* save Msub, iscol_sub and iscmap used in processor for next request */
3747: if (call == MAT_INITIAL_MATRIX) {
3748: *newmat = M;
3749: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubMatrix", (PetscObject)Msub));
3750: PetscCall(MatDestroy(&Msub));
3752: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubIScol", (PetscObject)iscol_sub));
3753: PetscCall(ISDestroy(&iscol_sub));
3755: PetscCall(PetscObjectCompose((PetscObject)*newmat, "Subcmap", (PetscObject)iscmap));
3756: PetscCall(ISDestroy(&iscmap));
3758: if (iscol_local) {
3759: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3760: PetscCall(ISDestroy(&iscol_local));
3761: }
3762: }
3763: PetscFunctionReturn(PETSC_SUCCESS);
3764: }
3766: /*
3767: Not great since it makes two copies of the submatrix, first an SeqAIJ
3768: in local and then by concatenating the local matrices the end result.
3769: Writing it directly would be much like MatCreateSubMatrices_MPIAIJ()
3771: This requires a sequential iscol with all indices.
3772: */
3773: PetscErrorCode MatCreateSubMatrix_MPIAIJ_nonscalable(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
3774: {
3775: PetscMPIInt rank, size;
3776: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs, cbs;
3777: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3778: Mat M, Mreuse;
3779: MatScalar *aa, *vwork;
3780: MPI_Comm comm;
3781: Mat_SeqAIJ *aij;
3782: PetscBool colflag, allcolumns = PETSC_FALSE;
3784: PetscFunctionBegin;
3785: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3786: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3787: PetscCallMPI(MPI_Comm_size(comm, &size));
3789: /* Check for special case: each processor gets entire matrix columns */
3790: PetscCall(ISIdentity(iscol, &colflag));
3791: PetscCall(ISGetLocalSize(iscol, &n));
3792: if (colflag && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3793: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3795: if (call == MAT_REUSE_MATRIX) {
3796: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
3797: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3798: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_REUSE_MATRIX, allcolumns, &Mreuse));
3799: } else {
3800: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_INITIAL_MATRIX, allcolumns, &Mreuse));
3801: }
3803: /*
3804: m - number of local rows
3805: n - number of columns (same on all processors)
3806: rstart - first row in new global matrix generated
3807: */
3808: PetscCall(MatGetSize(Mreuse, &m, &n));
3809: PetscCall(MatGetBlockSizes(Mreuse, &bs, &cbs));
3810: if (call == MAT_INITIAL_MATRIX) {
3811: aij = (Mat_SeqAIJ *)Mreuse->data;
3812: ii = aij->i;
3813: jj = aij->j;
3815: /*
3816: Determine the number of non-zeros in the diagonal and off-diagonal
3817: portions of the matrix in order to do correct preallocation
3818: */
3820: /* first get start and end of "diagonal" columns */
3821: if (csize == PETSC_DECIDE) {
3822: PetscCall(ISGetSize(isrow, &mglobal));
3823: if (mglobal == n) { /* square matrix */
3824: nlocal = m;
3825: } else {
3826: nlocal = n / size + ((n % size) > rank);
3827: }
3828: } else {
3829: nlocal = csize;
3830: }
3831: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3832: rstart = rend - nlocal;
3833: 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);
3835: /* next, compute all the lengths */
3836: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3837: olens = dlens + m;
3838: for (i = 0; i < m; i++) {
3839: jend = ii[i + 1] - ii[i];
3840: olen = 0;
3841: dlen = 0;
3842: for (j = 0; j < jend; j++) {
3843: if (*jj < rstart || *jj >= rend) olen++;
3844: else dlen++;
3845: jj++;
3846: }
3847: olens[i] = olen;
3848: dlens[i] = dlen;
3849: }
3850: PetscCall(MatCreate(comm, &M));
3851: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, n));
3852: PetscCall(MatSetBlockSizes(M, bs, cbs));
3853: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3854: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3855: PetscCall(PetscFree(dlens));
3856: } else {
3857: PetscInt ml, nl;
3859: M = *newmat;
3860: PetscCall(MatGetLocalSize(M, &ml, &nl));
3861: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3862: PetscCall(MatZeroEntries(M));
3863: /*
3864: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3865: rather than the slower MatSetValues().
3866: */
3867: M->was_assembled = PETSC_TRUE;
3868: M->assembled = PETSC_FALSE;
3869: }
3870: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
3871: aij = (Mat_SeqAIJ *)Mreuse->data;
3872: ii = aij->i;
3873: jj = aij->j;
3875: /* trigger copy to CPU if needed */
3876: PetscCall(MatSeqAIJGetArrayRead(Mreuse, (const PetscScalar **)&aa));
3877: for (i = 0; i < m; i++) {
3878: row = rstart + i;
3879: nz = ii[i + 1] - ii[i];
3880: cwork = jj;
3881: jj = PetscSafePointerPlusOffset(jj, nz);
3882: vwork = aa;
3883: aa = PetscSafePointerPlusOffset(aa, nz);
3884: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
3885: }
3886: PetscCall(MatSeqAIJRestoreArrayRead(Mreuse, (const PetscScalar **)&aa));
3888: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3889: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3890: *newmat = M;
3892: /* save submatrix used in processor for next request */
3893: if (call == MAT_INITIAL_MATRIX) {
3894: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
3895: PetscCall(MatDestroy(&Mreuse));
3896: }
3897: PetscFunctionReturn(PETSC_SUCCESS);
3898: }
3900: static PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
3901: {
3902: PetscInt m, cstart, cend, j, nnz, i, d, *ld;
3903: PetscInt *d_nnz, *o_nnz, nnz_max = 0, rstart, ii, irstart;
3904: const PetscInt *JJ;
3905: PetscBool nooffprocentries;
3906: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)B->data;
3908: PetscFunctionBegin;
3909: PetscCall(PetscLayoutSetUp(B->rmap));
3910: PetscCall(PetscLayoutSetUp(B->cmap));
3911: m = B->rmap->n;
3912: cstart = B->cmap->rstart;
3913: cend = B->cmap->rend;
3914: rstart = B->rmap->rstart;
3915: irstart = Ii[0];
3917: PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));
3919: if (PetscDefined(USE_DEBUG)) {
3920: for (i = 0; i < m; i++) {
3921: nnz = Ii[i + 1] - Ii[i];
3922: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3923: PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3924: 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]);
3925: 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);
3926: }
3927: }
3929: for (i = 0; i < m; i++) {
3930: nnz = Ii[i + 1] - Ii[i];
3931: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3932: nnz_max = PetscMax(nnz_max, nnz);
3933: d = 0;
3934: for (j = 0; j < nnz; j++) {
3935: if (cstart <= JJ[j] && JJ[j] < cend) d++;
3936: }
3937: d_nnz[i] = d;
3938: o_nnz[i] = nnz - d;
3939: }
3940: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3941: PetscCall(PetscFree2(d_nnz, o_nnz));
3943: for (i = 0; i < m; i++) {
3944: ii = i + rstart;
3945: PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], PetscSafePointerPlusOffset(J, Ii[i] - irstart), PetscSafePointerPlusOffset(v, Ii[i] - irstart), INSERT_VALUES));
3946: }
3947: nooffprocentries = B->nooffprocentries;
3948: B->nooffprocentries = PETSC_TRUE;
3949: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3950: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3951: B->nooffprocentries = nooffprocentries;
3953: /* count number of entries below block diagonal */
3954: PetscCall(PetscFree(Aij->ld));
3955: PetscCall(PetscCalloc1(m, &ld));
3956: Aij->ld = ld;
3957: for (i = 0; i < m; i++) {
3958: nnz = Ii[i + 1] - Ii[i];
3959: j = 0;
3960: while (j < nnz && J[j] < cstart) j++;
3961: ld[i] = j;
3962: if (J) J += nnz;
3963: }
3965: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3966: PetscFunctionReturn(PETSC_SUCCESS);
3967: }
3969: /*@
3970: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
3971: (the default parallel PETSc format).
3973: Collective
3975: Input Parameters:
3976: + B - the matrix
3977: . i - the indices into `j` for the start of each local row (indices start with zero)
3978: . j - the column indices for each local row (indices start with zero)
3979: - v - optional values in the matrix
3981: Level: developer
3983: Notes:
3984: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
3985: thus you CANNOT change the matrix entries by changing the values of `v` after you have
3986: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3988: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
3990: A convenience routine for this functionality is `MatCreateMPIAIJWithArrays()`.
3992: You can update the matrix with new numerical values using `MatUpdateMPIAIJWithArrays()` after this call if the column indices in `j` are sorted.
3994: If you do **not** use `MatUpdateMPIAIJWithArrays()`, the column indices in `j` do not need to be sorted. If you will use
3995: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
3997: The format which is used for the sparse matrix input, is equivalent to a
3998: row-major ordering.. i.e for the following matrix, the input data expected is
3999: as shown
4000: .vb
4001: 1 0 0
4002: 2 0 3 P0
4003: -------
4004: 4 5 6 P1
4006: Process0 [P0] rows_owned=[0,1]
4007: i = {0,1,3} [size = nrow+1 = 2+1]
4008: j = {0,0,2} [size = 3]
4009: v = {1,2,3} [size = 3]
4011: Process1 [P1] rows_owned=[2]
4012: i = {0,3} [size = nrow+1 = 1+1]
4013: j = {0,1,2} [size = 3]
4014: v = {4,5,6} [size = 3]
4015: .ve
4017: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`,
4018: `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`, `MatCreateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4019: @*/
4020: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4021: {
4022: PetscFunctionBegin;
4023: PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4024: PetscFunctionReturn(PETSC_SUCCESS);
4025: }
4027: /*@
4028: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
4029: (the default parallel PETSc format). For good matrix assembly performance
4030: the user should preallocate the matrix storage by setting the parameters
4031: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4033: Collective
4035: Input Parameters:
4036: + B - the matrix
4037: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4038: (same value is used for all local rows)
4039: . d_nnz - array containing the number of nonzeros in the various rows of the
4040: DIAGONAL portion of the local submatrix (possibly different for each row)
4041: or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4042: The size of this array is equal to the number of local rows, i.e 'm'.
4043: For matrices that will be factored, you must leave room for (and set)
4044: the diagonal entry even if it is zero.
4045: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4046: submatrix (same value is used for all local rows).
4047: - o_nnz - array containing the number of nonzeros in the various rows of the
4048: OFF-DIAGONAL portion of the local submatrix (possibly different for
4049: each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4050: structure. The size of this array is equal to the number
4051: of local rows, i.e 'm'.
4053: Example Usage:
4054: Consider the following 8x8 matrix with 34 non-zero values, that is
4055: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4056: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4057: as follows
4059: .vb
4060: 1 2 0 | 0 3 0 | 0 4
4061: Proc0 0 5 6 | 7 0 0 | 8 0
4062: 9 0 10 | 11 0 0 | 12 0
4063: -------------------------------------
4064: 13 0 14 | 15 16 17 | 0 0
4065: Proc1 0 18 0 | 19 20 21 | 0 0
4066: 0 0 0 | 22 23 0 | 24 0
4067: -------------------------------------
4068: Proc2 25 26 27 | 0 0 28 | 29 0
4069: 30 0 0 | 31 32 33 | 0 34
4070: .ve
4072: This can be represented as a collection of submatrices as
4073: .vb
4074: A B C
4075: D E F
4076: G H I
4077: .ve
4079: Where the submatrices A,B,C are owned by proc0, D,E,F are
4080: owned by proc1, G,H,I are owned by proc2.
4082: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4083: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4084: The 'M','N' parameters are 8,8, and have the same values on all procs.
4086: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4087: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4088: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4089: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4090: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4091: matrix, and [DF] as another `MATSEQAIJ` matrix.
4093: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4094: allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4095: storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4096: One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4097: the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4098: In this case, the values of `d_nz`, `o_nz` are
4099: .vb
4100: proc0 dnz = 2, o_nz = 2
4101: proc1 dnz = 3, o_nz = 2
4102: proc2 dnz = 1, o_nz = 4
4103: .ve
4104: We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4105: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4106: for proc3. i.e we are using 12+15+10=37 storage locations to store
4107: 34 values.
4109: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4110: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4111: In the above case the values for `d_nnz`, `o_nnz` are
4112: .vb
4113: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4114: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4115: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4116: .ve
4117: Here the space allocated is sum of all the above values i.e 34, and
4118: hence pre-allocation is perfect.
4120: Level: intermediate
4122: Notes:
4123: If the *_nnz parameter is given then the *_nz parameter is ignored
4125: The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4126: storage. The stored row and column indices begin with zero.
4127: See [Sparse Matrices](sec_matsparse) for details.
4129: The parallel matrix is partitioned such that the first m0 rows belong to
4130: process 0, the next m1 rows belong to process 1, the next m2 rows belong
4131: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
4133: The DIAGONAL portion of the local submatrix of a processor can be defined
4134: as the submatrix which is obtained by extraction the part corresponding to
4135: the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4136: first row that belongs to the processor, r2 is the last row belonging to
4137: the this processor, and c1-c2 is range of indices of the local part of a
4138: vector suitable for applying the matrix to. This is an mxn matrix. In the
4139: common case of a square matrix, the row and column ranges are the same and
4140: the DIAGONAL part is also square. The remaining portion of the local
4141: submatrix (mxN) constitute the OFF-DIAGONAL portion.
4143: If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.
4145: You can call `MatGetInfo()` to get information on how effective the preallocation was;
4146: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4147: You can also run with the option `-info` and look for messages with the string
4148: malloc in them to see if additional memory allocation was needed.
4150: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4151: `MatGetInfo()`, `PetscSplitOwnership()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4152: @*/
4153: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4154: {
4155: PetscFunctionBegin;
4158: PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4159: PetscFunctionReturn(PETSC_SUCCESS);
4160: }
4162: /*@
4163: MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4164: CSR format for the local rows.
4166: Collective
4168: Input Parameters:
4169: + comm - MPI communicator
4170: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4171: . n - This value should be the same as the local size used in creating the
4172: x vector for the matrix-vector product $ y = Ax$. (or `PETSC_DECIDE` to have
4173: calculated if `N` is given) For square matrices n is almost always `m`.
4174: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
4175: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
4176: . i - row indices (of length m+1); that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
4177: . j - global column indices
4178: - a - optional matrix values
4180: Output Parameter:
4181: . mat - the matrix
4183: Level: intermediate
4185: Notes:
4186: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4187: thus you CANNOT change the matrix entries by changing the values of `a[]` after you have
4188: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
4190: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
4192: Once you have created the matrix you can update it with new numerical values using `MatUpdateMPIAIJWithArray()`
4194: If you do **not** use `MatUpdateMPIAIJWithArray()`, the column indices in `j` do not need to be sorted. If you will use
4195: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
4197: The format which is used for the sparse matrix input, is equivalent to a
4198: row-major ordering, i.e., for the following matrix, the input data expected is
4199: as shown
4200: .vb
4201: 1 0 0
4202: 2 0 3 P0
4203: -------
4204: 4 5 6 P1
4206: Process0 [P0] rows_owned=[0,1]
4207: i = {0,1,3} [size = nrow+1 = 2+1]
4208: j = {0,0,2} [size = 3]
4209: v = {1,2,3} [size = 3]
4211: Process1 [P1] rows_owned=[2]
4212: i = {0,3} [size = nrow+1 = 1+1]
4213: j = {0,1,2} [size = 3]
4214: v = {4,5,6} [size = 3]
4215: .ve
4217: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4218: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4219: @*/
4220: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
4221: {
4222: PetscFunctionBegin;
4223: PetscCheck(!i || !i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4224: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4225: PetscCall(MatCreate(comm, mat));
4226: PetscCall(MatSetSizes(*mat, m, n, M, N));
4227: /* PetscCall(MatSetBlockSizes(M,bs,cbs)); */
4228: PetscCall(MatSetType(*mat, MATMPIAIJ));
4229: PetscCall(MatMPIAIJSetPreallocationCSR(*mat, i, j, a));
4230: PetscFunctionReturn(PETSC_SUCCESS);
4231: }
4233: /*@
4234: MatUpdateMPIAIJWithArrays - updates a `MATMPIAIJ` matrix using arrays that contain in standard
4235: CSR format for the local rows. Only the numerical values are updated the other arrays must be identical to what was passed
4236: from `MatCreateMPIAIJWithArrays()`
4238: Deprecated: Use `MatUpdateMPIAIJWithArray()`
4240: Collective
4242: Input Parameters:
4243: + mat - the matrix
4244: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4245: . n - This value should be the same as the local size used in creating the
4246: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4247: calculated if N is given) For square matrices n is almost always m.
