Actual source code: mpibaij.c
1: #include <../src/mat/impls/baij/mpi/mpibaij.h>
3: #include <petsc/private/hashseti.h>
4: #include <petscblaslapack.h>
5: #include <petscsf.h>
7: static PetscErrorCode MatDestroy_MPIBAIJ(Mat mat)
8: {
9: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
11: PetscFunctionBegin;
12: PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ",Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
13: PetscCall(MatStashDestroy_Private(&mat->stash));
14: PetscCall(MatStashDestroy_Private(&mat->bstash));
15: PetscCall(MatDestroy(&baij->A));
16: PetscCall(MatDestroy(&baij->B));
17: #if defined(PETSC_USE_CTABLE)
18: PetscCall(PetscHMapIDestroy(&baij->colmap));
19: #else
20: PetscCall(PetscFree(baij->colmap));
21: #endif
22: PetscCall(PetscFree(baij->garray));
23: PetscCall(VecDestroy(&baij->lvec));
24: PetscCall(VecScatterDestroy(&baij->Mvctx));
25: PetscCall(PetscFree2(baij->rowvalues, baij->rowindices));
26: PetscCall(PetscFree(baij->barray));
27: PetscCall(PetscFree2(baij->hd, baij->ht));
28: PetscCall(PetscFree(baij->rangebs));
29: PetscCall(PetscFree(mat->data));
31: PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
32: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
33: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
34: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIBAIJSetPreallocation_C", NULL));
35: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIBAIJSetPreallocationCSR_C", NULL));
36: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
37: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetHashTableFactor_C", NULL));
38: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_mpisbaij_C", NULL));
39: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_mpiadj_C", NULL));
40: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_mpiaij_C", NULL));
41: #if defined(PETSC_HAVE_HYPRE)
42: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_hypre_C", NULL));
43: #endif
44: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_is_C", NULL));
45: PetscFunctionReturn(PETSC_SUCCESS);
46: }
48: /* defines MatSetValues_MPI_Hash(), MatAssemblyBegin_MPI_Hash(), and MatAssemblyEnd_MPI_Hash() */
49: #define TYPE BAIJ
50: #include "../src/mat/impls/aij/mpi/mpihashmat.h"
51: #undef TYPE
53: #if defined(PETSC_HAVE_HYPRE)
54: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
55: #endif
57: static PetscErrorCode MatGetRowMaxAbs_MPIBAIJ(Mat A, Vec v, PetscInt idx[])
58: {
59: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
60: PetscInt i, *idxb = NULL, m = A->rmap->n, bs = A->cmap->bs;
61: PetscScalar *va, *vv;
62: Vec vB, vA;
63: const PetscScalar *vb;
65: PetscFunctionBegin;
66: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vA));
67: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
69: PetscCall(VecGetArrayWrite(vA, &va));
70: if (idx) {
71: for (i = 0; i < m; i++) {
72: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
73: }
74: }
76: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vB));
77: PetscCall(PetscMalloc1(m, &idxb));
78: PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));
80: PetscCall(VecGetArrayWrite(v, &vv));
81: PetscCall(VecGetArrayRead(vB, &vb));
82: for (i = 0; i < m; i++) {
83: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
84: vv[i] = vb[i];
85: if (idx) idx[i] = bs * a->garray[idxb[i] / bs] + (idxb[i] % bs);
86: } else {
87: vv[i] = va[i];
88: if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > bs * a->garray[idxb[i] / bs] + (idxb[i] % bs)) idx[i] = bs * a->garray[idxb[i] / bs] + (idxb[i] % bs);
89: }
90: }
91: PetscCall(VecRestoreArrayWrite(vA, &vv));
92: PetscCall(VecRestoreArrayWrite(vA, &va));
93: PetscCall(VecRestoreArrayRead(vB, &vb));
94: PetscCall(PetscFree(idxb));
95: PetscCall(VecDestroy(&vA));
96: PetscCall(VecDestroy(&vB));
97: PetscFunctionReturn(PETSC_SUCCESS);
98: }
100: static PetscErrorCode MatStoreValues_MPIBAIJ(Mat mat)
101: {
102: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
104: PetscFunctionBegin;
105: PetscCall(MatStoreValues(aij->A));
106: PetscCall(MatStoreValues(aij->B));
107: PetscFunctionReturn(PETSC_SUCCESS);
108: }
110: static PetscErrorCode MatRetrieveValues_MPIBAIJ(Mat mat)
111: {
112: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
114: PetscFunctionBegin;
115: PetscCall(MatRetrieveValues(aij->A));
116: PetscCall(MatRetrieveValues(aij->B));
117: PetscFunctionReturn(PETSC_SUCCESS);
118: }
120: /*
121: Local utility routine that creates a mapping from the global column
122: number to the local number in the off-diagonal part of the local
123: storage of the matrix. This is done in a non scalable way since the
124: length of colmap equals the global matrix length.
125: */
126: PetscErrorCode MatCreateColmap_MPIBAIJ_Private(Mat mat)
127: {
128: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
129: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)baij->B->data;
130: PetscInt nbs = B->nbs, i, bs = mat->rmap->bs;
132: PetscFunctionBegin;
133: #if defined(PETSC_USE_CTABLE)
134: PetscCall(PetscHMapICreateWithSize(baij->nbs, &baij->colmap));
135: for (i = 0; i < nbs; i++) PetscCall(PetscHMapISet(baij->colmap, baij->garray[i] + 1, i * bs + 1));
136: #else
137: PetscCall(PetscCalloc1(baij->Nbs + 1, &baij->colmap));
138: for (i = 0; i < nbs; i++) baij->colmap[baij->garray[i]] = i * bs + 1;
139: #endif
140: PetscFunctionReturn(PETSC_SUCCESS);
141: }
143: #define MatSetValues_SeqBAIJ_A_Private(row, col, value, addv, orow, ocol) \
144: do { \
145: brow = row / bs; \
146: rp = PetscSafePointerPlusOffset(aj, ai[brow]); \
147: ap = PetscSafePointerPlusOffset(aa, bs2 * ai[brow]); \
148: rmax = aimax[brow]; \
149: nrow = ailen[brow]; \
150: bcol = col / bs; \
151: ridx = row % bs; \
152: cidx = col % bs; \
153: low = 0; \
154: high = nrow; \
155: while (high - low > 3) { \
156: t = (low + high) / 2; \
157: if (rp[t] > bcol) high = t; \
158: else low = t; \
159: } \
160: for (_i = low; _i < high; _i++) { \
161: if (rp[_i] > bcol) break; \
162: if (rp[_i] == bcol) { \
163: bap = ap + bs2 * _i + bs * cidx + ridx; \
164: if (addv == ADD_VALUES) *bap += value; \
165: else *bap = value; \
166: goto a_noinsert; \
167: } \
168: } \
169: if (a->nonew == 1) goto a_noinsert; \
170: PetscCheck(a->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); \
171: MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, brow, bcol, rmax, aa, ai, aj, rp, ap, aimax, a->nonew, MatScalar); \
172: N = nrow++ - 1; \
173: /* shift up all the later entries in this row */ \
174: PetscCall(PetscArraymove(rp + _i + 1, rp + _i, N - _i + 1)); \
175: PetscCall(PetscArraymove(ap + bs2 * (_i + 1), ap + bs2 * _i, bs2 * (N - _i + 1))); \
176: PetscCall(PetscArrayzero(ap + bs2 * _i, bs2)); \
177: rp[_i] = bcol; \
178: ap[bs2 * _i + bs * cidx + ridx] = value; \
179: a_noinsert:; \
180: ailen[brow] = nrow; \
181: } while (0)
183: #define MatSetValues_SeqBAIJ_B_Private(row, col, value, addv, orow, ocol) \
184: do { \
185: brow = row / bs; \
186: rp = PetscSafePointerPlusOffset(bj, bi[brow]); \
187: ap = PetscSafePointerPlusOffset(ba, bs2 * bi[brow]); \
188: rmax = bimax[brow]; \
189: nrow = bilen[brow]; \
190: bcol = col / bs; \
191: ridx = row % bs; \
192: cidx = col % bs; \
193: low = 0; \
194: high = nrow; \
195: while (high - low > 3) { \
196: t = (low + high) / 2; \
197: if (rp[t] > bcol) high = t; \
198: else low = t; \
199: } \
200: for (_i = low; _i < high; _i++) { \
201: if (rp[_i] > bcol) break; \
202: if (rp[_i] == bcol) { \
203: bap = ap + bs2 * _i + bs * cidx + ridx; \
204: if (addv == ADD_VALUES) *bap += value; \
205: else *bap = value; \
206: goto b_noinsert; \
207: } \
208: } \
209: if (b->nonew == 1) goto b_noinsert; \
210: PetscCheck(b->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); \
211: MatSeqXAIJReallocateAIJ(B, b->mbs, bs2, nrow, brow, bcol, rmax, ba, bi, bj, rp, ap, bimax, b->nonew, MatScalar); \
212: N = nrow++ - 1; \
213: /* shift up all the later entries in this row */ \
214: PetscCall(PetscArraymove(rp + _i + 1, rp + _i, N - _i + 1)); \
215: PetscCall(PetscArraymove(ap + bs2 * (_i + 1), ap + bs2 * _i, bs2 * (N - _i + 1))); \
216: PetscCall(PetscArrayzero(ap + bs2 * _i, bs2)); \
217: rp[_i] = bcol; \
218: ap[bs2 * _i + bs * cidx + ridx] = value; \
219: b_noinsert:; \
220: bilen[brow] = nrow; \
221: } while (0)
223: PetscErrorCode MatSetValues_MPIBAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
224: {
225: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
226: MatScalar value;
227: PetscBool roworiented = baij->roworiented;
228: PetscInt i, j, row, col;
229: PetscInt rstart_orig = mat->rmap->rstart;
230: PetscInt rend_orig = mat->rmap->rend, cstart_orig = mat->cmap->rstart;
231: PetscInt cend_orig = mat->cmap->rend, bs = mat->rmap->bs;
233: /* Some Variables required in the macro */
234: Mat A = baij->A;
235: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)(A)->data;
236: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
237: MatScalar *aa = a->a;
239: Mat B = baij->B;
240: Mat_SeqBAIJ *b = (Mat_SeqBAIJ *)(B)->data;
241: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j;
242: MatScalar *ba = b->a;
244: PetscInt *rp, ii, nrow, _i, rmax, N, brow, bcol;
245: PetscInt low, high, t, ridx, cidx, bs2 = a->bs2;
246: MatScalar *ap, *bap;
248: PetscFunctionBegin;
249: for (i = 0; i < m; i++) {
250: if (im[i] < 0) continue;
251: 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);
252: if (im[i] >= rstart_orig && im[i] < rend_orig) {
253: row = im[i] - rstart_orig;
254: for (j = 0; j < n; j++) {
255: if (in[j] >= cstart_orig && in[j] < cend_orig) {
256: col = in[j] - cstart_orig;
257: if (roworiented) value = v[i * n + j];
258: else value = v[i + j * m];
259: MatSetValues_SeqBAIJ_A_Private(row, col, value, addv, im[i], in[j]);
260: } else if (in[j] < 0) {
261: continue;
262: } else {
263: 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);
264: if (mat->was_assembled) {
265: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
266: #if defined(PETSC_USE_CTABLE)
267: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] / bs + 1, 0, &col));
268: col = col - 1;
269: #else
270: col = baij->colmap[in[j] / bs] - 1;
271: #endif
272: if (col < 0 && !((Mat_SeqBAIJ *)baij->B->data)->nonew) {
273: PetscCall(MatDisAssemble_MPIBAIJ(mat));
274: col = in[j];
275: /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
276: B = baij->B;
277: b = (Mat_SeqBAIJ *)(B)->data;
278: bimax = b->imax;
279: bi = b->i;
280: bilen = b->ilen;
281: bj = b->j;
282: ba = b->a;
283: } else {
284: PetscCheck(col >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]);
285: col += in[j] % bs;
286: }
287: } else col = in[j];
288: if (roworiented) value = v[i * n + j];
289: else value = v[i + j * m];
290: MatSetValues_SeqBAIJ_B_Private(row, col, value, addv, im[i], in[j]);
291: /* PetscCall(MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv)); */
292: }
293: }
294: } else {
295: 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]);
296: if (!baij->donotstash) {
297: mat->assembled = PETSC_FALSE;
298: if (roworiented) {
299: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, PETSC_FALSE));
300: } else {
301: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, PETSC_FALSE));
302: }
303: }
304: }
305: }
306: PetscFunctionReturn(PETSC_SUCCESS);
307: }
309: static inline PetscErrorCode MatSetValuesBlocked_SeqBAIJ_Inlined(Mat A, PetscInt row, PetscInt col, const PetscScalar v[], InsertMode is, PetscInt orow, PetscInt ocol)
310: {
311: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
312: PetscInt *rp, low, high, t, ii, jj, nrow, i, rmax, N;
313: PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen;
314: PetscInt *aj = a->j, nonew = a->nonew, bs2 = a->bs2, bs = A->rmap->bs;
315: PetscBool roworiented = a->roworiented;
316: const PetscScalar *value = v;
317: MatScalar *ap, *aa = a->a, *bap;
319: PetscFunctionBegin;
320: rp = aj + ai[row];
321: ap = aa + bs2 * ai[row];
322: rmax = imax[row];
323: nrow = ailen[row];
324: value = v;
325: low = 0;
326: high = nrow;
327: while (high - low > 7) {
328: t = (low + high) / 2;
329: if (rp[t] > col) high = t;
330: else low = t;
331: }
332: for (i = low; i < high; i++) {
333: if (rp[i] > col) break;
334: if (rp[i] == col) {
335: bap = ap + bs2 * i;
336: if (roworiented) {
337: if (is == ADD_VALUES) {
338: for (ii = 0; ii < bs; ii++) {
339: for (jj = ii; jj < bs2; jj += bs) bap[jj] += *value++;
340: }
341: } else {
342: for (ii = 0; ii < bs; ii++) {
343: for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
344: }
345: }
346: } else {
347: if (is == ADD_VALUES) {
348: for (ii = 0; ii < bs; ii++, value += bs) {
349: for (jj = 0; jj < bs; jj++) bap[jj] += value[jj];
350: bap += bs;
351: }
352: } else {
353: for (ii = 0; ii < bs; ii++, value += bs) {
354: for (jj = 0; jj < bs; jj++) bap[jj] = value[jj];
355: bap += bs;
356: }
357: }
358: }
359: goto noinsert2;
360: }
361: }
362: if (nonew == 1) goto noinsert2;
363: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new global block indexed nonzero block (%" PetscInt_FMT ", %" PetscInt_FMT ") in the matrix", orow, ocol);
364: MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
365: N = nrow++ - 1;
366: high++;
367: /* shift up all the later entries in this row */
368: PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
369: PetscCall(PetscArraymove(ap + bs2 * (i + 1), ap + bs2 * i, bs2 * (N - i + 1)));
370: rp[i] = col;
371: bap = ap + bs2 * i;
372: if (roworiented) {
373: for (ii = 0; ii < bs; ii++) {
374: for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
375: }
376: } else {
377: for (ii = 0; ii < bs; ii++) {
378: for (jj = 0; jj < bs; jj++) *bap++ = *value++;
379: }
380: }
381: noinsert2:;
382: ailen[row] = nrow;
383: PetscFunctionReturn(PETSC_SUCCESS);
384: }
386: /*
387: This routine should be optimized so that the block copy at ** Here a copy is required ** below is not needed
388: by passing additional stride information into the MatSetValuesBlocked_SeqBAIJ_Inlined() routine
389: */
390: static PetscErrorCode MatSetValuesBlocked_MPIBAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
391: {
392: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
393: const PetscScalar *value;
394: MatScalar *barray = baij->barray;
395: PetscBool roworiented = baij->roworiented;
396: PetscInt i, j, ii, jj, row, col, rstart = baij->rstartbs;
397: PetscInt rend = baij->rendbs, cstart = baij->cstartbs, stepval;
398: PetscInt cend = baij->cendbs, bs = mat->rmap->bs, bs2 = baij->bs2;
400: PetscFunctionBegin;
401: if (!barray) {
402: PetscCall(PetscMalloc1(bs2, &barray));
403: baij->barray = barray;
404: }
406: if (roworiented) stepval = (n - 1) * bs;
407: else stepval = (m - 1) * bs;
409: for (i = 0; i < m; i++) {
410: if (im[i] < 0) continue;
411: PetscCheck(im[i] < baij->Mbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Block indexed row too large %" PetscInt_FMT " max %" PetscInt_FMT, im[i], baij->Mbs - 1);
412: if (im[i] >= rstart && im[i] < rend) {
413: row = im[i] - rstart;
414: for (j = 0; j < n; j++) {
415: /* If NumCol = 1 then a copy is not required */
416: if ((roworiented) && (n == 1)) {
417: barray = (MatScalar *)v + i * bs2;
418: } else if ((!roworiented) && (m == 1)) {
419: barray = (MatScalar *)v + j * bs2;
420: } else { /* Here a copy is required */
421: if (roworiented) {
422: value = v + (i * (stepval + bs) + j) * bs;
423: } else {
424: value = v + (j * (stepval + bs) + i) * bs;
425: }
426: for (ii = 0; ii < bs; ii++, value += bs + stepval) {
427: for (jj = 0; jj < bs; jj++) barray[jj] = value[jj];
428: barray += bs;
429: }
430: barray -= bs2;
431: }
433: if (in[j] >= cstart && in[j] < cend) {
434: col = in[j] - cstart;
435: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->A, row, col, barray, addv, im[i], in[j]));
436: } else if (in[j] < 0) {
437: continue;
438: } else {
439: PetscCheck(in[j] < baij->Nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Block indexed column too large %" PetscInt_FMT " max %" PetscInt_FMT, in[j], baij->Nbs - 1);
440: if (mat->was_assembled) {
441: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
