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;
309: PetscMPIInt in;
311: PetscFunctionBegin;
312: PetscCall(MatGetSize(A, &m, &n));
313: PetscCall(PetscCalloc1(n, &work));
314: PetscCall(MatSeqAIJGetArrayRead(aij->A, &dummy));
315: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &dummy));
316: PetscCall(MatSeqAIJGetArrayRead(aij->B, &dummy));
317: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &dummy));
318: if (type == NORM_2) {
319: 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]);
320: 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]);
321: } else if (type == NORM_1) {
322: 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]);
323: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]);
324: } else if (type == NORM_INFINITY) {
325: 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]]);
326: 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]]]);
327: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
328: 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]);
329: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscRealPart(b_aij->a[i]);
330: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
331: 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]);
332: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscImaginaryPart(b_aij->a[i]);
333: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
334: PetscCall(PetscMPIIntCast(n, &in));
335: if (type == NORM_INFINITY) {
336: PetscCallMPI(MPIU_Allreduce(work, reductions, in, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
337: } else {
338: PetscCallMPI(MPIU_Allreduce(work, reductions, in, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
339: }
340: PetscCall(PetscFree(work));
341: if (type == NORM_2) {
342: for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
343: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
344: for (i = 0; i < n; i++) reductions[i] /= m;
345: }
346: PetscFunctionReturn(PETSC_SUCCESS);
347: }
349: static PetscErrorCode MatFindOffBlockDiagonalEntries_MPIAIJ(Mat A, IS *is)
350: {
351: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
352: IS sis, gis;
353: const PetscInt *isis, *igis;
354: PetscInt n, *iis, nsis, ngis, rstart, i;
356: PetscFunctionBegin;
357: PetscCall(MatFindOffBlockDiagonalEntries(a->A, &sis));
358: PetscCall(MatFindNonzeroRows(a->B, &gis));
359: PetscCall(ISGetSize(gis, &ngis));
360: PetscCall(ISGetSize(sis, &nsis));
361: PetscCall(ISGetIndices(sis, &isis));
362: PetscCall(ISGetIndices(gis, &igis));
364: PetscCall(PetscMalloc1(ngis + nsis, &iis));
365: PetscCall(PetscArraycpy(iis, igis, ngis));
366: PetscCall(PetscArraycpy(iis + ngis, isis, nsis));
367: n = ngis + nsis;
368: PetscCall(PetscSortRemoveDupsInt(&n, iis));
369: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
370: for (i = 0; i < n; i++) iis[i] += rstart;
371: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), n, iis, PETSC_OWN_POINTER, is));
373: PetscCall(ISRestoreIndices(sis, &isis));
374: PetscCall(ISRestoreIndices(gis, &igis));
375: PetscCall(ISDestroy(&sis));
376: PetscCall(ISDestroy(&gis));
377: PetscFunctionReturn(PETSC_SUCCESS);
378: }
380: /*
381: Local utility routine that creates a mapping from the global column
382: number to the local number in the off-diagonal part of the local
383: storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at
384: a slightly higher hash table cost; without it it is not scalable (each processor
385: has an order N integer array but is fast to access.
386: */
387: PetscErrorCode MatCreateColmap_MPIAIJ_Private(Mat mat)
388: {
389: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
390: PetscInt n = aij->B->cmap->n, i;
392: PetscFunctionBegin;
393: PetscCheck(!n || aij->garray, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MPIAIJ Matrix was assembled but is missing garray");
394: #if defined(PETSC_USE_CTABLE)
395: PetscCall(PetscHMapICreateWithSize(n, &aij->colmap));
396: for (i = 0; i < n; i++) PetscCall(PetscHMapISet(aij->colmap, aij->garray[i] + 1, i + 1));
397: #else
398: PetscCall(PetscCalloc1(mat->cmap->N + 1, &aij->colmap));
399: for (i = 0; i < n; i++) aij->colmap[aij->garray[i]] = i + 1;
400: #endif
401: PetscFunctionReturn(PETSC_SUCCESS);
402: }
404: #define MatSetValues_SeqAIJ_A_Private(row, col, value, addv, orow, ocol) \
405: do { \
406: if (col <= lastcol1) low1 = 0; \
407: else high1 = nrow1; \
408: lastcol1 = col; \
409: while (high1 - low1 > 5) { \
410: t = (low1 + high1) / 2; \
411: if (rp1[t] > col) high1 = t; \
412: else low1 = t; \
413: } \
414: for (_i = low1; _i < high1; _i++) { \
415: if (rp1[_i] > col) break; \
416: if (rp1[_i] == col) { \
417: if (addv == ADD_VALUES) { \
418: ap1[_i] += value; \
419: /* Not sure LogFlops will slow dow the code or not */ \
420: (void)PetscLogFlops(1.0); \
421: } else ap1[_i] = value; \
422: goto a_noinsert; \
423: } \
424: } \
425: if (value == 0.0 && ignorezeroentries && row != col) { \
426: low1 = 0; \
427: high1 = nrow1; \
428: goto a_noinsert; \
429: } \
430: if (nonew == 1) { \
431: low1 = 0; \
432: high1 = nrow1; \
433: goto a_noinsert; \
434: } \
435: 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); \
436: MatSeqXAIJReallocateAIJ(A, am, 1, nrow1, row, col, rmax1, aa, ai, aj, rp1, ap1, aimax, nonew, MatScalar); \
437: N = nrow1++ - 1; \
438: a->nz++; \
439: high1++; \
440: /* shift up all the later entries in this row */ \
441: PetscCall(PetscArraymove(rp1 + _i + 1, rp1 + _i, N - _i + 1)); \
442: PetscCall(PetscArraymove(ap1 + _i + 1, ap1 + _i, N - _i + 1)); \
443: rp1[_i] = col; \
444: ap1[_i] = value; \
445: a_noinsert:; \
446: ailen[row] = nrow1; \
447: } while (0)
449: #define MatSetValues_SeqAIJ_B_Private(row, col, value, addv, orow, ocol) \
450: do { \
451: if (col <= lastcol2) low2 = 0; \
452: else high2 = nrow2; \
453: lastcol2 = col; \
454: while (high2 - low2 > 5) { \
455: t = (low2 + high2) / 2; \
456: if (rp2[t] > col) high2 = t; \
457: else low2 = t; \
458: } \
459: for (_i = low2; _i < high2; _i++) { \
460: if (rp2[_i] > col) break; \
461: if (rp2[_i] == col) { \
462: if (addv == ADD_VALUES) { \
463: ap2[_i] += value; \
464: (void)PetscLogFlops(1.0); \
465: } else ap2[_i] = value; \
466: goto b_noinsert; \
467: } \
468: } \
469: if (value == 0.0 && ignorezeroentries) { \
470: low2 = 0; \
471: high2 = nrow2; \
472: goto b_noinsert; \
473: } \
474: if (nonew == 1) { \
475: low2 = 0; \
476: high2 = nrow2; \
477: goto b_noinsert; \
478: } \
479: 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); \
480: MatSeqXAIJReallocateAIJ(B, bm, 1, nrow2, row, col, rmax2, ba, bi, bj, rp2, ap2, bimax, nonew, MatScalar); \
481: N = nrow2++ - 1; \
482: b->nz++; \
483: high2++; \
484: /* shift up all the later entries in this row */ \
485: PetscCall(PetscArraymove(rp2 + _i + 1, rp2 + _i, N - _i + 1)); \
486: PetscCall(PetscArraymove(ap2 + _i + 1, ap2 + _i, N - _i + 1)); \
487: rp2[_i] = col; \
488: ap2[_i] = value; \
489: b_noinsert:; \
490: bilen[row] = nrow2; \
491: } while (0)
493: static PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A, PetscInt row, const PetscScalar v[])
494: {
495: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
496: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data, *b = (Mat_SeqAIJ *)mat->B->data;
497: PetscInt l, *garray = mat->garray, diag;
498: PetscScalar *aa, *ba;
500: PetscFunctionBegin;
501: /* code only works for square matrices A */
503: /* find size of row to the left of the diagonal part */
504: PetscCall(MatGetOwnershipRange(A, &diag, NULL));
505: row = row - diag;
506: for (l = 0; l < b->i[row + 1] - b->i[row]; l++) {
507: if (garray[b->j[b->i[row] + l]] > diag) break;
508: }
509: if (l) {
510: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
511: PetscCall(PetscArraycpy(ba + b->i[row], v, l));
512: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
513: }
515: /* diagonal part */
516: if (a->i[row + 1] - a->i[row]) {
517: PetscCall(MatSeqAIJGetArray(mat->A, &aa));
518: PetscCall(PetscArraycpy(aa + a->i[row], v + l, a->i[row + 1] - a->i[row]));
519: PetscCall(MatSeqAIJRestoreArray(mat->A, &aa));
520: }
522: /* right of diagonal part */
523: if (b->i[row + 1] - b->i[row] - l) {
524: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
525: PetscCall(PetscArraycpy(ba + b->i[row] + l, v + l + a->i[row + 1] - a->i[row], b->i[row + 1] - b->i[row] - l));
526: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
527: }
528: PetscFunctionReturn(PETSC_SUCCESS);
529: }
531: PetscErrorCode MatSetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
532: {
533: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
534: PetscScalar value = 0.0;
535: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
536: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
537: PetscBool roworiented = aij->roworiented;
539: /* Some Variables required in the macro */
540: Mat A = aij->A;
541: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
542: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
543: PetscBool ignorezeroentries = a->ignorezeroentries;
544: Mat B = aij->B;
545: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
546: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
547: MatScalar *aa, *ba;
548: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
549: PetscInt nonew;
550: MatScalar *ap1, *ap2;
552: PetscFunctionBegin;
553: PetscCall(MatSeqAIJGetArray(A, &aa));
554: PetscCall(MatSeqAIJGetArray(B, &ba));
555: for (i = 0; i < m; i++) {
556: if (im[i] < 0) continue;
557: 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);
558: if (im[i] >= rstart && im[i] < rend) {
559: row = im[i] - rstart;
560: lastcol1 = -1;
561: rp1 = PetscSafePointerPlusOffset(aj, ai[row]);
562: ap1 = PetscSafePointerPlusOffset(aa, ai[row]);
563: rmax1 = aimax[row];
564: nrow1 = ailen[row];
565: low1 = 0;
566: high1 = nrow1;
567: lastcol2 = -1;
568: rp2 = PetscSafePointerPlusOffset(bj, bi[row]);
569: ap2 = PetscSafePointerPlusOffset(ba, bi[row]);
570: rmax2 = bimax[row];
571: nrow2 = bilen[row];
572: low2 = 0;
573: high2 = nrow2;
575: for (j = 0; j < n; j++) {
576: if (v) value = roworiented ? v[i * n + j] : v[i + j * m];
577: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
578: if (in[j] >= cstart && in[j] < cend) {
579: col = in[j] - cstart;
580: nonew = a->nonew;
581: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
582: } else if (in[j] < 0) {
583: continue;
584: } else {
585: 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);
586: if (mat->was_assembled) {
587: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
588: #if defined(PETSC_USE_CTABLE)
589: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col)); /* map global col ids to local ones */
590: col--;
591: #else
592: col = aij->colmap[in[j]] - 1;
593: #endif
594: if (col < 0 && !((Mat_SeqAIJ *)aij->B->data)->nonew) { /* col < 0 means in[j] is a new col for B */
595: PetscCall(MatDisAssemble_MPIAIJ(mat)); /* Change aij->B from reduced/local format to expanded/global format */
596: col = in[j];
597: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
598: B = aij->B;
599: b = (Mat_SeqAIJ *)B->data;
600: bimax = b->imax;
601: bi = b->i;
602: bilen = b->ilen;
603: bj = b->j;
604: ba = b->a;
605: rp2 = PetscSafePointerPlusOffset(bj, bi[row]);
606: ap2 = PetscSafePointerPlusOffset(ba, bi[row]);
607: rmax2 = bimax[row];
608: nrow2 = bilen[row];
609: low2 = 0;
610: high2 = nrow2;
611: bm = aij->B->rmap->n;
612: ba = b->a;
613: } else if (col < 0 && !(ignorezeroentries && value == 0.0)) {
614: if (1 == ((Mat_SeqAIJ *)aij->B->data)->nonew) {
615: 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]));
616: } 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]);
617: }
618: } else col = in[j];
619: nonew = b->nonew;
620: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
621: }
622: }
623: } else {
624: 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]);
625: if (!aij->donotstash) {
626: mat->assembled = PETSC_FALSE;
627: if (roworiented) {
628: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i * n), (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
629: } else {
630: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i), m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
631: }
632: }
633: }
634: }
635: PetscCall(MatSeqAIJRestoreArray(A, &aa)); /* aa, bb might have been free'd due to reallocation above. But we don't access them here */
636: PetscCall(MatSeqAIJRestoreArray(B, &ba));
637: PetscFunctionReturn(PETSC_SUCCESS);
638: }
640: /*
641: This function sets the j and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
642: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
643: No off-processor parts off the matrix are allowed here and mat->was_assembled has to be PETSC_FALSE.
644: */
645: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[])
646: {
647: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
648: Mat A = aij->A; /* diagonal part of the matrix */
649: Mat B = aij->B; /* off-diagonal part of the matrix */
650: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
651: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
652: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, col;
653: PetscInt *ailen = a->ilen, *aj = a->j;
654: PetscInt *bilen = b->ilen, *bj = b->j;
655: PetscInt am = aij->A->rmap->n, j;
656: PetscInt diag_so_far = 0, dnz;
657: PetscInt offd_so_far = 0, onz;
659: PetscFunctionBegin;
660: /* Iterate over all rows of the matrix */
661: for (j = 0; j < am; j++) {
662: dnz = onz = 0;
663: /* Iterate over all non-zero columns of the current row */
664: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
665: /* If column is in the diagonal */
666: if (mat_j[col] >= cstart && mat_j[col] < cend) {
667: aj[diag_so_far++] = mat_j[col] - cstart;
668: dnz++;
669: } else { /* off-diagonal entries */
670: bj[offd_so_far++] = mat_j[col];
671: onz++;
672: }
673: }
674: ailen[j] = dnz;
675: bilen[j] = onz;
676: }
677: PetscFunctionReturn(PETSC_SUCCESS);
678: }
680: /*
681: This function sets the local j, a and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
682: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
683: No off-processor parts off the matrix are allowed here, they are set at a later point by MatSetValues_MPIAIJ.
684: Also, mat->was_assembled has to be false, otherwise the statement aj[rowstart_diag+dnz_row] = mat_j[col] - cstart;
685: would not be true and the more complex MatSetValues_MPIAIJ has to be used.
686: */
687: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[], const PetscScalar mat_a[])
688: {
689: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
690: Mat A = aij->A; /* diagonal part of the matrix */
691: Mat B = aij->B; /* off-diagonal part of the matrix */
692: Mat_SeqAIJ *aijd = (Mat_SeqAIJ *)aij->A->data, *aijo = (Mat_SeqAIJ *)aij->B->data;
693: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
694: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
695: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend;
696: PetscInt *ailen = a->ilen, *aj = a->j;
697: PetscInt *bilen = b->ilen, *bj = b->j;
698: PetscInt am = aij->A->rmap->n, j;
699: 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. */
700: PetscInt col, dnz_row, onz_row, rowstart_diag, rowstart_offd;
701: PetscScalar *aa = a->a, *ba = b->a;
703: PetscFunctionBegin;
704: /* Iterate over all rows of the matrix */
705: for (j = 0; j < am; j++) {
706: dnz_row = onz_row = 0;
707: rowstart_offd = full_offd_i[j];
708: rowstart_diag = full_diag_i[j];
709: /* Iterate over all non-zero columns of the current row */
710: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
711: /* If column is in the diagonal */
712: if (mat_j[col] >= cstart && mat_j[col] < cend) {
713: aj[rowstart_diag + dnz_row] = mat_j[col] - cstart;
714: aa[rowstart_diag + dnz_row] = mat_a[col];
715: dnz_row++;
716: } else { /* off-diagonal entries */
717: bj[rowstart_offd + onz_row] = mat_j[col];
718: ba[rowstart_offd + onz_row] = mat_a[col];
719: onz_row++;
720: }
721: }
722: ailen[j] = dnz_row;
723: bilen[j] = onz_row;
724: }
725: PetscFunctionReturn(PETSC_SUCCESS);
726: }
728: static PetscErrorCode MatGetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
729: {
730: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
731: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
732: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
734: PetscFunctionBegin;
735: for (i = 0; i < m; i++) {
736: if (idxm[i] < 0) continue; /* negative row */
737: 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);
738: 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);
739: row = idxm[i] - rstart;
740: for (j = 0; j < n; j++) {
741: if (idxn[j] < 0) continue; /* negative column */
742: 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);
743: if (idxn[j] >= cstart && idxn[j] < cend) {
744: col = idxn[j] - cstart;
745: PetscCall(MatGetValues(aij->A, 1, &row, 1, &col, v + i * n + j));
746: } else {
747: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
748: #if defined(PETSC_USE_CTABLE)
749: PetscCall(PetscHMapIGetWithDefault(aij->colmap, idxn[j] + 1, 0, &col));
750: col--;
751: #else
752: col = aij->colmap[idxn[j]] - 1;
753: #endif
754: if ((col < 0) || (aij->garray[col] != idxn[j])) *(v + i * n + j) = 0.0;
755: else PetscCall(MatGetValues(aij->B, 1, &row, 1, &col, v + i * n + j));
756: }
757: }
758: }
759: PetscFunctionReturn(PETSC_SUCCESS);
760: }
762: static PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat, MatAssemblyType mode)
763: {
764: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
765: PetscInt nstash, reallocs;
767: PetscFunctionBegin;
768: if (aij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
770: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
771: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
772: PetscCall(PetscInfo(aij->A, "Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
773: PetscFunctionReturn(PETSC_SUCCESS);
774: }
776: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat, MatAssemblyType mode)
777: {
778: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
779: PetscMPIInt n;
780: PetscInt i, j, rstart, ncols, flg;
781: PetscInt *row, *col;
782: PetscBool other_disassembled;
783: PetscScalar *val;
785: /* do not use 'b = (Mat_SeqAIJ*)aij->B->data' as B can be reset in disassembly */
787: PetscFunctionBegin;
788: if (!aij->donotstash && !mat->nooffprocentries) {
789: while (1) {
790: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
791: if (!flg) break;
793: for (i = 0; i < n;) {
794: /* Now identify the consecutive vals belonging to the same row */
795: for (j = i, rstart = row[j]; j < n; j++) {
796: if (row[j] != rstart) break;
797: }
798: if (j < n) ncols = j - i;
799: else ncols = n - i;
800: /* Now assemble all these values with a single function call */
801: PetscCall(MatSetValues_MPIAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
802: i = j;
803: }
804: }
805: PetscCall(MatStashScatterEnd_Private(&mat->stash));
806: }
807: #if defined(PETSC_HAVE_DEVICE)
808: if (mat->offloadmask == PETSC_OFFLOAD_CPU) aij->A->offloadmask = PETSC_OFFLOAD_CPU;
809: /* We call MatBindToCPU() on aij->A and aij->B here, because if MatBindToCPU_MPIAIJ() is called before assembly, it cannot bind these. */
810: if (mat->boundtocpu) {
811: PetscCall(MatBindToCPU(aij->A, PETSC_TRUE));
812: PetscCall(MatBindToCPU(aij->B, PETSC_TRUE));
813: }
814: #endif
815: PetscCall(MatAssemblyBegin(aij->A, mode));
816: PetscCall(MatAssemblyEnd(aij->A, mode));
818: /* determine if any processor has disassembled, if so we must
819: also disassemble ourself, in order that we may reassemble. */
820: /*
821: if nonzero structure of submatrix B cannot change then we know that
822: no processor disassembled thus we can skip this stuff
823: */
824: if (!((Mat_SeqAIJ *)aij->B->data)->nonew) {
825: PetscCallMPI(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
826: if (mat->was_assembled && !other_disassembled) { /* mat on this rank has reduced off-diag B with local col ids, but globally it does not */
827: PetscCall(MatDisAssemble_MPIAIJ(mat));
828: }
829: }
830: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIAIJ(mat));
831: PetscCall(MatSetOption(aij->B, MAT_USE_INODES, PETSC_FALSE));
832: #if defined(PETSC_HAVE_DEVICE)
833: if (mat->offloadmask == PETSC_OFFLOAD_CPU && aij->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) aij->B->offloadmask = PETSC_OFFLOAD_CPU;
834: #endif
835: PetscCall(MatAssemblyBegin(aij->B, mode));
836: PetscCall(MatAssemblyEnd(aij->B, mode));
838: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
840: aij->rowvalues = NULL;
842: PetscCall(VecDestroy(&aij->diag));
844: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
845: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqAIJ *)aij->A->data)->nonew) {
846: PetscObjectState state = aij->A->nonzerostate + aij->B->nonzerostate;
847: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
848: }
849: #if defined(PETSC_HAVE_DEVICE)
850: mat->offloadmask = PETSC_OFFLOAD_BOTH;
851: #endif
852: PetscFunctionReturn(PETSC_SUCCESS);
853: }
855: static PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
856: {
857: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
859: PetscFunctionBegin;
860: PetscCall(MatZeroEntries(l->A));
861: PetscCall(MatZeroEntries(l->B));
862: PetscFunctionReturn(PETSC_SUCCESS);
863: }
865: static PetscErrorCode MatZeroRows_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
866: {
867: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
868: PetscInt *lrows;
869: PetscInt r, len;
870: PetscBool cong;
872: PetscFunctionBegin;
873: /* get locally owned rows */
874: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
875: PetscCall(MatHasCongruentLayouts(A, &cong));
876: /* fix right-hand side if needed */
877: if (x && b) {
878: const PetscScalar *xx;
879: PetscScalar *bb;
881: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
882: PetscCall(VecGetArrayRead(x, &xx));
883: PetscCall(VecGetArray(b, &bb));
884: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
885: PetscCall(VecRestoreArrayRead(x, &xx));
886: PetscCall(VecRestoreArray(b, &bb));
887: }
889: if (diag != 0.0 && cong) {
890: PetscCall(MatZeroRows(mat->A, len, lrows, diag, NULL, NULL));
891: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
892: } else if (diag != 0.0) { /* non-square or non congruent layouts -> if keepnonzeropattern is false, we allow for new insertion */
893: Mat_SeqAIJ *aijA = (Mat_SeqAIJ *)mat->A->data;
894: Mat_SeqAIJ *aijB = (Mat_SeqAIJ *)mat->B->data;
895: PetscInt nnwA, nnwB;
896: PetscBool nnzA, nnzB;
898: nnwA = aijA->nonew;
899: nnwB = aijB->nonew;
900: nnzA = aijA->keepnonzeropattern;
901: nnzB = aijB->keepnonzeropattern;
902: if (!nnzA) {
903: PetscCall(PetscInfo(mat->A, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on diagonal block.\n"));
904: aijA->nonew = 0;
905: }
906: if (!nnzB) {
907: PetscCall(PetscInfo(mat->B, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on off-diagonal block.\n"));
908: aijB->nonew = 0;
909: }
910: /* Must zero here before the next loop */
911: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
912: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
913: for (r = 0; r < len; ++r) {
914: const PetscInt row = lrows[r] + A->rmap->rstart;
915: if (row >= A->cmap->N) continue;
916: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
917: }
918: aijA->nonew = nnwA;
919: aijB->nonew = nnwB;
920: } else {
921: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
922: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
923: }
924: PetscCall(PetscFree(lrows));
925: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
926: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
928: /* only change matrix nonzero state if pattern was allowed to be changed */
929: if (!((Mat_SeqAIJ *)mat->A->data)->keepnonzeropattern || !((Mat_SeqAIJ *)mat->A->data)->nonew) {
930: PetscObjectState state = mat->A->nonzerostate + mat->B->nonzerostate;
931: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
932: }
933: PetscFunctionReturn(PETSC_SUCCESS);
934: }
936: static PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
937: {
938: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
939: PetscInt n = A->rmap->n;
940: PetscInt i, j, r, m, len = 0;
941: PetscInt *lrows, *owners = A->rmap->range;
942: PetscMPIInt p = 0;
943: PetscSFNode *rrows;
944: PetscSF sf;
945: const PetscScalar *xx;
946: PetscScalar *bb, *mask, *aij_a;
947: Vec xmask, lmask;
948: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)l->B->data;
949: const PetscInt *aj, *ii, *ridx;
950: PetscScalar *aa;
952: PetscFunctionBegin;
953: /* Create SF where leaves are input rows and roots are owned rows */
954: PetscCall(PetscMalloc1(n, &lrows));
955: for (r = 0; r < n; ++r) lrows[r] = -1;
956: PetscCall(PetscMalloc1(N, &rrows));
957: for (r = 0; r < N; ++r) {
958: const PetscInt idx = rows[r];
959: 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);
960: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
961: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
962: }
963: rrows[r].rank = p;
964: rrows[r].index = rows[r] - owners[p];
965: }
966: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
967: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
968: /* Collect flags for rows to be zeroed */
969: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
970: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
971: PetscCall(PetscSFDestroy(&sf));
972: /* Compress and put in row numbers */
973: for (r = 0; r < n; ++r)
974: if (lrows[r] >= 0) lrows[len++] = r;
975: /* zero diagonal part of matrix */
976: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
977: /* handle off-diagonal part of matrix */
978: PetscCall(MatCreateVecs(A, &xmask, NULL));
979: PetscCall(VecDuplicate(l->lvec, &lmask));
980: PetscCall(VecGetArray(xmask, &bb));
981: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
982: PetscCall(VecRestoreArray(xmask, &bb));
983: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
984: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
985: PetscCall(VecDestroy(&xmask));
986: if (x && b) { /* this code is buggy when the row and column layout don't match */
987: PetscBool cong;
989: PetscCall(MatHasCongruentLayouts(A, &cong));
990: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
991: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
992: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
993: PetscCall(VecGetArrayRead(l->lvec, &xx));
994: PetscCall(VecGetArray(b, &bb));
995: }
996: PetscCall(VecGetArray(lmask, &mask));
997: /* remove zeroed rows of off-diagonal matrix */
998: PetscCall(MatSeqAIJGetArray(l->B, &aij_a));
999: ii = aij->i;
1000: for (i = 0; i < len; i++) PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aij_a, ii[lrows[i]]), ii[lrows[i] + 1] - ii[lrows[i]]));
1001: /* loop over all elements of off process part of matrix zeroing removed columns*/
1002: if (aij->compressedrow.use) {
1003: m = aij->compressedrow.nrows;
1004: ii = aij->compressedrow.i;
1005: ridx = aij->compressedrow.rindex;
1006: for (i = 0; i < m; i++) {
1007: n = ii[i + 1] - ii[i];
1008: aj = aij->j + ii[i];
1009: aa = aij_a + ii[i];
1011: for (j = 0; j < n; j++) {
1012: if (PetscAbsScalar(mask[*aj])) {
1013: if (b) bb[*ridx] -= *aa * xx[*aj];
1014: *aa = 0.0;
1015: }
1016: aa++;
1017: aj++;
1018: }
1019: ridx++;
1020: }
1021: } else { /* do not use compressed row format */
1022: m = l->B->rmap->n;
1023: for (i = 0; i < m; i++) {
1024: n = ii[i + 1] - ii[i];
1025: aj = aij->j + ii[i];
1026: aa = aij_a + ii[i];
1027: for (j = 0; j < n; j++) {
1028: if (PetscAbsScalar(mask[*aj])) {
1029: if (b) bb[i] -= *aa * xx[*aj];
1030: *aa = 0.0;
1031: }
1032: aa++;
1033: aj++;
1034: }
1035: }
1036: }
1037: if (x && b) {
1038: PetscCall(VecRestoreArray(b, &bb));
1039: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1040: }
1041: PetscCall(MatSeqAIJRestoreArray(l->B, &aij_a));
1042: PetscCall(VecRestoreArray(lmask, &mask));
1043: PetscCall(VecDestroy(&lmask));
1044: PetscCall(PetscFree(lrows));
1046: /* only change matrix nonzero state if pattern was allowed to be changed */
1047: if (!((Mat_SeqAIJ *)l->A->data)->nonew) {
1048: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1049: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1050: }
1051: PetscFunctionReturn(PETSC_SUCCESS);
1052: }
1054: static PetscErrorCode MatMult_MPIAIJ(Mat A, Vec xx, Vec yy)
1055: {
1056: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1057: PetscInt nt;
1058: VecScatter Mvctx = a->Mvctx;
1060: PetscFunctionBegin;
1061: PetscCall(VecGetLocalSize(xx, &nt));
1062: 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);
1063: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1064: PetscUseTypeMethod(a->A, mult, xx, yy);
1065: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1066: PetscUseTypeMethod(a->B, multadd, a->lvec, yy, yy);
1067: PetscFunctionReturn(PETSC_SUCCESS);
1068: }
1070: static PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A, Vec bb, Vec xx)
1071: {
1072: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1074: PetscFunctionBegin;
1075: PetscCall(MatMultDiagonalBlock(a->A, bb, xx));
1076: PetscFunctionReturn(PETSC_SUCCESS);
1077: }
1079: static PetscErrorCode MatMultAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1080: {
1081: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1082: VecScatter Mvctx = a->Mvctx;
1084: PetscFunctionBegin;
1085: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1086: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1087: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1088: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1089: PetscFunctionReturn(PETSC_SUCCESS);
1090: }
1092: static PetscErrorCode MatMultTranspose_MPIAIJ(Mat A, Vec xx, Vec yy)
1093: {
1094: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1096: PetscFunctionBegin;
1097: /* do nondiagonal part */
1098: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1099: /* do local part */
1100: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1101: /* add partial results together */
1102: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1103: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1104: PetscFunctionReturn(PETSC_SUCCESS);
1105: }
1107: static PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f)
1108: {
1109: MPI_Comm comm;
1110: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)Amat->data, *Bij = (Mat_MPIAIJ *)Bmat->data;
1111: Mat Adia = Aij->A, Bdia = Bij->A, Aoff, Boff, *Aoffs, *Boffs;
1112: IS Me, Notme;
1113: PetscInt M, N, first, last, *notme, i;
1114: PetscBool lf;
1115: PetscMPIInt size;
1117: PetscFunctionBegin;
1118: /* Easy test: symmetric diagonal block */
1119: PetscCall(MatIsTranspose(Adia, Bdia, tol, &lf));
1120: PetscCallMPI(MPIU_Allreduce(&lf, f, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)Amat)));
1121: if (!*f) PetscFunctionReturn(PETSC_SUCCESS);
1122: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1123: PetscCallMPI(MPI_Comm_size(comm, &size));
1124: if (size == 1) PetscFunctionReturn(PETSC_SUCCESS);
1126: /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */
1127: PetscCall(MatGetSize(Amat, &M, &N));
1128: PetscCall(MatGetOwnershipRange(Amat, &first, &last));
1129: PetscCall(PetscMalloc1(N - last + first, ¬me));
1130: for (i = 0; i < first; i++) notme[i] = i;
1131: for (i = last; i < M; i++) notme[i - last + first] = i;
1132: PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme));
1133: PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me));
1134: PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs));
1135: Aoff = Aoffs[0];
1136: PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs));
1137: Boff = Boffs[0];
1138: PetscCall(MatIsTranspose(Aoff, Boff, tol, f));
1139: PetscCall(MatDestroyMatrices(1, &Aoffs));
1140: PetscCall(MatDestroyMatrices(1, &Boffs));
1141: PetscCall(ISDestroy(&Me));
1142: PetscCall(ISDestroy(&Notme));
1143: PetscCall(PetscFree(notme));
1144: PetscFunctionReturn(PETSC_SUCCESS);
1145: }
1147: static PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1148: {
1149: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1151: PetscFunctionBegin;
1152: /* do nondiagonal part */
1153: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1154: /* do local part */
1155: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1156: /* add partial results together */
1157: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1158: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1159: PetscFunctionReturn(PETSC_SUCCESS);
1160: }
1162: /*
1163: This only works correctly for square matrices where the subblock A->A is the
1164: diagonal block
1165: */
1166: static PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A, Vec v)
1167: {
1168: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1170: PetscFunctionBegin;
1171: PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1172: 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");
1173: PetscCall(MatGetDiagonal(a->A, v));
1174: PetscFunctionReturn(PETSC_SUCCESS);
1175: }
1177: static PetscErrorCode MatScale_MPIAIJ(Mat A, PetscScalar aa)
1178: {
1179: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1181: PetscFunctionBegin;
1182: PetscCall(MatScale(a->A, aa));
1183: PetscCall(MatScale(a->B, aa));
1184: PetscFunctionReturn(PETSC_SUCCESS);
1185: }
1187: static PetscErrorCode MatView_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
1188: {