4248: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4249: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4250: . Ii - row indices; that is Ii[0] = 0, Ii[row] = Ii[row-1] + number of elements in that row of the matrix
4251: . J - column indices
4252: - v - matrix values
4254: Level: deprecated
4256: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4257: `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4258: @*/
4259: PetscErrorCode MatUpdateMPIAIJWithArrays(Mat mat, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4260: {
4261: PetscInt nnz, i;
4262: PetscBool nooffprocentries;
4263: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4264: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4265: PetscScalar *ad, *ao;
4266: PetscInt ldi, Iii, md;
4267: const PetscInt *Adi = Ad->i;
4268: PetscInt *ld = Aij->ld;
4270: PetscFunctionBegin;
4271: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4272: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4273: PetscCheck(m == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of rows cannot change from call to MatUpdateMPIAIJWithArrays()");
4274: PetscCheck(n == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of columns cannot change from call to MatUpdateMPIAIJWithArrays()");
4276: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4277: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4279: for (i = 0; i < m; i++) {
4280: if (PetscDefined(USE_DEBUG)) {
4281: for (PetscInt j = Ii[i] + 1; j < Ii[i + 1]; ++j) {
4282: PetscCheck(J[j] >= J[j - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is not sorted", j - Ii[i], J[j], i);
4283: PetscCheck(J[j] != J[j - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is identical to previous entry", j - Ii[i], J[j], i);
4284: }
4285: }
4286: nnz = Ii[i + 1] - Ii[i];
4287: Iii = Ii[i];
4288: ldi = ld[i];
4289: md = Adi[i + 1] - Adi[i];
4290: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4291: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4292: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4293: ad += md;
4294: ao += nnz - md;
4295: }
4296: nooffprocentries = mat->nooffprocentries;
4297: mat->nooffprocentries = PETSC_TRUE;
4298: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4299: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4300: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4301: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4302: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4303: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4304: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4305: mat->nooffprocentries = nooffprocentries;
4306: PetscFunctionReturn(PETSC_SUCCESS);
4307: }
4309: /*@
4310: MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values
4312: Collective
4314: Input Parameters:
4315: + mat - the matrix
4316: - v - matrix values, stored by row
4318: Level: intermediate
4320: Notes:
4321: The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`
4323: The column indices in the call to `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()` must have been sorted for this call to work correctly
4325: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4326: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4327: @*/
4328: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4329: {
4330: PetscInt nnz, i, m;
4331: PetscBool nooffprocentries;
4332: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4333: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4334: Mat_SeqAIJ *Ao = (Mat_SeqAIJ *)Aij->B->data;
4335: PetscScalar *ad, *ao;
4336: const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4337: PetscInt ldi, Iii, md;
4338: PetscInt *ld = Aij->ld;
4340: PetscFunctionBegin;
4341: m = mat->rmap->n;
4343: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4344: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4345: Iii = 0;
4346: for (i = 0; i < m; i++) {
4347: nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4348: ldi = ld[i];
4349: md = Adi[i + 1] - Adi[i];
4350: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4351: ad += md;
4352: if (ao) {
4353: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4354: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4355: ao += nnz - md;
4356: }
4357: Iii += nnz;
4358: }
4359: nooffprocentries = mat->nooffprocentries;
4360: mat->nooffprocentries = PETSC_TRUE;
4361: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4362: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4363: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4364: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4365: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4366: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4367: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4368: mat->nooffprocentries = nooffprocentries;
4369: PetscFunctionReturn(PETSC_SUCCESS);
4370: }
4372: /*@
4373: MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4374: (the default parallel PETSc format). For good matrix assembly performance
4375: the user should preallocate the matrix storage by setting the parameters
4376: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4378: Collective
4380: Input Parameters:
4381: + comm - MPI communicator
4382: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4383: This value should be the same as the local size used in creating the
4384: y vector for the matrix-vector product y = Ax.
4385: . n - This value should be the same as the local size used in creating the
4386: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4387: calculated if N is given) For square matrices n is almost always m.
4388: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4389: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4390: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4391: (same value is used for all local rows)
4392: . d_nnz - array containing the number of nonzeros in the various rows of the
4393: DIAGONAL portion of the local submatrix (possibly different for each row)
4394: or `NULL`, if `d_nz` is used to specify the nonzero structure.
4395: The size of this array is equal to the number of local rows, i.e 'm'.
4396: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4397: submatrix (same value is used for all local rows).
4398: - o_nnz - array containing the number of nonzeros in the various rows of the
4399: OFF-DIAGONAL portion of the local submatrix (possibly different for
4400: each row) or `NULL`, if `o_nz` is used to specify the nonzero
4401: structure. The size of this array is equal to the number
4402: of local rows, i.e 'm'.
4404: Output Parameter:
4405: . A - the matrix
4407: Options Database Keys:
4408: + -mat_no_inode - Do not use inodes
4409: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
4410: - -matmult_vecscatter_view <viewer> - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4411: See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the `VecScatter`
4412: to be viewed as a matrix. Entry (i,j) is the size of message (in bytes) rank i sends to rank j in one `MatMult()` call.
4414: Level: intermediate
4416: Notes:
4417: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4418: MatXXXXSetPreallocation() paradigm instead of this routine directly.
4419: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
4421: If the *_nnz parameter is given then the *_nz parameter is ignored
4423: The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4424: processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4425: storage requirements for this matrix.
4427: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one
4428: processor than it must be used on all processors that share the object for
4429: that argument.
4431: If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by
4432: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`.
4434: The user MUST specify either the local or global matrix dimensions
4435: (possibly both).
4437: The parallel matrix is partitioned across processors such that the
4438: first `m0` rows belong to process 0, the next `m1` rows belong to
4439: process 1, the next `m2` rows belong to process 2, etc., where
4440: `m0`, `m1`, `m2`... are the input parameter `m` on each MPI process. I.e., each MPI process stores
4441: values corresponding to [m x N] submatrix.
4443: The columns are logically partitioned with the n0 columns belonging
4444: to 0th partition, the next n1 columns belonging to the next
4445: partition etc.. where n0,n1,n2... are the input parameter 'n'.
4447: The DIAGONAL portion of the local submatrix on any given processor
4448: is the submatrix corresponding to the rows and columns m,n
4449: corresponding to the given processor. i.e diagonal matrix on
4450: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4451: etc. The remaining portion of the local submatrix [m x (N-n)]
4452: constitute the OFF-DIAGONAL portion. The example below better
4453: illustrates this concept. The two matrices, the DIAGONAL portion and
4454: the OFF-DIAGONAL portion are each stored as `MATSEQAIJ` matrices.
4456: For a square global matrix we define each processor's diagonal portion
4457: to be its local rows and the corresponding columns (a square submatrix);
4458: each processor's off-diagonal portion encompasses the remainder of the
4459: local matrix (a rectangular submatrix).
4461: If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.
4463: When calling this routine with a single process communicator, a matrix of
4464: type `MATSEQAIJ` is returned. If a matrix of type `MATMPIAIJ` is desired for this
4465: type of communicator, use the construction mechanism
4466: .vb
4467: MatCreate(..., &A);
4468: MatSetType(A, MATMPIAIJ);
4469: MatSetSizes(A, m, n, M, N);
4470: MatMPIAIJSetPreallocation(A, ...);
4471: .ve
4473: By default, this format uses inodes (identical nodes) when possible.
4474: We search for consecutive rows with the same nonzero structure, thereby
4475: reusing matrix information to achieve increased efficiency.
4477: Example Usage:
4478: Consider the following 8x8 matrix with 34 non-zero values, that is
4479: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4480: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4481: as follows
4483: .vb
4484: 1 2 0 | 0 3 0 | 0 4
4485: Proc0 0 5 6 | 7 0 0 | 8 0
4486: 9 0 10 | 11 0 0 | 12 0
4487: -------------------------------------
4488: 13 0 14 | 15 16 17 | 0 0
4489: Proc1 0 18 0 | 19 20 21 | 0 0
4490: 0 0 0 | 22 23 0 | 24 0
4491: -------------------------------------
4492: Proc2 25 26 27 | 0 0 28 | 29 0
4493: 30 0 0 | 31 32 33 | 0 34
4494: .ve
4496: This can be represented as a collection of submatrices as
4498: .vb
4499: A B C
4500: D E F
4501: G H I
4502: .ve
4504: Where the submatrices A,B,C are owned by proc0, D,E,F are
4505: owned by proc1, G,H,I are owned by proc2.
4507: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4508: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4509: The 'M','N' parameters are 8,8, and have the same values on all procs.
4511: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4512: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4513: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4514: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4515: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4516: matrix, and [DF] as another SeqAIJ matrix.
4518: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4519: allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4520: storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4521: One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4522: the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4523: In this case, the values of `d_nz`,`o_nz` are
4524: .vb
4525: proc0 dnz = 2, o_nz = 2
4526: proc1 dnz = 3, o_nz = 2
4527: proc2 dnz = 1, o_nz = 4
4528: .ve
4529: We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4530: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4531: for proc3. i.e we are using 12+15+10=37 storage locations to store
4532: 34 values.
4534: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4535: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4536: In the above case the values for d_nnz,o_nnz are
4537: .vb
4538: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4539: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4540: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4541: .ve
4542: Here the space allocated is sum of all the above values i.e 34, and
4543: hence pre-allocation is perfect.
4545: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4546: `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`,
4547: `MatGetOwnershipRangesColumn()`, `PetscLayout`
4548: @*/
4549: 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)
4550: {
4551: PetscMPIInt size;
4553: PetscFunctionBegin;
4554: PetscCall(MatCreate(comm, A));
4555: PetscCall(MatSetSizes(*A, m, n, M, N));
4556: PetscCallMPI(MPI_Comm_size(comm, &size));
4557: if (size > 1) {
4558: PetscCall(MatSetType(*A, MATMPIAIJ));
4559: PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4560: } else {
4561: PetscCall(MatSetType(*A, MATSEQAIJ));
4562: PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4563: }
4564: PetscFunctionReturn(PETSC_SUCCESS);
4565: }
4567: /*MC
4568: MatMPIAIJGetSeqAIJF90 - Returns the local pieces of this distributed matrix
4570: Synopsis:
4571: MatMPIAIJGetSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)
4573: Not Collective
4575: Input Parameter:
4576: . A - the `MATMPIAIJ` matrix
4578: Output Parameters:
4579: + Ad - the diagonal portion of the matrix
4580: . Ao - the off-diagonal portion of the matrix
4581: . colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4582: - ierr - error code
4584: Level: advanced
4586: Note:
4587: Use `MatMPIAIJRestoreSeqAIJF90()` when you no longer need access to the matrices and `colmap`
4589: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJRestoreSeqAIJF90()`
4590: M*/
4592: /*MC
4593: MatMPIAIJRestoreSeqAIJF90 - call after `MatMPIAIJGetSeqAIJF90()` when you no longer need access to the matrices and `colmap`
4595: Synopsis:
4596: MatMPIAIJRestoreSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)
4598: Not Collective
4600: Input Parameters:
4601: + A - the `MATMPIAIJ` matrix
4602: . Ad - the diagonal portion of the matrix
4603: . Ao - the off-diagonal portion of the matrix
4604: . colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4605: - ierr - error code
4607: Level: advanced
4609: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJGetSeqAIJF90()`
4610: M*/
4612: /*@C
4613: MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix
4615: Not Collective
4617: Input Parameter:
4618: . A - The `MATMPIAIJ` matrix
4620: Output Parameters:
4621: + Ad - The local diagonal block as a `MATSEQAIJ` matrix
4622: . Ao - The local off-diagonal block as a `MATSEQAIJ` matrix
4623: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4625: Level: intermediate
4627: Note:
4628: The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4629: 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
4630: the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4631: local column numbers to global column numbers in the original matrix.
4633: Fortran Notes:
4634: `MatMPIAIJGetSeqAIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatMPIAIJGetSeqAIJF90()`
4636: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJF90()`, `MatMPIAIJRestoreSeqAIJF90()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATSEQAIJ`
4637: @*/
4638: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4639: {
4640: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4641: PetscBool flg;
4643: PetscFunctionBegin;
4644: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4645: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4646: if (Ad) *Ad = a->A;
4647: if (Ao) *Ao = a->B;
4648: if (colmap) *colmap = a->garray;
4649: PetscFunctionReturn(PETSC_SUCCESS);
4650: }
4652: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4653: {
4654: PetscInt m, N, i, rstart, nnz, Ii;
4655: PetscInt *indx;
4656: PetscScalar *values;
4657: MatType rootType;
4659: PetscFunctionBegin;
4660: PetscCall(MatGetSize(inmat, &m, &N));
4661: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4662: PetscInt *dnz, *onz, sum, bs, cbs;
4664: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4665: /* Check sum(n) = N */
4666: PetscCallMPI(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4667: PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);
4669: PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4670: rstart -= m;
4672: MatPreallocateBegin(comm, m, n, dnz, onz);
4673: for (i = 0; i < m; i++) {
4674: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4675: PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4676: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4677: }
4679: PetscCall(MatCreate(comm, outmat));
4680: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4681: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4682: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4683: PetscCall(MatGetRootType_Private(inmat, &rootType));
4684: PetscCall(MatSetType(*outmat, rootType));
4685: PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4686: PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4687: MatPreallocateEnd(dnz, onz);
4688: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4689: }
4691: /* numeric phase */
4692: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4693: for (i = 0; i < m; i++) {
4694: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4695: Ii = i + rstart;
4696: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4697: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4698: }
4699: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4700: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4701: PetscFunctionReturn(PETSC_SUCCESS);
4702: }
4704: static PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(void *data)
4705: {
4706: Mat_Merge_SeqsToMPI *merge = (Mat_Merge_SeqsToMPI *)data;
4708: PetscFunctionBegin;
4709: if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4710: PetscCall(PetscFree(merge->id_r));
4711: PetscCall(PetscFree(merge->len_s));
4712: PetscCall(PetscFree(merge->len_r));
4713: PetscCall(PetscFree(merge->bi));
4714: PetscCall(PetscFree(merge->bj));
4715: PetscCall(PetscFree(merge->buf_ri[0]));
4716: PetscCall(PetscFree(merge->buf_ri));
4717: PetscCall(PetscFree(merge->buf_rj[0]));
4718: PetscCall(PetscFree(merge->buf_rj));
4719: PetscCall(PetscFree(merge->coi));
4720: PetscCall(PetscFree(merge->coj));
4721: PetscCall(PetscFree(merge->owners_co));
4722: PetscCall(PetscLayoutDestroy(&merge->rowmap));
4723: PetscCall(PetscFree(merge));
4724: PetscFunctionReturn(PETSC_SUCCESS);
4725: }
4727: #include <../src/mat/utils/freespace.h>
4728: #include <petscbt.h>
4730: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4731: {
4732: MPI_Comm comm;
4733: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4734: PetscMPIInt size, rank, taga, *len_s;
4735: PetscInt N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj, m;
4736: PetscMPIInt proc, k;
4737: PetscInt **buf_ri, **buf_rj;
4738: PetscInt anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4739: PetscInt nrows, **buf_ri_k, **nextrow, **nextai;
4740: MPI_Request *s_waits, *r_waits;
4741: MPI_Status *status;
4742: const MatScalar *aa, *a_a;
4743: MatScalar **abuf_r, *ba_i;
4744: Mat_Merge_SeqsToMPI *merge;
4745: PetscContainer container;
4747: PetscFunctionBegin;
4748: PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4749: PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));
4751: PetscCallMPI(MPI_Comm_size(comm, &size));
4752: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4754: PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4755: PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4756: PetscCall(PetscContainerGetPointer(container, (void **)&merge));
4757: PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4758: aa = a_a;
4760: bi = merge->bi;
4761: bj = merge->bj;
4762: buf_ri = merge->buf_ri;
4763: buf_rj = merge->buf_rj;
4765: PetscCall(PetscMalloc1(size, &status));
4766: owners = merge->rowmap->range;
4767: len_s = merge->len_s;
4769: /* send and recv matrix values */
4770: PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4771: PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));
4773: PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4774: for (proc = 0, k = 0; proc < size; proc++) {
4775: if (!len_s[proc]) continue;
4776: i = owners[proc];
4777: PetscCallMPI(MPIU_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4778: k++;
4779: }
4781: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4782: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4783: PetscCall(PetscFree(status));
4785: PetscCall(PetscFree(s_waits));
4786: PetscCall(PetscFree(r_waits));
4788: /* insert mat values of mpimat */
4789: PetscCall(PetscMalloc1(N, &ba_i));
4790: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4792: for (k = 0; k < merge->nrecv; k++) {
4793: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4794: nrows = *buf_ri_k[k];
4795: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4796: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4797: }
4799: /* set values of ba */
4800: m = merge->rowmap->n;
4801: for (i = 0; i < m; i++) {
4802: arow = owners[rank] + i;
4803: bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4804: bnzi = bi[i + 1] - bi[i];
4805: PetscCall(PetscArrayzero(ba_i, bnzi));
4807: /* add local non-zero vals of this proc's seqmat into ba */
4808: anzi = ai[arow + 1] - ai[arow];
4809: aj = a->j + ai[arow];
4810: aa = a_a + ai[arow];
4811: nextaj = 0;
4812: for (j = 0; nextaj < anzi; j++) {
4813: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4814: ba_i[j] += aa[nextaj++];
4815: }
4816: }
4818: /* add received vals into ba */
4819: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4820: /* i-th row */
4821: if (i == *nextrow[k]) {
4822: anzi = *(nextai[k] + 1) - *nextai[k];
4823: aj = buf_rj[k] + *nextai[k];
4824: aa = abuf_r[k] + *nextai[k];
4825: nextaj = 0;
4826: for (j = 0; nextaj < anzi; j++) {
4827: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4828: ba_i[j] += aa[nextaj++];
4829: }
4830: }
4831: nextrow[k]++;
4832: nextai[k]++;
4833: }
4834: }
4835: PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4836: }
4837: PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4838: PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4839: PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));
4841: PetscCall(PetscFree(abuf_r[0]));
4842: PetscCall(PetscFree(abuf_r));
4843: PetscCall(PetscFree(ba_i));
4844: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4845: PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4846: PetscFunctionReturn(PETSC_SUCCESS);
4847: }
4849: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4850: {
