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: /* defines MatSetValues_MPI_Hash(), MatAssemblyBegin_MPI_Hash(), and MatAssemblyEnd_MPI_Hash() */
10: #define TYPE AIJ
11: #define TYPE_AIJ
12: #include "../src/mat/impls/aij/mpi/mpihashmat.h"
13: #undef TYPE
14: #undef TYPE_AIJ
16: static PetscErrorCode MatReset_MPIAIJ(Mat mat)
17: {
18: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
20: PetscFunctionBegin;
21: PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
22: PetscCall(MatStashDestroy_Private(&mat->stash));
23: PetscCall(VecDestroy(&aij->diag));
24: PetscCall(MatDestroy(&aij->A));
25: PetscCall(MatDestroy(&aij->B));
26: #if defined(PETSC_USE_CTABLE)
27: PetscCall(PetscHMapIDestroy(&aij->colmap));
28: #else
29: PetscCall(PetscFree(aij->colmap));
30: #endif
31: PetscCall(PetscFree(aij->garray));
32: PetscCall(VecDestroy(&aij->lvec));
33: PetscCall(VecScatterDestroy(&aij->Mvctx));
34: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
35: PetscCall(PetscFree(aij->ld));
36: PetscFunctionReturn(PETSC_SUCCESS);
37: }
39: static PetscErrorCode MatResetHash_MPIAIJ(Mat mat)
40: {
41: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
42: /* Save the nonzero states of the component matrices because those are what are used to determine
43: the nonzero state of mat */
44: PetscObjectState Astate = aij->A->nonzerostate, Bstate = aij->B->nonzerostate;
46: PetscFunctionBegin;
47: PetscCall(MatReset_MPIAIJ(mat));
48: PetscCall(MatSetUp_MPI_Hash(mat));
49: aij->A->nonzerostate = ++Astate, aij->B->nonzerostate = ++Bstate;
50: PetscFunctionReturn(PETSC_SUCCESS);
51: }
53: PetscErrorCode MatDestroy_MPIAIJ(Mat mat)
54: {
55: PetscFunctionBegin;
56: PetscCall(MatReset_MPIAIJ(mat));
58: PetscCall(PetscFree(mat->data));
60: /* may be created by MatCreateMPIAIJSumSeqAIJSymbolic */
61: PetscCall(PetscObjectCompose((PetscObject)mat, "MatMergeSeqsToMPI", NULL));
63: PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
64: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
65: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
66: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatIsTranspose_C", NULL));
67: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocation_C", NULL));
68: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatResetPreallocation_C", NULL));
69: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatResetHash_C", NULL));
70: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocationCSR_C", NULL));
71: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
72: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpibaij_C", NULL));
73: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisbaij_C", NULL));
74: #if defined(PETSC_HAVE_CUDA)
75: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcusparse_C", NULL));
76: #endif
77: #if defined(PETSC_HAVE_HIP)
78: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijhipsparse_C", NULL));
79: #endif
80: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
81: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijkokkos_C", NULL));
82: #endif
83: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpidense_C", NULL));
84: #if defined(PETSC_HAVE_ELEMENTAL)
85: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_elemental_C", NULL));
86: #endif
87: #if defined(PETSC_HAVE_SCALAPACK) && (defined(PETSC_USE_REAL_SINGLE) || defined(PETSC_USE_REAL_DOUBLE))
88: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_scalapack_C", NULL));
89: #endif
90: #if defined(PETSC_HAVE_HYPRE)
91: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_hypre_C", NULL));
92: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", NULL));
93: #endif
94: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
95: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_is_mpiaij_C", NULL));
96: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_mpiaij_mpiaij_C", NULL));
97: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetUseScalableIncreaseOverlap_C", NULL));
98: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijperm_C", NULL));
99: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijsell_C", NULL));
100: #if defined(PETSC_HAVE_MKL_SPARSE)
101: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijmkl_C", NULL));
102: #endif
103: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcrl_C", NULL));
104: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
105: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisell_C", NULL));
106: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetPreallocationCOO_C", NULL));
107: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetValuesCOO_C", NULL));
108: PetscFunctionReturn(PETSC_SUCCESS);
109: }
111: static PetscErrorCode MatGetRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
112: {
113: Mat B;
115: PetscFunctionBegin;
116: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, &B));
117: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject)B));
118: PetscCall(MatGetRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
119: PetscCall(MatDestroy(&B));
120: PetscFunctionReturn(PETSC_SUCCESS);
121: }
123: static PetscErrorCode MatRestoreRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
124: {
125: Mat B;
127: PetscFunctionBegin;
128: PetscCall(PetscObjectQuery((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject *)&B));
129: PetscCall(MatRestoreRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
130: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", NULL));
131: PetscFunctionReturn(PETSC_SUCCESS);
132: }
134: /*MC
135: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
137: This matrix type is identical to` MATSEQAIJ` when constructed with a single process communicator,
138: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
139: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
140: for communicators controlling multiple processes. It is recommended that you call both of
141: the above preallocation routines for simplicity.
143: Options Database Key:
144: . -mat_type aij - sets the matrix type to `MATAIJ` during a call to `MatSetFromOptions()`
146: Developer Note:
147: Level: beginner
149: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, `MATAIJKOKKOS`,and also automatically switches over to use inodes when
150: enough exist.
152: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATBAIJ`
153: M*/
155: /*MC
156: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
158: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
159: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
160: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
161: for communicators controlling multiple processes. It is recommended that you call both of
162: the above preallocation routines for simplicity.
164: Options Database Key:
165: . -mat_type aijcrl - sets the matrix type to `MATMPIAIJCRL` during a call to `MatSetFromOptions()`
167: Level: beginner
169: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJ`, `MATMPIAIJ`, `MATAIJ`
170: M*/
172: static PetscErrorCode MatBindToCPU_MPIAIJ(Mat A, PetscBool flg)
173: {
174: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
176: PetscFunctionBegin;
177: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP) || defined(PETSC_HAVE_VIENNACL)
178: A->boundtocpu = flg;
179: #endif
180: if (a->A) PetscCall(MatBindToCPU(a->A, flg));
181: if (a->B) PetscCall(MatBindToCPU(a->B, flg));
183: /* In addition to binding the diagonal and off-diagonal matrices, bind the local vectors used for matrix-vector products.
184: * This maybe seems a little odd for a MatBindToCPU() call to do, but it makes no sense for the binding of these vectors
185: * to differ from the parent matrix. */
186: if (a->lvec) PetscCall(VecBindToCPU(a->lvec, flg));
187: if (a->diag) PetscCall(VecBindToCPU(a->diag, flg));
188: PetscFunctionReturn(PETSC_SUCCESS);
189: }
191: static PetscErrorCode MatSetBlockSizes_MPIAIJ(Mat M, PetscInt rbs, PetscInt cbs)
192: {
193: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
195: PetscFunctionBegin;
196: if (mat->A) {
197: PetscCall(MatSetBlockSizes(mat->A, rbs, cbs));
198: PetscCall(MatSetBlockSizes(mat->B, rbs, 1));
199: }
200: PetscFunctionReturn(PETSC_SUCCESS);
201: }
203: static PetscErrorCode MatFindNonzeroRows_MPIAIJ(Mat M, IS *keptrows)
204: {
205: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
206: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data;
207: Mat_SeqAIJ *b = (Mat_SeqAIJ *)mat->B->data;
208: const PetscInt *ia, *ib;
209: const MatScalar *aa, *bb, *aav, *bav;
210: PetscInt na, nb, i, j, *rows, cnt = 0, n0rows;
211: PetscInt m = M->rmap->n, rstart = M->rmap->rstart;
213: PetscFunctionBegin;
214: *keptrows = NULL;
216: ia = a->i;
217: ib = b->i;
218: PetscCall(MatSeqAIJGetArrayRead(mat->A, &aav));
219: PetscCall(MatSeqAIJGetArrayRead(mat->B, &bav));
220: for (i = 0; i < m; i++) {
221: na = ia[i + 1] - ia[i];
222: nb = ib[i + 1] - ib[i];
223: if (!na && !nb) {
224: cnt++;
225: goto ok1;
226: }
227: aa = aav + ia[i];
228: for (j = 0; j < na; j++) {
229: if (aa[j] != 0.0) goto ok1;
230: }
231: bb = PetscSafePointerPlusOffset(bav, ib[i]);
232: for (j = 0; j < nb; j++) {
233: if (bb[j] != 0.0) goto ok1;
234: }
235: cnt++;
236: ok1:;
237: }
238: PetscCallMPI(MPIU_Allreduce(&cnt, &n0rows, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)M)));
239: if (!n0rows) {
240: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
241: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
242: PetscFunctionReturn(PETSC_SUCCESS);
243: }
244: PetscCall(PetscMalloc1(M->rmap->n - cnt, &rows));
245: cnt = 0;
246: for (i = 0; i < m; i++) {
247: na = ia[i + 1] - ia[i];
248: nb = ib[i + 1] - ib[i];
249: if (!na && !nb) continue;
250: aa = aav + ia[i];
251: for (j = 0; j < na; j++) {
252: if (aa[j] != 0.0) {
253: rows[cnt++] = rstart + i;
254: goto ok2;
255: }
256: }
257: bb = PetscSafePointerPlusOffset(bav, ib[i]);
258: for (j = 0; j < nb; j++) {
259: if (bb[j] != 0.0) {
260: rows[cnt++] = rstart + i;
261: goto ok2;
262: }
263: }
264: ok2:;
265: }
266: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), cnt, rows, PETSC_OWN_POINTER, keptrows));
267: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
268: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
269: PetscFunctionReturn(PETSC_SUCCESS);
270: }
272: static PetscErrorCode MatDiagonalSet_MPIAIJ(Mat Y, Vec D, InsertMode is)
273: {
274: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Y->data;
275: PetscBool cong;
277: PetscFunctionBegin;
278: PetscCall(MatHasCongruentLayouts(Y, &cong));
279: if (Y->assembled && cong) PetscCall(MatDiagonalSet(aij->A, D, is));
280: else PetscCall(MatDiagonalSet_Default(Y, D, is));
281: PetscFunctionReturn(PETSC_SUCCESS);
282: }
284: static PetscErrorCode MatFindZeroDiagonals_MPIAIJ(Mat M, IS *zrows)
285: {
286: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)M->data;
287: PetscInt i, rstart, nrows, *rows;
289: PetscFunctionBegin;
290: *zrows = NULL;
291: PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(aij->A, &nrows, &rows));
292: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
293: for (i = 0; i < nrows; i++) rows[i] += rstart;
294: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), nrows, rows, PETSC_OWN_POINTER, zrows));
295: PetscFunctionReturn(PETSC_SUCCESS);
296: }
298: static PetscErrorCode MatGetColumnReductions_MPIAIJ(Mat A, PetscInt type, PetscReal *reductions)
299: {
300: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)A->data;
301: PetscInt i, m, n, *garray = aij->garray;
302: Mat_SeqAIJ *a_aij = (Mat_SeqAIJ *)aij->A->data;
303: Mat_SeqAIJ *b_aij = (Mat_SeqAIJ *)aij->B->data;
304: PetscReal *work;
305: const PetscScalar *dummy;
307: PetscFunctionBegin;
308: PetscCall(MatGetSize(A, &m, &n));
309: PetscCall(PetscCalloc1(n, &work));
310: PetscCall(MatSeqAIJGetArrayRead(aij->A, &dummy));
311: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &dummy));
312: PetscCall(MatSeqAIJGetArrayRead(aij->B, &dummy));
313: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &dummy));
314: if (type == NORM_2) {
315: 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]);
316: 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]);
317: } else if (type == NORM_1) {
318: 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]);
319: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]);
320: } else if (type == NORM_INFINITY) {
321: 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]]);
322: 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]]]);
323: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
324: 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]);
325: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscRealPart(b_aij->a[i]);
326: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
327: 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]);
328: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscImaginaryPart(b_aij->a[i]);
329: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
330: if (type == NORM_INFINITY) {
331: PetscCallMPI(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
332: } else {
333: PetscCallMPI(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
334: }
335: PetscCall(PetscFree(work));
336: if (type == NORM_2) {
337: for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
338: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
339: for (i = 0; i < n; i++) reductions[i] /= m;
340: }
341: PetscFunctionReturn(PETSC_SUCCESS);
342: }
344: static PetscErrorCode MatFindOffBlockDiagonalEntries_MPIAIJ(Mat A, IS *is)
345: {
346: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
347: IS sis, gis;
348: const PetscInt *isis, *igis;
349: PetscInt n, *iis, nsis, ngis, rstart, i;
351: PetscFunctionBegin;
352: PetscCall(MatFindOffBlockDiagonalEntries(a->A, &sis));
353: PetscCall(MatFindNonzeroRows(a->B, &gis));
354: PetscCall(ISGetSize(gis, &ngis));
355: PetscCall(ISGetSize(sis, &nsis));
356: PetscCall(ISGetIndices(sis, &isis));
357: PetscCall(ISGetIndices(gis, &igis));
359: PetscCall(PetscMalloc1(ngis + nsis, &iis));
360: PetscCall(PetscArraycpy(iis, igis, ngis));
361: PetscCall(PetscArraycpy(iis + ngis, isis, nsis));
362: n = ngis + nsis;
363: PetscCall(PetscSortRemoveDupsInt(&n, iis));
364: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
365: for (i = 0; i < n; i++) iis[i] += rstart;
366: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), n, iis, PETSC_OWN_POINTER, is));
368: PetscCall(ISRestoreIndices(sis, &isis));
369: PetscCall(ISRestoreIndices(gis, &igis));
370: PetscCall(ISDestroy(&sis));
371: PetscCall(ISDestroy(&gis));
372: PetscFunctionReturn(PETSC_SUCCESS);
373: }
375: /*
376: Local utility routine that creates a mapping from the global column
377: number to the local number in the off-diagonal part of the local
378: storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at
379: a slightly higher hash table cost; without it it is not scalable (each processor
380: has an order N integer array but is fast to access.
381: */
382: PetscErrorCode MatCreateColmap_MPIAIJ_Private(Mat mat)
383: {
384: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
385: PetscInt n = aij->B->cmap->n, i;
387: PetscFunctionBegin;
388: PetscCheck(!n || aij->garray, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MPIAIJ Matrix was assembled but is missing garray");
389: #if defined(PETSC_USE_CTABLE)
390: PetscCall(PetscHMapICreateWithSize(n, &aij->colmap));
391: for (i = 0; i < n; i++) PetscCall(PetscHMapISet(aij->colmap, aij->garray[i] + 1, i + 1));
392: #else
393: PetscCall(PetscCalloc1(mat->cmap->N + 1, &aij->colmap));
394: for (i = 0; i < n; i++) aij->colmap[aij->garray[i]] = i + 1;
395: #endif
396: PetscFunctionReturn(PETSC_SUCCESS);
397: }
399: #define MatSetValues_SeqAIJ_A_Private(row, col, value, addv, orow, ocol) \
400: do { \
401: if (col <= lastcol1) low1 = 0; \
402: else high1 = nrow1; \
403: lastcol1 = col; \
404: while (high1 - low1 > 5) { \
405: t = (low1 + high1) / 2; \
406: if (rp1[t] > col) high1 = t; \
407: else low1 = t; \
408: } \
409: for (_i = low1; _i < high1; _i++) { \
410: if (rp1[_i] > col) break; \
411: if (rp1[_i] == col) { \
412: if (addv == ADD_VALUES) { \
413: ap1[_i] += value; \
414: /* Not sure LogFlops will slow down the code or not */ \
415: (void)PetscLogFlops(1.0); \
416: } else ap1[_i] = value; \
417: goto a_noinsert; \
418: } \
419: } \
420: if (value == 0.0 && ignorezeroentries && row != col) { \
421: low1 = 0; \
422: high1 = nrow1; \
423: goto a_noinsert; \
424: } \
425: if (nonew == 1) { \
426: low1 = 0; \
427: high1 = nrow1; \
428: goto a_noinsert; \
429: } \
430: 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); \
431: MatSeqXAIJReallocateAIJ(A, am, 1, nrow1, row, col, rmax1, aa, ai, aj, rp1, ap1, aimax, nonew, MatScalar); \
432: N = nrow1++ - 1; \
433: a->nz++; \
434: high1++; \
435: /* shift up all the later entries in this row */ \
436: PetscCall(PetscArraymove(rp1 + _i + 1, rp1 + _i, N - _i + 1)); \
437: PetscCall(PetscArraymove(ap1 + _i + 1, ap1 + _i, N - _i + 1)); \
438: rp1[_i] = col; \
439: ap1[_i] = value; \
440: a_noinsert:; \
441: ailen[row] = nrow1; \
442: } while (0)
444: #define MatSetValues_SeqAIJ_B_Private(row, col, value, addv, orow, ocol) \
445: do { \
446: if (col <= lastcol2) low2 = 0; \
447: else high2 = nrow2; \
448: lastcol2 = col; \
449: while (high2 - low2 > 5) { \
450: t = (low2 + high2) / 2; \
451: if (rp2[t] > col) high2 = t; \
452: else low2 = t; \
453: } \
454: for (_i = low2; _i < high2; _i++) { \
455: if (rp2[_i] > col) break; \
456: if (rp2[_i] == col) { \
457: if (addv == ADD_VALUES) { \
458: ap2[_i] += value; \
459: (void)PetscLogFlops(1.0); \
460: } else ap2[_i] = value; \
461: goto b_noinsert; \
462: } \
463: } \
464: if (value == 0.0 && ignorezeroentries) { \
465: low2 = 0; \
466: high2 = nrow2; \
467: goto b_noinsert; \
468: } \
469: if (nonew == 1) { \
470: low2 = 0; \
471: high2 = nrow2; \
472: goto b_noinsert; \
473: } \
474: 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); \
475: MatSeqXAIJReallocateAIJ(B, bm, 1, nrow2, row, col, rmax2, ba, bi, bj, rp2, ap2, bimax, nonew, MatScalar); \
476: N = nrow2++ - 1; \
477: b->nz++; \
478: high2++; \
479: /* shift up all the later entries in this row */ \
480: PetscCall(PetscArraymove(rp2 + _i + 1, rp2 + _i, N - _i + 1)); \
481: PetscCall(PetscArraymove(ap2 + _i + 1, ap2 + _i, N - _i + 1)); \
482: rp2[_i] = col; \
483: ap2[_i] = value; \
484: b_noinsert:; \
485: bilen[row] = nrow2; \
486: } while (0)
488: static PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A, PetscInt row, const PetscScalar v[])
489: {
490: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
491: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data, *b = (Mat_SeqAIJ *)mat->B->data;
492: PetscInt l, *garray = mat->garray, diag;
493: PetscScalar *aa, *ba;
495: PetscFunctionBegin;
496: /* code only works for square matrices A */
498: /* find size of row to the left of the diagonal part */
499: PetscCall(MatGetOwnershipRange(A, &diag, NULL));
500: row = row - diag;
501: for (l = 0; l < b->i[row + 1] - b->i[row]; l++) {
502: if (garray[b->j[b->i[row] + l]] > diag) break;
503: }
504: if (l) {
505: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
506: PetscCall(PetscArraycpy(ba + b->i[row], v, l));
507: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
508: }
510: /* diagonal part */
511: if (a->i[row + 1] - a->i[row]) {
512: PetscCall(MatSeqAIJGetArray(mat->A, &aa));
513: PetscCall(PetscArraycpy(aa + a->i[row], v + l, a->i[row + 1] - a->i[row]));
514: PetscCall(MatSeqAIJRestoreArray(mat->A, &aa));
515: }
517: /* right of diagonal part */
518: if (b->i[row + 1] - b->i[row] - l) {
519: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
520: PetscCall(PetscArraycpy(ba + b->i[row] + l, v + l + a->i[row + 1] - a->i[row], b->i[row + 1] - b->i[row] - l));
521: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
522: }
523: PetscFunctionReturn(PETSC_SUCCESS);
524: }
526: PetscErrorCode MatSetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
527: {
528: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
529: PetscScalar value = 0.0;
530: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
531: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
532: PetscBool roworiented = aij->roworiented;
534: /* Some Variables required in the macro */
535: Mat A = aij->A;
536: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
537: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
538: PetscBool ignorezeroentries = a->ignorezeroentries;
539: Mat B = aij->B;
540: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
541: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
542: MatScalar *aa, *ba;
543: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
544: PetscInt nonew;
545: MatScalar *ap1, *ap2;
547: PetscFunctionBegin;
548: PetscCall(MatSeqAIJGetArray(A, &aa));
549: PetscCall(MatSeqAIJGetArray(B, &ba));
550: for (i = 0; i < m; i++) {
551: if (im[i] < 0) continue;
552: 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);
553: if (im[i] >= rstart && im[i] < rend) {
554: row = im[i] - rstart;
555: lastcol1 = -1;
556: rp1 = PetscSafePointerPlusOffset(aj, ai[row]);
557: ap1 = PetscSafePointerPlusOffset(aa, ai[row]);
558: rmax1 = aimax[row];
559: nrow1 = ailen[row];
560: low1 = 0;
561: high1 = nrow1;
562: lastcol2 = -1;
563: rp2 = PetscSafePointerPlusOffset(bj, bi[row]);
564: ap2 = PetscSafePointerPlusOffset(ba, bi[row]);
565: rmax2 = bimax[row];
566: nrow2 = bilen[row];
567: low2 = 0;
568: high2 = nrow2;
570: for (j = 0; j < n; j++) {
571: if (v) value = roworiented ? v[i * n + j] : v[i + j * m];
572: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
573: if (in[j] >= cstart && in[j] < cend) {
574: col = in[j] - cstart;
575: nonew = a->nonew;
576: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
577: } else if (in[j] < 0) {
578: continue;
579: } else {
580: 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);
581: if (mat->was_assembled) {
582: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
583: #if defined(PETSC_USE_CTABLE)
584: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col)); /* map global col ids to local ones */
585: col--;
586: #else
587: col = aij->colmap[in[j]] - 1;
588: #endif
589: if (col < 0 && !((Mat_SeqAIJ *)aij->B->data)->nonew) { /* col < 0 means in[j] is a new col for B */
590: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE)); /* Change aij->B from reduced/local format to expanded/global format */
591: col = in[j];
592: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
593: B = aij->B;
594: b = (Mat_SeqAIJ *)B->data;
595: bimax = b->imax;
596: bi = b->i;
597: bilen = b->ilen;
598: bj = b->j;
599: ba = b->a;
600: rp2 = PetscSafePointerPlusOffset(bj, bi[row]);
601: ap2 = PetscSafePointerPlusOffset(ba, bi[row]);
602: rmax2 = bimax[row];
603: nrow2 = bilen[row];
604: low2 = 0;
605: high2 = nrow2;
606: bm = aij->B->rmap->n;
607: ba = b->a;
608: } else if (col < 0 && !(ignorezeroentries && value == 0.0)) {
609: PetscCheck(1 == ((Mat_SeqAIJ *)aij->B->data)->nonew, 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]);
610: 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]));
611: }
612: } else col = in[j];
613: nonew = b->nonew;
614: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
615: }
616: }
617: } else {
618: 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]);
619: if (!aij->donotstash) {
620: mat->assembled = PETSC_FALSE;
621: if (roworiented) {
622: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i * n), (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
623: } else {
624: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i), m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
625: }
626: }
627: }
628: }
629: PetscCall(MatSeqAIJRestoreArray(A, &aa)); /* aa, bb might have been free'd due to reallocation above. But we don't access them here */
630: PetscCall(MatSeqAIJRestoreArray(B, &ba));
631: PetscFunctionReturn(PETSC_SUCCESS);
632: }
634: /*
635: This function sets the j and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
636: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
637: No off-processor parts off the matrix are allowed here and mat->was_assembled has to be PETSC_FALSE.
638: */
639: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[])
640: {
641: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
642: Mat A = aij->A; /* diagonal part of the matrix */
643: Mat B = aij->B; /* off-diagonal part of the matrix */
644: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
645: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
646: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, col;
647: PetscInt *ailen = a->ilen, *aj = a->j;
648: PetscInt *bilen = b->ilen, *bj = b->j;
649: PetscInt am = aij->A->rmap->n, j;
650: PetscInt diag_so_far = 0, dnz;
651: PetscInt offd_so_far = 0, onz;
653: PetscFunctionBegin;
654: /* Iterate over all rows of the matrix */
655: for (j = 0; j < am; j++) {
656: dnz = onz = 0;
657: /* Iterate over all non-zero columns of the current row */
658: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
659: /* If column is in the diagonal */
660: if (mat_j[col] >= cstart && mat_j[col] < cend) {
661: aj[diag_so_far++] = mat_j[col] - cstart;
662: dnz++;
663: } else { /* off-diagonal entries */
664: bj[offd_so_far++] = mat_j[col];
665: onz++;
666: }
667: }
668: ailen[j] = dnz;
669: bilen[j] = onz;
670: }
671: PetscFunctionReturn(PETSC_SUCCESS);
672: }
674: /*
675: This function sets the local j, a and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
676: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
677: No off-processor parts off the matrix are allowed here, they are set at a later point by MatSetValues_MPIAIJ.
678: Also, mat->was_assembled has to be false, otherwise the statement aj[rowstart_diag+dnz_row] = mat_j[col] - cstart;
679: would not be true and the more complex MatSetValues_MPIAIJ has to be used.