443: #if defined(PETSC_USE_DEBUG)
444: #if defined(PETSC_USE_CTABLE)
445: {
446: PetscInt data;
447: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &data));
448: PetscCheck((data - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
449: }
450: #else
451: PetscCheck((baij->colmap[in[j]] - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
452: #endif
453: #endif
454: #if defined(PETSC_USE_CTABLE)
455: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &col));
456: col = (col - 1) / bs;
457: #else
458: col = (baij->colmap[in[j]] - 1) / bs;
459: #endif
460: if (col < 0 && !((Mat_SeqBAIJ *)baij->B->data)->nonew) {
461: PetscCall(MatDisAssemble_MPIBAIJ(mat));
462: col = in[j];
463: } else PetscCheck(col >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new blocked indexed nonzero block (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]);
464: } else col = in[j];
465: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->B, row, col, barray, addv, im[i], in[j]));
466: }
467: }
468: } else {
469: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Setting off process block indexed row %" PetscInt_FMT " even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set", im[i]);
470: if (!baij->donotstash) {
471: if (roworiented) {
472: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
473: } else {
474: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
475: }
476: }
477: }
478: }
479: PetscFunctionReturn(PETSC_SUCCESS);
480: }
482: #define HASH_KEY 0.6180339887
483: #define HASH(size, key, tmp) (tmp = (key) * HASH_KEY, (PetscInt)((size) * (tmp - (PetscInt)tmp)))
484: /* #define HASH(size,key) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
485: /* #define HASH(size,key,tmp) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
486: static PetscErrorCode MatSetValues_MPIBAIJ_HT(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
487: {
488: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
489: PetscBool roworiented = baij->roworiented;
490: PetscInt i, j, row, col;
491: PetscInt rstart_orig = mat->rmap->rstart;
492: PetscInt rend_orig = mat->rmap->rend, Nbs = baij->Nbs;
493: PetscInt h1, key, size = baij->ht_size, bs = mat->rmap->bs, *HT = baij->ht, idx;
494: PetscReal tmp;
495: MatScalar **HD = baij->hd, value;
496: PetscInt total_ct = baij->ht_total_ct, insert_ct = baij->ht_insert_ct;
498: PetscFunctionBegin;
499: for (i = 0; i < m; i++) {
500: if (PetscDefined(USE_DEBUG)) {
501: PetscCheck(im[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row");
502: 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);
503: }
504: row = im[i];
505: if (row >= rstart_orig && row < rend_orig) {
506: for (j = 0; j < n; j++) {
507: col = in[j];
508: if (roworiented) value = v[i * n + j];
509: else value = v[i + j * m];
510: /* Look up PetscInto the Hash Table */
511: key = (row / bs) * Nbs + (col / bs) + 1;
512: h1 = HASH(size, key, tmp);
514: idx = h1;
515: if (PetscDefined(USE_DEBUG)) {
516: insert_ct++;
517: total_ct++;
518: if (HT[idx] != key) {
519: for (idx = h1; (idx < size) && (HT[idx] != key); idx++, total_ct++);
520: if (idx == size) {
521: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++, total_ct++);
522: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
523: }
524: }
525: } else if (HT[idx] != key) {
526: for (idx = h1; (idx < size) && (HT[idx] != key); idx++);
527: if (idx == size) {
528: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++);
529: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
530: }
531: }
532: /* A HASH table entry is found, so insert the values at the correct address */
533: if (addv == ADD_VALUES) *(HD[idx] + (col % bs) * bs + (row % bs)) += value;
534: else *(HD[idx] + (col % bs) * bs + (row % bs)) = value;
535: }
536: } else if (!baij->donotstash) {
537: if (roworiented) {
538: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, PETSC_FALSE));
539: } else {
540: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, PETSC_FALSE));
541: }
542: }
543: }
544: if (PetscDefined(USE_DEBUG)) {
545: baij->ht_total_ct += total_ct;
546: baij->ht_insert_ct += insert_ct;
547: }
548: PetscFunctionReturn(PETSC_SUCCESS);
549: }
551: static PetscErrorCode MatSetValuesBlocked_MPIBAIJ_HT(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
552: {
553: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
554: PetscBool roworiented = baij->roworiented;
555: PetscInt i, j, ii, jj, row, col;
556: PetscInt rstart = baij->rstartbs;
557: PetscInt rend = mat->rmap->rend, stepval, bs = mat->rmap->bs, bs2 = baij->bs2, nbs2 = n * bs2;
558: PetscInt h1, key, size = baij->ht_size, idx, *HT = baij->ht, Nbs = baij->Nbs;
559: PetscReal tmp;
560: MatScalar **HD = baij->hd, *baij_a;
561: const PetscScalar *v_t, *value;
562: PetscInt total_ct = baij->ht_total_ct, insert_ct = baij->ht_insert_ct;
564: PetscFunctionBegin;
565: if (roworiented) stepval = (n - 1) * bs;
566: else stepval = (m - 1) * bs;
568: for (i = 0; i < m; i++) {
569: if (PetscDefined(USE_DEBUG)) {
570: PetscCheck(im[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row: %" PetscInt_FMT, im[i]);
571: PetscCheck(im[i] < baij->Mbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], baij->Mbs - 1);
572: }
573: row = im[i];
574: v_t = v + i * nbs2;
575: if (row >= rstart && row < rend) {
576: for (j = 0; j < n; j++) {
577: col = in[j];
579: /* Look up into the Hash Table */
580: key = row * Nbs + col + 1;
581: h1 = HASH(size, key, tmp);
583: idx = h1;
584: if (PetscDefined(USE_DEBUG)) {
585: total_ct++;
586: insert_ct++;
587: if (HT[idx] != key) {
588: for (idx = h1; (idx < size) && (HT[idx] != key); idx++, total_ct++);
589: if (idx == size) {
590: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++, total_ct++);
591: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
592: }
593: }
594: } else if (HT[idx] != key) {
595: for (idx = h1; (idx < size) && (HT[idx] != key); idx++);
596: if (idx == size) {
597: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++);
598: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
599: }
600: }
601: baij_a = HD[idx];
602: if (roworiented) {
603: /*value = v + i*(stepval+bs)*bs + j*bs;*/
604: /* value = v + (i*(stepval+bs)+j)*bs; */
605: value = v_t;
606: v_t += bs;
607: if (addv == ADD_VALUES) {
608: for (ii = 0; ii < bs; ii++, value += stepval) {
609: for (jj = ii; jj < bs2; jj += bs) baij_a[jj] += *value++;
610: }
611: } else {
612: for (ii = 0; ii < bs; ii++, value += stepval) {
613: for (jj = ii; jj < bs2; jj += bs) baij_a[jj] = *value++;
614: }
615: }
616: } else {
617: value = v + j * (stepval + bs) * bs + i * bs;
618: if (addv == ADD_VALUES) {
619: for (ii = 0; ii < bs; ii++, value += stepval, baij_a += bs) {
620: for (jj = 0; jj < bs; jj++) baij_a[jj] += *value++;
621: }
622: } else {
623: for (ii = 0; ii < bs; ii++, value += stepval, baij_a += bs) {
624: for (jj = 0; jj < bs; jj++) baij_a[jj] = *value++;
625: }
626: }
627: }
628: }
629: } else {
630: if (!baij->donotstash) {
631: if (roworiented) {
632: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
633: } else {
634: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
635: }
636: }
637: }
638: }
639: if (PetscDefined(USE_DEBUG)) {
640: baij->ht_total_ct += total_ct;
641: baij->ht_insert_ct += insert_ct;
642: }
643: PetscFunctionReturn(PETSC_SUCCESS);
644: }
646: static PetscErrorCode MatGetValues_MPIBAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
647: {
648: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
649: PetscInt bs = mat->rmap->bs, i, j, bsrstart = mat->rmap->rstart, bsrend = mat->rmap->rend;
650: PetscInt bscstart = mat->cmap->rstart, bscend = mat->cmap->rend, row, col, data;
652: PetscFunctionBegin;
653: for (i = 0; i < m; i++) {
654: if (idxm[i] < 0) continue; /* negative row */
655: 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);
656: if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
657: row = idxm[i] - bsrstart;
658: for (j = 0; j < n; j++) {
659: if (idxn[j] < 0) continue; /* negative column */
660: 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);
661: if (idxn[j] >= bscstart && idxn[j] < bscend) {
662: col = idxn[j] - bscstart;
663: PetscCall(MatGetValues_SeqBAIJ(baij->A, 1, &row, 1, &col, v + i * n + j));
664: } else {
665: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
666: #if defined(PETSC_USE_CTABLE)
667: PetscCall(PetscHMapIGetWithDefault(baij->colmap, idxn[j] / bs + 1, 0, &data));
668: data--;
669: #else
670: data = baij->colmap[idxn[j] / bs] - 1;
671: #endif
672: if ((data < 0) || (baij->garray[data / bs] != idxn[j] / bs)) *(v + i * n + j) = 0.0;
673: else {
674: col = data + idxn[j] % bs;
675: PetscCall(MatGetValues_SeqBAIJ(baij->B, 1, &row, 1, &col, v + i * n + j));
676: }
677: }
678: }
679: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local values currently supported");
680: }
681: PetscFunctionReturn(PETSC_SUCCESS);
682: }
684: static PetscErrorCode MatNorm_MPIBAIJ(Mat mat, NormType type, PetscReal *nrm)
685: {
686: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
687: Mat_SeqBAIJ *amat = (Mat_SeqBAIJ *)baij->A->data, *bmat = (Mat_SeqBAIJ *)baij->B->data;
688: PetscInt i, j, bs2 = baij->bs2, bs = baij->A->rmap->bs, nz, row, col;
689: PetscReal sum = 0.0;
690: MatScalar *v;
692: PetscFunctionBegin;
693: if (baij->size == 1) {
694: PetscCall(MatNorm(baij->A, type, nrm));
695: } else {
696: if (type == NORM_FROBENIUS) {
697: v = amat->a;
698: nz = amat->nz * bs2;
699: for (i = 0; i < nz; i++) {
700: sum += PetscRealPart(PetscConj(*v) * (*v));
701: v++;
702: }
703: v = bmat->a;
704: nz = bmat->nz * bs2;
705: for (i = 0; i < nz; i++) {
706: sum += PetscRealPart(PetscConj(*v) * (*v));
707: v++;
708: }
709: PetscCall(MPIU_Allreduce(&sum, nrm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
710: *nrm = PetscSqrtReal(*nrm);
711: } else if (type == NORM_1) { /* max column sum */
712: PetscReal *tmp, *tmp2;
713: PetscInt *jj, *garray = baij->garray, cstart = baij->rstartbs;
714: PetscCall(PetscCalloc1(mat->cmap->N, &tmp));
715: PetscCall(PetscMalloc1(mat->cmap->N, &tmp2));
716: v = amat->a;
717: jj = amat->j;
718: for (i = 0; i < amat->nz; i++) {
719: for (j = 0; j < bs; j++) {
720: col = bs * (cstart + *jj) + j; /* column index */
721: for (row = 0; row < bs; row++) {
722: tmp[col] += PetscAbsScalar(*v);
723: v++;
724: }
725: }
726: jj++;
727: }
728: v = bmat->a;
729: jj = bmat->j;
730: for (i = 0; i < bmat->nz; i++) {
731: for (j = 0; j < bs; j++) {
732: col = bs * garray[*jj] + j;
733: for (row = 0; row < bs; row++) {
734: tmp[col] += PetscAbsScalar(*v);
735: v++;
736: }
737: }
738: jj++;
739: }
740: PetscCall(MPIU_Allreduce(tmp, tmp2, mat->cmap->N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
741: *nrm = 0.0;
742: for (j = 0; j < mat->cmap->N; j++) {
743: if (tmp2[j] > *nrm) *nrm = tmp2[j];
744: }
745: PetscCall(PetscFree(tmp));
746: PetscCall(PetscFree(tmp2));
747: } else if (type == NORM_INFINITY) { /* max row sum */
748: PetscReal *sums;
749: PetscCall(PetscMalloc1(bs, &sums));
750: sum = 0.0;
751: for (j = 0; j < amat->mbs; j++) {
752: for (row = 0; row < bs; row++) sums[row] = 0.0;
753: v = amat->a + bs2 * amat->i[j];
754: nz = amat->i[j + 1] - amat->i[j];
755: for (i = 0; i < nz; i++) {
756: for (col = 0; col < bs; col++) {
757: for (row = 0; row < bs; row++) {
758: sums[row] += PetscAbsScalar(*v);
759: v++;
760: }
761: }
762: }
763: v = bmat->a + bs2 * bmat->i[j];
764: nz = bmat->i[j + 1] - bmat->i[j];
765: for (i = 0; i < nz; i++) {
766: for (col = 0; col < bs; col++) {
767: for (row = 0; row < bs; row++) {
768: sums[row] += PetscAbsScalar(*v);
769: v++;
770: }
771: }
772: }
773: for (row = 0; row < bs; row++) {
774: if (sums[row] > sum) sum = sums[row];
775: }
776: }
777: PetscCall(MPIU_Allreduce(&sum, nrm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
778: PetscCall(PetscFree(sums));
779: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for this norm yet");
780: }
781: PetscFunctionReturn(PETSC_SUCCESS);
782: }
784: /*
785: Creates the hash table, and sets the table
786: This table is created only once.
787: If new entried need to be added to the matrix
788: then the hash table has to be destroyed and
789: recreated.
790: */
791: static PetscErrorCode MatCreateHashTable_MPIBAIJ_Private(Mat mat, PetscReal factor)
792: {
793: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
794: Mat A = baij->A, B = baij->B;
795: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data, *b = (Mat_SeqBAIJ *)B->data;
796: PetscInt i, j, k, nz = a->nz + b->nz, h1, *ai = a->i, *aj = a->j, *bi = b->i, *bj = b->j;
797: PetscInt ht_size, bs2 = baij->bs2, rstart = baij->rstartbs;
798: PetscInt cstart = baij->cstartbs, *garray = baij->garray, row, col, Nbs = baij->Nbs;
799: PetscInt *HT, key;
800: MatScalar **HD;
801: PetscReal tmp;
802: #if defined(PETSC_USE_INFO)
803: PetscInt ct = 0, max = 0;
804: #endif
806: PetscFunctionBegin;
807: if (baij->ht) PetscFunctionReturn(PETSC_SUCCESS);
809: baij->ht_size = (PetscInt)(factor * nz);
810: ht_size = baij->ht_size;
812: /* Allocate Memory for Hash Table */
813: PetscCall(PetscCalloc2(ht_size, &baij->hd, ht_size, &baij->ht));
814: HD = baij->hd;
815: HT = baij->ht;
817: /* Loop Over A */
818: for (i = 0; i < a->mbs; i++) {
819: for (j = ai[i]; j < ai[i + 1]; j++) {
820: row = i + rstart;
821: col = aj[j] + cstart;
823: key = row * Nbs + col + 1;
824: h1 = HASH(ht_size, key, tmp);
825: for (k = 0; k < ht_size; k++) {
826: if (!HT[(h1 + k) % ht_size]) {
827: HT[(h1 + k) % ht_size] = key;
828: HD[(h1 + k) % ht_size] = a->a + j * bs2;
829: break;
830: #if defined(PETSC_USE_INFO)
831: } else {
832: ct++;
833: #endif
834: }
835: }
836: #if defined(PETSC_USE_INFO)
837: if (k > max) max = k;
838: #endif
839: }
840: }
841: /* Loop Over B */
842: for (i = 0; i < b->mbs; i++) {
843: for (j = bi[i]; j < bi[i + 1]; j++) {
844: row = i + rstart;
845: col = garray[bj[j]];
846: key = row * Nbs + col + 1;
847: h1 = HASH(ht_size, key, tmp);
848: for (k = 0; k < ht_size; k++) {
849: if (!HT[(h1 + k) % ht_size]) {
850: HT[(h1 + k) % ht_size] = key;
851: HD[(h1 + k) % ht_size] = b->a + j * bs2;
852: break;
853: #if defined(PETSC_USE_INFO)
854: } else {
855: ct++;
856: #endif
857: }
858: }
859: #if defined(PETSC_USE_INFO)
860: if (k > max) max = k;
861: #endif
862: }
863: }
865: /* Print Summary */
866: #if defined(PETSC_USE_INFO)
867: for (i = 0, j = 0; i < ht_size; i++) {
868: if (HT[i]) j++;
869: }
870: PetscCall(PetscInfo(mat, "Average Search = %5.2g,max search = %" PetscInt_FMT "\n", (!j) ? (double)0.0 : (double)(((PetscReal)(ct + j)) / (double)j), max));
871: #endif
872: PetscFunctionReturn(PETSC_SUCCESS);
873: }
875: static PetscErrorCode MatAssemblyBegin_MPIBAIJ(Mat mat, MatAssemblyType mode)
876: {
877: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
878: PetscInt nstash, reallocs;
880: PetscFunctionBegin;
881: if (baij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
883: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
884: PetscCall(MatStashScatterBegin_Private(mat, &mat->bstash, baij->rangebs));
885: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
886: PetscCall(PetscInfo(mat, "Stash has %" PetscInt_FMT " entries,uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
887: PetscCall(MatStashGetInfo_Private(&mat->bstash, &nstash, &reallocs));
888: PetscCall(PetscInfo(mat, "Block-Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
889: PetscFunctionReturn(PETSC_SUCCESS);
890: }
892: static PetscErrorCode MatAssemblyEnd_MPIBAIJ(Mat mat, MatAssemblyType mode)
893: {
894: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
895: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)baij->A->data;
896: PetscInt i, j, rstart, ncols, flg, bs2 = baij->bs2;
897: PetscInt *row, *col;
898: PetscBool r1, r2, r3, other_disassembled;
899: MatScalar *val;
900: PetscMPIInt n;
902: PetscFunctionBegin;
903: /* do not use 'b=(Mat_SeqBAIJ*)baij->B->data' as B can be reset in disassembly */
904: if (!baij->donotstash && !mat->nooffprocentries) {
905: while (1) {
906: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
