Actual source code: mpiaij.c
1: #include <../src/mat/impls/aij/mpi/mpiaij.h>
2: #include <petsc/private/vecimpl.h>
3: #include <petsc/private/sfimpl.h>
4: #include <petsc/private/isimpl.h>
5: #include <petscblaslapack.h>
6: #include <petscsf.h>
7: #include <petsc/private/hashmapi.h>
9: /* defines MatSetValues_MPI_Hash(), MatAssemblyBegin_MPI_Hash(), and MatAssemblyEnd_MPI_Hash() */
10: #define TYPE AIJ
11: #define TYPE_AIJ
12: #include "../src/mat/impls/aij/mpi/mpihashmat.h"
13: #undef TYPE
14: #undef TYPE_AIJ
16: static PetscErrorCode MatReset_MPIAIJ(Mat mat)
17: {
18: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
20: PetscFunctionBegin;
21: PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
22: PetscCall(MatStashDestroy_Private(&mat->stash));
23: PetscCall(VecDestroy(&aij->diag));
24: PetscCall(MatDestroy(&aij->A));
25: PetscCall(MatDestroy(&aij->B));
26: #if defined(PETSC_USE_CTABLE)
27: PetscCall(PetscHMapIDestroy(&aij->colmap));
28: #else
29: PetscCall(PetscFree(aij->colmap));
30: #endif
31: PetscCall(PetscFree(aij->garray));
32: PetscCall(VecDestroy(&aij->lvec));
33: PetscCall(VecScatterDestroy(&aij->Mvctx));
34: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
35: PetscCall(PetscFree(aij->ld));
36: PetscFunctionReturn(PETSC_SUCCESS);
37: }
39: static PetscErrorCode MatResetHash_MPIAIJ(Mat mat)
40: {
41: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
42: /* Save the nonzero states of the component matrices because those are what are used to determine
43: the nonzero state of mat */
44: PetscObjectState Astate = aij->A->nonzerostate, Bstate = aij->B->nonzerostate;
46: PetscFunctionBegin;
47: PetscCall(MatReset_MPIAIJ(mat));
48: PetscCall(MatSetUp_MPI_Hash(mat));
49: aij->A->nonzerostate = ++Astate, aij->B->nonzerostate = ++Bstate;
50: PetscFunctionReturn(PETSC_SUCCESS);
51: }
53: PetscErrorCode MatDestroy_MPIAIJ(Mat mat)
54: {
55: PetscFunctionBegin;
56: PetscCall(MatReset_MPIAIJ(mat));
58: PetscCall(PetscFree(mat->data));
60: /* may be created by MatCreateMPIAIJSumSeqAIJSymbolic */
61: PetscCall(PetscObjectCompose((PetscObject)mat, "MatMergeSeqsToMPI", NULL));
63: PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
64: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
65: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
66: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatIsTranspose_C", NULL));
67: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocation_C", NULL));
68: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatResetPreallocation_C", NULL));
69: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatResetHash_C", NULL));
70: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocationCSR_C", NULL));
71: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
72: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpibaij_C", NULL));
73: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisbaij_C", NULL));
74: #if defined(PETSC_HAVE_CUDA)
75: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcusparse_C", NULL));
76: #endif
77: #if defined(PETSC_HAVE_HIP)
78: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijhipsparse_C", NULL));
79: #endif
80: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
81: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijkokkos_C", NULL));
82: #endif
83: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpidense_C", NULL));
84: #if defined(PETSC_HAVE_ELEMENTAL)
85: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_elemental_C", NULL));
86: #endif
87: #if defined(PETSC_HAVE_SCALAPACK)
88: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_scalapack_C", NULL));
89: #endif
90: #if defined(PETSC_HAVE_HYPRE)
91: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_hypre_C", NULL));
92: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", NULL));
93: #endif
94: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
95: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_is_mpiaij_C", NULL));
96: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_mpiaij_mpiaij_C", NULL));
97: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetUseScalableIncreaseOverlap_C", NULL));
98: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijperm_C", NULL));
99: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijsell_C", NULL));
100: #if defined(PETSC_HAVE_MKL_SPARSE)
101: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijmkl_C", NULL));
102: #endif
103: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcrl_C", NULL));
104: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
105: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisell_C", NULL));
106: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetPreallocationCOO_C", NULL));
107: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetValuesCOO_C", NULL));
108: PetscFunctionReturn(PETSC_SUCCESS);
109: }
111: static PetscErrorCode MatGetRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
112: {
113: Mat B;
115: PetscFunctionBegin;
116: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, &B));
117: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject)B));
118: PetscCall(MatGetRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
119: PetscCall(MatDestroy(&B));
120: PetscFunctionReturn(PETSC_SUCCESS);
121: }
123: static PetscErrorCode MatRestoreRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
124: {
125: Mat B;
127: PetscFunctionBegin;
128: PetscCall(PetscObjectQuery((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject *)&B));
129: PetscCall(MatRestoreRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
130: PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", NULL));
131: PetscFunctionReturn(PETSC_SUCCESS);
132: }
134: /*MC
135: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
137: This matrix type is identical to` MATSEQAIJ` when constructed with a single process communicator,
138: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
139: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
140: for communicators controlling multiple processes. It is recommended that you call both of
141: the above preallocation routines for simplicity.
143: Options Database Key:
144: . -mat_type aij - sets the matrix type to `MATAIJ` during a call to `MatSetFromOptions()`
146: Developer Note:
147: Level: beginner
149: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, `MATAIJKOKKOS`,and also automatically switches over to use inodes when
150: enough exist.
152: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`
153: M*/
155: /*MC
156: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
158: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
159: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
160: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
161: for communicators controlling multiple processes. It is recommended that you call both of
162: the above preallocation routines for simplicity.
164: Options Database Key:
165: . -mat_type aijcrl - sets the matrix type to `MATMPIAIJCRL` during a call to `MatSetFromOptions()`
167: Level: beginner
169: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
170: M*/
172: static PetscErrorCode MatBindToCPU_MPIAIJ(Mat A, PetscBool flg)
173: {
174: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
176: PetscFunctionBegin;
177: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP) || defined(PETSC_HAVE_VIENNACL)
178: A->boundtocpu = flg;
179: #endif
180: if (a->A) PetscCall(MatBindToCPU(a->A, flg));
181: if (a->B) PetscCall(MatBindToCPU(a->B, flg));
183: /* In addition to binding the diagonal and off-diagonal matrices, bind the local vectors used for matrix-vector products.
184: * This maybe seems a little odd for a MatBindToCPU() call to do, but it makes no sense for the binding of these vectors
185: * to differ from the parent matrix. */
186: if (a->lvec) PetscCall(VecBindToCPU(a->lvec, flg));
187: if (a->diag) PetscCall(VecBindToCPU(a->diag, flg));
188: PetscFunctionReturn(PETSC_SUCCESS);
189: }
191: static PetscErrorCode MatSetBlockSizes_MPIAIJ(Mat M, PetscInt rbs, PetscInt cbs)
192: {
193: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
195: PetscFunctionBegin;
196: if (mat->A) {
197: PetscCall(MatSetBlockSizes(mat->A, rbs, cbs));
198: PetscCall(MatSetBlockSizes(mat->B, rbs, 1));
199: }
200: PetscFunctionReturn(PETSC_SUCCESS);
201: }
203: static PetscErrorCode MatFindNonzeroRows_MPIAIJ(Mat M, IS *keptrows)
204: {
205: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;
206: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data;
207: Mat_SeqAIJ *b = (Mat_SeqAIJ *)mat->B->data;
208: const PetscInt *ia, *ib;
209: const MatScalar *aa, *bb, *aav, *bav;
210: PetscInt na, nb, i, j, *rows, cnt = 0, n0rows;
211: PetscInt m = M->rmap->n, rstart = M->rmap->rstart;
213: PetscFunctionBegin;
214: *keptrows = NULL;
216: ia = a->i;
217: ib = b->i;
218: PetscCall(MatSeqAIJGetArrayRead(mat->A, &aav));
219: PetscCall(MatSeqAIJGetArrayRead(mat->B, &bav));
220: for (i = 0; i < m; i++) {
221: na = ia[i + 1] - ia[i];
222: nb = ib[i + 1] - ib[i];
223: if (!na && !nb) {
224: cnt++;
225: goto ok1;
226: }
227: aa = aav + ia[i];
228: for (j = 0; j < na; j++) {
229: if (aa[j] != 0.0) goto ok1;
230: }
231: bb = PetscSafePointerPlusOffset(bav, ib[i]);
232: for (j = 0; j < nb; j++) {
233: if (bb[j] != 0.0) goto ok1;
234: }
235: cnt++;
236: ok1:;
237: }
238: PetscCallMPI(MPIU_Allreduce(&cnt, &n0rows, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)M)));
239: if (!n0rows) {
240: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
241: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
242: PetscFunctionReturn(PETSC_SUCCESS);
243: }
244: PetscCall(PetscMalloc1(M->rmap->n - cnt, &rows));
245: cnt = 0;
246: for (i = 0; i < m; i++) {
247: na = ia[i + 1] - ia[i];
248: nb = ib[i + 1] - ib[i];
249: if (!na && !nb) continue;
250: aa = aav + ia[i];
251: for (j = 0; j < na; j++) {
252: if (aa[j] != 0.0) {
253: rows[cnt++] = rstart + i;
254: goto ok2;
255: }
256: }
257: bb = PetscSafePointerPlusOffset(bav, ib[i]);
258: for (j = 0; j < nb; j++) {
259: if (bb[j] != 0.0) {
260: rows[cnt++] = rstart + i;
261: goto ok2;
262: }
263: }
264: ok2:;
265: }
266: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), cnt, rows, PETSC_OWN_POINTER, keptrows));
267: PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
268: PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
269: PetscFunctionReturn(PETSC_SUCCESS);
270: }
272: static PetscErrorCode MatDiagonalSet_MPIAIJ(Mat Y, Vec D, InsertMode is)
273: {
274: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Y->data;
275: PetscBool cong;
277: PetscFunctionBegin;
278: PetscCall(MatHasCongruentLayouts(Y, &cong));
279: if (Y->assembled && cong) {
280: PetscCall(MatDiagonalSet(aij->A, D, is));
281: } else {
282: PetscCall(MatDiagonalSet_Default(Y, D, is));
283: }
284: PetscFunctionReturn(PETSC_SUCCESS);
285: }
287: static PetscErrorCode MatFindZeroDiagonals_MPIAIJ(Mat M, IS *zrows)
288: {
289: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)M->data;
290: PetscInt i, rstart, nrows, *rows;
292: PetscFunctionBegin;
293: *zrows = NULL;
294: PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(aij->A, &nrows, &rows));
295: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
296: for (i = 0; i < nrows; i++) rows[i] += rstart;
297: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), nrows, rows, PETSC_OWN_POINTER, zrows));
298: PetscFunctionReturn(PETSC_SUCCESS);
299: }
301: static PetscErrorCode MatGetColumnReductions_MPIAIJ(Mat A, PetscInt type, PetscReal *reductions)
302: {
303: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)A->data;
304: PetscInt i, m, n, *garray = aij->garray;
305: Mat_SeqAIJ *a_aij = (Mat_SeqAIJ *)aij->A->data;
306: Mat_SeqAIJ *b_aij = (Mat_SeqAIJ *)aij->B->data;
307: PetscReal *work;
308: const PetscScalar *dummy;
310: PetscFunctionBegin;
311: PetscCall(MatGetSize(A, &m, &n));
312: PetscCall(PetscCalloc1(n, &work));
313: PetscCall(MatSeqAIJGetArrayRead(aij->A, &dummy));
314: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &dummy));
315: PetscCall(MatSeqAIJGetArrayRead(aij->B, &dummy));
316: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &dummy));
317: if (type == NORM_2) {
318: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i] * a_aij->a[i]);
319: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i] * b_aij->a[i]);
320: } else if (type == NORM_1) {
321: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i]);
322: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]);
323: } else if (type == NORM_INFINITY) {
324: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] = PetscMax(PetscAbsScalar(a_aij->a[i]), work[A->cmap->rstart + a_aij->j[i]]);
325: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] = PetscMax(PetscAbsScalar(b_aij->a[i]), work[garray[b_aij->j[i]]]);
326: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
327: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscRealPart(a_aij->a[i]);
328: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscRealPart(b_aij->a[i]);
329: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
330: for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscImaginaryPart(a_aij->a[i]);
331: for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscImaginaryPart(b_aij->a[i]);
332: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
333: if (type == NORM_INFINITY) {
334: PetscCallMPI(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
335: } else {
336: PetscCallMPI(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
337: }
338: PetscCall(PetscFree(work));
339: if (type == NORM_2) {
340: for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
341: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
342: for (i = 0; i < n; i++) reductions[i] /= m;
343: }
344: PetscFunctionReturn(PETSC_SUCCESS);
345: }
347: static PetscErrorCode MatFindOffBlockDiagonalEntries_MPIAIJ(Mat A, IS *is)
348: {
349: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
350: IS sis, gis;
351: const PetscInt *isis, *igis;
352: PetscInt n, *iis, nsis, ngis, rstart, i;
354: PetscFunctionBegin;
355: PetscCall(MatFindOffBlockDiagonalEntries(a->A, &sis));
356: PetscCall(MatFindNonzeroRows(a->B, &gis));
357: PetscCall(ISGetSize(gis, &ngis));
358: PetscCall(ISGetSize(sis, &nsis));
359: PetscCall(ISGetIndices(sis, &isis));
360: PetscCall(ISGetIndices(gis, &igis));
362: PetscCall(PetscMalloc1(ngis + nsis, &iis));
363: PetscCall(PetscArraycpy(iis, igis, ngis));
364: PetscCall(PetscArraycpy(iis + ngis, isis, nsis));
365: n = ngis + nsis;
366: PetscCall(PetscSortRemoveDupsInt(&n, iis));
367: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
368: for (i = 0; i < n; i++) iis[i] += rstart;
369: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), n, iis, PETSC_OWN_POINTER, is));
371: PetscCall(ISRestoreIndices(sis, &isis));
372: PetscCall(ISRestoreIndices(gis, &igis));
373: PetscCall(ISDestroy(&sis));
374: PetscCall(ISDestroy(&gis));
375: PetscFunctionReturn(PETSC_SUCCESS);
376: }
378: /*
379: Local utility routine that creates a mapping from the global column
380: number to the local number in the off-diagonal part of the local
381: storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at
382: a slightly higher hash table cost; without it it is not scalable (each processor
383: has an order N integer array but is fast to access.
384: */
385: PetscErrorCode MatCreateColmap_MPIAIJ_Private(Mat mat)
386: {
387: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
388: PetscInt n = aij->B->cmap->n, i;
390: PetscFunctionBegin;
391: PetscCheck(!n || aij->garray, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MPIAIJ Matrix was assembled but is missing garray");
392: #if defined(PETSC_USE_CTABLE)
393: PetscCall(PetscHMapICreateWithSize(n, &aij->colmap));
394: for (i = 0; i < n; i++) PetscCall(PetscHMapISet(aij->colmap, aij->garray[i] + 1, i + 1));
395: #else
396: PetscCall(PetscCalloc1(mat->cmap->N + 1, &aij->colmap));
397: for (i = 0; i < n; i++) aij->colmap[aij->garray[i]] = i + 1;
398: #endif
399: PetscFunctionReturn(PETSC_SUCCESS);
400: }
402: #define MatSetValues_SeqAIJ_A_Private(row, col, value, addv, orow, ocol) \
403: do { \
404: if (col <= lastcol1) low1 = 0; \
405: else high1 = nrow1; \
406: lastcol1 = col; \
407: while (high1 - low1 > 5) { \
408: t = (low1 + high1) / 2; \
409: if (rp1[t] > col) high1 = t; \
410: else low1 = t; \
411: } \
412: for (_i = low1; _i < high1; _i++) { \
413: if (rp1[_i] > col) break; \
414: if (rp1[_i] == col) { \
415: if (addv == ADD_VALUES) { \
416: ap1[_i] += value; \
417: /* Not sure LogFlops will slow dow the code or not */ \
418: (void)PetscLogFlops(1.0); \
419: } else ap1[_i] = value; \
420: goto a_noinsert; \
421: } \
422: } \
423: if (value == 0.0 && ignorezeroentries && row != col) { \
424: low1 = 0; \
425: high1 = nrow1; \
426: goto a_noinsert; \
427: } \
428: if (nonew == 1) { \
429: low1 = 0; \
430: high1 = nrow1; \
431: goto a_noinsert; \
432: } \
433: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
434: MatSeqXAIJReallocateAIJ(A, am, 1, nrow1, row, col, rmax1, aa, ai, aj, rp1, ap1, aimax, nonew, MatScalar); \
435: N = nrow1++ - 1; \
436: a->nz++; \
437: high1++; \
438: /* shift up all the later entries in this row */ \
439: PetscCall(PetscArraymove(rp1 + _i + 1, rp1 + _i, N - _i + 1)); \
440: PetscCall(PetscArraymove(ap1 + _i + 1, ap1 + _i, N - _i + 1)); \
441: rp1[_i] = col; \
442: ap1[_i] = value; \
443: a_noinsert:; \
444: ailen[row] = nrow1; \
445: } while (0)
447: #define MatSetValues_SeqAIJ_B_Private(row, col, value, addv, orow, ocol) \
448: do { \
449: if (col <= lastcol2) low2 = 0; \
450: else high2 = nrow2; \
451: lastcol2 = col; \
452: while (high2 - low2 > 5) { \
453: t = (low2 + high2) / 2; \
454: if (rp2[t] > col) high2 = t; \
455: else low2 = t; \
456: } \
457: for (_i = low2; _i < high2; _i++) { \
458: if (rp2[_i] > col) break; \
459: if (rp2[_i] == col) { \
460: if (addv == ADD_VALUES) { \
461: ap2[_i] += value; \
462: (void)PetscLogFlops(1.0); \
463: } else ap2[_i] = value; \
464: goto b_noinsert; \
465: } \
466: } \
467: if (value == 0.0 && ignorezeroentries) { \
468: low2 = 0; \
469: high2 = nrow2; \
470: goto b_noinsert; \
471: } \
472: if (nonew == 1) { \
473: low2 = 0; \
474: high2 = nrow2; \
475: goto b_noinsert; \
476: } \
477: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
478: MatSeqXAIJReallocateAIJ(B, bm, 1, nrow2, row, col, rmax2, ba, bi, bj, rp2, ap2, bimax, nonew, MatScalar); \
479: N = nrow2++ - 1; \
480: b->nz++; \
481: high2++; \
482: /* shift up all the later entries in this row */ \
483: PetscCall(PetscArraymove(rp2 + _i + 1, rp2 + _i, N - _i + 1)); \
484: PetscCall(PetscArraymove(ap2 + _i + 1, ap2 + _i, N - _i + 1)); \
485: rp2[_i] = col; \
486: ap2[_i] = value; \
487: b_noinsert:; \
488: bilen[row] = nrow2; \
489: } while (0)
491: static PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A, PetscInt row, const PetscScalar v[])
492: {
493: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
494: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->A->data, *b = (Mat_SeqAIJ *)mat->B->data;
495: PetscInt l, *garray = mat->garray, diag;
496: PetscScalar *aa, *ba;
498: PetscFunctionBegin;
499: /* code only works for square matrices A */
501: /* find size of row to the left of the diagonal part */
502: PetscCall(MatGetOwnershipRange(A, &diag, NULL));
503: row = row - diag;
504: for (l = 0; l < b->i[row + 1] - b->i[row]; l++) {
505: if (garray[b->j[b->i[row] + l]] > diag) break;
506: }
507: if (l) {
508: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
509: PetscCall(PetscArraycpy(ba + b->i[row], v, l));
510: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
511: }
513: /* diagonal part */
514: if (a->i[row + 1] - a->i[row]) {
515: PetscCall(MatSeqAIJGetArray(mat->A, &aa));
516: PetscCall(PetscArraycpy(aa + a->i[row], v + l, a->i[row + 1] - a->i[row]));
517: PetscCall(MatSeqAIJRestoreArray(mat->A, &aa));
518: }
520: /* right of diagonal part */
521: if (b->i[row + 1] - b->i[row] - l) {
522: PetscCall(MatSeqAIJGetArray(mat->B, &ba));
523: PetscCall(PetscArraycpy(ba + b->i[row] + l, v + l + a->i[row + 1] - a->i[row], b->i[row + 1] - b->i[row] - l));
524: PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
525: }
526: PetscFunctionReturn(PETSC_SUCCESS);
527: }
529: PetscErrorCode MatSetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
530: {
531: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
532: PetscScalar value = 0.0;
533: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
534: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
535: PetscBool roworiented = aij->roworiented;
537: /* Some Variables required in the macro */
538: Mat A = aij->A;
539: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
540: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
541: PetscBool ignorezeroentries = a->ignorezeroentries;
542: Mat B = aij->B;
543: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
544: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
545: MatScalar *aa, *ba;
546: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
547: PetscInt nonew;
548: MatScalar *ap1, *ap2;
550: PetscFunctionBegin;
551: PetscCall(MatSeqAIJGetArray(A, &aa));
552: PetscCall(MatSeqAIJGetArray(B, &ba));
553: for (i = 0; i < m; i++) {
554: if (im[i] < 0) continue;
555: PetscCheck(im[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], mat->rmap->N - 1);
556: if (im[i] >= rstart && im[i] < rend) {
557: row = im[i] - rstart;
558: lastcol1 = -1;
559: rp1 = PetscSafePointerPlusOffset(aj, ai[row]);
560: ap1 = PetscSafePointerPlusOffset(aa, ai[row]);
561: rmax1 = aimax[row];
562: nrow1 = ailen[row];
563: low1 = 0;
564: high1 = nrow1;
565: lastcol2 = -1;
566: rp2 = PetscSafePointerPlusOffset(bj, bi[row]);
567: ap2 = PetscSafePointerPlusOffset(ba, bi[row]);
568: rmax2 = bimax[row];
569: nrow2 = bilen[row];
570: low2 = 0;
571: high2 = nrow2;
573: for (j = 0; j < n; j++) {
574: if (v) value = roworiented ? v[i * n + j] : v[i + j * m];
575: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
576: if (in[j] >= cstart && in[j] < cend) {
577: col = in[j] - cstart;
578: nonew = a->nonew;
579: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
580: } else if (in[j] < 0) {
581: continue;
582: } else {
583: PetscCheck(in[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
584: if (mat->was_assembled) {
585: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
586: #if defined(PETSC_USE_CTABLE)
587: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col)); /* map global col ids to local ones */
588: col--;
589: #else
590: col = aij->colmap[in[j]] - 1;
591: #endif
592: if (col < 0 && !((Mat_SeqAIJ *)aij->B->data)->nonew) { /* col < 0 means in[j] is a new col for B */
593: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE)); /* Change aij->B from reduced/local format to expanded/global format */
594: col = in[j];
595: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
596: B = aij->B;
597: b = (Mat_SeqAIJ *)B->data;
598: bimax = b->imax;
599: bi = b->i;
600: bilen = b->ilen;
601: bj = b->j;
602: ba = b->a;
603: rp2 = PetscSafePointerPlusOffset(bj, bi[row]);
604: ap2 = PetscSafePointerPlusOffset(ba, bi[row]);
605: rmax2 = bimax[row];
606: nrow2 = bilen[row];
607: low2 = 0;
608: high2 = nrow2;
609: bm = aij->B->rmap->n;
610: ba = b->a;
611: } else if (col < 0 && !(ignorezeroentries && value == 0.0)) {
612: if (1 == ((Mat_SeqAIJ *)aij->B->data)->nonew) {
613: PetscCall(PetscInfo(mat, "Skipping of insertion of new nonzero location in off-diagonal portion of matrix %g(%" PetscInt_FMT ",%" PetscInt_FMT ")\n", (double)PetscRealPart(value), im[i], in[j]));
614: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]);
615: }
616: } else col = in[j];
617: nonew = b->nonew;
618: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
619: }
620: }
621: } else {
622: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Setting off process row %" PetscInt_FMT " even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set", im[i]);
623: if (!aij->donotstash) {
624: mat->assembled = PETSC_FALSE;
625: if (roworiented) {
626: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i * n), (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
627: } else {
628: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i), m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
629: }
630: }
631: }
632: }
633: PetscCall(MatSeqAIJRestoreArray(A, &aa)); /* aa, bb might have been free'd due to reallocation above. But we don't access them here */
634: PetscCall(MatSeqAIJRestoreArray(B, &ba));
635: PetscFunctionReturn(PETSC_SUCCESS);
636: }
638: /*
639: This function sets the j and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
640: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
641: No off-processor parts off the matrix are allowed here and mat->was_assembled has to be PETSC_FALSE.
642: */
643: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[])
644: {
645: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
646: Mat A = aij->A; /* diagonal part of the matrix */
647: Mat B = aij->B; /* off-diagonal part of the matrix */
648: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
649: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
650: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, col;
651: PetscInt *ailen = a->ilen, *aj = a->j;
652: PetscInt *bilen = b->ilen, *bj = b->j;
653: PetscInt am = aij->A->rmap->n, j;
654: PetscInt diag_so_far = 0, dnz;
655: PetscInt offd_so_far = 0, onz;
657: PetscFunctionBegin;
658: /* Iterate over all rows of the matrix */
659: for (j = 0; j < am; j++) {
660: dnz = onz = 0;
661: /* Iterate over all non-zero columns of the current row */
662: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
663: /* If column is in the diagonal */
664: if (mat_j[col] >= cstart && mat_j[col] < cend) {
665: aj[diag_so_far++] = mat_j[col] - cstart;
666: dnz++;
667: } else { /* off-diagonal entries */
668: bj[offd_so_far++] = mat_j[col];
669: onz++;
670: }
671: }
672: ailen[j] = dnz;
673: bilen[j] = onz;
674: }
675: PetscFunctionReturn(PETSC_SUCCESS);
676: }
678: /*
679: This function sets the local j, a and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
680: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
681: No off-processor parts off the matrix are allowed here, they are set at a later point by MatSetValues_MPIAIJ.
682: Also, mat->was_assembled has to be false, otherwise the statement aj[rowstart_diag+dnz_row] = mat_j[col] - cstart;
683: would not be true and the more complex MatSetValues_MPIAIJ has to be used.
