Actual source code: mpiaij.c
1: #include <../src/mat/impls/aij/mpi/mpiaij.h>
2: #include <petsc/private/vecimpl.h>
3: #include <petsc/private/sfimpl.h>
4: #include <petsc/private/isimpl.h>
5: #include <petscblaslapack.h>
6: #include <petscsf.h>
7: #include <petsc/private/hashmapi.h>
9: PetscErrorCode MatDestroy_MPIAIJ(Mat mat)
10: {
11: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
13: PetscFunctionBegin;
14: PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
15: PetscCall(MatStashDestroy_Private(&mat->stash));
16: PetscCall(VecDestroy(&aij->diag));
17: PetscCall(MatDestroy(&aij->A));
18: PetscCall(MatDestroy(&aij->B));
19: #if defined(PETSC_USE_CTABLE)
20: PetscCall(PetscHMapIDestroy(&aij->colmap));
21: #else
22: PetscCall(PetscFree(aij->colmap));
23: #endif
24: PetscCall(PetscFree(aij->garray));
25: PetscCall(VecDestroy(&aij->lvec));
26: PetscCall(VecScatterDestroy(&aij->Mvctx));
27: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
28: PetscCall(PetscFree(aij->ld));
30: PetscCall(PetscFree(mat->data));
32: /* may be created by MatCreateMPIAIJSumSeqAIJSymbolic */
33: PetscCall(PetscObjectCompose((PetscObject)mat, "MatMergeSeqsToMPI", NULL));
35: PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
36: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
37: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
38: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatIsTranspose_C", NULL));
39: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocation_C", NULL));
40: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatResetPreallocation_C", NULL));
41: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocationCSR_C", NULL));
42: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
43: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpibaij_C", NULL));
44: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisbaij_C", NULL));
45: #if defined(PETSC_HAVE_CUDA)
46: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcusparse_C", NULL));
47: #endif
48: #if defined(PETSC_HAVE_HIP)
49: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijhipsparse_C", NULL));
50: #endif
51: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
52: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijkokkos_C", NULL));
53: #endif
54: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpidense_C", NULL));
55: #if defined(PETSC_HAVE_ELEMENTAL)
56: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_elemental_C", NULL));
57: #endif
58: #if defined(PETSC_HAVE_SCALAPACK)
59: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_scalapack_C", NULL));
60: #endif
61: #if defined(PETSC_HAVE_HYPRE)
62: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_hypre_C", NULL));
63: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", NULL));
64: #endif
65: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
66: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_is_mpiaij_C", NULL));
67: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_mpiaij_mpiaij_C", NULL));
68: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetUseScalableIncreaseOverlap_C", NULL));
69: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijperm_C", NULL));
70: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijsell_C", NULL));
71: #if defined(PETSC_HAVE_MKL_SPARSE)
72: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijmkl_C", NULL));
73: #endif
74: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcrl_C", NULL));
75: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
76: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisell_C", NULL));
77: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetPreallocationCOO_C", NULL));
78: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetValuesCOO_C", NULL));
79: PetscFunctionReturn(PETSC_SUCCESS);
80: }
82: /* defines MatSetValues_MPI_Hash(), MatAssemblyBegin_MPI_Hash(), and MatAssemblyEnd_MPI_Hash() */
83: #define TYPE AIJ
84: #define TYPE_AIJ
85: #include "../src/mat/impls/aij/mpi/mpihashmat.h"
86: #undef TYPE
87: #undef TYPE_AIJ
89: static PetscErrorCode MatGetRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
90: {
91: Mat B;
93: PetscFunctionBegin;
94: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, &B));
95: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject)B));
96: PetscCall(MatGetRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
97: PetscCall(MatDestroy(&B));
98: PetscFunctionReturn(PETSC_SUCCESS);
99: }
101: static PetscErrorCode MatRestoreRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
102: {
103: Mat B;
105: PetscFunctionBegin;
106: PetscCall(PetscObjectQuery((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject *)&B));
107: PetscCall(MatRestoreRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
108: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", NULL));
109: PetscFunctionReturn(PETSC_SUCCESS);
110: }
112: /*MC
113: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
115: This matrix type is identical to` MATSEQAIJ` when constructed with a single process communicator,
116: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
117: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
118: for communicators controlling multiple processes. It is recommended that you call both of
119: the above preallocation routines for simplicity.
121: Options Database Key:
122: . -mat_type aij - sets the matrix type to `MATAIJ` during a call to `MatSetFromOptions()`
124: Developer Note:
125: Level: beginner
127: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, `MATAIJKOKKOS`,and also automatically switches over to use inodes when
128: enough exist.
130: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`
131: M*/
133: /*MC
134: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
136: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
137: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
138: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
139: for communicators controlling multiple processes. It is recommended that you call both of
140: the above preallocation routines for simplicity.
142: Options Database Key:
143: . -mat_type aijcrl - sets the matrix type to `MATMPIAIJCRL` during a call to `MatSetFromOptions()`
145: Level: beginner
147: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
148: M*/
150: static PetscErrorCode MatBindToCPU_MPIAIJ(Mat A, PetscBool flg)
151: {
152: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
154: PetscFunctionBegin;
155: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP) || defined(PETSC_HAVE_VIENNACL)
156: A->boundtocpu = flg;
157: #endif
158: if (a->A) PetscCall(MatBindToCPU(a->A, flg));
159: if (a->B) PetscCall(MatBindToCPU(a->B, flg));
161: /* In addition to binding the diagonal and off-diagonal matrices, bind the local vectors used for matrix-vector products.
162: * This maybe seems a little odd for a MatBindToCPU() call to do, but it makes no sense for the binding of these vectors
163: * to differ from the parent matrix. */
164: if (a->lvec) PetscCall(VecBindToCPU(a->lvec, flg));
165: if (a->diag) PetscCall(VecBindToCPU(a->diag, flg));
166: PetscFunctionReturn(PETSC_SUCCESS);
167: }
169: static PetscErrorCode MatSetBlockSizes_MPIAIJ(Mat M, PetscInt rbs, PetscInt cbs)
170: {
171: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
173: PetscFunctionBegin;
174: if (mat->A) {
175: PetscCall(MatSetBlockSizes(mat->A, rbs, cbs));
176: PetscCall(MatSetBlockSizes(mat->B, rbs, 1));
177: }
178: PetscFunctionReturn(PETSC_SUCCESS);
179: }
181: static PetscErrorCode MatFindNonzeroRows_MPIAIJ(Mat M, IS *keptrows)
182: {
183: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
184: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data;
185: Mat_SeqAIJ *b = (Mat_SeqAIJ *)mat->B->data;
186: const PetscInt *ia, *ib;
187: const MatScalar *aa, *bb, *aav, *bav;
188: PetscInt na, nb, i, j, *rows, cnt = 0, n0rows;
189: PetscInt m = M->rmap->n, rstart = M->rmap->rstart;
191: PetscFunctionBegin;
192: *keptrows = NULL;
194: ia = a->i;
195: ib = b->i;
196: PetscCall(MatSeqAIJGetArrayRead(mat->A, &aav));
197: PetscCall(MatSeqAIJGetArrayRead(mat->B, &bav));
198: for (i = 0; i < m; i++) {
199: na = ia[i + 1] - ia[i];
200: nb = ib[i + 1] - ib[i];
201: if (!na && !nb) {
202: cnt++;
203: goto ok1;
204: }
205: aa = aav + ia[i];
206: for (j = 0; j < na; j++) {
207: if (aa[j] != 0.0) goto ok1;
208: }
209: bb = PetscSafePointerPlusOffset(bav, ib[i]);
210: for (j = 0; j < nb; j++) {
211: if (bb[j] != 0.0) goto ok1;
212: }
213: cnt++;
214: ok1:;
215: }
216: PetscCallMPI(MPIU_Allreduce(&cnt, &n0rows, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)M)));
217: if (!n0rows) {
218: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
219: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
220: PetscFunctionReturn(PETSC_SUCCESS);
221: }
222: PetscCall(PetscMalloc1(M->rmap->n - cnt, &rows));
223: cnt = 0;
224: for (i = 0; i < m; i++) {
225: na = ia[i + 1] - ia[i];
226: nb = ib[i + 1] - ib[i];
227: if (!na && !nb) continue;
228: aa = aav + ia[i];
229: for (j = 0; j < na; j++) {
230: if (aa[j] != 0.0) {
231: rows[cnt++] = rstart + i;
232: goto ok2;
233: }
234: }
235: bb = PetscSafePointerPlusOffset(bav, ib[i]);
236: for (j = 0; j < nb; j++) {
237: if (bb[j] != 0.0) {
238: rows[cnt++] = rstart + i;
239: goto ok2;
240: }
241: }
242: ok2:;
243: }
244: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), cnt, rows, PETSC_OWN_POINTER, keptrows));
245: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
246: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
247: PetscFunctionReturn(PETSC_SUCCESS);
248: }
250: static PetscErrorCode MatDiagonalSet_MPIAIJ(Mat Y, Vec D, InsertMode is)
251: {
252: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Y->data;
253: PetscBool cong;
255: PetscFunctionBegin;
256: PetscCall(MatHasCongruentLayouts(Y, &cong));
257: if (Y->assembled && cong) {
258: PetscCall(MatDiagonalSet(aij->A, D, is));
259: } else {
260: PetscCall(MatDiagonalSet_Default(Y, D, is));
261: }
262: PetscFunctionReturn(PETSC_SUCCESS);
263: }
265: static PetscErrorCode MatFindZeroDiagonals_MPIAIJ(Mat M, IS *zrows)
266: {
267: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)M->data;
268: PetscInt i, rstart, nrows, *rows;
270: PetscFunctionBegin;
271: *zrows = NULL;
272: PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(aij->A, &nrows, &rows));
273: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
274: for (i = 0; i < nrows; i++) rows[i] += rstart;
275: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), nrows, rows, PETSC_OWN_POINTER, zrows));
276: PetscFunctionReturn(PETSC_SUCCESS);
277: }
279: static PetscErrorCode MatGetColumnReductions_MPIAIJ(Mat A, PetscInt type, PetscReal *reductions)
280: {
281: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)A->data;
282: PetscInt i, m, n, *garray = aij->garray;
283: Mat_SeqAIJ *a_aij = (Mat_SeqAIJ *)aij->A->data;
284: Mat_SeqAIJ *b_aij = (Mat_SeqAIJ *)aij->B->data;
285: PetscReal *work;
286: const PetscScalar *dummy;
287: PetscMPIInt in;
289: PetscFunctionBegin;
290: PetscCall(MatGetSize(A, &m, &n));
291: PetscCall(PetscCalloc1(n, &work));
292: PetscCall(MatSeqAIJGetArrayRead(aij->A, &dummy));
293: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &dummy));
294: PetscCall(MatSeqAIJGetArrayRead(aij->B, &dummy));
295: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &dummy));
296: if (type == NORM_2) {
297: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i] * a_aij->a[i]);
298: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i] * b_aij->a[i]);
299: } else if (type == NORM_1) {
300: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i]);
301: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]);
302: } else if (type == NORM_INFINITY) {
303: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] = PetscMax(PetscAbsScalar(a_aij->a[i]), work[A->cmap->rstart + a_aij->j[i]]);
304: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] = PetscMax(PetscAbsScalar(b_aij->a[i]), work[garray[b_aij->j[i]]]);
305: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
306: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscRealPart(a_aij->a[i]);
307: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscRealPart(b_aij->a[i]);
308: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
309: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscImaginaryPart(a_aij->a[i]);
310: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscImaginaryPart(b_aij->a[i]);
311: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
312: PetscCall(PetscMPIIntCast(n, &in));
313: if (type == NORM_INFINITY) {
314: PetscCallMPI(MPIU_Allreduce(work, reductions, in, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
315: } else {
316: PetscCallMPI(MPIU_Allreduce(work, reductions, in, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
317: }
318: PetscCall(PetscFree(work));
319: if (type == NORM_2) {
320: for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
321: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
322: for (i = 0; i < n; i++) reductions[i] /= m;
323: }
324: PetscFunctionReturn(PETSC_SUCCESS);
325: }
327: static PetscErrorCode MatFindOffBlockDiagonalEntries_MPIAIJ(Mat A, IS *is)
328: {
329: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
330: IS sis, gis;
331: const PetscInt *isis, *igis;
332: PetscInt n, *iis, nsis, ngis, rstart, i;
334: PetscFunctionBegin;
335: PetscCall(MatFindOffBlockDiagonalEntries(a->A, &sis));
336: PetscCall(MatFindNonzeroRows(a->B, &gis));
337: PetscCall(ISGetSize(gis, &ngis));
338: PetscCall(ISGetSize(sis, &nsis));
339: PetscCall(ISGetIndices(sis, &isis));
340: PetscCall(ISGetIndices(gis, &igis));
342: PetscCall(PetscMalloc1(ngis + nsis, &iis));
343: PetscCall(PetscArraycpy(iis, igis, ngis));
344: PetscCall(PetscArraycpy(iis + ngis, isis, nsis));
345: n = ngis + nsis;
346: PetscCall(PetscSortRemoveDupsInt(&n, iis));
347: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
348: for (i = 0; i < n; i++) iis[i] += rstart;
349: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), n, iis, PETSC_OWN_POINTER, is));
351: PetscCall(ISRestoreIndices(sis, &isis));
352: PetscCall(ISRestoreIndices(gis, &igis));
353: PetscCall(ISDestroy(&sis));
354: PetscCall(ISDestroy(&gis));
355: PetscFunctionReturn(PETSC_SUCCESS);
356: }
358: /*
359: Local utility routine that creates a mapping from the global column
360: number to the local number in the off-diagonal part of the local
361: storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at
362: a slightly higher hash table cost; without it it is not scalable (each processor
363: has an order N integer array but is fast to access.
364: */
365: PetscErrorCode MatCreateColmap_MPIAIJ_Private(Mat mat)
366: {
367: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
368: PetscInt n = aij->B->cmap->n, i;
370: PetscFunctionBegin;
371: PetscCheck(!n || aij->garray, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MPIAIJ Matrix was assembled but is missing garray");
372: #if defined(PETSC_USE_CTABLE)
373: PetscCall(PetscHMapICreateWithSize(n, &aij->colmap));
374: for (i = 0; i < n; i++) PetscCall(PetscHMapISet(aij->colmap, aij->garray[i] + 1, i + 1));
375: #else
376: PetscCall(PetscCalloc1(mat->cmap->N + 1, &aij->colmap));
377: for (i = 0; i < n; i++) aij->colmap[aij->garray[i]] = i + 1;
378: #endif
379: PetscFunctionReturn(PETSC_SUCCESS);
380: }
382: #define MatSetValues_SeqAIJ_A_Private(row, col, value, addv, orow, ocol) \
383: do { \
384: if (col <= lastcol1) low1 = 0; \
385: else high1 = nrow1; \
386: lastcol1 = col; \
387: while (high1 - low1 > 5) { \
388: t = (low1 + high1) / 2; \
389: if (rp1[t] > col) high1 = t; \
390: else low1 = t; \
391: } \
392: for (_i = low1; _i < high1; _i++) { \
393: if (rp1[_i] > col) break; \
394: if (rp1[_i] == col) { \
395: if (addv == ADD_VALUES) { \
396: ap1[_i] += value; \
397: /* Not sure LogFlops will slow dow the code or not */ \
398: (void)PetscLogFlops(1.0); \
399: } else ap1[_i] = value; \
400: goto a_noinsert; \
401: } \
402: } \
403: if (value == 0.0 && ignorezeroentries && row != col) { \
404: low1 = 0; \
405: high1 = nrow1; \
406: goto a_noinsert; \
407: } \
408: if (nonew == 1) { \
409: low1 = 0; \
410: high1 = nrow1; \
411: goto a_noinsert; \
412: } \
413: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
414: MatSeqXAIJReallocateAIJ(A, am, 1, nrow1, row, col, rmax1, aa, ai, aj, rp1, ap1, aimax, nonew, MatScalar); \
415: N = nrow1++ - 1; \
416: a->nz++; \
417: high1++; \
418: /* shift up all the later entries in this row */ \
419: PetscCall(PetscArraymove(rp1 + _i + 1, rp1 + _i, N - _i + 1)); \
420: PetscCall(PetscArraymove(ap1 + _i + 1, ap1 + _i, N - _i + 1)); \
421: rp1[_i] = col; \
422: ap1[_i] = value; \
423: a_noinsert:; \
424: ailen[row] = nrow1; \
425: } while (0)
427: #define MatSetValues_SeqAIJ_B_Private(row, col, value, addv, orow, ocol) \
428: do { \
429: if (col <= lastcol2) low2 = 0; \
430: else high2 = nrow2; \
431: lastcol2 = col; \
432: while (high2 - low2 > 5) { \
433: t = (low2 + high2) / 2; \
434: if (rp2[t] > col) high2 = t; \
435: else low2 = t; \
436: } \
437: for (_i = low2; _i < high2; _i++) { \
438: if (rp2[_i] > col) break; \
439: if (rp2[_i] == col) { \
440: if (addv == ADD_VALUES) { \
441: ap2[_i] += value; \
442: (void)PetscLogFlops(1.0); \
443: } else ap2[_i] = value; \
444: goto b_noinsert; \
445: } \
446: } \
447: if (value == 0.0 && ignorezeroentries) { \
448: low2 = 0; \
449: high2 = nrow2; \
450: goto b_noinsert; \
451: } \
452: if (nonew == 1) { \
453: low2 = 0; \
454: high2 = nrow2; \
455: goto b_noinsert; \
456: } \
457: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
458: MatSeqXAIJReallocateAIJ(B, bm, 1, nrow2, row, col, rmax2, ba, bi, bj, rp2, ap2, bimax, nonew, MatScalar); \
459: N = nrow2++ - 1; \
460: b->nz++; \
461: high2++; \
462: /* shift up all the later entries in this row */ \
463: PetscCall(PetscArraymove(rp2 + _i + 1, rp2 + _i, N - _i + 1)); \
464: PetscCall(PetscArraymove(ap2 + _i + 1, ap2 + _i, N - _i + 1)); \
465: rp2[_i] = col; \
466: ap2[_i] = value; \
467: b_noinsert:; \
468: bilen[row] = nrow2; \
469: } while (0)
471: static PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A, PetscInt row, const PetscScalar v[])
472: {
473: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
474: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data, *b = (Mat_SeqAIJ *)mat->B->data;
475: PetscInt l, *garray = mat->garray, diag;
476: PetscScalar *aa, *ba;
478: PetscFunctionBegin;
479: /* code only works for square matrices A */
481: /* find size of row to the left of the diagonal part */
482: PetscCall(MatGetOwnershipRange(A, &diag, NULL));
483: row = row - diag;
484: for (l = 0; l < b->i[row + 1] - b->i[row]; l++) {
485: if (garray[b->j[b->i[row] + l]] > diag) break;
486: }
487: if (l) {
488: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
489: PetscCall(PetscArraycpy(ba + b->i[row], v, l));
490: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
491: }
493: /* diagonal part */
494: if (a->i[row + 1] - a->i[row]) {
495: PetscCall(MatSeqAIJGetArray(mat->A, &aa));
496: PetscCall(PetscArraycpy(aa + a->i[row], v + l, a->i[row + 1] - a->i[row]));
497: PetscCall(MatSeqAIJRestoreArray(mat->A, &aa));
498: }
500: /* right of diagonal part */
501: if (b->i[row + 1] - b->i[row] - l) {
502: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
503: PetscCall(PetscArraycpy(ba + b->i[row] + l, v + l + a->i[row + 1] - a->i[row], b->i[row + 1] - b->i[row] - l));
504: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
505: }
506: PetscFunctionReturn(PETSC_SUCCESS);
507: }
509: PetscErrorCode MatSetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
510: {
511: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
512: PetscScalar value = 0.0;
513: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
514: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
515: PetscBool roworiented = aij->roworiented;
517: /* Some Variables required in the macro */
518: Mat A = aij->A;
519: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
520: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
521: PetscBool ignorezeroentries = a->ignorezeroentries;
522: Mat B = aij->B;
523: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
524: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
525: MatScalar *aa, *ba;
526: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
527: PetscInt nonew;
528: MatScalar *ap1, *ap2;
530: PetscFunctionBegin;
531: PetscCall(MatSeqAIJGetArray(A, &aa));
532: PetscCall(MatSeqAIJGetArray(B, &ba));
533: for (i = 0; i < m; i++) {
534: if (im[i] < 0) continue;
535: PetscCheck(im[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], mat->rmap->N - 1);
536: if (im[i] >= rstart && im[i] < rend) {
537: row = im[i] - rstart;
538: lastcol1 = -1;
539: rp1 = PetscSafePointerPlusOffset(aj, ai[row]);
540: ap1 = PetscSafePointerPlusOffset(aa, ai[row]);
541: rmax1 = aimax[row];
542: nrow1 = ailen[row];
543: low1 = 0;
544: high1 = nrow1;
545: lastcol2 = -1;
546: rp2 = PetscSafePointerPlusOffset(bj, bi[row]);
547: ap2 = PetscSafePointerPlusOffset(ba, bi[row]);
548: rmax2 = bimax[row];
549: nrow2 = bilen[row];
550: low2 = 0;
551: high2 = nrow2;
553: for (j = 0; j < n; j++) {
554: if (v) value = roworiented ? v[i * n + j] : v[i + j * m];
555: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
556: if (in[j] >= cstart && in[j] < cend) {
557: col = in[j] - cstart;
558: nonew = a->nonew;
559: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
560: } else if (in[j] < 0) {
561: continue;
562: } else {
563: PetscCheck(in[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
564: if (mat->was_assembled) {
565: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
566: #if defined(PETSC_USE_CTABLE)
567: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col)); /* map global col ids to local ones */
568: col--;
569: #else
570: col = aij->colmap[in[j]] - 1;
571: #endif
572: if (col < 0 && !((Mat_SeqAIJ *)aij->B->data)->nonew) { /* col < 0 means in[j] is a new col for B */
573: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE)); /* Change aij->B from reduced/local format to expanded/global format */
574: col = in[j];
575: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
576: B = aij->B;
577: b = (Mat_SeqAIJ *)B->data;
578: bimax = b->imax;
579: bi = b->i;
580: bilen = b->ilen;
581: bj = b->j;
582: ba = b->a;
583: rp2 = PetscSafePointerPlusOffset(bj, bi[row]);
584: ap2 = PetscSafePointerPlusOffset(ba, bi[row]);
585: rmax2 = bimax[row];
586: nrow2 = bilen[row];
587: low2 = 0;
588: high2 = nrow2;
589: bm = aij->B->rmap->n;
590: ba = b->a;
591: } else if (col < 0 && !(ignorezeroentries && value == 0.0)) {
592: if (1 == ((Mat_SeqAIJ *)aij->B->data)->nonew) {
593: PetscCall(PetscInfo(mat, "Skipping of insertion of new nonzero location in off-diagonal portion of matrix %g(%" PetscInt_FMT ",%" PetscInt_FMT ")\n", (double)PetscRealPart(value), im[i], in[j]));
594: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]);
595: }
596: } else col = in[j];
597: nonew = b->nonew;
598: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
599: }
600: }
601: } else {
602: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Setting off process row %" PetscInt_FMT " even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set", im[i]);
603: if (!aij->donotstash) {
604: mat->assembled = PETSC_FALSE;
605: if (roworiented) {
606: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i * n), (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
607: } else {
608: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i), m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
609: }
610: }
611: }
612: }
613: PetscCall(MatSeqAIJRestoreArray(A, &aa)); /* aa, bb might have been free'd due to reallocation above. But we don't access them here */
614: PetscCall(MatSeqAIJRestoreArray(B, &ba));
615: PetscFunctionReturn(PETSC_SUCCESS);
616: }
618: /*
619: This function sets the j and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
620: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
621: No off-processor parts off the matrix are allowed here and mat->was_assembled has to be PETSC_FALSE.
622: */
623: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[])
624: {
625: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
626: Mat A = aij->A; /* diagonal part of the matrix */
627: Mat B = aij->B; /* off-diagonal part of the matrix */
628: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
629: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
630: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, col;
631: PetscInt *ailen = a->ilen, *aj = a->j;
632: PetscInt *bilen = b->ilen, *bj = b->j;
633: PetscInt am = aij->A->rmap->n, j;
634: PetscInt diag_so_far = 0, dnz;
635: PetscInt offd_so_far = 0, onz;
637: PetscFunctionBegin;
638: /* Iterate over all rows of the matrix */
639: for (j = 0; j < am; j++) {
640: dnz = onz = 0;
641: /* Iterate over all non-zero columns of the current row */
642: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
643: /* If column is in the diagonal */
644: if (mat_j[col] >= cstart && mat_j[col] < cend) {
645: aj[diag_so_far++] = mat_j[col] - cstart;
646: dnz++;
647: } else { /* off-diagonal entries */
648: bj[offd_so_far++] = mat_j[col];
649: onz++;
650: }
651: }
652: ailen[j] = dnz;
653: bilen[j] = onz;
654: }
655: PetscFunctionReturn(PETSC_SUCCESS);
656: }
658: /*
659: This function sets the local j, a and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
660: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
661: No off-processor parts off the matrix are allowed here, they are set at a later point by MatSetValues_MPIAIJ.
662: Also, mat->was_assembled has to be false, otherwise the statement aj[rowstart_diag+dnz_row] = mat_j[col] - cstart;
663: would not be true and the more complex MatSetValues_MPIAIJ has to be used.
