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: #if defined(PETSC_USE_LOG)
15: PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
16: #endif
17: PetscCall(MatStashDestroy_Private(&mat->stash));
18: PetscCall(VecDestroy(&aij->diag));
19: PetscCall(MatDestroy(&aij->A));
20: PetscCall(MatDestroy(&aij->B));
21: #if defined(PETSC_USE_CTABLE)
22: PetscCall(PetscHMapIDestroy(&aij->colmap));
23: #else
24: PetscCall(PetscFree(aij->colmap));
25: #endif
26: PetscCall(PetscFree(aij->garray));
27: PetscCall(VecDestroy(&aij->lvec));
28: PetscCall(VecScatterDestroy(&aij->Mvctx));
29: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
30: PetscCall(PetscFree(aij->ld));
32: /* Free COO */
33: PetscCall(MatResetPreallocationCOO_MPIAIJ(mat));
35: PetscCall(PetscFree(mat->data));
37: /* may be created by MatCreateMPIAIJSumSeqAIJSymbolic */
38: PetscCall(PetscObjectCompose((PetscObject)mat, "MatMergeSeqsToMPI", NULL));
40: PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
41: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
42: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
43: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatIsTranspose_C", NULL));
44: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocation_C", NULL));
45: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatResetPreallocation_C", NULL));
46: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocationCSR_C", NULL));
47: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
48: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpibaij_C", NULL));
49: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisbaij_C", NULL));
50: #if defined(PETSC_HAVE_CUDA)
51: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcusparse_C", NULL));
52: #endif
53: #if defined(PETSC_HAVE_HIP)
54: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijhipsparse_C", NULL));
55: #endif
56: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
57: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijkokkos_C", NULL));
58: #endif
59: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpidense_C", NULL));
60: #if defined(PETSC_HAVE_ELEMENTAL)
61: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_elemental_C", NULL));
62: #endif
63: #if defined(PETSC_HAVE_SCALAPACK)
64: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_scalapack_C", NULL));
65: #endif
66: #if defined(PETSC_HAVE_HYPRE)
67: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_hypre_C", NULL));
68: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", NULL));
69: #endif
70: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
71: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_is_mpiaij_C", NULL));
72: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_mpiaij_mpiaij_C", NULL));
73: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetUseScalableIncreaseOverlap_C", NULL));
74: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijperm_C", NULL));
75: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijsell_C", NULL));
76: #if defined(PETSC_HAVE_MKL_SPARSE)
77: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijmkl_C", NULL));
78: #endif
79: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcrl_C", NULL));
80: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
81: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisell_C", NULL));
82: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetPreallocationCOO_C", NULL));
83: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetValuesCOO_C", NULL));
84: PetscFunctionReturn(PETSC_SUCCESS);
85: }
87: /* defines MatSetValues_MPI_Hash(), MatAssemblyBegin_MPI_Hash(), and MatAssemblyEnd_MPI_Hash() */
88: #define TYPE AIJ
89: #define TYPE_AIJ
90: #include "../src/mat/impls/aij/mpi/mpihashmat.h"
91: #undef TYPE
92: #undef TYPE_AIJ
94: PetscErrorCode MatGetRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
95: {
96: Mat B;
98: PetscFunctionBegin;
99: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, &B));
100: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject)B));
101: PetscCall(MatGetRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
102: PetscCall(MatDestroy(&B));
103: PetscFunctionReturn(PETSC_SUCCESS);
104: }
106: PetscErrorCode MatRestoreRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
107: {
108: Mat B;
110: PetscFunctionBegin;
111: PetscCall(PetscObjectQuery((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject *)&B));
112: PetscCall(MatRestoreRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
113: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", NULL));
114: PetscFunctionReturn(PETSC_SUCCESS);
115: }
117: /*MC
118: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
120: This matrix type is identical to` MATSEQAIJ` when constructed with a single process communicator,
121: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
122: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
123: for communicators controlling multiple processes. It is recommended that you call both of
124: the above preallocation routines for simplicity.
126: Options Database Key:
127: . -mat_type aij - sets the matrix type to `MATAIJ` during a call to `MatSetFromOptions()`
129: Developer Note:
130: Level: beginner
132: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, `MATAIJKOKKOS`,and also automatically switches over to use inodes when
133: enough exist.
135: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`
136: M*/
138: /*MC
139: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
141: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
142: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
143: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
144: for communicators controlling multiple processes. It is recommended that you call both of
145: the above preallocation routines for simplicity.
147: Options Database Key:
148: . -mat_type aijcrl - sets the matrix type to `MATMPIAIJCRL` during a call to `MatSetFromOptions()`
150: Level: beginner
152: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
153: M*/
155: static PetscErrorCode MatBindToCPU_MPIAIJ(Mat A, PetscBool flg)
156: {
157: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
159: PetscFunctionBegin;
160: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP) || defined(PETSC_HAVE_VIENNACL)
161: A->boundtocpu = flg;
162: #endif
163: if (a->A) PetscCall(MatBindToCPU(a->A, flg));
164: if (a->B) PetscCall(MatBindToCPU(a->B, flg));
166: /* In addition to binding the diagonal and off-diagonal matrices, bind the local vectors used for matrix-vector products.
167: * This maybe seems a little odd for a MatBindToCPU() call to do, but it makes no sense for the binding of these vectors
168: * to differ from the parent matrix. */
169: if (a->lvec) PetscCall(VecBindToCPU(a->lvec, flg));
170: if (a->diag) PetscCall(VecBindToCPU(a->diag, flg));
172: PetscFunctionReturn(PETSC_SUCCESS);
173: }
175: PetscErrorCode MatSetBlockSizes_MPIAIJ(Mat M, PetscInt rbs, PetscInt cbs)
176: {
177: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
179: PetscFunctionBegin;
180: if (mat->A) {
181: PetscCall(MatSetBlockSizes(mat->A, rbs, cbs));
182: PetscCall(MatSetBlockSizes(mat->B, rbs, 1));
183: }
184: PetscFunctionReturn(PETSC_SUCCESS);
185: }
187: PetscErrorCode MatFindNonzeroRows_MPIAIJ(Mat M, IS *keptrows)
188: {
189: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
190: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data;
191: Mat_SeqAIJ *b = (Mat_SeqAIJ *)mat->B->data;
192: const PetscInt *ia, *ib;
193: const MatScalar *aa, *bb, *aav, *bav;
194: PetscInt na, nb, i, j, *rows, cnt = 0, n0rows;
195: PetscInt m = M->rmap->n, rstart = M->rmap->rstart;
197: PetscFunctionBegin;
198: *keptrows = NULL;
200: ia = a->i;
201: ib = b->i;
202: PetscCall(MatSeqAIJGetArrayRead(mat->A, &aav));
203: PetscCall(MatSeqAIJGetArrayRead(mat->B, &bav));
204: for (i = 0; i < m; i++) {
205: na = ia[i + 1] - ia[i];
206: nb = ib[i + 1] - ib[i];
207: if (!na && !nb) {
208: cnt++;
209: goto ok1;
210: }
211: aa = aav + ia[i];
212: for (j = 0; j < na; j++) {
213: if (aa[j] != 0.0) goto ok1;
214: }
215: bb = bav + ib[i];
216: for (j = 0; j < nb; j++) {
217: if (bb[j] != 0.0) goto ok1;
218: }
219: cnt++;
220: ok1:;
221: }
222: PetscCall(MPIU_Allreduce(&cnt, &n0rows, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)M)));
223: if (!n0rows) {
224: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
225: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
226: PetscFunctionReturn(PETSC_SUCCESS);
227: }
228: PetscCall(PetscMalloc1(M->rmap->n - cnt, &rows));
229: cnt = 0;
230: for (i = 0; i < m; i++) {
231: na = ia[i + 1] - ia[i];
232: nb = ib[i + 1] - ib[i];
233: if (!na && !nb) continue;
234: aa = aav + ia[i];
235: for (j = 0; j < na; j++) {
236: if (aa[j] != 0.0) {
237: rows[cnt++] = rstart + i;
238: goto ok2;
239: }
240: }
241: bb = bav + ib[i];
242: for (j = 0; j < nb; j++) {
243: if (bb[j] != 0.0) {
244: rows[cnt++] = rstart + i;
245: goto ok2;
246: }
247: }
248: ok2:;
249: }
250: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), cnt, rows, PETSC_OWN_POINTER, keptrows));
251: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
252: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
253: PetscFunctionReturn(PETSC_SUCCESS);
254: }
256: PetscErrorCode MatDiagonalSet_MPIAIJ(Mat Y, Vec D, InsertMode is)
257: {
258: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Y->data;
259: PetscBool cong;
261: PetscFunctionBegin;
262: PetscCall(MatHasCongruentLayouts(Y, &cong));
263: if (Y->assembled && cong) {
264: PetscCall(MatDiagonalSet(aij->A, D, is));
265: } else {
266: PetscCall(MatDiagonalSet_Default(Y, D, is));
267: }
268: PetscFunctionReturn(PETSC_SUCCESS);
269: }
271: PetscErrorCode MatFindZeroDiagonals_MPIAIJ(Mat M, IS *zrows)
272: {
273: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)M->data;
274: PetscInt i, rstart, nrows, *rows;
276: PetscFunctionBegin;
277: *zrows = NULL;
278: PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(aij->A, &nrows, &rows));
279: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
280: for (i = 0; i < nrows; i++) rows[i] += rstart;
281: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), nrows, rows, PETSC_OWN_POINTER, zrows));
282: PetscFunctionReturn(PETSC_SUCCESS);
283: }
285: PetscErrorCode MatGetColumnReductions_MPIAIJ(Mat A, PetscInt type, PetscReal *reductions)
286: {
287: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)A->data;
288: PetscInt i, m, n, *garray = aij->garray;
289: Mat_SeqAIJ *a_aij = (Mat_SeqAIJ *)aij->A->data;
290: Mat_SeqAIJ *b_aij = (Mat_SeqAIJ *)aij->B->data;
291: PetscReal *work;
292: const PetscScalar *dummy;
294: PetscFunctionBegin;
295: PetscCall(MatGetSize(A, &m, &n));
296: PetscCall(PetscCalloc1(n, &work));
297: PetscCall(MatSeqAIJGetArrayRead(aij->A, &dummy));
298: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &dummy));
299: PetscCall(MatSeqAIJGetArrayRead(aij->B, &dummy));
300: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &dummy));
301: if (type == NORM_2) {
302: 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]);
303: 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]);
304: } else if (type == NORM_1) {
305: 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]);
306: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]);
307: } else if (type == NORM_INFINITY) {
308: 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]]);
309: 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]]]);
310: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
311: 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]);
312: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscRealPart(b_aij->a[i]);
313: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
314: 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]);
315: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscImaginaryPart(b_aij->a[i]);
316: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
317: if (type == NORM_INFINITY) {
318: PetscCall(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
319: } else {
320: PetscCall(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
321: }
322: PetscCall(PetscFree(work));
323: if (type == NORM_2) {
324: for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
325: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
326: for (i = 0; i < n; i++) reductions[i] /= m;
327: }
328: PetscFunctionReturn(PETSC_SUCCESS);
329: }
331: PetscErrorCode MatFindOffBlockDiagonalEntries_MPIAIJ(Mat A, IS *is)
332: {
333: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
334: IS sis, gis;
335: const PetscInt *isis, *igis;
336: PetscInt n, *iis, nsis, ngis, rstart, i;
338: PetscFunctionBegin;
339: PetscCall(MatFindOffBlockDiagonalEntries(a->A, &sis));
340: PetscCall(MatFindNonzeroRows(a->B, &gis));
341: PetscCall(ISGetSize(gis, &ngis));
342: PetscCall(ISGetSize(sis, &nsis));
343: PetscCall(ISGetIndices(sis, &isis));
344: PetscCall(ISGetIndices(gis, &igis));
346: PetscCall(PetscMalloc1(ngis + nsis, &iis));
347: PetscCall(PetscArraycpy(iis, igis, ngis));
348: PetscCall(PetscArraycpy(iis + ngis, isis, nsis));
349: n = ngis + nsis;
350: PetscCall(PetscSortRemoveDupsInt(&n, iis));
351: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
352: for (i = 0; i < n; i++) iis[i] += rstart;
353: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), n, iis, PETSC_OWN_POINTER, is));
355: PetscCall(ISRestoreIndices(sis, &isis));
356: PetscCall(ISRestoreIndices(gis, &igis));
357: PetscCall(ISDestroy(&sis));
358: PetscCall(ISDestroy(&gis));
359: PetscFunctionReturn(PETSC_SUCCESS);
360: }
362: /*
363: Local utility routine that creates a mapping from the global column
364: number to the local number in the off-diagonal part of the local
365: storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at
366: a slightly higher hash table cost; without it it is not scalable (each processor
367: has an order N integer array but is fast to access.
368: */
369: PetscErrorCode MatCreateColmap_MPIAIJ_Private(Mat mat)
370: {
371: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
372: PetscInt n = aij->B->cmap->n, i;
374: PetscFunctionBegin;
375: PetscCheck(!n || aij->garray, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MPIAIJ Matrix was assembled but is missing garray");
376: #if defined(PETSC_USE_CTABLE)
377: PetscCall(PetscHMapICreateWithSize(n, &aij->colmap));
378: for (i = 0; i < n; i++) PetscCall(PetscHMapISet(aij->colmap, aij->garray[i] + 1, i + 1));
379: #else
380: PetscCall(PetscCalloc1(mat->cmap->N + 1, &aij->colmap));
381: for (i = 0; i < n; i++) aij->colmap[aij->garray[i]] = i + 1;
382: #endif
383: PetscFunctionReturn(PETSC_SUCCESS);
384: }
386: #define MatSetValues_SeqAIJ_A_Private(row, col, value, addv, orow, ocol) \
387: { \
388: if (col <= lastcol1) low1 = 0; \
389: else high1 = nrow1; \
390: lastcol1 = col; \
391: while (high1 - low1 > 5) { \
392: t = (low1 + high1) / 2; \
393: if (rp1[t] > col) high1 = t; \
394: else low1 = t; \
395: } \
396: for (_i = low1; _i < high1; _i++) { \
397: if (rp1[_i] > col) break; \
398: if (rp1[_i] == col) { \
399: if (addv == ADD_VALUES) { \
400: ap1[_i] += value; \
401: /* Not sure LogFlops will slow dow the code or not */ \
402: (void)PetscLogFlops(1.0); \
403: } else ap1[_i] = value; \
404: goto a_noinsert; \
405: } \
406: } \
407: if (value == 0.0 && ignorezeroentries && row != col) { \
408: low1 = 0; \
409: high1 = nrow1; \
410: goto a_noinsert; \
411: } \
412: if (nonew == 1) { \
413: low1 = 0; \
414: high1 = nrow1; \
415: goto a_noinsert; \
416: } \
417: 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); \
418: MatSeqXAIJReallocateAIJ(A, am, 1, nrow1, row, col, rmax1, aa, ai, aj, rp1, ap1, aimax, nonew, MatScalar); \
419: N = nrow1++ - 1; \
420: a->nz++; \
421: high1++; \
422: /* shift up all the later entries in this row */ \
423: PetscCall(PetscArraymove(rp1 + _i + 1, rp1 + _i, N - _i + 1)); \
424: PetscCall(PetscArraymove(ap1 + _i + 1, ap1 + _i, N - _i + 1)); \
425: rp1[_i] = col; \
426: ap1[_i] = value; \
427: A->nonzerostate++; \
428: a_noinsert:; \
429: ailen[row] = nrow1; \
430: }
432: #define MatSetValues_SeqAIJ_B_Private(row, col, value, addv, orow, ocol) \
433: { \
434: if (col <= lastcol2) low2 = 0; \
435: else high2 = nrow2; \
436: lastcol2 = col; \
437: while (high2 - low2 > 5) { \
438: t = (low2 + high2) / 2; \
439: if (rp2[t] > col) high2 = t; \
440: else low2 = t; \
441: } \
442: for (_i = low2; _i < high2; _i++) { \
443: if (rp2[_i] > col) break; \
444: if (rp2[_i] == col) { \
445: if (addv == ADD_VALUES) { \
446: ap2[_i] += value; \
447: (void)PetscLogFlops(1.0); \
448: } else ap2[_i] = value; \
449: goto b_noinsert; \
450: } \
451: } \
452: if (value == 0.0 && ignorezeroentries) { \
453: low2 = 0; \
454: high2 = nrow2; \
455: goto b_noinsert; \
456: } \
457: if (nonew == 1) { \
458: low2 = 0; \
459: high2 = nrow2; \
460: goto b_noinsert; \
461: } \
462: 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); \
463: MatSeqXAIJReallocateAIJ(B, bm, 1, nrow2, row, col, rmax2, ba, bi, bj, rp2, ap2, bimax, nonew, MatScalar); \
464: N = nrow2++ - 1; \
465: b->nz++; \
466: high2++; \
467: /* shift up all the later entries in this row */ \
468: PetscCall(PetscArraymove(rp2 + _i + 1, rp2 + _i, N - _i + 1)); \
469: PetscCall(PetscArraymove(ap2 + _i + 1, ap2 + _i, N - _i + 1)); \
470: rp2[_i] = col; \
471: ap2[_i] = value; \
472: B->nonzerostate++; \
473: b_noinsert:; \
474: bilen[row] = nrow2; \
475: }
477: PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A, PetscInt row, const PetscScalar v[])
478: {
479: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
480: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data, *b = (Mat_SeqAIJ *)mat->B->data;
481: PetscInt l, *garray = mat->garray, diag;
482: PetscScalar *aa, *ba;
484: PetscFunctionBegin;
485: /* code only works for square matrices A */
487: /* find size of row to the left of the diagonal part */
488: PetscCall(MatGetOwnershipRange(A, &diag, NULL));
489: row = row - diag;
490: for (l = 0; l < b->i[row + 1] - b->i[row]; l++) {
491: if (garray[b->j[b->i[row] + l]] > diag) break;
492: }
493: if (l) {
494: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
495: PetscCall(PetscArraycpy(ba + b->i[row], v, l));
496: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
497: }
499: /* diagonal part */
500: if (a->i[row + 1] - a->i[row]) {
501: PetscCall(MatSeqAIJGetArray(mat->A, &aa));
502: PetscCall(PetscArraycpy(aa + a->i[row], v + l, (a->i[row + 1] - a->i[row])));
503: PetscCall(MatSeqAIJRestoreArray(mat->A, &aa));
504: }
506: /* right of diagonal part */
507: if (b->i[row + 1] - b->i[row] - l) {
508: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
509: PetscCall(PetscArraycpy(ba + b->i[row] + l, v + l + a->i[row + 1] - a->i[row], b->i[row + 1] - b->i[row] - l));
510: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
511: }
512: PetscFunctionReturn(PETSC_SUCCESS);
513: }
515: PetscErrorCode MatSetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
516: {
517: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
518: PetscScalar value = 0.0;
519: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
520: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
521: PetscBool roworiented = aij->roworiented;
523: /* Some Variables required in the macro */
524: Mat A = aij->A;
525: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
526: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
527: PetscBool ignorezeroentries = a->ignorezeroentries;
528: Mat B = aij->B;
529: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
530: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
531: MatScalar *aa, *ba;
532: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
533: PetscInt nonew;
534: MatScalar *ap1, *ap2;
536: PetscFunctionBegin;
537: PetscCall(MatSeqAIJGetArray(A, &aa));
538: PetscCall(MatSeqAIJGetArray(B, &ba));
539: for (i = 0; i < m; i++) {
540: if (im[i] < 0) continue;
541: 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);
542: if (im[i] >= rstart && im[i] < rend) {
543: row = im[i] - rstart;
544: lastcol1 = -1;
545: rp1 = aj + ai[row];
546: ap1 = aa + ai[row];
547: rmax1 = aimax[row];
548: nrow1 = ailen[row];
549: low1 = 0;
550: high1 = nrow1;
551: lastcol2 = -1;
552: rp2 = bj + bi[row];
553: ap2 = ba + bi[row];
554: rmax2 = bimax[row];
555: nrow2 = bilen[row];
556: low2 = 0;
557: high2 = nrow2;
559: for (j = 0; j < n; j++) {
560: if (v) value = roworiented ? v[i * n + j] : v[i + j * m];
561: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
562: if (in[j] >= cstart && in[j] < cend) {
563: col = in[j] - cstart;
564: nonew = a->nonew;
565: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
566: } else if (in[j] < 0) {
567: continue;
568: } else {
569: 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);
570: if (mat->was_assembled) {
571: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
572: #if defined(PETSC_USE_CTABLE)
573: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col)); /* map global col ids to local ones */
574: col--;
575: #else
576: col = aij->colmap[in[j]] - 1;
577: #endif
578: if (col < 0 && !((Mat_SeqAIJ *)(aij->B->data))->nonew) { /* col < 0 means in[j] is a new col for B */
579: PetscCall(MatDisAssemble_MPIAIJ(mat)); /* Change aij->B from reduced/local format to expanded/global format */
580: col = in[j];
581: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
582: B = aij->B;
583: b = (Mat_SeqAIJ *)B->data;
584: bimax = b->imax;
585: bi = b->i;
586: bilen = b->ilen;
587: bj = b->j;
588: ba = b->a;
589: rp2 = bj + bi[row];
590: ap2 = ba + bi[row];
591: rmax2 = bimax[row];
592: nrow2 = bilen[row];
593: low2 = 0;
594: high2 = nrow2;
595: bm = aij->B->rmap->n;
596: ba = b->a;
597: } else if (col < 0 && !(ignorezeroentries && value == 0.0)) {
598: if (1 == ((Mat_SeqAIJ *)(aij->B->data))->nonew) {
599: 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]));
600: } 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]);
601: }
602: } else col = in[j];
603: nonew = b->nonew;
604: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
605: }
606: }
607: } else {
608: 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]);
609: if (!aij->donotstash) {
610: mat->assembled = PETSC_FALSE;
611: if (roworiented) {
612: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
613: } else {
614: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
615: }
616: }
617: }
618: }
619: PetscCall(MatSeqAIJRestoreArray(A, &aa)); /* aa, bb might have been free'd due to reallocation above. But we don't access them here */
620: PetscCall(MatSeqAIJRestoreArray(B, &ba));
621: PetscFunctionReturn(PETSC_SUCCESS);
622: }
624: /*
625: This function sets the j and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
626: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
627: No off-processor parts off the matrix are allowed here and mat->was_assembled has to be PETSC_FALSE.
628: */
629: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[])
630: {
631: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
632: Mat A = aij->A; /* diagonal part of the matrix */
633: Mat B = aij->B; /* offdiagonal part of the matrix */
634: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
635: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
636: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, col;
637: PetscInt *ailen = a->ilen, *aj = a->j;
638: PetscInt *bilen = b->ilen, *bj = b->j;
639: PetscInt am = aij->A->rmap->n, j;
640: PetscInt diag_so_far = 0, dnz;
641: PetscInt offd_so_far = 0, onz;
643: PetscFunctionBegin;
644: /* Iterate over all rows of the matrix */
645: for (j = 0; j < am; j++) {
646: dnz = onz = 0;
647: /* Iterate over all non-zero columns of the current row */
648: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
649: /* If column is in the diagonal */
650: if (mat_j[col] >= cstart && mat_j[col] < cend) {
651: aj[diag_so_far++] = mat_j[col] - cstart;
652: dnz++;
653: } else { /* off-diagonal entries */
654: bj[offd_so_far++] = mat_j[col];
655: onz++;
656: }
657: }
658: ailen[j] = dnz;
659: bilen[j] = onz;
660: }
661: PetscFunctionReturn(PETSC_SUCCESS);
662: }
664: /*
665: This function sets the local j, a and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
666: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
667: No off-processor parts off the matrix are allowed here, they are set at a later point by MatSetValues_MPIAIJ.
668: Also, mat->was_assembled has to be false, otherwise the statement aj[rowstart_diag+dnz_row] = mat_j[col] - cstart;
669: would not be true and the more complex MatSetValues_MPIAIJ has to be used.
670: */
671: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[], const PetscScalar mat_a[])
672: {
673: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
674: Mat A = aij->A; /* diagonal part of the matrix */
675: Mat B = aij->B; /* offdiagonal part of the matrix */
676: Mat_SeqAIJ *aijd = (Mat_SeqAIJ *)(aij->A)->data, *aijo = (Mat_SeqAIJ *)(aij->B)->data;
677: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
678: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
679: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend;
680: PetscInt *ailen = a->ilen, *aj = a->j;
681: PetscInt *bilen = b->ilen, *bj = b->j;
682: PetscInt am = aij->A->rmap->n, j;
683: 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. */
684: PetscInt col, dnz_row, onz_row, rowstart_diag, rowstart_offd;
685: PetscScalar *aa = a->a, *ba = b->a;
687: PetscFunctionBegin;
688: /* Iterate over all rows of the matrix */
689: for (j = 0; j < am; j++) {
690: dnz_row = onz_row = 0;
691: rowstart_offd = full_offd_i[j];
692: rowstart_diag = full_diag_i[j];
693: /* Iterate over all non-zero columns of the current row */
694: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
695: /* If column is in the diagonal */
696: if (mat_j[col] >= cstart && mat_j[col] < cend) {
697: aj[rowstart_diag + dnz_row] = mat_j[col] - cstart;
698: aa[rowstart_diag + dnz_row] = mat_a[col];
699: dnz_row++;
700: } else { /* off-diagonal entries */
701: bj[rowstart_offd + onz_row] = mat_j[col];
702: ba[rowstart_offd + onz_row] = mat_a[col];
703: onz_row++;
704: }
705: }
706: ailen[j] = dnz_row;
707: bilen[j] = onz_row;
708: }
709: PetscFunctionReturn(PETSC_SUCCESS);
710: }
712: PetscErrorCode MatGetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
713: {
714: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
715: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
716: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
718: PetscFunctionBegin;
719: for (i = 0; i < m; i++) {
720: if (idxm[i] < 0) continue; /* negative row */
721: 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);
722: if (idxm[i] >= rstart && idxm[i] < rend) {
723: row = idxm[i] - rstart;
724: for (j = 0; j < n; j++) {
725: if (idxn[j] < 0) continue; /* negative column */
726: 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);
727: if (idxn[j] >= cstart && idxn[j] < cend) {
728: col = idxn[j] - cstart;
729: PetscCall(MatGetValues(aij->A, 1, &row, 1, &col, v + i * n + j));
730: } else {
731: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
732: #if defined(PETSC_USE_CTABLE)
733: PetscCall(PetscHMapIGetWithDefault(aij->colmap, idxn[j] + 1, 0, &col));
734: col--;
735: #else
736: col = aij->colmap[idxn[j]] - 1;
737: #endif
738: if ((col < 0) || (aij->garray[col] != idxn[j])) *(v + i * n + j) = 0.0;
739: else PetscCall(MatGetValues(aij->B, 1, &row, 1, &col, v + i * n + j));
740: }
741: }
742: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local values currently supported");
743: }
744: PetscFunctionReturn(PETSC_SUCCESS);
745: }
747: PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat, MatAssemblyType mode)
748: {
749: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
750: PetscInt nstash, reallocs;
752: PetscFunctionBegin;
753: if (aij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
755: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
756: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
757: PetscCall(PetscInfo(aij->A, "Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
758: PetscFunctionReturn(PETSC_SUCCESS);
759: }
761: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat, MatAssemblyType mode)
762: {
763: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
764: PetscMPIInt n;
765: PetscInt i, j, rstart, ncols, flg;
766: PetscInt *row, *col;
767: PetscBool other_disassembled;
768: PetscScalar *val;
770: /* do not use 'b = (Mat_SeqAIJ*)aij->B->data' as B can be reset in disassembly */
772: PetscFunctionBegin;
773: if (!aij->donotstash && !mat->nooffprocentries) {
774: while (1) {
775: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
776: if (!flg) break;
778: for (i = 0; i < n;) {
779: /* Now identify the consecutive vals belonging to the same row */
780: for (j = i, rstart = row[j]; j < n; j++) {
781: if (row[j] != rstart) break;
782: }
783: if (j < n) ncols = j - i;
784: else ncols = n - i;
785: /* Now assemble all these values with a single function call */
786: PetscCall(MatSetValues_MPIAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
787: i = j;
788: }
789: }
790: PetscCall(MatStashScatterEnd_Private(&mat->stash));
791: }
792: #if defined(PETSC_HAVE_DEVICE)
793: if (mat->offloadmask == PETSC_OFFLOAD_CPU) aij->A->offloadmask = PETSC_OFFLOAD_CPU;
794: /* We call MatBindToCPU() on aij->A and aij->B here, because if MatBindToCPU_MPIAIJ() is called before assembly, it cannot bind these. */
795: if (mat->boundtocpu) {
796: PetscCall(MatBindToCPU(aij->A, PETSC_TRUE));
797: PetscCall(MatBindToCPU(aij->B, PETSC_TRUE));
798: }
799: #endif
800: PetscCall(MatAssemblyBegin(aij->A, mode));
801: PetscCall(MatAssemblyEnd(aij->A, mode));
803: /* determine if any processor has disassembled, if so we must
804: also disassemble ourself, in order that we may reassemble. */
805: /*
806: if nonzero structure of submatrix B cannot change then we know that
807: no processor disassembled thus we can skip this stuff
808: */
809: if (!((Mat_SeqAIJ *)aij->B->data)->nonew) {
810: PetscCall(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
811: if (mat->was_assembled && !other_disassembled) { /* mat on this rank has reduced off-diag B with local col ids, but globally it does not */
812: PetscCall(MatDisAssemble_MPIAIJ(mat));
813: }
814: }
815: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIAIJ(mat));
816: PetscCall(MatSetOption(aij->B, MAT_USE_INODES, PETSC_FALSE));
817: #if defined(PETSC_HAVE_DEVICE)
818: if (mat->offloadmask == PETSC_OFFLOAD_CPU && aij->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) aij->B->offloadmask = PETSC_OFFLOAD_CPU;
819: #endif
820: PetscCall(MatAssemblyBegin(aij->B, mode));
821: PetscCall(MatAssemblyEnd(aij->B, mode));
823: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
825: aij->rowvalues = NULL;
827: PetscCall(VecDestroy(&aij->diag));
829: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
830: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqAIJ *)(aij->A->data))->nonew) {
831: PetscObjectState state = aij->A->nonzerostate + aij->B->nonzerostate;
832: PetscCall(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
833: }
834: #if defined(PETSC_HAVE_DEVICE)
835: mat->offloadmask = PETSC_OFFLOAD_BOTH;
836: #endif
837: PetscFunctionReturn(PETSC_SUCCESS);
838: }
840: PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
841: {
842: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
844: PetscFunctionBegin;
845: PetscCall(MatZeroEntries(l->A));
846: PetscCall(MatZeroEntries(l->B));
847: PetscFunctionReturn(PETSC_SUCCESS);
848: }
850: PetscErrorCode MatZeroRows_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
851: {
852: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
853: PetscObjectState sA, sB;
854: PetscInt *lrows;
855: PetscInt r, len;
856: PetscBool cong, lch, gch;
858: PetscFunctionBegin;
859: /* get locally owned rows */
860: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
861: PetscCall(MatHasCongruentLayouts(A, &cong));
862: /* fix right hand side if needed */
863: if (x && b) {
864: const PetscScalar *xx;
865: PetscScalar *bb;
867: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
868: PetscCall(VecGetArrayRead(x, &xx));
869: PetscCall(VecGetArray(b, &bb));
870: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
871: PetscCall(VecRestoreArrayRead(x, &xx));
872: PetscCall(VecRestoreArray(b, &bb));
873: }
875: sA = mat->A->nonzerostate;
876: sB = mat->B->nonzerostate;
878: if (diag != 0.0 && cong) {
879: PetscCall(MatZeroRows(mat->A, len, lrows, diag, NULL, NULL));
880: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
881: } else if (diag != 0.0) { /* non-square or non congruent layouts -> if keepnonzeropattern is false, we allow for new insertion */
882: Mat_SeqAIJ *aijA = (Mat_SeqAIJ *)mat->A->data;
883: Mat_SeqAIJ *aijB = (Mat_SeqAIJ *)mat->B->data;
884: PetscInt nnwA, nnwB;
885: PetscBool nnzA, nnzB;
887: nnwA = aijA->nonew;
888: nnwB = aijB->nonew;
889: nnzA = aijA->keepnonzeropattern;
890: nnzB = aijB->keepnonzeropattern;
891: if (!nnzA) {
892: PetscCall(PetscInfo(mat->A, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on diagonal block.\n"));