684: */
685: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[], const PetscScalar mat_a[])
686: {
687: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
688: Mat A = aij->A; /* diagonal part of the matrix */
689: Mat B = aij->B; /* off-diagonal part of the matrix */
690: Mat_SeqAIJ *aijd = (Mat_SeqAIJ *)aij->A->data, *aijo = (Mat_SeqAIJ *)aij->B->data;
691: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
692: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
693: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend;
694: PetscInt *ailen = a->ilen, *aj = a->j;
695: PetscInt *bilen = b->ilen, *bj = b->j;
696: PetscInt am = aij->A->rmap->n, j;
697: PetscInt *full_diag_i = aijd->i, *full_offd_i = aijo->i; /* These variables can also include non-local elements, which are set at a later point. */
698: PetscInt col, dnz_row, onz_row, rowstart_diag, rowstart_offd;
699: PetscScalar *aa = a->a, *ba = b->a;
701: PetscFunctionBegin;
702: /* Iterate over all rows of the matrix */
703: for (j = 0; j < am; j++) {
704: dnz_row = onz_row = 0;
705: rowstart_offd = full_offd_i[j];
706: rowstart_diag = full_diag_i[j];
707: /* Iterate over all non-zero columns of the current row */
708: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
709: /* If column is in the diagonal */
710: if (mat_j[col] >= cstart && mat_j[col] < cend) {
711: aj[rowstart_diag + dnz_row] = mat_j[col] - cstart;
712: aa[rowstart_diag + dnz_row] = mat_a[col];
713: dnz_row++;
714: } else { /* off-diagonal entries */
715: bj[rowstart_offd + onz_row] = mat_j[col];
716: ba[rowstart_offd + onz_row] = mat_a[col];
717: onz_row++;
718: }
719: }
720: ailen[j] = dnz_row;
721: bilen[j] = onz_row;
722: }
723: PetscFunctionReturn(PETSC_SUCCESS);
724: }
726: static PetscErrorCode MatGetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
727: {
728: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
729: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
730: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
732: PetscFunctionBegin;
733: for (i = 0; i < m; i++) {
734: if (idxm[i] < 0) continue; /* negative row */
735: PetscCheck(idxm[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, idxm[i], mat->rmap->N - 1);
736: PetscCheck(idxm[i] >= rstart && idxm[i] < rend, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local values currently supported, row requested %" PetscInt_FMT " range [%" PetscInt_FMT " %" PetscInt_FMT ")", idxm[i], rstart, rend);
737: row = idxm[i] - rstart;
738: for (j = 0; j < n; j++) {
739: if (idxn[j] < 0) continue; /* negative column */
740: PetscCheck(idxn[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, idxn[j], mat->cmap->N - 1);
741: if (idxn[j] >= cstart && idxn[j] < cend) {
742: col = idxn[j] - cstart;
743: PetscCall(MatGetValues(aij->A, 1, &row, 1, &col, v + i * n + j));
744: } else {
745: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
746: #if defined(PETSC_USE_CTABLE)
747: PetscCall(PetscHMapIGetWithDefault(aij->colmap, idxn[j] + 1, 0, &col));
748: col--;
749: #else
750: col = aij->colmap[idxn[j]] - 1;
751: #endif
752: if ((col < 0) || (aij->garray[col] != idxn[j])) *(v + i * n + j) = 0.0;
753: else PetscCall(MatGetValues(aij->B, 1, &row, 1, &col, v + i * n + j));
754: }
755: }
756: }
757: PetscFunctionReturn(PETSC_SUCCESS);
758: }
760: static PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat, MatAssemblyType mode)
761: {
762: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
763: PetscInt nstash, reallocs;
765: PetscFunctionBegin;
766: if (aij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
768: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
769: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
770: PetscCall(PetscInfo(aij->A, "Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
771: PetscFunctionReturn(PETSC_SUCCESS);
772: }
774: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat, MatAssemblyType mode)
775: {
776: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
777: PetscMPIInt n;
778: PetscInt i, j, rstart, ncols, flg;
779: PetscInt *row, *col;
780: PetscBool other_disassembled;
781: PetscScalar *val;
783: /* do not use 'b = (Mat_SeqAIJ*)aij->B->data' as B can be reset in disassembly */
785: PetscFunctionBegin;
786: if (!aij->donotstash && !mat->nooffprocentries) {
787: while (1) {
788: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
789: if (!flg) break;
791: for (i = 0; i < n;) {
792: /* Now identify the consecutive vals belonging to the same row */
793: for (j = i, rstart = row[j]; j < n; j++) {
794: if (row[j] != rstart) break;
795: }
796: if (j < n) ncols = j - i;
797: else ncols = n - i;
798: /* Now assemble all these values with a single function call */
799: PetscCall(MatSetValues_MPIAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
800: i = j;
801: }
802: }
803: PetscCall(MatStashScatterEnd_Private(&mat->stash));
804: }
805: #if defined(PETSC_HAVE_DEVICE)
806: if (mat->offloadmask == PETSC_OFFLOAD_CPU) aij->A->offloadmask = PETSC_OFFLOAD_CPU;
807: /* We call MatBindToCPU() on aij->A and aij->B here, because if MatBindToCPU_MPIAIJ() is called before assembly, it cannot bind these. */
808: if (mat->boundtocpu) {
809: PetscCall(MatBindToCPU(aij->A, PETSC_TRUE));
810: PetscCall(MatBindToCPU(aij->B, PETSC_TRUE));
811: }
812: #endif
813: PetscCall(MatAssemblyBegin(aij->A, mode));
814: PetscCall(MatAssemblyEnd(aij->A, mode));
816: /* determine if any processor has disassembled, if so we must
817: also disassemble ourself, in order that we may reassemble. */
818: /*
819: if nonzero structure of submatrix B cannot change then we know that
820: no processor disassembled thus we can skip this stuff
821: */
822: if (!((Mat_SeqAIJ *)aij->B->data)->nonew) {
823: PetscCallMPI(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
824: if (mat->was_assembled && !other_disassembled) { /* mat on this rank has reduced off-diag B with local col ids, but globally it does not */
825: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));
826: }
827: }
828: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIAIJ(mat));
829: PetscCall(MatSetOption(aij->B, MAT_USE_INODES, PETSC_FALSE));
830: #if defined(PETSC_HAVE_DEVICE)
831: if (mat->offloadmask == PETSC_OFFLOAD_CPU && aij->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) aij->B->offloadmask = PETSC_OFFLOAD_CPU;
832: #endif
833: PetscCall(MatAssemblyBegin(aij->B, mode));
834: PetscCall(MatAssemblyEnd(aij->B, mode));
836: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
838: aij->rowvalues = NULL;
840: PetscCall(VecDestroy(&aij->diag));
842: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
843: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqAIJ *)aij->A->data)->nonew) {
844: PetscObjectState state = aij->A->nonzerostate + aij->B->nonzerostate;
845: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
846: }
847: #if defined(PETSC_HAVE_DEVICE)
848: mat->offloadmask = PETSC_OFFLOAD_BOTH;
849: #endif
850: PetscFunctionReturn(PETSC_SUCCESS);
851: }
853: static PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
854: {
855: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
857: PetscFunctionBegin;
858: PetscCall(MatZeroEntries(l->A));
859: PetscCall(MatZeroEntries(l->B));
860: PetscFunctionReturn(PETSC_SUCCESS);
861: }
863: static PetscErrorCode MatZeroRows_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
864: {
865: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
866: PetscInt *lrows;
867: PetscInt r, len;
868: PetscBool cong;
870: PetscFunctionBegin;
871: /* get locally owned rows */
872: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
873: PetscCall(MatHasCongruentLayouts(A, &cong));
874: /* fix right-hand side if needed */
875: if (x && b) {
876: const PetscScalar *xx;
877: PetscScalar *bb;
879: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
880: PetscCall(VecGetArrayRead(x, &xx));
881: PetscCall(VecGetArray(b, &bb));
882: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
883: PetscCall(VecRestoreArrayRead(x, &xx));
884: PetscCall(VecRestoreArray(b, &bb));
885: }
887: if (diag != 0.0 && cong) {
888: PetscCall(MatZeroRows(mat->A, len, lrows, diag, NULL, NULL));
889: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
890: } else if (diag != 0.0) { /* non-square or non congruent layouts -> if keepnonzeropattern is false, we allow for new insertion */
891: Mat_SeqAIJ *aijA = (Mat_SeqAIJ *)mat->A->data;
892: Mat_SeqAIJ *aijB = (Mat_SeqAIJ *)mat->B->data;
893: PetscInt nnwA, nnwB;
894: PetscBool nnzA, nnzB;
896: nnwA = aijA->nonew;
897: nnwB = aijB->nonew;
898: nnzA = aijA->keepnonzeropattern;
899: nnzB = aijB->keepnonzeropattern;
900: if (!nnzA) {
901: PetscCall(PetscInfo(mat->A, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on diagonal block.\n"));
902: aijA->nonew = 0;
903: }
904: if (!nnzB) {
905: PetscCall(PetscInfo(mat->B, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on off-diagonal block.\n"));
906: aijB->nonew = 0;
907: }
908: /* Must zero here before the next loop */
909: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
910: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
911: for (r = 0; r < len; ++r) {
912: const PetscInt row = lrows[r] + A->rmap->rstart;
913: if (row >= A->cmap->N) continue;
914: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
915: }
916: aijA->nonew = nnwA;
917: aijB->nonew = nnwB;
918: } else {
919: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
920: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
921: }
922: PetscCall(PetscFree(lrows));
923: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
924: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
926: /* only change matrix nonzero state if pattern was allowed to be changed */
927: if (!((Mat_SeqAIJ *)mat->A->data)->keepnonzeropattern || !((Mat_SeqAIJ *)mat->A->data)->nonew) {
928: PetscObjectState state = mat->A->nonzerostate + mat->B->nonzerostate;
929: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
930: }
931: PetscFunctionReturn(PETSC_SUCCESS);
932: }
934: static PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
935: {
936: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
937: PetscInt n = A->rmap->n;
938: PetscInt i, j, r, m, len = 0;
939: PetscInt *lrows, *owners = A->rmap->range;
940: PetscMPIInt p = 0;
941: PetscSFNode *rrows;
942: PetscSF sf;
943: const PetscScalar *xx;
944: PetscScalar *bb, *mask, *aij_a;
945: Vec xmask, lmask;
946: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)l->B->data;
947: const PetscInt *aj, *ii, *ridx;
948: PetscScalar *aa;
950: PetscFunctionBegin;
951: /* Create SF where leaves are input rows and roots are owned rows */
952: PetscCall(PetscMalloc1(n, &lrows));
953: for (r = 0; r < n; ++r) lrows[r] = -1;
954: PetscCall(PetscMalloc1(N, &rrows));
955: for (r = 0; r < N; ++r) {
956: const PetscInt idx = rows[r];
957: PetscCheck(idx >= 0 && A->rmap->N > idx, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row %" PetscInt_FMT " out of range [0,%" PetscInt_FMT ")", idx, A->rmap->N);
958: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
959: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
960: }
961: rrows[r].rank = p;
962: rrows[r].index = rows[r] - owners[p];
963: }
964: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
965: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
966: /* Collect flags for rows to be zeroed */
967: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
968: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
969: PetscCall(PetscSFDestroy(&sf));
970: /* Compress and put in row numbers */
971: for (r = 0; r < n; ++r)
972: if (lrows[r] >= 0) lrows[len++] = r;
973: /* zero diagonal part of matrix */
974: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
975: /* handle off-diagonal part of matrix */
976: PetscCall(MatCreateVecs(A, &xmask, NULL));
977: PetscCall(VecDuplicate(l->lvec, &lmask));
978: PetscCall(VecGetArray(xmask, &bb));
979: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
980: PetscCall(VecRestoreArray(xmask, &bb));
981: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
982: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
983: PetscCall(VecDestroy(&xmask));
984: if (x && b) { /* this code is buggy when the row and column layout don't match */
985: PetscBool cong;
987: PetscCall(MatHasCongruentLayouts(A, &cong));
988: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
989: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
990: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
991: PetscCall(VecGetArrayRead(l->lvec, &xx));
992: PetscCall(VecGetArray(b, &bb));
993: }
994: PetscCall(VecGetArray(lmask, &mask));
995: /* remove zeroed rows of off-diagonal matrix */
996: PetscCall(MatSeqAIJGetArray(l->B, &aij_a));
997: ii = aij->i;
998: for (i = 0; i < len; i++) PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aij_a, ii[lrows[i]]), ii[lrows[i] + 1] - ii[lrows[i]]));
999: /* loop over all elements of off process part of matrix zeroing removed columns*/
1000: if (aij->compressedrow.use) {
1001: m = aij->compressedrow.nrows;
1002: ii = aij->compressedrow.i;
1003: ridx = aij->compressedrow.rindex;
1004: for (i = 0; i < m; i++) {
1005: n = ii[i + 1] - ii[i];
1006: aj = aij->j + ii[i];
1007: aa = aij_a + ii[i];
1009: for (j = 0; j < n; j++) {
1010: if (PetscAbsScalar(mask[*aj])) {
1011: if (b) bb[*ridx] -= *aa * xx[*aj];
1012: *aa = 0.0;
1013: }
1014: aa++;
1015: aj++;
1016: }
1017: ridx++;
1018: }
1019: } else { /* do not use compressed row format */
1020: m = l->B->rmap->n;
1021: for (i = 0; i < m; i++) {
1022: n = ii[i + 1] - ii[i];
1023: aj = aij->j + ii[i];
1024: aa = aij_a + ii[i];
1025: for (j = 0; j < n; j++) {
1026: if (PetscAbsScalar(mask[*aj])) {
1027: if (b) bb[i] -= *aa * xx[*aj];
1028: *aa = 0.0;
1029: }
1030: aa++;
1031: aj++;
1032: }
1033: }
1034: }
1035: if (x && b) {
1036: PetscCall(VecRestoreArray(b, &bb));
1037: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1038: }
1039: PetscCall(MatSeqAIJRestoreArray(l->B, &aij_a));
1040: PetscCall(VecRestoreArray(lmask, &mask));
1041: PetscCall(VecDestroy(&lmask));
1042: PetscCall(PetscFree(lrows));
1044: /* only change matrix nonzero state if pattern was allowed to be changed */
1045: if (!((Mat_SeqAIJ *)l->A->data)->nonew) {
1046: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1047: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1048: }
1049: PetscFunctionReturn(PETSC_SUCCESS);
1050: }
1052: static PetscErrorCode MatMult_MPIAIJ(Mat A, Vec xx, Vec yy)
1053: {
1054: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1055: PetscInt nt;
1056: VecScatter Mvctx = a->Mvctx;
1058: PetscFunctionBegin;
1059: PetscCall(VecGetLocalSize(xx, &nt));
1060: PetscCheck(nt == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A (%" PetscInt_FMT ") and xx (%" PetscInt_FMT ")", A->cmap->n, nt);
1061: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1062: PetscUseTypeMethod(a->A, mult, xx, yy);
1063: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1064: PetscUseTypeMethod(a->B, multadd, a->lvec, yy, yy);
1065: PetscFunctionReturn(PETSC_SUCCESS);
1066: }
1068: static PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A, Vec bb, Vec xx)
1069: {
1070: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1072: PetscFunctionBegin;
1073: PetscCall(MatMultDiagonalBlock(a->A, bb, xx));
1074: PetscFunctionReturn(PETSC_SUCCESS);
1075: }
1077: static PetscErrorCode MatMultAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1078: {
1079: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1080: VecScatter Mvctx = a->Mvctx;
1082: PetscFunctionBegin;
1083: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1084: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1085: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1086: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1087: PetscFunctionReturn(PETSC_SUCCESS);
1088: }
1090: static PetscErrorCode MatMultTranspose_MPIAIJ(Mat A, Vec xx, Vec yy)
1091: {
1092: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1094: PetscFunctionBegin;
1095: /* do nondiagonal part */
1096: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1097: /* do local part */
1098: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1099: /* add partial results together */
1100: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1101: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1102: PetscFunctionReturn(PETSC_SUCCESS);
1103: }
1105: static PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f)
1106: {
1107: MPI_Comm comm;
1108: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)Amat->data, *Bij = (Mat_MPIAIJ *)Bmat->data;
1109: Mat Adia = Aij->A, Bdia = Bij->A, Aoff, Boff, *Aoffs, *Boffs;
1110: IS Me, Notme;
1111: PetscInt M, N, first, last, *notme, i;
1112: PetscBool lf;
1113: PetscMPIInt size;
1115: PetscFunctionBegin;
1116: /* Easy test: symmetric diagonal block */
1117: PetscCall(MatIsTranspose(Adia, Bdia, tol, &lf));
1118: PetscCallMPI(MPIU_Allreduce(&lf, f, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)Amat)));
1119: if (!*f) PetscFunctionReturn(PETSC_SUCCESS);
1120: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1121: PetscCallMPI(MPI_Comm_size(comm, &size));
1122: if (size == 1) PetscFunctionReturn(PETSC_SUCCESS);
1124: /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */
1125: PetscCall(MatGetSize(Amat, &M, &N));
1126: PetscCall(MatGetOwnershipRange(Amat, &first, &last));
1127: PetscCall(PetscMalloc1(N - last + first, ¬me));
1128: for (i = 0; i < first; i++) notme[i] = i;
1129: for (i = last; i < M; i++) notme[i - last + first] = i;
1130: PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme));
1131: PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me));
1132: PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs));
1133: Aoff = Aoffs[0];
1134: PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs));
1135: Boff = Boffs[0];
1136: PetscCall(MatIsTranspose(Aoff, Boff, tol, f));
1137: PetscCall(MatDestroyMatrices(1, &Aoffs));
1138: PetscCall(MatDestroyMatrices(1, &Boffs));
1139: PetscCall(ISDestroy(&Me));
1140: PetscCall(ISDestroy(&Notme));
1141: PetscCall(PetscFree(notme));
1142: PetscFunctionReturn(PETSC_SUCCESS);
1143: }
1145: static PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1146: {
1147: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1149: PetscFunctionBegin;
1150: /* do nondiagonal part */
1151: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1152: /* do local part */
1153: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1154: /* add partial results together */
1155: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1156: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1157: PetscFunctionReturn(PETSC_SUCCESS);
1158: }
1160: /*
1161: This only works correctly for square matrices where the subblock A->A is the
1162: diagonal block
1163: */
1164: static PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A, Vec v)
1165: {
1166: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1168: PetscFunctionBegin;
1169: PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1170: PetscCheck(A->rmap->rstart == A->cmap->rstart && A->rmap->rend == A->cmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "row partition must equal col partition");
1171: PetscCall(MatGetDiagonal(a->A, v));
1172: PetscFunctionReturn(PETSC_SUCCESS);
1173: }
1175: static PetscErrorCode MatScale_MPIAIJ(Mat A, PetscScalar aa)
1176: {
1177: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1179: PetscFunctionBegin;
1180: PetscCall(MatScale(a->A, aa));
1181: PetscCall(MatScale(a->B, aa));
1182: PetscFunctionReturn(PETSC_SUCCESS);
1183: }
1185: static PetscErrorCode MatView_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
1186: {
1187: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1188: Mat_SeqAIJ *A = (Mat_SeqAIJ *)aij->A->data;
1189: Mat_SeqAIJ *B = (Mat_SeqAIJ *)aij->B->data;
1190: const PetscInt *garray = aij->garray;
1191: const PetscScalar *aa, *ba;
1192: PetscInt header[4], M, N, m, rs, cs, cnt, i, ja, jb;
1193: PetscInt64 nz, hnz;
1194: PetscInt *rowlens;
1195: PetscInt *colidxs;
1196: PetscScalar *matvals;
1197: PetscMPIInt rank;
1199: PetscFunctionBegin;
1200: PetscCall(PetscViewerSetUp(viewer));
1202: M = mat->rmap->N;
1203: N = mat->cmap->N;
1204: m = mat->rmap->n;
1205: rs = mat->rmap->rstart;
1206: cs = mat->cmap->rstart;
1207: nz = A->nz + B->nz;
1209: /* write matrix header */
1210: header[0] = MAT_FILE_CLASSID;
1211: header[1] = M;
1212: header[2] = N;
1213: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1214: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1215: if (rank == 0) PetscCall(PetscIntCast(hnz, &header[3]));
1216: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1218: /* fill in and store row lengths */
1219: PetscCall(PetscMalloc1(m, &rowlens));
1220: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i];
1221: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1222: PetscCall(PetscFree(rowlens));
1224: /* fill in and store column indices */
1225: PetscCall(PetscMalloc1(nz, &colidxs));
1226: for (cnt = 0, i = 0; i < m; i++) {
1227: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1228: if (garray[B->j[jb]] > cs) break;
1229: colidxs[cnt++] = garray[B->j[jb]];
1230: }
1231: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) colidxs[cnt++] = A->j[ja] + cs;
1232: for (; jb < B->i[i + 1]; jb++) colidxs[cnt++] = garray[B->j[jb]];
1233: }
1234: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1235: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
1236: PetscCall(PetscFree(colidxs));
1238: /* fill in and store nonzero values */
1239: PetscCall(MatSeqAIJGetArrayRead(aij->A, &aa));
1240: PetscCall(MatSeqAIJGetArrayRead(aij->B, &ba));
1241: PetscCall(PetscMalloc1(nz, &matvals));
1242: for (cnt = 0, i = 0; i < m; i++) {
1243: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1244: if (garray[B->j[jb]] > cs) break;
1245: matvals[cnt++] = ba[jb];
1246: }
1247: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) matvals[cnt++] = aa[ja];
1248: for (; jb < B->i[i + 1]; jb++) matvals[cnt++] = ba[jb];
1249: }
1250: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &aa));
1251: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &ba));
1252: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1253: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
1254: PetscCall(PetscFree(matvals));
1256: /* write block size option to the viewer's .info file */
1257: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1258: PetscFunctionReturn(PETSC_SUCCESS);
1259: }
1261: #include <petscdraw.h>
1262: static PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1263: {
1264: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1265: PetscMPIInt rank = aij->rank, size = aij->size;
1266: PetscBool isdraw, iascii, isbinary;
1267: PetscViewer sviewer;
1268: PetscViewerFormat format;
1270: PetscFunctionBegin;
1271: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1272: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1273: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1274: if (iascii) {
1275: PetscCall(PetscViewerGetFormat(viewer, &format));
1276: if (format == PETSC_VIEWER_LOAD_BALANCE) {
1277: PetscInt i, nmax = 0, nmin = PETSC_INT_MAX, navg = 0, *nz, nzlocal = ((Mat_SeqAIJ *)aij->A->data)->nz + ((Mat_SeqAIJ *)aij->B->data)->nz;
1278: PetscCall(PetscMalloc1(size, &nz));
1279: PetscCallMPI(MPI_Allgather(&nzlocal, 1, MPIU_INT, nz, 1, MPIU_INT, PetscObjectComm((PetscObject)mat)));
1280: for (i = 0; i < size; i++) {
1281: nmax = PetscMax(nmax, nz[i]);
1282: nmin = PetscMin(nmin, nz[i]);
1283: navg += nz[i];
1284: }
1285: PetscCall(PetscFree(nz));
1286: navg = navg / size;
1287: PetscCall(PetscViewerASCIIPrintf(viewer, "Load Balance - Nonzeros: Min %" PetscInt_FMT " avg %" PetscInt_FMT " max %" PetscInt_FMT "\n", nmin, navg, nmax));
1288: PetscFunctionReturn(PETSC_SUCCESS);
1289: }
1290: PetscCall(PetscViewerGetFormat(viewer, &format));
1291: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1292: MatInfo info;
1293: PetscInt *inodes = NULL;
1295: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1296: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1297: PetscCall(MatInodeGetInodeSizes(aij->A, NULL, &inodes, NULL));
1298: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1299: if (!inodes) {
1300: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] Local rows %" PetscInt_FMT " nz %" PetscInt_FMT " nz alloced %" PetscInt_FMT " mem %g, not using I-node routines\n", rank, mat->rmap->n, (PetscInt)info.nz_used, (PetscInt)info.nz_allocated,
1301: info.memory));
1302: } else {
1303: PetscCall(
1304: PetscViewerASCIISynchronizedPrintf(viewer, "[%d] Local rows %" PetscInt_FMT " nz %" PetscInt_FMT " nz alloced %" PetscInt_FMT " mem %g, using I-node routines\n", rank, mat->rmap->n, (PetscInt)info.nz_used, (PetscInt)info.nz_allocated, info.memory));
1305: }
1306: PetscCall(MatGetInfo(aij->A, MAT_LOCAL, &info));
1307: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1308: PetscCall(MatGetInfo(aij->B, MAT_LOCAL, &info));
1309: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1310: PetscCall(PetscViewerFlush(viewer));
1311: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1312: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1313: PetscCall(VecScatterView(aij->Mvctx, viewer));
1314: PetscFunctionReturn(PETSC_SUCCESS);
1315: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1316: PetscInt inodecount, inodelimit, *inodes;
1317: PetscCall(MatInodeGetInodeSizes(aij->A, &inodecount, &inodes, &inodelimit));
1318: if (inodes) {
1319: PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
1320: } else {
1321: PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
1322: }
1323: PetscFunctionReturn(PETSC_SUCCESS);
1324: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1325: PetscFunctionReturn(PETSC_SUCCESS);
1326: }
1327: } else if (isbinary) {
1328: if (size == 1) {
1329: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1330: PetscCall(MatView(aij->A, viewer));
1331: } else {
1332: PetscCall(MatView_MPIAIJ_Binary(mat, viewer));
1333: }
1334: PetscFunctionReturn(PETSC_SUCCESS);
1335: } else if (iascii && size == 1) {
1336: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1337: PetscCall(MatView(aij->A, viewer));
1338: PetscFunctionReturn(PETSC_SUCCESS);
1339: } else if (isdraw) {
1340: PetscDraw draw;
1341: PetscBool isnull;
1342: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1343: PetscCall(PetscDrawIsNull(draw, &isnull));
1344: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1345: }
1347: { /* assemble the entire matrix onto first processor */
1348: Mat A = NULL, Av;
1349: IS isrow, iscol;
1351: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->rmap->N : 0, 0, 1, &isrow));
1352: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->cmap->N : 0, 0, 1, &iscol));
1353: PetscCall(MatCreateSubMatrix(mat, isrow, iscol, MAT_INITIAL_MATRIX, &A));
1354: PetscCall(MatMPIAIJGetSeqAIJ(A, &Av, NULL, NULL));
1355: /* The commented code uses MatCreateSubMatrices instead */
1356: /*
1357: Mat *AA, A = NULL, Av;
1358: IS isrow,iscol;
1360: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->rmap->N : 0,0,1,&isrow));
1361: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->cmap->N : 0,0,1,&iscol));
1362: PetscCall(MatCreateSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&AA));
1363: if (rank == 0) {
1364: PetscCall(PetscObjectReference((PetscObject)AA[0]));
1365: A = AA[0];
1366: Av = AA[0];
1367: }
1368: PetscCall(MatDestroySubMatrices(1,&AA));
1369: */
1370: PetscCall(ISDestroy(&iscol));
1371: PetscCall(ISDestroy(&isrow));
1372: /*
1373: Everyone has to call to draw the matrix since the graphics waits are
1374: synchronized across all processors that share the PetscDraw object
1375: */
1376: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1377: if (rank == 0) {
1378: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)Av, ((PetscObject)mat)->name));
1379: PetscCall(MatView_SeqAIJ(Av, sviewer));
1380: }
1381: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1382: PetscCall(MatDestroy(&A));
1383: }
1384: PetscFunctionReturn(PETSC_SUCCESS);
1385: }
1387: PetscErrorCode MatView_MPIAIJ(Mat mat, PetscViewer viewer)
1388: {
1389: PetscBool iascii, isdraw, issocket, isbinary;
1391: PetscFunctionBegin;
1392: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1393: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1394: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1395: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1396: if (iascii || isdraw || isbinary || issocket) PetscCall(MatView_MPIAIJ_ASCIIorDraworSocket(mat, viewer));
1397: PetscFunctionReturn(PETSC_SUCCESS);
1398: }
1400: static PetscErrorCode MatSOR_MPIAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1401: {
1402: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1403: Vec bb1 = NULL;
1404: PetscBool hasop;
1406: PetscFunctionBegin;
1407: if (flag == SOR_APPLY_UPPER) {
1408: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1409: PetscFunctionReturn(PETSC_SUCCESS);
1410: }
1412: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1));
1414: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
1415: if (flag & SOR_ZERO_INITIAL_GUESS) {
1416: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1417: its--;
1418: }
1420: while (its--) {
1421: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1422: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1424: /* update rhs: bb1 = bb - B*x */
1425: PetscCall(VecScale(mat->lvec, -1.0));
1426: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1428: /* local sweep */
1429: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
1430: }
1431: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1432: if (flag & SOR_ZERO_INITIAL_GUESS) {
1433: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1434: its--;
1435: }
1436: while (its--) {
1437: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1438: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1440: /* update rhs: bb1 = bb - B*x */
1441: PetscCall(VecScale(mat->lvec, -1.0));
1442: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1444: /* local sweep */
1445: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
1446: }
1447: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1448: if (flag & SOR_ZERO_INITIAL_GUESS) {
1449: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1450: its--;
1451: }
1452: while (its--) {
1453: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1454: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1456: /* update rhs: bb1 = bb - B*x */
1457: PetscCall(VecScale(mat->lvec, -1.0));
1458: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1460: /* local sweep */
1461: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
1462: }
1463: } else if (flag & SOR_EISENSTAT) {
1464: Vec xx1;
1466: PetscCall(VecDuplicate(bb, &xx1));
1467: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP), fshift, lits, 1, xx));
1469: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1470: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1471: if (!mat->diag) {
1472: PetscCall(MatCreateVecs(matin, &mat->diag, NULL));
1473: PetscCall(MatGetDiagonal(matin, mat->diag));
1474: }
1475: PetscCall(MatHasOperation(matin, MATOP_MULT_DIAGONAL_BLOCK, &hasop));
1476: if (hasop) {
1477: PetscCall(MatMultDiagonalBlock(matin, xx, bb1));
1478: } else {
1479: PetscCall(VecPointwiseMult(bb1, mat->diag, xx));
1480: }
1481: PetscCall(VecAYPX(bb1, (omega - 2.0) / omega, bb));
1483: PetscCall(MatMultAdd(mat->B, mat->lvec, bb1, bb1));
1485: /* local sweep */
1486: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP), fshift, lits, 1, xx1));
1487: PetscCall(VecAXPY(xx, 1.0, xx1));
1488: PetscCall(VecDestroy(&xx1));
1489: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");
1491: PetscCall(VecDestroy(&bb1));
1493: matin->factorerrortype = mat->A->factorerrortype;
1494: PetscFunctionReturn(PETSC_SUCCESS);
1495: }
1497: static PetscErrorCode MatPermute_MPIAIJ(Mat A, IS rowp, IS colp, Mat *B)
1498: {
1499: Mat aA, aB, Aperm;
1500: const PetscInt *rwant, *cwant, *gcols, *ai, *bi, *aj, *bj;
1501: PetscScalar *aa, *ba;
1502: PetscInt i, j, m, n, ng, anz, bnz, *dnnz, *onnz, *tdnnz, *tonnz, *rdest, *cdest, *work, *gcdest;
1503: PetscSF rowsf, sf;
1504: IS parcolp = NULL;
1505: PetscBool done;
1507: PetscFunctionBegin;
1508: PetscCall(MatGetLocalSize(A, &m, &n));
1509: PetscCall(ISGetIndices(rowp, &rwant));
1510: PetscCall(ISGetIndices(colp, &cwant));
1511: PetscCall(PetscMalloc3(PetscMax(m, n), &work, m, &rdest, n, &cdest));
1513: /* Invert row permutation to find out where my rows should go */
1514: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &rowsf));
1515: PetscCall(PetscSFSetGraphLayout(rowsf, A->rmap, A->rmap->n, NULL, PETSC_OWN_POINTER, rwant));
1516: PetscCall(PetscSFSetFromOptions(rowsf));
1517: for (i = 0; i < m; i++) work[i] = A->rmap->rstart + i;
1518: PetscCall(PetscSFReduceBegin(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1519: PetscCall(PetscSFReduceEnd(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1521: /* Invert column permutation to find out where my columns should go */
1522: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1523: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, A->cmap->n, NULL, PETSC_OWN_POINTER, cwant));
1524: PetscCall(PetscSFSetFromOptions(sf));
1525: for (i = 0; i < n; i++) work[i] = A->cmap->rstart + i;
1526: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1527: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1528: PetscCall(PetscSFDestroy(&sf));
1530: PetscCall(ISRestoreIndices(rowp, &rwant));
1531: PetscCall(ISRestoreIndices(colp, &cwant));
1532: PetscCall(MatMPIAIJGetSeqAIJ(A, &aA, &aB, &gcols));
1534: /* Find out where my gcols should go */
1535: PetscCall(MatGetSize(aB, NULL, &ng));
1536: PetscCall(PetscMalloc1(ng, &gcdest));
1537: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1538: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, ng, NULL, PETSC_OWN_POINTER, gcols));
1539: PetscCall(PetscSFSetFromOptions(sf));
1540: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1541: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1542: PetscCall(PetscSFDestroy(&sf));
1544: PetscCall(PetscCalloc4(m, &dnnz, m, &onnz, m, &tdnnz, m, &tonnz));
1545: PetscCall(MatGetRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1546: PetscCall(MatGetRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1547: for (i = 0; i < m; i++) {
1548: PetscInt row = rdest[i];
1549: PetscMPIInt rowner;
1550: PetscCall(PetscLayoutFindOwner(A->rmap, row, &rowner));
1551: for (j = ai[i]; j < ai[i + 1]; j++) {
1552: PetscInt col = cdest[aj[j]];
1553: PetscMPIInt cowner;
1554: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner)); /* Could build an index for the columns to eliminate this search */
1555: if (rowner == cowner) dnnz[i]++;
1556: else onnz[i]++;
1557: }
1558: for (j = bi[i]; j < bi[i + 1]; j++) {
1559: PetscInt col = gcdest[bj[j]];
1560: PetscMPIInt cowner;
1561: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner));
1562: if (rowner == cowner) dnnz[i]++;
1563: else onnz[i]++;
1564: }
1565: }
1566: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1567: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1568: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1569: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1570: PetscCall(PetscSFDestroy(&rowsf));
1572: PetscCall(MatCreateAIJ(PetscObjectComm((PetscObject)A), A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N, 0, tdnnz, 0, tonnz, &Aperm));
1573: PetscCall(MatSeqAIJGetArray(aA, &aa));
1574: PetscCall(MatSeqAIJGetArray(aB, &ba));
1575: for (i = 0; i < m; i++) {
1576: PetscInt *acols = dnnz, *bcols = onnz; /* Repurpose now-unneeded arrays */
1577: PetscInt j0, rowlen;
1578: rowlen = ai[i + 1] - ai[i];
1579: for (j0 = j = 0; j < rowlen; j0 = j) { /* rowlen could be larger than number of rows m, so sum in batches */
1580: for (; j < PetscMin(rowlen, j0 + m); j++) acols[j - j0] = cdest[aj[ai[i] + j]];
1581: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, acols, aa + ai[i] + j0, INSERT_VALUES));
1582: }
1583: rowlen = bi[i + 1] - bi[i];
1584: for (j0 = j = 0; j < rowlen; j0 = j) {
1585: for (; j < PetscMin(rowlen, j0 + m); j++) bcols[j - j0] = gcdest[bj[bi[i] + j]];
1586: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, bcols, ba + bi[i] + j0, INSERT_VALUES));
1587: }
1588: }
1589: PetscCall(MatAssemblyBegin(Aperm, MAT_FINAL_ASSEMBLY));
1590: PetscCall(MatAssemblyEnd(Aperm, MAT_FINAL_ASSEMBLY));
1591: PetscCall(MatRestoreRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1592: PetscCall(MatRestoreRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1593: PetscCall(MatSeqAIJRestoreArray(aA, &aa));
1594: PetscCall(MatSeqAIJRestoreArray(aB, &ba));
1595: PetscCall(PetscFree4(dnnz, onnz, tdnnz, tonnz));
1596: PetscCall(PetscFree3(work, rdest, cdest));
1597: PetscCall(PetscFree(gcdest));
1598: if (parcolp) PetscCall(ISDestroy(&colp));
1599: *B = Aperm;
1600: PetscFunctionReturn(PETSC_SUCCESS);
1601: }
1603: static PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
1604: {
1605: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1607: PetscFunctionBegin;
1608: PetscCall(MatGetSize(aij->B, NULL, nghosts));
1609: if (ghosts) *ghosts = aij->garray;
1610: PetscFunctionReturn(PETSC_SUCCESS);
1611: }
1613: static PetscErrorCode MatGetInfo_MPIAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1614: {
1615: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1616: Mat A = mat->A, B = mat->B;
1617: PetscLogDouble isend[5], irecv[5];
1619: PetscFunctionBegin;
1620: info->block_size = 1.0;
1621: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1623: isend[0] = info->nz_used;
1624: isend[1] = info->nz_allocated;
1625: isend[2] = info->nz_unneeded;
1626: isend[3] = info->memory;
1627: isend[4] = info->mallocs;
1629: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1631: isend[0] += info->nz_used;
1632: isend[1] += info->nz_allocated;
1633: isend[2] += info->nz_unneeded;
1634: isend[3] += info->memory;
1635: isend[4] += info->mallocs;
1636: if (flag == MAT_LOCAL) {
1637: info->nz_used = isend[0];
1638: info->nz_allocated = isend[1];
1639: info->nz_unneeded = isend[2];
1640: info->memory = isend[3];
1641: info->mallocs = isend[4];
1642: } else if (flag == MAT_GLOBAL_MAX) {
1643: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1645: info->nz_used = irecv[0];
1646: info->nz_allocated = irecv[1];
1647: info->nz_unneeded = irecv[2];
1648: info->memory = irecv[3];
1649: info->mallocs = irecv[4];
1650: } else if (flag == MAT_GLOBAL_SUM) {
1651: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1653: info->nz_used = irecv[0];
1654: info->nz_allocated = irecv[1];
1655: info->nz_unneeded = irecv[2];
1656: info->memory = irecv[3];
1657: info->mallocs = irecv[4];
1658: }
1659: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1660: info->fill_ratio_needed = 0;
1661: info->factor_mallocs = 0;
1662: PetscFunctionReturn(PETSC_SUCCESS);
1663: }
1665: PetscErrorCode MatSetOption_MPIAIJ(Mat A, MatOption op, PetscBool flg)
1666: {
1667: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1669: PetscFunctionBegin;
1670: switch (op) {
1671: case MAT_NEW_NONZERO_LOCATIONS:
1672: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1673: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1674: case MAT_KEEP_NONZERO_PATTERN:
1675: case MAT_NEW_NONZERO_LOCATION_ERR:
1676: case MAT_USE_INODES:
1677: case MAT_IGNORE_ZERO_ENTRIES:
1678: case MAT_FORM_EXPLICIT_TRANSPOSE:
1679: MatCheckPreallocated(A, 1);
1680: PetscCall(MatSetOption(a->A, op, flg));
1681: PetscCall(MatSetOption(a->B, op, flg));
1682: break;
1683: case MAT_ROW_ORIENTED:
1684: MatCheckPreallocated(A, 1);
1685: a->roworiented = flg;
1687: PetscCall(MatSetOption(a->A, op, flg));
1688: PetscCall(MatSetOption(a->B, op, flg));
1689: break;
1690: case MAT_IGNORE_OFF_PROC_ENTRIES:
1691: a->donotstash = flg;
1692: break;
1693: /* Symmetry flags are handled directly by MatSetOption() and they don't affect preallocation */
1694: case MAT_SPD:
1695: case MAT_SYMMETRIC:
1696: case MAT_STRUCTURALLY_SYMMETRIC:
1697: case MAT_HERMITIAN:
1698: case MAT_SYMMETRY_ETERNAL:
1699: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1700: case MAT_SPD_ETERNAL:
1701: /* if the diagonal matrix is square it inherits some of the properties above */
1702: if (a->A && A->rmap->n == A->cmap->n) PetscCall(MatSetOption(a->A, op, flg));
1703: break;
1704: case MAT_SUBMAT_SINGLEIS:
1705: A->submat_singleis = flg;
1706: break;
1707: default:
1708: break;
1709: }
1710: PetscFunctionReturn(PETSC_SUCCESS);
1711: }
1713: PetscErrorCode MatGetRow_MPIAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1714: {
1715: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1716: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1717: PetscInt i, *cworkA, *cworkB, **pcA, **pcB, cstart = matin->cmap->rstart;
1718: PetscInt nztot, nzA, nzB, lrow, rstart = matin->rmap->rstart, rend = matin->rmap->rend;
1719: PetscInt *cmap, *idx_p;
1721: PetscFunctionBegin;
1722: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1723: mat->getrowactive = PETSC_TRUE;
1725: if (!mat->rowvalues && (idx || v)) {
1726: /*
1727: allocate enough space to hold information from the longest row.