4851: Mat B_mpi;
4852: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4853: PetscMPIInt size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4854: PetscInt **buf_rj, **buf_ri, **buf_ri_k;
4855: PetscInt M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4856: PetscInt len, *dnz, *onz, bs, cbs;
4857: PetscInt k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4858: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4859: MPI_Request *si_waits, *sj_waits, *ri_waits, *rj_waits;
4860: MPI_Status *status;
4861: PetscFreeSpaceList free_space = NULL, current_space = NULL;
4862: PetscBT lnkbt;
4863: Mat_Merge_SeqsToMPI *merge;
4864: PetscContainer container;
4866: PetscFunctionBegin;
4867: PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));
4869: /* make sure it is a PETSc comm */
4870: PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4871: PetscCallMPI(MPI_Comm_size(comm, &size));
4872: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4874: PetscCall(PetscNew(&merge));
4875: PetscCall(PetscMalloc1(size, &status));
4877: /* determine row ownership */
4878: PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4879: PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4880: PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4881: PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4882: PetscCall(PetscLayoutSetUp(merge->rowmap));
4883: PetscCall(PetscMalloc1(size, &len_si));
4884: PetscCall(PetscMalloc1(size, &merge->len_s));
4886: m = merge->rowmap->n;
4887: owners = merge->rowmap->range;
4889: /* determine the number of messages to send, their lengths */
4890: len_s = merge->len_s;
4892: len = 0; /* length of buf_si[] */
4893: merge->nsend = 0;
4894: for (PetscMPIInt proc = 0; proc < size; proc++) {
4895: len_si[proc] = 0;
4896: if (proc == rank) {
4897: len_s[proc] = 0;
4898: } else {
4899: PetscCall(PetscMPIIntCast(owners[proc + 1] - owners[proc] + 1, &len_si[proc]));
4900: PetscCall(PetscMPIIntCast(ai[owners[proc + 1]] - ai[owners[proc]], &len_s[proc])); /* num of rows to be sent to [proc] */
4901: }
4902: if (len_s[proc]) {
4903: merge->nsend++;
4904: nrows = 0;
4905: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4906: if (ai[i + 1] > ai[i]) nrows++;
4907: }
4908: PetscCall(PetscMPIIntCast(2 * (nrows + 1), &len_si[proc]));
4909: len += len_si[proc];
4910: }
4911: }
4913: /* determine the number and length of messages to receive for ij-structure */
4914: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4915: PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));
4917: /* post the Irecv of j-structure */
4918: PetscCall(PetscCommGetNewTag(comm, &tagj));
4919: PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));
4921: /* post the Isend of j-structure */
4922: PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));
4924: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4925: if (!len_s[proc]) continue;
4926: i = owners[proc];
4927: PetscCallMPI(MPIU_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4928: k++;
4929: }
4931: /* receives and sends of j-structure are complete */
4932: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4933: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));
4935: /* send and recv i-structure */
4936: PetscCall(PetscCommGetNewTag(comm, &tagi));
4937: PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));
4939: PetscCall(PetscMalloc1(len + 1, &buf_s));
4940: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4941: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4942: if (!len_s[proc]) continue;
4943: /* form outgoing message for i-structure:
4944: buf_si[0]: nrows to be sent
4945: [1:nrows]: row index (global)
4946: [nrows+1:2*nrows+1]: i-structure index
4947: */
4948: nrows = len_si[proc] / 2 - 1;
4949: buf_si_i = buf_si + nrows + 1;
4950: buf_si[0] = nrows;
4951: buf_si_i[0] = 0;
4952: nrows = 0;
4953: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4954: anzi = ai[i + 1] - ai[i];
4955: if (anzi) {
4956: buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4957: buf_si[nrows + 1] = i - owners[proc]; /* local row index */
4958: nrows++;
4959: }
4960: }
4961: PetscCallMPI(MPIU_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4962: k++;
4963: buf_si += len_si[proc];
4964: }
4966: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4967: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));
4969: PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4970: 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]));
4972: PetscCall(PetscFree(len_si));
4973: PetscCall(PetscFree(len_ri));
4974: PetscCall(PetscFree(rj_waits));
4975: PetscCall(PetscFree2(si_waits, sj_waits));
4976: PetscCall(PetscFree(ri_waits));
4977: PetscCall(PetscFree(buf_s));
4978: PetscCall(PetscFree(status));
4980: /* compute a local seq matrix in each processor */
4981: /* allocate bi array and free space for accumulating nonzero column info */
4982: PetscCall(PetscMalloc1(m + 1, &bi));
4983: bi[0] = 0;
4985: /* create and initialize a linked list */
4986: nlnk = N + 1;
4987: PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));
4989: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4990: len = ai[owners[rank + 1]] - ai[owners[rank]];
4991: PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));
4993: current_space = free_space;
4995: /* determine symbolic info for each local row */
4996: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4998: for (k = 0; k < merge->nrecv; k++) {
4999: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
5000: nrows = *buf_ri_k[k];
5001: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
5002: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
5003: }
5005: MatPreallocateBegin(comm, m, n, dnz, onz);
5006: len = 0;
5007: for (i = 0; i < m; i++) {
5008: bnzi = 0;
5009: /* add local non-zero cols of this proc's seqmat into lnk */
5010: arow = owners[rank] + i;
5011: anzi = ai[arow + 1] - ai[arow];
5012: aj = a->j + ai[arow];
5013: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5014: bnzi += nlnk;
5015: /* add received col data into lnk */
5016: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
5017: if (i == *nextrow[k]) { /* i-th row */
5018: anzi = *(nextai[k] + 1) - *nextai[k];
5019: aj = buf_rj[k] + *nextai[k];
5020: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5021: bnzi += nlnk;
5022: nextrow[k]++;
5023: nextai[k]++;
5024: }
5025: }
5026: if (len < bnzi) len = bnzi; /* =max(bnzi) */
5028: /* if free space is not available, make more free space */
5029: if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), ¤t_space));
5030: /* copy data into free space, then initialize lnk */
5031: PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
5032: PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));
5034: current_space->array += bnzi;
5035: current_space->local_used += bnzi;
5036: current_space->local_remaining -= bnzi;
5038: bi[i + 1] = bi[i] + bnzi;
5039: }
5041: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
5043: PetscCall(PetscMalloc1(bi[m] + 1, &bj));
5044: PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
5045: PetscCall(PetscLLDestroy(lnk, lnkbt));
5047: /* create symbolic parallel matrix B_mpi */
5048: PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
5049: PetscCall(MatCreate(comm, &B_mpi));
5050: if (n == PETSC_DECIDE) {
5051: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
5052: } else {
5053: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
5054: }
5055: PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
5056: PetscCall(MatSetType(B_mpi, MATMPIAIJ));
5057: PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
5058: MatPreallocateEnd(dnz, onz);
5059: PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));
5061: /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
5062: B_mpi->assembled = PETSC_FALSE;
5063: merge->bi = bi;
5064: merge->bj = bj;
5065: merge->buf_ri = buf_ri;
5066: merge->buf_rj = buf_rj;
5067: merge->coi = NULL;
5068: merge->coj = NULL;
5069: merge->owners_co = NULL;
5071: PetscCall(PetscCommDestroy(&comm));
5073: /* attach the supporting struct to B_mpi for reuse */
5074: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5075: PetscCall(PetscContainerSetPointer(container, merge));
5076: PetscCall(PetscContainerSetUserDestroy(container, MatDestroy_MPIAIJ_SeqsToMPI));
5077: PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5078: PetscCall(PetscContainerDestroy(&container));
5079: *mpimat = B_mpi;
5081: PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5082: PetscFunctionReturn(PETSC_SUCCESS);
5083: }
5085: /*@
5086: MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5087: matrices from each processor
5089: Collective
5091: Input Parameters:
5092: + comm - the communicators the parallel matrix will live on
5093: . seqmat - the input sequential matrices
5094: . m - number of local rows (or `PETSC_DECIDE`)
5095: . n - number of local columns (or `PETSC_DECIDE`)
5096: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5098: Output Parameter:
5099: . mpimat - the parallel matrix generated
5101: Level: advanced
5103: Note:
5104: The dimensions of the sequential matrix in each processor MUST be the same.
5105: The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
5106: destroyed when `mpimat` is destroyed. Call `PetscObjectQuery()` to access `seqmat`.
5108: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`
5109: @*/
5110: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5111: {
5112: PetscMPIInt size;
5114: PetscFunctionBegin;
5115: PetscCallMPI(MPI_Comm_size(comm, &size));
5116: if (size == 1) {
5117: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5118: if (scall == MAT_INITIAL_MATRIX) {
5119: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5120: } else {
5121: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5122: }
5123: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5124: PetscFunctionReturn(PETSC_SUCCESS);
5125: }
5126: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5127: if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5128: PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5129: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5130: PetscFunctionReturn(PETSC_SUCCESS);
5131: }
5133: /*@
5134: MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix.
5136: Not Collective
5138: Input Parameter:
5139: . A - the matrix
5141: Output Parameter:
5142: . A_loc - the local sequential matrix generated
5144: Level: developer
5146: Notes:
5147: The matrix is created by taking `A`'s local rows and putting them into a sequential matrix
5148: with `mlocal` rows and `n` columns. Where `mlocal` is obtained with `MatGetLocalSize()` and
5149: `n` is the global column count obtained with `MatGetSize()`
5151: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5153: For parallel matrices this creates an entirely new matrix. If the matrix is sequential it merely increases the reference count.
5155: Destroy the matrix with `MatDestroy()`
5157: .seealso: [](ch_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5158: @*/
5159: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5160: {
5161: PetscBool mpi;
5163: PetscFunctionBegin;
5164: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5165: if (mpi) {
5166: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5167: } else {
5168: *A_loc = A;
5169: PetscCall(PetscObjectReference((PetscObject)*A_loc));
5170: }
5171: PetscFunctionReturn(PETSC_SUCCESS);
5172: }
5174: /*@
5175: MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix.
5177: Not Collective
5179: Input Parameters:
5180: + A - the matrix
5181: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5183: Output Parameter:
5184: . A_loc - the local sequential matrix generated
5186: Level: developer
5188: Notes:
5189: The matrix is created by taking all `A`'s local rows and putting them into a sequential
5190: matrix with `mlocal` rows and `n` columns.`mlocal` is the row count obtained with
5191: `MatGetLocalSize()` and `n` is the global column count obtained with `MatGetSize()`.
5193: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5195: When `A` is sequential and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A` (which contains the entire matrix),
5196: 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
5197: then `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called to fill `A_loc`. Thus one can preallocate the appropriate sequential matrix `A_loc`
5198: 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.
5200: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5201: @*/
5202: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5203: {
5204: Mat_MPIAIJ *mpimat = (Mat_MPIAIJ *)A->data;
5205: Mat_SeqAIJ *mat, *a, *b;
5206: PetscInt *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5207: const PetscScalar *aa, *ba, *aav, *bav;
5208: PetscScalar *ca, *cam;
5209: PetscMPIInt size;
5210: PetscInt am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5211: PetscInt *ci, *cj, col, ncols_d, ncols_o, jo;
5212: PetscBool match;
5214: PetscFunctionBegin;
5215: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5216: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5217: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5218: if (size == 1) {
5219: if (scall == MAT_INITIAL_MATRIX) {
5220: PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5221: *A_loc = mpimat->A;
5222: } else if (scall == MAT_REUSE_MATRIX) {
5223: PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5224: }
5225: PetscFunctionReturn(PETSC_SUCCESS);
5226: }
5228: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5229: a = (Mat_SeqAIJ *)mpimat->A->data;
5230: b = (Mat_SeqAIJ *)mpimat->B->data;
5231: ai = a->i;
5232: aj = a->j;
5233: bi = b->i;
5234: bj = b->j;
5235: PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5236: PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5237: aa = aav;
5238: ba = bav;
5239: if (scall == MAT_INITIAL_MATRIX) {
5240: PetscCall(PetscMalloc1(1 + am, &ci));
5241: ci[0] = 0;
5242: for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5243: PetscCall(PetscMalloc1(1 + ci[am], &cj));
5244: PetscCall(PetscMalloc1(1 + ci[am], &ca));
5245: k = 0;
5246: for (i = 0; i < am; i++) {
5247: ncols_o = bi[i + 1] - bi[i];
5248: ncols_d = ai[i + 1] - ai[i];
5249: /* off-diagonal portion of A */
5250: for (jo = 0; jo < ncols_o; jo++) {
5251: col = cmap[*bj];
5252: if (col >= cstart) break;
5253: cj[k] = col;
5254: bj++;
5255: ca[k++] = *ba++;
5256: }
5257: /* diagonal portion of A */
5258: for (j = 0; j < ncols_d; j++) {
5259: cj[k] = cstart + *aj++;
5260: ca[k++] = *aa++;
5261: }
5262: /* off-diagonal portion of A */
5263: for (j = jo; j < ncols_o; j++) {
5264: cj[k] = cmap[*bj++];
5265: ca[k++] = *ba++;
5266: }
5267: }
5268: /* put together the new matrix */
5269: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5270: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5271: /* Since these are PETSc arrays, change flags to free them as necessary. */
5272: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5273: mat->free_a = PETSC_TRUE;
5274: mat->free_ij = PETSC_TRUE;
5275: mat->nonew = 0;
5276: } else if (scall == MAT_REUSE_MATRIX) {
5277: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5278: ci = mat->i;
5279: cj = mat->j;
5280: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5281: for (i = 0; i < am; i++) {
5282: /* off-diagonal portion of A */
5283: ncols_o = bi[i + 1] - bi[i];
5284: for (jo = 0; jo < ncols_o; jo++) {
5285: col = cmap[*bj];
5286: if (col >= cstart) break;
5287: *cam++ = *ba++;
5288: bj++;
5289: }
5290: /* diagonal portion of A */
5291: ncols_d = ai[i + 1] - ai[i];
5292: for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5293: /* off-diagonal portion of A */
5294: for (j = jo; j < ncols_o; j++) {
5295: *cam++ = *ba++;
5296: bj++;
5297: }
5298: }
5299: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5300: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5301: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5302: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5303: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5304: PetscFunctionReturn(PETSC_SUCCESS);
5305: }
5307: /*@
5308: MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5309: mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and off-diagonal part
5311: Not Collective
5313: Input Parameters:
5314: + A - the matrix
5315: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5317: Output Parameters:
5318: + glob - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5319: - A_loc - the local sequential matrix generated
5321: Level: developer
5323: Note:
5324: This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5325: part, then those associated with the off-diagonal part (in its local ordering)
5327: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5328: @*/
5329: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5330: {
5331: Mat Ao, Ad;
5332: const PetscInt *cmap;
5333: PetscMPIInt size;
5334: PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);
5336: PetscFunctionBegin;
5337: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5338: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5339: if (size == 1) {
5340: if (scall == MAT_INITIAL_MATRIX) {
5341: PetscCall(PetscObjectReference((PetscObject)Ad));
5342: *A_loc = Ad;
5343: } else if (scall == MAT_REUSE_MATRIX) {
5344: PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5345: }
5346: if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5347: PetscFunctionReturn(PETSC_SUCCESS);
5348: }
5349: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5350: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5351: if (f) {
5352: PetscCall((*f)(A, scall, glob, A_loc));
5353: } else {
5354: Mat_SeqAIJ *a = (Mat_SeqAIJ *)Ad->data;
5355: Mat_SeqAIJ *b = (Mat_SeqAIJ *)Ao->data;
5356: Mat_SeqAIJ *c;
5357: PetscInt *ai = a->i, *aj = a->j;
5358: PetscInt *bi = b->i, *bj = b->j;
5359: PetscInt *ci, *cj;
5360: const PetscScalar *aa, *ba;
5361: PetscScalar *ca;
5362: PetscInt i, j, am, dn, on;
5364: PetscCall(MatGetLocalSize(Ad, &am, &dn));
5365: PetscCall(MatGetLocalSize(Ao, NULL, &on));
5366: PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5367: PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5368: if (scall == MAT_INITIAL_MATRIX) {
5369: PetscInt k;
5370: PetscCall(PetscMalloc1(1 + am, &ci));
5371: PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5372: PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5373: ci[0] = 0;
5374: for (i = 0, k = 0; i < am; i++) {
5375: const PetscInt ncols_o = bi[i + 1] - bi[i];
5376: const PetscInt ncols_d = ai[i + 1] - ai[i];
5377: ci[i + 1] = ci[i] + ncols_o + ncols_d;
5378: /* diagonal portion of A */
5379: for (j = 0; j < ncols_d; j++, k++) {
5380: cj[k] = *aj++;
5381: ca[k] = *aa++;
5382: }
5383: /* off-diagonal portion of A */
5384: for (j = 0; j < ncols_o; j++, k++) {
5385: cj[k] = dn + *bj++;
5386: ca[k] = *ba++;
5387: }
5388: }
5389: /* put together the new matrix */
5390: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5391: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5392: /* Since these are PETSc arrays, change flags to free them as necessary. */
5393: c = (Mat_SeqAIJ *)(*A_loc)->data;
5394: c->free_a = PETSC_TRUE;
5395: c->free_ij = PETSC_TRUE;
5396: c->nonew = 0;
5397: PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5398: } else if (scall == MAT_REUSE_MATRIX) {
5399: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5400: for (i = 0; i < am; i++) {
5401: const PetscInt ncols_d = ai[i + 1] - ai[i];
5402: const PetscInt ncols_o = bi[i + 1] - bi[i];
5403: /* diagonal portion of A */
5404: for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5405: /* off-diagonal portion of A */
5406: for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5407: }
5408: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5409: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5410: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5411: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5412: if (glob) {
5413: PetscInt cst, *gidx;
5415: PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5416: PetscCall(PetscMalloc1(dn + on, &gidx));
5417: for (i = 0; i < dn; i++) gidx[i] = cst + i;
5418: for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5419: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5420: }
5421: }
5422: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5423: PetscFunctionReturn(PETSC_SUCCESS);
5424: }
5426: /*@C
5427: MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns
5429: Not Collective
5431: Input Parameters:
5432: + A - the matrix
5433: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5434: . row - index set of rows to extract (or `NULL`)
5435: - col - index set of columns to extract (or `NULL`)
5437: Output Parameter:
5438: . A_loc - the local sequential matrix generated
5440: Level: developer
5442: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5443: @*/
5444: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5445: {
5446: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5447: PetscInt i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5448: IS isrowa, iscola;
5449: Mat *aloc;
5450: PetscBool match;
5452: PetscFunctionBegin;
5453: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5454: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5455: PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5456: if (!row) {
5457: start = A->rmap->rstart;
5458: end = A->rmap->rend;
5459: PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5460: } else {
5461: isrowa = *row;
5462: }
5463: if (!col) {
5464: start = A->cmap->rstart;
5465: cmap = a->garray;
5466: nzA = a->A->cmap->n;
5467: nzB = a->B->cmap->n;
5468: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5469: ncols = 0;
5470: for (i = 0; i < nzB; i++) {
5471: if (cmap[i] < start) idx[ncols++] = cmap[i];
5472: else break;
5473: }
5474: imark = i;
5475: for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5476: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5477: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5478: } else {
5479: iscola = *col;
5480: }
5481: if (scall != MAT_INITIAL_MATRIX) {
5482: PetscCall(PetscMalloc1(1, &aloc));
5483: aloc[0] = *A_loc;
5484: }
5485: PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5486: if (!col) { /* attach global id of condensed columns */
5487: PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5488: }
5489: *A_loc = aloc[0];
5490: PetscCall(PetscFree(aloc));
5491: if (!row) PetscCall(ISDestroy(&isrowa));
5492: if (!col) PetscCall(ISDestroy(&iscola));
5493: PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5494: PetscFunctionReturn(PETSC_SUCCESS);
5495: }
5497: /*
5498: * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5499: * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5500: * on a global size.
