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: [](chapter_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: [](chapter_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: [](chapter_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;
2645: PetscFunctionBegin;
2646: *nz = aaij->i[A->rmap->n] + baij->i[A->rmap->n];
2647: PetscFunctionReturn(PETSC_SUCCESS);
2648: }
2650: /*@
2651: MatMPIAIJSetUseScalableIncreaseOverlap - Determine if the matrix uses a scalable algorithm to compute the overlap
2653: Collective
2655: Input Parameters:
2656: + A - the matrix
2657: - sc - `PETSC_TRUE` indicates use the scalable algorithm (default is not to use the scalable algorithm)
2659: Level: advanced
2661: .seealso: [](chapter_matrices), `Mat`, `Mat`, `MATMPIAIJ`
2662: @*/
2663: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap(Mat A, PetscBool sc)
2664: {
2665: PetscFunctionBegin;
2666: PetscTryMethod(A, "MatMPIAIJSetUseScalableIncreaseOverlap_C", (Mat, PetscBool), (A, sc));
2667: PetscFunctionReturn(PETSC_SUCCESS);
2668: }
2670: PetscErrorCode MatSetFromOptions_MPIAIJ(Mat A, PetscOptionItems *PetscOptionsObject)
2671: {
2672: PetscBool sc = PETSC_FALSE, flg;
2674: PetscFunctionBegin;
2675: PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJ options");
2676: if (A->ops->increaseoverlap == MatIncreaseOverlap_MPIAIJ_Scalable) sc = PETSC_TRUE;
2677: PetscCall(PetscOptionsBool("-mat_increase_overlap_scalable", "Use a scalable algorithm to compute the overlap", "MatIncreaseOverlap", sc, &sc, &flg));
2678: if (flg) PetscCall(MatMPIAIJSetUseScalableIncreaseOverlap(A, sc));
2679: PetscOptionsHeadEnd();
2680: PetscFunctionReturn(PETSC_SUCCESS);
2681: }
2683: PetscErrorCode MatShift_MPIAIJ(Mat Y, PetscScalar a)
2684: {
2685: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)Y->data;
2686: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)maij->A->data;
2688: PetscFunctionBegin;
2689: if (!Y->preallocated) {
2690: PetscCall(MatMPIAIJSetPreallocation(Y, 1, NULL, 0, NULL));
2691: } else if (!aij->nz) { /* It does not matter if diagonals of Y only partially lie in maij->A. We just need an estimated preallocation. */
2692: PetscInt nonew = aij->nonew;
2693: PetscCall(MatSeqAIJSetPreallocation(maij->A, 1, NULL));
2694: aij->nonew = nonew;
2695: }
2696: PetscCall(MatShift_Basic(Y, a));
2697: PetscFunctionReturn(PETSC_SUCCESS);
2698: }
2700: PetscErrorCode MatMissingDiagonal_MPIAIJ(Mat A, PetscBool *missing, PetscInt *d)
2701: {
2702: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2704: PetscFunctionBegin;
2705: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2706: PetscCall(MatMissingDiagonal(a->A, missing, d));
2707: if (d) {
2708: PetscInt rstart;
2709: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2710: *d += rstart;
2711: }
2712: PetscFunctionReturn(PETSC_SUCCESS);
2713: }
2715: PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2716: {
2717: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2719: PetscFunctionBegin;
2720: PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2721: PetscFunctionReturn(PETSC_SUCCESS);
2722: }
2724: PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A)
2725: {
2726: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2728: PetscFunctionBegin;
2729: PetscCall(MatEliminateZeros(a->A));
2730: PetscCall(MatEliminateZeros(a->B));
2731: PetscFunctionReturn(PETSC_SUCCESS);
2732: }
2734: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2735: MatGetRow_MPIAIJ,
2736: MatRestoreRow_MPIAIJ,
2737: MatMult_MPIAIJ,
2738: /* 4*/ MatMultAdd_MPIAIJ,
2739: MatMultTranspose_MPIAIJ,
2740: MatMultTransposeAdd_MPIAIJ,
2741: NULL,
2742: NULL,
2743: NULL,
2744: /*10*/ NULL,
2745: NULL,
2746: NULL,
2747: MatSOR_MPIAIJ,
2748: MatTranspose_MPIAIJ,
2749: /*15*/ MatGetInfo_MPIAIJ,
2750: MatEqual_MPIAIJ,
2751: MatGetDiagonal_MPIAIJ,
2752: MatDiagonalScale_MPIAIJ,
2753: MatNorm_MPIAIJ,
2754: /*20*/ MatAssemblyBegin_MPIAIJ,
2755: MatAssemblyEnd_MPIAIJ,
2756: MatSetOption_MPIAIJ,
2757: MatZeroEntries_MPIAIJ,
2758: /*24*/ MatZeroRows_MPIAIJ,
2759: NULL,
2760: NULL,
2761: NULL,
2762: NULL,
2763: /*29*/ MatSetUp_MPI_Hash,
2764: NULL,
2765: NULL,
2766: MatGetDiagonalBlock_MPIAIJ,
2767: NULL,
2768: /*34*/ MatDuplicate_MPIAIJ,
2769: NULL,
2770: NULL,
2771: NULL,
2772: NULL,
2773: /*39*/ MatAXPY_MPIAIJ,
2774: MatCreateSubMatrices_MPIAIJ,
2775: MatIncreaseOverlap_MPIAIJ,
2776: MatGetValues_MPIAIJ,
2777: MatCopy_MPIAIJ,
2778: /*44*/ MatGetRowMax_MPIAIJ,
2779: MatScale_MPIAIJ,
2780: MatShift_MPIAIJ,
2781: MatDiagonalSet_MPIAIJ,
2782: MatZeroRowsColumns_MPIAIJ,
2783: /*49*/ MatSetRandom_MPIAIJ,
2784: MatGetRowIJ_MPIAIJ,
2785: MatRestoreRowIJ_MPIAIJ,
2786: NULL,
2787: NULL,
2788: /*54*/ MatFDColoringCreate_MPIXAIJ,
2789: NULL,
2790: MatSetUnfactored_MPIAIJ,
2791: MatPermute_MPIAIJ,
2792: NULL,
2793: /*59*/ MatCreateSubMatrix_MPIAIJ,
2794: MatDestroy_MPIAIJ,
2795: MatView_MPIAIJ,
2796: NULL,
2797: NULL,
2798: /*64*/ NULL,
2799: MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2800: NULL,
2801: NULL,
2802: NULL,
2803: /*69*/ MatGetRowMaxAbs_MPIAIJ,
2804: MatGetRowMinAbs_MPIAIJ,
2805: NULL,
2806: NULL,
2807: NULL,
2808: NULL,
2809: /*75*/ MatFDColoringApply_AIJ,
2810: MatSetFromOptions_MPIAIJ,
2811: NULL,
2812: NULL,
2813: MatFindZeroDiagonals_MPIAIJ,
2814: /*80*/ NULL,
2815: NULL,
2816: NULL,
2817: /*83*/ MatLoad_MPIAIJ,
2818: MatIsSymmetric_MPIAIJ,
2819: NULL,
2820: NULL,
2821: NULL,
2822: NULL,
2823: /*89*/ NULL,
2824: NULL,
2825: MatMatMultNumeric_MPIAIJ_MPIAIJ,
2826: NULL,
2827: NULL,
2828: /*94*/ MatPtAPNumeric_MPIAIJ_MPIAIJ,
2829: NULL,
2830: NULL,
2831: NULL,
2832: MatBindToCPU_MPIAIJ,
2833: /*99*/ MatProductSetFromOptions_MPIAIJ,
2834: NULL,
2835: NULL,
2836: MatConjugate_MPIAIJ,
2837: NULL,
2838: /*104*/ MatSetValuesRow_MPIAIJ,
2839: MatRealPart_MPIAIJ,
2840: MatImaginaryPart_MPIAIJ,
2841: NULL,
2842: NULL,
2843: /*109*/ NULL,
2844: NULL,
2845: MatGetRowMin_MPIAIJ,
2846: NULL,
2847: MatMissingDiagonal_MPIAIJ,
2848: /*114*/ MatGetSeqNonzeroStructure_MPIAIJ,
2849: NULL,
2850: MatGetGhosts_MPIAIJ,
2851: NULL,
2852: NULL,
2853: /*119*/ MatMultDiagonalBlock_MPIAIJ,
2854: NULL,
2855: NULL,
2856: NULL,
2857: MatGetMultiProcBlock_MPIAIJ,
2858: /*124*/ MatFindNonzeroRows_MPIAIJ,
2859: MatGetColumnReductions_MPIAIJ,
2860: MatInvertBlockDiagonal_MPIAIJ,
2861: MatInvertVariableBlockDiagonal_MPIAIJ,
2862: MatCreateSubMatricesMPI_MPIAIJ,
2863: /*129*/ NULL,
2864: NULL,
2865: NULL,
2866: MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2867: NULL,
2868: /*134*/ NULL,
2869: NULL,
2870: NULL,
2871: NULL,
2872: NULL,
2873: /*139*/ MatSetBlockSizes_MPIAIJ,
2874: NULL,
2875: NULL,
2876: MatFDColoringSetUp_MPIXAIJ,
2877: MatFindOffBlockDiagonalEntries_MPIAIJ,
2878: MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2879: /*145*/ NULL,
2880: NULL,
2881: NULL,
2882: MatCreateGraph_Simple_AIJ,
2883: NULL,
2884: /*150*/ NULL,
2885: MatEliminateZeros_MPIAIJ};
2887: PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2888: {
2889: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2891: PetscFunctionBegin;
2892: PetscCall(MatStoreValues(aij->A));
2893: PetscCall(MatStoreValues(aij->B));
2894: PetscFunctionReturn(PETSC_SUCCESS);
2895: }
2897: PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2898: {
2899: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2901: PetscFunctionBegin;
2902: PetscCall(MatRetrieveValues(aij->A));
2903: PetscCall(MatRetrieveValues(aij->B));
2904: PetscFunctionReturn(PETSC_SUCCESS);
2905: }
2907: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2908: {
2909: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2910: PetscMPIInt size;
2912: PetscFunctionBegin;
2913: if (B->hash_active) {
2914: PetscCall(PetscMemcpy(&B->ops, &b->cops, sizeof(*(B->ops))));
2915: B->hash_active = PETSC_FALSE;
2916: }
2917: PetscCall(PetscLayoutSetUp(B->rmap));
2918: PetscCall(PetscLayoutSetUp(B->cmap));
2920: #if defined(PETSC_USE_CTABLE)
2921: PetscCall(PetscHMapIDestroy(&b->colmap));
2922: #else
2923: PetscCall(PetscFree(b->colmap));
2924: #endif
2925: PetscCall(PetscFree(b->garray));
2926: PetscCall(VecDestroy(&b->lvec));
2927: PetscCall(VecScatterDestroy(&b->Mvctx));
2929: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2930: PetscCall(MatDestroy(&b->B));
2931: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2932: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2933: PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2934: PetscCall(MatSetType(b->B, MATSEQAIJ));
2936: PetscCall(MatDestroy(&b->A));
2937: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2938: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2939: PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2940: PetscCall(MatSetType(b->A, MATSEQAIJ));
2942: PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2943: PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2944: B->preallocated = PETSC_TRUE;
2945: B->was_assembled = PETSC_FALSE;
2946: B->assembled = PETSC_FALSE;
2947: PetscFunctionReturn(PETSC_SUCCESS);
2948: }
2950: PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2951: {
2952: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2954: PetscFunctionBegin;
2956: PetscCall(PetscLayoutSetUp(B->rmap));
2957: PetscCall(PetscLayoutSetUp(B->cmap));
2959: #if defined(PETSC_USE_CTABLE)
2960: PetscCall(PetscHMapIDestroy(&b->colmap));
2961: #else
2962: PetscCall(PetscFree(b->colmap));
2963: #endif
2964: PetscCall(PetscFree(b->garray));
2965: PetscCall(VecDestroy(&b->lvec));
2966: PetscCall(VecScatterDestroy(&b->Mvctx));
2968: PetscCall(MatResetPreallocation(b->A));
2969: PetscCall(MatResetPreallocation(b->B));
2970: B->preallocated = PETSC_TRUE;
2971: B->was_assembled = PETSC_FALSE;
2972: B->assembled = PETSC_FALSE;
2973: PetscFunctionReturn(PETSC_SUCCESS);
2974: }
2976: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2977: {
2978: Mat mat;
2979: Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;
2981: PetscFunctionBegin;
2982: *newmat = NULL;
2983: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2984: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2985: PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2986: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2987: a = (Mat_MPIAIJ *)mat->data;
2989: mat->factortype = matin->factortype;
2990: mat->assembled = matin->assembled;
2991: mat->insertmode = NOT_SET_VALUES;
2992: mat->preallocated = matin->preallocated;
2994: a->size = oldmat->size;
2995: a->rank = oldmat->rank;
2996: a->donotstash = oldmat->donotstash;
2997: a->roworiented = oldmat->roworiented;
2998: a->rowindices = NULL;
2999: a->rowvalues = NULL;
3000: a->getrowactive = PETSC_FALSE;
3002: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
3003: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
3005: if (oldmat->colmap) {
3006: #if defined(PETSC_USE_CTABLE)
3007: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3008: #else
3009: PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
3010: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
3011: #endif
3012: } else a->colmap = NULL;
3013: if (oldmat->garray) {
3014: PetscInt len;
3015: len = oldmat->B->cmap->n;
3016: PetscCall(PetscMalloc1(len + 1, &a->garray));
3017: if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3018: } else a->garray = NULL;
3020: /* It may happen MatDuplicate is called with a non-assembled matrix
3021: In fact, MatDuplicate only requires the matrix to be preallocated
3022: This may happen inside a DMCreateMatrix_Shell */
3023: if (oldmat->lvec) PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3024: if (oldmat->Mvctx) PetscCall(VecScatterCopy(oldmat->Mvctx, &a->Mvctx));
3025: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3026: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3027: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3028: *newmat = mat;
3029: PetscFunctionReturn(PETSC_SUCCESS);
3030: }
3032: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3033: {
3034: PetscBool isbinary, ishdf5;
3036: PetscFunctionBegin;
3039: /* force binary viewer to load .info file if it has not yet done so */
3040: PetscCall(PetscViewerSetUp(viewer));
3041: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3042: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3043: if (isbinary) {
3044: PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3045: } else if (ishdf5) {
3046: #if defined(PETSC_HAVE_HDF5)
3047: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3048: #else
3049: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3050: #endif
3051: } else {
3052: 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);
3053: }
3054: PetscFunctionReturn(PETSC_SUCCESS);
3055: }
3057: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3058: {
3059: PetscInt header[4], M, N, m, nz, rows, cols, sum, i;
3060: PetscInt *rowidxs, *colidxs;
3061: PetscScalar *matvals;
3063: PetscFunctionBegin;
3064: PetscCall(PetscViewerSetUp(viewer));
3066: /* read in matrix header */
3067: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3068: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3069: M = header[1];
3070: N = header[2];
3071: nz = header[3];
3072: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3073: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3074: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");
3076: /* set block sizes from the viewer's .info file */
3077: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3078: /* set global sizes if not set already */
3079: if (mat->rmap->N < 0) mat->rmap->N = M;
3080: if (mat->cmap->N < 0) mat->cmap->N = N;
3081: PetscCall(PetscLayoutSetUp(mat->rmap));
3082: PetscCall(PetscLayoutSetUp(mat->cmap));
3084: /* check if the matrix sizes are correct */
3085: PetscCall(MatGetSize(mat, &rows, &cols));
3086: 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);
3088: /* read in row lengths and build row indices */
3089: PetscCall(MatGetLocalSize(mat, &m, NULL));
3090: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3091: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3092: rowidxs[0] = 0;
3093: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3094: PetscCall(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3095: 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);
3096: /* read in column indices and matrix values */
3097: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3098: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3099: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3100: /* store matrix indices and values */
3101: PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3102: PetscCall(PetscFree(rowidxs));
3103: PetscCall(PetscFree2(colidxs, matvals));
3104: PetscFunctionReturn(PETSC_SUCCESS);
3105: }
3107: /* Not scalable because of ISAllGather() unless getting all columns. */
3108: PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3109: {
3110: IS iscol_local;
3111: PetscBool isstride;
3112: PetscMPIInt lisstride = 0, gisstride;
3114: PetscFunctionBegin;
3115: /* check if we are grabbing all columns*/
3116: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));
3118: if (isstride) {
3119: PetscInt start, len, mstart, mlen;
3120: PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3121: PetscCall(ISGetLocalSize(iscol, &len));
3122: PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3123: if (mstart == start && mlen - mstart == len) lisstride = 1;
3124: }
3126: PetscCall(MPIU_Allreduce(&lisstride, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3127: if (gisstride) {
3128: PetscInt N;
3129: PetscCall(MatGetSize(mat, NULL, &N));
3130: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3131: PetscCall(ISSetIdentity(iscol_local));
3132: PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3133: } else {
3134: PetscInt cbs;
3135: PetscCall(ISGetBlockSize(iscol, &cbs));
3136: PetscCall(ISAllGather(iscol, &iscol_local));
3137: PetscCall(ISSetBlockSize(iscol_local, cbs));
3138: }
3140: *isseq = iscol_local;
3141: PetscFunctionReturn(PETSC_SUCCESS);
3142: }
3144: /*
3145: Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3146: (see MatCreateSubMatrix_MPIAIJ_nonscalable)
3148: Input Parameters:
3149: + mat - matrix
3150: . isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3151: i.e., mat->rstart <= isrow[i] < mat->rend
3152: - iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3153: i.e., mat->cstart <= iscol[i] < mat->cend
3155: Output Parameters:
3156: + isrow_d - sequential row index set for retrieving mat->A
3157: . iscol_d - sequential column index set for retrieving mat->A
3158: . iscol_o - sequential column index set for retrieving mat->B
3159: - garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3160: */
3161: PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, const PetscInt *garray[])
3162: {
3163: Vec x, cmap;
3164: const PetscInt *is_idx;
3165: PetscScalar *xarray, *cmaparray;
3166: PetscInt ncols, isstart, *idx, m, rstart, *cmap1, count;
3167: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3168: Mat B = a->B;
3169: Vec lvec = a->lvec, lcmap;
3170: PetscInt i, cstart, cend, Bn = B->cmap->N;
3171: MPI_Comm comm;
3172: VecScatter Mvctx = a->Mvctx;
3174: PetscFunctionBegin;
3175: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3176: PetscCall(ISGetLocalSize(iscol, &ncols));
3178: /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3179: PetscCall(MatCreateVecs(mat, &x, NULL));
3180: PetscCall(VecSet(x, -1.0));
3181: PetscCall(VecDuplicate(x, &cmap));
3182: PetscCall(VecSet(cmap, -1.0));
3184: /* Get start indices */
3185: PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3186: isstart -= ncols;
3187: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
3189: PetscCall(ISGetIndices(iscol, &is_idx));
3190: PetscCall(VecGetArray(x, &xarray));
3191: PetscCall(VecGetArray(cmap, &cmaparray));
3192: PetscCall(PetscMalloc1(ncols, &idx));
3193: for (i = 0; i < ncols; i++) {
3194: xarray[is_idx[i] - cstart] = (PetscScalar)is_idx[i];
3195: cmaparray[is_idx[i] - cstart] = i + isstart; /* global index of iscol[i] */
3196: idx[i] = is_idx[i] - cstart; /* local index of iscol[i] */
3197: }
3198: PetscCall(VecRestoreArray(x, &xarray));
3199: PetscCall(VecRestoreArray(cmap, &cmaparray));
3200: PetscCall(ISRestoreIndices(iscol, &is_idx));
3202: /* Get iscol_d */
3203: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3204: PetscCall(ISGetBlockSize(iscol, &i));
3205: PetscCall(ISSetBlockSize(*iscol_d, i));
3207: /* Get isrow_d */
3208: PetscCall(ISGetLocalSize(isrow, &m));
3209: rstart = mat->rmap->rstart;
3210: PetscCall(PetscMalloc1(m, &idx));
3211: PetscCall(ISGetIndices(isrow, &is_idx));
3212: for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3213: PetscCall(ISRestoreIndices(isrow, &is_idx));
3215: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3216: PetscCall(ISGetBlockSize(isrow, &i));
3217: PetscCall(ISSetBlockSize(*isrow_d, i));
3219: /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3220: PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3221: PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3223: PetscCall(VecDuplicate(lvec, &lcmap));
3225: PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3226: PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3228: /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3229: /* off-process column indices */
3230: count = 0;
3231: PetscCall(PetscMalloc1(Bn, &idx));
3232: PetscCall(PetscMalloc1(Bn, &cmap1));
3234: PetscCall(VecGetArray(lvec, &xarray));
3235: PetscCall(VecGetArray(lcmap, &cmaparray));
3236: for (i = 0; i < Bn; i++) {
3237: if (PetscRealPart(xarray[i]) > -1.0) {
3238: idx[count] = i; /* local column index in off-diagonal part B */
3239: cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3240: count++;
3241: }
3242: }
3243: PetscCall(VecRestoreArray(lvec, &xarray));
3244: PetscCall(VecRestoreArray(lcmap, &cmaparray));
3246: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3247: /* cannot ensure iscol_o has same blocksize as iscol! */
3249: PetscCall(PetscFree(idx));
3250: *garray = cmap1;
3252: PetscCall(VecDestroy(&x));
3253: PetscCall(VecDestroy(&cmap));
3254: PetscCall(VecDestroy(&lcmap));
3255: PetscFunctionReturn(PETSC_SUCCESS);
3256: }
3258: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3259: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3260: {
3261: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3262: Mat M = NULL;
3263: MPI_Comm comm;
3264: IS iscol_d, isrow_d, iscol_o;
3265: Mat Asub = NULL, Bsub = NULL;
3266: PetscInt n;
3268: PetscFunctionBegin;
3269: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3271: if (call == MAT_REUSE_MATRIX) {
3272: /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3273: PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3274: PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");
3276: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3277: PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");
3279: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3280: PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");
3282: /* Update diagonal and off-diagonal portions of submat */
3283: asub = (Mat_MPIAIJ *)(*submat)->data;
3284: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3285: PetscCall(ISGetLocalSize(iscol_o, &n));
3286: if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3287: PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3288: PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));
3290: } else { /* call == MAT_INITIAL_MATRIX) */
3291: const PetscInt *garray;
3292: PetscInt BsubN;
3294: /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3295: PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));
3297: /* Create local submatrices Asub and Bsub */
3298: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3299: PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));
3301: /* Create submatrix M */
3302: PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, Asub, Bsub, garray, &M));
3304: /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3305: asub = (Mat_MPIAIJ *)M->data;
3307: PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3308: n = asub->B->cmap->N;
3309: if (BsubN > n) {
3310: /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3311: const PetscInt *idx;
3312: PetscInt i, j, *idx_new, *subgarray = asub->garray;
3313: PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));
3315: PetscCall(PetscMalloc1(n, &idx_new));
3316: j = 0;
3317: PetscCall(ISGetIndices(iscol_o, &idx));
3318: for (i = 0; i < n; i++) {
3319: if (j >= BsubN) break;
3320: while (subgarray[i] > garray[j]) j++;
3322: if (subgarray[i] == garray[j]) {
3323: idx_new[i] = idx[j++];
3324: } 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]);
3325: }
3326: PetscCall(ISRestoreIndices(iscol_o, &idx));
3328: PetscCall(ISDestroy(&iscol_o));
3329: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));
3331: } else if (BsubN < n) {
3332: 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);
3333: }
3335: PetscCall(PetscFree(garray));
3336: *submat = M;
3338: /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3339: PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3340: PetscCall(ISDestroy(&isrow_d));
3342: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3343: PetscCall(ISDestroy(&iscol_d));
3345: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3346: PetscCall(ISDestroy(&iscol_o));
3347: }
3348: PetscFunctionReturn(PETSC_SUCCESS);
3349: }
3351: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3352: {
3353: IS iscol_local = NULL, isrow_d;
3354: PetscInt csize;
3355: PetscInt n, i, j, start, end;
3356: PetscBool sameRowDist = PETSC_FALSE, sameDist[2], tsameDist[2];
3357: MPI_Comm comm;
3359: PetscFunctionBegin;
3360: /* If isrow has same processor distribution as mat,
3361: call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3362: if (call == MAT_REUSE_MATRIX) {
3363: PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3364: if (isrow_d) {
3365: sameRowDist = PETSC_TRUE;
3366: tsameDist[1] = PETSC_TRUE; /* sameColDist */
3367: } else {
3368: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3369: if (iscol_local) {
3370: sameRowDist = PETSC_TRUE;
3371: tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3372: }
3373: }
3374: } else {
3375: /* Check if isrow has same processor distribution as mat */
3376: sameDist[0] = PETSC_FALSE;
3377: PetscCall(ISGetLocalSize(isrow, &n));
3378: if (!n) {
3379: sameDist[0] = PETSC_TRUE;
3380: } else {
3381: PetscCall(ISGetMinMax(isrow, &i, &j));
3382: PetscCall(MatGetOwnershipRange(mat, &start, &end));
3383: if (i >= start && j < end) sameDist[0] = PETSC_TRUE;
3384: }
3386: /* Check if iscol has same processor distribution as mat */
3387: sameDist[1] = PETSC_FALSE;
3388: PetscCall(ISGetLocalSize(iscol, &n));
3389: if (!n) {
3390: sameDist[1] = PETSC_TRUE;
3391: } else {
3392: PetscCall(ISGetMinMax(iscol, &i, &j));
3393: PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3394: if (i >= start && j < end) sameDist[1] = PETSC_TRUE;
3395: }
3397: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3398: PetscCall(MPIU_Allreduce(&sameDist, &tsameDist, 2, MPIU_BOOL, MPI_LAND, comm));
3399: sameRowDist = tsameDist[0];
3400: }
3402: if (sameRowDist) {
3403: if (tsameDist[1]) { /* sameRowDist & sameColDist */
3404: /* isrow and iscol have same processor distribution as mat */
3405: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3406: PetscFunctionReturn(PETSC_SUCCESS);
3407: } else { /* sameRowDist */
3408: /* isrow has same processor distribution as mat */
3409: if (call == MAT_INITIAL_MATRIX) {
3410: PetscBool sorted;
3411: PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3412: PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3413: PetscCall(ISGetSize(iscol, &i));
3414: PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);
3416: PetscCall(ISSorted(iscol_local, &sorted));
3417: if (sorted) {
3418: /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3419: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3420: PetscFunctionReturn(PETSC_SUCCESS);
3421: }
3422: } else { /* call == MAT_REUSE_MATRIX */
3423: IS iscol_sub;
3424: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3425: if (iscol_sub) {
3426: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3427: PetscFunctionReturn(PETSC_SUCCESS);
3428: }
3429: }
3430: }
3431: }
3433: /* General case: iscol -> iscol_local which has global size of iscol */
3434: if (call == MAT_REUSE_MATRIX) {
3435: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3436: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3437: } else {
3438: if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3439: }
3441: PetscCall(ISGetLocalSize(iscol, &csize));
3442: PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));
3444: if (call == MAT_INITIAL_MATRIX) {
3445: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3446: PetscCall(ISDestroy(&iscol_local));
3447: }
3448: PetscFunctionReturn(PETSC_SUCCESS);
3449: }
3451: /*@C
3452: MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3453: and "off-diagonal" part of the matrix in CSR format.
