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 *vv;
62: Vec vB, vA;
63: const PetscScalar *va, *vb;
65: PetscFunctionBegin;
66: PetscCall(MatCreateVecs(a->A, NULL, &vA));
67: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
69: PetscCall(VecGetArrayRead(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(v, &vv));
92: PetscCall(VecRestoreArrayRead(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: PetscCheck(idxm[i] >= bsrstart && idxm[i] < bsrend, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local values currently supported");
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: }
686: PetscFunctionReturn(PETSC_SUCCESS);
687: }
689: static PetscErrorCode MatNorm_MPIBAIJ(Mat mat, NormType type, PetscReal *nrm)
690: {
691: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
692: Mat_SeqBAIJ *amat = (Mat_SeqBAIJ *)baij->A->data, *bmat = (Mat_SeqBAIJ *)baij->B->data;
693: PetscInt i, j, bs2 = baij->bs2, bs = baij->A->rmap->bs, nz, row, col;
694: PetscReal sum = 0.0;
695: MatScalar *v;
697: PetscFunctionBegin;
698: if (baij->size == 1) {
699: PetscCall(MatNorm(baij->A, type, nrm));
700: } else {
701: if (type == NORM_FROBENIUS) {
702: v = amat->a;
703: nz = amat->nz * bs2;
704: for (i = 0; i < nz; i++) {
705: sum += PetscRealPart(PetscConj(*v) * (*v));
706: v++;
707: }
708: v = bmat->a;
709: nz = bmat->nz * bs2;
710: for (i = 0; i < nz; i++) {
711: sum += PetscRealPart(PetscConj(*v) * (*v));
712: v++;
713: }
714: PetscCallMPI(MPIU_Allreduce(&sum, nrm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
715: *nrm = PetscSqrtReal(*nrm);
716: } else if (type == NORM_1) { /* max column sum */
717: Vec col, bcol;
718: PetscScalar *array;
719: PetscInt *jj, *garray = baij->garray;
721: PetscCall(MatCreateVecs(mat, &col, NULL));
722: PetscCall(VecSet(col, 0.0));
723: PetscCall(VecGetArrayWrite(col, &array));
724: v = amat->a;
725: jj = amat->j;
726: for (i = 0; i < amat->nz; i++) {
727: for (j = 0; j < bs; j++) {
728: PetscInt col = bs * *jj + j; /* column index */
730: for (row = 0; row < bs; row++) array[col] += PetscAbsScalar(*v++);
731: }
732: jj++;
733: }
734: PetscCall(VecRestoreArrayWrite(col, &array));
735: PetscCall(MatCreateVecs(baij->B, &bcol, NULL));
736: PetscCall(VecSet(bcol, 0.0));
737: PetscCall(VecGetArrayWrite(bcol, &array));
738: v = bmat->a;
739: jj = bmat->j;
740: for (i = 0; i < bmat->nz; i++) {
741: for (j = 0; j < bs; j++) {
742: PetscInt col = bs * *jj + j; /* column index */
744: for (row = 0; row < bs; row++) array[col] += PetscAbsScalar(*v++);
745: }
746: jj++;
747: }
748: PetscCall(VecSetValuesBlocked(col, bmat->nbs, garray, array, ADD_VALUES));
749: PetscCall(VecRestoreArrayWrite(bcol, &array));
750: PetscCall(VecDestroy(&bcol));
751: PetscCall(VecAssemblyBegin(col));
752: PetscCall(VecAssemblyEnd(col));
753: PetscCall(VecNorm(col, NORM_INFINITY, nrm));
754: PetscCall(VecDestroy(&col));
755: } else if (type == NORM_INFINITY) { /* max row sum */
756: PetscReal *sums;
757: PetscCall(PetscMalloc1(bs, &sums));
758: sum = 0.0;
759: for (j = 0; j < amat->mbs; j++) {
760: for (row = 0; row < bs; row++) sums[row] = 0.0;
761: v = amat->a + bs2 * amat->i[j];
762: nz = amat->i[j + 1] - amat->i[j];
763: for (i = 0; i < nz; i++) {
764: for (col = 0; col < bs; col++) {
765: for (row = 0; row < bs; row++) {
766: sums[row] += PetscAbsScalar(*v);
767: v++;
768: }
769: }
770: }
771: v = bmat->a + bs2 * bmat->i[j];
772: nz = bmat->i[j + 1] - bmat->i[j];
773: for (i = 0; i < nz; i++) {
774: for (col = 0; col < bs; col++) {
775: for (row = 0; row < bs; row++) {
776: sums[row] += PetscAbsScalar(*v);
777: v++;
778: }
779: }
780: }
781: for (row = 0; row < bs; row++) {
782: if (sums[row] > sum) sum = sums[row];
783: }
784: }
785: PetscCallMPI(MPIU_Allreduce(&sum, nrm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
786: PetscCall(PetscFree(sums));
787: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for this norm yet");
788: }
789: PetscFunctionReturn(PETSC_SUCCESS);
790: }
792: /*
793: Creates the hash table, and sets the table
794: This table is created only once.