680: */
681: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[], const PetscScalar mat_a[])
682: {
683: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
684: Mat A = aij->A; /* diagonal part of the matrix */
685: Mat B = aij->B; /* off-diagonal part of the matrix */
686: Mat_SeqAIJ *aijd = (Mat_SeqAIJ *)aij->A->data, *aijo = (Mat_SeqAIJ *)aij->B->data;
687: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
688: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
689: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend;
690: PetscInt *ailen = a->ilen, *aj = a->j;
691: PetscInt *bilen = b->ilen, *bj = b->j;
692: PetscInt am = aij->A->rmap->n, j;
693: 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. */
694: PetscInt col, dnz_row, onz_row, rowstart_diag, rowstart_offd;
695: PetscScalar *aa = a->a, *ba = b->a;
697: PetscFunctionBegin;
698: /* Iterate over all rows of the matrix */
699: for (j = 0; j < am; j++) {
700: dnz_row = onz_row = 0;
701: rowstart_offd = full_offd_i[j];
702: rowstart_diag = full_diag_i[j];
703: /* Iterate over all non-zero columns of the current row */
704: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
705: /* If column is in the diagonal */
706: if (mat_j[col] >= cstart && mat_j[col] < cend) {
707: aj[rowstart_diag + dnz_row] = mat_j[col] - cstart;
708: aa[rowstart_diag + dnz_row] = mat_a[col];
709: dnz_row++;
710: } else { /* off-diagonal entries */
711: bj[rowstart_offd + onz_row] = mat_j[col];
712: ba[rowstart_offd + onz_row] = mat_a[col];
713: onz_row++;
714: }
715: }
716: ailen[j] = dnz_row;
717: bilen[j] = onz_row;
718: }
719: PetscFunctionReturn(PETSC_SUCCESS);
720: }
722: static PetscErrorCode MatGetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
723: {
724: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
725: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
726: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
728: PetscFunctionBegin;
729: for (i = 0; i < m; i++) {
730: if (idxm[i] < 0) continue; /* negative row */
731: 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);
732: PetscCheck(idxm[i] >= rstart && idxm[i] < rend, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local values currently supported, row requested %" PetscInt_FMT " range [%" PetscInt_FMT " %" PetscInt_FMT ")", idxm[i], rstart, rend);
733: row = idxm[i] - rstart;
734: for (j = 0; j < n; j++) {
735: if (idxn[j] < 0) continue; /* negative column */
736: 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);
737: if (idxn[j] >= cstart && idxn[j] < cend) {
738: col = idxn[j] - cstart;
739: PetscCall(MatGetValues(aij->A, 1, &row, 1, &col, v + i * n + j));
740: } else {
741: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
742: #if defined(PETSC_USE_CTABLE)
743: PetscCall(PetscHMapIGetWithDefault(aij->colmap, idxn[j] + 1, 0, &col));
744: col--;
745: #else
746: col = aij->colmap[idxn[j]] - 1;
747: #endif
748: if ((col < 0) || (aij->garray[col] != idxn[j])) *(v + i * n + j) = 0.0;
749: else PetscCall(MatGetValues(aij->B, 1, &row, 1, &col, v + i * n + j));
750: }
751: }
752: }
753: PetscFunctionReturn(PETSC_SUCCESS);
754: }
756: static PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat, MatAssemblyType mode)
757: {
758: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
759: PetscInt nstash, reallocs;
761: PetscFunctionBegin;
762: if (aij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
764: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
765: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
766: PetscCall(PetscInfo(mat, "Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
767: PetscFunctionReturn(PETSC_SUCCESS);
768: }
770: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat, MatAssemblyType mode)
771: {
772: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
773: PetscMPIInt n;
774: PetscInt i, j, rstart, ncols, flg;
775: PetscInt *row, *col;
776: PetscBool all_assembled;
777: PetscScalar *val;
779: /* do not use 'b = (Mat_SeqAIJ*)aij->B->data' as B can be reset in disassembly */
781: PetscFunctionBegin;
782: if (!aij->donotstash && !mat->nooffprocentries) {
783: while (1) {
784: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
785: if (!flg) break;
787: for (i = 0; i < n;) {
788: /* Now identify the consecutive vals belonging to the same row */
789: for (j = i, rstart = row[j]; j < n; j++) {
790: if (row[j] != rstart) break;
791: }
792: if (j < n) ncols = j - i;
793: else ncols = n - i;
794: /* Now assemble all these values with a single function call */
795: PetscCall(MatSetValues_MPIAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
796: i = j;
797: }
798: }
799: PetscCall(MatStashScatterEnd_Private(&mat->stash));
800: }
801: #if defined(PETSC_HAVE_DEVICE)
802: if (mat->offloadmask == PETSC_OFFLOAD_CPU) aij->A->offloadmask = PETSC_OFFLOAD_CPU;
803: /* We call MatBindToCPU() on aij->A and aij->B here, because if MatBindToCPU_MPIAIJ() is called before assembly, it cannot bind these. */
804: if (mat->boundtocpu) {
805: PetscCall(MatBindToCPU(aij->A, PETSC_TRUE));
806: PetscCall(MatBindToCPU(aij->B, PETSC_TRUE));
807: }
808: #endif
809: PetscCall(MatAssemblyBegin(aij->A, mode));
810: PetscCall(MatAssemblyEnd(aij->A, mode));
812: /* determine if any process has disassembled, if so we must
813: also disassemble ourself, in order that we may reassemble. */
814: /*
815: if nonzero structure of submatrix B cannot change then we know that
816: no process disassembled thus we can skip this stuff
817: */
818: if (!((Mat_SeqAIJ *)aij->B->data)->nonew) {
819: PetscCallMPI(MPIU_Allreduce(&mat->was_assembled, &all_assembled, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
820: if (mat->was_assembled && !all_assembled) { /* mat on this rank has reduced off-diag B with local col ids, but globally it does not */
821: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));
822: }
823: }
824: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIAIJ(mat));
825: PetscCall(MatSetOption(aij->B, MAT_USE_INODES, PETSC_FALSE));
826: #if defined(PETSC_HAVE_DEVICE)
827: if (mat->offloadmask == PETSC_OFFLOAD_CPU && aij->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) aij->B->offloadmask = PETSC_OFFLOAD_CPU;
828: #endif
829: PetscCall(MatAssemblyBegin(aij->B, mode));
830: PetscCall(MatAssemblyEnd(aij->B, mode));
832: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
834: aij->rowvalues = NULL;
836: PetscCall(VecDestroy(&aij->diag));
838: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
839: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqAIJ *)aij->A->data)->nonew) {
840: PetscObjectState state = aij->A->nonzerostate + aij->B->nonzerostate;
841: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
842: }
843: #if defined(PETSC_HAVE_DEVICE)
844: mat->offloadmask = PETSC_OFFLOAD_BOTH;
845: #endif
846: PetscFunctionReturn(PETSC_SUCCESS);
847: }
849: static PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
850: {
851: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
853: PetscFunctionBegin;
854: PetscCall(MatZeroEntries(l->A));
855: PetscCall(MatZeroEntries(l->B));
856: PetscFunctionReturn(PETSC_SUCCESS);
857: }
859: static PetscErrorCode MatZeroRows_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
860: {
861: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
862: PetscInt *lrows;
863: PetscInt r, len;
864: PetscBool cong;
866: PetscFunctionBegin;
867: /* get locally owned rows */
868: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
869: PetscCall(MatHasCongruentLayouts(A, &cong));
870: /* fix right-hand side if needed */
871: if (x && b) {
872: const PetscScalar *xx;
873: PetscScalar *bb;
875: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
876: PetscCall(VecGetArrayRead(x, &xx));
877: PetscCall(VecGetArray(b, &bb));
878: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
879: PetscCall(VecRestoreArrayRead(x, &xx));
880: PetscCall(VecRestoreArray(b, &bb));
881: }
883: if (diag != 0.0 && cong) {
884: PetscCall(MatZeroRows(mat->A, len, lrows, diag, NULL, NULL));
885: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
886: } else if (diag != 0.0) { /* non-square or non congruent layouts -> if keepnonzeropattern is false, we allow for new insertion */
887: Mat_SeqAIJ *aijA = (Mat_SeqAIJ *)mat->A->data;
888: Mat_SeqAIJ *aijB = (Mat_SeqAIJ *)mat->B->data;
889: PetscInt nnwA, nnwB;
890: PetscBool nnzA, nnzB;
892: nnwA = aijA->nonew;
893: nnwB = aijB->nonew;
894: nnzA = aijA->keepnonzeropattern;
895: nnzB = aijB->keepnonzeropattern;
896: if (!nnzA) {
897: PetscCall(PetscInfo(mat->A, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on diagonal block.\n"));
898: aijA->nonew = 0;
899: }
900: if (!nnzB) {
901: PetscCall(PetscInfo(mat->B, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on off-diagonal block.\n"));
902: aijB->nonew = 0;
903: }
904: /* Must zero here before the next loop */
905: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
906: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
907: for (r = 0; r < len; ++r) {
908: const PetscInt row = lrows[r] + A->rmap->rstart;
909: if (row >= A->cmap->N) continue;
910: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
911: }
912: aijA->nonew = nnwA;
913: aijB->nonew = nnwB;
914: } else {
915: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
916: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
917: }
918: PetscCall(PetscFree(lrows));
919: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
920: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
922: /* only change matrix nonzero state if pattern was allowed to be changed */
923: if (!((Mat_SeqAIJ *)mat->A->data)->keepnonzeropattern || !((Mat_SeqAIJ *)mat->A->data)->nonew) {
924: PetscObjectState state = mat->A->nonzerostate + mat->B->nonzerostate;
925: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
926: }
927: PetscFunctionReturn(PETSC_SUCCESS);
928: }
930: static PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
931: {
932: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
933: PetscInt n = A->rmap->n;
934: PetscInt i, j, r, m, len = 0;
935: PetscInt *lrows, *owners = A->rmap->range;
936: PetscMPIInt p = 0;
937: PetscSFNode *rrows;
938: PetscSF sf;
939: const PetscScalar *xx;
940: PetscScalar *bb, *mask, *aij_a;
941: Vec xmask, lmask;
942: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)l->B->data;
943: const PetscInt *aj, *ii, *ridx;
944: PetscScalar *aa;
946: PetscFunctionBegin;
947: /* Create SF where leaves are input rows and roots are owned rows */
948: PetscCall(PetscMalloc1(n, &lrows));
949: for (r = 0; r < n; ++r) lrows[r] = -1;
950: PetscCall(PetscMalloc1(N, &rrows));
951: for (r = 0; r < N; ++r) {
952: const PetscInt idx = rows[r];
953: 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);
954: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
955: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
956: }
957: rrows[r].rank = p;
958: rrows[r].index = rows[r] - owners[p];
959: }
960: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
961: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
962: /* Collect flags for rows to be zeroed */
963: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
964: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
965: PetscCall(PetscSFDestroy(&sf));
966: /* Compress and put in row numbers */
967: for (r = 0; r < n; ++r)
968: if (lrows[r] >= 0) lrows[len++] = r;
969: /* zero diagonal part of matrix */
970: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
971: /* handle off-diagonal part of matrix */
972: PetscCall(MatCreateVecs(A, &xmask, NULL));
973: PetscCall(VecDuplicate(l->lvec, &lmask));
974: PetscCall(VecGetArray(xmask, &bb));
975: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
976: PetscCall(VecRestoreArray(xmask, &bb));
977: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
978: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
979: PetscCall(VecDestroy(&xmask));
980: if (x && b) { /* this code is buggy when the row and column layout don't match */
981: PetscBool cong;
983: PetscCall(MatHasCongruentLayouts(A, &cong));
984: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
985: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
986: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
987: PetscCall(VecGetArrayRead(l->lvec, &xx));
988: PetscCall(VecGetArray(b, &bb));
989: }
990: PetscCall(VecGetArray(lmask, &mask));
991: /* remove zeroed rows of off-diagonal matrix */
992: PetscCall(MatSeqAIJGetArray(l->B, &aij_a));
993: ii = aij->i;
994: for (i = 0; i < len; i++) PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aij_a, ii[lrows[i]]), ii[lrows[i] + 1] - ii[lrows[i]]));
995: /* loop over all elements of off process part of matrix zeroing removed columns*/
996: if (aij->compressedrow.use) {
997: m = aij->compressedrow.nrows;
998: ii = aij->compressedrow.i;
999: ridx = aij->compressedrow.rindex;
1000: for (i = 0; i < m; i++) {
1001: n = ii[i + 1] - ii[i];
1002: aj = aij->j + ii[i];
1003: aa = aij_a + ii[i];
1005: for (j = 0; j < n; j++) {
1006: if (PetscAbsScalar(mask[*aj])) {
1007: if (b) bb[*ridx] -= *aa * xx[*aj];
1008: *aa = 0.0;
1009: }
1010: aa++;
1011: aj++;
1012: }
1013: ridx++;
1014: }
1015: } else { /* do not use compressed row format */
1016: m = l->B->rmap->n;
1017: for (i = 0; i < m; i++) {
1018: n = ii[i + 1] - ii[i];
1019: aj = aij->j + ii[i];
1020: aa = aij_a + ii[i];
1021: for (j = 0; j < n; j++) {
1022: if (PetscAbsScalar(mask[*aj])) {
1023: if (b) bb[i] -= *aa * xx[*aj];
1024: *aa = 0.0;
1025: }
1026: aa++;
1027: aj++;
1028: }
1029: }
1030: }
1031: if (x && b) {
1032: PetscCall(VecRestoreArray(b, &bb));
1033: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1034: }
1035: PetscCall(MatSeqAIJRestoreArray(l->B, &aij_a));
1036: PetscCall(VecRestoreArray(lmask, &mask));
1037: PetscCall(VecDestroy(&lmask));
1038: PetscCall(PetscFree(lrows));
1040: /* only change matrix nonzero state if pattern was allowed to be changed */
1041: if (!((Mat_SeqAIJ *)l->A->data)->nonew) {
1042: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1043: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1044: }
1045: PetscFunctionReturn(PETSC_SUCCESS);
1046: }
1048: static PetscErrorCode MatMult_MPIAIJ(Mat A, Vec xx, Vec yy)
1049: {
1050: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1051: PetscInt nt;
1052: VecScatter Mvctx = a->Mvctx;
1054: PetscFunctionBegin;
1055: PetscCall(VecGetLocalSize(xx, &nt));
1056: 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);
1057: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1058: PetscUseTypeMethod(a->A, mult, xx, yy);
1059: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1060: PetscUseTypeMethod(a->B, multadd, a->lvec, yy, yy);
1061: PetscFunctionReturn(PETSC_SUCCESS);
1062: }
1064: static PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A, Vec bb, Vec xx)
1065: {
1066: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1068: PetscFunctionBegin;
1069: PetscCall(MatMultDiagonalBlock(a->A, bb, xx));
1070: PetscFunctionReturn(PETSC_SUCCESS);
1071: }
1073: static PetscErrorCode MatMultAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1074: {
1075: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1076: VecScatter Mvctx = a->Mvctx;
1078: PetscFunctionBegin;
1079: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1080: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1081: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1082: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1083: PetscFunctionReturn(PETSC_SUCCESS);
1084: }
1086: static PetscErrorCode MatMultTranspose_MPIAIJ(Mat A, Vec xx, Vec yy)
1087: {
1088: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1090: PetscFunctionBegin;
1091: /* do nondiagonal part */
1092: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1093: /* do local part */
1094: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1095: /* add partial results together */
1096: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1097: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1098: PetscFunctionReturn(PETSC_SUCCESS);
1099: }
1101: static PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f)
1102: {
1103: MPI_Comm comm;
1104: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)Amat->data, *Bij = (Mat_MPIAIJ *)Bmat->data;
1105: Mat Adia = Aij->A, Bdia = Bij->A, Aoff, Boff, *Aoffs, *Boffs;
1106: IS Me, Notme;
1107: PetscInt M, N, first, last, *notme, i;
1108: PetscBool lf;
1109: PetscMPIInt size;
1111: PetscFunctionBegin;
1112: /* Easy test: symmetric diagonal block */
1113: PetscCall(MatIsTranspose(Adia, Bdia, tol, &lf));
1114: PetscCallMPI(MPIU_Allreduce(&lf, f, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)Amat)));
1115: if (!*f) PetscFunctionReturn(PETSC_SUCCESS);
1116: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1117: PetscCallMPI(MPI_Comm_size(comm, &size));
1118: if (size == 1) PetscFunctionReturn(PETSC_SUCCESS);
1120: /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */
1121: PetscCall(MatGetSize(Amat, &M, &N));
1122: PetscCall(MatGetOwnershipRange(Amat, &first, &last));
1123: PetscCall(PetscMalloc1(N - last + first, ¬me));
1124: for (i = 0; i < first; i++) notme[i] = i;
1125: for (i = last; i < M; i++) notme[i - last + first] = i;
1126: PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme));
1127: PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me));
1128: PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs));
1129: Aoff = Aoffs[0];
1130: PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs));
1131: Boff = Boffs[0];
1132: PetscCall(MatIsTranspose(Aoff, Boff, tol, f));
1133: PetscCall(MatDestroyMatrices(1, &Aoffs));
1134: PetscCall(MatDestroyMatrices(1, &Boffs));
1135: PetscCall(ISDestroy(&Me));
1136: PetscCall(ISDestroy(&Notme));
1137: PetscCall(PetscFree(notme));
1138: PetscFunctionReturn(PETSC_SUCCESS);
1139: }
1141: static PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1142: {
1143: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1145: PetscFunctionBegin;
1146: /* do nondiagonal part */
1147: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1148: /* do local part */
1149: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1150: /* add partial results together */
1151: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1152: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1153: PetscFunctionReturn(PETSC_SUCCESS);
1154: }
1156: /*
1157: This only works correctly for square matrices where the subblock A->A is the
1158: diagonal block
1159: */
1160: static PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A, Vec v)
1161: {
1162: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1164: PetscFunctionBegin;
1165: PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1166: 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");
1167: PetscCall(MatGetDiagonal(a->A, v));
1168: PetscFunctionReturn(PETSC_SUCCESS);
1169: }
1171: static PetscErrorCode MatScale_MPIAIJ(Mat A, PetscScalar aa)
1172: {
1173: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1175: PetscFunctionBegin;
1176: PetscCall(MatScale(a->A, aa));
1177: PetscCall(MatScale(a->B, aa));
1178: PetscFunctionReturn(PETSC_SUCCESS);
1179: }
1181: static PetscErrorCode MatView_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
1182: {
1183: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1184: Mat_SeqAIJ *A = (Mat_SeqAIJ *)aij->A->data;
1185: Mat_SeqAIJ *B = (Mat_SeqAIJ *)aij->B->data;
1186: const PetscInt *garray = aij->garray;
1187: const PetscScalar *aa, *ba;
1188: PetscInt header[4], M, N, m, rs, cs, cnt, i, ja, jb;
1189: PetscInt64 nz, hnz;
1190: PetscInt *rowlens;
1191: PetscInt *colidxs;
1192: PetscScalar *matvals;
1193: PetscMPIInt rank;
1195: PetscFunctionBegin;
1196: PetscCall(PetscViewerSetUp(viewer));
1198: M = mat->rmap->N;
1199: N = mat->cmap->N;
1200: m = mat->rmap->n;
1201: rs = mat->rmap->rstart;
1202: cs = mat->cmap->rstart;
1203: nz = A->nz + B->nz;
1205: /* write matrix header */
1206: header[0] = MAT_FILE_CLASSID;
1207: header[1] = M;
1208: header[2] = N;
1209: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1210: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1211: if (rank == 0) PetscCall(PetscIntCast(hnz, &header[3]));
1212: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1214: /* fill in and store row lengths */
1215: PetscCall(PetscMalloc1(m, &rowlens));
1216: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i];
1217: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1218: PetscCall(PetscFree(rowlens));
1220: /* fill in and store column indices */
1221: PetscCall(PetscMalloc1(nz, &colidxs));
1222: for (cnt = 0, i = 0; i < m; i++) {
1223: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1224: if (garray[B->j[jb]] > cs) break;
1225: colidxs[cnt++] = garray[B->j[jb]];
1226: }
1227: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) colidxs[cnt++] = A->j[ja] + cs;
1228: for (; jb < B->i[i + 1]; jb++) colidxs[cnt++] = garray[B->j[jb]];
1229: }
1230: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1231: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
1232: PetscCall(PetscFree(colidxs));
1234: /* fill in and store nonzero values */
1235: PetscCall(MatSeqAIJGetArrayRead(aij->A, &aa));
1236: PetscCall(MatSeqAIJGetArrayRead(aij->B, &ba));
1237: PetscCall(PetscMalloc1(nz, &matvals));
1238: for (cnt = 0, i = 0; i < m; i++) {
1239: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1240: if (garray[B->j[jb]] > cs) break;
1241: matvals[cnt++] = ba[jb];
1242: }
1243: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) matvals[cnt++] = aa[ja];
1244: for (; jb < B->i[i + 1]; jb++) matvals[cnt++] = ba[jb];
1245: }
1246: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &aa));
1247: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &ba));
1248: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1249: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
1250: PetscCall(PetscFree(matvals));
1252: /* write block size option to the viewer's .info file */
1253: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1254: PetscFunctionReturn(PETSC_SUCCESS);
1255: }
1257: #include <petscdraw.h>
1258: static PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1259: {
1260: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1261: PetscMPIInt rank = aij->rank, size = aij->size;
1262: PetscBool isdraw, isascii, isbinary;
1263: PetscViewer sviewer;
1264: PetscViewerFormat format;
1266: PetscFunctionBegin;
1267: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1268: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1269: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1270: if (isascii) {
1271: PetscCall(PetscViewerGetFormat(viewer, &format));
1272: if (format == PETSC_VIEWER_LOAD_BALANCE) {
1273: PetscInt i, nmax = 0, nmin = PETSC_INT_MAX, navg = 0, *nz, nzlocal = ((Mat_SeqAIJ *)aij->A->data)->nz + ((Mat_SeqAIJ *)aij->B->data)->nz;
1274: PetscCall(PetscMalloc1(size, &nz));
1275: PetscCallMPI(MPI_Allgather(&nzlocal, 1, MPIU_INT, nz, 1, MPIU_INT, PetscObjectComm((PetscObject)mat)));
1276: for (i = 0; i < size; i++) {
1277: nmax = PetscMax(nmax, nz[i]);
1278: nmin = PetscMin(nmin, nz[i]);
1279: navg += nz[i];
1280: }
1281: PetscCall(PetscFree(nz));
1282: navg = navg / size;
1283: PetscCall(PetscViewerASCIIPrintf(viewer, "Load Balance - Nonzeros: Min %" PetscInt_FMT " avg %" PetscInt_FMT " max %" PetscInt_FMT "\n", nmin, navg, nmax));
1284: PetscFunctionReturn(PETSC_SUCCESS);
1285: }
1286: PetscCall(PetscViewerGetFormat(viewer, &format));
1287: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1288: MatInfo info;
1289: PetscInt *inodes = NULL;
1291: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1292: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1293: PetscCall(MatInodeGetInodeSizes(aij->A, NULL, &inodes, NULL));
1294: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1295: if (!inodes) {
1296: 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,
1297: info.memory));
1298: } else {
1299: PetscCall(
1300: 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, info.memory));
1301: }
1302: PetscCall(MatGetInfo(aij->A, MAT_LOCAL, &info));
1303: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1304: PetscCall(MatGetInfo(aij->B, MAT_LOCAL, &info));
1305: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1306: PetscCall(PetscViewerFlush(viewer));
1307: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1308: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1309: PetscCall(VecScatterView(aij->Mvctx, viewer));
1310: PetscFunctionReturn(PETSC_SUCCESS);
1311: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1312: PetscInt inodecount, inodelimit, *inodes;
1313: PetscCall(MatInodeGetInodeSizes(aij->A, &inodecount, &inodes, &inodelimit));
1314: if (inodes) {
1315: PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
1316: } else {
1317: PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
1318: }
1319: PetscFunctionReturn(PETSC_SUCCESS);
1320: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1321: PetscFunctionReturn(PETSC_SUCCESS);
1322: }
1323: } else if (isbinary) {
1324: if (size == 1) {
1325: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1326: PetscCall(MatView(aij->A, viewer));
1327: } else {
1328: PetscCall(MatView_MPIAIJ_Binary(mat, viewer));
1329: }
1330: PetscFunctionReturn(PETSC_SUCCESS);
1331: } else if (isascii && size == 1) {
1332: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1333: PetscCall(MatView(aij->A, viewer));
1334: PetscFunctionReturn(PETSC_SUCCESS);
1335: } else if (isdraw) {
1336: PetscDraw draw;
1337: PetscBool isnull;
1338: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1339: PetscCall(PetscDrawIsNull(draw, &isnull));
1340: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1341: }
1343: { /* assemble the entire matrix onto first processor */
1344: Mat A = NULL, Av;
1345: IS isrow, iscol;
1347: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->rmap->N : 0, 0, 1, &isrow));
1348: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->cmap->N : 0, 0, 1, &iscol));
1349: PetscCall(MatCreateSubMatrix(mat, isrow, iscol, MAT_INITIAL_MATRIX, &A));
1350: PetscCall(MatMPIAIJGetSeqAIJ(A, &Av, NULL, NULL));
1351: /* The commented code uses MatCreateSubMatrices instead */
1352: /*
1353: Mat *AA, A = NULL, Av;
1354: IS isrow,iscol;
1356: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->rmap->N : 0,0,1,&isrow));
1357: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->cmap->N : 0,0,1,&iscol));
1358: PetscCall(MatCreateSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&AA));
1359: if (rank == 0) {
1360: PetscCall(PetscObjectReference((PetscObject)AA[0]));
1361: A = AA[0];
1362: Av = AA[0];
1363: }
1364: PetscCall(MatDestroySubMatrices(1,&AA));
1365: */
1366: PetscCall(ISDestroy(&iscol));
1367: PetscCall(ISDestroy(&isrow));
1368: /*
1369: Everyone has to call to draw the matrix since the graphics waits are
1370: synchronized across all processors that share the PetscDraw object
1371: */
1372: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1373: if (rank == 0) {
1374: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)Av, ((PetscObject)mat)->name));
1375: PetscCall(MatView_SeqAIJ(Av, sviewer));
1376: }
1377: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1378: PetscCall(MatDestroy(&A));
1379: }
1380: PetscFunctionReturn(PETSC_SUCCESS);
1381: }
1383: PetscErrorCode MatView_MPIAIJ(Mat mat, PetscViewer viewer)
1384: {
1385: PetscBool isascii, isdraw, issocket, isbinary;
1387: PetscFunctionBegin;
1388: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1389: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1390: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1391: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1392: if (isascii || 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) PetscCall(MatMultDiagonalBlock(matin, xx, bb1));
1473: else PetscCall(VecPointwiseMult(bb1, mat->diag, xx));
1474: PetscCall(VecAYPX(bb1, (omega - 2.0) / omega, bb));
1476: PetscCall(MatMultAdd(mat->B, mat->lvec, bb1, bb1));
1478: /* local sweep */
1479: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP), fshift, lits, 1, xx1));
1480: PetscCall(VecAXPY(xx, 1.0, xx1));
1481: PetscCall(VecDestroy(&xx1));
1482: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");
1484: PetscCall(VecDestroy(&bb1));
1486: matin->factorerrortype = mat->A->factorerrortype;
1487: PetscFunctionReturn(PETSC_SUCCESS);
1488: }
1490: static PetscErrorCode MatPermute_MPIAIJ(Mat A, IS rowp, IS colp, Mat *B)
1491: {
1492: Mat aA, aB, Aperm;
1493: const PetscInt *rwant, *cwant, *gcols, *ai, *bi, *aj, *bj;
1494: PetscScalar *aa, *ba;
1495: PetscInt i, j, m, n, ng, anz, bnz, *dnnz, *onnz, *tdnnz, *tonnz, *rdest, *cdest, *work, *gcdest;
1496: PetscSF rowsf, sf;
1497: IS parcolp = NULL;
1498: PetscBool done;
1500: PetscFunctionBegin;
1501: PetscCall(MatGetLocalSize(A, &m, &n));
1502: PetscCall(ISGetIndices(rowp, &rwant));
1503: PetscCall(ISGetIndices(colp, &cwant));
1504: PetscCall(PetscMalloc3(PetscMax(m, n), &work, m, &rdest, n, &cdest));
1506: /* Invert row permutation to find out where my rows should go */
1507: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &rowsf));
1508: PetscCall(PetscSFSetGraphLayout(rowsf, A->rmap, A->rmap->n, NULL, PETSC_OWN_POINTER, rwant));
1509: PetscCall(PetscSFSetFromOptions(rowsf));
1510: for (i = 0; i < m; i++) work[i] = A->rmap->rstart + i;
1511: PetscCall(PetscSFReduceBegin(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1512: PetscCall(PetscSFReduceEnd(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1514: /* Invert column permutation to find out where my columns should go */
1515: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1516: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, A->cmap->n, NULL, PETSC_OWN_POINTER, cwant));
1517: PetscCall(PetscSFSetFromOptions(sf));
1518: for (i = 0; i < n; i++) work[i] = A->cmap->rstart + i;
1519: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1520: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1521: PetscCall(PetscSFDestroy(&sf));
1523: PetscCall(ISRestoreIndices(rowp, &rwant));
1524: PetscCall(ISRestoreIndices(colp, &cwant));
1525: PetscCall(MatMPIAIJGetSeqAIJ(A, &aA, &aB, &gcols));
1527: /* Find out where my gcols should go */
1528: PetscCall(MatGetSize(aB, NULL, &ng));
1529: PetscCall(PetscMalloc1(ng, &gcdest));
1530: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1531: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, ng, NULL, PETSC_OWN_POINTER, gcols));
1532: PetscCall(PetscSFSetFromOptions(sf));
1533: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1534: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1535: PetscCall(PetscSFDestroy(&sf));
1537: PetscCall(PetscCalloc4(m, &dnnz, m, &onnz, m, &tdnnz, m, &tonnz));
1538: PetscCall(MatGetRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1539: PetscCall(MatGetRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1540: for (i = 0; i < m; i++) {
1541: PetscInt row = rdest[i];
1542: PetscMPIInt rowner;
1543: PetscCall(PetscLayoutFindOwner(A->rmap, row, &rowner));
1544: for (j = ai[i]; j < ai[i + 1]; j++) {
1545: PetscInt col = cdest[aj[j]];
1546: PetscMPIInt cowner;
1547: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner)); /* Could build an index for the columns to eliminate this search */
1548: if (rowner == cowner) dnnz[i]++;
1549: else onnz[i]++;
1550: }
1551: for (j = bi[i]; j < bi[i + 1]; j++) {
1552: PetscInt col = gcdest[bj[j]];
1553: PetscMPIInt cowner;
1554: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner));
1555: if (rowner == cowner) dnnz[i]++;
1556: else onnz[i]++;
1557: }
1558: }
1559: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1560: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1561: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1562: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1563: PetscCall(PetscSFDestroy(&rowsf));
1565: PetscCall(MatCreateAIJ(PetscObjectComm((PetscObject)A), A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N, 0, tdnnz, 0, tonnz, &Aperm));
1566: PetscCall(MatSeqAIJGetArray(aA, &aa));
1567: PetscCall(MatSeqAIJGetArray(aB, &ba));
1568: for (i = 0; i < m; i++) {
1569: PetscInt *acols = dnnz, *bcols = onnz; /* Repurpose now-unneeded arrays */
1570: PetscInt j0, rowlen;
1571: rowlen = ai[i + 1] - ai[i];
1572: for (j0 = j = 0; j < rowlen; j0 = j) { /* rowlen could be larger than number of rows m, so sum in batches */
1573: for (; j < PetscMin(rowlen, j0 + m); j++) acols[j - j0] = cdest[aj[ai[i] + j]];
1574: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, acols, aa + ai[i] + j0, INSERT_VALUES));
1575: }
1576: rowlen = bi[i + 1] - bi[i];
1577: for (j0 = j = 0; j < rowlen; j0 = j) {
1578: for (; j < PetscMin(rowlen, j0 + m); j++) bcols[j - j0] = gcdest[bj[bi[i] + j]];
1579: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, bcols, ba + bi[i] + j0, INSERT_VALUES));
1580: }
1581: }
1582: PetscCall(MatAssemblyBegin(Aperm, MAT_FINAL_ASSEMBLY));
1583: PetscCall(MatAssemblyEnd(Aperm, MAT_FINAL_ASSEMBLY));
1584: PetscCall(MatRestoreRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1585: PetscCall(MatRestoreRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1586: PetscCall(MatSeqAIJRestoreArray(aA, &aa));
1587: PetscCall(MatSeqAIJRestoreArray(aB, &ba));
1588: PetscCall(PetscFree4(dnnz, onnz, tdnnz, tonnz));
1589: PetscCall(PetscFree3(work, rdest, cdest));
1590: PetscCall(PetscFree(gcdest));
1591: if (parcolp) PetscCall(ISDestroy(&colp));
1592: *B = Aperm;
1593: PetscFunctionReturn(PETSC_SUCCESS);
1594: }
1596: static PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
1597: {
1598: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1600: PetscFunctionBegin;
1601: PetscCall(MatGetSize(aij->B, NULL, nghosts));
1602: if (ghosts) *ghosts = aij->garray;
1603: PetscFunctionReturn(PETSC_SUCCESS);
1604: }
1606: static PetscErrorCode MatGetInfo_MPIAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1607: {
1608: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1609: Mat A = mat->A, B = mat->B;
1610: PetscLogDouble isend[5], irecv[5];
1612: PetscFunctionBegin;
1613: info->block_size = 1.0;
1614: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1616: isend[0] = info->nz_used;
1617: isend[1] = info->nz_allocated;
1618: isend[2] = info->nz_unneeded;
1619: isend[3] = info->memory;
1620: isend[4] = info->mallocs;
1622: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1624: isend[0] += info->nz_used;
1625: isend[1] += info->nz_allocated;
1626: isend[2] += info->nz_unneeded;
1627: isend[3] += info->memory;
1628: isend[4] += info->mallocs;
1629: if (flag == MAT_LOCAL) {
1630: info->nz_used = isend[0];
1631: info->nz_allocated = isend[1];
1632: info->nz_unneeded = isend[2];
1633: info->memory = isend[3];
1634: info->mallocs = isend[4];
1635: } else if (flag == MAT_GLOBAL_MAX) {
1636: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1638: info->nz_used = irecv[0];
1639: info->nz_allocated = irecv[1];
1640: info->nz_unneeded = irecv[2];
1641: info->memory = irecv[3];
1642: info->mallocs = irecv[4];
1643: } else if (flag == MAT_GLOBAL_SUM) {
1644: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1646: info->nz_used = irecv[0];
1647: info->nz_allocated = irecv[1];
1648: info->nz_unneeded = irecv[2];
1649: info->memory = irecv[3];
1650: info->mallocs = irecv[4];
1651: }
1652: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1653: info->fill_ratio_needed = 0;
1654: info->factor_mallocs = 0;
1655: PetscFunctionReturn(PETSC_SUCCESS);
1656: }
1658: PetscErrorCode MatSetOption_MPIAIJ(Mat A, MatOption op, PetscBool flg)
1659: {
1660: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1662: PetscFunctionBegin;
1663: switch (op) {
1664: case MAT_NEW_NONZERO_LOCATIONS:
1665: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1666: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1667: case MAT_KEEP_NONZERO_PATTERN:
1668: case MAT_NEW_NONZERO_LOCATION_ERR:
1669: case MAT_USE_INODES:
1670: case MAT_IGNORE_ZERO_ENTRIES:
1671: case MAT_FORM_EXPLICIT_TRANSPOSE:
1672: MatCheckPreallocated(A, 1);
1673: PetscCall(MatSetOption(a->A, op, flg));
1674: PetscCall(MatSetOption(a->B, op, flg));
1675: break;
1676: case MAT_ROW_ORIENTED:
1677: MatCheckPreallocated(A, 1);
1678: a->roworiented = flg;
1680: PetscCall(MatSetOption(a->A, op, flg));
1681: PetscCall(MatSetOption(a->B, op, flg));
1682: break;
1683: case MAT_IGNORE_OFF_PROC_ENTRIES:
1684: a->donotstash = flg;
1685: break;
1686: /* Symmetry flags are handled directly by MatSetOption() and they don't affect preallocation */
1687: case MAT_SPD:
1688: case MAT_SYMMETRIC:
1689: case MAT_STRUCTURALLY_SYMMETRIC:
1690: case MAT_HERMITIAN:
1691: case MAT_SYMMETRY_ETERNAL:
1692: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1693: case MAT_SPD_ETERNAL:
1694: /* if the diagonal matrix is square it inherits some of the properties above */
1695: if (a->A && A->rmap->n == A->cmap->n) PetscCall(MatSetOption(a->A, op, flg));
1696: break;
1697: case MAT_SUBMAT_SINGLEIS:
1698: A->submat_singleis = flg;
1699: break;
1700: default:
1701: break;
1702: }
1703: PetscFunctionReturn(PETSC_SUCCESS);
1704: }
1706: PetscErrorCode MatGetRow_MPIAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1707: {
1708: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1709: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1710: PetscInt i, *cworkA, *cworkB, **pcA, **pcB, cstart = matin->cmap->rstart;
1711: PetscInt nztot, nzA, nzB, lrow, rstart = matin->rmap->rstart, rend = matin->rmap->rend;
1712: PetscInt *cmap, *idx_p;
1714: PetscFunctionBegin;
1715: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1716: mat->getrowactive = PETSC_TRUE;
1718: if (!mat->rowvalues && (idx || v)) {
1719: /*
1720: allocate enough space to hold information from the longest row.
1721: */
1722: Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)mat->A->data, *Ba = (Mat_SeqAIJ *)mat->B->data;
1723: PetscInt max = 1, tmp;
1724: for (i = 0; i < matin->rmap->n; i++) {
1725: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1726: if (max < tmp) max = tmp;
1727: }
1728: PetscCall(PetscMalloc2(max, &mat->rowvalues, max, &mat->rowindices));
1729: }
1731: PetscCheck(row >= rstart && row < rend, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Only local rows");
1732: lrow = row - rstart;
1734: pvA = &vworkA;
1735: pcA = &cworkA;
1736: pvB = &vworkB;
1737: pcB = &cworkB;
1738: if (!v) {
1739: pvA = NULL;
1740: pvB = NULL;
1741: }
1742: if (!idx) {
1743: pcA = NULL;
1744: if (!v) pcB = NULL;
1745: }
1746: PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1747: PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1748: nztot = nzA + nzB;
1750: cmap = mat->garray;
1751: if (v || idx) {
1752: if (nztot) {
1753: /* Sort by increasing column numbers, assuming A and B already sorted */
1754: PetscInt imark = -1;
1755: if (v) {
1756: *v = v_p = mat->rowvalues;
1757: for (i = 0; i < nzB; i++) {
1758: if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1759: else break;
1760: }
1761: imark = i;
1762: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1763: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1764: }
1765: if (idx) {
1766: *idx = idx_p = mat->rowindices;
1767: if (imark > -1) {
1768: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i]];
1769: } else {
1770: for (i = 0; i < nzB; i++) {
1771: if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1772: else break;
1773: }
1774: imark = i;
1775: }
1776: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart + cworkA[i];
1777: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i]];
1778: }
1779: } else {
1780: if (idx) *idx = NULL;
1781: if (v) *v = NULL;
1782: }
1783: }
1784: *nz = nztot;
1785: PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1786: PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1787: PetscFunctionReturn(PETSC_SUCCESS);
1788: }
1790: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1791: {
1792: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1794: PetscFunctionBegin;
1795: PetscCheck(aij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow() must be called first");
1796: aij->getrowactive = PETSC_FALSE;
1797: PetscFunctionReturn(PETSC_SUCCESS);
1798: }
1800: static PetscErrorCode MatNorm_MPIAIJ(Mat mat, NormType type, PetscReal *norm)
1801: {
1802: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1803: Mat_SeqAIJ *amat = (Mat_SeqAIJ *)aij->A->data, *bmat = (Mat_SeqAIJ *)aij->B->data;
1804: PetscInt i, j;
1805: PetscReal sum = 0.0;
1806: const MatScalar *v, *amata, *bmata;
1808: PetscFunctionBegin;
1809: if (aij->size == 1) {
1810: PetscCall(MatNorm(aij->A, type, norm));
1811: } else {
1812: PetscCall(MatSeqAIJGetArrayRead(aij->A, &amata));
1813: PetscCall(MatSeqAIJGetArrayRead(aij->B, &bmata));
1814: if (type == NORM_FROBENIUS) {
1815: v = amata;
1816: for (i = 0; i < amat->nz; i++) {
1817: sum += PetscRealPart(PetscConj(*v) * (*v));
1818: v++;
1819: }
1820: v = bmata;
1821: for (i = 0; i < bmat->nz; i++) {
1822: sum += PetscRealPart(PetscConj(*v) * (*v));
1823: v++;
1824: }
1825: PetscCallMPI(MPIU_Allreduce(&sum, norm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1826: *norm = PetscSqrtReal(*norm);
1827: PetscCall(PetscLogFlops(2.0 * amat->nz + 2.0 * bmat->nz));
1828: } else if (type == NORM_1) { /* max column norm */
1829: Vec col, bcol;
1830: PetscScalar *array;
1831: PetscInt *jj, *garray = aij->garray;
1833: PetscCall(MatCreateVecs(mat, &col, NULL));
1834: PetscCall(VecSet(col, 0.0));
1835: PetscCall(VecGetArrayWrite(col, &array));
1836: v = amata;
1837: jj = amat->j;
1838: for (j = 0; j < amat->nz; j++) array[*jj++] += PetscAbsScalar(*v++);
1839: PetscCall(VecRestoreArrayWrite(col, &array));
1840: PetscCall(MatCreateVecs(aij->B, &bcol, NULL));
1841: PetscCall(VecSet(bcol, 0.0));
1842: PetscCall(VecGetArrayWrite(bcol, &array));
1843: v = bmata;
1844: jj = bmat->j;
1845: for (j = 0; j < bmat->nz; j++) array[*jj++] += PetscAbsScalar(*v++);
1846: PetscCall(VecSetValues(col, aij->B->cmap->n, garray, array, ADD_VALUES));
1847: PetscCall(VecRestoreArrayWrite(bcol, &array));
1848: PetscCall(VecDestroy(&bcol));
1849: PetscCall(VecAssemblyBegin(col));
1850: PetscCall(VecAssemblyEnd(col));
1851: PetscCall(VecNorm(col, NORM_INFINITY, norm));
1852: PetscCall(VecDestroy(&col));
1853: } else if (type == NORM_INFINITY) { /* max row norm */
1854: PetscReal ntemp = 0.0;
1855: for (j = 0; j < aij->A->rmap->n; j++) {
1856: v = PetscSafePointerPlusOffset(amata, amat->i[j]);
1857: sum = 0.0;
1858: for (i = 0; i < amat->i[j + 1] - amat->i[j]; i++) {
1859: sum += PetscAbsScalar(*v);
1860: v++;
1861: }
1862: v = PetscSafePointerPlusOffset(bmata, bmat->i[j]);
1863: for (i = 0; i < bmat->i[j + 1] - bmat->i[j]; i++) {
1864: sum += PetscAbsScalar(*v);
1865: v++;
1866: }
1867: if (sum > ntemp) ntemp = sum;
1868: }
1869: PetscCallMPI(MPIU_Allreduce(&ntemp, norm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
1870: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1871: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for two norm");
1872: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &amata));
1873: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &bmata));
1874: }
1875: PetscFunctionReturn(PETSC_SUCCESS);
1876: }
1878: static PetscErrorCode MatTranspose_MPIAIJ(Mat A, MatReuse reuse, Mat *matout)
1879: {
1880: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *b;
1881: Mat_SeqAIJ *Aloc = (Mat_SeqAIJ *)a->A->data, *Bloc = (Mat_SeqAIJ *)a->B->data, *sub_B_diag;
1882: 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;
1883: const PetscInt *ai, *aj, *bi, *bj, *B_diag_i;
1884: Mat B, A_diag, *B_diag;
1885: const MatScalar *pbv, *bv;
1887: PetscFunctionBegin;
1888: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1889: ma = A->rmap->n;
1890: na = A->cmap->n;
1891: mb = a->B->rmap->n;
1892: nb = a->B->cmap->n;
1893: ai = Aloc->i;
1894: aj = Aloc->j;
1895: bi = Bloc->i;
1896: bj = Bloc->j;
1897: if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1898: PetscInt *d_nnz, *g_nnz, *o_nnz;
1899: PetscSFNode *oloc;
1900: PETSC_UNUSED PetscSF sf;
1902: PetscCall(PetscMalloc4(na, &d_nnz, na, &o_nnz, nb, &g_nnz, nb, &oloc));
1903: /* compute d_nnz for preallocation */
1904: PetscCall(PetscArrayzero(d_nnz, na));
1905: for (i = 0; i < ai[ma]; i++) d_nnz[aj[i]]++;
1906: /* compute local off-diagonal contributions */
1907: PetscCall(PetscArrayzero(g_nnz, nb));
1908: for (i = 0; i < bi[ma]; i++) g_nnz[bj[i]]++;
1909: /* map those to global */
1910: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1911: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, nb, NULL, PETSC_USE_POINTER, a->garray));
1912: PetscCall(PetscSFSetFromOptions(sf));
1913: PetscCall(PetscArrayzero(o_nnz, na));
1914: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1915: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1916: PetscCall(PetscSFDestroy(&sf));
1918: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1919: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1920: PetscCall(MatSetBlockSizes(B, A->cmap->bs, A->rmap->bs));
1921: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1922: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
1923: PetscCall(PetscFree4(d_nnz, o_nnz, g_nnz, oloc));
1924: } else {
1925: B = *matout;
1926: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
1927: }
1929: b = (Mat_MPIAIJ *)B->data;
1930: A_diag = a->A;
1931: B_diag = &b->A;
1932: sub_B_diag = (Mat_SeqAIJ *)(*B_diag)->data;
1933: A_diag_ncol = A_diag->cmap->N;
1934: B_diag_ilen = sub_B_diag->ilen;
1935: B_diag_i = sub_B_diag->i;
1937: /* Set ilen for diagonal of B */
1938: for (i = 0; i < A_diag_ncol; i++) B_diag_ilen[i] = B_diag_i[i + 1] - B_diag_i[i];
1940: /* Transpose the diagonal part of the matrix. In contrast to the off-diagonal part, this can be done
1941: very quickly (=without using MatSetValues), because all writes are local. */
1942: PetscCall(MatTransposeSetPrecursor(A_diag, *B_diag));
1943: PetscCall(MatTranspose(A_diag, MAT_REUSE_MATRIX, B_diag));
1945: /* copy over the B part */
1946: PetscCall(PetscMalloc1(bi[mb], &cols));
1947: PetscCall(MatSeqAIJGetArrayRead(a->B, &bv));
1948: pbv = bv;
1949: row = A->rmap->rstart;
1950: for (i = 0; i < bi[mb]; i++) cols[i] = a->garray[bj[i]];
1951: cols_tmp = cols;
1952: for (i = 0; i < mb; i++) {
1953: ncol = bi[i + 1] - bi[i];
1954: PetscCall(MatSetValues(B, ncol, cols_tmp, 1, &row, pbv, INSERT_VALUES));
1955: row++;
1956: if (pbv) pbv += ncol;
1957: if (cols_tmp) cols_tmp += ncol;
1958: }
1959: PetscCall(PetscFree(cols));
1960: PetscCall(MatSeqAIJRestoreArrayRead(a->B, &bv));
1962: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1963: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1964: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1965: *matout = B;
1966: } else {
1967: PetscCall(MatHeaderMerge(A, &B));
1968: }
1969: PetscFunctionReturn(PETSC_SUCCESS);
1970: }
1972: static PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat, Vec ll, Vec rr)
1973: {
1974: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1975: Mat a = aij->A, b = aij->B;
1976: PetscInt s1, s2, s3;
1978: PetscFunctionBegin;
1979: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1980: if (rr) {
1981: PetscCall(VecGetLocalSize(rr, &s1));
1982: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1983: /* Overlap communication with computation. */
1984: PetscCall(VecScatterBegin(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1985: }
1986: if (ll) {
1987: PetscCall(VecGetLocalSize(ll, &s1));
1988: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1989: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1990: }
1991: /* scale the diagonal block */
1992: PetscUseTypeMethod(a, diagonalscale, ll, rr);
1994: if (rr) {
1995: /* Do a scatter end and then right scale the off-diagonal block */
1996: PetscCall(VecScatterEnd(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1997: PetscUseTypeMethod(b, diagonalscale, NULL, aij->lvec);
1998: }
1999: PetscFunctionReturn(PETSC_SUCCESS);
2000: }
2002: static PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2003: {
2004: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2006: PetscFunctionBegin;
2007: PetscCall(MatSetUnfactored(a->A));
2008: PetscFunctionReturn(PETSC_SUCCESS);
2009: }
2011: static PetscErrorCode MatEqual_MPIAIJ(Mat A, Mat B, PetscBool *flag)
2012: {
2013: Mat_MPIAIJ *matB = (Mat_MPIAIJ *)B->data, *matA = (Mat_MPIAIJ *)A->data;
2014: Mat a, b, c, d;
2015: PetscBool flg;
2017: PetscFunctionBegin;
2018: a = matA->A;
2019: b = matA->B;
2020: c = matB->A;
2021: d = matB->B;
2023: PetscCall(MatEqual(a, c, &flg));
2024: if (flg) PetscCall(MatEqual(b, d, &flg));
2025: PetscCallMPI(MPIU_Allreduce(&flg, flag, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
2026: PetscFunctionReturn(PETSC_SUCCESS);
2027: }
2029: static PetscErrorCode MatCopy_MPIAIJ(Mat A, Mat B, MatStructure str)
2030: {
2031: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2032: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2034: PetscFunctionBegin;
2035: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2036: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2037: /* because of the column compression in the off-processor part of the matrix a->B,
2038: the number of columns in a->B and b->B may be different, hence we cannot call
2039: the MatCopy() directly on the two parts. If need be, we can provide a more
2040: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2041: then copying the submatrices */
2042: PetscCall(MatCopy_Basic(A, B, str));
2043: } else {
2044: PetscCall(MatCopy(a->A, b->A, str));
2045: PetscCall(MatCopy(a->B, b->B, str));
2046: }
2047: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2048: PetscFunctionReturn(PETSC_SUCCESS);
2049: }
2051: /*
2052: Computes the number of nonzeros per row needed for preallocation when X and Y
2053: have different nonzero structure.