3455: Collective
3457: Input Parameters:
3458: + comm - MPI communicator
3459: . A - "diagonal" portion of matrix
3460: . B - "off-diagonal" portion of matrix, may have empty columns, will be destroyed by this routine
3461: - garray - global index of `B` columns
3463: Output Parameter:
3464: . mat - the matrix, with input `A` as its local diagonal matrix
3466: Level: advanced
3468: Notes:
3469: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.
3471: `A` becomes part of output mat, `B` is destroyed by this routine. The user cannot use `A` and `B` anymore.
3473: .seealso: [](chapter_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3474: @*/
3475: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, Mat A, Mat B, const PetscInt garray[], Mat *mat)
3476: {
3477: Mat_MPIAIJ *maij;
3478: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data, *bnew;
3479: PetscInt *oi = b->i, *oj = b->j, i, nz, col;
3480: const PetscScalar *oa;
3481: Mat Bnew;
3482: PetscInt m, n, N;
3483: MatType mpi_mat_type;
3485: PetscFunctionBegin;
3486: PetscCall(MatCreate(comm, mat));
3487: PetscCall(MatGetSize(A, &m, &n));
3488: PetscCheck(m == B->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Am %" PetscInt_FMT " != Bm %" PetscInt_FMT, m, B->rmap->N);
3489: 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);
3490: /* remove check below; When B is created using iscol_o from ISGetSeqIS_SameColDist_Private(), its bs may not be same as A */
3491: /* 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); */
3493: /* Get global columns of mat */
3494: PetscCall(MPIU_Allreduce(&n, &N, 1, MPIU_INT, MPI_SUM, comm));
3496: PetscCall(MatSetSizes(*mat, m, n, PETSC_DECIDE, N));
3497: /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3498: PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3499: PetscCall(MatSetType(*mat, mpi_mat_type));
3501: PetscCall(MatSetBlockSizes(*mat, A->rmap->bs, A->cmap->bs));
3502: maij = (Mat_MPIAIJ *)(*mat)->data;
3504: (*mat)->preallocated = PETSC_TRUE;
3506: PetscCall(PetscLayoutSetUp((*mat)->rmap));
3507: PetscCall(PetscLayoutSetUp((*mat)->cmap));
3509: /* Set A as diagonal portion of *mat */
3510: maij->A = A;
3512: nz = oi[m];
3513: for (i = 0; i < nz; i++) {
3514: col = oj[i];
3515: oj[i] = garray[col];
3516: }
3518: /* Set Bnew as off-diagonal portion of *mat */
3519: PetscCall(MatSeqAIJGetArrayRead(B, &oa));
3520: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, N, oi, oj, (PetscScalar *)oa, &Bnew));
3521: PetscCall(MatSeqAIJRestoreArrayRead(B, &oa));
3522: bnew = (Mat_SeqAIJ *)Bnew->data;
3523: bnew->maxnz = b->maxnz; /* allocated nonzeros of B */
3524: maij->B = Bnew;
3526: 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);
3528: b->singlemalloc = PETSC_FALSE; /* B arrays are shared by Bnew */
3529: b->free_a = PETSC_FALSE;
3530: b->free_ij = PETSC_FALSE;
3531: PetscCall(MatDestroy(&B));
3533: bnew->singlemalloc = PETSC_TRUE; /* arrays will be freed by MatDestroy(&Bnew) */
3534: bnew->free_a = PETSC_TRUE;
3535: bnew->free_ij = PETSC_TRUE;
3537: /* condense columns of maij->B */
3538: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3539: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
3540: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
3541: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
3542: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3543: PetscFunctionReturn(PETSC_SUCCESS);
3544: }
3546: extern PetscErrorCode MatCreateSubMatrices_MPIAIJ_SingleIS_Local(Mat, PetscInt, const IS[], const IS[], MatReuse, PetscBool, Mat *);
3548: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowDist(Mat mat, IS isrow, IS iscol, IS iscol_local, MatReuse call, Mat *newmat)
3549: {
3550: PetscInt i, m, n, rstart, row, rend, nz, j, bs, cbs;
3551: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3552: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3553: Mat M, Msub, B = a->B;
3554: MatScalar *aa;
3555: Mat_SeqAIJ *aij;
3556: PetscInt *garray = a->garray, *colsub, Ncols;
3557: PetscInt count, Bn = B->cmap->N, cstart = mat->cmap->rstart, cend = mat->cmap->rend;
3558: IS iscol_sub, iscmap;
3559: const PetscInt *is_idx, *cmap;
3560: PetscBool allcolumns = PETSC_FALSE;
3561: MPI_Comm comm;
3563: PetscFunctionBegin;
3564: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3565: if (call == MAT_REUSE_MATRIX) {
3566: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3567: PetscCheck(iscol_sub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "SubIScol passed in was not used before, cannot reuse");
3568: PetscCall(ISGetLocalSize(iscol_sub, &count));
3570: PetscCall(PetscObjectQuery((PetscObject)*newmat, "Subcmap", (PetscObject *)&iscmap));
3571: PetscCheck(iscmap, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Subcmap passed in was not used before, cannot reuse");
3573: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Msub));
3574: PetscCheck(Msub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3576: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_REUSE_MATRIX, PETSC_FALSE, &Msub));
3578: } else { /* call == MAT_INITIAL_MATRIX) */
3579: PetscBool flg;
3581: PetscCall(ISGetLocalSize(iscol, &n));
3582: PetscCall(ISGetSize(iscol, &Ncols));
3584: /* (1) iscol -> nonscalable iscol_local */
3585: /* Check for special case: each processor gets entire matrix columns */
3586: PetscCall(ISIdentity(iscol_local, &flg));
3587: if (flg && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3588: PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3589: if (allcolumns) {
3590: iscol_sub = iscol_local;
3591: PetscCall(PetscObjectReference((PetscObject)iscol_local));
3592: PetscCall(ISCreateStride(PETSC_COMM_SELF, n, 0, 1, &iscmap));
3594: } else {
3595: /* (2) iscol_local -> iscol_sub and iscmap. Implementation below requires iscol_local be sorted, it can have duplicate indices */
3596: PetscInt *idx, *cmap1, k;
3597: PetscCall(PetscMalloc1(Ncols, &idx));
3598: PetscCall(PetscMalloc1(Ncols, &cmap1));
3599: PetscCall(ISGetIndices(iscol_local, &is_idx));
3600: count = 0;
3601: k = 0;
3602: for (i = 0; i < Ncols; i++) {
3603: j = is_idx[i];
3604: if (j >= cstart && j < cend) {
3605: /* diagonal part of mat */
3606: idx[count] = j;
3607: cmap1[count++] = i; /* column index in submat */
3608: } else if (Bn) {
3609: /* off-diagonal part of mat */
3610: if (j == garray[k]) {
3611: idx[count] = j;
3612: cmap1[count++] = i; /* column index in submat */
3613: } else if (j > garray[k]) {
3614: while (j > garray[k] && k < Bn - 1) k++;
3615: if (j == garray[k]) {
3616: idx[count] = j;
3617: cmap1[count++] = i; /* column index in submat */
3618: }
3619: }
3620: }
3621: }
3622: PetscCall(ISRestoreIndices(iscol_local, &is_idx));
3624: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_OWN_POINTER, &iscol_sub));
3625: PetscCall(ISGetBlockSize(iscol, &cbs));
3626: PetscCall(ISSetBlockSize(iscol_sub, cbs));
3628: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)iscol_local), count, cmap1, PETSC_OWN_POINTER, &iscmap));
3629: }
3631: /* (3) Create sequential Msub */
3632: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_INITIAL_MATRIX, allcolumns, &Msub));
3633: }
3635: PetscCall(ISGetLocalSize(iscol_sub, &count));
3636: aij = (Mat_SeqAIJ *)(Msub)->data;
3637: ii = aij->i;
3638: PetscCall(ISGetIndices(iscmap, &cmap));
3640: /*
3641: m - number of local rows
3642: Ncols - number of columns (same on all processors)
3643: rstart - first row in new global matrix generated
3644: */
3645: PetscCall(MatGetSize(Msub, &m, NULL));
3647: if (call == MAT_INITIAL_MATRIX) {
3648: /* (4) Create parallel newmat */
3649: PetscMPIInt rank, size;
3650: PetscInt csize;
3652: PetscCallMPI(MPI_Comm_size(comm, &size));
3653: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3655: /*
3656: Determine the number of non-zeros in the diagonal and off-diagonal
3657: portions of the matrix in order to do correct preallocation
3658: */
3660: /* first get start and end of "diagonal" columns */
3661: PetscCall(ISGetLocalSize(iscol, &csize));
3662: if (csize == PETSC_DECIDE) {
3663: PetscCall(ISGetSize(isrow, &mglobal));
3664: if (mglobal == Ncols) { /* square matrix */
3665: nlocal = m;
3666: } else {
3667: nlocal = Ncols / size + ((Ncols % size) > rank);
3668: }
3669: } else {
3670: nlocal = csize;
3671: }
3672: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3673: rstart = rend - nlocal;
3674: 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);
3676: /* next, compute all the lengths */
3677: jj = aij->j;
3678: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3679: olens = dlens + m;
3680: for (i = 0; i < m; i++) {
3681: jend = ii[i + 1] - ii[i];
3682: olen = 0;
3683: dlen = 0;
3684: for (j = 0; j < jend; j++) {
3685: if (cmap[*jj] < rstart || cmap[*jj] >= rend) olen++;
3686: else dlen++;
3687: jj++;
3688: }
3689: olens[i] = olen;
3690: dlens[i] = dlen;
3691: }
3693: PetscCall(ISGetBlockSize(isrow, &bs));
3694: PetscCall(ISGetBlockSize(iscol, &cbs));
3696: PetscCall(MatCreate(comm, &M));
3697: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, Ncols));
3698: PetscCall(MatSetBlockSizes(M, bs, cbs));
3699: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3700: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3701: PetscCall(PetscFree(dlens));
3703: } else { /* call == MAT_REUSE_MATRIX */
3704: M = *newmat;
3705: PetscCall(MatGetLocalSize(M, &i, NULL));
3706: PetscCheck(i == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3707: PetscCall(MatZeroEntries(M));
3708: /*
3709: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3710: rather than the slower MatSetValues().
3711: */
3712: M->was_assembled = PETSC_TRUE;
3713: M->assembled = PETSC_FALSE;
3714: }
3716: /* (5) Set values of Msub to *newmat */
3717: PetscCall(PetscMalloc1(count, &colsub));
3718: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
3720: jj = aij->j;
3721: PetscCall(MatSeqAIJGetArrayRead(Msub, (const PetscScalar **)&aa));
3722: for (i = 0; i < m; i++) {
3723: row = rstart + i;
3724: nz = ii[i + 1] - ii[i];
3725: for (j = 0; j < nz; j++) colsub[j] = cmap[jj[j]];
3726: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, colsub, aa, INSERT_VALUES));
3727: jj += nz;
3728: aa += nz;
3729: }
3730: PetscCall(MatSeqAIJRestoreArrayRead(Msub, (const PetscScalar **)&aa));
3731: PetscCall(ISRestoreIndices(iscmap, &cmap));
3733: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3734: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3736: PetscCall(PetscFree(colsub));
3738: /* save Msub, iscol_sub and iscmap used in processor for next request */
3739: if (call == MAT_INITIAL_MATRIX) {
3740: *newmat = M;
3741: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "SubMatrix", (PetscObject)Msub));
3742: PetscCall(MatDestroy(&Msub));
3744: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "SubIScol", (PetscObject)iscol_sub));
3745: PetscCall(ISDestroy(&iscol_sub));
3747: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "Subcmap", (PetscObject)iscmap));
3748: PetscCall(ISDestroy(&iscmap));
3750: if (iscol_local) {
3751: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "ISAllGather", (PetscObject)iscol_local));
3752: PetscCall(ISDestroy(&iscol_local));
3753: }
3754: }
3755: PetscFunctionReturn(PETSC_SUCCESS);
3756: }
3758: /*
3759: Not great since it makes two copies of the submatrix, first an SeqAIJ
3760: in local and then by concatenating the local matrices the end result.
3761: Writing it directly would be much like MatCreateSubMatrices_MPIAIJ()
3763: This requires a sequential iscol with all indices.
3764: */
3765: PetscErrorCode MatCreateSubMatrix_MPIAIJ_nonscalable(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
3766: {
3767: PetscMPIInt rank, size;
3768: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs, cbs;
3769: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3770: Mat M, Mreuse;
3771: MatScalar *aa, *vwork;
3772: MPI_Comm comm;
3773: Mat_SeqAIJ *aij;
3774: PetscBool colflag, allcolumns = PETSC_FALSE;
3776: PetscFunctionBegin;
3777: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3778: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3779: PetscCallMPI(MPI_Comm_size(comm, &size));
3781: /* Check for special case: each processor gets entire matrix columns */
3782: PetscCall(ISIdentity(iscol, &colflag));
3783: PetscCall(ISGetLocalSize(iscol, &n));
3784: if (colflag && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3785: PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3787: if (call == MAT_REUSE_MATRIX) {
3788: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
3789: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3790: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_REUSE_MATRIX, allcolumns, &Mreuse));
3791: } else {
3792: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_INITIAL_MATRIX, allcolumns, &Mreuse));
3793: }
3795: /*
3796: m - number of local rows
3797: n - number of columns (same on all processors)
3798: rstart - first row in new global matrix generated
3799: */
3800: PetscCall(MatGetSize(Mreuse, &m, &n));
3801: PetscCall(MatGetBlockSizes(Mreuse, &bs, &cbs));
3802: if (call == MAT_INITIAL_MATRIX) {
3803: aij = (Mat_SeqAIJ *)(Mreuse)->data;
3804: ii = aij->i;
3805: jj = aij->j;
3807: /*
3808: Determine the number of non-zeros in the diagonal and off-diagonal
3809: portions of the matrix in order to do correct preallocation
3810: */
3812: /* first get start and end of "diagonal" columns */
3813: if (csize == PETSC_DECIDE) {
3814: PetscCall(ISGetSize(isrow, &mglobal));
3815: if (mglobal == n) { /* square matrix */
3816: nlocal = m;
3817: } else {
3818: nlocal = n / size + ((n % size) > rank);
3819: }
3820: } else {
3821: nlocal = csize;
3822: }
3823: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3824: rstart = rend - nlocal;
3825: 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);
3827: /* next, compute all the lengths */
3828: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3829: olens = dlens + m;
3830: for (i = 0; i < m; i++) {
3831: jend = ii[i + 1] - ii[i];
3832: olen = 0;
3833: dlen = 0;
3834: for (j = 0; j < jend; j++) {
3835: if (*jj < rstart || *jj >= rend) olen++;
3836: else dlen++;
3837: jj++;
3838: }
3839: olens[i] = olen;
3840: dlens[i] = dlen;
3841: }
3842: PetscCall(MatCreate(comm, &M));
3843: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, n));
3844: PetscCall(MatSetBlockSizes(M, bs, cbs));
3845: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3846: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3847: PetscCall(PetscFree(dlens));
3848: } else {
3849: PetscInt ml, nl;
3851: M = *newmat;
3852: PetscCall(MatGetLocalSize(M, &ml, &nl));
3853: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3854: PetscCall(MatZeroEntries(M));
3855: /*
3856: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3857: rather than the slower MatSetValues().
3858: */
3859: M->was_assembled = PETSC_TRUE;
3860: M->assembled = PETSC_FALSE;
3861: }
3862: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
3863: aij = (Mat_SeqAIJ *)(Mreuse)->data;
3864: ii = aij->i;
3865: jj = aij->j;
3867: /* trigger copy to CPU if needed */
3868: PetscCall(MatSeqAIJGetArrayRead(Mreuse, (const PetscScalar **)&aa));
3869: for (i = 0; i < m; i++) {
3870: row = rstart + i;
3871: nz = ii[i + 1] - ii[i];
3872: cwork = jj;
3873: jj += nz;
3874: vwork = aa;
3875: aa += nz;
3876: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
3877: }
3878: PetscCall(MatSeqAIJRestoreArrayRead(Mreuse, (const PetscScalar **)&aa));
3880: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3881: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3882: *newmat = M;
3884: /* save submatrix used in processor for next request */
3885: if (call == MAT_INITIAL_MATRIX) {
3886: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
3887: PetscCall(MatDestroy(&Mreuse));
3888: }
3889: PetscFunctionReturn(PETSC_SUCCESS);
3890: }
3892: PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
3893: {
3894: PetscInt m, cstart, cend, j, nnz, i, d, *ld;
3895: PetscInt *d_nnz, *o_nnz, nnz_max = 0, rstart, ii;
3896: const PetscInt *JJ;
3897: PetscBool nooffprocentries;
3898: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)B->data;
3900: PetscFunctionBegin;
3901: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);
3903: PetscCall(PetscLayoutSetUp(B->rmap));
3904: PetscCall(PetscLayoutSetUp(B->cmap));
3905: m = B->rmap->n;
3906: cstart = B->cmap->rstart;
3907: cend = B->cmap->rend;
3908: rstart = B->rmap->rstart;
3910: PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));
3912: if (PetscDefined(USE_DEBUG)) {
3913: for (i = 0; i < m; i++) {
3914: nnz = Ii[i + 1] - Ii[i];
3915: JJ = J + Ii[i];
3916: PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3917: 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]);
3918: 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);
3919: }
3920: }
3922: for (i = 0; i < m; i++) {
3923: nnz = Ii[i + 1] - Ii[i];
3924: JJ = J + Ii[i];
3925: nnz_max = PetscMax(nnz_max, nnz);
3926: d = 0;
3927: for (j = 0; j < nnz; j++) {
3928: if (cstart <= JJ[j] && JJ[j] < cend) d++;
3929: }
3930: d_nnz[i] = d;
3931: o_nnz[i] = nnz - d;
3932: }
3933: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3934: PetscCall(PetscFree2(d_nnz, o_nnz));
3936: for (i = 0; i < m; i++) {
3937: ii = i + rstart;
3938: PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], J + Ii[i], v ? v + Ii[i] : NULL, INSERT_VALUES));
3939: }
3940: nooffprocentries = B->nooffprocentries;
3941: B->nooffprocentries = PETSC_TRUE;
3942: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3943: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3944: B->nooffprocentries = nooffprocentries;
3946: /* count number of entries below block diagonal */
3947: PetscCall(PetscFree(Aij->ld));
3948: PetscCall(PetscCalloc1(m, &ld));
3949: Aij->ld = ld;
3950: for (i = 0; i < m; i++) {
3951: nnz = Ii[i + 1] - Ii[i];
3952: j = 0;
3953: while (j < nnz && J[j] < cstart) j++;
3954: ld[i] = j;
3955: J += nnz;
3956: }
3958: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3959: PetscFunctionReturn(PETSC_SUCCESS);
3960: }
3962: /*@
3963: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
3964: (the default parallel PETSc format).