907: if (!flg) break;
909: for (i = 0; i < n;) {
910: /* Now identify the consecutive vals belonging to the same row */
911: for (j = i, rstart = row[j]; j < n; j++) {
912: if (row[j] != rstart) break;
913: }
914: if (j < n) ncols = j - i;
915: else ncols = n - i;
916: /* Now assemble all these values with a single function call */
917: PetscCall(MatSetValues_MPIBAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
918: i = j;
919: }
920: }
921: PetscCall(MatStashScatterEnd_Private(&mat->stash));
922: /* Now process the block-stash. Since the values are stashed column-oriented,
923: set the row-oriented flag to column-oriented, and after MatSetValues()
924: restore the original flags */
925: r1 = baij->roworiented;
926: r2 = a->roworiented;
927: r3 = ((Mat_SeqBAIJ *)baij->B->data)->roworiented;
929: baij->roworiented = PETSC_FALSE;
930: a->roworiented = PETSC_FALSE;
931: ((Mat_SeqBAIJ *)baij->B->data)->roworiented = PETSC_FALSE;
932: while (1) {
933: PetscCall(MatStashScatterGetMesg_Private(&mat->bstash, &n, &row, &col, &val, &flg));
934: if (!flg) break;
936: for (i = 0; i < n;) {
937: /* Now identify the consecutive vals belonging to the same row */
938: for (j = i, rstart = row[j]; j < n; j++) {
939: if (row[j] != rstart) break;
940: }
941: if (j < n) ncols = j - i;
942: else ncols = n - i;
943: PetscCall(MatSetValuesBlocked_MPIBAIJ(mat, 1, row + i, ncols, col + i, val + i * bs2, mat->insertmode));
944: i = j;
945: }
946: }
947: PetscCall(MatStashScatterEnd_Private(&mat->bstash));
949: baij->roworiented = r1;
950: a->roworiented = r2;
951: ((Mat_SeqBAIJ *)baij->B->data)->roworiented = r3;
952: }
954: PetscCall(MatAssemblyBegin(baij->A, mode));
955: PetscCall(MatAssemblyEnd(baij->A, mode));
957: /* determine if any processor has disassembled, if so we must
958: also disassemble ourselves, in order that we may reassemble. */
959: /*
960: if nonzero structure of submatrix B cannot change then we know that
961: no processor disassembled thus we can skip this stuff
962: */
963: if (!((Mat_SeqBAIJ *)baij->B->data)->nonew) {
964: PetscCall(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
965: if (mat->was_assembled && !other_disassembled) PetscCall(MatDisAssemble_MPIBAIJ(mat));
966: }
968: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIBAIJ(mat));
969: PetscCall(MatAssemblyBegin(baij->B, mode));
970: PetscCall(MatAssemblyEnd(baij->B, mode));
972: #if defined(PETSC_USE_INFO)
973: if (baij->ht && mode == MAT_FINAL_ASSEMBLY) {
974: PetscCall(PetscInfo(mat, "Average Hash Table Search in MatSetValues = %5.2f\n", (double)((PetscReal)baij->ht_total_ct) / baij->ht_insert_ct));
976: baij->ht_total_ct = 0;
977: baij->ht_insert_ct = 0;
978: }
979: #endif
980: if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
981: PetscCall(MatCreateHashTable_MPIBAIJ_Private(mat, baij->ht_fact));
983: mat->ops->setvalues = MatSetValues_MPIBAIJ_HT;
984: mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
985: }
987: PetscCall(PetscFree2(baij->rowvalues, baij->rowindices));
989: baij->rowvalues = NULL;
991: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
992: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqBAIJ *)baij->A->data)->nonew) {
993: PetscObjectState state = baij->A->nonzerostate + baij->B->nonzerostate;
994: PetscCall(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
995: }
996: PetscFunctionReturn(PETSC_SUCCESS);
997: }
999: extern PetscErrorCode MatView_SeqBAIJ(Mat, PetscViewer);
1000: #include <petscdraw.h>
1001: static PetscErrorCode MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1002: {
1003: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1004: PetscMPIInt rank = baij->rank;
1005: PetscInt bs = mat->rmap->bs;
1006: PetscBool iascii, isdraw;
1007: PetscViewer sviewer;
1008: PetscViewerFormat format;
1010: PetscFunctionBegin;
1011: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1012: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1013: if (iascii) {
1014: PetscCall(PetscViewerGetFormat(viewer, &format));
1015: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1016: MatInfo info;
1017: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1018: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1019: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1020: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] Local rows %" PetscInt_FMT " nz %" PetscInt_FMT " nz alloced %" PetscInt_FMT " bs %" PetscInt_FMT " mem %g\n", rank, mat->rmap->n, (PetscInt)info.nz_used, (PetscInt)info.nz_allocated,
1021: mat->rmap->bs, (double)info.memory));
1022: PetscCall(MatGetInfo(baij->A, MAT_LOCAL, &info));
1023: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1024: PetscCall(MatGetInfo(baij->B, MAT_LOCAL, &info));
1025: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1026: PetscCall(PetscViewerFlush(viewer));
1027: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1028: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1029: PetscCall(VecScatterView(baij->Mvctx, viewer));
1030: PetscFunctionReturn(PETSC_SUCCESS);
1031: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1032: PetscCall(PetscViewerASCIIPrintf(viewer, " block size is %" PetscInt_FMT "\n", bs));
1033: PetscFunctionReturn(PETSC_SUCCESS);
1034: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1035: PetscFunctionReturn(PETSC_SUCCESS);
1036: }
1037: }
1039: if (isdraw) {
1040: PetscDraw draw;
1041: PetscBool isnull;
1042: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1043: PetscCall(PetscDrawIsNull(draw, &isnull));
1044: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1045: }
1047: {
1048: /* assemble the entire matrix onto first processor. */
1049: Mat A;
1050: Mat_SeqBAIJ *Aloc;
1051: PetscInt M = mat->rmap->N, N = mat->cmap->N, *ai, *aj, col, i, j, k, *rvals, mbs = baij->mbs;
1052: MatScalar *a;
1053: const char *matname;
1055: /* Here we are creating a temporary matrix, so will assume MPIBAIJ is acceptable */
1056: /* Perhaps this should be the type of mat? */
1057: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &A));
1058: if (rank == 0) {
1059: PetscCall(MatSetSizes(A, M, N, M, N));
1060: } else {
1061: PetscCall(MatSetSizes(A, 0, 0, M, N));
1062: }
1063: PetscCall(MatSetType(A, MATMPIBAIJ));
1064: PetscCall(MatMPIBAIJSetPreallocation(A, mat->rmap->bs, 0, NULL, 0, NULL));
1065: PetscCall(MatSetOption(A, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_FALSE));
1067: /* copy over the A part */
1068: Aloc = (Mat_SeqBAIJ *)baij->A->data;
1069: ai = Aloc->i;
1070: aj = Aloc->j;
1071: a = Aloc->a;
1072: PetscCall(PetscMalloc1(bs, &rvals));
1074: for (i = 0; i < mbs; i++) {
1075: rvals[0] = bs * (baij->rstartbs + i);
1076: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1077: for (j = ai[i]; j < ai[i + 1]; j++) {
1078: col = (baij->cstartbs + aj[j]) * bs;
1079: for (k = 0; k < bs; k++) {
1080: PetscCall(MatSetValues_MPIBAIJ(A, bs, rvals, 1, &col, a, INSERT_VALUES));
1081: col++;
1082: a += bs;
1083: }
1084: }
1085: }
1086: /* copy over the B part */
1087: Aloc = (Mat_SeqBAIJ *)baij->B->data;
1088: ai = Aloc->i;
1089: aj = Aloc->j;
1090: a = Aloc->a;
1091: for (i = 0; i < mbs; i++) {
1092: rvals[0] = bs * (baij->rstartbs + i);
1093: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1094: for (j = ai[i]; j < ai[i + 1]; j++) {
1095: col = baij->garray[aj[j]] * bs;
1096: for (k = 0; k < bs; k++) {
1097: PetscCall(MatSetValues_MPIBAIJ(A, bs, rvals, 1, &col, a, INSERT_VALUES));
1098: col++;
1099: a += bs;
1100: }
1101: }
1102: }
1103: PetscCall(PetscFree(rvals));
1104: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1105: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1106: /*
1107: Everyone has to call to draw the matrix since the graphics waits are
1108: synchronized across all processors that share the PetscDraw object
1109: */
1110: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1111: if (((PetscObject)mat)->name) PetscCall(PetscObjectGetName((PetscObject)mat, &matname));
1112: if (rank == 0) {
1113: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)((Mat_MPIBAIJ *)A->data)->A, matname));
1114: PetscCall(MatView_SeqBAIJ(((Mat_MPIBAIJ *)A->data)->A, sviewer));
1115: }
1116: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1117: PetscCall(MatDestroy(&A));
1118: }
1119: PetscFunctionReturn(PETSC_SUCCESS);
1120: }
1122: /* Used for both MPIBAIJ and MPISBAIJ matrices */
1123: PetscErrorCode MatView_MPIBAIJ_Binary(Mat mat, PetscViewer viewer)
1124: {
1125: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
1126: Mat_SeqBAIJ *A = (Mat_SeqBAIJ *)aij->A->data;
1127: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)aij->B->data;
1128: const PetscInt *garray = aij->garray;
1129: PetscInt header[4], M, N, m, rs, cs, bs, cnt, i, j, ja, jb, k, l;
1130: PetscInt64 nz, hnz;
1131: PetscInt *rowlens, *colidxs;
1132: PetscScalar *matvals;
1133: PetscMPIInt rank;
1135: PetscFunctionBegin;
1136: PetscCall(PetscViewerSetUp(viewer));
1138: M = mat->rmap->N;
1139: N = mat->cmap->N;
1140: m = mat->rmap->n;
1141: rs = mat->rmap->rstart;
1142: cs = mat->cmap->rstart;
1143: bs = mat->rmap->bs;
1144: nz = bs * bs * (A->nz + B->nz);
1146: /* write matrix header */
1147: header[0] = MAT_FILE_CLASSID;
1148: header[1] = M;
1149: header[2] = N;
1150: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1151: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1152: if (rank == 0) PetscCall(PetscIntCast(hnz, &header[3]));
1153: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1155: /* fill in and store row lengths */
1156: PetscCall(PetscMalloc1(m, &rowlens));
1157: for (cnt = 0, i = 0; i < A->mbs; i++)
1158: for (j = 0; j < bs; j++) rowlens[cnt++] = bs * (A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i]);
1159: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1160: PetscCall(PetscFree(rowlens));
1162: /* fill in and store column indices */
1163: PetscCall(PetscMalloc1(nz, &colidxs));
1164: for (cnt = 0, i = 0; i < A->mbs; i++) {
1165: for (k = 0; k < bs; k++) {
1166: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1167: if (garray[B->j[jb]] > cs / bs) break;
1168: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * garray[B->j[jb]] + l;
1169: }
1170: for (ja = A->i[i]; ja < A->i[i + 1]; ja++)
1171: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * A->j[ja] + l + cs;
1172: for (; jb < B->i[i + 1]; jb++)
1173: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * garray[B->j[jb]] + l;
1174: }
1175: }
1176: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1177: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DECIDE, PETSC_DECIDE, PETSC_INT));
1178: PetscCall(PetscFree(colidxs));
1180: /* fill in and store nonzero values */
1181: PetscCall(PetscMalloc1(nz, &matvals));
1182: for (cnt = 0, i = 0; i < A->mbs; i++) {
1183: for (k = 0; k < bs; k++) {
1184: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1185: if (garray[B->j[jb]] > cs / bs) break;
1186: for (l = 0; l < bs; l++) matvals[cnt++] = B->a[bs * (bs * jb + l) + k];
1187: }
1188: for (ja = A->i[i]; ja < A->i[i + 1]; ja++)
1189: for (l = 0; l < bs; l++) matvals[cnt++] = A->a[bs * (bs * ja + l) + k];
1190: for (; jb < B->i[i + 1]; jb++)
1191: for (l = 0; l < bs; l++) matvals[cnt++] = B->a[bs * (bs * jb + l) + k];
1192: }
1193: }
1194: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DECIDE, PETSC_DECIDE, PETSC_SCALAR));
1195: PetscCall(PetscFree(matvals));
1197: /* write block size option to the viewer's .info file */
1198: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1199: PetscFunctionReturn(PETSC_SUCCESS);
1200: }
1202: PetscErrorCode MatView_MPIBAIJ(Mat mat, PetscViewer viewer)
1203: {
1204: PetscBool iascii, isdraw, issocket, isbinary;
1206: PetscFunctionBegin;
1207: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1208: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1209: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1210: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1211: if (iascii || isdraw || issocket) {
1212: PetscCall(MatView_MPIBAIJ_ASCIIorDraworSocket(mat, viewer));
1213: } else if (isbinary) PetscCall(MatView_MPIBAIJ_Binary(mat, viewer));
1214: PetscFunctionReturn(PETSC_SUCCESS);
1215: }
1217: static PetscErrorCode MatMult_MPIBAIJ(Mat A, Vec xx, Vec yy)
1218: {
1219: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1220: PetscInt nt;
1222: PetscFunctionBegin;
1223: PetscCall(VecGetLocalSize(xx, &nt));
1224: PetscCheck(nt == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A and xx");
1225: PetscCall(VecGetLocalSize(yy, &nt));
1226: PetscCheck(nt == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A and yy");
1227: PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1228: PetscCall((*a->A->ops->mult)(a->A, xx, yy));
1229: PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1230: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, yy, yy));
1231: PetscFunctionReturn(PETSC_SUCCESS);
1232: }
1234: static PetscErrorCode MatMultAdd_MPIBAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1235: {
1236: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1238: PetscFunctionBegin;
1239: PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1240: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1241: PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1242: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1243: PetscFunctionReturn(PETSC_SUCCESS);
1244: }
1246: static PetscErrorCode MatMultTranspose_MPIBAIJ(Mat A, Vec xx, Vec yy)
1247: {
1248: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1250: PetscFunctionBegin;
1251: /* do nondiagonal part */
1252: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1253: /* do local part */
1254: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1255: /* add partial results together */
1256: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1257: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1258: PetscFunctionReturn(PETSC_SUCCESS);
1259: }
1261: static PetscErrorCode MatMultTransposeAdd_MPIBAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1262: {
1263: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1265: PetscFunctionBegin;
1266: /* do nondiagonal part */
1267: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1268: /* do local part */
1269: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1270: /* add partial results together */
1271: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1272: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1273: PetscFunctionReturn(PETSC_SUCCESS);
1274: }
1276: /*
1277: This only works correctly for square matrices where the subblock A->A is the
1278: diagonal block
1279: */
1280: static PetscErrorCode MatGetDiagonal_MPIBAIJ(Mat A, Vec v)
1281: {
1282: PetscFunctionBegin;
1283: PetscCheck(A->rmap->N == A->cmap->N, PETSC_COMM_SELF, PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1284: PetscCall(MatGetDiagonal(((Mat_MPIBAIJ *)A->data)->A, v));
1285: PetscFunctionReturn(PETSC_SUCCESS);
1286: }
1288: static PetscErrorCode MatScale_MPIBAIJ(Mat A, PetscScalar aa)
1289: {
1290: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1292: PetscFunctionBegin;
1293: PetscCall(MatScale(a->A, aa));
1294: PetscCall(MatScale(a->B, aa));
1295: PetscFunctionReturn(PETSC_SUCCESS);
1296: }
1298: PetscErrorCode MatGetRow_MPIBAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1299: {
1300: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)matin->data;
1301: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1302: PetscInt bs = matin->rmap->bs, bs2 = mat->bs2, i, *cworkA, *cworkB, **pcA, **pcB;
1303: PetscInt nztot, nzA, nzB, lrow, brstart = matin->rmap->rstart, brend = matin->rmap->rend;
1304: PetscInt *cmap, *idx_p, cstart = mat->cstartbs;
1306: PetscFunctionBegin;
1307: PetscCheck(row >= brstart && row < brend, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local rows");
1308: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1309: mat->getrowactive = PETSC_TRUE;
1311: if (!mat->rowvalues && (idx || v)) {
1312: /*
1313: allocate enough space to hold information from the longest row.