893: aijA->nonew = 0;
894: }
895: if (!nnzB) {
896: PetscCall(PetscInfo(mat->B, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on off-diagonal block.\n"));
897: aijB->nonew = 0;
898: }
899: /* Must zero here before the next loop */
900: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
901: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
902: for (r = 0; r < len; ++r) {
903: const PetscInt row = lrows[r] + A->rmap->rstart;
904: if (row >= A->cmap->N) continue;
905: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
906: }
907: aijA->nonew = nnwA;
908: aijB->nonew = nnwB;
909: } else {
910: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
911: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
912: }
913: PetscCall(PetscFree(lrows));
914: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
915: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
917: /* reduce nonzerostate */
918: lch = (PetscBool)(sA != mat->A->nonzerostate || sB != mat->B->nonzerostate);
919: PetscCall(MPIU_Allreduce(&lch, &gch, 1, MPIU_BOOL, MPI_LOR, PetscObjectComm((PetscObject)A)));
920: if (gch) A->nonzerostate++;
921: PetscFunctionReturn(PETSC_SUCCESS);
922: }
924: PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
925: {
926: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
927: PetscMPIInt n = A->rmap->n;
928: PetscInt i, j, r, m, len = 0;
929: PetscInt *lrows, *owners = A->rmap->range;
930: PetscMPIInt p = 0;
931: PetscSFNode *rrows;
932: PetscSF sf;
933: const PetscScalar *xx;
934: PetscScalar *bb, *mask, *aij_a;
935: Vec xmask, lmask;
936: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)l->B->data;
937: const PetscInt *aj, *ii, *ridx;
938: PetscScalar *aa;
940: PetscFunctionBegin;
941: /* Create SF where leaves are input rows and roots are owned rows */
942: PetscCall(PetscMalloc1(n, &lrows));
943: for (r = 0; r < n; ++r) lrows[r] = -1;
944: PetscCall(PetscMalloc1(N, &rrows));
945: for (r = 0; r < N; ++r) {
946: const PetscInt idx = rows[r];
947: 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);
948: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
949: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
950: }
951: rrows[r].rank = p;
952: rrows[r].index = rows[r] - owners[p];
953: }
954: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
955: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
956: /* Collect flags for rows to be zeroed */
957: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
958: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
959: PetscCall(PetscSFDestroy(&sf));
960: /* Compress and put in row numbers */
961: for (r = 0; r < n; ++r)
962: if (lrows[r] >= 0) lrows[len++] = r;
963: /* zero diagonal part of matrix */
964: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
965: /* handle off diagonal part of matrix */
966: PetscCall(MatCreateVecs(A, &xmask, NULL));
967: PetscCall(VecDuplicate(l->lvec, &lmask));
968: PetscCall(VecGetArray(xmask, &bb));
969: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
970: PetscCall(VecRestoreArray(xmask, &bb));
971: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
972: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
973: PetscCall(VecDestroy(&xmask));
974: if (x && b) { /* this code is buggy when the row and column layout don't match */
975: PetscBool cong;
977: PetscCall(MatHasCongruentLayouts(A, &cong));
978: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
979: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
980: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
981: PetscCall(VecGetArrayRead(l->lvec, &xx));
982: PetscCall(VecGetArray(b, &bb));
983: }
984: PetscCall(VecGetArray(lmask, &mask));
985: /* remove zeroed rows of off diagonal matrix */
986: PetscCall(MatSeqAIJGetArray(l->B, &aij_a));
987: ii = aij->i;
988: for (i = 0; i < len; i++) PetscCall(PetscArrayzero(aij_a + ii[lrows[i]], ii[lrows[i] + 1] - ii[lrows[i]]));
989: /* loop over all elements of off process part of matrix zeroing removed columns*/
990: if (aij->compressedrow.use) {
991: m = aij->compressedrow.nrows;
992: ii = aij->compressedrow.i;
993: ridx = aij->compressedrow.rindex;
994: for (i = 0; i < m; i++) {
995: n = ii[i + 1] - ii[i];
996: aj = aij->j + ii[i];
997: aa = aij_a + ii[i];
999: for (j = 0; j < n; j++) {
1000: if (PetscAbsScalar(mask[*aj])) {
1001: if (b) bb[*ridx] -= *aa * xx[*aj];
1002: *aa = 0.0;
1003: }
1004: aa++;
1005: aj++;
1006: }
1007: ridx++;
1008: }
1009: } else { /* do not use compressed row format */
1010: m = l->B->rmap->n;
1011: for (i = 0; i < m; i++) {
1012: n = ii[i + 1] - ii[i];
1013: aj = aij->j + ii[i];
1014: aa = aij_a + ii[i];
1015: for (j = 0; j < n; j++) {
1016: if (PetscAbsScalar(mask[*aj])) {
1017: if (b) bb[i] -= *aa * xx[*aj];
1018: *aa = 0.0;
1019: }
1020: aa++;
1021: aj++;
1022: }
1023: }
1024: }
1025: if (x && b) {
1026: PetscCall(VecRestoreArray(b, &bb));
1027: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1028: }
1029: PetscCall(MatSeqAIJRestoreArray(l->B, &aij_a));
1030: PetscCall(VecRestoreArray(lmask, &mask));
1031: PetscCall(VecDestroy(&lmask));
1032: PetscCall(PetscFree(lrows));
1034: /* only change matrix nonzero state if pattern was allowed to be changed */
1035: if (!((Mat_SeqAIJ *)(l->A->data))->keepnonzeropattern) {
1036: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1037: PetscCall(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1038: }
1039: PetscFunctionReturn(PETSC_SUCCESS);
1040: }
1042: PetscErrorCode MatMult_MPIAIJ(Mat A, Vec xx, Vec yy)
1043: {
1044: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1045: PetscInt nt;
1046: VecScatter Mvctx = a->Mvctx;
1048: PetscFunctionBegin;
1049: PetscCall(VecGetLocalSize(xx, &nt));
1050: 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);
1051: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1052: PetscUseTypeMethod(a->A, mult, xx, yy);
1053: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1054: PetscUseTypeMethod(a->B, multadd, a->lvec, yy, yy);
1055: PetscFunctionReturn(PETSC_SUCCESS);
1056: }
1058: PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A, Vec bb, Vec xx)
1059: {
1060: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1062: PetscFunctionBegin;
1063: PetscCall(MatMultDiagonalBlock(a->A, bb, xx));
1064: PetscFunctionReturn(PETSC_SUCCESS);
1065: }
1067: PetscErrorCode MatMultAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1068: {
1069: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1070: VecScatter Mvctx = a->Mvctx;
1072: PetscFunctionBegin;
1073: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1074: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1075: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1076: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1077: PetscFunctionReturn(PETSC_SUCCESS);
1078: }
1080: PetscErrorCode MatMultTranspose_MPIAIJ(Mat A, Vec xx, Vec yy)
1081: {
1082: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1084: PetscFunctionBegin;
1085: /* do nondiagonal part */
1086: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1087: /* do local part */
1088: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1089: /* add partial results together */
1090: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1091: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1092: PetscFunctionReturn(PETSC_SUCCESS);
1093: }
1095: PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f)
1096: {
1097: MPI_Comm comm;
1098: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)Amat->data, *Bij = (Mat_MPIAIJ *)Bmat->data;
1099: Mat Adia = Aij->A, Bdia = Bij->A, Aoff, Boff, *Aoffs, *Boffs;
1100: IS Me, Notme;
1101: PetscInt M, N, first, last, *notme, i;
1102: PetscBool lf;
1103: PetscMPIInt size;
1105: PetscFunctionBegin;
1106: /* Easy test: symmetric diagonal block */
1107: PetscCall(MatIsTranspose(Adia, Bdia, tol, &lf));
1108: PetscCall(MPIU_Allreduce(&lf, f, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)Amat)));
1109: if (!*f) PetscFunctionReturn(PETSC_SUCCESS);
1110: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1111: PetscCallMPI(MPI_Comm_size(comm, &size));
1112: if (size == 1) PetscFunctionReturn(PETSC_SUCCESS);
1114: /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */
1115: PetscCall(MatGetSize(Amat, &M, &N));
1116: PetscCall(MatGetOwnershipRange(Amat, &first, &last));
1117: PetscCall(PetscMalloc1(N - last + first, ¬me));
1118: for (i = 0; i < first; i++) notme[i] = i;
1119: for (i = last; i < M; i++) notme[i - last + first] = i;
1120: PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme));
1121: PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me));
1122: PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs));
1123: Aoff = Aoffs[0];
1124: PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs));
1125: Boff = Boffs[0];
1126: PetscCall(MatIsTranspose(Aoff, Boff, tol, f));
1127: PetscCall(MatDestroyMatrices(1, &Aoffs));
1128: PetscCall(MatDestroyMatrices(1, &Boffs));
1129: PetscCall(ISDestroy(&Me));
1130: PetscCall(ISDestroy(&Notme));
1131: PetscCall(PetscFree(notme));
1132: PetscFunctionReturn(PETSC_SUCCESS);
1133: }
1135: PetscErrorCode MatIsSymmetric_MPIAIJ(Mat A, PetscReal tol, PetscBool *f)
1136: {
1137: PetscFunctionBegin;
1138: PetscCall(MatIsTranspose_MPIAIJ(A, A, tol, f));
1139: PetscFunctionReturn(PETSC_SUCCESS);
1140: }
1142: PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1143: {
1144: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1146: PetscFunctionBegin;
1147: /* do nondiagonal part */
1148: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1149: /* do local part */
1150: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1151: /* add partial results together */
1152: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1153: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1154: PetscFunctionReturn(PETSC_SUCCESS);
1155: }
1157: /*
1158: This only works correctly for square matrices where the subblock A->A is the
1159: diagonal block
1160: */
1161: PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A, Vec v)
1162: {
1163: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1165: PetscFunctionBegin;
1166: PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1167: 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");
1168: PetscCall(MatGetDiagonal(a->A, v));
1169: PetscFunctionReturn(PETSC_SUCCESS);
1170: }
1172: PetscErrorCode MatScale_MPIAIJ(Mat A, PetscScalar aa)
1173: {
1174: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1176: PetscFunctionBegin;
1177: PetscCall(MatScale(a->A, aa));
1178: PetscCall(MatScale(a->B, aa));
1179: PetscFunctionReturn(PETSC_SUCCESS);
1180: }
1182: /* Free COO stuff; must match allocation methods in MatSetPreallocationCOO_MPIAIJ() */
1183: PETSC_INTERN PetscErrorCode MatResetPreallocationCOO_MPIAIJ(Mat mat)
1184: {
1185: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1187: PetscFunctionBegin;
1188: PetscCall(PetscSFDestroy(&aij->coo_sf));
1189: PetscCall(PetscFree(aij->Aperm1));
1190: PetscCall(PetscFree(aij->Bperm1));
1191: PetscCall(PetscFree(aij->Ajmap1));
1192: PetscCall(PetscFree(aij->Bjmap1));
1194: PetscCall(PetscFree(aij->Aimap2));
1195: PetscCall(PetscFree(aij->Bimap2));
1196: PetscCall(PetscFree(aij->Aperm2));
1197: PetscCall(PetscFree(aij->Bperm2));
1198: PetscCall(PetscFree(aij->Ajmap2));
1199: PetscCall(PetscFree(aij->Bjmap2));
1201: PetscCall(PetscFree2(aij->sendbuf, aij->recvbuf));
1202: PetscCall(PetscFree(aij->Cperm1));
1203: PetscFunctionReturn(PETSC_SUCCESS);
1204: }
1206: PetscErrorCode MatView_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
1207: {
1208: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1209: Mat_SeqAIJ *A = (Mat_SeqAIJ *)aij->A->data;
1210: Mat_SeqAIJ *B = (Mat_SeqAIJ *)aij->B->data;
1211: const PetscInt *garray = aij->garray;
1212: const PetscScalar *aa, *ba;
1213: PetscInt header[4], M, N, m, rs, cs, cnt, i, ja, jb;
1214: PetscInt64 nz, hnz;
1215: PetscInt *rowlens;
1216: PetscInt *colidxs;
1217: PetscScalar *matvals;
1218: PetscMPIInt rank;
1220: PetscFunctionBegin;
1221: PetscCall(PetscViewerSetUp(viewer));
1223: M = mat->rmap->N;
1224: N = mat->cmap->N;
1225: m = mat->rmap->n;
1226: rs = mat->rmap->rstart;
1227: cs = mat->cmap->rstart;
1228: nz = A->nz + B->nz;
1230: /* write matrix header */
1231: header[0] = MAT_FILE_CLASSID;
1232: header[1] = M;
1233: header[2] = N;
1234: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1235: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1236: if (rank == 0) PetscCall(PetscIntCast(hnz, &header[3]));
1237: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1239: /* fill in and store row lengths */
1240: PetscCall(PetscMalloc1(m, &rowlens));
1241: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i];
1242: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1243: PetscCall(PetscFree(rowlens));
1245: /* fill in and store column indices */
1246: PetscCall(PetscMalloc1(nz, &colidxs));
1247: for (cnt = 0, i = 0; i < m; i++) {
1248: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1249: if (garray[B->j[jb]] > cs) break;
1250: colidxs[cnt++] = garray[B->j[jb]];
1251: }
1252: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) colidxs[cnt++] = A->j[ja] + cs;
1253: for (; jb < B->i[i + 1]; jb++) colidxs[cnt++] = garray[B->j[jb]];
1254: }
1255: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1256: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
1257: PetscCall(PetscFree(colidxs));
1259: /* fill in and store nonzero values */
1260: PetscCall(MatSeqAIJGetArrayRead(aij->A, &aa));
1261: PetscCall(MatSeqAIJGetArrayRead(aij->B, &ba));
1262: PetscCall(PetscMalloc1(nz, &matvals));
1263: for (cnt = 0, i = 0; i < m; i++) {
1264: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1265: if (garray[B->j[jb]] > cs) break;
1266: matvals[cnt++] = ba[jb];
1267: }
1268: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) matvals[cnt++] = aa[ja];
1269: for (; jb < B->i[i + 1]; jb++) matvals[cnt++] = ba[jb];
1270: }
1271: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &aa));
1272: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &ba));
1273: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1274: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
1275: PetscCall(PetscFree(matvals));
1277: /* write block size option to the viewer's .info file */
1278: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1279: PetscFunctionReturn(PETSC_SUCCESS);
1280: }
1282: #include <petscdraw.h>
1283: PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1284: {
1285: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1286: PetscMPIInt rank = aij->rank, size = aij->size;
1287: PetscBool isdraw, iascii, isbinary;
1288: PetscViewer sviewer;
1289: PetscViewerFormat format;
1291: PetscFunctionBegin;
1292: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1293: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1294: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1295: if (iascii) {
1296: PetscCall(PetscViewerGetFormat(viewer, &format));
1297: if (format == PETSC_VIEWER_LOAD_BALANCE) {
1298: PetscInt i, nmax = 0, nmin = PETSC_MAX_INT, navg = 0, *nz, nzlocal = ((Mat_SeqAIJ *)(aij->A->data))->nz + ((Mat_SeqAIJ *)(aij->B->data))->nz;
1299: PetscCall(PetscMalloc1(size, &nz));
1300: PetscCallMPI(MPI_Allgather(&nzlocal, 1, MPIU_INT, nz, 1, MPIU_INT, PetscObjectComm((PetscObject)mat)));
1301: for (i = 0; i < (PetscInt)size; i++) {
1302: nmax = PetscMax(nmax, nz[i]);
1303: nmin = PetscMin(nmin, nz[i]);
1304: navg += nz[i];
1305: }
1306: PetscCall(PetscFree(nz));
1307: navg = navg / size;
1308: PetscCall(PetscViewerASCIIPrintf(viewer, "Load Balance - Nonzeros: Min %" PetscInt_FMT " avg %" PetscInt_FMT " max %" PetscInt_FMT "\n", nmin, navg, nmax));
1309: PetscFunctionReturn(PETSC_SUCCESS);
1310: }
1311: PetscCall(PetscViewerGetFormat(viewer, &format));
1312: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1313: MatInfo info;
1314: PetscInt *inodes = NULL;
1316: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1317: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1318: PetscCall(MatInodeGetInodeSizes(aij->A, NULL, &inodes, NULL));
1319: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1320: if (!inodes) {
1321: 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,
1322: (double)info.memory));
1323: } else {
1324: 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,
1325: (double)info.memory));
1326: }
1327: PetscCall(MatGetInfo(aij->A, MAT_LOCAL, &info));
1328: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1329: PetscCall(MatGetInfo(aij->B, MAT_LOCAL, &info));
1330: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1331: PetscCall(PetscViewerFlush(viewer));
1332: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1333: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1334: PetscCall(VecScatterView(aij->Mvctx, viewer));
1335: PetscFunctionReturn(PETSC_SUCCESS);
1336: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1337: PetscInt inodecount, inodelimit, *inodes;
1338: PetscCall(MatInodeGetInodeSizes(aij->A, &inodecount, &inodes, &inodelimit));
1339: if (inodes) {
1340: PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
1341: } else {
1342: PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
1343: }
1344: PetscFunctionReturn(PETSC_SUCCESS);
1345: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1346: PetscFunctionReturn(PETSC_SUCCESS);
1347: }
1348: } else if (isbinary) {
1349: if (size == 1) {
1350: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1351: PetscCall(MatView(aij->A, viewer));
1352: } else {
1353: PetscCall(MatView_MPIAIJ_Binary(mat, viewer));
1354: }
1355: PetscFunctionReturn(PETSC_SUCCESS);
1356: } else if (iascii && size == 1) {
1357: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1358: PetscCall(MatView(aij->A, viewer));
1359: PetscFunctionReturn(PETSC_SUCCESS);
1360: } else if (isdraw) {
1361: PetscDraw draw;
1362: PetscBool isnull;
1363: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1364: PetscCall(PetscDrawIsNull(draw, &isnull));
1365: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1366: }
1368: { /* assemble the entire matrix onto first processor */
1369: Mat A = NULL, Av;
1370: IS isrow, iscol;
1372: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->rmap->N : 0, 0, 1, &isrow));
1373: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->cmap->N : 0, 0, 1, &iscol));
1374: PetscCall(MatCreateSubMatrix(mat, isrow, iscol, MAT_INITIAL_MATRIX, &A));
1375: PetscCall(MatMPIAIJGetSeqAIJ(A, &Av, NULL, NULL));
1376: /* The commented code uses MatCreateSubMatrices instead */
1377: /*
1378: Mat *AA, A = NULL, Av;
1379: IS isrow,iscol;
1381: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->rmap->N : 0,0,1,&isrow));
1382: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->cmap->N : 0,0,1,&iscol));
1383: PetscCall(MatCreateSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&AA));
1384: if (rank == 0) {
1385: PetscCall(PetscObjectReference((PetscObject)AA[0]));
1386: A = AA[0];
1387: Av = AA[0];
1388: }
1389: PetscCall(MatDestroySubMatrices(1,&AA));
1390: */
1391: PetscCall(ISDestroy(&iscol));
1392: PetscCall(ISDestroy(&isrow));
1393: /*
1394: Everyone has to call to draw the matrix since the graphics waits are
1395: synchronized across all processors that share the PetscDraw object
1396: */
1397: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1398: if (rank == 0) {
1399: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)Av, ((PetscObject)mat)->name));
1400: PetscCall(MatView_SeqAIJ(Av, sviewer));
1401: }
1402: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1403: PetscCall(PetscViewerFlush(viewer));
1404: PetscCall(MatDestroy(&A));
1405: }
1406: PetscFunctionReturn(PETSC_SUCCESS);
1407: }
1409: PetscErrorCode MatView_MPIAIJ(Mat mat, PetscViewer viewer)
1410: {
1411: PetscBool iascii, isdraw, issocket, isbinary;
1413: PetscFunctionBegin;
1414: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1415: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1416: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1417: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1418: if (iascii || isdraw || isbinary || issocket) PetscCall(MatView_MPIAIJ_ASCIIorDraworSocket(mat, viewer));
1419: PetscFunctionReturn(PETSC_SUCCESS);
1420: }
1422: PetscErrorCode MatSOR_MPIAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1423: {
1424: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1425: Vec bb1 = NULL;
1426: PetscBool hasop;
1428: PetscFunctionBegin;
1429: if (flag == SOR_APPLY_UPPER) {
1430: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1431: PetscFunctionReturn(PETSC_SUCCESS);
1432: }
1434: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1));
1436: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
1437: if (flag & SOR_ZERO_INITIAL_GUESS) {
1438: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1439: its--;
1440: }
1442: while (its--) {
1443: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1444: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1446: /* update rhs: bb1 = bb - B*x */
1447: PetscCall(VecScale(mat->lvec, -1.0));
1448: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1450: /* local sweep */
1451: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
1452: }
1453: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1454: if (flag & SOR_ZERO_INITIAL_GUESS) {
1455: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1456: its--;
1457: }
1458: while (its--) {
1459: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1460: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1462: /* update rhs: bb1 = bb - B*x */
1463: PetscCall(VecScale(mat->lvec, -1.0));
1464: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1466: /* local sweep */
1467: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
1468: }
1469: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1470: if (flag & SOR_ZERO_INITIAL_GUESS) {
1471: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1472: its--;
1473: }
1474: while (its--) {
1475: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1476: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1478: /* update rhs: bb1 = bb - B*x */
1479: PetscCall(VecScale(mat->lvec, -1.0));
1480: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1482: /* local sweep */
1483: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
1484: }
1485: } else if (flag & SOR_EISENSTAT) {
1486: Vec xx1;
1488: PetscCall(VecDuplicate(bb, &xx1));
1489: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP), fshift, lits, 1, xx));
1491: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1492: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1493: if (!mat->diag) {
1494: PetscCall(MatCreateVecs(matin, &mat->diag, NULL));
1495: PetscCall(MatGetDiagonal(matin, mat->diag));
1496: }
1497: PetscCall(MatHasOperation(matin, MATOP_MULT_DIAGONAL_BLOCK, &hasop));
1498: if (hasop) {
1499: PetscCall(MatMultDiagonalBlock(matin, xx, bb1));
1500: } else {
1501: PetscCall(VecPointwiseMult(bb1, mat->diag, xx));
1502: }
1503: PetscCall(VecAYPX(bb1, (omega - 2.0) / omega, bb));
1505: PetscCall(MatMultAdd(mat->B, mat->lvec, bb1, bb1));
1507: /* local sweep */
1508: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP), fshift, lits, 1, xx1));
1509: PetscCall(VecAXPY(xx, 1.0, xx1));
1510: PetscCall(VecDestroy(&xx1));
1511: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");
1513: PetscCall(VecDestroy(&bb1));
1515: matin->factorerrortype = mat->A->factorerrortype;
1516: PetscFunctionReturn(PETSC_SUCCESS);
1517: }
1519: PetscErrorCode MatPermute_MPIAIJ(Mat A, IS rowp, IS colp, Mat *B)
1520: {
1521: Mat aA, aB, Aperm;
1522: const PetscInt *rwant, *cwant, *gcols, *ai, *bi, *aj, *bj;
1523: PetscScalar *aa, *ba;
1524: PetscInt i, j, m, n, ng, anz, bnz, *dnnz, *onnz, *tdnnz, *tonnz, *rdest, *cdest, *work, *gcdest;
1525: PetscSF rowsf, sf;
1526: IS parcolp = NULL;
1527: PetscBool done;
1529: PetscFunctionBegin;
1530: PetscCall(MatGetLocalSize(A, &m, &n));
1531: PetscCall(ISGetIndices(rowp, &rwant));
1532: PetscCall(ISGetIndices(colp, &cwant));
1533: PetscCall(PetscMalloc3(PetscMax(m, n), &work, m, &rdest, n, &cdest));
1535: /* Invert row permutation to find out where my rows should go */
1536: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &rowsf));
1537: PetscCall(PetscSFSetGraphLayout(rowsf, A->rmap, A->rmap->n, NULL, PETSC_OWN_POINTER, rwant));
1538: PetscCall(PetscSFSetFromOptions(rowsf));
1539: for (i = 0; i < m; i++) work[i] = A->rmap->rstart + i;
1540: PetscCall(PetscSFReduceBegin(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1541: PetscCall(PetscSFReduceEnd(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1543: /* Invert column permutation to find out where my columns should go */
1544: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1545: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, A->cmap->n, NULL, PETSC_OWN_POINTER, cwant));
1546: PetscCall(PetscSFSetFromOptions(sf));
1547: for (i = 0; i < n; i++) work[i] = A->cmap->rstart + i;
1548: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1549: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1550: PetscCall(PetscSFDestroy(&sf));
1552: PetscCall(ISRestoreIndices(rowp, &rwant));
1553: PetscCall(ISRestoreIndices(colp, &cwant));
1554: PetscCall(MatMPIAIJGetSeqAIJ(A, &aA, &aB, &gcols));
1556: /* Find out where my gcols should go */
1557: PetscCall(MatGetSize(aB, NULL, &ng));
1558: PetscCall(PetscMalloc1(ng, &gcdest));
1559: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1560: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, ng, NULL, PETSC_OWN_POINTER, gcols));
1561: PetscCall(PetscSFSetFromOptions(sf));
1562: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1563: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1564: PetscCall(PetscSFDestroy(&sf));
1566: PetscCall(PetscCalloc4(m, &dnnz, m, &onnz, m, &tdnnz, m, &tonnz));
1567: PetscCall(MatGetRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1568: PetscCall(MatGetRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1569: for (i = 0; i < m; i++) {
1570: PetscInt row = rdest[i];
1571: PetscMPIInt rowner;
1572: PetscCall(PetscLayoutFindOwner(A->rmap, row, &rowner));
1573: for (j = ai[i]; j < ai[i + 1]; j++) {
1574: PetscInt col = cdest[aj[j]];
1575: PetscMPIInt cowner;
1576: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner)); /* Could build an index for the columns to eliminate this search */
1577: if (rowner == cowner) dnnz[i]++;
1578: else onnz[i]++;
1579: }
1580: for (j = bi[i]; j < bi[i + 1]; j++) {
1581: PetscInt col = gcdest[bj[j]];
1582: PetscMPIInt cowner;
1583: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner));
1584: if (rowner == cowner) dnnz[i]++;
1585: else onnz[i]++;
1586: }
1587: }
1588: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1589: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1590: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1591: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1592: PetscCall(PetscSFDestroy(&rowsf));
1594: PetscCall(MatCreateAIJ(PetscObjectComm((PetscObject)A), A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N, 0, tdnnz, 0, tonnz, &Aperm));
1595: PetscCall(MatSeqAIJGetArray(aA, &aa));
1596: PetscCall(MatSeqAIJGetArray(aB, &ba));
1597: for (i = 0; i < m; i++) {
1598: PetscInt *acols = dnnz, *bcols = onnz; /* Repurpose now-unneeded arrays */
1599: PetscInt j0, rowlen;
1600: rowlen = ai[i + 1] - ai[i];
1601: for (j0 = j = 0; j < rowlen; j0 = j) { /* rowlen could be larger than number of rows m, so sum in batches */
1602: for (; j < PetscMin(rowlen, j0 + m); j++) acols[j - j0] = cdest[aj[ai[i] + j]];
1603: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, acols, aa + ai[i] + j0, INSERT_VALUES));
1604: }
1605: rowlen = bi[i + 1] - bi[i];
1606: for (j0 = j = 0; j < rowlen; j0 = j) {
1607: for (; j < PetscMin(rowlen, j0 + m); j++) bcols[j - j0] = gcdest[bj[bi[i] + j]];
1608: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, bcols, ba + bi[i] + j0, INSERT_VALUES));
1609: }
1610: }
1611: PetscCall(MatAssemblyBegin(Aperm, MAT_FINAL_ASSEMBLY));
1612: PetscCall(MatAssemblyEnd(Aperm, MAT_FINAL_ASSEMBLY));
1613: PetscCall(MatRestoreRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1614: PetscCall(MatRestoreRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1615: PetscCall(MatSeqAIJRestoreArray(aA, &aa));
1616: PetscCall(MatSeqAIJRestoreArray(aB, &ba));
1617: PetscCall(PetscFree4(dnnz, onnz, tdnnz, tonnz));
1618: PetscCall(PetscFree3(work, rdest, cdest));
1619: PetscCall(PetscFree(gcdest));
1620: if (parcolp) PetscCall(ISDestroy(&colp));
1621: *B = Aperm;
1622: PetscFunctionReturn(PETSC_SUCCESS);
1623: }
1625: PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
1626: {
1627: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1629: PetscFunctionBegin;
1630: PetscCall(MatGetSize(aij->B, NULL, nghosts));
1631: if (ghosts) *ghosts = aij->garray;
1632: PetscFunctionReturn(PETSC_SUCCESS);
1633: }
1635: PetscErrorCode MatGetInfo_MPIAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1636: {
1637: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1638: Mat A = mat->A, B = mat->B;
1639: PetscLogDouble isend[5], irecv[5];
1641: PetscFunctionBegin;
1642: info->block_size = 1.0;
1643: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1645: isend[0] = info->nz_used;
1646: isend[1] = info->nz_allocated;
1647: isend[2] = info->nz_unneeded;
1648: isend[3] = info->memory;
1649: isend[4] = info->mallocs;
1651: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1653: isend[0] += info->nz_used;
1654: isend[1] += info->nz_allocated;
1655: isend[2] += info->nz_unneeded;
1656: isend[3] += info->memory;
1657: isend[4] += info->mallocs;
1658: if (flag == MAT_LOCAL) {
1659: info->nz_used = isend[0];
1660: info->nz_allocated = isend[1];
1661: info->nz_unneeded = isend[2];
1662: info->memory = isend[3];
1663: info->mallocs = isend[4];
1664: } else if (flag == MAT_GLOBAL_MAX) {
1665: PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1667: info->nz_used = irecv[0];
1668: info->nz_allocated = irecv[1];
1669: info->nz_unneeded = irecv[2];
1670: info->memory = irecv[3];
1671: info->mallocs = irecv[4];
1672: } else if (flag == MAT_GLOBAL_SUM) {
1673: PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1675: info->nz_used = irecv[0];
1676: info->nz_allocated = irecv[1];
1677: info->nz_unneeded = irecv[2];
1678: info->memory = irecv[3];
1679: info->mallocs = irecv[4];
1680: }
1681: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1682: info->fill_ratio_needed = 0;
1683: info->factor_mallocs = 0;
1684: PetscFunctionReturn(PETSC_SUCCESS);
1685: }
1687: PetscErrorCode MatSetOption_MPIAIJ(Mat A, MatOption op, PetscBool flg)
1688: {
1689: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1691: PetscFunctionBegin;
1692: switch (op) {
1693: case MAT_NEW_NONZERO_LOCATIONS:
1694: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1695: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1696: case MAT_KEEP_NONZERO_PATTERN:
1697: case MAT_NEW_NONZERO_LOCATION_ERR:
1698: case MAT_USE_INODES:
1699: case MAT_IGNORE_ZERO_ENTRIES:
1700: case MAT_FORM_EXPLICIT_TRANSPOSE:
1701: MatCheckPreallocated(A, 1);
1702: PetscCall(MatSetOption(a->A, op, flg));
1703: PetscCall(MatSetOption(a->B, op, flg));
1704: break;
1705: case MAT_ROW_ORIENTED:
1706: MatCheckPreallocated(A, 1);
1707: a->roworiented = flg;
1709: PetscCall(MatSetOption(a->A, op, flg));
1710: PetscCall(MatSetOption(a->B, op, flg));
1711: break;
1712: case MAT_FORCE_DIAGONAL_ENTRIES:
1713: case MAT_SORTED_FULL:
1714: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1715: break;
1716: case MAT_IGNORE_OFF_PROC_ENTRIES:
1717: a->donotstash = flg;
1718: break;
1719: /* Symmetry flags are handled directly by MatSetOption() and they don't affect preallocation */
1720: case MAT_SPD:
1721: case MAT_SYMMETRIC:
1722: case MAT_STRUCTURALLY_SYMMETRIC:
1723: case MAT_HERMITIAN:
1724: case MAT_SYMMETRY_ETERNAL:
1725: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1726: case MAT_SPD_ETERNAL:
1727: /* if the diagonal matrix is square it inherits some of the properties above */
1728: break;
1729: case MAT_SUBMAT_SINGLEIS:
1730: A->submat_singleis = flg;
1731: break;
1732: case MAT_STRUCTURE_ONLY:
1733: /* The option is handled directly by MatSetOption() */
1734: break;
1735: default:
1736: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1737: }
1738: PetscFunctionReturn(PETSC_SUCCESS);
1739: }
1741: PetscErrorCode MatGetRow_MPIAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1742: {
1743: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1744: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1745: PetscInt i, *cworkA, *cworkB, **pcA, **pcB, cstart = matin->cmap->rstart;
1746: PetscInt nztot, nzA, nzB, lrow, rstart = matin->rmap->rstart, rend = matin->rmap->rend;
1747: PetscInt *cmap, *idx_p;
1749: PetscFunctionBegin;
1750: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1751: mat->getrowactive = PETSC_TRUE;
1753: if (!mat->rowvalues && (idx || v)) {
1754: /*
1755: allocate enough space to hold information from the longest row.