3966: Collective
3968: Input Parameters:
3969: + B - the matrix
3970: . i - the indices into j for the start of each local row (starts with zero)
3971: . j - the column indices for each local row (starts with zero)
3972: - v - optional values in the matrix
3974: Level: developer
3976: Notes:
3977: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
3978: thus you CANNOT change the matrix entries by changing the values of `v` after you have
3979: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3981: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
3983: The format which is used for the sparse matrix input, is equivalent to a
3984: row-major ordering.. i.e for the following matrix, the input data expected is
3985: as shown
3987: .vb
3988: 1 0 0
3989: 2 0 3 P0
3990: -------
3991: 4 5 6 P1
3993: Process0 [P0] rows_owned=[0,1]
3994: i = {0,1,3} [size = nrow+1 = 2+1]
3995: j = {0,0,2} [size = 3]
3996: v = {1,2,3} [size = 3]
3998: Process1 [P1] rows_owned=[2]
3999: i = {0,3} [size = nrow+1 = 1+1]
4000: j = {0,1,2} [size = 3]
4001: v = {4,5,6} [size = 3]
4002: .ve
4004: .seealso: [](chapter_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`, `MATMPIAIJ`,
4005: `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`
4006: @*/
4007: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4008: {
4009: PetscFunctionBegin;
4010: PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4011: PetscFunctionReturn(PETSC_SUCCESS);
4012: }
4014: /*@C
4015: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
4016: (the default parallel PETSc format). For good matrix assembly performance
4017: the user should preallocate the matrix storage by setting the parameters
4018: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4020: Collective
4022: Input Parameters:
4023: + B - the matrix
4024: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4025: (same value is used for all local rows)
4026: . d_nnz - array containing the number of nonzeros in the various rows of the
4027: DIAGONAL portion of the local submatrix (possibly different for each row)
4028: or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4029: The size of this array is equal to the number of local rows, i.e 'm'.
4030: For matrices that will be factored, you must leave room for (and set)
4031: the diagonal entry even if it is zero.
4032: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4033: submatrix (same value is used for all local rows).
4034: - o_nnz - array containing the number of nonzeros in the various rows of the
4035: OFF-DIAGONAL portion of the local submatrix (possibly different for
4036: each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4037: structure. The size of this array is equal to the number
4038: of local rows, i.e 'm'.
4040: Usage:
4041: Consider the following 8x8 matrix with 34 non-zero values, that is
4042: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4043: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4044: as follows
4046: .vb
4047: 1 2 0 | 0 3 0 | 0 4
4048: Proc0 0 5 6 | 7 0 0 | 8 0
4049: 9 0 10 | 11 0 0 | 12 0
4050: -------------------------------------
4051: 13 0 14 | 15 16 17 | 0 0
4052: Proc1 0 18 0 | 19 20 21 | 0 0
4053: 0 0 0 | 22 23 0 | 24 0
4054: -------------------------------------
4055: Proc2 25 26 27 | 0 0 28 | 29 0
4056: 30 0 0 | 31 32 33 | 0 34
4057: .ve
4059: This can be represented as a collection of submatrices as
4060: .vb
4061: A B C
4062: D E F
4063: G H I
4064: .ve
4066: Where the submatrices A,B,C are owned by proc0, D,E,F are
4067: owned by proc1, G,H,I are owned by proc2.
4069: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4070: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4071: The 'M','N' parameters are 8,8, and have the same values on all procs.
4073: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4074: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4075: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4076: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4077: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4078: matrix, ans [DF] as another `MATSEQAIJ` matrix.
4080: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4081: allocated for every row of the local diagonal submatrix, and `o_nz`
4082: storage locations are allocated for every row of the OFF-DIAGONAL submat.
4083: One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local
4084: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4085: In this case, the values of `d_nz`, `o_nz` are
4086: .vb
4087: proc0 dnz = 2, o_nz = 2
4088: proc1 dnz = 3, o_nz = 2
4089: proc2 dnz = 1, o_nz = 4
4090: .ve
4091: We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4092: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4093: for proc3. i.e we are using 12+15+10=37 storage locations to store
4094: 34 values.
4096: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4097: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4098: In the above case the values for `d_nnz`, `o_nnz` are
4099: .vb
4100: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4101: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4102: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4103: .ve
4104: Here the space allocated is sum of all the above values i.e 34, and
4105: hence pre-allocation is perfect.
4107: Level: intermediate
4109: Notes:
4110: If the *_nnz parameter is given then the *_nz parameter is ignored
4112: The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4113: storage. The stored row and column indices begin with zero.
4114: See [Sparse Matrices](sec_matsparse) for details.
4116: The parallel matrix is partitioned such that the first m0 rows belong to
4117: process 0, the next m1 rows belong to process 1, the next m2 rows belong
4118: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
4120: The DIAGONAL portion of the local submatrix of a processor can be defined
4121: as the submatrix which is obtained by extraction the part corresponding to
4122: the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4123: first row that belongs to the processor, r2 is the last row belonging to
4124: the this processor, and c1-c2 is range of indices of the local part of a
4125: vector suitable for applying the matrix to. This is an mxn matrix. In the
4126: common case of a square matrix, the row and column ranges are the same and
4127: the DIAGONAL part is also square. The remaining portion of the local
4128: submatrix (mxN) constitute the OFF-DIAGONAL portion.
4130: If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.
4132: You can call `MatGetInfo()` to get information on how effective the preallocation was;
4133: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4134: You can also run with the option `-info` and look for messages with the string
4135: malloc in them to see if additional memory allocation was needed.
4137: .seealso: [](chapter_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4138: `MATMPIAIJ`, `MatGetInfo()`, `PetscSplitOwnership()`
4139: @*/
4140: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4141: {
4142: PetscFunctionBegin;
4145: PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4146: PetscFunctionReturn(PETSC_SUCCESS);
4147: }
4149: /*@
4150: MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4151: CSR format for the local rows.
4153: Collective
4155: Input Parameters:
4156: + comm - MPI communicator
4157: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4158: . n - This value should be the same as the local size used in creating the
4159: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4160: calculated if N is given) For square matrices n is almost always m.
4161: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4162: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4163: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
4164: . j - column indices
4165: - a - optional matrix values
4167: Output Parameter:
4168: . mat - the matrix
4170: Level: intermediate
4172: Notes:
4173: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4174: thus you CANNOT change the matrix entries by changing the values of a[] after you have
4175: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
4177: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
4179: The format which is used for the sparse matrix input, is equivalent to a
4180: row-major ordering.. i.e for the following matrix, the input data expected is
4181: as shown
4183: Once you have created the matrix you can update it with new numerical values using MatUpdateMPIAIJWithArrays
4184: .vb
4185: 1 0 0
4186: 2 0 3 P0
4187: -------
4188: 4 5 6 P1
4190: Process0 [P0] rows_owned=[0,1]
4191: i = {0,1,3} [size = nrow+1 = 2+1]
4192: j = {0,0,2} [size = 3]
4193: v = {1,2,3} [size = 3]
4195: Process1 [P1] rows_owned=[2]
4196: i = {0,3} [size = nrow+1 = 1+1]
4197: j = {0,1,2} [size = 3]
4198: v = {4,5,6} [size = 3]
4199: .ve
4201: .seealso: [](chapter_matrices), `Mat`, `MATMPIAIK`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4202: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`
4203: @*/
4204: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
4205: {
4206: PetscFunctionBegin;
4207: PetscCheck(!i || !i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4208: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4209: PetscCall(MatCreate(comm, mat));
4210: PetscCall(MatSetSizes(*mat, m, n, M, N));
4211: /* PetscCall(MatSetBlockSizes(M,bs,cbs)); */
4212: PetscCall(MatSetType(*mat, MATMPIAIJ));
4213: PetscCall(MatMPIAIJSetPreallocationCSR(*mat, i, j, a));
4214: PetscFunctionReturn(PETSC_SUCCESS);
4215: }
4217: /*@
4218: MatUpdateMPIAIJWithArrays - updates a `MATMPIAIJ` matrix using arrays that contain in standard
4219: CSR format for the local rows. Only the numerical values are updated the other arrays must be identical to what was passed
4220: from `MatCreateMPIAIJWithArrays()`
4222: Deprecated: Use `MatUpdateMPIAIJWithArray()`
4224: Collective
4226: Input Parameters:
4227: + mat - the matrix
4228: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4229: . n - This value should be the same as the local size used in creating the
4230: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4231: calculated if N is given) For square matrices n is almost always m.
4232: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4233: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4234: . Ii - row indices; that is Ii[0] = 0, Ii[row] = Ii[row-1] + number of elements in that row of the matrix
4235: . J - column indices
4236: - v - matrix values
4238: Level: deprecated
4240: .seealso: [](chapter_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4241: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatUpdateMPIAIJWithArray()`
4242: @*/
4243: PetscErrorCode MatUpdateMPIAIJWithArrays(Mat mat, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4244: {
4245: PetscInt nnz, i;
4246: PetscBool nooffprocentries;
4247: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4248: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4249: PetscScalar *ad, *ao;
4250: PetscInt ldi, Iii, md;
4251: const PetscInt *Adi = Ad->i;
4252: PetscInt *ld = Aij->ld;
4254: PetscFunctionBegin;
4255: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4256: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4257: PetscCheck(m == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of rows cannot change from call to MatUpdateMPIAIJWithArrays()");
4258: PetscCheck(n == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of columns cannot change from call to MatUpdateMPIAIJWithArrays()");
4260: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4261: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4263: for (i = 0; i < m; i++) {
4264: nnz = Ii[i + 1] - Ii[i];
4265: Iii = Ii[i];
4266: ldi = ld[i];
4267: md = Adi[i + 1] - Adi[i];
4268: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4269: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4270: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4271: ad += md;
4272: ao += nnz - md;
4273: }
4274: nooffprocentries = mat->nooffprocentries;
4275: mat->nooffprocentries = PETSC_TRUE;
4276: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4277: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4278: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4279: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4280: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4281: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4282: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4283: mat->nooffprocentries = nooffprocentries;
4284: PetscFunctionReturn(PETSC_SUCCESS);
4285: }
4287: /*@
4288: MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values
4290: Collective
4292: Input Parameters:
4293: + mat - the matrix
4294: - v - matrix values, stored by row
4296: Level: intermediate
4298: Note:
4299: The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`
4301: .seealso: [](chapter_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4302: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatUpdateMPIAIJWithArrays()`
4303: @*/
4304: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4305: {
4306: PetscInt nnz, i, m;
4307: PetscBool nooffprocentries;
4308: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4309: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4310: Mat_SeqAIJ *Ao = (Mat_SeqAIJ *)Aij->B->data;
4311: PetscScalar *ad, *ao;
4312: const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4313: PetscInt ldi, Iii, md;
4314: PetscInt *ld = Aij->ld;
4316: PetscFunctionBegin;
4317: m = mat->rmap->n;
4319: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4320: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4321: Iii = 0;
4322: for (i = 0; i < m; i++) {
4323: nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4324: ldi = ld[i];
4325: md = Adi[i + 1] - Adi[i];
4326: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4327: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4328: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4329: ad += md;
4330: ao += nnz - md;
4331: Iii += nnz;
4332: }
4333: nooffprocentries = mat->nooffprocentries;
4334: mat->nooffprocentries = PETSC_TRUE;
4335: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4336: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4337: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4338: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4339: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4340: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4341: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4342: mat->nooffprocentries = nooffprocentries;
4343: PetscFunctionReturn(PETSC_SUCCESS);
4344: }
4346: /*@C
4347: MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4348: (the default parallel PETSc format). For good matrix assembly performance
4349: the user should preallocate the matrix storage by setting the parameters
4350: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4352: Collective
4354: Input Parameters:
4355: + comm - MPI communicator
4356: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4357: This value should be the same as the local size used in creating the
4358: y vector for the matrix-vector product y = Ax.
4359: . n - This value should be the same as the local size used in creating the
4360: x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
4361: calculated if N is given) For square matrices n is almost always m.
4362: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4363: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4364: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4365: (same value is used for all local rows)
4366: . d_nnz - array containing the number of nonzeros in the various rows of the
4367: DIAGONAL portion of the local submatrix (possibly different for each row)
4368: or `NULL`, if `d_nz` is used to specify the nonzero structure.
4369: The size of this array is equal to the number of local rows, i.e 'm'.
4370: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4371: submatrix (same value is used for all local rows).
4372: - o_nnz - array containing the number of nonzeros in the various rows of the
4373: OFF-DIAGONAL portion of the local submatrix (possibly different for
4374: each row) or `NULL`, if `o_nz` is used to specify the nonzero
4375: structure. The size of this array is equal to the number
4376: of local rows, i.e 'm'.
4378: Output Parameter:
4379: . A - the matrix
4381: Options Database Keys:
4382: + -mat_no_inode - Do not use inodes
4383: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
4384: - -matmult_vecscatter_view <viewer> - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4385: See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the vecscatter be viewed as a matrix.
4386: Entry (i,j) is the size of message (in bytes) rank i sends to rank j in one `MatMult()` call.
4388: Level: intermediate
4390: Notes:
4391: It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4392: MatXXXXSetPreallocation() paradigm instead of this routine directly.
4393: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
4395: If the *_nnz parameter is given then the *_nz parameter is ignored
4397: The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4398: processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4399: storage requirements for this matrix.
4401: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one
4402: processor than it must be used on all processors that share the object for
4403: that argument.
4405: The user MUST specify either the local or global matrix dimensions
4406: (possibly both).
4408: The parallel matrix is partitioned across processors such that the
4409: first m0 rows belong to process 0, the next m1 rows belong to
4410: process 1, the next m2 rows belong to process 2 etc.. where
4411: m0,m1,m2,.. are the input parameter 'm'. i.e each processor stores
4412: values corresponding to [m x N] submatrix.
4414: The columns are logically partitioned with the n0 columns belonging
4415: to 0th partition, the next n1 columns belonging to the next
4416: partition etc.. where n0,n1,n2... are the input parameter 'n'.
4418: The DIAGONAL portion of the local submatrix on any given processor
4419: is the submatrix corresponding to the rows and columns m,n
4420: corresponding to the given processor. i.e diagonal matrix on
4421: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4422: etc. The remaining portion of the local submatrix [m x (N-n)]
4423: constitute the OFF-DIAGONAL portion. The example below better
4424: illustrates this concept.
4426: For a square global matrix we define each processor's diagonal portion
4427: to be its local rows and the corresponding columns (a square submatrix);
4428: each processor's off-diagonal portion encompasses the remainder of the
4429: local matrix (a rectangular submatrix).
4431: If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.
4433: When calling this routine with a single process communicator, a matrix of
4434: type `MATSEQAIJ` is returned. If a matrix of type `MATMPIAIJ` is desired for this
4435: type of communicator, use the construction mechanism
4436: .vb
4437: MatCreate(...,&A);
4438: MatSetType(A,MATMPIAIJ);
4439: MatSetSizes(A, m,n,M,N);
4440: MatMPIAIJSetPreallocation(A,...);
4441: .ve
4443: By default, this format uses inodes (identical nodes) when possible.
4444: We search for consecutive rows with the same nonzero structure, thereby
4445: reusing matrix information to achieve increased efficiency.
4447: Usage:
4448: Consider the following 8x8 matrix with 34 non-zero values, that is
4449: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4450: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4451: as follows
4453: .vb
4454: 1 2 0 | 0 3 0 | 0 4
4455: Proc0 0 5 6 | 7 0 0 | 8 0
4456: 9 0 10 | 11 0 0 | 12 0
4457: -------------------------------------
4458: 13 0 14 | 15 16 17 | 0 0
4459: Proc1 0 18 0 | 19 20 21 | 0 0
4460: 0 0 0 | 22 23 0 | 24 0
4461: -------------------------------------
4462: Proc2 25 26 27 | 0 0 28 | 29 0
4463: 30 0 0 | 31 32 33 | 0 34
4464: .ve
4466: This can be represented as a collection of submatrices as
4468: .vb
4469: A B C
4470: D E F
4471: G H I
4472: .ve
4474: Where the submatrices A,B,C are owned by proc0, D,E,F are
4475: owned by proc1, G,H,I are owned by proc2.
4477: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4478: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4479: The 'M','N' parameters are 8,8, and have the same values on all procs.
4481: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4482: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4483: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4484: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4485: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4486: matrix, ans [DF] as another SeqAIJ matrix.
4488: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4489: allocated for every row of the local diagonal submatrix, and `o_nz`
4490: storage locations are allocated for every row of the OFF-DIAGONAL submat.
4491: One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local
4492: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4493: In this case, the values of `d_nz`,`o_nz` are
4494: .vb
4495: proc0 dnz = 2, o_nz = 2
4496: proc1 dnz = 3, o_nz = 2
4497: proc2 dnz = 1, o_nz = 4
4498: .ve
4499: We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4500: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4501: for proc3. i.e we are using 12+15+10=37 storage locations to store
4502: 34 values.
4504: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4505: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4506: In the above case the values for d_nnz,o_nnz are
4507: .vb
4508: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4509: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4510: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4511: .ve
4512: Here the space allocated is sum of all the above values i.e 34, and
4513: hence pre-allocation is perfect.
4515: .seealso: [](chapter_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4516: `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`
4517: @*/
4518: 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)
4519: {
4520: PetscMPIInt size;
4522: PetscFunctionBegin;
4523: PetscCall(MatCreate(comm, A));
4524: PetscCall(MatSetSizes(*A, m, n, M, N));
4525: PetscCallMPI(MPI_Comm_size(comm, &size));
4526: if (size > 1) {
4527: PetscCall(MatSetType(*A, MATMPIAIJ));
4528: PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4529: } else {
4530: PetscCall(MatSetType(*A, MATSEQAIJ));
4531: PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4532: }
4533: PetscFunctionReturn(PETSC_SUCCESS);
4534: }
4536: /*MC
4537: MatMPIAIJGetSeqAIJF90 - Returns the local pieces of this distributed matrix
4539: Synopsis:
4540: MatMPIAIJGetSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)
4542: Not Collective
4544: Input Parameter:
4545: . A - the `MATMPIAIJ` matrix
4547: Output Parameters:
4548: + Ad - the diagonal portion of the matrix
4549: . Ao - the off diagonal portion of the matrix
4550: . colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4551: - ierr - error code
4553: Level: advanced
4555: Note:
4556: Use `MatMPIAIJRestoreSeqAIJF90()` when you no longer need access to the matrices and `colmap`
4558: .seealso: [](chapter_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJRestoreSeqAIJF90()`
4559: M*/
4561: /*MC
4562: MatMPIAIJRestoreSeqAIJF90 - call after `MatMPIAIJGetSeqAIJF90()` when you no longer need access to the matrices and `colmap`
4564: Synopsis:
4565: MatMPIAIJRestoreSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)
4567: Not Collective
4569: Input Parameters:
4570: + A - the `MATMPIAIJ` matrix
4571: . Ad - the diagonal portion of the matrix
4572: . Ao - the off diagonal portion of the matrix
4573: . colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4574: - ierr - error code
4576: Level: advanced
4578: .seealso: [](chapter_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJGetSeqAIJF90()`
4579: M*/
4581: /*@C
4582: MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix
4584: Not Collective
4586: Input Parameter:
4587: . A - The `MATMPIAIJ` matrix
4589: Output Parameters:
4590: + Ad - The local diagonal block as a `MATSEQAIJ` matrix
4591: . Ao - The local off-diagonal block as a `MATSEQAIJ` matrix
4592: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4594: Level: intermediate
4596: Note:
4597: The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4598: 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
4599: the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4600: local column numbers to global column numbers in the original matrix.
