Actual source code: mpiaij.c
1: #include <../src/mat/impls/aij/mpi/mpiaij.h>
2: #include <petsc/private/vecimpl.h>
3: #include <petsc/private/sfimpl.h>
4: #include <petsc/private/isimpl.h>
5: #include <petscblaslapack.h>
6: #include <petscsf.h>
7: #include <petsc/private/hashmapi.h>
9: PetscErrorCode MatDestroy_MPIAIJ(Mat mat)
10: {
11: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
13: PetscFunctionBegin;
14: PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
15: PetscCall(MatStashDestroy_Private(&mat->stash));
16: PetscCall(VecDestroy(&aij->diag));
17: PetscCall(MatDestroy(&aij->A));
18: PetscCall(MatDestroy(&aij->B));
19: #if defined(PETSC_USE_CTABLE)
20: PetscCall(PetscHMapIDestroy(&aij->colmap));
21: #else
22: PetscCall(PetscFree(aij->colmap));
23: #endif
24: PetscCall(PetscFree(aij->garray));
25: PetscCall(VecDestroy(&aij->lvec));
26: PetscCall(VecScatterDestroy(&aij->Mvctx));
27: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
28: PetscCall(PetscFree(aij->ld));
30: PetscCall(PetscFree(mat->data));
32: /* may be created by MatCreateMPIAIJSumSeqAIJSymbolic */
33: PetscCall(PetscObjectCompose((PetscObject)mat, "MatMergeSeqsToMPI", NULL));
35: PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
36: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
37: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
38: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatIsTranspose_C", NULL));
39: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocation_C", NULL));
40: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatResetPreallocation_C", NULL));
41: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocationCSR_C", NULL));
42: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
43: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpibaij_C", NULL));
44: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisbaij_C", NULL));
45: #if defined(PETSC_HAVE_CUDA)
46: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcusparse_C", NULL));
47: #endif
48: #if defined(PETSC_HAVE_HIP)
49: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijhipsparse_C", NULL));
50: #endif
51: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
52: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijkokkos_C", NULL));
53: #endif
54: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpidense_C", NULL));
55: #if defined(PETSC_HAVE_ELEMENTAL)
56: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_elemental_C", NULL));
57: #endif
58: #if defined(PETSC_HAVE_SCALAPACK)
59: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_scalapack_C", NULL));
60: #endif
61: #if defined(PETSC_HAVE_HYPRE)
62: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_hypre_C", NULL));
63: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", NULL));
64: #endif
65: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
66: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_is_mpiaij_C", NULL));
67: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_mpiaij_mpiaij_C", NULL));
68: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetUseScalableIncreaseOverlap_C", NULL));
69: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijperm_C", NULL));
70: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijsell_C", NULL));
71: #if defined(PETSC_HAVE_MKL_SPARSE)
72: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijmkl_C", NULL));
73: #endif
74: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcrl_C", NULL));
75: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
76: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisell_C", NULL));
77: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetPreallocationCOO_C", NULL));
78: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetValuesCOO_C", NULL));
79: PetscFunctionReturn(PETSC_SUCCESS);
80: }
82: /* defines MatSetValues_MPI_Hash(), MatAssemblyBegin_MPI_Hash(), and MatAssemblyEnd_MPI_Hash() */
83: #define TYPE AIJ
84: #define TYPE_AIJ
85: #include "../src/mat/impls/aij/mpi/mpihashmat.h"
86: #undef TYPE
87: #undef TYPE_AIJ
89: static PetscErrorCode MatGetRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
90: {
91: Mat B;
93: PetscFunctionBegin;
94: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, &B));
95: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject)B));
96: PetscCall(MatGetRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
97: PetscCall(MatDestroy(&B));
98: PetscFunctionReturn(PETSC_SUCCESS);
99: }
101: static PetscErrorCode MatRestoreRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
102: {
103: Mat B;
105: PetscFunctionBegin;
106: PetscCall(PetscObjectQuery((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject *)&B));
107: PetscCall(MatRestoreRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
108: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", NULL));
109: PetscFunctionReturn(PETSC_SUCCESS);
110: }
112: /*MC
113: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
115: This matrix type is identical to` MATSEQAIJ` when constructed with a single process communicator,
116: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
117: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
118: for communicators controlling multiple processes. It is recommended that you call both of
119: the above preallocation routines for simplicity.
121: Options Database Key:
122: . -mat_type aij - sets the matrix type to `MATAIJ` during a call to `MatSetFromOptions()`
124: Developer Note:
125: Level: beginner
127: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, `MATAIJKOKKOS`,and also automatically switches over to use inodes when
128: enough exist.
130: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`
131: M*/
133: /*MC
134: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
136: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
137: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
138: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
139: for communicators controlling multiple processes. It is recommended that you call both of
140: the above preallocation routines for simplicity.
142: Options Database Key:
143: . -mat_type aijcrl - sets the matrix type to `MATMPIAIJCRL` during a call to `MatSetFromOptions()`
145: Level: beginner
147: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
148: M*/
150: static PetscErrorCode MatBindToCPU_MPIAIJ(Mat A, PetscBool flg)
151: {
152: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
154: PetscFunctionBegin;
155: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP) || defined(PETSC_HAVE_VIENNACL)
156: A->boundtocpu = flg;
157: #endif
158: if (a->A) PetscCall(MatBindToCPU(a->A, flg));
159: if (a->B) PetscCall(MatBindToCPU(a->B, flg));
161: /* In addition to binding the diagonal and off-diagonal matrices, bind the local vectors used for matrix-vector products.
162: * This maybe seems a little odd for a MatBindToCPU() call to do, but it makes no sense for the binding of these vectors
163: * to differ from the parent matrix. */
164: if (a->lvec) PetscCall(VecBindToCPU(a->lvec, flg));
165: if (a->diag) PetscCall(VecBindToCPU(a->diag, flg));
167: PetscFunctionReturn(PETSC_SUCCESS);
168: }
170: static PetscErrorCode MatSetBlockSizes_MPIAIJ(Mat M, PetscInt rbs, PetscInt cbs)
171: {
172: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
174: PetscFunctionBegin;
175: if (mat->A) {
176: PetscCall(MatSetBlockSizes(mat->A, rbs, cbs));
177: PetscCall(MatSetBlockSizes(mat->B, rbs, 1));
178: }
179: PetscFunctionReturn(PETSC_SUCCESS);
180: }
182: static PetscErrorCode MatFindNonzeroRows_MPIAIJ(Mat M, IS *keptrows)
183: {
184: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
185: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data;
186: Mat_SeqAIJ *b = (Mat_SeqAIJ *)mat->B->data;
187: const PetscInt *ia, *ib;
188: const MatScalar *aa, *bb, *aav, *bav;
189: PetscInt na, nb, i, j, *rows, cnt = 0, n0rows;
190: PetscInt m = M->rmap->n, rstart = M->rmap->rstart;
192: PetscFunctionBegin;
193: *keptrows = NULL;
195: ia = a->i;
196: ib = b->i;
197: PetscCall(MatSeqAIJGetArrayRead(mat->A, &aav));
198: PetscCall(MatSeqAIJGetArrayRead(mat->B, &bav));
199: for (i = 0; i < m; i++) {
200: na = ia[i + 1] - ia[i];
201: nb = ib[i + 1] - ib[i];
202: if (!na && !nb) {
203: cnt++;
204: goto ok1;
205: }
206: aa = aav + ia[i];
207: for (j = 0; j < na; j++) {
208: if (aa[j] != 0.0) goto ok1;
209: }
210: bb = bav ? bav + ib[i] : NULL;
211: for (j = 0; j < nb; j++) {
212: if (bb[j] != 0.0) goto ok1;
213: }
214: cnt++;
215: ok1:;
216: }
217: PetscCall(MPIU_Allreduce(&cnt, &n0rows, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)M)));
218: if (!n0rows) {
219: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
220: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
221: PetscFunctionReturn(PETSC_SUCCESS);
222: }
223: PetscCall(PetscMalloc1(M->rmap->n - cnt, &rows));
224: cnt = 0;
225: for (i = 0; i < m; i++) {
226: na = ia[i + 1] - ia[i];
227: nb = ib[i + 1] - ib[i];
228: if (!na && !nb) continue;
229: aa = aav + ia[i];
230: for (j = 0; j < na; j++) {
231: if (aa[j] != 0.0) {
232: rows[cnt++] = rstart + i;
233: goto ok2;
234: }
235: }
236: bb = bav ? bav + ib[i] : NULL;
237: for (j = 0; j < nb; j++) {
238: if (bb[j] != 0.0) {
239: rows[cnt++] = rstart + i;
240: goto ok2;
241: }
242: }
243: ok2:;
244: }
245: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), cnt, rows, PETSC_OWN_POINTER, keptrows));
246: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
247: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
248: PetscFunctionReturn(PETSC_SUCCESS);
249: }
251: static PetscErrorCode MatDiagonalSet_MPIAIJ(Mat Y, Vec D, InsertMode is)
252: {
253: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Y->data;
254: PetscBool cong;
256: PetscFunctionBegin;
257: PetscCall(MatHasCongruentLayouts(Y, &cong));
258: if (Y->assembled && cong) {
259: PetscCall(MatDiagonalSet(aij->A, D, is));
260: } else {
261: PetscCall(MatDiagonalSet_Default(Y, D, is));
262: }
263: PetscFunctionReturn(PETSC_SUCCESS);
264: }
266: static PetscErrorCode MatFindZeroDiagonals_MPIAIJ(Mat M, IS *zrows)
267: {
268: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)M->data;
269: PetscInt i, rstart, nrows, *rows;
271: PetscFunctionBegin;
272: *zrows = NULL;
273: PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(aij->A, &nrows, &rows));
274: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
275: for (i = 0; i < nrows; i++) rows[i] += rstart;
276: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), nrows, rows, PETSC_OWN_POINTER, zrows));
277: PetscFunctionReturn(PETSC_SUCCESS);
278: }
280: static PetscErrorCode MatGetColumnReductions_MPIAIJ(Mat A, PetscInt type, PetscReal *reductions)
281: {
282: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)A->data;
283: PetscInt i, m, n, *garray = aij->garray;
284: Mat_SeqAIJ *a_aij = (Mat_SeqAIJ *)aij->A->data;
285: Mat_SeqAIJ *b_aij = (Mat_SeqAIJ *)aij->B->data;
286: PetscReal *work;
287: const PetscScalar *dummy;
289: PetscFunctionBegin;
290: PetscCall(MatGetSize(A, &m, &n));
291: PetscCall(PetscCalloc1(n, &work));
292: PetscCall(MatSeqAIJGetArrayRead(aij->A, &dummy));
293: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &dummy));
294: PetscCall(MatSeqAIJGetArrayRead(aij->B, &dummy));
295: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &dummy));
296: if (type == NORM_2) {
297: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i] * a_aij->a[i]);
298: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i] * b_aij->a[i]);
299: } else if (type == NORM_1) {
300: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i]);
301: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]);
302: } else if (type == NORM_INFINITY) {
303: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] = PetscMax(PetscAbsScalar(a_aij->a[i]), work[A->cmap->rstart + a_aij->j[i]]);
304: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] = PetscMax(PetscAbsScalar(b_aij->a[i]), work[garray[b_aij->j[i]]]);
305: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
306: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscRealPart(a_aij->a[i]);
307: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscRealPart(b_aij->a[i]);
308: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
309: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscImaginaryPart(a_aij->a[i]);
310: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscImaginaryPart(b_aij->a[i]);
311: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
312: if (type == NORM_INFINITY) {
313: PetscCall(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
314: } else {
315: PetscCall(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
316: }
317: PetscCall(PetscFree(work));
318: if (type == NORM_2) {
319: for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
320: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
321: for (i = 0; i < n; i++) reductions[i] /= m;
322: }
323: PetscFunctionReturn(PETSC_SUCCESS);
324: }
326: static PetscErrorCode MatFindOffBlockDiagonalEntries_MPIAIJ(Mat A, IS *is)
327: {
328: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
329: IS sis, gis;
330: const PetscInt *isis, *igis;
331: PetscInt n, *iis, nsis, ngis, rstart, i;
333: PetscFunctionBegin;
334: PetscCall(MatFindOffBlockDiagonalEntries(a->A, &sis));
335: PetscCall(MatFindNonzeroRows(a->B, &gis));
336: PetscCall(ISGetSize(gis, &ngis));
337: PetscCall(ISGetSize(sis, &nsis));
338: PetscCall(ISGetIndices(sis, &isis));
339: PetscCall(ISGetIndices(gis, &igis));
341: PetscCall(PetscMalloc1(ngis + nsis, &iis));
342: PetscCall(PetscArraycpy(iis, igis, ngis));
343: PetscCall(PetscArraycpy(iis + ngis, isis, nsis));
344: n = ngis + nsis;
345: PetscCall(PetscSortRemoveDupsInt(&n, iis));
346: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
347: for (i = 0; i < n; i++) iis[i] += rstart;
348: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), n, iis, PETSC_OWN_POINTER, is));
350: PetscCall(ISRestoreIndices(sis, &isis));
351: PetscCall(ISRestoreIndices(gis, &igis));
352: PetscCall(ISDestroy(&sis));
353: PetscCall(ISDestroy(&gis));
354: PetscFunctionReturn(PETSC_SUCCESS);
355: }
357: /*
358: Local utility routine that creates a mapping from the global column
359: number to the local number in the off-diagonal part of the local
360: storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at
361: a slightly higher hash table cost; without it it is not scalable (each processor
362: has an order N integer array but is fast to access.
363: */
364: PetscErrorCode MatCreateColmap_MPIAIJ_Private(Mat mat)
365: {
366: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
367: PetscInt n = aij->B->cmap->n, i;
369: PetscFunctionBegin;
370: PetscCheck(!n || aij->garray, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MPIAIJ Matrix was assembled but is missing garray");
371: #if defined(PETSC_USE_CTABLE)
372: PetscCall(PetscHMapICreateWithSize(n, &aij->colmap));
373: for (i = 0; i < n; i++) PetscCall(PetscHMapISet(aij->colmap, aij->garray[i] + 1, i + 1));
374: #else
375: PetscCall(PetscCalloc1(mat->cmap->N + 1, &aij->colmap));
376: for (i = 0; i < n; i++) aij->colmap[aij->garray[i]] = i + 1;
377: #endif
378: PetscFunctionReturn(PETSC_SUCCESS);
379: }
381: #define MatSetValues_SeqAIJ_A_Private(row, col, value, addv, orow, ocol) \
382: do { \
383: if (col <= lastcol1) low1 = 0; \
384: else high1 = nrow1; \
385: lastcol1 = col; \
386: while (high1 - low1 > 5) { \
387: t = (low1 + high1) / 2; \
388: if (rp1[t] > col) high1 = t; \
389: else low1 = t; \
390: } \
391: for (_i = low1; _i < high1; _i++) { \
392: if (rp1[_i] > col) break; \
393: if (rp1[_i] == col) { \
394: if (addv == ADD_VALUES) { \
395: ap1[_i] += value; \
396: /* Not sure LogFlops will slow dow the code or not */ \
397: (void)PetscLogFlops(1.0); \
398: } else ap1[_i] = value; \
399: goto a_noinsert; \
400: } \
401: } \
402: if (value == 0.0 && ignorezeroentries && row != col) { \
403: low1 = 0; \
404: high1 = nrow1; \
405: goto a_noinsert; \
406: } \
407: if (nonew == 1) { \
408: low1 = 0; \
409: high1 = nrow1; \
410: goto a_noinsert; \
411: } \
412: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
413: MatSeqXAIJReallocateAIJ(A, am, 1, nrow1, row, col, rmax1, aa, ai, aj, rp1, ap1, aimax, nonew, MatScalar); \
414: N = nrow1++ - 1; \
415: a->nz++; \
416: high1++; \
417: /* shift up all the later entries in this row */ \
418: PetscCall(PetscArraymove(rp1 + _i + 1, rp1 + _i, N - _i + 1)); \
419: PetscCall(PetscArraymove(ap1 + _i + 1, ap1 + _i, N - _i + 1)); \
420: rp1[_i] = col; \
421: ap1[_i] = value; \
422: A->nonzerostate++; \
423: a_noinsert:; \
424: ailen[row] = nrow1; \
425: } while (0)
427: #define MatSetValues_SeqAIJ_B_Private(row, col, value, addv, orow, ocol) \
428: do { \
429: if (col <= lastcol2) low2 = 0; \
430: else high2 = nrow2; \
431: lastcol2 = col; \
432: while (high2 - low2 > 5) { \
433: t = (low2 + high2) / 2; \
434: if (rp2[t] > col) high2 = t; \
435: else low2 = t; \
436: } \
437: for (_i = low2; _i < high2; _i++) { \
438: if (rp2[_i] > col) break; \
439: if (rp2[_i] == col) { \
440: if (addv == ADD_VALUES) { \
441: ap2[_i] += value; \
442: (void)PetscLogFlops(1.0); \
443: } else ap2[_i] = value; \
444: goto b_noinsert; \
445: } \
446: } \
447: if (value == 0.0 && ignorezeroentries) { \
448: low2 = 0; \
449: high2 = nrow2; \
450: goto b_noinsert; \
451: } \
452: if (nonew == 1) { \
453: low2 = 0; \
454: high2 = nrow2; \
455: goto b_noinsert; \
456: } \
457: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
458: MatSeqXAIJReallocateAIJ(B, bm, 1, nrow2, row, col, rmax2, ba, bi, bj, rp2, ap2, bimax, nonew, MatScalar); \
459: N = nrow2++ - 1; \
460: b->nz++; \
461: high2++; \
462: /* shift up all the later entries in this row */ \
463: PetscCall(PetscArraymove(rp2 + _i + 1, rp2 + _i, N - _i + 1)); \
464: PetscCall(PetscArraymove(ap2 + _i + 1, ap2 + _i, N - _i + 1)); \
465: rp2[_i] = col; \
466: ap2[_i] = value; \
467: B->nonzerostate++; \
468: b_noinsert:; \
469: bilen[row] = nrow2; \
470: } while (0)
472: static PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A, PetscInt row, const PetscScalar v[])
473: {
474: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
475: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data, *b = (Mat_SeqAIJ *)mat->B->data;
476: PetscInt l, *garray = mat->garray, diag;
477: PetscScalar *aa, *ba;
479: PetscFunctionBegin;
480: /* code only works for square matrices A */
482: /* find size of row to the left of the diagonal part */
483: PetscCall(MatGetOwnershipRange(A, &diag, NULL));
484: row = row - diag;
485: for (l = 0; l < b->i[row + 1] - b->i[row]; l++) {
486: if (garray[b->j[b->i[row] + l]] > diag) break;
487: }
488: if (l) {
489: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
490: PetscCall(PetscArraycpy(ba + b->i[row], v, l));
491: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
492: }
494: /* diagonal part */
495: if (a->i[row + 1] - a->i[row]) {
496: PetscCall(MatSeqAIJGetArray(mat->A, &aa));
497: PetscCall(PetscArraycpy(aa + a->i[row], v + l, (a->i[row + 1] - a->i[row])));
498: PetscCall(MatSeqAIJRestoreArray(mat->A, &aa));
499: }
501: /* right of diagonal part */
502: if (b->i[row + 1] - b->i[row] - l) {
503: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
504: PetscCall(PetscArraycpy(ba + b->i[row] + l, v + l + a->i[row + 1] - a->i[row], b->i[row + 1] - b->i[row] - l));
505: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
506: }
507: PetscFunctionReturn(PETSC_SUCCESS);
508: }
510: PetscErrorCode MatSetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
511: {
512: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
513: PetscScalar value = 0.0;
514: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
515: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
516: PetscBool roworiented = aij->roworiented;
518: /* Some Variables required in the macro */
519: Mat A = aij->A;
520: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
521: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
522: PetscBool ignorezeroentries = a->ignorezeroentries;
523: Mat B = aij->B;
524: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
525: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
526: MatScalar *aa, *ba;
527: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
528: PetscInt nonew;
529: MatScalar *ap1, *ap2;
531: PetscFunctionBegin;
532: PetscCall(MatSeqAIJGetArray(A, &aa));
533: PetscCall(MatSeqAIJGetArray(B, &ba));
534: for (i = 0; i < m; i++) {
535: if (im[i] < 0) continue;
536: PetscCheck(im[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], mat->rmap->N - 1);
537: if (im[i] >= rstart && im[i] < rend) {
538: row = im[i] - rstart;
539: lastcol1 = -1;
540: rp1 = aj ? aj + ai[row] : NULL;
541: ap1 = aa ? aa + ai[row] : NULL;
542: rmax1 = aimax[row];
543: nrow1 = ailen[row];
544: low1 = 0;
545: high1 = nrow1;
546: lastcol2 = -1;
547: rp2 = bj ? bj + bi[row] : NULL;
548: ap2 = ba ? ba + bi[row] : NULL;
549: rmax2 = bimax[row];
550: nrow2 = bilen[row];
551: low2 = 0;
552: high2 = nrow2;
554: for (j = 0; j < n; j++) {
555: if (v) value = roworiented ? v[i * n + j] : v[i + j * m];
556: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
557: if (in[j] >= cstart && in[j] < cend) {
558: col = in[j] - cstart;
559: nonew = a->nonew;
560: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
561: } else if (in[j] < 0) {
562: continue;
563: } else {
564: PetscCheck(in[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
565: if (mat->was_assembled) {
566: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
567: #if defined(PETSC_USE_CTABLE)
568: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col)); /* map global col ids to local ones */
569: col--;
570: #else
571: col = aij->colmap[in[j]] - 1;
572: #endif
573: if (col < 0 && !((Mat_SeqAIJ *)(aij->B->data))->nonew) { /* col < 0 means in[j] is a new col for B */
574: PetscCall(MatDisAssemble_MPIAIJ(mat)); /* Change aij->B from reduced/local format to expanded/global format */
575: col = in[j];
576: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
577: B = aij->B;
578: b = (Mat_SeqAIJ *)B->data;
579: bimax = b->imax;
580: bi = b->i;
581: bilen = b->ilen;
582: bj = b->j;
583: ba = b->a;
584: rp2 = bj + bi[row];
585: ap2 = ba + bi[row];
586: rmax2 = bimax[row];
587: nrow2 = bilen[row];
588: low2 = 0;
589: high2 = nrow2;
590: bm = aij->B->rmap->n;
591: ba = b->a;
592: } else if (col < 0 && !(ignorezeroentries && value == 0.0)) {
593: if (1 == ((Mat_SeqAIJ *)(aij->B->data))->nonew) {
594: PetscCall(PetscInfo(mat, "Skipping of insertion of new nonzero location in off-diagonal portion of matrix %g(%" PetscInt_FMT ",%" PetscInt_FMT ")\n", (double)PetscRealPart(value), im[i], in[j]));
595: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]);
596: }
597: } else col = in[j];
598: nonew = b->nonew;
599: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
600: }
601: }
602: } else {
603: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Setting off process row %" PetscInt_FMT " even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set", im[i]);
604: if (!aij->donotstash) {
605: mat->assembled = PETSC_FALSE;
606: if (roworiented) {
607: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v ? v + i * n : NULL, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
608: } else {
609: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v ? v + i : NULL, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
610: }
611: }
612: }
613: }
614: PetscCall(MatSeqAIJRestoreArray(A, &aa)); /* aa, bb might have been free'd due to reallocation above. But we don't access them here */
615: PetscCall(MatSeqAIJRestoreArray(B, &ba));
616: PetscFunctionReturn(PETSC_SUCCESS);
617: }
619: /*
620: This function sets the j and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
621: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
622: No off-processor parts off the matrix are allowed here and mat->was_assembled has to be PETSC_FALSE.
623: */
624: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[])
625: {
626: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
627: Mat A = aij->A; /* diagonal part of the matrix */
628: Mat B = aij->B; /* off-diagonal part of the matrix */
629: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
630: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
631: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, col;
632: PetscInt *ailen = a->ilen, *aj = a->j;
633: PetscInt *bilen = b->ilen, *bj = b->j;
634: PetscInt am = aij->A->rmap->n, j;
635: PetscInt diag_so_far = 0, dnz;
636: PetscInt offd_so_far = 0, onz;
638: PetscFunctionBegin;
639: /* Iterate over all rows of the matrix */
640: for (j = 0; j < am; j++) {
641: dnz = onz = 0;
642: /* Iterate over all non-zero columns of the current row */
643: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
644: /* If column is in the diagonal */
645: if (mat_j[col] >= cstart && mat_j[col] < cend) {
646: aj[diag_so_far++] = mat_j[col] - cstart;
647: dnz++;
648: } else { /* off-diagonal entries */
649: bj[offd_so_far++] = mat_j[col];
650: onz++;
651: }
652: }
653: ailen[j] = dnz;
654: bilen[j] = onz;
655: }
656: PetscFunctionReturn(PETSC_SUCCESS);
657: }
659: /*
660: This function sets the local j, a and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
661: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
662: No off-processor parts off the matrix are allowed here, they are set at a later point by MatSetValues_MPIAIJ.
663: Also, mat->was_assembled has to be false, otherwise the statement aj[rowstart_diag+dnz_row] = mat_j[col] - cstart;
664: would not be true and the more complex MatSetValues_MPIAIJ has to be used.