1756: */
1757: Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)mat->A->data, *Ba = (Mat_SeqAIJ *)mat->B->data;
1758: PetscInt max = 1, tmp;
1759: for (i = 0; i < matin->rmap->n; i++) {
1760: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1761: if (max < tmp) max = tmp;
1762: }
1763: PetscCall(PetscMalloc2(max, &mat->rowvalues, max, &mat->rowindices));
1764: }
1766: PetscCheck(row >= rstart && row < rend, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Only local rows");
1767: lrow = row - rstart;
1769: pvA = &vworkA;
1770: pcA = &cworkA;
1771: pvB = &vworkB;
1772: pcB = &cworkB;
1773: if (!v) {
1774: pvA = NULL;
1775: pvB = NULL;
1776: }
1777: if (!idx) {
1778: pcA = NULL;
1779: if (!v) pcB = NULL;
1780: }
1781: PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1782: PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1783: nztot = nzA + nzB;
1785: cmap = mat->garray;
1786: if (v || idx) {
1787: if (nztot) {
1788: /* Sort by increasing column numbers, assuming A and B already sorted */
1789: PetscInt imark = -1;
1790: if (v) {
1791: *v = v_p = mat->rowvalues;
1792: for (i = 0; i < nzB; i++) {
1793: if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1794: else break;
1795: }
1796: imark = i;
1797: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1798: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1799: }
1800: if (idx) {
1801: *idx = idx_p = mat->rowindices;
1802: if (imark > -1) {
1803: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i]];
1804: } else {
1805: for (i = 0; i < nzB; i++) {
1806: if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1807: else break;
1808: }
1809: imark = i;
1810: }
1811: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart + cworkA[i];
1812: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i]];
1813: }
1814: } else {
1815: if (idx) *idx = NULL;
1816: if (v) *v = NULL;
1817: }
1818: }
1819: *nz = nztot;
1820: PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1821: PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1822: PetscFunctionReturn(PETSC_SUCCESS);
1823: }
1825: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1826: {
1827: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1829: PetscFunctionBegin;
1830: PetscCheck(aij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow() must be called first");
1831: aij->getrowactive = PETSC_FALSE;
1832: PetscFunctionReturn(PETSC_SUCCESS);
1833: }
1835: PetscErrorCode MatNorm_MPIAIJ(Mat mat, NormType type, PetscReal *norm)
1836: {
1837: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1838: Mat_SeqAIJ *amat = (Mat_SeqAIJ *)aij->A->data, *bmat = (Mat_SeqAIJ *)aij->B->data;
1839: PetscInt i, j, cstart = mat->cmap->rstart;
1840: PetscReal sum = 0.0;
1841: const MatScalar *v, *amata, *bmata;
1843: PetscFunctionBegin;
1844: if (aij->size == 1) {
1845: PetscCall(MatNorm(aij->A, type, norm));
1846: } else {
1847: PetscCall(MatSeqAIJGetArrayRead(aij->A, &amata));
1848: PetscCall(MatSeqAIJGetArrayRead(aij->B, &bmata));
1849: if (type == NORM_FROBENIUS) {
1850: v = amata;
1851: for (i = 0; i < amat->nz; i++) {
1852: sum += PetscRealPart(PetscConj(*v) * (*v));
1853: v++;
1854: }
1855: v = bmata;
1856: for (i = 0; i < bmat->nz; i++) {
1857: sum += PetscRealPart(PetscConj(*v) * (*v));
1858: v++;
1859: }
1860: PetscCall(MPIU_Allreduce(&sum, norm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1861: *norm = PetscSqrtReal(*norm);
1862: PetscCall(PetscLogFlops(2.0 * amat->nz + 2.0 * bmat->nz));
1863: } else if (type == NORM_1) { /* max column norm */
1864: PetscReal *tmp, *tmp2;
1865: PetscInt *jj, *garray = aij->garray;
1866: PetscCall(PetscCalloc1(mat->cmap->N + 1, &tmp));
1867: PetscCall(PetscMalloc1(mat->cmap->N + 1, &tmp2));
1868: *norm = 0.0;
1869: v = amata;
1870: jj = amat->j;
1871: for (j = 0; j < amat->nz; j++) {
1872: tmp[cstart + *jj++] += PetscAbsScalar(*v);
1873: v++;
1874: }
1875: v = bmata;
1876: jj = bmat->j;
1877: for (j = 0; j < bmat->nz; j++) {
1878: tmp[garray[*jj++]] += PetscAbsScalar(*v);
1879: v++;
1880: }
1881: PetscCall(MPIU_Allreduce(tmp, tmp2, mat->cmap->N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1882: for (j = 0; j < mat->cmap->N; j++) {
1883: if (tmp2[j] > *norm) *norm = tmp2[j];
1884: }
1885: PetscCall(PetscFree(tmp));
1886: PetscCall(PetscFree(tmp2));
1887: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1888: } else if (type == NORM_INFINITY) { /* max row norm */
1889: PetscReal ntemp = 0.0;
1890: for (j = 0; j < aij->A->rmap->n; j++) {
1891: v = amata + amat->i[j];
1892: sum = 0.0;
1893: for (i = 0; i < amat->i[j + 1] - amat->i[j]; i++) {
1894: sum += PetscAbsScalar(*v);
1895: v++;
1896: }
1897: v = bmata + bmat->i[j];
1898: for (i = 0; i < bmat->i[j + 1] - bmat->i[j]; i++) {
1899: sum += PetscAbsScalar(*v);
1900: v++;
1901: }
1902: if (sum > ntemp) ntemp = sum;
1903: }
1904: PetscCall(MPIU_Allreduce(&ntemp, norm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
1905: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1906: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for two norm");
1907: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &amata));
1908: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &bmata));
1909: }
1910: PetscFunctionReturn(PETSC_SUCCESS);
1911: }
1913: PetscErrorCode MatTranspose_MPIAIJ(Mat A, MatReuse reuse, Mat *matout)
1914: {
1915: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *b;
1916: Mat_SeqAIJ *Aloc = (Mat_SeqAIJ *)a->A->data, *Bloc = (Mat_SeqAIJ *)a->B->data, *sub_B_diag;
1917: 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;
1918: const PetscInt *ai, *aj, *bi, *bj, *B_diag_i;
1919: Mat B, A_diag, *B_diag;
1920: const MatScalar *pbv, *bv;
1922: PetscFunctionBegin;
1923: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1924: ma = A->rmap->n;
1925: na = A->cmap->n;
1926: mb = a->B->rmap->n;
1927: nb = a->B->cmap->n;
1928: ai = Aloc->i;
1929: aj = Aloc->j;
1930: bi = Bloc->i;
1931: bj = Bloc->j;
1932: if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1933: PetscInt *d_nnz, *g_nnz, *o_nnz;
1934: PetscSFNode *oloc;
1935: PETSC_UNUSED PetscSF sf;
1937: PetscCall(PetscMalloc4(na, &d_nnz, na, &o_nnz, nb, &g_nnz, nb, &oloc));
1938: /* compute d_nnz for preallocation */
1939: PetscCall(PetscArrayzero(d_nnz, na));
1940: for (i = 0; i < ai[ma]; i++) d_nnz[aj[i]]++;
1941: /* compute local off-diagonal contributions */
1942: PetscCall(PetscArrayzero(g_nnz, nb));
1943: for (i = 0; i < bi[ma]; i++) g_nnz[bj[i]]++;
1944: /* map those to global */
1945: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1946: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, nb, NULL, PETSC_USE_POINTER, a->garray));
1947: PetscCall(PetscSFSetFromOptions(sf));
1948: PetscCall(PetscArrayzero(o_nnz, na));
1949: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1950: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1951: PetscCall(PetscSFDestroy(&sf));
1953: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1954: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1955: PetscCall(MatSetBlockSizes(B, PetscAbs(A->cmap->bs), PetscAbs(A->rmap->bs)));
1956: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1957: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
1958: PetscCall(PetscFree4(d_nnz, o_nnz, g_nnz, oloc));
1959: } else {
1960: B = *matout;
1961: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
1962: }
1964: b = (Mat_MPIAIJ *)B->data;
1965: A_diag = a->A;
1966: B_diag = &b->A;
1967: sub_B_diag = (Mat_SeqAIJ *)(*B_diag)->data;
1968: A_diag_ncol = A_diag->cmap->N;
1969: B_diag_ilen = sub_B_diag->ilen;
1970: B_diag_i = sub_B_diag->i;
1972: /* Set ilen for diagonal of B */
1973: for (i = 0; i < A_diag_ncol; i++) B_diag_ilen[i] = B_diag_i[i + 1] - B_diag_i[i];
1975: /* Transpose the diagonal part of the matrix. In contrast to the offdiagonal part, this can be done
1976: very quickly (=without using MatSetValues), because all writes are local. */
1977: PetscCall(MatTransposeSetPrecursor(A_diag, *B_diag));
1978: PetscCall(MatTranspose(A_diag, MAT_REUSE_MATRIX, B_diag));
1980: /* copy over the B part */
1981: PetscCall(PetscMalloc1(bi[mb], &cols));
1982: PetscCall(MatSeqAIJGetArrayRead(a->B, &bv));
1983: pbv = bv;
1984: row = A->rmap->rstart;
1985: for (i = 0; i < bi[mb]; i++) cols[i] = a->garray[bj[i]];
1986: cols_tmp = cols;
1987: for (i = 0; i < mb; i++) {
1988: ncol = bi[i + 1] - bi[i];
1989: PetscCall(MatSetValues(B, ncol, cols_tmp, 1, &row, pbv, INSERT_VALUES));
1990: row++;
1991: pbv += ncol;
1992: cols_tmp += ncol;
1993: }
1994: PetscCall(PetscFree(cols));
1995: PetscCall(MatSeqAIJRestoreArrayRead(a->B, &bv));
1997: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1998: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1999: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
2000: *matout = B;
2001: } else {
2002: PetscCall(MatHeaderMerge(A, &B));
2003: }
2004: PetscFunctionReturn(PETSC_SUCCESS);
2005: }
2007: PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat, Vec ll, Vec rr)
2008: {
2009: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2010: Mat a = aij->A, b = aij->B;
2011: PetscInt s1, s2, s3;
2013: PetscFunctionBegin;
2014: PetscCall(MatGetLocalSize(mat, &s2, &s3));
2015: if (rr) {
2016: PetscCall(VecGetLocalSize(rr, &s1));
2017: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
2018: /* Overlap communication with computation. */
2019: PetscCall(VecScatterBegin(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
2020: }
2021: if (ll) {
2022: PetscCall(VecGetLocalSize(ll, &s1));
2023: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
2024: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
2025: }
2026: /* scale the diagonal block */
2027: PetscUseTypeMethod(a, diagonalscale, ll, rr);
2029: if (rr) {
2030: /* Do a scatter end and then right scale the off-diagonal block */
2031: PetscCall(VecScatterEnd(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
2032: PetscUseTypeMethod(b, diagonalscale, NULL, aij->lvec);
2033: }
2034: PetscFunctionReturn(PETSC_SUCCESS);
2035: }
2037: PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2038: {
2039: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2041: PetscFunctionBegin;
2042: PetscCall(MatSetUnfactored(a->A));
2043: PetscFunctionReturn(PETSC_SUCCESS);
2044: }
2046: PetscErrorCode MatEqual_MPIAIJ(Mat A, Mat B, PetscBool *flag)
2047: {
2048: Mat_MPIAIJ *matB = (Mat_MPIAIJ *)B->data, *matA = (Mat_MPIAIJ *)A->data;
2049: Mat a, b, c, d;
2050: PetscBool flg;
2052: PetscFunctionBegin;
2053: a = matA->A;
2054: b = matA->B;
2055: c = matB->A;
2056: d = matB->B;
2058: PetscCall(MatEqual(a, c, &flg));
2059: if (flg) PetscCall(MatEqual(b, d, &flg));
2060: PetscCall(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
2061: PetscFunctionReturn(PETSC_SUCCESS);
2062: }
2064: PetscErrorCode MatCopy_MPIAIJ(Mat A, Mat B, MatStructure str)
2065: {
2066: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2067: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2069: PetscFunctionBegin;
2070: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2071: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2072: /* because of the column compression in the off-processor part of the matrix a->B,
2073: the number of columns in a->B and b->B may be different, hence we cannot call
2074: the MatCopy() directly on the two parts. If need be, we can provide a more
2075: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2076: then copying the submatrices */
2077: PetscCall(MatCopy_Basic(A, B, str));
2078: } else {
2079: PetscCall(MatCopy(a->A, b->A, str));
2080: PetscCall(MatCopy(a->B, b->B, str));
2081: }
2082: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2083: PetscFunctionReturn(PETSC_SUCCESS);
2084: }
2086: /*
2087: Computes the number of nonzeros per row needed for preallocation when X and Y
2088: have different nonzero structure.
2089: */
2090: 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)
2091: {
2092: PetscInt i, j, k, nzx, nzy;
2094: PetscFunctionBegin;
2095: /* Set the number of nonzeros in the new matrix */
2096: for (i = 0; i < m; i++) {
2097: const PetscInt *xjj = xj + xi[i], *yjj = yj + yi[i];
2098: nzx = xi[i + 1] - xi[i];
2099: nzy = yi[i + 1] - yi[i];
2100: nnz[i] = 0;
2101: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2102: for (; k < nzy && yltog[yjj[k]] < xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2103: if (k < nzy && yltog[yjj[k]] == xltog[xjj[j]]) k++; /* Skip duplicate */
2104: nnz[i]++;
2105: }
2106: for (; k < nzy; k++) nnz[i]++;
2107: }
2108: PetscFunctionReturn(PETSC_SUCCESS);
2109: }
2111: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2112: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
2113: {
2114: PetscInt m = Y->rmap->N;
2115: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2116: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
2118: PetscFunctionBegin;
2119: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
2120: PetscFunctionReturn(PETSC_SUCCESS);
2121: }
2123: PetscErrorCode MatAXPY_MPIAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2124: {
2125: Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data, *yy = (Mat_MPIAIJ *)Y->data;
2127: PetscFunctionBegin;
2128: if (str == SAME_NONZERO_PATTERN) {
2129: PetscCall(MatAXPY(yy->A, a, xx->A, str));
2130: PetscCall(MatAXPY(yy->B, a, xx->B, str));
2131: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2132: PetscCall(MatAXPY_Basic(Y, a, X, str));
2133: } else {
2134: Mat B;
2135: PetscInt *nnz_d, *nnz_o;
2137: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
2138: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
2139: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2140: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2141: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2142: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2143: PetscCall(MatAXPYGetPreallocation_SeqAIJ(yy->A, xx->A, nnz_d));
2144: PetscCall(MatAXPYGetPreallocation_MPIAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
2145: PetscCall(MatMPIAIJSetPreallocation(B, 0, nnz_d, 0, nnz_o));
2146: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2147: PetscCall(MatHeaderMerge(Y, &B));
2148: PetscCall(PetscFree(nnz_d));
2149: PetscCall(PetscFree(nnz_o));
2150: }
2151: PetscFunctionReturn(PETSC_SUCCESS);
2152: }
2154: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);
2156: PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2157: {
2158: PetscFunctionBegin;
2159: if (PetscDefined(USE_COMPLEX)) {
2160: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2162: PetscCall(MatConjugate_SeqAIJ(aij->A));
2163: PetscCall(MatConjugate_SeqAIJ(aij->B));
2164: }
2165: PetscFunctionReturn(PETSC_SUCCESS);
2166: }
2168: PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2169: {
2170: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2172: PetscFunctionBegin;
2173: PetscCall(MatRealPart(a->A));
2174: PetscCall(MatRealPart(a->B));
2175: PetscFunctionReturn(PETSC_SUCCESS);
2176: }
2178: PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
2179: {
2180: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2182: PetscFunctionBegin;
2183: PetscCall(MatImaginaryPart(a->A));
2184: PetscCall(MatImaginaryPart(a->B));
2185: PetscFunctionReturn(PETSC_SUCCESS);
2186: }
2188: PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2189: {
2190: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2191: PetscInt i, *idxb = NULL, m = A->rmap->n;
2192: PetscScalar *va, *vv;
2193: Vec vB, vA;
2194: const PetscScalar *vb;
2196: PetscFunctionBegin;
2197: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vA));
2198: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
2200: PetscCall(VecGetArrayWrite(vA, &va));
2201: if (idx) {
2202: for (i = 0; i < m; i++) {
2203: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2204: }
2205: }
2207: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vB));
2208: PetscCall(PetscMalloc1(m, &idxb));
2209: PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));
2211: PetscCall(VecGetArrayWrite(v, &vv));
2212: PetscCall(VecGetArrayRead(vB, &vb));
2213: for (i = 0; i < m; i++) {
2214: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2215: vv[i] = vb[i];
2216: if (idx) idx[i] = a->garray[idxb[i]];
2217: } else {
2218: vv[i] = va[i];
2219: if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > a->garray[idxb[i]]) idx[i] = a->garray[idxb[i]];
2220: }
2221: }
2222: PetscCall(VecRestoreArrayWrite(vA, &vv));
2223: PetscCall(VecRestoreArrayWrite(vA, &va));
2224: PetscCall(VecRestoreArrayRead(vB, &vb));
2225: PetscCall(PetscFree(idxb));
2226: PetscCall(VecDestroy(&vA));
2227: PetscCall(VecDestroy(&vB));
2228: PetscFunctionReturn(PETSC_SUCCESS);
2229: }
2231: PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2232: {
2233: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2234: PetscInt m = A->rmap->n, n = A->cmap->n;
2235: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2236: PetscInt *cmap = mat->garray;
2237: PetscInt *diagIdx, *offdiagIdx;
2238: Vec diagV, offdiagV;
2239: PetscScalar *a, *diagA, *offdiagA;
2240: const PetscScalar *ba, *bav;
2241: PetscInt r, j, col, ncols, *bi, *bj;
2242: Mat B = mat->B;
2243: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2245: PetscFunctionBegin;
2246: /* When a process holds entire A and other processes have no entry */
2247: if (A->cmap->N == n) {
2248: PetscCall(VecGetArrayWrite(v, &diagA));
2249: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2250: PetscCall(MatGetRowMinAbs(mat->A, diagV, idx));
2251: PetscCall(VecDestroy(&diagV));
2252: PetscCall(VecRestoreArrayWrite(v, &diagA));
2253: PetscFunctionReturn(PETSC_SUCCESS);
2254: } else if (n == 0) {
2255: if (m) {
2256: PetscCall(VecGetArrayWrite(v, &a));
2257: for (r = 0; r < m; r++) {
2258: a[r] = 0.0;
2259: if (idx) idx[r] = -1;
2260: }
2261: PetscCall(VecRestoreArrayWrite(v, &a));
2262: }
2263: PetscFunctionReturn(PETSC_SUCCESS);
2264: }
2266: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2267: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2268: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2269: PetscCall(MatGetRowMinAbs(mat->A, diagV, diagIdx));
2271: /* Get offdiagIdx[] for implicit 0.0 */
2272: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2273: ba = bav;
2274: bi = b->i;
2275: bj = b->j;
2276: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2277: for (r = 0; r < m; r++) {
2278: ncols = bi[r + 1] - bi[r];
2279: if (ncols == A->cmap->N - n) { /* Brow is dense */
2280: offdiagA[r] = *ba;
2281: offdiagIdx[r] = cmap[0];
2282: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2283: offdiagA[r] = 0.0;
2285: /* Find first hole in the cmap */
2286: for (j = 0; j < ncols; j++) {
2287: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2288: if (col > j && j < cstart) {
2289: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2290: break;
2291: } else if (col > j + n && j >= cstart) {
2292: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2293: break;
2294: }
2295: }
2296: if (j == ncols && ncols < A->cmap->N - n) {
2297: /* a hole is outside compressed Bcols */
2298: if (ncols == 0) {
2299: if (cstart) {
2300: offdiagIdx[r] = 0;
2301: } else offdiagIdx[r] = cend;
2302: } else { /* ncols > 0 */
2303: offdiagIdx[r] = cmap[ncols - 1] + 1;
2304: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2305: }
2306: }
2307: }
2309: for (j = 0; j < ncols; j++) {
2310: if (PetscAbsScalar(offdiagA[r]) > PetscAbsScalar(*ba)) {
2311: offdiagA[r] = *ba;
2312: offdiagIdx[r] = cmap[*bj];
2313: }
2314: ba++;
2315: bj++;
2316: }
2317: }
2319: PetscCall(VecGetArrayWrite(v, &a));
2320: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2321: for (r = 0; r < m; ++r) {
2322: if (PetscAbsScalar(diagA[r]) < PetscAbsScalar(offdiagA[r])) {
2323: a[r] = diagA[r];
2324: if (idx) idx[r] = cstart + diagIdx[r];
2325: } else if (PetscAbsScalar(diagA[r]) == PetscAbsScalar(offdiagA[r])) {
2326: a[r] = diagA[r];
2327: if (idx) {
2328: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2329: idx[r] = cstart + diagIdx[r];
2330: } else idx[r] = offdiagIdx[r];
2331: }
2332: } else {
2333: a[r] = offdiagA[r];
2334: if (idx) idx[r] = offdiagIdx[r];
2335: }
2336: }
2337: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2338: PetscCall(VecRestoreArrayWrite(v, &a));
2339: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2340: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2341: PetscCall(VecDestroy(&diagV));
2342: PetscCall(VecDestroy(&offdiagV));
2343: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2344: PetscFunctionReturn(PETSC_SUCCESS);
2345: }
2347: PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2348: {
2349: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2350: PetscInt m = A->rmap->n, n = A->cmap->n;
2351: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2352: PetscInt *cmap = mat->garray;
2353: PetscInt *diagIdx, *offdiagIdx;
2354: Vec diagV, offdiagV;
2355: PetscScalar *a, *diagA, *offdiagA;
2356: const PetscScalar *ba, *bav;
2357: PetscInt r, j, col, ncols, *bi, *bj;
2358: Mat B = mat->B;
2359: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2361: PetscFunctionBegin;
2362: /* When a process holds entire A and other processes have no entry */
2363: if (A->cmap->N == n) {
2364: PetscCall(VecGetArrayWrite(v, &diagA));
2365: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2366: PetscCall(MatGetRowMin(mat->A, diagV, idx));
2367: PetscCall(VecDestroy(&diagV));
2368: PetscCall(VecRestoreArrayWrite(v, &diagA));
2369: PetscFunctionReturn(PETSC_SUCCESS);
2370: } else if (n == 0) {
2371: if (m) {
2372: PetscCall(VecGetArrayWrite(v, &a));
2373: for (r = 0; r < m; r++) {
2374: a[r] = PETSC_MAX_REAL;
2375: if (idx) idx[r] = -1;
2376: }
2377: PetscCall(VecRestoreArrayWrite(v, &a));
2378: }
2379: PetscFunctionReturn(PETSC_SUCCESS);
2380: }
2382: PetscCall(PetscCalloc2(m, &diagIdx, m, &offdiagIdx));
2383: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2384: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2385: PetscCall(MatGetRowMin(mat->A, diagV, diagIdx));
2387: /* Get offdiagIdx[] for implicit 0.0 */
2388: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2389: ba = bav;
2390: bi = b->i;
2391: bj = b->j;
2392: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2393: for (r = 0; r < m; r++) {
2394: ncols = bi[r + 1] - bi[r];
2395: if (ncols == A->cmap->N - n) { /* Brow is dense */
2396: offdiagA[r] = *ba;
2397: offdiagIdx[r] = cmap[0];
2398: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2399: offdiagA[r] = 0.0;
2401: /* Find first hole in the cmap */
2402: for (j = 0; j < ncols; j++) {
2403: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2404: if (col > j && j < cstart) {
2405: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2406: break;
2407: } else if (col > j + n && j >= cstart) {
2408: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2409: break;
2410: }
2411: }
2412: if (j == ncols && ncols < A->cmap->N - n) {
2413: /* a hole is outside compressed Bcols */
2414: if (ncols == 0) {
2415: if (cstart) {
2416: offdiagIdx[r] = 0;
2417: } else offdiagIdx[r] = cend;
2418: } else { /* ncols > 0 */
2419: offdiagIdx[r] = cmap[ncols - 1] + 1;
2420: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2421: }
2422: }
2423: }
2425: for (j = 0; j < ncols; j++) {
2426: if (PetscRealPart(offdiagA[r]) > PetscRealPart(*ba)) {
2427: offdiagA[r] = *ba;
2428: offdiagIdx[r] = cmap[*bj];
2429: }
2430: ba++;
2431: bj++;
2432: }
2433: }
2435: PetscCall(VecGetArrayWrite(v, &a));
2436: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2437: for (r = 0; r < m; ++r) {
2438: if (PetscRealPart(diagA[r]) < PetscRealPart(offdiagA[r])) {
2439: a[r] = diagA[r];
2440: if (idx) idx[r] = cstart + diagIdx[r];
2441: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2442: a[r] = diagA[r];
2443: if (idx) {
2444: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2445: idx[r] = cstart + diagIdx[r];
2446: } else idx[r] = offdiagIdx[r];
2447: }
2448: } else {
2449: a[r] = offdiagA[r];
2450: if (idx) idx[r] = offdiagIdx[r];
2451: }
2452: }
2453: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2454: PetscCall(VecRestoreArrayWrite(v, &a));
2455: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2456: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2457: PetscCall(VecDestroy(&diagV));
2458: PetscCall(VecDestroy(&offdiagV));
2459: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2460: PetscFunctionReturn(PETSC_SUCCESS);
2461: }
2463: PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2464: {
2465: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2466: PetscInt m = A->rmap->n, n = A->cmap->n;
2467: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2468: PetscInt *cmap = mat->garray;
2469: PetscInt *diagIdx, *offdiagIdx;
2470: Vec diagV, offdiagV;
2471: PetscScalar *a, *diagA, *offdiagA;
2472: const PetscScalar *ba, *bav;
2473: PetscInt r, j, col, ncols, *bi, *bj;
2474: Mat B = mat->B;
2475: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2477: PetscFunctionBegin;
2478: /* When a process holds entire A and other processes have no entry */
2479: if (A->cmap->N == n) {
2480: PetscCall(VecGetArrayWrite(v, &diagA));
2481: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2482: PetscCall(MatGetRowMax(mat->A, diagV, idx));
2483: PetscCall(VecDestroy(&diagV));
2484: PetscCall(VecRestoreArrayWrite(v, &diagA));
2485: PetscFunctionReturn(PETSC_SUCCESS);
2486: } else if (n == 0) {
2487: if (m) {
2488: PetscCall(VecGetArrayWrite(v, &a));
2489: for (r = 0; r < m; r++) {
2490: a[r] = PETSC_MIN_REAL;
2491: if (idx) idx[r] = -1;
2492: }
2493: PetscCall(VecRestoreArrayWrite(v, &a));
2494: }
2495: PetscFunctionReturn(PETSC_SUCCESS);
2496: }
2498: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2499: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2500: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2501: PetscCall(MatGetRowMax(mat->A, diagV, diagIdx));
2503: /* Get offdiagIdx[] for implicit 0.0 */
2504: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2505: ba = bav;
2506: bi = b->i;
2507: bj = b->j;
2508: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2509: for (r = 0; r < m; r++) {
2510: ncols = bi[r + 1] - bi[r];
2511: if (ncols == A->cmap->N - n) { /* Brow is dense */
2512: offdiagA[r] = *ba;
2513: offdiagIdx[r] = cmap[0];
2514: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2515: offdiagA[r] = 0.0;
2517: /* Find first hole in the cmap */
2518: for (j = 0; j < ncols; j++) {
2519: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2520: if (col > j && j < cstart) {
2521: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2522: break;
2523: } else if (col > j + n && j >= cstart) {
2524: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2525: break;
2526: }
2527: }
2528: if (j == ncols && ncols < A->cmap->N - n) {
2529: /* a hole is outside compressed Bcols */
2530: if (ncols == 0) {
2531: if (cstart) {
2532: offdiagIdx[r] = 0;
2533: } else offdiagIdx[r] = cend;
2534: } else { /* ncols > 0 */
2535: offdiagIdx[r] = cmap[ncols - 1] + 1;
2536: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2537: }
2538: }
2539: }
2541: for (j = 0; j < ncols; j++) {
2542: if (PetscRealPart(offdiagA[r]) < PetscRealPart(*ba)) {
2543: offdiagA[r] = *ba;
2544: offdiagIdx[r] = cmap[*bj];
2545: }
2546: ba++;
2547: bj++;
2548: }
2549: }
2551: PetscCall(VecGetArrayWrite(v, &a));
2552: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2553: for (r = 0; r < m; ++r) {
2554: if (PetscRealPart(diagA[r]) > PetscRealPart(offdiagA[r])) {
2555: a[r] = diagA[r];
2556: if (idx) idx[r] = cstart + diagIdx[r];
2557: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2558: a[r] = diagA[r];
2559: if (idx) {
2560: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2561: idx[r] = cstart + diagIdx[r];
2562: } else idx[r] = offdiagIdx[r];
2563: }
2564: } else {
2565: a[r] = offdiagA[r];
2566: if (idx) idx[r] = offdiagIdx[r];
2567: }
2568: }
2569: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2570: PetscCall(VecRestoreArrayWrite(v, &a));
2571: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2572: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2573: PetscCall(VecDestroy(&diagV));
2574: PetscCall(VecDestroy(&offdiagV));
2575: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2576: PetscFunctionReturn(PETSC_SUCCESS);
2577: }
2579: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat, Mat *newmat)
2580: {
2581: Mat *dummy;
2583: PetscFunctionBegin;
2584: PetscCall(MatCreateSubMatrix_MPIAIJ_All(mat, MAT_DO_NOT_GET_VALUES, MAT_INITIAL_MATRIX, &dummy));
2585: *newmat = *dummy;
2586: PetscCall(PetscFree(dummy));
2587: PetscFunctionReturn(PETSC_SUCCESS);
2588: }
2590: PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A, const PetscScalar **values)
2591: {
2592: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2594: PetscFunctionBegin;
2595: PetscCall(MatInvertBlockDiagonal(a->A, values));
2596: A->factorerrortype = a->A->factorerrortype;
2597: PetscFunctionReturn(PETSC_SUCCESS);
2598: }
2600: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x, PetscRandom rctx)
2601: {
2602: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)x->data;
2604: PetscFunctionBegin;
2605: PetscCheck(x->assembled || x->preallocated, PetscObjectComm((PetscObject)x), PETSC_ERR_ARG_WRONGSTATE, "MatSetRandom on an unassembled and unpreallocated MATMPIAIJ is not allowed");
2606: PetscCall(MatSetRandom(aij->A, rctx));
2607: if (x->assembled) {
2608: PetscCall(MatSetRandom(aij->B, rctx));
2609: } else {
2610: PetscCall(MatSetRandomSkipColumnRange_SeqAIJ_Private(aij->B, x->cmap->rstart, x->cmap->rend, rctx));
2611: }
2612: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
2613: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
2614: PetscFunctionReturn(PETSC_SUCCESS);
2615: }
2617: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ(Mat A, PetscBool sc)
2618: {
2619: PetscFunctionBegin;
2620: if (sc) A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ_Scalable;
2621: else A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ;
2622: PetscFunctionReturn(PETSC_SUCCESS);
2623: }
2625: /*@
2626: MatMPIAIJGetNumberNonzeros - gets the number of nonzeros in the matrix on this MPI rank
2628: Not Collective
2630: Input Parameter:
2631: . A - the matrix
2633: Output Parameter:
2634: . nz - the number of nonzeros
2636: Level: advanced
2638: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `Mat`
2639: @*/
2640: PetscErrorCode MatMPIAIJGetNumberNonzeros(Mat A, PetscCount *nz)
2641: {
2642: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)A->data;
2643: Mat_SeqAIJ *aaij = (Mat_SeqAIJ *)maij->A->data, *baij = (Mat_SeqAIJ *)maij->B->data;
2644: PetscBool isaij;
2646: PetscFunctionBegin;
2647: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATMPIAIJ, &isaij));
2648: PetscCheck(isaij, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for type %s", ((PetscObject)A)->type_name);
2649: *nz = aaij->i[A->rmap->n] + baij->i[A->rmap->n];
2650: PetscFunctionReturn(PETSC_SUCCESS);
2651: }
2653: /*@
2654: MatMPIAIJSetUseScalableIncreaseOverlap - Determine if the matrix uses a scalable algorithm to compute the overlap
2656: Collective
2658: Input Parameters:
2659: + A - the matrix
2660: - sc - `PETSC_TRUE` indicates use the scalable algorithm (default is not to use the scalable algorithm)
2662: Level: advanced
2664: .seealso: [](ch_matrices), `Mat`, `Mat`, `MATMPIAIJ`
2665: @*/
2666: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap(Mat A, PetscBool sc)
2667: {
2668: PetscFunctionBegin;
2669: PetscTryMethod(A, "MatMPIAIJSetUseScalableIncreaseOverlap_C", (Mat, PetscBool), (A, sc));
2670: PetscFunctionReturn(PETSC_SUCCESS);
2671: }
2673: PetscErrorCode MatSetFromOptions_MPIAIJ(Mat A, PetscOptionItems *PetscOptionsObject)
2674: {
2675: PetscBool sc = PETSC_FALSE, flg;
2677: PetscFunctionBegin;
2678: PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJ options");
2679: if (A->ops->increaseoverlap == MatIncreaseOverlap_MPIAIJ_Scalable) sc = PETSC_TRUE;
2680: PetscCall(PetscOptionsBool("-mat_increase_overlap_scalable", "Use a scalable algorithm to compute the overlap", "MatIncreaseOverlap", sc, &sc, &flg));
2681: if (flg) PetscCall(MatMPIAIJSetUseScalableIncreaseOverlap(A, sc));
2682: PetscOptionsHeadEnd();
2683: PetscFunctionReturn(PETSC_SUCCESS);
2684: }
2686: PetscErrorCode MatShift_MPIAIJ(Mat Y, PetscScalar a)
2687: {
2688: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)Y->data;
2689: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)maij->A->data;
2691: PetscFunctionBegin;
2692: if (!Y->preallocated) {
2693: PetscCall(MatMPIAIJSetPreallocation(Y, 1, NULL, 0, NULL));
2694: } else if (!aij->nz) { /* It does not matter if diagonals of Y only partially lie in maij->A. We just need an estimated preallocation. */
2695: PetscInt nonew = aij->nonew;
2696: PetscCall(MatSeqAIJSetPreallocation(maij->A, 1, NULL));
2697: aij->nonew = nonew;
2698: }
2699: PetscCall(MatShift_Basic(Y, a));
2700: PetscFunctionReturn(PETSC_SUCCESS);
2701: }
2703: PetscErrorCode MatMissingDiagonal_MPIAIJ(Mat A, PetscBool *missing, PetscInt *d)
2704: {
2705: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2707: PetscFunctionBegin;
2708: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2709: PetscCall(MatMissingDiagonal(a->A, missing, d));
2710: if (d) {
2711: PetscInt rstart;
2712: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2713: *d += rstart;
2714: }
2715: PetscFunctionReturn(PETSC_SUCCESS);
2716: }
2718: PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2719: {
2720: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2722: PetscFunctionBegin;
2723: PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2724: PetscFunctionReturn(PETSC_SUCCESS);
2725: }
2727: PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A)
2728: {
2729: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2731: PetscFunctionBegin;
2732: PetscCall(MatEliminateZeros(a->A));
2733: PetscCall(MatEliminateZeros(a->B));
2734: PetscFunctionReturn(PETSC_SUCCESS);
2735: }
2737: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2738: MatGetRow_MPIAIJ,
2739: MatRestoreRow_MPIAIJ,
2740: MatMult_MPIAIJ,
2741: /* 4*/ MatMultAdd_MPIAIJ,
2742: MatMultTranspose_MPIAIJ,
2743: MatMultTransposeAdd_MPIAIJ,
2744: NULL,
2745: NULL,
2746: NULL,
2747: /*10*/ NULL,
2748: NULL,
2749: NULL,
2750: MatSOR_MPIAIJ,
2751: MatTranspose_MPIAIJ,
2752: /*15*/ MatGetInfo_MPIAIJ,
2753: MatEqual_MPIAIJ,
2754: MatGetDiagonal_MPIAIJ,
2755: MatDiagonalScale_MPIAIJ,
2756: MatNorm_MPIAIJ,
2757: /*20*/ MatAssemblyBegin_MPIAIJ,
2758: MatAssemblyEnd_MPIAIJ,
2759: MatSetOption_MPIAIJ,
2760: MatZeroEntries_MPIAIJ,
2761: /*24*/ MatZeroRows_MPIAIJ,
2762: NULL,
2763: NULL,
2764: NULL,
2765: NULL,
2766: /*29*/ MatSetUp_MPI_Hash,
2767: NULL,
2768: NULL,
2769: MatGetDiagonalBlock_MPIAIJ,
2770: NULL,
2771: /*34*/ MatDuplicate_MPIAIJ,
2772: NULL,
2773: NULL,
2774: NULL,
2775: NULL,
2776: /*39*/ MatAXPY_MPIAIJ,
2777: MatCreateSubMatrices_MPIAIJ,
2778: MatIncreaseOverlap_MPIAIJ,
2779: MatGetValues_MPIAIJ,
2780: MatCopy_MPIAIJ,
2781: /*44*/ MatGetRowMax_MPIAIJ,
2782: MatScale_MPIAIJ,
2783: MatShift_MPIAIJ,
2784: MatDiagonalSet_MPIAIJ,
2785: MatZeroRowsColumns_MPIAIJ,
2786: /*49*/ MatSetRandom_MPIAIJ,
2787: MatGetRowIJ_MPIAIJ,
2788: MatRestoreRowIJ_MPIAIJ,
2789: NULL,
2790: NULL,
2791: /*54*/ MatFDColoringCreate_MPIXAIJ,
2792: NULL,
2793: MatSetUnfactored_MPIAIJ,
2794: MatPermute_MPIAIJ,
2795: NULL,
2796: /*59*/ MatCreateSubMatrix_MPIAIJ,
2797: MatDestroy_MPIAIJ,
2798: MatView_MPIAIJ,
2799: NULL,
2800: NULL,
2801: /*64*/ NULL,
2802: MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2803: NULL,
2804: NULL,
2805: NULL,
2806: /*69*/ MatGetRowMaxAbs_MPIAIJ,
2807: MatGetRowMinAbs_MPIAIJ,
2808: NULL,
2809: NULL,
2810: NULL,
2811: NULL,
2812: /*75*/ MatFDColoringApply_AIJ,
2813: MatSetFromOptions_MPIAIJ,
2814: NULL,
2815: NULL,
2816: MatFindZeroDiagonals_MPIAIJ,
2817: /*80*/ NULL,
2818: NULL,
2819: NULL,