4602: Fortran Note:
4603: `MatMPIAIJGetSeqAIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatMPIAIJGetSeqAIJF90()`
4605: .seealso: [](chapter_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJF90()`, `MatMPIAIJRestoreSeqAIJF90()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATMPIAIJ`, `MATSEQAIJ`
4606: @*/
4607: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4608: {
4609: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4610: PetscBool flg;
4612: PetscFunctionBegin;
4613: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4614: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4615: if (Ad) *Ad = a->A;
4616: if (Ao) *Ao = a->B;
4617: if (colmap) *colmap = a->garray;
4618: PetscFunctionReturn(PETSC_SUCCESS);
4619: }
4621: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4622: {
4623: PetscInt m, N, i, rstart, nnz, Ii;
4624: PetscInt *indx;
4625: PetscScalar *values;
4626: MatType rootType;
4628: PetscFunctionBegin;
4629: PetscCall(MatGetSize(inmat, &m, &N));
4630: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4631: PetscInt *dnz, *onz, sum, bs, cbs;
4633: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4634: /* Check sum(n) = N */
4635: PetscCall(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4636: PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);
4638: PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4639: rstart -= m;
4641: MatPreallocateBegin(comm, m, n, dnz, onz);
4642: for (i = 0; i < m; i++) {
4643: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4644: PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4645: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4646: }
4648: PetscCall(MatCreate(comm, outmat));
4649: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4650: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4651: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4652: PetscCall(MatGetRootType_Private(inmat, &rootType));
4653: PetscCall(MatSetType(*outmat, rootType));
4654: PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4655: PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4656: MatPreallocateEnd(dnz, onz);
4657: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4658: }
4660: /* numeric phase */
4661: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4662: for (i = 0; i < m; i++) {
4663: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4664: Ii = i + rstart;
4665: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4666: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4667: }
4668: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4669: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4670: PetscFunctionReturn(PETSC_SUCCESS);
4671: }
4673: PetscErrorCode MatFileSplit(Mat A, char *outfile)
4674: {
4675: PetscMPIInt rank;
4676: PetscInt m, N, i, rstart, nnz;
4677: size_t len;
4678: const PetscInt *indx;
4679: PetscViewer out;
4680: char *name;
4681: Mat B;
4682: const PetscScalar *values;
4684: PetscFunctionBegin;
4685: PetscCall(MatGetLocalSize(A, &m, NULL));
4686: PetscCall(MatGetSize(A, NULL, &N));
4687: /* Should this be the type of the diagonal block of A? */
4688: PetscCall(MatCreate(PETSC_COMM_SELF, &B));
4689: PetscCall(MatSetSizes(B, m, N, m, N));
4690: PetscCall(MatSetBlockSizesFromMats(B, A, A));
4691: PetscCall(MatSetType(B, MATSEQAIJ));
4692: PetscCall(MatSeqAIJSetPreallocation(B, 0, NULL));
4693: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
4694: for (i = 0; i < m; i++) {
4695: PetscCall(MatGetRow(A, i + rstart, &nnz, &indx, &values));
4696: PetscCall(MatSetValues(B, 1, &i, nnz, indx, values, INSERT_VALUES));
4697: PetscCall(MatRestoreRow(A, i + rstart, &nnz, &indx, &values));
4698: }
4699: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4700: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4702: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)A), &rank));
4703: PetscCall(PetscStrlen(outfile, &len));
4704: PetscCall(PetscMalloc1(len + 6, &name));
4705: PetscCall(PetscSNPrintf(name, len + 6, "%s.%d", outfile, rank));
4706: PetscCall(PetscViewerBinaryOpen(PETSC_COMM_SELF, name, FILE_MODE_APPEND, &out));
4707: PetscCall(PetscFree(name));
4708: PetscCall(MatView(B, out));
4709: PetscCall(PetscViewerDestroy(&out));
4710: PetscCall(MatDestroy(&B));
4711: PetscFunctionReturn(PETSC_SUCCESS);
4712: }
4714: static PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(void *data)
4715: {
4716: Mat_Merge_SeqsToMPI *merge = (Mat_Merge_SeqsToMPI *)data;
4718: PetscFunctionBegin;
4719: if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4720: PetscCall(PetscFree(merge->id_r));
4721: PetscCall(PetscFree(merge->len_s));
4722: PetscCall(PetscFree(merge->len_r));
4723: PetscCall(PetscFree(merge->bi));
4724: PetscCall(PetscFree(merge->bj));
4725: PetscCall(PetscFree(merge->buf_ri[0]));
4726: PetscCall(PetscFree(merge->buf_ri));
4727: PetscCall(PetscFree(merge->buf_rj[0]));
4728: PetscCall(PetscFree(merge->buf_rj));
4729: PetscCall(PetscFree(merge->coi));
4730: PetscCall(PetscFree(merge->coj));
4731: PetscCall(PetscFree(merge->owners_co));
4732: PetscCall(PetscLayoutDestroy(&merge->rowmap));
4733: PetscCall(PetscFree(merge));
4734: PetscFunctionReturn(PETSC_SUCCESS);
4735: }
4737: #include <../src/mat/utils/freespace.h>
4738: #include <petscbt.h>
4740: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4741: {
4742: MPI_Comm comm;
4743: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4744: PetscMPIInt size, rank, taga, *len_s;
4745: PetscInt N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj;
4746: PetscInt proc, m;
4747: PetscInt **buf_ri, **buf_rj;
4748: PetscInt k, anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4749: PetscInt nrows, **buf_ri_k, **nextrow, **nextai;
4750: MPI_Request *s_waits, *r_waits;
4751: MPI_Status *status;
4752: const MatScalar *aa, *a_a;
4753: MatScalar **abuf_r, *ba_i;
4754: Mat_Merge_SeqsToMPI *merge;
4755: PetscContainer container;
4757: PetscFunctionBegin;
4758: PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4759: PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));
4761: PetscCallMPI(MPI_Comm_size(comm, &size));
4762: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4764: PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4765: PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4766: PetscCall(PetscContainerGetPointer(container, (void **)&merge));
4767: PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4768: aa = a_a;
4770: bi = merge->bi;
4771: bj = merge->bj;
4772: buf_ri = merge->buf_ri;
4773: buf_rj = merge->buf_rj;
4775: PetscCall(PetscMalloc1(size, &status));
4776: owners = merge->rowmap->range;
4777: len_s = merge->len_s;
4779: /* send and recv matrix values */
4780: PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4781: PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));
4783: PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4784: for (proc = 0, k = 0; proc < size; proc++) {
4785: if (!len_s[proc]) continue;
4786: i = owners[proc];
4787: PetscCallMPI(MPI_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4788: k++;
4789: }
4791: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4792: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4793: PetscCall(PetscFree(status));
4795: PetscCall(PetscFree(s_waits));
4796: PetscCall(PetscFree(r_waits));
4798: /* insert mat values of mpimat */
4799: PetscCall(PetscMalloc1(N, &ba_i));
4800: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4802: for (k = 0; k < merge->nrecv; k++) {
4803: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4804: nrows = *(buf_ri_k[k]);
4805: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4806: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4807: }
4809: /* set values of ba */
4810: m = merge->rowmap->n;
4811: for (i = 0; i < m; i++) {
4812: arow = owners[rank] + i;
4813: bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4814: bnzi = bi[i + 1] - bi[i];
4815: PetscCall(PetscArrayzero(ba_i, bnzi));
4817: /* add local non-zero vals of this proc's seqmat into ba */
4818: anzi = ai[arow + 1] - ai[arow];
4819: aj = a->j + ai[arow];
4820: aa = a_a + ai[arow];
4821: nextaj = 0;
4822: for (j = 0; nextaj < anzi; j++) {
4823: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4824: ba_i[j] += aa[nextaj++];
4825: }
4826: }
4828: /* add received vals into ba */
4829: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4830: /* i-th row */
4831: if (i == *nextrow[k]) {
4832: anzi = *(nextai[k] + 1) - *nextai[k];
4833: aj = buf_rj[k] + *(nextai[k]);
4834: aa = abuf_r[k] + *(nextai[k]);
4835: nextaj = 0;
4836: for (j = 0; nextaj < anzi; j++) {
4837: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4838: ba_i[j] += aa[nextaj++];
4839: }
4840: }
4841: nextrow[k]++;
4842: nextai[k]++;
4843: }
4844: }
4845: PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4846: }
4847: PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4848: PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4849: PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));
4851: PetscCall(PetscFree(abuf_r[0]));
4852: PetscCall(PetscFree(abuf_r));
4853: PetscCall(PetscFree(ba_i));
4854: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4855: PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4856: PetscFunctionReturn(PETSC_SUCCESS);
4857: }
4859: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4860: {
4861: Mat B_mpi;
4862: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4863: PetscMPIInt size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4864: PetscInt **buf_rj, **buf_ri, **buf_ri_k;
4865: PetscInt M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4866: PetscInt len, proc, *dnz, *onz, bs, cbs;
4867: PetscInt k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4868: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4869: MPI_Request *si_waits, *sj_waits, *ri_waits, *rj_waits;
4870: MPI_Status *status;
4871: PetscFreeSpaceList free_space = NULL, current_space = NULL;
4872: PetscBT lnkbt;
4873: Mat_Merge_SeqsToMPI *merge;
4874: PetscContainer container;
4876: PetscFunctionBegin;
4877: PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));
4879: /* make sure it is a PETSc comm */
4880: PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4881: PetscCallMPI(MPI_Comm_size(comm, &size));
4882: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4884: PetscCall(PetscNew(&merge));
4885: PetscCall(PetscMalloc1(size, &status));
4887: /* determine row ownership */
4888: PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4889: PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4890: PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4891: PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4892: PetscCall(PetscLayoutSetUp(merge->rowmap));
4893: PetscCall(PetscMalloc1(size, &len_si));
4894: PetscCall(PetscMalloc1(size, &merge->len_s));
4896: m = merge->rowmap->n;
4897: owners = merge->rowmap->range;
4899: /* determine the number of messages to send, their lengths */
4900: len_s = merge->len_s;
4902: len = 0; /* length of buf_si[] */
4903: merge->nsend = 0;
4904: for (proc = 0; proc < size; proc++) {
4905: len_si[proc] = 0;
4906: if (proc == rank) {
4907: len_s[proc] = 0;
4908: } else {
4909: len_si[proc] = owners[proc + 1] - owners[proc] + 1;
4910: len_s[proc] = ai[owners[proc + 1]] - ai[owners[proc]]; /* num of rows to be sent to [proc] */
4911: }
4912: if (len_s[proc]) {
4913: merge->nsend++;
4914: nrows = 0;
4915: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4916: if (ai[i + 1] > ai[i]) nrows++;
4917: }
4918: len_si[proc] = 2 * (nrows + 1);
4919: len += len_si[proc];
4920: }
4921: }
4923: /* determine the number and length of messages to receive for ij-structure */
4924: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4925: PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));
4927: /* post the Irecv of j-structure */
4928: PetscCall(PetscCommGetNewTag(comm, &tagj));
4929: PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));
4931: /* post the Isend of j-structure */
4932: PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));
4934: for (proc = 0, k = 0; proc < size; proc++) {
4935: if (!len_s[proc]) continue;
4936: i = owners[proc];
4937: PetscCallMPI(MPI_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4938: k++;
4939: }
4941: /* receives and sends of j-structure are complete */
4942: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4943: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));
4945: /* send and recv i-structure */
4946: PetscCall(PetscCommGetNewTag(comm, &tagi));
4947: PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));
4949: PetscCall(PetscMalloc1(len + 1, &buf_s));
4950: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4951: for (proc = 0, k = 0; proc < size; proc++) {
4952: if (!len_s[proc]) continue;
4953: /* form outgoing message for i-structure:
4954: buf_si[0]: nrows to be sent
4955: [1:nrows]: row index (global)
4956: [nrows+1:2*nrows+1]: i-structure index
4957: */
4958: nrows = len_si[proc] / 2 - 1;
4959: buf_si_i = buf_si + nrows + 1;
4960: buf_si[0] = nrows;
4961: buf_si_i[0] = 0;
4962: nrows = 0;
4963: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4964: anzi = ai[i + 1] - ai[i];
4965: if (anzi) {
4966: buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4967: buf_si[nrows + 1] = i - owners[proc]; /* local row index */
4968: nrows++;
4969: }
4970: }
4971: PetscCallMPI(MPI_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4972: k++;
4973: buf_si += len_si[proc];
4974: }
4976: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4977: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));
4979: PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4980: 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]));
4982: PetscCall(PetscFree(len_si));
4983: PetscCall(PetscFree(len_ri));
4984: PetscCall(PetscFree(rj_waits));
4985: PetscCall(PetscFree2(si_waits, sj_waits));
4986: PetscCall(PetscFree(ri_waits));
4987: PetscCall(PetscFree(buf_s));
4988: PetscCall(PetscFree(status));
4990: /* compute a local seq matrix in each processor */
4991: /* allocate bi array and free space for accumulating nonzero column info */
4992: PetscCall(PetscMalloc1(m + 1, &bi));
4993: bi[0] = 0;
4995: /* create and initialize a linked list */
4996: nlnk = N + 1;
4997: PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));
4999: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
5000: len = ai[owners[rank + 1]] - ai[owners[rank]];
5001: PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));
5003: current_space = free_space;
5005: /* determine symbolic info for each local row */
5006: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
5008: for (k = 0; k < merge->nrecv; k++) {
5009: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
5010: nrows = *buf_ri_k[k];
5011: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
5012: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
5013: }
5015: MatPreallocateBegin(comm, m, n, dnz, onz);
5016: len = 0;
5017: for (i = 0; i < m; i++) {
5018: bnzi = 0;
5019: /* add local non-zero cols of this proc's seqmat into lnk */
5020: arow = owners[rank] + i;
5021: anzi = ai[arow + 1] - ai[arow];
5022: aj = a->j + ai[arow];
5023: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5024: bnzi += nlnk;
5025: /* add received col data into lnk */
5026: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
5027: if (i == *nextrow[k]) { /* i-th row */
5028: anzi = *(nextai[k] + 1) - *nextai[k];
5029: aj = buf_rj[k] + *nextai[k];
5030: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5031: bnzi += nlnk;
5032: nextrow[k]++;
5033: nextai[k]++;
5034: }
5035: }
5036: if (len < bnzi) len = bnzi; /* =max(bnzi) */
5038: /* if free space is not available, make more free space */
5039: if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), ¤t_space));
5040: /* copy data into free space, then initialize lnk */
5041: PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
5042: PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));
5044: current_space->array += bnzi;
5045: current_space->local_used += bnzi;
5046: current_space->local_remaining -= bnzi;
5048: bi[i + 1] = bi[i] + bnzi;
5049: }
5051: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
5053: PetscCall(PetscMalloc1(bi[m] + 1, &bj));
5054: PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
5055: PetscCall(PetscLLDestroy(lnk, lnkbt));
5057: /* create symbolic parallel matrix B_mpi */
5058: PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
5059: PetscCall(MatCreate(comm, &B_mpi));
5060: if (n == PETSC_DECIDE) {
5061: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
5062: } else {
5063: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
5064: }
5065: PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
5066: PetscCall(MatSetType(B_mpi, MATMPIAIJ));
5067: PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
5068: MatPreallocateEnd(dnz, onz);
5069: PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));
5071: /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
5072: B_mpi->assembled = PETSC_FALSE;
5073: merge->bi = bi;
5074: merge->bj = bj;
5075: merge->buf_ri = buf_ri;
5076: merge->buf_rj = buf_rj;
5077: merge->coi = NULL;
5078: merge->coj = NULL;
5079: merge->owners_co = NULL;
5081: PetscCall(PetscCommDestroy(&comm));
5083: /* attach the supporting struct to B_mpi for reuse */
5084: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5085: PetscCall(PetscContainerSetPointer(container, merge));
5086: PetscCall(PetscContainerSetUserDestroy(container, MatDestroy_MPIAIJ_SeqsToMPI));
5087: PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5088: PetscCall(PetscContainerDestroy(&container));
5089: *mpimat = B_mpi;
5091: PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5092: PetscFunctionReturn(PETSC_SUCCESS);
5093: }
5095: /*@C
5096: MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5097: matrices from each processor
5099: Collective
5101: Input Parameters:
5102: + comm - the communicators the parallel matrix will live on
5103: . seqmat - the input sequential matrices
5104: . m - number of local rows (or `PETSC_DECIDE`)
5105: . n - number of local columns (or `PETSC_DECIDE`)
5106: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5108: Output Parameter:
5109: . mpimat - the parallel matrix generated
5111: Level: advanced
5113: Note:
5114: The dimensions of the sequential matrix in each processor MUST be the same.
5115: The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
5116: destroyed when mpimat is destroyed. Call `PetscObjectQuery()` to access seqmat.
5118: seealso: [](chapter_matrices), `Mat`, `MatCreateAIJ()`
5119: @*/
5120: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5121: {
5122: PetscMPIInt size;
5124: PetscFunctionBegin;
5125: PetscCallMPI(MPI_Comm_size(comm, &size));
5126: if (size == 1) {
5127: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5128: if (scall == MAT_INITIAL_MATRIX) {
5129: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5130: } else {
5131: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5132: }
5133: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5134: PetscFunctionReturn(PETSC_SUCCESS);
5135: }
5136: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5137: if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5138: PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5139: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5140: PetscFunctionReturn(PETSC_SUCCESS);
5141: }
5143: /*@
5144: MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix by taking its local rows and putting them into a sequential matrix with
5145: mlocal rows and n columns. Where mlocal is obtained with `MatGetLocalSize()` and n is the global column count obtained
5146: with `MatGetSize()`
5148: Not Collective
5150: Input Parameter:
5151: . A - the matrix
5153: Output Parameter:
5154: . A_loc - the local sequential matrix generated
5156: Level: developer
5158: Notes:
5159: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5161: Destroy the matrix with `MatDestroy()`
5163: .seealso: [](chapter_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5164: @*/
5165: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5166: {
5167: PetscBool mpi;
5169: PetscFunctionBegin;
5170: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5171: if (mpi) {
5172: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5173: } else {
5174: *A_loc = A;
5175: PetscCall(PetscObjectReference((PetscObject)*A_loc));
5176: }
5177: PetscFunctionReturn(PETSC_SUCCESS);
5178: }
5180: /*@
5181: MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5182: mlocal rows and n columns. Where mlocal is the row count obtained with `MatGetLocalSize()` and n is the global column count obtained
5183: with `MatGetSize()`
5185: Not Collective
5187: Input Parameters:
5188: + A - the matrix
5189: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5191: Output Parameter:
5192: . A_loc - the local sequential matrix generated
5194: Level: developer
5196: Notes:
5197: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5199: When the communicator associated with `A` has size 1 and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A`.
5200: If `MAT_REUSE_MATRIX` is requested with comm size 1, `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called.
5201: This means that one can preallocate the proper sequential matrix first and then call this routine with `MAT_REUSE_MATRIX` to safely
5202: modify the values of the returned `A_loc`.
5204: .seealso: [](chapter_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5205: @*/
5206: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5207: {
5208: Mat_MPIAIJ *mpimat = (Mat_MPIAIJ *)A->data;
5209: Mat_SeqAIJ *mat, *a, *b;
5210: PetscInt *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5211: const PetscScalar *aa, *ba, *aav, *bav;
5212: PetscScalar *ca, *cam;
5213: PetscMPIInt size;
5214: PetscInt am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5215: PetscInt *ci, *cj, col, ncols_d, ncols_o, jo;
5216: PetscBool match;
5218: PetscFunctionBegin;
5219: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5220: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5221: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5222: if (size == 1) {
5223: if (scall == MAT_INITIAL_MATRIX) {
5224: PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5225: *A_loc = mpimat->A;
5226: } else if (scall == MAT_REUSE_MATRIX) {
5227: PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5228: }
5229: PetscFunctionReturn(PETSC_SUCCESS);
5230: }
5232: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5233: a = (Mat_SeqAIJ *)(mpimat->A)->data;
5234: b = (Mat_SeqAIJ *)(mpimat->B)->data;
5235: ai = a->i;
5236: aj = a->j;
5237: bi = b->i;
5238: bj = b->j;
5239: PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5240: PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5241: aa = aav;
5242: ba = bav;
5243: if (scall == MAT_INITIAL_MATRIX) {
5244: PetscCall(PetscMalloc1(1 + am, &ci));
5245: ci[0] = 0;
5246: for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5247: PetscCall(PetscMalloc1(1 + ci[am], &cj));
5248: PetscCall(PetscMalloc1(1 + ci[am], &ca));
5249: k = 0;
5250: for (i = 0; i < am; i++) {
5251: ncols_o = bi[i + 1] - bi[i];
5252: ncols_d = ai[i + 1] - ai[i];
5253: /* off-diagonal portion of A */
5254: for (jo = 0; jo < ncols_o; jo++) {
5255: col = cmap[*bj];
5256: if (col >= cstart) break;
5257: cj[k] = col;
5258: bj++;
5259: ca[k++] = *ba++;
5260: }
5261: /* diagonal portion of A */
5262: for (j = 0; j < ncols_d; j++) {
5263: cj[k] = cstart + *aj++;
5264: ca[k++] = *aa++;
5265: }
5266: /* off-diagonal portion of A */
5267: for (j = jo; j < ncols_o; j++) {
5268: cj[k] = cmap[*bj++];
5269: ca[k++] = *ba++;
5270: }
5271: }
5272: /* put together the new matrix */
5273: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5274: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5275: /* Since these are PETSc arrays, change flags to free them as necessary. */
5276: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5277: mat->free_a = PETSC_TRUE;
5278: mat->free_ij = PETSC_TRUE;
5279: mat->nonew = 0;
5280: } else if (scall == MAT_REUSE_MATRIX) {
5281: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5282: ci = mat->i;
5283: cj = mat->j;
5284: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5285: for (i = 0; i < am; i++) {
5286: /* off-diagonal portion of A */
5287: ncols_o = bi[i + 1] - bi[i];
5288: for (jo = 0; jo < ncols_o; jo++) {
5289: col = cmap[*bj];
5290: if (col >= cstart) break;
5291: *cam++ = *ba++;
5292: bj++;
5293: }
5294: /* diagonal portion of A */
5295: ncols_d = ai[i + 1] - ai[i];
5296: for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5297: /* off-diagonal portion of A */
5298: for (j = jo; j < ncols_o; j++) {
5299: *cam++ = *ba++;
5300: bj++;
5301: }
5302: }
5303: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5304: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5305: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5306: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5307: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5308: PetscFunctionReturn(PETSC_SUCCESS);
5309: }
5311: /*@
5312: MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5313: mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and offdiagonal part
5315: Not Collective
5317: Input Parameters:
5318: + A - the matrix
5319: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5321: Output Parameters:
5322: + glob - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5323: - A_loc - the local sequential matrix generated
5325: Level: developer
5327: Note:
5328: This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5329: part, then those associated with the off diagonal part (in its local ordering)
5331: .seealso: [](chapter_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5332: @*/
5333: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5334: {
5335: Mat Ao, Ad;
5336: const PetscInt *cmap;
5337: PetscMPIInt size;
5338: PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);
5340: PetscFunctionBegin;
5341: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5342: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5343: if (size == 1) {
5344: if (scall == MAT_INITIAL_MATRIX) {
5345: PetscCall(PetscObjectReference((PetscObject)Ad));
5346: *A_loc = Ad;
5347: } else if (scall == MAT_REUSE_MATRIX) {
5348: PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5349: }
5350: if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5351: PetscFunctionReturn(PETSC_SUCCESS);
5352: }
5353: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5354: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5355: if (f) {
5356: PetscCall((*f)(A, scall, glob, A_loc));
5357: } else {
5358: Mat_SeqAIJ *a = (Mat_SeqAIJ *)Ad->data;
5359: Mat_SeqAIJ *b = (Mat_SeqAIJ *)Ao->data;
5360: Mat_SeqAIJ *c;
5361: PetscInt *ai = a->i, *aj = a->j;
5362: PetscInt *bi = b->i, *bj = b->j;
5363: PetscInt *ci, *cj;
5364: const PetscScalar *aa, *ba;
5365: PetscScalar *ca;
5366: PetscInt i, j, am, dn, on;
5368: PetscCall(MatGetLocalSize(Ad, &am, &dn));
5369: PetscCall(MatGetLocalSize(Ao, NULL, &on));
5370: PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5371: PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5372: if (scall == MAT_INITIAL_MATRIX) {
5373: PetscInt k;
5374: PetscCall(PetscMalloc1(1 + am, &ci));
5375: PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5376: PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5377: ci[0] = 0;
5378: for (i = 0, k = 0; i < am; i++) {
5379: const PetscInt ncols_o = bi[i + 1] - bi[i];
5380: const PetscInt ncols_d = ai[i + 1] - ai[i];
5381: ci[i + 1] = ci[i] + ncols_o + ncols_d;
5382: /* diagonal portion of A */
5383: for (j = 0; j < ncols_d; j++, k++) {
5384: cj[k] = *aj++;
5385: ca[k] = *aa++;
5386: }
5387: /* off-diagonal portion of A */
5388: for (j = 0; j < ncols_o; j++, k++) {
5389: cj[k] = dn + *bj++;
5390: ca[k] = *ba++;
5391: }
5392: }
5393: /* put together the new matrix */
5394: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5395: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5396: /* Since these are PETSc arrays, change flags to free them as necessary. */
5397: c = (Mat_SeqAIJ *)(*A_loc)->data;
5398: c->free_a = PETSC_TRUE;
5399: c->free_ij = PETSC_TRUE;
5400: c->nonew = 0;
5401: PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5402: } else if (scall == MAT_REUSE_MATRIX) {
5403: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5404: for (i = 0; i < am; i++) {
5405: const PetscInt ncols_d = ai[i + 1] - ai[i];
5406: const PetscInt ncols_o = bi[i + 1] - bi[i];
5407: /* diagonal portion of A */
5408: for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5409: /* off-diagonal portion of A */
5410: for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5411: }
5412: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5413: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5414: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5415: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5416: if (glob) {
5417: PetscInt cst, *gidx;
5419: PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5420: PetscCall(PetscMalloc1(dn + on, &gidx));
5421: for (i = 0; i < dn; i++) gidx[i] = cst + i;
5422: for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5423: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5424: }
5425: }
5426: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5427: PetscFunctionReturn(PETSC_SUCCESS);
5428: }
5430: /*@C
5431: MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns
5433: Not Collective
5435: Input Parameters:
5436: + A - the matrix
5437: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5438: . row - index set of rows to extract (or `NULL`)
5439: - col - index set of columns to extract (or `NULL`)
5441: Output Parameter:
5442: . A_loc - the local sequential matrix generated
5444: Level: developer
5446: .seealso: [](chapter_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5447: @*/
5448: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5449: {
5450: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5451: PetscInt i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5452: IS isrowa, iscola;
5453: Mat *aloc;
5454: PetscBool match;
5456: PetscFunctionBegin;
5457: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5458: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5459: PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5460: if (!row) {
5461: start = A->rmap->rstart;
5462: end = A->rmap->rend;
5463: PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5464: } else {
5465: isrowa = *row;
5466: }
5467: if (!col) {
5468: start = A->cmap->rstart;
5469: cmap = a->garray;
5470: nzA = a->A->cmap->n;
5471: nzB = a->B->cmap->n;
5472: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5473: ncols = 0;
5474: for (i = 0; i < nzB; i++) {
5475: if (cmap[i] < start) idx[ncols++] = cmap[i];
5476: else break;
5477: }
5478: imark = i;
5479: for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5480: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5481: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5482: } else {
5483: iscola = *col;
5484: }
5485: if (scall != MAT_INITIAL_MATRIX) {
5486: PetscCall(PetscMalloc1(1, &aloc));
5487: aloc[0] = *A_loc;
5488: }
5489: PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5490: if (!col) { /* attach global id of condensed columns */
5491: PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5492: }
5493: *A_loc = aloc[0];
5494: PetscCall(PetscFree(aloc));
5495: if (!row) PetscCall(ISDestroy(&isrowa));
5496: if (!col) PetscCall(ISDestroy(&iscola));
5497: PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5498: PetscFunctionReturn(PETSC_SUCCESS);
5499: }
5501: /*
5502: * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5503: * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5504: * on a global size.