1314: */
1315: Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ *)mat->A->data, *Ba = (Mat_SeqBAIJ *)mat->B->data;
1316: PetscInt max = 1, mbs = mat->mbs, tmp;
1317: for (i = 0; i < mbs; i++) {
1318: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1319: if (max < tmp) max = tmp;
1320: }
1321: PetscCall(PetscMalloc2(max * bs2, &mat->rowvalues, max * bs2, &mat->rowindices));
1322: }
1323: lrow = row - brstart;
1325: pvA = &vworkA;
1326: pcA = &cworkA;
1327: pvB = &vworkB;
1328: pcB = &cworkB;
1329: if (!v) {
1330: pvA = NULL;
1331: pvB = NULL;
1332: }
1333: if (!idx) {
1334: pcA = NULL;
1335: if (!v) pcB = NULL;
1336: }
1337: PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1338: PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1339: nztot = nzA + nzB;
1341: cmap = mat->garray;
1342: if (v || idx) {
1343: if (nztot) {
1344: /* Sort by increasing column numbers, assuming A and B already sorted */
1345: PetscInt imark = -1;
1346: if (v) {
1347: *v = v_p = mat->rowvalues;
1348: for (i = 0; i < nzB; i++) {
1349: if (cmap[cworkB[i] / bs] < cstart) v_p[i] = vworkB[i];
1350: else break;
1351: }
1352: imark = i;
1353: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1354: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1355: }
1356: if (idx) {
1357: *idx = idx_p = mat->rowindices;
1358: if (imark > -1) {
1359: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i] / bs] * bs + cworkB[i] % bs;
1360: } else {
1361: for (i = 0; i < nzB; i++) {
1362: if (cmap[cworkB[i] / bs] < cstart) idx_p[i] = cmap[cworkB[i] / bs] * bs + cworkB[i] % bs;
1363: else break;
1364: }
1365: imark = i;
1366: }
1367: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart * bs + cworkA[i];
1368: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i] / bs] * bs + cworkB[i] % bs;
1369: }
1370: } else {
1371: if (idx) *idx = NULL;
1372: if (v) *v = NULL;
1373: }
1374: }
1375: *nz = nztot;
1376: PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1377: PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1378: PetscFunctionReturn(PETSC_SUCCESS);
1379: }
1381: PetscErrorCode MatRestoreRow_MPIBAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1382: {
1383: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1385: PetscFunctionBegin;
1386: PetscCheck(baij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow not called");
1387: baij->getrowactive = PETSC_FALSE;
1388: PetscFunctionReturn(PETSC_SUCCESS);
1389: }
1391: static PetscErrorCode MatZeroEntries_MPIBAIJ(Mat A)
1392: {
1393: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1395: PetscFunctionBegin;
1396: PetscCall(MatZeroEntries(l->A));
1397: PetscCall(MatZeroEntries(l->B));
1398: PetscFunctionReturn(PETSC_SUCCESS);
1399: }
1401: static PetscErrorCode MatGetInfo_MPIBAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1402: {
1403: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)matin->data;
1404: Mat A = a->A, B = a->B;
1405: PetscLogDouble isend[5], irecv[5];
1407: PetscFunctionBegin;
1408: info->block_size = (PetscReal)matin->rmap->bs;
1410: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1412: isend[0] = info->nz_used;
1413: isend[1] = info->nz_allocated;
1414: isend[2] = info->nz_unneeded;
1415: isend[3] = info->memory;
1416: isend[4] = info->mallocs;
1418: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1420: isend[0] += info->nz_used;
1421: isend[1] += info->nz_allocated;
1422: isend[2] += info->nz_unneeded;
1423: isend[3] += info->memory;
1424: isend[4] += info->mallocs;
1426: if (flag == MAT_LOCAL) {
1427: info->nz_used = isend[0];
1428: info->nz_allocated = isend[1];
1429: info->nz_unneeded = isend[2];
1430: info->memory = isend[3];
1431: info->mallocs = isend[4];
1432: } else if (flag == MAT_GLOBAL_MAX) {
1433: PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1435: info->nz_used = irecv[0];
1436: info->nz_allocated = irecv[1];
1437: info->nz_unneeded = irecv[2];
1438: info->memory = irecv[3];
1439: info->mallocs = irecv[4];
1440: } else if (flag == MAT_GLOBAL_SUM) {
1441: PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1443: info->nz_used = irecv[0];
1444: info->nz_allocated = irecv[1];
1445: info->nz_unneeded = irecv[2];
1446: info->memory = irecv[3];
1447: info->mallocs = irecv[4];
1448: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_ARG_WRONG, "Unknown MatInfoType argument %d", (int)flag);
1449: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1450: info->fill_ratio_needed = 0;
1451: info->factor_mallocs = 0;
1452: PetscFunctionReturn(PETSC_SUCCESS);
1453: }
1455: static PetscErrorCode MatSetOption_MPIBAIJ(Mat A, MatOption op, PetscBool flg)
1456: {
1457: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1459: PetscFunctionBegin;
1460: switch (op) {
1461: case MAT_NEW_NONZERO_LOCATIONS:
1462: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1463: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1464: case MAT_KEEP_NONZERO_PATTERN:
1465: case MAT_NEW_NONZERO_LOCATION_ERR:
1466: MatCheckPreallocated(A, 1);
1467: PetscCall(MatSetOption(a->A, op, flg));
1468: PetscCall(MatSetOption(a->B, op, flg));
1469: break;
1470: case MAT_ROW_ORIENTED:
1471: MatCheckPreallocated(A, 1);
1472: a->roworiented = flg;
1474: PetscCall(MatSetOption(a->A, op, flg));
1475: PetscCall(MatSetOption(a->B, op, flg));
1476: break;
1477: case MAT_FORCE_DIAGONAL_ENTRIES:
1478: case MAT_SORTED_FULL:
1479: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1480: break;
1481: case MAT_IGNORE_OFF_PROC_ENTRIES:
1482: a->donotstash = flg;
1483: break;
1484: case MAT_USE_HASH_TABLE:
1485: a->ht_flag = flg;
1486: a->ht_fact = 1.39;
1487: break;
1488: case MAT_SYMMETRIC:
1489: case MAT_STRUCTURALLY_SYMMETRIC:
1490: case MAT_HERMITIAN:
1491: case MAT_SUBMAT_SINGLEIS:
1492: case MAT_SYMMETRY_ETERNAL:
1493: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1494: case MAT_SPD_ETERNAL:
1495: /* if the diagonal matrix is square it inherits some of the properties above */
1496: break;
1497: default:
1498: SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "unknown option %d", op);
1499: }
1500: PetscFunctionReturn(PETSC_SUCCESS);
1501: }
1503: static PetscErrorCode MatTranspose_MPIBAIJ(Mat A, MatReuse reuse, Mat *matout)
1504: {
1505: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)A->data;
1506: Mat_SeqBAIJ *Aloc;
1507: Mat B;
1508: PetscInt M = A->rmap->N, N = A->cmap->N, *ai, *aj, i, *rvals, j, k, col;
1509: PetscInt bs = A->rmap->bs, mbs = baij->mbs;
1510: MatScalar *a;
1512: PetscFunctionBegin;
1513: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1514: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
1515: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1516: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1517: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1518: /* Do not know preallocation information, but must set block size */
1519: PetscCall(MatMPIBAIJSetPreallocation(B, A->rmap->bs, PETSC_DECIDE, NULL, PETSC_DECIDE, NULL));
1520: } else {
1521: B = *matout;
1522: }
1524: /* copy over the A part */
1525: Aloc = (Mat_SeqBAIJ *)baij->A->data;
1526: ai = Aloc->i;
1527: aj = Aloc->j;
1528: a = Aloc->a;
1529: PetscCall(PetscMalloc1(bs, &rvals));
1531: for (i = 0; i < mbs; i++) {
1532: rvals[0] = bs * (baij->rstartbs + i);
1533: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1534: for (j = ai[i]; j < ai[i + 1]; j++) {
1535: col = (baij->cstartbs + aj[j]) * bs;
1536: for (k = 0; k < bs; k++) {
1537: PetscCall(MatSetValues_MPIBAIJ(B, 1, &col, bs, rvals, a, INSERT_VALUES));
1539: col++;
1540: a += bs;
1541: }
1542: }
1543: }
1544: /* copy over the B part */
1545: Aloc = (Mat_SeqBAIJ *)baij->B->data;
1546: ai = Aloc->i;
1547: aj = Aloc->j;
1548: a = Aloc->a;
1549: for (i = 0; i < mbs; i++) {
1550: rvals[0] = bs * (baij->rstartbs + i);
1551: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1552: for (j = ai[i]; j < ai[i + 1]; j++) {
1553: col = baij->garray[aj[j]] * bs;
1554: for (k = 0; k < bs; k++) {
1555: PetscCall(MatSetValues_MPIBAIJ(B, 1, &col, bs, rvals, a, INSERT_VALUES));
1556: col++;
1557: a += bs;
1558: }
1559: }
1560: }
1561: PetscCall(PetscFree(rvals));
1562: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1563: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1565: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) *matout = B;
1566: else PetscCall(MatHeaderMerge(A, &B));
1567: PetscFunctionReturn(PETSC_SUCCESS);
1568: }
1570: static PetscErrorCode MatDiagonalScale_MPIBAIJ(Mat mat, Vec ll, Vec rr)
1571: {
1572: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1573: Mat a = baij->A, b = baij->B;
1574: PetscInt s1, s2, s3;
1576: PetscFunctionBegin;
1577: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1578: if (rr) {
1579: PetscCall(VecGetLocalSize(rr, &s1));
1580: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1581: /* Overlap communication with computation. */
1582: PetscCall(VecScatterBegin(baij->Mvctx, rr, baij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1583: }
1584: if (ll) {
1585: PetscCall(VecGetLocalSize(ll, &s1));
1586: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1587: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1588: }
1589: /* scale the diagonal block */
1590: PetscUseTypeMethod(a, diagonalscale, ll, rr);
1592: if (rr) {
1593: /* Do a scatter end and then right scale the off-diagonal block */
1594: PetscCall(VecScatterEnd(baij->Mvctx, rr, baij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1595: PetscUseTypeMethod(b, diagonalscale, NULL, baij->lvec);
1596: }
1597: PetscFunctionReturn(PETSC_SUCCESS);
1598: }
1600: static PetscErrorCode MatZeroRows_MPIBAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
1601: {
1602: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1603: PetscInt *lrows;
1604: PetscInt r, len;
1605: PetscBool cong;
1607: PetscFunctionBegin;
1608: /* get locally owned rows */
1609: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
1610: /* fix right-hand side if needed */
1611: if (x && b) {
1612: const PetscScalar *xx;
1613: PetscScalar *bb;
1615: PetscCall(VecGetArrayRead(x, &xx));
1616: PetscCall(VecGetArray(b, &bb));
1617: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
1618: PetscCall(VecRestoreArrayRead(x, &xx));
1619: PetscCall(VecRestoreArray(b, &bb));
1620: }
1622: /* actually zap the local rows */
1623: /*
1624: Zero the required rows. If the "diagonal block" of the matrix
1625: is square and the user wishes to set the diagonal we use separate
1626: code so that MatSetValues() is not called for each diagonal allocating
1627: new memory, thus calling lots of mallocs and slowing things down.
1629: */
1630: /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1631: PetscCall(MatZeroRows_SeqBAIJ(l->B, len, lrows, 0.0, NULL, NULL));
1632: PetscCall(MatHasCongruentLayouts(A, &cong));
1633: if ((diag != 0.0) && cong) {
1634: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, diag, NULL, NULL));
1635: } else if (diag != 0.0) {
1636: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, 0.0, NULL, NULL));
1637: PetscCheck(!((Mat_SeqBAIJ *)l->A->data)->nonew, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatZeroRows() on rectangular matrices cannot be used with the Mat options MAT_NEW_NONZERO_LOCATIONS, MAT_NEW_NONZERO_LOCATION_ERR, and MAT_NEW_NONZERO_ALLOCATION_ERR");
1638: for (r = 0; r < len; ++r) {
1639: const PetscInt row = lrows[r] + A->rmap->rstart;
1640: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
1641: }
1642: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1643: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1644: } else {
1645: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, 0.0, NULL, NULL));
1646: }
1647: PetscCall(PetscFree(lrows));
1649: /* only change matrix nonzero state if pattern was allowed to be changed */
1650: if (!((Mat_SeqBAIJ *)l->A->data)->keepnonzeropattern || !((Mat_SeqBAIJ *)l->A->data)->nonew) {
1651: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1652: PetscCall(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1653: }
1654: PetscFunctionReturn(PETSC_SUCCESS);
1655: }
1657: static PetscErrorCode MatZeroRowsColumns_MPIBAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
1658: {
1659: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1660: PetscMPIInt n = A->rmap->n, p = 0;
1661: PetscInt i, j, k, r, len = 0, row, col, count;
1662: PetscInt *lrows, *owners = A->rmap->range;
1663: PetscSFNode *rrows;
1664: PetscSF sf;
1665: const PetscScalar *xx;
1666: PetscScalar *bb, *mask;
1667: Vec xmask, lmask;
1668: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)l->B->data;
1669: PetscInt bs = A->rmap->bs, bs2 = baij->bs2;
1670: PetscScalar *aa;
1672: PetscFunctionBegin;
1673: /* Create SF where leaves are input rows and roots are owned rows */
1674: PetscCall(PetscMalloc1(n, &lrows));
1675: for (r = 0; r < n; ++r) lrows[r] = -1;
1676: PetscCall(PetscMalloc1(N, &rrows));
1677: for (r = 0; r < N; ++r) {
1678: const PetscInt idx = rows[r];
1679: 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);
1680: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
1681: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
1682: }
1683: rrows[r].rank = p;
1684: rrows[r].index = rows[r] - owners[p];
1685: }
1686: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1687: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
1688: /* Collect flags for rows to be zeroed */
1689: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
1690: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
1691: PetscCall(PetscSFDestroy(&sf));
1692: /* Compress and put in row numbers */
1693: for (r = 0; r < n; ++r)
1694: if (lrows[r] >= 0) lrows[len++] = r;
1695: /* zero diagonal part of matrix */
1696: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
1697: /* handle off-diagonal part of matrix */
1698: PetscCall(MatCreateVecs(A, &xmask, NULL));
1699: PetscCall(VecDuplicate(l->lvec, &lmask));
1700: PetscCall(VecGetArray(xmask, &bb));
1701: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
1702: PetscCall(VecRestoreArray(xmask, &bb));
1703: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
1704: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
1705: PetscCall(VecDestroy(&xmask));
1706: if (x) {
1707: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
1708: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
1709: PetscCall(VecGetArrayRead(l->lvec, &xx));
1710: PetscCall(VecGetArray(b, &bb));
1711: }
1712: PetscCall(VecGetArray(lmask, &mask));
1713: /* remove zeroed rows of off-diagonal matrix */
1714: for (i = 0; i < len; ++i) {
1715: row = lrows[i];
1716: count = (baij->i[row / bs + 1] - baij->i[row / bs]) * bs;
1717: aa = ((MatScalar *)baij->a) + baij->i[row / bs] * bs2 + (row % bs);
1718: for (k = 0; k < count; ++k) {
1719: aa[0] = 0.0;
1720: aa += bs;
1721: }
1722: }
1723: /* loop over all elements of off process part of matrix zeroing removed columns*/
1724: for (i = 0; i < l->B->rmap->N; ++i) {
1725: row = i / bs;
1726: for (j = baij->i[row]; j < baij->i[row + 1]; ++j) {
1727: for (k = 0; k < bs; ++k) {
1728: col = bs * baij->j[j] + k;
1729: if (PetscAbsScalar(mask[col])) {
1730: aa = ((MatScalar *)baij->a) + j * bs2 + (i % bs) + bs * k;
1731: if (x) bb[i] -= aa[0] * xx[col];
1732: aa[0] = 0.0;
1733: }
1734: }
1735: }
1736: }
1737: if (x) {
1738: PetscCall(VecRestoreArray(b, &bb));
1739: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1740: }
1741: PetscCall(VecRestoreArray(lmask, &mask));
1742: PetscCall(VecDestroy(&lmask));
1743: PetscCall(PetscFree(lrows));
1745: /* only change matrix nonzero state if pattern was allowed to be changed */
1746: if (!((Mat_SeqBAIJ *)l->A->data)->nonew) {
1747: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1748: PetscCall(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1749: }
1750: PetscFunctionReturn(PETSC_SUCCESS);
1751: }
1753: static PetscErrorCode MatSetUnfactored_MPIBAIJ(Mat A)
1754: {
1755: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1757: PetscFunctionBegin;
1758: PetscCall(MatSetUnfactored(a->A));
1759: PetscFunctionReturn(PETSC_SUCCESS);
1760: }
1762: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat, MatDuplicateOption, Mat *);
1764: static PetscErrorCode MatEqual_MPIBAIJ(Mat A, Mat B, PetscBool *flag)
1765: {
1766: Mat_MPIBAIJ *matB = (Mat_MPIBAIJ *)B->data, *matA = (Mat_MPIBAIJ *)A->data;
1767: Mat a, b, c, d;
1768: PetscBool flg;
1770: PetscFunctionBegin;
1771: a = matA->A;
1772: b = matA->B;
1773: c = matB->A;
1774: d = matB->B;
1776: PetscCall(MatEqual(a, c, &flg));
1777: if (flg) PetscCall(MatEqual(b, d, &flg));
1778: PetscCall(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
1779: PetscFunctionReturn(PETSC_SUCCESS);
1780: }
1782: static PetscErrorCode MatCopy_MPIBAIJ(Mat A, Mat B, MatStructure str)
1783: {
1784: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1785: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
1787: PetscFunctionBegin;
1788: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1789: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1790: PetscCall(MatCopy_Basic(A, B, str));
1791: } else {
1792: PetscCall(MatCopy(a->A, b->A, str));
1793: PetscCall(MatCopy(a->B, b->B, str));
1794: }
1795: PetscCall(PetscObjectStateIncrease((PetscObject)B));
1796: PetscFunctionReturn(PETSC_SUCCESS);
1797: }
1799: PetscErrorCode MatAXPYGetPreallocation_MPIBAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
1800: {
1801: PetscInt bs = Y->rmap->bs, m = Y->rmap->N / bs;
1802: Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data;
1803: Mat_SeqBAIJ *y = (Mat_SeqBAIJ *)Y->data;
1805: PetscFunctionBegin;
1806: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
1807: PetscFunctionReturn(PETSC_SUCCESS);
1808: }
1810: static PetscErrorCode MatAXPY_MPIBAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
1811: {
1812: Mat_MPIBAIJ *xx = (Mat_MPIBAIJ *)X->data, *yy = (Mat_MPIBAIJ *)Y->data;
1813: PetscBLASInt bnz, one = 1;
1814: Mat_SeqBAIJ *x, *y;
1815: PetscInt bs2 = Y->rmap->bs * Y->rmap->bs;
1817: PetscFunctionBegin;
1818: if (str == SAME_NONZERO_PATTERN) {
1819: PetscScalar alpha = a;
1820: x = (Mat_SeqBAIJ *)xx->A->data;
1821: y = (Mat_SeqBAIJ *)yy->A->data;
1822: PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz));
1823: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one));
1824: x = (Mat_SeqBAIJ *)xx->B->data;
1825: y = (Mat_SeqBAIJ *)yy->B->data;
1826: PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz));
1827: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one));
1828: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
1829: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
1830: PetscCall(MatAXPY_Basic(Y, a, X, str));
1831: } else {
1832: Mat B;
1833: PetscInt *nnz_d, *nnz_o, bs = Y->rmap->bs;
1834: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
1835: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
1836: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
1837: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
1838: PetscCall(MatSetSizes(B, Y->rmap->n, Y->cmap->n, Y->rmap->N, Y->cmap->N));
1839: PetscCall(MatSetBlockSizesFromMats(B, Y, Y));
1840: PetscCall(MatSetType(B, MATMPIBAIJ));
1841: PetscCall(MatAXPYGetPreallocation_SeqBAIJ(yy->A, xx->A, nnz_d));
1842: PetscCall(MatAXPYGetPreallocation_MPIBAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
1843: PetscCall(MatMPIBAIJSetPreallocation(B, bs, 0, nnz_d, 0, nnz_o));
1844: /* MatAXPY_BasicWithPreallocation() for BAIJ matrix is much slower than AIJ, even for bs=1 ! */
1845: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
1846: PetscCall(MatHeaderMerge(Y, &B));
1847: PetscCall(PetscFree(nnz_d));
1848: PetscCall(PetscFree(nnz_o));
1849: }
1850: PetscFunctionReturn(PETSC_SUCCESS);
1851: }
1853: static PetscErrorCode MatConjugate_MPIBAIJ(Mat mat)
1854: {
1855: PetscFunctionBegin;
1856: if (PetscDefined(USE_COMPLEX)) {
1857: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)mat->data;
1859: PetscCall(MatConjugate_SeqBAIJ(a->A));
1860: PetscCall(MatConjugate_SeqBAIJ(a->B));
1861: }
1862: PetscFunctionReturn(PETSC_SUCCESS);
1863: }
1865: static PetscErrorCode MatRealPart_MPIBAIJ(Mat A)
1866: {
1867: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1869: PetscFunctionBegin;
1870: PetscCall(MatRealPart(a->A));
1871: PetscCall(MatRealPart(a->B));
1872: PetscFunctionReturn(PETSC_SUCCESS);
1873: }
1875: static PetscErrorCode MatImaginaryPart_MPIBAIJ(Mat A)
1876: {
1877: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1879: PetscFunctionBegin;
1880: PetscCall(MatImaginaryPart(a->A));
1881: PetscCall(MatImaginaryPart(a->B));
1882: PetscFunctionReturn(PETSC_SUCCESS);
1883: }
1885: static PetscErrorCode MatCreateSubMatrix_MPIBAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
1886: {
1887: IS iscol_local;
1888: PetscInt csize;
1890: PetscFunctionBegin;
1891: PetscCall(ISGetLocalSize(iscol, &csize));
1892: if (call == MAT_REUSE_MATRIX) {
1893: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
1894: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
1895: } else {
1896: PetscCall(ISAllGather(iscol, &iscol_local));
1897: }
1898: PetscCall(MatCreateSubMatrix_MPIBAIJ_Private(mat, isrow, iscol_local, csize, call, newmat, PETSC_FALSE));
1899: if (call == MAT_INITIAL_MATRIX) {
1900: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
1901: PetscCall(ISDestroy(&iscol_local));
1902: }
1903: PetscFunctionReturn(PETSC_SUCCESS);
1904: }
1906: /*
1907: Not great since it makes two copies of the submatrix, first an SeqBAIJ
1908: in local and then by concatenating the local matrices the end result.