5501: * */
5502: static PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5503: {
5504: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
5505: Mat_SeqAIJ *pd = (Mat_SeqAIJ *)p->A->data, *po = (Mat_SeqAIJ *)p->B->data, *p_oth;
5506: PetscInt plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5507: PetscMPIInt owner;
5508: PetscSFNode *iremote, *oiremote;
5509: const PetscInt *lrowindices;
5510: PetscSF sf, osf;
5511: PetscInt pcstart, *roffsets, *loffsets, *pnnz, j;
5512: PetscInt ontotalcols, dntotalcols, ntotalcols, nout;
5513: MPI_Comm comm;
5514: ISLocalToGlobalMapping mapping;
5515: const PetscScalar *pd_a, *po_a;
5517: PetscFunctionBegin;
5518: PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5519: /* plocalsize is the number of roots
5520: * nrows is the number of leaves
5521: * */
5522: PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5523: PetscCall(ISGetLocalSize(rows, &nrows));
5524: PetscCall(PetscCalloc1(nrows, &iremote));
5525: PetscCall(ISGetIndices(rows, &lrowindices));
5526: for (i = 0; i < nrows; i++) {
5527: /* Find a remote index and an owner for a row
5528: * The row could be local or remote
5529: * */
5530: owner = 0;
5531: lidx = 0;
5532: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5533: iremote[i].index = lidx;
5534: iremote[i].rank = owner;
5535: }
5536: /* Create SF to communicate how many nonzero columns for each row */
5537: PetscCall(PetscSFCreate(comm, &sf));
5538: /* SF will figure out the number of nonzero columns for each row, and their
5539: * offsets
5540: * */
5541: PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5542: PetscCall(PetscSFSetFromOptions(sf));
5543: PetscCall(PetscSFSetUp(sf));
5545: PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5546: PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5547: PetscCall(PetscCalloc1(nrows, &pnnz));
5548: roffsets[0] = 0;
5549: roffsets[1] = 0;
5550: for (i = 0; i < plocalsize; i++) {
5551: /* diagonal */
5552: nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5553: /* off-diagonal */
5554: nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5555: /* compute offsets so that we relative location for each row */
5556: roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5557: roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5558: }
5559: PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5560: PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5561: /* 'r' means root, and 'l' means leaf */
5562: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5563: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5564: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5565: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5566: PetscCall(PetscSFDestroy(&sf));
5567: PetscCall(PetscFree(roffsets));
5568: PetscCall(PetscFree(nrcols));
5569: dntotalcols = 0;
5570: ontotalcols = 0;
5571: ncol = 0;
5572: for (i = 0; i < nrows; i++) {
5573: pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5574: ncol = PetscMax(pnnz[i], ncol);
5575: /* diagonal */
5576: dntotalcols += nlcols[i * 2 + 0];
5577: /* off-diagonal */
5578: ontotalcols += nlcols[i * 2 + 1];
5579: }
5580: /* We do not need to figure the right number of columns
5581: * since all the calculations will be done by going through the raw data
5582: * */
5583: PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5584: PetscCall(MatSetUp(*P_oth));
5585: PetscCall(PetscFree(pnnz));
5586: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5587: /* diagonal */
5588: PetscCall(PetscCalloc1(dntotalcols, &iremote));
5589: /* off-diagonal */
5590: PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5591: /* diagonal */
5592: PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5593: /* off-diagonal */
5594: PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5595: dntotalcols = 0;
5596: ontotalcols = 0;
5597: ntotalcols = 0;
5598: for (i = 0; i < nrows; i++) {
5599: owner = 0;
5600: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5601: /* Set iremote for diag matrix */
5602: for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5603: iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5604: iremote[dntotalcols].rank = owner;
5605: /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5606: ilocal[dntotalcols++] = ntotalcols++;
5607: }
5608: /* off-diagonal */
5609: for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5610: oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5611: oiremote[ontotalcols].rank = owner;
5612: oilocal[ontotalcols++] = ntotalcols++;
5613: }
5614: }
5615: PetscCall(ISRestoreIndices(rows, &lrowindices));
5616: PetscCall(PetscFree(loffsets));
5617: PetscCall(PetscFree(nlcols));
5618: PetscCall(PetscSFCreate(comm, &sf));
5619: /* P serves as roots and P_oth is leaves
5620: * Diag matrix
5621: * */
5622: PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5623: PetscCall(PetscSFSetFromOptions(sf));
5624: PetscCall(PetscSFSetUp(sf));
5626: PetscCall(PetscSFCreate(comm, &osf));
5627: /* off-diagonal */
5628: PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5629: PetscCall(PetscSFSetFromOptions(osf));
5630: PetscCall(PetscSFSetUp(osf));
5631: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5632: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5633: /* operate on the matrix internal data to save memory */
5634: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5635: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5636: PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5637: /* Convert to global indices for diag matrix */
5638: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5639: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5640: /* We want P_oth store global indices */
5641: PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5642: /* Use memory scalable approach */
5643: PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5644: PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5645: PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5646: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5647: /* Convert back to local indices */
5648: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5649: PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5650: nout = 0;
5651: PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5652: PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5653: PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5654: /* Exchange values */
5655: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5656: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5657: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5658: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5659: /* Stop PETSc from shrinking memory */
5660: for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5661: PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5662: PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5663: /* Attach PetscSF objects to P_oth so that we can reuse it later */
5664: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5665: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5666: PetscCall(PetscSFDestroy(&sf));
5667: PetscCall(PetscSFDestroy(&osf));
5668: PetscFunctionReturn(PETSC_SUCCESS);
5669: }
5671: /*
5672: * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5673: * This supports MPIAIJ and MAIJ
5674: * */
5675: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5676: {
5677: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5678: Mat_SeqAIJ *p_oth;
5679: IS rows, map;
5680: PetscHMapI hamp;
5681: PetscInt i, htsize, *rowindices, off, *mapping, key, count;
5682: MPI_Comm comm;
5683: PetscSF sf, osf;
5684: PetscBool has;
5686: PetscFunctionBegin;
5687: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5688: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5689: /* If it is the first time, create an index set of off-diag nonzero columns of A,
5690: * and then create a submatrix (that often is an overlapping matrix)
5691: * */
5692: if (reuse == MAT_INITIAL_MATRIX) {
5693: /* Use a hash table to figure out unique keys */
5694: PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5695: PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5696: count = 0;
5697: /* Assume that a->g is sorted, otherwise the following does not make sense */
5698: for (i = 0; i < a->B->cmap->n; i++) {
5699: key = a->garray[i] / dof;
5700: PetscCall(PetscHMapIHas(hamp, key, &has));
5701: if (!has) {
5702: mapping[i] = count;
5703: PetscCall(PetscHMapISet(hamp, key, count++));
5704: } else {
5705: /* Current 'i' has the same value the previous step */
5706: mapping[i] = count - 1;
5707: }
5708: }
5709: PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5710: PetscCall(PetscHMapIGetSize(hamp, &htsize));
5711: PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5712: PetscCall(PetscCalloc1(htsize, &rowindices));
5713: off = 0;
5714: PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5715: PetscCall(PetscHMapIDestroy(&hamp));
5716: PetscCall(PetscSortInt(htsize, rowindices));
5717: PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5718: /* In case, the matrix was already created but users want to recreate the matrix */
5719: PetscCall(MatDestroy(P_oth));
5720: PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5721: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5722: PetscCall(ISDestroy(&map));
5723: PetscCall(ISDestroy(&rows));
5724: } else if (reuse == MAT_REUSE_MATRIX) {
5725: /* If matrix was already created, we simply update values using SF objects
5726: * that as attached to the matrix earlier.
5727: */
5728: const PetscScalar *pd_a, *po_a;
5730: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5731: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5732: PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5733: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5734: /* Update values in place */
5735: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5736: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5737: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5738: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5739: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5740: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5741: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5742: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5743: } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5744: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5745: PetscFunctionReturn(PETSC_SUCCESS);
5746: }
5748: /*@C
5749: MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`
5751: Collective
5753: Input Parameters:
5754: + A - the first matrix in `MATMPIAIJ` format
5755: . B - the second matrix in `MATMPIAIJ` format
5756: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5758: Output Parameters:
5759: + rowb - On input index sets of rows of B to extract (or `NULL`), modified on output
5760: . colb - On input index sets of columns of B to extract (or `NULL`), modified on output
5761: - B_seq - the sequential matrix generated
5763: Level: developer
5765: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5766: @*/
5767: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5768: {
5769: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5770: PetscInt *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5771: IS isrowb, iscolb;
5772: Mat *bseq = NULL;
5774: PetscFunctionBegin;
5775: 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 ")",
5776: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5777: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));
5779: if (scall == MAT_INITIAL_MATRIX) {
5780: start = A->cmap->rstart;
5781: cmap = a->garray;
5782: nzA = a->A->cmap->n;
5783: nzB = a->B->cmap->n;
5784: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5785: ncols = 0;
5786: for (i = 0; i < nzB; i++) { /* row < local row index */
5787: if (cmap[i] < start) idx[ncols++] = cmap[i];
5788: else break;
5789: }
5790: imark = i;
5791: for (i = 0; i < nzA; i++) idx[ncols++] = start + i; /* local rows */
5792: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5793: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5794: PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5795: } else {
5796: PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5797: isrowb = *rowb;
5798: iscolb = *colb;
5799: PetscCall(PetscMalloc1(1, &bseq));
5800: bseq[0] = *B_seq;
5801: }
5802: PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5803: *B_seq = bseq[0];
5804: PetscCall(PetscFree(bseq));
5805: if (!rowb) {
5806: PetscCall(ISDestroy(&isrowb));
5807: } else {
5808: *rowb = isrowb;
5809: }
5810: if (!colb) {
5811: PetscCall(ISDestroy(&iscolb));
5812: } else {
5813: *colb = iscolb;
5814: }
5815: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5816: PetscFunctionReturn(PETSC_SUCCESS);
5817: }
5819: /*
5820: MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5821: of the OFF-DIAGONAL portion of local A
5823: Collective
5825: Input Parameters:
5826: + A,B - the matrices in `MATMPIAIJ` format
5827: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5829: Output Parameter:
5830: + startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5831: . startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5832: . bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5833: - B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N
5835: Developer Note:
5836: This directly accesses information inside the VecScatter associated with the matrix-vector product
5837: for this matrix. This is not desirable..
5839: Level: developer
5841: */
5843: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5844: {
5845: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5846: VecScatter ctx;
5847: MPI_Comm comm;
5848: const PetscMPIInt *rprocs, *sprocs;
5849: PetscMPIInt nrecvs, nsends;
5850: const PetscInt *srow, *rstarts, *sstarts;
5851: PetscInt *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5852: PetscInt i, j, k = 0, l, ll, nrows, *rstartsj = NULL, *sstartsj, len;
5853: PetscScalar *b_otha, *bufa, *bufA, *vals = NULL;
5854: MPI_Request *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5855: PetscMPIInt size, tag, rank, nreqs;
5857: PetscFunctionBegin;
5858: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5859: PetscCallMPI(MPI_Comm_size(comm, &size));
5861: 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 ")",
5862: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5863: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5864: PetscCallMPI(MPI_Comm_rank(comm, &rank));
5866: if (size == 1) {
5867: startsj_s = NULL;
5868: bufa_ptr = NULL;
5869: *B_oth = NULL;
5870: PetscFunctionReturn(PETSC_SUCCESS);
5871: }
5873: ctx = a->Mvctx;
5874: tag = ((PetscObject)ctx)->tag;
5876: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5877: /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5878: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5879: PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5880: PetscCall(PetscMalloc1(nreqs, &reqs));
5881: rwaits = reqs;
5882: swaits = PetscSafePointerPlusOffset(reqs, nrecvs);
5884: if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5885: if (scall == MAT_INITIAL_MATRIX) {
5886: /* i-array */
5887: /* post receives */
5888: if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5889: for (i = 0; i < nrecvs; i++) {
5890: rowlen = rvalues + rstarts[i] * rbs;
5891: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5892: PetscCallMPI(MPIU_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5893: }
5895: /* pack the outgoing message */
5896: PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));
5898: sstartsj[0] = 0;
5899: rstartsj[0] = 0;
5900: len = 0; /* total length of j or a array to be sent */
5901: if (nsends) {
5902: k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5903: PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5904: }
5905: for (i = 0; i < nsends; i++) {
5906: rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5907: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5908: for (j = 0; j < nrows; j++) {
5909: row = srow[k] + B->rmap->range[rank]; /* global row idx */
5910: for (l = 0; l < sbs; l++) {
5911: PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */
5913: rowlen[j * sbs + l] = ncols;
5915: len += ncols;
5916: PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5917: }
5918: k++;
5919: }
5920: PetscCallMPI(MPIU_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));
5922: sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5923: }
5924: /* recvs and sends of i-array are completed */
5925: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5926: PetscCall(PetscFree(svalues));
5928: /* allocate buffers for sending j and a arrays */
5929: PetscCall(PetscMalloc1(len + 1, &bufj));
5930: PetscCall(PetscMalloc1(len + 1, &bufa));
5932: /* create i-array of B_oth */
5933: PetscCall(PetscMalloc1(aBn + 2, &b_othi));
5935: b_othi[0] = 0;
5936: len = 0; /* total length of j or a array to be received */
5937: k = 0;
5938: for (i = 0; i < nrecvs; i++) {
5939: rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5940: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5941: for (j = 0; j < nrows; j++) {
5942: b_othi[k + 1] = b_othi[k] + rowlen[j];
5943: PetscCall(PetscIntSumError(rowlen[j], len, &len));
5944: k++;
5945: }
5946: rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5947: }
5948: PetscCall(PetscFree(rvalues));
5950: /* allocate space for j and a arrays of B_oth */
5951: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_othj));
5952: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_otha));
5954: /* j-array */
5955: /* post receives of j-array */
5956: for (i = 0; i < nrecvs; i++) {
5957: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5958: PetscCallMPI(MPIU_Irecv(b_othj + rstartsj[i], nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5959: }
5961: /* pack the outgoing message j-array */
5962: if (nsends) k = sstarts[0];
5963: for (i = 0; i < nsends; i++) {
5964: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5965: bufJ = bufj + sstartsj[i];
5966: for (j = 0; j < nrows; j++) {
5967: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5968: for (ll = 0; ll < sbs; ll++) {
5969: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5970: for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5971: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5972: }
5973: }
5974: PetscCallMPI(MPIU_Isend(bufj + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5975: }
5977: /* recvs and sends of j-array are completed */
5978: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5979: } else if (scall == MAT_REUSE_MATRIX) {
5980: sstartsj = *startsj_s;
5981: rstartsj = *startsj_r;
5982: bufa = *bufa_ptr;
5983: PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5984: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");
5986: /* a-array */
5987: /* post receives of a-array */
5988: for (i = 0; i < nrecvs; i++) {
5989: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5990: PetscCallMPI(MPIU_Irecv(b_otha + rstartsj[i], nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
5991: }
5993: /* pack the outgoing message a-array */
5994: if (nsends) k = sstarts[0];
5995: for (i = 0; i < nsends; i++) {
5996: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5997: bufA = bufa + sstartsj[i];
5998: for (j = 0; j < nrows; j++) {
5999: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
6000: for (ll = 0; ll < sbs; ll++) {
6001: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
6002: for (l = 0; l < ncols; l++) *bufA++ = vals[l];
6003: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
6004: }
6005: }
6006: PetscCallMPI(MPIU_Isend(bufa + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
6007: }
6008: /* recvs and sends of a-array are completed */
6009: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
6010: PetscCall(PetscFree(reqs));
6012: if (scall == MAT_INITIAL_MATRIX) {
6013: Mat_SeqAIJ *b_oth;
6015: /* put together the new matrix */
6016: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));
6018: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
6019: /* Since these are PETSc arrays, change flags to free them as necessary. */
6020: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
6021: b_oth->free_a = PETSC_TRUE;
6022: b_oth->free_ij = PETSC_TRUE;
6023: b_oth->nonew = 0;
6025: PetscCall(PetscFree(bufj));
6026: if (!startsj_s || !bufa_ptr) {
6027: PetscCall(PetscFree2(sstartsj, rstartsj));
6028: PetscCall(PetscFree(bufa_ptr));
6029: } else {
6030: *startsj_s = sstartsj;
6031: *startsj_r = rstartsj;
6032: *bufa_ptr = bufa;
6033: }
6034: } else if (scall == MAT_REUSE_MATRIX) {
6035: PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
6036: }
6038: PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
6039: PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
6040: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
6041: PetscFunctionReturn(PETSC_SUCCESS);
6042: }
6044: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
6045: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
6046: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
6047: #if defined(PETSC_HAVE_MKL_SPARSE)
6048: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
6049: #endif
6050: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
6051: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
6052: #if defined(PETSC_HAVE_ELEMENTAL)
6053: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
6054: #endif
6055: #if defined(PETSC_HAVE_SCALAPACK)
6056: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
6057: #endif
6058: #if defined(PETSC_HAVE_HYPRE)
6059: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
6060: #endif
6061: #if defined(PETSC_HAVE_CUDA)
6062: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
6063: #endif
6064: #if defined(PETSC_HAVE_HIP)
6065: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6066: #endif
6067: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6068: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6069: #endif
6070: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6071: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6072: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
6074: /*
6075: Computes (B'*A')' since computing B*A directly is untenable
6077: n p p
6078: [ ] [ ] [ ]
6079: m [ A ] * n [ B ] = m [ C ]
6080: [ ] [ ] [ ]
6082: */
6083: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6084: {
6085: Mat At, Bt, Ct;
6087: PetscFunctionBegin;
6088: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6089: PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6090: PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_CURRENT, &Ct));
6091: PetscCall(MatDestroy(&At));
6092: PetscCall(MatDestroy(&Bt));
6093: PetscCall(MatTransposeSetPrecursor(Ct, C));
6094: PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6095: PetscCall(MatDestroy(&Ct));
6096: PetscFunctionReturn(PETSC_SUCCESS);
6097: }
6099: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6100: {
6101: PetscBool cisdense;
6103: PetscFunctionBegin;
6104: 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);
6105: PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6106: PetscCall(MatSetBlockSizesFromMats(C, A, B));
6107: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6108: if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6109: PetscCall(MatSetUp(C));
6111: C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6112: PetscFunctionReturn(PETSC_SUCCESS);
6113: }
6115: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6116: {
6117: Mat_Product *product = C->product;
6118: Mat A = product->A, B = product->B;
6120: PetscFunctionBegin;
6121: 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 ")",
6122: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6123: C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6124: C->ops->productsymbolic = MatProductSymbolic_AB;
6125: PetscFunctionReturn(PETSC_SUCCESS);
6126: }
6128: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6129: {
6130: Mat_Product *product = C->product;
6132: PetscFunctionBegin;
6133: if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6134: PetscFunctionReturn(PETSC_SUCCESS);
6135: }
6137: /*
6138: Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix
6140: Input Parameters:
6142: j1,rowBegin1,rowEnd1,jmap1: describe the first set of nonzeros (Set1)
6143: j2,rowBegin2,rowEnd2,jmap2: describe the second set of nonzeros (Set2)
6145: mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat
6147: For Set1, j1[] contains column indices of the nonzeros.