1189: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1190: Mat_SeqAIJ *A = (Mat_SeqAIJ *)aij->A->data;
1191: Mat_SeqAIJ *B = (Mat_SeqAIJ *)aij->B->data;
1192: const PetscInt *garray = aij->garray;
1193: const PetscScalar *aa, *ba;
1194: PetscInt header[4], M, N, m, rs, cs, cnt, i, ja, jb;
1195: PetscInt64 nz, hnz;
1196: PetscInt *rowlens;
1197: PetscInt *colidxs;
1198: PetscScalar *matvals;
1199: PetscMPIInt rank;
1201: PetscFunctionBegin;
1202: PetscCall(PetscViewerSetUp(viewer));
1204: M = mat->rmap->N;
1205: N = mat->cmap->N;
1206: m = mat->rmap->n;
1207: rs = mat->rmap->rstart;
1208: cs = mat->cmap->rstart;
1209: nz = A->nz + B->nz;
1211: /* write matrix header */
1212: header[0] = MAT_FILE_CLASSID;
1213: header[1] = M;
1214: header[2] = N;
1215: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1216: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1217: if (rank == 0) PetscCall(PetscIntCast(hnz, &header[3]));
1218: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1220: /* fill in and store row lengths */
1221: PetscCall(PetscMalloc1(m, &rowlens));
1222: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i];
1223: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1224: PetscCall(PetscFree(rowlens));
1226: /* fill in and store column indices */
1227: PetscCall(PetscMalloc1(nz, &colidxs));
1228: for (cnt = 0, i = 0; i < m; i++) {
1229: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1230: if (garray[B->j[jb]] > cs) break;
1231: colidxs[cnt++] = garray[B->j[jb]];
1232: }
1233: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) colidxs[cnt++] = A->j[ja] + cs;
1234: for (; jb < B->i[i + 1]; jb++) colidxs[cnt++] = garray[B->j[jb]];
1235: }
1236: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1237: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
1238: PetscCall(PetscFree(colidxs));
1240: /* fill in and store nonzero values */
1241: PetscCall(MatSeqAIJGetArrayRead(aij->A, &aa));
1242: PetscCall(MatSeqAIJGetArrayRead(aij->B, &ba));
1243: PetscCall(PetscMalloc1(nz, &matvals));
1244: for (cnt = 0, i = 0; i < m; i++) {
1245: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1246: if (garray[B->j[jb]] > cs) break;
1247: matvals[cnt++] = ba[jb];
1248: }
1249: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) matvals[cnt++] = aa[ja];
1250: for (; jb < B->i[i + 1]; jb++) matvals[cnt++] = ba[jb];
1251: }
1252: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &aa));
1253: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &ba));
1254: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1255: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
1256: PetscCall(PetscFree(matvals));
1258: /* write block size option to the viewer's .info file */
1259: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1260: PetscFunctionReturn(PETSC_SUCCESS);
1261: }
1263: #include <petscdraw.h>
1264: static PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1265: {
1266: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1267: PetscMPIInt rank = aij->rank, size = aij->size;
1268: PetscBool isdraw, iascii, isbinary;
1269: PetscViewer sviewer;
1270: PetscViewerFormat format;
1272: PetscFunctionBegin;
1273: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1274: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1275: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1276: if (iascii) {
1277: PetscCall(PetscViewerGetFormat(viewer, &format));
1278: if (format == PETSC_VIEWER_LOAD_BALANCE) {
1279: PetscInt i, nmax = 0, nmin = PETSC_INT_MAX, navg = 0, *nz, nzlocal = ((Mat_SeqAIJ *)aij->A->data)->nz + ((Mat_SeqAIJ *)aij->B->data)->nz;
1280: PetscCall(PetscMalloc1(size, &nz));
1281: PetscCallMPI(MPI_Allgather(&nzlocal, 1, MPIU_INT, nz, 1, MPIU_INT, PetscObjectComm((PetscObject)mat)));
1282: for (i = 0; i < size; i++) {
1283: nmax = PetscMax(nmax, nz[i]);
1284: nmin = PetscMin(nmin, nz[i]);
1285: navg += nz[i];
1286: }
1287: PetscCall(PetscFree(nz));
1288: navg = navg / size;
1289: PetscCall(PetscViewerASCIIPrintf(viewer, "Load Balance - Nonzeros: Min %" PetscInt_FMT " avg %" PetscInt_FMT " max %" PetscInt_FMT "\n", nmin, navg, nmax));
1290: PetscFunctionReturn(PETSC_SUCCESS);
1291: }
1292: PetscCall(PetscViewerGetFormat(viewer, &format));
1293: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1294: MatInfo info;
1295: PetscInt *inodes = NULL;
1297: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1298: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1299: PetscCall(MatInodeGetInodeSizes(aij->A, NULL, &inodes, NULL));
1300: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1301: if (!inodes) {
1302: 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,
1303: info.memory));
1304: } else {
1305: PetscCall(
1306: 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));
1307: }
1308: PetscCall(MatGetInfo(aij->A, MAT_LOCAL, &info));
1309: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1310: PetscCall(MatGetInfo(aij->B, MAT_LOCAL, &info));
1311: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1312: PetscCall(PetscViewerFlush(viewer));
1313: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1314: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1315: PetscCall(VecScatterView(aij->Mvctx, viewer));
1316: PetscFunctionReturn(PETSC_SUCCESS);
1317: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1318: PetscInt inodecount, inodelimit, *inodes;
1319: PetscCall(MatInodeGetInodeSizes(aij->A, &inodecount, &inodes, &inodelimit));
1320: if (inodes) {
1321: PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
1322: } else {
1323: PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
1324: }
1325: PetscFunctionReturn(PETSC_SUCCESS);
1326: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1327: PetscFunctionReturn(PETSC_SUCCESS);
1328: }
1329: } else if (isbinary) {
1330: if (size == 1) {
1331: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1332: PetscCall(MatView(aij->A, viewer));
1333: } else {
1334: PetscCall(MatView_MPIAIJ_Binary(mat, viewer));
1335: }
1336: PetscFunctionReturn(PETSC_SUCCESS);
1337: } else if (iascii && size == 1) {
1338: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1339: PetscCall(MatView(aij->A, viewer));
1340: PetscFunctionReturn(PETSC_SUCCESS);
1341: } else if (isdraw) {
1342: PetscDraw draw;
1343: PetscBool isnull;
1344: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1345: PetscCall(PetscDrawIsNull(draw, &isnull));
1346: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1347: }
1349: { /* assemble the entire matrix onto first processor */
1350: Mat A = NULL, Av;
1351: IS isrow, iscol;
1353: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->rmap->N : 0, 0, 1, &isrow));
1354: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->cmap->N : 0, 0, 1, &iscol));
1355: PetscCall(MatCreateSubMatrix(mat, isrow, iscol, MAT_INITIAL_MATRIX, &A));
1356: PetscCall(MatMPIAIJGetSeqAIJ(A, &Av, NULL, NULL));
1357: /* The commented code uses MatCreateSubMatrices instead */
1358: /*
1359: Mat *AA, A = NULL, Av;
1360: IS isrow,iscol;
1362: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->rmap->N : 0,0,1,&isrow));
1363: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->cmap->N : 0,0,1,&iscol));
1364: PetscCall(MatCreateSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&AA));
1365: if (rank == 0) {
1366: PetscCall(PetscObjectReference((PetscObject)AA[0]));
1367: A = AA[0];
1368: Av = AA[0];
1369: }
1370: PetscCall(MatDestroySubMatrices(1,&AA));
1371: */
1372: PetscCall(ISDestroy(&iscol));
1373: PetscCall(ISDestroy(&isrow));
1374: /*
1375: Everyone has to call to draw the matrix since the graphics waits are
1376: synchronized across all processors that share the PetscDraw object
1377: */
1378: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1379: if (rank == 0) {
1380: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)Av, ((PetscObject)mat)->name));
1381: PetscCall(MatView_SeqAIJ(Av, sviewer));
1382: }
1383: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1384: PetscCall(MatDestroy(&A));
1385: }
1386: PetscFunctionReturn(PETSC_SUCCESS);
1387: }
1389: PetscErrorCode MatView_MPIAIJ(Mat mat, PetscViewer viewer)
1390: {
1391: PetscBool iascii, isdraw, issocket, isbinary;
1393: PetscFunctionBegin;
1394: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1395: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1396: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1397: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1398: if (iascii || isdraw || isbinary || issocket) PetscCall(MatView_MPIAIJ_ASCIIorDraworSocket(mat, viewer));
1399: PetscFunctionReturn(PETSC_SUCCESS);
1400: }
1402: static PetscErrorCode MatSOR_MPIAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1403: {
1404: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1405: Vec bb1 = NULL;
1406: PetscBool hasop;
1408: PetscFunctionBegin;
1409: if (flag == SOR_APPLY_UPPER) {
1410: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1411: PetscFunctionReturn(PETSC_SUCCESS);
1412: }
1414: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1));
1416: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
1417: if (flag & SOR_ZERO_INITIAL_GUESS) {
1418: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1419: its--;
1420: }
1422: while (its--) {
1423: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1424: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1426: /* update rhs: bb1 = bb - B*x */
1427: PetscCall(VecScale(mat->lvec, -1.0));
1428: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1430: /* local sweep */
1431: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
1432: }
1433: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1434: if (flag & SOR_ZERO_INITIAL_GUESS) {
1435: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1436: its--;
1437: }
1438: while (its--) {
1439: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1440: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1442: /* update rhs: bb1 = bb - B*x */
1443: PetscCall(VecScale(mat->lvec, -1.0));
1444: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1446: /* local sweep */
1447: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
1448: }
1449: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1450: if (flag & SOR_ZERO_INITIAL_GUESS) {
1451: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1452: its--;
1453: }
1454: while (its--) {
1455: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1456: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1458: /* update rhs: bb1 = bb - B*x */
1459: PetscCall(VecScale(mat->lvec, -1.0));
1460: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1462: /* local sweep */
1463: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
1464: }
1465: } else if (flag & SOR_EISENSTAT) {
1466: Vec xx1;
1468: PetscCall(VecDuplicate(bb, &xx1));
1469: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP), fshift, lits, 1, xx));
1471: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1472: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1473: if (!mat->diag) {
1474: PetscCall(MatCreateVecs(matin, &mat->diag, NULL));
1475: PetscCall(MatGetDiagonal(matin, mat->diag));
1476: }
1477: PetscCall(MatHasOperation(matin, MATOP_MULT_DIAGONAL_BLOCK, &hasop));
1478: if (hasop) {
1479: PetscCall(MatMultDiagonalBlock(matin, xx, bb1));
1480: } else {
1481: PetscCall(VecPointwiseMult(bb1, mat->diag, xx));
1482: }
1483: PetscCall(VecAYPX(bb1, (omega - 2.0) / omega, bb));
1485: PetscCall(MatMultAdd(mat->B, mat->lvec, bb1, bb1));
1487: /* local sweep */
1488: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP), fshift, lits, 1, xx1));
1489: PetscCall(VecAXPY(xx, 1.0, xx1));
1490: PetscCall(VecDestroy(&xx1));
1491: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");
1493: PetscCall(VecDestroy(&bb1));
1495: matin->factorerrortype = mat->A->factorerrortype;
1496: PetscFunctionReturn(PETSC_SUCCESS);
1497: }
1499: static PetscErrorCode MatPermute_MPIAIJ(Mat A, IS rowp, IS colp, Mat *B)
1500: {
1501: Mat aA, aB, Aperm;
1502: const PetscInt *rwant, *cwant, *gcols, *ai, *bi, *aj, *bj;
1503: PetscScalar *aa, *ba;
1504: PetscInt i, j, m, n, ng, anz, bnz, *dnnz, *onnz, *tdnnz, *tonnz, *rdest, *cdest, *work, *gcdest;
1505: PetscSF rowsf, sf;
1506: IS parcolp = NULL;
1507: PetscBool done;
1509: PetscFunctionBegin;
1510: PetscCall(MatGetLocalSize(A, &m, &n));
1511: PetscCall(ISGetIndices(rowp, &rwant));
1512: PetscCall(ISGetIndices(colp, &cwant));
1513: PetscCall(PetscMalloc3(PetscMax(m, n), &work, m, &rdest, n, &cdest));
1515: /* Invert row permutation to find out where my rows should go */
1516: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &rowsf));
1517: PetscCall(PetscSFSetGraphLayout(rowsf, A->rmap, A->rmap->n, NULL, PETSC_OWN_POINTER, rwant));
1518: PetscCall(PetscSFSetFromOptions(rowsf));
1519: for (i = 0; i < m; i++) work[i] = A->rmap->rstart + i;
1520: PetscCall(PetscSFReduceBegin(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1521: PetscCall(PetscSFReduceEnd(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1523: /* Invert column permutation to find out where my columns should go */
1524: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1525: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, A->cmap->n, NULL, PETSC_OWN_POINTER, cwant));
1526: PetscCall(PetscSFSetFromOptions(sf));
1527: for (i = 0; i < n; i++) work[i] = A->cmap->rstart + i;
1528: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1529: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1530: PetscCall(PetscSFDestroy(&sf));
1532: PetscCall(ISRestoreIndices(rowp, &rwant));
1533: PetscCall(ISRestoreIndices(colp, &cwant));
1534: PetscCall(MatMPIAIJGetSeqAIJ(A, &aA, &aB, &gcols));
1536: /* Find out where my gcols should go */
1537: PetscCall(MatGetSize(aB, NULL, &ng));
1538: PetscCall(PetscMalloc1(ng, &gcdest));
1539: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1540: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, ng, NULL, PETSC_OWN_POINTER, gcols));
1541: PetscCall(PetscSFSetFromOptions(sf));
1542: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1543: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1544: PetscCall(PetscSFDestroy(&sf));
1546: PetscCall(PetscCalloc4(m, &dnnz, m, &onnz, m, &tdnnz, m, &tonnz));
1547: PetscCall(MatGetRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1548: PetscCall(MatGetRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1549: for (i = 0; i < m; i++) {
1550: PetscInt row = rdest[i];
1551: PetscMPIInt rowner;
1552: PetscCall(PetscLayoutFindOwner(A->rmap, row, &rowner));
1553: for (j = ai[i]; j < ai[i + 1]; j++) {
1554: PetscInt col = cdest[aj[j]];
1555: PetscMPIInt cowner;
1556: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner)); /* Could build an index for the columns to eliminate this search */
1557: if (rowner == cowner) dnnz[i]++;
1558: else onnz[i]++;
1559: }
1560: for (j = bi[i]; j < bi[i + 1]; j++) {
1561: PetscInt col = gcdest[bj[j]];
1562: PetscMPIInt cowner;
1563: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner));
1564: if (rowner == cowner) dnnz[i]++;
1565: else onnz[i]++;
1566: }
1567: }
1568: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1569: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1570: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1571: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1572: PetscCall(PetscSFDestroy(&rowsf));
1574: PetscCall(MatCreateAIJ(PetscObjectComm((PetscObject)A), A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N, 0, tdnnz, 0, tonnz, &Aperm));
1575: PetscCall(MatSeqAIJGetArray(aA, &aa));
1576: PetscCall(MatSeqAIJGetArray(aB, &ba));
1577: for (i = 0; i < m; i++) {
1578: PetscInt *acols = dnnz, *bcols = onnz; /* Repurpose now-unneeded arrays */
1579: PetscInt j0, rowlen;
1580: rowlen = ai[i + 1] - ai[i];
1581: for (j0 = j = 0; j < rowlen; j0 = j) { /* rowlen could be larger than number of rows m, so sum in batches */
1582: for (; j < PetscMin(rowlen, j0 + m); j++) acols[j - j0] = cdest[aj[ai[i] + j]];
1583: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, acols, aa + ai[i] + j0, INSERT_VALUES));
1584: }
1585: rowlen = bi[i + 1] - bi[i];
1586: for (j0 = j = 0; j < rowlen; j0 = j) {
1587: for (; j < PetscMin(rowlen, j0 + m); j++) bcols[j - j0] = gcdest[bj[bi[i] + j]];
1588: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, bcols, ba + bi[i] + j0, INSERT_VALUES));
1589: }
1590: }
1591: PetscCall(MatAssemblyBegin(Aperm, MAT_FINAL_ASSEMBLY));
1592: PetscCall(MatAssemblyEnd(Aperm, MAT_FINAL_ASSEMBLY));
1593: PetscCall(MatRestoreRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1594: PetscCall(MatRestoreRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1595: PetscCall(MatSeqAIJRestoreArray(aA, &aa));
1596: PetscCall(MatSeqAIJRestoreArray(aB, &ba));
1597: PetscCall(PetscFree4(dnnz, onnz, tdnnz, tonnz));
1598: PetscCall(PetscFree3(work, rdest, cdest));
1599: PetscCall(PetscFree(gcdest));
1600: if (parcolp) PetscCall(ISDestroy(&colp));
1601: *B = Aperm;
1602: PetscFunctionReturn(PETSC_SUCCESS);
1603: }
1605: static PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
1606: {
1607: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1609: PetscFunctionBegin;
1610: PetscCall(MatGetSize(aij->B, NULL, nghosts));
1611: if (ghosts) *ghosts = aij->garray;
1612: PetscFunctionReturn(PETSC_SUCCESS);
1613: }
1615: static PetscErrorCode MatGetInfo_MPIAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1616: {
1617: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1618: Mat A = mat->A, B = mat->B;
1619: PetscLogDouble isend[5], irecv[5];
1621: PetscFunctionBegin;
1622: info->block_size = 1.0;
1623: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1625: isend[0] = info->nz_used;
1626: isend[1] = info->nz_allocated;
1627: isend[2] = info->nz_unneeded;
1628: isend[3] = info->memory;
1629: isend[4] = info->mallocs;
1631: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1633: isend[0] += info->nz_used;
1634: isend[1] += info->nz_allocated;
1635: isend[2] += info->nz_unneeded;
1636: isend[3] += info->memory;
1637: isend[4] += info->mallocs;
1638: if (flag == MAT_LOCAL) {
1639: info->nz_used = isend[0];
1640: info->nz_allocated = isend[1];
1641: info->nz_unneeded = isend[2];
1642: info->memory = isend[3];
1643: info->mallocs = isend[4];
1644: } else if (flag == MAT_GLOBAL_MAX) {
1645: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1647: info->nz_used = irecv[0];
1648: info->nz_allocated = irecv[1];
1649: info->nz_unneeded = irecv[2];
1650: info->memory = irecv[3];
1651: info->mallocs = irecv[4];
1652: } else if (flag == MAT_GLOBAL_SUM) {
1653: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1655: info->nz_used = irecv[0];
1656: info->nz_allocated = irecv[1];
1657: info->nz_unneeded = irecv[2];
1658: info->memory = irecv[3];
1659: info->mallocs = irecv[4];
1660: }
1661: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1662: info->fill_ratio_needed = 0;
1663: info->factor_mallocs = 0;
1664: PetscFunctionReturn(PETSC_SUCCESS);
1665: }
1667: PetscErrorCode MatSetOption_MPIAIJ(Mat A, MatOption op, PetscBool flg)
1668: {
1669: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1671: PetscFunctionBegin;
1672: switch (op) {
1673: case MAT_NEW_NONZERO_LOCATIONS:
1674: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1675: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1676: case MAT_KEEP_NONZERO_PATTERN:
1677: case MAT_NEW_NONZERO_LOCATION_ERR:
1678: case MAT_USE_INODES:
1679: case MAT_IGNORE_ZERO_ENTRIES:
1680: case MAT_FORM_EXPLICIT_TRANSPOSE:
1681: MatCheckPreallocated(A, 1);
1682: PetscCall(MatSetOption(a->A, op, flg));
1683: PetscCall(MatSetOption(a->B, op, flg));
1684: break;
1685: case MAT_ROW_ORIENTED:
1686: MatCheckPreallocated(A, 1);
1687: a->roworiented = flg;
1689: PetscCall(MatSetOption(a->A, op, flg));
1690: PetscCall(MatSetOption(a->B, op, flg));
1691: break;
1692: case MAT_FORCE_DIAGONAL_ENTRIES:
1693: case MAT_SORTED_FULL:
1694: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1695: break;
1696: case MAT_IGNORE_OFF_PROC_ENTRIES:
1697: a->donotstash = flg;
1698: break;
1699: /* Symmetry flags are handled directly by MatSetOption() and they don't affect preallocation */
1700: case MAT_SPD:
1701: case MAT_SYMMETRIC:
1702: case MAT_STRUCTURALLY_SYMMETRIC:
1703: case MAT_HERMITIAN:
1704: case MAT_SYMMETRY_ETERNAL:
1705: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1706: case MAT_SPD_ETERNAL:
1707: /* if the diagonal matrix is square it inherits some of the properties above */
1708: break;
1709: case MAT_SUBMAT_SINGLEIS:
1710: A->submat_singleis = flg;
1711: break;
1712: case MAT_STRUCTURE_ONLY:
1713: /* The option is handled directly by MatSetOption() */
1714: break;
1715: default:
1716: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1717: }
1718: PetscFunctionReturn(PETSC_SUCCESS);
1719: }
1721: PetscErrorCode MatGetRow_MPIAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1722: {
1723: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1724: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1725: PetscInt i, *cworkA, *cworkB, **pcA, **pcB, cstart = matin->cmap->rstart;
1726: PetscInt nztot, nzA, nzB, lrow, rstart = matin->rmap->rstart, rend = matin->rmap->rend;
1727: PetscInt *cmap, *idx_p;
1729: PetscFunctionBegin;
1730: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1731: mat->getrowactive = PETSC_TRUE;
1733: if (!mat->rowvalues && (idx || v)) {
1734: /*
1735: allocate enough space to hold information from the longest row.
1736: */
1737: Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)mat->A->data, *Ba = (Mat_SeqAIJ *)mat->B->data;
1738: PetscInt max = 1, tmp;
1739: for (i = 0; i < matin->rmap->n; i++) {
1740: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1741: if (max < tmp) max = tmp;
1742: }
1743: PetscCall(PetscMalloc2(max, &mat->rowvalues, max, &mat->rowindices));
1744: }
1746: PetscCheck(row >= rstart && row < rend, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Only local rows");
1747: lrow = row - rstart;
1749: pvA = &vworkA;
1750: pcA = &cworkA;
1751: pvB = &vworkB;
1752: pcB = &cworkB;
1753: if (!v) {
1754: pvA = NULL;
1755: pvB = NULL;
1756: }
1757: if (!idx) {
1758: pcA = NULL;
1759: if (!v) pcB = NULL;
1760: }
1761: PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1762: PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1763: nztot = nzA + nzB;
1765: cmap = mat->garray;
1766: if (v || idx) {
1767: if (nztot) {
1768: /* Sort by increasing column numbers, assuming A and B already sorted */
1769: PetscInt imark = -1;
1770: if (v) {
1771: *v = v_p = mat->rowvalues;
1772: for (i = 0; i < nzB; i++) {
1773: if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1774: else break;
1775: }
1776: imark = i;
1777: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1778: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1779: }
1780: if (idx) {
1781: *idx = idx_p = mat->rowindices;
1782: if (imark > -1) {
1783: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i]];
1784: } else {
1785: for (i = 0; i < nzB; i++) {
1786: if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1787: else break;
1788: }
1789: imark = i;
1790: }
1791: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart + cworkA[i];
1792: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i]];
1793: }
1794: } else {
1795: if (idx) *idx = NULL;
1796: if (v) *v = NULL;
1797: }
1798: }
1799: *nz = nztot;
1800: PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1801: PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1802: PetscFunctionReturn(PETSC_SUCCESS);
1803: }
1805: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1806: {
1807: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1809: PetscFunctionBegin;
1810: PetscCheck(aij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow() must be called first");
1811: aij->getrowactive = PETSC_FALSE;
1812: PetscFunctionReturn(PETSC_SUCCESS);
1813: }
1815: static PetscErrorCode MatNorm_MPIAIJ(Mat mat, NormType type, PetscReal *norm)
1816: {
1817: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1818: Mat_SeqAIJ *amat = (Mat_SeqAIJ *)aij->A->data, *bmat = (Mat_SeqAIJ *)aij->B->data;
1819: PetscInt i, j, cstart = mat->cmap->rstart;
1820: PetscReal sum = 0.0;
1821: const MatScalar *v, *amata, *bmata;
1822: PetscMPIInt iN;
1824: PetscFunctionBegin;
1825: if (aij->size == 1) {
1826: PetscCall(MatNorm(aij->A, type, norm));
1827: } else {
1828: PetscCall(MatSeqAIJGetArrayRead(aij->A, &amata));
1829: PetscCall(MatSeqAIJGetArrayRead(aij->B, &bmata));
1830: if (type == NORM_FROBENIUS) {
1831: v = amata;
1832: for (i = 0; i < amat->nz; i++) {
1833: sum += PetscRealPart(PetscConj(*v) * (*v));
1834: v++;
1835: }
1836: v = bmata;
1837: for (i = 0; i < bmat->nz; i++) {
1838: sum += PetscRealPart(PetscConj(*v) * (*v));
1839: v++;
1840: }
1841: PetscCallMPI(MPIU_Allreduce(&sum, norm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1842: *norm = PetscSqrtReal(*norm);
1843: PetscCall(PetscLogFlops(2.0 * amat->nz + 2.0 * bmat->nz));
1844: } else if (type == NORM_1) { /* max column norm */
1845: PetscReal *tmp, *tmp2;
1846: PetscInt *jj, *garray = aij->garray;
1847: PetscCall(PetscCalloc1(mat->cmap->N + 1, &tmp));
1848: PetscCall(PetscMalloc1(mat->cmap->N + 1, &tmp2));
1849: *norm = 0.0;
1850: v = amata;
1851: jj = amat->j;
1852: for (j = 0; j < amat->nz; j++) {
1853: tmp[cstart + *jj++] += PetscAbsScalar(*v);
1854: v++;
1855: }
1856: v = bmata;
1857: jj = bmat->j;
1858: for (j = 0; j < bmat->nz; j++) {
1859: tmp[garray[*jj++]] += PetscAbsScalar(*v);
1860: v++;
1861: }
1862: PetscCall(PetscMPIIntCast(mat->cmap->N, &iN));
1863: PetscCallMPI(MPIU_Allreduce(tmp, tmp2, iN, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1864: for (j = 0; j < mat->cmap->N; j++) {
1865: if (tmp2[j] > *norm) *norm = tmp2[j];
1866: }
1867: PetscCall(PetscFree(tmp));
1868: PetscCall(PetscFree(tmp2));
1869: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1870: } else if (type == NORM_INFINITY) { /* max row norm */
1871: PetscReal ntemp = 0.0;
1872: for (j = 0; j < aij->A->rmap->n; j++) {
1873: v = PetscSafePointerPlusOffset(amata, amat->i[j]);
1874: sum = 0.0;
1875: for (i = 0; i < amat->i[j + 1] - amat->i[j]; i++) {
1876: sum += PetscAbsScalar(*v);
1877: v++;
1878: }
1879: v = PetscSafePointerPlusOffset(bmata, bmat->i[j]);
1880: for (i = 0; i < bmat->i[j + 1] - bmat->i[j]; i++) {
1881: sum += PetscAbsScalar(*v);
1882: v++;
1883: }
1884: if (sum > ntemp) ntemp = sum;
1885: }
1886: PetscCallMPI(MPIU_Allreduce(&ntemp, norm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
1887: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1888: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for two norm");
1889: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &amata));
1890: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &bmata));
1891: }
1892: PetscFunctionReturn(PETSC_SUCCESS);
1893: }
1895: static PetscErrorCode MatTranspose_MPIAIJ(Mat A, MatReuse reuse, Mat *matout)
1896: {
1897: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *b;
1898: Mat_SeqAIJ *Aloc = (Mat_SeqAIJ *)a->A->data, *Bloc = (Mat_SeqAIJ *)a->B->data, *sub_B_diag;
1899: 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;
1900: const PetscInt *ai, *aj, *bi, *bj, *B_diag_i;
1901: Mat B, A_diag, *B_diag;
1902: const MatScalar *pbv, *bv;
1904: PetscFunctionBegin;
1905: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1906: ma = A->rmap->n;
1907: na = A->cmap->n;
1908: mb = a->B->rmap->n;
1909: nb = a->B->cmap->n;
1910: ai = Aloc->i;
1911: aj = Aloc->j;
1912: bi = Bloc->i;
1913: bj = Bloc->j;
1914: if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1915: PetscInt *d_nnz, *g_nnz, *o_nnz;
1916: PetscSFNode *oloc;
1917: PETSC_UNUSED PetscSF sf;
1919: PetscCall(PetscMalloc4(na, &d_nnz, na, &o_nnz, nb, &g_nnz, nb, &oloc));
1920: /* compute d_nnz for preallocation */
1921: PetscCall(PetscArrayzero(d_nnz, na));
1922: for (i = 0; i < ai[ma]; i++) d_nnz[aj[i]]++;
1923: /* compute local off-diagonal contributions */
1924: PetscCall(PetscArrayzero(g_nnz, nb));
1925: for (i = 0; i < bi[ma]; i++) g_nnz[bj[i]]++;
1926: /* map those to global */
1927: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1928: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, nb, NULL, PETSC_USE_POINTER, a->garray));
1929: PetscCall(PetscSFSetFromOptions(sf));
1930: PetscCall(PetscArrayzero(o_nnz, na));
1931: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1932: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1933: PetscCall(PetscSFDestroy(&sf));
1935: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1936: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1937: PetscCall(MatSetBlockSizes(B, PetscAbs(A->cmap->bs), PetscAbs(A->rmap->bs)));
1938: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1939: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
1940: PetscCall(PetscFree4(d_nnz, o_nnz, g_nnz, oloc));
1941: } else {
1942: B = *matout;
1943: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
1944: }
1946: b = (Mat_MPIAIJ *)B->data;
1947: A_diag = a->A;
1948: B_diag = &b->A;
1949: sub_B_diag = (Mat_SeqAIJ *)(*B_diag)->data;
1950: A_diag_ncol = A_diag->cmap->N;
1951: B_diag_ilen = sub_B_diag->ilen;
1952: B_diag_i = sub_B_diag->i;
1954: /* Set ilen for diagonal of B */
1955: for (i = 0; i < A_diag_ncol; i++) B_diag_ilen[i] = B_diag_i[i + 1] - B_diag_i[i];
1957: /* Transpose the diagonal part of the matrix. In contrast to the off-diagonal part, this can be done
1958: very quickly (=without using MatSetValues), because all writes are local. */
1959: PetscCall(MatTransposeSetPrecursor(A_diag, *B_diag));
1960: PetscCall(MatTranspose(A_diag, MAT_REUSE_MATRIX, B_diag));
1962: /* copy over the B part */
1963: PetscCall(PetscMalloc1(bi[mb], &cols));
1964: PetscCall(MatSeqAIJGetArrayRead(a->B, &bv));
1965: pbv = bv;
1966: row = A->rmap->rstart;
1967: for (i = 0; i < bi[mb]; i++) cols[i] = a->garray[bj[i]];
1968: cols_tmp = cols;
1969: for (i = 0; i < mb; i++) {
1970: ncol = bi[i + 1] - bi[i];
1971: PetscCall(MatSetValues(B, ncol, cols_tmp, 1, &row, pbv, INSERT_VALUES));
1972: row++;
1973: if (pbv) pbv += ncol;
1974: if (cols_tmp) cols_tmp += ncol;
1975: }
1976: PetscCall(PetscFree(cols));
1977: PetscCall(MatSeqAIJRestoreArrayRead(a->B, &bv));
1979: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1980: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1981: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1982: *matout = B;
1983: } else {
1984: PetscCall(MatHeaderMerge(A, &B));
1985: }
1986: PetscFunctionReturn(PETSC_SUCCESS);
1987: }
1989: static PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat, Vec ll, Vec rr)
1990: {
1991: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1992: Mat a = aij->A, b = aij->B;
1993: PetscInt s1, s2, s3;
1995: PetscFunctionBegin;
1996: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1997: if (rr) {
1998: PetscCall(VecGetLocalSize(rr, &s1));
1999: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
2000: /* Overlap communication with computation. */
2001: PetscCall(VecScatterBegin(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
2002: }
2003: if (ll) {
2004: PetscCall(VecGetLocalSize(ll, &s1));
2005: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
2006: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
2007: }
2008: /* scale the diagonal block */
2009: PetscUseTypeMethod(a, diagonalscale, ll, rr);
2011: if (rr) {
2012: /* Do a scatter end and then right scale the off-diagonal block */
2013: PetscCall(VecScatterEnd(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
2014: PetscUseTypeMethod(b, diagonalscale, NULL, aij->lvec);
2015: }
2016: PetscFunctionReturn(PETSC_SUCCESS);
2017: }
2019: static PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2020: {
2021: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2023: PetscFunctionBegin;
2024: PetscCall(MatSetUnfactored(a->A));
2025: PetscFunctionReturn(PETSC_SUCCESS);
2026: }
2028: static PetscErrorCode MatEqual_MPIAIJ(Mat A, Mat B, PetscBool *flag)
2029: {
2030: Mat_MPIAIJ *matB = (Mat_MPIAIJ *)B->data, *matA = (Mat_MPIAIJ *)A->data;
2031: Mat a, b, c, d;
2032: PetscBool flg;
2034: PetscFunctionBegin;
2035: a = matA->A;
2036: b = matA->B;
2037: c = matB->A;
2038: d = matB->B;
2040: PetscCall(MatEqual(a, c, &flg));
2041: if (flg) PetscCall(MatEqual(b, d, &flg));
2042: PetscCallMPI(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
2043: PetscFunctionReturn(PETSC_SUCCESS);
2044: }
2046: static PetscErrorCode MatCopy_MPIAIJ(Mat A, Mat B, MatStructure str)
2047: {
2048: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2049: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2051: PetscFunctionBegin;
2052: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2053: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2054: /* because of the column compression in the off-processor part of the matrix a->B,
2055: the number of columns in a->B and b->B may be different, hence we cannot call
2056: the MatCopy() directly on the two parts. If need be, we can provide a more
2057: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2058: then copying the submatrices */
2059: PetscCall(MatCopy_Basic(A, B, str));
2060: } else {
2061: PetscCall(MatCopy(a->A, b->A, str));
2062: PetscCall(MatCopy(a->B, b->B, str));
2063: }
2064: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2065: PetscFunctionReturn(PETSC_SUCCESS);
2066: }
2068: /*
2069: Computes the number of nonzeros per row needed for preallocation when X and Y
2070: have different nonzero structure.