5505: * */
5506: PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5507: {
5508: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
5509: Mat_SeqAIJ *pd = (Mat_SeqAIJ *)(p->A)->data, *po = (Mat_SeqAIJ *)(p->B)->data, *p_oth;
5510: PetscInt plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5511: PetscMPIInt owner;
5512: PetscSFNode *iremote, *oiremote;
5513: const PetscInt *lrowindices;
5514: PetscSF sf, osf;
5515: PetscInt pcstart, *roffsets, *loffsets, *pnnz, j;
5516: PetscInt ontotalcols, dntotalcols, ntotalcols, nout;
5517: MPI_Comm comm;
5518: ISLocalToGlobalMapping mapping;
5519: const PetscScalar *pd_a, *po_a;
5521: PetscFunctionBegin;
5522: PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5523: /* plocalsize is the number of roots
5524: * nrows is the number of leaves
5525: * */
5526: PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5527: PetscCall(ISGetLocalSize(rows, &nrows));
5528: PetscCall(PetscCalloc1(nrows, &iremote));
5529: PetscCall(ISGetIndices(rows, &lrowindices));
5530: for (i = 0; i < nrows; i++) {
5531: /* Find a remote index and an owner for a row
5532: * The row could be local or remote
5533: * */
5534: owner = 0;
5535: lidx = 0;
5536: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5537: iremote[i].index = lidx;
5538: iremote[i].rank = owner;
5539: }
5540: /* Create SF to communicate how many nonzero columns for each row */
5541: PetscCall(PetscSFCreate(comm, &sf));
5542: /* SF will figure out the number of nonzero colunms for each row, and their
5543: * offsets
5544: * */
5545: PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5546: PetscCall(PetscSFSetFromOptions(sf));
5547: PetscCall(PetscSFSetUp(sf));
5549: PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5550: PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5551: PetscCall(PetscCalloc1(nrows, &pnnz));
5552: roffsets[0] = 0;
5553: roffsets[1] = 0;
5554: for (i = 0; i < plocalsize; i++) {
5555: /* diag */
5556: nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5557: /* off diag */
5558: nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5559: /* compute offsets so that we relative location for each row */
5560: roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5561: roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5562: }
5563: PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5564: PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5565: /* 'r' means root, and 'l' means leaf */
5566: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5567: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5568: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5569: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5570: PetscCall(PetscSFDestroy(&sf));
5571: PetscCall(PetscFree(roffsets));
5572: PetscCall(PetscFree(nrcols));
5573: dntotalcols = 0;
5574: ontotalcols = 0;
5575: ncol = 0;
5576: for (i = 0; i < nrows; i++) {
5577: pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5578: ncol = PetscMax(pnnz[i], ncol);
5579: /* diag */
5580: dntotalcols += nlcols[i * 2 + 0];
5581: /* off diag */
5582: ontotalcols += nlcols[i * 2 + 1];
5583: }
5584: /* We do not need to figure the right number of columns
5585: * since all the calculations will be done by going through the raw data
5586: * */
5587: PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5588: PetscCall(MatSetUp(*P_oth));
5589: PetscCall(PetscFree(pnnz));
5590: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5591: /* diag */
5592: PetscCall(PetscCalloc1(dntotalcols, &iremote));
5593: /* off diag */
5594: PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5595: /* diag */
5596: PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5597: /* off diag */
5598: PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5599: dntotalcols = 0;
5600: ontotalcols = 0;
5601: ntotalcols = 0;
5602: for (i = 0; i < nrows; i++) {
5603: owner = 0;
5604: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5605: /* Set iremote for diag matrix */
5606: for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5607: iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5608: iremote[dntotalcols].rank = owner;
5609: /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5610: ilocal[dntotalcols++] = ntotalcols++;
5611: }
5612: /* off diag */
5613: for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5614: oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5615: oiremote[ontotalcols].rank = owner;
5616: oilocal[ontotalcols++] = ntotalcols++;
5617: }
5618: }
5619: PetscCall(ISRestoreIndices(rows, &lrowindices));
5620: PetscCall(PetscFree(loffsets));
5621: PetscCall(PetscFree(nlcols));
5622: PetscCall(PetscSFCreate(comm, &sf));
5623: /* P serves as roots and P_oth is leaves
5624: * Diag matrix
5625: * */
5626: PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5627: PetscCall(PetscSFSetFromOptions(sf));
5628: PetscCall(PetscSFSetUp(sf));
5630: PetscCall(PetscSFCreate(comm, &osf));
5631: /* Off diag */
5632: PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5633: PetscCall(PetscSFSetFromOptions(osf));
5634: PetscCall(PetscSFSetUp(osf));
5635: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5636: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5637: /* We operate on the matrix internal data for saving memory */
5638: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5639: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5640: PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5641: /* Convert to global indices for diag matrix */
5642: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5643: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5644: /* We want P_oth store global indices */
5645: PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5646: /* Use memory scalable approach */
5647: PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5648: PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5649: PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5650: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5651: /* Convert back to local indices */
5652: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5653: PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5654: nout = 0;
5655: PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5656: PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5657: PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5658: /* Exchange values */
5659: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5660: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5661: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5662: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5663: /* Stop PETSc from shrinking memory */
5664: for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5665: PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5666: PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5667: /* Attach PetscSF objects to P_oth so that we can reuse it later */
5668: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5669: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5670: PetscCall(PetscSFDestroy(&sf));
5671: PetscCall(PetscSFDestroy(&osf));
5672: PetscFunctionReturn(PETSC_SUCCESS);
5673: }
5675: /*
5676: * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5677: * This supports MPIAIJ and MAIJ
5678: * */
5679: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5680: {
5681: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5682: Mat_SeqAIJ *p_oth;
5683: IS rows, map;
5684: PetscHMapI hamp;
5685: PetscInt i, htsize, *rowindices, off, *mapping, key, count;
5686: MPI_Comm comm;
5687: PetscSF sf, osf;
5688: PetscBool has;
5690: PetscFunctionBegin;
5691: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5692: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5693: /* If it is the first time, create an index set of off-diag nonzero columns of A,
5694: * and then create a submatrix (that often is an overlapping matrix)
5695: * */
5696: if (reuse == MAT_INITIAL_MATRIX) {
5697: /* Use a hash table to figure out unique keys */
5698: PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5699: PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5700: count = 0;
5701: /* Assume that a->g is sorted, otherwise the following does not make sense */
5702: for (i = 0; i < a->B->cmap->n; i++) {
5703: key = a->garray[i] / dof;
5704: PetscCall(PetscHMapIHas(hamp, key, &has));
5705: if (!has) {
5706: mapping[i] = count;
5707: PetscCall(PetscHMapISet(hamp, key, count++));
5708: } else {
5709: /* Current 'i' has the same value the previous step */
5710: mapping[i] = count - 1;
5711: }
5712: }
5713: PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5714: PetscCall(PetscHMapIGetSize(hamp, &htsize));
5715: PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5716: PetscCall(PetscCalloc1(htsize, &rowindices));
5717: off = 0;
5718: PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5719: PetscCall(PetscHMapIDestroy(&hamp));
5720: PetscCall(PetscSortInt(htsize, rowindices));
5721: PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5722: /* In case, the matrix was already created but users want to recreate the matrix */
5723: PetscCall(MatDestroy(P_oth));
5724: PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5725: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5726: PetscCall(ISDestroy(&map));
5727: PetscCall(ISDestroy(&rows));
5728: } else if (reuse == MAT_REUSE_MATRIX) {
5729: /* If matrix was already created, we simply update values using SF objects
5730: * that as attached to the matrix earlier.
5731: */
5732: const PetscScalar *pd_a, *po_a;
5734: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5735: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5736: PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5737: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5738: /* Update values in place */
5739: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5740: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5741: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5742: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5743: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5744: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5745: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5746: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5747: } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5748: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5749: PetscFunctionReturn(PETSC_SUCCESS);
5750: }
5752: /*@C
5753: MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`
5755: Collective
5757: Input Parameters:
5758: + A - the first matrix in `MATMPIAIJ` format
5759: . B - the second matrix in `MATMPIAIJ` format
5760: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5762: Output Parameters:
5763: + rowb - On input index sets of rows of B to extract (or `NULL`), modified on output
5764: . colb - On input index sets of columns of B to extract (or `NULL`), modified on output
5765: - B_seq - the sequential matrix generated
5767: Level: developer
5769: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5770: @*/
5771: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5772: {
5773: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5774: PetscInt *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5775: IS isrowb, iscolb;
5776: Mat *bseq = NULL;
5778: PetscFunctionBegin;
5779: 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 ")",
5780: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5781: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));
5783: if (scall == MAT_INITIAL_MATRIX) {
5784: start = A->cmap->rstart;
5785: cmap = a->garray;
5786: nzA = a->A->cmap->n;
5787: nzB = a->B->cmap->n;
5788: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5789: ncols = 0;
5790: for (i = 0; i < nzB; i++) { /* row < local row index */
5791: if (cmap[i] < start) idx[ncols++] = cmap[i];
5792: else break;
5793: }
5794: imark = i;
5795: for (i = 0; i < nzA; i++) idx[ncols++] = start + i; /* local rows */
5796: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5797: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5798: PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5799: } else {
5800: PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5801: isrowb = *rowb;
5802: iscolb = *colb;
5803: PetscCall(PetscMalloc1(1, &bseq));
5804: bseq[0] = *B_seq;
5805: }
5806: PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5807: *B_seq = bseq[0];
5808: PetscCall(PetscFree(bseq));
5809: if (!rowb) {
5810: PetscCall(ISDestroy(&isrowb));
5811: } else {
5812: *rowb = isrowb;
5813: }
5814: if (!colb) {
5815: PetscCall(ISDestroy(&iscolb));
5816: } else {
5817: *colb = iscolb;
5818: }
5819: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5820: PetscFunctionReturn(PETSC_SUCCESS);
5821: }
5823: /*
5824: MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5825: of the OFF-DIAGONAL portion of local A
5827: Collective
5829: Input Parameters:
5830: + A,B - the matrices in `MATMPIAIJ` format
5831: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5833: Output Parameter:
5834: + startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5835: . startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5836: . bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5837: - B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N
5839: Developer Note:
5840: This directly accesses information inside the VecScatter associated with the matrix-vector product
5841: for this matrix. This is not desirable..
5843: Level: developer
5845: */
5846: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5847: {
5848: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5849: Mat_SeqAIJ *b_oth;
5850: VecScatter ctx;
5851: MPI_Comm comm;
5852: const PetscMPIInt *rprocs, *sprocs;
5853: const PetscInt *srow, *rstarts, *sstarts;
5854: PetscInt *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5855: PetscInt i, j, k = 0, l, ll, nrecvs, nsends, nrows, *rstartsj = NULL, *sstartsj, len;
5856: PetscScalar *b_otha, *bufa, *bufA, *vals = NULL;
5857: MPI_Request *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5858: PetscMPIInt size, tag, rank, nreqs;
5860: PetscFunctionBegin;
5861: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5862: PetscCallMPI(MPI_Comm_size(comm, &size));
5864: 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 ")",
5865: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5866: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5867: PetscCallMPI(MPI_Comm_rank(comm, &rank));
5869: if (size == 1) {
5870: startsj_s = NULL;
5871: bufa_ptr = NULL;
5872: *B_oth = NULL;
5873: PetscFunctionReturn(PETSC_SUCCESS);
5874: }
5876: ctx = a->Mvctx;
5877: tag = ((PetscObject)ctx)->tag;
5879: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5880: /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5881: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5882: PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5883: PetscCall(PetscMalloc1(nreqs, &reqs));
5884: rwaits = reqs;
5885: swaits = reqs + nrecvs;
5887: if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5888: if (scall == MAT_INITIAL_MATRIX) {
5889: /* i-array */
5890: /* post receives */
5891: if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5892: for (i = 0; i < nrecvs; i++) {
5893: rowlen = rvalues + rstarts[i] * rbs;
5894: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5895: PetscCallMPI(MPI_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5896: }
5898: /* pack the outgoing message */
5899: PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));
5901: sstartsj[0] = 0;
5902: rstartsj[0] = 0;
5903: len = 0; /* total length of j or a array to be sent */
5904: if (nsends) {
5905: k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5906: PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5907: }
5908: for (i = 0; i < nsends; i++) {
5909: rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5910: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5911: for (j = 0; j < nrows; j++) {
5912: row = srow[k] + B->rmap->range[rank]; /* global row idx */
5913: for (l = 0; l < sbs; l++) {
5914: PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */
5916: rowlen[j * sbs + l] = ncols;
5918: len += ncols;
5919: PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5920: }
5921: k++;
5922: }
5923: PetscCallMPI(MPI_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));
5925: sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5926: }
5927: /* recvs and sends of i-array are completed */
5928: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5929: PetscCall(PetscFree(svalues));
5931: /* allocate buffers for sending j and a arrays */
5932: PetscCall(PetscMalloc1(len + 1, &bufj));
5933: PetscCall(PetscMalloc1(len + 1, &bufa));
5935: /* create i-array of B_oth */
5936: PetscCall(PetscMalloc1(aBn + 2, &b_othi));
5938: b_othi[0] = 0;
5939: len = 0; /* total length of j or a array to be received */
5940: k = 0;
5941: for (i = 0; i < nrecvs; i++) {
5942: rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5943: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5944: for (j = 0; j < nrows; j++) {
5945: b_othi[k + 1] = b_othi[k] + rowlen[j];
5946: PetscCall(PetscIntSumError(rowlen[j], len, &len));
5947: k++;
5948: }
5949: rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5950: }
5951: PetscCall(PetscFree(rvalues));
5953: /* allocate space for j and a arrays of B_oth */
5954: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_othj));
5955: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_otha));
5957: /* j-array */
5958: /* post receives of j-array */
5959: for (i = 0; i < nrecvs; i++) {
5960: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5961: PetscCallMPI(MPI_Irecv(b_othj + rstartsj[i], nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5962: }
5964: /* pack the outgoing message j-array */
5965: if (nsends) k = sstarts[0];
5966: for (i = 0; i < nsends; i++) {
5967: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5968: bufJ = bufj + sstartsj[i];
5969: for (j = 0; j < nrows; j++) {
5970: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5971: for (ll = 0; ll < sbs; ll++) {
5972: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5973: for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5974: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5975: }
5976: }
5977: PetscCallMPI(MPI_Isend(bufj + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5978: }
5980: /* recvs and sends of j-array are completed */
5981: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5982: } else if (scall == MAT_REUSE_MATRIX) {
5983: sstartsj = *startsj_s;
5984: rstartsj = *startsj_r;
5985: bufa = *bufa_ptr;
5986: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
5987: PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5988: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");
5990: /* a-array */
5991: /* post receives of a-array */
5992: for (i = 0; i < nrecvs; i++) {
5993: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5994: PetscCallMPI(MPI_Irecv(b_otha + rstartsj[i], nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
5995: }
5997: /* pack the outgoing message a-array */
5998: if (nsends) k = sstarts[0];
5999: for (i = 0; i < nsends; i++) {
6000: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
6001: bufA = bufa + sstartsj[i];
6002: for (j = 0; j < nrows; j++) {
6003: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
6004: for (ll = 0; ll < sbs; ll++) {
6005: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
6006: for (l = 0; l < ncols; l++) *bufA++ = vals[l];
6007: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
6008: }
6009: }
6010: PetscCallMPI(MPI_Isend(bufa + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
6011: }
6012: /* recvs and sends of a-array are completed */
6013: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
6014: PetscCall(PetscFree(reqs));
6016: if (scall == MAT_INITIAL_MATRIX) {
6017: /* put together the new matrix */
6018: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));
6020: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
6021: /* Since these are PETSc arrays, change flags to free them as necessary. */
6022: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
6023: b_oth->free_a = PETSC_TRUE;
6024: b_oth->free_ij = PETSC_TRUE;
6025: b_oth->nonew = 0;
6027: PetscCall(PetscFree(bufj));
6028: if (!startsj_s || !bufa_ptr) {
6029: PetscCall(PetscFree2(sstartsj, rstartsj));
6030: PetscCall(PetscFree(bufa_ptr));
6031: } else {
6032: *startsj_s = sstartsj;
6033: *startsj_r = rstartsj;
6034: *bufa_ptr = bufa;
6035: }
6036: } else if (scall == MAT_REUSE_MATRIX) {
6037: PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
6038: }
6040: PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
6041: PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
6042: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
6043: PetscFunctionReturn(PETSC_SUCCESS);
6044: }
6046: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
6047: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
6048: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
6049: #if defined(PETSC_HAVE_MKL_SPARSE)
6050: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
6051: #endif
6052: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
6053: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
6054: #if defined(PETSC_HAVE_ELEMENTAL)
6055: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
6056: #endif
6057: #if defined(PETSC_HAVE_SCALAPACK)
6058: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
6059: #endif
6060: #if defined(PETSC_HAVE_HYPRE)
6061: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
6062: #endif
6063: #if defined(PETSC_HAVE_CUDA)
6064: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
6065: #endif
6066: #if defined(PETSC_HAVE_HIP)
6067: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6068: #endif
6069: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6070: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6071: #endif
6072: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6073: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6074: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
6076: /*
6077: Computes (B'*A')' since computing B*A directly is untenable
6079: n p p
6080: [ ] [ ] [ ]
6081: m [ A ] * n [ B ] = m [ C ]
6082: [ ] [ ] [ ]
6084: */
6085: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6086: {
6087: Mat At, Bt, Ct;
6089: PetscFunctionBegin;
6090: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6091: PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6092: PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_DEFAULT, &Ct));
6093: PetscCall(MatDestroy(&At));
6094: PetscCall(MatDestroy(&Bt));
6095: PetscCall(MatTransposeSetPrecursor(Ct, C));
6096: PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6097: PetscCall(MatDestroy(&Ct));
6098: PetscFunctionReturn(PETSC_SUCCESS);
6099: }
6101: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6102: {
6103: PetscBool cisdense;
6105: PetscFunctionBegin;
6106: 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);
6107: PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6108: PetscCall(MatSetBlockSizesFromMats(C, A, B));
6109: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6110: if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6111: PetscCall(MatSetUp(C));
6113: C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6114: PetscFunctionReturn(PETSC_SUCCESS);
6115: }
6117: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6118: {
6119: Mat_Product *product = C->product;
6120: Mat A = product->A, B = product->B;
6122: PetscFunctionBegin;
6123: 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 ")",
6124: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6125: C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6126: C->ops->productsymbolic = MatProductSymbolic_AB;
6127: PetscFunctionReturn(PETSC_SUCCESS);
6128: }
6130: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6131: {
6132: Mat_Product *product = C->product;
6134: PetscFunctionBegin;
6135: if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6136: PetscFunctionReturn(PETSC_SUCCESS);
6137: }
6139: /*
6140: Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix
6142: Input Parameters:
6144: j1,rowBegin1,rowEnd1,perm1,jmap1: describe the first set of nonzeros (Set1)
6145: j2,rowBegin2,rowEnd2,perm2,jmap2: describe the second set of nonzeros (Set2)
6147: mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat
6149: For Set1, j1[] contains column indices of the nonzeros.
6150: 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
6151: respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6152: but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.
6154: Similar for Set2.
6156: This routine merges the two sets of nonzeros row by row and removes repeats.
6158: Output Parameters: (memory is allocated by the caller)
6160: i[],j[]: the CSR of the merged matrix, which has m rows.
6161: imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6162: imap2[]: similar to imap1[], but for Set2.
6163: Note we order nonzeros row-by-row and from left to right.
