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 down 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: PetscCheck(1 == ((Mat_SeqAIJ *)aij->B->data)->nonew, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]);
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: }
615: } else col = in[j];
616: nonew = b->nonew;
617: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
618: }
619: }
620: } else {
621: 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]);
622: if (!aij->donotstash) {
623: mat->assembled = PETSC_FALSE;
624: if (roworiented) {
625: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i * n), (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
626: } else {
627: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i), m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
628: }
629: }
630: }
631: }
632: PetscCall(MatSeqAIJRestoreArray(A, &aa)); /* aa, bb might have been free'd due to reallocation above. But we don't access them here */
633: PetscCall(MatSeqAIJRestoreArray(B, &ba));
634: PetscFunctionReturn(PETSC_SUCCESS);
635: }
637: /*
638: This function sets the j and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
639: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
640: No off-processor parts off the matrix are allowed here and mat->was_assembled has to be PETSC_FALSE.
641: */
642: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[])
643: {
644: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
645: Mat A = aij->A; /* diagonal part of the matrix */
646: Mat B = aij->B; /* off-diagonal part of the matrix */
647: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
648: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
649: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, col;
650: PetscInt *ailen = a->ilen, *aj = a->j;
651: PetscInt *bilen = b->ilen, *bj = b->j;
652: PetscInt am = aij->A->rmap->n, j;
653: PetscInt diag_so_far = 0, dnz;
654: PetscInt offd_so_far = 0, onz;
656: PetscFunctionBegin;
657: /* Iterate over all rows of the matrix */
658: for (j = 0; j < am; j++) {
659: dnz = onz = 0;
660: /* Iterate over all non-zero columns of the current row */
661: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
662: /* If column is in the diagonal */
663: if (mat_j[col] >= cstart && mat_j[col] < cend) {
664: aj[diag_so_far++] = mat_j[col] - cstart;
665: dnz++;
666: } else { /* off-diagonal entries */
667: bj[offd_so_far++] = mat_j[col];
668: onz++;
669: }
670: }
671: ailen[j] = dnz;
672: bilen[j] = onz;
673: }
674: PetscFunctionReturn(PETSC_SUCCESS);
675: }
677: /*
678: This function sets the local j, a and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
679: The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
680: No off-processor parts off the matrix are allowed here, they are set at a later point by MatSetValues_MPIAIJ.
681: Also, mat->was_assembled has to be false, otherwise the statement aj[rowstart_diag+dnz_row] = mat_j[col] - cstart;
682: would not be true and the more complex MatSetValues_MPIAIJ has to be used.
683: */
684: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[], const PetscScalar mat_a[])
685: {
686: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
687: Mat A = aij->A; /* diagonal part of the matrix */
688: Mat B = aij->B; /* off-diagonal part of the matrix */
689: Mat_SeqAIJ *aijd = (Mat_SeqAIJ *)aij->A->data, *aijo = (Mat_SeqAIJ *)aij->B->data;
690: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
691: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
692: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend;
693: PetscInt *ailen = a->ilen, *aj = a->j;
694: PetscInt *bilen = b->ilen, *bj = b->j;
695: PetscInt am = aij->A->rmap->n, j;
696: 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. */
697: PetscInt col, dnz_row, onz_row, rowstart_diag, rowstart_offd;
698: PetscScalar *aa = a->a, *ba = b->a;
700: PetscFunctionBegin;
701: /* Iterate over all rows of the matrix */
702: for (j = 0; j < am; j++) {
703: dnz_row = onz_row = 0;
704: rowstart_offd = full_offd_i[j];
705: rowstart_diag = full_diag_i[j];
706: /* Iterate over all non-zero columns of the current row */
707: for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
708: /* If column is in the diagonal */
709: if (mat_j[col] >= cstart && mat_j[col] < cend) {
710: aj[rowstart_diag + dnz_row] = mat_j[col] - cstart;
711: aa[rowstart_diag + dnz_row] = mat_a[col];
712: dnz_row++;
713: } else { /* off-diagonal entries */
714: bj[rowstart_offd + onz_row] = mat_j[col];
715: ba[rowstart_offd + onz_row] = mat_a[col];
716: onz_row++;
717: }
718: }
719: ailen[j] = dnz_row;
720: bilen[j] = onz_row;
721: }
722: PetscFunctionReturn(PETSC_SUCCESS);
723: }
725: static PetscErrorCode MatGetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
726: {
727: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
728: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
729: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
731: PetscFunctionBegin;
732: for (i = 0; i < m; i++) {
733: if (idxm[i] < 0) continue; /* negative row */
734: 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);
735: 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);
736: row = idxm[i] - rstart;
737: for (j = 0; j < n; j++) {
738: if (idxn[j] < 0) continue; /* negative column */
739: 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);
740: if (idxn[j] >= cstart && idxn[j] < cend) {
741: col = idxn[j] - cstart;
742: PetscCall(MatGetValues(aij->A, 1, &row, 1, &col, v + i * n + j));
743: } else {
744: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
745: #if defined(PETSC_USE_CTABLE)
746: PetscCall(PetscHMapIGetWithDefault(aij->colmap, idxn[j] + 1, 0, &col));
747: col--;
748: #else
749: col = aij->colmap[idxn[j]] - 1;
750: #endif
751: if ((col < 0) || (aij->garray[col] != idxn[j])) *(v + i * n + j) = 0.0;
752: else PetscCall(MatGetValues(aij->B, 1, &row, 1, &col, v + i * n + j));
753: }
754: }
755: }
756: PetscFunctionReturn(PETSC_SUCCESS);
757: }
759: static PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat, MatAssemblyType mode)
760: {
761: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
762: PetscInt nstash, reallocs;
764: PetscFunctionBegin;
765: if (aij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
767: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
768: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
769: PetscCall(PetscInfo(aij->A, "Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
770: PetscFunctionReturn(PETSC_SUCCESS);
771: }
773: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat, MatAssemblyType mode)
774: {
775: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
776: PetscMPIInt n;
777: PetscInt i, j, rstart, ncols, flg;
778: PetscInt *row, *col;
779: PetscBool all_assembled;
780: PetscScalar *val;
782: /* do not use 'b = (Mat_SeqAIJ*)aij->B->data' as B can be reset in disassembly */
784: PetscFunctionBegin;
785: if (!aij->donotstash && !mat->nooffprocentries) {
786: while (1) {
787: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
788: if (!flg) break;
790: for (i = 0; i < n;) {
791: /* Now identify the consecutive vals belonging to the same row */
792: for (j = i, rstart = row[j]; j < n; j++) {
793: if (row[j] != rstart) break;
794: }
795: if (j < n) ncols = j - i;
796: else ncols = n - i;
797: /* Now assemble all these values with a single function call */
798: PetscCall(MatSetValues_MPIAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
799: i = j;
800: }
801: }
802: PetscCall(MatStashScatterEnd_Private(&mat->stash));
803: }
804: #if defined(PETSC_HAVE_DEVICE)
805: if (mat->offloadmask == PETSC_OFFLOAD_CPU) aij->A->offloadmask = PETSC_OFFLOAD_CPU;
806: /* We call MatBindToCPU() on aij->A and aij->B here, because if MatBindToCPU_MPIAIJ() is called before assembly, it cannot bind these. */
807: if (mat->boundtocpu) {
808: PetscCall(MatBindToCPU(aij->A, PETSC_TRUE));
809: PetscCall(MatBindToCPU(aij->B, PETSC_TRUE));
810: }
811: #endif
812: PetscCall(MatAssemblyBegin(aij->A, mode));
813: PetscCall(MatAssemblyEnd(aij->A, mode));
815: /* determine if any process has disassembled, if so we must
816: also disassemble ourself, in order that we may reassemble. */
817: /*
818: if nonzero structure of submatrix B cannot change then we know that
819: no process disassembled thus we can skip this stuff
820: */
821: if (!((Mat_SeqAIJ *)aij->B->data)->nonew) {
822: PetscCallMPI(MPIU_Allreduce(&mat->was_assembled, &all_assembled, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
823: if (mat->was_assembled && !all_assembled) { /* mat on this rank has reduced off-diag B with local col ids, but globally it does not */
824: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));
825: }
826: }
827: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIAIJ(mat));
828: PetscCall(MatSetOption(aij->B, MAT_USE_INODES, PETSC_FALSE));
829: #if defined(PETSC_HAVE_DEVICE)
830: if (mat->offloadmask == PETSC_OFFLOAD_CPU && aij->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) aij->B->offloadmask = PETSC_OFFLOAD_CPU;
831: #endif
832: PetscCall(MatAssemblyBegin(aij->B, mode));
833: PetscCall(MatAssemblyEnd(aij->B, mode));
835: PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
837: aij->rowvalues = NULL;
839: PetscCall(VecDestroy(&aij->diag));
841: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
842: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqAIJ *)aij->A->data)->nonew) {
843: PetscObjectState state = aij->A->nonzerostate + aij->B->nonzerostate;
844: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
845: }
846: #if defined(PETSC_HAVE_DEVICE)
847: mat->offloadmask = PETSC_OFFLOAD_BOTH;
848: #endif
849: PetscFunctionReturn(PETSC_SUCCESS);
850: }
852: static PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
853: {
854: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
856: PetscFunctionBegin;
857: PetscCall(MatZeroEntries(l->A));
858: PetscCall(MatZeroEntries(l->B));
859: PetscFunctionReturn(PETSC_SUCCESS);
860: }
862: static PetscErrorCode MatZeroRows_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
863: {
864: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
865: PetscInt *lrows;
866: PetscInt r, len;
867: PetscBool cong;
869: PetscFunctionBegin;
870: /* get locally owned rows */
871: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
872: PetscCall(MatHasCongruentLayouts(A, &cong));
873: /* fix right-hand side if needed */
874: if (x && b) {
875: const PetscScalar *xx;
876: PetscScalar *bb;
878: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
879: PetscCall(VecGetArrayRead(x, &xx));
880: PetscCall(VecGetArray(b, &bb));
881: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
882: PetscCall(VecRestoreArrayRead(x, &xx));
883: PetscCall(VecRestoreArray(b, &bb));
884: }
886: if (diag != 0.0 && cong) {
887: PetscCall(MatZeroRows(mat->A, len, lrows, diag, NULL, NULL));
888: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
889: } else if (diag != 0.0) { /* non-square or non congruent layouts -> if keepnonzeropattern is false, we allow for new insertion */
890: Mat_SeqAIJ *aijA = (Mat_SeqAIJ *)mat->A->data;
891: Mat_SeqAIJ *aijB = (Mat_SeqAIJ *)mat->B->data;
892: PetscInt nnwA, nnwB;
893: PetscBool nnzA, nnzB;
895: nnwA = aijA->nonew;
896: nnwB = aijB->nonew;
897: nnzA = aijA->keepnonzeropattern;
898: nnzB = aijB->keepnonzeropattern;
899: if (!nnzA) {
900: PetscCall(PetscInfo(mat->A, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on diagonal block.\n"));
901: aijA->nonew = 0;
902: }
903: if (!nnzB) {
904: PetscCall(PetscInfo(mat->B, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on off-diagonal block.\n"));
905: aijB->nonew = 0;
906: }
907: /* Must zero here before the next loop */
908: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
909: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
910: for (r = 0; r < len; ++r) {
911: const PetscInt row = lrows[r] + A->rmap->rstart;
912: if (row >= A->cmap->N) continue;
913: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
914: }
915: aijA->nonew = nnwA;
916: aijB->nonew = nnwB;
917: } else {
918: PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
919: PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
920: }
921: PetscCall(PetscFree(lrows));
922: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
923: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
925: /* only change matrix nonzero state if pattern was allowed to be changed */
926: if (!((Mat_SeqAIJ *)mat->A->data)->keepnonzeropattern || !((Mat_SeqAIJ *)mat->A->data)->nonew) {
927: PetscObjectState state = mat->A->nonzerostate + mat->B->nonzerostate;
928: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
929: }
930: PetscFunctionReturn(PETSC_SUCCESS);
931: }
933: static PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
934: {
935: Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;
936: PetscInt n = A->rmap->n;
937: PetscInt i, j, r, m, len = 0;
938: PetscInt *lrows, *owners = A->rmap->range;
939: PetscMPIInt p = 0;
940: PetscSFNode *rrows;
941: PetscSF sf;
942: const PetscScalar *xx;
943: PetscScalar *bb, *mask, *aij_a;
944: Vec xmask, lmask;
945: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)l->B->data;
946: const PetscInt *aj, *ii, *ridx;
947: PetscScalar *aa;
949: PetscFunctionBegin;
950: /* Create SF where leaves are input rows and roots are owned rows */
951: PetscCall(PetscMalloc1(n, &lrows));
952: for (r = 0; r < n; ++r) lrows[r] = -1;
953: PetscCall(PetscMalloc1(N, &rrows));
954: for (r = 0; r < N; ++r) {
955: const PetscInt idx = rows[r];
956: 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);
957: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
958: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
959: }
960: rrows[r].rank = p;
961: rrows[r].index = rows[r] - owners[p];
962: }
963: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
964: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
965: /* Collect flags for rows to be zeroed */
966: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
967: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
968: PetscCall(PetscSFDestroy(&sf));
969: /* Compress and put in row numbers */
970: for (r = 0; r < n; ++r)
971: if (lrows[r] >= 0) lrows[len++] = r;
972: /* zero diagonal part of matrix */
973: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
974: /* handle off-diagonal part of matrix */
975: PetscCall(MatCreateVecs(A, &xmask, NULL));
976: PetscCall(VecDuplicate(l->lvec, &lmask));
977: PetscCall(VecGetArray(xmask, &bb));
978: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
979: PetscCall(VecRestoreArray(xmask, &bb));
980: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
981: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
982: PetscCall(VecDestroy(&xmask));
983: if (x && b) { /* this code is buggy when the row and column layout don't match */
984: PetscBool cong;
986: PetscCall(MatHasCongruentLayouts(A, &cong));
987: PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
988: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
989: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
990: PetscCall(VecGetArrayRead(l->lvec, &xx));
991: PetscCall(VecGetArray(b, &bb));
992: }
993: PetscCall(VecGetArray(lmask, &mask));
994: /* remove zeroed rows of off-diagonal matrix */
995: PetscCall(MatSeqAIJGetArray(l->B, &aij_a));
996: ii = aij->i;
997: for (i = 0; i < len; i++) PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aij_a, ii[lrows[i]]), ii[lrows[i] + 1] - ii[lrows[i]]));
998: /* loop over all elements of off process part of matrix zeroing removed columns*/
999: if (aij->compressedrow.use) {
1000: m = aij->compressedrow.nrows;
1001: ii = aij->compressedrow.i;
1002: ridx = aij->compressedrow.rindex;
1003: for (i = 0; i < m; i++) {
1004: n = ii[i + 1] - ii[i];
1005: aj = aij->j + ii[i];
1006: aa = aij_a + ii[i];
1008: for (j = 0; j < n; j++) {
1009: if (PetscAbsScalar(mask[*aj])) {
1010: if (b) bb[*ridx] -= *aa * xx[*aj];
1011: *aa = 0.0;
1012: }
1013: aa++;
1014: aj++;
1015: }
1016: ridx++;
1017: }
1018: } else { /* do not use compressed row format */
1019: m = l->B->rmap->n;
1020: for (i = 0; i < m; i++) {
1021: n = ii[i + 1] - ii[i];
1022: aj = aij->j + ii[i];
1023: aa = aij_a + ii[i];
1024: for (j = 0; j < n; j++) {
1025: if (PetscAbsScalar(mask[*aj])) {
1026: if (b) bb[i] -= *aa * xx[*aj];
1027: *aa = 0.0;
1028: }
1029: aa++;
1030: aj++;
1031: }
1032: }
1033: }
1034: if (x && b) {
1035: PetscCall(VecRestoreArray(b, &bb));
1036: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1037: }
1038: PetscCall(MatSeqAIJRestoreArray(l->B, &aij_a));
1039: PetscCall(VecRestoreArray(lmask, &mask));
1040: PetscCall(VecDestroy(&lmask));
1041: PetscCall(PetscFree(lrows));
1043: /* only change matrix nonzero state if pattern was allowed to be changed */
1044: if (!((Mat_SeqAIJ *)l->A->data)->nonew) {
1045: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1046: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1047: }
1048: PetscFunctionReturn(PETSC_SUCCESS);
1049: }
1051: static PetscErrorCode MatMult_MPIAIJ(Mat A, Vec xx, Vec yy)
1052: {
1053: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1054: PetscInt nt;
1055: VecScatter Mvctx = a->Mvctx;
1057: PetscFunctionBegin;
1058: PetscCall(VecGetLocalSize(xx, &nt));
1059: 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);
1060: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1061: PetscUseTypeMethod(a->A, mult, xx, yy);
1062: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1063: PetscUseTypeMethod(a->B, multadd, a->lvec, yy, yy);
1064: PetscFunctionReturn(PETSC_SUCCESS);
1065: }
1067: static PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A, Vec bb, Vec xx)
1068: {
1069: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1071: PetscFunctionBegin;
1072: PetscCall(MatMultDiagonalBlock(a->A, bb, xx));
1073: PetscFunctionReturn(PETSC_SUCCESS);
1074: }
1076: static PetscErrorCode MatMultAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1077: {
1078: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1079: VecScatter Mvctx = a->Mvctx;
1081: PetscFunctionBegin;
1082: PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1083: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1084: PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1085: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1086: PetscFunctionReturn(PETSC_SUCCESS);
1087: }
1089: static PetscErrorCode MatMultTranspose_MPIAIJ(Mat A, Vec xx, Vec yy)
1090: {
1091: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1093: PetscFunctionBegin;
1094: /* do nondiagonal part */
1095: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1096: /* do local part */
1097: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1098: /* add partial results together */
1099: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1100: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1101: PetscFunctionReturn(PETSC_SUCCESS);
1102: }
1104: static PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f)
1105: {
1106: MPI_Comm comm;
1107: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)Amat->data, *Bij = (Mat_MPIAIJ *)Bmat->data;
1108: Mat Adia = Aij->A, Bdia = Bij->A, Aoff, Boff, *Aoffs, *Boffs;
1109: IS Me, Notme;
1110: PetscInt M, N, first, last, *notme, i;
1111: PetscBool lf;
1112: PetscMPIInt size;
1114: PetscFunctionBegin;
1115: /* Easy test: symmetric diagonal block */
1116: PetscCall(MatIsTranspose(Adia, Bdia, tol, &lf));
1117: PetscCallMPI(MPIU_Allreduce(&lf, f, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)Amat)));
1118: if (!*f) PetscFunctionReturn(PETSC_SUCCESS);
1119: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1120: PetscCallMPI(MPI_Comm_size(comm, &size));
1121: if (size == 1) PetscFunctionReturn(PETSC_SUCCESS);
1123: /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */
1124: PetscCall(MatGetSize(Amat, &M, &N));
1125: PetscCall(MatGetOwnershipRange(Amat, &first, &last));
1126: PetscCall(PetscMalloc1(N - last + first, ¬me));
1127: for (i = 0; i < first; i++) notme[i] = i;
1128: for (i = last; i < M; i++) notme[i - last + first] = i;
1129: PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme));
1130: PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me));
1131: PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs));
1132: Aoff = Aoffs[0];
1133: PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs));
1134: Boff = Boffs[0];
1135: PetscCall(MatIsTranspose(Aoff, Boff, tol, f));
1136: PetscCall(MatDestroyMatrices(1, &Aoffs));
1137: PetscCall(MatDestroyMatrices(1, &Boffs));
1138: PetscCall(ISDestroy(&Me));
1139: PetscCall(ISDestroy(&Notme));
1140: PetscCall(PetscFree(notme));
1141: PetscFunctionReturn(PETSC_SUCCESS);
1142: }
1144: static PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1145: {
1146: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1148: PetscFunctionBegin;
1149: /* do nondiagonal part */
1150: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1151: /* do local part */
1152: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1153: /* add partial results together */
1154: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1155: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1156: PetscFunctionReturn(PETSC_SUCCESS);
1157: }
1159: /*
1160: This only works correctly for square matrices where the subblock A->A is the
1161: diagonal block
1162: */
1163: static PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A, Vec v)
1164: {
1165: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1167: PetscFunctionBegin;
1168: PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1169: 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");
1170: PetscCall(MatGetDiagonal(a->A, v));
1171: PetscFunctionReturn(PETSC_SUCCESS);
1172: }
1174: static PetscErrorCode MatScale_MPIAIJ(Mat A, PetscScalar aa)
1175: {
1176: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1178: PetscFunctionBegin;
1179: PetscCall(MatScale(a->A, aa));
1180: PetscCall(MatScale(a->B, aa));
1181: PetscFunctionReturn(PETSC_SUCCESS);
1182: }
1184: static PetscErrorCode MatView_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
1185: {
1186: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1187: Mat_SeqAIJ *A = (Mat_SeqAIJ *)aij->A->data;
1188: Mat_SeqAIJ *B = (Mat_SeqAIJ *)aij->B->data;
1189: const PetscInt *garray = aij->garray;
1190: const PetscScalar *aa, *ba;
1191: PetscInt header[4], M, N, m, rs, cs, cnt, i, ja, jb;
1192: PetscInt64 nz, hnz;
1193: PetscInt *rowlens;
1194: PetscInt *colidxs;
1195: PetscScalar *matvals;
1196: PetscMPIInt rank;
1198: PetscFunctionBegin;
1199: PetscCall(PetscViewerSetUp(viewer));
1201: M = mat->rmap->N;
1202: N = mat->cmap->N;
1203: m = mat->rmap->n;
1204: rs = mat->rmap->rstart;
1205: cs = mat->cmap->rstart;
1206: nz = A->nz + B->nz;
1208: /* write matrix header */
1209: header[0] = MAT_FILE_CLASSID;
1210: header[1] = M;
1211: header[2] = N;
1212: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1213: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1214: if (rank == 0) PetscCall(PetscIntCast(hnz, &header[3]));
1215: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1217: /* fill in and store row lengths */
1218: PetscCall(PetscMalloc1(m, &rowlens));
1219: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i];
1220: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1221: PetscCall(PetscFree(rowlens));
1223: /* fill in and store column indices */
1224: PetscCall(PetscMalloc1(nz, &colidxs));
1225: for (cnt = 0, i = 0; i < m; i++) {
1226: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1227: if (garray[B->j[jb]] > cs) break;
1228: colidxs[cnt++] = garray[B->j[jb]];
1229: }
1230: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) colidxs[cnt++] = A->j[ja] + cs;
1231: for (; jb < B->i[i + 1]; jb++) colidxs[cnt++] = garray[B->j[jb]];
1232: }
1233: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1234: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
1235: PetscCall(PetscFree(colidxs));
1237: /* fill in and store nonzero values */
1238: PetscCall(MatSeqAIJGetArrayRead(aij->A, &aa));
1239: PetscCall(MatSeqAIJGetArrayRead(aij->B, &ba));
1240: PetscCall(PetscMalloc1(nz, &matvals));
1241: for (cnt = 0, i = 0; i < m; i++) {
1242: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1243: if (garray[B->j[jb]] > cs) break;
1244: matvals[cnt++] = ba[jb];
1245: }
1246: for (ja = A->i[i]; ja < A->i[i + 1]; ja++) matvals[cnt++] = aa[ja];
1247: for (; jb < B->i[i + 1]; jb++) matvals[cnt++] = ba[jb];
1248: }
1249: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &aa));
1250: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &ba));
1251: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1252: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
1253: PetscCall(PetscFree(matvals));
1255: /* write block size option to the viewer's .info file */
1256: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1257: PetscFunctionReturn(PETSC_SUCCESS);
1258: }
1260: #include <petscdraw.h>
1261: static PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1262: {
1263: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1264: PetscMPIInt rank = aij->rank, size = aij->size;
1265: PetscBool isdraw, isascii, isbinary;
1266: PetscViewer sviewer;
1267: PetscViewerFormat format;
1269: PetscFunctionBegin;
1270: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1271: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1272: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1273: if (isascii) {
1274: PetscCall(PetscViewerGetFormat(viewer, &format));
1275: if (format == PETSC_VIEWER_LOAD_BALANCE) {
1276: PetscInt i, nmax = 0, nmin = PETSC_INT_MAX, navg = 0, *nz, nzlocal = ((Mat_SeqAIJ *)aij->A->data)->nz + ((Mat_SeqAIJ *)aij->B->data)->nz;
1277: PetscCall(PetscMalloc1(size, &nz));
1278: PetscCallMPI(MPI_Allgather(&nzlocal, 1, MPIU_INT, nz, 1, MPIU_INT, PetscObjectComm((PetscObject)mat)));
1279: for (i = 0; i < size; i++) {
1280: nmax = PetscMax(nmax, nz[i]);
1281: nmin = PetscMin(nmin, nz[i]);
1282: navg += nz[i];
1283: }
1284: PetscCall(PetscFree(nz));
1285: navg = navg / size;
1286: PetscCall(PetscViewerASCIIPrintf(viewer, "Load Balance - Nonzeros: Min %" PetscInt_FMT " avg %" PetscInt_FMT " max %" PetscInt_FMT "\n", nmin, navg, nmax));
1287: PetscFunctionReturn(PETSC_SUCCESS);
1288: }
1289: PetscCall(PetscViewerGetFormat(viewer, &format));
1290: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1291: MatInfo info;
1292: PetscInt *inodes = NULL;
1294: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1295: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1296: PetscCall(MatInodeGetInodeSizes(aij->A, NULL, &inodes, NULL));
1297: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1298: if (!inodes) {
1299: 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,
1300: info.memory));
1301: } else {
1302: PetscCall(
1303: 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));
1304: }
1305: PetscCall(MatGetInfo(aij->A, MAT_LOCAL, &info));
1306: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1307: PetscCall(MatGetInfo(aij->B, MAT_LOCAL, &info));
1308: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1309: PetscCall(PetscViewerFlush(viewer));
1310: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1311: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1312: PetscCall(VecScatterView(aij->Mvctx, viewer));
1313: PetscFunctionReturn(PETSC_SUCCESS);
1314: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1315: PetscInt inodecount, inodelimit, *inodes;
1316: PetscCall(MatInodeGetInodeSizes(aij->A, &inodecount, &inodes, &inodelimit));
1317: if (inodes) {
1318: PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
1319: } else {
1320: PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
1321: }
1322: PetscFunctionReturn(PETSC_SUCCESS);
1323: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1324: PetscFunctionReturn(PETSC_SUCCESS);
1325: }
1326: } else if (isbinary) {
1327: if (size == 1) {
1328: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1329: PetscCall(MatView(aij->A, viewer));
1330: } else {
1331: PetscCall(MatView_MPIAIJ_Binary(mat, viewer));
1332: }
1333: PetscFunctionReturn(PETSC_SUCCESS);
1334: } else if (isascii && size == 1) {
1335: PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1336: PetscCall(MatView(aij->A, viewer));
1337: PetscFunctionReturn(PETSC_SUCCESS);
1338: } else if (isdraw) {
1339: PetscDraw draw;
1340: PetscBool isnull;
1341: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1342: PetscCall(PetscDrawIsNull(draw, &isnull));
1343: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1344: }
1346: { /* assemble the entire matrix onto first processor */
1347: Mat A = NULL, Av;
1348: IS isrow, iscol;
1350: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->rmap->N : 0, 0, 1, &isrow));
1351: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->cmap->N : 0, 0, 1, &iscol));
1352: PetscCall(MatCreateSubMatrix(mat, isrow, iscol, MAT_INITIAL_MATRIX, &A));
1353: PetscCall(MatMPIAIJGetSeqAIJ(A, &Av, NULL, NULL));
1354: /* The commented code uses MatCreateSubMatrices instead */
1355: /*
1356: Mat *AA, A = NULL, Av;
1357: IS isrow,iscol;
1359: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->rmap->N : 0,0,1,&isrow));
1360: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->cmap->N : 0,0,1,&iscol));
1361: PetscCall(MatCreateSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&AA));
1362: if (rank == 0) {
1363: PetscCall(PetscObjectReference((PetscObject)AA[0]));
1364: A = AA[0];
1365: Av = AA[0];
1366: }
1367: PetscCall(MatDestroySubMatrices(1,&AA));
1368: */
1369: PetscCall(ISDestroy(&iscol));
1370: PetscCall(ISDestroy(&isrow));
1371: /*
1372: Everyone has to call to draw the matrix since the graphics waits are
1373: synchronized across all processors that share the PetscDraw object
1374: */
1375: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1376: if (rank == 0) {
1377: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)Av, ((PetscObject)mat)->name));
1378: PetscCall(MatView_SeqAIJ(Av, sviewer));
1379: }
1380: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1381: PetscCall(MatDestroy(&A));
1382: }
1383: PetscFunctionReturn(PETSC_SUCCESS);
1384: }
1386: PetscErrorCode MatView_MPIAIJ(Mat mat, PetscViewer viewer)
1387: {
1388: PetscBool isascii, isdraw, issocket, isbinary;
1390: PetscFunctionBegin;
1391: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1392: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1393: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1394: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1395: if (isascii || isdraw || isbinary || issocket) PetscCall(MatView_MPIAIJ_ASCIIorDraworSocket(mat, viewer));
1396: PetscFunctionReturn(PETSC_SUCCESS);
1397: }
1399: static PetscErrorCode MatSOR_MPIAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1400: {
1401: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1402: Vec bb1 = NULL;
1403: PetscBool hasop;
1405: PetscFunctionBegin;
1406: if (flag == SOR_APPLY_UPPER) {
1407: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1408: PetscFunctionReturn(PETSC_SUCCESS);
1409: }
1411: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1));
1413: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
1414: if (flag & SOR_ZERO_INITIAL_GUESS) {
1415: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1416: its--;
1417: }
1419: while (its--) {
1420: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1421: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1423: /* update rhs: bb1 = bb - B*x */
1424: PetscCall(VecScale(mat->lvec, -1.0));
1425: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1427: /* local sweep */
1428: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
1429: }
1430: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1431: if (flag & SOR_ZERO_INITIAL_GUESS) {
1432: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1433: its--;
1434: }
1435: while (its--) {
1436: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1437: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1439: /* update rhs: bb1 = bb - B*x */
1440: PetscCall(VecScale(mat->lvec, -1.0));
1441: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1443: /* local sweep */
1444: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
1445: }
1446: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1447: if (flag & SOR_ZERO_INITIAL_GUESS) {
1448: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1449: its--;
1450: }
1451: while (its--) {
1452: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1453: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1455: /* update rhs: bb1 = bb - B*x */
1456: PetscCall(VecScale(mat->lvec, -1.0));
1457: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1459: /* local sweep */
1460: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
1461: }
1462: } else if (flag & SOR_EISENSTAT) {
1463: Vec xx1;
1465: PetscCall(VecDuplicate(bb, &xx1));
1466: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP), fshift, lits, 1, xx));
1468: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1469: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1470: if (!mat->diag) {
1471: PetscCall(MatCreateVecs(matin, &mat->diag, NULL));
1472: PetscCall(MatGetDiagonal(matin, mat->diag));
1473: }
1474: PetscCall(MatHasOperation(matin, MATOP_MULT_DIAGONAL_BLOCK, &hasop));
1475: if (hasop) {
1476: PetscCall(MatMultDiagonalBlock(matin, xx, bb1));
1477: } else {
1478: PetscCall(VecPointwiseMult(bb1, mat->diag, xx));
1479: }
1480: PetscCall(VecAYPX(bb1, (omega - 2.0) / omega, bb));
1482: PetscCall(MatMultAdd(mat->B, mat->lvec, bb1, bb1));
1484: /* local sweep */
1485: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP), fshift, lits, 1, xx1));
1486: PetscCall(VecAXPY(xx, 1.0, xx1));
1487: PetscCall(VecDestroy(&xx1));
1488: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");
1490: PetscCall(VecDestroy(&bb1));
1492: matin->factorerrortype = mat->A->factorerrortype;
1493: PetscFunctionReturn(PETSC_SUCCESS);
1494: }
1496: static PetscErrorCode MatPermute_MPIAIJ(Mat A, IS rowp, IS colp, Mat *B)
1497: {
1498: Mat aA, aB, Aperm;
1499: const PetscInt *rwant, *cwant, *gcols, *ai, *bi, *aj, *bj;
1500: PetscScalar *aa, *ba;
1501: PetscInt i, j, m, n, ng, anz, bnz, *dnnz, *onnz, *tdnnz, *tonnz, *rdest, *cdest, *work, *gcdest;
1502: PetscSF rowsf, sf;
1503: IS parcolp = NULL;
1504: PetscBool done;
1506: PetscFunctionBegin;
1507: PetscCall(MatGetLocalSize(A, &m, &n));
1508: PetscCall(ISGetIndices(rowp, &rwant));