2054: */
2055: 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)
2056: {
2057: PetscInt i, j, k, nzx, nzy;
2059: PetscFunctionBegin;
2060: /* Set the number of nonzeros in the new matrix */
2061: for (i = 0; i < m; i++) {
2062: const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2063: nzx = xi[i + 1] - xi[i];
2064: nzy = yi[i + 1] - yi[i];
2065: nnz[i] = 0;
2066: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2067: for (; k < nzy && yltog[yjj[k]] < xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2068: if (k < nzy && yltog[yjj[k]] == xltog[xjj[j]]) k++; /* Skip duplicate */
2069: nnz[i]++;
2070: }
2071: for (; k < nzy; k++) nnz[i]++;
2072: }
2073: PetscFunctionReturn(PETSC_SUCCESS);
2074: }
2076: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2077: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
2078: {
2079: PetscInt m = Y->rmap->N;
2080: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2081: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
2083: PetscFunctionBegin;
2084: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
2085: PetscFunctionReturn(PETSC_SUCCESS);
2086: }
2088: static PetscErrorCode MatAXPY_MPIAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2089: {
2090: Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data, *yy = (Mat_MPIAIJ *)Y->data;
2092: PetscFunctionBegin;
2093: if (str == SAME_NONZERO_PATTERN) {
2094: PetscCall(MatAXPY(yy->A, a, xx->A, str));
2095: PetscCall(MatAXPY(yy->B, a, xx->B, str));
2096: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2097: PetscCall(MatAXPY_Basic(Y, a, X, str));
2098: } else {
2099: Mat B;
2100: PetscInt *nnz_d, *nnz_o;
2102: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
2103: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
2104: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2105: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2106: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2107: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2108: PetscCall(MatAXPYGetPreallocation_SeqAIJ(yy->A, xx->A, nnz_d));
2109: PetscCall(MatAXPYGetPreallocation_MPIAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
2110: PetscCall(MatMPIAIJSetPreallocation(B, 0, nnz_d, 0, nnz_o));
2111: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2112: PetscCall(MatHeaderMerge(Y, &B));
2113: PetscCall(PetscFree(nnz_d));
2114: PetscCall(PetscFree(nnz_o));
2115: }
2116: PetscFunctionReturn(PETSC_SUCCESS);
2117: }
2119: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);
2121: static PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2122: {
2123: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2125: PetscFunctionBegin;
2126: PetscCall(MatConjugate_SeqAIJ(aij->A));
2127: PetscCall(MatConjugate_SeqAIJ(aij->B));
2128: PetscFunctionReturn(PETSC_SUCCESS);
2129: }
2131: static PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2132: {
2133: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2135: PetscFunctionBegin;
2136: PetscCall(MatRealPart(a->A));
2137: PetscCall(MatRealPart(a->B));
2138: PetscFunctionReturn(PETSC_SUCCESS);
2139: }
2141: static PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
2142: {
2143: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2145: PetscFunctionBegin;
2146: PetscCall(MatImaginaryPart(a->A));
2147: PetscCall(MatImaginaryPart(a->B));
2148: PetscFunctionReturn(PETSC_SUCCESS);
2149: }
2151: static PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2152: {
2153: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2154: PetscInt i, *idxb = NULL, m = A->rmap->n;
2155: PetscScalar *vv;
2156: Vec vB, vA;
2157: const PetscScalar *va, *vb;
2159: PetscFunctionBegin;
2160: PetscCall(MatCreateVecs(a->A, NULL, &vA));
2161: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
2163: PetscCall(VecGetArrayRead(vA, &va));
2164: if (idx) {
2165: for (i = 0; i < m; i++) {
2166: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2167: }
2168: }
2170: PetscCall(MatCreateVecs(a->B, NULL, &vB));
2171: PetscCall(PetscMalloc1(m, &idxb));
2172: PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));
2174: PetscCall(VecGetArrayWrite(v, &vv));
2175: PetscCall(VecGetArrayRead(vB, &vb));
2176: for (i = 0; i < m; i++) {
2177: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2178: vv[i] = vb[i];
2179: if (idx) idx[i] = a->garray[idxb[i]];
2180: } else {
2181: vv[i] = va[i];
2182: if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > a->garray[idxb[i]]) idx[i] = a->garray[idxb[i]];
2183: }
2184: }
2185: PetscCall(VecRestoreArrayWrite(v, &vv));
2186: PetscCall(VecRestoreArrayRead(vA, &va));
2187: PetscCall(VecRestoreArrayRead(vB, &vb));
2188: PetscCall(PetscFree(idxb));
2189: PetscCall(VecDestroy(&vA));
2190: PetscCall(VecDestroy(&vB));
2191: PetscFunctionReturn(PETSC_SUCCESS);
2192: }
2194: static PetscErrorCode MatGetRowSumAbs_MPIAIJ(Mat A, Vec v)
2195: {
2196: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2197: Vec vB, vA;
2199: PetscFunctionBegin;
2200: PetscCall(MatCreateVecs(a->A, NULL, &vA));
2201: PetscCall(MatGetRowSumAbs(a->A, vA));
2202: PetscCall(MatCreateVecs(a->B, NULL, &vB));
2203: PetscCall(MatGetRowSumAbs(a->B, vB));
2204: PetscCall(VecAXPY(vA, 1.0, vB));
2205: PetscCall(VecDestroy(&vB));
2206: PetscCall(VecCopy(vA, v));
2207: PetscCall(VecDestroy(&vA));
2208: PetscFunctionReturn(PETSC_SUCCESS);
2209: }
2211: static PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2212: {
2213: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2214: PetscInt m = A->rmap->n, n = A->cmap->n;
2215: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2216: PetscInt *cmap = mat->garray;
2217: PetscInt *diagIdx, *offdiagIdx;
2218: Vec diagV, offdiagV;
2219: PetscScalar *a, *diagA, *offdiagA;
2220: const PetscScalar *ba, *bav;
2221: PetscInt r, j, col, ncols, *bi, *bj;
2222: Mat B = mat->B;
2223: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2225: PetscFunctionBegin;
2226: /* When a process holds entire A and other processes have no entry */
2227: if (A->cmap->N == n) {
2228: PetscCall(VecGetArrayWrite(v, &diagA));
2229: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2230: PetscCall(MatGetRowMinAbs(mat->A, diagV, idx));
2231: PetscCall(VecDestroy(&diagV));
2232: PetscCall(VecRestoreArrayWrite(v, &diagA));
2233: PetscFunctionReturn(PETSC_SUCCESS);
2234: } else if (n == 0) {
2235: if (m) {
2236: PetscCall(VecGetArrayWrite(v, &a));
2237: for (r = 0; r < m; r++) {
2238: a[r] = 0.0;
2239: if (idx) idx[r] = -1;
2240: }
2241: PetscCall(VecRestoreArrayWrite(v, &a));
2242: }
2243: PetscFunctionReturn(PETSC_SUCCESS);
2244: }
2246: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2247: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2248: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2249: PetscCall(MatGetRowMinAbs(mat->A, diagV, diagIdx));
2251: /* Get offdiagIdx[] for implicit 0.0 */
2252: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2253: ba = bav;
2254: bi = b->i;
2255: bj = b->j;
2256: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2257: for (r = 0; r < m; r++) {
2258: ncols = bi[r + 1] - bi[r];
2259: if (ncols == A->cmap->N - n) { /* Brow is dense */
2260: offdiagA[r] = *ba;
2261: offdiagIdx[r] = cmap[0];
2262: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2263: offdiagA[r] = 0.0;
2265: /* Find first hole in the cmap */
2266: for (j = 0; j < ncols; j++) {
2267: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2268: if (col > j && j < cstart) {
2269: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2270: break;
2271: } else if (col > j + n && j >= cstart) {
2272: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2273: break;
2274: }
2275: }
2276: if (j == ncols && ncols < A->cmap->N - n) {
2277: /* a hole is outside compressed Bcols */
2278: if (ncols == 0) {
2279: if (cstart) {
2280: offdiagIdx[r] = 0;
2281: } else offdiagIdx[r] = cend;
2282: } else { /* ncols > 0 */
2283: offdiagIdx[r] = cmap[ncols - 1] + 1;
2284: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2285: }
2286: }
2287: }
2289: for (j = 0; j < ncols; j++) {
2290: if (PetscAbsScalar(offdiagA[r]) > PetscAbsScalar(*ba)) {
2291: offdiagA[r] = *ba;
2292: offdiagIdx[r] = cmap[*bj];
2293: }
2294: ba++;
2295: bj++;
2296: }
2297: }
2299: PetscCall(VecGetArrayWrite(v, &a));
2300: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2301: for (r = 0; r < m; ++r) {
2302: if (PetscAbsScalar(diagA[r]) < PetscAbsScalar(offdiagA[r])) {
2303: a[r] = diagA[r];
2304: if (idx) idx[r] = cstart + diagIdx[r];
2305: } else if (PetscAbsScalar(diagA[r]) == PetscAbsScalar(offdiagA[r])) {
2306: a[r] = diagA[r];
2307: if (idx) {
2308: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2309: idx[r] = cstart + diagIdx[r];
2310: } else idx[r] = offdiagIdx[r];
2311: }
2312: } else {
2313: a[r] = offdiagA[r];
2314: if (idx) idx[r] = offdiagIdx[r];
2315: }
2316: }
2317: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2318: PetscCall(VecRestoreArrayWrite(v, &a));
2319: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2320: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2321: PetscCall(VecDestroy(&diagV));
2322: PetscCall(VecDestroy(&offdiagV));
2323: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2324: PetscFunctionReturn(PETSC_SUCCESS);
2325: }
2327: static PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2328: {
2329: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2330: PetscInt m = A->rmap->n, n = A->cmap->n;
2331: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2332: PetscInt *cmap = mat->garray;
2333: PetscInt *diagIdx, *offdiagIdx;
2334: Vec diagV, offdiagV;
2335: PetscScalar *a, *diagA, *offdiagA;
2336: const PetscScalar *ba, *bav;
2337: PetscInt r, j, col, ncols, *bi, *bj;
2338: Mat B = mat->B;
2339: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2341: PetscFunctionBegin;
2342: /* When a process holds entire A and other processes have no entry */
2343: if (A->cmap->N == n) {
2344: PetscCall(VecGetArrayWrite(v, &diagA));
2345: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2346: PetscCall(MatGetRowMin(mat->A, diagV, idx));
2347: PetscCall(VecDestroy(&diagV));
2348: PetscCall(VecRestoreArrayWrite(v, &diagA));
2349: PetscFunctionReturn(PETSC_SUCCESS);
2350: } else if (n == 0) {
2351: if (m) {
2352: PetscCall(VecGetArrayWrite(v, &a));
2353: for (r = 0; r < m; r++) {
2354: a[r] = PETSC_MAX_REAL;
2355: if (idx) idx[r] = -1;
2356: }
2357: PetscCall(VecRestoreArrayWrite(v, &a));
2358: }
2359: PetscFunctionReturn(PETSC_SUCCESS);
2360: }
2362: PetscCall(PetscCalloc2(m, &diagIdx, m, &offdiagIdx));
2363: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2364: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2365: PetscCall(MatGetRowMin(mat->A, diagV, diagIdx));
2367: /* Get offdiagIdx[] for implicit 0.0 */
2368: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2369: ba = bav;
2370: bi = b->i;
2371: bj = b->j;
2372: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2373: for (r = 0; r < m; r++) {
2374: ncols = bi[r + 1] - bi[r];
2375: if (ncols == A->cmap->N - n) { /* Brow is dense */
2376: offdiagA[r] = *ba;
2377: offdiagIdx[r] = cmap[0];
2378: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2379: offdiagA[r] = 0.0;
2381: /* Find first hole in the cmap */
2382: for (j = 0; j < ncols; j++) {
2383: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2384: if (col > j && j < cstart) {
2385: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2386: break;
2387: } else if (col > j + n && j >= cstart) {
2388: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2389: break;
2390: }
2391: }
2392: if (j == ncols && ncols < A->cmap->N - n) {
2393: /* a hole is outside compressed Bcols */
2394: if (ncols == 0) {
2395: if (cstart) {
2396: offdiagIdx[r] = 0;
2397: } else offdiagIdx[r] = cend;
2398: } else { /* ncols > 0 */
2399: offdiagIdx[r] = cmap[ncols - 1] + 1;
2400: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2401: }
2402: }
2403: }
2405: for (j = 0; j < ncols; j++) {
2406: if (PetscRealPart(offdiagA[r]) > PetscRealPart(*ba)) {
2407: offdiagA[r] = *ba;
2408: offdiagIdx[r] = cmap[*bj];
2409: }
2410: ba++;
2411: bj++;
2412: }
2413: }
2415: PetscCall(VecGetArrayWrite(v, &a));
2416: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2417: for (r = 0; r < m; ++r) {
2418: if (PetscRealPart(diagA[r]) < PetscRealPart(offdiagA[r])) {
2419: a[r] = diagA[r];
2420: if (idx) idx[r] = cstart + diagIdx[r];
2421: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2422: a[r] = diagA[r];
2423: if (idx) {
2424: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2425: idx[r] = cstart + diagIdx[r];
2426: } else idx[r] = offdiagIdx[r];
2427: }
2428: } else {
2429: a[r] = offdiagA[r];
2430: if (idx) idx[r] = offdiagIdx[r];
2431: }
2432: }
2433: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2434: PetscCall(VecRestoreArrayWrite(v, &a));
2435: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2436: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2437: PetscCall(VecDestroy(&diagV));
2438: PetscCall(VecDestroy(&offdiagV));
2439: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2440: PetscFunctionReturn(PETSC_SUCCESS);
2441: }
2443: static PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2444: {
2445: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2446: PetscInt m = A->rmap->n, n = A->cmap->n;
2447: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2448: PetscInt *cmap = mat->garray;
2449: PetscInt *diagIdx, *offdiagIdx;
2450: Vec diagV, offdiagV;
2451: PetscScalar *a, *diagA, *offdiagA;
2452: const PetscScalar *ba, *bav;
2453: PetscInt r, j, col, ncols, *bi, *bj;
2454: Mat B = mat->B;
2455: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2457: PetscFunctionBegin;
2458: /* When a process holds entire A and other processes have no entry */
2459: if (A->cmap->N == n) {
2460: PetscCall(VecGetArrayWrite(v, &diagA));
2461: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2462: PetscCall(MatGetRowMax(mat->A, diagV, idx));
2463: PetscCall(VecDestroy(&diagV));
2464: PetscCall(VecRestoreArrayWrite(v, &diagA));
2465: PetscFunctionReturn(PETSC_SUCCESS);
2466: } else if (n == 0) {
2467: if (m) {
2468: PetscCall(VecGetArrayWrite(v, &a));
2469: for (r = 0; r < m; r++) {
2470: a[r] = PETSC_MIN_REAL;
2471: if (idx) idx[r] = -1;
2472: }
2473: PetscCall(VecRestoreArrayWrite(v, &a));
2474: }
2475: PetscFunctionReturn(PETSC_SUCCESS);
2476: }
2478: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2479: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2480: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2481: PetscCall(MatGetRowMax(mat->A, diagV, diagIdx));
2483: /* Get offdiagIdx[] for implicit 0.0 */
2484: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2485: ba = bav;
2486: bi = b->i;
2487: bj = b->j;
2488: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2489: for (r = 0; r < m; r++) {
2490: ncols = bi[r + 1] - bi[r];
2491: if (ncols == A->cmap->N - n) { /* Brow is dense */
2492: offdiagA[r] = *ba;
2493: offdiagIdx[r] = cmap[0];
2494: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2495: offdiagA[r] = 0.0;
2497: /* Find first hole in the cmap */
2498: for (j = 0; j < ncols; j++) {
2499: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2500: if (col > j && j < cstart) {
2501: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2502: break;
2503: } else if (col > j + n && j >= cstart) {
2504: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2505: break;
2506: }
2507: }
2508: if (j == ncols && ncols < A->cmap->N - n) {
2509: /* a hole is outside compressed Bcols */
2510: if (ncols == 0) {
2511: if (cstart) {
2512: offdiagIdx[r] = 0;
2513: } else offdiagIdx[r] = cend;
2514: } else { /* ncols > 0 */
2515: offdiagIdx[r] = cmap[ncols - 1] + 1;
2516: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2517: }
2518: }
2519: }
2521: for (j = 0; j < ncols; j++) {
2522: if (PetscRealPart(offdiagA[r]) < PetscRealPart(*ba)) {
2523: offdiagA[r] = *ba;
2524: offdiagIdx[r] = cmap[*bj];
2525: }
2526: ba++;
2527: bj++;
2528: }
2529: }
2531: PetscCall(VecGetArrayWrite(v, &a));
2532: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2533: for (r = 0; r < m; ++r) {
2534: if (PetscRealPart(diagA[r]) > PetscRealPart(offdiagA[r])) {
2535: a[r] = diagA[r];
2536: if (idx) idx[r] = cstart + diagIdx[r];
2537: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2538: a[r] = diagA[r];
2539: if (idx) {
2540: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2541: idx[r] = cstart + diagIdx[r];
2542: } else idx[r] = offdiagIdx[r];
2543: }
2544: } else {
2545: a[r] = offdiagA[r];
2546: if (idx) idx[r] = offdiagIdx[r];
2547: }
2548: }
2549: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2550: PetscCall(VecRestoreArrayWrite(v, &a));
2551: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2552: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2553: PetscCall(VecDestroy(&diagV));
2554: PetscCall(VecDestroy(&offdiagV));
2555: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2556: PetscFunctionReturn(PETSC_SUCCESS);
2557: }
2559: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat, Mat *newmat)
2560: {
2561: Mat *dummy;
2563: PetscFunctionBegin;
2564: PetscCall(MatCreateSubMatrix_MPIAIJ_All(mat, MAT_DO_NOT_GET_VALUES, MAT_INITIAL_MATRIX, &dummy));
2565: *newmat = *dummy;
2566: PetscCall(PetscFree(dummy));
2567: PetscFunctionReturn(PETSC_SUCCESS);
2568: }
2570: static PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A, const PetscScalar **values)
2571: {
2572: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2574: PetscFunctionBegin;
2575: PetscCall(MatInvertBlockDiagonal(a->A, values));
2576: A->factorerrortype = a->A->factorerrortype;
2577: PetscFunctionReturn(PETSC_SUCCESS);
2578: }
2580: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x, PetscRandom rctx)
2581: {
2582: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)x->data;
2584: PetscFunctionBegin;
2585: PetscCheck(x->assembled || x->preallocated, PetscObjectComm((PetscObject)x), PETSC_ERR_ARG_WRONGSTATE, "MatSetRandom on an unassembled and unpreallocated MATMPIAIJ is not allowed");
2586: PetscCall(MatSetRandom(aij->A, rctx));
2587: if (x->assembled) {
2588: PetscCall(MatSetRandom(aij->B, rctx));
2589: } else {
2590: PetscCall(MatSetRandomSkipColumnRange_SeqAIJ_Private(aij->B, x->cmap->rstart, x->cmap->rend, rctx));
2591: }
2592: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
2593: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
2594: PetscFunctionReturn(PETSC_SUCCESS);
2595: }
2597: static PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ(Mat A, PetscBool sc)
2598: {
2599: PetscFunctionBegin;
2600: if (sc) A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ_Scalable;
2601: else A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ;
2602: PetscFunctionReturn(PETSC_SUCCESS);
2603: }
2605: /*@
2606: MatMPIAIJGetNumberNonzeros - gets the number of nonzeros in the matrix on this MPI rank
2608: Not Collective
2610: Input Parameter:
2611: . A - the matrix
2613: Output Parameter:
2614: . nz - the number of nonzeros
2616: Level: advanced
2618: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2619: @*/
2620: PetscErrorCode MatMPIAIJGetNumberNonzeros(Mat A, PetscCount *nz)
2621: {
2622: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)A->data;
2623: Mat_SeqAIJ *aaij = (Mat_SeqAIJ *)maij->A->data, *baij = (Mat_SeqAIJ *)maij->B->data;
2624: PetscBool isaij;
2626: PetscFunctionBegin;
2627: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATMPIAIJ, &isaij));
2628: PetscCheck(isaij, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for type %s", ((PetscObject)A)->type_name);
2629: *nz = aaij->i[A->rmap->n] + baij->i[A->rmap->n];
2630: PetscFunctionReturn(PETSC_SUCCESS);
2631: }
2633: /*@
2634: MatMPIAIJSetUseScalableIncreaseOverlap - Determine if the matrix uses a scalable algorithm to compute the overlap
2636: Collective
2638: Input Parameters:
2639: + A - the matrix
2640: - sc - `PETSC_TRUE` indicates use the scalable algorithm (default is not to use the scalable algorithm)
2642: Level: advanced
2644: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2645: @*/
2646: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap(Mat A, PetscBool sc)
2647: {
2648: PetscFunctionBegin;
2649: PetscTryMethod(A, "MatMPIAIJSetUseScalableIncreaseOverlap_C", (Mat, PetscBool), (A, sc));
2650: PetscFunctionReturn(PETSC_SUCCESS);
2651: }
2653: PetscErrorCode MatSetFromOptions_MPIAIJ(Mat A, PetscOptionItems PetscOptionsObject)
2654: {
2655: PetscBool sc = PETSC_FALSE, flg;
2657: PetscFunctionBegin;
2658: PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJ options");
2659: if (A->ops->increaseoverlap == MatIncreaseOverlap_MPIAIJ_Scalable) sc = PETSC_TRUE;
2660: PetscCall(PetscOptionsBool("-mat_increase_overlap_scalable", "Use a scalable algorithm to compute the overlap", "MatIncreaseOverlap", sc, &sc, &flg));
2661: if (flg) PetscCall(MatMPIAIJSetUseScalableIncreaseOverlap(A, sc));
2662: PetscOptionsHeadEnd();
2663: PetscFunctionReturn(PETSC_SUCCESS);
2664: }
2666: static PetscErrorCode MatShift_MPIAIJ(Mat Y, PetscScalar a)
2667: {
2668: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)Y->data;
2669: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)maij->A->data;
2671: PetscFunctionBegin;
2672: if (!Y->preallocated) {
2673: PetscCall(MatMPIAIJSetPreallocation(Y, 1, NULL, 0, NULL));
2674: } else if (!aij->nz) { /* It does not matter if diagonals of Y only partially lie in maij->A. We just need an estimated preallocation. */
2675: PetscInt nonew = aij->nonew;
2676: PetscCall(MatSeqAIJSetPreallocation(maij->A, 1, NULL));
2677: aij->nonew = nonew;
2678: }
2679: PetscCall(MatShift_Basic(Y, a));
2680: PetscFunctionReturn(PETSC_SUCCESS);
2681: }
2683: static PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2684: {
2685: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2687: PetscFunctionBegin;
2688: PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2689: PetscFunctionReturn(PETSC_SUCCESS);
2690: }
2692: static PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A, PetscBool keep)
2693: {
2694: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2696: PetscFunctionBegin;
2697: PetscCall(MatEliminateZeros_SeqAIJ(a->A, keep)); // possibly keep zero diagonal coefficients
2698: PetscCall(MatEliminateZeros_SeqAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2699: PetscFunctionReturn(PETSC_SUCCESS);
2700: }
2702: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2703: MatGetRow_MPIAIJ,
2704: MatRestoreRow_MPIAIJ,
2705: MatMult_MPIAIJ,
2706: /* 4*/ MatMultAdd_MPIAIJ,
2707: MatMultTranspose_MPIAIJ,
2708: MatMultTransposeAdd_MPIAIJ,
2709: NULL,
2710: NULL,
2711: NULL,
2712: /*10*/ NULL,
2713: NULL,
2714: NULL,
2715: MatSOR_MPIAIJ,
2716: MatTranspose_MPIAIJ,
2717: /*15*/ MatGetInfo_MPIAIJ,
2718: MatEqual_MPIAIJ,
2719: MatGetDiagonal_MPIAIJ,
2720: MatDiagonalScale_MPIAIJ,
2721: MatNorm_MPIAIJ,
2722: /*20*/ MatAssemblyBegin_MPIAIJ,
2723: MatAssemblyEnd_MPIAIJ,
2724: MatSetOption_MPIAIJ,
2725: MatZeroEntries_MPIAIJ,
2726: /*24*/ MatZeroRows_MPIAIJ,
2727: NULL,
2728: NULL,
2729: NULL,
2730: NULL,
2731: /*29*/ MatSetUp_MPI_Hash,
2732: NULL,
2733: NULL,
2734: MatGetDiagonalBlock_MPIAIJ,
2735: NULL,
2736: /*34*/ MatDuplicate_MPIAIJ,
2737: NULL,
2738: NULL,
2739: NULL,
2740: NULL,
2741: /*39*/ MatAXPY_MPIAIJ,
2742: MatCreateSubMatrices_MPIAIJ,
2743: MatIncreaseOverlap_MPIAIJ,
2744: MatGetValues_MPIAIJ,
2745: MatCopy_MPIAIJ,
2746: /*44*/ MatGetRowMax_MPIAIJ,
2747: MatScale_MPIAIJ,
2748: MatShift_MPIAIJ,
2749: MatDiagonalSet_MPIAIJ,
2750: MatZeroRowsColumns_MPIAIJ,
2751: /*49*/ MatSetRandom_MPIAIJ,
2752: MatGetRowIJ_MPIAIJ,
2753: MatRestoreRowIJ_MPIAIJ,
2754: NULL,
2755: NULL,
2756: /*54*/ MatFDColoringCreate_MPIXAIJ,
2757: NULL,
2758: MatSetUnfactored_MPIAIJ,
2759: MatPermute_MPIAIJ,
2760: NULL,
2761: /*59*/ MatCreateSubMatrix_MPIAIJ,
2762: MatDestroy_MPIAIJ,
2763: MatView_MPIAIJ,
2764: NULL,
2765: NULL,
2766: /*64*/ MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2767: NULL,
2768: NULL,
2769: NULL,
2770: MatGetRowMaxAbs_MPIAIJ,
2771: /*69*/ MatGetRowMinAbs_MPIAIJ,
2772: NULL,
2773: NULL,
2774: MatFDColoringApply_AIJ,
2775: MatSetFromOptions_MPIAIJ,
2776: MatFindZeroDiagonals_MPIAIJ,
2777: /*75*/ NULL,
2778: NULL,
2779: NULL,
2780: MatLoad_MPIAIJ,
2781: NULL,
2782: /*80*/ NULL,
2783: NULL,
2784: NULL,
2785: /*83*/ NULL,
2786: NULL,
2787: MatMatMultNumeric_MPIAIJ_MPIAIJ,
2788: MatPtAPNumeric_MPIAIJ_MPIAIJ,
2789: NULL,
2790: NULL,
2791: /*89*/ MatBindToCPU_MPIAIJ,
2792: MatProductSetFromOptions_MPIAIJ,
2793: NULL,
2794: NULL,
2795: MatConjugate_MPIAIJ,
2796: /*94*/ NULL,
2797: MatSetValuesRow_MPIAIJ,
2798: MatRealPart_MPIAIJ,
2799: MatImaginaryPart_MPIAIJ,
2800: NULL,
2801: /*99*/ NULL,
2802: NULL,
2803: NULL,
2804: MatGetRowMin_MPIAIJ,
2805: NULL,
2806: /*104*/ MatGetSeqNonzeroStructure_MPIAIJ,
2807: NULL,
2808: MatGetGhosts_MPIAIJ,
2809: NULL,
2810: NULL,
2811: /*109*/ MatMultDiagonalBlock_MPIAIJ,
2812: NULL,
2813: NULL,
2814: NULL,
2815: MatGetMultiProcBlock_MPIAIJ,
2816: /*114*/ MatFindNonzeroRows_MPIAIJ,
2817: MatGetColumnReductions_MPIAIJ,
2818: MatInvertBlockDiagonal_MPIAIJ,
2819: MatInvertVariableBlockDiagonal_MPIAIJ,
2820: MatCreateSubMatricesMPI_MPIAIJ,
2821: /*119*/ NULL,
2822: NULL,
2823: MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2824: NULL,
2825: NULL,
2826: /*124*/ NULL,
2827: NULL,
2828: MatSetBlockSizes_MPIAIJ,
2829: NULL,
2830: MatFDColoringSetUp_MPIXAIJ,
2831: /*129*/ MatFindOffBlockDiagonalEntries_MPIAIJ,
2832: MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2833: NULL,
2834: NULL,
2835: NULL,
2836: /*134*/ MatCreateGraph_Simple_AIJ,
2837: NULL,
2838: MatEliminateZeros_MPIAIJ,
2839: MatGetRowSumAbs_MPIAIJ,
2840: NULL,
2841: /*139*/ NULL,
2842: NULL,
2843: MatCopyHashToXAIJ_MPI_Hash,
2844: MatGetCurrentMemType_MPIAIJ,
2845: NULL,
2846: /*144*/ MatADot_Default,
2847: MatANorm_Default,
2848: NULL,
2849: NULL};
2851: static PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2852: {
2853: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2855: PetscFunctionBegin;
2856: PetscCall(MatStoreValues(aij->A));
2857: PetscCall(MatStoreValues(aij->B));
2858: PetscFunctionReturn(PETSC_SUCCESS);
2859: }
2861: static PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2862: {
2863: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2865: PetscFunctionBegin;
2866: PetscCall(MatRetrieveValues(aij->A));
2867: PetscCall(MatRetrieveValues(aij->B));
2868: PetscFunctionReturn(PETSC_SUCCESS);
2869: }
2871: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2872: {
2873: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2874: PetscMPIInt size;
2876: PetscFunctionBegin;
2877: if (B->hash_active) {
2878: B->ops[0] = b->cops;
2879: B->hash_active = PETSC_FALSE;
2880: }
2881: PetscCall(PetscLayoutSetUp(B->rmap));
2882: PetscCall(PetscLayoutSetUp(B->cmap));
2884: #if defined(PETSC_USE_CTABLE)
2885: PetscCall(PetscHMapIDestroy(&b->colmap));
2886: #else
2887: PetscCall(PetscFree(b->colmap));
2888: #endif
2889: PetscCall(PetscFree(b->garray));
2890: PetscCall(VecDestroy(&b->lvec));
2891: PetscCall(VecScatterDestroy(&b->Mvctx));
2893: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2895: MatSeqXAIJGetOptions_Private(b->B);
2896: PetscCall(MatDestroy(&b->B));
2897: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2898: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2899: PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2900: PetscCall(MatSetType(b->B, MATSEQAIJ));
2901: MatSeqXAIJRestoreOptions_Private(b->B);
2903: MatSeqXAIJGetOptions_Private(b->A);
2904: PetscCall(MatDestroy(&b->A));
2905: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2906: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2907: PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2908: PetscCall(MatSetType(b->A, MATSEQAIJ));
2909: MatSeqXAIJRestoreOptions_Private(b->A);
2911: PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2912: PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2913: B->preallocated = PETSC_TRUE;
2914: B->was_assembled = PETSC_FALSE;
2915: B->assembled = PETSC_FALSE;
2916: PetscFunctionReturn(PETSC_SUCCESS);
2917: }
2919: static PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2920: {
2921: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2922: PetscBool ondiagreset, offdiagreset, memoryreset;
2924: PetscFunctionBegin;
2926: PetscCheck(B->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset preallocation after setting some values but not yet calling MatAssemblyBegin()/MatAssemblyEnd()");
2927: if (B->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
2929: PetscCall(MatResetPreallocation_SeqAIJ_Private(b->A, &ondiagreset));
2930: PetscCall(MatResetPreallocation_SeqAIJ_Private(b->B, &offdiagreset));
2931: memoryreset = (PetscBool)(ondiagreset || offdiagreset);
2932: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &memoryreset, 1, MPI_C_BOOL, MPI_LOR, PetscObjectComm((PetscObject)B)));
2933: if (!memoryreset) PetscFunctionReturn(PETSC_SUCCESS);
2935: PetscCall(PetscLayoutSetUp(B->rmap));
2936: PetscCall(PetscLayoutSetUp(B->cmap));
2937: PetscCheck(B->assembled || B->was_assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Should not need to reset preallocation if the matrix was never assembled");
2938: PetscCall(MatDisAssemble_MPIAIJ(B, PETSC_TRUE));
2939: PetscCall(VecScatterDestroy(&b->Mvctx));
2941: B->preallocated = PETSC_TRUE;
2942: B->was_assembled = PETSC_FALSE;
2943: B->assembled = PETSC_FALSE;
2944: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
2945: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2946: PetscFunctionReturn(PETSC_SUCCESS);
2947: }
2949: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2950: {
2951: Mat mat;
2952: Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;
2954: PetscFunctionBegin;
2955: *newmat = NULL;
2956: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2957: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2958: PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2959: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2960: a = (Mat_MPIAIJ *)mat->data;
2962: mat->factortype = matin->factortype;
2963: mat->assembled = matin->assembled;
2964: mat->insertmode = NOT_SET_VALUES;
2966: a->size = oldmat->size;
2967: a->rank = oldmat->rank;
2968: a->donotstash = oldmat->donotstash;
2969: a->roworiented = oldmat->roworiented;
2970: a->rowindices = NULL;
2971: a->rowvalues = NULL;
2972: a->getrowactive = PETSC_FALSE;
2974: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
2975: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
2976: if (matin->hash_active) PetscCall(MatSetUp(mat));
2977: else {
2978: mat->preallocated = matin->preallocated;
2979: if (oldmat->colmap) {
2980: #if defined(PETSC_USE_CTABLE)
2981: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
2982: #else
2983: PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
2984: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
2985: #endif
2986: } else a->colmap = NULL;
2987: if (oldmat->garray) {
2988: PetscInt len;
2989: len = oldmat->B->cmap->n;
2990: PetscCall(PetscMalloc1(len, &a->garray));
2991: if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
2992: } else a->garray = NULL;
2994: /* It may happen MatDuplicate is called with a non-assembled matrix
2995: In fact, MatDuplicate only requires the matrix to be preallocated
2996: This may happen inside a DMCreateMatrix_Shell */
