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: PetscErrorCode MatDestroy_MPIBAIJ(Mat mat)
8: {
9: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
11: PetscFunctionBegin;
12: #if defined(PETSC_USE_LOG)
13: PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ",Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
14: #endif
15: PetscCall(MatStashDestroy_Private(&mat->stash));
16: PetscCall(MatStashDestroy_Private(&mat->bstash));
17: PetscCall(MatDestroy(&baij->A));
18: PetscCall(MatDestroy(&baij->B));
19: #if defined(PETSC_USE_CTABLE)
20: PetscCall(PetscHMapIDestroy(&baij->colmap));
21: #else
22: PetscCall(PetscFree(baij->colmap));
23: #endif
24: PetscCall(PetscFree(baij->garray));
25: PetscCall(VecDestroy(&baij->lvec));
26: PetscCall(VecScatterDestroy(&baij->Mvctx));
27: PetscCall(PetscFree2(baij->rowvalues, baij->rowindices));
28: PetscCall(PetscFree(baij->barray));
29: PetscCall(PetscFree2(baij->hd, baij->ht));
30: PetscCall(PetscFree(baij->rangebs));
31: PetscCall(PetscFree(mat->data));
33: PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
34: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
35: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
36: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIBAIJSetPreallocation_C", NULL));
37: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIBAIJSetPreallocationCSR_C", NULL));
38: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
39: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetHashTableFactor_C", NULL));
40: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_mpisbaij_C", NULL));
41: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_mpiadj_C", NULL));
42: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_mpiaij_C", NULL));
43: #if defined(PETSC_HAVE_HYPRE)
44: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_hypre_C", NULL));
45: #endif
46: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_is_C", NULL));
47: PetscFunctionReturn(PETSC_SUCCESS);
48: }
50: /* defines MatSetValues_MPI_Hash(), MatAssemblyBegin_MPI_Hash(), and MatAssemblyEnd_MPI_Hash() */
51: #define TYPE BAIJ
52: #include "../src/mat/impls/aij/mpi/mpihashmat.h"
53: #undef TYPE
55: #if defined(PETSC_HAVE_HYPRE)
56: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
57: #endif
59: PetscErrorCode MatGetRowMaxAbs_MPIBAIJ(Mat A, Vec v, PetscInt idx[])
60: {
61: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
62: PetscInt i, *idxb = NULL, m = A->rmap->n, bs = A->cmap->bs;
63: PetscScalar *va, *vv;
64: Vec vB, vA;
65: const PetscScalar *vb;
67: PetscFunctionBegin;
68: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vA));
69: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
71: PetscCall(VecGetArrayWrite(vA, &va));
72: if (idx) {
73: for (i = 0; i < m; i++) {
74: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
75: }
76: }
78: PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vB));
79: PetscCall(PetscMalloc1(m, &idxb));
80: PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));
82: PetscCall(VecGetArrayWrite(v, &vv));
83: PetscCall(VecGetArrayRead(vB, &vb));
84: for (i = 0; i < m; i++) {
85: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
86: vv[i] = vb[i];
87: if (idx) idx[i] = bs * a->garray[idxb[i] / bs] + (idxb[i] % bs);
88: } else {
89: vv[i] = va[i];
90: 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);
91: }
92: }
93: PetscCall(VecRestoreArrayWrite(vA, &vv));
94: PetscCall(VecRestoreArrayWrite(vA, &va));
95: PetscCall(VecRestoreArrayRead(vB, &vb));
96: PetscCall(PetscFree(idxb));
97: PetscCall(VecDestroy(&vA));
98: PetscCall(VecDestroy(&vB));
99: PetscFunctionReturn(PETSC_SUCCESS);
100: }
102: PetscErrorCode MatStoreValues_MPIBAIJ(Mat mat)
103: {
104: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
106: PetscFunctionBegin;
107: PetscCall(MatStoreValues(aij->A));
108: PetscCall(MatStoreValues(aij->B));
109: PetscFunctionReturn(PETSC_SUCCESS);
110: }
112: PetscErrorCode MatRetrieveValues_MPIBAIJ(Mat mat)
113: {
114: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
116: PetscFunctionBegin;
117: PetscCall(MatRetrieveValues(aij->A));
118: PetscCall(MatRetrieveValues(aij->B));
119: PetscFunctionReturn(PETSC_SUCCESS);
120: }
122: /*
123: Local utility routine that creates a mapping from the global column
124: number to the local number in the off-diagonal part of the local
125: storage of the matrix. This is done in a non scalable way since the
126: length of colmap equals the global matrix length.
127: */
128: PetscErrorCode MatCreateColmap_MPIBAIJ_Private(Mat mat)
129: {
130: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
131: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)baij->B->data;
132: PetscInt nbs = B->nbs, i, bs = mat->rmap->bs;
134: PetscFunctionBegin;
135: #if defined(PETSC_USE_CTABLE)
136: PetscCall(PetscHMapICreateWithSize(baij->nbs, &baij->colmap));
137: for (i = 0; i < nbs; i++) PetscCall(PetscHMapISet(baij->colmap, baij->garray[i] + 1, i * bs + 1));
138: #else
139: PetscCall(PetscCalloc1(baij->Nbs + 1, &baij->colmap));
140: for (i = 0; i < nbs; i++) baij->colmap[baij->garray[i]] = i * bs + 1;
141: #endif
142: PetscFunctionReturn(PETSC_SUCCESS);
143: }
145: #define MatSetValues_SeqBAIJ_A_Private(row, col, value, addv, orow, ocol) \
146: { \
147: brow = row / bs; \
148: rp = aj + ai[brow]; \
149: ap = aa + bs2 * ai[brow]; \
150: rmax = aimax[brow]; \
151: nrow = ailen[brow]; \
152: bcol = col / bs; \
153: ridx = row % bs; \
154: cidx = col % bs; \
155: low = 0; \
156: high = nrow; \
157: while (high - low > 3) { \
158: t = (low + high) / 2; \
159: if (rp[t] > bcol) high = t; \
160: else low = t; \
161: } \
162: for (_i = low; _i < high; _i++) { \
163: if (rp[_i] > bcol) break; \
164: if (rp[_i] == bcol) { \
165: bap = ap + bs2 * _i + bs * cidx + ridx; \
166: if (addv == ADD_VALUES) *bap += value; \
167: else *bap = value; \
168: goto a_noinsert; \
169: } \
170: } \
171: if (a->nonew == 1) goto a_noinsert; \
172: 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); \
173: MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, brow, bcol, rmax, aa, ai, aj, rp, ap, aimax, a->nonew, MatScalar); \
174: N = nrow++ - 1; \
175: /* shift up all the later entries in this row */ \
176: PetscCall(PetscArraymove(rp + _i + 1, rp + _i, N - _i + 1)); \
177: PetscCall(PetscArraymove(ap + bs2 * (_i + 1), ap + bs2 * _i, bs2 * (N - _i + 1))); \
178: PetscCall(PetscArrayzero(ap + bs2 * _i, bs2)); \
179: rp[_i] = bcol; \
180: ap[bs2 * _i + bs * cidx + ridx] = value; \
181: a_noinsert:; \
182: ailen[brow] = nrow; \
183: }
185: #define MatSetValues_SeqBAIJ_B_Private(row, col, value, addv, orow, ocol) \
186: { \
187: brow = row / bs; \
188: rp = bj + bi[brow]; \
189: ap = ba + bs2 * bi[brow]; \
190: rmax = bimax[brow]; \
191: nrow = bilen[brow]; \
192: bcol = col / bs; \
193: ridx = row % bs; \
194: cidx = col % bs; \
195: low = 0; \
196: high = nrow; \
197: while (high - low > 3) { \
198: t = (low + high) / 2; \
199: if (rp[t] > bcol) high = t; \
200: else low = t; \
201: } \
202: for (_i = low; _i < high; _i++) { \
203: if (rp[_i] > bcol) break; \
204: if (rp[_i] == bcol) { \
205: bap = ap + bs2 * _i + bs * cidx + ridx; \
206: if (addv == ADD_VALUES) *bap += value; \
207: else *bap = value; \
208: goto b_noinsert; \
209: } \
210: } \
211: if (b->nonew == 1) goto b_noinsert; \
212: 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); \
213: MatSeqXAIJReallocateAIJ(B, b->mbs, bs2, nrow, brow, bcol, rmax, ba, bi, bj, rp, ap, bimax, b->nonew, MatScalar); \
214: N = nrow++ - 1; \
215: /* shift up all the later entries in this row */ \
216: PetscCall(PetscArraymove(rp + _i + 1, rp + _i, N - _i + 1)); \
217: PetscCall(PetscArraymove(ap + bs2 * (_i + 1), ap + bs2 * _i, bs2 * (N - _i + 1))); \
218: PetscCall(PetscArrayzero(ap + bs2 * _i, bs2)); \
219: rp[_i] = bcol; \
220: ap[bs2 * _i + bs * cidx + ridx] = value; \
221: b_noinsert:; \
222: bilen[brow] = nrow; \
223: }
225: PetscErrorCode MatSetValues_MPIBAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
226: {
227: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
228: MatScalar value;
229: PetscBool roworiented = baij->roworiented;
230: PetscInt i, j, row, col;
231: PetscInt rstart_orig = mat->rmap->rstart;
232: PetscInt rend_orig = mat->rmap->rend, cstart_orig = mat->cmap->rstart;
233: PetscInt cend_orig = mat->cmap->rend, bs = mat->rmap->bs;
235: /* Some Variables required in the macro */
236: Mat A = baij->A;
237: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)(A)->data;
238: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
239: MatScalar *aa = a->a;
241: Mat B = baij->B;
242: Mat_SeqBAIJ *b = (Mat_SeqBAIJ *)(B)->data;
243: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j;
244: MatScalar *ba = b->a;
246: PetscInt *rp, ii, nrow, _i, rmax, N, brow, bcol;
247: PetscInt low, high, t, ridx, cidx, bs2 = a->bs2;
248: MatScalar *ap, *bap;
250: PetscFunctionBegin;
251: for (i = 0; i < m; i++) {
252: if (im[i] < 0) continue;
253: 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);
254: if (im[i] >= rstart_orig && im[i] < rend_orig) {
255: row = im[i] - rstart_orig;
256: for (j = 0; j < n; j++) {
257: if (in[j] >= cstart_orig && in[j] < cend_orig) {
258: col = in[j] - cstart_orig;
259: if (roworiented) value = v[i * n + j];
260: else value = v[i + j * m];
261: MatSetValues_SeqBAIJ_A_Private(row, col, value, addv, im[i], in[j]);
262: } else if (in[j] < 0) {
263: continue;
264: } else {
265: 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);
266: if (mat->was_assembled) {
267: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
268: #if defined(PETSC_USE_CTABLE)
269: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] / bs + 1, 0, &col));
270: col = col - 1;
271: #else
272: col = baij->colmap[in[j] / bs] - 1;
273: #endif
274: if (col < 0 && !((Mat_SeqBAIJ *)(baij->B->data))->nonew) {
275: PetscCall(MatDisAssemble_MPIBAIJ(mat));
276: col = in[j];
277: /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
278: B = baij->B;
279: b = (Mat_SeqBAIJ *)(B)->data;
280: bimax = b->imax;
281: bi = b->i;
282: bilen = b->ilen;
283: bj = b->j;
284: ba = b->a;
285: } else {
286: PetscCheck(col >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]);
287: col += in[j] % bs;
288: }
289: } else col = in[j];
290: if (roworiented) value = v[i * n + j];
291: else value = v[i + j * m];
292: MatSetValues_SeqBAIJ_B_Private(row, col, value, addv, im[i], in[j]);
293: /* PetscCall(MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv)); */
294: }
295: }
296: } else {
297: 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]);
298: if (!baij->donotstash) {
299: mat->assembled = PETSC_FALSE;
300: if (roworiented) {
301: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, PETSC_FALSE));
302: } else {
303: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, PETSC_FALSE));
304: }
305: }
306: }
307: }
308: PetscFunctionReturn(PETSC_SUCCESS);
309: }
311: static inline PetscErrorCode MatSetValuesBlocked_SeqBAIJ_Inlined(Mat A, PetscInt row, PetscInt col, const PetscScalar v[], InsertMode is, PetscInt orow, PetscInt ocol)
312: {
313: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
314: PetscInt *rp, low, high, t, ii, jj, nrow, i, rmax, N;
315: PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen;
316: PetscInt *aj = a->j, nonew = a->nonew, bs2 = a->bs2, bs = A->rmap->bs;
317: PetscBool roworiented = a->roworiented;
318: const PetscScalar *value = v;
319: MatScalar *ap, *aa = a->a, *bap;
321: PetscFunctionBegin;
322: rp = aj + ai[row];
323: ap = aa + bs2 * ai[row];
324: rmax = imax[row];
325: nrow = ailen[row];
326: value = v;
327: low = 0;
328: high = nrow;
329: while (high - low > 7) {
330: t = (low + high) / 2;
331: if (rp[t] > col) high = t;
332: else low = t;
333: }
334: for (i = low; i < high; i++) {
335: if (rp[i] > col) break;
336: if (rp[i] == col) {
337: bap = ap + bs2 * i;
338: if (roworiented) {
339: if (is == ADD_VALUES) {
340: for (ii = 0; ii < bs; ii++) {
341: for (jj = ii; jj < bs2; jj += bs) bap[jj] += *value++;
342: }
343: } else {
344: for (ii = 0; ii < bs; ii++) {
345: for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
346: }
347: }
348: } else {
349: if (is == ADD_VALUES) {
350: for (ii = 0; ii < bs; ii++, value += bs) {
351: for (jj = 0; jj < bs; jj++) bap[jj] += value[jj];
352: bap += bs;
353: }
354: } else {
355: for (ii = 0; ii < bs; ii++, value += bs) {
356: for (jj = 0; jj < bs; jj++) bap[jj] = value[jj];
357: bap += bs;
358: }
359: }
360: }
361: goto noinsert2;
362: }
363: }
364: if (nonew == 1) goto noinsert2;
365: 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);
366: MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
367: N = nrow++ - 1;
368: high++;
369: /* shift up all the later entries in this row */
370: PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
371: PetscCall(PetscArraymove(ap + bs2 * (i + 1), ap + bs2 * i, bs2 * (N - i + 1)));
372: rp[i] = col;
373: bap = ap + bs2 * i;
374: if (roworiented) {
375: for (ii = 0; ii < bs; ii++) {
376: for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
377: }
378: } else {
379: for (ii = 0; ii < bs; ii++) {
380: for (jj = 0; jj < bs; jj++) *bap++ = *value++;
381: }
382: }
383: noinsert2:;
384: ailen[row] = nrow;
385: PetscFunctionReturn(PETSC_SUCCESS);
386: }
388: /*
389: This routine should be optimized so that the block copy at ** Here a copy is required ** below is not needed
390: by passing additional stride information into the MatSetValuesBlocked_SeqBAIJ_Inlined() routine
391: */
392: PetscErrorCode MatSetValuesBlocked_MPIBAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
393: {
394: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
395: const PetscScalar *value;
396: MatScalar *barray = baij->barray;
397: PetscBool roworiented = baij->roworiented;
398: PetscInt i, j, ii, jj, row, col, rstart = baij->rstartbs;
399: PetscInt rend = baij->rendbs, cstart = baij->cstartbs, stepval;
400: PetscInt cend = baij->cendbs, bs = mat->rmap->bs, bs2 = baij->bs2;
402: PetscFunctionBegin;
403: if (!barray) {
404: PetscCall(PetscMalloc1(bs2, &barray));
405: baij->barray = barray;
406: }
408: if (roworiented) stepval = (n - 1) * bs;
409: else stepval = (m - 1) * bs;
411: for (i = 0; i < m; i++) {
412: if (im[i] < 0) continue;
413: 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);
414: if (im[i] >= rstart && im[i] < rend) {
415: row = im[i] - rstart;
416: for (j = 0; j < n; j++) {
417: /* If NumCol = 1 then a copy is not required */
418: if ((roworiented) && (n == 1)) {
419: barray = (MatScalar *)v + i * bs2;
420: } else if ((!roworiented) && (m == 1)) {
421: barray = (MatScalar *)v + j * bs2;
422: } else { /* Here a copy is required */
423: if (roworiented) {
424: value = v + (i * (stepval + bs) + j) * bs;
425: } else {
426: value = v + (j * (stepval + bs) + i) * bs;
427: }
428: for (ii = 0; ii < bs; ii++, value += bs + stepval) {
429: for (jj = 0; jj < bs; jj++) barray[jj] = value[jj];
430: barray += bs;
431: }
432: barray -= bs2;
433: }
435: if (in[j] >= cstart && in[j] < cend) {
436: col = in[j] - cstart;
437: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->A, row, col, barray, addv, im[i], in[j]));
438: } else if (in[j] < 0) {
439: continue;
440: } else {
441: 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);
442: if (mat->was_assembled) {
443: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
445: #if defined(PETSC_USE_DEBUG)
446: #if defined(PETSC_USE_CTABLE)
447: {
448: PetscInt data;
449: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &data));
450: PetscCheck((data - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
451: }