2820: /*83*/ MatLoad_MPIAIJ,
2821: MatIsSymmetric_MPIAIJ,
2822: NULL,
2823: NULL,
2824: NULL,
2825: NULL,
2826: /*89*/ NULL,
2827: NULL,
2828: MatMatMultNumeric_MPIAIJ_MPIAIJ,
2829: NULL,
2830: NULL,
2831: /*94*/ MatPtAPNumeric_MPIAIJ_MPIAIJ,
2832: NULL,
2833: NULL,
2834: NULL,
2835: MatBindToCPU_MPIAIJ,
2836: /*99*/ MatProductSetFromOptions_MPIAIJ,
2837: NULL,
2838: NULL,
2839: MatConjugate_MPIAIJ,
2840: NULL,
2841: /*104*/ MatSetValuesRow_MPIAIJ,
2842: MatRealPart_MPIAIJ,
2843: MatImaginaryPart_MPIAIJ,
2844: NULL,
2845: NULL,
2846: /*109*/ NULL,
2847: NULL,
2848: MatGetRowMin_MPIAIJ,
2849: NULL,
2850: MatMissingDiagonal_MPIAIJ,
2851: /*114*/ MatGetSeqNonzeroStructure_MPIAIJ,
2852: NULL,
2853: MatGetGhosts_MPIAIJ,
2854: NULL,
2855: NULL,
2856: /*119*/ MatMultDiagonalBlock_MPIAIJ,
2857: NULL,
2858: NULL,
2859: NULL,
2860: MatGetMultiProcBlock_MPIAIJ,
2861: /*124*/ MatFindNonzeroRows_MPIAIJ,
2862: MatGetColumnReductions_MPIAIJ,
2863: MatInvertBlockDiagonal_MPIAIJ,
2864: MatInvertVariableBlockDiagonal_MPIAIJ,
2865: MatCreateSubMatricesMPI_MPIAIJ,
2866: /*129*/ NULL,
2867: NULL,
2868: NULL,
2869: MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2870: NULL,
2871: /*134*/ NULL,
2872: NULL,
2873: NULL,
2874: NULL,
2875: NULL,
2876: /*139*/ MatSetBlockSizes_MPIAIJ,
2877: NULL,
2878: NULL,
2879: MatFDColoringSetUp_MPIXAIJ,
2880: MatFindOffBlockDiagonalEntries_MPIAIJ,
2881: MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2882: /*145*/ NULL,
2883: NULL,
2884: NULL,
2885: MatCreateGraph_Simple_AIJ,
2886: NULL,
2887: /*150*/ NULL,
2888: MatEliminateZeros_MPIAIJ};
2890: PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2891: {
2892: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2894: PetscFunctionBegin;
2895: PetscCall(MatStoreValues(aij->A));
2896: PetscCall(MatStoreValues(aij->B));
2897: PetscFunctionReturn(PETSC_SUCCESS);
2898: }
2900: PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2901: {
2902: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2904: PetscFunctionBegin;
2905: PetscCall(MatRetrieveValues(aij->A));
2906: PetscCall(MatRetrieveValues(aij->B));
2907: PetscFunctionReturn(PETSC_SUCCESS);
2908: }
2910: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2911: {
2912: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2913: PetscMPIInt size;
2915: PetscFunctionBegin;
2916: if (B->hash_active) {
2917: PetscCall(PetscMemcpy(&B->ops, &b->cops, sizeof(*(B->ops))));
2918: B->hash_active = PETSC_FALSE;
2919: }
2920: PetscCall(PetscLayoutSetUp(B->rmap));
2921: PetscCall(PetscLayoutSetUp(B->cmap));
2923: #if defined(PETSC_USE_CTABLE)
2924: PetscCall(PetscHMapIDestroy(&b->colmap));
2925: #else
2926: PetscCall(PetscFree(b->colmap));
2927: #endif
2928: PetscCall(PetscFree(b->garray));
2929: PetscCall(VecDestroy(&b->lvec));
2930: PetscCall(VecScatterDestroy(&b->Mvctx));
2932: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2933: PetscCall(MatDestroy(&b->B));
2934: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2935: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2936: PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2937: PetscCall(MatSetType(b->B, MATSEQAIJ));
2939: PetscCall(MatDestroy(&b->A));
2940: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2941: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2942: PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2943: PetscCall(MatSetType(b->A, MATSEQAIJ));
2945: PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2946: PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2947: B->preallocated = PETSC_TRUE;
2948: B->was_assembled = PETSC_FALSE;
2949: B->assembled = PETSC_FALSE;
2950: PetscFunctionReturn(PETSC_SUCCESS);
2951: }
2953: PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2954: {
2955: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2957: PetscFunctionBegin;
2959: PetscCall(PetscLayoutSetUp(B->rmap));
2960: PetscCall(PetscLayoutSetUp(B->cmap));
2962: #if defined(PETSC_USE_CTABLE)
2963: PetscCall(PetscHMapIDestroy(&b->colmap));
2964: #else
2965: PetscCall(PetscFree(b->colmap));
2966: #endif
2967: PetscCall(PetscFree(b->garray));
2968: PetscCall(VecDestroy(&b->lvec));
2969: PetscCall(VecScatterDestroy(&b->Mvctx));
2971: PetscCall(MatResetPreallocation(b->A));
2972: PetscCall(MatResetPreallocation(b->B));
2973: B->preallocated = PETSC_TRUE;
2974: B->was_assembled = PETSC_FALSE;
2975: B->assembled = PETSC_FALSE;
2976: PetscFunctionReturn(PETSC_SUCCESS);
2977: }
2979: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2980: {
2981: Mat mat;
2982: Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;
2984: PetscFunctionBegin;
2985: *newmat = NULL;
2986: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2987: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2988: PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2989: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2990: a = (Mat_MPIAIJ *)mat->data;
2992: mat->factortype = matin->factortype;
2993: mat->assembled = matin->assembled;
2994: mat->insertmode = NOT_SET_VALUES;
2995: mat->preallocated = matin->preallocated;
2997: a->size = oldmat->size;
2998: a->rank = oldmat->rank;
2999: a->donotstash = oldmat->donotstash;
3000: a->roworiented = oldmat->roworiented;
3001: a->rowindices = NULL;
3002: a->rowvalues = NULL;
3003: a->getrowactive = PETSC_FALSE;
3005: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
3006: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
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) PetscCall(VecScatterCopy(oldmat->Mvctx, &a->Mvctx));
3028: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3029: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3030: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3031: *newmat = mat;
3032: PetscFunctionReturn(PETSC_SUCCESS);
3033: }
3035: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3036: {
3037: PetscBool isbinary, ishdf5;
3039: PetscFunctionBegin;
3042: /* force binary viewer to load .info file if it has not yet done so */
3043: PetscCall(PetscViewerSetUp(viewer));
3044: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3045: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3046: if (isbinary) {
3047: PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3048: } else if (ishdf5) {
3049: #if defined(PETSC_HAVE_HDF5)
3050: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3051: #else
3052: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3053: #endif
3054: } else {
3055: 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);
3056: }
3057: PetscFunctionReturn(PETSC_SUCCESS);
3058: }
3060: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3061: {
3062: PetscInt header[4], M, N, m, nz, rows, cols, sum, i;
3063: PetscInt *rowidxs, *colidxs;
3064: PetscScalar *matvals;
3066: PetscFunctionBegin;
3067: PetscCall(PetscViewerSetUp(viewer));
3069: /* read in matrix header */
3070: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3071: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3072: M = header[1];
3073: N = header[2];
3074: nz = header[3];
3075: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3076: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3077: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");
3079: /* set block sizes from the viewer's .info file */
3080: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3081: /* set global sizes if not set already */
3082: if (mat->rmap->N < 0) mat->rmap->N = M;
3083: if (mat->cmap->N < 0) mat->cmap->N = N;
3084: PetscCall(PetscLayoutSetUp(mat->rmap));
3085: PetscCall(PetscLayoutSetUp(mat->cmap));
3087: /* check if the matrix sizes are correct */
3088: PetscCall(MatGetSize(mat, &rows, &cols));
3089: 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);
3091: /* read in row lengths and build row indices */
3092: PetscCall(MatGetLocalSize(mat, &m, NULL));
3093: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3094: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3095: rowidxs[0] = 0;
3096: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3097: PetscCall(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3098: 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);
3099: /* read in column indices and matrix values */
3100: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3101: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3102: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3103: /* store matrix indices and values */
3104: PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3105: PetscCall(PetscFree(rowidxs));
3106: PetscCall(PetscFree2(colidxs, matvals));
3107: PetscFunctionReturn(PETSC_SUCCESS);
3108: }
3110: /* Not scalable because of ISAllGather() unless getting all columns. */
3111: PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3112: {
3113: IS iscol_local;
3114: PetscBool isstride;
3115: PetscMPIInt lisstride = 0, gisstride;
3117: PetscFunctionBegin;
3118: /* check if we are grabbing all columns*/
3119: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));
3121: if (isstride) {
3122: PetscInt start, len, mstart, mlen;
3123: PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3124: PetscCall(ISGetLocalSize(iscol, &len));
3125: PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3126: if (mstart == start && mlen - mstart == len) lisstride = 1;
3127: }
3129: PetscCall(MPIU_Allreduce(&lisstride, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3130: if (gisstride) {
3131: PetscInt N;
3132: PetscCall(MatGetSize(mat, NULL, &N));
3133: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3134: PetscCall(ISSetIdentity(iscol_local));
3135: PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3136: } else {
3137: PetscInt cbs;
3138: PetscCall(ISGetBlockSize(iscol, &cbs));
3139: PetscCall(ISAllGather(iscol, &iscol_local));
3140: PetscCall(ISSetBlockSize(iscol_local, cbs));
3141: }
3143: *isseq = iscol_local;
3144: PetscFunctionReturn(PETSC_SUCCESS);
3145: }
3147: /*
3148: Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3149: (see MatCreateSubMatrix_MPIAIJ_nonscalable)
3151: Input Parameters:
3152: + mat - matrix
3153: . isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3154: i.e., mat->rstart <= isrow[i] < mat->rend
3155: - iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3156: i.e., mat->cstart <= iscol[i] < mat->cend
3158: Output Parameters:
3159: + isrow_d - sequential row index set for retrieving mat->A
3160: . iscol_d - sequential column index set for retrieving mat->A
3161: . iscol_o - sequential column index set for retrieving mat->B
3162: - garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3163: */
3164: PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, const PetscInt *garray[])
3165: {
3166: Vec x, cmap;
3167: const PetscInt *is_idx;
3168: PetscScalar *xarray, *cmaparray;
3169: PetscInt ncols, isstart, *idx, m, rstart, *cmap1, count;
3170: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3171: Mat B = a->B;
3172: Vec lvec = a->lvec, lcmap;
3173: PetscInt i, cstart, cend, Bn = B->cmap->N;
3174: MPI_Comm comm;
3175: VecScatter Mvctx = a->Mvctx;
3177: PetscFunctionBegin;
3178: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3179: PetscCall(ISGetLocalSize(iscol, &ncols));
3181: /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3182: PetscCall(MatCreateVecs(mat, &x, NULL));
3183: PetscCall(VecSet(x, -1.0));
3184: PetscCall(VecDuplicate(x, &cmap));
3185: PetscCall(VecSet(cmap, -1.0));
3187: /* Get start indices */
3188: PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3189: isstart -= ncols;
3190: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
3192: PetscCall(ISGetIndices(iscol, &is_idx));
3193: PetscCall(VecGetArray(x, &xarray));
3194: PetscCall(VecGetArray(cmap, &cmaparray));
3195: PetscCall(PetscMalloc1(ncols, &idx));
3196: for (i = 0; i < ncols; i++) {
3197: xarray[is_idx[i] - cstart] = (PetscScalar)is_idx[i];
3198: cmaparray[is_idx[i] - cstart] = i + isstart; /* global index of iscol[i] */
3199: idx[i] = is_idx[i] - cstart; /* local index of iscol[i] */
3200: }
3201: PetscCall(VecRestoreArray(x, &xarray));
3202: PetscCall(VecRestoreArray(cmap, &cmaparray));
3203: PetscCall(ISRestoreIndices(iscol, &is_idx));
3205: /* Get iscol_d */
3206: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3207: PetscCall(ISGetBlockSize(iscol, &i));
3208: PetscCall(ISSetBlockSize(*iscol_d, i));
3210: /* Get isrow_d */
3211: PetscCall(ISGetLocalSize(isrow, &m));
3212: rstart = mat->rmap->rstart;
3213: PetscCall(PetscMalloc1(m, &idx));
3214: PetscCall(ISGetIndices(isrow, &is_idx));
3215: for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3216: PetscCall(ISRestoreIndices(isrow, &is_idx));
3218: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3219: PetscCall(ISGetBlockSize(isrow, &i));
3220: PetscCall(ISSetBlockSize(*isrow_d, i));
3222: /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3223: PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3224: PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3226: PetscCall(VecDuplicate(lvec, &lcmap));
3228: PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3229: PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3231: /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3232: /* off-process column indices */
3233: count = 0;
3234: PetscCall(PetscMalloc1(Bn, &idx));
3235: PetscCall(PetscMalloc1(Bn, &cmap1));
3237: PetscCall(VecGetArray(lvec, &xarray));
3238: PetscCall(VecGetArray(lcmap, &cmaparray));
3239: for (i = 0; i < Bn; i++) {
3240: if (PetscRealPart(xarray[i]) > -1.0) {
3241: idx[count] = i; /* local column index in off-diagonal part B */
3242: cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3243: count++;
3244: }
3245: }
3246: PetscCall(VecRestoreArray(lvec, &xarray));
3247: PetscCall(VecRestoreArray(lcmap, &cmaparray));
3249: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3250: /* cannot ensure iscol_o has same blocksize as iscol! */
3252: PetscCall(PetscFree(idx));
3253: *garray = cmap1;
3255: PetscCall(VecDestroy(&x));
3256: PetscCall(VecDestroy(&cmap));
3257: PetscCall(VecDestroy(&lcmap));
3258: PetscFunctionReturn(PETSC_SUCCESS);
3259: }
3261: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3262: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3263: {
3264: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3265: Mat M = NULL;
3266: MPI_Comm comm;
3267: IS iscol_d, isrow_d, iscol_o;
3268: Mat Asub = NULL, Bsub = NULL;
3269: PetscInt n;
3271: PetscFunctionBegin;
3272: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3274: if (call == MAT_REUSE_MATRIX) {
3275: /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3276: PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3277: PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");
3279: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3280: PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");
3282: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3283: PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");
3285: /* Update diagonal and off-diagonal portions of submat */
3286: asub = (Mat_MPIAIJ *)(*submat)->data;
3287: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3288: PetscCall(ISGetLocalSize(iscol_o, &n));
3289: if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3290: PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3291: PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));
3293: } else { /* call == MAT_INITIAL_MATRIX) */
3294: const PetscInt *garray;
3295: PetscInt BsubN;
3297: /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3298: PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));
3300: /* Create local submatrices Asub and Bsub */
3301: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3302: PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));
3304: /* Create submatrix M */
3305: PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, Asub, Bsub, garray, &M));
3307: /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3308: asub = (Mat_MPIAIJ *)M->data;
3310: PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3311: n = asub->B->cmap->N;
3312: if (BsubN > n) {
3313: /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3314: const PetscInt *idx;
3315: PetscInt i, j, *idx_new, *subgarray = asub->garray;
3316: PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));
3318: PetscCall(PetscMalloc1(n, &idx_new));
3319: j = 0;
3320: PetscCall(ISGetIndices(iscol_o, &idx));
3321: for (i = 0; i < n; i++) {
3322: if (j >= BsubN) break;
3323: while (subgarray[i] > garray[j]) j++;
3325: if (subgarray[i] == garray[j]) {
3326: idx_new[i] = idx[j++];
3327: } 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]);
3328: }
3329: PetscCall(ISRestoreIndices(iscol_o, &idx));
3331: PetscCall(ISDestroy(&iscol_o));
3332: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));
3334: } else if (BsubN < n) {
3335: 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);
3336: }
3338: PetscCall(PetscFree(garray));
3339: *submat = M;
3341: /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3342: PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3343: PetscCall(ISDestroy(&isrow_d));
3345: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3346: PetscCall(ISDestroy(&iscol_d));
3348: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3349: PetscCall(ISDestroy(&iscol_o));
3350: }
3351: PetscFunctionReturn(PETSC_SUCCESS);
3352: }
3354: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3355: {
3356: IS iscol_local = NULL, isrow_d;
3357: PetscInt csize;
3358: PetscInt n, i, j, start, end;
3359: PetscBool sameRowDist = PETSC_FALSE, sameDist[2], tsameDist[2];
3360: MPI_Comm comm;
3362: PetscFunctionBegin;
3363: /* If isrow has same processor distribution as mat,
3364: call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3365: if (call == MAT_REUSE_MATRIX) {
3366: PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3367: if (isrow_d) {
3368: sameRowDist = PETSC_TRUE;
3369: tsameDist[1] = PETSC_TRUE; /* sameColDist */
3370: } else {
3371: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3372: if (iscol_local) {
3373: sameRowDist = PETSC_TRUE;
3374: tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3375: }
3376: }
3377: } else {
3378: /* Check if isrow has same processor distribution as mat */
3379: sameDist[0] = PETSC_FALSE;
3380: PetscCall(ISGetLocalSize(isrow, &n));
3381: if (!n) {
3382: sameDist[0] = PETSC_TRUE;
3383: } else {
3384: PetscCall(ISGetMinMax(isrow, &i, &j));
3385: PetscCall(MatGetOwnershipRange(mat, &start, &end));
3386: if (i >= start && j < end) sameDist[0] = PETSC_TRUE;
3387: }
3389: /* Check if iscol has same processor distribution as mat */
3390: sameDist[1] = PETSC_FALSE;
3391: PetscCall(ISGetLocalSize(iscol, &n));
3392: if (!n) {
3393: sameDist[1] = PETSC_TRUE;
3394: } else {
3395: PetscCall(ISGetMinMax(iscol, &i, &j));
3396: PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3397: if (i >= start && j < end) sameDist[1] = PETSC_TRUE;
3398: }
3400: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3401: PetscCall(MPIU_Allreduce(&sameDist, &tsameDist, 2, MPIU_BOOL, MPI_LAND, comm));
3402: sameRowDist = tsameDist[0];
3403: }
3405: if (sameRowDist) {
3406: if (tsameDist[1]) { /* sameRowDist & sameColDist */
3407: /* isrow and iscol have same processor distribution as mat */
3408: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3409: PetscFunctionReturn(PETSC_SUCCESS);
3410: } else { /* sameRowDist */
3411: /* isrow has same processor distribution as mat */
3412: if (call == MAT_INITIAL_MATRIX) {
3413: PetscBool sorted;
3414: PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3415: PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3416: PetscCall(ISGetSize(iscol, &i));
3417: PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);
3419: PetscCall(ISSorted(iscol_local, &sorted));
3420: if (sorted) {
3421: /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3422: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3423: PetscFunctionReturn(PETSC_SUCCESS);
3424: }
3425: } else { /* call == MAT_REUSE_MATRIX */
3426: IS iscol_sub;
3427: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3428: if (iscol_sub) {
3429: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3430: PetscFunctionReturn(PETSC_SUCCESS);
3431: }
3432: }
3433: }
3434: }
3436: /* General case: iscol -> iscol_local which has global size of iscol */
3437: if (call == MAT_REUSE_MATRIX) {
3438: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3439: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3440: } else {
3441: if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3442: }
3444: PetscCall(ISGetLocalSize(iscol, &csize));
3445: PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));
3447: if (call == MAT_INITIAL_MATRIX) {
3448: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3449: PetscCall(ISDestroy(&iscol_local));
3450: }
3451: PetscFunctionReturn(PETSC_SUCCESS);
3452: }
3454: /*@C
3455: MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3456: and "off-diagonal" part of the matrix in CSR format.
3458: Collective
3460: Input Parameters:
3461: + comm - MPI communicator
3462: . A - "diagonal" portion of matrix
3463: . B - "off-diagonal" portion of matrix, may have empty columns, will be destroyed by this routine
3464: - garray - global index of `B` columns
3466: Output Parameter:
3467: . mat - the matrix, with input `A` as its local diagonal matrix
3469: Level: advanced
3471: Notes:
3472: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.
3474: `A` becomes part of output mat, `B` is destroyed by this routine. The user cannot use `A` and `B` anymore.
3476: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3477: @*/
3478: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, Mat A, Mat B, const PetscInt garray[], Mat *mat)
3479: {
3480: Mat_MPIAIJ *maij;
3481: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data, *bnew;
3482: PetscInt *oi = b->i, *oj = b->j, i, nz, col;
3483: const PetscScalar *oa;
3484: Mat Bnew;
3485: PetscInt m, n, N;
3486: MatType mpi_mat_type;
3488: PetscFunctionBegin;
3489: PetscCall(MatCreate(comm, mat));
3490: PetscCall(MatGetSize(A, &m, &n));
3491: PetscCheck(m == B->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Am %" PetscInt_FMT " != Bm %" PetscInt_FMT, m, B->rmap->N);
3492: PetscCheck(A->rmap->bs == B->rmap->bs, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "A row bs %" PetscInt_FMT " != B row bs %" PetscInt_FMT, A->rmap->bs, B->rmap->bs);
3493: /* remove check below; When B is created using iscol_o from ISGetSeqIS_SameColDist_Private(), its bs may not be same as A */
3494: /* 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); */
3496: /* Get global columns of mat */
3497: PetscCall(MPIU_Allreduce(&n, &N, 1, MPIU_INT, MPI_SUM, comm));
3499: PetscCall(MatSetSizes(*mat, m, n, PETSC_DECIDE, N));
3500: /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3501: PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3502: PetscCall(MatSetType(*mat, mpi_mat_type));
3504: PetscCall(MatSetBlockSizes(*mat, A->rmap->bs, A->cmap->bs));
3505: maij = (Mat_MPIAIJ *)(*mat)->data;
3507: (*mat)->preallocated = PETSC_TRUE;
3509: PetscCall(PetscLayoutSetUp((*mat)->rmap));
3510: PetscCall(PetscLayoutSetUp((*mat)->cmap));
3512: /* Set A as diagonal portion of *mat */
3513: maij->A = A;
3515: nz = oi[m];
3516: for (i = 0; i < nz; i++) {
3517: col = oj[i];
3518: oj[i] = garray[col];
3519: }
3521: /* Set Bnew as off-diagonal portion of *mat */
3522: PetscCall(MatSeqAIJGetArrayRead(B, &oa));
3523: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, N, oi, oj, (PetscScalar *)oa, &Bnew));
3524: PetscCall(MatSeqAIJRestoreArrayRead(B, &oa));
3525: bnew = (Mat_SeqAIJ *)Bnew->data;
3526: bnew->maxnz = b->maxnz; /* allocated nonzeros of B */
3527: maij->B = Bnew;
3529: 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);
3531: b->singlemalloc = PETSC_FALSE; /* B arrays are shared by Bnew */
3532: b->free_a = PETSC_FALSE;
3533: b->free_ij = PETSC_FALSE;
3534: PetscCall(MatDestroy(&B));
3536: bnew->singlemalloc = PETSC_TRUE; /* arrays will be freed by MatDestroy(&Bnew) */
3537: bnew->free_a = PETSC_TRUE;
3538: bnew->free_ij = PETSC_TRUE;
3540: /* condense columns of maij->B */
3541: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3542: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
3543: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
3544: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
3545: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3546: PetscFunctionReturn(PETSC_SUCCESS);
3547: }
3549: extern PetscErrorCode MatCreateSubMatrices_MPIAIJ_SingleIS_Local(Mat, PetscInt, const IS[], const IS[], MatReuse, PetscBool, Mat *);
3551: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowDist(Mat mat, IS isrow, IS iscol, IS iscol_local, MatReuse call, Mat *newmat)
3552: {
3553: PetscInt i, m, n, rstart, row, rend, nz, j, bs, cbs;
3554: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3555: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3556: Mat M, Msub, B = a->B;
3557: MatScalar *aa;
3558: Mat_SeqAIJ *aij;
3559: PetscInt *garray = a->garray, *colsub, Ncols;
3560: PetscInt count, Bn = B->cmap->N, cstart = mat->cmap->rstart, cend = mat->cmap->rend;
3561: IS iscol_sub, iscmap;
3562: const PetscInt *is_idx, *cmap;
3563: PetscBool allcolumns = PETSC_FALSE;
3564: MPI_Comm comm;
3566: PetscFunctionBegin;
3567: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3568: if (call == MAT_REUSE_MATRIX) {
3569: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3570: PetscCheck(iscol_sub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "SubIScol passed in was not used before, cannot reuse");
3571: PetscCall(ISGetLocalSize(iscol_sub, &count));
3573: PetscCall(PetscObjectQuery((PetscObject)*newmat, "Subcmap", (PetscObject *)&iscmap));
3574: PetscCheck(iscmap, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Subcmap passed in was not used before, cannot reuse");
3576: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Msub));
3577: PetscCheck(Msub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3579: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_REUSE_MATRIX, PETSC_FALSE, &Msub));
3581: } else { /* call == MAT_INITIAL_MATRIX) */
3582: PetscBool flg;
3584: PetscCall(ISGetLocalSize(iscol, &n));
3585: PetscCall(ISGetSize(iscol, &Ncols));
3587: /* (1) iscol -> nonscalable iscol_local */
3588: /* Check for special case: each processor gets entire matrix columns */
3589: PetscCall(ISIdentity(iscol_local, &flg));
3590: if (flg && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3591: PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3592: if (allcolumns) {
3593: iscol_sub = iscol_local;
3594: PetscCall(PetscObjectReference((PetscObject)iscol_local));
3595: PetscCall(ISCreateStride(PETSC_COMM_SELF, n, 0, 1, &iscmap));
3597: } else {
3598: /* (2) iscol_local -> iscol_sub and iscmap. Implementation below requires iscol_local be sorted, it can have duplicate indices */
3599: PetscInt *idx, *cmap1, k;
3600: PetscCall(PetscMalloc1(Ncols, &idx));
3601: PetscCall(PetscMalloc1(Ncols, &cmap1));
3602: PetscCall(ISGetIndices(iscol_local, &is_idx));
3603: count = 0;
3604: k = 0;
3605: for (i = 0; i < Ncols; i++) {
3606: j = is_idx[i];
3607: if (j >= cstart && j < cend) {
3608: /* diagonal part of mat */
3609: idx[count] = j;
3610: cmap1[count++] = i; /* column index in submat */
3611: } else if (Bn) {
3612: /* off-diagonal part of mat */
3613: if (j == garray[k]) {
3614: idx[count] = j;
3615: cmap1[count++] = i; /* column index in submat */
3616: } else if (j > garray[k]) {
3617: while (j > garray[k] && k < Bn - 1) k++;
3618: if (j == garray[k]) {
3619: idx[count] = j;
3620: cmap1[count++] = i; /* column index in submat */
3621: }
3622: }
3623: }
3624: }
3625: PetscCall(ISRestoreIndices(iscol_local, &is_idx));
3627: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_OWN_POINTER, &iscol_sub));
3628: PetscCall(ISGetBlockSize(iscol, &cbs));
3629: PetscCall(ISSetBlockSize(iscol_sub, cbs));
3631: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)iscol_local), count, cmap1, PETSC_OWN_POINTER, &iscmap));
3632: }
3634: /* (3) Create sequential Msub */
3635: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_INITIAL_MATRIX, allcolumns, &Msub));
3636: }
3638: PetscCall(ISGetLocalSize(iscol_sub, &count));
3639: aij = (Mat_SeqAIJ *)(Msub)->data;
3640: ii = aij->i;
3641: PetscCall(ISGetIndices(iscmap, &cmap));
3643: /*
3644: m - number of local rows
3645: Ncols - number of columns (same on all processors)
3646: rstart - first row in new global matrix generated
3647: */
3648: PetscCall(MatGetSize(Msub, &m, NULL));
3650: if (call == MAT_INITIAL_MATRIX) {
3651: /* (4) Create parallel newmat */
3652: PetscMPIInt rank, size;
3653: PetscInt csize;
3655: PetscCallMPI(MPI_Comm_size(comm, &size));
3656: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3658: /*
3659: Determine the number of non-zeros in the diagonal and off-diagonal
3660: portions of the matrix in order to do correct preallocation
3661: */
3663: /* first get start and end of "diagonal" columns */
3664: PetscCall(ISGetLocalSize(iscol, &csize));
3665: if (csize == PETSC_DECIDE) {
3666: PetscCall(ISGetSize(isrow, &mglobal));
3667: if (mglobal == Ncols) { /* square matrix */
3668: nlocal = m;
3669: } else {
3670: nlocal = Ncols / size + ((Ncols % size) > rank);
3671: }
3672: } else {
3673: nlocal = csize;
3674: }
3675: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3676: rstart = rend - nlocal;
3677: 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);
3679: /* next, compute all the lengths */
3680: jj = aij->j;
3681: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3682: olens = dlens + m;
3683: for (i = 0; i < m; i++) {
3684: jend = ii[i + 1] - ii[i];
3685: olen = 0;
3686: dlen = 0;
3687: for (j = 0; j < jend; j++) {
3688: if (cmap[*jj] < rstart || cmap[*jj] >= rend) olen++;
3689: else dlen++;
3690: jj++;
3691: }
3692: olens[i] = olen;
3693: dlens[i] = dlen;
3694: }
3696: PetscCall(ISGetBlockSize(isrow, &bs));
3697: PetscCall(ISGetBlockSize(iscol, &cbs));
3699: PetscCall(MatCreate(comm, &M));
3700: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, Ncols));
3701: PetscCall(MatSetBlockSizes(M, bs, cbs));
3702: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3703: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3704: PetscCall(PetscFree(dlens));
3706: } else { /* call == MAT_REUSE_MATRIX */
3707: M = *newmat;
3708: PetscCall(MatGetLocalSize(M, &i, NULL));
3709: PetscCheck(i == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3710: PetscCall(MatZeroEntries(M));
3711: /*
3712: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3713: rather than the slower MatSetValues().
3714: */
3715: M->was_assembled = PETSC_TRUE;
3716: M->assembled = PETSC_FALSE;
3717: }
3719: /* (5) Set values of Msub to *newmat */
3720: PetscCall(PetscMalloc1(count, &colsub));
3721: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
3723: jj = aij->j;
3724: PetscCall(MatSeqAIJGetArrayRead(Msub, (const PetscScalar **)&aa));
3725: for (i = 0; i < m; i++) {
3726: row = rstart + i;
3727: nz = ii[i + 1] - ii[i];
3728: for (j = 0; j < nz; j++) colsub[j] = cmap[jj[j]];
3729: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, colsub, aa, INSERT_VALUES));
3730: jj += nz;
3731: aa += nz;
3732: }
3733: PetscCall(MatSeqAIJRestoreArrayRead(Msub, (const PetscScalar **)&aa));
3734: PetscCall(ISRestoreIndices(iscmap, &cmap));
3736: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3737: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3739: PetscCall(PetscFree(colsub));
3741: /* save Msub, iscol_sub and iscmap used in processor for next request */
3742: if (call == MAT_INITIAL_MATRIX) {
3743: *newmat = M;
3744: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "SubMatrix", (PetscObject)Msub));
3745: PetscCall(MatDestroy(&Msub));
3747: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "SubIScol", (PetscObject)iscol_sub));
3748: PetscCall(ISDestroy(&iscol_sub));
3750: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "Subcmap", (PetscObject)iscmap));
3751: PetscCall(ISDestroy(&iscmap));
3753: if (iscol_local) {
3754: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "ISAllGather", (PetscObject)iscol_local));
3755: PetscCall(ISDestroy(&iscol_local));
3756: }
3757: }
3758: PetscFunctionReturn(PETSC_SUCCESS);
3759: }
3761: /*
3762: Not great since it makes two copies of the submatrix, first an SeqAIJ
3763: in local and then by concatenating the local matrices the end result.
3764: Writing it directly would be much like MatCreateSubMatrices_MPIAIJ()
3766: This requires a sequential iscol with all indices.
3767: */
3768: PetscErrorCode MatCreateSubMatrix_MPIAIJ_nonscalable(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
3769: {
3770: PetscMPIInt rank, size;
3771: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs, cbs;
3772: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3773: Mat M, Mreuse;
3774: MatScalar *aa, *vwork;
3775: MPI_Comm comm;
3776: Mat_SeqAIJ *aij;
3777: PetscBool colflag, allcolumns = PETSC_FALSE;
3779: PetscFunctionBegin;
3780: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3781: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3782: PetscCallMPI(MPI_Comm_size(comm, &size));
3784: /* Check for special case: each processor gets entire matrix columns */
3785: PetscCall(ISIdentity(iscol, &colflag));
3786: PetscCall(ISGetLocalSize(iscol, &n));
3787: if (colflag && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3788: PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3790: if (call == MAT_REUSE_MATRIX) {
3791: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
3792: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3793: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_REUSE_MATRIX, allcolumns, &Mreuse));
3794: } else {
3795: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_INITIAL_MATRIX, allcolumns, &Mreuse));
3796: }
3798: /*
3799: m - number of local rows
3800: n - number of columns (same on all processors)
3801: rstart - first row in new global matrix generated
3802: */
3803: PetscCall(MatGetSize(Mreuse, &m, &n));
3804: PetscCall(MatGetBlockSizes(Mreuse, &bs, &cbs));
3805: if (call == MAT_INITIAL_MATRIX) {
3806: aij = (Mat_SeqAIJ *)(Mreuse)->data;
3807: ii = aij->i;
3808: jj = aij->j;
3810: /*
3811: Determine the number of non-zeros in the diagonal and off-diagonal
3812: portions of the matrix in order to do correct preallocation
3813: */
3815: /* first get start and end of "diagonal" columns */
3816: if (csize == PETSC_DECIDE) {
3817: PetscCall(ISGetSize(isrow, &mglobal));
3818: if (mglobal == n) { /* square matrix */
3819: nlocal = m;
3820: } else {
3821: nlocal = n / size + ((n % size) > rank);
3822: }
3823: } else {
3824: nlocal = csize;
3825: }
3826: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3827: rstart = rend - nlocal;
3828: 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);
3830: /* next, compute all the lengths */
3831: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3832: olens = dlens + m;
3833: for (i = 0; i < m; i++) {
3834: jend = ii[i + 1] - ii[i];
3835: olen = 0;
3836: dlen = 0;
3837: for (j = 0; j < jend; j++) {
3838: if (*jj < rstart || *jj >= rend) olen++;
3839: else dlen++;
3840: jj++;
3841: }
3842: olens[i] = olen;
3843: dlens[i] = dlen;
3844: }
3845: PetscCall(MatCreate(comm, &M));
3846: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, n));
3847: PetscCall(MatSetBlockSizes(M, bs, cbs));
3848: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3849: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3850: PetscCall(PetscFree(dlens));
3851: } else {
3852: PetscInt ml, nl;
3854: M = *newmat;
3855: PetscCall(MatGetLocalSize(M, &ml, &nl));
3856: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3857: PetscCall(MatZeroEntries(M));
3858: /*
3859: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3860: rather than the slower MatSetValues().
