Actual source code: mpibaij.c
1: #include <../src/mat/impls/baij/mpi/mpibaij.h>
3: #include <petsc/private/hashseti.h>
4: #include <petscblaslapack.h>
5: #include <petscsf.h>
7: static PetscErrorCode MatDestroy_MPIBAIJ(Mat mat)
8: {
9: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
11: PetscFunctionBegin;
12: PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ",Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
13: PetscCall(MatStashDestroy_Private(&mat->stash));
14: PetscCall(MatStashDestroy_Private(&mat->bstash));
15: PetscCall(MatDestroy(&baij->A));
16: PetscCall(MatDestroy(&baij->B));
17: #if defined(PETSC_USE_CTABLE)
18: PetscCall(PetscHMapIDestroy(&baij->colmap));
19: #else
20: PetscCall(PetscFree(baij->colmap));
21: #endif
22: PetscCall(PetscFree(baij->garray));
23: PetscCall(VecDestroy(&baij->lvec));
24: PetscCall(VecScatterDestroy(&baij->Mvctx));
25: PetscCall(PetscFree2(baij->rowvalues, baij->rowindices));
26: PetscCall(PetscFree(baij->barray));
27: PetscCall(PetscFree2(baij->hd, baij->ht));
28: PetscCall(PetscFree(baij->rangebs));
29: PetscCall(PetscFree(mat->data));
31: PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
32: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
33: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
34: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIBAIJSetPreallocation_C", NULL));
35: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIBAIJSetPreallocationCSR_C", NULL));
36: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
37: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetHashTableFactor_C", NULL));
38: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_mpisbaij_C", NULL));
39: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_mpiadj_C", NULL));
40: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_mpiaij_C", NULL));
41: #if defined(PETSC_HAVE_HYPRE)
42: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_hypre_C", NULL));
43: #endif
44: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_is_C", NULL));
45: PetscFunctionReturn(PETSC_SUCCESS);
46: }
48: /* defines MatSetValues_MPI_Hash(), MatAssemblyBegin_MPI_Hash(), and MatAssemblyEnd_MPI_Hash() */
49: #define TYPE BAIJ
50: #include "../src/mat/impls/aij/mpi/mpihashmat.h"
51: #undef TYPE
53: #if defined(PETSC_HAVE_HYPRE)
54: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
55: #endif
57: static PetscErrorCode MatGetRowMaxAbs_MPIBAIJ(Mat A, Vec v, PetscInt idx[])
58: {
59: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
60: PetscInt i, *idxb = NULL, m = A->rmap->n, bs = A->cmap->bs;
61: PetscScalar *va, *vv;
62: Vec vB, vA;
63: const PetscScalar *vb;
65: PetscFunctionBegin;
66: PetscCall(MatCreateVecs(a->A, NULL, &vA));
67: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
69: PetscCall(VecGetArrayWrite(vA, &va));
70: if (idx) {
71: for (i = 0; i < m; i++) {
72: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
73: }
74: }
76: PetscCall(MatCreateVecs(a->B, NULL, &vB));
77: PetscCall(PetscMalloc1(m, &idxb));
78: PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));
80: PetscCall(VecGetArrayWrite(v, &vv));
81: PetscCall(VecGetArrayRead(vB, &vb));
82: for (i = 0; i < m; i++) {
83: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
84: vv[i] = vb[i];
85: if (idx) idx[i] = bs * a->garray[idxb[i] / bs] + (idxb[i] % bs);
86: } else {
87: vv[i] = va[i];
88: if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > bs * a->garray[idxb[i] / bs] + (idxb[i] % bs)) idx[i] = bs * a->garray[idxb[i] / bs] + (idxb[i] % bs);
89: }
90: }
91: PetscCall(VecRestoreArrayWrite(vA, &vv));
92: PetscCall(VecRestoreArrayWrite(vA, &va));
93: PetscCall(VecRestoreArrayRead(vB, &vb));
94: PetscCall(PetscFree(idxb));
95: PetscCall(VecDestroy(&vA));
96: PetscCall(VecDestroy(&vB));
97: PetscFunctionReturn(PETSC_SUCCESS);
98: }
100: static PetscErrorCode MatGetRowSumAbs_MPIBAIJ(Mat A, Vec v)
101: {
102: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
103: Vec vB, vA;
105: PetscFunctionBegin;
106: PetscCall(MatCreateVecs(a->A, NULL, &vA));
107: PetscCall(MatGetRowSumAbs(a->A, vA));
108: PetscCall(MatCreateVecs(a->B, NULL, &vB));
109: PetscCall(MatGetRowSumAbs(a->B, vB));
110: PetscCall(VecAXPY(vA, 1.0, vB));
111: PetscCall(VecDestroy(&vB));
112: PetscCall(VecCopy(vA, v));
113: PetscCall(VecDestroy(&vA));
114: PetscFunctionReturn(PETSC_SUCCESS);
115: }
117: static PetscErrorCode MatStoreValues_MPIBAIJ(Mat mat)
118: {
119: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
121: PetscFunctionBegin;
122: PetscCall(MatStoreValues(aij->A));
123: PetscCall(MatStoreValues(aij->B));
124: PetscFunctionReturn(PETSC_SUCCESS);
125: }
127: static PetscErrorCode MatRetrieveValues_MPIBAIJ(Mat mat)
128: {
129: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
131: PetscFunctionBegin;
132: PetscCall(MatRetrieveValues(aij->A));
133: PetscCall(MatRetrieveValues(aij->B));
134: PetscFunctionReturn(PETSC_SUCCESS);
135: }
137: /*
138: Local utility routine that creates a mapping from the global column
139: number to the local number in the off-diagonal part of the local
140: storage of the matrix. This is done in a non scalable way since the
141: length of colmap equals the global matrix length.
142: */
143: PetscErrorCode MatCreateColmap_MPIBAIJ_Private(Mat mat)
144: {
145: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
146: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)baij->B->data;
147: PetscInt nbs = B->nbs, i, bs = mat->rmap->bs;
149: PetscFunctionBegin;
150: #if defined(PETSC_USE_CTABLE)
151: PetscCall(PetscHMapICreateWithSize(baij->nbs, &baij->colmap));
152: for (i = 0; i < nbs; i++) PetscCall(PetscHMapISet(baij->colmap, baij->garray[i] + 1, i * bs + 1));
153: #else
154: PetscCall(PetscCalloc1(baij->Nbs + 1, &baij->colmap));
155: for (i = 0; i < nbs; i++) baij->colmap[baij->garray[i]] = i * bs + 1;
156: #endif
157: PetscFunctionReturn(PETSC_SUCCESS);
158: }
160: #define MatSetValues_SeqBAIJ_A_Private(row, col, value, addv, orow, ocol) \
161: do { \
162: brow = row / bs; \
163: rp = PetscSafePointerPlusOffset(aj, ai[brow]); \
164: ap = PetscSafePointerPlusOffset(aa, bs2 * ai[brow]); \
165: rmax = aimax[brow]; \
166: nrow = ailen[brow]; \
167: bcol = col / bs; \
168: ridx = row % bs; \
169: cidx = col % bs; \
170: low = 0; \
171: high = nrow; \
172: while (high - low > 3) { \
173: t = (low + high) / 2; \
174: if (rp[t] > bcol) high = t; \
175: else low = t; \
176: } \
177: for (_i = low; _i < high; _i++) { \
178: if (rp[_i] > bcol) break; \
179: if (rp[_i] == bcol) { \
180: bap = ap + bs2 * _i + bs * cidx + ridx; \
181: if (addv == ADD_VALUES) *bap += value; \
182: else *bap = value; \
183: goto a_noinsert; \
184: } \
185: } \
186: if (a->nonew == 1) goto a_noinsert; \
187: 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); \
188: MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, brow, bcol, rmax, aa, ai, aj, rp, ap, aimax, a->nonew, MatScalar); \
189: N = nrow++ - 1; \
190: /* shift up all the later entries in this row */ \
191: PetscCall(PetscArraymove(rp + _i + 1, rp + _i, N - _i + 1)); \
192: PetscCall(PetscArraymove(ap + bs2 * (_i + 1), ap + bs2 * _i, bs2 * (N - _i + 1))); \
193: PetscCall(PetscArrayzero(ap + bs2 * _i, bs2)); \
194: rp[_i] = bcol; \
195: ap[bs2 * _i + bs * cidx + ridx] = value; \
196: a_noinsert:; \
197: ailen[brow] = nrow; \
198: } while (0)
200: #define MatSetValues_SeqBAIJ_B_Private(row, col, value, addv, orow, ocol) \
201: do { \
202: brow = row / bs; \
203: rp = PetscSafePointerPlusOffset(bj, bi[brow]); \
204: ap = PetscSafePointerPlusOffset(ba, bs2 * bi[brow]); \
205: rmax = bimax[brow]; \
206: nrow = bilen[brow]; \
207: bcol = col / bs; \
208: ridx = row % bs; \
209: cidx = col % bs; \
210: low = 0; \
211: high = nrow; \
212: while (high - low > 3) { \
213: t = (low + high) / 2; \
214: if (rp[t] > bcol) high = t; \
215: else low = t; \
216: } \
217: for (_i = low; _i < high; _i++) { \
218: if (rp[_i] > bcol) break; \
219: if (rp[_i] == bcol) { \
220: bap = ap + bs2 * _i + bs * cidx + ridx; \
221: if (addv == ADD_VALUES) *bap += value; \
222: else *bap = value; \
223: goto b_noinsert; \
224: } \
225: } \
226: if (b->nonew == 1) goto b_noinsert; \
227: 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); \
228: MatSeqXAIJReallocateAIJ(B, b->mbs, bs2, nrow, brow, bcol, rmax, ba, bi, bj, rp, ap, bimax, b->nonew, MatScalar); \
229: N = nrow++ - 1; \
230: /* shift up all the later entries in this row */ \
231: PetscCall(PetscArraymove(rp + _i + 1, rp + _i, N - _i + 1)); \
232: PetscCall(PetscArraymove(ap + bs2 * (_i + 1), ap + bs2 * _i, bs2 * (N - _i + 1))); \
233: PetscCall(PetscArrayzero(ap + bs2 * _i, bs2)); \
234: rp[_i] = bcol; \
235: ap[bs2 * _i + bs * cidx + ridx] = value; \
236: b_noinsert:; \
237: bilen[brow] = nrow; \
238: } while (0)
240: PetscErrorCode MatSetValues_MPIBAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
241: {
242: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
243: MatScalar value;
244: PetscBool roworiented = baij->roworiented;
245: PetscInt i, j, row, col;
246: PetscInt rstart_orig = mat->rmap->rstart;
247: PetscInt rend_orig = mat->rmap->rend, cstart_orig = mat->cmap->rstart;
248: PetscInt cend_orig = mat->cmap->rend, bs = mat->rmap->bs;
250: /* Some Variables required in the macro */
251: Mat A = baij->A;
252: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)(A)->data;
253: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
254: MatScalar *aa = a->a;
256: Mat B = baij->B;
257: Mat_SeqBAIJ *b = (Mat_SeqBAIJ *)(B)->data;
258: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j;
259: MatScalar *ba = b->a;
261: PetscInt *rp, ii, nrow, _i, rmax, N, brow, bcol;
262: PetscInt low, high, t, ridx, cidx, bs2 = a->bs2;
263: MatScalar *ap, *bap;
265: PetscFunctionBegin;
266: for (i = 0; i < m; i++) {
267: if (im[i] < 0) continue;
268: 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);
269: if (im[i] >= rstart_orig && im[i] < rend_orig) {
270: row = im[i] - rstart_orig;
271: for (j = 0; j < n; j++) {
272: if (in[j] >= cstart_orig && in[j] < cend_orig) {
273: col = in[j] - cstart_orig;
274: if (roworiented) value = v[i * n + j];
275: else value = v[i + j * m];
276: MatSetValues_SeqBAIJ_A_Private(row, col, value, addv, im[i], in[j]);
277: } else if (in[j] < 0) {
278: continue;
279: } else {
280: 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);
281: if (mat->was_assembled) {
282: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
283: #if defined(PETSC_USE_CTABLE)
284: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] / bs + 1, 0, &col));
285: col = col - 1;
286: #else
287: col = baij->colmap[in[j] / bs] - 1;
288: #endif
289: if (col < 0 && !((Mat_SeqBAIJ *)baij->B->data)->nonew) {
290: PetscCall(MatDisAssemble_MPIBAIJ(mat));
291: col = in[j];
292: /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
293: B = baij->B;
294: b = (Mat_SeqBAIJ *)(B)->data;
295: bimax = b->imax;
296: bi = b->i;
297: bilen = b->ilen;
298: bj = b->j;
299: ba = b->a;
300: } else {
301: PetscCheck(col >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]);
302: col += in[j] % bs;
303: }
304: } else col = in[j];
305: if (roworiented) value = v[i * n + j];
306: else value = v[i + j * m];
307: MatSetValues_SeqBAIJ_B_Private(row, col, value, addv, im[i], in[j]);
308: /* PetscCall(MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv)); */
309: }
310: }
311: } else {
312: 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]);
313: if (!baij->donotstash) {
314: mat->assembled = PETSC_FALSE;
315: if (roworiented) {
316: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, PETSC_FALSE));
317: } else {
318: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, PETSC_FALSE));
319: }
320: }
321: }
322: }
323: PetscFunctionReturn(PETSC_SUCCESS);
324: }
326: static inline PetscErrorCode MatSetValuesBlocked_SeqBAIJ_Inlined(Mat A, PetscInt row, PetscInt col, const PetscScalar v[], InsertMode is, PetscInt orow, PetscInt ocol)
327: {
328: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
329: PetscInt *rp, low, high, t, ii, jj, nrow, i, rmax, N;
330: PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen;
331: PetscInt *aj = a->j, nonew = a->nonew, bs2 = a->bs2, bs = A->rmap->bs;
332: PetscBool roworiented = a->roworiented;
333: const PetscScalar *value = v;
334: MatScalar *ap, *aa = a->a, *bap;
336: PetscFunctionBegin;
337: rp = aj + ai[row];
338: ap = aa + bs2 * ai[row];
339: rmax = imax[row];
340: nrow = ailen[row];
341: value = v;
342: low = 0;
343: high = nrow;
344: while (high - low > 7) {
345: t = (low + high) / 2;
346: if (rp[t] > col) high = t;
347: else low = t;
348: }
349: for (i = low; i < high; i++) {
350: if (rp[i] > col) break;
351: if (rp[i] == col) {
352: bap = ap + bs2 * i;
353: if (roworiented) {
354: if (is == ADD_VALUES) {
355: for (ii = 0; ii < bs; ii++) {
356: for (jj = ii; jj < bs2; jj += bs) bap[jj] += *value++;
357: }
358: } else {
359: for (ii = 0; ii < bs; ii++) {
360: for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
361: }
362: }
363: } else {
364: if (is == ADD_VALUES) {
365: for (ii = 0; ii < bs; ii++, value += bs) {
366: for (jj = 0; jj < bs; jj++) bap[jj] += value[jj];
367: bap += bs;
368: }
369: } else {
370: for (ii = 0; ii < bs; ii++, value += bs) {
371: for (jj = 0; jj < bs; jj++) bap[jj] = value[jj];
372: bap += bs;
373: }
374: }
375: }
376: goto noinsert2;
377: }
378: }
379: if (nonew == 1) goto noinsert2;
380: 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);
381: MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
382: N = nrow++ - 1;
383: high++;
384: /* shift up all the later entries in this row */
385: PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
386: PetscCall(PetscArraymove(ap + bs2 * (i + 1), ap + bs2 * i, bs2 * (N - i + 1)));
387: rp[i] = col;
388: bap = ap + bs2 * i;
389: if (roworiented) {
390: for (ii = 0; ii < bs; ii++) {
391: for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
392: }
393: } else {
394: for (ii = 0; ii < bs; ii++) {
395: for (jj = 0; jj < bs; jj++) *bap++ = *value++;
396: }
397: }
398: noinsert2:;
399: ailen[row] = nrow;
400: PetscFunctionReturn(PETSC_SUCCESS);
401: }
403: /*
404: This routine should be optimized so that the block copy at ** Here a copy is required ** below is not needed
405: by passing additional stride information into the MatSetValuesBlocked_SeqBAIJ_Inlined() routine
406: */
407: static PetscErrorCode MatSetValuesBlocked_MPIBAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
408: {
409: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
410: const PetscScalar *value;
411: MatScalar *barray = baij->barray;
412: PetscBool roworiented = baij->roworiented;
413: PetscInt i, j, ii, jj, row, col, rstart = baij->rstartbs;
414: PetscInt rend = baij->rendbs, cstart = baij->cstartbs, stepval;
415: PetscInt cend = baij->cendbs, bs = mat->rmap->bs, bs2 = baij->bs2;
417: PetscFunctionBegin;
418: if (!barray) {
419: PetscCall(PetscMalloc1(bs2, &barray));
420: baij->barray = barray;
421: }
423: if (roworiented) stepval = (n - 1) * bs;
424: else stepval = (m - 1) * bs;
426: for (i = 0; i < m; i++) {
427: if (im[i] < 0) continue;