452: #else
453: PetscCheck((baij->colmap[in[j]] - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
454: #endif
455: #endif
456: #if defined(PETSC_USE_CTABLE)
457: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &col));
458: col = (col - 1) / bs;
459: #else
460: col = (baij->colmap[in[j]] - 1) / bs;
461: #endif
462: if (col < 0 && !((Mat_SeqBAIJ *)(baij->B->data))->nonew) {
463: PetscCall(MatDisAssemble_MPIBAIJ(mat));
464: col = in[j];
465: } 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]);
466: } else col = in[j];
467: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->B, row, col, barray, addv, im[i], in[j]));
468: }
469: }
470: } else {
471: 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]);
472: if (!baij->donotstash) {
473: if (roworiented) {
474: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
475: } else {
476: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
477: }
478: }
479: }
480: }
481: PetscFunctionReturn(PETSC_SUCCESS);
482: }
484: #define HASH_KEY 0.6180339887
485: #define HASH(size, key, tmp) (tmp = (key)*HASH_KEY, (PetscInt)((size) * (tmp - (PetscInt)tmp)))
486: /* #define HASH(size,key) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
487: /* #define HASH(size,key,tmp) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
488: PetscErrorCode MatSetValues_MPIBAIJ_HT(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
489: {
490: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
491: PetscBool roworiented = baij->roworiented;
492: PetscInt i, j, row, col;
493: PetscInt rstart_orig = mat->rmap->rstart;
494: PetscInt rend_orig = mat->rmap->rend, Nbs = baij->Nbs;
495: PetscInt h1, key, size = baij->ht_size, bs = mat->rmap->bs, *HT = baij->ht, idx;
496: PetscReal tmp;
497: MatScalar **HD = baij->hd, value;
498: PetscInt total_ct = baij->ht_total_ct, insert_ct = baij->ht_insert_ct;
500: PetscFunctionBegin;
501: for (i = 0; i < m; i++) {
502: if (PetscDefined(USE_DEBUG)) {
503: PetscCheck(im[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row");
504: 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);
505: }
506: row = im[i];
507: if (row >= rstart_orig && row < rend_orig) {
508: for (j = 0; j < n; j++) {
509: col = in[j];
510: if (roworiented) value = v[i * n + j];
511: else value = v[i + j * m];
512: /* Look up PetscInto the Hash Table */
513: key = (row / bs) * Nbs + (col / bs) + 1;
514: h1 = HASH(size, key, tmp);
516: idx = h1;
517: if (PetscDefined(USE_DEBUG)) {
518: insert_ct++;
519: total_ct++;
520: if (HT[idx] != key) {
521: for (idx = h1; (idx < size) && (HT[idx] != key); idx++, total_ct++)
522: ;
523: if (idx == size) {
524: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++, total_ct++)
525: ;
526: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
527: }
528: }
529: } else if (HT[idx] != key) {
530: for (idx = h1; (idx < size) && (HT[idx] != key); idx++)
531: ;
532: if (idx == size) {
533: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++)
534: ;
535: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
536: }
537: }
538: /* A HASH table entry is found, so insert the values at the correct address */
539: if (addv == ADD_VALUES) *(HD[idx] + (col % bs) * bs + (row % bs)) += value;
540: else *(HD[idx] + (col % bs) * bs + (row % bs)) = value;
541: }
542: } else if (!baij->donotstash) {
543: if (roworiented) {
544: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, PETSC_FALSE));
545: } else {
546: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, PETSC_FALSE));
547: }
548: }
549: }
550: if (PetscDefined(USE_DEBUG)) {
551: baij->ht_total_ct += total_ct;
552: baij->ht_insert_ct += insert_ct;
553: }
554: PetscFunctionReturn(PETSC_SUCCESS);
555: }
557: PetscErrorCode MatSetValuesBlocked_MPIBAIJ_HT(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
558: {
559: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
560: PetscBool roworiented = baij->roworiented;
561: PetscInt i, j, ii, jj, row, col;
562: PetscInt rstart = baij->rstartbs;
563: PetscInt rend = mat->rmap->rend, stepval, bs = mat->rmap->bs, bs2 = baij->bs2, nbs2 = n * bs2;
564: PetscInt h1, key, size = baij->ht_size, idx, *HT = baij->ht, Nbs = baij->Nbs;
565: PetscReal tmp;
566: MatScalar **HD = baij->hd, *baij_a;
567: const PetscScalar *v_t, *value;
568: PetscInt total_ct = baij->ht_total_ct, insert_ct = baij->ht_insert_ct;
570: PetscFunctionBegin;
571: if (roworiented) stepval = (n - 1) * bs;
572: else stepval = (m - 1) * bs;
574: for (i = 0; i < m; i++) {
575: if (PetscDefined(USE_DEBUG)) {
576: PetscCheck(im[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row: %" PetscInt_FMT, im[i]);
577: 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);
578: }
579: row = im[i];
580: v_t = v + i * nbs2;
581: if (row >= rstart && row < rend) {
582: for (j = 0; j < n; j++) {
583: col = in[j];
585: /* Look up into the Hash Table */
586: key = row * Nbs + col + 1;
587: h1 = HASH(size, key, tmp);
589: idx = h1;
590: if (PetscDefined(USE_DEBUG)) {
591: total_ct++;
592: insert_ct++;
593: if (HT[idx] != key) {
594: for (idx = h1; (idx < size) && (HT[idx] != key); idx++, total_ct++)
595: ;
596: if (idx == size) {
597: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++, total_ct++)
598: ;
599: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
600: }
601: }
602: } else if (HT[idx] != key) {
603: for (idx = h1; (idx < size) && (HT[idx] != key); idx++)
604: ;
605: if (idx == size) {
606: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++)
607: ;
608: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
609: }
610: }
611: baij_a = HD[idx];
612: if (roworiented) {
613: /*value = v + i*(stepval+bs)*bs + j*bs;*/
614: /* value = v + (i*(stepval+bs)+j)*bs; */
615: value = v_t;
616: v_t += bs;
617: if (addv == ADD_VALUES) {
618: for (ii = 0; ii < bs; ii++, value += stepval) {
619: for (jj = ii; jj < bs2; jj += bs) baij_a[jj] += *value++;
620: }
621: } else {
622: for (ii = 0; ii < bs; ii++, value += stepval) {
623: for (jj = ii; jj < bs2; jj += bs) baij_a[jj] = *value++;
624: }
625: }
626: } else {
627: value = v + j * (stepval + bs) * bs + i * bs;
628: if (addv == ADD_VALUES) {
629: for (ii = 0; ii < bs; ii++, value += stepval, baij_a += bs) {
630: for (jj = 0; jj < bs; jj++) baij_a[jj] += *value++;
631: }
632: } else {
633: for (ii = 0; ii < bs; ii++, value += stepval, baij_a += bs) {
634: for (jj = 0; jj < bs; jj++) baij_a[jj] = *value++;
635: }
636: }
637: }
638: }
639: } else {
640: if (!baij->donotstash) {
641: if (roworiented) {
642: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
643: } else {
644: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
645: }
646: }
647: }
648: }
649: if (PetscDefined(USE_DEBUG)) {
650: baij->ht_total_ct += total_ct;
651: baij->ht_insert_ct += insert_ct;
652: }
653: PetscFunctionReturn(PETSC_SUCCESS);
654: }
656: PetscErrorCode MatGetValues_MPIBAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
657: {
658: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
659: PetscInt bs = mat->rmap->bs, i, j, bsrstart = mat->rmap->rstart, bsrend = mat->rmap->rend;
660: PetscInt bscstart = mat->cmap->rstart, bscend = mat->cmap->rend, row, col, data;
662: PetscFunctionBegin;
663: for (i = 0; i < m; i++) {
664: if (idxm[i] < 0) continue; /* negative row */
665: 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);
666: if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
667: row = idxm[i] - bsrstart;
668: for (j = 0; j < n; j++) {
669: if (idxn[j] < 0) continue; /* negative column */
670: 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);
671: if (idxn[j] >= bscstart && idxn[j] < bscend) {
672: col = idxn[j] - bscstart;
673: PetscCall(MatGetValues_SeqBAIJ(baij->A, 1, &row, 1, &col, v + i * n + j));
674: } else {
675: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
676: #if defined(PETSC_USE_CTABLE)
677: PetscCall(PetscHMapIGetWithDefault(baij->colmap, idxn[j] / bs + 1, 0, &data));
678: data--;
679: #else
680: data = baij->colmap[idxn[j] / bs] - 1;
681: #endif
682: if ((data < 0) || (baij->garray[data / bs] != idxn[j] / bs)) *(v + i * n + j) = 0.0;
683: else {
684: col = data + idxn[j] % bs;
685: PetscCall(MatGetValues_SeqBAIJ(baij->B, 1, &row, 1, &col, v + i * n + j));
686: }
687: }
688: }
689: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local values currently supported");
690: }
691: PetscFunctionReturn(PETSC_SUCCESS);
692: }
694: PetscErrorCode MatNorm_MPIBAIJ(Mat mat, NormType type, PetscReal *nrm)
695: {
696: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
697: Mat_SeqBAIJ *amat = (Mat_SeqBAIJ *)baij->A->data, *bmat = (Mat_SeqBAIJ *)baij->B->data;
698: PetscInt i, j, bs2 = baij->bs2, bs = baij->A->rmap->bs, nz, row, col;
699: PetscReal sum = 0.0;
700: MatScalar *v;
702: PetscFunctionBegin;
703: if (baij->size == 1) {
704: PetscCall(MatNorm(baij->A, type, nrm));
705: } else {
706: if (type == NORM_FROBENIUS) {
707: v = amat->a;
708: nz = amat->nz * bs2;
709: for (i = 0; i < nz; i++) {
710: sum += PetscRealPart(PetscConj(*v) * (*v));
711: v++;
712: }
713: v = bmat->a;
714: nz = bmat->nz * bs2;
715: for (i = 0; i < nz; i++) {
716: sum += PetscRealPart(PetscConj(*v) * (*v));
717: v++;
718: }
719: PetscCall(MPIU_Allreduce(&sum, nrm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
720: *nrm = PetscSqrtReal(*nrm);
721: } else if (type == NORM_1) { /* max column sum */
722: PetscReal *tmp, *tmp2;
723: PetscInt *jj, *garray = baij->garray, cstart = baij->rstartbs;
724: PetscCall(PetscCalloc1(mat->cmap->N, &tmp));
725: PetscCall(PetscMalloc1(mat->cmap->N, &tmp2));
726: v = amat->a;
727: jj = amat->j;
728: for (i = 0; i < amat->nz; i++) {
729: for (j = 0; j < bs; j++) {
730: col = bs * (cstart + *jj) + j; /* column index */
731: for (row = 0; row < bs; row++) {
732: tmp[col] += PetscAbsScalar(*v);
733: v++;
734: }
735: }
736: jj++;
737: }
738: v = bmat->a;
739: jj = bmat->j;
740: for (i = 0; i < bmat->nz; i++) {
741: for (j = 0; j < bs; j++) {
742: col = bs * garray[*jj] + j;
743: for (row = 0; row < bs; row++) {
744: tmp[col] += PetscAbsScalar(*v);
745: v++;
746: }
747: }
748: jj++;
749: }
750: PetscCall(MPIU_Allreduce(tmp, tmp2, mat->cmap->N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
751: *nrm = 0.0;
752: for (j = 0; j < mat->cmap->N; j++) {
753: if (tmp2[j] > *nrm) *nrm = tmp2[j];
754: }
755: PetscCall(PetscFree(tmp));
756: PetscCall(PetscFree(tmp2));
757: } else if (type == NORM_INFINITY) { /* max row sum */
758: PetscReal *sums;
759: PetscCall(PetscMalloc1(bs, &sums));
760: sum = 0.0;
761: for (j = 0; j < amat->mbs; j++) {
762: for (row = 0; row < bs; row++) sums[row] = 0.0;
763: v = amat->a + bs2 * amat->i[j];
764: nz = amat->i[j + 1] - amat->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: v = bmat->a + bs2 * bmat->i[j];
774: nz = bmat->i[j + 1] - bmat->i[j];
775: for (i = 0; i < nz; i++) {
776: for (col = 0; col < bs; col++) {
777: for (row = 0; row < bs; row++) {
778: sums[row] += PetscAbsScalar(*v);
779: v++;
780: }
781: }
782: }
783: for (row = 0; row < bs; row++) {
784: if (sums[row] > sum) sum = sums[row];
785: }
786: }
787: PetscCall(MPIU_Allreduce(&sum, nrm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
788: PetscCall(PetscFree(sums));
789: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for this norm yet");
790: }
791: PetscFunctionReturn(PETSC_SUCCESS);
792: }
794: /*
795: Creates the hash table, and sets the table
796: This table is created only once.
797: If new entried need to be added to the matrix
798: then the hash table has to be destroyed and
799: recreated.
800: */
801: PetscErrorCode MatCreateHashTable_MPIBAIJ_Private(Mat mat, PetscReal factor)
802: {
803: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
804: Mat A = baij->A, B = baij->B;
805: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data, *b = (Mat_SeqBAIJ *)B->data;
806: PetscInt i, j, k, nz = a->nz + b->nz, h1, *ai = a->i, *aj = a->j, *bi = b->i, *bj = b->j;
807: PetscInt ht_size, bs2 = baij->bs2, rstart = baij->rstartbs;
808: PetscInt cstart = baij->cstartbs, *garray = baij->garray, row, col, Nbs = baij->Nbs;
809: PetscInt *HT, key;
810: MatScalar **HD;
811: PetscReal tmp;
812: #if defined(PETSC_USE_INFO)
813: PetscInt ct = 0, max = 0;
814: #endif
816: PetscFunctionBegin;
817: if (baij->ht) PetscFunctionReturn(PETSC_SUCCESS);
819: baij->ht_size = (PetscInt)(factor * nz);
820: ht_size = baij->ht_size;
822: /* Allocate Memory for Hash Table */
823: PetscCall(PetscCalloc2(ht_size, &baij->hd, ht_size, &baij->ht));
824: HD = baij->hd;
825: HT = baij->ht;
827: /* Loop Over A */
828: for (i = 0; i < a->mbs; i++) {
829: for (j = ai[i]; j < ai[i + 1]; j++) {
830: row = i + rstart;
831: col = aj[j] + cstart;
833: key = row * Nbs + col + 1;
834: h1 = HASH(ht_size, key, tmp);
835: for (k = 0; k < ht_size; k++) {
836: if (!HT[(h1 + k) % ht_size]) {
837: HT[(h1 + k) % ht_size] = key;
838: HD[(h1 + k) % ht_size] = a->a + j * bs2;
839: break;
840: #if defined(PETSC_USE_INFO)
841: } else {
842: ct++;
843: #endif
844: }
845: }
846: #if defined(PETSC_USE_INFO)
847: if (k > max) max = k;
848: #endif
849: }
850: }
851: /* Loop Over B */
852: for (i = 0; i < b->mbs; i++) {
853: for (j = bi[i]; j < bi[i + 1]; j++) {
854: row = i + rstart;
855: col = garray[bj[j]];
856: key = row * Nbs + col + 1;
857: h1 = HASH(ht_size, key, tmp);
858: for (k = 0; k < ht_size; k++) {
859: if (!HT[(h1 + k) % ht_size]) {
860: HT[(h1 + k) % ht_size] = key;
861: HD[(h1 + k) % ht_size] = b->a + j * bs2;
862: break;
863: #if defined(PETSC_USE_INFO)
864: } else {
865: ct++;
866: #endif
867: }
868: }
869: #if defined(PETSC_USE_INFO)
870: if (k > max) max = k;
871: #endif
872: }
873: }
875: /* Print Summary */
876: #if defined(PETSC_USE_INFO)
877: for (i = 0, j = 0; i < ht_size; i++) {
878: if (HT[i]) j++;
879: }
880: PetscCall(PetscInfo(mat, "Average Search = %5.2g,max search = %" PetscInt_FMT "\n", (!j) ? (double)0.0 : (double)(((PetscReal)(ct + j)) / (double)j), max));
881: #endif
882: PetscFunctionReturn(PETSC_SUCCESS);
883: }
885: PetscErrorCode MatAssemblyBegin_MPIBAIJ(Mat mat, MatAssemblyType mode)
886: {
887: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
888: PetscInt nstash, reallocs;
890: PetscFunctionBegin;
891: if (baij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
893: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
894: PetscCall(MatStashScatterBegin_Private(mat, &mat->bstash, baij->rangebs));
895: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
896: PetscCall(PetscInfo(mat, "Stash has %" PetscInt_FMT " entries,uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
897: PetscCall(MatStashGetInfo_Private(&mat->bstash, &nstash, &reallocs));
898: PetscCall(PetscInfo(mat, "Block-Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
899: PetscFunctionReturn(PETSC_SUCCESS);
900: }
902: PetscErrorCode MatAssemblyEnd_MPIBAIJ(Mat mat, MatAssemblyType mode)
903: {
904: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
905: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)baij->A->data;
906: PetscInt i, j, rstart, ncols, flg, bs2 = baij->bs2;
907: PetscInt *row, *col;
908: PetscBool r1, r2, r3, other_disassembled;
909: MatScalar *val;
910: PetscMPIInt n;
912: PetscFunctionBegin;
913: /* do not use 'b=(Mat_SeqBAIJ*)baij->B->data' as B can be reset in disassembly */
914: if (!baij->donotstash && !mat->nooffprocentries) {
915: while (1) {
916: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
917: if (!flg) break;
919: for (i = 0; i < n;) {
920: /* Now identify the consecutive vals belonging to the same row */
921: for (j = i, rstart = row[j]; j < n; j++) {
922: if (row[j] != rstart) break;
923: }
924: if (j < n) ncols = j - i;