1909: Writing it directly would be much like MatCreateSubMatrices_MPIBAIJ().
1910: This routine is used for BAIJ and SBAIJ matrices (unfortunate dependency).
1911: */
1912: PetscErrorCode MatCreateSubMatrix_MPIBAIJ_Private(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat, PetscBool sym)
1913: {
1914: PetscMPIInt rank, size;
1915: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs;
1916: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
1917: Mat M, Mreuse;
1918: MatScalar *vwork, *aa;
1919: MPI_Comm comm;
1920: IS isrow_new, iscol_new;
1921: Mat_SeqBAIJ *aij;
1923: PetscFunctionBegin;
1924: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
1925: PetscCallMPI(MPI_Comm_rank(comm, &rank));
1926: PetscCallMPI(MPI_Comm_size(comm, &size));
1927: /* The compression and expansion should be avoided. Doesn't point
1928: out errors, might change the indices, hence buggey */
1929: PetscCall(ISCompressIndicesGeneral(mat->rmap->N, mat->rmap->n, mat->rmap->bs, 1, &isrow, &isrow_new));
1930: if (isrow == iscol) {
1931: iscol_new = isrow_new;
1932: PetscCall(PetscObjectReference((PetscObject)iscol_new));
1933: } else PetscCall(ISCompressIndicesGeneral(mat->cmap->N, mat->cmap->n, mat->cmap->bs, 1, &iscol, &iscol_new));
1935: if (call == MAT_REUSE_MATRIX) {
1936: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
1937: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
1938: PetscCall(MatCreateSubMatrices_MPIBAIJ_local(mat, 1, &isrow_new, &iscol_new, MAT_REUSE_MATRIX, &Mreuse, sym));
1939: } else {
1940: PetscCall(MatCreateSubMatrices_MPIBAIJ_local(mat, 1, &isrow_new, &iscol_new, MAT_INITIAL_MATRIX, &Mreuse, sym));
1941: }
1942: PetscCall(ISDestroy(&isrow_new));
1943: PetscCall(ISDestroy(&iscol_new));
1944: /*
1945: m - number of local rows
1946: n - number of columns (same on all processors)
1947: rstart - first row in new global matrix generated
1948: */
1949: PetscCall(MatGetBlockSize(mat, &bs));
1950: PetscCall(MatGetSize(Mreuse, &m, &n));
1951: m = m / bs;
1952: n = n / bs;
1954: if (call == MAT_INITIAL_MATRIX) {
1955: aij = (Mat_SeqBAIJ *)(Mreuse)->data;
1956: ii = aij->i;
1957: jj = aij->j;
1959: /*
1960: Determine the number of non-zeros in the diagonal and off-diagonal
1961: portions of the matrix in order to do correct preallocation
1962: */
1964: /* first get start and end of "diagonal" columns */
1965: if (csize == PETSC_DECIDE) {
1966: PetscCall(ISGetSize(isrow, &mglobal));
1967: if (mglobal == n * bs) { /* square matrix */
1968: nlocal = m;
1969: } else {
1970: nlocal = n / size + ((n % size) > rank);
1971: }
1972: } else {
1973: nlocal = csize / bs;
1974: }
1975: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
1976: rstart = rend - nlocal;
1977: 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);
1979: /* next, compute all the lengths */
1980: PetscCall(PetscMalloc2(m + 1, &dlens, m + 1, &olens));
1981: for (i = 0; i < m; i++) {
1982: jend = ii[i + 1] - ii[i];
1983: olen = 0;
1984: dlen = 0;
1985: for (j = 0; j < jend; j++) {
1986: if (*jj < rstart || *jj >= rend) olen++;
1987: else dlen++;
1988: jj++;
1989: }
1990: olens[i] = olen;
1991: dlens[i] = dlen;
1992: }
1993: PetscCall(MatCreate(comm, &M));
1994: PetscCall(MatSetSizes(M, bs * m, bs * nlocal, PETSC_DECIDE, bs * n));
1995: PetscCall(MatSetType(M, sym ? ((PetscObject)mat)->type_name : MATMPIBAIJ));
1996: PetscCall(MatMPIBAIJSetPreallocation(M, bs, 0, dlens, 0, olens));
1997: PetscCall(MatMPISBAIJSetPreallocation(M, bs, 0, dlens, 0, olens));
1998: PetscCall(PetscFree2(dlens, olens));
1999: } else {
2000: PetscInt ml, nl;
2002: M = *newmat;
2003: PetscCall(MatGetLocalSize(M, &ml, &nl));
2004: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
2005: PetscCall(MatZeroEntries(M));
2006: /*
2007: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
2008: rather than the slower MatSetValues().
2009: */
2010: M->was_assembled = PETSC_TRUE;
2011: M->assembled = PETSC_FALSE;
2012: }
2013: PetscCall(MatSetOption(M, MAT_ROW_ORIENTED, PETSC_FALSE));
2014: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
2015: aij = (Mat_SeqBAIJ *)(Mreuse)->data;
2016: ii = aij->i;
2017: jj = aij->j;
2018: aa = aij->a;
2019: for (i = 0; i < m; i++) {
2020: row = rstart / bs + i;
2021: nz = ii[i + 1] - ii[i];
2022: cwork = jj;
2023: jj = PetscSafePointerPlusOffset(jj, nz);
2024: vwork = aa;
2025: aa = PetscSafePointerPlusOffset(aa, nz * bs * bs);
2026: PetscCall(MatSetValuesBlocked_MPIBAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
2027: }
2029: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
2030: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
2031: *newmat = M;
2033: /* save submatrix used in processor for next request */
2034: if (call == MAT_INITIAL_MATRIX) {
2035: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
2036: PetscCall(PetscObjectDereference((PetscObject)Mreuse));
2037: }
2038: PetscFunctionReturn(PETSC_SUCCESS);
2039: }
2041: static PetscErrorCode MatPermute_MPIBAIJ(Mat A, IS rowp, IS colp, Mat *B)
2042: {
2043: MPI_Comm comm, pcomm;
2044: PetscInt clocal_size, nrows;
2045: const PetscInt *rows;
2046: PetscMPIInt size;
2047: IS crowp, lcolp;
2049: PetscFunctionBegin;
2050: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
2051: /* make a collective version of 'rowp' */
2052: PetscCall(PetscObjectGetComm((PetscObject)rowp, &pcomm));
2053: if (pcomm == comm) {
2054: crowp = rowp;
2055: } else {
2056: PetscCall(ISGetSize(rowp, &nrows));
2057: PetscCall(ISGetIndices(rowp, &rows));
2058: PetscCall(ISCreateGeneral(comm, nrows, rows, PETSC_COPY_VALUES, &crowp));
2059: PetscCall(ISRestoreIndices(rowp, &rows));
2060: }
2061: PetscCall(ISSetPermutation(crowp));
2062: /* make a local version of 'colp' */
2063: PetscCall(PetscObjectGetComm((PetscObject)colp, &pcomm));
2064: PetscCallMPI(MPI_Comm_size(pcomm, &size));
2065: if (size == 1) {
2066: lcolp = colp;
2067: } else {
2068: PetscCall(ISAllGather(colp, &lcolp));
2069: }
2070: PetscCall(ISSetPermutation(lcolp));
2071: /* now we just get the submatrix */
2072: PetscCall(MatGetLocalSize(A, NULL, &clocal_size));
2073: PetscCall(MatCreateSubMatrix_MPIBAIJ_Private(A, crowp, lcolp, clocal_size, MAT_INITIAL_MATRIX, B, PETSC_FALSE));
2074: /* clean up */
2075: if (pcomm != comm) PetscCall(ISDestroy(&crowp));
2076: if (size > 1) PetscCall(ISDestroy(&lcolp));
2077: PetscFunctionReturn(PETSC_SUCCESS);
2078: }
2080: static PetscErrorCode MatGetGhosts_MPIBAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
2081: {
2082: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
2083: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)baij->B->data;
2085: PetscFunctionBegin;
2086: if (nghosts) *nghosts = B->nbs;
2087: if (ghosts) *ghosts = baij->garray;
2088: PetscFunctionReturn(PETSC_SUCCESS);
2089: }
2091: static PetscErrorCode MatGetSeqNonzeroStructure_MPIBAIJ(Mat A, Mat *newmat)
2092: {
2093: Mat B;
2094: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2095: Mat_SeqBAIJ *ad = (Mat_SeqBAIJ *)a->A->data, *bd = (Mat_SeqBAIJ *)a->B->data;
2096: Mat_SeqAIJ *b;
2097: PetscMPIInt size, rank, *recvcounts = NULL, *displs = NULL;
2098: PetscInt sendcount, i, *rstarts = A->rmap->range, n, cnt, j, bs = A->rmap->bs;
2099: PetscInt m, *garray = a->garray, *lens, *jsendbuf, *a_jsendbuf, *b_jsendbuf;
2101: PetscFunctionBegin;
2102: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
2103: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)A), &rank));
2105: /* Tell every processor the number of nonzeros per row */
2106: PetscCall(PetscMalloc1(A->rmap->N / bs, &lens));
2107: for (i = A->rmap->rstart / bs; i < A->rmap->rend / bs; i++) lens[i] = ad->i[i - A->rmap->rstart / bs + 1] - ad->i[i - A->rmap->rstart / bs] + bd->i[i - A->rmap->rstart / bs + 1] - bd->i[i - A->rmap->rstart / bs];
2108: PetscCall(PetscMalloc1(2 * size, &recvcounts));
2109: displs = recvcounts + size;
2110: for (i = 0; i < size; i++) {
2111: recvcounts[i] = A->rmap->range[i + 1] / bs - A->rmap->range[i] / bs;
2112: displs[i] = A->rmap->range[i] / bs;
2113: }
2114: PetscCallMPI(MPI_Allgatherv(MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, lens, recvcounts, displs, MPIU_INT, PetscObjectComm((PetscObject)A)));
2115: /* Create the sequential matrix of the same type as the local block diagonal */
2116: PetscCall(MatCreate(PETSC_COMM_SELF, &B));
2117: PetscCall(MatSetSizes(B, A->rmap->N / bs, A->cmap->N / bs, PETSC_DETERMINE, PETSC_DETERMINE));
2118: PetscCall(MatSetType(B, MATSEQAIJ));
2119: PetscCall(MatSeqAIJSetPreallocation(B, 0, lens));
2120: b = (Mat_SeqAIJ *)B->data;
2122: /* Copy my part of matrix column indices over */
2123: sendcount = ad->nz + bd->nz;
2124: jsendbuf = b->j + b->i[rstarts[rank] / bs];
2125: a_jsendbuf = ad->j;
2126: b_jsendbuf = bd->j;
2127: n = A->rmap->rend / bs - A->rmap->rstart / bs;
2128: cnt = 0;
2129: for (i = 0; i < n; i++) {
2130: /* put in lower diagonal portion */
2131: m = bd->i[i + 1] - bd->i[i];
2132: while (m > 0) {
2133: /* is it above diagonal (in bd (compressed) numbering) */
2134: if (garray[*b_jsendbuf] > A->rmap->rstart / bs + i) break;
2135: jsendbuf[cnt++] = garray[*b_jsendbuf++];
2136: m--;
2137: }
2139: /* put in diagonal portion */
2140: for (j = ad->i[i]; j < ad->i[i + 1]; j++) jsendbuf[cnt++] = A->rmap->rstart / bs + *a_jsendbuf++;
2142: /* put in upper diagonal portion */
2143: while (m-- > 0) jsendbuf[cnt++] = garray[*b_jsendbuf++];
2144: }
2145: PetscCheck(cnt == sendcount, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Corrupted PETSc matrix: nz given %" PetscInt_FMT " actual nz %" PetscInt_FMT, sendcount, cnt);
2147: /* Gather all column indices to all processors */
2148: for (i = 0; i < size; i++) {
2149: recvcounts[i] = 0;
2150: for (j = A->rmap->range[i] / bs; j < A->rmap->range[i + 1] / bs; j++) recvcounts[i] += lens[j];
2151: }
2152: displs[0] = 0;
2153: for (i = 1; i < size; i++) displs[i] = displs[i - 1] + recvcounts[i - 1];
2154: PetscCallMPI(MPI_Allgatherv(MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, b->j, recvcounts, displs, MPIU_INT, PetscObjectComm((PetscObject)A)));
2155: /* Assemble the matrix into usable form (note numerical values not yet set) */
2156: /* set the b->ilen (length of each row) values */
2157: PetscCall(PetscArraycpy(b->ilen, lens, A->rmap->N / bs));
2158: /* set the b->i indices */
2159: b->i[0] = 0;
2160: for (i = 1; i <= A->rmap->N / bs; i++) b->i[i] = b->i[i - 1] + lens[i - 1];
2161: PetscCall(PetscFree(lens));
2162: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2163: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2164: PetscCall(PetscFree(recvcounts));
2166: PetscCall(MatPropagateSymmetryOptions(A, B));
2167: *newmat = B;
2168: PetscFunctionReturn(PETSC_SUCCESS);
2169: }
2171: static PetscErrorCode MatSOR_MPIBAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
2172: {
2173: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)matin->data;
2174: Vec bb1 = NULL;
2176: PetscFunctionBegin;
2177: if (flag == SOR_APPLY_UPPER) {
2178: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2179: PetscFunctionReturn(PETSC_SUCCESS);
2180: }
2182: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS) PetscCall(VecDuplicate(bb, &bb1));
2184: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
2185: if (flag & SOR_ZERO_INITIAL_GUESS) {
2186: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2187: its--;
2188: }
2190: while (its--) {
2191: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2192: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2194: /* update rhs: bb1 = bb - B*x */
2195: PetscCall(VecScale(mat->lvec, -1.0));
2196: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2198: /* local sweep */
2199: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
2200: }
2201: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
2202: if (flag & SOR_ZERO_INITIAL_GUESS) {
2203: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2204: its--;
2205: }
2206: while (its--) {
2207: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2208: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2210: /* update rhs: bb1 = bb - B*x */
2211: PetscCall(VecScale(mat->lvec, -1.0));
2212: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2214: /* local sweep */
2215: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
2216: }
2217: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
2218: if (flag & SOR_ZERO_INITIAL_GUESS) {
2219: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2220: its--;
2221: }
2222: while (its--) {
2223: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2224: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2226: /* update rhs: bb1 = bb - B*x */
2227: PetscCall(VecScale(mat->lvec, -1.0));
2228: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2230: /* local sweep */
2231: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
2232: }
2233: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel version of SOR requested not supported");
2235: PetscCall(VecDestroy(&bb1));
2236: PetscFunctionReturn(PETSC_SUCCESS);
2237: }
2239: static PetscErrorCode MatGetColumnReductions_MPIBAIJ(Mat A, PetscInt type, PetscReal *reductions)
2240: {
2241: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)A->data;
2242: PetscInt m, N, i, *garray = aij->garray;
2243: PetscInt ib, jb, bs = A->rmap->bs;
2244: Mat_SeqBAIJ *a_aij = (Mat_SeqBAIJ *)aij->A->data;
2245: MatScalar *a_val = a_aij->a;
2246: Mat_SeqBAIJ *b_aij = (Mat_SeqBAIJ *)aij->B->data;
2247: MatScalar *b_val = b_aij->a;
2248: PetscReal *work;
2250: PetscFunctionBegin;
2251: PetscCall(MatGetSize(A, &m, &N));
2252: PetscCall(PetscCalloc1(N, &work));
2253: if (type == NORM_2) {
2254: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2255: for (jb = 0; jb < bs; jb++) {
2256: for (ib = 0; ib < bs; ib++) {
2257: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val * *a_val);
2258: a_val++;
2259: }
2260: }
2261: }
2262: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2263: for (jb = 0; jb < bs; jb++) {
2264: for (ib = 0; ib < bs; ib++) {
2265: work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val * *b_val);
2266: b_val++;
2267: }
2268: }
2269: }
2270: } else if (type == NORM_1) {
2271: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2272: for (jb = 0; jb < bs; jb++) {
2273: for (ib = 0; ib < bs; ib++) {
2274: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val);
2275: a_val++;
2276: }
2277: }
2278: }
2279: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2280: for (jb = 0; jb < bs; jb++) {
2281: for (ib = 0; ib < bs; ib++) {
2282: work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val);
2283: b_val++;
2284: }
2285: }
2286: }
2287: } else if (type == NORM_INFINITY) {
2288: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2289: for (jb = 0; jb < bs; jb++) {
2290: for (ib = 0; ib < bs; ib++) {
2291: int col = A->cmap->rstart + a_aij->j[i] * bs + jb;
2292: work[col] = PetscMax(PetscAbsScalar(*a_val), work[col]);
2293: a_val++;
2294: }
2295: }
2296: }
2297: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2298: for (jb = 0; jb < bs; jb++) {
2299: for (ib = 0; ib < bs; ib++) {
2300: int col = garray[b_aij->j[i]] * bs + jb;
2301: work[col] = PetscMax(PetscAbsScalar(*b_val), work[col]);
2302: b_val++;
2303: }
2304: }
2305: }
2306: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
2307: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2308: for (jb = 0; jb < bs; jb++) {
2309: for (ib = 0; ib < bs; ib++) {
2310: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscRealPart(*a_val);
2311: a_val++;
2312: }
2313: }
2314: }
2315: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2316: for (jb = 0; jb < bs; jb++) {
2317: for (ib = 0; ib < bs; ib++) {
2318: work[garray[b_aij->j[i]] * bs + jb] += PetscRealPart(*b_val);
2319: b_val++;
2320: }
2321: }
2322: }
2323: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
2324: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2325: for (jb = 0; jb < bs; jb++) {
2326: for (ib = 0; ib < bs; ib++) {
2327: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscImaginaryPart(*a_val);
2328: a_val++;
2329: }
2330: }
2331: }
2332: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2333: for (jb = 0; jb < bs; jb++) {
2334: for (ib = 0; ib < bs; ib++) {
2335: work[garray[b_aij->j[i]] * bs + jb] += PetscImaginaryPart(*b_val);
2336: b_val++;
2337: }
2338: }
2339: }
2340: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
2341: if (type == NORM_INFINITY) {
2342: PetscCall(MPIU_Allreduce(work, reductions, N, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
2343: } else {
2344: PetscCall(MPIU_Allreduce(work, reductions, N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
2345: }
2346: PetscCall(PetscFree(work));
2347: if (type == NORM_2) {
2348: for (i = 0; i < N; i++) reductions[i] = PetscSqrtReal(reductions[i]);
2349: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
2350: for (i = 0; i < N; i++) reductions[i] /= m;
2351: }
2352: PetscFunctionReturn(PETSC_SUCCESS);
2353: }
2355: static PetscErrorCode MatInvertBlockDiagonal_MPIBAIJ(Mat A, const PetscScalar **values)