665: */
666: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[], const PetscScalar mat_a[])
667: {
668: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
669: Mat A = aij->A; /* diagonal part of the matrix */
670: Mat B = aij->B; /* off-diagonal part of the matrix */
671: Mat_SeqAIJ *aijd = (Mat_SeqAIJ *)(aij->A)->data, *aijo = (Mat_SeqAIJ *)(aij->B)->data;
672: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
673: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
674: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend;
675: PetscInt *ailen = a->ilen, *aj = a->j;
676: PetscInt *bilen = b->ilen, *bj = b->j;
677: PetscInt am = aij->A->rmap->n, j;
678: PetscInt *full_diag_i = aijd->i, *full_offd_i = aijo->i; /* These variables can also include non-local elements, which are set at a later point. */
679: PetscInt col, dnz_row, onz_row, rowstart_diag, rowstart_offd;
680: PetscScalar *aa = a->a, *ba = b->a;
682: PetscFunctionBegin;
683: /* Iterate over all rows of the matrix */
684: for (j = 0; j < am; j++) {
685: dnz_row = onz_row = 0;
686: rowstart_offd = full_offd_i[j];
687: rowstart_diag = full_diag_i[j];
688: /* Iterate over all non-zero columns of the current row */
689: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
690: /* If column is in the diagonal */
691: if (mat_j[col] >= cstart && mat_j[col] < cend) {
692: aj[rowstart_diag + dnz_row] = mat_j[col] - cstart;
693: aa[rowstart_diag + dnz_row] = mat_a[col];
694: dnz_row++;
695: } else { /* off-diagonal entries */
696: bj[rowstart_offd + onz_row] = mat_j[col];
697: ba[rowstart_offd + onz_row] = mat_a[col];
698: onz_row++;
699: }
700: }
701: ailen[j] = dnz_row;
702: bilen[j] = onz_row;
703: }
704: PetscFunctionReturn(PETSC_SUCCESS);
705: }
707: static PetscErrorCode MatGetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
708: {
709: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
710: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
711: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
713: PetscFunctionBegin;
714: for (i = 0; i < m; i++) {
715: if (idxm[i] < 0) continue; /* negative row */
716: PetscCheck(idxm[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, idxm[i], mat->rmap->N - 1);
717: if (idxm[i] >= rstart && idxm[i] < rend) {
718: row = idxm[i] - rstart;
719: for (j = 0; j < n; j++) {
720: if (idxn[j] < 0) continue; /* negative column */
721: PetscCheck(idxn[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, idxn[j], mat->cmap->N - 1);
722: if (idxn[j] >= cstart && idxn[j] < cend) {
723: col = idxn[j] - cstart;
724: PetscCall(MatGetValues(aij->A, 1, &row, 1, &col, v + i * n + j));
725: } else {
726: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
727: #if defined(PETSC_USE_CTABLE)
728: PetscCall(PetscHMapIGetWithDefault(aij->colmap, idxn[j] + 1, 0, &col));
729: col--;
730: #else
731: col = aij->colmap[idxn[j]] - 1;
732: #endif
733: if ((col < 0) || (aij->garray[col] != idxn[j])) *(v + i * n + j) = 0.0;
734: else PetscCall(MatGetValues(aij->B, 1, &row, 1, &col, v + i * n + j));
735: }
736: }
737: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local values currently supported");
738: }
739: PetscFunctionReturn(PETSC_SUCCESS);
740: }
742: static PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat, MatAssemblyType mode)
743: {
744: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
745: PetscInt nstash, reallocs;
747: PetscFunctionBegin;
748: if (aij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
750: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
751: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
752: PetscCall(PetscInfo(aij->A, "Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
753: PetscFunctionReturn(PETSC_SUCCESS);
754: }
756: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat, MatAssemblyType mode)
757: {
758: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
759: PetscMPIInt n;
760: PetscInt i, j, rstart, ncols, flg;
761: PetscInt *row, *col;
762: PetscBool other_disassembled;
763: PetscScalar *val;
765: /* do not use 'b = (Mat_SeqAIJ*)aij->B->data' as B can be reset in disassembly */
767: PetscFunctionBegin;
768: if (!aij->donotstash && !mat->nooffprocentries) {
769: while (1) {
770: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
771: if (!flg) break;
773: for (i = 0; i < n;) {
774: /* Now identify the consecutive vals belonging to the same row */
775: for (j = i, rstart = row[j]; j < n; j++) {
776: if (row[j] != rstart) break;
777: }
778: if (j < n) ncols = j - i;
779: else ncols = n - i;
780: /* Now assemble all these values with a single function call */
781: PetscCall(MatSetValues_MPIAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
782: i = j;
783: }
784: }
785: PetscCall(MatStashScatterEnd_Private(&mat->stash));
786: }
787: #if defined(PETSC_HAVE_DEVICE)
788: if (mat->offloadmask == PETSC_OFFLOAD_CPU) aij->A->offloadmask = PETSC_OFFLOAD_CPU;
789: /* We call MatBindToCPU() on aij->A and aij->B here, because if MatBindToCPU_MPIAIJ() is called before assembly, it cannot bind these. */
790: if (mat->boundtocpu) {
791: PetscCall(MatBindToCPU(aij->A, PETSC_TRUE));
792: PetscCall(MatBindToCPU(aij->B, PETSC_TRUE));
793: }
794: #endif
795: PetscCall(MatAssemblyBegin(aij->A, mode));
796: PetscCall(MatAssemblyEnd(aij->A, mode));
798: /* determine if any processor has disassembled, if so we must
799: also disassemble ourself, in order that we may reassemble. */
800: /*
801: if nonzero structure of submatrix B cannot change then we know that
802: no processor disassembled thus we can skip this stuff
803: */
804: if (!((Mat_SeqAIJ *)aij->B->data)->nonew) {
805: PetscCall(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
806: if (mat->was_assembled && !other_disassembled) { /* mat on this rank has reduced off-diag B with local col ids, but globally it does not */
807: PetscCall(MatDisAssemble_MPIAIJ(mat));
808: }
809: }
810: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIAIJ(mat));
811: PetscCall(MatSetOption(aij->B, MAT_USE_INODES, PETSC_FALSE));
812: #if defined(PETSC_HAVE_DEVICE)
813: if (mat->offloadmask == PETSC_OFFLOAD_CPU && aij->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) aij->B->offloadmask = PETSC_OFFLOAD_CPU;
814: #endif
815: PetscCall(MatAssemblyBegin(aij->B, mode));
816: PetscCall(MatAssemblyEnd(aij->B, mode));
818: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
820: aij->rowvalues = NULL;
822: PetscCall(VecDestroy(&aij->diag));
824: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
825: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqAIJ *)(aij->A->data))->nonew) {
826: PetscObjectState state = aij->A->nonzerostate + aij->B->nonzerostate;
827: PetscCall(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
828: }
829: #if defined(PETSC_HAVE_DEVICE)
830: mat->offloadmask = PETSC_OFFLOAD_BOTH;
831: #endif
832: PetscFunctionReturn(PETSC_SUCCESS);
833: }
835: static PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
836: {
837: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
839: PetscFunctionBegin;
840: PetscCall(MatZeroEntries(l->A));
841: PetscCall(MatZeroEntries(l->B));
842: PetscFunctionReturn(PETSC_SUCCESS);
843: }
845: static PetscErrorCode MatZeroRows_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
846: {
847: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
848: PetscObjectState sA, sB;
849: PetscInt *lrows;
850: PetscInt r, len;
851: PetscBool cong, lch, gch;
853: PetscFunctionBegin;
854: /* get locally owned rows */
855: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
856: PetscCall(MatHasCongruentLayouts(A, &cong));
857: /* fix right hand side if needed */
858: if (x && b) {
859: const PetscScalar *xx;
860: PetscScalar *bb;
862: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
863: PetscCall(VecGetArrayRead(x, &xx));
864: PetscCall(VecGetArray(b, &bb));
865: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
866: PetscCall(VecRestoreArrayRead(x, &xx));
867: PetscCall(VecRestoreArray(b, &bb));
868: }
870: sA = mat->A->nonzerostate;
871: sB = mat->B->nonzerostate;
873: if (diag != 0.0 && cong) {
874: PetscCall(MatZeroRows(mat->A, len, lrows, diag, NULL, NULL));
875: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
876: } else if (diag != 0.0) { /* non-square or non congruent layouts -> if keepnonzeropattern is false, we allow for new insertion */
877: Mat_SeqAIJ *aijA = (Mat_SeqAIJ *)mat->A->data;
878: Mat_SeqAIJ *aijB = (Mat_SeqAIJ *)mat->B->data;
879: PetscInt nnwA, nnwB;
880: PetscBool nnzA, nnzB;
882: nnwA = aijA->nonew;
883: nnwB = aijB->nonew;
884: nnzA = aijA->keepnonzeropattern;
885: nnzB = aijB->keepnonzeropattern;
886: if (!nnzA) {
887: PetscCall(PetscInfo(mat->A, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on diagonal block.\n"));
888: aijA->nonew = 0;
889: }
890: if (!nnzB) {
891: PetscCall(PetscInfo(mat->B, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on off-diagonal block.\n"));
892: aijB->nonew = 0;
893: }
894: /* Must zero here before the next loop */
895: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
896: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
897: for (r = 0; r < len; ++r) {
898: const PetscInt row = lrows[r] + A->rmap->rstart;
899: if (row >= A->cmap->N) continue;
900: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
901: }
902: aijA->nonew = nnwA;
903: aijB->nonew = nnwB;
904: } else {
905: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
906: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
907: }
908: PetscCall(PetscFree(lrows));
909: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
910: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
912: /* reduce nonzerostate */
913: lch = (PetscBool)(sA != mat->A->nonzerostate || sB != mat->B->nonzerostate);
914: PetscCall(MPIU_Allreduce(&lch, &gch, 1, MPIU_BOOL, MPI_LOR, PetscObjectComm((PetscObject)A)));
915: if (gch) A->nonzerostate++;
916: PetscFunctionReturn(PETSC_SUCCESS);
917: }
919: static PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
920: {
921: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
922: PetscMPIInt n = A->rmap->n;
923: PetscInt i, j, r, m, len = 0;
924: PetscInt *lrows, *owners = A->rmap->range;
925: PetscMPIInt p = 0;
926: PetscSFNode *rrows;
927: PetscSF sf;
928: const PetscScalar *xx;
929: PetscScalar *bb, *mask, *aij_a;
930: Vec xmask, lmask;
931: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)l->B->data;
932: const PetscInt *aj, *ii, *ridx;
933: PetscScalar *aa;
935: PetscFunctionBegin;
936: /* Create SF where leaves are input rows and roots are owned rows */
937: PetscCall(PetscMalloc1(n, &lrows));
938: for (r = 0; r < n; ++r) lrows[r] = -1;
939: PetscCall(PetscMalloc1(N, &rrows));
940: for (r = 0; r < N; ++r) {
941: const PetscInt idx = rows[r];
942: 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);
943: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
944: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
945: }
946: rrows[r].rank = p;
947: rrows[r].index = rows[r] - owners[p];
948: }
949: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
950: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
951: /* Collect flags for rows to be zeroed */
952: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
953: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
954: PetscCall(PetscSFDestroy(&sf));
955: /* Compress and put in row numbers */
956: for (r = 0; r < n; ++r)
957: if (lrows[r] >= 0) lrows[len++] = r;
958: /* zero diagonal part of matrix */
959: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
960: /* handle off-diagonal part of matrix */
961: PetscCall(MatCreateVecs(A, &xmask, NULL));
962: PetscCall(VecDuplicate(l->lvec, &lmask));
963: PetscCall(VecGetArray(xmask, &bb));
964: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
965: PetscCall(VecRestoreArray(xmask, &bb));
966: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
967: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
968: PetscCall(VecDestroy(&xmask));
969: if (x && b) { /* this code is buggy when the row and column layout don't match */
970: PetscBool cong;
972: PetscCall(MatHasCongruentLayouts(A, &cong));
973: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
974: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
975: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
976: PetscCall(VecGetArrayRead(l->lvec, &xx));
977: PetscCall(VecGetArray(b, &bb));
978: }
979: PetscCall(VecGetArray(lmask, &mask));
980: /* remove zeroed rows of off-diagonal matrix */
981: PetscCall(MatSeqAIJGetArray(l->B, &aij_a));
982: ii = aij->i;
983: for (i = 0; i < len; i++) PetscCall(PetscArrayzero(aij_a + ii[lrows[i]], ii[lrows[i] + 1] - ii[lrows[i]]));
984: /* loop over all elements of off process part of matrix zeroing removed columns*/
985: if (aij->compressedrow.use) {
986: m = aij->compressedrow.nrows;
987: ii = aij->compressedrow.i;
988: ridx = aij->compressedrow.rindex;
989: for (i = 0; i < m; i++) {
990: n = ii[i + 1] - ii[i];
991: aj = aij->j + ii[i];
992: aa = aij_a + ii[i];
994: for (j = 0; j < n; j++) {
995: if (PetscAbsScalar(mask[*aj])) {
996: if (b) bb[*ridx] -= *aa * xx[*aj];
997: *aa = 0.0;
998: }
999: aa++;
1000: aj++;
1001: }
1002: ridx++;
1003: }
1004: } else { /* do not use compressed row format */
1005: m = l->B->rmap->n;
1006: for (i = 0; i < m; i++) {
1007: n = ii[i + 1] - ii[i];
1008: aj = aij->j + ii[i];
1009: aa = aij_a + ii[i];
1010: for (j = 0; j < n; j++) {
1011: if (PetscAbsScalar(mask[*aj])) {
1012: if (b) bb[i] -= *aa * xx[*aj];
1013: *aa = 0.0;
1014: }
1015: aa++;
1016: aj++;
1017: }
1018: }
1019: }
1020: if (x && b) {
1021: PetscCall(VecRestoreArray(b, &bb));
1022: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1023: }
1024: PetscCall(MatSeqAIJRestoreArray(l->B, &aij_a));
1025: PetscCall(VecRestoreArray(lmask, &mask));
1026: PetscCall(VecDestroy(&lmask));
1027: PetscCall(PetscFree(lrows));
1029: /* only change matrix nonzero state if pattern was allowed to be changed */
1030: if (!((Mat_SeqAIJ *)(l->A->data))->keepnonzeropattern) {
1031: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1032: PetscCall(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1033: }
1034: PetscFunctionReturn(PETSC_SUCCESS);
1035: }
1037: static PetscErrorCode MatMult_MPIAIJ(Mat A, Vec xx, Vec yy)
1038: {
1039: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1040: PetscInt nt;
1041: VecScatter Mvctx = a->Mvctx;
1043: PetscFunctionBegin;
1044: PetscCall(VecGetLocalSize(xx, &nt));
1045: 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);
1046: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1047: PetscUseTypeMethod(a->A, mult, xx, yy);
1048: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1049: PetscUseTypeMethod(a->B, multadd, a->lvec, yy, yy);
1050: PetscFunctionReturn(PETSC_SUCCESS);
1051: }
1053: static PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A, Vec bb, Vec xx)
1054: {
1055: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1057: PetscFunctionBegin;
1058: PetscCall(MatMultDiagonalBlock(a->A, bb, xx));
1059: PetscFunctionReturn(PETSC_SUCCESS);
1060: }
1062: static PetscErrorCode MatMultAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1063: {
1064: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1065: VecScatter Mvctx = a->Mvctx;
1067: PetscFunctionBegin;
1068: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1069: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1070: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1071: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1072: PetscFunctionReturn(PETSC_SUCCESS);
1073: }
1075: static PetscErrorCode MatMultTranspose_MPIAIJ(Mat A, Vec xx, Vec yy)
1076: {
1077: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1079: PetscFunctionBegin;
1080: /* do nondiagonal part */
1081: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1082: /* do local part */
1083: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1084: /* add partial results together */
1085: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1086: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1087: PetscFunctionReturn(PETSC_SUCCESS);
1088: }
1090: static PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f)
1091: {
1092: MPI_Comm comm;
1093: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)Amat->data, *Bij = (Mat_MPIAIJ *)Bmat->data;
1094: Mat Adia = Aij->A, Bdia = Bij->A, Aoff, Boff, *Aoffs, *Boffs;
1095: IS Me, Notme;
1096: PetscInt M, N, first, last, *notme, i;
1097: PetscBool lf;
1098: PetscMPIInt size;
1100: PetscFunctionBegin;
1101: /* Easy test: symmetric diagonal block */
1102: PetscCall(MatIsTranspose(Adia, Bdia, tol, &lf));
1103: PetscCall(MPIU_Allreduce(&lf, f, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)Amat)));
1104: if (!*f) PetscFunctionReturn(PETSC_SUCCESS);
1105: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1106: PetscCallMPI(MPI_Comm_size(comm, &size));
1107: if (size == 1) PetscFunctionReturn(PETSC_SUCCESS);
1109: /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */
1110: PetscCall(MatGetSize(Amat, &M, &N));
1111: PetscCall(MatGetOwnershipRange(Amat, &first, &last));
1112: PetscCall(PetscMalloc1(N - last + first, ¬me));
1113: for (i = 0; i < first; i++) notme[i] = i;
1114: for (i = last; i < M; i++) notme[i - last + first] = i;
1115: PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme));
1116: PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me));
1117: PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs));
1118: Aoff = Aoffs[0];
1119: PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs));
1120: Boff = Boffs[0];
1121: PetscCall(MatIsTranspose(Aoff, Boff, tol, f));
1122: PetscCall(MatDestroyMatrices(1, &Aoffs));
1123: PetscCall(MatDestroyMatrices(1, &Boffs));
1124: PetscCall(ISDestroy(&Me));
1125: PetscCall(ISDestroy(&Notme));
1126: PetscCall(PetscFree(notme));
1127: PetscFunctionReturn(PETSC_SUCCESS);
1128: }
1130: static PetscErrorCode MatIsSymmetric_MPIAIJ(Mat A, PetscReal tol, PetscBool *f)
1131: {
1132: PetscFunctionBegin;
1133: PetscCall(MatIsTranspose_MPIAIJ(A, A, tol, f));
1134: PetscFunctionReturn(PETSC_SUCCESS);
1135: }
1137: static PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1138: {
1139: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1141: PetscFunctionBegin;
1142: /* do nondiagonal part */
1143: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1144: /* do local part */
1145: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1146: /* add partial results together */
1147: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1148: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1149: PetscFunctionReturn(PETSC_SUCCESS);
1150: }
1152: /*
1153: This only works correctly for square matrices where the subblock A->A is the
1154: diagonal block
1155: */
1156: static PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A, Vec v)
1157: {
1158: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1160: PetscFunctionBegin;
1161: PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1162: 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");
1163: PetscCall(MatGetDiagonal(a->A, v));
1164: PetscFunctionReturn(PETSC_SUCCESS);
1165: }
1167: static PetscErrorCode MatScale_MPIAIJ(Mat A, PetscScalar aa)
1168: {
1169: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1171: PetscFunctionBegin;
1172: PetscCall(MatScale(a->A, aa));
1173: PetscCall(MatScale(a->B, aa));
1174: PetscFunctionReturn(PETSC_SUCCESS);
1175: }
1177: static PetscErrorCode MatView_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
1178: {
1179: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1180: Mat_SeqAIJ *A = (Mat_SeqAIJ *)aij->A->data;
1181: Mat_SeqAIJ *B = (Mat_SeqAIJ *)aij->B->data;
1182: const PetscInt *garray = aij->garray;
1183: const PetscScalar *aa, *ba;
1184: PetscInt header[4], M, N, m, rs, cs, cnt, i, ja, jb;
1185: PetscInt64 nz, hnz;
1186: PetscInt *rowlens;
1187: PetscInt *colidxs;
1188: PetscScalar *matvals;
1189: PetscMPIInt rank;
1191: PetscFunctionBegin;
1192: PetscCall(PetscViewerSetUp(viewer));
1194: M = mat->rmap->N;
1195: N = mat->cmap->N;
1196: m = mat->rmap->n;
1197: rs = mat->rmap->rstart;
1198: cs = mat->cmap->rstart;
1199: nz = A->nz + B->nz;
1201: /* write matrix header */
1202: header[0] = MAT_FILE_CLASSID;
1203: header[1] = M;
1204: header[2] = N;
1205: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1206: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1207: if (rank == 0) {
1208: if (hnz > PETSC_MAX_INT) header[3] = PETSC_MAX_INT;
1209: else header[3] = (PetscInt)hnz;
1210: }
1211: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1213: /* fill in and store row lengths */
1214: PetscCall(PetscMalloc1(m, &rowlens));
1215: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i];
1216: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1217: PetscCall(PetscFree(rowlens));
1219: /* fill in and store column indices */
1220: PetscCall(PetscMalloc1(nz, &colidxs));
1221: for (cnt = 0, i = 0; i < m; i++) {
1222: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1223: if (garray[B->j[jb]] > cs) break;
1224: colidxs[cnt++] = garray[B->j[jb]];
1225: }
1226: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) colidxs[cnt++] = A->j[ja] + cs;
1227: for (; jb < B->i[i + 1]; jb++) colidxs[cnt++] = garray[B->j[jb]];
1228: }
1229: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1230: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
1231: PetscCall(PetscFree(colidxs));
1233: /* fill in and store nonzero values */
1234: PetscCall(MatSeqAIJGetArrayRead(aij->A, &aa));
1235: PetscCall(MatSeqAIJGetArrayRead(aij->B, &ba));
1236: PetscCall(PetscMalloc1(nz, &matvals));
1237: for (cnt = 0, i = 0; i < m; i++) {
1238: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1239: if (garray[B->j[jb]] > cs) break;
1240: matvals[cnt++] = ba[jb];
1241: }
1242: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) matvals[cnt++] = aa[ja];
1243: for (; jb < B->i[i + 1]; jb++) matvals[cnt++] = ba[jb];
1244: }
1245: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &aa));
1246: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &ba));
1247: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1248: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
1249: PetscCall(PetscFree(matvals));
1251: /* write block size option to the viewer's .info file */
1252: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1253: PetscFunctionReturn(PETSC_SUCCESS);
1254: }
1256: #include <petscdraw.h>
1257: static PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1258: {
1259: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1260: PetscMPIInt rank = aij->rank, size = aij->size;
1261: PetscBool isdraw, iascii, isbinary;
1262: PetscViewer sviewer;
1263: PetscViewerFormat format;
1265: PetscFunctionBegin;
1266: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1267: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1268: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1269: if (iascii) {
1270: PetscCall(PetscViewerGetFormat(viewer, &format));
1271: if (format == PETSC_VIEWER_LOAD_BALANCE) {
1272: PetscInt i, nmax = 0, nmin = PETSC_MAX_INT, navg = 0, *nz, nzlocal = ((Mat_SeqAIJ *)(aij->A->data))->nz + ((Mat_SeqAIJ *)(aij->B->data))->nz;
1273: PetscCall(PetscMalloc1(size, &nz));
1274: PetscCallMPI(MPI_Allgather(&nzlocal, 1, MPIU_INT, nz, 1, MPIU_INT, PetscObjectComm((PetscObject)mat)));
1275: for (i = 0; i < (PetscInt)size; i++) {
1276: nmax = PetscMax(nmax, nz[i]);
1277: nmin = PetscMin(nmin, nz[i]);
1278: navg += nz[i];
1279: }
1280: PetscCall(PetscFree(nz));
1281: navg = navg / size;
1282: PetscCall(PetscViewerASCIIPrintf(viewer, "Load Balance - Nonzeros: Min %" PetscInt_FMT " avg %" PetscInt_FMT " max %" PetscInt_FMT "\n", nmin, navg, nmax));
1283: PetscFunctionReturn(PETSC_SUCCESS);
1284: }
1285: PetscCall(PetscViewerGetFormat(viewer, &format));
1286: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1287: MatInfo info;
1288: PetscInt *inodes = NULL;
1290: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1291: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1292: PetscCall(MatInodeGetInodeSizes(aij->A, NULL, &inodes, NULL));
1293: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1294: if (!inodes) {
1295: 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,
1296: (double)info.memory));
1297: } else {
1298: 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,
1299: (double)info.memory));
1300: }
1301: PetscCall(MatGetInfo(aij->A, MAT_LOCAL, &info));
1302: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1303: PetscCall(MatGetInfo(aij->B, MAT_LOCAL, &info));
1304: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1305: PetscCall(PetscViewerFlush(viewer));
1306: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1307: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1308: PetscCall(VecScatterView(aij->Mvctx, viewer));
1309: PetscFunctionReturn(PETSC_SUCCESS);
1310: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1311: PetscInt inodecount, inodelimit, *inodes;
1312: PetscCall(MatInodeGetInodeSizes(aij->A, &inodecount, &inodes, &inodelimit));
1313: if (inodes) {
1314: PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
1315: } else {
1316: PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
1317: }
1318: PetscFunctionReturn(PETSC_SUCCESS);
1319: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1320: PetscFunctionReturn(PETSC_SUCCESS);
1321: }
1322: } else if (isbinary) {
1323: if (size == 1) {
1324: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1325: PetscCall(MatView(aij->A, viewer));
1326: } else {
1327: PetscCall(MatView_MPIAIJ_Binary(mat, viewer));
1328: }
1329: PetscFunctionReturn(PETSC_SUCCESS);
1330: } else if (iascii && size == 1) {
1331: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1332: PetscCall(MatView(aij->A, viewer));
1333: PetscFunctionReturn(PETSC_SUCCESS);
1334: } else if (isdraw) {
1335: PetscDraw draw;
1336: PetscBool isnull;
1337: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1338: PetscCall(PetscDrawIsNull(draw, &isnull));
1339: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1340: }
1342: { /* assemble the entire matrix onto first processor */
1343: Mat A = NULL, Av;
1344: IS isrow, iscol;
1346: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->rmap->N : 0, 0, 1, &isrow));
1347: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->cmap->N : 0, 0, 1, &iscol));
1348: PetscCall(MatCreateSubMatrix(mat, isrow, iscol, MAT_INITIAL_MATRIX, &A));
1349: PetscCall(MatMPIAIJGetSeqAIJ(A, &Av, NULL, NULL));
1350: /* The commented code uses MatCreateSubMatrices instead */
1351: /*
1352: Mat *AA, A = NULL, Av;
1353: IS isrow,iscol;
1355: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->rmap->N : 0,0,1,&isrow));
1356: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->cmap->N : 0,0,1,&iscol));
1357: PetscCall(MatCreateSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&AA));
1358: if (rank == 0) {
1359: PetscCall(PetscObjectReference((PetscObject)AA[0]));
1360: A = AA[0];
1361: Av = AA[0];
1362: }
1363: PetscCall(MatDestroySubMatrices(1,&AA));
1364: */
1365: PetscCall(ISDestroy(&iscol));
1366: PetscCall(ISDestroy(&isrow));
1367: /*
1368: Everyone has to call to draw the matrix since the graphics waits are
1369: synchronized across all processors that share the PetscDraw object
1370: */
1371: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1372: if (rank == 0) {
1373: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)Av, ((PetscObject)mat)->name));
1374: PetscCall(MatView_SeqAIJ(Av, sviewer));
1375: }
1376: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1377: PetscCall(PetscViewerFlush(viewer));
1378: PetscCall(MatDestroy(&A));
1379: }
1380: PetscFunctionReturn(PETSC_SUCCESS);
1381: }
1383: PetscErrorCode MatView_MPIAIJ(Mat mat, PetscViewer viewer)
1384: {
1385: PetscBool iascii, isdraw, issocket, isbinary;
1387: PetscFunctionBegin;
1388: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1389: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1390: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1391: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1392: if (iascii || isdraw || isbinary || issocket) PetscCall(MatView_MPIAIJ_ASCIIorDraworSocket(mat, viewer));
1393: PetscFunctionReturn(PETSC_SUCCESS);
1394: }
1396: static PetscErrorCode MatSOR_MPIAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1397: {
1398: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1399: Vec bb1 = NULL;
1400: PetscBool hasop;
1402: PetscFunctionBegin;
1403: if (flag == SOR_APPLY_UPPER) {
1404: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1405: PetscFunctionReturn(PETSC_SUCCESS);
1406: }
1408: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1));
1410: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
1411: if (flag & SOR_ZERO_INITIAL_GUESS) {
1412: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1413: its--;
1414: }
1416: while (its--) {
1417: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1418: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1420: /* update rhs: bb1 = bb - B*x */
1421: PetscCall(VecScale(mat->lvec, -1.0));
1422: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1424: /* local sweep */
1425: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
1426: }
1427: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1428: if (flag & SOR_ZERO_INITIAL_GUESS) {
1429: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1430: its--;
1431: }
1432: while (its--) {
1433: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1434: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1436: /* update rhs: bb1 = bb - B*x */
1437: PetscCall(VecScale(mat->lvec, -1.0));
1438: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1440: /* local sweep */
1441: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
1442: }
1443: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1444: if (flag & SOR_ZERO_INITIAL_GUESS) {
1445: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1446: its--;
1447: }
1448: while (its--) {
1449: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1450: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1452: /* update rhs: bb1 = bb - B*x */
1453: PetscCall(VecScale(mat->lvec, -1.0));
1454: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1456: /* local sweep */
1457: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
1458: }
1459: } else if (flag & SOR_EISENSTAT) {
1460: Vec xx1;
1462: PetscCall(VecDuplicate(bb, &xx1));
1463: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP), fshift, lits, 1, xx));
1465: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1466: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1467: if (!mat->diag) {
1468: PetscCall(MatCreateVecs(matin, &mat->diag, NULL));
1469: PetscCall(MatGetDiagonal(matin, mat->diag));
1470: }
1471: PetscCall(MatHasOperation(matin, MATOP_MULT_DIAGONAL_BLOCK, &hasop));
1472: if (hasop) {
1473: PetscCall(MatMultDiagonalBlock(matin, xx, bb1));
1474: } else {
1475: PetscCall(VecPointwiseMult(bb1, mat->diag, xx));
1476: }
1477: PetscCall(VecAYPX(bb1, (omega - 2.0) / omega, bb));
1479: PetscCall(MatMultAdd(mat->B, mat->lvec, bb1, bb1));
1481: /* local sweep */
1482: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP), fshift, lits, 1, xx1));
1483: PetscCall(VecAXPY(xx, 1.0, xx1));
1484: PetscCall(VecDestroy(&xx1));
1485: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");
1487: PetscCall(VecDestroy(&bb1));
1489: matin->factorerrortype = mat->A->factorerrortype;
1490: PetscFunctionReturn(PETSC_SUCCESS);
1491: }
1493: static PetscErrorCode MatPermute_MPIAIJ(Mat A, IS rowp, IS colp, Mat *B)
1494: {
1495: Mat aA, aB, Aperm;
1496: const PetscInt *rwant, *cwant, *gcols, *ai, *bi, *aj, *bj;
1497: PetscScalar *aa, *ba;
1498: PetscInt i, j, m, n, ng, anz, bnz, *dnnz, *onnz, *tdnnz, *tonnz, *rdest, *cdest, *work, *gcdest;
1499: PetscSF rowsf, sf;
1500: IS parcolp = NULL;
1501: PetscBool done;
1503: PetscFunctionBegin;
1504: PetscCall(MatGetLocalSize(A, &m, &n));
1505: PetscCall(ISGetIndices(rowp, &rwant));
1506: PetscCall(ISGetIndices(colp, &cwant));
1507: PetscCall(PetscMalloc3(PetscMax(m, n), &work, m, &rdest, n, &cdest));
1509: /* Invert row permutation to find out where my rows should go */
1510: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &rowsf));
1511: PetscCall(PetscSFSetGraphLayout(rowsf, A->rmap, A->rmap->n, NULL, PETSC_OWN_POINTER, rwant));
1512: PetscCall(PetscSFSetFromOptions(rowsf));
1513: for (i = 0; i < m; i++) work[i] = A->rmap->rstart + i;
1514: PetscCall(PetscSFReduceBegin(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1515: PetscCall(PetscSFReduceEnd(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1517: /* Invert column permutation to find out where my columns should go */
1518: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1519: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, A->cmap->n, NULL, PETSC_OWN_POINTER, cwant));
1520: PetscCall(PetscSFSetFromOptions(sf));
1521: for (i = 0; i < n; i++) work[i] = A->cmap->rstart + i;
1522: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1523: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1524: PetscCall(PetscSFDestroy(&sf));
1526: PetscCall(ISRestoreIndices(rowp, &rwant));
1527: PetscCall(ISRestoreIndices(colp, &cwant));
1528: PetscCall(MatMPIAIJGetSeqAIJ(A, &aA, &aB, &gcols));
1530: /* Find out where my gcols should go */
1531: PetscCall(MatGetSize(aB, NULL, &ng));
1532: PetscCall(PetscMalloc1(ng, &gcdest));
1533: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1534: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, ng, NULL, PETSC_OWN_POINTER, gcols));
1535: PetscCall(PetscSFSetFromOptions(sf));
1536: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1537: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1538: PetscCall(PetscSFDestroy(&sf));
1540: PetscCall(PetscCalloc4(m, &dnnz, m, &onnz, m, &tdnnz, m, &tonnz));
1541: PetscCall(MatGetRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1542: PetscCall(MatGetRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1543: for (i = 0; i < m; i++) {
1544: PetscInt row = rdest[i];
1545: PetscMPIInt rowner;
1546: PetscCall(PetscLayoutFindOwner(A->rmap, row, &rowner));
1547: for (j = ai[i]; j < ai[i + 1]; j++) {
1548: PetscInt col = cdest[aj[j]];
1549: PetscMPIInt cowner;
1550: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner)); /* Could build an index for the columns to eliminate this search */
1551: if (rowner == cowner) dnnz[i]++;
1552: else onnz[i]++;
1553: }
1554: for (j = bi[i]; j < bi[i + 1]; j++) {
1555: PetscInt col = gcdest[bj[j]];
1556: PetscMPIInt cowner;
1557: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner));
1558: if (rowner == cowner) dnnz[i]++;
1559: else onnz[i]++;
1560: }
1561: }
1562: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1563: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1564: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1565: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1566: PetscCall(PetscSFDestroy(&rowsf));
1568: PetscCall(MatCreateAIJ(PetscObjectComm((PetscObject)A), A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N, 0, tdnnz, 0, tonnz, &Aperm));
1569: PetscCall(MatSeqAIJGetArray(aA, &aa));
1570: PetscCall(MatSeqAIJGetArray(aB, &ba));
1571: for (i = 0; i < m; i++) {
1572: PetscInt *acols = dnnz, *bcols = onnz; /* Repurpose now-unneeded arrays */
1573: PetscInt j0, rowlen;
1574: rowlen = ai[i + 1] - ai[i];
1575: for (j0 = j = 0; j < rowlen; j0 = j) { /* rowlen could be larger than number of rows m, so sum in batches */
1576: for (; j < PetscMin(rowlen, j0 + m); j++) acols[j - j0] = cdest[aj[ai[i] + j]];
1577: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, acols, aa + ai[i] + j0, INSERT_VALUES));
1578: }
1579: rowlen = bi[i + 1] - bi[i];
1580: for (j0 = j = 0; j < rowlen; j0 = j) {
1581: for (; j < PetscMin(rowlen, j0 + m); j++) bcols[j - j0] = gcdest[bj[bi[i] + j]];
1582: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, bcols, ba + bi[i] + j0, INSERT_VALUES));
1583: }
1584: }
1585: PetscCall(MatAssemblyBegin(Aperm, MAT_FINAL_ASSEMBLY));
1586: PetscCall(MatAssemblyEnd(Aperm, MAT_FINAL_ASSEMBLY));
1587: PetscCall(MatRestoreRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1588: PetscCall(MatRestoreRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1589: PetscCall(MatSeqAIJRestoreArray(aA, &aa));
1590: PetscCall(MatSeqAIJRestoreArray(aB, &ba));
1591: PetscCall(PetscFree4(dnnz, onnz, tdnnz, tonnz));
1592: PetscCall(PetscFree3(work, rdest, cdest));
1593: PetscCall(PetscFree(gcdest));
1594: if (parcolp) PetscCall(ISDestroy(&colp));
1595: *B = Aperm;
1596: PetscFunctionReturn(PETSC_SUCCESS);
1597: }
1599: static PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
1600: {
1601: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1603: PetscFunctionBegin;
1604: PetscCall(MatGetSize(aij->B, NULL, nghosts));
1605: if (ghosts) *ghosts = aij->garray;
1606: PetscFunctionReturn(PETSC_SUCCESS);
1607: }
1609: static PetscErrorCode MatGetInfo_MPIAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1610: {
1611: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1612: Mat A = mat->A, B = mat->B;
1613: PetscLogDouble isend[5], irecv[5];
1615: PetscFunctionBegin;
1616: info->block_size = 1.0;
1617: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1619: isend[0] = info->nz_used;
1620: isend[1] = info->nz_allocated;
1621: isend[2] = info->nz_unneeded;
1622: isend[3] = info->memory;
1623: isend[4] = info->mallocs;
1625: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1627: isend[0] += info->nz_used;
1628: isend[1] += info->nz_allocated;
1629: isend[2] += info->nz_unneeded;
1630: isend[3] += info->memory;
1631: isend[4] += info->mallocs;
1632: if (flag == MAT_LOCAL) {
1633: info->nz_used = isend[0];
1634: info->nz_allocated = isend[1];
1635: info->nz_unneeded = isend[2];
1636: info->memory = isend[3];
1637: info->mallocs = isend[4];
1638: } else if (flag == MAT_GLOBAL_MAX) {
1639: PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1641: info->nz_used = irecv[0];
1642: info->nz_allocated = irecv[1];
1643: info->nz_unneeded = irecv[2];
1644: info->memory = irecv[3];
1645: info->mallocs = irecv[4];
1646: } else if (flag == MAT_GLOBAL_SUM) {
1647: PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1649: info->nz_used = irecv[0];
1650: info->nz_allocated = irecv[1];
1651: info->nz_unneeded = irecv[2];
1652: info->memory = irecv[3];
1653: info->mallocs = irecv[4];
1654: }
1655: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1656: info->fill_ratio_needed = 0;
1657: info->factor_mallocs = 0;
1658: PetscFunctionReturn(PETSC_SUCCESS);
1659: }
1661: PetscErrorCode MatSetOption_MPIAIJ(Mat A, MatOption op, PetscBool flg)
1662: {
1663: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1665: PetscFunctionBegin;
1666: switch (op) {
1667: case MAT_NEW_NONZERO_LOCATIONS:
1668: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1669: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1670: case MAT_KEEP_NONZERO_PATTERN:
1671: case MAT_NEW_NONZERO_LOCATION_ERR:
1672: case MAT_USE_INODES:
1673: case MAT_IGNORE_ZERO_ENTRIES:
1674: case MAT_FORM_EXPLICIT_TRANSPOSE:
1675: MatCheckPreallocated(A, 1);
1676: PetscCall(MatSetOption(a->A, op, flg));
1677: PetscCall(MatSetOption(a->B, op, flg));
1678: break;
1679: case MAT_ROW_ORIENTED:
1680: MatCheckPreallocated(A, 1);
1681: a->roworiented = flg;
1683: PetscCall(MatSetOption(a->A, op, flg));
1684: PetscCall(MatSetOption(a->B, op, flg));
1685: break;
1686: case MAT_FORCE_DIAGONAL_ENTRIES:
1687: case MAT_SORTED_FULL:
1688: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1689: break;
1690: case MAT_IGNORE_OFF_PROC_ENTRIES:
1691: a->donotstash = flg;
1692: break;
1693: /* Symmetry flags are handled directly by MatSetOption() and they don't affect preallocation */
1694: case MAT_SPD:
1695: case MAT_SYMMETRIC:
1696: case MAT_STRUCTURALLY_SYMMETRIC:
1697: case MAT_HERMITIAN:
1698: case MAT_SYMMETRY_ETERNAL:
1699: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1700: case MAT_SPD_ETERNAL:
1701: /* if the diagonal matrix is square it inherits some of the properties above */
1702: break;
1703: case MAT_SUBMAT_SINGLEIS:
1704: A->submat_singleis = flg;
1705: break;
1706: case MAT_STRUCTURE_ONLY:
1707: /* The option is handled directly by MatSetOption() */
1708: break;
1709: default:
1710: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1711: }
1712: PetscFunctionReturn(PETSC_SUCCESS);
1713: }
1715: PetscErrorCode MatGetRow_MPIAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1716: {
1717: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1718: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1719: PetscInt i, *cworkA, *cworkB, **pcA, **pcB, cstart = matin->cmap->rstart;
1720: PetscInt nztot, nzA, nzB, lrow, rstart = matin->rmap->rstart, rend = matin->rmap->rend;
1721: PetscInt *cmap, *idx_p;
1723: PetscFunctionBegin;
1724: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1725: mat->getrowactive = PETSC_TRUE;
1727: if (!mat->rowvalues && (idx || v)) {
1728: /*
1729: allocate enough space to hold information from the longest row.
