Actual source code: mpiaij.c
1: #include <../src/mat/impls/aij/mpi/mpiaij.h>
2: #include <petsc/private/vecimpl.h>
3: #include <petsc/private/sfimpl.h>
4: #include <petsc/private/isimpl.h>
5: #include <petscblaslapack.h>
6: #include <petscsf.h>
7: #include <petsc/private/hashmapi.h>
9: /* defines MatSetValues_MPI_Hash(), MatAssemblyBegin_MPI_Hash(), and MatAssemblyEnd_MPI_Hash() */
10: #define TYPE AIJ
11: #define TYPE_AIJ
12: #include "../src/mat/impls/aij/mpi/mpihashmat.h"
13: #undef TYPE
14: #undef TYPE_AIJ
16: static PetscErrorCode MatReset_MPIAIJ(Mat mat)
17: {
18: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
20: PetscFunctionBegin;
21: PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
22: PetscCall(MatStashDestroy_Private(&mat->stash));
23: PetscCall(VecDestroy(&aij->diag));
24: PetscCall(MatDestroy(&aij->A));
25: PetscCall(MatDestroy(&aij->B));
26: #if defined(PETSC_USE_CTABLE)
27: PetscCall(PetscHMapIDestroy(&aij->colmap));
28: #else
29: PetscCall(PetscFree(aij->colmap));
30: #endif
31: PetscCall(PetscFree(aij->garray));
32: PetscCall(VecDestroy(&aij->lvec));
33: PetscCall(VecScatterDestroy(&aij->Mvctx));
34: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
35: PetscCall(PetscFree(aij->ld));
36: PetscFunctionReturn(PETSC_SUCCESS);
37: }
39: static PetscErrorCode MatResetHash_MPIAIJ(Mat mat)
40: {
41: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
42: /* Save the nonzero states of the component matrices because those are what are used to determine
43: the nonzero state of mat */
44: PetscObjectState Astate = aij->A->nonzerostate, Bstate = aij->B->nonzerostate;
46: PetscFunctionBegin;
47: PetscCall(MatReset_MPIAIJ(mat));
48: PetscCall(MatSetUp_MPI_Hash(mat));
49: aij->A->nonzerostate = ++Astate, aij->B->nonzerostate = ++Bstate;
50: PetscFunctionReturn(PETSC_SUCCESS);
51: }
53: PetscErrorCode MatDestroy_MPIAIJ(Mat mat)
54: {
55: PetscFunctionBegin;
56: PetscCall(MatReset_MPIAIJ(mat));
58: PetscCall(PetscFree(mat->data));
60: /* may be created by MatCreateMPIAIJSumSeqAIJSymbolic */
61: PetscCall(PetscObjectCompose((PetscObject)mat, "MatMergeSeqsToMPI", NULL));
63: PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
64: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
65: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
66: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatIsTranspose_C", NULL));
67: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocation_C", NULL));
68: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatResetPreallocation_C", NULL));
69: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatResetHash_C", NULL));
70: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocationCSR_C", NULL));
71: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
72: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpibaij_C", NULL));
73: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisbaij_C", NULL));
74: #if defined(PETSC_HAVE_CUDA)
75: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcusparse_C", NULL));
76: #endif
77: #if defined(PETSC_HAVE_HIP)
78: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijhipsparse_C", NULL));
79: #endif
80: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
81: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijkokkos_C", NULL));
82: #endif
83: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpidense_C", NULL));
84: #if defined(PETSC_HAVE_ELEMENTAL)
85: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_elemental_C", NULL));
86: #endif
87: #if defined(PETSC_HAVE_SCALAPACK)
88: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_scalapack_C", NULL));
89: #endif
90: #if defined(PETSC_HAVE_HYPRE)
91: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_hypre_C", NULL));
92: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", NULL));
93: #endif
94: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
95: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_is_mpiaij_C", NULL));
96: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_mpiaij_mpiaij_C", NULL));
97: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetUseScalableIncreaseOverlap_C", NULL));
98: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijperm_C", NULL));
99: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijsell_C", NULL));
100: #if defined(PETSC_HAVE_MKL_SPARSE)
101: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijmkl_C", NULL));
102: #endif
103: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcrl_C", NULL));
104: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
105: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisell_C", NULL));
106: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetPreallocationCOO_C", NULL));
107: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetValuesCOO_C", NULL));
108: PetscFunctionReturn(PETSC_SUCCESS);
109: }
111: static PetscErrorCode MatGetRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
112: {
113: Mat B;
115: PetscFunctionBegin;
116: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, &B));
117: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject)B));
118: PetscCall(MatGetRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
119: PetscCall(MatDestroy(&B));
120: PetscFunctionReturn(PETSC_SUCCESS);
121: }
123: static PetscErrorCode MatRestoreRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
124: {
125: Mat B;
127: PetscFunctionBegin;
128: PetscCall(PetscObjectQuery((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject *)&B));
129: PetscCall(MatRestoreRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
130: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", NULL));
131: PetscFunctionReturn(PETSC_SUCCESS);
132: }
134: /*MC
135: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
137: This matrix type is identical to` MATSEQAIJ` when constructed with a single process communicator,
138: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
139: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
140: for communicators controlling multiple processes. It is recommended that you call both of
141: the above preallocation routines for simplicity.
143: Options Database Key:
144: . -mat_type aij - sets the matrix type to `MATAIJ` during a call to `MatSetFromOptions()`
146: Developer Note:
147: Level: beginner
149: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, `MATAIJKOKKOS`,and also automatically switches over to use inodes when
150: enough exist.
152: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`
153: M*/
155: /*MC
156: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
158: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
159: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
160: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
161: for communicators controlling multiple processes. It is recommended that you call both of
162: the above preallocation routines for simplicity.
164: Options Database Key:
165: . -mat_type aijcrl - sets the matrix type to `MATMPIAIJCRL` during a call to `MatSetFromOptions()`
167: Level: beginner
169: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
170: M*/
172: static PetscErrorCode MatBindToCPU_MPIAIJ(Mat A, PetscBool flg)
173: {
174: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
176: PetscFunctionBegin;
177: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP) || defined(PETSC_HAVE_VIENNACL)
178: A->boundtocpu = flg;
179: #endif
180: if (a->A) PetscCall(MatBindToCPU(a->A, flg));
181: if (a->B) PetscCall(MatBindToCPU(a->B, flg));
183: /* In addition to binding the diagonal and off-diagonal matrices, bind the local vectors used for matrix-vector products.
184: * This maybe seems a little odd for a MatBindToCPU() call to do, but it makes no sense for the binding of these vectors
185: * to differ from the parent matrix. */
186: if (a->lvec) PetscCall(VecBindToCPU(a->lvec, flg));
187: if (a->diag) PetscCall(VecBindToCPU(a->diag, flg));
188: PetscFunctionReturn(PETSC_SUCCESS);
189: }
191: static PetscErrorCode MatSetBlockSizes_MPIAIJ(Mat M, PetscInt rbs, PetscInt cbs)
192: {
193: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
195: PetscFunctionBegin;
196: if (mat->A) {
197: PetscCall(MatSetBlockSizes(mat->A, rbs, cbs));
198: PetscCall(MatSetBlockSizes(mat->B, rbs, 1));
199: }
200: PetscFunctionReturn(PETSC_SUCCESS);
201: }
203: static PetscErrorCode MatFindNonzeroRows_MPIAIJ(Mat M, IS *keptrows)
204: {
205: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
206: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data;
207: Mat_SeqAIJ *b = (Mat_SeqAIJ *)mat->B->data;
208: const PetscInt *ia, *ib;
209: const MatScalar *aa, *bb, *aav, *bav;
210: PetscInt na, nb, i, j, *rows, cnt = 0, n0rows;
211: PetscInt m = M->rmap->n, rstart = M->rmap->rstart;
213: PetscFunctionBegin;
214: *keptrows = NULL;
216: ia = a->i;
217: ib = b->i;
218: PetscCall(MatSeqAIJGetArrayRead(mat->A, &aav));
219: PetscCall(MatSeqAIJGetArrayRead(mat->B, &bav));
220: for (i = 0; i < m; i++) {
221: na = ia[i + 1] - ia[i];
222: nb = ib[i + 1] - ib[i];
223: if (!na && !nb) {
224: cnt++;
225: goto ok1;
226: }
227: aa = aav + ia[i];
228: for (j = 0; j < na; j++) {
229: if (aa[j] != 0.0) goto ok1;
230: }
231: bb = PetscSafePointerPlusOffset(bav, ib[i]);
232: for (j = 0; j < nb; j++) {
233: if (bb[j] != 0.0) goto ok1;
234: }
235: cnt++;
236: ok1:;
237: }
238: PetscCallMPI(MPIU_Allreduce(&cnt, &n0rows, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)M)));
239: if (!n0rows) {
240: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
241: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
242: PetscFunctionReturn(PETSC_SUCCESS);
243: }
244: PetscCall(PetscMalloc1(M->rmap->n - cnt, &rows));
245: cnt = 0;
246: for (i = 0; i < m; i++) {
247: na = ia[i + 1] - ia[i];
248: nb = ib[i + 1] - ib[i];
249: if (!na && !nb) continue;
250: aa = aav + ia[i];
251: for (j = 0; j < na; j++) {
252: if (aa[j] != 0.0) {
253: rows[cnt++] = rstart + i;
254: goto ok2;
255: }
256: }
257: bb = PetscSafePointerPlusOffset(bav, ib[i]);
258: for (j = 0; j < nb; j++) {
259: if (bb[j] != 0.0) {
260: rows[cnt++] = rstart + i;
261: goto ok2;
262: }
263: }
264: ok2:;
265: }
266: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), cnt, rows, PETSC_OWN_POINTER, keptrows));
267: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
268: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
269: PetscFunctionReturn(PETSC_SUCCESS);
270: }
272: static PetscErrorCode MatDiagonalSet_MPIAIJ(Mat Y, Vec D, InsertMode is)
273: {
274: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Y->data;
275: PetscBool cong;
277: PetscFunctionBegin;
278: PetscCall(MatHasCongruentLayouts(Y, &cong));
279: if (Y->assembled && cong) {
280: PetscCall(MatDiagonalSet(aij->A, D, is));
281: } else {
282: PetscCall(MatDiagonalSet_Default(Y, D, is));
283: }
284: PetscFunctionReturn(PETSC_SUCCESS);
285: }
287: static PetscErrorCode MatFindZeroDiagonals_MPIAIJ(Mat M, IS *zrows)
288: {
289: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)M->data;
290: PetscInt i, rstart, nrows, *rows;
292: PetscFunctionBegin;
293: *zrows = NULL;
294: PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(aij->A, &nrows, &rows));
295: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
296: for (i = 0; i < nrows; i++) rows[i] += rstart;
297: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), nrows, rows, PETSC_OWN_POINTER, zrows));
298: PetscFunctionReturn(PETSC_SUCCESS);
299: }
301: static PetscErrorCode MatGetColumnReductions_MPIAIJ(Mat A, PetscInt type, PetscReal *reductions)
302: {
303: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)A->data;
304: PetscInt i, m, n, *garray = aij->garray;
305: Mat_SeqAIJ *a_aij = (Mat_SeqAIJ *)aij->A->data;
306: Mat_SeqAIJ *b_aij = (Mat_SeqAIJ *)aij->B->data;
307: PetscReal *work;
308: const PetscScalar *dummy;
310: PetscFunctionBegin;
311: PetscCall(MatGetSize(A, &m, &n));
312: PetscCall(PetscCalloc1(n, &work));
313: PetscCall(MatSeqAIJGetArrayRead(aij->A, &dummy));
314: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &dummy));
315: PetscCall(MatSeqAIJGetArrayRead(aij->B, &dummy));
316: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &dummy));
317: if (type == NORM_2) {
318: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i] * a_aij->a[i]);
319: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i] * b_aij->a[i]);
320: } else if (type == NORM_1) {
321: 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]);
322: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]);
323: } else if (type == NORM_INFINITY) {
324: 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]]);
325: 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]]]);
326: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
327: 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]);
328: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscRealPart(b_aij->a[i]);
329: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
330: 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]);
331: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscImaginaryPart(b_aij->a[i]);
332: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
333: if (type == NORM_INFINITY) {
334: PetscCallMPI(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
335: } else {
336: PetscCallMPI(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
337: }
338: PetscCall(PetscFree(work));
339: if (type == NORM_2) {
340: for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
341: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
342: for (i = 0; i < n; i++) reductions[i] /= m;
343: }
344: PetscFunctionReturn(PETSC_SUCCESS);
345: }
347: static PetscErrorCode MatFindOffBlockDiagonalEntries_MPIAIJ(Mat A, IS *is)
348: {
349: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
350: IS sis, gis;
351: const PetscInt *isis, *igis;
352: PetscInt n, *iis, nsis, ngis, rstart, i;
354: PetscFunctionBegin;
355: PetscCall(MatFindOffBlockDiagonalEntries(a->A, &sis));
356: PetscCall(MatFindNonzeroRows(a->B, &gis));
357: PetscCall(ISGetSize(gis, &ngis));
358: PetscCall(ISGetSize(sis, &nsis));
359: PetscCall(ISGetIndices(sis, &isis));
360: PetscCall(ISGetIndices(gis, &igis));
362: PetscCall(PetscMalloc1(ngis + nsis, &iis));
363: PetscCall(PetscArraycpy(iis, igis, ngis));
364: PetscCall(PetscArraycpy(iis + ngis, isis, nsis));
365: n = ngis + nsis;
366: PetscCall(PetscSortRemoveDupsInt(&n, iis));
367: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
368: for (i = 0; i < n; i++) iis[i] += rstart;
369: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), n, iis, PETSC_OWN_POINTER, is));
371: PetscCall(ISRestoreIndices(sis, &isis));
372: PetscCall(ISRestoreIndices(gis, &igis));
373: PetscCall(ISDestroy(&sis));
374: PetscCall(ISDestroy(&gis));
375: PetscFunctionReturn(PETSC_SUCCESS);
376: }
378: /*
379: Local utility routine that creates a mapping from the global column
380: number to the local number in the off-diagonal part of the local
381: storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at
382: a slightly higher hash table cost; without it it is not scalable (each processor
383: has an order N integer array but is fast to access.
384: */
385: PetscErrorCode MatCreateColmap_MPIAIJ_Private(Mat mat)
386: {
387: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
388: PetscInt n = aij->B->cmap->n, i;
390: PetscFunctionBegin;
391: PetscCheck(!n || aij->garray, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MPIAIJ Matrix was assembled but is missing garray");
392: #if defined(PETSC_USE_CTABLE)
393: PetscCall(PetscHMapICreateWithSize(n, &aij->colmap));
394: for (i = 0; i < n; i++) PetscCall(PetscHMapISet(aij->colmap, aij->garray[i] + 1, i + 1));
395: #else
396: PetscCall(PetscCalloc1(mat->cmap->N + 1, &aij->colmap));
397: for (i = 0; i < n; i++) aij->colmap[aij->garray[i]] = i + 1;
398: #endif
399: PetscFunctionReturn(PETSC_SUCCESS);
400: }
402: #define MatSetValues_SeqAIJ_A_Private(row, col, value, addv, orow, ocol) \
403: do { \
404: if (col <= lastcol1) low1 = 0; \
405: else high1 = nrow1; \
406: lastcol1 = col; \
407: while (high1 - low1 > 5) { \
408: t = (low1 + high1) / 2; \
409: if (rp1[t] > col) high1 = t; \
410: else low1 = t; \
411: } \
412: for (_i = low1; _i < high1; _i++) { \
413: if (rp1[_i] > col) break; \
414: if (rp1[_i] == col) { \
415: if (addv == ADD_VALUES) { \
416: ap1[_i] += value; \
417: /* Not sure LogFlops will slow dow the code or not */ \
418: (void)PetscLogFlops(1.0); \
419: } else ap1[_i] = value; \
420: goto a_noinsert; \
421: } \
422: } \
423: if (value == 0.0 && ignorezeroentries && row != col) { \
424: low1 = 0; \
425: high1 = nrow1; \
426: goto a_noinsert; \
427: } \
428: if (nonew == 1) { \
429: low1 = 0; \
430: high1 = nrow1; \
431: goto a_noinsert; \
432: } \
433: 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); \
434: MatSeqXAIJReallocateAIJ(A, am, 1, nrow1, row, col, rmax1, aa, ai, aj, rp1, ap1, aimax, nonew, MatScalar); \
435: N = nrow1++ - 1; \
436: a->nz++; \
437: high1++; \
438: /* shift up all the later entries in this row */ \
439: PetscCall(PetscArraymove(rp1 + _i + 1, rp1 + _i, N - _i + 1)); \
440: PetscCall(PetscArraymove(ap1 + _i + 1, ap1 + _i, N - _i + 1)); \
441: rp1[_i] = col; \
442: ap1[_i] = value; \
443: a_noinsert:; \
444: ailen[row] = nrow1; \
445: } while (0)
447: #define MatSetValues_SeqAIJ_B_Private(row, col, value, addv, orow, ocol) \
448: do { \
449: if (col <= lastcol2) low2 = 0; \
450: else high2 = nrow2; \
451: lastcol2 = col; \
452: while (high2 - low2 > 5) { \
453: t = (low2 + high2) / 2; \
454: if (rp2[t] > col) high2 = t; \
455: else low2 = t; \
456: } \
457: for (_i = low2; _i < high2; _i++) { \
458: if (rp2[_i] > col) break; \
459: if (rp2[_i] == col) { \
460: if (addv == ADD_VALUES) { \
461: ap2[_i] += value; \
462: (void)PetscLogFlops(1.0); \
463: } else ap2[_i] = value; \
464: goto b_noinsert; \
465: } \
466: } \
467: if (value == 0.0 && ignorezeroentries) { \
468: low2 = 0; \
469: high2 = nrow2; \
470: goto b_noinsert; \
471: } \
472: if (nonew == 1) { \
473: low2 = 0; \
474: high2 = nrow2; \
475: goto b_noinsert; \
476: } \
477: 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); \
478: MatSeqXAIJReallocateAIJ(B, bm, 1, nrow2, row, col, rmax2, ba, bi, bj, rp2, ap2, bimax, nonew, MatScalar); \
479: N = nrow2++ - 1; \
480: b->nz++; \
481: high2++; \
482: /* shift up all the later entries in this row */ \
483: PetscCall(PetscArraymove(rp2 + _i + 1, rp2 + _i, N - _i + 1)); \
484: PetscCall(PetscArraymove(ap2 + _i + 1, ap2 + _i, N - _i + 1)); \
485: rp2[_i] = col; \
486: ap2[_i] = value; \
487: b_noinsert:; \
488: bilen[row] = nrow2; \
489: } while (0)
491: static PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A, PetscInt row, const PetscScalar v[])
492: {
493: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
494: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data, *b = (Mat_SeqAIJ *)mat->B->data;
495: PetscInt l, *garray = mat->garray, diag;
496: PetscScalar *aa, *ba;
498: PetscFunctionBegin;
499: /* code only works for square matrices A */
501: /* find size of row to the left of the diagonal part */
502: PetscCall(MatGetOwnershipRange(A, &diag, NULL));
503: row = row - diag;
504: for (l = 0; l < b->i[row + 1] - b->i[row]; l++) {
505: if (garray[b->j[b->i[row] + l]] > diag) break;
506: }
507: if (l) {
508: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
509: PetscCall(PetscArraycpy(ba + b->i[row], v, l));
510: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
511: }
513: /* diagonal part */
514: if (a->i[row + 1] - a->i[row]) {
515: PetscCall(MatSeqAIJGetArray(mat->A, &aa));
516: PetscCall(PetscArraycpy(aa + a->i[row], v + l, a->i[row + 1] - a->i[row]));
517: PetscCall(MatSeqAIJRestoreArray(mat->A, &aa));
518: }
520: /* right of diagonal part */
521: if (b->i[row + 1] - b->i[row] - l) {
522: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
523: PetscCall(PetscArraycpy(ba + b->i[row] + l, v + l + a->i[row + 1] - a->i[row], b->i[row + 1] - b->i[row] - l));
524: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
525: }
526: PetscFunctionReturn(PETSC_SUCCESS);
527: }
529: PetscErrorCode MatSetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
530: {
531: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
532: PetscScalar value = 0.0;
533: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
534: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
535: PetscBool roworiented = aij->roworiented;
537: /* Some Variables required in the macro */
538: Mat A = aij->A;
539: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
540: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
541: PetscBool ignorezeroentries = a->ignorezeroentries;
542: Mat B = aij->B;
543: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
544: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
545: MatScalar *aa, *ba;
546: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
547: PetscInt nonew;
548: MatScalar *ap1, *ap2;
550: PetscFunctionBegin;
551: PetscCall(MatSeqAIJGetArray(A, &aa));
552: PetscCall(MatSeqAIJGetArray(B, &ba));
553: for (i = 0; i < m; i++) {
554: if (im[i] < 0) continue;
555: 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);
556: if (im[i] >= rstart && im[i] < rend) {
557: row = im[i] - rstart;
558: lastcol1 = -1;
559: rp1 = PetscSafePointerPlusOffset(aj, ai[row]);
560: ap1 = PetscSafePointerPlusOffset(aa, ai[row]);
561: rmax1 = aimax[row];
562: nrow1 = ailen[row];
563: low1 = 0;
564: high1 = nrow1;
565: lastcol2 = -1;
566: rp2 = PetscSafePointerPlusOffset(bj, bi[row]);
567: ap2 = PetscSafePointerPlusOffset(ba, bi[row]);
568: rmax2 = bimax[row];
569: nrow2 = bilen[row];
570: low2 = 0;
571: high2 = nrow2;
573: for (j = 0; j < n; j++) {
574: if (v) value = roworiented ? v[i * n + j] : v[i + j * m];
575: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
576: if (in[j] >= cstart && in[j] < cend) {
577: col = in[j] - cstart;
578: nonew = a->nonew;
579: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
580: } else if (in[j] < 0) {
581: continue;
582: } else {
583: 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);
584: if (mat->was_assembled) {
585: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
586: #if defined(PETSC_USE_CTABLE)
587: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col)); /* map global col ids to local ones */
588: col--;
589: #else
590: col = aij->colmap[in[j]] - 1;
591: #endif
592: if (col < 0 && !((Mat_SeqAIJ *)aij->B->data)->nonew) { /* col < 0 means in[j] is a new col for B */
593: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE)); /* Change aij->B from reduced/local format to expanded/global format */
594: col = in[j];
595: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
596: B = aij->B;
597: b = (Mat_SeqAIJ *)B->data;
598: bimax = b->imax;
599: bi = b->i;
600: bilen = b->ilen;
601: bj = b->j;
602: ba = b->a;
603: rp2 = PetscSafePointerPlusOffset(bj, bi[row]);
604: ap2 = PetscSafePointerPlusOffset(ba, bi[row]);
605: rmax2 = bimax[row];
606: nrow2 = bilen[row];
607: low2 = 0;
608: high2 = nrow2;
609: bm = aij->B->rmap->n;
610: ba = b->a;
611: } else if (col < 0 && !(ignorezeroentries && value == 0.0)) {
612: if (1 == ((Mat_SeqAIJ *)aij->B->data)->nonew) {
613: 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]));
614: } 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]);
615: }
616: } else col = in[j];
617: nonew = b->nonew;
618: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
619: }
620: }
621: } else {
622: 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]);
623: if (!aij->donotstash) {
624: mat->assembled = PETSC_FALSE;
625: if (roworiented) {
626: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i * n), (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
627: } else {
628: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i), m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
629: }
630: }
631: }
632: }
633: PetscCall(MatSeqAIJRestoreArray(A, &aa)); /* aa, bb might have been free'd due to reallocation above. But we don't access them here */
634: PetscCall(MatSeqAIJRestoreArray(B, &ba));
635: PetscFunctionReturn(PETSC_SUCCESS);
636: }
638: /*
639: This function sets the j and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
640: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
641: No off-processor parts off the matrix are allowed here and mat->was_assembled has to be PETSC_FALSE.
642: */
643: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[])
644: {
645: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
646: Mat A = aij->A; /* diagonal part of the matrix */
647: Mat B = aij->B; /* off-diagonal part of the matrix */
648: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
649: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
650: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, col;
651: PetscInt *ailen = a->ilen, *aj = a->j;
652: PetscInt *bilen = b->ilen, *bj = b->j;
653: PetscInt am = aij->A->rmap->n, j;
654: PetscInt diag_so_far = 0, dnz;
655: PetscInt offd_so_far = 0, onz;
657: PetscFunctionBegin;
658: /* Iterate over all rows of the matrix */
659: for (j = 0; j < am; j++) {
660: dnz = onz = 0;
661: /* Iterate over all non-zero columns of the current row */
662: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
663: /* If column is in the diagonal */
664: if (mat_j[col] >= cstart && mat_j[col] < cend) {
665: aj[diag_so_far++] = mat_j[col] - cstart;
666: dnz++;
667: } else { /* off-diagonal entries */
668: bj[offd_so_far++] = mat_j[col];
669: onz++;
670: }
671: }
672: ailen[j] = dnz;
673: bilen[j] = onz;
674: }
675: PetscFunctionReturn(PETSC_SUCCESS);
676: }
678: /*
679: This function sets the local j, a and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
680: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
681: No off-processor parts off the matrix are allowed here, they are set at a later point by MatSetValues_MPIAIJ.
682: Also, mat->was_assembled has to be false, otherwise the statement aj[rowstart_diag+dnz_row] = mat_j[col] - cstart;
683: would not be true and the more complex MatSetValues_MPIAIJ has to be used.