2997: if (oldmat->lvec) PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
2998: if (oldmat->Mvctx) {
2999: a->Mvctx = oldmat->Mvctx;
3000: PetscCall(PetscObjectReference((PetscObject)oldmat->Mvctx));
3001: }
3002: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3003: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3004: }
3005: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3006: *newmat = mat;
3007: PetscFunctionReturn(PETSC_SUCCESS);
3008: }
3010: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3011: {
3012: PetscBool isbinary, ishdf5;
3014: PetscFunctionBegin;
3017: /* force binary viewer to load .info file if it has not yet done so */
3018: PetscCall(PetscViewerSetUp(viewer));
3019: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3020: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3021: if (isbinary) {
3022: PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3023: } else if (ishdf5) {
3024: #if defined(PETSC_HAVE_HDF5)
3025: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3026: #else
3027: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3028: #endif
3029: } else {
3030: 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);
3031: }
3032: PetscFunctionReturn(PETSC_SUCCESS);
3033: }
3035: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3036: {
3037: PetscInt header[4], M, N, m, nz, rows, cols, sum, i;
3038: PetscInt *rowidxs, *colidxs;
3039: PetscScalar *matvals;
3041: PetscFunctionBegin;
3042: PetscCall(PetscViewerSetUp(viewer));
3044: /* read in matrix header */
3045: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3046: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3047: M = header[1];
3048: N = header[2];
3049: nz = header[3];
3050: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3051: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3052: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");
3054: /* set block sizes from the viewer's .info file */
3055: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3056: /* set global sizes if not set already */
3057: if (mat->rmap->N < 0) mat->rmap->N = M;
3058: if (mat->cmap->N < 0) mat->cmap->N = N;
3059: PetscCall(PetscLayoutSetUp(mat->rmap));
3060: PetscCall(PetscLayoutSetUp(mat->cmap));
3062: /* check if the matrix sizes are correct */
3063: PetscCall(MatGetSize(mat, &rows, &cols));
3064: 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);
3066: /* read in row lengths and build row indices */
3067: PetscCall(MatGetLocalSize(mat, &m, NULL));
3068: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3069: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3070: rowidxs[0] = 0;
3071: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3072: if (nz != PETSC_INT_MAX) {
3073: PetscCallMPI(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3074: 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);
3075: }
3077: /* read in column indices and matrix values */
3078: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3079: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3080: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3081: /* store matrix indices and values */
3082: PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3083: PetscCall(PetscFree(rowidxs));
3084: PetscCall(PetscFree2(colidxs, matvals));
3085: PetscFunctionReturn(PETSC_SUCCESS);
3086: }
3088: /* Not scalable because of ISAllGather() unless getting all columns. */
3089: static PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3090: {
3091: IS iscol_local;
3092: PetscBool isstride;
3093: PetscMPIInt gisstride = 0;
3095: PetscFunctionBegin;
3096: /* check if we are grabbing all columns*/
3097: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));
3099: if (isstride) {
3100: PetscInt start, len, mstart, mlen;
3101: PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3102: PetscCall(ISGetLocalSize(iscol, &len));
3103: PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3104: if (mstart == start && mlen - mstart == len) gisstride = 1;
3105: }
3107: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3108: if (gisstride) {
3109: PetscInt N;
3110: PetscCall(MatGetSize(mat, NULL, &N));
3111: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3112: PetscCall(ISSetIdentity(iscol_local));
3113: PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3114: } else {
3115: PetscInt cbs;
3116: PetscCall(ISGetBlockSize(iscol, &cbs));
3117: PetscCall(ISAllGather(iscol, &iscol_local));
3118: PetscCall(ISSetBlockSize(iscol_local, cbs));
3119: }
3121: *isseq = iscol_local;
3122: PetscFunctionReturn(PETSC_SUCCESS);
3123: }
3125: /*
3126: Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3127: (see MatCreateSubMatrix_MPIAIJ_nonscalable)
3129: Input Parameters:
3130: + mat - matrix
3131: . isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3132: i.e., mat->rstart <= isrow[i] < mat->rend
3133: - iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3134: i.e., mat->cstart <= iscol[i] < mat->cend
3136: Output Parameters:
3137: + isrow_d - sequential row index set for retrieving mat->A
3138: . iscol_d - sequential column index set for retrieving mat->A
3139: . iscol_o - sequential column index set for retrieving mat->B
3140: - garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3141: */
3142: static PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, PetscInt *garray[])
3143: {
3144: Vec x, cmap;
3145: const PetscInt *is_idx;
3146: PetscScalar *xarray, *cmaparray;
3147: PetscInt ncols, isstart, *idx, m, rstart, *cmap1, count;
3148: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3149: Mat B = a->B;
3150: Vec lvec = a->lvec, lcmap;
3151: PetscInt i, cstart, cend, Bn = B->cmap->N;
3152: MPI_Comm comm;
3153: VecScatter Mvctx = a->Mvctx;
3155: PetscFunctionBegin;
3156: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3157: PetscCall(ISGetLocalSize(iscol, &ncols));
3159: /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3160: PetscCall(MatCreateVecs(mat, &x, NULL));
3161: PetscCall(VecSet(x, -1.0));
3162: PetscCall(VecDuplicate(x, &cmap));
3163: PetscCall(VecSet(cmap, -1.0));
3165: /* Get start indices */
3166: PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3167: isstart -= ncols;
3168: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
3170: PetscCall(ISGetIndices(iscol, &is_idx));
3171: PetscCall(VecGetArray(x, &xarray));
3172: PetscCall(VecGetArray(cmap, &cmaparray));
3173: PetscCall(PetscMalloc1(ncols, &idx));
3174: for (i = 0; i < ncols; i++) {
3175: xarray[is_idx[i] - cstart] = (PetscScalar)is_idx[i];
3176: cmaparray[is_idx[i] - cstart] = i + isstart; /* global index of iscol[i] */
3177: idx[i] = is_idx[i] - cstart; /* local index of iscol[i] */
3178: }
3179: PetscCall(VecRestoreArray(x, &xarray));
3180: PetscCall(VecRestoreArray(cmap, &cmaparray));
3181: PetscCall(ISRestoreIndices(iscol, &is_idx));
3183: /* Get iscol_d */
3184: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3185: PetscCall(ISGetBlockSize(iscol, &i));
3186: PetscCall(ISSetBlockSize(*iscol_d, i));
3188: /* Get isrow_d */
3189: PetscCall(ISGetLocalSize(isrow, &m));
3190: rstart = mat->rmap->rstart;
3191: PetscCall(PetscMalloc1(m, &idx));
3192: PetscCall(ISGetIndices(isrow, &is_idx));
3193: for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3194: PetscCall(ISRestoreIndices(isrow, &is_idx));
3196: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3197: PetscCall(ISGetBlockSize(isrow, &i));
3198: PetscCall(ISSetBlockSize(*isrow_d, i));
3200: /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3201: PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3202: PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3204: PetscCall(VecDuplicate(lvec, &lcmap));
3206: PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3207: PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3209: /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3210: /* off-process column indices */
3211: count = 0;
3212: PetscCall(PetscMalloc1(Bn, &idx));
3213: PetscCall(PetscMalloc1(Bn, &cmap1));
3215: PetscCall(VecGetArray(lvec, &xarray));
3216: PetscCall(VecGetArray(lcmap, &cmaparray));
3217: for (i = 0; i < Bn; i++) {
3218: if (PetscRealPart(xarray[i]) > -1.0) {
3219: idx[count] = i; /* local column index in off-diagonal part B */
3220: cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3221: count++;
3222: }
3223: }
3224: PetscCall(VecRestoreArray(lvec, &xarray));
3225: PetscCall(VecRestoreArray(lcmap, &cmaparray));
3227: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3228: /* cannot ensure iscol_o has same blocksize as iscol! */
3230: PetscCall(PetscFree(idx));
3231: *garray = cmap1;
3233: PetscCall(VecDestroy(&x));
3234: PetscCall(VecDestroy(&cmap));
3235: PetscCall(VecDestroy(&lcmap));
3236: PetscFunctionReturn(PETSC_SUCCESS);
3237: }
3239: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3240: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3241: {
3242: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3243: Mat M = NULL;
3244: MPI_Comm comm;
3245: IS iscol_d, isrow_d, iscol_o;
3246: Mat Asub = NULL, Bsub = NULL;
3247: PetscInt n, count, M_size, N_size;
3249: PetscFunctionBegin;
3250: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3252: if (call == MAT_REUSE_MATRIX) {
3253: /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3254: PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3255: PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");
3257: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3258: PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");
3260: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3261: PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");
3263: /* Update diagonal and off-diagonal portions of submat */
3264: asub = (Mat_MPIAIJ *)(*submat)->data;
3265: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3266: PetscCall(ISGetLocalSize(iscol_o, &n));
3267: if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3268: PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3269: PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));
3271: } else { /* call == MAT_INITIAL_MATRIX) */
3272: PetscInt *garray, *garray_compact;
3273: PetscInt BsubN;
3275: /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3276: PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));
3278: /* Create local submatrices Asub and Bsub */
3279: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3280: PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));
3282: // Compact garray so its not of size Bn
3283: PetscCall(ISGetSize(iscol_o, &count));
3284: PetscCall(PetscMalloc1(count, &garray_compact));
3285: PetscCall(PetscArraycpy(garray_compact, garray, count));
3287: /* Create submatrix M */
3288: PetscCall(ISGetSize(isrow, &M_size));
3289: PetscCall(ISGetSize(iscol, &N_size));
3290: PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, M_size, N_size, Asub, Bsub, garray_compact, &M));
3292: /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3293: asub = (Mat_MPIAIJ *)M->data;
3295: PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3296: n = asub->B->cmap->N;
3297: if (BsubN > n) {
3298: /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3299: const PetscInt *idx;
3300: PetscInt i, j, *idx_new, *subgarray = asub->garray;
3301: PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));
3303: PetscCall(PetscMalloc1(n, &idx_new));
3304: j = 0;
3305: PetscCall(ISGetIndices(iscol_o, &idx));
3306: for (i = 0; i < n; i++) {
3307: if (j >= BsubN) break;
3308: while (subgarray[i] > garray[j]) j++;
3310: PetscCheck(subgarray[i] == garray[j], PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "subgarray[%" PetscInt_FMT "]=%" PetscInt_FMT " cannot < garray[%" PetscInt_FMT "]=%" PetscInt_FMT, i, subgarray[i], j, garray[j]);
3311: idx_new[i] = idx[j++];
3312: }
3313: PetscCall(ISRestoreIndices(iscol_o, &idx));
3315: PetscCall(ISDestroy(&iscol_o));
3316: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));
3318: } else PetscCheck(BsubN >= n, 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);
3320: PetscCall(PetscFree(garray));
3321: *submat = M;
3323: /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3324: PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3325: PetscCall(ISDestroy(&isrow_d));
3327: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3328: PetscCall(ISDestroy(&iscol_d));
3330: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3331: PetscCall(ISDestroy(&iscol_o));
3332: }
3333: PetscFunctionReturn(PETSC_SUCCESS);
3334: }
3336: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3337: {
3338: IS iscol_local = NULL, isrow_d;
3339: PetscInt csize;
3340: PetscInt n, i, j, start, end;
3341: PetscBool sameRowDist = PETSC_FALSE, sameDist[2], tsameDist[2];
3342: MPI_Comm comm;
3344: PetscFunctionBegin;
3345: /* If isrow has same processor distribution as mat,
3346: call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3347: if (call == MAT_REUSE_MATRIX) {
3348: PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3349: if (isrow_d) {
3350: sameRowDist = PETSC_TRUE;
3351: tsameDist[1] = PETSC_TRUE; /* sameColDist */
3352: } else {
3353: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3354: if (iscol_local) {
3355: sameRowDist = PETSC_TRUE;
3356: tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3357: }
3358: }
3359: } else {
3360: /* Check if isrow has same processor distribution as mat */
3361: sameDist[0] = PETSC_FALSE;
3362: PetscCall(ISGetLocalSize(isrow, &n));
3363: if (!n) {
3364: sameDist[0] = PETSC_TRUE;
3365: } else {
3366: PetscCall(ISGetMinMax(isrow, &i, &j));
3367: PetscCall(MatGetOwnershipRange(mat, &start, &end));
3368: if (i >= start && j < end) sameDist[0] = PETSC_TRUE;
3369: }
3371: /* Check if iscol has same processor distribution as mat */
3372: sameDist[1] = PETSC_FALSE;
3373: PetscCall(ISGetLocalSize(iscol, &n));
3374: if (!n) {
3375: sameDist[1] = PETSC_TRUE;
3376: } else {
3377: PetscCall(ISGetMinMax(iscol, &i, &j));
3378: PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3379: if (i >= start && j < end) sameDist[1] = PETSC_TRUE;
3380: }
3382: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3383: PetscCallMPI(MPIU_Allreduce(&sameDist, &tsameDist, 2, MPI_C_BOOL, MPI_LAND, comm));
3384: sameRowDist = tsameDist[0];
3385: }
3387: if (sameRowDist) {
3388: if (tsameDist[1]) { /* sameRowDist & sameColDist */
3389: /* isrow and iscol have same processor distribution as mat */
3390: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3391: PetscFunctionReturn(PETSC_SUCCESS);
3392: } else { /* sameRowDist */
3393: /* isrow has same processor distribution as mat */
3394: if (call == MAT_INITIAL_MATRIX) {
3395: PetscBool sorted;
3396: PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3397: PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3398: PetscCall(ISGetSize(iscol, &i));
3399: PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);
3401: PetscCall(ISSorted(iscol_local, &sorted));
3402: if (sorted) {
3403: /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3404: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3405: PetscFunctionReturn(PETSC_SUCCESS);
3406: }
3407: } else { /* call == MAT_REUSE_MATRIX */
3408: IS iscol_sub;
3409: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3410: if (iscol_sub) {
3411: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3412: PetscFunctionReturn(PETSC_SUCCESS);
3413: }
3414: }
3415: }
3416: }
3418: /* General case: iscol -> iscol_local which has global size of iscol */
3419: if (call == MAT_REUSE_MATRIX) {
3420: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3421: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3422: } else {
3423: if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3424: }
3426: PetscCall(ISGetLocalSize(iscol, &csize));
3427: PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));
3429: if (call == MAT_INITIAL_MATRIX) {
3430: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3431: PetscCall(ISDestroy(&iscol_local));
3432: }
3433: PetscFunctionReturn(PETSC_SUCCESS);
3434: }
3436: /*@C
3437: MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3438: and "off-diagonal" part of the matrix in CSR format.
3440: Collective
3442: Input Parameters:
3443: + comm - MPI communicator
3444: . M - the global row size
3445: . N - the global column size
3446: . A - "diagonal" portion of matrix
3447: . B - if garray is `NULL`, B should be the offdiag matrix using global col ids and of size N - if garray is not `NULL`, B should be the offdiag matrix using local col ids and of size garray
3448: - garray - either `NULL` or the global index of `B` columns. If not `NULL`, it should be allocated by `PetscMalloc1()` and will be owned by `mat` thereafter.
3450: Output Parameter:
3451: . mat - the matrix, with input `A` as its local diagonal matrix
3453: Level: advanced
3455: Notes:
3456: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.
3458: `A` and `B` becomes part of output mat. The user cannot use `A` and `B` anymore.
3460: If `garray` is `NULL`, `B` will be compacted to use local indices. In this sense, `B`'s sparsity pattern (nonzerostate) will be changed. If `B` is a device matrix, we need to somehow also update
3461: `B`'s copy on device. We do so by increasing `B`'s nonzerostate. In use of `B` on device, device matrix types should detect this change (ref. internal routines `MatSeqAIJCUSPARSECopyToGPU()` or
3462: `MatAssemblyEnd_SeqAIJKokkos()`) and will just destroy and then recreate the device copy of `B`. It is not optimal, but is easy to implement and less hacky. To avoid this overhead, try to compute `garray`
3463: yourself, see algorithms in the private function `MatSetUpMultiply_MPIAIJ()`.
3465: The `NULL`-ness of `garray` doesn't need to be collective, in other words, `garray` can be `NULL` on some processes while not on others.
3467: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3468: @*/
3469: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, PetscInt M, PetscInt N, Mat A, Mat B, PetscInt *garray, Mat *mat)
3470: {
3471: PetscInt m, n;
3472: MatType mpi_mat_type;
3473: Mat_MPIAIJ *mpiaij;
3474: Mat C;
3476: PetscFunctionBegin;
3477: PetscCall(MatCreate(comm, &C));
3478: PetscCall(MatGetSize(A, &m, &n));
3479: PetscCheck(m == B->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Am %" PetscInt_FMT " != Bm %" PetscInt_FMT, m, B->rmap->N);
3480: PetscCheck(A->rmap->bs == B->rmap->bs, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "A row bs %" PetscInt_FMT " != B row bs %" PetscInt_FMT, A->rmap->bs, B->rmap->bs);
3482: PetscCall(MatSetSizes(C, m, n, M, N));
3483: /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3484: PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3485: PetscCall(MatSetType(C, mpi_mat_type));
3486: if (!garray) {
3487: const PetscScalar *ba;
3489: B->nonzerostate++;
3490: PetscCall(MatSeqAIJGetArrayRead(B, &ba)); /* Since we will destroy B's device copy, we need to make sure the host copy is up to date */
3491: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
3492: }
3494: PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
3495: PetscCall(PetscLayoutSetUp(C->rmap));
3496: PetscCall(PetscLayoutSetUp(C->cmap));
3498: mpiaij = (Mat_MPIAIJ *)C->data;
3499: mpiaij->A = A;
3500: mpiaij->B = B;
3501: mpiaij->garray = garray;
3502: C->preallocated = PETSC_TRUE;
3503: C->nooffprocentries = PETSC_TRUE; /* See MatAssemblyBegin_MPIAIJ. In effect, making MatAssemblyBegin a nop */
3505: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3506: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
3507: /* MatAssemblyEnd is critical here. It sets mat->offloadmask according to A and B's, and
3508: also gets mpiaij->B compacted (if garray is NULL), with its col ids and size reduced
3509: */
3510: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
3511: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
3512: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3513: *mat = C;
3514: PetscFunctionReturn(PETSC_SUCCESS);
3515: }
3517: extern PetscErrorCode MatCreateSubMatrices_MPIAIJ_SingleIS_Local(Mat, PetscInt, const IS[], const IS[], MatReuse, PetscBool, Mat *);
3519: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowDist(Mat mat, IS isrow, IS iscol, IS iscol_local, MatReuse call, Mat *newmat)
3520: {
3521: PetscInt i, m, n, rstart, row, rend, nz, j, bs, cbs;
3522: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3523: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3524: Mat M, Msub, B = a->B;
3525: MatScalar *aa;
3526: Mat_SeqAIJ *aij;
3527: PetscInt *garray = a->garray, *colsub, Ncols;
3528: PetscInt count, Bn = B->cmap->N, cstart = mat->cmap->rstart, cend = mat->cmap->rend;
3529: IS iscol_sub, iscmap;
3530: const PetscInt *is_idx, *cmap;
3531: PetscBool allcolumns = PETSC_FALSE;
3532: MPI_Comm comm;
3534: PetscFunctionBegin;
3535: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3536: if (call == MAT_REUSE_MATRIX) {
3537: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3538: PetscCheck(iscol_sub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "SubIScol passed in was not used before, cannot reuse");
3539: PetscCall(ISGetLocalSize(iscol_sub, &count));
3541: PetscCall(PetscObjectQuery((PetscObject)*newmat, "Subcmap", (PetscObject *)&iscmap));
3542: PetscCheck(iscmap, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Subcmap passed in was not used before, cannot reuse");
3544: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Msub));
3545: PetscCheck(Msub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3547: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_REUSE_MATRIX, PETSC_FALSE, &Msub));
3549: } else { /* call == MAT_INITIAL_MATRIX) */
3550: PetscBool flg;
3552: PetscCall(ISGetLocalSize(iscol, &n));
3553: PetscCall(ISGetSize(iscol, &Ncols));
3555: /* (1) iscol -> nonscalable iscol_local */
3556: /* Check for special case: each processor gets entire matrix columns */
3557: PetscCall(ISIdentity(iscol_local, &flg));
3558: if (flg && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3559: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3560: if (allcolumns) {
3561: iscol_sub = iscol_local;
3562: PetscCall(PetscObjectReference((PetscObject)iscol_local));
3563: PetscCall(ISCreateStride(PETSC_COMM_SELF, n, 0, 1, &iscmap));
3565: } else {
3566: /* (2) iscol_local -> iscol_sub and iscmap. Implementation below requires iscol_local be sorted, it can have duplicate indices */
3567: PetscInt *idx, *cmap1, k;
3568: PetscCall(PetscMalloc1(Ncols, &idx));
3569: PetscCall(PetscMalloc1(Ncols, &cmap1));
3570: PetscCall(ISGetIndices(iscol_local, &is_idx));
3571: count = 0;
3572: k = 0;
3573: for (i = 0; i < Ncols; i++) {
3574: j = is_idx[i];
3575: if (j >= cstart && j < cend) {
3576: /* diagonal part of mat */
3577: idx[count] = j;
3578: cmap1[count++] = i; /* column index in submat */
3579: } else if (Bn) {
3580: /* off-diagonal part of mat */
3581: if (j == garray[k]) {
3582: idx[count] = j;
3583: cmap1[count++] = i; /* column index in submat */
3584: } else if (j > garray[k]) {
3585: while (j > garray[k] && k < Bn - 1) k++;
3586: if (j == garray[k]) {
3587: idx[count] = j;
3588: cmap1[count++] = i; /* column index in submat */
3589: }
3590: }
3591: }
3592: }
3593: PetscCall(ISRestoreIndices(iscol_local, &is_idx));
3595: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_OWN_POINTER, &iscol_sub));
3596: PetscCall(ISGetBlockSize(iscol, &cbs));
3597: PetscCall(ISSetBlockSize(iscol_sub, cbs));
3599: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)iscol_local), count, cmap1, PETSC_OWN_POINTER, &iscmap));
3600: }
3602: /* (3) Create sequential Msub */
3603: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_INITIAL_MATRIX, allcolumns, &Msub));
3604: }
3606: PetscCall(ISGetLocalSize(iscol_sub, &count));
3607: aij = (Mat_SeqAIJ *)Msub->data;
3608: ii = aij->i;
3609: PetscCall(ISGetIndices(iscmap, &cmap));
3611: /*
3612: m - number of local rows
3613: Ncols - number of columns (same on all processors)
3614: rstart - first row in new global matrix generated
3615: */
3616: PetscCall(MatGetSize(Msub, &m, NULL));
3618: if (call == MAT_INITIAL_MATRIX) {
3619: /* (4) Create parallel newmat */
3620: PetscMPIInt rank, size;
3621: PetscInt csize;
3623: PetscCallMPI(MPI_Comm_size(comm, &size));
3624: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3626: /*
3627: Determine the number of non-zeros in the diagonal and off-diagonal
3628: portions of the matrix in order to do correct preallocation
3629: */
3631: /* first get start and end of "diagonal" columns */
3632: PetscCall(ISGetLocalSize(iscol, &csize));
3633: if (csize == PETSC_DECIDE) {
3634: PetscCall(ISGetSize(isrow, &mglobal));
3635: if (mglobal == Ncols) { /* square matrix */
3636: nlocal = m;
3637: } else {
3638: nlocal = Ncols / size + ((Ncols % size) > rank);
3639: }
3640: } else {
3641: nlocal = csize;
3642: }
3643: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3644: rstart = rend - nlocal;
3645: 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);
3647: /* next, compute all the lengths */
3648: jj = aij->j;
3649: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3650: olens = dlens + m;
3651: for (i = 0; i < m; i++) {
3652: jend = ii[i + 1] - ii[i];
3653: olen = 0;
3654: dlen = 0;
3655: for (j = 0; j < jend; j++) {
3656: if (cmap[*jj] < rstart || cmap[*jj] >= rend) olen++;
3657: else dlen++;
3658: jj++;
3659: }
3660: olens[i] = olen;
3661: dlens[i] = dlen;
3662: }
3664: PetscCall(ISGetBlockSize(isrow, &bs));
3665: PetscCall(ISGetBlockSize(iscol, &cbs));
3667: PetscCall(MatCreate(comm, &M));
3668: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, Ncols));
3669: PetscCall(MatSetBlockSizes(M, bs, cbs));
3670: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3671: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3672: PetscCall(PetscFree(dlens));
3674: } else { /* call == MAT_REUSE_MATRIX */
3675: M = *newmat;
3676: PetscCall(MatGetLocalSize(M, &i, NULL));
3677: PetscCheck(i == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3678: PetscCall(MatZeroEntries(M));
3679: /*
3680: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3681: rather than the slower MatSetValues().
3682: */
3683: M->was_assembled = PETSC_TRUE;
3684: M->assembled = PETSC_FALSE;
3685: }
3687: /* (5) Set values of Msub to *newmat */
3688: PetscCall(PetscMalloc1(count, &colsub));
3689: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
3691: jj = aij->j;
3692: PetscCall(MatSeqAIJGetArrayRead(Msub, (const PetscScalar **)&aa));
3693: for (i = 0; i < m; i++) {
3694: row = rstart + i;
3695: nz = ii[i + 1] - ii[i];
3696: for (j = 0; j < nz; j++) colsub[j] = cmap[jj[j]];
3697: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, colsub, aa, INSERT_VALUES));
3698: jj += nz;
3699: aa += nz;
3700: }
3701: PetscCall(MatSeqAIJRestoreArrayRead(Msub, (const PetscScalar **)&aa));
3702: PetscCall(ISRestoreIndices(iscmap, &cmap));
3704: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3705: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3707: PetscCall(PetscFree(colsub));
3709: /* save Msub, iscol_sub and iscmap used in processor for next request */
3710: if (call == MAT_INITIAL_MATRIX) {
3711: *newmat = M;
3712: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubMatrix", (PetscObject)Msub));
3713: PetscCall(MatDestroy(&Msub));
3715: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubIScol", (PetscObject)iscol_sub));
3716: PetscCall(ISDestroy(&iscol_sub));
3718: PetscCall(PetscObjectCompose((PetscObject)*newmat, "Subcmap", (PetscObject)iscmap));
3719: PetscCall(ISDestroy(&iscmap));
3721: if (iscol_local) {
3722: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3723: PetscCall(ISDestroy(&iscol_local));
3724: }
3725: }
3726: PetscFunctionReturn(PETSC_SUCCESS);
3727: }
3729: /*
3730: Not great since it makes two copies of the submatrix, first an SeqAIJ
3731: in local and then by concatenating the local matrices the end result.
3732: Writing it directly would be much like MatCreateSubMatrices_MPIAIJ()
3734: This requires a sequential iscol with all indices.
3735: */
3736: PetscErrorCode MatCreateSubMatrix_MPIAIJ_nonscalable(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
3737: {
3738: PetscMPIInt rank, size;
3739: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs, cbs;
3740: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3741: Mat M, Mreuse;
3742: MatScalar *aa, *vwork;
3743: MPI_Comm comm;
3744: Mat_SeqAIJ *aij;
3745: PetscBool colflag, allcolumns = PETSC_FALSE;
3747: PetscFunctionBegin;
3748: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3749: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3750: PetscCallMPI(MPI_Comm_size(comm, &size));
3752: /* Check for special case: each processor gets entire matrix columns */
3753: PetscCall(ISIdentity(iscol, &colflag));
3754: PetscCall(ISGetLocalSize(iscol, &n));
3755: if (colflag && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3756: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3758: if (call == MAT_REUSE_MATRIX) {
3759: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
3760: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3761: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_REUSE_MATRIX, allcolumns, &Mreuse));
3762: } else {
3763: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_INITIAL_MATRIX, allcolumns, &Mreuse));
3764: }
3766: /*
3767: m - number of local rows
3768: n - number of columns (same on all processors)
3769: rstart - first row in new global matrix generated
3770: */
3771: PetscCall(MatGetSize(Mreuse, &m, &n));
3772: PetscCall(MatGetBlockSizes(Mreuse, &bs, &cbs));
3773: if (call == MAT_INITIAL_MATRIX) {
3774: aij = (Mat_SeqAIJ *)Mreuse->data;
3775: ii = aij->i;
3776: jj = aij->j;
3778: /*
3779: Determine the number of non-zeros in the diagonal and off-diagonal
3780: portions of the matrix in order to do correct preallocation
3781: */
3783: /* first get start and end of "diagonal" columns */
3784: if (csize == PETSC_DECIDE) {
3785: PetscCall(ISGetSize(isrow, &mglobal));
3786: if (mglobal == n) { /* square matrix */
3787: nlocal = m;
3788: } else {
3789: nlocal = n / size + ((n % size) > rank);
3790: }
3791: } else {
3792: nlocal = csize;
3793: }
3794: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3795: rstart = rend - nlocal;
3796: 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);
3798: /* next, compute all the lengths */
3799: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3800: olens = dlens + m;
3801: for (i = 0; i < m; i++) {
3802: jend = ii[i + 1] - ii[i];
3803: olen = 0;
3804: dlen = 0;
3805: for (j = 0; j < jend; j++) {
3806: if (*jj < rstart || *jj >= rend) olen++;
3807: else dlen++;
3808: jj++;
3809: }
3810: olens[i] = olen;
3811: dlens[i] = dlen;
3812: }
3813: PetscCall(MatCreate(comm, &M));
3814: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, n));
3815: PetscCall(MatSetBlockSizes(M, bs, cbs));
3816: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3817: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3818: PetscCall(PetscFree(dlens));
3819: } else {
3820: PetscInt ml, nl;
3822: M = *newmat;
3823: PetscCall(MatGetLocalSize(M, &ml, &nl));
3824: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3825: PetscCall(MatZeroEntries(M));
3826: /*
3827: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3828: rather than the slower MatSetValues().