1730: */
1731: Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)mat->A->data, *Ba = (Mat_SeqAIJ *)mat->B->data;
1732: PetscInt max = 1, tmp;
1733: for (i = 0; i < matin->rmap->n; i++) {
1734: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1735: if (max < tmp) max = tmp;
1736: }
1737: PetscCall(PetscMalloc2(max, &mat->rowvalues, max, &mat->rowindices));
1738: }
1740: PetscCheck(row >= rstart && row < rend, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Only local rows");
1741: lrow = row - rstart;
1743: pvA = &vworkA;
1744: pcA = &cworkA;
1745: pvB = &vworkB;
1746: pcB = &cworkB;
1747: if (!v) {
1748: pvA = NULL;
1749: pvB = NULL;
1750: }
1751: if (!idx) {
1752: pcA = NULL;
1753: if (!v) pcB = NULL;
1754: }
1755: PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1756: PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1757: nztot = nzA + nzB;
1759: cmap = mat->garray;
1760: if (v || idx) {
1761: if (nztot) {
1762: /* Sort by increasing column numbers, assuming A and B already sorted */
1763: PetscInt imark = -1;
1764: if (v) {
1765: *v = v_p = mat->rowvalues;
1766: for (i = 0; i < nzB; i++) {
1767: if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1768: else break;
1769: }
1770: imark = i;
1771: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1772: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1773: }
1774: if (idx) {
1775: *idx = idx_p = mat->rowindices;
1776: if (imark > -1) {
1777: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i]];
1778: } else {
1779: for (i = 0; i < nzB; i++) {
1780: if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1781: else break;
1782: }
1783: imark = i;
1784: }
1785: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart + cworkA[i];
1786: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i]];
1787: }
1788: } else {
1789: if (idx) *idx = NULL;
1790: if (v) *v = NULL;
1791: }
1792: }
1793: *nz = nztot;
1794: PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1795: PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1796: PetscFunctionReturn(PETSC_SUCCESS);
1797: }
1799: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1800: {
1801: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1803: PetscFunctionBegin;
1804: PetscCheck(aij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow() must be called first");
1805: aij->getrowactive = PETSC_FALSE;
1806: PetscFunctionReturn(PETSC_SUCCESS);
1807: }
1809: static PetscErrorCode MatNorm_MPIAIJ(Mat mat, NormType type, PetscReal *norm)
1810: {
1811: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1812: Mat_SeqAIJ *amat = (Mat_SeqAIJ *)aij->A->data, *bmat = (Mat_SeqAIJ *)aij->B->data;
1813: PetscInt i, j, cstart = mat->cmap->rstart;
1814: PetscReal sum = 0.0;
1815: const MatScalar *v, *amata, *bmata;
1817: PetscFunctionBegin;
1818: if (aij->size == 1) {
1819: PetscCall(MatNorm(aij->A, type, norm));
1820: } else {
1821: PetscCall(MatSeqAIJGetArrayRead(aij->A, &amata));
1822: PetscCall(MatSeqAIJGetArrayRead(aij->B, &bmata));
1823: if (type == NORM_FROBENIUS) {
1824: v = amata;
1825: for (i = 0; i < amat->nz; i++) {
1826: sum += PetscRealPart(PetscConj(*v) * (*v));
1827: v++;
1828: }
1829: v = bmata;
1830: for (i = 0; i < bmat->nz; i++) {
1831: sum += PetscRealPart(PetscConj(*v) * (*v));
1832: v++;
1833: }
1834: PetscCall(MPIU_Allreduce(&sum, norm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1835: *norm = PetscSqrtReal(*norm);
1836: PetscCall(PetscLogFlops(2.0 * amat->nz + 2.0 * bmat->nz));
1837: } else if (type == NORM_1) { /* max column norm */
1838: PetscReal *tmp, *tmp2;
1839: PetscInt *jj, *garray = aij->garray;
1840: PetscCall(PetscCalloc1(mat->cmap->N + 1, &tmp));
1841: PetscCall(PetscMalloc1(mat->cmap->N + 1, &tmp2));
1842: *norm = 0.0;
1843: v = amata;
1844: jj = amat->j;
1845: for (j = 0; j < amat->nz; j++) {
1846: tmp[cstart + *jj++] += PetscAbsScalar(*v);
1847: v++;
1848: }
1849: v = bmata;
1850: jj = bmat->j;
1851: for (j = 0; j < bmat->nz; j++) {
1852: tmp[garray[*jj++]] += PetscAbsScalar(*v);
1853: v++;
1854: }
1855: PetscCall(MPIU_Allreduce(tmp, tmp2, mat->cmap->N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1856: for (j = 0; j < mat->cmap->N; j++) {
1857: if (tmp2[j] > *norm) *norm = tmp2[j];
1858: }
1859: PetscCall(PetscFree(tmp));
1860: PetscCall(PetscFree(tmp2));
1861: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1862: } else if (type == NORM_INFINITY) { /* max row norm */
1863: PetscReal ntemp = 0.0;
1864: for (j = 0; j < aij->A->rmap->n; j++) {
1865: v = amata + amat->i[j];
1866: sum = 0.0;
1867: for (i = 0; i < amat->i[j + 1] - amat->i[j]; i++) {
1868: sum += PetscAbsScalar(*v);
1869: v++;
1870: }
1871: v = bmata + bmat->i[j];
1872: for (i = 0; i < bmat->i[j + 1] - bmat->i[j]; i++) {
1873: sum += PetscAbsScalar(*v);
1874: v++;
1875: }
1876: if (sum > ntemp) ntemp = sum;
1877: }
1878: PetscCall(MPIU_Allreduce(&ntemp, norm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
1879: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1880: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for two norm");
1881: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &amata));
1882: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &bmata));
1883: }
1884: PetscFunctionReturn(PETSC_SUCCESS);
1885: }
1887: static PetscErrorCode MatTranspose_MPIAIJ(Mat A, MatReuse reuse, Mat *matout)
1888: {
1889: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *b;
1890: Mat_SeqAIJ *Aloc = (Mat_SeqAIJ *)a->A->data, *Bloc = (Mat_SeqAIJ *)a->B->data, *sub_B_diag;
1891: 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;
1892: const PetscInt *ai, *aj, *bi, *bj, *B_diag_i;
1893: Mat B, A_diag, *B_diag;
1894: const MatScalar *pbv, *bv;
1896: PetscFunctionBegin;
1897: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1898: ma = A->rmap->n;
1899: na = A->cmap->n;
1900: mb = a->B->rmap->n;
1901: nb = a->B->cmap->n;
1902: ai = Aloc->i;
1903: aj = Aloc->j;
1904: bi = Bloc->i;
1905: bj = Bloc->j;
1906: if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1907: PetscInt *d_nnz, *g_nnz, *o_nnz;
1908: PetscSFNode *oloc;
1909: PETSC_UNUSED PetscSF sf;
1911: PetscCall(PetscMalloc4(na, &d_nnz, na, &o_nnz, nb, &g_nnz, nb, &oloc));
1912: /* compute d_nnz for preallocation */
1913: PetscCall(PetscArrayzero(d_nnz, na));
1914: for (i = 0; i < ai[ma]; i++) d_nnz[aj[i]]++;
1915: /* compute local off-diagonal contributions */
1916: PetscCall(PetscArrayzero(g_nnz, nb));
1917: for (i = 0; i < bi[ma]; i++) g_nnz[bj[i]]++;
1918: /* map those to global */
1919: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1920: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, nb, NULL, PETSC_USE_POINTER, a->garray));
1921: PetscCall(PetscSFSetFromOptions(sf));
1922: PetscCall(PetscArrayzero(o_nnz, na));
1923: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1924: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1925: PetscCall(PetscSFDestroy(&sf));
1927: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1928: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1929: PetscCall(MatSetBlockSizes(B, PetscAbs(A->cmap->bs), PetscAbs(A->rmap->bs)));
1930: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1931: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
1932: PetscCall(PetscFree4(d_nnz, o_nnz, g_nnz, oloc));
1933: } else {
1934: B = *matout;
1935: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
1936: }
1938: b = (Mat_MPIAIJ *)B->data;
1939: A_diag = a->A;
1940: B_diag = &b->A;
1941: sub_B_diag = (Mat_SeqAIJ *)(*B_diag)->data;
1942: A_diag_ncol = A_diag->cmap->N;
1943: B_diag_ilen = sub_B_diag->ilen;
1944: B_diag_i = sub_B_diag->i;
1946: /* Set ilen for diagonal of B */
1947: for (i = 0; i < A_diag_ncol; i++) B_diag_ilen[i] = B_diag_i[i + 1] - B_diag_i[i];
1949: /* Transpose the diagonal part of the matrix. In contrast to the off-diagonal part, this can be done
1950: very quickly (=without using MatSetValues), because all writes are local. */
1951: PetscCall(MatTransposeSetPrecursor(A_diag, *B_diag));
1952: PetscCall(MatTranspose(A_diag, MAT_REUSE_MATRIX, B_diag));
1954: /* copy over the B part */
1955: PetscCall(PetscMalloc1(bi[mb], &cols));
1956: PetscCall(MatSeqAIJGetArrayRead(a->B, &bv));
1957: pbv = bv;
1958: row = A->rmap->rstart;
1959: for (i = 0; i < bi[mb]; i++) cols[i] = a->garray[bj[i]];
1960: cols_tmp = cols;
1961: for (i = 0; i < mb; i++) {
1962: ncol = bi[i + 1] - bi[i];
1963: PetscCall(MatSetValues(B, ncol, cols_tmp, 1, &row, pbv, INSERT_VALUES));
1964: row++;
1965: if (pbv) pbv += ncol;
1966: if (cols_tmp) cols_tmp += ncol;
1967: }
1968: PetscCall(PetscFree(cols));
1969: PetscCall(MatSeqAIJRestoreArrayRead(a->B, &bv));
1971: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1972: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1973: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1974: *matout = B;
1975: } else {
1976: PetscCall(MatHeaderMerge(A, &B));
1977: }
1978: PetscFunctionReturn(PETSC_SUCCESS);
1979: }
1981: static PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat, Vec ll, Vec rr)
1982: {
1983: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1984: Mat a = aij->A, b = aij->B;
1985: PetscInt s1, s2, s3;
1987: PetscFunctionBegin;
1988: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1989: if (rr) {
1990: PetscCall(VecGetLocalSize(rr, &s1));
1991: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1992: /* Overlap communication with computation. */
1993: PetscCall(VecScatterBegin(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1994: }
1995: if (ll) {
1996: PetscCall(VecGetLocalSize(ll, &s1));
1997: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1998: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1999: }
2000: /* scale the diagonal block */
2001: PetscUseTypeMethod(a, diagonalscale, ll, rr);
2003: if (rr) {
2004: /* Do a scatter end and then right scale the off-diagonal block */
2005: PetscCall(VecScatterEnd(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
2006: PetscUseTypeMethod(b, diagonalscale, NULL, aij->lvec);
2007: }
2008: PetscFunctionReturn(PETSC_SUCCESS);
2009: }
2011: static PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2012: {
2013: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2015: PetscFunctionBegin;
2016: PetscCall(MatSetUnfactored(a->A));
2017: PetscFunctionReturn(PETSC_SUCCESS);
2018: }
2020: static PetscErrorCode MatEqual_MPIAIJ(Mat A, Mat B, PetscBool *flag)
2021: {
2022: Mat_MPIAIJ *matB = (Mat_MPIAIJ *)B->data, *matA = (Mat_MPIAIJ *)A->data;
2023: Mat a, b, c, d;
2024: PetscBool flg;
2026: PetscFunctionBegin;
2027: a = matA->A;
2028: b = matA->B;
2029: c = matB->A;
2030: d = matB->B;
2032: PetscCall(MatEqual(a, c, &flg));
2033: if (flg) PetscCall(MatEqual(b, d, &flg));
2034: PetscCall(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
2035: PetscFunctionReturn(PETSC_SUCCESS);
2036: }
2038: static PetscErrorCode MatCopy_MPIAIJ(Mat A, Mat B, MatStructure str)
2039: {
2040: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2041: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2043: PetscFunctionBegin;
2044: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2045: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2046: /* because of the column compression in the off-processor part of the matrix a->B,
2047: the number of columns in a->B and b->B may be different, hence we cannot call
2048: the MatCopy() directly on the two parts. If need be, we can provide a more
2049: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2050: then copying the submatrices */
2051: PetscCall(MatCopy_Basic(A, B, str));
2052: } else {
2053: PetscCall(MatCopy(a->A, b->A, str));
2054: PetscCall(MatCopy(a->B, b->B, str));
2055: }
2056: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2057: PetscFunctionReturn(PETSC_SUCCESS);
2058: }
2060: /*
2061: Computes the number of nonzeros per row needed for preallocation when X and Y
2062: have different nonzero structure.
2063: */
2064: 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)
2065: {
2066: PetscInt i, j, k, nzx, nzy;
2068: PetscFunctionBegin;
2069: /* Set the number of nonzeros in the new matrix */
2070: for (i = 0; i < m; i++) {
2071: const PetscInt *xjj = xj + xi[i], *yjj = yj + yi[i];
2072: nzx = xi[i + 1] - xi[i];
2073: nzy = yi[i + 1] - yi[i];
2074: nnz[i] = 0;
2075: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2076: for (; k < nzy && yltog[yjj[k]] < xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2077: if (k < nzy && yltog[yjj[k]] == xltog[xjj[j]]) k++; /* Skip duplicate */
2078: nnz[i]++;
2079: }
2080: for (; k < nzy; k++) nnz[i]++;
2081: }
2082: PetscFunctionReturn(PETSC_SUCCESS);
2083: }
2085: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2086: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
2087: {
2088: PetscInt m = Y->rmap->N;
2089: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2090: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
2092: PetscFunctionBegin;
2093: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
2094: PetscFunctionReturn(PETSC_SUCCESS);
2095: }
2097: static PetscErrorCode MatAXPY_MPIAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2098: {
2099: Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data, *yy = (Mat_MPIAIJ *)Y->data;
2101: PetscFunctionBegin;
2102: if (str == SAME_NONZERO_PATTERN) {
2103: PetscCall(MatAXPY(yy->A, a, xx->A, str));
2104: PetscCall(MatAXPY(yy->B, a, xx->B, str));
2105: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2106: PetscCall(MatAXPY_Basic(Y, a, X, str));
2107: } else {
2108: Mat B;
2109: PetscInt *nnz_d, *nnz_o;
2111: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
2112: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
2113: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2114: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2115: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2116: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2117: PetscCall(MatAXPYGetPreallocation_SeqAIJ(yy->A, xx->A, nnz_d));
2118: PetscCall(MatAXPYGetPreallocation_MPIAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
2119: PetscCall(MatMPIAIJSetPreallocation(B, 0, nnz_d, 0, nnz_o));
2120: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2121: PetscCall(MatHeaderMerge(Y, &B));
2122: PetscCall(PetscFree(nnz_d));
2123: PetscCall(PetscFree(nnz_o));
2124: }
2125: PetscFunctionReturn(PETSC_SUCCESS);
2126: }
2128: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);
2130: static PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2131: {
2132: PetscFunctionBegin;
2133: if (PetscDefined(USE_COMPLEX)) {
2134: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2136: PetscCall(MatConjugate_SeqAIJ(aij->A));
2137: PetscCall(MatConjugate_SeqAIJ(aij->B));
2138: }
2139: PetscFunctionReturn(PETSC_SUCCESS);
2140: }
2142: static PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2143: {
2144: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2146: PetscFunctionBegin;
2147: PetscCall(MatRealPart(a->A));
2148: PetscCall(MatRealPart(a->B));
2149: PetscFunctionReturn(PETSC_SUCCESS);
2150: }
2152: static PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
2153: {
2154: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2156: PetscFunctionBegin;
2157: PetscCall(MatImaginaryPart(a->A));
2158: PetscCall(MatImaginaryPart(a->B));
2159: PetscFunctionReturn(PETSC_SUCCESS);
2160: }
2162: static PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2163: {
2164: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2165: PetscInt i, *idxb = NULL, m = A->rmap->n;
2166: PetscScalar *va, *vv;
2167: Vec vB, vA;
2168: const PetscScalar *vb;
2170: PetscFunctionBegin;
2171: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vA));
2172: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
2174: PetscCall(VecGetArrayWrite(vA, &va));
2175: if (idx) {
2176: for (i = 0; i < m; i++) {
2177: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2178: }
2179: }
2181: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vB));
2182: PetscCall(PetscMalloc1(m, &idxb));
2183: PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));
2185: PetscCall(VecGetArrayWrite(v, &vv));
2186: PetscCall(VecGetArrayRead(vB, &vb));
2187: for (i = 0; i < m; i++) {
2188: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2189: vv[i] = vb[i];
2190: if (idx) idx[i] = a->garray[idxb[i]];
2191: } else {
2192: vv[i] = va[i];
2193: if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > a->garray[idxb[i]]) idx[i] = a->garray[idxb[i]];
2194: }
2195: }
2196: PetscCall(VecRestoreArrayWrite(vA, &vv));
2197: PetscCall(VecRestoreArrayWrite(vA, &va));
2198: PetscCall(VecRestoreArrayRead(vB, &vb));
2199: PetscCall(PetscFree(idxb));
2200: PetscCall(VecDestroy(&vA));
2201: PetscCall(VecDestroy(&vB));
2202: PetscFunctionReturn(PETSC_SUCCESS);
2203: }
2205: static PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2206: {
2207: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2208: PetscInt m = A->rmap->n, n = A->cmap->n;
2209: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2210: PetscInt *cmap = mat->garray;
2211: PetscInt *diagIdx, *offdiagIdx;
2212: Vec diagV, offdiagV;
2213: PetscScalar *a, *diagA, *offdiagA;
2214: const PetscScalar *ba, *bav;
2215: PetscInt r, j, col, ncols, *bi, *bj;
2216: Mat B = mat->B;
2217: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2219: PetscFunctionBegin;
2220: /* When a process holds entire A and other processes have no entry */
2221: if (A->cmap->N == n) {
2222: PetscCall(VecGetArrayWrite(v, &diagA));
2223: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2224: PetscCall(MatGetRowMinAbs(mat->A, diagV, idx));
2225: PetscCall(VecDestroy(&diagV));
2226: PetscCall(VecRestoreArrayWrite(v, &diagA));
2227: PetscFunctionReturn(PETSC_SUCCESS);
2228: } else if (n == 0) {
2229: if (m) {
2230: PetscCall(VecGetArrayWrite(v, &a));
2231: for (r = 0; r < m; r++) {
2232: a[r] = 0.0;
2233: if (idx) idx[r] = -1;
2234: }
2235: PetscCall(VecRestoreArrayWrite(v, &a));
2236: }
2237: PetscFunctionReturn(PETSC_SUCCESS);
2238: }
2240: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2241: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2242: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2243: PetscCall(MatGetRowMinAbs(mat->A, diagV, diagIdx));
2245: /* Get offdiagIdx[] for implicit 0.0 */
2246: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2247: ba = bav;
2248: bi = b->i;
2249: bj = b->j;
2250: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2251: for (r = 0; r < m; r++) {
2252: ncols = bi[r + 1] - bi[r];
2253: if (ncols == A->cmap->N - n) { /* Brow is dense */
2254: offdiagA[r] = *ba;
2255: offdiagIdx[r] = cmap[0];
2256: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2257: offdiagA[r] = 0.0;
2259: /* Find first hole in the cmap */
2260: for (j = 0; j < ncols; j++) {
2261: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2262: if (col > j && j < cstart) {
2263: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2264: break;
2265: } else if (col > j + n && j >= cstart) {
2266: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2267: break;
2268: }
2269: }
2270: if (j == ncols && ncols < A->cmap->N - n) {
2271: /* a hole is outside compressed Bcols */
2272: if (ncols == 0) {
2273: if (cstart) {
2274: offdiagIdx[r] = 0;
2275: } else offdiagIdx[r] = cend;
2276: } else { /* ncols > 0 */
2277: offdiagIdx[r] = cmap[ncols - 1] + 1;
2278: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2279: }
2280: }
2281: }
2283: for (j = 0; j < ncols; j++) {
2284: if (PetscAbsScalar(offdiagA[r]) > PetscAbsScalar(*ba)) {
2285: offdiagA[r] = *ba;
2286: offdiagIdx[r] = cmap[*bj];
2287: }
2288: ba++;
2289: bj++;
2290: }
2291: }
2293: PetscCall(VecGetArrayWrite(v, &a));
2294: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2295: for (r = 0; r < m; ++r) {
2296: if (PetscAbsScalar(diagA[r]) < PetscAbsScalar(offdiagA[r])) {
2297: a[r] = diagA[r];
2298: if (idx) idx[r] = cstart + diagIdx[r];
2299: } else if (PetscAbsScalar(diagA[r]) == PetscAbsScalar(offdiagA[r])) {
2300: a[r] = diagA[r];
2301: if (idx) {
2302: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2303: idx[r] = cstart + diagIdx[r];
2304: } else idx[r] = offdiagIdx[r];
2305: }
2306: } else {
2307: a[r] = offdiagA[r];
2308: if (idx) idx[r] = offdiagIdx[r];
2309: }
2310: }
2311: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2312: PetscCall(VecRestoreArrayWrite(v, &a));
2313: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2314: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2315: PetscCall(VecDestroy(&diagV));
2316: PetscCall(VecDestroy(&offdiagV));
2317: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2318: PetscFunctionReturn(PETSC_SUCCESS);
2319: }
2321: static PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2322: {
2323: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2324: PetscInt m = A->rmap->n, n = A->cmap->n;
2325: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2326: PetscInt *cmap = mat->garray;
2327: PetscInt *diagIdx, *offdiagIdx;
2328: Vec diagV, offdiagV;
2329: PetscScalar *a, *diagA, *offdiagA;
2330: const PetscScalar *ba, *bav;
2331: PetscInt r, j, col, ncols, *bi, *bj;
2332: Mat B = mat->B;
2333: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2335: PetscFunctionBegin;
2336: /* When a process holds entire A and other processes have no entry */
2337: if (A->cmap->N == n) {
2338: PetscCall(VecGetArrayWrite(v, &diagA));
2339: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2340: PetscCall(MatGetRowMin(mat->A, diagV, idx));
2341: PetscCall(VecDestroy(&diagV));
2342: PetscCall(VecRestoreArrayWrite(v, &diagA));
2343: PetscFunctionReturn(PETSC_SUCCESS);
2344: } else if (n == 0) {
2345: if (m) {
2346: PetscCall(VecGetArrayWrite(v, &a));
2347: for (r = 0; r < m; r++) {
2348: a[r] = PETSC_MAX_REAL;
2349: if (idx) idx[r] = -1;
2350: }
2351: PetscCall(VecRestoreArrayWrite(v, &a));
2352: }
2353: PetscFunctionReturn(PETSC_SUCCESS);
2354: }
2356: PetscCall(PetscCalloc2(m, &diagIdx, m, &offdiagIdx));
2357: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2358: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2359: PetscCall(MatGetRowMin(mat->A, diagV, diagIdx));
2361: /* Get offdiagIdx[] for implicit 0.0 */
2362: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2363: ba = bav;
2364: bi = b->i;
2365: bj = b->j;
2366: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2367: for (r = 0; r < m; r++) {
2368: ncols = bi[r + 1] - bi[r];
2369: if (ncols == A->cmap->N - n) { /* Brow is dense */
2370: offdiagA[r] = *ba;
2371: offdiagIdx[r] = cmap[0];
2372: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2373: offdiagA[r] = 0.0;
2375: /* Find first hole in the cmap */
2376: for (j = 0; j < ncols; j++) {
2377: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2378: if (col > j && j < cstart) {
2379: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2380: break;
2381: } else if (col > j + n && j >= cstart) {
2382: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2383: break;
2384: }
2385: }
2386: if (j == ncols && ncols < A->cmap->N - n) {
2387: /* a hole is outside compressed Bcols */
2388: if (ncols == 0) {
2389: if (cstart) {
2390: offdiagIdx[r] = 0;
2391: } else offdiagIdx[r] = cend;
2392: } else { /* ncols > 0 */
2393: offdiagIdx[r] = cmap[ncols - 1] + 1;
2394: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2395: }
2396: }
2397: }
2399: for (j = 0; j < ncols; j++) {
2400: if (PetscRealPart(offdiagA[r]) > PetscRealPart(*ba)) {
2401: offdiagA[r] = *ba;
2402: offdiagIdx[r] = cmap[*bj];
2403: }
2404: ba++;
2405: bj++;
2406: }
2407: }
2409: PetscCall(VecGetArrayWrite(v, &a));
2410: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2411: for (r = 0; r < m; ++r) {
2412: if (PetscRealPart(diagA[r]) < PetscRealPart(offdiagA[r])) {
2413: a[r] = diagA[r];
2414: if (idx) idx[r] = cstart + diagIdx[r];
2415: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2416: a[r] = diagA[r];
2417: if (idx) {
2418: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2419: idx[r] = cstart + diagIdx[r];
2420: } else idx[r] = offdiagIdx[r];
2421: }
2422: } else {
2423: a[r] = offdiagA[r];
2424: if (idx) idx[r] = offdiagIdx[r];
2425: }
2426: }
2427: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2428: PetscCall(VecRestoreArrayWrite(v, &a));
2429: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2430: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2431: PetscCall(VecDestroy(&diagV));
2432: PetscCall(VecDestroy(&offdiagV));
2433: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2434: PetscFunctionReturn(PETSC_SUCCESS);
2435: }
2437: static PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2438: {
2439: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2440: PetscInt m = A->rmap->n, n = A->cmap->n;
2441: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2442: PetscInt *cmap = mat->garray;
2443: PetscInt *diagIdx, *offdiagIdx;
2444: Vec diagV, offdiagV;
2445: PetscScalar *a, *diagA, *offdiagA;
2446: const PetscScalar *ba, *bav;
2447: PetscInt r, j, col, ncols, *bi, *bj;
2448: Mat B = mat->B;
2449: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2451: PetscFunctionBegin;
2452: /* When a process holds entire A and other processes have no entry */
2453: if (A->cmap->N == n) {
2454: PetscCall(VecGetArrayWrite(v, &diagA));
2455: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2456: PetscCall(MatGetRowMax(mat->A, diagV, idx));
2457: PetscCall(VecDestroy(&diagV));
2458: PetscCall(VecRestoreArrayWrite(v, &diagA));
2459: PetscFunctionReturn(PETSC_SUCCESS);
2460: } else if (n == 0) {
2461: if (m) {
2462: PetscCall(VecGetArrayWrite(v, &a));
2463: for (r = 0; r < m; r++) {
2464: a[r] = PETSC_MIN_REAL;
2465: if (idx) idx[r] = -1;
2466: }
2467: PetscCall(VecRestoreArrayWrite(v, &a));
2468: }
2469: PetscFunctionReturn(PETSC_SUCCESS);
2470: }
2472: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2473: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2474: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2475: PetscCall(MatGetRowMax(mat->A, diagV, diagIdx));
2477: /* Get offdiagIdx[] for implicit 0.0 */
2478: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2479: ba = bav;
2480: bi = b->i;
2481: bj = b->j;
2482: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2483: for (r = 0; r < m; r++) {
2484: ncols = bi[r + 1] - bi[r];
2485: if (ncols == A->cmap->N - n) { /* Brow is dense */
2486: offdiagA[r] = *ba;
2487: offdiagIdx[r] = cmap[0];
2488: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2489: offdiagA[r] = 0.0;
2491: /* Find first hole in the cmap */
2492: for (j = 0; j < ncols; j++) {
2493: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2494: if (col > j && j < cstart) {
2495: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2496: break;
2497: } else if (col > j + n && j >= cstart) {
2498: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2499: break;
2500: }
2501: }
2502: if (j == ncols && ncols < A->cmap->N - n) {
2503: /* a hole is outside compressed Bcols */
2504: if (ncols == 0) {
2505: if (cstart) {
2506: offdiagIdx[r] = 0;
2507: } else offdiagIdx[r] = cend;
2508: } else { /* ncols > 0 */
2509: offdiagIdx[r] = cmap[ncols - 1] + 1;
2510: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2511: }
2512: }
2513: }
2515: for (j = 0; j < ncols; j++) {
2516: if (PetscRealPart(offdiagA[r]) < PetscRealPart(*ba)) {
2517: offdiagA[r] = *ba;
2518: offdiagIdx[r] = cmap[*bj];
2519: }
2520: ba++;
2521: bj++;
2522: }
2523: }
2525: PetscCall(VecGetArrayWrite(v, &a));
2526: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2527: for (r = 0; r < m; ++r) {
2528: if (PetscRealPart(diagA[r]) > PetscRealPart(offdiagA[r])) {
2529: a[r] = diagA[r];
2530: if (idx) idx[r] = cstart + diagIdx[r];
2531: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2532: a[r] = diagA[r];
2533: if (idx) {
2534: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2535: idx[r] = cstart + diagIdx[r];
2536: } else idx[r] = offdiagIdx[r];
2537: }
2538: } else {
2539: a[r] = offdiagA[r];
2540: if (idx) idx[r] = offdiagIdx[r];
2541: }
2542: }
2543: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2544: PetscCall(VecRestoreArrayWrite(v, &a));
2545: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2546: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2547: PetscCall(VecDestroy(&diagV));
2548: PetscCall(VecDestroy(&offdiagV));
2549: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2550: PetscFunctionReturn(PETSC_SUCCESS);
2551: }
2553: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat, Mat *newmat)
2554: {
2555: Mat *dummy;
2557: PetscFunctionBegin;
2558: PetscCall(MatCreateSubMatrix_MPIAIJ_All(mat, MAT_DO_NOT_GET_VALUES, MAT_INITIAL_MATRIX, &dummy));
2559: *newmat = *dummy;
2560: PetscCall(PetscFree(dummy));
2561: PetscFunctionReturn(PETSC_SUCCESS);
2562: }
2564: static PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A, const PetscScalar **values)
2565: {
2566: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2568: PetscFunctionBegin;
2569: PetscCall(MatInvertBlockDiagonal(a->A, values));
2570: A->factorerrortype = a->A->factorerrortype;
2571: PetscFunctionReturn(PETSC_SUCCESS);
2572: }
2574: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x, PetscRandom rctx)
2575: {
2576: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)x->data;
2578: PetscFunctionBegin;
2579: PetscCheck(x->assembled || x->preallocated, PetscObjectComm((PetscObject)x), PETSC_ERR_ARG_WRONGSTATE, "MatSetRandom on an unassembled and unpreallocated MATMPIAIJ is not allowed");
2580: PetscCall(MatSetRandom(aij->A, rctx));
2581: if (x->assembled) {
2582: PetscCall(MatSetRandom(aij->B, rctx));
2583: } else {
2584: PetscCall(MatSetRandomSkipColumnRange_SeqAIJ_Private(aij->B, x->cmap->rstart, x->cmap->rend, rctx));
2585: }
2586: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
2587: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
2588: PetscFunctionReturn(PETSC_SUCCESS);
2589: }
2591: static PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ(Mat A, PetscBool sc)
2592: {
2593: PetscFunctionBegin;
2594: if (sc) A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ_Scalable;
2595: else A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ;
2596: PetscFunctionReturn(PETSC_SUCCESS);
2597: }
2599: /*@
2600: MatMPIAIJGetNumberNonzeros - gets the number of nonzeros in the matrix on this MPI rank
2602: Not Collective
2604: Input Parameter:
2605: . A - the matrix
2607: Output Parameter:
2608: . nz - the number of nonzeros
2610: Level: advanced
2612: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2613: @*/
2614: PetscErrorCode MatMPIAIJGetNumberNonzeros(Mat A, PetscCount *nz)
2615: {
2616: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)A->data;
2617: Mat_SeqAIJ *aaij = (Mat_SeqAIJ *)maij->A->data, *baij = (Mat_SeqAIJ *)maij->B->data;
2618: PetscBool isaij;
2620: PetscFunctionBegin;
2621: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATMPIAIJ, &isaij));
2622: PetscCheck(isaij, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for type %s", ((PetscObject)A)->type_name);
2623: *nz = aaij->i[A->rmap->n] + baij->i[A->rmap->n];
2624: PetscFunctionReturn(PETSC_SUCCESS);
2625: }
2627: /*@
2628: MatMPIAIJSetUseScalableIncreaseOverlap - Determine if the matrix uses a scalable algorithm to compute the overlap
2630: Collective
2632: Input Parameters:
2633: + A - the matrix
2634: - sc - `PETSC_TRUE` indicates use the scalable algorithm (default is not to use the scalable algorithm)
2636: Level: advanced
2638: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2639: @*/
2640: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap(Mat A, PetscBool sc)
2641: {
2642: PetscFunctionBegin;
2643: PetscTryMethod(A, "MatMPIAIJSetUseScalableIncreaseOverlap_C", (Mat, PetscBool), (A, sc));
2644: PetscFunctionReturn(PETSC_SUCCESS);
2645: }
2647: PetscErrorCode MatSetFromOptions_MPIAIJ(Mat A, PetscOptionItems *PetscOptionsObject)
2648: {
2649: PetscBool sc = PETSC_FALSE, flg;
2651: PetscFunctionBegin;
2652: PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJ options");
2653: if (A->ops->increaseoverlap == MatIncreaseOverlap_MPIAIJ_Scalable) sc = PETSC_TRUE;
2654: PetscCall(PetscOptionsBool("-mat_increase_overlap_scalable", "Use a scalable algorithm to compute the overlap", "MatIncreaseOverlap", sc, &sc, &flg));
2655: if (flg) PetscCall(MatMPIAIJSetUseScalableIncreaseOverlap(A, sc));
2656: PetscOptionsHeadEnd();
2657: PetscFunctionReturn(PETSC_SUCCESS);
2658: }
2660: static PetscErrorCode MatShift_MPIAIJ(Mat Y, PetscScalar a)
2661: {
2662: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)Y->data;
2663: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)maij->A->data;
2665: PetscFunctionBegin;
2666: if (!Y->preallocated) {
2667: PetscCall(MatMPIAIJSetPreallocation(Y, 1, NULL, 0, NULL));
2668: } else if (!aij->nz) { /* It does not matter if diagonals of Y only partially lie in maij->A. We just need an estimated preallocation. */
2669: PetscInt nonew = aij->nonew;
2670: PetscCall(MatSeqAIJSetPreallocation(maij->A, 1, NULL));
2671: aij->nonew = nonew;
2672: }
2673: PetscCall(MatShift_Basic(Y, a));
2674: PetscFunctionReturn(PETSC_SUCCESS);
2675: }
2677: static PetscErrorCode MatMissingDiagonal_MPIAIJ(Mat A, PetscBool *missing, PetscInt *d)
2678: {
2679: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2681: PetscFunctionBegin;
2682: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2683: PetscCall(MatMissingDiagonal(a->A, missing, d));
2684: if (d) {
2685: PetscInt rstart;
2686: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2687: *d += rstart;
2688: }
2689: PetscFunctionReturn(PETSC_SUCCESS);
2690: }
2692: static PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2693: {
2694: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2696: PetscFunctionBegin;
2697: PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2698: PetscFunctionReturn(PETSC_SUCCESS);
2699: }
2701: static PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A, PetscBool keep)
2702: {
2703: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2705: PetscFunctionBegin;