3861: */
3862: M->was_assembled = PETSC_TRUE;
3863: M->assembled = PETSC_FALSE;
3864: }
3865: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
3866: aij = (Mat_SeqAIJ *)(Mreuse)->data;
3867: ii = aij->i;
3868: jj = aij->j;
3870: /* trigger copy to CPU if needed */
3871: PetscCall(MatSeqAIJGetArrayRead(Mreuse, (const PetscScalar **)&aa));
3872: for (i = 0; i < m; i++) {
3873: row = rstart + i;
3874: nz = ii[i + 1] - ii[i];
3875: cwork = jj;
3876: jj += nz;
3877: vwork = aa;
3878: aa += nz;
3879: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
3880: }
3881: PetscCall(MatSeqAIJRestoreArrayRead(Mreuse, (const PetscScalar **)&aa));
3883: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3884: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3885: *newmat = M;
3887: /* save submatrix used in processor for next request */
3888: if (call == MAT_INITIAL_MATRIX) {
3889: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
3890: PetscCall(MatDestroy(&Mreuse));
3891: }
3892: PetscFunctionReturn(PETSC_SUCCESS);
3893: }
3895: PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
3896: {
3897: PetscInt m, cstart, cend, j, nnz, i, d, *ld;
3898: PetscInt *d_nnz, *o_nnz, nnz_max = 0, rstart, ii;
3899: const PetscInt *JJ;
3900: PetscBool nooffprocentries;
3901: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)B->data;
3903: PetscFunctionBegin;
3904: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);
3906: PetscCall(PetscLayoutSetUp(B->rmap));
3907: PetscCall(PetscLayoutSetUp(B->cmap));
3908: m = B->rmap->n;
3909: cstart = B->cmap->rstart;
3910: cend = B->cmap->rend;
3911: rstart = B->rmap->rstart;
3913: PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));
3915: if (PetscDefined(USE_DEBUG)) {
3916: for (i = 0; i < m; i++) {
3917: nnz = Ii[i + 1] - Ii[i];
3918: JJ = J + Ii[i];
3919: PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3920: 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]);
3921: 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);
3922: }
3923: }
3925: for (i = 0; i < m; i++) {
3926: nnz = Ii[i + 1] - Ii[i];
3927: JJ = J + Ii[i];
3928: nnz_max = PetscMax(nnz_max, nnz);
3929: d = 0;
3930: for (j = 0; j < nnz; j++) {
3931: if (cstart <= JJ[j] && JJ[j] < cend) d++;
3932: }
3933: d_nnz[i] = d;
3934: o_nnz[i] = nnz - d;
3935: }
3936: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3937: PetscCall(PetscFree2(d_nnz, o_nnz));
3939: for (i = 0; i < m; i++) {
3940: ii = i + rstart;
3941: PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], J + Ii[i], v ? v + Ii[i] : NULL, INSERT_VALUES));
3942: }
3943: nooffprocentries = B->nooffprocentries;
3944: B->nooffprocentries = PETSC_TRUE;
3945: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3946: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3947: B->nooffprocentries = nooffprocentries;
3949: /* count number of entries below block diagonal */
3950: PetscCall(PetscFree(Aij->ld));
3951: PetscCall(PetscCalloc1(m, &ld));
3952: Aij->ld = ld;
3953: for (i = 0; i < m; i++) {
3954: nnz = Ii[i + 1] - Ii[i];
3955: j = 0;
3956: while (j < nnz && J[j] < cstart) j++;
3957: ld[i] = j;
3958: J += nnz;
3959: }
3961: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3962: PetscFunctionReturn(PETSC_SUCCESS);
3963: }
3965: /*@
3966: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
3967: (the default parallel PETSc format).
3969: Collective
3971: Input Parameters:
3972: + B - the matrix
3973: . i - the indices into j for the start of each local row (starts with zero)
3974: . j - the column indices for each local row (starts with zero)
3975: - v - optional values in the matrix
3977: Level: developer
3979: Notes:
3980: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
3981: thus you CANNOT change the matrix entries by changing the values of `v` after you have
3982: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3984: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
3986: The format which is used for the sparse matrix input, is equivalent to a
3987: row-major ordering.. i.e for the following matrix, the input data expected is
3988: as shown
3990: .vb
3991: 1 0 0
3992: 2 0 3 P0
3993: -------
3994: 4 5 6 P1
3996: Process0 [P0] rows_owned=[0,1]
3997: i = {0,1,3} [size = nrow+1 = 2+1]
3998: j = {0,0,2} [size = 3]
3999: v = {1,2,3} [size = 3]
4001: Process1 [P1] rows_owned=[2]
4002: i = {0,3} [size = nrow+1 = 1+1]
4003: j = {0,1,2} [size = 3]
4004: v = {4,5,6} [size = 3]
4005: .ve
4007: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`, `MATMPIAIJ`,
4008: `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`
4009: @*/
4010: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4011: {
4012: PetscFunctionBegin;
4013: PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4014: PetscFunctionReturn(PETSC_SUCCESS);
4015: }
4017: /*@C
4018: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
4019: (the default parallel PETSc format). For good matrix assembly performance
4020: the user should preallocate the matrix storage by setting the parameters
4021: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4023: Collective
4025: Input Parameters:
4026: + B - the matrix
4027: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4028: (same value is used for all local rows)
4029: . d_nnz - array containing the number of nonzeros in the various rows of the
4030: DIAGONAL portion of the local submatrix (possibly different for each row)
4031: or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4032: The size of this array is equal to the number of local rows, i.e 'm'.
4033: For matrices that will be factored, you must leave room for (and set)
4034: the diagonal entry even if it is zero.
4035: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4036: submatrix (same value is used for all local rows).
4037: - o_nnz - array containing the number of nonzeros in the various rows of the
4038: OFF-DIAGONAL portion of the local submatrix (possibly different for
4039: each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4040: structure. The size of this array is equal to the number
4041: of local rows, i.e 'm'.
4043: Usage:
4044: Consider the following 8x8 matrix with 34 non-zero values, that is
4045: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4046: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4047: as follows
4049: .vb
4050: 1 2 0 | 0 3 0 | 0 4
4051: Proc0 0 5 6 | 7 0 0 | 8 0
4052: 9 0 10 | 11 0 0 | 12 0
4053: -------------------------------------
4054: 13 0 14 | 15 16 17 | 0 0
4055: Proc1 0 18 0 | 19 20 21 | 0 0
4056: 0 0 0 | 22 23 0 | 24 0
4057: -------------------------------------
4058: Proc2 25 26 27 | 0 0 28 | 29 0
4059: 30 0 0 | 31 32 33 | 0 34
4060: .ve
4062: This can be represented as a collection of submatrices as
4063: .vb
4064: A B C
4065: D E F
4066: G H I
4067: .ve
4069: Where the submatrices A,B,C are owned by proc0, D,E,F are
4070: owned by proc1, G,H,I are owned by proc2.
4072: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4073: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4074: The 'M','N' parameters are 8,8, and have the same values on all procs.
4076: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4077: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4078: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4079: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4080: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4081: matrix, ans [DF] as another `MATSEQAIJ` matrix.
4083: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4084: allocated for every row of the local diagonal submatrix, and `o_nz`
4085: storage locations are allocated for every row of the OFF-DIAGONAL submat.
4086: One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local
4087: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4088: In this case, the values of `d_nz`, `o_nz` are
4089: .vb
4090: proc0 dnz = 2, o_nz = 2
4091: proc1 dnz = 3, o_nz = 2
4092: proc2 dnz = 1, o_nz = 4
4093: .ve
4094: We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4095: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4096: for proc3. i.e we are using 12+15+10=37 storage locations to store
4097: 34 values.
4099: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4100: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4101: In the above case the values for `d_nnz`, `o_nnz` are
4102: .vb
4103: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4104: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4105: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4106: .ve
4107: Here the space allocated is sum of all the above values i.e 34, and
4108: hence pre-allocation is perfect.
4110: Level: intermediate
4112: Notes:
4113: If the *_nnz parameter is given then the *_nz parameter is ignored
4115: The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4116: storage. The stored row and column indices begin with zero.
4117: See [Sparse Matrices](sec_matsparse) for details.
4119: The parallel matrix is partitioned such that the first m0 rows belong to
4120: process 0, the next m1 rows belong to process 1, the next m2 rows belong
4121: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
4123: The DIAGONAL portion of the local submatrix of a processor can be defined
4124: as the submatrix which is obtained by extraction the part corresponding to
4125: the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4126: first row that belongs to the processor, r2 is the last row belonging to
4127: the this processor, and c1-c2 is range of indices of the local part of a
4128: vector suitable for applying the matrix to. This is an mxn matrix. In the
4129: common case of a square matrix, the row and column ranges are the same and
4130: the DIAGONAL part is also square. The remaining portion of the local
4131: submatrix (mxN) constitute the OFF-DIAGONAL portion.
4133: If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.
4135: You can call `MatGetInfo()` to get information on how effective the preallocation was;
4136: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4137: You can also run with the option `-info` and look for messages with the string
4138: malloc in them to see if additional memory allocation was needed.
4140: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4141: `MATMPIAIJ`, `MatGetInfo()`, `PetscSplitOwnership()`
4142: @*/
4143: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4144: {
4145: PetscFunctionBegin;
4148: PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4149: PetscFunctionReturn(PETSC_SUCCESS);
4150: }
4152: /*@
4153: MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4154: CSR format for the local rows.
4156: Collective
4158: Input Parameters:
4159: + comm - MPI communicator
4160: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4161: . n - This value should be the same as the local size used in creating the
4162: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4163: calculated if N is given) For square matrices n is almost always m.
4164: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4165: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4166: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
4167: . j - column indices
4168: - a - optional matrix values
4170: Output Parameter:
4171: . mat - the matrix
4173: Level: intermediate
4175: Notes:
4176: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4177: thus you CANNOT change the matrix entries by changing the values of a[] after you have
4178: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
4180: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
4182: The format which is used for the sparse matrix input, is equivalent to a
4183: row-major ordering.. i.e for the following matrix, the input data expected is
4184: as shown
4186: Once you have created the matrix you can update it with new numerical values using MatUpdateMPIAIJWithArrays
4187: .vb
4188: 1 0 0
4189: 2 0 3 P0
4190: -------
4191: 4 5 6 P1
4193: Process0 [P0] rows_owned=[0,1]
4194: i = {0,1,3} [size = nrow+1 = 2+1]
4195: j = {0,0,2} [size = 3]
4196: v = {1,2,3} [size = 3]
4198: Process1 [P1] rows_owned=[2]
4199: i = {0,3} [size = nrow+1 = 1+1]
4200: j = {0,1,2} [size = 3]
4201: v = {4,5,6} [size = 3]
4202: .ve
4204: .seealso: [](ch_matrices), `Mat`, `MATMPIAIK`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4205: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`
4206: @*/
4207: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
4208: {
4209: PetscFunctionBegin;
4210: PetscCheck(!i || !i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4211: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4212: PetscCall(MatCreate(comm, mat));
4213: PetscCall(MatSetSizes(*mat, m, n, M, N));
4214: /* PetscCall(MatSetBlockSizes(M,bs,cbs)); */
4215: PetscCall(MatSetType(*mat, MATMPIAIJ));
4216: PetscCall(MatMPIAIJSetPreallocationCSR(*mat, i, j, a));
4217: PetscFunctionReturn(PETSC_SUCCESS);
4218: }
4220: /*@
4221: MatUpdateMPIAIJWithArrays - updates a `MATMPIAIJ` matrix using arrays that contain in standard
4222: CSR format for the local rows. Only the numerical values are updated the other arrays must be identical to what was passed
4223: from `MatCreateMPIAIJWithArrays()`
4225: Deprecated: Use `MatUpdateMPIAIJWithArray()`
4227: Collective
4229: Input Parameters:
4230: + mat - the matrix
4231: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4232: . n - This value should be the same as the local size used in creating the
4233: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4234: calculated if N is given) For square matrices n is almost always m.
4235: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4236: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4237: . Ii - row indices; that is Ii[0] = 0, Ii[row] = Ii[row-1] + number of elements in that row of the matrix
4238: . J - column indices
4239: - v - matrix values
4241: Level: deprecated
4243: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4244: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatUpdateMPIAIJWithArray()`
4245: @*/
4246: PetscErrorCode MatUpdateMPIAIJWithArrays(Mat mat, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4247: {
4248: PetscInt nnz, i;
4249: PetscBool nooffprocentries;
4250: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4251: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4252: PetscScalar *ad, *ao;
4253: PetscInt ldi, Iii, md;
4254: const PetscInt *Adi = Ad->i;
4255: PetscInt *ld = Aij->ld;
4257: PetscFunctionBegin;
4258: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4259: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4260: PetscCheck(m == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of rows cannot change from call to MatUpdateMPIAIJWithArrays()");
4261: PetscCheck(n == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of columns cannot change from call to MatUpdateMPIAIJWithArrays()");
4263: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4264: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4266: for (i = 0; i < m; i++) {
4267: nnz = Ii[i + 1] - Ii[i];
4268: Iii = Ii[i];
4269: ldi = ld[i];
4270: md = Adi[i + 1] - Adi[i];
4271: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4272: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4273: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4274: ad += md;
4275: ao += nnz - md;
4276: }
4277: nooffprocentries = mat->nooffprocentries;
4278: mat->nooffprocentries = PETSC_TRUE;
4279: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4280: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4281: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4282: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4283: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4284: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4285: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4286: mat->nooffprocentries = nooffprocentries;
4287: PetscFunctionReturn(PETSC_SUCCESS);
4288: }
4290: /*@
4291: MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values
4293: Collective
4295: Input Parameters:
4296: + mat - the matrix
4297: - v - matrix values, stored by row
4299: Level: intermediate
4301: Note:
4302: The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`
4304: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4305: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatUpdateMPIAIJWithArrays()`
4306: @*/
4307: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4308: {
4309: PetscInt nnz, i, m;
4310: PetscBool nooffprocentries;
4311: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4312: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4313: Mat_SeqAIJ *Ao = (Mat_SeqAIJ *)Aij->B->data;
4314: PetscScalar *ad, *ao;
4315: const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4316: PetscInt ldi, Iii, md;
4317: PetscInt *ld = Aij->ld;
4319: PetscFunctionBegin;
4320: m = mat->rmap->n;
4322: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4323: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4324: Iii = 0;
4325: for (i = 0; i < m; i++) {
4326: nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4327: ldi = ld[i];
4328: md = Adi[i + 1] - Adi[i];
4329: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4330: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4331: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4332: ad += md;
4333: ao += nnz - md;
4334: Iii += nnz;
4335: }
4336: nooffprocentries = mat->nooffprocentries;
4337: mat->nooffprocentries = PETSC_TRUE;
4338: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4339: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4340: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4341: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4342: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4343: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4344: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4345: mat->nooffprocentries = nooffprocentries;
4346: PetscFunctionReturn(PETSC_SUCCESS);
4347: }
4349: /*@C
4350: MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4351: (the default parallel PETSc format). For good matrix assembly performance
4352: the user should preallocate the matrix storage by setting the parameters
4353: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4355: Collective
4357: Input Parameters:
4358: + comm - MPI communicator
4359: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4360: This value should be the same as the local size used in creating the
4361: y vector for the matrix-vector product y = Ax.
4362: . n - This value should be the same as the local size used in creating the
4363: x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
4364: calculated if N is given) For square matrices n is almost always m.
4365: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4366: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4367: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4368: (same value is used for all local rows)
4369: . d_nnz - array containing the number of nonzeros in the various rows of the
4370: DIAGONAL portion of the local submatrix (possibly different for each row)
4371: or `NULL`, if `d_nz` is used to specify the nonzero structure.
4372: The size of this array is equal to the number of local rows, i.e 'm'.
4373: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4374: submatrix (same value is used for all local rows).
4375: - o_nnz - array containing the number of nonzeros in the various rows of the
4376: OFF-DIAGONAL portion of the local submatrix (possibly different for
4377: each row) or `NULL`, if `o_nz` is used to specify the nonzero
4378: structure. The size of this array is equal to the number
4379: of local rows, i.e 'm'.
4381: Output Parameter:
4382: . A - the matrix
4384: Options Database Keys:
4385: + -mat_no_inode - Do not use inodes
4386: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
4387: - -matmult_vecscatter_view <viewer> - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4388: See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the vecscatter be viewed as a matrix.
4389: Entry (i,j) is the size of message (in bytes) rank i sends to rank j in one `MatMult()` call.
4391: Level: intermediate
4393: Notes:
4394: It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4395: MatXXXXSetPreallocation() paradigm instead of this routine directly.
4396: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
4398: If the *_nnz parameter is given then the *_nz parameter is ignored
4400: The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4401: processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4402: storage requirements for this matrix.
4404: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one
4405: processor than it must be used on all processors that share the object for
4406: that argument.
4408: The user MUST specify either the local or global matrix dimensions
4409: (possibly both).
4411: The parallel matrix is partitioned across processors such that the
4412: first m0 rows belong to process 0, the next m1 rows belong to
4413: process 1, the next m2 rows belong to process 2 etc.. where
4414: m0,m1,m2,.. are the input parameter 'm'. i.e each processor stores
4415: values corresponding to [m x N] submatrix.
4417: The columns are logically partitioned with the n0 columns belonging
4418: to 0th partition, the next n1 columns belonging to the next
4419: partition etc.. where n0,n1,n2... are the input parameter 'n'.
4421: The DIAGONAL portion of the local submatrix on any given processor
4422: is the submatrix corresponding to the rows and columns m,n
4423: corresponding to the given processor. i.e diagonal matrix on
4424: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4425: etc. The remaining portion of the local submatrix [m x (N-n)]
4426: constitute the OFF-DIAGONAL portion. The example below better
4427: illustrates this concept.
4429: For a square global matrix we define each processor's diagonal portion
4430: to be its local rows and the corresponding columns (a square submatrix);
4431: each processor's off-diagonal portion encompasses the remainder of the
4432: local matrix (a rectangular submatrix).
4434: If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.
4436: When calling this routine with a single process communicator, a matrix of
4437: type `MATSEQAIJ` is returned. If a matrix of type `MATMPIAIJ` is desired for this
4438: type of communicator, use the construction mechanism
4439: .vb
4440: MatCreate(...,&A);
4441: MatSetType(A,MATMPIAIJ);
4442: MatSetSizes(A, m,n,M,N);
4443: MatMPIAIJSetPreallocation(A,...);
4444: .ve
4446: By default, this format uses inodes (identical nodes) when possible.
4447: We search for consecutive rows with the same nonzero structure, thereby
4448: reusing matrix information to achieve increased efficiency.
4450: Usage:
4451: Consider the following 8x8 matrix with 34 non-zero values, that is
4452: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4453: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4454: as follows
4456: .vb
4457: 1 2 0 | 0 3 0 | 0 4
4458: Proc0 0 5 6 | 7 0 0 | 8 0
4459: 9 0 10 | 11 0 0 | 12 0
4460: -------------------------------------
4461: 13 0 14 | 15 16 17 | 0 0
4462: Proc1 0 18 0 | 19 20 21 | 0 0
4463: 0 0 0 | 22 23 0 | 24 0
4464: -------------------------------------
4465: Proc2 25 26 27 | 0 0 28 | 29 0
4466: 30 0 0 | 31 32 33 | 0 34
4467: .ve
4469: This can be represented as a collection of submatrices as
4471: .vb
4472: A B C
4473: D E F
4474: G H I
4475: .ve
4477: Where the submatrices A,B,C are owned by proc0, D,E,F are
4478: owned by proc1, G,H,I are owned by proc2.
4480: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4481: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4482: The 'M','N' parameters are 8,8, and have the same values on all procs.
4484: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4485: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4486: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4487: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4488: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4489: matrix, ans [DF] as another SeqAIJ matrix.
4491: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4492: allocated for every row of the local diagonal submatrix, and `o_nz`
4493: storage locations are allocated for every row of the OFF-DIAGONAL submat.
4494: One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local
4495: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4496: In this case, the values of `d_nz`,`o_nz` are
4497: .vb
4498: proc0 dnz = 2, o_nz = 2
4499: proc1 dnz = 3, o_nz = 2
4500: proc2 dnz = 1, o_nz = 4
4501: .ve
4502: We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4503: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4504: for proc3. i.e we are using 12+15+10=37 storage locations to store
4505: 34 values.
4507: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4508: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4509: In the above case the values for d_nnz,o_nnz are
4510: .vb
4511: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4512: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4513: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4514: .ve
4515: Here the space allocated is sum of all the above values i.e 34, and
4516: hence pre-allocation is perfect.
4518: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4519: `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`
4520: @*/
4521: 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)
4522: {
4523: PetscMPIInt size;
4525: PetscFunctionBegin;
4526: PetscCall(MatCreate(comm, A));
4527: PetscCall(MatSetSizes(*A, m, n, M, N));
4528: PetscCallMPI(MPI_Comm_size(comm, &size));
4529: if (size > 1) {
4530: PetscCall(MatSetType(*A, MATMPIAIJ));
4531: PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4532: } else {
4533: PetscCall(MatSetType(*A, MATSEQAIJ));
4534: PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4535: }
4536: PetscFunctionReturn(PETSC_SUCCESS);
4537: }
4539: /*MC
4540: MatMPIAIJGetSeqAIJF90 - Returns the local pieces of this distributed matrix
4542: Synopsis:
4543: MatMPIAIJGetSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)
4545: Not Collective
4547: Input Parameter:
4548: . A - the `MATMPIAIJ` matrix
4550: Output Parameters:
4551: + Ad - the diagonal portion of the matrix
4552: . Ao - the off diagonal portion of the matrix
4553: . colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4554: - ierr - error code
4556: Level: advanced
4558: Note:
4559: Use `MatMPIAIJRestoreSeqAIJF90()` when you no longer need access to the matrices and `colmap`
4561: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJRestoreSeqAIJF90()`
4562: M*/
4564: /*MC
4565: MatMPIAIJRestoreSeqAIJF90 - call after `MatMPIAIJGetSeqAIJF90()` when you no longer need access to the matrices and `colmap`
4567: Synopsis:
4568: MatMPIAIJRestoreSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)
4570: Not Collective
4572: Input Parameters:
4573: + A - the `MATMPIAIJ` matrix
4574: . Ad - the diagonal portion of the matrix
4575: . Ao - the off diagonal portion of the matrix
4576: . colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4577: - ierr - error code
4579: Level: advanced
4581: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJGetSeqAIJF90()`
4582: M*/
4584: /*@C
4585: MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix
4587: Not Collective
4589: Input Parameter:
4590: . A - The `MATMPIAIJ` matrix
4592: Output Parameters:
4593: + Ad - The local diagonal block as a `MATSEQAIJ` matrix
4594: . Ao - The local off-diagonal block as a `MATSEQAIJ` matrix
4595: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4597: Level: intermediate
4599: Note:
4600: The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4601: 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
4602: the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4603: local column numbers to global column numbers in the original matrix.
4605: Fortran Note:
4606: `MatMPIAIJGetSeqAIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatMPIAIJGetSeqAIJF90()`
4608: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJF90()`, `MatMPIAIJRestoreSeqAIJF90()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATMPIAIJ`, `MATSEQAIJ`
4609: @*/
4610: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4611: {
4612: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4613: PetscBool flg;
4615: PetscFunctionBegin;
4616: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4617: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4618: if (Ad) *Ad = a->A;
4619: if (Ao) *Ao = a->B;
4620: if (colmap) *colmap = a->garray;
4621: PetscFunctionReturn(PETSC_SUCCESS);
4622: }
4624: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4625: {
4626: PetscInt m, N, i, rstart, nnz, Ii;
4627: PetscInt *indx;
4628: PetscScalar *values;
4629: MatType rootType;
4631: PetscFunctionBegin;
4632: PetscCall(MatGetSize(inmat, &m, &N));
4633: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4634: PetscInt *dnz, *onz, sum, bs, cbs;
4636: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4637: /* Check sum(n) = N */
4638: PetscCall(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4639: PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);
4641: PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4642: rstart -= m;
4644: MatPreallocateBegin(comm, m, n, dnz, onz);
4645: for (i = 0; i < m; i++) {
4646: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4647: PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4648: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4649: }
4651: PetscCall(MatCreate(comm, outmat));
4652: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4653: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4654: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4655: PetscCall(MatGetRootType_Private(inmat, &rootType));
4656: PetscCall(MatSetType(*outmat, rootType));
4657: PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4658: PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4659: MatPreallocateEnd(dnz, onz);
4660: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4661: }
4663: /* numeric phase */
4664: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4665: for (i = 0; i < m; i++) {
4666: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4667: Ii = i + rstart;
4668: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4669: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4670: }
4671: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4672: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4673: PetscFunctionReturn(PETSC_SUCCESS);
4674: }
4676: PetscErrorCode MatFileSplit(Mat A, char *outfile)
4677: {
4678: PetscMPIInt rank;
4679: PetscInt m, N, i, rstart, nnz;
4680: size_t len;
4681: const PetscInt *indx;
4682: PetscViewer out;
4683: char *name;
4684: Mat B;
4685: const PetscScalar *values;
4687: PetscFunctionBegin;
4688: PetscCall(MatGetLocalSize(A, &m, NULL));
4689: PetscCall(MatGetSize(A, NULL, &N));
4690: /* Should this be the type of the diagonal block of A? */
4691: PetscCall(MatCreate(PETSC_COMM_SELF, &B));
4692: PetscCall(MatSetSizes(B, m, N, m, N));
4693: PetscCall(MatSetBlockSizesFromMats(B, A, A));
4694: PetscCall(MatSetType(B, MATSEQAIJ));
4695: PetscCall(MatSeqAIJSetPreallocation(B, 0, NULL));
4696: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
4697: for (i = 0; i < m; i++) {
4698: PetscCall(MatGetRow(A, i + rstart, &nnz, &indx, &values));
4699: PetscCall(MatSetValues(B, 1, &i, nnz, indx, values, INSERT_VALUES));
4700: PetscCall(MatRestoreRow(A, i + rstart, &nnz, &indx, &values));
4701: }
4702: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4703: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4705: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)A), &rank));
4706: PetscCall(PetscStrlen(outfile, &len));
4707: PetscCall(PetscMalloc1(len + 6, &name));
4708: PetscCall(PetscSNPrintf(name, len + 6, "%s.%d", outfile, rank));
4709: PetscCall(PetscViewerBinaryOpen(PETSC_COMM_SELF, name, FILE_MODE_APPEND, &out));
4710: PetscCall(PetscFree(name));
4711: PetscCall(MatView(B, out));
4712: PetscCall(PetscViewerDestroy(&out));
4713: PetscCall(MatDestroy(&B));
4714: PetscFunctionReturn(PETSC_SUCCESS);
4715: }
4717: static PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(void *data)
4718: {
4719: Mat_Merge_SeqsToMPI *merge = (Mat_Merge_SeqsToMPI *)data;
4721: PetscFunctionBegin;
4722: if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4723: PetscCall(PetscFree(merge->id_r));
4724: PetscCall(PetscFree(merge->len_s));
4725: PetscCall(PetscFree(merge->len_r));
4726: PetscCall(PetscFree(merge->bi));
4727: PetscCall(PetscFree(merge->bj));
4728: PetscCall(PetscFree(merge->buf_ri[0]));
4729: PetscCall(PetscFree(merge->buf_ri));
4730: PetscCall(PetscFree(merge->buf_rj[0]));
4731: PetscCall(PetscFree(merge->buf_rj));
4732: PetscCall(PetscFree(merge->coi));
4733: PetscCall(PetscFree(merge->coj));
4734: PetscCall(PetscFree(merge->owners_co));
4735: PetscCall(PetscLayoutDestroy(&merge->rowmap));
4736: PetscCall(PetscFree(merge));
4737: PetscFunctionReturn(PETSC_SUCCESS);
4738: }
4740: #include <../src/mat/utils/freespace.h>
4741: #include <petscbt.h>
4743: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4744: {
4745: MPI_Comm comm;
4746: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4747: PetscMPIInt size, rank, taga, *len_s;
4748: PetscInt N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj;
4749: PetscInt proc, m;
4750: PetscInt **buf_ri, **buf_rj;
4751: PetscInt k, anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4752: PetscInt nrows, **buf_ri_k, **nextrow, **nextai;
4753: MPI_Request *s_waits, *r_waits;
4754: MPI_Status *status;
4755: const MatScalar *aa, *a_a;
4756: MatScalar **abuf_r, *ba_i;
4757: Mat_Merge_SeqsToMPI *merge;
4758: PetscContainer container;
4760: PetscFunctionBegin;
4761: PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4762: PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));
4764: PetscCallMPI(MPI_Comm_size(comm, &size));
4765: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4767: PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4768: PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4769: PetscCall(PetscContainerGetPointer(container, (void **)&merge));
4770: PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4771: aa = a_a;
4773: bi = merge->bi;
4774: bj = merge->bj;
4775: buf_ri = merge->buf_ri;
4776: buf_rj = merge->buf_rj;
4778: PetscCall(PetscMalloc1(size, &status));
4779: owners = merge->rowmap->range;
4780: len_s = merge->len_s;
4782: /* send and recv matrix values */
4783: PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4784: PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));
4786: PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4787: for (proc = 0, k = 0; proc < size; proc++) {
4788: if (!len_s[proc]) continue;
4789: i = owners[proc];
4790: PetscCallMPI(MPI_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4791: k++;
4792: }
4794: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4795: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4796: PetscCall(PetscFree(status));
4798: PetscCall(PetscFree(s_waits));
4799: PetscCall(PetscFree(r_waits));
4801: /* insert mat values of mpimat */
4802: PetscCall(PetscMalloc1(N, &ba_i));
4803: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4805: for (k = 0; k < merge->nrecv; k++) {
4806: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4807: nrows = *(buf_ri_k[k]);
4808: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4809: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4810: }
4812: /* set values of ba */
4813: m = merge->rowmap->n;
4814: for (i = 0; i < m; i++) {
4815: arow = owners[rank] + i;
4816: bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4817: bnzi = bi[i + 1] - bi[i];
4818: PetscCall(PetscArrayzero(ba_i, bnzi));
4820: /* add local non-zero vals of this proc's seqmat into ba */
4821: anzi = ai[arow + 1] - ai[arow];
4822: aj = a->j + ai[arow];
4823: aa = a_a + ai[arow];
4824: nextaj = 0;
4825: for (j = 0; nextaj < anzi; j++) {
4826: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4827: ba_i[j] += aa[nextaj++];
4828: }
4829: }
4831: /* add received vals into ba */
4832: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4833: /* i-th row */
4834: if (i == *nextrow[k]) {
4835: anzi = *(nextai[k] + 1) - *nextai[k];
4836: aj = buf_rj[k] + *(nextai[k]);
4837: aa = abuf_r[k] + *(nextai[k]);
4838: nextaj = 0;
4839: for (j = 0; nextaj < anzi; j++) {
4840: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4841: ba_i[j] += aa[nextaj++];
4842: }
4843: }
4844: nextrow[k]++;
4845: nextai[k]++;
4846: }
4847: }
4848: PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4849: }
4850: PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4851: PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4852: PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));
4854: PetscCall(PetscFree(abuf_r[0]));
4855: PetscCall(PetscFree(abuf_r));
4856: PetscCall(PetscFree(ba_i));
4857: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4858: PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4859: PetscFunctionReturn(PETSC_SUCCESS);
4860: }
4862: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4863: {
4864: Mat B_mpi;
4865: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4866: PetscMPIInt size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4867: PetscInt **buf_rj, **buf_ri, **buf_ri_k;
4868: PetscInt M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4869: PetscInt len, proc, *dnz, *onz, bs, cbs;
4870: PetscInt k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4871: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4872: MPI_Request *si_waits, *sj_waits, *ri_waits, *rj_waits;
4873: MPI_Status *status;
4874: PetscFreeSpaceList free_space = NULL, current_space = NULL;
4875: PetscBT lnkbt;
4876: Mat_Merge_SeqsToMPI *merge;
4877: PetscContainer container;
4879: PetscFunctionBegin;
4880: PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));
4882: /* make sure it is a PETSc comm */
4883: PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4884: PetscCallMPI(MPI_Comm_size(comm, &size));
4885: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4887: PetscCall(PetscNew(&merge));
4888: PetscCall(PetscMalloc1(size, &status));
4890: /* determine row ownership */
4891: PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4892: PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4893: PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4894: PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4895: PetscCall(PetscLayoutSetUp(merge->rowmap));
4896: PetscCall(PetscMalloc1(size, &len_si));
4897: PetscCall(PetscMalloc1(size, &merge->len_s));
4899: m = merge->rowmap->n;
4900: owners = merge->rowmap->range;
4902: /* determine the number of messages to send, their lengths */
4903: len_s = merge->len_s;
4905: len = 0; /* length of buf_si[] */
4906: merge->nsend = 0;
4907: for (proc = 0; proc < size; proc++) {
4908: len_si[proc] = 0;
4909: if (proc == rank) {
4910: len_s[proc] = 0;
4911: } else {
4912: len_si[proc] = owners[proc + 1] - owners[proc] + 1;
4913: len_s[proc] = ai[owners[proc + 1]] - ai[owners[proc]]; /* num of rows to be sent to [proc] */
4914: }
4915: if (len_s[proc]) {
4916: merge->nsend++;
4917: nrows = 0;
4918: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4919: if (ai[i + 1] > ai[i]) nrows++;
4920: }
4921: len_si[proc] = 2 * (nrows + 1);
4922: len += len_si[proc];
4923: }
4924: }
4926: /* determine the number and length of messages to receive for ij-structure */
4927: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4928: PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));
4930: /* post the Irecv of j-structure */
4931: PetscCall(PetscCommGetNewTag(comm, &tagj));
4932: PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));
4934: /* post the Isend of j-structure */
4935: PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));
4937: for (proc = 0, k = 0; proc < size; proc++) {
4938: if (!len_s[proc]) continue;
4939: i = owners[proc];
4940: PetscCallMPI(MPI_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4941: k++;
4942: }
4944: /* receives and sends of j-structure are complete */
4945: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4946: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));
4948: /* send and recv i-structure */
4949: PetscCall(PetscCommGetNewTag(comm, &tagi));
4950: PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));
4952: PetscCall(PetscMalloc1(len + 1, &buf_s));
4953: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4954: for (proc = 0, k = 0; proc < size; proc++) {
4955: if (!len_s[proc]) continue;
4956: /* form outgoing message for i-structure:
4957: buf_si[0]: nrows to be sent
4958: [1:nrows]: row index (global)
4959: [nrows+1:2*nrows+1]: i-structure index
4960: */
4961: nrows = len_si[proc] / 2 - 1;
4962: buf_si_i = buf_si + nrows + 1;
4963: buf_si[0] = nrows;
4964: buf_si_i[0] = 0;
4965: nrows = 0;
4966: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4967: anzi = ai[i + 1] - ai[i];
4968: if (anzi) {
4969: buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4970: buf_si[nrows + 1] = i - owners[proc]; /* local row index */
4971: nrows++;
4972: }
4973: }
4974: PetscCallMPI(MPI_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4975: k++;
4976: buf_si += len_si[proc];
4977: }
4979: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4980: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));
4982: PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4983: 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]));
4985: PetscCall(PetscFree(len_si));
4986: PetscCall(PetscFree(len_ri));
4987: PetscCall(PetscFree(rj_waits));
4988: PetscCall(PetscFree2(si_waits, sj_waits));
4989: PetscCall(PetscFree(ri_waits));
4990: PetscCall(PetscFree(buf_s));
4991: PetscCall(PetscFree(status));
4993: /* compute a local seq matrix in each processor */
4994: /* allocate bi array and free space for accumulating nonzero column info */
4995: PetscCall(PetscMalloc1(m + 1, &bi));
4996: bi[0] = 0;
4998: /* create and initialize a linked list */
4999: nlnk = N + 1;
5000: PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));
5002: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
5003: len = ai[owners[rank + 1]] - ai[owners[rank]];
5004: PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));
5006: current_space = free_space;
5008: /* determine symbolic info for each local row */
5009: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
5011: for (k = 0; k < merge->nrecv; k++) {
5012: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
5013: nrows = *buf_ri_k[k];
5014: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
5015: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
5016: }
5018: MatPreallocateBegin(comm, m, n, dnz, onz);
5019: len = 0;
5020: for (i = 0; i < m; i++) {
5021: bnzi = 0;
5022: /* add local non-zero cols of this proc's seqmat into lnk */
5023: arow = owners[rank] + i;
5024: anzi = ai[arow + 1] - ai[arow];
5025: aj = a->j + ai[arow];
5026: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5027: bnzi += nlnk;
5028: /* add received col data into lnk */
5029: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
5030: if (i == *nextrow[k]) { /* i-th row */
5031: anzi = *(nextai[k] + 1) - *nextai[k];
5032: aj = buf_rj[k] + *nextai[k];
5033: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5034: bnzi += nlnk;
5035: nextrow[k]++;
5036: nextai[k]++;
5037: }
5038: }
5039: if (len < bnzi) len = bnzi; /* =max(bnzi) */
5041: /* if free space is not available, make more free space */
5042: if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), ¤t_space));
5043: /* copy data into free space, then initialize lnk */
5044: PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
5045: PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));
5047: current_space->array += bnzi;
5048: current_space->local_used += bnzi;
5049: current_space->local_remaining -= bnzi;
5051: bi[i + 1] = bi[i] + bnzi;
5052: }
5054: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
5056: PetscCall(PetscMalloc1(bi[m] + 1, &bj));
5057: PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
5058: PetscCall(PetscLLDestroy(lnk, lnkbt));
5060: /* create symbolic parallel matrix B_mpi */
5061: PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
5062: PetscCall(MatCreate(comm, &B_mpi));
5063: if (n == PETSC_DECIDE) {
5064: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
5065: } else {
5066: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
5067: }
5068: PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
5069: PetscCall(MatSetType(B_mpi, MATMPIAIJ));
5070: PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
5071: MatPreallocateEnd(dnz, onz);
5072: PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));
5074: /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
5075: B_mpi->assembled = PETSC_FALSE;
5076: merge->bi = bi;
5077: merge->bj = bj;
5078: merge->buf_ri = buf_ri;
5079: merge->buf_rj = buf_rj;
5080: merge->coi = NULL;
5081: merge->coj = NULL;
5082: merge->owners_co = NULL;
5084: PetscCall(PetscCommDestroy(&comm));
5086: /* attach the supporting struct to B_mpi for reuse */
5087: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5088: PetscCall(PetscContainerSetPointer(container, merge));
5089: PetscCall(PetscContainerSetUserDestroy(container, MatDestroy_MPIAIJ_SeqsToMPI));
5090: PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5091: PetscCall(PetscContainerDestroy(&container));
5092: *mpimat = B_mpi;
5094: PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5095: PetscFunctionReturn(PETSC_SUCCESS);
5096: }
5098: /*@C
5099: MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5100: matrices from each processor
5102: Collective
5104: Input Parameters:
5105: + comm - the communicators the parallel matrix will live on
5106: . seqmat - the input sequential matrices
5107: . m - number of local rows (or `PETSC_DECIDE`)
5108: . n - number of local columns (or `PETSC_DECIDE`)
5109: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5111: Output Parameter:
5112: . mpimat - the parallel matrix generated
5114: Level: advanced
5116: Note:
5117: The dimensions of the sequential matrix in each processor MUST be the same.