664: */
665: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[], const PetscScalar mat_a[])
666: {
667: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
668: Mat A = aij->A; /* diagonal part of the matrix */
669: Mat B = aij->B; /* off-diagonal part of the matrix */
670: Mat_SeqAIJ *aijd = (Mat_SeqAIJ *)aij->A->data, *aijo = (Mat_SeqAIJ *)aij->B->data;
671: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
672: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
673: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend;
674: PetscInt *ailen = a->ilen, *aj = a->j;
675: PetscInt *bilen = b->ilen, *bj = b->j;
676: PetscInt am = aij->A->rmap->n, j;
677: PetscInt *full_diag_i = aijd->i, *full_offd_i = aijo->i; /* These variables can also include non-local elements, which are set at a later point. */
678: PetscInt col, dnz_row, onz_row, rowstart_diag, rowstart_offd;
679: PetscScalar *aa = a->a, *ba = b->a;
681: PetscFunctionBegin;
682: /* Iterate over all rows of the matrix */
683: for (j = 0; j < am; j++) {
684: dnz_row = onz_row = 0;
685: rowstart_offd = full_offd_i[j];
686: rowstart_diag = full_diag_i[j];
687: /* Iterate over all non-zero columns of the current row */
688: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
689: /* If column is in the diagonal */
690: if (mat_j[col] >= cstart && mat_j[col] < cend) {
691: aj[rowstart_diag + dnz_row] = mat_j[col] - cstart;
692: aa[rowstart_diag + dnz_row] = mat_a[col];
693: dnz_row++;
694: } else { /* off-diagonal entries */
695: bj[rowstart_offd + onz_row] = mat_j[col];
696: ba[rowstart_offd + onz_row] = mat_a[col];
697: onz_row++;
698: }
699: }
700: ailen[j] = dnz_row;
701: bilen[j] = onz_row;
702: }
703: PetscFunctionReturn(PETSC_SUCCESS);
704: }
706: static PetscErrorCode MatGetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
707: {
708: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
709: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
710: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
712: PetscFunctionBegin;
713: for (i = 0; i < m; i++) {
714: if (idxm[i] < 0) continue; /* negative row */
715: PetscCheck(idxm[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, idxm[i], mat->rmap->N - 1);
716: PetscCheck(idxm[i] >= rstart && idxm[i] < rend, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local values currently supported, row requested %" PetscInt_FMT " range [%" PetscInt_FMT " %" PetscInt_FMT ")", idxm[i], rstart, rend);
717: row = idxm[i] - rstart;
718: for (j = 0; j < n; j++) {
719: if (idxn[j] < 0) continue; /* negative column */
720: PetscCheck(idxn[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, idxn[j], mat->cmap->N - 1);
721: if (idxn[j] >= cstart && idxn[j] < cend) {
722: col = idxn[j] - cstart;
723: PetscCall(MatGetValues(aij->A, 1, &row, 1, &col, v + i * n + j));
724: } else {
725: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
726: #if defined(PETSC_USE_CTABLE)
727: PetscCall(PetscHMapIGetWithDefault(aij->colmap, idxn[j] + 1, 0, &col));
728: col--;
729: #else
730: col = aij->colmap[idxn[j]] - 1;
731: #endif
732: if ((col < 0) || (aij->garray[col] != idxn[j])) *(v + i * n + j) = 0.0;
733: else PetscCall(MatGetValues(aij->B, 1, &row, 1, &col, v + i * n + j));
734: }
735: }
736: }
737: PetscFunctionReturn(PETSC_SUCCESS);
738: }
740: static PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat, MatAssemblyType mode)
741: {
742: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
743: PetscInt nstash, reallocs;
745: PetscFunctionBegin;
746: if (aij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
748: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
749: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
750: PetscCall(PetscInfo(aij->A, "Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
751: PetscFunctionReturn(PETSC_SUCCESS);
752: }
754: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat, MatAssemblyType mode)
755: {
756: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
757: PetscMPIInt n;
758: PetscInt i, j, rstart, ncols, flg;
759: PetscInt *row, *col;
760: PetscBool other_disassembled;
761: PetscScalar *val;
763: /* do not use 'b = (Mat_SeqAIJ*)aij->B->data' as B can be reset in disassembly */
765: PetscFunctionBegin;
766: if (!aij->donotstash && !mat->nooffprocentries) {
767: while (1) {
768: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
769: if (!flg) break;
771: for (i = 0; i < n;) {
772: /* Now identify the consecutive vals belonging to the same row */
773: for (j = i, rstart = row[j]; j < n; j++) {
774: if (row[j] != rstart) break;
775: }
776: if (j < n) ncols = j - i;
777: else ncols = n - i;
778: /* Now assemble all these values with a single function call */
779: PetscCall(MatSetValues_MPIAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
780: i = j;
781: }
782: }
783: PetscCall(MatStashScatterEnd_Private(&mat->stash));
784: }
785: #if defined(PETSC_HAVE_DEVICE)
786: if (mat->offloadmask == PETSC_OFFLOAD_CPU) aij->A->offloadmask = PETSC_OFFLOAD_CPU;
787: /* We call MatBindToCPU() on aij->A and aij->B here, because if MatBindToCPU_MPIAIJ() is called before assembly, it cannot bind these. */
788: if (mat->boundtocpu) {
789: PetscCall(MatBindToCPU(aij->A, PETSC_TRUE));
790: PetscCall(MatBindToCPU(aij->B, PETSC_TRUE));
791: }
792: #endif
793: PetscCall(MatAssemblyBegin(aij->A, mode));
794: PetscCall(MatAssemblyEnd(aij->A, mode));
796: /* determine if any processor has disassembled, if so we must
797: also disassemble ourself, in order that we may reassemble. */
798: /*
799: if nonzero structure of submatrix B cannot change then we know that
800: no processor disassembled thus we can skip this stuff
801: */
802: if (!((Mat_SeqAIJ *)aij->B->data)->nonew) {
803: PetscCallMPI(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
804: if (mat->was_assembled && !other_disassembled) { /* mat on this rank has reduced off-diag B with local col ids, but globally it does not */
805: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));
806: }
807: }
808: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIAIJ(mat));
809: PetscCall(MatSetOption(aij->B, MAT_USE_INODES, PETSC_FALSE));
810: #if defined(PETSC_HAVE_DEVICE)
811: if (mat->offloadmask == PETSC_OFFLOAD_CPU && aij->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) aij->B->offloadmask = PETSC_OFFLOAD_CPU;
812: #endif
813: PetscCall(MatAssemblyBegin(aij->B, mode));
814: PetscCall(MatAssemblyEnd(aij->B, mode));
816: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
818: aij->rowvalues = NULL;
820: PetscCall(VecDestroy(&aij->diag));
822: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
823: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqAIJ *)aij->A->data)->nonew) {
824: PetscObjectState state = aij->A->nonzerostate + aij->B->nonzerostate;
825: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
826: }
827: #if defined(PETSC_HAVE_DEVICE)
828: mat->offloadmask = PETSC_OFFLOAD_BOTH;
829: #endif
830: PetscFunctionReturn(PETSC_SUCCESS);
831: }
833: static PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
834: {
835: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
837: PetscFunctionBegin;
838: PetscCall(MatZeroEntries(l->A));
839: PetscCall(MatZeroEntries(l->B));
840: PetscFunctionReturn(PETSC_SUCCESS);
841: }
843: static PetscErrorCode MatZeroRows_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
844: {
845: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
846: PetscInt *lrows;
847: PetscInt r, len;
848: PetscBool cong;
850: PetscFunctionBegin;
851: /* get locally owned rows */
852: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
853: PetscCall(MatHasCongruentLayouts(A, &cong));
854: /* fix right-hand side if needed */
855: if (x && b) {
856: const PetscScalar *xx;
857: PetscScalar *bb;
859: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
860: PetscCall(VecGetArrayRead(x, &xx));
861: PetscCall(VecGetArray(b, &bb));
862: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
863: PetscCall(VecRestoreArrayRead(x, &xx));
864: PetscCall(VecRestoreArray(b, &bb));
865: }
867: if (diag != 0.0 && cong) {
868: PetscCall(MatZeroRows(mat->A, len, lrows, diag, NULL, NULL));
869: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
870: } else if (diag != 0.0) { /* non-square or non congruent layouts -> if keepnonzeropattern is false, we allow for new insertion */
871: Mat_SeqAIJ *aijA = (Mat_SeqAIJ *)mat->A->data;
872: Mat_SeqAIJ *aijB = (Mat_SeqAIJ *)mat->B->data;
873: PetscInt nnwA, nnwB;
874: PetscBool nnzA, nnzB;
876: nnwA = aijA->nonew;
877: nnwB = aijB->nonew;
878: nnzA = aijA->keepnonzeropattern;
879: nnzB = aijB->keepnonzeropattern;
880: if (!nnzA) {
881: PetscCall(PetscInfo(mat->A, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on diagonal block.\n"));
882: aijA->nonew = 0;
883: }
884: if (!nnzB) {
885: PetscCall(PetscInfo(mat->B, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on off-diagonal block.\n"));
886: aijB->nonew = 0;
887: }
888: /* Must zero here before the next loop */
889: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
890: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
891: for (r = 0; r < len; ++r) {
892: const PetscInt row = lrows[r] + A->rmap->rstart;
893: if (row >= A->cmap->N) continue;
894: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
895: }
896: aijA->nonew = nnwA;
897: aijB->nonew = nnwB;
898: } else {
899: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
900: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
901: }
902: PetscCall(PetscFree(lrows));
903: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
904: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
906: /* only change matrix nonzero state if pattern was allowed to be changed */
907: if (!((Mat_SeqAIJ *)mat->A->data)->keepnonzeropattern || !((Mat_SeqAIJ *)mat->A->data)->nonew) {
908: PetscObjectState state = mat->A->nonzerostate + mat->B->nonzerostate;
909: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
910: }
911: PetscFunctionReturn(PETSC_SUCCESS);
912: }
914: static PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
915: {
916: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
917: PetscInt n = A->rmap->n;
918: PetscInt i, j, r, m, len = 0;
919: PetscInt *lrows, *owners = A->rmap->range;
920: PetscMPIInt p = 0;
921: PetscSFNode *rrows;
922: PetscSF sf;
923: const PetscScalar *xx;
924: PetscScalar *bb, *mask, *aij_a;
925: Vec xmask, lmask;
926: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)l->B->data;
927: const PetscInt *aj, *ii, *ridx;
928: PetscScalar *aa;
930: PetscFunctionBegin;
931: /* Create SF where leaves are input rows and roots are owned rows */
932: PetscCall(PetscMalloc1(n, &lrows));
933: for (r = 0; r < n; ++r) lrows[r] = -1;
934: PetscCall(PetscMalloc1(N, &rrows));
935: for (r = 0; r < N; ++r) {
936: const PetscInt idx = rows[r];
937: PetscCheck(idx >= 0 && A->rmap->N > idx, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row %" PetscInt_FMT " out of range [0,%" PetscInt_FMT ")", idx, A->rmap->N);
938: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
939: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
940: }
941: rrows[r].rank = p;
942: rrows[r].index = rows[r] - owners[p];
943: }
944: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
945: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
946: /* Collect flags for rows to be zeroed */
947: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
948: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
949: PetscCall(PetscSFDestroy(&sf));
950: /* Compress and put in row numbers */
951: for (r = 0; r < n; ++r)
952: if (lrows[r] >= 0) lrows[len++] = r;
953: /* zero diagonal part of matrix */
954: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
955: /* handle off-diagonal part of matrix */
956: PetscCall(MatCreateVecs(A, &xmask, NULL));
957: PetscCall(VecDuplicate(l->lvec, &lmask));
958: PetscCall(VecGetArray(xmask, &bb));
959: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
960: PetscCall(VecRestoreArray(xmask, &bb));
961: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
962: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
963: PetscCall(VecDestroy(&xmask));
964: if (x && b) { /* this code is buggy when the row and column layout don't match */
965: PetscBool cong;
967: PetscCall(MatHasCongruentLayouts(A, &cong));
968: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
969: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
970: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
971: PetscCall(VecGetArrayRead(l->lvec, &xx));
972: PetscCall(VecGetArray(b, &bb));
973: }
974: PetscCall(VecGetArray(lmask, &mask));
975: /* remove zeroed rows of off-diagonal matrix */
976: PetscCall(MatSeqAIJGetArray(l->B, &aij_a));
977: ii = aij->i;
978: for (i = 0; i < len; i++) PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aij_a, ii[lrows[i]]), ii[lrows[i] + 1] - ii[lrows[i]]));
979: /* loop over all elements of off process part of matrix zeroing removed columns*/
980: if (aij->compressedrow.use) {
981: m = aij->compressedrow.nrows;
982: ii = aij->compressedrow.i;
983: ridx = aij->compressedrow.rindex;
984: for (i = 0; i < m; i++) {
985: n = ii[i + 1] - ii[i];
986: aj = aij->j + ii[i];
987: aa = aij_a + ii[i];
989: for (j = 0; j < n; j++) {
990: if (PetscAbsScalar(mask[*aj])) {
991: if (b) bb[*ridx] -= *aa * xx[*aj];
992: *aa = 0.0;
993: }
994: aa++;
995: aj++;
996: }
997: ridx++;
998: }
999: } else { /* do not use compressed row format */
1000: m = l->B->rmap->n;
1001: for (i = 0; i < m; i++) {
1002: n = ii[i + 1] - ii[i];
1003: aj = aij->j + ii[i];
1004: aa = aij_a + ii[i];
1005: for (j = 0; j < n; j++) {
1006: if (PetscAbsScalar(mask[*aj])) {
1007: if (b) bb[i] -= *aa * xx[*aj];
1008: *aa = 0.0;
1009: }
1010: aa++;
1011: aj++;
1012: }
1013: }
1014: }
1015: if (x && b) {
1016: PetscCall(VecRestoreArray(b, &bb));
1017: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1018: }
1019: PetscCall(MatSeqAIJRestoreArray(l->B, &aij_a));
1020: PetscCall(VecRestoreArray(lmask, &mask));
1021: PetscCall(VecDestroy(&lmask));
1022: PetscCall(PetscFree(lrows));
1024: /* only change matrix nonzero state if pattern was allowed to be changed */
1025: if (!((Mat_SeqAIJ *)l->A->data)->nonew) {
1026: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1027: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1028: }
1029: PetscFunctionReturn(PETSC_SUCCESS);
1030: }
1032: static PetscErrorCode MatMult_MPIAIJ(Mat A, Vec xx, Vec yy)
1033: {
1034: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1035: PetscInt nt;
1036: VecScatter Mvctx = a->Mvctx;
1038: PetscFunctionBegin;
1039: PetscCall(VecGetLocalSize(xx, &nt));
1040: PetscCheck(nt == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A (%" PetscInt_FMT ") and xx (%" PetscInt_FMT ")", A->cmap->n, nt);
1041: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1042: PetscUseTypeMethod(a->A, mult, xx, yy);
1043: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1044: PetscUseTypeMethod(a->B, multadd, a->lvec, yy, yy);
1045: PetscFunctionReturn(PETSC_SUCCESS);
1046: }
1048: static PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A, Vec bb, Vec xx)
1049: {
1050: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1052: PetscFunctionBegin;
1053: PetscCall(MatMultDiagonalBlock(a->A, bb, xx));
1054: PetscFunctionReturn(PETSC_SUCCESS);
1055: }
1057: static PetscErrorCode MatMultAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1058: {
1059: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1060: VecScatter Mvctx = a->Mvctx;
1062: PetscFunctionBegin;
1063: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1064: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1065: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1066: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1067: PetscFunctionReturn(PETSC_SUCCESS);
1068: }
1070: static PetscErrorCode MatMultTranspose_MPIAIJ(Mat A, Vec xx, Vec yy)
1071: {
1072: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1074: PetscFunctionBegin;
1075: /* do nondiagonal part */
1076: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1077: /* do local part */
1078: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1079: /* add partial results together */
1080: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1081: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1082: PetscFunctionReturn(PETSC_SUCCESS);
1083: }
1085: static PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f)
1086: {
1087: MPI_Comm comm;
1088: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)Amat->data, *Bij = (Mat_MPIAIJ *)Bmat->data;
1089: Mat Adia = Aij->A, Bdia = Bij->A, Aoff, Boff, *Aoffs, *Boffs;
1090: IS Me, Notme;
1091: PetscInt M, N, first, last, *notme, i;
1092: PetscBool lf;
1093: PetscMPIInt size;
1095: PetscFunctionBegin;
1096: /* Easy test: symmetric diagonal block */
1097: PetscCall(MatIsTranspose(Adia, Bdia, tol, &lf));
1098: PetscCallMPI(MPIU_Allreduce(&lf, f, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)Amat)));
1099: if (!*f) PetscFunctionReturn(PETSC_SUCCESS);
1100: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1101: PetscCallMPI(MPI_Comm_size(comm, &size));
1102: if (size == 1) PetscFunctionReturn(PETSC_SUCCESS);
1104: /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */
1105: PetscCall(MatGetSize(Amat, &M, &N));
1106: PetscCall(MatGetOwnershipRange(Amat, &first, &last));
1107: PetscCall(PetscMalloc1(N - last + first, ¬me));
1108: for (i = 0; i < first; i++) notme[i] = i;
1109: for (i = last; i < M; i++) notme[i - last + first] = i;
1110: PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme));
1111: PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me));
1112: PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs));
1113: Aoff = Aoffs[0];
1114: PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs));
1115: Boff = Boffs[0];
1116: PetscCall(MatIsTranspose(Aoff, Boff, tol, f));
1117: PetscCall(MatDestroyMatrices(1, &Aoffs));
1118: PetscCall(MatDestroyMatrices(1, &Boffs));
1119: PetscCall(ISDestroy(&Me));
1120: PetscCall(ISDestroy(&Notme));
1121: PetscCall(PetscFree(notme));
1122: PetscFunctionReturn(PETSC_SUCCESS);
1123: }
1125: static PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1126: {
1127: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1129: PetscFunctionBegin;
1130: /* do nondiagonal part */
1131: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1132: /* do local part */
1133: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1134: /* add partial results together */
1135: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1136: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1137: PetscFunctionReturn(PETSC_SUCCESS);
1138: }
1140: /*
1141: This only works correctly for square matrices where the subblock A->A is the
1142: diagonal block
1143: */
1144: static PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A, Vec v)
1145: {
1146: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1148: PetscFunctionBegin;
1149: PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1150: PetscCheck(A->rmap->rstart == A->cmap->rstart && A->rmap->rend == A->cmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "row partition must equal col partition");
1151: PetscCall(MatGetDiagonal(a->A, v));
1152: PetscFunctionReturn(PETSC_SUCCESS);
1153: }
1155: static PetscErrorCode MatScale_MPIAIJ(Mat A, PetscScalar aa)
1156: {
1157: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1159: PetscFunctionBegin;
1160: PetscCall(MatScale(a->A, aa));
1161: PetscCall(MatScale(a->B, aa));
1162: PetscFunctionReturn(PETSC_SUCCESS);
1163: }
1165: static PetscErrorCode MatView_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
1166: {
1167: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1168: Mat_SeqAIJ *A = (Mat_SeqAIJ *)aij->A->data;
1169: Mat_SeqAIJ *B = (Mat_SeqAIJ *)aij->B->data;
1170: const PetscInt *garray = aij->garray;
1171: const PetscScalar *aa, *ba;
1172: PetscInt header[4], M, N, m, rs, cs, cnt, i, ja, jb;
1173: PetscInt64 nz, hnz;
1174: PetscInt *rowlens;
1175: PetscInt *colidxs;
1176: PetscScalar *matvals;
1177: PetscMPIInt rank;
1179: PetscFunctionBegin;
1180: PetscCall(PetscViewerSetUp(viewer));
1182: M = mat->rmap->N;
1183: N = mat->cmap->N;
1184: m = mat->rmap->n;
1185: rs = mat->rmap->rstart;
1186: cs = mat->cmap->rstart;
1187: nz = A->nz + B->nz;
1189: /* write matrix header */
1190: header[0] = MAT_FILE_CLASSID;
1191: header[1] = M;
1192: header[2] = N;
1193: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1194: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1195: if (rank == 0) {
1196: if (hnz > PETSC_INT_MAX) header[3] = PETSC_INT_MAX;
1197: else header[3] = (PetscInt)hnz;
1198: }
1199: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1201: /* fill in and store row lengths */
1202: PetscCall(PetscMalloc1(m, &rowlens));
1203: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i];
1204: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1205: PetscCall(PetscFree(rowlens));
1207: /* fill in and store column indices */
1208: PetscCall(PetscMalloc1(nz, &colidxs));
1209: for (cnt = 0, i = 0; i < m; i++) {
1210: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1211: if (garray[B->j[jb]] > cs) break;
1212: colidxs[cnt++] = garray[B->j[jb]];
1213: }
1214: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) colidxs[cnt++] = A->j[ja] + cs;
1215: for (; jb < B->i[i + 1]; jb++) colidxs[cnt++] = garray[B->j[jb]];
1216: }
1217: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1218: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
1219: PetscCall(PetscFree(colidxs));
1221: /* fill in and store nonzero values */
1222: PetscCall(MatSeqAIJGetArrayRead(aij->A, &aa));
1223: PetscCall(MatSeqAIJGetArrayRead(aij->B, &ba));
1224: PetscCall(PetscMalloc1(nz, &matvals));
1225: for (cnt = 0, i = 0; i < m; i++) {
1226: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1227: if (garray[B->j[jb]] > cs) break;
1228: matvals[cnt++] = ba[jb];
1229: }
1230: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) matvals[cnt++] = aa[ja];
1231: for (; jb < B->i[i + 1]; jb++) matvals[cnt++] = ba[jb];
1232: }
1233: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &aa));
1234: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &ba));
1235: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1236: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
1237: PetscCall(PetscFree(matvals));
1239: /* write block size option to the viewer's .info file */
1240: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1241: PetscFunctionReturn(PETSC_SUCCESS);
1242: }
1244: #include <petscdraw.h>
1245: static PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1246: {
1247: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1248: PetscMPIInt rank = aij->rank, size = aij->size;
1249: PetscBool isdraw, iascii, isbinary;
1250: PetscViewer sviewer;
1251: PetscViewerFormat format;
1253: PetscFunctionBegin;
1254: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1255: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1256: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1257: if (iascii) {
1258: PetscCall(PetscViewerGetFormat(viewer, &format));
1259: if (format == PETSC_VIEWER_LOAD_BALANCE) {
1260: PetscInt i, nmax = 0, nmin = PETSC_INT_MAX, navg = 0, *nz, nzlocal = ((Mat_SeqAIJ *)aij->A->data)->nz + ((Mat_SeqAIJ *)aij->B->data)->nz;
1261: PetscCall(PetscMalloc1(size, &nz));
1262: PetscCallMPI(MPI_Allgather(&nzlocal, 1, MPIU_INT, nz, 1, MPIU_INT, PetscObjectComm((PetscObject)mat)));
1263: for (i = 0; i < (PetscInt)size; i++) {
1264: nmax = PetscMax(nmax, nz[i]);
1265: nmin = PetscMin(nmin, nz[i]);
1266: navg += nz[i];
1267: }
1268: PetscCall(PetscFree(nz));
1269: navg = navg / size;
1270: PetscCall(PetscViewerASCIIPrintf(viewer, "Load Balance - Nonzeros: Min %" PetscInt_FMT " avg %" PetscInt_FMT " max %" PetscInt_FMT "\n", nmin, navg, nmax));
1271: PetscFunctionReturn(PETSC_SUCCESS);
1272: }
1273: PetscCall(PetscViewerGetFormat(viewer, &format));
1274: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1275: MatInfo info;
1276: PetscInt *inodes = NULL;
1278: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1279: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1280: PetscCall(MatInodeGetInodeSizes(aij->A, NULL, &inodes, NULL));
1281: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1282: if (!inodes) {
1283: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] Local rows %" PetscInt_FMT " nz %" PetscInt_FMT " nz alloced %" PetscInt_FMT " mem %g, not using I-node routines\n", rank, mat->rmap->n, (PetscInt)info.nz_used, (PetscInt)info.nz_allocated,
1284: (double)info.memory));
1285: } else {
1286: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] Local rows %" PetscInt_FMT " nz %" PetscInt_FMT " nz alloced %" PetscInt_FMT " mem %g, using I-node routines\n", rank, mat->rmap->n, (PetscInt)info.nz_used, (PetscInt)info.nz_allocated,
1287: (double)info.memory));
1288: }
1289: PetscCall(MatGetInfo(aij->A, MAT_LOCAL, &info));
1290: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1291: PetscCall(MatGetInfo(aij->B, MAT_LOCAL, &info));
1292: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1293: PetscCall(PetscViewerFlush(viewer));
1294: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1295: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1296: PetscCall(VecScatterView(aij->Mvctx, viewer));
1297: PetscFunctionReturn(PETSC_SUCCESS);
1298: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1299: PetscInt inodecount, inodelimit, *inodes;
1300: PetscCall(MatInodeGetInodeSizes(aij->A, &inodecount, &inodes, &inodelimit));
1301: if (inodes) {
1302: PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
1303: } else {
1304: PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
1305: }
1306: PetscFunctionReturn(PETSC_SUCCESS);
1307: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1308: PetscFunctionReturn(PETSC_SUCCESS);
1309: }
1310: } else if (isbinary) {
1311: if (size == 1) {
1312: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1313: PetscCall(MatView(aij->A, viewer));
1314: } else {
1315: PetscCall(MatView_MPIAIJ_Binary(mat, viewer));
1316: }
1317: PetscFunctionReturn(PETSC_SUCCESS);
1318: } else if (iascii && size == 1) {
1319: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1320: PetscCall(MatView(aij->A, viewer));
1321: PetscFunctionReturn(PETSC_SUCCESS);
1322: } else if (isdraw) {
1323: PetscDraw draw;
1324: PetscBool isnull;
1325: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1326: PetscCall(PetscDrawIsNull(draw, &isnull));
1327: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1328: }
1330: { /* assemble the entire matrix onto first processor */
1331: Mat A = NULL, Av;
1332: IS isrow, iscol;
1334: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->rmap->N : 0, 0, 1, &isrow));
1335: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->cmap->N : 0, 0, 1, &iscol));
1336: PetscCall(MatCreateSubMatrix(mat, isrow, iscol, MAT_INITIAL_MATRIX, &A));
1337: PetscCall(MatMPIAIJGetSeqAIJ(A, &Av, NULL, NULL));
1338: /* The commented code uses MatCreateSubMatrices instead */
1339: /*
1340: Mat *AA, A = NULL, Av;
1341: IS isrow,iscol;
1343: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->rmap->N : 0,0,1,&isrow));
1344: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->cmap->N : 0,0,1,&iscol));
1345: PetscCall(MatCreateSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&AA));
1346: if (rank == 0) {
1347: PetscCall(PetscObjectReference((PetscObject)AA[0]));
1348: A = AA[0];
1349: Av = AA[0];
1350: }
1351: PetscCall(MatDestroySubMatrices(1,&AA));
1352: */
1353: PetscCall(ISDestroy(&iscol));
1354: PetscCall(ISDestroy(&isrow));
1355: /*
1356: Everyone has to call to draw the matrix since the graphics waits are
1357: synchronized across all processors that share the PetscDraw object
1358: */
1359: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1360: if (rank == 0) {
1361: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)Av, ((PetscObject)mat)->name));
1362: PetscCall(MatView_SeqAIJ(Av, sviewer));
1363: }
1364: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1365: PetscCall(MatDestroy(&A));
1366: }
1367: PetscFunctionReturn(PETSC_SUCCESS);
1368: }
1370: PetscErrorCode MatView_MPIAIJ(Mat mat, PetscViewer viewer)
1371: {
1372: PetscBool iascii, isdraw, issocket, isbinary;
1374: PetscFunctionBegin;
1375: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1376: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1377: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1378: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1379: if (iascii || isdraw || isbinary || issocket) PetscCall(MatView_MPIAIJ_ASCIIorDraworSocket(mat, viewer));
1380: PetscFunctionReturn(PETSC_SUCCESS);
1381: }
1383: static PetscErrorCode MatSOR_MPIAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1384: {
1385: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1386: Vec bb1 = NULL;
1387: PetscBool hasop;
1389: PetscFunctionBegin;
1390: if (flag == SOR_APPLY_UPPER) {
1391: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1392: PetscFunctionReturn(PETSC_SUCCESS);
1393: }
1395: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1));
1397: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
1398: if (flag & SOR_ZERO_INITIAL_GUESS) {
1399: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1400: its--;
1401: }
1403: while (its--) {
1404: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1405: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1407: /* update rhs: bb1 = bb - B*x */
1408: PetscCall(VecScale(mat->lvec, -1.0));
1409: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1411: /* local sweep */
1412: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
1413: }
1414: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1415: if (flag & SOR_ZERO_INITIAL_GUESS) {
1416: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1417: its--;
1418: }
1419: while (its--) {
1420: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1421: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1423: /* update rhs: bb1 = bb - B*x */
1424: PetscCall(VecScale(mat->lvec, -1.0));
1425: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1427: /* local sweep */
1428: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
1429: }
1430: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1431: if (flag & SOR_ZERO_INITIAL_GUESS) {
1432: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1433: its--;
1434: }
1435: while (its--) {
1436: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1437: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1439: /* update rhs: bb1 = bb - B*x */
1440: PetscCall(VecScale(mat->lvec, -1.0));
1441: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1443: /* local sweep */
1444: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
1445: }
1446: } else if (flag & SOR_EISENSTAT) {
1447: Vec xx1;
1449: PetscCall(VecDuplicate(bb, &xx1));
1450: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP), fshift, lits, 1, xx));
1452: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1453: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1454: if (!mat->diag) {
1455: PetscCall(MatCreateVecs(matin, &mat->diag, NULL));
1456: PetscCall(MatGetDiagonal(matin, mat->diag));
1457: }
1458: PetscCall(MatHasOperation(matin, MATOP_MULT_DIAGONAL_BLOCK, &hasop));
1459: if (hasop) {
1460: PetscCall(MatMultDiagonalBlock(matin, xx, bb1));
1461: } else {
1462: PetscCall(VecPointwiseMult(bb1, mat->diag, xx));
1463: }
1464: PetscCall(VecAYPX(bb1, (omega - 2.0) / omega, bb));
1466: PetscCall(MatMultAdd(mat->B, mat->lvec, bb1, bb1));
1468: /* local sweep */
1469: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP), fshift, lits, 1, xx1));
1470: PetscCall(VecAXPY(xx, 1.0, xx1));
1471: PetscCall(VecDestroy(&xx1));
1472: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");
1474: PetscCall(VecDestroy(&bb1));
1476: matin->factorerrortype = mat->A->factorerrortype;
1477: PetscFunctionReturn(PETSC_SUCCESS);
1478: }
1480: static PetscErrorCode MatPermute_MPIAIJ(Mat A, IS rowp, IS colp, Mat *B)
1481: {
1482: Mat aA, aB, Aperm;
1483: const PetscInt *rwant, *cwant, *gcols, *ai, *bi, *aj, *bj;
1484: PetscScalar *aa, *ba;
1485: PetscInt i, j, m, n, ng, anz, bnz, *dnnz, *onnz, *tdnnz, *tonnz, *rdest, *cdest, *work, *gcdest;
1486: PetscSF rowsf, sf;
1487: IS parcolp = NULL;
1488: PetscBool done;
1490: PetscFunctionBegin;
1491: PetscCall(MatGetLocalSize(A, &m, &n));
1492: PetscCall(ISGetIndices(rowp, &rwant));
1493: PetscCall(ISGetIndices(colp, &cwant));
1494: PetscCall(PetscMalloc3(PetscMax(m, n), &work, m, &rdest, n, &cdest));
1496: /* Invert row permutation to find out where my rows should go */
1497: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &rowsf));
1498: PetscCall(PetscSFSetGraphLayout(rowsf, A->rmap, A->rmap->n, NULL, PETSC_OWN_POINTER, rwant));
1499: PetscCall(PetscSFSetFromOptions(rowsf));
1500: for (i = 0; i < m; i++) work[i] = A->rmap->rstart + i;
1501: PetscCall(PetscSFReduceBegin(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1502: PetscCall(PetscSFReduceEnd(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1504: /* Invert column permutation to find out where my columns should go */
1505: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1506: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, A->cmap->n, NULL, PETSC_OWN_POINTER, cwant));
1507: PetscCall(PetscSFSetFromOptions(sf));
1508: for (i = 0; i < n; i++) work[i] = A->cmap->rstart + i;
1509: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1510: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1511: PetscCall(PetscSFDestroy(&sf));
1513: PetscCall(ISRestoreIndices(rowp, &rwant));
1514: PetscCall(ISRestoreIndices(colp, &cwant));
1515: PetscCall(MatMPIAIJGetSeqAIJ(A, &aA, &aB, &gcols));
1517: /* Find out where my gcols should go */
1518: PetscCall(MatGetSize(aB, NULL, &ng));
1519: PetscCall(PetscMalloc1(ng, &gcdest));
1520: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1521: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, ng, NULL, PETSC_OWN_POINTER, gcols));
1522: PetscCall(PetscSFSetFromOptions(sf));
1523: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1524: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1525: PetscCall(PetscSFDestroy(&sf));
1527: PetscCall(PetscCalloc4(m, &dnnz, m, &onnz, m, &tdnnz, m, &tonnz));
1528: PetscCall(MatGetRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1529: PetscCall(MatGetRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1530: for (i = 0; i < m; i++) {
1531: PetscInt row = rdest[i];
1532: PetscMPIInt rowner;
1533: PetscCall(PetscLayoutFindOwner(A->rmap, row, &rowner));
1534: for (j = ai[i]; j < ai[i + 1]; j++) {
1535: PetscInt col = cdest[aj[j]];
1536: PetscMPIInt cowner;
1537: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner)); /* Could build an index for the columns to eliminate this search */
1538: if (rowner == cowner) dnnz[i]++;
1539: else onnz[i]++;
1540: }
1541: for (j = bi[i]; j < bi[i + 1]; j++) {
1542: PetscInt col = gcdest[bj[j]];
1543: PetscMPIInt cowner;
1544: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner));
1545: if (rowner == cowner) dnnz[i]++;
1546: else onnz[i]++;
1547: }
1548: }
1549: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1550: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1551: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1552: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1553: PetscCall(PetscSFDestroy(&rowsf));
1555: PetscCall(MatCreateAIJ(PetscObjectComm((PetscObject)A), A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N, 0, tdnnz, 0, tonnz, &Aperm));
1556: PetscCall(MatSeqAIJGetArray(aA, &aa));
1557: PetscCall(MatSeqAIJGetArray(aB, &ba));
1558: for (i = 0; i < m; i++) {
1559: PetscInt *acols = dnnz, *bcols = onnz; /* Repurpose now-unneeded arrays */
1560: PetscInt j0, rowlen;
1561: rowlen = ai[i + 1] - ai[i];
1562: for (j0 = j = 0; j < rowlen; j0 = j) { /* rowlen could be larger than number of rows m, so sum in batches */
1563: for (; j < PetscMin(rowlen, j0 + m); j++) acols[j - j0] = cdest[aj[ai[i] + j]];
1564: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, acols, aa + ai[i] + j0, INSERT_VALUES));
1565: }
1566: rowlen = bi[i + 1] - bi[i];
1567: for (j0 = j = 0; j < rowlen; j0 = j) {
1568: for (; j < PetscMin(rowlen, j0 + m); j++) bcols[j - j0] = gcdest[bj[bi[i] + j]];
1569: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, bcols, ba + bi[i] + j0, INSERT_VALUES));
1570: }
1571: }
1572: PetscCall(MatAssemblyBegin(Aperm, MAT_FINAL_ASSEMBLY));
1573: PetscCall(MatAssemblyEnd(Aperm, MAT_FINAL_ASSEMBLY));
1574: PetscCall(MatRestoreRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1575: PetscCall(MatRestoreRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1576: PetscCall(MatSeqAIJRestoreArray(aA, &aa));
1577: PetscCall(MatSeqAIJRestoreArray(aB, &ba));
1578: PetscCall(PetscFree4(dnnz, onnz, tdnnz, tonnz));
1579: PetscCall(PetscFree3(work, rdest, cdest));
1580: PetscCall(PetscFree(gcdest));
1581: if (parcolp) PetscCall(ISDestroy(&colp));
1582: *B = Aperm;
1583: PetscFunctionReturn(PETSC_SUCCESS);
1584: }
1586: static PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
1587: {
1588: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1590: PetscFunctionBegin;
1591: PetscCall(MatGetSize(aij->B, NULL, nghosts));
1592: if (ghosts) *ghosts = aij->garray;
1593: PetscFunctionReturn(PETSC_SUCCESS);
1594: }
1596: static PetscErrorCode MatGetInfo_MPIAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1597: {
1598: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1599: Mat A = mat->A, B = mat->B;
1600: PetscLogDouble isend[5], irecv[5];
1602: PetscFunctionBegin;
1603: info->block_size = 1.0;
1604: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1606: isend[0] = info->nz_used;
1607: isend[1] = info->nz_allocated;
1608: isend[2] = info->nz_unneeded;
1609: isend[3] = info->memory;
1610: isend[4] = info->mallocs;
1612: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1614: isend[0] += info->nz_used;
1615: isend[1] += info->nz_allocated;
1616: isend[2] += info->nz_unneeded;
1617: isend[3] += info->memory;
1618: isend[4] += info->mallocs;
1619: if (flag == MAT_LOCAL) {
1620: info->nz_used = isend[0];
1621: info->nz_allocated = isend[1];
1622: info->nz_unneeded = isend[2];
1623: info->memory = isend[3];
1624: info->mallocs = isend[4];
1625: } else if (flag == MAT_GLOBAL_MAX) {
1626: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1628: info->nz_used = irecv[0];
1629: info->nz_allocated = irecv[1];
1630: info->nz_unneeded = irecv[2];
1631: info->memory = irecv[3];
1632: info->mallocs = irecv[4];
1633: } else if (flag == MAT_GLOBAL_SUM) {
1634: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1636: info->nz_used = irecv[0];
1637: info->nz_allocated = irecv[1];
1638: info->nz_unneeded = irecv[2];
1639: info->memory = irecv[3];
1640: info->mallocs = irecv[4];
1641: }
1642: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1643: info->fill_ratio_needed = 0;
1644: info->factor_mallocs = 0;
1645: PetscFunctionReturn(PETSC_SUCCESS);
1646: }
1648: PetscErrorCode MatSetOption_MPIAIJ(Mat A, MatOption op, PetscBool flg)
1649: {
1650: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1652: PetscFunctionBegin;
1653: switch (op) {
1654: case MAT_NEW_NONZERO_LOCATIONS:
1655: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1656: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1657: case MAT_KEEP_NONZERO_PATTERN:
1658: case MAT_NEW_NONZERO_LOCATION_ERR:
1659: case MAT_USE_INODES:
1660: case MAT_IGNORE_ZERO_ENTRIES:
1661: case MAT_FORM_EXPLICIT_TRANSPOSE:
1662: MatCheckPreallocated(A, 1);
1663: PetscCall(MatSetOption(a->A, op, flg));
1664: PetscCall(MatSetOption(a->B, op, flg));
1665: break;
1666: case MAT_ROW_ORIENTED:
1667: MatCheckPreallocated(A, 1);
1668: a->roworiented = flg;
1670: PetscCall(MatSetOption(a->A, op, flg));
1671: PetscCall(MatSetOption(a->B, op, flg));
1672: break;
1673: case MAT_FORCE_DIAGONAL_ENTRIES:
1674: case MAT_SORTED_FULL:
1675: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1676: break;
1677: case MAT_IGNORE_OFF_PROC_ENTRIES:
1678: a->donotstash = flg;
1679: break;
1680: /* Symmetry flags are handled directly by MatSetOption() and they don't affect preallocation */
1681: case MAT_SPD:
1682: case MAT_SYMMETRIC:
1683: case MAT_STRUCTURALLY_SYMMETRIC:
1684: case MAT_HERMITIAN:
1685: case MAT_SYMMETRY_ETERNAL:
1686: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1687: case MAT_SPD_ETERNAL:
1688: /* if the diagonal matrix is square it inherits some of the properties above */
1689: break;
1690: case MAT_SUBMAT_SINGLEIS:
1691: A->submat_singleis = flg;
1692: break;
1693: case MAT_STRUCTURE_ONLY:
1694: /* The option is handled directly by MatSetOption() */
1695: break;
1696: default:
1697: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1698: }
1699: PetscFunctionReturn(PETSC_SUCCESS);
1700: }
1702: PetscErrorCode MatGetRow_MPIAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1703: {
1704: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1705: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1706: PetscInt i, *cworkA, *cworkB, **pcA, **pcB, cstart = matin->cmap->rstart;
1707: PetscInt nztot, nzA, nzB, lrow, rstart = matin->rmap->rstart, rend = matin->rmap->rend;
1708: PetscInt *cmap, *idx_p;
1710: PetscFunctionBegin;
1711: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1712: mat->getrowactive = PETSC_TRUE;
1714: if (!mat->rowvalues && (idx || v)) {
1715: /*
1716: allocate enough space to hold information from the longest row.