1728: */
1729: Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)mat->A->data, *Ba = (Mat_SeqAIJ *)mat->B->data;
1730: PetscInt max = 1, tmp;
1731: for (i = 0; i < matin->rmap->n; i++) {
1732: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1733: if (max < tmp) max = tmp;
1734: }
1735: PetscCall(PetscMalloc2(max, &mat->rowvalues, max, &mat->rowindices));
1736: }
1738: PetscCheck(row >= rstart && row < rend, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Only local rows");
1739: lrow = row - rstart;
1741: pvA = &vworkA;
1742: pcA = &cworkA;
1743: pvB = &vworkB;
1744: pcB = &cworkB;
1745: if (!v) {
1746: pvA = NULL;
1747: pvB = NULL;
1748: }
1749: if (!idx) {
1750: pcA = NULL;
1751: if (!v) pcB = NULL;
1752: }
1753: PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1754: PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1755: nztot = nzA + nzB;
1757: cmap = mat->garray;
1758: if (v || idx) {
1759: if (nztot) {
1760: /* Sort by increasing column numbers, assuming A and B already sorted */
1761: PetscInt imark = -1;
1762: if (v) {
1763: *v = v_p = mat->rowvalues;
1764: for (i = 0; i < nzB; i++) {
1765: if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1766: else break;
1767: }
1768: imark = i;
1769: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1770: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1771: }
1772: if (idx) {
1773: *idx = idx_p = mat->rowindices;
1774: if (imark > -1) {
1775: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i]];
1776: } else {
1777: for (i = 0; i < nzB; i++) {
1778: if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1779: else break;
1780: }
1781: imark = i;
1782: }
1783: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart + cworkA[i];
1784: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i]];
1785: }
1786: } else {
1787: if (idx) *idx = NULL;
1788: if (v) *v = NULL;
1789: }
1790: }
1791: *nz = nztot;
1792: PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1793: PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1794: PetscFunctionReturn(PETSC_SUCCESS);
1795: }
1797: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1798: {
1799: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1801: PetscFunctionBegin;
1802: PetscCheck(aij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow() must be called first");
1803: aij->getrowactive = PETSC_FALSE;
1804: PetscFunctionReturn(PETSC_SUCCESS);
1805: }
1807: static PetscErrorCode MatNorm_MPIAIJ(Mat mat, NormType type, PetscReal *norm)
1808: {
1809: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1810: Mat_SeqAIJ *amat = (Mat_SeqAIJ *)aij->A->data, *bmat = (Mat_SeqAIJ *)aij->B->data;
1811: PetscInt i, j, cstart = mat->cmap->rstart;
1812: PetscReal sum = 0.0;
1813: const MatScalar *v, *amata, *bmata;
1815: PetscFunctionBegin;
1816: if (aij->size == 1) {
1817: PetscCall(MatNorm(aij->A, type, norm));
1818: } else {
1819: PetscCall(MatSeqAIJGetArrayRead(aij->A, &amata));
1820: PetscCall(MatSeqAIJGetArrayRead(aij->B, &bmata));
1821: if (type == NORM_FROBENIUS) {
1822: v = amata;
1823: for (i = 0; i < amat->nz; i++) {
1824: sum += PetscRealPart(PetscConj(*v) * (*v));
1825: v++;
1826: }
1827: v = bmata;
1828: for (i = 0; i < bmat->nz; i++) {
1829: sum += PetscRealPart(PetscConj(*v) * (*v));
1830: v++;
1831: }
1832: PetscCallMPI(MPIU_Allreduce(&sum, norm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1833: *norm = PetscSqrtReal(*norm);
1834: PetscCall(PetscLogFlops(2.0 * amat->nz + 2.0 * bmat->nz));
1835: } else if (type == NORM_1) { /* max column norm */
1836: PetscReal *tmp;
1837: PetscInt *jj, *garray = aij->garray;
1838: PetscCall(PetscCalloc1(mat->cmap->N + 1, &tmp));
1839: *norm = 0.0;
1840: v = amata;
1841: jj = amat->j;
1842: for (j = 0; j < amat->nz; j++) {
1843: tmp[cstart + *jj++] += PetscAbsScalar(*v);
1844: v++;
1845: }
1846: v = bmata;
1847: jj = bmat->j;
1848: for (j = 0; j < bmat->nz; j++) {
1849: tmp[garray[*jj++]] += PetscAbsScalar(*v);
1850: v++;
1851: }
1852: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, tmp, mat->cmap->N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1853: for (j = 0; j < mat->cmap->N; j++) {
1854: if (tmp[j] > *norm) *norm = tmp[j];
1855: }
1856: PetscCall(PetscFree(tmp));
1857: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1858: } else if (type == NORM_INFINITY) { /* max row norm */
1859: PetscReal ntemp = 0.0;
1860: for (j = 0; j < aij->A->rmap->n; j++) {
1861: v = PetscSafePointerPlusOffset(amata, amat->i[j]);
1862: sum = 0.0;
1863: for (i = 0; i < amat->i[j + 1] - amat->i[j]; i++) {
1864: sum += PetscAbsScalar(*v);
1865: v++;
1866: }
1867: v = PetscSafePointerPlusOffset(bmata, bmat->i[j]);
1868: for (i = 0; i < bmat->i[j + 1] - bmat->i[j]; i++) {
1869: sum += PetscAbsScalar(*v);
1870: v++;
1871: }
1872: if (sum > ntemp) ntemp = sum;
1873: }
1874: PetscCallMPI(MPIU_Allreduce(&ntemp, norm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
1875: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1876: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for two norm");
1877: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &amata));
1878: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &bmata));
1879: }
1880: PetscFunctionReturn(PETSC_SUCCESS);
1881: }
1883: static PetscErrorCode MatTranspose_MPIAIJ(Mat A, MatReuse reuse, Mat *matout)
1884: {
1885: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *b;
1886: Mat_SeqAIJ *Aloc = (Mat_SeqAIJ *)a->A->data, *Bloc = (Mat_SeqAIJ *)a->B->data, *sub_B_diag;
1887: PetscInt M = A->rmap->N, N = A->cmap->N, ma, na, mb, nb, row, *cols, *cols_tmp, *B_diag_ilen, i, ncol, A_diag_ncol;
1888: const PetscInt *ai, *aj, *bi, *bj, *B_diag_i;
1889: Mat B, A_diag, *B_diag;
1890: const MatScalar *pbv, *bv;
1892: PetscFunctionBegin;
1893: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1894: ma = A->rmap->n;
1895: na = A->cmap->n;
1896: mb = a->B->rmap->n;
1897: nb = a->B->cmap->n;
1898: ai = Aloc->i;
1899: aj = Aloc->j;
1900: bi = Bloc->i;
1901: bj = Bloc->j;
1902: if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1903: PetscInt *d_nnz, *g_nnz, *o_nnz;
1904: PetscSFNode *oloc;
1905: PETSC_UNUSED PetscSF sf;
1907: PetscCall(PetscMalloc4(na, &d_nnz, na, &o_nnz, nb, &g_nnz, nb, &oloc));
1908: /* compute d_nnz for preallocation */
1909: PetscCall(PetscArrayzero(d_nnz, na));
1910: for (i = 0; i < ai[ma]; i++) d_nnz[aj[i]]++;
1911: /* compute local off-diagonal contributions */
1912: PetscCall(PetscArrayzero(g_nnz, nb));
1913: for (i = 0; i < bi[ma]; i++) g_nnz[bj[i]]++;
1914: /* map those to global */
1915: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1916: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, nb, NULL, PETSC_USE_POINTER, a->garray));
1917: PetscCall(PetscSFSetFromOptions(sf));
1918: PetscCall(PetscArrayzero(o_nnz, na));
1919: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1920: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1921: PetscCall(PetscSFDestroy(&sf));
1923: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1924: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1925: PetscCall(MatSetBlockSizes(B, PetscAbs(A->cmap->bs), PetscAbs(A->rmap->bs)));
1926: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1927: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
1928: PetscCall(PetscFree4(d_nnz, o_nnz, g_nnz, oloc));
1929: } else {
1930: B = *matout;
1931: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
1932: }
1934: b = (Mat_MPIAIJ *)B->data;
1935: A_diag = a->A;
1936: B_diag = &b->A;
1937: sub_B_diag = (Mat_SeqAIJ *)(*B_diag)->data;
1938: A_diag_ncol = A_diag->cmap->N;
1939: B_diag_ilen = sub_B_diag->ilen;
1940: B_diag_i = sub_B_diag->i;
1942: /* Set ilen for diagonal of B */
1943: for (i = 0; i < A_diag_ncol; i++) B_diag_ilen[i] = B_diag_i[i + 1] - B_diag_i[i];
1945: /* Transpose the diagonal part of the matrix. In contrast to the off-diagonal part, this can be done
1946: very quickly (=without using MatSetValues), because all writes are local. */
1947: PetscCall(MatTransposeSetPrecursor(A_diag, *B_diag));
1948: PetscCall(MatTranspose(A_diag, MAT_REUSE_MATRIX, B_diag));
1950: /* copy over the B part */
1951: PetscCall(PetscMalloc1(bi[mb], &cols));
1952: PetscCall(MatSeqAIJGetArrayRead(a->B, &bv));
1953: pbv = bv;
1954: row = A->rmap->rstart;
1955: for (i = 0; i < bi[mb]; i++) cols[i] = a->garray[bj[i]];
1956: cols_tmp = cols;
1957: for (i = 0; i < mb; i++) {
1958: ncol = bi[i + 1] - bi[i];
1959: PetscCall(MatSetValues(B, ncol, cols_tmp, 1, &row, pbv, INSERT_VALUES));
1960: row++;
1961: if (pbv) pbv += ncol;
1962: if (cols_tmp) cols_tmp += ncol;
1963: }
1964: PetscCall(PetscFree(cols));
1965: PetscCall(MatSeqAIJRestoreArrayRead(a->B, &bv));
1967: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1968: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1969: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1970: *matout = B;
1971: } else {
1972: PetscCall(MatHeaderMerge(A, &B));
1973: }
1974: PetscFunctionReturn(PETSC_SUCCESS);
1975: }
1977: static PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat, Vec ll, Vec rr)
1978: {
1979: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1980: Mat a = aij->A, b = aij->B;
1981: PetscInt s1, s2, s3;
1983: PetscFunctionBegin;
1984: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1985: if (rr) {
1986: PetscCall(VecGetLocalSize(rr, &s1));
1987: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1988: /* Overlap communication with computation. */
1989: PetscCall(VecScatterBegin(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1990: }
1991: if (ll) {
1992: PetscCall(VecGetLocalSize(ll, &s1));
1993: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1994: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1995: }
1996: /* scale the diagonal block */
1997: PetscUseTypeMethod(a, diagonalscale, ll, rr);
1999: if (rr) {
2000: /* Do a scatter end and then right scale the off-diagonal block */
2001: PetscCall(VecScatterEnd(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
2002: PetscUseTypeMethod(b, diagonalscale, NULL, aij->lvec);
2003: }
2004: PetscFunctionReturn(PETSC_SUCCESS);
2005: }
2007: static PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2008: {
2009: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2011: PetscFunctionBegin;
2012: PetscCall(MatSetUnfactored(a->A));
2013: PetscFunctionReturn(PETSC_SUCCESS);
2014: }
2016: static PetscErrorCode MatEqual_MPIAIJ(Mat A, Mat B, PetscBool *flag)
2017: {
2018: Mat_MPIAIJ *matB = (Mat_MPIAIJ *)B->data, *matA = (Mat_MPIAIJ *)A->data;
2019: Mat a, b, c, d;
2020: PetscBool flg;
2022: PetscFunctionBegin;
2023: a = matA->A;
2024: b = matA->B;
2025: c = matB->A;
2026: d = matB->B;
2028: PetscCall(MatEqual(a, c, &flg));
2029: if (flg) PetscCall(MatEqual(b, d, &flg));
2030: PetscCallMPI(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
2031: PetscFunctionReturn(PETSC_SUCCESS);
2032: }
2034: static PetscErrorCode MatCopy_MPIAIJ(Mat A, Mat B, MatStructure str)
2035: {
2036: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2037: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2039: PetscFunctionBegin;
2040: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2041: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2042: /* because of the column compression in the off-processor part of the matrix a->B,
2043: the number of columns in a->B and b->B may be different, hence we cannot call
2044: the MatCopy() directly on the two parts. If need be, we can provide a more
2045: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2046: then copying the submatrices */
2047: PetscCall(MatCopy_Basic(A, B, str));
2048: } else {
2049: PetscCall(MatCopy(a->A, b->A, str));
2050: PetscCall(MatCopy(a->B, b->B, str));
2051: }
2052: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2053: PetscFunctionReturn(PETSC_SUCCESS);
2054: }
2056: /*
2057: Computes the number of nonzeros per row needed for preallocation when X and Y
2058: have different nonzero structure.
2059: */
2060: PetscErrorCode MatAXPYGetPreallocation_MPIX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *xltog, const PetscInt *yi, const PetscInt *yj, const PetscInt *yltog, PetscInt *nnz)
2061: {
2062: PetscInt i, j, k, nzx, nzy;
2064: PetscFunctionBegin;
2065: /* Set the number of nonzeros in the new matrix */
2066: for (i = 0; i < m; i++) {
2067: const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2068: nzx = xi[i + 1] - xi[i];
2069: nzy = yi[i + 1] - yi[i];
2070: nnz[i] = 0;
2071: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2072: for (; k < nzy && yltog[yjj[k]] < xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2073: if (k < nzy && yltog[yjj[k]] == xltog[xjj[j]]) k++; /* Skip duplicate */
2074: nnz[i]++;
2075: }
2076: for (; k < nzy; k++) nnz[i]++;
2077: }
2078: PetscFunctionReturn(PETSC_SUCCESS);
2079: }
2081: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2082: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
2083: {
2084: PetscInt m = Y->rmap->N;
2085: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2086: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
2088: PetscFunctionBegin;
2089: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
2090: PetscFunctionReturn(PETSC_SUCCESS);
2091: }
2093: static PetscErrorCode MatAXPY_MPIAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2094: {
2095: Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data, *yy = (Mat_MPIAIJ *)Y->data;
2097: PetscFunctionBegin;
2098: if (str == SAME_NONZERO_PATTERN) {
2099: PetscCall(MatAXPY(yy->A, a, xx->A, str));
2100: PetscCall(MatAXPY(yy->B, a, xx->B, str));
2101: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2102: PetscCall(MatAXPY_Basic(Y, a, X, str));
2103: } else {
2104: Mat B;
2105: PetscInt *nnz_d, *nnz_o;
2107: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
2108: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
2109: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2110: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2111: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2112: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2113: PetscCall(MatAXPYGetPreallocation_SeqAIJ(yy->A, xx->A, nnz_d));
2114: PetscCall(MatAXPYGetPreallocation_MPIAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
2115: PetscCall(MatMPIAIJSetPreallocation(B, 0, nnz_d, 0, nnz_o));
2116: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2117: PetscCall(MatHeaderMerge(Y, &B));
2118: PetscCall(PetscFree(nnz_d));
2119: PetscCall(PetscFree(nnz_o));
2120: }
2121: PetscFunctionReturn(PETSC_SUCCESS);
2122: }
2124: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);
2126: static PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2127: {
2128: PetscFunctionBegin;
2129: if (PetscDefined(USE_COMPLEX)) {
2130: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2132: PetscCall(MatConjugate_SeqAIJ(aij->A));
2133: PetscCall(MatConjugate_SeqAIJ(aij->B));
2134: }
2135: PetscFunctionReturn(PETSC_SUCCESS);
2136: }
2138: static PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2139: {
2140: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2142: PetscFunctionBegin;
2143: PetscCall(MatRealPart(a->A));
2144: PetscCall(MatRealPart(a->B));
2145: PetscFunctionReturn(PETSC_SUCCESS);
2146: }
2148: static PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
2149: {
2150: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2152: PetscFunctionBegin;
2153: PetscCall(MatImaginaryPart(a->A));
2154: PetscCall(MatImaginaryPart(a->B));
2155: PetscFunctionReturn(PETSC_SUCCESS);
2156: }
2158: static PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2159: {
2160: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2161: PetscInt i, *idxb = NULL, m = A->rmap->n;
2162: PetscScalar *vv;
2163: Vec vB, vA;
2164: const PetscScalar *va, *vb;
2166: PetscFunctionBegin;
2167: PetscCall(MatCreateVecs(a->A, NULL, &vA));
2168: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
2170: PetscCall(VecGetArrayRead(vA, &va));
2171: if (idx) {
2172: for (i = 0; i < m; i++) {
2173: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2174: }
2175: }
2177: PetscCall(MatCreateVecs(a->B, NULL, &vB));
2178: PetscCall(PetscMalloc1(m, &idxb));
2179: PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));
2181: PetscCall(VecGetArrayWrite(v, &vv));
2182: PetscCall(VecGetArrayRead(vB, &vb));
2183: for (i = 0; i < m; i++) {
2184: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2185: vv[i] = vb[i];
2186: if (idx) idx[i] = a->garray[idxb[i]];
2187: } else {
2188: vv[i] = va[i];
2189: if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > a->garray[idxb[i]]) idx[i] = a->garray[idxb[i]];
2190: }
2191: }
2192: PetscCall(VecRestoreArrayWrite(v, &vv));
2193: PetscCall(VecRestoreArrayRead(vA, &va));
2194: PetscCall(VecRestoreArrayRead(vB, &vb));
2195: PetscCall(PetscFree(idxb));
2196: PetscCall(VecDestroy(&vA));
2197: PetscCall(VecDestroy(&vB));
2198: PetscFunctionReturn(PETSC_SUCCESS);
2199: }
2201: static PetscErrorCode MatGetRowSumAbs_MPIAIJ(Mat A, Vec v)
2202: {
2203: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2204: Vec vB, vA;
2206: PetscFunctionBegin;
2207: PetscCall(MatCreateVecs(a->A, NULL, &vA));
2208: PetscCall(MatGetRowSumAbs(a->A, vA));
2209: PetscCall(MatCreateVecs(a->B, NULL, &vB));
2210: PetscCall(MatGetRowSumAbs(a->B, vB));
2211: PetscCall(VecAXPY(vA, 1.0, vB));
2212: PetscCall(VecDestroy(&vB));
2213: PetscCall(VecCopy(vA, v));
2214: PetscCall(VecDestroy(&vA));
2215: PetscFunctionReturn(PETSC_SUCCESS);
2216: }
2218: static PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2219: {
2220: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2221: PetscInt m = A->rmap->n, n = A->cmap->n;
2222: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2223: PetscInt *cmap = mat->garray;
2224: PetscInt *diagIdx, *offdiagIdx;
2225: Vec diagV, offdiagV;
2226: PetscScalar *a, *diagA, *offdiagA;
2227: const PetscScalar *ba, *bav;
2228: PetscInt r, j, col, ncols, *bi, *bj;
2229: Mat B = mat->B;
2230: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2232: PetscFunctionBegin;
2233: /* When a process holds entire A and other processes have no entry */
2234: if (A->cmap->N == n) {
2235: PetscCall(VecGetArrayWrite(v, &diagA));
2236: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2237: PetscCall(MatGetRowMinAbs(mat->A, diagV, idx));
2238: PetscCall(VecDestroy(&diagV));
2239: PetscCall(VecRestoreArrayWrite(v, &diagA));
2240: PetscFunctionReturn(PETSC_SUCCESS);
2241: } else if (n == 0) {
2242: if (m) {
2243: PetscCall(VecGetArrayWrite(v, &a));
2244: for (r = 0; r < m; r++) {
2245: a[r] = 0.0;
2246: if (idx) idx[r] = -1;
2247: }
2248: PetscCall(VecRestoreArrayWrite(v, &a));
2249: }
2250: PetscFunctionReturn(PETSC_SUCCESS);
2251: }
2253: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2254: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2255: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2256: PetscCall(MatGetRowMinAbs(mat->A, diagV, diagIdx));
2258: /* Get offdiagIdx[] for implicit 0.0 */
2259: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2260: ba = bav;
2261: bi = b->i;
2262: bj = b->j;
2263: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2264: for (r = 0; r < m; r++) {
2265: ncols = bi[r + 1] - bi[r];
2266: if (ncols == A->cmap->N - n) { /* Brow is dense */
2267: offdiagA[r] = *ba;
2268: offdiagIdx[r] = cmap[0];
2269: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2270: offdiagA[r] = 0.0;
2272: /* Find first hole in the cmap */
2273: for (j = 0; j < ncols; j++) {
2274: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2275: if (col > j && j < cstart) {
2276: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2277: break;
2278: } else if (col > j + n && j >= cstart) {
2279: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2280: break;
2281: }
2282: }
2283: if (j == ncols && ncols < A->cmap->N - n) {
2284: /* a hole is outside compressed Bcols */
2285: if (ncols == 0) {
2286: if (cstart) {
2287: offdiagIdx[r] = 0;
2288: } else offdiagIdx[r] = cend;
2289: } else { /* ncols > 0 */
2290: offdiagIdx[r] = cmap[ncols - 1] + 1;
2291: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2292: }
2293: }
2294: }
2296: for (j = 0; j < ncols; j++) {
2297: if (PetscAbsScalar(offdiagA[r]) > PetscAbsScalar(*ba)) {
2298: offdiagA[r] = *ba;
2299: offdiagIdx[r] = cmap[*bj];
2300: }
2301: ba++;
2302: bj++;
2303: }
2304: }
2306: PetscCall(VecGetArrayWrite(v, &a));
2307: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2308: for (r = 0; r < m; ++r) {
2309: if (PetscAbsScalar(diagA[r]) < PetscAbsScalar(offdiagA[r])) {
2310: a[r] = diagA[r];
2311: if (idx) idx[r] = cstart + diagIdx[r];
2312: } else if (PetscAbsScalar(diagA[r]) == PetscAbsScalar(offdiagA[r])) {
2313: a[r] = diagA[r];
2314: if (idx) {
2315: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2316: idx[r] = cstart + diagIdx[r];
2317: } else idx[r] = offdiagIdx[r];
2318: }
2319: } else {
2320: a[r] = offdiagA[r];
2321: if (idx) idx[r] = offdiagIdx[r];
2322: }
2323: }
2324: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2325: PetscCall(VecRestoreArrayWrite(v, &a));
2326: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2327: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2328: PetscCall(VecDestroy(&diagV));
2329: PetscCall(VecDestroy(&offdiagV));
2330: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2331: PetscFunctionReturn(PETSC_SUCCESS);
2332: }
2334: static PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2335: {
2336: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2337: PetscInt m = A->rmap->n, n = A->cmap->n;
2338: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2339: PetscInt *cmap = mat->garray;
2340: PetscInt *diagIdx, *offdiagIdx;
2341: Vec diagV, offdiagV;
2342: PetscScalar *a, *diagA, *offdiagA;
2343: const PetscScalar *ba, *bav;
2344: PetscInt r, j, col, ncols, *bi, *bj;
2345: Mat B = mat->B;
2346: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2348: PetscFunctionBegin;
2349: /* When a process holds entire A and other processes have no entry */
2350: if (A->cmap->N == n) {
2351: PetscCall(VecGetArrayWrite(v, &diagA));
2352: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2353: PetscCall(MatGetRowMin(mat->A, diagV, idx));
2354: PetscCall(VecDestroy(&diagV));
2355: PetscCall(VecRestoreArrayWrite(v, &diagA));
2356: PetscFunctionReturn(PETSC_SUCCESS);
2357: } else if (n == 0) {
2358: if (m) {
2359: PetscCall(VecGetArrayWrite(v, &a));
2360: for (r = 0; r < m; r++) {
2361: a[r] = PETSC_MAX_REAL;
2362: if (idx) idx[r] = -1;
2363: }
2364: PetscCall(VecRestoreArrayWrite(v, &a));
2365: }
2366: PetscFunctionReturn(PETSC_SUCCESS);
2367: }
2369: PetscCall(PetscCalloc2(m, &diagIdx, m, &offdiagIdx));
2370: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2371: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2372: PetscCall(MatGetRowMin(mat->A, diagV, diagIdx));
2374: /* Get offdiagIdx[] for implicit 0.0 */
2375: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2376: ba = bav;
2377: bi = b->i;
2378: bj = b->j;
2379: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2380: for (r = 0; r < m; r++) {
2381: ncols = bi[r + 1] - bi[r];
2382: if (ncols == A->cmap->N - n) { /* Brow is dense */
2383: offdiagA[r] = *ba;
2384: offdiagIdx[r] = cmap[0];
2385: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2386: offdiagA[r] = 0.0;
2388: /* Find first hole in the cmap */
2389: for (j = 0; j < ncols; j++) {
2390: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2391: if (col > j && j < cstart) {
2392: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2393: break;
2394: } else if (col > j + n && j >= cstart) {
2395: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2396: break;
2397: }
2398: }
2399: if (j == ncols && ncols < A->cmap->N - n) {
2400: /* a hole is outside compressed Bcols */
2401: if (ncols == 0) {
2402: if (cstart) {
2403: offdiagIdx[r] = 0;
2404: } else offdiagIdx[r] = cend;
2405: } else { /* ncols > 0 */
2406: offdiagIdx[r] = cmap[ncols - 1] + 1;
2407: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2408: }
2409: }
2410: }
2412: for (j = 0; j < ncols; j++) {
2413: if (PetscRealPart(offdiagA[r]) > PetscRealPart(*ba)) {
2414: offdiagA[r] = *ba;
2415: offdiagIdx[r] = cmap[*bj];
2416: }
2417: ba++;
2418: bj++;
2419: }
2420: }
2422: PetscCall(VecGetArrayWrite(v, &a));
2423: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2424: for (r = 0; r < m; ++r) {
2425: if (PetscRealPart(diagA[r]) < PetscRealPart(offdiagA[r])) {
2426: a[r] = diagA[r];
2427: if (idx) idx[r] = cstart + diagIdx[r];
2428: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2429: a[r] = diagA[r];
2430: if (idx) {
2431: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2432: idx[r] = cstart + diagIdx[r];
2433: } else idx[r] = offdiagIdx[r];
2434: }
2435: } else {
2436: a[r] = offdiagA[r];
2437: if (idx) idx[r] = offdiagIdx[r];
2438: }
2439: }
2440: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2441: PetscCall(VecRestoreArrayWrite(v, &a));
2442: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2443: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2444: PetscCall(VecDestroy(&diagV));
2445: PetscCall(VecDestroy(&offdiagV));
2446: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2447: PetscFunctionReturn(PETSC_SUCCESS);
2448: }
2450: static PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2451: {
2452: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2453: PetscInt m = A->rmap->n, n = A->cmap->n;
2454: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2455: PetscInt *cmap = mat->garray;
2456: PetscInt *diagIdx, *offdiagIdx;
2457: Vec diagV, offdiagV;
2458: PetscScalar *a, *diagA, *offdiagA;
2459: const PetscScalar *ba, *bav;
2460: PetscInt r, j, col, ncols, *bi, *bj;
2461: Mat B = mat->B;
2462: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2464: PetscFunctionBegin;
2465: /* When a process holds entire A and other processes have no entry */
2466: if (A->cmap->N == n) {
2467: PetscCall(VecGetArrayWrite(v, &diagA));
2468: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2469: PetscCall(MatGetRowMax(mat->A, diagV, idx));
2470: PetscCall(VecDestroy(&diagV));
2471: PetscCall(VecRestoreArrayWrite(v, &diagA));
2472: PetscFunctionReturn(PETSC_SUCCESS);
2473: } else if (n == 0) {
2474: if (m) {
2475: PetscCall(VecGetArrayWrite(v, &a));
2476: for (r = 0; r < m; r++) {
2477: a[r] = PETSC_MIN_REAL;
2478: if (idx) idx[r] = -1;
2479: }
2480: PetscCall(VecRestoreArrayWrite(v, &a));
2481: }
2482: PetscFunctionReturn(PETSC_SUCCESS);
2483: }
2485: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2486: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2487: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2488: PetscCall(MatGetRowMax(mat->A, diagV, diagIdx));
2490: /* Get offdiagIdx[] for implicit 0.0 */
2491: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2492: ba = bav;
2493: bi = b->i;
2494: bj = b->j;
2495: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2496: for (r = 0; r < m; r++) {
2497: ncols = bi[r + 1] - bi[r];
2498: if (ncols == A->cmap->N - n) { /* Brow is dense */
2499: offdiagA[r] = *ba;
2500: offdiagIdx[r] = cmap[0];
2501: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2502: offdiagA[r] = 0.0;
2504: /* Find first hole in the cmap */
2505: for (j = 0; j < ncols; j++) {
2506: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2507: if (col > j && j < cstart) {
2508: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2509: break;
2510: } else if (col > j + n && j >= cstart) {
2511: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2512: break;
2513: }
2514: }
2515: if (j == ncols && ncols < A->cmap->N - n) {
2516: /* a hole is outside compressed Bcols */
2517: if (ncols == 0) {
2518: if (cstart) {
2519: offdiagIdx[r] = 0;
2520: } else offdiagIdx[r] = cend;
2521: } else { /* ncols > 0 */
2522: offdiagIdx[r] = cmap[ncols - 1] + 1;
2523: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2524: }
2525: }
2526: }
2528: for (j = 0; j < ncols; j++) {
2529: if (PetscRealPart(offdiagA[r]) < PetscRealPart(*ba)) {
2530: offdiagA[r] = *ba;
2531: offdiagIdx[r] = cmap[*bj];
2532: }
2533: ba++;
2534: bj++;
2535: }
2536: }
2538: PetscCall(VecGetArrayWrite(v, &a));
2539: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2540: for (r = 0; r < m; ++r) {
2541: if (PetscRealPart(diagA[r]) > PetscRealPart(offdiagA[r])) {
2542: a[r] = diagA[r];
2543: if (idx) idx[r] = cstart + diagIdx[r];
2544: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2545: a[r] = diagA[r];
2546: if (idx) {
2547: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2548: idx[r] = cstart + diagIdx[r];
2549: } else idx[r] = offdiagIdx[r];
2550: }
2551: } else {
2552: a[r] = offdiagA[r];
2553: if (idx) idx[r] = offdiagIdx[r];
2554: }
2555: }
2556: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2557: PetscCall(VecRestoreArrayWrite(v, &a));
2558: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2559: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2560: PetscCall(VecDestroy(&diagV));
2561: PetscCall(VecDestroy(&offdiagV));
2562: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2563: PetscFunctionReturn(PETSC_SUCCESS);
2564: }
2566: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat, Mat *newmat)
2567: {
2568: Mat *dummy;
2570: PetscFunctionBegin;
2571: PetscCall(MatCreateSubMatrix_MPIAIJ_All(mat, MAT_DO_NOT_GET_VALUES, MAT_INITIAL_MATRIX, &dummy));
2572: *newmat = *dummy;
2573: PetscCall(PetscFree(dummy));
2574: PetscFunctionReturn(PETSC_SUCCESS);
2575: }
2577: static PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A, const PetscScalar **values)
2578: {
2579: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2581: PetscFunctionBegin;
2582: PetscCall(MatInvertBlockDiagonal(a->A, values));
2583: A->factorerrortype = a->A->factorerrortype;
2584: PetscFunctionReturn(PETSC_SUCCESS);
2585: }
2587: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x, PetscRandom rctx)
2588: {
2589: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)x->data;
2591: PetscFunctionBegin;
2592: PetscCheck(x->assembled || x->preallocated, PetscObjectComm((PetscObject)x), PETSC_ERR_ARG_WRONGSTATE, "MatSetRandom on an unassembled and unpreallocated MATMPIAIJ is not allowed");
2593: PetscCall(MatSetRandom(aij->A, rctx));
2594: if (x->assembled) {
2595: PetscCall(MatSetRandom(aij->B, rctx));
2596: } else {
2597: PetscCall(MatSetRandomSkipColumnRange_SeqAIJ_Private(aij->B, x->cmap->rstart, x->cmap->rend, rctx));
2598: }
2599: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
2600: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
2601: PetscFunctionReturn(PETSC_SUCCESS);
2602: }
2604: static PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ(Mat A, PetscBool sc)
2605: {
2606: PetscFunctionBegin;
2607: if (sc) A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ_Scalable;
2608: else A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ;
2609: PetscFunctionReturn(PETSC_SUCCESS);
2610: }
2612: /*@
2613: MatMPIAIJGetNumberNonzeros - gets the number of nonzeros in the matrix on this MPI rank
2615: Not Collective
2617: Input Parameter:
2618: . A - the matrix
2620: Output Parameter:
2621: . nz - the number of nonzeros
2623: Level: advanced
2625: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2626: @*/
2627: PetscErrorCode MatMPIAIJGetNumberNonzeros(Mat A, PetscCount *nz)
2628: {
2629: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)A->data;
2630: Mat_SeqAIJ *aaij = (Mat_SeqAIJ *)maij->A->data, *baij = (Mat_SeqAIJ *)maij->B->data;
2631: PetscBool isaij;
2633: PetscFunctionBegin;
2634: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATMPIAIJ, &isaij));
2635: PetscCheck(isaij, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for type %s", ((PetscObject)A)->type_name);
2636: *nz = aaij->i[A->rmap->n] + baij->i[A->rmap->n];
2637: PetscFunctionReturn(PETSC_SUCCESS);
2638: }
2640: /*@
2641: MatMPIAIJSetUseScalableIncreaseOverlap - Determine if the matrix uses a scalable algorithm to compute the overlap
2643: Collective
2645: Input Parameters:
2646: + A - the matrix
2647: - sc - `PETSC_TRUE` indicates use the scalable algorithm (default is not to use the scalable algorithm)
2649: Level: advanced
2651: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2652: @*/
2653: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap(Mat A, PetscBool sc)
2654: {
2655: PetscFunctionBegin;
2656: PetscTryMethod(A, "MatMPIAIJSetUseScalableIncreaseOverlap_C", (Mat, PetscBool), (A, sc));
2657: PetscFunctionReturn(PETSC_SUCCESS);
2658: }
2660: PetscErrorCode MatSetFromOptions_MPIAIJ(Mat A, PetscOptionItems PetscOptionsObject)
2661: {
2662: PetscBool sc = PETSC_FALSE, flg;
2664: PetscFunctionBegin;
2665: PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJ options");
2666: if (A->ops->increaseoverlap == MatIncreaseOverlap_MPIAIJ_Scalable) sc = PETSC_TRUE;
2667: PetscCall(PetscOptionsBool("-mat_increase_overlap_scalable", "Use a scalable algorithm to compute the overlap", "MatIncreaseOverlap", sc, &sc, &flg));
2668: if (flg) PetscCall(MatMPIAIJSetUseScalableIncreaseOverlap(A, sc));
2669: PetscOptionsHeadEnd();
2670: PetscFunctionReturn(PETSC_SUCCESS);
2671: }
2673: static PetscErrorCode MatShift_MPIAIJ(Mat Y, PetscScalar a)
2674: {
2675: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)Y->data;
2676: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)maij->A->data;
2678: PetscFunctionBegin;
2679: if (!Y->preallocated) {
2680: PetscCall(MatMPIAIJSetPreallocation(Y, 1, NULL, 0, NULL));
2681: } else if (!aij->nz) { /* It does not matter if diagonals of Y only partially lie in maij->A. We just need an estimated preallocation. */
2682: PetscInt nonew = aij->nonew;
2683: PetscCall(MatSeqAIJSetPreallocation(maij->A, 1, NULL));
2684: aij->nonew = nonew;
2685: }
2686: PetscCall(MatShift_Basic(Y, a));
2687: PetscFunctionReturn(PETSC_SUCCESS);
2688: }
2690: static PetscErrorCode MatMissingDiagonal_MPIAIJ(Mat A, PetscBool *missing, PetscInt *d)
2691: {
2692: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2694: PetscFunctionBegin;
2695: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2696: PetscCall(MatMissingDiagonal(a->A, missing, d));
2697: if (d) {
2698: PetscInt rstart;
2699: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2700: *d += rstart;
2701: }
2702: PetscFunctionReturn(PETSC_SUCCESS);
2703: }
2705: static PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2706: {
2707: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2709: PetscFunctionBegin;
2710: PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2711: PetscFunctionReturn(PETSC_SUCCESS);
2712: }
2714: static PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A, PetscBool keep)
2715: {
2716: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2718: PetscFunctionBegin;
2719: PetscCall(MatEliminateZeros_SeqAIJ(a->A, keep)); // possibly keep zero diagonal coefficients
2720: PetscCall(MatEliminateZeros_SeqAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2721: PetscFunctionReturn(PETSC_SUCCESS);
2722: }
2724: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2725: MatGetRow_MPIAIJ,
2726: MatRestoreRow_MPIAIJ,
2727: MatMult_MPIAIJ,
2728: /* 4*/ MatMultAdd_MPIAIJ,
2729: MatMultTranspose_MPIAIJ,
2730: MatMultTransposeAdd_MPIAIJ,
2731: NULL,
2732: NULL,
2733: NULL,
2734: /*10*/ NULL,
2735: NULL,
2736: NULL,
2737: MatSOR_MPIAIJ,
2738: MatTranspose_MPIAIJ,
2739: /*15*/ MatGetInfo_MPIAIJ,
2740: MatEqual_MPIAIJ,
2741: MatGetDiagonal_MPIAIJ,
2742: MatDiagonalScale_MPIAIJ,
2743: MatNorm_MPIAIJ,
2744: /*20*/ MatAssemblyBegin_MPIAIJ,
2745: MatAssemblyEnd_MPIAIJ,
2746: MatSetOption_MPIAIJ,
2747: MatZeroEntries_MPIAIJ,
2748: /*24*/ MatZeroRows_MPIAIJ,
2749: NULL,
2750: NULL,
2751: NULL,
2752: NULL,
2753: /*29*/ MatSetUp_MPI_Hash,
2754: NULL,
2755: NULL,
2756: MatGetDiagonalBlock_MPIAIJ,
2757: NULL,
2758: /*34*/ MatDuplicate_MPIAIJ,
2759: NULL,
2760: NULL,
2761: NULL,
2762: NULL,
2763: /*39*/ MatAXPY_MPIAIJ,
2764: MatCreateSubMatrices_MPIAIJ,
2765: MatIncreaseOverlap_MPIAIJ,
2766: MatGetValues_MPIAIJ,
2767: MatCopy_MPIAIJ,
2768: /*44*/ MatGetRowMax_MPIAIJ,
2769: MatScale_MPIAIJ,