2356: {
2357: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2359: PetscFunctionBegin;
2360: PetscCall(MatInvertBlockDiagonal(a->A, values));
2361: A->factorerrortype = a->A->factorerrortype;
2362: A->factorerror_zeropivot_value = a->A->factorerror_zeropivot_value;
2363: A->factorerror_zeropivot_row = a->A->factorerror_zeropivot_row;
2364: PetscFunctionReturn(PETSC_SUCCESS);
2365: }
2367: static PetscErrorCode MatShift_MPIBAIJ(Mat Y, PetscScalar a)
2368: {
2369: Mat_MPIBAIJ *maij = (Mat_MPIBAIJ *)Y->data;
2370: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)maij->A->data;
2372: PetscFunctionBegin;
2373: if (!Y->preallocated) {
2374: PetscCall(MatMPIBAIJSetPreallocation(Y, Y->rmap->bs, 1, NULL, 0, NULL));
2375: } else if (!aij->nz) {
2376: PetscInt nonew = aij->nonew;
2377: PetscCall(MatSeqBAIJSetPreallocation(maij->A, Y->rmap->bs, 1, NULL));
2378: aij->nonew = nonew;
2379: }
2380: PetscCall(MatShift_Basic(Y, a));
2381: PetscFunctionReturn(PETSC_SUCCESS);
2382: }
2384: static PetscErrorCode MatMissingDiagonal_MPIBAIJ(Mat A, PetscBool *missing, PetscInt *d)
2385: {
2386: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2388: PetscFunctionBegin;
2389: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2390: PetscCall(MatMissingDiagonal(a->A, missing, d));
2391: if (d) {
2392: PetscInt rstart;
2393: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2394: *d += rstart / A->rmap->bs;
2395: }
2396: PetscFunctionReturn(PETSC_SUCCESS);
2397: }
2399: static PetscErrorCode MatGetDiagonalBlock_MPIBAIJ(Mat A, Mat *a)
2400: {
2401: PetscFunctionBegin;
2402: *a = ((Mat_MPIBAIJ *)A->data)->A;
2403: PetscFunctionReturn(PETSC_SUCCESS);
2404: }
2406: static PetscErrorCode MatEliminateZeros_MPIBAIJ(Mat A, PetscBool keep)
2407: {
2408: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2410: PetscFunctionBegin;
2411: PetscCall(MatEliminateZeros_SeqBAIJ(a->A, keep)); // possibly keep zero diagonal coefficients
2412: PetscCall(MatEliminateZeros_SeqBAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2413: PetscFunctionReturn(PETSC_SUCCESS);
2414: }
2416: static struct _MatOps MatOps_Values = {MatSetValues_MPIBAIJ,
2417: MatGetRow_MPIBAIJ,
2418: MatRestoreRow_MPIBAIJ,
2419: MatMult_MPIBAIJ,
2420: /* 4*/ MatMultAdd_MPIBAIJ,
2421: MatMultTranspose_MPIBAIJ,
2422: MatMultTransposeAdd_MPIBAIJ,
2423: NULL,
2424: NULL,
2425: NULL,
2426: /*10*/ NULL,
2427: NULL,
2428: NULL,
2429: MatSOR_MPIBAIJ,
2430: MatTranspose_MPIBAIJ,
2431: /*15*/ MatGetInfo_MPIBAIJ,
2432: MatEqual_MPIBAIJ,
2433: MatGetDiagonal_MPIBAIJ,
2434: MatDiagonalScale_MPIBAIJ,
2435: MatNorm_MPIBAIJ,
2436: /*20*/ MatAssemblyBegin_MPIBAIJ,
2437: MatAssemblyEnd_MPIBAIJ,
2438: MatSetOption_MPIBAIJ,
2439: MatZeroEntries_MPIBAIJ,
2440: /*24*/ MatZeroRows_MPIBAIJ,
2441: NULL,
2442: NULL,
2443: NULL,
2444: NULL,
2445: /*29*/ MatSetUp_MPI_Hash,
2446: NULL,
2447: NULL,
2448: MatGetDiagonalBlock_MPIBAIJ,
2449: NULL,
2450: /*34*/ MatDuplicate_MPIBAIJ,
2451: NULL,
2452: NULL,
2453: NULL,
2454: NULL,
2455: /*39*/ MatAXPY_MPIBAIJ,
2456: MatCreateSubMatrices_MPIBAIJ,
2457: MatIncreaseOverlap_MPIBAIJ,
2458: MatGetValues_MPIBAIJ,
2459: MatCopy_MPIBAIJ,
2460: /*44*/ NULL,
2461: MatScale_MPIBAIJ,
2462: MatShift_MPIBAIJ,
2463: NULL,
2464: MatZeroRowsColumns_MPIBAIJ,
2465: /*49*/ NULL,
2466: NULL,
2467: NULL,
2468: NULL,
2469: NULL,
2470: /*54*/ MatFDColoringCreate_MPIXAIJ,
2471: NULL,
2472: MatSetUnfactored_MPIBAIJ,
2473: MatPermute_MPIBAIJ,
2474: MatSetValuesBlocked_MPIBAIJ,
2475: /*59*/ MatCreateSubMatrix_MPIBAIJ,
2476: MatDestroy_MPIBAIJ,
2477: MatView_MPIBAIJ,
2478: NULL,
2479: NULL,
2480: /*64*/ NULL,
2481: NULL,
2482: NULL,
2483: NULL,
2484: NULL,
2485: /*69*/ MatGetRowMaxAbs_MPIBAIJ,
2486: NULL,
2487: NULL,
2488: NULL,
2489: NULL,
2490: /*74*/ NULL,
2491: MatFDColoringApply_BAIJ,
2492: NULL,
2493: NULL,
2494: NULL,
2495: /*79*/ NULL,
2496: NULL,
2497: NULL,
2498: NULL,
2499: MatLoad_MPIBAIJ,
2500: /*84*/ NULL,
2501: NULL,
2502: NULL,
2503: NULL,
2504: NULL,
2505: /*89*/ NULL,
2506: NULL,
2507: NULL,
2508: NULL,
2509: NULL,
2510: /*94*/ NULL,
2511: NULL,
2512: NULL,
2513: NULL,
2514: NULL,
2515: /*99*/ NULL,
2516: NULL,
2517: NULL,
2518: MatConjugate_MPIBAIJ,
2519: NULL,
2520: /*104*/ NULL,
2521: MatRealPart_MPIBAIJ,
2522: MatImaginaryPart_MPIBAIJ,
2523: NULL,
2524: NULL,
2525: /*109*/ NULL,
2526: NULL,
2527: NULL,
2528: NULL,
2529: MatMissingDiagonal_MPIBAIJ,
2530: /*114*/ MatGetSeqNonzeroStructure_MPIBAIJ,
2531: NULL,
2532: MatGetGhosts_MPIBAIJ,
2533: NULL,
2534: NULL,
2535: /*119*/ NULL,
2536: NULL,
2537: NULL,
2538: NULL,
2539: MatGetMultiProcBlock_MPIBAIJ,
2540: /*124*/ NULL,
2541: MatGetColumnReductions_MPIBAIJ,
2542: MatInvertBlockDiagonal_MPIBAIJ,
2543: NULL,
2544: NULL,
2545: /*129*/ NULL,
2546: NULL,
2547: NULL,
2548: NULL,
2549: NULL,
2550: /*134*/ NULL,
2551: NULL,
2552: NULL,
2553: NULL,
2554: NULL,
2555: /*139*/ MatSetBlockSizes_Default,
2556: NULL,
2557: NULL,
2558: MatFDColoringSetUp_MPIXAIJ,
2559: NULL,
2560: /*144*/ MatCreateMPIMatConcatenateSeqMat_MPIBAIJ,
2561: NULL,
2562: NULL,
2563: NULL,
2564: NULL,
2565: NULL,
2566: /*150*/ NULL,
2567: MatEliminateZeros_MPIBAIJ,
2568: NULL};
2570: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
2571: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
2573: static PetscErrorCode MatMPIBAIJSetPreallocationCSR_MPIBAIJ(Mat B, PetscInt bs, const PetscInt ii[], const PetscInt jj[], const PetscScalar V[])
2574: {
2575: PetscInt m, rstart, cstart, cend;
2576: PetscInt i, j, dlen, olen, nz, nz_max = 0, *d_nnz = NULL, *o_nnz = NULL;
2577: const PetscInt *JJ = NULL;
2578: PetscScalar *values = NULL;
2579: PetscBool roworiented = ((Mat_MPIBAIJ *)B->data)->roworiented;
2580: PetscBool nooffprocentries;
2582: PetscFunctionBegin;
2583: PetscCall(PetscLayoutSetBlockSize(B->rmap, bs));
2584: PetscCall(PetscLayoutSetBlockSize(B->cmap, bs));
2585: PetscCall(PetscLayoutSetUp(B->rmap));
2586: PetscCall(PetscLayoutSetUp(B->cmap));
2587: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
2588: m = B->rmap->n / bs;
2589: rstart = B->rmap->rstart / bs;
2590: cstart = B->cmap->rstart / bs;
2591: cend = B->cmap->rend / bs;
2593: PetscCheck(!ii[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "ii[0] must be 0 but it is %" PetscInt_FMT, ii[0]);
2594: PetscCall(PetscMalloc2(m, &d_nnz, m, &o_nnz));
2595: for (i = 0; i < m; i++) {
2596: nz = ii[i + 1] - ii[i];
2597: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
2598: nz_max = PetscMax(nz_max, nz);
2599: dlen = 0;
2600: olen = 0;
2601: JJ = jj + ii[i];
2602: for (j = 0; j < nz; j++) {
2603: if (*JJ < cstart || *JJ >= cend) olen++;
2604: else dlen++;
2605: JJ++;
2606: }
2607: d_nnz[i] = dlen;
2608: o_nnz[i] = olen;
2609: }
2610: PetscCall(MatMPIBAIJSetPreallocation(B, bs, 0, d_nnz, 0, o_nnz));
2611: PetscCall(PetscFree2(d_nnz, o_nnz));
2613: values = (PetscScalar *)V;
2614: if (!values) PetscCall(PetscCalloc1(bs * bs * nz_max, &values));
2615: for (i = 0; i < m; i++) {
2616: PetscInt row = i + rstart;
2617: PetscInt ncols = ii[i + 1] - ii[i];
2618: const PetscInt *icols = jj + ii[i];
2619: if (bs == 1 || !roworiented) { /* block ordering matches the non-nested layout of MatSetValues so we can insert entire rows */
2620: const PetscScalar *svals = values + (V ? (bs * bs * ii[i]) : 0);
2621: PetscCall(MatSetValuesBlocked_MPIBAIJ(B, 1, &row, ncols, icols, svals, INSERT_VALUES));
2622: } else { /* block ordering does not match so we can only insert one block at a time. */
2623: PetscInt j;
2624: for (j = 0; j < ncols; j++) {
2625: const PetscScalar *svals = values + (V ? (bs * bs * (ii[i] + j)) : 0);
2626: PetscCall(MatSetValuesBlocked_MPIBAIJ(B, 1, &row, 1, &icols[j], svals, INSERT_VALUES));
2627: }
2628: }
2629: }
2631: if (!V) PetscCall(PetscFree(values));
2632: nooffprocentries = B->nooffprocentries;
2633: B->nooffprocentries = PETSC_TRUE;
2634: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2635: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2636: B->nooffprocentries = nooffprocentries;
2638: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
2639: PetscFunctionReturn(PETSC_SUCCESS);
2640: }
2642: /*@C
2643: MatMPIBAIJSetPreallocationCSR - Creates a sparse parallel matrix in `MATBAIJ` format using the given nonzero structure and (optional) numerical values
2645: Collective
2647: Input Parameters:
2648: + B - the matrix
2649: . bs - the block size
2650: . i - the indices into `j` for the start of each local row (starts with zero)
2651: . j - the column indices for each local row (starts with zero) these must be sorted for each row
2652: - v - optional values in the matrix, use `NULL` if not provided
2654: Level: advanced
2656: Notes:
2657: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
2658: thus you CANNOT change the matrix entries by changing the values of `v` after you have
2659: called this routine.
2661: The order of the entries in values is specified by the `MatOption` `MAT_ROW_ORIENTED`. For example, C programs
2662: may want to use the default `MAT_ROW_ORIENTED` with value `PETSC_TRUE` and use an array v[nnz][bs][bs] where the second index is
2663: over rows within a block and the last index is over columns within a block row. Fortran programs will likely set
2664: `MAT_ROW_ORIENTED` with value `PETSC_FALSE` and use a Fortran array v(bs,bs,nnz) in which the first index is over rows within a
2665: block column and the second index is over columns within a block.
2667: Though this routine has Preallocation() in the name it also sets the exact nonzero locations of the matrix entries and usually the numerical values as well
2669: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIBAIJSetPreallocation()`, `MatCreateAIJ()`, `MATMPIAIJ`, `MatCreateMPIBAIJWithArrays()`, `MATMPIBAIJ`
2670: @*/
2671: PetscErrorCode MatMPIBAIJSetPreallocationCSR(Mat B, PetscInt bs, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
2672: {
2673: PetscFunctionBegin;
2677: PetscTryMethod(B, "MatMPIBAIJSetPreallocationCSR_C", (Mat, PetscInt, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, bs, i, j, v));
2678: PetscFunctionReturn(PETSC_SUCCESS);
2679: }
2681: PetscErrorCode MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B, PetscInt bs, PetscInt d_nz, const PetscInt *d_nnz, PetscInt o_nz, const PetscInt *o_nnz)
2682: {
2683: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
2684: PetscInt i;
2685: PetscMPIInt size;
2687: PetscFunctionBegin;
2688: if (B->hash_active) {
2689: B->ops[0] = b->cops;
2690: B->hash_active = PETSC_FALSE;
2691: }
2692: if (!B->preallocated) PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), bs, &B->bstash));
2693: PetscCall(MatSetBlockSize(B, PetscAbs(bs)));
2694: PetscCall(PetscLayoutSetUp(B->rmap));
2695: PetscCall(PetscLayoutSetUp(B->cmap));
2696: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
2698: if (d_nnz) {
2699: for (i = 0; i < B->rmap->n / bs; i++) PetscCheck(d_nnz[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "d_nnz cannot be less than -1: local row %" PetscInt_FMT " value %" PetscInt_FMT, i, d_nnz[i]);
2700: }
2701: if (o_nnz) {
2702: for (i = 0; i < B->rmap->n / bs; i++) PetscCheck(o_nnz[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "o_nnz cannot be less than -1: local row %" PetscInt_FMT " value %" PetscInt_FMT, i, o_nnz[i]);
2703: }
2705: b->bs2 = bs * bs;
2706: b->mbs = B->rmap->n / bs;
2707: b->nbs = B->cmap->n / bs;
2708: b->Mbs = B->rmap->N / bs;
2709: b->Nbs = B->cmap->N / bs;
2711: for (i = 0; i <= b->size; i++) b->rangebs[i] = B->rmap->range[i] / bs;
2712: b->rstartbs = B->rmap->rstart / bs;
2713: b->rendbs = B->rmap->rend / bs;
2714: b->cstartbs = B->cmap->rstart / bs;
2715: b->cendbs = B->cmap->rend / bs;
2717: #if defined(PETSC_USE_CTABLE)
2718: PetscCall(PetscHMapIDestroy(&b->colmap));
2719: #else
2720: PetscCall(PetscFree(b->colmap));
2721: #endif
2722: PetscCall(PetscFree(b->garray));
2723: PetscCall(VecDestroy(&b->lvec));
2724: PetscCall(VecScatterDestroy(&b->Mvctx));
2726: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2728: MatSeqXAIJGetOptions_Private(b->B);
2729: PetscCall(MatDestroy(&b->B));
2730: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2731: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2732: PetscCall(MatSetType(b->B, MATSEQBAIJ));
2733: MatSeqXAIJRestoreOptions_Private(b->B);
2735: MatSeqXAIJGetOptions_Private(b->A);
2736: PetscCall(MatDestroy(&b->A));
2737: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2738: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2739: PetscCall(MatSetType(b->A, MATSEQBAIJ));
2740: MatSeqXAIJRestoreOptions_Private(b->A);
2742: PetscCall(MatSeqBAIJSetPreallocation(b->A, bs, d_nz, d_nnz));
2743: PetscCall(MatSeqBAIJSetPreallocation(b->B, bs, o_nz, o_nnz));
2744: B->preallocated = PETSC_TRUE;
2745: B->was_assembled = PETSC_FALSE;
2746: B->assembled = PETSC_FALSE;
2747: PetscFunctionReturn(PETSC_SUCCESS);
2748: }
2750: extern PetscErrorCode MatDiagonalScaleLocal_MPIBAIJ(Mat, Vec);
2751: extern PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat, PetscReal);
2753: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAdj(Mat B, MatType newtype, MatReuse reuse, Mat *adj)
2754: {
2755: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
2756: Mat_SeqBAIJ *d = (Mat_SeqBAIJ *)b->A->data, *o = (Mat_SeqBAIJ *)b->B->data;
2757: PetscInt M = B->rmap->n / B->rmap->bs, i, *ii, *jj, cnt, j, k, rstart = B->rmap->rstart / B->rmap->bs;
2758: const PetscInt *id = d->i, *jd = d->j, *io = o->i, *jo = o->j, *garray = b->garray;
2760: PetscFunctionBegin;
2761: PetscCall(PetscMalloc1(M + 1, &ii));
2762: ii[0] = 0;
2763: for (i = 0; i < M; i++) {
2764: PetscCheck((id[i + 1] - id[i]) >= 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Indices wrong %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT, i, id[i], id[i + 1]);
2765: PetscCheck((io[i + 1] - io[i]) >= 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Indices wrong %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT, i, io[i], io[i + 1]);
2766: ii[i + 1] = ii[i] + id[i + 1] - id[i] + io[i + 1] - io[i];
2767: /* remove one from count of matrix has diagonal */
2768: for (j = id[i]; j < id[i + 1]; j++) {
2769: if (jd[j] == i) {
2770: ii[i + 1]--;
2771: break;
2772: }
2773: }
2774: }
2775: PetscCall(PetscMalloc1(ii[M], &jj));
2776: cnt = 0;
2777: for (i = 0; i < M; i++) {
2778: for (j = io[i]; j < io[i + 1]; j++) {
2779: if (garray[jo[j]] > rstart) break;
2780: jj[cnt++] = garray[jo[j]];
2781: }
2782: for (k = id[i]; k < id[i + 1]; k++) {
2783: if (jd[k] != i) jj[cnt++] = rstart + jd[k];
2784: }
2785: for (; j < io[i + 1]; j++) jj[cnt++] = garray[jo[j]];
2786: }
2787: PetscCall(MatCreateMPIAdj(PetscObjectComm((PetscObject)B), M, B->cmap->N / B->rmap->bs, ii, jj, NULL, adj));
2788: PetscFunctionReturn(PETSC_SUCCESS);
2789: }
2791: #include <../src/mat/impls/aij/mpi/mpiaij.h>
2793: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat, MatType, MatReuse, Mat *);
2795: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAIJ(Mat A, MatType newtype, MatReuse reuse, Mat *newmat)
2796: {
2797: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2798: Mat_MPIAIJ *b;
2799: Mat B;
2801: PetscFunctionBegin;
2802: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Matrix must be assembled");
2804: if (reuse == MAT_REUSE_MATRIX) {
2805: B = *newmat;
2806: } else {
2807: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
2808: PetscCall(MatSetType(B, MATMPIAIJ));
2809: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
2810: PetscCall(MatSetBlockSizes(B, A->rmap->bs, A->cmap->bs));
2811: PetscCall(MatSeqAIJSetPreallocation(B, 0, NULL));
2812: PetscCall(MatMPIAIJSetPreallocation(B, 0, NULL, 0, NULL));
2813: }
2814: b = (Mat_MPIAIJ *)B->data;
2816: if (reuse == MAT_REUSE_MATRIX) {
2817: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_REUSE_MATRIX, &b->A));
2818: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_REUSE_MATRIX, &b->B));
2819: } else {
2820: PetscInt *garray = a->garray;
2821: Mat_SeqAIJ *bB;
2822: PetscInt bs, nnz;
2823: PetscCall(MatDestroy(&b->A));
2824: PetscCall(MatDestroy(&b->B));
2825: /* just clear out the data structure */
2826: PetscCall(MatDisAssemble_MPIAIJ(B));
2827: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->A));
2828: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->B));
2830: /* Global numbering for b->B columns */
2831: bB = (Mat_SeqAIJ *)b->B->data;
2832: bs = A->rmap->bs;
2833: nnz = bB->i[A->rmap->n];
2834: for (PetscInt k = 0; k < nnz; k++) {
2835: PetscInt bj = bB->j[k] / bs;
2836: PetscInt br = bB->j[k] % bs;
2837: bB->j[k] = garray[bj] * bs + br;
2838: }
2839: }
2840: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2841: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2843: if (reuse == MAT_INPLACE_MATRIX) {
2844: PetscCall(MatHeaderReplace(A, &B));
2845: } else {
2846: *newmat = B;
2847: }
2848: PetscFunctionReturn(PETSC_SUCCESS);
2849: }
2851: /*MC
2852: MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.