1717: */
1718: Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)mat->A->data, *Ba = (Mat_SeqAIJ *)mat->B->data;
1719: PetscInt max = 1, tmp;
1720: for (i = 0; i < matin->rmap->n; i++) {
1721: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1722: if (max < tmp) max = tmp;
1723: }
1724: PetscCall(PetscMalloc2(max, &mat->rowvalues, max, &mat->rowindices));
1725: }
1727: PetscCheck(row >= rstart && row < rend, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Only local rows");
1728: lrow = row - rstart;
1730: pvA = &vworkA;
1731: pcA = &cworkA;
1732: pvB = &vworkB;
1733: pcB = &cworkB;
1734: if (!v) {
1735: pvA = NULL;
1736: pvB = NULL;
1737: }
1738: if (!idx) {
1739: pcA = NULL;
1740: if (!v) pcB = NULL;
1741: }
1742: PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1743: PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1744: nztot = nzA + nzB;
1746: cmap = mat->garray;
1747: if (v || idx) {
1748: if (nztot) {
1749: /* Sort by increasing column numbers, assuming A and B already sorted */
1750: PetscInt imark = -1;
1751: if (v) {
1752: *v = v_p = mat->rowvalues;
1753: for (i = 0; i < nzB; i++) {
1754: if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1755: else break;
1756: }
1757: imark = i;
1758: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1759: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1760: }
1761: if (idx) {
1762: *idx = idx_p = mat->rowindices;
1763: if (imark > -1) {
1764: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i]];
1765: } else {
1766: for (i = 0; i < nzB; i++) {
1767: if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1768: else break;
1769: }
1770: imark = i;
1771: }
1772: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart + cworkA[i];
1773: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i]];
1774: }
1775: } else {
1776: if (idx) *idx = NULL;
1777: if (v) *v = NULL;
1778: }
1779: }
1780: *nz = nztot;
1781: PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1782: PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1783: PetscFunctionReturn(PETSC_SUCCESS);
1784: }
1786: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1787: {
1788: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1790: PetscFunctionBegin;
1791: PetscCheck(aij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow() must be called first");
1792: aij->getrowactive = PETSC_FALSE;
1793: PetscFunctionReturn(PETSC_SUCCESS);
1794: }
1796: static PetscErrorCode MatNorm_MPIAIJ(Mat mat, NormType type, PetscReal *norm)
1797: {
1798: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1799: Mat_SeqAIJ *amat = (Mat_SeqAIJ *)aij->A->data, *bmat = (Mat_SeqAIJ *)aij->B->data;
1800: PetscInt i, j, cstart = mat->cmap->rstart;
1801: PetscReal sum = 0.0;
1802: const MatScalar *v, *amata, *bmata;
1803: PetscMPIInt iN;
1805: PetscFunctionBegin;
1806: if (aij->size == 1) {
1807: PetscCall(MatNorm(aij->A, type, norm));
1808: } else {
1809: PetscCall(MatSeqAIJGetArrayRead(aij->A, &amata));
1810: PetscCall(MatSeqAIJGetArrayRead(aij->B, &bmata));
1811: if (type == NORM_FROBENIUS) {
1812: v = amata;
1813: for (i = 0; i < amat->nz; i++) {
1814: sum += PetscRealPart(PetscConj(*v) * (*v));
1815: v++;
1816: }
1817: v = bmata;
1818: for (i = 0; i < bmat->nz; i++) {
1819: sum += PetscRealPart(PetscConj(*v) * (*v));
1820: v++;
1821: }
1822: PetscCallMPI(MPIU_Allreduce(&sum, norm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1823: *norm = PetscSqrtReal(*norm);
1824: PetscCall(PetscLogFlops(2.0 * amat->nz + 2.0 * bmat->nz));
1825: } else if (type == NORM_1) { /* max column norm */
1826: PetscReal *tmp, *tmp2;
1827: PetscInt *jj, *garray = aij->garray;
1828: PetscCall(PetscCalloc1(mat->cmap->N + 1, &tmp));
1829: PetscCall(PetscMalloc1(mat->cmap->N + 1, &tmp2));
1830: *norm = 0.0;
1831: v = amata;
1832: jj = amat->j;
1833: for (j = 0; j < amat->nz; j++) {
1834: tmp[cstart + *jj++] += PetscAbsScalar(*v);
1835: v++;
1836: }
1837: v = bmata;
1838: jj = bmat->j;
1839: for (j = 0; j < bmat->nz; j++) {
1840: tmp[garray[*jj++]] += PetscAbsScalar(*v);
1841: v++;
1842: }
1843: PetscCall(PetscMPIIntCast(mat->cmap->N, &iN));
1844: PetscCallMPI(MPIU_Allreduce(tmp, tmp2, iN, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1845: for (j = 0; j < mat->cmap->N; j++) {
1846: if (tmp2[j] > *norm) *norm = tmp2[j];
1847: }
1848: PetscCall(PetscFree(tmp));
1849: PetscCall(PetscFree(tmp2));
1850: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1851: } else if (type == NORM_INFINITY) { /* max row norm */
1852: PetscReal ntemp = 0.0;
1853: for (j = 0; j < aij->A->rmap->n; j++) {
1854: v = PetscSafePointerPlusOffset(amata, amat->i[j]);
1855: sum = 0.0;
1856: for (i = 0; i < amat->i[j + 1] - amat->i[j]; i++) {
1857: sum += PetscAbsScalar(*v);
1858: v++;
1859: }
1860: v = PetscSafePointerPlusOffset(bmata, bmat->i[j]);
1861: for (i = 0; i < bmat->i[j + 1] - bmat->i[j]; i++) {
1862: sum += PetscAbsScalar(*v);
1863: v++;
1864: }
1865: if (sum > ntemp) ntemp = sum;
1866: }
1867: PetscCallMPI(MPIU_Allreduce(&ntemp, norm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
1868: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1869: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for two norm");
1870: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &amata));
1871: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &bmata));
1872: }
1873: PetscFunctionReturn(PETSC_SUCCESS);
1874: }
1876: static PetscErrorCode MatTranspose_MPIAIJ(Mat A, MatReuse reuse, Mat *matout)
1877: {
1878: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *b;
1879: Mat_SeqAIJ *Aloc = (Mat_SeqAIJ *)a->A->data, *Bloc = (Mat_SeqAIJ *)a->B->data, *sub_B_diag;
1880: 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;
1881: const PetscInt *ai, *aj, *bi, *bj, *B_diag_i;
1882: Mat B, A_diag, *B_diag;
1883: const MatScalar *pbv, *bv;
1885: PetscFunctionBegin;
1886: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1887: ma = A->rmap->n;
1888: na = A->cmap->n;
1889: mb = a->B->rmap->n;
1890: nb = a->B->cmap->n;
1891: ai = Aloc->i;
1892: aj = Aloc->j;
1893: bi = Bloc->i;
1894: bj = Bloc->j;
1895: if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1896: PetscInt *d_nnz, *g_nnz, *o_nnz;
1897: PetscSFNode *oloc;
1898: PETSC_UNUSED PetscSF sf;
1900: PetscCall(PetscMalloc4(na, &d_nnz, na, &o_nnz, nb, &g_nnz, nb, &oloc));
1901: /* compute d_nnz for preallocation */
1902: PetscCall(PetscArrayzero(d_nnz, na));
1903: for (i = 0; i < ai[ma]; i++) d_nnz[aj[i]]++;
1904: /* compute local off-diagonal contributions */
1905: PetscCall(PetscArrayzero(g_nnz, nb));
1906: for (i = 0; i < bi[ma]; i++) g_nnz[bj[i]]++;
1907: /* map those to global */
1908: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1909: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, nb, NULL, PETSC_USE_POINTER, a->garray));
1910: PetscCall(PetscSFSetFromOptions(sf));
1911: PetscCall(PetscArrayzero(o_nnz, na));
1912: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1913: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1914: PetscCall(PetscSFDestroy(&sf));
1916: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1917: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1918: PetscCall(MatSetBlockSizes(B, PetscAbs(A->cmap->bs), PetscAbs(A->rmap->bs)));
1919: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1920: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
1921: PetscCall(PetscFree4(d_nnz, o_nnz, g_nnz, oloc));
1922: } else {
1923: B = *matout;
1924: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
1925: }
1927: b = (Mat_MPIAIJ *)B->data;
1928: A_diag = a->A;
1929: B_diag = &b->A;
1930: sub_B_diag = (Mat_SeqAIJ *)(*B_diag)->data;
1931: A_diag_ncol = A_diag->cmap->N;
1932: B_diag_ilen = sub_B_diag->ilen;
1933: B_diag_i = sub_B_diag->i;
1935: /* Set ilen for diagonal of B */
1936: for (i = 0; i < A_diag_ncol; i++) B_diag_ilen[i] = B_diag_i[i + 1] - B_diag_i[i];
1938: /* Transpose the diagonal part of the matrix. In contrast to the off-diagonal part, this can be done
1939: very quickly (=without using MatSetValues), because all writes are local. */
1940: PetscCall(MatTransposeSetPrecursor(A_diag, *B_diag));
1941: PetscCall(MatTranspose(A_diag, MAT_REUSE_MATRIX, B_diag));
1943: /* copy over the B part */
1944: PetscCall(PetscMalloc1(bi[mb], &cols));
1945: PetscCall(MatSeqAIJGetArrayRead(a->B, &bv));
1946: pbv = bv;
1947: row = A->rmap->rstart;
1948: for (i = 0; i < bi[mb]; i++) cols[i] = a->garray[bj[i]];
1949: cols_tmp = cols;
1950: for (i = 0; i < mb; i++) {
1951: ncol = bi[i + 1] - bi[i];
1952: PetscCall(MatSetValues(B, ncol, cols_tmp, 1, &row, pbv, INSERT_VALUES));
1953: row++;
1954: if (pbv) pbv += ncol;
1955: if (cols_tmp) cols_tmp += ncol;
1956: }
1957: PetscCall(PetscFree(cols));
1958: PetscCall(MatSeqAIJRestoreArrayRead(a->B, &bv));
1960: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1961: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1962: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1963: *matout = B;
1964: } else {
1965: PetscCall(MatHeaderMerge(A, &B));
1966: }
1967: PetscFunctionReturn(PETSC_SUCCESS);
1968: }
1970: static PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat, Vec ll, Vec rr)
1971: {
1972: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1973: Mat a = aij->A, b = aij->B;
1974: PetscInt s1, s2, s3;
1976: PetscFunctionBegin;
1977: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1978: if (rr) {
1979: PetscCall(VecGetLocalSize(rr, &s1));
1980: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1981: /* Overlap communication with computation. */
1982: PetscCall(VecScatterBegin(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1983: }
1984: if (ll) {
1985: PetscCall(VecGetLocalSize(ll, &s1));
1986: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1987: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1988: }
1989: /* scale the diagonal block */
1990: PetscUseTypeMethod(a, diagonalscale, ll, rr);
1992: if (rr) {
1993: /* Do a scatter end and then right scale the off-diagonal block */
1994: PetscCall(VecScatterEnd(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1995: PetscUseTypeMethod(b, diagonalscale, NULL, aij->lvec);
1996: }
1997: PetscFunctionReturn(PETSC_SUCCESS);
1998: }
2000: static PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2001: {
2002: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2004: PetscFunctionBegin;
2005: PetscCall(MatSetUnfactored(a->A));
2006: PetscFunctionReturn(PETSC_SUCCESS);
2007: }
2009: static PetscErrorCode MatEqual_MPIAIJ(Mat A, Mat B, PetscBool *flag)
2010: {
2011: Mat_MPIAIJ *matB = (Mat_MPIAIJ *)B->data, *matA = (Mat_MPIAIJ *)A->data;
2012: Mat a, b, c, d;
2013: PetscBool flg;
2015: PetscFunctionBegin;
2016: a = matA->A;
2017: b = matA->B;
2018: c = matB->A;
2019: d = matB->B;
2021: PetscCall(MatEqual(a, c, &flg));
2022: if (flg) PetscCall(MatEqual(b, d, &flg));
2023: PetscCallMPI(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
2024: PetscFunctionReturn(PETSC_SUCCESS);
2025: }
2027: static PetscErrorCode MatCopy_MPIAIJ(Mat A, Mat B, MatStructure str)
2028: {
2029: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2030: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2032: PetscFunctionBegin;
2033: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2034: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2035: /* because of the column compression in the off-processor part of the matrix a->B,
2036: the number of columns in a->B and b->B may be different, hence we cannot call
2037: the MatCopy() directly on the two parts. If need be, we can provide a more
2038: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2039: then copying the submatrices */
2040: PetscCall(MatCopy_Basic(A, B, str));
2041: } else {
2042: PetscCall(MatCopy(a->A, b->A, str));
2043: PetscCall(MatCopy(a->B, b->B, str));
2044: }
2045: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2046: PetscFunctionReturn(PETSC_SUCCESS);
2047: }
2049: /*
2050: Computes the number of nonzeros per row needed for preallocation when X and Y
2051: have different nonzero structure.
2052: */
2053: 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)
2054: {
2055: PetscInt i, j, k, nzx, nzy;
2057: PetscFunctionBegin;
2058: /* Set the number of nonzeros in the new matrix */
2059: for (i = 0; i < m; i++) {
2060: const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2061: nzx = xi[i + 1] - xi[i];
2062: nzy = yi[i + 1] - yi[i];
2063: nnz[i] = 0;
2064: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2065: for (; k < nzy && yltog[yjj[k]] < xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2066: if (k < nzy && yltog[yjj[k]] == xltog[xjj[j]]) k++; /* Skip duplicate */
2067: nnz[i]++;
2068: }
2069: for (; k < nzy; k++) nnz[i]++;
2070: }
2071: PetscFunctionReturn(PETSC_SUCCESS);
2072: }
2074: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2075: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
2076: {
2077: PetscInt m = Y->rmap->N;
2078: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2079: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
2081: PetscFunctionBegin;
2082: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
2083: PetscFunctionReturn(PETSC_SUCCESS);
2084: }
2086: static PetscErrorCode MatAXPY_MPIAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2087: {
2088: Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data, *yy = (Mat_MPIAIJ *)Y->data;
2090: PetscFunctionBegin;
2091: if (str == SAME_NONZERO_PATTERN) {
2092: PetscCall(MatAXPY(yy->A, a, xx->A, str));
2093: PetscCall(MatAXPY(yy->B, a, xx->B, str));
2094: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2095: PetscCall(MatAXPY_Basic(Y, a, X, str));
2096: } else {
2097: Mat B;
2098: PetscInt *nnz_d, *nnz_o;
2100: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
2101: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
2102: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2103: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2104: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2105: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2106: PetscCall(MatAXPYGetPreallocation_SeqAIJ(yy->A, xx->A, nnz_d));
2107: PetscCall(MatAXPYGetPreallocation_MPIAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
2108: PetscCall(MatMPIAIJSetPreallocation(B, 0, nnz_d, 0, nnz_o));
2109: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2110: PetscCall(MatHeaderMerge(Y, &B));
2111: PetscCall(PetscFree(nnz_d));
2112: PetscCall(PetscFree(nnz_o));
2113: }
2114: PetscFunctionReturn(PETSC_SUCCESS);
2115: }
2117: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);
2119: static PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2120: {
2121: PetscFunctionBegin;
2122: if (PetscDefined(USE_COMPLEX)) {
2123: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2125: PetscCall(MatConjugate_SeqAIJ(aij->A));
2126: PetscCall(MatConjugate_SeqAIJ(aij->B));
2127: }
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 *va, *vv;
2156: Vec vB, vA;
2157: const PetscScalar *vb;
2159: PetscFunctionBegin;
2160: PetscCall(MatCreateVecs(a->A, NULL, &vA));
2161: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
2163: PetscCall(VecGetArrayWrite(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(vA, &vv));
2186: PetscCall(VecRestoreArrayWrite(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 MatMissingDiagonal_MPIAIJ(Mat A, PetscBool *missing, PetscInt *d)
2684: {
2685: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2687: PetscFunctionBegin;
2688: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2689: PetscCall(MatMissingDiagonal(a->A, missing, d));
2690: if (d) {
2691: PetscInt rstart;
2692: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2693: *d += rstart;
2694: }
2695: PetscFunctionReturn(PETSC_SUCCESS);
2696: }
2698: static PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2699: {
2700: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2702: PetscFunctionBegin;
2703: PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2704: PetscFunctionReturn(PETSC_SUCCESS);
2705: }
2707: static PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A, PetscBool keep)
2708: {
2709: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2711: PetscFunctionBegin;
2712: PetscCall(MatEliminateZeros_SeqAIJ(a->A, keep)); // possibly keep zero diagonal coefficients
2713: PetscCall(MatEliminateZeros_SeqAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2714: PetscFunctionReturn(PETSC_SUCCESS);
2715: }
2717: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2718: MatGetRow_MPIAIJ,
2719: MatRestoreRow_MPIAIJ,
2720: MatMult_MPIAIJ,
2721: /* 4*/ MatMultAdd_MPIAIJ,
2722: MatMultTranspose_MPIAIJ,
2723: MatMultTransposeAdd_MPIAIJ,
2724: NULL,
2725: NULL,
2726: NULL,
2727: /*10*/ NULL,
2728: NULL,
2729: NULL,
2730: MatSOR_MPIAIJ,
2731: MatTranspose_MPIAIJ,
2732: /*15*/ MatGetInfo_MPIAIJ,
2733: MatEqual_MPIAIJ,
2734: MatGetDiagonal_MPIAIJ,
2735: MatDiagonalScale_MPIAIJ,
2736: MatNorm_MPIAIJ,
2737: /*20*/ MatAssemblyBegin_MPIAIJ,
2738: MatAssemblyEnd_MPIAIJ,
2739: MatSetOption_MPIAIJ,
2740: MatZeroEntries_MPIAIJ,
2741: /*24*/ MatZeroRows_MPIAIJ,
2742: NULL,
2743: NULL,
2744: NULL,
2745: NULL,
2746: /*29*/ MatSetUp_MPI_Hash,
2747: NULL,
2748: NULL,
2749: MatGetDiagonalBlock_MPIAIJ,
2750: NULL,
2751: /*34*/ MatDuplicate_MPIAIJ,
2752: NULL,
2753: NULL,
2754: NULL,
2755: NULL,
2756: /*39*/ MatAXPY_MPIAIJ,
2757: MatCreateSubMatrices_MPIAIJ,
2758: MatIncreaseOverlap_MPIAIJ,
2759: MatGetValues_MPIAIJ,
2760: MatCopy_MPIAIJ,
2761: /*44*/ MatGetRowMax_MPIAIJ,
2762: MatScale_MPIAIJ,
2763: MatShift_MPIAIJ,
2764: MatDiagonalSet_MPIAIJ,
2765: MatZeroRowsColumns_MPIAIJ,
2766: /*49*/ MatSetRandom_MPIAIJ,
2767: MatGetRowIJ_MPIAIJ,
2768: MatRestoreRowIJ_MPIAIJ,
2769: NULL,
2770: NULL,
2771: /*54*/ MatFDColoringCreate_MPIXAIJ,
2772: NULL,
2773: MatSetUnfactored_MPIAIJ,
2774: MatPermute_MPIAIJ,
2775: NULL,
2776: /*59*/ MatCreateSubMatrix_MPIAIJ,
2777: MatDestroy_MPIAIJ,
2778: MatView_MPIAIJ,
2779: NULL,
2780: NULL,
2781: /*64*/ NULL,
2782: MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2783: NULL,
2784: NULL,
2785: NULL,
2786: /*69*/ MatGetRowMaxAbs_MPIAIJ,
2787: MatGetRowMinAbs_MPIAIJ,
2788: NULL,
2789: NULL,
2790: NULL,
2791: NULL,
2792: /*75*/ MatFDColoringApply_AIJ,
2793: MatSetFromOptions_MPIAIJ,
2794: NULL,
2795: NULL,
2796: MatFindZeroDiagonals_MPIAIJ,
2797: /*80*/ NULL,
2798: NULL,
2799: NULL,
2800: /*83*/ MatLoad_MPIAIJ,
2801: NULL,
2802: NULL,
2803: NULL,
2804: NULL,
2805: NULL,
2806: /*89*/ NULL,
2807: NULL,
2808: MatMatMultNumeric_MPIAIJ_MPIAIJ,
2809: NULL,
2810: NULL,
2811: /*94*/ MatPtAPNumeric_MPIAIJ_MPIAIJ,
2812: NULL,
2813: NULL,
2814: NULL,
2815: MatBindToCPU_MPIAIJ,
2816: /*99*/ MatProductSetFromOptions_MPIAIJ,
2817: NULL,
2818: NULL,
2819: MatConjugate_MPIAIJ,
2820: NULL,
2821: /*104*/ MatSetValuesRow_MPIAIJ,
2822: MatRealPart_MPIAIJ,
2823: MatImaginaryPart_MPIAIJ,
2824: NULL,
2825: NULL,
2826: /*109*/ NULL,
2827: NULL,
2828: MatGetRowMin_MPIAIJ,
2829: NULL,
2830: MatMissingDiagonal_MPIAIJ,
2831: /*114*/ MatGetSeqNonzeroStructure_MPIAIJ,
2832: NULL,
2833: MatGetGhosts_MPIAIJ,
2834: NULL,
2835: NULL,
2836: /*119*/ MatMultDiagonalBlock_MPIAIJ,
2837: NULL,
2838: NULL,
2839: NULL,
2840: MatGetMultiProcBlock_MPIAIJ,
2841: /*124*/ MatFindNonzeroRows_MPIAIJ,
2842: MatGetColumnReductions_MPIAIJ,
2843: MatInvertBlockDiagonal_MPIAIJ,
2844: MatInvertVariableBlockDiagonal_MPIAIJ,
2845: MatCreateSubMatricesMPI_MPIAIJ,
2846: /*129*/ NULL,
2847: NULL,
2848: NULL,
2849: MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2850: NULL,
2851: /*134*/ NULL,
2852: NULL,
2853: NULL,
2854: NULL,
2855: NULL,
2856: /*139*/ MatSetBlockSizes_MPIAIJ,
2857: NULL,
2858: NULL,
2859: MatFDColoringSetUp_MPIXAIJ,
2860: MatFindOffBlockDiagonalEntries_MPIAIJ,
2861: MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2862: /*145*/ NULL,
2863: NULL,
2864: NULL,
2865: MatCreateGraph_Simple_AIJ,
2866: NULL,
2867: /*150*/ NULL,
2868: MatEliminateZeros_MPIAIJ,
2869: MatGetRowSumAbs_MPIAIJ,
2870: NULL,
2871: NULL,
2872: NULL};
2874: static PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2875: {
2876: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2878: PetscFunctionBegin;
2879: PetscCall(MatStoreValues(aij->A));
2880: PetscCall(MatStoreValues(aij->B));
2881: PetscFunctionReturn(PETSC_SUCCESS);
2882: }
2884: static PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2885: {
2886: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2888: PetscFunctionBegin;
2889: PetscCall(MatRetrieveValues(aij->A));
2890: PetscCall(MatRetrieveValues(aij->B));
2891: PetscFunctionReturn(PETSC_SUCCESS);
2892: }
2894: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2895: {
2896: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2897: PetscMPIInt size;
2899: PetscFunctionBegin;
2900: if (B->hash_active) {
2901: B->ops[0] = b->cops;
2902: B->hash_active = PETSC_FALSE;
2903: }
2904: PetscCall(PetscLayoutSetUp(B->rmap));
2905: PetscCall(PetscLayoutSetUp(B->cmap));
2907: #if defined(PETSC_USE_CTABLE)
2908: PetscCall(PetscHMapIDestroy(&b->colmap));
2909: #else
2910: PetscCall(PetscFree(b->colmap));
2911: #endif
2912: PetscCall(PetscFree(b->garray));
2913: PetscCall(VecDestroy(&b->lvec));
2914: PetscCall(VecScatterDestroy(&b->Mvctx));
2916: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2918: MatSeqXAIJGetOptions_Private(b->B);
2919: PetscCall(MatDestroy(&b->B));
2920: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2921: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2922: PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2923: PetscCall(MatSetType(b->B, MATSEQAIJ));
2924: MatSeqXAIJRestoreOptions_Private(b->B);
2926: MatSeqXAIJGetOptions_Private(b->A);
2927: PetscCall(MatDestroy(&b->A));
2928: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2929: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2930: PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2931: PetscCall(MatSetType(b->A, MATSEQAIJ));
2932: MatSeqXAIJRestoreOptions_Private(b->A);
2934: PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2935: PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2936: B->preallocated = PETSC_TRUE;
2937: B->was_assembled = PETSC_FALSE;
2938: B->assembled = PETSC_FALSE;
2939: PetscFunctionReturn(PETSC_SUCCESS);
2940: }
2942: static PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2943: {
2944: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2946: PetscFunctionBegin;
2948: PetscCall(PetscLayoutSetUp(B->rmap));
2949: PetscCall(PetscLayoutSetUp(B->cmap));
2950: if (B->assembled || B->was_assembled) PetscCall(MatDisAssemble_MPIAIJ(B, PETSC_TRUE));
2951: else {
2952: #if defined(PETSC_USE_CTABLE)
2953: PetscCall(PetscHMapIDestroy(&b->colmap));
2954: #else
2955: PetscCall(PetscFree(b->colmap));
2956: #endif
2957: PetscCall(PetscFree(b->garray));
2958: PetscCall(VecDestroy(&b->lvec));
2959: }
2960: PetscCall(VecScatterDestroy(&b->Mvctx));
2962: PetscCall(MatResetPreallocation(b->A));
2963: PetscCall(MatResetPreallocation(b->B));
2964: B->preallocated = PETSC_TRUE;
2965: B->was_assembled = PETSC_FALSE;
2966: B->assembled = PETSC_FALSE;
2967: PetscFunctionReturn(PETSC_SUCCESS);
2968: }
2970: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2971: {
2972: Mat mat;
2973: Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;
2975: PetscFunctionBegin;
2976: *newmat = NULL;
2977: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2978: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2979: PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2980: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2981: a = (Mat_MPIAIJ *)mat->data;
2983: mat->factortype = matin->factortype;
2984: mat->assembled = matin->assembled;
2985: mat->insertmode = NOT_SET_VALUES;
2987: a->size = oldmat->size;
2988: a->rank = oldmat->rank;
2989: a->donotstash = oldmat->donotstash;
2990: a->roworiented = oldmat->roworiented;
2991: a->rowindices = NULL;
2992: a->rowvalues = NULL;
2993: a->getrowactive = PETSC_FALSE;
2995: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
2996: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
2997: if (matin->hash_active) {
2998: PetscCall(MatSetUp(mat));
2999: } else {
3000: mat->preallocated = matin->preallocated;
3001: if (oldmat->colmap) {
3002: #if defined(PETSC_USE_CTABLE)
3003: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3004: #else
3005: PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
3006: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
3007: #endif
3008: } else a->colmap = NULL;
3009: if (oldmat->garray) {
3010: PetscInt len;
3011: len = oldmat->B->cmap->n;
3012: PetscCall(PetscMalloc1(len + 1, &a->garray));
3013: if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3014: } else a->garray = NULL;
3016: /* It may happen MatDuplicate is called with a non-assembled matrix
3017: In fact, MatDuplicate only requires the matrix to be preallocated
3018: This may happen inside a DMCreateMatrix_Shell */
3019: if (oldmat->lvec) PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3020: if (oldmat->Mvctx) {
3021: a->Mvctx = oldmat->Mvctx;
3022: PetscCall(PetscObjectReference((PetscObject)oldmat->Mvctx));
3023: }
3024: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3025: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3026: }
3027: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3028: *newmat = mat;
3029: PetscFunctionReturn(PETSC_SUCCESS);
3030: }
3032: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3033: {
3034: PetscBool isbinary, ishdf5;
3036: PetscFunctionBegin;
3039: /* force binary viewer to load .info file if it has not yet done so */
3040: PetscCall(PetscViewerSetUp(viewer));
3041: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3042: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3043: if (isbinary) {
3044: PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3045: } else if (ishdf5) {
3046: #if defined(PETSC_HAVE_HDF5)
3047: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3048: #else
3049: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3050: #endif
3051: } else {
3052: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "Viewer type %s not yet supported for reading %s matrices", ((PetscObject)viewer)->type_name, ((PetscObject)newMat)->type_name);
3053: }
3054: PetscFunctionReturn(PETSC_SUCCESS);
3055: }
3057: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3058: {
3059: PetscInt header[4], M, N, m, nz, rows, cols, sum, i;
3060: PetscInt *rowidxs, *colidxs;
3061: PetscScalar *matvals;
3063: PetscFunctionBegin;
3064: PetscCall(PetscViewerSetUp(viewer));
3066: /* read in matrix header */
3067: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3068: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3069: M = header[1];
3070: N = header[2];
3071: nz = header[3];
3072: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3073: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3074: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");
3076: /* set block sizes from the viewer's .info file */
3077: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3078: /* set global sizes if not set already */
3079: if (mat->rmap->N < 0) mat->rmap->N = M;
3080: if (mat->cmap->N < 0) mat->cmap->N = N;
3081: PetscCall(PetscLayoutSetUp(mat->rmap));
3082: PetscCall(PetscLayoutSetUp(mat->cmap));
3084: /* check if the matrix sizes are correct */
3085: PetscCall(MatGetSize(mat, &rows, &cols));
3086: PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different sizes (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols);
3088: /* read in row lengths and build row indices */
3089: PetscCall(MatGetLocalSize(mat, &m, NULL));
3090: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3091: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3092: rowidxs[0] = 0;
3093: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3094: if (nz != PETSC_INT_MAX) {
3095: PetscCallMPI(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3096: 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);
3097: }
3099: /* read in column indices and matrix values */