2770: MatShift_MPIAIJ,
2771: MatDiagonalSet_MPIAIJ,
2772: MatZeroRowsColumns_MPIAIJ,
2773: /*49*/ MatSetRandom_MPIAIJ,
2774: MatGetRowIJ_MPIAIJ,
2775: MatRestoreRowIJ_MPIAIJ,
2776: NULL,
2777: NULL,
2778: /*54*/ MatFDColoringCreate_MPIXAIJ,
2779: NULL,
2780: MatSetUnfactored_MPIAIJ,
2781: MatPermute_MPIAIJ,
2782: NULL,
2783: /*59*/ MatCreateSubMatrix_MPIAIJ,
2784: MatDestroy_MPIAIJ,
2785: MatView_MPIAIJ,
2786: NULL,
2787: NULL,
2788: /*64*/ NULL,
2789: MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2790: NULL,
2791: NULL,
2792: NULL,
2793: /*69*/ MatGetRowMaxAbs_MPIAIJ,
2794: MatGetRowMinAbs_MPIAIJ,
2795: NULL,
2796: NULL,
2797: NULL,
2798: NULL,
2799: /*75*/ MatFDColoringApply_AIJ,
2800: MatSetFromOptions_MPIAIJ,
2801: NULL,
2802: NULL,
2803: MatFindZeroDiagonals_MPIAIJ,
2804: /*80*/ NULL,
2805: NULL,
2806: NULL,
2807: /*83*/ MatLoad_MPIAIJ,
2808: NULL,
2809: NULL,
2810: NULL,
2811: NULL,
2812: NULL,
2813: /*89*/ NULL,
2814: NULL,
2815: MatMatMultNumeric_MPIAIJ_MPIAIJ,
2816: NULL,
2817: NULL,
2818: /*94*/ MatPtAPNumeric_MPIAIJ_MPIAIJ,
2819: NULL,
2820: NULL,
2821: NULL,
2822: MatBindToCPU_MPIAIJ,
2823: /*99*/ MatProductSetFromOptions_MPIAIJ,
2824: NULL,
2825: NULL,
2826: MatConjugate_MPIAIJ,
2827: NULL,
2828: /*104*/ MatSetValuesRow_MPIAIJ,
2829: MatRealPart_MPIAIJ,
2830: MatImaginaryPart_MPIAIJ,
2831: NULL,
2832: NULL,
2833: /*109*/ NULL,
2834: NULL,
2835: MatGetRowMin_MPIAIJ,
2836: NULL,
2837: MatMissingDiagonal_MPIAIJ,
2838: /*114*/ MatGetSeqNonzeroStructure_MPIAIJ,
2839: NULL,
2840: MatGetGhosts_MPIAIJ,
2841: NULL,
2842: NULL,
2843: /*119*/ MatMultDiagonalBlock_MPIAIJ,
2844: NULL,
2845: NULL,
2846: NULL,
2847: MatGetMultiProcBlock_MPIAIJ,
2848: /*124*/ MatFindNonzeroRows_MPIAIJ,
2849: MatGetColumnReductions_MPIAIJ,
2850: MatInvertBlockDiagonal_MPIAIJ,
2851: MatInvertVariableBlockDiagonal_MPIAIJ,
2852: MatCreateSubMatricesMPI_MPIAIJ,
2853: /*129*/ NULL,
2854: NULL,
2855: NULL,
2856: MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2857: NULL,
2858: /*134*/ NULL,
2859: NULL,
2860: NULL,
2861: NULL,
2862: NULL,
2863: /*139*/ MatSetBlockSizes_MPIAIJ,
2864: NULL,
2865: NULL,
2866: MatFDColoringSetUp_MPIXAIJ,
2867: MatFindOffBlockDiagonalEntries_MPIAIJ,
2868: MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2869: /*145*/ NULL,
2870: NULL,
2871: NULL,
2872: MatCreateGraph_Simple_AIJ,
2873: NULL,
2874: /*150*/ NULL,
2875: MatEliminateZeros_MPIAIJ,
2876: MatGetRowSumAbs_MPIAIJ,
2877: NULL,
2878: NULL,
2879: /*155*/ NULL,
2880: MatCopyHashToXAIJ_MPI_Hash};
2882: static PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2883: {
2884: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2886: PetscFunctionBegin;
2887: PetscCall(MatStoreValues(aij->A));
2888: PetscCall(MatStoreValues(aij->B));
2889: PetscFunctionReturn(PETSC_SUCCESS);
2890: }
2892: static PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2893: {
2894: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2896: PetscFunctionBegin;
2897: PetscCall(MatRetrieveValues(aij->A));
2898: PetscCall(MatRetrieveValues(aij->B));
2899: PetscFunctionReturn(PETSC_SUCCESS);
2900: }
2902: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2903: {
2904: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2905: PetscMPIInt size;
2907: PetscFunctionBegin;
2908: if (B->hash_active) {
2909: B->ops[0] = b->cops;
2910: B->hash_active = PETSC_FALSE;
2911: }
2912: PetscCall(PetscLayoutSetUp(B->rmap));
2913: PetscCall(PetscLayoutSetUp(B->cmap));
2915: #if defined(PETSC_USE_CTABLE)
2916: PetscCall(PetscHMapIDestroy(&b->colmap));
2917: #else
2918: PetscCall(PetscFree(b->colmap));
2919: #endif
2920: PetscCall(PetscFree(b->garray));
2921: PetscCall(VecDestroy(&b->lvec));
2922: PetscCall(VecScatterDestroy(&b->Mvctx));
2924: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2926: MatSeqXAIJGetOptions_Private(b->B);
2927: PetscCall(MatDestroy(&b->B));
2928: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2929: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2930: PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2931: PetscCall(MatSetType(b->B, MATSEQAIJ));
2932: MatSeqXAIJRestoreOptions_Private(b->B);
2934: MatSeqXAIJGetOptions_Private(b->A);
2935: PetscCall(MatDestroy(&b->A));
2936: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2937: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2938: PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2939: PetscCall(MatSetType(b->A, MATSEQAIJ));
2940: MatSeqXAIJRestoreOptions_Private(b->A);
2942: PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2943: PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2944: B->preallocated = PETSC_TRUE;
2945: B->was_assembled = PETSC_FALSE;
2946: B->assembled = PETSC_FALSE;
2947: PetscFunctionReturn(PETSC_SUCCESS);
2948: }
2950: static PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2951: {
2952: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2953: /* Save the nonzero states of the component matrices because those are what are used to determine
2954: the nonzero state of mat */
2955: PetscObjectState diagstate = b->A->nonzerostate, offdiagstate = b->B->nonzerostate;
2957: PetscFunctionBegin;
2959: PetscCall(PetscLayoutSetUp(B->rmap));
2960: PetscCall(PetscLayoutSetUp(B->cmap));
2961: if (B->assembled || B->was_assembled) PetscCall(MatDisAssemble_MPIAIJ(B, PETSC_TRUE));
2962: else {
2963: #if defined(PETSC_USE_CTABLE)
2964: PetscCall(PetscHMapIDestroy(&b->colmap));
2965: #else
2966: PetscCall(PetscFree(b->colmap));
2967: #endif
2968: PetscCall(PetscFree(b->garray));
2969: PetscCall(VecDestroy(&b->lvec));
2970: }
2971: PetscCall(VecScatterDestroy(&b->Mvctx));
2973: PetscCall(MatResetPreallocation(b->A));
2974: PetscCall(MatResetPreallocation(b->B));
2975: B->preallocated = PETSC_TRUE;
2976: B->was_assembled = PETSC_FALSE;
2977: B->assembled = PETSC_FALSE;
2978: b->A->nonzerostate = ++diagstate, b->B->nonzerostate = ++offdiagstate;
2979: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
2980: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2981: PetscFunctionReturn(PETSC_SUCCESS);
2982: }
2984: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2985: {
2986: Mat mat;
2987: Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;
2989: PetscFunctionBegin;
2990: *newmat = NULL;
2991: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2992: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2993: PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2994: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2995: a = (Mat_MPIAIJ *)mat->data;
2997: mat->factortype = matin->factortype;
2998: mat->assembled = matin->assembled;
2999: mat->insertmode = NOT_SET_VALUES;
3001: a->size = oldmat->size;
3002: a->rank = oldmat->rank;
3003: a->donotstash = oldmat->donotstash;
3004: a->roworiented = oldmat->roworiented;
3005: a->rowindices = NULL;
3006: a->rowvalues = NULL;
3007: a->getrowactive = PETSC_FALSE;
3009: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
3010: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
3011: if (matin->hash_active) {
3012: PetscCall(MatSetUp(mat));
3013: } else {
3014: mat->preallocated = matin->preallocated;
3015: if (oldmat->colmap) {
3016: #if defined(PETSC_USE_CTABLE)
3017: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3018: #else
3019: PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
3020: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
3021: #endif
3022: } else a->colmap = NULL;
3023: if (oldmat->garray) {
3024: PetscInt len;
3025: len = oldmat->B->cmap->n;
3026: PetscCall(PetscMalloc1(len + 1, &a->garray));
3027: if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3028: } else a->garray = NULL;
3030: /* It may happen MatDuplicate is called with a non-assembled matrix
3031: In fact, MatDuplicate only requires the matrix to be preallocated
3032: This may happen inside a DMCreateMatrix_Shell */
3033: if (oldmat->lvec) PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3034: if (oldmat->Mvctx) {
3035: a->Mvctx = oldmat->Mvctx;
3036: PetscCall(PetscObjectReference((PetscObject)oldmat->Mvctx));
3037: }
3038: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3039: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3040: }
3041: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3042: *newmat = mat;
3043: PetscFunctionReturn(PETSC_SUCCESS);
3044: }
3046: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3047: {
3048: PetscBool isbinary, ishdf5;
3050: PetscFunctionBegin;
3053: /* force binary viewer to load .info file if it has not yet done so */
3054: PetscCall(PetscViewerSetUp(viewer));
3055: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3056: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3057: if (isbinary) {
3058: PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3059: } else if (ishdf5) {
3060: #if defined(PETSC_HAVE_HDF5)
3061: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3062: #else
3063: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3064: #endif
3065: } else {
3066: 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);
3067: }
3068: PetscFunctionReturn(PETSC_SUCCESS);
3069: }
3071: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3072: {
3073: PetscInt header[4], M, N, m, nz, rows, cols, sum, i;
3074: PetscInt *rowidxs, *colidxs;
3075: PetscScalar *matvals;
3077: PetscFunctionBegin;
3078: PetscCall(PetscViewerSetUp(viewer));
3080: /* read in matrix header */
3081: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3082: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3083: M = header[1];
3084: N = header[2];
3085: nz = header[3];
3086: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3087: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3088: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");
3090: /* set block sizes from the viewer's .info file */
3091: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3092: /* set global sizes if not set already */
3093: if (mat->rmap->N < 0) mat->rmap->N = M;
3094: if (mat->cmap->N < 0) mat->cmap->N = N;
3095: PetscCall(PetscLayoutSetUp(mat->rmap));
3096: PetscCall(PetscLayoutSetUp(mat->cmap));
3098: /* check if the matrix sizes are correct */
3099: PetscCall(MatGetSize(mat, &rows, &cols));
3100: 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);
3102: /* read in row lengths and build row indices */
3103: PetscCall(MatGetLocalSize(mat, &m, NULL));
3104: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3105: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3106: rowidxs[0] = 0;
3107: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3108: if (nz != PETSC_INT_MAX) {
3109: PetscCallMPI(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3110: 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);
3111: }
3113: /* read in column indices and matrix values */
3114: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3115: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3116: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3117: /* store matrix indices and values */
3118: PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3119: PetscCall(PetscFree(rowidxs));
3120: PetscCall(PetscFree2(colidxs, matvals));
3121: PetscFunctionReturn(PETSC_SUCCESS);
3122: }
3124: /* Not scalable because of ISAllGather() unless getting all columns. */
3125: static PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3126: {
3127: IS iscol_local;
3128: PetscBool isstride;
3129: PetscMPIInt gisstride = 0;
3131: PetscFunctionBegin;
3132: /* check if we are grabbing all columns*/
3133: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));
3135: if (isstride) {
3136: PetscInt start, len, mstart, mlen;
3137: PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3138: PetscCall(ISGetLocalSize(iscol, &len));
3139: PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3140: if (mstart == start && mlen - mstart == len) gisstride = 1;
3141: }
3143: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3144: if (gisstride) {
3145: PetscInt N;
3146: PetscCall(MatGetSize(mat, NULL, &N));
3147: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3148: PetscCall(ISSetIdentity(iscol_local));
3149: PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3150: } else {
3151: PetscInt cbs;
3152: PetscCall(ISGetBlockSize(iscol, &cbs));
3153: PetscCall(ISAllGather(iscol, &iscol_local));
3154: PetscCall(ISSetBlockSize(iscol_local, cbs));
3155: }
3157: *isseq = iscol_local;
3158: PetscFunctionReturn(PETSC_SUCCESS);
3159: }
3161: /*
3162: Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3163: (see MatCreateSubMatrix_MPIAIJ_nonscalable)
3165: Input Parameters:
3166: + mat - matrix
3167: . isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3168: i.e., mat->rstart <= isrow[i] < mat->rend
3169: - iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3170: i.e., mat->cstart <= iscol[i] < mat->cend
3172: Output Parameters:
3173: + isrow_d - sequential row index set for retrieving mat->A
3174: . iscol_d - sequential column index set for retrieving mat->A
3175: . iscol_o - sequential column index set for retrieving mat->B
3176: - garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3177: */
3178: static PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, PetscInt *garray[])
3179: {
3180: Vec x, cmap;
3181: const PetscInt *is_idx;
3182: PetscScalar *xarray, *cmaparray;
3183: PetscInt ncols, isstart, *idx, m, rstart, *cmap1, count;
3184: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3185: Mat B = a->B;
3186: Vec lvec = a->lvec, lcmap;
3187: PetscInt i, cstart, cend, Bn = B->cmap->N;
3188: MPI_Comm comm;
3189: VecScatter Mvctx = a->Mvctx;
3191: PetscFunctionBegin;
3192: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3193: PetscCall(ISGetLocalSize(iscol, &ncols));
3195: /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3196: PetscCall(MatCreateVecs(mat, &x, NULL));
3197: PetscCall(VecSet(x, -1.0));
3198: PetscCall(VecDuplicate(x, &cmap));
3199: PetscCall(VecSet(cmap, -1.0));
3201: /* Get start indices */
3202: PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3203: isstart -= ncols;
3204: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
3206: PetscCall(ISGetIndices(iscol, &is_idx));
3207: PetscCall(VecGetArray(x, &xarray));
3208: PetscCall(VecGetArray(cmap, &cmaparray));
3209: PetscCall(PetscMalloc1(ncols, &idx));
3210: for (i = 0; i < ncols; i++) {
3211: xarray[is_idx[i] - cstart] = (PetscScalar)is_idx[i];
3212: cmaparray[is_idx[i] - cstart] = i + isstart; /* global index of iscol[i] */
3213: idx[i] = is_idx[i] - cstart; /* local index of iscol[i] */
3214: }
3215: PetscCall(VecRestoreArray(x, &xarray));
3216: PetscCall(VecRestoreArray(cmap, &cmaparray));
3217: PetscCall(ISRestoreIndices(iscol, &is_idx));
3219: /* Get iscol_d */
3220: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3221: PetscCall(ISGetBlockSize(iscol, &i));
3222: PetscCall(ISSetBlockSize(*iscol_d, i));
3224: /* Get isrow_d */
3225: PetscCall(ISGetLocalSize(isrow, &m));
3226: rstart = mat->rmap->rstart;
3227: PetscCall(PetscMalloc1(m, &idx));
3228: PetscCall(ISGetIndices(isrow, &is_idx));
3229: for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3230: PetscCall(ISRestoreIndices(isrow, &is_idx));
3232: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3233: PetscCall(ISGetBlockSize(isrow, &i));
3234: PetscCall(ISSetBlockSize(*isrow_d, i));
3236: /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3237: PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3238: PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3240: PetscCall(VecDuplicate(lvec, &lcmap));
3242: PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3243: PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3245: /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3246: /* off-process column indices */
3247: count = 0;
3248: PetscCall(PetscMalloc1(Bn, &idx));
3249: PetscCall(PetscMalloc1(Bn, &cmap1));
3251: PetscCall(VecGetArray(lvec, &xarray));
3252: PetscCall(VecGetArray(lcmap, &cmaparray));
3253: for (i = 0; i < Bn; i++) {
3254: if (PetscRealPart(xarray[i]) > -1.0) {
3255: idx[count] = i; /* local column index in off-diagonal part B */
3256: cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3257: count++;
3258: }
3259: }
3260: PetscCall(VecRestoreArray(lvec, &xarray));
3261: PetscCall(VecRestoreArray(lcmap, &cmaparray));
3263: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3264: /* cannot ensure iscol_o has same blocksize as iscol! */
3266: PetscCall(PetscFree(idx));
3267: *garray = cmap1;
3269: PetscCall(VecDestroy(&x));
3270: PetscCall(VecDestroy(&cmap));
3271: PetscCall(VecDestroy(&lcmap));
3272: PetscFunctionReturn(PETSC_SUCCESS);
3273: }
3275: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3276: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3277: {
3278: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3279: Mat M = NULL;
3280: MPI_Comm comm;
3281: IS iscol_d, isrow_d, iscol_o;
3282: Mat Asub = NULL, Bsub = NULL;
3283: PetscInt n, count, M_size, N_size;
3285: PetscFunctionBegin;
3286: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3288: if (call == MAT_REUSE_MATRIX) {
3289: /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3290: PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3291: PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");
3293: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3294: PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");
3296: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3297: PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");
3299: /* Update diagonal and off-diagonal portions of submat */
3300: asub = (Mat_MPIAIJ *)(*submat)->data;
3301: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3302: PetscCall(ISGetLocalSize(iscol_o, &n));
3303: if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3304: PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3305: PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));
3307: } else { /* call == MAT_INITIAL_MATRIX) */
3308: PetscInt *garray, *garray_compact;
3309: PetscInt BsubN;
3311: /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3312: PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));
3314: /* Create local submatrices Asub and Bsub */
3315: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3316: PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));
3318: // Compact garray so its not of size Bn
3319: PetscCall(ISGetSize(iscol_o, &count));
3320: PetscCall(PetscMalloc1(count, &garray_compact));
3321: PetscCall(PetscArraycpy(garray_compact, garray, count));
3323: /* Create submatrix M */
3324: PetscCall(ISGetSize(isrow, &M_size));
3325: PetscCall(ISGetSize(iscol, &N_size));
3326: PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, M_size, N_size, Asub, Bsub, garray_compact, &M));
3328: /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3329: asub = (Mat_MPIAIJ *)M->data;
3331: PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3332: n = asub->B->cmap->N;
3333: if (BsubN > n) {
3334: /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3335: const PetscInt *idx;
3336: PetscInt i, j, *idx_new, *subgarray = asub->garray;
3337: PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));
3339: PetscCall(PetscMalloc1(n, &idx_new));
3340: j = 0;
3341: PetscCall(ISGetIndices(iscol_o, &idx));
3342: for (i = 0; i < n; i++) {
3343: if (j >= BsubN) break;
3344: while (subgarray[i] > garray[j]) j++;
3346: if (subgarray[i] == garray[j]) {
3347: idx_new[i] = idx[j++];
3348: } 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]);
3349: }
3350: PetscCall(ISRestoreIndices(iscol_o, &idx));
3352: PetscCall(ISDestroy(&iscol_o));
3353: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));
3355: } else if (BsubN < n) {
3356: 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);
3357: }
3359: PetscCall(PetscFree(garray));
3360: *submat = M;
3362: /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3363: PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3364: PetscCall(ISDestroy(&isrow_d));
3366: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3367: PetscCall(ISDestroy(&iscol_d));
3369: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3370: PetscCall(ISDestroy(&iscol_o));
3371: }
3372: PetscFunctionReturn(PETSC_SUCCESS);
3373: }
3375: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3376: {
3377: IS iscol_local = NULL, isrow_d;
3378: PetscInt csize;
3379: PetscInt n, i, j, start, end;
3380: PetscBool sameRowDist = PETSC_FALSE, sameDist[2], tsameDist[2];
3381: MPI_Comm comm;
3383: PetscFunctionBegin;
3384: /* If isrow has same processor distribution as mat,
3385: call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3386: if (call == MAT_REUSE_MATRIX) {
3387: PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3388: if (isrow_d) {
3389: sameRowDist = PETSC_TRUE;
3390: tsameDist[1] = PETSC_TRUE; /* sameColDist */
3391: } else {
3392: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3393: if (iscol_local) {
3394: sameRowDist = PETSC_TRUE;
3395: tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3396: }
3397: }
3398: } else {
3399: /* Check if isrow has same processor distribution as mat */
3400: sameDist[0] = PETSC_FALSE;
3401: PetscCall(ISGetLocalSize(isrow, &n));
3402: if (!n) {
3403: sameDist[0] = PETSC_TRUE;
3404: } else {
3405: PetscCall(ISGetMinMax(isrow, &i, &j));
3406: PetscCall(MatGetOwnershipRange(mat, &start, &end));
3407: if (i >= start && j < end) sameDist[0] = PETSC_TRUE;
3408: }
3410: /* Check if iscol has same processor distribution as mat */
3411: sameDist[1] = PETSC_FALSE;
3412: PetscCall(ISGetLocalSize(iscol, &n));
3413: if (!n) {
3414: sameDist[1] = PETSC_TRUE;
3415: } else {
3416: PetscCall(ISGetMinMax(iscol, &i, &j));
3417: PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3418: if (i >= start && j < end) sameDist[1] = PETSC_TRUE;
3419: }
3421: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3422: PetscCallMPI(MPIU_Allreduce(&sameDist, &tsameDist, 2, MPIU_BOOL, MPI_LAND, comm));
3423: sameRowDist = tsameDist[0];
3424: }
3426: if (sameRowDist) {
3427: if (tsameDist[1]) { /* sameRowDist & sameColDist */
3428: /* isrow and iscol have same processor distribution as mat */
3429: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3430: PetscFunctionReturn(PETSC_SUCCESS);
3431: } else { /* sameRowDist */
3432: /* isrow has same processor distribution as mat */
3433: if (call == MAT_INITIAL_MATRIX) {
3434: PetscBool sorted;
3435: PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3436: PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3437: PetscCall(ISGetSize(iscol, &i));
3438: PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);
3440: PetscCall(ISSorted(iscol_local, &sorted));
3441: if (sorted) {
3442: /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3443: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3444: PetscFunctionReturn(PETSC_SUCCESS);
3445: }
3446: } else { /* call == MAT_REUSE_MATRIX */
3447: IS iscol_sub;
3448: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3449: if (iscol_sub) {
3450: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3451: PetscFunctionReturn(PETSC_SUCCESS);
3452: }
3453: }
3454: }
3455: }
3457: /* General case: iscol -> iscol_local which has global size of iscol */
3458: if (call == MAT_REUSE_MATRIX) {
3459: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3460: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3461: } else {
3462: if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3463: }
3465: PetscCall(ISGetLocalSize(iscol, &csize));
3466: PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));
3468: if (call == MAT_INITIAL_MATRIX) {
3469: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3470: PetscCall(ISDestroy(&iscol_local));
3471: }
3472: PetscFunctionReturn(PETSC_SUCCESS);
3473: }
3475: /*@C
3476: MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3477: and "off-diagonal" part of the matrix in CSR format.
3479: Collective
3481: Input Parameters:
3482: + comm - MPI communicator
3483: . M - the global row size
3484: . N - the global column size
3485: . A - "diagonal" portion of matrix
3486: . B - if garray is `NULL`, B should be the offdiag matrix using global col ids and of size N - if garray is not `NULL`, B should be the offdiag matrix using local col ids and of size garray
3487: - garray - either `NULL` or the global index of `B` columns
3489: Output Parameter:
3490: . mat - the matrix, with input `A` as its local diagonal matrix
3492: Level: advanced
3494: Notes:
3495: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.
3497: `A` and `B` becomes part of output mat. The user cannot use `A` and `B` anymore.
3499: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3500: @*/
3501: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, PetscInt M, PetscInt N, Mat A, Mat B, PetscInt *garray, Mat *mat)
3502: {
3503: PetscInt m, 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);
3512: PetscCall(MatSetSizes(*mat, m, n, M, N));
3513: /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3514: PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3515: PetscCall(MatSetType(*mat, mpi_mat_type));
3517: if (A->rmap->bs > 1 || A->cmap->bs > 1) PetscCall(MatSetBlockSizes(*mat, A->rmap->bs, A->cmap->bs));
3519: PetscCall(PetscLayoutSetUp((*mat)->rmap));
3520: PetscCall(PetscLayoutSetUp((*mat)->cmap));
3521: PetscCall(MatSetMPIAIJWithSplitSeqAIJ(*mat, A, B, garray));
3522: PetscFunctionReturn(PETSC_SUCCESS);
3523: }
3525: /*
3526: MatSetMPIAIJWithSplitSeqAIJ - Set the diag and offdiag matrices of a `MATMPIAIJ` matrix.
3527: It is similar to `MatCreateMPIAIJWithSplitArrays()`. This routine allows passing in
3528: B with local indices and the correct size, along with the accompanying
3529: garray, hence skipping compactification
3531: Collective
3533: Input Parameters:
3534: + mat - the MATMPIAIJ matrix, which should have its type and layout set, but should not have its diag, offdiag matrices set
3535: . A - the diag matrix using local col ids
3536: . B - if garray is `NULL`, B should be the offdiag matrix using global col ids and of size N - if garray is not `NULL`, B should be the offdiag matrix using local col ids and of size garray
3537: - garray - either `NULL` or the global index of `B` columns
3539: Output Parameter:
3540: . mat - the updated `MATMPIAIJ` matrix
3542: Level: advanced
3544: Notes:
3545: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.
3547: `A` and `B` become part of output mat. The user cannot use `A` and `B` anymore.
3549: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3550: */
3551: PETSC_INTERN PetscErrorCode MatSetMPIAIJWithSplitSeqAIJ(Mat mat, Mat A, Mat B, PetscInt *garray)
3552: {
3553: PetscFunctionBegin;
3554: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
3555: PetscInt m, n, M, N, Am, An, Bm, Bn;
3557: PetscCall(MatGetSize(mat, &M, &N));
3558: PetscCall(MatGetLocalSize(mat, &m, &n));
3559: PetscCall(MatGetLocalSize(A, &Am, &An));
3560: PetscCall(MatGetLocalSize(B, &Bm, &Bn));
3562: PetscCheck(m == Am && m == Bm, PETSC_COMM_SELF, PETSC_ERR_PLIB, "local number of rows do not match");
3563: PetscCheck(n == An, PETSC_COMM_SELF, PETSC_ERR_PLIB, "local number of columns do not match");
3564: PetscCheck(!mpiaij->A && !mpiaij->B, PETSC_COMM_SELF, PETSC_ERR_PLIB, "A, B of the MPIAIJ matrix are not empty");
3565: mpiaij->A = A;
3566: mpiaij->B = B;
3567: mpiaij->garray = garray;
3569: mat->preallocated = PETSC_TRUE;
3570: mat->nooffprocentries = PETSC_TRUE; /* See MatAssemblyBegin_MPIAIJ. In effect, making MatAssemblyBegin a nop */
3572: PetscCall(MatSetOption(mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3573: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
3574: /* MatAssemblyEnd is critical here. It sets mat->offloadmask according to A and B's, and
3575: also gets mpiaij->B compacted (if garray is NULL), with its col ids and size reduced
3576: */
3577: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
3578: PetscCall(MatSetOption(mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
3579: PetscCall(MatSetOption(mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3580: PetscFunctionReturn(PETSC_SUCCESS);
3581: }
3583: extern PetscErrorCode MatCreateSubMatrices_MPIAIJ_SingleIS_Local(Mat, PetscInt, const IS[], const IS[], MatReuse, PetscBool, Mat *);
3585: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowDist(Mat mat, IS isrow, IS iscol, IS iscol_local, MatReuse call, Mat *newmat)
3586: {
3587: PetscInt i, m, n, rstart, row, rend, nz, j, bs, cbs;
3588: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3589: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3590: Mat M, Msub, B = a->B;
3591: MatScalar *aa;
3592: Mat_SeqAIJ *aij;
3593: PetscInt *garray = a->garray, *colsub, Ncols;
3594: PetscInt count, Bn = B->cmap->N, cstart = mat->cmap->rstart, cend = mat->cmap->rend;
3595: IS iscol_sub, iscmap;
3596: const PetscInt *is_idx, *cmap;
3597: PetscBool allcolumns = PETSC_FALSE;
3598: MPI_Comm comm;
3600: PetscFunctionBegin;
3601: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3602: if (call == MAT_REUSE_MATRIX) {
3603: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3604: PetscCheck(iscol_sub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "SubIScol passed in was not used before, cannot reuse");
3605: PetscCall(ISGetLocalSize(iscol_sub, &count));
3607: PetscCall(PetscObjectQuery((PetscObject)*newmat, "Subcmap", (PetscObject *)&iscmap));
3608: PetscCheck(iscmap, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Subcmap passed in was not used before, cannot reuse");
3610: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Msub));
3611: PetscCheck(Msub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3613: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_REUSE_MATRIX, PETSC_FALSE, &Msub));
3615: } else { /* call == MAT_INITIAL_MATRIX) */
3616: PetscBool flg;
3618: PetscCall(ISGetLocalSize(iscol, &n));
3619: PetscCall(ISGetSize(iscol, &Ncols));
3621: /* (1) iscol -> nonscalable iscol_local */
3622: /* Check for special case: each processor gets entire matrix columns */
3623: PetscCall(ISIdentity(iscol_local, &flg));
3624: if (flg && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3625: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3626: if (allcolumns) {
3627: iscol_sub = iscol_local;
3628: PetscCall(PetscObjectReference((PetscObject)iscol_local));
3629: PetscCall(ISCreateStride(PETSC_COMM_SELF, n, 0, 1, &iscmap));
3631: } else {
3632: /* (2) iscol_local -> iscol_sub and iscmap. Implementation below requires iscol_local be sorted, it can have duplicate indices */
3633: PetscInt *idx, *cmap1, k;
3634: PetscCall(PetscMalloc1(Ncols, &idx));
3635: PetscCall(PetscMalloc1(Ncols, &cmap1));
3636: PetscCall(ISGetIndices(iscol_local, &is_idx));
3637: count = 0;
3638: k = 0;
3639: for (i = 0; i < Ncols; i++) {
3640: j = is_idx[i];
3641: if (j >= cstart && j < cend) {
3642: /* diagonal part of mat */
3643: idx[count] = j;
3644: cmap1[count++] = i; /* column index in submat */
3645: } else if (Bn) {
3646: /* off-diagonal part of mat */
3647: if (j == garray[k]) {
3648: idx[count] = j;
3649: cmap1[count++] = i; /* column index in submat */
3650: } else if (j > garray[k]) {
3651: while (j > garray[k] && k < Bn - 1) k++;
3652: if (j == garray[k]) {
3653: idx[count] = j;
3654: cmap1[count++] = i; /* column index in submat */
3655: }
3656: }
3657: }
3658: }
3659: PetscCall(ISRestoreIndices(iscol_local, &is_idx));
3661: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_OWN_POINTER, &iscol_sub));
3662: PetscCall(ISGetBlockSize(iscol, &cbs));
3663: PetscCall(ISSetBlockSize(iscol_sub, cbs));
3665: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)iscol_local), count, cmap1, PETSC_OWN_POINTER, &iscmap));
3666: }
3668: /* (3) Create sequential Msub */
3669: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_INITIAL_MATRIX, allcolumns, &Msub));
3670: }
3672: PetscCall(ISGetLocalSize(iscol_sub, &count));
3673: aij = (Mat_SeqAIJ *)Msub->data;
3674: ii = aij->i;
3675: PetscCall(ISGetIndices(iscmap, &cmap));
3677: /*
3678: m - number of local rows
3679: Ncols - number of columns (same on all processors)
3680: rstart - first row in new global matrix generated
3681: */
3682: PetscCall(MatGetSize(Msub, &m, NULL));
3684: if (call == MAT_INITIAL_MATRIX) {
3685: /* (4) Create parallel newmat */
3686: PetscMPIInt rank, size;
3687: PetscInt csize;
3689: PetscCallMPI(MPI_Comm_size(comm, &size));
3690: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3692: /*
3693: Determine the number of non-zeros in the diagonal and off-diagonal
3694: portions of the matrix in order to do correct preallocation
3695: */
3697: /* first get start and end of "diagonal" columns */
3698: PetscCall(ISGetLocalSize(iscol, &csize));
3699: if (csize == PETSC_DECIDE) {
3700: PetscCall(ISGetSize(isrow, &mglobal));
3701: if (mglobal == Ncols) { /* square matrix */
3702: nlocal = m;
3703: } else {
3704: nlocal = Ncols / size + ((Ncols % size) > rank);
3705: }
3706: } else {
3707: nlocal = csize;
3708: }
3709: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3710: rstart = rend - nlocal;
3711: 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);
3713: /* next, compute all the lengths */
3714: jj = aij->j;
3715: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3716: olens = dlens + m;
3717: for (i = 0; i < m; i++) {
3718: jend = ii[i + 1] - ii[i];
3719: olen = 0;
3720: dlen = 0;
3721: for (j = 0; j < jend; j++) {
3722: if (cmap[*jj] < rstart || cmap[*jj] >= rend) olen++;
3723: else dlen++;
3724: jj++;
3725: }
3726: olens[i] = olen;
3727: dlens[i] = dlen;
3728: }
3730: PetscCall(ISGetBlockSize(isrow, &bs));
3731: PetscCall(ISGetBlockSize(iscol, &cbs));
3733: PetscCall(MatCreate(comm, &M));
3734: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, Ncols));
3735: PetscCall(MatSetBlockSizes(M, bs, cbs));
3736: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3737: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3738: PetscCall(PetscFree(dlens));
3740: } else { /* call == MAT_REUSE_MATRIX */
3741: M = *newmat;
3742: PetscCall(MatGetLocalSize(M, &i, NULL));
3743: PetscCheck(i == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3744: PetscCall(MatZeroEntries(M));
3745: /*
3746: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3747: rather than the slower MatSetValues().
3748: */
3749: M->was_assembled = PETSC_TRUE;
3750: M->assembled = PETSC_FALSE;
3751: }
3753: /* (5) Set values of Msub to *newmat */
3754: PetscCall(PetscMalloc1(count, &colsub));
3755: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
3757: jj = aij->j;
3758: PetscCall(MatSeqAIJGetArrayRead(Msub, (const PetscScalar **)&aa));
3759: for (i = 0; i < m; i++) {
3760: row = rstart + i;
3761: nz = ii[i + 1] - ii[i];
3762: for (j = 0; j < nz; j++) colsub[j] = cmap[jj[j]];
3763: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, colsub, aa, INSERT_VALUES));
3764: jj += nz;
3765: aa += nz;
3766: }
3767: PetscCall(MatSeqAIJRestoreArrayRead(Msub, (const PetscScalar **)&aa));
3768: PetscCall(ISRestoreIndices(iscmap, &cmap));
3770: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3771: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3773: PetscCall(PetscFree(colsub));
3775: /* save Msub, iscol_sub and iscmap used in processor for next request */
3776: if (call == MAT_INITIAL_MATRIX) {
3777: *newmat = M;
3778: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubMatrix", (PetscObject)Msub));
3779: PetscCall(MatDestroy(&Msub));
3781: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubIScol", (PetscObject)iscol_sub));
3782: PetscCall(ISDestroy(&iscol_sub));
3784: PetscCall(PetscObjectCompose((PetscObject)*newmat, "Subcmap", (PetscObject)iscmap));
3785: PetscCall(ISDestroy(&iscmap));
3787: if (iscol_local) {
3788: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3789: PetscCall(ISDestroy(&iscol_local));
3790: }
3791: }
3792: PetscFunctionReturn(PETSC_SUCCESS);
3793: }
3795: /*
3796: Not great since it makes two copies of the submatrix, first an SeqAIJ
3797: in local and then by concatenating the local matrices the end result.