5118: The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
5119: destroyed when mpimat is destroyed. Call `PetscObjectQuery()` to access seqmat.
5121: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`
5122: @*/
5123: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5124: {
5125: PetscMPIInt size;
5127: PetscFunctionBegin;
5128: PetscCallMPI(MPI_Comm_size(comm, &size));
5129: if (size == 1) {
5130: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5131: if (scall == MAT_INITIAL_MATRIX) {
5132: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5133: } else {
5134: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5135: }
5136: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5137: PetscFunctionReturn(PETSC_SUCCESS);
5138: }
5139: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5140: if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5141: PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5142: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5143: PetscFunctionReturn(PETSC_SUCCESS);
5144: }
5146: /*@
5147: MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix by taking its local rows and putting them into a sequential matrix with
5148: mlocal rows and n columns. Where mlocal is obtained with `MatGetLocalSize()` and n is the global column count obtained
5149: with `MatGetSize()`
5151: Not Collective
5153: Input Parameter:
5154: . A - the matrix
5156: Output Parameter:
5157: . A_loc - the local sequential matrix generated
5159: Level: developer
5161: Notes:
5162: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5164: Destroy the matrix with `MatDestroy()`
5166: .seealso: [](ch_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5167: @*/
5168: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5169: {
5170: PetscBool mpi;
5172: PetscFunctionBegin;
5173: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5174: if (mpi) {
5175: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5176: } else {
5177: *A_loc = A;
5178: PetscCall(PetscObjectReference((PetscObject)*A_loc));
5179: }
5180: PetscFunctionReturn(PETSC_SUCCESS);
5181: }
5183: /*@
5184: MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5185: mlocal rows and n columns. Where mlocal is the row count obtained with `MatGetLocalSize()` and n is the global column count obtained
5186: with `MatGetSize()`
5188: Not Collective
5190: Input Parameters:
5191: + A - the matrix
5192: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5194: Output Parameter:
5195: . A_loc - the local sequential matrix generated
5197: Level: developer
5199: Notes:
5200: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5202: When the communicator associated with `A` has size 1 and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A`.
5203: If `MAT_REUSE_MATRIX` is requested with comm size 1, `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called.
5204: This means that one can preallocate the proper sequential matrix first and then call this routine with `MAT_REUSE_MATRIX` to safely
5205: modify the values of the returned `A_loc`.
5207: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5208: @*/
5209: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5210: {
5211: Mat_MPIAIJ *mpimat = (Mat_MPIAIJ *)A->data;
5212: Mat_SeqAIJ *mat, *a, *b;
5213: PetscInt *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5214: const PetscScalar *aa, *ba, *aav, *bav;
5215: PetscScalar *ca, *cam;
5216: PetscMPIInt size;
5217: PetscInt am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5218: PetscInt *ci, *cj, col, ncols_d, ncols_o, jo;
5219: PetscBool match;
5221: PetscFunctionBegin;
5222: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5223: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5224: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5225: if (size == 1) {
5226: if (scall == MAT_INITIAL_MATRIX) {
5227: PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5228: *A_loc = mpimat->A;
5229: } else if (scall == MAT_REUSE_MATRIX) {
5230: PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5231: }
5232: PetscFunctionReturn(PETSC_SUCCESS);
5233: }
5235: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5236: a = (Mat_SeqAIJ *)(mpimat->A)->data;
5237: b = (Mat_SeqAIJ *)(mpimat->B)->data;
5238: ai = a->i;
5239: aj = a->j;
5240: bi = b->i;
5241: bj = b->j;
5242: PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5243: PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5244: aa = aav;
5245: ba = bav;
5246: if (scall == MAT_INITIAL_MATRIX) {
5247: PetscCall(PetscMalloc1(1 + am, &ci));
5248: ci[0] = 0;
5249: for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5250: PetscCall(PetscMalloc1(1 + ci[am], &cj));
5251: PetscCall(PetscMalloc1(1 + ci[am], &ca));
5252: k = 0;
5253: for (i = 0; i < am; i++) {
5254: ncols_o = bi[i + 1] - bi[i];
5255: ncols_d = ai[i + 1] - ai[i];
5256: /* off-diagonal portion of A */
5257: for (jo = 0; jo < ncols_o; jo++) {
5258: col = cmap[*bj];
5259: if (col >= cstart) break;
5260: cj[k] = col;
5261: bj++;
5262: ca[k++] = *ba++;
5263: }
5264: /* diagonal portion of A */
5265: for (j = 0; j < ncols_d; j++) {
5266: cj[k] = cstart + *aj++;
5267: ca[k++] = *aa++;
5268: }
5269: /* off-diagonal portion of A */
5270: for (j = jo; j < ncols_o; j++) {
5271: cj[k] = cmap[*bj++];
5272: ca[k++] = *ba++;
5273: }
5274: }
5275: /* put together the new matrix */
5276: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5277: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5278: /* Since these are PETSc arrays, change flags to free them as necessary. */
5279: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5280: mat->free_a = PETSC_TRUE;
5281: mat->free_ij = PETSC_TRUE;
5282: mat->nonew = 0;
5283: } else if (scall == MAT_REUSE_MATRIX) {
5284: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5285: ci = mat->i;
5286: cj = mat->j;
5287: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5288: for (i = 0; i < am; i++) {
5289: /* off-diagonal portion of A */
5290: ncols_o = bi[i + 1] - bi[i];
5291: for (jo = 0; jo < ncols_o; jo++) {
5292: col = cmap[*bj];
5293: if (col >= cstart) break;
5294: *cam++ = *ba++;
5295: bj++;
5296: }
5297: /* diagonal portion of A */
5298: ncols_d = ai[i + 1] - ai[i];
5299: for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5300: /* off-diagonal portion of A */
5301: for (j = jo; j < ncols_o; j++) {
5302: *cam++ = *ba++;
5303: bj++;
5304: }
5305: }
5306: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5307: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5308: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5309: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5310: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5311: PetscFunctionReturn(PETSC_SUCCESS);
5312: }
5314: /*@
5315: MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5316: mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and offdiagonal part
5318: Not Collective
5320: Input Parameters:
5321: + A - the matrix
5322: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5324: Output Parameters:
5325: + glob - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5326: - A_loc - the local sequential matrix generated
5328: Level: developer
5330: Note:
5331: This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5332: part, then those associated with the off diagonal part (in its local ordering)
5334: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5335: @*/
5336: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5337: {
5338: Mat Ao, Ad;
5339: const PetscInt *cmap;
5340: PetscMPIInt size;
5341: PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);
5343: PetscFunctionBegin;
5344: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5345: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5346: if (size == 1) {
5347: if (scall == MAT_INITIAL_MATRIX) {
5348: PetscCall(PetscObjectReference((PetscObject)Ad));
5349: *A_loc = Ad;
5350: } else if (scall == MAT_REUSE_MATRIX) {
5351: PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5352: }
5353: if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5354: PetscFunctionReturn(PETSC_SUCCESS);
5355: }
5356: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5357: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5358: if (f) {
5359: PetscCall((*f)(A, scall, glob, A_loc));
5360: } else {
5361: Mat_SeqAIJ *a = (Mat_SeqAIJ *)Ad->data;
5362: Mat_SeqAIJ *b = (Mat_SeqAIJ *)Ao->data;
5363: Mat_SeqAIJ *c;
5364: PetscInt *ai = a->i, *aj = a->j;
5365: PetscInt *bi = b->i, *bj = b->j;
5366: PetscInt *ci, *cj;
5367: const PetscScalar *aa, *ba;
5368: PetscScalar *ca;
5369: PetscInt i, j, am, dn, on;
5371: PetscCall(MatGetLocalSize(Ad, &am, &dn));
5372: PetscCall(MatGetLocalSize(Ao, NULL, &on));
5373: PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5374: PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5375: if (scall == MAT_INITIAL_MATRIX) {
5376: PetscInt k;
5377: PetscCall(PetscMalloc1(1 + am, &ci));
5378: PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5379: PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5380: ci[0] = 0;
5381: for (i = 0, k = 0; i < am; i++) {
5382: const PetscInt ncols_o = bi[i + 1] - bi[i];
5383: const PetscInt ncols_d = ai[i + 1] - ai[i];
5384: ci[i + 1] = ci[i] + ncols_o + ncols_d;
5385: /* diagonal portion of A */
5386: for (j = 0; j < ncols_d; j++, k++) {
5387: cj[k] = *aj++;
5388: ca[k] = *aa++;
5389: }
5390: /* off-diagonal portion of A */
5391: for (j = 0; j < ncols_o; j++, k++) {
5392: cj[k] = dn + *bj++;
5393: ca[k] = *ba++;
5394: }
5395: }
5396: /* put together the new matrix */
5397: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5398: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5399: /* Since these are PETSc arrays, change flags to free them as necessary. */
5400: c = (Mat_SeqAIJ *)(*A_loc)->data;
5401: c->free_a = PETSC_TRUE;
5402: c->free_ij = PETSC_TRUE;
5403: c->nonew = 0;
5404: PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5405: } else if (scall == MAT_REUSE_MATRIX) {
5406: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5407: for (i = 0; i < am; i++) {
5408: const PetscInt ncols_d = ai[i + 1] - ai[i];
5409: const PetscInt ncols_o = bi[i + 1] - bi[i];
5410: /* diagonal portion of A */
5411: for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5412: /* off-diagonal portion of A */
5413: for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5414: }
5415: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5416: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5417: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5418: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5419: if (glob) {
5420: PetscInt cst, *gidx;
5422: PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5423: PetscCall(PetscMalloc1(dn + on, &gidx));
5424: for (i = 0; i < dn; i++) gidx[i] = cst + i;
5425: for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5426: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5427: }
5428: }
5429: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5430: PetscFunctionReturn(PETSC_SUCCESS);
5431: }
5433: /*@C
5434: MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns
5436: Not Collective
5438: Input Parameters:
5439: + A - the matrix
5440: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5441: . row - index set of rows to extract (or `NULL`)
5442: - col - index set of columns to extract (or `NULL`)
5444: Output Parameter:
5445: . A_loc - the local sequential matrix generated
5447: Level: developer
5449: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5450: @*/
5451: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5452: {
5453: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5454: PetscInt i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5455: IS isrowa, iscola;
5456: Mat *aloc;
5457: PetscBool match;
5459: PetscFunctionBegin;
5460: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5461: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5462: PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5463: if (!row) {
5464: start = A->rmap->rstart;
5465: end = A->rmap->rend;
5466: PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5467: } else {
5468: isrowa = *row;
5469: }
5470: if (!col) {
5471: start = A->cmap->rstart;
5472: cmap = a->garray;
5473: nzA = a->A->cmap->n;
5474: nzB = a->B->cmap->n;
5475: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5476: ncols = 0;
5477: for (i = 0; i < nzB; i++) {
5478: if (cmap[i] < start) idx[ncols++] = cmap[i];
5479: else break;
5480: }
5481: imark = i;
5482: for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5483: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5484: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5485: } else {
5486: iscola = *col;
5487: }
5488: if (scall != MAT_INITIAL_MATRIX) {
5489: PetscCall(PetscMalloc1(1, &aloc));
5490: aloc[0] = *A_loc;
5491: }
5492: PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5493: if (!col) { /* attach global id of condensed columns */
5494: PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5495: }
5496: *A_loc = aloc[0];
5497: PetscCall(PetscFree(aloc));
5498: if (!row) PetscCall(ISDestroy(&isrowa));
5499: if (!col) PetscCall(ISDestroy(&iscola));
5500: PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5501: PetscFunctionReturn(PETSC_SUCCESS);
5502: }
5504: /*
5505: * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5506: * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5507: * on a global size.
5508: * */
5509: PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5510: {
5511: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
5512: Mat_SeqAIJ *pd = (Mat_SeqAIJ *)(p->A)->data, *po = (Mat_SeqAIJ *)(p->B)->data, *p_oth;
5513: PetscInt plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5514: PetscMPIInt owner;
5515: PetscSFNode *iremote, *oiremote;
5516: const PetscInt *lrowindices;
5517: PetscSF sf, osf;
5518: PetscInt pcstart, *roffsets, *loffsets, *pnnz, j;
5519: PetscInt ontotalcols, dntotalcols, ntotalcols, nout;
5520: MPI_Comm comm;
5521: ISLocalToGlobalMapping mapping;
5522: const PetscScalar *pd_a, *po_a;
5524: PetscFunctionBegin;
5525: PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5526: /* plocalsize is the number of roots
5527: * nrows is the number of leaves
5528: * */
5529: PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5530: PetscCall(ISGetLocalSize(rows, &nrows));
5531: PetscCall(PetscCalloc1(nrows, &iremote));
5532: PetscCall(ISGetIndices(rows, &lrowindices));
5533: for (i = 0; i < nrows; i++) {
5534: /* Find a remote index and an owner for a row
5535: * The row could be local or remote
5536: * */
5537: owner = 0;
5538: lidx = 0;
5539: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5540: iremote[i].index = lidx;
5541: iremote[i].rank = owner;
5542: }
5543: /* Create SF to communicate how many nonzero columns for each row */
5544: PetscCall(PetscSFCreate(comm, &sf));
5545: /* SF will figure out the number of nonzero colunms for each row, and their
5546: * offsets
5547: * */
5548: PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5549: PetscCall(PetscSFSetFromOptions(sf));
5550: PetscCall(PetscSFSetUp(sf));
5552: PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5553: PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5554: PetscCall(PetscCalloc1(nrows, &pnnz));
5555: roffsets[0] = 0;
5556: roffsets[1] = 0;
5557: for (i = 0; i < plocalsize; i++) {
5558: /* diag */
5559: nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5560: /* off diag */
5561: nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5562: /* compute offsets so that we relative location for each row */
5563: roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5564: roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5565: }
5566: PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5567: PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5568: /* 'r' means root, and 'l' means leaf */
5569: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5570: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5571: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5572: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5573: PetscCall(PetscSFDestroy(&sf));
5574: PetscCall(PetscFree(roffsets));
5575: PetscCall(PetscFree(nrcols));
5576: dntotalcols = 0;
5577: ontotalcols = 0;
5578: ncol = 0;
5579: for (i = 0; i < nrows; i++) {
5580: pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5581: ncol = PetscMax(pnnz[i], ncol);
5582: /* diag */
5583: dntotalcols += nlcols[i * 2 + 0];
5584: /* off diag */
5585: ontotalcols += nlcols[i * 2 + 1];
5586: }
5587: /* We do not need to figure the right number of columns
5588: * since all the calculations will be done by going through the raw data
5589: * */
5590: PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5591: PetscCall(MatSetUp(*P_oth));
5592: PetscCall(PetscFree(pnnz));
5593: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5594: /* diag */
5595: PetscCall(PetscCalloc1(dntotalcols, &iremote));
5596: /* off diag */
5597: PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5598: /* diag */
5599: PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5600: /* off diag */
5601: PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5602: dntotalcols = 0;
5603: ontotalcols = 0;
5604: ntotalcols = 0;
5605: for (i = 0; i < nrows; i++) {
5606: owner = 0;
5607: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5608: /* Set iremote for diag matrix */
5609: for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5610: iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5611: iremote[dntotalcols].rank = owner;
5612: /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5613: ilocal[dntotalcols++] = ntotalcols++;
5614: }
5615: /* off diag */
5616: for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5617: oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5618: oiremote[ontotalcols].rank = owner;
5619: oilocal[ontotalcols++] = ntotalcols++;
5620: }
5621: }
5622: PetscCall(ISRestoreIndices(rows, &lrowindices));
5623: PetscCall(PetscFree(loffsets));
5624: PetscCall(PetscFree(nlcols));
5625: PetscCall(PetscSFCreate(comm, &sf));
5626: /* P serves as roots and P_oth is leaves
5627: * Diag matrix
5628: * */
5629: PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5630: PetscCall(PetscSFSetFromOptions(sf));
5631: PetscCall(PetscSFSetUp(sf));
5633: PetscCall(PetscSFCreate(comm, &osf));
5634: /* Off diag */
5635: PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5636: PetscCall(PetscSFSetFromOptions(osf));
5637: PetscCall(PetscSFSetUp(osf));
5638: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5639: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5640: /* We operate on the matrix internal data for saving memory */
5641: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5642: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5643: PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5644: /* Convert to global indices for diag matrix */
5645: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5646: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5647: /* We want P_oth store global indices */
5648: PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5649: /* Use memory scalable approach */
5650: PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5651: PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5652: PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5653: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5654: /* Convert back to local indices */
5655: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5656: PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5657: nout = 0;
5658: PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5659: PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5660: PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5661: /* Exchange values */
5662: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5663: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5664: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5665: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5666: /* Stop PETSc from shrinking memory */
5667: for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5668: PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5669: PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5670: /* Attach PetscSF objects to P_oth so that we can reuse it later */
5671: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5672: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5673: PetscCall(PetscSFDestroy(&sf));
5674: PetscCall(PetscSFDestroy(&osf));
5675: PetscFunctionReturn(PETSC_SUCCESS);
5676: }
5678: /*
5679: * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5680: * This supports MPIAIJ and MAIJ
5681: * */
5682: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5683: {
5684: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5685: Mat_SeqAIJ *p_oth;
5686: IS rows, map;
5687: PetscHMapI hamp;
5688: PetscInt i, htsize, *rowindices, off, *mapping, key, count;
5689: MPI_Comm comm;
5690: PetscSF sf, osf;
5691: PetscBool has;
5693: PetscFunctionBegin;
5694: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5695: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5696: /* If it is the first time, create an index set of off-diag nonzero columns of A,
5697: * and then create a submatrix (that often is an overlapping matrix)
5698: * */
5699: if (reuse == MAT_INITIAL_MATRIX) {
5700: /* Use a hash table to figure out unique keys */
5701: PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5702: PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5703: count = 0;
5704: /* Assume that a->g is sorted, otherwise the following does not make sense */
5705: for (i = 0; i < a->B->cmap->n; i++) {
5706: key = a->garray[i] / dof;
5707: PetscCall(PetscHMapIHas(hamp, key, &has));
5708: if (!has) {
5709: mapping[i] = count;
5710: PetscCall(PetscHMapISet(hamp, key, count++));
5711: } else {
5712: /* Current 'i' has the same value the previous step */
5713: mapping[i] = count - 1;
5714: }
5715: }
5716: PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5717: PetscCall(PetscHMapIGetSize(hamp, &htsize));
5718: PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5719: PetscCall(PetscCalloc1(htsize, &rowindices));
5720: off = 0;
5721: PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5722: PetscCall(PetscHMapIDestroy(&hamp));
5723: PetscCall(PetscSortInt(htsize, rowindices));
5724: PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5725: /* In case, the matrix was already created but users want to recreate the matrix */
5726: PetscCall(MatDestroy(P_oth));
5727: PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5728: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5729: PetscCall(ISDestroy(&map));
5730: PetscCall(ISDestroy(&rows));
5731: } else if (reuse == MAT_REUSE_MATRIX) {
5732: /* If matrix was already created, we simply update values using SF objects
5733: * that as attached to the matrix earlier.
5734: */
5735: const PetscScalar *pd_a, *po_a;
5737: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5738: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5739: PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5740: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5741: /* Update values in place */
5742: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5743: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5744: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5745: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5746: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5747: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5748: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5749: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5750: } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5751: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5752: PetscFunctionReturn(PETSC_SUCCESS);
5753: }
5755: /*@C
5756: MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`
5758: Collective
5760: Input Parameters:
5761: + A - the first matrix in `MATMPIAIJ` format
5762: . B - the second matrix in `MATMPIAIJ` format
5763: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5765: Output Parameters:
5766: + rowb - On input index sets of rows of B to extract (or `NULL`), modified on output
5767: . colb - On input index sets of columns of B to extract (or `NULL`), modified on output
5768: - B_seq - the sequential matrix generated
5770: Level: developer
5772: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5773: @*/
5774: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5775: {
5776: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5777: PetscInt *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5778: IS isrowb, iscolb;
5779: Mat *bseq = NULL;
5781: PetscFunctionBegin;
5782: 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 ")",
5783: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5784: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));
5786: if (scall == MAT_INITIAL_MATRIX) {
5787: start = A->cmap->rstart;
5788: cmap = a->garray;
5789: nzA = a->A->cmap->n;
5790: nzB = a->B->cmap->n;
5791: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5792: ncols = 0;
5793: for (i = 0; i < nzB; i++) { /* row < local row index */
5794: if (cmap[i] < start) idx[ncols++] = cmap[i];
5795: else break;
5796: }
5797: imark = i;
5798: for (i = 0; i < nzA; i++) idx[ncols++] = start + i; /* local rows */
5799: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5800: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5801: PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5802: } else {
5803: PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5804: isrowb = *rowb;
5805: iscolb = *colb;
5806: PetscCall(PetscMalloc1(1, &bseq));
5807: bseq[0] = *B_seq;
5808: }
5809: PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5810: *B_seq = bseq[0];
5811: PetscCall(PetscFree(bseq));
5812: if (!rowb) {
5813: PetscCall(ISDestroy(&isrowb));
5814: } else {
5815: *rowb = isrowb;
5816: }
5817: if (!colb) {
5818: PetscCall(ISDestroy(&iscolb));
5819: } else {
5820: *colb = iscolb;
5821: }
5822: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5823: PetscFunctionReturn(PETSC_SUCCESS);
5824: }
5826: /*
5827: MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5828: of the OFF-DIAGONAL portion of local A
5830: Collective
5832: Input Parameters:
5833: + A,B - the matrices in `MATMPIAIJ` format
5834: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5836: Output Parameter:
5837: + startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5838: . startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5839: . bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5840: - B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N
5842: Developer Note:
5843: This directly accesses information inside the VecScatter associated with the matrix-vector product
5844: for this matrix. This is not desirable..
5846: Level: developer
5848: */
5849: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5850: {
5851: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5852: Mat_SeqAIJ *b_oth;
5853: VecScatter ctx;
5854: MPI_Comm comm;
5855: const PetscMPIInt *rprocs, *sprocs;
5856: const PetscInt *srow, *rstarts, *sstarts;
5857: PetscInt *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5858: PetscInt i, j, k = 0, l, ll, nrecvs, nsends, nrows, *rstartsj = NULL, *sstartsj, len;
5859: PetscScalar *b_otha, *bufa, *bufA, *vals = NULL;
5860: MPI_Request *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5861: PetscMPIInt size, tag, rank, nreqs;
5863: PetscFunctionBegin;
5864: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5865: PetscCallMPI(MPI_Comm_size(comm, &size));
5867: 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 ")",
5868: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5869: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5870: PetscCallMPI(MPI_Comm_rank(comm, &rank));
5872: if (size == 1) {
5873: startsj_s = NULL;
5874: bufa_ptr = NULL;
5875: *B_oth = NULL;
5876: PetscFunctionReturn(PETSC_SUCCESS);
5877: }
5879: ctx = a->Mvctx;
5880: tag = ((PetscObject)ctx)->tag;
5882: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5883: /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5884: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5885: PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5886: PetscCall(PetscMalloc1(nreqs, &reqs));
5887: rwaits = reqs;
5888: swaits = reqs + nrecvs;
5890: if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5891: if (scall == MAT_INITIAL_MATRIX) {
5892: /* i-array */
5893: /* post receives */
5894: if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5895: for (i = 0; i < nrecvs; i++) {
5896: rowlen = rvalues + rstarts[i] * rbs;
5897: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5898: PetscCallMPI(MPI_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5899: }
5901: /* pack the outgoing message */
5902: PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));
5904: sstartsj[0] = 0;
5905: rstartsj[0] = 0;
5906: len = 0; /* total length of j or a array to be sent */
5907: if (nsends) {
5908: k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5909: PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5910: }
5911: for (i = 0; i < nsends; i++) {
5912: rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5913: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5914: for (j = 0; j < nrows; j++) {
5915: row = srow[k] + B->rmap->range[rank]; /* global row idx */
5916: for (l = 0; l < sbs; l++) {
5917: PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */
5919: rowlen[j * sbs + l] = ncols;
5921: len += ncols;
5922: PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5923: }
5924: k++;
5925: }
5926: PetscCallMPI(MPI_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));
5928: sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5929: }
5930: /* recvs and sends of i-array are completed */
5931: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5932: PetscCall(PetscFree(svalues));
5934: /* allocate buffers for sending j and a arrays */
5935: PetscCall(PetscMalloc1(len + 1, &bufj));
5936: PetscCall(PetscMalloc1(len + 1, &bufa));
5938: /* create i-array of B_oth */
5939: PetscCall(PetscMalloc1(aBn + 2, &b_othi));
5941: b_othi[0] = 0;
5942: len = 0; /* total length of j or a array to be received */
5943: k = 0;
5944: for (i = 0; i < nrecvs; i++) {
5945: rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5946: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5947: for (j = 0; j < nrows; j++) {
5948: b_othi[k + 1] = b_othi[k] + rowlen[j];
5949: PetscCall(PetscIntSumError(rowlen[j], len, &len));
5950: k++;
5951: }
5952: rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5953: }
5954: PetscCall(PetscFree(rvalues));
5956: /* allocate space for j and a arrays of B_oth */
5957: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_othj));
5958: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_otha));
5960: /* j-array */
5961: /* post receives of j-array */
5962: for (i = 0; i < nrecvs; i++) {
5963: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5964: PetscCallMPI(MPI_Irecv(b_othj + rstartsj[i], nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5965: }
5967: /* pack the outgoing message j-array */
5968: if (nsends) k = sstarts[0];
5969: for (i = 0; i < nsends; i++) {
5970: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5971: bufJ = bufj + sstartsj[i];
5972: for (j = 0; j < nrows; j++) {
5973: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5974: for (ll = 0; ll < sbs; ll++) {
5975: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5976: for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5977: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5978: }
5979: }
5980: PetscCallMPI(MPI_Isend(bufj + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5981: }
5983: /* recvs and sends of j-array are completed */
5984: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5985: } else if (scall == MAT_REUSE_MATRIX) {
5986: sstartsj = *startsj_s;
5987: rstartsj = *startsj_r;
5988: bufa = *bufa_ptr;
5989: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
5990: PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5991: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");
5993: /* a-array */
5994: /* post receives of a-array */
5995: for (i = 0; i < nrecvs; i++) {
5996: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5997: PetscCallMPI(MPI_Irecv(b_otha + rstartsj[i], nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
5998: }
6000: /* pack the outgoing message a-array */
6001: if (nsends) k = sstarts[0];
6002: for (i = 0; i < nsends; i++) {
6003: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
6004: bufA = bufa + sstartsj[i];
6005: for (j = 0; j < nrows; j++) {
6006: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
6007: for (ll = 0; ll < sbs; ll++) {
6008: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
6009: for (l = 0; l < ncols; l++) *bufA++ = vals[l];
6010: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
6011: }
6012: }
6013: PetscCallMPI(MPI_Isend(bufa + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
6014: }
6015: /* recvs and sends of a-array are completed */
6016: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
6017: PetscCall(PetscFree(reqs));
6019: if (scall == MAT_INITIAL_MATRIX) {
6020: /* put together the new matrix */
6021: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));
6023: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
6024: /* Since these are PETSc arrays, change flags to free them as necessary. */
6025: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
6026: b_oth->free_a = PETSC_TRUE;
6027: b_oth->free_ij = PETSC_TRUE;
6028: b_oth->nonew = 0;
6030: PetscCall(PetscFree(bufj));
6031: if (!startsj_s || !bufa_ptr) {
6032: PetscCall(PetscFree2(sstartsj, rstartsj));
6033: PetscCall(PetscFree(bufa_ptr));
6034: } else {
6035: *startsj_s = sstartsj;
6036: *startsj_r = rstartsj;
6037: *bufa_ptr = bufa;
6038: }
6039: } else if (scall == MAT_REUSE_MATRIX) {
6040: PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
6041: }
6043: PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
6044: PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
6045: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
6046: PetscFunctionReturn(PETSC_SUCCESS);
6047: }
6049: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
6050: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
6051: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
6052: #if defined(PETSC_HAVE_MKL_SPARSE)
6053: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
6054: #endif
6055: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
6056: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
6057: #if defined(PETSC_HAVE_ELEMENTAL)
6058: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
6059: #endif
6060: #if defined(PETSC_HAVE_SCALAPACK)
6061: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
6062: #endif
6063: #if defined(PETSC_HAVE_HYPRE)
6064: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
6065: #endif
6066: #if defined(PETSC_HAVE_CUDA)
6067: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
6068: #endif
6069: #if defined(PETSC_HAVE_HIP)
6070: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6071: #endif
6072: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6073: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6074: #endif
6075: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6076: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6077: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
6079: /*
6080: Computes (B'*A')' since computing B*A directly is untenable
6082: n p p
6083: [ ] [ ] [ ]
6084: m [ A ] * n [ B ] = m [ C ]
6085: [ ] [ ] [ ]
6087: */
6088: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6089: {
6090: Mat At, Bt, Ct;
6092: PetscFunctionBegin;
6093: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6094: PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6095: PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_DEFAULT, &Ct));
6096: PetscCall(MatDestroy(&At));
6097: PetscCall(MatDestroy(&Bt));
6098: PetscCall(MatTransposeSetPrecursor(Ct, C));
6099: PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6100: PetscCall(MatDestroy(&Ct));
6101: PetscFunctionReturn(PETSC_SUCCESS);
6102: }
6104: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6105: {
6106: PetscBool cisdense;
6108: PetscFunctionBegin;
6109: 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);
6110: PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6111: PetscCall(MatSetBlockSizesFromMats(C, A, B));
6112: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6113: if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6114: PetscCall(MatSetUp(C));
6116: C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6117: PetscFunctionReturn(PETSC_SUCCESS);
6118: }
6120: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6121: {
6122: Mat_Product *product = C->product;
6123: Mat A = product->A, B = product->B;
6125: PetscFunctionBegin;
6126: 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 ")",
6127: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6128: C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6129: C->ops->productsymbolic = MatProductSymbolic_AB;
6130: PetscFunctionReturn(PETSC_SUCCESS);
6131: }
6133: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6134: {
6135: Mat_Product *product = C->product;
6137: PetscFunctionBegin;
6138: if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6139: PetscFunctionReturn(PETSC_SUCCESS);
6140: }
6142: /*
6143: Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix
6145: Input Parameters:
6147: j1,rowBegin1,rowEnd1,perm1,jmap1: describe the first set of nonzeros (Set1)
6148: j2,rowBegin2,rowEnd2,perm2,jmap2: describe the second set of nonzeros (Set2)
6150: mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat
6152: For Set1, j1[] contains column indices of the nonzeros.