6148: 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
6149: respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6150: but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.
6152: Similar for Set2.
6154: This routine merges the two sets of nonzeros row by row and removes repeats.
6156: Output Parameters: (memory is allocated by the caller)
6158: i[],j[]: the CSR of the merged matrix, which has m rows.
6159: imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6160: imap2[]: similar to imap1[], but for Set2.
6161: Note we order nonzeros row-by-row and from left to right.
6162: */
6163: 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[])
6164: {
6165: PetscInt r, m; /* Row index of mat */
6166: PetscCount t, t1, t2, b1, e1, b2, e2;
6168: PetscFunctionBegin;
6169: PetscCall(MatGetLocalSize(mat, &m, NULL));
6170: t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6171: i[0] = 0;
6172: for (r = 0; r < m; r++) { /* Do row by row merging */
6173: b1 = rowBegin1[r];
6174: e1 = rowEnd1[r];
6175: b2 = rowBegin2[r];
6176: e2 = rowEnd2[r];
6177: while (b1 < e1 && b2 < e2) {
6178: if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6179: j[t] = j1[b1];
6180: imap1[t1] = t;
6181: imap2[t2] = t;
6182: b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6183: b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6184: t1++;
6185: t2++;
6186: t++;
6187: } else if (j1[b1] < j2[b2]) {
6188: j[t] = j1[b1];
6189: imap1[t1] = t;
6190: b1 += jmap1[t1 + 1] - jmap1[t1];
6191: t1++;
6192: t++;
6193: } else {
6194: j[t] = j2[b2];
6195: imap2[t2] = t;
6196: b2 += jmap2[t2 + 1] - jmap2[t2];
6197: t2++;
6198: t++;
6199: }
6200: }
6201: /* Merge the remaining in either j1[] or j2[] */
6202: while (b1 < e1) {
6203: j[t] = j1[b1];
6204: imap1[t1] = t;
6205: b1 += jmap1[t1 + 1] - jmap1[t1];
6206: t1++;
6207: t++;
6208: }
6209: while (b2 < e2) {
6210: j[t] = j2[b2];
6211: imap2[t2] = t;
6212: b2 += jmap2[t2 + 1] - jmap2[t2];
6213: t2++;
6214: t++;
6215: }
6216: PetscCall(PetscIntCast(t, i + r + 1));
6217: }
6218: PetscFunctionReturn(PETSC_SUCCESS);
6219: }
6221: /*
6222: Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block
6224: Input Parameters:
6225: mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6226: 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[]
6227: respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.
6229: i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6230: i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.
6232: Output Parameters:
6233: j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6234: rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6235: 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,
6236: and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.
6238: Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6239: Atot: number of entries belonging to the diagonal block.
6240: Annz: number of unique nonzeros belonging to the diagonal block.
6241: Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6242: repeats (i.e., same 'i,j' pair).
6243: Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6244: is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.
6246: Atot: number of entries belonging to the diagonal block
6247: Annz: number of unique nonzeros belonging to the diagonal block.
6249: Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.
6251: Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6252: */
6253: 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_)
6254: {
6255: PetscInt cstart, cend, rstart, rend, row, col;
6256: PetscCount Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6257: PetscCount Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6258: PetscCount k, m, p, q, r, s, mid;
6259: PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;
6261: PetscFunctionBegin;
6262: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6263: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6264: m = rend - rstart;
6266: /* Skip negative rows */
6267: for (k = 0; k < n; k++)
6268: if (i[k] >= 0) break;
6270: /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6271: fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6272: */
6273: while (k < n) {
6274: row = i[k];
6275: /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6276: for (s = k; s < n; s++)
6277: if (i[s] != row) break;
6279: /* Shift diag columns to range of [-PETSC_INT_MAX, -1] */
6280: for (p = k; p < s; p++) {
6281: if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_INT_MAX;
6282: 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]);
6283: }
6284: PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6285: PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6286: rowBegin[row - rstart] = k;
6287: rowMid[row - rstart] = mid;
6288: rowEnd[row - rstart] = s;
6290: /* Count nonzeros of this diag/offdiag row, which might have repeats */
6291: Atot += mid - k;
6292: Btot += s - mid;
6294: /* Count unique nonzeros of this diag row */
6295: for (p = k; p < mid;) {
6296: col = j[p];
6297: do {
6298: j[p] += PETSC_INT_MAX; /* Revert the modified diagonal indices */
6299: p++;
6300: } while (p < mid && j[p] == col);
6301: Annz++;
6302: }
6304: /* Count unique nonzeros of this offdiag row */
6305: for (p = mid; p < s;) {
6306: col = j[p];
6307: do {
6308: p++;
6309: } while (p < s && j[p] == col);
6310: Bnnz++;
6311: }
6312: k = s;
6313: }
6315: /* Allocation according to Atot, Btot, Annz, Bnnz */
6316: PetscCall(PetscMalloc1(Atot, &Aperm));
6317: PetscCall(PetscMalloc1(Btot, &Bperm));
6318: PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6319: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));
6321: /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6322: Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6323: for (r = 0; r < m; r++) {
6324: k = rowBegin[r];
6325: mid = rowMid[r];
6326: s = rowEnd[r];
6327: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Aperm, Atot), PetscSafePointerPlusOffset(perm, k), mid - k));
6328: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Bperm, Btot), PetscSafePointerPlusOffset(perm, mid), s - mid));
6329: Atot += mid - k;
6330: Btot += s - mid;
6332: /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6333: for (p = k; p < mid;) {
6334: col = j[p];
6335: q = p;
6336: do {
6337: p++;
6338: } while (p < mid && j[p] == col);
6339: Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6340: Annz++;
6341: }
6343: for (p = mid; p < s;) {
6344: col = j[p];
6345: q = p;
6346: do {
6347: p++;
6348: } while (p < s && j[p] == col);
6349: Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6350: Bnnz++;
6351: }
6352: }
6353: /* Output */
6354: *Aperm_ = Aperm;
6355: *Annz_ = Annz;
6356: *Atot_ = Atot;
6357: *Ajmap_ = Ajmap;
6358: *Bperm_ = Bperm;
6359: *Bnnz_ = Bnnz;
6360: *Btot_ = Btot;
6361: *Bjmap_ = Bjmap;
6362: PetscFunctionReturn(PETSC_SUCCESS);
6363: }
6365: /*
6366: Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix
6368: Input Parameters:
6369: nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6370: nnz: number of unique nonzeros in the merged matrix
6371: imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6372: jmap[nnz1+1]: i-th nonzero in the set has jmap[i+1] - jmap[i] repeats in the set
6374: Output Parameter: (memory is allocated by the caller)
6375: jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set
6377: Example:
6378: nnz1 = 4
6379: nnz = 6
6380: imap = [1,3,4,5]
6381: jmap = [0,3,5,6,7]
6382: then,
6383: jmap_new = [0,0,3,3,5,6,7]
6384: */
6385: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6386: {
6387: PetscCount k, p;
6389: PetscFunctionBegin;
6390: jmap_new[0] = 0;
6391: p = nnz; /* p loops over jmap_new[] backwards */
6392: for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6393: for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6394: }
6395: for (; p >= 0; p--) jmap_new[p] = jmap[0];
6396: PetscFunctionReturn(PETSC_SUCCESS);
6397: }
6399: static PetscErrorCode MatCOOStructDestroy_MPIAIJ(void *data)
6400: {
6401: MatCOOStruct_MPIAIJ *coo = (MatCOOStruct_MPIAIJ *)data;
6403: PetscFunctionBegin;
6404: PetscCall(PetscSFDestroy(&coo->sf));
6405: PetscCall(PetscFree(coo->Aperm1));
6406: PetscCall(PetscFree(coo->Bperm1));
6407: PetscCall(PetscFree(coo->Ajmap1));
6408: PetscCall(PetscFree(coo->Bjmap1));
6409: PetscCall(PetscFree(coo->Aimap2));
6410: PetscCall(PetscFree(coo->Bimap2));
6411: PetscCall(PetscFree(coo->Aperm2));
6412: PetscCall(PetscFree(coo->Bperm2));
6413: PetscCall(PetscFree(coo->Ajmap2));
6414: PetscCall(PetscFree(coo->Bjmap2));
6415: PetscCall(PetscFree(coo->Cperm1));
6416: PetscCall(PetscFree2(coo->sendbuf, coo->recvbuf));
6417: PetscCall(PetscFree(coo));
6418: PetscFunctionReturn(PETSC_SUCCESS);
6419: }
6421: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6422: {
6423: MPI_Comm comm;
6424: PetscMPIInt rank, size;
6425: PetscInt m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6426: PetscCount k, p, q, rem; /* Loop variables over coo arrays */
6427: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6428: PetscContainer container;
6429: MatCOOStruct_MPIAIJ *coo;
6431: PetscFunctionBegin;
6432: PetscCall(PetscFree(mpiaij->garray));
6433: PetscCall(VecDestroy(&mpiaij->lvec));
6434: #if defined(PETSC_USE_CTABLE)
6435: PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6436: #else
6437: PetscCall(PetscFree(mpiaij->colmap));
6438: #endif
6439: PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6440: mat->assembled = PETSC_FALSE;
6441: mat->was_assembled = PETSC_FALSE;
6443: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6444: PetscCallMPI(MPI_Comm_size(comm, &size));
6445: PetscCallMPI(MPI_Comm_rank(comm, &rank));
6446: PetscCall(PetscLayoutSetUp(mat->rmap));
6447: PetscCall(PetscLayoutSetUp(mat->cmap));
6448: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6449: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6450: PetscCall(MatGetLocalSize(mat, &m, &n));
6451: PetscCall(MatGetSize(mat, &M, &N));
6453: /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6454: /* entries come first, then local rows, then remote rows. */
6455: PetscCount n1 = coo_n, *perm1;
6456: PetscInt *i1 = coo_i, *j1 = coo_j;
6458: PetscCall(PetscMalloc1(n1, &perm1));
6459: for (k = 0; k < n1; k++) perm1[k] = k;
6461: /* Manipulate indices so that entries with negative row or col indices will have smallest
6462: row indices, local entries will have greater but negative row indices, and remote entries
6463: will have positive row indices.
6464: */
6465: for (k = 0; k < n1; k++) {
6466: if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_INT_MIN; /* e.g., -2^31, minimal to move them ahead */
6467: else if (i1[k] >= rstart && i1[k] < rend) i1[k] -= PETSC_INT_MAX; /* e.g., minus 2^31-1 to shift local rows to range of [-PETSC_INT_MAX, -1] */
6468: else {
6469: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6470: if (mpiaij->donotstash) i1[k] = PETSC_INT_MIN; /* Ignore offproc entries as if they had negative indices */
6471: }
6472: }
6474: /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6475: PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));
6477: /* Advance k to the first entry we need to take care of */
6478: for (k = 0; k < n1; k++)
6479: if (i1[k] > PETSC_INT_MIN) break;
6480: PetscCount i1start = k;
6482: PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_INT_MAX, &rem)); /* rem is upper bound of the last local row */
6483: for (; k < rem; k++) i1[k] += PETSC_INT_MAX; /* Revert row indices of local rows*/
6485: /* Send remote rows to their owner */
6486: /* Find which rows should be sent to which remote ranks*/
6487: PetscInt nsend = 0; /* Number of MPI ranks to send data to */
6488: PetscMPIInt *sendto; /* [nsend], storing remote ranks */
6489: PetscInt *nentries; /* [nsend], storing number of entries sent to remote ranks; Assume PetscInt is big enough for this count, and error if not */
6490: const PetscInt *ranges;
6491: PetscInt maxNsend = size >= 128 ? 128 : size; /* Assume max 128 neighbors; realloc when needed */
6493: PetscCall(PetscLayoutGetRanges(mat->rmap, &ranges));
6494: PetscCall(PetscMalloc2(maxNsend, &sendto, maxNsend, &nentries));
6495: for (k = rem; k < n1;) {
6496: PetscMPIInt owner;
6497: PetscInt firstRow, lastRow;
6499: /* Locate a row range */
6500: firstRow = i1[k]; /* first row of this owner */
6501: PetscCall(PetscLayoutFindOwner(mat->rmap, firstRow, &owner));
6502: lastRow = ranges[owner + 1] - 1; /* last row of this owner */
6504: /* Find the first index 'p' in [k,n) with i[p] belonging to next owner */
6505: PetscCall(PetscSortedIntUpperBound(i1, k, n1, lastRow, &p));
6507: /* All entries in [k,p) belong to this remote owner */
6508: if (nsend >= maxNsend) { /* Double the remote ranks arrays if not long enough */
6509: PetscMPIInt *sendto2;
6510: PetscInt *nentries2;
6511: PetscInt maxNsend2 = (maxNsend <= size / 2) ? maxNsend * 2 : size;
6513: PetscCall(PetscMalloc2(maxNsend2, &sendto2, maxNsend2, &nentries2));
6514: PetscCall(PetscArraycpy(sendto2, sendto, maxNsend));
6515: PetscCall(PetscArraycpy(nentries2, nentries2, maxNsend + 1));
6516: PetscCall(PetscFree2(sendto, nentries2));
6517: sendto = sendto2;
6518: nentries = nentries2;
6519: maxNsend = maxNsend2;
6520: }
6521: sendto[nsend] = owner;
6522: PetscCall(PetscIntCast(p - k, &nentries[nsend]));
6523: nsend++;
6524: k = p;
6525: }
6527: /* Build 1st SF to know offsets on remote to send data */
6528: PetscSF sf1;
6529: PetscInt nroots = 1, nroots2 = 0;
6530: PetscInt nleaves = nsend, nleaves2 = 0;
6531: PetscInt *offsets;
6532: PetscSFNode *iremote;
6534: PetscCall(PetscSFCreate(comm, &sf1));
6535: PetscCall(PetscMalloc1(nsend, &iremote));
6536: PetscCall(PetscMalloc1(nsend, &offsets));
6537: for (k = 0; k < nsend; k++) {
6538: iremote[k].rank = sendto[k];
6539: iremote[k].index = 0;
6540: nleaves2 += nentries[k];
6541: PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6542: }
6543: PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6544: PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6545: PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6546: PetscCall(PetscSFDestroy(&sf1));
6547: PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT, nleaves2, n1 - rem);
6549: /* Build 2nd SF to send remote COOs to their owner */
6550: PetscSF sf2;
6551: nroots = nroots2;
6552: nleaves = nleaves2;
6553: PetscCall(PetscSFCreate(comm, &sf2));
6554: PetscCall(PetscSFSetFromOptions(sf2));
6555: PetscCall(PetscMalloc1(nleaves, &iremote));
6556: p = 0;
6557: for (k = 0; k < nsend; k++) {
6558: PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6559: for (q = 0; q < nentries[k]; q++, p++) {
6560: iremote[p].rank = sendto[k];
6561: PetscCall(PetscIntCast(offsets[k] + q, &iremote[p].index));
6562: }
6563: }
6564: PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6566: /* Send the remote COOs to their owner */
6567: PetscInt n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6568: PetscCount *perm2; /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6569: PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6570: PetscAssert(rem == 0 || i1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6571: PetscAssert(rem == 0 || j1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6572: PetscInt *i1prem = PetscSafePointerPlusOffset(i1, rem);
6573: PetscInt *j1prem = PetscSafePointerPlusOffset(j1, rem);
6574: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1prem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6575: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1prem, i2, MPI_REPLACE));
6576: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1prem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6577: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1prem, j2, MPI_REPLACE));
6579: PetscCall(PetscFree(offsets));
6580: PetscCall(PetscFree2(sendto, nentries));
6582: /* Sort received COOs by row along with the permutation array */
6583: for (k = 0; k < n2; k++) perm2[k] = k;
6584: PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));
6586: /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6587: PetscCount *Cperm1;
6588: PetscAssert(rem == 0 || perm1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6589: PetscCount *perm1prem = PetscSafePointerPlusOffset(perm1, rem);
6590: PetscCall(PetscMalloc1(nleaves, &Cperm1));
6591: PetscCall(PetscArraycpy(Cperm1, perm1prem, nleaves));
6593: /* Support for HYPRE matrices, kind of a hack.