2706: PetscCall(MatEliminateZeros_SeqAIJ(a->A, keep)); // possibly keep zero diagonal coefficients
2707: PetscCall(MatEliminateZeros_SeqAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2708: PetscFunctionReturn(PETSC_SUCCESS);
2709: }
2711: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2712: MatGetRow_MPIAIJ,
2713: MatRestoreRow_MPIAIJ,
2714: MatMult_MPIAIJ,
2715: /* 4*/ MatMultAdd_MPIAIJ,
2716: MatMultTranspose_MPIAIJ,
2717: MatMultTransposeAdd_MPIAIJ,
2718: NULL,
2719: NULL,
2720: NULL,
2721: /*10*/ NULL,
2722: NULL,
2723: NULL,
2724: MatSOR_MPIAIJ,
2725: MatTranspose_MPIAIJ,
2726: /*15*/ MatGetInfo_MPIAIJ,
2727: MatEqual_MPIAIJ,
2728: MatGetDiagonal_MPIAIJ,
2729: MatDiagonalScale_MPIAIJ,
2730: MatNorm_MPIAIJ,
2731: /*20*/ MatAssemblyBegin_MPIAIJ,
2732: MatAssemblyEnd_MPIAIJ,
2733: MatSetOption_MPIAIJ,
2734: MatZeroEntries_MPIAIJ,
2735: /*24*/ MatZeroRows_MPIAIJ,
2736: NULL,
2737: NULL,
2738: NULL,
2739: NULL,
2740: /*29*/ MatSetUp_MPI_Hash,
2741: NULL,
2742: NULL,
2743: MatGetDiagonalBlock_MPIAIJ,
2744: NULL,
2745: /*34*/ MatDuplicate_MPIAIJ,
2746: NULL,
2747: NULL,
2748: NULL,
2749: NULL,
2750: /*39*/ MatAXPY_MPIAIJ,
2751: MatCreateSubMatrices_MPIAIJ,
2752: MatIncreaseOverlap_MPIAIJ,
2753: MatGetValues_MPIAIJ,
2754: MatCopy_MPIAIJ,
2755: /*44*/ MatGetRowMax_MPIAIJ,
2756: MatScale_MPIAIJ,
2757: MatShift_MPIAIJ,
2758: MatDiagonalSet_MPIAIJ,
2759: MatZeroRowsColumns_MPIAIJ,
2760: /*49*/ MatSetRandom_MPIAIJ,
2761: MatGetRowIJ_MPIAIJ,
2762: MatRestoreRowIJ_MPIAIJ,
2763: NULL,
2764: NULL,
2765: /*54*/ MatFDColoringCreate_MPIXAIJ,
2766: NULL,
2767: MatSetUnfactored_MPIAIJ,
2768: MatPermute_MPIAIJ,
2769: NULL,
2770: /*59*/ MatCreateSubMatrix_MPIAIJ,
2771: MatDestroy_MPIAIJ,
2772: MatView_MPIAIJ,
2773: NULL,
2774: NULL,
2775: /*64*/ NULL,
2776: MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2777: NULL,
2778: NULL,
2779: NULL,
2780: /*69*/ MatGetRowMaxAbs_MPIAIJ,
2781: MatGetRowMinAbs_MPIAIJ,
2782: NULL,
2783: NULL,
2784: NULL,
2785: NULL,
2786: /*75*/ MatFDColoringApply_AIJ,
2787: MatSetFromOptions_MPIAIJ,
2788: NULL,
2789: NULL,
2790: MatFindZeroDiagonals_MPIAIJ,
2791: /*80*/ NULL,
2792: NULL,
2793: NULL,
2794: /*83*/ MatLoad_MPIAIJ,
2795: MatIsSymmetric_MPIAIJ,
2796: NULL,
2797: NULL,
2798: NULL,
2799: NULL,
2800: /*89*/ NULL,
2801: NULL,
2802: MatMatMultNumeric_MPIAIJ_MPIAIJ,
2803: NULL,
2804: NULL,
2805: /*94*/ MatPtAPNumeric_MPIAIJ_MPIAIJ,
2806: NULL,
2807: NULL,
2808: NULL,
2809: MatBindToCPU_MPIAIJ,
2810: /*99*/ MatProductSetFromOptions_MPIAIJ,
2811: NULL,
2812: NULL,
2813: MatConjugate_MPIAIJ,
2814: NULL,
2815: /*104*/ MatSetValuesRow_MPIAIJ,
2816: MatRealPart_MPIAIJ,
2817: MatImaginaryPart_MPIAIJ,
2818: NULL,
2819: NULL,
2820: /*109*/ NULL,
2821: NULL,
2822: MatGetRowMin_MPIAIJ,
2823: NULL,
2824: MatMissingDiagonal_MPIAIJ,
2825: /*114*/ MatGetSeqNonzeroStructure_MPIAIJ,
2826: NULL,
2827: MatGetGhosts_MPIAIJ,
2828: NULL,
2829: NULL,
2830: /*119*/ MatMultDiagonalBlock_MPIAIJ,
2831: NULL,
2832: NULL,
2833: NULL,
2834: MatGetMultiProcBlock_MPIAIJ,
2835: /*124*/ MatFindNonzeroRows_MPIAIJ,
2836: MatGetColumnReductions_MPIAIJ,
2837: MatInvertBlockDiagonal_MPIAIJ,
2838: MatInvertVariableBlockDiagonal_MPIAIJ,
2839: MatCreateSubMatricesMPI_MPIAIJ,
2840: /*129*/ NULL,
2841: NULL,
2842: NULL,
2843: MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2844: NULL,
2845: /*134*/ NULL,
2846: NULL,
2847: NULL,
2848: NULL,
2849: NULL,
2850: /*139*/ MatSetBlockSizes_MPIAIJ,
2851: NULL,
2852: NULL,
2853: MatFDColoringSetUp_MPIXAIJ,
2854: MatFindOffBlockDiagonalEntries_MPIAIJ,
2855: MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2856: /*145*/ NULL,
2857: NULL,
2858: NULL,
2859: MatCreateGraph_Simple_AIJ,
2860: NULL,
2861: /*150*/ NULL,
2862: MatEliminateZeros_MPIAIJ};
2864: static PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2865: {
2866: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2868: PetscFunctionBegin;
2869: PetscCall(MatStoreValues(aij->A));
2870: PetscCall(MatStoreValues(aij->B));
2871: PetscFunctionReturn(PETSC_SUCCESS);
2872: }
2874: static PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2875: {
2876: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2878: PetscFunctionBegin;
2879: PetscCall(MatRetrieveValues(aij->A));
2880: PetscCall(MatRetrieveValues(aij->B));
2881: PetscFunctionReturn(PETSC_SUCCESS);
2882: }
2884: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2885: {
2886: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2887: PetscMPIInt size;
2889: PetscFunctionBegin;
2890: if (B->hash_active) {
2891: B->ops[0] = b->cops;
2892: B->hash_active = PETSC_FALSE;
2893: }
2894: PetscCall(PetscLayoutSetUp(B->rmap));
2895: PetscCall(PetscLayoutSetUp(B->cmap));
2897: #if defined(PETSC_USE_CTABLE)
2898: PetscCall(PetscHMapIDestroy(&b->colmap));
2899: #else
2900: PetscCall(PetscFree(b->colmap));
2901: #endif
2902: PetscCall(PetscFree(b->garray));
2903: PetscCall(VecDestroy(&b->lvec));
2904: PetscCall(VecScatterDestroy(&b->Mvctx));
2906: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2907: PetscCall(MatDestroy(&b->B));
2908: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2909: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2910: PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2911: PetscCall(MatSetType(b->B, MATSEQAIJ));
2913: PetscCall(MatDestroy(&b->A));
2914: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2915: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2916: PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2917: PetscCall(MatSetType(b->A, MATSEQAIJ));
2919: PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2920: PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2921: B->preallocated = PETSC_TRUE;
2922: B->was_assembled = PETSC_FALSE;
2923: B->assembled = PETSC_FALSE;
2924: PetscFunctionReturn(PETSC_SUCCESS);
2925: }
2927: static PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2928: {
2929: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2931: PetscFunctionBegin;
2933: PetscCall(PetscLayoutSetUp(B->rmap));
2934: PetscCall(PetscLayoutSetUp(B->cmap));
2936: #if defined(PETSC_USE_CTABLE)
2937: PetscCall(PetscHMapIDestroy(&b->colmap));
2938: #else
2939: PetscCall(PetscFree(b->colmap));
2940: #endif
2941: PetscCall(PetscFree(b->garray));
2942: PetscCall(VecDestroy(&b->lvec));
2943: PetscCall(VecScatterDestroy(&b->Mvctx));
2945: PetscCall(MatResetPreallocation(b->A));
2946: PetscCall(MatResetPreallocation(b->B));
2947: B->preallocated = PETSC_TRUE;
2948: B->was_assembled = PETSC_FALSE;
2949: B->assembled = PETSC_FALSE;
2950: PetscFunctionReturn(PETSC_SUCCESS);
2951: }
2953: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2954: {
2955: Mat mat;
2956: Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;
2958: PetscFunctionBegin;
2959: *newmat = NULL;
2960: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2961: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2962: PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2963: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2964: a = (Mat_MPIAIJ *)mat->data;
2966: mat->factortype = matin->factortype;
2967: mat->assembled = matin->assembled;
2968: mat->insertmode = NOT_SET_VALUES;
2969: mat->preallocated = matin->preallocated;
2971: a->size = oldmat->size;
2972: a->rank = oldmat->rank;
2973: a->donotstash = oldmat->donotstash;
2974: a->roworiented = oldmat->roworiented;
2975: a->rowindices = NULL;
2976: a->rowvalues = NULL;
2977: a->getrowactive = PETSC_FALSE;
2979: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
2980: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
2982: if (oldmat->colmap) {
2983: #if defined(PETSC_USE_CTABLE)
2984: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
2985: #else
2986: PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
2987: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
2988: #endif
2989: } else a->colmap = NULL;
2990: if (oldmat->garray) {
2991: PetscInt len;
2992: len = oldmat->B->cmap->n;
2993: PetscCall(PetscMalloc1(len + 1, &a->garray));
2994: if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
2995: } else a->garray = NULL;
2997: /* It may happen MatDuplicate is called with a non-assembled matrix
2998: In fact, MatDuplicate only requires the matrix to be preallocated
2999: This may happen inside a DMCreateMatrix_Shell */
3000: if (oldmat->lvec) PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3001: if (oldmat->Mvctx) PetscCall(VecScatterCopy(oldmat->Mvctx, &a->Mvctx));
3002: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3003: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3004: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3005: *newmat = mat;
3006: PetscFunctionReturn(PETSC_SUCCESS);
3007: }
3009: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3010: {
3011: PetscBool isbinary, ishdf5;
3013: PetscFunctionBegin;
3016: /* force binary viewer to load .info file if it has not yet done so */
3017: PetscCall(PetscViewerSetUp(viewer));
3018: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3019: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3020: if (isbinary) {
3021: PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3022: } else if (ishdf5) {
3023: #if defined(PETSC_HAVE_HDF5)
3024: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3025: #else
3026: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3027: #endif
3028: } else {
3029: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "Viewer type %s not yet supported for reading %s matrices", ((PetscObject)viewer)->type_name, ((PetscObject)newMat)->type_name);
3030: }
3031: PetscFunctionReturn(PETSC_SUCCESS);
3032: }
3034: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3035: {
3036: PetscInt header[4], M, N, m, nz, rows, cols, sum, i;
3037: PetscInt *rowidxs, *colidxs;
3038: PetscScalar *matvals;
3040: PetscFunctionBegin;
3041: PetscCall(PetscViewerSetUp(viewer));
3043: /* read in matrix header */
3044: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3045: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3046: M = header[1];
3047: N = header[2];
3048: nz = header[3];
3049: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3050: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3051: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");
3053: /* set block sizes from the viewer's .info file */
3054: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3055: /* set global sizes if not set already */
3056: if (mat->rmap->N < 0) mat->rmap->N = M;
3057: if (mat->cmap->N < 0) mat->cmap->N = N;
3058: PetscCall(PetscLayoutSetUp(mat->rmap));
3059: PetscCall(PetscLayoutSetUp(mat->cmap));
3061: /* check if the matrix sizes are correct */
3062: PetscCall(MatGetSize(mat, &rows, &cols));
3063: PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different sizes (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols);
3065: /* read in row lengths and build row indices */
3066: PetscCall(MatGetLocalSize(mat, &m, NULL));
3067: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3068: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3069: rowidxs[0] = 0;
3070: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3071: if (nz != PETSC_MAX_INT) {
3072: PetscCall(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3073: PetscCheck(sum == nz, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Inconsistent matrix data in file: nonzeros = %" PetscInt_FMT ", sum-row-lengths = %" PetscInt_FMT, nz, sum);
3074: }
3076: /* read in column indices and matrix values */
3077: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3078: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3079: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3080: /* store matrix indices and values */
3081: PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3082: PetscCall(PetscFree(rowidxs));
3083: PetscCall(PetscFree2(colidxs, matvals));
3084: PetscFunctionReturn(PETSC_SUCCESS);
3085: }
3087: /* Not scalable because of ISAllGather() unless getting all columns. */
3088: static PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3089: {
3090: IS iscol_local;
3091: PetscBool isstride;
3092: PetscMPIInt lisstride = 0, gisstride;
3094: PetscFunctionBegin;
3095: /* check if we are grabbing all columns*/
3096: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));
3098: if (isstride) {
3099: PetscInt start, len, mstart, mlen;
3100: PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3101: PetscCall(ISGetLocalSize(iscol, &len));
3102: PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3103: if (mstart == start && mlen - mstart == len) lisstride = 1;
3104: }
3106: PetscCall(MPIU_Allreduce(&lisstride, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3107: if (gisstride) {
3108: PetscInt N;
3109: PetscCall(MatGetSize(mat, NULL, &N));
3110: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3111: PetscCall(ISSetIdentity(iscol_local));
3112: PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3113: } else {
3114: PetscInt cbs;
3115: PetscCall(ISGetBlockSize(iscol, &cbs));
3116: PetscCall(ISAllGather(iscol, &iscol_local));
3117: PetscCall(ISSetBlockSize(iscol_local, cbs));
3118: }
3120: *isseq = iscol_local;
3121: PetscFunctionReturn(PETSC_SUCCESS);
3122: }
3124: /*
3125: Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3126: (see MatCreateSubMatrix_MPIAIJ_nonscalable)
3128: Input Parameters:
3129: + mat - matrix
3130: . isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3131: i.e., mat->rstart <= isrow[i] < mat->rend
3132: - iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3133: i.e., mat->cstart <= iscol[i] < mat->cend
3135: Output Parameters:
3136: + isrow_d - sequential row index set for retrieving mat->A
3137: . iscol_d - sequential column index set for retrieving mat->A
3138: . iscol_o - sequential column index set for retrieving mat->B
3139: - garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3140: */
3141: static PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, const PetscInt *garray[])
3142: {
3143: Vec x, cmap;
3144: const PetscInt *is_idx;
3145: PetscScalar *xarray, *cmaparray;
3146: PetscInt ncols, isstart, *idx, m, rstart, *cmap1, count;
3147: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3148: Mat B = a->B;
3149: Vec lvec = a->lvec, lcmap;
3150: PetscInt i, cstart, cend, Bn = B->cmap->N;
3151: MPI_Comm comm;
3152: VecScatter Mvctx = a->Mvctx;
3154: PetscFunctionBegin;
3155: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3156: PetscCall(ISGetLocalSize(iscol, &ncols));
3158: /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3159: PetscCall(MatCreateVecs(mat, &x, NULL));
3160: PetscCall(VecSet(x, -1.0));
3161: PetscCall(VecDuplicate(x, &cmap));
3162: PetscCall(VecSet(cmap, -1.0));
3164: /* Get start indices */
3165: PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3166: isstart -= ncols;
3167: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
3169: PetscCall(ISGetIndices(iscol, &is_idx));
3170: PetscCall(VecGetArray(x, &xarray));
3171: PetscCall(VecGetArray(cmap, &cmaparray));
3172: PetscCall(PetscMalloc1(ncols, &idx));
3173: for (i = 0; i < ncols; i++) {
3174: xarray[is_idx[i] - cstart] = (PetscScalar)is_idx[i];
3175: cmaparray[is_idx[i] - cstart] = i + isstart; /* global index of iscol[i] */
3176: idx[i] = is_idx[i] - cstart; /* local index of iscol[i] */
3177: }
3178: PetscCall(VecRestoreArray(x, &xarray));
3179: PetscCall(VecRestoreArray(cmap, &cmaparray));
3180: PetscCall(ISRestoreIndices(iscol, &is_idx));
3182: /* Get iscol_d */
3183: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3184: PetscCall(ISGetBlockSize(iscol, &i));
3185: PetscCall(ISSetBlockSize(*iscol_d, i));
3187: /* Get isrow_d */
3188: PetscCall(ISGetLocalSize(isrow, &m));
3189: rstart = mat->rmap->rstart;
3190: PetscCall(PetscMalloc1(m, &idx));
3191: PetscCall(ISGetIndices(isrow, &is_idx));
3192: for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3193: PetscCall(ISRestoreIndices(isrow, &is_idx));
3195: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3196: PetscCall(ISGetBlockSize(isrow, &i));
3197: PetscCall(ISSetBlockSize(*isrow_d, i));
3199: /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3200: PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3201: PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3203: PetscCall(VecDuplicate(lvec, &lcmap));
3205: PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3206: PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3208: /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3209: /* off-process column indices */
3210: count = 0;
3211: PetscCall(PetscMalloc1(Bn, &idx));
3212: PetscCall(PetscMalloc1(Bn, &cmap1));
3214: PetscCall(VecGetArray(lvec, &xarray));
3215: PetscCall(VecGetArray(lcmap, &cmaparray));
3216: for (i = 0; i < Bn; i++) {
3217: if (PetscRealPart(xarray[i]) > -1.0) {
3218: idx[count] = i; /* local column index in off-diagonal part B */
3219: cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3220: count++;
3221: }
3222: }
3223: PetscCall(VecRestoreArray(lvec, &xarray));
3224: PetscCall(VecRestoreArray(lcmap, &cmaparray));
3226: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3227: /* cannot ensure iscol_o has same blocksize as iscol! */
3229: PetscCall(PetscFree(idx));
3230: *garray = cmap1;
3232: PetscCall(VecDestroy(&x));
3233: PetscCall(VecDestroy(&cmap));
3234: PetscCall(VecDestroy(&lcmap));
3235: PetscFunctionReturn(PETSC_SUCCESS);
3236: }
3238: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3239: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3240: {
3241: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3242: Mat M = NULL;
3243: MPI_Comm comm;
3244: IS iscol_d, isrow_d, iscol_o;
3245: Mat Asub = NULL, Bsub = NULL;
3246: PetscInt n;
3248: PetscFunctionBegin;
3249: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3251: if (call == MAT_REUSE_MATRIX) {
3252: /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3253: PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3254: PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");
3256: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3257: PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");
3259: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3260: PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");
3262: /* Update diagonal and off-diagonal portions of submat */
3263: asub = (Mat_MPIAIJ *)(*submat)->data;
3264: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3265: PetscCall(ISGetLocalSize(iscol_o, &n));
3266: if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3267: PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3268: PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));
3270: } else { /* call == MAT_INITIAL_MATRIX) */
3271: const PetscInt *garray;
3272: PetscInt BsubN;
3274: /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3275: PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));
3277: /* Create local submatrices Asub and Bsub */
3278: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3279: PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));
3281: /* Create submatrix M */
3282: PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, Asub, Bsub, garray, &M));
3284: /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3285: asub = (Mat_MPIAIJ *)M->data;
3287: PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3288: n = asub->B->cmap->N;
3289: if (BsubN > n) {
3290: /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3291: const PetscInt *idx;
3292: PetscInt i, j, *idx_new, *subgarray = asub->garray;
3293: PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));
3295: PetscCall(PetscMalloc1(n, &idx_new));
3296: j = 0;
3297: PetscCall(ISGetIndices(iscol_o, &idx));
3298: for (i = 0; i < n; i++) {
3299: if (j >= BsubN) break;
3300: while (subgarray[i] > garray[j]) j++;
3302: if (subgarray[i] == garray[j]) {
3303: idx_new[i] = idx[j++];
3304: } 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]);
3305: }
3306: PetscCall(ISRestoreIndices(iscol_o, &idx));
3308: PetscCall(ISDestroy(&iscol_o));
3309: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));
3311: } else if (BsubN < n) {
3312: 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);
3313: }
3315: PetscCall(PetscFree(garray));
3316: *submat = M;
3318: /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3319: PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3320: PetscCall(ISDestroy(&isrow_d));
3322: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3323: PetscCall(ISDestroy(&iscol_d));
3325: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3326: PetscCall(ISDestroy(&iscol_o));
3327: }
3328: PetscFunctionReturn(PETSC_SUCCESS);
3329: }
3331: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3332: {
3333: IS iscol_local = NULL, isrow_d;
3334: PetscInt csize;
3335: PetscInt n, i, j, start, end;
3336: PetscBool sameRowDist = PETSC_FALSE, sameDist[2], tsameDist[2];
3337: MPI_Comm comm;
3339: PetscFunctionBegin;
3340: /* If isrow has same processor distribution as mat,
3341: call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3342: if (call == MAT_REUSE_MATRIX) {
3343: PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3344: if (isrow_d) {
3345: sameRowDist = PETSC_TRUE;
3346: tsameDist[1] = PETSC_TRUE; /* sameColDist */
3347: } else {
3348: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3349: if (iscol_local) {
3350: sameRowDist = PETSC_TRUE;
3351: tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3352: }
3353: }
3354: } else {
3355: /* Check if isrow has same processor distribution as mat */
3356: sameDist[0] = PETSC_FALSE;
3357: PetscCall(ISGetLocalSize(isrow, &n));
3358: if (!n) {
3359: sameDist[0] = PETSC_TRUE;
3360: } else {
3361: PetscCall(ISGetMinMax(isrow, &i, &j));
3362: PetscCall(MatGetOwnershipRange(mat, &start, &end));
3363: if (i >= start && j < end) sameDist[0] = PETSC_TRUE;
3364: }
3366: /* Check if iscol has same processor distribution as mat */
3367: sameDist[1] = PETSC_FALSE;
3368: PetscCall(ISGetLocalSize(iscol, &n));
3369: if (!n) {
3370: sameDist[1] = PETSC_TRUE;
3371: } else {
3372: PetscCall(ISGetMinMax(iscol, &i, &j));
3373: PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3374: if (i >= start && j < end) sameDist[1] = PETSC_TRUE;
3375: }
3377: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3378: PetscCall(MPIU_Allreduce(&sameDist, &tsameDist, 2, MPIU_BOOL, MPI_LAND, comm));
3379: sameRowDist = tsameDist[0];
3380: }
3382: if (sameRowDist) {
3383: if (tsameDist[1]) { /* sameRowDist & sameColDist */
3384: /* isrow and iscol have same processor distribution as mat */
3385: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3386: PetscFunctionReturn(PETSC_SUCCESS);
3387: } else { /* sameRowDist */
3388: /* isrow has same processor distribution as mat */
3389: if (call == MAT_INITIAL_MATRIX) {
3390: PetscBool sorted;
3391: PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3392: PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3393: PetscCall(ISGetSize(iscol, &i));
3394: PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);
3396: PetscCall(ISSorted(iscol_local, &sorted));
3397: if (sorted) {
3398: /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3399: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3400: PetscFunctionReturn(PETSC_SUCCESS);
3401: }
3402: } else { /* call == MAT_REUSE_MATRIX */
3403: IS iscol_sub;
3404: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3405: if (iscol_sub) {
3406: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3407: PetscFunctionReturn(PETSC_SUCCESS);
3408: }
3409: }
3410: }
3411: }
3413: /* General case: iscol -> iscol_local which has global size of iscol */
3414: if (call == MAT_REUSE_MATRIX) {
3415: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3416: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3417: } else {
3418: if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3419: }
3421: PetscCall(ISGetLocalSize(iscol, &csize));
3422: PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));
3424: if (call == MAT_INITIAL_MATRIX) {
3425: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3426: PetscCall(ISDestroy(&iscol_local));
3427: }
3428: PetscFunctionReturn(PETSC_SUCCESS);
3429: }
3431: /*@C
3432: MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3433: and "off-diagonal" part of the matrix in CSR format.
3435: Collective
3437: Input Parameters:
3438: + comm - MPI communicator
3439: . A - "diagonal" portion of matrix
3440: . B - "off-diagonal" portion of matrix, may have empty columns, will be destroyed by this routine
3441: - garray - global index of `B` columns
3443: Output Parameter:
3444: . mat - the matrix, with input `A` as its local diagonal matrix
3446: Level: advanced
3448: Notes:
3449: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.
3451: `A` becomes part of output mat, `B` is destroyed by this routine. The user cannot use `A` and `B` anymore.
3453: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3454: @*/
3455: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, Mat A, Mat B, const PetscInt garray[], Mat *mat)
3456: {
3457: Mat_MPIAIJ *maij;
3458: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data, *bnew;
3459: PetscInt *oi = b->i, *oj = b->j, i, nz, col;
3460: const PetscScalar *oa;
3461: Mat Bnew;
3462: PetscInt m, n, N;
3463: MatType mpi_mat_type;
3465: PetscFunctionBegin;
3466: PetscCall(MatCreate(comm, mat));
3467: PetscCall(MatGetSize(A, &m, &n));
3468: PetscCheck(m == B->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Am %" PetscInt_FMT " != Bm %" PetscInt_FMT, m, B->rmap->N);
3469: PetscCheck(PetscAbs(A->rmap->bs) == PetscAbs(B->rmap->bs), PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "A row bs %" PetscInt_FMT " != B row bs %" PetscInt_FMT, A->rmap->bs, B->rmap->bs);
3470: /* remove check below; When B is created using iscol_o from ISGetSeqIS_SameColDist_Private(), its bs may not be same as A */
3471: /* 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); */
3473: /* Get global columns of mat */
3474: PetscCall(MPIU_Allreduce(&n, &N, 1, MPIU_INT, MPI_SUM, comm));
3476: PetscCall(MatSetSizes(*mat, m, n, PETSC_DECIDE, N));
3477: /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3478: PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3479: PetscCall(MatSetType(*mat, mpi_mat_type));
3481: if (A->rmap->bs > 1 || A->cmap->bs > 1) PetscCall(MatSetBlockSizes(*mat, A->rmap->bs, A->cmap->bs));
3482: maij = (Mat_MPIAIJ *)(*mat)->data;
3484: (*mat)->preallocated = PETSC_TRUE;
3486: PetscCall(PetscLayoutSetUp((*mat)->rmap));
3487: PetscCall(PetscLayoutSetUp((*mat)->cmap));
3489: /* Set A as diagonal portion of *mat */
3490: maij->A = A;
3492: nz = oi[m];
3493: for (i = 0; i < nz; i++) {
3494: col = oj[i];
3495: oj[i] = garray[col];
3496: }
3498: /* Set Bnew as off-diagonal portion of *mat */
3499: PetscCall(MatSeqAIJGetArrayRead(B, &oa));
3500: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, N, oi, oj, (PetscScalar *)oa, &Bnew));
3501: PetscCall(MatSeqAIJRestoreArrayRead(B, &oa));
3502: bnew = (Mat_SeqAIJ *)Bnew->data;
3503: bnew->maxnz = b->maxnz; /* allocated nonzeros of B */
3504: maij->B = Bnew;
3506: 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);
3508: b->singlemalloc = PETSC_FALSE; /* B arrays are shared by Bnew */
3509: b->free_a = PETSC_FALSE;
3510: b->free_ij = PETSC_FALSE;
3511: PetscCall(MatDestroy(&B));
3513: bnew->singlemalloc = PETSC_TRUE; /* arrays will be freed by MatDestroy(&Bnew) */
3514: bnew->free_a = PETSC_TRUE;
3515: bnew->free_ij = PETSC_TRUE;
3517: /* condense columns of maij->B */
3518: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3519: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
3520: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
3521: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
3522: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3523: PetscFunctionReturn(PETSC_SUCCESS);
3524: }
3526: extern PetscErrorCode MatCreateSubMatrices_MPIAIJ_SingleIS_Local(Mat, PetscInt, const IS[], const IS[], MatReuse, PetscBool, Mat *);
3528: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowDist(Mat mat, IS isrow, IS iscol, IS iscol_local, MatReuse call, Mat *newmat)
3529: {
3530: PetscInt i, m, n, rstart, row, rend, nz, j, bs, cbs;
3531: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3532: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3533: Mat M, Msub, B = a->B;
3534: MatScalar *aa;
3535: Mat_SeqAIJ *aij;
3536: PetscInt *garray = a->garray, *colsub, Ncols;
3537: PetscInt count, Bn = B->cmap->N, cstart = mat->cmap->rstart, cend = mat->cmap->rend;
3538: IS iscol_sub, iscmap;
3539: const PetscInt *is_idx, *cmap;
3540: PetscBool allcolumns = PETSC_FALSE;
3541: MPI_Comm comm;
3543: PetscFunctionBegin;
3544: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3545: if (call == MAT_REUSE_MATRIX) {
3546: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3547: PetscCheck(iscol_sub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "SubIScol passed in was not used before, cannot reuse");
3548: PetscCall(ISGetLocalSize(iscol_sub, &count));
3550: PetscCall(PetscObjectQuery((PetscObject)*newmat, "Subcmap", (PetscObject *)&iscmap));
3551: PetscCheck(iscmap, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Subcmap passed in was not used before, cannot reuse");
3553: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Msub));
3554: PetscCheck(Msub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3556: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_REUSE_MATRIX, PETSC_FALSE, &Msub));
3558: } else { /* call == MAT_INITIAL_MATRIX) */
3559: PetscBool flg;
3561: PetscCall(ISGetLocalSize(iscol, &n));
3562: PetscCall(ISGetSize(iscol, &Ncols));
3564: /* (1) iscol -> nonscalable iscol_local */
3565: /* Check for special case: each processor gets entire matrix columns */
3566: PetscCall(ISIdentity(iscol_local, &flg));
3567: if (flg && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3568: PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3569: if (allcolumns) {
3570: iscol_sub = iscol_local;
3571: PetscCall(PetscObjectReference((PetscObject)iscol_local));
3572: PetscCall(ISCreateStride(PETSC_COMM_SELF, n, 0, 1, &iscmap));
3574: } else {
3575: /* (2) iscol_local -> iscol_sub and iscmap. Implementation below requires iscol_local be sorted, it can have duplicate indices */
3576: PetscInt *idx, *cmap1, k;
3577: PetscCall(PetscMalloc1(Ncols, &idx));
3578: PetscCall(PetscMalloc1(Ncols, &cmap1));
3579: PetscCall(ISGetIndices(iscol_local, &is_idx));
3580: count = 0;
3581: k = 0;
3582: for (i = 0; i < Ncols; i++) {
3583: j = is_idx[i];
3584: if (j >= cstart && j < cend) {
3585: /* diagonal part of mat */
3586: idx[count] = j;
3587: cmap1[count++] = i; /* column index in submat */
3588: } else if (Bn) {
3589: /* off-diagonal part of mat */
3590: if (j == garray[k]) {
3591: idx[count] = j;
3592: cmap1[count++] = i; /* column index in submat */
3593: } else if (j > garray[k]) {
3594: while (j > garray[k] && k < Bn - 1) k++;
3595: if (j == garray[k]) {
3596: idx[count] = j;
3597: cmap1[count++] = i; /* column index in submat */
3598: }
3599: }
3600: }
3601: }
3602: PetscCall(ISRestoreIndices(iscol_local, &is_idx));
3604: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_OWN_POINTER, &iscol_sub));
3605: PetscCall(ISGetBlockSize(iscol, &cbs));
3606: PetscCall(ISSetBlockSize(iscol_sub, cbs));
3608: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)iscol_local), count, cmap1, PETSC_OWN_POINTER, &iscmap));
3609: }
3611: /* (3) Create sequential Msub */
3612: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_INITIAL_MATRIX, allcolumns, &Msub));
3613: }
3615: PetscCall(ISGetLocalSize(iscol_sub, &count));
3616: aij = (Mat_SeqAIJ *)(Msub)->data;
3617: ii = aij->i;
3618: PetscCall(ISGetIndices(iscmap, &cmap));
3620: /*
3621: m - number of local rows
3622: Ncols - number of columns (same on all processors)
3623: rstart - first row in new global matrix generated
3624: */
3625: PetscCall(MatGetSize(Msub, &m, NULL));
3627: if (call == MAT_INITIAL_MATRIX) {
3628: /* (4) Create parallel newmat */
3629: PetscMPIInt rank, size;
3630: PetscInt csize;
3632: PetscCallMPI(MPI_Comm_size(comm, &size));
3633: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3635: /*
3636: Determine the number of non-zeros in the diagonal and off-diagonal
3637: portions of the matrix in order to do correct preallocation
3638: */
3640: /* first get start and end of "diagonal" columns */
3641: PetscCall(ISGetLocalSize(iscol, &csize));
3642: if (csize == PETSC_DECIDE) {
3643: PetscCall(ISGetSize(isrow, &mglobal));
3644: if (mglobal == Ncols) { /* square matrix */
3645: nlocal = m;
3646: } else {
3647: nlocal = Ncols / size + ((Ncols % size) > rank);
3648: }
3649: } else {
3650: nlocal = csize;
3651: }
3652: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3653: rstart = rend - nlocal;
3654: 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);
3656: /* next, compute all the lengths */
3657: jj = aij->j;
3658: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3659: olens = dlens + m;
3660: for (i = 0; i < m; i++) {
3661: jend = ii[i + 1] - ii[i];
3662: olen = 0;
3663: dlen = 0;
3664: for (j = 0; j < jend; j++) {
3665: if (cmap[*jj] < rstart || cmap[*jj] >= rend) olen++;
3666: else dlen++;
3667: jj++;
3668: }
3669: olens[i] = olen;
3670: dlens[i] = dlen;
3671: }
3673: PetscCall(ISGetBlockSize(isrow, &bs));
3674: PetscCall(ISGetBlockSize(iscol, &cbs));
3676: PetscCall(MatCreate(comm, &M));
3677: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, Ncols));
3678: PetscCall(MatSetBlockSizes(M, bs, cbs));
3679: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3680: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3681: PetscCall(PetscFree(dlens));
3683: } else { /* call == MAT_REUSE_MATRIX */
3684: M = *newmat;
3685: PetscCall(MatGetLocalSize(M, &i, NULL));
3686: PetscCheck(i == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3687: PetscCall(MatZeroEntries(M));
3688: /*
3689: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3690: rather than the slower MatSetValues().