3100: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3101: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3102: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3103: /* store matrix indices and values */
3104: PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3105: PetscCall(PetscFree(rowidxs));
3106: PetscCall(PetscFree2(colidxs, matvals));
3107: PetscFunctionReturn(PETSC_SUCCESS);
3108: }
3110: /* Not scalable because of ISAllGather() unless getting all columns. */
3111: static PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3112: {
3113: IS iscol_local;
3114: PetscBool isstride;
3115: PetscMPIInt lisstride = 0, gisstride;
3117: PetscFunctionBegin;
3118: /* check if we are grabbing all columns*/
3119: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));
3121: if (isstride) {
3122: PetscInt start, len, mstart, mlen;
3123: PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3124: PetscCall(ISGetLocalSize(iscol, &len));
3125: PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3126: if (mstart == start && mlen - mstart == len) lisstride = 1;
3127: }
3129: PetscCallMPI(MPIU_Allreduce(&lisstride, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3130: if (gisstride) {
3131: PetscInt N;
3132: PetscCall(MatGetSize(mat, NULL, &N));
3133: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3134: PetscCall(ISSetIdentity(iscol_local));
3135: PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3136: } else {
3137: PetscInt cbs;
3138: PetscCall(ISGetBlockSize(iscol, &cbs));
3139: PetscCall(ISAllGather(iscol, &iscol_local));
3140: PetscCall(ISSetBlockSize(iscol_local, cbs));
3141: }
3143: *isseq = iscol_local;
3144: PetscFunctionReturn(PETSC_SUCCESS);
3145: }
3147: /*
3148: Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3149: (see MatCreateSubMatrix_MPIAIJ_nonscalable)
3151: Input Parameters:
3152: + mat - matrix
3153: . isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3154: i.e., mat->rstart <= isrow[i] < mat->rend
3155: - iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3156: i.e., mat->cstart <= iscol[i] < mat->cend
3158: Output Parameters:
3159: + isrow_d - sequential row index set for retrieving mat->A
3160: . iscol_d - sequential column index set for retrieving mat->A
3161: . iscol_o - sequential column index set for retrieving mat->B
3162: - garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3163: */
3164: static PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, const PetscInt *garray[])
3165: {
3166: Vec x, cmap;
3167: const PetscInt *is_idx;
3168: PetscScalar *xarray, *cmaparray;
3169: PetscInt ncols, isstart, *idx, m, rstart, *cmap1, count;
3170: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3171: Mat B = a->B;
3172: Vec lvec = a->lvec, lcmap;
3173: PetscInt i, cstart, cend, Bn = B->cmap->N;
3174: MPI_Comm comm;
3175: VecScatter Mvctx = a->Mvctx;
3177: PetscFunctionBegin;
3178: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3179: PetscCall(ISGetLocalSize(iscol, &ncols));
3181: /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3182: PetscCall(MatCreateVecs(mat, &x, NULL));
3183: PetscCall(VecSet(x, -1.0));
3184: PetscCall(VecDuplicate(x, &cmap));
3185: PetscCall(VecSet(cmap, -1.0));
3187: /* Get start indices */
3188: PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3189: isstart -= ncols;
3190: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
3192: PetscCall(ISGetIndices(iscol, &is_idx));
3193: PetscCall(VecGetArray(x, &xarray));
3194: PetscCall(VecGetArray(cmap, &cmaparray));
3195: PetscCall(PetscMalloc1(ncols, &idx));
3196: for (i = 0; i < ncols; i++) {
3197: xarray[is_idx[i] - cstart] = (PetscScalar)is_idx[i];
3198: cmaparray[is_idx[i] - cstart] = i + isstart; /* global index of iscol[i] */
3199: idx[i] = is_idx[i] - cstart; /* local index of iscol[i] */
3200: }
3201: PetscCall(VecRestoreArray(x, &xarray));
3202: PetscCall(VecRestoreArray(cmap, &cmaparray));
3203: PetscCall(ISRestoreIndices(iscol, &is_idx));
3205: /* Get iscol_d */
3206: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3207: PetscCall(ISGetBlockSize(iscol, &i));
3208: PetscCall(ISSetBlockSize(*iscol_d, i));
3210: /* Get isrow_d */
3211: PetscCall(ISGetLocalSize(isrow, &m));
3212: rstart = mat->rmap->rstart;
3213: PetscCall(PetscMalloc1(m, &idx));
3214: PetscCall(ISGetIndices(isrow, &is_idx));
3215: for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3216: PetscCall(ISRestoreIndices(isrow, &is_idx));
3218: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3219: PetscCall(ISGetBlockSize(isrow, &i));
3220: PetscCall(ISSetBlockSize(*isrow_d, i));
3222: /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3223: PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3224: PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3226: PetscCall(VecDuplicate(lvec, &lcmap));
3228: PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3229: PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3231: /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3232: /* off-process column indices */
3233: count = 0;
3234: PetscCall(PetscMalloc1(Bn, &idx));
3235: PetscCall(PetscMalloc1(Bn, &cmap1));
3237: PetscCall(VecGetArray(lvec, &xarray));
3238: PetscCall(VecGetArray(lcmap, &cmaparray));
3239: for (i = 0; i < Bn; i++) {
3240: if (PetscRealPart(xarray[i]) > -1.0) {
3241: idx[count] = i; /* local column index in off-diagonal part B */
3242: cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3243: count++;
3244: }
3245: }
3246: PetscCall(VecRestoreArray(lvec, &xarray));
3247: PetscCall(VecRestoreArray(lcmap, &cmaparray));
3249: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3250: /* cannot ensure iscol_o has same blocksize as iscol! */
3252: PetscCall(PetscFree(idx));
3253: *garray = cmap1;
3255: PetscCall(VecDestroy(&x));
3256: PetscCall(VecDestroy(&cmap));
3257: PetscCall(VecDestroy(&lcmap));
3258: PetscFunctionReturn(PETSC_SUCCESS);
3259: }
3261: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3262: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3263: {
3264: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3265: Mat M = NULL;
3266: MPI_Comm comm;
3267: IS iscol_d, isrow_d, iscol_o;
3268: Mat Asub = NULL, Bsub = NULL;
3269: PetscInt n;
3271: PetscFunctionBegin;
3272: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3274: if (call == MAT_REUSE_MATRIX) {
3275: /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3276: PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3277: PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");
3279: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3280: PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");
3282: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3283: PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");
3285: /* Update diagonal and off-diagonal portions of submat */
3286: asub = (Mat_MPIAIJ *)(*submat)->data;
3287: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3288: PetscCall(ISGetLocalSize(iscol_o, &n));
3289: if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3290: PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3291: PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));
3293: } else { /* call == MAT_INITIAL_MATRIX) */
3294: const PetscInt *garray;
3295: PetscInt BsubN;
3297: /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3298: PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));
3300: /* Create local submatrices Asub and Bsub */
3301: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3302: PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));
3304: /* Create submatrix M */
3305: PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, Asub, Bsub, garray, &M));
3307: /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3308: asub = (Mat_MPIAIJ *)M->data;
3310: PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3311: n = asub->B->cmap->N;
3312: if (BsubN > n) {
3313: /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3314: const PetscInt *idx;
3315: PetscInt i, j, *idx_new, *subgarray = asub->garray;
3316: PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));
3318: PetscCall(PetscMalloc1(n, &idx_new));
3319: j = 0;
3320: PetscCall(ISGetIndices(iscol_o, &idx));
3321: for (i = 0; i < n; i++) {
3322: if (j >= BsubN) break;
3323: while (subgarray[i] > garray[j]) j++;
3325: if (subgarray[i] == garray[j]) {
3326: idx_new[i] = idx[j++];
3327: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "subgarray[%" PetscInt_FMT "]=%" PetscInt_FMT " cannot < garray[%" PetscInt_FMT "]=%" PetscInt_FMT, i, subgarray[i], j, garray[j]);
3328: }
3329: PetscCall(ISRestoreIndices(iscol_o, &idx));
3331: PetscCall(ISDestroy(&iscol_o));
3332: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));
3334: } else if (BsubN < n) {
3335: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Columns of Bsub (%" PetscInt_FMT ") cannot be smaller than B's (%" PetscInt_FMT ")", BsubN, asub->B->cmap->N);
3336: }
3338: PetscCall(PetscFree(garray));
3339: *submat = M;
3341: /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3342: PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3343: PetscCall(ISDestroy(&isrow_d));
3345: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3346: PetscCall(ISDestroy(&iscol_d));
3348: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3349: PetscCall(ISDestroy(&iscol_o));
3350: }
3351: PetscFunctionReturn(PETSC_SUCCESS);
3352: }
3354: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3355: {
3356: IS iscol_local = NULL, isrow_d;
3357: PetscInt csize;
3358: PetscInt n, i, j, start, end;
3359: PetscBool sameRowDist = PETSC_FALSE, sameDist[2], tsameDist[2];
3360: MPI_Comm comm;
3362: PetscFunctionBegin;
3363: /* If isrow has same processor distribution as mat,
3364: call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3365: if (call == MAT_REUSE_MATRIX) {
3366: PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3367: if (isrow_d) {
3368: sameRowDist = PETSC_TRUE;
3369: tsameDist[1] = PETSC_TRUE; /* sameColDist */
3370: } else {
3371: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3372: if (iscol_local) {
3373: sameRowDist = PETSC_TRUE;
3374: tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3375: }
3376: }
3377: } else {
3378: /* Check if isrow has same processor distribution as mat */
3379: sameDist[0] = PETSC_FALSE;
3380: PetscCall(ISGetLocalSize(isrow, &n));
3381: if (!n) {
3382: sameDist[0] = PETSC_TRUE;
3383: } else {
3384: PetscCall(ISGetMinMax(isrow, &i, &j));
3385: PetscCall(MatGetOwnershipRange(mat, &start, &end));
3386: if (i >= start && j < end) sameDist[0] = PETSC_TRUE;
3387: }
3389: /* Check if iscol has same processor distribution as mat */
3390: sameDist[1] = PETSC_FALSE;
3391: PetscCall(ISGetLocalSize(iscol, &n));
3392: if (!n) {
3393: sameDist[1] = PETSC_TRUE;
3394: } else {
3395: PetscCall(ISGetMinMax(iscol, &i, &j));
3396: PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3397: if (i >= start && j < end) sameDist[1] = PETSC_TRUE;
3398: }
3400: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3401: PetscCallMPI(MPIU_Allreduce(&sameDist, &tsameDist, 2, MPIU_BOOL, MPI_LAND, comm));
3402: sameRowDist = tsameDist[0];
3403: }
3405: if (sameRowDist) {
3406: if (tsameDist[1]) { /* sameRowDist & sameColDist */
3407: /* isrow and iscol have same processor distribution as mat */
3408: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3409: PetscFunctionReturn(PETSC_SUCCESS);
3410: } else { /* sameRowDist */
3411: /* isrow has same processor distribution as mat */
3412: if (call == MAT_INITIAL_MATRIX) {
3413: PetscBool sorted;
3414: PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3415: PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3416: PetscCall(ISGetSize(iscol, &i));
3417: PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);
3419: PetscCall(ISSorted(iscol_local, &sorted));
3420: if (sorted) {
3421: /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3422: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3423: PetscFunctionReturn(PETSC_SUCCESS);
3424: }
3425: } else { /* call == MAT_REUSE_MATRIX */
3426: IS iscol_sub;
3427: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3428: if (iscol_sub) {
3429: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3430: PetscFunctionReturn(PETSC_SUCCESS);
3431: }
3432: }
3433: }
3434: }
3436: /* General case: iscol -> iscol_local which has global size of iscol */
3437: if (call == MAT_REUSE_MATRIX) {
3438: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3439: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3440: } else {
3441: if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3442: }
3444: PetscCall(ISGetLocalSize(iscol, &csize));
3445: PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));
3447: if (call == MAT_INITIAL_MATRIX) {
3448: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3449: PetscCall(ISDestroy(&iscol_local));
3450: }
3451: PetscFunctionReturn(PETSC_SUCCESS);
3452: }
3454: /*@C
3455: MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3456: and "off-diagonal" part of the matrix in CSR format.
3458: Collective
3460: Input Parameters:
3461: + comm - MPI communicator
3462: . A - "diagonal" portion of matrix
3463: . B - "off-diagonal" portion of matrix, may have empty columns, will be destroyed by this routine
3464: - garray - global index of `B` columns
3466: Output Parameter:
3467: . mat - the matrix, with input `A` as its local diagonal matrix
3469: Level: advanced
3471: Notes:
3472: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.
3474: `A` becomes part of output mat, `B` is destroyed by this routine. The user cannot use `A` and `B` anymore.
3476: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3477: @*/
3478: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, Mat A, Mat B, const PetscInt garray[], Mat *mat)
3479: {
3480: Mat_MPIAIJ *maij;
3481: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data, *bnew;
3482: PetscInt *oi = b->i, *oj = b->j, i, nz, col;
3483: const PetscScalar *oa;
3484: Mat Bnew;
3485: PetscInt m, n, N;
3486: MatType mpi_mat_type;
3488: PetscFunctionBegin;
3489: PetscCall(MatCreate(comm, mat));
3490: PetscCall(MatGetSize(A, &m, &n));
3491: PetscCheck(m == B->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Am %" PetscInt_FMT " != Bm %" PetscInt_FMT, m, B->rmap->N);
3492: PetscCheck(PetscAbs(A->rmap->bs) == PetscAbs(B->rmap->bs), PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "A row bs %" PetscInt_FMT " != B row bs %" PetscInt_FMT, A->rmap->bs, B->rmap->bs);
3493: /* remove check below; When B is created using iscol_o from ISGetSeqIS_SameColDist_Private(), its bs may not be same as A */
3494: /* PetscCheck(A->cmap->bs == B->cmap->bs,PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"A column bs %" PetscInt_FMT " != B column bs %" PetscInt_FMT,A->cmap->bs,B->cmap->bs); */
3496: /* Get global columns of mat */
3497: PetscCallMPI(MPIU_Allreduce(&n, &N, 1, MPIU_INT, MPI_SUM, comm));
3499: PetscCall(MatSetSizes(*mat, m, n, PETSC_DECIDE, N));
3500: /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3501: PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3502: PetscCall(MatSetType(*mat, mpi_mat_type));
3504: if (A->rmap->bs > 1 || A->cmap->bs > 1) PetscCall(MatSetBlockSizes(*mat, A->rmap->bs, A->cmap->bs));
3505: maij = (Mat_MPIAIJ *)(*mat)->data;
3507: (*mat)->preallocated = PETSC_TRUE;
3509: PetscCall(PetscLayoutSetUp((*mat)->rmap));
3510: PetscCall(PetscLayoutSetUp((*mat)->cmap));
3512: /* Set A as diagonal portion of *mat */
3513: maij->A = A;
3515: nz = oi[m];
3516: for (i = 0; i < nz; i++) {
3517: col = oj[i];
3518: oj[i] = garray[col];
3519: }
3521: /* Set Bnew as off-diagonal portion of *mat */
3522: PetscCall(MatSeqAIJGetArrayRead(B, &oa));
3523: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, N, oi, oj, (PetscScalar *)oa, &Bnew));
3524: PetscCall(MatSeqAIJRestoreArrayRead(B, &oa));
3525: bnew = (Mat_SeqAIJ *)Bnew->data;
3526: bnew->maxnz = b->maxnz; /* allocated nonzeros of B */
3527: maij->B = Bnew;
3529: PetscCheck(B->rmap->N == Bnew->rmap->N, PETSC_COMM_SELF, PETSC_ERR_PLIB, "BN %" PetscInt_FMT " != BnewN %" PetscInt_FMT, B->rmap->N, Bnew->rmap->N);
3531: b->free_a = PETSC_FALSE;
3532: b->free_ij = PETSC_FALSE;
3533: PetscCall(MatDestroy(&B));
3535: bnew->free_a = PETSC_TRUE;
3536: bnew->free_ij = PETSC_TRUE;
3538: /* condense columns of maij->B */
3539: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3540: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
3541: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
3542: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
3543: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3544: PetscFunctionReturn(PETSC_SUCCESS);
3545: }
3547: extern PetscErrorCode MatCreateSubMatrices_MPIAIJ_SingleIS_Local(Mat, PetscInt, const IS[], const IS[], MatReuse, PetscBool, Mat *);
3549: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowDist(Mat mat, IS isrow, IS iscol, IS iscol_local, MatReuse call, Mat *newmat)
3550: {
3551: PetscInt i, m, n, rstart, row, rend, nz, j, bs, cbs;
3552: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3553: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3554: Mat M, Msub, B = a->B;
3555: MatScalar *aa;
3556: Mat_SeqAIJ *aij;
3557: PetscInt *garray = a->garray, *colsub, Ncols;
3558: PetscInt count, Bn = B->cmap->N, cstart = mat->cmap->rstart, cend = mat->cmap->rend;
3559: IS iscol_sub, iscmap;
3560: const PetscInt *is_idx, *cmap;
3561: PetscBool allcolumns = PETSC_FALSE;
3562: MPI_Comm comm;
3564: PetscFunctionBegin;
3565: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3566: if (call == MAT_REUSE_MATRIX) {
3567: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3568: PetscCheck(iscol_sub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "SubIScol passed in was not used before, cannot reuse");
3569: PetscCall(ISGetLocalSize(iscol_sub, &count));
3571: PetscCall(PetscObjectQuery((PetscObject)*newmat, "Subcmap", (PetscObject *)&iscmap));
3572: PetscCheck(iscmap, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Subcmap passed in was not used before, cannot reuse");
3574: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Msub));
3575: PetscCheck(Msub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3577: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_REUSE_MATRIX, PETSC_FALSE, &Msub));
3579: } else { /* call == MAT_INITIAL_MATRIX) */
3580: PetscBool flg;
3582: PetscCall(ISGetLocalSize(iscol, &n));
3583: PetscCall(ISGetSize(iscol, &Ncols));
3585: /* (1) iscol -> nonscalable iscol_local */
3586: /* Check for special case: each processor gets entire matrix columns */
3587: PetscCall(ISIdentity(iscol_local, &flg));
3588: if (flg && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3589: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3590: if (allcolumns) {
3591: iscol_sub = iscol_local;
3592: PetscCall(PetscObjectReference((PetscObject)iscol_local));
3593: PetscCall(ISCreateStride(PETSC_COMM_SELF, n, 0, 1, &iscmap));
3595: } else {
3596: /* (2) iscol_local -> iscol_sub and iscmap. Implementation below requires iscol_local be sorted, it can have duplicate indices */
3597: PetscInt *idx, *cmap1, k;
3598: PetscCall(PetscMalloc1(Ncols, &idx));
3599: PetscCall(PetscMalloc1(Ncols, &cmap1));
3600: PetscCall(ISGetIndices(iscol_local, &is_idx));
3601: count = 0;
3602: k = 0;
3603: for (i = 0; i < Ncols; i++) {
3604: j = is_idx[i];
3605: if (j >= cstart && j < cend) {
3606: /* diagonal part of mat */
3607: idx[count] = j;
3608: cmap1[count++] = i; /* column index in submat */
3609: } else if (Bn) {
3610: /* off-diagonal part of mat */
3611: if (j == garray[k]) {
3612: idx[count] = j;
3613: cmap1[count++] = i; /* column index in submat */
3614: } else if (j > garray[k]) {
3615: while (j > garray[k] && k < Bn - 1) k++;
3616: if (j == garray[k]) {
3617: idx[count] = j;
3618: cmap1[count++] = i; /* column index in submat */
3619: }
3620: }
3621: }
3622: }
3623: PetscCall(ISRestoreIndices(iscol_local, &is_idx));
3625: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_OWN_POINTER, &iscol_sub));
3626: PetscCall(ISGetBlockSize(iscol, &cbs));
3627: PetscCall(ISSetBlockSize(iscol_sub, cbs));
3629: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)iscol_local), count, cmap1, PETSC_OWN_POINTER, &iscmap));
3630: }
3632: /* (3) Create sequential Msub */
3633: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_INITIAL_MATRIX, allcolumns, &Msub));
3634: }
3636: PetscCall(ISGetLocalSize(iscol_sub, &count));
3637: aij = (Mat_SeqAIJ *)Msub->data;
3638: ii = aij->i;
3639: PetscCall(ISGetIndices(iscmap, &cmap));
3641: /*
3642: m - number of local rows
3643: Ncols - number of columns (same on all processors)
3644: rstart - first row in new global matrix generated
3645: */
3646: PetscCall(MatGetSize(Msub, &m, NULL));
3648: if (call == MAT_INITIAL_MATRIX) {
3649: /* (4) Create parallel newmat */
3650: PetscMPIInt rank, size;
3651: PetscInt csize;
3653: PetscCallMPI(MPI_Comm_size(comm, &size));
3654: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3656: /*
3657: Determine the number of non-zeros in the diagonal and off-diagonal
3658: portions of the matrix in order to do correct preallocation
3659: */
3661: /* first get start and end of "diagonal" columns */
3662: PetscCall(ISGetLocalSize(iscol, &csize));
3663: if (csize == PETSC_DECIDE) {
3664: PetscCall(ISGetSize(isrow, &mglobal));
3665: if (mglobal == Ncols) { /* square matrix */
3666: nlocal = m;
3667: } else {
3668: nlocal = Ncols / size + ((Ncols % size) > rank);
3669: }
3670: } else {
3671: nlocal = csize;
3672: }
3673: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3674: rstart = rend - nlocal;
3675: 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);
3677: /* next, compute all the lengths */
3678: jj = aij->j;
3679: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3680: olens = dlens + m;
3681: for (i = 0; i < m; i++) {
3682: jend = ii[i + 1] - ii[i];
3683: olen = 0;
3684: dlen = 0;
3685: for (j = 0; j < jend; j++) {
3686: if (cmap[*jj] < rstart || cmap[*jj] >= rend) olen++;
3687: else dlen++;
3688: jj++;
3689: }
3690: olens[i] = olen;
3691: dlens[i] = dlen;
3692: }
3694: PetscCall(ISGetBlockSize(isrow, &bs));
3695: PetscCall(ISGetBlockSize(iscol, &cbs));
3697: PetscCall(MatCreate(comm, &M));
3698: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, Ncols));
3699: PetscCall(MatSetBlockSizes(M, bs, cbs));
3700: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3701: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3702: PetscCall(PetscFree(dlens));
3704: } else { /* call == MAT_REUSE_MATRIX */
3705: M = *newmat;
3706: PetscCall(MatGetLocalSize(M, &i, NULL));
3707: PetscCheck(i == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3708: PetscCall(MatZeroEntries(M));
3709: /*
3710: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3711: rather than the slower MatSetValues().
3712: */
3713: M->was_assembled = PETSC_TRUE;
3714: M->assembled = PETSC_FALSE;
3715: }
3717: /* (5) Set values of Msub to *newmat */
3718: PetscCall(PetscMalloc1(count, &colsub));
3719: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
3721: jj = aij->j;
3722: PetscCall(MatSeqAIJGetArrayRead(Msub, (const PetscScalar **)&aa));
3723: for (i = 0; i < m; i++) {
3724: row = rstart + i;
3725: nz = ii[i + 1] - ii[i];
3726: for (j = 0; j < nz; j++) colsub[j] = cmap[jj[j]];
3727: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, colsub, aa, INSERT_VALUES));
3728: jj += nz;
3729: aa += nz;
3730: }
3731: PetscCall(MatSeqAIJRestoreArrayRead(Msub, (const PetscScalar **)&aa));
3732: PetscCall(ISRestoreIndices(iscmap, &cmap));
3734: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3735: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3737: PetscCall(PetscFree(colsub));
3739: /* save Msub, iscol_sub and iscmap used in processor for next request */
3740: if (call == MAT_INITIAL_MATRIX) {
3741: *newmat = M;
3742: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubMatrix", (PetscObject)Msub));
3743: PetscCall(MatDestroy(&Msub));
3745: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubIScol", (PetscObject)iscol_sub));
3746: PetscCall(ISDestroy(&iscol_sub));
3748: PetscCall(PetscObjectCompose((PetscObject)*newmat, "Subcmap", (PetscObject)iscmap));
3749: PetscCall(ISDestroy(&iscmap));
3751: if (iscol_local) {
3752: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3753: PetscCall(ISDestroy(&iscol_local));
3754: }
3755: }
3756: PetscFunctionReturn(PETSC_SUCCESS);
3757: }
3759: /*
3760: Not great since it makes two copies of the submatrix, first an SeqAIJ
3761: in local and then by concatenating the local matrices the end result.
3762: Writing it directly would be much like MatCreateSubMatrices_MPIAIJ()
3764: This requires a sequential iscol with all indices.
3765: */
3766: PetscErrorCode MatCreateSubMatrix_MPIAIJ_nonscalable(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
3767: {
3768: PetscMPIInt rank, size;
3769: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs, cbs;
3770: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3771: Mat M, Mreuse;
3772: MatScalar *aa, *vwork;
3773: MPI_Comm comm;
3774: Mat_SeqAIJ *aij;
3775: PetscBool colflag, allcolumns = PETSC_FALSE;
3777: PetscFunctionBegin;
3778: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3779: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3780: PetscCallMPI(MPI_Comm_size(comm, &size));
3782: /* Check for special case: each processor gets entire matrix columns */
3783: PetscCall(ISIdentity(iscol, &colflag));
3784: PetscCall(ISGetLocalSize(iscol, &n));
3785: if (colflag && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3786: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3788: if (call == MAT_REUSE_MATRIX) {
3789: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
3790: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3791: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_REUSE_MATRIX, allcolumns, &Mreuse));
3792: } else {
3793: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_INITIAL_MATRIX, allcolumns, &Mreuse));
3794: }
3796: /*
3797: m - number of local rows
3798: n - number of columns (same on all processors)
3799: rstart - first row in new global matrix generated
3800: */
3801: PetscCall(MatGetSize(Mreuse, &m, &n));
3802: PetscCall(MatGetBlockSizes(Mreuse, &bs, &cbs));
3803: if (call == MAT_INITIAL_MATRIX) {
3804: aij = (Mat_SeqAIJ *)Mreuse->data;
3805: ii = aij->i;
3806: jj = aij->j;
3808: /*
3809: Determine the number of non-zeros in the diagonal and off-diagonal
3810: portions of the matrix in order to do correct preallocation
3811: */
3813: /* first get start and end of "diagonal" columns */
3814: if (csize == PETSC_DECIDE) {
3815: PetscCall(ISGetSize(isrow, &mglobal));
3816: if (mglobal == n) { /* square matrix */
3817: nlocal = m;
3818: } else {
3819: nlocal = n / size + ((n % size) > rank);
3820: }
3821: } else {
3822: nlocal = csize;
3823: }
3824: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3825: rstart = rend - nlocal;
3826: 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);
3828: /* next, compute all the lengths */
3829: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3830: olens = dlens + m;
3831: for (i = 0; i < m; i++) {
3832: jend = ii[i + 1] - ii[i];
3833: olen = 0;
3834: dlen = 0;
3835: for (j = 0; j < jend; j++) {
3836: if (*jj < rstart || *jj >= rend) olen++;
3837: else dlen++;
3838: jj++;
3839: }
3840: olens[i] = olen;
3841: dlens[i] = dlen;
3842: }
3843: PetscCall(MatCreate(comm, &M));
3844: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, n));
3845: PetscCall(MatSetBlockSizes(M, bs, cbs));
3846: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3847: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3848: PetscCall(PetscFree(dlens));
3849: } else {
3850: PetscInt ml, nl;
3852: M = *newmat;
3853: PetscCall(MatGetLocalSize(M, &ml, &nl));
3854: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3855: PetscCall(MatZeroEntries(M));
3856: /*
3857: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3858: rather than the slower MatSetValues().