925: else ncols = n - i;
926: /* Now assemble all these values with a single function call */
927: PetscCall(MatSetValues_MPIBAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
928: i = j;
929: }
930: }
931: PetscCall(MatStashScatterEnd_Private(&mat->stash));
932: /* Now process the block-stash. Since the values are stashed column-oriented,
933: set the roworiented flag to column oriented, and after MatSetValues()
934: restore the original flags */
935: r1 = baij->roworiented;
936: r2 = a->roworiented;
937: r3 = ((Mat_SeqBAIJ *)baij->B->data)->roworiented;
939: baij->roworiented = PETSC_FALSE;
940: a->roworiented = PETSC_FALSE;
942: (((Mat_SeqBAIJ *)baij->B->data))->roworiented = PETSC_FALSE; /* b->roworiented */
943: while (1) {
944: PetscCall(MatStashScatterGetMesg_Private(&mat->bstash, &n, &row, &col, &val, &flg));
945: if (!flg) break;
947: for (i = 0; i < n;) {
948: /* Now identify the consecutive vals belonging to the same row */
949: for (j = i, rstart = row[j]; j < n; j++) {
950: if (row[j] != rstart) break;
951: }
952: if (j < n) ncols = j - i;
953: else ncols = n - i;
954: PetscCall(MatSetValuesBlocked_MPIBAIJ(mat, 1, row + i, ncols, col + i, val + i * bs2, mat->insertmode));
955: i = j;
956: }
957: }
958: PetscCall(MatStashScatterEnd_Private(&mat->bstash));
960: baij->roworiented = r1;
961: a->roworiented = r2;
963: ((Mat_SeqBAIJ *)baij->B->data)->roworiented = r3; /* b->roworiented */
964: }
966: PetscCall(MatAssemblyBegin(baij->A, mode));
967: PetscCall(MatAssemblyEnd(baij->A, mode));
969: /* determine if any processor has disassembled, if so we must
970: also disassemble ourselves, in order that we may reassemble. */
971: /*
972: if nonzero structure of submatrix B cannot change then we know that
973: no processor disassembled thus we can skip this stuff
974: */
975: if (!((Mat_SeqBAIJ *)baij->B->data)->nonew) {
976: PetscCall(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
977: if (mat->was_assembled && !other_disassembled) PetscCall(MatDisAssemble_MPIBAIJ(mat));
978: }
980: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIBAIJ(mat));
981: PetscCall(MatAssemblyBegin(baij->B, mode));
982: PetscCall(MatAssemblyEnd(baij->B, mode));
984: #if defined(PETSC_USE_INFO)
985: if (baij->ht && mode == MAT_FINAL_ASSEMBLY) {
986: PetscCall(PetscInfo(mat, "Average Hash Table Search in MatSetValues = %5.2f\n", (double)((PetscReal)baij->ht_total_ct) / baij->ht_insert_ct));
988: baij->ht_total_ct = 0;
989: baij->ht_insert_ct = 0;
990: }
991: #endif
992: if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
993: PetscCall(MatCreateHashTable_MPIBAIJ_Private(mat, baij->ht_fact));
995: mat->ops->setvalues = MatSetValues_MPIBAIJ_HT;
996: mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
997: }
999: PetscCall(PetscFree2(baij->rowvalues, baij->rowindices));
1001: baij->rowvalues = NULL;
1003: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
1004: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqBAIJ *)(baij->A->data))->nonew) {
1005: PetscObjectState state = baij->A->nonzerostate + baij->B->nonzerostate;
1006: PetscCall(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
1007: }
1008: PetscFunctionReturn(PETSC_SUCCESS);
1009: }
1011: extern PetscErrorCode MatView_SeqBAIJ(Mat, PetscViewer);
1012: #include <petscdraw.h>
1013: static PetscErrorCode MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1014: {
1015: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1016: PetscMPIInt rank = baij->rank;
1017: PetscInt bs = mat->rmap->bs;
1018: PetscBool iascii, isdraw;
1019: PetscViewer sviewer;
1020: PetscViewerFormat format;
1022: PetscFunctionBegin;
1023: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1024: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1025: if (iascii) {
1026: PetscCall(PetscViewerGetFormat(viewer, &format));
1027: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1028: MatInfo info;
1029: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1030: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1031: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1032: 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,
1033: mat->rmap->bs, (double)info.memory));
1034: PetscCall(MatGetInfo(baij->A, MAT_LOCAL, &info));
1035: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1036: PetscCall(MatGetInfo(baij->B, MAT_LOCAL, &info));
1037: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1038: PetscCall(PetscViewerFlush(viewer));
1039: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1040: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1041: PetscCall(VecScatterView(baij->Mvctx, viewer));
1042: PetscFunctionReturn(PETSC_SUCCESS);
1043: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1044: PetscCall(PetscViewerASCIIPrintf(viewer, " block size is %" PetscInt_FMT "\n", bs));
1045: PetscFunctionReturn(PETSC_SUCCESS);
1046: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1047: PetscFunctionReturn(PETSC_SUCCESS);
1048: }
1049: }
1051: if (isdraw) {
1052: PetscDraw draw;
1053: PetscBool isnull;
1054: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1055: PetscCall(PetscDrawIsNull(draw, &isnull));
1056: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1057: }
1059: {
1060: /* assemble the entire matrix onto first processor. */
1061: Mat A;
1062: Mat_SeqBAIJ *Aloc;
1063: PetscInt M = mat->rmap->N, N = mat->cmap->N, *ai, *aj, col, i, j, k, *rvals, mbs = baij->mbs;
1064: MatScalar *a;
1065: const char *matname;
1067: /* Here we are creating a temporary matrix, so will assume MPIBAIJ is acceptable */
1068: /* Perhaps this should be the type of mat? */
1069: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &A));
1070: if (rank == 0) {
1071: PetscCall(MatSetSizes(A, M, N, M, N));
1072: } else {
1073: PetscCall(MatSetSizes(A, 0, 0, M, N));
1074: }
1075: PetscCall(MatSetType(A, MATMPIBAIJ));
1076: PetscCall(MatMPIBAIJSetPreallocation(A, mat->rmap->bs, 0, NULL, 0, NULL));
1077: PetscCall(MatSetOption(A, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_FALSE));
1079: /* copy over the A part */
1080: Aloc = (Mat_SeqBAIJ *)baij->A->data;
1081: ai = Aloc->i;
1082: aj = Aloc->j;
1083: a = Aloc->a;
1084: PetscCall(PetscMalloc1(bs, &rvals));
1086: for (i = 0; i < mbs; i++) {
1087: rvals[0] = bs * (baij->rstartbs + i);
1088: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1089: for (j = ai[i]; j < ai[i + 1]; j++) {
1090: col = (baij->cstartbs + aj[j]) * bs;
1091: for (k = 0; k < bs; k++) {
1092: PetscCall(MatSetValues_MPIBAIJ(A, bs, rvals, 1, &col, a, INSERT_VALUES));
1093: col++;
1094: a += bs;
1095: }
1096: }
1097: }
1098: /* copy over the B part */
1099: Aloc = (Mat_SeqBAIJ *)baij->B->data;
1100: ai = Aloc->i;
1101: aj = Aloc->j;
1102: a = Aloc->a;
1103: for (i = 0; i < mbs; i++) {
1104: rvals[0] = bs * (baij->rstartbs + i);
1105: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1106: for (j = ai[i]; j < ai[i + 1]; j++) {
1107: col = baij->garray[aj[j]] * bs;
1108: for (k = 0; k < bs; k++) {
1109: PetscCall(MatSetValues_MPIBAIJ(A, bs, rvals, 1, &col, a, INSERT_VALUES));
1110: col++;
1111: a += bs;
1112: }
1113: }
1114: }
1115: PetscCall(PetscFree(rvals));
1116: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1117: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1118: /*
1119: Everyone has to call to draw the matrix since the graphics waits are
1120: synchronized across all processors that share the PetscDraw object
1121: */
1122: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1123: if (((PetscObject)mat)->name) PetscCall(PetscObjectGetName((PetscObject)mat, &matname));
1124: if (rank == 0) {
1125: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)((Mat_MPIBAIJ *)(A->data))->A, matname));
1126: PetscCall(MatView_SeqBAIJ(((Mat_MPIBAIJ *)(A->data))->A, sviewer));
1127: }
1128: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1129: PetscCall(PetscViewerFlush(viewer));
1130: PetscCall(MatDestroy(&A));
1131: }
1132: PetscFunctionReturn(PETSC_SUCCESS);
1133: }
1135: /* Used for both MPIBAIJ and MPISBAIJ matrices */
1136: PetscErrorCode MatView_MPIBAIJ_Binary(Mat mat, PetscViewer viewer)
1137: {
1138: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
1139: Mat_SeqBAIJ *A = (Mat_SeqBAIJ *)aij->A->data;
1140: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)aij->B->data;
1141: const PetscInt *garray = aij->garray;
1142: PetscInt header[4], M, N, m, rs, cs, bs, nz, cnt, i, j, ja, jb, k, l;
1143: PetscInt *rowlens, *colidxs;
1144: PetscScalar *matvals;
1146: PetscFunctionBegin;
1147: PetscCall(PetscViewerSetUp(viewer));
1149: M = mat->rmap->N;
1150: N = mat->cmap->N;
1151: m = mat->rmap->n;
1152: rs = mat->rmap->rstart;
1153: cs = mat->cmap->rstart;
1154: bs = mat->rmap->bs;
1155: nz = bs * bs * (A->nz + B->nz);
1157: /* write matrix header */
1158: header[0] = MAT_FILE_CLASSID;
1159: header[1] = M;
1160: header[2] = N;
1161: header[3] = nz;
1162: PetscCallMPI(MPI_Reduce(&nz, &header[3], 1, MPIU_INT, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1163: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1165: /* fill in and store row lengths */
1166: PetscCall(PetscMalloc1(m, &rowlens));
1167: for (cnt = 0, i = 0; i < A->mbs; i++)
1168: for (j = 0; j < bs; j++) rowlens[cnt++] = bs * (A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i]);
1169: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1170: PetscCall(PetscFree(rowlens));
1172: /* fill in and store column indices */
1173: PetscCall(PetscMalloc1(nz, &colidxs));
1174: for (cnt = 0, i = 0; i < A->mbs; i++) {
1175: for (k = 0; k < bs; k++) {
1176: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1177: if (garray[B->j[jb]] > cs / bs) break;
1178: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * garray[B->j[jb]] + l;
1179: }
1180: for (ja = A->i[i]; ja < A->i[i + 1]; ja++)
1181: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * A->j[ja] + l + cs;
1182: for (; jb < B->i[i + 1]; jb++)
1183: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * garray[B->j[jb]] + l;
1184: }
1185: }
1186: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt_FMT, cnt, nz);
1187: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DECIDE, PETSC_DECIDE, PETSC_INT));
1188: PetscCall(PetscFree(colidxs));
1190: /* fill in and store nonzero values */
1191: PetscCall(PetscMalloc1(nz, &matvals));
1192: for (cnt = 0, i = 0; i < A->mbs; i++) {
1193: for (k = 0; k < bs; k++) {
1194: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1195: if (garray[B->j[jb]] > cs / bs) break;
1196: for (l = 0; l < bs; l++) matvals[cnt++] = B->a[bs * (bs * jb + l) + k];
1197: }
1198: for (ja = A->i[i]; ja < A->i[i + 1]; ja++)
1199: for (l = 0; l < bs; l++) matvals[cnt++] = A->a[bs * (bs * ja + l) + k];
1200: for (; jb < B->i[i + 1]; jb++)
1201: for (l = 0; l < bs; l++) matvals[cnt++] = B->a[bs * (bs * jb + l) + k];
1202: }
1203: }
1204: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DECIDE, PETSC_DECIDE, PETSC_SCALAR));
1205: PetscCall(PetscFree(matvals));
1207: /* write block size option to the viewer's .info file */
1208: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1209: PetscFunctionReturn(PETSC_SUCCESS);
1210: }
1212: PetscErrorCode MatView_MPIBAIJ(Mat mat, PetscViewer viewer)
1213: {
1214: PetscBool iascii, isdraw, issocket, isbinary;
1216: PetscFunctionBegin;
1217: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1218: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1219: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1220: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1221: if (iascii || isdraw || issocket) {
1222: PetscCall(MatView_MPIBAIJ_ASCIIorDraworSocket(mat, viewer));
1223: } else if (isbinary) PetscCall(MatView_MPIBAIJ_Binary(mat, viewer));
1224: PetscFunctionReturn(PETSC_SUCCESS);
1225: }
1227: PetscErrorCode MatMult_MPIBAIJ(Mat A, Vec xx, Vec yy)
1228: {
1229: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1230: PetscInt nt;
1232: PetscFunctionBegin;
1233: PetscCall(VecGetLocalSize(xx, &nt));
1234: PetscCheck(nt == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A and xx");
1235: PetscCall(VecGetLocalSize(yy, &nt));
1236: PetscCheck(nt == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A and yy");
1237: PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1238: PetscCall((*a->A->ops->mult)(a->A, xx, yy));
1239: PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1240: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, yy, yy));
1241: PetscFunctionReturn(PETSC_SUCCESS);
1242: }
1244: PetscErrorCode MatMultAdd_MPIBAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1245: {
1246: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1248: PetscFunctionBegin;
1249: PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1250: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1251: PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1252: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1253: PetscFunctionReturn(PETSC_SUCCESS);
1254: }
1256: PetscErrorCode MatMultTranspose_MPIBAIJ(Mat A, Vec xx, Vec yy)
1257: {
1258: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1260: PetscFunctionBegin;
1261: /* do nondiagonal part */
1262: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1263: /* do local part */
1264: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1265: /* add partial results together */
1266: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1267: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1268: PetscFunctionReturn(PETSC_SUCCESS);
1269: }
1271: PetscErrorCode MatMultTransposeAdd_MPIBAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1272: {
1273: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1275: PetscFunctionBegin;
1276: /* do nondiagonal part */
1277: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1278: /* do local part */
1279: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1280: /* add partial results together */
1281: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1282: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1283: PetscFunctionReturn(PETSC_SUCCESS);
1284: }
1286: /*
1287: This only works correctly for square matrices where the subblock A->A is the
1288: diagonal block
1289: */
1290: PetscErrorCode MatGetDiagonal_MPIBAIJ(Mat A, Vec v)
1291: {
1292: PetscFunctionBegin;
1293: PetscCheck(A->rmap->N == A->cmap->N, PETSC_COMM_SELF, PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1294: PetscCall(MatGetDiagonal(((Mat_MPIBAIJ *)A->data)->A, v));
1295: PetscFunctionReturn(PETSC_SUCCESS);
1296: }
1298: PetscErrorCode MatScale_MPIBAIJ(Mat A, PetscScalar aa)
1299: {
1300: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1302: PetscFunctionBegin;
1303: PetscCall(MatScale(a->A, aa));
1304: PetscCall(MatScale(a->B, aa));
1305: PetscFunctionReturn(PETSC_SUCCESS);
1306: }
1308: PetscErrorCode MatGetRow_MPIBAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1309: {
1310: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)matin->data;
1311: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1312: PetscInt bs = matin->rmap->bs, bs2 = mat->bs2, i, *cworkA, *cworkB, **pcA, **pcB;
1313: PetscInt nztot, nzA, nzB, lrow, brstart = matin->rmap->rstart, brend = matin->rmap->rend;
1314: PetscInt *cmap, *idx_p, cstart = mat->cstartbs;
1316: PetscFunctionBegin;
1317: PetscCheck(row >= brstart && row < brend, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local rows");
1318: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1319: mat->getrowactive = PETSC_TRUE;
1321: if (!mat->rowvalues && (idx || v)) {
1322: /*
1323: allocate enough space to hold information from the longest row.