684: */
685: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[], const PetscScalar mat_a[])
686: {
687: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
688: Mat A = aij->A; /* diagonal part of the matrix */
689: Mat B = aij->B; /* off-diagonal part of the matrix */
690: Mat_SeqAIJ *aijd = (Mat_SeqAIJ *)aij->A->data, *aijo = (Mat_SeqAIJ *)aij->B->data;
691: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
692: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
693: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend;
694: PetscInt *ailen = a->ilen, *aj = a->j;
695: PetscInt *bilen = b->ilen, *bj = b->j;
696: PetscInt am = aij->A->rmap->n, j;
697: 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. */
698: PetscInt col, dnz_row, onz_row, rowstart_diag, rowstart_offd;
699: PetscScalar *aa = a->a, *ba = b->a;
701: PetscFunctionBegin;
702: /* Iterate over all rows of the matrix */
703: for (j = 0; j < am; j++) {
704: dnz_row = onz_row = 0;
705: rowstart_offd = full_offd_i[j];
706: rowstart_diag = full_diag_i[j];
707: /* Iterate over all non-zero columns of the current row */
708: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
709: /* If column is in the diagonal */
710: if (mat_j[col] >= cstart && mat_j[col] < cend) {
711: aj[rowstart_diag + dnz_row] = mat_j[col] - cstart;
712: aa[rowstart_diag + dnz_row] = mat_a[col];
713: dnz_row++;
714: } else { /* off-diagonal entries */
715: bj[rowstart_offd + onz_row] = mat_j[col];
716: ba[rowstart_offd + onz_row] = mat_a[col];
717: onz_row++;
718: }
719: }
720: ailen[j] = dnz_row;
721: bilen[j] = onz_row;
722: }
723: PetscFunctionReturn(PETSC_SUCCESS);
724: }
726: static PetscErrorCode MatGetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
727: {
728: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
729: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
730: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
732: PetscFunctionBegin;
733: for (i = 0; i < m; i++) {
734: if (idxm[i] < 0) continue; /* negative row */
735: 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);
736: 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);
737: row = idxm[i] - rstart;
738: for (j = 0; j < n; j++) {
739: if (idxn[j] < 0) continue; /* negative column */
740: 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);
741: if (idxn[j] >= cstart && idxn[j] < cend) {
742: col = idxn[j] - cstart;
743: PetscCall(MatGetValues(aij->A, 1, &row, 1, &col, v + i * n + j));
744: } else {
745: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
746: #if defined(PETSC_USE_CTABLE)
747: PetscCall(PetscHMapIGetWithDefault(aij->colmap, idxn[j] + 1, 0, &col));
748: col--;
749: #else
750: col = aij->colmap[idxn[j]] - 1;
751: #endif
752: if ((col < 0) || (aij->garray[col] != idxn[j])) *(v + i * n + j) = 0.0;
753: else PetscCall(MatGetValues(aij->B, 1, &row, 1, &col, v + i * n + j));
754: }
755: }
756: }
757: PetscFunctionReturn(PETSC_SUCCESS);
758: }
760: static PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat, MatAssemblyType mode)
761: {
762: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
763: PetscInt nstash, reallocs;
765: PetscFunctionBegin;
766: if (aij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
768: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
769: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
770: PetscCall(PetscInfo(aij->A, "Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
771: PetscFunctionReturn(PETSC_SUCCESS);
772: }
774: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat, MatAssemblyType mode)
775: {
776: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
777: PetscMPIInt n;
778: PetscInt i, j, rstart, ncols, flg;
779: PetscInt *row, *col;
780: PetscBool other_disassembled;
781: PetscScalar *val;
783: /* do not use 'b = (Mat_SeqAIJ*)aij->B->data' as B can be reset in disassembly */
785: PetscFunctionBegin;
786: if (!aij->donotstash && !mat->nooffprocentries) {
787: while (1) {
788: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
789: if (!flg) break;
791: for (i = 0; i < n;) {
792: /* Now identify the consecutive vals belonging to the same row */
793: for (j = i, rstart = row[j]; j < n; j++) {
794: if (row[j] != rstart) break;
795: }
796: if (j < n) ncols = j - i;
797: else ncols = n - i;
798: /* Now assemble all these values with a single function call */
799: PetscCall(MatSetValues_MPIAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
800: i = j;
801: }
802: }
803: PetscCall(MatStashScatterEnd_Private(&mat->stash));
804: }
805: #if defined(PETSC_HAVE_DEVICE)
806: if (mat->offloadmask == PETSC_OFFLOAD_CPU) aij->A->offloadmask = PETSC_OFFLOAD_CPU;
807: /* We call MatBindToCPU() on aij->A and aij->B here, because if MatBindToCPU_MPIAIJ() is called before assembly, it cannot bind these. */
808: if (mat->boundtocpu) {
809: PetscCall(MatBindToCPU(aij->A, PETSC_TRUE));
810: PetscCall(MatBindToCPU(aij->B, PETSC_TRUE));
811: }
812: #endif
813: PetscCall(MatAssemblyBegin(aij->A, mode));
814: PetscCall(MatAssemblyEnd(aij->A, mode));
816: /* determine if any processor has disassembled, if so we must
817: also disassemble ourself, in order that we may reassemble. */
818: /*
819: if nonzero structure of submatrix B cannot change then we know that
820: no processor disassembled thus we can skip this stuff
821: */
822: if (!((Mat_SeqAIJ *)aij->B->data)->nonew) {
823: PetscCallMPI(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
824: if (mat->was_assembled && !other_disassembled) { /* mat on this rank has reduced off-diag B with local col ids, but globally it does not */
825: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));
826: }
827: }
828: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIAIJ(mat));
829: PetscCall(MatSetOption(aij->B, MAT_USE_INODES, PETSC_FALSE));
830: #if defined(PETSC_HAVE_DEVICE)
831: if (mat->offloadmask == PETSC_OFFLOAD_CPU && aij->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) aij->B->offloadmask = PETSC_OFFLOAD_CPU;
832: #endif
833: PetscCall(MatAssemblyBegin(aij->B, mode));
834: PetscCall(MatAssemblyEnd(aij->B, mode));
836: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
838: aij->rowvalues = NULL;
840: PetscCall(VecDestroy(&aij->diag));
842: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
843: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqAIJ *)aij->A->data)->nonew) {
844: PetscObjectState state = aij->A->nonzerostate + aij->B->nonzerostate;
845: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
846: }
847: #if defined(PETSC_HAVE_DEVICE)
848: mat->offloadmask = PETSC_OFFLOAD_BOTH;
849: #endif
850: PetscFunctionReturn(PETSC_SUCCESS);
851: }
853: static PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
854: {
855: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
857: PetscFunctionBegin;
858: PetscCall(MatZeroEntries(l->A));
859: PetscCall(MatZeroEntries(l->B));
860: PetscFunctionReturn(PETSC_SUCCESS);
861: }
863: static PetscErrorCode MatZeroRows_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
864: {
865: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
866: PetscInt *lrows;
867: PetscInt r, len;
868: PetscBool cong;
870: PetscFunctionBegin;
871: /* get locally owned rows */
872: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
873: PetscCall(MatHasCongruentLayouts(A, &cong));
874: /* fix right-hand side if needed */
875: if (x && b) {
876: const PetscScalar *xx;
877: PetscScalar *bb;
879: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
880: PetscCall(VecGetArrayRead(x, &xx));
881: PetscCall(VecGetArray(b, &bb));
882: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
883: PetscCall(VecRestoreArrayRead(x, &xx));
884: PetscCall(VecRestoreArray(b, &bb));
885: }
887: if (diag != 0.0 && cong) {
888: PetscCall(MatZeroRows(mat->A, len, lrows, diag, NULL, NULL));
889: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
890: } else if (diag != 0.0) { /* non-square or non congruent layouts -> if keepnonzeropattern is false, we allow for new insertion */
891: Mat_SeqAIJ *aijA = (Mat_SeqAIJ *)mat->A->data;
892: Mat_SeqAIJ *aijB = (Mat_SeqAIJ *)mat->B->data;
893: PetscInt nnwA, nnwB;
894: PetscBool nnzA, nnzB;
896: nnwA = aijA->nonew;
897: nnwB = aijB->nonew;
898: nnzA = aijA->keepnonzeropattern;
899: nnzB = aijB->keepnonzeropattern;
900: if (!nnzA) {
901: PetscCall(PetscInfo(mat->A, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on diagonal block.\n"));
902: aijA->nonew = 0;
903: }
904: if (!nnzB) {
905: PetscCall(PetscInfo(mat->B, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on off-diagonal block.\n"));
906: aijB->nonew = 0;
907: }
908: /* Must zero here before the next loop */
909: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
910: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
911: for (r = 0; r < len; ++r) {
912: const PetscInt row = lrows[r] + A->rmap->rstart;
913: if (row >= A->cmap->N) continue;
914: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
915: }
916: aijA->nonew = nnwA;
917: aijB->nonew = nnwB;
918: } else {
919: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
920: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
921: }
922: PetscCall(PetscFree(lrows));
923: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
924: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
926: /* only change matrix nonzero state if pattern was allowed to be changed */
927: if (!((Mat_SeqAIJ *)mat->A->data)->keepnonzeropattern || !((Mat_SeqAIJ *)mat->A->data)->nonew) {
928: PetscObjectState state = mat->A->nonzerostate + mat->B->nonzerostate;
929: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
930: }
931: PetscFunctionReturn(PETSC_SUCCESS);
932: }
934: static PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
935: {
936: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
937: PetscInt n = A->rmap->n;
938: PetscInt i, j, r, m, len = 0;
939: PetscInt *lrows, *owners = A->rmap->range;
940: PetscMPIInt p = 0;
941: PetscSFNode *rrows;
942: PetscSF sf;
943: const PetscScalar *xx;
944: PetscScalar *bb, *mask, *aij_a;
945: Vec xmask, lmask;
946: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)l->B->data;
947: const PetscInt *aj, *ii, *ridx;
948: PetscScalar *aa;
950: PetscFunctionBegin;
951: /* Create SF where leaves are input rows and roots are owned rows */
952: PetscCall(PetscMalloc1(n, &lrows));
953: for (r = 0; r < n; ++r) lrows[r] = -1;
954: PetscCall(PetscMalloc1(N, &rrows));
955: for (r = 0; r < N; ++r) {
956: const PetscInt idx = rows[r];
957: 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);
958: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
959: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
960: }
961: rrows[r].rank = p;
962: rrows[r].index = rows[r] - owners[p];
963: }
964: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
965: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
966: /* Collect flags for rows to be zeroed */
967: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
968: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
969: PetscCall(PetscSFDestroy(&sf));
970: /* Compress and put in row numbers */
971: for (r = 0; r < n; ++r)
972: if (lrows[r] >= 0) lrows[len++] = r;
973: /* zero diagonal part of matrix */
974: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
975: /* handle off-diagonal part of matrix */
976: PetscCall(MatCreateVecs(A, &xmask, NULL));
977: PetscCall(VecDuplicate(l->lvec, &lmask));
978: PetscCall(VecGetArray(xmask, &bb));
979: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
980: PetscCall(VecRestoreArray(xmask, &bb));
981: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
982: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
983: PetscCall(VecDestroy(&xmask));
984: if (x && b) { /* this code is buggy when the row and column layout don't match */
985: PetscBool cong;
987: PetscCall(MatHasCongruentLayouts(A, &cong));
988: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
989: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
990: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
991: PetscCall(VecGetArrayRead(l->lvec, &xx));
992: PetscCall(VecGetArray(b, &bb));
993: }
994: PetscCall(VecGetArray(lmask, &mask));
995: /* remove zeroed rows of off-diagonal matrix */
996: PetscCall(MatSeqAIJGetArray(l->B, &aij_a));
997: ii = aij->i;
998: for (i = 0; i < len; i++) PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aij_a, ii[lrows[i]]), ii[lrows[i] + 1] - ii[lrows[i]]));
999: /* loop over all elements of off process part of matrix zeroing removed columns*/
1000: if (aij->compressedrow.use) {
1001: m = aij->compressedrow.nrows;
1002: ii = aij->compressedrow.i;
1003: ridx = aij->compressedrow.rindex;
1004: for (i = 0; i < m; i++) {
1005: n = ii[i + 1] - ii[i];
1006: aj = aij->j + ii[i];
1007: aa = aij_a + ii[i];
1009: for (j = 0; j < n; j++) {
1010: if (PetscAbsScalar(mask[*aj])) {
1011: if (b) bb[*ridx] -= *aa * xx[*aj];
1012: *aa = 0.0;
1013: }
1014: aa++;
1015: aj++;
1016: }
1017: ridx++;
1018: }
1019: } else { /* do not use compressed row format */
1020: m = l->B->rmap->n;
1021: for (i = 0; i < m; i++) {
1022: n = ii[i + 1] - ii[i];
1023: aj = aij->j + ii[i];
1024: aa = aij_a + ii[i];
1025: for (j = 0; j < n; j++) {
1026: if (PetscAbsScalar(mask[*aj])) {
1027: if (b) bb[i] -= *aa * xx[*aj];
1028: *aa = 0.0;
1029: }
1030: aa++;
1031: aj++;
1032: }
1033: }
1034: }
1035: if (x && b) {
1036: PetscCall(VecRestoreArray(b, &bb));
1037: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1038: }
1039: PetscCall(MatSeqAIJRestoreArray(l->B, &aij_a));
1040: PetscCall(VecRestoreArray(lmask, &mask));
1041: PetscCall(VecDestroy(&lmask));
1042: PetscCall(PetscFree(lrows));
1044: /* only change matrix nonzero state if pattern was allowed to be changed */
1045: if (!((Mat_SeqAIJ *)l->A->data)->nonew) {
1046: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1047: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1048: }
1049: PetscFunctionReturn(PETSC_SUCCESS);
1050: }
1052: static PetscErrorCode MatMult_MPIAIJ(Mat A, Vec xx, Vec yy)
1053: {
1054: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1055: PetscInt nt;
1056: VecScatter Mvctx = a->Mvctx;
1058: PetscFunctionBegin;
1059: PetscCall(VecGetLocalSize(xx, &nt));
1060: 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);
1061: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1062: PetscUseTypeMethod(a->A, mult, xx, yy);
1063: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1064: PetscUseTypeMethod(a->B, multadd, a->lvec, yy, yy);
1065: PetscFunctionReturn(PETSC_SUCCESS);
1066: }
1068: static PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A, Vec bb, Vec xx)
1069: {
1070: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1072: PetscFunctionBegin;
1073: PetscCall(MatMultDiagonalBlock(a->A, bb, xx));
1074: PetscFunctionReturn(PETSC_SUCCESS);
1075: }
1077: static PetscErrorCode MatMultAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1078: {
1079: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1080: VecScatter Mvctx = a->Mvctx;
1082: PetscFunctionBegin;
1083: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1084: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1085: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1086: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1087: PetscFunctionReturn(PETSC_SUCCESS);
1088: }
1090: static PetscErrorCode MatMultTranspose_MPIAIJ(Mat A, Vec xx, Vec yy)
1091: {
1092: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1094: PetscFunctionBegin;
1095: /* do nondiagonal part */
1096: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1097: /* do local part */
1098: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1099: /* add partial results together */
1100: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1101: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1102: PetscFunctionReturn(PETSC_SUCCESS);
1103: }
1105: static PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f)
1106: {
1107: MPI_Comm comm;
1108: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)Amat->data, *Bij = (Mat_MPIAIJ *)Bmat->data;
1109: Mat Adia = Aij->A, Bdia = Bij->A, Aoff, Boff, *Aoffs, *Boffs;
1110: IS Me, Notme;
1111: PetscInt M, N, first, last, *notme, i;
1112: PetscBool lf;
1113: PetscMPIInt size;
1115: PetscFunctionBegin;
1116: /* Easy test: symmetric diagonal block */
1117: PetscCall(MatIsTranspose(Adia, Bdia, tol, &lf));
1118: PetscCallMPI(MPIU_Allreduce(&lf, f, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)Amat)));
1119: if (!*f) PetscFunctionReturn(PETSC_SUCCESS);
1120: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1121: PetscCallMPI(MPI_Comm_size(comm, &size));
1122: if (size == 1) PetscFunctionReturn(PETSC_SUCCESS);
1124: /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */
1125: PetscCall(MatGetSize(Amat, &M, &N));
1126: PetscCall(MatGetOwnershipRange(Amat, &first, &last));
1127: PetscCall(PetscMalloc1(N - last + first, ¬me));
1128: for (i = 0; i < first; i++) notme[i] = i;
1129: for (i = last; i < M; i++) notme[i - last + first] = i;
1130: PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme));
1131: PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me));
1132: PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs));
1133: Aoff = Aoffs[0];
1134: PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs));
1135: Boff = Boffs[0];
1136: PetscCall(MatIsTranspose(Aoff, Boff, tol, f));
1137: PetscCall(MatDestroyMatrices(1, &Aoffs));
1138: PetscCall(MatDestroyMatrices(1, &Boffs));
1139: PetscCall(ISDestroy(&Me));
1140: PetscCall(ISDestroy(&Notme));
1141: PetscCall(PetscFree(notme));
1142: PetscFunctionReturn(PETSC_SUCCESS);
1143: }
1145: static PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1146: {
1147: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1149: PetscFunctionBegin;
1150: /* do nondiagonal part */
1151: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1152: /* do local part */
1153: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1154: /* add partial results together */
1155: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1156: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1157: PetscFunctionReturn(PETSC_SUCCESS);
1158: }
1160: /*
1161: This only works correctly for square matrices where the subblock A->A is the
1162: diagonal block
1163: */
1164: static PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A, Vec v)
1165: {
1166: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1168: PetscFunctionBegin;
1169: PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1170: 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");
1171: PetscCall(MatGetDiagonal(a->A, v));
1172: PetscFunctionReturn(PETSC_SUCCESS);
1173: }
1175: static PetscErrorCode MatScale_MPIAIJ(Mat A, PetscScalar aa)
1176: {
1177: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1179: PetscFunctionBegin;
1180: PetscCall(MatScale(a->A, aa));
1181: PetscCall(MatScale(a->B, aa));
1182: PetscFunctionReturn(PETSC_SUCCESS);
1183: }
1185: static PetscErrorCode MatView_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
1186: {
1187: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1188: Mat_SeqAIJ *A = (Mat_SeqAIJ *)aij->A->data;
1189: Mat_SeqAIJ *B = (Mat_SeqAIJ *)aij->B->data;
1190: const PetscInt *garray = aij->garray;
1191: const PetscScalar *aa, *ba;
1192: PetscInt header[4], M, N, m, rs, cs, cnt, i, ja, jb;
1193: PetscInt64 nz, hnz;
1194: PetscInt *rowlens;
1195: PetscInt *colidxs;
1196: PetscScalar *matvals;
1197: PetscMPIInt rank;
1199: PetscFunctionBegin;
1200: PetscCall(PetscViewerSetUp(viewer));
1202: M = mat->rmap->N;
1203: N = mat->cmap->N;
1204: m = mat->rmap->n;
1205: rs = mat->rmap->rstart;
1206: cs = mat->cmap->rstart;
1207: nz = A->nz + B->nz;
1209: /* write matrix header */
1210: header[0] = MAT_FILE_CLASSID;
1211: header[1] = M;
1212: header[2] = N;
1213: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1214: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1215: if (rank == 0) PetscCall(PetscIntCast(hnz, &header[3]));
1216: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1218: /* fill in and store row lengths */
1219: PetscCall(PetscMalloc1(m, &rowlens));
1220: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i];
1221: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1222: PetscCall(PetscFree(rowlens));
1224: /* fill in and store column indices */
1225: PetscCall(PetscMalloc1(nz, &colidxs));
1226: for (cnt = 0, i = 0; i < m; i++) {
1227: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1228: if (garray[B->j[jb]] > cs) break;
1229: colidxs[cnt++] = garray[B->j[jb]];
1230: }
1231: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) colidxs[cnt++] = A->j[ja] + cs;
1232: for (; jb < B->i[i + 1]; jb++) colidxs[cnt++] = garray[B->j[jb]];
1233: }
1234: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1235: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
1236: PetscCall(PetscFree(colidxs));
1238: /* fill in and store nonzero values */
1239: PetscCall(MatSeqAIJGetArrayRead(aij->A, &aa));
1240: PetscCall(MatSeqAIJGetArrayRead(aij->B, &ba));
1241: PetscCall(PetscMalloc1(nz, &matvals));
1242: for (cnt = 0, i = 0; i < m; i++) {
1243: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1244: if (garray[B->j[jb]] > cs) break;
1245: matvals[cnt++] = ba[jb];
1246: }
1247: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) matvals[cnt++] = aa[ja];
1248: for (; jb < B->i[i + 1]; jb++) matvals[cnt++] = ba[jb];
1249: }
1250: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &aa));
1251: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &ba));
1252: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1253: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
1254: PetscCall(PetscFree(matvals));
1256: /* write block size option to the viewer's .info file */
1257: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1258: PetscFunctionReturn(PETSC_SUCCESS);
1259: }
1261: #include <petscdraw.h>
1262: static PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1263: {
1264: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1265: PetscMPIInt rank = aij->rank, size = aij->size;
1266: PetscBool isdraw, iascii, isbinary;
1267: PetscViewer sviewer;
1268: PetscViewerFormat format;
1270: PetscFunctionBegin;
1271: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1272: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1273: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1274: if (iascii) {
1275: PetscCall(PetscViewerGetFormat(viewer, &format));
1276: if (format == PETSC_VIEWER_LOAD_BALANCE) {
1277: PetscInt i, nmax = 0, nmin = PETSC_INT_MAX, navg = 0, *nz, nzlocal = ((Mat_SeqAIJ *)aij->A->data)->nz + ((Mat_SeqAIJ *)aij->B->data)->nz;
1278: PetscCall(PetscMalloc1(size, &nz));
1279: PetscCallMPI(MPI_Allgather(&nzlocal, 1, MPIU_INT, nz, 1, MPIU_INT, PetscObjectComm((PetscObject)mat)));
1280: for (i = 0; i < size; i++) {
1281: nmax = PetscMax(nmax, nz[i]);
1282: nmin = PetscMin(nmin, nz[i]);
1283: navg += nz[i];
1284: }
1285: PetscCall(PetscFree(nz));
1286: navg = navg / size;
1287: PetscCall(PetscViewerASCIIPrintf(viewer, "Load Balance - Nonzeros: Min %" PetscInt_FMT " avg %" PetscInt_FMT " max %" PetscInt_FMT "\n", nmin, navg, nmax));
1288: PetscFunctionReturn(PETSC_SUCCESS);
1289: }
1290: PetscCall(PetscViewerGetFormat(viewer, &format));
1291: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1292: MatInfo info;
1293: PetscInt *inodes = NULL;
1295: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1296: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1297: PetscCall(MatInodeGetInodeSizes(aij->A, NULL, &inodes, NULL));
1298: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1299: if (!inodes) {
1300: 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,
1301: info.memory));
1302: } else {
1303: PetscCall(
1304: PetscViewerASCIISynchronizedPrintf(viewer, "[%d] Local rows %" PetscInt_FMT " nz %" PetscInt_FMT " nz alloced %" PetscInt_FMT " mem %g, using I-node routines\n", rank, mat->rmap->n, (PetscInt)info.nz_used, (PetscInt)info.nz_allocated, info.memory));
1305: }
1306: PetscCall(MatGetInfo(aij->A, MAT_LOCAL, &info));
1307: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1308: PetscCall(MatGetInfo(aij->B, MAT_LOCAL, &info));
1309: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1310: PetscCall(PetscViewerFlush(viewer));
1311: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1312: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1313: PetscCall(VecScatterView(aij->Mvctx, viewer));
1314: PetscFunctionReturn(PETSC_SUCCESS);
1315: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1316: PetscInt inodecount, inodelimit, *inodes;
1317: PetscCall(MatInodeGetInodeSizes(aij->A, &inodecount, &inodes, &inodelimit));
1318: if (inodes) {
1319: PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
1320: } else {
1321: PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
1322: }
1323: PetscFunctionReturn(PETSC_SUCCESS);
1324: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1325: PetscFunctionReturn(PETSC_SUCCESS);
1326: }
1327: } else if (isbinary) {
1328: if (size == 1) {
1329: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1330: PetscCall(MatView(aij->A, viewer));
1331: } else {
1332: PetscCall(MatView_MPIAIJ_Binary(mat, viewer));
1333: }
1334: PetscFunctionReturn(PETSC_SUCCESS);
1335: } else if (iascii && size == 1) {
1336: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1337: PetscCall(MatView(aij->A, viewer));
1338: PetscFunctionReturn(PETSC_SUCCESS);
1339: } else if (isdraw) {
1340: PetscDraw draw;
1341: PetscBool isnull;
1342: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1343: PetscCall(PetscDrawIsNull(draw, &isnull));
1344: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1345: }
1347: { /* assemble the entire matrix onto first processor */
1348: Mat A = NULL, Av;
1349: IS isrow, iscol;
1351: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->rmap->N : 0, 0, 1, &isrow));
1352: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->cmap->N : 0, 0, 1, &iscol));
1353: PetscCall(MatCreateSubMatrix(mat, isrow, iscol, MAT_INITIAL_MATRIX, &A));
1354: PetscCall(MatMPIAIJGetSeqAIJ(A, &Av, NULL, NULL));
1355: /* The commented code uses MatCreateSubMatrices instead */
1356: /*
1357: Mat *AA, A = NULL, Av;
1358: IS isrow,iscol;
1360: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->rmap->N : 0,0,1,&isrow));
1361: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->cmap->N : 0,0,1,&iscol));
1362: PetscCall(MatCreateSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&AA));
1363: if (rank == 0) {
1364: PetscCall(PetscObjectReference((PetscObject)AA[0]));
1365: A = AA[0];
1366: Av = AA[0];
1367: }
1368: PetscCall(MatDestroySubMatrices(1,&AA));
1369: */
1370: PetscCall(ISDestroy(&iscol));
1371: PetscCall(ISDestroy(&isrow));
1372: /*
1373: Everyone has to call to draw the matrix since the graphics waits are
1374: synchronized across all processors that share the PetscDraw object
1375: */
1376: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1377: if (rank == 0) {
1378: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)Av, ((PetscObject)mat)->name));
1379: PetscCall(MatView_SeqAIJ(Av, sviewer));
1380: }
1381: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1382: PetscCall(MatDestroy(&A));
1383: }
1384: PetscFunctionReturn(PETSC_SUCCESS);
1385: }
1387: PetscErrorCode MatView_MPIAIJ(Mat mat, PetscViewer viewer)
1388: {
1389: PetscBool iascii, isdraw, issocket, isbinary;
1391: PetscFunctionBegin;
1392: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1393: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1394: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1395: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1396: if (iascii || isdraw || isbinary || issocket) PetscCall(MatView_MPIAIJ_ASCIIorDraworSocket(mat, viewer));
1397: PetscFunctionReturn(PETSC_SUCCESS);
1398: }
1400: static PetscErrorCode MatSOR_MPIAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1401: {
1402: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1403: Vec bb1 = NULL;
1404: PetscBool hasop;
1406: PetscFunctionBegin;
1407: if (flag == SOR_APPLY_UPPER) {
1408: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1409: PetscFunctionReturn(PETSC_SUCCESS);
1410: }
1412: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1));
1414: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_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: }
1420: while (its--) {
1421: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1422: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1424: /* update rhs: bb1 = bb - B*x */
1425: PetscCall(VecScale(mat->lvec, -1.0));
1426: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1428: /* local sweep */
1429: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
1430: }
1431: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1432: if (flag & SOR_ZERO_INITIAL_GUESS) {
1433: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1434: its--;
1435: }
1436: while (its--) {
1437: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1438: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1440: /* update rhs: bb1 = bb - B*x */
1441: PetscCall(VecScale(mat->lvec, -1.0));
1442: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1444: /* local sweep */
1445: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
1446: }
1447: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1448: if (flag & SOR_ZERO_INITIAL_GUESS) {
1449: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1450: its--;
1451: }
1452: while (its--) {
1453: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1454: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1456: /* update rhs: bb1 = bb - B*x */
1457: PetscCall(VecScale(mat->lvec, -1.0));
1458: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1460: /* local sweep */
1461: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
1462: }
1463: } else if (flag & SOR_EISENSTAT) {
1464: Vec xx1;
1466: PetscCall(VecDuplicate(bb, &xx1));
1467: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP), fshift, lits, 1, xx));
1469: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1470: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1471: if (!mat->diag) {
1472: PetscCall(MatCreateVecs(matin, &mat->diag, NULL));
1473: PetscCall(MatGetDiagonal(matin, mat->diag));
1474: }
1475: PetscCall(MatHasOperation(matin, MATOP_MULT_DIAGONAL_BLOCK, &hasop));
1476: if (hasop) {
1477: PetscCall(MatMultDiagonalBlock(matin, xx, bb1));
1478: } else {
1479: PetscCall(VecPointwiseMult(bb1, mat->diag, xx));
1480: }
1481: PetscCall(VecAYPX(bb1, (omega - 2.0) / omega, bb));
1483: PetscCall(MatMultAdd(mat->B, mat->lvec, bb1, bb1));
1485: /* local sweep */
1486: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP), fshift, lits, 1, xx1));
1487: PetscCall(VecAXPY(xx, 1.0, xx1));
1488: PetscCall(VecDestroy(&xx1));
1489: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");
1491: PetscCall(VecDestroy(&bb1));
1493: matin->factorerrortype = mat->A->factorerrortype;
1494: PetscFunctionReturn(PETSC_SUCCESS);
1495: }
1497: static PetscErrorCode MatPermute_MPIAIJ(Mat A, IS rowp, IS colp, Mat *B)
1498: {
1499: Mat aA, aB, Aperm;
1500: const PetscInt *rwant, *cwant, *gcols, *ai, *bi, *aj, *bj;
1501: PetscScalar *aa, *ba;
1502: PetscInt i, j, m, n, ng, anz, bnz, *dnnz, *onnz, *tdnnz, *tonnz, *rdest, *cdest, *work, *gcdest;
1503: PetscSF rowsf, sf;
1504: IS parcolp = NULL;
1505: PetscBool done;
1507: PetscFunctionBegin;
1508: PetscCall(MatGetLocalSize(A, &m, &n));
1509: PetscCall(ISGetIndices(rowp, &rwant));
1510: PetscCall(ISGetIndices(colp, &cwant));
1511: PetscCall(PetscMalloc3(PetscMax(m, n), &work, m, &rdest, n, &cdest));
1513: /* Invert row permutation to find out where my rows should go */
1514: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &rowsf));
1515: PetscCall(PetscSFSetGraphLayout(rowsf, A->rmap, A->rmap->n, NULL, PETSC_OWN_POINTER, rwant));
1516: PetscCall(PetscSFSetFromOptions(rowsf));
1517: for (i = 0; i < m; i++) work[i] = A->rmap->rstart + i;
1518: PetscCall(PetscSFReduceBegin(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1519: PetscCall(PetscSFReduceEnd(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1521: /* Invert column permutation to find out where my columns should go */
1522: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1523: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, A->cmap->n, NULL, PETSC_OWN_POINTER, cwant));
1524: PetscCall(PetscSFSetFromOptions(sf));
1525: for (i = 0; i < n; i++) work[i] = A->cmap->rstart + i;
1526: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1527: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1528: PetscCall(PetscSFDestroy(&sf));
1530: PetscCall(ISRestoreIndices(rowp, &rwant));
1531: PetscCall(ISRestoreIndices(colp, &cwant));
1532: PetscCall(MatMPIAIJGetSeqAIJ(A, &aA, &aB, &gcols));
1534: /* Find out where my gcols should go */
1535: PetscCall(MatGetSize(aB, NULL, &ng));
1536: PetscCall(PetscMalloc1(ng, &gcdest));
1537: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1538: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, ng, NULL, PETSC_OWN_POINTER, gcols));
1539: PetscCall(PetscSFSetFromOptions(sf));
1540: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1541: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1542: PetscCall(PetscSFDestroy(&sf));
1544: PetscCall(PetscCalloc4(m, &dnnz, m, &onnz, m, &tdnnz, m, &tonnz));
1545: PetscCall(MatGetRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1546: PetscCall(MatGetRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1547: for (i = 0; i < m; i++) {
1548: PetscInt row = rdest[i];
1549: PetscMPIInt rowner;
1550: PetscCall(PetscLayoutFindOwner(A->rmap, row, &rowner));
1551: for (j = ai[i]; j < ai[i + 1]; j++) {
1552: PetscInt col = cdest[aj[j]];
1553: PetscMPIInt cowner;
1554: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner)); /* Could build an index for the columns to eliminate this search */
1555: if (rowner == cowner) dnnz[i]++;
1556: else onnz[i]++;
1557: }
1558: for (j = bi[i]; j < bi[i + 1]; j++) {
1559: PetscInt col = gcdest[bj[j]];
1560: PetscMPIInt cowner;
1561: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner));
1562: if (rowner == cowner) dnnz[i]++;
1563: else onnz[i]++;
1564: }
1565: }
1566: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1567: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1568: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1569: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1570: PetscCall(PetscSFDestroy(&rowsf));
1572: PetscCall(MatCreateAIJ(PetscObjectComm((PetscObject)A), A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N, 0, tdnnz, 0, tonnz, &Aperm));
1573: PetscCall(MatSeqAIJGetArray(aA, &aa));
1574: PetscCall(MatSeqAIJGetArray(aB, &ba));
1575: for (i = 0; i < m; i++) {
1576: PetscInt *acols = dnnz, *bcols = onnz; /* Repurpose now-unneeded arrays */
1577: PetscInt j0, rowlen;
1578: rowlen = ai[i + 1] - ai[i];
1579: for (j0 = j = 0; j < rowlen; j0 = j) { /* rowlen could be larger than number of rows m, so sum in batches */
1580: for (; j < PetscMin(rowlen, j0 + m); j++) acols[j - j0] = cdest[aj[ai[i] + j]];
1581: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, acols, aa + ai[i] + j0, INSERT_VALUES));
1582: }
1583: rowlen = bi[i + 1] - bi[i];
1584: for (j0 = j = 0; j < rowlen; j0 = j) {
1585: for (; j < PetscMin(rowlen, j0 + m); j++) bcols[j - j0] = gcdest[bj[bi[i] + j]];
1586: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, bcols, ba + bi[i] + j0, INSERT_VALUES));
1587: }
1588: }
1589: PetscCall(MatAssemblyBegin(Aperm, MAT_FINAL_ASSEMBLY));
1590: PetscCall(MatAssemblyEnd(Aperm, MAT_FINAL_ASSEMBLY));
1591: PetscCall(MatRestoreRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1592: PetscCall(MatRestoreRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1593: PetscCall(MatSeqAIJRestoreArray(aA, &aa));
1594: PetscCall(MatSeqAIJRestoreArray(aB, &ba));
1595: PetscCall(PetscFree4(dnnz, onnz, tdnnz, tonnz));
1596: PetscCall(PetscFree3(work, rdest, cdest));
1597: PetscCall(PetscFree(gcdest));
1598: if (parcolp) PetscCall(ISDestroy(&colp));
1599: *B = Aperm;
1600: PetscFunctionReturn(PETSC_SUCCESS);
1601: }
1603: static PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
1604: {
1605: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1607: PetscFunctionBegin;
1608: PetscCall(MatGetSize(aij->B, NULL, nghosts));
1609: if (ghosts) *ghosts = aij->garray;
1610: PetscFunctionReturn(PETSC_SUCCESS);
1611: }
1613: static PetscErrorCode MatGetInfo_MPIAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1614: {
1615: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1616: Mat A = mat->A, B = mat->B;
1617: PetscLogDouble isend[5], irecv[5];
1619: PetscFunctionBegin;
1620: info->block_size = 1.0;
1621: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1623: isend[0] = info->nz_used;
1624: isend[1] = info->nz_allocated;
1625: isend[2] = info->nz_unneeded;
1626: isend[3] = info->memory;
1627: isend[4] = info->mallocs;
1629: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1631: isend[0] += info->nz_used;
1632: isend[1] += info->nz_allocated;
1633: isend[2] += info->nz_unneeded;
1634: isend[3] += info->memory;
1635: isend[4] += info->mallocs;
1636: if (flag == MAT_LOCAL) {
1637: info->nz_used = isend[0];
1638: info->nz_allocated = isend[1];
1639: info->nz_unneeded = isend[2];
1640: info->memory = isend[3];
1641: info->mallocs = isend[4];
1642: } else if (flag == MAT_GLOBAL_MAX) {
1643: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1645: info->nz_used = irecv[0];
1646: info->nz_allocated = irecv[1];
1647: info->nz_unneeded = irecv[2];
1648: info->memory = irecv[3];
1649: info->mallocs = irecv[4];
1650: } else if (flag == MAT_GLOBAL_SUM) {
1651: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1653: info->nz_used = irecv[0];
1654: info->nz_allocated = irecv[1];
1655: info->nz_unneeded = irecv[2];
1656: info->memory = irecv[3];
1657: info->mallocs = irecv[4];
1658: }
1659: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1660: info->fill_ratio_needed = 0;
1661: info->factor_mallocs = 0;
1662: PetscFunctionReturn(PETSC_SUCCESS);
1663: }
1665: PetscErrorCode MatSetOption_MPIAIJ(Mat A, MatOption op, PetscBool flg)
1666: {
1667: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1669: PetscFunctionBegin;
1670: switch (op) {
1671: case MAT_NEW_NONZERO_LOCATIONS:
1672: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1673: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1674: case MAT_KEEP_NONZERO_PATTERN:
1675: case MAT_NEW_NONZERO_LOCATION_ERR:
1676: case MAT_USE_INODES:
1677: case MAT_IGNORE_ZERO_ENTRIES:
1678: case MAT_FORM_EXPLICIT_TRANSPOSE:
1679: MatCheckPreallocated(A, 1);
1680: PetscCall(MatSetOption(a->A, op, flg));
1681: PetscCall(MatSetOption(a->B, op, flg));
1682: break;
1683: case MAT_ROW_ORIENTED:
1684: MatCheckPreallocated(A, 1);
1685: a->roworiented = flg;
1687: PetscCall(MatSetOption(a->A, op, flg));
1688: PetscCall(MatSetOption(a->B, op, flg));
1689: break;
1690: case MAT_IGNORE_OFF_PROC_ENTRIES:
1691: a->donotstash = flg;
1692: break;
1693: /* Symmetry flags are handled directly by MatSetOption() and they don't affect preallocation */
1694: case MAT_SPD:
1695: case MAT_SYMMETRIC:
1696: case MAT_STRUCTURALLY_SYMMETRIC:
1697: case MAT_HERMITIAN:
1698: case MAT_SYMMETRY_ETERNAL:
1699: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1700: case MAT_SPD_ETERNAL:
1701: /* if the diagonal matrix is square it inherits some of the properties above */
1702: if (a->A && A->rmap->n == A->cmap->n) PetscCall(MatSetOption(a->A, op, flg));
1703: break;
1704: case MAT_SUBMAT_SINGLEIS:
1705: A->submat_singleis = flg;
1706: break;
1707: default:
1708: break;
1709: }
1710: PetscFunctionReturn(PETSC_SUCCESS);
1711: }
1713: PetscErrorCode MatGetRow_MPIAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1714: {
1715: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1716: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1717: PetscInt i, *cworkA, *cworkB, **pcA, **pcB, cstart = matin->cmap->rstart;
1718: PetscInt nztot, nzA, nzB, lrow, rstart = matin->rmap->rstart, rend = matin->rmap->rend;
1719: PetscInt *cmap, *idx_p;
1721: PetscFunctionBegin;
1722: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1723: mat->getrowactive = PETSC_TRUE;
1725: if (!mat->rowvalues && (idx || v)) {
1726: /*
1727: allocate enough space to hold information from the longest row.