428: 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);
429: if (im[i] >= rstart && im[i] < rend) {
430: row = im[i] - rstart;
431: for (j = 0; j < n; j++) {
432: /* If NumCol = 1 then a copy is not required */
433: if ((roworiented) && (n == 1)) {
434: barray = (MatScalar *)v + i * bs2;
435: } else if ((!roworiented) && (m == 1)) {
436: barray = (MatScalar *)v + j * bs2;
437: } else { /* Here a copy is required */
438: if (roworiented) {
439: value = v + (i * (stepval + bs) + j) * bs;
440: } else {
441: value = v + (j * (stepval + bs) + i) * bs;
442: }
443: for (ii = 0; ii < bs; ii++, value += bs + stepval) {
444: for (jj = 0; jj < bs; jj++) barray[jj] = value[jj];
445: barray += bs;
446: }
447: barray -= bs2;
448: }
450: if (in[j] >= cstart && in[j] < cend) {
451: col = in[j] - cstart;
452: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->A, row, col, barray, addv, im[i], in[j]));
453: } else if (in[j] < 0) {
454: continue;
455: } else {
456: 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);
457: if (mat->was_assembled) {
458: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
460: #if defined(PETSC_USE_CTABLE)
461: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &col));
462: col = col < 1 ? -1 : (col - 1) / bs;
463: #else
464: col = baij->colmap[in[j]] < 1 ? -1 : (baij->colmap[in[j]] - 1) / bs;
465: #endif
466: if (col < 0 && !((Mat_SeqBAIJ *)baij->B->data)->nonew) {
467: PetscCall(MatDisAssemble_MPIBAIJ(mat));
468: col = in[j];
469: } 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]);
470: } else col = in[j];
471: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->B, row, col, barray, addv, im[i], in[j]));
472: }
473: }
474: } else {
475: 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]);
476: if (!baij->donotstash) {
477: if (roworiented) {
478: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
479: } else {
480: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
481: }
482: }
483: }
484: }
485: PetscFunctionReturn(PETSC_SUCCESS);
486: }
488: #define HASH_KEY 0.6180339887
489: #define HASH(size, key, tmp) (tmp = (key) * HASH_KEY, (PetscInt)((size) * (tmp - (PetscInt)tmp)))
490: /* #define HASH(size,key) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
491: /* #define HASH(size,key,tmp) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
492: static PetscErrorCode MatSetValues_MPIBAIJ_HT(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
493: {
494: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
495: PetscBool roworiented = baij->roworiented;
496: PetscInt i, j, row, col;
497: PetscInt rstart_orig = mat->rmap->rstart;
498: PetscInt rend_orig = mat->rmap->rend, Nbs = baij->Nbs;
499: PetscInt h1, key, size = baij->ht_size, bs = mat->rmap->bs, *HT = baij->ht, idx;
500: PetscReal tmp;
501: MatScalar **HD = baij->hd, value;
502: PetscInt total_ct = baij->ht_total_ct, insert_ct = baij->ht_insert_ct;
504: PetscFunctionBegin;
505: for (i = 0; i < m; i++) {
506: if (PetscDefined(USE_DEBUG)) {
507: PetscCheck(im[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row");
508: 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);
509: }
510: row = im[i];
511: if (row >= rstart_orig && row < rend_orig) {
512: for (j = 0; j < n; j++) {
513: col = in[j];
514: if (roworiented) value = v[i * n + j];
515: else value = v[i + j * m];
516: /* Look up PetscInto the Hash Table */
517: key = (row / bs) * Nbs + (col / bs) + 1;
518: h1 = HASH(size, key, tmp);
520: idx = h1;
521: if (PetscDefined(USE_DEBUG)) {
522: insert_ct++;
523: total_ct++;
524: if (HT[idx] != key) {
525: for (idx = h1; (idx < size) && (HT[idx] != key); idx++, total_ct++);
526: if (idx == size) {
527: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++, total_ct++);
528: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
529: }
530: }
531: } else if (HT[idx] != key) {
532: for (idx = h1; (idx < size) && (HT[idx] != key); idx++);
533: if (idx == size) {
534: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++);
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: static 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: if (idx == size) {
596: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++, total_ct++);
597: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
598: }
599: }
600: } else if (HT[idx] != key) {
601: for (idx = h1; (idx < size) && (HT[idx] != key); idx++);
602: if (idx == size) {
603: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++);
604: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
605: }
606: }
607: baij_a = HD[idx];
608: if (roworiented) {
609: /*value = v + i*(stepval+bs)*bs + j*bs;*/
610: /* value = v + (i*(stepval+bs)+j)*bs; */
611: value = v_t;
612: v_t += bs;
613: if (addv == ADD_VALUES) {
614: for (ii = 0; ii < bs; ii++, value += stepval) {
615: for (jj = ii; jj < bs2; jj += bs) baij_a[jj] += *value++;
616: }
617: } else {
618: for (ii = 0; ii < bs; ii++, value += stepval) {
619: for (jj = ii; jj < bs2; jj += bs) baij_a[jj] = *value++;
620: }
621: }
622: } else {
623: value = v + j * (stepval + bs) * bs + i * bs;
624: if (addv == ADD_VALUES) {
625: for (ii = 0; ii < bs; ii++, value += stepval, baij_a += bs) {
626: for (jj = 0; jj < bs; jj++) baij_a[jj] += *value++;
627: }
628: } else {
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: }
633: }
634: }
635: } else {
636: if (!baij->donotstash) {
637: if (roworiented) {
638: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
639: } else {
640: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
641: }
642: }
643: }
644: }
645: if (PetscDefined(USE_DEBUG)) {
646: baij->ht_total_ct += total_ct;
647: baij->ht_insert_ct += insert_ct;
648: }
649: PetscFunctionReturn(PETSC_SUCCESS);
650: }
652: static PetscErrorCode MatGetValues_MPIBAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
653: {
654: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
655: PetscInt bs = mat->rmap->bs, i, j, bsrstart = mat->rmap->rstart, bsrend = mat->rmap->rend;
656: PetscInt bscstart = mat->cmap->rstart, bscend = mat->cmap->rend, row, col, data;
658: PetscFunctionBegin;
659: for (i = 0; i < m; i++) {
660: if (idxm[i] < 0) continue; /* negative row */
661: 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);
662: if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
663: row = idxm[i] - bsrstart;
664: for (j = 0; j < n; j++) {
665: if (idxn[j] < 0) continue; /* negative column */
666: 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);
667: if (idxn[j] >= bscstart && idxn[j] < bscend) {
668: col = idxn[j] - bscstart;
669: PetscCall(MatGetValues_SeqBAIJ(baij->A, 1, &row, 1, &col, v + i * n + j));
670: } else {
671: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
672: #if defined(PETSC_USE_CTABLE)
673: PetscCall(PetscHMapIGetWithDefault(baij->colmap, idxn[j] / bs + 1, 0, &data));
674: data--;
675: #else
676: data = baij->colmap[idxn[j] / bs] - 1;
677: #endif
678: if ((data < 0) || (baij->garray[data / bs] != idxn[j] / bs)) *(v + i * n + j) = 0.0;
679: else {
680: col = data + idxn[j] % bs;
681: PetscCall(MatGetValues_SeqBAIJ(baij->B, 1, &row, 1, &col, v + i * n + j));
682: }
683: }
684: }
685: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local values currently supported");
686: }
687: PetscFunctionReturn(PETSC_SUCCESS);
688: }
690: static PetscErrorCode MatNorm_MPIBAIJ(Mat mat, NormType type, PetscReal *nrm)
691: {
692: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
693: Mat_SeqBAIJ *amat = (Mat_SeqBAIJ *)baij->A->data, *bmat = (Mat_SeqBAIJ *)baij->B->data;
694: PetscInt i, j, bs2 = baij->bs2, bs = baij->A->rmap->bs, nz, row, col;
695: PetscReal sum = 0.0;
696: MatScalar *v;
698: PetscFunctionBegin;
699: if (baij->size == 1) {
700: PetscCall(MatNorm(baij->A, type, nrm));
701: } else {
702: if (type == NORM_FROBENIUS) {
703: v = amat->a;
704: nz = amat->nz * bs2;
705: for (i = 0; i < nz; i++) {
706: sum += PetscRealPart(PetscConj(*v) * (*v));
707: v++;
708: }
709: v = bmat->a;
710: nz = bmat->nz * bs2;
711: for (i = 0; i < nz; i++) {
712: sum += PetscRealPart(PetscConj(*v) * (*v));
713: v++;
714: }
715: PetscCall(MPIU_Allreduce(&sum, nrm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
716: *nrm = PetscSqrtReal(*nrm);
717: } else if (type == NORM_1) { /* max column sum */
718: PetscReal *tmp, *tmp2;
719: PetscInt *jj, *garray = baij->garray, cstart = baij->rstartbs;
720: PetscCall(PetscCalloc1(mat->cmap->N, &tmp));
721: PetscCall(PetscMalloc1(mat->cmap->N, &tmp2));
722: v = amat->a;
723: jj = amat->j;
724: for (i = 0; i < amat->nz; i++) {
725: for (j = 0; j < bs; j++) {
726: col = bs * (cstart + *jj) + j; /* column index */
727: for (row = 0; row < bs; row++) {
728: tmp[col] += PetscAbsScalar(*v);
729: v++;
730: }
731: }
732: jj++;
733: }
734: v = bmat->a;
735: jj = bmat->j;
736: for (i = 0; i < bmat->nz; i++) {
737: for (j = 0; j < bs; j++) {
738: col = bs * garray[*jj] + j;
739: for (row = 0; row < bs; row++) {
740: tmp[col] += PetscAbsScalar(*v);
741: v++;
742: }
743: }
744: jj++;
745: }
746: PetscCall(MPIU_Allreduce(tmp, tmp2, mat->cmap->N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
747: *nrm = 0.0;
748: for (j = 0; j < mat->cmap->N; j++) {
749: if (tmp2[j] > *nrm) *nrm = tmp2[j];
750: }
751: PetscCall(PetscFree(tmp));
752: PetscCall(PetscFree(tmp2));
753: } else if (type == NORM_INFINITY) { /* max row sum */
754: PetscReal *sums;
755: PetscCall(PetscMalloc1(bs, &sums));
756: sum = 0.0;
757: for (j = 0; j < amat->mbs; j++) {
758: for (row = 0; row < bs; row++) sums[row] = 0.0;
759: v = amat->a + bs2 * amat->i[j];
760: nz = amat->i[j + 1] - amat->i[j];
761: for (i = 0; i < nz; i++) {
762: for (col = 0; col < bs; col++) {
763: for (row = 0; row < bs; row++) {
764: sums[row] += PetscAbsScalar(*v);
765: v++;
766: }
767: }
768: }
769: v = bmat->a + bs2 * bmat->i[j];
770: nz = bmat->i[j + 1] - bmat->i[j];
771: for (i = 0; i < nz; i++) {
772: for (col = 0; col < bs; col++) {
773: for (row = 0; row < bs; row++) {
774: sums[row] += PetscAbsScalar(*v);
775: v++;
776: }
777: }
778: }
779: for (row = 0; row < bs; row++) {
780: if (sums[row] > sum) sum = sums[row];
781: }
782: }
783: PetscCall(MPIU_Allreduce(&sum, nrm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
784: PetscCall(PetscFree(sums));
785: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for this norm yet");
786: }
787: PetscFunctionReturn(PETSC_SUCCESS);
788: }
790: /*
791: Creates the hash table, and sets the table
792: This table is created only once.
793: If new entried need to be added to the matrix
794: then the hash table has to be destroyed and
795: recreated.
796: */
797: static PetscErrorCode MatCreateHashTable_MPIBAIJ_Private(Mat mat, PetscReal factor)
798: {
799: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
800: Mat A = baij->A, B = baij->B;
801: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data, *b = (Mat_SeqBAIJ *)B->data;
802: PetscInt i, j, k, nz = a->nz + b->nz, h1, *ai = a->i, *aj = a->j, *bi = b->i, *bj = b->j;
803: PetscInt ht_size, bs2 = baij->bs2, rstart = baij->rstartbs;
804: PetscInt cstart = baij->cstartbs, *garray = baij->garray, row, col, Nbs = baij->Nbs;
805: PetscInt *HT, key;
806: MatScalar **HD;
807: PetscReal tmp;
808: #if defined(PETSC_USE_INFO)
809: PetscInt ct = 0, max = 0;
810: #endif
812: PetscFunctionBegin;
813: if (baij->ht) PetscFunctionReturn(PETSC_SUCCESS);
815: baij->ht_size = (PetscInt)(factor * nz);
816: ht_size = baij->ht_size;
818: /* Allocate Memory for Hash Table */
819: PetscCall(PetscCalloc2(ht_size, &baij->hd, ht_size, &baij->ht));
820: HD = baij->hd;
821: HT = baij->ht;
823: /* Loop Over A */
824: for (i = 0; i < a->mbs; i++) {
825: for (j = ai[i]; j < ai[i + 1]; j++) {
826: row = i + rstart;
827: col = aj[j] + cstart;
829: key = row * Nbs + col + 1;
830: h1 = HASH(ht_size, key, tmp);
831: for (k = 0; k < ht_size; k++) {
832: if (!HT[(h1 + k) % ht_size]) {
833: HT[(h1 + k) % ht_size] = key;
834: HD[(h1 + k) % ht_size] = a->a + j * bs2;
835: break;
836: #if defined(PETSC_USE_INFO)
837: } else {
838: ct++;
839: #endif
840: }
841: }
842: #if defined(PETSC_USE_INFO)
843: if (k > max) max = k;
844: #endif
845: }
846: }
847: /* Loop Over B */
848: for (i = 0; i < b->mbs; i++) {
849: for (j = bi[i]; j < bi[i + 1]; j++) {
850: row = i + rstart;
851: col = garray[bj[j]];
852: key = row * Nbs + col + 1;
853: h1 = HASH(ht_size, key, tmp);
854: for (k = 0; k < ht_size; k++) {
855: if (!HT[(h1 + k) % ht_size]) {
856: HT[(h1 + k) % ht_size] = key;
857: HD[(h1 + k) % ht_size] = b->a + j * bs2;
858: break;
859: #if defined(PETSC_USE_INFO)
860: } else {
861: ct++;
862: #endif
863: }
864: }
865: #if defined(PETSC_USE_INFO)
866: if (k > max) max = k;
867: #endif
868: }
869: }
871: /* Print Summary */
872: #if defined(PETSC_USE_INFO)
873: for (i = 0, j = 0; i < ht_size; i++) {
874: if (HT[i]) j++;
875: }
876: PetscCall(PetscInfo(mat, "Average Search = %5.2g,max search = %" PetscInt_FMT "\n", (!j) ? (double)0.0 : (double)(((PetscReal)(ct + j)) / (double)j), max));
877: #endif
878: PetscFunctionReturn(PETSC_SUCCESS);
879: }
881: static PetscErrorCode MatAssemblyBegin_MPIBAIJ(Mat mat, MatAssemblyType mode)
882: {
883: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
884: PetscInt nstash, reallocs;
886: PetscFunctionBegin;
887: if (baij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
889: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
890: PetscCall(MatStashScatterBegin_Private(mat, &mat->bstash, baij->rangebs));
891: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
892: PetscCall(PetscInfo(mat, "Stash has %" PetscInt_FMT " entries,uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
893: PetscCall(MatStashGetInfo_Private(&mat->bstash, &nstash, &reallocs));
894: PetscCall(PetscInfo(mat, "Block-Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
895: PetscFunctionReturn(PETSC_SUCCESS);
896: }
898: static PetscErrorCode MatAssemblyEnd_MPIBAIJ(Mat mat, MatAssemblyType mode)
899: {
900: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
901: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)baij->A->data;
902: PetscInt i, j, rstart, ncols, flg, bs2 = baij->bs2;
903: PetscInt *row, *col;
904: PetscBool r1, r2, r3, other_disassembled;
905: MatScalar *val;
906: PetscMPIInt n;
908: PetscFunctionBegin;
909: /* do not use 'b=(Mat_SeqBAIJ*)baij->B->data' as B can be reset in disassembly */
910: if (!baij->donotstash && !mat->nooffprocentries) {
911: while (1) {
912: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
913: if (!flg) break;
915: for (i = 0; i < n;) {
916: /* Now identify the consecutive vals belonging to the same row */
917: for (j = i, rstart = row[j]; j < n; j++) {
918: if (row[j] != rstart) break;
919: }
920: if (j < n) ncols = j - i;
921: else ncols = n - i;
922: /* Now assemble all these values with a single function call */
923: PetscCall(MatSetValues_MPIBAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
924: i = j;
925: }
926: }
927: PetscCall(MatStashScatterEnd_Private(&mat->stash));
928: /* Now process the block-stash. Since the values are stashed column-oriented,
929: set the row-oriented flag to column-oriented, and after MatSetValues()
930: restore the original flags */
931: r1 = baij->roworiented;