6594: Swap min column with diagonal so that diagonal values will go first */
6595: PetscBool hypre;
6596: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", ((PetscObject)mat)->name, &hypre));
6597: if (hypre) {
6598: PetscInt *minj;
6599: PetscBT hasdiag;
6601: PetscCall(PetscBTCreate(m, &hasdiag));
6602: PetscCall(PetscMalloc1(m, &minj));
6603: for (k = 0; k < m; k++) minj[k] = PETSC_INT_MAX;
6604: for (k = i1start; k < rem; k++) {
6605: if (j1[k] < cstart || j1[k] >= cend) continue;
6606: const PetscInt rindex = i1[k] - rstart;
6607: if ((j1[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6608: minj[rindex] = PetscMin(minj[rindex], j1[k]);
6609: }
6610: for (k = 0; k < n2; k++) {
6611: if (j2[k] < cstart || j2[k] >= cend) continue;
6612: const PetscInt rindex = i2[k] - rstart;
6613: if ((j2[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6614: minj[rindex] = PetscMin(minj[rindex], j2[k]);
6615: }
6616: for (k = i1start; k < rem; k++) {
6617: const PetscInt rindex = i1[k] - rstart;
6618: if (j1[k] < cstart || j1[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6619: if (j1[k] == minj[rindex]) j1[k] = i1[k] + (cstart - rstart);
6620: else if ((j1[k] - cstart) == rindex) j1[k] = minj[rindex];
6621: }
6622: for (k = 0; k < n2; k++) {
6623: const PetscInt rindex = i2[k] - rstart;
6624: if (j2[k] < cstart || j2[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6625: if (j2[k] == minj[rindex]) j2[k] = i2[k] + (cstart - rstart);
6626: else if ((j2[k] - cstart) == rindex) j2[k] = minj[rindex];
6627: }
6628: PetscCall(PetscBTDestroy(&hasdiag));
6629: PetscCall(PetscFree(minj));
6630: }
6632: /* Split local COOs and received COOs into diag/offdiag portions */
6633: PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6634: PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1;
6635: PetscCount Annz1, Bnnz1, Atot1, Btot1;
6636: PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6637: PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6638: PetscCount Annz2, Bnnz2, Atot2, Btot2;
6640: PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6641: PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6642: PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6643: PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));
6645: /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6646: PetscInt *Ai, *Bi;
6647: PetscInt *Aj, *Bj;
6649: PetscCall(PetscMalloc1(m + 1, &Ai));
6650: PetscCall(PetscMalloc1(m + 1, &Bi));
6651: PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6652: PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));
6654: PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6655: PetscCall(PetscMalloc1(Annz1, &Aimap1));
6656: PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6657: PetscCall(PetscMalloc1(Annz2, &Aimap2));
6658: PetscCall(PetscMalloc1(Bnnz2, &Bimap2));
6660: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6661: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));
6663: /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we */
6664: /* expect nonzeros in A/B most likely have local contributing entries */
6665: PetscInt Annz = Ai[m];
6666: PetscInt Bnnz = Bi[m];
6667: PetscCount *Ajmap1_new, *Bjmap1_new;
6669: PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6670: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));
6672: PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6673: PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));
6675: PetscCall(PetscFree(Aimap1));
6676: PetscCall(PetscFree(Ajmap1));
6677: PetscCall(PetscFree(Bimap1));
6678: PetscCall(PetscFree(Bjmap1));
6679: PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6680: PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6681: PetscCall(PetscFree(perm1));
6682: PetscCall(PetscFree3(i2, j2, perm2));
6684: Ajmap1 = Ajmap1_new;
6685: Bjmap1 = Bjmap1_new;
6687: /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6688: if (Annz < Annz1 + Annz2) {
6689: PetscInt *Aj_new;
6690: PetscCall(PetscMalloc1(Annz, &Aj_new));
6691: PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6692: PetscCall(PetscFree(Aj));
6693: Aj = Aj_new;
6694: }
6696: if (Bnnz < Bnnz1 + Bnnz2) {
6697: PetscInt *Bj_new;
6698: PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6699: PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6700: PetscCall(PetscFree(Bj));
6701: Bj = Bj_new;
6702: }
6704: /* Create new submatrices for on-process and off-process coupling */
6705: PetscScalar *Aa, *Ba;
6706: MatType rtype;
6707: Mat_SeqAIJ *a, *b;
6708: PetscObjectState state;
6709: PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6710: PetscCall(PetscCalloc1(Bnnz, &Ba));
6711: /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6712: if (cstart) {
6713: for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6714: }
6716: PetscCall(MatGetRootType_Private(mat, &rtype));
6718: MatSeqXAIJGetOptions_Private(mpiaij->A);
6719: PetscCall(MatDestroy(&mpiaij->A));
6720: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6721: PetscCall(MatSetBlockSizesFromMats(mpiaij->A, mat, mat));
6722: MatSeqXAIJRestoreOptions_Private(mpiaij->A);
6724: MatSeqXAIJGetOptions_Private(mpiaij->B);
6725: PetscCall(MatDestroy(&mpiaij->B));
6726: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6727: PetscCall(MatSetBlockSizesFromMats(mpiaij->B, mat, mat));
6728: MatSeqXAIJRestoreOptions_Private(mpiaij->B);
6730: PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6731: mat->was_assembled = PETSC_TRUE; // was_assembled in effect means the Mvctx is built; doing so avoids redundant MatSetUpMultiply_MPIAIJ
6732: state = mpiaij->A->nonzerostate + mpiaij->B->nonzerostate;
6733: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
6735: a = (Mat_SeqAIJ *)mpiaij->A->data;
6736: b = (Mat_SeqAIJ *)mpiaij->B->data;
6737: a->free_a = PETSC_TRUE;
6738: a->free_ij = PETSC_TRUE;
6739: b->free_a = PETSC_TRUE;
6740: b->free_ij = PETSC_TRUE;
6741: a->maxnz = a->nz;
6742: b->maxnz = b->nz;
6744: /* conversion must happen AFTER multiply setup */
6745: PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6746: PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6747: PetscCall(VecDestroy(&mpiaij->lvec));
6748: PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));
6750: // Put the COO struct in a container and then attach that to the matrix
6751: PetscCall(PetscMalloc1(1, &coo));
6752: coo->n = coo_n;
6753: coo->sf = sf2;
6754: coo->sendlen = nleaves;
6755: coo->recvlen = nroots;
6756: coo->Annz = Annz;
6757: coo->Bnnz = Bnnz;
6758: coo->Annz2 = Annz2;
6759: coo->Bnnz2 = Bnnz2;
6760: coo->Atot1 = Atot1;
6761: coo->Atot2 = Atot2;
6762: coo->Btot1 = Btot1;
6763: coo->Btot2 = Btot2;
6764: coo->Ajmap1 = Ajmap1;
6765: coo->Aperm1 = Aperm1;
6766: coo->Bjmap1 = Bjmap1;
6767: coo->Bperm1 = Bperm1;
6768: coo->Aimap2 = Aimap2;
6769: coo->Ajmap2 = Ajmap2;
6770: coo->Aperm2 = Aperm2;
6771: coo->Bimap2 = Bimap2;
6772: coo->Bjmap2 = Bjmap2;
6773: coo->Bperm2 = Bperm2;
6774: coo->Cperm1 = Cperm1;
6775: // Allocate in preallocation. If not used, it has zero cost on host
6776: PetscCall(PetscMalloc2(coo->sendlen, &coo->sendbuf, coo->recvlen, &coo->recvbuf));
6777: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
6778: PetscCall(PetscContainerSetPointer(container, coo));
6779: PetscCall(PetscContainerSetUserDestroy(container, MatCOOStructDestroy_MPIAIJ));
6780: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
6781: PetscCall(PetscContainerDestroy(&container));
6782: PetscFunctionReturn(PETSC_SUCCESS);
6783: }
6785: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6786: {
6787: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6788: Mat A = mpiaij->A, B = mpiaij->B;
6789: PetscScalar *Aa, *Ba;
6790: PetscScalar *sendbuf, *recvbuf;
6791: const PetscCount *Ajmap1, *Ajmap2, *Aimap2;
6792: const PetscCount *Bjmap1, *Bjmap2, *Bimap2;
6793: const PetscCount *Aperm1, *Aperm2, *Bperm1, *Bperm2;
6794: const PetscCount *Cperm1;
6795: PetscContainer container;
6796: MatCOOStruct_MPIAIJ *coo;
6798: PetscFunctionBegin;
6799: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
6800: PetscCheck(container, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
6801: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
6802: sendbuf = coo->sendbuf;
6803: recvbuf = coo->recvbuf;
6804: Ajmap1 = coo->Ajmap1;
6805: Ajmap2 = coo->Ajmap2;
6806: Aimap2 = coo->Aimap2;
6807: Bjmap1 = coo->Bjmap1;
6808: Bjmap2 = coo->Bjmap2;
6809: Bimap2 = coo->Bimap2;
6810: Aperm1 = coo->Aperm1;
6811: Aperm2 = coo->Aperm2;
6812: Bperm1 = coo->Bperm1;
6813: Bperm2 = coo->Bperm2;
6814: Cperm1 = coo->Cperm1;
6816: PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6817: PetscCall(MatSeqAIJGetArray(B, &Ba));
6819: /* Pack entries to be sent to remote */
6820: for (PetscCount i = 0; i < coo->sendlen; i++) sendbuf[i] = v[Cperm1[i]];
6822: /* Send remote entries to their owner and overlap the communication with local computation */
6823: PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6824: /* Add local entries to A and B */
6825: for (PetscCount i = 0; i < coo->Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6826: PetscScalar sum = 0.0; /* Do partial summation first to improve numerical stability */
6827: for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6828: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6829: }
6830: for (PetscCount i = 0; i < coo->Bnnz; i++) {
6831: PetscScalar sum = 0.0;
6832: for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6833: Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6834: }
6835: PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));
6837: /* Add received remote entries to A and B */
6838: for (PetscCount i = 0; i < coo->Annz2; i++) {
6839: for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6840: }
6841: for (PetscCount i = 0; i < coo->Bnnz2; i++) {
6842: for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6843: }
6844: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6845: PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6846: PetscFunctionReturn(PETSC_SUCCESS);
6847: }
6849: /*MC
6850: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
6852: Options Database Keys:
6853: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`
6855: Level: beginner
6857: Notes:
6858: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
6859: in this case the values associated with the rows and columns one passes in are set to zero
6860: in the matrix
6862: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
6863: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
6865: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6866: M*/
6867: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6868: {
6869: Mat_MPIAIJ *b;
6870: PetscMPIInt size;
6872: PetscFunctionBegin;
6873: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
6875: PetscCall(PetscNew(&b));
6876: B->data = (void *)b;
6877: B->ops[0] = MatOps_Values;
6878: B->assembled = PETSC_FALSE;
6879: B->insertmode = NOT_SET_VALUES;
6880: b->size = size;
6882: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
6884: /* build cache for off array entries formed */
6885: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
6887: b->donotstash = PETSC_FALSE;
6888: b->colmap = NULL;
6889: b->garray = NULL;
6890: b->roworiented = PETSC_TRUE;
6892: /* stuff used for matrix vector multiply */
6893: b->lvec = NULL;
6894: b->Mvctx = NULL;
6896: /* stuff for MatGetRow() */
6897: b->rowindices = NULL;
6898: b->rowvalues = NULL;
6899: b->getrowactive = PETSC_FALSE;
6901: /* flexible pointer used in CUSPARSE classes */
6902: b->spptr = NULL;
6904: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6905: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6906: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6907: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6908: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6909: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6910: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6911: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6912: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6913: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6914: #if defined(PETSC_HAVE_CUDA)
6915: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6916: #endif
6917: #if defined(PETSC_HAVE_HIP)
6918: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6919: #endif
6920: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6921: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6922: #endif
6923: #if defined(PETSC_HAVE_MKL_SPARSE)
6924: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6925: #endif
6926: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6927: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6928: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6929: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6930: #if defined(PETSC_HAVE_ELEMENTAL)
6931: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6932: #endif
6933: #if defined(PETSC_HAVE_SCALAPACK)
6934: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6935: #endif
6936: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6937: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6938: #if defined(PETSC_HAVE_HYPRE)
6939: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6940: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6941: #endif
6942: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6943: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6944: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6945: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6946: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6947: PetscFunctionReturn(PETSC_SUCCESS);
6948: }
6950: /*@
6951: MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6952: and "off-diagonal" part of the matrix in CSR format.
6954: Collective
6956: Input Parameters:
6957: + comm - MPI communicator
6958: . m - number of local rows (Cannot be `PETSC_DECIDE`)
6959: . n - This value should be the same as the local size used in creating the
6960: x vector for the matrix-vector product $y = Ax$. (or `PETSC_DECIDE` to have
6961: calculated if `N` is given) For square matrices `n` is almost always `m`.
6962: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6963: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6964: . 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
6965: . j - column indices, which must be local, i.e., based off the start column of the diagonal portion
6966: . a - matrix values
6967: . 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
6968: . oj - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6969: - oa - matrix values
6971: Output Parameter:
6972: . mat - the matrix
6974: Level: advanced
6976: Notes:
6977: The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc (even in Fortran). The user
6978: must free the arrays once the matrix has been destroyed and not before.
6980: The `i` and `j` indices are 0 based
6982: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix
6984: This sets local rows and cannot be used to set off-processor values.
6986: Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6987: legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6988: not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
6989: the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
6990: keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
6991: communication if it is known that only local entries will be set.
6993: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
6994: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
6995: @*/
6996: 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)
6997: {
6998: Mat_MPIAIJ *maij;
7000: PetscFunctionBegin;
7001: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
7002: PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
7003: PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
7004: PetscCall(MatCreate(comm, mat));
7005: PetscCall(MatSetSizes(*mat, m, n, M, N));
7006: PetscCall(MatSetType(*mat, MATMPIAIJ));
7007: maij = (Mat_MPIAIJ *)(*mat)->data;
7009: (*mat)->preallocated = PETSC_TRUE;
7011: PetscCall(PetscLayoutSetUp((*mat)->rmap));
7012: PetscCall(PetscLayoutSetUp((*mat)->cmap));
7014: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
7015: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));
7017: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
7018: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
7019: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
7020: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
7021: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
7022: PetscFunctionReturn(PETSC_SUCCESS);
7023: }
7025: typedef struct {
7026: Mat *mp; /* intermediate products */
7027: PetscBool *mptmp; /* is the intermediate product temporary ? */
7028: PetscInt cp; /* number of intermediate products */
7030: /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
7031: PetscInt *startsj_s, *startsj_r;
7032: PetscScalar *bufa;
7033: Mat P_oth;
7035: /* may take advantage of merging product->B */
7036: Mat Bloc; /* B-local by merging diag and off-diag */
7038: /* cusparse does not have support to split between symbolic and numeric phases.
7039: When api_user is true, we don't need to update the numerical values
7040: of the temporary storage */
7041: PetscBool reusesym;
7043: /* support for COO values insertion */
7044: PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
7045: PetscInt **own; /* own[i] points to address of on-process COO indices for Mat mp[i] */
7046: PetscInt **off; /* off[i] points to address of off-process COO indices for Mat mp[i] */
7047: PetscBool hasoffproc; /* if true, have off-process values insertion (i.e. AtB or PtAP) */
7048: PetscSF sf; /* used for non-local values insertion and memory malloc */
7049: PetscMemType mtype;
7051: /* customization */
7052: PetscBool abmerge;
7053: PetscBool P_oth_bind;
7054: } MatMatMPIAIJBACKEND;
7056: static PetscErrorCode MatDestroy_MatMatMPIAIJBACKEND(void *data)
7057: {
7058: MatMatMPIAIJBACKEND *mmdata = (MatMatMPIAIJBACKEND *)data;
7059: PetscInt i;
7061: PetscFunctionBegin;
7062: PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
7063: PetscCall(PetscFree(mmdata->bufa));
7064: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
7065: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
7066: PetscCall(MatDestroy(&mmdata->P_oth));
7067: PetscCall(MatDestroy(&mmdata->Bloc));
7068: PetscCall(PetscSFDestroy(&mmdata->sf));
7069: for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
7070: PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
7071: PetscCall(PetscFree(mmdata->own[0]));
7072: PetscCall(PetscFree(mmdata->own));
7073: PetscCall(PetscFree(mmdata->off[0]));
7074: PetscCall(PetscFree(mmdata->off));
7075: PetscCall(PetscFree(mmdata));
7076: PetscFunctionReturn(PETSC_SUCCESS);
7077: }
7079: /* Copy selected n entries with indices in idx[] of A to v[].