2071: */
2072: 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)
2073: {
2074: PetscInt i, j, k, nzx, nzy;
2076: PetscFunctionBegin;
2077: /* Set the number of nonzeros in the new matrix */
2078: for (i = 0; i < m; i++) {
2079: const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2080: nzx = xi[i + 1] - xi[i];
2081: nzy = yi[i + 1] - yi[i];
2082: nnz[i] = 0;
2083: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2084: for (; k < nzy && yltog[yjj[k]] < xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2085: if (k < nzy && yltog[yjj[k]] == xltog[xjj[j]]) k++; /* Skip duplicate */
2086: nnz[i]++;
2087: }
2088: for (; k < nzy; k++) nnz[i]++;
2089: }
2090: PetscFunctionReturn(PETSC_SUCCESS);
2091: }
2093: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2094: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
2095: {
2096: PetscInt m = Y->rmap->N;
2097: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2098: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
2100: PetscFunctionBegin;
2101: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
2102: PetscFunctionReturn(PETSC_SUCCESS);
2103: }
2105: static PetscErrorCode MatAXPY_MPIAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2106: {
2107: Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data, *yy = (Mat_MPIAIJ *)Y->data;
2109: PetscFunctionBegin;
2110: if (str == SAME_NONZERO_PATTERN) {
2111: PetscCall(MatAXPY(yy->A, a, xx->A, str));
2112: PetscCall(MatAXPY(yy->B, a, xx->B, str));
2113: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2114: PetscCall(MatAXPY_Basic(Y, a, X, str));
2115: } else {
2116: Mat B;
2117: PetscInt *nnz_d, *nnz_o;
2119: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
2120: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
2121: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2122: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2123: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2124: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2125: PetscCall(MatAXPYGetPreallocation_SeqAIJ(yy->A, xx->A, nnz_d));
2126: PetscCall(MatAXPYGetPreallocation_MPIAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
2127: PetscCall(MatMPIAIJSetPreallocation(B, 0, nnz_d, 0, nnz_o));
2128: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2129: PetscCall(MatHeaderMerge(Y, &B));
2130: PetscCall(PetscFree(nnz_d));
2131: PetscCall(PetscFree(nnz_o));
2132: }
2133: PetscFunctionReturn(PETSC_SUCCESS);
2134: }
2136: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);
2138: static PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2139: {
2140: PetscFunctionBegin;
2141: if (PetscDefined(USE_COMPLEX)) {
2142: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2144: PetscCall(MatConjugate_SeqAIJ(aij->A));
2145: PetscCall(MatConjugate_SeqAIJ(aij->B));
2146: }
2147: PetscFunctionReturn(PETSC_SUCCESS);
2148: }
2150: static PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2151: {
2152: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2154: PetscFunctionBegin;
2155: PetscCall(MatRealPart(a->A));
2156: PetscCall(MatRealPart(a->B));
2157: PetscFunctionReturn(PETSC_SUCCESS);
2158: }
2160: static PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
2161: {
2162: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2164: PetscFunctionBegin;
2165: PetscCall(MatImaginaryPart(a->A));
2166: PetscCall(MatImaginaryPart(a->B));
2167: PetscFunctionReturn(PETSC_SUCCESS);
2168: }
2170: static PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2171: {
2172: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2173: PetscInt i, *idxb = NULL, m = A->rmap->n;
2174: PetscScalar *va, *vv;
2175: Vec vB, vA;
2176: const PetscScalar *vb;
2178: PetscFunctionBegin;
2179: PetscCall(MatCreateVecs(a->A, NULL, &vA));
2180: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
2182: PetscCall(VecGetArrayWrite(vA, &va));
2183: if (idx) {
2184: for (i = 0; i < m; i++) {
2185: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2186: }
2187: }
2189: PetscCall(MatCreateVecs(a->B, NULL, &vB));
2190: PetscCall(PetscMalloc1(m, &idxb));
2191: PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));
2193: PetscCall(VecGetArrayWrite(v, &vv));
2194: PetscCall(VecGetArrayRead(vB, &vb));
2195: for (i = 0; i < m; i++) {
2196: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2197: vv[i] = vb[i];
2198: if (idx) idx[i] = a->garray[idxb[i]];
2199: } else {
2200: vv[i] = va[i];
2201: if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > a->garray[idxb[i]]) idx[i] = a->garray[idxb[i]];
2202: }
2203: }
2204: PetscCall(VecRestoreArrayWrite(vA, &vv));
2205: PetscCall(VecRestoreArrayWrite(vA, &va));
2206: PetscCall(VecRestoreArrayRead(vB, &vb));
2207: PetscCall(PetscFree(idxb));
2208: PetscCall(VecDestroy(&vA));
2209: PetscCall(VecDestroy(&vB));
2210: PetscFunctionReturn(PETSC_SUCCESS);
2211: }
2213: static PetscErrorCode MatGetRowSumAbs_MPIAIJ(Mat A, Vec v)
2214: {
2215: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2216: Vec vB, vA;
2218: PetscFunctionBegin;
2219: PetscCall(MatCreateVecs(a->A, NULL, &vA));
2220: PetscCall(MatGetRowSumAbs(a->A, vA));
2221: PetscCall(MatCreateVecs(a->B, NULL, &vB));
2222: PetscCall(MatGetRowSumAbs(a->B, vB));
2223: PetscCall(VecAXPY(vA, 1.0, vB));
2224: PetscCall(VecDestroy(&vB));
2225: PetscCall(VecCopy(vA, v));
2226: PetscCall(VecDestroy(&vA));
2227: PetscFunctionReturn(PETSC_SUCCESS);
2228: }
2230: static PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2231: {
2232: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2233: PetscInt m = A->rmap->n, n = A->cmap->n;
2234: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2235: PetscInt *cmap = mat->garray;
2236: PetscInt *diagIdx, *offdiagIdx;
2237: Vec diagV, offdiagV;
2238: PetscScalar *a, *diagA, *offdiagA;
2239: const PetscScalar *ba, *bav;
2240: PetscInt r, j, col, ncols, *bi, *bj;
2241: Mat B = mat->B;
2242: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2244: PetscFunctionBegin;
2245: /* When a process holds entire A and other processes have no entry */
2246: if (A->cmap->N == n) {
2247: PetscCall(VecGetArrayWrite(v, &diagA));
2248: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2249: PetscCall(MatGetRowMinAbs(mat->A, diagV, idx));
2250: PetscCall(VecDestroy(&diagV));
2251: PetscCall(VecRestoreArrayWrite(v, &diagA));
2252: PetscFunctionReturn(PETSC_SUCCESS);
2253: } else if (n == 0) {
2254: if (m) {
2255: PetscCall(VecGetArrayWrite(v, &a));
2256: for (r = 0; r < m; r++) {
2257: a[r] = 0.0;
2258: if (idx) idx[r] = -1;
2259: }
2260: PetscCall(VecRestoreArrayWrite(v, &a));
2261: }
2262: PetscFunctionReturn(PETSC_SUCCESS);
2263: }
2265: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2266: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2267: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2268: PetscCall(MatGetRowMinAbs(mat->A, diagV, diagIdx));
2270: /* Get offdiagIdx[] for implicit 0.0 */
2271: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2272: ba = bav;
2273: bi = b->i;
2274: bj = b->j;
2275: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2276: for (r = 0; r < m; r++) {
2277: ncols = bi[r + 1] - bi[r];
2278: if (ncols == A->cmap->N - n) { /* Brow is dense */
2279: offdiagA[r] = *ba;
2280: offdiagIdx[r] = cmap[0];
2281: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2282: offdiagA[r] = 0.0;
2284: /* Find first hole in the cmap */
2285: for (j = 0; j < ncols; j++) {
2286: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2287: if (col > j && j < cstart) {
2288: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2289: break;
2290: } else if (col > j + n && j >= cstart) {
2291: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2292: break;
2293: }
2294: }
2295: if (j == ncols && ncols < A->cmap->N - n) {
2296: /* a hole is outside compressed Bcols */
2297: if (ncols == 0) {
2298: if (cstart) {
2299: offdiagIdx[r] = 0;
2300: } else offdiagIdx[r] = cend;
2301: } else { /* ncols > 0 */
2302: offdiagIdx[r] = cmap[ncols - 1] + 1;
2303: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2304: }
2305: }
2306: }
2308: for (j = 0; j < ncols; j++) {
2309: if (PetscAbsScalar(offdiagA[r]) > PetscAbsScalar(*ba)) {
2310: offdiagA[r] = *ba;
2311: offdiagIdx[r] = cmap[*bj];
2312: }
2313: ba++;
2314: bj++;
2315: }
2316: }
2318: PetscCall(VecGetArrayWrite(v, &a));
2319: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2320: for (r = 0; r < m; ++r) {
2321: if (PetscAbsScalar(diagA[r]) < PetscAbsScalar(offdiagA[r])) {
2322: a[r] = diagA[r];
2323: if (idx) idx[r] = cstart + diagIdx[r];
2324: } else if (PetscAbsScalar(diagA[r]) == PetscAbsScalar(offdiagA[r])) {
2325: a[r] = diagA[r];
2326: if (idx) {
2327: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2328: idx[r] = cstart + diagIdx[r];
2329: } else idx[r] = offdiagIdx[r];
2330: }
2331: } else {
2332: a[r] = offdiagA[r];
2333: if (idx) idx[r] = offdiagIdx[r];
2334: }
2335: }
2336: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2337: PetscCall(VecRestoreArrayWrite(v, &a));
2338: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2339: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2340: PetscCall(VecDestroy(&diagV));
2341: PetscCall(VecDestroy(&offdiagV));
2342: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2343: PetscFunctionReturn(PETSC_SUCCESS);
2344: }
2346: static PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2347: {
2348: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2349: PetscInt m = A->rmap->n, n = A->cmap->n;
2350: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2351: PetscInt *cmap = mat->garray;
2352: PetscInt *diagIdx, *offdiagIdx;
2353: Vec diagV, offdiagV;
2354: PetscScalar *a, *diagA, *offdiagA;
2355: const PetscScalar *ba, *bav;
2356: PetscInt r, j, col, ncols, *bi, *bj;
2357: Mat B = mat->B;
2358: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2360: PetscFunctionBegin;
2361: /* When a process holds entire A and other processes have no entry */
2362: if (A->cmap->N == n) {
2363: PetscCall(VecGetArrayWrite(v, &diagA));
2364: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2365: PetscCall(MatGetRowMin(mat->A, diagV, idx));
2366: PetscCall(VecDestroy(&diagV));
2367: PetscCall(VecRestoreArrayWrite(v, &diagA));
2368: PetscFunctionReturn(PETSC_SUCCESS);
2369: } else if (n == 0) {
2370: if (m) {
2371: PetscCall(VecGetArrayWrite(v, &a));
2372: for (r = 0; r < m; r++) {
2373: a[r] = PETSC_MAX_REAL;
2374: if (idx) idx[r] = -1;
2375: }
2376: PetscCall(VecRestoreArrayWrite(v, &a));
2377: }
2378: PetscFunctionReturn(PETSC_SUCCESS);
2379: }
2381: PetscCall(PetscCalloc2(m, &diagIdx, m, &offdiagIdx));
2382: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2383: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2384: PetscCall(MatGetRowMin(mat->A, diagV, diagIdx));
2386: /* Get offdiagIdx[] for implicit 0.0 */
2387: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2388: ba = bav;
2389: bi = b->i;
2390: bj = b->j;
2391: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2392: for (r = 0; r < m; r++) {
2393: ncols = bi[r + 1] - bi[r];
2394: if (ncols == A->cmap->N - n) { /* Brow is dense */
2395: offdiagA[r] = *ba;
2396: offdiagIdx[r] = cmap[0];
2397: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2398: offdiagA[r] = 0.0;
2400: /* Find first hole in the cmap */
2401: for (j = 0; j < ncols; j++) {
2402: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2403: if (col > j && j < cstart) {
2404: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2405: break;
2406: } else if (col > j + n && j >= cstart) {
2407: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2408: break;
2409: }
2410: }
2411: if (j == ncols && ncols < A->cmap->N - n) {
2412: /* a hole is outside compressed Bcols */
2413: if (ncols == 0) {
2414: if (cstart) {
2415: offdiagIdx[r] = 0;
2416: } else offdiagIdx[r] = cend;
2417: } else { /* ncols > 0 */
2418: offdiagIdx[r] = cmap[ncols - 1] + 1;
2419: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2420: }
2421: }
2422: }
2424: for (j = 0; j < ncols; j++) {
2425: if (PetscRealPart(offdiagA[r]) > PetscRealPart(*ba)) {
2426: offdiagA[r] = *ba;
2427: offdiagIdx[r] = cmap[*bj];
2428: }
2429: ba++;
2430: bj++;
2431: }
2432: }
2434: PetscCall(VecGetArrayWrite(v, &a));
2435: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2436: for (r = 0; r < m; ++r) {
2437: if (PetscRealPart(diagA[r]) < PetscRealPart(offdiagA[r])) {
2438: a[r] = diagA[r];
2439: if (idx) idx[r] = cstart + diagIdx[r];
2440: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2441: a[r] = diagA[r];
2442: if (idx) {
2443: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2444: idx[r] = cstart + diagIdx[r];
2445: } else idx[r] = offdiagIdx[r];
2446: }
2447: } else {
2448: a[r] = offdiagA[r];
2449: if (idx) idx[r] = offdiagIdx[r];
2450: }
2451: }
2452: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2453: PetscCall(VecRestoreArrayWrite(v, &a));
2454: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2455: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2456: PetscCall(VecDestroy(&diagV));
2457: PetscCall(VecDestroy(&offdiagV));
2458: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2459: PetscFunctionReturn(PETSC_SUCCESS);
2460: }
2462: static PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2463: {
2464: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2465: PetscInt m = A->rmap->n, n = A->cmap->n;
2466: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2467: PetscInt *cmap = mat->garray;
2468: PetscInt *diagIdx, *offdiagIdx;
2469: Vec diagV, offdiagV;
2470: PetscScalar *a, *diagA, *offdiagA;
2471: const PetscScalar *ba, *bav;
2472: PetscInt r, j, col, ncols, *bi, *bj;
2473: Mat B = mat->B;
2474: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2476: PetscFunctionBegin;
2477: /* When a process holds entire A and other processes have no entry */
2478: if (A->cmap->N == n) {
2479: PetscCall(VecGetArrayWrite(v, &diagA));
2480: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2481: PetscCall(MatGetRowMax(mat->A, diagV, idx));
2482: PetscCall(VecDestroy(&diagV));
2483: PetscCall(VecRestoreArrayWrite(v, &diagA));
2484: PetscFunctionReturn(PETSC_SUCCESS);
2485: } else if (n == 0) {
2486: if (m) {
2487: PetscCall(VecGetArrayWrite(v, &a));
2488: for (r = 0; r < m; r++) {
2489: a[r] = PETSC_MIN_REAL;
2490: if (idx) idx[r] = -1;
2491: }
2492: PetscCall(VecRestoreArrayWrite(v, &a));
2493: }
2494: PetscFunctionReturn(PETSC_SUCCESS);
2495: }
2497: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2498: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2499: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2500: PetscCall(MatGetRowMax(mat->A, diagV, diagIdx));
2502: /* Get offdiagIdx[] for implicit 0.0 */
2503: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2504: ba = bav;
2505: bi = b->i;
2506: bj = b->j;
2507: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2508: for (r = 0; r < m; r++) {
2509: ncols = bi[r + 1] - bi[r];
2510: if (ncols == A->cmap->N - n) { /* Brow is dense */
2511: offdiagA[r] = *ba;
2512: offdiagIdx[r] = cmap[0];
2513: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2514: offdiagA[r] = 0.0;
2516: /* Find first hole in the cmap */
2517: for (j = 0; j < ncols; j++) {
2518: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2519: if (col > j && j < cstart) {
2520: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2521: break;
2522: } else if (col > j + n && j >= cstart) {
2523: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2524: break;
2525: }
2526: }
2527: if (j == ncols && ncols < A->cmap->N - n) {
2528: /* a hole is outside compressed Bcols */
2529: if (ncols == 0) {
2530: if (cstart) {
2531: offdiagIdx[r] = 0;
2532: } else offdiagIdx[r] = cend;
2533: } else { /* ncols > 0 */
2534: offdiagIdx[r] = cmap[ncols - 1] + 1;
2535: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2536: }
2537: }
2538: }
2540: for (j = 0; j < ncols; j++) {
2541: if (PetscRealPart(offdiagA[r]) < PetscRealPart(*ba)) {
2542: offdiagA[r] = *ba;
2543: offdiagIdx[r] = cmap[*bj];
2544: }
2545: ba++;
2546: bj++;
2547: }
2548: }
2550: PetscCall(VecGetArrayWrite(v, &a));
2551: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2552: for (r = 0; r < m; ++r) {
2553: if (PetscRealPart(diagA[r]) > PetscRealPart(offdiagA[r])) {
2554: a[r] = diagA[r];
2555: if (idx) idx[r] = cstart + diagIdx[r];
2556: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2557: a[r] = diagA[r];
2558: if (idx) {
2559: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2560: idx[r] = cstart + diagIdx[r];
2561: } else idx[r] = offdiagIdx[r];
2562: }
2563: } else {
2564: a[r] = offdiagA[r];
2565: if (idx) idx[r] = offdiagIdx[r];
2566: }
2567: }
2568: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2569: PetscCall(VecRestoreArrayWrite(v, &a));
2570: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2571: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2572: PetscCall(VecDestroy(&diagV));
2573: PetscCall(VecDestroy(&offdiagV));
2574: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2575: PetscFunctionReturn(PETSC_SUCCESS);
2576: }
2578: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat, Mat *newmat)
2579: {
2580: Mat *dummy;
2582: PetscFunctionBegin;
2583: PetscCall(MatCreateSubMatrix_MPIAIJ_All(mat, MAT_DO_NOT_GET_VALUES, MAT_INITIAL_MATRIX, &dummy));
2584: *newmat = *dummy;
2585: PetscCall(PetscFree(dummy));
2586: PetscFunctionReturn(PETSC_SUCCESS);
2587: }
2589: static PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A, const PetscScalar **values)
2590: {
2591: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2593: PetscFunctionBegin;
2594: PetscCall(MatInvertBlockDiagonal(a->A, values));
2595: A->factorerrortype = a->A->factorerrortype;
2596: PetscFunctionReturn(PETSC_SUCCESS);
2597: }
2599: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x, PetscRandom rctx)
2600: {
2601: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)x->data;
2603: PetscFunctionBegin;
2604: PetscCheck(x->assembled || x->preallocated, PetscObjectComm((PetscObject)x), PETSC_ERR_ARG_WRONGSTATE, "MatSetRandom on an unassembled and unpreallocated MATMPIAIJ is not allowed");
2605: PetscCall(MatSetRandom(aij->A, rctx));
2606: if (x->assembled) {
2607: PetscCall(MatSetRandom(aij->B, rctx));
2608: } else {
2609: PetscCall(MatSetRandomSkipColumnRange_SeqAIJ_Private(aij->B, x->cmap->rstart, x->cmap->rend, rctx));
2610: }
2611: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
2612: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
2613: PetscFunctionReturn(PETSC_SUCCESS);
2614: }
2616: static PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ(Mat A, PetscBool sc)
2617: {
2618: PetscFunctionBegin;
2619: if (sc) A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ_Scalable;
2620: else A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ;
2621: PetscFunctionReturn(PETSC_SUCCESS);
2622: }
2624: /*@
2625: MatMPIAIJGetNumberNonzeros - gets the number of nonzeros in the matrix on this MPI rank
2627: Not Collective
2629: Input Parameter:
2630: . A - the matrix
2632: Output Parameter:
2633: . nz - the number of nonzeros
2635: Level: advanced
2637: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2638: @*/
2639: PetscErrorCode MatMPIAIJGetNumberNonzeros(Mat A, PetscCount *nz)
2640: {
2641: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)A->data;
2642: Mat_SeqAIJ *aaij = (Mat_SeqAIJ *)maij->A->data, *baij = (Mat_SeqAIJ *)maij->B->data;
2643: PetscBool isaij;
2645: PetscFunctionBegin;
2646: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATMPIAIJ, &isaij));
2647: PetscCheck(isaij, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for type %s", ((PetscObject)A)->type_name);
2648: *nz = aaij->i[A->rmap->n] + baij->i[A->rmap->n];
2649: PetscFunctionReturn(PETSC_SUCCESS);
2650: }
2652: /*@
2653: MatMPIAIJSetUseScalableIncreaseOverlap - Determine if the matrix uses a scalable algorithm to compute the overlap
2655: Collective
2657: Input Parameters:
2658: + A - the matrix
2659: - sc - `PETSC_TRUE` indicates use the scalable algorithm (default is not to use the scalable algorithm)
2661: Level: advanced
2663: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2664: @*/
2665: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap(Mat A, PetscBool sc)
2666: {
2667: PetscFunctionBegin;
2668: PetscTryMethod(A, "MatMPIAIJSetUseScalableIncreaseOverlap_C", (Mat, PetscBool), (A, sc));
2669: PetscFunctionReturn(PETSC_SUCCESS);
2670: }
2672: PetscErrorCode MatSetFromOptions_MPIAIJ(Mat A, PetscOptionItems *PetscOptionsObject)
2673: {
2674: PetscBool sc = PETSC_FALSE, flg;
2676: PetscFunctionBegin;
2677: PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJ options");
2678: if (A->ops->increaseoverlap == MatIncreaseOverlap_MPIAIJ_Scalable) sc = PETSC_TRUE;
2679: PetscCall(PetscOptionsBool("-mat_increase_overlap_scalable", "Use a scalable algorithm to compute the overlap", "MatIncreaseOverlap", sc, &sc, &flg));
2680: if (flg) PetscCall(MatMPIAIJSetUseScalableIncreaseOverlap(A, sc));
2681: PetscOptionsHeadEnd();
2682: PetscFunctionReturn(PETSC_SUCCESS);
2683: }
2685: static PetscErrorCode MatShift_MPIAIJ(Mat Y, PetscScalar a)
2686: {
2687: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)Y->data;
2688: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)maij->A->data;
2690: PetscFunctionBegin;
2691: if (!Y->preallocated) {
2692: PetscCall(MatMPIAIJSetPreallocation(Y, 1, NULL, 0, NULL));
2693: } else if (!aij->nz) { /* It does not matter if diagonals of Y only partially lie in maij->A. We just need an estimated preallocation. */
2694: PetscInt nonew = aij->nonew;
2695: PetscCall(MatSeqAIJSetPreallocation(maij->A, 1, NULL));
2696: aij->nonew = nonew;
2697: }
2698: PetscCall(MatShift_Basic(Y, a));
2699: PetscFunctionReturn(PETSC_SUCCESS);
2700: }
2702: static PetscErrorCode MatMissingDiagonal_MPIAIJ(Mat A, PetscBool *missing, PetscInt *d)
2703: {
2704: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2706: PetscFunctionBegin;
2707: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2708: PetscCall(MatMissingDiagonal(a->A, missing, d));
2709: if (d) {
2710: PetscInt rstart;
2711: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2712: *d += rstart;
2713: }
2714: PetscFunctionReturn(PETSC_SUCCESS);
2715: }
2717: static PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2718: {
2719: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2721: PetscFunctionBegin;
2722: PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2723: PetscFunctionReturn(PETSC_SUCCESS);
2724: }
2726: static PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A, PetscBool keep)
2727: {
2728: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2730: PetscFunctionBegin;
2731: PetscCall(MatEliminateZeros_SeqAIJ(a->A, keep)); // possibly keep zero diagonal coefficients
2732: PetscCall(MatEliminateZeros_SeqAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2733: PetscFunctionReturn(PETSC_SUCCESS);
2734: }
2736: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2737: MatGetRow_MPIAIJ,
2738: MatRestoreRow_MPIAIJ,
2739: MatMult_MPIAIJ,
2740: /* 4*/ MatMultAdd_MPIAIJ,
2741: MatMultTranspose_MPIAIJ,
2742: MatMultTransposeAdd_MPIAIJ,
2743: NULL,
2744: NULL,
2745: NULL,
2746: /*10*/ NULL,
2747: NULL,
2748: NULL,
2749: MatSOR_MPIAIJ,
2750: MatTranspose_MPIAIJ,
2751: /*15*/ MatGetInfo_MPIAIJ,
2752: MatEqual_MPIAIJ,
2753: MatGetDiagonal_MPIAIJ,
2754: MatDiagonalScale_MPIAIJ,
2755: MatNorm_MPIAIJ,
2756: /*20*/ MatAssemblyBegin_MPIAIJ,
2757: MatAssemblyEnd_MPIAIJ,
2758: MatSetOption_MPIAIJ,
2759: MatZeroEntries_MPIAIJ,
2760: /*24*/ MatZeroRows_MPIAIJ,
2761: NULL,
2762: NULL,
2763: NULL,
2764: NULL,
2765: /*29*/ MatSetUp_MPI_Hash,
2766: NULL,
2767: NULL,
2768: MatGetDiagonalBlock_MPIAIJ,
2769: NULL,
2770: /*34*/ MatDuplicate_MPIAIJ,
2771: NULL,
2772: NULL,
2773: NULL,
2774: NULL,
2775: /*39*/ MatAXPY_MPIAIJ,
2776: MatCreateSubMatrices_MPIAIJ,
2777: MatIncreaseOverlap_MPIAIJ,
2778: MatGetValues_MPIAIJ,
2779: MatCopy_MPIAIJ,
2780: /*44*/ MatGetRowMax_MPIAIJ,
2781: MatScale_MPIAIJ,
2782: MatShift_MPIAIJ,
2783: MatDiagonalSet_MPIAIJ,
2784: MatZeroRowsColumns_MPIAIJ,
2785: /*49*/ MatSetRandom_MPIAIJ,
2786: MatGetRowIJ_MPIAIJ,
2787: MatRestoreRowIJ_MPIAIJ,
2788: NULL,
2789: NULL,
2790: /*54*/ MatFDColoringCreate_MPIXAIJ,
2791: NULL,
2792: MatSetUnfactored_MPIAIJ,
2793: MatPermute_MPIAIJ,
2794: NULL,
2795: /*59*/ MatCreateSubMatrix_MPIAIJ,
2796: MatDestroy_MPIAIJ,
2797: MatView_MPIAIJ,
2798: NULL,
2799: NULL,
2800: /*64*/ NULL,
2801: MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2802: NULL,
2803: NULL,
2804: NULL,
2805: /*69*/ MatGetRowMaxAbs_MPIAIJ,
2806: MatGetRowMinAbs_MPIAIJ,
2807: NULL,
2808: NULL,
2809: NULL,
2810: NULL,
2811: /*75*/ MatFDColoringApply_AIJ,
2812: MatSetFromOptions_MPIAIJ,
2813: NULL,
2814: NULL,
2815: MatFindZeroDiagonals_MPIAIJ,
2816: /*80*/ NULL,
2817: NULL,
2818: NULL,
2819: /*83*/ MatLoad_MPIAIJ,
2820: NULL,
2821: NULL,
2822: NULL,
2823: NULL,
2824: NULL,
2825: /*89*/ NULL,
2826: NULL,
2827: MatMatMultNumeric_MPIAIJ_MPIAIJ,
2828: NULL,
2829: NULL,
2830: /*94*/ MatPtAPNumeric_MPIAIJ_MPIAIJ,
2831: NULL,
2832: NULL,
2833: NULL,
2834: MatBindToCPU_MPIAIJ,
2835: /*99*/ MatProductSetFromOptions_MPIAIJ,
2836: NULL,
2837: NULL,
2838: MatConjugate_MPIAIJ,
2839: NULL,
2840: /*104*/ MatSetValuesRow_MPIAIJ,
2841: MatRealPart_MPIAIJ,
2842: MatImaginaryPart_MPIAIJ,
2843: NULL,
2844: NULL,
2845: /*109*/ NULL,
2846: NULL,
2847: MatGetRowMin_MPIAIJ,
2848: NULL,
2849: MatMissingDiagonal_MPIAIJ,
2850: /*114*/ MatGetSeqNonzeroStructure_MPIAIJ,
2851: NULL,
2852: MatGetGhosts_MPIAIJ,
2853: NULL,
2854: NULL,
2855: /*119*/ MatMultDiagonalBlock_MPIAIJ,
2856: NULL,
2857: NULL,
2858: NULL,
2859: MatGetMultiProcBlock_MPIAIJ,
2860: /*124*/ MatFindNonzeroRows_MPIAIJ,
2861: MatGetColumnReductions_MPIAIJ,
2862: MatInvertBlockDiagonal_MPIAIJ,
2863: MatInvertVariableBlockDiagonal_MPIAIJ,
2864: MatCreateSubMatricesMPI_MPIAIJ,
2865: /*129*/ NULL,
2866: NULL,
2867: NULL,
2868: MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2869: NULL,
2870: /*134*/ NULL,
2871: NULL,
2872: NULL,
2873: NULL,
2874: NULL,
2875: /*139*/ MatSetBlockSizes_MPIAIJ,
2876: NULL,
2877: NULL,
2878: MatFDColoringSetUp_MPIXAIJ,
2879: MatFindOffBlockDiagonalEntries_MPIAIJ,
2880: MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2881: /*145*/ NULL,
2882: NULL,
2883: NULL,
2884: MatCreateGraph_Simple_AIJ,
2885: NULL,
2886: /*150*/ NULL,
2887: MatEliminateZeros_MPIAIJ,
2888: MatGetRowSumAbs_MPIAIJ,
2889: NULL,
2890: NULL,
2891: /*155*/ NULL,
2892: MatCopyHashToXAIJ_MPI_Hash};
2894: static PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2895: {
2896: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2898: PetscFunctionBegin;
2899: PetscCall(MatStoreValues(aij->A));
2900: PetscCall(MatStoreValues(aij->B));
2901: PetscFunctionReturn(PETSC_SUCCESS);
2902: }
2904: static PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2905: {
2906: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2908: PetscFunctionBegin;
2909: PetscCall(MatRetrieveValues(aij->A));
2910: PetscCall(MatRetrieveValues(aij->B));
2911: PetscFunctionReturn(PETSC_SUCCESS);
2912: }
2914: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2915: {
2916: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2917: PetscMPIInt size;
2919: PetscFunctionBegin;
2920: if (B->hash_active) {
2921: B->ops[0] = b->cops;
2922: B->hash_active = PETSC_FALSE;
2923: }
2924: PetscCall(PetscLayoutSetUp(B->rmap));
2925: PetscCall(PetscLayoutSetUp(B->cmap));
2927: #if defined(PETSC_USE_CTABLE)
2928: PetscCall(PetscHMapIDestroy(&b->colmap));
2929: #else
2930: PetscCall(PetscFree(b->colmap));
2931: #endif
2932: PetscCall(PetscFree(b->garray));
2933: PetscCall(VecDestroy(&b->lvec));
2934: PetscCall(VecScatterDestroy(&b->Mvctx));
2936: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2938: MatSeqXAIJGetOptions_Private(b->B);
2939: PetscCall(MatDestroy(&b->B));
2940: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2941: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2942: PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2943: PetscCall(MatSetType(b->B, MATSEQAIJ));
2944: MatSeqXAIJRestoreOptions_Private(b->B);
2946: MatSeqXAIJGetOptions_Private(b->A);
2947: PetscCall(MatDestroy(&b->A));
2948: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2949: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2950: PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2951: PetscCall(MatSetType(b->A, MATSEQAIJ));
2952: MatSeqXAIJRestoreOptions_Private(b->A);
2954: PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2955: PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2956: B->preallocated = PETSC_TRUE;
2957: B->was_assembled = PETSC_FALSE;
2958: B->assembled = PETSC_FALSE;
2959: PetscFunctionReturn(PETSC_SUCCESS);
2960: }
2962: static PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2963: {
2964: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2966: PetscFunctionBegin;
2968: PetscCall(PetscLayoutSetUp(B->rmap));
2969: PetscCall(PetscLayoutSetUp(B->cmap));
2971: #if defined(PETSC_USE_CTABLE)
2972: PetscCall(PetscHMapIDestroy(&b->colmap));
2973: #else
2974: PetscCall(PetscFree(b->colmap));
2975: #endif
2976: PetscCall(PetscFree(b->garray));
2977: PetscCall(VecDestroy(&b->lvec));
2978: PetscCall(VecScatterDestroy(&b->Mvctx));
2980: PetscCall(MatResetPreallocation(b->A));
2981: PetscCall(MatResetPreallocation(b->B));
2982: B->preallocated = PETSC_TRUE;
2983: B->was_assembled = PETSC_FALSE;
2984: B->assembled = PETSC_FALSE;
2985: PetscFunctionReturn(PETSC_SUCCESS);
2986: }
2988: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2989: {
2990: Mat mat;
2991: Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;
2993: PetscFunctionBegin;
2994: *newmat = NULL;
2995: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2996: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2997: PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2998: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2999: a = (Mat_MPIAIJ *)mat->data;
3001: mat->factortype = matin->factortype;
3002: mat->assembled = matin->assembled;
3003: mat->insertmode = NOT_SET_VALUES;
3005: a->size = oldmat->size;
3006: a->rank = oldmat->rank;
3007: a->donotstash = oldmat->donotstash;
3008: a->roworiented = oldmat->roworiented;
3009: a->rowindices = NULL;
3010: a->rowvalues = NULL;
3011: a->getrowactive = PETSC_FALSE;
3013: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
3014: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
3015: if (matin->hash_active) {
3016: PetscCall(MatSetUp(mat));
3017: } else {
3018: mat->preallocated = matin->preallocated;
3019: if (oldmat->colmap) {
3020: #if defined(PETSC_USE_CTABLE)
3021: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3022: #else
3023: PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
3024: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
3025: #endif
3026: } else a->colmap = NULL;
3027: if (oldmat->garray) {
3028: PetscInt len;
3029: len = oldmat->B->cmap->n;
3030: PetscCall(PetscMalloc1(len + 1, &a->garray));
3031: if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3032: } else a->garray = NULL;
3034: /* It may happen MatDuplicate is called with a non-assembled matrix
3035: In fact, MatDuplicate only requires the matrix to be preallocated
3036: This may happen inside a DMCreateMatrix_Shell */
3037: if (oldmat->lvec) PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3038: if (oldmat->Mvctx) {
3039: a->Mvctx = oldmat->Mvctx;
3040: PetscCall(PetscObjectReference((PetscObject)oldmat->Mvctx));
3041: }
3042: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3043: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3044: }
3045: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3046: *newmat = mat;
3047: PetscFunctionReturn(PETSC_SUCCESS);
3048: }
3050: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3051: {
3052: PetscBool isbinary, ishdf5;
3054: PetscFunctionBegin;
3057: /* force binary viewer to load .info file if it has not yet done so */
3058: PetscCall(PetscViewerSetUp(viewer));
3059: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3060: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3061: if (isbinary) {
3062: PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3063: } else if (ishdf5) {
3064: #if defined(PETSC_HAVE_HDF5)
3065: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3066: #else
3067: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3068: #endif
3069: } else {
3070: 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);
3071: }
3072: PetscFunctionReturn(PETSC_SUCCESS);
3073: }
3075: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3076: {
3077: PetscInt header[4], M, N, m, nz, rows, cols, sum, i;
3078: PetscInt *rowidxs, *colidxs;
3079: PetscScalar *matvals;
3081: PetscFunctionBegin;