1509: PetscCall(ISGetIndices(colp, &cwant));
1510: PetscCall(PetscMalloc3(PetscMax(m, n), &work, m, &rdest, n, &cdest));
1512: /* Invert row permutation to find out where my rows should go */
1513: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &rowsf));
1514: PetscCall(PetscSFSetGraphLayout(rowsf, A->rmap, A->rmap->n, NULL, PETSC_OWN_POINTER, rwant));
1515: PetscCall(PetscSFSetFromOptions(rowsf));
1516: for (i = 0; i < m; i++) work[i] = A->rmap->rstart + i;
1517: PetscCall(PetscSFReduceBegin(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1518: PetscCall(PetscSFReduceEnd(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1520: /* Invert column permutation to find out where my columns should go */
1521: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1522: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, A->cmap->n, NULL, PETSC_OWN_POINTER, cwant));
1523: PetscCall(PetscSFSetFromOptions(sf));
1524: for (i = 0; i < n; i++) work[i] = A->cmap->rstart + i;
1525: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1526: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1527: PetscCall(PetscSFDestroy(&sf));
1529: PetscCall(ISRestoreIndices(rowp, &rwant));
1530: PetscCall(ISRestoreIndices(colp, &cwant));
1531: PetscCall(MatMPIAIJGetSeqAIJ(A, &aA, &aB, &gcols));
1533: /* Find out where my gcols should go */
1534: PetscCall(MatGetSize(aB, NULL, &ng));
1535: PetscCall(PetscMalloc1(ng, &gcdest));
1536: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1537: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, ng, NULL, PETSC_OWN_POINTER, gcols));
1538: PetscCall(PetscSFSetFromOptions(sf));
1539: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1540: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1541: PetscCall(PetscSFDestroy(&sf));
1543: PetscCall(PetscCalloc4(m, &dnnz, m, &onnz, m, &tdnnz, m, &tonnz));
1544: PetscCall(MatGetRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1545: PetscCall(MatGetRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1546: for (i = 0; i < m; i++) {
1547: PetscInt row = rdest[i];
1548: PetscMPIInt rowner;
1549: PetscCall(PetscLayoutFindOwner(A->rmap, row, &rowner));
1550: for (j = ai[i]; j < ai[i + 1]; j++) {
1551: PetscInt col = cdest[aj[j]];
1552: PetscMPIInt cowner;
1553: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner)); /* Could build an index for the columns to eliminate this search */
1554: if (rowner == cowner) dnnz[i]++;
1555: else onnz[i]++;
1556: }
1557: for (j = bi[i]; j < bi[i + 1]; j++) {
1558: PetscInt col = gcdest[bj[j]];
1559: PetscMPIInt cowner;
1560: PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner));
1561: if (rowner == cowner) dnnz[i]++;
1562: else onnz[i]++;
1563: }
1564: }
1565: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1566: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1567: PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1568: PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1569: PetscCall(PetscSFDestroy(&rowsf));
1571: PetscCall(MatCreateAIJ(PetscObjectComm((PetscObject)A), A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N, 0, tdnnz, 0, tonnz, &Aperm));
1572: PetscCall(MatSeqAIJGetArray(aA, &aa));
1573: PetscCall(MatSeqAIJGetArray(aB, &ba));
1574: for (i = 0; i < m; i++) {
1575: PetscInt *acols = dnnz, *bcols = onnz; /* Repurpose now-unneeded arrays */
1576: PetscInt j0, rowlen;
1577: rowlen = ai[i + 1] - ai[i];
1578: for (j0 = j = 0; j < rowlen; j0 = j) { /* rowlen could be larger than number of rows m, so sum in batches */
1579: for (; j < PetscMin(rowlen, j0 + m); j++) acols[j - j0] = cdest[aj[ai[i] + j]];
1580: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, acols, aa + ai[i] + j0, INSERT_VALUES));
1581: }
1582: rowlen = bi[i + 1] - bi[i];
1583: for (j0 = j = 0; j < rowlen; j0 = j) {
1584: for (; j < PetscMin(rowlen, j0 + m); j++) bcols[j - j0] = gcdest[bj[bi[i] + j]];
1585: PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, bcols, ba + bi[i] + j0, INSERT_VALUES));
1586: }
1587: }
1588: PetscCall(MatAssemblyBegin(Aperm, MAT_FINAL_ASSEMBLY));
1589: PetscCall(MatAssemblyEnd(Aperm, MAT_FINAL_ASSEMBLY));
1590: PetscCall(MatRestoreRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1591: PetscCall(MatRestoreRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1592: PetscCall(MatSeqAIJRestoreArray(aA, &aa));
1593: PetscCall(MatSeqAIJRestoreArray(aB, &ba));
1594: PetscCall(PetscFree4(dnnz, onnz, tdnnz, tonnz));
1595: PetscCall(PetscFree3(work, rdest, cdest));
1596: PetscCall(PetscFree(gcdest));
1597: if (parcolp) PetscCall(ISDestroy(&colp));
1598: *B = Aperm;
1599: PetscFunctionReturn(PETSC_SUCCESS);
1600: }
1602: static PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
1603: {
1604: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1606: PetscFunctionBegin;
1607: PetscCall(MatGetSize(aij->B, NULL, nghosts));
1608: if (ghosts) *ghosts = aij->garray;
1609: PetscFunctionReturn(PETSC_SUCCESS);
1610: }
1612: static PetscErrorCode MatGetInfo_MPIAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1613: {
1614: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1615: Mat A = mat->A, B = mat->B;
1616: PetscLogDouble isend[5], irecv[5];
1618: PetscFunctionBegin;
1619: info->block_size = 1.0;
1620: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1622: isend[0] = info->nz_used;
1623: isend[1] = info->nz_allocated;
1624: isend[2] = info->nz_unneeded;
1625: isend[3] = info->memory;
1626: isend[4] = info->mallocs;
1628: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1630: isend[0] += info->nz_used;
1631: isend[1] += info->nz_allocated;
1632: isend[2] += info->nz_unneeded;
1633: isend[3] += info->memory;
1634: isend[4] += info->mallocs;
1635: if (flag == MAT_LOCAL) {
1636: info->nz_used = isend[0];
1637: info->nz_allocated = isend[1];
1638: info->nz_unneeded = isend[2];
1639: info->memory = isend[3];
1640: info->mallocs = isend[4];
1641: } else if (flag == MAT_GLOBAL_MAX) {
1642: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1644: info->nz_used = irecv[0];
1645: info->nz_allocated = irecv[1];
1646: info->nz_unneeded = irecv[2];
1647: info->memory = irecv[3];
1648: info->mallocs = irecv[4];
1649: } else if (flag == MAT_GLOBAL_SUM) {
1650: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1652: info->nz_used = irecv[0];
1653: info->nz_allocated = irecv[1];
1654: info->nz_unneeded = irecv[2];
1655: info->memory = irecv[3];
1656: info->mallocs = irecv[4];
1657: }
1658: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1659: info->fill_ratio_needed = 0;
1660: info->factor_mallocs = 0;
1661: PetscFunctionReturn(PETSC_SUCCESS);
1662: }
1664: PetscErrorCode MatSetOption_MPIAIJ(Mat A, MatOption op, PetscBool flg)
1665: {
1666: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1668: PetscFunctionBegin;
1669: switch (op) {
1670: case MAT_NEW_NONZERO_LOCATIONS:
1671: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1672: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1673: case MAT_KEEP_NONZERO_PATTERN:
1674: case MAT_NEW_NONZERO_LOCATION_ERR:
1675: case MAT_USE_INODES:
1676: case MAT_IGNORE_ZERO_ENTRIES:
1677: case MAT_FORM_EXPLICIT_TRANSPOSE:
1678: MatCheckPreallocated(A, 1);
1679: PetscCall(MatSetOption(a->A, op, flg));
1680: PetscCall(MatSetOption(a->B, op, flg));
1681: break;
1682: case MAT_ROW_ORIENTED:
1683: MatCheckPreallocated(A, 1);
1684: a->roworiented = flg;
1686: PetscCall(MatSetOption(a->A, op, flg));
1687: PetscCall(MatSetOption(a->B, op, flg));
1688: break;
1689: case MAT_IGNORE_OFF_PROC_ENTRIES:
1690: a->donotstash = flg;
1691: break;
1692: /* Symmetry flags are handled directly by MatSetOption() and they don't affect preallocation */
1693: case MAT_SPD:
1694: case MAT_SYMMETRIC:
1695: case MAT_STRUCTURALLY_SYMMETRIC:
1696: case MAT_HERMITIAN:
1697: case MAT_SYMMETRY_ETERNAL:
1698: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1699: case MAT_SPD_ETERNAL:
1700: /* if the diagonal matrix is square it inherits some of the properties above */
1701: if (a->A && A->rmap->n == A->cmap->n) PetscCall(MatSetOption(a->A, op, flg));
1702: break;
1703: case MAT_SUBMAT_SINGLEIS:
1704: A->submat_singleis = flg;
1705: break;
1706: default:
1707: break;
1708: }
1709: PetscFunctionReturn(PETSC_SUCCESS);
1710: }
1712: PetscErrorCode MatGetRow_MPIAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1713: {
1714: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1715: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1716: PetscInt i, *cworkA, *cworkB, **pcA, **pcB, cstart = matin->cmap->rstart;
1717: PetscInt nztot, nzA, nzB, lrow, rstart = matin->rmap->rstart, rend = matin->rmap->rend;
1718: PetscInt *cmap, *idx_p;
1720: PetscFunctionBegin;
1721: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1722: mat->getrowactive = PETSC_TRUE;
1724: if (!mat->rowvalues && (idx || v)) {
1725: /*
1726: allocate enough space to hold information from the longest row.
1727: */
1728: Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)mat->A->data, *Ba = (Mat_SeqAIJ *)mat->B->data;
1729: PetscInt max = 1, tmp;
1730: for (i = 0; i < matin->rmap->n; i++) {
1731: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1732: if (max < tmp) max = tmp;
1733: }
1734: PetscCall(PetscMalloc2(max, &mat->rowvalues, max, &mat->rowindices));
1735: }
1737: PetscCheck(row >= rstart && row < rend, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Only local rows");
1738: lrow = row - rstart;
1740: pvA = &vworkA;
1741: pcA = &cworkA;
1742: pvB = &vworkB;
1743: pcB = &cworkB;
1744: if (!v) {
1745: pvA = NULL;
1746: pvB = NULL;
1747: }
1748: if (!idx) {
1749: pcA = NULL;
1750: if (!v) pcB = NULL;
1751: }
1752: PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1753: PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1754: nztot = nzA + nzB;
1756: cmap = mat->garray;
1757: if (v || idx) {
1758: if (nztot) {
1759: /* Sort by increasing column numbers, assuming A and B already sorted */
1760: PetscInt imark = -1;
1761: if (v) {
1762: *v = v_p = mat->rowvalues;
1763: for (i = 0; i < nzB; i++) {
1764: if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1765: else break;
1766: }
1767: imark = i;
1768: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1769: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1770: }
1771: if (idx) {
1772: *idx = idx_p = mat->rowindices;
1773: if (imark > -1) {
1774: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i]];
1775: } else {
1776: for (i = 0; i < nzB; i++) {
1777: if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1778: else break;
1779: }
1780: imark = i;
1781: }
1782: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart + cworkA[i];
1783: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i]];
1784: }
1785: } else {
1786: if (idx) *idx = NULL;
1787: if (v) *v = NULL;
1788: }
1789: }
1790: *nz = nztot;
1791: PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1792: PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1793: PetscFunctionReturn(PETSC_SUCCESS);
1794: }
1796: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1797: {
1798: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1800: PetscFunctionBegin;
1801: PetscCheck(aij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow() must be called first");
1802: aij->getrowactive = PETSC_FALSE;
1803: PetscFunctionReturn(PETSC_SUCCESS);
1804: }
1806: static PetscErrorCode MatNorm_MPIAIJ(Mat mat, NormType type, PetscReal *norm)
1807: {
1808: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1809: Mat_SeqAIJ *amat = (Mat_SeqAIJ *)aij->A->data, *bmat = (Mat_SeqAIJ *)aij->B->data;
1810: PetscInt i, j, cstart = mat->cmap->rstart;
1811: PetscReal sum = 0.0;
1812: const MatScalar *v, *amata, *bmata;
1814: PetscFunctionBegin;
1815: if (aij->size == 1) {
1816: PetscCall(MatNorm(aij->A, type, norm));
1817: } else {
1818: PetscCall(MatSeqAIJGetArrayRead(aij->A, &amata));
1819: PetscCall(MatSeqAIJGetArrayRead(aij->B, &bmata));
1820: if (type == NORM_FROBENIUS) {
1821: v = amata;
1822: for (i = 0; i < amat->nz; i++) {
1823: sum += PetscRealPart(PetscConj(*v) * (*v));
1824: v++;
1825: }
1826: v = bmata;
1827: for (i = 0; i < bmat->nz; i++) {
1828: sum += PetscRealPart(PetscConj(*v) * (*v));
1829: v++;
1830: }
1831: PetscCallMPI(MPIU_Allreduce(&sum, norm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1832: *norm = PetscSqrtReal(*norm);
1833: PetscCall(PetscLogFlops(2.0 * amat->nz + 2.0 * bmat->nz));
1834: } else if (type == NORM_1) { /* max column norm */
1835: PetscReal *tmp;
1836: PetscInt *jj, *garray = aij->garray;
1837: PetscCall(PetscCalloc1(mat->cmap->N + 1, &tmp));
1838: *norm = 0.0;
1839: v = amata;
1840: jj = amat->j;
1841: for (j = 0; j < amat->nz; j++) {
1842: tmp[cstart + *jj++] += PetscAbsScalar(*v);
1843: v++;
1844: }
1845: v = bmata;
1846: jj = bmat->j;
1847: for (j = 0; j < bmat->nz; j++) {
1848: tmp[garray[*jj++]] += PetscAbsScalar(*v);
1849: v++;
1850: }
1851: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, tmp, mat->cmap->N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1852: for (j = 0; j < mat->cmap->N; j++) {
1853: if (tmp[j] > *norm) *norm = tmp[j];
1854: }
1855: PetscCall(PetscFree(tmp));
1856: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1857: } else if (type == NORM_INFINITY) { /* max row norm */
1858: PetscReal ntemp = 0.0;
1859: for (j = 0; j < aij->A->rmap->n; j++) {
1860: v = PetscSafePointerPlusOffset(amata, amat->i[j]);
1861: sum = 0.0;
1862: for (i = 0; i < amat->i[j + 1] - amat->i[j]; i++) {
1863: sum += PetscAbsScalar(*v);
1864: v++;
1865: }
1866: v = PetscSafePointerPlusOffset(bmata, bmat->i[j]);
1867: for (i = 0; i < bmat->i[j + 1] - bmat->i[j]; i++) {
1868: sum += PetscAbsScalar(*v);
1869: v++;
1870: }
1871: if (sum > ntemp) ntemp = sum;
1872: }
1873: PetscCallMPI(MPIU_Allreduce(&ntemp, norm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
1874: PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1875: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for two norm");
1876: PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &amata));
1877: PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &bmata));
1878: }
1879: PetscFunctionReturn(PETSC_SUCCESS);
1880: }
1882: static PetscErrorCode MatTranspose_MPIAIJ(Mat A, MatReuse reuse, Mat *matout)
1883: {
1884: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *b;
1885: Mat_SeqAIJ *Aloc = (Mat_SeqAIJ *)a->A->data, *Bloc = (Mat_SeqAIJ *)a->B->data, *sub_B_diag;
1886: 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;
1887: const PetscInt *ai, *aj, *bi, *bj, *B_diag_i;
1888: Mat B, A_diag, *B_diag;
1889: const MatScalar *pbv, *bv;
1891: PetscFunctionBegin;
1892: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1893: ma = A->rmap->n;
1894: na = A->cmap->n;
1895: mb = a->B->rmap->n;
1896: nb = a->B->cmap->n;
1897: ai = Aloc->i;
1898: aj = Aloc->j;
1899: bi = Bloc->i;
1900: bj = Bloc->j;
1901: if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1902: PetscInt *d_nnz, *g_nnz, *o_nnz;
1903: PetscSFNode *oloc;
1904: PETSC_UNUSED PetscSF sf;
1906: PetscCall(PetscMalloc4(na, &d_nnz, na, &o_nnz, nb, &g_nnz, nb, &oloc));
1907: /* compute d_nnz for preallocation */
1908: PetscCall(PetscArrayzero(d_nnz, na));
1909: for (i = 0; i < ai[ma]; i++) d_nnz[aj[i]]++;
1910: /* compute local off-diagonal contributions */
1911: PetscCall(PetscArrayzero(g_nnz, nb));
1912: for (i = 0; i < bi[ma]; i++) g_nnz[bj[i]]++;
1913: /* map those to global */
1914: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1915: PetscCall(PetscSFSetGraphLayout(sf, A->cmap, nb, NULL, PETSC_USE_POINTER, a->garray));
1916: PetscCall(PetscSFSetFromOptions(sf));
1917: PetscCall(PetscArrayzero(o_nnz, na));
1918: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1919: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1920: PetscCall(PetscSFDestroy(&sf));
1922: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1923: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1924: PetscCall(MatSetBlockSizes(B, A->cmap->bs, A->rmap->bs));
1925: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1926: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
1927: PetscCall(PetscFree4(d_nnz, o_nnz, g_nnz, oloc));
1928: } else {
1929: B = *matout;
1930: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
1931: }
1933: b = (Mat_MPIAIJ *)B->data;
1934: A_diag = a->A;
1935: B_diag = &b->A;
1936: sub_B_diag = (Mat_SeqAIJ *)(*B_diag)->data;
1937: A_diag_ncol = A_diag->cmap->N;
1938: B_diag_ilen = sub_B_diag->ilen;
1939: B_diag_i = sub_B_diag->i;
1941: /* Set ilen for diagonal of B */
1942: for (i = 0; i < A_diag_ncol; i++) B_diag_ilen[i] = B_diag_i[i + 1] - B_diag_i[i];
1944: /* Transpose the diagonal part of the matrix. In contrast to the off-diagonal part, this can be done
1945: very quickly (=without using MatSetValues), because all writes are local. */
1946: PetscCall(MatTransposeSetPrecursor(A_diag, *B_diag));
1947: PetscCall(MatTranspose(A_diag, MAT_REUSE_MATRIX, B_diag));
1949: /* copy over the B part */
1950: PetscCall(PetscMalloc1(bi[mb], &cols));
1951: PetscCall(MatSeqAIJGetArrayRead(a->B, &bv));
1952: pbv = bv;
1953: row = A->rmap->rstart;
1954: for (i = 0; i < bi[mb]; i++) cols[i] = a->garray[bj[i]];
1955: cols_tmp = cols;
1956: for (i = 0; i < mb; i++) {
1957: ncol = bi[i + 1] - bi[i];
1958: PetscCall(MatSetValues(B, ncol, cols_tmp, 1, &row, pbv, INSERT_VALUES));
1959: row++;
1960: if (pbv) pbv += ncol;
1961: if (cols_tmp) cols_tmp += ncol;
1962: }
1963: PetscCall(PetscFree(cols));
1964: PetscCall(MatSeqAIJRestoreArrayRead(a->B, &bv));
1966: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1967: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1968: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1969: *matout = B;
1970: } else {
1971: PetscCall(MatHeaderMerge(A, &B));
1972: }
1973: PetscFunctionReturn(PETSC_SUCCESS);
1974: }
1976: static PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat, Vec ll, Vec rr)
1977: {
1978: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1979: Mat a = aij->A, b = aij->B;
1980: PetscInt s1, s2, s3;
1982: PetscFunctionBegin;
1983: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1984: if (rr) {
1985: PetscCall(VecGetLocalSize(rr, &s1));
1986: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1987: /* Overlap communication with computation. */
1988: PetscCall(VecScatterBegin(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1989: }
1990: if (ll) {
1991: PetscCall(VecGetLocalSize(ll, &s1));
1992: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1993: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1994: }
1995: /* scale the diagonal block */
1996: PetscUseTypeMethod(a, diagonalscale, ll, rr);
1998: if (rr) {
1999: /* Do a scatter end and then right scale the off-diagonal block */
2000: PetscCall(VecScatterEnd(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
2001: PetscUseTypeMethod(b, diagonalscale, NULL, aij->lvec);
2002: }
2003: PetscFunctionReturn(PETSC_SUCCESS);
2004: }
2006: static PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2007: {
2008: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2010: PetscFunctionBegin;
2011: PetscCall(MatSetUnfactored(a->A));
2012: PetscFunctionReturn(PETSC_SUCCESS);
2013: }
2015: static PetscErrorCode MatEqual_MPIAIJ(Mat A, Mat B, PetscBool *flag)
2016: {
2017: Mat_MPIAIJ *matB = (Mat_MPIAIJ *)B->data, *matA = (Mat_MPIAIJ *)A->data;
2018: Mat a, b, c, d;
2019: PetscBool flg;
2021: PetscFunctionBegin;
2022: a = matA->A;
2023: b = matA->B;
2024: c = matB->A;
2025: d = matB->B;
2027: PetscCall(MatEqual(a, c, &flg));
2028: if (flg) PetscCall(MatEqual(b, d, &flg));
2029: PetscCallMPI(MPIU_Allreduce(&flg, flag, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
2030: PetscFunctionReturn(PETSC_SUCCESS);
2031: }
2033: static PetscErrorCode MatCopy_MPIAIJ(Mat A, Mat B, MatStructure str)
2034: {
2035: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2036: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2038: PetscFunctionBegin;
2039: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2040: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2041: /* because of the column compression in the off-processor part of the matrix a->B,
2042: the number of columns in a->B and b->B may be different, hence we cannot call
2043: the MatCopy() directly on the two parts. If need be, we can provide a more
2044: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2045: then copying the submatrices */
2046: PetscCall(MatCopy_Basic(A, B, str));
2047: } else {
2048: PetscCall(MatCopy(a->A, b->A, str));
2049: PetscCall(MatCopy(a->B, b->B, str));
2050: }
2051: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2052: PetscFunctionReturn(PETSC_SUCCESS);
2053: }
2055: /*
2056: Computes the number of nonzeros per row needed for preallocation when X and Y
2057: have different nonzero structure.
2058: */
2059: 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)
2060: {
2061: PetscInt i, j, k, nzx, nzy;
2063: PetscFunctionBegin;
2064: /* Set the number of nonzeros in the new matrix */
2065: for (i = 0; i < m; i++) {
2066: const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2067: nzx = xi[i + 1] - xi[i];
2068: nzy = yi[i + 1] - yi[i];
2069: nnz[i] = 0;
2070: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2071: for (; k < nzy && yltog[yjj[k]] < xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2072: if (k < nzy && yltog[yjj[k]] == xltog[xjj[j]]) k++; /* Skip duplicate */
2073: nnz[i]++;
2074: }
2075: for (; k < nzy; k++) nnz[i]++;
2076: }
2077: PetscFunctionReturn(PETSC_SUCCESS);
2078: }
2080: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2081: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
2082: {
2083: PetscInt m = Y->rmap->N;
2084: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2085: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
2087: PetscFunctionBegin;
2088: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
2089: PetscFunctionReturn(PETSC_SUCCESS);
2090: }
2092: static PetscErrorCode MatAXPY_MPIAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2093: {
2094: Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data, *yy = (Mat_MPIAIJ *)Y->data;
2096: PetscFunctionBegin;
2097: if (str == SAME_NONZERO_PATTERN) {
2098: PetscCall(MatAXPY(yy->A, a, xx->A, str));
2099: PetscCall(MatAXPY(yy->B, a, xx->B, str));
2100: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2101: PetscCall(MatAXPY_Basic(Y, a, X, str));
2102: } else {
2103: Mat B;
2104: PetscInt *nnz_d, *nnz_o;
2106: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
2107: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
2108: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2109: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2110: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2111: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2112: PetscCall(MatAXPYGetPreallocation_SeqAIJ(yy->A, xx->A, nnz_d));
2113: PetscCall(MatAXPYGetPreallocation_MPIAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
2114: PetscCall(MatMPIAIJSetPreallocation(B, 0, nnz_d, 0, nnz_o));
2115: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2116: PetscCall(MatHeaderMerge(Y, &B));
2117: PetscCall(PetscFree(nnz_d));
2118: PetscCall(PetscFree(nnz_o));
2119: }
2120: PetscFunctionReturn(PETSC_SUCCESS);
2121: }
2123: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);
2125: static PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2126: {
2127: PetscFunctionBegin;
2128: if (PetscDefined(USE_COMPLEX)) {
2129: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2131: PetscCall(MatConjugate_SeqAIJ(aij->A));
2132: PetscCall(MatConjugate_SeqAIJ(aij->B));
2133: }
2134: PetscFunctionReturn(PETSC_SUCCESS);
2135: }
2137: static PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2138: {
2139: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2141: PetscFunctionBegin;
2142: PetscCall(MatRealPart(a->A));
2143: PetscCall(MatRealPart(a->B));
2144: PetscFunctionReturn(PETSC_SUCCESS);
2145: }
2147: static PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
2148: {
2149: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2151: PetscFunctionBegin;
2152: PetscCall(MatImaginaryPart(a->A));
2153: PetscCall(MatImaginaryPart(a->B));
2154: PetscFunctionReturn(PETSC_SUCCESS);
2155: }
2157: static PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2158: {
2159: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2160: PetscInt i, *idxb = NULL, m = A->rmap->n;
2161: PetscScalar *vv;
2162: Vec vB, vA;
2163: const PetscScalar *va, *vb;
2165: PetscFunctionBegin;
2166: PetscCall(MatCreateVecs(a->A, NULL, &vA));
2167: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
2169: PetscCall(VecGetArrayRead(vA, &va));
2170: if (idx) {
2171: for (i = 0; i < m; i++) {
2172: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2173: }
2174: }
2176: PetscCall(MatCreateVecs(a->B, NULL, &vB));
2177: PetscCall(PetscMalloc1(m, &idxb));
2178: PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));
2180: PetscCall(VecGetArrayWrite(v, &vv));
2181: PetscCall(VecGetArrayRead(vB, &vb));
2182: for (i = 0; i < m; i++) {
2183: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2184: vv[i] = vb[i];
2185: if (idx) idx[i] = a->garray[idxb[i]];
2186: } else {
2187: vv[i] = va[i];
2188: if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > a->garray[idxb[i]]) idx[i] = a->garray[idxb[i]];
2189: }
2190: }
2191: PetscCall(VecRestoreArrayWrite(v, &vv));
2192: PetscCall(VecRestoreArrayRead(vA, &va));
2193: PetscCall(VecRestoreArrayRead(vB, &vb));
2194: PetscCall(PetscFree(idxb));
2195: PetscCall(VecDestroy(&vA));
2196: PetscCall(VecDestroy(&vB));
2197: PetscFunctionReturn(PETSC_SUCCESS);
2198: }
2200: static PetscErrorCode MatGetRowSumAbs_MPIAIJ(Mat A, Vec v)
2201: {
2202: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2203: Vec vB, vA;
2205: PetscFunctionBegin;
2206: PetscCall(MatCreateVecs(a->A, NULL, &vA));
2207: PetscCall(MatGetRowSumAbs(a->A, vA));
2208: PetscCall(MatCreateVecs(a->B, NULL, &vB));
2209: PetscCall(MatGetRowSumAbs(a->B, vB));
2210: PetscCall(VecAXPY(vA, 1.0, vB));
2211: PetscCall(VecDestroy(&vB));
2212: PetscCall(VecCopy(vA, v));
2213: PetscCall(VecDestroy(&vA));
2214: PetscFunctionReturn(PETSC_SUCCESS);
2215: }
2217: static PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2218: {
2219: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2220: PetscInt m = A->rmap->n, n = A->cmap->n;
2221: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2222: PetscInt *cmap = mat->garray;
2223: PetscInt *diagIdx, *offdiagIdx;
2224: Vec diagV, offdiagV;
2225: PetscScalar *a, *diagA, *offdiagA;
2226: const PetscScalar *ba, *bav;
2227: PetscInt r, j, col, ncols, *bi, *bj;
2228: Mat B = mat->B;
2229: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2231: PetscFunctionBegin;
2232: /* When a process holds entire A and other processes have no entry */
2233: if (A->cmap->N == n) {
2234: PetscCall(VecGetArrayWrite(v, &diagA));
2235: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2236: PetscCall(MatGetRowMinAbs(mat->A, diagV, idx));
2237: PetscCall(VecDestroy(&diagV));
2238: PetscCall(VecRestoreArrayWrite(v, &diagA));
2239: PetscFunctionReturn(PETSC_SUCCESS);
2240: } else if (n == 0) {
2241: if (m) {
2242: PetscCall(VecGetArrayWrite(v, &a));
2243: for (r = 0; r < m; r++) {
2244: a[r] = 0.0;
2245: if (idx) idx[r] = -1;
2246: }
2247: PetscCall(VecRestoreArrayWrite(v, &a));
2248: }
2249: PetscFunctionReturn(PETSC_SUCCESS);
2250: }
2252: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2253: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2254: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2255: PetscCall(MatGetRowMinAbs(mat->A, diagV, diagIdx));
2257: /* Get offdiagIdx[] for implicit 0.0 */
2258: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2259: ba = bav;
2260: bi = b->i;
2261: bj = b->j;
2262: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2263: for (r = 0; r < m; r++) {
2264: ncols = bi[r + 1] - bi[r];
2265: if (ncols == A->cmap->N - n) { /* Brow is dense */
2266: offdiagA[r] = *ba;
2267: offdiagIdx[r] = cmap[0];
2268: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2269: offdiagA[r] = 0.0;
2271: /* Find first hole in the cmap */
2272: for (j = 0; j < ncols; j++) {
2273: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2274: if (col > j && j < cstart) {
2275: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2276: break;
2277: } else if (col > j + n && j >= cstart) {
2278: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2279: break;
2280: }
2281: }
2282: if (j == ncols && ncols < A->cmap->N - n) {
2283: /* a hole is outside compressed Bcols */
2284: if (ncols == 0) {
2285: if (cstart) {
2286: offdiagIdx[r] = 0;
2287: } else offdiagIdx[r] = cend;
2288: } else { /* ncols > 0 */
2289: offdiagIdx[r] = cmap[ncols - 1] + 1;
2290: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2291: }
2292: }
2293: }
2295: for (j = 0; j < ncols; j++) {
2296: if (PetscAbsScalar(offdiagA[r]) > PetscAbsScalar(*ba)) {
2297: offdiagA[r] = *ba;
2298: offdiagIdx[r] = cmap[*bj];
2299: }
2300: ba++;
2301: bj++;
2302: }
2303: }
2305: PetscCall(VecGetArrayWrite(v, &a));
2306: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2307: for (r = 0; r < m; ++r) {
2308: if (PetscAbsScalar(diagA[r]) < PetscAbsScalar(offdiagA[r])) {
2309: a[r] = diagA[r];
2310: if (idx) idx[r] = cstart + diagIdx[r];
2311: } else if (PetscAbsScalar(diagA[r]) == PetscAbsScalar(offdiagA[r])) {
2312: a[r] = diagA[r];
2313: if (idx) {
2314: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2315: idx[r] = cstart + diagIdx[r];
2316: } else idx[r] = offdiagIdx[r];
2317: }
2318: } else {
2319: a[r] = offdiagA[r];
2320: if (idx) idx[r] = offdiagIdx[r];
2321: }
2322: }
2323: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2324: PetscCall(VecRestoreArrayWrite(v, &a));
2325: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2326: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2327: PetscCall(VecDestroy(&diagV));
2328: PetscCall(VecDestroy(&offdiagV));
2329: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2330: PetscFunctionReturn(PETSC_SUCCESS);
2331: }
2333: static PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2334: {
2335: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2336: PetscInt m = A->rmap->n, n = A->cmap->n;
2337: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2338: PetscInt *cmap = mat->garray;
2339: PetscInt *diagIdx, *offdiagIdx;
2340: Vec diagV, offdiagV;
2341: PetscScalar *a, *diagA, *offdiagA;
2342: const PetscScalar *ba, *bav;
2343: PetscInt r, j, col, ncols, *bi, *bj;
2344: Mat B = mat->B;
2345: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2347: PetscFunctionBegin;
2348: /* When a process holds entire A and other processes have no entry */
2349: if (A->cmap->N == n) {
2350: PetscCall(VecGetArrayWrite(v, &diagA));
2351: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2352: PetscCall(MatGetRowMin(mat->A, diagV, idx));
2353: PetscCall(VecDestroy(&diagV));
2354: PetscCall(VecRestoreArrayWrite(v, &diagA));
2355: PetscFunctionReturn(PETSC_SUCCESS);
2356: } else if (n == 0) {
2357: if (m) {
2358: PetscCall(VecGetArrayWrite(v, &a));
2359: for (r = 0; r < m; r++) {
2360: a[r] = PETSC_MAX_REAL;
2361: if (idx) idx[r] = -1;
2362: }
2363: PetscCall(VecRestoreArrayWrite(v, &a));
2364: }
2365: PetscFunctionReturn(PETSC_SUCCESS);
2366: }
2368: PetscCall(PetscCalloc2(m, &diagIdx, m, &offdiagIdx));
2369: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2370: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2371: PetscCall(MatGetRowMin(mat->A, diagV, diagIdx));
2373: /* Get offdiagIdx[] for implicit 0.0 */
2374: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2375: ba = bav;
2376: bi = b->i;
2377: bj = b->j;
2378: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2379: for (r = 0; r < m; r++) {
2380: ncols = bi[r + 1] - bi[r];
2381: if (ncols == A->cmap->N - n) { /* Brow is dense */
2382: offdiagA[r] = *ba;
2383: offdiagIdx[r] = cmap[0];
2384: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2385: offdiagA[r] = 0.0;
2387: /* Find first hole in the cmap */
2388: for (j = 0; j < ncols; j++) {
2389: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2390: if (col > j && j < cstart) {
2391: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2392: break;
2393: } else if (col > j + n && j >= cstart) {
2394: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2395: break;
2396: }
2397: }
2398: if (j == ncols && ncols < A->cmap->N - n) {
2399: /* a hole is outside compressed Bcols */
2400: if (ncols == 0) {
2401: if (cstart) {
2402: offdiagIdx[r] = 0;
2403: } else offdiagIdx[r] = cend;
2404: } else { /* ncols > 0 */
2405: offdiagIdx[r] = cmap[ncols - 1] + 1;
2406: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2407: }
2408: }
2409: }
2411: for (j = 0; j < ncols; j++) {
2412: if (PetscRealPart(offdiagA[r]) > PetscRealPart(*ba)) {
2413: offdiagA[r] = *ba;
2414: offdiagIdx[r] = cmap[*bj];
2415: }
2416: ba++;
2417: bj++;
2418: }
2419: }
2421: PetscCall(VecGetArrayWrite(v, &a));
2422: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2423: for (r = 0; r < m; ++r) {
2424: if (PetscRealPart(diagA[r]) < PetscRealPart(offdiagA[r])) {
2425: a[r] = diagA[r];
2426: if (idx) idx[r] = cstart + diagIdx[r];
2427: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2428: a[r] = diagA[r];
2429: if (idx) {
2430: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2431: idx[r] = cstart + diagIdx[r];
2432: } else idx[r] = offdiagIdx[r];
2433: }
2434: } else {
2435: a[r] = offdiagA[r];
2436: if (idx) idx[r] = offdiagIdx[r];
2437: }
2438: }
2439: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2440: PetscCall(VecRestoreArrayWrite(v, &a));
2441: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2442: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2443: PetscCall(VecDestroy(&diagV));
2444: PetscCall(VecDestroy(&offdiagV));
2445: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2446: PetscFunctionReturn(PETSC_SUCCESS);
2447: }
2449: static PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2450: {
2451: Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
2452: PetscInt m = A->rmap->n, n = A->cmap->n;
2453: PetscInt cstart = A->cmap->rstart, cend = A->cmap->rend;
2454: PetscInt *cmap = mat->garray;
2455: PetscInt *diagIdx, *offdiagIdx;
2456: Vec diagV, offdiagV;
2457: PetscScalar *a, *diagA, *offdiagA;
2458: const PetscScalar *ba, *bav;
2459: PetscInt r, j, col, ncols, *bi, *bj;
2460: Mat B = mat->B;