2854: Options Database Keys:
2855: + -mat_type mpibaij - sets the matrix type to `MATMPIBAIJ` during a call to `MatSetFromOptions()`
2856: . -mat_block_size <bs> - set the blocksize used to store the matrix
2857: . -mat_baij_mult_version version - indicate the version of the matrix-vector product to use (0 often indicates using BLAS)
2858: - -mat_use_hash_table <fact> - set hash table factor
2860: Level: beginner
2862: Note:
2863: `MatSetOption(A, MAT_STRUCTURE_ONLY, PETSC_TRUE)` may be called for this matrix type. In this no
2864: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
2866: .seealso: `Mat`, `MATBAIJ`, `MATSEQBAIJ`, `MatCreateBAIJ`
2867: M*/
2869: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIBSTRM(Mat, MatType, MatReuse, Mat *);
2871: PETSC_EXTERN PetscErrorCode MatCreate_MPIBAIJ(Mat B)
2872: {
2873: Mat_MPIBAIJ *b;
2874: PetscBool flg = PETSC_FALSE;
2876: PetscFunctionBegin;
2877: PetscCall(PetscNew(&b));
2878: B->data = (void *)b;
2879: B->ops[0] = MatOps_Values;
2880: B->assembled = PETSC_FALSE;
2882: B->insertmode = NOT_SET_VALUES;
2883: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
2884: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &b->size));
2886: /* build local table of row and column ownerships */
2887: PetscCall(PetscMalloc1(b->size + 1, &b->rangebs));
2889: /* build cache for off array entries formed */
2890: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
2892: b->donotstash = PETSC_FALSE;
2893: b->colmap = NULL;
2894: b->garray = NULL;
2895: b->roworiented = PETSC_TRUE;
2897: /* stuff used in block assembly */
2898: b->barray = NULL;
2900: /* stuff used for matrix vector multiply */
2901: b->lvec = NULL;
2902: b->Mvctx = NULL;
2904: /* stuff for MatGetRow() */
2905: b->rowindices = NULL;
2906: b->rowvalues = NULL;
2907: b->getrowactive = PETSC_FALSE;
2909: /* hash table stuff */
2910: b->ht = NULL;
2911: b->hd = NULL;
2912: b->ht_size = 0;
2913: b->ht_flag = PETSC_FALSE;
2914: b->ht_fact = 0;
2915: b->ht_total_ct = 0;
2916: b->ht_insert_ct = 0;
2918: /* stuff for MatCreateSubMatrices_MPIBAIJ_local() */
2919: b->ijonly = PETSC_FALSE;
2921: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpiadj_C", MatConvert_MPIBAIJ_MPIAdj));
2922: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpiaij_C", MatConvert_MPIBAIJ_MPIAIJ));
2923: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpisbaij_C", MatConvert_MPIBAIJ_MPISBAIJ));
2924: #if defined(PETSC_HAVE_HYPRE)
2925: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_hypre_C", MatConvert_AIJ_HYPRE));
2926: #endif
2927: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIBAIJ));
2928: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIBAIJ));
2929: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIBAIJSetPreallocation_C", MatMPIBAIJSetPreallocation_MPIBAIJ));
2930: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIBAIJSetPreallocationCSR_C", MatMPIBAIJSetPreallocationCSR_MPIBAIJ));
2931: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIBAIJ));
2932: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetHashTableFactor_C", MatSetHashTableFactor_MPIBAIJ));
2933: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_is_C", MatConvert_XAIJ_IS));
2934: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIBAIJ));
2936: PetscOptionsBegin(PetscObjectComm((PetscObject)B), NULL, "Options for loading MPIBAIJ matrix 1", "Mat");
2937: PetscCall(PetscOptionsName("-mat_use_hash_table", "Use hash table to save time in constructing matrix", "MatSetOption", &flg));
2938: if (flg) {
2939: PetscReal fact = 1.39;
2940: PetscCall(MatSetOption(B, MAT_USE_HASH_TABLE, PETSC_TRUE));
2941: PetscCall(PetscOptionsReal("-mat_use_hash_table", "Use hash table factor", "MatMPIBAIJSetHashTableFactor", fact, &fact, NULL));
2942: if (fact <= 1.0) fact = 1.39;
2943: PetscCall(MatMPIBAIJSetHashTableFactor(B, fact));
2944: PetscCall(PetscInfo(B, "Hash table Factor used %5.2g\n", (double)fact));
2945: }
2946: PetscOptionsEnd();
2947: PetscFunctionReturn(PETSC_SUCCESS);
2948: }
2950: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
2951: /*MC
2952: MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.
2954: This matrix type is identical to `MATSEQBAIJ` when constructed with a single process communicator,
2955: and `MATMPIBAIJ` otherwise.
2957: Options Database Keys:
2958: . -mat_type baij - sets the matrix type to `MATBAIJ` during a call to `MatSetFromOptions()`
2960: Level: beginner
2962: .seealso: `Mat`, `MatCreateBAIJ()`, `MATSEQBAIJ`, `MATMPIBAIJ`, `MatMPIBAIJSetPreallocation()`, `MatMPIBAIJSetPreallocationCSR()`
2963: M*/
2965: /*@C
2966: MatMPIBAIJSetPreallocation - Allocates memory for a sparse parallel matrix in `MATMPIBAIJ` format
2967: (block compressed row).
2969: Collective
2971: Input Parameters:
2972: + B - the matrix
2973: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
2974: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
2975: . d_nz - number of block nonzeros per block row in diagonal portion of local
2976: submatrix (same for all local rows)
2977: . d_nnz - array containing the number of block nonzeros in the various block rows
2978: of the in diagonal portion of the local (possibly different for each block
2979: row) or `NULL`. If you plan to factor the matrix you must leave room for the diagonal entry and
2980: set it even if it is zero.
2981: . o_nz - number of block nonzeros per block row in the off-diagonal portion of local
2982: submatrix (same for all local rows).
2983: - o_nnz - array containing the number of nonzeros in the various block rows of the
2984: off-diagonal portion of the local submatrix (possibly different for
2985: each block row) or `NULL`.
2987: If the *_nnz parameter is given then the *_nz parameter is ignored
2989: Options Database Keys:
2990: + -mat_block_size - size of the blocks to use
2991: - -mat_use_hash_table <fact> - set hash table factor
2993: Level: intermediate
2995: Notes:
2996: For good matrix assembly performance
2997: the user should preallocate the matrix storage by setting the parameters
2998: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`). By setting these parameters accurately,
2999: performance can be increased by more than a factor of 50.
3001: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one processor
3002: than it must be used on all processors that share the object for that argument.
3004: Storage Information:
3005: For a square global matrix we define each processor's diagonal portion
3006: to be its local rows and the corresponding columns (a square submatrix);
3007: each processor's off-diagonal portion encompasses the remainder of the
3008: local matrix (a rectangular submatrix).
3010: The user can specify preallocated storage for the diagonal part of
3011: the local submatrix with either `d_nz` or `d_nnz` (not both). Set
3012: `d_nz` = `PETSC_DEFAULT` and `d_nnz` = `NULL` for PETSc to control dynamic
3013: memory allocation. Likewise, specify preallocated storage for the
3014: off-diagonal part of the local submatrix with `o_nz` or `o_nnz` (not both).
3016: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
3017: the figure below we depict these three local rows and all columns (0-11).
3019: .vb
3020: 0 1 2 3 4 5 6 7 8 9 10 11
3021: --------------------------
3022: row 3 |o o o d d d o o o o o o
3023: row 4 |o o o d d d o o o o o o
3024: row 5 |o o o d d d o o o o o o
3025: --------------------------
3026: .ve
3028: Thus, any entries in the d locations are stored in the d (diagonal)
3029: submatrix, and any entries in the o locations are stored in the
3030: o (off-diagonal) submatrix. Note that the d and the o submatrices are
3031: stored simply in the `MATSEQBAIJ` format for compressed row storage.
3033: Now `d_nz` should indicate the number of block nonzeros per row in the d matrix,
3034: and `o_nz` should indicate the number of block nonzeros per row in the o matrix.
3035: In general, for PDE problems in which most nonzeros are near the diagonal,
3036: one expects `d_nz` >> `o_nz`.
3038: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3039: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3040: You can also run with the option `-info` and look for messages with the string
3041: malloc in them to see if additional memory allocation was needed.
3043: .seealso: `Mat`, `MATMPIBAIJ`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`, `MatMPIBAIJSetPreallocationCSR()`, `PetscSplitOwnership()`
3044: @*/
3045: PetscErrorCode MatMPIBAIJSetPreallocation(Mat B, PetscInt bs, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
3046: {
3047: PetscFunctionBegin;
3051: PetscTryMethod(B, "MatMPIBAIJSetPreallocation_C", (Mat, PetscInt, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, bs, d_nz, d_nnz, o_nz, o_nnz));
3052: PetscFunctionReturn(PETSC_SUCCESS);
3053: }
3055: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
3056: /*@C
3057: MatCreateBAIJ - Creates a sparse parallel matrix in `MATBAIJ` format
3058: (block compressed row).
3060: Collective
3062: Input Parameters:
3063: + comm - MPI communicator
3064: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
3065: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
3066: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
3067: This value should be the same as the local size used in creating the
3068: y vector for the matrix-vector product y = Ax.
3069: . n - number of local columns (or `PETSC_DECIDE` to have calculated if N is given)
3070: This value should be the same as the local size used in creating the
3071: x vector for the matrix-vector product y = Ax.
3072: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
3073: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
3074: . d_nz - number of nonzero blocks per block row in diagonal portion of local
3075: submatrix (same for all local rows)
3076: . d_nnz - array containing the number of nonzero blocks in the various block rows
3077: of the in diagonal portion of the local (possibly different for each block
3078: row) or NULL. If you plan to factor the matrix you must leave room for the diagonal entry
3079: and set it even if it is zero.
3080: . o_nz - number of nonzero blocks per block row in the off-diagonal portion of local
3081: submatrix (same for all local rows).
3082: - o_nnz - array containing the number of nonzero blocks in the various block rows of the
3083: off-diagonal portion of the local submatrix (possibly different for
3084: each block row) or NULL.
3086: Output Parameter:
3087: . A - the matrix
3089: Options Database Keys:
3090: + -mat_block_size - size of the blocks to use
3091: - -mat_use_hash_table <fact> - set hash table factor
3093: Level: intermediate
3095: Notes:
3096: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
3097: MatXXXXSetPreallocation() paradigm instead of this routine directly.
3098: [MatXXXXSetPreallocation() is, for example, `MatSeqBAIJSetPreallocation()`]
3100: For good matrix assembly performance
3101: the user should preallocate the matrix storage by setting the parameters
3102: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`). By setting these parameters accurately,
3103: performance can be increased by more than a factor of 50.
3105: If the *_nnz parameter is given then the *_nz parameter is ignored
3107: A nonzero block is any block that as 1 or more nonzeros in it
3109: The user MUST specify either the local or global matrix dimensions
3110: (possibly both).
3112: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one processor
3113: than it must be used on all processors that share the object for that argument.
3115: Storage Information:
3116: For a square global matrix we define each processor's diagonal portion
3117: to be its local rows and the corresponding columns (a square submatrix);
3118: each processor's off-diagonal portion encompasses the remainder of the
3119: local matrix (a rectangular submatrix).
3121: The user can specify preallocated storage for the diagonal part of
3122: the local submatrix with either d_nz or d_nnz (not both). Set
3123: `d_nz` = `PETSC_DEFAULT` and `d_nnz` = `NULL` for PETSc to control dynamic
3124: memory allocation. Likewise, specify preallocated storage for the
3125: off-diagonal part of the local submatrix with `o_nz` or `o_nnz` (not both).
3127: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
3128: the figure below we depict these three local rows and all columns (0-11).
3130: .vb
3131: 0 1 2 3 4 5 6 7 8 9 10 11
3132: --------------------------
3133: row 3 |o o o d d d o o o o o o
3134: row 4 |o o o d d d o o o o o o
3135: row 5 |o o o d d d o o o o o o
3136: --------------------------
3137: .ve
3139: Thus, any entries in the d locations are stored in the d (diagonal)
3140: submatrix, and any entries in the o locations are stored in the
3141: o (off-diagonal) submatrix. Note that the d and the o submatrices are
3142: stored simply in the `MATSEQBAIJ` format for compressed row storage.
3144: Now `d_nz` should indicate the number of block nonzeros per row in the d matrix,
3145: and `o_nz` should indicate the number of block nonzeros per row in the o matrix.
3146: In general, for PDE problems in which most nonzeros are near the diagonal,
3147: one expects `d_nz` >> `o_nz`.