3691: */
3692: M->was_assembled = PETSC_TRUE;
3693: M->assembled = PETSC_FALSE;
3694: }
3696: /* (5) Set values of Msub to *newmat */
3697: PetscCall(PetscMalloc1(count, &colsub));
3698: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
3700: jj = aij->j;
3701: PetscCall(MatSeqAIJGetArrayRead(Msub, (const PetscScalar **)&aa));
3702: for (i = 0; i < m; i++) {
3703: row = rstart + i;
3704: nz = ii[i + 1] - ii[i];
3705: for (j = 0; j < nz; j++) colsub[j] = cmap[jj[j]];
3706: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, colsub, aa, INSERT_VALUES));
3707: jj += nz;
3708: aa += nz;
3709: }
3710: PetscCall(MatSeqAIJRestoreArrayRead(Msub, (const PetscScalar **)&aa));
3711: PetscCall(ISRestoreIndices(iscmap, &cmap));
3713: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3714: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3716: PetscCall(PetscFree(colsub));
3718: /* save Msub, iscol_sub and iscmap used in processor for next request */
3719: if (call == MAT_INITIAL_MATRIX) {
3720: *newmat = M;
3721: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "SubMatrix", (PetscObject)Msub));
3722: PetscCall(MatDestroy(&Msub));
3724: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "SubIScol", (PetscObject)iscol_sub));
3725: PetscCall(ISDestroy(&iscol_sub));
3727: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "Subcmap", (PetscObject)iscmap));
3728: PetscCall(ISDestroy(&iscmap));
3730: if (iscol_local) {
3731: PetscCall(PetscObjectCompose((PetscObject)(*newmat), "ISAllGather", (PetscObject)iscol_local));
3732: PetscCall(ISDestroy(&iscol_local));
3733: }
3734: }
3735: PetscFunctionReturn(PETSC_SUCCESS);
3736: }
3738: /*
3739: Not great since it makes two copies of the submatrix, first an SeqAIJ
3740: in local and then by concatenating the local matrices the end result.
3741: Writing it directly would be much like MatCreateSubMatrices_MPIAIJ()
3743: This requires a sequential iscol with all indices.
3744: */
3745: PetscErrorCode MatCreateSubMatrix_MPIAIJ_nonscalable(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
3746: {
3747: PetscMPIInt rank, size;
3748: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs, cbs;
3749: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3750: Mat M, Mreuse;
3751: MatScalar *aa, *vwork;
3752: MPI_Comm comm;
3753: Mat_SeqAIJ *aij;
3754: PetscBool colflag, allcolumns = PETSC_FALSE;
3756: PetscFunctionBegin;
3757: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3758: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3759: PetscCallMPI(MPI_Comm_size(comm, &size));
3761: /* Check for special case: each processor gets entire matrix columns */
3762: PetscCall(ISIdentity(iscol, &colflag));
3763: PetscCall(ISGetLocalSize(iscol, &n));
3764: if (colflag && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3765: PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3767: if (call == MAT_REUSE_MATRIX) {
3768: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
3769: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3770: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_REUSE_MATRIX, allcolumns, &Mreuse));
3771: } else {
3772: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_INITIAL_MATRIX, allcolumns, &Mreuse));
3773: }
3775: /*
3776: m - number of local rows
3777: n - number of columns (same on all processors)
3778: rstart - first row in new global matrix generated
3779: */
3780: PetscCall(MatGetSize(Mreuse, &m, &n));
3781: PetscCall(MatGetBlockSizes(Mreuse, &bs, &cbs));
3782: if (call == MAT_INITIAL_MATRIX) {
3783: aij = (Mat_SeqAIJ *)(Mreuse)->data;
3784: ii = aij->i;
3785: jj = aij->j;
3787: /*
3788: Determine the number of non-zeros in the diagonal and off-diagonal
3789: portions of the matrix in order to do correct preallocation
3790: */
3792: /* first get start and end of "diagonal" columns */
3793: if (csize == PETSC_DECIDE) {
3794: PetscCall(ISGetSize(isrow, &mglobal));
3795: if (mglobal == n) { /* square matrix */
3796: nlocal = m;
3797: } else {
3798: nlocal = n / size + ((n % size) > rank);
3799: }
3800: } else {
3801: nlocal = csize;
3802: }
3803: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3804: rstart = rend - nlocal;
3805: 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);
3807: /* next, compute all the lengths */
3808: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3809: olens = dlens + m;
3810: for (i = 0; i < m; i++) {
3811: jend = ii[i + 1] - ii[i];
3812: olen = 0;
3813: dlen = 0;
3814: for (j = 0; j < jend; j++) {
3815: if (*jj < rstart || *jj >= rend) olen++;
3816: else dlen++;
3817: jj++;
3818: }
3819: olens[i] = olen;
3820: dlens[i] = dlen;
3821: }
3822: PetscCall(MatCreate(comm, &M));
3823: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, n));
3824: PetscCall(MatSetBlockSizes(M, bs, cbs));
3825: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3826: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3827: PetscCall(PetscFree(dlens));
3828: } else {
3829: PetscInt ml, nl;
3831: M = *newmat;
3832: PetscCall(MatGetLocalSize(M, &ml, &nl));
3833: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3834: PetscCall(MatZeroEntries(M));
3835: /*
3836: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3837: rather than the slower MatSetValues().
3838: */
3839: M->was_assembled = PETSC_TRUE;
3840: M->assembled = PETSC_FALSE;
3841: }
3842: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
3843: aij = (Mat_SeqAIJ *)(Mreuse)->data;
3844: ii = aij->i;
3845: jj = aij->j;
3847: /* trigger copy to CPU if needed */
3848: PetscCall(MatSeqAIJGetArrayRead(Mreuse, (const PetscScalar **)&aa));
3849: for (i = 0; i < m; i++) {
3850: row = rstart + i;
3851: nz = ii[i + 1] - ii[i];
3852: cwork = jj;
3853: jj += nz;
3854: vwork = aa;
3855: aa += nz;
3856: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
3857: }
3858: PetscCall(MatSeqAIJRestoreArrayRead(Mreuse, (const PetscScalar **)&aa));
3860: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3861: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3862: *newmat = M;
3864: /* save submatrix used in processor for next request */
3865: if (call == MAT_INITIAL_MATRIX) {
3866: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
3867: PetscCall(MatDestroy(&Mreuse));
3868: }
3869: PetscFunctionReturn(PETSC_SUCCESS);
3870: }
3872: static PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
3873: {
3874: PetscInt m, cstart, cend, j, nnz, i, d, *ld;
3875: PetscInt *d_nnz, *o_nnz, nnz_max = 0, rstart, ii;
3876: const PetscInt *JJ;
3877: PetscBool nooffprocentries;
3878: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)B->data;
3880: PetscFunctionBegin;
3881: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);
3883: PetscCall(PetscLayoutSetUp(B->rmap));
3884: PetscCall(PetscLayoutSetUp(B->cmap));
3885: m = B->rmap->n;
3886: cstart = B->cmap->rstart;
3887: cend = B->cmap->rend;
3888: rstart = B->rmap->rstart;
3890: PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));
3892: if (PetscDefined(USE_DEBUG)) {
3893: for (i = 0; i < m; i++) {
3894: nnz = Ii[i + 1] - Ii[i];
3895: JJ = J ? J + Ii[i] : NULL;
3896: PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3897: 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]);
3898: 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);
3899: }
3900: }
3902: for (i = 0; i < m; i++) {
3903: nnz = Ii[i + 1] - Ii[i];
3904: JJ = J ? J + Ii[i] : NULL;
3905: nnz_max = PetscMax(nnz_max, nnz);
3906: d = 0;
3907: for (j = 0; j < nnz; j++) {
3908: if (cstart <= JJ[j] && JJ[j] < cend) d++;
3909: }
3910: d_nnz[i] = d;
3911: o_nnz[i] = nnz - d;
3912: }
3913: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3914: PetscCall(PetscFree2(d_nnz, o_nnz));
3916: for (i = 0; i < m; i++) {
3917: ii = i + rstart;
3918: PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], J ? J + Ii[i] : NULL, v ? v + Ii[i] : NULL, INSERT_VALUES));
3919: }
3920: nooffprocentries = B->nooffprocentries;
3921: B->nooffprocentries = PETSC_TRUE;
3922: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3923: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3924: B->nooffprocentries = nooffprocentries;
3926: /* count number of entries below block diagonal */
3927: PetscCall(PetscFree(Aij->ld));
3928: PetscCall(PetscCalloc1(m, &ld));
3929: Aij->ld = ld;
3930: for (i = 0; i < m; i++) {
3931: nnz = Ii[i + 1] - Ii[i];
3932: j = 0;
3933: while (j < nnz && J[j] < cstart) j++;
3934: ld[i] = j;
3935: if (J) J += nnz;
3936: }
3938: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3939: PetscFunctionReturn(PETSC_SUCCESS);
3940: }
3942: /*@
3943: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
3944: (the default parallel PETSc format).
3946: Collective
3948: Input Parameters:
3949: + B - the matrix
3950: . i - the indices into j for the start of each local row (starts with zero)
3951: . j - the column indices for each local row (starts with zero)
3952: - v - optional values in the matrix
3954: Level: developer
3956: Notes:
3957: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
3958: thus you CANNOT change the matrix entries by changing the values of `v` after you have
3959: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3961: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
3963: The format which is used for the sparse matrix input, is equivalent to a
3964: row-major ordering.. i.e for the following matrix, the input data expected is
3965: as shown
3967: .vb
3968: 1 0 0
3969: 2 0 3 P0
3970: -------
3971: 4 5 6 P1
3973: Process0 [P0] rows_owned=[0,1]
3974: i = {0,1,3} [size = nrow+1 = 2+1]
3975: j = {0,0,2} [size = 3]
3976: v = {1,2,3} [size = 3]
3978: Process1 [P1] rows_owned=[2]
3979: i = {0,3} [size = nrow+1 = 1+1]
3980: j = {0,1,2} [size = 3]
3981: v = {4,5,6} [size = 3]
3982: .ve
3984: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`,
3985: `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`
3986: @*/
3987: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
3988: {
3989: PetscFunctionBegin;
3990: PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
3991: PetscFunctionReturn(PETSC_SUCCESS);
3992: }
3994: /*@C
3995: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
3996: (the default parallel PETSc format). For good matrix assembly performance
3997: the user should preallocate the matrix storage by setting the parameters
3998: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4000: Collective
4002: Input Parameters:
4003: + B - the matrix
4004: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4005: (same value is used for all local rows)
4006: . d_nnz - array containing the number of nonzeros in the various rows of the
4007: DIAGONAL portion of the local submatrix (possibly different for each row)
4008: or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4009: The size of this array is equal to the number of local rows, i.e 'm'.
4010: For matrices that will be factored, you must leave room for (and set)
4011: the diagonal entry even if it is zero.
4012: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4013: submatrix (same value is used for all local rows).
4014: - o_nnz - array containing the number of nonzeros in the various rows of the
4015: OFF-DIAGONAL portion of the local submatrix (possibly different for
4016: each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4017: structure. The size of this array is equal to the number
4018: of local rows, i.e 'm'.
4020: Example Usage:
4021: Consider the following 8x8 matrix with 34 non-zero values, that is
4022: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4023: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4024: as follows
4026: .vb
4027: 1 2 0 | 0 3 0 | 0 4
4028: Proc0 0 5 6 | 7 0 0 | 8 0
4029: 9 0 10 | 11 0 0 | 12 0
4030: -------------------------------------
4031: 13 0 14 | 15 16 17 | 0 0
4032: Proc1 0 18 0 | 19 20 21 | 0 0
4033: 0 0 0 | 22 23 0 | 24 0
4034: -------------------------------------
4035: Proc2 25 26 27 | 0 0 28 | 29 0
4036: 30 0 0 | 31 32 33 | 0 34
4037: .ve
4039: This can be represented as a collection of submatrices as
4040: .vb
4041: A B C
4042: D E F
4043: G H I
4044: .ve
4046: Where the submatrices A,B,C are owned by proc0, D,E,F are
4047: owned by proc1, G,H,I are owned by proc2.
4049: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4050: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4051: The 'M','N' parameters are 8,8, and have the same values on all procs.
4053: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4054: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4055: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4056: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4057: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4058: matrix, ans [DF] as another `MATSEQAIJ` matrix.
4060: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4061: allocated for every row of the local diagonal submatrix, and `o_nz`
4062: storage locations are allocated for every row of the OFF-DIAGONAL submat.
4063: One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local
4064: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4065: In this case, the values of `d_nz`, `o_nz` are
4066: .vb
4067: proc0 dnz = 2, o_nz = 2
4068: proc1 dnz = 3, o_nz = 2
4069: proc2 dnz = 1, o_nz = 4
4070: .ve
4071: We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4072: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4073: for proc3. i.e we are using 12+15+10=37 storage locations to store
4074: 34 values.
4076: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4077: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4078: In the above case the values for `d_nnz`, `o_nnz` are
4079: .vb
4080: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4081: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4082: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4083: .ve
4084: Here the space allocated is sum of all the above values i.e 34, and
4085: hence pre-allocation is perfect.
4087: Level: intermediate
4089: Notes:
4090: If the *_nnz parameter is given then the *_nz parameter is ignored
4092: The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4093: storage. The stored row and column indices begin with zero.
4094: See [Sparse Matrices](sec_matsparse) for details.
4096: The parallel matrix is partitioned such that the first m0 rows belong to
4097: process 0, the next m1 rows belong to process 1, the next m2 rows belong
4098: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
4100: The DIAGONAL portion of the local submatrix of a processor can be defined
4101: as the submatrix which is obtained by extraction the part corresponding to
4102: the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4103: first row that belongs to the processor, r2 is the last row belonging to
4104: the this processor, and c1-c2 is range of indices of the local part of a
4105: vector suitable for applying the matrix to. This is an mxn matrix. In the
4106: common case of a square matrix, the row and column ranges are the same and
4107: the DIAGONAL part is also square. The remaining portion of the local
4108: submatrix (mxN) constitute the OFF-DIAGONAL portion.
4110: If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.
4112: You can call `MatGetInfo()` to get information on how effective the preallocation was;
4113: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4114: You can also run with the option `-info` and look for messages with the string
4115: malloc in them to see if additional memory allocation was needed.
4117: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4118: `MatGetInfo()`, `PetscSplitOwnership()`
4119: @*/
4120: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4121: {
4122: PetscFunctionBegin;
4125: PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4126: PetscFunctionReturn(PETSC_SUCCESS);
4127: }
4129: /*@
4130: MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4131: CSR format for the local rows.
4133: Collective
4135: Input Parameters:
4136: + comm - MPI communicator
4137: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4138: . n - This value should be the same as the local size used in creating the
4139: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4140: calculated if N is given) For square matrices n is almost always m.
4141: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4142: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4143: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
4144: . j - column indices
4145: - a - optional matrix values
4147: Output Parameter:
4148: . mat - the matrix
4150: Level: intermediate
4152: Notes:
4153: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4154: thus you CANNOT change the matrix entries by changing the values of a[] after you have
4155: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
4157: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
4159: The format which is used for the sparse matrix input, is equivalent to a
4160: row-major ordering.. i.e for the following matrix, the input data expected is
4161: as shown
4163: Once you have created the matrix you can update it with new numerical values using MatUpdateMPIAIJWithArrays
4164: .vb
4165: 1 0 0
4166: 2 0 3 P0
4167: -------
4168: 4 5 6 P1
4170: Process0 [P0] rows_owned=[0,1]
4171: i = {0,1,3} [size = nrow+1 = 2+1]
4172: j = {0,0,2} [size = 3]
4173: v = {1,2,3} [size = 3]
4175: Process1 [P1] rows_owned=[2]
4176: i = {0,3} [size = nrow+1 = 1+1]
4177: j = {0,1,2} [size = 3]
4178: v = {4,5,6} [size = 3]
4179: .ve
4181: .seealso: [](ch_matrices), `Mat`, `MATMPIAIK`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4182: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`
4183: @*/
4184: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
4185: {
4186: PetscFunctionBegin;
4187: PetscCheck(!i || !i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4188: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4189: PetscCall(MatCreate(comm, mat));
4190: PetscCall(MatSetSizes(*mat, m, n, M, N));
4191: /* PetscCall(MatSetBlockSizes(M,bs,cbs)); */
4192: PetscCall(MatSetType(*mat, MATMPIAIJ));
4193: PetscCall(MatMPIAIJSetPreallocationCSR(*mat, i, j, a));
4194: PetscFunctionReturn(PETSC_SUCCESS);
4195: }
4197: /*@
4198: MatUpdateMPIAIJWithArrays - updates a `MATMPIAIJ` matrix using arrays that contain in standard
4199: CSR format for the local rows. Only the numerical values are updated the other arrays must be identical to what was passed
4200: from `MatCreateMPIAIJWithArrays()`
4202: Deprecated: Use `MatUpdateMPIAIJWithArray()`
4204: Collective
4206: Input Parameters:
4207: + mat - the matrix
4208: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4209: . n - This value should be the same as the local size used in creating the
4210: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4211: calculated if N is given) For square matrices n is almost always m.
4212: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4213: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4214: . Ii - row indices; that is Ii[0] = 0, Ii[row] = Ii[row-1] + number of elements in that row of the matrix
4215: . J - column indices
4216: - v - matrix values
4218: Level: deprecated
4220: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4221: `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`
4222: @*/
4223: PetscErrorCode MatUpdateMPIAIJWithArrays(Mat mat, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4224: {
4225: PetscInt nnz, i;
4226: PetscBool nooffprocentries;
4227: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4228: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4229: PetscScalar *ad, *ao;
4230: PetscInt ldi, Iii, md;
4231: const PetscInt *Adi = Ad->i;
4232: PetscInt *ld = Aij->ld;
4234: PetscFunctionBegin;
4235: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4236: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4237: PetscCheck(m == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of rows cannot change from call to MatUpdateMPIAIJWithArrays()");
4238: PetscCheck(n == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of columns cannot change from call to MatUpdateMPIAIJWithArrays()");
4240: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4241: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4243: for (i = 0; i < m; i++) {
4244: nnz = Ii[i + 1] - Ii[i];
4245: Iii = Ii[i];
4246: ldi = ld[i];
4247: md = Adi[i + 1] - Adi[i];
4248: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4249: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4250: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4251: ad += md;
4252: ao += nnz - md;
4253: }
4254: nooffprocentries = mat->nooffprocentries;
4255: mat->nooffprocentries = PETSC_TRUE;
4256: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4257: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4258: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4259: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4260: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4261: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4262: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4263: mat->nooffprocentries = nooffprocentries;
4264: PetscFunctionReturn(PETSC_SUCCESS);
4265: }
4267: /*@
4268: MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values
4270: Collective
4272: Input Parameters:
4273: + mat - the matrix
4274: - v - matrix values, stored by row
4276: Level: intermediate
4278: Note:
4279: The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`
4281: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4282: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`
4283: @*/
4284: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4285: {
4286: PetscInt nnz, i, m;
4287: PetscBool nooffprocentries;
4288: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4289: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4290: Mat_SeqAIJ *Ao = (Mat_SeqAIJ *)Aij->B->data;
4291: PetscScalar *ad, *ao;
4292: const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4293: PetscInt ldi, Iii, md;
4294: PetscInt *ld = Aij->ld;
4296: PetscFunctionBegin;
4297: m = mat->rmap->n;
4299: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4300: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4301: Iii = 0;
4302: for (i = 0; i < m; i++) {
4303: nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4304: ldi = ld[i];
4305: md = Adi[i + 1] - Adi[i];
4306: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4307: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4308: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4309: ad += md;
4310: ao += nnz - md;
4311: Iii += nnz;
4312: }
4313: nooffprocentries = mat->nooffprocentries;
4314: mat->nooffprocentries = PETSC_TRUE;
4315: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4316: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4317: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4318: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4319: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4320: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4321: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4322: mat->nooffprocentries = nooffprocentries;
4323: PetscFunctionReturn(PETSC_SUCCESS);
4324: }
4326: /*@C
4327: MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4328: (the default parallel PETSc format). For good matrix assembly performance
4329: the user should preallocate the matrix storage by setting the parameters
4330: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4332: Collective
4334: Input Parameters:
4335: + comm - MPI communicator
4336: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4337: This value should be the same as the local size used in creating the
4338: y vector for the matrix-vector product y = Ax.
4339: . n - This value should be the same as the local size used in creating the
4340: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4341: calculated if N is given) For square matrices n is almost always m.
4342: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4343: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4344: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4345: (same value is used for all local rows)
4346: . d_nnz - array containing the number of nonzeros in the various rows of the
4347: DIAGONAL portion of the local submatrix (possibly different for each row)
4348: or `NULL`, if `d_nz` is used to specify the nonzero structure.
4349: The size of this array is equal to the number of local rows, i.e 'm'.
4350: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4351: submatrix (same value is used for all local rows).
4352: - o_nnz - array containing the number of nonzeros in the various rows of the
4353: OFF-DIAGONAL portion of the local submatrix (possibly different for
4354: each row) or `NULL`, if `o_nz` is used to specify the nonzero
4355: structure. The size of this array is equal to the number
4356: of local rows, i.e 'm'.
4358: Output Parameter:
4359: . A - the matrix
4361: Options Database Keys:
4362: + -mat_no_inode - Do not use inodes
4363: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
4364: - -matmult_vecscatter_view <viewer> - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4365: See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the vecscatter be viewed as a matrix.
4366: Entry (i,j) is the size of message (in bytes) rank i sends to rank j in one `MatMult()` call.
4368: Level: intermediate
4370: Notes:
4371: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4372: MatXXXXSetPreallocation() paradigm instead of this routine directly.
4373: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
4375: If the *_nnz parameter is given then the *_nz parameter is ignored
4377: The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4378: processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4379: storage requirements for this matrix.
4381: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one
4382: processor than it must be used on all processors that share the object for
4383: that argument.
4385: The user MUST specify either the local or global matrix dimensions
4386: (possibly both).
4388: The parallel matrix is partitioned across processors such that the
4389: first m0 rows belong to process 0, the next m1 rows belong to
4390: process 1, the next m2 rows belong to process 2 etc.. where
4391: m0,m1,m2,.. are the input parameter 'm'. i.e each processor stores
4392: values corresponding to [m x N] submatrix.
4394: The columns are logically partitioned with the n0 columns belonging
4395: to 0th partition, the next n1 columns belonging to the next
4396: partition etc.. where n0,n1,n2... are the input parameter 'n'.
4398: The DIAGONAL portion of the local submatrix on any given processor
4399: is the submatrix corresponding to the rows and columns m,n
4400: corresponding to the given processor. i.e diagonal matrix on
4401: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4402: etc. The remaining portion of the local submatrix [m x (N-n)]
4403: constitute the OFF-DIAGONAL portion. The example below better
4404: illustrates this concept.
4406: For a square global matrix we define each processor's diagonal portion
4407: to be its local rows and the corresponding columns (a square submatrix);
4408: each processor's off-diagonal portion encompasses the remainder of the
4409: local matrix (a rectangular submatrix).
4411: If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.
4413: When calling this routine with a single process communicator, a matrix of
4414: type `MATSEQAIJ` is returned. If a matrix of type `MATMPIAIJ` is desired for this
4415: type of communicator, use the construction mechanism
4416: .vb
4417: MatCreate(..., &A);
4418: MatSetType(A, MATMPIAIJ);
4419: MatSetSizes(A, m, n, M, N);
4420: MatMPIAIJSetPreallocation(A, ...);
4421: .ve
4423: By default, this format uses inodes (identical nodes) when possible.
4424: We search for consecutive rows with the same nonzero structure, thereby
4425: reusing matrix information to achieve increased efficiency.
4427: Example Usage:
4428: Consider the following 8x8 matrix with 34 non-zero values, that is
4429: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4430: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4431: as follows
4433: .vb
4434: 1 2 0 | 0 3 0 | 0 4
4435: Proc0 0 5 6 | 7 0 0 | 8 0
4436: 9 0 10 | 11 0 0 | 12 0
4437: -------------------------------------
4438: 13 0 14 | 15 16 17 | 0 0
4439: Proc1 0 18 0 | 19 20 21 | 0 0
4440: 0 0 0 | 22 23 0 | 24 0
4441: -------------------------------------
4442: Proc2 25 26 27 | 0 0 28 | 29 0
4443: 30 0 0 | 31 32 33 | 0 34
4444: .ve
4446: This can be represented as a collection of submatrices as
4448: .vb
4449: A B C
4450: D E F
4451: G H I
4452: .ve
4454: Where the submatrices A,B,C are owned by proc0, D,E,F are
4455: owned by proc1, G,H,I are owned by proc2.
4457: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4458: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4459: The 'M','N' parameters are 8,8, and have the same values on all procs.
4461: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4462: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4463: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4464: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4465: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4466: matrix, ans [DF] as another SeqAIJ matrix.
4468: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4469: allocated for every row of the local diagonal submatrix, and `o_nz`
4470: storage locations are allocated for every row of the OFF-DIAGONAL submat.
4471: One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local
4472: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4473: In this case, the values of `d_nz`,`o_nz` are
4474: .vb
4475: proc0 dnz = 2, o_nz = 2
4476: proc1 dnz = 3, o_nz = 2
4477: proc2 dnz = 1, o_nz = 4
4478: .ve
4479: We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4480: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4481: for proc3. i.e we are using 12+15+10=37 storage locations to store
4482: 34 values.
4484: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4485: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4486: In the above case the values for d_nnz,o_nnz are
4487: .vb
4488: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4489: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4490: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4491: .ve
4492: Here the space allocated is sum of all the above values i.e 34, and
4493: hence pre-allocation is perfect.
4495: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4496: `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`
4497: @*/
4498: 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)
4499: {
4500: PetscMPIInt size;
4502: PetscFunctionBegin;
4503: PetscCall(MatCreate(comm, A));
4504: PetscCall(MatSetSizes(*A, m, n, M, N));
4505: PetscCallMPI(MPI_Comm_size(comm, &size));
4506: if (size > 1) {
4507: PetscCall(MatSetType(*A, MATMPIAIJ));
4508: PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4509: } else {
4510: PetscCall(MatSetType(*A, MATSEQAIJ));
4511: PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4512: }
4513: PetscFunctionReturn(PETSC_SUCCESS);
4514: }
4516: /*MC
4517: MatMPIAIJGetSeqAIJF90 - Returns the local pieces of this distributed matrix
4519: Synopsis:
4520: MatMPIAIJGetSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)
4522: Not Collective
4524: Input Parameter:
4525: . A - the `MATMPIAIJ` matrix
4527: Output Parameters:
4528: + Ad - the diagonal portion of the matrix
4529: . Ao - the off-diagonal portion of the matrix
4530: . colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4531: - ierr - error code
4533: Level: advanced
4535: Note:
4536: Use `MatMPIAIJRestoreSeqAIJF90()` when you no longer need access to the matrices and `colmap`
4538: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJRestoreSeqAIJF90()`
4539: M*/
4541: /*MC
4542: MatMPIAIJRestoreSeqAIJF90 - call after `MatMPIAIJGetSeqAIJF90()` when you no longer need access to the matrices and `colmap`
4544: Synopsis:
4545: MatMPIAIJRestoreSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)
4547: Not Collective
4549: Input Parameters:
4550: + A - the `MATMPIAIJ` matrix
4551: . Ad - the diagonal portion of the matrix
4552: . Ao - the off-diagonal portion of the matrix
4553: . colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4554: - ierr - error code
4556: Level: advanced
4558: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJGetSeqAIJF90()`
4559: M*/
4561: /*@C
4562: MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix
4564: Not Collective
4566: Input Parameter:
4567: . A - The `MATMPIAIJ` matrix
4569: Output Parameters:
4570: + Ad - The local diagonal block as a `MATSEQAIJ` matrix
4571: . Ao - The local off-diagonal block as a `MATSEQAIJ` matrix
4572: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4574: Level: intermediate
4576: Note:
4577: The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4578: 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
4579: the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4580: local column numbers to global column numbers in the original matrix.