1728: */
1729: Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)mat->A->data, *Ba = (Mat_SeqAIJ *)mat->B->data;
1730: PetscInt max = 1, tmp;
1731: for (i = 0; i < matin->rmap->n; i++) {
1732: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1733: if (max < tmp) max = tmp;
1734: }
1735: PetscCall(PetscMalloc2(max, &mat->rowvalues, max, &mat->rowindices));
1736: }
1738: PetscCheck(row >= rstart && row < rend, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Only local rows");
1739: lrow = row - rstart;
1741: pvA = &vworkA;
1742: pcA = &cworkA;
1743: pvB = &vworkB;
1744: pcB = &cworkB;
1745: if (!v) {
1746: pvA = NULL;
1747: pvB = NULL;
1748: }
1749: if (!idx) {
1750: pcA = NULL;
1751: if (!v) pcB = NULL;
1752: }
1753: PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1754: PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1755: nztot = nzA + nzB;
1757: cmap = mat->garray;
1758: if (v || idx) {
1759: if (nztot) {
1760: /* Sort by increasing column numbers, assuming A and B already sorted */
1761: PetscInt imark = -1;
1762: if (v) {
1763: *v = v_p = mat->rowvalues;
1764: for (i = 0; i < nzB; i++) {
1765: if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1766: else break;
1767: }
1768: imark = i;
1769: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1770: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1771: }
1772: if (idx) {
1773: *idx = idx_p = mat->rowindices;
1774: if (imark > -1) {
1775: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i]];
1776: } else {
1777: for (i = 0; i < nzB; i++) {
1778: if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1779: else break;
1780: }
1781: imark = i;
1782: }
1783: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart + cworkA[i];
1784: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i]];
1785: }
1786: } else {
1787: if (idx) *idx = NULL;
1788: if (v) *v = NULL;
1789: }
1790: }
1791: *nz = nztot;
1792: PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1793: PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1794: PetscFunctionReturn(PETSC_SUCCESS);
1795: }
1797: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1798: {
1799: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1801: PetscFunctionBegin;
1802: PetscCheck(aij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow() must be called first");
1803: aij->getrowactive = PETSC_FALSE;
1804: PetscFunctionReturn(PETSC_SUCCESS);
1805: }
1807: static PetscErrorCode MatNorm_MPIAIJ(Mat mat, NormType type, PetscReal *norm)
1808: {
1809: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1810: Mat_SeqAIJ *amat = (Mat_SeqAIJ *)aij->A->data, *bmat = (Mat_SeqAIJ *)aij->B->data;
1811: PetscInt i, j, cstart = mat->cmap->rstart;
1812: PetscReal sum = 0.0;
1813: const MatScalar *v, *amata, *bmata;
1815: PetscFunctionBegin;
1816: if (aij->size == 1) {
1817: PetscCall(MatNorm(aij->A, type, norm));
1818: } else {
1819: PetscCall(MatSeqAIJGetArrayRead(aij->A, &amata));
1820: PetscCall(MatSeqAIJGetArrayRead(aij->B, &bmata));
1821: if (type == NORM_FROBENIUS) {
1822: v = amata;
1823: for (i = 0; i < amat->nz; i++) {
1824: sum += PetscRealPart(PetscConj(*v) * (*v));
1825: v++;
1826: }
1827: v = bmata;
1828: for (i = 0; i < bmat->nz; i++) {
1829: sum += PetscRealPart(PetscConj(*v) * (*v));
1830: v++;
1831: }
1832: PetscCallMPI(MPIU_Allreduce(&sum, norm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1833: *norm = PetscSqrtReal(*norm);
1834: PetscCall(PetscLogFlops(2.0 * amat->nz + 2.0 * bmat->nz));
1835: } else if (type == NORM_1) { /* max column norm */
1836: PetscReal *tmp;
1837: PetscInt *jj, *garray = aij->garray;
1838: PetscCall(PetscCalloc1(mat->cmap->N + 1, &tmp));
1839: *norm = 0.0;
1840: v = amata;
1841: jj = amat->j;
1842: for (j = 0; j < amat->nz; j++) {
1843: tmp[cstart + *jj++] += PetscAbsScalar(*v);
1844: v++;
1845: }
1846: v = bmata;
1847: jj = bmat->j;
1848: for (j = 0; j < bmat->nz; j++) {
1849: tmp[garray[*jj++]] += PetscAbsScalar(*v);
1850: v++;
1851: }
1852: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, tmp, mat->cmap->N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1853: for (j = 0; j < mat->cmap->N; j++) {
1854: if (tmp[j] > *norm) *norm = tmp[j];
1855: }
1856: PetscCall(PetscFree(tmp));
1857: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1858: } else if (type == NORM_INFINITY) { /* max row norm */
1859: PetscReal ntemp = 0.0;
1860: for (j = 0; j < aij->A->rmap->n; j++) {
1861: v = PetscSafePointerPlusOffset(amata, amat->i[j]);
1862: sum = 0.0;
1863: for (i = 0; i < amat->i[j + 1] - amat->i[j]; i++) {
1864: sum += PetscAbsScalar(*v);
1865: v++;
1866: }
1867: v = PetscSafePointerPlusOffset(bmata, bmat->i[j]);
1868: for (i = 0; i < bmat->i[j + 1] - bmat->i[j]; i++) {
1869: sum += PetscAbsScalar(*v);
1870: v++;
1871: }
1872: if (sum > ntemp) ntemp = sum;
1873: }
1874: PetscCallMPI(MPIU_Allreduce(&ntemp, norm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
1875: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1876: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for two norm");
1877: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &amata));
1878: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &bmata));
1879: }
1880: PetscFunctionReturn(PETSC_SUCCESS);
1881: }
1883: static PetscErrorCode MatTranspose_MPIAIJ(Mat A, MatReuse reuse, Mat *matout)
1884: {
1885: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *b;
1886: Mat_SeqAIJ *Aloc = (Mat_SeqAIJ *)a->A->data, *Bloc = (Mat_SeqAIJ *)a->B->data, *sub_B_diag;
1887: 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;
1888: const PetscInt *ai, *aj, *bi, *bj, *B_diag_i;
1889: Mat B, A_diag, *B_diag;
1890: const MatScalar *pbv, *bv;
1892: PetscFunctionBegin;
1893: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1894: ma = A->rmap->n;
1895: na = A->cmap->n;
1896: mb = a->B->rmap->n;
1897: nb = a->B->cmap->n;
1898: ai = Aloc->i;
1899: aj = Aloc->j;
1900: bi = Bloc->i;
1901: bj = Bloc->j;
1902: if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1903: PetscInt *d_nnz, *g_nnz, *o_nnz;
1904: PetscSFNode *oloc;
1905: PETSC_UNUSED PetscSF sf;
1907: PetscCall(PetscMalloc4(na, &d_nnz, na, &o_nnz, nb, &g_nnz, nb, &oloc));
1908: /* compute d_nnz for preallocation */
1909: PetscCall(PetscArrayzero(d_nnz, na));
1910: for (i = 0; i < ai[ma]; i++) d_nnz[aj[i]]++;
1911: /* compute local off-diagonal contributions */
1912: PetscCall(PetscArrayzero(g_nnz, nb));
1913: for (i = 0; i < bi[ma]; i++) g_nnz[bj[i]]++;
1914: /* map those to global */
1915: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1916: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, nb, NULL, PETSC_USE_POINTER, a->garray));
1917: PetscCall(PetscSFSetFromOptions(sf));
1918: PetscCall(PetscArrayzero(o_nnz, na));
1919: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1920: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1921: PetscCall(PetscSFDestroy(&sf));
1923: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1924: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1925: PetscCall(MatSetBlockSizes(B, A->cmap->bs, A->rmap->bs));
1926: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1927: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
1928: PetscCall(PetscFree4(d_nnz, o_nnz, g_nnz, oloc));
1929: } else {
1930: B = *matout;
1931: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
1932: }
1934: b = (Mat_MPIAIJ *)B->data;
1935: A_diag = a->A;
1936: B_diag = &b->A;
1937: sub_B_diag = (Mat_SeqAIJ *)(*B_diag)->data;
1938: A_diag_ncol = A_diag->cmap->N;
1939: B_diag_ilen = sub_B_diag->ilen;
1940: B_diag_i = sub_B_diag->i;
1942: /* Set ilen for diagonal of B */
1943: for (i = 0; i < A_diag_ncol; i++) B_diag_ilen[i] = B_diag_i[i + 1] - B_diag_i[i];
1945: /* Transpose the diagonal part of the matrix. In contrast to the off-diagonal part, this can be done
1946: very quickly (=without using MatSetValues), because all writes are local. */
1947: PetscCall(MatTransposeSetPrecursor(A_diag, *B_diag));
1948: PetscCall(MatTranspose(A_diag, MAT_REUSE_MATRIX, B_diag));
1950: /* copy over the B part */
1951: PetscCall(PetscMalloc1(bi[mb], &cols));
1952: PetscCall(MatSeqAIJGetArrayRead(a->B, &bv));
1953: pbv = bv;
1954: row = A->rmap->rstart;
1955: for (i = 0; i < bi[mb]; i++) cols[i] = a->garray[bj[i]];
1956: cols_tmp = cols;
1957: for (i = 0; i < mb; i++) {
1958: ncol = bi[i + 1] - bi[i];
1959: PetscCall(MatSetValues(B, ncol, cols_tmp, 1, &row, pbv, INSERT_VALUES));
1960: row++;
1961: if (pbv) pbv += ncol;
1962: if (cols_tmp) cols_tmp += ncol;
1963: }
1964: PetscCall(PetscFree(cols));
1965: PetscCall(MatSeqAIJRestoreArrayRead(a->B, &bv));
1967: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1968: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1969: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1970: *matout = B;
1971: } else {
1972: PetscCall(MatHeaderMerge(A, &B));
1973: }
1974: PetscFunctionReturn(PETSC_SUCCESS);
1975: }
1977: static PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat, Vec ll, Vec rr)
1978: {
1979: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1980: Mat a = aij->A, b = aij->B;
1981: PetscInt s1, s2, s3;
1983: PetscFunctionBegin;
1984: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1985: if (rr) {
1986: PetscCall(VecGetLocalSize(rr, &s1));
1987: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1988: /* Overlap communication with computation. */
1989: PetscCall(VecScatterBegin(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1990: }
1991: if (ll) {
1992: PetscCall(VecGetLocalSize(ll, &s1));
1993: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1994: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1995: }
1996: /* scale the diagonal block */
1997: PetscUseTypeMethod(a, diagonalscale, ll, rr);
1999: if (rr) {
2000: /* Do a scatter end and then right scale the off-diagonal block */
2001: PetscCall(VecScatterEnd(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
2002: PetscUseTypeMethod(b, diagonalscale, NULL, aij->lvec);
2003: }
2004: PetscFunctionReturn(PETSC_SUCCESS);
2005: }
2007: static PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2008: {
2009: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2011: PetscFunctionBegin;
2012: PetscCall(MatSetUnfactored(a->A));
2013: PetscFunctionReturn(PETSC_SUCCESS);
2014: }
2016: static PetscErrorCode MatEqual_MPIAIJ(Mat A, Mat B, PetscBool *flag)
2017: {
2018: Mat_MPIAIJ *matB = (Mat_MPIAIJ *)B->data, *matA = (Mat_MPIAIJ *)A->data;
2019: Mat a, b, c, d;
2020: PetscBool flg;
2022: PetscFunctionBegin;
2023: a = matA->A;
2024: b = matA->B;
2025: c = matB->A;
2026: d = matB->B;
2028: PetscCall(MatEqual(a, c, &flg));
2029: if (flg) PetscCall(MatEqual(b, d, &flg));
2030: PetscCallMPI(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
2031: PetscFunctionReturn(PETSC_SUCCESS);
2032: }
2034: static PetscErrorCode MatCopy_MPIAIJ(Mat A, Mat B, MatStructure str)
2035: {
2036: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2037: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2039: PetscFunctionBegin;
2040: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2041: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2042: /* because of the column compression in the off-processor part of the matrix a->B,
2043: the number of columns in a->B and b->B may be different, hence we cannot call
2044: the MatCopy() directly on the two parts. If need be, we can provide a more
2045: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2046: then copying the submatrices */
2047: PetscCall(MatCopy_Basic(A, B, str));
2048: } else {
2049: PetscCall(MatCopy(a->A, b->A, str));
2050: PetscCall(MatCopy(a->B, b->B, str));
2051: }
2052: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2053: PetscFunctionReturn(PETSC_SUCCESS);
2054: }
2056: /*
2057: Computes the number of nonzeros per row needed for preallocation when X and Y
2058: have different nonzero structure.
2059: */
2060: 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)
2061: {
2062: PetscInt i, j, k, nzx, nzy;
2064: PetscFunctionBegin;
2065: /* Set the number of nonzeros in the new matrix */
2066: for (i = 0; i < m; i++) {
2067: const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2068: nzx = xi[i + 1] - xi[i];
2069: nzy = yi[i + 1] - yi[i];
2070: nnz[i] = 0;
2071: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2072: for (; k < nzy && yltog[yjj[k]] < xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2073: if (k < nzy && yltog[yjj[k]] == xltog[xjj[j]]) k++; /* Skip duplicate */
2074: nnz[i]++;
2075: }
2076: for (; k < nzy; k++) nnz[i]++;
2077: }
2078: PetscFunctionReturn(PETSC_SUCCESS);
2079: }
2081: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2082: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
2083: {
2084: PetscInt m = Y->rmap->N;
2085: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2086: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
2088: PetscFunctionBegin;
2089: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
2090: PetscFunctionReturn(PETSC_SUCCESS);
2091: }
2093: static PetscErrorCode MatAXPY_MPIAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2094: {
2095: Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data, *yy = (Mat_MPIAIJ *)Y->data;
2097: PetscFunctionBegin;
2098: if (str == SAME_NONZERO_PATTERN) {
2099: PetscCall(MatAXPY(yy->A, a, xx->A, str));
2100: PetscCall(MatAXPY(yy->B, a, xx->B, str));
2101: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2102: PetscCall(MatAXPY_Basic(Y, a, X, str));
2103: } else {
2104: Mat B;
2105: PetscInt *nnz_d, *nnz_o;
2107: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
2108: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
2109: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2110: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2111: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2112: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2113: PetscCall(MatAXPYGetPreallocation_SeqAIJ(yy->A, xx->A, nnz_d));
2114: PetscCall(MatAXPYGetPreallocation_MPIAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
2115: PetscCall(MatMPIAIJSetPreallocation(B, 0, nnz_d, 0, nnz_o));
2116: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2117: PetscCall(MatHeaderMerge(Y, &B));
2118: PetscCall(PetscFree(nnz_d));
2119: PetscCall(PetscFree(nnz_o));
2120: }
2121: PetscFunctionReturn(PETSC_SUCCESS);
2122: }
2124: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);
2126: static PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2127: {
2128: PetscFunctionBegin;
2129: if (PetscDefined(USE_COMPLEX)) {
2130: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2132: PetscCall(MatConjugate_SeqAIJ(aij->A));
2133: PetscCall(MatConjugate_SeqAIJ(aij->B));
2134: }
2135: PetscFunctionReturn(PETSC_SUCCESS);
2136: }
2138: static PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2139: {
2140: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2142: PetscFunctionBegin;
2143: PetscCall(MatRealPart(a->A));
2144: PetscCall(MatRealPart(a->B));
2145: PetscFunctionReturn(PETSC_SUCCESS);
2146: }
2148: static PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
2149: {
2150: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2152: PetscFunctionBegin;
2153: PetscCall(MatImaginaryPart(a->A));
2154: PetscCall(MatImaginaryPart(a->B));
2155: PetscFunctionReturn(PETSC_SUCCESS);
2156: }
2158: static PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2159: {
2160: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2161: PetscInt i, *idxb = NULL, m = A->rmap->n;
2162: PetscScalar *vv;
2163: Vec vB, vA;
2164: const PetscScalar *va, *vb;
2166: PetscFunctionBegin;
2167: PetscCall(MatCreateVecs(a->A, NULL, &vA));
2168: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
2170: PetscCall(VecGetArrayRead(vA, &va));
2171: if (idx) {
2172: for (i = 0; i < m; i++) {
2173: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2174: }
2175: }
2177: PetscCall(MatCreateVecs(a->B, NULL, &vB));
2178: PetscCall(PetscMalloc1(m, &idxb));
2179: PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));
2181: PetscCall(VecGetArrayWrite(v, &vv));
2182: PetscCall(VecGetArrayRead(vB, &vb));
2183: for (i = 0; i < m; i++) {
2184: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2185: vv[i] = vb[i];
2186: if (idx) idx[i] = a->garray[idxb[i]];
2187: } else {
2188: vv[i] = va[i];
2189: if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > a->garray[idxb[i]]) idx[i] = a->garray[idxb[i]];
2190: }
2191: }
2192: PetscCall(VecRestoreArrayWrite(v, &vv));
2193: PetscCall(VecRestoreArrayRead(vA, &va));
2194: PetscCall(VecRestoreArrayRead(vB, &vb));
2195: PetscCall(PetscFree(idxb));
2196: PetscCall(VecDestroy(&vA));
2197: PetscCall(VecDestroy(&vB));
2198: PetscFunctionReturn(PETSC_SUCCESS);
2199: }
2201: static PetscErrorCode MatGetRowSumAbs_MPIAIJ(Mat A, Vec v)
2202: {
2203: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2204: Vec vB, vA;
2206: PetscFunctionBegin;
2207: PetscCall(MatCreateVecs(a->A, NULL, &vA));
2208: PetscCall(MatGetRowSumAbs(a->A, vA));
2209: PetscCall(MatCreateVecs(a->B, NULL, &vB));
2210: PetscCall(MatGetRowSumAbs(a->B, vB));
2211: PetscCall(VecAXPY(vA, 1.0, vB));
2212: PetscCall(VecDestroy(&vB));
2213: PetscCall(VecCopy(vA, v));
2214: PetscCall(VecDestroy(&vA));
2215: PetscFunctionReturn(PETSC_SUCCESS);
2216: }
2218: static PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2219: {
2220: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2221: PetscInt m = A->rmap->n, n = A->cmap->n;
2222: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2223: PetscInt *cmap = mat->garray;
2224: PetscInt *diagIdx, *offdiagIdx;
2225: Vec diagV, offdiagV;
2226: PetscScalar *a, *diagA, *offdiagA;
2227: const PetscScalar *ba, *bav;
2228: PetscInt r, j, col, ncols, *bi, *bj;
2229: Mat B = mat->B;
2230: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2232: PetscFunctionBegin;
2233: /* When a process holds entire A and other processes have no entry */
2234: if (A->cmap->N == n) {
2235: PetscCall(VecGetArrayWrite(v, &diagA));
2236: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2237: PetscCall(MatGetRowMinAbs(mat->A, diagV, idx));
2238: PetscCall(VecDestroy(&diagV));
2239: PetscCall(VecRestoreArrayWrite(v, &diagA));
2240: PetscFunctionReturn(PETSC_SUCCESS);
2241: } else if (n == 0) {
2242: if (m) {
2243: PetscCall(VecGetArrayWrite(v, &a));
2244: for (r = 0; r < m; r++) {
2245: a[r] = 0.0;
2246: if (idx) idx[r] = -1;
2247: }
2248: PetscCall(VecRestoreArrayWrite(v, &a));
2249: }
2250: PetscFunctionReturn(PETSC_SUCCESS);
2251: }
2253: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2254: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2255: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2256: PetscCall(MatGetRowMinAbs(mat->A, diagV, diagIdx));
2258: /* Get offdiagIdx[] for implicit 0.0 */
2259: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2260: ba = bav;
2261: bi = b->i;
2262: bj = b->j;
2263: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2264: for (r = 0; r < m; r++) {
2265: ncols = bi[r + 1] - bi[r];
2266: if (ncols == A->cmap->N - n) { /* Brow is dense */
2267: offdiagA[r] = *ba;
2268: offdiagIdx[r] = cmap[0];
2269: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2270: offdiagA[r] = 0.0;
2272: /* Find first hole in the cmap */
2273: for (j = 0; j < ncols; j++) {
2274: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2275: if (col > j && j < cstart) {
2276: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2277: break;
2278: } else if (col > j + n && j >= cstart) {
2279: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2280: break;
2281: }
2282: }
2283: if (j == ncols && ncols < A->cmap->N - n) {
2284: /* a hole is outside compressed Bcols */
2285: if (ncols == 0) {
2286: if (cstart) {
2287: offdiagIdx[r] = 0;
2288: } else offdiagIdx[r] = cend;
2289: } else { /* ncols > 0 */
2290: offdiagIdx[r] = cmap[ncols - 1] + 1;
2291: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2292: }
2293: }
2294: }
2296: for (j = 0; j < ncols; j++) {
2297: if (PetscAbsScalar(offdiagA[r]) > PetscAbsScalar(*ba)) {
2298: offdiagA[r] = *ba;
2299: offdiagIdx[r] = cmap[*bj];
2300: }
2301: ba++;
2302: bj++;
2303: }
2304: }
2306: PetscCall(VecGetArrayWrite(v, &a));
2307: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2308: for (r = 0; r < m; ++r) {
2309: if (PetscAbsScalar(diagA[r]) < PetscAbsScalar(offdiagA[r])) {
2310: a[r] = diagA[r];
2311: if (idx) idx[r] = cstart + diagIdx[r];
2312: } else if (PetscAbsScalar(diagA[r]) == PetscAbsScalar(offdiagA[r])) {
2313: a[r] = diagA[r];
2314: if (idx) {
2315: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2316: idx[r] = cstart + diagIdx[r];
2317: } else idx[r] = offdiagIdx[r];
2318: }
2319: } else {
2320: a[r] = offdiagA[r];
2321: if (idx) idx[r] = offdiagIdx[r];
2322: }
2323: }
2324: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2325: PetscCall(VecRestoreArrayWrite(v, &a));
2326: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2327: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2328: PetscCall(VecDestroy(&diagV));
2329: PetscCall(VecDestroy(&offdiagV));
2330: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2331: PetscFunctionReturn(PETSC_SUCCESS);
2332: }
2334: static PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2335: {
2336: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2337: PetscInt m = A->rmap->n, n = A->cmap->n;
2338: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2339: PetscInt *cmap = mat->garray;
2340: PetscInt *diagIdx, *offdiagIdx;
2341: Vec diagV, offdiagV;
2342: PetscScalar *a, *diagA, *offdiagA;
2343: const PetscScalar *ba, *bav;
2344: PetscInt r, j, col, ncols, *bi, *bj;
2345: Mat B = mat->B;
2346: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2348: PetscFunctionBegin;
2349: /* When a process holds entire A and other processes have no entry */
2350: if (A->cmap->N == n) {
2351: PetscCall(VecGetArrayWrite(v, &diagA));
2352: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2353: PetscCall(MatGetRowMin(mat->A, diagV, idx));
2354: PetscCall(VecDestroy(&diagV));
2355: PetscCall(VecRestoreArrayWrite(v, &diagA));
2356: PetscFunctionReturn(PETSC_SUCCESS);
2357: } else if (n == 0) {
2358: if (m) {
2359: PetscCall(VecGetArrayWrite(v, &a));
2360: for (r = 0; r < m; r++) {
2361: a[r] = PETSC_MAX_REAL;
2362: if (idx) idx[r] = -1;
2363: }
2364: PetscCall(VecRestoreArrayWrite(v, &a));
2365: }
2366: PetscFunctionReturn(PETSC_SUCCESS);
2367: }
2369: PetscCall(PetscCalloc2(m, &diagIdx, m, &offdiagIdx));
2370: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2371: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2372: PetscCall(MatGetRowMin(mat->A, diagV, diagIdx));
2374: /* Get offdiagIdx[] for implicit 0.0 */
2375: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2376: ba = bav;
2377: bi = b->i;
2378: bj = b->j;
2379: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2380: for (r = 0; r < m; r++) {
2381: ncols = bi[r + 1] - bi[r];
2382: if (ncols == A->cmap->N - n) { /* Brow is dense */
2383: offdiagA[r] = *ba;
2384: offdiagIdx[r] = cmap[0];
2385: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2386: offdiagA[r] = 0.0;
2388: /* Find first hole in the cmap */
2389: for (j = 0; j < ncols; j++) {
2390: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2391: if (col > j && j < cstart) {
2392: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2393: break;
2394: } else if (col > j + n && j >= cstart) {
2395: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2396: break;
2397: }
2398: }
2399: if (j == ncols && ncols < A->cmap->N - n) {
2400: /* a hole is outside compressed Bcols */
2401: if (ncols == 0) {
2402: if (cstart) {
2403: offdiagIdx[r] = 0;
2404: } else offdiagIdx[r] = cend;
2405: } else { /* ncols > 0 */
2406: offdiagIdx[r] = cmap[ncols - 1] + 1;
2407: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2408: }
2409: }
2410: }
2412: for (j = 0; j < ncols; j++) {
2413: if (PetscRealPart(offdiagA[r]) > PetscRealPart(*ba)) {
2414: offdiagA[r] = *ba;
2415: offdiagIdx[r] = cmap[*bj];
2416: }
2417: ba++;
2418: bj++;
2419: }
2420: }
2422: PetscCall(VecGetArrayWrite(v, &a));
2423: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2424: for (r = 0; r < m; ++r) {
2425: if (PetscRealPart(diagA[r]) < PetscRealPart(offdiagA[r])) {
2426: a[r] = diagA[r];
2427: if (idx) idx[r] = cstart + diagIdx[r];
2428: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2429: a[r] = diagA[r];
2430: if (idx) {
2431: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2432: idx[r] = cstart + diagIdx[r];
2433: } else idx[r] = offdiagIdx[r];
2434: }
2435: } else {
2436: a[r] = offdiagA[r];
2437: if (idx) idx[r] = offdiagIdx[r];
2438: }
2439: }
2440: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2441: PetscCall(VecRestoreArrayWrite(v, &a));
2442: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2443: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2444: PetscCall(VecDestroy(&diagV));
2445: PetscCall(VecDestroy(&offdiagV));
2446: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2447: PetscFunctionReturn(PETSC_SUCCESS);
2448: }
2450: static PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2451: {
2452: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2453: PetscInt m = A->rmap->n, n = A->cmap->n;
2454: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2455: PetscInt *cmap = mat->garray;
2456: PetscInt *diagIdx, *offdiagIdx;
2457: Vec diagV, offdiagV;
2458: PetscScalar *a, *diagA, *offdiagA;
2459: const PetscScalar *ba, *bav;
2460: PetscInt r, j, col, ncols, *bi, *bj;
2461: Mat B = mat->B;
2462: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2464: PetscFunctionBegin;
2465: /* When a process holds entire A and other processes have no entry */
2466: if (A->cmap->N == n) {
2467: PetscCall(VecGetArrayWrite(v, &diagA));
2468: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2469: PetscCall(MatGetRowMax(mat->A, diagV, idx));
2470: PetscCall(VecDestroy(&diagV));
2471: PetscCall(VecRestoreArrayWrite(v, &diagA));
2472: PetscFunctionReturn(PETSC_SUCCESS);
2473: } else if (n == 0) {
2474: if (m) {
2475: PetscCall(VecGetArrayWrite(v, &a));
2476: for (r = 0; r < m; r++) {
2477: a[r] = PETSC_MIN_REAL;
2478: if (idx) idx[r] = -1;
2479: }
2480: PetscCall(VecRestoreArrayWrite(v, &a));
2481: }
2482: PetscFunctionReturn(PETSC_SUCCESS);
2483: }
2485: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2486: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2487: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2488: PetscCall(MatGetRowMax(mat->A, diagV, diagIdx));
2490: /* Get offdiagIdx[] for implicit 0.0 */
2491: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2492: ba = bav;
2493: bi = b->i;
2494: bj = b->j;
2495: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2496: for (r = 0; r < m; r++) {
2497: ncols = bi[r + 1] - bi[r];
2498: if (ncols == A->cmap->N - n) { /* Brow is dense */
2499: offdiagA[r] = *ba;
2500: offdiagIdx[r] = cmap[0];
2501: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2502: offdiagA[r] = 0.0;
2504: /* Find first hole in the cmap */
2505: for (j = 0; j < ncols; j++) {
2506: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2507: if (col > j && j < cstart) {
2508: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2509: break;
2510: } else if (col > j + n && j >= cstart) {
2511: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2512: break;
2513: }
2514: }
2515: if (j == ncols && ncols < A->cmap->N - n) {
2516: /* a hole is outside compressed Bcols */
2517: if (ncols == 0) {
2518: if (cstart) {
2519: offdiagIdx[r] = 0;
2520: } else offdiagIdx[r] = cend;
2521: } else { /* ncols > 0 */
2522: offdiagIdx[r] = cmap[ncols - 1] + 1;
2523: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2524: }
2525: }
2526: }
2528: for (j = 0; j < ncols; j++) {
2529: if (PetscRealPart(offdiagA[r]) < PetscRealPart(*ba)) {
2530: offdiagA[r] = *ba;
2531: offdiagIdx[r] = cmap[*bj];
2532: }
2533: ba++;
2534: bj++;
2535: }
2536: }
2538: PetscCall(VecGetArrayWrite(v, &a));
2539: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2540: for (r = 0; r < m; ++r) {
2541: if (PetscRealPart(diagA[r]) > PetscRealPart(offdiagA[r])) {
2542: a[r] = diagA[r];
2543: if (idx) idx[r] = cstart + diagIdx[r];
2544: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2545: a[r] = diagA[r];
2546: if (idx) {
2547: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2548: idx[r] = cstart + diagIdx[r];
2549: } else idx[r] = offdiagIdx[r];
2550: }
2551: } else {
2552: a[r] = offdiagA[r];
2553: if (idx) idx[r] = offdiagIdx[r];
2554: }
2555: }
2556: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2557: PetscCall(VecRestoreArrayWrite(v, &a));
2558: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2559: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2560: PetscCall(VecDestroy(&diagV));
2561: PetscCall(VecDestroy(&offdiagV));
2562: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2563: PetscFunctionReturn(PETSC_SUCCESS);
2564: }
2566: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat, Mat *newmat)
2567: {
2568: Mat *dummy;
2570: PetscFunctionBegin;
2571: PetscCall(MatCreateSubMatrix_MPIAIJ_All(mat, MAT_DO_NOT_GET_VALUES, MAT_INITIAL_MATRIX, &dummy));
2572: *newmat = *dummy;
2573: PetscCall(PetscFree(dummy));
2574: PetscFunctionReturn(PETSC_SUCCESS);
2575: }
2577: static PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A, const PetscScalar **values)
2578: {
2579: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2581: PetscFunctionBegin;
2582: PetscCall(MatInvertBlockDiagonal(a->A, values));
2583: A->factorerrortype = a->A->factorerrortype;
2584: PetscFunctionReturn(PETSC_SUCCESS);
2585: }
2587: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x, PetscRandom rctx)
2588: {
2589: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)x->data;
2591: PetscFunctionBegin;
2592: PetscCheck(x->assembled || x->preallocated, PetscObjectComm((PetscObject)x), PETSC_ERR_ARG_WRONGSTATE, "MatSetRandom on an unassembled and unpreallocated MATMPIAIJ is not allowed");
2593: PetscCall(MatSetRandom(aij->A, rctx));
2594: if (x->assembled) {
2595: PetscCall(MatSetRandom(aij->B, rctx));
2596: } else {
2597: PetscCall(MatSetRandomSkipColumnRange_SeqAIJ_Private(aij->B, x->cmap->rstart, x->cmap->rend, rctx));
2598: }
2599: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
2600: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
2601: PetscFunctionReturn(PETSC_SUCCESS);
2602: }
2604: static PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ(Mat A, PetscBool sc)
2605: {
2606: PetscFunctionBegin;
2607: if (sc) A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ_Scalable;
2608: else A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ;
2609: PetscFunctionReturn(PETSC_SUCCESS);
2610: }
2612: /*@
2613: MatMPIAIJGetNumberNonzeros - gets the number of nonzeros in the matrix on this MPI rank
2615: Not Collective
2617: Input Parameter:
2618: . A - the matrix
2620: Output Parameter:
2621: . nz - the number of nonzeros
2623: Level: advanced
2625: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2626: @*/
2627: PetscErrorCode MatMPIAIJGetNumberNonzeros(Mat A, PetscCount *nz)
2628: {
2629: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)A->data;
2630: Mat_SeqAIJ *aaij = (Mat_SeqAIJ *)maij->A->data, *baij = (Mat_SeqAIJ *)maij->B->data;
2631: PetscBool isaij;
2633: PetscFunctionBegin;
2634: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATMPIAIJ, &isaij));
2635: PetscCheck(isaij, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for type %s", ((PetscObject)A)->type_name);
2636: *nz = aaij->i[A->rmap->n] + baij->i[A->rmap->n];
2637: PetscFunctionReturn(PETSC_SUCCESS);
2638: }
2640: /*@
2641: MatMPIAIJSetUseScalableIncreaseOverlap - Determine if the matrix uses a scalable algorithm to compute the overlap
2643: Collective
2645: Input Parameters:
2646: + A - the matrix
2647: - sc - `PETSC_TRUE` indicates use the scalable algorithm (default is not to use the scalable algorithm)
2649: Level: advanced
2651: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2652: @*/
2653: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap(Mat A, PetscBool sc)
2654: {
2655: PetscFunctionBegin;
2656: PetscTryMethod(A, "MatMPIAIJSetUseScalableIncreaseOverlap_C", (Mat, PetscBool), (A, sc));
2657: PetscFunctionReturn(PETSC_SUCCESS);
2658: }
2660: PetscErrorCode MatSetFromOptions_MPIAIJ(Mat A, PetscOptionItems PetscOptionsObject)
2661: {
2662: PetscBool sc = PETSC_FALSE, flg;
2664: PetscFunctionBegin;
2665: PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJ options");
2666: if (A->ops->increaseoverlap == MatIncreaseOverlap_MPIAIJ_Scalable) sc = PETSC_TRUE;
2667: PetscCall(PetscOptionsBool("-mat_increase_overlap_scalable", "Use a scalable algorithm to compute the overlap", "MatIncreaseOverlap", sc, &sc, &flg));
2668: if (flg) PetscCall(MatMPIAIJSetUseScalableIncreaseOverlap(A, sc));
2669: PetscOptionsHeadEnd();
2670: PetscFunctionReturn(PETSC_SUCCESS);
2671: }
2673: static PetscErrorCode MatShift_MPIAIJ(Mat Y, PetscScalar a)
2674: {
2675: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)Y->data;
2676: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)maij->A->data;
2678: PetscFunctionBegin;
2679: if (!Y->preallocated) {
2680: PetscCall(MatMPIAIJSetPreallocation(Y, 1, NULL, 0, NULL));
2681: } else if (!aij->nz) { /* It does not matter if diagonals of Y only partially lie in maij->A. We just need an estimated preallocation. */
2682: PetscInt nonew = aij->nonew;
2683: PetscCall(MatSeqAIJSetPreallocation(maij->A, 1, NULL));
2684: aij->nonew = nonew;
2685: }
2686: PetscCall(MatShift_Basic(Y, a));
2687: PetscFunctionReturn(PETSC_SUCCESS);
2688: }
2690: static PetscErrorCode MatMissingDiagonal_MPIAIJ(Mat A, PetscBool *missing, PetscInt *d)
2691: {
2692: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2694: PetscFunctionBegin;
2695: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2696: PetscCall(MatMissingDiagonal(a->A, missing, d));
2697: if (d) {
2698: PetscInt rstart;
2699: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2700: *d += rstart;
2701: }
2702: PetscFunctionReturn(PETSC_SUCCESS);
2703: }
2705: static PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2706: {
2707: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2709: PetscFunctionBegin;
2710: PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2711: PetscFunctionReturn(PETSC_SUCCESS);
2712: }
2714: static PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A, PetscBool keep)
2715: {
2716: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2718: PetscFunctionBegin;
2719: PetscCall(MatEliminateZeros_SeqAIJ(a->A, keep)); // possibly keep zero diagonal coefficients
2720: PetscCall(MatEliminateZeros_SeqAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2721: PetscFunctionReturn(PETSC_SUCCESS);
2722: }
2724: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2725: MatGetRow_MPIAIJ,
2726: MatRestoreRow_MPIAIJ,
2727: MatMult_MPIAIJ,
2728: /* 4*/ MatMultAdd_MPIAIJ,
2729: MatMultTranspose_MPIAIJ,
2730: MatMultTransposeAdd_MPIAIJ,
2731: NULL,
2732: NULL,
2733: NULL,
2734: /*10*/ NULL,
2735: NULL,
2736: NULL,
2737: MatSOR_MPIAIJ,
2738: MatTranspose_MPIAIJ,
2739: /*15*/ MatGetInfo_MPIAIJ,
2740: MatEqual_MPIAIJ,
2741: MatGetDiagonal_MPIAIJ,
2742: MatDiagonalScale_MPIAIJ,
2743: MatNorm_MPIAIJ,
2744: /*20*/ MatAssemblyBegin_MPIAIJ,
2745: MatAssemblyEnd_MPIAIJ,
2746: MatSetOption_MPIAIJ,
2747: MatZeroEntries_MPIAIJ,
2748: /*24*/ MatZeroRows_MPIAIJ,
2749: NULL,
2750: NULL,
2751: NULL,
2752: NULL,
2753: /*29*/ MatSetUp_MPI_Hash,
2754: NULL,
2755: NULL,
2756: MatGetDiagonalBlock_MPIAIJ,
2757: NULL,
2758: /*34*/ MatDuplicate_MPIAIJ,
2759: NULL,
2760: NULL,
2761: NULL,
2762: NULL,
2763: /*39*/ MatAXPY_MPIAIJ,
2764: MatCreateSubMatrices_MPIAIJ,
2765: MatIncreaseOverlap_MPIAIJ,
2766: MatGetValues_MPIAIJ,
2767: MatCopy_MPIAIJ,
2768: /*44*/ MatGetRowMax_MPIAIJ,
2769: MatScale_MPIAIJ,
2770: MatShift_MPIAIJ,
2771: MatDiagonalSet_MPIAIJ,
2772: MatZeroRowsColumns_MPIAIJ,
2773: /*49*/ MatSetRandom_MPIAIJ,
2774: MatGetRowIJ_MPIAIJ,
2775: MatRestoreRowIJ_MPIAIJ,
2776: NULL,
2777: NULL,
2778: /*54*/ MatFDColoringCreate_MPIXAIJ,
2779: NULL,
2780: MatSetUnfactored_MPIAIJ,
2781: MatPermute_MPIAIJ,
2782: NULL,
2783: /*59*/ MatCreateSubMatrix_MPIAIJ,
2784: MatDestroy_MPIAIJ,
2785: MatView_MPIAIJ,
2786: NULL,
2787: NULL,
2788: /*64*/ NULL,