1324: */
1325: Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ *)mat->A->data, *Ba = (Mat_SeqBAIJ *)mat->B->data;
1326: PetscInt max = 1, mbs = mat->mbs, tmp;
1327: for (i = 0; i < mbs; i++) {
1328: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1329: if (max < tmp) max = tmp;
1330: }
1331: PetscCall(PetscMalloc2(max * bs2, &mat->rowvalues, max * bs2, &mat->rowindices));
1332: }
1333: lrow = row - brstart;
1335: pvA = &vworkA;
1336: pcA = &cworkA;
1337: pvB = &vworkB;
1338: pcB = &cworkB;
1339: if (!v) {
1340: pvA = NULL;
1341: pvB = NULL;
1342: }
1343: if (!idx) {
1344: pcA = NULL;
1345: if (!v) pcB = NULL;
1346: }
1347: PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1348: PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1349: nztot = nzA + nzB;
1351: cmap = mat->garray;
1352: if (v || idx) {
1353: if (nztot) {
1354: /* Sort by increasing column numbers, assuming A and B already sorted */
1355: PetscInt imark = -1;
1356: if (v) {
1357: *v = v_p = mat->rowvalues;
1358: for (i = 0; i < nzB; i++) {
1359: if (cmap[cworkB[i] / bs] < cstart) v_p[i] = vworkB[i];
1360: else break;
1361: }
1362: imark = i;
1363: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1364: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1365: }
1366: if (idx) {
1367: *idx = idx_p = mat->rowindices;
1368: if (imark > -1) {
1369: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i] / bs] * bs + cworkB[i] % bs;
1370: } else {
1371: for (i = 0; i < nzB; i++) {
1372: if (cmap[cworkB[i] / bs] < cstart) idx_p[i] = cmap[cworkB[i] / bs] * bs + cworkB[i] % bs;
1373: else break;
1374: }
1375: imark = i;
1376: }
1377: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart * bs + cworkA[i];
1378: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i] / bs] * bs + cworkB[i] % bs;
1379: }
1380: } else {
1381: if (idx) *idx = NULL;
1382: if (v) *v = NULL;
1383: }
1384: }
1385: *nz = nztot;
1386: PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1387: PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1388: PetscFunctionReturn(PETSC_SUCCESS);
1389: }
1391: PetscErrorCode MatRestoreRow_MPIBAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1392: {
1393: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1395: PetscFunctionBegin;
1396: PetscCheck(baij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow not called");
1397: baij->getrowactive = PETSC_FALSE;
1398: PetscFunctionReturn(PETSC_SUCCESS);
1399: }
1401: PetscErrorCode MatZeroEntries_MPIBAIJ(Mat A)
1402: {
1403: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1405: PetscFunctionBegin;
1406: PetscCall(MatZeroEntries(l->A));
1407: PetscCall(MatZeroEntries(l->B));
1408: PetscFunctionReturn(PETSC_SUCCESS);
1409: }
1411: PetscErrorCode MatGetInfo_MPIBAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1412: {
1413: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)matin->data;
1414: Mat A = a->A, B = a->B;
1415: PetscLogDouble isend[5], irecv[5];
1417: PetscFunctionBegin;
1418: info->block_size = (PetscReal)matin->rmap->bs;
1420: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1422: isend[0] = info->nz_used;
1423: isend[1] = info->nz_allocated;
1424: isend[2] = info->nz_unneeded;
1425: isend[3] = info->memory;
1426: isend[4] = info->mallocs;
1428: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1430: isend[0] += info->nz_used;
1431: isend[1] += info->nz_allocated;
1432: isend[2] += info->nz_unneeded;
1433: isend[3] += info->memory;
1434: isend[4] += info->mallocs;
1436: if (flag == MAT_LOCAL) {
1437: info->nz_used = isend[0];
1438: info->nz_allocated = isend[1];
1439: info->nz_unneeded = isend[2];
1440: info->memory = isend[3];
1441: info->mallocs = isend[4];
1442: } else if (flag == MAT_GLOBAL_MAX) {
1443: PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1445: info->nz_used = irecv[0];
1446: info->nz_allocated = irecv[1];
1447: info->nz_unneeded = irecv[2];
1448: info->memory = irecv[3];
1449: info->mallocs = irecv[4];
1450: } else if (flag == MAT_GLOBAL_SUM) {
1451: PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1453: info->nz_used = irecv[0];
1454: info->nz_allocated = irecv[1];
1455: info->nz_unneeded = irecv[2];
1456: info->memory = irecv[3];
1457: info->mallocs = irecv[4];
1458: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_ARG_WRONG, "Unknown MatInfoType argument %d", (int)flag);
1459: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1460: info->fill_ratio_needed = 0;
1461: info->factor_mallocs = 0;
1462: PetscFunctionReturn(PETSC_SUCCESS);
1463: }
1465: PetscErrorCode MatSetOption_MPIBAIJ(Mat A, MatOption op, PetscBool flg)
1466: {
1467: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1469: PetscFunctionBegin;
1470: switch (op) {
1471: case MAT_NEW_NONZERO_LOCATIONS:
1472: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1473: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1474: case MAT_KEEP_NONZERO_PATTERN:
1475: case MAT_NEW_NONZERO_LOCATION_ERR:
1476: MatCheckPreallocated(A, 1);
1477: PetscCall(MatSetOption(a->A, op, flg));
1478: PetscCall(MatSetOption(a->B, op, flg));
1479: break;
1480: case MAT_ROW_ORIENTED:
1481: MatCheckPreallocated(A, 1);
1482: a->roworiented = flg;
1484: PetscCall(MatSetOption(a->A, op, flg));
1485: PetscCall(MatSetOption(a->B, op, flg));
1486: break;
1487: case MAT_FORCE_DIAGONAL_ENTRIES:
1488: case MAT_SORTED_FULL:
1489: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1490: break;
1491: case MAT_IGNORE_OFF_PROC_ENTRIES:
1492: a->donotstash = flg;
1493: break;
1494: case MAT_USE_HASH_TABLE:
1495: a->ht_flag = flg;
1496: a->ht_fact = 1.39;
1497: break;
1498: case MAT_SYMMETRIC:
1499: case MAT_STRUCTURALLY_SYMMETRIC:
1500: case MAT_HERMITIAN:
1501: case MAT_SUBMAT_SINGLEIS:
1502: case MAT_SYMMETRY_ETERNAL:
1503: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1504: case MAT_SPD_ETERNAL:
1505: /* if the diagonal matrix is square it inherits some of the properties above */
1506: break;
1507: default:
1508: SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "unknown option %d", op);
1509: }
1510: PetscFunctionReturn(PETSC_SUCCESS);
1511: }
1513: PetscErrorCode MatTranspose_MPIBAIJ(Mat A, MatReuse reuse, Mat *matout)
1514: {
1515: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)A->data;
1516: Mat_SeqBAIJ *Aloc;
1517: Mat B;
1518: PetscInt M = A->rmap->N, N = A->cmap->N, *ai, *aj, i, *rvals, j, k, col;
1519: PetscInt bs = A->rmap->bs, mbs = baij->mbs;
1520: MatScalar *a;
1522: PetscFunctionBegin;
1523: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1524: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
1525: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1526: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1527: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1528: /* Do not know preallocation information, but must set block size */
1529: PetscCall(MatMPIBAIJSetPreallocation(B, A->rmap->bs, PETSC_DECIDE, NULL, PETSC_DECIDE, NULL));
1530: } else {
1531: B = *matout;
1532: }
1534: /* copy over the A part */
1535: Aloc = (Mat_SeqBAIJ *)baij->A->data;
1536: ai = Aloc->i;
1537: aj = Aloc->j;
1538: a = Aloc->a;
1539: PetscCall(PetscMalloc1(bs, &rvals));
1541: for (i = 0; i < mbs; i++) {
1542: rvals[0] = bs * (baij->rstartbs + i);
1543: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1544: for (j = ai[i]; j < ai[i + 1]; j++) {
1545: col = (baij->cstartbs + aj[j]) * bs;
1546: for (k = 0; k < bs; k++) {
1547: PetscCall(MatSetValues_MPIBAIJ(B, 1, &col, bs, rvals, a, INSERT_VALUES));
1549: col++;
1550: a += bs;
1551: }
1552: }
1553: }
1554: /* copy over the B part */
1555: Aloc = (Mat_SeqBAIJ *)baij->B->data;
1556: ai = Aloc->i;
1557: aj = Aloc->j;
1558: a = Aloc->a;
1559: for (i = 0; i < mbs; i++) {
1560: rvals[0] = bs * (baij->rstartbs + i);
1561: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1562: for (j = ai[i]; j < ai[i + 1]; j++) {
1563: col = baij->garray[aj[j]] * bs;
1564: for (k = 0; k < bs; k++) {
1565: PetscCall(MatSetValues_MPIBAIJ(B, 1, &col, bs, rvals, a, INSERT_VALUES));
1566: col++;
1567: a += bs;
1568: }
1569: }
1570: }
1571: PetscCall(PetscFree(rvals));
1572: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1573: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1575: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) *matout = B;
1576: else PetscCall(MatHeaderMerge(A, &B));
1577: PetscFunctionReturn(PETSC_SUCCESS);
1578: }
1580: PetscErrorCode MatDiagonalScale_MPIBAIJ(Mat mat, Vec ll, Vec rr)
1581: {
1582: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1583: Mat a = baij->A, b = baij->B;
1584: PetscInt s1, s2, s3;
1586: PetscFunctionBegin;
1587: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1588: if (rr) {
1589: PetscCall(VecGetLocalSize(rr, &s1));
1590: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1591: /* Overlap communication with computation. */
1592: PetscCall(VecScatterBegin(baij->Mvctx, rr, baij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1593: }
1594: if (ll) {
1595: PetscCall(VecGetLocalSize(ll, &s1));
1596: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1597: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1598: }
1599: /* scale the diagonal block */
1600: PetscUseTypeMethod(a, diagonalscale, ll, rr);
1602: if (rr) {
1603: /* Do a scatter end and then right scale the off-diagonal block */
1604: PetscCall(VecScatterEnd(baij->Mvctx, rr, baij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1605: PetscUseTypeMethod(b, diagonalscale, NULL, baij->lvec);
1606: }
1607: PetscFunctionReturn(PETSC_SUCCESS);
1608: }
1610: PetscErrorCode MatZeroRows_MPIBAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
1611: {
1612: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1613: PetscInt *lrows;
1614: PetscInt r, len;
1615: PetscBool cong;
1617: PetscFunctionBegin;
1618: /* get locally owned rows */
1619: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
1620: /* fix right hand side if needed */
1621: if (x && b) {
1622: const PetscScalar *xx;
1623: PetscScalar *bb;
1625: PetscCall(VecGetArrayRead(x, &xx));
1626: PetscCall(VecGetArray(b, &bb));
1627: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
1628: PetscCall(VecRestoreArrayRead(x, &xx));
1629: PetscCall(VecRestoreArray(b, &bb));
1630: }
1632: /* actually zap the local rows */
1633: /*
1634: Zero the required rows. If the "diagonal block" of the matrix
1635: is square and the user wishes to set the diagonal we use separate
1636: code so that MatSetValues() is not called for each diagonal allocating
1637: new memory, thus calling lots of mallocs and slowing things down.
1639: */
1640: /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1641: PetscCall(MatZeroRows_SeqBAIJ(l->B, len, lrows, 0.0, NULL, NULL));
1642: PetscCall(MatHasCongruentLayouts(A, &cong));
1643: if ((diag != 0.0) && cong) {
1644: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, diag, NULL, NULL));
1645: } else if (diag != 0.0) {
1646: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, 0.0, NULL, NULL));
1647: PetscCheck(!((Mat_SeqBAIJ *)l->A->data)->nonew, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatZeroRows() on rectangular matrices cannot be used with the Mat options \n\
1648: MAT_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
1649: for (r = 0; r < len; ++r) {
1650: const PetscInt row = lrows[r] + A->rmap->rstart;
1651: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
1652: }
1653: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1654: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1655: } else {
1656: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, 0.0, NULL, NULL));
1657: }
1658: PetscCall(PetscFree(lrows));
1660: /* only change matrix nonzero state if pattern was allowed to be changed */
1661: if (!((Mat_SeqBAIJ *)(l->A->data))->keepnonzeropattern) {
1662: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1663: PetscCall(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1664: }
1665: PetscFunctionReturn(PETSC_SUCCESS);
1666: }
1668: PetscErrorCode MatZeroRowsColumns_MPIBAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
1669: {
1670: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1671: PetscMPIInt n = A->rmap->n, p = 0;
1672: PetscInt i, j, k, r, len = 0, row, col, count;
1673: PetscInt *lrows, *owners = A->rmap->range;
1674: PetscSFNode *rrows;
1675: PetscSF sf;
1676: const PetscScalar *xx;
1677: PetscScalar *bb, *mask;
1678: Vec xmask, lmask;
1679: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)l->B->data;
1680: PetscInt bs = A->rmap->bs, bs2 = baij->bs2;
1681: PetscScalar *aa;
1683: PetscFunctionBegin;
1684: /* Create SF where leaves are input rows and roots are owned rows */
1685: PetscCall(PetscMalloc1(n, &lrows));
1686: for (r = 0; r < n; ++r) lrows[r] = -1;
1687: PetscCall(PetscMalloc1(N, &rrows));
1688: for (r = 0; r < N; ++r) {
1689: const PetscInt idx = rows[r];
1690: 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);
1691: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
1692: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
1693: }
1694: rrows[r].rank = p;
1695: rrows[r].index = rows[r] - owners[p];
1696: }
1697: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1698: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
1699: /* Collect flags for rows to be zeroed */
1700: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
1701: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
1702: PetscCall(PetscSFDestroy(&sf));
1703: /* Compress and put in row numbers */
1704: for (r = 0; r < n; ++r)
1705: if (lrows[r] >= 0) lrows[len++] = r;
1706: /* zero diagonal part of matrix */
1707: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
1708: /* handle off diagonal part of matrix */
1709: PetscCall(MatCreateVecs(A, &xmask, NULL));
1710: PetscCall(VecDuplicate(l->lvec, &lmask));
1711: PetscCall(VecGetArray(xmask, &bb));
1712: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
1713: PetscCall(VecRestoreArray(xmask, &bb));
1714: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
1715: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
1716: PetscCall(VecDestroy(&xmask));
1717: if (x) {
1718: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
1719: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
1720: PetscCall(VecGetArrayRead(l->lvec, &xx));
1721: PetscCall(VecGetArray(b, &bb));
1722: }
1723: PetscCall(VecGetArray(lmask, &mask));
1724: /* remove zeroed rows of off diagonal matrix */
1725: for (i = 0; i < len; ++i) {
1726: row = lrows[i];
1727: count = (baij->i[row / bs + 1] - baij->i[row / bs]) * bs;
1728: aa = ((MatScalar *)(baij->a)) + baij->i[row / bs] * bs2 + (row % bs);
1729: for (k = 0; k < count; ++k) {
1730: aa[0] = 0.0;
1731: aa += bs;
1732: }
1733: }
1734: /* loop over all elements of off process part of matrix zeroing removed columns*/
1735: for (i = 0; i < l->B->rmap->N; ++i) {
1736: row = i / bs;
1737: for (j = baij->i[row]; j < baij->i[row + 1]; ++j) {
1738: for (k = 0; k < bs; ++k) {
1739: col = bs * baij->j[j] + k;
1740: if (PetscAbsScalar(mask[col])) {
1741: aa = ((MatScalar *)(baij->a)) + j * bs2 + (i % bs) + bs * k;
1742: if (x) bb[i] -= aa[0] * xx[col];
1743: aa[0] = 0.0;
1744: }
1745: }
1746: }
1747: }
1748: if (x) {
1749: PetscCall(VecRestoreArray(b, &bb));
1750: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1751: }
1752: PetscCall(VecRestoreArray(lmask, &mask));
1753: PetscCall(VecDestroy(&lmask));
1754: PetscCall(PetscFree(lrows));
1756: /* only change matrix nonzero state if pattern was allowed to be changed */
1757: if (!((Mat_SeqBAIJ *)(l->A->data))->keepnonzeropattern) {
1758: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1759: PetscCall(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1760: }
1761: PetscFunctionReturn(PETSC_SUCCESS);
1762: }
1764: PetscErrorCode MatSetUnfactored_MPIBAIJ(Mat A)
1765: {
1766: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1768: PetscFunctionBegin;
1769: PetscCall(MatSetUnfactored(a->A));
1770: PetscFunctionReturn(PETSC_SUCCESS);
1771: }
1773: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat, MatDuplicateOption, Mat *);
1775: PetscErrorCode MatEqual_MPIBAIJ(Mat A, Mat B, PetscBool *flag)
1776: {
1777: Mat_MPIBAIJ *matB = (Mat_MPIBAIJ *)B->data, *matA = (Mat_MPIBAIJ *)A->data;
1778: Mat a, b, c, d;
1779: PetscBool flg;
1781: PetscFunctionBegin;
1782: a = matA->A;
1783: b = matA->B;
1784: c = matB->A;
1785: d = matB->B;
1787: PetscCall(MatEqual(a, c, &flg));
1788: if (flg) PetscCall(MatEqual(b, d, &flg));
1789: PetscCall(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
1790: PetscFunctionReturn(PETSC_SUCCESS);
1791: }
1793: PetscErrorCode MatCopy_MPIBAIJ(Mat A, Mat B, MatStructure str)
1794: {
1795: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1796: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
1798: PetscFunctionBegin;
1799: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1800: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1801: PetscCall(MatCopy_Basic(A, B, str));
1802: } else {
1803: PetscCall(MatCopy(a->A, b->A, str));
1804: PetscCall(MatCopy(a->B, b->B, str));
1805: }
1806: PetscCall(PetscObjectStateIncrease((PetscObject)B));
1807: PetscFunctionReturn(PETSC_SUCCESS);
1808: }
1810: PetscErrorCode MatAXPYGetPreallocation_MPIBAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
1811: {
1812: PetscInt bs = Y->rmap->bs, m = Y->rmap->N / bs;
1813: Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data;
1814: Mat_SeqBAIJ *y = (Mat_SeqBAIJ *)Y->data;
1816: PetscFunctionBegin;
1817: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
1818: PetscFunctionReturn(PETSC_SUCCESS);
1819: }
1821: PetscErrorCode MatAXPY_MPIBAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
1822: {
1823: Mat_MPIBAIJ *xx = (Mat_MPIBAIJ *)X->data, *yy = (Mat_MPIBAIJ *)Y->data;
1824: PetscBLASInt bnz, one = 1;
1825: Mat_SeqBAIJ *x, *y;
1826: PetscInt bs2 = Y->rmap->bs * Y->rmap->bs;
1828: PetscFunctionBegin;
1829: if (str == SAME_NONZERO_PATTERN) {
1830: PetscScalar alpha = a;
1831: x = (Mat_SeqBAIJ *)xx->A->data;
1832: y = (Mat_SeqBAIJ *)yy->A->data;
1833: PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz));
1834: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one));
1835: x = (Mat_SeqBAIJ *)xx->B->data;
1836: y = (Mat_SeqBAIJ *)yy->B->data;
1837: PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz));
1838: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one));
1839: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
1840: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
1841: PetscCall(MatAXPY_Basic(Y, a, X, str));
1842: } else {
1843: Mat B;
1844: PetscInt *nnz_d, *nnz_o, bs = Y->rmap->bs;
1845: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
1846: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
1847: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
1848: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
1849: PetscCall(MatSetSizes(B, Y->rmap->n, Y->cmap->n, Y->rmap->N, Y->cmap->N));
1850: PetscCall(MatSetBlockSizesFromMats(B, Y, Y));
1851: PetscCall(MatSetType(B, MATMPIBAIJ));
1852: PetscCall(MatAXPYGetPreallocation_SeqBAIJ(yy->A, xx->A, nnz_d));
1853: PetscCall(MatAXPYGetPreallocation_MPIBAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
1854: PetscCall(MatMPIBAIJSetPreallocation(B, bs, 0, nnz_d, 0, nnz_o));
1855: /* MatAXPY_BasicWithPreallocation() for BAIJ matrix is much slower than AIJ, even for bs=1 ! */
1856: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
1857: PetscCall(MatHeaderMerge(Y, &B));
1858: PetscCall(PetscFree(nnz_d));
1859: PetscCall(PetscFree(nnz_o));
1860: }
1861: PetscFunctionReturn(PETSC_SUCCESS);
1862: }
1864: PetscErrorCode MatConjugate_MPIBAIJ(Mat mat)
1865: {
1866: PetscFunctionBegin;
1867: if (PetscDefined(USE_COMPLEX)) {
1868: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)mat->data;
1870: PetscCall(MatConjugate_SeqBAIJ(a->A));
1871: PetscCall(MatConjugate_SeqBAIJ(a->B));
1872: }
1873: PetscFunctionReturn(PETSC_SUCCESS);
1874: }
1876: PetscErrorCode MatRealPart_MPIBAIJ(Mat A)
1877: {
1878: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1880: PetscFunctionBegin;
1881: PetscCall(MatRealPart(a->A));
1882: PetscCall(MatRealPart(a->B));
1883: PetscFunctionReturn(PETSC_SUCCESS);
1884: }
1886: PetscErrorCode MatImaginaryPart_MPIBAIJ(Mat A)
1887: {
1888: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1890: PetscFunctionBegin;
1891: PetscCall(MatImaginaryPart(a->A));
1892: PetscCall(MatImaginaryPart(a->B));
1893: PetscFunctionReturn(PETSC_SUCCESS);
1894: }
1896: PetscErrorCode MatCreateSubMatrix_MPIBAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
1897: {
1898: IS iscol_local;
1899: PetscInt csize;
1901: PetscFunctionBegin;
1902: PetscCall(ISGetLocalSize(iscol, &csize));
1903: if (call == MAT_REUSE_MATRIX) {
1904: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
1905: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
1906: } else {
1907: PetscCall(ISAllGather(iscol, &iscol_local));
1908: }
1909: PetscCall(MatCreateSubMatrix_MPIBAIJ_Private(mat, isrow, iscol_local, csize, call, newmat));
1910: if (call == MAT_INITIAL_MATRIX) {
1911: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
1912: PetscCall(ISDestroy(&iscol_local));
1913: }
1914: PetscFunctionReturn(PETSC_SUCCESS);
1915: }
1917: /*
1918: Not great since it makes two copies of the submatrix, first an SeqBAIJ
1919: in local and then by concatenating the local matrices the end result.