3082: PetscCall(PetscViewerSetUp(viewer));
3084: /* read in matrix header */
3085: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3086: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3087: M = header[1];
3088: N = header[2];
3089: nz = header[3];
3090: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3091: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3092: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");
3094: /* set block sizes from the viewer's .info file */
3095: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3096: /* set global sizes if not set already */
3097: if (mat->rmap->N < 0) mat->rmap->N = M;
3098: if (mat->cmap->N < 0) mat->cmap->N = N;
3099: PetscCall(PetscLayoutSetUp(mat->rmap));
3100: PetscCall(PetscLayoutSetUp(mat->cmap));
3102: /* check if the matrix sizes are correct */
3103: PetscCall(MatGetSize(mat, &rows, &cols));
3104: 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);
3106: /* read in row lengths and build row indices */
3107: PetscCall(MatGetLocalSize(mat, &m, NULL));
3108: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3109: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3110: rowidxs[0] = 0;
3111: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3112: if (nz != PETSC_INT_MAX) {
3113: PetscCallMPI(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3114: 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);
3115: }
3117: /* read in column indices and matrix values */
3118: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3119: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3120: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3121: /* store matrix indices and values */
3122: PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3123: PetscCall(PetscFree(rowidxs));
3124: PetscCall(PetscFree2(colidxs, matvals));
3125: PetscFunctionReturn(PETSC_SUCCESS);
3126: }
3128: /* Not scalable because of ISAllGather() unless getting all columns. */
3129: static PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3130: {
3131: IS iscol_local;
3132: PetscBool isstride;
3133: PetscMPIInt lisstride = 0, gisstride;
3135: PetscFunctionBegin;
3136: /* check if we are grabbing all columns*/
3137: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));
3139: if (isstride) {
3140: PetscInt start, len, mstart, mlen;
3141: PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3142: PetscCall(ISGetLocalSize(iscol, &len));
3143: PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3144: if (mstart == start && mlen - mstart == len) lisstride = 1;
3145: }
3147: PetscCallMPI(MPIU_Allreduce(&lisstride, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3148: if (gisstride) {
3149: PetscInt N;
3150: PetscCall(MatGetSize(mat, NULL, &N));
3151: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3152: PetscCall(ISSetIdentity(iscol_local));
3153: PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3154: } else {
3155: PetscInt cbs;
3156: PetscCall(ISGetBlockSize(iscol, &cbs));
3157: PetscCall(ISAllGather(iscol, &iscol_local));
3158: PetscCall(ISSetBlockSize(iscol_local, cbs));
3159: }
3161: *isseq = iscol_local;
3162: PetscFunctionReturn(PETSC_SUCCESS);
3163: }
3165: /*
3166: Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3167: (see MatCreateSubMatrix_MPIAIJ_nonscalable)
3169: Input Parameters:
3170: + mat - matrix
3171: . isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3172: i.e., mat->rstart <= isrow[i] < mat->rend
3173: - iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3174: i.e., mat->cstart <= iscol[i] < mat->cend
3176: Output Parameters:
3177: + isrow_d - sequential row index set for retrieving mat->A
3178: . iscol_d - sequential column index set for retrieving mat->A
3179: . iscol_o - sequential column index set for retrieving mat->B
3180: - garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3181: */
3182: static PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, PetscInt *garray[])
3183: {
3184: Vec x, cmap;
3185: const PetscInt *is_idx;
3186: PetscScalar *xarray, *cmaparray;
3187: PetscInt ncols, isstart, *idx, m, rstart, *cmap1, count;
3188: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3189: Mat B = a->B;
3190: Vec lvec = a->lvec, lcmap;
3191: PetscInt i, cstart, cend, Bn = B->cmap->N;
3192: MPI_Comm comm;
3193: VecScatter Mvctx = a->Mvctx;
3195: PetscFunctionBegin;
3196: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3197: PetscCall(ISGetLocalSize(iscol, &ncols));
3199: /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3200: PetscCall(MatCreateVecs(mat, &x, NULL));
3201: PetscCall(VecSet(x, -1.0));
3202: PetscCall(VecDuplicate(x, &cmap));
3203: PetscCall(VecSet(cmap, -1.0));
3205: /* Get start indices */
3206: PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3207: isstart -= ncols;
3208: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
3210: PetscCall(ISGetIndices(iscol, &is_idx));
3211: PetscCall(VecGetArray(x, &xarray));
3212: PetscCall(VecGetArray(cmap, &cmaparray));
3213: PetscCall(PetscMalloc1(ncols, &idx));
3214: for (i = 0; i < ncols; i++) {
3215: xarray[is_idx[i] - cstart] = (PetscScalar)is_idx[i];
3216: cmaparray[is_idx[i] - cstart] = i + isstart; /* global index of iscol[i] */
3217: idx[i] = is_idx[i] - cstart; /* local index of iscol[i] */
3218: }
3219: PetscCall(VecRestoreArray(x, &xarray));
3220: PetscCall(VecRestoreArray(cmap, &cmaparray));
3221: PetscCall(ISRestoreIndices(iscol, &is_idx));
3223: /* Get iscol_d */
3224: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3225: PetscCall(ISGetBlockSize(iscol, &i));
3226: PetscCall(ISSetBlockSize(*iscol_d, i));
3228: /* Get isrow_d */
3229: PetscCall(ISGetLocalSize(isrow, &m));
3230: rstart = mat->rmap->rstart;
3231: PetscCall(PetscMalloc1(m, &idx));
3232: PetscCall(ISGetIndices(isrow, &is_idx));
3233: for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3234: PetscCall(ISRestoreIndices(isrow, &is_idx));
3236: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3237: PetscCall(ISGetBlockSize(isrow, &i));
3238: PetscCall(ISSetBlockSize(*isrow_d, i));
3240: /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3241: PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3242: PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3244: PetscCall(VecDuplicate(lvec, &lcmap));
3246: PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3247: PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3249: /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3250: /* off-process column indices */
3251: count = 0;
3252: PetscCall(PetscMalloc1(Bn, &idx));
3253: PetscCall(PetscMalloc1(Bn, &cmap1));
3255: PetscCall(VecGetArray(lvec, &xarray));
3256: PetscCall(VecGetArray(lcmap, &cmaparray));
3257: for (i = 0; i < Bn; i++) {
3258: if (PetscRealPart(xarray[i]) > -1.0) {
3259: idx[count] = i; /* local column index in off-diagonal part B */
3260: cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3261: count++;
3262: }
3263: }
3264: PetscCall(VecRestoreArray(lvec, &xarray));
3265: PetscCall(VecRestoreArray(lcmap, &cmaparray));
3267: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3268: /* cannot ensure iscol_o has same blocksize as iscol! */
3270: PetscCall(PetscFree(idx));
3271: *garray = cmap1;
3273: PetscCall(VecDestroy(&x));
3274: PetscCall(VecDestroy(&cmap));
3275: PetscCall(VecDestroy(&lcmap));
3276: PetscFunctionReturn(PETSC_SUCCESS);
3277: }
3279: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3280: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3281: {
3282: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3283: Mat M = NULL;
3284: MPI_Comm comm;
3285: IS iscol_d, isrow_d, iscol_o;
3286: Mat Asub = NULL, Bsub = NULL;
3287: PetscInt n;
3289: PetscFunctionBegin;
3290: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3292: if (call == MAT_REUSE_MATRIX) {
3293: /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3294: PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3295: PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");
3297: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3298: PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");
3300: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3301: PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");
3303: /* Update diagonal and off-diagonal portions of submat */
3304: asub = (Mat_MPIAIJ *)(*submat)->data;
3305: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3306: PetscCall(ISGetLocalSize(iscol_o, &n));
3307: if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3308: PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3309: PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));
3311: } else { /* call == MAT_INITIAL_MATRIX) */
3312: PetscInt *garray;
3313: PetscInt BsubN;
3315: /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3316: PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));
3318: /* Create local submatrices Asub and Bsub */
3319: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3320: PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));
3322: /* Create submatrix M */
3323: PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, Asub, Bsub, garray, &M));
3325: /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3326: asub = (Mat_MPIAIJ *)M->data;
3328: PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3329: n = asub->B->cmap->N;
3330: if (BsubN > n) {
3331: /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3332: const PetscInt *idx;
3333: PetscInt i, j, *idx_new, *subgarray = asub->garray;
3334: PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));
3336: PetscCall(PetscMalloc1(n, &idx_new));
3337: j = 0;
3338: PetscCall(ISGetIndices(iscol_o, &idx));
3339: for (i = 0; i < n; i++) {
3340: if (j >= BsubN) break;
3341: while (subgarray[i] > garray[j]) j++;
3343: if (subgarray[i] == garray[j]) {
3344: idx_new[i] = idx[j++];
3345: } 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]);
3346: }
3347: PetscCall(ISRestoreIndices(iscol_o, &idx));
3349: PetscCall(ISDestroy(&iscol_o));
3350: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));
3352: } else if (BsubN < n) {
3353: 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);
3354: }
3356: PetscCall(PetscFree(garray));
3357: *submat = M;
3359: /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3360: PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3361: PetscCall(ISDestroy(&isrow_d));
3363: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3364: PetscCall(ISDestroy(&iscol_d));
3366: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3367: PetscCall(ISDestroy(&iscol_o));
3368: }
3369: PetscFunctionReturn(PETSC_SUCCESS);
3370: }
3372: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3373: {
3374: IS iscol_local = NULL, isrow_d;
3375: PetscInt csize;
3376: PetscInt n, i, j, start, end;
3377: PetscBool sameRowDist = PETSC_FALSE, sameDist[2], tsameDist[2];
3378: MPI_Comm comm;
3380: PetscFunctionBegin;
3381: /* If isrow has same processor distribution as mat,
3382: call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3383: if (call == MAT_REUSE_MATRIX) {
3384: PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3385: if (isrow_d) {
3386: sameRowDist = PETSC_TRUE;
3387: tsameDist[1] = PETSC_TRUE; /* sameColDist */
3388: } else {
3389: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3390: if (iscol_local) {
3391: sameRowDist = PETSC_TRUE;
3392: tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3393: }
3394: }
3395: } else {
3396: /* Check if isrow has same processor distribution as mat */
3397: sameDist[0] = PETSC_FALSE;
3398: PetscCall(ISGetLocalSize(isrow, &n));
3399: if (!n) {
3400: sameDist[0] = PETSC_TRUE;
3401: } else {
3402: PetscCall(ISGetMinMax(isrow, &i, &j));
3403: PetscCall(MatGetOwnershipRange(mat, &start, &end));
3404: if (i >= start && j < end) sameDist[0] = PETSC_TRUE;
3405: }
3407: /* Check if iscol has same processor distribution as mat */
3408: sameDist[1] = PETSC_FALSE;
3409: PetscCall(ISGetLocalSize(iscol, &n));
3410: if (!n) {
3411: sameDist[1] = PETSC_TRUE;
3412: } else {
3413: PetscCall(ISGetMinMax(iscol, &i, &j));
3414: PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3415: if (i >= start && j < end) sameDist[1] = PETSC_TRUE;
3416: }
3418: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3419: PetscCallMPI(MPIU_Allreduce(&sameDist, &tsameDist, 2, MPIU_BOOL, MPI_LAND, comm));
3420: sameRowDist = tsameDist[0];
3421: }
3423: if (sameRowDist) {
3424: if (tsameDist[1]) { /* sameRowDist & sameColDist */
3425: /* isrow and iscol have same processor distribution as mat */
3426: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3427: PetscFunctionReturn(PETSC_SUCCESS);
3428: } else { /* sameRowDist */
3429: /* isrow has same processor distribution as mat */
3430: if (call == MAT_INITIAL_MATRIX) {
3431: PetscBool sorted;
3432: PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3433: PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3434: PetscCall(ISGetSize(iscol, &i));
3435: PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);
3437: PetscCall(ISSorted(iscol_local, &sorted));
3438: if (sorted) {
3439: /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3440: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3441: PetscFunctionReturn(PETSC_SUCCESS);
3442: }
3443: } else { /* call == MAT_REUSE_MATRIX */
3444: IS iscol_sub;
3445: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3446: if (iscol_sub) {
3447: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3448: PetscFunctionReturn(PETSC_SUCCESS);
3449: }
3450: }
3451: }
3452: }
3454: /* General case: iscol -> iscol_local which has global size of iscol */
3455: if (call == MAT_REUSE_MATRIX) {
3456: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3457: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3458: } else {
3459: if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3460: }
3462: PetscCall(ISGetLocalSize(iscol, &csize));
3463: PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));
3465: if (call == MAT_INITIAL_MATRIX) {
3466: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3467: PetscCall(ISDestroy(&iscol_local));
3468: }
3469: PetscFunctionReturn(PETSC_SUCCESS);
3470: }
3472: /*@C
3473: MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3474: and "off-diagonal" part of the matrix in CSR format.
3476: Collective
3478: Input Parameters:
3479: + comm - MPI communicator
3480: . A - "diagonal" portion of matrix
3481: . B - "off-diagonal" portion of matrix, may have empty columns, will be destroyed by this routine
3482: - garray - global index of `B` columns
3484: Output Parameter:
3485: . mat - the matrix, with input `A` as its local diagonal matrix
3487: Level: advanced
3489: Notes:
3490: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.
3492: `A` becomes part of output mat, `B` is destroyed by this routine. The user cannot use `A` and `B` anymore.
3494: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3495: @*/
3496: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, Mat A, Mat B, const PetscInt garray[], Mat *mat)
3497: {
3498: Mat_MPIAIJ *maij;
3499: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data, *bnew;
3500: PetscInt *oi = b->i, *oj = b->j, i, nz, col;
3501: const PetscScalar *oa;
3502: Mat Bnew;
3503: PetscInt m, n, N;
3504: MatType mpi_mat_type;
3506: PetscFunctionBegin;
3507: PetscCall(MatCreate(comm, mat));
3508: PetscCall(MatGetSize(A, &m, &n));
3509: PetscCheck(m == B->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Am %" PetscInt_FMT " != Bm %" PetscInt_FMT, m, B->rmap->N);
3510: PetscCheck(PetscAbs(A->rmap->bs) == PetscAbs(B->rmap->bs), PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "A row bs %" PetscInt_FMT " != B row bs %" PetscInt_FMT, A->rmap->bs, B->rmap->bs);
3511: /* remove check below; When B is created using iscol_o from ISGetSeqIS_SameColDist_Private(), its bs may not be same as A */
3512: /* PetscCheck(A->cmap->bs == B->cmap->bs,PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"A column bs %" PetscInt_FMT " != B column bs %" PetscInt_FMT,A->cmap->bs,B->cmap->bs); */
3514: /* Get global columns of mat */
3515: PetscCallMPI(MPIU_Allreduce(&n, &N, 1, MPIU_INT, MPI_SUM, comm));
3517: PetscCall(MatSetSizes(*mat, m, n, PETSC_DECIDE, N));
3518: /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3519: PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3520: PetscCall(MatSetType(*mat, mpi_mat_type));
3522: if (A->rmap->bs > 1 || A->cmap->bs > 1) PetscCall(MatSetBlockSizes(*mat, A->rmap->bs, A->cmap->bs));
3523: maij = (Mat_MPIAIJ *)(*mat)->data;
3525: (*mat)->preallocated = PETSC_TRUE;
3527: PetscCall(PetscLayoutSetUp((*mat)->rmap));
3528: PetscCall(PetscLayoutSetUp((*mat)->cmap));
3530: /* Set A as diagonal portion of *mat */
3531: maij->A = A;
3533: nz = oi[m];
3534: for (i = 0; i < nz; i++) {
3535: col = oj[i];
3536: oj[i] = garray[col];
3537: }
3539: /* Set Bnew as off-diagonal portion of *mat */
3540: PetscCall(MatSeqAIJGetArrayRead(B, &oa));
3541: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, N, oi, oj, (PetscScalar *)oa, &Bnew));
3542: PetscCall(MatSeqAIJRestoreArrayRead(B, &oa));
3543: bnew = (Mat_SeqAIJ *)Bnew->data;
3544: bnew->maxnz = b->maxnz; /* allocated nonzeros of B */
3545: maij->B = Bnew;
3547: PetscCheck(B->rmap->N == Bnew->rmap->N, PETSC_COMM_SELF, PETSC_ERR_PLIB, "BN %" PetscInt_FMT " != BnewN %" PetscInt_FMT, B->rmap->N, Bnew->rmap->N);
3549: b->free_a = PETSC_FALSE;
3550: b->free_ij = PETSC_FALSE;
3551: PetscCall(MatDestroy(&B));
3553: bnew->free_a = PETSC_TRUE;
3554: bnew->free_ij = PETSC_TRUE;
3556: /* condense columns of maij->B */
3557: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3558: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
3559: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
3560: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
3561: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3562: PetscFunctionReturn(PETSC_SUCCESS);
3563: }
3565: extern PetscErrorCode MatCreateSubMatrices_MPIAIJ_SingleIS_Local(Mat, PetscInt, const IS[], const IS[], MatReuse, PetscBool, Mat *);
3567: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowDist(Mat mat, IS isrow, IS iscol, IS iscol_local, MatReuse call, Mat *newmat)
3568: {
3569: PetscInt i, m, n, rstart, row, rend, nz, j, bs, cbs;
3570: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3571: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3572: Mat M, Msub, B = a->B;
3573: MatScalar *aa;
3574: Mat_SeqAIJ *aij;
3575: PetscInt *garray = a->garray, *colsub, Ncols;
3576: PetscInt count, Bn = B->cmap->N, cstart = mat->cmap->rstart, cend = mat->cmap->rend;
3577: IS iscol_sub, iscmap;
3578: const PetscInt *is_idx, *cmap;
3579: PetscBool allcolumns = PETSC_FALSE;
3580: MPI_Comm comm;
3582: PetscFunctionBegin;
3583: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3584: if (call == MAT_REUSE_MATRIX) {
3585: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3586: PetscCheck(iscol_sub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "SubIScol passed in was not used before, cannot reuse");
3587: PetscCall(ISGetLocalSize(iscol_sub, &count));
3589: PetscCall(PetscObjectQuery((PetscObject)*newmat, "Subcmap", (PetscObject *)&iscmap));
3590: PetscCheck(iscmap, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Subcmap passed in was not used before, cannot reuse");
3592: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Msub));
3593: PetscCheck(Msub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3595: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_REUSE_MATRIX, PETSC_FALSE, &Msub));
3597: } else { /* call == MAT_INITIAL_MATRIX) */
3598: PetscBool flg;
3600: PetscCall(ISGetLocalSize(iscol, &n));
3601: PetscCall(ISGetSize(iscol, &Ncols));
3603: /* (1) iscol -> nonscalable iscol_local */
3604: /* Check for special case: each processor gets entire matrix columns */
3605: PetscCall(ISIdentity(iscol_local, &flg));
3606: if (flg && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3607: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3608: if (allcolumns) {
3609: iscol_sub = iscol_local;
3610: PetscCall(PetscObjectReference((PetscObject)iscol_local));
3611: PetscCall(ISCreateStride(PETSC_COMM_SELF, n, 0, 1, &iscmap));
3613: } else {
3614: /* (2) iscol_local -> iscol_sub and iscmap. Implementation below requires iscol_local be sorted, it can have duplicate indices */
3615: PetscInt *idx, *cmap1, k;
3616: PetscCall(PetscMalloc1(Ncols, &idx));
3617: PetscCall(PetscMalloc1(Ncols, &cmap1));
3618: PetscCall(ISGetIndices(iscol_local, &is_idx));
3619: count = 0;
3620: k = 0;
3621: for (i = 0; i < Ncols; i++) {
3622: j = is_idx[i];
3623: if (j >= cstart && j < cend) {
3624: /* diagonal part of mat */
3625: idx[count] = j;
3626: cmap1[count++] = i; /* column index in submat */
3627: } else if (Bn) {
3628: /* off-diagonal part of mat */
3629: if (j == garray[k]) {
3630: idx[count] = j;
3631: cmap1[count++] = i; /* column index in submat */
3632: } else if (j > garray[k]) {
3633: while (j > garray[k] && k < Bn - 1) k++;
3634: if (j == garray[k]) {
3635: idx[count] = j;
3636: cmap1[count++] = i; /* column index in submat */
3637: }
3638: }
3639: }
3640: }
3641: PetscCall(ISRestoreIndices(iscol_local, &is_idx));
3643: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_OWN_POINTER, &iscol_sub));
3644: PetscCall(ISGetBlockSize(iscol, &cbs));
3645: PetscCall(ISSetBlockSize(iscol_sub, cbs));
3647: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)iscol_local), count, cmap1, PETSC_OWN_POINTER, &iscmap));
3648: }
3650: /* (3) Create sequential Msub */
3651: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_INITIAL_MATRIX, allcolumns, &Msub));
3652: }
3654: PetscCall(ISGetLocalSize(iscol_sub, &count));
3655: aij = (Mat_SeqAIJ *)Msub->data;
3656: ii = aij->i;
3657: PetscCall(ISGetIndices(iscmap, &cmap));
3659: /*
3660: m - number of local rows
3661: Ncols - number of columns (same on all processors)
3662: rstart - first row in new global matrix generated
3663: */
3664: PetscCall(MatGetSize(Msub, &m, NULL));
3666: if (call == MAT_INITIAL_MATRIX) {
3667: /* (4) Create parallel newmat */
3668: PetscMPIInt rank, size;
3669: PetscInt csize;
3671: PetscCallMPI(MPI_Comm_size(comm, &size));
3672: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3674: /*
3675: Determine the number of non-zeros in the diagonal and off-diagonal
3676: portions of the matrix in order to do correct preallocation
3677: */
3679: /* first get start and end of "diagonal" columns */
3680: PetscCall(ISGetLocalSize(iscol, &csize));
3681: if (csize == PETSC_DECIDE) {
3682: PetscCall(ISGetSize(isrow, &mglobal));
3683: if (mglobal == Ncols) { /* square matrix */
3684: nlocal = m;
3685: } else {
3686: nlocal = Ncols / size + ((Ncols % size) > rank);
3687: }
3688: } else {
3689: nlocal = csize;
3690: }
3691: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3692: rstart = rend - nlocal;
3693: 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);
3695: /* next, compute all the lengths */
3696: jj = aij->j;
3697: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3698: olens = dlens + m;
3699: for (i = 0; i < m; i++) {
3700: jend = ii[i + 1] - ii[i];
3701: olen = 0;
3702: dlen = 0;
3703: for (j = 0; j < jend; j++) {
3704: if (cmap[*jj] < rstart || cmap[*jj] >= rend) olen++;
3705: else dlen++;
3706: jj++;
3707: }
3708: olens[i] = olen;
3709: dlens[i] = dlen;
3710: }
3712: PetscCall(ISGetBlockSize(isrow, &bs));
3713: PetscCall(ISGetBlockSize(iscol, &cbs));
3715: PetscCall(MatCreate(comm, &M));
3716: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, Ncols));
3717: PetscCall(MatSetBlockSizes(M, bs, cbs));
3718: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3719: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3720: PetscCall(PetscFree(dlens));
3722: } else { /* call == MAT_REUSE_MATRIX */
3723: M = *newmat;
3724: PetscCall(MatGetLocalSize(M, &i, NULL));
3725: PetscCheck(i == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3726: PetscCall(MatZeroEntries(M));
3727: /*
3728: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3729: rather than the slower MatSetValues().
3730: */
3731: M->was_assembled = PETSC_TRUE;
3732: M->assembled = PETSC_FALSE;
3733: }
3735: /* (5) Set values of Msub to *newmat */
3736: PetscCall(PetscMalloc1(count, &colsub));
3737: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
3739: jj = aij->j;
3740: PetscCall(MatSeqAIJGetArrayRead(Msub, (const PetscScalar **)&aa));
3741: for (i = 0; i < m; i++) {
3742: row = rstart + i;
3743: nz = ii[i + 1] - ii[i];
3744: for (j = 0; j < nz; j++) colsub[j] = cmap[jj[j]];
3745: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, colsub, aa, INSERT_VALUES));
3746: jj += nz;
3747: aa += nz;
3748: }
3749: PetscCall(MatSeqAIJRestoreArrayRead(Msub, (const PetscScalar **)&aa));
3750: PetscCall(ISRestoreIndices(iscmap, &cmap));
3752: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3753: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3755: PetscCall(PetscFree(colsub));
3757: /* save Msub, iscol_sub and iscmap used in processor for next request */
3758: if (call == MAT_INITIAL_MATRIX) {
3759: *newmat = M;
3760: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubMatrix", (PetscObject)Msub));
3761: PetscCall(MatDestroy(&Msub));
3763: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubIScol", (PetscObject)iscol_sub));
3764: PetscCall(ISDestroy(&iscol_sub));
3766: PetscCall(PetscObjectCompose((PetscObject)*newmat, "Subcmap", (PetscObject)iscmap));
3767: PetscCall(ISDestroy(&iscmap));
3769: if (iscol_local) {
3770: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3771: PetscCall(ISDestroy(&iscol_local));
3772: }
3773: }
3774: PetscFunctionReturn(PETSC_SUCCESS);
3775: }
3777: /*
3778: Not great since it makes two copies of the submatrix, first an SeqAIJ
3779: in local and then by concatenating the local matrices the end result.
3780: Writing it directly would be much like MatCreateSubMatrices_MPIAIJ()
3782: This requires a sequential iscol with all indices.
3783: */
3784: PetscErrorCode MatCreateSubMatrix_MPIAIJ_nonscalable(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
3785: {
3786: PetscMPIInt rank, size;
3787: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs, cbs;
3788: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3789: Mat M, Mreuse;
3790: MatScalar *aa, *vwork;
3791: MPI_Comm comm;
3792: Mat_SeqAIJ *aij;
3793: PetscBool colflag, allcolumns = PETSC_FALSE;
3795: PetscFunctionBegin;
3796: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3797: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3798: PetscCallMPI(MPI_Comm_size(comm, &size));
3800: /* Check for special case: each processor gets entire matrix columns */
3801: PetscCall(ISIdentity(iscol, &colflag));
3802: PetscCall(ISGetLocalSize(iscol, &n));
3803: if (colflag && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3804: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3806: if (call == MAT_REUSE_MATRIX) {
3807: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
3808: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3809: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_REUSE_MATRIX, allcolumns, &Mreuse));
3810: } else {
3811: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_INITIAL_MATRIX, allcolumns, &Mreuse));
3812: }
3814: /*
3815: m - number of local rows
3816: n - number of columns (same on all processors)
3817: rstart - first row in new global matrix generated
3818: */
3819: PetscCall(MatGetSize(Mreuse, &m, &n));
3820: PetscCall(MatGetBlockSizes(Mreuse, &bs, &cbs));
3821: if (call == MAT_INITIAL_MATRIX) {
3822: aij = (Mat_SeqAIJ *)Mreuse->data;
3823: ii = aij->i;
3824: jj = aij->j;
3826: /*
3827: Determine the number of non-zeros in the diagonal and off-diagonal
3828: portions of the matrix in order to do correct preallocation
3829: */
3831: /* first get start and end of "diagonal" columns */
3832: if (csize == PETSC_DECIDE) {
3833: PetscCall(ISGetSize(isrow, &mglobal));
3834: if (mglobal == n) { /* square matrix */
3835: nlocal = m;
3836: } else {
3837: nlocal = n / size + ((n % size) > rank);
3838: }
3839: } else {
3840: nlocal = csize;
3841: }
3842: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3843: rstart = rend - nlocal;
3844: 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);
3846: /* next, compute all the lengths */
3847: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3848: olens = dlens + m;
3849: for (i = 0; i < m; i++) {
3850: jend = ii[i + 1] - ii[i];
3851: olen = 0;
3852: dlen = 0;
3853: for (j = 0; j < jend; j++) {
3854: if (*jj < rstart || *jj >= rend) olen++;
3855: else dlen++;
3856: jj++;
3857: }
3858: olens[i] = olen;
3859: dlens[i] = dlen;
3860: }
3861: PetscCall(MatCreate(comm, &M));
3862: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, n));
3863: PetscCall(MatSetBlockSizes(M, bs, cbs));
3864: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3865: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3866: PetscCall(PetscFree(dlens));
3867: } else {
3868: PetscInt ml, nl;
3870: M = *newmat;
3871: PetscCall(MatGetLocalSize(M, &ml, &nl));
3872: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3873: PetscCall(MatZeroEntries(M));
3874: /*
3875: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3876: rather than the slower MatSetValues().
3877: */
3878: M->was_assembled = PETSC_TRUE;
3879: M->assembled = PETSC_FALSE;
3880: }
3881: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
3882: aij = (Mat_SeqAIJ *)Mreuse->data;
3883: ii = aij->i;
3884: jj = aij->j;
3886: /* trigger copy to CPU if needed */
3887: PetscCall(MatSeqAIJGetArrayRead(Mreuse, (const PetscScalar **)&aa));
3888: for (i = 0; i < m; i++) {
3889: row = rstart + i;
3890: nz = ii[i + 1] - ii[i];
3891: cwork = jj;
3892: jj = PetscSafePointerPlusOffset(jj, nz);
3893: vwork = aa;
3894: aa = PetscSafePointerPlusOffset(aa, nz);
3895: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
3896: }
3897: PetscCall(MatSeqAIJRestoreArrayRead(Mreuse, (const PetscScalar **)&aa));
3899: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3900: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3901: *newmat = M;
3903: /* save submatrix used in processor for next request */
3904: if (call == MAT_INITIAL_MATRIX) {
3905: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
3906: PetscCall(MatDestroy(&Mreuse));
3907: }
3908: PetscFunctionReturn(PETSC_SUCCESS);
3909: }
3911: static PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
3912: {
3913: PetscInt m, cstart, cend, j, nnz, i, d, *ld;
3914: PetscInt *d_nnz, *o_nnz, nnz_max = 0, rstart, ii, irstart;
3915: const PetscInt *JJ;
3916: PetscBool nooffprocentries;
3917: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)B->data;
3919: PetscFunctionBegin;
3920: PetscCall(PetscLayoutSetUp(B->rmap));
3921: PetscCall(PetscLayoutSetUp(B->cmap));
3922: m = B->rmap->n;
3923: cstart = B->cmap->rstart;
3924: cend = B->cmap->rend;
3925: rstart = B->rmap->rstart;
3926: irstart = Ii[0];
3928: PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));
3930: if (PetscDefined(USE_DEBUG)) {
3931: for (i = 0; i < m; i++) {
3932: nnz = Ii[i + 1] - Ii[i];
3933: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3934: PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3935: 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]);
3936: 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);
3937: }
3938: }
3940: for (i = 0; i < m; i++) {
3941: nnz = Ii[i + 1] - Ii[i];
3942: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3943: nnz_max = PetscMax(nnz_max, nnz);
3944: d = 0;
3945: for (j = 0; j < nnz; j++) {
3946: if (cstart <= JJ[j] && JJ[j] < cend) d++;
3947: }
3948: d_nnz[i] = d;
3949: o_nnz[i] = nnz - d;
3950: }
3951: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3952: PetscCall(PetscFree2(d_nnz, o_nnz));
3954: for (i = 0; i < m; i++) {
3955: ii = i + rstart;
3956: PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], PetscSafePointerPlusOffset(J, Ii[i] - irstart), PetscSafePointerPlusOffset(v, Ii[i] - irstart), INSERT_VALUES));
3957: }
3958: nooffprocentries = B->nooffprocentries;
3959: B->nooffprocentries = PETSC_TRUE;
3960: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3961: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3962: B->nooffprocentries = nooffprocentries;
3964: /* count number of entries below block diagonal */
3965: PetscCall(PetscFree(Aij->ld));
3966: PetscCall(PetscCalloc1(m, &ld));
3967: Aij->ld = ld;
3968: for (i = 0; i < m; i++) {
3969: nnz = Ii[i + 1] - Ii[i];
3970: j = 0;
3971: while (j < nnz && J[j] < cstart) j++;
3972: ld[i] = j;
3973: if (J) J += nnz;
3974: }
3976: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3977: PetscFunctionReturn(PETSC_SUCCESS);
3978: }
3980: /*@
3981: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
3982: (the default parallel PETSc format).