2461: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2463: PetscFunctionBegin;
2464: /* When a process holds entire A and other processes have no entry */
2465: if (A->cmap->N == n) {
2466: PetscCall(VecGetArrayWrite(v, &diagA));
2467: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2468: PetscCall(MatGetRowMax(mat->A, diagV, idx));
2469: PetscCall(VecDestroy(&diagV));
2470: PetscCall(VecRestoreArrayWrite(v, &diagA));
2471: PetscFunctionReturn(PETSC_SUCCESS);
2472: } else if (n == 0) {
2473: if (m) {
2474: PetscCall(VecGetArrayWrite(v, &a));
2475: for (r = 0; r < m; r++) {
2476: a[r] = PETSC_MIN_REAL;
2477: if (idx) idx[r] = -1;
2478: }
2479: PetscCall(VecRestoreArrayWrite(v, &a));
2480: }
2481: PetscFunctionReturn(PETSC_SUCCESS);
2482: }
2484: PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2485: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2486: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2487: PetscCall(MatGetRowMax(mat->A, diagV, diagIdx));
2489: /* Get offdiagIdx[] for implicit 0.0 */
2490: PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2491: ba = bav;
2492: bi = b->i;
2493: bj = b->j;
2494: PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2495: for (r = 0; r < m; r++) {
2496: ncols = bi[r + 1] - bi[r];
2497: if (ncols == A->cmap->N - n) { /* Brow is dense */
2498: offdiagA[r] = *ba;
2499: offdiagIdx[r] = cmap[0];
2500: } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2501: offdiagA[r] = 0.0;
2503: /* Find first hole in the cmap */
2504: for (j = 0; j < ncols; j++) {
2505: col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2506: if (col > j && j < cstart) {
2507: offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2508: break;
2509: } else if (col > j + n && j >= cstart) {
2510: offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2511: break;
2512: }
2513: }
2514: if (j == ncols && ncols < A->cmap->N - n) {
2515: /* a hole is outside compressed Bcols */
2516: if (ncols == 0) {
2517: if (cstart) {
2518: offdiagIdx[r] = 0;
2519: } else offdiagIdx[r] = cend;
2520: } else { /* ncols > 0 */
2521: offdiagIdx[r] = cmap[ncols - 1] + 1;
2522: if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2523: }
2524: }
2525: }
2527: for (j = 0; j < ncols; j++) {
2528: if (PetscRealPart(offdiagA[r]) < PetscRealPart(*ba)) {
2529: offdiagA[r] = *ba;
2530: offdiagIdx[r] = cmap[*bj];
2531: }
2532: ba++;
2533: bj++;
2534: }
2535: }
2537: PetscCall(VecGetArrayWrite(v, &a));
2538: PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2539: for (r = 0; r < m; ++r) {
2540: if (PetscRealPart(diagA[r]) > PetscRealPart(offdiagA[r])) {
2541: a[r] = diagA[r];
2542: if (idx) idx[r] = cstart + diagIdx[r];
2543: } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2544: a[r] = diagA[r];
2545: if (idx) {
2546: if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2547: idx[r] = cstart + diagIdx[r];
2548: } else idx[r] = offdiagIdx[r];
2549: }
2550: } else {
2551: a[r] = offdiagA[r];
2552: if (idx) idx[r] = offdiagIdx[r];
2553: }
2554: }
2555: PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2556: PetscCall(VecRestoreArrayWrite(v, &a));
2557: PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2558: PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2559: PetscCall(VecDestroy(&diagV));
2560: PetscCall(VecDestroy(&offdiagV));
2561: PetscCall(PetscFree2(diagIdx, offdiagIdx));
2562: PetscFunctionReturn(PETSC_SUCCESS);
2563: }
2565: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat, Mat *newmat)
2566: {
2567: Mat *dummy;
2569: PetscFunctionBegin;
2570: PetscCall(MatCreateSubMatrix_MPIAIJ_All(mat, MAT_DO_NOT_GET_VALUES, MAT_INITIAL_MATRIX, &dummy));
2571: *newmat = *dummy;
2572: PetscCall(PetscFree(dummy));
2573: PetscFunctionReturn(PETSC_SUCCESS);
2574: }
2576: static PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A, const PetscScalar **values)
2577: {
2578: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2580: PetscFunctionBegin;
2581: PetscCall(MatInvertBlockDiagonal(a->A, values));
2582: A->factorerrortype = a->A->factorerrortype;
2583: PetscFunctionReturn(PETSC_SUCCESS);
2584: }
2586: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x, PetscRandom rctx)
2587: {
2588: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)x->data;
2590: PetscFunctionBegin;
2591: PetscCheck(x->assembled || x->preallocated, PetscObjectComm((PetscObject)x), PETSC_ERR_ARG_WRONGSTATE, "MatSetRandom on an unassembled and unpreallocated MATMPIAIJ is not allowed");
2592: PetscCall(MatSetRandom(aij->A, rctx));
2593: if (x->assembled) {
2594: PetscCall(MatSetRandom(aij->B, rctx));
2595: } else {
2596: PetscCall(MatSetRandomSkipColumnRange_SeqAIJ_Private(aij->B, x->cmap->rstart, x->cmap->rend, rctx));
2597: }
2598: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
2599: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
2600: PetscFunctionReturn(PETSC_SUCCESS);
2601: }
2603: static PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ(Mat A, PetscBool sc)
2604: {
2605: PetscFunctionBegin;
2606: if (sc) A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ_Scalable;
2607: else A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ;
2608: PetscFunctionReturn(PETSC_SUCCESS);
2609: }
2611: /*@
2612: MatMPIAIJGetNumberNonzeros - gets the number of nonzeros in the matrix on this MPI rank
2614: Not Collective
2616: Input Parameter:
2617: . A - the matrix
2619: Output Parameter:
2620: . nz - the number of nonzeros
2622: Level: advanced
2624: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2625: @*/
2626: PetscErrorCode MatMPIAIJGetNumberNonzeros(Mat A, PetscCount *nz)
2627: {
2628: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)A->data;
2629: Mat_SeqAIJ *aaij = (Mat_SeqAIJ *)maij->A->data, *baij = (Mat_SeqAIJ *)maij->B->data;
2630: PetscBool isaij;
2632: PetscFunctionBegin;
2633: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATMPIAIJ, &isaij));
2634: PetscCheck(isaij, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for type %s", ((PetscObject)A)->type_name);
2635: *nz = aaij->i[A->rmap->n] + baij->i[A->rmap->n];
2636: PetscFunctionReturn(PETSC_SUCCESS);
2637: }
2639: /*@
2640: MatMPIAIJSetUseScalableIncreaseOverlap - Determine if the matrix uses a scalable algorithm to compute the overlap
2642: Collective
2644: Input Parameters:
2645: + A - the matrix
2646: - sc - `PETSC_TRUE` indicates use the scalable algorithm (default is not to use the scalable algorithm)
2648: Level: advanced
2650: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2651: @*/
2652: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap(Mat A, PetscBool sc)
2653: {
2654: PetscFunctionBegin;
2655: PetscTryMethod(A, "MatMPIAIJSetUseScalableIncreaseOverlap_C", (Mat, PetscBool), (A, sc));
2656: PetscFunctionReturn(PETSC_SUCCESS);
2657: }
2659: PetscErrorCode MatSetFromOptions_MPIAIJ(Mat A, PetscOptionItems PetscOptionsObject)
2660: {
2661: PetscBool sc = PETSC_FALSE, flg;
2663: PetscFunctionBegin;
2664: PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJ options");
2665: if (A->ops->increaseoverlap == MatIncreaseOverlap_MPIAIJ_Scalable) sc = PETSC_TRUE;
2666: PetscCall(PetscOptionsBool("-mat_increase_overlap_scalable", "Use a scalable algorithm to compute the overlap", "MatIncreaseOverlap", sc, &sc, &flg));
2667: if (flg) PetscCall(MatMPIAIJSetUseScalableIncreaseOverlap(A, sc));
2668: PetscOptionsHeadEnd();
2669: PetscFunctionReturn(PETSC_SUCCESS);
2670: }
2672: static PetscErrorCode MatShift_MPIAIJ(Mat Y, PetscScalar a)
2673: {
2674: Mat_MPIAIJ *maij = (Mat_MPIAIJ *)Y->data;
2675: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)maij->A->data;
2677: PetscFunctionBegin;
2678: if (!Y->preallocated) {
2679: PetscCall(MatMPIAIJSetPreallocation(Y, 1, NULL, 0, NULL));
2680: } else if (!aij->nz) { /* It does not matter if diagonals of Y only partially lie in maij->A. We just need an estimated preallocation. */
2681: PetscInt nonew = aij->nonew;
2682: PetscCall(MatSeqAIJSetPreallocation(maij->A, 1, NULL));
2683: aij->nonew = nonew;
2684: }
2685: PetscCall(MatShift_Basic(Y, a));
2686: PetscFunctionReturn(PETSC_SUCCESS);
2687: }
2689: static PetscErrorCode MatMissingDiagonal_MPIAIJ(Mat A, PetscBool *missing, PetscInt *d)
2690: {
2691: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2693: PetscFunctionBegin;
2694: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2695: PetscCall(MatMissingDiagonal(a->A, missing, d));
2696: if (d) {
2697: PetscInt rstart;
2698: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2699: *d += rstart;
2700: }
2701: PetscFunctionReturn(PETSC_SUCCESS);
2702: }
2704: static PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2705: {
2706: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2708: PetscFunctionBegin;
2709: PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2710: PetscFunctionReturn(PETSC_SUCCESS);
2711: }
2713: static PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A, PetscBool keep)
2714: {
2715: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2717: PetscFunctionBegin;
2718: PetscCall(MatEliminateZeros_SeqAIJ(a->A, keep)); // possibly keep zero diagonal coefficients
2719: PetscCall(MatEliminateZeros_SeqAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2720: PetscFunctionReturn(PETSC_SUCCESS);
2721: }
2723: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2724: MatGetRow_MPIAIJ,
2725: MatRestoreRow_MPIAIJ,
2726: MatMult_MPIAIJ,
2727: /* 4*/ MatMultAdd_MPIAIJ,
2728: MatMultTranspose_MPIAIJ,
2729: MatMultTransposeAdd_MPIAIJ,
2730: NULL,
2731: NULL,
2732: NULL,
2733: /*10*/ NULL,
2734: NULL,
2735: NULL,
2736: MatSOR_MPIAIJ,
2737: MatTranspose_MPIAIJ,
2738: /*15*/ MatGetInfo_MPIAIJ,
2739: MatEqual_MPIAIJ,
2740: MatGetDiagonal_MPIAIJ,
2741: MatDiagonalScale_MPIAIJ,
2742: MatNorm_MPIAIJ,
2743: /*20*/ MatAssemblyBegin_MPIAIJ,
2744: MatAssemblyEnd_MPIAIJ,
2745: MatSetOption_MPIAIJ,
2746: MatZeroEntries_MPIAIJ,
2747: /*24*/ MatZeroRows_MPIAIJ,
2748: NULL,
2749: NULL,
2750: NULL,
2751: NULL,
2752: /*29*/ MatSetUp_MPI_Hash,
2753: NULL,
2754: NULL,
2755: MatGetDiagonalBlock_MPIAIJ,
2756: NULL,
2757: /*34*/ MatDuplicate_MPIAIJ,
2758: NULL,
2759: NULL,
2760: NULL,
2761: NULL,
2762: /*39*/ MatAXPY_MPIAIJ,
2763: MatCreateSubMatrices_MPIAIJ,
2764: MatIncreaseOverlap_MPIAIJ,
2765: MatGetValues_MPIAIJ,
2766: MatCopy_MPIAIJ,
2767: /*44*/ MatGetRowMax_MPIAIJ,
2768: MatScale_MPIAIJ,
2769: MatShift_MPIAIJ,
2770: MatDiagonalSet_MPIAIJ,
2771: MatZeroRowsColumns_MPIAIJ,
2772: /*49*/ MatSetRandom_MPIAIJ,
2773: MatGetRowIJ_MPIAIJ,
2774: MatRestoreRowIJ_MPIAIJ,
2775: NULL,
2776: NULL,
2777: /*54*/ MatFDColoringCreate_MPIXAIJ,
2778: NULL,
2779: MatSetUnfactored_MPIAIJ,
2780: MatPermute_MPIAIJ,
2781: NULL,
2782: /*59*/ MatCreateSubMatrix_MPIAIJ,
2783: MatDestroy_MPIAIJ,
2784: MatView_MPIAIJ,
2785: NULL,
2786: NULL,
2787: /*64*/ MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2788: NULL,
2789: NULL,
2790: NULL,
2791: MatGetRowMaxAbs_MPIAIJ,
2792: /*69*/ MatGetRowMinAbs_MPIAIJ,
2793: NULL,
2794: NULL,
2795: MatFDColoringApply_AIJ,
2796: MatSetFromOptions_MPIAIJ,
2797: MatFindZeroDiagonals_MPIAIJ,
2798: /*75*/ NULL,
2799: NULL,
2800: NULL,
2801: MatLoad_MPIAIJ,
2802: NULL,
2803: /*80*/ NULL,
2804: NULL,
2805: NULL,
2806: /*83*/ NULL,
2807: NULL,
2808: MatMatMultNumeric_MPIAIJ_MPIAIJ,
2809: MatPtAPNumeric_MPIAIJ_MPIAIJ,
2810: NULL,
2811: NULL,
2812: /*89*/ MatBindToCPU_MPIAIJ,
2813: MatProductSetFromOptions_MPIAIJ,
2814: NULL,
2815: NULL,
2816: MatConjugate_MPIAIJ,
2817: /*94*/ NULL,
2818: MatSetValuesRow_MPIAIJ,
2819: MatRealPart_MPIAIJ,
2820: MatImaginaryPart_MPIAIJ,
2821: NULL,
2822: /*99*/ NULL,
2823: NULL,
2824: NULL,
2825: MatGetRowMin_MPIAIJ,
2826: NULL,
2827: /*104*/ MatMissingDiagonal_MPIAIJ,
2828: MatGetSeqNonzeroStructure_MPIAIJ,
2829: NULL,
2830: MatGetGhosts_MPIAIJ,
2831: NULL,
2832: /*109*/ NULL,
2833: MatMultDiagonalBlock_MPIAIJ,
2834: NULL,
2835: NULL,
2836: NULL,
2837: /*114*/ MatGetMultiProcBlock_MPIAIJ,
2838: MatFindNonzeroRows_MPIAIJ,
2839: MatGetColumnReductions_MPIAIJ,
2840: MatInvertBlockDiagonal_MPIAIJ,
2841: MatInvertVariableBlockDiagonal_MPIAIJ,
2842: /*119*/ MatCreateSubMatricesMPI_MPIAIJ,
2843: NULL,
2844: NULL,
2845: MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2846: NULL,
2847: /*124*/ NULL,
2848: NULL,
2849: NULL,
2850: MatSetBlockSizes_MPIAIJ,
2851: NULL,
2852: /*129*/ MatFDColoringSetUp_MPIXAIJ,
2853: MatFindOffBlockDiagonalEntries_MPIAIJ,
2854: MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2855: NULL,
2856: NULL,
2857: /*134*/ NULL,
2858: MatCreateGraph_Simple_AIJ,
2859: NULL,
2860: MatEliminateZeros_MPIAIJ,
2861: MatGetRowSumAbs_MPIAIJ,
2862: /*139*/ NULL,
2863: NULL,
2864: NULL,
2865: MatCopyHashToXAIJ_MPI_Hash,
2866: MatGetCurrentMemType_MPIAIJ,
2867: NULL};
2869: static PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2870: {
2871: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2873: PetscFunctionBegin;
2874: PetscCall(MatStoreValues(aij->A));
2875: PetscCall(MatStoreValues(aij->B));
2876: PetscFunctionReturn(PETSC_SUCCESS);
2877: }
2879: static PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2880: {
2881: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2883: PetscFunctionBegin;
2884: PetscCall(MatRetrieveValues(aij->A));
2885: PetscCall(MatRetrieveValues(aij->B));
2886: PetscFunctionReturn(PETSC_SUCCESS);
2887: }
2889: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2890: {
2891: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2892: PetscMPIInt size;
2894: PetscFunctionBegin;
2895: if (B->hash_active) {
2896: B->ops[0] = b->cops;
2897: B->hash_active = PETSC_FALSE;
2898: }
2899: PetscCall(PetscLayoutSetUp(B->rmap));
2900: PetscCall(PetscLayoutSetUp(B->cmap));
2902: #if defined(PETSC_USE_CTABLE)
2903: PetscCall(PetscHMapIDestroy(&b->colmap));
2904: #else
2905: PetscCall(PetscFree(b->colmap));
2906: #endif
2907: PetscCall(PetscFree(b->garray));
2908: PetscCall(VecDestroy(&b->lvec));
2909: PetscCall(VecScatterDestroy(&b->Mvctx));
2911: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2913: MatSeqXAIJGetOptions_Private(b->B);
2914: PetscCall(MatDestroy(&b->B));
2915: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2916: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2917: PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2918: PetscCall(MatSetType(b->B, MATSEQAIJ));
2919: MatSeqXAIJRestoreOptions_Private(b->B);
2921: MatSeqXAIJGetOptions_Private(b->A);
2922: PetscCall(MatDestroy(&b->A));
2923: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2924: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2925: PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2926: PetscCall(MatSetType(b->A, MATSEQAIJ));
2927: MatSeqXAIJRestoreOptions_Private(b->A);
2929: PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2930: PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2931: B->preallocated = PETSC_TRUE;
2932: B->was_assembled = PETSC_FALSE;
2933: B->assembled = PETSC_FALSE;
2934: PetscFunctionReturn(PETSC_SUCCESS);
2935: }
2937: static PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2938: {
2939: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2940: PetscBool ondiagreset, offdiagreset, memoryreset;
2942: PetscFunctionBegin;
2944: PetscCheck(B->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset preallocation after setting some values but not yet calling MatAssemblyBegin()/MatAssemblyEnd()");
2945: if (B->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
2947: PetscCall(MatResetPreallocation_SeqAIJ_Private(b->A, &ondiagreset));
2948: PetscCall(MatResetPreallocation_SeqAIJ_Private(b->B, &offdiagreset));
2949: memoryreset = (PetscBool)(ondiagreset || offdiagreset);
2950: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &memoryreset, 1, MPI_C_BOOL, MPI_LOR, PetscObjectComm((PetscObject)B)));
2951: if (!memoryreset) PetscFunctionReturn(PETSC_SUCCESS);
2953: PetscCall(PetscLayoutSetUp(B->rmap));
2954: PetscCall(PetscLayoutSetUp(B->cmap));
2955: PetscCheck(B->assembled || B->was_assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Should not need to reset preallocation if the matrix was never assembled");
2956: PetscCall(MatDisAssemble_MPIAIJ(B, PETSC_TRUE));
2957: PetscCall(VecScatterDestroy(&b->Mvctx));
2959: B->preallocated = PETSC_TRUE;
2960: B->was_assembled = PETSC_FALSE;
2961: B->assembled = PETSC_FALSE;
2962: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
2963: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2964: PetscFunctionReturn(PETSC_SUCCESS);
2965: }
2967: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2968: {
2969: Mat mat;
2970: Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;
2972: PetscFunctionBegin;
2973: *newmat = NULL;
2974: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2975: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2976: PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2977: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2978: a = (Mat_MPIAIJ *)mat->data;
2980: mat->factortype = matin->factortype;
2981: mat->assembled = matin->assembled;
2982: mat->insertmode = NOT_SET_VALUES;
2984: a->size = oldmat->size;
2985: a->rank = oldmat->rank;
2986: a->donotstash = oldmat->donotstash;
2987: a->roworiented = oldmat->roworiented;
2988: a->rowindices = NULL;
2989: a->rowvalues = NULL;
2990: a->getrowactive = PETSC_FALSE;
2992: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
2993: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
2994: if (matin->hash_active) {
2995: PetscCall(MatSetUp(mat));
2996: } else {
2997: mat->preallocated = matin->preallocated;
2998: if (oldmat->colmap) {
2999: #if defined(PETSC_USE_CTABLE)
3000: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3001: #else
3002: PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
3003: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
3004: #endif
3005: } else a->colmap = NULL;
3006: if (oldmat->garray) {
3007: PetscInt len;
3008: len = oldmat->B->cmap->n;
3009: PetscCall(PetscMalloc1(len + 1, &a->garray));
3010: if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3011: } else a->garray = NULL;
3013: /* It may happen MatDuplicate is called with a non-assembled matrix
3014: In fact, MatDuplicate only requires the matrix to be preallocated
3015: This may happen inside a DMCreateMatrix_Shell */
3016: if (oldmat->lvec) PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3017: if (oldmat->Mvctx) {
3018: a->Mvctx = oldmat->Mvctx;
3019: PetscCall(PetscObjectReference((PetscObject)oldmat->Mvctx));
3020: }
3021: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3022: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3023: }
3024: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3025: *newmat = mat;
3026: PetscFunctionReturn(PETSC_SUCCESS);
3027: }
3029: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3030: {
3031: PetscBool isbinary, ishdf5;
3033: PetscFunctionBegin;
3036: /* force binary viewer to load .info file if it has not yet done so */
3037: PetscCall(PetscViewerSetUp(viewer));
3038: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3039: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3040: if (isbinary) {
3041: PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3042: } else if (ishdf5) {
3043: #if defined(PETSC_HAVE_HDF5)
3044: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3045: #else
3046: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3047: #endif
3048: } else {
3049: 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);
3050: }
3051: PetscFunctionReturn(PETSC_SUCCESS);
3052: }
3054: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3055: {
3056: PetscInt header[4], M, N, m, nz, rows, cols, sum, i;
3057: PetscInt *rowidxs, *colidxs;
3058: PetscScalar *matvals;
3060: PetscFunctionBegin;
3061: PetscCall(PetscViewerSetUp(viewer));
3063: /* read in matrix header */
3064: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3065: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3066: M = header[1];
3067: N = header[2];
3068: nz = header[3];
3069: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3070: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3071: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");
3073: /* set block sizes from the viewer's .info file */
3074: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3075: /* set global sizes if not set already */
3076: if (mat->rmap->N < 0) mat->rmap->N = M;
3077: if (mat->cmap->N < 0) mat->cmap->N = N;
3078: PetscCall(PetscLayoutSetUp(mat->rmap));
3079: PetscCall(PetscLayoutSetUp(mat->cmap));
3081: /* check if the matrix sizes are correct */
3082: PetscCall(MatGetSize(mat, &rows, &cols));
3083: 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);
3085: /* read in row lengths and build row indices */
3086: PetscCall(MatGetLocalSize(mat, &m, NULL));
3087: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3088: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3089: rowidxs[0] = 0;
3090: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3091: if (nz != PETSC_INT_MAX) {
3092: PetscCallMPI(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3093: 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);
3094: }
3096: /* read in column indices and matrix values */
3097: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3098: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3099: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3100: /* store matrix indices and values */
3101: PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3102: PetscCall(PetscFree(rowidxs));
3103: PetscCall(PetscFree2(colidxs, matvals));
3104: PetscFunctionReturn(PETSC_SUCCESS);
3105: }
3107: /* Not scalable because of ISAllGather() unless getting all columns. */
3108: static PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3109: {
3110: IS iscol_local;
3111: PetscBool isstride;
3112: PetscMPIInt gisstride = 0;
3114: PetscFunctionBegin;
3115: /* check if we are grabbing all columns*/
3116: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));
3118: if (isstride) {
3119: PetscInt start, len, mstart, mlen;
3120: PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3121: PetscCall(ISGetLocalSize(iscol, &len));
3122: PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3123: if (mstart == start && mlen - mstart == len) gisstride = 1;
3124: }
3126: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3127: if (gisstride) {
3128: PetscInt N;
3129: PetscCall(MatGetSize(mat, NULL, &N));
3130: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3131: PetscCall(ISSetIdentity(iscol_local));
3132: PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3133: } else {
3134: PetscInt cbs;
3135: PetscCall(ISGetBlockSize(iscol, &cbs));
3136: PetscCall(ISAllGather(iscol, &iscol_local));
3137: PetscCall(ISSetBlockSize(iscol_local, cbs));
3138: }
3140: *isseq = iscol_local;
3141: PetscFunctionReturn(PETSC_SUCCESS);
3142: }
3144: /*
3145: Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3146: (see MatCreateSubMatrix_MPIAIJ_nonscalable)
3148: Input Parameters:
3149: + mat - matrix
3150: . isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3151: i.e., mat->rstart <= isrow[i] < mat->rend
3152: - iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3153: i.e., mat->cstart <= iscol[i] < mat->cend
3155: Output Parameters:
3156: + isrow_d - sequential row index set for retrieving mat->A
3157: . iscol_d - sequential column index set for retrieving mat->A
3158: . iscol_o - sequential column index set for retrieving mat->B
3159: - garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3160: */
3161: static PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, PetscInt *garray[])
3162: {
3163: Vec x, cmap;
3164: const PetscInt *is_idx;
3165: PetscScalar *xarray, *cmaparray;
3166: PetscInt ncols, isstart, *idx, m, rstart, *cmap1, count;
3167: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3168: Mat B = a->B;
3169: Vec lvec = a->lvec, lcmap;
3170: PetscInt i, cstart, cend, Bn = B->cmap->N;
3171: MPI_Comm comm;
3172: VecScatter Mvctx = a->Mvctx;
3174: PetscFunctionBegin;
3175: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3176: PetscCall(ISGetLocalSize(iscol, &ncols));
3178: /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3179: PetscCall(MatCreateVecs(mat, &x, NULL));
3180: PetscCall(VecSet(x, -1.0));
3181: PetscCall(VecDuplicate(x, &cmap));
3182: PetscCall(VecSet(cmap, -1.0));
3184: /* Get start indices */
3185: PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3186: isstart -= ncols;
3187: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
3189: PetscCall(ISGetIndices(iscol, &is_idx));
3190: PetscCall(VecGetArray(x, &xarray));
3191: PetscCall(VecGetArray(cmap, &cmaparray));
3192: PetscCall(PetscMalloc1(ncols, &idx));
3193: for (i = 0; i < ncols; i++) {
3194: xarray[is_idx[i] - cstart] = (PetscScalar)is_idx[i];
3195: cmaparray[is_idx[i] - cstart] = i + isstart; /* global index of iscol[i] */
3196: idx[i] = is_idx[i] - cstart; /* local index of iscol[i] */
3197: }
3198: PetscCall(VecRestoreArray(x, &xarray));
3199: PetscCall(VecRestoreArray(cmap, &cmaparray));
3200: PetscCall(ISRestoreIndices(iscol, &is_idx));
3202: /* Get iscol_d */
3203: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3204: PetscCall(ISGetBlockSize(iscol, &i));
3205: PetscCall(ISSetBlockSize(*iscol_d, i));
3207: /* Get isrow_d */
3208: PetscCall(ISGetLocalSize(isrow, &m));
3209: rstart = mat->rmap->rstart;
3210: PetscCall(PetscMalloc1(m, &idx));
3211: PetscCall(ISGetIndices(isrow, &is_idx));
3212: for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3213: PetscCall(ISRestoreIndices(isrow, &is_idx));
3215: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3216: PetscCall(ISGetBlockSize(isrow, &i));
3217: PetscCall(ISSetBlockSize(*isrow_d, i));
3219: /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3220: PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3221: PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3223: PetscCall(VecDuplicate(lvec, &lcmap));
3225: PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3226: PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3228: /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3229: /* off-process column indices */
3230: count = 0;
3231: PetscCall(PetscMalloc1(Bn, &idx));
3232: PetscCall(PetscMalloc1(Bn, &cmap1));
3234: PetscCall(VecGetArray(lvec, &xarray));
3235: PetscCall(VecGetArray(lcmap, &cmaparray));
3236: for (i = 0; i < Bn; i++) {
3237: if (PetscRealPart(xarray[i]) > -1.0) {
3238: idx[count] = i; /* local column index in off-diagonal part B */
3239: cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3240: count++;
3241: }
3242: }
3243: PetscCall(VecRestoreArray(lvec, &xarray));
3244: PetscCall(VecRestoreArray(lcmap, &cmaparray));
3246: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3247: /* cannot ensure iscol_o has same blocksize as iscol! */
3249: PetscCall(PetscFree(idx));
3250: *garray = cmap1;
3252: PetscCall(VecDestroy(&x));
3253: PetscCall(VecDestroy(&cmap));
3254: PetscCall(VecDestroy(&lcmap));
3255: PetscFunctionReturn(PETSC_SUCCESS);
3256: }
3258: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3259: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3260: {
3261: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3262: Mat M = NULL;
3263: MPI_Comm comm;
3264: IS iscol_d, isrow_d, iscol_o;
3265: Mat Asub = NULL, Bsub = NULL;
3266: PetscInt n, count, M_size, N_size;
3268: PetscFunctionBegin;
3269: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3271: if (call == MAT_REUSE_MATRIX) {
3272: /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3273: PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3274: PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");
3276: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3277: PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");
3279: PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3280: PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");
3282: /* Update diagonal and off-diagonal portions of submat */
3283: asub = (Mat_MPIAIJ *)(*submat)->data;
3284: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3285: PetscCall(ISGetLocalSize(iscol_o, &n));
3286: if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3287: PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3288: PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));
3290: } else { /* call == MAT_INITIAL_MATRIX) */
3291: PetscInt *garray, *garray_compact;
3292: PetscInt BsubN;
3294: /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3295: PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));
3297: /* Create local submatrices Asub and Bsub */
3298: PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3299: PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));
3301: // Compact garray so its not of size Bn
3302: PetscCall(ISGetSize(iscol_o, &count));
3303: PetscCall(PetscMalloc1(count, &garray_compact));
3304: PetscCall(PetscArraycpy(garray_compact, garray, count));
3306: /* Create submatrix M */
3307: PetscCall(ISGetSize(isrow, &M_size));
3308: PetscCall(ISGetSize(iscol, &N_size));
3309: PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, M_size, N_size, Asub, Bsub, garray_compact, &M));
3311: /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3312: asub = (Mat_MPIAIJ *)M->data;
3314: PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3315: n = asub->B->cmap->N;
3316: if (BsubN > n) {
3317: /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3318: const PetscInt *idx;
3319: PetscInt i, j, *idx_new, *subgarray = asub->garray;
3320: PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));
3322: PetscCall(PetscMalloc1(n, &idx_new));
3323: j = 0;
3324: PetscCall(ISGetIndices(iscol_o, &idx));
3325: for (i = 0; i < n; i++) {
3326: if (j >= BsubN) break;
3327: while (subgarray[i] > garray[j]) j++;
3329: PetscCheck(subgarray[i] == garray[j], PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "subgarray[%" PetscInt_FMT "]=%" PetscInt_FMT " cannot < garray[%" PetscInt_FMT "]=%" PetscInt_FMT, i, subgarray[i], j, garray[j]);
3330: idx_new[i] = idx[j++];
3331: }
3332: PetscCall(ISRestoreIndices(iscol_o, &idx));
3334: PetscCall(ISDestroy(&iscol_o));
3335: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));
3337: } else PetscCheck(BsubN >= n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Columns of Bsub (%" PetscInt_FMT ") cannot be smaller than B's (%" PetscInt_FMT ")", BsubN, asub->B->cmap->N);
3339: PetscCall(PetscFree(garray));
3340: *submat = M;
3342: /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3343: PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3344: PetscCall(ISDestroy(&isrow_d));
3346: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3347: PetscCall(ISDestroy(&iscol_d));
3349: PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3350: PetscCall(ISDestroy(&iscol_o));
3351: }
3352: PetscFunctionReturn(PETSC_SUCCESS);
3353: }
3355: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3356: {
3357: IS iscol_local = NULL, isrow_d;
3358: PetscInt csize;
3359: PetscInt n, i, j, start, end;
3360: PetscBool sameRowDist = PETSC_FALSE, sameDist[2], tsameDist[2];
3361: MPI_Comm comm;
3363: PetscFunctionBegin;
3364: /* If isrow has same processor distribution as mat,
3365: call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3366: if (call == MAT_REUSE_MATRIX) {
3367: PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3368: if (isrow_d) {
3369: sameRowDist = PETSC_TRUE;
3370: tsameDist[1] = PETSC_TRUE; /* sameColDist */
3371: } else {
3372: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3373: if (iscol_local) {
3374: sameRowDist = PETSC_TRUE;
3375: tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3376: }
3377: }
3378: } else {
3379: /* Check if isrow has same processor distribution as mat */
3380: sameDist[0] = PETSC_FALSE;
3381: PetscCall(ISGetLocalSize(isrow, &n));
3382: if (!n) {
3383: sameDist[0] = PETSC_TRUE;
3384: } else {
3385: PetscCall(ISGetMinMax(isrow, &i, &j));
3386: PetscCall(MatGetOwnershipRange(mat, &start, &end));
3387: if (i >= start && j < end) sameDist[0] = PETSC_TRUE;
3388: }
3390: /* Check if iscol has same processor distribution as mat */
3391: sameDist[1] = PETSC_FALSE;
3392: PetscCall(ISGetLocalSize(iscol, &n));
3393: if (!n) {
3394: sameDist[1] = PETSC_TRUE;
3395: } else {
3396: PetscCall(ISGetMinMax(iscol, &i, &j));
3397: PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3398: if (i >= start && j < end) sameDist[1] = PETSC_TRUE;
3399: }
3401: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3402: PetscCallMPI(MPIU_Allreduce(&sameDist, &tsameDist, 2, MPI_C_BOOL, MPI_LAND, comm));
3403: sameRowDist = tsameDist[0];
3404: }
3406: if (sameRowDist) {
3407: if (tsameDist[1]) { /* sameRowDist & sameColDist */
3408: /* isrow and iscol have same processor distribution as mat */
3409: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3410: PetscFunctionReturn(PETSC_SUCCESS);
3411: } else { /* sameRowDist */
3412: /* isrow has same processor distribution as mat */
3413: if (call == MAT_INITIAL_MATRIX) {
3414: PetscBool sorted;
3415: PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3416: PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3417: PetscCall(ISGetSize(iscol, &i));
3418: PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);
3420: PetscCall(ISSorted(iscol_local, &sorted));
3421: if (sorted) {
3422: /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3423: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3424: PetscFunctionReturn(PETSC_SUCCESS);
3425: }
3426: } else { /* call == MAT_REUSE_MATRIX */
3427: IS iscol_sub;
3428: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3429: if (iscol_sub) {
3430: PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3431: PetscFunctionReturn(PETSC_SUCCESS);
3432: }
3433: }
3434: }
3435: }
3437: /* General case: iscol -> iscol_local which has global size of iscol */
3438: if (call == MAT_REUSE_MATRIX) {
3439: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3440: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3441: } else {
3442: if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3443: }
3445: PetscCall(ISGetLocalSize(iscol, &csize));
3446: PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));
3448: if (call == MAT_INITIAL_MATRIX) {
3449: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3450: PetscCall(ISDestroy(&iscol_local));
3451: }
3452: PetscFunctionReturn(PETSC_SUCCESS);
3453: }
3455: /*@C
3456: MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3457: and "off-diagonal" part of the matrix in CSR format.