4582: Fortran Notes:
4583: `MatMPIAIJGetSeqAIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatMPIAIJGetSeqAIJF90()`
4585: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJF90()`, `MatMPIAIJRestoreSeqAIJF90()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATSEQAIJ`
4586: @*/
4587: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4588: {
4589: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4590: PetscBool flg;
4592: PetscFunctionBegin;
4593: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4594: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4595: if (Ad) *Ad = a->A;
4596: if (Ao) *Ao = a->B;
4597: if (colmap) *colmap = a->garray;
4598: PetscFunctionReturn(PETSC_SUCCESS);
4599: }
4601: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4602: {
4603: PetscInt m, N, i, rstart, nnz, Ii;
4604: PetscInt *indx;
4605: PetscScalar *values;
4606: MatType rootType;
4608: PetscFunctionBegin;
4609: PetscCall(MatGetSize(inmat, &m, &N));
4610: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4611: PetscInt *dnz, *onz, sum, bs, cbs;
4613: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4614: /* Check sum(n) = N */
4615: PetscCall(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4616: PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);
4618: PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4619: rstart -= m;
4621: MatPreallocateBegin(comm, m, n, dnz, onz);
4622: for (i = 0; i < m; i++) {
4623: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4624: PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4625: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4626: }
4628: PetscCall(MatCreate(comm, outmat));
4629: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4630: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4631: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4632: PetscCall(MatGetRootType_Private(inmat, &rootType));
4633: PetscCall(MatSetType(*outmat, rootType));
4634: PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4635: PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4636: MatPreallocateEnd(dnz, onz);
4637: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4638: }
4640: /* numeric phase */
4641: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4642: for (i = 0; i < m; i++) {
4643: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4644: Ii = i + rstart;
4645: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4646: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4647: }
4648: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4649: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4650: PetscFunctionReturn(PETSC_SUCCESS);
4651: }
4653: static PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(void *data)
4654: {
4655: Mat_Merge_SeqsToMPI *merge = (Mat_Merge_SeqsToMPI *)data;
4657: PetscFunctionBegin;
4658: if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4659: PetscCall(PetscFree(merge->id_r));
4660: PetscCall(PetscFree(merge->len_s));
4661: PetscCall(PetscFree(merge->len_r));
4662: PetscCall(PetscFree(merge->bi));
4663: PetscCall(PetscFree(merge->bj));
4664: PetscCall(PetscFree(merge->buf_ri[0]));
4665: PetscCall(PetscFree(merge->buf_ri));
4666: PetscCall(PetscFree(merge->buf_rj[0]));
4667: PetscCall(PetscFree(merge->buf_rj));
4668: PetscCall(PetscFree(merge->coi));
4669: PetscCall(PetscFree(merge->coj));
4670: PetscCall(PetscFree(merge->owners_co));
4671: PetscCall(PetscLayoutDestroy(&merge->rowmap));
4672: PetscCall(PetscFree(merge));
4673: PetscFunctionReturn(PETSC_SUCCESS);
4674: }
4676: #include <../src/mat/utils/freespace.h>
4677: #include <petscbt.h>
4679: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4680: {
4681: MPI_Comm comm;
4682: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4683: PetscMPIInt size, rank, taga, *len_s;
4684: PetscInt N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj;
4685: PetscInt proc, m;
4686: PetscInt **buf_ri, **buf_rj;
4687: PetscInt k, anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4688: PetscInt nrows, **buf_ri_k, **nextrow, **nextai;
4689: MPI_Request *s_waits, *r_waits;
4690: MPI_Status *status;
4691: const MatScalar *aa, *a_a;
4692: MatScalar **abuf_r, *ba_i;
4693: Mat_Merge_SeqsToMPI *merge;
4694: PetscContainer container;
4696: PetscFunctionBegin;
4697: PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4698: PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));
4700: PetscCallMPI(MPI_Comm_size(comm, &size));
4701: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4703: PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4704: PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4705: PetscCall(PetscContainerGetPointer(container, (void **)&merge));
4706: PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4707: aa = a_a;
4709: bi = merge->bi;
4710: bj = merge->bj;
4711: buf_ri = merge->buf_ri;
4712: buf_rj = merge->buf_rj;
4714: PetscCall(PetscMalloc1(size, &status));
4715: owners = merge->rowmap->range;
4716: len_s = merge->len_s;
4718: /* send and recv matrix values */
4719: PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4720: PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));
4722: PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4723: for (proc = 0, k = 0; proc < size; proc++) {
4724: if (!len_s[proc]) continue;
4725: i = owners[proc];
4726: PetscCallMPI(MPI_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4727: k++;
4728: }
4730: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4731: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4732: PetscCall(PetscFree(status));
4734: PetscCall(PetscFree(s_waits));
4735: PetscCall(PetscFree(r_waits));
4737: /* insert mat values of mpimat */
4738: PetscCall(PetscMalloc1(N, &ba_i));
4739: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4741: for (k = 0; k < merge->nrecv; k++) {
4742: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4743: nrows = *(buf_ri_k[k]);
4744: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4745: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4746: }
4748: /* set values of ba */
4749: m = merge->rowmap->n;
4750: for (i = 0; i < m; i++) {
4751: arow = owners[rank] + i;
4752: bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4753: bnzi = bi[i + 1] - bi[i];
4754: PetscCall(PetscArrayzero(ba_i, bnzi));
4756: /* add local non-zero vals of this proc's seqmat into ba */
4757: anzi = ai[arow + 1] - ai[arow];
4758: aj = a->j + ai[arow];
4759: aa = a_a + ai[arow];
4760: nextaj = 0;
4761: for (j = 0; nextaj < anzi; j++) {
4762: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4763: ba_i[j] += aa[nextaj++];
4764: }
4765: }
4767: /* add received vals into ba */
4768: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4769: /* i-th row */
4770: if (i == *nextrow[k]) {
4771: anzi = *(nextai[k] + 1) - *nextai[k];
4772: aj = buf_rj[k] + *(nextai[k]);
4773: aa = abuf_r[k] + *(nextai[k]);
4774: nextaj = 0;
4775: for (j = 0; nextaj < anzi; j++) {
4776: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4777: ba_i[j] += aa[nextaj++];
4778: }
4779: }
4780: nextrow[k]++;
4781: nextai[k]++;
4782: }
4783: }
4784: PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4785: }
4786: PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4787: PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4788: PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));
4790: PetscCall(PetscFree(abuf_r[0]));
4791: PetscCall(PetscFree(abuf_r));
4792: PetscCall(PetscFree(ba_i));
4793: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4794: PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4795: PetscFunctionReturn(PETSC_SUCCESS);
4796: }
4798: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4799: {
4800: Mat B_mpi;
4801: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4802: PetscMPIInt size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4803: PetscInt **buf_rj, **buf_ri, **buf_ri_k;
4804: PetscInt M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4805: PetscInt len, proc, *dnz, *onz, bs, cbs;
4806: PetscInt k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4807: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4808: MPI_Request *si_waits, *sj_waits, *ri_waits, *rj_waits;
4809: MPI_Status *status;
4810: PetscFreeSpaceList free_space = NULL, current_space = NULL;
4811: PetscBT lnkbt;
4812: Mat_Merge_SeqsToMPI *merge;
4813: PetscContainer container;
4815: PetscFunctionBegin;
4816: PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));
4818: /* make sure it is a PETSc comm */
4819: PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4820: PetscCallMPI(MPI_Comm_size(comm, &size));
4821: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4823: PetscCall(PetscNew(&merge));
4824: PetscCall(PetscMalloc1(size, &status));
4826: /* determine row ownership */
4827: PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4828: PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4829: PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4830: PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4831: PetscCall(PetscLayoutSetUp(merge->rowmap));
4832: PetscCall(PetscMalloc1(size, &len_si));
4833: PetscCall(PetscMalloc1(size, &merge->len_s));
4835: m = merge->rowmap->n;
4836: owners = merge->rowmap->range;
4838: /* determine the number of messages to send, their lengths */
4839: len_s = merge->len_s;
4841: len = 0; /* length of buf_si[] */
4842: merge->nsend = 0;
4843: for (proc = 0; proc < size; proc++) {
4844: len_si[proc] = 0;
4845: if (proc == rank) {
4846: len_s[proc] = 0;
4847: } else {
4848: len_si[proc] = owners[proc + 1] - owners[proc] + 1;
4849: len_s[proc] = ai[owners[proc + 1]] - ai[owners[proc]]; /* num of rows to be sent to [proc] */
4850: }
4851: if (len_s[proc]) {
4852: merge->nsend++;
4853: nrows = 0;
4854: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4855: if (ai[i + 1] > ai[i]) nrows++;
4856: }
4857: len_si[proc] = 2 * (nrows + 1);
4858: len += len_si[proc];
4859: }
4860: }
4862: /* determine the number and length of messages to receive for ij-structure */
4863: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4864: PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));
4866: /* post the Irecv of j-structure */
4867: PetscCall(PetscCommGetNewTag(comm, &tagj));
4868: PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));
4870: /* post the Isend of j-structure */
4871: PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));
4873: for (proc = 0, k = 0; proc < size; proc++) {
4874: if (!len_s[proc]) continue;
4875: i = owners[proc];
4876: PetscCallMPI(MPI_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4877: k++;
4878: }
4880: /* receives and sends of j-structure are complete */
4881: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4882: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));
4884: /* send and recv i-structure */
4885: PetscCall(PetscCommGetNewTag(comm, &tagi));
4886: PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));
4888: PetscCall(PetscMalloc1(len + 1, &buf_s));
4889: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4890: for (proc = 0, k = 0; proc < size; proc++) {
4891: if (!len_s[proc]) continue;
4892: /* form outgoing message for i-structure:
4893: buf_si[0]: nrows to be sent
4894: [1:nrows]: row index (global)
4895: [nrows+1:2*nrows+1]: i-structure index
4896: */
4897: nrows = len_si[proc] / 2 - 1;
4898: buf_si_i = buf_si + nrows + 1;
4899: buf_si[0] = nrows;
4900: buf_si_i[0] = 0;
4901: nrows = 0;
4902: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4903: anzi = ai[i + 1] - ai[i];
4904: if (anzi) {
4905: buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4906: buf_si[nrows + 1] = i - owners[proc]; /* local row index */
4907: nrows++;
4908: }
4909: }
4910: PetscCallMPI(MPI_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4911: k++;
4912: buf_si += len_si[proc];
4913: }
4915: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4916: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));
4918: PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4919: 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]));
4921: PetscCall(PetscFree(len_si));
4922: PetscCall(PetscFree(len_ri));
4923: PetscCall(PetscFree(rj_waits));
4924: PetscCall(PetscFree2(si_waits, sj_waits));
4925: PetscCall(PetscFree(ri_waits));
4926: PetscCall(PetscFree(buf_s));
4927: PetscCall(PetscFree(status));
4929: /* compute a local seq matrix in each processor */
4930: /* allocate bi array and free space for accumulating nonzero column info */
4931: PetscCall(PetscMalloc1(m + 1, &bi));
4932: bi[0] = 0;
4934: /* create and initialize a linked list */
4935: nlnk = N + 1;
4936: PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));
4938: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4939: len = ai[owners[rank + 1]] - ai[owners[rank]];
4940: PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));
4942: current_space = free_space;
4944: /* determine symbolic info for each local row */
4945: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4947: for (k = 0; k < merge->nrecv; k++) {
4948: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4949: nrows = *buf_ri_k[k];
4950: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4951: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4952: }
4954: MatPreallocateBegin(comm, m, n, dnz, onz);
4955: len = 0;
4956: for (i = 0; i < m; i++) {
4957: bnzi = 0;
4958: /* add local non-zero cols of this proc's seqmat into lnk */
4959: arow = owners[rank] + i;
4960: anzi = ai[arow + 1] - ai[arow];
4961: aj = a->j + ai[arow];
4962: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
4963: bnzi += nlnk;
4964: /* add received col data into lnk */
4965: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4966: if (i == *nextrow[k]) { /* i-th row */
4967: anzi = *(nextai[k] + 1) - *nextai[k];
4968: aj = buf_rj[k] + *nextai[k];
4969: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
4970: bnzi += nlnk;
4971: nextrow[k]++;
4972: nextai[k]++;
4973: }
4974: }
4975: if (len < bnzi) len = bnzi; /* =max(bnzi) */
4977: /* if free space is not available, make more free space */
4978: if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), ¤t_space));
4979: /* copy data into free space, then initialize lnk */
4980: PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
4981: PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));
4983: current_space->array += bnzi;
4984: current_space->local_used += bnzi;
4985: current_space->local_remaining -= bnzi;
4987: bi[i + 1] = bi[i] + bnzi;
4988: }
4990: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4992: PetscCall(PetscMalloc1(bi[m] + 1, &bj));
4993: PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
4994: PetscCall(PetscLLDestroy(lnk, lnkbt));
4996: /* create symbolic parallel matrix B_mpi */
4997: PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
4998: PetscCall(MatCreate(comm, &B_mpi));
4999: if (n == PETSC_DECIDE) {
5000: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
5001: } else {
5002: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
5003: }
5004: PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
5005: PetscCall(MatSetType(B_mpi, MATMPIAIJ));
5006: PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
5007: MatPreallocateEnd(dnz, onz);
5008: PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));
5010: /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
5011: B_mpi->assembled = PETSC_FALSE;
5012: merge->bi = bi;
5013: merge->bj = bj;
5014: merge->buf_ri = buf_ri;
5015: merge->buf_rj = buf_rj;
5016: merge->coi = NULL;
5017: merge->coj = NULL;
5018: merge->owners_co = NULL;
5020: PetscCall(PetscCommDestroy(&comm));
5022: /* attach the supporting struct to B_mpi for reuse */
5023: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5024: PetscCall(PetscContainerSetPointer(container, merge));
5025: PetscCall(PetscContainerSetUserDestroy(container, MatDestroy_MPIAIJ_SeqsToMPI));
5026: PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5027: PetscCall(PetscContainerDestroy(&container));
5028: *mpimat = B_mpi;
5030: PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5031: PetscFunctionReturn(PETSC_SUCCESS);
5032: }
5034: /*@C
5035: MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5036: matrices from each processor
5038: Collective
5040: Input Parameters:
5041: + comm - the communicators the parallel matrix will live on
5042: . seqmat - the input sequential matrices
5043: . m - number of local rows (or `PETSC_DECIDE`)
5044: . n - number of local columns (or `PETSC_DECIDE`)
5045: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5047: Output Parameter:
5048: . mpimat - the parallel matrix generated
5050: Level: advanced
5052: Note:
5053: The dimensions of the sequential matrix in each processor MUST be the same.
5054: The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
5055: destroyed when mpimat is destroyed. Call `PetscObjectQuery()` to access seqmat.
5057: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`
5058: @*/
5059: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5060: {
5061: PetscMPIInt size;
5063: PetscFunctionBegin;
5064: PetscCallMPI(MPI_Comm_size(comm, &size));
5065: if (size == 1) {
5066: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5067: if (scall == MAT_INITIAL_MATRIX) {
5068: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5069: } else {
5070: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5071: }
5072: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5073: PetscFunctionReturn(PETSC_SUCCESS);
5074: }
5075: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5076: if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5077: PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5078: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5079: PetscFunctionReturn(PETSC_SUCCESS);
5080: }
5082: /*@
5083: MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix.
5085: Not Collective
5087: Input Parameter:
5088: . A - the matrix
5090: Output Parameter:
5091: . A_loc - the local sequential matrix generated
5093: Level: developer
5095: Notes:
5096: The matrix is created by taking `A`'s local rows and putting them into a sequential matrix
5097: with `mlocal` rows and `n` columns. Where `mlocal` is obtained with `MatGetLocalSize()` and
5098: `n` is the global column count obtained with `MatGetSize()`
5100: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5102: For parallel matrices this creates an entirely new matrix. If the matrix is sequential it merely increases the reference count.
5104: Destroy the matrix with `MatDestroy()`
5106: .seealso: [](ch_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5107: @*/
5108: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5109: {
5110: PetscBool mpi;
5112: PetscFunctionBegin;
5113: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5114: if (mpi) {
5115: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5116: } else {
5117: *A_loc = A;
5118: PetscCall(PetscObjectReference((PetscObject)*A_loc));
5119: }
5120: PetscFunctionReturn(PETSC_SUCCESS);
5121: }
5123: /*@
5124: MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix.
5126: Not Collective
5128: Input Parameters:
5129: + A - the matrix
5130: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5132: Output Parameter:
5133: . A_loc - the local sequential matrix generated
5135: Level: developer
5137: Notes:
5138: The matrix is created by taking all `A`'s local rows and putting them into a sequential
5139: matrix with `mlocal` rows and `n` columns.`mlocal` is the row count obtained with
5140: `MatGetLocalSize()` and `n` is the global column count obtained with `MatGetSize()`.
5142: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5144: When `A` is sequential and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A` (which contains the entire matrix),
5145: with its reference count increased by one. Hence changing values of `A_loc` changes `A`. If `MAT_REUSE_MATRIX` is requested on a sequential matrix
5146: then `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called to fill `A_loc`. Thus one can preallocate the appropriate sequential matrix `A_loc`
5147: and then call this routine with `MAT_REUSE_MATRIX`. In this case, one can modify the values of `A_loc` without affecting the original sequential matrix.
5149: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5150: @*/
5151: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5152: {
5153: Mat_MPIAIJ *mpimat = (Mat_MPIAIJ *)A->data;
5154: Mat_SeqAIJ *mat, *a, *b;
5155: PetscInt *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5156: const PetscScalar *aa, *ba, *aav, *bav;
5157: PetscScalar *ca, *cam;
5158: PetscMPIInt size;
5159: PetscInt am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5160: PetscInt *ci, *cj, col, ncols_d, ncols_o, jo;
5161: PetscBool match;
5163: PetscFunctionBegin;
5164: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5165: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5166: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5167: if (size == 1) {
5168: if (scall == MAT_INITIAL_MATRIX) {
5169: PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5170: *A_loc = mpimat->A;
5171: } else if (scall == MAT_REUSE_MATRIX) {
5172: PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5173: }
5174: PetscFunctionReturn(PETSC_SUCCESS);
5175: }
5177: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5178: a = (Mat_SeqAIJ *)(mpimat->A)->data;
5179: b = (Mat_SeqAIJ *)(mpimat->B)->data;
5180: ai = a->i;
5181: aj = a->j;
5182: bi = b->i;
5183: bj = b->j;
5184: PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5185: PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5186: aa = aav;
5187: ba = bav;
5188: if (scall == MAT_INITIAL_MATRIX) {
5189: PetscCall(PetscMalloc1(1 + am, &ci));
5190: ci[0] = 0;
5191: for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5192: PetscCall(PetscMalloc1(1 + ci[am], &cj));
5193: PetscCall(PetscMalloc1(1 + ci[am], &ca));
5194: k = 0;
5195: for (i = 0; i < am; i++) {
5196: ncols_o = bi[i + 1] - bi[i];
5197: ncols_d = ai[i + 1] - ai[i];
5198: /* off-diagonal portion of A */
5199: for (jo = 0; jo < ncols_o; jo++) {
5200: col = cmap[*bj];
5201: if (col >= cstart) break;
5202: cj[k] = col;
5203: bj++;
5204: ca[k++] = *ba++;
5205: }
5206: /* diagonal portion of A */
5207: for (j = 0; j < ncols_d; j++) {
5208: cj[k] = cstart + *aj++;
5209: ca[k++] = *aa++;
5210: }
5211: /* off-diagonal portion of A */
5212: for (j = jo; j < ncols_o; j++) {
5213: cj[k] = cmap[*bj++];
5214: ca[k++] = *ba++;
5215: }
5216: }
5217: /* put together the new matrix */
5218: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5219: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5220: /* Since these are PETSc arrays, change flags to free them as necessary. */
5221: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5222: mat->free_a = PETSC_TRUE;
5223: mat->free_ij = PETSC_TRUE;
5224: mat->nonew = 0;
5225: } else if (scall == MAT_REUSE_MATRIX) {
5226: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5227: ci = mat->i;
5228: cj = mat->j;
5229: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5230: for (i = 0; i < am; i++) {
5231: /* off-diagonal portion of A */
5232: ncols_o = bi[i + 1] - bi[i];
5233: for (jo = 0; jo < ncols_o; jo++) {
5234: col = cmap[*bj];
5235: if (col >= cstart) break;
5236: *cam++ = *ba++;
5237: bj++;
5238: }
5239: /* diagonal portion of A */
5240: ncols_d = ai[i + 1] - ai[i];
5241: for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5242: /* off-diagonal portion of A */
5243: for (j = jo; j < ncols_o; j++) {
5244: *cam++ = *ba++;
5245: bj++;
5246: }
5247: }
5248: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5249: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5250: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5251: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5252: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5253: PetscFunctionReturn(PETSC_SUCCESS);
5254: }
5256: /*@
5257: MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5258: mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and off-diagonal part
5260: Not Collective
5262: Input Parameters:
5263: + A - the matrix
5264: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5266: Output Parameters:
5267: + glob - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5268: - A_loc - the local sequential matrix generated
5270: Level: developer
5272: Note:
5273: This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5274: part, then those associated with the off-diagonal part (in its local ordering)
5276: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5277: @*/
5278: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5279: {
5280: Mat Ao, Ad;
5281: const PetscInt *cmap;
5282: PetscMPIInt size;
5283: PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);
5285: PetscFunctionBegin;
5286: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5287: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5288: if (size == 1) {
5289: if (scall == MAT_INITIAL_MATRIX) {
5290: PetscCall(PetscObjectReference((PetscObject)Ad));
5291: *A_loc = Ad;
5292: } else if (scall == MAT_REUSE_MATRIX) {
5293: PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5294: }
5295: if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5296: PetscFunctionReturn(PETSC_SUCCESS);
5297: }
5298: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5299: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5300: if (f) {
5301: PetscCall((*f)(A, scall, glob, A_loc));
5302: } else {
5303: Mat_SeqAIJ *a = (Mat_SeqAIJ *)Ad->data;
5304: Mat_SeqAIJ *b = (Mat_SeqAIJ *)Ao->data;
5305: Mat_SeqAIJ *c;
5306: PetscInt *ai = a->i, *aj = a->j;
5307: PetscInt *bi = b->i, *bj = b->j;
5308: PetscInt *ci, *cj;
5309: const PetscScalar *aa, *ba;
5310: PetscScalar *ca;
5311: PetscInt i, j, am, dn, on;
5313: PetscCall(MatGetLocalSize(Ad, &am, &dn));
5314: PetscCall(MatGetLocalSize(Ao, NULL, &on));
5315: PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5316: PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5317: if (scall == MAT_INITIAL_MATRIX) {
5318: PetscInt k;
5319: PetscCall(PetscMalloc1(1 + am, &ci));
5320: PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5321: PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5322: ci[0] = 0;
5323: for (i = 0, k = 0; i < am; i++) {
5324: const PetscInt ncols_o = bi[i + 1] - bi[i];
5325: const PetscInt ncols_d = ai[i + 1] - ai[i];
5326: ci[i + 1] = ci[i] + ncols_o + ncols_d;
5327: /* diagonal portion of A */
5328: for (j = 0; j < ncols_d; j++, k++) {
5329: cj[k] = *aj++;
5330: ca[k] = *aa++;
5331: }
5332: /* off-diagonal portion of A */
5333: for (j = 0; j < ncols_o; j++, k++) {
5334: cj[k] = dn + *bj++;
5335: ca[k] = *ba++;
5336: }
5337: }
5338: /* put together the new matrix */
5339: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5340: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5341: /* Since these are PETSc arrays, change flags to free them as necessary. */
5342: c = (Mat_SeqAIJ *)(*A_loc)->data;
5343: c->free_a = PETSC_TRUE;
5344: c->free_ij = PETSC_TRUE;
5345: c->nonew = 0;
5346: PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5347: } else if (scall == MAT_REUSE_MATRIX) {
5348: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5349: for (i = 0; i < am; i++) {
5350: const PetscInt ncols_d = ai[i + 1] - ai[i];
5351: const PetscInt ncols_o = bi[i + 1] - bi[i];
5352: /* diagonal portion of A */
5353: for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5354: /* off-diagonal portion of A */
5355: for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5356: }
5357: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5358: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5359: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5360: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5361: if (glob) {
5362: PetscInt cst, *gidx;
5364: PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5365: PetscCall(PetscMalloc1(dn + on, &gidx));
5366: for (i = 0; i < dn; i++) gidx[i] = cst + i;
5367: for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5368: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5369: }
5370: }
5371: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5372: PetscFunctionReturn(PETSC_SUCCESS);
5373: }
5375: /*@C
5376: MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns
5378: Not Collective
5380: Input Parameters:
5381: + A - the matrix
5382: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5383: . row - index set of rows to extract (or `NULL`)
5384: - col - index set of columns to extract (or `NULL`)
5386: Output Parameter:
5387: . A_loc - the local sequential matrix generated
5389: Level: developer
5391: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5392: @*/
5393: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5394: {
5395: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5396: PetscInt i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5397: IS isrowa, iscola;
5398: Mat *aloc;
5399: PetscBool match;
5401: PetscFunctionBegin;
5402: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5403: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5404: PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5405: if (!row) {
5406: start = A->rmap->rstart;
5407: end = A->rmap->rend;
5408: PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5409: } else {
5410: isrowa = *row;
5411: }
5412: if (!col) {
5413: start = A->cmap->rstart;
5414: cmap = a->garray;
5415: nzA = a->A->cmap->n;
5416: nzB = a->B->cmap->n;
5417: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5418: ncols = 0;
5419: for (i = 0; i < nzB; i++) {
5420: if (cmap[i] < start) idx[ncols++] = cmap[i];
5421: else break;
5422: }
5423: imark = i;
5424: for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5425: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5426: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5427: } else {
5428: iscola = *col;
5429: }
5430: if (scall != MAT_INITIAL_MATRIX) {
5431: PetscCall(PetscMalloc1(1, &aloc));
5432: aloc[0] = *A_loc;
5433: }
5434: PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5435: if (!col) { /* attach global id of condensed columns */
5436: PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5437: }
5438: *A_loc = aloc[0];
5439: PetscCall(PetscFree(aloc));
5440: if (!row) PetscCall(ISDestroy(&isrowa));
5441: if (!col) PetscCall(ISDestroy(&iscola));
5442: PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5443: PetscFunctionReturn(PETSC_SUCCESS);
5444: }
5446: /*
5447: * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5448: * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5449: * on a global size.
5450: * */
5451: static PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5452: {
5453: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
5454: Mat_SeqAIJ *pd = (Mat_SeqAIJ *)(p->A)->data, *po = (Mat_SeqAIJ *)(p->B)->data, *p_oth;
5455: PetscInt plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5456: PetscMPIInt owner;
5457: PetscSFNode *iremote, *oiremote;
5458: const PetscInt *lrowindices;
5459: PetscSF sf, osf;
5460: PetscInt pcstart, *roffsets, *loffsets, *pnnz, j;
5461: PetscInt ontotalcols, dntotalcols, ntotalcols, nout;
5462: MPI_Comm comm;
5463: ISLocalToGlobalMapping mapping;
5464: const PetscScalar *pd_a, *po_a;
5466: PetscFunctionBegin;
5467: PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5468: /* plocalsize is the number of roots
5469: * nrows is the number of leaves
5470: * */
5471: PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5472: PetscCall(ISGetLocalSize(rows, &nrows));
5473: PetscCall(PetscCalloc1(nrows, &iremote));
5474: PetscCall(ISGetIndices(rows, &lrowindices));
5475: for (i = 0; i < nrows; i++) {
5476: /* Find a remote index and an owner for a row
5477: * The row could be local or remote
5478: * */
5479: owner = 0;
5480: lidx = 0;
5481: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5482: iremote[i].index = lidx;
5483: iremote[i].rank = owner;
5484: }
5485: /* Create SF to communicate how many nonzero columns for each row */
5486: PetscCall(PetscSFCreate(comm, &sf));
5487: /* SF will figure out the number of nonzero columns for each row, and their
5488: * offsets
5489: * */
5490: PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5491: PetscCall(PetscSFSetFromOptions(sf));
5492: PetscCall(PetscSFSetUp(sf));
5494: PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5495: PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5496: PetscCall(PetscCalloc1(nrows, &pnnz));
5497: roffsets[0] = 0;
5498: roffsets[1] = 0;
5499: for (i = 0; i < plocalsize; i++) {
5500: /* diagonal */
5501: nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5502: /* off-diagonal */
5503: nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5504: /* compute offsets so that we relative location for each row */
5505: roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5506: roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5507: }
5508: PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5509: PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5510: /* 'r' means root, and 'l' means leaf */
5511: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5512: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5513: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5514: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5515: PetscCall(PetscSFDestroy(&sf));
5516: PetscCall(PetscFree(roffsets));
5517: PetscCall(PetscFree(nrcols));
5518: dntotalcols = 0;
5519: ontotalcols = 0;
5520: ncol = 0;
5521: for (i = 0; i < nrows; i++) {
5522: pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5523: ncol = PetscMax(pnnz[i], ncol);
5524: /* diagonal */
5525: dntotalcols += nlcols[i * 2 + 0];
5526: /* off-diagonal */
5527: ontotalcols += nlcols[i * 2 + 1];
5528: }
5529: /* We do not need to figure the right number of columns
5530: * since all the calculations will be done by going through the raw data
5531: * */
5532: PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5533: PetscCall(MatSetUp(*P_oth));
5534: PetscCall(PetscFree(pnnz));
5535: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5536: /* diagonal */
5537: PetscCall(PetscCalloc1(dntotalcols, &iremote));
5538: /* off-diagonal */
5539: PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5540: /* diagonal */
5541: PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5542: /* off-diagonal */
5543: PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5544: dntotalcols = 0;
5545: ontotalcols = 0;
5546: ntotalcols = 0;
5547: for (i = 0; i < nrows; i++) {
5548: owner = 0;
5549: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5550: /* Set iremote for diag matrix */
5551: for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5552: iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5553: iremote[dntotalcols].rank = owner;
5554: /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5555: ilocal[dntotalcols++] = ntotalcols++;
5556: }
5557: /* off-diagonal */
5558: for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5559: oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5560: oiremote[ontotalcols].rank = owner;
5561: oilocal[ontotalcols++] = ntotalcols++;
5562: }
5563: }
5564: PetscCall(ISRestoreIndices(rows, &lrowindices));
5565: PetscCall(PetscFree(loffsets));
5566: PetscCall(PetscFree(nlcols));
5567: PetscCall(PetscSFCreate(comm, &sf));
5568: /* P serves as roots and P_oth is leaves
5569: * Diag matrix
5570: * */
5571: PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5572: PetscCall(PetscSFSetFromOptions(sf));
5573: PetscCall(PetscSFSetUp(sf));
5575: PetscCall(PetscSFCreate(comm, &osf));
5576: /* off-diagonal */
5577: PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5578: PetscCall(PetscSFSetFromOptions(osf));
5579: PetscCall(PetscSFSetUp(osf));
5580: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5581: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5582: /* operate on the matrix internal data to save memory */
5583: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5584: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5585: PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5586: /* Convert to global indices for diag matrix */
5587: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5588: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5589: /* We want P_oth store global indices */
5590: PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5591: /* Use memory scalable approach */
5592: PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5593: PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5594: PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5595: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5596: /* Convert back to local indices */
5597: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5598: PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5599: nout = 0;
5600: PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5601: PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5602: PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5603: /* Exchange values */
5604: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5605: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5606: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5607: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5608: /* Stop PETSc from shrinking memory */
5609: for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5610: PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5611: PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5612: /* Attach PetscSF objects to P_oth so that we can reuse it later */
5613: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5614: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5615: PetscCall(PetscSFDestroy(&sf));
5616: PetscCall(PetscSFDestroy(&osf));
5617: PetscFunctionReturn(PETSC_SUCCESS);
5618: }
5620: /*
5621: * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5622: * This supports MPIAIJ and MAIJ
5623: * */
5624: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5625: {
5626: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5627: Mat_SeqAIJ *p_oth;
5628: IS rows, map;
5629: PetscHMapI hamp;
5630: PetscInt i, htsize, *rowindices, off, *mapping, key, count;
5631: MPI_Comm comm;
5632: PetscSF sf, osf;
5633: PetscBool has;
5635: PetscFunctionBegin;
5636: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5637: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5638: /* If it is the first time, create an index set of off-diag nonzero columns of A,
5639: * and then create a submatrix (that often is an overlapping matrix)
5640: * */
5641: if (reuse == MAT_INITIAL_MATRIX) {
5642: /* Use a hash table to figure out unique keys */
5643: PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5644: PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5645: count = 0;
5646: /* Assume that a->g is sorted, otherwise the following does not make sense */
5647: for (i = 0; i < a->B->cmap->n; i++) {
5648: key = a->garray[i] / dof;
5649: PetscCall(PetscHMapIHas(hamp, key, &has));
5650: if (!has) {
5651: mapping[i] = count;
5652: PetscCall(PetscHMapISet(hamp, key, count++));
5653: } else {
5654: /* Current 'i' has the same value the previous step */
5655: mapping[i] = count - 1;
5656: }
5657: }
5658: PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5659: PetscCall(PetscHMapIGetSize(hamp, &htsize));
5660: PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5661: PetscCall(PetscCalloc1(htsize, &rowindices));
5662: off = 0;
5663: PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5664: PetscCall(PetscHMapIDestroy(&hamp));
5665: PetscCall(PetscSortInt(htsize, rowindices));
5666: PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5667: /* In case, the matrix was already created but users want to recreate the matrix */
5668: PetscCall(MatDestroy(P_oth));
5669: PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5670: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5671: PetscCall(ISDestroy(&map));
5672: PetscCall(ISDestroy(&rows));
5673: } else if (reuse == MAT_REUSE_MATRIX) {
5674: /* If matrix was already created, we simply update values using SF objects
5675: * that as attached to the matrix earlier.
5676: */
5677: const PetscScalar *pd_a, *po_a;
5679: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5680: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5681: PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5682: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5683: /* Update values in place */
5684: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5685: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5686: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5687: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5688: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5689: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5690: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5691: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5692: } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5693: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5694: PetscFunctionReturn(PETSC_SUCCESS);
5695: }
5697: /*@C
5698: MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`
5700: Collective
5702: Input Parameters:
5703: + A - the first matrix in `MATMPIAIJ` format
5704: . B - the second matrix in `MATMPIAIJ` format
5705: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5707: Output Parameters:
5708: + rowb - On input index sets of rows of B to extract (or `NULL`), modified on output
5709: . colb - On input index sets of columns of B to extract (or `NULL`), modified on output
5710: - B_seq - the sequential matrix generated
5712: Level: developer
5714: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5715: @*/
5716: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5717: {
5718: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5719: PetscInt *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5720: IS isrowb, iscolb;
5721: Mat *bseq = NULL;
5723: PetscFunctionBegin;
5724: 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 ")",
5725: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5726: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));
5728: if (scall == MAT_INITIAL_MATRIX) {
5729: start = A->cmap->rstart;
5730: cmap = a->garray;
5731: nzA = a->A->cmap->n;
5732: nzB = a->B->cmap->n;
5733: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5734: ncols = 0;
5735: for (i = 0; i < nzB; i++) { /* row < local row index */
5736: if (cmap[i] < start) idx[ncols++] = cmap[i];
5737: else break;
5738: }
5739: imark = i;
5740: for (i = 0; i < nzA; i++) idx[ncols++] = start + i; /* local rows */
5741: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5742: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5743: PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5744: } else {
5745: PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5746: isrowb = *rowb;
5747: iscolb = *colb;
5748: PetscCall(PetscMalloc1(1, &bseq));
5749: bseq[0] = *B_seq;
5750: }
5751: PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5752: *B_seq = bseq[0];
5753: PetscCall(PetscFree(bseq));
5754: if (!rowb) {
5755: PetscCall(ISDestroy(&isrowb));
5756: } else {
5757: *rowb = isrowb;
5758: }
5759: if (!colb) {
5760: PetscCall(ISDestroy(&iscolb));
5761: } else {
5762: *colb = iscolb;
5763: }
5764: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5765: PetscFunctionReturn(PETSC_SUCCESS);
5766: }
5768: /*
5769: MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5770: of the OFF-DIAGONAL portion of local A
5772: Collective
5774: Input Parameters:
5775: + A,B - the matrices in `MATMPIAIJ` format
5776: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5778: Output Parameter:
5779: + startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5780: . startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5781: . bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5782: - B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N
5784: Developer Note:
5785: This directly accesses information inside the VecScatter associated with the matrix-vector product
5786: for this matrix. This is not desirable..