6153: 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
6154: respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6155: but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.
6157: Similar for Set2.
6159: This routine merges the two sets of nonzeros row by row and removes repeats.
6161: Output Parameters: (memory is allocated by the caller)
6163: i[],j[]: the CSR of the merged matrix, which has m rows.
6164: imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6165: imap2[]: similar to imap1[], but for Set2.
6166: Note we order nonzeros row-by-row and from left to right.
6167: */
6168: 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[])
6169: {
6170: PetscInt r, m; /* Row index of mat */
6171: PetscCount t, t1, t2, b1, e1, b2, e2;
6173: PetscFunctionBegin;
6174: PetscCall(MatGetLocalSize(mat, &m, NULL));
6175: t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6176: i[0] = 0;
6177: for (r = 0; r < m; r++) { /* Do row by row merging */
6178: b1 = rowBegin1[r];
6179: e1 = rowEnd1[r];
6180: b2 = rowBegin2[r];
6181: e2 = rowEnd2[r];
6182: while (b1 < e1 && b2 < e2) {
6183: if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6184: j[t] = j1[b1];
6185: imap1[t1] = t;
6186: imap2[t2] = t;
6187: b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6188: b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6189: t1++;
6190: t2++;
6191: t++;
6192: } else if (j1[b1] < j2[b2]) {
6193: j[t] = j1[b1];
6194: imap1[t1] = t;
6195: b1 += jmap1[t1 + 1] - jmap1[t1];
6196: t1++;
6197: t++;
6198: } else {
6199: j[t] = j2[b2];
6200: imap2[t2] = t;
6201: b2 += jmap2[t2 + 1] - jmap2[t2];
6202: t2++;
6203: t++;
6204: }
6205: }
6206: /* Merge the remaining in either j1[] or j2[] */
6207: while (b1 < e1) {
6208: j[t] = j1[b1];
6209: imap1[t1] = t;
6210: b1 += jmap1[t1 + 1] - jmap1[t1];
6211: t1++;
6212: t++;
6213: }
6214: while (b2 < e2) {
6215: j[t] = j2[b2];
6216: imap2[t2] = t;
6217: b2 += jmap2[t2 + 1] - jmap2[t2];
6218: t2++;
6219: t++;
6220: }
6221: i[r + 1] = t;
6222: }
6223: PetscFunctionReturn(PETSC_SUCCESS);
6224: }
6226: /*
6227: Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block
6229: Input Parameters:
6230: mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6231: 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[]
6232: respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.
6234: i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6235: i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.
6237: Output Parameters:
6238: j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6239: rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6240: 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,
6241: and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.
6243: Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6244: Atot: number of entries belonging to the diagonal block.
6245: Annz: number of unique nonzeros belonging to the diagonal block.
6246: Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6247: repeats (i.e., same 'i,j' pair).
6248: Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6249: is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.
6251: Atot: number of entries belonging to the diagonal block
6252: Annz: number of unique nonzeros belonging to the diagonal block.
6254: Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.
6256: Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6257: */
6258: 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_)
6259: {
6260: PetscInt cstart, cend, rstart, rend, row, col;
6261: PetscCount Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6262: PetscCount Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6263: PetscCount k, m, p, q, r, s, mid;
6264: PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;
6266: PetscFunctionBegin;
6267: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6268: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6269: m = rend - rstart;
6271: for (k = 0; k < n; k++) {
6272: if (i[k] >= 0) break;
6273: } /* Skip negative rows */
6275: /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6276: fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6277: */
6278: while (k < n) {
6279: row = i[k];
6280: /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6281: for (s = k; s < n; s++)
6282: if (i[s] != row) break;
6283: for (p = k; p < s; p++) {
6284: if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_MAX_INT; /* Shift diag columns to range of [-PETSC_MAX_INT, -1] */
6285: 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]);
6286: }
6287: PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6288: PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6289: rowBegin[row - rstart] = k;
6290: rowMid[row - rstart] = mid;
6291: rowEnd[row - rstart] = s;
6293: /* Count nonzeros of this diag/offdiag row, which might have repeats */
6294: Atot += mid - k;
6295: Btot += s - mid;
6297: /* Count unique nonzeros of this diag/offdiag row */
6298: for (p = k; p < mid;) {
6299: col = j[p];
6300: do {
6301: j[p] += PETSC_MAX_INT;
6302: p++;
6303: } while (p < mid && j[p] == col); /* Revert the modified diagonal indices */
6304: Annz++;
6305: }
6307: for (p = mid; p < s;) {
6308: col = j[p];
6309: do {
6310: p++;
6311: } while (p < s && j[p] == col);
6312: Bnnz++;
6313: }
6314: k = s;
6315: }
6317: /* Allocation according to Atot, Btot, Annz, Bnnz */
6318: PetscCall(PetscMalloc1(Atot, &Aperm));
6319: PetscCall(PetscMalloc1(Btot, &Bperm));
6320: PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6321: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));
6323: /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6324: Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6325: for (r = 0; r < m; r++) {
6326: k = rowBegin[r];
6327: mid = rowMid[r];
6328: s = rowEnd[r];
6329: PetscCall(PetscArraycpy(Aperm + Atot, perm + k, mid - k));
6330: PetscCall(PetscArraycpy(Bperm + Btot, perm + mid, s - mid));
6331: Atot += mid - k;
6332: Btot += s - mid;
6334: /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6335: for (p = k; p < mid;) {
6336: col = j[p];
6337: q = p;
6338: do {
6339: p++;
6340: } while (p < mid && j[p] == col);
6341: Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6342: Annz++;
6343: }
6345: for (p = mid; p < s;) {
6346: col = j[p];
6347: q = p;
6348: do {
6349: p++;
6350: } while (p < s && j[p] == col);
6351: Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6352: Bnnz++;
6353: }
6354: }
6355: /* Output */
6356: *Aperm_ = Aperm;
6357: *Annz_ = Annz;
6358: *Atot_ = Atot;
6359: *Ajmap_ = Ajmap;
6360: *Bperm_ = Bperm;
6361: *Bnnz_ = Bnnz;
6362: *Btot_ = Btot;
6363: *Bjmap_ = Bjmap;
6364: PetscFunctionReturn(PETSC_SUCCESS);
6365: }
6367: /*
6368: Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix
6370: Input Parameters:
6371: nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6372: nnz: number of unique nonzeros in the merged matrix
6373: imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6374: jmap[nnz1+1]: i-th nonzeron in the set has jmap[i+1] - jmap[i] repeats in the set
6376: Output Parameter: (memory is allocated by the caller)
6377: jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set
6379: Example:
6380: nnz1 = 4
6381: nnz = 6
6382: imap = [1,3,4,5]
6383: jmap = [0,3,5,6,7]
6384: then,
6385: jmap_new = [0,0,3,3,5,6,7]
6386: */
6387: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6388: {
6389: PetscCount k, p;
6391: PetscFunctionBegin;
6392: jmap_new[0] = 0;
6393: p = nnz; /* p loops over jmap_new[] backwards */
6394: for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6395: for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6396: }
6397: for (; p >= 0; p--) jmap_new[p] = jmap[0];
6398: PetscFunctionReturn(PETSC_SUCCESS);
6399: }
6401: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6402: {
6403: MPI_Comm comm;
6404: PetscMPIInt rank, size;
6405: PetscInt m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6406: PetscCount k, p, q, rem; /* Loop variables over coo arrays */
6407: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6409: PetscFunctionBegin;
6410: PetscCall(PetscFree(mpiaij->garray));
6411: PetscCall(VecDestroy(&mpiaij->lvec));
6412: #if defined(PETSC_USE_CTABLE)
6413: PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6414: #else
6415: PetscCall(PetscFree(mpiaij->colmap));
6416: #endif
6417: PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6418: mat->assembled = PETSC_FALSE;
6419: mat->was_assembled = PETSC_FALSE;
6420: PetscCall(MatResetPreallocationCOO_MPIAIJ(mat));
6422: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6423: PetscCallMPI(MPI_Comm_size(comm, &size));
6424: PetscCallMPI(MPI_Comm_rank(comm, &rank));
6425: PetscCall(PetscLayoutSetUp(mat->rmap));
6426: PetscCall(PetscLayoutSetUp(mat->cmap));
6427: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6428: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6429: PetscCall(MatGetLocalSize(mat, &m, &n));
6430: PetscCall(MatGetSize(mat, &M, &N));
6432: /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6433: /* entries come first, then local rows, then remote rows. */
6434: PetscCount n1 = coo_n, *perm1;
6435: PetscInt *i1 = coo_i, *j1 = coo_j;
6437: PetscCall(PetscMalloc1(n1, &perm1));
6438: for (k = 0; k < n1; k++) perm1[k] = k;
6440: /* Manipulate indices so that entries with negative row or col indices will have smallest
6441: row indices, local entries will have greater but negative row indices, and remote entries
6442: will have positive row indices.
6443: */
6444: for (k = 0; k < n1; k++) {
6445: if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_MIN_INT; /* e.g., -2^31, minimal to move them ahead */
6446: else if (i1[k] >= rstart && i1[k] < rend) i1[k] -= PETSC_MAX_INT; /* e.g., minus 2^31-1 to shift local rows to range of [-PETSC_MAX_INT, -1] */
6447: else {
6448: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6449: if (mpiaij->donotstash) i1[k] = PETSC_MIN_INT; /* Ignore offproc entries as if they had negative indices */
6450: }
6451: }
6453: /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6454: PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));
6455: for (k = 0; k < n1; k++) {
6456: if (i1[k] > PETSC_MIN_INT) break;
6457: } /* Advance k to the first entry we need to take care of */
6458: PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_MAX_INT, &rem)); /* rem is upper bound of the last local row */
6459: for (; k < rem; k++) i1[k] += PETSC_MAX_INT; /* Revert row indices of local rows*/
6461: /* Split local rows into diag/offdiag portions */
6462: PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6463: PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1, *Cperm1;
6464: PetscCount Annz1, Bnnz1, Atot1, Btot1;
6466: PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6467: PetscCall(PetscMalloc1(n1 - rem, &Cperm1));
6468: PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6470: /* Send remote rows to their owner */
6471: /* Find which rows should be sent to which remote ranks*/
6472: PetscInt nsend = 0; /* Number of MPI ranks to send data to */
6473: PetscMPIInt *sendto; /* [nsend], storing remote ranks */
6474: PetscInt *nentries; /* [nsend], storing number of entries sent to remote ranks; Assume PetscInt is big enough for this count, and error if not */
6475: const PetscInt *ranges;
6476: PetscInt maxNsend = size >= 128 ? 128 : size; /* Assume max 128 neighbors; realloc when needed */
6478: PetscCall(PetscLayoutGetRanges(mat->rmap, &ranges));
6479: PetscCall(PetscMalloc2(maxNsend, &sendto, maxNsend, &nentries));
6480: for (k = rem; k < n1;) {
6481: PetscMPIInt owner;
6482: PetscInt firstRow, lastRow;
6484: /* Locate a row range */
6485: firstRow = i1[k]; /* first row of this owner */
6486: PetscCall(PetscLayoutFindOwner(mat->rmap, firstRow, &owner));
6487: lastRow = ranges[owner + 1] - 1; /* last row of this owner */
6489: /* Find the first index 'p' in [k,n) with i[p] belonging to next owner */
6490: PetscCall(PetscSortedIntUpperBound(i1, k, n1, lastRow, &p));
6492: /* All entries in [k,p) belong to this remote owner */
6493: if (nsend >= maxNsend) { /* Double the remote ranks arrays if not long enough */
6494: PetscMPIInt *sendto2;
6495: PetscInt *nentries2;
6496: PetscInt maxNsend2 = (maxNsend <= size / 2) ? maxNsend * 2 : size;
6498: PetscCall(PetscMalloc2(maxNsend2, &sendto2, maxNsend2, &nentries2));
6499: PetscCall(PetscArraycpy(sendto2, sendto, maxNsend));
6500: PetscCall(PetscArraycpy(nentries2, nentries2, maxNsend + 1));
6501: PetscCall(PetscFree2(sendto, nentries2));
6502: sendto = sendto2;
6503: nentries = nentries2;
6504: maxNsend = maxNsend2;
6505: }
6506: sendto[nsend] = owner;
6507: nentries[nsend] = p - k;
6508: PetscCall(PetscCountCast(p - k, &nentries[nsend]));
6509: nsend++;
6510: k = p;
6511: }
6513: /* Build 1st SF to know offsets on remote to send data */
6514: PetscSF sf1;
6515: PetscInt nroots = 1, nroots2 = 0;
6516: PetscInt nleaves = nsend, nleaves2 = 0;
6517: PetscInt *offsets;
6518: PetscSFNode *iremote;
6520: PetscCall(PetscSFCreate(comm, &sf1));
6521: PetscCall(PetscMalloc1(nsend, &iremote));
6522: PetscCall(PetscMalloc1(nsend, &offsets));
6523: for (k = 0; k < nsend; k++) {
6524: iremote[k].rank = sendto[k];
6525: iremote[k].index = 0;
6526: nleaves2 += nentries[k];
6527: PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6528: }
6529: PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6530: PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6531: PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6532: PetscCall(PetscSFDestroy(&sf1));
6533: PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT "", nleaves2, n1 - rem);
6535: /* Build 2nd SF to send remote COOs to their owner */
6536: PetscSF sf2;
6537: nroots = nroots2;
6538: nleaves = nleaves2;
6539: PetscCall(PetscSFCreate(comm, &sf2));
6540: PetscCall(PetscSFSetFromOptions(sf2));
6541: PetscCall(PetscMalloc1(nleaves, &iremote));
6542: p = 0;
6543: for (k = 0; k < nsend; k++) {
6544: PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6545: for (q = 0; q < nentries[k]; q++, p++) {
6546: iremote[p].rank = sendto[k];
6547: iremote[p].index = offsets[k] + q;
6548: }
6549: }
6550: PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6552: /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6553: PetscCall(PetscArraycpy(Cperm1, perm1 + rem, n1 - rem));
6555: /* Send the remote COOs to their owner */
6556: PetscInt n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6557: PetscCount *perm2; /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6558: PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6559: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1 + rem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6560: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1 + rem, i2, MPI_REPLACE));
6561: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1 + rem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6562: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1 + rem, j2, MPI_REPLACE));
6564: PetscCall(PetscFree(offsets));
6565: PetscCall(PetscFree2(sendto, nentries));
6567: /* Sort received COOs by row along with the permutation array */
6568: for (k = 0; k < n2; k++) perm2[k] = k;
6569: PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));
6571: /* Split received COOs into diag/offdiag portions */
6572: PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6573: PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6574: PetscCount Annz2, Bnnz2, Atot2, Btot2;
6576: PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6577: PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));
6579: /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6580: PetscInt *Ai, *Bi;
6581: PetscInt *Aj, *Bj;
6583: PetscCall(PetscMalloc1(m + 1, &Ai));
6584: PetscCall(PetscMalloc1(m + 1, &Bi));
6585: PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6586: PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));
6588: PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6589: PetscCall(PetscMalloc1(Annz1, &Aimap1));
6590: PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6591: PetscCall(PetscMalloc1(Annz2, &Aimap2));
6592: PetscCall(PetscMalloc1(Bnnz2, &Bimap2));
6594: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6595: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));
6597: /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we */
6598: /* expect nonzeros in A/B most likely have local contributing entries */
6599: PetscInt Annz = Ai[m];
6600: PetscInt Bnnz = Bi[m];
6601: PetscCount *Ajmap1_new, *Bjmap1_new;
6603: PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6604: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));
6606: PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6607: PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));
6609: PetscCall(PetscFree(Aimap1));
6610: PetscCall(PetscFree(Ajmap1));
6611: PetscCall(PetscFree(Bimap1));
6612: PetscCall(PetscFree(Bjmap1));
6613: PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6614: PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6615: PetscCall(PetscFree(perm1));
6616: PetscCall(PetscFree3(i2, j2, perm2));
6618: Ajmap1 = Ajmap1_new;
6619: Bjmap1 = Bjmap1_new;
6621: /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6622: if (Annz < Annz1 + Annz2) {
6623: PetscInt *Aj_new;
6624: PetscCall(PetscMalloc1(Annz, &Aj_new));
6625: PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6626: PetscCall(PetscFree(Aj));
6627: Aj = Aj_new;
6628: }
6630: if (Bnnz < Bnnz1 + Bnnz2) {
6631: PetscInt *Bj_new;
6632: PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6633: PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6634: PetscCall(PetscFree(Bj));
6635: Bj = Bj_new;
6636: }
6638: /* Create new submatrices for on-process and off-process coupling */
6639: PetscScalar *Aa, *Ba;
6640: MatType rtype;
6641: Mat_SeqAIJ *a, *b;
6642: PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6643: PetscCall(PetscCalloc1(Bnnz, &Ba));
6644: /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6645: if (cstart) {
6646: for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6647: }
6648: PetscCall(MatDestroy(&mpiaij->A));
6649: PetscCall(MatDestroy(&mpiaij->B));
6650: PetscCall(MatGetRootType_Private(mat, &rtype));
6651: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6652: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6653: PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6655: a = (Mat_SeqAIJ *)mpiaij->A->data;
6656: b = (Mat_SeqAIJ *)mpiaij->B->data;
6657: a->singlemalloc = b->singlemalloc = PETSC_FALSE; /* Let newmat own Ai,Aj,Aa,Bi,Bj,Ba */
6658: a->free_a = b->free_a = PETSC_TRUE;
6659: a->free_ij = b->free_ij = PETSC_TRUE;
6661: /* conversion must happen AFTER multiply setup */
6662: PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6663: PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6664: PetscCall(VecDestroy(&mpiaij->lvec));
6665: PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));
6667: mpiaij->coo_n = coo_n;
6668: mpiaij->coo_sf = sf2;
6669: mpiaij->sendlen = nleaves;
6670: mpiaij->recvlen = nroots;
6672: mpiaij->Annz = Annz;
6673: mpiaij->Bnnz = Bnnz;
6675: mpiaij->Annz2 = Annz2;
6676: mpiaij->Bnnz2 = Bnnz2;
6678: mpiaij->Atot1 = Atot1;
6679: mpiaij->Atot2 = Atot2;
6680: mpiaij->Btot1 = Btot1;
6681: mpiaij->Btot2 = Btot2;
6683: mpiaij->Ajmap1 = Ajmap1;
6684: mpiaij->Aperm1 = Aperm1;
6686: mpiaij->Bjmap1 = Bjmap1;
6687: mpiaij->Bperm1 = Bperm1;
6689: mpiaij->Aimap2 = Aimap2;
6690: mpiaij->Ajmap2 = Ajmap2;
6691: mpiaij->Aperm2 = Aperm2;
6693: mpiaij->Bimap2 = Bimap2;
6694: mpiaij->Bjmap2 = Bjmap2;
6695: mpiaij->Bperm2 = Bperm2;
6697: mpiaij->Cperm1 = Cperm1;
6699: /* Allocate in preallocation. If not used, it has zero cost on host */
6700: PetscCall(PetscMalloc2(mpiaij->sendlen, &mpiaij->sendbuf, mpiaij->recvlen, &mpiaij->recvbuf));
6701: PetscFunctionReturn(PETSC_SUCCESS);
6702: }
6704: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6705: {
6706: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6707: Mat A = mpiaij->A, B = mpiaij->B;
6708: PetscCount Annz = mpiaij->Annz, Annz2 = mpiaij->Annz2, Bnnz = mpiaij->Bnnz, Bnnz2 = mpiaij->Bnnz2;
6709: PetscScalar *Aa, *Ba;
6710: PetscScalar *sendbuf = mpiaij->sendbuf;
6711: PetscScalar *recvbuf = mpiaij->recvbuf;
6712: const PetscCount *Ajmap1 = mpiaij->Ajmap1, *Ajmap2 = mpiaij->Ajmap2, *Aimap2 = mpiaij->Aimap2;
6713: const PetscCount *Bjmap1 = mpiaij->Bjmap1, *Bjmap2 = mpiaij->Bjmap2, *Bimap2 = mpiaij->Bimap2;
6714: const PetscCount *Aperm1 = mpiaij->Aperm1, *Aperm2 = mpiaij->Aperm2, *Bperm1 = mpiaij->Bperm1, *Bperm2 = mpiaij->Bperm2;
6715: const PetscCount *Cperm1 = mpiaij->Cperm1;
6717: PetscFunctionBegin;
6718: PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6719: PetscCall(MatSeqAIJGetArray(B, &Ba));
6721: /* Pack entries to be sent to remote */
6722: for (PetscCount i = 0; i < mpiaij->sendlen; i++) sendbuf[i] = v[Cperm1[i]];
6724: /* Send remote entries to their owner and overlap the communication with local computation */
6725: PetscCall(PetscSFReduceWithMemTypeBegin(mpiaij->coo_sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6726: /* Add local entries to A and B */
6727: for (PetscCount i = 0; i < Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6728: PetscScalar sum = 0.0; /* Do partial summation first to improve numerical stability */
6729: for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6730: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6731: }
6732: for (PetscCount i = 0; i < Bnnz; i++) {
6733: PetscScalar sum = 0.0;
6734: for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6735: Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6736: }
6737: PetscCall(PetscSFReduceEnd(mpiaij->coo_sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));
6739: /* Add received remote entries to A and B */
6740: for (PetscCount i = 0; i < Annz2; i++) {
6741: for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6742: }
6743: for (PetscCount i = 0; i < Bnnz2; i++) {
6744: for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6745: }
6746: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6747: PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6748: PetscFunctionReturn(PETSC_SUCCESS);
6749: }
6751: /*MC
6752: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
6754: Options Database Keys:
6755: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`
6757: Level: beginner
6759: Notes:
6760: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
6761: in this case the values associated with the rows and columns one passes in are set to zero
6762: in the matrix
6764: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
6765: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
6767: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6768: M*/
6769: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6770: {
6771: Mat_MPIAIJ *b;
6772: PetscMPIInt size;
6774: PetscFunctionBegin;
6775: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
6777: PetscCall(PetscNew(&b));
6778: B->data = (void *)b;
6779: PetscCall(PetscMemcpy(B->ops, &MatOps_Values, sizeof(struct _MatOps)));
6780: B->assembled = PETSC_FALSE;
6781: B->insertmode = NOT_SET_VALUES;
6782: b->size = size;
6784: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
6786: /* build cache for off array entries formed */
6787: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
6789: b->donotstash = PETSC_FALSE;
6790: b->colmap = NULL;
6791: b->garray = NULL;
6792: b->roworiented = PETSC_TRUE;
6794: /* stuff used for matrix vector multiply */
6795: b->lvec = NULL;
6796: b->Mvctx = NULL;
6798: /* stuff for MatGetRow() */
6799: b->rowindices = NULL;
6800: b->rowvalues = NULL;
6801: b->getrowactive = PETSC_FALSE;
6803: /* flexible pointer used in CUSPARSE classes */
6804: b->spptr = NULL;
6806: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6807: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6808: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6809: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6810: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6811: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6812: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6813: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6814: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6815: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6816: #if defined(PETSC_HAVE_CUDA)
6817: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6818: #endif
6819: #if defined(PETSC_HAVE_HIP)
6820: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6821: #endif
6822: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6823: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6824: #endif
6825: #if defined(PETSC_HAVE_MKL_SPARSE)
6826: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6827: #endif
6828: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6829: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6830: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6831: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6832: #if defined(PETSC_HAVE_ELEMENTAL)
6833: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6834: #endif
6835: #if defined(PETSC_HAVE_SCALAPACK)
6836: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6837: #endif
6838: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6839: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6840: #if defined(PETSC_HAVE_HYPRE)
6841: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6842: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6843: #endif
6844: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6845: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6846: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6847: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6848: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6849: PetscFunctionReturn(PETSC_SUCCESS);
6850: }
6852: /*@C
6853: MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6854: and "off-diagonal" part of the matrix in CSR format.
6856: Collective
6858: Input Parameters:
6859: + comm - MPI communicator
6860: . m - number of local rows (Cannot be `PETSC_DECIDE`)
6861: . n - This value should be the same as the local size used in creating the
6862: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
6863: calculated if `N` is given) For square matrices `n` is almost always `m`.
6864: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6865: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6866: . 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
6867: . j - column indices, which must be local, i.e., based off the start column of the diagonal portion
6868: . a - matrix values
6869: . 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
6870: . oj - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6871: - oa - matrix values
6873: Output Parameter:
6874: . mat - the matrix
6876: Level: advanced
6878: Notes:
6879: The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc. The user
6880: must free the arrays once the matrix has been destroyed and not before.
6882: The `i` and `j` indices are 0 based
6884: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix
6886: This sets local rows and cannot be used to set off-processor values.
6888: Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6889: legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6890: not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
6891: the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
6892: keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
6893: communication if it is known that only local entries will be set.
6895: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
6896: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
6897: @*/
6898: 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)
6899: {
6900: Mat_MPIAIJ *maij;
6902: PetscFunctionBegin;
6903: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
6904: PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
6905: PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
6906: PetscCall(MatCreate(comm, mat));
6907: PetscCall(MatSetSizes(*mat, m, n, M, N));
6908: PetscCall(MatSetType(*mat, MATMPIAIJ));
6909: maij = (Mat_MPIAIJ *)(*mat)->data;
6911: (*mat)->preallocated = PETSC_TRUE;
6913: PetscCall(PetscLayoutSetUp((*mat)->rmap));
6914: PetscCall(PetscLayoutSetUp((*mat)->cmap));
6916: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
6917: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));
6919: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
6920: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
6921: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
6922: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
6923: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
6924: PetscFunctionReturn(PETSC_SUCCESS);
6925: }
6927: typedef struct {
6928: Mat *mp; /* intermediate products */
6929: PetscBool *mptmp; /* is the intermediate product temporary ? */
6930: PetscInt cp; /* number of intermediate products */
6932: /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
6933: PetscInt *startsj_s, *startsj_r;
6934: PetscScalar *bufa;
6935: Mat P_oth;
6937: /* may take advantage of merging product->B */
6938: Mat Bloc; /* B-local by merging diag and off-diag */
6940: /* cusparse does not have support to split between symbolic and numeric phases.
6941: When api_user is true, we don't need to update the numerical values
6942: of the temporary storage */
6943: PetscBool reusesym;
6945: /* support for COO values insertion */
6946: PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
6947: PetscInt **own; /* own[i] points to address of on-process COO indices for Mat mp[i] */
6948: PetscInt **off; /* off[i] points to address of off-process COO indices for Mat mp[i] */
6949: PetscBool hasoffproc; /* if true, have off-process values insertion (i.e. AtB or PtAP) */
6950: PetscSF sf; /* used for non-local values insertion and memory malloc */
6951: PetscMemType mtype;
6953: /* customization */
6954: PetscBool abmerge;
6955: PetscBool P_oth_bind;
6956: } MatMatMPIAIJBACKEND;
6958: PetscErrorCode MatDestroy_MatMatMPIAIJBACKEND(void *data)
6959: {
6960: MatMatMPIAIJBACKEND *mmdata = (MatMatMPIAIJBACKEND *)data;
6961: PetscInt i;
6963: PetscFunctionBegin;
6964: PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
6965: PetscCall(PetscFree(mmdata->bufa));
6966: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
6967: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
6968: PetscCall(MatDestroy(&mmdata->P_oth));
6969: PetscCall(MatDestroy(&mmdata->Bloc));
6970: PetscCall(PetscSFDestroy(&mmdata->sf));
6971: for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
6972: PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
6973: PetscCall(PetscFree(mmdata->own[0]));
6974: PetscCall(PetscFree(mmdata->own));
6975: PetscCall(PetscFree(mmdata->off[0]));
6976: PetscCall(PetscFree(mmdata->off));
6977: PetscCall(PetscFree(mmdata));
6978: PetscFunctionReturn(PETSC_SUCCESS);
6979: }
6981: /* Copy selected n entries with indices in idx[] of A to v[].