7080: If idx is NULL, copy the whole data array of A to v[]
7081: */
7082: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
7083: {
7084: PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);
7086: PetscFunctionBegin;
7087: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
7088: if (f) {
7089: PetscCall((*f)(A, n, idx, v));
7090: } else {
7091: const PetscScalar *vv;
7093: PetscCall(MatSeqAIJGetArrayRead(A, &vv));
7094: if (n && idx) {
7095: PetscScalar *w = v;
7096: const PetscInt *oi = idx;
7097: PetscInt j;
7099: for (j = 0; j < n; j++) *w++ = vv[*oi++];
7100: } else {
7101: PetscCall(PetscArraycpy(v, vv, n));
7102: }
7103: PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7104: }
7105: PetscFunctionReturn(PETSC_SUCCESS);
7106: }
7108: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7109: {
7110: MatMatMPIAIJBACKEND *mmdata;
7111: PetscInt i, n_d, n_o;
7113: PetscFunctionBegin;
7114: MatCheckProduct(C, 1);
7115: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7116: mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7117: if (!mmdata->reusesym) { /* update temporary matrices */
7118: 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));
7119: if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7120: }
7121: mmdata->reusesym = PETSC_FALSE;
7123: for (i = 0; i < mmdata->cp; i++) {
7124: 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]);
7125: PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7126: }
7127: for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7128: PetscInt noff;
7130: PetscCall(PetscIntCast(mmdata->off[i + 1] - mmdata->off[i], &noff));
7131: if (mmdata->mptmp[i]) continue;
7132: if (noff) {
7133: PetscInt nown;
7135: PetscCall(PetscIntCast(mmdata->own[i + 1] - mmdata->own[i], &nown));
7136: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7137: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7138: n_o += noff;
7139: n_d += nown;
7140: } else {
7141: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;
7143: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7144: n_d += mm->nz;
7145: }
7146: }
7147: if (mmdata->hasoffproc) { /* offprocess insertion */
7148: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7149: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7150: }
7151: PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7152: PetscFunctionReturn(PETSC_SUCCESS);
7153: }
7155: /* Support for Pt * A, A * P, or Pt * A * P */
7156: #define MAX_NUMBER_INTERMEDIATE 4
7157: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7158: {
7159: Mat_Product *product = C->product;
7160: Mat A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7161: Mat_MPIAIJ *a, *p;
7162: MatMatMPIAIJBACKEND *mmdata;
7163: ISLocalToGlobalMapping P_oth_l2g = NULL;
7164: IS glob = NULL;
7165: const char *prefix;
7166: char pprefix[256];
7167: const PetscInt *globidx, *P_oth_idx;
7168: PetscInt i, j, cp, m, n, M, N, *coo_i, *coo_j;
7169: PetscCount ncoo, ncoo_d, ncoo_o, ncoo_oown;
7170: PetscInt cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7171: /* type-0: consecutive, start from 0; type-1: consecutive with */
7172: /* a base offset; type-2: sparse with a local to global map table */
7173: const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE]; /* col/row local to global map array (table) for type-2 map type */
7175: MatProductType ptype;
7176: PetscBool mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7177: PetscMPIInt size;
7179: PetscFunctionBegin;
7180: MatCheckProduct(C, 1);
7181: PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7182: ptype = product->type;
7183: if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7184: ptype = MATPRODUCT_AB;
7185: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7186: }
7187: switch (ptype) {
7188: case MATPRODUCT_AB:
7189: A = product->A;
7190: P = product->B;
7191: m = A->rmap->n;
7192: n = P->cmap->n;
7193: M = A->rmap->N;
7194: N = P->cmap->N;
7195: hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7196: break;
7197: case MATPRODUCT_AtB:
7198: P = product->A;
7199: A = product->B;
7200: m = P->cmap->n;
7201: n = A->cmap->n;
7202: M = P->cmap->N;
7203: N = A->cmap->N;
7204: hasoffproc = PETSC_TRUE;
7205: break;
7206: case MATPRODUCT_PtAP:
7207: A = product->A;
7208: P = product->B;
7209: m = P->cmap->n;
7210: n = P->cmap->n;
7211: M = P->cmap->N;
7212: N = P->cmap->N;
7213: hasoffproc = PETSC_TRUE;
7214: break;
7215: default:
7216: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7217: }
7218: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7219: if (size == 1) hasoffproc = PETSC_FALSE;
7221: /* defaults */
7222: for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7223: mp[i] = NULL;
7224: mptmp[i] = PETSC_FALSE;
7225: rmapt[i] = -1;
7226: cmapt[i] = -1;
7227: rmapa[i] = NULL;
7228: cmapa[i] = NULL;
7229: }
7231: /* customization */
7232: PetscCall(PetscNew(&mmdata));
7233: mmdata->reusesym = product->api_user;
7234: if (ptype == MATPRODUCT_AB) {
7235: if (product->api_user) {
7236: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7237: PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7238: PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7239: PetscOptionsEnd();
7240: } else {
7241: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7242: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7243: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7244: PetscOptionsEnd();
7245: }
7246: } else if (ptype == MATPRODUCT_PtAP) {
7247: if (product->api_user) {
7248: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7249: PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7250: PetscOptionsEnd();
7251: } else {
7252: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7253: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7254: PetscOptionsEnd();
7255: }
7256: }
7257: a = (Mat_MPIAIJ *)A->data;
7258: p = (Mat_MPIAIJ *)P->data;
7259: PetscCall(MatSetSizes(C, m, n, M, N));
7260: PetscCall(PetscLayoutSetUp(C->rmap));
7261: PetscCall(PetscLayoutSetUp(C->cmap));
7262: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7263: PetscCall(MatGetOptionsPrefix(C, &prefix));
7265: cp = 0;
7266: switch (ptype) {
7267: case MATPRODUCT_AB: /* A * P */
7268: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7270: /* A_diag * P_local (merged or not) */
7271: if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7272: /* P is product->B */
7273: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7274: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7275: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7276: PetscCall(MatProductSetFill(mp[cp], product->fill));
7277: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7278: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7279: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7280: mp[cp]->product->api_user = product->api_user;
7281: PetscCall(MatProductSetFromOptions(mp[cp]));
7282: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7283: PetscCall(ISGetIndices(glob, &globidx));
7284: rmapt[cp] = 1;
7285: cmapt[cp] = 2;
7286: cmapa[cp] = globidx;
7287: mptmp[cp] = PETSC_FALSE;
7288: cp++;
7289: } else { /* A_diag * P_diag and A_diag * P_off */
7290: PetscCall(MatProductCreate(a->A, p->A, NULL, &mp[cp]));
7291: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7292: PetscCall(MatProductSetFill(mp[cp], product->fill));
7293: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7294: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7295: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7296: mp[cp]->product->api_user = product->api_user;
7297: PetscCall(MatProductSetFromOptions(mp[cp]));
7298: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7299: rmapt[cp] = 1;
7300: cmapt[cp] = 1;
7301: mptmp[cp] = PETSC_FALSE;
7302: cp++;
7303: PetscCall(MatProductCreate(a->A, p->B, NULL, &mp[cp]));
7304: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7305: PetscCall(MatProductSetFill(mp[cp], product->fill));
7306: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7307: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7308: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7309: mp[cp]->product->api_user = product->api_user;
7310: PetscCall(MatProductSetFromOptions(mp[cp]));
7311: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7312: rmapt[cp] = 1;
7313: cmapt[cp] = 2;
7314: cmapa[cp] = p->garray;
7315: mptmp[cp] = PETSC_FALSE;
7316: cp++;
7317: }
7319: /* A_off * P_other */
7320: if (mmdata->P_oth) {
7321: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7322: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7323: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7324: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7325: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7326: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7327: PetscCall(MatProductSetFill(mp[cp], product->fill));
7328: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7329: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7330: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7331: mp[cp]->product->api_user = product->api_user;
7332: PetscCall(MatProductSetFromOptions(mp[cp]));
7333: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7334: rmapt[cp] = 1;
7335: cmapt[cp] = 2;
7336: cmapa[cp] = P_oth_idx;
7337: mptmp[cp] = PETSC_FALSE;
7338: cp++;
7339: }
7340: break;
7342: case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7343: /* A is product->B */
7344: PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7345: if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7346: PetscCall(MatProductCreate(mmdata->Bloc, mmdata->Bloc, NULL, &mp[cp]));
7347: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7348: PetscCall(MatProductSetFill(mp[cp], product->fill));
7349: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7350: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7351: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7352: mp[cp]->product->api_user = product->api_user;
7353: PetscCall(MatProductSetFromOptions(mp[cp]));
7354: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7355: PetscCall(ISGetIndices(glob, &globidx));
7356: rmapt[cp] = 2;
7357: rmapa[cp] = globidx;
7358: cmapt[cp] = 2;
7359: cmapa[cp] = globidx;
7360: mptmp[cp] = PETSC_FALSE;
7361: cp++;
7362: } else {
7363: PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7364: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7365: PetscCall(MatProductSetFill(mp[cp], product->fill));
7366: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7367: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7368: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7369: mp[cp]->product->api_user = product->api_user;
7370: PetscCall(MatProductSetFromOptions(mp[cp]));
7371: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7372: PetscCall(ISGetIndices(glob, &globidx));
7373: rmapt[cp] = 1;
7374: cmapt[cp] = 2;
7375: cmapa[cp] = globidx;
7376: mptmp[cp] = PETSC_FALSE;
7377: cp++;
7378: PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7379: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7380: PetscCall(MatProductSetFill(mp[cp], product->fill));
7381: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7382: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7383: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7384: mp[cp]->product->api_user = product->api_user;
7385: PetscCall(MatProductSetFromOptions(mp[cp]));
7386: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7387: rmapt[cp] = 2;
7388: rmapa[cp] = p->garray;
7389: cmapt[cp] = 2;
7390: cmapa[cp] = globidx;
7391: mptmp[cp] = PETSC_FALSE;
7392: cp++;
7393: }
7394: break;
7395: case MATPRODUCT_PtAP:
7396: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7397: /* P is product->B */
7398: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7399: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7400: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
7401: PetscCall(MatProductSetFill(mp[cp], product->fill));
7402: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7403: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7404: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7405: mp[cp]->product->api_user = product->api_user;
7406: PetscCall(MatProductSetFromOptions(mp[cp]));
7407: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7408: PetscCall(ISGetIndices(glob, &globidx));
7409: rmapt[cp] = 2;
7410: rmapa[cp] = globidx;
7411: cmapt[cp] = 2;
7412: cmapa[cp] = globidx;
7413: mptmp[cp] = PETSC_FALSE;
7414: cp++;
7415: if (mmdata->P_oth) {
7416: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7417: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7418: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7419: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7420: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7421: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7422: PetscCall(MatProductSetFill(mp[cp], product->fill));
7423: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7424: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7425: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7426: mp[cp]->product->api_user = product->api_user;
7427: PetscCall(MatProductSetFromOptions(mp[cp]));
7428: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7429: mptmp[cp] = PETSC_TRUE;
7430: cp++;
7431: PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7432: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7433: PetscCall(MatProductSetFill(mp[cp], product->fill));
7434: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7435: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7436: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7437: mp[cp]->product->api_user = product->api_user;
7438: PetscCall(MatProductSetFromOptions(mp[cp]));
7439: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7440: rmapt[cp] = 2;
7441: rmapa[cp] = globidx;
7442: cmapt[cp] = 2;
7443: cmapa[cp] = P_oth_idx;
7444: mptmp[cp] = PETSC_FALSE;
7445: cp++;
7446: }
7447: break;
7448: default:
7449: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7450: }
7451: /* sanity check */
7452: if (size > 1)
7453: 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);
7455: PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7456: for (i = 0; i < cp; i++) {
7457: mmdata->mp[i] = mp[i];
7458: mmdata->mptmp[i] = mptmp[i];
7459: }
7460: mmdata->cp = cp;
7461: C->product->data = mmdata;
7462: C->product->destroy = MatDestroy_MatMatMPIAIJBACKEND;
7463: C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;
7465: /* memory type */
7466: mmdata->mtype = PETSC_MEMTYPE_HOST;
7467: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7468: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7469: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7470: if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7471: else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7472: else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;
7474: /* prepare coo coordinates for values insertion */
7476: /* count total nonzeros of those intermediate seqaij Mats
7477: ncoo_d: # of nonzeros of matrices that do not have offproc entries
7478: ncoo_o: # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7479: ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7480: */
7481: for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7482: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7483: if (mptmp[cp]) continue;
7484: if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7485: const PetscInt *rmap = rmapa[cp];
7486: const PetscInt mr = mp[cp]->rmap->n;
7487: const PetscInt rs = C->rmap->rstart;
7488: const PetscInt re = C->rmap->rend;
7489: const PetscInt *ii = mm->i;
7490: for (i = 0; i < mr; i++) {
7491: const PetscInt gr = rmap[i];
7492: const PetscInt nz = ii[i + 1] - ii[i];
7493: if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7494: else ncoo_oown += nz; /* this row is local */
7495: }
7496: } else ncoo_d += mm->nz;
7497: }
7499: /*
7500: ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc
7502: ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.
7504: off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].
7506: off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7507: own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7508: so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.
7510: coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7511: 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.