3149: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatMPIBAIJSetPreallocation()`, `MatMPIBAIJSetPreallocationCSR()`
3150: @*/
3151: PetscErrorCode MatCreateBAIJ(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt M, PetscInt N, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[], Mat *A)
3152: {
3153: PetscMPIInt size;
3155: PetscFunctionBegin;
3156: PetscCall(MatCreate(comm, A));
3157: PetscCall(MatSetSizes(*A, m, n, M, N));
3158: PetscCallMPI(MPI_Comm_size(comm, &size));
3159: if (size > 1) {
3160: PetscCall(MatSetType(*A, MATMPIBAIJ));
3161: PetscCall(MatMPIBAIJSetPreallocation(*A, bs, d_nz, d_nnz, o_nz, o_nnz));
3162: } else {
3163: PetscCall(MatSetType(*A, MATSEQBAIJ));
3164: PetscCall(MatSeqBAIJSetPreallocation(*A, bs, d_nz, d_nnz));
3165: }
3166: PetscFunctionReturn(PETSC_SUCCESS);
3167: }
3169: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
3170: {
3171: Mat mat;
3172: Mat_MPIBAIJ *a, *oldmat = (Mat_MPIBAIJ *)matin->data;
3173: PetscInt len = 0;
3175: PetscFunctionBegin;
3176: *newmat = NULL;
3177: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
3178: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
3179: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
3181: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
3182: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
3183: if (matin->hash_active) {
3184: PetscCall(MatSetUp(mat));
3185: } else {
3186: mat->factortype = matin->factortype;
3187: mat->preallocated = PETSC_TRUE;
3188: mat->assembled = PETSC_TRUE;
3189: mat->insertmode = NOT_SET_VALUES;
3191: a = (Mat_MPIBAIJ *)mat->data;
3192: mat->rmap->bs = matin->rmap->bs;
3193: a->bs2 = oldmat->bs2;
3194: a->mbs = oldmat->mbs;
3195: a->nbs = oldmat->nbs;
3196: a->Mbs = oldmat->Mbs;
3197: a->Nbs = oldmat->Nbs;
3199: a->size = oldmat->size;
3200: a->rank = oldmat->rank;
3201: a->donotstash = oldmat->donotstash;
3202: a->roworiented = oldmat->roworiented;
3203: a->rowindices = NULL;
3204: a->rowvalues = NULL;
3205: a->getrowactive = PETSC_FALSE;
3206: a->barray = NULL;
3207: a->rstartbs = oldmat->rstartbs;
3208: a->rendbs = oldmat->rendbs;
3209: a->cstartbs = oldmat->cstartbs;
3210: a->cendbs = oldmat->cendbs;
3212: /* hash table stuff */
3213: a->ht = NULL;
3214: a->hd = NULL;
3215: a->ht_size = 0;
3216: a->ht_flag = oldmat->ht_flag;
3217: a->ht_fact = oldmat->ht_fact;
3218: a->ht_total_ct = 0;
3219: a->ht_insert_ct = 0;
3221: PetscCall(PetscArraycpy(a->rangebs, oldmat->rangebs, a->size + 1));
3222: if (oldmat->colmap) {
3223: #if defined(PETSC_USE_CTABLE)
3224: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3225: #else
3226: PetscCall(PetscMalloc1(a->Nbs, &a->colmap));
3227: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, a->Nbs));
3228: #endif
3229: } else a->colmap = NULL;
3231: if (oldmat->garray && (len = ((Mat_SeqBAIJ *)oldmat->B->data)->nbs)) {
3232: PetscCall(PetscMalloc1(len, &a->garray));
3233: PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3234: } else a->garray = NULL;
3236: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)matin), matin->rmap->bs, &mat->bstash));
3237: PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3238: PetscCall(VecScatterCopy(oldmat->Mvctx, &a->Mvctx));
3240: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3241: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3242: }
3243: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3244: *newmat = mat;
3245: PetscFunctionReturn(PETSC_SUCCESS);
3246: }
3248: /* Used for both MPIBAIJ and MPISBAIJ matrices */
3249: PetscErrorCode MatLoad_MPIBAIJ_Binary(Mat mat, PetscViewer viewer)
3250: {
3251: PetscInt header[4], M, N, nz, bs, m, n, mbs, nbs, rows, cols, sum, i, j, k;
3252: PetscInt *rowidxs, *colidxs, rs, cs, ce;
3253: PetscScalar *matvals;
3255: PetscFunctionBegin;
3256: PetscCall(PetscViewerSetUp(viewer));
3258: /* read in matrix header */
3259: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3260: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3261: M = header[1];
3262: N = header[2];
3263: nz = header[3];
3264: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3265: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3266: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIBAIJ");
3268: /* set block sizes from the viewer's .info file */
3269: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3270: /* set local sizes if not set already */
3271: if (mat->rmap->n < 0 && M == N) mat->rmap->n = mat->cmap->n;
3272: if (mat->cmap->n < 0 && M == N) mat->cmap->n = mat->rmap->n;
3273: /* set global sizes if not set already */
3274: if (mat->rmap->N < 0) mat->rmap->N = M;
3275: if (mat->cmap->N < 0) mat->cmap->N = N;
3276: PetscCall(PetscLayoutSetUp(mat->rmap));
3277: PetscCall(PetscLayoutSetUp(mat->cmap));
3279: /* check if the matrix sizes are correct */
3280: PetscCall(MatGetSize(mat, &rows, &cols));
3281: 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);
3282: PetscCall(MatGetBlockSize(mat, &bs));
3283: PetscCall(MatGetLocalSize(mat, &m, &n));
3284: PetscCall(PetscLayoutGetRange(mat->rmap, &rs, NULL));
3285: PetscCall(PetscLayoutGetRange(mat->cmap, &cs, &ce));
3286: mbs = m / bs;
3287: nbs = n / bs;
3289: /* read in row lengths and build row indices */
3290: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3291: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3292: rowidxs[0] = 0;
3293: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3294: PetscCall(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3295: 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);
3297: /* read in column indices and matrix values */
3298: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3299: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3300: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3302: { /* preallocate matrix storage */
3303: PetscBT bt; /* helper bit set to count diagonal nonzeros */
3304: PetscHSetI ht; /* helper hash set to count off-diagonal nonzeros */
3305: PetscBool sbaij, done;
3306: PetscInt *d_nnz, *o_nnz;
3308: PetscCall(PetscBTCreate(nbs, &bt));
3309: PetscCall(PetscHSetICreate(&ht));
3310: PetscCall(PetscCalloc2(mbs, &d_nnz, mbs, &o_nnz));
3311: PetscCall(PetscObjectTypeCompare((PetscObject)mat, MATMPISBAIJ, &sbaij));
3312: for (i = 0; i < mbs; i++) {
3313: PetscCall(PetscBTMemzero(nbs, bt));
3314: PetscCall(PetscHSetIClear(ht));
3315: for (k = 0; k < bs; k++) {
3316: PetscInt row = bs * i + k;
3317: for (j = rowidxs[row]; j < rowidxs[row + 1]; j++) {
3318: PetscInt col = colidxs[j];
3319: if (!sbaij || col >= row) {
3320: if (col >= cs && col < ce) {
3321: if (!PetscBTLookupSet(bt, (col - cs) / bs)) d_nnz[i]++;
3322: } else {
3323: PetscCall(PetscHSetIQueryAdd(ht, col / bs, &done));
3324: if (done) o_nnz[i]++;
3325: }
3326: }
3327: }
3328: }
3329: }
3330: PetscCall(PetscBTDestroy(&bt));
3331: PetscCall(PetscHSetIDestroy(&ht));
3332: PetscCall(MatMPIBAIJSetPreallocation(mat, bs, 0, d_nnz, 0, o_nnz));
3333: PetscCall(MatMPISBAIJSetPreallocation(mat, bs, 0, d_nnz, 0, o_nnz));
3334: PetscCall(PetscFree2(d_nnz, o_nnz));
3335: }
3337: /* store matrix values */
3338: for (i = 0; i < m; i++) {
3339: PetscInt row = rs + i, s = rowidxs[i], e = rowidxs[i + 1];
3340: PetscUseTypeMethod(mat, setvalues, 1, &row, e - s, colidxs + s, matvals + s, INSERT_VALUES);
3341: }
3343: PetscCall(PetscFree(rowidxs));
3344: PetscCall(PetscFree2(colidxs, matvals));
3345: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
3346: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
3347: PetscFunctionReturn(PETSC_SUCCESS);
3348: }
3350: PetscErrorCode MatLoad_MPIBAIJ(Mat mat, PetscViewer viewer)
3351: {
3352: PetscBool isbinary;
3354: PetscFunctionBegin;
3355: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3356: PetscCheck(isbinary, PetscObjectComm((PetscObject)viewer), PETSC_ERR_SUP, "Viewer type %s not yet supported for reading %s matrices", ((PetscObject)viewer)->type_name, ((PetscObject)mat)->type_name);
3357: PetscCall(MatLoad_MPIBAIJ_Binary(mat, viewer));
3358: PetscFunctionReturn(PETSC_SUCCESS);
3359: }
3361: /*@
3362: MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the matrices hash table
3364: Input Parameters:
3365: + mat - the matrix
3366: - fact - factor
3368: Options Database Key:
3369: . -mat_use_hash_table <fact> - provide the factor
3371: Level: advanced
3373: .seealso: `Mat`, `MATMPIBAIJ`, `MatSetOption()`
3374: @*/
3375: PetscErrorCode MatMPIBAIJSetHashTableFactor(Mat mat, PetscReal fact)
3376: {
3377: PetscFunctionBegin;
3378: PetscTryMethod(mat, "MatSetHashTableFactor_C", (Mat, PetscReal), (mat, fact));
3379: PetscFunctionReturn(PETSC_SUCCESS);
3380: }
3382: PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat mat, PetscReal fact)
3383: {
3384: Mat_MPIBAIJ *baij;
3386: PetscFunctionBegin;
3387: baij = (Mat_MPIBAIJ *)mat->data;
3388: baij->ht_fact = fact;
3389: PetscFunctionReturn(PETSC_SUCCESS);
3390: }
3392: PetscErrorCode MatMPIBAIJGetSeqBAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
3393: {
3394: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
3395: PetscBool flg;
3397: PetscFunctionBegin;
3398: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIBAIJ, &flg));
3399: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIBAIJ matrix as input");
3400: if (Ad) *Ad = a->A;
3401: if (Ao) *Ao = a->B;
3402: if (colmap) *colmap = a->garray;
3403: PetscFunctionReturn(PETSC_SUCCESS);
3404: }
3406: /*
3407: Special version for direct calls from Fortran (to eliminate two function call overheads
3408: */
3409: #if defined(PETSC_HAVE_FORTRAN_CAPS)
3410: #define matmpibaijsetvaluesblocked_ MATMPIBAIJSETVALUESBLOCKED
3411: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
3412: #define matmpibaijsetvaluesblocked_ matmpibaijsetvaluesblocked
3413: #endif
3415: // PetscClangLinter pragma disable: -fdoc-synopsis-matching-symbol-name
3416: /*@C
3417: MatMPIBAIJSetValuesBlocked - Direct Fortran call to replace call to `MatSetValuesBlocked()`
3419: Collective
3421: Input Parameters:
3422: + matin - the matrix
3423: . min - number of input rows
3424: . im - input rows
3425: . nin - number of input columns
3426: . in - input columns
3427: . v - numerical values input
3428: - addvin - `INSERT_VALUES` or `ADD_VALUES`
3430: Level: advanced
3432: Developer Notes:
3433: This has a complete copy of `MatSetValuesBlocked_MPIBAIJ()` which is terrible code un-reuse.
3435: .seealso: `Mat`, `MatSetValuesBlocked()`
3436: @*/
3437: PETSC_EXTERN PetscErrorCode matmpibaijsetvaluesblocked_(Mat *matin, PetscInt *min, const PetscInt im[], PetscInt *nin, const PetscInt in[], const MatScalar v[], InsertMode *addvin)
3438: {
3439: /* convert input arguments to C version */
3440: Mat mat = *matin;
3441: PetscInt m = *min, n = *nin;
3442: InsertMode addv = *addvin;
3444: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
3445: const MatScalar *value;
3446: MatScalar *barray = baij->barray;
3447: PetscBool roworiented = baij->roworiented;
3448: PetscInt i, j, ii, jj, row, col, rstart = baij->rstartbs;
3449: PetscInt rend = baij->rendbs, cstart = baij->cstartbs, stepval;
3450: PetscInt cend = baij->cendbs, bs = mat->rmap->bs, bs2 = baij->bs2;
3452: PetscFunctionBegin;
3453: /* tasks normally handled by MatSetValuesBlocked() */
3454: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
3455: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
3456: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3457: if (mat->assembled) {
3458: mat->was_assembled = PETSC_TRUE;
3459: mat->assembled = PETSC_FALSE;
3460: }
3461: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
3463: if (!barray) {
3464: PetscCall(PetscMalloc1(bs2, &barray));
3465: baij->barray = barray;
3466: }
3468: if (roworiented) stepval = (n - 1) * bs;
3469: else stepval = (m - 1) * bs;
3471: for (i = 0; i < m; i++) {
3472: if (im[i] < 0) continue;
3473: PetscCheck(im[i] < baij->Mbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large, row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], baij->Mbs - 1);
3474: if (im[i] >= rstart && im[i] < rend) {
3475: row = im[i] - rstart;
3476: for (j = 0; j < n; j++) {
3477: /* If NumCol = 1 then a copy is not required */
3478: if ((roworiented) && (n == 1)) {
3479: barray = (MatScalar *)v + i * bs2;
3480: } else if ((!roworiented) && (m == 1)) {
3481: barray = (MatScalar *)v + j * bs2;
3482: } else { /* Here a copy is required */
3483: if (roworiented) {
3484: value = v + i * (stepval + bs) * bs + j * bs;
3485: } else {
3486: value = v + j * (stepval + bs) * bs + i * bs;
3487: }
3488: for (ii = 0; ii < bs; ii++, value += stepval) {
3489: for (jj = 0; jj < bs; jj++) *barray++ = *value++;
3490: }
3491: barray -= bs2;
3492: }
3494: if (in[j] >= cstart && in[j] < cend) {
3495: col = in[j] - cstart;
3496: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->A, row, col, barray, addv, im[i], in[j]));
3497: } else if (in[j] < 0) {
3498: continue;
3499: } else {
3500: PetscCheck(in[j] < baij->Nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large, col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], baij->Nbs - 1);
3501: if (mat->was_assembled) {
3502: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
3504: #if defined(PETSC_USE_DEBUG)
3505: #if defined(PETSC_USE_CTABLE)
3506: {
3507: PetscInt data;
3508: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &data));
3509: PetscCheck((data - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
3510: }
3511: #else
3512: PetscCheck((baij->colmap[in[j]] - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
3513: #endif
3514: #endif
3515: #if defined(PETSC_USE_CTABLE)
3516: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &col));
3517: col = (col - 1) / bs;
3518: #else
3519: col = (baij->colmap[in[j]] - 1) / bs;
3520: #endif
3521: if (col < 0 && !((Mat_SeqBAIJ *)baij->A->data)->nonew) {
3522: PetscCall(MatDisAssemble_MPIBAIJ(mat));
3523: col = in[j];
3524: }
3525: } else col = in[j];
3526: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->B, row, col, barray, addv, im[i], in[j]));
3527: }
3528: }
3529: } else {
3530: if (!baij->donotstash) {
3531: if (roworiented) {
3532: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
3533: } else {
3534: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
3535: }
3536: }
3537: }
3538: }
3540: /* task normally handled by MatSetValuesBlocked() */
3541: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
3542: PetscFunctionReturn(PETSC_SUCCESS);
3543: }
3545: /*@
3546: MatCreateMPIBAIJWithArrays - creates a `MATMPIBAIJ` matrix using arrays that contain in standard block CSR format for the local rows.
3548: Collective
3550: Input Parameters:
3551: + comm - MPI communicator
3552: . bs - the block size, only a block size of 1 is supported
3553: . m - number of local rows (Cannot be `PETSC_DECIDE`)
3554: . n - This value should be the same as the local size used in creating the
3555: x vector for the matrix-vector product $ y = Ax $. (or `PETSC_DECIDE` to have
3556: calculated if `N` is given) For square matrices `n` is almost always `m`.
3557: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
3558: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
3559: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of block elements in that rowth block row of the matrix
3560: . j - column indices
3561: - a - matrix values
3563: Output Parameter:
3564: . mat - the matrix
3566: Level: intermediate
3568: Notes:
3569: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
3570: thus you CANNOT change the matrix entries by changing the values of a[] after you have
3571: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3573: The order of the entries in values is the same as the block compressed sparse row storage format; that is, it is
3574: the same as a three dimensional array in Fortran values(bs,bs,nnz) that contains the first column of the first
3575: block, followed by the second column of the first block etc etc. That is, the blocks are contiguous in memory
3576: with column-major ordering within blocks.
3578: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
3580: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
3581: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`
3582: @*/
3583: PetscErrorCode MatCreateMPIBAIJWithArrays(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
3584: {
3585: PetscFunctionBegin;
3586: PetscCheck(!i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
3587: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
3588: PetscCall(MatCreate(comm, mat));
3589: PetscCall(MatSetSizes(*mat, m, n, M, N));
3590: PetscCall(MatSetType(*mat, MATMPIBAIJ));
3591: PetscCall(MatSetBlockSize(*mat, bs));
3592: PetscCall(MatSetUp(*mat));
3593: PetscCall(MatSetOption(*mat, MAT_ROW_ORIENTED, PETSC_FALSE));
3594: PetscCall(MatMPIBAIJSetPreallocationCSR(*mat, bs, i, j, a));
3595: PetscCall(MatSetOption(*mat, MAT_ROW_ORIENTED, PETSC_TRUE));
3596: PetscFunctionReturn(PETSC_SUCCESS);
3597: }
3599: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIBAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
3600: {
3601: PetscInt m, N, i, rstart, nnz, Ii, bs, cbs;
3602: PetscInt *indx;
3603: PetscScalar *values;
3605: PetscFunctionBegin;
3606: PetscCall(MatGetSize(inmat, &m, &N));
3607: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
3608: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)inmat->data;
3609: PetscInt *dnz, *onz, mbs, Nbs, nbs;
3610: PetscInt *bindx, rmax = a->rmax, j;
3611: PetscMPIInt rank, size;
3613: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
3614: mbs = m / bs;
3615: Nbs = N / cbs;
3616: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnershipBlock(comm, cbs, &n, &N));
3617: nbs = n / cbs;
3619: PetscCall(PetscMalloc1(rmax, &bindx));
3620: MatPreallocateBegin(comm, mbs, nbs, dnz, onz); /* inline function, output __end and __rstart are used below */
3622: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3623: PetscCallMPI(MPI_Comm_rank(comm, &size));
3624: if (rank == size - 1) {
3625: /* Check sum(nbs) = Nbs */
3626: PetscCheck(__end == Nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local block columns %" PetscInt_FMT " != global block columns %" PetscInt_FMT, __end, Nbs);
3627: }
3629: rstart = __rstart; /* block rstart of *outmat; see inline function MatPreallocateBegin */
3630: for (i = 0; i < mbs; i++) {
3631: PetscCall(MatGetRow_SeqBAIJ(inmat, i * bs, &nnz, &indx, NULL)); /* non-blocked nnz and indx */
3632: nnz = nnz / bs;
3633: for (j = 0; j < nnz; j++) bindx[j] = indx[j * bs] / bs;
3634: PetscCall(MatPreallocateSet(i + rstart, nnz, bindx, dnz, onz));
3635: PetscCall(MatRestoreRow_SeqBAIJ(inmat, i * bs, &nnz, &indx, NULL));
3636: }
3637: PetscCall(PetscFree(bindx));
3639: PetscCall(MatCreate(comm, outmat));
3640: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
3641: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
3642: PetscCall(MatSetType(*outmat, MATBAIJ));
3643: PetscCall(MatSeqBAIJSetPreallocation(*outmat, bs, 0, dnz));
3644: PetscCall(MatMPIBAIJSetPreallocation(*outmat, bs, 0, dnz, 0, onz));
3645: MatPreallocateEnd(dnz, onz);
3646: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3647: }
3649: /* numeric phase */
3650: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
3651: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
3653: for (i = 0; i < m; i++) {
3654: PetscCall(MatGetRow_SeqBAIJ(inmat, i, &nnz, &indx, &values));
3655: Ii = i + rstart;
3656: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
3657: PetscCall(MatRestoreRow_SeqBAIJ(inmat, i, &nnz, &indx, &values));
3658: }
3659: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
3660: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
3661: PetscFunctionReturn(PETSC_SUCCESS);
3662: }