5788: Level: developer
5790: */
5791: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5792: {
5793: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5794: Mat_SeqAIJ *b_oth;
5795: VecScatter ctx;
5796: MPI_Comm comm;
5797: const PetscMPIInt *rprocs, *sprocs;
5798: const PetscInt *srow, *rstarts, *sstarts;
5799: PetscInt *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5800: PetscInt i, j, k = 0, l, ll, nrecvs, nsends, nrows, *rstartsj = NULL, *sstartsj, len;
5801: PetscScalar *b_otha, *bufa, *bufA, *vals = NULL;
5802: MPI_Request *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5803: PetscMPIInt size, tag, rank, nreqs;
5805: PetscFunctionBegin;
5806: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5807: PetscCallMPI(MPI_Comm_size(comm, &size));
5809: 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 ")",
5810: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5811: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5812: PetscCallMPI(MPI_Comm_rank(comm, &rank));
5814: if (size == 1) {
5815: startsj_s = NULL;
5816: bufa_ptr = NULL;
5817: *B_oth = NULL;
5818: PetscFunctionReturn(PETSC_SUCCESS);
5819: }
5821: ctx = a->Mvctx;
5822: tag = ((PetscObject)ctx)->tag;
5824: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5825: /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5826: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5827: PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5828: PetscCall(PetscMalloc1(nreqs, &reqs));
5829: rwaits = reqs;
5830: swaits = reqs + nrecvs;
5832: if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5833: if (scall == MAT_INITIAL_MATRIX) {
5834: /* i-array */
5835: /* post receives */
5836: if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5837: for (i = 0; i < nrecvs; i++) {
5838: rowlen = rvalues + rstarts[i] * rbs;
5839: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5840: PetscCallMPI(MPI_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5841: }
5843: /* pack the outgoing message */
5844: PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));
5846: sstartsj[0] = 0;
5847: rstartsj[0] = 0;
5848: len = 0; /* total length of j or a array to be sent */
5849: if (nsends) {
5850: k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5851: PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5852: }
5853: for (i = 0; i < nsends; i++) {
5854: rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5855: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5856: for (j = 0; j < nrows; j++) {
5857: row = srow[k] + B->rmap->range[rank]; /* global row idx */
5858: for (l = 0; l < sbs; l++) {
5859: PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */
5861: rowlen[j * sbs + l] = ncols;
5863: len += ncols;
5864: PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5865: }
5866: k++;
5867: }
5868: PetscCallMPI(MPI_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));
5870: sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5871: }
5872: /* recvs and sends of i-array are completed */
5873: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5874: PetscCall(PetscFree(svalues));
5876: /* allocate buffers for sending j and a arrays */
5877: PetscCall(PetscMalloc1(len + 1, &bufj));
5878: PetscCall(PetscMalloc1(len + 1, &bufa));
5880: /* create i-array of B_oth */
5881: PetscCall(PetscMalloc1(aBn + 2, &b_othi));
5883: b_othi[0] = 0;
5884: len = 0; /* total length of j or a array to be received */
5885: k = 0;
5886: for (i = 0; i < nrecvs; i++) {
5887: rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5888: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5889: for (j = 0; j < nrows; j++) {
5890: b_othi[k + 1] = b_othi[k] + rowlen[j];
5891: PetscCall(PetscIntSumError(rowlen[j], len, &len));
5892: k++;
5893: }
5894: rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5895: }
5896: PetscCall(PetscFree(rvalues));
5898: /* allocate space for j and a arrays of B_oth */
5899: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_othj));
5900: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_otha));
5902: /* j-array */
5903: /* post receives of j-array */
5904: for (i = 0; i < nrecvs; i++) {
5905: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5906: PetscCallMPI(MPI_Irecv(b_othj + rstartsj[i], nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5907: }
5909: /* pack the outgoing message j-array */
5910: if (nsends) k = sstarts[0];
5911: for (i = 0; i < nsends; i++) {
5912: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5913: bufJ = bufj + sstartsj[i];
5914: for (j = 0; j < nrows; j++) {
5915: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5916: for (ll = 0; ll < sbs; ll++) {
5917: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5918: for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5919: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5920: }
5921: }
5922: PetscCallMPI(MPI_Isend(bufj + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5923: }
5925: /* recvs and sends of j-array are completed */
5926: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5927: } else if (scall == MAT_REUSE_MATRIX) {
5928: sstartsj = *startsj_s;
5929: rstartsj = *startsj_r;
5930: bufa = *bufa_ptr;
5931: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
5932: PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5933: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");
5935: /* a-array */
5936: /* post receives of a-array */
5937: for (i = 0; i < nrecvs; i++) {
5938: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5939: PetscCallMPI(MPI_Irecv(b_otha + rstartsj[i], nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
5940: }
5942: /* pack the outgoing message a-array */
5943: if (nsends) k = sstarts[0];
5944: for (i = 0; i < nsends; i++) {
5945: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5946: bufA = bufa + sstartsj[i];
5947: for (j = 0; j < nrows; j++) {
5948: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5949: for (ll = 0; ll < sbs; ll++) {
5950: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5951: for (l = 0; l < ncols; l++) *bufA++ = vals[l];
5952: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5953: }
5954: }
5955: PetscCallMPI(MPI_Isend(bufa + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
5956: }
5957: /* recvs and sends of a-array are completed */
5958: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5959: PetscCall(PetscFree(reqs));
5961: if (scall == MAT_INITIAL_MATRIX) {
5962: /* put together the new matrix */
5963: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));
5965: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5966: /* Since these are PETSc arrays, change flags to free them as necessary. */
5967: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
5968: b_oth->free_a = PETSC_TRUE;
5969: b_oth->free_ij = PETSC_TRUE;
5970: b_oth->nonew = 0;
5972: PetscCall(PetscFree(bufj));
5973: if (!startsj_s || !bufa_ptr) {
5974: PetscCall(PetscFree2(sstartsj, rstartsj));
5975: PetscCall(PetscFree(bufa_ptr));
5976: } else {
5977: *startsj_s = sstartsj;
5978: *startsj_r = rstartsj;
5979: *bufa_ptr = bufa;
5980: }
5981: } else if (scall == MAT_REUSE_MATRIX) {
5982: PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
5983: }
5985: PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
5986: PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
5987: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
5988: PetscFunctionReturn(PETSC_SUCCESS);
5989: }
5991: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
5992: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
5993: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
5994: #if defined(PETSC_HAVE_MKL_SPARSE)
5995: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
5996: #endif
5997: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
5998: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
5999: #if defined(PETSC_HAVE_ELEMENTAL)
6000: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
6001: #endif
6002: #if defined(PETSC_HAVE_SCALAPACK)
6003: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
6004: #endif
6005: #if defined(PETSC_HAVE_HYPRE)
6006: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
6007: #endif
6008: #if defined(PETSC_HAVE_CUDA)
6009: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
6010: #endif
6011: #if defined(PETSC_HAVE_HIP)
6012: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6013: #endif
6014: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6015: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6016: #endif
6017: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6018: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6019: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
6021: /*
6022: Computes (B'*A')' since computing B*A directly is untenable
6024: n p p
6025: [ ] [ ] [ ]
6026: m [ A ] * n [ B ] = m [ C ]
6027: [ ] [ ] [ ]
6029: */
6030: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6031: {
6032: Mat At, Bt, Ct;
6034: PetscFunctionBegin;
6035: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6036: PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6037: PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_DEFAULT, &Ct));
6038: PetscCall(MatDestroy(&At));
6039: PetscCall(MatDestroy(&Bt));
6040: PetscCall(MatTransposeSetPrecursor(Ct, C));
6041: PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6042: PetscCall(MatDestroy(&Ct));
6043: PetscFunctionReturn(PETSC_SUCCESS);
6044: }
6046: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6047: {
6048: PetscBool cisdense;
6050: PetscFunctionBegin;
6051: 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);
6052: PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6053: PetscCall(MatSetBlockSizesFromMats(C, A, B));
6054: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6055: if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6056: PetscCall(MatSetUp(C));
6058: C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6059: PetscFunctionReturn(PETSC_SUCCESS);
6060: }
6062: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6063: {
6064: Mat_Product *product = C->product;
6065: Mat A = product->A, B = product->B;
6067: PetscFunctionBegin;
6068: 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 ")",
6069: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6070: C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6071: C->ops->productsymbolic = MatProductSymbolic_AB;
6072: PetscFunctionReturn(PETSC_SUCCESS);
6073: }
6075: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6076: {
6077: Mat_Product *product = C->product;
6079: PetscFunctionBegin;
6080: if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6081: PetscFunctionReturn(PETSC_SUCCESS);
6082: }
6084: /*
6085: Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix
6087: Input Parameters:
6089: j1,rowBegin1,rowEnd1,jmap1: describe the first set of nonzeros (Set1)
6090: j2,rowBegin2,rowEnd2,jmap2: describe the second set of nonzeros (Set2)
6092: mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat
6094: For Set1, j1[] contains column indices of the nonzeros.
6095: 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
6096: respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6097: but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.
6099: Similar for Set2.
6101: This routine merges the two sets of nonzeros row by row and removes repeats.
6103: Output Parameters: (memory is allocated by the caller)
6105: i[],j[]: the CSR of the merged matrix, which has m rows.
6106: imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6107: imap2[]: similar to imap1[], but for Set2.
6108: Note we order nonzeros row-by-row and from left to right.
6109: */
6110: 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[])
6111: {
6112: PetscInt r, m; /* Row index of mat */
6113: PetscCount t, t1, t2, b1, e1, b2, e2;
6115: PetscFunctionBegin;
6116: PetscCall(MatGetLocalSize(mat, &m, NULL));
6117: t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6118: i[0] = 0;
6119: for (r = 0; r < m; r++) { /* Do row by row merging */
6120: b1 = rowBegin1[r];
6121: e1 = rowEnd1[r];
6122: b2 = rowBegin2[r];
6123: e2 = rowEnd2[r];
6124: while (b1 < e1 && b2 < e2) {
6125: if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6126: j[t] = j1[b1];
6127: imap1[t1] = t;
6128: imap2[t2] = t;
6129: b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6130: b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6131: t1++;
6132: t2++;
6133: t++;
6134: } else if (j1[b1] < j2[b2]) {
6135: j[t] = j1[b1];
6136: imap1[t1] = t;
6137: b1 += jmap1[t1 + 1] - jmap1[t1];
6138: t1++;
6139: t++;
6140: } else {
6141: j[t] = j2[b2];
6142: imap2[t2] = t;
6143: b2 += jmap2[t2 + 1] - jmap2[t2];
6144: t2++;
6145: t++;
6146: }
6147: }
6148: /* Merge the remaining in either j1[] or j2[] */
6149: while (b1 < e1) {
6150: j[t] = j1[b1];
6151: imap1[t1] = t;
6152: b1 += jmap1[t1 + 1] - jmap1[t1];
6153: t1++;
6154: t++;
6155: }
6156: while (b2 < e2) {
6157: j[t] = j2[b2];
6158: imap2[t2] = t;
6159: b2 += jmap2[t2 + 1] - jmap2[t2];
6160: t2++;
6161: t++;
6162: }
6163: i[r + 1] = t;
6164: }
6165: PetscFunctionReturn(PETSC_SUCCESS);
6166: }
6168: /*
6169: Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block
6171: Input Parameters:
6172: mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6173: 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[]
6174: respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.
6176: i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6177: i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.
6179: Output Parameters:
6180: j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6181: rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6182: 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,
6183: and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.
6185: Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6186: Atot: number of entries belonging to the diagonal block.
6187: Annz: number of unique nonzeros belonging to the diagonal block.
6188: Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6189: repeats (i.e., same 'i,j' pair).
6190: Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6191: is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.
6193: Atot: number of entries belonging to the diagonal block
6194: Annz: number of unique nonzeros belonging to the diagonal block.
6196: Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.
6198: Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6199: */
6200: 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_)
6201: {
6202: PetscInt cstart, cend, rstart, rend, row, col;
6203: PetscCount Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6204: PetscCount Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6205: PetscCount k, m, p, q, r, s, mid;
6206: PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;
6208: PetscFunctionBegin;
6209: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6210: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6211: m = rend - rstart;
6213: /* Skip negative rows */
6214: for (k = 0; k < n; k++)
6215: if (i[k] >= 0) break;
6217: /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6218: fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6219: */
6220: while (k < n) {
6221: row = i[k];
6222: /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6223: for (s = k; s < n; s++)
6224: if (i[s] != row) break;
6226: /* Shift diag columns to range of [-PETSC_MAX_INT, -1] */
6227: for (p = k; p < s; p++) {
6228: if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_MAX_INT;
6229: 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]);
6230: }
6231: PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6232: PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6233: rowBegin[row - rstart] = k;
6234: rowMid[row - rstart] = mid;
6235: rowEnd[row - rstart] = s;
6237: /* Count nonzeros of this diag/offdiag row, which might have repeats */
6238: Atot += mid - k;
6239: Btot += s - mid;
6241: /* Count unique nonzeros of this diag row */
6242: for (p = k; p < mid;) {
6243: col = j[p];
6244: do {
6245: j[p] += PETSC_MAX_INT; /* Revert the modified diagonal indices */
6246: p++;
6247: } while (p < mid && j[p] == col);
6248: Annz++;
6249: }
6251: /* Count unique nonzeros of this offdiag row */
6252: for (p = mid; p < s;) {
6253: col = j[p];
6254: do {
6255: p++;
6256: } while (p < s && j[p] == col);
6257: Bnnz++;
6258: }
6259: k = s;
6260: }
6262: /* Allocation according to Atot, Btot, Annz, Bnnz */
6263: PetscCall(PetscMalloc1(Atot, &Aperm));
6264: PetscCall(PetscMalloc1(Btot, &Bperm));
6265: PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6266: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));
6268: /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6269: Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6270: for (r = 0; r < m; r++) {
6271: k = rowBegin[r];
6272: mid = rowMid[r];
6273: s = rowEnd[r];
6274: PetscCall(PetscArraycpy(Aperm + Atot, perm + k, mid - k));
6275: PetscCall(PetscArraycpy(Bperm + Btot, perm + mid, s - mid));
6276: Atot += mid - k;
6277: Btot += s - mid;
6279: /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6280: for (p = k; p < mid;) {
6281: col = j[p];
6282: q = p;
6283: do {
6284: p++;
6285: } while (p < mid && j[p] == col);
6286: Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6287: Annz++;
6288: }
6290: for (p = mid; p < s;) {
6291: col = j[p];
6292: q = p;
6293: do {
6294: p++;
6295: } while (p < s && j[p] == col);
6296: Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6297: Bnnz++;
6298: }
6299: }
6300: /* Output */
6301: *Aperm_ = Aperm;
6302: *Annz_ = Annz;
6303: *Atot_ = Atot;
6304: *Ajmap_ = Ajmap;
6305: *Bperm_ = Bperm;
6306: *Bnnz_ = Bnnz;
6307: *Btot_ = Btot;
6308: *Bjmap_ = Bjmap;
6309: PetscFunctionReturn(PETSC_SUCCESS);
6310: }
6312: /*
6313: Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix
6315: Input Parameters:
6316: nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6317: nnz: number of unique nonzeros in the merged matrix
6318: imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6319: jmap[nnz1+1]: i-th nonzero in the set has jmap[i+1] - jmap[i] repeats in the set
6321: Output Parameter: (memory is allocated by the caller)
6322: jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set
6324: Example:
6325: nnz1 = 4
6326: nnz = 6
6327: imap = [1,3,4,5]
6328: jmap = [0,3,5,6,7]
6329: then,
6330: jmap_new = [0,0,3,3,5,6,7]
6331: */
6332: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6333: {
6334: PetscCount k, p;
6336: PetscFunctionBegin;
6337: jmap_new[0] = 0;
6338: p = nnz; /* p loops over jmap_new[] backwards */
6339: for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6340: for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6341: }
6342: for (; p >= 0; p--) jmap_new[p] = jmap[0];
6343: PetscFunctionReturn(PETSC_SUCCESS);
6344: }
6346: static PetscErrorCode MatCOOStructDestroy_MPIAIJ(void *data)
6347: {
6348: MatCOOStruct_MPIAIJ *coo = (MatCOOStruct_MPIAIJ *)data;
6350: PetscFunctionBegin;
6351: PetscCall(PetscSFDestroy(&coo->sf));
6352: PetscCall(PetscFree(coo->Aperm1));
6353: PetscCall(PetscFree(coo->Bperm1));
6354: PetscCall(PetscFree(coo->Ajmap1));
6355: PetscCall(PetscFree(coo->Bjmap1));
6356: PetscCall(PetscFree(coo->Aimap2));
6357: PetscCall(PetscFree(coo->Bimap2));
6358: PetscCall(PetscFree(coo->Aperm2));
6359: PetscCall(PetscFree(coo->Bperm2));
6360: PetscCall(PetscFree(coo->Ajmap2));
6361: PetscCall(PetscFree(coo->Bjmap2));
6362: PetscCall(PetscFree(coo->Cperm1));
6363: PetscCall(PetscFree2(coo->sendbuf, coo->recvbuf));
6364: PetscCall(PetscFree(coo));
6365: PetscFunctionReturn(PETSC_SUCCESS);
6366: }
6368: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6369: {
6370: MPI_Comm comm;
6371: PetscMPIInt rank, size;
6372: PetscInt m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6373: PetscCount k, p, q, rem; /* Loop variables over coo arrays */
6374: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6375: PetscContainer container;
6376: MatCOOStruct_MPIAIJ *coo;
6378: PetscFunctionBegin;
6379: PetscCall(PetscFree(mpiaij->garray));
6380: PetscCall(VecDestroy(&mpiaij->lvec));
6381: #if defined(PETSC_USE_CTABLE)
6382: PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6383: #else
6384: PetscCall(PetscFree(mpiaij->colmap));
6385: #endif
6386: PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6387: mat->assembled = PETSC_FALSE;
6388: mat->was_assembled = PETSC_FALSE;
6390: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6391: PetscCallMPI(MPI_Comm_size(comm, &size));
6392: PetscCallMPI(MPI_Comm_rank(comm, &rank));
6393: PetscCall(PetscLayoutSetUp(mat->rmap));
6394: PetscCall(PetscLayoutSetUp(mat->cmap));
6395: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6396: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6397: PetscCall(MatGetLocalSize(mat, &m, &n));
6398: PetscCall(MatGetSize(mat, &M, &N));
6400: /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6401: /* entries come first, then local rows, then remote rows. */
6402: PetscCount n1 = coo_n, *perm1;
6403: PetscInt *i1 = coo_i, *j1 = coo_j;
6405: PetscCall(PetscMalloc1(n1, &perm1));
6406: for (k = 0; k < n1; k++) perm1[k] = k;
6408: /* Manipulate indices so that entries with negative row or col indices will have smallest
6409: row indices, local entries will have greater but negative row indices, and remote entries
6410: will have positive row indices.
6411: */
6412: for (k = 0; k < n1; k++) {
6413: if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_MIN_INT; /* e.g., -2^31, minimal to move them ahead */
6414: 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] */
6415: else {
6416: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6417: if (mpiaij->donotstash) i1[k] = PETSC_MIN_INT; /* Ignore offproc entries as if they had negative indices */
6418: }
6419: }
6421: /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6422: PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));
6424: /* Advance k to the first entry we need to take care of */
6425: for (k = 0; k < n1; k++)
6426: if (i1[k] > PETSC_MIN_INT) break;
6427: PetscInt i1start = k;
6429: PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_MAX_INT, &rem)); /* rem is upper bound of the last local row */
6430: for (; k < rem; k++) i1[k] += PETSC_MAX_INT; /* Revert row indices of local rows*/
6432: /* Send remote rows to their owner */
6433: /* Find which rows should be sent to which remote ranks*/
6434: PetscInt nsend = 0; /* Number of MPI ranks to send data to */
6435: PetscMPIInt *sendto; /* [nsend], storing remote ranks */
6436: PetscInt *nentries; /* [nsend], storing number of entries sent to remote ranks; Assume PetscInt is big enough for this count, and error if not */
6437: const PetscInt *ranges;
6438: PetscInt maxNsend = size >= 128 ? 128 : size; /* Assume max 128 neighbors; realloc when needed */
6440: PetscCall(PetscLayoutGetRanges(mat->rmap, &ranges));
6441: PetscCall(PetscMalloc2(maxNsend, &sendto, maxNsend, &nentries));
6442: for (k = rem; k < n1;) {
6443: PetscMPIInt owner;
6444: PetscInt firstRow, lastRow;
6446: /* Locate a row range */
6447: firstRow = i1[k]; /* first row of this owner */
6448: PetscCall(PetscLayoutFindOwner(mat->rmap, firstRow, &owner));
6449: lastRow = ranges[owner + 1] - 1; /* last row of this owner */
6451: /* Find the first index 'p' in [k,n) with i[p] belonging to next owner */
6452: PetscCall(PetscSortedIntUpperBound(i1, k, n1, lastRow, &p));
6454: /* All entries in [k,p) belong to this remote owner */
6455: if (nsend >= maxNsend) { /* Double the remote ranks arrays if not long enough */
6456: PetscMPIInt *sendto2;
6457: PetscInt *nentries2;
6458: PetscInt maxNsend2 = (maxNsend <= size / 2) ? maxNsend * 2 : size;
6460: PetscCall(PetscMalloc2(maxNsend2, &sendto2, maxNsend2, &nentries2));
6461: PetscCall(PetscArraycpy(sendto2, sendto, maxNsend));
6462: PetscCall(PetscArraycpy(nentries2, nentries2, maxNsend + 1));
6463: PetscCall(PetscFree2(sendto, nentries2));
6464: sendto = sendto2;
6465: nentries = nentries2;
6466: maxNsend = maxNsend2;
6467: }
6468: sendto[nsend] = owner;
6469: nentries[nsend] = p - k;
6470: PetscCall(PetscCountCast(p - k, &nentries[nsend]));
6471: nsend++;
6472: k = p;
6473: }
6475: /* Build 1st SF to know offsets on remote to send data */
6476: PetscSF sf1;
6477: PetscInt nroots = 1, nroots2 = 0;
6478: PetscInt nleaves = nsend, nleaves2 = 0;
6479: PetscInt *offsets;
6480: PetscSFNode *iremote;
6482: PetscCall(PetscSFCreate(comm, &sf1));
6483: PetscCall(PetscMalloc1(nsend, &iremote));
6484: PetscCall(PetscMalloc1(nsend, &offsets));
6485: for (k = 0; k < nsend; k++) {
6486: iremote[k].rank = sendto[k];
6487: iremote[k].index = 0;
6488: nleaves2 += nentries[k];
6489: PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6490: }
6491: PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6492: PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6493: PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6494: PetscCall(PetscSFDestroy(&sf1));
6495: PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT "", nleaves2, n1 - rem);
6497: /* Build 2nd SF to send remote COOs to their owner */
6498: PetscSF sf2;
6499: nroots = nroots2;
6500: nleaves = nleaves2;
6501: PetscCall(PetscSFCreate(comm, &sf2));
6502: PetscCall(PetscSFSetFromOptions(sf2));
6503: PetscCall(PetscMalloc1(nleaves, &iremote));
6504: p = 0;
6505: for (k = 0; k < nsend; k++) {
6506: PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6507: for (q = 0; q < nentries[k]; q++, p++) {
6508: iremote[p].rank = sendto[k];
6509: iremote[p].index = offsets[k] + q;
6510: }
6511: }
6512: PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6514: /* Send the remote COOs to their owner */
6515: PetscInt n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6516: PetscCount *perm2; /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6517: PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6518: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1 + rem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6519: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1 + rem, i2, MPI_REPLACE));
6520: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1 + rem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6521: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1 + rem, j2, MPI_REPLACE));
6523: PetscCall(PetscFree(offsets));
6524: PetscCall(PetscFree2(sendto, nentries));
6526: /* Sort received COOs by row along with the permutation array */
6527: for (k = 0; k < n2; k++) perm2[k] = k;
6528: PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));
6530: /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6531: PetscCount *Cperm1;
6532: PetscCall(PetscMalloc1(nleaves, &Cperm1));
6533: PetscCall(PetscArraycpy(Cperm1, perm1 + rem, nleaves));
6535: /* Support for HYPRE matrices, kind of a hack.
6536: Swap min column with diagonal so that diagonal values will go first */
6537: PetscBool hypre;
6538: const char *name;
6539: PetscCall(PetscObjectGetName((PetscObject)mat, &name));
6540: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", name, &hypre));
6541: if (hypre) {
6542: PetscInt *minj;
6543: PetscBT hasdiag;
6545: PetscCall(PetscBTCreate(m, &hasdiag));
6546: PetscCall(PetscMalloc1(m, &minj));
6547: for (k = 0; k < m; k++) minj[k] = PETSC_MAX_INT;
6548: for (k = i1start; k < rem; k++) {
6549: if (j1[k] < cstart || j1[k] >= cend) continue;
6550: const PetscInt rindex = i1[k] - rstart;
6551: if ((j1[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6552: minj[rindex] = PetscMin(minj[rindex], j1[k]);
6553: }
6554: for (k = 0; k < n2; k++) {
6555: if (j2[k] < cstart || j2[k] >= cend) continue;
6556: const PetscInt rindex = i2[k] - rstart;
6557: if ((j2[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6558: minj[rindex] = PetscMin(minj[rindex], j2[k]);
6559: }
6560: for (k = i1start; k < rem; k++) {
6561: const PetscInt rindex = i1[k] - rstart;
6562: if (j1[k] < cstart || j1[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6563: if (j1[k] == minj[rindex]) j1[k] = i1[k] + (cstart - rstart);
6564: else if ((j1[k] - cstart) == rindex) j1[k] = minj[rindex];
6565: }
6566: for (k = 0; k < n2; k++) {
6567: const PetscInt rindex = i2[k] - rstart;
6568: if (j2[k] < cstart || j2[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6569: if (j2[k] == minj[rindex]) j2[k] = i2[k] + (cstart - rstart);
6570: else if ((j2[k] - cstart) == rindex) j2[k] = minj[rindex];
6571: }
6572: PetscCall(PetscBTDestroy(&hasdiag));
6573: PetscCall(PetscFree(minj));
6574: }
6576: /* Split local COOs and received COOs into diag/offdiag portions */
6577: PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6578: PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1;
6579: PetscCount Annz1, Bnnz1, Atot1, Btot1;
6580: PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6581: PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6582: PetscCount Annz2, Bnnz2, Atot2, Btot2;
6584: PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6585: PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6586: PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6587: PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));
6589: /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6590: PetscInt *Ai, *Bi;
6591: PetscInt *Aj, *Bj;
6593: PetscCall(PetscMalloc1(m + 1, &Ai));
6594: PetscCall(PetscMalloc1(m + 1, &Bi));
6595: PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6596: PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));
6598: PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6599: PetscCall(PetscMalloc1(Annz1, &Aimap1));
6600: PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6601: PetscCall(PetscMalloc1(Annz2, &Aimap2));
6602: PetscCall(PetscMalloc1(Bnnz2, &Bimap2));
6604: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6605: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));
6607: /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we */
6608: /* expect nonzeros in A/B most likely have local contributing entries */
6609: PetscInt Annz = Ai[m];
6610: PetscInt Bnnz = Bi[m];
6611: PetscCount *Ajmap1_new, *Bjmap1_new;
6613: PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6614: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));
6616: PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6617: PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));
6619: PetscCall(PetscFree(Aimap1));
6620: PetscCall(PetscFree(Ajmap1));
6621: PetscCall(PetscFree(Bimap1));
6622: PetscCall(PetscFree(Bjmap1));
6623: PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6624: PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6625: PetscCall(PetscFree(perm1));
6626: PetscCall(PetscFree3(i2, j2, perm2));
6628: Ajmap1 = Ajmap1_new;
6629: Bjmap1 = Bjmap1_new;
6631: /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6632: if (Annz < Annz1 + Annz2) {
6633: PetscInt *Aj_new;
6634: PetscCall(PetscMalloc1(Annz, &Aj_new));
6635: PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6636: PetscCall(PetscFree(Aj));
6637: Aj = Aj_new;
6638: }
6640: if (Bnnz < Bnnz1 + Bnnz2) {
6641: PetscInt *Bj_new;
6642: PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6643: PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6644: PetscCall(PetscFree(Bj));
6645: Bj = Bj_new;
6646: }
6648: /* Create new submatrices for on-process and off-process coupling */
6649: PetscScalar *Aa, *Ba;
6650: MatType rtype;
6651: Mat_SeqAIJ *a, *b;
6652: PetscObjectState state;
6653: PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6654: PetscCall(PetscCalloc1(Bnnz, &Ba));
6655: /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6656: if (cstart) {
6657: for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6658: }
6659: PetscCall(MatDestroy(&mpiaij->A));
6660: PetscCall(MatDestroy(&mpiaij->B));
6661: PetscCall(MatGetRootType_Private(mat, &rtype));
6662: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6663: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6664: PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6665: mat->was_assembled = PETSC_TRUE; // was_assembled in effect means the Mvctx is built; doing so avoids redundant MatSetUpMultiply_MPIAIJ
6666: state = mpiaij->A->nonzerostate + mpiaij->B->nonzerostate;
6667: PetscCall(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
6669: a = (Mat_SeqAIJ *)mpiaij->A->data;
6670: b = (Mat_SeqAIJ *)mpiaij->B->data;
6671: a->singlemalloc = b->singlemalloc = PETSC_FALSE; /* Let newmat own Ai,Aj,Aa,Bi,Bj,Ba */
6672: a->free_a = b->free_a = PETSC_TRUE;
6673: a->free_ij = b->free_ij = PETSC_TRUE;
6675: /* conversion must happen AFTER multiply setup */
6676: PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6677: PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6678: PetscCall(VecDestroy(&mpiaij->lvec));
6679: PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));
6681: // Put the COO struct in a container and then attach that to the matrix
6682: PetscCall(PetscMalloc1(1, &coo));
6683: coo->n = coo_n;
6684: coo->sf = sf2;
6685: coo->sendlen = nleaves;
6686: coo->recvlen = nroots;
6687: coo->Annz = Annz;
6688: coo->Bnnz = Bnnz;
6689: coo->Annz2 = Annz2;
6690: coo->Bnnz2 = Bnnz2;
6691: coo->Atot1 = Atot1;
6692: coo->Atot2 = Atot2;
6693: coo->Btot1 = Btot1;
6694: coo->Btot2 = Btot2;
6695: coo->Ajmap1 = Ajmap1;
6696: coo->Aperm1 = Aperm1;
6697: coo->Bjmap1 = Bjmap1;
6698: coo->Bperm1 = Bperm1;
6699: coo->Aimap2 = Aimap2;
6700: coo->Ajmap2 = Ajmap2;
6701: coo->Aperm2 = Aperm2;
6702: coo->Bimap2 = Bimap2;
6703: coo->Bjmap2 = Bjmap2;
6704: coo->Bperm2 = Bperm2;
6705: coo->Cperm1 = Cperm1;
6706: // Allocate in preallocation. If not used, it has zero cost on host
6707: PetscCall(PetscMalloc2(coo->sendlen, &coo->sendbuf, coo->recvlen, &coo->recvbuf));
6708: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
6709: PetscCall(PetscContainerSetPointer(container, coo));
6710: PetscCall(PetscContainerSetUserDestroy(container, MatCOOStructDestroy_MPIAIJ));
6711: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
6712: PetscCall(PetscContainerDestroy(&container));
6713: PetscFunctionReturn(PETSC_SUCCESS);
6714: }
6716: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6717: {
6718: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6719: Mat A = mpiaij->A, B = mpiaij->B;
6720: PetscScalar *Aa, *Ba;
6721: PetscScalar *sendbuf, *recvbuf;
6722: const PetscCount *Ajmap1, *Ajmap2, *Aimap2;
6723: const PetscCount *Bjmap1, *Bjmap2, *Bimap2;
6724: const PetscCount *Aperm1, *Aperm2, *Bperm1, *Bperm2;
6725: const PetscCount *Cperm1;
6726: PetscContainer container;
6727: MatCOOStruct_MPIAIJ *coo;
6729: PetscFunctionBegin;
6730: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
6731: PetscCheck(container, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
6732: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
6733: sendbuf = coo->sendbuf;
6734: recvbuf = coo->recvbuf;
6735: Ajmap1 = coo->Ajmap1;
6736: Ajmap2 = coo->Ajmap2;
6737: Aimap2 = coo->Aimap2;
6738: Bjmap1 = coo->Bjmap1;
6739: Bjmap2 = coo->Bjmap2;
6740: Bimap2 = coo->Bimap2;
6741: Aperm1 = coo->Aperm1;
6742: Aperm2 = coo->Aperm2;
6743: Bperm1 = coo->Bperm1;
6744: Bperm2 = coo->Bperm2;
6745: Cperm1 = coo->Cperm1;
6747: PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6748: PetscCall(MatSeqAIJGetArray(B, &Ba));
6750: /* Pack entries to be sent to remote */
6751: for (PetscCount i = 0; i < coo->sendlen; i++) sendbuf[i] = v[Cperm1[i]];
6753: /* Send remote entries to their owner and overlap the communication with local computation */
6754: PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6755: /* Add local entries to A and B */
6756: for (PetscCount i = 0; i < coo->Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6757: PetscScalar sum = 0.0; /* Do partial summation first to improve numerical stability */
6758: for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6759: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6760: }
6761: for (PetscCount i = 0; i < coo->Bnnz; i++) {
6762: PetscScalar sum = 0.0;
6763: for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6764: Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6765: }
6766: PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));
6768: /* Add received remote entries to A and B */
6769: for (PetscCount i = 0; i < coo->Annz2; i++) {
6770: for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6771: }
6772: for (PetscCount i = 0; i < coo->Bnnz2; i++) {
6773: for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6774: }
6775: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6776: PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6777: PetscFunctionReturn(PETSC_SUCCESS);
6778: }
6780: /*MC
6781: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
6783: Options Database Keys:
6784: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`
6786: Level: beginner
6788: Notes:
6789: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
6790: in this case the values associated with the rows and columns one passes in are set to zero
6791: in the matrix
6793: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
6794: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
6796: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6797: M*/
6798: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6799: {
6800: Mat_MPIAIJ *b;
6801: PetscMPIInt size;
6803: PetscFunctionBegin;
6804: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
6806: PetscCall(PetscNew(&b));
6807: B->data = (void *)b;
6808: B->ops[0] = MatOps_Values;
6809: B->assembled = PETSC_FALSE;
6810: B->insertmode = NOT_SET_VALUES;
6811: b->size = size;
6813: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
6815: /* build cache for off array entries formed */
6816: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
6818: b->donotstash = PETSC_FALSE;
6819: b->colmap = NULL;
6820: b->garray = NULL;
6821: b->roworiented = PETSC_TRUE;
6823: /* stuff used for matrix vector multiply */
6824: b->lvec = NULL;
6825: b->Mvctx = NULL;
6827: /* stuff for MatGetRow() */
6828: b->rowindices = NULL;
6829: b->rowvalues = NULL;
6830: b->getrowactive = PETSC_FALSE;
6832: /* flexible pointer used in CUSPARSE classes */
6833: b->spptr = NULL;
6835: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6836: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6837: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6838: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6839: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6840: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6841: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6842: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6843: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6844: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6845: #if defined(PETSC_HAVE_CUDA)
6846: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6847: #endif
6848: #if defined(PETSC_HAVE_HIP)
6849: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6850: #endif
6851: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6852: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6853: #endif
6854: #if defined(PETSC_HAVE_MKL_SPARSE)
6855: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6856: #endif
6857: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6858: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6859: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6860: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6861: #if defined(PETSC_HAVE_ELEMENTAL)
6862: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6863: #endif
6864: #if defined(PETSC_HAVE_SCALAPACK)
6865: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6866: #endif
6867: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6868: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6869: #if defined(PETSC_HAVE_HYPRE)
6870: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6871: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6872: #endif
6873: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6874: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6875: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6876: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6877: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6878: PetscFunctionReturn(PETSC_SUCCESS);
6879: }
6881: /*@C
6882: MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6883: and "off-diagonal" part of the matrix in CSR format.
6885: Collective
6887: Input Parameters:
6888: + comm - MPI communicator
6889: . m - number of local rows (Cannot be `PETSC_DECIDE`)
6890: . n - This value should be the same as the local size used in creating the
6891: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
6892: calculated if `N` is given) For square matrices `n` is almost always `m`.
6893: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6894: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6895: . 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
6896: . j - column indices, which must be local, i.e., based off the start column of the diagonal portion
6897: . a - matrix values
6898: . 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
6899: . oj - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6900: - oa - matrix values
6902: Output Parameter:
6903: . mat - the matrix
6905: Level: advanced
6907: Notes:
6908: The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc. The user
6909: must free the arrays once the matrix has been destroyed and not before.
6911: The `i` and `j` indices are 0 based
6913: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix
6915: This sets local rows and cannot be used to set off-processor values.