3859: */
3860: M->was_assembled = PETSC_TRUE;
3861: M->assembled = PETSC_FALSE;
3862: }
3863: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
3864: aij = (Mat_SeqAIJ *)Mreuse->data;
3865: ii = aij->i;
3866: jj = aij->j;
3868: /* trigger copy to CPU if needed */
3869: PetscCall(MatSeqAIJGetArrayRead(Mreuse, (const PetscScalar **)&aa));
3870: for (i = 0; i < m; i++) {
3871: row = rstart + i;
3872: nz = ii[i + 1] - ii[i];
3873: cwork = jj;
3874: jj = PetscSafePointerPlusOffset(jj, nz);
3875: vwork = aa;
3876: aa = PetscSafePointerPlusOffset(aa, nz);
3877: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
3878: }
3879: PetscCall(MatSeqAIJRestoreArrayRead(Mreuse, (const PetscScalar **)&aa));
3881: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3882: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3883: *newmat = M;
3885: /* save submatrix used in processor for next request */
3886: if (call == MAT_INITIAL_MATRIX) {
3887: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
3888: PetscCall(MatDestroy(&Mreuse));
3889: }
3890: PetscFunctionReturn(PETSC_SUCCESS);
3891: }
3893: static PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
3894: {
3895: PetscInt m, cstart, cend, j, nnz, i, d, *ld;
3896: PetscInt *d_nnz, *o_nnz, nnz_max = 0, rstart, ii, irstart;
3897: const PetscInt *JJ;
3898: PetscBool nooffprocentries;
3899: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)B->data;
3901: PetscFunctionBegin;
3902: PetscCall(PetscLayoutSetUp(B->rmap));
3903: PetscCall(PetscLayoutSetUp(B->cmap));
3904: m = B->rmap->n;
3905: cstart = B->cmap->rstart;
3906: cend = B->cmap->rend;
3907: rstart = B->rmap->rstart;
3908: irstart = Ii[0];
3910: PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));
3912: if (PetscDefined(USE_DEBUG)) {
3913: for (i = 0; i < m; i++) {
3914: nnz = Ii[i + 1] - Ii[i];
3915: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3916: PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3917: PetscCheck(!nnz || !(JJ[0] < 0), PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Row %" PetscInt_FMT " starts with negative column index %" PetscInt_FMT, i, JJ[0]);
3918: PetscCheck(!nnz || !(JJ[nnz - 1] >= B->cmap->N), PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Row %" PetscInt_FMT " ends with too large a column index %" PetscInt_FMT " (max allowed %" PetscInt_FMT ")", i, JJ[nnz - 1], B->cmap->N);
3919: }
3920: }
3922: for (i = 0; i < m; i++) {
3923: nnz = Ii[i + 1] - Ii[i];
3924: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3925: nnz_max = PetscMax(nnz_max, nnz);
3926: d = 0;
3927: for (j = 0; j < nnz; j++) {
3928: if (cstart <= JJ[j] && JJ[j] < cend) d++;
3929: }
3930: d_nnz[i] = d;
3931: o_nnz[i] = nnz - d;
3932: }
3933: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3934: PetscCall(PetscFree2(d_nnz, o_nnz));
3936: for (i = 0; i < m; i++) {
3937: ii = i + rstart;
3938: PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], PetscSafePointerPlusOffset(J, Ii[i] - irstart), PetscSafePointerPlusOffset(v, Ii[i] - irstart), INSERT_VALUES));
3939: }
3940: nooffprocentries = B->nooffprocentries;
3941: B->nooffprocentries = PETSC_TRUE;
3942: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3943: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3944: B->nooffprocentries = nooffprocentries;
3946: /* count number of entries below block diagonal */
3947: PetscCall(PetscFree(Aij->ld));
3948: PetscCall(PetscCalloc1(m, &ld));
3949: Aij->ld = ld;
3950: for (i = 0; i < m; i++) {
3951: nnz = Ii[i + 1] - Ii[i];
3952: j = 0;
3953: while (j < nnz && J[j] < cstart) j++;
3954: ld[i] = j;
3955: if (J) J += nnz;
3956: }
3958: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3959: PetscFunctionReturn(PETSC_SUCCESS);
3960: }
3962: /*@
3963: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
3964: (the default parallel PETSc format).
3966: Collective
3968: Input Parameters:
3969: + B - the matrix
3970: . i - the indices into `j` for the start of each local row (indices start with zero)
3971: . j - the column indices for each local row (indices start with zero)
3972: - v - optional values in the matrix
3974: Level: developer
3976: Notes:
3977: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
3978: thus you CANNOT change the matrix entries by changing the values of `v` after you have
3979: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3981: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
3983: A convenience routine for this functionality is `MatCreateMPIAIJWithArrays()`.
3985: You can update the matrix with new numerical values using `MatUpdateMPIAIJWithArrays()` after this call if the column indices in `j` are sorted.
3987: If you do **not** use `MatUpdateMPIAIJWithArrays()`, the column indices in `j` do not need to be sorted. If you will use
3988: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
3990: The format which is used for the sparse matrix input, is equivalent to a
3991: row-major ordering.. i.e for the following matrix, the input data expected is
3992: as shown
3993: .vb
3994: 1 0 0
3995: 2 0 3 P0
3996: -------
3997: 4 5 6 P1
3999: Process0 [P0] rows_owned=[0,1]
4000: i = {0,1,3} [size = nrow+1 = 2+1]
4001: j = {0,0,2} [size = 3]
4002: v = {1,2,3} [size = 3]
4004: Process1 [P1] rows_owned=[2]
4005: i = {0,3} [size = nrow+1 = 1+1]
4006: j = {0,1,2} [size = 3]
4007: v = {4,5,6} [size = 3]
4008: .ve
4010: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`,
4011: `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`, `MatCreateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4012: @*/
4013: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4014: {
4015: PetscFunctionBegin;
4016: PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4017: PetscFunctionReturn(PETSC_SUCCESS);
4018: }
4020: /*@
4021: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
4022: (the default parallel PETSc format). For good matrix assembly performance
4023: the user should preallocate the matrix storage by setting the parameters
4024: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4026: Collective
4028: Input Parameters:
4029: + B - the matrix
4030: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4031: (same value is used for all local rows)
4032: . d_nnz - array containing the number of nonzeros in the various rows of the
4033: DIAGONAL portion of the local submatrix (possibly different for each row)
4034: or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4035: The size of this array is equal to the number of local rows, i.e 'm'.
4036: For matrices that will be factored, you must leave room for (and set)
4037: the diagonal entry even if it is zero.
4038: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4039: submatrix (same value is used for all local rows).
4040: - o_nnz - array containing the number of nonzeros in the various rows of the
4041: OFF-DIAGONAL portion of the local submatrix (possibly different for
4042: each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4043: structure. The size of this array is equal to the number
4044: of local rows, i.e 'm'.
4046: Example Usage:
4047: Consider the following 8x8 matrix with 34 non-zero values, that is
4048: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4049: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4050: as follows
4052: .vb
4053: 1 2 0 | 0 3 0 | 0 4
4054: Proc0 0 5 6 | 7 0 0 | 8 0
4055: 9 0 10 | 11 0 0 | 12 0
4056: -------------------------------------
4057: 13 0 14 | 15 16 17 | 0 0
4058: Proc1 0 18 0 | 19 20 21 | 0 0
4059: 0 0 0 | 22 23 0 | 24 0
4060: -------------------------------------
4061: Proc2 25 26 27 | 0 0 28 | 29 0
4062: 30 0 0 | 31 32 33 | 0 34
4063: .ve
4065: This can be represented as a collection of submatrices as
4066: .vb
4067: A B C
4068: D E F
4069: G H I
4070: .ve
4072: Where the submatrices A,B,C are owned by proc0, D,E,F are
4073: owned by proc1, G,H,I are owned by proc2.
4075: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4076: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4077: The 'M','N' parameters are 8,8, and have the same values on all procs.
4079: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4080: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4081: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4082: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4083: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4084: matrix, and [DF] as another `MATSEQAIJ` matrix.
4086: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4087: allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4088: storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4089: One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4090: the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4091: In this case, the values of `d_nz`, `o_nz` are
4092: .vb
4093: proc0 dnz = 2, o_nz = 2
4094: proc1 dnz = 3, o_nz = 2
4095: proc2 dnz = 1, o_nz = 4
4096: .ve
4097: We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4098: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4099: for proc3. i.e we are using 12+15+10=37 storage locations to store
4100: 34 values.
4102: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4103: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4104: In the above case the values for `d_nnz`, `o_nnz` are
4105: .vb
4106: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4107: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4108: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4109: .ve
4110: Here the space allocated is sum of all the above values i.e 34, and
4111: hence pre-allocation is perfect.
4113: Level: intermediate
4115: Notes:
4116: If the *_nnz parameter is given then the *_nz parameter is ignored
4118: The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4119: storage. The stored row and column indices begin with zero.
4120: See [Sparse Matrices](sec_matsparse) for details.
4122: The parallel matrix is partitioned such that the first m0 rows belong to
4123: process 0, the next m1 rows belong to process 1, the next m2 rows belong
4124: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
4126: The DIAGONAL portion of the local submatrix of a processor can be defined
4127: as the submatrix which is obtained by extraction the part corresponding to
4128: the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4129: first row that belongs to the processor, r2 is the last row belonging to
4130: the this processor, and c1-c2 is range of indices of the local part of a
4131: vector suitable for applying the matrix to. This is an mxn matrix. In the
4132: common case of a square matrix, the row and column ranges are the same and
4133: the DIAGONAL part is also square. The remaining portion of the local
4134: submatrix (mxN) constitute the OFF-DIAGONAL portion.
4136: If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.
4138: You can call `MatGetInfo()` to get information on how effective the preallocation was;
4139: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4140: You can also run with the option `-info` and look for messages with the string
4141: malloc in them to see if additional memory allocation was needed.
4143: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4144: `MatGetInfo()`, `PetscSplitOwnership()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4145: @*/
4146: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4147: {
4148: PetscFunctionBegin;
4151: PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4152: PetscFunctionReturn(PETSC_SUCCESS);
4153: }
4155: /*@
4156: MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4157: CSR format for the local rows.
4159: Collective
4161: Input Parameters:
4162: + comm - MPI communicator
4163: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4164: . n - This value should be the same as the local size used in creating the
4165: x vector for the matrix-vector product $ y = Ax$. (or `PETSC_DECIDE` to have
4166: calculated if `N` is given) For square matrices n is almost always `m`.
4167: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
4168: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
4169: . 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
4170: . j - global column indices
4171: - a - optional matrix values
4173: Output Parameter:
4174: . mat - the matrix
4176: Level: intermediate
4178: Notes:
4179: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4180: thus you CANNOT change the matrix entries by changing the values of `a[]` after you have
4181: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
4183: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
4185: Once you have created the matrix you can update it with new numerical values using `MatUpdateMPIAIJWithArray()`
4187: If you do **not** use `MatUpdateMPIAIJWithArray()`, the column indices in `j` do not need to be sorted. If you will use
4188: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
4190: The format which is used for the sparse matrix input, is equivalent to a
4191: row-major ordering, i.e., for the following matrix, the input data expected is
4192: as shown
4193: .vb
4194: 1 0 0
4195: 2 0 3 P0
4196: -------
4197: 4 5 6 P1
4199: Process0 [P0] rows_owned=[0,1]
4200: i = {0,1,3} [size = nrow+1 = 2+1]
4201: j = {0,0,2} [size = 3]
4202: v = {1,2,3} [size = 3]
4204: Process1 [P1] rows_owned=[2]
4205: i = {0,3} [size = nrow+1 = 1+1]
4206: j = {0,1,2} [size = 3]
4207: v = {4,5,6} [size = 3]
4208: .ve
4210: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4211: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4212: @*/
4213: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
4214: {
4215: PetscFunctionBegin;
4216: PetscCheck(!i || !i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4217: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4218: PetscCall(MatCreate(comm, mat));
4219: PetscCall(MatSetSizes(*mat, m, n, M, N));
4220: /* PetscCall(MatSetBlockSizes(M,bs,cbs)); */
4221: PetscCall(MatSetType(*mat, MATMPIAIJ));
4222: PetscCall(MatMPIAIJSetPreallocationCSR(*mat, i, j, a));
4223: PetscFunctionReturn(PETSC_SUCCESS);
4224: }
4226: /*@
4227: MatUpdateMPIAIJWithArrays - updates a `MATMPIAIJ` matrix using arrays that contain in standard
4228: CSR format for the local rows. Only the numerical values are updated the other arrays must be identical to what was passed
4229: from `MatCreateMPIAIJWithArrays()`
4231: Deprecated: Use `MatUpdateMPIAIJWithArray()`
4233: Collective
4235: Input Parameters:
4236: + mat - the matrix
4237: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4238: . n - This value should be the same as the local size used in creating the
4239: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4240: calculated if N is given) For square matrices n is almost always m.
4241: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4242: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4243: . Ii - row indices; that is Ii[0] = 0, Ii[row] = Ii[row-1] + number of elements in that row of the matrix
4244: . J - column indices
4245: - v - matrix values
4247: Level: deprecated
4249: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4250: `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4251: @*/
4252: PetscErrorCode MatUpdateMPIAIJWithArrays(Mat mat, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4253: {
4254: PetscInt nnz, i;
4255: PetscBool nooffprocentries;
4256: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4257: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4258: PetscScalar *ad, *ao;
4259: PetscInt ldi, Iii, md;
4260: const PetscInt *Adi = Ad->i;
4261: PetscInt *ld = Aij->ld;
4263: PetscFunctionBegin;
4264: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4265: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4266: PetscCheck(m == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of rows cannot change from call to MatUpdateMPIAIJWithArrays()");
4267: PetscCheck(n == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of columns cannot change from call to MatUpdateMPIAIJWithArrays()");
4269: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4270: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4272: for (i = 0; i < m; i++) {
4273: if (PetscDefined(USE_DEBUG)) {
4274: for (PetscInt j = Ii[i] + 1; j < Ii[i + 1]; ++j) {
4275: 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);
4276: 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);
4277: }
4278: }
4279: nnz = Ii[i + 1] - Ii[i];
4280: Iii = Ii[i];
4281: ldi = ld[i];
4282: md = Adi[i + 1] - Adi[i];
4283: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4284: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4285: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4286: ad += md;
4287: ao += nnz - md;
4288: }
4289: nooffprocentries = mat->nooffprocentries;
4290: mat->nooffprocentries = PETSC_TRUE;
4291: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4292: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4293: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4294: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4295: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4296: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4297: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4298: mat->nooffprocentries = nooffprocentries;
4299: PetscFunctionReturn(PETSC_SUCCESS);
4300: }
4302: /*@
4303: MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values
4305: Collective
4307: Input Parameters:
4308: + mat - the matrix
4309: - v - matrix values, stored by row
4311: Level: intermediate
4313: Notes:
4314: The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`
4316: The column indices in the call to `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()` must have been sorted for this call to work correctly
4318: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4319: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4320: @*/
4321: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4322: {
4323: PetscInt nnz, i, m;
4324: PetscBool nooffprocentries;
4325: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4326: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4327: Mat_SeqAIJ *Ao = (Mat_SeqAIJ *)Aij->B->data;
4328: PetscScalar *ad, *ao;
4329: const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4330: PetscInt ldi, Iii, md;
4331: PetscInt *ld = Aij->ld;
4333: PetscFunctionBegin;
4334: m = mat->rmap->n;
4336: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4337: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4338: Iii = 0;
4339: for (i = 0; i < m; i++) {
4340: nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4341: ldi = ld[i];
4342: md = Adi[i + 1] - Adi[i];
4343: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4344: ad += md;
4345: if (ao) {
4346: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4347: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4348: ao += nnz - md;
4349: }
4350: Iii += nnz;
4351: }
4352: nooffprocentries = mat->nooffprocentries;
4353: mat->nooffprocentries = PETSC_TRUE;
4354: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4355: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4356: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4357: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4358: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4359: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4360: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4361: mat->nooffprocentries = nooffprocentries;
4362: PetscFunctionReturn(PETSC_SUCCESS);
4363: }
4365: /*@
4366: MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4367: (the default parallel PETSc format). For good matrix assembly performance
4368: the user should preallocate the matrix storage by setting the parameters
4369: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4371: Collective
4373: Input Parameters:
4374: + comm - MPI communicator
4375: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4376: This value should be the same as the local size used in creating the
4377: y vector for the matrix-vector product y = Ax.
4378: . n - This value should be the same as the local size used in creating the
4379: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4380: calculated if N is given) For square matrices n is almost always m.
4381: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4382: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4383: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4384: (same value is used for all local rows)
4385: . d_nnz - array containing the number of nonzeros in the various rows of the
4386: DIAGONAL portion of the local submatrix (possibly different for each row)
4387: or `NULL`, if `d_nz` is used to specify the nonzero structure.
4388: The size of this array is equal to the number of local rows, i.e 'm'.
4389: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4390: submatrix (same value is used for all local rows).
4391: - o_nnz - array containing the number of nonzeros in the various rows of the
4392: OFF-DIAGONAL portion of the local submatrix (possibly different for
4393: each row) or `NULL`, if `o_nz` is used to specify the nonzero
4394: structure. The size of this array is equal to the number
4395: of local rows, i.e 'm'.
4397: Output Parameter:
4398: . A - the matrix
4400: Options Database Keys:
4401: + -mat_no_inode - Do not use inodes
4402: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
4403: - -matmult_vecscatter_view <viewer> - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4404: See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the `VecScatter`
4405: 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.
4407: Level: intermediate
4409: Notes:
4410: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4411: MatXXXXSetPreallocation() paradigm instead of this routine directly.
4412: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
4414: If the *_nnz parameter is given then the *_nz parameter is ignored
4416: The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4417: processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4418: storage requirements for this matrix.
4420: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one
4421: processor than it must be used on all processors that share the object for
4422: that argument.
4424: If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by
4425: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`.
4427: The user MUST specify either the local or global matrix dimensions
4428: (possibly both).
4430: The parallel matrix is partitioned across processors such that the
4431: first `m0` rows belong to process 0, the next `m1` rows belong to
4432: process 1, the next `m2` rows belong to process 2, etc., where
4433: `m0`, `m1`, `m2`... are the input parameter `m` on each MPI process. I.e., each MPI process stores
4434: values corresponding to [m x N] submatrix.
4436: The columns are logically partitioned with the n0 columns belonging
4437: to 0th partition, the next n1 columns belonging to the next
4438: partition etc.. where n0,n1,n2... are the input parameter 'n'.
4440: The DIAGONAL portion of the local submatrix on any given processor
4441: is the submatrix corresponding to the rows and columns m,n
4442: corresponding to the given processor. i.e diagonal matrix on
4443: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4444: etc. The remaining portion of the local submatrix [m x (N-n)]
4445: constitute the OFF-DIAGONAL portion. The example below better
4446: illustrates this concept.
4448: For a square global matrix we define each processor's diagonal portion
4449: to be its local rows and the corresponding columns (a square submatrix);
4450: each processor's off-diagonal portion encompasses the remainder of the
4451: local matrix (a rectangular submatrix).
4453: If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.
4455: When calling this routine with a single process communicator, a matrix of
4456: type `MATSEQAIJ` is returned. If a matrix of type `MATMPIAIJ` is desired for this
4457: type of communicator, use the construction mechanism
4458: .vb
4459: MatCreate(..., &A);
4460: MatSetType(A, MATMPIAIJ);
4461: MatSetSizes(A, m, n, M, N);
4462: MatMPIAIJSetPreallocation(A, ...);
4463: .ve
4465: By default, this format uses inodes (identical nodes) when possible.
4466: We search for consecutive rows with the same nonzero structure, thereby
4467: reusing matrix information to achieve increased efficiency.
4469: Example Usage:
4470: Consider the following 8x8 matrix with 34 non-zero values, that is
4471: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4472: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4473: as follows
4475: .vb
4476: 1 2 0 | 0 3 0 | 0 4
4477: Proc0 0 5 6 | 7 0 0 | 8 0
4478: 9 0 10 | 11 0 0 | 12 0
4479: -------------------------------------
4480: 13 0 14 | 15 16 17 | 0 0
4481: Proc1 0 18 0 | 19 20 21 | 0 0
4482: 0 0 0 | 22 23 0 | 24 0
4483: -------------------------------------
4484: Proc2 25 26 27 | 0 0 28 | 29 0
4485: 30 0 0 | 31 32 33 | 0 34
4486: .ve
4488: This can be represented as a collection of submatrices as
4490: .vb
4491: A B C
4492: D E F
4493: G H I
4494: .ve
4496: Where the submatrices A,B,C are owned by proc0, D,E,F are
4497: owned by proc1, G,H,I are owned by proc2.
4499: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4500: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4501: The 'M','N' parameters are 8,8, and have the same values on all procs.
4503: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4504: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4505: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4506: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4507: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4508: matrix, and [DF] as another SeqAIJ matrix.
4510: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4511: allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4512: storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4513: One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4514: the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4515: In this case, the values of `d_nz`,`o_nz` are
4516: .vb
4517: proc0 dnz = 2, o_nz = 2
4518: proc1 dnz = 3, o_nz = 2
4519: proc2 dnz = 1, o_nz = 4
4520: .ve
4521: We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4522: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4523: for proc3. i.e we are using 12+15+10=37 storage locations to store
4524: 34 values.
4526: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4527: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4528: In the above case the values for d_nnz,o_nnz are
4529: .vb
4530: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4531: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4532: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4533: .ve
4534: Here the space allocated is sum of all the above values i.e 34, and
4535: hence pre-allocation is perfect.
4537: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4538: `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`,
4539: `MatGetOwnershipRangesColumn()`, `PetscLayout`
4540: @*/
4541: 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)
4542: {
4543: PetscMPIInt size;
4545: PetscFunctionBegin;
4546: PetscCall(MatCreate(comm, A));
4547: PetscCall(MatSetSizes(*A, m, n, M, N));
4548: PetscCallMPI(MPI_Comm_size(comm, &size));
4549: if (size > 1) {
4550: PetscCall(MatSetType(*A, MATMPIAIJ));
4551: PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4552: } else {
4553: PetscCall(MatSetType(*A, MATSEQAIJ));
4554: PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4555: }
4556: PetscFunctionReturn(PETSC_SUCCESS);
4557: }
4559: /*MC
4560: MatMPIAIJGetSeqAIJF90 - Returns the local pieces of this distributed matrix
4562: Synopsis:
4563: MatMPIAIJGetSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)
4565: Not Collective
4567: Input Parameter:
4568: . A - the `MATMPIAIJ` matrix
4570: Output Parameters:
4571: + Ad - the diagonal portion of the matrix
4572: . Ao - the off-diagonal portion of the matrix
4573: . colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4574: - ierr - error code
4576: Level: advanced
4578: Note:
4579: Use `MatMPIAIJRestoreSeqAIJF90()` when you no longer need access to the matrices and `colmap`
4581: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJRestoreSeqAIJF90()`
4582: M*/
4584: /*MC
4585: MatMPIAIJRestoreSeqAIJF90 - call after `MatMPIAIJGetSeqAIJF90()` when you no longer need access to the matrices and `colmap`
4587: Synopsis:
4588: MatMPIAIJRestoreSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)
4590: Not Collective
4592: Input Parameters:
4593: + A - the `MATMPIAIJ` matrix
4594: . Ad - the diagonal portion of the matrix
4595: . Ao - the off-diagonal portion of the matrix
4596: . colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4597: - ierr - error code
4599: Level: advanced
4601: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJGetSeqAIJF90()`
4602: M*/
4604: /*@C
4605: MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix
4607: Not Collective
4609: Input Parameter:
4610: . A - The `MATMPIAIJ` matrix
4612: Output Parameters:
4613: + Ad - The local diagonal block as a `MATSEQAIJ` matrix
4614: . Ao - The local off-diagonal block as a `MATSEQAIJ` matrix
4615: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4617: Level: intermediate
4619: Note:
4620: The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4621: 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
4622: the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4623: local column numbers to global column numbers in the original matrix.
4625: Fortran Notes:
4626: `MatMPIAIJGetSeqAIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatMPIAIJGetSeqAIJF90()`
4628: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJF90()`, `MatMPIAIJRestoreSeqAIJF90()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATSEQAIJ`
4629: @*/
4630: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4631: {
4632: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4633: PetscBool flg;
4635: PetscFunctionBegin;
4636: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4637: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4638: if (Ad) *Ad = a->A;
4639: if (Ao) *Ao = a->B;
4640: if (colmap) *colmap = a->garray;
4641: PetscFunctionReturn(PETSC_SUCCESS);
4642: }
4644: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4645: {
4646: PetscInt m, N, i, rstart, nnz, Ii;
4647: PetscInt *indx;
4648: PetscScalar *values;
4649: MatType rootType;
4651: PetscFunctionBegin;
4652: PetscCall(MatGetSize(inmat, &m, &N));
4653: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4654: PetscInt *dnz, *onz, sum, bs, cbs;
4656: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4657: /* Check sum(n) = N */
4658: PetscCallMPI(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4659: PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);
4661: PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4662: rstart -= m;
4664: MatPreallocateBegin(comm, m, n, dnz, onz);
4665: for (i = 0; i < m; i++) {
4666: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4667: PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4668: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4669: }
4671: PetscCall(MatCreate(comm, outmat));
4672: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4673: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4674: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4675: PetscCall(MatGetRootType_Private(inmat, &rootType));
4676: PetscCall(MatSetType(*outmat, rootType));
4677: PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4678: PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4679: MatPreallocateEnd(dnz, onz);
4680: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4681: }
4683: /* numeric phase */
4684: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4685: for (i = 0; i < m; i++) {
4686: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4687: Ii = i + rstart;
4688: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4689: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4690: }
4691: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4692: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4693: PetscFunctionReturn(PETSC_SUCCESS);
4694: }
4696: static PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(void *data)
4697: {
4698: Mat_Merge_SeqsToMPI *merge = (Mat_Merge_SeqsToMPI *)data;
4700: PetscFunctionBegin;
4701: if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4702: PetscCall(PetscFree(merge->id_r));
4703: PetscCall(PetscFree(merge->len_s));
4704: PetscCall(PetscFree(merge->len_r));
4705: PetscCall(PetscFree(merge->bi));
4706: PetscCall(PetscFree(merge->bj));
4707: PetscCall(PetscFree(merge->buf_ri[0]));
4708: PetscCall(PetscFree(merge->buf_ri));
4709: PetscCall(PetscFree(merge->buf_rj[0]));
4710: PetscCall(PetscFree(merge->buf_rj));
4711: PetscCall(PetscFree(merge->coi));
4712: PetscCall(PetscFree(merge->coj));
4713: PetscCall(PetscFree(merge->owners_co));
4714: PetscCall(PetscLayoutDestroy(&merge->rowmap));
4715: PetscCall(PetscFree(merge));
4716: PetscFunctionReturn(PETSC_SUCCESS);
4717: }
4719: #include <../src/mat/utils/freespace.h>
4720: #include <petscbt.h>
4722: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4723: {
4724: MPI_Comm comm;
4725: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4726: PetscMPIInt size, rank, taga, *len_s;
4727: PetscInt N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj, m;
4728: PetscMPIInt proc, k;
4729: PetscInt **buf_ri, **buf_rj;
4730: PetscInt anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4731: PetscInt nrows, **buf_ri_k, **nextrow, **nextai;
4732: MPI_Request *s_waits, *r_waits;
4733: MPI_Status *status;
4734: const MatScalar *aa, *a_a;
4735: MatScalar **abuf_r, *ba_i;
4736: Mat_Merge_SeqsToMPI *merge;
4737: PetscContainer container;
4739: PetscFunctionBegin;
4740: PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4741: PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));
4743: PetscCallMPI(MPI_Comm_size(comm, &size));
4744: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4746: PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4747: PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4748: PetscCall(PetscContainerGetPointer(container, (void **)&merge));
4749: PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4750: aa = a_a;
4752: bi = merge->bi;
4753: bj = merge->bj;
4754: buf_ri = merge->buf_ri;
4755: buf_rj = merge->buf_rj;
4757: PetscCall(PetscMalloc1(size, &status));
4758: owners = merge->rowmap->range;
4759: len_s = merge->len_s;
4761: /* send and recv matrix values */
4762: PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4763: PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));
4765: PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4766: for (proc = 0, k = 0; proc < size; proc++) {
4767: if (!len_s[proc]) continue;
4768: i = owners[proc];
4769: PetscCallMPI(MPIU_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4770: k++;
4771: }
4773: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4774: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4775: PetscCall(PetscFree(status));
4777: PetscCall(PetscFree(s_waits));
4778: PetscCall(PetscFree(r_waits));
4780: /* insert mat values of mpimat */
4781: PetscCall(PetscMalloc1(N, &ba_i));
4782: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4784: for (k = 0; k < merge->nrecv; k++) {
4785: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4786: nrows = *buf_ri_k[k];
4787: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4788: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4789: }
4791: /* set values of ba */
4792: m = merge->rowmap->n;
4793: for (i = 0; i < m; i++) {
4794: arow = owners[rank] + i;
4795: bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4796: bnzi = bi[i + 1] - bi[i];
4797: PetscCall(PetscArrayzero(ba_i, bnzi));
4799: /* add local non-zero vals of this proc's seqmat into ba */
4800: anzi = ai[arow + 1] - ai[arow];
4801: aj = a->j + ai[arow];
4802: aa = a_a + ai[arow];
4803: nextaj = 0;
4804: for (j = 0; nextaj < anzi; j++) {
4805: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4806: ba_i[j] += aa[nextaj++];
4807: }
4808: }
4810: /* add received vals into ba */
4811: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4812: /* i-th row */
4813: if (i == *nextrow[k]) {
4814: anzi = *(nextai[k] + 1) - *nextai[k];
4815: aj = buf_rj[k] + *nextai[k];
4816: aa = abuf_r[k] + *nextai[k];
4817: nextaj = 0;
4818: for (j = 0; nextaj < anzi; j++) {
4819: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4820: ba_i[j] += aa[nextaj++];
4821: }
4822: }
4823: nextrow[k]++;
4824: nextai[k]++;
4825: }
4826: }
4827: PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4828: }
4829: PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4830: PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4831: PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));
4833: PetscCall(PetscFree(abuf_r[0]));
4834: PetscCall(PetscFree(abuf_r));
4835: PetscCall(PetscFree(ba_i));
4836: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4837: PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4838: PetscFunctionReturn(PETSC_SUCCESS);
4839: }
4841: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4842: {
4843: Mat B_mpi;
4844: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4845: PetscMPIInt size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4846: PetscInt **buf_rj, **buf_ri, **buf_ri_k;
4847: PetscInt M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4848: PetscInt len, *dnz, *onz, bs, cbs;
4849: PetscInt k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4850: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4851: MPI_Request *si_waits, *sj_waits, *ri_waits, *rj_waits;
4852: MPI_Status *status;
4853: PetscFreeSpaceList free_space = NULL, current_space = NULL;
4854: PetscBT lnkbt;
4855: Mat_Merge_SeqsToMPI *merge;
4856: PetscContainer container;
4858: PetscFunctionBegin;
4859: PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));
4861: /* make sure it is a PETSc comm */
4862: PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4863: PetscCallMPI(MPI_Comm_size(comm, &size));
4864: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4866: PetscCall(PetscNew(&merge));
4867: PetscCall(PetscMalloc1(size, &status));
4869: /* determine row ownership */
4870: PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4871: PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4872: PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4873: PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4874: PetscCall(PetscLayoutSetUp(merge->rowmap));
4875: PetscCall(PetscMalloc1(size, &len_si));
4876: PetscCall(PetscMalloc1(size, &merge->len_s));
4878: m = merge->rowmap->n;
4879: owners = merge->rowmap->range;
4881: /* determine the number of messages to send, their lengths */
4882: len_s = merge->len_s;
4884: len = 0; /* length of buf_si[] */
4885: merge->nsend = 0;
4886: for (PetscMPIInt proc = 0; proc < size; proc++) {
4887: len_si[proc] = 0;
4888: if (proc == rank) {
4889: len_s[proc] = 0;
4890: } else {
4891: PetscCall(PetscMPIIntCast(owners[proc + 1] - owners[proc] + 1, &len_si[proc]));
4892: PetscCall(PetscMPIIntCast(ai[owners[proc + 1]] - ai[owners[proc]], &len_s[proc])); /* num of rows to be sent to [proc] */
4893: }
4894: if (len_s[proc]) {
4895: merge->nsend++;
4896: nrows = 0;
4897: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4898: if (ai[i + 1] > ai[i]) nrows++;
4899: }
4900: PetscCall(PetscMPIIntCast(2 * (nrows + 1), &len_si[proc]));
4901: len += len_si[proc];
4902: }
4903: }
4905: /* determine the number and length of messages to receive for ij-structure */
4906: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4907: PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));
4909: /* post the Irecv of j-structure */
4910: PetscCall(PetscCommGetNewTag(comm, &tagj));
4911: PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));
4913: /* post the Isend of j-structure */
4914: PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));
4916: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4917: if (!len_s[proc]) continue;
4918: i = owners[proc];
4919: PetscCallMPI(MPIU_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4920: k++;
4921: }
4923: /* receives and sends of j-structure are complete */
4924: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4925: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));
4927: /* send and recv i-structure */
4928: PetscCall(PetscCommGetNewTag(comm, &tagi));
4929: PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));
4931: PetscCall(PetscMalloc1(len + 1, &buf_s));
4932: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4933: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4934: if (!len_s[proc]) continue;
4935: /* form outgoing message for i-structure:
4936: buf_si[0]: nrows to be sent
4937: [1:nrows]: row index (global)
4938: [nrows+1:2*nrows+1]: i-structure index
4939: */
4940: nrows = len_si[proc] / 2 - 1;
4941: buf_si_i = buf_si + nrows + 1;
4942: buf_si[0] = nrows;
4943: buf_si_i[0] = 0;
4944: nrows = 0;
4945: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4946: anzi = ai[i + 1] - ai[i];
4947: if (anzi) {
4948: buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4949: buf_si[nrows + 1] = i - owners[proc]; /* local row index */
4950: nrows++;
4951: }
4952: }
4953: PetscCallMPI(MPIU_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4954: k++;
4955: buf_si += len_si[proc];
4956: }
4958: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4959: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));
4961: PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4962: 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]));
4964: PetscCall(PetscFree(len_si));
4965: PetscCall(PetscFree(len_ri));
4966: PetscCall(PetscFree(rj_waits));
4967: PetscCall(PetscFree2(si_waits, sj_waits));
4968: PetscCall(PetscFree(ri_waits));
4969: PetscCall(PetscFree(buf_s));
4970: PetscCall(PetscFree(status));
4972: /* compute a local seq matrix in each processor */
4973: /* allocate bi array and free space for accumulating nonzero column info */
4974: PetscCall(PetscMalloc1(m + 1, &bi));
4975: bi[0] = 0;
4977: /* create and initialize a linked list */
4978: nlnk = N + 1;
4979: PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));
4981: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4982: len = ai[owners[rank + 1]] - ai[owners[rank]];
4983: PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));
4985: current_space = free_space;
4987: /* determine symbolic info for each local row */
4988: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4990: for (k = 0; k < merge->nrecv; k++) {
4991: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4992: nrows = *buf_ri_k[k];
4993: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4994: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4995: }
4997: MatPreallocateBegin(comm, m, n, dnz, onz);
4998: len = 0;
4999: for (i = 0; i < m; i++) {
5000: bnzi = 0;
5001: /* add local non-zero cols of this proc's seqmat into lnk */
5002: arow = owners[rank] + i;
5003: anzi = ai[arow + 1] - ai[arow];
5004: aj = a->j + ai[arow];
5005: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5006: bnzi += nlnk;
5007: /* add received col data into lnk */
5008: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
5009: if (i == *nextrow[k]) { /* i-th row */
5010: anzi = *(nextai[k] + 1) - *nextai[k];
5011: aj = buf_rj[k] + *nextai[k];
5012: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5013: bnzi += nlnk;
5014: nextrow[k]++;
5015: nextai[k]++;
5016: }
5017: }
5018: if (len < bnzi) len = bnzi; /* =max(bnzi) */
5020: /* if free space is not available, make more free space */
5021: if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), ¤t_space));
5022: /* copy data into free space, then initialize lnk */
5023: PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
5024: PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));
5026: current_space->array += bnzi;
5027: current_space->local_used += bnzi;
5028: current_space->local_remaining -= bnzi;
5030: bi[i + 1] = bi[i] + bnzi;
5031: }
5033: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
5035: PetscCall(PetscMalloc1(bi[m] + 1, &bj));
5036: PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
5037: PetscCall(PetscLLDestroy(lnk, lnkbt));
5039: /* create symbolic parallel matrix B_mpi */
5040: PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
5041: PetscCall(MatCreate(comm, &B_mpi));
5042: if (n == PETSC_DECIDE) {
5043: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
5044: } else {
5045: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
5046: }
5047: PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
5048: PetscCall(MatSetType(B_mpi, MATMPIAIJ));
5049: PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
5050: MatPreallocateEnd(dnz, onz);
5051: PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));
5053: /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
5054: B_mpi->assembled = PETSC_FALSE;
5055: merge->bi = bi;
5056: merge->bj = bj;
5057: merge->buf_ri = buf_ri;
5058: merge->buf_rj = buf_rj;
5059: merge->coi = NULL;
5060: merge->coj = NULL;
5061: merge->owners_co = NULL;
5063: PetscCall(PetscCommDestroy(&comm));
5065: /* attach the supporting struct to B_mpi for reuse */
5066: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5067: PetscCall(PetscContainerSetPointer(container, merge));
5068: PetscCall(PetscContainerSetUserDestroy(container, MatDestroy_MPIAIJ_SeqsToMPI));
5069: PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5070: PetscCall(PetscContainerDestroy(&container));
5071: *mpimat = B_mpi;
5073: PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5074: PetscFunctionReturn(PETSC_SUCCESS);
5075: }
5077: /*@
5078: MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5079: matrices from each processor
5081: Collective
5083: Input Parameters:
5084: + comm - the communicators the parallel matrix will live on
5085: . seqmat - the input sequential matrices
5086: . m - number of local rows (or `PETSC_DECIDE`)
5087: . n - number of local columns (or `PETSC_DECIDE`)
5088: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5090: Output Parameter:
5091: . mpimat - the parallel matrix generated
5093: Level: advanced
5095: Note:
5096: The dimensions of the sequential matrix in each processor MUST be the same.