3984: Collective
3986: Input Parameters:
3987: + B - the matrix
3988: . i - the indices into `j` for the start of each local row (indices start with zero)
3989: . j - the column indices for each local row (indices start with zero)
3990: - v - optional values in the matrix
3992: Level: developer
3994: Notes:
3995: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
3996: thus you CANNOT change the matrix entries by changing the values of `v` after you have
3997: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3999: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
4001: A convenience routine for this functionality is `MatCreateMPIAIJWithArrays()`.
4003: You can update the matrix with new numerical values using `MatUpdateMPIAIJWithArrays()` after this call if the column indices in `j` are sorted.
4005: If you do **not** use `MatUpdateMPIAIJWithArrays()`, the column indices in `j` do not need to be sorted. If you will use
4006: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
4008: The format which is used for the sparse matrix input, is equivalent to a
4009: row-major ordering.. i.e for the following matrix, the input data expected is
4010: as shown
4011: .vb
4012: 1 0 0
4013: 2 0 3 P0
4014: -------
4015: 4 5 6 P1
4017: Process0 [P0] rows_owned=[0,1]
4018: i = {0,1,3} [size = nrow+1 = 2+1]
4019: j = {0,0,2} [size = 3]
4020: v = {1,2,3} [size = 3]
4022: Process1 [P1] rows_owned=[2]
4023: i = {0,3} [size = nrow+1 = 1+1]
4024: j = {0,1,2} [size = 3]
4025: v = {4,5,6} [size = 3]
4026: .ve
4028: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`,
4029: `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`, `MatCreateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4030: @*/
4031: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4032: {
4033: PetscFunctionBegin;
4034: PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4035: PetscFunctionReturn(PETSC_SUCCESS);
4036: }
4038: /*@
4039: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
4040: (the default parallel PETSc format). For good matrix assembly performance
4041: the user should preallocate the matrix storage by setting the parameters
4042: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4044: Collective
4046: Input Parameters:
4047: + B - the matrix
4048: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4049: (same value is used for all local rows)
4050: . d_nnz - array containing the number of nonzeros in the various rows of the
4051: DIAGONAL portion of the local submatrix (possibly different for each row)
4052: or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4053: The size of this array is equal to the number of local rows, i.e 'm'.
4054: For matrices that will be factored, you must leave room for (and set)
4055: the diagonal entry even if it is zero.
4056: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4057: submatrix (same value is used for all local rows).
4058: - o_nnz - array containing the number of nonzeros in the various rows of the
4059: OFF-DIAGONAL portion of the local submatrix (possibly different for
4060: each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4061: structure. The size of this array is equal to the number
4062: of local rows, i.e 'm'.
4064: Example Usage:
4065: Consider the following 8x8 matrix with 34 non-zero values, that is
4066: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4067: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4068: as follows
4070: .vb
4071: 1 2 0 | 0 3 0 | 0 4
4072: Proc0 0 5 6 | 7 0 0 | 8 0
4073: 9 0 10 | 11 0 0 | 12 0
4074: -------------------------------------
4075: 13 0 14 | 15 16 17 | 0 0
4076: Proc1 0 18 0 | 19 20 21 | 0 0
4077: 0 0 0 | 22 23 0 | 24 0
4078: -------------------------------------
4079: Proc2 25 26 27 | 0 0 28 | 29 0
4080: 30 0 0 | 31 32 33 | 0 34
4081: .ve
4083: This can be represented as a collection of submatrices as
4084: .vb
4085: A B C
4086: D E F
4087: G H I
4088: .ve
4090: Where the submatrices A,B,C are owned by proc0, D,E,F are
4091: owned by proc1, G,H,I are owned by proc2.
4093: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4094: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4095: The 'M','N' parameters are 8,8, and have the same values on all procs.
4097: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4098: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4099: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4100: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4101: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4102: matrix, ans [DF] as another `MATSEQAIJ` matrix.
4104: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4105: allocated for every row of the local diagonal submatrix, and `o_nz`
4106: storage locations are allocated for every row of the OFF-DIAGONAL submat.
4107: One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local
4108: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4109: In this case, the values of `d_nz`, `o_nz` are
4110: .vb
4111: proc0 dnz = 2, o_nz = 2
4112: proc1 dnz = 3, o_nz = 2
4113: proc2 dnz = 1, o_nz = 4
4114: .ve
4115: We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4116: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4117: for proc3. i.e we are using 12+15+10=37 storage locations to store
4118: 34 values.
4120: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4121: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4122: In the above case the values for `d_nnz`, `o_nnz` are
4123: .vb
4124: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4125: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4126: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4127: .ve
4128: Here the space allocated is sum of all the above values i.e 34, and
4129: hence pre-allocation is perfect.
4131: Level: intermediate
4133: Notes:
4134: If the *_nnz parameter is given then the *_nz parameter is ignored
4136: The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4137: storage. The stored row and column indices begin with zero.
4138: See [Sparse Matrices](sec_matsparse) for details.
4140: The parallel matrix is partitioned such that the first m0 rows belong to
4141: process 0, the next m1 rows belong to process 1, the next m2 rows belong
4142: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
4144: The DIAGONAL portion of the local submatrix of a processor can be defined
4145: as the submatrix which is obtained by extraction the part corresponding to
4146: the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4147: first row that belongs to the processor, r2 is the last row belonging to
4148: the this processor, and c1-c2 is range of indices of the local part of a
4149: vector suitable for applying the matrix to. This is an mxn matrix. In the
4150: common case of a square matrix, the row and column ranges are the same and
4151: the DIAGONAL part is also square. The remaining portion of the local
4152: submatrix (mxN) constitute the OFF-DIAGONAL portion.
4154: If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.
4156: You can call `MatGetInfo()` to get information on how effective the preallocation was;
4157: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4158: You can also run with the option `-info` and look for messages with the string
4159: malloc in them to see if additional memory allocation was needed.
4161: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4162: `MatGetInfo()`, `PetscSplitOwnership()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4163: @*/
4164: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4165: {
4166: PetscFunctionBegin;
4169: PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4170: PetscFunctionReturn(PETSC_SUCCESS);
4171: }
4173: /*@
4174: MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4175: CSR format for the local rows.
4177: Collective
4179: Input Parameters:
4180: + comm - MPI communicator
4181: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4182: . n - This value should be the same as the local size used in creating the
4183: x vector for the matrix-vector product $ y = Ax$. (or `PETSC_DECIDE` to have
4184: calculated if `N` is given) For square matrices n is almost always `m`.
4185: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
4186: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
4187: . 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
4188: . j - global column indices
4189: - a - optional matrix values
4191: Output Parameter:
4192: . mat - the matrix
4194: Level: intermediate
4196: Notes:
4197: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4198: thus you CANNOT change the matrix entries by changing the values of `a[]` after you have
4199: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
4201: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
4203: Once you have created the matrix you can update it with new numerical values using `MatUpdateMPIAIJWithArray()`
4205: If you do **not** use `MatUpdateMPIAIJWithArray()`, the column indices in `j` do not need to be sorted. If you will use
4206: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
4208: The format which is used for the sparse matrix input, is equivalent to a
4209: row-major ordering, i.e., for the following matrix, the input data expected is
4210: as shown
4211: .vb
4212: 1 0 0
4213: 2 0 3 P0
4214: -------
4215: 4 5 6 P1
4217: Process0 [P0] rows_owned=[0,1]
4218: i = {0,1,3} [size = nrow+1 = 2+1]
4219: j = {0,0,2} [size = 3]
4220: v = {1,2,3} [size = 3]
4222: Process1 [P1] rows_owned=[2]
4223: i = {0,3} [size = nrow+1 = 1+1]
4224: j = {0,1,2} [size = 3]
4225: v = {4,5,6} [size = 3]
4226: .ve
4228: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4229: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4230: @*/
4231: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
4232: {
4233: PetscFunctionBegin;
4234: PetscCheck(!i || !i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4235: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4236: PetscCall(MatCreate(comm, mat));
4237: PetscCall(MatSetSizes(*mat, m, n, M, N));
4238: /* PetscCall(MatSetBlockSizes(M,bs,cbs)); */
4239: PetscCall(MatSetType(*mat, MATMPIAIJ));
4240: PetscCall(MatMPIAIJSetPreallocationCSR(*mat, i, j, a));
4241: PetscFunctionReturn(PETSC_SUCCESS);
4242: }
4244: /*@
4245: MatUpdateMPIAIJWithArrays - updates a `MATMPIAIJ` matrix using arrays that contain in standard
4246: CSR format for the local rows. Only the numerical values are updated the other arrays must be identical to what was passed
4247: from `MatCreateMPIAIJWithArrays()`
4249: Deprecated: Use `MatUpdateMPIAIJWithArray()`
4251: Collective
4253: Input Parameters:
4254: + mat - the matrix
4255: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4256: . n - This value should be the same as the local size used in creating the
4257: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4258: calculated if N is given) For square matrices n is almost always m.
4259: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4260: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4261: . Ii - row indices; that is Ii[0] = 0, Ii[row] = Ii[row-1] + number of elements in that row of the matrix
4262: . J - column indices
4263: - v - matrix values
4265: Level: deprecated
4267: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4268: `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4269: @*/
4270: PetscErrorCode MatUpdateMPIAIJWithArrays(Mat mat, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4271: {
4272: PetscInt nnz, i;
4273: PetscBool nooffprocentries;
4274: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4275: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4276: PetscScalar *ad, *ao;
4277: PetscInt ldi, Iii, md;
4278: const PetscInt *Adi = Ad->i;
4279: PetscInt *ld = Aij->ld;
4281: PetscFunctionBegin;
4282: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4283: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4284: PetscCheck(m == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of rows cannot change from call to MatUpdateMPIAIJWithArrays()");
4285: PetscCheck(n == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of columns cannot change from call to MatUpdateMPIAIJWithArrays()");
4287: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4288: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4290: for (i = 0; i < m; i++) {
4291: if (PetscDefined(USE_DEBUG)) {
4292: for (PetscInt j = Ii[i] + 1; j < Ii[i + 1]; ++j) {
4293: 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);
4294: 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);
4295: }
4296: }
4297: nnz = Ii[i + 1] - Ii[i];
4298: Iii = Ii[i];
4299: ldi = ld[i];
4300: md = Adi[i + 1] - Adi[i];
4301: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4302: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4303: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4304: ad += md;
4305: ao += nnz - md;
4306: }
4307: nooffprocentries = mat->nooffprocentries;
4308: mat->nooffprocentries = PETSC_TRUE;
4309: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4310: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4311: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4312: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4313: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4314: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4315: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4316: mat->nooffprocentries = nooffprocentries;
4317: PetscFunctionReturn(PETSC_SUCCESS);
4318: }
4320: /*@
4321: MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values
4323: Collective
4325: Input Parameters:
4326: + mat - the matrix
4327: - v - matrix values, stored by row
4329: Level: intermediate
4331: Notes:
4332: The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`
4334: The column indices in the call to `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()` must have been sorted for this call to work correctly
4336: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4337: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4338: @*/
4339: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4340: {
4341: PetscInt nnz, i, m;
4342: PetscBool nooffprocentries;
4343: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4344: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4345: Mat_SeqAIJ *Ao = (Mat_SeqAIJ *)Aij->B->data;
4346: PetscScalar *ad, *ao;
4347: const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4348: PetscInt ldi, Iii, md;
4349: PetscInt *ld = Aij->ld;
4351: PetscFunctionBegin;
4352: m = mat->rmap->n;
4354: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4355: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4356: Iii = 0;
4357: for (i = 0; i < m; i++) {
4358: nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4359: ldi = ld[i];
4360: md = Adi[i + 1] - Adi[i];
4361: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4362: ad += md;
4363: if (ao) {
4364: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4365: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4366: ao += nnz - md;
4367: }
4368: Iii += nnz;
4369: }
4370: nooffprocentries = mat->nooffprocentries;
4371: mat->nooffprocentries = PETSC_TRUE;
4372: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4373: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4374: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4375: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4376: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4377: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4378: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4379: mat->nooffprocentries = nooffprocentries;
4380: PetscFunctionReturn(PETSC_SUCCESS);
4381: }
4383: /*@
4384: MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4385: (the default parallel PETSc format). For good matrix assembly performance
4386: the user should preallocate the matrix storage by setting the parameters
4387: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4389: Collective
4391: Input Parameters:
4392: + comm - MPI communicator
4393: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4394: This value should be the same as the local size used in creating the
4395: y vector for the matrix-vector product y = Ax.
4396: . n - This value should be the same as the local size used in creating the
4397: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4398: calculated if N is given) For square matrices n is almost always m.
4399: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4400: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4401: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4402: (same value is used for all local rows)
4403: . d_nnz - array containing the number of nonzeros in the various rows of the
4404: DIAGONAL portion of the local submatrix (possibly different for each row)
4405: or `NULL`, if `d_nz` is used to specify the nonzero structure.
4406: The size of this array is equal to the number of local rows, i.e 'm'.
4407: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4408: submatrix (same value is used for all local rows).
4409: - o_nnz - array containing the number of nonzeros in the various rows of the
4410: OFF-DIAGONAL portion of the local submatrix (possibly different for
4411: each row) or `NULL`, if `o_nz` is used to specify the nonzero
4412: structure. The size of this array is equal to the number
4413: of local rows, i.e 'm'.
4415: Output Parameter:
4416: . A - the matrix
4418: Options Database Keys:
4419: + -mat_no_inode - Do not use inodes
4420: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
4421: - -matmult_vecscatter_view <viewer> - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4422: See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the `VecScatter`
4423: 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.
4425: Level: intermediate
4427: Notes:
4428: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4429: MatXXXXSetPreallocation() paradigm instead of this routine directly.
4430: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
4432: If the *_nnz parameter is given then the *_nz parameter is ignored
4434: The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4435: processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4436: storage requirements for this matrix.
4438: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one
4439: processor than it must be used on all processors that share the object for
4440: that argument.
4442: If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by
4443: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`.
4445: The user MUST specify either the local or global matrix dimensions
4446: (possibly both).
4448: The parallel matrix is partitioned across processors such that the
4449: first `m0` rows belong to process 0, the next `m1` rows belong to
4450: process 1, the next `m2` rows belong to process 2, etc., where
4451: `m0`, `m1`, `m2`... are the input parameter `m` on each MPI process. I.e., each MPI process stores
4452: values corresponding to [m x N] submatrix.
4454: The columns are logically partitioned with the n0 columns belonging
4455: to 0th partition, the next n1 columns belonging to the next
4456: partition etc.. where n0,n1,n2... are the input parameter 'n'.
4458: The DIAGONAL portion of the local submatrix on any given processor
4459: is the submatrix corresponding to the rows and columns m,n
4460: corresponding to the given processor. i.e diagonal matrix on
4461: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4462: etc. The remaining portion of the local submatrix [m x (N-n)]
4463: constitute the OFF-DIAGONAL portion. The example below better
4464: illustrates this concept.
4466: For a square global matrix we define each processor's diagonal portion
4467: to be its local rows and the corresponding columns (a square submatrix);
4468: each processor's off-diagonal portion encompasses the remainder of the
4469: local matrix (a rectangular submatrix).
4471: If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.
4473: When calling this routine with a single process communicator, a matrix of
4474: type `MATSEQAIJ` is returned. If a matrix of type `MATMPIAIJ` is desired for this
4475: type of communicator, use the construction mechanism
4476: .vb
4477: MatCreate(..., &A);
4478: MatSetType(A, MATMPIAIJ);
4479: MatSetSizes(A, m, n, M, N);
4480: MatMPIAIJSetPreallocation(A, ...);
4481: .ve
4483: By default, this format uses inodes (identical nodes) when possible.
4484: We search for consecutive rows with the same nonzero structure, thereby
4485: reusing matrix information to achieve increased efficiency.
4487: Example Usage:
4488: Consider the following 8x8 matrix with 34 non-zero values, that is
4489: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4490: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4491: as follows
4493: .vb
4494: 1 2 0 | 0 3 0 | 0 4
4495: Proc0 0 5 6 | 7 0 0 | 8 0
4496: 9 0 10 | 11 0 0 | 12 0
4497: -------------------------------------
4498: 13 0 14 | 15 16 17 | 0 0
4499: Proc1 0 18 0 | 19 20 21 | 0 0
4500: 0 0 0 | 22 23 0 | 24 0
4501: -------------------------------------
4502: Proc2 25 26 27 | 0 0 28 | 29 0
4503: 30 0 0 | 31 32 33 | 0 34
4504: .ve
4506: This can be represented as a collection of submatrices as
4508: .vb
4509: A B C
4510: D E F
4511: G H I
4512: .ve
4514: Where the submatrices A,B,C are owned by proc0, D,E,F are
4515: owned by proc1, G,H,I are owned by proc2.
4517: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4518: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4519: The 'M','N' parameters are 8,8, and have the same values on all procs.
4521: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4522: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4523: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4524: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4525: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4526: matrix, ans [DF] as another SeqAIJ matrix.
4528: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4529: allocated for every row of the local diagonal submatrix, and `o_nz`
4530: storage locations are allocated for every row of the OFF-DIAGONAL submat.
4531: One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local
4532: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4533: In this case, the values of `d_nz`,`o_nz` are
4534: .vb
4535: proc0 dnz = 2, o_nz = 2
4536: proc1 dnz = 3, o_nz = 2
4537: proc2 dnz = 1, o_nz = 4
4538: .ve
4539: We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4540: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4541: for proc3. i.e we are using 12+15+10=37 storage locations to store
4542: 34 values.
4544: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4545: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4546: In the above case the values for d_nnz,o_nnz are
4547: .vb
4548: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4549: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4550: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4551: .ve
4552: Here the space allocated is sum of all the above values i.e 34, and
4553: hence pre-allocation is perfect.
4555: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4556: `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`,
4557: `MatGetOwnershipRangesColumn()`, `PetscLayout`
4558: @*/
4559: 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)
4560: {
4561: PetscMPIInt size;
4563: PetscFunctionBegin;
4564: PetscCall(MatCreate(comm, A));
4565: PetscCall(MatSetSizes(*A, m, n, M, N));
4566: PetscCallMPI(MPI_Comm_size(comm, &size));
4567: if (size > 1) {
4568: PetscCall(MatSetType(*A, MATMPIAIJ));
4569: PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4570: } else {
4571: PetscCall(MatSetType(*A, MATSEQAIJ));
4572: PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4573: }
4574: PetscFunctionReturn(PETSC_SUCCESS);
4575: }
4577: /*MC
4578: MatMPIAIJGetSeqAIJF90 - Returns the local pieces of this distributed matrix
4580: Synopsis:
4581: MatMPIAIJGetSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)
4583: Not Collective
4585: Input Parameter:
4586: . A - the `MATMPIAIJ` matrix
4588: Output Parameters:
4589: + Ad - the diagonal portion of the matrix
4590: . Ao - the off-diagonal portion of the matrix
4591: . colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4592: - ierr - error code
4594: Level: advanced
4596: Note:
4597: Use `MatMPIAIJRestoreSeqAIJF90()` when you no longer need access to the matrices and `colmap`
4599: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJRestoreSeqAIJF90()`
4600: M*/
4602: /*MC
4603: MatMPIAIJRestoreSeqAIJF90 - call after `MatMPIAIJGetSeqAIJF90()` when you no longer need access to the matrices and `colmap`
4605: Synopsis:
4606: MatMPIAIJRestoreSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)
4608: Not Collective
4610: Input Parameters:
4611: + A - the `MATMPIAIJ` matrix
4612: . Ad - the diagonal portion of the matrix
4613: . Ao - the off-diagonal portion of the matrix
4614: . colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4615: - ierr - error code
4617: Level: advanced
4619: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJGetSeqAIJF90()`
4620: M*/
4622: /*@C
4623: MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix
4625: Not Collective
4627: Input Parameter:
4628: . A - The `MATMPIAIJ` matrix
4630: Output Parameters:
4631: + Ad - The local diagonal block as a `MATSEQAIJ` matrix
4632: . Ao - The local off-diagonal block as a `MATSEQAIJ` matrix
4633: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4635: Level: intermediate
4637: Note:
4638: The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4639: 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
4640: the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4641: local column numbers to global column numbers in the original matrix.
4643: Fortran Notes:
4644: `MatMPIAIJGetSeqAIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatMPIAIJGetSeqAIJF90()`
4646: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJF90()`, `MatMPIAIJRestoreSeqAIJF90()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATSEQAIJ`
4647: @*/
4648: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4649: {
4650: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4651: PetscBool flg;
4653: PetscFunctionBegin;
4654: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4655: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4656: if (Ad) *Ad = a->A;
4657: if (Ao) *Ao = a->B;
4658: if (colmap) *colmap = a->garray;
4659: PetscFunctionReturn(PETSC_SUCCESS);
4660: }
4662: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4663: {
4664: PetscInt m, N, i, rstart, nnz, Ii;
4665: PetscInt *indx;
4666: PetscScalar *values;
4667: MatType rootType;
4669: PetscFunctionBegin;
4670: PetscCall(MatGetSize(inmat, &m, &N));
4671: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4672: PetscInt *dnz, *onz, sum, bs, cbs;
4674: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4675: /* Check sum(n) = N */
4676: PetscCallMPI(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4677: PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);
4679: PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4680: rstart -= m;
4682: MatPreallocateBegin(comm, m, n, dnz, onz);
4683: for (i = 0; i < m; i++) {
4684: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4685: PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4686: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4687: }
4689: PetscCall(MatCreate(comm, outmat));
4690: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4691: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4692: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4693: PetscCall(MatGetRootType_Private(inmat, &rootType));
4694: PetscCall(MatSetType(*outmat, rootType));
4695: PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4696: PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4697: MatPreallocateEnd(dnz, onz);
4698: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4699: }
4701: /* numeric phase */
4702: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4703: for (i = 0; i < m; i++) {
4704: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4705: Ii = i + rstart;
4706: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4707: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4708: }
4709: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4710: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4711: PetscFunctionReturn(PETSC_SUCCESS);
4712: }
4714: static PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(void **data)
4715: {
4716: Mat_Merge_SeqsToMPI *merge = (Mat_Merge_SeqsToMPI *)*data;
4718: PetscFunctionBegin;
4719: if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4720: PetscCall(PetscFree(merge->id_r));
4721: PetscCall(PetscFree(merge->len_s));
4722: PetscCall(PetscFree(merge->len_r));
4723: PetscCall(PetscFree(merge->bi));
4724: PetscCall(PetscFree(merge->bj));
4725: PetscCall(PetscFree(merge->buf_ri[0]));
4726: PetscCall(PetscFree(merge->buf_ri));
4727: PetscCall(PetscFree(merge->buf_rj[0]));
4728: PetscCall(PetscFree(merge->buf_rj));
4729: PetscCall(PetscFree(merge->coi));
4730: PetscCall(PetscFree(merge->coj));
4731: PetscCall(PetscFree(merge->owners_co));
4732: PetscCall(PetscLayoutDestroy(&merge->rowmap));
4733: PetscCall(PetscFree(merge));
4734: PetscFunctionReturn(PETSC_SUCCESS);
4735: }
4737: #include <../src/mat/utils/freespace.h>
4738: #include <petscbt.h>
4740: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4741: {
4742: MPI_Comm comm;
4743: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4744: PetscMPIInt size, rank, taga, *len_s;
4745: PetscInt N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj, m;
4746: PetscMPIInt proc, k;
4747: PetscInt **buf_ri, **buf_rj;
4748: PetscInt anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4749: PetscInt nrows, **buf_ri_k, **nextrow, **nextai;
4750: MPI_Request *s_waits, *r_waits;
4751: MPI_Status *status;
4752: const MatScalar *aa, *a_a;
4753: MatScalar **abuf_r, *ba_i;
4754: Mat_Merge_SeqsToMPI *merge;
4755: PetscContainer container;
4757: PetscFunctionBegin;
4758: PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4759: PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));
4761: PetscCallMPI(MPI_Comm_size(comm, &size));
4762: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4764: PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4765: PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4766: PetscCall(PetscContainerGetPointer(container, (void **)&merge));
4767: PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4768: aa = a_a;
4770: bi = merge->bi;
4771: bj = merge->bj;
4772: buf_ri = merge->buf_ri;
4773: buf_rj = merge->buf_rj;
4775: PetscCall(PetscMalloc1(size, &status));
4776: owners = merge->rowmap->range;
4777: len_s = merge->len_s;
4779: /* send and recv matrix values */
4780: PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4781: PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));
4783: PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4784: for (proc = 0, k = 0; proc < size; proc++) {
4785: if (!len_s[proc]) continue;
4786: i = owners[proc];
4787: PetscCallMPI(MPIU_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4788: k++;
4789: }
4791: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4792: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4793: PetscCall(PetscFree(status));
4795: PetscCall(PetscFree(s_waits));
4796: PetscCall(PetscFree(r_waits));
4798: /* insert mat values of mpimat */
4799: PetscCall(PetscMalloc1(N, &ba_i));
4800: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4802: for (k = 0; k < merge->nrecv; k++) {
4803: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4804: nrows = *buf_ri_k[k];
4805: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4806: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4807: }
4809: /* set values of ba */
4810: m = merge->rowmap->n;
4811: for (i = 0; i < m; i++) {
4812: arow = owners[rank] + i;
4813: bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4814: bnzi = bi[i + 1] - bi[i];
4815: PetscCall(PetscArrayzero(ba_i, bnzi));
4817: /* add local non-zero vals of this proc's seqmat into ba */
4818: anzi = ai[arow + 1] - ai[arow];
4819: aj = a->j + ai[arow];
4820: aa = a_a + ai[arow];
4821: nextaj = 0;
4822: for (j = 0; nextaj < anzi; j++) {
4823: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4824: ba_i[j] += aa[nextaj++];
4825: }
4826: }
4828: /* add received vals into ba */
4829: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4830: /* i-th row */
4831: if (i == *nextrow[k]) {
4832: anzi = *(nextai[k] + 1) - *nextai[k];
4833: aj = buf_rj[k] + *nextai[k];
4834: aa = abuf_r[k] + *nextai[k];
4835: nextaj = 0;
4836: for (j = 0; nextaj < anzi; j++) {
4837: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4838: ba_i[j] += aa[nextaj++];
4839: }
4840: }
4841: nextrow[k]++;
4842: nextai[k]++;
4843: }
4844: }
4845: PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4846: }
4847: PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4848: PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4849: PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));
4851: PetscCall(PetscFree(abuf_r[0]));
4852: PetscCall(PetscFree(abuf_r));
4853: PetscCall(PetscFree(ba_i));
4854: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4855: PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4856: PetscFunctionReturn(PETSC_SUCCESS);
4857: }
4859: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4860: {
4861: Mat B_mpi;
4862: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4863: PetscMPIInt size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4864: PetscInt **buf_rj, **buf_ri, **buf_ri_k;
4865: PetscInt M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4866: PetscInt len, *dnz, *onz, bs, cbs;
4867: PetscInt k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4868: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4869: MPI_Request *si_waits, *sj_waits, *ri_waits, *rj_waits;
4870: MPI_Status *status;
4871: PetscFreeSpaceList free_space = NULL, current_space = NULL;
4872: PetscBT lnkbt;
4873: Mat_Merge_SeqsToMPI *merge;
4874: PetscContainer container;
4876: PetscFunctionBegin;
4877: PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));
4879: /* make sure it is a PETSc comm */
4880: PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4881: PetscCallMPI(MPI_Comm_size(comm, &size));
4882: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4884: PetscCall(PetscNew(&merge));
4885: PetscCall(PetscMalloc1(size, &status));
4887: /* determine row ownership */
4888: PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4889: PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4890: PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4891: PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4892: PetscCall(PetscLayoutSetUp(merge->rowmap));
4893: PetscCall(PetscMalloc1(size, &len_si));
4894: PetscCall(PetscMalloc1(size, &merge->len_s));
4896: m = merge->rowmap->n;
4897: owners = merge->rowmap->range;
4899: /* determine the number of messages to send, their lengths */
4900: len_s = merge->len_s;
4902: len = 0; /* length of buf_si[] */
4903: merge->nsend = 0;
4904: for (PetscMPIInt proc = 0; proc < size; proc++) {
4905: len_si[proc] = 0;
4906: if (proc == rank) {
4907: len_s[proc] = 0;
4908: } else {
4909: PetscCall(PetscMPIIntCast(owners[proc + 1] - owners[proc] + 1, &len_si[proc]));
4910: PetscCall(PetscMPIIntCast(ai[owners[proc + 1]] - ai[owners[proc]], &len_s[proc])); /* num of rows to be sent to [proc] */
4911: }
4912: if (len_s[proc]) {
4913: merge->nsend++;
4914: nrows = 0;
4915: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4916: if (ai[i + 1] > ai[i]) nrows++;
4917: }
4918: PetscCall(PetscMPIIntCast(2 * (nrows + 1), &len_si[proc]));
4919: len += len_si[proc];
4920: }
4921: }
4923: /* determine the number and length of messages to receive for ij-structure */
4924: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4925: PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));
4927: /* post the Irecv of j-structure */
4928: PetscCall(PetscCommGetNewTag(comm, &tagj));
4929: PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));
4931: /* post the Isend of j-structure */
4932: PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));
4934: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4935: if (!len_s[proc]) continue;
4936: i = owners[proc];
4937: PetscCallMPI(MPIU_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4938: k++;
4939: }
4941: /* receives and sends of j-structure are complete */
4942: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4943: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));
4945: /* send and recv i-structure */
4946: PetscCall(PetscCommGetNewTag(comm, &tagi));
4947: PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));
4949: PetscCall(PetscMalloc1(len + 1, &buf_s));
4950: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4951: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4952: if (!len_s[proc]) continue;
4953: /* form outgoing message for i-structure:
4954: buf_si[0]: nrows to be sent
4955: [1:nrows]: row index (global)
4956: [nrows+1:2*nrows+1]: i-structure index
4957: */
4958: nrows = len_si[proc] / 2 - 1;
4959: buf_si_i = buf_si + nrows + 1;
4960: buf_si[0] = nrows;
4961: buf_si_i[0] = 0;
4962: nrows = 0;
4963: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4964: anzi = ai[i + 1] - ai[i];
4965: if (anzi) {
4966: buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4967: buf_si[nrows + 1] = i - owners[proc]; /* local row index */
4968: nrows++;
4969: }
4970: }
4971: PetscCallMPI(MPIU_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4972: k++;
4973: buf_si += len_si[proc];
4974: }
4976: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4977: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));
4979: PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4980: for (i = 0; i < merge->nrecv; i++) PetscCall(PetscInfo(seqmat, "recv len_ri=%d, len_rj=%d from [%d]\n", len_ri[i], merge->len_r[i], merge->id_r[i]));
4982: PetscCall(PetscFree(len_si));
4983: PetscCall(PetscFree(len_ri));
4984: PetscCall(PetscFree(rj_waits));
4985: PetscCall(PetscFree2(si_waits, sj_waits));
4986: PetscCall(PetscFree(ri_waits));
4987: PetscCall(PetscFree(buf_s));
4988: PetscCall(PetscFree(status));
4990: /* compute a local seq matrix in each processor */
4991: /* allocate bi array and free space for accumulating nonzero column info */
4992: PetscCall(PetscMalloc1(m + 1, &bi));
4993: bi[0] = 0;
4995: /* create and initialize a linked list */
4996: nlnk = N + 1;
4997: PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));
4999: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
5000: len = ai[owners[rank + 1]] - ai[owners[rank]];
5001: PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));
5003: current_space = free_space;
5005: /* determine symbolic info for each local row */
5006: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
5008: for (k = 0; k < merge->nrecv; k++) {
5009: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
5010: nrows = *buf_ri_k[k];
5011: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
5012: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
5013: }
5015: MatPreallocateBegin(comm, m, n, dnz, onz);
5016: len = 0;
5017: for (i = 0; i < m; i++) {
5018: bnzi = 0;
5019: /* add local non-zero cols of this proc's seqmat into lnk */
5020: arow = owners[rank] + i;
5021: anzi = ai[arow + 1] - ai[arow];
5022: aj = a->j + ai[arow];
5023: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5024: bnzi += nlnk;
5025: /* add received col data into lnk */
5026: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
5027: if (i == *nextrow[k]) { /* i-th row */
5028: anzi = *(nextai[k] + 1) - *nextai[k];
5029: aj = buf_rj[k] + *nextai[k];
5030: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5031: bnzi += nlnk;
5032: nextrow[k]++;
5033: nextai[k]++;
5034: }
5035: }
5036: if (len < bnzi) len = bnzi; /* =max(bnzi) */
5038: /* if free space is not available, make more free space */
5039: if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), ¤t_space));
5040: /* copy data into free space, then initialize lnk */
5041: PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
5042: PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));
5044: current_space->array += bnzi;
5045: current_space->local_used += bnzi;
5046: current_space->local_remaining -= bnzi;
5048: bi[i + 1] = bi[i] + bnzi;
5049: }
5051: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
5053: PetscCall(PetscMalloc1(bi[m] + 1, &bj));
5054: PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
5055: PetscCall(PetscLLDestroy(lnk, lnkbt));
5057: /* create symbolic parallel matrix B_mpi */
5058: PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
5059: PetscCall(MatCreate(comm, &B_mpi));
5060: if (n == PETSC_DECIDE) {
5061: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
5062: } else {
5063: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
5064: }
5065: PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
5066: PetscCall(MatSetType(B_mpi, MATMPIAIJ));
5067: PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
5068: MatPreallocateEnd(dnz, onz);
5069: PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));
5071: /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
5072: B_mpi->assembled = PETSC_FALSE;
5073: merge->bi = bi;
5074: merge->bj = bj;
5075: merge->buf_ri = buf_ri;
5076: merge->buf_rj = buf_rj;
5077: merge->coi = NULL;
5078: merge->coj = NULL;
5079: merge->owners_co = NULL;
5081: PetscCall(PetscCommDestroy(&comm));
5083: /* attach the supporting struct to B_mpi for reuse */
5084: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5085: PetscCall(PetscContainerSetPointer(container, merge));
5086: PetscCall(PetscContainerSetCtxDestroy(container, MatDestroy_MPIAIJ_SeqsToMPI));
5087: PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5088: PetscCall(PetscContainerDestroy(&container));
5089: *mpimat = B_mpi;
5091: PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5092: PetscFunctionReturn(PETSC_SUCCESS);
5093: }
5095: /*@
5096: MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5097: matrices from each processor
5099: Collective
5101: Input Parameters:
5102: + comm - the communicators the parallel matrix will live on
5103: . seqmat - the input sequential matrices
5104: . m - number of local rows (or `PETSC_DECIDE`)
5105: . n - number of local columns (or `PETSC_DECIDE`)
5106: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5108: Output Parameter:
5109: . mpimat - the parallel matrix generated
5111: Level: advanced
5113: Note:
5114: The dimensions of the sequential matrix in each processor MUST be the same.