3798: Writing it directly would be much like MatCreateSubMatrices_MPIAIJ()
3800: This requires a sequential iscol with all indices.
3801: */
3802: PetscErrorCode MatCreateSubMatrix_MPIAIJ_nonscalable(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
3803: {
3804: PetscMPIInt rank, size;
3805: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs, cbs;
3806: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3807: Mat M, Mreuse;
3808: MatScalar *aa, *vwork;
3809: MPI_Comm comm;
3810: Mat_SeqAIJ *aij;
3811: PetscBool colflag, allcolumns = PETSC_FALSE;
3813: PetscFunctionBegin;
3814: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3815: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3816: PetscCallMPI(MPI_Comm_size(comm, &size));
3818: /* Check for special case: each processor gets entire matrix columns */
3819: PetscCall(ISIdentity(iscol, &colflag));
3820: PetscCall(ISGetLocalSize(iscol, &n));
3821: if (colflag && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3822: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3824: if (call == MAT_REUSE_MATRIX) {
3825: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
3826: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3827: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_REUSE_MATRIX, allcolumns, &Mreuse));
3828: } else {
3829: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_INITIAL_MATRIX, allcolumns, &Mreuse));
3830: }
3832: /*
3833: m - number of local rows
3834: n - number of columns (same on all processors)
3835: rstart - first row in new global matrix generated
3836: */
3837: PetscCall(MatGetSize(Mreuse, &m, &n));
3838: PetscCall(MatGetBlockSizes(Mreuse, &bs, &cbs));
3839: if (call == MAT_INITIAL_MATRIX) {
3840: aij = (Mat_SeqAIJ *)Mreuse->data;
3841: ii = aij->i;
3842: jj = aij->j;
3844: /*
3845: Determine the number of non-zeros in the diagonal and off-diagonal
3846: portions of the matrix in order to do correct preallocation
3847: */
3849: /* first get start and end of "diagonal" columns */
3850: if (csize == PETSC_DECIDE) {
3851: PetscCall(ISGetSize(isrow, &mglobal));
3852: if (mglobal == n) { /* square matrix */
3853: nlocal = m;
3854: } else {
3855: nlocal = n / size + ((n % size) > rank);
3856: }
3857: } else {
3858: nlocal = csize;
3859: }
3860: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3861: rstart = rend - nlocal;
3862: 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);
3864: /* next, compute all the lengths */
3865: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3866: olens = dlens + m;
3867: for (i = 0; i < m; i++) {
3868: jend = ii[i + 1] - ii[i];
3869: olen = 0;
3870: dlen = 0;
3871: for (j = 0; j < jend; j++) {
3872: if (*jj < rstart || *jj >= rend) olen++;
3873: else dlen++;
3874: jj++;
3875: }
3876: olens[i] = olen;
3877: dlens[i] = dlen;
3878: }
3879: PetscCall(MatCreate(comm, &M));
3880: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, n));
3881: PetscCall(MatSetBlockSizes(M, bs, cbs));
3882: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3883: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3884: PetscCall(PetscFree(dlens));
3885: } else {
3886: PetscInt ml, nl;
3888: M = *newmat;
3889: PetscCall(MatGetLocalSize(M, &ml, &nl));
3890: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3891: PetscCall(MatZeroEntries(M));
3892: /*
3893: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3894: rather than the slower MatSetValues().
3895: */
3896: M->was_assembled = PETSC_TRUE;
3897: M->assembled = PETSC_FALSE;
3898: }
3899: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
3900: aij = (Mat_SeqAIJ *)Mreuse->data;
3901: ii = aij->i;
3902: jj = aij->j;
3904: /* trigger copy to CPU if needed */
3905: PetscCall(MatSeqAIJGetArrayRead(Mreuse, (const PetscScalar **)&aa));
3906: for (i = 0; i < m; i++) {
3907: row = rstart + i;
3908: nz = ii[i + 1] - ii[i];
3909: cwork = jj;
3910: jj = PetscSafePointerPlusOffset(jj, nz);
3911: vwork = aa;
3912: aa = PetscSafePointerPlusOffset(aa, nz);
3913: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
3914: }
3915: PetscCall(MatSeqAIJRestoreArrayRead(Mreuse, (const PetscScalar **)&aa));
3917: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3918: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3919: *newmat = M;
3921: /* save submatrix used in processor for next request */
3922: if (call == MAT_INITIAL_MATRIX) {
3923: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
3924: PetscCall(MatDestroy(&Mreuse));
3925: }
3926: PetscFunctionReturn(PETSC_SUCCESS);
3927: }
3929: static PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
3930: {
3931: PetscInt m, cstart, cend, j, nnz, i, d, *ld;
3932: PetscInt *d_nnz, *o_nnz, nnz_max = 0, rstart, ii, irstart;
3933: const PetscInt *JJ;
3934: PetscBool nooffprocentries;
3935: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)B->data;
3937: PetscFunctionBegin;
3938: PetscCall(PetscLayoutSetUp(B->rmap));
3939: PetscCall(PetscLayoutSetUp(B->cmap));
3940: m = B->rmap->n;
3941: cstart = B->cmap->rstart;
3942: cend = B->cmap->rend;
3943: rstart = B->rmap->rstart;
3944: irstart = Ii[0];
3946: PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));
3948: if (PetscDefined(USE_DEBUG)) {
3949: for (i = 0; i < m; i++) {
3950: nnz = Ii[i + 1] - Ii[i];
3951: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3952: PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3953: 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]);
3954: 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);
3955: }
3956: }
3958: for (i = 0; i < m; i++) {
3959: nnz = Ii[i + 1] - Ii[i];
3960: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3961: nnz_max = PetscMax(nnz_max, nnz);
3962: d = 0;
3963: for (j = 0; j < nnz; j++) {
3964: if (cstart <= JJ[j] && JJ[j] < cend) d++;
3965: }
3966: d_nnz[i] = d;
3967: o_nnz[i] = nnz - d;
3968: }
3969: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3970: PetscCall(PetscFree2(d_nnz, o_nnz));
3972: for (i = 0; i < m; i++) {
3973: ii = i + rstart;
3974: PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], PetscSafePointerPlusOffset(J, Ii[i] - irstart), PetscSafePointerPlusOffset(v, Ii[i] - irstart), INSERT_VALUES));
3975: }
3976: nooffprocentries = B->nooffprocentries;
3977: B->nooffprocentries = PETSC_TRUE;
3978: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3979: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3980: B->nooffprocentries = nooffprocentries;
3982: /* count number of entries below block diagonal */
3983: PetscCall(PetscFree(Aij->ld));
3984: PetscCall(PetscCalloc1(m, &ld));
3985: Aij->ld = ld;
3986: for (i = 0; i < m; i++) {
3987: nnz = Ii[i + 1] - Ii[i];
3988: j = 0;
3989: while (j < nnz && J[j] < cstart) j++;
3990: ld[i] = j;
3991: if (J) J += nnz;
3992: }
3994: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3995: PetscFunctionReturn(PETSC_SUCCESS);
3996: }
3998: /*@
3999: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
4000: (the default parallel PETSc format).
4002: Collective
4004: Input Parameters:
4005: + B - the matrix
4006: . i - the indices into `j` for the start of each local row (indices start with zero)
4007: . j - the column indices for each local row (indices start with zero)
4008: - v - optional values in the matrix
4010: Level: developer
4012: Notes:
4013: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
4014: thus you CANNOT change the matrix entries by changing the values of `v` after you have
4015: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
4017: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
4019: A convenience routine for this functionality is `MatCreateMPIAIJWithArrays()`.
4021: You can update the matrix with new numerical values using `MatUpdateMPIAIJWithArrays()` after this call if the column indices in `j` are sorted.
4023: If you do **not** use `MatUpdateMPIAIJWithArrays()`, the column indices in `j` do not need to be sorted. If you will use
4024: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
4026: The format which is used for the sparse matrix input, is equivalent to a
4027: row-major ordering.. i.e for the following matrix, the input data expected is
4028: as shown
4029: .vb
4030: 1 0 0
4031: 2 0 3 P0
4032: -------
4033: 4 5 6 P1
4035: Process0 [P0] rows_owned=[0,1]
4036: i = {0,1,3} [size = nrow+1 = 2+1]
4037: j = {0,0,2} [size = 3]
4038: v = {1,2,3} [size = 3]
4040: Process1 [P1] rows_owned=[2]
4041: i = {0,3} [size = nrow+1 = 1+1]
4042: j = {0,1,2} [size = 3]
4043: v = {4,5,6} [size = 3]
4044: .ve
4046: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`,
4047: `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`, `MatCreateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4048: @*/
4049: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4050: {
4051: PetscFunctionBegin;
4052: PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4053: PetscFunctionReturn(PETSC_SUCCESS);
4054: }
4056: /*@
4057: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
4058: (the default parallel PETSc format). For good matrix assembly performance
4059: the user should preallocate the matrix storage by setting the parameters
4060: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4062: Collective
4064: Input Parameters:
4065: + B - the matrix
4066: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4067: (same value is used for all local rows)
4068: . d_nnz - array containing the number of nonzeros in the various rows of the
4069: DIAGONAL portion of the local submatrix (possibly different for each row)
4070: or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4071: The size of this array is equal to the number of local rows, i.e 'm'.
4072: For matrices that will be factored, you must leave room for (and set)
4073: the diagonal entry even if it is zero.
4074: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4075: submatrix (same value is used for all local rows).
4076: - o_nnz - array containing the number of nonzeros in the various rows of the
4077: OFF-DIAGONAL portion of the local submatrix (possibly different for
4078: each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4079: structure. The size of this array is equal to the number
4080: of local rows, i.e 'm'.
4082: Example Usage:
4083: Consider the following 8x8 matrix with 34 non-zero values, that is
4084: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4085: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4086: as follows
4088: .vb
4089: 1 2 0 | 0 3 0 | 0 4
4090: Proc0 0 5 6 | 7 0 0 | 8 0
4091: 9 0 10 | 11 0 0 | 12 0
4092: -------------------------------------
4093: 13 0 14 | 15 16 17 | 0 0
4094: Proc1 0 18 0 | 19 20 21 | 0 0
4095: 0 0 0 | 22 23 0 | 24 0
4096: -------------------------------------
4097: Proc2 25 26 27 | 0 0 28 | 29 0
4098: 30 0 0 | 31 32 33 | 0 34
4099: .ve
4101: This can be represented as a collection of submatrices as
4102: .vb
4103: A B C
4104: D E F
4105: G H I
4106: .ve
4108: Where the submatrices A,B,C are owned by proc0, D,E,F are
4109: owned by proc1, G,H,I are owned by proc2.
4111: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4112: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4113: The 'M','N' parameters are 8,8, and have the same values on all procs.
4115: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4116: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4117: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4118: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4119: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4120: matrix, and [DF] as another `MATSEQAIJ` matrix.
4122: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4123: allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4124: storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4125: One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4126: the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4127: In this case, the values of `d_nz`, `o_nz` are
4128: .vb
4129: proc0 dnz = 2, o_nz = 2
4130: proc1 dnz = 3, o_nz = 2
4131: proc2 dnz = 1, o_nz = 4
4132: .ve
4133: We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4134: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4135: for proc3. i.e we are using 12+15+10=37 storage locations to store
4136: 34 values.
4138: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4139: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4140: In the above case the values for `d_nnz`, `o_nnz` are
4141: .vb
4142: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4143: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4144: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4145: .ve
4146: Here the space allocated is sum of all the above values i.e 34, and
4147: hence pre-allocation is perfect.
4149: Level: intermediate
4151: Notes:
4152: If the *_nnz parameter is given then the *_nz parameter is ignored
4154: The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4155: storage. The stored row and column indices begin with zero.
4156: See [Sparse Matrices](sec_matsparse) for details.
4158: The parallel matrix is partitioned such that the first m0 rows belong to
4159: process 0, the next m1 rows belong to process 1, the next m2 rows belong
4160: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
4162: The DIAGONAL portion of the local submatrix of a processor can be defined
4163: as the submatrix which is obtained by extraction the part corresponding to
4164: the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4165: first row that belongs to the processor, r2 is the last row belonging to
4166: the this processor, and c1-c2 is range of indices of the local part of a
4167: vector suitable for applying the matrix to. This is an mxn matrix. In the
4168: common case of a square matrix, the row and column ranges are the same and
4169: the DIAGONAL part is also square. The remaining portion of the local
4170: submatrix (mxN) constitute the OFF-DIAGONAL portion.
4172: If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.
4174: You can call `MatGetInfo()` to get information on how effective the preallocation was;
4175: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4176: You can also run with the option `-info` and look for messages with the string
4177: malloc in them to see if additional memory allocation was needed.
4179: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4180: `MatGetInfo()`, `PetscSplitOwnership()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4181: @*/
4182: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4183: {
4184: PetscFunctionBegin;
4187: PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4188: PetscFunctionReturn(PETSC_SUCCESS);
4189: }
4191: /*@
4192: MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4193: CSR format for the local rows.
4195: Collective
4197: Input Parameters:
4198: + comm - MPI communicator
4199: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4200: . n - This value should be the same as the local size used in creating the
4201: x vector for the matrix-vector product $ y = Ax$. (or `PETSC_DECIDE` to have
4202: calculated if `N` is given) For square matrices n is almost always `m`.
4203: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
4204: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
4205: . 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
4206: . j - global column indices
4207: - a - optional matrix values
4209: Output Parameter:
4210: . mat - the matrix
4212: Level: intermediate
4214: Notes:
4215: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4216: thus you CANNOT change the matrix entries by changing the values of `a[]` after you have
4217: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
4219: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
4221: Once you have created the matrix you can update it with new numerical values using `MatUpdateMPIAIJWithArray()`
4223: If you do **not** use `MatUpdateMPIAIJWithArray()`, the column indices in `j` do not need to be sorted. If you will use
4224: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
4226: The format which is used for the sparse matrix input, is equivalent to a
4227: row-major ordering, i.e., for the following matrix, the input data expected is
4228: as shown
4229: .vb
4230: 1 0 0
4231: 2 0 3 P0
4232: -------
4233: 4 5 6 P1
4235: Process0 [P0] rows_owned=[0,1]
4236: i = {0,1,3} [size = nrow+1 = 2+1]
4237: j = {0,0,2} [size = 3]
4238: v = {1,2,3} [size = 3]
4240: Process1 [P1] rows_owned=[2]
4241: i = {0,3} [size = nrow+1 = 1+1]
4242: j = {0,1,2} [size = 3]
4243: v = {4,5,6} [size = 3]
4244: .ve
4246: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4247: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4248: @*/
4249: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
4250: {
4251: PetscFunctionBegin;
4252: PetscCheck(!i || !i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4253: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4254: PetscCall(MatCreate(comm, mat));
4255: PetscCall(MatSetSizes(*mat, m, n, M, N));
4256: /* PetscCall(MatSetBlockSizes(M,bs,cbs)); */
4257: PetscCall(MatSetType(*mat, MATMPIAIJ));
4258: PetscCall(MatMPIAIJSetPreallocationCSR(*mat, i, j, a));
4259: PetscFunctionReturn(PETSC_SUCCESS);
4260: }
4262: /*@
4263: MatUpdateMPIAIJWithArrays - updates a `MATMPIAIJ` matrix using arrays that contain in standard
4264: CSR format for the local rows. Only the numerical values are updated the other arrays must be identical to what was passed
4265: from `MatCreateMPIAIJWithArrays()`
4267: Deprecated: Use `MatUpdateMPIAIJWithArray()`
4269: Collective
4271: Input Parameters:
4272: + mat - the matrix
4273: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4274: . n - This value should be the same as the local size used in creating the
4275: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4276: calculated if N is given) For square matrices n is almost always m.
4277: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4278: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4279: . Ii - row indices; that is Ii[0] = 0, Ii[row] = Ii[row-1] + number of elements in that row of the matrix
4280: . J - column indices
4281: - v - matrix values
4283: Level: deprecated
4285: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4286: `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4287: @*/
4288: PetscErrorCode MatUpdateMPIAIJWithArrays(Mat mat, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4289: {
4290: PetscInt nnz, i;
4291: PetscBool nooffprocentries;
4292: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4293: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4294: PetscScalar *ad, *ao;
4295: PetscInt ldi, Iii, md;
4296: const PetscInt *Adi = Ad->i;
4297: PetscInt *ld = Aij->ld;
4299: PetscFunctionBegin;
4300: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4301: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4302: PetscCheck(m == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of rows cannot change from call to MatUpdateMPIAIJWithArrays()");
4303: PetscCheck(n == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of columns cannot change from call to MatUpdateMPIAIJWithArrays()");
4305: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4306: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4308: for (i = 0; i < m; i++) {
4309: if (PetscDefined(USE_DEBUG)) {
4310: for (PetscInt j = Ii[i] + 1; j < Ii[i + 1]; ++j) {
4311: 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);
4312: 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);
4313: }
4314: }
4315: nnz = Ii[i + 1] - Ii[i];
4316: Iii = Ii[i];
4317: ldi = ld[i];
4318: md = Adi[i + 1] - Adi[i];
4319: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4320: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4321: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4322: ad += md;
4323: ao += nnz - md;
4324: }
4325: nooffprocentries = mat->nooffprocentries;
4326: mat->nooffprocentries = PETSC_TRUE;
4327: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4328: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4329: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4330: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4331: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4332: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4333: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4334: mat->nooffprocentries = nooffprocentries;
4335: PetscFunctionReturn(PETSC_SUCCESS);
4336: }
4338: /*@
4339: MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values
4341: Collective
4343: Input Parameters:
4344: + mat - the matrix
4345: - v - matrix values, stored by row
4347: Level: intermediate
4349: Notes:
4350: The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`
4352: The column indices in the call to `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()` must have been sorted for this call to work correctly
4354: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4355: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4356: @*/
4357: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4358: {
4359: PetscInt nnz, i, m;
4360: PetscBool nooffprocentries;
4361: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4362: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4363: Mat_SeqAIJ *Ao = (Mat_SeqAIJ *)Aij->B->data;
4364: PetscScalar *ad, *ao;
4365: const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4366: PetscInt ldi, Iii, md;
4367: PetscInt *ld = Aij->ld;
4369: PetscFunctionBegin;
4370: m = mat->rmap->n;
4372: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4373: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4374: Iii = 0;
4375: for (i = 0; i < m; i++) {
4376: nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4377: ldi = ld[i];
4378: md = Adi[i + 1] - Adi[i];
4379: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4380: ad += md;
4381: if (ao) {
4382: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4383: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4384: ao += nnz - md;
4385: }
4386: Iii += nnz;
4387: }
4388: nooffprocentries = mat->nooffprocentries;
4389: mat->nooffprocentries = PETSC_TRUE;
4390: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4391: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4392: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4393: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4394: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4395: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4396: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4397: mat->nooffprocentries = nooffprocentries;
4398: PetscFunctionReturn(PETSC_SUCCESS);
4399: }
4401: /*@
4402: MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4403: (the default parallel PETSc format). For good matrix assembly performance
4404: the user should preallocate the matrix storage by setting the parameters
4405: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4407: Collective
4409: Input Parameters:
4410: + comm - MPI communicator
4411: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4412: This value should be the same as the local size used in creating the
4413: y vector for the matrix-vector product y = Ax.
4414: . n - This value should be the same as the local size used in creating the
4415: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4416: calculated if N is given) For square matrices n is almost always m.
4417: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4418: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4419: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4420: (same value is used for all local rows)
4421: . d_nnz - array containing the number of nonzeros in the various rows of the
4422: DIAGONAL portion of the local submatrix (possibly different for each row)
4423: or `NULL`, if `d_nz` is used to specify the nonzero structure.
4424: The size of this array is equal to the number of local rows, i.e 'm'.
4425: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4426: submatrix (same value is used for all local rows).
4427: - o_nnz - array containing the number of nonzeros in the various rows of the
4428: OFF-DIAGONAL portion of the local submatrix (possibly different for
4429: each row) or `NULL`, if `o_nz` is used to specify the nonzero
4430: structure. The size of this array is equal to the number
4431: of local rows, i.e 'm'.
4433: Output Parameter:
4434: . A - the matrix
4436: Options Database Keys:
4437: + -mat_no_inode - Do not use inodes
4438: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
4439: - -matmult_vecscatter_view <viewer> - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4440: See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the `VecScatter`
4441: 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.
4443: Level: intermediate
4445: Notes:
4446: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4447: MatXXXXSetPreallocation() paradigm instead of this routine directly.
4448: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
4450: If the *_nnz parameter is given then the *_nz parameter is ignored
4452: The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4453: processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4454: storage requirements for this matrix.
4456: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one
4457: processor than it must be used on all processors that share the object for
4458: that argument.
4460: If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by
4461: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`.
4463: The user MUST specify either the local or global matrix dimensions
4464: (possibly both).
4466: The parallel matrix is partitioned across processors such that the
4467: first `m0` rows belong to process 0, the next `m1` rows belong to
4468: process 1, the next `m2` rows belong to process 2, etc., where
4469: `m0`, `m1`, `m2`... are the input parameter `m` on each MPI process. I.e., each MPI process stores
4470: values corresponding to [m x N] submatrix.
4472: The columns are logically partitioned with the n0 columns belonging
4473: to 0th partition, the next n1 columns belonging to the next
4474: partition etc.. where n0,n1,n2... are the input parameter 'n'.
4476: The DIAGONAL portion of the local submatrix on any given processor
4477: is the submatrix corresponding to the rows and columns m,n
4478: corresponding to the given processor. i.e diagonal matrix on
4479: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4480: etc. The remaining portion of the local submatrix [m x (N-n)]
4481: constitute the OFF-DIAGONAL portion. The example below better
4482: illustrates this concept. The two matrices, the DIAGONAL portion and
4483: the OFF-DIAGONAL portion are each stored as `MATSEQAIJ` matrices.
4485: For a square global matrix we define each processor's diagonal portion
4486: to be its local rows and the corresponding columns (a square submatrix);
4487: each processor's off-diagonal portion encompasses the remainder of the
4488: local matrix (a rectangular submatrix).
4490: If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.
4492: When calling this routine with a single process communicator, a matrix of
4493: type `MATSEQAIJ` is returned. If a matrix of type `MATMPIAIJ` is desired for this
4494: type of communicator, use the construction mechanism
4495: .vb
4496: MatCreate(..., &A);
4497: MatSetType(A, MATMPIAIJ);
4498: MatSetSizes(A, m, n, M, N);
4499: MatMPIAIJSetPreallocation(A, ...);
4500: .ve
4502: By default, this format uses inodes (identical nodes) when possible.
4503: We search for consecutive rows with the same nonzero structure, thereby
4504: reusing matrix information to achieve increased efficiency.
4506: Example Usage:
4507: Consider the following 8x8 matrix with 34 non-zero values, that is
4508: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4509: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4510: as follows
4512: .vb
4513: 1 2 0 | 0 3 0 | 0 4
4514: Proc0 0 5 6 | 7 0 0 | 8 0
4515: 9 0 10 | 11 0 0 | 12 0
4516: -------------------------------------
4517: 13 0 14 | 15 16 17 | 0 0
4518: Proc1 0 18 0 | 19 20 21 | 0 0
4519: 0 0 0 | 22 23 0 | 24 0
4520: -------------------------------------
4521: Proc2 25 26 27 | 0 0 28 | 29 0
4522: 30 0 0 | 31 32 33 | 0 34
4523: .ve
4525: This can be represented as a collection of submatrices as
4527: .vb
4528: A B C
4529: D E F
4530: G H I
4531: .ve
4533: Where the submatrices A,B,C are owned by proc0, D,E,F are
4534: owned by proc1, G,H,I are owned by proc2.
4536: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4537: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4538: The 'M','N' parameters are 8,8, and have the same values on all procs.
4540: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4541: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4542: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4543: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4544: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4545: matrix, and [DF] as another SeqAIJ matrix.
4547: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4548: allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4549: storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4550: One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4551: the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4552: In this case, the values of `d_nz`,`o_nz` are
4553: .vb
4554: proc0 dnz = 2, o_nz = 2
4555: proc1 dnz = 3, o_nz = 2
4556: proc2 dnz = 1, o_nz = 4
4557: .ve
4558: We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4559: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4560: for proc3. i.e we are using 12+15+10=37 storage locations to store
4561: 34 values.
4563: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4564: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4565: In the above case the values for d_nnz,o_nnz are
4566: .vb
4567: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4568: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4569: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4570: .ve
4571: Here the space allocated is sum of all the above values i.e 34, and
4572: hence pre-allocation is perfect.
4574: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4575: `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`,
4576: `MatGetOwnershipRangesColumn()`, `PetscLayout`
4577: @*/
4578: 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)
4579: {
4580: PetscMPIInt size;
4582: PetscFunctionBegin;
4583: PetscCall(MatCreate(comm, A));
4584: PetscCall(MatSetSizes(*A, m, n, M, N));
4585: PetscCallMPI(MPI_Comm_size(comm, &size));
4586: if (size > 1) {
4587: PetscCall(MatSetType(*A, MATMPIAIJ));
4588: PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4589: } else {
4590: PetscCall(MatSetType(*A, MATSEQAIJ));
4591: PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4592: }
4593: PetscFunctionReturn(PETSC_SUCCESS);
4594: }
4596: /*@C
4597: MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix
4599: Not Collective
4601: Input Parameter:
4602: . A - The `MATMPIAIJ` matrix
4604: Output Parameters:
4605: + Ad - The local diagonal block as a `MATSEQAIJ` matrix
4606: . Ao - The local off-diagonal block as a `MATSEQAIJ` matrix
4607: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4609: Level: intermediate
4611: Note:
4612: The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4613: 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
4614: the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4615: local column numbers to global column numbers in the original matrix.
4617: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATSEQAIJ`
4618: @*/
4619: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4620: {
4621: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4622: PetscBool flg;
4624: PetscFunctionBegin;
4625: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4626: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4627: if (Ad) *Ad = a->A;
4628: if (Ao) *Ao = a->B;
4629: if (colmap) *colmap = a->garray;
4630: PetscFunctionReturn(PETSC_SUCCESS);
4631: }
4633: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4634: {
4635: PetscInt m, N, i, rstart, nnz, Ii;
4636: PetscInt *indx;
4637: PetscScalar *values;
4638: MatType rootType;
4640: PetscFunctionBegin;
4641: PetscCall(MatGetSize(inmat, &m, &N));
4642: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4643: PetscInt *dnz, *onz, sum, bs, cbs;
4645: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4646: /* Check sum(n) = N */
4647: PetscCallMPI(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4648: PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);
4650: PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4651: rstart -= m;
4653: MatPreallocateBegin(comm, m, n, dnz, onz);
4654: for (i = 0; i < m; i++) {
4655: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4656: PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4657: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4658: }
4660: PetscCall(MatCreate(comm, outmat));
4661: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4662: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4663: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4664: PetscCall(MatGetRootType_Private(inmat, &rootType));
4665: PetscCall(MatSetType(*outmat, rootType));
4666: PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4667: PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4668: MatPreallocateEnd(dnz, onz);
4669: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4670: }
4672: /* numeric phase */
4673: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4674: for (i = 0; i < m; i++) {
4675: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4676: Ii = i + rstart;
4677: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4678: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4679: }
4680: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4681: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4682: PetscFunctionReturn(PETSC_SUCCESS);
4683: }
4685: static PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(void **data)
4686: {
4687: Mat_Merge_SeqsToMPI *merge = (Mat_Merge_SeqsToMPI *)*data;
4689: PetscFunctionBegin;
4690: if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4691: PetscCall(PetscFree(merge->id_r));
4692: PetscCall(PetscFree(merge->len_s));
4693: PetscCall(PetscFree(merge->len_r));
4694: PetscCall(PetscFree(merge->bi));
4695: PetscCall(PetscFree(merge->bj));
4696: PetscCall(PetscFree(merge->buf_ri[0]));
4697: PetscCall(PetscFree(merge->buf_ri));
4698: PetscCall(PetscFree(merge->buf_rj[0]));
4699: PetscCall(PetscFree(merge->buf_rj));
4700: PetscCall(PetscFree(merge->coi));
4701: PetscCall(PetscFree(merge->coj));
4702: PetscCall(PetscFree(merge->owners_co));
4703: PetscCall(PetscLayoutDestroy(&merge->rowmap));
4704: PetscCall(PetscFree(merge));
4705: PetscFunctionReturn(PETSC_SUCCESS);
4706: }
4708: #include <../src/mat/utils/freespace.h>
4709: #include <petscbt.h>
4711: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4712: {
4713: MPI_Comm comm;
4714: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4715: PetscMPIInt size, rank, taga, *len_s;
4716: PetscInt N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj, m;
4717: PetscMPIInt proc, k;
4718: PetscInt **buf_ri, **buf_rj;
4719: PetscInt anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4720: PetscInt nrows, **buf_ri_k, **nextrow, **nextai;
4721: MPI_Request *s_waits, *r_waits;
4722: MPI_Status *status;
4723: const MatScalar *aa, *a_a;
4724: MatScalar **abuf_r, *ba_i;
4725: Mat_Merge_SeqsToMPI *merge;
4726: PetscContainer container;
4728: PetscFunctionBegin;
4729: PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4730: PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));
4732: PetscCallMPI(MPI_Comm_size(comm, &size));
4733: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4735: PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4736: PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4737: PetscCall(PetscContainerGetPointer(container, (void **)&merge));
4738: PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4739: aa = a_a;
4741: bi = merge->bi;
4742: bj = merge->bj;
4743: buf_ri = merge->buf_ri;
4744: buf_rj = merge->buf_rj;
4746: PetscCall(PetscMalloc1(size, &status));
4747: owners = merge->rowmap->range;
4748: len_s = merge->len_s;
4750: /* send and recv matrix values */
4751: PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4752: PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));
4754: PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4755: for (proc = 0, k = 0; proc < size; proc++) {
4756: if (!len_s[proc]) continue;
4757: i = owners[proc];
4758: PetscCallMPI(MPIU_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4759: k++;
4760: }
4762: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4763: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4764: PetscCall(PetscFree(status));
4766: PetscCall(PetscFree(s_waits));
4767: PetscCall(PetscFree(r_waits));
4769: /* insert mat values of mpimat */
4770: PetscCall(PetscMalloc1(N, &ba_i));
4771: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4773: for (k = 0; k < merge->nrecv; k++) {
4774: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4775: nrows = *buf_ri_k[k];
4776: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4777: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4778: }
4780: /* set values of ba */
4781: m = merge->rowmap->n;
4782: for (i = 0; i < m; i++) {
4783: arow = owners[rank] + i;
4784: bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4785: bnzi = bi[i + 1] - bi[i];
4786: PetscCall(PetscArrayzero(ba_i, bnzi));
4788: /* add local non-zero vals of this proc's seqmat into ba */
4789: anzi = ai[arow + 1] - ai[arow];
4790: aj = a->j + ai[arow];
4791: aa = a_a + ai[arow];
4792: nextaj = 0;
4793: for (j = 0; nextaj < anzi; j++) {
4794: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4795: ba_i[j] += aa[nextaj++];
4796: }
4797: }
4799: /* add received vals into ba */
4800: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4801: /* i-th row */
4802: if (i == *nextrow[k]) {
4803: anzi = *(nextai[k] + 1) - *nextai[k];
4804: aj = buf_rj[k] + *nextai[k];
4805: aa = abuf_r[k] + *nextai[k];
4806: nextaj = 0;
4807: for (j = 0; nextaj < anzi; j++) {
4808: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4809: ba_i[j] += aa[nextaj++];
4810: }
4811: }
4812: nextrow[k]++;
4813: nextai[k]++;
4814: }
4815: }
4816: PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4817: }
4818: PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4819: PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4820: PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));
4822: PetscCall(PetscFree(abuf_r[0]));
4823: PetscCall(PetscFree(abuf_r));
4824: PetscCall(PetscFree(ba_i));
4825: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4826: PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4827: PetscFunctionReturn(PETSC_SUCCESS);
4828: }
4830: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4831: {
4832: Mat B_mpi;
4833: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4834: PetscMPIInt size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4835: PetscInt **buf_rj, **buf_ri, **buf_ri_k;
4836: PetscInt M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4837: PetscInt len, *dnz, *onz, bs, cbs;
4838: PetscInt k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4839: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4840: MPI_Request *si_waits, *sj_waits, *ri_waits, *rj_waits;
4841: MPI_Status *status;
4842: PetscFreeSpaceList free_space = NULL, current_space = NULL;
4843: PetscBT lnkbt;
4844: Mat_Merge_SeqsToMPI *merge;
4845: PetscContainer container;
4847: PetscFunctionBegin;
4848: PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));
4850: /* make sure it is a PETSc comm */
4851: PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4852: PetscCallMPI(MPI_Comm_size(comm, &size));
4853: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4855: PetscCall(PetscNew(&merge));
4856: PetscCall(PetscMalloc1(size, &status));
4858: /* determine row ownership */
4859: PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4860: PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4861: PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4862: PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4863: PetscCall(PetscLayoutSetUp(merge->rowmap));
4864: PetscCall(PetscMalloc1(size, &len_si));
4865: PetscCall(PetscMalloc1(size, &merge->len_s));
4867: m = merge->rowmap->n;
4868: owners = merge->rowmap->range;
4870: /* determine the number of messages to send, their lengths */
4871: len_s = merge->len_s;
4873: len = 0; /* length of buf_si[] */
4874: merge->nsend = 0;
4875: for (PetscMPIInt proc = 0; proc < size; proc++) {
4876: len_si[proc] = 0;
4877: if (proc == rank) {
4878: len_s[proc] = 0;
4879: } else {
4880: PetscCall(PetscMPIIntCast(owners[proc + 1] - owners[proc] + 1, &len_si[proc]));
4881: PetscCall(PetscMPIIntCast(ai[owners[proc + 1]] - ai[owners[proc]], &len_s[proc])); /* num of rows to be sent to [proc] */
4882: }
4883: if (len_s[proc]) {
4884: merge->nsend++;
4885: nrows = 0;
4886: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4887: if (ai[i + 1] > ai[i]) nrows++;
4888: }
4889: PetscCall(PetscMPIIntCast(2 * (nrows + 1), &len_si[proc]));
4890: len += len_si[proc];
4891: }
4892: }
4894: /* determine the number and length of messages to receive for ij-structure */
4895: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4896: PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));
4898: /* post the Irecv of j-structure */
4899: PetscCall(PetscCommGetNewTag(comm, &tagj));
4900: PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));
4902: /* post the Isend of j-structure */
4903: PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));
4905: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4906: if (!len_s[proc]) continue;
4907: i = owners[proc];
4908: PetscCallMPI(MPIU_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4909: k++;
4910: }
4912: /* receives and sends of j-structure are complete */
4913: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4914: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));
4916: /* send and recv i-structure */
4917: PetscCall(PetscCommGetNewTag(comm, &tagi));
4918: PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));
4920: PetscCall(PetscMalloc1(len + 1, &buf_s));
4921: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4922: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4923: if (!len_s[proc]) continue;
4924: /* form outgoing message for i-structure:
4925: buf_si[0]: nrows to be sent
4926: [1:nrows]: row index (global)
4927: [nrows+1:2*nrows+1]: i-structure index
4928: */
4929: nrows = len_si[proc] / 2 - 1;
4930: buf_si_i = buf_si + nrows + 1;
4931: buf_si[0] = nrows;
4932: buf_si_i[0] = 0;
4933: nrows = 0;
4934: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4935: anzi = ai[i + 1] - ai[i];
4936: if (anzi) {
4937: buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4938: buf_si[nrows + 1] = i - owners[proc]; /* local row index */
4939: nrows++;
4940: }
4941: }
4942: PetscCallMPI(MPIU_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4943: k++;
4944: buf_si += len_si[proc];
4945: }
4947: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4948: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));
4950: PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4951: 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]));
4953: PetscCall(PetscFree(len_si));
4954: PetscCall(PetscFree(len_ri));
4955: PetscCall(PetscFree(rj_waits));
4956: PetscCall(PetscFree2(si_waits, sj_waits));
4957: PetscCall(PetscFree(ri_waits));
4958: PetscCall(PetscFree(buf_s));
4959: PetscCall(PetscFree(status));
4961: /* compute a local seq matrix in each processor */
4962: /* allocate bi array and free space for accumulating nonzero column info */
4963: PetscCall(PetscMalloc1(m + 1, &bi));
4964: bi[0] = 0;
4966: /* create and initialize a linked list */
4967: nlnk = N + 1;
4968: PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));
4970: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4971: len = ai[owners[rank + 1]] - ai[owners[rank]];
4972: PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));
4974: current_space = free_space;
4976: /* determine symbolic info for each local row */
4977: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4979: for (k = 0; k < merge->nrecv; k++) {
4980: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4981: nrows = *buf_ri_k[k];
4982: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4983: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4984: }
4986: MatPreallocateBegin(comm, m, n, dnz, onz);
4987: len = 0;
4988: for (i = 0; i < m; i++) {
4989: bnzi = 0;
4990: /* add local non-zero cols of this proc's seqmat into lnk */
4991: arow = owners[rank] + i;
4992: anzi = ai[arow + 1] - ai[arow];
4993: aj = a->j + ai[arow];
4994: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
4995: bnzi += nlnk;
4996: /* add received col data into lnk */
4997: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4998: if (i == *nextrow[k]) { /* i-th row */
4999: anzi = *(nextai[k] + 1) - *nextai[k];
5000: aj = buf_rj[k] + *nextai[k];
5001: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5002: bnzi += nlnk;
5003: nextrow[k]++;
5004: nextai[k]++;
5005: }
5006: }
5007: if (len < bnzi) len = bnzi; /* =max(bnzi) */
5009: /* if free space is not available, make more free space */
5010: if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), ¤t_space));
5011: /* copy data into free space, then initialize lnk */
5012: PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
5013: PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));
5015: current_space->array += bnzi;
5016: current_space->local_used += bnzi;
5017: current_space->local_remaining -= bnzi;
5019: bi[i + 1] = bi[i] + bnzi;
5020: }
5022: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
5024: PetscCall(PetscMalloc1(bi[m] + 1, &bj));
5025: PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
5026: PetscCall(PetscLLDestroy(lnk, lnkbt));
5028: /* create symbolic parallel matrix B_mpi */
5029: PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
5030: PetscCall(MatCreate(comm, &B_mpi));
5031: if (n == PETSC_DECIDE) {
5032: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
5033: } else {
5034: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
5035: }
5036: PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
5037: PetscCall(MatSetType(B_mpi, MATMPIAIJ));
5038: PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
5039: MatPreallocateEnd(dnz, onz);
5040: PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));
5042: /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
5043: B_mpi->assembled = PETSC_FALSE;
5044: merge->bi = bi;
5045: merge->bj = bj;
5046: merge->buf_ri = buf_ri;
5047: merge->buf_rj = buf_rj;
5048: merge->coi = NULL;
5049: merge->coj = NULL;
5050: merge->owners_co = NULL;
5052: PetscCall(PetscCommDestroy(&comm));
5054: /* attach the supporting struct to B_mpi for reuse */
5055: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5056: PetscCall(PetscContainerSetPointer(container, merge));
5057: PetscCall(PetscContainerSetCtxDestroy(container, MatDestroy_MPIAIJ_SeqsToMPI));
5058: PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5059: PetscCall(PetscContainerDestroy(&container));
5060: *mpimat = B_mpi;
5062: PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5063: PetscFunctionReturn(PETSC_SUCCESS);
5064: }
5066: /*@
5067: MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5068: matrices from each processor
5070: Collective
5072: Input Parameters:
5073: + comm - the communicators the parallel matrix will live on
5074: . seqmat - the input sequential matrices
5075: . m - number of local rows (or `PETSC_DECIDE`)
5076: . n - number of local columns (or `PETSC_DECIDE`)
5077: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5079: Output Parameter:
5080: . mpimat - the parallel matrix generated
5082: Level: advanced
5084: Note:
5085: The dimensions of the sequential matrix in each processor MUST be the same.