2789: MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2790: NULL,
2791: NULL,
2792: NULL,
2793: /*69*/ MatGetRowMaxAbs_MPIAIJ,
2794: MatGetRowMinAbs_MPIAIJ,
2795: NULL,
2796: NULL,
2797: NULL,
2798: NULL,
2799: /*75*/ MatFDColoringApply_AIJ,
2800: MatSetFromOptions_MPIAIJ,
2801: NULL,
2802: NULL,
2803: MatFindZeroDiagonals_MPIAIJ,
2804: /*80*/ NULL,
2805: NULL,
2806: NULL,
2807: /*83*/ MatLoad_MPIAIJ,
2808: NULL,
2809: NULL,
2810: NULL,
2811: NULL,
2812: NULL,
2813: /*89*/ NULL,
2814: NULL,
2815: MatMatMultNumeric_MPIAIJ_MPIAIJ,
2816: NULL,
2817: NULL,
2818: /*94*/ MatPtAPNumeric_MPIAIJ_MPIAIJ,
2819: NULL,
2820: NULL,
2821: NULL,
2822: MatBindToCPU_MPIAIJ,
2823: /*99*/ MatProductSetFromOptions_MPIAIJ,
2824: NULL,
2825: NULL,
2826: MatConjugate_MPIAIJ,
2827: NULL,
2828: /*104*/ MatSetValuesRow_MPIAIJ,
2829: MatRealPart_MPIAIJ,
2830: MatImaginaryPart_MPIAIJ,
2831: NULL,
2832: NULL,
2833: /*109*/ NULL,
2834: NULL,
2835: MatGetRowMin_MPIAIJ,
2836: NULL,
2837: MatMissingDiagonal_MPIAIJ,
2838: /*114*/ MatGetSeqNonzeroStructure_MPIAIJ,
2839: NULL,
2840: MatGetGhosts_MPIAIJ,
2841: NULL,
2842: NULL,
2843: /*119*/ MatMultDiagonalBlock_MPIAIJ,
2844: NULL,
2845: NULL,
2846: NULL,
2847: MatGetMultiProcBlock_MPIAIJ,
2848: /*124*/ MatFindNonzeroRows_MPIAIJ,
2849: MatGetColumnReductions_MPIAIJ,
2850: MatInvertBlockDiagonal_MPIAIJ,
2851: MatInvertVariableBlockDiagonal_MPIAIJ,
2852: MatCreateSubMatricesMPI_MPIAIJ,
2853: /*129*/ NULL,
2854: NULL,
2855: NULL,
2856: MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2857: NULL,
2858: /*134*/ NULL,
2859: NULL,
2860: NULL,
2861: NULL,
2862: NULL,
2863: /*139*/ MatSetBlockSizes_MPIAIJ,
2864: NULL,
2865: NULL,
2866: MatFDColoringSetUp_MPIXAIJ,
2867: MatFindOffBlockDiagonalEntries_MPIAIJ,
2868: MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2869: /*145*/ NULL,
2870: NULL,
2871: NULL,
2872: MatCreateGraph_Simple_AIJ,
2873: NULL,
2874: /*150*/ NULL,
2875: MatEliminateZeros_MPIAIJ,
2876: MatGetRowSumAbs_MPIAIJ,
2877: NULL,
2878: NULL,
2879: /*155*/ NULL,
2880: MatCopyHashToXAIJ_MPI_Hash};
2882: static PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2883: {
2884: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2886: PetscFunctionBegin;
2887: PetscCall(MatStoreValues(aij->A));
2888: PetscCall(MatStoreValues(aij->B));
2889: PetscFunctionReturn(PETSC_SUCCESS);
2890: }
2892: static PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2893: {
2894: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2896: PetscFunctionBegin;
2897: PetscCall(MatRetrieveValues(aij->A));
2898: PetscCall(MatRetrieveValues(aij->B));
2899: PetscFunctionReturn(PETSC_SUCCESS);
2900: }
2902: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2903: {
2904: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2905: PetscMPIInt size;
2907: PetscFunctionBegin;
2908: if (B->hash_active) {
2909: B->ops[0] = b->cops;
2910: B->hash_active = PETSC_FALSE;
2911: }
2912: PetscCall(PetscLayoutSetUp(B->rmap));
2913: PetscCall(PetscLayoutSetUp(B->cmap));
2915: #if defined(PETSC_USE_CTABLE)
2916: PetscCall(PetscHMapIDestroy(&b->colmap));
2917: #else
2918: PetscCall(PetscFree(b->colmap));
2919: #endif
2920: PetscCall(PetscFree(b->garray));
2921: PetscCall(VecDestroy(&b->lvec));
2922: PetscCall(VecScatterDestroy(&b->Mvctx));
2924: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2926: MatSeqXAIJGetOptions_Private(b->B);
2927: PetscCall(MatDestroy(&b->B));
2928: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2929: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2930: PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2931: PetscCall(MatSetType(b->B, MATSEQAIJ));
2932: MatSeqXAIJRestoreOptions_Private(b->B);
2934: MatSeqXAIJGetOptions_Private(b->A);
2935: PetscCall(MatDestroy(&b->A));
2936: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2937: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2938: PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2939: PetscCall(MatSetType(b->A, MATSEQAIJ));
2940: MatSeqXAIJRestoreOptions_Private(b->A);
2942: PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2943: PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2944: B->preallocated = PETSC_TRUE;
2945: B->was_assembled = PETSC_FALSE;
2946: B->assembled = PETSC_FALSE;
2947: PetscFunctionReturn(PETSC_SUCCESS);
2948: }
2950: static PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2951: {
2952: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2953: PetscBool ondiagreset, offdiagreset, memoryreset;
2955: PetscFunctionBegin;
2957: PetscCheck(B->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset preallocation after setting some values but not yet calling MatAssemblyBegin()/MatAssemblyEnd()");
2958: if (B->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
2960: PetscCall(MatResetPreallocation_SeqAIJ_Private(b->A, &ondiagreset));
2961: PetscCall(MatResetPreallocation_SeqAIJ_Private(b->B, &offdiagreset));
2962: memoryreset = (PetscBool)(ondiagreset || offdiagreset);
2963: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &memoryreset, 1, MPIU_BOOL, MPI_LOR, PetscObjectComm((PetscObject)B)));
2964: if (!memoryreset) PetscFunctionReturn(PETSC_SUCCESS);
2966: PetscCall(PetscLayoutSetUp(B->rmap));
2967: PetscCall(PetscLayoutSetUp(B->cmap));
2968: PetscCheck(B->assembled || B->was_assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Should not need to reset preallocation if the matrix was never assembled");
2969: PetscCall(MatDisAssemble_MPIAIJ(B, PETSC_TRUE));
2970: PetscCall(VecScatterDestroy(&b->Mvctx));
2972: B->preallocated = PETSC_TRUE;
2973: B->was_assembled = PETSC_FALSE;
2974: B->assembled = PETSC_FALSE;
2975: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
2976: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2977: PetscFunctionReturn(PETSC_SUCCESS);
2978: }
2980: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2981: {
2982: Mat mat;
2983: Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;
2985: PetscFunctionBegin;
2986: *newmat = NULL;
2987: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2988: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2989: PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2990: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2991: a = (Mat_MPIAIJ *)mat->data;
2993: mat->factortype = matin->factortype;
2994: mat->assembled = matin->assembled;
2995: mat->insertmode = NOT_SET_VALUES;
2997: a->size = oldmat->size;
2998: a->rank = oldmat->rank;
2999: a->donotstash = oldmat->donotstash;
3000: a->roworiented = oldmat->roworiented;
3001: a->rowindices = NULL;
3002: a->rowvalues = NULL;
3003: a->getrowactive = PETSC_FALSE;
3005: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
3006: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
3007: if (matin->hash_active) {
3008: PetscCall(MatSetUp(mat));
3009: } else {
3010: mat->preallocated = matin->preallocated;
3011: if (oldmat->colmap) {
3012: #if defined(PETSC_USE_CTABLE)
3013: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3014: #else
3015: PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
3016: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
3017: #endif
3018: } else a->colmap = NULL;
3019: if (oldmat->garray) {
3020: PetscInt len;
3021: len = oldmat->B->cmap->n;
3022: PetscCall(PetscMalloc1(len + 1, &a->garray));
3023: if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3024: } else a->garray = NULL;
3026: /* It may happen MatDuplicate is called with a non-assembled matrix
3027: In fact, MatDuplicate only requires the matrix to be preallocated
3028: This may happen inside a DMCreateMatrix_Shell */
3029: if (oldmat->lvec) PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3030: if (oldmat->Mvctx) {
3031: a->Mvctx = oldmat->Mvctx;
3032: PetscCall(PetscObjectReference((PetscObject)oldmat->Mvctx));
3033: }
3034: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3035: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3036: }
3037: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3038: *newmat = mat;
3039: PetscFunctionReturn(PETSC_SUCCESS);
3040: }
3042: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3043: {
3044: PetscBool isbinary, ishdf5;
3046: PetscFunctionBegin;
3049: /* force binary viewer to load .info file if it has not yet done so */
3050: PetscCall(PetscViewerSetUp(viewer));
3051: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3052: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3053: if (isbinary) {
3054: PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3055: } else if (ishdf5) {
3056: #if defined(PETSC_HAVE_HDF5)
3057: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3058: #else
3059: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3060: #endif
3061: } else {
3062: 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);
3063: }
3064: PetscFunctionReturn(PETSC_SUCCESS);
3065: }
3067: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3068: {
3069: PetscInt header[4], M, N, m, nz, rows, cols, sum, i;
3070: PetscInt *rowidxs, *colidxs;
3071: PetscScalar *matvals;
3073: PetscFunctionBegin;
3074: PetscCall(PetscViewerSetUp(viewer));
3076: /* read in matrix header */
3077: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3078: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3079: M = header[1];
3080: N = header[2];
3081: nz = header[3];
3082: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3083: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3084: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");
3086: /* set block sizes from the viewer's .info file */
3087: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3088: /* set global sizes if not set already */
3089: if (mat->rmap->N < 0) mat->rmap->N = M;
3090: if (mat->cmap->N < 0) mat->cmap->N = N;
3091: PetscCall(PetscLayoutSetUp(mat->rmap));
3092: PetscCall(PetscLayoutSetUp(mat->cmap));
3094: /* check if the matrix sizes are correct */
3095: PetscCall(MatGetSize(mat, &rows, &cols));
3096: 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);
3098: /* read in row lengths and build row indices */
3099: PetscCall(MatGetLocalSize(mat, &m, NULL));
3100: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3101: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3102: rowidxs[0] = 0;
3103: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3104: if (nz != PETSC_INT_MAX) {
3105: PetscCallMPI(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3106: 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);
3107: }
3109: /* read in column indices and matrix values */
3110: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3111: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3112: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3113: /* store matrix indices and values */
3114: PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3115: PetscCall(PetscFree(rowidxs));
3116: PetscCall(PetscFree2(colidxs, matvals));
3117: PetscFunctionReturn(PETSC_SUCCESS);
3118: }
3120: /* Not scalable because of ISAllGather() unless getting all columns. */
3121: static PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3122: {
3123: IS iscol_local;
3124: PetscBool isstride;
3125: PetscMPIInt gisstride = 0;
3127: PetscFunctionBegin;
3128: /* check if we are grabbing all columns*/
3129: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));
3131: if (isstride) {
3132: PetscInt start, len, mstart, mlen;
3133: PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3134: PetscCall(ISGetLocalSize(iscol, &len));
3135: PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3136: if (mstart == start && mlen - mstart == len) gisstride = 1;
3137: }
3139: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3140: if (gisstride) {
3141: PetscInt N;
3142: PetscCall(MatGetSize(mat, NULL, &N));
3143: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3144: PetscCall(ISSetIdentity(iscol_local));
3145: PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3146: } else {
3147: PetscInt cbs;
3148: PetscCall(ISGetBlockSize(iscol, &cbs));
3149: PetscCall(ISAllGather(iscol, &iscol_local));
3150: PetscCall(ISSetBlockSize(iscol_local, cbs));
3151: }
3153: *isseq = iscol_local;
3154: PetscFunctionReturn(PETSC_SUCCESS);
3155: }
3157: /*
3158: Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3159: (see MatCreateSubMatrix_MPIAIJ_nonscalable)
3161: Input Parameters:
3162: + mat - matrix
3163: . isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3164: i.e., mat->rstart <= isrow[i] < mat->rend
3165: - iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3166: i.e., mat->cstart <= iscol[i] < mat->cend
3168: Output Parameters:
3169: + isrow_d - sequential row index set for retrieving mat->A
3170: . iscol_d - sequential column index set for retrieving mat->A
3171: . iscol_o - sequential column index set for retrieving mat->B
3172: - garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3173: */
3174: static PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, PetscInt *garray[])
3175: {
3176: Vec x, cmap;
3177: const PetscInt *is_idx;
3178: PetscScalar *xarray, *cmaparray;
3179: PetscInt ncols, isstart, *idx, m, rstart, *cmap1, count;
3180: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3181: Mat B = a->B;
3182: Vec lvec = a->lvec, lcmap;
3183: PetscInt i, cstart, cend, Bn = B->cmap->N;
3184: MPI_Comm comm;
3185: VecScatter Mvctx = a->Mvctx;
3187: PetscFunctionBegin;
3188: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3189: PetscCall(ISGetLocalSize(iscol, &ncols));
3191: /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3192: PetscCall(MatCreateVecs(mat, &x, NULL));
3193: PetscCall(VecSet(x, -1.0));
3194: PetscCall(VecDuplicate(x, &cmap));
3195: PetscCall(VecSet(cmap, -1.0));
3197: /* Get start indices */
3198: PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3199: isstart -= ncols;
3200: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
3202: PetscCall(ISGetIndices(iscol, &is_idx));
3203: PetscCall(VecGetArray(x, &xarray));
3204: PetscCall(VecGetArray(cmap, &cmaparray));
3205: PetscCall(PetscMalloc1(ncols, &idx));
3206: for (i = 0; i < ncols; i++) {
3207: xarray[is_idx[i] - cstart] = (PetscScalar)is_idx[i];
3208: cmaparray[is_idx[i] - cstart] = i + isstart; /* global index of iscol[i] */
3209: idx[i] = is_idx[i] - cstart; /* local index of iscol[i] */
3210: }
3211: PetscCall(VecRestoreArray(x, &xarray));
3212: PetscCall(VecRestoreArray(cmap, &cmaparray));
3213: PetscCall(ISRestoreIndices(iscol, &is_idx));
3215: /* Get iscol_d */
3216: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3217: PetscCall(ISGetBlockSize(iscol, &i));
3218: PetscCall(ISSetBlockSize(*iscol_d, i));
3220: /* Get isrow_d */
3221: PetscCall(ISGetLocalSize(isrow, &m));
3222: rstart = mat->rmap->rstart;
3223: PetscCall(PetscMalloc1(m, &idx));
3224: PetscCall(ISGetIndices(isrow, &is_idx));
3225: for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3226: PetscCall(ISRestoreIndices(isrow, &is_idx));
3228: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3229: PetscCall(ISGetBlockSize(isrow, &i));
3230: PetscCall(ISSetBlockSize(*isrow_d, i));
3232: /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3233: PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3234: PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3236: PetscCall(VecDuplicate(lvec, &lcmap));
3238: PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3239: PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3241: /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3242: /* off-process column indices */
3243: count = 0;
3244: PetscCall(PetscMalloc1(Bn, &idx));
3245: PetscCall(PetscMalloc1(Bn, &cmap1));
3247: PetscCall(VecGetArray(lvec, &xarray));
3248: PetscCall(VecGetArray(lcmap, &cmaparray));
3249: for (i = 0; i < Bn; i++) {
3250: if (PetscRealPart(xarray[i]) > -1.0) {
3251: idx[count] = i; /* local column index in off-diagonal part B */
3252: cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3253: count++;
3254: }
3255: }
3256: PetscCall(VecRestoreArray(lvec, &xarray));
3257: PetscCall(VecRestoreArray(lcmap, &cmaparray));
3259: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3260: /* cannot ensure iscol_o has same blocksize as iscol! */
3262: PetscCall(PetscFree(idx));
3263: *garray = cmap1;
3265: PetscCall(VecDestroy(&x));
3266: PetscCall(VecDestroy(&cmap));
3267: PetscCall(VecDestroy(&lcmap));
3268: PetscFunctionReturn(PETSC_SUCCESS);
3269: }
3271: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3272: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3273: {
3274: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3275: Mat M = NULL;
3276: MPI_Comm comm;
3277: IS iscol_d, isrow_d, iscol_o;
3278: Mat Asub = NULL, Bsub = NULL;
3279: PetscInt n, count, M_size, N_size;
3281: PetscFunctionBegin;
3282: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3284: if (call == MAT_REUSE_MATRIX) {
3285: /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3286: PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3287: PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");
3289: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3290: PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");
3292: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3293: PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");
3295: /* Update diagonal and off-diagonal portions of submat */
3296: asub = (Mat_MPIAIJ *)(*submat)->data;
3297: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3298: PetscCall(ISGetLocalSize(iscol_o, &n));
3299: if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3300: PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3301: PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));
3303: } else { /* call == MAT_INITIAL_MATRIX) */
3304: PetscInt *garray, *garray_compact;
3305: PetscInt BsubN;
3307: /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3308: PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));
3310: /* Create local submatrices Asub and Bsub */
3311: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3312: PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));
3314: // Compact garray so its not of size Bn
3315: PetscCall(ISGetSize(iscol_o, &count));
3316: PetscCall(PetscMalloc1(count, &garray_compact));
3317: PetscCall(PetscArraycpy(garray_compact, garray, count));
3319: /* Create submatrix M */
3320: PetscCall(ISGetSize(isrow, &M_size));
3321: PetscCall(ISGetSize(iscol, &N_size));
3322: PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, M_size, N_size, Asub, Bsub, garray_compact, &M));
3324: /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3325: asub = (Mat_MPIAIJ *)M->data;
3327: PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3328: n = asub->B->cmap->N;
3329: if (BsubN > n) {
3330: /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3331: const PetscInt *idx;
3332: PetscInt i, j, *idx_new, *subgarray = asub->garray;
3333: PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));
3335: PetscCall(PetscMalloc1(n, &idx_new));
3336: j = 0;
3337: PetscCall(ISGetIndices(iscol_o, &idx));
3338: for (i = 0; i < n; i++) {
3339: if (j >= BsubN) break;
3340: while (subgarray[i] > garray[j]) j++;
3342: if (subgarray[i] == garray[j]) {
3343: idx_new[i] = idx[j++];
3344: } 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]);
3345: }
3346: PetscCall(ISRestoreIndices(iscol_o, &idx));
3348: PetscCall(ISDestroy(&iscol_o));
3349: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));
3351: } else if (BsubN < n) {
3352: 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);
3353: }
3355: PetscCall(PetscFree(garray));
3356: *submat = M;
3358: /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3359: PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3360: PetscCall(ISDestroy(&isrow_d));
3362: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3363: PetscCall(ISDestroy(&iscol_d));
3365: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3366: PetscCall(ISDestroy(&iscol_o));
3367: }
3368: PetscFunctionReturn(PETSC_SUCCESS);
3369: }
3371: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3372: {
3373: IS iscol_local = NULL, isrow_d;
3374: PetscInt csize;
3375: PetscInt n, i, j, start, end;
3376: PetscBool sameRowDist = PETSC_FALSE, sameDist[2], tsameDist[2];
3377: MPI_Comm comm;
3379: PetscFunctionBegin;
3380: /* If isrow has same processor distribution as mat,
3381: call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3382: if (call == MAT_REUSE_MATRIX) {
3383: PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3384: if (isrow_d) {
3385: sameRowDist = PETSC_TRUE;
3386: tsameDist[1] = PETSC_TRUE; /* sameColDist */
3387: } else {
3388: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3389: if (iscol_local) {
3390: sameRowDist = PETSC_TRUE;
3391: tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3392: }
3393: }
3394: } else {
3395: /* Check if isrow has same processor distribution as mat */
3396: sameDist[0] = PETSC_FALSE;
3397: PetscCall(ISGetLocalSize(isrow, &n));
3398: if (!n) {
3399: sameDist[0] = PETSC_TRUE;
3400: } else {
3401: PetscCall(ISGetMinMax(isrow, &i, &j));
3402: PetscCall(MatGetOwnershipRange(mat, &start, &end));
3403: if (i >= start && j < end) sameDist[0] = PETSC_TRUE;
3404: }
3406: /* Check if iscol has same processor distribution as mat */
3407: sameDist[1] = PETSC_FALSE;
3408: PetscCall(ISGetLocalSize(iscol, &n));
3409: if (!n) {
3410: sameDist[1] = PETSC_TRUE;
3411: } else {
3412: PetscCall(ISGetMinMax(iscol, &i, &j));
3413: PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3414: if (i >= start && j < end) sameDist[1] = PETSC_TRUE;
3415: }
3417: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3418: PetscCallMPI(MPIU_Allreduce(&sameDist, &tsameDist, 2, MPIU_BOOL, MPI_LAND, comm));
3419: sameRowDist = tsameDist[0];
3420: }
3422: if (sameRowDist) {
3423: if (tsameDist[1]) { /* sameRowDist & sameColDist */
3424: /* isrow and iscol have same processor distribution as mat */
3425: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3426: PetscFunctionReturn(PETSC_SUCCESS);
3427: } else { /* sameRowDist */
3428: /* isrow has same processor distribution as mat */
3429: if (call == MAT_INITIAL_MATRIX) {
3430: PetscBool sorted;
3431: PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3432: PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3433: PetscCall(ISGetSize(iscol, &i));
3434: PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);
3436: PetscCall(ISSorted(iscol_local, &sorted));
3437: if (sorted) {
3438: /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3439: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3440: PetscFunctionReturn(PETSC_SUCCESS);
3441: }
3442: } else { /* call == MAT_REUSE_MATRIX */
3443: IS iscol_sub;
3444: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3445: if (iscol_sub) {
3446: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3447: PetscFunctionReturn(PETSC_SUCCESS);
3448: }
3449: }
3450: }
3451: }
3453: /* General case: iscol -> iscol_local which has global size of iscol */
3454: if (call == MAT_REUSE_MATRIX) {
3455: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3456: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3457: } else {
3458: if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3459: }
3461: PetscCall(ISGetLocalSize(iscol, &csize));
3462: PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));
3464: if (call == MAT_INITIAL_MATRIX) {
3465: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3466: PetscCall(ISDestroy(&iscol_local));
3467: }
3468: PetscFunctionReturn(PETSC_SUCCESS);
3469: }
3471: /*@C
3472: MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3473: and "off-diagonal" part of the matrix in CSR format.
3475: Collective
3477: Input Parameters:
3478: + comm - MPI communicator
3479: . M - the global row size
3480: . N - the global column size
3481: . A - "diagonal" portion of matrix
3482: . B - if garray is `NULL`, B should be the offdiag matrix using global col ids and of size N - if garray is not `NULL`, B should be the offdiag matrix using local col ids and of size garray
3483: - garray - either `NULL` or the global index of `B` columns
3485: Output Parameter:
3486: . mat - the matrix, with input `A` as its local diagonal matrix
3488: Level: advanced
3490: Notes:
3491: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.
3493: `A` and `B` becomes part of output mat. The user cannot use `A` and `B` anymore.
3495: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3496: @*/
3497: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, PetscInt M, PetscInt N, Mat A, Mat B, PetscInt *garray, Mat *mat)
3498: {
3499: PetscInt m, n;
3500: MatType mpi_mat_type;
3502: PetscFunctionBegin;
3503: PetscCall(MatCreate(comm, mat));
3504: PetscCall(MatGetSize(A, &m, &n));
3505: PetscCheck(m == B->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Am %" PetscInt_FMT " != Bm %" PetscInt_FMT, m, B->rmap->N);
3506: PetscCheck(A->rmap->bs == B->rmap->bs, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "A row bs %" PetscInt_FMT " != B row bs %" PetscInt_FMT, A->rmap->bs, B->rmap->bs);
3508: PetscCall(MatSetSizes(*mat, m, n, M, N));
3509: /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3510: PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3511: PetscCall(MatSetType(*mat, mpi_mat_type));
3513: PetscCall(MatSetBlockSizes(*mat, A->rmap->bs, A->cmap->bs));
3515: PetscCall(PetscLayoutSetUp((*mat)->rmap));
3516: PetscCall(PetscLayoutSetUp((*mat)->cmap));
3517: PetscCall(MatSetMPIAIJWithSplitSeqAIJ(*mat, A, B, garray));
3518: PetscFunctionReturn(PETSC_SUCCESS);
3519: }
3521: /*
3522: MatSetMPIAIJWithSplitSeqAIJ - Set the diag and offdiag matrices of a `MATMPIAIJ` matrix.
3523: It is similar to `MatCreateMPIAIJWithSplitArrays()`. This routine allows passing in
3524: B with local indices and the correct size, along with the accompanying
3525: garray, hence skipping compactification
3527: Collective
3529: Input Parameters:
3530: + mat - the MATMPIAIJ matrix, which should have its type and layout set, but should not have its diag, offdiag matrices set
3531: . A - the diag matrix using local col ids
3532: . B - if garray is `NULL`, B should be the offdiag matrix using global col ids and of size N - if garray is not `NULL`, B should be the offdiag matrix using local col ids and of size garray
3533: - garray - either `NULL` or the global index of `B` columns
3535: Output Parameter:
3536: . mat - the updated `MATMPIAIJ` matrix
3538: Level: advanced
3540: Notes:
3541: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.
3543: `A` and `B` become part of output mat. The user cannot use `A` and `B` anymore.
3545: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3546: */
3547: PETSC_INTERN PetscErrorCode MatSetMPIAIJWithSplitSeqAIJ(Mat mat, Mat A, Mat B, PetscInt *garray)
3548: {
3549: PetscFunctionBegin;
3550: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
3551: PetscInt m, n, M, N, Am, An, Bm, Bn;
3553: PetscCall(MatGetSize(mat, &M, &N));
3554: PetscCall(MatGetLocalSize(mat, &m, &n));
3555: PetscCall(MatGetLocalSize(A, &Am, &An));
3556: PetscCall(MatGetLocalSize(B, &Bm, &Bn));
3558: PetscCheck(m == Am && m == Bm, PETSC_COMM_SELF, PETSC_ERR_PLIB, "local number of rows do not match");
3559: PetscCheck(n == An, PETSC_COMM_SELF, PETSC_ERR_PLIB, "local number of columns do not match");
3560: PetscCheck(!mpiaij->A && !mpiaij->B, PETSC_COMM_SELF, PETSC_ERR_PLIB, "A, B of the MPIAIJ matrix are not empty");
3561: mpiaij->A = A;
3562: mpiaij->B = B;
3563: mpiaij->garray = garray;
3565: mat->preallocated = PETSC_TRUE;
3566: mat->nooffprocentries = PETSC_TRUE; /* See MatAssemblyBegin_MPIAIJ. In effect, making MatAssemblyBegin a nop */
3568: PetscCall(MatSetOption(mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3569: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
3570: /* MatAssemblyEnd is critical here. It sets mat->offloadmask according to A and B's, and
3571: also gets mpiaij->B compacted (if garray is NULL), with its col ids and size reduced
3572: */
3573: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
3574: PetscCall(MatSetOption(mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
3575: PetscCall(MatSetOption(mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3576: PetscFunctionReturn(PETSC_SUCCESS);
3577: }
3579: extern PetscErrorCode MatCreateSubMatrices_MPIAIJ_SingleIS_Local(Mat, PetscInt, const IS[], const IS[], MatReuse, PetscBool, Mat *);
3581: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowDist(Mat mat, IS isrow, IS iscol, IS iscol_local, MatReuse call, Mat *newmat)
3582: {
3583: PetscInt i, m, n, rstart, row, rend, nz, j, bs, cbs;
3584: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3585: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3586: Mat M, Msub, B = a->B;
3587: MatScalar *aa;
3588: Mat_SeqAIJ *aij;
3589: PetscInt *garray = a->garray, *colsub, Ncols;
3590: PetscInt count, Bn = B->cmap->N, cstart = mat->cmap->rstart, cend = mat->cmap->rend;
3591: IS iscol_sub, iscmap;
3592: const PetscInt *is_idx, *cmap;
3593: PetscBool allcolumns = PETSC_FALSE;
3594: MPI_Comm comm;
3596: PetscFunctionBegin;
3597: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3598: if (call == MAT_REUSE_MATRIX) {
3599: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3600: PetscCheck(iscol_sub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "SubIScol passed in was not used before, cannot reuse");
3601: PetscCall(ISGetLocalSize(iscol_sub, &count));
3603: PetscCall(PetscObjectQuery((PetscObject)*newmat, "Subcmap", (PetscObject *)&iscmap));
3604: PetscCheck(iscmap, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Subcmap passed in was not used before, cannot reuse");
3606: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Msub));
3607: PetscCheck(Msub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3609: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_REUSE_MATRIX, PETSC_FALSE, &Msub));
3611: } else { /* call == MAT_INITIAL_MATRIX) */
3612: PetscBool flg;
3614: PetscCall(ISGetLocalSize(iscol, &n));
3615: PetscCall(ISGetSize(iscol, &Ncols));
3617: /* (1) iscol -> nonscalable iscol_local */
3618: /* Check for special case: each processor gets entire matrix columns */
3619: PetscCall(ISIdentity(iscol_local, &flg));
3620: if (flg && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3621: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3622: if (allcolumns) {
3623: iscol_sub = iscol_local;
3624: PetscCall(PetscObjectReference((PetscObject)iscol_local));
3625: PetscCall(ISCreateStride(PETSC_COMM_SELF, n, 0, 1, &iscmap));
3627: } else {
3628: /* (2) iscol_local -> iscol_sub and iscmap. Implementation below requires iscol_local be sorted, it can have duplicate indices */
3629: PetscInt *idx, *cmap1, k;
3630: PetscCall(PetscMalloc1(Ncols, &idx));
3631: PetscCall(PetscMalloc1(Ncols, &cmap1));
3632: PetscCall(ISGetIndices(iscol_local, &is_idx));
3633: count = 0;
3634: k = 0;
3635: for (i = 0; i < Ncols; i++) {
3636: j = is_idx[i];
3637: if (j >= cstart && j < cend) {
3638: /* diagonal part of mat */
3639: idx[count] = j;
3640: cmap1[count++] = i; /* column index in submat */
3641: } else if (Bn) {
3642: /* off-diagonal part of mat */
3643: if (j == garray[k]) {
3644: idx[count] = j;
3645: cmap1[count++] = i; /* column index in submat */
3646: } else if (j > garray[k]) {
3647: while (j > garray[k] && k < Bn - 1) k++;
3648: if (j == garray[k]) {
3649: idx[count] = j;
3650: cmap1[count++] = i; /* column index in submat */
3651: }
3652: }
3653: }
3654: }
3655: PetscCall(ISRestoreIndices(iscol_local, &is_idx));
3657: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_OWN_POINTER, &iscol_sub));
3658: PetscCall(ISGetBlockSize(iscol, &cbs));
3659: PetscCall(ISSetBlockSize(iscol_sub, cbs));
3661: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)iscol_local), count, cmap1, PETSC_OWN_POINTER, &iscmap));
3662: }
3664: /* (3) Create sequential Msub */
3665: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_INITIAL_MATRIX, allcolumns, &Msub));
3666: }
3668: PetscCall(ISGetLocalSize(iscol_sub, &count));
3669: aij = (Mat_SeqAIJ *)Msub->data;
3670: ii = aij->i;
3671: PetscCall(ISGetIndices(iscmap, &cmap));
3673: /*
3674: m - number of local rows
3675: Ncols - number of columns (same on all processors)
3676: rstart - first row in new global matrix generated
3677: */
3678: PetscCall(MatGetSize(Msub, &m, NULL));
3680: if (call == MAT_INITIAL_MATRIX) {
3681: /* (4) Create parallel newmat */
3682: PetscMPIInt rank, size;
3683: PetscInt csize;
3685: PetscCallMPI(MPI_Comm_size(comm, &size));
3686: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3688: /*
3689: Determine the number of non-zeros in the diagonal and off-diagonal
3690: portions of the matrix in order to do correct preallocation
3691: */
3693: /* first get start and end of "diagonal" columns */
3694: PetscCall(ISGetLocalSize(iscol, &csize));
3695: if (csize == PETSC_DECIDE) {
3696: PetscCall(ISGetSize(isrow, &mglobal));
3697: if (mglobal == Ncols) { /* square matrix */
3698: nlocal = m;
3699: } else {
3700: nlocal = Ncols / size + ((Ncols % size) > rank);
3701: }
3702: } else {
3703: nlocal = csize;
3704: }
3705: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3706: rstart = rend - nlocal;
3707: 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);
3709: /* next, compute all the lengths */
3710: jj = aij->j;
3711: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3712: olens = dlens + m;
3713: for (i = 0; i < m; i++) {
3714: jend = ii[i + 1] - ii[i];
3715: olen = 0;
3716: dlen = 0;
3717: for (j = 0; j < jend; j++) {
3718: if (cmap[*jj] < rstart || cmap[*jj] >= rend) olen++;
3719: else dlen++;
3720: jj++;
3721: }
3722: olens[i] = olen;
3723: dlens[i] = dlen;
3724: }
3726: PetscCall(ISGetBlockSize(isrow, &bs));
3727: PetscCall(ISGetBlockSize(iscol, &cbs));
3729: PetscCall(MatCreate(comm, &M));
3730: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, Ncols));
3731: PetscCall(MatSetBlockSizes(M, bs, cbs));
3732: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3733: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3734: PetscCall(PetscFree(dlens));
3736: } else { /* call == MAT_REUSE_MATRIX */
3737: M = *newmat;
3738: PetscCall(MatGetLocalSize(M, &i, NULL));
3739: PetscCheck(i == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3740: PetscCall(MatZeroEntries(M));
3741: /*
3742: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3743: rather than the slower MatSetValues().
3744: */
3745: M->was_assembled = PETSC_TRUE;
3746: M->assembled = PETSC_FALSE;
3747: }
3749: /* (5) Set values of Msub to *newmat */
3750: PetscCall(PetscMalloc1(count, &colsub));
3751: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
3753: jj = aij->j;
3754: PetscCall(MatSeqAIJGetArrayRead(Msub, (const PetscScalar **)&aa));
3755: for (i = 0; i < m; i++) {
3756: row = rstart + i;
3757: nz = ii[i + 1] - ii[i];
3758: for (j = 0; j < nz; j++) colsub[j] = cmap[jj[j]];
3759: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, colsub, aa, INSERT_VALUES));
3760: jj += nz;
3761: aa += nz;
3762: }
3763: PetscCall(MatSeqAIJRestoreArrayRead(Msub, (const PetscScalar **)&aa));
3764: PetscCall(ISRestoreIndices(iscmap, &cmap));
3766: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3767: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3769: PetscCall(PetscFree(colsub));
3771: /* save Msub, iscol_sub and iscmap used in processor for next request */
3772: if (call == MAT_INITIAL_MATRIX) {
3773: *newmat = M;
3774: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubMatrix", (PetscObject)Msub));
3775: PetscCall(MatDestroy(&Msub));
3777: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubIScol", (PetscObject)iscol_sub));
3778: PetscCall(ISDestroy(&iscol_sub));
3780: PetscCall(PetscObjectCompose((PetscObject)*newmat, "Subcmap", (PetscObject)iscmap));
3781: PetscCall(ISDestroy(&iscmap));
3783: if (iscol_local) {
3784: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3785: PetscCall(ISDestroy(&iscol_local));
3786: }
3787: }
3788: PetscFunctionReturn(PETSC_SUCCESS);
3789: }
3791: /*
3792: Not great since it makes two copies of the submatrix, first an SeqAIJ
3793: in local and then by concatenating the local matrices the end result.
3794: Writing it directly would be much like MatCreateSubMatrices_MPIAIJ()
3796: This requires a sequential iscol with all indices.