6164: */
6165: 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[])
6166: {
6167: PetscInt r, m; /* Row index of mat */
6168: PetscCount t, t1, t2, b1, e1, b2, e2;
6170: PetscFunctionBegin;
6171: PetscCall(MatGetLocalSize(mat, &m, NULL));
6172: t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6173: i[0] = 0;
6174: for (r = 0; r < m; r++) { /* Do row by row merging */
6175: b1 = rowBegin1[r];
6176: e1 = rowEnd1[r];
6177: b2 = rowBegin2[r];
6178: e2 = rowEnd2[r];
6179: while (b1 < e1 && b2 < e2) {
6180: if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6181: j[t] = j1[b1];
6182: imap1[t1] = t;
6183: imap2[t2] = t;
6184: b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6185: b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6186: t1++;
6187: t2++;
6188: t++;
6189: } else if (j1[b1] < j2[b2]) {
6190: j[t] = j1[b1];
6191: imap1[t1] = t;
6192: b1 += jmap1[t1 + 1] - jmap1[t1];
6193: t1++;
6194: t++;
6195: } else {
6196: j[t] = j2[b2];
6197: imap2[t2] = t;
6198: b2 += jmap2[t2 + 1] - jmap2[t2];
6199: t2++;
6200: t++;
6201: }
6202: }
6203: /* Merge the remaining in either j1[] or j2[] */
6204: while (b1 < e1) {
6205: j[t] = j1[b1];
6206: imap1[t1] = t;
6207: b1 += jmap1[t1 + 1] - jmap1[t1];
6208: t1++;
6209: t++;
6210: }
6211: while (b2 < e2) {
6212: j[t] = j2[b2];
6213: imap2[t2] = t;
6214: b2 += jmap2[t2 + 1] - jmap2[t2];
6215: t2++;
6216: t++;
6217: }
6218: i[r + 1] = t;
6219: }
6220: PetscFunctionReturn(PETSC_SUCCESS);
6221: }
6223: /*
6224: Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block
6226: Input Parameters:
6227: mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6228: 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[]
6229: respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.
6231: i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6232: i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.
6234: Output Parameters:
6235: j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6236: rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6237: 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,
6238: and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.
6240: Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6241: Atot: number of entries belonging to the diagonal block.
6242: Annz: number of unique nonzeros belonging to the diagonal block.
6243: Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6244: repeats (i.e., same 'i,j' pair).
6245: Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6246: is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.
6248: Atot: number of entries belonging to the diagonal block
6249: Annz: number of unique nonzeros belonging to the diagonal block.
6251: Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.
6253: Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6254: */
6255: 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_)
6256: {
6257: PetscInt cstart, cend, rstart, rend, row, col;
6258: PetscCount Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6259: PetscCount Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6260: PetscCount k, m, p, q, r, s, mid;
6261: PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;
6263: PetscFunctionBegin;
6264: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6265: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6266: m = rend - rstart;
6268: for (k = 0; k < n; k++) {
6269: if (i[k] >= 0) break;
6270: } /* Skip negative rows */
6272: /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6273: fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6274: */
6275: while (k < n) {
6276: row = i[k];
6277: /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6278: for (s = k; s < n; s++)
6279: if (i[s] != row) break;
6280: for (p = k; p < s; p++) {
6281: if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_MAX_INT; /* Shift diag columns to range of [-PETSC_MAX_INT, -1] */
6282: else PetscAssert((j[p] >= 0) && (j[p] <= mat->cmap->N), PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index %" PetscInt_FMT " is out of range", j[p]);
6283: }
6284: PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6285: PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6286: rowBegin[row - rstart] = k;
6287: rowMid[row - rstart] = mid;
6288: rowEnd[row - rstart] = s;
6290: /* Count nonzeros of this diag/offdiag row, which might have repeats */
6291: Atot += mid - k;
6292: Btot += s - mid;
6294: /* Count unique nonzeros of this diag/offdiag row */
6295: for (p = k; p < mid;) {
6296: col = j[p];
6297: do {
6298: j[p] += PETSC_MAX_INT;
6299: p++;
6300: } while (p < mid && j[p] == col); /* Revert the modified diagonal indices */
6301: Annz++;
6302: }
6304: for (p = mid; p < s;) {
6305: col = j[p];
6306: do {
6307: p++;
6308: } while (p < s && j[p] == col);
6309: Bnnz++;
6310: }
6311: k = s;
6312: }
6314: /* Allocation according to Atot, Btot, Annz, Bnnz */
6315: PetscCall(PetscMalloc1(Atot, &Aperm));
6316: PetscCall(PetscMalloc1(Btot, &Bperm));
6317: PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6318: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));
6320: /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6321: Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6322: for (r = 0; r < m; r++) {
6323: k = rowBegin[r];
6324: mid = rowMid[r];
6325: s = rowEnd[r];
6326: PetscCall(PetscArraycpy(Aperm + Atot, perm + k, mid - k));
6327: PetscCall(PetscArraycpy(Bperm + Btot, perm + mid, s - mid));
6328: Atot += mid - k;
6329: Btot += s - mid;
6331: /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6332: for (p = k; p < mid;) {
6333: col = j[p];
6334: q = p;
6335: do {
6336: p++;
6337: } while (p < mid && j[p] == col);
6338: Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6339: Annz++;
6340: }
6342: for (p = mid; p < s;) {
6343: col = j[p];
6344: q = p;
6345: do {
6346: p++;
6347: } while (p < s && j[p] == col);
6348: Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6349: Bnnz++;
6350: }
6351: }
6352: /* Output */
6353: *Aperm_ = Aperm;
6354: *Annz_ = Annz;
6355: *Atot_ = Atot;
6356: *Ajmap_ = Ajmap;
6357: *Bperm_ = Bperm;
6358: *Bnnz_ = Bnnz;
6359: *Btot_ = Btot;
6360: *Bjmap_ = Bjmap;
6361: PetscFunctionReturn(PETSC_SUCCESS);
6362: }
6364: /*
6365: Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix
6367: Input Parameters:
6368: nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6369: nnz: number of unique nonzeros in the merged matrix
6370: imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6371: jmap[nnz1+1]: i-th nonzeron in the set has jmap[i+1] - jmap[i] repeats in the set
6373: Output Parameter: (memory is allocated by the caller)
6374: jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set
6376: Example:
6377: nnz1 = 4
6378: nnz = 6
6379: imap = [1,3,4,5]
6380: jmap = [0,3,5,6,7]
6381: then,
6382: jmap_new = [0,0,3,3,5,6,7]
6383: */
6384: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6385: {
6386: PetscCount k, p;
6388: PetscFunctionBegin;
6389: jmap_new[0] = 0;
6390: p = nnz; /* p loops over jmap_new[] backwards */
6391: for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6392: for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6393: }
6394: for (; p >= 0; p--) jmap_new[p] = jmap[0];
6395: PetscFunctionReturn(PETSC_SUCCESS);
6396: }
6398: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6399: {
6400: MPI_Comm comm;
6401: PetscMPIInt rank, size;
6402: PetscInt m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6403: PetscCount k, p, q, rem; /* Loop variables over coo arrays */
6404: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6406: PetscFunctionBegin;
6407: PetscCall(PetscFree(mpiaij->garray));
6408: PetscCall(VecDestroy(&mpiaij->lvec));
6409: #if defined(PETSC_USE_CTABLE)
6410: PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6411: #else
6412: PetscCall(PetscFree(mpiaij->colmap));
6413: #endif
6414: PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6415: mat->assembled = PETSC_FALSE;
6416: mat->was_assembled = PETSC_FALSE;
6417: PetscCall(MatResetPreallocationCOO_MPIAIJ(mat));
6419: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6420: PetscCallMPI(MPI_Comm_size(comm, &size));
6421: PetscCallMPI(MPI_Comm_rank(comm, &rank));
6422: PetscCall(PetscLayoutSetUp(mat->rmap));
6423: PetscCall(PetscLayoutSetUp(mat->cmap));
6424: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6425: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6426: PetscCall(MatGetLocalSize(mat, &m, &n));
6427: PetscCall(MatGetSize(mat, &M, &N));
6429: /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6430: /* entries come first, then local rows, then remote rows. */
6431: PetscCount n1 = coo_n, *perm1;
6432: PetscInt *i1 = coo_i, *j1 = coo_j;
6434: PetscCall(PetscMalloc1(n1, &perm1));
6435: for (k = 0; k < n1; k++) perm1[k] = k;
6437: /* Manipulate indices so that entries with negative row or col indices will have smallest
6438: row indices, local entries will have greater but negative row indices, and remote entries
6439: will have positive row indices.
6440: */
6441: for (k = 0; k < n1; k++) {
6442: if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_MIN_INT; /* e.g., -2^31, minimal to move them ahead */
6443: 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] */
6444: else {
6445: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6446: if (mpiaij->donotstash) i1[k] = PETSC_MIN_INT; /* Ignore offproc entries as if they had negative indices */
6447: }
6448: }
6450: /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6451: PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));
6452: for (k = 0; k < n1; k++) {
6453: if (i1[k] > PETSC_MIN_INT) break;
6454: } /* Advance k to the first entry we need to take care of */
6455: PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_MAX_INT, &rem)); /* rem is upper bound of the last local row */
6456: for (; k < rem; k++) i1[k] += PETSC_MAX_INT; /* Revert row indices of local rows*/
6458: /* Split local rows into diag/offdiag portions */
6459: PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6460: PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1, *Cperm1;
6461: PetscCount Annz1, Bnnz1, Atot1, Btot1;
6463: PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6464: PetscCall(PetscMalloc1(n1 - rem, &Cperm1));
6465: PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6467: /* Send remote rows to their owner */
6468: /* Find which rows should be sent to which remote ranks*/
6469: PetscInt nsend = 0; /* Number of MPI ranks to send data to */
6470: PetscMPIInt *sendto; /* [nsend], storing remote ranks */
6471: PetscInt *nentries; /* [nsend], storing number of entries sent to remote ranks; Assume PetscInt is big enough for this count, and error if not */
6472: const PetscInt *ranges;
6473: PetscInt maxNsend = size >= 128 ? 128 : size; /* Assume max 128 neighbors; realloc when needed */
6475: PetscCall(PetscLayoutGetRanges(mat->rmap, &ranges));
6476: PetscCall(PetscMalloc2(maxNsend, &sendto, maxNsend, &nentries));
6477: for (k = rem; k < n1;) {
6478: PetscMPIInt owner;
6479: PetscInt firstRow, lastRow;
6481: /* Locate a row range */
6482: firstRow = i1[k]; /* first row of this owner */
6483: PetscCall(PetscLayoutFindOwner(mat->rmap, firstRow, &owner));
6484: lastRow = ranges[owner + 1] - 1; /* last row of this owner */
6486: /* Find the first index 'p' in [k,n) with i[p] belonging to next owner */
6487: PetscCall(PetscSortedIntUpperBound(i1, k, n1, lastRow, &p));
6489: /* All entries in [k,p) belong to this remote owner */
6490: if (nsend >= maxNsend) { /* Double the remote ranks arrays if not long enough */
6491: PetscMPIInt *sendto2;
6492: PetscInt *nentries2;
6493: PetscInt maxNsend2 = (maxNsend <= size / 2) ? maxNsend * 2 : size;
6495: PetscCall(PetscMalloc2(maxNsend2, &sendto2, maxNsend2, &nentries2));
6496: PetscCall(PetscArraycpy(sendto2, sendto, maxNsend));
6497: PetscCall(PetscArraycpy(nentries2, nentries2, maxNsend + 1));
6498: PetscCall(PetscFree2(sendto, nentries2));
6499: sendto = sendto2;
6500: nentries = nentries2;
6501: maxNsend = maxNsend2;
6502: }
6503: sendto[nsend] = owner;
6504: nentries[nsend] = p - k;
6505: PetscCall(PetscCountCast(p - k, &nentries[nsend]));
6506: nsend++;
6507: k = p;
6508: }
6510: /* Build 1st SF to know offsets on remote to send data */
6511: PetscSF sf1;
6512: PetscInt nroots = 1, nroots2 = 0;
6513: PetscInt nleaves = nsend, nleaves2 = 0;
6514: PetscInt *offsets;
6515: PetscSFNode *iremote;
6517: PetscCall(PetscSFCreate(comm, &sf1));
6518: PetscCall(PetscMalloc1(nsend, &iremote));
6519: PetscCall(PetscMalloc1(nsend, &offsets));
6520: for (k = 0; k < nsend; k++) {
6521: iremote[k].rank = sendto[k];
6522: iremote[k].index = 0;
6523: nleaves2 += nentries[k];
6524: PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6525: }
6526: PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6527: PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6528: PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6529: PetscCall(PetscSFDestroy(&sf1));
6530: PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT "", nleaves2, n1 - rem);
6532: /* Build 2nd SF to send remote COOs to their owner */
6533: PetscSF sf2;
6534: nroots = nroots2;
6535: nleaves = nleaves2;
6536: PetscCall(PetscSFCreate(comm, &sf2));
6537: PetscCall(PetscSFSetFromOptions(sf2));
6538: PetscCall(PetscMalloc1(nleaves, &iremote));
6539: p = 0;
6540: for (k = 0; k < nsend; k++) {
6541: PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6542: for (q = 0; q < nentries[k]; q++, p++) {
6543: iremote[p].rank = sendto[k];
6544: iremote[p].index = offsets[k] + q;
6545: }
6546: }
6547: PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6549: /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6550: PetscCall(PetscArraycpy(Cperm1, perm1 + rem, n1 - rem));
6552: /* Send the remote COOs to their owner */
6553: PetscInt n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6554: PetscCount *perm2; /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6555: PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6556: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1 + rem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6557: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1 + rem, i2, MPI_REPLACE));
6558: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1 + rem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6559: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1 + rem, j2, MPI_REPLACE));
6561: PetscCall(PetscFree(offsets));
6562: PetscCall(PetscFree2(sendto, nentries));
6564: /* Sort received COOs by row along with the permutation array */
6565: for (k = 0; k < n2; k++) perm2[k] = k;
6566: PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));
6568: /* Split received COOs into diag/offdiag portions */
6569: PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6570: PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6571: PetscCount Annz2, Bnnz2, Atot2, Btot2;
6573: PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6574: PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));
6576: /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6577: PetscInt *Ai, *Bi;
6578: PetscInt *Aj, *Bj;
6580: PetscCall(PetscMalloc1(m + 1, &Ai));
6581: PetscCall(PetscMalloc1(m + 1, &Bi));
6582: PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6583: PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));
6585: PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6586: PetscCall(PetscMalloc1(Annz1, &Aimap1));
6587: PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6588: PetscCall(PetscMalloc1(Annz2, &Aimap2));
6589: PetscCall(PetscMalloc1(Bnnz2, &Bimap2));
6591: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6592: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));
6594: /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we */
6595: /* expect nonzeros in A/B most likely have local contributing entries */
6596: PetscInt Annz = Ai[m];
6597: PetscInt Bnnz = Bi[m];
6598: PetscCount *Ajmap1_new, *Bjmap1_new;
6600: PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6601: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));
6603: PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6604: PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));
6606: PetscCall(PetscFree(Aimap1));
6607: PetscCall(PetscFree(Ajmap1));
6608: PetscCall(PetscFree(Bimap1));
6609: PetscCall(PetscFree(Bjmap1));
6610: PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6611: PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6612: PetscCall(PetscFree(perm1));
6613: PetscCall(PetscFree3(i2, j2, perm2));
6615: Ajmap1 = Ajmap1_new;
6616: Bjmap1 = Bjmap1_new;
6618: /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6619: if (Annz < Annz1 + Annz2) {
6620: PetscInt *Aj_new;
6621: PetscCall(PetscMalloc1(Annz, &Aj_new));
6622: PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6623: PetscCall(PetscFree(Aj));
6624: Aj = Aj_new;
6625: }
6627: if (Bnnz < Bnnz1 + Bnnz2) {
6628: PetscInt *Bj_new;
6629: PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6630: PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6631: PetscCall(PetscFree(Bj));
6632: Bj = Bj_new;
6633: }
6635: /* Create new submatrices for on-process and off-process coupling */
6636: PetscScalar *Aa, *Ba;
6637: MatType rtype;
6638: Mat_SeqAIJ *a, *b;
6639: PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6640: PetscCall(PetscCalloc1(Bnnz, &Ba));
6641: /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6642: if (cstart) {
6643: for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6644: }
6645: PetscCall(MatDestroy(&mpiaij->A));
6646: PetscCall(MatDestroy(&mpiaij->B));
6647: PetscCall(MatGetRootType_Private(mat, &rtype));
6648: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6649: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6650: PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6652: a = (Mat_SeqAIJ *)mpiaij->A->data;
6653: b = (Mat_SeqAIJ *)mpiaij->B->data;
6654: a->singlemalloc = b->singlemalloc = PETSC_FALSE; /* Let newmat own Ai,Aj,Aa,Bi,Bj,Ba */
6655: a->free_a = b->free_a = PETSC_TRUE;
6656: a->free_ij = b->free_ij = PETSC_TRUE;
6658: /* conversion must happen AFTER multiply setup */
6659: PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6660: PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6661: PetscCall(VecDestroy(&mpiaij->lvec));
6662: PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));
6664: mpiaij->coo_n = coo_n;
6665: mpiaij->coo_sf = sf2;
6666: mpiaij->sendlen = nleaves;
6667: mpiaij->recvlen = nroots;
6669: mpiaij->Annz = Annz;
6670: mpiaij->Bnnz = Bnnz;
6672: mpiaij->Annz2 = Annz2;
6673: mpiaij->Bnnz2 = Bnnz2;
6675: mpiaij->Atot1 = Atot1;
6676: mpiaij->Atot2 = Atot2;
6677: mpiaij->Btot1 = Btot1;
6678: mpiaij->Btot2 = Btot2;
6680: mpiaij->Ajmap1 = Ajmap1;
6681: mpiaij->Aperm1 = Aperm1;
6683: mpiaij->Bjmap1 = Bjmap1;
6684: mpiaij->Bperm1 = Bperm1;
6686: mpiaij->Aimap2 = Aimap2;
6687: mpiaij->Ajmap2 = Ajmap2;
6688: mpiaij->Aperm2 = Aperm2;
6690: mpiaij->Bimap2 = Bimap2;
6691: mpiaij->Bjmap2 = Bjmap2;
6692: mpiaij->Bperm2 = Bperm2;
6694: mpiaij->Cperm1 = Cperm1;
6696: /* Allocate in preallocation. If not used, it has zero cost on host */
6697: PetscCall(PetscMalloc2(mpiaij->sendlen, &mpiaij->sendbuf, mpiaij->recvlen, &mpiaij->recvbuf));
6698: PetscFunctionReturn(PETSC_SUCCESS);
6699: }
6701: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6702: {
6703: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6704: Mat A = mpiaij->A, B = mpiaij->B;
6705: PetscCount Annz = mpiaij->Annz, Annz2 = mpiaij->Annz2, Bnnz = mpiaij->Bnnz, Bnnz2 = mpiaij->Bnnz2;
6706: PetscScalar *Aa, *Ba;
6707: PetscScalar *sendbuf = mpiaij->sendbuf;
6708: PetscScalar *recvbuf = mpiaij->recvbuf;
6709: const PetscCount *Ajmap1 = mpiaij->Ajmap1, *Ajmap2 = mpiaij->Ajmap2, *Aimap2 = mpiaij->Aimap2;
6710: const PetscCount *Bjmap1 = mpiaij->Bjmap1, *Bjmap2 = mpiaij->Bjmap2, *Bimap2 = mpiaij->Bimap2;
6711: const PetscCount *Aperm1 = mpiaij->Aperm1, *Aperm2 = mpiaij->Aperm2, *Bperm1 = mpiaij->Bperm1, *Bperm2 = mpiaij->Bperm2;
6712: const PetscCount *Cperm1 = mpiaij->Cperm1;
6714: PetscFunctionBegin;
6715: PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6716: PetscCall(MatSeqAIJGetArray(B, &Ba));
6718: /* Pack entries to be sent to remote */
6719: for (PetscCount i = 0; i < mpiaij->sendlen; i++) sendbuf[i] = v[Cperm1[i]];
6721: /* Send remote entries to their owner and overlap the communication with local computation */
6722: PetscCall(PetscSFReduceWithMemTypeBegin(mpiaij->coo_sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6723: /* Add local entries to A and B */
6724: for (PetscCount i = 0; i < Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6725: PetscScalar sum = 0.0; /* Do partial summation first to improve numerical stability */
6726: for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6727: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6728: }
6729: for (PetscCount i = 0; i < Bnnz; i++) {
6730: PetscScalar sum = 0.0;
6731: for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6732: Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6733: }
6734: PetscCall(PetscSFReduceEnd(mpiaij->coo_sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));
6736: /* Add received remote entries to A and B */
6737: for (PetscCount i = 0; i < Annz2; i++) {
6738: for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6739: }
6740: for (PetscCount i = 0; i < Bnnz2; i++) {
6741: for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6742: }
6743: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6744: PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6745: PetscFunctionReturn(PETSC_SUCCESS);
6746: }
6748: /*MC
6749: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
6751: Options Database Keys:
6752: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`
6754: Level: beginner
6756: Notes:
6757: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
6758: in this case the values associated with the rows and columns one passes in are set to zero
6759: in the matrix
6761: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
6762: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
6764: .seealso: [](chapter_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6765: M*/
6766: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6767: {
6768: Mat_MPIAIJ *b;
6769: PetscMPIInt size;
6771: PetscFunctionBegin;
6772: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
6774: PetscCall(PetscNew(&b));
6775: B->data = (void *)b;
6776: PetscCall(PetscMemcpy(B->ops, &MatOps_Values, sizeof(struct _MatOps)));
6777: B->assembled = PETSC_FALSE;
6778: B->insertmode = NOT_SET_VALUES;
6779: b->size = size;
6781: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
6783: /* build cache for off array entries formed */
6784: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
6786: b->donotstash = PETSC_FALSE;
6787: b->colmap = NULL;
6788: b->garray = NULL;
6789: b->roworiented = PETSC_TRUE;
6791: /* stuff used for matrix vector multiply */
6792: b->lvec = NULL;
6793: b->Mvctx = NULL;
6795: /* stuff for MatGetRow() */
6796: b->rowindices = NULL;
6797: b->rowvalues = NULL;
6798: b->getrowactive = PETSC_FALSE;
6800: /* flexible pointer used in CUSPARSE classes */
6801: b->spptr = NULL;
6803: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6804: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6805: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6806: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6807: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6808: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6809: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6810: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6811: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6812: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6813: #if defined(PETSC_HAVE_CUDA)
6814: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6815: #endif
6816: #if defined(PETSC_HAVE_HIP)
6817: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6818: #endif
6819: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6820: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6821: #endif
6822: #if defined(PETSC_HAVE_MKL_SPARSE)
6823: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6824: #endif
6825: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6826: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6827: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6828: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6829: #if defined(PETSC_HAVE_ELEMENTAL)
6830: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6831: #endif
6832: #if defined(PETSC_HAVE_SCALAPACK)
6833: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6834: #endif
6835: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6836: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6837: #if defined(PETSC_HAVE_HYPRE)
6838: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6839: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6840: #endif
6841: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6842: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6843: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6844: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6845: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6846: PetscFunctionReturn(PETSC_SUCCESS);
6847: }
6849: /*@C
6850: MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6851: and "off-diagonal" part of the matrix in CSR format.