932: r2 = a->roworiented;
933: r3 = ((Mat_SeqBAIJ *)baij->B->data)->roworiented;
935: baij->roworiented = PETSC_FALSE;
936: a->roworiented = PETSC_FALSE;
937: ((Mat_SeqBAIJ *)baij->B->data)->roworiented = PETSC_FALSE;
938: while (1) {
939: PetscCall(MatStashScatterGetMesg_Private(&mat->bstash, &n, &row, &col, &val, &flg));
940: if (!flg) break;
942: for (i = 0; i < n;) {
943: /* Now identify the consecutive vals belonging to the same row */
944: for (j = i, rstart = row[j]; j < n; j++) {
945: if (row[j] != rstart) break;
946: }
947: if (j < n) ncols = j - i;
948: else ncols = n - i;
949: PetscCall(MatSetValuesBlocked_MPIBAIJ(mat, 1, row + i, ncols, col + i, val + i * bs2, mat->insertmode));
950: i = j;
951: }
952: }
953: PetscCall(MatStashScatterEnd_Private(&mat->bstash));
955: baij->roworiented = r1;
956: a->roworiented = r2;
957: ((Mat_SeqBAIJ *)baij->B->data)->roworiented = r3;
958: }
960: PetscCall(MatAssemblyBegin(baij->A, mode));
961: PetscCall(MatAssemblyEnd(baij->A, mode));
963: /* determine if any processor has disassembled, if so we must
964: also disassemble ourselves, in order that we may reassemble. */
965: /*
966: if nonzero structure of submatrix B cannot change then we know that
967: no processor disassembled thus we can skip this stuff
968: */
969: if (!((Mat_SeqBAIJ *)baij->B->data)->nonew) {
970: PetscCall(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
971: if (mat->was_assembled && !other_disassembled) PetscCall(MatDisAssemble_MPIBAIJ(mat));
972: }
974: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIBAIJ(mat));
975: PetscCall(MatAssemblyBegin(baij->B, mode));
976: PetscCall(MatAssemblyEnd(baij->B, mode));
978: #if defined(PETSC_USE_INFO)
979: if (baij->ht && mode == MAT_FINAL_ASSEMBLY) {
980: PetscCall(PetscInfo(mat, "Average Hash Table Search in MatSetValues = %5.2f\n", (double)((PetscReal)baij->ht_total_ct) / baij->ht_insert_ct));
982: baij->ht_total_ct = 0;
983: baij->ht_insert_ct = 0;
984: }
985: #endif
986: if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
987: PetscCall(MatCreateHashTable_MPIBAIJ_Private(mat, baij->ht_fact));
989: mat->ops->setvalues = MatSetValues_MPIBAIJ_HT;
990: mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
991: }
993: PetscCall(PetscFree2(baij->rowvalues, baij->rowindices));
995: baij->rowvalues = NULL;
997: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
998: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqBAIJ *)baij->A->data)->nonew) {
999: PetscObjectState state = baij->A->nonzerostate + baij->B->nonzerostate;
1000: PetscCall(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
1001: }
1002: PetscFunctionReturn(PETSC_SUCCESS);
1003: }
1005: extern PetscErrorCode MatView_SeqBAIJ(Mat, PetscViewer);
1006: #include <petscdraw.h>
1007: static PetscErrorCode MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1008: {
1009: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1010: PetscMPIInt rank = baij->rank;
1011: PetscInt bs = mat->rmap->bs;
1012: PetscBool iascii, isdraw;
1013: PetscViewer sviewer;
1014: PetscViewerFormat format;
1016: PetscFunctionBegin;
1017: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1018: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1019: if (iascii) {
1020: PetscCall(PetscViewerGetFormat(viewer, &format));
1021: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1022: MatInfo info;
1023: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1024: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1025: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1026: 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,
1027: mat->rmap->bs, (double)info.memory));
1028: PetscCall(MatGetInfo(baij->A, MAT_LOCAL, &info));
1029: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1030: PetscCall(MatGetInfo(baij->B, MAT_LOCAL, &info));
1031: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1032: PetscCall(PetscViewerFlush(viewer));
1033: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1034: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1035: PetscCall(VecScatterView(baij->Mvctx, viewer));
1036: PetscFunctionReturn(PETSC_SUCCESS);
1037: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1038: PetscCall(PetscViewerASCIIPrintf(viewer, " block size is %" PetscInt_FMT "\n", bs));
1039: PetscFunctionReturn(PETSC_SUCCESS);
1040: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1041: PetscFunctionReturn(PETSC_SUCCESS);
1042: }
1043: }
1045: if (isdraw) {
1046: PetscDraw draw;
1047: PetscBool isnull;
1048: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1049: PetscCall(PetscDrawIsNull(draw, &isnull));
1050: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1051: }
1053: {
1054: /* assemble the entire matrix onto first processor. */
1055: Mat A;
1056: Mat_SeqBAIJ *Aloc;
1057: PetscInt M = mat->rmap->N, N = mat->cmap->N, *ai, *aj, col, i, j, k, *rvals, mbs = baij->mbs;
1058: MatScalar *a;
1059: const char *matname;
1061: /* Here we are creating a temporary matrix, so will assume MPIBAIJ is acceptable */
1062: /* Perhaps this should be the type of mat? */
1063: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &A));
1064: if (rank == 0) {
1065: PetscCall(MatSetSizes(A, M, N, M, N));
1066: } else {
1067: PetscCall(MatSetSizes(A, 0, 0, M, N));
1068: }
1069: PetscCall(MatSetType(A, MATMPIBAIJ));
1070: PetscCall(MatMPIBAIJSetPreallocation(A, mat->rmap->bs, 0, NULL, 0, NULL));
1071: PetscCall(MatSetOption(A, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_FALSE));
1073: /* copy over the A part */
1074: Aloc = (Mat_SeqBAIJ *)baij->A->data;
1075: ai = Aloc->i;
1076: aj = Aloc->j;
1077: a = Aloc->a;
1078: PetscCall(PetscMalloc1(bs, &rvals));
1080: for (i = 0; i < mbs; i++) {
1081: rvals[0] = bs * (baij->rstartbs + i);
1082: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1083: for (j = ai[i]; j < ai[i + 1]; j++) {
1084: col = (baij->cstartbs + aj[j]) * bs;
1085: for (k = 0; k < bs; k++) {
1086: PetscCall(MatSetValues_MPIBAIJ(A, bs, rvals, 1, &col, a, INSERT_VALUES));
1087: col++;
1088: a += bs;
1089: }
1090: }
1091: }
1092: /* copy over the B part */
1093: Aloc = (Mat_SeqBAIJ *)baij->B->data;
1094: ai = Aloc->i;
1095: aj = Aloc->j;
1096: a = Aloc->a;
1097: for (i = 0; i < mbs; i++) {
1098: rvals[0] = bs * (baij->rstartbs + i);
1099: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1100: for (j = ai[i]; j < ai[i + 1]; j++) {
1101: col = baij->garray[aj[j]] * bs;
1102: for (k = 0; k < bs; k++) {
1103: PetscCall(MatSetValues_MPIBAIJ(A, bs, rvals, 1, &col, a, INSERT_VALUES));
1104: col++;
1105: a += bs;
1106: }
1107: }
1108: }
1109: PetscCall(PetscFree(rvals));
1110: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1111: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1112: /*
1113: Everyone has to call to draw the matrix since the graphics waits are
1114: synchronized across all processors that share the PetscDraw object
1115: */
1116: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1117: if (((PetscObject)mat)->name) PetscCall(PetscObjectGetName((PetscObject)mat, &matname));
1118: if (rank == 0) {
1119: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)((Mat_MPIBAIJ *)A->data)->A, matname));
1120: PetscCall(MatView_SeqBAIJ(((Mat_MPIBAIJ *)A->data)->A, sviewer));
1121: }
1122: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1123: PetscCall(MatDestroy(&A));
1124: }
1125: PetscFunctionReturn(PETSC_SUCCESS);
1126: }
1128: /* Used for both MPIBAIJ and MPISBAIJ matrices */
1129: PetscErrorCode MatView_MPIBAIJ_Binary(Mat mat, PetscViewer viewer)
1130: {
1131: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
1132: Mat_SeqBAIJ *A = (Mat_SeqBAIJ *)aij->A->data;
1133: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)aij->B->data;
1134: const PetscInt *garray = aij->garray;
1135: PetscInt header[4], M, N, m, rs, cs, bs, cnt, i, j, ja, jb, k, l;
1136: PetscInt64 nz, hnz;
1137: PetscInt *rowlens, *colidxs;
1138: PetscScalar *matvals;
1139: PetscMPIInt rank;
1141: PetscFunctionBegin;
1142: PetscCall(PetscViewerSetUp(viewer));
1144: M = mat->rmap->N;
1145: N = mat->cmap->N;
1146: m = mat->rmap->n;
1147: rs = mat->rmap->rstart;
1148: cs = mat->cmap->rstart;
1149: bs = mat->rmap->bs;
1150: nz = bs * bs * (A->nz + B->nz);
1152: /* write matrix header */
1153: header[0] = MAT_FILE_CLASSID;
1154: header[1] = M;
1155: header[2] = N;
1156: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1157: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1158: if (rank == 0) PetscCall(PetscIntCast(hnz, &header[3]));
1159: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1161: /* fill in and store row lengths */
1162: PetscCall(PetscMalloc1(m, &rowlens));
1163: for (cnt = 0, i = 0; i < A->mbs; i++)
1164: for (j = 0; j < bs; j++) rowlens[cnt++] = bs * (A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i]);
1165: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1166: PetscCall(PetscFree(rowlens));
1168: /* fill in and store column indices */
1169: PetscCall(PetscMalloc1(nz, &colidxs));
1170: for (cnt = 0, i = 0; i < A->mbs; i++) {
1171: for (k = 0; k < bs; k++) {
1172: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1173: if (garray[B->j[jb]] > cs / bs) break;
1174: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * garray[B->j[jb]] + l;
1175: }
1176: for (ja = A->i[i]; ja < A->i[i + 1]; ja++)
1177: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * A->j[ja] + l + cs;
1178: for (; jb < B->i[i + 1]; jb++)
1179: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * garray[B->j[jb]] + l;
1180: }
1181: }
1182: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1183: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DECIDE, PETSC_DECIDE, PETSC_INT));
1184: PetscCall(PetscFree(colidxs));
1186: /* fill in and store nonzero values */
1187: PetscCall(PetscMalloc1(nz, &matvals));
1188: for (cnt = 0, i = 0; i < A->mbs; i++) {
1189: for (k = 0; k < bs; k++) {
1190: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1191: if (garray[B->j[jb]] > cs / bs) break;
1192: for (l = 0; l < bs; l++) matvals[cnt++] = B->a[bs * (bs * jb + l) + k];
1193: }
1194: for (ja = A->i[i]; ja < A->i[i + 1]; ja++)
1195: for (l = 0; l < bs; l++) matvals[cnt++] = A->a[bs * (bs * ja + l) + k];
1196: for (; jb < B->i[i + 1]; jb++)
1197: for (l = 0; l < bs; l++) matvals[cnt++] = B->a[bs * (bs * jb + l) + k];
1198: }
1199: }
1200: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DECIDE, PETSC_DECIDE, PETSC_SCALAR));
1201: PetscCall(PetscFree(matvals));
1203: /* write block size option to the viewer's .info file */
1204: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1205: PetscFunctionReturn(PETSC_SUCCESS);
1206: }
1208: PetscErrorCode MatView_MPIBAIJ(Mat mat, PetscViewer viewer)
1209: {
1210: PetscBool iascii, isdraw, issocket, isbinary;
1212: PetscFunctionBegin;
1213: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1214: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1215: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1216: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1217: if (iascii || isdraw || issocket) {
1218: PetscCall(MatView_MPIBAIJ_ASCIIorDraworSocket(mat, viewer));
1219: } else if (isbinary) PetscCall(MatView_MPIBAIJ_Binary(mat, viewer));
1220: PetscFunctionReturn(PETSC_SUCCESS);
1221: }
1223: static PetscErrorCode MatMult_MPIBAIJ(Mat A, Vec xx, Vec yy)
1224: {
1225: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1226: PetscInt nt;
1228: PetscFunctionBegin;
1229: PetscCall(VecGetLocalSize(xx, &nt));
1230: PetscCheck(nt == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A and xx");
1231: PetscCall(VecGetLocalSize(yy, &nt));
1232: PetscCheck(nt == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A and yy");
1233: PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1234: PetscCall((*a->A->ops->mult)(a->A, xx, yy));
1235: PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1236: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, yy, yy));
1237: PetscFunctionReturn(PETSC_SUCCESS);
1238: }
1240: static PetscErrorCode MatMultAdd_MPIBAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1241: {
1242: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1244: PetscFunctionBegin;
1245: PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1246: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1247: PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1248: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1249: PetscFunctionReturn(PETSC_SUCCESS);
1250: }
1252: static PetscErrorCode MatMultTranspose_MPIBAIJ(Mat A, Vec xx, Vec yy)
1253: {
1254: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1256: PetscFunctionBegin;
1257: /* do nondiagonal part */
1258: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1259: /* do local part */
1260: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1261: /* add partial results together */
1262: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1263: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1264: PetscFunctionReturn(PETSC_SUCCESS);
1265: }
1267: static PetscErrorCode MatMultTransposeAdd_MPIBAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1268: {
1269: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1271: PetscFunctionBegin;
1272: /* do nondiagonal part */
1273: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1274: /* do local part */
1275: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1276: /* add partial results together */
1277: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1278: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1279: PetscFunctionReturn(PETSC_SUCCESS);
1280: }
1282: /*
1283: This only works correctly for square matrices where the subblock A->A is the
1284: diagonal block
1285: */
1286: static PetscErrorCode MatGetDiagonal_MPIBAIJ(Mat A, Vec v)
1287: {
1288: PetscFunctionBegin;
1289: PetscCheck(A->rmap->N == A->cmap->N, PETSC_COMM_SELF, PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1290: PetscCall(MatGetDiagonal(((Mat_MPIBAIJ *)A->data)->A, v));
1291: PetscFunctionReturn(PETSC_SUCCESS);
1292: }
1294: static PetscErrorCode MatScale_MPIBAIJ(Mat A, PetscScalar aa)
1295: {
1296: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1298: PetscFunctionBegin;
1299: PetscCall(MatScale(a->A, aa));
1300: PetscCall(MatScale(a->B, aa));
1301: PetscFunctionReturn(PETSC_SUCCESS);
1302: }
1304: PetscErrorCode MatGetRow_MPIBAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1305: {
1306: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)matin->data;
1307: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1308: PetscInt bs = matin->rmap->bs, bs2 = mat->bs2, i, *cworkA, *cworkB, **pcA, **pcB;
1309: PetscInt nztot, nzA, nzB, lrow, brstart = matin->rmap->rstart, brend = matin->rmap->rend;
1310: PetscInt *cmap, *idx_p, cstart = mat->cstartbs;
1312: PetscFunctionBegin;
1313: PetscCheck(row >= brstart && row < brend, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local rows");
1314: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1315: mat->getrowactive = PETSC_TRUE;
1317: if (!mat->rowvalues && (idx || v)) {
1318: /*
1319: allocate enough space to hold information from the longest row.