795: If new entries need to be added to the matrix
796: then the hash table has to be destroyed and
797: recreated.
798: */
799: static PetscErrorCode MatCreateHashTable_MPIBAIJ_Private(Mat mat, PetscReal factor)
800: {
801: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
802: Mat A = baij->A, B = baij->B;
803: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data, *b = (Mat_SeqBAIJ *)B->data;
804: PetscInt i, j, k, nz = a->nz + b->nz, h1, *ai = a->i, *aj = a->j, *bi = b->i, *bj = b->j;
805: PetscInt ht_size, bs2 = baij->bs2, rstart = baij->rstartbs;
806: PetscInt cstart = baij->cstartbs, *garray = baij->garray, row, col, Nbs = baij->Nbs;
807: PetscInt *HT, key;
808: MatScalar **HD;
809: PetscReal tmp;
810: #if defined(PETSC_USE_INFO)
811: PetscInt ct = 0, max = 0;
812: #endif
814: PetscFunctionBegin;
815: if (baij->ht) PetscFunctionReturn(PETSC_SUCCESS);
817: baij->ht_size = (PetscInt)(factor * nz);
818: ht_size = baij->ht_size;
820: /* Allocate Memory for Hash Table */
821: PetscCall(PetscCalloc2(ht_size, &baij->hd, ht_size, &baij->ht));
822: HD = baij->hd;
823: HT = baij->ht;
825: /* Loop Over A */
826: for (i = 0; i < a->mbs; i++) {
827: for (j = ai[i]; j < ai[i + 1]; j++) {
828: row = i + rstart;
829: col = aj[j] + cstart;
831: key = row * Nbs + col + 1;
832: h1 = HASH(ht_size, key, tmp);
833: for (k = 0; k < ht_size; k++) {
834: if (!HT[(h1 + k) % ht_size]) {
835: HT[(h1 + k) % ht_size] = key;
836: HD[(h1 + k) % ht_size] = a->a + j * bs2;
837: break;
838: #if defined(PETSC_USE_INFO)
839: } else {
840: ct++;
841: #endif
842: }
843: }
844: #if defined(PETSC_USE_INFO)
845: if (k > max) max = k;
846: #endif
847: }
848: }
849: /* Loop Over B */
850: for (i = 0; i < b->mbs; i++) {
851: for (j = bi[i]; j < bi[i + 1]; j++) {
852: row = i + rstart;
853: col = garray[bj[j]];
854: key = row * Nbs + col + 1;
855: h1 = HASH(ht_size, key, tmp);
856: for (k = 0; k < ht_size; k++) {
857: if (!HT[(h1 + k) % ht_size]) {
858: HT[(h1 + k) % ht_size] = key;
859: HD[(h1 + k) % ht_size] = b->a + j * bs2;
860: break;
861: #if defined(PETSC_USE_INFO)
862: } else {
863: ct++;
864: #endif
865: }
866: }
867: #if defined(PETSC_USE_INFO)
868: if (k > max) max = k;
869: #endif
870: }
871: }
873: /* Print Summary */
874: #if defined(PETSC_USE_INFO)
875: for (i = 0, j = 0; i < ht_size; i++) {
876: if (HT[i]) j++;
877: }
878: PetscCall(PetscInfo(mat, "Average Search = %5.2g,max search = %" PetscInt_FMT "\n", (!j) ? 0.0 : (double)(((PetscReal)(ct + j)) / j), max));
879: #endif
880: PetscFunctionReturn(PETSC_SUCCESS);
881: }
883: static PetscErrorCode MatAssemblyBegin_MPIBAIJ(Mat mat, MatAssemblyType mode)
884: {
885: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
886: PetscInt nstash, reallocs;
888: PetscFunctionBegin;
889: if (baij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
891: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
892: PetscCall(MatStashScatterBegin_Private(mat, &mat->bstash, baij->rangebs));
893: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
894: PetscCall(PetscInfo(mat, "Stash has %" PetscInt_FMT " entries,uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
895: PetscCall(MatStashGetInfo_Private(&mat->bstash, &nstash, &reallocs));
896: PetscCall(PetscInfo(mat, "Block-Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
897: PetscFunctionReturn(PETSC_SUCCESS);
898: }
900: static PetscErrorCode MatAssemblyEnd_MPIBAIJ(Mat mat, MatAssemblyType mode)
901: {
902: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
903: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)baij->A->data;
904: PetscInt i, j, rstart, ncols, flg, bs2 = baij->bs2;
905: PetscInt *row, *col;
906: PetscBool r1, r2, r3, all_assembled;
907: MatScalar *val;
908: PetscMPIInt n;
910: PetscFunctionBegin;
911: /* do not use 'b=(Mat_SeqBAIJ*)baij->B->data' as B can be reset in disassembly */
912: if (!baij->donotstash && !mat->nooffprocentries) {
913: while (1) {
914: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
915: if (!flg) break;
917: for (i = 0; i < n;) {
918: /* Now identify the consecutive vals belonging to the same row */
919: for (j = i, rstart = row[j]; j < n; j++) {
920: if (row[j] != rstart) break;
921: }
922: if (j < n) ncols = j - i;
923: else ncols = n - i;
924: /* Now assemble all these values with a single function call */