6853: Collective
6855: Input Parameters:
6856: + comm - MPI communicator
6857: . m - number of local rows (Cannot be `PETSC_DECIDE`)
6858: . n - This value should be the same as the local size used in creating the
6859: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
6860: calculated if `N` is given) For square matrices `n` is almost always `m`.
6861: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6862: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6863: . 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
6864: . j - column indices, which must be local, i.e., based off the start column of the diagonal portion
6865: . a - matrix values
6866: . 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
6867: . oj - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6868: - oa - matrix values
6870: Output Parameter:
6871: . mat - the matrix
6873: Level: advanced
6875: Notes:
6876: The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc. The user
6877: must free the arrays once the matrix has been destroyed and not before.
6879: The `i` and `j` indices are 0 based
6881: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix
6883: This sets local rows and cannot be used to set off-processor values.
6885: Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6886: legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6887: not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
6888: the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
6889: keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
6890: communication if it is known that only local entries will be set.
6892: .seealso: [](chapter_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
6893: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
6894: @*/
6895: 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)
6896: {
6897: Mat_MPIAIJ *maij;
6899: PetscFunctionBegin;
6900: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
6901: PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
6902: PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
6903: PetscCall(MatCreate(comm, mat));
6904: PetscCall(MatSetSizes(*mat, m, n, M, N));
6905: PetscCall(MatSetType(*mat, MATMPIAIJ));
6906: maij = (Mat_MPIAIJ *)(*mat)->data;
6908: (*mat)->preallocated = PETSC_TRUE;
6910: PetscCall(PetscLayoutSetUp((*mat)->rmap));
6911: PetscCall(PetscLayoutSetUp((*mat)->cmap));
6913: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
6914: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));
6916: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
6917: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
6918: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
6919: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
6920: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
6921: PetscFunctionReturn(PETSC_SUCCESS);
6922: }
6924: typedef struct {
6925: Mat *mp; /* intermediate products */
6926: PetscBool *mptmp; /* is the intermediate product temporary ? */
6927: PetscInt cp; /* number of intermediate products */
6929: /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
6930: PetscInt *startsj_s, *startsj_r;
6931: PetscScalar *bufa;
6932: Mat P_oth;
6934: /* may take advantage of merging product->B */
6935: Mat Bloc; /* B-local by merging diag and off-diag */
6937: /* cusparse does not have support to split between symbolic and numeric phases.
6938: When api_user is true, we don't need to update the numerical values
6939: of the temporary storage */
6940: PetscBool reusesym;
6942: /* support for COO values insertion */
6943: PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
6944: PetscInt **own; /* own[i] points to address of on-process COO indices for Mat mp[i] */
6945: PetscInt **off; /* off[i] points to address of off-process COO indices for Mat mp[i] */
6946: PetscBool hasoffproc; /* if true, have off-process values insertion (i.e. AtB or PtAP) */
6947: PetscSF sf; /* used for non-local values insertion and memory malloc */
6948: PetscMemType mtype;
6950: /* customization */
6951: PetscBool abmerge;
6952: PetscBool P_oth_bind;
6953: } MatMatMPIAIJBACKEND;
6955: PetscErrorCode MatDestroy_MatMatMPIAIJBACKEND(void *data)
6956: {
6957: MatMatMPIAIJBACKEND *mmdata = (MatMatMPIAIJBACKEND *)data;
6958: PetscInt i;
6960: PetscFunctionBegin;
6961: PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
6962: PetscCall(PetscFree(mmdata->bufa));
6963: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
6964: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
6965: PetscCall(MatDestroy(&mmdata->P_oth));
6966: PetscCall(MatDestroy(&mmdata->Bloc));
6967: PetscCall(PetscSFDestroy(&mmdata->sf));
6968: for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
6969: PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
6970: PetscCall(PetscFree(mmdata->own[0]));
6971: PetscCall(PetscFree(mmdata->own));
6972: PetscCall(PetscFree(mmdata->off[0]));
6973: PetscCall(PetscFree(mmdata->off));
6974: PetscCall(PetscFree(mmdata));
6975: PetscFunctionReturn(PETSC_SUCCESS);
6976: }
6978: /* Copy selected n entries with indices in idx[] of A to v[].
6979: If idx is NULL, copy the whole data array of A to v[]
6980: */
6981: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
6982: {
6983: PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);
6985: PetscFunctionBegin;
6986: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
6987: if (f) {
6988: PetscCall((*f)(A, n, idx, v));
6989: } else {
6990: const PetscScalar *vv;
6992: PetscCall(MatSeqAIJGetArrayRead(A, &vv));
6993: if (n && idx) {
6994: PetscScalar *w = v;
6995: const PetscInt *oi = idx;
6996: PetscInt j;
6998: for (j = 0; j < n; j++) *w++ = vv[*oi++];
6999: } else {
7000: PetscCall(PetscArraycpy(v, vv, n));
7001: }
7002: PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7003: }
7004: PetscFunctionReturn(PETSC_SUCCESS);
7005: }
7007: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7008: {
7009: MatMatMPIAIJBACKEND *mmdata;
7010: PetscInt i, n_d, n_o;
7012: PetscFunctionBegin;
7013: MatCheckProduct(C, 1);
7014: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7015: mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7016: if (!mmdata->reusesym) { /* update temporary matrices */
7017: 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));
7018: if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7019: }
7020: mmdata->reusesym = PETSC_FALSE;
7022: for (i = 0; i < mmdata->cp; i++) {
7023: 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]);
7024: PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7025: }
7026: for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7027: PetscInt noff = mmdata->off[i + 1] - mmdata->off[i];
7029: if (mmdata->mptmp[i]) continue;
7030: if (noff) {
7031: PetscInt nown = mmdata->own[i + 1] - mmdata->own[i];
7033: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7034: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7035: n_o += noff;
7036: n_d += nown;
7037: } else {
7038: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;
7040: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7041: n_d += mm->nz;
7042: }
7043: }
7044: if (mmdata->hasoffproc) { /* offprocess insertion */
7045: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7046: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7047: }
7048: PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7049: PetscFunctionReturn(PETSC_SUCCESS);
7050: }
7052: /* Support for Pt * A, A * P, or Pt * A * P */
7053: #define MAX_NUMBER_INTERMEDIATE 4
7054: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7055: {
7056: Mat_Product *product = C->product;
7057: Mat A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7058: Mat_MPIAIJ *a, *p;
7059: MatMatMPIAIJBACKEND *mmdata;
7060: ISLocalToGlobalMapping P_oth_l2g = NULL;
7061: IS glob = NULL;
7062: const char *prefix;
7063: char pprefix[256];
7064: const PetscInt *globidx, *P_oth_idx;
7065: PetscInt i, j, cp, m, n, M, N, *coo_i, *coo_j;
7066: PetscCount ncoo, ncoo_d, ncoo_o, ncoo_oown;
7067: PetscInt cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7068: /* type-0: consecutive, start from 0; type-1: consecutive with */
7069: /* a base offset; type-2: sparse with a local to global map table */
7070: const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE]; /* col/row local to global map array (table) for type-2 map type */
7072: MatProductType ptype;
7073: PetscBool mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7074: PetscMPIInt size;
7076: PetscFunctionBegin;
7077: MatCheckProduct(C, 1);
7078: PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7079: ptype = product->type;
7080: if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7081: ptype = MATPRODUCT_AB;
7082: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7083: }
7084: switch (ptype) {
7085: case MATPRODUCT_AB:
7086: A = product->A;
7087: P = product->B;
7088: m = A->rmap->n;
7089: n = P->cmap->n;
7090: M = A->rmap->N;
7091: N = P->cmap->N;
7092: hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7093: break;
7094: case MATPRODUCT_AtB:
7095: P = product->A;
7096: A = product->B;
7097: m = P->cmap->n;
7098: n = A->cmap->n;
7099: M = P->cmap->N;
7100: N = A->cmap->N;
7101: hasoffproc = PETSC_TRUE;
7102: break;
7103: case MATPRODUCT_PtAP:
7104: A = product->A;
7105: P = product->B;
7106: m = P->cmap->n;
7107: n = P->cmap->n;
7108: M = P->cmap->N;
7109: N = P->cmap->N;
7110: hasoffproc = PETSC_TRUE;
7111: break;
7112: default:
7113: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7114: }
7115: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7116: if (size == 1) hasoffproc = PETSC_FALSE;
7118: /* defaults */
7119: for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7120: mp[i] = NULL;
7121: mptmp[i] = PETSC_FALSE;
7122: rmapt[i] = -1;
7123: cmapt[i] = -1;
7124: rmapa[i] = NULL;
7125: cmapa[i] = NULL;
7126: }
7128: /* customization */
7129: PetscCall(PetscNew(&mmdata));
7130: mmdata->reusesym = product->api_user;
7131: if (ptype == MATPRODUCT_AB) {
7132: if (product->api_user) {
7133: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7134: PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7135: PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7136: PetscOptionsEnd();
7137: } else {
7138: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7139: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7140: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7141: PetscOptionsEnd();
7142: }
7143: } else if (ptype == MATPRODUCT_PtAP) {
7144: if (product->api_user) {
7145: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7146: PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7147: PetscOptionsEnd();
7148: } else {
7149: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7150: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7151: PetscOptionsEnd();
7152: }
7153: }
7154: a = (Mat_MPIAIJ *)A->data;
7155: p = (Mat_MPIAIJ *)P->data;
7156: PetscCall(MatSetSizes(C, m, n, M, N));
7157: PetscCall(PetscLayoutSetUp(C->rmap));
7158: PetscCall(PetscLayoutSetUp(C->cmap));
7159: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7160: PetscCall(MatGetOptionsPrefix(C, &prefix));
7162: cp = 0;
7163: switch (ptype) {
7164: case MATPRODUCT_AB: /* A * P */
7165: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7167: /* A_diag * P_local (merged or not) */
7168: if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7169: /* P is product->B */
7170: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7171: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7172: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7173: PetscCall(MatProductSetFill(mp[cp], product->fill));
7174: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7175: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7176: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7177: mp[cp]->product->api_user = product->api_user;
7178: PetscCall(MatProductSetFromOptions(mp[cp]));
7179: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7180: PetscCall(ISGetIndices(glob, &globidx));
7181: rmapt[cp] = 1;
7182: cmapt[cp] = 2;
7183: cmapa[cp] = globidx;
7184: mptmp[cp] = PETSC_FALSE;
7185: cp++;
7186: } else { /* A_diag * P_diag and A_diag * P_off */
7187: PetscCall(MatProductCreate(a->A, p->A, NULL, &mp[cp]));
7188: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7189: PetscCall(MatProductSetFill(mp[cp], product->fill));
7190: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7191: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7192: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7193: mp[cp]->product->api_user = product->api_user;
7194: PetscCall(MatProductSetFromOptions(mp[cp]));
7195: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7196: rmapt[cp] = 1;
7197: cmapt[cp] = 1;
7198: mptmp[cp] = PETSC_FALSE;
7199: cp++;
7200: PetscCall(MatProductCreate(a->A, p->B, NULL, &mp[cp]));
7201: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7202: PetscCall(MatProductSetFill(mp[cp], product->fill));
7203: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7204: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7205: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7206: mp[cp]->product->api_user = product->api_user;
7207: PetscCall(MatProductSetFromOptions(mp[cp]));
7208: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7209: rmapt[cp] = 1;
7210: cmapt[cp] = 2;
7211: cmapa[cp] = p->garray;
7212: mptmp[cp] = PETSC_FALSE;
7213: cp++;
7214: }
7216: /* A_off * P_other */
7217: if (mmdata->P_oth) {
7218: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7219: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7220: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)(a->B))->type_name));
7221: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7222: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7223: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7224: PetscCall(MatProductSetFill(mp[cp], product->fill));
7225: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7226: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7227: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7228: mp[cp]->product->api_user = product->api_user;
7229: PetscCall(MatProductSetFromOptions(mp[cp]));
7230: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7231: rmapt[cp] = 1;
7232: cmapt[cp] = 2;
7233: cmapa[cp] = P_oth_idx;
7234: mptmp[cp] = PETSC_FALSE;
7235: cp++;
7236: }
7237: break;
7239: case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7240: /* A is product->B */
7241: PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7242: if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7243: PetscCall(MatProductCreate(mmdata->Bloc, mmdata->Bloc, NULL, &mp[cp]));
7244: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7245: PetscCall(MatProductSetFill(mp[cp], product->fill));
7246: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7247: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7248: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7249: mp[cp]->product->api_user = product->api_user;
7250: PetscCall(MatProductSetFromOptions(mp[cp]));
7251: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7252: PetscCall(ISGetIndices(glob, &globidx));
7253: rmapt[cp] = 2;
7254: rmapa[cp] = globidx;
7255: cmapt[cp] = 2;
7256: cmapa[cp] = globidx;
7257: mptmp[cp] = PETSC_FALSE;
7258: cp++;
7259: } else {
7260: PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7261: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7262: PetscCall(MatProductSetFill(mp[cp], product->fill));
7263: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7264: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7265: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7266: mp[cp]->product->api_user = product->api_user;
7267: PetscCall(MatProductSetFromOptions(mp[cp]));
7268: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7269: PetscCall(ISGetIndices(glob, &globidx));
7270: rmapt[cp] = 1;
7271: cmapt[cp] = 2;
7272: cmapa[cp] = globidx;
7273: mptmp[cp] = PETSC_FALSE;
7274: cp++;
7275: PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7276: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7277: PetscCall(MatProductSetFill(mp[cp], product->fill));
7278: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7279: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7280: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7281: mp[cp]->product->api_user = product->api_user;
7282: PetscCall(MatProductSetFromOptions(mp[cp]));
7283: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7284: rmapt[cp] = 2;
7285: rmapa[cp] = p->garray;
7286: cmapt[cp] = 2;
7287: cmapa[cp] = globidx;
7288: mptmp[cp] = PETSC_FALSE;
7289: cp++;
7290: }
7291: break;
7292: case MATPRODUCT_PtAP:
7293: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7294: /* P is product->B */
7295: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7296: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7297: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
7298: PetscCall(MatProductSetFill(mp[cp], product->fill));
7299: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7300: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7301: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7302: mp[cp]->product->api_user = product->api_user;
7303: PetscCall(MatProductSetFromOptions(mp[cp]));
7304: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7305: PetscCall(ISGetIndices(glob, &globidx));
7306: rmapt[cp] = 2;
7307: rmapa[cp] = globidx;
7308: cmapt[cp] = 2;
7309: cmapa[cp] = globidx;
7310: mptmp[cp] = PETSC_FALSE;
7311: cp++;
7312: if (mmdata->P_oth) {
7313: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7314: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7315: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)(a->B))->type_name));
7316: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7317: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7318: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7319: PetscCall(MatProductSetFill(mp[cp], product->fill));
7320: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7321: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7322: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7323: mp[cp]->product->api_user = product->api_user;
7324: PetscCall(MatProductSetFromOptions(mp[cp]));
7325: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7326: mptmp[cp] = PETSC_TRUE;
7327: cp++;
7328: PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7329: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7330: PetscCall(MatProductSetFill(mp[cp], product->fill));
7331: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7332: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7333: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7334: mp[cp]->product->api_user = product->api_user;
7335: PetscCall(MatProductSetFromOptions(mp[cp]));
7336: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7337: rmapt[cp] = 2;
7338: rmapa[cp] = globidx;
7339: cmapt[cp] = 2;
7340: cmapa[cp] = P_oth_idx;
7341: mptmp[cp] = PETSC_FALSE;
7342: cp++;
7343: }
7344: break;
7345: default:
7346: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7347: }
7348: /* sanity check */
7349: if (size > 1)
7350: 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);
7352: PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7353: for (i = 0; i < cp; i++) {
7354: mmdata->mp[i] = mp[i];
7355: mmdata->mptmp[i] = mptmp[i];
7356: }
7357: mmdata->cp = cp;
7358: C->product->data = mmdata;
7359: C->product->destroy = MatDestroy_MatMatMPIAIJBACKEND;
7360: C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;
7362: /* memory type */
7363: mmdata->mtype = PETSC_MEMTYPE_HOST;
7364: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7365: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7366: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7367: if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7368: else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7369: else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;
7371: /* prepare coo coordinates for values insertion */
7373: /* count total nonzeros of those intermediate seqaij Mats
7374: ncoo_d: # of nonzeros of matrices that do not have offproc entries
7375: ncoo_o: # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7376: ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7377: */
7378: for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7379: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7380: if (mptmp[cp]) continue;
7381: if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7382: const PetscInt *rmap = rmapa[cp];
7383: const PetscInt mr = mp[cp]->rmap->n;
7384: const PetscInt rs = C->rmap->rstart;
7385: const PetscInt re = C->rmap->rend;
7386: const PetscInt *ii = mm->i;
7387: for (i = 0; i < mr; i++) {
7388: const PetscInt gr = rmap[i];
7389: const PetscInt nz = ii[i + 1] - ii[i];
7390: if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7391: else ncoo_oown += nz; /* this row is local */
7392: }
7393: } else ncoo_d += mm->nz;
7394: }
7396: /*
7397: ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc
7399: ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.
7401: off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].
7403: off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7404: own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7405: so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.
7407: coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7408: 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.
7409: */
7410: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7411: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));
7413: /* gather (i,j) of nonzeros inserted by remote procs */
7414: if (hasoffproc) {
7415: PetscSF msf;
7416: PetscInt ncoo2, *coo_i2, *coo_j2;
7418: PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7419: PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7420: PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */
7422: for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7423: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7424: PetscInt *idxoff = mmdata->off[cp];
7425: PetscInt *idxown = mmdata->own[cp];
7426: if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7427: const PetscInt *rmap = rmapa[cp];
7428: const PetscInt *cmap = cmapa[cp];
7429: const PetscInt *ii = mm->i;
7430: PetscInt *coi = coo_i + ncoo_o;
7431: PetscInt *coj = coo_j + ncoo_o;
7432: const PetscInt mr = mp[cp]->rmap->n;
7433: const PetscInt rs = C->rmap->rstart;
7434: const PetscInt re = C->rmap->rend;
7435: const PetscInt cs = C->cmap->rstart;
7436: for (i = 0; i < mr; i++) {
7437: const PetscInt *jj = mm->j + ii[i];
7438: const PetscInt gr = rmap[i];
7439: const PetscInt nz = ii[i + 1] - ii[i];
7440: if (gr < rs || gr >= re) { /* this is an offproc row */
7441: for (j = ii[i]; j < ii[i + 1]; j++) {
7442: *coi++ = gr;
7443: *idxoff++ = j;
7444: }
7445: if (!cmapt[cp]) { /* already global */
7446: for (j = 0; j < nz; j++) *coj++ = jj[j];
7447: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7448: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7449: } else { /* offdiag */
7450: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7451: }
7452: ncoo_o += nz;
7453: } else { /* this is a local row */
7454: for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7455: }
7456: }
7457: }
7458: mmdata->off[cp + 1] = idxoff;
7459: mmdata->own[cp + 1] = idxown;
7460: }
7462: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7463: PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, ncoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7464: PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7465: PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7466: ncoo = ncoo_d + ncoo_oown + ncoo2;
7467: PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7468: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7469: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7470: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7471: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7472: PetscCall(PetscFree2(coo_i, coo_j));
7473: /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7474: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7475: coo_i = coo_i2;
7476: coo_j = coo_j2;
7477: } else { /* no offproc values insertion */
7478: ncoo = ncoo_d;
7479: PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));
7481: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7482: PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7483: PetscCall(PetscSFSetUp(mmdata->sf));
7484: }
7485: mmdata->hasoffproc = hasoffproc;
7487: /* gather (i,j) of nonzeros inserted locally */
7488: for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7489: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7490: PetscInt *coi = coo_i + ncoo_d;
7491: PetscInt *coj = coo_j + ncoo_d;
7492: const PetscInt *jj = mm->j;
7493: const PetscInt *ii = mm->i;
7494: const PetscInt *cmap = cmapa[cp];
7495: const PetscInt *rmap = rmapa[cp];
7496: const PetscInt mr = mp[cp]->rmap->n;
7497: const PetscInt rs = C->rmap->rstart;
7498: const PetscInt re = C->rmap->rend;
7499: const PetscInt cs = C->cmap->rstart;
7501: if (mptmp[cp]) continue;
7502: if (rmapt[cp] == 1) { /* consecutive rows */
7503: /* fill coo_i */
7504: for (i = 0; i < mr; i++) {
7505: const PetscInt gr = i + rs;
7506: for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7507: }
7508: /* fill coo_j */
7509: if (!cmapt[cp]) { /* type-0, already global */
7510: PetscCall(PetscArraycpy(coj, jj, mm->nz));
7511: } else if (cmapt[cp] == 1) { /* type-1, local to global for consecutive columns of C */
7512: for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7513: } else { /* type-2, local to global for sparse columns */
7514: for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7515: }
7516: ncoo_d += mm->nz;
7517: } else if (rmapt[cp] == 2) { /* sparse rows */
7518: for (i = 0; i < mr; i++) {
7519: const PetscInt *jj = mm->j + ii[i];
7520: const PetscInt gr = rmap[i];
7521: const PetscInt nz = ii[i + 1] - ii[i];
7522: if (gr >= rs && gr < re) { /* local rows */
7523: for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7524: if (!cmapt[cp]) { /* type-0, already global */
7525: for (j = 0; j < nz; j++) *coj++ = jj[j];
7526: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7527: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7528: } else { /* type-2, local to global for sparse columns */
7529: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7530: }
7531: ncoo_d += nz;
7532: }
7533: }
7534: }
7535: }
7536: if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7537: PetscCall(ISDestroy(&glob));
7538: if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7539: PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7540: /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7541: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));
7543: /* preallocate with COO data */
7544: PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7545: PetscCall(PetscFree2(coo_i, coo_j));
7546: PetscFunctionReturn(PETSC_SUCCESS);
7547: }
7549: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7550: {
7551: Mat_Product *product = mat->product;
7552: #if defined(PETSC_HAVE_DEVICE)
7553: PetscBool match = PETSC_FALSE;
7554: PetscBool usecpu = PETSC_FALSE;
7555: #else
7556: PetscBool match = PETSC_TRUE;
7557: #endif
7559: PetscFunctionBegin;
7560: MatCheckProduct(mat, 1);
7561: #if defined(PETSC_HAVE_DEVICE)
7562: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7563: if (match) { /* we can always fallback to the CPU if requested */
7564: switch (product->type) {
7565: case MATPRODUCT_AB:
7566: if (product->api_user) {
7567: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7568: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7569: PetscOptionsEnd();
7570: } else {
7571: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7572: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7573: PetscOptionsEnd();
7574: }
7575: break;
7576: case MATPRODUCT_AtB:
7577: if (product->api_user) {
7578: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7579: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7580: PetscOptionsEnd();
7581: } else {
7582: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7583: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7584: PetscOptionsEnd();
7585: }
7586: break;
7587: case MATPRODUCT_PtAP:
7588: if (product->api_user) {
7589: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7590: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7591: PetscOptionsEnd();
7592: } else {
7593: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7594: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7595: PetscOptionsEnd();
7596: }
7597: break;
7598: default:
7599: break;
7600: }
7601: match = (PetscBool)!usecpu;
7602: }
7603: #endif
7604: if (match) {
7605: switch (product->type) {
7606: case MATPRODUCT_AB:
7607: case MATPRODUCT_AtB:
7608: case MATPRODUCT_PtAP:
7609: mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7610: break;
7611: default:
7612: break;
7613: }
7614: }
7615: /* fallback to MPIAIJ ops */
7616: if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7617: PetscFunctionReturn(PETSC_SUCCESS);
7618: }
7620: /*
7621: Produces a set of block column indices of the matrix row, one for each block represented in the original row
7623: n - the number of block indices in cc[]
7624: cc - the block indices (must be large enough to contain the indices)
7625: */
7626: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7627: {
7628: PetscInt cnt = -1, nidx, j;
7629: const PetscInt *idx;
7631: PetscFunctionBegin;
7632: PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7633: if (nidx) {
7634: cnt = 0;
7635: cc[cnt] = idx[0] / bs;
7636: for (j = 1; j < nidx; j++) {
7637: if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7638: }
7639: }
7640: PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7641: *n = cnt + 1;
7642: PetscFunctionReturn(PETSC_SUCCESS);
7643: }
7645: /*
7646: Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows
7648: ncollapsed - the number of block indices
7649: collapsed - the block indices (must be large enough to contain the indices)
7650: */
7651: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7652: {
7653: PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;
7655: PetscFunctionBegin;
7656: PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7657: for (i = start + 1; i < start + bs; i++) {
7658: PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7659: PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7660: cprevtmp = cprev;
7661: cprev = merged;
7662: merged = cprevtmp;
7663: }
7664: *ncollapsed = nprev;
7665: if (collapsed) *collapsed = cprev;
7666: PetscFunctionReturn(PETSC_SUCCESS);
7667: }
7669: /*
7670: This will eventually be folded into MatCreateGraph_AIJ() for optimal performance
7671: */
7672: static PetscErrorCode MatFilter_AIJ(Mat Gmat, PetscReal vfilter, Mat *filteredG)
7673: {
7674: PetscInt Istart, Iend, ncols, nnz0, nnz1, NN, MM, nloc;
7675: Mat tGmat;
7676: MPI_Comm comm;
7677: const PetscScalar *vals;
7678: const PetscInt *idx;
7679: PetscInt *d_nnz, *o_nnz, kk, *garray = NULL, *AJ, maxcols = 0;
7680: MatScalar *AA; // this is checked in graph
7681: PetscBool isseqaij;
7682: Mat a, b, c;
7683: MatType jtype;
7685: PetscFunctionBegin;
7686: PetscCall(PetscObjectGetComm((PetscObject)Gmat, &comm));
7687: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Gmat, MATSEQAIJ, &isseqaij));
7688: PetscCall(MatGetType(Gmat, &jtype));
7689: PetscCall(MatCreate(comm, &tGmat));
7690: PetscCall(MatSetType(tGmat, jtype));
7692: /* TODO GPU: this can be called when filter = 0 -> Probably provide MatAIJThresholdCompress that compresses the entries below a threshold?