3829: */
3830: M->was_assembled = PETSC_TRUE;
3831: M->assembled = PETSC_FALSE;
3832: }
3833: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
3834: aij = (Mat_SeqAIJ *)Mreuse->data;
3835: ii = aij->i;
3836: jj = aij->j;
3838: /* trigger copy to CPU if needed */
3839: PetscCall(MatSeqAIJGetArrayRead(Mreuse, (const PetscScalar **)&aa));
3840: for (i = 0; i < m; i++) {
3841: row = rstart + i;
3842: nz = ii[i + 1] - ii[i];
3843: cwork = jj;
3844: jj = PetscSafePointerPlusOffset(jj, nz);
3845: vwork = aa;
3846: aa = PetscSafePointerPlusOffset(aa, nz);
3847: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
3848: }
3849: PetscCall(MatSeqAIJRestoreArrayRead(Mreuse, (const PetscScalar **)&aa));
3851: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3852: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3853: *newmat = M;
3855: /* save submatrix used in processor for next request */
3856: if (call == MAT_INITIAL_MATRIX) {
3857: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
3858: PetscCall(MatDestroy(&Mreuse));
3859: }
3860: PetscFunctionReturn(PETSC_SUCCESS);
3861: }
3863: static PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
3864: {
3865: PetscInt m, cstart, cend, j, nnz, i, d, *ld;
3866: PetscInt *d_nnz, *o_nnz, nnz_max = 0, rstart, ii, irstart;
3867: const PetscInt *JJ;
3868: PetscBool nooffprocentries;
3869: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)B->data;
3871: PetscFunctionBegin;
3872: PetscCall(PetscLayoutSetUp(B->rmap));
3873: PetscCall(PetscLayoutSetUp(B->cmap));
3874: m = B->rmap->n;
3875: cstart = B->cmap->rstart;
3876: cend = B->cmap->rend;
3877: rstart = B->rmap->rstart;
3878: irstart = Ii[0];
3880: PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));
3882: if (PetscDefined(USE_DEBUG)) {
3883: for (i = 0; i < m; i++) {
3884: nnz = Ii[i + 1] - Ii[i];
3885: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3886: PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3887: 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]);
3888: 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);
3889: }
3890: }
3892: for (i = 0; i < m; i++) {
3893: nnz = Ii[i + 1] - Ii[i];
3894: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3895: nnz_max = PetscMax(nnz_max, nnz);
3896: d = 0;
3897: for (j = 0; j < nnz; j++) {
3898: if (cstart <= JJ[j] && JJ[j] < cend) d++;
3899: }
3900: d_nnz[i] = d;
3901: o_nnz[i] = nnz - d;
3902: }
3903: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3904: PetscCall(PetscFree2(d_nnz, o_nnz));
3906: for (i = 0; i < m; i++) {
3907: ii = i + rstart;
3908: PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], PetscSafePointerPlusOffset(J, Ii[i] - irstart), PetscSafePointerPlusOffset(v, Ii[i] - irstart), INSERT_VALUES));
3909: }
3910: nooffprocentries = B->nooffprocentries;
3911: B->nooffprocentries = PETSC_TRUE;
3912: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3913: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3914: B->nooffprocentries = nooffprocentries;
3916: /* count number of entries below block diagonal */
3917: PetscCall(PetscFree(Aij->ld));
3918: PetscCall(PetscCalloc1(m, &ld));
3919: Aij->ld = ld;
3920: for (i = 0; i < m; i++) {
3921: nnz = Ii[i + 1] - Ii[i];
3922: j = 0;
3923: while (j < nnz && J[j] < cstart) j++;
3924: ld[i] = j;
3925: if (J) J += nnz;
3926: }
3928: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3929: PetscFunctionReturn(PETSC_SUCCESS);
3930: }
3932: /*@
3933: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
3934: (the default parallel PETSc format).
3936: Collective
3938: Input Parameters:
3939: + B - the matrix
3940: . i - the indices into `j` for the start of each local row (indices start with zero)
3941: . j - the column indices for each local row (indices start with zero)
3942: - v - optional values in the matrix
3944: Level: developer
3946: Notes:
3947: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
3948: thus you CANNOT change the matrix entries by changing the values of `v` after you have
3949: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3951: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
3953: A convenience routine for this functionality is `MatCreateMPIAIJWithArrays()`.
3955: You can update the matrix with new numerical values using `MatUpdateMPIAIJWithArrays()` after this call if the column indices in `j` are sorted.
3957: If you do **not** use `MatUpdateMPIAIJWithArrays()`, the column indices in `j` do not need to be sorted. If you will use
3958: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
3960: The format which is used for the sparse matrix input, is equivalent to a
3961: row-major ordering.. i.e for the following matrix, the input data expected is
3962: as shown
3963: .vb
3964: 1 0 0
3965: 2 0 3 P0
3966: -------
3967: 4 5 6 P1
3969: Process0 [P0] rows_owned=[0,1]
3970: i = {0,1,3} [size = nrow+1 = 2+1]
3971: j = {0,0,2} [size = 3]
3972: v = {1,2,3} [size = 3]
3974: Process1 [P1] rows_owned=[2]
3975: i = {0,3} [size = nrow+1 = 1+1]
3976: j = {0,1,2} [size = 3]
3977: v = {4,5,6} [size = 3]
3978: .ve
3980: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`,
3981: `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`, `MatCreateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
3982: @*/
3983: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
3984: {
3985: PetscFunctionBegin;
3986: PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
3987: PetscFunctionReturn(PETSC_SUCCESS);
3988: }
3990: /*@
3991: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
3992: (the default parallel PETSc format). For good matrix assembly performance
3993: the user should preallocate the matrix storage by setting the parameters
3994: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
3996: Collective
3998: Input Parameters:
3999: + B - the matrix
4000: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4001: (same value is used for all local rows)
4002: . d_nnz - array containing the number of nonzeros in the various rows of the
4003: DIAGONAL portion of the local submatrix (possibly different for each row)
4004: or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4005: The size of this array is equal to the number of local rows, i.e 'm'.
4006: For matrices that will be factored, you must leave room for (and set)
4007: the diagonal entry even if it is zero.
4008: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4009: submatrix (same value is used for all local rows).
4010: - o_nnz - array containing the number of nonzeros in the various rows of the
4011: OFF-DIAGONAL portion of the local submatrix (possibly different for
4012: each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4013: structure. The size of this array is equal to the number
4014: of local rows, i.e 'm'.
4016: Example Usage:
4017: Consider the following 8x8 matrix with 34 non-zero values, that is
4018: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4019: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4020: as follows
4022: .vb
4023: 1 2 0 | 0 3 0 | 0 4
4024: Proc0 0 5 6 | 7 0 0 | 8 0
4025: 9 0 10 | 11 0 0 | 12 0
4026: -------------------------------------
4027: 13 0 14 | 15 16 17 | 0 0
4028: Proc1 0 18 0 | 19 20 21 | 0 0
4029: 0 0 0 | 22 23 0 | 24 0
4030: -------------------------------------
4031: Proc2 25 26 27 | 0 0 28 | 29 0
4032: 30 0 0 | 31 32 33 | 0 34
4033: .ve
4035: This can be represented as a collection of submatrices as
4036: .vb
4037: A B C
4038: D E F
4039: G H I
4040: .ve
4042: Where the submatrices A,B,C are owned by proc0, D,E,F are
4043: owned by proc1, G,H,I are owned by proc2.
4045: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4046: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4047: The 'M','N' parameters are 8,8, and have the same values on all procs.
4049: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4050: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4051: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4052: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4053: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4054: matrix, and [DF] as another `MATSEQAIJ` matrix.
4056: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4057: allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4058: storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4059: One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4060: the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4061: In this case, the values of `d_nz`, `o_nz` are
4062: .vb
4063: proc0 dnz = 2, o_nz = 2
4064: proc1 dnz = 3, o_nz = 2
4065: proc2 dnz = 1, o_nz = 4
4066: .ve
4067: We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4068: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4069: for proc3. i.e we are using 12+15+10=37 storage locations to store
4070: 34 values.
4072: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4073: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4074: In the above case the values for `d_nnz`, `o_nnz` are
4075: .vb
4076: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4077: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4078: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4079: .ve
4080: Here the space allocated is sum of all the above values i.e 34, and
4081: hence pre-allocation is perfect.
4083: Level: intermediate
4085: Notes:
4086: If the *_nnz parameter is given then the *_nz parameter is ignored
4088: The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4089: storage. The stored row and column indices begin with zero.
4090: See [Sparse Matrices](sec_matsparse) for details.
4092: The parallel matrix is partitioned such that the first m0 rows belong to
4093: process 0, the next m1 rows belong to process 1, the next m2 rows belong
4094: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
4096: The DIAGONAL portion of the local submatrix of a processor can be defined
4097: as the submatrix which is obtained by extraction the part corresponding to
4098: the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4099: first row that belongs to the processor, r2 is the last row belonging to
4100: the this processor, and c1-c2 is range of indices of the local part of a
4101: vector suitable for applying the matrix to. This is an mxn matrix. In the
4102: common case of a square matrix, the row and column ranges are the same and
4103: the DIAGONAL part is also square. The remaining portion of the local
4104: submatrix (mxN) constitute the OFF-DIAGONAL portion.
4106: If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.
4108: You can call `MatGetInfo()` to get information on how effective the preallocation was;
4109: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4110: You can also run with the option `-info` and look for messages with the string
4111: malloc in them to see if additional memory allocation was needed.
4113: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4114: `MatGetInfo()`, `PetscSplitOwnership()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4115: @*/
4116: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4117: {
4118: PetscFunctionBegin;
4121: PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4122: PetscFunctionReturn(PETSC_SUCCESS);
4123: }
4125: /*@
4126: MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4127: CSR format for the local rows.
4129: Collective
4131: Input Parameters:
4132: + comm - MPI communicator
4133: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4134: . n - This value should be the same as the local size used in creating the
4135: x vector for the matrix-vector product $ y = Ax$. (or `PETSC_DECIDE` to have
4136: calculated if `N` is given) For square matrices n is almost always `m`.
4137: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
4138: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
4139: . i - row indices (of length m+1); that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
4140: . j - global column indices
4141: - a - optional matrix values
4143: Output Parameter:
4144: . mat - the matrix
4146: Level: intermediate
4148: Notes:
4149: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4150: thus you CANNOT change the matrix entries by changing the values of `a[]` after you have
4151: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
4153: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
4155: Once you have created the matrix you can update it with new numerical values using `MatUpdateMPIAIJWithArray()`
4157: If you do **not** use `MatUpdateMPIAIJWithArray()`, the column indices in `j` do not need to be sorted. If you will use
4158: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
4160: The format which is used for the sparse matrix input, is equivalent to a
4161: row-major ordering, i.e., for the following matrix, the input data expected is
4162: as shown
4163: .vb
4164: 1 0 0
4165: 2 0 3 P0
4166: -------
4167: 4 5 6 P1
4169: Process0 [P0] rows_owned=[0,1]
4170: i = {0,1,3} [size = nrow+1 = 2+1]
4171: j = {0,0,2} [size = 3]
4172: v = {1,2,3} [size = 3]
4174: Process1 [P1] rows_owned=[2]
4175: i = {0,3} [size = nrow+1 = 1+1]
4176: j = {0,1,2} [size = 3]
4177: v = {4,5,6} [size = 3]
4178: .ve
4180: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4181: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4182: @*/
4183: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
4184: {
4185: PetscFunctionBegin;
4186: PetscCheck(!i || !i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4187: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4188: PetscCall(MatCreate(comm, mat));
4189: PetscCall(MatSetSizes(*mat, m, n, M, N));
4190: /* PetscCall(MatSetBlockSizes(M,bs,cbs)); */
4191: PetscCall(MatSetType(*mat, MATMPIAIJ));
4192: PetscCall(MatMPIAIJSetPreallocationCSR(*mat, i, j, a));
4193: PetscFunctionReturn(PETSC_SUCCESS);
4194: }
4196: /*@
4197: MatUpdateMPIAIJWithArrays - updates a `MATMPIAIJ` matrix using arrays that contain in standard
4198: CSR format for the local rows. Only the numerical values are updated the other arrays must be identical to what was passed
4199: from `MatCreateMPIAIJWithArrays()`
4201: Deprecated: Use `MatUpdateMPIAIJWithArray()`
4203: Collective
4205: Input Parameters:
4206: + mat - the matrix
4207: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4208: . n - This value should be the same as the local size used in creating the
4209: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4210: calculated if N is given) For square matrices n is almost always m.
4211: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4212: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4213: . Ii - row indices; that is Ii[0] = 0, Ii[row] = Ii[row-1] + number of elements in that row of the matrix
4214: . J - column indices
4215: - v - matrix values
4217: Level: deprecated
4219: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4220: `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4221: @*/
4222: PetscErrorCode MatUpdateMPIAIJWithArrays(Mat mat, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4223: {
4224: PetscInt nnz, i;
4225: PetscBool nooffprocentries;
4226: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4227: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4228: PetscScalar *ad, *ao;
4229: PetscInt ldi, Iii, md;
4230: const PetscInt *Adi = Ad->i;
4231: PetscInt *ld = Aij->ld;
4233: PetscFunctionBegin;
4234: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4235: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4236: PetscCheck(m == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of rows cannot change from call to MatUpdateMPIAIJWithArrays()");
4237: PetscCheck(n == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of columns cannot change from call to MatUpdateMPIAIJWithArrays()");
4239: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4240: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4242: for (i = 0; i < m; i++) {
4243: if (PetscDefined(USE_DEBUG)) {
4244: for (PetscInt j = Ii[i] + 1; j < Ii[i + 1]; ++j) {
4245: PetscCheck(J[j] >= J[j - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is not sorted", j - Ii[i], J[j], i);
4246: PetscCheck(J[j] != J[j - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is identical to previous entry", j - Ii[i], J[j], i);
4247: }
4248: }
4249: nnz = Ii[i + 1] - Ii[i];
4250: Iii = Ii[i];
4251: ldi = ld[i];
4252: md = Adi[i + 1] - Adi[i];
4253: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4254: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4255: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4256: ad += md;
4257: ao += nnz - md;
4258: }
4259: nooffprocentries = mat->nooffprocentries;
4260: mat->nooffprocentries = PETSC_TRUE;
4261: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4262: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4263: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4264: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4265: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4266: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4267: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4268: mat->nooffprocentries = nooffprocentries;
4269: PetscFunctionReturn(PETSC_SUCCESS);
4270: }
4272: /*@
4273: MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values
4275: Collective
4277: Input Parameters:
4278: + mat - the matrix
4279: - v - matrix values, stored by row
4281: Level: intermediate
4283: Notes:
4284: The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`
4286: The column indices in the call to `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()` must have been sorted for this call to work correctly
4288: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4289: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4290: @*/
4291: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4292: {
4293: PetscInt nnz, i, m;
4294: PetscBool nooffprocentries;
4295: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4296: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4297: Mat_SeqAIJ *Ao = (Mat_SeqAIJ *)Aij->B->data;
4298: PetscScalar *ad, *ao;
4299: const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4300: PetscInt ldi, Iii, md;
4301: PetscInt *ld = Aij->ld;
4303: PetscFunctionBegin;
4304: m = mat->rmap->n;
4306: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4307: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4308: Iii = 0;
4309: for (i = 0; i < m; i++) {
4310: nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4311: ldi = ld[i];
4312: md = Adi[i + 1] - Adi[i];
4313: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4314: ad += md;
4315: if (ao) {
4316: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4317: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4318: ao += nnz - md;
4319: }
4320: Iii += nnz;
4321: }
4322: nooffprocentries = mat->nooffprocentries;
4323: mat->nooffprocentries = PETSC_TRUE;
4324: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4325: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4326: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4327: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4328: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4329: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4330: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4331: mat->nooffprocentries = nooffprocentries;
4332: PetscFunctionReturn(PETSC_SUCCESS);
4333: }
4335: /*@
4336: MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4337: (the default parallel PETSc format). For good matrix assembly performance
4338: the user should preallocate the matrix storage by setting the parameters
4339: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4341: Collective
4343: Input Parameters:
4344: + comm - MPI communicator
4345: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4346: This value should be the same as the local size used in creating the
4347: y vector for the matrix-vector product y = Ax.
4348: . n - This value should be the same as the local size used in creating the
4349: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4350: calculated if N is given) For square matrices n is almost always m.
4351: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4352: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4353: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4354: (same value is used for all local rows)
4355: . d_nnz - array containing the number of nonzeros in the various rows of the
4356: DIAGONAL portion of the local submatrix (possibly different for each row)
4357: or `NULL`, if `d_nz` is used to specify the nonzero structure.
4358: The size of this array is equal to the number of local rows, i.e 'm'.
4359: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4360: submatrix (same value is used for all local rows).
4361: - o_nnz - array containing the number of nonzeros in the various rows of the
4362: OFF-DIAGONAL portion of the local submatrix (possibly different for
4363: each row) or `NULL`, if `o_nz` is used to specify the nonzero
4364: structure. The size of this array is equal to the number
4365: of local rows, i.e 'm'.
4367: Output Parameter:
4368: . A - the matrix
4370: Options Database Keys:
4371: + -mat_no_inode - Do not use inodes
4372: . -mat_inode_limit limit - Sets inode limit (max limit=5)
4373: - -matmult_vecscatter_view viewer - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4374: See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the `VecScatter`
4375: to be viewed as a matrix. Entry (i,j) is the size of message (in bytes) rank i sends to rank j in one `MatMult()` call.
4377: Level: intermediate
4379: Notes:
4380: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4381: MatXXXXSetPreallocation() paradigm instead of this routine directly.
4382: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
4384: If the *_nnz parameter is given then the *_nz parameter is ignored
4386: The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4387: processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4388: storage requirements for this matrix.
4390: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one
4391: processor than it must be used on all processors that share the object for
4392: that argument.
4394: If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by
4395: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`.
4397: The user MUST specify either the local or global matrix dimensions
4398: (possibly both).
4400: The parallel matrix is partitioned across processors such that the
4401: first `m0` rows belong to process 0, the next `m1` rows belong to
4402: process 1, the next `m2` rows belong to process 2, etc., where
4403: `m0`, `m1`, `m2`... are the input parameter `m` on each MPI process. I.e., each MPI process stores
4404: values corresponding to [m x N] submatrix.
4406: The columns are logically partitioned with the n0 columns belonging
4407: to 0th partition, the next n1 columns belonging to the next
4408: partition etc.. where n0,n1,n2... are the input parameter 'n'.
4410: The DIAGONAL portion of the local submatrix on any given processor
4411: is the submatrix corresponding to the rows and columns m,n
4412: corresponding to the given processor. i.e diagonal matrix on
4413: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4414: etc. The remaining portion of the local submatrix [m x (N-n)]
4415: constitute the OFF-DIAGONAL portion. The example below better
4416: illustrates this concept. The two matrices, the DIAGONAL portion and
4417: the OFF-DIAGONAL portion are each stored as `MATSEQAIJ` matrices.
4419: For a square global matrix we define each processor's diagonal portion
4420: to be its local rows and the corresponding columns (a square submatrix);
4421: each processor's off-diagonal portion encompasses the remainder of the
4422: local matrix (a rectangular submatrix).
4424: If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.
4426: When calling this routine with a single process communicator, a matrix of
4427: type `MATSEQAIJ` is returned. If a matrix of type `MATMPIAIJ` is desired for this
4428: type of communicator, use the construction mechanism
4429: .vb
4430: MatCreate(..., &A);
4431: MatSetType(A, MATMPIAIJ);
4432: MatSetSizes(A, m, n, M, N);
4433: MatMPIAIJSetPreallocation(A, ...);
4434: .ve
4436: By default, this format uses inodes (identical nodes) when possible.
4437: We search for consecutive rows with the same nonzero structure, thereby
4438: reusing matrix information to achieve increased efficiency.
4440: Example Usage:
4441: Consider the following 8x8 matrix with 34 non-zero values, that is
4442: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4443: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4444: as follows
4446: .vb
4447: 1 2 0 | 0 3 0 | 0 4
4448: Proc0 0 5 6 | 7 0 0 | 8 0
4449: 9 0 10 | 11 0 0 | 12 0
4450: -------------------------------------
4451: 13 0 14 | 15 16 17 | 0 0
4452: Proc1 0 18 0 | 19 20 21 | 0 0
4453: 0 0 0 | 22 23 0 | 24 0
4454: -------------------------------------
4455: Proc2 25 26 27 | 0 0 28 | 29 0
4456: 30 0 0 | 31 32 33 | 0 34
4457: .ve
4459: This can be represented as a collection of submatrices as
4461: .vb
4462: A B C
4463: D E F
4464: G H I
4465: .ve
4467: Where the submatrices A,B,C are owned by proc0, D,E,F are
4468: owned by proc1, G,H,I are owned by proc2.
4470: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4471: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4472: The 'M','N' parameters are 8,8, and have the same values on all procs.
4474: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4475: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4476: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4477: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4478: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4479: matrix, and [DF] as another SeqAIJ matrix.
4481: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4482: allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4483: storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4484: One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4485: the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4486: In this case, the values of `d_nz`,`o_nz` are
4487: .vb
4488: proc0 dnz = 2, o_nz = 2
4489: proc1 dnz = 3, o_nz = 2
4490: proc2 dnz = 1, o_nz = 4
4491: .ve
4492: We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4493: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4494: for proc3. i.e we are using 12+15+10=37 storage locations to store
4495: 34 values.
4497: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4498: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4499: In the above case the values for d_nnz,o_nnz are
4500: .vb
4501: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4502: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4503: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4504: .ve
4505: Here the space allocated is sum of all the above values i.e 34, and
4506: hence pre-allocation is perfect.
4508: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4509: `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`,
4510: `MatGetOwnershipRangesColumn()`, `PetscLayout`
4511: @*/
4512: 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)
4513: {
4514: PetscMPIInt size;
4516: PetscFunctionBegin;
4517: PetscCall(MatCreate(comm, A));
4518: PetscCall(MatSetSizes(*A, m, n, M, N));
4519: PetscCallMPI(MPI_Comm_size(comm, &size));
4520: if (size > 1) {
4521: PetscCall(MatSetType(*A, MATMPIAIJ));
4522: PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4523: } else {
4524: PetscCall(MatSetType(*A, MATSEQAIJ));
4525: PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4526: }
4527: PetscFunctionReturn(PETSC_SUCCESS);
4528: }
4530: /*@C
4531: MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix
4533: Not Collective
4535: Input Parameter:
4536: . A - The `MATMPIAIJ` matrix
4538: Output Parameters:
4539: + Ad - The local diagonal block as a `MATSEQAIJ` matrix
4540: . Ao - The local off-diagonal block as a `MATSEQAIJ` matrix
4541: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4543: Level: intermediate
4545: Note:
4546: The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4547: 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
4548: the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4549: local column numbers to global column numbers in the original matrix.
4551: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATSEQAIJ`
4552: @*/
4553: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4554: {
4555: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4556: PetscBool flg;
4558: PetscFunctionBegin;
4559: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4560: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4561: if (Ad) *Ad = a->A;
4562: if (Ao) *Ao = a->B;
4563: if (colmap) *colmap = a->garray;
4564: PetscFunctionReturn(PETSC_SUCCESS);
4565: }
4567: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4568: {
4569: PetscInt m, N, i, rstart, nnz, Ii;
4570: PetscInt *indx;
4571: PetscScalar *values;
4572: MatType rootType;
4574: PetscFunctionBegin;
4575: PetscCall(MatGetSize(inmat, &m, &N));
4576: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4577: PetscInt *dnz, *onz, sum, bs, cbs;
4579: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4580: /* Check sum(n) = N */
4581: PetscCallMPI(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4582: PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);
4584: PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4585: rstart -= m;
4587: MatPreallocateBegin(comm, m, n, dnz, onz);
4588: for (i = 0; i < m; i++) {
4589: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4590: PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4591: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4592: }
4594: PetscCall(MatCreate(comm, outmat));
4595: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4596: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4597: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4598: PetscCall(MatGetRootType_Private(inmat, &rootType));
4599: PetscCall(MatSetType(*outmat, rootType));
4600: PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4601: PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4602: MatPreallocateEnd(dnz, onz);
4603: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4604: }
4606: /* numeric phase */
4607: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4608: for (i = 0; i < m; i++) {
4609: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4610: Ii = i + rstart;
4611: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4612: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4613: }
4614: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4615: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4616: PetscFunctionReturn(PETSC_SUCCESS);
4617: }
4619: static PetscErrorCode MatMergeSeqsToMPIDestroy(PetscCtxRt data)
4620: {
4621: MatMergeSeqsToMPI *merge = *(MatMergeSeqsToMPI **)data;
4623: PetscFunctionBegin;
4624: if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4625: PetscCall(PetscFree(merge->id_r));
4626: PetscCall(PetscFree(merge->len_s));
4627: PetscCall(PetscFree(merge->len_r));
4628: PetscCall(PetscFree(merge->bi));
4629: PetscCall(PetscFree(merge->bj));
4630: PetscCall(PetscFree(merge->buf_ri[0]));
4631: PetscCall(PetscFree(merge->buf_ri));
4632: PetscCall(PetscFree(merge->buf_rj[0]));
4633: PetscCall(PetscFree(merge->buf_rj));
4634: PetscCall(PetscFree(merge->coi));
4635: PetscCall(PetscFree(merge->coj));
4636: PetscCall(PetscFree(merge->owners_co));
4637: PetscCall(PetscLayoutDestroy(&merge->rowmap));
4638: PetscCall(PetscFree(merge));
4639: PetscFunctionReturn(PETSC_SUCCESS);
4640: }
4642: #include <../src/mat/utils/freespace.h>
4643: #include <petscbt.h>
4645: /*@
4646: MatCreateMPIAIJSumSeqAIJNumeric - Fill the numerical values of an `MATMPIAIJ` matrix previously created by
4647: `MatCreateMPIAIJSumSeqAIJSymbolic()` by summing the local `MATSEQAIJ` contributions from each process.
4649: Collective
4651: Input Parameters:
4652: + seqmat - the local `MATSEQAIJ` contribution from this process
4653: - mpimat - the target `MATMPIAIJ` matrix created by `MatCreateMPIAIJSumSeqAIJSymbolic()`
4655: Level: developer
4657: .seealso: `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJSumSeqAIJSymbolic()`, `MatCreateMPIAIJSumSeqAIJ()`
4658: @*/
4659: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4660: {
4661: MPI_Comm comm;
4662: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4663: PetscMPIInt size, rank, taga, *len_s;
4664: PetscInt N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj, m;
4665: PetscMPIInt proc, k;
4666: PetscInt **buf_ri, **buf_rj;
4667: PetscInt anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4668: PetscInt nrows, **buf_ri_k, **nextrow, **nextai;
4669: MPI_Request *s_waits, *r_waits;
4670: MPI_Status *status;
4671: const MatScalar *aa, *a_a;
4672: MatScalar **abuf_r, *ba_i;
4673: MatMergeSeqsToMPI *merge;
4674: PetscContainer container;
4676: PetscFunctionBegin;
4677: PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4678: PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));
4680: PetscCallMPI(MPI_Comm_size(comm, &size));
4681: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4683: PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4684: PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4685: PetscCall(PetscContainerGetPointer(container, &merge));
4686: PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4687: aa = a_a;
4689: bi = merge->bi;
4690: bj = merge->bj;
4691: buf_ri = merge->buf_ri;
4692: buf_rj = merge->buf_rj;
4694: PetscCall(PetscMalloc1(size, &status));
4695: owners = merge->rowmap->range;
4696: len_s = merge->len_s;
4698: /* send and recv matrix values */
4699: PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4700: PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));
4702: PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4703: for (proc = 0, k = 0; proc < size; proc++) {
4704: if (!len_s[proc]) continue;
4705: i = owners[proc];
4706: PetscCallMPI(MPIU_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4707: k++;
4708: }
4710: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4711: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4712: PetscCall(PetscFree(status));
4714: PetscCall(PetscFree(s_waits));
4715: PetscCall(PetscFree(r_waits));
4717: /* insert mat values of mpimat */
4718: PetscCall(PetscMalloc1(N, &ba_i));
4719: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4721: for (k = 0; k < merge->nrecv; k++) {
4722: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4723: nrows = *buf_ri_k[k];
4724: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4725: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4726: }
4728: /* set values of ba */
4729: m = merge->rowmap->n;
4730: for (i = 0; i < m; i++) {
4731: arow = owners[rank] + i;
4732: bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4733: bnzi = bi[i + 1] - bi[i];
4734: PetscCall(PetscArrayzero(ba_i, bnzi));
4736: /* add local non-zero vals of this proc's seqmat into ba */
4737: anzi = ai[arow + 1] - ai[arow];
4738: aj = a->j + ai[arow];
4739: aa = a_a + ai[arow];
4740: nextaj = 0;
4741: for (j = 0; nextaj < anzi; j++) {
4742: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4743: ba_i[j] += aa[nextaj++];
4744: }
4745: }
4747: /* add received vals into ba */
4748: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4749: /* i-th row */
4750: if (i == *nextrow[k]) {
4751: anzi = *(nextai[k] + 1) - *nextai[k];
4752: aj = buf_rj[k] + *nextai[k];
4753: aa = abuf_r[k] + *nextai[k];
4754: nextaj = 0;
4755: for (j = 0; nextaj < anzi; j++) {
4756: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4757: ba_i[j] += aa[nextaj++];
4758: }
4759: }
4760: nextrow[k]++;
4761: nextai[k]++;
4762: }
4763: }
4764: PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4765: }
4766: PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4767: PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4768: PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));
4770: PetscCall(PetscFree(abuf_r[0]));
4771: PetscCall(PetscFree(abuf_r));
4772: PetscCall(PetscFree(ba_i));
4773: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4774: PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4775: PetscFunctionReturn(PETSC_SUCCESS);
4776: }
4778: /*@
4779: MatCreateMPIAIJSumSeqAIJSymbolic - Create the symbolic (nonzero-pattern) portion of an `MATMPIAIJ` matrix
4780: obtained by summing local `MATSEQAIJ` contributions from each process.
4782: Collective
4784: Input Parameters:
4785: + comm - the communicator
4786: . seqmat - the local `MATSEQAIJ` contribution from this process
4787: . m - the number of local rows for the resulting `MATMPIAIJ` matrix, or `PETSC_DECIDE`
4788: - n - the number of local columns for the resulting `MATMPIAIJ` matrix, or `PETSC_DECIDE`
4790: Output Parameter:
4791: . mpimat - the newly created `MATMPIAIJ` matrix
4793: Level: developer
4795: Note:
4796: The numerical values are filled in by a subsequent call to `MatCreateMPIAIJSumSeqAIJNumeric()`.
4798: .seealso: `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJSumSeqAIJNumeric()`, `MatCreateMPIAIJSumSeqAIJ()`
4799: @*/
4800: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4801: {
4802: Mat B_mpi;
4803: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4804: PetscMPIInt size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4805: PetscInt **buf_rj, **buf_ri, **buf_ri_k;
4806: PetscInt M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4807: PetscInt len, *dnz, *onz, bs, cbs;
4808: PetscInt k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4809: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4810: MPI_Request *si_waits, *sj_waits, *ri_waits, *rj_waits;
4811: MPI_Status *status;
4812: PetscFreeSpaceList free_space = NULL, current_space = NULL;
4813: PetscBT lnkbt;
4814: MatMergeSeqsToMPI *merge;
4815: PetscContainer container;
4817: PetscFunctionBegin;
4818: PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));
4820: /* make sure it is a PETSc comm */
4821: PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4822: PetscCallMPI(MPI_Comm_size(comm, &size));
4823: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4825: PetscCall(PetscNew(&merge));
4826: PetscCall(PetscMalloc1(size, &status));
4828: /* determine row ownership */
4829: PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4830: PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4831: PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4832: PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4833: PetscCall(PetscLayoutSetUp(merge->rowmap));
4834: PetscCall(PetscMalloc1(size, &len_si));
4835: PetscCall(PetscMalloc1(size, &merge->len_s));
4837: m = merge->rowmap->n;
4838: owners = merge->rowmap->range;
4840: /* determine the number of messages to send, their lengths */
4841: len_s = merge->len_s;
4843: len = 0; /* length of buf_si[] */
4844: merge->nsend = 0;
4845: for (PetscMPIInt proc = 0; proc < size; proc++) {
4846: len_si[proc] = 0;
4847: if (proc == rank) {
4848: len_s[proc] = 0;
4849: } else {
4850: PetscCall(PetscMPIIntCast(owners[proc + 1] - owners[proc] + 1, &len_si[proc]));
4851: PetscCall(PetscMPIIntCast(ai[owners[proc + 1]] - ai[owners[proc]], &len_s[proc])); /* num of rows to be sent to [proc] */
4852: }
4853: if (len_s[proc]) {
4854: merge->nsend++;
4855: nrows = 0;
4856: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4857: if (ai[i + 1] > ai[i]) nrows++;
4858: }
4859: PetscCall(PetscMPIIntCast(2 * (nrows + 1), &len_si[proc]));
4860: len += len_si[proc];
4861: }
4862: }
4864: /* determine the number and length of messages to receive for ij-structure */
4865: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4866: PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));
4868: /* post the Irecv of j-structure */
4869: PetscCall(PetscCommGetNewTag(comm, &tagj));
4870: PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));
4872: /* post the Isend of j-structure */
4873: PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));
4875: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4876: if (!len_s[proc]) continue;
4877: i = owners[proc];
4878: PetscCallMPI(MPIU_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4879: k++;
4880: }
4882: /* receives and sends of j-structure are complete */
4883: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4884: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));
4886: /* send and recv i-structure */
4887: PetscCall(PetscCommGetNewTag(comm, &tagi));
4888: PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));
4890: PetscCall(PetscMalloc1(len + 1, &buf_s));
4891: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4892: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4893: if (!len_s[proc]) continue;
4894: /* form outgoing message for i-structure:
4895: buf_si[0]: nrows to be sent
4896: [1:nrows]: row index (global)
4897: [nrows+1:2*nrows+1]: i-structure index
4898: */
4899: nrows = len_si[proc] / 2 - 1;
4900: buf_si_i = buf_si + nrows + 1;
4901: buf_si[0] = nrows;
4902: buf_si_i[0] = 0;
4903: nrows = 0;
4904: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4905: anzi = ai[i + 1] - ai[i];
4906: if (anzi) {
4907: buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4908: buf_si[nrows + 1] = i - owners[proc]; /* local row index */
4909: nrows++;
4910: }
4911: }
4912: PetscCallMPI(MPIU_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4913: k++;
4914: buf_si += len_si[proc];
4915: }
4917: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4918: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));
4920: PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4921: for (i = 0; i < merge->nrecv; i++) PetscCall(PetscInfo(seqmat, "recv len_ri=%d, len_rj=%d from [%d]\n", len_ri[i], merge->len_r[i], merge->id_r[i]));
4923: PetscCall(PetscFree(len_si));
4924: PetscCall(PetscFree(len_ri));
4925: PetscCall(PetscFree(rj_waits));
4926: PetscCall(PetscFree2(si_waits, sj_waits));
4927: PetscCall(PetscFree(ri_waits));
4928: PetscCall(PetscFree(buf_s));
4929: PetscCall(PetscFree(status));
4931: /* compute a local seq matrix in each processor */
4932: /* allocate bi array and free space for accumulating nonzero column info */
4933: PetscCall(PetscMalloc1(m + 1, &bi));
4934: bi[0] = 0;
4936: /* create and initialize a linked list */
4937: nlnk = N + 1;
4938: PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));
4940: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4941: len = ai[owners[rank + 1]] - ai[owners[rank]];
4942: PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));
4944: current_space = free_space;
4946: /* determine symbolic info for each local row */
4947: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4949: for (k = 0; k < merge->nrecv; k++) {
4950: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4951: nrows = *buf_ri_k[k];
4952: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4953: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4954: }
4956: MatPreallocateBegin(comm, m, n, dnz, onz);
4957: len = 0;
4958: for (i = 0; i < m; i++) {
4959: bnzi = 0;
4960: /* add local non-zero cols of this proc's seqmat into lnk */
4961: arow = owners[rank] + i;
4962: anzi = ai[arow + 1] - ai[arow];
4963: aj = a->j + ai[arow];
4964: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
4965: bnzi += nlnk;
4966: /* add received col data into lnk */
4967: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4968: if (i == *nextrow[k]) { /* i-th row */
4969: anzi = *(nextai[k] + 1) - *nextai[k];
4970: aj = buf_rj[k] + *nextai[k];
4971: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
4972: bnzi += nlnk;
4973: nextrow[k]++;
4974: nextai[k]++;
4975: }
4976: }
4977: if (len < bnzi) len = bnzi; /* =max(bnzi) */
4979: /* if free space is not available, make more free space */
4980: if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), ¤t_space));
4981: /* copy data into free space, then initialize lnk */
4982: PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
4983: PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));
4985: current_space->array += bnzi;
4986: current_space->local_used += bnzi;
4987: current_space->local_remaining -= bnzi;
4989: bi[i + 1] = bi[i] + bnzi;
4990: }
4992: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4994: PetscCall(PetscMalloc1(bi[m], &bj));
4995: PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
4996: PetscCall(PetscLLDestroy(lnk, lnkbt));
4998: /* create symbolic parallel matrix B_mpi */
4999: PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
5000: PetscCall(MatCreate(comm, &B_mpi));
5001: if (n == PETSC_DECIDE) PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
5002: else PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
5003: PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
5004: PetscCall(MatSetType(B_mpi, MATMPIAIJ));
5005: PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
5006: MatPreallocateEnd(dnz, onz);
5007: PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));
5009: /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
5010: B_mpi->assembled = PETSC_FALSE;
5011: merge->bi = bi;
5012: merge->bj = bj;
5013: merge->buf_ri = buf_ri;
5014: merge->buf_rj = buf_rj;
5015: merge->coi = NULL;
5016: merge->coj = NULL;
5017: merge->owners_co = NULL;
5019: PetscCall(PetscCommDestroy(&comm));
5021: /* attach the supporting struct to B_mpi for reuse */
5022: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5023: PetscCall(PetscContainerSetPointer(container, merge));
5024: PetscCall(PetscContainerSetCtxDestroy(container, MatMergeSeqsToMPIDestroy));
5025: PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5026: PetscCall(PetscContainerDestroy(&container));
5027: *mpimat = B_mpi;
5029: PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5030: PetscFunctionReturn(PETSC_SUCCESS);
5031: }
5033: /*@
5034: MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5035: matrices from each processor
5037: Collective
5039: Input Parameters:
5040: + comm - the communicators the parallel matrix will live on
5041: . seqmat - the input sequential matrices
5042: . m - number of local rows (or `PETSC_DECIDE`)
5043: . n - number of local columns (or `PETSC_DECIDE`)
5044: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5046: Output Parameter:
5047: . mpimat - the parallel matrix generated
5049: Level: advanced
5051: Note:
5052: The dimensions of the sequential matrix in each processor MUST be the same.