5097: The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
5098: destroyed when `mpimat` is destroyed. Call `PetscObjectQuery()` to access `seqmat`.
5100: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`
5101: @*/
5102: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5103: {
5104: PetscMPIInt size;
5106: PetscFunctionBegin;
5107: PetscCallMPI(MPI_Comm_size(comm, &size));
5108: if (size == 1) {
5109: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5110: if (scall == MAT_INITIAL_MATRIX) {
5111: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5112: } else {
5113: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5114: }
5115: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5116: PetscFunctionReturn(PETSC_SUCCESS);
5117: }
5118: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5119: if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5120: PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5121: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5122: PetscFunctionReturn(PETSC_SUCCESS);
5123: }
5125: /*@
5126: MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix.
5128: Not Collective
5130: Input Parameter:
5131: . A - the matrix
5133: Output Parameter:
5134: . A_loc - the local sequential matrix generated
5136: Level: developer
5138: Notes:
5139: The matrix is created by taking `A`'s local rows and putting them into a sequential matrix
5140: with `mlocal` rows and `n` columns. Where `mlocal` is obtained with `MatGetLocalSize()` and
5141: `n` is the global column count obtained with `MatGetSize()`
5143: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5145: For parallel matrices this creates an entirely new matrix. If the matrix is sequential it merely increases the reference count.
5147: Destroy the matrix with `MatDestroy()`
5149: .seealso: [](ch_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5150: @*/
5151: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5152: {
5153: PetscBool mpi;
5155: PetscFunctionBegin;
5156: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5157: if (mpi) {
5158: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5159: } else {
5160: *A_loc = A;
5161: PetscCall(PetscObjectReference((PetscObject)*A_loc));
5162: }
5163: PetscFunctionReturn(PETSC_SUCCESS);
5164: }
5166: /*@
5167: MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix.
5169: Not Collective
5171: Input Parameters:
5172: + A - the matrix
5173: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5175: Output Parameter:
5176: . A_loc - the local sequential matrix generated
5178: Level: developer
5180: Notes:
5181: The matrix is created by taking all `A`'s local rows and putting them into a sequential
5182: matrix with `mlocal` rows and `n` columns.`mlocal` is the row count obtained with
5183: `MatGetLocalSize()` and `n` is the global column count obtained with `MatGetSize()`.
5185: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5187: When `A` is sequential and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A` (which contains the entire matrix),
5188: 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
5189: then `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called to fill `A_loc`. Thus one can preallocate the appropriate sequential matrix `A_loc`
5190: 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.
5192: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5193: @*/
5194: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5195: {
5196: Mat_MPIAIJ *mpimat = (Mat_MPIAIJ *)A->data;
5197: Mat_SeqAIJ *mat, *a, *b;
5198: PetscInt *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5199: const PetscScalar *aa, *ba, *aav, *bav;
5200: PetscScalar *ca, *cam;
5201: PetscMPIInt size;
5202: PetscInt am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5203: PetscInt *ci, *cj, col, ncols_d, ncols_o, jo;
5204: PetscBool match;
5206: PetscFunctionBegin;
5207: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5208: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5209: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5210: if (size == 1) {
5211: if (scall == MAT_INITIAL_MATRIX) {
5212: PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5213: *A_loc = mpimat->A;
5214: } else if (scall == MAT_REUSE_MATRIX) {
5215: PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5216: }
5217: PetscFunctionReturn(PETSC_SUCCESS);
5218: }
5220: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5221: a = (Mat_SeqAIJ *)mpimat->A->data;
5222: b = (Mat_SeqAIJ *)mpimat->B->data;
5223: ai = a->i;
5224: aj = a->j;
5225: bi = b->i;
5226: bj = b->j;
5227: PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5228: PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5229: aa = aav;
5230: ba = bav;
5231: if (scall == MAT_INITIAL_MATRIX) {
5232: PetscCall(PetscMalloc1(1 + am, &ci));
5233: ci[0] = 0;
5234: for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5235: PetscCall(PetscMalloc1(1 + ci[am], &cj));
5236: PetscCall(PetscMalloc1(1 + ci[am], &ca));
5237: k = 0;
5238: for (i = 0; i < am; i++) {
5239: ncols_o = bi[i + 1] - bi[i];
5240: ncols_d = ai[i + 1] - ai[i];
5241: /* off-diagonal portion of A */
5242: for (jo = 0; jo < ncols_o; jo++) {
5243: col = cmap[*bj];
5244: if (col >= cstart) break;
5245: cj[k] = col;
5246: bj++;
5247: ca[k++] = *ba++;
5248: }
5249: /* diagonal portion of A */
5250: for (j = 0; j < ncols_d; j++) {
5251: cj[k] = cstart + *aj++;
5252: ca[k++] = *aa++;
5253: }
5254: /* off-diagonal portion of A */
5255: for (j = jo; j < ncols_o; j++) {
5256: cj[k] = cmap[*bj++];
5257: ca[k++] = *ba++;
5258: }
5259: }
5260: /* put together the new matrix */
5261: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5262: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5263: /* Since these are PETSc arrays, change flags to free them as necessary. */
5264: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5265: mat->free_a = PETSC_TRUE;
5266: mat->free_ij = PETSC_TRUE;
5267: mat->nonew = 0;
5268: } else if (scall == MAT_REUSE_MATRIX) {
5269: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5270: ci = mat->i;
5271: cj = mat->j;
5272: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5273: for (i = 0; i < am; i++) {
5274: /* off-diagonal portion of A */
5275: ncols_o = bi[i + 1] - bi[i];
5276: for (jo = 0; jo < ncols_o; jo++) {
5277: col = cmap[*bj];
5278: if (col >= cstart) break;
5279: *cam++ = *ba++;
5280: bj++;
5281: }
5282: /* diagonal portion of A */
5283: ncols_d = ai[i + 1] - ai[i];
5284: for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5285: /* off-diagonal portion of A */
5286: for (j = jo; j < ncols_o; j++) {
5287: *cam++ = *ba++;
5288: bj++;
5289: }
5290: }
5291: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5292: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5293: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5294: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5295: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5296: PetscFunctionReturn(PETSC_SUCCESS);
5297: }
5299: /*@
5300: MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5301: mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and off-diagonal part
5303: Not Collective
5305: Input Parameters:
5306: + A - the matrix
5307: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5309: Output Parameters:
5310: + glob - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5311: - A_loc - the local sequential matrix generated
5313: Level: developer
5315: Note:
5316: This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5317: part, then those associated with the off-diagonal part (in its local ordering)
5319: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5320: @*/
5321: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5322: {
5323: Mat Ao, Ad;
5324: const PetscInt *cmap;
5325: PetscMPIInt size;
5326: PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);
5328: PetscFunctionBegin;
5329: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5330: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5331: if (size == 1) {
5332: if (scall == MAT_INITIAL_MATRIX) {
5333: PetscCall(PetscObjectReference((PetscObject)Ad));
5334: *A_loc = Ad;
5335: } else if (scall == MAT_REUSE_MATRIX) {
5336: PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5337: }
5338: if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5339: PetscFunctionReturn(PETSC_SUCCESS);
5340: }
5341: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5342: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5343: if (f) {
5344: PetscCall((*f)(A, scall, glob, A_loc));
5345: } else {
5346: Mat_SeqAIJ *a = (Mat_SeqAIJ *)Ad->data;
5347: Mat_SeqAIJ *b = (Mat_SeqAIJ *)Ao->data;
5348: Mat_SeqAIJ *c;
5349: PetscInt *ai = a->i, *aj = a->j;
5350: PetscInt *bi = b->i, *bj = b->j;
5351: PetscInt *ci, *cj;
5352: const PetscScalar *aa, *ba;
5353: PetscScalar *ca;
5354: PetscInt i, j, am, dn, on;
5356: PetscCall(MatGetLocalSize(Ad, &am, &dn));
5357: PetscCall(MatGetLocalSize(Ao, NULL, &on));
5358: PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5359: PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5360: if (scall == MAT_INITIAL_MATRIX) {
5361: PetscInt k;
5362: PetscCall(PetscMalloc1(1 + am, &ci));
5363: PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5364: PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5365: ci[0] = 0;
5366: for (i = 0, k = 0; i < am; i++) {
5367: const PetscInt ncols_o = bi[i + 1] - bi[i];
5368: const PetscInt ncols_d = ai[i + 1] - ai[i];
5369: ci[i + 1] = ci[i] + ncols_o + ncols_d;
5370: /* diagonal portion of A */
5371: for (j = 0; j < ncols_d; j++, k++) {
5372: cj[k] = *aj++;
5373: ca[k] = *aa++;
5374: }
5375: /* off-diagonal portion of A */
5376: for (j = 0; j < ncols_o; j++, k++) {
5377: cj[k] = dn + *bj++;
5378: ca[k] = *ba++;
5379: }
5380: }
5381: /* put together the new matrix */
5382: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5383: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5384: /* Since these are PETSc arrays, change flags to free them as necessary. */
5385: c = (Mat_SeqAIJ *)(*A_loc)->data;
5386: c->free_a = PETSC_TRUE;
5387: c->free_ij = PETSC_TRUE;
5388: c->nonew = 0;
5389: PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5390: } else if (scall == MAT_REUSE_MATRIX) {
5391: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5392: for (i = 0; i < am; i++) {
5393: const PetscInt ncols_d = ai[i + 1] - ai[i];
5394: const PetscInt ncols_o = bi[i + 1] - bi[i];
5395: /* diagonal portion of A */
5396: for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5397: /* off-diagonal portion of A */
5398: for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5399: }
5400: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5401: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5402: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5403: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5404: if (glob) {
5405: PetscInt cst, *gidx;
5407: PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5408: PetscCall(PetscMalloc1(dn + on, &gidx));
5409: for (i = 0; i < dn; i++) gidx[i] = cst + i;
5410: for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5411: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5412: }
5413: }
5414: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5415: PetscFunctionReturn(PETSC_SUCCESS);
5416: }
5418: /*@C
5419: MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns
5421: Not Collective
5423: Input Parameters:
5424: + A - the matrix
5425: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5426: . row - index set of rows to extract (or `NULL`)
5427: - col - index set of columns to extract (or `NULL`)
5429: Output Parameter:
5430: . A_loc - the local sequential matrix generated
5432: Level: developer
5434: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5435: @*/
5436: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5437: {
5438: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5439: PetscInt i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5440: IS isrowa, iscola;
5441: Mat *aloc;
5442: PetscBool match;
5444: PetscFunctionBegin;
5445: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5446: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5447: PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5448: if (!row) {
5449: start = A->rmap->rstart;
5450: end = A->rmap->rend;
5451: PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5452: } else {
5453: isrowa = *row;
5454: }
5455: if (!col) {
5456: start = A->cmap->rstart;
5457: cmap = a->garray;
5458: nzA = a->A->cmap->n;
5459: nzB = a->B->cmap->n;
5460: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5461: ncols = 0;
5462: for (i = 0; i < nzB; i++) {
5463: if (cmap[i] < start) idx[ncols++] = cmap[i];
5464: else break;
5465: }
5466: imark = i;
5467: for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5468: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5469: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5470: } else {
5471: iscola = *col;
5472: }
5473: if (scall != MAT_INITIAL_MATRIX) {
5474: PetscCall(PetscMalloc1(1, &aloc));
5475: aloc[0] = *A_loc;
5476: }
5477: PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5478: if (!col) { /* attach global id of condensed columns */
5479: PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5480: }
5481: *A_loc = aloc[0];
5482: PetscCall(PetscFree(aloc));
5483: if (!row) PetscCall(ISDestroy(&isrowa));
5484: if (!col) PetscCall(ISDestroy(&iscola));
5485: PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5486: PetscFunctionReturn(PETSC_SUCCESS);
5487: }
5489: /*
5490: * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5491: * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5492: * on a global size.
5493: * */
5494: static PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5495: {
5496: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
5497: Mat_SeqAIJ *pd = (Mat_SeqAIJ *)p->A->data, *po = (Mat_SeqAIJ *)p->B->data, *p_oth;
5498: PetscInt plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5499: PetscMPIInt owner;
5500: PetscSFNode *iremote, *oiremote;
5501: const PetscInt *lrowindices;
5502: PetscSF sf, osf;
5503: PetscInt pcstart, *roffsets, *loffsets, *pnnz, j;
5504: PetscInt ontotalcols, dntotalcols, ntotalcols, nout;
5505: MPI_Comm comm;
5506: ISLocalToGlobalMapping mapping;
5507: const PetscScalar *pd_a, *po_a;
5509: PetscFunctionBegin;
5510: PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5511: /* plocalsize is the number of roots
5512: * nrows is the number of leaves
5513: * */
5514: PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5515: PetscCall(ISGetLocalSize(rows, &nrows));
5516: PetscCall(PetscCalloc1(nrows, &iremote));
5517: PetscCall(ISGetIndices(rows, &lrowindices));
5518: for (i = 0; i < nrows; i++) {
5519: /* Find a remote index and an owner for a row
5520: * The row could be local or remote
5521: * */
5522: owner = 0;
5523: lidx = 0;
5524: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5525: iremote[i].index = lidx;
5526: iremote[i].rank = owner;
5527: }
5528: /* Create SF to communicate how many nonzero columns for each row */
5529: PetscCall(PetscSFCreate(comm, &sf));
5530: /* SF will figure out the number of nonzero columns for each row, and their
5531: * offsets
5532: * */
5533: PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5534: PetscCall(PetscSFSetFromOptions(sf));
5535: PetscCall(PetscSFSetUp(sf));
5537: PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5538: PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5539: PetscCall(PetscCalloc1(nrows, &pnnz));
5540: roffsets[0] = 0;
5541: roffsets[1] = 0;
5542: for (i = 0; i < plocalsize; i++) {
5543: /* diagonal */
5544: nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5545: /* off-diagonal */
5546: nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5547: /* compute offsets so that we relative location for each row */
5548: roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5549: roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5550: }
5551: PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5552: PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5553: /* 'r' means root, and 'l' means leaf */
5554: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5555: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5556: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5557: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5558: PetscCall(PetscSFDestroy(&sf));
5559: PetscCall(PetscFree(roffsets));
5560: PetscCall(PetscFree(nrcols));
5561: dntotalcols = 0;
5562: ontotalcols = 0;
5563: ncol = 0;
5564: for (i = 0; i < nrows; i++) {
5565: pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5566: ncol = PetscMax(pnnz[i], ncol);
5567: /* diagonal */
5568: dntotalcols += nlcols[i * 2 + 0];
5569: /* off-diagonal */
5570: ontotalcols += nlcols[i * 2 + 1];
5571: }
5572: /* We do not need to figure the right number of columns
5573: * since all the calculations will be done by going through the raw data
5574: * */
5575: PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5576: PetscCall(MatSetUp(*P_oth));
5577: PetscCall(PetscFree(pnnz));
5578: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5579: /* diagonal */
5580: PetscCall(PetscCalloc1(dntotalcols, &iremote));
5581: /* off-diagonal */
5582: PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5583: /* diagonal */
5584: PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5585: /* off-diagonal */
5586: PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5587: dntotalcols = 0;
5588: ontotalcols = 0;
5589: ntotalcols = 0;
5590: for (i = 0; i < nrows; i++) {
5591: owner = 0;
5592: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5593: /* Set iremote for diag matrix */
5594: for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5595: iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5596: iremote[dntotalcols].rank = owner;
5597: /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5598: ilocal[dntotalcols++] = ntotalcols++;
5599: }
5600: /* off-diagonal */
5601: for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5602: oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5603: oiremote[ontotalcols].rank = owner;
5604: oilocal[ontotalcols++] = ntotalcols++;
5605: }
5606: }
5607: PetscCall(ISRestoreIndices(rows, &lrowindices));
5608: PetscCall(PetscFree(loffsets));
5609: PetscCall(PetscFree(nlcols));
5610: PetscCall(PetscSFCreate(comm, &sf));
5611: /* P serves as roots and P_oth is leaves
5612: * Diag matrix
5613: * */
5614: PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5615: PetscCall(PetscSFSetFromOptions(sf));
5616: PetscCall(PetscSFSetUp(sf));
5618: PetscCall(PetscSFCreate(comm, &osf));
5619: /* off-diagonal */
5620: PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5621: PetscCall(PetscSFSetFromOptions(osf));
5622: PetscCall(PetscSFSetUp(osf));
5623: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5624: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5625: /* operate on the matrix internal data to save memory */
5626: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5627: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5628: PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5629: /* Convert to global indices for diag matrix */
5630: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5631: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5632: /* We want P_oth store global indices */
5633: PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5634: /* Use memory scalable approach */
5635: PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5636: PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5637: PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5638: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5639: /* Convert back to local indices */
5640: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5641: PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5642: nout = 0;
5643: PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5644: PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5645: PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5646: /* Exchange values */
5647: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5648: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5649: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5650: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5651: /* Stop PETSc from shrinking memory */
5652: for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5653: PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5654: PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5655: /* Attach PetscSF objects to P_oth so that we can reuse it later */
5656: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5657: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5658: PetscCall(PetscSFDestroy(&sf));
5659: PetscCall(PetscSFDestroy(&osf));
5660: PetscFunctionReturn(PETSC_SUCCESS);
5661: }
5663: /*
5664: * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5665: * This supports MPIAIJ and MAIJ
5666: * */
5667: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5668: {
5669: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5670: Mat_SeqAIJ *p_oth;
5671: IS rows, map;
5672: PetscHMapI hamp;
5673: PetscInt i, htsize, *rowindices, off, *mapping, key, count;
5674: MPI_Comm comm;
5675: PetscSF sf, osf;
5676: PetscBool has;
5678: PetscFunctionBegin;
5679: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5680: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5681: /* If it is the first time, create an index set of off-diag nonzero columns of A,
5682: * and then create a submatrix (that often is an overlapping matrix)
5683: * */
5684: if (reuse == MAT_INITIAL_MATRIX) {
5685: /* Use a hash table to figure out unique keys */
5686: PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5687: PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5688: count = 0;
5689: /* Assume that a->g is sorted, otherwise the following does not make sense */
5690: for (i = 0; i < a->B->cmap->n; i++) {
5691: key = a->garray[i] / dof;
5692: PetscCall(PetscHMapIHas(hamp, key, &has));
5693: if (!has) {
5694: mapping[i] = count;
5695: PetscCall(PetscHMapISet(hamp, key, count++));
5696: } else {
5697: /* Current 'i' has the same value the previous step */
5698: mapping[i] = count - 1;
5699: }
5700: }
5701: PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5702: PetscCall(PetscHMapIGetSize(hamp, &htsize));
5703: PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5704: PetscCall(PetscCalloc1(htsize, &rowindices));
5705: off = 0;
5706: PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5707: PetscCall(PetscHMapIDestroy(&hamp));
5708: PetscCall(PetscSortInt(htsize, rowindices));
5709: PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5710: /* In case, the matrix was already created but users want to recreate the matrix */
5711: PetscCall(MatDestroy(P_oth));
5712: PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5713: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5714: PetscCall(ISDestroy(&map));
5715: PetscCall(ISDestroy(&rows));
5716: } else if (reuse == MAT_REUSE_MATRIX) {
5717: /* If matrix was already created, we simply update values using SF objects
5718: * that as attached to the matrix earlier.
5719: */
5720: const PetscScalar *pd_a, *po_a;
5722: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5723: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5724: PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5725: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5726: /* Update values in place */
5727: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5728: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5729: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5730: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5731: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5732: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5733: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5734: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5735: } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5736: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5737: PetscFunctionReturn(PETSC_SUCCESS);
5738: }
5740: /*@C
5741: MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`
5743: Collective
5745: Input Parameters:
5746: + A - the first matrix in `MATMPIAIJ` format
5747: . B - the second matrix in `MATMPIAIJ` format
5748: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5750: Output Parameters:
5751: + rowb - On input index sets of rows of B to extract (or `NULL`), modified on output
5752: . colb - On input index sets of columns of B to extract (or `NULL`), modified on output
5753: - B_seq - the sequential matrix generated
5755: Level: developer
5757: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5758: @*/
5759: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5760: {
5761: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5762: PetscInt *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5763: IS isrowb, iscolb;
5764: Mat *bseq = NULL;
5766: PetscFunctionBegin;
5767: 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 ")",
5768: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5769: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));
5771: if (scall == MAT_INITIAL_MATRIX) {
5772: start = A->cmap->rstart;
5773: cmap = a->garray;
5774: nzA = a->A->cmap->n;
5775: nzB = a->B->cmap->n;
5776: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5777: ncols = 0;
5778: for (i = 0; i < nzB; i++) { /* row < local row index */
5779: if (cmap[i] < start) idx[ncols++] = cmap[i];
5780: else break;
5781: }
5782: imark = i;
5783: for (i = 0; i < nzA; i++) idx[ncols++] = start + i; /* local rows */
5784: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5785: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5786: PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5787: } else {
5788: PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5789: isrowb = *rowb;
5790: iscolb = *colb;
5791: PetscCall(PetscMalloc1(1, &bseq));
5792: bseq[0] = *B_seq;
5793: }
5794: PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5795: *B_seq = bseq[0];
5796: PetscCall(PetscFree(bseq));
5797: if (!rowb) {
5798: PetscCall(ISDestroy(&isrowb));
5799: } else {
5800: *rowb = isrowb;
5801: }
5802: if (!colb) {
5803: PetscCall(ISDestroy(&iscolb));
5804: } else {
5805: *colb = iscolb;
5806: }
5807: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5808: PetscFunctionReturn(PETSC_SUCCESS);
5809: }
5811: /*
5812: MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5813: of the OFF-DIAGONAL portion of local A
5815: Collective
5817: Input Parameters:
5818: + A,B - the matrices in `MATMPIAIJ` format
5819: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5821: Output Parameter:
5822: + startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5823: . startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5824: . bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5825: - B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N
5827: Developer Note:
5828: This directly accesses information inside the VecScatter associated with the matrix-vector product
5829: for this matrix. This is not desirable..