1320: */
1321: Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ *)mat->A->data, *Ba = (Mat_SeqBAIJ *)mat->B->data;
1322: PetscInt max = 1, mbs = mat->mbs, tmp;
1323: for (i = 0; i < mbs; i++) {
1324: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1325: if (max < tmp) max = tmp;
1326: }
1327: PetscCall(PetscMalloc2(max * bs2, &mat->rowvalues, max * bs2, &mat->rowindices));
1328: }
1329: lrow = row - brstart;
1331: pvA = &vworkA;
1332: pcA = &cworkA;
1333: pvB = &vworkB;
1334: pcB = &cworkB;
1335: if (!v) {
1336: pvA = NULL;
1337: pvB = NULL;
1338: }
1339: if (!idx) {
1340: pcA = NULL;
1341: if (!v) pcB = NULL;
1342: }
1343: PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1344: PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1345: nztot = nzA + nzB;
1347: cmap = mat->garray;
1348: if (v || idx) {
1349: if (nztot) {
1350: /* Sort by increasing column numbers, assuming A and B already sorted */
1351: PetscInt imark = -1;
1352: if (v) {
1353: *v = v_p = mat->rowvalues;
1354: for (i = 0; i < nzB; i++) {
1355: if (cmap[cworkB[i] / bs] < cstart) v_p[i] = vworkB[i];
1356: else break;
1357: }
1358: imark = i;
1359: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1360: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1361: }
1362: if (idx) {
1363: *idx = idx_p = mat->rowindices;
1364: if (imark > -1) {
1365: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i] / bs] * bs + cworkB[i] % bs;
1366: } else {
1367: for (i = 0; i < nzB; i++) {
1368: if (cmap[cworkB[i] / bs] < cstart) idx_p[i] = cmap[cworkB[i] / bs] * bs + cworkB[i] % bs;
1369: else break;
1370: }
1371: imark = i;
1372: }
1373: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart * bs + cworkA[i];
1374: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i] / bs] * bs + cworkB[i] % bs;
1375: }
1376: } else {
1377: if (idx) *idx = NULL;
1378: if (v) *v = NULL;
1379: }
1380: }
1381: *nz = nztot;
1382: PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1383: PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1384: PetscFunctionReturn(PETSC_SUCCESS);
1385: }
1387: PetscErrorCode MatRestoreRow_MPIBAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1388: {
1389: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1391: PetscFunctionBegin;
1392: PetscCheck(baij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow not called");
1393: baij->getrowactive = PETSC_FALSE;
1394: PetscFunctionReturn(PETSC_SUCCESS);
1395: }
1397: static PetscErrorCode MatZeroEntries_MPIBAIJ(Mat A)
1398: {
1399: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1401: PetscFunctionBegin;
1402: PetscCall(MatZeroEntries(l->A));
1403: PetscCall(MatZeroEntries(l->B));
1404: PetscFunctionReturn(PETSC_SUCCESS);
1405: }
1407: static PetscErrorCode MatGetInfo_MPIBAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1408: {
1409: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)matin->data;
1410: Mat A = a->A, B = a->B;
1411: PetscLogDouble isend[5], irecv[5];
1413: PetscFunctionBegin;
1414: info->block_size = (PetscReal)matin->rmap->bs;
1416: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1418: isend[0] = info->nz_used;
1419: isend[1] = info->nz_allocated;
1420: isend[2] = info->nz_unneeded;
1421: isend[3] = info->memory;
1422: isend[4] = info->mallocs;
1424: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1426: isend[0] += info->nz_used;
1427: isend[1] += info->nz_allocated;
1428: isend[2] += info->nz_unneeded;
1429: isend[3] += info->memory;
1430: isend[4] += info->mallocs;
1432: if (flag == MAT_LOCAL) {
1433: info->nz_used = isend[0];
1434: info->nz_allocated = isend[1];
1435: info->nz_unneeded = isend[2];
1436: info->memory = isend[3];
1437: info->mallocs = isend[4];
1438: } else if (flag == MAT_GLOBAL_MAX) {
1439: PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1441: info->nz_used = irecv[0];
1442: info->nz_allocated = irecv[1];
1443: info->nz_unneeded = irecv[2];
1444: info->memory = irecv[3];
1445: info->mallocs = irecv[4];
1446: } else if (flag == MAT_GLOBAL_SUM) {
1447: PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1449: info->nz_used = irecv[0];
1450: info->nz_allocated = irecv[1];
1451: info->nz_unneeded = irecv[2];
1452: info->memory = irecv[3];
1453: info->mallocs = irecv[4];
1454: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_ARG_WRONG, "Unknown MatInfoType argument %d", (int)flag);
1455: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1456: info->fill_ratio_needed = 0;
1457: info->factor_mallocs = 0;
1458: PetscFunctionReturn(PETSC_SUCCESS);
1459: }
1461: static PetscErrorCode MatSetOption_MPIBAIJ(Mat A, MatOption op, PetscBool flg)
1462: {
1463: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1465: PetscFunctionBegin;
1466: switch (op) {
1467: case MAT_NEW_NONZERO_LOCATIONS:
1468: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1469: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1470: case MAT_KEEP_NONZERO_PATTERN:
1471: case MAT_NEW_NONZERO_LOCATION_ERR:
1472: MatCheckPreallocated(A, 1);
1473: PetscCall(MatSetOption(a->A, op, flg));
1474: PetscCall(MatSetOption(a->B, op, flg));
1475: break;
1476: case MAT_ROW_ORIENTED:
1477: MatCheckPreallocated(A, 1);
1478: a->roworiented = flg;
1480: PetscCall(MatSetOption(a->A, op, flg));
1481: PetscCall(MatSetOption(a->B, op, flg));
1482: break;
1483: case MAT_FORCE_DIAGONAL_ENTRIES:
1484: case MAT_SORTED_FULL:
1485: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1486: break;
1487: case MAT_IGNORE_OFF_PROC_ENTRIES:
1488: a->donotstash = flg;
1489: break;
1490: case MAT_USE_HASH_TABLE:
1491: a->ht_flag = flg;
1492: a->ht_fact = 1.39;
1493: break;
1494: case MAT_SYMMETRIC:
1495: case MAT_STRUCTURALLY_SYMMETRIC:
1496: case MAT_HERMITIAN:
1497: case MAT_SUBMAT_SINGLEIS:
1498: case MAT_SYMMETRY_ETERNAL:
1499: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1500: case MAT_SPD_ETERNAL:
1501: /* if the diagonal matrix is square it inherits some of the properties above */
1502: break;
1503: default:
1504: SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "unknown option %d", op);
1505: }
1506: PetscFunctionReturn(PETSC_SUCCESS);
1507: }
1509: static PetscErrorCode MatTranspose_MPIBAIJ(Mat A, MatReuse reuse, Mat *matout)
1510: {
1511: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)A->data;
1512: Mat_SeqBAIJ *Aloc;
1513: Mat B;
1514: PetscInt M = A->rmap->N, N = A->cmap->N, *ai, *aj, i, *rvals, j, k, col;
1515: PetscInt bs = A->rmap->bs, mbs = baij->mbs;
1516: MatScalar *a;
1518: PetscFunctionBegin;
1519: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1520: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
1521: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1522: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1523: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1524: /* Do not know preallocation information, but must set block size */
1525: PetscCall(MatMPIBAIJSetPreallocation(B, A->rmap->bs, PETSC_DECIDE, NULL, PETSC_DECIDE, NULL));
1526: } else {
1527: B = *matout;
1528: }
1530: /* copy over the A part */
1531: Aloc = (Mat_SeqBAIJ *)baij->A->data;
1532: ai = Aloc->i;
1533: aj = Aloc->j;
1534: a = Aloc->a;
1535: PetscCall(PetscMalloc1(bs, &rvals));
1537: for (i = 0; i < mbs; i++) {
1538: rvals[0] = bs * (baij->rstartbs + i);
1539: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1540: for (j = ai[i]; j < ai[i + 1]; j++) {
1541: col = (baij->cstartbs + aj[j]) * bs;
1542: for (k = 0; k < bs; k++) {
1543: PetscCall(MatSetValues_MPIBAIJ(B, 1, &col, bs, rvals, a, INSERT_VALUES));
1545: col++;
1546: a += bs;
1547: }
1548: }
1549: }
1550: /* copy over the B part */
1551: Aloc = (Mat_SeqBAIJ *)baij->B->data;
1552: ai = Aloc->i;
1553: aj = Aloc->j;
1554: a = Aloc->a;
1555: for (i = 0; i < mbs; i++) {
1556: rvals[0] = bs * (baij->rstartbs + i);
1557: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1558: for (j = ai[i]; j < ai[i + 1]; j++) {
1559: col = baij->garray[aj[j]] * bs;
1560: for (k = 0; k < bs; k++) {
1561: PetscCall(MatSetValues_MPIBAIJ(B, 1, &col, bs, rvals, a, INSERT_VALUES));
1562: col++;
1563: a += bs;
1564: }
1565: }
1566: }
1567: PetscCall(PetscFree(rvals));
1568: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1569: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1571: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) *matout = B;
1572: else PetscCall(MatHeaderMerge(A, &B));
1573: PetscFunctionReturn(PETSC_SUCCESS);
1574: }
1576: static PetscErrorCode MatDiagonalScale_MPIBAIJ(Mat mat, Vec ll, Vec rr)
1577: {
1578: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1579: Mat a = baij->A, b = baij->B;
1580: PetscInt s1, s2, s3;
1582: PetscFunctionBegin;
1583: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1584: if (rr) {
1585: PetscCall(VecGetLocalSize(rr, &s1));
1586: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1587: /* Overlap communication with computation. */
1588: PetscCall(VecScatterBegin(baij->Mvctx, rr, baij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1589: }
1590: if (ll) {
1591: PetscCall(VecGetLocalSize(ll, &s1));
1592: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1593: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1594: }
1595: /* scale the diagonal block */
1596: PetscUseTypeMethod(a, diagonalscale, ll, rr);
1598: if (rr) {
1599: /* Do a scatter end and then right scale the off-diagonal block */
1600: PetscCall(VecScatterEnd(baij->Mvctx, rr, baij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1601: PetscUseTypeMethod(b, diagonalscale, NULL, baij->lvec);
1602: }
1603: PetscFunctionReturn(PETSC_SUCCESS);
1604: }
1606: static PetscErrorCode MatZeroRows_MPIBAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
1607: {
1608: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1609: PetscInt *lrows;
1610: PetscInt r, len;
1611: PetscBool cong;
1613: PetscFunctionBegin;
1614: /* get locally owned rows */
1615: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
1616: /* fix right-hand side if needed */
1617: if (x && b) {
1618: const PetscScalar *xx;
1619: PetscScalar *bb;
1621: PetscCall(VecGetArrayRead(x, &xx));
1622: PetscCall(VecGetArray(b, &bb));
1623: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
1624: PetscCall(VecRestoreArrayRead(x, &xx));
1625: PetscCall(VecRestoreArray(b, &bb));
1626: }
1628: /* actually zap the local rows */
1629: /*
1630: Zero the required rows. If the "diagonal block" of the matrix
1631: is square and the user wishes to set the diagonal we use separate
1632: code so that MatSetValues() is not called for each diagonal allocating
1633: new memory, thus calling lots of mallocs and slowing things down.
1635: */
1636: /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1637: PetscCall(MatZeroRows_SeqBAIJ(l->B, len, lrows, 0.0, NULL, NULL));
1638: PetscCall(MatHasCongruentLayouts(A, &cong));
1639: if ((diag != 0.0) && cong) {
1640: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, diag, NULL, NULL));
1641: } else if (diag != 0.0) {
1642: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, 0.0, NULL, NULL));
1643: PetscCheck(!((Mat_SeqBAIJ *)l->A->data)->nonew, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatZeroRows() on rectangular matrices cannot be used with the Mat options MAT_NEW_NONZERO_LOCATIONS, MAT_NEW_NONZERO_LOCATION_ERR, and MAT_NEW_NONZERO_ALLOCATION_ERR");
1644: for (r = 0; r < len; ++r) {
1645: const PetscInt row = lrows[r] + A->rmap->rstart;
1646: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
1647: }
1648: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1649: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1650: } else {
1651: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, 0.0, NULL, NULL));
1652: }
1653: PetscCall(PetscFree(lrows));
1655: /* only change matrix nonzero state if pattern was allowed to be changed */
1656: if (!((Mat_SeqBAIJ *)l->A->data)->keepnonzeropattern || !((Mat_SeqBAIJ *)l->A->data)->nonew) {
1657: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1658: PetscCall(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1659: }
1660: PetscFunctionReturn(PETSC_SUCCESS);
1661: }
1663: static PetscErrorCode MatZeroRowsColumns_MPIBAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
1664: {
1665: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1666: PetscMPIInt n = A->rmap->n, p = 0;
1667: PetscInt i, j, k, r, len = 0, row, col, count;
1668: PetscInt *lrows, *owners = A->rmap->range;
1669: PetscSFNode *rrows;
1670: PetscSF sf;
1671: const PetscScalar *xx;
1672: PetscScalar *bb, *mask;
1673: Vec xmask, lmask;
1674: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)l->B->data;
1675: PetscInt bs = A->rmap->bs, bs2 = baij->bs2;
1676: PetscScalar *aa;
1678: PetscFunctionBegin;
1679: /* Create SF where leaves are input rows and roots are owned rows */
1680: PetscCall(PetscMalloc1(n, &lrows));
1681: for (r = 0; r < n; ++r) lrows[r] = -1;
1682: PetscCall(PetscMalloc1(N, &rrows));
1683: for (r = 0; r < N; ++r) {
1684: const PetscInt idx = rows[r];
1685: 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);
1686: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
1687: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
1688: }
1689: rrows[r].rank = p;
1690: rrows[r].index = rows[r] - owners[p];
1691: }
1692: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1693: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
1694: /* Collect flags for rows to be zeroed */
1695: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
1696: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
1697: PetscCall(PetscSFDestroy(&sf));
1698: /* Compress and put in row numbers */
1699: for (r = 0; r < n; ++r)
1700: if (lrows[r] >= 0) lrows[len++] = r;
1701: /* zero diagonal part of matrix */
1702: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
1703: /* handle off-diagonal part of matrix */
1704: PetscCall(MatCreateVecs(A, &xmask, NULL));
1705: PetscCall(VecDuplicate(l->lvec, &lmask));
1706: PetscCall(VecGetArray(xmask, &bb));
1707: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
1708: PetscCall(VecRestoreArray(xmask, &bb));
1709: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
1710: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
1711: PetscCall(VecDestroy(&xmask));
1712: if (x) {
1713: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
1714: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
1715: PetscCall(VecGetArrayRead(l->lvec, &xx));
1716: PetscCall(VecGetArray(b, &bb));
1717: }
1718: PetscCall(VecGetArray(lmask, &mask));
1719: /* remove zeroed rows of off-diagonal matrix */
1720: for (i = 0; i < len; ++i) {
1721: row = lrows[i];
1722: count = (baij->i[row / bs + 1] - baij->i[row / bs]) * bs;
1723: aa = ((MatScalar *)baij->a) + baij->i[row / bs] * bs2 + (row % bs);
1724: for (k = 0; k < count; ++k) {
1725: aa[0] = 0.0;
1726: aa += bs;
1727: }
1728: }
1729: /* loop over all elements of off process part of matrix zeroing removed columns*/
1730: for (i = 0; i < l->B->rmap->N; ++i) {
1731: row = i / bs;
1732: for (j = baij->i[row]; j < baij->i[row + 1]; ++j) {
1733: for (k = 0; k < bs; ++k) {
1734: col = bs * baij->j[j] + k;
1735: if (PetscAbsScalar(mask[col])) {
1736: aa = ((MatScalar *)baij->a) + j * bs2 + (i % bs) + bs * k;
1737: if (x) bb[i] -= aa[0] * xx[col];
1738: aa[0] = 0.0;
1739: }
1740: }
1741: }
1742: }
1743: if (x) {
1744: PetscCall(VecRestoreArray(b, &bb));
1745: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1746: }
1747: PetscCall(VecRestoreArray(lmask, &mask));
1748: PetscCall(VecDestroy(&lmask));
1749: PetscCall(PetscFree(lrows));
1751: /* only change matrix nonzero state if pattern was allowed to be changed */
1752: if (!((Mat_SeqBAIJ *)l->A->data)->nonew) {
1753: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1754: PetscCall(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1755: }
1756: PetscFunctionReturn(PETSC_SUCCESS);
1757: }
1759: static PetscErrorCode MatSetUnfactored_MPIBAIJ(Mat A)
1760: {
1761: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1763: PetscFunctionBegin;
1764: PetscCall(MatSetUnfactored(a->A));
1765: PetscFunctionReturn(PETSC_SUCCESS);
1766: }
1768: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat, MatDuplicateOption, Mat *);
1770: static PetscErrorCode MatEqual_MPIBAIJ(Mat A, Mat B, PetscBool *flag)
1771: {
1772: Mat_MPIBAIJ *matB = (Mat_MPIBAIJ *)B->data, *matA = (Mat_MPIBAIJ *)A->data;
1773: Mat a, b, c, d;
1774: PetscBool flg;
1776: PetscFunctionBegin;
1777: a = matA->A;
1778: b = matA->B;
1779: c = matB->A;
1780: d = matB->B;
1782: PetscCall(MatEqual(a, c, &flg));
1783: if (flg) PetscCall(MatEqual(b, d, &flg));
1784: PetscCall(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
1785: PetscFunctionReturn(PETSC_SUCCESS);
1786: }
1788: static PetscErrorCode MatCopy_MPIBAIJ(Mat A, Mat B, MatStructure str)
1789: {
1790: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1791: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
1793: PetscFunctionBegin;
1794: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1795: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1796: PetscCall(MatCopy_Basic(A, B, str));
1797: } else {
1798: PetscCall(MatCopy(a->A, b->A, str));
1799: PetscCall(MatCopy(a->B, b->B, str));
1800: }
1801: PetscCall(PetscObjectStateIncrease((PetscObject)B));
1802: PetscFunctionReturn(PETSC_SUCCESS);
1803: }
1805: PetscErrorCode MatAXPYGetPreallocation_MPIBAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
1806: {
1807: PetscInt bs = Y->rmap->bs, m = Y->rmap->N / bs;
1808: Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data;
1809: Mat_SeqBAIJ *y = (Mat_SeqBAIJ *)Y->data;
1811: PetscFunctionBegin;
1812: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
1813: PetscFunctionReturn(PETSC_SUCCESS);
1814: }
1816: static PetscErrorCode MatAXPY_MPIBAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
1817: {
1818: Mat_MPIBAIJ *xx = (Mat_MPIBAIJ *)X->data, *yy = (Mat_MPIBAIJ *)Y->data;
1819: PetscBLASInt bnz, one = 1;
1820: Mat_SeqBAIJ *x, *y;
1821: PetscInt bs2 = Y->rmap->bs * Y->rmap->bs;
1823: PetscFunctionBegin;
1824: if (str == SAME_NONZERO_PATTERN) {
1825: PetscScalar alpha = a;
1826: x = (Mat_SeqBAIJ *)xx->A->data;
1827: y = (Mat_SeqBAIJ *)yy->A->data;
1828: PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz));
1829: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one));
1830: x = (Mat_SeqBAIJ *)xx->B->data;
1831: y = (Mat_SeqBAIJ *)yy->B->data;
1832: PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz));
1833: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one));
1834: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
1835: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
1836: PetscCall(MatAXPY_Basic(Y, a, X, str));
1837: } else {
1838: Mat B;
1839: PetscInt *nnz_d, *nnz_o, bs = Y->rmap->bs;
1840: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
1841: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
1842: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
1843: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
1844: PetscCall(MatSetSizes(B, Y->rmap->n, Y->cmap->n, Y->rmap->N, Y->cmap->N));
1845: PetscCall(MatSetBlockSizesFromMats(B, Y, Y));
1846: PetscCall(MatSetType(B, MATMPIBAIJ));
1847: PetscCall(MatAXPYGetPreallocation_SeqBAIJ(yy->A, xx->A, nnz_d));
1848: PetscCall(MatAXPYGetPreallocation_MPIBAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
1849: PetscCall(MatMPIBAIJSetPreallocation(B, bs, 0, nnz_d, 0, nnz_o));
1850: /* MatAXPY_BasicWithPreallocation() for BAIJ matrix is much slower than AIJ, even for bs=1 ! */
1851: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
1852: PetscCall(MatHeaderMerge(Y, &B));
1853: PetscCall(PetscFree(nnz_d));
1854: PetscCall(PetscFree(nnz_o));
1855: }
1856: PetscFunctionReturn(PETSC_SUCCESS);
1857: }
1859: static PetscErrorCode MatConjugate_MPIBAIJ(Mat mat)
1860: {
1861: PetscFunctionBegin;
1862: if (PetscDefined(USE_COMPLEX)) {
1863: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)mat->data;
1865: PetscCall(MatConjugate_SeqBAIJ(a->A));
1866: PetscCall(MatConjugate_SeqBAIJ(a->B));
1867: }
1868: PetscFunctionReturn(PETSC_SUCCESS);
1869: }
1871: static PetscErrorCode MatRealPart_MPIBAIJ(Mat A)
1872: {
1873: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1875: PetscFunctionBegin;
1876: PetscCall(MatRealPart(a->A));
1877: PetscCall(MatRealPart(a->B));
1878: PetscFunctionReturn(PETSC_SUCCESS);
1879: }
1881: static PetscErrorCode MatImaginaryPart_MPIBAIJ(Mat A)
1882: {
1883: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1885: PetscFunctionBegin;
1886: PetscCall(MatImaginaryPart(a->A));
1887: PetscCall(MatImaginaryPart(a->B));
1888: PetscFunctionReturn(PETSC_SUCCESS);
1889: }
1891: static PetscErrorCode MatCreateSubMatrix_MPIBAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
1892: {
1893: IS iscol_local;
1894: PetscInt csize;
1896: PetscFunctionBegin;
1897: PetscCall(ISGetLocalSize(iscol, &csize));
1898: if (call == MAT_REUSE_MATRIX) {
1899: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
1900: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
1901: } else {
1902: PetscCall(ISAllGather(iscol, &iscol_local));
1903: }
1904: PetscCall(MatCreateSubMatrix_MPIBAIJ_Private(mat, isrow, iscol_local, csize, call, newmat, PETSC_FALSE));
1905: if (call == MAT_INITIAL_MATRIX) {
1906: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
1907: PetscCall(ISDestroy(&iscol_local));
1908: }
1909: PetscFunctionReturn(PETSC_SUCCESS);
1910: }
1912: /*
1913: Not great since it makes two copies of the submatrix, first an SeqBAIJ
1914: in local and then by concatenating the local matrices the end result.