3459: Collective
3461: Input Parameters:
3462: + comm - MPI communicator
3463: . M - the global row size
3464: . N - the global column size
3465: . A - "diagonal" portion of matrix
3466: . 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
3467: - garray - either `NULL` or the global index of `B` columns. If not `NULL`, it should be allocated by `PetscMalloc1()` and will be owned by `mat` thereafter.
3469: Output Parameter:
3470: . mat - the matrix, with input `A` as its local diagonal matrix
3472: Level: advanced
3474: Notes:
3475: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.
3477: `A` and `B` becomes part of output mat. The user cannot use `A` and `B` anymore.
3479: If `garray` is `NULL`, `B` will be compacted to use local indices. In this sense, `B`'s sparsity pattern (nonzerostate) will be changed. If `B` is a device matrix, we need to somehow also update
3480: `B`'s copy on device. We do so by increasing `B`'s nonzerostate. In use of `B` on device, device matrix types should detect this change (ref. internal routines `MatSeqAIJCUSPARSECopyToGPU()` or
3481: `MatAssemblyEnd_SeqAIJKokkos()`) and will just destroy and then recreate the device copy of `B`. It is not optimal, but is easy to implement and less hacky. To avoid this overhead, try to compute `garray`
3482: yourself, see algorithms in the private function `MatSetUpMultiply_MPIAIJ()`.
3484: The `NULL`-ness of `garray` doesn't need to be collective, in other words, `garray` can be `NULL` on some processes while not on others.
3486: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3487: @*/
3488: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, PetscInt M, PetscInt N, Mat A, Mat B, PetscInt *garray, Mat *mat)
3489: {
3490: PetscInt m, n;
3491: MatType mpi_mat_type;
3492: Mat_MPIAIJ *mpiaij;
3493: Mat C;
3495: PetscFunctionBegin;
3496: PetscCall(MatCreate(comm, &C));
3497: PetscCall(MatGetSize(A, &m, &n));
3498: PetscCheck(m == B->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Am %" PetscInt_FMT " != Bm %" PetscInt_FMT, m, B->rmap->N);
3499: PetscCheck(A->rmap->bs == B->rmap->bs, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "A row bs %" PetscInt_FMT " != B row bs %" PetscInt_FMT, A->rmap->bs, B->rmap->bs);
3501: PetscCall(MatSetSizes(C, m, n, M, N));
3502: /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3503: PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3504: PetscCall(MatSetType(C, mpi_mat_type));
3505: if (!garray) {
3506: const PetscScalar *ba;
3508: B->nonzerostate++;
3509: PetscCall(MatSeqAIJGetArrayRead(B, &ba)); /* Since we will destroy B's device copy, we need to make sure the host copy is up to date */
3510: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
3511: }
3513: PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
3514: PetscCall(PetscLayoutSetUp(C->rmap));
3515: PetscCall(PetscLayoutSetUp(C->cmap));
3517: mpiaij = (Mat_MPIAIJ *)C->data;
3518: mpiaij->A = A;
3519: mpiaij->B = B;
3520: mpiaij->garray = garray;
3521: C->preallocated = PETSC_TRUE;
3522: C->nooffprocentries = PETSC_TRUE; /* See MatAssemblyBegin_MPIAIJ. In effect, making MatAssemblyBegin a nop */
3524: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3525: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
3526: /* MatAssemblyEnd is critical here. It sets mat->offloadmask according to A and B's, and
3527: also gets mpiaij->B compacted (if garray is NULL), with its col ids and size reduced
3528: */
3529: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
3530: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
3531: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3532: *mat = C;
3533: PetscFunctionReturn(PETSC_SUCCESS);
3534: }
3536: extern PetscErrorCode MatCreateSubMatrices_MPIAIJ_SingleIS_Local(Mat, PetscInt, const IS[], const IS[], MatReuse, PetscBool, Mat *);
3538: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowDist(Mat mat, IS isrow, IS iscol, IS iscol_local, MatReuse call, Mat *newmat)
3539: {
3540: PetscInt i, m, n, rstart, row, rend, nz, j, bs, cbs;
3541: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3542: Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data;
3543: Mat M, Msub, B = a->B;
3544: MatScalar *aa;
3545: Mat_SeqAIJ *aij;
3546: PetscInt *garray = a->garray, *colsub, Ncols;
3547: PetscInt count, Bn = B->cmap->N, cstart = mat->cmap->rstart, cend = mat->cmap->rend;
3548: IS iscol_sub, iscmap;
3549: const PetscInt *is_idx, *cmap;
3550: PetscBool allcolumns = PETSC_FALSE;
3551: MPI_Comm comm;
3553: PetscFunctionBegin;
3554: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3555: if (call == MAT_REUSE_MATRIX) {
3556: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3557: PetscCheck(iscol_sub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "SubIScol passed in was not used before, cannot reuse");
3558: PetscCall(ISGetLocalSize(iscol_sub, &count));
3560: PetscCall(PetscObjectQuery((PetscObject)*newmat, "Subcmap", (PetscObject *)&iscmap));
3561: PetscCheck(iscmap, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Subcmap passed in was not used before, cannot reuse");
3563: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Msub));
3564: PetscCheck(Msub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3566: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_REUSE_MATRIX, PETSC_FALSE, &Msub));
3568: } else { /* call == MAT_INITIAL_MATRIX) */
3569: PetscBool flg;
3571: PetscCall(ISGetLocalSize(iscol, &n));
3572: PetscCall(ISGetSize(iscol, &Ncols));
3574: /* (1) iscol -> nonscalable iscol_local */
3575: /* Check for special case: each processor gets entire matrix columns */
3576: PetscCall(ISIdentity(iscol_local, &flg));
3577: if (flg && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3578: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3579: if (allcolumns) {
3580: iscol_sub = iscol_local;
3581: PetscCall(PetscObjectReference((PetscObject)iscol_local));
3582: PetscCall(ISCreateStride(PETSC_COMM_SELF, n, 0, 1, &iscmap));
3584: } else {
3585: /* (2) iscol_local -> iscol_sub and iscmap. Implementation below requires iscol_local be sorted, it can have duplicate indices */
3586: PetscInt *idx, *cmap1, k;
3587: PetscCall(PetscMalloc1(Ncols, &idx));
3588: PetscCall(PetscMalloc1(Ncols, &cmap1));
3589: PetscCall(ISGetIndices(iscol_local, &is_idx));
3590: count = 0;
3591: k = 0;
3592: for (i = 0; i < Ncols; i++) {
3593: j = is_idx[i];
3594: if (j >= cstart && j < cend) {
3595: /* diagonal part of mat */
3596: idx[count] = j;
3597: cmap1[count++] = i; /* column index in submat */
3598: } else if (Bn) {
3599: /* off-diagonal part of mat */
3600: if (j == garray[k]) {
3601: idx[count] = j;
3602: cmap1[count++] = i; /* column index in submat */
3603: } else if (j > garray[k]) {
3604: while (j > garray[k] && k < Bn - 1) k++;
3605: if (j == garray[k]) {
3606: idx[count] = j;
3607: cmap1[count++] = i; /* column index in submat */
3608: }
3609: }
3610: }
3611: }
3612: PetscCall(ISRestoreIndices(iscol_local, &is_idx));
3614: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_OWN_POINTER, &iscol_sub));
3615: PetscCall(ISGetBlockSize(iscol, &cbs));
3616: PetscCall(ISSetBlockSize(iscol_sub, cbs));
3618: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)iscol_local), count, cmap1, PETSC_OWN_POINTER, &iscmap));
3619: }
3621: /* (3) Create sequential Msub */
3622: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_INITIAL_MATRIX, allcolumns, &Msub));
3623: }
3625: PetscCall(ISGetLocalSize(iscol_sub, &count));
3626: aij = (Mat_SeqAIJ *)Msub->data;
3627: ii = aij->i;
3628: PetscCall(ISGetIndices(iscmap, &cmap));
3630: /*
3631: m - number of local rows
3632: Ncols - number of columns (same on all processors)
3633: rstart - first row in new global matrix generated
3634: */
3635: PetscCall(MatGetSize(Msub, &m, NULL));
3637: if (call == MAT_INITIAL_MATRIX) {
3638: /* (4) Create parallel newmat */
3639: PetscMPIInt rank, size;
3640: PetscInt csize;
3642: PetscCallMPI(MPI_Comm_size(comm, &size));
3643: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3645: /*
3646: Determine the number of non-zeros in the diagonal and off-diagonal
3647: portions of the matrix in order to do correct preallocation
3648: */
3650: /* first get start and end of "diagonal" columns */
3651: PetscCall(ISGetLocalSize(iscol, &csize));
3652: if (csize == PETSC_DECIDE) {
3653: PetscCall(ISGetSize(isrow, &mglobal));
3654: if (mglobal == Ncols) { /* square matrix */
3655: nlocal = m;
3656: } else {
3657: nlocal = Ncols / size + ((Ncols % size) > rank);
3658: }
3659: } else {
3660: nlocal = csize;
3661: }
3662: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3663: rstart = rend - nlocal;
3664: 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);
3666: /* next, compute all the lengths */
3667: jj = aij->j;
3668: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3669: olens = dlens + m;
3670: for (i = 0; i < m; i++) {
3671: jend = ii[i + 1] - ii[i];
3672: olen = 0;
3673: dlen = 0;
3674: for (j = 0; j < jend; j++) {
3675: if (cmap[*jj] < rstart || cmap[*jj] >= rend) olen++;
3676: else dlen++;
3677: jj++;
3678: }
3679: olens[i] = olen;
3680: dlens[i] = dlen;
3681: }
3683: PetscCall(ISGetBlockSize(isrow, &bs));
3684: PetscCall(ISGetBlockSize(iscol, &cbs));
3686: PetscCall(MatCreate(comm, &M));
3687: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, Ncols));
3688: PetscCall(MatSetBlockSizes(M, bs, cbs));
3689: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3690: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3691: PetscCall(PetscFree(dlens));
3693: } else { /* call == MAT_REUSE_MATRIX */
3694: M = *newmat;
3695: PetscCall(MatGetLocalSize(M, &i, NULL));
3696: PetscCheck(i == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3697: PetscCall(MatZeroEntries(M));
3698: /*
3699: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3700: rather than the slower MatSetValues().
3701: */
3702: M->was_assembled = PETSC_TRUE;
3703: M->assembled = PETSC_FALSE;
3704: }
3706: /* (5) Set values of Msub to *newmat */
3707: PetscCall(PetscMalloc1(count, &colsub));
3708: PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
3710: jj = aij->j;
3711: PetscCall(MatSeqAIJGetArrayRead(Msub, (const PetscScalar **)&aa));
3712: for (i = 0; i < m; i++) {
3713: row = rstart + i;
3714: nz = ii[i + 1] - ii[i];
3715: for (j = 0; j < nz; j++) colsub[j] = cmap[jj[j]];
3716: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, colsub, aa, INSERT_VALUES));
3717: jj += nz;
3718: aa += nz;
3719: }
3720: PetscCall(MatSeqAIJRestoreArrayRead(Msub, (const PetscScalar **)&aa));
3721: PetscCall(ISRestoreIndices(iscmap, &cmap));
3723: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3724: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3726: PetscCall(PetscFree(colsub));
3728: /* save Msub, iscol_sub and iscmap used in processor for next request */
3729: if (call == MAT_INITIAL_MATRIX) {
3730: *newmat = M;
3731: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubMatrix", (PetscObject)Msub));
3732: PetscCall(MatDestroy(&Msub));
3734: PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubIScol", (PetscObject)iscol_sub));
3735: PetscCall(ISDestroy(&iscol_sub));
3737: PetscCall(PetscObjectCompose((PetscObject)*newmat, "Subcmap", (PetscObject)iscmap));
3738: PetscCall(ISDestroy(&iscmap));
3740: if (iscol_local) {
3741: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3742: PetscCall(ISDestroy(&iscol_local));
3743: }
3744: }
3745: PetscFunctionReturn(PETSC_SUCCESS);
3746: }
3748: /*
3749: Not great since it makes two copies of the submatrix, first an SeqAIJ
3750: in local and then by concatenating the local matrices the end result.
3751: Writing it directly would be much like MatCreateSubMatrices_MPIAIJ()
3753: This requires a sequential iscol with all indices.
3754: */
3755: PetscErrorCode MatCreateSubMatrix_MPIAIJ_nonscalable(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
3756: {
3757: PetscMPIInt rank, size;
3758: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs, cbs;
3759: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3760: Mat M, Mreuse;
3761: MatScalar *aa, *vwork;
3762: MPI_Comm comm;
3763: Mat_SeqAIJ *aij;
3764: PetscBool colflag, allcolumns = PETSC_FALSE;
3766: PetscFunctionBegin;
3767: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3768: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3769: PetscCallMPI(MPI_Comm_size(comm, &size));
3771: /* Check for special case: each processor gets entire matrix columns */
3772: PetscCall(ISIdentity(iscol, &colflag));
3773: PetscCall(ISGetLocalSize(iscol, &n));
3774: if (colflag && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3775: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3777: if (call == MAT_REUSE_MATRIX) {
3778: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
3779: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3780: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_REUSE_MATRIX, allcolumns, &Mreuse));
3781: } else {
3782: PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_INITIAL_MATRIX, allcolumns, &Mreuse));
3783: }
3785: /*
3786: m - number of local rows
3787: n - number of columns (same on all processors)
3788: rstart - first row in new global matrix generated
3789: */
3790: PetscCall(MatGetSize(Mreuse, &m, &n));
3791: PetscCall(MatGetBlockSizes(Mreuse, &bs, &cbs));
3792: if (call == MAT_INITIAL_MATRIX) {
3793: aij = (Mat_SeqAIJ *)Mreuse->data;
3794: ii = aij->i;
3795: jj = aij->j;
3797: /*
3798: Determine the number of non-zeros in the diagonal and off-diagonal
3799: portions of the matrix in order to do correct preallocation
3800: */
3802: /* first get start and end of "diagonal" columns */
3803: if (csize == PETSC_DECIDE) {
3804: PetscCall(ISGetSize(isrow, &mglobal));
3805: if (mglobal == n) { /* square matrix */
3806: nlocal = m;
3807: } else {
3808: nlocal = n / size + ((n % size) > rank);
3809: }
3810: } else {
3811: nlocal = csize;
3812: }
3813: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3814: rstart = rend - nlocal;
3815: 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);
3817: /* next, compute all the lengths */
3818: PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3819: olens = dlens + m;
3820: for (i = 0; i < m; i++) {
3821: jend = ii[i + 1] - ii[i];
3822: olen = 0;
3823: dlen = 0;
3824: for (j = 0; j < jend; j++) {
3825: if (*jj < rstart || *jj >= rend) olen++;
3826: else dlen++;
3827: jj++;
3828: }
3829: olens[i] = olen;
3830: dlens[i] = dlen;
3831: }
3832: PetscCall(MatCreate(comm, &M));
3833: PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, n));
3834: PetscCall(MatSetBlockSizes(M, bs, cbs));
3835: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3836: PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3837: PetscCall(PetscFree(dlens));
3838: } else {
3839: PetscInt ml, nl;
3841: M = *newmat;
3842: PetscCall(MatGetLocalSize(M, &ml, &nl));
3843: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3844: PetscCall(MatZeroEntries(M));
3845: /*
3846: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3847: rather than the slower MatSetValues().
3848: */
3849: M->was_assembled = PETSC_TRUE;
3850: M->assembled = PETSC_FALSE;
3851: }
3852: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
3853: aij = (Mat_SeqAIJ *)Mreuse->data;
3854: ii = aij->i;
3855: jj = aij->j;
3857: /* trigger copy to CPU if needed */
3858: PetscCall(MatSeqAIJGetArrayRead(Mreuse, (const PetscScalar **)&aa));
3859: for (i = 0; i < m; i++) {
3860: row = rstart + i;
3861: nz = ii[i + 1] - ii[i];
3862: cwork = jj;
3863: jj = PetscSafePointerPlusOffset(jj, nz);
3864: vwork = aa;
3865: aa = PetscSafePointerPlusOffset(aa, nz);
3866: PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
3867: }
3868: PetscCall(MatSeqAIJRestoreArrayRead(Mreuse, (const PetscScalar **)&aa));
3870: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3871: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3872: *newmat = M;
3874: /* save submatrix used in processor for next request */
3875: if (call == MAT_INITIAL_MATRIX) {
3876: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
3877: PetscCall(MatDestroy(&Mreuse));
3878: }
3879: PetscFunctionReturn(PETSC_SUCCESS);
3880: }
3882: static PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
3883: {
3884: PetscInt m, cstart, cend, j, nnz, i, d, *ld;
3885: PetscInt *d_nnz, *o_nnz, nnz_max = 0, rstart, ii, irstart;
3886: const PetscInt *JJ;
3887: PetscBool nooffprocentries;
3888: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)B->data;
3890: PetscFunctionBegin;
3891: PetscCall(PetscLayoutSetUp(B->rmap));
3892: PetscCall(PetscLayoutSetUp(B->cmap));
3893: m = B->rmap->n;
3894: cstart = B->cmap->rstart;
3895: cend = B->cmap->rend;
3896: rstart = B->rmap->rstart;
3897: irstart = Ii[0];
3899: PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));
3901: if (PetscDefined(USE_DEBUG)) {
3902: for (i = 0; i < m; i++) {
3903: nnz = Ii[i + 1] - Ii[i];
3904: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3905: PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3906: 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]);
3907: 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);
3908: }
3909: }
3911: for (i = 0; i < m; i++) {
3912: nnz = Ii[i + 1] - Ii[i];
3913: JJ = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3914: nnz_max = PetscMax(nnz_max, nnz);
3915: d = 0;
3916: for (j = 0; j < nnz; j++) {
3917: if (cstart <= JJ[j] && JJ[j] < cend) d++;
3918: }
3919: d_nnz[i] = d;
3920: o_nnz[i] = nnz - d;
3921: }
3922: PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3923: PetscCall(PetscFree2(d_nnz, o_nnz));
3925: for (i = 0; i < m; i++) {
3926: ii = i + rstart;
3927: PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], PetscSafePointerPlusOffset(J, Ii[i] - irstart), PetscSafePointerPlusOffset(v, Ii[i] - irstart), INSERT_VALUES));
3928: }
3929: nooffprocentries = B->nooffprocentries;
3930: B->nooffprocentries = PETSC_TRUE;
3931: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3932: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3933: B->nooffprocentries = nooffprocentries;
3935: /* count number of entries below block diagonal */
3936: PetscCall(PetscFree(Aij->ld));
3937: PetscCall(PetscCalloc1(m, &ld));
3938: Aij->ld = ld;
3939: for (i = 0; i < m; i++) {
3940: nnz = Ii[i + 1] - Ii[i];
3941: j = 0;
3942: while (j < nnz && J[j] < cstart) j++;
3943: ld[i] = j;
3944: if (J) J += nnz;
3945: }
3947: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3948: PetscFunctionReturn(PETSC_SUCCESS);
3949: }
3951: /*@
3952: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
3953: (the default parallel PETSc format).
3955: Collective
3957: Input Parameters:
3958: + B - the matrix
3959: . i - the indices into `j` for the start of each local row (indices start with zero)
3960: . j - the column indices for each local row (indices start with zero)
3961: - v - optional values in the matrix
3963: Level: developer
3965: Notes:
3966: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
3967: thus you CANNOT change the matrix entries by changing the values of `v` after you have
3968: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3970: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
3972: A convenience routine for this functionality is `MatCreateMPIAIJWithArrays()`.
3974: You can update the matrix with new numerical values using `MatUpdateMPIAIJWithArrays()` after this call if the column indices in `j` are sorted.
3976: If you do **not** use `MatUpdateMPIAIJWithArrays()`, the column indices in `j` do not need to be sorted. If you will use
3977: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
3979: The format which is used for the sparse matrix input, is equivalent to a
3980: row-major ordering.. i.e for the following matrix, the input data expected is
3981: as shown
3982: .vb
3983: 1 0 0
3984: 2 0 3 P0
3985: -------
3986: 4 5 6 P1
3988: Process0 [P0] rows_owned=[0,1]
3989: i = {0,1,3} [size = nrow+1 = 2+1]
3990: j = {0,0,2} [size = 3]
3991: v = {1,2,3} [size = 3]
3993: Process1 [P1] rows_owned=[2]
3994: i = {0,3} [size = nrow+1 = 1+1]
3995: j = {0,1,2} [size = 3]
3996: v = {4,5,6} [size = 3]
3997: .ve
3999: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`,
4000: `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`, `MatCreateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4001: @*/
4002: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4003: {
4004: PetscFunctionBegin;
4005: PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4006: PetscFunctionReturn(PETSC_SUCCESS);
4007: }
4009: /*@
4010: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
4011: (the default parallel PETSc format). For good matrix assembly performance
4012: the user should preallocate the matrix storage by setting the parameters
4013: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4015: Collective
4017: Input Parameters:
4018: + B - the matrix
4019: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4020: (same value is used for all local rows)
4021: . d_nnz - array containing the number of nonzeros in the various rows of the
4022: DIAGONAL portion of the local submatrix (possibly different for each row)
4023: or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4024: The size of this array is equal to the number of local rows, i.e 'm'.
4025: For matrices that will be factored, you must leave room for (and set)
4026: the diagonal entry even if it is zero.
4027: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4028: submatrix (same value is used for all local rows).
4029: - o_nnz - array containing the number of nonzeros in the various rows of the
4030: OFF-DIAGONAL portion of the local submatrix (possibly different for
4031: each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4032: structure. The size of this array is equal to the number
4033: of local rows, i.e 'm'.
4035: Example Usage:
4036: Consider the following 8x8 matrix with 34 non-zero values, that is
4037: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4038: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4039: as follows
4041: .vb
4042: 1 2 0 | 0 3 0 | 0 4
4043: Proc0 0 5 6 | 7 0 0 | 8 0
4044: 9 0 10 | 11 0 0 | 12 0
4045: -------------------------------------
4046: 13 0 14 | 15 16 17 | 0 0
4047: Proc1 0 18 0 | 19 20 21 | 0 0
4048: 0 0 0 | 22 23 0 | 24 0
4049: -------------------------------------
4050: Proc2 25 26 27 | 0 0 28 | 29 0
4051: 30 0 0 | 31 32 33 | 0 34
4052: .ve
4054: This can be represented as a collection of submatrices as
4055: .vb
4056: A B C
4057: D E F
4058: G H I
4059: .ve
4061: Where the submatrices A,B,C are owned by proc0, D,E,F are
4062: owned by proc1, G,H,I are owned by proc2.
4064: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4065: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4066: The 'M','N' parameters are 8,8, and have the same values on all procs.
4068: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4069: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4070: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4071: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4072: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4073: matrix, and [DF] as another `MATSEQAIJ` matrix.
4075: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4076: allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4077: storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4078: One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4079: the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4080: In this case, the values of `d_nz`, `o_nz` are
4081: .vb
4082: proc0 dnz = 2, o_nz = 2
4083: proc1 dnz = 3, o_nz = 2
4084: proc2 dnz = 1, o_nz = 4
4085: .ve
4086: We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4087: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4088: for proc3. i.e we are using 12+15+10=37 storage locations to store
4089: 34 values.
4091: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4092: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4093: In the above case the values for `d_nnz`, `o_nnz` are
4094: .vb
4095: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4096: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4097: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4098: .ve
4099: Here the space allocated is sum of all the above values i.e 34, and
4100: hence pre-allocation is perfect.
4102: Level: intermediate
4104: Notes:
4105: If the *_nnz parameter is given then the *_nz parameter is ignored
4107: The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4108: storage. The stored row and column indices begin with zero.
4109: See [Sparse Matrices](sec_matsparse) for details.
4111: The parallel matrix is partitioned such that the first m0 rows belong to
4112: process 0, the next m1 rows belong to process 1, the next m2 rows belong
4113: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
4115: The DIAGONAL portion of the local submatrix of a processor can be defined
4116: as the submatrix which is obtained by extraction the part corresponding to
4117: the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4118: first row that belongs to the processor, r2 is the last row belonging to
4119: the this processor, and c1-c2 is range of indices of the local part of a
4120: vector suitable for applying the matrix to. This is an mxn matrix. In the
4121: common case of a square matrix, the row and column ranges are the same and
4122: the DIAGONAL part is also square. The remaining portion of the local
4123: submatrix (mxN) constitute the OFF-DIAGONAL portion.
4125: If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.
4127: You can call `MatGetInfo()` to get information on how effective the preallocation was;
4128: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4129: You can also run with the option `-info` and look for messages with the string
4130: malloc in them to see if additional memory allocation was needed.
4132: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4133: `MatGetInfo()`, `PetscSplitOwnership()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4134: @*/
4135: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4136: {
4137: PetscFunctionBegin;
4140: PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4141: PetscFunctionReturn(PETSC_SUCCESS);
4142: }
4144: /*@
4145: MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4146: CSR format for the local rows.
4148: Collective
4150: Input Parameters:
4151: + comm - MPI communicator
4152: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4153: . n - This value should be the same as the local size used in creating the
4154: x vector for the matrix-vector product $ y = Ax$. (or `PETSC_DECIDE` to have
4155: calculated if `N` is given) For square matrices n is almost always `m`.