5086: The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
5087: destroyed when `mpimat` is destroyed. Call `PetscObjectQuery()` to access `seqmat`.
5089: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`
5090: @*/
5091: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5092: {
5093: PetscMPIInt size;
5095: PetscFunctionBegin;
5096: PetscCallMPI(MPI_Comm_size(comm, &size));
5097: if (size == 1) {
5098: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5099: if (scall == MAT_INITIAL_MATRIX) {
5100: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5101: } else {
5102: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5103: }
5104: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5105: PetscFunctionReturn(PETSC_SUCCESS);
5106: }
5107: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5108: if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5109: PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5110: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5111: PetscFunctionReturn(PETSC_SUCCESS);
5112: }
5114: /*@
5115: MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix.
5117: Not Collective
5119: Input Parameter:
5120: . A - the matrix
5122: Output Parameter:
5123: . A_loc - the local sequential matrix generated
5125: Level: developer
5127: Notes:
5128: The matrix is created by taking `A`'s local rows and putting them into a sequential matrix
5129: with `mlocal` rows and `n` columns. Where `mlocal` is obtained with `MatGetLocalSize()` and
5130: `n` is the global column count obtained with `MatGetSize()`
5132: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5134: For parallel matrices this creates an entirely new matrix. If the matrix is sequential it merely increases the reference count.
5136: Destroy the matrix with `MatDestroy()`
5138: .seealso: [](ch_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5139: @*/
5140: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5141: {
5142: PetscBool mpi;
5144: PetscFunctionBegin;
5145: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5146: if (mpi) {
5147: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5148: } else {
5149: *A_loc = A;
5150: PetscCall(PetscObjectReference((PetscObject)*A_loc));
5151: }
5152: PetscFunctionReturn(PETSC_SUCCESS);
5153: }
5155: /*@
5156: MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix.
5158: Not Collective
5160: Input Parameters:
5161: + A - the matrix
5162: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5164: Output Parameter:
5165: . A_loc - the local sequential matrix generated
5167: Level: developer
5169: Notes:
5170: The matrix is created by taking all `A`'s local rows and putting them into a sequential
5171: matrix with `mlocal` rows and `n` columns.`mlocal` is the row count obtained with
5172: `MatGetLocalSize()` and `n` is the global column count obtained with `MatGetSize()`.
5174: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5176: When `A` is sequential and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A` (which contains the entire matrix),
5177: 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
5178: then `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called to fill `A_loc`. Thus one can preallocate the appropriate sequential matrix `A_loc`
5179: 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.
5181: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5182: @*/
5183: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5184: {
5185: Mat_MPIAIJ *mpimat = (Mat_MPIAIJ *)A->data;
5186: Mat_SeqAIJ *mat, *a, *b;
5187: PetscInt *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5188: const PetscScalar *aa, *ba, *aav, *bav;
5189: PetscScalar *ca, *cam;
5190: PetscMPIInt size;
5191: PetscInt am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5192: PetscInt *ci, *cj, col, ncols_d, ncols_o, jo;
5193: PetscBool match;
5195: PetscFunctionBegin;
5196: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5197: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5198: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5199: if (size == 1) {
5200: if (scall == MAT_INITIAL_MATRIX) {
5201: PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5202: *A_loc = mpimat->A;
5203: } else if (scall == MAT_REUSE_MATRIX) {
5204: PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5205: }
5206: PetscFunctionReturn(PETSC_SUCCESS);
5207: }
5209: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5210: a = (Mat_SeqAIJ *)mpimat->A->data;
5211: b = (Mat_SeqAIJ *)mpimat->B->data;
5212: ai = a->i;
5213: aj = a->j;
5214: bi = b->i;
5215: bj = b->j;
5216: PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5217: PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5218: aa = aav;
5219: ba = bav;
5220: if (scall == MAT_INITIAL_MATRIX) {
5221: PetscCall(PetscMalloc1(1 + am, &ci));
5222: ci[0] = 0;
5223: for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5224: PetscCall(PetscMalloc1(1 + ci[am], &cj));
5225: PetscCall(PetscMalloc1(1 + ci[am], &ca));
5226: k = 0;
5227: for (i = 0; i < am; i++) {
5228: ncols_o = bi[i + 1] - bi[i];
5229: ncols_d = ai[i + 1] - ai[i];
5230: /* off-diagonal portion of A */
5231: for (jo = 0; jo < ncols_o; jo++) {
5232: col = cmap[*bj];
5233: if (col >= cstart) break;
5234: cj[k] = col;
5235: bj++;
5236: ca[k++] = *ba++;
5237: }
5238: /* diagonal portion of A */
5239: for (j = 0; j < ncols_d; j++) {
5240: cj[k] = cstart + *aj++;
5241: ca[k++] = *aa++;
5242: }
5243: /* off-diagonal portion of A */
5244: for (j = jo; j < ncols_o; j++) {
5245: cj[k] = cmap[*bj++];
5246: ca[k++] = *ba++;
5247: }
5248: }
5249: /* put together the new matrix */
5250: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5251: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5252: /* Since these are PETSc arrays, change flags to free them as necessary. */
5253: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5254: mat->free_a = PETSC_TRUE;
5255: mat->free_ij = PETSC_TRUE;
5256: mat->nonew = 0;
5257: } else if (scall == MAT_REUSE_MATRIX) {
5258: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5259: ci = mat->i;
5260: cj = mat->j;
5261: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5262: for (i = 0; i < am; i++) {
5263: /* off-diagonal portion of A */
5264: ncols_o = bi[i + 1] - bi[i];
5265: for (jo = 0; jo < ncols_o; jo++) {
5266: col = cmap[*bj];
5267: if (col >= cstart) break;
5268: *cam++ = *ba++;
5269: bj++;
5270: }
5271: /* diagonal portion of A */
5272: ncols_d = ai[i + 1] - ai[i];
5273: for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5274: /* off-diagonal portion of A */
5275: for (j = jo; j < ncols_o; j++) {
5276: *cam++ = *ba++;
5277: bj++;
5278: }
5279: }
5280: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5281: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5282: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5283: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5284: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5285: PetscFunctionReturn(PETSC_SUCCESS);
5286: }
5288: /*@
5289: MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5290: mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and off-diagonal part
5292: Not Collective
5294: Input Parameters:
5295: + A - the matrix
5296: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5298: Output Parameters:
5299: + glob - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5300: - A_loc - the local sequential matrix generated
5302: Level: developer
5304: Note:
5305: This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5306: part, then those associated with the off-diagonal part (in its local ordering)
5308: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5309: @*/
5310: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5311: {
5312: Mat Ao, Ad;
5313: const PetscInt *cmap;
5314: PetscMPIInt size;
5315: PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);
5317: PetscFunctionBegin;
5318: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5319: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5320: if (size == 1) {
5321: if (scall == MAT_INITIAL_MATRIX) {
5322: PetscCall(PetscObjectReference((PetscObject)Ad));
5323: *A_loc = Ad;
5324: } else if (scall == MAT_REUSE_MATRIX) {
5325: PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5326: }
5327: if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5328: PetscFunctionReturn(PETSC_SUCCESS);
5329: }
5330: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5331: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5332: if (f) {
5333: PetscCall((*f)(A, scall, glob, A_loc));
5334: } else {
5335: Mat_SeqAIJ *a = (Mat_SeqAIJ *)Ad->data;
5336: Mat_SeqAIJ *b = (Mat_SeqAIJ *)Ao->data;
5337: Mat_SeqAIJ *c;
5338: PetscInt *ai = a->i, *aj = a->j;
5339: PetscInt *bi = b->i, *bj = b->j;
5340: PetscInt *ci, *cj;
5341: const PetscScalar *aa, *ba;
5342: PetscScalar *ca;
5343: PetscInt i, j, am, dn, on;
5345: PetscCall(MatGetLocalSize(Ad, &am, &dn));
5346: PetscCall(MatGetLocalSize(Ao, NULL, &on));
5347: PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5348: PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5349: if (scall == MAT_INITIAL_MATRIX) {
5350: PetscInt k;
5351: PetscCall(PetscMalloc1(1 + am, &ci));
5352: PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5353: PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5354: ci[0] = 0;
5355: for (i = 0, k = 0; i < am; i++) {
5356: const PetscInt ncols_o = bi[i + 1] - bi[i];
5357: const PetscInt ncols_d = ai[i + 1] - ai[i];
5358: ci[i + 1] = ci[i] + ncols_o + ncols_d;
5359: /* diagonal portion of A */
5360: for (j = 0; j < ncols_d; j++, k++) {
5361: cj[k] = *aj++;
5362: ca[k] = *aa++;
5363: }
5364: /* off-diagonal portion of A */
5365: for (j = 0; j < ncols_o; j++, k++) {
5366: cj[k] = dn + *bj++;
5367: ca[k] = *ba++;
5368: }
5369: }
5370: /* put together the new matrix */
5371: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5372: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5373: /* Since these are PETSc arrays, change flags to free them as necessary. */
5374: c = (Mat_SeqAIJ *)(*A_loc)->data;
5375: c->free_a = PETSC_TRUE;
5376: c->free_ij = PETSC_TRUE;
5377: c->nonew = 0;
5378: PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5379: } else if (scall == MAT_REUSE_MATRIX) {
5380: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5381: for (i = 0; i < am; i++) {
5382: const PetscInt ncols_d = ai[i + 1] - ai[i];
5383: const PetscInt ncols_o = bi[i + 1] - bi[i];
5384: /* diagonal portion of A */
5385: for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5386: /* off-diagonal portion of A */
5387: for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5388: }
5389: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5390: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5391: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5392: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5393: if (glob) {
5394: PetscInt cst, *gidx;
5396: PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5397: PetscCall(PetscMalloc1(dn + on, &gidx));
5398: for (i = 0; i < dn; i++) gidx[i] = cst + i;
5399: for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5400: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5401: }
5402: }
5403: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5404: PetscFunctionReturn(PETSC_SUCCESS);
5405: }
5407: /*@C
5408: MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns
5410: Not Collective
5412: Input Parameters:
5413: + A - the matrix
5414: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5415: . row - index set of rows to extract (or `NULL`)
5416: - col - index set of columns to extract (or `NULL`)
5418: Output Parameter:
5419: . A_loc - the local sequential matrix generated
5421: Level: developer
5423: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5424: @*/
5425: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5426: {
5427: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5428: PetscInt i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5429: IS isrowa, iscola;
5430: Mat *aloc;
5431: PetscBool match;
5433: PetscFunctionBegin;
5434: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5435: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5436: PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5437: if (!row) {
5438: start = A->rmap->rstart;
5439: end = A->rmap->rend;
5440: PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5441: } else {
5442: isrowa = *row;
5443: }
5444: if (!col) {
5445: start = A->cmap->rstart;
5446: cmap = a->garray;
5447: nzA = a->A->cmap->n;
5448: nzB = a->B->cmap->n;
5449: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5450: ncols = 0;
5451: for (i = 0; i < nzB; i++) {
5452: if (cmap[i] < start) idx[ncols++] = cmap[i];
5453: else break;
5454: }
5455: imark = i;
5456: for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5457: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5458: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5459: } else {
5460: iscola = *col;
5461: }
5462: if (scall != MAT_INITIAL_MATRIX) {
5463: PetscCall(PetscMalloc1(1, &aloc));
5464: aloc[0] = *A_loc;
5465: }
5466: PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5467: if (!col) { /* attach global id of condensed columns */
5468: PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5469: }
5470: *A_loc = aloc[0];
5471: PetscCall(PetscFree(aloc));
5472: if (!row) PetscCall(ISDestroy(&isrowa));
5473: if (!col) PetscCall(ISDestroy(&iscola));
5474: PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5475: PetscFunctionReturn(PETSC_SUCCESS);
5476: }
5478: /*
5479: * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5480: * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5481: * on a global size.
5482: * */
5483: static PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5484: {
5485: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
5486: Mat_SeqAIJ *pd = (Mat_SeqAIJ *)p->A->data, *po = (Mat_SeqAIJ *)p->B->data, *p_oth;
5487: PetscInt plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5488: PetscMPIInt owner;
5489: PetscSFNode *iremote, *oiremote;
5490: const PetscInt *lrowindices;
5491: PetscSF sf, osf;
5492: PetscInt pcstart, *roffsets, *loffsets, *pnnz, j;
5493: PetscInt ontotalcols, dntotalcols, ntotalcols, nout;
5494: MPI_Comm comm;
5495: ISLocalToGlobalMapping mapping;
5496: const PetscScalar *pd_a, *po_a;
5498: PetscFunctionBegin;
5499: PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5500: /* plocalsize is the number of roots
5501: * nrows is the number of leaves
5502: * */
5503: PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5504: PetscCall(ISGetLocalSize(rows, &nrows));
5505: PetscCall(PetscCalloc1(nrows, &iremote));
5506: PetscCall(ISGetIndices(rows, &lrowindices));
5507: for (i = 0; i < nrows; i++) {
5508: /* Find a remote index and an owner for a row
5509: * The row could be local or remote
5510: * */
5511: owner = 0;
5512: lidx = 0;
5513: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5514: iremote[i].index = lidx;
5515: iremote[i].rank = owner;
5516: }
5517: /* Create SF to communicate how many nonzero columns for each row */
5518: PetscCall(PetscSFCreate(comm, &sf));
5519: /* SF will figure out the number of nonzero columns for each row, and their
5520: * offsets
5521: * */
5522: PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5523: PetscCall(PetscSFSetFromOptions(sf));
5524: PetscCall(PetscSFSetUp(sf));
5526: PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5527: PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5528: PetscCall(PetscCalloc1(nrows, &pnnz));
5529: roffsets[0] = 0;
5530: roffsets[1] = 0;
5531: for (i = 0; i < plocalsize; i++) {
5532: /* diagonal */
5533: nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5534: /* off-diagonal */
5535: nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5536: /* compute offsets so that we relative location for each row */
5537: roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5538: roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5539: }
5540: PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5541: PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5542: /* 'r' means root, and 'l' means leaf */
5543: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5544: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5545: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5546: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5547: PetscCall(PetscSFDestroy(&sf));
5548: PetscCall(PetscFree(roffsets));
5549: PetscCall(PetscFree(nrcols));
5550: dntotalcols = 0;
5551: ontotalcols = 0;
5552: ncol = 0;
5553: for (i = 0; i < nrows; i++) {
5554: pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5555: ncol = PetscMax(pnnz[i], ncol);
5556: /* diagonal */
5557: dntotalcols += nlcols[i * 2 + 0];
5558: /* off-diagonal */
5559: ontotalcols += nlcols[i * 2 + 1];
5560: }
5561: /* We do not need to figure the right number of columns
5562: * since all the calculations will be done by going through the raw data
5563: * */
5564: PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5565: PetscCall(MatSetUp(*P_oth));
5566: PetscCall(PetscFree(pnnz));
5567: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5568: /* diagonal */
5569: PetscCall(PetscCalloc1(dntotalcols, &iremote));
5570: /* off-diagonal */
5571: PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5572: /* diagonal */
5573: PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5574: /* off-diagonal */
5575: PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5576: dntotalcols = 0;
5577: ontotalcols = 0;
5578: ntotalcols = 0;
5579: for (i = 0; i < nrows; i++) {
5580: owner = 0;
5581: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5582: /* Set iremote for diag matrix */
5583: for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5584: iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5585: iremote[dntotalcols].rank = owner;
5586: /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5587: ilocal[dntotalcols++] = ntotalcols++;
5588: }
5589: /* off-diagonal */
5590: for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5591: oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5592: oiremote[ontotalcols].rank = owner;
5593: oilocal[ontotalcols++] = ntotalcols++;
5594: }
5595: }
5596: PetscCall(ISRestoreIndices(rows, &lrowindices));
5597: PetscCall(PetscFree(loffsets));
5598: PetscCall(PetscFree(nlcols));
5599: PetscCall(PetscSFCreate(comm, &sf));
5600: /* P serves as roots and P_oth is leaves
5601: * Diag matrix
5602: * */
5603: PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5604: PetscCall(PetscSFSetFromOptions(sf));
5605: PetscCall(PetscSFSetUp(sf));
5607: PetscCall(PetscSFCreate(comm, &osf));
5608: /* off-diagonal */
5609: PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5610: PetscCall(PetscSFSetFromOptions(osf));
5611: PetscCall(PetscSFSetUp(osf));
5612: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5613: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5614: /* operate on the matrix internal data to save memory */
5615: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5616: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5617: PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5618: /* Convert to global indices for diag matrix */
5619: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5620: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5621: /* We want P_oth store global indices */
5622: PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5623: /* Use memory scalable approach */
5624: PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5625: PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5626: PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5627: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5628: /* Convert back to local indices */
5629: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5630: PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5631: nout = 0;
5632: PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5633: PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5634: PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5635: /* Exchange values */
5636: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5637: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5638: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5639: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5640: /* Stop PETSc from shrinking memory */
5641: for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5642: PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5643: PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5644: /* Attach PetscSF objects to P_oth so that we can reuse it later */
5645: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5646: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5647: PetscCall(PetscSFDestroy(&sf));
5648: PetscCall(PetscSFDestroy(&osf));
5649: PetscFunctionReturn(PETSC_SUCCESS);
5650: }
5652: /*
5653: * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5654: * This supports MPIAIJ and MAIJ
5655: * */
5656: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5657: {
5658: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5659: Mat_SeqAIJ *p_oth;
5660: IS rows, map;
5661: PetscHMapI hamp;
5662: PetscInt i, htsize, *rowindices, off, *mapping, key, count;
5663: MPI_Comm comm;
5664: PetscSF sf, osf;
5665: PetscBool has;
5667: PetscFunctionBegin;
5668: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5669: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5670: /* If it is the first time, create an index set of off-diag nonzero columns of A,
5671: * and then create a submatrix (that often is an overlapping matrix)
5672: * */
5673: if (reuse == MAT_INITIAL_MATRIX) {
5674: /* Use a hash table to figure out unique keys */
5675: PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5676: PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5677: count = 0;
5678: /* Assume that a->g is sorted, otherwise the following does not make sense */
5679: for (i = 0; i < a->B->cmap->n; i++) {
5680: key = a->garray[i] / dof;
5681: PetscCall(PetscHMapIHas(hamp, key, &has));
5682: if (!has) {
5683: mapping[i] = count;
5684: PetscCall(PetscHMapISet(hamp, key, count++));
5685: } else {
5686: /* Current 'i' has the same value the previous step */
5687: mapping[i] = count - 1;
5688: }
5689: }
5690: PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5691: PetscCall(PetscHMapIGetSize(hamp, &htsize));
5692: PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5693: PetscCall(PetscCalloc1(htsize, &rowindices));
5694: off = 0;
5695: PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5696: PetscCall(PetscHMapIDestroy(&hamp));
5697: PetscCall(PetscSortInt(htsize, rowindices));
5698: PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5699: /* In case, the matrix was already created but users want to recreate the matrix */
5700: PetscCall(MatDestroy(P_oth));
5701: PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5702: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5703: PetscCall(ISDestroy(&map));
5704: PetscCall(ISDestroy(&rows));
5705: } else if (reuse == MAT_REUSE_MATRIX) {
5706: /* If matrix was already created, we simply update values using SF objects
5707: * that as attached to the matrix earlier.
5708: */
5709: const PetscScalar *pd_a, *po_a;
5711: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5712: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5713: PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5714: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5715: /* Update values in place */
5716: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5717: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5718: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5719: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5720: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5721: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5722: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5723: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5724: } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5725: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5726: PetscFunctionReturn(PETSC_SUCCESS);
5727: }
5729: /*@C
5730: MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`
5732: Collective
5734: Input Parameters:
5735: + A - the first matrix in `MATMPIAIJ` format
5736: . B - the second matrix in `MATMPIAIJ` format
5737: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5739: Output Parameters:
5740: + rowb - On input index sets of rows of B to extract (or `NULL`), modified on output
5741: . colb - On input index sets of columns of B to extract (or `NULL`), modified on output
5742: - B_seq - the sequential matrix generated
5744: Level: developer
5746: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5747: @*/
5748: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5749: {
5750: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5751: PetscInt *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5752: IS isrowb, iscolb;
5753: Mat *bseq = NULL;
5755: PetscFunctionBegin;
5756: 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 ")",
5757: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5758: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));
5760: if (scall == MAT_INITIAL_MATRIX) {
5761: start = A->cmap->rstart;
5762: cmap = a->garray;
5763: nzA = a->A->cmap->n;
5764: nzB = a->B->cmap->n;
5765: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5766: ncols = 0;
5767: for (i = 0; i < nzB; i++) { /* row < local row index */
5768: if (cmap[i] < start) idx[ncols++] = cmap[i];
5769: else break;
5770: }
5771: imark = i;
5772: for (i = 0; i < nzA; i++) idx[ncols++] = start + i; /* local rows */
5773: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5774: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5775: PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5776: } else {
5777: PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5778: isrowb = *rowb;
5779: iscolb = *colb;
5780: PetscCall(PetscMalloc1(1, &bseq));
5781: bseq[0] = *B_seq;
5782: }
5783: PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5784: *B_seq = bseq[0];
5785: PetscCall(PetscFree(bseq));
5786: if (!rowb) {
5787: PetscCall(ISDestroy(&isrowb));
5788: } else {
5789: *rowb = isrowb;
5790: }
5791: if (!colb) {
5792: PetscCall(ISDestroy(&iscolb));
5793: } else {
5794: *colb = iscolb;
5795: }
5796: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5797: PetscFunctionReturn(PETSC_SUCCESS);
5798: }
5800: /*
5801: MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5802: of the OFF-DIAGONAL portion of local A
5804: Collective
5806: Input Parameters:
5807: + A,B - the matrices in `MATMPIAIJ` format
5808: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5810: Output Parameter:
5811: + startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5812: . startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5813: . bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5814: - B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N
5816: Developer Note:
5817: This directly accesses information inside the VecScatter associated with the matrix-vector product
5818: for this matrix. This is not desirable..
5820: Level: developer
5822: */
5824: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5825: {
5826: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5827: VecScatter ctx;
5828: MPI_Comm comm;
5829: const PetscMPIInt *rprocs, *sprocs;
5830: PetscMPIInt nrecvs, nsends;
5831: const PetscInt *srow, *rstarts, *sstarts;
5832: PetscInt *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5833: PetscInt i, j, k = 0, l, ll, nrows, *rstartsj = NULL, *sstartsj, len;
5834: PetscScalar *b_otha, *bufa, *bufA, *vals = NULL;
5835: MPI_Request *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5836: PetscMPIInt size, tag, rank, nreqs;
5838: PetscFunctionBegin;
5839: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5840: PetscCallMPI(MPI_Comm_size(comm, &size));
5842: 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 ")",
5843: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5844: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5845: PetscCallMPI(MPI_Comm_rank(comm, &rank));
5847: if (size == 1) {
5848: startsj_s = NULL;
5849: bufa_ptr = NULL;
5850: *B_oth = NULL;
5851: PetscFunctionReturn(PETSC_SUCCESS);
5852: }
5854: ctx = a->Mvctx;
5855: tag = ((PetscObject)ctx)->tag;
5857: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5858: /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5859: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5860: PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5861: PetscCall(PetscMalloc1(nreqs, &reqs));
5862: rwaits = reqs;
5863: swaits = PetscSafePointerPlusOffset(reqs, nrecvs);
5865: if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5866: if (scall == MAT_INITIAL_MATRIX) {
5867: /* i-array */
5868: /* post receives */
5869: if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5870: for (i = 0; i < nrecvs; i++) {
5871: rowlen = rvalues + rstarts[i] * rbs;
5872: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5873: PetscCallMPI(MPIU_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5874: }
5876: /* pack the outgoing message */
5877: PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));
5879: sstartsj[0] = 0;
5880: rstartsj[0] = 0;
5881: len = 0; /* total length of j or a array to be sent */
5882: if (nsends) {
5883: k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5884: PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5885: }
5886: for (i = 0; i < nsends; i++) {
5887: rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5888: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5889: for (j = 0; j < nrows; j++) {
5890: row = srow[k] + B->rmap->range[rank]; /* global row idx */
5891: for (l = 0; l < sbs; l++) {
5892: PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */
5894: rowlen[j * sbs + l] = ncols;
5896: len += ncols;
5897: PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5898: }
5899: k++;
5900: }
5901: PetscCallMPI(MPIU_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));
5903: sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5904: }
5905: /* recvs and sends of i-array are completed */
5906: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5907: PetscCall(PetscFree(svalues));
5909: /* allocate buffers for sending j and a arrays */
5910: PetscCall(PetscMalloc1(len + 1, &bufj));
5911: PetscCall(PetscMalloc1(len + 1, &bufa));
5913: /* create i-array of B_oth */
5914: PetscCall(PetscMalloc1(aBn + 2, &b_othi));
5916: b_othi[0] = 0;
5917: len = 0; /* total length of j or a array to be received */
5918: k = 0;
5919: for (i = 0; i < nrecvs; i++) {
5920: rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5921: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5922: for (j = 0; j < nrows; j++) {
5923: b_othi[k + 1] = b_othi[k] + rowlen[j];
5924: PetscCall(PetscIntSumError(rowlen[j], len, &len));
5925: k++;
5926: }
5927: rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5928: }
5929: PetscCall(PetscFree(rvalues));
5931: /* allocate space for j and a arrays of B_oth */
5932: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_othj));
5933: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_otha));
5935: /* j-array */
5936: /* post receives of j-array */
5937: for (i = 0; i < nrecvs; i++) {
5938: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5939: PetscCallMPI(MPIU_Irecv(b_othj + rstartsj[i], nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5940: }
5942: /* pack the outgoing message j-array */
5943: if (nsends) k = sstarts[0];
5944: for (i = 0; i < nsends; i++) {
5945: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5946: bufJ = bufj + sstartsj[i];
5947: for (j = 0; j < nrows; j++) {
5948: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5949: for (ll = 0; ll < sbs; ll++) {
5950: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5951: for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5952: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5953: }
5954: }
5955: PetscCallMPI(MPIU_Isend(bufj + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5956: }
5958: /* recvs and sends of j-array are completed */
5959: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5960: } else if (scall == MAT_REUSE_MATRIX) {
5961: sstartsj = *startsj_s;
5962: rstartsj = *startsj_r;
5963: bufa = *bufa_ptr;
5964: PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5965: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");
5967: /* a-array */
5968: /* post receives of a-array */
5969: for (i = 0; i < nrecvs; i++) {
5970: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5971: PetscCallMPI(MPIU_Irecv(b_otha + rstartsj[i], nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
5972: }
5974: /* pack the outgoing message a-array */
5975: if (nsends) k = sstarts[0];
5976: for (i = 0; i < nsends; i++) {
5977: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5978: bufA = bufa + sstartsj[i];
5979: for (j = 0; j < nrows; j++) {
5980: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5981: for (ll = 0; ll < sbs; ll++) {
5982: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5983: for (l = 0; l < ncols; l++) *bufA++ = vals[l];
5984: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5985: }
5986: }
5987: PetscCallMPI(MPIU_Isend(bufa + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
5988: }
5989: /* recvs and sends of a-array are completed */
5990: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5991: PetscCall(PetscFree(reqs));
5993: if (scall == MAT_INITIAL_MATRIX) {
5994: Mat_SeqAIJ *b_oth;
5996: /* put together the new matrix */
5997: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));
5999: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
6000: /* Since these are PETSc arrays, change flags to free them as necessary. */
6001: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
6002: b_oth->free_a = PETSC_TRUE;
6003: b_oth->free_ij = PETSC_TRUE;
6004: b_oth->nonew = 0;
6006: PetscCall(PetscFree(bufj));
6007: if (!startsj_s || !bufa_ptr) {
6008: PetscCall(PetscFree2(sstartsj, rstartsj));
6009: PetscCall(PetscFree(bufa_ptr));
6010: } else {
6011: *startsj_s = sstartsj;
6012: *startsj_r = rstartsj;
6013: *bufa_ptr = bufa;
6014: }
6015: } else if (scall == MAT_REUSE_MATRIX) {
6016: PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
6017: }
6019: PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
6020: PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
6021: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
6022: PetscFunctionReturn(PETSC_SUCCESS);
6023: }
6025: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
6026: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
6027: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
6028: #if defined(PETSC_HAVE_MKL_SPARSE)
6029: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
6030: #endif
6031: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
6032: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
6033: #if defined(PETSC_HAVE_ELEMENTAL)
6034: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
6035: #endif
6036: #if defined(PETSC_HAVE_SCALAPACK)
6037: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
6038: #endif
6039: #if defined(PETSC_HAVE_HYPRE)
6040: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
6041: #endif
6042: #if defined(PETSC_HAVE_CUDA)
6043: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
6044: #endif
6045: #if defined(PETSC_HAVE_HIP)
6046: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6047: #endif
6048: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6049: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6050: #endif
6051: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6052: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6053: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
6055: /*
6056: Computes (B'*A')' since computing B*A directly is untenable
6058: n p p
6059: [ ] [ ] [ ]
6060: m [ A ] * n [ B ] = m [ C ]
6061: [ ] [ ] [ ]
6063: */
6064: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6065: {
6066: Mat At, Bt, Ct;
6068: PetscFunctionBegin;
6069: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6070: PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6071: PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_CURRENT, &Ct));
6072: PetscCall(MatDestroy(&At));
6073: PetscCall(MatDestroy(&Bt));
6074: PetscCall(MatTransposeSetPrecursor(Ct, C));
6075: PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6076: PetscCall(MatDestroy(&Ct));
6077: PetscFunctionReturn(PETSC_SUCCESS);
6078: }
6080: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6081: {
6082: PetscBool cisdense;
6084: PetscFunctionBegin;
6085: 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);
6086: PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6087: PetscCall(MatSetBlockSizesFromMats(C, A, B));
6088: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6089: if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6090: PetscCall(MatSetUp(C));
6092: C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6093: PetscFunctionReturn(PETSC_SUCCESS);
6094: }
6096: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6097: {
6098: Mat_Product *product = C->product;
6099: Mat A = product->A, B = product->B;
6101: PetscFunctionBegin;
6102: 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 ")",
6103: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6104: C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6105: C->ops->productsymbolic = MatProductSymbolic_AB;
6106: PetscFunctionReturn(PETSC_SUCCESS);
6107: }
6109: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6110: {
6111: Mat_Product *product = C->product;
6113: PetscFunctionBegin;
6114: if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6115: PetscFunctionReturn(PETSC_SUCCESS);
6116: }
6118: /*
6119: Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix
6121: Input Parameters:
6123: j1,rowBegin1,rowEnd1,jmap1: describe the first set of nonzeros (Set1)
6124: j2,rowBegin2,rowEnd2,jmap2: describe the second set of nonzeros (Set2)
6126: mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat
6128: For Set1, j1[] contains column indices of the nonzeros.