3797: */
3798: PetscErrorCode MatCreateSubMatrix_MPIAIJ_nonscalable(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
3799: {
3800: PetscMPIInt rank, size;
3801: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs, cbs;
3802: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3803: Mat M, Mreuse;
3804: MatScalar *aa, *vwork;
3805: MPI_Comm comm;
3806: Mat_SeqAIJ *aij;
3807: PetscBool colflag, allcolumns = PETSC_FALSE;
3809: PetscFunctionBegin;
3810: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3811: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3812: PetscCallMPI(MPI_Comm_size(comm, &size));
3814: /* Check for special case: each processor gets entire matrix columns */
3815: PetscCall(ISIdentity(iscol, &colflag));
3816: PetscCall(ISGetLocalSize(iscol, &n));
3817: if (colflag && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3818: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3820: if (call == MAT_REUSE_MATRIX) {
3821: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
3822: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3823: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_REUSE_MATRIX, allcolumns, &Mreuse));
3824: } else {
3825: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_INITIAL_MATRIX, allcolumns, &Mreuse));
3826: }
3828: /*
3829: m - number of local rows
3830: n - number of columns (same on all processors)
3831: rstart - first row in new global matrix generated
3832: */
3833: PetscCall(MatGetSize(Mreuse, &m, &n));
3834: PetscCall(MatGetBlockSizes(Mreuse, &bs, &cbs));
3835: if (call == MAT_INITIAL_MATRIX) {
3836: aij = (Mat_SeqAIJ *)Mreuse->data;
3837: ii = aij->i;
3838: jj = aij->j;
3840: /*
3841: Determine the number of non-zeros in the diagonal and off-diagonal
3842: portions of the matrix in order to do correct preallocation
3843: */
3845: /* first get start and end of "diagonal" columns */
3846: if (csize == PETSC_DECIDE) {
3847: PetscCall(ISGetSize(isrow, &mglobal));
3848: if (mglobal == n) { /* square matrix */
3849: nlocal = m;
3850: } else {
3851: nlocal = n / size + ((n % size) > rank);
3852: }
3853: } else {
3854: nlocal = csize;
3855: }
3856: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3857: rstart = rend - nlocal;
3858: 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);
3860: /* next, compute all the lengths */
3861: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3862: olens = dlens + m;
3863: for (i = 0; i < m; i++) {
3864: jend = ii[i + 1] - ii[i];
3865: olen = 0;
3866: dlen = 0;
3867: for (j = 0; j < jend; j++) {
3868: if (*jj < rstart || *jj >= rend) olen++;
3869: else dlen++;
3870: jj++;
3871: }
3872: olens[i] = olen;
3873: dlens[i] = dlen;
3874: }
3875: PetscCall(MatCreate(comm, &M));
3876: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, n));
3877: PetscCall(MatSetBlockSizes(M, bs, cbs));
3878: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3879: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3880: PetscCall(PetscFree(dlens));
3881: } else {
3882: PetscInt ml, nl;
3884: M = *newmat;
3885: PetscCall(MatGetLocalSize(M, &ml, &nl));
3886: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3887: PetscCall(MatZeroEntries(M));
3888: /*
3889: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3890: rather than the slower MatSetValues().
3891: */
3892: M->was_assembled = PETSC_TRUE;
3893: M->assembled = PETSC_FALSE;
3894: }
3895: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
3896: aij = (Mat_SeqAIJ *)Mreuse->data;
3897: ii = aij->i;
3898: jj = aij->j;
3900: /* trigger copy to CPU if needed */
3901: PetscCall(MatSeqAIJGetArrayRead(Mreuse, (const PetscScalar **)&aa));
3902: for (i = 0; i < m; i++) {
3903: row = rstart + i;
3904: nz = ii[i + 1] - ii[i];
3905: cwork = jj;
3906: jj = PetscSafePointerPlusOffset(jj, nz);
3907: vwork = aa;
3908: aa = PetscSafePointerPlusOffset(aa, nz);
3909: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
3910: }
3911: PetscCall(MatSeqAIJRestoreArrayRead(Mreuse, (const PetscScalar **)&aa));
3913: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3914: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3915: *newmat = M;
3917: /* save submatrix used in processor for next request */
3918: if (call == MAT_INITIAL_MATRIX) {
3919: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
3920: PetscCall(MatDestroy(&Mreuse));
3921: }
3922: PetscFunctionReturn(PETSC_SUCCESS);
3923: }
3925: static PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
3926: {
3927: PetscInt m, cstart, cend, j, nnz, i, d, *ld;
3928: PetscInt *d_nnz, *o_nnz, nnz_max = 0, rstart, ii, irstart;
3929: const PetscInt *JJ;
3930: PetscBool nooffprocentries;
3931: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)B->data;
3933: PetscFunctionBegin;
3934: PetscCall(PetscLayoutSetUp(B->rmap));
3935: PetscCall(PetscLayoutSetUp(B->cmap));
3936: m = B->rmap->n;
3937: cstart = B->cmap->rstart;
3938: cend = B->cmap->rend;
3939: rstart = B->rmap->rstart;
3940: irstart = Ii[0];
3942: PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));
3944: if (PetscDefined(USE_DEBUG)) {
3945: for (i = 0; i < m; i++) {
3946: nnz = Ii[i + 1] - Ii[i];
3947: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3948: PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3949: 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]);
3950: 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);
3951: }
3952: }
3954: for (i = 0; i < m; i++) {
3955: nnz = Ii[i + 1] - Ii[i];
3956: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3957: nnz_max = PetscMax(nnz_max, nnz);
3958: d = 0;
3959: for (j = 0; j < nnz; j++) {
3960: if (cstart <= JJ[j] && JJ[j] < cend) d++;
3961: }
3962: d_nnz[i] = d;
3963: o_nnz[i] = nnz - d;
3964: }
3965: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3966: PetscCall(PetscFree2(d_nnz, o_nnz));
3968: for (i = 0; i < m; i++) {
3969: ii = i + rstart;
3970: PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], PetscSafePointerPlusOffset(J, Ii[i] - irstart), PetscSafePointerPlusOffset(v, Ii[i] - irstart), INSERT_VALUES));
3971: }
3972: nooffprocentries = B->nooffprocentries;
3973: B->nooffprocentries = PETSC_TRUE;
3974: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3975: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3976: B->nooffprocentries = nooffprocentries;
3978: /* count number of entries below block diagonal */
3979: PetscCall(PetscFree(Aij->ld));
3980: PetscCall(PetscCalloc1(m, &ld));
3981: Aij->ld = ld;
3982: for (i = 0; i < m; i++) {
3983: nnz = Ii[i + 1] - Ii[i];
3984: j = 0;
3985: while (j < nnz && J[j] < cstart) j++;
3986: ld[i] = j;
3987: if (J) J += nnz;
3988: }
3990: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3991: PetscFunctionReturn(PETSC_SUCCESS);
3992: }
3994: /*@
3995: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
3996: (the default parallel PETSc format).
3998: Collective
4000: Input Parameters:
4001: + B - the matrix
4002: . i - the indices into `j` for the start of each local row (indices start with zero)
4003: . j - the column indices for each local row (indices start with zero)
4004: - v - optional values in the matrix
4006: Level: developer
4008: Notes:
4009: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
4010: thus you CANNOT change the matrix entries by changing the values of `v` after you have
4011: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
4013: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
4015: A convenience routine for this functionality is `MatCreateMPIAIJWithArrays()`.
4017: You can update the matrix with new numerical values using `MatUpdateMPIAIJWithArrays()` after this call if the column indices in `j` are sorted.
4019: If you do **not** use `MatUpdateMPIAIJWithArrays()`, the column indices in `j` do not need to be sorted. If you will use
4020: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
4022: The format which is used for the sparse matrix input, is equivalent to a
4023: row-major ordering.. i.e for the following matrix, the input data expected is
4024: as shown
4025: .vb
4026: 1 0 0
4027: 2 0 3 P0
4028: -------
4029: 4 5 6 P1
4031: Process0 [P0] rows_owned=[0,1]
4032: i = {0,1,3} [size = nrow+1 = 2+1]
4033: j = {0,0,2} [size = 3]
4034: v = {1,2,3} [size = 3]
4036: Process1 [P1] rows_owned=[2]
4037: i = {0,3} [size = nrow+1 = 1+1]
4038: j = {0,1,2} [size = 3]
4039: v = {4,5,6} [size = 3]
4040: .ve
4042: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`,
4043: `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`, `MatCreateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4044: @*/
4045: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4046: {
4047: PetscFunctionBegin;
4048: PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4049: PetscFunctionReturn(PETSC_SUCCESS);
4050: }
4052: /*@
4053: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
4054: (the default parallel PETSc format). For good matrix assembly performance
4055: the user should preallocate the matrix storage by setting the parameters
4056: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4058: Collective
4060: Input Parameters:
4061: + B - the matrix
4062: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4063: (same value is used for all local rows)
4064: . d_nnz - array containing the number of nonzeros in the various rows of the
4065: DIAGONAL portion of the local submatrix (possibly different for each row)
4066: or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4067: The size of this array is equal to the number of local rows, i.e 'm'.
4068: For matrices that will be factored, you must leave room for (and set)
4069: the diagonal entry even if it is zero.
4070: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4071: submatrix (same value is used for all local rows).
4072: - o_nnz - array containing the number of nonzeros in the various rows of the
4073: OFF-DIAGONAL portion of the local submatrix (possibly different for
4074: each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4075: structure. The size of this array is equal to the number
4076: of local rows, i.e 'm'.
4078: Example Usage:
4079: Consider the following 8x8 matrix with 34 non-zero values, that is
4080: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4081: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4082: as follows
4084: .vb
4085: 1 2 0 | 0 3 0 | 0 4
4086: Proc0 0 5 6 | 7 0 0 | 8 0
4087: 9 0 10 | 11 0 0 | 12 0
4088: -------------------------------------
4089: 13 0 14 | 15 16 17 | 0 0
4090: Proc1 0 18 0 | 19 20 21 | 0 0
4091: 0 0 0 | 22 23 0 | 24 0
4092: -------------------------------------
4093: Proc2 25 26 27 | 0 0 28 | 29 0
4094: 30 0 0 | 31 32 33 | 0 34
4095: .ve
4097: This can be represented as a collection of submatrices as
4098: .vb
4099: A B C
4100: D E F
4101: G H I
4102: .ve
4104: Where the submatrices A,B,C are owned by proc0, D,E,F are
4105: owned by proc1, G,H,I are owned by proc2.
4107: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4108: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4109: The 'M','N' parameters are 8,8, and have the same values on all procs.
4111: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4112: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4113: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4114: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4115: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4116: matrix, and [DF] as another `MATSEQAIJ` matrix.
4118: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4119: allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4120: storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4121: One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4122: the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4123: In this case, the values of `d_nz`, `o_nz` are
4124: .vb
4125: proc0 dnz = 2, o_nz = 2
4126: proc1 dnz = 3, o_nz = 2
4127: proc2 dnz = 1, o_nz = 4
4128: .ve
4129: We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4130: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4131: for proc3. i.e we are using 12+15+10=37 storage locations to store
4132: 34 values.
4134: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4135: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4136: In the above case the values for `d_nnz`, `o_nnz` are
4137: .vb
4138: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4139: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4140: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4141: .ve
4142: Here the space allocated is sum of all the above values i.e 34, and
4143: hence pre-allocation is perfect.
4145: Level: intermediate
4147: Notes:
4148: If the *_nnz parameter is given then the *_nz parameter is ignored
4150: The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4151: storage. The stored row and column indices begin with zero.
4152: See [Sparse Matrices](sec_matsparse) for details.
4154: The parallel matrix is partitioned such that the first m0 rows belong to
4155: process 0, the next m1 rows belong to process 1, the next m2 rows belong
4156: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
4158: The DIAGONAL portion of the local submatrix of a processor can be defined
4159: as the submatrix which is obtained by extraction the part corresponding to
4160: the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4161: first row that belongs to the processor, r2 is the last row belonging to
4162: the this processor, and c1-c2 is range of indices of the local part of a
4163: vector suitable for applying the matrix to. This is an mxn matrix. In the
4164: common case of a square matrix, the row and column ranges are the same and
4165: the DIAGONAL part is also square. The remaining portion of the local
4166: submatrix (mxN) constitute the OFF-DIAGONAL portion.
4168: If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.
4170: You can call `MatGetInfo()` to get information on how effective the preallocation was;
4171: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4172: You can also run with the option `-info` and look for messages with the string
4173: malloc in them to see if additional memory allocation was needed.
4175: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4176: `MatGetInfo()`, `PetscSplitOwnership()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4177: @*/
4178: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4179: {
4180: PetscFunctionBegin;
4183: PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4184: PetscFunctionReturn(PETSC_SUCCESS);
4185: }
4187: /*@
4188: MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4189: CSR format for the local rows.
4191: Collective
4193: Input Parameters:
4194: + comm - MPI communicator
4195: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4196: . n - This value should be the same as the local size used in creating the
4197: x vector for the matrix-vector product $ y = Ax$. (or `PETSC_DECIDE` to have
4198: calculated if `N` is given) For square matrices n is almost always `m`.
4199: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
4200: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
4201: . 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
4202: . j - global column indices
4203: - a - optional matrix values
4205: Output Parameter:
4206: . mat - the matrix
4208: Level: intermediate
4210: Notes:
4211: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4212: thus you CANNOT change the matrix entries by changing the values of `a[]` after you have
4213: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
4215: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
4217: Once you have created the matrix you can update it with new numerical values using `MatUpdateMPIAIJWithArray()`
4219: If you do **not** use `MatUpdateMPIAIJWithArray()`, the column indices in `j` do not need to be sorted. If you will use
4220: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
4222: The format which is used for the sparse matrix input, is equivalent to a
4223: row-major ordering, i.e., for the following matrix, the input data expected is
4224: as shown
4225: .vb
4226: 1 0 0
4227: 2 0 3 P0
4228: -------
4229: 4 5 6 P1
4231: Process0 [P0] rows_owned=[0,1]
4232: i = {0,1,3} [size = nrow+1 = 2+1]
4233: j = {0,0,2} [size = 3]
4234: v = {1,2,3} [size = 3]
4236: Process1 [P1] rows_owned=[2]
4237: i = {0,3} [size = nrow+1 = 1+1]
4238: j = {0,1,2} [size = 3]
4239: v = {4,5,6} [size = 3]
4240: .ve
4242: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4243: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4244: @*/
4245: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
4246: {
4247: PetscFunctionBegin;
4248: PetscCheck(!i || !i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4249: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4250: PetscCall(MatCreate(comm, mat));
4251: PetscCall(MatSetSizes(*mat, m, n, M, N));
4252: /* PetscCall(MatSetBlockSizes(M,bs,cbs)); */
4253: PetscCall(MatSetType(*mat, MATMPIAIJ));
4254: PetscCall(MatMPIAIJSetPreallocationCSR(*mat, i, j, a));
4255: PetscFunctionReturn(PETSC_SUCCESS);
4256: }
4258: /*@
4259: MatUpdateMPIAIJWithArrays - updates a `MATMPIAIJ` matrix using arrays that contain in standard
4260: CSR format for the local rows. Only the numerical values are updated the other arrays must be identical to what was passed
4261: from `MatCreateMPIAIJWithArrays()`
4263: Deprecated: Use `MatUpdateMPIAIJWithArray()`
4265: Collective
4267: Input Parameters:
4268: + mat - the matrix
4269: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4270: . n - This value should be the same as the local size used in creating the
4271: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4272: calculated if N is given) For square matrices n is almost always m.
4273: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4274: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4275: . Ii - row indices; that is Ii[0] = 0, Ii[row] = Ii[row-1] + number of elements in that row of the matrix
4276: . J - column indices
4277: - v - matrix values
4279: Level: deprecated
4281: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4282: `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4283: @*/
4284: PetscErrorCode MatUpdateMPIAIJWithArrays(Mat mat, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4285: {
4286: PetscInt nnz, i;
4287: PetscBool nooffprocentries;
4288: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4289: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4290: PetscScalar *ad, *ao;
4291: PetscInt ldi, Iii, md;
4292: const PetscInt *Adi = Ad->i;
4293: PetscInt *ld = Aij->ld;
4295: PetscFunctionBegin;
4296: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4297: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4298: PetscCheck(m == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of rows cannot change from call to MatUpdateMPIAIJWithArrays()");
4299: PetscCheck(n == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of columns cannot change from call to MatUpdateMPIAIJWithArrays()");
4301: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4302: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4304: for (i = 0; i < m; i++) {
4305: if (PetscDefined(USE_DEBUG)) {
4306: for (PetscInt j = Ii[i] + 1; j < Ii[i + 1]; ++j) {
4307: 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);
4308: 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);
4309: }
4310: }
4311: nnz = Ii[i + 1] - Ii[i];
4312: Iii = Ii[i];
4313: ldi = ld[i];
4314: md = Adi[i + 1] - Adi[i];
4315: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4316: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4317: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4318: ad += md;
4319: ao += nnz - md;
4320: }
4321: nooffprocentries = mat->nooffprocentries;
4322: mat->nooffprocentries = PETSC_TRUE;
4323: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4324: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4325: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4326: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4327: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4328: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4329: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4330: mat->nooffprocentries = nooffprocentries;
4331: PetscFunctionReturn(PETSC_SUCCESS);
4332: }
4334: /*@
4335: MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values
4337: Collective
4339: Input Parameters:
4340: + mat - the matrix
4341: - v - matrix values, stored by row
4343: Level: intermediate
4345: Notes:
4346: The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`
4348: The column indices in the call to `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()` must have been sorted for this call to work correctly
4350: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4351: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4352: @*/
4353: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4354: {
4355: PetscInt nnz, i, m;
4356: PetscBool nooffprocentries;
4357: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4358: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4359: Mat_SeqAIJ *Ao = (Mat_SeqAIJ *)Aij->B->data;
4360: PetscScalar *ad, *ao;
4361: const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4362: PetscInt ldi, Iii, md;
4363: PetscInt *ld = Aij->ld;
4365: PetscFunctionBegin;
4366: m = mat->rmap->n;
4368: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4369: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4370: Iii = 0;
4371: for (i = 0; i < m; i++) {
4372: nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4373: ldi = ld[i];
4374: md = Adi[i + 1] - Adi[i];
4375: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4376: ad += md;
4377: if (ao) {
4378: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4379: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4380: ao += nnz - md;
4381: }
4382: Iii += nnz;
4383: }
4384: nooffprocentries = mat->nooffprocentries;
4385: mat->nooffprocentries = PETSC_TRUE;
4386: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4387: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4388: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4389: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4390: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4391: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4392: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4393: mat->nooffprocentries = nooffprocentries;
4394: PetscFunctionReturn(PETSC_SUCCESS);
4395: }
4397: /*@
4398: MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4399: (the default parallel PETSc format). For good matrix assembly performance
4400: the user should preallocate the matrix storage by setting the parameters
4401: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4403: Collective
4405: Input Parameters:
4406: + comm - MPI communicator
4407: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4408: This value should be the same as the local size used in creating the
4409: y vector for the matrix-vector product y = Ax.
4410: . n - This value should be the same as the local size used in creating the
4411: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4412: calculated if N is given) For square matrices n is almost always m.
4413: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4414: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4415: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4416: (same value is used for all local rows)
4417: . d_nnz - array containing the number of nonzeros in the various rows of the
4418: DIAGONAL portion of the local submatrix (possibly different for each row)
4419: or `NULL`, if `d_nz` is used to specify the nonzero structure.
4420: The size of this array is equal to the number of local rows, i.e 'm'.
4421: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4422: submatrix (same value is used for all local rows).
4423: - o_nnz - array containing the number of nonzeros in the various rows of the
4424: OFF-DIAGONAL portion of the local submatrix (possibly different for
4425: each row) or `NULL`, if `o_nz` is used to specify the nonzero
4426: structure. The size of this array is equal to the number
4427: of local rows, i.e 'm'.
4429: Output Parameter:
4430: . A - the matrix
4432: Options Database Keys:
4433: + -mat_no_inode - Do not use inodes
4434: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
4435: - -matmult_vecscatter_view <viewer> - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4436: See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the `VecScatter`
4437: 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.
4439: Level: intermediate
4441: Notes:
4442: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4443: MatXXXXSetPreallocation() paradigm instead of this routine directly.
4444: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
4446: If the *_nnz parameter is given then the *_nz parameter is ignored
4448: The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4449: processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4450: storage requirements for this matrix.
4452: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one
4453: processor than it must be used on all processors that share the object for
4454: that argument.
4456: If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by
4457: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`.
4459: The user MUST specify either the local or global matrix dimensions
4460: (possibly both).
4462: The parallel matrix is partitioned across processors such that the
4463: first `m0` rows belong to process 0, the next `m1` rows belong to
4464: process 1, the next `m2` rows belong to process 2, etc., where
4465: `m0`, `m1`, `m2`... are the input parameter `m` on each MPI process. I.e., each MPI process stores
4466: values corresponding to [m x N] submatrix.
4468: The columns are logically partitioned with the n0 columns belonging
4469: to 0th partition, the next n1 columns belonging to the next
4470: partition etc.. where n0,n1,n2... are the input parameter 'n'.
4472: The DIAGONAL portion of the local submatrix on any given processor
4473: is the submatrix corresponding to the rows and columns m,n
4474: corresponding to the given processor. i.e diagonal matrix on
4475: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4476: etc. The remaining portion of the local submatrix [m x (N-n)]
4477: constitute the OFF-DIAGONAL portion. The example below better
4478: illustrates this concept. The two matrices, the DIAGONAL portion and
4479: the OFF-DIAGONAL portion are each stored as `MATSEQAIJ` matrices.
4481: For a square global matrix we define each processor's diagonal portion
4482: to be its local rows and the corresponding columns (a square submatrix);
4483: each processor's off-diagonal portion encompasses the remainder of the
4484: local matrix (a rectangular submatrix).
4486: If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.
4488: When calling this routine with a single process communicator, a matrix of
4489: type `MATSEQAIJ` is returned. If a matrix of type `MATMPIAIJ` is desired for this
4490: type of communicator, use the construction mechanism
4491: .vb
4492: MatCreate(..., &A);
4493: MatSetType(A, MATMPIAIJ);
4494: MatSetSizes(A, m, n, M, N);
4495: MatMPIAIJSetPreallocation(A, ...);
4496: .ve
4498: By default, this format uses inodes (identical nodes) when possible.
4499: We search for consecutive rows with the same nonzero structure, thereby
4500: reusing matrix information to achieve increased efficiency.
4502: Example Usage:
4503: Consider the following 8x8 matrix with 34 non-zero values, that is
4504: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4505: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4506: as follows
4508: .vb
4509: 1 2 0 | 0 3 0 | 0 4
4510: Proc0 0 5 6 | 7 0 0 | 8 0
4511: 9 0 10 | 11 0 0 | 12 0
4512: -------------------------------------
4513: 13 0 14 | 15 16 17 | 0 0
4514: Proc1 0 18 0 | 19 20 21 | 0 0
4515: 0 0 0 | 22 23 0 | 24 0
4516: -------------------------------------
4517: Proc2 25 26 27 | 0 0 28 | 29 0
4518: 30 0 0 | 31 32 33 | 0 34
4519: .ve
4521: This can be represented as a collection of submatrices as
4523: .vb
4524: A B C
4525: D E F
4526: G H I
4527: .ve
4529: Where the submatrices A,B,C are owned by proc0, D,E,F are
4530: owned by proc1, G,H,I are owned by proc2.
4532: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4533: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4534: The 'M','N' parameters are 8,8, and have the same values on all procs.
4536: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4537: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4538: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4539: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4540: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4541: matrix, and [DF] as another SeqAIJ matrix.
4543: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4544: allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4545: storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4546: One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4547: the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4548: In this case, the values of `d_nz`,`o_nz` are
4549: .vb
4550: proc0 dnz = 2, o_nz = 2
4551: proc1 dnz = 3, o_nz = 2
4552: proc2 dnz = 1, o_nz = 4
4553: .ve
4554: We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4555: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4556: for proc3. i.e we are using 12+15+10=37 storage locations to store
4557: 34 values.
4559: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4560: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4561: In the above case the values for d_nnz,o_nnz are
4562: .vb
4563: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4564: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4565: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4566: .ve
4567: Here the space allocated is sum of all the above values i.e 34, and
4568: hence pre-allocation is perfect.
4570: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4571: `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`,
4572: `MatGetOwnershipRangesColumn()`, `PetscLayout`
4573: @*/
4574: 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)
4575: {
4576: PetscMPIInt size;
4578: PetscFunctionBegin;
4579: PetscCall(MatCreate(comm, A));
4580: PetscCall(MatSetSizes(*A, m, n, M, N));
4581: PetscCallMPI(MPI_Comm_size(comm, &size));
4582: if (size > 1) {
4583: PetscCall(MatSetType(*A, MATMPIAIJ));
4584: PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4585: } else {
4586: PetscCall(MatSetType(*A, MATSEQAIJ));
4587: PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4588: }
4589: PetscFunctionReturn(PETSC_SUCCESS);
4590: }
4592: /*@C
4593: MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix
4595: Not Collective
4597: Input Parameter:
4598: . A - The `MATMPIAIJ` matrix
4600: Output Parameters:
4601: + Ad - The local diagonal block as a `MATSEQAIJ` matrix
4602: . Ao - The local off-diagonal block as a `MATSEQAIJ` matrix
4603: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4605: Level: intermediate
4607: Note:
4608: The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4609: 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
4610: the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4611: local column numbers to global column numbers in the original matrix.
4613: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATSEQAIJ`
4614: @*/
4615: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4616: {
4617: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4618: PetscBool flg;
4620: PetscFunctionBegin;
4621: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4622: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4623: if (Ad) *Ad = a->A;
4624: if (Ao) *Ao = a->B;
4625: if (colmap) *colmap = a->garray;
4626: PetscFunctionReturn(PETSC_SUCCESS);
4627: }
4629: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4630: {
4631: PetscInt m, N, i, rstart, nnz, Ii;
4632: PetscInt *indx;
4633: PetscScalar *values;
4634: MatType rootType;
4636: PetscFunctionBegin;
4637: PetscCall(MatGetSize(inmat, &m, &N));
4638: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4639: PetscInt *dnz, *onz, sum, bs, cbs;
4641: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4642: /* Check sum(n) = N */
4643: PetscCallMPI(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4644: PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);
4646: PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4647: rstart -= m;
4649: MatPreallocateBegin(comm, m, n, dnz, onz);
4650: for (i = 0; i < m; i++) {
4651: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4652: PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4653: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4654: }
4656: PetscCall(MatCreate(comm, outmat));
4657: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4658: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4659: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4660: PetscCall(MatGetRootType_Private(inmat, &rootType));
4661: PetscCall(MatSetType(*outmat, rootType));
4662: PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4663: PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4664: MatPreallocateEnd(dnz, onz);
4665: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4666: }
4668: /* numeric phase */
4669: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4670: for (i = 0; i < m; i++) {
4671: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4672: Ii = i + rstart;
4673: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4674: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4675: }
4676: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4677: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4678: PetscFunctionReturn(PETSC_SUCCESS);
4679: }
4681: static PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(void **data)
4682: {
4683: Mat_Merge_SeqsToMPI *merge = (Mat_Merge_SeqsToMPI *)*data;
4685: PetscFunctionBegin;
4686: if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4687: PetscCall(PetscFree(merge->id_r));
4688: PetscCall(PetscFree(merge->len_s));
4689: PetscCall(PetscFree(merge->len_r));
4690: PetscCall(PetscFree(merge->bi));
4691: PetscCall(PetscFree(merge->bj));
4692: PetscCall(PetscFree(merge->buf_ri[0]));
4693: PetscCall(PetscFree(merge->buf_ri));
4694: PetscCall(PetscFree(merge->buf_rj[0]));
4695: PetscCall(PetscFree(merge->buf_rj));
4696: PetscCall(PetscFree(merge->coi));
4697: PetscCall(PetscFree(merge->coj));
4698: PetscCall(PetscFree(merge->owners_co));
4699: PetscCall(PetscLayoutDestroy(&merge->rowmap));
4700: PetscCall(PetscFree(merge));
4701: PetscFunctionReturn(PETSC_SUCCESS);
4702: }
4704: #include <../src/mat/utils/freespace.h>
4705: #include <petscbt.h>
4707: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4708: {
4709: MPI_Comm comm;
4710: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4711: PetscMPIInt size, rank, taga, *len_s;
4712: PetscInt N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj, m;
4713: PetscMPIInt proc, k;
4714: PetscInt **buf_ri, **buf_rj;
4715: PetscInt anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4716: PetscInt nrows, **buf_ri_k, **nextrow, **nextai;
4717: MPI_Request *s_waits, *r_waits;
4718: MPI_Status *status;
4719: const MatScalar *aa, *a_a;
4720: MatScalar **abuf_r, *ba_i;
4721: Mat_Merge_SeqsToMPI *merge;
4722: PetscContainer container;
4724: PetscFunctionBegin;
4725: PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4726: PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));
4728: PetscCallMPI(MPI_Comm_size(comm, &size));
4729: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4731: PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4732: PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4733: PetscCall(PetscContainerGetPointer(container, (void **)&merge));
4734: PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4735: aa = a_a;
4737: bi = merge->bi;
4738: bj = merge->bj;
4739: buf_ri = merge->buf_ri;
4740: buf_rj = merge->buf_rj;
4742: PetscCall(PetscMalloc1(size, &status));
4743: owners = merge->rowmap->range;
4744: len_s = merge->len_s;
4746: /* send and recv matrix values */
4747: PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4748: PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));
4750: PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4751: for (proc = 0, k = 0; proc < size; proc++) {
4752: if (!len_s[proc]) continue;
4753: i = owners[proc];
4754: PetscCallMPI(MPIU_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4755: k++;
4756: }
4758: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4759: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4760: PetscCall(PetscFree(status));
4762: PetscCall(PetscFree(s_waits));
4763: PetscCall(PetscFree(r_waits));
4765: /* insert mat values of mpimat */
4766: PetscCall(PetscMalloc1(N, &ba_i));
4767: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4769: for (k = 0; k < merge->nrecv; k++) {
4770: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4771: nrows = *buf_ri_k[k];
4772: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4773: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4774: }
4776: /* set values of ba */
4777: m = merge->rowmap->n;
4778: for (i = 0; i < m; i++) {
4779: arow = owners[rank] + i;
4780: bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4781: bnzi = bi[i + 1] - bi[i];
4782: PetscCall(PetscArrayzero(ba_i, bnzi));
4784: /* add local non-zero vals of this proc's seqmat into ba */
4785: anzi = ai[arow + 1] - ai[arow];
4786: aj = a->j + ai[arow];
4787: aa = a_a + ai[arow];
4788: nextaj = 0;
4789: for (j = 0; nextaj < anzi; j++) {
4790: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4791: ba_i[j] += aa[nextaj++];
4792: }
4793: }
4795: /* add received vals into ba */
4796: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4797: /* i-th row */
4798: if (i == *nextrow[k]) {
4799: anzi = *(nextai[k] + 1) - *nextai[k];
4800: aj = buf_rj[k] + *nextai[k];
4801: aa = abuf_r[k] + *nextai[k];
4802: nextaj = 0;
4803: for (j = 0; nextaj < anzi; j++) {
4804: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4805: ba_i[j] += aa[nextaj++];
4806: }
4807: }
4808: nextrow[k]++;
4809: nextai[k]++;
4810: }
4811: }
4812: PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4813: }
4814: PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4815: PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4816: PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));
4818: PetscCall(PetscFree(abuf_r[0]));
4819: PetscCall(PetscFree(abuf_r));
4820: PetscCall(PetscFree(ba_i));
4821: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4822: PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4823: PetscFunctionReturn(PETSC_SUCCESS);
4824: }
4826: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4827: {
4828: Mat B_mpi;
4829: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4830: PetscMPIInt size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4831: PetscInt **buf_rj, **buf_ri, **buf_ri_k;
4832: PetscInt M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4833: PetscInt len, *dnz, *onz, bs, cbs;
4834: PetscInt k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4835: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4836: MPI_Request *si_waits, *sj_waits, *ri_waits, *rj_waits;
4837: MPI_Status *status;
4838: PetscFreeSpaceList free_space = NULL, current_space = NULL;
4839: PetscBT lnkbt;
4840: Mat_Merge_SeqsToMPI *merge;
4841: PetscContainer container;
4843: PetscFunctionBegin;
4844: PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));
4846: /* make sure it is a PETSc comm */
4847: PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4848: PetscCallMPI(MPI_Comm_size(comm, &size));
4849: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4851: PetscCall(PetscNew(&merge));
4852: PetscCall(PetscMalloc1(size, &status));
4854: /* determine row ownership */
4855: PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4856: PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4857: PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4858: PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4859: PetscCall(PetscLayoutSetUp(merge->rowmap));
4860: PetscCall(PetscMalloc1(size, &len_si));
4861: PetscCall(PetscMalloc1(size, &merge->len_s));
4863: m = merge->rowmap->n;
4864: owners = merge->rowmap->range;
4866: /* determine the number of messages to send, their lengths */
4867: len_s = merge->len_s;
4869: len = 0; /* length of buf_si[] */
4870: merge->nsend = 0;
4871: for (PetscMPIInt proc = 0; proc < size; proc++) {
4872: len_si[proc] = 0;
4873: if (proc == rank) {
4874: len_s[proc] = 0;
4875: } else {
4876: PetscCall(PetscMPIIntCast(owners[proc + 1] - owners[proc] + 1, &len_si[proc]));
4877: PetscCall(PetscMPIIntCast(ai[owners[proc + 1]] - ai[owners[proc]], &len_s[proc])); /* num of rows to be sent to [proc] */
4878: }
4879: if (len_s[proc]) {
4880: merge->nsend++;
4881: nrows = 0;
4882: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4883: if (ai[i + 1] > ai[i]) nrows++;
4884: }
4885: PetscCall(PetscMPIIntCast(2 * (nrows + 1), &len_si[proc]));
4886: len += len_si[proc];
4887: }
4888: }
4890: /* determine the number and length of messages to receive for ij-structure */
4891: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4892: PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));
4894: /* post the Irecv of j-structure */
4895: PetscCall(PetscCommGetNewTag(comm, &tagj));
4896: PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));
4898: /* post the Isend of j-structure */
4899: PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));
4901: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4902: if (!len_s[proc]) continue;
4903: i = owners[proc];
4904: PetscCallMPI(MPIU_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4905: k++;
4906: }
4908: /* receives and sends of j-structure are complete */
4909: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4910: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));
4912: /* send and recv i-structure */
4913: PetscCall(PetscCommGetNewTag(comm, &tagi));
4914: PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));
4916: PetscCall(PetscMalloc1(len + 1, &buf_s));
4917: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4918: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4919: if (!len_s[proc]) continue;
4920: /* form outgoing message for i-structure:
4921: buf_si[0]: nrows to be sent
4922: [1:nrows]: row index (global)
4923: [nrows+1:2*nrows+1]: i-structure index
4924: */
4925: nrows = len_si[proc] / 2 - 1;
4926: buf_si_i = buf_si + nrows + 1;
4927: buf_si[0] = nrows;
4928: buf_si_i[0] = 0;
4929: nrows = 0;
4930: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4931: anzi = ai[i + 1] - ai[i];
4932: if (anzi) {
4933: buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4934: buf_si[nrows + 1] = i - owners[proc]; /* local row index */
4935: nrows++;
4936: }
4937: }
4938: PetscCallMPI(MPIU_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4939: k++;
4940: buf_si += len_si[proc];
4941: }
4943: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4944: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));
4946: PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4947: 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]));
4949: PetscCall(PetscFree(len_si));
4950: PetscCall(PetscFree(len_ri));
4951: PetscCall(PetscFree(rj_waits));
4952: PetscCall(PetscFree2(si_waits, sj_waits));
4953: PetscCall(PetscFree(ri_waits));
4954: PetscCall(PetscFree(buf_s));
4955: PetscCall(PetscFree(status));
4957: /* compute a local seq matrix in each processor */
4958: /* allocate bi array and free space for accumulating nonzero column info */
4959: PetscCall(PetscMalloc1(m + 1, &bi));
4960: bi[0] = 0;
4962: /* create and initialize a linked list */
4963: nlnk = N + 1;
4964: PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));
4966: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4967: len = ai[owners[rank + 1]] - ai[owners[rank]];
4968: PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));
4970: current_space = free_space;
4972: /* determine symbolic info for each local row */
4973: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4975: for (k = 0; k < merge->nrecv; k++) {
4976: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4977: nrows = *buf_ri_k[k];
4978: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4979: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4980: }
4982: MatPreallocateBegin(comm, m, n, dnz, onz);
4983: len = 0;
4984: for (i = 0; i < m; i++) {
4985: bnzi = 0;
4986: /* add local non-zero cols of this proc's seqmat into lnk */
4987: arow = owners[rank] + i;
4988: anzi = ai[arow + 1] - ai[arow];
4989: aj = a->j + ai[arow];
4990: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
4991: bnzi += nlnk;
4992: /* add received col data into lnk */
4993: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4994: if (i == *nextrow[k]) { /* i-th row */
4995: anzi = *(nextai[k] + 1) - *nextai[k];
4996: aj = buf_rj[k] + *nextai[k];
4997: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
4998: bnzi += nlnk;
4999: nextrow[k]++;
5000: nextai[k]++;
5001: }
5002: }
5003: if (len < bnzi) len = bnzi; /* =max(bnzi) */
5005: /* if free space is not available, make more free space */
5006: if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), ¤t_space));
5007: /* copy data into free space, then initialize lnk */
5008: PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
5009: PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));
5011: current_space->array += bnzi;
5012: current_space->local_used += bnzi;
5013: current_space->local_remaining -= bnzi;
5015: bi[i + 1] = bi[i] + bnzi;
5016: }
5018: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
5020: PetscCall(PetscMalloc1(bi[m] + 1, &bj));
5021: PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
5022: PetscCall(PetscLLDestroy(lnk, lnkbt));
5024: /* create symbolic parallel matrix B_mpi */
5025: PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
5026: PetscCall(MatCreate(comm, &B_mpi));
5027: if (n == PETSC_DECIDE) {
5028: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
5029: } else {
5030: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
5031: }
5032: PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
5033: PetscCall(MatSetType(B_mpi, MATMPIAIJ));
5034: PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
5035: MatPreallocateEnd(dnz, onz);
5036: PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));
5038: /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
5039: B_mpi->assembled = PETSC_FALSE;
5040: merge->bi = bi;
5041: merge->bj = bj;
5042: merge->buf_ri = buf_ri;
5043: merge->buf_rj = buf_rj;
5044: merge->coi = NULL;
5045: merge->coj = NULL;
5046: merge->owners_co = NULL;
5048: PetscCall(PetscCommDestroy(&comm));
5050: /* attach the supporting struct to B_mpi for reuse */
5051: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5052: PetscCall(PetscContainerSetPointer(container, merge));
5053: PetscCall(PetscContainerSetCtxDestroy(container, MatDestroy_MPIAIJ_SeqsToMPI));
5054: PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5055: PetscCall(PetscContainerDestroy(&container));
5056: *mpimat = B_mpi;
5058: PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5059: PetscFunctionReturn(PETSC_SUCCESS);
5060: }
5062: /*@
5063: MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5064: matrices from each processor
5066: Collective
5068: Input Parameters:
5069: + comm - the communicators the parallel matrix will live on
5070: . seqmat - the input sequential matrices
5071: . m - number of local rows (or `PETSC_DECIDE`)
5072: . n - number of local columns (or `PETSC_DECIDE`)
5073: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5075: Output Parameter:
5076: . mpimat - the parallel matrix generated
5078: Level: advanced
5080: Note:
5081: The dimensions of the sequential matrix in each processor MUST be the same.