4156: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
4157: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
4158: . 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
4159: . j - global column indices
4160: - a - optional matrix values
4162: Output Parameter:
4163: . mat - the matrix
4165: Level: intermediate
4167: Notes:
4168: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4169: thus you CANNOT change the matrix entries by changing the values of `a[]` after you have
4170: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
4172: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
4174: Once you have created the matrix you can update it with new numerical values using `MatUpdateMPIAIJWithArray()`
4176: If you do **not** use `MatUpdateMPIAIJWithArray()`, the column indices in `j` do not need to be sorted. If you will use
4177: `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.
4179: The format which is used for the sparse matrix input, is equivalent to a
4180: row-major ordering, i.e., for the following matrix, the input data expected is
4181: as shown
4182: .vb
4183: 1 0 0
4184: 2 0 3 P0
4185: -------
4186: 4 5 6 P1
4188: Process0 [P0] rows_owned=[0,1]
4189: i = {0,1,3} [size = nrow+1 = 2+1]
4190: j = {0,0,2} [size = 3]
4191: v = {1,2,3} [size = 3]
4193: Process1 [P1] rows_owned=[2]
4194: i = {0,3} [size = nrow+1 = 1+1]
4195: j = {0,1,2} [size = 3]
4196: v = {4,5,6} [size = 3]
4197: .ve
4199: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4200: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4201: @*/
4202: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
4203: {
4204: PetscFunctionBegin;
4205: PetscCheck(!i || !i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4206: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4207: PetscCall(MatCreate(comm, mat));
4208: PetscCall(MatSetSizes(*mat, m, n, M, N));
4209: /* PetscCall(MatSetBlockSizes(M,bs,cbs)); */
4210: PetscCall(MatSetType(*mat, MATMPIAIJ));
4211: PetscCall(MatMPIAIJSetPreallocationCSR(*mat, i, j, a));
4212: PetscFunctionReturn(PETSC_SUCCESS);
4213: }
4215: /*@
4216: MatUpdateMPIAIJWithArrays - updates a `MATMPIAIJ` matrix using arrays that contain in standard
4217: CSR format for the local rows. Only the numerical values are updated the other arrays must be identical to what was passed
4218: from `MatCreateMPIAIJWithArrays()`
4220: Deprecated: Use `MatUpdateMPIAIJWithArray()`
4222: Collective
4224: Input Parameters:
4225: + mat - the matrix
4226: . m - number of local rows (Cannot be `PETSC_DECIDE`)
4227: . n - This value should be the same as the local size used in creating the
4228: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4229: calculated if N is given) For square matrices n is almost always m.
4230: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4231: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4232: . Ii - row indices; that is Ii[0] = 0, Ii[row] = Ii[row-1] + number of elements in that row of the matrix
4233: . J - column indices
4234: - v - matrix values
4236: Level: deprecated
4238: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4239: `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4240: @*/
4241: PetscErrorCode MatUpdateMPIAIJWithArrays(Mat mat, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4242: {
4243: PetscInt nnz, i;
4244: PetscBool nooffprocentries;
4245: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4246: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4247: PetscScalar *ad, *ao;
4248: PetscInt ldi, Iii, md;
4249: const PetscInt *Adi = Ad->i;
4250: PetscInt *ld = Aij->ld;
4252: PetscFunctionBegin;
4253: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4254: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4255: PetscCheck(m == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of rows cannot change from call to MatUpdateMPIAIJWithArrays()");
4256: PetscCheck(n == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of columns cannot change from call to MatUpdateMPIAIJWithArrays()");
4258: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4259: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4261: for (i = 0; i < m; i++) {
4262: if (PetscDefined(USE_DEBUG)) {
4263: for (PetscInt j = Ii[i] + 1; j < Ii[i + 1]; ++j) {
4264: 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);
4265: 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);
4266: }
4267: }
4268: nnz = Ii[i + 1] - Ii[i];
4269: Iii = Ii[i];
4270: ldi = ld[i];
4271: md = Adi[i + 1] - Adi[i];
4272: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4273: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4274: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4275: ad += md;
4276: ao += nnz - md;
4277: }
4278: nooffprocentries = mat->nooffprocentries;
4279: mat->nooffprocentries = PETSC_TRUE;
4280: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4281: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4282: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4283: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4284: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4285: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4286: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4287: mat->nooffprocentries = nooffprocentries;
4288: PetscFunctionReturn(PETSC_SUCCESS);
4289: }
4291: /*@
4292: MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values
4294: Collective
4296: Input Parameters:
4297: + mat - the matrix
4298: - v - matrix values, stored by row
4300: Level: intermediate
4302: Notes:
4303: The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`
4305: The column indices in the call to `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()` must have been sorted for this call to work correctly
4307: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4308: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4309: @*/
4310: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4311: {
4312: PetscInt nnz, i, m;
4313: PetscBool nooffprocentries;
4314: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)mat->data;
4315: Mat_SeqAIJ *Ad = (Mat_SeqAIJ *)Aij->A->data;
4316: Mat_SeqAIJ *Ao = (Mat_SeqAIJ *)Aij->B->data;
4317: PetscScalar *ad, *ao;
4318: const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4319: PetscInt ldi, Iii, md;
4320: PetscInt *ld = Aij->ld;
4322: PetscFunctionBegin;
4323: m = mat->rmap->n;
4325: PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4326: PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4327: Iii = 0;
4328: for (i = 0; i < m; i++) {
4329: nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4330: ldi = ld[i];
4331: md = Adi[i + 1] - Adi[i];
4332: PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4333: ad += md;
4334: if (ao) {
4335: PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4336: PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4337: ao += nnz - md;
4338: }
4339: Iii += nnz;
4340: }
4341: nooffprocentries = mat->nooffprocentries;
4342: mat->nooffprocentries = PETSC_TRUE;
4343: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4344: PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4345: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4346: PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4347: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4348: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4349: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4350: mat->nooffprocentries = nooffprocentries;
4351: PetscFunctionReturn(PETSC_SUCCESS);
4352: }
4354: /*@
4355: MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4356: (the default parallel PETSc format). For good matrix assembly performance
4357: the user should preallocate the matrix storage by setting the parameters
4358: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
4360: Collective
4362: Input Parameters:
4363: + comm - MPI communicator
4364: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4365: This value should be the same as the local size used in creating the
4366: y vector for the matrix-vector product y = Ax.
4367: . n - This value should be the same as the local size used in creating the
4368: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4369: calculated if N is given) For square matrices n is almost always m.
4370: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4371: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4372: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4373: (same value is used for all local rows)
4374: . d_nnz - array containing the number of nonzeros in the various rows of the
4375: DIAGONAL portion of the local submatrix (possibly different for each row)
4376: or `NULL`, if `d_nz` is used to specify the nonzero structure.
4377: The size of this array is equal to the number of local rows, i.e 'm'.
4378: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4379: submatrix (same value is used for all local rows).
4380: - o_nnz - array containing the number of nonzeros in the various rows of the
4381: OFF-DIAGONAL portion of the local submatrix (possibly different for
4382: each row) or `NULL`, if `o_nz` is used to specify the nonzero
4383: structure. The size of this array is equal to the number
4384: of local rows, i.e 'm'.
4386: Output Parameter:
4387: . A - the matrix
4389: Options Database Keys:
4390: + -mat_no_inode - Do not use inodes
4391: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
4392: - -matmult_vecscatter_view <viewer> - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4393: See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the `VecScatter`
4394: 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.
4396: Level: intermediate
4398: Notes:
4399: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4400: MatXXXXSetPreallocation() paradigm instead of this routine directly.
4401: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
4403: If the *_nnz parameter is given then the *_nz parameter is ignored
4405: The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4406: processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4407: storage requirements for this matrix.
4409: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one
4410: processor than it must be used on all processors that share the object for
4411: that argument.
4413: If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by
4414: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`.
4416: The user MUST specify either the local or global matrix dimensions
4417: (possibly both).
4419: The parallel matrix is partitioned across processors such that the
4420: first `m0` rows belong to process 0, the next `m1` rows belong to
4421: process 1, the next `m2` rows belong to process 2, etc., where
4422: `m0`, `m1`, `m2`... are the input parameter `m` on each MPI process. I.e., each MPI process stores
4423: values corresponding to [m x N] submatrix.
4425: The columns are logically partitioned with the n0 columns belonging
4426: to 0th partition, the next n1 columns belonging to the next
4427: partition etc.. where n0,n1,n2... are the input parameter 'n'.
4429: The DIAGONAL portion of the local submatrix on any given processor
4430: is the submatrix corresponding to the rows and columns m,n
4431: corresponding to the given processor. i.e diagonal matrix on
4432: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4433: etc. The remaining portion of the local submatrix [m x (N-n)]
4434: constitute the OFF-DIAGONAL portion. The example below better
4435: illustrates this concept. The two matrices, the DIAGONAL portion and
4436: the OFF-DIAGONAL portion are each stored as `MATSEQAIJ` matrices.
4438: For a square global matrix we define each processor's diagonal portion
4439: to be its local rows and the corresponding columns (a square submatrix);
4440: each processor's off-diagonal portion encompasses the remainder of the
4441: local matrix (a rectangular submatrix).
4443: If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.
4445: When calling this routine with a single process communicator, a matrix of
4446: type `MATSEQAIJ` is returned. If a matrix of type `MATMPIAIJ` is desired for this
4447: type of communicator, use the construction mechanism
4448: .vb
4449: MatCreate(..., &A);
4450: MatSetType(A, MATMPIAIJ);
4451: MatSetSizes(A, m, n, M, N);
4452: MatMPIAIJSetPreallocation(A, ...);
4453: .ve
4455: By default, this format uses inodes (identical nodes) when possible.
4456: We search for consecutive rows with the same nonzero structure, thereby
4457: reusing matrix information to achieve increased efficiency.
4459: Example Usage:
4460: Consider the following 8x8 matrix with 34 non-zero values, that is
4461: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4462: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4463: as follows
4465: .vb
4466: 1 2 0 | 0 3 0 | 0 4
4467: Proc0 0 5 6 | 7 0 0 | 8 0
4468: 9 0 10 | 11 0 0 | 12 0
4469: -------------------------------------
4470: 13 0 14 | 15 16 17 | 0 0
4471: Proc1 0 18 0 | 19 20 21 | 0 0
4472: 0 0 0 | 22 23 0 | 24 0
4473: -------------------------------------
4474: Proc2 25 26 27 | 0 0 28 | 29 0
4475: 30 0 0 | 31 32 33 | 0 34
4476: .ve
4478: This can be represented as a collection of submatrices as
4480: .vb
4481: A B C
4482: D E F
4483: G H I
4484: .ve
4486: Where the submatrices A,B,C are owned by proc0, D,E,F are
4487: owned by proc1, G,H,I are owned by proc2.
4489: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4490: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4491: The 'M','N' parameters are 8,8, and have the same values on all procs.
4493: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4494: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4495: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4496: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4497: part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4498: matrix, and [DF] as another SeqAIJ matrix.
4500: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4501: allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4502: storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4503: One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4504: the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4505: In this case, the values of `d_nz`,`o_nz` are
4506: .vb
4507: proc0 dnz = 2, o_nz = 2
4508: proc1 dnz = 3, o_nz = 2
4509: proc2 dnz = 1, o_nz = 4
4510: .ve
4511: We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4512: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4513: for proc3. i.e we are using 12+15+10=37 storage locations to store
4514: 34 values.
4516: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4517: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4518: In the above case the values for d_nnz,o_nnz are
4519: .vb
4520: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4521: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4522: proc2 d_nnz = [1,1] and o_nnz = [4,4]
4523: .ve
4524: Here the space allocated is sum of all the above values i.e 34, and
4525: hence pre-allocation is perfect.
4527: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4528: `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`,
4529: `MatGetOwnershipRangesColumn()`, `PetscLayout`
4530: @*/
4531: 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)
4532: {
4533: PetscMPIInt size;
4535: PetscFunctionBegin;
4536: PetscCall(MatCreate(comm, A));
4537: PetscCall(MatSetSizes(*A, m, n, M, N));
4538: PetscCallMPI(MPI_Comm_size(comm, &size));
4539: if (size > 1) {
4540: PetscCall(MatSetType(*A, MATMPIAIJ));
4541: PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4542: } else {
4543: PetscCall(MatSetType(*A, MATSEQAIJ));
4544: PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4545: }
4546: PetscFunctionReturn(PETSC_SUCCESS);
4547: }
4549: /*@C
4550: MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix
4552: Not Collective
4554: Input Parameter:
4555: . A - The `MATMPIAIJ` matrix
4557: Output Parameters:
4558: + Ad - The local diagonal block as a `MATSEQAIJ` matrix
4559: . Ao - The local off-diagonal block as a `MATSEQAIJ` matrix
4560: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4562: Level: intermediate
4564: Note:
4565: The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4566: 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
4567: the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4568: local column numbers to global column numbers in the original matrix.
4570: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATSEQAIJ`
4571: @*/
4572: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4573: {
4574: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4575: PetscBool flg;
4577: PetscFunctionBegin;
4578: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4579: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4580: if (Ad) *Ad = a->A;
4581: if (Ao) *Ao = a->B;
4582: if (colmap) *colmap = a->garray;
4583: PetscFunctionReturn(PETSC_SUCCESS);
4584: }
4586: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4587: {
4588: PetscInt m, N, i, rstart, nnz, Ii;
4589: PetscInt *indx;
4590: PetscScalar *values;
4591: MatType rootType;
4593: PetscFunctionBegin;
4594: PetscCall(MatGetSize(inmat, &m, &N));
4595: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4596: PetscInt *dnz, *onz, sum, bs, cbs;
4598: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4599: /* Check sum(n) = N */
4600: PetscCallMPI(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4601: PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);
4603: PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4604: rstart -= m;
4606: MatPreallocateBegin(comm, m, n, dnz, onz);
4607: for (i = 0; i < m; i++) {
4608: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4609: PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4610: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4611: }
4613: PetscCall(MatCreate(comm, outmat));
4614: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4615: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4616: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4617: PetscCall(MatGetRootType_Private(inmat, &rootType));
4618: PetscCall(MatSetType(*outmat, rootType));
4619: PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4620: PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4621: MatPreallocateEnd(dnz, onz);
4622: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4623: }
4625: /* numeric phase */
4626: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4627: for (i = 0; i < m; i++) {
4628: PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4629: Ii = i + rstart;
4630: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4631: PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4632: }
4633: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4634: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4635: PetscFunctionReturn(PETSC_SUCCESS);
4636: }
4638: static PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(void **data)
4639: {
4640: Mat_Merge_SeqsToMPI *merge = (Mat_Merge_SeqsToMPI *)*data;
4642: PetscFunctionBegin;
4643: if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4644: PetscCall(PetscFree(merge->id_r));
4645: PetscCall(PetscFree(merge->len_s));
4646: PetscCall(PetscFree(merge->len_r));
4647: PetscCall(PetscFree(merge->bi));
4648: PetscCall(PetscFree(merge->bj));
4649: PetscCall(PetscFree(merge->buf_ri[0]));
4650: PetscCall(PetscFree(merge->buf_ri));
4651: PetscCall(PetscFree(merge->buf_rj[0]));
4652: PetscCall(PetscFree(merge->buf_rj));
4653: PetscCall(PetscFree(merge->coi));
4654: PetscCall(PetscFree(merge->coj));
4655: PetscCall(PetscFree(merge->owners_co));
4656: PetscCall(PetscLayoutDestroy(&merge->rowmap));
4657: PetscCall(PetscFree(merge));
4658: PetscFunctionReturn(PETSC_SUCCESS);
4659: }
4661: #include <../src/mat/utils/freespace.h>
4662: #include <petscbt.h>
4664: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4665: {
4666: MPI_Comm comm;
4667: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4668: PetscMPIInt size, rank, taga, *len_s;
4669: PetscInt N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj, m;
4670: PetscMPIInt proc, k;
4671: PetscInt **buf_ri, **buf_rj;
4672: PetscInt anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4673: PetscInt nrows, **buf_ri_k, **nextrow, **nextai;
4674: MPI_Request *s_waits, *r_waits;
4675: MPI_Status *status;
4676: const MatScalar *aa, *a_a;
4677: MatScalar **abuf_r, *ba_i;
4678: Mat_Merge_SeqsToMPI *merge;
4679: PetscContainer container;
4681: PetscFunctionBegin;
4682: PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4683: PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));
4685: PetscCallMPI(MPI_Comm_size(comm, &size));
4686: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4688: PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4689: PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4690: PetscCall(PetscContainerGetPointer(container, (void **)&merge));
4691: PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4692: aa = a_a;
4694: bi = merge->bi;
4695: bj = merge->bj;
4696: buf_ri = merge->buf_ri;
4697: buf_rj = merge->buf_rj;
4699: PetscCall(PetscMalloc1(size, &status));
4700: owners = merge->rowmap->range;
4701: len_s = merge->len_s;
4703: /* send and recv matrix values */
4704: PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4705: PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));
4707: PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4708: for (proc = 0, k = 0; proc < size; proc++) {
4709: if (!len_s[proc]) continue;
4710: i = owners[proc];
4711: PetscCallMPI(MPIU_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4712: k++;
4713: }
4715: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4716: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4717: PetscCall(PetscFree(status));
4719: PetscCall(PetscFree(s_waits));
4720: PetscCall(PetscFree(r_waits));
4722: /* insert mat values of mpimat */
4723: PetscCall(PetscMalloc1(N, &ba_i));
4724: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4726: for (k = 0; k < merge->nrecv; k++) {
4727: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4728: nrows = *buf_ri_k[k];
4729: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4730: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4731: }
4733: /* set values of ba */
4734: m = merge->rowmap->n;
4735: for (i = 0; i < m; i++) {
4736: arow = owners[rank] + i;
4737: bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4738: bnzi = bi[i + 1] - bi[i];
4739: PetscCall(PetscArrayzero(ba_i, bnzi));
4741: /* add local non-zero vals of this proc's seqmat into ba */
4742: anzi = ai[arow + 1] - ai[arow];
4743: aj = a->j + ai[arow];
4744: aa = a_a + ai[arow];
4745: nextaj = 0;
4746: for (j = 0; nextaj < anzi; j++) {
4747: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4748: ba_i[j] += aa[nextaj++];
4749: }
4750: }
4752: /* add received vals into ba */
4753: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4754: /* i-th row */
4755: if (i == *nextrow[k]) {
4756: anzi = *(nextai[k] + 1) - *nextai[k];
4757: aj = buf_rj[k] + *nextai[k];
4758: aa = abuf_r[k] + *nextai[k];
4759: nextaj = 0;
4760: for (j = 0; nextaj < anzi; j++) {
4761: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4762: ba_i[j] += aa[nextaj++];
4763: }
4764: }
4765: nextrow[k]++;
4766: nextai[k]++;
4767: }
4768: }
4769: PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4770: }
4771: PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4772: PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4773: PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));
4775: PetscCall(PetscFree(abuf_r[0]));
4776: PetscCall(PetscFree(abuf_r));
4777: PetscCall(PetscFree(ba_i));
4778: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4779: PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4780: PetscFunctionReturn(PETSC_SUCCESS);
4781: }
4783: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4784: {
4785: Mat B_mpi;
4786: Mat_SeqAIJ *a = (Mat_SeqAIJ *)seqmat->data;
4787: PetscMPIInt size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4788: PetscInt **buf_rj, **buf_ri, **buf_ri_k;
4789: PetscInt M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4790: PetscInt len, *dnz, *onz, bs, cbs;
4791: PetscInt k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4792: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4793: MPI_Request *si_waits, *sj_waits, *ri_waits, *rj_waits;
4794: MPI_Status *status;
4795: PetscFreeSpaceList free_space = NULL, current_space = NULL;
4796: PetscBT lnkbt;
4797: Mat_Merge_SeqsToMPI *merge;
4798: PetscContainer container;
4800: PetscFunctionBegin;
4801: PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));
4803: /* make sure it is a PETSc comm */
4804: PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4805: PetscCallMPI(MPI_Comm_size(comm, &size));
4806: PetscCallMPI(MPI_Comm_rank(comm, &rank));
4808: PetscCall(PetscNew(&merge));
4809: PetscCall(PetscMalloc1(size, &status));
4811: /* determine row ownership */
4812: PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4813: PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4814: PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4815: PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4816: PetscCall(PetscLayoutSetUp(merge->rowmap));
4817: PetscCall(PetscMalloc1(size, &len_si));
4818: PetscCall(PetscMalloc1(size, &merge->len_s));
4820: m = merge->rowmap->n;
4821: owners = merge->rowmap->range;
4823: /* determine the number of messages to send, their lengths */
4824: len_s = merge->len_s;
4826: len = 0; /* length of buf_si[] */
4827: merge->nsend = 0;
4828: for (PetscMPIInt proc = 0; proc < size; proc++) {
4829: len_si[proc] = 0;
4830: if (proc == rank) {
4831: len_s[proc] = 0;
4832: } else {
4833: PetscCall(PetscMPIIntCast(owners[proc + 1] - owners[proc] + 1, &len_si[proc]));
4834: PetscCall(PetscMPIIntCast(ai[owners[proc + 1]] - ai[owners[proc]], &len_s[proc])); /* num of rows to be sent to [proc] */
4835: }
4836: if (len_s[proc]) {
4837: merge->nsend++;
4838: nrows = 0;
4839: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4840: if (ai[i + 1] > ai[i]) nrows++;
4841: }
4842: PetscCall(PetscMPIIntCast(2 * (nrows + 1), &len_si[proc]));
4843: len += len_si[proc];
4844: }
4845: }
4847: /* determine the number and length of messages to receive for ij-structure */
4848: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4849: PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));
4851: /* post the Irecv of j-structure */
4852: PetscCall(PetscCommGetNewTag(comm, &tagj));
4853: PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));
4855: /* post the Isend of j-structure */
4856: PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));
4858: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4859: if (!len_s[proc]) continue;
4860: i = owners[proc];
4861: PetscCallMPI(MPIU_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4862: k++;
4863: }
4865: /* receives and sends of j-structure are complete */
4866: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4867: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));
4869: /* send and recv i-structure */
4870: PetscCall(PetscCommGetNewTag(comm, &tagi));
4871: PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));
4873: PetscCall(PetscMalloc1(len + 1, &buf_s));
4874: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4875: for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4876: if (!len_s[proc]) continue;
4877: /* form outgoing message for i-structure:
4878: buf_si[0]: nrows to be sent
4879: [1:nrows]: row index (global)
4880: [nrows+1:2*nrows+1]: i-structure index
4881: */
4882: nrows = len_si[proc] / 2 - 1;
4883: buf_si_i = buf_si + nrows + 1;
4884: buf_si[0] = nrows;
4885: buf_si_i[0] = 0;
4886: nrows = 0;
4887: for (i = owners[proc]; i < owners[proc + 1]; i++) {
4888: anzi = ai[i + 1] - ai[i];
4889: if (anzi) {
4890: buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4891: buf_si[nrows + 1] = i - owners[proc]; /* local row index */
4892: nrows++;
4893: }
4894: }
4895: PetscCallMPI(MPIU_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4896: k++;
4897: buf_si += len_si[proc];
4898: }
4900: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4901: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));
4903: PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4904: 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]));
4906: PetscCall(PetscFree(len_si));
4907: PetscCall(PetscFree(len_ri));
4908: PetscCall(PetscFree(rj_waits));
4909: PetscCall(PetscFree2(si_waits, sj_waits));
4910: PetscCall(PetscFree(ri_waits));
4911: PetscCall(PetscFree(buf_s));
4912: PetscCall(PetscFree(status));
4914: /* compute a local seq matrix in each processor */
4915: /* allocate bi array and free space for accumulating nonzero column info */
4916: PetscCall(PetscMalloc1(m + 1, &bi));
4917: bi[0] = 0;
4919: /* create and initialize a linked list */
4920: nlnk = N + 1;
4921: PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));
4923: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4924: len = ai[owners[rank + 1]] - ai[owners[rank]];
4925: PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));
4927: current_space = free_space;
4929: /* determine symbolic info for each local row */
4930: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));
4932: for (k = 0; k < merge->nrecv; k++) {
4933: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4934: nrows = *buf_ri_k[k];
4935: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4936: nextai[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
4937: }
4939: MatPreallocateBegin(comm, m, n, dnz, onz);
4940: len = 0;
4941: for (i = 0; i < m; i++) {
4942: bnzi = 0;
4943: /* add local non-zero cols of this proc's seqmat into lnk */
4944: arow = owners[rank] + i;
4945: anzi = ai[arow + 1] - ai[arow];
4946: aj = a->j + ai[arow];
4947: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
4948: bnzi += nlnk;
4949: /* add received col data into lnk */
4950: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4951: if (i == *nextrow[k]) { /* i-th row */
4952: anzi = *(nextai[k] + 1) - *nextai[k];
4953: aj = buf_rj[k] + *nextai[k];
4954: PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
4955: bnzi += nlnk;
4956: nextrow[k]++;
4957: nextai[k]++;
4958: }
4959: }
4960: if (len < bnzi) len = bnzi; /* =max(bnzi) */
4962: /* if free space is not available, make more free space */
4963: if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), ¤t_space));
4964: /* copy data into free space, then initialize lnk */
4965: PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
4966: PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));
4968: current_space->array += bnzi;
4969: current_space->local_used += bnzi;
4970: current_space->local_remaining -= bnzi;
4972: bi[i + 1] = bi[i] + bnzi;
4973: }
4975: PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4977: PetscCall(PetscMalloc1(bi[m] + 1, &bj));
4978: PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
4979: PetscCall(PetscLLDestroy(lnk, lnkbt));
4981: /* create symbolic parallel matrix B_mpi */
4982: PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
4983: PetscCall(MatCreate(comm, &B_mpi));
4984: if (n == PETSC_DECIDE) {
4985: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
4986: } else {
4987: PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4988: }
4989: PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
4990: PetscCall(MatSetType(B_mpi, MATMPIAIJ));
4991: PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
4992: MatPreallocateEnd(dnz, onz);
4993: PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));
4995: /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
4996: B_mpi->assembled = PETSC_FALSE;
4997: merge->bi = bi;
4998: merge->bj = bj;
4999: merge->buf_ri = buf_ri;
5000: merge->buf_rj = buf_rj;
5001: merge->coi = NULL;
5002: merge->coj = NULL;
5003: merge->owners_co = NULL;
5005: PetscCall(PetscCommDestroy(&comm));
5007: /* attach the supporting struct to B_mpi for reuse */
5008: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5009: PetscCall(PetscContainerSetPointer(container, merge));
5010: PetscCall(PetscContainerSetCtxDestroy(container, MatDestroy_MPIAIJ_SeqsToMPI));
5011: PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5012: PetscCall(PetscContainerDestroy(&container));
5013: *mpimat = B_mpi;
5015: PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5016: PetscFunctionReturn(PETSC_SUCCESS);
5017: }
5019: /*@
5020: MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5021: matrices from each processor
5023: Collective
5025: Input Parameters:
5026: + comm - the communicators the parallel matrix will live on
5027: . seqmat - the input sequential matrices
5028: . m - number of local rows (or `PETSC_DECIDE`)
5029: . n - number of local columns (or `PETSC_DECIDE`)
5030: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5032: Output Parameter:
5033: . mpimat - the parallel matrix generated
5035: Level: advanced
5037: Note:
5038: The dimensions of the sequential matrix in each processor MUST be the same.
5039: The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
5040: destroyed when `mpimat` is destroyed. Call `PetscObjectQuery()` to access `seqmat`.
5042: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`
5043: @*/
5044: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5045: {
5046: PetscMPIInt size;
5048: PetscFunctionBegin;
5049: PetscCallMPI(MPI_Comm_size(comm, &size));
5050: if (size == 1) {
5051: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5052: if (scall == MAT_INITIAL_MATRIX) {
5053: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5054: } else {
5055: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5056: }
5057: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5058: PetscFunctionReturn(PETSC_SUCCESS);
5059: }
5060: PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5061: if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5062: PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5063: PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5064: PetscFunctionReturn(PETSC_SUCCESS);
5065: }
5067: /*@
5068: MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix.
5070: Not Collective
5072: Input Parameter:
5073: . A - the matrix
5075: Output Parameter:
5076: . A_loc - the local sequential matrix generated
5078: Level: developer
5080: Notes:
5081: The matrix is created by taking `A`'s local rows and putting them into a sequential matrix
5082: with `mlocal` rows and `n` columns. Where `mlocal` is obtained with `MatGetLocalSize()` and
5083: `n` is the global column count obtained with `MatGetSize()`
5085: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5087: For parallel matrices this creates an entirely new matrix. If the matrix is sequential it merely increases the reference count.
5089: Destroy the matrix with `MatDestroy()`
5091: .seealso: [](ch_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5092: @*/
5093: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5094: {
5095: PetscBool mpi;
5097: PetscFunctionBegin;
5098: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5099: if (mpi) {
5100: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5101: } else {
5102: *A_loc = A;
5103: PetscCall(PetscObjectReference((PetscObject)*A_loc));
5104: }
5105: PetscFunctionReturn(PETSC_SUCCESS);
5106: }
5108: /*@
5109: MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix.
5111: Not Collective
5113: Input Parameters:
5114: + A - the matrix
5115: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5117: Output Parameter:
5118: . A_loc - the local sequential matrix generated
5120: Level: developer
5122: Notes:
5123: The matrix is created by taking all `A`'s local rows and putting them into a sequential
5124: matrix with `mlocal` rows and `n` columns.`mlocal` is the row count obtained with
5125: `MatGetLocalSize()` and `n` is the global column count obtained with `MatGetSize()`.
5127: In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.
5129: When `A` is sequential and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A` (which contains the entire matrix),
5130: 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
5131: then `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called to fill `A_loc`. Thus one can preallocate the appropriate sequential matrix `A_loc`
5132: 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.