1915: Writing it directly would be much like MatCreateSubMatrices_MPIBAIJ().
1916: This routine is used for BAIJ and SBAIJ matrices (unfortunate dependency).
1917: */
1918: PetscErrorCode MatCreateSubMatrix_MPIBAIJ_Private(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat, PetscBool sym)
1919: {
1920: PetscMPIInt rank, size;
1921: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs;
1922: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
1923: Mat M, Mreuse;
1924: MatScalar *vwork, *aa;
1925: MPI_Comm comm;
1926: IS isrow_new, iscol_new;
1927: Mat_SeqBAIJ *aij;
1929: PetscFunctionBegin;
1930: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
1931: PetscCallMPI(MPI_Comm_rank(comm, &rank));
1932: PetscCallMPI(MPI_Comm_size(comm, &size));
1933: /* The compression and expansion should be avoided. Doesn't point
1934: out errors, might change the indices, hence buggey */
1935: PetscCall(ISCompressIndicesGeneral(mat->rmap->N, mat->rmap->n, mat->rmap->bs, 1, &isrow, &isrow_new));
1936: if (isrow == iscol) {
1937: iscol_new = isrow_new;
1938: PetscCall(PetscObjectReference((PetscObject)iscol_new));
1939: } else PetscCall(ISCompressIndicesGeneral(mat->cmap->N, mat->cmap->n, mat->cmap->bs, 1, &iscol, &iscol_new));
1941: if (call == MAT_REUSE_MATRIX) {
1942: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
1943: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
1944: PetscCall(MatCreateSubMatrices_MPIBAIJ_local(mat, 1, &isrow_new, &iscol_new, MAT_REUSE_MATRIX, &Mreuse, sym));
1945: } else {
1946: PetscCall(MatCreateSubMatrices_MPIBAIJ_local(mat, 1, &isrow_new, &iscol_new, MAT_INITIAL_MATRIX, &Mreuse, sym));
1947: }
1948: PetscCall(ISDestroy(&isrow_new));
1949: PetscCall(ISDestroy(&iscol_new));
1950: /*
1951: m - number of local rows
1952: n - number of columns (same on all processors)
1953: rstart - first row in new global matrix generated
1954: */
1955: PetscCall(MatGetBlockSize(mat, &bs));
1956: PetscCall(MatGetSize(Mreuse, &m, &n));
1957: m = m / bs;
1958: n = n / bs;
1960: if (call == MAT_INITIAL_MATRIX) {
1961: aij = (Mat_SeqBAIJ *)(Mreuse)->data;
1962: ii = aij->i;
1963: jj = aij->j;
1965: /*
1966: Determine the number of non-zeros in the diagonal and off-diagonal
1967: portions of the matrix in order to do correct preallocation
1968: */
1970: /* first get start and end of "diagonal" columns */
1971: if (csize == PETSC_DECIDE) {
1972: PetscCall(ISGetSize(isrow, &mglobal));
1973: if (mglobal == n * bs) { /* square matrix */
1974: nlocal = m;
1975: } else {
1976: nlocal = n / size + ((n % size) > rank);
1977: }
1978: } else {
1979: nlocal = csize / bs;
1980: }
1981: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
1982: rstart = rend - nlocal;
1983: 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);
1985: /* next, compute all the lengths */
1986: PetscCall(PetscMalloc2(m + 1, &dlens, m + 1, &olens));
1987: for (i = 0; i < m; i++) {
1988: jend = ii[i + 1] - ii[i];
1989: olen = 0;
1990: dlen = 0;
1991: for (j = 0; j < jend; j++) {
1992: if (*jj < rstart || *jj >= rend) olen++;
1993: else dlen++;
1994: jj++;
1995: }
1996: olens[i] = olen;
1997: dlens[i] = dlen;
1998: }
1999: PetscCall(MatCreate(comm, &M));
2000: PetscCall(MatSetSizes(M, bs * m, bs * nlocal, PETSC_DECIDE, bs * n));
2001: PetscCall(MatSetType(M, sym ? ((PetscObject)mat)->type_name : MATMPIBAIJ));
2002: PetscCall(MatMPIBAIJSetPreallocation(M, bs, 0, dlens, 0, olens));
2003: PetscCall(MatMPISBAIJSetPreallocation(M, bs, 0, dlens, 0, olens));
2004: PetscCall(PetscFree2(dlens, olens));
2005: } else {
2006: PetscInt ml, nl;
2008: M = *newmat;
2009: PetscCall(MatGetLocalSize(M, &ml, &nl));
2010: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
2011: PetscCall(MatZeroEntries(M));
2012: /*
2013: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
2014: rather than the slower MatSetValues().
2015: */
2016: M->was_assembled = PETSC_TRUE;
2017: M->assembled = PETSC_FALSE;
2018: }
2019: PetscCall(MatSetOption(M, MAT_ROW_ORIENTED, PETSC_FALSE));
2020: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
2021: aij = (Mat_SeqBAIJ *)(Mreuse)->data;
2022: ii = aij->i;
2023: jj = aij->j;
2024: aa = aij->a;
2025: for (i = 0; i < m; i++) {
2026: row = rstart / bs + i;
2027: nz = ii[i + 1] - ii[i];
2028: cwork = jj;
2029: jj = PetscSafePointerPlusOffset(jj, nz);
2030: vwork = aa;
2031: aa = PetscSafePointerPlusOffset(aa, nz * bs * bs);
2032: PetscCall(MatSetValuesBlocked_MPIBAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
2033: }
2035: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
2036: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
2037: *newmat = M;
2039: /* save submatrix used in processor for next request */
2040: if (call == MAT_INITIAL_MATRIX) {
2041: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
2042: PetscCall(PetscObjectDereference((PetscObject)Mreuse));
2043: }
2044: PetscFunctionReturn(PETSC_SUCCESS);
2045: }
2047: static PetscErrorCode MatPermute_MPIBAIJ(Mat A, IS rowp, IS colp, Mat *B)
2048: {
2049: MPI_Comm comm, pcomm;
2050: PetscInt clocal_size, nrows;
2051: const PetscInt *rows;
2052: PetscMPIInt size;
2053: IS crowp, lcolp;
2055: PetscFunctionBegin;
2056: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
2057: /* make a collective version of 'rowp' */
2058: PetscCall(PetscObjectGetComm((PetscObject)rowp, &pcomm));
2059: if (pcomm == comm) {
2060: crowp = rowp;
2061: } else {
2062: PetscCall(ISGetSize(rowp, &nrows));
2063: PetscCall(ISGetIndices(rowp, &rows));
2064: PetscCall(ISCreateGeneral(comm, nrows, rows, PETSC_COPY_VALUES, &crowp));
2065: PetscCall(ISRestoreIndices(rowp, &rows));
2066: }
2067: PetscCall(ISSetPermutation(crowp));
2068: /* make a local version of 'colp' */
2069: PetscCall(PetscObjectGetComm((PetscObject)colp, &pcomm));
2070: PetscCallMPI(MPI_Comm_size(pcomm, &size));
2071: if (size == 1) {
2072: lcolp = colp;
2073: } else {
2074: PetscCall(ISAllGather(colp, &lcolp));
2075: }
2076: PetscCall(ISSetPermutation(lcolp));
2077: /* now we just get the submatrix */
2078: PetscCall(MatGetLocalSize(A, NULL, &clocal_size));
2079: PetscCall(MatCreateSubMatrix_MPIBAIJ_Private(A, crowp, lcolp, clocal_size, MAT_INITIAL_MATRIX, B, PETSC_FALSE));
2080: /* clean up */
2081: if (pcomm != comm) PetscCall(ISDestroy(&crowp));
2082: if (size > 1) PetscCall(ISDestroy(&lcolp));
2083: PetscFunctionReturn(PETSC_SUCCESS);
2084: }
2086: static PetscErrorCode MatGetGhosts_MPIBAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
2087: {
2088: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
2089: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)baij->B->data;
2091: PetscFunctionBegin;
2092: if (nghosts) *nghosts = B->nbs;
2093: if (ghosts) *ghosts = baij->garray;
2094: PetscFunctionReturn(PETSC_SUCCESS);
2095: }
2097: static PetscErrorCode MatGetSeqNonzeroStructure_MPIBAIJ(Mat A, Mat *newmat)
2098: {
2099: Mat B;
2100: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2101: Mat_SeqBAIJ *ad = (Mat_SeqBAIJ *)a->A->data, *bd = (Mat_SeqBAIJ *)a->B->data;
2102: Mat_SeqAIJ *b;
2103: PetscMPIInt size, rank, *recvcounts = NULL, *displs = NULL;
2104: PetscInt sendcount, i, *rstarts = A->rmap->range, n, cnt, j, bs = A->rmap->bs;
2105: PetscInt m, *garray = a->garray, *lens, *jsendbuf, *a_jsendbuf, *b_jsendbuf;
2107: PetscFunctionBegin;
2108: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
2109: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)A), &rank));
2111: /* Tell every processor the number of nonzeros per row */
2112: PetscCall(PetscMalloc1(A->rmap->N / bs, &lens));
2113: 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];
2114: PetscCall(PetscMalloc1(2 * size, &recvcounts));
2115: displs = recvcounts + size;
2116: for (i = 0; i < size; i++) {
2117: recvcounts[i] = A->rmap->range[i + 1] / bs - A->rmap->range[i] / bs;
2118: displs[i] = A->rmap->range[i] / bs;
2119: }
2120: PetscCallMPI(MPI_Allgatherv(MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, lens, recvcounts, displs, MPIU_INT, PetscObjectComm((PetscObject)A)));
2121: /* Create the sequential matrix of the same type as the local block diagonal */
2122: PetscCall(MatCreate(PETSC_COMM_SELF, &B));
2123: PetscCall(MatSetSizes(B, A->rmap->N / bs, A->cmap->N / bs, PETSC_DETERMINE, PETSC_DETERMINE));
2124: PetscCall(MatSetType(B, MATSEQAIJ));
2125: PetscCall(MatSeqAIJSetPreallocation(B, 0, lens));
2126: b = (Mat_SeqAIJ *)B->data;
2128: /* Copy my part of matrix column indices over */
2129: sendcount = ad->nz + bd->nz;
2130: jsendbuf = b->j + b->i[rstarts[rank] / bs];
2131: a_jsendbuf = ad->j;
2132: b_jsendbuf = bd->j;
2133: n = A->rmap->rend / bs - A->rmap->rstart / bs;
2134: cnt = 0;
2135: for (i = 0; i < n; i++) {
2136: /* put in lower diagonal portion */
2137: m = bd->i[i + 1] - bd->i[i];
2138: while (m > 0) {
2139: /* is it above diagonal (in bd (compressed) numbering) */
2140: if (garray[*b_jsendbuf] > A->rmap->rstart / bs + i) break;
2141: jsendbuf[cnt++] = garray[*b_jsendbuf++];
2142: m--;
2143: }
2145: /* put in diagonal portion */
2146: for (j = ad->i[i]; j < ad->i[i + 1]; j++) jsendbuf[cnt++] = A->rmap->rstart / bs + *a_jsendbuf++;
2148: /* put in upper diagonal portion */
2149: while (m-- > 0) jsendbuf[cnt++] = garray[*b_jsendbuf++];
2150: }
2151: PetscCheck(cnt == sendcount, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Corrupted PETSc matrix: nz given %" PetscInt_FMT " actual nz %" PetscInt_FMT, sendcount, cnt);
2153: /* Gather all column indices to all processors */
2154: for (i = 0; i < size; i++) {
2155: recvcounts[i] = 0;
2156: for (j = A->rmap->range[i] / bs; j < A->rmap->range[i + 1] / bs; j++) recvcounts[i] += lens[j];
2157: }
2158: displs[0] = 0;
2159: for (i = 1; i < size; i++) displs[i] = displs[i - 1] + recvcounts[i - 1];
2160: PetscCallMPI(MPI_Allgatherv(MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, b->j, recvcounts, displs, MPIU_INT, PetscObjectComm((PetscObject)A)));
2161: /* Assemble the matrix into usable form (note numerical values not yet set) */
2162: /* set the b->ilen (length of each row) values */
2163: PetscCall(PetscArraycpy(b->ilen, lens, A->rmap->N / bs));
2164: /* set the b->i indices */
2165: b->i[0] = 0;
2166: for (i = 1; i <= A->rmap->N / bs; i++) b->i[i] = b->i[i - 1] + lens[i - 1];
2167: PetscCall(PetscFree(lens));
2168: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2169: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2170: PetscCall(PetscFree(recvcounts));
2172: PetscCall(MatPropagateSymmetryOptions(A, B));
2173: *newmat = B;
2174: PetscFunctionReturn(PETSC_SUCCESS);
2175: }
2177: static PetscErrorCode MatSOR_MPIBAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
2178: {
2179: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)matin->data;
2180: Vec bb1 = NULL;
2182: PetscFunctionBegin;
2183: if (flag == SOR_APPLY_UPPER) {
2184: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2185: PetscFunctionReturn(PETSC_SUCCESS);
2186: }
2188: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS) PetscCall(VecDuplicate(bb, &bb1));
2190: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
2191: if (flag & SOR_ZERO_INITIAL_GUESS) {
2192: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2193: its--;
2194: }
2196: while (its--) {
2197: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2198: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2200: /* update rhs: bb1 = bb - B*x */
2201: PetscCall(VecScale(mat->lvec, -1.0));
2202: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2204: /* local sweep */
2205: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
2206: }
2207: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
2208: if (flag & SOR_ZERO_INITIAL_GUESS) {
2209: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2210: its--;
2211: }
2212: while (its--) {
2213: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2214: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2216: /* update rhs: bb1 = bb - B*x */
2217: PetscCall(VecScale(mat->lvec, -1.0));
2218: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2220: /* local sweep */
2221: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
2222: }
2223: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
2224: if (flag & SOR_ZERO_INITIAL_GUESS) {
2225: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2226: its--;
2227: }
2228: while (its--) {
2229: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2230: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2232: /* update rhs: bb1 = bb - B*x */
2233: PetscCall(VecScale(mat->lvec, -1.0));
2234: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2236: /* local sweep */
2237: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
2238: }
2239: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel version of SOR requested not supported");
2241: PetscCall(VecDestroy(&bb1));
2242: PetscFunctionReturn(PETSC_SUCCESS);
2243: }
2245: static PetscErrorCode MatGetColumnReductions_MPIBAIJ(Mat A, PetscInt type, PetscReal *reductions)
2246: {
2247: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)A->data;
2248: PetscInt m, N, i, *garray = aij->garray;
2249: PetscInt ib, jb, bs = A->rmap->bs;
2250: Mat_SeqBAIJ *a_aij = (Mat_SeqBAIJ *)aij->A->data;
2251: MatScalar *a_val = a_aij->a;
2252: Mat_SeqBAIJ *b_aij = (Mat_SeqBAIJ *)aij->B->data;
2253: MatScalar *b_val = b_aij->a;
2254: PetscReal *work;
2256: PetscFunctionBegin;
2257: PetscCall(MatGetSize(A, &m, &N));
2258: PetscCall(PetscCalloc1(N, &work));
2259: if (type == NORM_2) {
2260: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2261: for (jb = 0; jb < bs; jb++) {
2262: for (ib = 0; ib < bs; ib++) {
2263: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val * *a_val);
2264: a_val++;
2265: }
2266: }
2267: }
2268: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2269: for (jb = 0; jb < bs; jb++) {
2270: for (ib = 0; ib < bs; ib++) {
2271: work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val * *b_val);
2272: b_val++;
2273: }
2274: }
2275: }
2276: } else if (type == NORM_1) {
2277: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2278: for (jb = 0; jb < bs; jb++) {
2279: for (ib = 0; ib < bs; ib++) {
2280: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val);
2281: a_val++;
2282: }
2283: }
2284: }
2285: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2286: for (jb = 0; jb < bs; jb++) {
2287: for (ib = 0; ib < bs; ib++) {
2288: work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val);
2289: b_val++;
2290: }
2291: }
2292: }
2293: } else if (type == NORM_INFINITY) {
2294: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2295: for (jb = 0; jb < bs; jb++) {
2296: for (ib = 0; ib < bs; ib++) {
2297: int col = A->cmap->rstart + a_aij->j[i] * bs + jb;
2298: work[col] = PetscMax(PetscAbsScalar(*a_val), work[col]);
2299: a_val++;
2300: }
2301: }
2302: }
2303: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2304: for (jb = 0; jb < bs; jb++) {
2305: for (ib = 0; ib < bs; ib++) {
2306: int col = garray[b_aij->j[i]] * bs + jb;
2307: work[col] = PetscMax(PetscAbsScalar(*b_val), work[col]);
2308: b_val++;
2309: }
2310: }
2311: }
2312: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
2313: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2314: for (jb = 0; jb < bs; jb++) {
2315: for (ib = 0; ib < bs; ib++) {
2316: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscRealPart(*a_val);
2317: a_val++;
2318: }
2319: }
2320: }
2321: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2322: for (jb = 0; jb < bs; jb++) {
2323: for (ib = 0; ib < bs; ib++) {
2324: work[garray[b_aij->j[i]] * bs + jb] += PetscRealPart(*b_val);
2325: b_val++;
2326: }
2327: }
2328: }
2329: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
2330: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2331: for (jb = 0; jb < bs; jb++) {
2332: for (ib = 0; ib < bs; ib++) {
2333: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscImaginaryPart(*a_val);
2334: a_val++;
2335: }
2336: }
2337: }
2338: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2339: for (jb = 0; jb < bs; jb++) {
2340: for (ib = 0; ib < bs; ib++) {
2341: work[garray[b_aij->j[i]] * bs + jb] += PetscImaginaryPart(*b_val);
2342: b_val++;
2343: }
2344: }
2345: }
2346: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
2347: if (type == NORM_INFINITY) {
2348: PetscCall(MPIU_Allreduce(work, reductions, N, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
2349: } else {
2350: PetscCall(MPIU_Allreduce(work, reductions, N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
2351: }
2352: PetscCall(PetscFree(work));
2353: if (type == NORM_2) {