7512: */
7513: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7514: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));
7516: /* gather (i,j) of nonzeros inserted by remote procs */
7517: if (hasoffproc) {
7518: PetscSF msf;
7519: PetscInt ncoo2, *coo_i2, *coo_j2;
7521: PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7522: PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7523: PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */
7525: for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7526: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7527: PetscInt *idxoff = mmdata->off[cp];
7528: PetscInt *idxown = mmdata->own[cp];
7529: if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7530: const PetscInt *rmap = rmapa[cp];
7531: const PetscInt *cmap = cmapa[cp];
7532: const PetscInt *ii = mm->i;
7533: PetscInt *coi = coo_i + ncoo_o;
7534: PetscInt *coj = coo_j + ncoo_o;
7535: const PetscInt mr = mp[cp]->rmap->n;
7536: const PetscInt rs = C->rmap->rstart;
7537: const PetscInt re = C->rmap->rend;
7538: const PetscInt cs = C->cmap->rstart;
7539: for (i = 0; i < mr; i++) {
7540: const PetscInt *jj = mm->j + ii[i];
7541: const PetscInt gr = rmap[i];
7542: const PetscInt nz = ii[i + 1] - ii[i];
7543: if (gr < rs || gr >= re) { /* this is an offproc row */
7544: for (j = ii[i]; j < ii[i + 1]; j++) {
7545: *coi++ = gr;
7546: *idxoff++ = j;
7547: }
7548: if (!cmapt[cp]) { /* already global */
7549: for (j = 0; j < nz; j++) *coj++ = jj[j];
7550: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7551: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7552: } else { /* offdiag */
7553: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7554: }
7555: ncoo_o += nz;
7556: } else { /* this is a local row */
7557: for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7558: }
7559: }
7560: }
7561: mmdata->off[cp + 1] = idxoff;
7562: mmdata->own[cp + 1] = idxown;
7563: }
7565: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7566: PetscInt incoo_o;
7567: PetscCall(PetscIntCast(ncoo_o, &incoo_o));
7568: PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, incoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7569: PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7570: PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7571: ncoo = ncoo_d + ncoo_oown + ncoo2;
7572: PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7573: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7574: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7575: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7576: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7577: PetscCall(PetscFree2(coo_i, coo_j));
7578: /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7579: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7580: coo_i = coo_i2;
7581: coo_j = coo_j2;
7582: } else { /* no offproc values insertion */
7583: ncoo = ncoo_d;
7584: PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));
7586: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7587: PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7588: PetscCall(PetscSFSetUp(mmdata->sf));
7589: }
7590: mmdata->hasoffproc = hasoffproc;
7592: /* gather (i,j) of nonzeros inserted locally */
7593: for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7594: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7595: PetscInt *coi = coo_i + ncoo_d;
7596: PetscInt *coj = coo_j + ncoo_d;
7597: const PetscInt *jj = mm->j;
7598: const PetscInt *ii = mm->i;
7599: const PetscInt *cmap = cmapa[cp];
7600: const PetscInt *rmap = rmapa[cp];
7601: const PetscInt mr = mp[cp]->rmap->n;
7602: const PetscInt rs = C->rmap->rstart;
7603: const PetscInt re = C->rmap->rend;
7604: const PetscInt cs = C->cmap->rstart;
7606: if (mptmp[cp]) continue;
7607: if (rmapt[cp] == 1) { /* consecutive rows */
7608: /* fill coo_i */
7609: for (i = 0; i < mr; i++) {
7610: const PetscInt gr = i + rs;
7611: for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7612: }
7613: /* fill coo_j */
7614: if (!cmapt[cp]) { /* type-0, already global */
7615: PetscCall(PetscArraycpy(coj, jj, mm->nz));
7616: } else if (cmapt[cp] == 1) { /* type-1, local to global for consecutive columns of C */
7617: for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7618: } else { /* type-2, local to global for sparse columns */
7619: for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7620: }
7621: ncoo_d += mm->nz;
7622: } else if (rmapt[cp] == 2) { /* sparse rows */
7623: for (i = 0; i < mr; i++) {
7624: const PetscInt *jj = mm->j + ii[i];
7625: const PetscInt gr = rmap[i];
7626: const PetscInt nz = ii[i + 1] - ii[i];
7627: if (gr >= rs && gr < re) { /* local rows */
7628: for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7629: if (!cmapt[cp]) { /* type-0, already global */
7630: for (j = 0; j < nz; j++) *coj++ = jj[j];
7631: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7632: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7633: } else { /* type-2, local to global for sparse columns */
7634: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7635: }
7636: ncoo_d += nz;
7637: }
7638: }
7639: }
7640: }
7641: if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7642: PetscCall(ISDestroy(&glob));
7643: if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7644: PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7645: /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7646: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));
7648: /* preallocate with COO data */
7649: PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7650: PetscCall(PetscFree2(coo_i, coo_j));
7651: PetscFunctionReturn(PETSC_SUCCESS);
7652: }
7654: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7655: {
7656: Mat_Product *product = mat->product;
7657: #if defined(PETSC_HAVE_DEVICE)
7658: PetscBool match = PETSC_FALSE;
7659: PetscBool usecpu = PETSC_FALSE;
7660: #else
7661: PetscBool match = PETSC_TRUE;
7662: #endif
7664: PetscFunctionBegin;
7665: MatCheckProduct(mat, 1);
7666: #if defined(PETSC_HAVE_DEVICE)
7667: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7668: if (match) { /* we can always fallback to the CPU if requested */
7669: switch (product->type) {
7670: case MATPRODUCT_AB:
7671: if (product->api_user) {
7672: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7673: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7674: PetscOptionsEnd();
7675: } else {
7676: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7677: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7678: PetscOptionsEnd();
7679: }
7680: break;
7681: case MATPRODUCT_AtB:
7682: if (product->api_user) {
7683: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7684: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7685: PetscOptionsEnd();
7686: } else {
7687: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7688: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7689: PetscOptionsEnd();
7690: }
7691: break;
7692: case MATPRODUCT_PtAP:
7693: if (product->api_user) {
7694: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7695: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7696: PetscOptionsEnd();
7697: } else {
7698: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7699: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7700: PetscOptionsEnd();
7701: }
7702: break;
7703: default:
7704: break;
7705: }
7706: match = (PetscBool)!usecpu;
7707: }
7708: #endif
7709: if (match) {
7710: switch (product->type) {
7711: case MATPRODUCT_AB:
7712: case MATPRODUCT_AtB:
7713: case MATPRODUCT_PtAP:
7714: mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7715: break;
7716: default:
7717: break;
7718: }
7719: }
7720: /* fallback to MPIAIJ ops */
7721: if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7722: PetscFunctionReturn(PETSC_SUCCESS);
7723: }
7725: /*
7726: Produces a set of block column indices of the matrix row, one for each block represented in the original row
7728: n - the number of block indices in cc[]
7729: cc - the block indices (must be large enough to contain the indices)
7730: */
7731: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7732: {
7733: PetscInt cnt = -1, nidx, j;
7734: const PetscInt *idx;
7736: PetscFunctionBegin;
7737: PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7738: if (nidx) {
7739: cnt = 0;
7740: cc[cnt] = idx[0] / bs;
7741: for (j = 1; j < nidx; j++) {
7742: if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7743: }
7744: }
7745: PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7746: *n = cnt + 1;
7747: PetscFunctionReturn(PETSC_SUCCESS);
7748: }
7750: /*
7751: Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows
7753: ncollapsed - the number of block indices
7754: collapsed - the block indices (must be large enough to contain the indices)
7755: */
7756: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7757: {
7758: PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;
7760: PetscFunctionBegin;
7761: PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7762: for (i = start + 1; i < start + bs; i++) {
7763: PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7764: PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7765: cprevtmp = cprev;
7766: cprev = merged;
7767: merged = cprevtmp;
7768: }
7769: *ncollapsed = nprev;
7770: if (collapsed) *collapsed = cprev;
7771: PetscFunctionReturn(PETSC_SUCCESS);
7772: }
7774: /*
7775: MatCreateGraph_Simple_AIJ - create simple scalar matrix (graph) from potentially blocked matrix
7777: Input Parameter:
7778: . Amat - matrix
7779: - symmetrize - make the result symmetric
7780: + scale - scale with diagonal
7782: Output Parameter:
7783: . a_Gmat - output scalar graph >= 0
7785: */
7786: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, PetscInt index_size, PetscInt index[], Mat *a_Gmat)
7787: {
7788: PetscInt Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7789: MPI_Comm comm;
7790: Mat Gmat;
7791: PetscBool ismpiaij, isseqaij;
7792: Mat a, b, c;
7793: MatType jtype;
7795: PetscFunctionBegin;
7796: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7797: PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7798: PetscCall(MatGetSize(Amat, &MM, &NN));
7799: PetscCall(MatGetBlockSize(Amat, &bs));
7800: nloc = (Iend - Istart) / bs;
7802: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7803: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7804: PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");
7806: /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7807: /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7808: implementation */
7809: if (bs > 1) {
7810: PetscCall(MatGetType(Amat, &jtype));
7811: PetscCall(MatCreate(comm, &Gmat));
7812: PetscCall(MatSetType(Gmat, jtype));
7813: PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7814: PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7815: if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7816: PetscInt *d_nnz, *o_nnz;
7817: MatScalar *aa, val, *AA;
7818: PetscInt *aj, *ai, *AJ, nc, nmax = 0;
7820: if (isseqaij) {
7821: a = Amat;
7822: b = NULL;
7823: } else {
7824: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7825: a = d->A;
7826: b = d->B;
7827: }
7828: PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7829: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7830: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7831: PetscInt *nnz = (c == a) ? d_nnz : o_nnz;
7832: const PetscInt *cols1, *cols2;
7834: for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7835: PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7836: nnz[brow / bs] = nc2 / bs;
7837: if (nc2 % bs) ok = 0;
7838: if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7839: for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7840: PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7841: if (nc1 != nc2) ok = 0;
7842: else {
7843: for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7844: if (cols1[jj] != cols2[jj]) ok = 0;
7845: if (cols1[jj] % bs != jj % bs) ok = 0;
7846: }
7847: }
7848: PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7849: }
7850: PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7851: if (!ok) {
7852: PetscCall(PetscFree2(d_nnz, o_nnz));
7853: PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7854: goto old_bs;
7855: }
7856: }
7857: }
7858: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7859: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7860: PetscCall(PetscFree2(d_nnz, o_nnz));
7861: PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7862: // diag
7863: for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7864: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;
7866: ai = aseq->i;
7867: n = ai[brow + 1] - ai[brow];
7868: aj = aseq->j + ai[brow];
7869: for (PetscInt k = 0; k < n; k += bs) { // block columns
7870: AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7871: val = 0;
7872: if (index_size == 0) {
7873: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7874: aa = aseq->a + ai[brow + ii] + k;
7875: for (PetscInt jj = 0; jj < bs; jj++) { // columns in block
7876: val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7877: }
7878: }
7879: } else { // use (index,index) value if provided
7880: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7881: PetscInt ii = index[iii];
7882: aa = aseq->a + ai[brow + ii] + k;
7883: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // columns in block
7884: PetscInt jj = index[jjj];
7885: val += PetscAbs(PetscRealPart(aa[jj]));
7886: }
7887: }
7888: }
7889: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7890: AA[k / bs] = val;
7891: }
7892: grow = Istart / bs + brow / bs;
7893: PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, ADD_VALUES));
7894: }
7895: // off-diag
7896: if (ismpiaij) {
7897: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Amat->data;
7898: const PetscScalar *vals;
7899: const PetscInt *cols, *garray = aij->garray;
7901: PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7902: for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7903: PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7904: for (PetscInt k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7905: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7906: AA[k / bs] = 0;
7907: AJ[cidx] = garray[cols[k]] / bs;
7908: }
7909: nc = ncols / bs;
7910: PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7911: if (index_size == 0) {
7912: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7913: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7914: for (PetscInt k = 0; k < ncols; k += bs) {
7915: for (PetscInt jj = 0; jj < bs; jj++) { // cols in block
7916: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7917: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7918: }
7919: }
7920: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7921: }
7922: } else { // use (index,index) value if provided
7923: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7924: PetscInt ii = index[iii];
7925: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7926: for (PetscInt k = 0; k < ncols; k += bs) {
7927: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // cols in block
7928: PetscInt jj = index[jjj];
7929: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7930: }
7931: }
7932: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7933: }
7934: }
7935: grow = Istart / bs + brow / bs;
7936: PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, ADD_VALUES));
7937: }
7938: }
7939: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7940: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7941: PetscCall(PetscFree2(AA, AJ));
7942: } else {
7943: const PetscScalar *vals;
7944: const PetscInt *idx;
7945: PetscInt *d_nnz, *o_nnz, *w0, *w1, *w2;
7946: old_bs:
7947: /*
7948: Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7949: */
7950: PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7951: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7952: if (isseqaij) {
7953: PetscInt max_d_nnz;
7955: /*
7956: Determine exact preallocation count for (sequential) scalar matrix
7957: */
7958: PetscCall(MatSeqAIJGetMaxRowNonzeros(Amat, &max_d_nnz));
7959: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7960: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7961: for (Ii = 0, jj = 0; Ii < Iend; Ii += bs, jj++) PetscCall(MatCollapseRows(Amat, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7962: PetscCall(PetscFree3(w0, w1, w2));
7963: } else if (ismpiaij) {
7964: Mat Daij, Oaij;
7965: const PetscInt *garray;
7966: PetscInt max_d_nnz;
7968: PetscCall(MatMPIAIJGetSeqAIJ(Amat, &Daij, &Oaij, &garray));
7969: /*
7970: Determine exact preallocation count for diagonal block portion of scalar matrix
7971: */
7972: PetscCall(MatSeqAIJGetMaxRowNonzeros(Daij, &max_d_nnz));
7973: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7974: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7975: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) PetscCall(MatCollapseRows(Daij, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7976: PetscCall(PetscFree3(w0, w1, w2));
7977: /*
7978: Over estimate (usually grossly over), preallocation count for off-diagonal portion of scalar matrix
7979: */
7980: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) {
7981: o_nnz[jj] = 0;
7982: for (kk = 0; kk < bs; kk++) { /* rows that get collapsed to a single row */
7983: PetscCall(MatGetRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7984: o_nnz[jj] += ncols;
7985: PetscCall(MatRestoreRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7986: }
7987: if (o_nnz[jj] > (NN / bs - nloc)) o_nnz[jj] = NN / bs - nloc;
7988: }
7989: } else SETERRQ(comm, PETSC_ERR_USER, "Require AIJ matrix type");
7990: /* get scalar copy (norms) of matrix */
7991: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7992: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7993: PetscCall(PetscFree2(d_nnz, o_nnz));
7994: for (Ii = Istart; Ii < Iend; Ii++) {
7995: PetscInt dest_row = Ii / bs;
7997: PetscCall(MatGetRow(Amat, Ii, &ncols, &idx, &vals));
7998: for (jj = 0; jj < ncols; jj++) {
7999: PetscInt dest_col = idx[jj] / bs;
8000: PetscScalar sv = PetscAbs(PetscRealPart(vals[jj]));
8002: PetscCall(MatSetValues(Gmat, 1, &dest_row, 1, &dest_col, &sv, ADD_VALUES));
8003: }
8004: PetscCall(MatRestoreRow(Amat, Ii, &ncols, &idx, &vals));
8005: }
8006: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
8007: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
8008: }
8009: } else {
8010: if (symmetrize || filter >= 0 || scale) PetscCall(MatDuplicate(Amat, MAT_COPY_VALUES, &Gmat));
8011: else {
8012: Gmat = Amat;
8013: PetscCall(PetscObjectReference((PetscObject)Gmat));
8014: }
8015: if (isseqaij) {
8016: a = Gmat;
8017: b = NULL;
8018: } else {
8019: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
8020: a = d->A;
8021: b = d->B;
8022: }
8023: if (filter >= 0 || scale) {
8024: /* take absolute value of each entry */
8025: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
8026: MatInfo info;
8027: PetscScalar *avals;
8029: PetscCall(MatGetInfo(c, MAT_LOCAL, &info));
8030: PetscCall(MatSeqAIJGetArray(c, &avals));
8031: for (int jj = 0; jj < info.nz_used; jj++) avals[jj] = PetscAbsScalar(avals[jj]);
8032: PetscCall(MatSeqAIJRestoreArray(c, &avals));
8033: }
8034: }
8035: }
8036: if (symmetrize) {
8037: PetscBool isset, issym;
8039: PetscCall(MatIsSymmetricKnown(Amat, &isset, &issym));
8040: if (!isset || !issym) {
8041: Mat matTrans;
8043: PetscCall(MatTranspose(Gmat, MAT_INITIAL_MATRIX, &matTrans));
8044: PetscCall(MatAXPY(Gmat, 1.0, matTrans, Gmat->structurally_symmetric == PETSC_BOOL3_TRUE ? SAME_NONZERO_PATTERN : DIFFERENT_NONZERO_PATTERN));
8045: PetscCall(MatDestroy(&matTrans));
8046: }
8047: PetscCall(MatSetOption(Gmat, MAT_SYMMETRIC, PETSC_TRUE));
8048: } else if (Amat != Gmat) PetscCall(MatPropagateSymmetryOptions(Amat, Gmat));
8049: if (scale) {
8050: /* scale c for all diagonal values = 1 or -1 */
8051: Vec diag;
8053: PetscCall(MatCreateVecs(Gmat, &diag, NULL));
8054: PetscCall(MatGetDiagonal(Gmat, diag));
8055: PetscCall(VecReciprocal(diag));
8056: PetscCall(VecSqrtAbs(diag));
8057: PetscCall(MatDiagonalScale(Gmat, diag, diag));
8058: PetscCall(VecDestroy(&diag));
8059: }
8060: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_graph_view"));
8061: if (filter >= 0) {
8062: PetscCall(MatFilter(Gmat, filter, PETSC_TRUE, PETSC_TRUE));
8063: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_filter_graph_view"));
8064: }
8065: *a_Gmat = Gmat;
8066: PetscFunctionReturn(PETSC_SUCCESS);
8067: }
8069: /*
8070: Special version for direct calls from Fortran
8071: */
8073: /* Change these macros so can be used in void function */
8074: /* Identical to PetscCallVoid, except it assigns to *_ierr */
8075: #undef PetscCall
8076: #define PetscCall(...) \
8077: do { \
8078: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
8079: if (PetscUnlikely(ierr_msv_mpiaij)) { \
8080: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
8081: return; \
8082: } \
8083: } while (0)
8085: #undef SETERRQ
8086: #define SETERRQ(comm, ierr, ...) \
8087: do { \
8088: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
8089: return; \
8090: } while (0)
8092: #if defined(PETSC_HAVE_FORTRAN_CAPS)
8093: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
8094: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
8095: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
8096: #else
8097: #endif
8098: PETSC_EXTERN void matsetvaluesmpiaij_(Mat *mmat, PetscInt *mm, const PetscInt im[], PetscInt *mn, const PetscInt in[], const PetscScalar v[], InsertMode *maddv, PetscErrorCode *_ierr)
8099: {
8100: Mat mat = *mmat;
8101: PetscInt m = *mm, n = *mn;
8102: InsertMode addv = *maddv;
8103: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
8104: PetscScalar value;
8106: MatCheckPreallocated(mat, 1);
8107: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
8108: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
8109: {
8110: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
8111: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
8112: PetscBool roworiented = aij->roworiented;
8114: /* Some Variables required in the macro */
8115: Mat A = aij->A;
8116: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
8117: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
8118: MatScalar *aa;
8119: PetscBool ignorezeroentries = ((a->ignorezeroentries && (addv == ADD_VALUES)) ? PETSC_TRUE : PETSC_FALSE);
8120: Mat B = aij->B;
8121: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
8122: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
8123: MatScalar *ba;
8124: /* This variable below is only for the PETSC_HAVE_VIENNACL or PETSC_HAVE_CUDA cases, but we define it in all cases because we
8125: * cannot use "#if defined" inside a macro. */
8126: PETSC_UNUSED PetscBool inserted = PETSC_FALSE;
8128: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
8129: PetscInt nonew = a->nonew;
8130: MatScalar *ap1, *ap2;
8132: PetscFunctionBegin;
8133: PetscCall(MatSeqAIJGetArray(A, &aa));
8134: PetscCall(MatSeqAIJGetArray(B, &ba));
8135: for (i = 0; i < m; i++) {
8136: if (im[i] < 0) continue;
8137: 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);
8138: if (im[i] >= rstart && im[i] < rend) {
8139: row = im[i] - rstart;
8140: lastcol1 = -1;
8141: rp1 = aj + ai[row];
8142: ap1 = aa + ai[row];
8143: rmax1 = aimax[row];
8144: nrow1 = ailen[row];
8145: low1 = 0;
8146: high1 = nrow1;
8147: lastcol2 = -1;
8148: rp2 = bj + bi[row];
8149: ap2 = ba + bi[row];
8150: rmax2 = bimax[row];
8151: nrow2 = bilen[row];
8152: low2 = 0;
8153: high2 = nrow2;
8155: for (j = 0; j < n; j++) {
8156: if (roworiented) value = v[i * n + j];
8157: else value = v[i + j * m];
8158: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
8159: if (in[j] >= cstart && in[j] < cend) {
8160: col = in[j] - cstart;
8161: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
8162: } else if (in[j] < 0) continue;
8163: else if (PetscUnlikelyDebug(in[j] >= mat->cmap->N)) {
8164: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
8165: } else {
8166: if (mat->was_assembled) {
8167: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
8168: #if defined(PETSC_USE_CTABLE)
8169: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col));
8170: col--;
8171: #else
8172: col = aij->colmap[in[j]] - 1;
8173: #endif
8174: if (col < 0 && !((Mat_SeqAIJ *)aij->A->data)->nonew) {
8175: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));
8176: col = in[j];
8177: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
8178: B = aij->B;
8179: b = (Mat_SeqAIJ *)B->data;
8180: bimax = b->imax;
8181: bi = b->i;
8182: bilen = b->ilen;
8183: bj = b->j;
8184: rp2 = bj + bi[row];
8185: ap2 = ba + bi[row];
8186: rmax2 = bimax[row];
8187: nrow2 = bilen[row];
8188: low2 = 0;
8189: high2 = nrow2;
8190: bm = aij->B->rmap->n;
8191: ba = b->a;
8192: inserted = PETSC_FALSE;
8193: }
8194: } else col = in[j];
8195: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
8196: }
8197: }
8198: } else if (!aij->donotstash) {
8199: if (roworiented) {
8200: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8201: } else {
8202: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8203: }
8204: }
8205: }
8206: PetscCall(MatSeqAIJRestoreArray(A, &aa));
8207: PetscCall(MatSeqAIJRestoreArray(B, &ba));
8208: }
8209: PetscFunctionReturnVoid();
8210: }
8212: /* Undefining these here since they were redefined from their original definition above! No
8213: * other PETSc functions should be defined past this point, as it is impossible to recover the
8214: * original definitions */
8215: #undef PetscCall
8216: #undef SETERRQ