5134: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5135: @*/
5136: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5137: {
5138: Mat_MPIAIJ *mpimat = (Mat_MPIAIJ *)A->data;
5139: Mat_SeqAIJ *mat, *a, *b;
5140: PetscInt *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5141: const PetscScalar *aa, *ba, *aav, *bav;
5142: PetscScalar *ca, *cam;
5143: PetscMPIInt size;
5144: PetscInt am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5145: PetscInt *ci, *cj, col, ncols_d, ncols_o, jo;
5146: PetscBool match;
5148: PetscFunctionBegin;
5149: PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5150: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5151: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5152: if (size == 1) {
5153: if (scall == MAT_INITIAL_MATRIX) {
5154: PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5155: *A_loc = mpimat->A;
5156: } else if (scall == MAT_REUSE_MATRIX) {
5157: PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5158: }
5159: PetscFunctionReturn(PETSC_SUCCESS);
5160: }
5162: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5163: a = (Mat_SeqAIJ *)mpimat->A->data;
5164: b = (Mat_SeqAIJ *)mpimat->B->data;
5165: ai = a->i;
5166: aj = a->j;
5167: bi = b->i;
5168: bj = b->j;
5169: PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5170: PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5171: aa = aav;
5172: ba = bav;
5173: if (scall == MAT_INITIAL_MATRIX) {
5174: PetscCall(PetscMalloc1(1 + am, &ci));
5175: ci[0] = 0;
5176: for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5177: PetscCall(PetscMalloc1(1 + ci[am], &cj));
5178: PetscCall(PetscMalloc1(1 + ci[am], &ca));
5179: k = 0;
5180: for (i = 0; i < am; i++) {
5181: ncols_o = bi[i + 1] - bi[i];
5182: ncols_d = ai[i + 1] - ai[i];
5183: /* off-diagonal portion of A */
5184: for (jo = 0; jo < ncols_o; jo++) {
5185: col = cmap[*bj];
5186: if (col >= cstart) break;
5187: cj[k] = col;
5188: bj++;
5189: ca[k++] = *ba++;
5190: }
5191: /* diagonal portion of A */
5192: for (j = 0; j < ncols_d; j++) {
5193: cj[k] = cstart + *aj++;
5194: ca[k++] = *aa++;
5195: }
5196: /* off-diagonal portion of A */
5197: for (j = jo; j < ncols_o; j++) {
5198: cj[k] = cmap[*bj++];
5199: ca[k++] = *ba++;
5200: }
5201: }
5202: /* put together the new matrix */
5203: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5204: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5205: /* Since these are PETSc arrays, change flags to free them as necessary. */
5206: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5207: mat->free_a = PETSC_TRUE;
5208: mat->free_ij = PETSC_TRUE;
5209: mat->nonew = 0;
5210: } else if (scall == MAT_REUSE_MATRIX) {
5211: mat = (Mat_SeqAIJ *)(*A_loc)->data;
5212: ci = mat->i;
5213: cj = mat->j;
5214: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5215: for (i = 0; i < am; i++) {
5216: /* off-diagonal portion of A */
5217: ncols_o = bi[i + 1] - bi[i];
5218: for (jo = 0; jo < ncols_o; jo++) {
5219: col = cmap[*bj];
5220: if (col >= cstart) break;
5221: *cam++ = *ba++;
5222: bj++;
5223: }
5224: /* diagonal portion of A */
5225: ncols_d = ai[i + 1] - ai[i];
5226: for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5227: /* off-diagonal portion of A */
5228: for (j = jo; j < ncols_o; j++) {
5229: *cam++ = *ba++;
5230: bj++;
5231: }
5232: }
5233: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5234: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5235: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5236: PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5237: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5238: PetscFunctionReturn(PETSC_SUCCESS);
5239: }
5241: /*@
5242: MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5243: mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and off-diagonal part
5245: Not Collective
5247: Input Parameters:
5248: + A - the matrix
5249: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5251: Output Parameters:
5252: + glob - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5253: - A_loc - the local sequential matrix generated
5255: Level: developer
5257: Note:
5258: This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5259: part, then those associated with the off-diagonal part (in its local ordering)
5261: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5262: @*/
5263: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5264: {
5265: Mat Ao, Ad;
5266: const PetscInt *cmap;
5267: PetscMPIInt size;
5268: PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);
5270: PetscFunctionBegin;
5271: PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5272: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5273: if (size == 1) {
5274: if (scall == MAT_INITIAL_MATRIX) {
5275: PetscCall(PetscObjectReference((PetscObject)Ad));
5276: *A_loc = Ad;
5277: } else if (scall == MAT_REUSE_MATRIX) {
5278: PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5279: }
5280: if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5281: PetscFunctionReturn(PETSC_SUCCESS);
5282: }
5283: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5284: PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5285: if (f) {
5286: PetscCall((*f)(A, scall, glob, A_loc));
5287: } else {
5288: Mat_SeqAIJ *a = (Mat_SeqAIJ *)Ad->data;
5289: Mat_SeqAIJ *b = (Mat_SeqAIJ *)Ao->data;
5290: Mat_SeqAIJ *c;
5291: PetscInt *ai = a->i, *aj = a->j;
5292: PetscInt *bi = b->i, *bj = b->j;
5293: PetscInt *ci, *cj;
5294: const PetscScalar *aa, *ba;
5295: PetscScalar *ca;
5296: PetscInt i, j, am, dn, on;
5298: PetscCall(MatGetLocalSize(Ad, &am, &dn));
5299: PetscCall(MatGetLocalSize(Ao, NULL, &on));
5300: PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5301: PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5302: if (scall == MAT_INITIAL_MATRIX) {
5303: PetscInt k;
5304: PetscCall(PetscMalloc1(1 + am, &ci));
5305: PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5306: PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5307: ci[0] = 0;
5308: for (i = 0, k = 0; i < am; i++) {
5309: const PetscInt ncols_o = bi[i + 1] - bi[i];
5310: const PetscInt ncols_d = ai[i + 1] - ai[i];
5311: ci[i + 1] = ci[i] + ncols_o + ncols_d;
5312: /* diagonal portion of A */
5313: for (j = 0; j < ncols_d; j++, k++) {
5314: cj[k] = *aj++;
5315: ca[k] = *aa++;
5316: }
5317: /* off-diagonal portion of A */
5318: for (j = 0; j < ncols_o; j++, k++) {
5319: cj[k] = dn + *bj++;
5320: ca[k] = *ba++;
5321: }
5322: }
5323: /* put together the new matrix */
5324: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5325: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5326: /* Since these are PETSc arrays, change flags to free them as necessary. */
5327: c = (Mat_SeqAIJ *)(*A_loc)->data;
5328: c->free_a = PETSC_TRUE;
5329: c->free_ij = PETSC_TRUE;
5330: c->nonew = 0;
5331: PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5332: } else if (scall == MAT_REUSE_MATRIX) {
5333: PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5334: for (i = 0; i < am; i++) {
5335: const PetscInt ncols_d = ai[i + 1] - ai[i];
5336: const PetscInt ncols_o = bi[i + 1] - bi[i];
5337: /* diagonal portion of A */
5338: for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5339: /* off-diagonal portion of A */
5340: for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5341: }
5342: PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5343: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5344: PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5345: PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5346: if (glob) {
5347: PetscInt cst, *gidx;
5349: PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5350: PetscCall(PetscMalloc1(dn + on, &gidx));
5351: for (i = 0; i < dn; i++) gidx[i] = cst + i;
5352: for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5353: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5354: }
5355: }
5356: PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5357: PetscFunctionReturn(PETSC_SUCCESS);
5358: }
5360: /*@C
5361: MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns
5363: Not Collective
5365: Input Parameters:
5366: + A - the matrix
5367: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5368: . row - index set of rows to extract (or `NULL`)
5369: - col - index set of columns to extract (or `NULL`)
5371: Output Parameter:
5372: . A_loc - the local sequential matrix generated
5374: Level: developer
5376: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5377: @*/
5378: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5379: {
5380: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5381: PetscInt i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5382: IS isrowa, iscola;
5383: Mat *aloc;
5384: PetscBool match;
5386: PetscFunctionBegin;
5387: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5388: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5389: PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5390: if (!row) {
5391: start = A->rmap->rstart;
5392: end = A->rmap->rend;
5393: PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5394: } else {
5395: isrowa = *row;
5396: }
5397: if (!col) {
5398: start = A->cmap->rstart;
5399: cmap = a->garray;
5400: nzA = a->A->cmap->n;
5401: nzB = a->B->cmap->n;
5402: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5403: ncols = 0;
5404: for (i = 0; i < nzB; i++) {
5405: if (cmap[i] < start) idx[ncols++] = cmap[i];
5406: else break;
5407: }
5408: imark = i;
5409: for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5410: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5411: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5412: } else {
5413: iscola = *col;
5414: }
5415: if (scall != MAT_INITIAL_MATRIX) {
5416: PetscCall(PetscMalloc1(1, &aloc));
5417: aloc[0] = *A_loc;
5418: }
5419: PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5420: if (!col) { /* attach global id of condensed columns */
5421: PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5422: }
5423: *A_loc = aloc[0];
5424: PetscCall(PetscFree(aloc));
5425: if (!row) PetscCall(ISDestroy(&isrowa));
5426: if (!col) PetscCall(ISDestroy(&iscola));
5427: PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5428: PetscFunctionReturn(PETSC_SUCCESS);
5429: }
5431: /*
5432: * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5433: * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5434: * on a global size.
5435: * */
5436: static PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5437: {
5438: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
5439: Mat_SeqAIJ *pd = (Mat_SeqAIJ *)p->A->data, *po = (Mat_SeqAIJ *)p->B->data, *p_oth;
5440: PetscInt plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5441: PetscMPIInt owner;
5442: PetscSFNode *iremote, *oiremote;
5443: const PetscInt *lrowindices;
5444: PetscSF sf, osf;
5445: PetscInt pcstart, *roffsets, *loffsets, *pnnz, j;
5446: PetscInt ontotalcols, dntotalcols, ntotalcols, nout;
5447: MPI_Comm comm;
5448: ISLocalToGlobalMapping mapping;
5449: const PetscScalar *pd_a, *po_a;
5451: PetscFunctionBegin;
5452: PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5453: /* plocalsize is the number of roots
5454: * nrows is the number of leaves
5455: * */
5456: PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5457: PetscCall(ISGetLocalSize(rows, &nrows));
5458: PetscCall(PetscCalloc1(nrows, &iremote));
5459: PetscCall(ISGetIndices(rows, &lrowindices));
5460: for (i = 0; i < nrows; i++) {
5461: /* Find a remote index and an owner for a row
5462: * The row could be local or remote
5463: * */
5464: owner = 0;
5465: lidx = 0;
5466: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5467: iremote[i].index = lidx;
5468: iremote[i].rank = owner;
5469: }
5470: /* Create SF to communicate how many nonzero columns for each row */
5471: PetscCall(PetscSFCreate(comm, &sf));
5472: /* SF will figure out the number of nonzero columns for each row, and their
5473: * offsets
5474: * */
5475: PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5476: PetscCall(PetscSFSetFromOptions(sf));
5477: PetscCall(PetscSFSetUp(sf));
5479: PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5480: PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5481: PetscCall(PetscCalloc1(nrows, &pnnz));
5482: roffsets[0] = 0;
5483: roffsets[1] = 0;
5484: for (i = 0; i < plocalsize; i++) {
5485: /* diagonal */
5486: nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5487: /* off-diagonal */
5488: nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5489: /* compute offsets so that we relative location for each row */
5490: roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5491: roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5492: }
5493: PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5494: PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5495: /* 'r' means root, and 'l' means leaf */
5496: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5497: PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5498: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5499: PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5500: PetscCall(PetscSFDestroy(&sf));
5501: PetscCall(PetscFree(roffsets));
5502: PetscCall(PetscFree(nrcols));
5503: dntotalcols = 0;
5504: ontotalcols = 0;
5505: ncol = 0;
5506: for (i = 0; i < nrows; i++) {
5507: pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5508: ncol = PetscMax(pnnz[i], ncol);
5509: /* diagonal */
5510: dntotalcols += nlcols[i * 2 + 0];
5511: /* off-diagonal */
5512: ontotalcols += nlcols[i * 2 + 1];
5513: }
5514: /* We do not need to figure the right number of columns
5515: * since all the calculations will be done by going through the raw data
5516: * */
5517: PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5518: PetscCall(MatSetUp(*P_oth));
5519: PetscCall(PetscFree(pnnz));
5520: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5521: /* diagonal */
5522: PetscCall(PetscCalloc1(dntotalcols, &iremote));
5523: /* off-diagonal */
5524: PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5525: /* diagonal */
5526: PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5527: /* off-diagonal */
5528: PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5529: dntotalcols = 0;
5530: ontotalcols = 0;
5531: ntotalcols = 0;
5532: for (i = 0; i < nrows; i++) {
5533: owner = 0;
5534: PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5535: /* Set iremote for diag matrix */
5536: for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5537: iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5538: iremote[dntotalcols].rank = owner;
5539: /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5540: ilocal[dntotalcols++] = ntotalcols++;
5541: }
5542: /* off-diagonal */
5543: for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5544: oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5545: oiremote[ontotalcols].rank = owner;
5546: oilocal[ontotalcols++] = ntotalcols++;
5547: }
5548: }
5549: PetscCall(ISRestoreIndices(rows, &lrowindices));
5550: PetscCall(PetscFree(loffsets));
5551: PetscCall(PetscFree(nlcols));
5552: PetscCall(PetscSFCreate(comm, &sf));
5553: /* P serves as roots and P_oth is leaves
5554: * Diag matrix
5555: * */
5556: PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5557: PetscCall(PetscSFSetFromOptions(sf));
5558: PetscCall(PetscSFSetUp(sf));
5560: PetscCall(PetscSFCreate(comm, &osf));
5561: /* off-diagonal */
5562: PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5563: PetscCall(PetscSFSetFromOptions(osf));
5564: PetscCall(PetscSFSetUp(osf));
5565: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5566: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5567: /* operate on the matrix internal data to save memory */
5568: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5569: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5570: PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5571: /* Convert to global indices for diag matrix */
5572: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5573: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5574: /* We want P_oth store global indices */
5575: PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5576: /* Use memory scalable approach */
5577: PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5578: PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5579: PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5580: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5581: /* Convert back to local indices */
5582: for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5583: PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5584: nout = 0;
5585: PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5586: PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5587: PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5588: /* Exchange values */
5589: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5590: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5591: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5592: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5593: /* Stop PETSc from shrinking memory */
5594: for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5595: PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5596: PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5597: /* Attach PetscSF objects to P_oth so that we can reuse it later */
5598: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5599: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5600: PetscCall(PetscSFDestroy(&sf));
5601: PetscCall(PetscSFDestroy(&osf));
5602: PetscFunctionReturn(PETSC_SUCCESS);
5603: }
5605: /*
5606: * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5607: * This supports MPIAIJ and MAIJ
5608: * */
5609: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5610: {
5611: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5612: Mat_SeqAIJ *p_oth;
5613: IS rows, map;
5614: PetscHMapI hamp;
5615: PetscInt i, htsize, *rowindices, off, *mapping, key, count;
5616: MPI_Comm comm;
5617: PetscSF sf, osf;
5618: PetscBool has;
5620: PetscFunctionBegin;
5621: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5622: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5623: /* If it is the first time, create an index set of off-diag nonzero columns of A,
5624: * and then create a submatrix (that often is an overlapping matrix)
5625: * */
5626: if (reuse == MAT_INITIAL_MATRIX) {
5627: /* Use a hash table to figure out unique keys */
5628: PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5629: PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5630: count = 0;
5631: /* Assume that a->g is sorted, otherwise the following does not make sense */
5632: for (i = 0; i < a->B->cmap->n; i++) {
5633: key = a->garray[i] / dof;
5634: PetscCall(PetscHMapIHas(hamp, key, &has));
5635: if (!has) {
5636: mapping[i] = count;
5637: PetscCall(PetscHMapISet(hamp, key, count++));
5638: } else {
5639: /* Current 'i' has the same value the previous step */
5640: mapping[i] = count - 1;
5641: }
5642: }
5643: PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5644: PetscCall(PetscHMapIGetSize(hamp, &htsize));
5645: PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5646: PetscCall(PetscCalloc1(htsize, &rowindices));
5647: off = 0;
5648: PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5649: PetscCall(PetscHMapIDestroy(&hamp));
5650: PetscCall(PetscSortInt(htsize, rowindices));
5651: PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5652: /* In case, the matrix was already created but users want to recreate the matrix */
5653: PetscCall(MatDestroy(P_oth));
5654: PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5655: PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5656: PetscCall(ISDestroy(&map));
5657: PetscCall(ISDestroy(&rows));
5658: } else if (reuse == MAT_REUSE_MATRIX) {
5659: /* If matrix was already created, we simply update values using SF objects
5660: * that as attached to the matrix earlier.
5661: */
5662: const PetscScalar *pd_a, *po_a;
5664: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5665: PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5666: PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5667: p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5668: /* Update values in place */
5669: PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5670: PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5671: PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5672: PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5673: PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5674: PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5675: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5676: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5677: } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5678: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5679: PetscFunctionReturn(PETSC_SUCCESS);
5680: }
5682: /*@C
5683: MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`
5685: Collective
5687: Input Parameters:
5688: + A - the first matrix in `MATMPIAIJ` format
5689: . B - the second matrix in `MATMPIAIJ` format
5690: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5692: Output Parameters:
5693: + rowb - On input index sets of rows of B to extract (or `NULL`), modified on output
5694: . colb - On input index sets of columns of B to extract (or `NULL`), modified on output
5695: - B_seq - the sequential matrix generated
5697: Level: developer
5699: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5700: @*/
5701: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5702: {
5703: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5704: PetscInt *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5705: IS isrowb, iscolb;
5706: Mat *bseq = NULL;
5708: PetscFunctionBegin;
5709: 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 ")",
5710: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5711: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));
5713: if (scall == MAT_INITIAL_MATRIX) {
5714: start = A->cmap->rstart;
5715: cmap = a->garray;
5716: nzA = a->A->cmap->n;
5717: nzB = a->B->cmap->n;
5718: PetscCall(PetscMalloc1(nzA + nzB, &idx));
5719: ncols = 0;
5720: for (i = 0; i < nzB; i++) { /* row < local row index */
5721: if (cmap[i] < start) idx[ncols++] = cmap[i];
5722: else break;
5723: }
5724: imark = i;
5725: for (i = 0; i < nzA; i++) idx[ncols++] = start + i; /* local rows */
5726: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5727: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5728: PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5729: } else {
5730: PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5731: isrowb = *rowb;
5732: iscolb = *colb;
5733: PetscCall(PetscMalloc1(1, &bseq));
5734: bseq[0] = *B_seq;
5735: }
5736: PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5737: *B_seq = bseq[0];
5738: PetscCall(PetscFree(bseq));
5739: if (!rowb) {
5740: PetscCall(ISDestroy(&isrowb));
5741: } else {
5742: *rowb = isrowb;
5743: }
5744: if (!colb) {
5745: PetscCall(ISDestroy(&iscolb));
5746: } else {
5747: *colb = iscolb;
5748: }
5749: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5750: PetscFunctionReturn(PETSC_SUCCESS);
5751: }
5753: /*
5754: MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5755: of the OFF-DIAGONAL portion of local A
5757: Collective
5759: Input Parameters:
5760: + A,B - the matrices in `MATMPIAIJ` format
5761: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5763: Output Parameter:
5764: + startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5765: . startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5766: . bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5767: - B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N
5769: Developer Note:
5770: This directly accesses information inside the VecScatter associated with the matrix-vector product
5771: for this matrix. This is not desirable..
5773: Level: developer
5775: */
5777: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5778: {
5779: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5780: VecScatter ctx;
5781: MPI_Comm comm;
5782: const PetscMPIInt *rprocs, *sprocs;
5783: PetscMPIInt nrecvs, nsends;
5784: const PetscInt *srow, *rstarts, *sstarts;
5785: PetscInt *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5786: PetscInt i, j, k = 0, l, ll, nrows, *rstartsj = NULL, *sstartsj, len;
5787: PetscScalar *b_otha, *bufa, *bufA, *vals = NULL;
5788: MPI_Request *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5789: PetscMPIInt size, tag, rank, nreqs;
5791: PetscFunctionBegin;
5792: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5793: PetscCallMPI(MPI_Comm_size(comm, &size));
5795: 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 ")",
5796: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5797: PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5798: PetscCallMPI(MPI_Comm_rank(comm, &rank));
5800: if (size == 1) {
5801: startsj_s = NULL;
5802: bufa_ptr = NULL;
5803: *B_oth = NULL;
5804: PetscFunctionReturn(PETSC_SUCCESS);
5805: }
5807: ctx = a->Mvctx;
5808: tag = ((PetscObject)ctx)->tag;
5810: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5811: /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5812: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5813: PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5814: PetscCall(PetscMalloc1(nreqs, &reqs));
5815: rwaits = reqs;
5816: swaits = PetscSafePointerPlusOffset(reqs, nrecvs);
5818: if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5819: if (scall == MAT_INITIAL_MATRIX) {
5820: /* i-array */
5821: /* post receives */
5822: if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5823: for (i = 0; i < nrecvs; i++) {
5824: rowlen = rvalues + rstarts[i] * rbs;
5825: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5826: PetscCallMPI(MPIU_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5827: }
5829: /* pack the outgoing message */
5830: PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));
5832: sstartsj[0] = 0;
5833: rstartsj[0] = 0;
5834: len = 0; /* total length of j or a array to be sent */
5835: if (nsends) {
5836: k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5837: PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5838: }
5839: for (i = 0; i < nsends; i++) {
5840: rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5841: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5842: for (j = 0; j < nrows; j++) {
5843: row = srow[k] + B->rmap->range[rank]; /* global row idx */
5844: for (l = 0; l < sbs; l++) {
5845: PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */
5847: rowlen[j * sbs + l] = ncols;
5849: len += ncols;
5850: PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5851: }
5852: k++;
5853: }
5854: PetscCallMPI(MPIU_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));
5856: sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5857: }
5858: /* recvs and sends of i-array are completed */
5859: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5860: PetscCall(PetscFree(svalues));
5862: /* allocate buffers for sending j and a arrays */
5863: PetscCall(PetscMalloc1(len + 1, &bufj));
5864: PetscCall(PetscMalloc1(len + 1, &bufa));
5866: /* create i-array of B_oth */
5867: PetscCall(PetscMalloc1(aBn + 2, &b_othi));
5869: b_othi[0] = 0;
5870: len = 0; /* total length of j or a array to be received */
5871: k = 0;
5872: for (i = 0; i < nrecvs; i++) {
5873: rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5874: nrows = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5875: for (j = 0; j < nrows; j++) {
5876: b_othi[k + 1] = b_othi[k] + rowlen[j];
5877: PetscCall(PetscIntSumError(rowlen[j], len, &len));
5878: k++;
5879: }
5880: rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5881: }
5882: PetscCall(PetscFree(rvalues));
5884: /* allocate space for j and a arrays of B_oth */
5885: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_othj));
5886: PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_otha));
5888: /* j-array */
5889: /* post receives of j-array */
5890: for (i = 0; i < nrecvs; i++) {
5891: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5892: PetscCallMPI(MPIU_Irecv(b_othj + rstartsj[i], nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5893: }
5895: /* pack the outgoing message j-array */
5896: if (nsends) k = sstarts[0];
5897: for (i = 0; i < nsends; i++) {
5898: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5899: bufJ = bufj + sstartsj[i];
5900: for (j = 0; j < nrows; j++) {
5901: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5902: for (ll = 0; ll < sbs; ll++) {
5903: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5904: for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5905: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5906: }
5907: }
5908: PetscCallMPI(MPIU_Isend(bufj + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5909: }
5911: /* recvs and sends of j-array are completed */
5912: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5913: } else if (scall == MAT_REUSE_MATRIX) {
5914: sstartsj = *startsj_s;
5915: rstartsj = *startsj_r;
5916: bufa = *bufa_ptr;
5917: PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5918: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");
5920: /* a-array */
5921: /* post receives of a-array */
5922: for (i = 0; i < nrecvs; i++) {
5923: nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5924: PetscCallMPI(MPIU_Irecv(b_otha + rstartsj[i], nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
5925: }
5927: /* pack the outgoing message a-array */
5928: if (nsends) k = sstarts[0];
5929: for (i = 0; i < nsends; i++) {
5930: nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5931: bufA = bufa + sstartsj[i];
5932: for (j = 0; j < nrows; j++) {
5933: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5934: for (ll = 0; ll < sbs; ll++) {
5935: PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5936: for (l = 0; l < ncols; l++) *bufA++ = vals[l];
5937: PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5938: }
5939: }
5940: PetscCallMPI(MPIU_Isend(bufa + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
5941: }
5942: /* recvs and sends of a-array are completed */
5943: if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5944: PetscCall(PetscFree(reqs));
5946: if (scall == MAT_INITIAL_MATRIX) {
5947: Mat_SeqAIJ *b_oth;
5949: /* put together the new matrix */
5950: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));
5952: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5953: /* Since these are PETSc arrays, change flags to free them as necessary. */
5954: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
5955: b_oth->free_a = PETSC_TRUE;
5956: b_oth->free_ij = PETSC_TRUE;
5957: b_oth->nonew = 0;
5959: PetscCall(PetscFree(bufj));
5960: if (!startsj_s || !bufa_ptr) {
5961: PetscCall(PetscFree2(sstartsj, rstartsj));
5962: PetscCall(PetscFree(bufa_ptr));
5963: } else {
5964: *startsj_s = sstartsj;
5965: *startsj_r = rstartsj;
5966: *bufa_ptr = bufa;
5967: }
5968: } else if (scall == MAT_REUSE_MATRIX) {
5969: PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
5970: }
5972: PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
5973: PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
5974: PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
5975: PetscFunctionReturn(PETSC_SUCCESS);
5976: }
5978: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
5979: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
5980: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
5981: #if defined(PETSC_HAVE_MKL_SPARSE)
5982: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
5983: #endif
5984: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
5985: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
5986: #if defined(PETSC_HAVE_ELEMENTAL)
5987: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
5988: #endif
5989: #if defined(PETSC_HAVE_SCALAPACK)
5990: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
5991: #endif
5992: #if defined(PETSC_HAVE_HYPRE)
5993: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
5994: #endif
5995: #if defined(PETSC_HAVE_CUDA)
5996: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
5997: #endif
5998: #if defined(PETSC_HAVE_HIP)
5999: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6000: #endif
6001: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6002: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6003: #endif
6004: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6005: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6006: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
6008: /*
6009: Computes (B'*A')' since computing B*A directly is untenable
6011: n p p
6012: [ ] [ ] [ ]
6013: m [ A ] * n [ B ] = m [ C ]
6014: [ ] [ ] [ ]
6016: */
6017: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6018: {
6019: Mat At, Bt, Ct;
6021: PetscFunctionBegin;
6022: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6023: PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6024: PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_CURRENT, &Ct));
6025: PetscCall(MatDestroy(&At));
6026: PetscCall(MatDestroy(&Bt));
6027: PetscCall(MatTransposeSetPrecursor(Ct, C));
6028: PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6029: PetscCall(MatDestroy(&Ct));
6030: PetscFunctionReturn(PETSC_SUCCESS);
6031: }
6033: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6034: {
6035: PetscBool cisdense;
6037: PetscFunctionBegin;
6038: 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);
6039: PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6040: PetscCall(MatSetBlockSizesFromMats(C, A, B));
6041: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6042: if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6043: PetscCall(MatSetUp(C));
6045: C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6046: PetscFunctionReturn(PETSC_SUCCESS);
6047: }
6049: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6050: {
6051: Mat_Product *product = C->product;
6052: Mat A = product->A, B = product->B;
6054: PetscFunctionBegin;
6055: 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 ")",
6056: A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6057: C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6058: C->ops->productsymbolic = MatProductSymbolic_AB;
6059: PetscFunctionReturn(PETSC_SUCCESS);
6060: }
6062: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6063: {
6064: Mat_Product *product = C->product;
6066: PetscFunctionBegin;
6067: if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6068: PetscFunctionReturn(PETSC_SUCCESS);
6069: }
6071: /*
6072: Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix
6074: Input Parameters:
6076: j1,rowBegin1,rowEnd1,jmap1: describe the first set of nonzeros (Set1)
6077: j2,rowBegin2,rowEnd2,jmap2: describe the second set of nonzeros (Set2)
6079: mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat
6081: For Set1, j1[] contains column indices of the nonzeros.
6082: 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
6083: respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6084: but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.
6086: Similar for Set2.
6088: This routine merges the two sets of nonzeros row by row and removes repeats.
6090: Output Parameters: (memory is allocated by the caller)
6092: i[],j[]: the CSR of the merged matrix, which has m rows.
6093: imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6094: imap2[]: similar to imap1[], but for Set2.
6095: Note we order nonzeros row-by-row and from left to right.
6096: */
6097: 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[])
6098: {
6099: PetscInt r, m; /* Row index of mat */
6100: PetscCount t, t1, t2, b1, e1, b2, e2;
6102: PetscFunctionBegin;
6103: PetscCall(MatGetLocalSize(mat, &m, NULL));
6104: t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6105: i[0] = 0;
6106: for (r = 0; r < m; r++) { /* Do row by row merging */
6107: b1 = rowBegin1[r];
6108: e1 = rowEnd1[r];
6109: b2 = rowBegin2[r];
6110: e2 = rowEnd2[r];
6111: while (b1 < e1 && b2 < e2) {
6112: if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6113: j[t] = j1[b1];
6114: imap1[t1] = t;
6115: imap2[t2] = t;
6116: b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6117: b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6118: t1++;
6119: t2++;
6120: t++;
6121: } else if (j1[b1] < j2[b2]) {
6122: j[t] = j1[b1];
6123: imap1[t1] = t;
6124: b1 += jmap1[t1 + 1] - jmap1[t1];
6125: t1++;
6126: t++;
6127: } else {
6128: j[t] = j2[b2];
6129: imap2[t2] = t;
6130: b2 += jmap2[t2 + 1] - jmap2[t2];
6131: t2++;
6132: t++;
6133: }
6134: }
6135: /* Merge the remaining in either j1[] or j2[] */
6136: while (b1 < e1) {
6137: j[t] = j1[b1];
6138: imap1[t1] = t;
6139: b1 += jmap1[t1 + 1] - jmap1[t1];
6140: t1++;
6141: t++;
6142: }
6143: while (b2 < e2) {
6144: j[t] = j2[b2];
6145: imap2[t2] = t;
6146: b2 += jmap2[t2 + 1] - jmap2[t2];
6147: t2++;
6148: t++;
6149: }
6150: PetscCall(PetscIntCast(t, i + r + 1));
6151: }
6152: PetscFunctionReturn(PETSC_SUCCESS);
6153: }
6155: /*
6156: Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block
6158: Input Parameters:
6159: mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6160: 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[]
6161: respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.
6163: i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6164: i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.
6166: Output Parameters:
6167: j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6168: rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6169: 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,
6170: and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.
6172: Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6173: Atot: number of entries belonging to the diagonal block.
6174: Annz: number of unique nonzeros belonging to the diagonal block.
6175: Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6176: repeats (i.e., same 'i,j' pair).
6177: Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6178: is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.
6180: Atot: number of entries belonging to the diagonal block
6181: Annz: number of unique nonzeros belonging to the diagonal block.
6183: Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.