5082: The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
5083: destroyed when `mpimat` is destroyed. Call `PetscObjectQuery()` to access `seqmat`.
5085: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`
5086: @*/
5087: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5088: {
5089: PetscMPIInt size;
5091: PetscFunctionBegin;
5092: PetscCallMPI(MPI_Comm_size(comm, &size));
5093: if (size == 1) {
5094: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5095: if (scall == MAT_INITIAL_MATRIX) {
5096: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5097: } else {
5098: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5099: }
5100: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5101: PetscFunctionReturn(PETSC_SUCCESS);
5102: }
5103: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5104: if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5105: PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5106: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5107: PetscFunctionReturn(PETSC_SUCCESS);
5108: }
5110: /*@
5111: MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix.
5113: Not Collective
5115: Input Parameter:
5116: . A - the matrix
5118: Output Parameter:
5119: . A_loc - the local sequential matrix generated
5121: Level: developer
5123: Notes:
5124: The matrix is created by taking `A`'s local rows and putting them into a sequential matrix
5125: with `mlocal` rows and `n` columns. Where `mlocal` is obtained with `MatGetLocalSize()` and
5126: `n` is the global column count obtained with `MatGetSize()`
5128: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5130: For parallel matrices this creates an entirely new matrix. If the matrix is sequential it merely increases the reference count.
5132: Destroy the matrix with `MatDestroy()`
5134: .seealso: [](ch_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5135: @*/
5136: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5137: {
5138: PetscBool mpi;
5140: PetscFunctionBegin;
5141: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5142: if (mpi) {
5143: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5144: } else {
5145: *A_loc = A;
5146: PetscCall(PetscObjectReference((PetscObject)*A_loc));
5147: }
5148: PetscFunctionReturn(PETSC_SUCCESS);
5149: }
5151: /*@
5152: MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix.
5154: Not Collective
5156: Input Parameters:
5157: + A - the matrix
5158: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5160: Output Parameter:
5161: . A_loc - the local sequential matrix generated
5163: Level: developer
5165: Notes:
5166: The matrix is created by taking all `A`'s local rows and putting them into a sequential
5167: matrix with `mlocal` rows and `n` columns.`mlocal` is the row count obtained with
5168: `MatGetLocalSize()` and `n` is the global column count obtained with `MatGetSize()`.
5170: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5172: When `A` is sequential and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A` (which contains the entire matrix),
5173: 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
5174: then `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called to fill `A_loc`. Thus one can preallocate the appropriate sequential matrix `A_loc`
5175: 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.
5177: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5178: @*/
5179: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5180: {
5181: Mat_MPIAIJ *mpimat = (Mat_MPIAIJ *)A->data;
5182: Mat_SeqAIJ *mat, *a, *b;
5183: PetscInt *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5184: const PetscScalar *aa, *ba, *aav, *bav;
5185: PetscScalar *ca, *cam;
5186: PetscMPIInt size;
5187: PetscInt am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5188: PetscInt *ci, *cj, col, ncols_d, ncols_o, jo;
5189: PetscBool match;
5191: PetscFunctionBegin;
5192: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5193: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5194: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5195: if (size == 1) {
5196: if (scall == MAT_INITIAL_MATRIX) {
5197: PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5198: *A_loc = mpimat->A;
5199: } else if (scall == MAT_REUSE_MATRIX) {
5200: PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5201: }
5202: PetscFunctionReturn(PETSC_SUCCESS);
5203: }
5205: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5206: a = (Mat_SeqAIJ *)mpimat->A->data;
5207: b = (Mat_SeqAIJ *)mpimat->B->data;
5208: ai = a->i;
5209: aj = a->j;
5210: bi = b->i;
5211: bj = b->j;
5212: PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5213: PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5214: aa = aav;
5215: ba = bav;
5216: if (scall == MAT_INITIAL_MATRIX) {
5217: PetscCall(PetscMalloc1(1 + am, &ci));
5218: ci[0] = 0;
5219: for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5220: PetscCall(PetscMalloc1(1 + ci[am], &cj));
5221: PetscCall(PetscMalloc1(1 + ci[am], &ca));
5222: k = 0;
5223: for (i = 0; i < am; i++) {
5224: ncols_o = bi[i + 1] - bi[i];
5225: ncols_d = ai[i + 1] - ai[i];
5226: /* off-diagonal portion of A */
5227: for (jo = 0; jo < ncols_o; jo++) {
5228: col = cmap[*bj];
5229: if (col >= cstart) break;
5230: cj[k] = col;
5231: bj++;
5232: ca[k++] = *ba++;
5233: }
5234: /* diagonal portion of A */
5235: for (j = 0; j < ncols_d; j++) {
5236: cj[k] = cstart + *aj++;
5237: ca[k++] = *aa++;
5238: }
5239: /* off-diagonal portion of A */
5240: for (j = jo; j < ncols_o; j++) {
5241: cj[k] = cmap[*bj++];
5242: ca[k++] = *ba++;
5243: }
5244: }
5245: /* put together the new matrix */
5246: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5247: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5248: /* Since these are PETSc arrays, change flags to free them as necessary. */
5249: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5250: mat->free_a = PETSC_TRUE;
5251: mat->free_ij = PETSC_TRUE;
5252: mat->nonew = 0;
5253: } else if (scall == MAT_REUSE_MATRIX) {
5254: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5255: ci = mat->i;
5256: cj = mat->j;
5257: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5258: for (i = 0; i < am; i++) {
5259: /* off-diagonal portion of A */
5260: ncols_o = bi[i + 1] - bi[i];
5261: for (jo = 0; jo < ncols_o; jo++) {
5262: col = cmap[*bj];
5263: if (col >= cstart) break;
5264: *cam++ = *ba++;
5265: bj++;
5266: }
5267: /* diagonal portion of A */
5268: ncols_d = ai[i + 1] - ai[i];
5269: for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5270: /* off-diagonal portion of A */
5271: for (j = jo; j < ncols_o; j++) {
5272: *cam++ = *ba++;
5273: bj++;
5274: }
5275: }
5276: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5277: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5278: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5279: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5280: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5281: PetscFunctionReturn(PETSC_SUCCESS);
5282: }
5284: /*@
5285: MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5286: mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and off-diagonal part
5288: Not Collective
5290: Input Parameters:
5291: + A - the matrix
5292: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5294: Output Parameters:
5295: + glob - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5296: - A_loc - the local sequential matrix generated
5298: Level: developer
5300: Note:
5301: This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5302: part, then those associated with the off-diagonal part (in its local ordering)
5304: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5305: @*/
5306: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5307: {
5308: Mat Ao, Ad;
5309: const PetscInt *cmap;
5310: PetscMPIInt size;
5311: PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);
5313: PetscFunctionBegin;
5314: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5315: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5316: if (size == 1) {
5317: if (scall == MAT_INITIAL_MATRIX) {
5318: PetscCall(PetscObjectReference((PetscObject)Ad));
5319: *A_loc = Ad;
5320: } else if (scall == MAT_REUSE_MATRIX) {
5321: PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5322: }
5323: if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5324: PetscFunctionReturn(PETSC_SUCCESS);
5325: }
5326: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5327: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5328: if (f) {
5329: PetscCall((*f)(A, scall, glob, A_loc));
5330: } else {
5331: Mat_SeqAIJ *a = (Mat_SeqAIJ *)Ad->data;
5332: Mat_SeqAIJ *b = (Mat_SeqAIJ *)Ao->data;
5333: Mat_SeqAIJ *c;
5334: PetscInt *ai = a->i, *aj = a->j;
5335: PetscInt *bi = b->i, *bj = b->j;
5336: PetscInt *ci, *cj;
5337: const PetscScalar *aa, *ba;
5338: PetscScalar *ca;
5339: PetscInt i, j, am, dn, on;
5341: PetscCall(MatGetLocalSize(Ad, &am, &dn));
5342: PetscCall(MatGetLocalSize(Ao, NULL, &on));
5343: PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5344: PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5345: if (scall == MAT_INITIAL_MATRIX) {
5346: PetscInt k;
5347: PetscCall(PetscMalloc1(1 + am, &ci));
5348: PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5349: PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5350: ci[0] = 0;
5351: for (i = 0, k = 0; i < am; i++) {
5352: const PetscInt ncols_o = bi[i + 1] - bi[i];
5353: const PetscInt ncols_d = ai[i + 1] - ai[i];
5354: ci[i + 1] = ci[i] + ncols_o + ncols_d;
5355: /* diagonal portion of A */
5356: for (j = 0; j < ncols_d; j++, k++) {
5357: cj[k] = *aj++;
5358: ca[k] = *aa++;
5359: }
5360: /* off-diagonal portion of A */
5361: for (j = 0; j < ncols_o; j++, k++) {
5362: cj[k] = dn + *bj++;
5363: ca[k] = *ba++;
5364: }
5365: }
5366: /* put together the new matrix */
5367: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5368: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5369: /* Since these are PETSc arrays, change flags to free them as necessary. */
5370: c = (Mat_SeqAIJ *)(*A_loc)->data;
5371: c->free_a = PETSC_TRUE;
5372: c->free_ij = PETSC_TRUE;
5373: c->nonew = 0;
5374: PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5375: } else if (scall == MAT_REUSE_MATRIX) {
5376: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5377: for (i = 0; i < am; i++) {
5378: const PetscInt ncols_d = ai[i + 1] - ai[i];
5379: const PetscInt ncols_o = bi[i + 1] - bi[i];
5380: /* diagonal portion of A */
5381: for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5382: /* off-diagonal portion of A */
5383: for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5384: }
5385: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5386: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5387: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5388: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5389: if (glob) {
5390: PetscInt cst, *gidx;
5392: PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5393: PetscCall(PetscMalloc1(dn + on, &gidx));
5394: for (i = 0; i < dn; i++) gidx[i] = cst + i;
5395: for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5396: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5397: }
5398: }
5399: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5400: PetscFunctionReturn(PETSC_SUCCESS);
5401: }
5403: /*@C
5404: MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns
5406: Not Collective
5408: Input Parameters:
5409: + A - the matrix
5410: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5411: . row - index set of rows to extract (or `NULL`)
5412: - col - index set of columns to extract (or `NULL`)
5414: Output Parameter:
5415: . A_loc - the local sequential matrix generated
5417: Level: developer
5419: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5420: @*/
5421: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5422: {
5423: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5424: PetscInt i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5425: IS isrowa, iscola;
5426: Mat *aloc;
5427: PetscBool match;
5429: PetscFunctionBegin;
5430: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5431: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5432: PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5433: if (!row) {
5434: start = A->rmap->rstart;
5435: end = A->rmap->rend;
5436: PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5437: } else {
5438: isrowa = *row;
5439: }
5440: if (!col) {
5441: start = A->cmap->rstart;
5442: cmap = a->garray;
5443: nzA = a->A->cmap->n;
5444: nzB = a->B->cmap->n;
5445: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5446: ncols = 0;
5447: for (i = 0; i < nzB; i++) {
5448: if (cmap[i] < start) idx[ncols++] = cmap[i];
5449: else break;
5450: }
5451: imark = i;
5452: for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5453: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5454: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5455: } else {
5456: iscola = *col;
5457: }
5458: if (scall != MAT_INITIAL_MATRIX) {
5459: PetscCall(PetscMalloc1(1, &aloc));
5460: aloc[0] = *A_loc;
5461: }
5462: PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5463: if (!col) { /* attach global id of condensed columns */
5464: PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5465: }
5466: *A_loc = aloc[0];
5467: PetscCall(PetscFree(aloc));
5468: if (!row) PetscCall(ISDestroy(&isrowa));
5469: if (!col) PetscCall(ISDestroy(&iscola));
5470: PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5471: PetscFunctionReturn(PETSC_SUCCESS);
5472: }
5474: /*
5475: * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5476: * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5477: * on a global size.
5478: * */
5479: static PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5480: {
5481: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
5482: Mat_SeqAIJ *pd = (Mat_SeqAIJ *)p->A->data, *po = (Mat_SeqAIJ *)p->B->data, *p_oth;
5483: PetscInt plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5484: PetscMPIInt owner;
5485: PetscSFNode *iremote, *oiremote;
5486: const PetscInt *lrowindices;
5487: PetscSF sf, osf;
5488: PetscInt pcstart, *roffsets, *loffsets, *pnnz, j;
5489: PetscInt ontotalcols, dntotalcols, ntotalcols, nout;
5490: MPI_Comm comm;
5491: ISLocalToGlobalMapping mapping;
5492: const PetscScalar *pd_a, *po_a;
5494: PetscFunctionBegin;
5495: PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5496: /* plocalsize is the number of roots
5497: * nrows is the number of leaves
5498: * */
5499: PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5500: PetscCall(ISGetLocalSize(rows, &nrows));
5501: PetscCall(PetscCalloc1(nrows, &iremote));
5502: PetscCall(ISGetIndices(rows, &lrowindices));
5503: for (i = 0; i < nrows; i++) {
5504: /* Find a remote index and an owner for a row
5505: * The row could be local or remote
5506: * */
5507: owner = 0;
5508: lidx = 0;
5509: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5510: iremote[i].index = lidx;
5511: iremote[i].rank = owner;
5512: }
5513: /* Create SF to communicate how many nonzero columns for each row */
5514: PetscCall(PetscSFCreate(comm, &sf));
5515: /* SF will figure out the number of nonzero columns for each row, and their
5516: * offsets
5517: * */
5518: PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5519: PetscCall(PetscSFSetFromOptions(sf));
5520: PetscCall(PetscSFSetUp(sf));
5522: PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5523: PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5524: PetscCall(PetscCalloc1(nrows, &pnnz));
5525: roffsets[0] = 0;
5526: roffsets[1] = 0;
5527: for (i = 0; i < plocalsize; i++) {
5528: /* diagonal */
5529: nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5530: /* off-diagonal */
5531: nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5532: /* compute offsets so that we relative location for each row */
5533: roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5534: roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5535: }
5536: PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5537: PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5538: /* 'r' means root, and 'l' means leaf */
5539: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5540: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5541: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5542: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5543: PetscCall(PetscSFDestroy(&sf));
5544: PetscCall(PetscFree(roffsets));
5545: PetscCall(PetscFree(nrcols));
5546: dntotalcols = 0;
5547: ontotalcols = 0;
5548: ncol = 0;
5549: for (i = 0; i < nrows; i++) {
5550: pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5551: ncol = PetscMax(pnnz[i], ncol);
5552: /* diagonal */
5553: dntotalcols += nlcols[i * 2 + 0];
5554: /* off-diagonal */
5555: ontotalcols += nlcols[i * 2 + 1];
5556: }
5557: /* We do not need to figure the right number of columns
5558: * since all the calculations will be done by going through the raw data
5559: * */
5560: PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5561: PetscCall(MatSetUp(*P_oth));
5562: PetscCall(PetscFree(pnnz));
5563: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5564: /* diagonal */
5565: PetscCall(PetscCalloc1(dntotalcols, &iremote));
5566: /* off-diagonal */
5567: PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5568: /* diagonal */
5569: PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5570: /* off-diagonal */
5571: PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5572: dntotalcols = 0;
5573: ontotalcols = 0;
5574: ntotalcols = 0;
5575: for (i = 0; i < nrows; i++) {
5576: owner = 0;
5577: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5578: /* Set iremote for diag matrix */
5579: for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5580: iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5581: iremote[dntotalcols].rank = owner;
5582: /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5583: ilocal[dntotalcols++] = ntotalcols++;
5584: }
5585: /* off-diagonal */
5586: for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5587: oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5588: oiremote[ontotalcols].rank = owner;
5589: oilocal[ontotalcols++] = ntotalcols++;
5590: }
5591: }
5592: PetscCall(ISRestoreIndices(rows, &lrowindices));
5593: PetscCall(PetscFree(loffsets));
5594: PetscCall(PetscFree(nlcols));
5595: PetscCall(PetscSFCreate(comm, &sf));
5596: /* P serves as roots and P_oth is leaves
5597: * Diag matrix
5598: * */
5599: PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5600: PetscCall(PetscSFSetFromOptions(sf));
5601: PetscCall(PetscSFSetUp(sf));
5603: PetscCall(PetscSFCreate(comm, &osf));
5604: /* off-diagonal */
5605: PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5606: PetscCall(PetscSFSetFromOptions(osf));
5607: PetscCall(PetscSFSetUp(osf));
5608: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5609: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5610: /* operate on the matrix internal data to save memory */
5611: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5612: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5613: PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5614: /* Convert to global indices for diag matrix */
5615: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5616: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5617: /* We want P_oth store global indices */
5618: PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5619: /* Use memory scalable approach */
5620: PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5621: PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5622: PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5623: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5624: /* Convert back to local indices */
5625: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5626: PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5627: nout = 0;
5628: PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5629: PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5630: PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5631: /* Exchange values */
5632: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5633: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5634: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5635: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5636: /* Stop PETSc from shrinking memory */
5637: for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5638: PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5639: PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5640: /* Attach PetscSF objects to P_oth so that we can reuse it later */
5641: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5642: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5643: PetscCall(PetscSFDestroy(&sf));
5644: PetscCall(PetscSFDestroy(&osf));
5645: PetscFunctionReturn(PETSC_SUCCESS);
5646: }
5648: /*
5649: * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5650: * This supports MPIAIJ and MAIJ
5651: * */
5652: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5653: {
5654: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5655: Mat_SeqAIJ *p_oth;
5656: IS rows, map;
5657: PetscHMapI hamp;
5658: PetscInt i, htsize, *rowindices, off, *mapping, key, count;
5659: MPI_Comm comm;
5660: PetscSF sf, osf;
5661: PetscBool has;
5663: PetscFunctionBegin;
5664: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5665: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5666: /* If it is the first time, create an index set of off-diag nonzero columns of A,
5667: * and then create a submatrix (that often is an overlapping matrix)
5668: * */
5669: if (reuse == MAT_INITIAL_MATRIX) {
5670: /* Use a hash table to figure out unique keys */
5671: PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5672: PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5673: count = 0;
5674: /* Assume that a->g is sorted, otherwise the following does not make sense */
5675: for (i = 0; i < a->B->cmap->n; i++) {
5676: key = a->garray[i] / dof;
5677: PetscCall(PetscHMapIHas(hamp, key, &has));
5678: if (!has) {
5679: mapping[i] = count;
5680: PetscCall(PetscHMapISet(hamp, key, count++));
5681: } else {
5682: /* Current 'i' has the same value the previous step */
5683: mapping[i] = count - 1;
5684: }
5685: }
5686: PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5687: PetscCall(PetscHMapIGetSize(hamp, &htsize));
5688: PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5689: PetscCall(PetscCalloc1(htsize, &rowindices));
5690: off = 0;
5691: PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5692: PetscCall(PetscHMapIDestroy(&hamp));
5693: PetscCall(PetscSortInt(htsize, rowindices));
5694: PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5695: /* In case, the matrix was already created but users want to recreate the matrix */
5696: PetscCall(MatDestroy(P_oth));
5697: PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5698: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5699: PetscCall(ISDestroy(&map));
5700: PetscCall(ISDestroy(&rows));
5701: } else if (reuse == MAT_REUSE_MATRIX) {
5702: /* If matrix was already created, we simply update values using SF objects
5703: * that as attached to the matrix earlier.
5704: */
5705: const PetscScalar *pd_a, *po_a;
5707: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5708: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5709: PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5710: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5711: /* Update values in place */
5712: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5713: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5714: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5715: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5716: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5717: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5718: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5719: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5720: } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5721: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5722: PetscFunctionReturn(PETSC_SUCCESS);
5723: }
5725: /*@C
5726: MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`
5728: Collective
5730: Input Parameters:
5731: + A - the first matrix in `MATMPIAIJ` format
5732: . B - the second matrix in `MATMPIAIJ` format
5733: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5735: Output Parameters:
5736: + rowb - On input index sets of rows of B to extract (or `NULL`), modified on output
5737: . colb - On input index sets of columns of B to extract (or `NULL`), modified on output
5738: - B_seq - the sequential matrix generated
5740: Level: developer
5742: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5743: @*/
5744: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5745: {
5746: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5747: PetscInt *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5748: IS isrowb, iscolb;
5749: Mat *bseq = NULL;
5751: PetscFunctionBegin;
5752: 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 ")",
5753: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5754: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));
5756: if (scall == MAT_INITIAL_MATRIX) {
5757: start = A->cmap->rstart;
5758: cmap = a->garray;
5759: nzA = a->A->cmap->n;
5760: nzB = a->B->cmap->n;
5761: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5762: ncols = 0;
5763: for (i = 0; i < nzB; i++) { /* row < local row index */
5764: if (cmap[i] < start) idx[ncols++] = cmap[i];
5765: else break;
5766: }
5767: imark = i;
5768: for (i = 0; i < nzA; i++) idx[ncols++] = start + i; /* local rows */
5769: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5770: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5771: PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5772: } else {
5773: PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5774: isrowb = *rowb;
5775: iscolb = *colb;
5776: PetscCall(PetscMalloc1(1, &bseq));
5777: bseq[0] = *B_seq;
5778: }
5779: PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5780: *B_seq = bseq[0];
5781: PetscCall(PetscFree(bseq));
5782: if (!rowb) {
5783: PetscCall(ISDestroy(&isrowb));
5784: } else {
5785: *rowb = isrowb;
5786: }
5787: if (!colb) {
5788: PetscCall(ISDestroy(&iscolb));
5789: } else {
5790: *colb = iscolb;
5791: }
5792: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5793: PetscFunctionReturn(PETSC_SUCCESS);
5794: }
5796: /*
5797: MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5798: of the OFF-DIAGONAL portion of local A
5800: Collective
5802: Input Parameters:
5803: + A,B - the matrices in `MATMPIAIJ` format
5804: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5806: Output Parameter:
5807: + startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5808: . startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5809: . bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5810: - B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N
5812: Developer Note:
5813: This directly accesses information inside the VecScatter associated with the matrix-vector product
5814: for this matrix. This is not desirable..
5816: Level: developer
5818: */
5820: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5821: {
5822: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5823: VecScatter ctx;
5824: MPI_Comm comm;
5825: const PetscMPIInt *rprocs, *sprocs;
5826: PetscMPIInt nrecvs, nsends;
5827: const PetscInt *srow, *rstarts, *sstarts;
5828: PetscInt *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5829: PetscInt i, j, k = 0, l, ll, nrows, *rstartsj = NULL, *sstartsj, len;
5830: PetscScalar *b_otha, *bufa, *bufA, *vals = NULL;
5831: MPI_Request *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5832: PetscMPIInt size, tag, rank, nreqs;
5834: PetscFunctionBegin;
5835: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5836: PetscCallMPI(MPI_Comm_size(comm, &size));
5838: 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 ")",
5839: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5840: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5841: PetscCallMPI(MPI_Comm_rank(comm, &rank));
5843: if (size == 1) {
5844: startsj_s = NULL;
5845: bufa_ptr = NULL;
5846: *B_oth = NULL;
5847: PetscFunctionReturn(PETSC_SUCCESS);
5848: }
5850: ctx = a->Mvctx;
5851: tag = ((PetscObject)ctx)->tag;
5853: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5854: /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5855: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5856: PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5857: PetscCall(PetscMalloc1(nreqs, &reqs));
5858: rwaits = reqs;
5859: swaits = PetscSafePointerPlusOffset(reqs, nrecvs);
5861: if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5862: if (scall == MAT_INITIAL_MATRIX) {
5863: /* i-array */
5864: /* post receives */
5865: if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5866: for (i = 0; i < nrecvs; i++) {
5867: rowlen = rvalues + rstarts[i] * rbs;
5868: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5869: PetscCallMPI(MPIU_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5870: }
5872: /* pack the outgoing message */
5873: PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));
5875: sstartsj[0] = 0;
5876: rstartsj[0] = 0;
5877: len = 0; /* total length of j or a array to be sent */
5878: if (nsends) {
5879: k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5880: PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5881: }
5882: for (i = 0; i < nsends; i++) {
5883: rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5884: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5885: for (j = 0; j < nrows; j++) {
5886: row = srow[k] + B->rmap->range[rank]; /* global row idx */
5887: for (l = 0; l < sbs; l++) {
5888: PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */
5890: rowlen[j * sbs + l] = ncols;
5892: len += ncols;
5893: PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5894: }
5895: k++;
5896: }
5897: PetscCallMPI(MPIU_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));
5899: sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5900: }
5901: /* recvs and sends of i-array are completed */
5902: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5903: PetscCall(PetscFree(svalues));
5905: /* allocate buffers for sending j and a arrays */
5906: PetscCall(PetscMalloc1(len + 1, &bufj));
5907: PetscCall(PetscMalloc1(len + 1, &bufa));
5909: /* create i-array of B_oth */
5910: PetscCall(PetscMalloc1(aBn + 2, &b_othi));
5912: b_othi[0] = 0;
5913: len = 0; /* total length of j or a array to be received */
5914: k = 0;
5915: for (i = 0; i < nrecvs; i++) {
5916: rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5917: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5918: for (j = 0; j < nrows; j++) {
5919: b_othi[k + 1] = b_othi[k] + rowlen[j];
5920: PetscCall(PetscIntSumError(rowlen[j], len, &len));
5921: k++;
5922: }
5923: rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5924: }
5925: PetscCall(PetscFree(rvalues));
5927: /* allocate space for j and a arrays of B_oth */
5928: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_othj));
5929: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_otha));
5931: /* j-array */
5932: /* post receives of j-array */
5933: for (i = 0; i < nrecvs; i++) {
5934: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5935: PetscCallMPI(MPIU_Irecv(b_othj + rstartsj[i], nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5936: }
5938: /* pack the outgoing message j-array */
5939: if (nsends) k = sstarts[0];
5940: for (i = 0; i < nsends; i++) {
5941: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5942: bufJ = bufj + sstartsj[i];
5943: for (j = 0; j < nrows; j++) {
5944: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5945: for (ll = 0; ll < sbs; ll++) {
5946: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5947: for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5948: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5949: }
5950: }
5951: PetscCallMPI(MPIU_Isend(bufj + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5952: }
5954: /* recvs and sends of j-array are completed */
5955: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5956: } else if (scall == MAT_REUSE_MATRIX) {
5957: sstartsj = *startsj_s;
5958: rstartsj = *startsj_r;
5959: bufa = *bufa_ptr;
5960: PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5961: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");
5963: /* a-array */
5964: /* post receives of a-array */
5965: for (i = 0; i < nrecvs; i++) {
5966: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5967: PetscCallMPI(MPIU_Irecv(b_otha + rstartsj[i], nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
5968: }
5970: /* pack the outgoing message a-array */
5971: if (nsends) k = sstarts[0];
5972: for (i = 0; i < nsends; i++) {
5973: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5974: bufA = bufa + sstartsj[i];
5975: for (j = 0; j < nrows; j++) {
5976: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5977: for (ll = 0; ll < sbs; ll++) {
5978: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5979: for (l = 0; l < ncols; l++) *bufA++ = vals[l];
5980: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5981: }
5982: }
5983: PetscCallMPI(MPIU_Isend(bufa + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
5984: }
5985: /* recvs and sends of a-array are completed */
5986: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5987: PetscCall(PetscFree(reqs));
5989: if (scall == MAT_INITIAL_MATRIX) {
5990: Mat_SeqAIJ *b_oth;
5992: /* put together the new matrix */
5993: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));
5995: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5996: /* Since these are PETSc arrays, change flags to free them as necessary. */
5997: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
5998: b_oth->free_a = PETSC_TRUE;
5999: b_oth->free_ij = PETSC_TRUE;
6000: b_oth->nonew = 0;
6002: PetscCall(PetscFree(bufj));
6003: if (!startsj_s || !bufa_ptr) {
6004: PetscCall(PetscFree2(sstartsj, rstartsj));
6005: PetscCall(PetscFree(bufa_ptr));
6006: } else {
6007: *startsj_s = sstartsj;
6008: *startsj_r = rstartsj;
6009: *bufa_ptr = bufa;
6010: }
6011: } else if (scall == MAT_REUSE_MATRIX) {
6012: PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
6013: }
6015: PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
6016: PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
6017: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
6018: PetscFunctionReturn(PETSC_SUCCESS);
6019: }
6021: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
6022: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
6023: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
6024: #if defined(PETSC_HAVE_MKL_SPARSE)
6025: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
6026: #endif
6027: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
6028: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
6029: #if defined(PETSC_HAVE_ELEMENTAL)
6030: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
6031: #endif
6032: #if defined(PETSC_HAVE_SCALAPACK)
6033: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
6034: #endif
6035: #if defined(PETSC_HAVE_HYPRE)
6036: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
6037: #endif
6038: #if defined(PETSC_HAVE_CUDA)
6039: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
6040: #endif
6041: #if defined(PETSC_HAVE_HIP)
6042: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6043: #endif
6044: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6045: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6046: #endif
6047: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6048: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6049: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
6051: /*
6052: Computes (B'*A')' since computing B*A directly is untenable
6054: n p p
6055: [ ] [ ] [ ]
6056: m [ A ] * n [ B ] = m [ C ]
6057: [ ] [ ] [ ]
6059: */
6060: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6061: {
6062: Mat At, Bt, Ct;
6064: PetscFunctionBegin;
6065: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6066: PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6067: PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_CURRENT, &Ct));
6068: PetscCall(MatDestroy(&At));
6069: PetscCall(MatDestroy(&Bt));
6070: PetscCall(MatTransposeSetPrecursor(Ct, C));
6071: PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6072: PetscCall(MatDestroy(&Ct));
6073: PetscFunctionReturn(PETSC_SUCCESS);
6074: }
6076: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6077: {
6078: PetscBool cisdense;
6080: PetscFunctionBegin;
6081: 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);
6082: PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6083: PetscCall(MatSetBlockSizesFromMats(C, A, B));
6084: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6085: if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6086: PetscCall(MatSetUp(C));
6088: C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6089: PetscFunctionReturn(PETSC_SUCCESS);
6090: }
6092: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6093: {
6094: Mat_Product *product = C->product;
6095: Mat A = product->A, B = product->B;
6097: PetscFunctionBegin;
6098: 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 ")",
6099: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6100: C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6101: C->ops->productsymbolic = MatProductSymbolic_AB;
6102: PetscFunctionReturn(PETSC_SUCCESS);
6103: }
6105: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6106: {
6107: Mat_Product *product = C->product;
6109: PetscFunctionBegin;
6110: if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6111: PetscFunctionReturn(PETSC_SUCCESS);
6112: }
6114: /*
6115: Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix
6117: Input Parameters:
6119: j1,rowBegin1,rowEnd1,jmap1: describe the first set of nonzeros (Set1)
6120: j2,rowBegin2,rowEnd2,jmap2: describe the second set of nonzeros (Set2)
6122: mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat
6124: For Set1, j1[] contains column indices of the nonzeros.