6982: If idx is NULL, copy the whole data array of A to v[]
6983: */
6984: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
6985: {
6986: PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);
6988: PetscFunctionBegin;
6989: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
6990: if (f) {
6991: PetscCall((*f)(A, n, idx, v));
6992: } else {
6993: const PetscScalar *vv;
6995: PetscCall(MatSeqAIJGetArrayRead(A, &vv));
6996: if (n && idx) {
6997: PetscScalar *w = v;
6998: const PetscInt *oi = idx;
6999: PetscInt j;
7001: for (j = 0; j < n; j++) *w++ = vv[*oi++];
7002: } else {
7003: PetscCall(PetscArraycpy(v, vv, n));
7004: }
7005: PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7006: }
7007: PetscFunctionReturn(PETSC_SUCCESS);
7008: }
7010: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7011: {
7012: MatMatMPIAIJBACKEND *mmdata;
7013: PetscInt i, n_d, n_o;
7015: PetscFunctionBegin;
7016: MatCheckProduct(C, 1);
7017: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7018: mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7019: if (!mmdata->reusesym) { /* update temporary matrices */
7020: 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));
7021: if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7022: }
7023: mmdata->reusesym = PETSC_FALSE;
7025: for (i = 0; i < mmdata->cp; i++) {
7026: 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]);
7027: PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7028: }
7029: for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7030: PetscInt noff = mmdata->off[i + 1] - mmdata->off[i];
7032: if (mmdata->mptmp[i]) continue;
7033: if (noff) {
7034: PetscInt nown = mmdata->own[i + 1] - mmdata->own[i];
7036: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7037: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7038: n_o += noff;
7039: n_d += nown;
7040: } else {
7041: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;
7043: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7044: n_d += mm->nz;
7045: }
7046: }
7047: if (mmdata->hasoffproc) { /* offprocess insertion */
7048: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7049: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7050: }
7051: PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7052: PetscFunctionReturn(PETSC_SUCCESS);
7053: }
7055: /* Support for Pt * A, A * P, or Pt * A * P */
7056: #define MAX_NUMBER_INTERMEDIATE 4
7057: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7058: {
7059: Mat_Product *product = C->product;
7060: Mat A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7061: Mat_MPIAIJ *a, *p;
7062: MatMatMPIAIJBACKEND *mmdata;
7063: ISLocalToGlobalMapping P_oth_l2g = NULL;
7064: IS glob = NULL;
7065: const char *prefix;
7066: char pprefix[256];
7067: const PetscInt *globidx, *P_oth_idx;
7068: PetscInt i, j, cp, m, n, M, N, *coo_i, *coo_j;
7069: PetscCount ncoo, ncoo_d, ncoo_o, ncoo_oown;
7070: PetscInt cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7071: /* type-0: consecutive, start from 0; type-1: consecutive with */
7072: /* a base offset; type-2: sparse with a local to global map table */
7073: const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE]; /* col/row local to global map array (table) for type-2 map type */
7075: MatProductType ptype;
7076: PetscBool mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7077: PetscMPIInt size;
7079: PetscFunctionBegin;
7080: MatCheckProduct(C, 1);
7081: PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7082: ptype = product->type;
7083: if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7084: ptype = MATPRODUCT_AB;
7085: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7086: }
7087: switch (ptype) {
7088: case MATPRODUCT_AB:
7089: A = product->A;
7090: P = product->B;
7091: m = A->rmap->n;
7092: n = P->cmap->n;
7093: M = A->rmap->N;
7094: N = P->cmap->N;
7095: hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7096: break;
7097: case MATPRODUCT_AtB:
7098: P = product->A;
7099: A = product->B;
7100: m = P->cmap->n;
7101: n = A->cmap->n;
7102: M = P->cmap->N;
7103: N = A->cmap->N;
7104: hasoffproc = PETSC_TRUE;
7105: break;
7106: case MATPRODUCT_PtAP:
7107: A = product->A;
7108: P = product->B;
7109: m = P->cmap->n;
7110: n = P->cmap->n;
7111: M = P->cmap->N;
7112: N = P->cmap->N;
7113: hasoffproc = PETSC_TRUE;
7114: break;
7115: default:
7116: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7117: }
7118: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7119: if (size == 1) hasoffproc = PETSC_FALSE;
7121: /* defaults */
7122: for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7123: mp[i] = NULL;
7124: mptmp[i] = PETSC_FALSE;
7125: rmapt[i] = -1;
7126: cmapt[i] = -1;
7127: rmapa[i] = NULL;
7128: cmapa[i] = NULL;
7129: }
7131: /* customization */
7132: PetscCall(PetscNew(&mmdata));
7133: mmdata->reusesym = product->api_user;
7134: if (ptype == MATPRODUCT_AB) {
7135: if (product->api_user) {
7136: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7137: PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7138: PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7139: PetscOptionsEnd();
7140: } else {
7141: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7142: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7143: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7144: PetscOptionsEnd();
7145: }
7146: } else if (ptype == MATPRODUCT_PtAP) {
7147: if (product->api_user) {
7148: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7149: PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7150: PetscOptionsEnd();
7151: } else {
7152: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7153: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7154: PetscOptionsEnd();
7155: }
7156: }
7157: a = (Mat_MPIAIJ *)A->data;
7158: p = (Mat_MPIAIJ *)P->data;
7159: PetscCall(MatSetSizes(C, m, n, M, N));
7160: PetscCall(PetscLayoutSetUp(C->rmap));
7161: PetscCall(PetscLayoutSetUp(C->cmap));
7162: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7163: PetscCall(MatGetOptionsPrefix(C, &prefix));
7165: cp = 0;
7166: switch (ptype) {
7167: case MATPRODUCT_AB: /* A * P */
7168: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7170: /* A_diag * P_local (merged or not) */
7171: if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7172: /* P is product->B */
7173: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7174: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7175: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7176: PetscCall(MatProductSetFill(mp[cp], product->fill));
7177: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7178: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7179: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7180: mp[cp]->product->api_user = product->api_user;
7181: PetscCall(MatProductSetFromOptions(mp[cp]));
7182: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7183: PetscCall(ISGetIndices(glob, &globidx));
7184: rmapt[cp] = 1;
7185: cmapt[cp] = 2;
7186: cmapa[cp] = globidx;
7187: mptmp[cp] = PETSC_FALSE;
7188: cp++;
7189: } else { /* A_diag * P_diag and A_diag * P_off */
7190: PetscCall(MatProductCreate(a->A, p->A, NULL, &mp[cp]));
7191: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7192: PetscCall(MatProductSetFill(mp[cp], product->fill));
7193: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7194: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7195: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7196: mp[cp]->product->api_user = product->api_user;
7197: PetscCall(MatProductSetFromOptions(mp[cp]));
7198: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7199: rmapt[cp] = 1;
7200: cmapt[cp] = 1;
7201: mptmp[cp] = PETSC_FALSE;
7202: cp++;
7203: PetscCall(MatProductCreate(a->A, p->B, NULL, &mp[cp]));
7204: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7205: PetscCall(MatProductSetFill(mp[cp], product->fill));
7206: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7207: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7208: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7209: mp[cp]->product->api_user = product->api_user;
7210: PetscCall(MatProductSetFromOptions(mp[cp]));
7211: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7212: rmapt[cp] = 1;
7213: cmapt[cp] = 2;
7214: cmapa[cp] = p->garray;
7215: mptmp[cp] = PETSC_FALSE;
7216: cp++;
7217: }
7219: /* A_off * P_other */
7220: if (mmdata->P_oth) {
7221: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7222: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7223: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)(a->B))->type_name));
7224: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7225: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7226: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7227: PetscCall(MatProductSetFill(mp[cp], product->fill));
7228: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7229: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7230: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7231: mp[cp]->product->api_user = product->api_user;
7232: PetscCall(MatProductSetFromOptions(mp[cp]));
7233: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7234: rmapt[cp] = 1;
7235: cmapt[cp] = 2;
7236: cmapa[cp] = P_oth_idx;
7237: mptmp[cp] = PETSC_FALSE;
7238: cp++;
7239: }
7240: break;
7242: case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7243: /* A is product->B */
7244: PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7245: if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7246: PetscCall(MatProductCreate(mmdata->Bloc, mmdata->Bloc, NULL, &mp[cp]));
7247: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7248: PetscCall(MatProductSetFill(mp[cp], product->fill));
7249: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7250: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7251: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7252: mp[cp]->product->api_user = product->api_user;
7253: PetscCall(MatProductSetFromOptions(mp[cp]));
7254: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7255: PetscCall(ISGetIndices(glob, &globidx));
7256: rmapt[cp] = 2;
7257: rmapa[cp] = globidx;
7258: cmapt[cp] = 2;
7259: cmapa[cp] = globidx;
7260: mptmp[cp] = PETSC_FALSE;
7261: cp++;
7262: } else {
7263: PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7264: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7265: PetscCall(MatProductSetFill(mp[cp], product->fill));
7266: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7267: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7268: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7269: mp[cp]->product->api_user = product->api_user;
7270: PetscCall(MatProductSetFromOptions(mp[cp]));
7271: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7272: PetscCall(ISGetIndices(glob, &globidx));
7273: rmapt[cp] = 1;
7274: cmapt[cp] = 2;
7275: cmapa[cp] = globidx;
7276: mptmp[cp] = PETSC_FALSE;
7277: cp++;
7278: PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7279: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7280: PetscCall(MatProductSetFill(mp[cp], product->fill));
7281: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7282: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7283: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7284: mp[cp]->product->api_user = product->api_user;
7285: PetscCall(MatProductSetFromOptions(mp[cp]));
7286: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7287: rmapt[cp] = 2;
7288: rmapa[cp] = p->garray;
7289: cmapt[cp] = 2;
7290: cmapa[cp] = globidx;
7291: mptmp[cp] = PETSC_FALSE;
7292: cp++;
7293: }
7294: break;
7295: case MATPRODUCT_PtAP:
7296: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7297: /* P is product->B */
7298: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7299: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7300: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
7301: PetscCall(MatProductSetFill(mp[cp], product->fill));
7302: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7303: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7304: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7305: mp[cp]->product->api_user = product->api_user;
7306: PetscCall(MatProductSetFromOptions(mp[cp]));
7307: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7308: PetscCall(ISGetIndices(glob, &globidx));
7309: rmapt[cp] = 2;
7310: rmapa[cp] = globidx;
7311: cmapt[cp] = 2;
7312: cmapa[cp] = globidx;
7313: mptmp[cp] = PETSC_FALSE;
7314: cp++;
7315: if (mmdata->P_oth) {
7316: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7317: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7318: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)(a->B))->type_name));
7319: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7320: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7321: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7322: PetscCall(MatProductSetFill(mp[cp], product->fill));
7323: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7324: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7325: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7326: mp[cp]->product->api_user = product->api_user;
7327: PetscCall(MatProductSetFromOptions(mp[cp]));
7328: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7329: mptmp[cp] = PETSC_TRUE;
7330: cp++;
7331: PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7332: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7333: PetscCall(MatProductSetFill(mp[cp], product->fill));
7334: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7335: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7336: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7337: mp[cp]->product->api_user = product->api_user;
7338: PetscCall(MatProductSetFromOptions(mp[cp]));
7339: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7340: rmapt[cp] = 2;
7341: rmapa[cp] = globidx;
7342: cmapt[cp] = 2;
7343: cmapa[cp] = P_oth_idx;
7344: mptmp[cp] = PETSC_FALSE;
7345: cp++;
7346: }
7347: break;
7348: default:
7349: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7350: }
7351: /* sanity check */
7352: if (size > 1)
7353: 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);
7355: PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7356: for (i = 0; i < cp; i++) {
7357: mmdata->mp[i] = mp[i];
7358: mmdata->mptmp[i] = mptmp[i];
7359: }
7360: mmdata->cp = cp;
7361: C->product->data = mmdata;
7362: C->product->destroy = MatDestroy_MatMatMPIAIJBACKEND;
7363: C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;
7365: /* memory type */
7366: mmdata->mtype = PETSC_MEMTYPE_HOST;
7367: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7368: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7369: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7370: if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7371: else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7372: else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;
7374: /* prepare coo coordinates for values insertion */
7376: /* count total nonzeros of those intermediate seqaij Mats
7377: ncoo_d: # of nonzeros of matrices that do not have offproc entries
7378: ncoo_o: # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7379: ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7380: */
7381: for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7382: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7383: if (mptmp[cp]) continue;
7384: if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7385: const PetscInt *rmap = rmapa[cp];
7386: const PetscInt mr = mp[cp]->rmap->n;
7387: const PetscInt rs = C->rmap->rstart;
7388: const PetscInt re = C->rmap->rend;
7389: const PetscInt *ii = mm->i;
7390: for (i = 0; i < mr; i++) {
7391: const PetscInt gr = rmap[i];
7392: const PetscInt nz = ii[i + 1] - ii[i];
7393: if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7394: else ncoo_oown += nz; /* this row is local */
7395: }
7396: } else ncoo_d += mm->nz;
7397: }
7399: /*
7400: ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc
7402: ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.
7404: off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].
7406: off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7407: own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7408: so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.
7410: coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7411: 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.
7412: */
7413: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7414: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));
7416: /* gather (i,j) of nonzeros inserted by remote procs */
7417: if (hasoffproc) {
7418: PetscSF msf;
7419: PetscInt ncoo2, *coo_i2, *coo_j2;
7421: PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7422: PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7423: PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */
7425: for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7426: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7427: PetscInt *idxoff = mmdata->off[cp];
7428: PetscInt *idxown = mmdata->own[cp];
7429: if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7430: const PetscInt *rmap = rmapa[cp];
7431: const PetscInt *cmap = cmapa[cp];
7432: const PetscInt *ii = mm->i;
7433: PetscInt *coi = coo_i + ncoo_o;
7434: PetscInt *coj = coo_j + ncoo_o;
7435: const PetscInt mr = mp[cp]->rmap->n;
7436: const PetscInt rs = C->rmap->rstart;
7437: const PetscInt re = C->rmap->rend;
7438: const PetscInt cs = C->cmap->rstart;
7439: for (i = 0; i < mr; i++) {
7440: const PetscInt *jj = mm->j + ii[i];
7441: const PetscInt gr = rmap[i];
7442: const PetscInt nz = ii[i + 1] - ii[i];
7443: if (gr < rs || gr >= re) { /* this is an offproc row */
7444: for (j = ii[i]; j < ii[i + 1]; j++) {
7445: *coi++ = gr;
7446: *idxoff++ = j;
7447: }
7448: if (!cmapt[cp]) { /* already global */
7449: for (j = 0; j < nz; j++) *coj++ = jj[j];
7450: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7451: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7452: } else { /* offdiag */
7453: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7454: }
7455: ncoo_o += nz;
7456: } else { /* this is a local row */
7457: for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7458: }
7459: }
7460: }
7461: mmdata->off[cp + 1] = idxoff;
7462: mmdata->own[cp + 1] = idxown;
7463: }
7465: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7466: PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, ncoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7467: PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7468: PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7469: ncoo = ncoo_d + ncoo_oown + ncoo2;
7470: PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7471: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7472: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7473: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7474: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7475: PetscCall(PetscFree2(coo_i, coo_j));
7476: /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7477: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7478: coo_i = coo_i2;
7479: coo_j = coo_j2;
7480: } else { /* no offproc values insertion */
7481: ncoo = ncoo_d;
7482: PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));
7484: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7485: PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7486: PetscCall(PetscSFSetUp(mmdata->sf));
7487: }
7488: mmdata->hasoffproc = hasoffproc;
7490: /* gather (i,j) of nonzeros inserted locally */
7491: for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7492: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7493: PetscInt *coi = coo_i + ncoo_d;
7494: PetscInt *coj = coo_j + ncoo_d;
7495: const PetscInt *jj = mm->j;
7496: const PetscInt *ii = mm->i;
7497: const PetscInt *cmap = cmapa[cp];
7498: const PetscInt *rmap = rmapa[cp];
7499: const PetscInt mr = mp[cp]->rmap->n;
7500: const PetscInt rs = C->rmap->rstart;
7501: const PetscInt re = C->rmap->rend;
7502: const PetscInt cs = C->cmap->rstart;
7504: if (mptmp[cp]) continue;
7505: if (rmapt[cp] == 1) { /* consecutive rows */
7506: /* fill coo_i */
7507: for (i = 0; i < mr; i++) {
7508: const PetscInt gr = i + rs;
7509: for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7510: }
7511: /* fill coo_j */
7512: if (!cmapt[cp]) { /* type-0, already global */
7513: PetscCall(PetscArraycpy(coj, jj, mm->nz));
7514: } else if (cmapt[cp] == 1) { /* type-1, local to global for consecutive columns of C */
7515: for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7516: } else { /* type-2, local to global for sparse columns */
7517: for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7518: }
7519: ncoo_d += mm->nz;
7520: } else if (rmapt[cp] == 2) { /* sparse rows */
7521: for (i = 0; i < mr; i++) {
7522: const PetscInt *jj = mm->j + ii[i];
7523: const PetscInt gr = rmap[i];
7524: const PetscInt nz = ii[i + 1] - ii[i];
7525: if (gr >= rs && gr < re) { /* local rows */
7526: for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7527: if (!cmapt[cp]) { /* type-0, already global */
7528: for (j = 0; j < nz; j++) *coj++ = jj[j];
7529: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7530: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7531: } else { /* type-2, local to global for sparse columns */
7532: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7533: }
7534: ncoo_d += nz;
7535: }
7536: }
7537: }
7538: }
7539: if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7540: PetscCall(ISDestroy(&glob));
7541: if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7542: PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7543: /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7544: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));
7546: /* preallocate with COO data */
7547: PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7548: PetscCall(PetscFree2(coo_i, coo_j));
7549: PetscFunctionReturn(PETSC_SUCCESS);
7550: }
7552: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7553: {
7554: Mat_Product *product = mat->product;
7555: #if defined(PETSC_HAVE_DEVICE)
7556: PetscBool match = PETSC_FALSE;
7557: PetscBool usecpu = PETSC_FALSE;
7558: #else
7559: PetscBool match = PETSC_TRUE;
7560: #endif
7562: PetscFunctionBegin;
7563: MatCheckProduct(mat, 1);
7564: #if defined(PETSC_HAVE_DEVICE)
7565: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7566: if (match) { /* we can always fallback to the CPU if requested */
7567: switch (product->type) {
7568: case MATPRODUCT_AB:
7569: if (product->api_user) {
7570: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7571: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7572: PetscOptionsEnd();
7573: } else {
7574: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7575: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7576: PetscOptionsEnd();
7577: }
7578: break;
7579: case MATPRODUCT_AtB:
7580: if (product->api_user) {
7581: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7582: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7583: PetscOptionsEnd();
7584: } else {
7585: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7586: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7587: PetscOptionsEnd();
7588: }
7589: break;
7590: case MATPRODUCT_PtAP:
7591: if (product->api_user) {
7592: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7593: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7594: PetscOptionsEnd();
7595: } else {
7596: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7597: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7598: PetscOptionsEnd();
7599: }
7600: break;
7601: default:
7602: break;
7603: }
7604: match = (PetscBool)!usecpu;
7605: }
7606: #endif
7607: if (match) {
7608: switch (product->type) {
7609: case MATPRODUCT_AB:
7610: case MATPRODUCT_AtB:
7611: case MATPRODUCT_PtAP:
7612: mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7613: break;
7614: default:
7615: break;
7616: }
7617: }
7618: /* fallback to MPIAIJ ops */
7619: if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7620: PetscFunctionReturn(PETSC_SUCCESS);
7621: }
7623: /*
7624: Produces a set of block column indices of the matrix row, one for each block represented in the original row
7626: n - the number of block indices in cc[]
7627: cc - the block indices (must be large enough to contain the indices)
7628: */
7629: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7630: {
7631: PetscInt cnt = -1, nidx, j;
7632: const PetscInt *idx;
7634: PetscFunctionBegin;
7635: PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7636: if (nidx) {
7637: cnt = 0;
7638: cc[cnt] = idx[0] / bs;
7639: for (j = 1; j < nidx; j++) {
7640: if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7641: }
7642: }
7643: PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7644: *n = cnt + 1;
7645: PetscFunctionReturn(PETSC_SUCCESS);
7646: }
7648: /*
7649: Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows
7651: ncollapsed - the number of block indices
7652: collapsed - the block indices (must be large enough to contain the indices)
7653: */
7654: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7655: {
7656: PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;
7658: PetscFunctionBegin;
7659: PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7660: for (i = start + 1; i < start + bs; i++) {
7661: PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7662: PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7663: cprevtmp = cprev;
7664: cprev = merged;
7665: merged = cprevtmp;
7666: }
7667: *ncollapsed = nprev;
7668: if (collapsed) *collapsed = cprev;
7669: PetscFunctionReturn(PETSC_SUCCESS);
7670: }
7672: /*
7673: This will eventually be folded into MatCreateGraph_AIJ() for optimal performance
7674: */
7675: static PetscErrorCode MatFilter_AIJ(Mat Gmat, PetscReal vfilter, Mat *filteredG)
7676: {
7677: PetscInt Istart, Iend, ncols, nnz0, nnz1, NN, MM, nloc;
7678: Mat tGmat;
7679: MPI_Comm comm;
7680: const PetscScalar *vals;
7681: const PetscInt *idx;
7682: PetscInt *d_nnz, *o_nnz, kk, *garray = NULL, *AJ, maxcols = 0;
7683: MatScalar *AA; // this is checked in graph
7684: PetscBool isseqaij;
7685: Mat a, b, c;
7686: MatType jtype;
7688: PetscFunctionBegin;
7689: PetscCall(PetscObjectGetComm((PetscObject)Gmat, &comm));
7690: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Gmat, MATSEQAIJ, &isseqaij));
7691: PetscCall(MatGetType(Gmat, &jtype));
7692: PetscCall(MatCreate(comm, &tGmat));
7693: PetscCall(MatSetType(tGmat, jtype));
7695: /* TODO GPU: this can be called when filter = 0 -> Probably provide MatAIJThresholdCompress that compresses the entries below a threshold?
7696: Also, if the matrix is symmetric, can we skip this
7697: operation? It can be very expensive on large matrices. */
7699: // global sizes
7700: PetscCall(MatGetSize(Gmat, &MM, &NN));
7701: PetscCall(MatGetOwnershipRange(Gmat, &Istart, &Iend));
7702: nloc = Iend - Istart;
7703: PetscCall(PetscMalloc2(nloc, &d_nnz, nloc, &o_nnz));
7704: if (isseqaij) {
7705: a = Gmat;
7706: b = NULL;
7707: } else {
7708: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
7709: a = d->A;
7710: b = d->B;
7711: garray = d->garray;
7712: }
7713: /* Determine upper bound on non-zeros needed in new filtered matrix */
7714: for (PetscInt row = 0; row < nloc; row++) {
7715: PetscCall(MatGetRow(a, row, &ncols, NULL, NULL));
7716: d_nnz[row] = ncols;
7717: if (ncols > maxcols) maxcols = ncols;
7718: PetscCall(MatRestoreRow(a, row, &ncols, NULL, NULL));
7719: }
7720: if (b) {
7721: for (PetscInt row = 0; row < nloc; row++) {
7722: PetscCall(MatGetRow(b, row, &ncols, NULL, NULL));
7723: o_nnz[row] = ncols;
7724: if (ncols > maxcols) maxcols = ncols;
7725: PetscCall(MatRestoreRow(b, row, &ncols, NULL, NULL));
7726: }
7727: }
7728: PetscCall(MatSetSizes(tGmat, nloc, nloc, MM, MM));
7729: PetscCall(MatSetBlockSizes(tGmat, 1, 1));
7730: PetscCall(MatSeqAIJSetPreallocation(tGmat, 0, d_nnz));
7731: PetscCall(MatMPIAIJSetPreallocation(tGmat, 0, d_nnz, 0, o_nnz));
7732: PetscCall(MatSetOption(tGmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
7733: PetscCall(PetscFree2(d_nnz, o_nnz));
7734: //
7735: PetscCall(PetscMalloc2(maxcols, &AA, maxcols, &AJ));
7736: nnz0 = nnz1 = 0;
7737: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7738: for (PetscInt row = 0, grow = Istart, ncol_row, jj; row < nloc; row++, grow++) {
7739: PetscCall(MatGetRow(c, row, &ncols, &idx, &vals));
7740: for (ncol_row = jj = 0; jj < ncols; jj++, nnz0++) {
7741: PetscScalar sv = PetscAbs(PetscRealPart(vals[jj]));
7742: if (PetscRealPart(sv) > vfilter) {
7743: nnz1++;
7744: PetscInt cid = idx[jj] + Istart; //diag
7745: if (c != a) cid = garray[idx[jj]];
7746: AA[ncol_row] = vals[jj];
7747: AJ[ncol_row] = cid;
7748: ncol_row++;
7749: }
7750: }
7751: PetscCall(MatRestoreRow(c, row, &ncols, &idx, &vals));
7752: PetscCall(MatSetValues(tGmat, 1, &grow, ncol_row, AJ, AA, INSERT_VALUES));
7753: }
7754: }
7755: PetscCall(PetscFree2(AA, AJ));
7756: PetscCall(MatAssemblyBegin(tGmat, MAT_FINAL_ASSEMBLY));
7757: PetscCall(MatAssemblyEnd(tGmat, MAT_FINAL_ASSEMBLY));
7758: PetscCall(MatPropagateSymmetryOptions(Gmat, tGmat)); /* Normal Mat options are not relevant ? */
7760: PetscCall(PetscInfo(tGmat, "\t %g%% nnz after filtering, with threshold %g, %g nnz ave. (N=%" PetscInt_FMT ", max row size %d)\n", (!nnz0) ? 1. : 100. * (double)nnz1 / (double)nnz0, (double)vfilter, (!nloc) ? 1. : (double)nnz0 / (double)nloc, MM, (int)maxcols));
7762: *filteredG = tGmat;
7763: PetscCall(MatViewFromOptions(tGmat, NULL, "-mat_filter_graph_view"));
7764: PetscFunctionReturn(PETSC_SUCCESS);
7765: }
7767: /*
7768: MatCreateGraph_Simple_AIJ - create simple scalar matrix (graph) from potentially blocked matrix
7770: Input Parameter:
7771: . Amat - matrix
7772: - symmetrize - make the result symmetric
7773: + scale - scale with diagonal
7775: Output Parameter:
7776: . a_Gmat - output scalar graph >= 0
7778: */
7779: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, Mat *a_Gmat)
7780: {
7781: PetscInt Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7782: MPI_Comm comm;
7783: Mat Gmat;
7784: PetscBool ismpiaij, isseqaij;
7785: Mat a, b, c;
7786: MatType jtype;
7788: PetscFunctionBegin;
7789: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7790: PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7791: PetscCall(MatGetSize(Amat, &MM, &NN));
7792: PetscCall(MatGetBlockSize(Amat, &bs));
7793: nloc = (Iend - Istart) / bs;
7795: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7796: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7797: PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");
7799: /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7800: /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7801: implementation */
7802: if (bs > 1) {
7803: PetscCall(MatGetType(Amat, &jtype));
7804: PetscCall(MatCreate(comm, &Gmat));
7805: PetscCall(MatSetType(Gmat, jtype));
7806: PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7807: PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7808: if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7809: PetscInt *d_nnz, *o_nnz;
7810: MatScalar *aa, val, *AA;
7811: PetscInt *aj, *ai, *AJ, nc, nmax = 0;
7812: if (isseqaij) {
7813: a = Amat;
7814: b = NULL;
7815: } else {
7816: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7817: a = d->A;
7818: b = d->B;
7819: }
7820: PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7821: PetscCall(PetscMalloc2(nloc, &d_nnz, isseqaij ? 0 : nloc, &o_nnz));
7822: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7823: PetscInt *nnz = (c == a) ? d_nnz : o_nnz;
7824: const PetscInt *cols1, *cols2;
7825: for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7826: PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7827: nnz[brow / bs] = nc2 / bs;
7828: if (nc2 % bs) ok = 0;
7829: if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7830: for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7831: PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7832: if (nc1 != nc2) ok = 0;
7833: else {
7834: for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7835: if (cols1[jj] != cols2[jj]) ok = 0;
7836: if (cols1[jj] % bs != jj % bs) ok = 0;
7837: }
7838: }
7839: PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7840: }
7841: PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7842: if (!ok) {
7843: PetscCall(PetscFree2(d_nnz, o_nnz));
7844: PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7845: goto old_bs;
7846: }
7847: }
7848: }
7849: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7850: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7851: PetscCall(PetscFree2(d_nnz, o_nnz));
7852: PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7853: // diag
7854: for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7855: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;
7856: ai = aseq->i;
7857: n = ai[brow + 1] - ai[brow];
7858: aj = aseq->j + ai[brow];
7859: for (int k = 0; k < n; k += bs) { // block columns
7860: AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7861: val = 0;
7862: for (int ii = 0; ii < bs; ii++) { // rows in block
7863: aa = aseq->a + ai[brow + ii] + k;
7864: for (int jj = 0; jj < bs; jj++) { // columns in block
7865: val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7866: }
7867: }
7868: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7869: AA[k / bs] = val;
7870: }
7871: grow = Istart / bs + brow / bs;
7872: PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, INSERT_VALUES));
7873: }
7874: // off-diag
7875: if (ismpiaij) {
7876: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Amat->data;
7877: const PetscScalar *vals;
7878: const PetscInt *cols, *garray = aij->garray;
7879: PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7880: for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7881: PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7882: for (int k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7883: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7884: AA[k / bs] = 0;
7885: AJ[cidx] = garray[cols[k]] / bs;
7886: }
7887: nc = ncols / bs;
7888: PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7889: for (int ii = 0; ii < bs; ii++) { // rows in block
7890: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7891: for (int k = 0; k < ncols; k += bs) {
7892: for (int jj = 0; jj < bs; jj++) { // cols in block
7893: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7894: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7895: }
7896: }
7897: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7898: }
7899: grow = Istart / bs + brow / bs;
7900: PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, INSERT_VALUES));
7901: }
7902: }
7903: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7904: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7905: PetscCall(PetscFree2(AA, AJ));
7906: } else {
7907: const PetscScalar *vals;
7908: const PetscInt *idx;
7909: PetscInt *d_nnz, *o_nnz, *w0, *w1, *w2;
7910: old_bs:
7911: /*
7912: Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7913: */
7914: PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7915: PetscCall(PetscMalloc2(nloc, &d_nnz, isseqaij ? 0 : nloc, &o_nnz));
7916: if (isseqaij) {
7917: PetscInt max_d_nnz;
7918: /*
7919: Determine exact preallocation count for (sequential) scalar matrix
7920: */
7921: PetscCall(MatSeqAIJGetMaxRowNonzeros(Amat, &max_d_nnz));
7922: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7923: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7924: for (Ii = 0, jj = 0; Ii < Iend; Ii += bs, jj++) PetscCall(MatCollapseRows(Amat, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7925: PetscCall(PetscFree3(w0, w1, w2));
7926: } else if (ismpiaij) {
7927: Mat Daij, Oaij;
7928: const PetscInt *garray;
7929: PetscInt max_d_nnz;
7930: PetscCall(MatMPIAIJGetSeqAIJ(Amat, &Daij, &Oaij, &garray));
7931: /*
7932: Determine exact preallocation count for diagonal block portion of scalar matrix
7933: */
7934: PetscCall(MatSeqAIJGetMaxRowNonzeros(Daij, &max_d_nnz));
7935: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7936: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7937: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) PetscCall(MatCollapseRows(Daij, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7938: PetscCall(PetscFree3(w0, w1, w2));
7939: /*
7940: Over estimate (usually grossly over), preallocation count for off-diagonal portion of scalar matrix
7941: */
7942: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) {
7943: o_nnz[jj] = 0;
7944: for (kk = 0; kk < bs; kk++) { /* rows that get collapsed to a single row */
7945: PetscCall(MatGetRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7946: o_nnz[jj] += ncols;
7947: PetscCall(MatRestoreRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7948: }
7949: if (o_nnz[jj] > (NN / bs - nloc)) o_nnz[jj] = NN / bs - nloc;
7950: }
7951: } else SETERRQ(comm, PETSC_ERR_USER, "Require AIJ matrix type");
7952: /* get scalar copy (norms) of matrix */
7953: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7954: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7955: PetscCall(PetscFree2(d_nnz, o_nnz));
7956: for (Ii = Istart; Ii < Iend; Ii++) {
7957: PetscInt dest_row = Ii / bs;
7958: PetscCall(MatGetRow(Amat, Ii, &ncols, &idx, &vals));
7959: for (jj = 0; jj < ncols; jj++) {
7960: PetscInt dest_col = idx[jj] / bs;
7961: PetscScalar sv = PetscAbs(PetscRealPart(vals[jj]));
7962: PetscCall(MatSetValues(Gmat, 1, &dest_row, 1, &dest_col, &sv, ADD_VALUES));
7963: }
7964: PetscCall(MatRestoreRow(Amat, Ii, &ncols, &idx, &vals));
7965: }
7966: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7967: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7968: }
7969: } else {
7970: if (symmetrize || filter >= 0 || scale) PetscCall(MatDuplicate(Amat, MAT_COPY_VALUES, &Gmat));
7971: else {
7972: Gmat = Amat;
7973: PetscCall(PetscObjectReference((PetscObject)Gmat));
7974: }
7975: if (isseqaij) {
7976: a = Gmat;
7977: b = NULL;
7978: } else {
7979: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
7980: a = d->A;
7981: b = d->B;
7982: }
7983: if (filter >= 0 || scale) {
7984: /* take absolute value of each entry */
7985: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7986: MatInfo info;
7987: PetscScalar *avals;
7988: PetscCall(MatGetInfo(c, MAT_LOCAL, &info));
7989: PetscCall(MatSeqAIJGetArray(c, &avals));
7990: for (int jj = 0; jj < info.nz_used; jj++) avals[jj] = PetscAbsScalar(avals[jj]);
7991: PetscCall(MatSeqAIJRestoreArray(c, &avals));
7992: }
7993: }
7994: }
7995: if (symmetrize) {
7996: PetscBool isset, issym;
7997: PetscCall(MatIsSymmetricKnown(Amat, &isset, &issym));
7998: if (!isset || !issym) {
7999: Mat matTrans;
8000: PetscCall(MatTranspose(Gmat, MAT_INITIAL_MATRIX, &matTrans));
8001: PetscCall(MatAXPY(Gmat, 1.0, matTrans, Gmat->structurally_symmetric == PETSC_BOOL3_TRUE ? SAME_NONZERO_PATTERN : DIFFERENT_NONZERO_PATTERN));
8002: PetscCall(MatDestroy(&matTrans));
8003: }
8004: PetscCall(MatSetOption(Gmat, MAT_SYMMETRIC, PETSC_TRUE));
8005: } else if (Amat != Gmat) PetscCall(MatPropagateSymmetryOptions(Amat, Gmat));
8006: if (scale) {
8007: /* scale c for all diagonal values = 1 or -1 */
8008: Vec diag;
8009: PetscCall(MatCreateVecs(Gmat, &diag, NULL));
8010: PetscCall(MatGetDiagonal(Gmat, diag));
8011: PetscCall(VecReciprocal(diag));
8012: PetscCall(VecSqrtAbs(diag));
8013: PetscCall(MatDiagonalScale(Gmat, diag, diag));
8014: PetscCall(VecDestroy(&diag));
8015: }
8016: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_graph_view"));
8018: if (filter >= 0) {
8019: Mat Fmat = NULL; /* some silly compiler needs this */
8021: PetscCall(MatFilter_AIJ(Gmat, filter, &Fmat));
8022: PetscCall(MatDestroy(&Gmat));
8023: Gmat = Fmat;
8024: }
8025: *a_Gmat = Gmat;
8026: PetscFunctionReturn(PETSC_SUCCESS);
8027: }
8029: /*
8030: Special version for direct calls from Fortran
8031: */
8032: #include <petsc/private/fortranimpl.h>
8034: /* Change these macros so can be used in void function */
8035: /* Identical to PetscCallVoid, except it assigns to *_ierr */
8036: #undef PetscCall
8037: #define PetscCall(...) \
8038: do { \
8039: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
8040: if (PetscUnlikely(ierr_msv_mpiaij)) { \
8041: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
8042: return; \
8043: } \
8044: } while (0)
8046: #undef SETERRQ
8047: #define SETERRQ(comm, ierr, ...) \
8048: do { \
8049: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
8050: return; \
8051: } while (0)
8053: #if defined(PETSC_HAVE_FORTRAN_CAPS)
8054: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
8055: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
8056: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
8057: #else
8058: #endif
8059: PETSC_EXTERN void matsetvaluesmpiaij_(Mat *mmat, PetscInt *mm, const PetscInt im[], PetscInt *mn, const PetscInt in[], const PetscScalar v[], InsertMode *maddv, PetscErrorCode *_ierr)
8060: {
8061: Mat mat = *mmat;
8062: PetscInt m = *mm, n = *mn;
8063: InsertMode addv = *maddv;
8064: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
8065: PetscScalar value;
8067: MatCheckPreallocated(mat, 1);
8068: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
8069: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
8070: {
8071: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
8072: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
8073: PetscBool roworiented = aij->roworiented;
8075: /* Some Variables required in the macro */
8076: Mat A = aij->A;
8077: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
8078: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
8079: MatScalar *aa;
8080: PetscBool ignorezeroentries = (((a->ignorezeroentries) && (addv == ADD_VALUES)) ? PETSC_TRUE : PETSC_FALSE);
8081: Mat B = aij->B;
8082: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
8083: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
8084: MatScalar *ba;
8085: /* This variable below is only for the PETSC_HAVE_VIENNACL or PETSC_HAVE_CUDA cases, but we define it in all cases because we
8086: * cannot use "#if defined" inside a macro. */
8087: PETSC_UNUSED PetscBool inserted = PETSC_FALSE;
8089: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
8090: PetscInt nonew = a->nonew;
8091: MatScalar *ap1, *ap2;
8093: PetscFunctionBegin;
8094: PetscCall(MatSeqAIJGetArray(A, &aa));
8095: PetscCall(MatSeqAIJGetArray(B, &ba));
8096: for (i = 0; i < m; i++) {
8097: if (im[i] < 0) continue;
8098: 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);
8099: if (im[i] >= rstart && im[i] < rend) {
8100: row = im[i] - rstart;
8101: lastcol1 = -1;
8102: rp1 = aj + ai[row];
8103: ap1 = aa + ai[row];
8104: rmax1 = aimax[row];
8105: nrow1 = ailen[row];
8106: low1 = 0;
8107: high1 = nrow1;
8108: lastcol2 = -1;
8109: rp2 = bj + bi[row];
8110: ap2 = ba + bi[row];
8111: rmax2 = bimax[row];
8112: nrow2 = bilen[row];
8113: low2 = 0;
8114: high2 = nrow2;
8116: for (j = 0; j < n; j++) {
8117: if (roworiented) value = v[i * n + j];
8118: else value = v[i + j * m];
8119: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
8120: if (in[j] >= cstart && in[j] < cend) {
8121: col = in[j] - cstart;
8122: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
8123: } else if (in[j] < 0) continue;
8124: else if (PetscUnlikelyDebug(in[j] >= mat->cmap->N)) {
8125: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
8126: } else {
8127: if (mat->was_assembled) {
8128: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
8129: #if defined(PETSC_USE_CTABLE)
8130: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col));
8131: col--;
8132: #else
8133: col = aij->colmap[in[j]] - 1;
8134: #endif
8135: if (col < 0 && !((Mat_SeqAIJ *)(aij->A->data))->nonew) {
8136: PetscCall(MatDisAssemble_MPIAIJ(mat));
8137: col = in[j];
8138: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
8139: B = aij->B;
8140: b = (Mat_SeqAIJ *)B->data;
8141: bimax = b->imax;
8142: bi = b->i;
8143: bilen = b->ilen;
8144: bj = b->j;
8145: rp2 = bj + bi[row];
8146: ap2 = ba + bi[row];
8147: rmax2 = bimax[row];
8148: nrow2 = bilen[row];
8149: low2 = 0;
8150: high2 = nrow2;
8151: bm = aij->B->rmap->n;
8152: ba = b->a;
8153: inserted = PETSC_FALSE;
8154: }
8155: } else col = in[j];
8156: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
8157: }
8158: }
8159: } else if (!aij->donotstash) {
8160: if (roworiented) {
8161: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8162: } else {
8163: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8164: }
8165: }
8166: }
8167: PetscCall(MatSeqAIJRestoreArray(A, &aa));
8168: PetscCall(MatSeqAIJRestoreArray(B, &ba));
8169: }
8170: PetscFunctionReturnVoid();
8171: }
8173: /* Undefining these here since they were redefined from their original definition above! No
8174: * other PETSc functions should be defined past this point, as it is impossible to recover the
8175: * original definitions */
8176: #undef PetscCall
8177: #undef SETERRQ