5115: The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
5116: destroyed when `mpimat` is destroyed. Call `PetscObjectQuery()` to access `seqmat`.
5118: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`
5119: @*/
5120: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5121: {
5122: PetscMPIInt size;
5124: PetscFunctionBegin;
5125: PetscCallMPI(MPI_Comm_size(comm, &size));
5126: if (size == 1) {
5127: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5128: if (scall == MAT_INITIAL_MATRIX) {
5129: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5130: } else {
5131: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5132: }
5133: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5134: PetscFunctionReturn(PETSC_SUCCESS);
5135: }
5136: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5137: if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5138: PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5139: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5140: PetscFunctionReturn(PETSC_SUCCESS);
5141: }
5143: /*@
5144: MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix.
5146: Not Collective
5148: Input Parameter:
5149: . A - the matrix
5151: Output Parameter:
5152: . A_loc - the local sequential matrix generated
5154: Level: developer
5156: Notes:
5157: The matrix is created by taking `A`'s local rows and putting them into a sequential matrix
5158: with `mlocal` rows and `n` columns. Where `mlocal` is obtained with `MatGetLocalSize()` and
5159: `n` is the global column count obtained with `MatGetSize()`
5161: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5163: For parallel matrices this creates an entirely new matrix. If the matrix is sequential it merely increases the reference count.
5165: Destroy the matrix with `MatDestroy()`
5167: .seealso: [](ch_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5168: @*/
5169: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5170: {
5171: PetscBool mpi;
5173: PetscFunctionBegin;
5174: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5175: if (mpi) {
5176: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5177: } else {
5178: *A_loc = A;
5179: PetscCall(PetscObjectReference((PetscObject)*A_loc));
5180: }
5181: PetscFunctionReturn(PETSC_SUCCESS);
5182: }
5184: /*@
5185: MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix.
5187: Not Collective
5189: Input Parameters:
5190: + A - the matrix
5191: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5193: Output Parameter:
5194: . A_loc - the local sequential matrix generated
5196: Level: developer
5198: Notes:
5199: The matrix is created by taking all `A`'s local rows and putting them into a sequential
5200: matrix with `mlocal` rows and `n` columns.`mlocal` is the row count obtained with
5201: `MatGetLocalSize()` and `n` is the global column count obtained with `MatGetSize()`.
5203: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5205: When `A` is sequential and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A` (which contains the entire matrix),
5206: 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
5207: then `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called to fill `A_loc`. Thus one can preallocate the appropriate sequential matrix `A_loc`
5208: 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.
5210: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5211: @*/
5212: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5213: {
5214: Mat_MPIAIJ *mpimat = (Mat_MPIAIJ *)A->data;
5215: Mat_SeqAIJ *mat, *a, *b;
5216: PetscInt *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5217: const PetscScalar *aa, *ba, *aav, *bav;
5218: PetscScalar *ca, *cam;
5219: PetscMPIInt size;
5220: PetscInt am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5221: PetscInt *ci, *cj, col, ncols_d, ncols_o, jo;
5222: PetscBool match;
5224: PetscFunctionBegin;
5225: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5226: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5227: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5228: if (size == 1) {
5229: if (scall == MAT_INITIAL_MATRIX) {
5230: PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5231: *A_loc = mpimat->A;
5232: } else if (scall == MAT_REUSE_MATRIX) {
5233: PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5234: }
5235: PetscFunctionReturn(PETSC_SUCCESS);
5236: }
5238: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5239: a = (Mat_SeqAIJ *)mpimat->A->data;
5240: b = (Mat_SeqAIJ *)mpimat->B->data;
5241: ai = a->i;
5242: aj = a->j;
5243: bi = b->i;
5244: bj = b->j;
5245: PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5246: PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5247: aa = aav;
5248: ba = bav;
5249: if (scall == MAT_INITIAL_MATRIX) {
5250: PetscCall(PetscMalloc1(1 + am, &ci));
5251: ci[0] = 0;
5252: for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5253: PetscCall(PetscMalloc1(1 + ci[am], &cj));
5254: PetscCall(PetscMalloc1(1 + ci[am], &ca));
5255: k = 0;
5256: for (i = 0; i < am; i++) {
5257: ncols_o = bi[i + 1] - bi[i];
5258: ncols_d = ai[i + 1] - ai[i];
5259: /* off-diagonal portion of A */
5260: for (jo = 0; jo < ncols_o; jo++) {
5261: col = cmap[*bj];
5262: if (col >= cstart) break;
5263: cj[k] = col;
5264: bj++;
5265: ca[k++] = *ba++;
5266: }
5267: /* diagonal portion of A */
5268: for (j = 0; j < ncols_d; j++) {
5269: cj[k] = cstart + *aj++;
5270: ca[k++] = *aa++;
5271: }
5272: /* off-diagonal portion of A */
5273: for (j = jo; j < ncols_o; j++) {
5274: cj[k] = cmap[*bj++];
5275: ca[k++] = *ba++;
5276: }
5277: }
5278: /* put together the new matrix */
5279: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5280: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5281: /* Since these are PETSc arrays, change flags to free them as necessary. */
5282: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5283: mat->free_a = PETSC_TRUE;
5284: mat->free_ij = PETSC_TRUE;
5285: mat->nonew = 0;
5286: } else if (scall == MAT_REUSE_MATRIX) {
5287: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5288: ci = mat->i;
5289: cj = mat->j;
5290: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5291: for (i = 0; i < am; i++) {
5292: /* off-diagonal portion of A */
5293: ncols_o = bi[i + 1] - bi[i];
5294: for (jo = 0; jo < ncols_o; jo++) {
5295: col = cmap[*bj];
5296: if (col >= cstart) break;
5297: *cam++ = *ba++;
5298: bj++;
5299: }
5300: /* diagonal portion of A */
5301: ncols_d = ai[i + 1] - ai[i];
5302: for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5303: /* off-diagonal portion of A */
5304: for (j = jo; j < ncols_o; j++) {
5305: *cam++ = *ba++;
5306: bj++;
5307: }
5308: }
5309: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5310: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5311: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5312: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5313: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5314: PetscFunctionReturn(PETSC_SUCCESS);
5315: }
5317: /*@
5318: MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5319: mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and off-diagonal part
5321: Not Collective
5323: Input Parameters:
5324: + A - the matrix
5325: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5327: Output Parameters:
5328: + glob - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5329: - A_loc - the local sequential matrix generated
5331: Level: developer
5333: Note:
5334: This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5335: part, then those associated with the off-diagonal part (in its local ordering)
5337: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5338: @*/
5339: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5340: {
5341: Mat Ao, Ad;
5342: const PetscInt *cmap;
5343: PetscMPIInt size;
5344: PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);
5346: PetscFunctionBegin;
5347: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5348: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5349: if (size == 1) {
5350: if (scall == MAT_INITIAL_MATRIX) {
5351: PetscCall(PetscObjectReference((PetscObject)Ad));
5352: *A_loc = Ad;
5353: } else if (scall == MAT_REUSE_MATRIX) {
5354: PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5355: }
5356: if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5357: PetscFunctionReturn(PETSC_SUCCESS);
5358: }
5359: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5360: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5361: if (f) {
5362: PetscCall((*f)(A, scall, glob, A_loc));
5363: } else {
5364: Mat_SeqAIJ *a = (Mat_SeqAIJ *)Ad->data;
5365: Mat_SeqAIJ *b = (Mat_SeqAIJ *)Ao->data;
5366: Mat_SeqAIJ *c;
5367: PetscInt *ai = a->i, *aj = a->j;
5368: PetscInt *bi = b->i, *bj = b->j;
5369: PetscInt *ci, *cj;
5370: const PetscScalar *aa, *ba;
5371: PetscScalar *ca;
5372: PetscInt i, j, am, dn, on;
5374: PetscCall(MatGetLocalSize(Ad, &am, &dn));
5375: PetscCall(MatGetLocalSize(Ao, NULL, &on));
5376: PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5377: PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5378: if (scall == MAT_INITIAL_MATRIX) {
5379: PetscInt k;
5380: PetscCall(PetscMalloc1(1 + am, &ci));
5381: PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5382: PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5383: ci[0] = 0;
5384: for (i = 0, k = 0; i < am; i++) {
5385: const PetscInt ncols_o = bi[i + 1] - bi[i];
5386: const PetscInt ncols_d = ai[i + 1] - ai[i];
5387: ci[i + 1] = ci[i] + ncols_o + ncols_d;
5388: /* diagonal portion of A */
5389: for (j = 0; j < ncols_d; j++, k++) {
5390: cj[k] = *aj++;
5391: ca[k] = *aa++;
5392: }
5393: /* off-diagonal portion of A */
5394: for (j = 0; j < ncols_o; j++, k++) {
5395: cj[k] = dn + *bj++;
5396: ca[k] = *ba++;
5397: }
5398: }
5399: /* put together the new matrix */
5400: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5401: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5402: /* Since these are PETSc arrays, change flags to free them as necessary. */
5403: c = (Mat_SeqAIJ *)(*A_loc)->data;
5404: c->free_a = PETSC_TRUE;
5405: c->free_ij = PETSC_TRUE;
5406: c->nonew = 0;
5407: PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5408: } else if (scall == MAT_REUSE_MATRIX) {
5409: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5410: for (i = 0; i < am; i++) {
5411: const PetscInt ncols_d = ai[i + 1] - ai[i];
5412: const PetscInt ncols_o = bi[i + 1] - bi[i];
5413: /* diagonal portion of A */
5414: for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5415: /* off-diagonal portion of A */
5416: for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5417: }
5418: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5419: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5420: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5421: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5422: if (glob) {
5423: PetscInt cst, *gidx;
5425: PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5426: PetscCall(PetscMalloc1(dn + on, &gidx));
5427: for (i = 0; i < dn; i++) gidx[i] = cst + i;
5428: for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5429: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5430: }
5431: }
5432: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5433: PetscFunctionReturn(PETSC_SUCCESS);
5434: }
5436: /*@C
5437: MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns
5439: Not Collective
5441: Input Parameters:
5442: + A - the matrix
5443: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5444: . row - index set of rows to extract (or `NULL`)
5445: - col - index set of columns to extract (or `NULL`)
5447: Output Parameter:
5448: . A_loc - the local sequential matrix generated
5450: Level: developer
5452: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5453: @*/
5454: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5455: {
5456: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5457: PetscInt i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5458: IS isrowa, iscola;
5459: Mat *aloc;
5460: PetscBool match;
5462: PetscFunctionBegin;
5463: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5464: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5465: PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5466: if (!row) {
5467: start = A->rmap->rstart;
5468: end = A->rmap->rend;
5469: PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5470: } else {
5471: isrowa = *row;
5472: }
5473: if (!col) {
5474: start = A->cmap->rstart;
5475: cmap = a->garray;
5476: nzA = a->A->cmap->n;
5477: nzB = a->B->cmap->n;
5478: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5479: ncols = 0;
5480: for (i = 0; i < nzB; i++) {
5481: if (cmap[i] < start) idx[ncols++] = cmap[i];
5482: else break;
5483: }
5484: imark = i;
5485: for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5486: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5487: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5488: } else {
5489: iscola = *col;
5490: }
5491: if (scall != MAT_INITIAL_MATRIX) {
5492: PetscCall(PetscMalloc1(1, &aloc));
5493: aloc[0] = *A_loc;
5494: }
5495: PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5496: if (!col) { /* attach global id of condensed columns */
5497: PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5498: }
5499: *A_loc = aloc[0];
5500: PetscCall(PetscFree(aloc));
5501: if (!row) PetscCall(ISDestroy(&isrowa));
5502: if (!col) PetscCall(ISDestroy(&iscola));
5503: PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5504: PetscFunctionReturn(PETSC_SUCCESS);
5505: }
5507: /*
5508: * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5509: * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5510: * on a global size.
5511: * */
5512: static PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5513: {
5514: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
5515: Mat_SeqAIJ *pd = (Mat_SeqAIJ *)p->A->data, *po = (Mat_SeqAIJ *)p->B->data, *p_oth;
5516: PetscInt plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5517: PetscMPIInt owner;
5518: PetscSFNode *iremote, *oiremote;
5519: const PetscInt *lrowindices;
5520: PetscSF sf, osf;
5521: PetscInt pcstart, *roffsets, *loffsets, *pnnz, j;
5522: PetscInt ontotalcols, dntotalcols, ntotalcols, nout;
5523: MPI_Comm comm;
5524: ISLocalToGlobalMapping mapping;
5525: const PetscScalar *pd_a, *po_a;
5527: PetscFunctionBegin;
5528: PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5529: /* plocalsize is the number of roots
5530: * nrows is the number of leaves
5531: * */
5532: PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5533: PetscCall(ISGetLocalSize(rows, &nrows));
5534: PetscCall(PetscCalloc1(nrows, &iremote));
5535: PetscCall(ISGetIndices(rows, &lrowindices));
5536: for (i = 0; i < nrows; i++) {
5537: /* Find a remote index and an owner for a row
5538: * The row could be local or remote
5539: * */
5540: owner = 0;
5541: lidx = 0;
5542: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5543: iremote[i].index = lidx;
5544: iremote[i].rank = owner;
5545: }
5546: /* Create SF to communicate how many nonzero columns for each row */
5547: PetscCall(PetscSFCreate(comm, &sf));
5548: /* SF will figure out the number of nonzero columns for each row, and their
5549: * offsets
5550: * */
5551: PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5552: PetscCall(PetscSFSetFromOptions(sf));
5553: PetscCall(PetscSFSetUp(sf));
5555: PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5556: PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5557: PetscCall(PetscCalloc1(nrows, &pnnz));
5558: roffsets[0] = 0;
5559: roffsets[1] = 0;
5560: for (i = 0; i < plocalsize; i++) {
5561: /* diagonal */
5562: nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5563: /* off-diagonal */
5564: nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5565: /* compute offsets so that we relative location for each row */
5566: roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5567: roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5568: }
5569: PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5570: PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5571: /* 'r' means root, and 'l' means leaf */
5572: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5573: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5574: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5575: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5576: PetscCall(PetscSFDestroy(&sf));
5577: PetscCall(PetscFree(roffsets));
5578: PetscCall(PetscFree(nrcols));
5579: dntotalcols = 0;
5580: ontotalcols = 0;
5581: ncol = 0;
5582: for (i = 0; i < nrows; i++) {
5583: pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5584: ncol = PetscMax(pnnz[i], ncol);
5585: /* diagonal */
5586: dntotalcols += nlcols[i * 2 + 0];
5587: /* off-diagonal */
5588: ontotalcols += nlcols[i * 2 + 1];
5589: }
5590: /* We do not need to figure the right number of columns
5591: * since all the calculations will be done by going through the raw data
5592: * */
5593: PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5594: PetscCall(MatSetUp(*P_oth));
5595: PetscCall(PetscFree(pnnz));
5596: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5597: /* diagonal */
5598: PetscCall(PetscCalloc1(dntotalcols, &iremote));
5599: /* off-diagonal */
5600: PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5601: /* diagonal */
5602: PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5603: /* off-diagonal */
5604: PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5605: dntotalcols = 0;
5606: ontotalcols = 0;
5607: ntotalcols = 0;
5608: for (i = 0; i < nrows; i++) {
5609: owner = 0;
5610: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5611: /* Set iremote for diag matrix */
5612: for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5613: iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5614: iremote[dntotalcols].rank = owner;
5615: /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5616: ilocal[dntotalcols++] = ntotalcols++;
5617: }
5618: /* off-diagonal */
5619: for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5620: oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5621: oiremote[ontotalcols].rank = owner;
5622: oilocal[ontotalcols++] = ntotalcols++;
5623: }
5624: }
5625: PetscCall(ISRestoreIndices(rows, &lrowindices));
5626: PetscCall(PetscFree(loffsets));
5627: PetscCall(PetscFree(nlcols));
5628: PetscCall(PetscSFCreate(comm, &sf));
5629: /* P serves as roots and P_oth is leaves
5630: * Diag matrix
5631: * */
5632: PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5633: PetscCall(PetscSFSetFromOptions(sf));
5634: PetscCall(PetscSFSetUp(sf));
5636: PetscCall(PetscSFCreate(comm, &osf));
5637: /* off-diagonal */
5638: PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5639: PetscCall(PetscSFSetFromOptions(osf));
5640: PetscCall(PetscSFSetUp(osf));
5641: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5642: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5643: /* operate on the matrix internal data to save memory */
5644: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5645: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5646: PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5647: /* Convert to global indices for diag matrix */
5648: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5649: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5650: /* We want P_oth store global indices */
5651: PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5652: /* Use memory scalable approach */
5653: PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5654: PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5655: PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5656: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5657: /* Convert back to local indices */
5658: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5659: PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5660: nout = 0;
5661: PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5662: PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5663: PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5664: /* Exchange values */
5665: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5666: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5667: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5668: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5669: /* Stop PETSc from shrinking memory */
5670: for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5671: PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5672: PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5673: /* Attach PetscSF objects to P_oth so that we can reuse it later */
5674: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5675: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5676: PetscCall(PetscSFDestroy(&sf));
5677: PetscCall(PetscSFDestroy(&osf));
5678: PetscFunctionReturn(PETSC_SUCCESS);
5679: }
5681: /*
5682: * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5683: * This supports MPIAIJ and MAIJ
5684: * */
5685: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5686: {
5687: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5688: Mat_SeqAIJ *p_oth;
5689: IS rows, map;
5690: PetscHMapI hamp;
5691: PetscInt i, htsize, *rowindices, off, *mapping, key, count;
5692: MPI_Comm comm;
5693: PetscSF sf, osf;
5694: PetscBool has;
5696: PetscFunctionBegin;
5697: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5698: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5699: /* If it is the first time, create an index set of off-diag nonzero columns of A,
5700: * and then create a submatrix (that often is an overlapping matrix)
5701: * */
5702: if (reuse == MAT_INITIAL_MATRIX) {
5703: /* Use a hash table to figure out unique keys */
5704: PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5705: PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5706: count = 0;
5707: /* Assume that a->g is sorted, otherwise the following does not make sense */
5708: for (i = 0; i < a->B->cmap->n; i++) {
5709: key = a->garray[i] / dof;
5710: PetscCall(PetscHMapIHas(hamp, key, &has));
5711: if (!has) {
5712: mapping[i] = count;
5713: PetscCall(PetscHMapISet(hamp, key, count++));
5714: } else {
5715: /* Current 'i' has the same value the previous step */
5716: mapping[i] = count - 1;
5717: }
5718: }
5719: PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5720: PetscCall(PetscHMapIGetSize(hamp, &htsize));
5721: PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5722: PetscCall(PetscCalloc1(htsize, &rowindices));
5723: off = 0;
5724: PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5725: PetscCall(PetscHMapIDestroy(&hamp));
5726: PetscCall(PetscSortInt(htsize, rowindices));
5727: PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5728: /* In case, the matrix was already created but users want to recreate the matrix */
5729: PetscCall(MatDestroy(P_oth));
5730: PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5731: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5732: PetscCall(ISDestroy(&map));
5733: PetscCall(ISDestroy(&rows));
5734: } else if (reuse == MAT_REUSE_MATRIX) {
5735: /* If matrix was already created, we simply update values using SF objects
5736: * that as attached to the matrix earlier.
5737: */
5738: const PetscScalar *pd_a, *po_a;
5740: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5741: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5742: PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5743: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5744: /* Update values in place */
5745: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5746: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5747: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5748: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5749: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5750: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5751: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5752: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5753: } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5754: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5755: PetscFunctionReturn(PETSC_SUCCESS);
5756: }
5758: /*@C
5759: MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`
5761: Collective
5763: Input Parameters:
5764: + A - the first matrix in `MATMPIAIJ` format
5765: . B - the second matrix in `MATMPIAIJ` format
5766: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5768: Output Parameters:
5769: + rowb - On input index sets of rows of B to extract (or `NULL`), modified on output
5770: . colb - On input index sets of columns of B to extract (or `NULL`), modified on output
5771: - B_seq - the sequential matrix generated
5773: Level: developer
5775: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5776: @*/
5777: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5778: {
5779: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5780: PetscInt *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5781: IS isrowb, iscolb;
5782: Mat *bseq = NULL;
5784: PetscFunctionBegin;
5785: 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 ")",
5786: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5787: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));
5789: if (scall == MAT_INITIAL_MATRIX) {
5790: start = A->cmap->rstart;
5791: cmap = a->garray;
5792: nzA = a->A->cmap->n;
5793: nzB = a->B->cmap->n;
5794: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5795: ncols = 0;
5796: for (i = 0; i < nzB; i++) { /* row < local row index */
5797: if (cmap[i] < start) idx[ncols++] = cmap[i];
5798: else break;
5799: }
5800: imark = i;
5801: for (i = 0; i < nzA; i++) idx[ncols++] = start + i; /* local rows */
5802: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5803: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5804: PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5805: } else {
5806: PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5807: isrowb = *rowb;
5808: iscolb = *colb;
5809: PetscCall(PetscMalloc1(1, &bseq));
5810: bseq[0] = *B_seq;
5811: }
5812: PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5813: *B_seq = bseq[0];
5814: PetscCall(PetscFree(bseq));
5815: if (!rowb) {
5816: PetscCall(ISDestroy(&isrowb));
5817: } else {
5818: *rowb = isrowb;
5819: }
5820: if (!colb) {
5821: PetscCall(ISDestroy(&iscolb));
5822: } else {
5823: *colb = iscolb;
5824: }
5825: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5826: PetscFunctionReturn(PETSC_SUCCESS);
5827: }
5829: /*
5830: MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5831: of the OFF-DIAGONAL portion of local A
5833: Collective
5835: Input Parameters:
5836: + A,B - the matrices in `MATMPIAIJ` format
5837: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5839: Output Parameter:
5840: + startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5841: . startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5842: . bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5843: - B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N
5845: Developer Note:
5846: This directly accesses information inside the VecScatter associated with the matrix-vector product
5847: for this matrix. This is not desirable..
5849: Level: developer
5851: */
5853: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5854: {
5855: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5856: VecScatter ctx;
5857: MPI_Comm comm;
5858: const PetscMPIInt *rprocs, *sprocs;
5859: PetscMPIInt nrecvs, nsends;
5860: const PetscInt *srow, *rstarts, *sstarts;
5861: PetscInt *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5862: PetscInt i, j, k = 0, l, ll, nrows, *rstartsj = NULL, *sstartsj, len;
5863: PetscScalar *b_otha, *bufa, *bufA, *vals = NULL;
5864: MPI_Request *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5865: PetscMPIInt size, tag, rank, nreqs;
5867: PetscFunctionBegin;
5868: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5869: PetscCallMPI(MPI_Comm_size(comm, &size));
5871: 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 ")",
5872: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5873: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5874: PetscCallMPI(MPI_Comm_rank(comm, &rank));
5876: if (size == 1) {
5877: startsj_s = NULL;
5878: bufa_ptr = NULL;
5879: *B_oth = NULL;
5880: PetscFunctionReturn(PETSC_SUCCESS);
5881: }
5883: ctx = a->Mvctx;
5884: tag = ((PetscObject)ctx)->tag;
5886: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5887: /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5888: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5889: PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5890: PetscCall(PetscMalloc1(nreqs, &reqs));
5891: rwaits = reqs;
5892: swaits = PetscSafePointerPlusOffset(reqs, nrecvs);
5894: if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5895: if (scall == MAT_INITIAL_MATRIX) {
5896: /* i-array */
5897: /* post receives */
5898: if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5899: for (i = 0; i < nrecvs; i++) {
5900: rowlen = rvalues + rstarts[i] * rbs;
5901: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5902: PetscCallMPI(MPIU_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5903: }
5905: /* pack the outgoing message */
5906: PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));
5908: sstartsj[0] = 0;
5909: rstartsj[0] = 0;
5910: len = 0; /* total length of j or a array to be sent */
5911: if (nsends) {
5912: k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5913: PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5914: }
5915: for (i = 0; i < nsends; i++) {
5916: rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5917: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5918: for (j = 0; j < nrows; j++) {
5919: row = srow[k] + B->rmap->range[rank]; /* global row idx */
5920: for (l = 0; l < sbs; l++) {
5921: PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */
5923: rowlen[j * sbs + l] = ncols;
5925: len += ncols;
5926: PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5927: }
5928: k++;
5929: }
5930: PetscCallMPI(MPIU_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));
5932: sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5933: }
5934: /* recvs and sends of i-array are completed */
5935: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5936: PetscCall(PetscFree(svalues));
5938: /* allocate buffers for sending j and a arrays */
5939: PetscCall(PetscMalloc1(len + 1, &bufj));
5940: PetscCall(PetscMalloc1(len + 1, &bufa));
5942: /* create i-array of B_oth */
5943: PetscCall(PetscMalloc1(aBn + 2, &b_othi));
5945: b_othi[0] = 0;
5946: len = 0; /* total length of j or a array to be received */
5947: k = 0;
5948: for (i = 0; i < nrecvs; i++) {
5949: rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5950: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5951: for (j = 0; j < nrows; j++) {
5952: b_othi[k + 1] = b_othi[k] + rowlen[j];
5953: PetscCall(PetscIntSumError(rowlen[j], len, &len));
5954: k++;
5955: }
5956: rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5957: }
5958: PetscCall(PetscFree(rvalues));
5960: /* allocate space for j and a arrays of B_oth */
5961: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_othj));
5962: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_otha));
5964: /* j-array */
5965: /* post receives of j-array */
5966: for (i = 0; i < nrecvs; i++) {
5967: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5968: PetscCallMPI(MPIU_Irecv(b_othj + rstartsj[i], nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5969: }
5971: /* pack the outgoing message j-array */
5972: if (nsends) k = sstarts[0];
5973: for (i = 0; i < nsends; i++) {
5974: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5975: bufJ = bufj + sstartsj[i];
5976: for (j = 0; j < nrows; j++) {
5977: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5978: for (ll = 0; ll < sbs; ll++) {
5979: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5980: for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5981: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5982: }
5983: }
5984: PetscCallMPI(MPIU_Isend(bufj + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5985: }
5987: /* recvs and sends of j-array are completed */
5988: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5989: } else if (scall == MAT_REUSE_MATRIX) {
5990: sstartsj = *startsj_s;
5991: rstartsj = *startsj_r;
5992: bufa = *bufa_ptr;
5993: PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5994: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");
5996: /* a-array */
5997: /* post receives of a-array */
5998: for (i = 0; i < nrecvs; i++) {
5999: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
6000: PetscCallMPI(MPIU_Irecv(b_otha + rstartsj[i], nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
6001: }
6003: /* pack the outgoing message a-array */
6004: if (nsends) k = sstarts[0];
6005: for (i = 0; i < nsends; i++) {
6006: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
6007: bufA = bufa + sstartsj[i];
6008: for (j = 0; j < nrows; j++) {
6009: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
6010: for (ll = 0; ll < sbs; ll++) {
6011: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
6012: for (l = 0; l < ncols; l++) *bufA++ = vals[l];
6013: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
6014: }
6015: }
6016: PetscCallMPI(MPIU_Isend(bufa + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
6017: }
6018: /* recvs and sends of a-array are completed */
6019: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
6020: PetscCall(PetscFree(reqs));
6022: if (scall == MAT_INITIAL_MATRIX) {
6023: Mat_SeqAIJ *b_oth;
6025: /* put together the new matrix */
6026: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));
6028: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
6029: /* Since these are PETSc arrays, change flags to free them as necessary. */
6030: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
6031: b_oth->free_a = PETSC_TRUE;
6032: b_oth->free_ij = PETSC_TRUE;
6033: b_oth->nonew = 0;
6035: PetscCall(PetscFree(bufj));
6036: if (!startsj_s || !bufa_ptr) {
6037: PetscCall(PetscFree2(sstartsj, rstartsj));
6038: PetscCall(PetscFree(bufa_ptr));
6039: } else {
6040: *startsj_s = sstartsj;
6041: *startsj_r = rstartsj;
6042: *bufa_ptr = bufa;
6043: }
6044: } else if (scall == MAT_REUSE_MATRIX) {
6045: PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
6046: }
6048: PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
6049: PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
6050: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
6051: PetscFunctionReturn(PETSC_SUCCESS);
6052: }
6054: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
6055: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
6056: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
6057: #if defined(PETSC_HAVE_MKL_SPARSE)
6058: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
6059: #endif
6060: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
6061: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
6062: #if defined(PETSC_HAVE_ELEMENTAL)
6063: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
6064: #endif
6065: #if defined(PETSC_HAVE_SCALAPACK)
6066: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
6067: #endif
6068: #if defined(PETSC_HAVE_HYPRE)
6069: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
6070: #endif
6071: #if defined(PETSC_HAVE_CUDA)
6072: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
6073: #endif
6074: #if defined(PETSC_HAVE_HIP)
6075: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6076: #endif
6077: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6078: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6079: #endif
6080: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6081: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6082: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
6084: /*
6085: Computes (B'*A')' since computing B*A directly is untenable
6087: n p p
6088: [ ] [ ] [ ]
6089: m [ A ] * n [ B ] = m [ C ]
6090: [ ] [ ] [ ]
6092: */
6093: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6094: {
6095: Mat At, Bt, Ct;
6097: PetscFunctionBegin;
6098: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6099: PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6100: PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_CURRENT, &Ct));
6101: PetscCall(MatDestroy(&At));
6102: PetscCall(MatDestroy(&Bt));
6103: PetscCall(MatTransposeSetPrecursor(Ct, C));
6104: PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6105: PetscCall(MatDestroy(&Ct));
6106: PetscFunctionReturn(PETSC_SUCCESS);
6107: }
6109: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6110: {
6111: PetscBool cisdense;
6113: PetscFunctionBegin;
6114: 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);
6115: PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6116: PetscCall(MatSetBlockSizesFromMats(C, A, B));
6117: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6118: if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6119: PetscCall(MatSetUp(C));
6121: C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6122: PetscFunctionReturn(PETSC_SUCCESS);
6123: }
6125: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6126: {
6127: Mat_Product *product = C->product;
6128: Mat A = product->A, B = product->B;
6130: PetscFunctionBegin;
6131: 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 ")",
6132: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6133: C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6134: C->ops->productsymbolic = MatProductSymbolic_AB;
6135: PetscFunctionReturn(PETSC_SUCCESS);
6136: }
6138: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6139: {
6140: Mat_Product *product = C->product;
6142: PetscFunctionBegin;
6143: if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6144: PetscFunctionReturn(PETSC_SUCCESS);
6145: }
6147: /*
6148: Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix
6150: Input Parameters:
6152: j1,rowBegin1,rowEnd1,jmap1: describe the first set of nonzeros (Set1)
6153: j2,rowBegin2,rowEnd2,jmap2: describe the second set of nonzeros (Set2)
6155: mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat
6157: For Set1, j1[] contains column indices of the nonzeros.
6158: 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
6159: respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6160: but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.
6162: Similar for Set2.
6164: This routine merges the two sets of nonzeros row by row and removes repeats.
6166: Output Parameters: (memory is allocated by the caller)
6168: i[],j[]: the CSR of the merged matrix, which has m rows.
6169: imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6170: imap2[]: similar to imap1[], but for Set2.
6171: Note we order nonzeros row-by-row and from left to right.