1920: Writing it directly would be much like MatCreateSubMatrices_MPIBAIJ().
1921: This routine is used for BAIJ and SBAIJ matrices (unfortunate dependency).
1922: */
1923: PetscErrorCode MatCreateSubMatrix_MPIBAIJ_Private(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
1924: {
1925: PetscMPIInt rank, size;
1926: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs;
1927: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
1928: Mat M, Mreuse;
1929: MatScalar *vwork, *aa;
1930: MPI_Comm comm;
1931: IS isrow_new, iscol_new;
1932: Mat_SeqBAIJ *aij;
1934: PetscFunctionBegin;
1935: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
1936: PetscCallMPI(MPI_Comm_rank(comm, &rank));
1937: PetscCallMPI(MPI_Comm_size(comm, &size));
1938: /* The compression and expansion should be avoided. Doesn't point
1939: out errors, might change the indices, hence buggey */
1940: PetscCall(ISCompressIndicesGeneral(mat->rmap->N, mat->rmap->n, mat->rmap->bs, 1, &isrow, &isrow_new));
1941: PetscCall(ISCompressIndicesGeneral(mat->cmap->N, mat->cmap->n, mat->cmap->bs, 1, &iscol, &iscol_new));
1943: if (call == MAT_REUSE_MATRIX) {
1944: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
1945: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
1946: PetscCall(MatCreateSubMatrices_MPIBAIJ_local(mat, 1, &isrow_new, &iscol_new, MAT_REUSE_MATRIX, &Mreuse));
1947: } else {
1948: PetscCall(MatCreateSubMatrices_MPIBAIJ_local(mat, 1, &isrow_new, &iscol_new, MAT_INITIAL_MATRIX, &Mreuse));
1949: }
1950: PetscCall(ISDestroy(&isrow_new));
1951: PetscCall(ISDestroy(&iscol_new));
1952: /*
1953: m - number of local rows
1954: n - number of columns (same on all processors)
1955: rstart - first row in new global matrix generated
1956: */
1957: PetscCall(MatGetBlockSize(mat, &bs));
1958: PetscCall(MatGetSize(Mreuse, &m, &n));
1959: m = m / bs;
1960: n = n / bs;
1962: if (call == MAT_INITIAL_MATRIX) {
1963: aij = (Mat_SeqBAIJ *)(Mreuse)->data;
1964: ii = aij->i;
1965: jj = aij->j;
1967: /*
1968: Determine the number of non-zeros in the diagonal and off-diagonal
1969: portions of the matrix in order to do correct preallocation
1970: */
1972: /* first get start and end of "diagonal" columns */
1973: if (csize == PETSC_DECIDE) {
1974: PetscCall(ISGetSize(isrow, &mglobal));
1975: if (mglobal == n * bs) { /* square matrix */
1976: nlocal = m;
1977: } else {
1978: nlocal = n / size + ((n % size) > rank);
1979: }
1980: } else {
1981: nlocal = csize / bs;
1982: }
1983: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
1984: rstart = rend - nlocal;
1985: 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);
1987: /* next, compute all the lengths */
1988: PetscCall(PetscMalloc2(m + 1, &dlens, m + 1, &olens));
1989: for (i = 0; i < m; i++) {
1990: jend = ii[i + 1] - ii[i];
1991: olen = 0;
1992: dlen = 0;
1993: for (j = 0; j < jend; j++) {
1994: if (*jj < rstart || *jj >= rend) olen++;
1995: else dlen++;
1996: jj++;
1997: }
1998: olens[i] = olen;
1999: dlens[i] = dlen;
2000: }
2001: PetscCall(MatCreate(comm, &M));
2002: PetscCall(MatSetSizes(M, bs * m, bs * nlocal, PETSC_DECIDE, bs * n));
2003: PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
2004: PetscCall(MatMPIBAIJSetPreallocation(M, bs, 0, dlens, 0, olens));
2005: PetscCall(MatMPISBAIJSetPreallocation(M, bs, 0, dlens, 0, olens));
2006: PetscCall(PetscFree2(dlens, olens));
2007: } else {
2008: PetscInt ml, nl;
2010: M = *newmat;
2011: PetscCall(MatGetLocalSize(M, &ml, &nl));
2012: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
2013: PetscCall(MatZeroEntries(M));
2014: /*
2015: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
2016: rather than the slower MatSetValues().
2017: */
2018: M->was_assembled = PETSC_TRUE;
2019: M->assembled = PETSC_FALSE;
2020: }
2021: PetscCall(MatSetOption(M, MAT_ROW_ORIENTED, PETSC_FALSE));
2022: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
2023: aij = (Mat_SeqBAIJ *)(Mreuse)->data;
2024: ii = aij->i;
2025: jj = aij->j;
2026: aa = aij->a;
2027: for (i = 0; i < m; i++) {
2028: row = rstart / bs + i;
2029: nz = ii[i + 1] - ii[i];
2030: cwork = jj;
2031: jj += nz;
2032: vwork = aa;
2033: aa += nz * bs * bs;
2034: PetscCall(MatSetValuesBlocked_MPIBAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
2035: }
2037: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
2038: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
2039: *newmat = M;
2041: /* save submatrix used in processor for next request */
2042: if (call == MAT_INITIAL_MATRIX) {
2043: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
2044: PetscCall(PetscObjectDereference((PetscObject)Mreuse));
2045: }
2046: PetscFunctionReturn(PETSC_SUCCESS);
2047: }
2049: PetscErrorCode MatPermute_MPIBAIJ(Mat A, IS rowp, IS colp, Mat *B)
2050: {
2051: MPI_Comm comm, pcomm;
2052: PetscInt clocal_size, nrows;
2053: const PetscInt *rows;
2054: PetscMPIInt size;
2055: IS crowp, lcolp;
2057: PetscFunctionBegin;
2058: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
2059: /* make a collective version of 'rowp' */
2060: PetscCall(PetscObjectGetComm((PetscObject)rowp, &pcomm));
2061: if (pcomm == comm) {
2062: crowp = rowp;
2063: } else {
2064: PetscCall(ISGetSize(rowp, &nrows));
2065: PetscCall(ISGetIndices(rowp, &rows));
2066: PetscCall(ISCreateGeneral(comm, nrows, rows, PETSC_COPY_VALUES, &crowp));
2067: PetscCall(ISRestoreIndices(rowp, &rows));
2068: }
2069: PetscCall(ISSetPermutation(crowp));
2070: /* make a local version of 'colp' */
2071: PetscCall(PetscObjectGetComm((PetscObject)colp, &pcomm));
2072: PetscCallMPI(MPI_Comm_size(pcomm, &size));
2073: if (size == 1) {
2074: lcolp = colp;
2075: } else {
2076: PetscCall(ISAllGather(colp, &lcolp));
2077: }
2078: PetscCall(ISSetPermutation(lcolp));
2079: /* now we just get the submatrix */
2080: PetscCall(MatGetLocalSize(A, NULL, &clocal_size));
2081: PetscCall(MatCreateSubMatrix_MPIBAIJ_Private(A, crowp, lcolp, clocal_size, MAT_INITIAL_MATRIX, B));
2082: /* clean up */
2083: if (pcomm != comm) PetscCall(ISDestroy(&crowp));
2084: if (size > 1) PetscCall(ISDestroy(&lcolp));
2085: PetscFunctionReturn(PETSC_SUCCESS);
2086: }
2088: PetscErrorCode MatGetGhosts_MPIBAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
2089: {
2090: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
2091: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)baij->B->data;
2093: PetscFunctionBegin;
2094: if (nghosts) *nghosts = B->nbs;
2095: if (ghosts) *ghosts = baij->garray;
2096: PetscFunctionReturn(PETSC_SUCCESS);
2097: }
2099: PetscErrorCode MatGetSeqNonzeroStructure_MPIBAIJ(Mat A, Mat *newmat)
2100: {
2101: Mat B;
2102: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2103: Mat_SeqBAIJ *ad = (Mat_SeqBAIJ *)a->A->data, *bd = (Mat_SeqBAIJ *)a->B->data;
2104: Mat_SeqAIJ *b;
2105: PetscMPIInt size, rank, *recvcounts = NULL, *displs = NULL;
2106: PetscInt sendcount, i, *rstarts = A->rmap->range, n, cnt, j, bs = A->rmap->bs;
2107: PetscInt m, *garray = a->garray, *lens, *jsendbuf, *a_jsendbuf, *b_jsendbuf;
2109: PetscFunctionBegin;
2110: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
2111: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)A), &rank));
2113: /* Tell every processor the number of nonzeros per row */
2114: PetscCall(PetscMalloc1(A->rmap->N / bs, &lens));
2115: 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];
2116: PetscCall(PetscMalloc1(2 * size, &recvcounts));
2117: displs = recvcounts + size;
2118: for (i = 0; i < size; i++) {
2119: recvcounts[i] = A->rmap->range[i + 1] / bs - A->rmap->range[i] / bs;
2120: displs[i] = A->rmap->range[i] / bs;
2121: }
2122: PetscCallMPI(MPI_Allgatherv(MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, lens, recvcounts, displs, MPIU_INT, PetscObjectComm((PetscObject)A)));
2123: /* Create the sequential matrix of the same type as the local block diagonal */
2124: PetscCall(MatCreate(PETSC_COMM_SELF, &B));
2125: PetscCall(MatSetSizes(B, A->rmap->N / bs, A->cmap->N / bs, PETSC_DETERMINE, PETSC_DETERMINE));
2126: PetscCall(MatSetType(B, MATSEQAIJ));
2127: PetscCall(MatSeqAIJSetPreallocation(B, 0, lens));
2128: b = (Mat_SeqAIJ *)B->data;
2130: /* Copy my part of matrix column indices over */
2131: sendcount = ad->nz + bd->nz;
2132: jsendbuf = b->j + b->i[rstarts[rank] / bs];
2133: a_jsendbuf = ad->j;
2134: b_jsendbuf = bd->j;
2135: n = A->rmap->rend / bs - A->rmap->rstart / bs;
2136: cnt = 0;
2137: for (i = 0; i < n; i++) {
2138: /* put in lower diagonal portion */
2139: m = bd->i[i + 1] - bd->i[i];
2140: while (m > 0) {
2141: /* is it above diagonal (in bd (compressed) numbering) */
2142: if (garray[*b_jsendbuf] > A->rmap->rstart / bs + i) break;
2143: jsendbuf[cnt++] = garray[*b_jsendbuf++];
2144: m--;
2145: }
2147: /* put in diagonal portion */
2148: for (j = ad->i[i]; j < ad->i[i + 1]; j++) jsendbuf[cnt++] = A->rmap->rstart / bs + *a_jsendbuf++;
2150: /* put in upper diagonal portion */
2151: while (m-- > 0) jsendbuf[cnt++] = garray[*b_jsendbuf++];
2152: }
2153: PetscCheck(cnt == sendcount, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Corrupted PETSc matrix: nz given %" PetscInt_FMT " actual nz %" PetscInt_FMT, sendcount, cnt);
2155: /* Gather all column indices to all processors */
2156: for (i = 0; i < size; i++) {
2157: recvcounts[i] = 0;
2158: for (j = A->rmap->range[i] / bs; j < A->rmap->range[i + 1] / bs; j++) recvcounts[i] += lens[j];
2159: }
2160: displs[0] = 0;
2161: for (i = 1; i < size; i++) displs[i] = displs[i - 1] + recvcounts[i - 1];
2162: PetscCallMPI(MPI_Allgatherv(MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, b->j, recvcounts, displs, MPIU_INT, PetscObjectComm((PetscObject)A)));
2163: /* Assemble the matrix into useable form (note numerical values not yet set) */
2164: /* set the b->ilen (length of each row) values */
2165: PetscCall(PetscArraycpy(b->ilen, lens, A->rmap->N / bs));
2166: /* set the b->i indices */
2167: b->i[0] = 0;
2168: for (i = 1; i <= A->rmap->N / bs; i++) b->i[i] = b->i[i - 1] + lens[i - 1];
2169: PetscCall(PetscFree(lens));
2170: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2171: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2172: PetscCall(PetscFree(recvcounts));
2174: PetscCall(MatPropagateSymmetryOptions(A, B));
2175: *newmat = B;
2176: PetscFunctionReturn(PETSC_SUCCESS);
2177: }
2179: PetscErrorCode MatSOR_MPIBAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
2180: {
2181: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)matin->data;
2182: Vec bb1 = NULL;
2184: PetscFunctionBegin;
2185: if (flag == SOR_APPLY_UPPER) {
2186: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2187: PetscFunctionReturn(PETSC_SUCCESS);
2188: }
2190: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS) PetscCall(VecDuplicate(bb, &bb1));
2192: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
2193: if (flag & SOR_ZERO_INITIAL_GUESS) {
2194: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2195: its--;
2196: }
2198: while (its--) {
2199: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2200: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2202: /* update rhs: bb1 = bb - B*x */
2203: PetscCall(VecScale(mat->lvec, -1.0));
2204: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2206: /* local sweep */
2207: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
2208: }
2209: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
2210: if (flag & SOR_ZERO_INITIAL_GUESS) {
2211: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2212: its--;
2213: }
2214: while (its--) {
2215: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2216: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2218: /* update rhs: bb1 = bb - B*x */
2219: PetscCall(VecScale(mat->lvec, -1.0));
2220: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2222: /* local sweep */
2223: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
2224: }
2225: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
2226: if (flag & SOR_ZERO_INITIAL_GUESS) {
2227: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2228: its--;
2229: }
2230: while (its--) {
2231: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2232: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2234: /* update rhs: bb1 = bb - B*x */
2235: PetscCall(VecScale(mat->lvec, -1.0));
2236: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2238: /* local sweep */
2239: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
2240: }
2241: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel version of SOR requested not supported");
2243: PetscCall(VecDestroy(&bb1));
2244: PetscFunctionReturn(PETSC_SUCCESS);
2245: }
2247: PetscErrorCode MatGetColumnReductions_MPIBAIJ(Mat A, PetscInt type, PetscReal *reductions)
2248: {
2249: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)A->data;
2250: PetscInt m, N, i, *garray = aij->garray;
2251: PetscInt ib, jb, bs = A->rmap->bs;
2252: Mat_SeqBAIJ *a_aij = (Mat_SeqBAIJ *)aij->A->data;
2253: MatScalar *a_val = a_aij->a;
2254: Mat_SeqBAIJ *b_aij = (Mat_SeqBAIJ *)aij->B->data;
2255: MatScalar *b_val = b_aij->a;
2256: PetscReal *work;
2258: PetscFunctionBegin;
2259: PetscCall(MatGetSize(A, &m, &N));
2260: PetscCall(PetscCalloc1(N, &work));
2261: if (type == NORM_2) {
2262: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2263: for (jb = 0; jb < bs; jb++) {
2264: for (ib = 0; ib < bs; ib++) {
2265: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val * *a_val);
2266: a_val++;
2267: }
2268: }
2269: }
2270: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2271: for (jb = 0; jb < bs; jb++) {
2272: for (ib = 0; ib < bs; ib++) {
2273: work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val * *b_val);
2274: b_val++;
2275: }
2276: }
2277: }
2278: } else if (type == NORM_1) {
2279: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2280: for (jb = 0; jb < bs; jb++) {
2281: for (ib = 0; ib < bs; ib++) {
2282: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val);
2283: a_val++;
2284: }
2285: }
2286: }
2287: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2288: for (jb = 0; jb < bs; jb++) {
2289: for (ib = 0; ib < bs; ib++) {
2290: work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val);
2291: b_val++;
2292: }
2293: }
2294: }
2295: } else if (type == NORM_INFINITY) {
2296: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2297: for (jb = 0; jb < bs; jb++) {
2298: for (ib = 0; ib < bs; ib++) {
2299: int col = A->cmap->rstart + a_aij->j[i] * bs + jb;
2300: work[col] = PetscMax(PetscAbsScalar(*a_val), work[col]);
2301: a_val++;
2302: }
2303: }
2304: }
2305: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2306: for (jb = 0; jb < bs; jb++) {
2307: for (ib = 0; ib < bs; ib++) {
2308: int col = garray[b_aij->j[i]] * bs + jb;
2309: work[col] = PetscMax(PetscAbsScalar(*b_val), work[col]);
2310: b_val++;
2311: }
2312: }
2313: }
2314: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
2315: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2316: for (jb = 0; jb < bs; jb++) {
2317: for (ib = 0; ib < bs; ib++) {
2318: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscRealPart(*a_val);
2319: a_val++;
2320: }
2321: }
2322: }
2323: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2324: for (jb = 0; jb < bs; jb++) {
2325: for (ib = 0; ib < bs; ib++) {
2326: work[garray[b_aij->j[i]] * bs + jb] += PetscRealPart(*b_val);
2327: b_val++;
2328: }
2329: }
2330: }
2331: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
2332: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2333: for (jb = 0; jb < bs; jb++) {
2334: for (ib = 0; ib < bs; ib++) {
2335: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscImaginaryPart(*a_val);
2336: a_val++;
2337: }
2338: }
2339: }
2340: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2341: for (jb = 0; jb < bs; jb++) {
2342: for (ib = 0; ib < bs; ib++) {
2343: work[garray[b_aij->j[i]] * bs + jb] += PetscImaginaryPart(*b_val);
2344: b_val++;
2345: }
2346: }
2347: }
2348: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