6125: 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
6126: respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6127: but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.
6129: Similar for Set2.
6131: This routine merges the two sets of nonzeros row by row and removes repeats.
6133: Output Parameters: (memory is allocated by the caller)
6135: i[],j[]: the CSR of the merged matrix, which has m rows.
6136: imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6137: imap2[]: similar to imap1[], but for Set2.
6138: Note we order nonzeros row-by-row and from left to right.
6139: */
6140: 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[])
6141: {
6142: PetscInt r, m; /* Row index of mat */
6143: PetscCount t, t1, t2, b1, e1, b2, e2;
6145: PetscFunctionBegin;
6146: PetscCall(MatGetLocalSize(mat, &m, NULL));
6147: t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6148: i[0] = 0;
6149: for (r = 0; r < m; r++) { /* Do row by row merging */
6150: b1 = rowBegin1[r];
6151: e1 = rowEnd1[r];
6152: b2 = rowBegin2[r];
6153: e2 = rowEnd2[r];
6154: while (b1 < e1 && b2 < e2) {
6155: if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6156: j[t] = j1[b1];
6157: imap1[t1] = t;
6158: imap2[t2] = t;
6159: b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6160: b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6161: t1++;
6162: t2++;
6163: t++;
6164: } else if (j1[b1] < j2[b2]) {
6165: j[t] = j1[b1];
6166: imap1[t1] = t;
6167: b1 += jmap1[t1 + 1] - jmap1[t1];
6168: t1++;
6169: t++;
6170: } else {
6171: j[t] = j2[b2];
6172: imap2[t2] = t;
6173: b2 += jmap2[t2 + 1] - jmap2[t2];
6174: t2++;
6175: t++;
6176: }
6177: }
6178: /* Merge the remaining in either j1[] or j2[] */
6179: while (b1 < e1) {
6180: j[t] = j1[b1];
6181: imap1[t1] = t;
6182: b1 += jmap1[t1 + 1] - jmap1[t1];
6183: t1++;
6184: t++;
6185: }
6186: while (b2 < e2) {
6187: j[t] = j2[b2];
6188: imap2[t2] = t;
6189: b2 += jmap2[t2 + 1] - jmap2[t2];
6190: t2++;
6191: t++;
6192: }
6193: PetscCall(PetscIntCast(t, i + r + 1));
6194: }
6195: PetscFunctionReturn(PETSC_SUCCESS);
6196: }
6198: /*
6199: Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block
6201: Input Parameters:
6202: mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6203: 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[]
6204: respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.
6206: i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6207: i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.
6209: Output Parameters:
6210: j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6211: rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6212: 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,
6213: and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.
6215: Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6216: Atot: number of entries belonging to the diagonal block.
6217: Annz: number of unique nonzeros belonging to the diagonal block.
6218: Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6219: repeats (i.e., same 'i,j' pair).
6220: Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6221: is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.
6223: Atot: number of entries belonging to the diagonal block
6224: Annz: number of unique nonzeros belonging to the diagonal block.
6226: Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.
6228: Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6229: */
6230: 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_)
6231: {
6232: PetscInt cstart, cend, rstart, rend, row, col;
6233: PetscCount Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6234: PetscCount Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6235: PetscCount k, m, p, q, r, s, mid;
6236: PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;
6238: PetscFunctionBegin;
6239: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6240: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6241: m = rend - rstart;
6243: /* Skip negative rows */
6244: for (k = 0; k < n; k++)
6245: if (i[k] >= 0) break;
6247: /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6248: fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6249: */
6250: while (k < n) {
6251: row = i[k];
6252: /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6253: for (s = k; s < n; s++)
6254: if (i[s] != row) break;
6256: /* Shift diag columns to range of [-PETSC_INT_MAX, -1] */
6257: for (p = k; p < s; p++) {
6258: if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_INT_MAX;
6259: 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]);
6260: }
6261: PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6262: PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6263: rowBegin[row - rstart] = k;
6264: rowMid[row - rstart] = mid;
6265: rowEnd[row - rstart] = s;
6267: /* Count nonzeros of this diag/offdiag row, which might have repeats */
6268: Atot += mid - k;
6269: Btot += s - mid;
6271: /* Count unique nonzeros of this diag row */
6272: for (p = k; p < mid;) {
6273: col = j[p];
6274: do {
6275: j[p] += PETSC_INT_MAX; /* Revert the modified diagonal indices */
6276: p++;
6277: } while (p < mid && j[p] == col);
6278: Annz++;
6279: }
6281: /* Count unique nonzeros of this offdiag row */
6282: for (p = mid; p < s;) {
6283: col = j[p];
6284: do {
6285: p++;
6286: } while (p < s && j[p] == col);
6287: Bnnz++;
6288: }
6289: k = s;
6290: }
6292: /* Allocation according to Atot, Btot, Annz, Bnnz */
6293: PetscCall(PetscMalloc1(Atot, &Aperm));
6294: PetscCall(PetscMalloc1(Btot, &Bperm));
6295: PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6296: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));
6298: /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6299: Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6300: for (r = 0; r < m; r++) {
6301: k = rowBegin[r];
6302: mid = rowMid[r];
6303: s = rowEnd[r];
6304: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Aperm, Atot), PetscSafePointerPlusOffset(perm, k), mid - k));
6305: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Bperm, Btot), PetscSafePointerPlusOffset(perm, mid), s - mid));
6306: Atot += mid - k;
6307: Btot += s - mid;
6309: /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6310: for (p = k; p < mid;) {
6311: col = j[p];
6312: q = p;
6313: do {
6314: p++;
6315: } while (p < mid && j[p] == col);
6316: Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6317: Annz++;
6318: }
6320: for (p = mid; p < s;) {
6321: col = j[p];
6322: q = p;
6323: do {
6324: p++;
6325: } while (p < s && j[p] == col);
6326: Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6327: Bnnz++;
6328: }
6329: }
6330: /* Output */
6331: *Aperm_ = Aperm;
6332: *Annz_ = Annz;
6333: *Atot_ = Atot;
6334: *Ajmap_ = Ajmap;
6335: *Bperm_ = Bperm;
6336: *Bnnz_ = Bnnz;
6337: *Btot_ = Btot;
6338: *Bjmap_ = Bjmap;
6339: PetscFunctionReturn(PETSC_SUCCESS);
6340: }
6342: /*
6343: Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix
6345: Input Parameters:
6346: nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6347: nnz: number of unique nonzeros in the merged matrix
6348: imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6349: jmap[nnz1+1]: i-th nonzero in the set has jmap[i+1] - jmap[i] repeats in the set
6351: Output Parameter: (memory is allocated by the caller)
6352: jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set
6354: Example:
6355: nnz1 = 4
6356: nnz = 6
6357: imap = [1,3,4,5]
6358: jmap = [0,3,5,6,7]
6359: then,
6360: jmap_new = [0,0,3,3,5,6,7]
6361: */
6362: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6363: {
6364: PetscCount k, p;
6366: PetscFunctionBegin;
6367: jmap_new[0] = 0;
6368: p = nnz; /* p loops over jmap_new[] backwards */
6369: for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6370: for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6371: }
6372: for (; p >= 0; p--) jmap_new[p] = jmap[0];
6373: PetscFunctionReturn(PETSC_SUCCESS);
6374: }
6376: static PetscErrorCode MatCOOStructDestroy_MPIAIJ(void **data)
6377: {
6378: MatCOOStruct_MPIAIJ *coo = (MatCOOStruct_MPIAIJ *)*data;
6380: PetscFunctionBegin;
6381: PetscCall(PetscSFDestroy(&coo->sf));
6382: PetscCall(PetscFree(coo->Aperm1));
6383: PetscCall(PetscFree(coo->Bperm1));
6384: PetscCall(PetscFree(coo->Ajmap1));
6385: PetscCall(PetscFree(coo->Bjmap1));
6386: PetscCall(PetscFree(coo->Aimap2));
6387: PetscCall(PetscFree(coo->Bimap2));
6388: PetscCall(PetscFree(coo->Aperm2));
6389: PetscCall(PetscFree(coo->Bperm2));
6390: PetscCall(PetscFree(coo->Ajmap2));
6391: PetscCall(PetscFree(coo->Bjmap2));
6392: PetscCall(PetscFree(coo->Cperm1));
6393: PetscCall(PetscFree2(coo->sendbuf, coo->recvbuf));
6394: PetscCall(PetscFree(coo));
6395: PetscFunctionReturn(PETSC_SUCCESS);
6396: }
6398: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6399: {
6400: MPI_Comm comm;
6401: PetscMPIInt rank, size;
6402: PetscInt m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6403: PetscCount k, p, q, rem; /* Loop variables over coo arrays */
6404: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6405: PetscContainer container;
6406: MatCOOStruct_MPIAIJ *coo;
6408: PetscFunctionBegin;
6409: PetscCall(PetscFree(mpiaij->garray));
6410: PetscCall(VecDestroy(&mpiaij->lvec));
6411: #if defined(PETSC_USE_CTABLE)
6412: PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6413: #else
6414: PetscCall(PetscFree(mpiaij->colmap));
6415: #endif
6416: PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6417: mat->assembled = PETSC_FALSE;
6418: mat->was_assembled = PETSC_FALSE;
6420: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6421: PetscCallMPI(MPI_Comm_size(comm, &size));
6422: PetscCallMPI(MPI_Comm_rank(comm, &rank));
6423: PetscCall(PetscLayoutSetUp(mat->rmap));
6424: PetscCall(PetscLayoutSetUp(mat->cmap));
6425: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6426: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6427: PetscCall(MatGetLocalSize(mat, &m, &n));
6428: PetscCall(MatGetSize(mat, &M, &N));
6430: /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6431: /* entries come first, then local rows, then remote rows. */
6432: PetscCount n1 = coo_n, *perm1;
6433: PetscInt *i1 = coo_i, *j1 = coo_j;
6435: PetscCall(PetscMalloc1(n1, &perm1));
6436: for (k = 0; k < n1; k++) perm1[k] = k;
6438: /* Manipulate indices so that entries with negative row or col indices will have smallest
6439: row indices, local entries will have greater but negative row indices, and remote entries
6440: will have positive row indices.
6441: */
6442: for (k = 0; k < n1; k++) {
6443: if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_INT_MIN; /* e.g., -2^31, minimal to move them ahead */
6444: 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] */
6445: else {
6446: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6447: if (mpiaij->donotstash) i1[k] = PETSC_INT_MIN; /* Ignore offproc entries as if they had negative indices */
6448: }
6449: }
6451: /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6452: PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));
6454: /* Advance k to the first entry we need to take care of */
6455: for (k = 0; k < n1; k++)
6456: if (i1[k] > PETSC_INT_MIN) break;
6457: PetscCount i1start = k;
6459: PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_INT_MAX, &rem)); /* rem is upper bound of the last local row */
6460: for (; k < rem; k++) i1[k] += PETSC_INT_MAX; /* Revert row indices of local rows*/
6462: /* Send remote rows to their owner */
6463: /* Find which rows should be sent to which remote ranks*/
6464: PetscInt nsend = 0; /* Number of MPI ranks to send data to */
6465: PetscMPIInt *sendto; /* [nsend], storing remote ranks */
6466: PetscInt *nentries; /* [nsend], storing number of entries sent to remote ranks; Assume PetscInt is big enough for this count, and error if not */
6467: const PetscInt *ranges;
6468: PetscInt maxNsend = size >= 128 ? 128 : size; /* Assume max 128 neighbors; realloc when needed */
6470: PetscCall(PetscLayoutGetRanges(mat->rmap, &ranges));
6471: PetscCall(PetscMalloc2(maxNsend, &sendto, maxNsend, &nentries));
6472: for (k = rem; k < n1;) {
6473: PetscMPIInt owner;
6474: PetscInt firstRow, lastRow;
6476: /* Locate a row range */
6477: firstRow = i1[k]; /* first row of this owner */
6478: PetscCall(PetscLayoutFindOwner(mat->rmap, firstRow, &owner));
6479: lastRow = ranges[owner + 1] - 1; /* last row of this owner */
6481: /* Find the first index 'p' in [k,n) with i[p] belonging to next owner */
6482: PetscCall(PetscSortedIntUpperBound(i1, k, n1, lastRow, &p));
6484: /* All entries in [k,p) belong to this remote owner */
6485: if (nsend >= maxNsend) { /* Double the remote ranks arrays if not long enough */
6486: PetscMPIInt *sendto2;
6487: PetscInt *nentries2;
6488: PetscInt maxNsend2 = (maxNsend <= size / 2) ? maxNsend * 2 : size;
6490: PetscCall(PetscMalloc2(maxNsend2, &sendto2, maxNsend2, &nentries2));
6491: PetscCall(PetscArraycpy(sendto2, sendto, maxNsend));
6492: PetscCall(PetscArraycpy(nentries2, nentries2, maxNsend + 1));
6493: PetscCall(PetscFree2(sendto, nentries2));
6494: sendto = sendto2;
6495: nentries = nentries2;
6496: maxNsend = maxNsend2;
6497: }
6498: sendto[nsend] = owner;
6499: PetscCall(PetscIntCast(p - k, &nentries[nsend]));
6500: nsend++;
6501: k = p;
6502: }
6504: /* Build 1st SF to know offsets on remote to send data */
6505: PetscSF sf1;
6506: PetscInt nroots = 1, nroots2 = 0;
6507: PetscInt nleaves = nsend, nleaves2 = 0;
6508: PetscInt *offsets;
6509: PetscSFNode *iremote;
6511: PetscCall(PetscSFCreate(comm, &sf1));
6512: PetscCall(PetscMalloc1(nsend, &iremote));
6513: PetscCall(PetscMalloc1(nsend, &offsets));
6514: for (k = 0; k < nsend; k++) {
6515: iremote[k].rank = sendto[k];
6516: iremote[k].index = 0;
6517: nleaves2 += nentries[k];
6518: PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6519: }
6520: PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6521: PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6522: PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6523: PetscCall(PetscSFDestroy(&sf1));
6524: PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT, nleaves2, n1 - rem);
6526: /* Build 2nd SF to send remote COOs to their owner */
6527: PetscSF sf2;
6528: nroots = nroots2;
6529: nleaves = nleaves2;
6530: PetscCall(PetscSFCreate(comm, &sf2));
6531: PetscCall(PetscSFSetFromOptions(sf2));
6532: PetscCall(PetscMalloc1(nleaves, &iremote));
6533: p = 0;
6534: for (k = 0; k < nsend; k++) {
6535: PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6536: for (q = 0; q < nentries[k]; q++, p++) {
6537: iremote[p].rank = sendto[k];
6538: PetscCall(PetscIntCast(offsets[k] + q, &iremote[p].index));
6539: }
6540: }
6541: PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6543: /* Send the remote COOs to their owner */
6544: PetscInt n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6545: PetscCount *perm2; /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6546: PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6547: PetscAssert(rem == 0 || i1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6548: PetscAssert(rem == 0 || j1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6549: PetscInt *i1prem = PetscSafePointerPlusOffset(i1, rem);
6550: PetscInt *j1prem = PetscSafePointerPlusOffset(j1, rem);
6551: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1prem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6552: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1prem, i2, MPI_REPLACE));
6553: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1prem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6554: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1prem, j2, MPI_REPLACE));
6556: PetscCall(PetscFree(offsets));
6557: PetscCall(PetscFree2(sendto, nentries));
6559: /* Sort received COOs by row along with the permutation array */
6560: for (k = 0; k < n2; k++) perm2[k] = k;
6561: PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));
6563: /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6564: PetscCount *Cperm1;
6565: PetscAssert(rem == 0 || perm1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6566: PetscCount *perm1prem = PetscSafePointerPlusOffset(perm1, rem);
6567: PetscCall(PetscMalloc1(nleaves, &Cperm1));
6568: PetscCall(PetscArraycpy(Cperm1, perm1prem, nleaves));
6570: /* Support for HYPRE matrices, kind of a hack.
6571: Swap min column with diagonal so that diagonal values will go first */
6572: PetscBool hypre;
6573: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", ((PetscObject)mat)->name, &hypre));
6574: if (hypre) {
6575: PetscInt *minj;
6576: PetscBT hasdiag;
6578: PetscCall(PetscBTCreate(m, &hasdiag));
6579: PetscCall(PetscMalloc1(m, &minj));
6580: for (k = 0; k < m; k++) minj[k] = PETSC_INT_MAX;
6581: for (k = i1start; k < rem; k++) {
6582: if (j1[k] < cstart || j1[k] >= cend) continue;
6583: const PetscInt rindex = i1[k] - rstart;
6584: if ((j1[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6585: minj[rindex] = PetscMin(minj[rindex], j1[k]);
6586: }
6587: for (k = 0; k < n2; k++) {
6588: if (j2[k] < cstart || j2[k] >= cend) continue;
6589: const PetscInt rindex = i2[k] - rstart;
6590: if ((j2[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6591: minj[rindex] = PetscMin(minj[rindex], j2[k]);
6592: }
6593: for (k = i1start; k < rem; k++) {
6594: const PetscInt rindex = i1[k] - rstart;
6595: if (j1[k] < cstart || j1[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6596: if (j1[k] == minj[rindex]) j1[k] = i1[k] + (cstart - rstart);
6597: else if ((j1[k] - cstart) == rindex) j1[k] = minj[rindex];
6598: }
6599: for (k = 0; k < n2; k++) {
6600: const PetscInt rindex = i2[k] - rstart;
6601: if (j2[k] < cstart || j2[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6602: if (j2[k] == minj[rindex]) j2[k] = i2[k] + (cstart - rstart);
6603: else if ((j2[k] - cstart) == rindex) j2[k] = minj[rindex];
6604: }
6605: PetscCall(PetscBTDestroy(&hasdiag));
6606: PetscCall(PetscFree(minj));
6607: }
6609: /* Split local COOs and received COOs into diag/offdiag portions */
6610: PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6611: PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1;
6612: PetscCount Annz1, Bnnz1, Atot1, Btot1;
6613: PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6614: PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6615: PetscCount Annz2, Bnnz2, Atot2, Btot2;
6617: PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6618: PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6619: PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6620: PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));
6622: /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6623: PetscInt *Ai, *Bi;
6624: PetscInt *Aj, *Bj;
6626: PetscCall(PetscMalloc1(m + 1, &Ai));
6627: PetscCall(PetscMalloc1(m + 1, &Bi));
6628: PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6629: PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));
6631: PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6632: PetscCall(PetscMalloc1(Annz1, &Aimap1));
6633: PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6634: PetscCall(PetscMalloc1(Annz2, &Aimap2));
6635: PetscCall(PetscMalloc1(Bnnz2, &Bimap2));
6637: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6638: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));
6640: /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we */
6641: /* expect nonzeros in A/B most likely have local contributing entries */
6642: PetscInt Annz = Ai[m];
6643: PetscInt Bnnz = Bi[m];
6644: PetscCount *Ajmap1_new, *Bjmap1_new;
6646: PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6647: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));
6649: PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6650: PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));
6652: PetscCall(PetscFree(Aimap1));
6653: PetscCall(PetscFree(Ajmap1));
6654: PetscCall(PetscFree(Bimap1));
6655: PetscCall(PetscFree(Bjmap1));
6656: PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6657: PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6658: PetscCall(PetscFree(perm1));
6659: PetscCall(PetscFree3(i2, j2, perm2));
6661: Ajmap1 = Ajmap1_new;
6662: Bjmap1 = Bjmap1_new;
6664: /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6665: if (Annz < Annz1 + Annz2) {
6666: PetscInt *Aj_new;
6667: PetscCall(PetscMalloc1(Annz, &Aj_new));
6668: PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6669: PetscCall(PetscFree(Aj));
6670: Aj = Aj_new;
6671: }
6673: if (Bnnz < Bnnz1 + Bnnz2) {
6674: PetscInt *Bj_new;
6675: PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6676: PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6677: PetscCall(PetscFree(Bj));
6678: Bj = Bj_new;
6679: }
6681: /* Create new submatrices for on-process and off-process coupling */
6682: PetscScalar *Aa, *Ba;
6683: MatType rtype;
6684: Mat_SeqAIJ *a, *b;
6685: PetscObjectState state;
6686: PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6687: PetscCall(PetscCalloc1(Bnnz, &Ba));
6688: /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6689: if (cstart) {
6690: for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6691: }
6693: PetscCall(MatGetRootType_Private(mat, &rtype));
6695: MatSeqXAIJGetOptions_Private(mpiaij->A);
6696: PetscCall(MatDestroy(&mpiaij->A));
6697: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6698: PetscCall(MatSetBlockSizesFromMats(mpiaij->A, mat, mat));
6699: MatSeqXAIJRestoreOptions_Private(mpiaij->A);
6701: MatSeqXAIJGetOptions_Private(mpiaij->B);
6702: PetscCall(MatDestroy(&mpiaij->B));
6703: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6704: PetscCall(MatSetBlockSizesFromMats(mpiaij->B, mat, mat));
6705: MatSeqXAIJRestoreOptions_Private(mpiaij->B);
6707: PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6708: mat->was_assembled = PETSC_TRUE; // was_assembled in effect means the Mvctx is built; doing so avoids redundant MatSetUpMultiply_MPIAIJ
6709: state = mpiaij->A->nonzerostate + mpiaij->B->nonzerostate;
6710: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
6712: a = (Mat_SeqAIJ *)mpiaij->A->data;
6713: b = (Mat_SeqAIJ *)mpiaij->B->data;
6714: a->free_a = PETSC_TRUE;
6715: a->free_ij = PETSC_TRUE;
6716: b->free_a = PETSC_TRUE;
6717: b->free_ij = PETSC_TRUE;
6718: a->maxnz = a->nz;
6719: b->maxnz = b->nz;
6721: /* conversion must happen AFTER multiply setup */
6722: PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6723: PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6724: PetscCall(VecDestroy(&mpiaij->lvec));
6725: PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));
6727: // Put the COO struct in a container and then attach that to the matrix
6728: PetscCall(PetscMalloc1(1, &coo));
6729: coo->n = coo_n;
6730: coo->sf = sf2;
6731: coo->sendlen = nleaves;
6732: coo->recvlen = nroots;
6733: coo->Annz = Annz;
6734: coo->Bnnz = Bnnz;
6735: coo->Annz2 = Annz2;
6736: coo->Bnnz2 = Bnnz2;
6737: coo->Atot1 = Atot1;
6738: coo->Atot2 = Atot2;
6739: coo->Btot1 = Btot1;
6740: coo->Btot2 = Btot2;
6741: coo->Ajmap1 = Ajmap1;
6742: coo->Aperm1 = Aperm1;
6743: coo->Bjmap1 = Bjmap1;
6744: coo->Bperm1 = Bperm1;
6745: coo->Aimap2 = Aimap2;
6746: coo->Ajmap2 = Ajmap2;
6747: coo->Aperm2 = Aperm2;
6748: coo->Bimap2 = Bimap2;
6749: coo->Bjmap2 = Bjmap2;
6750: coo->Bperm2 = Bperm2;
6751: coo->Cperm1 = Cperm1;
6752: // Allocate in preallocation. If not used, it has zero cost on host
6753: PetscCall(PetscMalloc2(coo->sendlen, &coo->sendbuf, coo->recvlen, &coo->recvbuf));
6754: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
6755: PetscCall(PetscContainerSetPointer(container, coo));
6756: PetscCall(PetscContainerSetCtxDestroy(container, MatCOOStructDestroy_MPIAIJ));
6757: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
6758: PetscCall(PetscContainerDestroy(&container));
6759: PetscFunctionReturn(PETSC_SUCCESS);
6760: }
6762: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6763: {
6764: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6765: Mat A = mpiaij->A, B = mpiaij->B;
6766: PetscScalar *Aa, *Ba;
6767: PetscScalar *sendbuf, *recvbuf;
6768: const PetscCount *Ajmap1, *Ajmap2, *Aimap2;
6769: const PetscCount *Bjmap1, *Bjmap2, *Bimap2;
6770: const PetscCount *Aperm1, *Aperm2, *Bperm1, *Bperm2;
6771: const PetscCount *Cperm1;
6772: PetscContainer container;
6773: MatCOOStruct_MPIAIJ *coo;
6775: PetscFunctionBegin;
6776: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
6777: PetscCheck(container, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
6778: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
6779: sendbuf = coo->sendbuf;
6780: recvbuf = coo->recvbuf;
6781: Ajmap1 = coo->Ajmap1;
6782: Ajmap2 = coo->Ajmap2;
6783: Aimap2 = coo->Aimap2;
6784: Bjmap1 = coo->Bjmap1;
6785: Bjmap2 = coo->Bjmap2;
6786: Bimap2 = coo->Bimap2;
6787: Aperm1 = coo->Aperm1;
6788: Aperm2 = coo->Aperm2;
6789: Bperm1 = coo->Bperm1;
6790: Bperm2 = coo->Bperm2;
6791: Cperm1 = coo->Cperm1;
6793: PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6794: PetscCall(MatSeqAIJGetArray(B, &Ba));
6796: /* Pack entries to be sent to remote */
6797: for (PetscCount i = 0; i < coo->sendlen; i++) sendbuf[i] = v[Cperm1[i]];
6799: /* Send remote entries to their owner and overlap the communication with local computation */
6800: PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6801: /* Add local entries to A and B */
6802: for (PetscCount i = 0; i < coo->Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6803: PetscScalar sum = 0.0; /* Do partial summation first to improve numerical stability */
6804: for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6805: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6806: }
6807: for (PetscCount i = 0; i < coo->Bnnz; i++) {
6808: PetscScalar sum = 0.0;
6809: for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6810: Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6811: }
6812: PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));
6814: /* Add received remote entries to A and B */
6815: for (PetscCount i = 0; i < coo->Annz2; i++) {
6816: for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6817: }
6818: for (PetscCount i = 0; i < coo->Bnnz2; i++) {
6819: for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6820: }
6821: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6822: PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6823: PetscFunctionReturn(PETSC_SUCCESS);
6824: }
6826: /*MC
6827: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
6829: Options Database Keys:
6830: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`
6832: Level: beginner
6834: Notes:
6835: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
6836: in this case the values associated with the rows and columns one passes in are set to zero
6837: in the matrix
6839: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
6840: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
6842: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6843: M*/
6844: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6845: {
6846: Mat_MPIAIJ *b;
6847: PetscMPIInt size;
6849: PetscFunctionBegin;
6850: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
6852: PetscCall(PetscNew(&b));
6853: B->data = (void *)b;
6854: B->ops[0] = MatOps_Values;
6855: B->assembled = PETSC_FALSE;
6856: B->insertmode = NOT_SET_VALUES;
6857: b->size = size;
6859: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
6861: /* build cache for off array entries formed */
6862: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
6864: b->donotstash = PETSC_FALSE;
6865: b->colmap = NULL;
6866: b->garray = NULL;
6867: b->roworiented = PETSC_TRUE;
6869: /* stuff used for matrix vector multiply */
6870: b->lvec = NULL;
6871: b->Mvctx = NULL;
6873: /* stuff for MatGetRow() */
6874: b->rowindices = NULL;
6875: b->rowvalues = NULL;
6876: b->getrowactive = PETSC_FALSE;
6878: /* flexible pointer used in CUSPARSE classes */
6879: b->spptr = NULL;
6881: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6882: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6883: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6884: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6885: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6886: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6887: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetHash_C", MatResetHash_MPIAIJ));
6888: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6889: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6890: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6891: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6892: #if defined(PETSC_HAVE_CUDA)
6893: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6894: #endif
6895: #if defined(PETSC_HAVE_HIP)
6896: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6897: #endif
6898: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6899: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6900: #endif
6901: #if defined(PETSC_HAVE_MKL_SPARSE)
6902: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6903: #endif
6904: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6905: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6906: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6907: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6908: #if defined(PETSC_HAVE_ELEMENTAL)
6909: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6910: #endif
6911: #if defined(PETSC_HAVE_SCALAPACK)
6912: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6913: #endif
6914: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6915: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6916: #if defined(PETSC_HAVE_HYPRE)
6917: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6918: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6919: #endif
6920: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6921: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6922: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6923: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6924: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6925: PetscFunctionReturn(PETSC_SUCCESS);
6926: }
6928: /*@
6929: MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6930: and "off-diagonal" part of the matrix in CSR format.
6932: Collective
6934: Input Parameters:
6935: + comm - MPI communicator
6936: . m - number of local rows (Cannot be `PETSC_DECIDE`)
6937: . n - This value should be the same as the local size used in creating the
6938: x vector for the matrix-vector product $y = Ax$. (or `PETSC_DECIDE` to have
6939: calculated if `N` is given) For square matrices `n` is almost always `m`.
6940: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6941: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6942: . 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
6943: . j - column indices, which must be local, i.e., based off the start column of the diagonal portion
6944: . a - matrix values
6945: . 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
6946: . oj - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6947: - oa - matrix values
6949: Output Parameter:
6950: . mat - the matrix
6952: Level: advanced
6954: Notes:
6955: The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc (even in Fortran). The user
6956: must free the arrays once the matrix has been destroyed and not before.
6958: The `i` and `j` indices are 0 based
6960: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix
6962: This sets local rows and cannot be used to set off-processor values.
6964: Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6965: legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6966: not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
6967: the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
6968: keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
6969: communication if it is known that only local entries will be set.
6971: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
6972: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
6973: @*/
6974: 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)
6975: {
6976: Mat_MPIAIJ *maij;
6978: PetscFunctionBegin;
6979: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
6980: PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
6981: PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
6982: PetscCall(MatCreate(comm, mat));
6983: PetscCall(MatSetSizes(*mat, m, n, M, N));
6984: PetscCall(MatSetType(*mat, MATMPIAIJ));
6985: maij = (Mat_MPIAIJ *)(*mat)->data;
6987: (*mat)->preallocated = PETSC_TRUE;
6989: PetscCall(PetscLayoutSetUp((*mat)->rmap));
6990: PetscCall(PetscLayoutSetUp((*mat)->cmap));
6992: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
6993: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));
6995: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
6996: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
6997: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
6998: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
6999: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
7000: PetscFunctionReturn(PETSC_SUCCESS);
7001: }
7003: typedef struct {
7004: Mat *mp; /* intermediate products */
7005: PetscBool *mptmp; /* is the intermediate product temporary ? */
7006: PetscInt cp; /* number of intermediate products */
7008: /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
7009: PetscInt *startsj_s, *startsj_r;
7010: PetscScalar *bufa;
7011: Mat P_oth;
7013: /* may take advantage of merging product->B */
7014: Mat Bloc; /* B-local by merging diag and off-diag */
7016: /* cusparse does not have support to split between symbolic and numeric phases.
7017: When api_user is true, we don't need to update the numerical values
7018: of the temporary storage */
7019: PetscBool reusesym;
7021: /* support for COO values insertion */
7022: PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
7023: PetscInt **own; /* own[i] points to address of on-process COO indices for Mat mp[i] */
7024: PetscInt **off; /* off[i] points to address of off-process COO indices for Mat mp[i] */
7025: PetscBool hasoffproc; /* if true, have off-process values insertion (i.e. AtB or PtAP) */
7026: PetscSF sf; /* used for non-local values insertion and memory malloc */
7027: PetscMemType mtype;
7029: /* customization */
7030: PetscBool abmerge;
7031: PetscBool P_oth_bind;
7032: } MatMatMPIAIJBACKEND;
7034: static PetscErrorCode MatDestroy_MatMatMPIAIJBACKEND(void *data)
7035: {
7036: MatMatMPIAIJBACKEND *mmdata = (MatMatMPIAIJBACKEND *)data;
7037: PetscInt i;
7039: PetscFunctionBegin;
7040: PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
7041: PetscCall(PetscFree(mmdata->bufa));
7042: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
7043: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
7044: PetscCall(MatDestroy(&mmdata->P_oth));
7045: PetscCall(MatDestroy(&mmdata->Bloc));
7046: PetscCall(PetscSFDestroy(&mmdata->sf));
7047: for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
7048: PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
7049: PetscCall(PetscFree(mmdata->own[0]));
7050: PetscCall(PetscFree(mmdata->own));
7051: PetscCall(PetscFree(mmdata->off[0]));
7052: PetscCall(PetscFree(mmdata->off));
7053: PetscCall(PetscFree(mmdata));
7054: PetscFunctionReturn(PETSC_SUCCESS);
7055: }
7057: /* Copy selected n entries with indices in idx[] of A to v[].