6129: 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
6130: respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6131: but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.
6133: Similar for Set2.
6135: This routine merges the two sets of nonzeros row by row and removes repeats.
6137: Output Parameters: (memory is allocated by the caller)
6139: i[],j[]: the CSR of the merged matrix, which has m rows.
6140: imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6141: imap2[]: similar to imap1[], but for Set2.
6142: Note we order nonzeros row-by-row and from left to right.
6143: */
6144: 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[])
6145: {
6146: PetscInt r, m; /* Row index of mat */
6147: PetscCount t, t1, t2, b1, e1, b2, e2;
6149: PetscFunctionBegin;
6150: PetscCall(MatGetLocalSize(mat, &m, NULL));
6151: t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6152: i[0] = 0;
6153: for (r = 0; r < m; r++) { /* Do row by row merging */
6154: b1 = rowBegin1[r];
6155: e1 = rowEnd1[r];
6156: b2 = rowBegin2[r];
6157: e2 = rowEnd2[r];
6158: while (b1 < e1 && b2 < e2) {
6159: if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6160: j[t] = j1[b1];
6161: imap1[t1] = t;
6162: imap2[t2] = t;
6163: b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6164: b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6165: t1++;
6166: t2++;
6167: t++;
6168: } else if (j1[b1] < j2[b2]) {
6169: j[t] = j1[b1];
6170: imap1[t1] = t;
6171: b1 += jmap1[t1 + 1] - jmap1[t1];
6172: t1++;
6173: t++;
6174: } else {
6175: j[t] = j2[b2];
6176: imap2[t2] = t;
6177: b2 += jmap2[t2 + 1] - jmap2[t2];
6178: t2++;
6179: t++;
6180: }
6181: }
6182: /* Merge the remaining in either j1[] or j2[] */
6183: while (b1 < e1) {
6184: j[t] = j1[b1];
6185: imap1[t1] = t;
6186: b1 += jmap1[t1 + 1] - jmap1[t1];
6187: t1++;
6188: t++;
6189: }
6190: while (b2 < e2) {
6191: j[t] = j2[b2];
6192: imap2[t2] = t;
6193: b2 += jmap2[t2 + 1] - jmap2[t2];
6194: t2++;
6195: t++;
6196: }
6197: PetscCall(PetscIntCast(t, i + r + 1));
6198: }
6199: PetscFunctionReturn(PETSC_SUCCESS);
6200: }
6202: /*
6203: Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block
6205: Input Parameters:
6206: mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6207: 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[]
6208: respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.
6210: i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6211: i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.
6213: Output Parameters:
6214: j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6215: rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6216: 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,
6217: and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.
6219: Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6220: Atot: number of entries belonging to the diagonal block.
6221: Annz: number of unique nonzeros belonging to the diagonal block.
6222: Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6223: repeats (i.e., same 'i,j' pair).
6224: Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6225: is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.
6227: Atot: number of entries belonging to the diagonal block
6228: Annz: number of unique nonzeros belonging to the diagonal block.
6230: Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.
6232: Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6233: */
6234: 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_)
6235: {
6236: PetscInt cstart, cend, rstart, rend, row, col;
6237: PetscCount Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6238: PetscCount Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6239: PetscCount k, m, p, q, r, s, mid;
6240: PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;
6242: PetscFunctionBegin;
6243: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6244: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6245: m = rend - rstart;
6247: /* Skip negative rows */
6248: for (k = 0; k < n; k++)
6249: if (i[k] >= 0) break;
6251: /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6252: fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6253: */
6254: while (k < n) {
6255: row = i[k];
6256: /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6257: for (s = k; s < n; s++)
6258: if (i[s] != row) break;
6260: /* Shift diag columns to range of [-PETSC_INT_MAX, -1] */
6261: for (p = k; p < s; p++) {
6262: if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_INT_MAX;
6263: 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]);
6264: }
6265: PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6266: PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6267: rowBegin[row - rstart] = k;
6268: rowMid[row - rstart] = mid;
6269: rowEnd[row - rstart] = s;
6271: /* Count nonzeros of this diag/offdiag row, which might have repeats */
6272: Atot += mid - k;
6273: Btot += s - mid;
6275: /* Count unique nonzeros of this diag row */
6276: for (p = k; p < mid;) {
6277: col = j[p];
6278: do {
6279: j[p] += PETSC_INT_MAX; /* Revert the modified diagonal indices */
6280: p++;
6281: } while (p < mid && j[p] == col);
6282: Annz++;
6283: }
6285: /* Count unique nonzeros of this offdiag row */
6286: for (p = mid; p < s;) {
6287: col = j[p];
6288: do {
6289: p++;
6290: } while (p < s && j[p] == col);
6291: Bnnz++;
6292: }
6293: k = s;
6294: }
6296: /* Allocation according to Atot, Btot, Annz, Bnnz */
6297: PetscCall(PetscMalloc1(Atot, &Aperm));
6298: PetscCall(PetscMalloc1(Btot, &Bperm));
6299: PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6300: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));
6302: /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6303: Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6304: for (r = 0; r < m; r++) {
6305: k = rowBegin[r];
6306: mid = rowMid[r];
6307: s = rowEnd[r];
6308: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Aperm, Atot), PetscSafePointerPlusOffset(perm, k), mid - k));
6309: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Bperm, Btot), PetscSafePointerPlusOffset(perm, mid), s - mid));
6310: Atot += mid - k;
6311: Btot += s - mid;
6313: /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6314: for (p = k; p < mid;) {
6315: col = j[p];
6316: q = p;
6317: do {
6318: p++;
6319: } while (p < mid && j[p] == col);
6320: Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6321: Annz++;
6322: }
6324: for (p = mid; p < s;) {
6325: col = j[p];
6326: q = p;
6327: do {
6328: p++;
6329: } while (p < s && j[p] == col);
6330: Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6331: Bnnz++;
6332: }
6333: }
6334: /* Output */
6335: *Aperm_ = Aperm;
6336: *Annz_ = Annz;
6337: *Atot_ = Atot;
6338: *Ajmap_ = Ajmap;
6339: *Bperm_ = Bperm;
6340: *Bnnz_ = Bnnz;
6341: *Btot_ = Btot;
6342: *Bjmap_ = Bjmap;
6343: PetscFunctionReturn(PETSC_SUCCESS);
6344: }
6346: /*
6347: Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix
6349: Input Parameters:
6350: nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6351: nnz: number of unique nonzeros in the merged matrix
6352: imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6353: jmap[nnz1+1]: i-th nonzero in the set has jmap[i+1] - jmap[i] repeats in the set
6355: Output Parameter: (memory is allocated by the caller)
6356: jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set
6358: Example:
6359: nnz1 = 4
6360: nnz = 6
6361: imap = [1,3,4,5]
6362: jmap = [0,3,5,6,7]
6363: then,
6364: jmap_new = [0,0,3,3,5,6,7]
6365: */
6366: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6367: {
6368: PetscCount k, p;
6370: PetscFunctionBegin;
6371: jmap_new[0] = 0;
6372: p = nnz; /* p loops over jmap_new[] backwards */
6373: for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6374: for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6375: }
6376: for (; p >= 0; p--) jmap_new[p] = jmap[0];
6377: PetscFunctionReturn(PETSC_SUCCESS);
6378: }
6380: static PetscErrorCode MatCOOStructDestroy_MPIAIJ(void **data)
6381: {
6382: MatCOOStruct_MPIAIJ *coo = (MatCOOStruct_MPIAIJ *)*data;
6384: PetscFunctionBegin;
6385: PetscCall(PetscSFDestroy(&coo->sf));
6386: PetscCall(PetscFree(coo->Aperm1));
6387: PetscCall(PetscFree(coo->Bperm1));
6388: PetscCall(PetscFree(coo->Ajmap1));
6389: PetscCall(PetscFree(coo->Bjmap1));
6390: PetscCall(PetscFree(coo->Aimap2));
6391: PetscCall(PetscFree(coo->Bimap2));
6392: PetscCall(PetscFree(coo->Aperm2));
6393: PetscCall(PetscFree(coo->Bperm2));
6394: PetscCall(PetscFree(coo->Ajmap2));
6395: PetscCall(PetscFree(coo->Bjmap2));
6396: PetscCall(PetscFree(coo->Cperm1));
6397: PetscCall(PetscFree2(coo->sendbuf, coo->recvbuf));
6398: PetscCall(PetscFree(coo));
6399: PetscFunctionReturn(PETSC_SUCCESS);
6400: }
6402: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6403: {
6404: MPI_Comm comm;
6405: PetscMPIInt rank, size;
6406: PetscInt m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6407: PetscCount k, p, q, rem; /* Loop variables over coo arrays */
6408: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6409: PetscContainer container;
6410: MatCOOStruct_MPIAIJ *coo;
6412: PetscFunctionBegin;
6413: PetscCall(PetscFree(mpiaij->garray));
6414: PetscCall(VecDestroy(&mpiaij->lvec));
6415: #if defined(PETSC_USE_CTABLE)
6416: PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6417: #else
6418: PetscCall(PetscFree(mpiaij->colmap));
6419: #endif
6420: PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6421: mat->assembled = PETSC_FALSE;
6422: mat->was_assembled = PETSC_FALSE;
6424: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6425: PetscCallMPI(MPI_Comm_size(comm, &size));
6426: PetscCallMPI(MPI_Comm_rank(comm, &rank));
6427: PetscCall(PetscLayoutSetUp(mat->rmap));
6428: PetscCall(PetscLayoutSetUp(mat->cmap));
6429: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6430: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6431: PetscCall(MatGetLocalSize(mat, &m, &n));
6432: PetscCall(MatGetSize(mat, &M, &N));
6434: /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6435: /* entries come first, then local rows, then remote rows. */
6436: PetscCount n1 = coo_n, *perm1;
6437: PetscInt *i1 = coo_i, *j1 = coo_j;
6439: PetscCall(PetscMalloc1(n1, &perm1));
6440: for (k = 0; k < n1; k++) perm1[k] = k;
6442: /* Manipulate indices so that entries with negative row or col indices will have smallest
6443: row indices, local entries will have greater but negative row indices, and remote entries
6444: will have positive row indices.
6445: */
6446: for (k = 0; k < n1; k++) {
6447: if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_INT_MIN; /* e.g., -2^31, minimal to move them ahead */
6448: 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] */
6449: else {
6450: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6451: if (mpiaij->donotstash) i1[k] = PETSC_INT_MIN; /* Ignore offproc entries as if they had negative indices */
6452: }
6453: }
6455: /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6456: PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));
6458: /* Advance k to the first entry we need to take care of */
6459: for (k = 0; k < n1; k++)
6460: if (i1[k] > PETSC_INT_MIN) break;
6461: PetscCount i1start = k;
6463: PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_INT_MAX, &rem)); /* rem is upper bound of the last local row */
6464: for (; k < rem; k++) i1[k] += PETSC_INT_MAX; /* Revert row indices of local rows*/
6466: /* Send remote rows to their owner */
6467: /* Find which rows should be sent to which remote ranks*/
6468: PetscInt nsend = 0; /* Number of MPI ranks to send data to */
6469: PetscMPIInt *sendto; /* [nsend], storing remote ranks */
6470: PetscInt *nentries; /* [nsend], storing number of entries sent to remote ranks; Assume PetscInt is big enough for this count, and error if not */
6471: const PetscInt *ranges;
6472: PetscInt maxNsend = size >= 128 ? 128 : size; /* Assume max 128 neighbors; realloc when needed */
6474: PetscCall(PetscLayoutGetRanges(mat->rmap, &ranges));
6475: PetscCall(PetscMalloc2(maxNsend, &sendto, maxNsend, &nentries));
6476: for (k = rem; k < n1;) {
6477: PetscMPIInt owner;
6478: PetscInt firstRow, lastRow;
6480: /* Locate a row range */
6481: firstRow = i1[k]; /* first row of this owner */
6482: PetscCall(PetscLayoutFindOwner(mat->rmap, firstRow, &owner));
6483: lastRow = ranges[owner + 1] - 1; /* last row of this owner */
6485: /* Find the first index 'p' in [k,n) with i[p] belonging to next owner */
6486: PetscCall(PetscSortedIntUpperBound(i1, k, n1, lastRow, &p));
6488: /* All entries in [k,p) belong to this remote owner */
6489: if (nsend >= maxNsend) { /* Double the remote ranks arrays if not long enough */
6490: PetscMPIInt *sendto2;
6491: PetscInt *nentries2;
6492: PetscInt maxNsend2 = (maxNsend <= size / 2) ? maxNsend * 2 : size;
6494: PetscCall(PetscMalloc2(maxNsend2, &sendto2, maxNsend2, &nentries2));
6495: PetscCall(PetscArraycpy(sendto2, sendto, maxNsend));
6496: PetscCall(PetscArraycpy(nentries2, nentries2, maxNsend + 1));
6497: PetscCall(PetscFree2(sendto, nentries2));
6498: sendto = sendto2;
6499: nentries = nentries2;
6500: maxNsend = maxNsend2;
6501: }
6502: sendto[nsend] = owner;
6503: PetscCall(PetscIntCast(p - k, &nentries[nsend]));
6504: nsend++;
6505: k = p;
6506: }
6508: /* Build 1st SF to know offsets on remote to send data */
6509: PetscSF sf1;
6510: PetscInt nroots = 1, nroots2 = 0;
6511: PetscInt nleaves = nsend, nleaves2 = 0;
6512: PetscInt *offsets;
6513: PetscSFNode *iremote;
6515: PetscCall(PetscSFCreate(comm, &sf1));
6516: PetscCall(PetscMalloc1(nsend, &iremote));
6517: PetscCall(PetscMalloc1(nsend, &offsets));
6518: for (k = 0; k < nsend; k++) {
6519: iremote[k].rank = sendto[k];
6520: iremote[k].index = 0;
6521: nleaves2 += nentries[k];
6522: PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6523: }
6524: PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6525: PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6526: PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6527: PetscCall(PetscSFDestroy(&sf1));
6528: PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT, nleaves2, n1 - rem);
6530: /* Build 2nd SF to send remote COOs to their owner */
6531: PetscSF sf2;
6532: nroots = nroots2;
6533: nleaves = nleaves2;
6534: PetscCall(PetscSFCreate(comm, &sf2));
6535: PetscCall(PetscSFSetFromOptions(sf2));
6536: PetscCall(PetscMalloc1(nleaves, &iremote));
6537: p = 0;
6538: for (k = 0; k < nsend; k++) {
6539: PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6540: for (q = 0; q < nentries[k]; q++, p++) {
6541: iremote[p].rank = sendto[k];
6542: PetscCall(PetscIntCast(offsets[k] + q, &iremote[p].index));
6543: }
6544: }
6545: PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6547: /* Send the remote COOs to their owner */
6548: PetscInt n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6549: PetscCount *perm2; /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6550: PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6551: PetscAssert(rem == 0 || i1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6552: PetscAssert(rem == 0 || j1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6553: PetscInt *i1prem = PetscSafePointerPlusOffset(i1, rem);
6554: PetscInt *j1prem = PetscSafePointerPlusOffset(j1, rem);
6555: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1prem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6556: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1prem, i2, MPI_REPLACE));
6557: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1prem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6558: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1prem, j2, MPI_REPLACE));
6560: PetscCall(PetscFree(offsets));
6561: PetscCall(PetscFree2(sendto, nentries));
6563: /* Sort received COOs by row along with the permutation array */
6564: for (k = 0; k < n2; k++) perm2[k] = k;
6565: PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));
6567: /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6568: PetscCount *Cperm1;
6569: PetscAssert(rem == 0 || perm1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6570: PetscCount *perm1prem = PetscSafePointerPlusOffset(perm1, rem);
6571: PetscCall(PetscMalloc1(nleaves, &Cperm1));
6572: PetscCall(PetscArraycpy(Cperm1, perm1prem, nleaves));
6574: /* Support for HYPRE matrices, kind of a hack.
6575: Swap min column with diagonal so that diagonal values will go first */
6576: PetscBool hypre;
6577: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", ((PetscObject)mat)->name, &hypre));
6578: if (hypre) {
6579: PetscInt *minj;
6580: PetscBT hasdiag;
6582: PetscCall(PetscBTCreate(m, &hasdiag));
6583: PetscCall(PetscMalloc1(m, &minj));
6584: for (k = 0; k < m; k++) minj[k] = PETSC_INT_MAX;
6585: for (k = i1start; k < rem; k++) {
6586: if (j1[k] < cstart || j1[k] >= cend) continue;
6587: const PetscInt rindex = i1[k] - rstart;
6588: if ((j1[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6589: minj[rindex] = PetscMin(minj[rindex], j1[k]);
6590: }
6591: for (k = 0; k < n2; k++) {
6592: if (j2[k] < cstart || j2[k] >= cend) continue;
6593: const PetscInt rindex = i2[k] - rstart;
6594: if ((j2[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6595: minj[rindex] = PetscMin(minj[rindex], j2[k]);
6596: }
6597: for (k = i1start; k < rem; k++) {
6598: const PetscInt rindex = i1[k] - rstart;
6599: if (j1[k] < cstart || j1[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6600: if (j1[k] == minj[rindex]) j1[k] = i1[k] + (cstart - rstart);
6601: else if ((j1[k] - cstart) == rindex) j1[k] = minj[rindex];
6602: }
6603: for (k = 0; k < n2; k++) {
6604: const PetscInt rindex = i2[k] - rstart;
6605: if (j2[k] < cstart || j2[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6606: if (j2[k] == minj[rindex]) j2[k] = i2[k] + (cstart - rstart);
6607: else if ((j2[k] - cstart) == rindex) j2[k] = minj[rindex];
6608: }
6609: PetscCall(PetscBTDestroy(&hasdiag));
6610: PetscCall(PetscFree(minj));
6611: }
6613: /* Split local COOs and received COOs into diag/offdiag portions */
6614: PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6615: PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1;
6616: PetscCount Annz1, Bnnz1, Atot1, Btot1;
6617: PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6618: PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6619: PetscCount Annz2, Bnnz2, Atot2, Btot2;
6621: PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6622: PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6623: PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6624: PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));
6626: /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6627: PetscInt *Ai, *Bi;
6628: PetscInt *Aj, *Bj;
6630: PetscCall(PetscMalloc1(m + 1, &Ai));
6631: PetscCall(PetscMalloc1(m + 1, &Bi));
6632: PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6633: PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));
6635: PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6636: PetscCall(PetscMalloc1(Annz1, &Aimap1));
6637: PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6638: PetscCall(PetscMalloc1(Annz2, &Aimap2));
6639: PetscCall(PetscMalloc1(Bnnz2, &Bimap2));
6641: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6642: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));
6644: /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we */
6645: /* expect nonzeros in A/B most likely have local contributing entries */
6646: PetscInt Annz = Ai[m];
6647: PetscInt Bnnz = Bi[m];
6648: PetscCount *Ajmap1_new, *Bjmap1_new;
6650: PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6651: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));
6653: PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6654: PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));
6656: PetscCall(PetscFree(Aimap1));
6657: PetscCall(PetscFree(Ajmap1));
6658: PetscCall(PetscFree(Bimap1));
6659: PetscCall(PetscFree(Bjmap1));
6660: PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6661: PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6662: PetscCall(PetscFree(perm1));
6663: PetscCall(PetscFree3(i2, j2, perm2));
6665: Ajmap1 = Ajmap1_new;
6666: Bjmap1 = Bjmap1_new;
6668: /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6669: if (Annz < Annz1 + Annz2) {
6670: PetscInt *Aj_new;
6671: PetscCall(PetscMalloc1(Annz, &Aj_new));
6672: PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6673: PetscCall(PetscFree(Aj));
6674: Aj = Aj_new;
6675: }
6677: if (Bnnz < Bnnz1 + Bnnz2) {
6678: PetscInt *Bj_new;
6679: PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6680: PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6681: PetscCall(PetscFree(Bj));
6682: Bj = Bj_new;
6683: }
6685: /* Create new submatrices for on-process and off-process coupling */
6686: PetscScalar *Aa, *Ba;
6687: MatType rtype;
6688: Mat_SeqAIJ *a, *b;
6689: PetscObjectState state;
6690: PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6691: PetscCall(PetscCalloc1(Bnnz, &Ba));
6692: /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6693: if (cstart) {
6694: for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6695: }
6697: PetscCall(MatGetRootType_Private(mat, &rtype));
6699: MatSeqXAIJGetOptions_Private(mpiaij->A);
6700: PetscCall(MatDestroy(&mpiaij->A));
6701: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6702: PetscCall(MatSetBlockSizesFromMats(mpiaij->A, mat, mat));
6703: MatSeqXAIJRestoreOptions_Private(mpiaij->A);
6705: MatSeqXAIJGetOptions_Private(mpiaij->B);
6706: PetscCall(MatDestroy(&mpiaij->B));
6707: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6708: PetscCall(MatSetBlockSizesFromMats(mpiaij->B, mat, mat));
6709: MatSeqXAIJRestoreOptions_Private(mpiaij->B);
6711: PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6712: mat->was_assembled = PETSC_TRUE; // was_assembled in effect means the Mvctx is built; doing so avoids redundant MatSetUpMultiply_MPIAIJ
6713: state = mpiaij->A->nonzerostate + mpiaij->B->nonzerostate;
6714: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
6716: a = (Mat_SeqAIJ *)mpiaij->A->data;
6717: b = (Mat_SeqAIJ *)mpiaij->B->data;
6718: a->free_a = PETSC_TRUE;
6719: a->free_ij = PETSC_TRUE;
6720: b->free_a = PETSC_TRUE;
6721: b->free_ij = PETSC_TRUE;
6722: a->maxnz = a->nz;
6723: b->maxnz = b->nz;
6725: /* conversion must happen AFTER multiply setup */
6726: PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6727: PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6728: PetscCall(VecDestroy(&mpiaij->lvec));
6729: PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));
6731: // Put the COO struct in a container and then attach that to the matrix
6732: PetscCall(PetscMalloc1(1, &coo));
6733: coo->n = coo_n;
6734: coo->sf = sf2;
6735: coo->sendlen = nleaves;
6736: coo->recvlen = nroots;
6737: coo->Annz = Annz;
6738: coo->Bnnz = Bnnz;
6739: coo->Annz2 = Annz2;
6740: coo->Bnnz2 = Bnnz2;
6741: coo->Atot1 = Atot1;
6742: coo->Atot2 = Atot2;
6743: coo->Btot1 = Btot1;
6744: coo->Btot2 = Btot2;
6745: coo->Ajmap1 = Ajmap1;
6746: coo->Aperm1 = Aperm1;
6747: coo->Bjmap1 = Bjmap1;
6748: coo->Bperm1 = Bperm1;
6749: coo->Aimap2 = Aimap2;
6750: coo->Ajmap2 = Ajmap2;
6751: coo->Aperm2 = Aperm2;
6752: coo->Bimap2 = Bimap2;
6753: coo->Bjmap2 = Bjmap2;
6754: coo->Bperm2 = Bperm2;
6755: coo->Cperm1 = Cperm1;
6756: // Allocate in preallocation. If not used, it has zero cost on host
6757: PetscCall(PetscMalloc2(coo->sendlen, &coo->sendbuf, coo->recvlen, &coo->recvbuf));
6758: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
6759: PetscCall(PetscContainerSetPointer(container, coo));
6760: PetscCall(PetscContainerSetCtxDestroy(container, MatCOOStructDestroy_MPIAIJ));
6761: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
6762: PetscCall(PetscContainerDestroy(&container));
6763: PetscFunctionReturn(PETSC_SUCCESS);
6764: }
6766: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6767: {
6768: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6769: Mat A = mpiaij->A, B = mpiaij->B;
6770: PetscScalar *Aa, *Ba;
6771: PetscScalar *sendbuf, *recvbuf;
6772: const PetscCount *Ajmap1, *Ajmap2, *Aimap2;
6773: const PetscCount *Bjmap1, *Bjmap2, *Bimap2;
6774: const PetscCount *Aperm1, *Aperm2, *Bperm1, *Bperm2;
6775: const PetscCount *Cperm1;
6776: PetscContainer container;
6777: MatCOOStruct_MPIAIJ *coo;
6779: PetscFunctionBegin;
6780: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
6781: PetscCheck(container, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
6782: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
6783: sendbuf = coo->sendbuf;
6784: recvbuf = coo->recvbuf;
6785: Ajmap1 = coo->Ajmap1;
6786: Ajmap2 = coo->Ajmap2;
6787: Aimap2 = coo->Aimap2;
6788: Bjmap1 = coo->Bjmap1;
6789: Bjmap2 = coo->Bjmap2;
6790: Bimap2 = coo->Bimap2;
6791: Aperm1 = coo->Aperm1;
6792: Aperm2 = coo->Aperm2;
6793: Bperm1 = coo->Bperm1;
6794: Bperm2 = coo->Bperm2;
6795: Cperm1 = coo->Cperm1;
6797: PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6798: PetscCall(MatSeqAIJGetArray(B, &Ba));
6800: /* Pack entries to be sent to remote */
6801: for (PetscCount i = 0; i < coo->sendlen; i++) sendbuf[i] = v[Cperm1[i]];
6803: /* Send remote entries to their owner and overlap the communication with local computation */
6804: PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6805: /* Add local entries to A and B */
6806: for (PetscCount i = 0; i < coo->Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6807: PetscScalar sum = 0.0; /* Do partial summation first to improve numerical stability */
6808: for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6809: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6810: }
6811: for (PetscCount i = 0; i < coo->Bnnz; i++) {
6812: PetscScalar sum = 0.0;
6813: for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6814: Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6815: }
6816: PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));
6818: /* Add received remote entries to A and B */
6819: for (PetscCount i = 0; i < coo->Annz2; i++) {
6820: for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6821: }
6822: for (PetscCount i = 0; i < coo->Bnnz2; i++) {
6823: for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6824: }
6825: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6826: PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6827: PetscFunctionReturn(PETSC_SUCCESS);
6828: }
6830: /*MC
6831: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
6833: Options Database Keys:
6834: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`
6836: Level: beginner
6838: Notes:
6839: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
6840: in this case the values associated with the rows and columns one passes in are set to zero
6841: in the matrix
6843: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
6844: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
6846: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6847: M*/
6848: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6849: {
6850: Mat_MPIAIJ *b;
6851: PetscMPIInt size;
6853: PetscFunctionBegin;
6854: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
6856: PetscCall(PetscNew(&b));
6857: B->data = (void *)b;
6858: B->ops[0] = MatOps_Values;
6859: B->assembled = PETSC_FALSE;
6860: B->insertmode = NOT_SET_VALUES;
6861: b->size = size;
6863: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
6865: /* build cache for off array entries formed */
6866: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
6868: b->donotstash = PETSC_FALSE;
6869: b->colmap = NULL;
6870: b->garray = NULL;
6871: b->roworiented = PETSC_TRUE;
6873: /* stuff used for matrix vector multiply */
6874: b->lvec = NULL;
6875: b->Mvctx = NULL;
6877: /* stuff for MatGetRow() */
6878: b->rowindices = NULL;
6879: b->rowvalues = NULL;
6880: b->getrowactive = PETSC_FALSE;
6882: /* flexible pointer used in CUSPARSE classes */
6883: b->spptr = NULL;
6885: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6886: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6887: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6888: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6889: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6890: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6891: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetHash_C", MatResetHash_MPIAIJ));
6892: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6893: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6894: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6895: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6896: #if defined(PETSC_HAVE_CUDA)
6897: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6898: #endif
6899: #if defined(PETSC_HAVE_HIP)
6900: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6901: #endif
6902: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6903: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6904: #endif
6905: #if defined(PETSC_HAVE_MKL_SPARSE)
6906: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6907: #endif
6908: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6909: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6910: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6911: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6912: #if defined(PETSC_HAVE_ELEMENTAL)
6913: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6914: #endif
6915: #if defined(PETSC_HAVE_SCALAPACK)
6916: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6917: #endif
6918: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6919: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6920: #if defined(PETSC_HAVE_HYPRE)
6921: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6922: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6923: #endif
6924: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6925: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6926: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6927: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6928: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6929: PetscFunctionReturn(PETSC_SUCCESS);
6930: }
6932: /*@
6933: MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6934: and "off-diagonal" part of the matrix in CSR format.
6936: Collective
6938: Input Parameters:
6939: + comm - MPI communicator
6940: . m - number of local rows (Cannot be `PETSC_DECIDE`)
6941: . n - This value should be the same as the local size used in creating the
6942: x vector for the matrix-vector product $y = Ax$. (or `PETSC_DECIDE` to have
6943: calculated if `N` is given) For square matrices `n` is almost always `m`.
6944: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6945: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6946: . 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
6947: . j - column indices, which must be local, i.e., based off the start column of the diagonal portion
6948: . a - matrix values
6949: . 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
6950: . oj - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6951: - oa - matrix values
6953: Output Parameter:
6954: . mat - the matrix
6956: Level: advanced
6958: Notes:
6959: The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc (even in Fortran). The user
6960: must free the arrays once the matrix has been destroyed and not before.
6962: The `i` and `j` indices are 0 based
6964: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix
6966: This sets local rows and cannot be used to set off-processor values.
6968: Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6969: legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6970: not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
6971: the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
6972: keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
6973: communication if it is known that only local entries will be set.
6975: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
6976: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
6977: @*/
6978: 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)
6979: {
6980: Mat_MPIAIJ *maij;
6982: PetscFunctionBegin;
6983: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
6984: PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
6985: PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
6986: PetscCall(MatCreate(comm, mat));
6987: PetscCall(MatSetSizes(*mat, m, n, M, N));
6988: PetscCall(MatSetType(*mat, MATMPIAIJ));
6989: maij = (Mat_MPIAIJ *)(*mat)->data;
6991: (*mat)->preallocated = PETSC_TRUE;
6993: PetscCall(PetscLayoutSetUp((*mat)->rmap));
6994: PetscCall(PetscLayoutSetUp((*mat)->cmap));
6996: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
6997: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));
6999: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
7000: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
7001: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
7002: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
7003: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
7004: PetscFunctionReturn(PETSC_SUCCESS);
7005: }
7007: typedef struct {
7008: Mat *mp; /* intermediate products */
7009: PetscBool *mptmp; /* is the intermediate product temporary ? */
7010: PetscInt cp; /* number of intermediate products */
7012: /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
7013: PetscInt *startsj_s, *startsj_r;
7014: PetscScalar *bufa;
7015: Mat P_oth;
7017: /* may take advantage of merging product->B */
7018: Mat Bloc; /* B-local by merging diag and off-diag */
7020: /* cusparse does not have support to split between symbolic and numeric phases.
7021: When api_user is true, we don't need to update the numerical values
7022: of the temporary storage */
7023: PetscBool reusesym;
7025: /* support for COO values insertion */
7026: PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
7027: PetscInt **own; /* own[i] points to address of on-process COO indices for Mat mp[i] */
7028: PetscInt **off; /* off[i] points to address of off-process COO indices for Mat mp[i] */
7029: PetscBool hasoffproc; /* if true, have off-process values insertion (i.e. AtB or PtAP) */
7030: PetscSF sf; /* used for non-local values insertion and memory malloc */
7031: PetscMemType mtype;
7033: /* customization */
7034: PetscBool abmerge;
7035: PetscBool P_oth_bind;
7036: } MatMatMPIAIJBACKEND;
7038: static PetscErrorCode MatDestroy_MatMatMPIAIJBACKEND(void *data)
7039: {
7040: MatMatMPIAIJBACKEND *mmdata = (MatMatMPIAIJBACKEND *)data;
7041: PetscInt i;
7043: PetscFunctionBegin;
7044: PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
7045: PetscCall(PetscFree(mmdata->bufa));
7046: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
7047: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
7048: PetscCall(MatDestroy(&mmdata->P_oth));
7049: PetscCall(MatDestroy(&mmdata->Bloc));
7050: PetscCall(PetscSFDestroy(&mmdata->sf));
7051: for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
7052: PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
7053: PetscCall(PetscFree(mmdata->own[0]));
7054: PetscCall(PetscFree(mmdata->own));
7055: PetscCall(PetscFree(mmdata->off[0]));
7056: PetscCall(PetscFree(mmdata->off));
7057: PetscCall(PetscFree(mmdata));
7058: PetscFunctionReturn(PETSC_SUCCESS);
7059: }
7061: /* Copy selected n entries with indices in idx[] of A to v[].