2349: if (type == NORM_INFINITY) {
2350: PetscCall(MPIU_Allreduce(work, reductions, N, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
2351: } else {
2352: PetscCall(MPIU_Allreduce(work, reductions, N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
2353: }
2354: PetscCall(PetscFree(work));
2355: if (type == NORM_2) {
2356: for (i = 0; i < N; i++) reductions[i] = PetscSqrtReal(reductions[i]);
2357: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
2358: for (i = 0; i < N; i++) reductions[i] /= m;
2359: }
2360: PetscFunctionReturn(PETSC_SUCCESS);
2361: }
2363: PetscErrorCode MatInvertBlockDiagonal_MPIBAIJ(Mat A, const PetscScalar **values)
2364: {
2365: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2367: PetscFunctionBegin;
2368: PetscCall(MatInvertBlockDiagonal(a->A, values));
2369: A->factorerrortype = a->A->factorerrortype;
2370: A->factorerror_zeropivot_value = a->A->factorerror_zeropivot_value;
2371: A->factorerror_zeropivot_row = a->A->factorerror_zeropivot_row;
2372: PetscFunctionReturn(PETSC_SUCCESS);
2373: }
2375: PetscErrorCode MatShift_MPIBAIJ(Mat Y, PetscScalar a)
2376: {
2377: Mat_MPIBAIJ *maij = (Mat_MPIBAIJ *)Y->data;
2378: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)maij->A->data;
2380: PetscFunctionBegin;
2381: if (!Y->preallocated) {
2382: PetscCall(MatMPIBAIJSetPreallocation(Y, Y->rmap->bs, 1, NULL, 0, NULL));
2383: } else if (!aij->nz) {
2384: PetscInt nonew = aij->nonew;
2385: PetscCall(MatSeqBAIJSetPreallocation(maij->A, Y->rmap->bs, 1, NULL));
2386: aij->nonew = nonew;
2387: }
2388: PetscCall(MatShift_Basic(Y, a));
2389: PetscFunctionReturn(PETSC_SUCCESS);
2390: }
2392: PetscErrorCode MatMissingDiagonal_MPIBAIJ(Mat A, PetscBool *missing, PetscInt *d)
2393: {
2394: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2396: PetscFunctionBegin;
2397: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2398: PetscCall(MatMissingDiagonal(a->A, missing, d));
2399: if (d) {
2400: PetscInt rstart;
2401: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2402: *d += rstart / A->rmap->bs;
2403: }
2404: PetscFunctionReturn(PETSC_SUCCESS);
2405: }
2407: PetscErrorCode MatGetDiagonalBlock_MPIBAIJ(Mat A, Mat *a)
2408: {
2409: PetscFunctionBegin;
2410: *a = ((Mat_MPIBAIJ *)A->data)->A;
2411: PetscFunctionReturn(PETSC_SUCCESS);
2412: }
2414: static struct _MatOps MatOps_Values = {MatSetValues_MPIBAIJ,
2415: MatGetRow_MPIBAIJ,
2416: MatRestoreRow_MPIBAIJ,
2417: MatMult_MPIBAIJ,
2418: /* 4*/ MatMultAdd_MPIBAIJ,
2419: MatMultTranspose_MPIBAIJ,
2420: MatMultTransposeAdd_MPIBAIJ,
2421: NULL,
2422: NULL,
2423: NULL,
2424: /*10*/ NULL,
2425: NULL,
2426: NULL,
2427: MatSOR_MPIBAIJ,
2428: MatTranspose_MPIBAIJ,
2429: /*15*/ MatGetInfo_MPIBAIJ,
2430: MatEqual_MPIBAIJ,
2431: MatGetDiagonal_MPIBAIJ,
2432: MatDiagonalScale_MPIBAIJ,
2433: MatNorm_MPIBAIJ,
2434: /*20*/ MatAssemblyBegin_MPIBAIJ,
2435: MatAssemblyEnd_MPIBAIJ,
2436: MatSetOption_MPIBAIJ,
2437: MatZeroEntries_MPIBAIJ,
2438: /*24*/ MatZeroRows_MPIBAIJ,
2439: NULL,
2440: NULL,
2441: NULL,
2442: NULL,
2443: /*29*/ MatSetUp_MPI_Hash,
2444: NULL,
2445: NULL,
2446: MatGetDiagonalBlock_MPIBAIJ,
2447: NULL,
2448: /*34*/ MatDuplicate_MPIBAIJ,
2449: NULL,
2450: NULL,
2451: NULL,
2452: NULL,
2453: /*39*/ MatAXPY_MPIBAIJ,
2454: MatCreateSubMatrices_MPIBAIJ,
2455: MatIncreaseOverlap_MPIBAIJ,
2456: MatGetValues_MPIBAIJ,
2457: MatCopy_MPIBAIJ,
2458: /*44*/ NULL,
2459: MatScale_MPIBAIJ,
2460: MatShift_MPIBAIJ,
2461: NULL,
2462: MatZeroRowsColumns_MPIBAIJ,
2463: /*49*/ NULL,
2464: NULL,
2465: NULL,
2466: NULL,
2467: NULL,
2468: /*54*/ MatFDColoringCreate_MPIXAIJ,
2469: NULL,
2470: MatSetUnfactored_MPIBAIJ,
2471: MatPermute_MPIBAIJ,
2472: MatSetValuesBlocked_MPIBAIJ,
2473: /*59*/ MatCreateSubMatrix_MPIBAIJ,
2474: MatDestroy_MPIBAIJ,
2475: MatView_MPIBAIJ,
2476: NULL,
2477: NULL,
2478: /*64*/ NULL,
2479: NULL,
2480: NULL,
2481: NULL,
2482: NULL,
2483: /*69*/ MatGetRowMaxAbs_MPIBAIJ,
2484: NULL,
2485: NULL,
2486: NULL,
2487: NULL,
2488: /*74*/ NULL,
2489: MatFDColoringApply_BAIJ,
2490: NULL,
2491: NULL,
2492: NULL,
2493: /*79*/ NULL,
2494: NULL,
2495: NULL,
2496: NULL,
2497: MatLoad_MPIBAIJ,
2498: /*84*/ NULL,
2499: NULL,
2500: NULL,
2501: NULL,
2502: NULL,
2503: /*89*/ NULL,
2504: NULL,
2505: NULL,
2506: NULL,
2507: NULL,
2508: /*94*/ NULL,
2509: NULL,
2510: NULL,
2511: NULL,
2512: NULL,
2513: /*99*/ NULL,
2514: NULL,
2515: NULL,
2516: MatConjugate_MPIBAIJ,
2517: NULL,
2518: /*104*/ NULL,
2519: MatRealPart_MPIBAIJ,
2520: MatImaginaryPart_MPIBAIJ,
2521: NULL,
2522: NULL,
2523: /*109*/ NULL,
2524: NULL,
2525: NULL,
2526: NULL,
2527: MatMissingDiagonal_MPIBAIJ,
2528: /*114*/ MatGetSeqNonzeroStructure_MPIBAIJ,
2529: NULL,
2530: MatGetGhosts_MPIBAIJ,
2531: NULL,
2532: NULL,
2533: /*119*/ NULL,
2534: NULL,
2535: NULL,
2536: NULL,
2537: MatGetMultiProcBlock_MPIBAIJ,
2538: /*124*/ NULL,
2539: MatGetColumnReductions_MPIBAIJ,
2540: MatInvertBlockDiagonal_MPIBAIJ,
2541: NULL,
2542: NULL,
2543: /*129*/ NULL,
2544: NULL,
2545: NULL,
2546: NULL,
2547: NULL,
2548: /*134*/ NULL,
2549: NULL,
2550: NULL,
2551: NULL,
2552: NULL,
2553: /*139*/ MatSetBlockSizes_Default,
2554: NULL,
2555: NULL,
2556: MatFDColoringSetUp_MPIXAIJ,
2557: NULL,
2558: /*144*/ MatCreateMPIMatConcatenateSeqMat_MPIBAIJ,
2559: NULL,
2560: NULL,
2561: NULL,
2562: NULL,
2563: NULL,
2564: /*150*/ NULL,
2565: NULL};
2567: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
2568: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
2570: PetscErrorCode MatMPIBAIJSetPreallocationCSR_MPIBAIJ(Mat B, PetscInt bs, const PetscInt ii[], const PetscInt jj[], const PetscScalar V[])
2571: {
2572: PetscInt m, rstart, cstart, cend;
2573: PetscInt i, j, dlen, olen, nz, nz_max = 0, *d_nnz = NULL, *o_nnz = NULL;
2574: const PetscInt *JJ = NULL;
2575: PetscScalar *values = NULL;
2576: PetscBool roworiented = ((Mat_MPIBAIJ *)B->data)->roworiented;
2577: PetscBool nooffprocentries;
2579: PetscFunctionBegin;
2580: PetscCall(PetscLayoutSetBlockSize(B->rmap, bs));
2581: PetscCall(PetscLayoutSetBlockSize(B->cmap, bs));
2582: PetscCall(PetscLayoutSetUp(B->rmap));
2583: PetscCall(PetscLayoutSetUp(B->cmap));
2584: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
2585: m = B->rmap->n / bs;
2586: rstart = B->rmap->rstart / bs;
2587: cstart = B->cmap->rstart / bs;
2588: cend = B->cmap->rend / bs;
2590: PetscCheck(!ii[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "ii[0] must be 0 but it is %" PetscInt_FMT, ii[0]);
2591: PetscCall(PetscMalloc2(m, &d_nnz, m, &o_nnz));
2592: for (i = 0; i < m; i++) {
2593: nz = ii[i + 1] - ii[i];
2594: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
2595: nz_max = PetscMax(nz_max, nz);
2596: dlen = 0;
2597: olen = 0;
2598: JJ = jj + ii[i];
2599: for (j = 0; j < nz; j++) {
2600: if (*JJ < cstart || *JJ >= cend) olen++;
2601: else dlen++;
2602: JJ++;
2603: }
2604: d_nnz[i] = dlen;
2605: o_nnz[i] = olen;
2606: }
2607: PetscCall(MatMPIBAIJSetPreallocation(B, bs, 0, d_nnz, 0, o_nnz));
2608: PetscCall(PetscFree2(d_nnz, o_nnz));
2610: values = (PetscScalar *)V;
2611: if (!values) PetscCall(PetscCalloc1(bs * bs * nz_max, &values));
2612: for (i = 0; i < m; i++) {
2613: PetscInt row = i + rstart;
2614: PetscInt ncols = ii[i + 1] - ii[i];
2615: const PetscInt *icols = jj + ii[i];
2616: if (bs == 1 || !roworiented) { /* block ordering matches the non-nested layout of MatSetValues so we can insert entire rows */
2617: const PetscScalar *svals = values + (V ? (bs * bs * ii[i]) : 0);
2618: PetscCall(MatSetValuesBlocked_MPIBAIJ(B, 1, &row, ncols, icols, svals, INSERT_VALUES));
2619: } else { /* block ordering does not match so we can only insert one block at a time. */
2620: PetscInt j;
2621: for (j = 0; j < ncols; j++) {
2622: const PetscScalar *svals = values + (V ? (bs * bs * (ii[i] + j)) : 0);
2623: PetscCall(MatSetValuesBlocked_MPIBAIJ(B, 1, &row, 1, &icols[j], svals, INSERT_VALUES));
2624: }
2625: }
2626: }
2628: if (!V) PetscCall(PetscFree(values));
2629: nooffprocentries = B->nooffprocentries;
2630: B->nooffprocentries = PETSC_TRUE;
2631: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2632: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2633: B->nooffprocentries = nooffprocentries;
2635: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
2636: PetscFunctionReturn(PETSC_SUCCESS);
2637: }
2639: /*@C
2640: MatMPIBAIJSetPreallocationCSR - Creates a sparse parallel matrix in `MATBAIJ` format using the given nonzero structure and (optional) numerical values
2642: Collective
2644: Input Parameters:
2645: + B - the matrix
2646: . bs - the block size
2647: . i - the indices into j for the start of each local row (starts with zero)
2648: . j - the column indices for each local row (starts with zero) these must be sorted for each row
2649: - v - optional values in the matrix
2651: Level: advanced
2653: Notes:
2654: The order of the entries in values is specified by the `MatOption` `MAT_ROW_ORIENTED`. For example, C programs
2655: 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
2656: over rows within a block and the last index is over columns within a block row. Fortran programs will likely set
2657: `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
2658: block column and the second index is over columns within a block.
2660: 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
2662: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIBAIJSetPreallocation()`, `MatCreateAIJ()`, `MPIAIJ`, `MatCreateMPIBAIJWithArrays()`, `MPIBAIJ`
2663: @*/
2664: PetscErrorCode MatMPIBAIJSetPreallocationCSR(Mat B, PetscInt bs, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
2665: {
2666: PetscFunctionBegin;
2670: PetscTryMethod(B, "MatMPIBAIJSetPreallocationCSR_C", (Mat, PetscInt, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, bs, i, j, v));
2671: PetscFunctionReturn(PETSC_SUCCESS);
2672: }
2674: PetscErrorCode MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B, PetscInt bs, PetscInt d_nz, const PetscInt *d_nnz, PetscInt o_nz, const PetscInt *o_nnz)
2675: {
2676: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
2677: PetscInt i;
2678: PetscMPIInt size;
2680: PetscFunctionBegin;
2681: if (B->hash_active) {
2682: PetscCall(PetscMemcpy(&B->ops, &b->cops, sizeof(*(B->ops))));
2683: B->hash_active = PETSC_FALSE;
2684: }
2685: if (!B->preallocated) PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), bs, &B->bstash));
2686: PetscCall(MatSetBlockSize(B, PetscAbs(bs)));
2687: PetscCall(PetscLayoutSetUp(B->rmap));
2688: PetscCall(PetscLayoutSetUp(B->cmap));
2689: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
2691: if (d_nnz) {
2692: 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]);
2693: }
2694: if (o_nnz) {
2695: 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]);
2696: }
2698: b->bs2 = bs * bs;
2699: b->mbs = B->rmap->n / bs;
2700: b->nbs = B->cmap->n / bs;
2701: b->Mbs = B->rmap->N / bs;
2702: b->Nbs = B->cmap->N / bs;
2704: for (i = 0; i <= b->size; i++) b->rangebs[i] = B->rmap->range[i] / bs;
2705: b->rstartbs = B->rmap->rstart / bs;
2706: b->rendbs = B->rmap->rend / bs;
2707: b->cstartbs = B->cmap->rstart / bs;
2708: b->cendbs = B->cmap->rend / bs;
2710: #if defined(PETSC_USE_CTABLE)
2711: PetscCall(PetscHMapIDestroy(&b->colmap));
2712: #else
2713: PetscCall(PetscFree(b->colmap));
2714: #endif
2715: PetscCall(PetscFree(b->garray));
2716: PetscCall(VecDestroy(&b->lvec));
2717: PetscCall(VecScatterDestroy(&b->Mvctx));
2719: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2720: PetscCall(MatDestroy(&b->B));
2721: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2722: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2723: PetscCall(MatSetType(b->B, MATSEQBAIJ));
2725: PetscCall(MatDestroy(&b->A));
2726: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2727: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2728: PetscCall(MatSetType(b->A, MATSEQBAIJ));
2730: PetscCall(MatSeqBAIJSetPreallocation(b->A, bs, d_nz, d_nnz));
2731: PetscCall(MatSeqBAIJSetPreallocation(b->B, bs, o_nz, o_nnz));
2732: B->preallocated = PETSC_TRUE;
2733: B->was_assembled = PETSC_FALSE;
2734: B->assembled = PETSC_FALSE;
2735: PetscFunctionReturn(PETSC_SUCCESS);
2736: }
2738: extern PetscErrorCode MatDiagonalScaleLocal_MPIBAIJ(Mat, Vec);
2739: extern PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat, PetscReal);
2741: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAdj(Mat B, MatType newtype, MatReuse reuse, Mat *adj)
2742: {
2743: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
2744: Mat_SeqBAIJ *d = (Mat_SeqBAIJ *)b->A->data, *o = (Mat_SeqBAIJ *)b->B->data;
2745: PetscInt M = B->rmap->n / B->rmap->bs, i, *ii, *jj, cnt, j, k, rstart = B->rmap->rstart / B->rmap->bs;
2746: const PetscInt *id = d->i, *jd = d->j, *io = o->i, *jo = o->j, *garray = b->garray;
2748: PetscFunctionBegin;
2749: PetscCall(PetscMalloc1(M + 1, &ii));
2750: ii[0] = 0;
2751: for (i = 0; i < M; i++) {
2752: 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]);
2753: 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]);
2754: ii[i + 1] = ii[i] + id[i + 1] - id[i] + io[i + 1] - io[i];
2755: /* remove one from count of matrix has diagonal */
2756: for (j = id[i]; j < id[i + 1]; j++) {
2757: if (jd[j] == i) {
2758: ii[i + 1]--;
2759: break;
2760: }
2761: }
2762: }
2763: PetscCall(PetscMalloc1(ii[M], &jj));
2764: cnt = 0;
2765: for (i = 0; i < M; i++) {
2766: for (j = io[i]; j < io[i + 1]; j++) {
2767: if (garray[jo[j]] > rstart) break;
2768: jj[cnt++] = garray[jo[j]];
2769: }
2770: for (k = id[i]; k < id[i + 1]; k++) {
2771: if (jd[k] != i) jj[cnt++] = rstart + jd[k];
2772: }
2773: for (; j < io[i + 1]; j++) jj[cnt++] = garray[jo[j]];
2774: }
2775: PetscCall(MatCreateMPIAdj(PetscObjectComm((PetscObject)B), M, B->cmap->N / B->rmap->bs, ii, jj, NULL, adj));
2776: PetscFunctionReturn(PETSC_SUCCESS);
2777: }
2779: #include <../src/mat/impls/aij/mpi/mpiaij.h>
2781: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat, MatType, MatReuse, Mat *);
2783: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAIJ(Mat A, MatType newtype, MatReuse reuse, Mat *newmat)
2784: {
2785: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2786: Mat_MPIAIJ *b;
2787: Mat B;
2789: PetscFunctionBegin;
2790: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Matrix must be assembled");
2792: if (reuse == MAT_REUSE_MATRIX) {
2793: B = *newmat;
2794: } else {
2795: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
2796: PetscCall(MatSetType(B, MATMPIAIJ));
2797: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
2798: PetscCall(MatSetBlockSizes(B, A->rmap->bs, A->cmap->bs));
2799: PetscCall(MatSeqAIJSetPreallocation(B, 0, NULL));
2800: PetscCall(MatMPIAIJSetPreallocation(B, 0, NULL, 0, NULL));
2801: }
2802: b = (Mat_MPIAIJ *)B->data;
2804: if (reuse == MAT_REUSE_MATRIX) {
2805: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_REUSE_MATRIX, &b->A));
2806: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_REUSE_MATRIX, &b->B));
2807: } else {
2808: PetscBool3 sym = A->symmetric, hermitian = A->hermitian, structurally_symmetric = A->structurally_symmetric, spd = A->spd;
2809: PetscCall(MatDestroy(&b->A));
2810: PetscCall(MatDestroy(&b->B));
2811: PetscCall(MatDisAssemble_MPIBAIJ(A));
2812: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->A));
2813: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->B));
2814: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
2815: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
2816: A->symmetric = sym;
2817: A->hermitian = hermitian;
2818: A->structurally_symmetric = structurally_symmetric;
2819: A->spd = spd;
2820: }
2821: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2822: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2824: if (reuse == MAT_INPLACE_MATRIX) {
2825: PetscCall(MatHeaderReplace(A, &B));
2826: } else {
2827: *newmat = B;
2828: }
2829: PetscFunctionReturn(PETSC_SUCCESS);
2830: }
2832: /*MC
2833: MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.