2354: for (i = 0; i < N; i++) reductions[i] = PetscSqrtReal(reductions[i]);
2355: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
2356: for (i = 0; i < N; i++) reductions[i] /= m;
2357: }
2358: PetscFunctionReturn(PETSC_SUCCESS);
2359: }
2361: static PetscErrorCode MatInvertBlockDiagonal_MPIBAIJ(Mat A, const PetscScalar **values)
2362: {
2363: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2365: PetscFunctionBegin;
2366: PetscCall(MatInvertBlockDiagonal(a->A, values));
2367: A->factorerrortype = a->A->factorerrortype;
2368: A->factorerror_zeropivot_value = a->A->factorerror_zeropivot_value;
2369: A->factorerror_zeropivot_row = a->A->factorerror_zeropivot_row;
2370: PetscFunctionReturn(PETSC_SUCCESS);
2371: }
2373: static PetscErrorCode MatShift_MPIBAIJ(Mat Y, PetscScalar a)
2374: {
2375: Mat_MPIBAIJ *maij = (Mat_MPIBAIJ *)Y->data;
2376: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)maij->A->data;
2378: PetscFunctionBegin;
2379: if (!Y->preallocated) {
2380: PetscCall(MatMPIBAIJSetPreallocation(Y, Y->rmap->bs, 1, NULL, 0, NULL));
2381: } else if (!aij->nz) {
2382: PetscInt nonew = aij->nonew;
2383: PetscCall(MatSeqBAIJSetPreallocation(maij->A, Y->rmap->bs, 1, NULL));
2384: aij->nonew = nonew;
2385: }
2386: PetscCall(MatShift_Basic(Y, a));
2387: PetscFunctionReturn(PETSC_SUCCESS);
2388: }
2390: static PetscErrorCode MatMissingDiagonal_MPIBAIJ(Mat A, PetscBool *missing, PetscInt *d)
2391: {
2392: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2394: PetscFunctionBegin;
2395: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2396: PetscCall(MatMissingDiagonal(a->A, missing, d));
2397: if (d) {
2398: PetscInt rstart;
2399: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2400: *d += rstart / A->rmap->bs;
2401: }
2402: PetscFunctionReturn(PETSC_SUCCESS);
2403: }
2405: static PetscErrorCode MatGetDiagonalBlock_MPIBAIJ(Mat A, Mat *a)
2406: {
2407: PetscFunctionBegin;
2408: *a = ((Mat_MPIBAIJ *)A->data)->A;
2409: PetscFunctionReturn(PETSC_SUCCESS);
2410: }
2412: static PetscErrorCode MatEliminateZeros_MPIBAIJ(Mat A, PetscBool keep)
2413: {
2414: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2416: PetscFunctionBegin;
2417: PetscCall(MatEliminateZeros_SeqBAIJ(a->A, keep)); // possibly keep zero diagonal coefficients
2418: PetscCall(MatEliminateZeros_SeqBAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2419: PetscFunctionReturn(PETSC_SUCCESS);
2420: }
2422: static struct _MatOps MatOps_Values = {MatSetValues_MPIBAIJ,
2423: MatGetRow_MPIBAIJ,
2424: MatRestoreRow_MPIBAIJ,
2425: MatMult_MPIBAIJ,
2426: /* 4*/ MatMultAdd_MPIBAIJ,
2427: MatMultTranspose_MPIBAIJ,
2428: MatMultTransposeAdd_MPIBAIJ,
2429: NULL,
2430: NULL,
2431: NULL,
2432: /*10*/ NULL,
2433: NULL,
2434: NULL,
2435: MatSOR_MPIBAIJ,
2436: MatTranspose_MPIBAIJ,
2437: /*15*/ MatGetInfo_MPIBAIJ,
2438: MatEqual_MPIBAIJ,
2439: MatGetDiagonal_MPIBAIJ,
2440: MatDiagonalScale_MPIBAIJ,
2441: MatNorm_MPIBAIJ,
2442: /*20*/ MatAssemblyBegin_MPIBAIJ,
2443: MatAssemblyEnd_MPIBAIJ,
2444: MatSetOption_MPIBAIJ,
2445: MatZeroEntries_MPIBAIJ,
2446: /*24*/ MatZeroRows_MPIBAIJ,
2447: NULL,
2448: NULL,
2449: NULL,
2450: NULL,
2451: /*29*/ MatSetUp_MPI_Hash,
2452: NULL,
2453: NULL,
2454: MatGetDiagonalBlock_MPIBAIJ,
2455: NULL,
2456: /*34*/ MatDuplicate_MPIBAIJ,
2457: NULL,
2458: NULL,
2459: NULL,
2460: NULL,
2461: /*39*/ MatAXPY_MPIBAIJ,
2462: MatCreateSubMatrices_MPIBAIJ,
2463: MatIncreaseOverlap_MPIBAIJ,
2464: MatGetValues_MPIBAIJ,
2465: MatCopy_MPIBAIJ,
2466: /*44*/ NULL,
2467: MatScale_MPIBAIJ,
2468: MatShift_MPIBAIJ,
2469: NULL,
2470: MatZeroRowsColumns_MPIBAIJ,
2471: /*49*/ NULL,
2472: NULL,
2473: NULL,
2474: NULL,
2475: NULL,
2476: /*54*/ MatFDColoringCreate_MPIXAIJ,
2477: NULL,
2478: MatSetUnfactored_MPIBAIJ,
2479: MatPermute_MPIBAIJ,
2480: MatSetValuesBlocked_MPIBAIJ,
2481: /*59*/ MatCreateSubMatrix_MPIBAIJ,
2482: MatDestroy_MPIBAIJ,
2483: MatView_MPIBAIJ,
2484: NULL,
2485: NULL,
2486: /*64*/ NULL,
2487: NULL,
2488: NULL,
2489: NULL,
2490: NULL,
2491: /*69*/ MatGetRowMaxAbs_MPIBAIJ,
2492: NULL,
2493: NULL,
2494: NULL,
2495: NULL,
2496: /*74*/ NULL,
2497: MatFDColoringApply_BAIJ,
2498: NULL,
2499: NULL,
2500: NULL,
2501: /*79*/ NULL,
2502: NULL,
2503: NULL,
2504: NULL,
2505: MatLoad_MPIBAIJ,
2506: /*84*/ NULL,
2507: NULL,
2508: NULL,
2509: NULL,
2510: NULL,
2511: /*89*/ NULL,
2512: NULL,
2513: NULL,
2514: NULL,
2515: NULL,
2516: /*94*/ NULL,
2517: NULL,
2518: NULL,
2519: NULL,
2520: NULL,
2521: /*99*/ NULL,
2522: NULL,
2523: NULL,
2524: MatConjugate_MPIBAIJ,
2525: NULL,
2526: /*104*/ NULL,
2527: MatRealPart_MPIBAIJ,
2528: MatImaginaryPart_MPIBAIJ,
2529: NULL,
2530: NULL,
2531: /*109*/ NULL,
2532: NULL,
2533: NULL,
2534: NULL,
2535: MatMissingDiagonal_MPIBAIJ,
2536: /*114*/ MatGetSeqNonzeroStructure_MPIBAIJ,
2537: NULL,
2538: MatGetGhosts_MPIBAIJ,
2539: NULL,
2540: NULL,
2541: /*119*/ NULL,
2542: NULL,
2543: NULL,
2544: NULL,
2545: MatGetMultiProcBlock_MPIBAIJ,
2546: /*124*/ NULL,
2547: MatGetColumnReductions_MPIBAIJ,
2548: MatInvertBlockDiagonal_MPIBAIJ,
2549: NULL,
2550: NULL,
2551: /*129*/ NULL,
2552: NULL,
2553: NULL,
2554: NULL,
2555: NULL,
2556: /*134*/ NULL,
2557: NULL,
2558: NULL,
2559: NULL,
2560: NULL,
2561: /*139*/ MatSetBlockSizes_Default,
2562: NULL,
2563: NULL,
2564: MatFDColoringSetUp_MPIXAIJ,
2565: NULL,
2566: /*144*/ MatCreateMPIMatConcatenateSeqMat_MPIBAIJ,
2567: NULL,
2568: NULL,
2569: NULL,
2570: NULL,
2571: NULL,
2572: /*150*/ NULL,
2573: MatEliminateZeros_MPIBAIJ,
2574: MatGetRowSumAbs_MPIBAIJ,
2575: NULL};
2577: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
2578: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
2580: static PetscErrorCode MatMPIBAIJSetPreallocationCSR_MPIBAIJ(Mat B, PetscInt bs, const PetscInt ii[], const PetscInt jj[], const PetscScalar V[])
2581: {
2582: PetscInt m, rstart, cstart, cend;
2583: PetscInt i, j, dlen, olen, nz, nz_max = 0, *d_nnz = NULL, *o_nnz = NULL;
2584: const PetscInt *JJ = NULL;
2585: PetscScalar *values = NULL;
2586: PetscBool roworiented = ((Mat_MPIBAIJ *)B->data)->roworiented;
2587: PetscBool nooffprocentries;
2589: PetscFunctionBegin;
2590: PetscCall(PetscLayoutSetBlockSize(B->rmap, bs));
2591: PetscCall(PetscLayoutSetBlockSize(B->cmap, bs));
2592: PetscCall(PetscLayoutSetUp(B->rmap));
2593: PetscCall(PetscLayoutSetUp(B->cmap));
2594: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
2595: m = B->rmap->n / bs;
2596: rstart = B->rmap->rstart / bs;
2597: cstart = B->cmap->rstart / bs;
2598: cend = B->cmap->rend / bs;
2600: PetscCheck(!ii[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "ii[0] must be 0 but it is %" PetscInt_FMT, ii[0]);
2601: PetscCall(PetscMalloc2(m, &d_nnz, m, &o_nnz));
2602: for (i = 0; i < m; i++) {
2603: nz = ii[i + 1] - ii[i];
2604: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
2605: nz_max = PetscMax(nz_max, nz);
2606: dlen = 0;
2607: olen = 0;
2608: JJ = jj + ii[i];
2609: for (j = 0; j < nz; j++) {
2610: if (*JJ < cstart || *JJ >= cend) olen++;
2611: else dlen++;
2612: JJ++;
2613: }
2614: d_nnz[i] = dlen;
2615: o_nnz[i] = olen;
2616: }
2617: PetscCall(MatMPIBAIJSetPreallocation(B, bs, 0, d_nnz, 0, o_nnz));
2618: PetscCall(PetscFree2(d_nnz, o_nnz));
2620: values = (PetscScalar *)V;
2621: if (!values) PetscCall(PetscCalloc1(bs * bs * nz_max, &values));
2622: for (i = 0; i < m; i++) {
2623: PetscInt row = i + rstart;
2624: PetscInt ncols = ii[i + 1] - ii[i];
2625: const PetscInt *icols = jj + ii[i];
2626: if (bs == 1 || !roworiented) { /* block ordering matches the non-nested layout of MatSetValues so we can insert entire rows */
2627: const PetscScalar *svals = values + (V ? (bs * bs * ii[i]) : 0);
2628: PetscCall(MatSetValuesBlocked_MPIBAIJ(B, 1, &row, ncols, icols, svals, INSERT_VALUES));
2629: } else { /* block ordering does not match so we can only insert one block at a time. */
2630: PetscInt j;
2631: for (j = 0; j < ncols; j++) {
2632: const PetscScalar *svals = values + (V ? (bs * bs * (ii[i] + j)) : 0);
2633: PetscCall(MatSetValuesBlocked_MPIBAIJ(B, 1, &row, 1, &icols[j], svals, INSERT_VALUES));
2634: }
2635: }
2636: }
2638: if (!V) PetscCall(PetscFree(values));
2639: nooffprocentries = B->nooffprocentries;
2640: B->nooffprocentries = PETSC_TRUE;
2641: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2642: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2643: B->nooffprocentries = nooffprocentries;
2645: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
2646: PetscFunctionReturn(PETSC_SUCCESS);
2647: }
2649: /*@C
2650: MatMPIBAIJSetPreallocationCSR - Creates a sparse parallel matrix in `MATBAIJ` format using the given nonzero structure and (optional) numerical values
2652: Collective
2654: Input Parameters:
2655: + B - the matrix
2656: . bs - the block size
2657: . i - the indices into `j` for the start of each local row (starts with zero)
2658: . j - the column indices for each local row (starts with zero) these must be sorted for each row
2659: - v - optional values in the matrix, use `NULL` if not provided
2661: Level: advanced
2663: Notes:
2664: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
2665: thus you CANNOT change the matrix entries by changing the values of `v` after you have
2666: called this routine.
2668: The order of the entries in values is specified by the `MatOption` `MAT_ROW_ORIENTED`. For example, C programs
2669: 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
2670: over rows within a block and the last index is over columns within a block row. Fortran programs will likely set
2671: `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
2672: block column and the second index is over columns within a block.
2674: 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
2676: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIBAIJSetPreallocation()`, `MatCreateAIJ()`, `MATMPIAIJ`, `MatCreateMPIBAIJWithArrays()`, `MATMPIBAIJ`
2677: @*/
2678: PetscErrorCode MatMPIBAIJSetPreallocationCSR(Mat B, PetscInt bs, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
2679: {
2680: PetscFunctionBegin;
2684: PetscTryMethod(B, "MatMPIBAIJSetPreallocationCSR_C", (Mat, PetscInt, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, bs, i, j, v));
2685: PetscFunctionReturn(PETSC_SUCCESS);
2686: }
2688: PetscErrorCode MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B, PetscInt bs, PetscInt d_nz, const PetscInt *d_nnz, PetscInt o_nz, const PetscInt *o_nnz)
2689: {
2690: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
2691: PetscInt i;
2692: PetscMPIInt size;
2694: PetscFunctionBegin;
2695: if (B->hash_active) {
2696: B->ops[0] = b->cops;
2697: B->hash_active = PETSC_FALSE;
2698: }
2699: if (!B->preallocated) PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), bs, &B->bstash));
2700: PetscCall(MatSetBlockSize(B, PetscAbs(bs)));
2701: PetscCall(PetscLayoutSetUp(B->rmap));
2702: PetscCall(PetscLayoutSetUp(B->cmap));
2703: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
2705: if (d_nnz) {
2706: 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]);
2707: }
2708: if (o_nnz) {
2709: 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]);
2710: }
2712: b->bs2 = bs * bs;
2713: b->mbs = B->rmap->n / bs;
2714: b->nbs = B->cmap->n / bs;
2715: b->Mbs = B->rmap->N / bs;
2716: b->Nbs = B->cmap->N / bs;
2718: for (i = 0; i <= b->size; i++) b->rangebs[i] = B->rmap->range[i] / bs;
2719: b->rstartbs = B->rmap->rstart / bs;
2720: b->rendbs = B->rmap->rend / bs;
2721: b->cstartbs = B->cmap->rstart / bs;
2722: b->cendbs = B->cmap->rend / bs;
2724: #if defined(PETSC_USE_CTABLE)
2725: PetscCall(PetscHMapIDestroy(&b->colmap));
2726: #else
2727: PetscCall(PetscFree(b->colmap));
2728: #endif
2729: PetscCall(PetscFree(b->garray));
2730: PetscCall(VecDestroy(&b->lvec));
2731: PetscCall(VecScatterDestroy(&b->Mvctx));
2733: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2735: MatSeqXAIJGetOptions_Private(b->B);
2736: PetscCall(MatDestroy(&b->B));
2737: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2738: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2739: PetscCall(MatSetType(b->B, MATSEQBAIJ));
2740: MatSeqXAIJRestoreOptions_Private(b->B);
2742: MatSeqXAIJGetOptions_Private(b->A);
2743: PetscCall(MatDestroy(&b->A));
2744: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2745: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2746: PetscCall(MatSetType(b->A, MATSEQBAIJ));
2747: MatSeqXAIJRestoreOptions_Private(b->A);
2749: PetscCall(MatSeqBAIJSetPreallocation(b->A, bs, d_nz, d_nnz));
2750: PetscCall(MatSeqBAIJSetPreallocation(b->B, bs, o_nz, o_nnz));
2751: B->preallocated = PETSC_TRUE;
2752: B->was_assembled = PETSC_FALSE;
2753: B->assembled = PETSC_FALSE;
2754: PetscFunctionReturn(PETSC_SUCCESS);
2755: }
2757: extern PetscErrorCode MatDiagonalScaleLocal_MPIBAIJ(Mat, Vec);
2758: extern PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat, PetscReal);
2760: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAdj(Mat B, MatType newtype, MatReuse reuse, Mat *adj)
2761: {
2762: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
2763: Mat_SeqBAIJ *d = (Mat_SeqBAIJ *)b->A->data, *o = (Mat_SeqBAIJ *)b->B->data;
2764: PetscInt M = B->rmap->n / B->rmap->bs, i, *ii, *jj, cnt, j, k, rstart = B->rmap->rstart / B->rmap->bs;
2765: const PetscInt *id = d->i, *jd = d->j, *io = o->i, *jo = o->j, *garray = b->garray;
2767: PetscFunctionBegin;
2768: PetscCall(PetscMalloc1(M + 1, &ii));
2769: ii[0] = 0;
2770: for (i = 0; i < M; i++) {
2771: 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]);
2772: 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]);
2773: ii[i + 1] = ii[i] + id[i + 1] - id[i] + io[i + 1] - io[i];
2774: /* remove one from count of matrix has diagonal */
2775: for (j = id[i]; j < id[i + 1]; j++) {
2776: if (jd[j] == i) {
2777: ii[i + 1]--;
2778: break;
2779: }
2780: }
2781: }
2782: PetscCall(PetscMalloc1(ii[M], &jj));
2783: cnt = 0;
2784: for (i = 0; i < M; i++) {
2785: for (j = io[i]; j < io[i + 1]; j++) {
2786: if (garray[jo[j]] > rstart) break;
2787: jj[cnt++] = garray[jo[j]];
2788: }
2789: for (k = id[i]; k < id[i + 1]; k++) {
2790: if (jd[k] != i) jj[cnt++] = rstart + jd[k];
2791: }
2792: for (; j < io[i + 1]; j++) jj[cnt++] = garray[jo[j]];
2793: }
2794: PetscCall(MatCreateMPIAdj(PetscObjectComm((PetscObject)B), M, B->cmap->N / B->rmap->bs, ii, jj, NULL, adj));
2795: PetscFunctionReturn(PETSC_SUCCESS);
2796: }
2798: #include <../src/mat/impls/aij/mpi/mpiaij.h>
2800: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat, MatType, MatReuse, Mat *);
2802: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAIJ(Mat A, MatType newtype, MatReuse reuse, Mat *newmat)
2803: {
2804: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2805: Mat_MPIAIJ *b;
2806: Mat B;
2808: PetscFunctionBegin;
2809: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Matrix must be assembled");
2811: if (reuse == MAT_REUSE_MATRIX) {
2812: B = *newmat;
2813: } else {
2814: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
2815: PetscCall(MatSetType(B, MATMPIAIJ));
2816: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
2817: PetscCall(MatSetBlockSizes(B, A->rmap->bs, A->cmap->bs));
2818: PetscCall(MatSeqAIJSetPreallocation(B, 0, NULL));
2819: PetscCall(MatMPIAIJSetPreallocation(B, 0, NULL, 0, NULL));
2820: }
2821: b = (Mat_MPIAIJ *)B->data;
2823: if (reuse == MAT_REUSE_MATRIX) {
2824: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_REUSE_MATRIX, &b->A));
2825: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_REUSE_MATRIX, &b->B));
2826: } else {
2827: PetscInt *garray = a->garray;
2828: Mat_SeqAIJ *bB;
2829: PetscInt bs, nnz;
2830: PetscCall(MatDestroy(&b->A));
2831: PetscCall(MatDestroy(&b->B));
2832: /* just clear out the data structure */
2833: PetscCall(MatDisAssemble_MPIAIJ(B));
2834: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->A));
2835: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->B));
2837: /* Global numbering for b->B columns */
2838: bB = (Mat_SeqAIJ *)b->B->data;
2839: bs = A->rmap->bs;
2840: nnz = bB->i[A->rmap->n];
2841: for (PetscInt k = 0; k < nnz; k++) {
2842: PetscInt bj = bB->j[k] / bs;
2843: PetscInt br = bB->j[k] % bs;
2844: bB->j[k] = garray[bj] * bs + br;
2845: }
2846: }
2847: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2848: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2850: if (reuse == MAT_INPLACE_MATRIX) {
2851: PetscCall(MatHeaderReplace(A, &B));
2852: } else {
2853: *newmat = B;
2854: }
2855: PetscFunctionReturn(PETSC_SUCCESS);
2856: }
2858: /*MC
2859: MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.