5831: Level: developer
5833: */
5835: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5836: {
5837: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5838: VecScatter ctx;
5839: MPI_Comm comm;
5840: const PetscMPIInt *rprocs, *sprocs;
5841: PetscMPIInt nrecvs, nsends;
5842: const PetscInt *srow, *rstarts, *sstarts;
5843: PetscInt *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5844: PetscInt i, j, k = 0, l, ll, nrows, *rstartsj = NULL, *sstartsj, len;
5845: PetscScalar *b_otha, *bufa, *bufA, *vals = NULL;
5846: MPI_Request *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5847: PetscMPIInt size, tag, rank, nreqs;
5849: PetscFunctionBegin;
5850: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5851: PetscCallMPI(MPI_Comm_size(comm, &size));
5853: 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 ")",
5854: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5855: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5856: PetscCallMPI(MPI_Comm_rank(comm, &rank));
5858: if (size == 1) {
5859: startsj_s = NULL;
5860: bufa_ptr = NULL;
5861: *B_oth = NULL;
5862: PetscFunctionReturn(PETSC_SUCCESS);
5863: }
5865: ctx = a->Mvctx;
5866: tag = ((PetscObject)ctx)->tag;
5868: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5869: /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5870: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5871: PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5872: PetscCall(PetscMalloc1(nreqs, &reqs));
5873: rwaits = reqs;
5874: swaits = PetscSafePointerPlusOffset(reqs, nrecvs);
5876: if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5877: if (scall == MAT_INITIAL_MATRIX) {
5878: /* i-array */
5879: /* post receives */
5880: if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5881: for (i = 0; i < nrecvs; i++) {
5882: rowlen = rvalues + rstarts[i] * rbs;
5883: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5884: PetscCallMPI(MPIU_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5885: }
5887: /* pack the outgoing message */
5888: PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));
5890: sstartsj[0] = 0;
5891: rstartsj[0] = 0;
5892: len = 0; /* total length of j or a array to be sent */
5893: if (nsends) {
5894: k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5895: PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5896: }
5897: for (i = 0; i < nsends; i++) {
5898: rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5899: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5900: for (j = 0; j < nrows; j++) {
5901: row = srow[k] + B->rmap->range[rank]; /* global row idx */
5902: for (l = 0; l < sbs; l++) {
5903: PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */
5905: rowlen[j * sbs + l] = ncols;
5907: len += ncols;
5908: PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5909: }
5910: k++;
5911: }
5912: PetscCallMPI(MPIU_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));
5914: sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5915: }
5916: /* recvs and sends of i-array are completed */
5917: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5918: PetscCall(PetscFree(svalues));
5920: /* allocate buffers for sending j and a arrays */
5921: PetscCall(PetscMalloc1(len + 1, &bufj));
5922: PetscCall(PetscMalloc1(len + 1, &bufa));
5924: /* create i-array of B_oth */
5925: PetscCall(PetscMalloc1(aBn + 2, &b_othi));
5927: b_othi[0] = 0;
5928: len = 0; /* total length of j or a array to be received */
5929: k = 0;
5930: for (i = 0; i < nrecvs; i++) {
5931: rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5932: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5933: for (j = 0; j < nrows; j++) {
5934: b_othi[k + 1] = b_othi[k] + rowlen[j];
5935: PetscCall(PetscIntSumError(rowlen[j], len, &len));
5936: k++;
5937: }
5938: rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5939: }
5940: PetscCall(PetscFree(rvalues));
5942: /* allocate space for j and a arrays of B_oth */
5943: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_othj));
5944: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_otha));
5946: /* j-array */
5947: /* post receives of j-array */
5948: for (i = 0; i < nrecvs; i++) {
5949: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5950: PetscCallMPI(MPIU_Irecv(b_othj + rstartsj[i], nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5951: }
5953: /* pack the outgoing message j-array */
5954: if (nsends) k = sstarts[0];
5955: for (i = 0; i < nsends; i++) {
5956: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5957: bufJ = bufj + sstartsj[i];
5958: for (j = 0; j < nrows; j++) {
5959: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5960: for (ll = 0; ll < sbs; ll++) {
5961: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5962: for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5963: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5964: }
5965: }
5966: PetscCallMPI(MPIU_Isend(bufj + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5967: }
5969: /* recvs and sends of j-array are completed */
5970: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5971: } else if (scall == MAT_REUSE_MATRIX) {
5972: sstartsj = *startsj_s;
5973: rstartsj = *startsj_r;
5974: bufa = *bufa_ptr;
5975: PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5976: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");
5978: /* a-array */
5979: /* post receives of a-array */
5980: for (i = 0; i < nrecvs; i++) {
5981: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5982: PetscCallMPI(MPIU_Irecv(b_otha + rstartsj[i], nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
5983: }
5985: /* pack the outgoing message a-array */
5986: if (nsends) k = sstarts[0];
5987: for (i = 0; i < nsends; i++) {
5988: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5989: bufA = bufa + sstartsj[i];
5990: for (j = 0; j < nrows; j++) {
5991: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5992: for (ll = 0; ll < sbs; ll++) {
5993: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5994: for (l = 0; l < ncols; l++) *bufA++ = vals[l];
5995: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5996: }
5997: }
5998: PetscCallMPI(MPIU_Isend(bufa + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
5999: }
6000: /* recvs and sends of a-array are completed */
6001: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
6002: PetscCall(PetscFree(reqs));
6004: if (scall == MAT_INITIAL_MATRIX) {
6005: Mat_SeqAIJ *b_oth;
6007: /* put together the new matrix */
6008: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));
6010: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
6011: /* Since these are PETSc arrays, change flags to free them as necessary. */
6012: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
6013: b_oth->free_a = PETSC_TRUE;
6014: b_oth->free_ij = PETSC_TRUE;
6015: b_oth->nonew = 0;
6017: PetscCall(PetscFree(bufj));
6018: if (!startsj_s || !bufa_ptr) {
6019: PetscCall(PetscFree2(sstartsj, rstartsj));
6020: PetscCall(PetscFree(bufa_ptr));
6021: } else {
6022: *startsj_s = sstartsj;
6023: *startsj_r = rstartsj;
6024: *bufa_ptr = bufa;
6025: }
6026: } else if (scall == MAT_REUSE_MATRIX) {
6027: PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
6028: }
6030: PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
6031: PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
6032: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
6033: PetscFunctionReturn(PETSC_SUCCESS);
6034: }
6036: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
6037: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
6038: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
6039: #if defined(PETSC_HAVE_MKL_SPARSE)
6040: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
6041: #endif
6042: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
6043: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
6044: #if defined(PETSC_HAVE_ELEMENTAL)
6045: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
6046: #endif
6047: #if defined(PETSC_HAVE_SCALAPACK)
6048: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
6049: #endif
6050: #if defined(PETSC_HAVE_HYPRE)
6051: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
6052: #endif
6053: #if defined(PETSC_HAVE_CUDA)
6054: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
6055: #endif
6056: #if defined(PETSC_HAVE_HIP)
6057: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6058: #endif
6059: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6060: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6061: #endif
6062: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6063: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6064: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
6066: /*
6067: Computes (B'*A')' since computing B*A directly is untenable
6069: n p p
6070: [ ] [ ] [ ]
6071: m [ A ] * n [ B ] = m [ C ]
6072: [ ] [ ] [ ]
6074: */
6075: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6076: {
6077: Mat At, Bt, Ct;
6079: PetscFunctionBegin;
6080: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6081: PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6082: PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_CURRENT, &Ct));
6083: PetscCall(MatDestroy(&At));
6084: PetscCall(MatDestroy(&Bt));
6085: PetscCall(MatTransposeSetPrecursor(Ct, C));
6086: PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6087: PetscCall(MatDestroy(&Ct));
6088: PetscFunctionReturn(PETSC_SUCCESS);
6089: }
6091: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6092: {
6093: PetscBool cisdense;
6095: PetscFunctionBegin;
6096: 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);
6097: PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6098: PetscCall(MatSetBlockSizesFromMats(C, A, B));
6099: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6100: if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6101: PetscCall(MatSetUp(C));
6103: C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6104: PetscFunctionReturn(PETSC_SUCCESS);
6105: }
6107: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6108: {
6109: Mat_Product *product = C->product;
6110: Mat A = product->A, B = product->B;
6112: PetscFunctionBegin;
6113: 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 ")",
6114: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6115: C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6116: C->ops->productsymbolic = MatProductSymbolic_AB;
6117: PetscFunctionReturn(PETSC_SUCCESS);
6118: }
6120: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6121: {
6122: Mat_Product *product = C->product;
6124: PetscFunctionBegin;
6125: if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6126: PetscFunctionReturn(PETSC_SUCCESS);
6127: }
6129: /*
6130: Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix
6132: Input Parameters:
6134: j1,rowBegin1,rowEnd1,jmap1: describe the first set of nonzeros (Set1)
6135: j2,rowBegin2,rowEnd2,jmap2: describe the second set of nonzeros (Set2)
6137: mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat
6139: For Set1, j1[] contains column indices of the nonzeros.
6140: 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
6141: respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6142: but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.
6144: Similar for Set2.
6146: This routine merges the two sets of nonzeros row by row and removes repeats.
6148: Output Parameters: (memory is allocated by the caller)
6150: i[],j[]: the CSR of the merged matrix, which has m rows.
6151: imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6152: imap2[]: similar to imap1[], but for Set2.
6153: Note we order nonzeros row-by-row and from left to right.
6154: */
6155: 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[])
6156: {
6157: PetscInt r, m; /* Row index of mat */
6158: PetscCount t, t1, t2, b1, e1, b2, e2;
6160: PetscFunctionBegin;
6161: PetscCall(MatGetLocalSize(mat, &m, NULL));
6162: t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6163: i[0] = 0;
6164: for (r = 0; r < m; r++) { /* Do row by row merging */
6165: b1 = rowBegin1[r];
6166: e1 = rowEnd1[r];
6167: b2 = rowBegin2[r];
6168: e2 = rowEnd2[r];
6169: while (b1 < e1 && b2 < e2) {
6170: if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6171: j[t] = j1[b1];
6172: imap1[t1] = t;
6173: imap2[t2] = t;
6174: b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6175: b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6176: t1++;
6177: t2++;
6178: t++;
6179: } else if (j1[b1] < j2[b2]) {
6180: j[t] = j1[b1];
6181: imap1[t1] = t;
6182: b1 += jmap1[t1 + 1] - jmap1[t1];
6183: t1++;
6184: t++;
6185: } else {
6186: j[t] = j2[b2];
6187: imap2[t2] = t;
6188: b2 += jmap2[t2 + 1] - jmap2[t2];
6189: t2++;
6190: t++;
6191: }
6192: }
6193: /* Merge the remaining in either j1[] or j2[] */
6194: while (b1 < e1) {
6195: j[t] = j1[b1];
6196: imap1[t1] = t;
6197: b1 += jmap1[t1 + 1] - jmap1[t1];
6198: t1++;
6199: t++;
6200: }
6201: while (b2 < e2) {
6202: j[t] = j2[b2];
6203: imap2[t2] = t;
6204: b2 += jmap2[t2 + 1] - jmap2[t2];
6205: t2++;
6206: t++;
6207: }
6208: PetscCall(PetscIntCast(t, i + r + 1));
6209: }
6210: PetscFunctionReturn(PETSC_SUCCESS);
6211: }
6213: /*
6214: Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block
6216: Input Parameters:
6217: mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6218: 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[]
6219: respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.
6221: i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6222: i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.
6224: Output Parameters:
6225: j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6226: rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6227: 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,
6228: and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.
6230: Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6231: Atot: number of entries belonging to the diagonal block.
6232: Annz: number of unique nonzeros belonging to the diagonal block.
6233: Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6234: repeats (i.e., same 'i,j' pair).
6235: Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6236: is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.
6238: Atot: number of entries belonging to the diagonal block
6239: Annz: number of unique nonzeros belonging to the diagonal block.
6241: Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.
6243: Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6244: */
6245: 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_)
6246: {
6247: PetscInt cstart, cend, rstart, rend, row, col;
6248: PetscCount Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6249: PetscCount Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6250: PetscCount k, m, p, q, r, s, mid;
6251: PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;
6253: PetscFunctionBegin;
6254: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6255: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6256: m = rend - rstart;
6258: /* Skip negative rows */
6259: for (k = 0; k < n; k++)
6260: if (i[k] >= 0) break;
6262: /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6263: fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6264: */
6265: while (k < n) {
6266: row = i[k];
6267: /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6268: for (s = k; s < n; s++)
6269: if (i[s] != row) break;
6271: /* Shift diag columns to range of [-PETSC_INT_MAX, -1] */
6272: for (p = k; p < s; p++) {
6273: if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_INT_MAX;
6274: else PetscAssert((j[p] >= 0) && (j[p] <= mat->cmap->N), PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index %" PetscInt_FMT " is out of range", j[p]);
6275: }
6276: PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6277: PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6278: rowBegin[row - rstart] = k;
6279: rowMid[row - rstart] = mid;
6280: rowEnd[row - rstart] = s;
6282: /* Count nonzeros of this diag/offdiag row, which might have repeats */
6283: Atot += mid - k;
6284: Btot += s - mid;
6286: /* Count unique nonzeros of this diag row */
6287: for (p = k; p < mid;) {
6288: col = j[p];
6289: do {
6290: j[p] += PETSC_INT_MAX; /* Revert the modified diagonal indices */
6291: p++;
6292: } while (p < mid && j[p] == col);
6293: Annz++;
6294: }
6296: /* Count unique nonzeros of this offdiag row */
6297: for (p = mid; p < s;) {
6298: col = j[p];
6299: do {
6300: p++;
6301: } while (p < s && j[p] == col);
6302: Bnnz++;
6303: }
6304: k = s;
6305: }
6307: /* Allocation according to Atot, Btot, Annz, Bnnz */
6308: PetscCall(PetscMalloc1(Atot, &Aperm));
6309: PetscCall(PetscMalloc1(Btot, &Bperm));
6310: PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6311: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));
6313: /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6314: Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6315: for (r = 0; r < m; r++) {
6316: k = rowBegin[r];
6317: mid = rowMid[r];
6318: s = rowEnd[r];
6319: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Aperm, Atot), PetscSafePointerPlusOffset(perm, k), mid - k));
6320: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Bperm, Btot), PetscSafePointerPlusOffset(perm, mid), s - mid));
6321: Atot += mid - k;
6322: Btot += s - mid;
6324: /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6325: for (p = k; p < mid;) {
6326: col = j[p];
6327: q = p;
6328: do {
6329: p++;
6330: } while (p < mid && j[p] == col);
6331: Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6332: Annz++;
6333: }
6335: for (p = mid; p < s;) {
6336: col = j[p];
6337: q = p;
6338: do {
6339: p++;
6340: } while (p < s && j[p] == col);
6341: Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6342: Bnnz++;
6343: }
6344: }
6345: /* Output */
6346: *Aperm_ = Aperm;
6347: *Annz_ = Annz;
6348: *Atot_ = Atot;
6349: *Ajmap_ = Ajmap;
6350: *Bperm_ = Bperm;
6351: *Bnnz_ = Bnnz;
6352: *Btot_ = Btot;
6353: *Bjmap_ = Bjmap;
6354: PetscFunctionReturn(PETSC_SUCCESS);
6355: }
6357: /*
6358: Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix
6360: Input Parameters:
6361: nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6362: nnz: number of unique nonzeros in the merged matrix
6363: imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6364: jmap[nnz1+1]: i-th nonzero in the set has jmap[i+1] - jmap[i] repeats in the set
6366: Output Parameter: (memory is allocated by the caller)
6367: jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set
6369: Example:
6370: nnz1 = 4
6371: nnz = 6
6372: imap = [1,3,4,5]
6373: jmap = [0,3,5,6,7]
6374: then,
6375: jmap_new = [0,0,3,3,5,6,7]
6376: */
6377: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6378: {
6379: PetscCount k, p;
6381: PetscFunctionBegin;
6382: jmap_new[0] = 0;
6383: p = nnz; /* p loops over jmap_new[] backwards */
6384: for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6385: for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6386: }
6387: for (; p >= 0; p--) jmap_new[p] = jmap[0];
6388: PetscFunctionReturn(PETSC_SUCCESS);
6389: }
6391: static PetscErrorCode MatCOOStructDestroy_MPIAIJ(void *data)
6392: {
6393: MatCOOStruct_MPIAIJ *coo = (MatCOOStruct_MPIAIJ *)data;
6395: PetscFunctionBegin;
6396: PetscCall(PetscSFDestroy(&coo->sf));
6397: PetscCall(PetscFree(coo->Aperm1));
6398: PetscCall(PetscFree(coo->Bperm1));
6399: PetscCall(PetscFree(coo->Ajmap1));
6400: PetscCall(PetscFree(coo->Bjmap1));
6401: PetscCall(PetscFree(coo->Aimap2));
6402: PetscCall(PetscFree(coo->Bimap2));
6403: PetscCall(PetscFree(coo->Aperm2));
6404: PetscCall(PetscFree(coo->Bperm2));
6405: PetscCall(PetscFree(coo->Ajmap2));
6406: PetscCall(PetscFree(coo->Bjmap2));
6407: PetscCall(PetscFree(coo->Cperm1));
6408: PetscCall(PetscFree2(coo->sendbuf, coo->recvbuf));
6409: PetscCall(PetscFree(coo));
6410: PetscFunctionReturn(PETSC_SUCCESS);
6411: }
6413: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6414: {
6415: MPI_Comm comm;
6416: PetscMPIInt rank, size;
6417: PetscInt m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6418: PetscCount k, p, q, rem; /* Loop variables over coo arrays */
6419: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6420: PetscContainer container;
6421: MatCOOStruct_MPIAIJ *coo;
6423: PetscFunctionBegin;
6424: PetscCall(PetscFree(mpiaij->garray));
6425: PetscCall(VecDestroy(&mpiaij->lvec));
6426: #if defined(PETSC_USE_CTABLE)
6427: PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6428: #else
6429: PetscCall(PetscFree(mpiaij->colmap));
6430: #endif
6431: PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6432: mat->assembled = PETSC_FALSE;
6433: mat->was_assembled = PETSC_FALSE;
6435: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6436: PetscCallMPI(MPI_Comm_size(comm, &size));
6437: PetscCallMPI(MPI_Comm_rank(comm, &rank));
6438: PetscCall(PetscLayoutSetUp(mat->rmap));
6439: PetscCall(PetscLayoutSetUp(mat->cmap));
6440: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6441: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6442: PetscCall(MatGetLocalSize(mat, &m, &n));
6443: PetscCall(MatGetSize(mat, &M, &N));
6445: /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6446: /* entries come first, then local rows, then remote rows. */
6447: PetscCount n1 = coo_n, *perm1;
6448: PetscInt *i1 = coo_i, *j1 = coo_j;
6450: PetscCall(PetscMalloc1(n1, &perm1));
6451: for (k = 0; k < n1; k++) perm1[k] = k;
6453: /* Manipulate indices so that entries with negative row or col indices will have smallest
6454: row indices, local entries will have greater but negative row indices, and remote entries
6455: will have positive row indices.
6456: */
6457: for (k = 0; k < n1; k++) {
6458: if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_INT_MIN; /* e.g., -2^31, minimal to move them ahead */
6459: 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] */
6460: else {
6461: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6462: if (mpiaij->donotstash) i1[k] = PETSC_INT_MIN; /* Ignore offproc entries as if they had negative indices */
6463: }
6464: }
6466: /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6467: PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));
6469: /* Advance k to the first entry we need to take care of */
6470: for (k = 0; k < n1; k++)
6471: if (i1[k] > PETSC_INT_MIN) break;
6472: PetscCount i1start = k;
6474: PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_INT_MAX, &rem)); /* rem is upper bound of the last local row */
6475: for (; k < rem; k++) i1[k] += PETSC_INT_MAX; /* Revert row indices of local rows*/
6477: /* Send remote rows to their owner */
6478: /* Find which rows should be sent to which remote ranks*/
6479: PetscInt nsend = 0; /* Number of MPI ranks to send data to */
6480: PetscMPIInt *sendto; /* [nsend], storing remote ranks */
6481: PetscInt *nentries; /* [nsend], storing number of entries sent to remote ranks; Assume PetscInt is big enough for this count, and error if not */
6482: const PetscInt *ranges;
6483: PetscInt maxNsend = size >= 128 ? 128 : size; /* Assume max 128 neighbors; realloc when needed */
6485: PetscCall(PetscLayoutGetRanges(mat->rmap, &ranges));
6486: PetscCall(PetscMalloc2(maxNsend, &sendto, maxNsend, &nentries));
6487: for (k = rem; k < n1;) {
6488: PetscMPIInt owner;
6489: PetscInt firstRow, lastRow;
6491: /* Locate a row range */
6492: firstRow = i1[k]; /* first row of this owner */
6493: PetscCall(PetscLayoutFindOwner(mat->rmap, firstRow, &owner));
6494: lastRow = ranges[owner + 1] - 1; /* last row of this owner */
6496: /* Find the first index 'p' in [k,n) with i[p] belonging to next owner */
6497: PetscCall(PetscSortedIntUpperBound(i1, k, n1, lastRow, &p));
6499: /* All entries in [k,p) belong to this remote owner */
6500: if (nsend >= maxNsend) { /* Double the remote ranks arrays if not long enough */
6501: PetscMPIInt *sendto2;
6502: PetscInt *nentries2;
6503: PetscInt maxNsend2 = (maxNsend <= size / 2) ? maxNsend * 2 : size;
6505: PetscCall(PetscMalloc2(maxNsend2, &sendto2, maxNsend2, &nentries2));
6506: PetscCall(PetscArraycpy(sendto2, sendto, maxNsend));
6507: PetscCall(PetscArraycpy(nentries2, nentries2, maxNsend + 1));
6508: PetscCall(PetscFree2(sendto, nentries2));
6509: sendto = sendto2;
6510: nentries = nentries2;
6511: maxNsend = maxNsend2;
6512: }
6513: sendto[nsend] = owner;
6514: PetscCall(PetscIntCast(p - k, &nentries[nsend]));
6515: nsend++;
6516: k = p;
6517: }
6519: /* Build 1st SF to know offsets on remote to send data */
6520: PetscSF sf1;
6521: PetscInt nroots = 1, nroots2 = 0;
6522: PetscInt nleaves = nsend, nleaves2 = 0;
6523: PetscInt *offsets;
6524: PetscSFNode *iremote;
6526: PetscCall(PetscSFCreate(comm, &sf1));
6527: PetscCall(PetscMalloc1(nsend, &iremote));
6528: PetscCall(PetscMalloc1(nsend, &offsets));
6529: for (k = 0; k < nsend; k++) {
6530: iremote[k].rank = sendto[k];
6531: iremote[k].index = 0;
6532: nleaves2 += nentries[k];
6533: PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6534: }
6535: PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6536: PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6537: PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6538: PetscCall(PetscSFDestroy(&sf1));
6539: PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT, nleaves2, n1 - rem);
6541: /* Build 2nd SF to send remote COOs to their owner */
6542: PetscSF sf2;
6543: nroots = nroots2;
6544: nleaves = nleaves2;
6545: PetscCall(PetscSFCreate(comm, &sf2));
6546: PetscCall(PetscSFSetFromOptions(sf2));
6547: PetscCall(PetscMalloc1(nleaves, &iremote));
6548: p = 0;
6549: for (k = 0; k < nsend; k++) {
6550: PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6551: for (q = 0; q < nentries[k]; q++, p++) {
6552: iremote[p].rank = sendto[k];
6553: PetscCall(PetscIntCast(offsets[k] + q, &iremote[p].index));
6554: }
6555: }
6556: PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6558: /* Send the remote COOs to their owner */
6559: PetscInt n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6560: PetscCount *perm2; /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6561: PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6562: PetscAssert(rem == 0 || i1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6563: PetscAssert(rem == 0 || j1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6564: PetscInt *i1prem = PetscSafePointerPlusOffset(i1, rem);
6565: PetscInt *j1prem = PetscSafePointerPlusOffset(j1, rem);
6566: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1prem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6567: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1prem, i2, MPI_REPLACE));
6568: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1prem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6569: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1prem, j2, MPI_REPLACE));
6571: PetscCall(PetscFree(offsets));
6572: PetscCall(PetscFree2(sendto, nentries));
6574: /* Sort received COOs by row along with the permutation array */
6575: for (k = 0; k < n2; k++) perm2[k] = k;
6576: PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));
6578: /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6579: PetscCount *Cperm1;
6580: PetscAssert(rem == 0 || perm1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6581: PetscCount *perm1prem = PetscSafePointerPlusOffset(perm1, rem);
6582: PetscCall(PetscMalloc1(nleaves, &Cperm1));
6583: PetscCall(PetscArraycpy(Cperm1, perm1prem, nleaves));
6585: /* Support for HYPRE matrices, kind of a hack.
6586: Swap min column with diagonal so that diagonal values will go first */
6587: PetscBool hypre;
6588: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", ((PetscObject)mat)->name, &hypre));
6589: if (hypre) {
6590: PetscInt *minj;
6591: PetscBT hasdiag;
6593: PetscCall(PetscBTCreate(m, &hasdiag));
6594: PetscCall(PetscMalloc1(m, &minj));
6595: for (k = 0; k < m; k++) minj[k] = PETSC_INT_MAX;
6596: for (k = i1start; k < rem; k++) {
6597: if (j1[k] < cstart || j1[k] >= cend) continue;
6598: const PetscInt rindex = i1[k] - rstart;
6599: if ((j1[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6600: minj[rindex] = PetscMin(minj[rindex], j1[k]);
6601: }
6602: for (k = 0; k < n2; k++) {
6603: if (j2[k] < cstart || j2[k] >= cend) continue;
6604: const PetscInt rindex = i2[k] - rstart;
6605: if ((j2[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6606: minj[rindex] = PetscMin(minj[rindex], j2[k]);
6607: }
6608: for (k = i1start; k < rem; k++) {
6609: const PetscInt rindex = i1[k] - rstart;
6610: if (j1[k] < cstart || j1[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6611: if (j1[k] == minj[rindex]) j1[k] = i1[k] + (cstart - rstart);
6612: else if ((j1[k] - cstart) == rindex) j1[k] = minj[rindex];
6613: }
6614: for (k = 0; k < n2; k++) {
6615: const PetscInt rindex = i2[k] - rstart;
6616: if (j2[k] < cstart || j2[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6617: if (j2[k] == minj[rindex]) j2[k] = i2[k] + (cstart - rstart);
6618: else if ((j2[k] - cstart) == rindex) j2[k] = minj[rindex];
6619: }
6620: PetscCall(PetscBTDestroy(&hasdiag));
6621: PetscCall(PetscFree(minj));
6622: }
6624: /* Split local COOs and received COOs into diag/offdiag portions */
6625: PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6626: PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1;
6627: PetscCount Annz1, Bnnz1, Atot1, Btot1;
6628: PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6629: PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6630: PetscCount Annz2, Bnnz2, Atot2, Btot2;
6632: PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6633: PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6634: PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6635: PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));
6637: /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6638: PetscInt *Ai, *Bi;
6639: PetscInt *Aj, *Bj;
6641: PetscCall(PetscMalloc1(m + 1, &Ai));
6642: PetscCall(PetscMalloc1(m + 1, &Bi));
6643: PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6644: PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));
6646: PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6647: PetscCall(PetscMalloc1(Annz1, &Aimap1));
6648: PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6649: PetscCall(PetscMalloc1(Annz2, &Aimap2));
6650: PetscCall(PetscMalloc1(Bnnz2, &Bimap2));
6652: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6653: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));
6655: /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we */
6656: /* expect nonzeros in A/B most likely have local contributing entries */
6657: PetscInt Annz = Ai[m];
6658: PetscInt Bnnz = Bi[m];
6659: PetscCount *Ajmap1_new, *Bjmap1_new;
6661: PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6662: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));
6664: PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6665: PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));
6667: PetscCall(PetscFree(Aimap1));
6668: PetscCall(PetscFree(Ajmap1));
6669: PetscCall(PetscFree(Bimap1));
6670: PetscCall(PetscFree(Bjmap1));
6671: PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6672: PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6673: PetscCall(PetscFree(perm1));
6674: PetscCall(PetscFree3(i2, j2, perm2));
6676: Ajmap1 = Ajmap1_new;
6677: Bjmap1 = Bjmap1_new;
6679: /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6680: if (Annz < Annz1 + Annz2) {
6681: PetscInt *Aj_new;
6682: PetscCall(PetscMalloc1(Annz, &Aj_new));
6683: PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6684: PetscCall(PetscFree(Aj));
6685: Aj = Aj_new;
6686: }
6688: if (Bnnz < Bnnz1 + Bnnz2) {
6689: PetscInt *Bj_new;
6690: PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6691: PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6692: PetscCall(PetscFree(Bj));
6693: Bj = Bj_new;
6694: }
6696: /* Create new submatrices for on-process and off-process coupling */
6697: PetscScalar *Aa, *Ba;
6698: MatType rtype;
6699: Mat_SeqAIJ *a, *b;
6700: PetscObjectState state;
6701: PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6702: PetscCall(PetscCalloc1(Bnnz, &Ba));
6703: /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6704: if (cstart) {
6705: for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6706: }
6708: PetscCall(MatGetRootType_Private(mat, &rtype));
6710: MatSeqXAIJGetOptions_Private(mpiaij->A);
6711: PetscCall(MatDestroy(&mpiaij->A));
6712: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6713: PetscCall(MatSetBlockSizesFromMats(mpiaij->A, mat, mat));
6714: MatSeqXAIJRestoreOptions_Private(mpiaij->A);
6716: MatSeqXAIJGetOptions_Private(mpiaij->B);
6717: PetscCall(MatDestroy(&mpiaij->B));
6718: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6719: PetscCall(MatSetBlockSizesFromMats(mpiaij->B, mat, mat));
6720: MatSeqXAIJRestoreOptions_Private(mpiaij->B);
6722: PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6723: mat->was_assembled = PETSC_TRUE; // was_assembled in effect means the Mvctx is built; doing so avoids redundant MatSetUpMultiply_MPIAIJ
6724: state = mpiaij->A->nonzerostate + mpiaij->B->nonzerostate;
6725: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
6727: a = (Mat_SeqAIJ *)mpiaij->A->data;
6728: b = (Mat_SeqAIJ *)mpiaij->B->data;
6729: a->free_a = PETSC_TRUE;
6730: a->free_ij = PETSC_TRUE;
6731: b->free_a = PETSC_TRUE;
6732: b->free_ij = PETSC_TRUE;
6733: a->maxnz = a->nz;
6734: b->maxnz = b->nz;
6736: /* conversion must happen AFTER multiply setup */
6737: PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6738: PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6739: PetscCall(VecDestroy(&mpiaij->lvec));
6740: PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));
6742: // Put the COO struct in a container and then attach that to the matrix
6743: PetscCall(PetscMalloc1(1, &coo));
6744: coo->n = coo_n;
6745: coo->sf = sf2;
6746: coo->sendlen = nleaves;
6747: coo->recvlen = nroots;
6748: coo->Annz = Annz;
6749: coo->Bnnz = Bnnz;
6750: coo->Annz2 = Annz2;
6751: coo->Bnnz2 = Bnnz2;
6752: coo->Atot1 = Atot1;
6753: coo->Atot2 = Atot2;
6754: coo->Btot1 = Btot1;
6755: coo->Btot2 = Btot2;
6756: coo->Ajmap1 = Ajmap1;
6757: coo->Aperm1 = Aperm1;
6758: coo->Bjmap1 = Bjmap1;
6759: coo->Bperm1 = Bperm1;
6760: coo->Aimap2 = Aimap2;
6761: coo->Ajmap2 = Ajmap2;
6762: coo->Aperm2 = Aperm2;
6763: coo->Bimap2 = Bimap2;
6764: coo->Bjmap2 = Bjmap2;
6765: coo->Bperm2 = Bperm2;
6766: coo->Cperm1 = Cperm1;
6767: // Allocate in preallocation. If not used, it has zero cost on host
6768: PetscCall(PetscMalloc2(coo->sendlen, &coo->sendbuf, coo->recvlen, &coo->recvbuf));
6769: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
6770: PetscCall(PetscContainerSetPointer(container, coo));
6771: PetscCall(PetscContainerSetUserDestroy(container, MatCOOStructDestroy_MPIAIJ));
6772: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
6773: PetscCall(PetscContainerDestroy(&container));
6774: PetscFunctionReturn(PETSC_SUCCESS);
6775: }
6777: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6778: {
6779: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6780: Mat A = mpiaij->A, B = mpiaij->B;
6781: PetscScalar *Aa, *Ba;
6782: PetscScalar *sendbuf, *recvbuf;
6783: const PetscCount *Ajmap1, *Ajmap2, *Aimap2;
6784: const PetscCount *Bjmap1, *Bjmap2, *Bimap2;
6785: const PetscCount *Aperm1, *Aperm2, *Bperm1, *Bperm2;
6786: const PetscCount *Cperm1;
6787: PetscContainer container;
6788: MatCOOStruct_MPIAIJ *coo;
6790: PetscFunctionBegin;
6791: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
6792: PetscCheck(container, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
6793: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
6794: sendbuf = coo->sendbuf;
6795: recvbuf = coo->recvbuf;
6796: Ajmap1 = coo->Ajmap1;
6797: Ajmap2 = coo->Ajmap2;
6798: Aimap2 = coo->Aimap2;
6799: Bjmap1 = coo->Bjmap1;
6800: Bjmap2 = coo->Bjmap2;
6801: Bimap2 = coo->Bimap2;
6802: Aperm1 = coo->Aperm1;
6803: Aperm2 = coo->Aperm2;
6804: Bperm1 = coo->Bperm1;
6805: Bperm2 = coo->Bperm2;
6806: Cperm1 = coo->Cperm1;
6808: PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6809: PetscCall(MatSeqAIJGetArray(B, &Ba));
6811: /* Pack entries to be sent to remote */
6812: for (PetscCount i = 0; i < coo->sendlen; i++) sendbuf[i] = v[Cperm1[i]];
6814: /* Send remote entries to their owner and overlap the communication with local computation */
6815: PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6816: /* Add local entries to A and B */
6817: for (PetscCount i = 0; i < coo->Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6818: PetscScalar sum = 0.0; /* Do partial summation first to improve numerical stability */
6819: for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6820: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6821: }
6822: for (PetscCount i = 0; i < coo->Bnnz; i++) {
6823: PetscScalar sum = 0.0;
6824: for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6825: Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6826: }
6827: PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));
6829: /* Add received remote entries to A and B */
6830: for (PetscCount i = 0; i < coo->Annz2; i++) {
6831: for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6832: }
6833: for (PetscCount i = 0; i < coo->Bnnz2; i++) {
6834: for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6835: }
6836: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6837: PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6838: PetscFunctionReturn(PETSC_SUCCESS);
6839: }
6841: /*MC
6842: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
6844: Options Database Keys:
6845: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`
6847: Level: beginner
6849: Notes:
6850: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
6851: in this case the values associated with the rows and columns one passes in are set to zero
6852: in the matrix
6854: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
6855: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
6857: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6858: M*/
6859: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6860: {
6861: Mat_MPIAIJ *b;
6862: PetscMPIInt size;
6864: PetscFunctionBegin;
6865: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
6867: PetscCall(PetscNew(&b));
6868: B->data = (void *)b;
6869: B->ops[0] = MatOps_Values;
6870: B->assembled = PETSC_FALSE;
6871: B->insertmode = NOT_SET_VALUES;
6872: b->size = size;
6874: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
6876: /* build cache for off array entries formed */
6877: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
6879: b->donotstash = PETSC_FALSE;
6880: b->colmap = NULL;
6881: b->garray = NULL;
6882: b->roworiented = PETSC_TRUE;
6884: /* stuff used for matrix vector multiply */
6885: b->lvec = NULL;
6886: b->Mvctx = NULL;
6888: /* stuff for MatGetRow() */
6889: b->rowindices = NULL;
6890: b->rowvalues = NULL;
6891: b->getrowactive = PETSC_FALSE;
6893: /* flexible pointer used in CUSPARSE classes */
6894: b->spptr = NULL;
6896: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6897: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6898: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6899: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6900: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6901: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6902: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6903: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6904: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6905: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6906: #if defined(PETSC_HAVE_CUDA)
6907: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6908: #endif
6909: #if defined(PETSC_HAVE_HIP)
6910: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6911: #endif
6912: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6913: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6914: #endif
6915: #if defined(PETSC_HAVE_MKL_SPARSE)
6916: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6917: #endif
6918: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6919: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6920: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6921: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6922: #if defined(PETSC_HAVE_ELEMENTAL)
6923: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6924: #endif
6925: #if defined(PETSC_HAVE_SCALAPACK)
6926: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6927: #endif
6928: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6929: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6930: #if defined(PETSC_HAVE_HYPRE)
6931: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6932: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6933: #endif
6934: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6935: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6936: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6937: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6938: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6939: PetscFunctionReturn(PETSC_SUCCESS);
6940: }
6942: /*@
6943: MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6944: and "off-diagonal" part of the matrix in CSR format.