6917: Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6918: legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6919: not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
6920: the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
6921: keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
6922: communication if it is known that only local entries will be set.
6924: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
6925: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
6926: @*/
6927: 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)
6928: {
6929: Mat_MPIAIJ *maij;
6931: PetscFunctionBegin;
6932: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
6933: PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
6934: PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
6935: PetscCall(MatCreate(comm, mat));
6936: PetscCall(MatSetSizes(*mat, m, n, M, N));
6937: PetscCall(MatSetType(*mat, MATMPIAIJ));
6938: maij = (Mat_MPIAIJ *)(*mat)->data;
6940: (*mat)->preallocated = PETSC_TRUE;
6942: PetscCall(PetscLayoutSetUp((*mat)->rmap));
6943: PetscCall(PetscLayoutSetUp((*mat)->cmap));
6945: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
6946: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));
6948: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
6949: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
6950: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
6951: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
6952: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
6953: PetscFunctionReturn(PETSC_SUCCESS);
6954: }
6956: typedef struct {
6957: Mat *mp; /* intermediate products */
6958: PetscBool *mptmp; /* is the intermediate product temporary ? */
6959: PetscInt cp; /* number of intermediate products */
6961: /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
6962: PetscInt *startsj_s, *startsj_r;
6963: PetscScalar *bufa;
6964: Mat P_oth;
6966: /* may take advantage of merging product->B */
6967: Mat Bloc; /* B-local by merging diag and off-diag */
6969: /* cusparse does not have support to split between symbolic and numeric phases.
6970: When api_user is true, we don't need to update the numerical values
6971: of the temporary storage */
6972: PetscBool reusesym;
6974: /* support for COO values insertion */
6975: PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
6976: PetscInt **own; /* own[i] points to address of on-process COO indices for Mat mp[i] */
6977: PetscInt **off; /* off[i] points to address of off-process COO indices for Mat mp[i] */
6978: PetscBool hasoffproc; /* if true, have off-process values insertion (i.e. AtB or PtAP) */
6979: PetscSF sf; /* used for non-local values insertion and memory malloc */
6980: PetscMemType mtype;
6982: /* customization */
6983: PetscBool abmerge;
6984: PetscBool P_oth_bind;
6985: } MatMatMPIAIJBACKEND;
6987: static PetscErrorCode MatDestroy_MatMatMPIAIJBACKEND(void *data)
6988: {
6989: MatMatMPIAIJBACKEND *mmdata = (MatMatMPIAIJBACKEND *)data;
6990: PetscInt i;
6992: PetscFunctionBegin;
6993: PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
6994: PetscCall(PetscFree(mmdata->bufa));
6995: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
6996: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
6997: PetscCall(MatDestroy(&mmdata->P_oth));
6998: PetscCall(MatDestroy(&mmdata->Bloc));
6999: PetscCall(PetscSFDestroy(&mmdata->sf));
7000: for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
7001: PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
7002: PetscCall(PetscFree(mmdata->own[0]));
7003: PetscCall(PetscFree(mmdata->own));
7004: PetscCall(PetscFree(mmdata->off[0]));
7005: PetscCall(PetscFree(mmdata->off));
7006: PetscCall(PetscFree(mmdata));
7007: PetscFunctionReturn(PETSC_SUCCESS);
7008: }
7010: /* Copy selected n entries with indices in idx[] of A to v[].
7011: If idx is NULL, copy the whole data array of A to v[]
7012: */
7013: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
7014: {
7015: PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);
7017: PetscFunctionBegin;
7018: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
7019: if (f) {
7020: PetscCall((*f)(A, n, idx, v));
7021: } else {
7022: const PetscScalar *vv;
7024: PetscCall(MatSeqAIJGetArrayRead(A, &vv));
7025: if (n && idx) {
7026: PetscScalar *w = v;
7027: const PetscInt *oi = idx;
7028: PetscInt j;
7030: for (j = 0; j < n; j++) *w++ = vv[*oi++];
7031: } else {
7032: PetscCall(PetscArraycpy(v, vv, n));
7033: }
7034: PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7035: }
7036: PetscFunctionReturn(PETSC_SUCCESS);
7037: }
7039: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7040: {
7041: MatMatMPIAIJBACKEND *mmdata;
7042: PetscInt i, n_d, n_o;
7044: PetscFunctionBegin;
7045: MatCheckProduct(C, 1);
7046: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7047: mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7048: if (!mmdata->reusesym) { /* update temporary matrices */
7049: 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));
7050: if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7051: }
7052: mmdata->reusesym = PETSC_FALSE;
7054: for (i = 0; i < mmdata->cp; i++) {
7055: 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]);
7056: PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7057: }
7058: for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7059: PetscInt noff = mmdata->off[i + 1] - mmdata->off[i];
7061: if (mmdata->mptmp[i]) continue;
7062: if (noff) {
7063: PetscInt nown = mmdata->own[i + 1] - mmdata->own[i];
7065: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7066: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7067: n_o += noff;
7068: n_d += nown;
7069: } else {
7070: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;
7072: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7073: n_d += mm->nz;
7074: }
7075: }
7076: if (mmdata->hasoffproc) { /* offprocess insertion */
7077: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7078: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7079: }
7080: PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7081: PetscFunctionReturn(PETSC_SUCCESS);
7082: }
7084: /* Support for Pt * A, A * P, or Pt * A * P */
7085: #define MAX_NUMBER_INTERMEDIATE 4
7086: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7087: {
7088: Mat_Product *product = C->product;
7089: Mat A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7090: Mat_MPIAIJ *a, *p;
7091: MatMatMPIAIJBACKEND *mmdata;
7092: ISLocalToGlobalMapping P_oth_l2g = NULL;
7093: IS glob = NULL;
7094: const char *prefix;
7095: char pprefix[256];
7096: const PetscInt *globidx, *P_oth_idx;
7097: PetscInt i, j, cp, m, n, M, N, *coo_i, *coo_j;
7098: PetscCount ncoo, ncoo_d, ncoo_o, ncoo_oown;
7099: PetscInt cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7100: /* type-0: consecutive, start from 0; type-1: consecutive with */
7101: /* a base offset; type-2: sparse with a local to global map table */
7102: const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE]; /* col/row local to global map array (table) for type-2 map type */
7104: MatProductType ptype;
7105: PetscBool mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7106: PetscMPIInt size;
7108: PetscFunctionBegin;
7109: MatCheckProduct(C, 1);
7110: PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7111: ptype = product->type;
7112: if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7113: ptype = MATPRODUCT_AB;
7114: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7115: }
7116: switch (ptype) {
7117: case MATPRODUCT_AB:
7118: A = product->A;
7119: P = product->B;
7120: m = A->rmap->n;
7121: n = P->cmap->n;
7122: M = A->rmap->N;
7123: N = P->cmap->N;
7124: hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7125: break;
7126: case MATPRODUCT_AtB:
7127: P = product->A;
7128: A = product->B;
7129: m = P->cmap->n;
7130: n = A->cmap->n;
7131: M = P->cmap->N;
7132: N = A->cmap->N;
7133: hasoffproc = PETSC_TRUE;
7134: break;
7135: case MATPRODUCT_PtAP:
7136: A = product->A;
7137: P = product->B;
7138: m = P->cmap->n;
7139: n = P->cmap->n;
7140: M = P->cmap->N;
7141: N = P->cmap->N;
7142: hasoffproc = PETSC_TRUE;
7143: break;
7144: default:
7145: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7146: }
7147: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7148: if (size == 1) hasoffproc = PETSC_FALSE;
7150: /* defaults */
7151: for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7152: mp[i] = NULL;
7153: mptmp[i] = PETSC_FALSE;
7154: rmapt[i] = -1;
7155: cmapt[i] = -1;
7156: rmapa[i] = NULL;
7157: cmapa[i] = NULL;
7158: }
7160: /* customization */
7161: PetscCall(PetscNew(&mmdata));
7162: mmdata->reusesym = product->api_user;
7163: if (ptype == MATPRODUCT_AB) {
7164: if (product->api_user) {
7165: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7166: PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7167: PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7168: PetscOptionsEnd();
7169: } else {
7170: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7171: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7172: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7173: PetscOptionsEnd();
7174: }
7175: } else if (ptype == MATPRODUCT_PtAP) {
7176: if (product->api_user) {
7177: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7178: PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7179: PetscOptionsEnd();
7180: } else {
7181: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7182: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7183: PetscOptionsEnd();
7184: }
7185: }
7186: a = (Mat_MPIAIJ *)A->data;
7187: p = (Mat_MPIAIJ *)P->data;
7188: PetscCall(MatSetSizes(C, m, n, M, N));
7189: PetscCall(PetscLayoutSetUp(C->rmap));
7190: PetscCall(PetscLayoutSetUp(C->cmap));
7191: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7192: PetscCall(MatGetOptionsPrefix(C, &prefix));
7194: cp = 0;
7195: switch (ptype) {
7196: case MATPRODUCT_AB: /* A * P */
7197: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7199: /* A_diag * P_local (merged or not) */
7200: if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7201: /* P is product->B */
7202: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7203: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7204: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7205: PetscCall(MatProductSetFill(mp[cp], product->fill));
7206: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7207: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7208: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7209: mp[cp]->product->api_user = product->api_user;
7210: PetscCall(MatProductSetFromOptions(mp[cp]));
7211: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7212: PetscCall(ISGetIndices(glob, &globidx));
7213: rmapt[cp] = 1;
7214: cmapt[cp] = 2;
7215: cmapa[cp] = globidx;
7216: mptmp[cp] = PETSC_FALSE;
7217: cp++;
7218: } else { /* A_diag * P_diag and A_diag * P_off */
7219: PetscCall(MatProductCreate(a->A, p->A, NULL, &mp[cp]));
7220: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7221: PetscCall(MatProductSetFill(mp[cp], product->fill));
7222: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7223: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7224: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7225: mp[cp]->product->api_user = product->api_user;
7226: PetscCall(MatProductSetFromOptions(mp[cp]));
7227: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7228: rmapt[cp] = 1;
7229: cmapt[cp] = 1;
7230: mptmp[cp] = PETSC_FALSE;
7231: cp++;
7232: PetscCall(MatProductCreate(a->A, p->B, NULL, &mp[cp]));
7233: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7234: PetscCall(MatProductSetFill(mp[cp], product->fill));
7235: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7236: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7237: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7238: mp[cp]->product->api_user = product->api_user;
7239: PetscCall(MatProductSetFromOptions(mp[cp]));
7240: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7241: rmapt[cp] = 1;
7242: cmapt[cp] = 2;
7243: cmapa[cp] = p->garray;
7244: mptmp[cp] = PETSC_FALSE;
7245: cp++;
7246: }
7248: /* A_off * P_other */
7249: if (mmdata->P_oth) {
7250: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7251: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7252: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)(a->B))->type_name));
7253: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7254: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7255: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7256: PetscCall(MatProductSetFill(mp[cp], product->fill));
7257: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7258: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7259: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7260: mp[cp]->product->api_user = product->api_user;
7261: PetscCall(MatProductSetFromOptions(mp[cp]));
7262: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7263: rmapt[cp] = 1;
7264: cmapt[cp] = 2;
7265: cmapa[cp] = P_oth_idx;
7266: mptmp[cp] = PETSC_FALSE;
7267: cp++;
7268: }
7269: break;
7271: case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7272: /* A is product->B */
7273: PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7274: if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7275: PetscCall(MatProductCreate(mmdata->Bloc, 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: PetscCall(ISGetIndices(glob, &globidx));
7285: rmapt[cp] = 2;
7286: rmapa[cp] = globidx;
7287: cmapt[cp] = 2;
7288: cmapa[cp] = globidx;
7289: mptmp[cp] = PETSC_FALSE;
7290: cp++;
7291: } else {
7292: PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7293: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7294: PetscCall(MatProductSetFill(mp[cp], product->fill));
7295: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7296: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7297: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7298: mp[cp]->product->api_user = product->api_user;
7299: PetscCall(MatProductSetFromOptions(mp[cp]));
7300: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7301: PetscCall(ISGetIndices(glob, &globidx));
7302: rmapt[cp] = 1;
7303: cmapt[cp] = 2;
7304: cmapa[cp] = globidx;
7305: mptmp[cp] = PETSC_FALSE;
7306: cp++;
7307: PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7308: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7309: PetscCall(MatProductSetFill(mp[cp], product->fill));
7310: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7311: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7312: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7313: mp[cp]->product->api_user = product->api_user;
7314: PetscCall(MatProductSetFromOptions(mp[cp]));
7315: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7316: rmapt[cp] = 2;
7317: rmapa[cp] = p->garray;
7318: cmapt[cp] = 2;
7319: cmapa[cp] = globidx;
7320: mptmp[cp] = PETSC_FALSE;
7321: cp++;
7322: }
7323: break;
7324: case MATPRODUCT_PtAP:
7325: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7326: /* P is product->B */
7327: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7328: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7329: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
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: PetscCall(ISGetIndices(glob, &globidx));
7338: rmapt[cp] = 2;
7339: rmapa[cp] = globidx;
7340: cmapt[cp] = 2;
7341: cmapa[cp] = globidx;
7342: mptmp[cp] = PETSC_FALSE;
7343: cp++;
7344: if (mmdata->P_oth) {
7345: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7346: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7347: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)(a->B))->type_name));
7348: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7349: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7350: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7351: PetscCall(MatProductSetFill(mp[cp], product->fill));
7352: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7353: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7354: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7355: mp[cp]->product->api_user = product->api_user;
7356: PetscCall(MatProductSetFromOptions(mp[cp]));
7357: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7358: mptmp[cp] = PETSC_TRUE;
7359: cp++;
7360: PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7361: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7362: PetscCall(MatProductSetFill(mp[cp], product->fill));
7363: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7364: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7365: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7366: mp[cp]->product->api_user = product->api_user;
7367: PetscCall(MatProductSetFromOptions(mp[cp]));
7368: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7369: rmapt[cp] = 2;
7370: rmapa[cp] = globidx;
7371: cmapt[cp] = 2;
7372: cmapa[cp] = P_oth_idx;
7373: mptmp[cp] = PETSC_FALSE;
7374: cp++;
7375: }
7376: break;
7377: default:
7378: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7379: }
7380: /* sanity check */
7381: if (size > 1)
7382: 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);
7384: PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7385: for (i = 0; i < cp; i++) {
7386: mmdata->mp[i] = mp[i];
7387: mmdata->mptmp[i] = mptmp[i];
7388: }
7389: mmdata->cp = cp;
7390: C->product->data = mmdata;
7391: C->product->destroy = MatDestroy_MatMatMPIAIJBACKEND;
7392: C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;
7394: /* memory type */
7395: mmdata->mtype = PETSC_MEMTYPE_HOST;
7396: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7397: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7398: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7399: if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7400: else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7401: else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;
7403: /* prepare coo coordinates for values insertion */
7405: /* count total nonzeros of those intermediate seqaij Mats
7406: ncoo_d: # of nonzeros of matrices that do not have offproc entries
7407: ncoo_o: # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7408: ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7409: */
7410: for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7411: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7412: if (mptmp[cp]) continue;
7413: if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7414: const PetscInt *rmap = rmapa[cp];
7415: const PetscInt mr = mp[cp]->rmap->n;
7416: const PetscInt rs = C->rmap->rstart;
7417: const PetscInt re = C->rmap->rend;
7418: const PetscInt *ii = mm->i;
7419: for (i = 0; i < mr; i++) {
7420: const PetscInt gr = rmap[i];
7421: const PetscInt nz = ii[i + 1] - ii[i];
7422: if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7423: else ncoo_oown += nz; /* this row is local */
7424: }
7425: } else ncoo_d += mm->nz;
7426: }
7428: /*
7429: ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc
7431: ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.
7433: off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].
7435: off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7436: own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7437: so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.
7439: coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7440: 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.
7441: */
7442: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7443: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));
7445: /* gather (i,j) of nonzeros inserted by remote procs */
7446: if (hasoffproc) {
7447: PetscSF msf;
7448: PetscInt ncoo2, *coo_i2, *coo_j2;
7450: PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7451: PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7452: PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */
7454: for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7455: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7456: PetscInt *idxoff = mmdata->off[cp];
7457: PetscInt *idxown = mmdata->own[cp];
7458: if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7459: const PetscInt *rmap = rmapa[cp];
7460: const PetscInt *cmap = cmapa[cp];
7461: const PetscInt *ii = mm->i;
7462: PetscInt *coi = coo_i + ncoo_o;
7463: PetscInt *coj = coo_j + ncoo_o;
7464: const PetscInt mr = mp[cp]->rmap->n;
7465: const PetscInt rs = C->rmap->rstart;
7466: const PetscInt re = C->rmap->rend;
7467: const PetscInt cs = C->cmap->rstart;
7468: for (i = 0; i < mr; i++) {
7469: const PetscInt *jj = mm->j + ii[i];
7470: const PetscInt gr = rmap[i];
7471: const PetscInt nz = ii[i + 1] - ii[i];
7472: if (gr < rs || gr >= re) { /* this is an offproc row */
7473: for (j = ii[i]; j < ii[i + 1]; j++) {
7474: *coi++ = gr;
7475: *idxoff++ = j;
7476: }
7477: if (!cmapt[cp]) { /* already global */
7478: for (j = 0; j < nz; j++) *coj++ = jj[j];
7479: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7480: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7481: } else { /* offdiag */
7482: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7483: }
7484: ncoo_o += nz;
7485: } else { /* this is a local row */
7486: for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7487: }
7488: }
7489: }
7490: mmdata->off[cp + 1] = idxoff;
7491: mmdata->own[cp + 1] = idxown;
7492: }
7494: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7495: PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, ncoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7496: PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7497: PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7498: ncoo = ncoo_d + ncoo_oown + ncoo2;
7499: PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7500: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7501: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7502: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7503: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7504: PetscCall(PetscFree2(coo_i, coo_j));
7505: /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7506: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7507: coo_i = coo_i2;
7508: coo_j = coo_j2;
7509: } else { /* no offproc values insertion */
7510: ncoo = ncoo_d;
7511: PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));
7513: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7514: PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7515: PetscCall(PetscSFSetUp(mmdata->sf));
7516: }
7517: mmdata->hasoffproc = hasoffproc;
7519: /* gather (i,j) of nonzeros inserted locally */
7520: for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7521: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7522: PetscInt *coi = coo_i + ncoo_d;
7523: PetscInt *coj = coo_j + ncoo_d;
7524: const PetscInt *jj = mm->j;
7525: const PetscInt *ii = mm->i;
7526: const PetscInt *cmap = cmapa[cp];
7527: const PetscInt *rmap = rmapa[cp];
7528: const PetscInt mr = mp[cp]->rmap->n;
7529: const PetscInt rs = C->rmap->rstart;
7530: const PetscInt re = C->rmap->rend;
7531: const PetscInt cs = C->cmap->rstart;
7533: if (mptmp[cp]) continue;
7534: if (rmapt[cp] == 1) { /* consecutive rows */
7535: /* fill coo_i */
7536: for (i = 0; i < mr; i++) {
7537: const PetscInt gr = i + rs;
7538: for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7539: }
7540: /* fill coo_j */
7541: if (!cmapt[cp]) { /* type-0, already global */
7542: PetscCall(PetscArraycpy(coj, jj, mm->nz));
7543: } else if (cmapt[cp] == 1) { /* type-1, local to global for consecutive columns of C */
7544: for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7545: } else { /* type-2, local to global for sparse columns */
7546: for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7547: }
7548: ncoo_d += mm->nz;
7549: } else if (rmapt[cp] == 2) { /* sparse rows */
7550: for (i = 0; i < mr; i++) {
7551: const PetscInt *jj = mm->j + ii[i];
7552: const PetscInt gr = rmap[i];
7553: const PetscInt nz = ii[i + 1] - ii[i];
7554: if (gr >= rs && gr < re) { /* local rows */
7555: for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7556: if (!cmapt[cp]) { /* type-0, already global */
7557: for (j = 0; j < nz; j++) *coj++ = jj[j];
7558: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7559: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7560: } else { /* type-2, local to global for sparse columns */
7561: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7562: }
7563: ncoo_d += nz;
7564: }
7565: }
7566: }
7567: }
7568: if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7569: PetscCall(ISDestroy(&glob));
7570: if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7571: PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7572: /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7573: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));
7575: /* preallocate with COO data */
7576: PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7577: PetscCall(PetscFree2(coo_i, coo_j));
7578: PetscFunctionReturn(PETSC_SUCCESS);
7579: }
7581: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7582: {
7583: Mat_Product *product = mat->product;
7584: #if defined(PETSC_HAVE_DEVICE)
7585: PetscBool match = PETSC_FALSE;
7586: PetscBool usecpu = PETSC_FALSE;
7587: #else
7588: PetscBool match = PETSC_TRUE;
7589: #endif
7591: PetscFunctionBegin;
7592: MatCheckProduct(mat, 1);
7593: #if defined(PETSC_HAVE_DEVICE)
7594: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7595: if (match) { /* we can always fallback to the CPU if requested */
7596: switch (product->type) {
7597: case MATPRODUCT_AB:
7598: if (product->api_user) {
7599: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7600: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7601: PetscOptionsEnd();
7602: } else {
7603: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7604: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7605: PetscOptionsEnd();
7606: }
7607: break;
7608: case MATPRODUCT_AtB:
7609: if (product->api_user) {
7610: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7611: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7612: PetscOptionsEnd();
7613: } else {
7614: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7615: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7616: PetscOptionsEnd();
7617: }
7618: break;
7619: case MATPRODUCT_PtAP:
7620: if (product->api_user) {
7621: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7622: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7623: PetscOptionsEnd();
7624: } else {
7625: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7626: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7627: PetscOptionsEnd();
7628: }
7629: break;
7630: default:
7631: break;
7632: }
7633: match = (PetscBool)!usecpu;
7634: }
7635: #endif
7636: if (match) {
7637: switch (product->type) {
7638: case MATPRODUCT_AB:
7639: case MATPRODUCT_AtB:
7640: case MATPRODUCT_PtAP:
7641: mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7642: break;
7643: default:
7644: break;
7645: }
7646: }
7647: /* fallback to MPIAIJ ops */
7648: if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7649: PetscFunctionReturn(PETSC_SUCCESS);
7650: }
7652: /*
7653: Produces a set of block column indices of the matrix row, one for each block represented in the original row
7655: n - the number of block indices in cc[]
7656: cc - the block indices (must be large enough to contain the indices)
7657: */
7658: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7659: {
7660: PetscInt cnt = -1, nidx, j;
7661: const PetscInt *idx;
7663: PetscFunctionBegin;
7664: PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7665: if (nidx) {
7666: cnt = 0;
7667: cc[cnt] = idx[0] / bs;
7668: for (j = 1; j < nidx; j++) {
7669: if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7670: }
7671: }
7672: PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7673: *n = cnt + 1;
7674: PetscFunctionReturn(PETSC_SUCCESS);
7675: }
7677: /*
7678: Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows
7680: ncollapsed - the number of block indices
7681: collapsed - the block indices (must be large enough to contain the indices)
7682: */
7683: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7684: {
7685: PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;
7687: PetscFunctionBegin;
7688: PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7689: for (i = start + 1; i < start + bs; i++) {
7690: PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7691: PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7692: cprevtmp = cprev;
7693: cprev = merged;
7694: merged = cprevtmp;
7695: }
7696: *ncollapsed = nprev;
7697: if (collapsed) *collapsed = cprev;
7698: PetscFunctionReturn(PETSC_SUCCESS);
7699: }
7701: /*
7702: MatCreateGraph_Simple_AIJ - create simple scalar matrix (graph) from potentially blocked matrix
7704: Input Parameter:
7705: . Amat - matrix
7706: - symmetrize - make the result symmetric
7707: + scale - scale with diagonal
7709: Output Parameter:
7710: . a_Gmat - output scalar graph >= 0
7712: */
7713: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, Mat *a_Gmat)
7714: {
7715: PetscInt Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7716: MPI_Comm comm;
7717: Mat Gmat;
7718: PetscBool ismpiaij, isseqaij;
7719: Mat a, b, c;
7720: MatType jtype;
7722: PetscFunctionBegin;
7723: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7724: PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7725: PetscCall(MatGetSize(Amat, &MM, &NN));
7726: PetscCall(MatGetBlockSize(Amat, &bs));
7727: nloc = (Iend - Istart) / bs;
7729: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7730: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7731: PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");
7733: /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7734: /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7735: implementation */
7736: if (bs > 1) {
7737: PetscCall(MatGetType(Amat, &jtype));
7738: PetscCall(MatCreate(comm, &Gmat));
7739: PetscCall(MatSetType(Gmat, jtype));
7740: PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7741: PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7742: if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7743: PetscInt *d_nnz, *o_nnz;
7744: MatScalar *aa, val, *AA;
7745: PetscInt *aj, *ai, *AJ, nc, nmax = 0;
7746: if (isseqaij) {
7747: a = Amat;
7748: b = NULL;
7749: } else {
7750: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7751: a = d->A;
7752: b = d->B;
7753: }
7754: PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7755: PetscCall(PetscMalloc2(nloc, &d_nnz, isseqaij ? 0 : nloc, &o_nnz));
7756: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7757: PetscInt *nnz = (c == a) ? d_nnz : o_nnz;
7758: const PetscInt *cols1, *cols2;
7759: for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7760: PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7761: nnz[brow / bs] = nc2 / bs;
7762: if (nc2 % bs) ok = 0;
7763: if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7764: for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7765: PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7766: if (nc1 != nc2) ok = 0;
7767: else {
7768: for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7769: if (cols1[jj] != cols2[jj]) ok = 0;
7770: if (cols1[jj] % bs != jj % bs) ok = 0;
7771: }
7772: }
7773: PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7774: }
7775: PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7776: if (!ok) {
7777: PetscCall(PetscFree2(d_nnz, o_nnz));
7778: PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7779: goto old_bs;
7780: }
7781: }
7782: }
7783: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7784: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7785: PetscCall(PetscFree2(d_nnz, o_nnz));
7786: PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7787: // diag
7788: for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7789: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;
7790: ai = aseq->i;
7791: n = ai[brow + 1] - ai[brow];
7792: aj = aseq->j + ai[brow];
7793: for (int k = 0; k < n; k += bs) { // block columns
7794: AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7795: val = 0;
7796: for (int ii = 0; ii < bs; ii++) { // rows in block
7797: aa = aseq->a + ai[brow + ii] + k;
7798: for (int jj = 0; jj < bs; jj++) { // columns in block
7799: val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7800: }
7801: }
7802: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7803: AA[k / bs] = val;
7804: }
7805: grow = Istart / bs + brow / bs;
7806: PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, INSERT_VALUES));
7807: }
7808: // off-diag
7809: if (ismpiaij) {
7810: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Amat->data;
7811: const PetscScalar *vals;
7812: const PetscInt *cols, *garray = aij->garray;
7813: PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7814: for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7815: PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7816: for (int k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7817: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7818: AA[k / bs] = 0;
7819: AJ[cidx] = garray[cols[k]] / bs;
7820: }
7821: nc = ncols / bs;
7822: PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7823: for (int ii = 0; ii < bs; ii++) { // rows in block
7824: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7825: for (int k = 0; k < ncols; k += bs) {
7826: for (int jj = 0; jj < bs; jj++) { // cols in block
7827: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7828: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7829: }
7830: }
7831: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7832: }
7833: grow = Istart / bs + brow / bs;
7834: PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, INSERT_VALUES));
7835: }
7836: }
7837: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7838: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7839: PetscCall(PetscFree2(AA, AJ));
7840: } else {
7841: const PetscScalar *vals;
7842: const PetscInt *idx;
7843: PetscInt *d_nnz, *o_nnz, *w0, *w1, *w2;
7844: old_bs:
7845: /*
7846: Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7847: */
7848: PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7849: PetscCall(PetscMalloc2(nloc, &d_nnz, isseqaij ? 0 : nloc, &o_nnz));
7850: if (isseqaij) {
7851: PetscInt max_d_nnz;
7852: /*
7853: Determine exact preallocation count for (sequential) scalar matrix
7854: */
7855: PetscCall(MatSeqAIJGetMaxRowNonzeros(Amat, &max_d_nnz));
7856: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7857: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7858: for (Ii = 0, jj = 0; Ii < Iend; Ii += bs, jj++) PetscCall(MatCollapseRows(Amat, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7859: PetscCall(PetscFree3(w0, w1, w2));
7860: } else if (ismpiaij) {
7861: Mat Daij, Oaij;
7862: const PetscInt *garray;
7863: PetscInt max_d_nnz;
7864: PetscCall(MatMPIAIJGetSeqAIJ(Amat, &Daij, &Oaij, &garray));
7865: /*
7866: Determine exact preallocation count for diagonal block portion of scalar matrix
7867: */
7868: PetscCall(MatSeqAIJGetMaxRowNonzeros(Daij, &max_d_nnz));
7869: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7870: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7871: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) PetscCall(MatCollapseRows(Daij, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7872: PetscCall(PetscFree3(w0, w1, w2));
7873: /*
7874: Over estimate (usually grossly over), preallocation count for off-diagonal portion of scalar matrix
7875: */
7876: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) {
7877: o_nnz[jj] = 0;
7878: for (kk = 0; kk < bs; kk++) { /* rows that get collapsed to a single row */
7879: PetscCall(MatGetRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7880: o_nnz[jj] += ncols;
7881: PetscCall(MatRestoreRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7882: }
7883: if (o_nnz[jj] > (NN / bs - nloc)) o_nnz[jj] = NN / bs - nloc;
7884: }
7885: } else SETERRQ(comm, PETSC_ERR_USER, "Require AIJ matrix type");
7886: /* get scalar copy (norms) of matrix */
7887: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7888: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7889: PetscCall(PetscFree2(d_nnz, o_nnz));
7890: for (Ii = Istart; Ii < Iend; Ii++) {
7891: PetscInt dest_row = Ii / bs;
7892: PetscCall(MatGetRow(Amat, Ii, &ncols, &idx, &vals));
7893: for (jj = 0; jj < ncols; jj++) {
7894: PetscInt dest_col = idx[jj] / bs;
7895: PetscScalar sv = PetscAbs(PetscRealPart(vals[jj]));
7896: PetscCall(MatSetValues(Gmat, 1, &dest_row, 1, &dest_col, &sv, ADD_VALUES));
7897: }
7898: PetscCall(MatRestoreRow(Amat, Ii, &ncols, &idx, &vals));
7899: }
7900: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7901: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7902: }
7903: } else {
7904: if (symmetrize || filter >= 0 || scale) PetscCall(MatDuplicate(Amat, MAT_COPY_VALUES, &Gmat));
7905: else {
7906: Gmat = Amat;
7907: PetscCall(PetscObjectReference((PetscObject)Gmat));
7908: }
7909: if (isseqaij) {
7910: a = Gmat;
7911: b = NULL;
7912: } else {
7913: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
7914: a = d->A;
7915: b = d->B;
7916: }
7917: if (filter >= 0 || scale) {
7918: /* take absolute value of each entry */
7919: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7920: MatInfo info;
7921: PetscScalar *avals;
7922: PetscCall(MatGetInfo(c, MAT_LOCAL, &info));
7923: PetscCall(MatSeqAIJGetArray(c, &avals));
7924: for (int jj = 0; jj < info.nz_used; jj++) avals[jj] = PetscAbsScalar(avals[jj]);
7925: PetscCall(MatSeqAIJRestoreArray(c, &avals));
7926: }
7927: }
7928: }
7929: if (symmetrize) {
7930: PetscBool isset, issym;
7931: PetscCall(MatIsSymmetricKnown(Amat, &isset, &issym));
7932: if (!isset || !issym) {
7933: Mat matTrans;
7934: PetscCall(MatTranspose(Gmat, MAT_INITIAL_MATRIX, &matTrans));
7935: PetscCall(MatAXPY(Gmat, 1.0, matTrans, Gmat->structurally_symmetric == PETSC_BOOL3_TRUE ? SAME_NONZERO_PATTERN : DIFFERENT_NONZERO_PATTERN));
7936: PetscCall(MatDestroy(&matTrans));
7937: }
7938: PetscCall(MatSetOption(Gmat, MAT_SYMMETRIC, PETSC_TRUE));
7939: } else if (Amat != Gmat) PetscCall(MatPropagateSymmetryOptions(Amat, Gmat));
7940: if (scale) {
7941: /* scale c for all diagonal values = 1 or -1 */
7942: Vec diag;
7943: PetscCall(MatCreateVecs(Gmat, &diag, NULL));
7944: PetscCall(MatGetDiagonal(Gmat, diag));
7945: PetscCall(VecReciprocal(diag));
7946: PetscCall(VecSqrtAbs(diag));
7947: PetscCall(MatDiagonalScale(Gmat, diag, diag));
7948: PetscCall(VecDestroy(&diag));
7949: }
7950: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_graph_view"));
7952: if (filter >= 0) {
7953: PetscCall(MatFilter(Gmat, filter, PETSC_TRUE, PETSC_TRUE));
7954: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_filter_graph_view"));
7955: }
7956: *a_Gmat = Gmat;
7957: PetscFunctionReturn(PETSC_SUCCESS);
7958: }
7960: /*
7961: Special version for direct calls from Fortran
7962: */
7963: #include <petsc/private/fortranimpl.h>
7965: /* Change these macros so can be used in void function */
7966: /* Identical to PetscCallVoid, except it assigns to *_ierr */
7967: #undef PetscCall
7968: #define PetscCall(...) \
7969: do { \
7970: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
7971: if (PetscUnlikely(ierr_msv_mpiaij)) { \
7972: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
7973: return; \
7974: } \
7975: } while (0)
7977: #undef SETERRQ
7978: #define SETERRQ(comm, ierr, ...) \
7979: do { \
7980: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
7981: return; \
7982: } while (0)
7984: #if defined(PETSC_HAVE_FORTRAN_CAPS)
7985: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
7986: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
7987: #define matsetvaluesmpiaij_ matsetvaluesmpiaij