6172: */
6173: 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[])
6174: {
6175: PetscInt r, m; /* Row index of mat */
6176: PetscCount t, t1, t2, b1, e1, b2, e2;
6178: PetscFunctionBegin;
6179: PetscCall(MatGetLocalSize(mat, &m, NULL));
6180: t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6181: i[0] = 0;
6182: for (r = 0; r < m; r++) { /* Do row by row merging */
6183: b1 = rowBegin1[r];
6184: e1 = rowEnd1[r];
6185: b2 = rowBegin2[r];
6186: e2 = rowEnd2[r];
6187: while (b1 < e1 && b2 < e2) {
6188: if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6189: j[t] = j1[b1];
6190: imap1[t1] = t;
6191: imap2[t2] = t;
6192: b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6193: b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6194: t1++;
6195: t2++;
6196: t++;
6197: } else if (j1[b1] < j2[b2]) {
6198: j[t] = j1[b1];
6199: imap1[t1] = t;
6200: b1 += jmap1[t1 + 1] - jmap1[t1];
6201: t1++;
6202: t++;
6203: } else {
6204: j[t] = j2[b2];
6205: imap2[t2] = t;
6206: b2 += jmap2[t2 + 1] - jmap2[t2];
6207: t2++;
6208: t++;
6209: }
6210: }
6211: /* Merge the remaining in either j1[] or j2[] */
6212: while (b1 < e1) {
6213: j[t] = j1[b1];
6214: imap1[t1] = t;
6215: b1 += jmap1[t1 + 1] - jmap1[t1];
6216: t1++;
6217: t++;
6218: }
6219: while (b2 < e2) {
6220: j[t] = j2[b2];
6221: imap2[t2] = t;
6222: b2 += jmap2[t2 + 1] - jmap2[t2];
6223: t2++;
6224: t++;
6225: }
6226: PetscCall(PetscIntCast(t, i + r + 1));
6227: }
6228: PetscFunctionReturn(PETSC_SUCCESS);
6229: }
6231: /*
6232: Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block
6234: Input Parameters:
6235: mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6236: 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[]
6237: respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.
6239: i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6240: i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.
6242: Output Parameters:
6243: j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6244: rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6245: 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,
6246: and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.
6248: Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6249: Atot: number of entries belonging to the diagonal block.
6250: Annz: number of unique nonzeros belonging to the diagonal block.
6251: Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6252: repeats (i.e., same 'i,j' pair).
6253: Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6254: is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.
6256: Atot: number of entries belonging to the diagonal block
6257: Annz: number of unique nonzeros belonging to the diagonal block.
6259: Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.
6261: Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6262: */
6263: 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_)
6264: {
6265: PetscInt cstart, cend, rstart, rend, row, col;
6266: PetscCount Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6267: PetscCount Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6268: PetscCount k, m, p, q, r, s, mid;
6269: PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;
6271: PetscFunctionBegin;
6272: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6273: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6274: m = rend - rstart;
6276: /* Skip negative rows */
6277: for (k = 0; k < n; k++)
6278: if (i[k] >= 0) break;
6280: /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6281: fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6282: */
6283: while (k < n) {
6284: row = i[k];
6285: /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6286: for (s = k; s < n; s++)
6287: if (i[s] != row) break;
6289: /* Shift diag columns to range of [-PETSC_INT_MAX, -1] */
6290: for (p = k; p < s; p++) {
6291: if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_INT_MAX;
6292: 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]);
6293: }
6294: PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6295: PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6296: rowBegin[row - rstart] = k;
6297: rowMid[row - rstart] = mid;
6298: rowEnd[row - rstart] = s;
6300: /* Count nonzeros of this diag/offdiag row, which might have repeats */
6301: Atot += mid - k;
6302: Btot += s - mid;
6304: /* Count unique nonzeros of this diag row */
6305: for (p = k; p < mid;) {
6306: col = j[p];
6307: do {
6308: j[p] += PETSC_INT_MAX; /* Revert the modified diagonal indices */
6309: p++;
6310: } while (p < mid && j[p] == col);
6311: Annz++;
6312: }
6314: /* Count unique nonzeros of this offdiag row */
6315: for (p = mid; p < s;) {
6316: col = j[p];
6317: do {
6318: p++;
6319: } while (p < s && j[p] == col);
6320: Bnnz++;
6321: }
6322: k = s;
6323: }
6325: /* Allocation according to Atot, Btot, Annz, Bnnz */
6326: PetscCall(PetscMalloc1(Atot, &Aperm));
6327: PetscCall(PetscMalloc1(Btot, &Bperm));
6328: PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6329: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));
6331: /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6332: Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6333: for (r = 0; r < m; r++) {
6334: k = rowBegin[r];
6335: mid = rowMid[r];
6336: s = rowEnd[r];
6337: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Aperm, Atot), PetscSafePointerPlusOffset(perm, k), mid - k));
6338: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Bperm, Btot), PetscSafePointerPlusOffset(perm, mid), s - mid));
6339: Atot += mid - k;
6340: Btot += s - mid;
6342: /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6343: for (p = k; p < mid;) {
6344: col = j[p];
6345: q = p;
6346: do {
6347: p++;
6348: } while (p < mid && j[p] == col);
6349: Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6350: Annz++;
6351: }
6353: for (p = mid; p < s;) {
6354: col = j[p];
6355: q = p;
6356: do {
6357: p++;
6358: } while (p < s && j[p] == col);
6359: Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6360: Bnnz++;
6361: }
6362: }
6363: /* Output */
6364: *Aperm_ = Aperm;
6365: *Annz_ = Annz;
6366: *Atot_ = Atot;
6367: *Ajmap_ = Ajmap;
6368: *Bperm_ = Bperm;
6369: *Bnnz_ = Bnnz;
6370: *Btot_ = Btot;
6371: *Bjmap_ = Bjmap;
6372: PetscFunctionReturn(PETSC_SUCCESS);
6373: }
6375: /*
6376: Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix
6378: Input Parameters:
6379: nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6380: nnz: number of unique nonzeros in the merged matrix
6381: imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6382: jmap[nnz1+1]: i-th nonzero in the set has jmap[i+1] - jmap[i] repeats in the set
6384: Output Parameter: (memory is allocated by the caller)
6385: jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set
6387: Example:
6388: nnz1 = 4
6389: nnz = 6
6390: imap = [1,3,4,5]
6391: jmap = [0,3,5,6,7]
6392: then,
6393: jmap_new = [0,0,3,3,5,6,7]
6394: */
6395: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6396: {
6397: PetscCount k, p;
6399: PetscFunctionBegin;
6400: jmap_new[0] = 0;
6401: p = nnz; /* p loops over jmap_new[] backwards */
6402: for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6403: for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6404: }
6405: for (; p >= 0; p--) jmap_new[p] = jmap[0];
6406: PetscFunctionReturn(PETSC_SUCCESS);
6407: }
6409: static PetscErrorCode MatCOOStructDestroy_MPIAIJ(void **data)
6410: {
6411: MatCOOStruct_MPIAIJ *coo = (MatCOOStruct_MPIAIJ *)*data;
6413: PetscFunctionBegin;
6414: PetscCall(PetscSFDestroy(&coo->sf));
6415: PetscCall(PetscFree(coo->Aperm1));
6416: PetscCall(PetscFree(coo->Bperm1));
6417: PetscCall(PetscFree(coo->Ajmap1));
6418: PetscCall(PetscFree(coo->Bjmap1));
6419: PetscCall(PetscFree(coo->Aimap2));
6420: PetscCall(PetscFree(coo->Bimap2));
6421: PetscCall(PetscFree(coo->Aperm2));
6422: PetscCall(PetscFree(coo->Bperm2));
6423: PetscCall(PetscFree(coo->Ajmap2));
6424: PetscCall(PetscFree(coo->Bjmap2));
6425: PetscCall(PetscFree(coo->Cperm1));
6426: PetscCall(PetscFree2(coo->sendbuf, coo->recvbuf));
6427: PetscCall(PetscFree(coo));
6428: PetscFunctionReturn(PETSC_SUCCESS);
6429: }
6431: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6432: {
6433: MPI_Comm comm;
6434: PetscMPIInt rank, size;
6435: PetscInt m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6436: PetscCount k, p, q, rem; /* Loop variables over coo arrays */
6437: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6438: PetscContainer container;
6439: MatCOOStruct_MPIAIJ *coo;
6441: PetscFunctionBegin;
6442: PetscCall(PetscFree(mpiaij->garray));
6443: PetscCall(VecDestroy(&mpiaij->lvec));
6444: #if defined(PETSC_USE_CTABLE)
6445: PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6446: #else
6447: PetscCall(PetscFree(mpiaij->colmap));
6448: #endif
6449: PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6450: mat->assembled = PETSC_FALSE;
6451: mat->was_assembled = PETSC_FALSE;
6453: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6454: PetscCallMPI(MPI_Comm_size(comm, &size));
6455: PetscCallMPI(MPI_Comm_rank(comm, &rank));
6456: PetscCall(PetscLayoutSetUp(mat->rmap));
6457: PetscCall(PetscLayoutSetUp(mat->cmap));
6458: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6459: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6460: PetscCall(MatGetLocalSize(mat, &m, &n));
6461: PetscCall(MatGetSize(mat, &M, &N));
6463: /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6464: /* entries come first, then local rows, then remote rows. */
6465: PetscCount n1 = coo_n, *perm1;
6466: PetscInt *i1 = coo_i, *j1 = coo_j;
6468: PetscCall(PetscMalloc1(n1, &perm1));
6469: for (k = 0; k < n1; k++) perm1[k] = k;
6471: /* Manipulate indices so that entries with negative row or col indices will have smallest
6472: row indices, local entries will have greater but negative row indices, and remote entries
6473: will have positive row indices.
6474: */
6475: for (k = 0; k < n1; k++) {
6476: if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_INT_MIN; /* e.g., -2^31, minimal to move them ahead */
6477: 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] */
6478: else {
6479: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6480: if (mpiaij->donotstash) i1[k] = PETSC_INT_MIN; /* Ignore offproc entries as if they had negative indices */
6481: }
6482: }
6484: /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6485: PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));
6487: /* Advance k to the first entry we need to take care of */
6488: for (k = 0; k < n1; k++)
6489: if (i1[k] > PETSC_INT_MIN) break;
6490: PetscCount i1start = k;
6492: PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_INT_MAX, &rem)); /* rem is upper bound of the last local row */
6493: for (; k < rem; k++) i1[k] += PETSC_INT_MAX; /* Revert row indices of local rows*/
6495: /* Send remote rows to their owner */
6496: /* Find which rows should be sent to which remote ranks*/
6497: PetscInt nsend = 0; /* Number of MPI ranks to send data to */
6498: PetscMPIInt *sendto; /* [nsend], storing remote ranks */
6499: PetscInt *nentries; /* [nsend], storing number of entries sent to remote ranks; Assume PetscInt is big enough for this count, and error if not */
6500: const PetscInt *ranges;
6501: PetscInt maxNsend = size >= 128 ? 128 : size; /* Assume max 128 neighbors; realloc when needed */
6503: PetscCall(PetscLayoutGetRanges(mat->rmap, &ranges));
6504: PetscCall(PetscMalloc2(maxNsend, &sendto, maxNsend, &nentries));
6505: for (k = rem; k < n1;) {
6506: PetscMPIInt owner;
6507: PetscInt firstRow, lastRow;
6509: /* Locate a row range */
6510: firstRow = i1[k]; /* first row of this owner */
6511: PetscCall(PetscLayoutFindOwner(mat->rmap, firstRow, &owner));
6512: lastRow = ranges[owner + 1] - 1; /* last row of this owner */
6514: /* Find the first index 'p' in [k,n) with i[p] belonging to next owner */
6515: PetscCall(PetscSortedIntUpperBound(i1, k, n1, lastRow, &p));
6517: /* All entries in [k,p) belong to this remote owner */
6518: if (nsend >= maxNsend) { /* Double the remote ranks arrays if not long enough */
6519: PetscMPIInt *sendto2;
6520: PetscInt *nentries2;
6521: PetscInt maxNsend2 = (maxNsend <= size / 2) ? maxNsend * 2 : size;
6523: PetscCall(PetscMalloc2(maxNsend2, &sendto2, maxNsend2, &nentries2));
6524: PetscCall(PetscArraycpy(sendto2, sendto, maxNsend));
6525: PetscCall(PetscArraycpy(nentries2, nentries2, maxNsend + 1));
6526: PetscCall(PetscFree2(sendto, nentries2));
6527: sendto = sendto2;
6528: nentries = nentries2;
6529: maxNsend = maxNsend2;
6530: }
6531: sendto[nsend] = owner;
6532: PetscCall(PetscIntCast(p - k, &nentries[nsend]));
6533: nsend++;
6534: k = p;
6535: }
6537: /* Build 1st SF to know offsets on remote to send data */
6538: PetscSF sf1;
6539: PetscInt nroots = 1, nroots2 = 0;
6540: PetscInt nleaves = nsend, nleaves2 = 0;
6541: PetscInt *offsets;
6542: PetscSFNode *iremote;
6544: PetscCall(PetscSFCreate(comm, &sf1));
6545: PetscCall(PetscMalloc1(nsend, &iremote));
6546: PetscCall(PetscMalloc1(nsend, &offsets));
6547: for (k = 0; k < nsend; k++) {
6548: iremote[k].rank = sendto[k];
6549: iremote[k].index = 0;
6550: nleaves2 += nentries[k];
6551: PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6552: }
6553: PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6554: PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6555: PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6556: PetscCall(PetscSFDestroy(&sf1));
6557: PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT, nleaves2, n1 - rem);
6559: /* Build 2nd SF to send remote COOs to their owner */
6560: PetscSF sf2;
6561: nroots = nroots2;
6562: nleaves = nleaves2;
6563: PetscCall(PetscSFCreate(comm, &sf2));
6564: PetscCall(PetscSFSetFromOptions(sf2));
6565: PetscCall(PetscMalloc1(nleaves, &iremote));
6566: p = 0;
6567: for (k = 0; k < nsend; k++) {
6568: PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6569: for (q = 0; q < nentries[k]; q++, p++) {
6570: iremote[p].rank = sendto[k];
6571: PetscCall(PetscIntCast(offsets[k] + q, &iremote[p].index));
6572: }
6573: }
6574: PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6576: /* Send the remote COOs to their owner */
6577: PetscInt n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6578: PetscCount *perm2; /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6579: PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6580: PetscAssert(rem == 0 || i1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6581: PetscAssert(rem == 0 || j1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6582: PetscInt *i1prem = PetscSafePointerPlusOffset(i1, rem);
6583: PetscInt *j1prem = PetscSafePointerPlusOffset(j1, rem);
6584: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1prem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6585: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1prem, i2, MPI_REPLACE));
6586: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1prem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6587: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1prem, j2, MPI_REPLACE));
6589: PetscCall(PetscFree(offsets));
6590: PetscCall(PetscFree2(sendto, nentries));
6592: /* Sort received COOs by row along with the permutation array */
6593: for (k = 0; k < n2; k++) perm2[k] = k;
6594: PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));
6596: /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6597: PetscCount *Cperm1;
6598: PetscAssert(rem == 0 || perm1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6599: PetscCount *perm1prem = PetscSafePointerPlusOffset(perm1, rem);
6600: PetscCall(PetscMalloc1(nleaves, &Cperm1));
6601: PetscCall(PetscArraycpy(Cperm1, perm1prem, nleaves));
6603: /* Support for HYPRE matrices, kind of a hack.
6604: Swap min column with diagonal so that diagonal values will go first */
6605: PetscBool hypre;
6606: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", ((PetscObject)mat)->name, &hypre));
6607: if (hypre) {
6608: PetscInt *minj;
6609: PetscBT hasdiag;
6611: PetscCall(PetscBTCreate(m, &hasdiag));
6612: PetscCall(PetscMalloc1(m, &minj));
6613: for (k = 0; k < m; k++) minj[k] = PETSC_INT_MAX;
6614: for (k = i1start; k < rem; k++) {
6615: if (j1[k] < cstart || j1[k] >= cend) continue;
6616: const PetscInt rindex = i1[k] - rstart;
6617: if ((j1[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6618: minj[rindex] = PetscMin(minj[rindex], j1[k]);
6619: }
6620: for (k = 0; k < n2; k++) {
6621: if (j2[k] < cstart || j2[k] >= cend) continue;
6622: const PetscInt rindex = i2[k] - rstart;
6623: if ((j2[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6624: minj[rindex] = PetscMin(minj[rindex], j2[k]);
6625: }
6626: for (k = i1start; k < rem; k++) {
6627: const PetscInt rindex = i1[k] - rstart;
6628: if (j1[k] < cstart || j1[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6629: if (j1[k] == minj[rindex]) j1[k] = i1[k] + (cstart - rstart);
6630: else if ((j1[k] - cstart) == rindex) j1[k] = minj[rindex];
6631: }
6632: for (k = 0; k < n2; k++) {
6633: const PetscInt rindex = i2[k] - rstart;
6634: if (j2[k] < cstart || j2[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6635: if (j2[k] == minj[rindex]) j2[k] = i2[k] + (cstart - rstart);
6636: else if ((j2[k] - cstart) == rindex) j2[k] = minj[rindex];
6637: }
6638: PetscCall(PetscBTDestroy(&hasdiag));
6639: PetscCall(PetscFree(minj));
6640: }
6642: /* Split local COOs and received COOs into diag/offdiag portions */
6643: PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6644: PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1;
6645: PetscCount Annz1, Bnnz1, Atot1, Btot1;
6646: PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6647: PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6648: PetscCount Annz2, Bnnz2, Atot2, Btot2;
6650: PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6651: PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6652: PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6653: PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));
6655: /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6656: PetscInt *Ai, *Bi;
6657: PetscInt *Aj, *Bj;
6659: PetscCall(PetscMalloc1(m + 1, &Ai));
6660: PetscCall(PetscMalloc1(m + 1, &Bi));
6661: PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6662: PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));
6664: PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6665: PetscCall(PetscMalloc1(Annz1, &Aimap1));
6666: PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6667: PetscCall(PetscMalloc1(Annz2, &Aimap2));
6668: PetscCall(PetscMalloc1(Bnnz2, &Bimap2));
6670: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6671: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));
6673: /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we */
6674: /* expect nonzeros in A/B most likely have local contributing entries */
6675: PetscInt Annz = Ai[m];
6676: PetscInt Bnnz = Bi[m];
6677: PetscCount *Ajmap1_new, *Bjmap1_new;
6679: PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6680: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));
6682: PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6683: PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));
6685: PetscCall(PetscFree(Aimap1));
6686: PetscCall(PetscFree(Ajmap1));
6687: PetscCall(PetscFree(Bimap1));
6688: PetscCall(PetscFree(Bjmap1));
6689: PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6690: PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6691: PetscCall(PetscFree(perm1));
6692: PetscCall(PetscFree3(i2, j2, perm2));
6694: Ajmap1 = Ajmap1_new;
6695: Bjmap1 = Bjmap1_new;
6697: /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6698: if (Annz < Annz1 + Annz2) {
6699: PetscInt *Aj_new;
6700: PetscCall(PetscMalloc1(Annz, &Aj_new));
6701: PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6702: PetscCall(PetscFree(Aj));
6703: Aj = Aj_new;
6704: }
6706: if (Bnnz < Bnnz1 + Bnnz2) {
6707: PetscInt *Bj_new;
6708: PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6709: PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6710: PetscCall(PetscFree(Bj));
6711: Bj = Bj_new;
6712: }
6714: /* Create new submatrices for on-process and off-process coupling */
6715: PetscScalar *Aa, *Ba;
6716: MatType rtype;
6717: Mat_SeqAIJ *a, *b;
6718: PetscObjectState state;
6719: PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6720: PetscCall(PetscCalloc1(Bnnz, &Ba));
6721: /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6722: if (cstart) {
6723: for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6724: }
6726: PetscCall(MatGetRootType_Private(mat, &rtype));
6728: MatSeqXAIJGetOptions_Private(mpiaij->A);
6729: PetscCall(MatDestroy(&mpiaij->A));
6730: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6731: PetscCall(MatSetBlockSizesFromMats(mpiaij->A, mat, mat));
6732: MatSeqXAIJRestoreOptions_Private(mpiaij->A);
6734: MatSeqXAIJGetOptions_Private(mpiaij->B);
6735: PetscCall(MatDestroy(&mpiaij->B));
6736: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6737: PetscCall(MatSetBlockSizesFromMats(mpiaij->B, mat, mat));
6738: MatSeqXAIJRestoreOptions_Private(mpiaij->B);
6740: PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6741: mat->was_assembled = PETSC_TRUE; // was_assembled in effect means the Mvctx is built; doing so avoids redundant MatSetUpMultiply_MPIAIJ
6742: state = mpiaij->A->nonzerostate + mpiaij->B->nonzerostate;
6743: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
6745: a = (Mat_SeqAIJ *)mpiaij->A->data;
6746: b = (Mat_SeqAIJ *)mpiaij->B->data;
6747: a->free_a = PETSC_TRUE;
6748: a->free_ij = PETSC_TRUE;
6749: b->free_a = PETSC_TRUE;
6750: b->free_ij = PETSC_TRUE;
6751: a->maxnz = a->nz;
6752: b->maxnz = b->nz;
6754: /* conversion must happen AFTER multiply setup */
6755: PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6756: PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6757: PetscCall(VecDestroy(&mpiaij->lvec));
6758: PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));
6760: // Put the COO struct in a container and then attach that to the matrix
6761: PetscCall(PetscMalloc1(1, &coo));
6762: coo->n = coo_n;
6763: coo->sf = sf2;
6764: coo->sendlen = nleaves;
6765: coo->recvlen = nroots;
6766: coo->Annz = Annz;
6767: coo->Bnnz = Bnnz;
6768: coo->Annz2 = Annz2;
6769: coo->Bnnz2 = Bnnz2;
6770: coo->Atot1 = Atot1;
6771: coo->Atot2 = Atot2;
6772: coo->Btot1 = Btot1;
6773: coo->Btot2 = Btot2;
6774: coo->Ajmap1 = Ajmap1;
6775: coo->Aperm1 = Aperm1;
6776: coo->Bjmap1 = Bjmap1;
6777: coo->Bperm1 = Bperm1;
6778: coo->Aimap2 = Aimap2;
6779: coo->Ajmap2 = Ajmap2;
6780: coo->Aperm2 = Aperm2;
6781: coo->Bimap2 = Bimap2;
6782: coo->Bjmap2 = Bjmap2;
6783: coo->Bperm2 = Bperm2;
6784: coo->Cperm1 = Cperm1;
6785: // Allocate in preallocation. If not used, it has zero cost on host
6786: PetscCall(PetscMalloc2(coo->sendlen, &coo->sendbuf, coo->recvlen, &coo->recvbuf));
6787: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
6788: PetscCall(PetscContainerSetPointer(container, coo));
6789: PetscCall(PetscContainerSetCtxDestroy(container, MatCOOStructDestroy_MPIAIJ));
6790: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
6791: PetscCall(PetscContainerDestroy(&container));
6792: PetscFunctionReturn(PETSC_SUCCESS);
6793: }
6795: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6796: {
6797: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6798: Mat A = mpiaij->A, B = mpiaij->B;
6799: PetscScalar *Aa, *Ba;
6800: PetscScalar *sendbuf, *recvbuf;
6801: const PetscCount *Ajmap1, *Ajmap2, *Aimap2;
6802: const PetscCount *Bjmap1, *Bjmap2, *Bimap2;
6803: const PetscCount *Aperm1, *Aperm2, *Bperm1, *Bperm2;
6804: const PetscCount *Cperm1;
6805: PetscContainer container;
6806: MatCOOStruct_MPIAIJ *coo;
6808: PetscFunctionBegin;
6809: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
6810: PetscCheck(container, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
6811: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
6812: sendbuf = coo->sendbuf;
6813: recvbuf = coo->recvbuf;
6814: Ajmap1 = coo->Ajmap1;
6815: Ajmap2 = coo->Ajmap2;
6816: Aimap2 = coo->Aimap2;
6817: Bjmap1 = coo->Bjmap1;
6818: Bjmap2 = coo->Bjmap2;
6819: Bimap2 = coo->Bimap2;
6820: Aperm1 = coo->Aperm1;
6821: Aperm2 = coo->Aperm2;
6822: Bperm1 = coo->Bperm1;
6823: Bperm2 = coo->Bperm2;
6824: Cperm1 = coo->Cperm1;
6826: PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6827: PetscCall(MatSeqAIJGetArray(B, &Ba));
6829: /* Pack entries to be sent to remote */
6830: for (PetscCount i = 0; i < coo->sendlen; i++) sendbuf[i] = v[Cperm1[i]];
6832: /* Send remote entries to their owner and overlap the communication with local computation */
6833: PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6834: /* Add local entries to A and B */
6835: for (PetscCount i = 0; i < coo->Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6836: PetscScalar sum = 0.0; /* Do partial summation first to improve numerical stability */
6837: for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6838: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6839: }
6840: for (PetscCount i = 0; i < coo->Bnnz; i++) {
6841: PetscScalar sum = 0.0;
6842: for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6843: Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6844: }
6845: PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));
6847: /* Add received remote entries to A and B */
6848: for (PetscCount i = 0; i < coo->Annz2; i++) {
6849: for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6850: }
6851: for (PetscCount i = 0; i < coo->Bnnz2; i++) {
6852: for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6853: }
6854: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6855: PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6856: PetscFunctionReturn(PETSC_SUCCESS);
6857: }
6859: /*MC
6860: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
6862: Options Database Keys:
6863: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`
6865: Level: beginner
6867: Notes:
6868: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
6869: in this case the values associated with the rows and columns one passes in are set to zero
6870: in the matrix
6872: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
6873: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
6875: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6876: M*/
6877: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6878: {
6879: Mat_MPIAIJ *b;
6880: PetscMPIInt size;
6882: PetscFunctionBegin;
6883: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
6885: PetscCall(PetscNew(&b));
6886: B->data = (void *)b;
6887: B->ops[0] = MatOps_Values;
6888: B->assembled = PETSC_FALSE;
6889: B->insertmode = NOT_SET_VALUES;
6890: b->size = size;
6892: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
6894: /* build cache for off array entries formed */
6895: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
6897: b->donotstash = PETSC_FALSE;
6898: b->colmap = NULL;
6899: b->garray = NULL;
6900: b->roworiented = PETSC_TRUE;
6902: /* stuff used for matrix vector multiply */
6903: b->lvec = NULL;
6904: b->Mvctx = NULL;
6906: /* stuff for MatGetRow() */
6907: b->rowindices = NULL;
6908: b->rowvalues = NULL;
6909: b->getrowactive = PETSC_FALSE;
6911: /* flexible pointer used in CUSPARSE classes */
6912: b->spptr = NULL;
6914: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6915: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6916: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6917: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6918: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6919: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6920: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetHash_C", MatResetHash_MPIAIJ));
6921: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6922: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6923: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6924: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6925: #if defined(PETSC_HAVE_CUDA)
6926: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6927: #endif
6928: #if defined(PETSC_HAVE_HIP)
6929: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6930: #endif
6931: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6932: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6933: #endif
6934: #if defined(PETSC_HAVE_MKL_SPARSE)
6935: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6936: #endif
6937: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6938: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6939: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6940: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6941: #if defined(PETSC_HAVE_ELEMENTAL)
6942: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6943: #endif
6944: #if defined(PETSC_HAVE_SCALAPACK)
6945: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6946: #endif
6947: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6948: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6949: #if defined(PETSC_HAVE_HYPRE)
6950: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6951: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6952: #endif
6953: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6954: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6955: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6956: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6957: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6958: PetscFunctionReturn(PETSC_SUCCESS);
6959: }
6961: /*@
6962: MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6963: and "off-diagonal" part of the matrix in CSR format.
6965: Collective
6967: Input Parameters:
6968: + comm - MPI communicator
6969: . m - number of local rows (Cannot be `PETSC_DECIDE`)
6970: . n - This value should be the same as the local size used in creating the
6971: x vector for the matrix-vector product $y = Ax$. (or `PETSC_DECIDE` to have
6972: calculated if `N` is given) For square matrices `n` is almost always `m`.
6973: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6974: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6975: . 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
6976: . j - column indices, which must be local, i.e., based off the start column of the diagonal portion
6977: . a - matrix values
6978: . 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
6979: . oj - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6980: - oa - matrix values
6982: Output Parameter:
6983: . mat - the matrix
6985: Level: advanced
6987: Notes:
6988: The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc (even in Fortran). The user
6989: must free the arrays once the matrix has been destroyed and not before.
6991: The `i` and `j` indices are 0 based
6993: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix
6995: This sets local rows and cannot be used to set off-processor values.
6997: Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6998: legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6999: not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
7000: the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
7001: keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
7002: communication if it is known that only local entries will be set.
7004: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
7005: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
7006: @*/
7007: 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)
7008: {
7009: Mat_MPIAIJ *maij;
7011: PetscFunctionBegin;
7012: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
7013: PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
7014: PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
7015: PetscCall(MatCreate(comm, mat));
7016: PetscCall(MatSetSizes(*mat, m, n, M, N));
7017: PetscCall(MatSetType(*mat, MATMPIAIJ));
7018: maij = (Mat_MPIAIJ *)(*mat)->data;
7020: (*mat)->preallocated = PETSC_TRUE;
7022: PetscCall(PetscLayoutSetUp((*mat)->rmap));
7023: PetscCall(PetscLayoutSetUp((*mat)->cmap));
7025: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
7026: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));
7028: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
7029: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
7030: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
7031: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
7032: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
7033: PetscFunctionReturn(PETSC_SUCCESS);
7034: }
7036: typedef struct {
7037: Mat *mp; /* intermediate products */
7038: PetscBool *mptmp; /* is the intermediate product temporary ? */
7039: PetscInt cp; /* number of intermediate products */
7041: /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
7042: PetscInt *startsj_s, *startsj_r;
7043: PetscScalar *bufa;
7044: Mat P_oth;
7046: /* may take advantage of merging product->B */
7047: Mat Bloc; /* B-local by merging diag and off-diag */
7049: /* cusparse does not have support to split between symbolic and numeric phases.
7050: When api_user is true, we don't need to update the numerical values
7051: of the temporary storage */
7052: PetscBool reusesym;
7054: /* support for COO values insertion */
7055: PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
7056: PetscInt **own; /* own[i] points to address of on-process COO indices for Mat mp[i] */
7057: PetscInt **off; /* off[i] points to address of off-process COO indices for Mat mp[i] */
7058: PetscBool hasoffproc; /* if true, have off-process values insertion (i.e. AtB or PtAP) */
7059: PetscSF sf; /* used for non-local values insertion and memory malloc */
7060: PetscMemType mtype;
7062: /* customization */
7063: PetscBool abmerge;
7064: PetscBool P_oth_bind;
7065: } MatMatMPIAIJBACKEND;
7067: static PetscErrorCode MatDestroy_MatMatMPIAIJBACKEND(void *data)
7068: {
7069: MatMatMPIAIJBACKEND *mmdata = (MatMatMPIAIJBACKEND *)data;
7070: PetscInt i;
7072: PetscFunctionBegin;
7073: PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
7074: PetscCall(PetscFree(mmdata->bufa));
7075: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
7076: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
7077: PetscCall(MatDestroy(&mmdata->P_oth));
7078: PetscCall(MatDestroy(&mmdata->Bloc));
7079: PetscCall(PetscSFDestroy(&mmdata->sf));
7080: for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
7081: PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
7082: PetscCall(PetscFree(mmdata->own[0]));
7083: PetscCall(PetscFree(mmdata->own));
7084: PetscCall(PetscFree(mmdata->off[0]));
7085: PetscCall(PetscFree(mmdata->off));
7086: PetscCall(PetscFree(mmdata));
7087: PetscFunctionReturn(PETSC_SUCCESS);
7088: }
7090: /* Copy selected n entries with indices in idx[] of A to v[].