6946: Collective
6948: Input Parameters:
6949: + comm - MPI communicator
6950: . m - number of local rows (Cannot be `PETSC_DECIDE`)
6951: . n - This value should be the same as the local size used in creating the
6952: x vector for the matrix-vector product $y = Ax$. (or `PETSC_DECIDE` to have
6953: calculated if `N` is given) For square matrices `n` is almost always `m`.
6954: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6955: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6956: . 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
6957: . j - column indices, which must be local, i.e., based off the start column of the diagonal portion
6958: . a - matrix values
6959: . 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
6960: . oj - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6961: - oa - matrix values
6963: Output Parameter:
6964: . mat - the matrix
6966: Level: advanced
6968: Notes:
6969: The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc (even in Fortran). The user
6970: must free the arrays once the matrix has been destroyed and not before.
6972: The `i` and `j` indices are 0 based
6974: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix
6976: This sets local rows and cannot be used to set off-processor values.
6978: Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6979: legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6980: not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
6981: the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
6982: keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
6983: communication if it is known that only local entries will be set.
6985: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
6986: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
6987: @*/
6988: 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)
6989: {
6990: Mat_MPIAIJ *maij;
6992: PetscFunctionBegin;
6993: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
6994: PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
6995: PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
6996: PetscCall(MatCreate(comm, mat));
6997: PetscCall(MatSetSizes(*mat, m, n, M, N));
6998: PetscCall(MatSetType(*mat, MATMPIAIJ));
6999: maij = (Mat_MPIAIJ *)(*mat)->data;
7001: (*mat)->preallocated = PETSC_TRUE;
7003: PetscCall(PetscLayoutSetUp((*mat)->rmap));
7004: PetscCall(PetscLayoutSetUp((*mat)->cmap));
7006: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
7007: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));
7009: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
7010: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
7011: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
7012: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
7013: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
7014: PetscFunctionReturn(PETSC_SUCCESS);
7015: }
7017: typedef struct {
7018: Mat *mp; /* intermediate products */
7019: PetscBool *mptmp; /* is the intermediate product temporary ? */
7020: PetscInt cp; /* number of intermediate products */
7022: /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
7023: PetscInt *startsj_s, *startsj_r;
7024: PetscScalar *bufa;
7025: Mat P_oth;
7027: /* may take advantage of merging product->B */
7028: Mat Bloc; /* B-local by merging diag and off-diag */
7030: /* cusparse does not have support to split between symbolic and numeric phases.
7031: When api_user is true, we don't need to update the numerical values
7032: of the temporary storage */
7033: PetscBool reusesym;
7035: /* support for COO values insertion */
7036: PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
7037: PetscInt **own; /* own[i] points to address of on-process COO indices for Mat mp[i] */
7038: PetscInt **off; /* off[i] points to address of off-process COO indices for Mat mp[i] */
7039: PetscBool hasoffproc; /* if true, have off-process values insertion (i.e. AtB or PtAP) */
7040: PetscSF sf; /* used for non-local values insertion and memory malloc */
7041: PetscMemType mtype;
7043: /* customization */
7044: PetscBool abmerge;
7045: PetscBool P_oth_bind;
7046: } MatMatMPIAIJBACKEND;
7048: static PetscErrorCode MatDestroy_MatMatMPIAIJBACKEND(void *data)
7049: {
7050: MatMatMPIAIJBACKEND *mmdata = (MatMatMPIAIJBACKEND *)data;
7051: PetscInt i;
7053: PetscFunctionBegin;
7054: PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
7055: PetscCall(PetscFree(mmdata->bufa));
7056: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
7057: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
7058: PetscCall(MatDestroy(&mmdata->P_oth));
7059: PetscCall(MatDestroy(&mmdata->Bloc));
7060: PetscCall(PetscSFDestroy(&mmdata->sf));
7061: for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
7062: PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
7063: PetscCall(PetscFree(mmdata->own[0]));
7064: PetscCall(PetscFree(mmdata->own));
7065: PetscCall(PetscFree(mmdata->off[0]));
7066: PetscCall(PetscFree(mmdata->off));
7067: PetscCall(PetscFree(mmdata));
7068: PetscFunctionReturn(PETSC_SUCCESS);
7069: }
7071: /* Copy selected n entries with indices in idx[] of A to v[].
7072: If idx is NULL, copy the whole data array of A to v[]
7073: */
7074: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
7075: {
7076: PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);
7078: PetscFunctionBegin;
7079: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
7080: if (f) {
7081: PetscCall((*f)(A, n, idx, v));
7082: } else {
7083: const PetscScalar *vv;
7085: PetscCall(MatSeqAIJGetArrayRead(A, &vv));
7086: if (n && idx) {
7087: PetscScalar *w = v;
7088: const PetscInt *oi = idx;
7089: PetscInt j;
7091: for (j = 0; j < n; j++) *w++ = vv[*oi++];
7092: } else {
7093: PetscCall(PetscArraycpy(v, vv, n));
7094: }
7095: PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7096: }
7097: PetscFunctionReturn(PETSC_SUCCESS);
7098: }
7100: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7101: {
7102: MatMatMPIAIJBACKEND *mmdata;
7103: PetscInt i, n_d, n_o;
7105: PetscFunctionBegin;
7106: MatCheckProduct(C, 1);
7107: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7108: mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7109: if (!mmdata->reusesym) { /* update temporary matrices */
7110: 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));
7111: if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7112: }
7113: mmdata->reusesym = PETSC_FALSE;
7115: for (i = 0; i < mmdata->cp; i++) {
7116: 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]);
7117: PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7118: }
7119: for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7120: PetscInt noff;
7122: PetscCall(PetscIntCast(mmdata->off[i + 1] - mmdata->off[i], &noff));
7123: if (mmdata->mptmp[i]) continue;
7124: if (noff) {
7125: PetscInt nown;
7127: PetscCall(PetscIntCast(mmdata->own[i + 1] - mmdata->own[i], &nown));
7128: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7129: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7130: n_o += noff;
7131: n_d += nown;
7132: } else {
7133: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;
7135: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7136: n_d += mm->nz;
7137: }
7138: }
7139: if (mmdata->hasoffproc) { /* offprocess insertion */
7140: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7141: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7142: }
7143: PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7144: PetscFunctionReturn(PETSC_SUCCESS);
7145: }
7147: /* Support for Pt * A, A * P, or Pt * A * P */
7148: #define MAX_NUMBER_INTERMEDIATE 4
7149: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7150: {
7151: Mat_Product *product = C->product;
7152: Mat A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7153: Mat_MPIAIJ *a, *p;
7154: MatMatMPIAIJBACKEND *mmdata;
7155: ISLocalToGlobalMapping P_oth_l2g = NULL;
7156: IS glob = NULL;
7157: const char *prefix;
7158: char pprefix[256];
7159: const PetscInt *globidx, *P_oth_idx;
7160: PetscInt i, j, cp, m, n, M, N, *coo_i, *coo_j;
7161: PetscCount ncoo, ncoo_d, ncoo_o, ncoo_oown;
7162: PetscInt cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7163: /* type-0: consecutive, start from 0; type-1: consecutive with */
7164: /* a base offset; type-2: sparse with a local to global map table */
7165: const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE]; /* col/row local to global map array (table) for type-2 map type */
7167: MatProductType ptype;
7168: PetscBool mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7169: PetscMPIInt size;
7171: PetscFunctionBegin;
7172: MatCheckProduct(C, 1);
7173: PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7174: ptype = product->type;
7175: if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7176: ptype = MATPRODUCT_AB;
7177: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7178: }
7179: switch (ptype) {
7180: case MATPRODUCT_AB:
7181: A = product->A;
7182: P = product->B;
7183: m = A->rmap->n;
7184: n = P->cmap->n;
7185: M = A->rmap->N;
7186: N = P->cmap->N;
7187: hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7188: break;
7189: case MATPRODUCT_AtB:
7190: P = product->A;
7191: A = product->B;
7192: m = P->cmap->n;
7193: n = A->cmap->n;
7194: M = P->cmap->N;
7195: N = A->cmap->N;
7196: hasoffproc = PETSC_TRUE;
7197: break;
7198: case MATPRODUCT_PtAP:
7199: A = product->A;
7200: P = product->B;
7201: m = P->cmap->n;
7202: n = P->cmap->n;
7203: M = P->cmap->N;
7204: N = P->cmap->N;
7205: hasoffproc = PETSC_TRUE;
7206: break;
7207: default:
7208: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7209: }
7210: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7211: if (size == 1) hasoffproc = PETSC_FALSE;
7213: /* defaults */
7214: for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7215: mp[i] = NULL;
7216: mptmp[i] = PETSC_FALSE;
7217: rmapt[i] = -1;
7218: cmapt[i] = -1;
7219: rmapa[i] = NULL;
7220: cmapa[i] = NULL;
7221: }
7223: /* customization */
7224: PetscCall(PetscNew(&mmdata));
7225: mmdata->reusesym = product->api_user;
7226: if (ptype == MATPRODUCT_AB) {
7227: if (product->api_user) {
7228: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7229: PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7230: PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7231: PetscOptionsEnd();
7232: } else {
7233: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7234: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7235: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7236: PetscOptionsEnd();
7237: }
7238: } else if (ptype == MATPRODUCT_PtAP) {
7239: if (product->api_user) {
7240: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7241: PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7242: PetscOptionsEnd();
7243: } else {
7244: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7245: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7246: PetscOptionsEnd();
7247: }
7248: }
7249: a = (Mat_MPIAIJ *)A->data;
7250: p = (Mat_MPIAIJ *)P->data;
7251: PetscCall(MatSetSizes(C, m, n, M, N));
7252: PetscCall(PetscLayoutSetUp(C->rmap));
7253: PetscCall(PetscLayoutSetUp(C->cmap));
7254: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7255: PetscCall(MatGetOptionsPrefix(C, &prefix));
7257: cp = 0;
7258: switch (ptype) {
7259: case MATPRODUCT_AB: /* A * P */
7260: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7262: /* A_diag * P_local (merged or not) */
7263: if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7264: /* P is product->B */
7265: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7266: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7267: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7268: PetscCall(MatProductSetFill(mp[cp], product->fill));
7269: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7270: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7271: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7272: mp[cp]->product->api_user = product->api_user;
7273: PetscCall(MatProductSetFromOptions(mp[cp]));
7274: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7275: PetscCall(ISGetIndices(glob, &globidx));
7276: rmapt[cp] = 1;
7277: cmapt[cp] = 2;
7278: cmapa[cp] = globidx;
7279: mptmp[cp] = PETSC_FALSE;
7280: cp++;
7281: } else { /* A_diag * P_diag and A_diag * P_off */
7282: PetscCall(MatProductCreate(a->A, p->A, NULL, &mp[cp]));
7283: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7284: PetscCall(MatProductSetFill(mp[cp], product->fill));
7285: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7286: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7287: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7288: mp[cp]->product->api_user = product->api_user;
7289: PetscCall(MatProductSetFromOptions(mp[cp]));
7290: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7291: rmapt[cp] = 1;
7292: cmapt[cp] = 1;
7293: mptmp[cp] = PETSC_FALSE;
7294: cp++;
7295: PetscCall(MatProductCreate(a->A, p->B, NULL, &mp[cp]));
7296: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7297: PetscCall(MatProductSetFill(mp[cp], product->fill));
7298: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7299: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7300: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7301: mp[cp]->product->api_user = product->api_user;
7302: PetscCall(MatProductSetFromOptions(mp[cp]));
7303: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7304: rmapt[cp] = 1;
7305: cmapt[cp] = 2;
7306: cmapa[cp] = p->garray;
7307: mptmp[cp] = PETSC_FALSE;
7308: cp++;
7309: }
7311: /* A_off * P_other */
7312: if (mmdata->P_oth) {
7313: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7314: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7315: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7316: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7317: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7318: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7319: PetscCall(MatProductSetFill(mp[cp], product->fill));
7320: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7321: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7322: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7323: mp[cp]->product->api_user = product->api_user;
7324: PetscCall(MatProductSetFromOptions(mp[cp]));
7325: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7326: rmapt[cp] = 1;
7327: cmapt[cp] = 2;
7328: cmapa[cp] = P_oth_idx;
7329: mptmp[cp] = PETSC_FALSE;
7330: cp++;
7331: }
7332: break;
7334: case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7335: /* A is product->B */
7336: PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7337: if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7338: PetscCall(MatProductCreate(mmdata->Bloc, mmdata->Bloc, NULL, &mp[cp]));
7339: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7340: PetscCall(MatProductSetFill(mp[cp], product->fill));
7341: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7342: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7343: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7344: mp[cp]->product->api_user = product->api_user;
7345: PetscCall(MatProductSetFromOptions(mp[cp]));
7346: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7347: PetscCall(ISGetIndices(glob, &globidx));
7348: rmapt[cp] = 2;
7349: rmapa[cp] = globidx;
7350: cmapt[cp] = 2;
7351: cmapa[cp] = globidx;
7352: mptmp[cp] = PETSC_FALSE;
7353: cp++;
7354: } else {
7355: PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7356: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7357: PetscCall(MatProductSetFill(mp[cp], product->fill));
7358: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7359: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7360: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7361: mp[cp]->product->api_user = product->api_user;
7362: PetscCall(MatProductSetFromOptions(mp[cp]));
7363: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7364: PetscCall(ISGetIndices(glob, &globidx));
7365: rmapt[cp] = 1;
7366: cmapt[cp] = 2;
7367: cmapa[cp] = globidx;
7368: mptmp[cp] = PETSC_FALSE;
7369: cp++;
7370: PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7371: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7372: PetscCall(MatProductSetFill(mp[cp], product->fill));
7373: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7374: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7375: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7376: mp[cp]->product->api_user = product->api_user;
7377: PetscCall(MatProductSetFromOptions(mp[cp]));
7378: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7379: rmapt[cp] = 2;
7380: rmapa[cp] = p->garray;
7381: cmapt[cp] = 2;
7382: cmapa[cp] = globidx;
7383: mptmp[cp] = PETSC_FALSE;
7384: cp++;
7385: }
7386: break;
7387: case MATPRODUCT_PtAP:
7388: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7389: /* P is product->B */
7390: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7391: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7392: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
7393: PetscCall(MatProductSetFill(mp[cp], product->fill));
7394: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7395: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7396: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7397: mp[cp]->product->api_user = product->api_user;
7398: PetscCall(MatProductSetFromOptions(mp[cp]));
7399: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7400: PetscCall(ISGetIndices(glob, &globidx));
7401: rmapt[cp] = 2;
7402: rmapa[cp] = globidx;
7403: cmapt[cp] = 2;
7404: cmapa[cp] = globidx;
7405: mptmp[cp] = PETSC_FALSE;
7406: cp++;
7407: if (mmdata->P_oth) {
7408: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7409: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7410: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7411: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7412: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7413: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7414: PetscCall(MatProductSetFill(mp[cp], product->fill));
7415: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7416: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7417: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7418: mp[cp]->product->api_user = product->api_user;
7419: PetscCall(MatProductSetFromOptions(mp[cp]));
7420: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7421: mptmp[cp] = PETSC_TRUE;
7422: cp++;
7423: PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7424: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7425: PetscCall(MatProductSetFill(mp[cp], product->fill));
7426: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7427: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7428: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7429: mp[cp]->product->api_user = product->api_user;
7430: PetscCall(MatProductSetFromOptions(mp[cp]));
7431: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7432: rmapt[cp] = 2;
7433: rmapa[cp] = globidx;
7434: cmapt[cp] = 2;
7435: cmapa[cp] = P_oth_idx;
7436: mptmp[cp] = PETSC_FALSE;
7437: cp++;
7438: }
7439: break;
7440: default:
7441: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7442: }
7443: /* sanity check */
7444: if (size > 1)
7445: 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);
7447: PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7448: for (i = 0; i < cp; i++) {
7449: mmdata->mp[i] = mp[i];
7450: mmdata->mptmp[i] = mptmp[i];
7451: }
7452: mmdata->cp = cp;
7453: C->product->data = mmdata;
7454: C->product->destroy = MatDestroy_MatMatMPIAIJBACKEND;
7455: C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;
7457: /* memory type */
7458: mmdata->mtype = PETSC_MEMTYPE_HOST;
7459: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7460: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7461: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7462: if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7463: else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7464: else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;
7466: /* prepare coo coordinates for values insertion */
7468: /* count total nonzeros of those intermediate seqaij Mats
7469: ncoo_d: # of nonzeros of matrices that do not have offproc entries
7470: ncoo_o: # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7471: ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7472: */
7473: for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7474: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7475: if (mptmp[cp]) continue;
7476: if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7477: const PetscInt *rmap = rmapa[cp];
7478: const PetscInt mr = mp[cp]->rmap->n;
7479: const PetscInt rs = C->rmap->rstart;
7480: const PetscInt re = C->rmap->rend;
7481: const PetscInt *ii = mm->i;
7482: for (i = 0; i < mr; i++) {
7483: const PetscInt gr = rmap[i];
7484: const PetscInt nz = ii[i + 1] - ii[i];
7485: if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7486: else ncoo_oown += nz; /* this row is local */
7487: }
7488: } else ncoo_d += mm->nz;
7489: }
7491: /*
7492: ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc
7494: ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.
7496: off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].
7498: off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7499: own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7500: so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.
7502: coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7503: 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.
7504: */
7505: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7506: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));
7508: /* gather (i,j) of nonzeros inserted by remote procs */
7509: if (hasoffproc) {
7510: PetscSF msf;
7511: PetscInt ncoo2, *coo_i2, *coo_j2;
7513: PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7514: PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7515: PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */
7517: for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7518: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7519: PetscInt *idxoff = mmdata->off[cp];
7520: PetscInt *idxown = mmdata->own[cp];
7521: if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7522: const PetscInt *rmap = rmapa[cp];
7523: const PetscInt *cmap = cmapa[cp];
7524: const PetscInt *ii = mm->i;
7525: PetscInt *coi = coo_i + ncoo_o;
7526: PetscInt *coj = coo_j + ncoo_o;
7527: const PetscInt mr = mp[cp]->rmap->n;
7528: const PetscInt rs = C->rmap->rstart;
7529: const PetscInt re = C->rmap->rend;
7530: const PetscInt cs = C->cmap->rstart;
7531: for (i = 0; i < mr; i++) {
7532: const PetscInt *jj = mm->j + ii[i];
7533: const PetscInt gr = rmap[i];
7534: const PetscInt nz = ii[i + 1] - ii[i];
7535: if (gr < rs || gr >= re) { /* this is an offproc row */
7536: for (j = ii[i]; j < ii[i + 1]; j++) {
7537: *coi++ = gr;
7538: *idxoff++ = j;
7539: }
7540: if (!cmapt[cp]) { /* already global */
7541: for (j = 0; j < nz; j++) *coj++ = jj[j];
7542: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7543: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7544: } else { /* offdiag */
7545: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7546: }
7547: ncoo_o += nz;
7548: } else { /* this is a local row */
7549: for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7550: }
7551: }
7552: }
7553: mmdata->off[cp + 1] = idxoff;
7554: mmdata->own[cp + 1] = idxown;
7555: }
7557: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7558: PetscInt incoo_o;
7559: PetscCall(PetscIntCast(ncoo_o, &incoo_o));
7560: PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, incoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7561: PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7562: PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7563: ncoo = ncoo_d + ncoo_oown + ncoo2;
7564: PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7565: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7566: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7567: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7568: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7569: PetscCall(PetscFree2(coo_i, coo_j));
7570: /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7571: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7572: coo_i = coo_i2;
7573: coo_j = coo_j2;
7574: } else { /* no offproc values insertion */
7575: ncoo = ncoo_d;
7576: PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));
7578: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7579: PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7580: PetscCall(PetscSFSetUp(mmdata->sf));
7581: }
7582: mmdata->hasoffproc = hasoffproc;
7584: /* gather (i,j) of nonzeros inserted locally */
7585: for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7586: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7587: PetscInt *coi = coo_i + ncoo_d;
7588: PetscInt *coj = coo_j + ncoo_d;
7589: const PetscInt *jj = mm->j;
7590: const PetscInt *ii = mm->i;
7591: const PetscInt *cmap = cmapa[cp];
7592: const PetscInt *rmap = rmapa[cp];
7593: const PetscInt mr = mp[cp]->rmap->n;
7594: const PetscInt rs = C->rmap->rstart;
7595: const PetscInt re = C->rmap->rend;
7596: const PetscInt cs = C->cmap->rstart;
7598: if (mptmp[cp]) continue;
7599: if (rmapt[cp] == 1) { /* consecutive rows */
7600: /* fill coo_i */
7601: for (i = 0; i < mr; i++) {
7602: const PetscInt gr = i + rs;
7603: for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7604: }
7605: /* fill coo_j */
7606: if (!cmapt[cp]) { /* type-0, already global */
7607: PetscCall(PetscArraycpy(coj, jj, mm->nz));
7608: } else if (cmapt[cp] == 1) { /* type-1, local to global for consecutive columns of C */
7609: for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7610: } else { /* type-2, local to global for sparse columns */
7611: for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7612: }
7613: ncoo_d += mm->nz;
7614: } else if (rmapt[cp] == 2) { /* sparse rows */
7615: for (i = 0; i < mr; i++) {
7616: const PetscInt *jj = mm->j + ii[i];
7617: const PetscInt gr = rmap[i];
7618: const PetscInt nz = ii[i + 1] - ii[i];
7619: if (gr >= rs && gr < re) { /* local rows */
7620: for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7621: if (!cmapt[cp]) { /* type-0, already global */
7622: for (j = 0; j < nz; j++) *coj++ = jj[j];
7623: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7624: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7625: } else { /* type-2, local to global for sparse columns */
7626: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7627: }
7628: ncoo_d += nz;
7629: }
7630: }
7631: }
7632: }
7633: if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7634: PetscCall(ISDestroy(&glob));
7635: if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7636: PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7637: /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7638: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));
7640: /* preallocate with COO data */
7641: PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7642: PetscCall(PetscFree2(coo_i, coo_j));
7643: PetscFunctionReturn(PETSC_SUCCESS);
7644: }
7646: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7647: {
7648: Mat_Product *product = mat->product;
7649: #if defined(PETSC_HAVE_DEVICE)
7650: PetscBool match = PETSC_FALSE;
7651: PetscBool usecpu = PETSC_FALSE;
7652: #else
7653: PetscBool match = PETSC_TRUE;
7654: #endif
7656: PetscFunctionBegin;
7657: MatCheckProduct(mat, 1);
7658: #if defined(PETSC_HAVE_DEVICE)
7659: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7660: if (match) { /* we can always fallback to the CPU if requested */
7661: switch (product->type) {
7662: case MATPRODUCT_AB:
7663: if (product->api_user) {
7664: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7665: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7666: PetscOptionsEnd();
7667: } else {
7668: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7669: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7670: PetscOptionsEnd();
7671: }
7672: break;
7673: case MATPRODUCT_AtB:
7674: if (product->api_user) {
7675: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7676: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7677: PetscOptionsEnd();
7678: } else {
7679: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7680: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7681: PetscOptionsEnd();
7682: }
7683: break;
7684: case MATPRODUCT_PtAP:
7685: if (product->api_user) {
7686: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7687: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7688: PetscOptionsEnd();
7689: } else {
7690: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7691: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7692: PetscOptionsEnd();
7693: }
7694: break;
7695: default:
7696: break;
7697: }
7698: match = (PetscBool)!usecpu;
7699: }
7700: #endif
7701: if (match) {
7702: switch (product->type) {
7703: case MATPRODUCT_AB:
7704: case MATPRODUCT_AtB:
7705: case MATPRODUCT_PtAP:
7706: mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7707: break;
7708: default:
7709: break;
7710: }
7711: }
7712: /* fallback to MPIAIJ ops */
7713: if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7714: PetscFunctionReturn(PETSC_SUCCESS);
7715: }
7717: /*
7718: Produces a set of block column indices of the matrix row, one for each block represented in the original row
7720: n - the number of block indices in cc[]
7721: cc - the block indices (must be large enough to contain the indices)
7722: */
7723: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7724: {
7725: PetscInt cnt = -1, nidx, j;
7726: const PetscInt *idx;
7728: PetscFunctionBegin;
7729: PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7730: if (nidx) {
7731: cnt = 0;
7732: cc[cnt] = idx[0] / bs;
7733: for (j = 1; j < nidx; j++) {
7734: if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7735: }
7736: }
7737: PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7738: *n = cnt + 1;
7739: PetscFunctionReturn(PETSC_SUCCESS);
7740: }
7742: /*
7743: Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows
7745: ncollapsed - the number of block indices
7746: collapsed - the block indices (must be large enough to contain the indices)
7747: */
7748: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7749: {
7750: PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;
7752: PetscFunctionBegin;
7753: PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7754: for (i = start + 1; i < start + bs; i++) {
7755: PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7756: PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7757: cprevtmp = cprev;
7758: cprev = merged;
7759: merged = cprevtmp;
7760: }
7761: *ncollapsed = nprev;
7762: if (collapsed) *collapsed = cprev;
7763: PetscFunctionReturn(PETSC_SUCCESS);
7764: }
7766: /*
7767: MatCreateGraph_Simple_AIJ - create simple scalar matrix (graph) from potentially blocked matrix
7769: Input Parameter:
7770: . Amat - matrix
7771: - symmetrize - make the result symmetric
7772: + scale - scale with diagonal
7774: Output Parameter:
7775: . a_Gmat - output scalar graph >= 0
7777: */
7778: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, PetscInt index_size, PetscInt index[], Mat *a_Gmat)
7779: {
7780: PetscInt Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7781: MPI_Comm comm;
7782: Mat Gmat;
7783: PetscBool ismpiaij, isseqaij;
7784: Mat a, b, c;
7785: MatType jtype;
7787: PetscFunctionBegin;
7788: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7789: PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7790: PetscCall(MatGetSize(Amat, &MM, &NN));
7791: PetscCall(MatGetBlockSize(Amat, &bs));
7792: nloc = (Iend - Istart) / bs;
7794: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7795: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7796: PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");
7798: /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7799: /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7800: implementation */
7801: if (bs > 1) {
7802: PetscCall(MatGetType(Amat, &jtype));
7803: PetscCall(MatCreate(comm, &Gmat));
7804: PetscCall(MatSetType(Gmat, jtype));
7805: PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7806: PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7807: if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7808: PetscInt *d_nnz, *o_nnz;
7809: MatScalar *aa, val, *AA;
7810: PetscInt *aj, *ai, *AJ, nc, nmax = 0;
7812: if (isseqaij) {
7813: a = Amat;
7814: b = NULL;
7815: } else {
7816: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7817: a = d->A;
7818: b = d->B;
7819: }
7820: PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7821: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7822: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7823: PetscInt *nnz = (c == a) ? d_nnz : o_nnz;
7824: const PetscInt *cols1, *cols2;
7826: for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7827: PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7828: nnz[brow / bs] = nc2 / bs;
7829: if (nc2 % bs) ok = 0;
7830: if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7831: for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7832: PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7833: if (nc1 != nc2) ok = 0;
7834: else {
7835: for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7836: if (cols1[jj] != cols2[jj]) ok = 0;
7837: if (cols1[jj] % bs != jj % bs) ok = 0;
7838: }
7839: }
7840: PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7841: }
7842: PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7843: if (!ok) {
7844: PetscCall(PetscFree2(d_nnz, o_nnz));
7845: PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7846: goto old_bs;
7847: }
7848: }
7849: }
7850: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7851: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7852: PetscCall(PetscFree2(d_nnz, o_nnz));
7853: PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7854: // diag
7855: for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7856: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;
7858: ai = aseq->i;
7859: n = ai[brow + 1] - ai[brow];
7860: aj = aseq->j + ai[brow];
7861: for (PetscInt k = 0; k < n; k += bs) { // block columns
7862: AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7863: val = 0;
7864: if (index_size == 0) {
7865: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7866: aa = aseq->a + ai[brow + ii] + k;
7867: for (PetscInt jj = 0; jj < bs; jj++) { // columns in block
7868: val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7869: }
7870: }
7871: } else { // use (index,index) value if provided
7872: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7873: PetscInt ii = index[iii];
7874: aa = aseq->a + ai[brow + ii] + k;
7875: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // columns in block
7876: PetscInt jj = index[jjj];
7877: val += PetscAbs(PetscRealPart(aa[jj]));
7878: }
7879: }
7880: }
7881: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7882: AA[k / bs] = val;
7883: }
7884: grow = Istart / bs + brow / bs;
7885: PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, ADD_VALUES));
7886: }
7887: // off-diag
7888: if (ismpiaij) {
7889: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Amat->data;
7890: const PetscScalar *vals;
7891: const PetscInt *cols, *garray = aij->garray;
7893: PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7894: for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7895: PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7896: for (PetscInt k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7897: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7898: AA[k / bs] = 0;
7899: AJ[cidx] = garray[cols[k]] / bs;
7900: }
7901: nc = ncols / bs;
7902: PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7903: if (index_size == 0) {
7904: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7905: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7906: for (PetscInt k = 0; k < ncols; k += bs) {
7907: for (PetscInt jj = 0; jj < bs; jj++) { // cols in block
7908: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7909: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7910: }
7911: }
7912: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7913: }
7914: } else { // use (index,index) value if provided
7915: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7916: PetscInt ii = index[iii];
7917: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7918: for (PetscInt k = 0; k < ncols; k += bs) {
7919: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // cols in block
7920: PetscInt jj = index[jjj];
7921: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7922: }
7923: }
7924: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7925: }
7926: }
7927: grow = Istart / bs + brow / bs;
7928: PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, ADD_VALUES));
7929: }
7930: }
7931: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7932: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7933: PetscCall(PetscFree2(AA, AJ));
7934: } else {
7935: const PetscScalar *vals;
7936: const PetscInt *idx;
7937: PetscInt *d_nnz, *o_nnz, *w0, *w1, *w2;
7938: old_bs:
7939: /*
7940: Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7941: */
7942: PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7943: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7944: if (isseqaij) {
7945: PetscInt max_d_nnz;
7947: /*
7948: Determine exact preallocation count for (sequential) scalar matrix
7949: */
7950: PetscCall(