5053: The input seqmat is included into the container `MatMergeSeqsToMPIDestroy`, and will be
5054: destroyed when `mpimat` is destroyed. Call `PetscObjectQuery()` to access `seqmat`.
5056: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`
5057: @*/
5058: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5059: {
5060: PetscMPIInt size;
5062: PetscFunctionBegin;
5063: PetscCallMPI(MPI_Comm_size(comm, &size));
5064: if (size == 1) {
5065: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5066: if (scall == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5067: else PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5068: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5069: PetscFunctionReturn(PETSC_SUCCESS);
5070: }
5071: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5072: if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5073: PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5074: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5075: PetscFunctionReturn(PETSC_SUCCESS);
5076: }
5078: /*@
5079: MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix.
5081: Not Collective
5083: Input Parameter:
5084: . A - the matrix
5086: Output Parameter:
5087: . A_loc - the local sequential matrix generated
5089: Level: developer
5091: Notes:
5092: The matrix is created by taking `A`'s local rows and putting them into a sequential matrix
5093: with `mlocal` rows and `n` columns. Where `mlocal` is obtained with `MatGetLocalSize()` and
5094: `n` is the global column count obtained with `MatGetSize()`
5096: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5098: For parallel matrices this creates an entirely new matrix. If the matrix is sequential it merely increases the reference count.
5100: Destroy the matrix with `MatDestroy()`
5102: .seealso: [](ch_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5103: @*/
5104: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5105: {
5106: PetscBool mpi;
5108: PetscFunctionBegin;
5109: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5110: if (mpi) PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5111: else {
5112: *A_loc = A;
5113: PetscCall(PetscObjectReference((PetscObject)*A_loc));
5114: }
5115: PetscFunctionReturn(PETSC_SUCCESS);
5116: }
5118: /*@
5119: MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix.
5121: Not Collective
5123: Input Parameters:
5124: + A - the matrix
5125: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5127: Output Parameter:
5128: . A_loc - the local sequential matrix generated
5130: Level: developer
5132: Notes:
5133: The matrix is created by taking all `A`'s local rows and putting them into a sequential
5134: matrix with `mlocal` rows and `n` columns.`mlocal` is the row count obtained with
5135: `MatGetLocalSize()` and `n` is the global column count obtained with `MatGetSize()`.
5137: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5139: When `A` is sequential and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A` (which contains the entire matrix),
5140: 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
5141: then `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called to fill `A_loc`. Thus one can preallocate the appropriate sequential matrix `A_loc`
5142: 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.
5144: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5145: @*/
5146: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5147: {
5148: Mat_MPIAIJ *mpimat = (Mat_MPIAIJ *)A->data;
5149: Mat_SeqAIJ *mat, *a, *b;
5150: PetscInt *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5151: const PetscScalar *aa, *ba, *aav, *bav;
5152: PetscScalar *ca, *cam;
5153: PetscMPIInt size;
5154: PetscInt am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5155: PetscInt *ci, *cj, col, ncols_d, ncols_o, jo;
5156: PetscBool match;
5158: PetscFunctionBegin;
5159: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5160: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5161: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5162: if (size == 1) {
5163: if (scall == MAT_INITIAL_MATRIX) {
5164: PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5165: *A_loc = mpimat->A;
5166: } else if (scall == MAT_REUSE_MATRIX) {
5167: PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5168: }
5169: PetscFunctionReturn(PETSC_SUCCESS);
5170: }
5172: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5173: a = (Mat_SeqAIJ *)mpimat->A->data;
5174: b = (Mat_SeqAIJ *)mpimat->B->data;
5175: ai = a->i;
5176: aj = a->j;
5177: bi = b->i;
5178: bj = b->j;
5179: PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5180: PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5181: aa = aav;
5182: ba = bav;
5183: if (scall == MAT_INITIAL_MATRIX) {
5184: PetscCall(PetscMalloc1(1 + am, &ci));
5185: ci[0] = 0;
5186: for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5187: PetscCall(PetscMalloc1(1 + ci[am], &cj));
5188: PetscCall(PetscMalloc1(1 + ci[am], &ca));
5189: k = 0;
5190: for (i = 0; i < am; i++) {
5191: ncols_o = bi[i + 1] - bi[i];
5192: ncols_d = ai[i + 1] - ai[i];
5193: /* off-diagonal portion of A */
5194: for (jo = 0; jo < ncols_o; jo++) {
5195: col = cmap[*bj];
5196: if (col >= cstart) break;
5197: cj[k] = col;
5198: bj++;
5199: ca[k++] = *ba++;
5200: }
5201: /* diagonal portion of A */
5202: for (j = 0; j < ncols_d; j++) {
5203: cj[k] = cstart + *aj++;
5204: ca[k++] = *aa++;
5205: }
5206: /* off-diagonal portion of A */
5207: for (j = jo; j < ncols_o; j++) {
5208: cj[k] = cmap[*bj++];
5209: ca[k++] = *ba++;
5210: }
5211: }
5212: /* put together the new matrix */
5213: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5214: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5215: /* Since these are PETSc arrays, change flags to free them as necessary. */
5216: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5217: mat->free_a = PETSC_TRUE;
5218: mat->free_ij = PETSC_TRUE;
5219: mat->nonew = 0;
5220: } else if (scall == MAT_REUSE_MATRIX) {
5221: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5222: ci = mat->i;
5223: cj = mat->j;
5224: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5225: for (i = 0; i < am; i++) {
5226: /* off-diagonal portion of A */
5227: ncols_o = bi[i + 1] - bi[i];
5228: for (jo = 0; jo < ncols_o; jo++) {
5229: col = cmap[*bj];
5230: if (col >= cstart) break;
5231: *cam++ = *ba++;
5232: bj++;
5233: }
5234: /* diagonal portion of A */
5235: ncols_d = ai[i + 1] - ai[i];
5236: for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5237: /* off-diagonal portion of A */
5238: for (j = jo; j < ncols_o; j++) {
5239: *cam++ = *ba++;
5240: bj++;
5241: }
5242: }
5243: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5244: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5245: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5246: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5247: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5248: PetscFunctionReturn(PETSC_SUCCESS);
5249: }
5251: /*@
5252: MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5253: mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and off-diagonal part
5255: Not Collective
5257: Input Parameters:
5258: + A - the matrix
5259: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5261: Output Parameters:
5262: + glob - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5263: - A_loc - the local sequential matrix generated
5265: Level: developer
5267: Note:
5268: This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5269: part, then those associated with the off-diagonal part (in its local ordering)
5271: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5272: @*/
5273: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5274: {
5275: Mat Ao, Ad;
5276: const PetscInt *cmap;
5277: PetscMPIInt size;
5278: PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);
5280: PetscFunctionBegin;
5281: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5282: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5283: if (size == 1) {
5284: if (scall == MAT_INITIAL_MATRIX) {
5285: PetscCall(PetscObjectReference((PetscObject)Ad));
5286: *A_loc = Ad;
5287: } else if (scall == MAT_REUSE_MATRIX) {
5288: PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5289: }
5290: if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5291: PetscFunctionReturn(PETSC_SUCCESS);
5292: }
5293: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5294: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5295: if (f) PetscCall((*f)(A, scall, glob, A_loc));
5296: else {
5297: Mat_SeqAIJ *a = (Mat_SeqAIJ *)Ad->data;
5298: Mat_SeqAIJ *b = (Mat_SeqAIJ *)Ao->data;
5299: Mat_SeqAIJ *c;
5300: PetscInt *ai = a->i, *aj = a->j;
5301: PetscInt *bi = b->i, *bj = b->j;
5302: PetscInt *ci, *cj;
5303: const PetscScalar *aa, *ba;
5304: PetscScalar *ca;
5305: PetscInt i, j, am, dn, on;
5307: PetscCall(MatGetLocalSize(Ad, &am, &dn));
5308: PetscCall(MatGetLocalSize(Ao, NULL, &on));
5309: PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5310: PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5311: if (scall == MAT_INITIAL_MATRIX) {
5312: PetscInt k;
5313: PetscCall(PetscMalloc1(1 + am, &ci));
5314: PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5315: PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5316: ci[0] = 0;
5317: for (i = 0, k = 0; i < am; i++) {
5318: const PetscInt ncols_o = bi[i + 1] - bi[i];
5319: const PetscInt ncols_d = ai[i + 1] - ai[i];
5320: ci[i + 1] = ci[i] + ncols_o + ncols_d;
5321: /* diagonal portion of A */
5322: for (j = 0; j < ncols_d; j++, k++) {
5323: cj[k] = *aj++;
5324: ca[k] = *aa++;
5325: }
5326: /* off-diagonal portion of A */
5327: for (j = 0; j < ncols_o; j++, k++) {
5328: cj[k] = dn + *bj++;
5329: ca[k] = *ba++;
5330: }
5331: }
5332: /* put together the new matrix */
5333: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5334: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5335: /* Since these are PETSc arrays, change flags to free them as necessary. */
5336: c = (Mat_SeqAIJ *)(*A_loc)->data;
5337: c->free_a = PETSC_TRUE;
5338: c->free_ij = PETSC_TRUE;
5339: c->nonew = 0;
5340: PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5341: } else if (scall == MAT_REUSE_MATRIX) {
5342: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5343: for (i = 0; i < am; i++) {
5344: const PetscInt ncols_d = ai[i + 1] - ai[i];
5345: const PetscInt ncols_o = bi[i + 1] - bi[i];
5346: /* diagonal portion of A */
5347: for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5348: /* off-diagonal portion of A */
5349: for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5350: }
5351: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5352: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5353: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5354: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5355: if (glob) {
5356: PetscInt cst, *gidx;
5358: PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5359: PetscCall(PetscMalloc1(dn + on, &gidx));
5360: for (i = 0; i < dn; i++) gidx[i] = cst + i;
5361: for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5362: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5363: }
5364: }
5365: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5366: PetscFunctionReturn(PETSC_SUCCESS);
5367: }
5369: /*@C
5370: MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns
5372: Not Collective
5374: Input Parameters:
5375: + A - the matrix
5376: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5377: . row - index set of rows to extract (or `NULL`)
5378: - col - index set of columns to extract (or `NULL`)
5380: Output Parameter:
5381: . A_loc - the local sequential matrix generated
5383: Level: developer
5385: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5386: @*/
5387: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5388: {
5389: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5390: PetscInt i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5391: IS isrowa, iscola;
5392: Mat *aloc;
5393: PetscBool match;
5395: PetscFunctionBegin;
5396: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5397: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5398: PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5399: if (!row) {
5400: start = A->rmap->rstart;
5401: end = A->rmap->rend;
5402: PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5403: } else {
5404: isrowa = *row;
5405: }
5406: if (!col) {
5407: start = A->cmap->rstart;
5408: cmap = a->garray;
5409: nzA = a->A->cmap->n;
5410: nzB = a->B->cmap->n;
5411: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5412: ncols = 0;
5413: for (i = 0; i < nzB; i++) {
5414: if (cmap[i] < start) idx[ncols++] = cmap[i];
5415: else break;
5416: }
5417: imark = i;
5418: for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5419: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5420: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5421: } else {
5422: iscola = *col;
5423: }
5424: if (scall != MAT_INITIAL_MATRIX) {
5425: PetscCall(PetscMalloc1(1, &aloc));
5426: aloc[0] = *A_loc;
5427: }
5428: PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5429: if (!col) { /* attach global id of condensed columns */
5430: PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5431: }
5432: *A_loc = aloc[0];
5433: PetscCall(PetscFree(aloc));
5434: if (!row) PetscCall(ISDestroy(&isrowa));
5435: if (!col) PetscCall(ISDestroy(&iscola));
5436: PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5437: PetscFunctionReturn(PETSC_SUCCESS);
5438: }
5440: /*
5441: * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5442: * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5443: * on a global size.
5444: * */
5445: static PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5446: {
5447: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
5448: Mat_SeqAIJ *pd = (Mat_SeqAIJ *)p->A->data, *po = (Mat_SeqAIJ *)p->B->data, *p_oth;
5449: PetscInt plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5450: PetscMPIInt owner;
5451: PetscSFNode *iremote, *oiremote;
5452: const PetscInt *lrowindices;
5453: PetscSF sf, osf;
5454: PetscInt pcstart, *roffsets, *loffsets, *pnnz, j;
5455: PetscInt ontotalcols, dntotalcols, ntotalcols, nout;
5456: MPI_Comm comm;
5457: ISLocalToGlobalMapping mapping;
5458: const PetscScalar *pd_a, *po_a;
5460: PetscFunctionBegin;
5461: PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5462: /* plocalsize is the number of roots
5463: * nrows is the number of leaves
5464: * */
5465: PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5466: PetscCall(ISGetLocalSize(rows, &nrows));
5467: PetscCall(PetscCalloc1(nrows, &iremote));
5468: PetscCall(ISGetIndices(rows, &lrowindices));
5469: for (i = 0; i < nrows; i++) {
5470: /* Find a remote index and an owner for a row
5471: * The row could be local or remote
5472: * */
5473: owner = 0;
5474: lidx = 0;
5475: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5476: iremote[i].index = lidx;
5477: iremote[i].rank = owner;
5478: }
5479: /* Create SF to communicate how many nonzero columns for each row */
5480: PetscCall(PetscSFCreate(comm, &sf));
5481: /* SF will figure out the number of nonzero columns for each row, and their
5482: * offsets
5483: * */
5484: PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5485: PetscCall(PetscSFSetFromOptions(sf));
5486: PetscCall(PetscSFSetUp(sf));
5488: PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5489: PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5490: PetscCall(PetscCalloc1(nrows, &pnnz));
5491: roffsets[0] = 0;
5492: roffsets[1] = 0;
5493: for (i = 0; i < plocalsize; i++) {
5494: /* diagonal */
5495: nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5496: /* off-diagonal */
5497: nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5498: /* compute offsets so that we relative location for each row */
5499: roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5500: roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5501: }
5502: PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5503: PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5504: /* 'r' means root, and 'l' means leaf */
5505: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5506: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5507: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5508: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5509: PetscCall(PetscSFDestroy(&sf));
5510: PetscCall(PetscFree(roffsets));
5511: PetscCall(PetscFree(nrcols));
5512: dntotalcols = 0;
5513: ontotalcols = 0;
5514: ncol = 0;
5515: for (i = 0; i < nrows; i++) {
5516: pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5517: ncol = PetscMax(pnnz[i], ncol);
5518: /* diagonal */
5519: dntotalcols += nlcols[i * 2 + 0];
5520: /* off-diagonal */
5521: ontotalcols += nlcols[i * 2 + 1];
5522: }
5523: /* We do not need to figure the right number of columns
5524: * since all the calculations will be done by going through the raw data
5525: * */
5526: PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5527: PetscCall(MatSetUp(*P_oth));
5528: PetscCall(PetscFree(pnnz));
5529: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5530: /* diagonal */
5531: PetscCall(PetscCalloc1(dntotalcols, &iremote));
5532: /* off-diagonal */
5533: PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5534: /* diagonal */
5535: PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5536: /* off-diagonal */
5537: PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5538: dntotalcols = 0;
5539: ontotalcols = 0;
5540: ntotalcols = 0;
5541: for (i = 0; i < nrows; i++) {
5542: owner = 0;
5543: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5544: /* Set iremote for diag matrix */
5545: for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5546: iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5547: iremote[dntotalcols].rank = owner;
5548: /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5549: ilocal[dntotalcols++] = ntotalcols++;
5550: }
5551: /* off-diagonal */
5552: for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5553: oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5554: oiremote[ontotalcols].rank = owner;
5555: oilocal[ontotalcols++] = ntotalcols++;
5556: }
5557: }
5558: PetscCall(ISRestoreIndices(rows, &lrowindices));
5559: PetscCall(PetscFree(loffsets));
5560: PetscCall(PetscFree(nlcols));
5561: PetscCall(PetscSFCreate(comm, &sf));
5562: /* P serves as roots and P_oth is leaves
5563: * Diag matrix
5564: * */
5565: PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5566: PetscCall(PetscSFSetFromOptions(sf));
5567: PetscCall(PetscSFSetUp(sf));
5569: PetscCall(PetscSFCreate(comm, &osf));
5570: /* off-diagonal */
5571: PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5572: PetscCall(PetscSFSetFromOptions(osf));
5573: PetscCall(PetscSFSetUp(osf));
5574: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5575: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5576: /* operate on the matrix internal data to save memory */
5577: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5578: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5579: PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5580: /* Convert to global indices for diag matrix */
5581: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5582: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5583: /* We want P_oth store global indices */
5584: PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5585: /* Use memory scalable approach */
5586: PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5587: PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5588: PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5589: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5590: /* Convert back to local indices */
5591: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5592: PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5593: nout = 0;
5594: PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5595: PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5596: PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5597: /* Exchange values */
5598: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5599: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5600: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5601: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5602: /* Stop PETSc from shrinking memory */
5603: for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5604: PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5605: PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5606: /* Attach PetscSF objects to P_oth so that we can reuse it later */
5607: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5608: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5609: PetscCall(PetscSFDestroy(&sf));
5610: PetscCall(PetscSFDestroy(&osf));
5611: PetscFunctionReturn(PETSC_SUCCESS);
5612: }
5614: /*
5615: * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5616: * This supports MPIAIJ and MAIJ
5617: * */
5618: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5619: {
5620: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5621: Mat_SeqAIJ *p_oth;
5622: IS rows, map;
5623: PetscHMapI hamp;
5624: PetscInt i, htsize, *rowindices, off, *mapping, key, count;
5625: MPI_Comm comm;
5626: PetscSF sf, osf;
5627: PetscBool has;
5629: PetscFunctionBegin;
5630: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5631: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5632: /* If it is the first time, create an index set of off-diag nonzero columns of A,
5633: * and then create a submatrix (that often is an overlapping matrix)
5634: * */
5635: if (reuse == MAT_INITIAL_MATRIX) {
5636: /* Use a hash table to figure out unique keys */
5637: PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5638: PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5639: count = 0;
5640: /* Assume that a->g is sorted, otherwise the following does not make sense */
5641: for (i = 0; i < a->B->cmap->n; i++) {
5642: key = a->garray[i] / dof;
5643: PetscCall(PetscHMapIHas(hamp, key, &has));
5644: if (!has) {
5645: mapping[i] = count;
5646: PetscCall(PetscHMapISet(hamp, key, count++));
5647: } else {
5648: /* Current 'i' has the same value the previous step */
5649: mapping[i] = count - 1;
5650: }
5651: }
5652: PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5653: PetscCall(PetscHMapIGetSize(hamp, &htsize));
5654: PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5655: PetscCall(PetscCalloc1(htsize, &rowindices));
5656: off = 0;
5657: PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5658: PetscCall(PetscHMapIDestroy(&hamp));
5659: PetscCall(PetscSortInt(htsize, rowindices));
5660: PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5661: /* In case, the matrix was already created but users want to recreate the matrix */
5662: PetscCall(MatDestroy(P_oth));
5663: PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5664: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5665: PetscCall(ISDestroy(&map));
5666: PetscCall(ISDestroy(&rows));
5667: } else if (reuse == MAT_REUSE_MATRIX) {
5668: /* If matrix was already created, we simply update values using SF objects
5669: * that as attached to the matrix earlier.
5670: */
5671: const PetscScalar *pd_a, *po_a;
5673: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5674: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5675: PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5676: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5677: /* Update values in place */
5678: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5679: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5680: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5681: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5682: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5683: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5684: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5685: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5686: } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5687: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5688: PetscFunctionReturn(PETSC_SUCCESS);
5689: }
5691: /*@C
5692: MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`
5694: Collective
5696: Input Parameters:
5697: + A - the first matrix in `MATMPIAIJ` format
5698: . B - the second matrix in `MATMPIAIJ` format
5699: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5701: Output Parameters:
5702: + rowb - On input index sets of rows of B to extract (or `NULL`), modified on output
5703: . colb - On input index sets of columns of B to extract (or `NULL`), modified on output
5704: - B_seq - the sequential matrix generated
5706: Level: developer
5708: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5709: @*/
5710: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5711: {
5712: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5713: PetscInt *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5714: IS isrowb, iscolb;
5715: Mat *bseq = NULL;
5717: PetscFunctionBegin;
5718: 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 ")",
5719: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5720: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));
5722: if (scall == MAT_INITIAL_MATRIX) {
5723: start = A->cmap->rstart;
5724: cmap = a->garray;
5725: nzA = a->A->cmap->n;
5726: nzB = a->B->cmap->n;
5727: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5728: ncols = 0;
5729: for (i = 0; i < nzB; i++) { /* row < local row index */
5730: if (cmap[i] < start) idx[ncols++] = cmap[i];
5731: else break;
5732: }
5733: imark = i;
5734: for (i = 0; i < nzA; i++) idx[ncols++] = start + i; /* local rows */
5735: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5736: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5737: PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5738: } else {
5739: PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5740: isrowb = *rowb;
5741: iscolb = *colb;
5742: PetscCall(PetscMalloc1(1, &bseq));
5743: bseq[0] = *B_seq;
5744: }
5745: PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5746: *B_seq = bseq[0];
5747: PetscCall(PetscFree(bseq));
5748: if (!rowb) {
5749: PetscCall(ISDestroy(&isrowb));
5750: } else {
5751: *rowb = isrowb;
5752: }
5753: if (!colb) {
5754: PetscCall(ISDestroy(&iscolb));
5755: } else {
5756: *colb = iscolb;
5757: }
5758: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5759: PetscFunctionReturn(PETSC_SUCCESS);
5760: }
5762: // PetscClangLinter pragma disable: -fdoc-sowing-chars
5763: /*
5764: MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5765: of the OFF-DIAGONAL portion of local A
5767: Collective
5769: Input Parameters:
5770: + A - the first matrix in `MATMPIAIJ` format
5771: . B - the second matrix in `MATMPIAIJ` format
5772: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5774: Output Parameters:
5775: + startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5776: . startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5777: . bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5778: - B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N
5780: Level: developer
5782: Developer Note:
5783: This directly accesses information inside the VecScatter associated with the matrix-vector product
5784: for this matrix. This is not desirable.
5786: .seealso: [](ch_mat), `Mat`, `MATMPIAIJ`
5787: */
5788: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5789: {
5790: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5791: VecScatter ctx;
5792: MPI_Comm comm;
5793: const PetscMPIInt *rprocs, *sprocs;
5794: PetscMPIInt nrecvs, nsends;
5795: const PetscInt *srow, *rstarts, *sstarts;
5796: PetscInt *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5797: PetscInt i, j, k = 0, l, ll, nrows, *rstartsj = NULL, *sstartsj, len;
5798: PetscScalar *b_otha, *bufa, *bufA, *vals = NULL;
5799: MPI_Request *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5800: PetscMPIInt size, tag, rank, nreqs;
5802: PetscFunctionBegin;
5803: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5804: PetscCallMPI(MPI_Comm_size(comm, &size));
5806: 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 ")",
5807: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5808: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5809: PetscCallMPI(MPI_Comm_rank(comm, &rank));
5811: if (size == 1) {
5812: startsj_s = NULL;
5813: bufa_ptr = NULL;
5814: *B_oth = NULL;
5815: PetscFunctionReturn(PETSC_SUCCESS);
5816: }
5818: ctx = a->Mvctx;
5819: tag = ((PetscObject)ctx)->tag;
5821: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5822: /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5823: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5824: PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5825: PetscCall(PetscMalloc1(nreqs, &reqs));
5826: rwaits = reqs;
5827: swaits = PetscSafePointerPlusOffset(reqs, nrecvs);
5829: if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5830: if (scall == MAT_INITIAL_MATRIX) {
5831: /* i-array */
5832: /* post receives */
5833: if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5834: for (i = 0; i < nrecvs; i++) {
5835: rowlen = rvalues + rstarts[i] * rbs;
5836: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5837: PetscCallMPI(MPIU_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5838: }
5840: /* pack the outgoing message */
5841: PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));
5843: sstartsj[0] = 0;
5844: rstartsj[0] = 0;
5845: len = 0; /* total length of j or a array to be sent */
5846: if (nsends) {
5847: k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5848: PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5849: }
5850: for (i = 0; i < nsends; i++) {
5851: rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5852: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5853: for (j = 0; j < nrows; j++) {
5854: row = srow[k] + B->rmap->range[rank]; /* global row idx */
5855: for (l = 0; l < sbs; l++) {
5856: PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */
5858: rowlen[j * sbs + l] = ncols;
5860: len += ncols;
5861: PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5862: }
5863: k++;
5864: }
5865: PetscCallMPI(MPIU_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));
5867: sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5868: }
5869: /* recvs and sends of i-array are completed */
5870: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5871: PetscCall(PetscFree(svalues));
5873: /* allocate buffers for sending j and a arrays */
5874: PetscCall(PetscMalloc1(len, &bufj));
5875: PetscCall(PetscMalloc1(len, &bufa));
5877: /* create i-array of B_oth */
5878: PetscCall(PetscMalloc1(aBn + 1, &b_othi));
5880: b_othi[0] = 0;
5881: len = 0; /* total length of j or a array to be received */
5882: k = 0;
5883: for (i = 0; i < nrecvs; i++) {
5884: rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5885: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5886: for (j = 0; j < nrows; j++) {
5887: b_othi[k + 1] = b_othi[k] + rowlen[j];
5888: PetscCall(PetscIntSumError(rowlen[j], len, &len));
5889: k++;
5890: }
5891: rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5892: }
5893: PetscCall(PetscFree(rvalues));
5895: /* allocate space for j and a arrays of B_oth */
5896: PetscCall(PetscMalloc1(b_othi[aBn], &b_othj));
5897: PetscCall(PetscMalloc1(b_othi[aBn], &b_otha));
5899: /* j-array */
5900: /* post receives of j-array */
5901: for (i = 0; i < nrecvs; i++) {
5902: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5903: PetscCallMPI(MPIU_Irecv(PetscSafePointerPlusOffset(b_othj, rstartsj[i]), nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5904: }
5906: /* pack the outgoing message j-array */
5907: if (nsends) k = sstarts[0];
5908: for (i = 0; i < nsends; i++) {
5909: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5910: bufJ = PetscSafePointerPlusOffset(bufj, sstartsj[i]);
5911: for (j = 0; j < nrows; j++) {
5912: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5913: for (ll = 0; ll < sbs; ll++) {
5914: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5915: for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5916: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5917: }
5918: }
5919: PetscCallMPI(MPIU_Isend(PetscSafePointerPlusOffset(bufj, sstartsj[i]), sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5920: }
5922: /* recvs and sends of j-array are completed */
5923: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5924: } else if (scall == MAT_REUSE_MATRIX) {
5925: sstartsj = *startsj_s;
5926: rstartsj = *startsj_r;
5927: bufa = *bufa_ptr;
5928: PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5929: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");
5931: /* a-array */
5932: /* post receives of a-array */
5933: for (i = 0; i < nrecvs; i++) {
5934: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5935: PetscCallMPI(MPIU_Irecv(PetscSafePointerPlusOffset(b_otha, rstartsj[i]), nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
5936: }
5938: /* pack the outgoing message a-array */
5939: if (nsends) k = sstarts[0];
5940: for (i = 0; i < nsends; i++) {
5941: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5942: bufA = PetscSafePointerPlusOffset(bufa, sstartsj[i]);
5943: for (j = 0; j < nrows; j++) {
5944: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5945: for (ll = 0; ll < sbs; ll++) {
5946: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5947: for (l = 0; l < ncols; l++) *bufA++ = vals[l];
5948: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5949: }
5950: }
5951: PetscCallMPI(MPIU_Isend(PetscSafePointerPlusOffset(bufa, sstartsj[i]), sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
5952: }
5953: /* recvs and sends of a-array are completed */
5954: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5955: PetscCall(PetscFree(reqs));
5957: if (scall == MAT_INITIAL_MATRIX) {
5958: Mat_SeqAIJ *b_oth;
5960: /* put together the new matrix */
5961: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));
5963: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5964: /* Since these are PETSc arrays, change flags to free them as necessary. */
5965: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
5966: b_oth->free_a = PETSC_TRUE;
5967: b_oth->free_ij = PETSC_TRUE;
5968: b_oth->nonew = 0;
5970: PetscCall(PetscFree(bufj));
5971: if (!startsj_s || !bufa_ptr) {
5972: PetscCall(PetscFree2(sstartsj, rstartsj));
5973: PetscCall(PetscFree(bufa_ptr));
5974: } else {
5975: *startsj_s = sstartsj;
5976: *startsj_r = rstartsj;
5977: *bufa_ptr = bufa;
5978: }
5979: } else if (scall == MAT_REUSE_MATRIX) {
5980: PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
5981: }
5983: PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
5984: PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
5985: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
5986: PetscFunctionReturn(PETSC_SUCCESS);
5987: }
5989: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
5990: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
5991: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
5992: #if defined(PETSC_HAVE_MKL_SPARSE)
5993: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
5994: #endif
5995: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
5996: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
5997: #if defined(PETSC_HAVE_ELEMENTAL)
5998: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
5999: #endif
6000: #if defined(PETSC_HAVE_SCALAPACK) && (defined(PETSC_USE_REAL_SINGLE) || defined(PETSC_USE_REAL_DOUBLE))
6001: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
6002: #endif
6003: #if defined(PETSC_HAVE_HYPRE)
6004: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
6005: #endif
6006: #if defined(PETSC_HAVE_CUDA)
6007: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
6008: #endif
6009: #if defined(PETSC_HAVE_HIP)
6010: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6011: #endif
6012: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6013: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6014: #endif
6015: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6016: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6017: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
6019: /*
6020: Computes (B'*A')' since computing B*A directly is untenable
6022: n p p
6023: [ ] [ ] [ ]
6024: m [ A ] * n [ B ] = m [ C ]
6025: [ ] [ ] [ ]
6027: */
6028: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6029: {
6030: Mat At, Bt, Ct;
6032: PetscFunctionBegin;
6033: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6034: PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6035: PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_CURRENT, &Ct));
6036: PetscCall(MatDestroy(&At));
6037: PetscCall(MatDestroy(&Bt));
6038: PetscCall(MatTransposeSetPrecursor(Ct, C));
6039: PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6040: PetscCall(MatDestroy(&Ct));
6041: PetscFunctionReturn(PETSC_SUCCESS);
6042: }
6044: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6045: {
6046: PetscBool cisdense;
6048: PetscFunctionBegin;
6049: 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);
6050: PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6051: PetscCall(MatSetBlockSizesFromMats(C, A, B));
6052: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6053: if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6054: PetscCall(MatSetUp(C));
6056: C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6057: PetscFunctionReturn(PETSC_SUCCESS);
6058: }
6060: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6061: {
6062: Mat_Product *product = C->product;
6063: Mat A = product->A, B = product->B;
6065: PetscFunctionBegin;
6066: 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 ")",
6067: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6068: C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6069: C->ops->productsymbolic = MatProductSymbolic_AB;
6070: PetscFunctionReturn(PETSC_SUCCESS);
6071: }
6073: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6074: {
6075: Mat_Product *product = C->product;
6077: PetscFunctionBegin;
6078: if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6079: PetscFunctionReturn(PETSC_SUCCESS);
6080: }
6082: /*
6083: Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix
6085: Input Parameters:
6087: j1,rowBegin1,rowEnd1,jmap1: describe the first set of nonzeros (Set1)
6088: j2,rowBegin2,rowEnd2,jmap2: describe the second set of nonzeros (Set2)
6090: mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat
6092: For Set1, j1[] contains column indices of the nonzeros.