7091: If idx is NULL, copy the whole data array of A to v[]
7092: */
7093: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
7094: {
7095: PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);
7097: PetscFunctionBegin;
7098: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
7099: if (f) {
7100: PetscCall((*f)(A, n, idx, v));
7101: } else {
7102: const PetscScalar *vv;
7104: PetscCall(MatSeqAIJGetArrayRead(A, &vv));
7105: if (n && idx) {
7106: PetscScalar *w = v;
7107: const PetscInt *oi = idx;
7108: PetscInt j;
7110: for (j = 0; j < n; j++) *w++ = vv[*oi++];
7111: } else {
7112: PetscCall(PetscArraycpy(v, vv, n));
7113: }
7114: PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7115: }
7116: PetscFunctionReturn(PETSC_SUCCESS);
7117: }
7119: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7120: {
7121: MatMatMPIAIJBACKEND *mmdata;
7122: PetscInt i, n_d, n_o;
7124: PetscFunctionBegin;
7125: MatCheckProduct(C, 1);
7126: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7127: mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7128: if (!mmdata->reusesym) { /* update temporary matrices */
7129: 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));
7130: if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7131: }
7132: mmdata->reusesym = PETSC_FALSE;
7134: for (i = 0; i < mmdata->cp; i++) {
7135: 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]);
7136: PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7137: }
7138: for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7139: PetscInt noff;
7141: PetscCall(PetscIntCast(mmdata->off[i + 1] - mmdata->off[i], &noff));
7142: if (mmdata->mptmp[i]) continue;
7143: if (noff) {
7144: PetscInt nown;
7146: PetscCall(PetscIntCast(mmdata->own[i + 1] - mmdata->own[i], &nown));
7147: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7148: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7149: n_o += noff;
7150: n_d += nown;
7151: } else {
7152: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;
7154: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7155: n_d += mm->nz;
7156: }
7157: }
7158: if (mmdata->hasoffproc) { /* offprocess insertion */
7159: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7160: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7161: }
7162: PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7163: PetscFunctionReturn(PETSC_SUCCESS);
7164: }
7166: /* Support for Pt * A, A * P, or Pt * A * P */
7167: #define MAX_NUMBER_INTERMEDIATE 4
7168: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7169: {
7170: Mat_Product *product = C->product;
7171: Mat A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7172: Mat_MPIAIJ *a, *p;
7173: MatMatMPIAIJBACKEND *mmdata;
7174: ISLocalToGlobalMapping P_oth_l2g = NULL;
7175: IS glob = NULL;
7176: const char *prefix;
7177: char pprefix[256];
7178: const PetscInt *globidx, *P_oth_idx;
7179: PetscInt i, j, cp, m, n, M, N, *coo_i, *coo_j;
7180: PetscCount ncoo, ncoo_d, ncoo_o, ncoo_oown;
7181: PetscInt cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7182: /* type-0: consecutive, start from 0; type-1: consecutive with */
7183: /* a base offset; type-2: sparse with a local to global map table */
7184: const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE]; /* col/row local to global map array (table) for type-2 map type */
7186: MatProductType ptype;
7187: PetscBool mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7188: PetscMPIInt size;
7190: PetscFunctionBegin;
7191: MatCheckProduct(C, 1);
7192: PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7193: ptype = product->type;
7194: if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7195: ptype = MATPRODUCT_AB;
7196: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7197: }
7198: switch (ptype) {
7199: case MATPRODUCT_AB:
7200: A = product->A;
7201: P = product->B;
7202: m = A->rmap->n;
7203: n = P->cmap->n;
7204: M = A->rmap->N;
7205: N = P->cmap->N;
7206: hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7207: break;
7208: case MATPRODUCT_AtB:
7209: P = product->A;
7210: A = product->B;
7211: m = P->cmap->n;
7212: n = A->cmap->n;
7213: M = P->cmap->N;
7214: N = A->cmap->N;
7215: hasoffproc = PETSC_TRUE;
7216: break;
7217: case MATPRODUCT_PtAP:
7218: A = product->A;
7219: P = product->B;
7220: m = P->cmap->n;
7221: n = P->cmap->n;
7222: M = P->cmap->N;
7223: N = P->cmap->N;
7224: hasoffproc = PETSC_TRUE;
7225: break;
7226: default:
7227: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7228: }
7229: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7230: if (size == 1) hasoffproc = PETSC_FALSE;
7232: /* defaults */
7233: for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7234: mp[i] = NULL;
7235: mptmp[i] = PETSC_FALSE;
7236: rmapt[i] = -1;
7237: cmapt[i] = -1;
7238: rmapa[i] = NULL;
7239: cmapa[i] = NULL;
7240: }
7242: /* customization */
7243: PetscCall(PetscNew(&mmdata));
7244: mmdata->reusesym = product->api_user;
7245: if (ptype == MATPRODUCT_AB) {
7246: if (product->api_user) {
7247: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7248: PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7249: PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7250: PetscOptionsEnd();
7251: } else {
7252: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7253: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7254: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7255: PetscOptionsEnd();
7256: }
7257: } else if (ptype == MATPRODUCT_PtAP) {
7258: if (product->api_user) {
7259: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7260: PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7261: PetscOptionsEnd();
7262: } else {
7263: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7264: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7265: PetscOptionsEnd();
7266: }
7267: }
7268: a = (Mat_MPIAIJ *)A->data;
7269: p = (Mat_MPIAIJ *)P->data;
7270: PetscCall(MatSetSizes(C, m, n, M, N));
7271: PetscCall(PetscLayoutSetUp(C->rmap));
7272: PetscCall(PetscLayoutSetUp(C->cmap));
7273: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7274: PetscCall(MatGetOptionsPrefix(C, &prefix));
7276: cp = 0;
7277: switch (ptype) {
7278: case MATPRODUCT_AB: /* A * P */
7279: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7281: /* A_diag * P_local (merged or not) */
7282: if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7283: /* P is product->B */
7284: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7285: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7286: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7287: PetscCall(MatProductSetFill(mp[cp], product->fill));
7288: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7289: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7290: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7291: mp[cp]->product->api_user = product->api_user;
7292: PetscCall(MatProductSetFromOptions(mp[cp]));
7293: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7294: PetscCall(ISGetIndices(glob, &globidx));
7295: rmapt[cp] = 1;
7296: cmapt[cp] = 2;
7297: cmapa[cp] = globidx;
7298: mptmp[cp] = PETSC_FALSE;
7299: cp++;
7300: } else { /* A_diag * P_diag and A_diag * P_off */
7301: PetscCall(MatProductCreate(a->A, p->A, NULL, &mp[cp]));
7302: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7303: PetscCall(MatProductSetFill(mp[cp], product->fill));
7304: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7305: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7306: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7307: mp[cp]->product->api_user = product->api_user;
7308: PetscCall(MatProductSetFromOptions(mp[cp]));
7309: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7310: rmapt[cp] = 1;
7311: cmapt[cp] = 1;
7312: mptmp[cp] = PETSC_FALSE;
7313: cp++;
7314: PetscCall(MatProductCreate(a->A, p->B, NULL, &mp[cp]));
7315: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7316: PetscCall(MatProductSetFill(mp[cp], product->fill));
7317: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7318: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7319: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7320: mp[cp]->product->api_user = product->api_user;
7321: PetscCall(MatProductSetFromOptions(mp[cp]));
7322: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7323: rmapt[cp] = 1;
7324: cmapt[cp] = 2;
7325: cmapa[cp] = p->garray;
7326: mptmp[cp] = PETSC_FALSE;
7327: cp++;
7328: }
7330: /* A_off * P_other */
7331: if (mmdata->P_oth) {
7332: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7333: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7334: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7335: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7336: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7337: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7338: PetscCall(MatProductSetFill(mp[cp], product->fill));
7339: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7340: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7341: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7342: mp[cp]->product->api_user = product->api_user;
7343: PetscCall(MatProductSetFromOptions(mp[cp]));
7344: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7345: rmapt[cp] = 1;
7346: cmapt[cp] = 2;
7347: cmapa[cp] = P_oth_idx;
7348: mptmp[cp] = PETSC_FALSE;
7349: cp++;
7350: }
7351: break;
7353: case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7354: /* A is product->B */
7355: PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7356: if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7357: PetscCall(MatProductCreate(mmdata->Bloc, mmdata->Bloc, NULL, &mp[cp]));
7358: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7359: PetscCall(MatProductSetFill(mp[cp], product->fill));
7360: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7361: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7362: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7363: mp[cp]->product->api_user = product->api_user;
7364: PetscCall(MatProductSetFromOptions(mp[cp]));
7365: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7366: PetscCall(ISGetIndices(glob, &globidx));
7367: rmapt[cp] = 2;
7368: rmapa[cp] = globidx;
7369: cmapt[cp] = 2;
7370: cmapa[cp] = globidx;
7371: mptmp[cp] = PETSC_FALSE;
7372: cp++;
7373: } else {
7374: PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7375: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7376: PetscCall(MatProductSetFill(mp[cp], product->fill));
7377: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7378: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7379: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7380: mp[cp]->product->api_user = product->api_user;
7381: PetscCall(MatProductSetFromOptions(mp[cp]));
7382: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7383: PetscCall(ISGetIndices(glob, &globidx));
7384: rmapt[cp] = 1;
7385: cmapt[cp] = 2;
7386: cmapa[cp] = globidx;
7387: mptmp[cp] = PETSC_FALSE;
7388: cp++;
7389: PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7390: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7391: PetscCall(MatProductSetFill(mp[cp], product->fill));
7392: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7393: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7394: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7395: mp[cp]->product->api_user = product->api_user;
7396: PetscCall(MatProductSetFromOptions(mp[cp]));
7397: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7398: rmapt[cp] = 2;
7399: rmapa[cp] = p->garray;
7400: cmapt[cp] = 2;
7401: cmapa[cp] = globidx;
7402: mptmp[cp] = PETSC_FALSE;
7403: cp++;
7404: }
7405: break;
7406: case MATPRODUCT_PtAP:
7407: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7408: /* P is product->B */
7409: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7410: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7411: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
7412: PetscCall(MatProductSetFill(mp[cp], product->fill));
7413: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7414: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7415: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7416: mp[cp]->product->api_user = product->api_user;
7417: PetscCall(MatProductSetFromOptions(mp[cp]));
7418: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7419: PetscCall(ISGetIndices(glob, &globidx));
7420: rmapt[cp] = 2;
7421: rmapa[cp] = globidx;
7422: cmapt[cp] = 2;
7423: cmapa[cp] = globidx;
7424: mptmp[cp] = PETSC_FALSE;
7425: cp++;
7426: if (mmdata->P_oth) {
7427: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7428: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7429: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7430: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7431: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7432: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7433: PetscCall(MatProductSetFill(mp[cp], product->fill));
7434: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7435: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7436: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7437: mp[cp]->product->api_user = product->api_user;
7438: PetscCall(MatProductSetFromOptions(mp[cp]));
7439: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7440: mptmp[cp] = PETSC_TRUE;
7441: cp++;
7442: PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7443: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7444: PetscCall(MatProductSetFill(mp[cp], product->fill));
7445: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7446: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7447: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7448: mp[cp]->product->api_user = product->api_user;
7449: PetscCall(MatProductSetFromOptions(mp[cp]));
7450: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7451: rmapt[cp] = 2;
7452: rmapa[cp] = globidx;
7453: cmapt[cp] = 2;
7454: cmapa[cp] = P_oth_idx;
7455: mptmp[cp] = PETSC_FALSE;
7456: cp++;
7457: }
7458: break;
7459: default:
7460: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7461: }
7462: /* sanity check */
7463: if (size > 1)
7464: 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);
7466: PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7467: for (i = 0; i < cp; i++) {
7468: mmdata->mp[i] = mp[i];
7469: mmdata->mptmp[i] = mptmp[i];
7470: }
7471: mmdata->cp = cp;
7472: C->product->data = mmdata;
7473: C->product->destroy = MatDestroy_MatMatMPIAIJBACKEND;
7474: C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;
7476: /* memory type */
7477: mmdata->mtype = PETSC_MEMTYPE_HOST;
7478: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7479: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7480: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7481: if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7482: else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7483: else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;
7485: /* prepare coo coordinates for values insertion */
7487: /* count total nonzeros of those intermediate seqaij Mats
7488: ncoo_d: # of nonzeros of matrices that do not have offproc entries
7489: ncoo_o: # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7490: ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7491: */
7492: for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7493: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7494: if (mptmp[cp]) continue;
7495: if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7496: const PetscInt *rmap = rmapa[cp];
7497: const PetscInt mr = mp[cp]->rmap->n;
7498: const PetscInt rs = C->rmap->rstart;
7499: const PetscInt re = C->rmap->rend;
7500: const PetscInt *ii = mm->i;
7501: for (i = 0; i < mr; i++) {
7502: const PetscInt gr = rmap[i];
7503: const PetscInt nz = ii[i + 1] - ii[i];
7504: if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7505: else ncoo_oown += nz; /* this row is local */
7506: }
7507: } else ncoo_d += mm->nz;
7508: }
7510: /*
7511: ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc
7513: ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.
7515: off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].
7517: off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7518: own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7519: so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.
7521: coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7522: 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.
7523: */
7524: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7525: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));
7527: /* gather (i,j) of nonzeros inserted by remote procs */
7528: if (hasoffproc) {
7529: PetscSF msf;
7530: PetscInt ncoo2, *coo_i2, *coo_j2;
7532: PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7533: PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7534: PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */
7536: for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7537: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7538: PetscInt *idxoff = mmdata->off[cp];
7539: PetscInt *idxown = mmdata->own[cp];
7540: if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7541: const PetscInt *rmap = rmapa[cp];
7542: const PetscInt *cmap = cmapa[cp];
7543: const PetscInt *ii = mm->i;
7544: PetscInt *coi = coo_i + ncoo_o;
7545: PetscInt *coj = coo_j + ncoo_o;
7546: const PetscInt mr = mp[cp]->rmap->n;
7547: const PetscInt rs = C->rmap->rstart;
7548: const PetscInt re = C->rmap->rend;
7549: const PetscInt cs = C->cmap->rstart;
7550: for (i = 0; i < mr; i++) {
7551: const PetscInt *jj = mm->j + ii[i];
7552: const PetscInt gr = rmap[i];
7553: const PetscInt nz = ii[i + 1] - ii[i];
7554: if (gr < rs || gr >= re) { /* this is an offproc row */
7555: for (j = ii[i]; j < ii[i + 1]; j++) {
7556: *coi++ = gr;
7557: *idxoff++ = j;
7558: }
7559: if (!cmapt[cp]) { /* already global */
7560: for (j = 0; j < nz; j++) *coj++ = jj[j];
7561: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7562: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7563: } else { /* offdiag */
7564: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7565: }
7566: ncoo_o += nz;
7567: } else { /* this is a local row */
7568: for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7569: }
7570: }
7571: }
7572: mmdata->off[cp + 1] = idxoff;
7573: mmdata->own[cp + 1] = idxown;
7574: }
7576: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7577: PetscInt incoo_o;
7578: PetscCall(PetscIntCast(ncoo_o, &incoo_o));
7579: PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, incoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7580: PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7581: PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7582: ncoo = ncoo_d + ncoo_oown + ncoo2;
7583: PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7584: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7585: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7586: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7587: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7588: PetscCall(PetscFree2(coo_i, coo_j));
7589: /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7590: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7591: coo_i = coo_i2;
7592: coo_j = coo_j2;
7593: } else { /* no offproc values insertion */
7594: ncoo = ncoo_d;
7595: PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));
7597: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7598: PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7599: PetscCall(PetscSFSetUp(mmdata->sf));
7600: }
7601: mmdata->hasoffproc = hasoffproc;
7603: /* gather (i,j) of nonzeros inserted locally */
7604: for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7605: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7606: PetscInt *coi = coo_i + ncoo_d;
7607: PetscInt *coj = coo_j + ncoo_d;
7608: const PetscInt *jj = mm->j;
7609: const PetscInt *ii = mm->i;
7610: const PetscInt *cmap = cmapa[cp];
7611: const PetscInt *rmap = rmapa[cp];
7612: const PetscInt mr = mp[cp]->rmap->n;
7613: const PetscInt rs = C->rmap->rstart;
7614: const PetscInt re = C->rmap->rend;
7615: const PetscInt cs = C->cmap->rstart;
7617: if (mptmp[cp]) continue;
7618: if (rmapt[cp] == 1) { /* consecutive rows */
7619: /* fill coo_i */
7620: for (i = 0; i < mr; i++) {
7621: const PetscInt gr = i + rs;
7622: for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7623: }
7624: /* fill coo_j */
7625: if (!cmapt[cp]) { /* type-0, already global */
7626: PetscCall(PetscArraycpy(coj, jj, mm->nz));
7627: } else if (cmapt[cp] == 1) { /* type-1, local to global for consecutive columns of C */
7628: for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7629: } else { /* type-2, local to global for sparse columns */
7630: for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7631: }
7632: ncoo_d += mm->nz;
7633: } else if (rmapt[cp] == 2) { /* sparse rows */
7634: for (i = 0; i < mr; i++) {
7635: const PetscInt *jj = mm->j + ii[i];
7636: const PetscInt gr = rmap[i];
7637: const PetscInt nz = ii[i + 1] - ii[i];
7638: if (gr >= rs && gr < re) { /* local rows */
7639: for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7640: if (!cmapt[cp]) { /* type-0, already global */
7641: for (j = 0; j < nz; j++) *coj++ = jj[j];
7642: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7643: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7644: } else { /* type-2, local to global for sparse columns */
7645: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7646: }
7647: ncoo_d += nz;
7648: }
7649: }
7650: }
7651: }
7652: if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7653: PetscCall(ISDestroy(&glob));
7654: if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7655: PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7656: /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7657: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));
7659: /* preallocate with COO data */
7660: PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7661: PetscCall(PetscFree2(coo_i, coo_j));
7662: PetscFunctionReturn(PETSC_SUCCESS);
7663: }
7665: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7666: {
7667: Mat_Product *product = mat->product;
7668: #if defined(PETSC_HAVE_DEVICE)
7669: PetscBool match = PETSC_FALSE;
7670: PetscBool usecpu = PETSC_FALSE;
7671: #else
7672: PetscBool match = PETSC_TRUE;
7673: #endif
7675: PetscFunctionBegin;
7676: MatCheckProduct(mat, 1);
7677: #if defined(PETSC_HAVE_DEVICE)
7678: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7679: if (match) { /* we can always fallback to the CPU if requested */
7680: switch (product->type) {
7681: case MATPRODUCT_AB:
7682: if (product->api_user) {
7683: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7684: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7685: PetscOptionsEnd();
7686: } else {
7687: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7688: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7689: PetscOptionsEnd();
7690: }
7691: break;
7692: case MATPRODUCT_AtB:
7693: if (product->api_user) {
7694: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7695: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7696: PetscOptionsEnd();
7697: } else {
7698: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7699: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7700: PetscOptionsEnd();
7701: }
7702: break;
7703: case MATPRODUCT_PtAP:
7704: if (product->api_user) {
7705: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7706: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7707: PetscOptionsEnd();
7708: } else {
7709: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7710: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7711: PetscOptionsEnd();
7712: }
7713: break;
7714: default:
7715: break;
7716: }
7717: match = (PetscBool)!usecpu;
7718: }
7719: #endif
7720: if (match) {
7721: switch (product->type) {
7722: case MATPRODUCT_AB:
7723: case MATPRODUCT_AtB:
7724: case MATPRODUCT_PtAP:
7725: mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7726: break;
7727: default:
7728: break;
7729: }
7730: }
7731: /* fallback to MPIAIJ ops */
7732: if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7733: PetscFunctionReturn(PETSC_SUCCESS);
7734: }
7736: /*
7737: Produces a set of block column indices of the matrix row, one for each block represented in the original row
7739: n - the number of block indices in cc[]
7740: cc - the block indices (must be large enough to contain the indices)
7741: */
7742: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7743: {
7744: PetscInt cnt = -1, nidx, j;
7745: const PetscInt *idx;
7747: PetscFunctionBegin;
7748: PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7749: if (nidx) {
7750: cnt = 0;
7751: cc[cnt] = idx[0] / bs;
7752: for (j = 1; j < nidx; j++) {
7753: if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7754: }
7755: }
7756: PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7757: *n = cnt + 1;
7758: PetscFunctionReturn(PETSC_SUCCESS);
7759: }
7761: /*
7762: Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows
7764: ncollapsed - the number of block indices
7765: collapsed - the block indices (must be large enough to contain the indices)
7766: */
7767: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7768: {
7769: PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;
7771: PetscFunctionBegin;
7772: PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7773: for (i = start + 1; i < start + bs; i++) {
7774: PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7775: PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7776: cprevtmp = cprev;
7777: cprev = merged;
7778: merged = cprevtmp;
7779: }
7780: *ncollapsed = nprev;
7781: if (collapsed) *collapsed = cprev;
7782: PetscFunctionReturn(PETSC_SUCCESS);
7783: }
7785: /*
7786: MatCreateGraph_Simple_AIJ - create simple scalar matrix (graph) from potentially blocked matrix
7788: Input Parameter:
7789: . Amat - matrix
7790: - symmetrize - make the result symmetric
7791: + scale - scale with diagonal
7793: Output Parameter:
7794: . a_Gmat - output scalar graph >= 0
7796: */
7797: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, PetscInt index_size, PetscInt index[], Mat *a_Gmat)
7798: {
7799: PetscInt Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7800: MPI_Comm comm;
7801: Mat Gmat;
7802: PetscBool ismpiaij, isseqaij;
7803: Mat a, b, c;
7804: MatType jtype;
7806: PetscFunctionBegin;
7807: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7808: PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7809: PetscCall(MatGetSize(Amat, &MM, &NN));
7810: PetscCall(MatGetBlockSize(Amat, &bs));
7811: nloc = (Iend - Istart) / bs;
7813: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7814: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7815: PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");
7817: /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7818: /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7819: implementation */
7820: if (bs > 1) {
7821: PetscCall(MatGetType(Amat, &jtype));
7822: PetscCall(MatCreate(comm, &Gmat));
7823: PetscCall(MatSetType(Gmat, jtype));
7824: PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7825: PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7826: if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7827: PetscInt *d_nnz, *o_nnz;
7828: MatScalar *aa, val, *AA;
7829: PetscInt *aj, *ai, *AJ, nc, nmax = 0;
7831: if (isseqaij) {
7832: a = Amat;
7833: b = NULL;
7834: } else {
7835: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7836: a = d->A;
7837: b = d->B;
7838: }
7839: PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7840: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7841: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7842: PetscInt *nnz = (c == a) ? d_nnz : o_nnz;
7843: const PetscInt *cols1, *cols2;
7845: for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7846: PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7847: nnz[brow / bs] = nc2 / bs;
7848: if (nc2 % bs) ok = 0;
7849: if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7850: for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7851: PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7852: if (nc1 != nc2) ok = 0;
7853: else {
7854: for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7855: if (cols1[jj] != cols2[jj]) ok = 0;
7856: if (cols1[jj] % bs != jj % bs) ok = 0;
7857: }
7858: }
7859: PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7860: }
7861: PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7862: if (!ok) {
7863: PetscCall(PetscFree2(d_nnz, o_nnz));
7864: PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7865: goto old_bs;
7866: }
7867: }
7868: }
7869: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7870: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7871: PetscCall(PetscFree2(d_nnz, o_nnz));
7872: PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7873: // diag
7874: for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7875: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;
7877: ai = aseq->i;
7878: n = ai[brow + 1] - ai[brow];
7879: aj = aseq->j + ai[brow];
7880: for (PetscInt k = 0; k < n; k += bs) { // block columns
7881: AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7882: val = 0;
7883: if (index_size == 0) {
7884: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7885: aa = aseq->a + ai[brow + ii] + k;
7886: for (PetscInt jj = 0; jj < bs; jj++) { // columns in block
7887: val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7888: }
7889: }
7890: } else { // use (index,index) value if provided
7891: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7892: PetscInt ii = index[iii];
7893: aa = aseq->a + ai[brow + ii] + k;
7894: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // columns in block
7895: PetscInt jj = index[jjj];
7896: val += PetscAbs(PetscRealPart(aa[jj]));
7897: }
7898: }
7899: }
7900: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%" PetscInt_FMT ") >= nmax (%" PetscInt_FMT ")", k / bs, nmax);
7901: AA[k / bs] = val;
7902: }
7903: grow = Istart / bs + brow / bs;
7904: PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, ADD_VALUES));
7905: }
7906: // off-diag
7907: if (ismpiaij) {
7908: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Amat->data;
7909: const PetscScalar *vals;
7910: const PetscInt *cols, *garray = aij->garray;
7912: PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7913: for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7914: PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7915: for (PetscInt k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7916: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7917: AA[k / bs] = 0;
7918: AJ[cidx] = garray[cols[k]] / bs;
7919: }
7920: nc = ncols / bs;
7921: PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7922: if (index_size == 0) {
7923: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7924: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7925: for (PetscInt k = 0; k < ncols; k += bs) {
7926: for (PetscInt jj = 0; jj < bs; jj++) { // cols in block
7927: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%" PetscInt_FMT ") >= nmax (%" PetscInt_FMT ")", k / bs, nmax);
7928: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7929: }
7930: }
7931: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7932: }
7933: } else { // use (index,index) value if provided
7934: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7935: PetscInt ii = index[iii];
7936: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7937: for (PetscInt k = 0; k < ncols; k += bs) {
7938: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // cols in block
7939: PetscInt jj = index[jjj];
7940: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7941: }
7942: }
7943: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7944: }
7945: }
7946: grow = Istart / bs + brow / bs;
7947: PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, ADD_VALUES));
7948: }
7949: }
7950: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7951: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7952: PetscCall(PetscFree2(AA, AJ));
7953: } else {
7954: const PetscScalar *vals;
7955: const PetscInt *idx;
7956: PetscInt *d_nnz, *o_nnz, *w0, *w1, *w2;
7957: old_bs:
7958: /*
7959: Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7960: */
7961: PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7962: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7963: if (isseqaij) {
7964: PetscInt max_d_nnz;
7966: /*
7967: Determine exact preallocation count for (sequential) scalar matrix
7968: */
7969: PetscCall(MatSeqAIJGetMaxRowNonzeros(Amat, &max_d_nnz));
7970: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7971: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7972: for (Ii = 0, jj = 0; Ii < Iend; Ii += bs, jj++) PetscCall(MatCollapseRows(Amat, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7973: PetscCall(PetscFree3(w0, w1, w2));
7974: } else if (ismpiaij) {
7975: Mat Daij, Oaij;
7976: const PetscInt *garray;
7977: PetscInt max_d_nnz;
7979: PetscCall(MatMPIAIJGetSeqAIJ(Amat, &Daij, &Oaij, &garray));
7980: /*
7981: Determine exact preallocation count for diagonal block portion of scalar matrix
7982: */
7983: PetscCall(MatSeqAIJGetMaxRowNonzeros(Daij, &max_d_nnz));
7984: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7985: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7986: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) PetscCall(MatCollapseRows(Daij, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7987: PetscCall(PetscFree3(w0, w1, w2));
7988: /*
7989: Over estimate (usually grossly over), preallocation count for off-diagonal portion of scalar matrix
7990: */
7991: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) {
7992: o_nnz[jj] = 0;
7993: for (kk = 0; kk < bs; kk++) { /* rows that get collapsed to a single row */
7994: PetscCall(MatGetRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7995: o_nnz[jj] += ncols;
7996: PetscCall(MatRestoreRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7997: }
7998: if (o_nnz[jj] > (NN / bs - nloc)) o_nnz[jj] = NN / bs - nloc;
7999: }
8000: } else SETERRQ(comm, PETSC_ERR_USER, "Require AIJ matrix type");
8001: /* get scalar copy (norms) of matrix */
8002: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
8003: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
8004: PetscCall(PetscFree2(d_nnz, o_nnz));
8005: for (Ii = Istart; Ii < Iend; Ii++) {
8006: PetscInt dest_row = Ii / bs;
8008: PetscCall(MatGetRow(Amat, Ii, &ncols, &idx, &vals));
8009: for (jj = 0; jj < ncols; jj++) {
8010: PetscInt dest_col = idx[jj] / bs;
8011: PetscScalar sv = PetscAbs(PetscRealPart(vals[jj]));
8013: PetscCall(MatSetValues(Gmat, 1, &dest_row, 1, &dest_col, &sv, ADD_VALUES));
8014: }
8015: PetscCall(MatRestoreRow(Amat, Ii, &ncols, &idx, &vals));
8016: }
8017: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
8018: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
8019: }
8020: } else {
8021: if (symmetrize || filter >= 0 || scale) PetscCall(MatDuplicate(Amat, MAT_COPY_VALUES, &Gmat));
8022: else {
8023: Gmat = Amat;
8024: PetscCall(PetscObjectReference((PetscObject)Gmat));
8025: }
8026: if (isseqaij) {
8027: a = Gmat;
8028: b = NULL;
8029: } else {
8030: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
8031: a = d->A;
8032: b = d->B;
8033: }
8034: if (filter >= 0 || scale) {
8035: /* take absolute value of each entry */
8036: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
8037: MatInfo info;
8038: PetscScalar *avals;
8040: PetscCall(MatGetInfo(c, MAT_LOCAL, &info));
8041: PetscCall(MatSeqAIJGetArray(c, &avals));
8042: for (int jj = 0; jj < info.nz_used; jj++) avals[jj] = PetscAbsScalar(avals[jj]);
8043: PetscCall(MatSeqAIJRestoreArray(c, &avals));
8044: }
8045: }
8046: }
8047: if (symmetrize) {
8048: PetscBool isset, issym;
8050: PetscCall(MatIsSymmetricKnown(Amat, &isset, &issym));
8051: if (!isset || !issym) {
8052: Mat matTrans;
8054: PetscCall(MatTranspose(Gmat, MAT_INITIAL_MATRIX, &matTrans));
8055: PetscCall(MatAXPY(Gmat, 1.0, matTrans, Gmat->structurally_symmetric == PETSC_BOOL3_TRUE ? SAME_NONZERO_PATTERN : DIFFERENT_NONZERO_PATTERN));
8056: PetscCall(MatDestroy(&matTrans));
8057: }
8058: PetscCall(MatSetOption(Gmat, MAT_SYMMETRIC, PETSC_TRUE));
8059: } else if (Amat != Gmat) PetscCall(MatPropagateSymmetryOptions(Amat, Gmat));
8060: if (scale) {
8061: /* scale c for all diagonal values = 1 or -1 */
8062: Vec diag;
8064: PetscCall(MatCreateVecs(Gmat, &diag, NULL));
8065: PetscCall(MatGetDiagonal(Gmat, diag));
8066: PetscCall(VecReciprocal(diag));
8067: PetscCall(VecSqrtAbs(diag));
8068: PetscCall(MatDiagonalScale(Gmat, diag, diag));
8069: PetscCall(VecDestroy(&diag));
8070: }
8071: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_graph_view"));
8072: if (filter >= 0) {
8073: PetscCall(MatFilter(Gmat, filter, PETSC_TRUE, PETSC_TRUE));
8074: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_filter_graph_view"));
8075: }
8076: *a_Gmat = Gmat;
8077: PetscFunctionReturn(PETSC_SUCCESS);
8078: }
8080: /*
8081: Special version for direct calls from Fortran
8082: */
8084: /* Change these macros so can be used in void function */
8085: /* Identical to PetscCallVoid, except it assigns to *_ierr */
8086: #undef PetscCall
8087: #define PetscCall(...) \
8088: do { \
8089: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
8090: if (PetscUnlikely(ierr_msv_mpiaij)) { \
8091: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
8092: return; \
8093: } \
8094: } while (0)
8096: #undef SETERRQ
8097: #define SETERRQ(comm, ierr, ...) \
8098: do { \
8099: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
8100: return; \
8101: } while (0)
8103: #if defined(PETSC_HAVE_FORTRAN_CAPS)
8104: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
8105: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
8106: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
8107: #else
8108: #endif
8109: PETSC_EXTERN void matsetvaluesmpiaij_(Mat *mmat, PetscInt *mm, const PetscInt im[], PetscInt *mn, const PetscInt in[], const PetscScalar v[], InsertMode *maddv, PetscErrorCode *_ierr)
8110: {
8111: Mat mat = *mmat;
8112: PetscInt m = *mm, n = *mn;
8113: InsertMode addv = *maddv;
8114: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
8115: PetscScalar value;
8117: MatCheckPreallocated(mat, 1);
8118: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
8119: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
8120: {
8121: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
8122: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
8123: PetscBool roworiented = aij->roworiented;
8125: /* Some Variables required in the macro */
8126: Mat A = aij->A;
8127: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
8128: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
8129: MatScalar *aa;
8130: PetscBool ignorezeroentries = ((a->ignorezeroentries && (addv == ADD_VALUES)) ? PETSC_TRUE : PETSC_FALSE);
8131: Mat B = aij->B;
8132: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
8133: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
8134: MatScalar *ba;
8135: /* This variable below is only for the PETSC_HAVE_VIENNACL or PETSC_HAVE_CUDA cases, but we define it in all cases because we
8136: * cannot use "#if defined" inside a macro. */
8137: PETSC_UNUSED PetscBool inserted = PETSC_FALSE;
8139: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
8140: PetscInt nonew = a->nonew;
8141: MatScalar *ap1, *ap2;
8143: PetscFunctionBegin;
8144: PetscCall(MatSeqAIJGetArray(A, &aa));
8145: PetscCall(MatSeqAIJGetArray(B, &ba));
8146: for (i = 0; i < m; i++) {
8147: if (im[i] < 0) continue;
8148: 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);
8149: if (im[i] >= rstart && im[i] < rend) {
8150: row = im[i] - rstart;
8151: lastcol1 = -1;
8152: rp1 = aj + ai[row];
8153: ap1 = aa + ai[row];
8154: rmax1 = aimax[row];
8155: nrow1 = ailen[row];
8156: low1 = 0;
8157: high1 = nrow1;
8158: lastcol2 = -1;
8159: rp2 = bj + bi[row];
8160: ap2 = ba + bi[row];
8161: rmax2 = bimax[row];
8162: nrow2 = bilen[row];
8163: low2 = 0;
8164: high2 = nrow2;
8166: for (j = 0; j < n; j++) {
8167: if (roworiented) value = v[i * n + j];
8168: else value = v[i + j * m];
8169: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
8170: if (in[j] >= cstart && in[j] < cend) {
8171: col = in[j] - cstart;
8172: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
8173: } else if (in[j] < 0) continue;
8174: else if (PetscUnlikelyDebug(in[j] >= mat->cmap->N)) {
8175: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
8176: } else {
8177: if (mat->was_assembled) {
8178: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
8179: #if defined(PETSC_USE_CTABLE)
8180: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col));
8181: col--;
8182: #else
8183: col = aij->colmap[in[j]] - 1;
8184: #endif
8185: if (col < 0 && !((Mat_SeqAIJ *)aij->A->data)->nonew) {
8186: PetscCall(MatDisAssemble_MPIAIJ(mat));
8187: col = in[j];
8188: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
8189: B = aij->B;
8190: b = (Mat_SeqAIJ *)B->data;
8191: bimax = b->imax;
8192: bi = b->i;
8193: bilen = b->ilen;
8194: bj = b->j;
8195: rp2 = bj + bi[row];
8196: ap2 = ba + bi[row];
8197: rmax2 = bimax[row];
8198: nrow2 = bilen[row];
8199: low2 = 0;
8200: high2 = nrow2;
8201: bm = aij->B->rmap->n;
8202: ba = b->a;
8203: inserted = PETSC_FALSE;
8204: }
8205: } else col = in[j];
8206: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
8207: }
8208: }
8209: } else if (!aij->donotstash) {
8210: if (roworiented) {
8211: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8212: } else {
8213: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8214: }
8215: }
8216: }
8217: PetscCall(MatSeqAIJRestoreArray(A, &aa));
8218: PetscCall(MatSeqAIJRestoreArray(B, &ba));
8219: }
8220: PetscFunctionReturnVoid();
8221: }
8223: /* Undefining these here since they were redefined from their original definition above! No
8224: * other PETSc functions should be defined past this point, as it is impossible to recover the
8225: * original definitions */
8226: #undef PetscCall
8227: #undef SETERRQ