2835: Options Database Keys:
2836: + -mat_type mpibaij - sets the matrix type to `MATMPIBAIJ` during a call to `MatSetFromOptions()`
2837: . -mat_block_size <bs> - set the blocksize used to store the matrix
2838: . -mat_baij_mult_version version - indicate the version of the matrix-vector product to use (0 often indicates using BLAS)
2839: - -mat_use_hash_table <fact> - set hash table factor
2841: Level: beginner
2843: Note:
2844: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
2845: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
2847: .seealso: `Mat`, MATBAIJ`, MATSEQBAIJ`, `MatCreateBAIJ`
2848: M*/
2850: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIBSTRM(Mat, MatType, MatReuse, Mat *);
2852: PETSC_EXTERN PetscErrorCode MatCreate_MPIBAIJ(Mat B)
2853: {
2854: Mat_MPIBAIJ *b;
2855: PetscBool flg = PETSC_FALSE;
2857: PetscFunctionBegin;
2858: PetscCall(PetscNew(&b));
2859: B->data = (void *)b;
2861: PetscCall(PetscMemcpy(B->ops, &MatOps_Values, sizeof(struct _MatOps)));
2862: B->assembled = PETSC_FALSE;
2864: B->insertmode = NOT_SET_VALUES;
2865: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
2866: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &b->size));
2868: /* build local table of row and column ownerships */
2869: PetscCall(PetscMalloc1(b->size + 1, &b->rangebs));
2871: /* build cache for off array entries formed */
2872: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
2874: b->donotstash = PETSC_FALSE;
2875: b->colmap = NULL;
2876: b->garray = NULL;
2877: b->roworiented = PETSC_TRUE;
2879: /* stuff used in block assembly */
2880: b->barray = NULL;
2882: /* stuff used for matrix vector multiply */
2883: b->lvec = NULL;
2884: b->Mvctx = NULL;
2886: /* stuff for MatGetRow() */
2887: b->rowindices = NULL;
2888: b->rowvalues = NULL;
2889: b->getrowactive = PETSC_FALSE;
2891: /* hash table stuff */
2892: b->ht = NULL;
2893: b->hd = NULL;
2894: b->ht_size = 0;
2895: b->ht_flag = PETSC_FALSE;
2896: b->ht_fact = 0;
2897: b->ht_total_ct = 0;
2898: b->ht_insert_ct = 0;
2900: /* stuff for MatCreateSubMatrices_MPIBAIJ_local() */
2901: b->ijonly = PETSC_FALSE;
2903: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpiadj_C", MatConvert_MPIBAIJ_MPIAdj));
2904: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpiaij_C", MatConvert_MPIBAIJ_MPIAIJ));
2905: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpisbaij_C", MatConvert_MPIBAIJ_MPISBAIJ));
2906: #if defined(PETSC_HAVE_HYPRE)
2907: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_hypre_C", MatConvert_AIJ_HYPRE));
2908: #endif
2909: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIBAIJ));
2910: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIBAIJ));
2911: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIBAIJSetPreallocation_C", MatMPIBAIJSetPreallocation_MPIBAIJ));
2912: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIBAIJSetPreallocationCSR_C", MatMPIBAIJSetPreallocationCSR_MPIBAIJ));
2913: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIBAIJ));
2914: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetHashTableFactor_C", MatSetHashTableFactor_MPIBAIJ));
2915: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_is_C", MatConvert_XAIJ_IS));
2916: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIBAIJ));
2918: PetscOptionsBegin(PetscObjectComm((PetscObject)B), NULL, "Options for loading MPIBAIJ matrix 1", "Mat");
2919: PetscCall(PetscOptionsName("-mat_use_hash_table", "Use hash table to save time in constructing matrix", "MatSetOption", &flg));
2920: if (flg) {
2921: PetscReal fact = 1.39;
2922: PetscCall(MatSetOption(B, MAT_USE_HASH_TABLE, PETSC_TRUE));
2923: PetscCall(PetscOptionsReal("-mat_use_hash_table", "Use hash table factor", "MatMPIBAIJSetHashTableFactor", fact, &fact, NULL));
2924: if (fact <= 1.0) fact = 1.39;
2925: PetscCall(MatMPIBAIJSetHashTableFactor(B, fact));
2926: PetscCall(PetscInfo(B, "Hash table Factor used %5.2g\n", (double)fact));
2927: }
2928: PetscOptionsEnd();
2929: PetscFunctionReturn(PETSC_SUCCESS);
2930: }
2932: /*MC
2933: MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.
2935: This matrix type is identical to `MATSEQBAIJ` when constructed with a single process communicator,
2936: and `MATMPIBAIJ` otherwise.
2938: Options Database Keys:
2939: . -mat_type baij - sets the matrix type to `MATBAIJ` during a call to `MatSetFromOptions()`
2941: Level: beginner
2943: .seealso: `Mat`, `MatCreateBAIJ()`, `MATSEQBAIJ`, `MATMPIBAIJ`, `MatMPIBAIJSetPreallocation()`, `MatMPIBAIJSetPreallocationCSR()`
2944: M*/
2946: /*@C
2947: MatMPIBAIJSetPreallocation - Allocates memory for a sparse parallel matrix in `MATMPIBAIJ` format
2948: (block compressed row).
2950: Collective
2952: Input Parameters:
2953: + B - the matrix
2954: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
2955: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
2956: . d_nz - number of block nonzeros per block row in diagonal portion of local
2957: submatrix (same for all local rows)
2958: . d_nnz - array containing the number of block nonzeros in the various block rows
2959: of the in diagonal portion of the local (possibly different for each block
2960: row) or `NULL`. If you plan to factor the matrix you must leave room for the diagonal entry and
2961: set it even if it is zero.
2962: . o_nz - number of block nonzeros per block row in the off-diagonal portion of local
2963: submatrix (same for all local rows).
2964: - o_nnz - array containing the number of nonzeros in the various block rows of the
2965: off-diagonal portion of the local submatrix (possibly different for
2966: each block row) or `NULL`.
2968: If the *_nnz parameter is given then the *_nz parameter is ignored
2970: Options Database Keys:
2971: + -mat_block_size - size of the blocks to use
2972: - -mat_use_hash_table <fact> - set hash table factor
2974: Level: intermediate
2976: Notes:
2977: For good matrix assembly performance
2978: the user should preallocate the matrix storage by setting the parameters
2979: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`). By setting these parameters accurately,
2980: performance can be increased by more than a factor of 50.
2982: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one processor
2983: than it must be used on all processors that share the object for that argument.
2985: Storage Information:
2986: For a square global matrix we define each processor's diagonal portion
2987: to be its local rows and the corresponding columns (a square submatrix);
2988: each processor's off-diagonal portion encompasses the remainder of the
2989: local matrix (a rectangular submatrix).
2991: The user can specify preallocated storage for the diagonal part of
2992: the local submatrix with either `d_nz` or `d_nnz` (not both). Set
2993: `d_nz` = `PETSC_DEFAULT` and `d_nnz` = `NULL` for PETSc to control dynamic
2994: memory allocation. Likewise, specify preallocated storage for the
2995: off-diagonal part of the local submatrix with `o_nz` or `o_nnz` (not both).
2997: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2998: the figure below we depict these three local rows and all columns (0-11).
3000: .vb
3001: 0 1 2 3 4 5 6 7 8 9 10 11
3002: --------------------------
3003: row 3 |o o o d d d o o o o o o
3004: row 4 |o o o d d d o o o o o o
3005: row 5 |o o o d d d o o o o o o
3006: --------------------------
3007: .ve
3009: Thus, any entries in the d locations are stored in the d (diagonal)
3010: submatrix, and any entries in the o locations are stored in the
3011: o (off-diagonal) submatrix. Note that the d and the o submatrices are
3012: stored simply in the `MATSEQBAIJ` format for compressed row storage.
3014: Now `d_nz` should indicate the number of block nonzeros per row in the d matrix,
3015: and `o_nz` should indicate the number of block nonzeros per row in the o matrix.
3016: In general, for PDE problems in which most nonzeros are near the diagonal,
3017: one expects `d_nz` >> `o_nz`. For large problems you MUST preallocate memory
3018: or you will get TERRIBLE performance; see the users' manual chapter on
3019: matrices.
3021: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3022: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3023: You can also run with the option `-info` and look for messages with the string
3024: malloc in them to see if additional memory allocation was needed.
3026: .seealso: `Mat`, `MATMPIBAIJ`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`, `MatMPIBAIJSetPreallocationCSR()`, `PetscSplitOwnership()`
3027: @*/
3028: PetscErrorCode MatMPIBAIJSetPreallocation(Mat B, PetscInt bs, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
3029: {
3030: PetscFunctionBegin;
3034: PetscTryMethod(B, "MatMPIBAIJSetPreallocation_C", (Mat, PetscInt, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, bs, d_nz, d_nnz, o_nz, o_nnz));
3035: PetscFunctionReturn(PETSC_SUCCESS);
3036: }
3038: /*@C
3039: MatCreateBAIJ - Creates a sparse parallel matrix in `MATBAIJ` format
3040: (block compressed row).
3042: Collective
3044: Input Parameters:
3045: + comm - MPI communicator
3046: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
3047: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
3048: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
3049: This value should be the same as the local size used in creating the
3050: y vector for the matrix-vector product y = Ax.
3051: . n - number of local columns (or `PETSC_DECIDE` to have calculated if N is given)
3052: This value should be the same as the local size used in creating the
3053: x vector for the matrix-vector product y = Ax.
3054: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
3055: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
3056: . d_nz - number of nonzero blocks per block row in diagonal portion of local
3057: submatrix (same for all local rows)
3058: . d_nnz - array containing the number of nonzero blocks in the various block rows
3059: of the in diagonal portion of the local (possibly different for each block
3060: row) or NULL. If you plan to factor the matrix you must leave room for the diagonal entry
3061: and set it even if it is zero.
3062: . o_nz - number of nonzero blocks per block row in the off-diagonal portion of local
3063: submatrix (same for all local rows).
3064: - o_nnz - array containing the number of nonzero blocks in the various block rows of the
3065: off-diagonal portion of the local submatrix (possibly different for
3066: each block row) or NULL.
3068: Output Parameter:
3069: . A - the matrix
3071: Options Database Keys:
3072: + -mat_block_size - size of the blocks to use
3073: - -mat_use_hash_table <fact> - set hash table factor
3075: Level: intermediate
3077: Notes:
3078: For good matrix assembly performance
3079: the user should preallocate the matrix storage by setting the parameters
3080: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`). By setting these parameters accurately,
3081: performance can be increased by more than a factor of 50.
3083: It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
3084: MatXXXXSetPreallocation() paradigm instead of this routine directly.
3085: [MatXXXXSetPreallocation() is, for example, `MatSeqBAIJSetPreallocation()`]
3087: If the *_nnz parameter is given then the *_nz parameter is ignored
3089: A nonzero block is any block that as 1 or more nonzeros in it
3091: The user MUST specify either the local or global matrix dimensions
3092: (possibly both).
3094: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one processor
3095: than it must be used on all processors that share the object for that argument.
3097: Storage Information:
3098: For a square global matrix we define each processor's diagonal portion
3099: to be its local rows and the corresponding columns (a square submatrix);
3100: each processor's off-diagonal portion encompasses the remainder of the
3101: local matrix (a rectangular submatrix).
3103: The user can specify preallocated storage for the diagonal part of
3104: the local submatrix with either d_nz or d_nnz (not both). Set
3105: `d_nz` = `PETSC_DEFAULT` and `d_nnz` = `NULL` for PETSc to control dynamic
3106: memory allocation. Likewise, specify preallocated storage for the
3107: off-diagonal part of the local submatrix with `o_nz` or `o_nnz` (not both).
3109: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
3110: the figure below we depict these three local rows and all columns (0-11).
3112: .vb
3113: 0 1 2 3 4 5 6 7 8 9 10 11
3114: --------------------------
3115: row 3 |o o o d d d o o o o o o
3116: row 4 |o o o d d d o o o o o o
3117: row 5 |o o o d d d o o o o o o
3118: --------------------------
3119: .ve
3121: Thus, any entries in the d locations are stored in the d (diagonal)
3122: submatrix, and any entries in the o locations are stored in the
3123: o (off-diagonal) submatrix. Note that the d and the o submatrices are
3124: stored simply in the `MATSEQBAIJ` format for compressed row storage.
3126: Now `d_nz` should indicate the number of block nonzeros per row in the d matrix,
3127: and `o_nz` should indicate the number of block nonzeros per row in the o matrix.
3128: In general, for PDE problems in which most nonzeros are near the diagonal,
3129: one expects `d_nz` >> `o_nz`. For large problems you MUST preallocate memory
3130: or you will get TERRIBLE performance; see the users' manual chapter on
3131: matrices.