6093: 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
6094: respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6095: but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.
6097: Similar for Set2.
6099: This routine merges the two sets of nonzeros row by row and removes repeats.
6101: Output Parameters: (memory is allocated by the caller)
6103: i[],j[]: the CSR of the merged matrix, which has m rows.
6104: imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6105: imap2[]: similar to imap1[], but for Set2.
6106: Note we order nonzeros row-by-row and from left to right.
6107: */
6108: 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[])
6109: {
6110: PetscInt r, m; /* Row index of mat */
6111: PetscCount t, t1, t2, b1, e1, b2, e2;
6113: PetscFunctionBegin;
6114: PetscCall(MatGetLocalSize(mat, &m, NULL));
6115: t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6116: i[0] = 0;
6117: for (r = 0; r < m; r++) { /* Do row by row merging */
6118: b1 = rowBegin1[r];
6119: e1 = rowEnd1[r];
6120: b2 = rowBegin2[r];
6121: e2 = rowEnd2[r];
6122: while (b1 < e1 && b2 < e2) {
6123: if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6124: j[t] = j1[b1];
6125: imap1[t1] = t;
6126: imap2[t2] = t;
6127: b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6128: b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6129: t1++;
6130: t2++;
6131: t++;
6132: } else if (j1[b1] < j2[b2]) {
6133: j[t] = j1[b1];
6134: imap1[t1] = t;
6135: b1 += jmap1[t1 + 1] - jmap1[t1];
6136: t1++;
6137: t++;
6138: } else {
6139: j[t] = j2[b2];
6140: imap2[t2] = t;
6141: b2 += jmap2[t2 + 1] - jmap2[t2];
6142: t2++;
6143: t++;
6144: }
6145: }
6146: /* Merge the remaining in either j1[] or j2[] */
6147: while (b1 < e1) {
6148: j[t] = j1[b1];
6149: imap1[t1] = t;
6150: b1 += jmap1[t1 + 1] - jmap1[t1];
6151: t1++;
6152: t++;
6153: }
6154: while (b2 < e2) {
6155: j[t] = j2[b2];
6156: imap2[t2] = t;
6157: b2 += jmap2[t2 + 1] - jmap2[t2];
6158: t2++;
6159: t++;
6160: }
6161: PetscCall(PetscIntCast(t, i + r + 1));
6162: }
6163: PetscFunctionReturn(PETSC_SUCCESS);
6164: }
6166: /*
6167: Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block
6169: Input Parameters:
6170: mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6171: 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[]
6172: respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.
6174: i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6175: i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.
6177: Output Parameters:
6178: j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6179: rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6180: 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,
6181: and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.
6183: Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6184: Atot: number of entries belonging to the diagonal block.
6185: Annz: number of unique nonzeros belonging to the diagonal block.
6186: Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6187: repeats (i.e., same 'i,j' pair).
6188: Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6189: is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.
6191: Atot: number of entries belonging to the diagonal block
6192: Annz: number of unique nonzeros belonging to the diagonal block.
6194: Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.
6196: Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6197: */
6198: 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_)
6199: {
6200: PetscInt cstart, cend, rstart, rend, row, col;
6201: PetscCount Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6202: PetscCount Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6203: PetscCount k, m, p, q, r, s, mid;
6204: PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;
6206: PetscFunctionBegin;
6207: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6208: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6209: m = rend - rstart;
6211: /* Skip negative rows */
6212: for (k = 0; k < n; k++)
6213: if (i[k] >= 0) break;
6215: /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6216: fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6217: */
6218: while (k < n) {
6219: row = i[k];
6220: /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6221: for (s = k; s < n; s++)
6222: if (i[s] != row) break;
6224: /* Shift diag columns to range of [-PETSC_INT_MAX, -1] */
6225: for (p = k; p < s; p++) {
6226: if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_INT_MAX;
6227: }
6228: PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6229: PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6230: rowBegin[row - rstart] = k;
6231: rowMid[row - rstart] = mid;
6232: rowEnd[row - rstart] = s;
6233: PetscCheck(k == s || j[s - 1] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index %" PetscInt_FMT " is >= matrix column size %" PetscInt_FMT, j[s - 1], mat->cmap->N);
6235: /* Count nonzeros of this diag/offdiag row, which might have repeats */
6236: Atot += mid - k;
6237: Btot += s - mid;
6239: /* Count unique nonzeros of this diag row */
6240: for (p = k; p < mid;) {
6241: col = j[p];
6242: do {
6243: j[p] += PETSC_INT_MAX; /* Revert the modified diagonal indices */
6244: p++;
6245: } while (p < mid && j[p] == col);
6246: Annz++;
6247: }
6249: /* Count unique nonzeros of this offdiag row */
6250: for (p = mid; p < s;) {
6251: col = j[p];
6252: do {
6253: p++;
6254: } while (p < s && j[p] == col);
6255: Bnnz++;
6256: }
6257: k = s;
6258: }
6260: /* Allocation according to Atot, Btot, Annz, Bnnz */
6261: PetscCall(PetscMalloc1(Atot, &Aperm));
6262: PetscCall(PetscMalloc1(Btot, &Bperm));
6263: PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6264: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));
6266: /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6267: Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6268: for (r = 0; r < m; r++) {
6269: k = rowBegin[r];
6270: mid = rowMid[r];
6271: s = rowEnd[r];
6272: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Aperm, Atot), PetscSafePointerPlusOffset(perm, k), mid - k));
6273: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Bperm, Btot), PetscSafePointerPlusOffset(perm, mid), s - mid));
6274: Atot += mid - k;
6275: Btot += s - mid;
6277: /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6278: for (p = k; p < mid;) {
6279: col = j[p];
6280: q = p;
6281: do {
6282: p++;
6283: } while (p < mid && j[p] == col);
6284: Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6285: Annz++;
6286: }
6288: for (p = mid; p < s;) {
6289: col = j[p];
6290: q = p;
6291: do {
6292: p++;
6293: } while (p < s && j[p] == col);
6294: Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6295: Bnnz++;
6296: }
6297: }
6298: /* Output */
6299: *Aperm_ = Aperm;
6300: *Annz_ = Annz;
6301: *Atot_ = Atot;
6302: *Ajmap_ = Ajmap;
6303: *Bperm_ = Bperm;
6304: *Bnnz_ = Bnnz;
6305: *Btot_ = Btot;
6306: *Bjmap_ = Bjmap;
6307: PetscFunctionReturn(PETSC_SUCCESS);
6308: }
6310: /*
6311: Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix
6313: Input Parameters:
6314: nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6315: nnz: number of unique nonzeros in the merged matrix
6316: imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6317: jmap[nnz1+1]: i-th nonzero in the set has jmap[i+1] - jmap[i] repeats in the set
6319: Output Parameter: (memory is allocated by the caller)
6320: jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set
6322: Example:
6323: nnz1 = 4
6324: nnz = 6
6325: imap = [1,3,4,5]
6326: jmap = [0,3,5,6,7]
6327: then,
6328: jmap_new = [0,0,3,3,5,6,7]
6329: */
6330: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6331: {
6332: PetscCount k, p;
6334: PetscFunctionBegin;
6335: jmap_new[0] = 0;
6336: p = nnz; /* p loops over jmap_new[] backwards */
6337: for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6338: for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6339: }
6340: for (; p >= 0; p--) jmap_new[p] = jmap[0];
6341: PetscFunctionReturn(PETSC_SUCCESS);
6342: }
6344: static PetscErrorCode MatCOOStructDestroy_MPIAIJ(PetscCtxRt data)
6345: {
6346: MatCOOStruct_MPIAIJ *coo = *(MatCOOStruct_MPIAIJ **)data;
6348: PetscFunctionBegin;
6349: PetscCall(PetscSFDestroy(&coo->sf));
6350: PetscCall(PetscFree(coo->Aperm1));
6351: PetscCall(PetscFree(coo->Bperm1));
6352: PetscCall(PetscFree(coo->Ajmap1));
6353: PetscCall(PetscFree(coo->Bjmap1));
6354: PetscCall(PetscFree(coo->Aimap2));
6355: PetscCall(PetscFree(coo->Bimap2));
6356: PetscCall(PetscFree(coo->Aperm2));
6357: PetscCall(PetscFree(coo->Bperm2));
6358: PetscCall(PetscFree(coo->Ajmap2));
6359: PetscCall(PetscFree(coo->Bjmap2));
6360: PetscCall(PetscFree(coo->Cperm1));
6361: PetscCall(PetscFree2(coo->sendbuf, coo->recvbuf));
6362: PetscCall(PetscFree(coo));
6363: PetscFunctionReturn(PETSC_SUCCESS);
6364: }
6366: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6367: {
6368: MPI_Comm comm;
6369: PetscMPIInt rank, size;
6370: PetscInt m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6371: PetscCount k, p, q, rem; /* Loop variables over coo arrays */
6372: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6373: PetscContainer container;
6374: MatCOOStruct_MPIAIJ *coo;
6376: PetscFunctionBegin;
6377: PetscCall(PetscFree(mpiaij->garray));
6378: PetscCall(VecDestroy(&mpiaij->lvec));
6379: #if defined(PETSC_USE_CTABLE)
6380: PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6381: #else
6382: PetscCall(PetscFree(mpiaij->colmap));
6383: #endif
6384: PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6385: mat->assembled = PETSC_FALSE;
6386: mat->was_assembled = PETSC_FALSE;
6388: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6389: PetscCallMPI(MPI_Comm_size(comm, &size));
6390: PetscCallMPI(MPI_Comm_rank(comm, &rank));
6391: PetscCall(PetscLayoutSetUp(mat->rmap));
6392: PetscCall(PetscLayoutSetUp(mat->cmap));
6393: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6394: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6395: PetscCall(MatGetLocalSize(mat, &m, &n));
6396: PetscCall(MatGetSize(mat, &M, &N));
6398: /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6399: /* entries come first, then local rows, then remote rows. */
6400: PetscCount n1 = coo_n, *perm1;
6401: PetscInt *i1 = coo_i, *j1 = coo_j;
6403: PetscCall(PetscMalloc1(n1, &perm1));
6404: for (k = 0; k < n1; k++) perm1[k] = k;
6406: /* Manipulate indices so that entries with negative row or col indices will have smallest
6407: row indices, local entries will have greater but negative row indices, and remote entries
6408: will have positive row indices.
6409: */
6410: for (k = 0; k < n1; k++) {
6411: if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_INT_MIN; /* e.g., -2^31, minimal to move them ahead */
6412: else if (i1[k] >= rstart && i1[k] < rend) i1[k] -= PETSC_INT_MAX; /* e.g., minus 2^31-1 to shift local rows to range of [-PETSC_INT_MAX, -1] */
6413: else {
6414: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6415: if (mpiaij->donotstash) i1[k] = PETSC_INT_MIN; /* Ignore offproc entries as if they had negative indices */
6416: }
6417: }
6419: /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6420: PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));
6422: /* Advance k to the first entry we need to take care of */
6423: for (k = 0; k < n1; k++)
6424: if (i1[k] > PETSC_INT_MIN) break;
6425: PetscCount i1start = k;
6427: PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_INT_MAX, &rem)); /* rem is upper bound of the last local row */
6428: for (; k < rem; k++) i1[k] += PETSC_INT_MAX; /* Revert row indices of local rows*/
6430: PetscCheck(n1 == 0 || i1[n1 - 1] < M, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "COO row index %" PetscInt_FMT " is >= the matrix row size %" PetscInt_FMT, i1[n1 - 1], M);
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 i1[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, nentries, maxNsend));
6463: PetscCall(PetscFree2(sendto, nentries));
6464: sendto = sendto2;
6465: nentries = nentries2;
6466: maxNsend = maxNsend2;
6467: }
6468: sendto[nsend] = owner;
6469: PetscCall(PetscIntCast(p - k, &nentries[nsend]));
6470: nsend++;
6471: k = p;
6472: }
6474: /* Build 1st SF to know offsets on remote to send data */
6475: PetscSF sf1;
6476: PetscInt nroots = 1, nroots2 = 0;
6477: PetscInt nleaves = nsend, nleaves2 = 0;
6478: PetscInt *offsets;
6479: PetscSFNode *iremote;
6481: PetscCall(PetscSFCreate(comm, &sf1));
6482: PetscCall(PetscMalloc1(nsend, &iremote));
6483: PetscCall(PetscMalloc1(nsend, &offsets));
6484: for (k = 0; k < nsend; k++) {
6485: iremote[k].rank = sendto[k];
6486: iremote[k].index = 0;
6487: nleaves2 += nentries[k];
6488: PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6489: }
6490: PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6491: PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6492: PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6493: PetscCall(PetscSFDestroy(&sf1));
6494: PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT, nleaves2, n1 - rem);
6496: /* Build 2nd SF to send remote COOs to their owner */
6497: PetscSF sf2;
6498: nroots = nroots2;
6499: nleaves = nleaves2;
6500: PetscCall(PetscSFCreate(comm, &sf2));
6501: PetscCall(PetscSFSetFromOptions(sf2));
6502: PetscCall(PetscMalloc1(nleaves, &iremote));
6503: p = 0;
6504: for (k = 0; k < nsend; k++) {
6505: PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6506: for (q = 0; q < nentries[k]; q++, p++) {
6507: iremote[p].rank = sendto[k];
6508: PetscCall(PetscIntCast(offsets[k] + q, &iremote[p].index));
6509: }
6510: }
6511: PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6513: /* Send the remote COOs to their owner */
6514: PetscInt n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6515: PetscCount *perm2; /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6516: PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6517: PetscAssert(rem == 0 || i1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6518: PetscAssert(rem == 0 || j1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6519: PetscInt *i1prem = PetscSafePointerPlusOffset(i1, rem);
6520: PetscInt *j1prem = PetscSafePointerPlusOffset(j1, rem);
6521: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1prem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6522: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1prem, i2, MPI_REPLACE));
6523: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1prem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6524: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1prem, j2, MPI_REPLACE));
6526: PetscCall(PetscFree(offsets));
6527: PetscCall(PetscFree2(sendto, nentries));
6529: /* Sort received COOs by row along with the permutation array */
6530: for (k = 0; k < n2; k++) perm2[k] = k;
6531: PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));
6533: /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6534: PetscCount *Cperm1;
6535: PetscAssert(rem == 0 || perm1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6536: PetscCount *perm1prem = PetscSafePointerPlusOffset(perm1, rem);
6537: PetscCall(PetscMalloc1(nleaves, &Cperm1));
6538: PetscCall(PetscArraycpy(Cperm1, perm1prem, nleaves));
6540: /* Support for HYPRE matrices, kind of a hack.
6541: Swap min column with diagonal so that diagonal values will go first */
6542: PetscBool hypre;
6543: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", ((PetscObject)mat)->name, &hypre));
6544: if (hypre) {
6545: PetscInt *minj;
6546: PetscBT hasdiag;
6548: PetscCall(PetscBTCreate(m, &hasdiag));
6549: PetscCall(PetscMalloc1(m, &minj));
6550: for (k = 0; k < m; k++) minj[k] = PETSC_INT_MAX;
6551: for (k = i1start; k < rem; k++) {
6552: if (j1[k] < cstart || j1[k] >= cend) continue;
6553: const PetscInt rindex = i1[k] - rstart;
6554: if ((j1[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6555: minj[rindex] = PetscMin(minj[rindex], j1[k]);
6556: }
6557: for (k = 0; k < n2; k++) {
6558: if (j2[k] < cstart || j2[k] >= cend) continue;
6559: const PetscInt rindex = i2[k] - rstart;
6560: if ((j2[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6561: minj[rindex] = PetscMin(minj[rindex], j2[k]);
6562: }
6563: for (k = i1start; k < rem; k++) {
6564: const PetscInt rindex = i1[k] - rstart;
6565: if (j1[k] < cstart || j1[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6566: if (j1[k] == minj[rindex]) j1[k] = i1[k] + (cstart - rstart);
6567: else if ((j1[k] - cstart) == rindex) j1[k] = minj[rindex];
6568: }
6569: for (k = 0; k < n2; k++) {
6570: const PetscInt rindex = i2[k] - rstart;
6571: if (j2[k] < cstart || j2[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6572: if (j2[k] == minj[rindex]) j2[k] = i2[k] + (cstart - rstart);
6573: else if ((j2[k] - cstart) == rindex) j2[k] = minj[rindex];
6574: }
6575: PetscCall(PetscBTDestroy(&hasdiag));
6576: PetscCall(PetscFree(minj));
6577: }
6579: /* Split local COOs and received COOs into diag/offdiag portions */
6580: PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6581: PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1;
6582: PetscCount Annz1, Bnnz1, Atot1, Btot1;
6583: PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6584: PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6585: PetscCount Annz2, Bnnz2, Atot2, Btot2;
6587: PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6588: PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6589: PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6590: PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));
6592: /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6593: PetscInt *Ai, *Bi;
6594: PetscInt *Aj, *Bj;
6596: PetscCall(PetscMalloc1(m + 1, &Ai));
6597: PetscCall(PetscMalloc1(m + 1, &Bi));
6598: PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6599: PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));
6601: PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6602: PetscCall(PetscMalloc1(Annz1, &Aimap1));
6603: PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6604: PetscCall(PetscMalloc1(Annz2, &Aimap2));
6605: PetscCall(PetscMalloc1(Bnnz2, &Bimap2));
6607: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6608: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));
6610: /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we */
6611: /* expect nonzeros in A/B most likely have local contributing entries */
6612: PetscInt Annz = Ai[m];
6613: PetscInt Bnnz = Bi[m];
6614: PetscCount *Ajmap1_new, *Bjmap1_new;
6616: PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6617: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));
6619: PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6620: PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));
6622: PetscCall(PetscFree(Aimap1));
6623: PetscCall(PetscFree(Ajmap1));
6624: PetscCall(PetscFree(Bimap1));
6625: PetscCall(PetscFree(Bjmap1));
6626: PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6627: PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6628: PetscCall(PetscFree(perm1));
6629: PetscCall(PetscFree3(i2, j2, perm2));
6631: Ajmap1 = Ajmap1_new;
6632: Bjmap1 = Bjmap1_new;
6634: /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6635: if (Annz < Annz1 + Annz2) {
6636: PetscInt *Aj_new;
6637: PetscCall(PetscMalloc1(Annz, &Aj_new));
6638: PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6639: PetscCall(PetscFree(Aj));
6640: Aj = Aj_new;
6641: }
6643: if (Bnnz < Bnnz1 + Bnnz2) {
6644: PetscInt *Bj_new;
6645: PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6646: PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6647: PetscCall(PetscFree(Bj));
6648: Bj = Bj_new;
6649: }
6651: /* Create new submatrices for on-process and off-process coupling */
6652: PetscScalar *Aa, *Ba;
6653: MatType rtype;
6654: Mat_SeqAIJ *a, *b;
6655: PetscObjectState state;
6656: PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6657: PetscCall(PetscCalloc1(Bnnz, &Ba));
6658: /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6659: if (cstart) {
6660: for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6661: }
6663: PetscCall(MatGetRootType_Private(mat, &rtype));
6665: MatSeqXAIJGetOptions_Private(mpiaij->A);
6666: PetscCall(MatDestroy(&mpiaij->A));
6667: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6668: PetscCall(MatSetBlockSizesFromMats(mpiaij->A, mat, mat));
6669: MatSeqXAIJRestoreOptions_Private(mpiaij->A);
6671: MatSeqXAIJGetOptions_Private(mpiaij->B);
6672: PetscCall(MatDestroy(&mpiaij->B));
6673: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6674: PetscCall(MatSetBlockSizesFromMats(mpiaij->B, mat, mat));
6675: MatSeqXAIJRestoreOptions_Private(mpiaij->B);
6677: PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6678: mat->was_assembled = PETSC_TRUE; // was_assembled in effect means the Mvctx is built; doing so avoids redundant MatSetUpMultiply_MPIAIJ
6679: state = mpiaij->A->nonzerostate + mpiaij->B->nonzerostate;
6680: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
6682: a = (Mat_SeqAIJ *)mpiaij->A->data;
6683: b = (Mat_SeqAIJ *)mpiaij->B->data;
6684: a->free_a = PETSC_TRUE;
6685: a->free_ij = PETSC_TRUE;
6686: b->free_a = PETSC_TRUE;
6687: b->free_ij = PETSC_TRUE;
6688: a->maxnz = a->nz;
6689: b->maxnz = b->nz;
6691: /* conversion must happen AFTER multiply setup */
6692: PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6693: PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6694: PetscCall(VecDestroy(&mpiaij->lvec));
6695: PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));
6697: // Put the COO struct in a container and then attach that to the matrix
6698: PetscCall(PetscMalloc1(1, &coo));
6699: coo->n = coo_n;
6700: coo->sf = sf2;
6701: coo->sendlen = nleaves;
6702: coo->recvlen = nroots;
6703: coo->Annz = Annz;
6704: coo->Bnnz = Bnnz;
6705: coo->Annz2 = Annz2;
6706: coo->Bnnz2 = Bnnz2;
6707: coo->Atot1 = Atot1;
6708: coo->Atot2 = Atot2;
6709: coo->Btot1 = Btot1;
6710: coo->Btot2 = Btot2;
6711: coo->Ajmap1 = Ajmap1;
6712: coo->Aperm1 = Aperm1;
6713: coo->Bjmap1 = Bjmap1;
6714: coo->Bperm1 = Bperm1;
6715: coo->Aimap2 = Aimap2;
6716: coo->Ajmap2 = Ajmap2;
6717: coo->Aperm2 = Aperm2;
6718: coo->Bimap2 = Bimap2;
6719: coo->Bjmap2 = Bjmap2;
6720: coo->Bperm2 = Bperm2;
6721: coo->Cperm1 = Cperm1;
6722: // Allocate in preallocation. If not used, it has zero cost on host
6723: PetscCall(PetscMalloc2(coo->sendlen, &coo->sendbuf, coo->recvlen, &coo->recvbuf));
6724: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
6725: PetscCall(PetscContainerSetPointer(container, coo));
6726: PetscCall(PetscContainerSetCtxDestroy(container, MatCOOStructDestroy_MPIAIJ));
6727: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
6728: PetscCall(PetscContainerDestroy(&container));
6729: PetscFunctionReturn(PETSC_SUCCESS);
6730: }
6732: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6733: {
6734: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6735: Mat A = mpiaij->A, B = mpiaij->B;
6736: PetscScalar *Aa, *Ba;
6737: PetscScalar *sendbuf, *recvbuf;
6738: const PetscCount *Ajmap1, *Ajmap2, *Aimap2;
6739: const PetscCount *Bjmap1, *Bjmap2, *Bimap2;
6740: const PetscCount *Aperm1, *Aperm2, *Bperm1, *Bperm2;
6741: const PetscCount *Cperm1;
6742: PetscContainer container;
6743: MatCOOStruct_MPIAIJ *coo;
6745: PetscFunctionBegin;
6746: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
6747: PetscCheck(container, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
6748: PetscCall(PetscContainerGetPointer(container, &coo));
6749: sendbuf = coo->sendbuf;
6750: recvbuf = coo->recvbuf;
6751: Ajmap1 = coo->Ajmap1;
6752: Ajmap2 = coo->Ajmap2;
6753: Aimap2 = coo->Aimap2;
6754: Bjmap1 = coo->Bjmap1;
6755: Bjmap2 = coo->Bjmap2;
6756: Bimap2 = coo->Bimap2;
6757: Aperm1 = coo->Aperm1;
6758: Aperm2 = coo->Aperm2;
6759: Bperm1 = coo->Bperm1;
6760: Bperm2 = coo->Bperm2;
6761: Cperm1 = coo->Cperm1;
6763: PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6764: PetscCall(MatSeqAIJGetArray(B, &Ba));
6766: /* Pack entries to be sent to remote */
6767: for (PetscCount i = 0; i < coo->sendlen; i++) sendbuf[i] = v[Cperm1[i]];
6769: /* Send remote entries to their owner and overlap the communication with local computation */
6770: PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6771: /* Add local entries to A and B */
6772: for (PetscCount i = 0; i < coo->Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6773: PetscScalar sum = 0.0; /* Do partial summation first to improve numerical stability */
6774: for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6775: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6776: }
6777: for (PetscCount i = 0; i < coo->Bnnz; i++) {
6778: PetscScalar sum = 0.0;
6779: for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6780: Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6781: }
6782: PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));
6784: /* Add received remote entries to A and B */
6785: for (PetscCount i = 0; i < coo->Annz2; i++) {
6786: for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6787: }
6788: for (PetscCount i = 0; i < coo->Bnnz2; i++) {
6789: for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6790: }
6791: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6792: PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6793: PetscFunctionReturn(PETSC_SUCCESS);
6794: }
6796: /*MC
6797: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
6799: Options Database Keys:
6800: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`
6802: Level: beginner
6804: Notes:
6805: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
6806: in this case the values associated with the rows and columns one passes in are set to zero
6807: in the matrix
6809: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
6810: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
6812: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6813: M*/
6814: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6815: {
6816: Mat_MPIAIJ *b;
6817: PetscMPIInt size;
6819: PetscFunctionBegin;
6820: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
6822: PetscCall(PetscNew(&b));
6823: B->data = (void *)b;
6824: B->ops[0] = MatOps_Values;
6825: B->assembled = PETSC_FALSE;
6826: B->insertmode = NOT_SET_VALUES;
6827: b->size = size;
6829: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
6831: /* build cache for off array entries formed */
6832: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
6834: b->donotstash = PETSC_FALSE;
6835: b->colmap = NULL;
6836: b->garray = NULL;
6837: b->roworiented = PETSC_TRUE;
6839: /* stuff used for matrix vector multiply */
6840: b->lvec = NULL;
6841: b->Mvctx = NULL;
6843: /* stuff for MatGetRow() */
6844: b->rowindices = NULL;
6845: b->rowvalues = NULL;
6846: b->getrowactive = PETSC_FALSE;
6848: /* flexible pointer used in CUSPARSE classes */
6849: b->spptr = NULL;
6851: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6852: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6853: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6854: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6855: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6856: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6857: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetHash_C", MatResetHash_MPIAIJ));
6858: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6859: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6860: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6861: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6862: #if defined(PETSC_HAVE_CUDA)
6863: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6864: #endif
6865: #if defined(PETSC_HAVE_HIP)
6866: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6867: #endif
6868: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6869: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6870: #endif
6871: #if defined(PETSC_HAVE_MKL_SPARSE)
6872: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6873: #endif
6874: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6875: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6876: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6877: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6878: #if defined(PETSC_HAVE_ELEMENTAL)
6879: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6880: #endif
6881: #if defined(PETSC_HAVE_SCALAPACK) && (defined(PETSC_USE_REAL_SINGLE) || defined(PETSC_USE_REAL_DOUBLE))
6882: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6883: #endif
6884: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6885: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6886: #if defined(PETSC_HAVE_HYPRE)
6887: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6888: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6889: #endif
6890: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6891: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6892: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6893: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6894: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6895: PetscFunctionReturn(PETSC_SUCCESS);
6896: }
6898: /*@
6899: MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6900: and "off-diagonal" part of the matrix in CSR format.
6902: Collective
6904: Input Parameters:
6905: + comm - MPI communicator
6906: . m - number of local rows (Cannot be `PETSC_DECIDE`)
6907: . n - This value should be the same as the local size used in creating the
6908: x vector for the matrix-vector product $y = Ax$. (or `PETSC_DECIDE` to have
6909: calculated if `N` is given) For square matrices `n` is almost always `m`.
6910: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6911: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6912: . 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
6913: . j - column indices, which must be local, i.e., based off the start column of the diagonal portion
6914: . a - matrix values
6915: . 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
6916: . oj - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6917: - oa - matrix values
6919: Output Parameter:
6920: . mat - the matrix
6922: Level: advanced
6924: Notes:
6925: The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc (even in Fortran). The user
6926: must free the arrays once the matrix has been destroyed and not before.
6928: The `i` and `j` indices are 0 based
6930: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix
6932: This sets local rows and cannot be used to set off-processor values.
6934: Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6935: legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6936: not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
6937: the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
6938: keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
6939: communication if it is known that only local entries will be set.
6941: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
6942: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
6943: @*/
6944: 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)
6945: {
6946: Mat_MPIAIJ *maij;
6948: PetscFunctionBegin;
6949: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
6950: PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
6951: PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
6952: PetscCall(MatCreate(comm, mat));
6953: PetscCall(MatSetSizes(*mat, m, n, M, N));
6954: PetscCall(MatSetType(*mat, MATMPIAIJ));
6955: maij = (Mat_MPIAIJ *)(*mat)->data;
6957: (*mat)->preallocated = PETSC_TRUE;
6959: PetscCall(PetscLayoutSetUp((*mat)->rmap));
6960: PetscCall(PetscLayoutSetUp((*mat)->cmap));
6962: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
6963: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));
6965: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
6966: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
6967: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
6968: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
6969: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
6970: PetscFunctionReturn(PETSC_SUCCESS);
6971: }
6973: typedef struct {
6974: Mat *mp; /* intermediate products */
6975: PetscBool *mptmp; /* is the intermediate product temporary ? */
6976: PetscInt cp; /* number of intermediate products */
6978: /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
6979: PetscInt *startsj_s, *startsj_r;
6980: PetscScalar *bufa;
6981: Mat P_oth;
6983: /* may take advantage of merging product->B */
6984: Mat Bloc; /* B-local by merging diag and off-diag */
6986: /* cusparse does not have support to split between symbolic and numeric phases.
6987: When api_user is true, we don't need to update the numerical values
6988: of the temporary storage */
6989: PetscBool reusesym;
6991: /* support for COO values insertion */
6992: PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
6993: PetscInt **own; /* own[i] points to address of on-process COO indices for Mat mp[i] */
6994: PetscInt **off; /* off[i] points to address of off-process COO indices for Mat mp[i] */
6995: PetscBool hasoffproc; /* if true, have off-process values insertion (i.e. AtB or PtAP) */
6996: PetscSF sf; /* used for non-local values insertion and memory malloc */
6997: PetscMemType mtype;
6999: /* customization */
7000: PetscBool abmerge;
7001: PetscBool P_oth_bind;
7002: } MatMatMPIAIJBACKEND;
7004: static PetscErrorCode MatProductCtxDestroy_MatMatMPIAIJBACKEND(PetscCtxRt data)
7005: {
7006: MatMatMPIAIJBACKEND *mmdata = *(MatMatMPIAIJBACKEND **)data;
7007: PetscInt i;
7009: PetscFunctionBegin;
7010: PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
7011: PetscCall(PetscFree(mmdata->bufa));
7012: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
7013: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
7014: PetscCall(MatDestroy(&mmdata->P_oth));
7015: PetscCall(MatDestroy(&mmdata->Bloc));
7016: PetscCall(PetscSFDestroy(&mmdata->sf));
7017: for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
7018: PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
7019: PetscCall(PetscFree(mmdata->own[0]));
7020: PetscCall(PetscFree(mmdata->own));
7021: PetscCall(PetscFree(mmdata->off[0]));
7022: PetscCall(PetscFree(mmdata->off));
7023: PetscCall(PetscFree(mmdata));
7024: PetscFunctionReturn(PETSC_SUCCESS);
7025: }
7027: /* Copy selected n entries with indices in idx[] of A to v[].