6185: Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6186: */
6187: 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_)
6188: {
6189: PetscInt cstart, cend, rstart, rend, row, col;
6190: PetscCount Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6191: PetscCount Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6192: PetscCount k, m, p, q, r, s, mid;
6193: PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;
6195: PetscFunctionBegin;
6196: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6197: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6198: m = rend - rstart;
6200: /* Skip negative rows */
6201: for (k = 0; k < n; k++)
6202: if (i[k] >= 0) break;
6204: /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6205: fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6206: */
6207: while (k < n) {
6208: row = i[k];
6209: /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6210: for (s = k; s < n; s++)
6211: if (i[s] != row) break;
6213: /* Shift diag columns to range of [-PETSC_INT_MAX, -1] */
6214: for (p = k; p < s; p++) {
6215: if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_INT_MAX;
6216: }
6217: PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6218: PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6219: rowBegin[row - rstart] = k;
6220: rowMid[row - rstart] = mid;
6221: rowEnd[row - rstart] = s;
6222: PetscCheck(k == s || j[s - 1] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index %" PetscInt_FMT " is >= matrix column size %" PetscInt_FMT, j[s - 1], mat->cmap->N);
6224: /* Count nonzeros of this diag/offdiag row, which might have repeats */
6225: Atot += mid - k;
6226: Btot += s - mid;
6228: /* Count unique nonzeros of this diag row */
6229: for (p = k; p < mid;) {
6230: col = j[p];
6231: do {
6232: j[p] += PETSC_INT_MAX; /* Revert the modified diagonal indices */
6233: p++;
6234: } while (p < mid && j[p] == col);
6235: Annz++;
6236: }
6238: /* Count unique nonzeros of this offdiag row */
6239: for (p = mid; p < s;) {
6240: col = j[p];
6241: do {
6242: p++;
6243: } while (p < s && j[p] == col);
6244: Bnnz++;
6245: }
6246: k = s;
6247: }
6249: /* Allocation according to Atot, Btot, Annz, Bnnz */
6250: PetscCall(PetscMalloc1(Atot, &Aperm));
6251: PetscCall(PetscMalloc1(Btot, &Bperm));
6252: PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6253: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));
6255: /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6256: Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6257: for (r = 0; r < m; r++) {
6258: k = rowBegin[r];
6259: mid = rowMid[r];
6260: s = rowEnd[r];
6261: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Aperm, Atot), PetscSafePointerPlusOffset(perm, k), mid - k));
6262: PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Bperm, Btot), PetscSafePointerPlusOffset(perm, mid), s - mid));
6263: Atot += mid - k;
6264: Btot += s - mid;
6266: /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6267: for (p = k; p < mid;) {
6268: col = j[p];
6269: q = p;
6270: do {
6271: p++;
6272: } while (p < mid && j[p] == col);
6273: Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6274: Annz++;
6275: }
6277: for (p = mid; p < s;) {
6278: col = j[p];
6279: q = p;
6280: do {
6281: p++;
6282: } while (p < s && j[p] == col);
6283: Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6284: Bnnz++;
6285: }
6286: }
6287: /* Output */
6288: *Aperm_ = Aperm;
6289: *Annz_ = Annz;
6290: *Atot_ = Atot;
6291: *Ajmap_ = Ajmap;
6292: *Bperm_ = Bperm;
6293: *Bnnz_ = Bnnz;
6294: *Btot_ = Btot;
6295: *Bjmap_ = Bjmap;
6296: PetscFunctionReturn(PETSC_SUCCESS);
6297: }
6299: /*
6300: Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix
6302: Input Parameters:
6303: nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6304: nnz: number of unique nonzeros in the merged matrix
6305: imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6306: jmap[nnz1+1]: i-th nonzero in the set has jmap[i+1] - jmap[i] repeats in the set
6308: Output Parameter: (memory is allocated by the caller)
6309: jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set
6311: Example:
6312: nnz1 = 4
6313: nnz = 6
6314: imap = [1,3,4,5]
6315: jmap = [0,3,5,6,7]
6316: then,
6317: jmap_new = [0,0,3,3,5,6,7]
6318: */
6319: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6320: {
6321: PetscCount k, p;
6323: PetscFunctionBegin;
6324: jmap_new[0] = 0;
6325: p = nnz; /* p loops over jmap_new[] backwards */
6326: for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6327: for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6328: }
6329: for (; p >= 0; p--) jmap_new[p] = jmap[0];
6330: PetscFunctionReturn(PETSC_SUCCESS);
6331: }
6333: static PetscErrorCode MatCOOStructDestroy_MPIAIJ(void **data)
6334: {
6335: MatCOOStruct_MPIAIJ *coo = (MatCOOStruct_MPIAIJ *)*data;
6337: PetscFunctionBegin;
6338: PetscCall(PetscSFDestroy(&coo->sf));
6339: PetscCall(PetscFree(coo->Aperm1));
6340: PetscCall(PetscFree(coo->Bperm1));
6341: PetscCall(PetscFree(coo->Ajmap1));
6342: PetscCall(PetscFree(coo->Bjmap1));
6343: PetscCall(PetscFree(coo->Aimap2));
6344: PetscCall(PetscFree(coo->Bimap2));
6345: PetscCall(PetscFree(coo->Aperm2));
6346: PetscCall(PetscFree(coo->Bperm2));
6347: PetscCall(PetscFree(coo->Ajmap2));
6348: PetscCall(PetscFree(coo->Bjmap2));
6349: PetscCall(PetscFree(coo->Cperm1));
6350: PetscCall(PetscFree2(coo->sendbuf, coo->recvbuf));
6351: PetscCall(PetscFree(coo));
6352: PetscFunctionReturn(PETSC_SUCCESS);
6353: }
6355: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6356: {
6357: MPI_Comm comm;
6358: PetscMPIInt rank, size;
6359: PetscInt m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6360: PetscCount k, p, q, rem; /* Loop variables over coo arrays */
6361: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6362: PetscContainer container;
6363: MatCOOStruct_MPIAIJ *coo;
6365: PetscFunctionBegin;
6366: PetscCall(PetscFree(mpiaij->garray));
6367: PetscCall(VecDestroy(&mpiaij->lvec));
6368: #if defined(PETSC_USE_CTABLE)
6369: PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6370: #else
6371: PetscCall(PetscFree(mpiaij->colmap));
6372: #endif
6373: PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6374: mat->assembled = PETSC_FALSE;
6375: mat->was_assembled = PETSC_FALSE;
6377: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6378: PetscCallMPI(MPI_Comm_size(comm, &size));
6379: PetscCallMPI(MPI_Comm_rank(comm, &rank));
6380: PetscCall(PetscLayoutSetUp(mat->rmap));
6381: PetscCall(PetscLayoutSetUp(mat->cmap));
6382: PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6383: PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6384: PetscCall(MatGetLocalSize(mat, &m, &n));
6385: PetscCall(MatGetSize(mat, &M, &N));
6387: /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6388: /* entries come first, then local rows, then remote rows. */
6389: PetscCount n1 = coo_n, *perm1;
6390: PetscInt *i1 = coo_i, *j1 = coo_j;
6392: PetscCall(PetscMalloc1(n1, &perm1));
6393: for (k = 0; k < n1; k++) perm1[k] = k;
6395: /* Manipulate indices so that entries with negative row or col indices will have smallest
6396: row indices, local entries will have greater but negative row indices, and remote entries
6397: will have positive row indices.
6398: */
6399: for (k = 0; k < n1; k++) {
6400: if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_INT_MIN; /* e.g., -2^31, minimal to move them ahead */
6401: 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] */
6402: else {
6403: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6404: if (mpiaij->donotstash) i1[k] = PETSC_INT_MIN; /* Ignore offproc entries as if they had negative indices */
6405: }
6406: }
6408: /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6409: PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));
6411: /* Advance k to the first entry we need to take care of */
6412: for (k = 0; k < n1; k++)
6413: if (i1[k] > PETSC_INT_MIN) break;
6414: PetscCount i1start = k;
6416: PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_INT_MAX, &rem)); /* rem is upper bound of the last local row */
6417: for (; k < rem; k++) i1[k] += PETSC_INT_MAX; /* Revert row indices of local rows*/
6419: PetscCheck(n1 == 0 || i1[n1 - 1] < M, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "COO row index %" PetscInt_FMT " is >= the matrix row size %" PetscInt_FMT, i1[n1 - 1], M);
6421: /* Send remote rows to their owner */
6422: /* Find which rows should be sent to which remote ranks*/
6423: PetscInt nsend = 0; /* Number of MPI ranks to send data to */
6424: PetscMPIInt *sendto; /* [nsend], storing remote ranks */
6425: PetscInt *nentries; /* [nsend], storing number of entries sent to remote ranks; Assume PetscInt is big enough for this count, and error if not */
6426: const PetscInt *ranges;
6427: PetscInt maxNsend = size >= 128 ? 128 : size; /* Assume max 128 neighbors; realloc when needed */
6429: PetscCall(PetscLayoutGetRanges(mat->rmap, &ranges));
6430: PetscCall(PetscMalloc2(maxNsend, &sendto, maxNsend, &nentries));
6431: for (k = rem; k < n1;) {
6432: PetscMPIInt owner;
6433: PetscInt firstRow, lastRow;
6435: /* Locate a row range */
6436: firstRow = i1[k]; /* first row of this owner */
6437: PetscCall(PetscLayoutFindOwner(mat->rmap, firstRow, &owner));
6438: lastRow = ranges[owner + 1] - 1; /* last row of this owner */
6440: /* Find the first index 'p' in [k,n) with i1[p] belonging to next owner */
6441: PetscCall(PetscSortedIntUpperBound(i1, k, n1, lastRow, &p));
6443: /* All entries in [k,p) belong to this remote owner */
6444: if (nsend >= maxNsend) { /* Double the remote ranks arrays if not long enough */
6445: PetscMPIInt *sendto2;
6446: PetscInt *nentries2;
6447: PetscInt maxNsend2 = (maxNsend <= size / 2) ? maxNsend * 2 : size;
6449: PetscCall(PetscMalloc2(maxNsend2, &sendto2, maxNsend2, &nentries2));
6450: PetscCall(PetscArraycpy(sendto2, sendto, maxNsend));
6451: PetscCall(PetscArraycpy(nentries2, nentries2, maxNsend + 1));
6452: PetscCall(PetscFree2(sendto, nentries2));
6453: sendto = sendto2;
6454: nentries = nentries2;
6455: maxNsend = maxNsend2;
6456: }
6457: sendto[nsend] = owner;
6458: PetscCall(PetscIntCast(p - k, &nentries[nsend]));
6459: nsend++;
6460: k = p;
6461: }
6463: /* Build 1st SF to know offsets on remote to send data */
6464: PetscSF sf1;
6465: PetscInt nroots = 1, nroots2 = 0;
6466: PetscInt nleaves = nsend, nleaves2 = 0;
6467: PetscInt *offsets;
6468: PetscSFNode *iremote;
6470: PetscCall(PetscSFCreate(comm, &sf1));
6471: PetscCall(PetscMalloc1(nsend, &iremote));
6472: PetscCall(PetscMalloc1(nsend, &offsets));
6473: for (k = 0; k < nsend; k++) {
6474: iremote[k].rank = sendto[k];
6475: iremote[k].index = 0;
6476: nleaves2 += nentries[k];
6477: PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6478: }
6479: PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6480: PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6481: PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6482: PetscCall(PetscSFDestroy(&sf1));
6483: PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT, nleaves2, n1 - rem);
6485: /* Build 2nd SF to send remote COOs to their owner */
6486: PetscSF sf2;
6487: nroots = nroots2;
6488: nleaves = nleaves2;
6489: PetscCall(PetscSFCreate(comm, &sf2));
6490: PetscCall(PetscSFSetFromOptions(sf2));
6491: PetscCall(PetscMalloc1(nleaves, &iremote));
6492: p = 0;
6493: for (k = 0; k < nsend; k++) {
6494: PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6495: for (q = 0; q < nentries[k]; q++, p++) {
6496: iremote[p].rank = sendto[k];
6497: PetscCall(PetscIntCast(offsets[k] + q, &iremote[p].index));
6498: }
6499: }
6500: PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6502: /* Send the remote COOs to their owner */
6503: PetscInt n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6504: PetscCount *perm2; /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6505: PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6506: PetscAssert(rem == 0 || i1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6507: PetscAssert(rem == 0 || j1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6508: PetscInt *i1prem = PetscSafePointerPlusOffset(i1, rem);
6509: PetscInt *j1prem = PetscSafePointerPlusOffset(j1, rem);
6510: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1prem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6511: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1prem, i2, MPI_REPLACE));
6512: PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1prem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6513: PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1prem, j2, MPI_REPLACE));
6515: PetscCall(PetscFree(offsets));
6516: PetscCall(PetscFree2(sendto, nentries));
6518: /* Sort received COOs by row along with the permutation array */
6519: for (k = 0; k < n2; k++) perm2[k] = k;
6520: PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));
6522: /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6523: PetscCount *Cperm1;
6524: PetscAssert(rem == 0 || perm1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6525: PetscCount *perm1prem = PetscSafePointerPlusOffset(perm1, rem);
6526: PetscCall(PetscMalloc1(nleaves, &Cperm1));
6527: PetscCall(PetscArraycpy(Cperm1, perm1prem, nleaves));
6529: /* Support for HYPRE matrices, kind of a hack.
6530: Swap min column with diagonal so that diagonal values will go first */
6531: PetscBool hypre;
6532: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", ((PetscObject)mat)->name, &hypre));
6533: if (hypre) {
6534: PetscInt *minj;
6535: PetscBT hasdiag;
6537: PetscCall(PetscBTCreate(m, &hasdiag));
6538: PetscCall(PetscMalloc1(m, &minj));
6539: for (k = 0; k < m; k++) minj[k] = PETSC_INT_MAX;
6540: for (k = i1start; k < rem; k++) {
6541: if (j1[k] < cstart || j1[k] >= cend) continue;
6542: const PetscInt rindex = i1[k] - rstart;
6543: if ((j1[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6544: minj[rindex] = PetscMin(minj[rindex], j1[k]);
6545: }
6546: for (k = 0; k < n2; k++) {
6547: if (j2[k] < cstart || j2[k] >= cend) continue;
6548: const PetscInt rindex = i2[k] - rstart;
6549: if ((j2[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6550: minj[rindex] = PetscMin(minj[rindex], j2[k]);
6551: }
6552: for (k = i1start; k < rem; k++) {
6553: const PetscInt rindex = i1[k] - rstart;
6554: if (j1[k] < cstart || j1[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6555: if (j1[k] == minj[rindex]) j1[k] = i1[k] + (cstart - rstart);
6556: else if ((j1[k] - cstart) == rindex) j1[k] = minj[rindex];
6557: }
6558: for (k = 0; k < n2; k++) {
6559: const PetscInt rindex = i2[k] - rstart;
6560: if (j2[k] < cstart || j2[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6561: if (j2[k] == minj[rindex]) j2[k] = i2[k] + (cstart - rstart);
6562: else if ((j2[k] - cstart) == rindex) j2[k] = minj[rindex];
6563: }
6564: PetscCall(PetscBTDestroy(&hasdiag));
6565: PetscCall(PetscFree(minj));
6566: }
6568: /* Split local COOs and received COOs into diag/offdiag portions */
6569: PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6570: PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1;
6571: PetscCount Annz1, Bnnz1, Atot1, Btot1;
6572: PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6573: PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6574: PetscCount Annz2, Bnnz2, Atot2, Btot2;
6576: PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6577: PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6578: PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6579: PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));
6581: /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6582: PetscInt *Ai, *Bi;
6583: PetscInt *Aj, *Bj;
6585: PetscCall(PetscMalloc1(m + 1, &Ai));
6586: PetscCall(PetscMalloc1(m + 1, &Bi));
6587: PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6588: PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));
6590: PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6591: PetscCall(PetscMalloc1(Annz1, &Aimap1));
6592: PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6593: PetscCall(PetscMalloc1(Annz2, &Aimap2));
6594: PetscCall(PetscMalloc1(Bnnz2, &Bimap2));
6596: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6597: PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));
6599: /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we */
6600: /* expect nonzeros in A/B most likely have local contributing entries */
6601: PetscInt Annz = Ai[m];
6602: PetscInt Bnnz = Bi[m];
6603: PetscCount *Ajmap1_new, *Bjmap1_new;
6605: PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6606: PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));
6608: PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6609: PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));
6611: PetscCall(PetscFree(Aimap1));
6612: PetscCall(PetscFree(Ajmap1));
6613: PetscCall(PetscFree(Bimap1));
6614: PetscCall(PetscFree(Bjmap1));
6615: PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6616: PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6617: PetscCall(PetscFree(perm1));
6618: PetscCall(PetscFree3(i2, j2, perm2));
6620: Ajmap1 = Ajmap1_new;
6621: Bjmap1 = Bjmap1_new;
6623: /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6624: if (Annz < Annz1 + Annz2) {
6625: PetscInt *Aj_new;
6626: PetscCall(PetscMalloc1(Annz, &Aj_new));
6627: PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6628: PetscCall(PetscFree(Aj));
6629: Aj = Aj_new;
6630: }
6632: if (Bnnz < Bnnz1 + Bnnz2) {
6633: PetscInt *Bj_new;
6634: PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6635: PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6636: PetscCall(PetscFree(Bj));
6637: Bj = Bj_new;
6638: }
6640: /* Create new submatrices for on-process and off-process coupling */
6641: PetscScalar *Aa, *Ba;
6642: MatType rtype;
6643: Mat_SeqAIJ *a, *b;
6644: PetscObjectState state;
6645: PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6646: PetscCall(PetscCalloc1(Bnnz, &Ba));
6647: /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6648: if (cstart) {
6649: for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6650: }
6652: PetscCall(MatGetRootType_Private(mat, &rtype));
6654: MatSeqXAIJGetOptions_Private(mpiaij->A);
6655: PetscCall(MatDestroy(&mpiaij->A));
6656: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6657: PetscCall(MatSetBlockSizesFromMats(mpiaij->A, mat, mat));
6658: MatSeqXAIJRestoreOptions_Private(mpiaij->A);
6660: MatSeqXAIJGetOptions_Private(mpiaij->B);
6661: PetscCall(MatDestroy(&mpiaij->B));
6662: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6663: PetscCall(MatSetBlockSizesFromMats(mpiaij->B, mat, mat));
6664: MatSeqXAIJRestoreOptions_Private(mpiaij->B);
6666: PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6667: mat->was_assembled = PETSC_TRUE; // was_assembled in effect means the Mvctx is built; doing so avoids redundant MatSetUpMultiply_MPIAIJ
6668: state = mpiaij->A->nonzerostate + mpiaij->B->nonzerostate;
6669: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
6671: a = (Mat_SeqAIJ *)mpiaij->A->data;
6672: b = (Mat_SeqAIJ *)mpiaij->B->data;
6673: a->free_a = PETSC_TRUE;
6674: a->free_ij = PETSC_TRUE;
6675: b->free_a = PETSC_TRUE;
6676: b->free_ij = PETSC_TRUE;
6677: a->maxnz = a->nz;
6678: b->maxnz = b->nz;
6680: /* conversion must happen AFTER multiply setup */
6681: PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6682: PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6683: PetscCall(VecDestroy(&mpiaij->lvec));
6684: PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));
6686: // Put the COO struct in a container and then attach that to the matrix
6687: PetscCall(PetscMalloc1(1, &coo));
6688: coo->n = coo_n;
6689: coo->sf = sf2;
6690: coo->sendlen = nleaves;
6691: coo->recvlen = nroots;
6692: coo->Annz = Annz;
6693: coo->Bnnz = Bnnz;
6694: coo->Annz2 = Annz2;
6695: coo->Bnnz2 = Bnnz2;
6696: coo->Atot1 = Atot1;
6697: coo->Atot2 = Atot2;
6698: coo->Btot1 = Btot1;
6699: coo->Btot2 = Btot2;
6700: coo->Ajmap1 = Ajmap1;
6701: coo->Aperm1 = Aperm1;
6702: coo->Bjmap1 = Bjmap1;
6703: coo->Bperm1 = Bperm1;
6704: coo->Aimap2 = Aimap2;
6705: coo->Ajmap2 = Ajmap2;
6706: coo->Aperm2 = Aperm2;
6707: coo->Bimap2 = Bimap2;
6708: coo->Bjmap2 = Bjmap2;
6709: coo->Bperm2 = Bperm2;
6710: coo->Cperm1 = Cperm1;
6711: // Allocate in preallocation. If not used, it has zero cost on host
6712: PetscCall(PetscMalloc2(coo->sendlen, &coo->sendbuf, coo->recvlen, &coo->recvbuf));
6713: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
6714: PetscCall(PetscContainerSetPointer(container, coo));
6715: PetscCall(PetscContainerSetCtxDestroy(container, MatCOOStructDestroy_MPIAIJ));
6716: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
6717: PetscCall(PetscContainerDestroy(&container));
6718: PetscFunctionReturn(PETSC_SUCCESS);
6719: }
6721: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6722: {
6723: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
6724: Mat A = mpiaij->A, B = mpiaij->B;
6725: PetscScalar *Aa, *Ba;
6726: PetscScalar *sendbuf, *recvbuf;
6727: const PetscCount *Ajmap1, *Ajmap2, *Aimap2;
6728: const PetscCount *Bjmap1, *Bjmap2, *Bimap2;
6729: const PetscCount *Aperm1, *Aperm2, *Bperm1, *Bperm2;
6730: const PetscCount *Cperm1;
6731: PetscContainer container;
6732: MatCOOStruct_MPIAIJ *coo;
6734: PetscFunctionBegin;
6735: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
6736: PetscCheck(container, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
6737: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
6738: sendbuf = coo->sendbuf;
6739: recvbuf = coo->recvbuf;
6740: Ajmap1 = coo->Ajmap1;
6741: Ajmap2 = coo->Ajmap2;
6742: Aimap2 = coo->Aimap2;
6743: Bjmap1 = coo->Bjmap1;
6744: Bjmap2 = coo->Bjmap2;
6745: Bimap2 = coo->Bimap2;
6746: Aperm1 = coo->Aperm1;
6747: Aperm2 = coo->Aperm2;
6748: Bperm1 = coo->Bperm1;
6749: Bperm2 = coo->Bperm2;
6750: Cperm1 = coo->Cperm1;
6752: PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6753: PetscCall(MatSeqAIJGetArray(B, &Ba));
6755: /* Pack entries to be sent to remote */
6756: for (PetscCount i = 0; i < coo->sendlen; i++) sendbuf[i] = v[Cperm1[i]];
6758: /* Send remote entries to their owner and overlap the communication with local computation */
6759: PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6760: /* Add local entries to A and B */
6761: for (PetscCount i = 0; i < coo->Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6762: PetscScalar sum = 0.0; /* Do partial summation first to improve numerical stability */
6763: for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6764: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6765: }
6766: for (PetscCount i = 0; i < coo->Bnnz; i++) {
6767: PetscScalar sum = 0.0;
6768: for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6769: Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6770: }
6771: PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));
6773: /* Add received remote entries to A and B */
6774: for (PetscCount i = 0; i < coo->Annz2; i++) {
6775: for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6776: }
6777: for (PetscCount i = 0; i < coo->Bnnz2; i++) {
6778: for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6779: }
6780: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6781: PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6782: PetscFunctionReturn(PETSC_SUCCESS);
6783: }
6785: /*MC
6786: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
6788: Options Database Keys:
6789: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`
6791: Level: beginner
6793: Notes:
6794: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
6795: in this case the values associated with the rows and columns one passes in are set to zero
6796: in the matrix
6798: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
6799: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
6801: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6802: M*/
6803: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6804: {
6805: Mat_MPIAIJ *b;
6806: PetscMPIInt size;
6808: PetscFunctionBegin;
6809: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
6811: PetscCall(PetscNew(&b));
6812: B->data = (void *)b;
6813: B->ops[0] = MatOps_Values;
6814: B->assembled = PETSC_FALSE;
6815: B->insertmode = NOT_SET_VALUES;
6816: b->size = size;
6818: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
6820: /* build cache for off array entries formed */
6821: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
6823: b->donotstash = PETSC_FALSE;
6824: b->colmap = NULL;
6825: b->garray = NULL;
6826: b->roworiented = PETSC_TRUE;
6828: /* stuff used for matrix vector multiply */
6829: b->lvec = NULL;
6830: b->Mvctx = NULL;
6832: /* stuff for MatGetRow() */
6833: b->rowindices = NULL;
6834: b->rowvalues = NULL;
6835: b->getrowactive = PETSC_FALSE;
6837: /* flexible pointer used in CUSPARSE classes */
6838: b->spptr = NULL;
6840: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6841: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6842: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6843: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6844: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6845: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6846: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetHash_C", MatResetHash_MPIAIJ));
6847: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6848: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6849: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6850: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6851: #if defined(PETSC_HAVE_CUDA)
6852: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6853: #endif
6854: #if defined(PETSC_HAVE_HIP)
6855: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6856: #endif
6857: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6858: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6859: #endif
6860: #if defined(PETSC_HAVE_MKL_SPARSE)
6861: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6862: #endif
6863: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6864: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6865: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6866: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6867: #if defined(PETSC_HAVE_ELEMENTAL)
6868: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6869: #endif
6870: #if defined(PETSC_HAVE_SCALAPACK)
6871: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6872: #endif
6873: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6874: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6875: #if defined(PETSC_HAVE_HYPRE)
6876: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6877: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6878: #endif
6879: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6880: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6881: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6882: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6883: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6884: PetscFunctionReturn(PETSC_SUCCESS);
6885: }
6887: /*@
6888: MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6889: and "off-diagonal" part of the matrix in CSR format.
6891: Collective
6893: Input Parameters:
6894: + comm - MPI communicator
6895: . m - number of local rows (Cannot be `PETSC_DECIDE`)
6896: . n - This value should be the same as the local size used in creating the
6897: x vector for the matrix-vector product $y = Ax$. (or `PETSC_DECIDE` to have
6898: calculated if `N` is given) For square matrices `n` is almost always `m`.
6899: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6900: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6901: . 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
6902: . j - column indices, which must be local, i.e., based off the start column of the diagonal portion
6903: . a - matrix values
6904: . 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
6905: . oj - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6906: - oa - matrix values
6908: Output Parameter:
6909: . mat - the matrix
6911: Level: advanced
6913: Notes:
6914: The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc (even in Fortran). The user
6915: must free the arrays once the matrix has been destroyed and not before.
6917: The `i` and `j` indices are 0 based
6919: See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix
6921: This sets local rows and cannot be used to set off-processor values.
6923: Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6924: legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6925: not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
6926: the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
6927: keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
6928: communication if it is known that only local entries will be set.
6930: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
6931: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
6932: @*/
6933: 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)
6934: {
6935: Mat_MPIAIJ *maij;
6937: PetscFunctionBegin;
6938: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
6939: PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
6940: PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
6941: PetscCall(MatCreate(comm, mat));
6942: PetscCall(MatSetSizes(*mat, m, n, M, N));
6943: PetscCall(MatSetType(*mat, MATMPIAIJ));
6944: maij = (Mat_MPIAIJ *)(*mat)->data;
6946: (*mat)->preallocated = PETSC_TRUE;
6948: PetscCall(PetscLayoutSetUp((*mat)->rmap));
6949: PetscCall(PetscLayoutSetUp((*mat)->cmap));
6951: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
6952: PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));
6954: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
6955: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
6956: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
6957: PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
6958: PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
6959: PetscFunctionReturn(PETSC_SUCCESS);
6960: }
6962: typedef struct {
6963: Mat *mp; /* intermediate products */
6964: PetscBool *mptmp; /* is the intermediate product temporary ? */
6965: PetscInt cp; /* number of intermediate products */
6967: /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
6968: PetscInt *startsj_s, *startsj_r;
6969: PetscScalar *bufa;
6970: Mat P_oth;
6972: /* may take advantage of merging product->B */
6973: Mat Bloc; /* B-local by merging diag and off-diag */
6975: /* cusparse does not have support to split between symbolic and numeric phases.
6976: When api_user is true, we don't need to update the numerical values
6977: of the temporary storage */
6978: PetscBool reusesym;
6980: /* support for COO values insertion */
6981: PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
6982: PetscInt **own; /* own[i] points to address of on-process COO indices for Mat mp[i] */
6983: PetscInt **off; /* off[i] points to address of off-process COO indices for Mat mp[i] */
6984: PetscBool hasoffproc; /* if true, have off-process values insertion (i.e. AtB or PtAP) */
6985: PetscSF sf; /* used for non-local values insertion and memory malloc */
6986: PetscMemType mtype;
6988: /* customization */
6989: PetscBool abmerge;
6990: PetscBool P_oth_bind;
6991: } MatMatMPIAIJBACKEND;
6993: static PetscErrorCode MatDestroy_MatMatMPIAIJBACKEND(void *data)
6994: {
6995: MatMatMPIAIJBACKEND *mmdata = (MatMatMPIAIJBACKEND *)data;
6996: PetscInt i;
6998: PetscFunctionBegin;
6999: PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
7000: PetscCall(PetscFree(mmdata->bufa));
7001: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
7002: PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
7003: PetscCall(MatDestroy(&mmdata->P_oth));
7004: PetscCall(MatDestroy(&mmdata->Bloc));
7005: PetscCall(PetscSFDestroy(&mmdata->sf));
7006: for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
7007: PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
7008: PetscCall(PetscFree(mmdata->own[0]));
7009: PetscCall(PetscFree(mmdata->own));
7010: PetscCall(PetscFree(mmdata->off[0]));
7011: PetscCall(PetscFree(mmdata->off));
7012: PetscCall(PetscFree(mmdata));
7013: PetscFunctionReturn(PETSC_SUCCESS);
7014: }
7016: /* Copy selected n entries with indices in idx[] of A to v[].