2861: Options Database Keys:
2862: + -mat_type mpibaij - sets the matrix type to `MATMPIBAIJ` during a call to `MatSetFromOptions()`
2863: . -mat_block_size <bs> - set the blocksize used to store the matrix
2864: . -mat_baij_mult_version version - indicate the version of the matrix-vector product to use (0 often indicates using BLAS)
2865: - -mat_use_hash_table <fact> - set hash table factor
2867: Level: beginner
2869: Note:
2870: `MatSetOption(A, MAT_STRUCTURE_ONLY, PETSC_TRUE)` may be called for this matrix type. In this no
2871: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
2873: .seealso: `Mat`, `MATBAIJ`, `MATSEQBAIJ`, `MatCreateBAIJ`
2874: M*/
2876: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIBSTRM(Mat, MatType, MatReuse, Mat *);
2878: PETSC_EXTERN PetscErrorCode MatCreate_MPIBAIJ(Mat B)
2879: {
2880: Mat_MPIBAIJ *b;
2881: PetscBool flg = PETSC_FALSE;
2883: PetscFunctionBegin;
2884: PetscCall(PetscNew(&b));
2885: B->data = (void *)b;
2886: B->ops[0] = MatOps_Values;
2887: B->assembled = PETSC_FALSE;
2889: B->insertmode = NOT_SET_VALUES;
2890: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
2891: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &b->size));
2893: /* build local table of row and column ownerships */
2894: PetscCall(PetscMalloc1(b->size + 1, &b->rangebs));
2896: /* build cache for off array entries formed */
2897: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
2899: b->donotstash = PETSC_FALSE;
2900: b->colmap = NULL;
2901: b->garray = NULL;
2902: b->roworiented = PETSC_TRUE;
2904: /* stuff used in block assembly */
2905: b->barray = NULL;
2907: /* stuff used for matrix vector multiply */
2908: b->lvec = NULL;
2909: b->Mvctx = NULL;
2911: /* stuff for MatGetRow() */
2912: b->rowindices = NULL;
2913: b->rowvalues = NULL;
2914: b->getrowactive = PETSC_FALSE;
2916: /* hash table stuff */
2917: b->ht = NULL;
2918: b->hd = NULL;
2919: b->ht_size = 0;
2920: b->ht_flag = PETSC_FALSE;
2921: b->ht_fact = 0;
2922: b->ht_total_ct = 0;
2923: b->ht_insert_ct = 0;
2925: /* stuff for MatCreateSubMatrices_MPIBAIJ_local() */
2926: b->ijonly = PETSC_FALSE;
2928: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpiadj_C", MatConvert_MPIBAIJ_MPIAdj));
2929: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpiaij_C", MatConvert_MPIBAIJ_MPIAIJ));
2930: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpisbaij_C", MatConvert_MPIBAIJ_MPISBAIJ));
2931: #if defined(PETSC_HAVE_HYPRE)
2932: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_hypre_C", MatConvert_AIJ_HYPRE));
2933: #endif
2934: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIBAIJ));
2935: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIBAIJ));
2936: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIBAIJSetPreallocation_C", MatMPIBAIJSetPreallocation_MPIBAIJ));
2937: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIBAIJSetPreallocationCSR_C", MatMPIBAIJSetPreallocationCSR_MPIBAIJ));
2938: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIBAIJ));
2939: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetHashTableFactor_C", MatSetHashTableFactor_MPIBAIJ));
2940: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_is_C", MatConvert_XAIJ_IS));
2941: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIBAIJ));
2943: PetscOptionsBegin(PetscObjectComm((PetscObject)B), NULL, "Options for loading MPIBAIJ matrix 1", "Mat");
2944: PetscCall(PetscOptionsName("-mat_use_hash_table", "Use hash table to save time in constructing matrix", "MatSetOption", &flg));
2945: if (flg) {
2946: PetscReal fact = 1.39;
2947: PetscCall(MatSetOption(B, MAT_USE_HASH_TABLE, PETSC_TRUE));
2948: PetscCall(PetscOptionsReal("-mat_use_hash_table", "Use hash table factor", "MatMPIBAIJSetHashTableFactor", fact, &fact, NULL));
2949: if (fact <= 1.0) fact = 1.39;
2950: PetscCall(MatMPIBAIJSetHashTableFactor(B, fact));
2951: PetscCall(PetscInfo(B, "Hash table Factor used %5.2g\n", (double)fact));
2952: }
2953: PetscOptionsEnd();
2954: PetscFunctionReturn(PETSC_SUCCESS);
2955: }
2957: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
2958: /*MC
2959: MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.
2961: This matrix type is identical to `MATSEQBAIJ` when constructed with a single process communicator,
2962: and `MATMPIBAIJ` otherwise.
2964: Options Database Keys:
2965: . -mat_type baij - sets the matrix type to `MATBAIJ` during a call to `MatSetFromOptions()`
2967: Level: beginner
2969: .seealso: `Mat`, `MatCreateBAIJ()`, `MATSEQBAIJ`, `MATMPIBAIJ`, `MatMPIBAIJSetPreallocation()`, `MatMPIBAIJSetPreallocationCSR()`
2970: M*/
2972: /*@
2973: MatMPIBAIJSetPreallocation - Allocates memory for a sparse parallel matrix in `MATMPIBAIJ` format
2974: (block compressed row).
2976: Collective
2978: Input Parameters:
2979: + B - the matrix
2980: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
2981: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
2982: . d_nz - number of block nonzeros per block row in diagonal portion of local
2983: submatrix (same for all local rows)
2984: . d_nnz - array containing the number of block nonzeros in the various block rows
2985: of the in diagonal portion of the local (possibly different for each block
2986: row) or `NULL`. If you plan to factor the matrix you must leave room for the diagonal entry and
2987: set it even if it is zero.
2988: . o_nz - number of block nonzeros per block row in the off-diagonal portion of local
2989: submatrix (same for all local rows).
2990: - o_nnz - array containing the number of nonzeros in the various block rows of the
2991: off-diagonal portion of the local submatrix (possibly different for
2992: each block row) or `NULL`.
2994: If the *_nnz parameter is given then the *_nz parameter is ignored
2996: Options Database Keys:
2997: + -mat_block_size - size of the blocks to use
2998: - -mat_use_hash_table <fact> - set hash table factor
3000: Level: intermediate
3002: Notes:
3003: For good matrix assembly performance
3004: the user should preallocate the matrix storage by setting the parameters
3005: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`). By setting these parameters accurately,
3006: performance can be increased by more than a factor of 50.
3008: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one processor
3009: than it must be used on all processors that share the object for that argument.
3011: Storage Information:
3012: For a square global matrix we define each processor's diagonal portion
3013: to be its local rows and the corresponding columns (a square submatrix);
3014: each processor's off-diagonal portion encompasses the remainder of the
3015: local matrix (a rectangular submatrix).
3017: The user can specify preallocated storage for the diagonal part of
3018: the local submatrix with either `d_nz` or `d_nnz` (not both). Set
3019: `d_nz` = `PETSC_DEFAULT` and `d_nnz` = `NULL` for PETSc to control dynamic
3020: memory allocation. Likewise, specify preallocated storage for the
3021: off-diagonal part of the local submatrix with `o_nz` or `o_nnz` (not both).
3023: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
3024: the figure below we depict these three local rows and all columns (0-11).
3026: .vb
3027: 0 1 2 3 4 5 6 7 8 9 10 11
3028: --------------------------
3029: row 3 |o o o d d d o o o o o o
3030: row 4 |o o o d d d o o o o o o
3031: row 5 |o o o d d d o o o o o o
3032: --------------------------
3033: .ve
3035: Thus, any entries in the d locations are stored in the d (diagonal)
3036: submatrix, and any entries in the o locations are stored in the
3037: o (off-diagonal) submatrix. Note that the d and the o submatrices are
3038: stored simply in the `MATSEQBAIJ` format for compressed row storage.
3040: Now `d_nz` should indicate the number of block nonzeros per row in the d matrix,
3041: and `o_nz` should indicate the number of block nonzeros per row in the o matrix.
3042: In general, for PDE problems in which most nonzeros are near the diagonal,
3043: one expects `d_nz` >> `o_nz`.
3045: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3046: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3047: You can also run with the option `-info` and look for messages with the string
3048: malloc in them to see if additional memory allocation was needed.
3050: .seealso: `Mat`, `MATMPIBAIJ`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`, `MatMPIBAIJSetPreallocationCSR()`, `PetscSplitOwnership()`
3051: @*/
3052: PetscErrorCode MatMPIBAIJSetPreallocation(Mat B, PetscInt bs, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
3053: {
3054: PetscFunctionBegin;
3058: PetscTryMethod(B, "MatMPIBAIJSetPreallocation_C", (Mat, PetscInt, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, bs, d_nz, d_nnz, o_nz, o_nnz));
3059: PetscFunctionReturn(PETSC_SUCCESS);
3060: }
3062: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
3063: /*@
3064: MatCreateBAIJ - Creates a sparse parallel matrix in `MATBAIJ` format
3065: (block compressed row).
3067: Collective
3069: Input Parameters:
3070: + comm - MPI communicator
3071: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
3072: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
3073: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
3074: This value should be the same as the local size used in creating the
3075: y vector for the matrix-vector product y = Ax.
3076: . n - number of local columns (or `PETSC_DECIDE` to have calculated if N is given)
3077: This value should be the same as the local size used in creating the
3078: x vector for the matrix-vector product y = Ax.
3079: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
3080: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
3081: . d_nz - number of nonzero blocks per block row in diagonal portion of local
3082: submatrix (same for all local rows)
3083: . d_nnz - array containing the number of nonzero blocks in the various block rows
3084: of the in diagonal portion of the local (possibly different for each block
3085: row) or NULL. If you plan to factor the matrix you must leave room for the diagonal entry
3086: and set it even if it is zero.
3087: . o_nz - number of nonzero blocks per block row in the off-diagonal portion of local
3088: submatrix (same for all local rows).
3089: - o_nnz - array containing the number of nonzero blocks in the various block rows of the
3090: off-diagonal portion of the local submatrix (possibly different for
3091: each block row) or NULL.
3093: Output Parameter:
3094: . A - the matrix
3096: Options Database Keys:
3097: + -mat_block_size - size of the blocks to use
3098: - -mat_use_hash_table <fact> - set hash table factor
3100: Level: intermediate
3102: Notes:
3103: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
3104: MatXXXXSetPreallocation() paradigm instead of this routine directly.
3105: [MatXXXXSetPreallocation() is, for example, `MatSeqBAIJSetPreallocation()`]
3107: For good matrix assembly performance
3108: the user should preallocate the matrix storage by setting the parameters
3109: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`). By setting these parameters accurately,
3110: performance can be increased by more than a factor of 50.
3112: If the *_nnz parameter is given then the *_nz parameter is ignored
3114: A nonzero block is any block that as 1 or more nonzeros in it
3116: The user MUST specify either the local or global matrix dimensions
3117: (possibly both).
3119: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one processor
3120: than it must be used on all processors that share the object for that argument.
3122: If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by
3123: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`.
3125: Storage Information:
3126: For a square global matrix we define each processor's diagonal portion
3127: to be its local rows and the corresponding columns (a square submatrix);
3128: each processor's off-diagonal portion encompasses the remainder of the
3129: local matrix (a rectangular submatrix).
3131: The user can specify preallocated storage for the diagonal part of
3132: the local submatrix with either d_nz or d_nnz (not both). Set
3133: `d_nz` = `PETSC_DEFAULT` and `d_nnz` = `NULL` for PETSc to control dynamic
3134: memory allocation. Likewise, specify preallocated storage for the
3135: off-diagonal part of the local submatrix with `o_nz` or `o_nnz` (not both).
3137: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
3138: the figure below we depict these three local rows and all columns (0-11).
3140: .vb
3141: 0 1 2 3 4 5 6 7 8 9 10 11
3142: --------------------------
3143: row 3 |o o o d d d o o o o o o
3144: row 4 |o o o d d d o o o o o o
3145: row 5 |o o o d d d o o o o o o
3146: --------------------------
3147: .ve
3149: Thus, any entries in the d locations are stored in the d (diagonal)
3150: submatrix, and any entries in the o locations are stored in the
3151: o (off-diagonal) submatrix. Note that the d and the o submatrices are
3152: stored simply in the `MATSEQBAIJ` format for compressed row storage.
3154: Now `d_nz` should indicate the number of block nonzeros per row in the d matrix,
3155: and `o_nz` should indicate the number of block nonzeros per row in the o matrix.
3156: In general, for PDE problems in which most nonzeros are near the diagonal,
3157: one expects `d_nz` >> `o_nz`.