3133: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`, `MatMPIBAIJSetPreallocation()`, `MatMPIBAIJSetPreallocationCSR()`
3134: @*/
3135: 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)
3136: {
3137: PetscMPIInt size;
3139: PetscFunctionBegin;
3140: PetscCall(MatCreate(comm, A));
3141: PetscCall(MatSetSizes(*A, m, n, M, N));
3142: PetscCallMPI(MPI_Comm_size(comm, &size));
3143: if (size > 1) {
3144: PetscCall(MatSetType(*A, MATMPIBAIJ));
3145: PetscCall(MatMPIBAIJSetPreallocation(*A, bs, d_nz, d_nnz, o_nz, o_nnz));
3146: } else {
3147: PetscCall(MatSetType(*A, MATSEQBAIJ));
3148: PetscCall(MatSeqBAIJSetPreallocation(*A, bs, d_nz, d_nnz));
3149: }
3150: PetscFunctionReturn(PETSC_SUCCESS);
3151: }
3153: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
3154: {
3155: Mat mat;
3156: Mat_MPIBAIJ *a, *oldmat = (Mat_MPIBAIJ *)matin->data;
3157: PetscInt len = 0;
3159: PetscFunctionBegin;
3160: *newmat = NULL;
3161: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
3162: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
3163: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
3165: mat->factortype = matin->factortype;
3166: mat->preallocated = PETSC_TRUE;
3167: mat->assembled = PETSC_TRUE;
3168: mat->insertmode = NOT_SET_VALUES;
3170: a = (Mat_MPIBAIJ *)mat->data;
3171: mat->rmap->bs = matin->rmap->bs;
3172: a->bs2 = oldmat->bs2;
3173: a->mbs = oldmat->mbs;
3174: a->nbs = oldmat->nbs;
3175: a->Mbs = oldmat->Mbs;
3176: a->Nbs = oldmat->Nbs;
3178: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
3179: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
3181: a->size = oldmat->size;
3182: a->rank = oldmat->rank;
3183: a->donotstash = oldmat->donotstash;
3184: a->roworiented = oldmat->roworiented;
3185: a->rowindices = NULL;
3186: a->rowvalues = NULL;
3187: a->getrowactive = PETSC_FALSE;
3188: a->barray = NULL;
3189: a->rstartbs = oldmat->rstartbs;
3190: a->rendbs = oldmat->rendbs;
3191: a->cstartbs = oldmat->cstartbs;
3192: a->cendbs = oldmat->cendbs;
3194: /* hash table stuff */
3195: a->ht = NULL;
3196: a->hd = NULL;
3197: a->ht_size = 0;
3198: a->ht_flag = oldmat->ht_flag;
3199: a->ht_fact = oldmat->ht_fact;
3200: a->ht_total_ct = 0;
3201: a->ht_insert_ct = 0;
3203: PetscCall(PetscArraycpy(a->rangebs, oldmat->rangebs, a->size + 1));
3204: if (oldmat->colmap) {
3205: #if defined(PETSC_USE_CTABLE)
3206: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3207: #else
3208: PetscCall(PetscMalloc1(a->Nbs, &a->colmap));
3209: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, a->Nbs));
3210: #endif
3211: } else a->colmap = NULL;
3213: if (oldmat->garray && (len = ((Mat_SeqBAIJ *)(oldmat->B->data))->nbs)) {
3214: PetscCall(PetscMalloc1(len, &a->garray));
3215: PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3216: } else a->garray = NULL;
3218: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)matin), matin->rmap->bs, &mat->bstash));
3219: PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3220: PetscCall(VecScatterCopy(oldmat->Mvctx, &a->Mvctx));
3222: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3223: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3224: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3225: *newmat = mat;
3226: PetscFunctionReturn(PETSC_SUCCESS);
3227: }
3229: /* Used for both MPIBAIJ and MPISBAIJ matrices */
3230: PetscErrorCode MatLoad_MPIBAIJ_Binary(Mat mat, PetscViewer viewer)
3231: {
3232: PetscInt header[4], M, N, nz, bs, m, n, mbs, nbs, rows, cols, sum, i, j, k;
3233: PetscInt *rowidxs, *colidxs, rs, cs, ce;
3234: PetscScalar *matvals;
3236: PetscFunctionBegin;
3237: PetscCall(PetscViewerSetUp(viewer));
3239: /* read in matrix header */
3240: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3241: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3242: M = header[1];
3243: N = header[2];
3244: nz = header[3];
3245: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3246: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3247: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIBAIJ");
3249: /* set block sizes from the viewer's .info file */
3250: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3251: /* set local sizes if not set already */
3252: if (mat->rmap->n < 0 && M == N) mat->rmap->n = mat->cmap->n;
3253: if (mat->cmap->n < 0 && M == N) mat->cmap->n = mat->rmap->n;
3254: /* set global sizes if not set already */
3255: if (mat->rmap->N < 0) mat->rmap->N = M;
3256: if (mat->cmap->N < 0) mat->cmap->N = N;
3257: PetscCall(PetscLayoutSetUp(mat->rmap));
3258: PetscCall(PetscLayoutSetUp(mat->cmap));
3260: /* check if the matrix sizes are correct */
3261: PetscCall(MatGetSize(mat, &rows, &cols));
3262: 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);
3263: PetscCall(MatGetBlockSize(mat, &bs));
3264: PetscCall(MatGetLocalSize(mat, &m, &n));
3265: PetscCall(PetscLayoutGetRange(mat->rmap, &rs, NULL));
3266: PetscCall(PetscLayoutGetRange(mat->cmap, &cs, &ce));
3267: mbs = m / bs;
3268: nbs = n / bs;
3270: /* read in row lengths and build row indices */
3271: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3272: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3273: rowidxs[0] = 0;
3274: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3275: PetscCall(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3276: 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);
3278: /* read in column indices and matrix values */
3279: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3280: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3281: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3283: { /* preallocate matrix storage */
3284: PetscBT bt; /* helper bit set to count diagonal nonzeros */
3285: PetscHSetI ht; /* helper hash set to count off-diagonal nonzeros */
3286: PetscBool sbaij, done;
3287: PetscInt *d_nnz, *o_nnz;
3289: PetscCall(PetscBTCreate(nbs, &bt));
3290: PetscCall(PetscHSetICreate(&ht));
3291: PetscCall(PetscCalloc2(mbs, &d_nnz, mbs, &o_nnz));
3292: PetscCall(PetscObjectTypeCompare((PetscObject)mat, MATMPISBAIJ, &sbaij));
3293: for (i = 0; i < mbs; i++) {
3294: PetscCall(PetscBTMemzero(nbs, bt));
3295: PetscCall(PetscHSetIClear(ht));
3296: for (k = 0; k < bs; k++) {
3297: PetscInt row = bs * i + k;
3298: for (j = rowidxs[row]; j < rowidxs[row + 1]; j++) {
3299: PetscInt col = colidxs[j];
3300: if (!sbaij || col >= row) {
3301: if (col >= cs && col < ce) {
3302: if (!PetscBTLookupSet(bt, (col - cs) / bs)) d_nnz[i]++;
3303: } else {
3304: PetscCall(PetscHSetIQueryAdd(ht, col / bs, &done));
3305: if (done) o_nnz[i]++;
3306: }
3307: }
3308: }
3309: }
3310: }
3311: PetscCall(PetscBTDestroy(&bt));
3312: PetscCall(PetscHSetIDestroy(&ht));
3313: PetscCall(MatMPIBAIJSetPreallocation(mat, bs, 0, d_nnz, 0, o_nnz));
3314: PetscCall(MatMPISBAIJSetPreallocation(mat, bs, 0, d_nnz, 0, o_nnz));
3315: PetscCall(PetscFree2(d_nnz, o_nnz));
3316: }
3318: /* store matrix values */
3319: for (i = 0; i < m; i++) {
3320: PetscInt row = rs + i, s = rowidxs[i], e = rowidxs[i + 1];
3321: PetscCall((*mat->ops->setvalues)(mat, 1, &row, e - s, colidxs + s, matvals + s, INSERT_VALUES));
3322: }
3324: PetscCall(PetscFree(rowidxs));
3325: PetscCall(PetscFree2(colidxs, matvals));
3326: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
3327: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
3328: PetscFunctionReturn(PETSC_SUCCESS);
3329: }
3331: PetscErrorCode MatLoad_MPIBAIJ(Mat mat, PetscViewer viewer)
3332: {
3333: PetscBool isbinary;
3335: PetscFunctionBegin;
3336: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3337: 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);
3338: PetscCall(MatLoad_MPIBAIJ_Binary(mat, viewer));
3339: PetscFunctionReturn(PETSC_SUCCESS);
3340: }
3342: /*@
3343: MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the matrices hash table
3345: Input Parameters:
3346: + mat - the matrix
3347: - fact - factor
3349: Options Database Key:
3350: . -mat_use_hash_table <fact> - provide the factor
3352: Level: advanced
3354: .seealso: `Mat`, `MATMPIBAIJ`, `MatSetOption()`
3355: @*/
3356: PetscErrorCode MatMPIBAIJSetHashTableFactor(Mat mat, PetscReal fact)
3357: {
3358: PetscFunctionBegin;
3359: PetscTryMethod(mat, "MatSetHashTableFactor_C", (Mat, PetscReal), (mat, fact));
3360: PetscFunctionReturn(PETSC_SUCCESS);
3361: }
3363: PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat mat, PetscReal fact)
3364: {
3365: Mat_MPIBAIJ *baij;
3367: PetscFunctionBegin;
3368: baij = (Mat_MPIBAIJ *)mat->data;
3369: baij->ht_fact = fact;
3370: PetscFunctionReturn(PETSC_SUCCESS);
3371: }
3373: PetscErrorCode MatMPIBAIJGetSeqBAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
3374: {
3375: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
3376: PetscBool flg;
3378: PetscFunctionBegin;
3379: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIBAIJ, &flg));
3380: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIBAIJ matrix as input");
3381: if (Ad) *Ad = a->A;
3382: if (Ao) *Ao = a->B;
3383: if (colmap) *colmap = a->garray;
3384: PetscFunctionReturn(PETSC_SUCCESS);
3385: }
3387: /*
3388: Special version for direct calls from Fortran (to eliminate two function call overheads
3389: */
3390: #if defined(PETSC_HAVE_FORTRAN_CAPS)
3391: #define matmpibaijsetvaluesblocked_ MATMPIBAIJSETVALUESBLOCKED
3392: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
3393: #define matmpibaijsetvaluesblocked_ matmpibaijsetvaluesblocked
3394: #endif
3396: /*@C
3397: MatMPIBAIJSetValuesBlocked - Direct Fortran call to replace call to `MatSetValuesBlocked()`
3399: Collective
3401: Input Parameters:
3402: + mat - the matrix
3403: . min - number of input rows
3404: . im - input rows
3405: . nin - number of input columns
3406: . in - input columns
3407: . v - numerical values input
3408: - addvin - `INSERT_VALUES` or `ADD_VALUES`
3410: Developer Note:
3411: This has a complete copy of `MatSetValuesBlocked_MPIBAIJ()` which is terrible code un-reuse.
3413: Level: advanced
3415: .seealso: `Mat`, `MatSetValuesBlocked()`
3416: @*/
3417: PetscErrorCode matmpibaijsetvaluesblocked_(Mat *matin, PetscInt *min, const PetscInt im[], PetscInt *nin, const PetscInt in[], const MatScalar v[], InsertMode *addvin)
3418: {
3419: /* convert input arguments to C version */
3420: Mat mat = *matin;
3421: PetscInt m = *min, n = *nin;
3422: InsertMode addv = *addvin;
3424: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
3425: const MatScalar *value;
3426: MatScalar *barray = baij->barray;
3427: PetscBool roworiented = baij->roworiented;
3428: PetscInt i, j, ii, jj, row, col, rstart = baij->rstartbs;
3429: PetscInt rend = baij->rendbs, cstart = baij->cstartbs, stepval;
3430: PetscInt cend = baij->cendbs, bs = mat->rmap->bs, bs2 = baij->bs2;
3432: PetscFunctionBegin;
3433: /* tasks normally handled by MatSetValuesBlocked() */
3434: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
3435: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
3436: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3437: if (mat->assembled) {
3438: mat->was_assembled = PETSC_TRUE;
3439: mat->assembled = PETSC_FALSE;
3440: }
3441: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
3443: if (!barray) {
3444: PetscCall(PetscMalloc1(bs2, &barray));
3445: baij->barray = barray;
3446: }
3448: if (roworiented) stepval = (n - 1) * bs;
3449: else stepval = (m - 1) * bs;
3451: for (i = 0; i < m; i++) {
3452: if (im[i] < 0) continue;
3453: 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);
3454: if (im[i] >= rstart && im[i] < rend) {
3455: row = im[i] - rstart;
3456: for (j = 0; j < n; j++) {
3457: /* If NumCol = 1 then a copy is not required */
3458: if ((roworiented) && (n == 1)) {
3459: barray = (MatScalar *)v + i * bs2;
3460: } else if ((!roworiented) && (m == 1)) {
3461: barray = (MatScalar *)v + j * bs2;
3462: } else { /* Here a copy is required */
3463: if (roworiented) {
3464: value = v + i * (stepval + bs) * bs + j * bs;
3465: } else {
3466: value = v + j * (stepval + bs) * bs + i * bs;
3467: }
3468: for (ii = 0; ii < bs; ii++, value += stepval) {
3469: for (jj = 0; jj < bs; jj++) *barray++ = *value++;
3470: }
3471: barray -= bs2;
3472: }
3474: if (in[j] >= cstart && in[j] < cend) {
3475: col = in[j] - cstart;
3476: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->A, row, col, barray, addv, im[i], in[j]));
3477: } else if (in[j] < 0) {
3478: continue;
3479: } else {
3480: 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);
3481: if (mat->was_assembled) {
3482: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
3484: #if defined(PETSC_USE_DEBUG)
3485: #if defined(PETSC_USE_CTABLE)
3486: {
3487: PetscInt data;
3488: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &data));
3489: PetscCheck((data - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
3490: }
3491: #else
3492: PetscCheck((baij->colmap[in[j]] - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
3493: #endif
3494: #endif
3495: #if defined(PETSC_USE_CTABLE)
3496: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &col));
3497: col = (col - 1) / bs;
3498: #else
3499: col = (baij->colmap[in[j]] - 1) / bs;
3500: #endif
3501: if (col < 0 && !((Mat_SeqBAIJ *)(baij->A->data))->nonew) {
3502: PetscCall(MatDisAssemble_MPIBAIJ(mat));
3503: col = in[j];
3504: }
3505: } else col = in[j];
3506: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->B, row, col, barray, addv, im[i], in[j]));
3507: }
3508: }
3509: } else {
3510: if (!baij->donotstash) {
3511: if (roworiented) {
3512: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
3513: } else {
3514: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
3515: }
3516: }
3517: }
3518: }
3520: /* task normally handled by MatSetValuesBlocked() */
3521: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
3522: PetscFunctionReturn(PETSC_SUCCESS);
3523: }
3525: /*@
3526: MatCreateMPIBAIJWithArrays - creates a `MATMPIBAIJ` matrix using arrays that contain in standard block
3527: CSR format the local rows.
3529: Collective
3531: Input Parameters:
3532: + comm - MPI communicator
3533: . bs - the block size, only a block size of 1 is supported
3534: . m - number of local rows (Cannot be `PETSC_DECIDE`)
3535: . n - This value should be the same as the local size used in creating the
3536: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
3537: calculated if N is given) For square matrices n is almost always m.
3538: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
3539: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
3540: . 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
3541: . j - column indices
3542: - a - matrix values
3544: Output Parameter:
3545: . mat - the matrix
3547: Level: intermediate
3549: Notes:
3550: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
3551: thus you CANNOT change the matrix entries by changing the values of a[] after you have
3552: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3554: The order of the entries in values is the same as the block compressed sparse row storage format; that is, it is
3555: the same as a three dimensional array in Fortran values(bs,bs,nnz) that contains the first column of the first
3556: block, followed by the second column of the first block etc etc. That is, the blocks are contiguous in memory
3557: with column-major ordering within blocks.
3559: The `i` and `j` indices are 0 based, and i indices are indices corresponding to the local `j` array.
3561: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
3562: `MPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`
3563: @*/
3564: 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)
3565: {
3566: PetscFunctionBegin;
3567: PetscCheck(!i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
3568: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
3569: PetscCall(MatCreate(comm, mat));
3570: PetscCall(MatSetSizes(*mat, m, n, M, N));
3571: PetscCall(MatSetType(*mat, MATMPIBAIJ));
3572: PetscCall(MatSetBlockSize(*mat, bs));
3573: PetscCall(MatSetUp(*mat));
3574: PetscCall(MatSetOption(*mat, MAT_ROW_ORIENTED, PETSC_FALSE));
3575: PetscCall(MatMPIBAIJSetPreallocationCSR(*mat, bs, i, j, a));
3576: PetscCall(MatSetOption(*mat, MAT_ROW_ORIENTED, PETSC_TRUE));
3577: PetscFunctionReturn(PETSC_SUCCESS);
3578: }
3580: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIBAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
3581: {
3582: PetscInt m, N, i, rstart, nnz, Ii, bs, cbs;
3583: PetscInt *indx;
3584: PetscScalar *values;
3586: PetscFunctionBegin;
3587: PetscCall(MatGetSize(inmat, &m, &N));
3588: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
3589: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)inmat->data;
3590: PetscInt *dnz, *onz, mbs, Nbs, nbs;
3591: PetscInt *bindx, rmax = a->rmax, j;
3592: PetscMPIInt rank, size;
3594: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
3595: mbs = m / bs;
3596: Nbs = N / cbs;
3597: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnershipBlock(comm, cbs, &n, &N));
3598: nbs = n / cbs;
3600: PetscCall(PetscMalloc1(rmax, &bindx));
3601: MatPreallocateBegin(comm, mbs, nbs, dnz, onz); /* inline function, output __end and __rstart are used below */
3603: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3604: PetscCallMPI(MPI_Comm_rank(comm, &size));
3605: if (rank == size - 1) {
3606: /* Check sum(nbs) = Nbs */
3607: PetscCheck(__end == Nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local block columns %" PetscInt_FMT " != global block columns %" PetscInt_FMT, __end, Nbs);
3608: }
3610: rstart = __rstart; /* block rstart of *outmat; see inline function MatPreallocateBegin */
3611: for (i = 0; i < mbs; i++) {
3612: PetscCall(MatGetRow_SeqBAIJ(inmat, i * bs, &nnz, &indx, NULL)); /* non-blocked nnz and indx */
3613: nnz = nnz / bs;
3614: for (j = 0; j < nnz; j++) bindx[j] = indx[j * bs] / bs;
3615: PetscCall(MatPreallocateSet(i + rstart, nnz, bindx, dnz, onz));
3616: PetscCall(MatRestoreRow_SeqBAIJ(inmat, i * bs, &nnz, &indx, NULL));
3617: }
3618: PetscCall(PetscFree(bindx));
3620: PetscCall(MatCreate(comm, outmat));
3621: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
3622: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
3623: PetscCall(MatSetType(*outmat, MATBAIJ));
3624: PetscCall(MatSeqBAIJSetPreallocation(*outmat, bs, 0, dnz));
3625: PetscCall(MatMPIBAIJSetPreallocation(*outmat, bs, 0, dnz, 0, onz));
3626: MatPreallocateEnd(dnz, onz);
3627: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3628: }
3630: /* numeric phase */
3631: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
3632: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
3634: for (i = 0; i < m; i++) {
3635: PetscCall(MatGetRow_SeqBAIJ(inmat, i, &nnz, &indx, &values));
3636: Ii = i + rstart;
3637: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
3638: PetscCall(MatRestoreRow_SeqBAIJ(inmat, i, &nnz, &indx, &values));
3639: }
3640: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
3641: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
3642: PetscFunctionReturn(PETSC_SUCCESS);
3643: }