7028: If idx is NULL, copy the whole data array of A to v[]
7029: */
7030: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
7031: {
7032: PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);
7034: PetscFunctionBegin;
7035: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
7036: if (f) PetscCall((*f)(A, n, idx, v));
7037: else {
7038: const PetscScalar *vv;
7040: PetscCall(MatSeqAIJGetArrayRead(A, &vv));
7041: if (n && idx) {
7042: PetscScalar *w = v;
7043: const PetscInt *oi = idx;
7044: PetscInt j;
7046: for (j = 0; j < n; j++) *w++ = vv[*oi++];
7047: } else {
7048: PetscCall(PetscArraycpy(v, vv, n));
7049: }
7050: PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7051: }
7052: PetscFunctionReturn(PETSC_SUCCESS);
7053: }
7055: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7056: {
7057: MatMatMPIAIJBACKEND *mmdata;
7058: PetscInt i, n_d, n_o;
7060: PetscFunctionBegin;
7061: MatCheckProduct(C, 1);
7062: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7063: mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7064: if (!mmdata->reusesym) { /* update temporary matrices */
7065: 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));
7066: if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7067: }
7068: mmdata->reusesym = PETSC_FALSE;
7070: for (i = 0; i < mmdata->cp; i++) {
7071: 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]);
7072: PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7073: }
7074: for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7075: PetscInt noff;
7077: PetscCall(PetscIntCast(mmdata->off[i + 1] - mmdata->off[i], &noff));
7078: if (mmdata->mptmp[i]) continue;
7079: if (noff) {
7080: PetscInt nown;
7082: PetscCall(PetscIntCast(mmdata->own[i + 1] - mmdata->own[i], &nown));
7083: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7084: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7085: n_o += noff;
7086: n_d += nown;
7087: } else {
7088: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;
7090: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7091: n_d += mm->nz;
7092: }
7093: }
7094: if (mmdata->hasoffproc) { /* offprocess insertion */
7095: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7096: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7097: }
7098: PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7099: PetscFunctionReturn(PETSC_SUCCESS);
7100: }
7102: /* Support for Pt * A, A * P, or Pt * A * P */
7103: #define MAX_NUMBER_INTERMEDIATE 4
7104: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7105: {
7106: Mat_Product *product = C->product;
7107: Mat A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7108: Mat_MPIAIJ *a, *p;
7109: MatMatMPIAIJBACKEND *mmdata;
7110: ISLocalToGlobalMapping P_oth_l2g = NULL;
7111: IS glob = NULL;
7112: const char *prefix;
7113: char pprefix[256];
7114: const PetscInt *globidx, *P_oth_idx;
7115: PetscInt i, j, cp, m, n, M, N, *coo_i, *coo_j;
7116: PetscCount ncoo, ncoo_d, ncoo_o, ncoo_oown;
7117: PetscInt cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7118: /* type-0: consecutive, start from 0; type-1: consecutive with */
7119: /* a base offset; type-2: sparse with a local to global map table */
7120: const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE]; /* col/row local to global map array (table) for type-2 map type */
7122: MatProductType ptype;
7123: PetscBool mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7124: PetscMPIInt size;
7126: PetscFunctionBegin;
7127: MatCheckProduct(C, 1);
7128: PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7129: ptype = product->type;
7130: if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7131: ptype = MATPRODUCT_AB;
7132: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7133: }
7134: switch (ptype) {
7135: case MATPRODUCT_AB:
7136: A = product->A;
7137: P = product->B;
7138: m = A->rmap->n;
7139: n = P->cmap->n;
7140: M = A->rmap->N;
7141: N = P->cmap->N;
7142: hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7143: break;
7144: case MATPRODUCT_AtB:
7145: P = product->A;
7146: A = product->B;
7147: m = P->cmap->n;
7148: n = A->cmap->n;
7149: M = P->cmap->N;
7150: N = A->cmap->N;
7151: hasoffproc = PETSC_TRUE;
7152: break;
7153: case MATPRODUCT_PtAP:
7154: A = product->A;
7155: P = product->B;
7156: m = P->cmap->n;
7157: n = P->cmap->n;
7158: M = P->cmap->N;
7159: N = P->cmap->N;
7160: hasoffproc = PETSC_TRUE;
7161: break;
7162: default:
7163: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7164: }
7165: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7166: if (size == 1) hasoffproc = PETSC_FALSE;
7168: /* defaults */
7169: for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7170: mp[i] = NULL;
7171: mptmp[i] = PETSC_FALSE;
7172: rmapt[i] = -1;
7173: cmapt[i] = -1;
7174: rmapa[i] = NULL;
7175: cmapa[i] = NULL;
7176: }
7178: /* customization */
7179: PetscCall(PetscNew(&mmdata));
7180: mmdata->reusesym = product->api_user;
7181: if (ptype == MATPRODUCT_AB) {
7182: if (product->api_user) {
7183: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7184: PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7185: PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7186: PetscOptionsEnd();
7187: } else {
7188: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7189: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7190: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7191: PetscOptionsEnd();
7192: }
7193: } else if (ptype == MATPRODUCT_PtAP) {
7194: if (product->api_user) {
7195: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7196: PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7197: PetscOptionsEnd();
7198: } else {
7199: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7200: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7201: PetscOptionsEnd();
7202: }
7203: }
7204: a = (Mat_MPIAIJ *)A->data;
7205: p = (Mat_MPIAIJ *)P->data;
7206: PetscCall(MatSetSizes(C, m, n, M, N));
7207: PetscCall(PetscLayoutSetUp(C->rmap));
7208: PetscCall(PetscLayoutSetUp(C->cmap));
7209: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7210: PetscCall(MatGetOptionsPrefix(C, &prefix));
7212: cp = 0;
7213: switch (ptype) {
7214: case MATPRODUCT_AB: /* A * P */
7215: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7217: /* A_diag * P_local (merged or not) */
7218: if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7219: /* P is product->B */
7220: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7221: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7222: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7223: PetscCall(MatProductSetFill(mp[cp], product->fill));
7224: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7225: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7226: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7227: mp[cp]->product->api_user = product->api_user;
7228: PetscCall(MatProductSetFromOptions(mp[cp]));
7229: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7230: PetscCall(ISGetIndices(glob, &globidx));
7231: rmapt[cp] = 1;
7232: cmapt[cp] = 2;
7233: cmapa[cp] = globidx;
7234: mptmp[cp] = PETSC_FALSE;
7235: cp++;
7236: } else { /* A_diag * P_diag and A_diag * P_off */
7237: PetscCall(MatProductCreate(a->A, p->A, NULL, &mp[cp]));
7238: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7239: PetscCall(MatProductSetFill(mp[cp], product->fill));
7240: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7241: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7242: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7243: mp[cp]->product->api_user = product->api_user;
7244: PetscCall(MatProductSetFromOptions(mp[cp]));
7245: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7246: rmapt[cp] = 1;
7247: cmapt[cp] = 1;
7248: mptmp[cp] = PETSC_FALSE;
7249: cp++;
7250: PetscCall(MatProductCreate(a->A, p->B, NULL, &mp[cp]));
7251: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7252: PetscCall(MatProductSetFill(mp[cp], product->fill));
7253: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7254: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7255: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7256: mp[cp]->product->api_user = product->api_user;
7257: PetscCall(MatProductSetFromOptions(mp[cp]));
7258: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7259: rmapt[cp] = 1;
7260: cmapt[cp] = 2;
7261: cmapa[cp] = p->garray;
7262: mptmp[cp] = PETSC_FALSE;
7263: cp++;
7264: }
7266: /* A_off * P_other */
7267: if (mmdata->P_oth) {
7268: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7269: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7270: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7271: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7272: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7273: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7274: PetscCall(MatProductSetFill(mp[cp], product->fill));
7275: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7276: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7277: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7278: mp[cp]->product->api_user = product->api_user;
7279: PetscCall(MatProductSetFromOptions(mp[cp]));
7280: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7281: rmapt[cp] = 1;
7282: cmapt[cp] = 2;
7283: cmapa[cp] = P_oth_idx;
7284: mptmp[cp] = PETSC_FALSE;
7285: cp++;
7286: }
7287: break;
7289: case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7290: /* A is product->B */
7291: PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7292: if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7293: PetscCall(MatProductCreate(mmdata->Bloc, mmdata->Bloc, NULL, &mp[cp]));
7294: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7295: PetscCall(MatProductSetFill(mp[cp], product->fill));
7296: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7297: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7298: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7299: mp[cp]->product->api_user = product->api_user;
7300: PetscCall(MatProductSetFromOptions(mp[cp]));
7301: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7302: PetscCall(ISGetIndices(glob, &globidx));
7303: rmapt[cp] = 2;
7304: rmapa[cp] = globidx;
7305: cmapt[cp] = 2;
7306: cmapa[cp] = globidx;
7307: mptmp[cp] = PETSC_FALSE;
7308: cp++;
7309: } else {
7310: PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7311: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7312: PetscCall(MatProductSetFill(mp[cp], product->fill));
7313: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7314: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7315: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7316: mp[cp]->product->api_user = product->api_user;
7317: PetscCall(MatProductSetFromOptions(mp[cp]));
7318: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7319: PetscCall(ISGetIndices(glob, &globidx));
7320: rmapt[cp] = 1;
7321: cmapt[cp] = 2;
7322: cmapa[cp] = globidx;
7323: mptmp[cp] = PETSC_FALSE;
7324: cp++;
7325: PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7326: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7327: PetscCall(MatProductSetFill(mp[cp], product->fill));
7328: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7329: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7330: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7331: mp[cp]->product->api_user = product->api_user;
7332: PetscCall(MatProductSetFromOptions(mp[cp]));
7333: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7334: rmapt[cp] = 2;
7335: rmapa[cp] = p->garray;
7336: cmapt[cp] = 2;
7337: cmapa[cp] = globidx;
7338: mptmp[cp] = PETSC_FALSE;
7339: cp++;
7340: }
7341: break;
7342: case MATPRODUCT_PtAP:
7343: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7344: /* P is product->B */
7345: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7346: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7347: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
7348: PetscCall(MatProductSetFill(mp[cp], product->fill));
7349: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7350: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7351: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7352: mp[cp]->product->api_user = product->api_user;
7353: PetscCall(MatProductSetFromOptions(mp[cp]));
7354: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7355: PetscCall(ISGetIndices(glob, &globidx));
7356: rmapt[cp] = 2;
7357: rmapa[cp] = globidx;
7358: cmapt[cp] = 2;
7359: cmapa[cp] = globidx;
7360: mptmp[cp] = PETSC_FALSE;
7361: cp++;
7362: if (mmdata->P_oth) {
7363: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7364: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7365: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7366: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7367: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7368: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7369: PetscCall(MatProductSetFill(mp[cp], product->fill));
7370: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7371: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7372: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7373: mp[cp]->product->api_user = product->api_user;
7374: PetscCall(MatProductSetFromOptions(mp[cp]));
7375: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7376: mptmp[cp] = PETSC_TRUE;
7377: cp++;
7378: PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7379: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7380: PetscCall(MatProductSetFill(mp[cp], product->fill));
7381: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7382: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7383: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7384: mp[cp]->product->api_user = product->api_user;
7385: PetscCall(MatProductSetFromOptions(mp[cp]));
7386: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7387: rmapt[cp] = 2;
7388: rmapa[cp] = globidx;
7389: cmapt[cp] = 2;
7390: cmapa[cp] = P_oth_idx;
7391: mptmp[cp] = PETSC_FALSE;
7392: cp++;
7393: }
7394: break;
7395: default:
7396: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7397: }
7398: /* sanity check */
7399: if (size > 1)
7400: 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);
7402: PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7403: for (i = 0; i < cp; i++) {
7404: mmdata->mp[i] = mp[i];
7405: mmdata->mptmp[i] = mptmp[i];
7406: }
7407: mmdata->cp = cp;
7408: C->product->data = mmdata;
7409: C->product->destroy = MatProductCtxDestroy_MatMatMPIAIJBACKEND;
7410: C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;
7412: /* memory type */
7413: mmdata->mtype = PETSC_MEMTYPE_HOST;
7414: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7415: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7416: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7417: if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7418: else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7419: else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;
7421: /* prepare coo coordinates for values insertion */
7423: /* count total nonzeros of those intermediate seqaij Mats
7424: ncoo_d: # of nonzeros of matrices that do not have offproc entries
7425: ncoo_o: # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7426: ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7427: */
7428: for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7429: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7430: if (mptmp[cp]) continue;
7431: if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7432: const PetscInt *rmap = rmapa[cp];
7433: const PetscInt mr = mp[cp]->rmap->n;
7434: const PetscInt rs = C->rmap->rstart;
7435: const PetscInt re = C->rmap->rend;
7436: const PetscInt *ii = mm->i;
7437: for (i = 0; i < mr; i++) {
7438: const PetscInt gr = rmap[i];
7439: const PetscInt nz = ii[i + 1] - ii[i];
7440: if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7441: else ncoo_oown += nz; /* this row is local */
7442: }
7443: } else ncoo_d += mm->nz;
7444: }
7446: /*
7447: ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc
7449: ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.
7451: off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].
7453: off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7454: own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7455: so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.
7457: coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7458: 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.
7459: */
7460: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7461: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));
7463: /* gather (i,j) of nonzeros inserted by remote procs */
7464: if (hasoffproc) {
7465: PetscSF msf;
7466: PetscInt ncoo2, *coo_i2, *coo_j2;
7468: PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7469: PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7470: PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */
7472: for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7473: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7474: PetscInt *idxoff = mmdata->off[cp];
7475: PetscInt *idxown = mmdata->own[cp];
7476: if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7477: const PetscInt *rmap = rmapa[cp];
7478: const PetscInt *cmap = cmapa[cp];
7479: const PetscInt *ii = mm->i;
7480: PetscInt *coi = coo_i + ncoo_o;
7481: PetscInt *coj = coo_j + ncoo_o;
7482: const PetscInt mr = mp[cp]->rmap->n;
7483: const PetscInt rs = C->rmap->rstart;
7484: const PetscInt re = C->rmap->rend;
7485: const PetscInt cs = C->cmap->rstart;
7486: for (i = 0; i < mr; i++) {
7487: const PetscInt *jj = mm->j + ii[i];
7488: const PetscInt gr = rmap[i];
7489: const PetscInt nz = ii[i + 1] - ii[i];
7490: if (gr < rs || gr >= re) { /* this is an offproc row */
7491: for (j = ii[i]; j < ii[i + 1]; j++) {
7492: *coi++ = gr;
7493: *idxoff++ = j;
7494: }
7495: if (!cmapt[cp]) { /* already global */
7496: for (j = 0; j < nz; j++) *coj++ = jj[j];
7497: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7498: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7499: } else { /* offdiag */
7500: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7501: }
7502: ncoo_o += nz;
7503: } else { /* this is a local row */
7504: for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7505: }
7506: }
7507: }
7508: mmdata->off[cp + 1] = idxoff;
7509: mmdata->own[cp + 1] = idxown;
7510: }
7512: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7513: PetscInt incoo_o;
7514: PetscCall(PetscIntCast(ncoo_o, &incoo_o));
7515: PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, incoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7516: PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7517: PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7518: ncoo = ncoo_d + ncoo_oown + ncoo2;
7519: PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7520: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7521: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7522: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7523: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7524: PetscCall(PetscFree2(coo_i, coo_j));
7525: /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7526: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7527: coo_i = coo_i2;
7528: coo_j = coo_j2;
7529: } else { /* no offproc values insertion */
7530: ncoo = ncoo_d;
7531: PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));
7533: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7534: PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7535: PetscCall(PetscSFSetUp(mmdata->sf));
7536: }
7537: mmdata->hasoffproc = hasoffproc;
7539: /* gather (i,j) of nonzeros inserted locally */
7540: for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7541: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7542: PetscInt *coi = coo_i + ncoo_d;
7543: PetscInt *coj = coo_j + ncoo_d;
7544: const PetscInt *jj = mm->j;
7545: const PetscInt *ii = mm->i;
7546: const PetscInt *cmap = cmapa[cp];
7547: const PetscInt *rmap = rmapa[cp];
7548: const PetscInt mr = mp[cp]->rmap->n;
7549: const PetscInt rs = C->rmap->rstart;
7550: const PetscInt re = C->rmap->rend;
7551: const PetscInt cs = C->cmap->rstart;
7553: if (mptmp[cp]) continue;
7554: if (rmapt[cp] == 1) { /* consecutive rows */
7555: /* fill coo_i */
7556: for (i = 0; i < mr; i++) {
7557: const PetscInt gr = i + rs;
7558: for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7559: }
7560: /* fill coo_j */
7561: if (!cmapt[cp]) { /* type-0, already global */
7562: PetscCall(PetscArraycpy(coj, jj, mm->nz));
7563: } else if (cmapt[cp] == 1) { /* type-1, local to global for consecutive columns of C */
7564: for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7565: } else { /* type-2, local to global for sparse columns */
7566: for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7567: }
7568: ncoo_d += mm->nz;
7569: } else if (rmapt[cp] == 2) { /* sparse rows */
7570: for (i = 0; i < mr; i++) {
7571: const PetscInt *jj = mm->j + ii[i];
7572: const PetscInt gr = rmap[i];
7573: const PetscInt nz = ii[i + 1] - ii[i];
7574: if (gr >= rs && gr < re) { /* local rows */
7575: for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7576: if (!cmapt[cp]) { /* type-0, already global */
7577: for (j = 0; j < nz; j++) *coj++ = jj[j];
7578: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7579: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7580: } else { /* type-2, local to global for sparse columns */
7581: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7582: }
7583: ncoo_d += nz;
7584: }
7585: }
7586: }
7587: }
7588: if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7589: PetscCall(ISDestroy(&glob));
7590: if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7591: PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7592: /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7593: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));
7595: /* set block sizes */
7596: A = product->A;
7597: P = product->B;
7598: switch (ptype) {
7599: case MATPRODUCT_PtAP:
7600: PetscCall(MatSetBlockSizes(C, P->cmap->bs, P->cmap->bs));
7601: break;
7602: case MATPRODUCT_RARt:
7603: PetscCall(MatSetBlockSizes(C, P->rmap->bs, P->rmap->bs));
7604: break;
7605: case MATPRODUCT_ABC:
7606: PetscCall(MatSetBlockSizesFromMats(C, A, product->C));
7607: break;
7608: case MATPRODUCT_AB:
7609: PetscCall(MatSetBlockSizesFromMats(C, A, P));
7610: break;
7611: case MATPRODUCT_AtB:
7612: PetscCall(MatSetBlockSizes(C, A->cmap->bs, P->cmap->bs));
7613: break;
7614: case MATPRODUCT_ABt:
7615: PetscCall(MatSetBlockSizes(C, A->rmap->bs, P->rmap->bs));
7616: break;
7617: default:
7618: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for ProductType %s", MatProductTypes[ptype]);
7619: }
7621: /* preallocate with COO data */
7622: PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7623: PetscCall(PetscFree2(coo_i, coo_j));
7624: PetscFunctionReturn(PETSC_SUCCESS);
7625: }
7627: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7628: {
7629: Mat_Product *product = mat->product;
7630: #if defined(PETSC_HAVE_DEVICE)
7631: PetscBool match = PETSC_FALSE;
7632: PetscBool usecpu = PETSC_FALSE;
7633: #else
7634: PetscBool match = PETSC_TRUE;
7635: #endif
7637: PetscFunctionBegin;
7638: MatCheckProduct(mat, 1);
7639: #if defined(PETSC_HAVE_DEVICE)
7640: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7641: if (match) { /* we can always fallback to the CPU if requested */
7642: switch (product->type) {
7643: case MATPRODUCT_AB:
7644: if (product->api_user) {
7645: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7646: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7647: PetscOptionsEnd();
7648: } else {
7649: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7650: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7651: PetscOptionsEnd();
7652: }
7653: break;
7654: case MATPRODUCT_AtB:
7655: if (product->api_user) {
7656: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7657: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7658: PetscOptionsEnd();
7659: } else {
7660: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7661: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7662: PetscOptionsEnd();
7663: }
7664: break;
7665: case MATPRODUCT_PtAP:
7666: if (product->api_user) {
7667: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7668: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7669: PetscOptionsEnd();
7670: } else {
7671: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7672: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7673: PetscOptionsEnd();
7674: }
7675: break;
7676: default:
7677: break;
7678: }
7679: match = (PetscBool)!usecpu;
7680: }
7681: #endif
7682: if (match) {
7683: switch (product->type) {
7684: case MATPRODUCT_AB:
7685: case MATPRODUCT_AtB:
7686: case MATPRODUCT_PtAP:
7687: mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7688: break;
7689: default:
7690: break;
7691: }
7692: }
7693: /* fallback to MPIAIJ ops */
7694: if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7695: PetscFunctionReturn(PETSC_SUCCESS);
7696: }
7698: /*
7699: Produces a set of block column indices of the matrix row, one for each block represented in the original row
7701: n - the number of block indices in cc[]
7702: cc - the block indices (must be large enough to contain the indices)
7703: */
7704: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7705: {
7706: PetscInt cnt = -1, nidx, j;
7707: const PetscInt *idx;
7709: PetscFunctionBegin;
7710: PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7711: if (nidx) {
7712: cnt = 0;
7713: cc[cnt] = idx[0] / bs;
7714: for (j = 1; j < nidx; j++) {
7715: if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7716: }
7717: }
7718: PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7719: *n = cnt + 1;
7720: PetscFunctionReturn(PETSC_SUCCESS);
7721: }
7723: /*
7724: Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows
7726: ncollapsed - the number of block indices
7727: collapsed - the block indices (must be large enough to contain the indices)
7728: */
7729: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7730: {
7731: PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;
7733: PetscFunctionBegin;
7734: PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7735: for (i = start + 1; i < start + bs; i++) {
7736: PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7737: PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7738: cprevtmp = cprev;
7739: cprev = merged;
7740: merged = cprevtmp;
7741: }
7742: *ncollapsed = nprev;
7743: if (collapsed) *collapsed = cprev;
7744: PetscFunctionReturn(PETSC_SUCCESS);
7745: }
7747: // PetscClangLinter pragma disable: -fdoc-sowing-chars
7748: /*
7749: MatCreateGraph_Simple_AIJ - create simple scalar matrix (graph) from potentially blocked matrix
7751: Input Parameters:
7752: + Amat - matrix
7753: . symmetrize - make the result symmetric
7754: . scale - scale with diagonal
7755: . filter - threshold for filter
7756: . index_size - length of `index`
7757: - index - indices of unknown purpose
7759: Output Parameter:
7760: . a_Gmat - output scalar graph >= 0
7762: Level: developer
7764: .seealso: `MATAIJ`
7765: */
7766: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, PetscInt index_size, PetscInt index[], Mat *a_Gmat)
7767: {
7768: PetscInt Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7769: MPI_Comm comm;
7770: Mat Gmat;
7771: PetscBool ismpiaij, isseqaij;
7772: Mat a, b, c;
7773: MatType jtype;
7775: PetscFunctionBegin;
7776: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7777: PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7778: PetscCall(MatGetSize(Amat, &MM, &NN));
7779: PetscCall(MatGetBlockSize(Amat, &bs));
7780: nloc = (Iend - Istart) / bs;
7782: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7783: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7784: PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");
7786: /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7787: /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7788: implementation */
7789: if (bs > 1) {
7790: PetscCall(MatGetType(Amat, &jtype));
7791: PetscCall(MatCreate(comm, &Gmat));
7792: PetscCall(MatSetType(Gmat, jtype));
7793: PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7794: PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7795: if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7796: PetscInt *d_nnz, *o_nnz;
7797: MatScalar *aa, val, *AA;
7798: PetscInt *aj, *ai, *AJ, nc, nmax = 0;
7800: if (isseqaij) {
7801: a = Amat;
7802: b = NULL;
7803: } else {
7804: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7805: a = d->A;
7806: b = d->B;
7807: }
7808: PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7809: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7810: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7811: PetscInt *nnz = (c == a) ? d_nnz : o_nnz;
7812: const PetscInt *cols1, *cols2;
7814: for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7815: PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7816: nnz[brow / bs] = nc2 / bs;
7817: if (nc2 % bs) ok = 0;
7818: if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7819: for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7820: PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7821: if (nc1 != nc2) ok = 0;
7822: else {
7823: for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7824: if (cols1[jj] != cols2[jj]) ok = 0;
7825: if (cols1[jj] % bs != jj % bs) ok = 0;
7826: }
7827: }
7828: PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7829: }
7830: PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7831: if (!ok) {
7832: PetscCall(PetscFree2(d_nnz, o_nnz));
7833: PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7834: goto old_bs;
7835: }
7836: }
7837: }
7838: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7839: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7840: PetscCall(PetscFree2(d_nnz, o_nnz));
7841: PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7842: // diag
7843: for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7844: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;
7846: ai = aseq->i;
7847: n = ai[brow + 1] - ai[brow];
7848: aj = aseq->j + ai[brow];
7849: for (PetscInt k = 0; k < n; k += bs) { // block columns
7850: AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7851: val = 0;
7852: if (index_size == 0) {
7853: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7854: aa = aseq->a + ai[brow + ii] + k;
7855: for (PetscInt jj = 0; jj < bs; jj++) { // columns in block
7856: val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7857: }
7858: }
7859: } else { // use (index,index) value if provided
7860: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7861: PetscInt ii = index[iii];
7862: aa = aseq->a + ai[brow + ii] + k;
7863: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // columns in block
7864: PetscInt jj = index[jjj];
7865: val += PetscAbs(PetscRealPart(aa[jj]));
7866: }
7867: }
7868: }
7869: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%" PetscInt_FMT ") >= nmax (%" PetscInt_FMT ")", k / bs, nmax);
7870: AA[k / bs] = val;
7871: }
7872: grow = Istart / bs + brow / bs;
7873: PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, ADD_VALUES));
7874: }
7875: // off-diag
7876: if (ismpiaij) {
7877: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Amat->data;
7878: const PetscScalar *vals;
7879: const PetscInt *cols, *garray = aij->garray;
7881: PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7882: for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7883: PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7884: for (PetscInt k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7885: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7886: AA[k / bs] = 0;
7887: AJ[cidx] = garray[cols[k]] / bs;
7888: }
7889: nc = ncols / bs;
7890: PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7891: if (index_size == 0) {
7892: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7893: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7894: for (PetscInt k = 0; k < ncols; k += bs) {
7895: for (PetscInt jj = 0; jj < bs; jj++) { // cols in block
7896: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%" PetscInt_FMT ") >= nmax (%" PetscInt_FMT ")", k / bs, nmax);
7897: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7898: }
7899: }
7900: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7901: }
7902: } else { // use (index,index) value if provided
7903: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7904: PetscInt ii = index[iii];
7905: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7906: for (PetscInt k = 0; k < ncols; k += bs) {
7907: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // cols in block
7908: PetscInt jj = index[jjj];
7909: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7910: }
7911: }
7912: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7913: }
7914: }
7915: grow = Istart / bs + brow / bs;
7916: PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, ADD_VALUES));
7917: }
7918: }
7919: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7920: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7921: PetscCall(PetscFree2(AA, AJ));
7922: } else {
7923: const PetscScalar *vals;
7924: const PetscInt *idx;
7925: PetscInt *d_nnz, *o_nnz, *w0, *w1, *w2;
7926: old_bs:
7927: /*
7928: Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7929: */
7930: PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7931: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7932: if (isseqaij) {
7933: PetscInt max_d_nnz;
7935: /*
7936: Determine exact preallocation count for (sequential) scalar matrix
7937: */
7938: PetscCall(MatSeqAIJGetMaxRowNonzeros(Amat, &max_d_nnz));
7939: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7940: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7941: for (Ii = 0, jj = 0; Ii < Iend; Ii += bs, jj++) PetscCall(MatCollapseRows(Amat, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7942: PetscCall(PetscFree3(w0, w1, w2));
7943: } else if (ismpiaij) {
7944: Mat Daij, Oaij;
7945: const PetscInt *garray;
7946: PetscInt max_d_nnz;
7948: PetscCall(MatMPIAIJGetSeqAIJ(Amat, &Daij, &Oaij, &garray));
7949: /*
7950: Determine exact preallocation count for diagonal block portion of scalar matrix
7951: */
7952: PetscCall(MatSeqAIJGetMaxRowNonzeros(Daij, &max_d_nnz));
7953: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7954: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7955: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) PetscCall(MatCollapseRows(Daij, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7956: PetscCall(PetscFree3(w0, w1, w2));
7957: /*
7958: Over estimate (usually grossly over), preallocation count for off-diagonal portion of scalar matrix
7959: */
7960: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) {
7961: o_nnz[jj] = 0;
7962: for (kk = 0; kk < bs; kk++) { /* rows that get collapsed to a single row */
7963: PetscCall(MatGetRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7964: o_nnz[jj] += ncols;
7965: PetscCall(MatRestoreRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7966: }
7967: if (o_nnz[jj] > (NN / bs - nloc)) o_nnz[jj] = NN / bs - nloc;
7968: }
7969: } else SETERRQ(comm, PETSC_ERR_USER, "Require AIJ matrix type");
7970: /* get scalar copy (norms) of matrix */
7971: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7972: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7973: PetscCall(PetscFree2(d_nnz, o_nnz));
7974: for (Ii = Istart; Ii < Iend; Ii++) {
7975: PetscInt dest_row = Ii / bs;
7977: PetscCall(MatGetRow(Amat, Ii, &ncols, &idx, &vals));
7978: for (jj = 0; jj < ncols; jj++) {
7979: PetscInt dest_col = idx[jj] / bs;
7980: PetscScalar sv = PetscAbs(PetscRealPart(vals[jj]));
7982: PetscCall(MatSetValues(Gmat, 1, &dest_row, 1, &dest_col, &sv, ADD_VALUES));
7983: }
7984: PetscCall(MatRestoreRow(Amat, Ii, &ncols, &idx, &vals));
7985: }
7986: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7987: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7988: }
7989: } else {
7990: if (symmetrize || filter >= 0 || scale) PetscCall(MatDuplicate(Amat, MAT_COPY_VALUES, &Gmat));
7991: else {
7992: Gmat = Amat;
7993: PetscCall(PetscObjectReference((PetscObject)Gmat));
7994: }
7995: if (isseqaij) {
7996: a = Gmat;
7997: b = NULL;
7998: } else {
7999: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
8000: a = d->A;
8001: b = d->B;
8002: }
8003: if (filter >= 0 || scale) {
8004: /* take absolute value of each entry */
8005: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
8006: MatInfo info;
8007: PetscScalar *avals;
8009: PetscCall(MatGetInfo(c, MAT_LOCAL, &info));
8010: PetscCall(MatSeqAIJGetArray(c, &avals));
8011: for (int jj = 0; jj < info.nz_used; jj++) avals[jj] = PetscAbsScalar(avals[jj]);
8012: PetscCall(MatSeqAIJRestoreArray(c, &avals));
8013: }
8014: }
8015: }
8016: if (symmetrize) {
8017: PetscBool isset, issym;
8019: PetscCall(MatIsSymmetricKnown(Amat, &isset, &issym));
8020: if (!isset || !issym) {
8021: Mat matTrans;
8023: PetscCall(MatTranspose(Gmat, MAT_INITIAL_MATRIX, &matTrans));
8024: PetscCall(MatAXPY(Gmat, 1.0, matTrans, Gmat->structurally_symmetric == PETSC_BOOL3_TRUE ? SAME_NONZERO_PATTERN : DIFFERENT_NONZERO_PATTERN));
8025: PetscCall(MatDestroy(&matTrans));
8026: }
8027: PetscCall(MatSetOption(Gmat, MAT_SYMMETRIC, PETSC_TRUE));
8028: } else if (Amat != Gmat) PetscCall(MatPropagateSymmetryOptions(Amat, Gmat));
8029: if (scale) {
8030: /* scale c for all diagonal values = 1 or -1 */
8031: Vec diag;
8033: PetscCall(MatCreateVecs(Gmat, &diag, NULL));
8034: PetscCall(MatGetDiagonal(Gmat, diag));
8035: PetscCall(VecReciprocal(diag));
8036: PetscCall(VecSqrtAbs(diag));
8037: PetscCall(MatDiagonalScale(Gmat, diag, diag));
8038: PetscCall(VecDestroy(&diag));
8039: }
8040: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_graph_view"));
8041: if (filter >= 0) {
8042: PetscCall(MatFilter(Gmat, filter, PETSC_TRUE, PETSC_TRUE));
8043: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_filter_graph_view"));
8044: }
8045: *a_Gmat = Gmat;
8046: PetscFunctionReturn(PETSC_SUCCESS);
8047: }
8049: PETSC_INTERN PetscErrorCode MatGetCurrentMemType_MPIAIJ(Mat A, PetscMemType *memtype)
8050: {
8051: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)A->data;
8052: PetscMemType mD = PETSC_MEMTYPE_HOST, mO = PETSC_MEMTYPE_HOST;
8054: PetscFunctionBegin;
8055: if (mpiaij->A) PetscCall(MatGetCurrentMemType(mpiaij->A, &mD));
8056: if (mpiaij->B) PetscCall(MatGetCurrentMemType(mpiaij->B, &mO));
8057: *memtype = (mD == mO) ? mD : PETSC_MEMTYPE_HOST;
8058: PetscFunctionReturn(PETSC_SUCCESS);
8059: }
8061: /*
8062: Special version for direct calls from Fortran
8063: */
8065: /* Change these macros so can be used in void function */
8066: /* Identical to PetscCallVoid, except it assigns to *_ierr */
8067: #undef PetscCall
8068: #define PetscCall(...) \
8069: do { \
8070: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
8071: if (PetscUnlikely(ierr_msv_mpiaij)) { \
8072: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
8073: return; \
8074: } \
8075: } while (0)
8077: #undef SETERRQ
8078: #define SETERRQ(comm, ierr, ...) \
8079: do { \
8080: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
8081: return; \
8082: } while (0)
8084: #if defined(PETSC_HAVE_FORTRAN_CAPS)
8085: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
8086: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
8087: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
8088: #else
8089: #endif
8090: PETSC_EXTERN void matsetvaluesmpiaij_(Mat *mmat, PetscInt *mm, const PetscInt im[], PetscInt *mn, const PetscInt in[], const PetscScalar v[], InsertMode *maddv, PetscErrorCode *_ierr)
8091: {
8092: Mat mat = *mmat;
8093: PetscInt m = *mm, n = *mn;
8094: InsertMode addv = *maddv;
8095: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
8096: PetscScalar value;
8098: MatCheckPreallocated(mat, 1);
8099: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
8100: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
8101: {
8102: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
8103: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
8104: PetscBool roworiented = aij->roworiented;
8106: /* Some Variables required in the macro */
8107: Mat A = aij->A;
8108: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
8109: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
8110: MatScalar *aa;
8111: PetscBool ignorezeroentries = ((a->ignorezeroentries && (addv == ADD_VALUES)) ? PETSC_TRUE : PETSC_FALSE);
8112: Mat B = aij->B;
8113: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
8114: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
8115: MatScalar *ba;
8116: /* This variable below is only for the PETSC_HAVE_VIENNACL or PETSC_HAVE_CUDA cases, but we define it in all cases because we
8117: * cannot use "#if defined" inside a macro. */
8118: PETSC_UNUSED PetscBool inserted = PETSC_FALSE;
8120: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
8121: PetscInt nonew = a->nonew;
8122: MatScalar *ap1, *ap2;
8124: PetscFunctionBegin;
8125: PetscCall(MatSeqAIJGetArray(A, &aa));
8126: PetscCall(MatSeqAIJGetArray(B, &ba));
8127: for (i = 0; i < m; i++) {
8128: if (im[i] < 0) continue;
8129: 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);
8130: if (im[i] >= rstart && im[i] < rend) {
8131: row = im[i] - rstart;
8132: lastcol1 = -1;
8133: rp1 = aj + ai[row];
8134: ap1 = aa + ai[row];
8135: rmax1 = aimax[row];
8136: nrow1 = ailen[row];
8137: low1 = 0;
8138: high1 = nrow1;
8139: lastcol2 = -1;
8140: rp2 = bj + bi[row];
8141: ap2 = ba + bi[row];
8142: rmax2 = bimax[row];
8143: nrow2 = bilen[row];
8144: low2 = 0;
8145: high2 = nrow2;
8147: for (j = 0; j < n; j++) {
8148: if (roworiented) value = v[i * n + j];
8149: else value = v[i + j * m];
8150: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
8151: if (in[j] >= cstart && in[j] < cend) {
8152: col = in[j] - cstart;
8153: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
8154: } else if (in[j] < 0) continue;
8155: else if (PetscUnlikelyDebug(in[j] >= mat->cmap->N)) {
8156: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
8157: } else {
8158: if (mat->was_assembled) {
8159: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
8160: #if defined(PETSC_USE_CTABLE)
8161: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col));
8162: col--;
8163: #else
8164: col = aij->colmap[in[j]] - 1;
8165: #endif
8166: if (col < 0 && !((Mat_SeqAIJ *)aij->A->data)->nonew) {
8167: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));
8168: col = in[j];
8169: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
8170: B = aij->B;
8171: b = (Mat_SeqAIJ *)B->data;
8172: bimax = b->imax;
8173: bi = b->i;
8174: bilen = b->ilen;
8175: bj = b->j;
8176: rp2 = bj + bi[row];
8177: ap2 = ba + bi[row];
8178: rmax2 = bimax[row];
8179: nrow2 = bilen[row];
8180: low2 = 0;
8181: high2 = nrow2;
8182: bm = aij->B->rmap->n;
8183: ba = b->a;
8184: inserted = PETSC_FALSE;
8185: }
8186: } else col = in[j];
8187: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
8188: }
8189: }
8190: } else if (!aij->donotstash) {
8191: if (roworiented) {
8192: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8193: } else {
8194: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8195: }
8196: }
8197: }
8198: PetscCall(MatSeqAIJRestoreArray(A, &aa));
8199: PetscCall(MatSeqAIJRestoreArray(B, &ba));
8200: }
8201: PetscFunctionReturnVoid();
8202: }
8204: /* Undefining these here since they were redefined from their original definition above! No
8205: * other PETSc functions should be defined past this point, as it is impossible to recover the
8206: * original definitions */
8207: #undef PetscCall
8208: #undef SETERRQ