3159: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatMPIBAIJSetPreallocation()`, `MatMPIBAIJSetPreallocationCSR()`,
3160: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`
3161: @*/
3162: 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)
3163: {
3164: PetscMPIInt size;
3166: PetscFunctionBegin;
3167: PetscCall(MatCreate(comm, A));
3168: PetscCall(MatSetSizes(*A, m, n, M, N));
3169: PetscCallMPI(MPI_Comm_size(comm, &size));
3170: if (size > 1) {
3171: PetscCall(MatSetType(*A, MATMPIBAIJ));
3172: PetscCall(MatMPIBAIJSetPreallocation(*A, bs, d_nz, d_nnz, o_nz, o_nnz));
3173: } else {
3174: PetscCall(MatSetType(*A, MATSEQBAIJ));
3175: PetscCall(MatSeqBAIJSetPreallocation(*A, bs, d_nz, d_nnz));
3176: }
3177: PetscFunctionReturn(PETSC_SUCCESS);
3178: }
3180: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
3181: {
3182: Mat mat;
3183: Mat_MPIBAIJ *a, *oldmat = (Mat_MPIBAIJ *)matin->data;
3184: PetscInt len = 0;
3186: PetscFunctionBegin;
3187: *newmat = NULL;
3188: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
3189: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
3190: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
3192: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
3193: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
3194: if (matin->hash_active) {
3195: PetscCall(MatSetUp(mat));
3196: } else {
3197: mat->factortype = matin->factortype;
3198: mat->preallocated = PETSC_TRUE;
3199: mat->assembled = PETSC_TRUE;
3200: mat->insertmode = NOT_SET_VALUES;
3202: a = (Mat_MPIBAIJ *)mat->data;
3203: mat->rmap->bs = matin->rmap->bs;
3204: a->bs2 = oldmat->bs2;
3205: a->mbs = oldmat->mbs;
3206: a->nbs = oldmat->nbs;
3207: a->Mbs = oldmat->Mbs;
3208: a->Nbs = oldmat->Nbs;
3210: a->size = oldmat->size;
3211: a->rank = oldmat->rank;
3212: a->donotstash = oldmat->donotstash;
3213: a->roworiented = oldmat->roworiented;
3214: a->rowindices = NULL;
3215: a->rowvalues = NULL;
3216: a->getrowactive = PETSC_FALSE;
3217: a->barray = NULL;
3218: a->rstartbs = oldmat->rstartbs;
3219: a->rendbs = oldmat->rendbs;
3220: a->cstartbs = oldmat->cstartbs;
3221: a->cendbs = oldmat->cendbs;
3223: /* hash table stuff */
3224: a->ht = NULL;
3225: a->hd = NULL;
3226: a->ht_size = 0;
3227: a->ht_flag = oldmat->ht_flag;
3228: a->ht_fact = oldmat->ht_fact;
3229: a->ht_total_ct = 0;
3230: a->ht_insert_ct = 0;
3232: PetscCall(PetscArraycpy(a->rangebs, oldmat->rangebs, a->size + 1));
3233: if (oldmat->colmap) {
3234: #if defined(PETSC_USE_CTABLE)
3235: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3236: #else
3237: PetscCall(PetscMalloc1(a->Nbs, &a->colmap));
3238: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, a->Nbs));
3239: #endif
3240: } else a->colmap = NULL;
3242: if (oldmat->garray && (len = ((Mat_SeqBAIJ *)oldmat->B->data)->nbs)) {
3243: PetscCall(PetscMalloc1(len, &a->garray));
3244: PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3245: } else a->garray = NULL;
3247: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)matin), matin->rmap->bs, &mat->bstash));
3248: PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3249: PetscCall(VecScatterCopy(oldmat->Mvctx, &a->Mvctx));
3251: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3252: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3253: }
3254: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3255: *newmat = mat;
3256: PetscFunctionReturn(PETSC_SUCCESS);
3257: }
3259: /* Used for both MPIBAIJ and MPISBAIJ matrices */
3260: PetscErrorCode MatLoad_MPIBAIJ_Binary(Mat mat, PetscViewer viewer)
3261: {
3262: PetscInt header[4], M, N, nz, bs, m, n, mbs, nbs, rows, cols, sum, i, j, k;
3263: PetscInt *rowidxs, *colidxs, rs, cs, ce;
3264: PetscScalar *matvals;
3266: PetscFunctionBegin;
3267: PetscCall(PetscViewerSetUp(viewer));
3269: /* read in matrix header */
3270: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3271: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3272: M = header[1];
3273: N = header[2];
3274: nz = header[3];
3275: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3276: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3277: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIBAIJ");
3279: /* set block sizes from the viewer's .info file */
3280: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3281: /* set local sizes if not set already */
3282: if (mat->rmap->n < 0 && M == N) mat->rmap->n = mat->cmap->n;
3283: if (mat->cmap->n < 0 && M == N) mat->cmap->n = mat->rmap->n;
3284: /* set global sizes if not set already */
3285: if (mat->rmap->N < 0) mat->rmap->N = M;
3286: if (mat->cmap->N < 0) mat->cmap->N = N;
3287: PetscCall(PetscLayoutSetUp(mat->rmap));
3288: PetscCall(PetscLayoutSetUp(mat->cmap));
3290: /* check if the matrix sizes are correct */
3291: PetscCall(MatGetSize(mat, &rows, &cols));
3292: 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);
3293: PetscCall(MatGetBlockSize(mat, &bs));
3294: PetscCall(MatGetLocalSize(mat, &m, &n));
3295: PetscCall(PetscLayoutGetRange(mat->rmap, &rs, NULL));
3296: PetscCall(PetscLayoutGetRange(mat->cmap, &cs, &ce));
3297: mbs = m / bs;
3298: nbs = n / bs;
3300: /* read in row lengths and build row indices */
3301: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3302: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3303: rowidxs[0] = 0;
3304: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3305: PetscCall(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3306: 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);
3308: /* read in column indices and matrix values */
3309: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3310: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3311: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3313: { /* preallocate matrix storage */
3314: PetscBT bt; /* helper bit set to count diagonal nonzeros */
3315: PetscHSetI ht; /* helper hash set to count off-diagonal nonzeros */
3316: PetscBool sbaij, done;
3317: PetscInt *d_nnz, *o_nnz;
3319: PetscCall(PetscBTCreate(nbs, &bt));
3320: PetscCall(PetscHSetICreate(&ht));
3321: PetscCall(PetscCalloc2(mbs, &d_nnz, mbs, &o_nnz));
3322: PetscCall(PetscObjectTypeCompare((PetscObject)mat, MATMPISBAIJ, &sbaij));
3323: for (i = 0; i < mbs; i++) {
3324: PetscCall(PetscBTMemzero(nbs, bt));
3325: PetscCall(PetscHSetIClear(ht));
3326: for (k = 0; k < bs; k++) {
3327: PetscInt row = bs * i + k;
3328: for (j = rowidxs[row]; j < rowidxs[row + 1]; j++) {
3329: PetscInt col = colidxs[j];
3330: if (!sbaij || col >= row) {
3331: if (col >= cs && col < ce) {
3332: if (!PetscBTLookupSet(bt, (col - cs) / bs)) d_nnz[i]++;
3333: } else {
3334: PetscCall(PetscHSetIQueryAdd(ht, col / bs, &done));
3335: if (done) o_nnz[i]++;
3336: }
3337: }
3338: }
3339: }
3340: }
3341: PetscCall(PetscBTDestroy(&bt));
3342: PetscCall(PetscHSetIDestroy(&ht));
3343: PetscCall(MatMPIBAIJSetPreallocation(mat, bs, 0, d_nnz, 0, o_nnz));
3344: PetscCall(MatMPISBAIJSetPreallocation(mat, bs, 0, d_nnz, 0, o_nnz));
3345: PetscCall(PetscFree2(d_nnz, o_nnz));
3346: }
3348: /* store matrix values */
3349: for (i = 0; i < m; i++) {
3350: PetscInt row = rs + i, s = rowidxs[i], e = rowidxs[i + 1];
3351: PetscUseTypeMethod(mat, setvalues, 1, &row, e - s, colidxs + s, matvals + s, INSERT_VALUES);
3352: }
3354: PetscCall(PetscFree(rowidxs));
3355: PetscCall(PetscFree2(colidxs, matvals));
3356: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
3357: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
3358: PetscFunctionReturn(PETSC_SUCCESS);
3359: }
3361: PetscErrorCode MatLoad_MPIBAIJ(Mat mat, PetscViewer viewer)
3362: {
3363: PetscBool isbinary;
3365: PetscFunctionBegin;
3366: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3367: 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);
3368: PetscCall(MatLoad_MPIBAIJ_Binary(mat, viewer));
3369: PetscFunctionReturn(PETSC_SUCCESS);
3370: }
3372: /*@
3373: MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the matrices hash table
3375: Input Parameters:
3376: + mat - the matrix
3377: - fact - factor
3379: Options Database Key:
3380: . -mat_use_hash_table <fact> - provide the factor
3382: Level: advanced
3384: .seealso: `Mat`, `MATMPIBAIJ`, `MatSetOption()`
3385: @*/
3386: PetscErrorCode MatMPIBAIJSetHashTableFactor(Mat mat, PetscReal fact)
3387: {
3388: PetscFunctionBegin;
3389: PetscTryMethod(mat, "MatSetHashTableFactor_C", (Mat, PetscReal), (mat, fact));
3390: PetscFunctionReturn(PETSC_SUCCESS);
3391: }
3393: PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat mat, PetscReal fact)
3394: {
3395: Mat_MPIBAIJ *baij;
3397: PetscFunctionBegin;
3398: baij = (Mat_MPIBAIJ *)mat->data;
3399: baij->ht_fact = fact;
3400: PetscFunctionReturn(PETSC_SUCCESS);
3401: }
3403: PetscErrorCode MatMPIBAIJGetSeqBAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
3404: {
3405: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
3406: PetscBool flg;
3408: PetscFunctionBegin;
3409: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIBAIJ, &flg));
3410: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIBAIJ matrix as input");
3411: if (Ad) *Ad = a->A;
3412: if (Ao) *Ao = a->B;
3413: if (colmap) *colmap = a->garray;
3414: PetscFunctionReturn(PETSC_SUCCESS);
3415: }
3417: /*
3418: Special version for direct calls from Fortran (to eliminate two function call overheads
3419: */
3420: #if defined(PETSC_HAVE_FORTRAN_CAPS)
3421: #define matmpibaijsetvaluesblocked_ MATMPIBAIJSETVALUESBLOCKED
3422: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
3423: #define matmpibaijsetvaluesblocked_ matmpibaijsetvaluesblocked
3424: #endif
3426: // PetscClangLinter pragma disable: -fdoc-synopsis-matching-symbol-name
3427: /*@C
3428: MatMPIBAIJSetValuesBlocked - Direct Fortran call to replace call to `MatSetValuesBlocked()`
3430: Collective
3432: Input Parameters:
3433: + matin - the matrix
3434: . min - number of input rows
3435: . im - input rows
3436: . nin - number of input columns
3437: . in - input columns
3438: . v - numerical values input
3439: - addvin - `INSERT_VALUES` or `ADD_VALUES`
3441: Level: advanced
3443: Developer Notes:
3444: This has a complete copy of `MatSetValuesBlocked_MPIBAIJ()` which is terrible code un-reuse.
3446: .seealso: `Mat`, `MatSetValuesBlocked()`
3447: @*/
3448: PETSC_EXTERN PetscErrorCode matmpibaijsetvaluesblocked_(Mat *matin, PetscInt *min, const PetscInt im[], PetscInt *nin, const PetscInt in[], const MatScalar v[], InsertMode *addvin)
3449: {
3450: /* convert input arguments to C version */
3451: Mat mat = *matin;
3452: PetscInt m = *min, n = *nin;
3453: InsertMode addv = *addvin;
3455: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
3456: const MatScalar *value;
3457: MatScalar *barray = baij->barray;
3458: PetscBool roworiented = baij->roworiented;
3459: PetscInt i, j, ii, jj, row, col, rstart = baij->rstartbs;
3460: PetscInt rend = baij->rendbs, cstart = baij->cstartbs, stepval;
3461: PetscInt cend = baij->cendbs, bs = mat->rmap->bs, bs2 = baij->bs2;
3463: PetscFunctionBegin;
3464: /* tasks normally handled by MatSetValuesBlocked() */
3465: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
3466: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
3467: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3468: if (mat->assembled) {
3469: mat->was_assembled = PETSC_TRUE;
3470: mat->assembled = PETSC_FALSE;
3471: }
3472: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
3474: if (!barray) {
3475: PetscCall(PetscMalloc1(bs2, &barray));
3476: baij->barray = barray;
3477: }
3479: if (roworiented) stepval = (n - 1) * bs;
3480: else stepval = (m - 1) * bs;
3482: for (i = 0; i < m; i++) {
3483: if (im[i] < 0) continue;
3484: 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);
3485: if (im[i] >= rstart && im[i] < rend) {
3486: row = im[i] - rstart;
3487: for (j = 0; j < n; j++) {
3488: /* If NumCol = 1 then a copy is not required */
3489: if ((roworiented) && (n == 1)) {
3490: barray = (MatScalar *)v + i * bs2;
3491: } else if ((!roworiented) && (m == 1)) {
3492: barray = (MatScalar *)v + j * bs2;
3493: } else { /* Here a copy is required */
3494: if (roworiented) {
3495: value = v + i * (stepval + bs) * bs + j * bs;
3496: } else {
3497: value = v + j * (stepval + bs) * bs + i * bs;
3498: }
3499: for (ii = 0; ii < bs; ii++, value += stepval) {
3500: for (jj = 0; jj < bs; jj++) *barray++ = *value++;
3501: }
3502: barray -= bs2;
3503: }
3505: if (in[j] >= cstart && in[j] < cend) {
3506: col = in[j] - cstart;
3507: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->A, row, col, barray, addv, im[i], in[j]));
3508: } else if (in[j] < 0) {
3509: continue;
3510: } else {
3511: 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);
3512: if (mat->was_assembled) {
3513: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
3515: #if defined(PETSC_USE_DEBUG)
3516: #if defined(PETSC_USE_CTABLE)
3517: {
3518: PetscInt data;
3519: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &data));
3520: PetscCheck((data - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
3521: }
3522: #else
3523: PetscCheck((baij->colmap[in[j]] - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
3524: #endif
3525: #endif
3526: #if defined(PETSC_USE_CTABLE)
3527: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &col));
3528: col = (col - 1) / bs;
3529: #else
3530: col = (baij->colmap[in[j]] - 1) / bs;
3531: #endif
3532: if (col < 0 && !((Mat_SeqBAIJ *)baij->A->data)->nonew) {
3533: PetscCall(MatDisAssemble_MPIBAIJ(mat));
3534: col = in[j];
3535: }
3536: } else col = in[j];
3537: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->B, row, col, barray, addv, im[i], in[j]));
3538: }
3539: }
3540: } else {
3541: if (!baij->donotstash) {
3542: if (roworiented) {
3543: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
3544: } else {
3545: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
3546: }
3547: }
3548: }
3549: }
3551: /* task normally handled by MatSetValuesBlocked() */
3552: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
3553: PetscFunctionReturn(PETSC_SUCCESS);
3554: }
3556: /*@
3557: MatCreateMPIBAIJWithArrays - creates a `MATMPIBAIJ` matrix using arrays that contain in standard block CSR format for the local rows.
3559: Collective
3561: Input Parameters:
3562: + comm - MPI communicator
3563: . bs - the block size, only a block size of 1 is supported
3564: . m - number of local rows (Cannot be `PETSC_DECIDE`)
3565: . n - This value should be the same as the local size used in creating the
3566: x vector for the matrix-vector product $ y = Ax $. (or `PETSC_DECIDE` to have
3567: calculated if `N` is given) For square matrices `n` is almost always `m`.
3568: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
3569: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
3570: . 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
3571: . j - column indices
3572: - a - matrix values
3574: Output Parameter:
3575: . mat - the matrix
3577: Level: intermediate
3579: Notes:
3580: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
3581: thus you CANNOT change the matrix entries by changing the values of a[] after you have
3582: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3584: The order of the entries in values is the same as the block compressed sparse row storage format; that is, it is
3585: the same as a three dimensional array in Fortran values(bs,bs,nnz) that contains the first column of the first
3586: block, followed by the second column of the first block etc etc. That is, the blocks are contiguous in memory
3587: with column-major ordering within blocks.
3589: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
3591: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
3592: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`
3593: @*/
3594: 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)
3595: {
3596: PetscFunctionBegin;
3597: PetscCheck(!i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
3598: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
3599: PetscCall(MatCreate(comm, mat));
3600: PetscCall(MatSetSizes(*mat, m, n, M, N));
3601: PetscCall(MatSetType(*mat, MATMPIBAIJ));
3602: PetscCall(MatSetBlockSize(*mat, bs));
3603: PetscCall(MatSetUp(*mat));
3604: PetscCall(MatSetOption(*mat, MAT_ROW_ORIENTED, PETSC_FALSE));
3605: PetscCall(MatMPIBAIJSetPreallocationCSR(*mat, bs, i, j, a));
3606: PetscCall(MatSetOption(*mat, MAT_ROW_ORIENTED, PETSC_TRUE));
3607: PetscFunctionReturn(PETSC_SUCCESS);
3608: }
3610: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIBAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
3611: {
3612: PetscInt m, N, i, rstart, nnz, Ii, bs, cbs;
3613: PetscInt *indx;
3614: PetscScalar *values;
3616: PetscFunctionBegin;
3617: PetscCall(MatGetSize(inmat, &m, &N));
3618: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
3619: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)inmat->data;
3620: PetscInt *dnz, *onz, mbs, Nbs, nbs;
3621: PetscInt *bindx, rmax = a->rmax, j;
3622: PetscMPIInt rank, size;
3624: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
3625: mbs = m / bs;
3626: Nbs = N / cbs;
3627: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnershipBlock(comm, cbs, &n, &N));
3628: nbs = n / cbs;
3630: PetscCall(PetscMalloc1(rmax, &bindx));
3631: MatPreallocateBegin(comm, mbs, nbs, dnz, onz); /* inline function, output __end and __rstart are used below */
3633: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3634: PetscCallMPI(MPI_Comm_rank(comm, &size));
3635: if (rank == size - 1) {
3636: /* Check sum(nbs) = Nbs */
3637: PetscCheck(__end == Nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local block columns %" PetscInt_FMT " != global block columns %" PetscInt_FMT, __end, Nbs);
3638: }
3640: rstart = __rstart; /* block rstart of *outmat; see inline function MatPreallocateBegin */
3641: for (i = 0; i < mbs; i++) {
3642: PetscCall(MatGetRow_SeqBAIJ(inmat, i * bs, &nnz, &indx, NULL)); /* non-blocked nnz and indx */
3643: nnz = nnz / bs;
3644: for (j = 0; j < nnz; j++) bindx[j] = indx[j * bs] / bs;
3645: PetscCall(MatPreallocateSet(i + rstart, nnz, bindx, dnz, onz));
3646: PetscCall(MatRestoreRow_SeqBAIJ(inmat, i * bs, &nnz, &indx, NULL));
3647: }
3648: PetscCall(PetscFree(bindx));
3650: PetscCall(MatCreate(comm, outmat));
3651: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
3652: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
3653: PetscCall(MatSetType(*outmat, MATBAIJ));
3654: PetscCall(MatSeqBAIJSetPreallocation(*outmat, bs, 0, dnz));
3655: PetscCall(MatMPIBAIJSetPreallocation(*outmat, bs, 0, dnz, 0, onz));
3656: MatPreallocateEnd(dnz, onz);
3657: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3658: }
3660: /* numeric phase */
3661: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
3662: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
3664: for (i = 0; i < m; i++) {
3665: PetscCall(MatGetRow_SeqBAIJ(inmat, i, &nnz, &indx, &values));
3666: Ii = i + rstart;
3667: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
3668: PetscCall(MatRestoreRow_SeqBAIJ(inmat, i, &nnz, &indx, &values));
3669: }
3670: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
3671: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
3672: PetscFunctionReturn(PETSC_SUCCESS);
3673: }