7058: If idx is NULL, copy the whole data array of A to v[]
7059: */
7060: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
7061: {
7062: PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);
7064: PetscFunctionBegin;
7065: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
7066: if (f) {
7067: PetscCall((*f)(A, n, idx, v));
7068: } else {
7069: const PetscScalar *vv;
7071: PetscCall(MatSeqAIJGetArrayRead(A, &vv));
7072: if (n && idx) {
7073: PetscScalar *w = v;
7074: const PetscInt *oi = idx;
7075: PetscInt j;
7077: for (j = 0; j < n; j++) *w++ = vv[*oi++];
7078: } else {
7079: PetscCall(PetscArraycpy(v, vv, n));
7080: }
7081: PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7082: }
7083: PetscFunctionReturn(PETSC_SUCCESS);
7084: }
7086: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7087: {
7088: MatMatMPIAIJBACKEND *mmdata;
7089: PetscInt i, n_d, n_o;
7091: PetscFunctionBegin;
7092: MatCheckProduct(C, 1);
7093: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7094: mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7095: if (!mmdata->reusesym) { /* update temporary matrices */
7096: 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));
7097: if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7098: }
7099: mmdata->reusesym = PETSC_FALSE;
7101: for (i = 0; i < mmdata->cp; i++) {
7102: 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]);
7103: PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7104: }
7105: for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7106: PetscInt noff;
7108: PetscCall(PetscIntCast(mmdata->off[i + 1] - mmdata->off[i], &noff));
7109: if (mmdata->mptmp[i]) continue;
7110: if (noff) {
7111: PetscInt nown;
7113: PetscCall(PetscIntCast(mmdata->own[i + 1] - mmdata->own[i], &nown));
7114: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7115: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7116: n_o += noff;
7117: n_d += nown;
7118: } else {
7119: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;
7121: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7122: n_d += mm->nz;
7123: }
7124: }
7125: if (mmdata->hasoffproc) { /* offprocess insertion */
7126: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7127: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7128: }
7129: PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7130: PetscFunctionReturn(PETSC_SUCCESS);
7131: }
7133: /* Support for Pt * A, A * P, or Pt * A * P */
7134: #define MAX_NUMBER_INTERMEDIATE 4
7135: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7136: {
7137: Mat_Product *product = C->product;
7138: Mat A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7139: Mat_MPIAIJ *a, *p;
7140: MatMatMPIAIJBACKEND *mmdata;
7141: ISLocalToGlobalMapping P_oth_l2g = NULL;
7142: IS glob = NULL;
7143: const char *prefix;
7144: char pprefix[256];
7145: const PetscInt *globidx, *P_oth_idx;
7146: PetscInt i, j, cp, m, n, M, N, *coo_i, *coo_j;
7147: PetscCount ncoo, ncoo_d, ncoo_o, ncoo_oown;
7148: PetscInt cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7149: /* type-0: consecutive, start from 0; type-1: consecutive with */
7150: /* a base offset; type-2: sparse with a local to global map table */
7151: const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE]; /* col/row local to global map array (table) for type-2 map type */
7153: MatProductType ptype;
7154: PetscBool mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7155: PetscMPIInt size;
7157: PetscFunctionBegin;
7158: MatCheckProduct(C, 1);
7159: PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7160: ptype = product->type;
7161: if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7162: ptype = MATPRODUCT_AB;
7163: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7164: }
7165: switch (ptype) {
7166: case MATPRODUCT_AB:
7167: A = product->A;
7168: P = product->B;
7169: m = A->rmap->n;
7170: n = P->cmap->n;
7171: M = A->rmap->N;
7172: N = P->cmap->N;
7173: hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7174: break;
7175: case MATPRODUCT_AtB:
7176: P = product->A;
7177: A = product->B;
7178: m = P->cmap->n;
7179: n = A->cmap->n;
7180: M = P->cmap->N;
7181: N = A->cmap->N;
7182: hasoffproc = PETSC_TRUE;
7183: break;
7184: case MATPRODUCT_PtAP:
7185: A = product->A;
7186: P = product->B;
7187: m = P->cmap->n;
7188: n = P->cmap->n;
7189: M = P->cmap->N;
7190: N = P->cmap->N;
7191: hasoffproc = PETSC_TRUE;
7192: break;
7193: default:
7194: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7195: }
7196: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7197: if (size == 1) hasoffproc = PETSC_FALSE;
7199: /* defaults */
7200: for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7201: mp[i] = NULL;
7202: mptmp[i] = PETSC_FALSE;
7203: rmapt[i] = -1;
7204: cmapt[i] = -1;
7205: rmapa[i] = NULL;
7206: cmapa[i] = NULL;
7207: }
7209: /* customization */
7210: PetscCall(PetscNew(&mmdata));
7211: mmdata->reusesym = product->api_user;
7212: if (ptype == MATPRODUCT_AB) {
7213: if (product->api_user) {
7214: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7215: PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7216: PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7217: PetscOptionsEnd();
7218: } else {
7219: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7220: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7221: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7222: PetscOptionsEnd();
7223: }
7224: } else if (ptype == MATPRODUCT_PtAP) {
7225: if (product->api_user) {
7226: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7227: PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7228: PetscOptionsEnd();
7229: } else {
7230: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7231: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7232: PetscOptionsEnd();
7233: }
7234: }
7235: a = (Mat_MPIAIJ *)A->data;
7236: p = (Mat_MPIAIJ *)P->data;
7237: PetscCall(MatSetSizes(C, m, n, M, N));
7238: PetscCall(PetscLayoutSetUp(C->rmap));
7239: PetscCall(PetscLayoutSetUp(C->cmap));
7240: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7241: PetscCall(MatGetOptionsPrefix(C, &prefix));
7243: cp = 0;
7244: switch (ptype) {
7245: case MATPRODUCT_AB: /* A * P */
7246: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7248: /* A_diag * P_local (merged or not) */
7249: if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7250: /* P is product->B */
7251: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7252: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7253: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7254: PetscCall(MatProductSetFill(mp[cp], product->fill));
7255: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7256: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7257: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7258: mp[cp]->product->api_user = product->api_user;
7259: PetscCall(MatProductSetFromOptions(mp[cp]));
7260: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7261: PetscCall(ISGetIndices(glob, &globidx));
7262: rmapt[cp] = 1;
7263: cmapt[cp] = 2;
7264: cmapa[cp] = globidx;
7265: mptmp[cp] = PETSC_FALSE;
7266: cp++;
7267: } else { /* A_diag * P_diag and A_diag * P_off */
7268: PetscCall(MatProductCreate(a->A, p->A, NULL, &mp[cp]));
7269: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7270: PetscCall(MatProductSetFill(mp[cp], product->fill));
7271: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7272: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7273: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7274: mp[cp]->product->api_user = product->api_user;
7275: PetscCall(MatProductSetFromOptions(mp[cp]));
7276: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7277: rmapt[cp] = 1;
7278: cmapt[cp] = 1;
7279: mptmp[cp] = PETSC_FALSE;
7280: cp++;
7281: PetscCall(MatProductCreate(a->A, p->B, NULL, &mp[cp]));
7282: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7283: PetscCall(MatProductSetFill(mp[cp], product->fill));
7284: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7285: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7286: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7287: mp[cp]->product->api_user = product->api_user;
7288: PetscCall(MatProductSetFromOptions(mp[cp]));
7289: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7290: rmapt[cp] = 1;
7291: cmapt[cp] = 2;
7292: cmapa[cp] = p->garray;
7293: mptmp[cp] = PETSC_FALSE;
7294: cp++;
7295: }
7297: /* A_off * P_other */
7298: if (mmdata->P_oth) {
7299: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7300: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7301: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7302: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7303: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7304: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7305: PetscCall(MatProductSetFill(mp[cp], product->fill));
7306: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7307: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7308: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7309: mp[cp]->product->api_user = product->api_user;
7310: PetscCall(MatProductSetFromOptions(mp[cp]));
7311: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7312: rmapt[cp] = 1;
7313: cmapt[cp] = 2;
7314: cmapa[cp] = P_oth_idx;
7315: mptmp[cp] = PETSC_FALSE;
7316: cp++;
7317: }
7318: break;
7320: case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7321: /* A is product->B */
7322: PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7323: if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7324: PetscCall(MatProductCreate(mmdata->Bloc, mmdata->Bloc, NULL, &mp[cp]));
7325: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7326: PetscCall(MatProductSetFill(mp[cp], product->fill));
7327: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7328: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7329: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7330: mp[cp]->product->api_user = product->api_user;
7331: PetscCall(MatProductSetFromOptions(mp[cp]));
7332: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7333: PetscCall(ISGetIndices(glob, &globidx));
7334: rmapt[cp] = 2;
7335: rmapa[cp] = globidx;
7336: cmapt[cp] = 2;
7337: cmapa[cp] = globidx;
7338: mptmp[cp] = PETSC_FALSE;
7339: cp++;
7340: } else {
7341: PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7342: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7343: PetscCall(MatProductSetFill(mp[cp], product->fill));
7344: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7345: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7346: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7347: mp[cp]->product->api_user = product->api_user;
7348: PetscCall(MatProductSetFromOptions(mp[cp]));
7349: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7350: PetscCall(ISGetIndices(glob, &globidx));
7351: rmapt[cp] = 1;
7352: cmapt[cp] = 2;
7353: cmapa[cp] = globidx;
7354: mptmp[cp] = PETSC_FALSE;
7355: cp++;
7356: PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7357: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7358: PetscCall(MatProductSetFill(mp[cp], product->fill));
7359: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7360: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7361: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7362: mp[cp]->product->api_user = product->api_user;
7363: PetscCall(MatProductSetFromOptions(mp[cp]));
7364: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7365: rmapt[cp] = 2;
7366: rmapa[cp] = p->garray;
7367: cmapt[cp] = 2;
7368: cmapa[cp] = globidx;
7369: mptmp[cp] = PETSC_FALSE;
7370: cp++;
7371: }
7372: break;
7373: case MATPRODUCT_PtAP:
7374: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7375: /* P is product->B */
7376: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7377: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7378: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
7379: PetscCall(MatProductSetFill(mp[cp], product->fill));
7380: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7381: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7382: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7383: mp[cp]->product->api_user = product->api_user;
7384: PetscCall(MatProductSetFromOptions(mp[cp]));
7385: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7386: PetscCall(ISGetIndices(glob, &globidx));
7387: rmapt[cp] = 2;
7388: rmapa[cp] = globidx;
7389: cmapt[cp] = 2;
7390: cmapa[cp] = globidx;
7391: mptmp[cp] = PETSC_FALSE;
7392: cp++;
7393: if (mmdata->P_oth) {
7394: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7395: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7396: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7397: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7398: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7399: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7400: PetscCall(MatProductSetFill(mp[cp], product->fill));
7401: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7402: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7403: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7404: mp[cp]->product->api_user = product->api_user;
7405: PetscCall(MatProductSetFromOptions(mp[cp]));
7406: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7407: mptmp[cp] = PETSC_TRUE;
7408: cp++;
7409: PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7410: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7411: PetscCall(MatProductSetFill(mp[cp], product->fill));
7412: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7413: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7414: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7415: mp[cp]->product->api_user = product->api_user;
7416: PetscCall(MatProductSetFromOptions(mp[cp]));
7417: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7418: rmapt[cp] = 2;
7419: rmapa[cp] = globidx;
7420: cmapt[cp] = 2;
7421: cmapa[cp] = P_oth_idx;
7422: mptmp[cp] = PETSC_FALSE;
7423: cp++;
7424: }
7425: break;
7426: default:
7427: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7428: }
7429: /* sanity check */
7430: if (size > 1)
7431: 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);
7433: PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7434: for (i = 0; i < cp; i++) {
7435: mmdata->mp[i] = mp[i];
7436: mmdata->mptmp[i] = mptmp[i];
7437: }
7438: mmdata->cp = cp;
7439: C->product->data = mmdata;
7440: C->product->destroy = MatDestroy_MatMatMPIAIJBACKEND;
7441: C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;
7443: /* memory type */
7444: mmdata->mtype = PETSC_MEMTYPE_HOST;
7445: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7446: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7447: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7448: if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7449: else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7450: else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;
7452: /* prepare coo coordinates for values insertion */
7454: /* count total nonzeros of those intermediate seqaij Mats
7455: ncoo_d: # of nonzeros of matrices that do not have offproc entries
7456: ncoo_o: # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7457: ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7458: */
7459: for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7460: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7461: if (mptmp[cp]) continue;
7462: if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7463: const PetscInt *rmap = rmapa[cp];
7464: const PetscInt mr = mp[cp]->rmap->n;
7465: const PetscInt rs = C->rmap->rstart;
7466: const PetscInt re = C->rmap->rend;
7467: const PetscInt *ii = mm->i;
7468: for (i = 0; i < mr; i++) {
7469: const PetscInt gr = rmap[i];
7470: const PetscInt nz = ii[i + 1] - ii[i];
7471: if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7472: else ncoo_oown += nz; /* this row is local */
7473: }
7474: } else ncoo_d += mm->nz;
7475: }
7477: /*
7478: ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc
7480: ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.
7482: off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].
7484: off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7485: own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7486: so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.
7488: coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7489: 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.
7490: */
7491: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7492: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));
7494: /* gather (i,j) of nonzeros inserted by remote procs */
7495: if (hasoffproc) {
7496: PetscSF msf;
7497: PetscInt ncoo2, *coo_i2, *coo_j2;
7499: PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7500: PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7501: PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */
7503: for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7504: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7505: PetscInt *idxoff = mmdata->off[cp];
7506: PetscInt *idxown = mmdata->own[cp];
7507: if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7508: const PetscInt *rmap = rmapa[cp];
7509: const PetscInt *cmap = cmapa[cp];
7510: const PetscInt *ii = mm->i;
7511: PetscInt *coi = coo_i + ncoo_o;
7512: PetscInt *coj = coo_j + ncoo_o;
7513: const PetscInt mr = mp[cp]->rmap->n;
7514: const PetscInt rs = C->rmap->rstart;
7515: const PetscInt re = C->rmap->rend;
7516: const PetscInt cs = C->cmap->rstart;
7517: for (i = 0; i < mr; i++) {
7518: const PetscInt *jj = mm->j + ii[i];
7519: const PetscInt gr = rmap[i];
7520: const PetscInt nz = ii[i + 1] - ii[i];
7521: if (gr < rs || gr >= re) { /* this is an offproc row */
7522: for (j = ii[i]; j < ii[i + 1]; j++) {
7523: *coi++ = gr;
7524: *idxoff++ = j;
7525: }
7526: if (!cmapt[cp]) { /* already global */
7527: for (j = 0; j < nz; j++) *coj++ = jj[j];
7528: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7529: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7530: } else { /* offdiag */
7531: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7532: }
7533: ncoo_o += nz;
7534: } else { /* this is a local row */
7535: for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7536: }
7537: }
7538: }
7539: mmdata->off[cp + 1] = idxoff;
7540: mmdata->own[cp + 1] = idxown;
7541: }
7543: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7544: PetscInt incoo_o;
7545: PetscCall(PetscIntCast(ncoo_o, &incoo_o));
7546: PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, incoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7547: PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7548: PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7549: ncoo = ncoo_d + ncoo_oown + ncoo2;
7550: PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7551: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7552: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7553: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7554: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7555: PetscCall(PetscFree2(coo_i, coo_j));
7556: /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7557: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7558: coo_i = coo_i2;
7559: coo_j = coo_j2;
7560: } else { /* no offproc values insertion */
7561: ncoo = ncoo_d;
7562: PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));
7564: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7565: PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7566: PetscCall(PetscSFSetUp(mmdata->sf));
7567: }
7568: mmdata->hasoffproc = hasoffproc;
7570: /* gather (i,j) of nonzeros inserted locally */
7571: for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7572: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7573: PetscInt *coi = coo_i + ncoo_d;
7574: PetscInt *coj = coo_j + ncoo_d;
7575: const PetscInt *jj = mm->j;
7576: const PetscInt *ii = mm->i;
7577: const PetscInt *cmap = cmapa[cp];
7578: const PetscInt *rmap = rmapa[cp];
7579: const PetscInt mr = mp[cp]->rmap->n;
7580: const PetscInt rs = C->rmap->rstart;
7581: const PetscInt re = C->rmap->rend;
7582: const PetscInt cs = C->cmap->rstart;
7584: if (mptmp[cp]) continue;
7585: if (rmapt[cp] == 1) { /* consecutive rows */
7586: /* fill coo_i */
7587: for (i = 0; i < mr; i++) {
7588: const PetscInt gr = i + rs;
7589: for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7590: }
7591: /* fill coo_j */
7592: if (!cmapt[cp]) { /* type-0, already global */
7593: PetscCall(PetscArraycpy(coj, jj, mm->nz));
7594: } else if (cmapt[cp] == 1) { /* type-1, local to global for consecutive columns of C */
7595: for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7596: } else { /* type-2, local to global for sparse columns */
7597: for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7598: }
7599: ncoo_d += mm->nz;
7600: } else if (rmapt[cp] == 2) { /* sparse rows */
7601: for (i = 0; i < mr; i++) {
7602: const PetscInt *jj = mm->j + ii[i];
7603: const PetscInt gr = rmap[i];
7604: const PetscInt nz = ii[i + 1] - ii[i];
7605: if (gr >= rs && gr < re) { /* local rows */
7606: for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7607: if (!cmapt[cp]) { /* type-0, already global */
7608: for (j = 0; j < nz; j++) *coj++ = jj[j];
7609: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7610: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7611: } else { /* type-2, local to global for sparse columns */
7612: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7613: }
7614: ncoo_d += nz;
7615: }
7616: }
7617: }
7618: }
7619: if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7620: PetscCall(ISDestroy(&glob));
7621: if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7622: PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7623: /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7624: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));
7626: /* set block sizes */
7627: A = product->A;
7628: P = product->B;
7629: switch (ptype) {
7630: case MATPRODUCT_PtAP:
7631: PetscCall(MatSetBlockSizes(C, P->cmap->bs, P->cmap->bs));
7632: break;
7633: case MATPRODUCT_RARt:
7634: PetscCall(MatSetBlockSizes(C, P->rmap->bs, P->rmap->bs));
7635: break;
7636: case MATPRODUCT_ABC:
7637: PetscCall(MatSetBlockSizesFromMats(C, A, product->C));
7638: break;
7639: case MATPRODUCT_AB:
7640: PetscCall(MatSetBlockSizesFromMats(C, A, P));
7641: break;
7642: case MATPRODUCT_AtB:
7643: PetscCall(MatSetBlockSizes(C, A->cmap->bs, P->cmap->bs));
7644: break;
7645: case MATPRODUCT_ABt:
7646: PetscCall(MatSetBlockSizes(C, A->rmap->bs, P->rmap->bs));
7647: break;
7648: default:
7649: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for ProductType %s", MatProductTypes[ptype]);
7650: }
7652: /* preallocate with COO data */
7653: PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7654: PetscCall(PetscFree2(coo_i, coo_j));
7655: PetscFunctionReturn(PETSC_SUCCESS);
7656: }
7658: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7659: {
7660: Mat_Product *product = mat->product;
7661: #if defined(PETSC_HAVE_DEVICE)
7662: PetscBool match = PETSC_FALSE;
7663: PetscBool usecpu = PETSC_FALSE;
7664: #else
7665: PetscBool match = PETSC_TRUE;
7666: #endif
7668: PetscFunctionBegin;
7669: MatCheckProduct(mat, 1);
7670: #if defined(PETSC_HAVE_DEVICE)
7671: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7672: if (match) { /* we can always fallback to the CPU if requested */
7673: switch (product->type) {
7674: case MATPRODUCT_AB:
7675: if (product->api_user) {
7676: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7677: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7678: PetscOptionsEnd();
7679: } else {
7680: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7681: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7682: PetscOptionsEnd();
7683: }
7684: break;
7685: case MATPRODUCT_AtB:
7686: if (product->api_user) {
7687: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7688: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7689: PetscOptionsEnd();
7690: } else {
7691: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7692: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7693: PetscOptionsEnd();
7694: }
7695: break;
7696: case MATPRODUCT_PtAP:
7697: if (product->api_user) {
7698: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7699: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7700: PetscOptionsEnd();
7701: } else {
7702: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7703: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7704: PetscOptionsEnd();
7705: }
7706: break;
7707: default:
7708: break;
7709: }
7710: match = (PetscBool)!usecpu;
7711: }
7712: #endif
7713: if (match) {
7714: switch (product->type) {
7715: case MATPRODUCT_AB:
7716: case MATPRODUCT_AtB:
7717: case MATPRODUCT_PtAP:
7718: mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7719: break;
7720: default:
7721: break;
7722: }
7723: }
7724: /* fallback to MPIAIJ ops */
7725: if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7726: PetscFunctionReturn(PETSC_SUCCESS);
7727: }
7729: /*
7730: Produces a set of block column indices of the matrix row, one for each block represented in the original row
7732: n - the number of block indices in cc[]
7733: cc - the block indices (must be large enough to contain the indices)
7734: */
7735: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7736: {
7737: PetscInt cnt = -1, nidx, j;
7738: const PetscInt *idx;
7740: PetscFunctionBegin;
7741: PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7742: if (nidx) {
7743: cnt = 0;
7744: cc[cnt] = idx[0] / bs;
7745: for (j = 1; j < nidx; j++) {
7746: if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7747: }
7748: }
7749: PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7750: *n = cnt + 1;
7751: PetscFunctionReturn(PETSC_SUCCESS);
7752: }
7754: /*
7755: Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows
7757: ncollapsed - the number of block indices
7758: collapsed - the block indices (must be large enough to contain the indices)
7759: */
7760: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7761: {
7762: PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;
7764: PetscFunctionBegin;
7765: PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7766: for (i = start + 1; i < start + bs; i++) {
7767: PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7768: PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7769: cprevtmp = cprev;
7770: cprev = merged;
7771: merged = cprevtmp;
7772: }
7773: *ncollapsed = nprev;
7774: if (collapsed) *collapsed = cprev;
7775: PetscFunctionReturn(PETSC_SUCCESS);
7776: }
7778: /*
7779: MatCreateGraph_Simple_AIJ - create simple scalar matrix (graph) from potentially blocked matrix
7781: Input Parameter:
7782: . Amat - matrix
7783: - symmetrize - make the result symmetric
7784: + scale - scale with diagonal
7786: Output Parameter:
7787: . a_Gmat - output scalar graph >= 0
7789: */
7790: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, PetscInt index_size, PetscInt index[], Mat *a_Gmat)
7791: {
7792: PetscInt Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7793: MPI_Comm comm;
7794: Mat Gmat;
7795: PetscBool ismpiaij, isseqaij;
7796: Mat a, b, c;
7797: MatType jtype;
7799: PetscFunctionBegin;
7800: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7801: PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7802: PetscCall(MatGetSize(Amat, &MM, &NN));
7803: PetscCall(MatGetBlockSize(Amat, &bs));
7804: nloc = (Iend - Istart) / bs;
7806: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7807: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7808: PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");
7810: /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7811: /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7812: implementation */
7813: if (bs > 1) {
7814: PetscCall(MatGetType(Amat, &jtype));
7815: PetscCall(MatCreate(comm, &Gmat));
7816: PetscCall(MatSetType(Gmat, jtype));
7817: PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7818: PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7819: if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7820: PetscInt *d_nnz, *o_nnz;
7821: MatScalar *aa, val, *AA;
7822: PetscInt *aj, *ai, *AJ, nc, nmax = 0;
7824: if (isseqaij) {
7825: a = Amat;
7826: b = NULL;
7827: } else {
7828: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7829: a = d->A;
7830: b = d->B;
7831: }
7832: PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7833: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7834: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7835: PetscInt *nnz = (c == a) ? d_nnz : o_nnz;
7836: const PetscInt *cols1, *cols2;
7838: for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7839: PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7840: nnz[brow / bs] = nc2 / bs;
7841: if (nc2 % bs) ok = 0;
7842: if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7843: for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7844: PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7845: if (nc1 != nc2) ok = 0;
7846: else {
7847: for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7848: if (cols1[jj] != cols2[jj]) ok = 0;
7849: if (cols1[jj] % bs != jj % bs) ok = 0;
7850: }
7851: }
7852: PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7853: }
7854: PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7855: if (!ok) {
7856: PetscCall(PetscFree2(d_nnz, o_nnz));
7857: PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7858: goto old_bs;
7859: }
7860: }
7861: }
7862: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7863: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7864: PetscCall(PetscFree2(d_nnz, o_nnz));
7865: PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7866: // diag
7867: for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7868: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;
7870: ai = aseq->i;
7871: n = ai[brow + 1] - ai[brow];
7872: aj = aseq->j + ai[brow];
7873: for (PetscInt k = 0; k < n; k += bs) { // block columns
7874: AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7875: val = 0;
7876: if (index_size == 0) {
7877: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7878: aa = aseq->a + ai[brow + ii] + k;
7879: for (PetscInt jj = 0; jj < bs; jj++) { // columns in block
7880: val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7881: }
7882: }
7883: } else { // use (index,index) value if provided
7884: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7885: PetscInt ii = index[iii];
7886: aa = aseq->a + ai[brow + ii] + k;
7887: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // columns in block
7888: PetscInt jj = index[jjj];
7889: val += PetscAbs(PetscRealPart(aa[jj]));
7890: }
7891: }
7892: }
7893: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%" PetscInt_FMT ") >= nmax (%" PetscInt_FMT ")", k / bs, nmax);
7894: AA[k / bs] = val;
7895: }
7896: grow = Istart / bs + brow / bs;
7897: PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, ADD_VALUES));
7898: }
7899: // off-diag
7900: if (ismpiaij) {
7901: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Amat->data;
7902: const PetscScalar *vals;
7903: const PetscInt *cols, *garray = aij->garray;
7905: PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7906: for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7907: PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7908: for (PetscInt k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7909: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7910: AA[k / bs] = 0;
7911: AJ[cidx] = garray[cols[k]] / bs;
7912: }
7913: nc = ncols / bs;
7914: PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7915: if (index_size == 0) {
7916: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7917: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7918: for (PetscInt k = 0; k < ncols; k += bs) {
7919: for (PetscInt jj = 0; jj < bs; jj++) { // cols in block
7920: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%" PetscInt_FMT ") >= nmax (%" PetscInt_FMT ")", k / bs, nmax);
7921: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7922: }
7923: }
7924: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7925: }
7926: } else { // use (index,index) value if provided
7927: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7928: PetscInt ii = index[iii];
7929: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7930: for (PetscInt k = 0; k < ncols; k += bs) {
7931: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // cols in block
7932: PetscInt jj = index[jjj];
7933: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7934: }
7935: }
7936: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7937: }
7938: }
7939: grow = Istart / bs + brow / bs;
7940: PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, ADD_VALUES));
7941: }
7942: }
7943: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7944: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7945: PetscCall(PetscFree2(AA, AJ));
7946: } else {
7947: const PetscScalar *vals;
7948: const PetscInt *idx;
7949: PetscInt *d_nnz, *o_nnz, *w0, *w1, *w2;
7950: old_bs:
7951: /*
7952: Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7953: */
7954: PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7955: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7956: if (isseqaij) {
7957: PetscInt max_d_nnz;
7959: /*
7960: Determine exact preallocation count for (sequential) scalar matrix
7961: */
7962: PetscCall(MatSeqAIJGetMaxRowNonzeros(Amat, &max_d_nnz));
7963: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7964: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7965: for (Ii = 0, jj = 0; Ii < Iend; Ii += bs, jj++) PetscCall(MatCollapseRows(Amat, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7966: PetscCall(PetscFree3(w0, w1, w2));
7967: } else if (ismpiaij) {
7968: Mat Daij, Oaij;
7969: const PetscInt *garray;
7970: PetscInt max_d_nnz;
7972: PetscCall(MatMPIAIJGetSeqAIJ(Amat, &Daij, &Oaij, &garray));
7973: /*
7974: Determine exact preallocation count for diagonal block portion of scalar matrix
7975: */
7976: PetscCall(MatSeqAIJGetMaxRowNonzeros(Daij, &max_d_nnz));
7977: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7978: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7979: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) PetscCall(MatCollapseRows(Daij, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7980: PetscCall(PetscFree3(w0, w1, w2));
7981: /*
7982: Over estimate (usually grossly over), preallocation count for off-diagonal portion of scalar matrix
7983: */
7984: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) {
7985: o_nnz[jj] = 0;
7986: for (kk = 0; kk < bs; kk++) { /* rows that get collapsed to a single row */
7987: PetscCall(MatGetRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7988: o_nnz[jj] += ncols;
7989: PetscCall(MatRestoreRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7990: }
7991: if (o_nnz[jj] > (NN / bs - nloc)) o_nnz[jj] = NN / bs - nloc;
7992: }
7993: } else SETERRQ(comm, PETSC_ERR_USER, "Require AIJ matrix type");
7994: /* get scalar copy (norms) of matrix */
7995: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7996: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7997: PetscCall(PetscFree2(d_nnz, o_nnz));
7998: for (Ii = Istart; Ii < Iend; Ii++) {
7999: PetscInt dest_row = Ii / bs;
8001: PetscCall(MatGetRow(Amat, Ii, &ncols, &idx, &vals));
8002: for (jj = 0; jj < ncols; jj++) {
8003: PetscInt dest_col = idx[jj] / bs;
8004: PetscScalar sv = PetscAbs(PetscRealPart(vals[jj]));
8006: PetscCall(MatSetValues(Gmat, 1, &dest_row, 1, &dest_col, &sv, ADD_VALUES));
8007: }
8008: PetscCall(MatRestoreRow(Amat, Ii, &ncols, &idx, &vals));
8009: }
8010: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
8011: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
8012: }
8013: } else {
8014: if (symmetrize || filter >= 0 || scale) PetscCall(MatDuplicate(Amat, MAT_COPY_VALUES, &Gmat));
8015: else {
8016: Gmat = Amat;
8017: PetscCall(PetscObjectReference((PetscObject)Gmat));
8018: }
8019: if (isseqaij) {
8020: a = Gmat;
8021: b = NULL;
8022: } else {
8023: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
8024: a = d->A;
8025: b = d->B;
8026: }
8027: if (filter >= 0 || scale) {
8028: /* take absolute value of each entry */
8029: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
8030: MatInfo info;
8031: PetscScalar *avals;
8033: PetscCall(MatGetInfo(c, MAT_LOCAL, &info));
8034: PetscCall(MatSeqAIJGetArray(c, &avals));
8035: for (int jj = 0; jj < info.nz_used; jj++) avals[jj] = PetscAbsScalar(avals[jj]);
8036: PetscCall(MatSeqAIJRestoreArray(c, &avals));
8037: }
8038: }
8039: }
8040: if (symmetrize) {
8041: PetscBool isset, issym;
8043: PetscCall(MatIsSymmetricKnown(Amat, &isset, &issym));
8044: if (!isset || !issym) {
8045: Mat matTrans;
8047: PetscCall(MatTranspose(Gmat, MAT_INITIAL_MATRIX, &matTrans));
8048: PetscCall(MatAXPY(Gmat, 1.0, matTrans, Gmat->structurally_symmetric == PETSC_BOOL3_TRUE ? SAME_NONZERO_PATTERN : DIFFERENT_NONZERO_PATTERN));
8049: PetscCall(MatDestroy(&matTrans));
8050: }
8051: PetscCall(MatSetOption(Gmat, MAT_SYMMETRIC, PETSC_TRUE));
8052: } else if (Amat != Gmat) PetscCall(MatPropagateSymmetryOptions(Amat, Gmat));
8053: if (scale) {
8054: /* scale c for all diagonal values = 1 or -1 */
8055: Vec diag;
8057: PetscCall(MatCreateVecs(Gmat, &diag, NULL));
8058: PetscCall(MatGetDiagonal(Gmat, diag));
8059: PetscCall(VecReciprocal(diag));
8060: PetscCall(VecSqrtAbs(diag));
8061: PetscCall(MatDiagonalScale(Gmat, diag, diag));
8062: PetscCall(VecDestroy(&diag));
8063: }
8064: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_graph_view"));
8065: if (filter >= 0) {
8066: PetscCall(MatFilter(Gmat, filter, PETSC_TRUE, PETSC_TRUE));
8067: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_filter_graph_view"));
8068: }
8069: *a_Gmat = Gmat;
8070: PetscFunctionReturn(PETSC_SUCCESS);
8071: }
8073: /*
8074: Special version for direct calls from Fortran
8075: */
8077: /* Change these macros so can be used in void function */
8078: /* Identical to PetscCallVoid, except it assigns to *_ierr */
8079: #undef PetscCall
8080: #define PetscCall(...) \
8081: do { \
8082: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
8083: if (PetscUnlikely(ierr_msv_mpiaij)) { \
8084: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
8085: return; \
8086: } \
8087: } while (0)
8089: #undef SETERRQ
8090: #define SETERRQ(comm, ierr, ...) \
8091: do { \
8092: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
8093: return; \
8094: } while (0)
8096: #if defined(PETSC_HAVE_FORTRAN_CAPS)
8097: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
8098: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
8099: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
8100: #else
8101: #endif
8102: PETSC_EXTERN void matsetvaluesmpiaij_(Mat *mmat, PetscInt *mm, const PetscInt im[], PetscInt *mn, const PetscInt in[], const PetscScalar v[], InsertMode *maddv, PetscErrorCode *_ierr)
8103: {
8104: Mat mat = *mmat;
8105: PetscInt m = *mm, n = *mn;
8106: InsertMode addv = *maddv;
8107: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
8108: PetscScalar value;
8110: MatCheckPreallocated(mat, 1);
8111: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
8112: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
8113: {
8114: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
8115: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
8116: PetscBool roworiented = aij->roworiented;
8118: /* Some Variables required in the macro */
8119: Mat A = aij->A;
8120: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
8121: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
8122: MatScalar *aa;
8123: PetscBool ignorezeroentries = ((a->ignorezeroentries && (addv == ADD_VALUES)) ? PETSC_TRUE : PETSC_FALSE);
8124: Mat B = aij->B;
8125: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
8126: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
8127: MatScalar *ba;
8128: /* This variable below is only for the PETSC_HAVE_VIENNACL or PETSC_HAVE_CUDA cases, but we define it in all cases because we
8129: * cannot use "#if defined" inside a macro. */
8130: PETSC_UNUSED PetscBool inserted = PETSC_FALSE;
8132: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
8133: PetscInt nonew = a->nonew;
8134: MatScalar *ap1, *ap2;
8136: PetscFunctionBegin;
8137: PetscCall(MatSeqAIJGetArray(A, &aa));
8138: PetscCall(MatSeqAIJGetArray(B, &ba));
8139: for (i = 0; i < m; i++) {
8140: if (im[i] < 0) continue;
8141: 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);
8142: if (im[i] >= rstart && im[i] < rend) {
8143: row = im[i] - rstart;
8144: lastcol1 = -1;
8145: rp1 = aj + ai[row];
8146: ap1 = aa + ai[row];
8147: rmax1 = aimax[row];
8148: nrow1 = ailen[row];
8149: low1 = 0;
8150: high1 = nrow1;
8151: lastcol2 = -1;
8152: rp2 = bj + bi[row];
8153: ap2 = ba + bi[row];
8154: rmax2 = bimax[row];
8155: nrow2 = bilen[row];
8156: low2 = 0;
8157: high2 = nrow2;
8159: for (j = 0; j < n; j++) {
8160: if (roworiented) value = v[i * n + j];
8161: else value = v[i + j * m];
8162: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
8163: if (in[j] >= cstart && in[j] < cend) {
8164: col = in[j] - cstart;
8165: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
8166: } else if (in[j] < 0) continue;
8167: else if (PetscUnlikelyDebug(in[j] >= mat->cmap->N)) {
8168: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
8169: } else {
8170: if (mat->was_assembled) {
8171: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
8172: #if defined(PETSC_USE_CTABLE)
8173: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col));
8174: col--;
8175: #else
8176: col = aij->colmap[in[j]] - 1;
8177: #endif
8178: if (col < 0 && !((Mat_SeqAIJ *)aij->A->data)->nonew) {
8179: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));
8180: col = in[j];
8181: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
8182: B = aij->B;
8183: b = (Mat_SeqAIJ *)B->data;
8184: bimax = b->imax;
8185: bi = b->i;
8186: bilen = b->ilen;
8187: bj = b->j;
8188: rp2 = bj + bi[row];
8189: ap2 = ba + bi[row];
8190: rmax2 = bimax[row];
8191: nrow2 = bilen[row];
8192: low2 = 0;
8193: high2 = nrow2;
8194: bm = aij->B->rmap->n;
8195: ba = b->a;
8196: inserted = PETSC_FALSE;
8197: }
8198: } else col = in[j];
8199: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
8200: }
8201: }
8202: } else if (!aij->donotstash) {
8203: if (roworiented) {
8204: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8205: } else {
8206: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8207: }
8208: }
8209: }
8210: PetscCall(MatSeqAIJRestoreArray(A, &aa));
8211: PetscCall(MatSeqAIJRestoreArray(B, &ba));
8212: }
8213: PetscFunctionReturnVoid();
8214: }
8216: /* Undefining these here since they were redefined from their original definition above! No
8217: * other PETSc functions should be defined past this point, as it is impossible to recover the
8218: * original definitions */
8219: #undef PetscCall
8220: #undef SETERRQ