7693: Also, if the matrix is symmetric, can we skip this
7694: operation? It can be very expensive on large matrices. */
7696: // global sizes
7697: PetscCall(MatGetSize(Gmat, &MM, &NN));
7698: PetscCall(MatGetOwnershipRange(Gmat, &Istart, &Iend));
7699: nloc = Iend - Istart;
7700: PetscCall(PetscMalloc2(nloc, &d_nnz, nloc, &o_nnz));
7701: if (isseqaij) {
7702: a = Gmat;
7703: b = NULL;
7704: } else {
7705: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
7706: a = d->A;
7707: b = d->B;
7708: garray = d->garray;
7709: }
7710: /* Determine upper bound on non-zeros needed in new filtered matrix */
7711: for (PetscInt row = 0; row < nloc; row++) {
7712: PetscCall(MatGetRow(a, row, &ncols, NULL, NULL));
7713: d_nnz[row] = ncols;
7714: if (ncols > maxcols) maxcols = ncols;
7715: PetscCall(MatRestoreRow(a, row, &ncols, NULL, NULL));
7716: }
7717: if (b) {
7718: for (PetscInt row = 0; row < nloc; row++) {
7719: PetscCall(MatGetRow(b, row, &ncols, NULL, NULL));
7720: o_nnz[row] = ncols;
7721: if (ncols > maxcols) maxcols = ncols;
7722: PetscCall(MatRestoreRow(b, row, &ncols, NULL, NULL));
7723: }
7724: }
7725: PetscCall(MatSetSizes(tGmat, nloc, nloc, MM, MM));
7726: PetscCall(MatSetBlockSizes(tGmat, 1, 1));
7727: PetscCall(MatSeqAIJSetPreallocation(tGmat, 0, d_nnz));
7728: PetscCall(MatMPIAIJSetPreallocation(tGmat, 0, d_nnz, 0, o_nnz));
7729: PetscCall(MatSetOption(tGmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
7730: PetscCall(PetscFree2(d_nnz, o_nnz));
7731: //
7732: PetscCall(PetscMalloc2(maxcols, &AA, maxcols, &AJ));
7733: nnz0 = nnz1 = 0;
7734: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7735: for (PetscInt row = 0, grow = Istart, ncol_row, jj; row < nloc; row++, grow++) {
7736: PetscCall(MatGetRow(c, row, &ncols, &idx, &vals));
7737: for (ncol_row = jj = 0; jj < ncols; jj++, nnz0++) {
7738: PetscScalar sv = PetscAbs(PetscRealPart(vals[jj]));
7739: if (PetscRealPart(sv) > vfilter) {
7740: nnz1++;
7741: PetscInt cid = idx[jj] + Istart; //diag
7742: if (c != a) cid = garray[idx[jj]];
7743: AA[ncol_row] = vals[jj];
7744: AJ[ncol_row] = cid;
7745: ncol_row++;
7746: }
7747: }
7748: PetscCall(MatRestoreRow(c, row, &ncols, &idx, &vals));
7749: PetscCall(MatSetValues(tGmat, 1, &grow, ncol_row, AJ, AA, INSERT_VALUES));
7750: }
7751: }
7752: PetscCall(PetscFree2(AA, AJ));
7753: PetscCall(MatAssemblyBegin(tGmat, MAT_FINAL_ASSEMBLY));
7754: PetscCall(MatAssemblyEnd(tGmat, MAT_FINAL_ASSEMBLY));
7755: PetscCall(MatPropagateSymmetryOptions(Gmat, tGmat)); /* Normal Mat options are not relevant ? */
7757: 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));
7759: *filteredG = tGmat;
7760: PetscCall(MatViewFromOptions(tGmat, NULL, "-mat_filter_graph_view"));
7761: PetscFunctionReturn(PETSC_SUCCESS);
7762: }
7764: /*
7765: MatCreateGraph_Simple_AIJ - create simple scalar matrix (graph) from potentially blocked matrix
7767: Input Parameter:
7768: . Amat - matrix
7769: - symmetrize - make the result symmetric
7770: + scale - scale with diagonal
7772: Output Parameter:
7773: . a_Gmat - output scalar graph >= 0
7775: */
7776: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, Mat *a_Gmat)
7777: {
7778: PetscInt Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7779: MPI_Comm comm;
7780: Mat Gmat;
7781: PetscBool ismpiaij, isseqaij;
7782: Mat a, b, c;
7783: MatType jtype;
7785: PetscFunctionBegin;
7786: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7787: PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7788: PetscCall(MatGetSize(Amat, &MM, &NN));
7789: PetscCall(MatGetBlockSize(Amat, &bs));
7790: nloc = (Iend - Istart) / bs;
7792: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7793: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7794: PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");
7796: /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7797: /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7798: implementation */
7799: if (bs > 1) {
7800: PetscCall(MatGetType(Amat, &jtype));
7801: PetscCall(MatCreate(comm, &Gmat));
7802: PetscCall(MatSetType(Gmat, jtype));
7803: PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7804: PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7805: if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7806: PetscInt *d_nnz, *o_nnz;
7807: MatScalar *aa, val, *AA;
7808: PetscInt *aj, *ai, *AJ, nc, nmax = 0;
7809: if (isseqaij) {
7810: a = Amat;
7811: b = NULL;
7812: } else {
7813: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7814: a = d->A;
7815: b = d->B;
7816: }
7817: PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7818: PetscCall(PetscMalloc2(nloc, &d_nnz, isseqaij ? 0 : nloc, &o_nnz));
7819: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7820: PetscInt *nnz = (c == a) ? d_nnz : o_nnz;
7821: const PetscInt *cols1, *cols2;
7822: for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7823: PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7824: nnz[brow / bs] = nc2 / bs;
7825: if (nc2 % bs) ok = 0;
7826: if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7827: for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7828: PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7829: if (nc1 != nc2) ok = 0;
7830: else {
7831: for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7832: if (cols1[jj] != cols2[jj]) ok = 0;
7833: if (cols1[jj] % bs != jj % bs) ok = 0;
7834: }
7835: }
7836: PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7837: }
7838: PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7839: if (!ok) {
7840: PetscCall(PetscFree2(d_nnz, o_nnz));
7841: PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7842: goto old_bs;
7843: }
7844: }
7845: }
7846: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7847: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7848: PetscCall(PetscFree2(d_nnz, o_nnz));
7849: PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7850: // diag
7851: for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7852: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;
7853: ai = aseq->i;
7854: n = ai[brow + 1] - ai[brow];
7855: aj = aseq->j + ai[brow];
7856: for (int k = 0; k < n; k += bs) { // block columns
7857: AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7858: val = 0;
7859: for (int ii = 0; ii < bs; ii++) { // rows in block
7860: aa = aseq->a + ai[brow + ii] + k;
7861: for (int jj = 0; jj < bs; jj++) { // columns in block
7862: val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7863: }
7864: }
7865: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7866: AA[k / bs] = val;
7867: }
7868: grow = Istart / bs + brow / bs;
7869: PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, INSERT_VALUES));
7870: }
7871: // off-diag
7872: if (ismpiaij) {
7873: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Amat->data;
7874: const PetscScalar *vals;
7875: const PetscInt *cols, *garray = aij->garray;
7876: PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7877: for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7878: PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7879: for (int k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7880: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7881: AA[k / bs] = 0;
7882: AJ[cidx] = garray[cols[k]] / bs;
7883: }
7884: nc = ncols / bs;
7885: PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7886: for (int ii = 0; ii < bs; ii++) { // rows in block
7887: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7888: for (int k = 0; k < ncols; k += bs) {
7889: for (int jj = 0; jj < bs; jj++) { // cols in block
7890: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7891: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7892: }
7893: }
7894: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7895: }
7896: grow = Istart / bs + brow / bs;
7897: PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, INSERT_VALUES));
7898: }
7899: }
7900: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7901: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7902: PetscCall(PetscFree2(AA, AJ));
7903: } else {
7904: const PetscScalar *vals;
7905: const PetscInt *idx;
7906: PetscInt *d_nnz, *o_nnz, *w0, *w1, *w2;
7907: old_bs:
7908: /*
7909: Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7910: */
7911: PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7912: PetscCall(PetscMalloc2(nloc, &d_nnz, isseqaij ? 0 : nloc, &o_nnz));
7913: if (isseqaij) {
7914: PetscInt max_d_nnz;
7915: /*
7916: Determine exact preallocation count for (sequential) scalar matrix
7917: */
7918: PetscCall(MatSeqAIJGetMaxRowNonzeros(Amat, &max_d_nnz));
7919: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7920: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7921: for (Ii = 0, jj = 0; Ii < Iend; Ii += bs, jj++) PetscCall(MatCollapseRows(Amat, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7922: PetscCall(PetscFree3(w0, w1, w2));
7923: } else if (ismpiaij) {
7924: Mat Daij, Oaij;
7925: const PetscInt *garray;
7926: PetscInt max_d_nnz;
7927: PetscCall(MatMPIAIJGetSeqAIJ(Amat, &Daij, &Oaij, &garray));
7928: /*
7929: Determine exact preallocation count for diagonal block portion of scalar matrix
7930: */
7931: PetscCall(MatSeqAIJGetMaxRowNonzeros(Daij, &max_d_nnz));
7932: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7933: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7934: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) PetscCall(MatCollapseRows(Daij, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7935: PetscCall(PetscFree3(w0, w1, w2));
7936: /*
7937: Over estimate (usually grossly over), preallocation count for off-diagonal portion of scalar matrix
7938: */
7939: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) {
7940: o_nnz[jj] = 0;
7941: for (kk = 0; kk < bs; kk++) { /* rows that get collapsed to a single row */
7942: PetscCall(MatGetRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7943: o_nnz[jj] += ncols;
7944: PetscCall(MatRestoreRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7945: }
7946: if (o_nnz[jj] > (NN / bs - nloc)) o_nnz[jj] = NN / bs - nloc;
7947: }
7948: } else SETERRQ(comm, PETSC_ERR_USER, "Require AIJ matrix type");
7949: /* get scalar copy (norms) of matrix */
7950: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7951: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7952: PetscCall(PetscFree2(d_nnz, o_nnz));
7953: for (Ii = Istart; Ii < Iend; Ii++) {
7954: PetscInt dest_row = Ii / bs;
7955: PetscCall(MatGetRow(Amat, Ii, &ncols, &idx, &vals));
7956: for (jj = 0; jj < ncols; jj++) {
7957: PetscInt dest_col = idx[jj] / bs;
7958: PetscScalar sv = PetscAbs(PetscRealPart(vals[jj]));
7959: PetscCall(MatSetValues(Gmat, 1, &dest_row, 1, &dest_col, &sv, ADD_VALUES));
7960: }
7961: PetscCall(MatRestoreRow(Amat, Ii, &ncols, &idx, &vals));
7962: }
7963: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7964: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7965: }
7966: } else {
7967: if (symmetrize || filter >= 0 || scale) PetscCall(MatDuplicate(Amat, MAT_COPY_VALUES, &Gmat));
7968: else {
7969: Gmat = Amat;
7970: PetscCall(PetscObjectReference((PetscObject)Gmat));
7971: }
7972: if (isseqaij) {
7973: a = Gmat;
7974: b = NULL;
7975: } else {
7976: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
7977: a = d->A;
7978: b = d->B;
7979: }
7980: if (filter >= 0 || scale) {
7981: /* take absolute value of each entry */
7982: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7983: MatInfo info;
7984: PetscScalar *avals;
7985: PetscCall(MatGetInfo(c, MAT_LOCAL, &info));
7986: PetscCall(MatSeqAIJGetArray(c, &avals));
7987: for (int jj = 0; jj < info.nz_used; jj++) avals[jj] = PetscAbsScalar(avals[jj]);
7988: PetscCall(MatSeqAIJRestoreArray(c, &avals));
7989: }
7990: }
7991: }
7992: if (symmetrize) {
7993: PetscBool isset, issym;
7994: PetscCall(MatIsSymmetricKnown(Amat, &isset, &issym));
7995: if (!isset || !issym) {
7996: Mat matTrans;
7997: PetscCall(MatTranspose(Gmat, MAT_INITIAL_MATRIX, &matTrans));
7998: PetscCall(MatAXPY(Gmat, 1.0, matTrans, Gmat->structurally_symmetric == PETSC_BOOL3_TRUE ? SAME_NONZERO_PATTERN : DIFFERENT_NONZERO_PATTERN));
7999: PetscCall(MatDestroy(&matTrans));
8000: }
8001: PetscCall(MatSetOption(Gmat, MAT_SYMMETRIC, PETSC_TRUE));
8002: } else if (Amat != Gmat) PetscCall(MatPropagateSymmetryOptions(Amat, Gmat));
8003: if (scale) {
8004: /* scale c for all diagonal values = 1 or -1 */
8005: Vec diag;
8006: PetscCall(MatCreateVecs(Gmat, &diag, NULL));
8007: PetscCall(MatGetDiagonal(Gmat, diag));
8008: PetscCall(VecReciprocal(diag));
8009: PetscCall(VecSqrtAbs(diag));
8010: PetscCall(MatDiagonalScale(Gmat, diag, diag));
8011: PetscCall(VecDestroy(&diag));
8012: }
8013: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_graph_view"));
8015: if (filter >= 0) {
8016: Mat Fmat = NULL; /* some silly compiler needs this */
8018: PetscCall(MatFilter_AIJ(Gmat, filter, &Fmat));
8019: PetscCall(MatDestroy(&Gmat));
8020: Gmat = Fmat;
8021: }
8022: *a_Gmat = Gmat;
8023: PetscFunctionReturn(PETSC_SUCCESS);
8024: }
8026: /*
8027: Special version for direct calls from Fortran
8028: */
8029: #include <petsc/private/fortranimpl.h>
8031: /* Change these macros so can be used in void function */
8032: /* Identical to PetscCallVoid, except it assigns to *_ierr */
8033: #undef PetscCall
8034: #define PetscCall(...) \
8035: do { \
8036: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
8037: if (PetscUnlikely(ierr_msv_mpiaij)) { \
8038: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
8039: return; \
8040: } \
8041: } while (0)
8043: #undef SETERRQ
8044: #define SETERRQ(comm, ierr, ...) \
8045: do { \
8046: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
8047: return; \
8048: } while (0)
8050: #if defined(PETSC_HAVE_FORTRAN_CAPS)
8051: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
8052: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
8053: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
8054: #else
8055: #endif
8056: PETSC_EXTERN void matsetvaluesmpiaij_(Mat *mmat, PetscInt *mm, const PetscInt im[], PetscInt *mn, const PetscInt in[], const PetscScalar v[], InsertMode *maddv, PetscErrorCode *_ierr)
8057: {
8058: Mat mat = *mmat;
8059: PetscInt m = *mm, n = *mn;
8060: InsertMode addv = *maddv;
8061: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
8062: PetscScalar value;
8064: MatCheckPreallocated(mat, 1);
8065: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
8066: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
8067: {
8068: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
8069: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
8070: PetscBool roworiented = aij->roworiented;
8072: /* Some Variables required in the macro */
8073: Mat A = aij->A;
8074: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
8075: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
8076: MatScalar *aa;
8077: PetscBool ignorezeroentries = (((a->ignorezeroentries) && (addv == ADD_VALUES)) ? PETSC_TRUE : PETSC_FALSE);
8078: Mat B = aij->B;
8079: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
8080: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
8081: MatScalar *ba;
8082: /* This variable below is only for the PETSC_HAVE_VIENNACL or PETSC_HAVE_CUDA cases, but we define it in all cases because we
8083: * cannot use "#if defined" inside a macro. */
8084: PETSC_UNUSED PetscBool inserted = PETSC_FALSE;
8086: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
8087: PetscInt nonew = a->nonew;
8088: MatScalar *ap1, *ap2;
8090: PetscFunctionBegin;
8091: PetscCall(MatSeqAIJGetArray(A, &aa));
8092: PetscCall(MatSeqAIJGetArray(B, &ba));
8093: for (i = 0; i < m; i++) {
8094: if (im[i] < 0) continue;
8095: 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);
8096: if (im[i] >= rstart && im[i] < rend) {
8097: row = im[i] - rstart;
8098: lastcol1 = -1;
8099: rp1 = aj + ai[row];
8100: ap1 = aa + ai[row];
8101: rmax1 = aimax[row];
8102: nrow1 = ailen[row];
8103: low1 = 0;
8104: high1 = nrow1;
8105: lastcol2 = -1;
8106: rp2 = bj + bi[row];
8107: ap2 = ba + bi[row];
8108: rmax2 = bimax[row];
8109: nrow2 = bilen[row];
8110: low2 = 0;
8111: high2 = nrow2;
8113: for (j = 0; j < n; j++) {
8114: if (roworiented) value = v[i * n + j];
8115: else value = v[i + j * m];
8116: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
8117: if (in[j] >= cstart && in[j] < cend) {
8118: col = in[j] - cstart;
8119: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
8120: } else if (in[j] < 0) continue;
8121: else if (PetscUnlikelyDebug(in[j] >= mat->cmap->N)) {
8122: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
8123: } else {
8124: if (mat->was_assembled) {
8125: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
8126: #if defined(PETSC_USE_CTABLE)
8127: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col));
8128: col--;
8129: #else
8130: col = aij->colmap[in[j]] - 1;
8131: #endif
8132: if (col < 0 && !((Mat_SeqAIJ *)(aij->A->data))->nonew) {
8133: PetscCall(MatDisAssemble_MPIAIJ(mat));
8134: col = in[j];
8135: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
8136: B = aij->B;
8137: b = (Mat_SeqAIJ *)B->data;
8138: bimax = b->imax;
8139: bi = b->i;
8140: bilen = b->ilen;
8141: bj = b->j;
8142: rp2 = bj + bi[row];
8143: ap2 = ba + bi[row];
8144: rmax2 = bimax[row];
8145: nrow2 = bilen[row];
8146: low2 = 0;
8147: high2 = nrow2;
8148: bm = aij->B->rmap->n;
8149: ba = b->a;
8150: inserted = PETSC_FALSE;
8151: }
8152: } else col = in[j];
8153: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
8154: }
8155: }
8156: } else if (!aij->donotstash) {
8157: if (roworiented) {
8158: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8159: } else {
8160: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8161: }
8162: }
8163: }
8164: PetscCall(MatSeqAIJRestoreArray(A, &aa));
8165: PetscCall(MatSeqAIJRestoreArray(B, &ba));
8166: }
8167: PetscFunctionReturnVoid();
8168: }
8170: /* Undefining these here since they were redefined from their original definition above! No
8171: * other PETSc functions should be defined past this point, as it is impossible to recover the
8172: * original definitions */
8173: #undef PetscCall
8174: #undef SETERRQ