7017: If idx is NULL, copy the whole data array of A to v[]
7018: */
7019: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
7020: {
7021: PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);
7023: PetscFunctionBegin;
7024: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
7025: if (f) {
7026: PetscCall((*f)(A, n, idx, v));
7027: } else {
7028: const PetscScalar *vv;
7030: PetscCall(MatSeqAIJGetArrayRead(A, &vv));
7031: if (n && idx) {
7032: PetscScalar *w = v;
7033: const PetscInt *oi = idx;
7034: PetscInt j;
7036: for (j = 0; j < n; j++) *w++ = vv[*oi++];
7037: } else {
7038: PetscCall(PetscArraycpy(v, vv, n));
7039: }
7040: PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7041: }
7042: PetscFunctionReturn(PETSC_SUCCESS);
7043: }
7045: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7046: {
7047: MatMatMPIAIJBACKEND *mmdata;
7048: PetscInt i, n_d, n_o;
7050: PetscFunctionBegin;
7051: MatCheckProduct(C, 1);
7052: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7053: mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7054: if (!mmdata->reusesym) { /* update temporary matrices */
7055: 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));
7056: if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7057: }
7058: mmdata->reusesym = PETSC_FALSE;
7060: for (i = 0; i < mmdata->cp; i++) {
7061: 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]);
7062: PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7063: }
7064: for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7065: PetscInt noff;
7067: PetscCall(PetscIntCast(mmdata->off[i + 1] - mmdata->off[i], &noff));
7068: if (mmdata->mptmp[i]) continue;
7069: if (noff) {
7070: PetscInt nown;
7072: PetscCall(PetscIntCast(mmdata->own[i + 1] - mmdata->own[i], &nown));
7073: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7074: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7075: n_o += noff;
7076: n_d += nown;
7077: } else {
7078: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;
7080: PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7081: n_d += mm->nz;
7082: }
7083: }
7084: if (mmdata->hasoffproc) { /* offprocess insertion */
7085: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7086: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7087: }
7088: PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7089: PetscFunctionReturn(PETSC_SUCCESS);
7090: }
7092: /* Support for Pt * A, A * P, or Pt * A * P */
7093: #define MAX_NUMBER_INTERMEDIATE 4
7094: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7095: {
7096: Mat_Product *product = C->product;
7097: Mat A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7098: Mat_MPIAIJ *a, *p;
7099: MatMatMPIAIJBACKEND *mmdata;
7100: ISLocalToGlobalMapping P_oth_l2g = NULL;
7101: IS glob = NULL;
7102: const char *prefix;
7103: char pprefix[256];
7104: const PetscInt *globidx, *P_oth_idx;
7105: PetscInt i, j, cp, m, n, M, N, *coo_i, *coo_j;
7106: PetscCount ncoo, ncoo_d, ncoo_o, ncoo_oown;
7107: PetscInt cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7108: /* type-0: consecutive, start from 0; type-1: consecutive with */
7109: /* a base offset; type-2: sparse with a local to global map table */
7110: const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE]; /* col/row local to global map array (table) for type-2 map type */
7112: MatProductType ptype;
7113: PetscBool mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7114: PetscMPIInt size;
7116: PetscFunctionBegin;
7117: MatCheckProduct(C, 1);
7118: PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7119: ptype = product->type;
7120: if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7121: ptype = MATPRODUCT_AB;
7122: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7123: }
7124: switch (ptype) {
7125: case MATPRODUCT_AB:
7126: A = product->A;
7127: P = product->B;
7128: m = A->rmap->n;
7129: n = P->cmap->n;
7130: M = A->rmap->N;
7131: N = P->cmap->N;
7132: hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7133: break;
7134: case MATPRODUCT_AtB:
7135: P = product->A;
7136: A = product->B;
7137: m = P->cmap->n;
7138: n = A->cmap->n;
7139: M = P->cmap->N;
7140: N = A->cmap->N;
7141: hasoffproc = PETSC_TRUE;
7142: break;
7143: case MATPRODUCT_PtAP:
7144: A = product->A;
7145: P = product->B;
7146: m = P->cmap->n;
7147: n = P->cmap->n;
7148: M = P->cmap->N;
7149: N = P->cmap->N;
7150: hasoffproc = PETSC_TRUE;
7151: break;
7152: default:
7153: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7154: }
7155: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7156: if (size == 1) hasoffproc = PETSC_FALSE;
7158: /* defaults */
7159: for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7160: mp[i] = NULL;
7161: mptmp[i] = PETSC_FALSE;
7162: rmapt[i] = -1;
7163: cmapt[i] = -1;
7164: rmapa[i] = NULL;
7165: cmapa[i] = NULL;
7166: }
7168: /* customization */
7169: PetscCall(PetscNew(&mmdata));
7170: mmdata->reusesym = product->api_user;
7171: if (ptype == MATPRODUCT_AB) {
7172: if (product->api_user) {
7173: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7174: PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7175: PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7176: PetscOptionsEnd();
7177: } else {
7178: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7179: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7180: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7181: PetscOptionsEnd();
7182: }
7183: } else if (ptype == MATPRODUCT_PtAP) {
7184: if (product->api_user) {
7185: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7186: PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7187: PetscOptionsEnd();
7188: } else {
7189: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7190: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7191: PetscOptionsEnd();
7192: }
7193: }
7194: a = (Mat_MPIAIJ *)A->data;
7195: p = (Mat_MPIAIJ *)P->data;
7196: PetscCall(MatSetSizes(C, m, n, M, N));
7197: PetscCall(PetscLayoutSetUp(C->rmap));
7198: PetscCall(PetscLayoutSetUp(C->cmap));
7199: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7200: PetscCall(MatGetOptionsPrefix(C, &prefix));
7202: cp = 0;
7203: switch (ptype) {
7204: case MATPRODUCT_AB: /* A * P */
7205: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7207: /* A_diag * P_local (merged or not) */
7208: if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7209: /* P is product->B */
7210: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7211: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7212: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7213: PetscCall(MatProductSetFill(mp[cp], product->fill));
7214: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7215: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7216: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7217: mp[cp]->product->api_user = product->api_user;
7218: PetscCall(MatProductSetFromOptions(mp[cp]));
7219: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7220: PetscCall(ISGetIndices(glob, &globidx));
7221: rmapt[cp] = 1;
7222: cmapt[cp] = 2;
7223: cmapa[cp] = globidx;
7224: mptmp[cp] = PETSC_FALSE;
7225: cp++;
7226: } else { /* A_diag * P_diag and A_diag * P_off */
7227: PetscCall(MatProductCreate(a->A, p->A, NULL, &mp[cp]));
7228: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7229: PetscCall(MatProductSetFill(mp[cp], product->fill));
7230: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7231: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7232: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7233: mp[cp]->product->api_user = product->api_user;
7234: PetscCall(MatProductSetFromOptions(mp[cp]));
7235: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7236: rmapt[cp] = 1;
7237: cmapt[cp] = 1;
7238: mptmp[cp] = PETSC_FALSE;
7239: cp++;
7240: PetscCall(MatProductCreate(a->A, p->B, NULL, &mp[cp]));
7241: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7242: PetscCall(MatProductSetFill(mp[cp], product->fill));
7243: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7244: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7245: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7246: mp[cp]->product->api_user = product->api_user;
7247: PetscCall(MatProductSetFromOptions(mp[cp]));
7248: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7249: rmapt[cp] = 1;
7250: cmapt[cp] = 2;
7251: cmapa[cp] = p->garray;
7252: mptmp[cp] = PETSC_FALSE;
7253: cp++;
7254: }
7256: /* A_off * P_other */
7257: if (mmdata->P_oth) {
7258: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7259: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7260: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7261: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7262: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7263: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7264: PetscCall(MatProductSetFill(mp[cp], product->fill));
7265: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7266: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7267: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7268: mp[cp]->product->api_user = product->api_user;
7269: PetscCall(MatProductSetFromOptions(mp[cp]));
7270: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7271: rmapt[cp] = 1;
7272: cmapt[cp] = 2;
7273: cmapa[cp] = P_oth_idx;
7274: mptmp[cp] = PETSC_FALSE;
7275: cp++;
7276: }
7277: break;
7279: case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7280: /* A is product->B */
7281: PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7282: if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7283: PetscCall(MatProductCreate(mmdata->Bloc, mmdata->Bloc, NULL, &mp[cp]));
7284: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7285: PetscCall(MatProductSetFill(mp[cp], product->fill));
7286: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7287: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7288: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7289: mp[cp]->product->api_user = product->api_user;
7290: PetscCall(MatProductSetFromOptions(mp[cp]));
7291: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7292: PetscCall(ISGetIndices(glob, &globidx));
7293: rmapt[cp] = 2;
7294: rmapa[cp] = globidx;
7295: cmapt[cp] = 2;
7296: cmapa[cp] = globidx;
7297: mptmp[cp] = PETSC_FALSE;
7298: cp++;
7299: } else {
7300: PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7301: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7302: PetscCall(MatProductSetFill(mp[cp], product->fill));
7303: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7304: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7305: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7306: mp[cp]->product->api_user = product->api_user;
7307: PetscCall(MatProductSetFromOptions(mp[cp]));
7308: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7309: PetscCall(ISGetIndices(glob, &globidx));
7310: rmapt[cp] = 1;
7311: cmapt[cp] = 2;
7312: cmapa[cp] = globidx;
7313: mptmp[cp] = PETSC_FALSE;
7314: cp++;
7315: PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7316: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7317: PetscCall(MatProductSetFill(mp[cp], product->fill));
7318: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7319: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7320: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7321: mp[cp]->product->api_user = product->api_user;
7322: PetscCall(MatProductSetFromOptions(mp[cp]));
7323: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7324: rmapt[cp] = 2;
7325: rmapa[cp] = p->garray;
7326: cmapt[cp] = 2;
7327: cmapa[cp] = globidx;
7328: mptmp[cp] = PETSC_FALSE;
7329: cp++;
7330: }
7331: break;
7332: case MATPRODUCT_PtAP:
7333: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7334: /* P is product->B */
7335: PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7336: PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7337: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
7338: PetscCall(MatProductSetFill(mp[cp], product->fill));
7339: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7340: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7341: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7342: mp[cp]->product->api_user = product->api_user;
7343: PetscCall(MatProductSetFromOptions(mp[cp]));
7344: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7345: PetscCall(ISGetIndices(glob, &globidx));
7346: rmapt[cp] = 2;
7347: rmapa[cp] = globidx;
7348: cmapt[cp] = 2;
7349: cmapa[cp] = globidx;
7350: mptmp[cp] = PETSC_FALSE;
7351: cp++;
7352: if (mmdata->P_oth) {
7353: PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7354: PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7355: PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7356: PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7357: PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7358: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7359: PetscCall(MatProductSetFill(mp[cp], product->fill));
7360: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7361: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7362: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7363: mp[cp]->product->api_user = product->api_user;
7364: PetscCall(MatProductSetFromOptions(mp[cp]));
7365: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7366: mptmp[cp] = PETSC_TRUE;
7367: cp++;
7368: PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7369: PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7370: PetscCall(MatProductSetFill(mp[cp], product->fill));
7371: PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7372: PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7373: PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7374: mp[cp]->product->api_user = product->api_user;
7375: PetscCall(MatProductSetFromOptions(mp[cp]));
7376: PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7377: rmapt[cp] = 2;
7378: rmapa[cp] = globidx;
7379: cmapt[cp] = 2;
7380: cmapa[cp] = P_oth_idx;
7381: mptmp[cp] = PETSC_FALSE;
7382: cp++;
7383: }
7384: break;
7385: default:
7386: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7387: }
7388: /* sanity check */
7389: if (size > 1)
7390: 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);
7392: PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7393: for (i = 0; i < cp; i++) {
7394: mmdata->mp[i] = mp[i];
7395: mmdata->mptmp[i] = mptmp[i];
7396: }
7397: mmdata->cp = cp;
7398: C->product->data = mmdata;
7399: C->product->destroy = MatDestroy_MatMatMPIAIJBACKEND;
7400: C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;
7402: /* memory type */
7403: mmdata->mtype = PETSC_MEMTYPE_HOST;
7404: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7405: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7406: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7407: if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7408: else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7409: else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;
7411: /* prepare coo coordinates for values insertion */
7413: /* count total nonzeros of those intermediate seqaij Mats
7414: ncoo_d: # of nonzeros of matrices that do not have offproc entries
7415: ncoo_o: # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7416: ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7417: */
7418: for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7419: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7420: if (mptmp[cp]) continue;
7421: if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7422: const PetscInt *rmap = rmapa[cp];
7423: const PetscInt mr = mp[cp]->rmap->n;
7424: const PetscInt rs = C->rmap->rstart;
7425: const PetscInt re = C->rmap->rend;
7426: const PetscInt *ii = mm->i;
7427: for (i = 0; i < mr; i++) {
7428: const PetscInt gr = rmap[i];
7429: const PetscInt nz = ii[i + 1] - ii[i];
7430: if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7431: else ncoo_oown += nz; /* this row is local */
7432: }
7433: } else ncoo_d += mm->nz;
7434: }
7436: /*
7437: ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc
7439: ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.
7441: off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].
7443: off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7444: own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7445: so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.
7447: coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7448: 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.
7449: */
7450: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7451: PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));
7453: /* gather (i,j) of nonzeros inserted by remote procs */
7454: if (hasoffproc) {
7455: PetscSF msf;
7456: PetscInt ncoo2, *coo_i2, *coo_j2;
7458: PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7459: PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7460: PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */
7462: for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7463: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7464: PetscInt *idxoff = mmdata->off[cp];
7465: PetscInt *idxown = mmdata->own[cp];
7466: if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7467: const PetscInt *rmap = rmapa[cp];
7468: const PetscInt *cmap = cmapa[cp];
7469: const PetscInt *ii = mm->i;
7470: PetscInt *coi = coo_i + ncoo_o;
7471: PetscInt *coj = coo_j + ncoo_o;
7472: const PetscInt mr = mp[cp]->rmap->n;
7473: const PetscInt rs = C->rmap->rstart;
7474: const PetscInt re = C->rmap->rend;
7475: const PetscInt cs = C->cmap->rstart;
7476: for (i = 0; i < mr; i++) {
7477: const PetscInt *jj = mm->j + ii[i];
7478: const PetscInt gr = rmap[i];
7479: const PetscInt nz = ii[i + 1] - ii[i];
7480: if (gr < rs || gr >= re) { /* this is an offproc row */
7481: for (j = ii[i]; j < ii[i + 1]; j++) {
7482: *coi++ = gr;
7483: *idxoff++ = j;
7484: }
7485: if (!cmapt[cp]) { /* already global */
7486: for (j = 0; j < nz; j++) *coj++ = jj[j];
7487: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7488: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7489: } else { /* offdiag */
7490: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7491: }
7492: ncoo_o += nz;
7493: } else { /* this is a local row */
7494: for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7495: }
7496: }
7497: }
7498: mmdata->off[cp + 1] = idxoff;
7499: mmdata->own[cp + 1] = idxown;
7500: }
7502: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7503: PetscInt incoo_o;
7504: PetscCall(PetscIntCast(ncoo_o, &incoo_o));
7505: PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, incoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7506: PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7507: PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7508: ncoo = ncoo_d + ncoo_oown + ncoo2;
7509: PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7510: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7511: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7512: PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7513: PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7514: PetscCall(PetscFree2(coo_i, coo_j));
7515: /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7516: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7517: coo_i = coo_i2;
7518: coo_j = coo_j2;
7519: } else { /* no offproc values insertion */
7520: ncoo = ncoo_d;
7521: PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));
7523: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7524: PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7525: PetscCall(PetscSFSetUp(mmdata->sf));
7526: }
7527: mmdata->hasoffproc = hasoffproc;
7529: /* gather (i,j) of nonzeros inserted locally */
7530: for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7531: Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7532: PetscInt *coi = coo_i + ncoo_d;
7533: PetscInt *coj = coo_j + ncoo_d;
7534: const PetscInt *jj = mm->j;
7535: const PetscInt *ii = mm->i;
7536: const PetscInt *cmap = cmapa[cp];
7537: const PetscInt *rmap = rmapa[cp];
7538: const PetscInt mr = mp[cp]->rmap->n;
7539: const PetscInt rs = C->rmap->rstart;
7540: const PetscInt re = C->rmap->rend;
7541: const PetscInt cs = C->cmap->rstart;
7543: if (mptmp[cp]) continue;
7544: if (rmapt[cp] == 1) { /* consecutive rows */
7545: /* fill coo_i */
7546: for (i = 0; i < mr; i++) {
7547: const PetscInt gr = i + rs;
7548: for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7549: }
7550: /* fill coo_j */
7551: if (!cmapt[cp]) { /* type-0, already global */
7552: PetscCall(PetscArraycpy(coj, jj, mm->nz));
7553: } else if (cmapt[cp] == 1) { /* type-1, local to global for consecutive columns of C */
7554: for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7555: } else { /* type-2, local to global for sparse columns */
7556: for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7557: }
7558: ncoo_d += mm->nz;
7559: } else if (rmapt[cp] == 2) { /* sparse rows */
7560: for (i = 0; i < mr; i++) {
7561: const PetscInt *jj = mm->j + ii[i];
7562: const PetscInt gr = rmap[i];
7563: const PetscInt nz = ii[i + 1] - ii[i];
7564: if (gr >= rs && gr < re) { /* local rows */
7565: for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7566: if (!cmapt[cp]) { /* type-0, already global */
7567: for (j = 0; j < nz; j++) *coj++ = jj[j];
7568: } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7569: for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7570: } else { /* type-2, local to global for sparse columns */
7571: for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7572: }
7573: ncoo_d += nz;
7574: }
7575: }
7576: }
7577: }
7578: if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7579: PetscCall(ISDestroy(&glob));
7580: if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7581: PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7582: /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7583: PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));
7585: /* set block sizes */
7586: A = product->A;
7587: P = product->B;
7588: switch (ptype) {
7589: case MATPRODUCT_PtAP:
7590: PetscCall(MatSetBlockSizes(C, P->cmap->bs, P->cmap->bs));
7591: break;
7592: case MATPRODUCT_RARt:
7593: PetscCall(MatSetBlockSizes(C, P->rmap->bs, P->rmap->bs));
7594: break;
7595: case MATPRODUCT_ABC:
7596: PetscCall(MatSetBlockSizesFromMats(C, A, product->C));
7597: break;
7598: case MATPRODUCT_AB:
7599: PetscCall(MatSetBlockSizesFromMats(C, A, P));
7600: break;
7601: case MATPRODUCT_AtB:
7602: PetscCall(MatSetBlockSizes(C, A->cmap->bs, P->cmap->bs));
7603: break;
7604: case MATPRODUCT_ABt:
7605: PetscCall(MatSetBlockSizes(C, A->rmap->bs, P->rmap->bs));
7606: break;
7607: default:
7608: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for ProductType %s", MatProductTypes[ptype]);
7609: }
7611: /* preallocate with COO data */
7612: PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7613: PetscCall(PetscFree2(coo_i, coo_j));
7614: PetscFunctionReturn(PETSC_SUCCESS);
7615: }
7617: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7618: {
7619: Mat_Product *product = mat->product;
7620: #if defined(PETSC_HAVE_DEVICE)
7621: PetscBool match = PETSC_FALSE;
7622: PetscBool usecpu = PETSC_FALSE;
7623: #else
7624: PetscBool match = PETSC_TRUE;
7625: #endif
7627: PetscFunctionBegin;
7628: MatCheckProduct(mat, 1);
7629: #if defined(PETSC_HAVE_DEVICE)
7630: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7631: if (match) { /* we can always fallback to the CPU if requested */
7632: switch (product->type) {
7633: case MATPRODUCT_AB:
7634: if (product->api_user) {
7635: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7636: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7637: PetscOptionsEnd();
7638: } else {
7639: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7640: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7641: PetscOptionsEnd();
7642: }
7643: break;
7644: case MATPRODUCT_AtB:
7645: if (product->api_user) {
7646: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7647: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7648: PetscOptionsEnd();
7649: } else {
7650: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7651: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7652: PetscOptionsEnd();
7653: }
7654: break;
7655: case MATPRODUCT_PtAP:
7656: if (product->api_user) {
7657: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7658: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7659: PetscOptionsEnd();
7660: } else {
7661: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7662: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7663: PetscOptionsEnd();
7664: }
7665: break;
7666: default:
7667: break;
7668: }
7669: match = (PetscBool)!usecpu;
7670: }
7671: #endif
7672: if (match) {
7673: switch (product->type) {
7674: case MATPRODUCT_AB:
7675: case MATPRODUCT_AtB:
7676: case MATPRODUCT_PtAP:
7677: mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7678: break;
7679: default:
7680: break;
7681: }
7682: }
7683: /* fallback to MPIAIJ ops */
7684: if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7685: PetscFunctionReturn(PETSC_SUCCESS);
7686: }
7688: /*
7689: Produces a set of block column indices of the matrix row, one for each block represented in the original row
7691: n - the number of block indices in cc[]
7692: cc - the block indices (must be large enough to contain the indices)
7693: */
7694: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7695: {
7696: PetscInt cnt = -1, nidx, j;
7697: const PetscInt *idx;
7699: PetscFunctionBegin;
7700: PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7701: if (nidx) {
7702: cnt = 0;
7703: cc[cnt] = idx[0] / bs;
7704: for (j = 1; j < nidx; j++) {
7705: if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7706: }
7707: }
7708: PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7709: *n = cnt + 1;
7710: PetscFunctionReturn(PETSC_SUCCESS);
7711: }
7713: /*
7714: Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows
7716: ncollapsed - the number of block indices
7717: collapsed - the block indices (must be large enough to contain the indices)
7718: */
7719: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7720: {
7721: PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;
7723: PetscFunctionBegin;
7724: PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7725: for (i = start + 1; i < start + bs; i++) {
7726: PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7727: PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7728: cprevtmp = cprev;
7729: cprev = merged;
7730: merged = cprevtmp;
7731: }
7732: *ncollapsed = nprev;
7733: if (collapsed) *collapsed = cprev;
7734: PetscFunctionReturn(PETSC_SUCCESS);
7735: }
7737: /*
7738: MatCreateGraph_Simple_AIJ - create simple scalar matrix (graph) from potentially blocked matrix
7740: Input Parameter:
7741: . Amat - matrix
7742: - symmetrize - make the result symmetric
7743: + scale - scale with diagonal
7745: Output Parameter:
7746: . a_Gmat - output scalar graph >= 0
7748: */
7749: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, PetscInt index_size, PetscInt index[], Mat *a_Gmat)
7750: {
7751: PetscInt Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7752: MPI_Comm comm;
7753: Mat Gmat;
7754: PetscBool ismpiaij, isseqaij;
7755: Mat a, b, c;
7756: MatType jtype;
7758: PetscFunctionBegin;
7759: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7760: PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7761: PetscCall(MatGetSize(Amat, &MM, &NN));
7762: PetscCall(MatGetBlockSize(Amat, &bs));
7763: nloc = (Iend - Istart) / bs;
7765: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7766: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7767: PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");
7769: /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7770: /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7771: implementation */
7772: if (bs > 1) {
7773: PetscCall(MatGetType(Amat, &jtype));
7774: PetscCall(MatCreate(comm, &Gmat));
7775: PetscCall(MatSetType(Gmat, jtype));
7776: PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7777: PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7778: if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7779: PetscInt *d_nnz, *o_nnz;
7780: MatScalar *aa, val, *AA;
7781: PetscInt *aj, *ai, *AJ, nc, nmax = 0;
7783: if (isseqaij) {
7784: a = Amat;
7785: b = NULL;
7786: } else {
7787: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7788: a = d->A;
7789: b = d->B;
7790: }
7791: PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7792: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7793: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7794: PetscInt *nnz = (c == a) ? d_nnz : o_nnz;
7795: const PetscInt *cols1, *cols2;
7797: for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7798: PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7799: nnz[brow / bs] = nc2 / bs;
7800: if (nc2 % bs) ok = 0;
7801: if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7802: for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7803: PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7804: if (nc1 != nc2) ok = 0;
7805: else {
7806: for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7807: if (cols1[jj] != cols2[jj]) ok = 0;
7808: if (cols1[jj] % bs != jj % bs) ok = 0;
7809: }
7810: }
7811: PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7812: }
7813: PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7814: if (!ok) {
7815: PetscCall(PetscFree2(d_nnz, o_nnz));
7816: PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7817: goto old_bs;
7818: }
7819: }
7820: }
7821: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7822: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7823: PetscCall(PetscFree2(d_nnz, o_nnz));
7824: PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7825: // diag
7826: for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7827: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;
7829: ai = aseq->i;
7830: n = ai[brow + 1] - ai[brow];
7831: aj = aseq->j + ai[brow];
7832: for (PetscInt k = 0; k < n; k += bs) { // block columns
7833: AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7834: val = 0;
7835: if (index_size == 0) {
7836: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7837: aa = aseq->a + ai[brow + ii] + k;
7838: for (PetscInt jj = 0; jj < bs; jj++) { // columns in block
7839: val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7840: }
7841: }
7842: } else { // use (index,index) value if provided
7843: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7844: PetscInt ii = index[iii];
7845: aa = aseq->a + ai[brow + ii] + k;
7846: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // columns in block
7847: PetscInt jj = index[jjj];
7848: val += PetscAbs(PetscRealPart(aa[jj]));
7849: }
7850: }
7851: }
7852: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%" PetscInt_FMT ") >= nmax (%" PetscInt_FMT ")", k / bs, nmax);
7853: AA[k / bs] = val;
7854: }
7855: grow = Istart / bs + brow / bs;
7856: PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, ADD_VALUES));
7857: }
7858: // off-diag
7859: if (ismpiaij) {
7860: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Amat->data;
7861: const PetscScalar *vals;
7862: const PetscInt *cols, *garray = aij->garray;
7864: PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7865: for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7866: PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7867: for (PetscInt k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7868: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7869: AA[k / bs] = 0;
7870: AJ[cidx] = garray[cols[k]] / bs;
7871: }
7872: nc = ncols / bs;
7873: PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7874: if (index_size == 0) {
7875: for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7876: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7877: for (PetscInt k = 0; k < ncols; k += bs) {
7878: for (PetscInt jj = 0; jj < bs; jj++) { // cols in block
7879: PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%" PetscInt_FMT ") >= nmax (%" PetscInt_FMT ")", k / bs, nmax);
7880: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7881: }
7882: }
7883: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7884: }
7885: } else { // use (index,index) value if provided
7886: for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7887: PetscInt ii = index[iii];
7888: PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7889: for (PetscInt k = 0; k < ncols; k += bs) {
7890: for (PetscInt jjj = 0; jjj < index_size; jjj++) { // cols in block
7891: PetscInt jj = index[jjj];
7892: AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7893: }
7894: }
7895: PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7896: }
7897: }
7898: grow = Istart / bs + brow / bs;
7899: PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, ADD_VALUES));
7900: }
7901: }
7902: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7903: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7904: PetscCall(PetscFree2(AA, AJ));
7905: } else {
7906: const PetscScalar *vals;
7907: const PetscInt *idx;
7908: PetscInt *d_nnz, *o_nnz, *w0, *w1, *w2;
7909: old_bs:
7910: /*
7911: Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7912: */
7913: PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7914: PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7915: if (isseqaij) {
7916: PetscInt max_d_nnz;
7918: /*
7919: Determine exact preallocation count for (sequential) scalar matrix
7920: */
7921: PetscCall(MatSeqAIJGetMaxRowNonzeros(Amat, &max_d_nnz));
7922: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7923: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7924: for (Ii = 0, jj = 0; Ii < Iend; Ii += bs, jj++) PetscCall(MatCollapseRows(Amat, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7925: PetscCall(PetscFree3(w0, w1, w2));
7926: } else if (ismpiaij) {
7927: Mat Daij, Oaij;
7928: const PetscInt *garray;
7929: PetscInt max_d_nnz;
7931: PetscCall(MatMPIAIJGetSeqAIJ(Amat, &Daij, &Oaij, &garray));
7932: /*
7933: Determine exact preallocation count for diagonal block portion of scalar matrix
7934: */
7935: PetscCall(MatSeqAIJGetMaxRowNonzeros(Daij, &max_d_nnz));
7936: max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7937: PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7938: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) PetscCall(MatCollapseRows(Daij, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7939: PetscCall(PetscFree3(w0, w1, w2));
7940: /*
7941: Over estimate (usually grossly over), preallocation count for off-diagonal portion of scalar matrix
7942: */
7943: for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) {
7944: o_nnz[jj] = 0;
7945: for (kk = 0; kk < bs; kk++) { /* rows that get collapsed to a single row */
7946: PetscCall(MatGetRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7947: o_nnz[jj] += ncols;
7948: PetscCall(MatRestoreRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7949: }
7950: if (o_nnz[jj] > (NN / bs - nloc)) o_nnz[jj] = NN / bs - nloc;
7951: }
7952: } else SETERRQ(comm, PETSC_ERR_USER, "Require AIJ matrix type");
7953: /* get scalar copy (norms) of matrix */
7954: PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7955: PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7956: PetscCall(PetscFree2(d_nnz, o_nnz));
7957: for (Ii = Istart; Ii < Iend; Ii++) {
7958: PetscInt dest_row = Ii / bs;
7960: PetscCall(MatGetRow(Amat, Ii, &ncols, &idx, &vals));
7961: for (jj = 0; jj < ncols; jj++) {
7962: PetscInt dest_col = idx[jj] / bs;
7963: PetscScalar sv = PetscAbs(PetscRealPart(vals[jj]));
7965: PetscCall(MatSetValues(Gmat, 1, &dest_row, 1, &dest_col, &sv, ADD_VALUES));
7966: }
7967: PetscCall(MatRestoreRow(Amat, Ii, &ncols, &idx, &vals));
7968: }
7969: PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7970: PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7971: }
7972: } else {
7973: if (symmetrize || filter >= 0 || scale) PetscCall(MatDuplicate(Amat, MAT_COPY_VALUES, &Gmat));
7974: else {
7975: Gmat = Amat;
7976: PetscCall(PetscObjectReference((PetscObject)Gmat));
7977: }
7978: if (isseqaij) {
7979: a = Gmat;
7980: b = NULL;
7981: } else {
7982: Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
7983: a = d->A;
7984: b = d->B;
7985: }
7986: if (filter >= 0 || scale) {
7987: /* take absolute value of each entry */
7988: for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7989: MatInfo info;
7990: PetscScalar *avals;
7992: PetscCall(MatGetInfo(c, MAT_LOCAL, &info));
7993: PetscCall(MatSeqAIJGetArray(c, &avals));
7994: for (int jj = 0; jj < info.nz_used; jj++) avals[jj] = PetscAbsScalar(avals[jj]);
7995: PetscCall(MatSeqAIJRestoreArray(c, &avals));
7996: }
7997: }
7998: }
7999: if (symmetrize) {
8000: PetscBool isset, issym;
8002: PetscCall(MatIsSymmetricKnown(Amat, &isset, &issym));
8003: if (!isset || !issym) {
8004: Mat matTrans;
8006: PetscCall(MatTranspose(Gmat, MAT_INITIAL_MATRIX, &matTrans));
8007: PetscCall(MatAXPY(Gmat, 1.0, matTrans, Gmat->structurally_symmetric == PETSC_BOOL3_TRUE ? SAME_NONZERO_PATTERN : DIFFERENT_NONZERO_PATTERN));
8008: PetscCall(MatDestroy(&matTrans));
8009: }
8010: PetscCall(MatSetOption(Gmat, MAT_SYMMETRIC, PETSC_TRUE));
8011: } else if (Amat != Gmat) PetscCall(MatPropagateSymmetryOptions(Amat, Gmat));
8012: if (scale) {
8013: /* scale c for all diagonal values = 1 or -1 */
8014: Vec diag;
8016: PetscCall(MatCreateVecs(Gmat, &diag, NULL));
8017: PetscCall(MatGetDiagonal(Gmat, diag));
8018: PetscCall(VecReciprocal(diag));
8019: PetscCall(VecSqrtAbs(diag));
8020: PetscCall(MatDiagonalScale(Gmat, diag, diag));
8021: PetscCall(VecDestroy(&diag));
8022: }
8023: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_graph_view"));
8024: if (filter >= 0) {
8025: PetscCall(MatFilter(Gmat, filter, PETSC_TRUE, PETSC_TRUE));
8026: PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_filter_graph_view"));
8027: }
8028: *a_Gmat = Gmat;
8029: PetscFunctionReturn(PETSC_SUCCESS);
8030: }
8032: PETSC_INTERN PetscErrorCode MatGetCurrentMemType_MPIAIJ(Mat A, PetscMemType *memtype)
8033: {
8034: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)A->data;
8035: PetscMemType mD = PETSC_MEMTYPE_HOST, mO = PETSC_MEMTYPE_HOST;
8037: PetscFunctionBegin;
8038: if (mpiaij->A) PetscCall(MatGetCurrentMemType(mpiaij->A, &mD));
8039: if (mpiaij->B) PetscCall(MatGetCurrentMemType(mpiaij->B, &mO));
8040: *memtype = (mD == mO) ? mD : PETSC_MEMTYPE_HOST;
8041: PetscFunctionReturn(PETSC_SUCCESS);
8042: }
8044: /*
8045: Special version for direct calls from Fortran
8046: */
8048: /* Change these macros so can be used in void function */
8049: /* Identical to PetscCallVoid, except it assigns to *_ierr */
8050: #undef PetscCall
8051: #define PetscCall(...) \
8052: do { \
8053: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
8054: if (PetscUnlikely(ierr_msv_mpiaij)) { \
8055: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
8056: return; \
8057: } \
8058: } while (0)
8060: #undef SETERRQ
8061: #define SETERRQ(comm, ierr, ...) \
8062: do { \
8063: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
8064: return; \
8065: } while (0)
8067: #if defined(PETSC_HAVE_FORTRAN_CAPS)
8068: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
8069: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
8070: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
8071: #else
8072: #endif
8073: PETSC_EXTERN void matsetvaluesmpiaij_(Mat *mmat, PetscInt *mm, const PetscInt im[], PetscInt *mn, const PetscInt in[], const PetscScalar v[], InsertMode *maddv, PetscErrorCode *_ierr)
8074: {
8075: Mat mat = *mmat;
8076: PetscInt m = *mm, n = *mn;
8077: InsertMode addv = *maddv;
8078: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
8079: PetscScalar value;
8081: MatCheckPreallocated(mat, 1);
8082: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
8083: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
8084: {
8085: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
8086: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
8087: PetscBool roworiented = aij->roworiented;
8089: /* Some Variables required in the macro */
8090: Mat A = aij->A;
8091: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
8092: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
8093: MatScalar *aa;
8094: PetscBool ignorezeroentries = ((a->ignorezeroentries && (addv == ADD_VALUES)) ? PETSC_TRUE : PETSC_FALSE);
8095: Mat B = aij->B;
8096: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
8097: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
8098: MatScalar *ba;
8099: /* This variable below is only for the PETSC_HAVE_VIENNACL or PETSC_HAVE_CUDA cases, but we define it in all cases because we
8100: * cannot use "#if defined" inside a macro. */
8101: PETSC_UNUSED PetscBool inserted = PETSC_FALSE;
8103: PetscInt *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
8104: PetscInt nonew = a->nonew;
8105: MatScalar *ap1, *ap2;
8107: PetscFunctionBegin;
8108: PetscCall(MatSeqAIJGetArray(A, &aa));
8109: PetscCall(MatSeqAIJGetArray(B, &ba));
8110: for (i = 0; i < m; i++) {
8111: if (im[i] < 0) continue;
8112: 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);
8113: if (im[i] >= rstart && im[i] < rend) {
8114: row = im[i] - rstart;
8115: lastcol1 = -1;
8116: rp1 = aj + ai[row];
8117: ap1 = aa + ai[row];
8118: rmax1 = aimax[row];
8119: nrow1 = ailen[row];
8120: low1 = 0;
8121: high1 = nrow1;
8122: lastcol2 = -1;
8123: rp2 = bj + bi[row];
8124: ap2 = ba + bi[row];
8125: rmax2 = bimax[row];
8126: nrow2 = bilen[row];
8127: low2 = 0;
8128: high2 = nrow2;
8130: for (j = 0; j < n; j++) {
8131: if (roworiented) value = v[i * n + j];
8132: else value = v[i + j * m];
8133: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
8134: if (in[j] >= cstart && in[j] < cend) {
8135: col = in[j] - cstart;
8136: MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
8137: } else if (in[j] < 0) continue;
8138: else if (PetscUnlikelyDebug(in[j] >= mat->cmap->N)) {
8139: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
8140: } else {
8141: if (mat->was_assembled) {
8142: if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
8143: #if defined(PETSC_USE_CTABLE)
8144: PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col));
8145: col--;
8146: #else
8147: col = aij->colmap[in[j]] - 1;
8148: #endif
8149: if (col < 0 && !((Mat_SeqAIJ *)aij->A->data)->nonew) {
8150: PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));
8151: col = in[j];
8152: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
8153: B = aij->B;
8154: b = (Mat_SeqAIJ *)B->data;
8155: bimax = b->imax;
8156: bi = b->i;
8157: bilen = b->ilen;
8158: bj = b->j;
8159: rp2 = bj + bi[row];
8160: ap2 = ba + bi[row];
8161: rmax2 = bimax[row];
8162: nrow2 = bilen[row];
8163: low2 = 0;
8164: high2 = nrow2;
8165: bm = aij->B->rmap->n;
8166: ba = b->a;
8167: inserted = PETSC_FALSE;
8168: }
8169: } else col = in[j];
8170: MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
8171: }
8172: }
8173: } else if (!aij->donotstash) {
8174: if (roworiented) {
8175: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8176: } else {
8177: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8178: }
8179: }
8180: }
8181: PetscCall(MatSeqAIJRestoreArray(A, &aa));
8182: PetscCall(MatSeqAIJRestoreArray(B, &ba));
8183: }
8184: PetscFunctionReturnVoid();
8185: }
8187: /* Undefining these here since they were redefined from their original definition above! No
8188: * other PETSc functions should be defined past this point, as it is impossible to recover the
8189: * original definitions */
8190: #undef PetscCall
8191: #undef SETERRQ