7062: If idx is NULL, copy the whole data array of A to v[]
7063: */
7064: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
7065: {
7066: PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);
7068: PetscFunctionBegin;
7069: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
7070: if (f) {
7071: PetscCall((*f)(A, n, idx, v));
7072: } else {
7073: const PetscScalar *vv;
7075: PetscCall(MatSeqAIJGetArrayRead(A, &vv));
7076: if (n && idx) {
7077: PetscScalar *w = v;
7078: const PetscInt *oi = idx;
7079: PetscInt j;
7081: for (j = 0; j < n; j++) *w++ = vv[*oi++];
7082: } else {
7083: PetscCall(PetscArraycpy(v, vv, n));
7084: }
7085: PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7086: }
7087: PetscFunctionReturn(PETSC_SUCCESS);
7088: }
7090: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7091: {
7092: MatMatMPIAIJBACKEND *mmdata;
7093: PetscInt i, n_d, n_o;
7095: PetscFunctionBegin;
7096: MatCheckProduct(C, 1);
7097: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7098: mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7099: if (!mmdata->reusesym) { /* update temporary matrices */
7100: 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));
7101: if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7102: }
7103: mmdata->reusesym = PETSC_FALSE;
7105: for (i = 0; i < mmdata->cp; i++) {
7106: 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]);
7107: PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7108: }
7109: for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7110: PetscInt noff;
7112: PetscCall(PetscIntCast(mmdata->off[i + 1] - mmdata->off[i], &noff));
7113: if (mmdata->mptmp[i]) continue;
7114: if (noff) {
7115: PetscInt nown;
7117: PetscCall(PetscIntCast(mmdata->own[i + 1] - mmdata->own[i], &nown));
7118: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7119: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7120: n_o += noff;
7121: n_d += nown;
7122: } else {
7123: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;
7125: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7126: n_d += mm->nz;
7127: }
7128: }
7129: if (mmdata->hasoffproc) { /* offprocess insertion */
7130: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7131: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7132: }
7133: PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7134: PetscFunctionReturn(PETSC_SUCCESS);
7135: }
7137: /* Support for Pt * A, A * P, or Pt * A * P */
7138: #define MAX_NUMBER_INTERMEDIATE 4
7139: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7140: {
7141: Mat_Product *product = C->product;
7142: Mat A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7143: Mat_MPIAIJ *a, *p;
7144: MatMatMPIAIJBACKEND *mmdata;
7145: ISLocalToGlobalMapping P_oth_l2g = NULL;
7146: IS glob = NULL;
7147: const char *prefix;
7148: char pprefix[256];
7149: const PetscInt *globidx, *P_oth_idx;
7150: PetscInt i, j, cp, m, n, M, N, *coo_i, *coo_j;
7151: PetscCount ncoo, ncoo_d, ncoo_o, ncoo_oown;
7152: PetscInt cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7153: /* type-0: consecutive, start from 0; type-1: consecutive with */
7154: /* a base offset; type-2: sparse with a local to global map table */
7155: const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE]; /* col/row local to global map array (table) for type-2 map type */
7157: MatProductType ptype;
7158: PetscBool mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7159: PetscMPIInt size;
7161: PetscFunctionBegin;
7162: MatCheckProduct(C, 1);
7163: PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7164: ptype = product->type;
7165: if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7166: ptype = MATPRODUCT_AB;
7167: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7168: }
7169: switch (ptype) {
7170: case MATPRODUCT_AB:
7171: A = product->A;
7172: P = product->B;
7173: m = A->rmap->n;
7174: n = P->cmap->n;
7175: M = A->rmap->N;
7176: N = P->cmap->N;
7177: hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7178: break;
7179: case MATPRODUCT_AtB:
7180: P = product->A;
7181: A = product->B;
7182: m = P->cmap->n;
7183: n = A->cmap->n;
7184: M = P->cmap->N;
7185: N = A->cmap->N;
7186: hasoffproc = PETSC_TRUE;
7187: break;
7188: case MATPRODUCT_PtAP:
7189: A = product->A;
7190: P = product->B;
7191: m = P->cmap->n;
7192: n = P->cmap->n;
7193: M = P->cmap->N;
7194: N = P->cmap->N;
7195: hasoffproc = PETSC_TRUE;
7196: break;
7197: default:
7198: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7199: }
7200: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7201: if (size == 1) hasoffproc = PETSC_FALSE;
7203: /* defaults */
7204: for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7205: mp[i] = NULL;
7206: mptmp[i] = PETSC_FALSE;
7207: rmapt[i] = -1;
7208: cmapt[i] = -1;
7209: rmapa[i] = NULL;
7210: cmapa[i] = NULL;
7211: }
7213: /* customization */
7214: PetscCall(PetscNew(&mmdata));
7215: mmdata->reusesym = product->api_user;
7216: if (ptype == MATPRODUCT_AB) {
7217: if (product->api_user) {
7218: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7219: PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7220: PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7221: PetscOptionsEnd();
7222: } else {
7223: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7224: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7225: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7226: PetscOptionsEnd();
7227: }
7228: } else if (ptype == MATPRODUCT_PtAP) {
7229: if (product->api_user) {
7230: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7231: PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7232: PetscOptionsEnd();
7233: } else {
7234: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7235: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7236: PetscOptionsEnd();
7237: }
7238: }
7239: a = (Mat_MPIAIJ *)A->data;
7240: p = (Mat_MPIAIJ *)P->data;
7241: PetscCall(MatSetSizes(C, m, n, M, N));
7242: PetscCall(PetscLayoutSetUp(C->rmap));
7243: PetscCall(PetscLayoutSetUp(C->cmap));
7244: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7245: PetscCall(MatGetOptionsPrefix(C, &prefix));
7247: cp = 0;
7248: switch (ptype) {
7249: case MATPRODUCT_AB: /* A * P */
7250: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7252: /* A_diag * P_local (merged or not) */
7253: if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7254: /* P is product->B */
7255: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7256: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7257: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7258: PetscCall(MatProductSetFill(mp[cp], product->fill));
7259: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7260: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7261: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7262: mp[cp]->product->api_user = product->api_user;
7263: PetscCall(MatProductSetFromOptions(mp[cp]));
7264: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7265: PetscCall(ISGetIndices(glob, &globidx));
7266: rmapt[cp] = 1;
7267: cmapt[cp] = 2;
7268: cmapa[cp] = globidx;
7269: mptmp[cp] = PETSC_FALSE;
7270: cp++;
7271: } else { /* A_diag * P_diag and A_diag * P_off */
7272: PetscCall(MatProductCreate(a->A, p->A, NULL, &mp[cp]));
7273: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7274: PetscCall(MatProductSetFill(mp[cp], product->fill));
7275: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7276: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7277: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7278: mp[cp]->product->api_user = product->api_user;
7279: PetscCall(MatProductSetFromOptions(mp[cp]));
7280: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7281: rmapt[cp] = 1;
7282: cmapt[cp] = 1;
7283: mptmp[cp] = PETSC_FALSE;
7284: cp++;
7285: PetscCall(MatProductCreate(a->A, p->B, 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: rmapt[cp] = 1;
7295: cmapt[cp] = 2;
7296: cmapa[cp] = p->garray;
7297: mptmp[cp] = PETSC_FALSE;
7298: cp++;
7299: }
7301: /* A_off * P_other */
7302: if (mmdata->P_oth) {
7303: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7304: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7305: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7306: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7307: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7308: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7309: PetscCall(MatProductSetFill(mp[cp], product->fill));
7310: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7311: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7312: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7313: mp[cp]->product->api_user = product->api_user;
7314: PetscCall(MatProductSetFromOptions(mp[cp]));
7315: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7316: rmapt[cp] = 1;
7317: cmapt[cp] = 2;
7318: cmapa[cp] = P_oth_idx;
7319: mptmp[cp] = PETSC_FALSE;
7320: cp++;
7321: }
7322: break;
7324: case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7325: /* A is product->B */
7326: PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7327: if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7328: PetscCall(MatProductCreate(mmdata->Bloc, mmdata->Bloc, NULL, &mp[cp]));
7329: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7330: PetscCall(MatProductSetFill(mp[cp], product->fill));
7331: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7332: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7333: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7334: mp[cp]->product->api_user = product->api_user;
7335: PetscCall(MatProductSetFromOptions(mp[cp]));
7336: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7337: PetscCall(ISGetIndices(glob, &globidx));
7338: rmapt[cp] = 2;
7339: rmapa[cp] = globidx;
7340: cmapt[cp] = 2;
7341: cmapa[cp] = globidx;
7342: mptmp[cp] = PETSC_FALSE;
7343: cp++;
7344: } else {
7345: PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7346: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7347: PetscCall(MatProductSetFill(mp[cp], product->fill));
7348: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7349: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7350: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7351: mp[cp]->product->api_user = product->api_user;
7352: PetscCall(MatProductSetFromOptions(mp[cp]));
7353: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7354: PetscCall(ISGetIndices(glob, &globidx));
7355: rmapt[cp] = 1;
7356: cmapt[cp] = 2;
7357: cmapa[cp] = globidx;
7358: mptmp[cp] = PETSC_FALSE;
7359: cp++;
7360: PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7361: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7362: PetscCall(MatProductSetFill(mp[cp], product->fill));
7363: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7364: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7365: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7366: mp[cp]->product->api_user = product->api_user;
7367: PetscCall(MatProductSetFromOptions(mp[cp]));
7368: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7369: rmapt[cp] = 2;
7370: rmapa[cp] = p->garray;
7371: cmapt[cp] = 2;
7372: cmapa[cp] = globidx;
7373: mptmp[cp] = PETSC_FALSE;
7374: cp++;
7375: }
7376: break;
7377: case MATPRODUCT_PtAP:
7378: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7379: /* P is product->B */
7380: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7381: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7382: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
7383: PetscCall(MatProductSetFill(mp[cp], product->fill));
7384: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7385: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7386: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7387: mp[cp]->product->api_user = product->api_user;
7388: PetscCall(MatProductSetFromOptions(mp[cp]));
7389: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7390: PetscCall(ISGetIndices(glob, &globidx));
7391: rmapt[cp] = 2;
7392: rmapa[cp] = globidx;
7393: cmapt[cp] = 2;
7394: cmapa[cp] = globidx;
7395: mptmp[cp] = PETSC_FALSE;
7396: cp++;
7397: if (mmdata->P_oth) {
7398: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7399: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7400: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7401: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7402: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7403: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7404: PetscCall(MatProductSetFill(mp[cp], product->fill));
7405: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7406: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7407: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7408: mp[cp]->product->api_user = product->api_user;
7409: PetscCall(MatProductSetFromOptions(mp[cp]));
7410: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7411: mptmp[cp] = PETSC_TRUE;
7412: cp++;
7413: PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7414: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7415: PetscCall(MatProductSetFill(mp[cp], product->fill));
7416: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7417: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7418: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7419: mp[cp]->product->api_user = product->api_user;
7420: PetscCall(MatProductSetFromOptions(mp[cp]));
7421: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7422: rmapt[cp] = 2;
7423: rmapa[cp] = globidx;
7424: cmapt[cp] = 2;
7425: cmapa[cp] = P_oth_idx;
7426: mptmp[cp] = PETSC_FALSE;
7427: cp++;
7428: }
7429: break;
7430: default:
7431: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7432: }
7433: /* sanity check */
7434: if (size > 1)
7435: 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);
7437: PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7438: for (i = 0; i < cp; i++) {
7439: mmdata->mp[i] = mp[i];
7440: mmdata->mptmp[i] = mptmp[i];
7441: }
7442: mmdata->cp = cp;
7443: C->product->data = mmdata;
7444: C->product->destroy = MatDestroy_MatMatMPIAIJBACKEND;
7445: C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;
7447: /* memory type */
7448: mmdata->mtype = PETSC_MEMTYPE_HOST;
7449: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7450: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7451: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7452: if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7453: else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7454: else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;
7456: /* prepare coo coordinates for values insertion */
7458: /* count total nonzeros of those intermediate seqaij Mats
7459: ncoo_d: # of nonzeros of matrices that do not have offproc entries
7460: ncoo_o: # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7461: ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7462: */
7463: for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7464: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7465: if (mptmp[cp]) continue;
7466: if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7467: const PetscInt *rmap = rmapa[cp];
7468: const PetscInt mr = mp[cp]->rmap->n;
7469: const PetscInt rs = C->rmap->rstart;
7470: const PetscInt re = C->rmap->rend;
7471: const PetscInt *ii = mm->i;
7472: for (i = 0; i < mr; i++) {
7473: const PetscInt gr = rmap[i];
7474: const PetscInt nz = ii[i + 1] - ii[i];
7475: if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7476: else ncoo_oown += nz; /* this row is local */
7477: }
7478: } else ncoo_d += mm->nz;
7479: }
7481: /*
7482: ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc
7484: ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.
7486: off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].
7488: off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7489: own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7490: so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.
7492: coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7493: 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.
7494: */
7495: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7496: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));
7498: /* gather (i,j) of nonzeros inserted by remote procs */
7499: if (hasoffproc) {
7500: PetscSF msf;
7501: PetscInt ncoo2, *coo_i2, *coo_j2;
7503: PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7504: PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7505: PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */
7507: for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7508: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7509: PetscInt *idxoff = mmdata->off[cp];
7510: PetscInt *idxown = mmdata->own[cp];
7511: if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7512: const PetscInt *rmap = rmapa[cp];
7513: const PetscInt *cmap = cmapa[cp];
7514: const PetscInt *ii = mm->i;
7515: PetscInt *coi = coo_i + ncoo_o;
7516: PetscInt *coj = coo_j + ncoo_o;
7517: const PetscInt mr = mp[cp]->rmap->n;
7518: const PetscInt rs = C->rmap->rstart;
7519: const PetscInt re = C->rmap->rend;
7520: const PetscInt cs = C->cmap->rstart;
7521: for (i = 0; i < mr; i++) {
7522: const PetscInt *jj = mm->j + ii[i];
7523: const PetscInt gr = rmap[i];
7524: const PetscInt nz = ii[i + 1] - ii[i];
7525: if (gr < rs || gr >= re) { /* this is an offproc row */
7526: for (j = ii[i]; j < ii[i + 1]; j++) {
7527: *coi++ = gr;
7528: *idxoff++ = j;
7529: }
7530: if (!cmapt[cp]) { /* already global */
7531: for (j = 0; j < nz; j++) *coj++ = jj[j];
7532: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7533: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7534: } else { /* offdiag */
7535: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7536: }
7537: ncoo_o += nz;
7538: } else { /* this is a local row */
7539: for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7540: }
7541: }
7542: }
7543: mmdata->off[cp + 1] = idxoff;
7544: mmdata->own[cp + 1] = idxown;
7545: }
7547: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7548: PetscInt incoo_o;
7549: PetscCall(PetscIntCast(ncoo_o, &incoo_o));
7550: PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, incoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7551: PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7552: PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7553: ncoo = ncoo_d + ncoo_oown + ncoo2;
7554: PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7555: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7556: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7557: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7558: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7559: PetscCall(PetscFree2(coo_i, coo_j));
7560: /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7561: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7562: coo_i = coo_i2;
7563: coo_j = coo_j2;
7564: } else { /* no offproc values insertion */
7565: ncoo = ncoo_d;
7566: PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));
7568: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7569: PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7570: PetscCall(PetscSFSetUp(mmdata->sf));
7571: }
7572: mmdata->hasoffproc = hasoffproc;
7574: /* gather (i,j) of nonzeros inserted locally */
7575: for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7576: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7577: PetscInt *coi = coo_i + ncoo_d;
7578: PetscInt *coj = coo_j + ncoo_d;
7579: const PetscInt *jj = mm->j;
7580: const PetscInt *ii = mm->i;
7581: const PetscInt *cmap = cmapa[cp];
7582: const PetscInt *rmap = rmapa[cp];
7583: const PetscInt mr = mp[cp]->rmap->n;
7584: const PetscInt rs = C->rmap->rstart;
7585: const PetscInt re = C->rmap->rend;
7586: const PetscInt cs = C->cmap->rstart;
7588: if (mptmp[cp]) continue;
7589: if (rmapt[cp] == 1) { /* consecutive rows */
7590: /* fill coo_i */
7591: for (i = 0; i < mr; i++) {
7592: const PetscInt gr = i + rs;
7593: for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7594: }
7595: /* fill coo_j */
7596: if (!cmapt[cp]) { /* type-0, already global */
7597: PetscCall(PetscArraycpy(coj, jj, mm->nz));
7598: } else if (cmapt[cp] == 1) { /* type-1, local to global for consecutive columns of C */
7599: for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7600: } else { /* type-2, local to global for sparse columns */
7601: for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7602: }
7603: ncoo_d += mm->nz;
7604: } else if (rmapt[cp] == 2) { /* sparse rows */
7605: for (i = 0; i < mr; i++) {
7606: const PetscInt *jj = mm->j + ii[i];
7607: const PetscInt gr = rmap[i];
7608: const PetscInt nz = ii[i + 1] - ii[i];
7609: if (gr >= rs && gr < re) { /* local rows */
7610: for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7611: if (!cmapt[cp]) { /* type-0, already global */
7612: for (j = 0; j < nz; j++) *coj++ = jj[j];
7613: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7614: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7615: } else { /* type-2, local to global for sparse columns */
7616: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7617: }
7618: ncoo_d += nz;
7619: }
7620: }
7621: }
7622: }
7623: if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7624: PetscCall(ISDestroy(&glob));
7625: if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7626: PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7627: /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7628: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));
7630: /* set block sizes */
7631: A = product->A;
7632: P = product->B;
7633: switch (ptype) {
7634: case MATPRODUCT_PtAP:
7635: if (P->cmap->bs > 1) PetscCall(MatSetBlockSizes(C, P->cmap->bs, P->cmap->bs));
7636: break;
7637: case MATPRODUCT_RARt:
7638: if (P->rmap->bs > 1) PetscCall(MatSetBlockSizes(C, P->rmap->bs, P->rmap->bs));
7639: break;
7640: case MATPRODUCT_ABC:
7641: PetscCall(MatSetBlockSizesFromMats(C, A, product->C));
7642: break;
7643: case MATPRODUCT_AB:
7644: PetscCall(MatSetBlockSizesFromMats(C, A, P));
7645: break;
7646: case MATPRODUCT_AtB:
7647: if (A->cmap->bs > 1 || P->cmap->bs > 1) PetscCall(MatSetBlockSizes(C, A->cmap->bs, P->cmap->bs));
7648: break;
7649: case MATPRODUCT_ABt:
7650: if (A->rmap->bs > 1 || P->rmap->bs > 1) PetscCall(MatSetBlockSizes(C, A->rmap->bs, P->rmap->bs));
7651: break;
7652: default:
7653: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for ProductType %s", MatProductTypes[ptype]);
7654: }
7656: /* preallocate with COO data */
7657: PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7658: PetscCall(PetscFree2(coo_i, coo_j));
7659: PetscFunctionReturn(PETSC_SUCCESS);
7660: }
7662: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7663: {
7664: Mat_Product *product = mat->product;
7665: #if defined(PETSC_HAVE_DEVICE)
7666: PetscBool match = PETSC_FALSE;
7667: PetscBool usecpu = PETSC_FALSE;
7668: #else
7669: PetscBool match = PETSC_TRUE;
7670: #endif
7672: PetscFunctionBegin;
7673: MatCheckProduct(mat, 1);
7674: #if defined(PETSC_HAVE_DEVICE)
7675: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7676: if (match) { /* we can always fallback to the CPU if requested */
7677: switch (product->type) {
7678: case MATPRODUCT_AB:
7679: if (product->api_user) {
7680: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7681: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7682: PetscOptionsEnd();
7683: } else {
7684: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7685: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7686: PetscOptionsEnd();
7687: }
7688: break;
7689: case MATPRODUCT_AtB:
7690: if (product->api_user) {
7691: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7692: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7693: PetscOptionsEnd();
7694: } else {
7695: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7696: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7697: PetscOptionsEnd();
7698: }
7699: break;
7700: case MATPRODUCT_PtAP:
7701: if (product->api_user) {
7702: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7703: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7704: PetscOptionsEnd();
7705: } else {
7706: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7707: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7708: PetscOptionsEnd();
7709: }
7710: break;
7711: default:
7712: break;
7713: }
7714: match = (PetscBool)!usecpu;
7715: }
7716: #endif
7717: if (match) {
7718: switch (product->type) {
7719: case MATPRODUCT_AB:
7720: case MATPRODUCT_AtB:
7721: case MATPRODUCT_PtAP:
7722: mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7723: break;
7724: default:
7725: break;
7726: }
7727: }
7728: /* fallback to MPIAIJ ops */
7729: if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7730: PetscFunctionReturn(PETSC_SUCCESS);
7731: }
7733: /*
7734: Produces a set of block column indices of the matrix row, one for each block represented in the original row
7736: n - the number of block indices in cc[]
7737: cc - the block indices (must be large enough to contain the indices)
7738: */
7739: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7740: {
7741: PetscInt cnt = -1, nidx, j;
7742: const PetscInt *idx;
7744: PetscFunctionBegin;
7745: PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7746: if (nidx) {
7747: cnt = 0;
7748: cc[cnt] = idx[0] / bs;
7749: for (j = 1; j < nidx; j++) {
7750: if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7751: }
7752: }
7753: PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7754: *n = cnt + 1;
7755: PetscFunctionReturn(PETSC_SUCCESS);
7756: }
7758: /*
7759: Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows
7761: ncollapsed - the number of block indices
7762: collapsed - the block indices (must be large enough to contain the indices)
7763: */
7764: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7765: {
7766: PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;
7768: PetscFunctionBegin;
7769: PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7770: for (i = start + 1; i < start + bs; i++) {
7771: PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7772: PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7773: cprevtmp = cprev;
7774: cprev = merged;
7775: merged = cprevtmp;
7776: }
7777: *ncollapsed = nprev;
7778: if (collapsed) *collapsed = cprev;
7779: PetscFunctionReturn(PETSC_SUCCESS);
7780: }
7782: /*
7783: MatCreateGraph_Simple_AIJ - create simple scalar matrix (graph) from potentially blocked matrix
7785: Input Parameter:
7786: . Amat - matrix
7787: - symmetrize - make the result symmetric
7788: + scale - scale with diagonal
7790: Output Parameter:
7791: . a_Gmat - output scalar graph >= 0
7793: */
7794: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, PetscInt index_size, PetscInt index[], Mat *a_Gmat)
7795: {
7796: PetscInt Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7797: MPI_Comm comm;
7798: Mat Gmat;
7799: PetscBool ismpiaij, isseqaij;
7800: Mat a, b, c;
7801: MatType jtype;
7803: PetscFunctionBegin;
7804: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7805: PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7806: PetscCall(MatGetSize(Amat, &MM, &NN));
7807: PetscCall(MatGetBlockSize(Amat, &bs));
7808: nloc = (Iend - Istart) / bs;
7810: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7811: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7812: PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");
7814: /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7815: /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7816: implementation */
7817: if (bs > 1) {
7818: PetscCall(MatGetType(Amat, &jtype));
7819: PetscCall(MatCreate(comm, &Gmat));
7820: PetscCall(MatSetType(Gmat, jtype));
7821: PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7822: PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7823: if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7824: PetscInt *d_nnz, *o_nnz;
7825: MatScalar *aa, val, *AA;
7826: PetscInt *aj, *ai, *AJ, nc, nmax = 0;
7828: if (isseqaij) {
7829: a = Amat;
7830: b = NULL;
7831: } else {
7832: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7833: a = d->A;
7834: b = d->B;
7835: }
7836: PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7837: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7838: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7839: PetscInt *nnz = (c == a) ? d_nnz : o_nnz;
7840: const PetscInt *cols1, *cols2;
7842: for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7843: PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7844: nnz[brow / bs] = nc2 / bs;
7845: if (nc2 % bs) ok = 0;
7846: if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7847: for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7848: PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7849: if (nc1 != nc2) ok = 0;
7850: else {
7851: for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7852: if (cols1[jj] != cols2[jj]) ok = 0;
7853: if (cols1[jj] % bs != jj % bs) ok = 0;
7854: }
7855: }
7856: PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7857: }
7858: PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7859: if (!ok) {
7860: PetscCall(PetscFree2(d_nnz, o_nnz));
7861: PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7862: goto old_bs;
7863: }
7864: }
7865: }
7866: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7867: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7868: PetscCall(PetscFree2(d_nnz, o_nnz));
7869: PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7870: // diag
7871: for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7872: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;
7874: ai = aseq->i;
7875: n = ai[brow + 1] - ai[brow];
7876: aj = aseq->j + ai[brow];
7877: for (PetscInt k = 0; k < n; k += bs) { // block columns
7878: AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7879: val = 0;
7880: if (index_size == 0) {
7881: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7882: aa = aseq->a + ai[brow + ii] + k;
7883: for (PetscInt jj = 0; jj < bs; jj++) { // columns in block
7884: val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7885: }
7886: }
7887: } else { // use (index,index) value if provided
7888: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7889: PetscInt ii = index[iii];
7890: aa = aseq->a + ai[brow + ii] + k;
7891: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // columns in block
7892: PetscInt jj = index[jjj];
7893: val += PetscAbs(PetscRealPart(aa[jj]));
7894: }
7895: }
7896: }
7897: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%" PetscInt_FMT ") >= nmax (%" PetscInt_FMT ")", k / bs, nmax);
7898: AA[k / bs] = val;
7899: }
7900: grow = Istart / bs + brow / bs;
7901: PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, ADD_VALUES));
7902: }
7903: // off-diag
7904: if (ismpiaij) {
7905: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Amat->data;
7906: const PetscScalar *vals;
7907: const PetscInt *cols, *garray = aij->garray;
7909: PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7910: for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7911: PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7912: for (PetscInt k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7913: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7914: AA[k / bs] = 0;
7915: AJ[cidx] = garray[cols[k]] / bs;
7916: }
7917: nc = ncols / bs;
7918: PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7919: if (index_size == 0) {
7920: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7921: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7922: for (PetscInt k = 0; k < ncols; k += bs) {
7923: for (PetscInt jj = 0; jj < bs; jj++) { // cols in block
7924: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%" PetscInt_FMT ") >= nmax (%" PetscInt_FMT ")", k / bs, nmax);
7925: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7926: }
7927: }
7928: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7929: }
7930: } else { // use (index,index) value if provided
7931: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7932: PetscInt ii = index[iii];
7933: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7934: for (PetscInt k = 0; k < ncols; k += bs) {
7935: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // cols in block
7936: PetscInt jj = index[jjj];
7937: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7938: }
7939: }
7940: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7941: }
7942: }
7943: grow = Istart / bs + brow / bs;
7944: PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, ADD_VALUES));
7945: }
7946: }
7947: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7948: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7949: PetscCall(PetscFree2(AA, AJ));
7950: } else {
7951: const PetscScalar *vals;
7952: const PetscInt *idx;
7953: PetscInt *d_nnz, *o_nnz, *w0, *w1, *w2;
7954: old_bs:
7955: /*
7956: Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7957: */
7958: PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7959: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7960: if (isseqaij) {
7961: PetscInt max_d_nnz;
7963: /*
7964: Determine exact preallocation count for (sequential) scalar matrix
7965: */
7966: PetscCall(MatSeqAIJGetMaxRowNonzeros(Amat, &max_d_nnz));
7967: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7968: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7969: for (Ii = 0, jj = 0; Ii < Iend; Ii += bs, jj++) PetscCall(MatCollapseRows(Amat, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7970: PetscCall(PetscFree3(w0, w1, w2));
7971: } else if (ismpiaij) {
7972: Mat Daij, Oaij;
7973: const PetscInt *garray;
7974: PetscInt max_d_nnz;
7976: PetscCall(MatMPIAIJGetSeqAIJ(Amat, &Daij, &Oaij, &garray));
7977: /*
7978: Determine exact preallocation count for diagonal block portion of scalar matrix
7979: */
7980: PetscCall(MatSeqAIJGetMaxRowNonzeros(Daij, &max_d_nnz));
7981: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7982: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7983: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) PetscCall(MatCollapseRows(Daij, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7984: PetscCall(PetscFree3(w0, w1, w2));
7985: /*
7986: Over estimate (usually grossly over), preallocation count for off-diagonal portion of scalar matrix
7987: */
7988: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) {
7989: o_nnz[jj] = 0;
7990: for (kk = 0; kk < bs; kk++) { /* rows that get collapsed to a single row */
7991: PetscCall(MatGetRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7992: o_nnz[jj] += ncols;
7993: PetscCall(MatRestoreRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7994: }
7995: if (o_nnz[jj] > (NN / bs - nloc)) o_nnz[jj] = NN / bs - nloc;
7996: }
7997: } else SETERRQ(comm, PETSC_ERR_USER, "Require AIJ matrix type");
7998: /* get scalar copy (norms) of matrix */
7999: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
8000: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
8001: PetscCall(PetscFree2(d_nnz, o_nnz));
8002: for (Ii = Istart; Ii < Iend; Ii++) {
8003: PetscInt dest_row = Ii / bs;
8005: PetscCall(MatGetRow(Amat, Ii, &ncols, &idx, &vals));
8006: for (jj = 0; jj < ncols; jj++) {
8007: PetscInt dest_col = idx[jj] / bs;
8008: PetscScalar sv = PetscAbs(PetscRealPart(vals[jj]));
8010: PetscCall(MatSetValues(Gmat, 1, &dest_row, 1, &dest_col, &sv, ADD_VALUES));
8011: }
8012: PetscCall(MatRestoreRow(Amat, Ii, &ncols, &idx, &vals));
8013: }
8014: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
8015: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
8016: }
8017: } else {
8018: if (symmetrize || filter >= 0 || scale) PetscCall(MatDuplicate(Amat, MAT_COPY_VALUES, &Gmat));
8019: else {
8020: Gmat = Amat;
8021: PetscCall(PetscObjectReference((PetscObject)Gmat));
8022: }
8023: if (isseqaij) {
8024: a = Gmat;
8025: b = NULL;
8026: } else {
8027: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
8028: a = d->A;
8029: b = d->B;
8030: }
8031: if (filter >= 0 || scale) {
8032: /* take absolute value of each entry */
8033: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
8034: MatInfo info;
8035: PetscScalar *avals;
8037: PetscCall(MatGetInfo(c, MAT_LOCAL, &info));
8038: PetscCall(MatSeqAIJGetArray(c, &avals));
8039: for (int jj = 0; jj < info.nz_used; jj++) avals[jj] = PetscAbsScalar(avals[jj]);
8040: PetscCall(MatSeqAIJRestoreArray(c, &avals));
8041: }
8042: }
8043: }
8044: if (symmetrize) {
8045: PetscBool isset, issym;
8047: PetscCall(MatIsSymmetricKnown(Amat, &isset, &issym));
8048: if (!isset || !issym) {
8049: Mat matTrans;
8051: PetscCall(MatTranspose(Gmat, MAT_INITIAL_MATRIX, &matTrans));
8052: PetscCall(MatAXPY(Gmat, 1.0, matTrans, Gmat->structurally_symmetric == PETSC_BOOL3_TRUE ? SAME_NONZERO_PATTERN : DIFFERENT_NONZERO_PATTERN));
8053: PetscCall(MatDestroy(&matTrans));
8054: }
8055: PetscCall(MatSetOption(Gmat, MAT_SYMMETRIC, PETSC_TRUE));
8056: } else if (Amat != Gmat) PetscCall(MatPropagateSymmetryOptions(Amat, Gmat));
8057: if (scale) {
8058: /* scale c for all diagonal values = 1 or -1 */
8059: Vec diag;
8061: PetscCall(MatCreateVecs(Gmat, &diag, NULL));
8062: PetscCall(MatGetDiagonal(Gmat, diag));
8063: PetscCall(VecReciprocal(diag));
8064: PetscCall(VecSqrtAbs(diag));
8065: PetscCall(MatDiagonalScale(Gmat, diag, diag));
8066: PetscCall(VecDestroy(&diag));
8067: }
8068: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_graph_view"));
8069: if (filter >= 0) {
8070: PetscCall(MatFilter(Gmat, filter, PETSC_TRUE, PETSC_TRUE));
8071: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_filter_graph_view"));
8072: }
8073: *a_Gmat = Gmat;
8074: PetscFunctionReturn(PETSC_SUCCESS);
8075: }
8077: /*
8078: Special version for direct calls from Fortran
8079: */
8081: /* Change these macros so can be used in void function */
8082: /* Identical to PetscCallVoid, except it assigns to *_ierr */
8083: #undef PetscCall
8084: #define PetscCall(...) \
8085: do { \
8086: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
8087: if (PetscUnlikely(ierr_msv_mpiaij)) { \
8088: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
8089: return; \
8090: } \
8091: } while (0)
8093: #undef SETERRQ
8094: #define SETERRQ(comm, ierr, ...) \
8095: do { \
8096: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
8097: return; \
8098: } while (0)
8100: #if defined(PETSC_HAVE_FORTRAN_CAPS)
8101: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
8102: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
8103: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
8104: #else
8105: #endif
8106: PETSC_EXTERN void matsetvaluesmpiaij_(Mat *mmat, PetscInt *mm, const PetscInt im[], PetscInt *mn, const PetscInt in[], const PetscScalar v[], InsertMode *maddv, PetscErrorCode *_ierr)
8107: {
8108: Mat mat = *mmat;
8109: PetscInt m = *mm, n = *mn;
8110: InsertMode addv = *maddv;
8111: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
8112: PetscScalar value;
8114: MatCheckPreallocated(mat, 1);
8115: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
8116: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
8117: {
8118: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
8119: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
8120: PetscBool roworiented = aij->roworiented;
8122: /* Some Variables required in the macro */
8123: Mat A = aij->A;
8124: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
8125: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
8126: MatScalar *aa;
8127: PetscBool ignorezeroentries = ((a->ignorezeroentries && (addv == ADD_VALUES)) ? PETSC_TRUE : PETSC_FALSE);
8128: Mat B = aij->B;
8129: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
8130: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
8131: MatScalar *ba;
8132: /* This variable below is only for the PETSC_HAVE_VIENNACL or PETSC_HAVE_CUDA cases, but we define it in all cases because we
8133: * cannot use "#if defined" inside a macro. */
8134: PETSC_UNUSED PetscBool inserted = PETSC_FALSE;
8136: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
8137: PetscInt nonew = a->nonew;
8138: MatScalar *ap1, *ap2;
8140: PetscFunctionBegin;
8141: PetscCall(MatSeqAIJGetArray(A, &aa));
8142: PetscCall(MatSeqAIJGetArray(B, &ba));
8143: for (i = 0; i < m; i++) {
8144: if (im[i] < 0) continue;
8145: 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);
8146: if (im[i] >= rstart && im[i] < rend) {
8147: row = im[i] - rstart;
8148: lastcol1 = -1;
8149: rp1 = aj + ai[row];
8150: ap1 = aa + ai[row];
8151: rmax1 = aimax[row];
8152: nrow1 = ailen[row];
8153: low1 = 0;
8154: high1 = nrow1;
8155: lastcol2 = -1;
8156: rp2 = bj + bi[row];
8157: ap2 = ba + bi[row];
8158: rmax2 = bimax[row];
8159: nrow2 = bilen[row];
8160: low2 = 0;
8161: high2 = nrow2;
8163: for (j = 0; j < n; j++) {
8164: if (roworiented) value = v[i * n + j];
8165: else value = v[i + j * m];
8166: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
8167: if (in[j] >= cstart && in[j] < cend) {
8168: col = in[j] - cstart;
8169: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
8170: } else if (in[j] < 0) continue;
8171: else if (PetscUnlikelyDebug(in[j] >= mat->cmap->N)) {
8172: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
8173: } else {
8174: if (mat->was_assembled) {
8175: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
8176: #if defined(PETSC_USE_CTABLE)
8177: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col));
8178: col--;
8179: #else
8180: col = aij->colmap[in[j]] - 1;
8181: #endif
8182: if (col < 0 && !((Mat_SeqAIJ *)aij->A->data)->nonew) {
8183: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));
8184: col = in[j];
8185: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
8186: B = aij->B;
8187: b = (Mat_SeqAIJ *)B->data;
8188: bimax = b->imax;
8189: bi = b->i;
8190: bilen = b->ilen;
8191: bj = b->j;
8192: rp2 = bj + bi[row];
8193: ap2 = ba + bi[row];
8194: rmax2 = bimax[row];
8195: nrow2 = bilen[row];
8196: low2 = 0;
8197: high2 = nrow2;
8198: bm = aij->B->rmap->n;
8199: ba = b->a;
8200: inserted = PETSC_FALSE;
8201: }
8202: } else col = in[j];
8203: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
8204: }
8205: }
8206: } else if (!aij->donotstash) {
8207: if (roworiented) {
8208: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8209: } else {
8210: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8211: }
8212: }
8213: }
8214: PetscCall(MatSeqAIJRestoreArray(A, &aa));
8215: PetscCall(MatSeqAIJRestoreArray(B, &ba));
8216: }
8217: PetscFunctionReturnVoid();
8218: }
8220: /* Undefining these here since they were redefined from their original definition above! No
8221: * other PETSc functions should be defined past this point, as it is impossible to recover the
8222: * original definitions */
8223: #undef PetscCall
8224: #undef SETERRQ