925: PetscCall(MatSetValues_MPIBAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
926: i = j;
927: }
928: }
929: PetscCall(MatStashScatterEnd_Private(&mat->stash));
930: /* Now process the block-stash. Since the values are stashed column-oriented,
931: set the row-oriented flag to column-oriented, and after MatSetValues()
932: restore the original flags */
933: r1 = baij->roworiented;
934: r2 = a->roworiented;
935: r3 = ((Mat_SeqBAIJ *)baij->B->data)->roworiented;
937: baij->roworiented = PETSC_FALSE;
938: a->roworiented = PETSC_FALSE;
939: ((Mat_SeqBAIJ *)baij->B->data)->roworiented = PETSC_FALSE;
940: while (1) {
941: PetscCall(MatStashScatterGetMesg_Private(&mat->bstash, &n, &row, &col, &val, &flg));
942: if (!flg) break;
944: for (i = 0; i < n;) {
945: /* Now identify the consecutive vals belonging to the same row */
946: for (j = i, rstart = row[j]; j < n; j++) {
947: if (row[j] != rstart) break;
948: }
949: if (j < n) ncols = j - i;
950: else ncols = n - i;
951: PetscCall(MatSetValuesBlocked_MPIBAIJ(mat, 1, row + i, ncols, col + i, val + i * bs2, mat->insertmode));
952: i = j;
953: }
954: }
955: PetscCall(MatStashScatterEnd_Private(&mat->bstash));
957: baij->roworiented = r1;
958: a->roworiented = r2;
959: ((Mat_SeqBAIJ *)baij->B->data)->roworiented = r3;
960: }
962: PetscCall(MatAssemblyBegin(baij->A, mode));
963: PetscCall(MatAssemblyEnd(baij->A, mode));
965: /* determine if any process has disassembled, if so we must
966: also disassemble ourselves, in order that we may reassemble. */
967: /*
968: if nonzero structure of submatrix B cannot change then we know that
969: no process disassembled thus we can skip this stuff
970: */
971: if (!((Mat_SeqBAIJ *)baij->B->data)->nonew) {
972: PetscCallMPI(MPIU_Allreduce(&mat->was_assembled, &all_assembled, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
973: if (mat->was_assembled && !all_assembled) PetscCall(MatDisAssemble_MPIBAIJ(mat));
974: }
976: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIBAIJ(mat));
977: PetscCall(MatAssemblyBegin(baij->B, mode));
978: PetscCall(MatAssemblyEnd(baij->B, mode));
980: #if defined(PETSC_USE_INFO)
981: if (baij->ht && mode == MAT_FINAL_ASSEMBLY) {
982: PetscCall(PetscInfo(mat, "Average Hash Table Search in MatSetValues = %5.2f\n", (double)((PetscReal)baij->ht_total_ct) / baij->ht_insert_ct));
984: baij->ht_total_ct = 0;
985: baij->ht_insert_ct = 0;
986: }
987: #endif
988: if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
989: PetscCall(MatCreateHashTable_MPIBAIJ_Private(mat, baij->ht_fact));
991: mat->ops->setvalues = MatSetValues_MPIBAIJ_HT;
992: mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
993: }
995: PetscCall(PetscFree2(baij->rowvalues, baij->rowindices));
997: baij->rowvalues = NULL;
999: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
1000: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqBAIJ *)baij->A->data)->nonew) {
1001: PetscObjectState state = baij->A->nonzerostate + baij->B->nonzerostate;
1002: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
1003: }
1004: PetscFunctionReturn(PETSC_SUCCESS);
1005: }
1007: #include <petscdraw.h>
1008: static PetscErrorCode MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1009: {
1010: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1011: PetscMPIInt rank = baij->rank;
1012: PetscInt bs = mat->rmap->bs;
1013: PetscBool isascii, isdraw;
1014: PetscViewer sviewer;
1015: PetscViewerFormat format;
1017: PetscFunctionBegin;
1018: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1019: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1020: if (isascii) {
1021: PetscCall(PetscViewerGetFormat(viewer, &format));
1022: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1023: MatInfo info;
1024: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1025: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1026: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1027: 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,
1028: mat->rmap->bs, info.memory));
1029: PetscCall(MatGetInfo(baij->A, MAT_LOCAL, &info));
1030: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1031: PetscCall(MatGetInfo(baij->B, MAT_LOCAL, &info));
1032: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1033: PetscCall(PetscViewerFlush(viewer));
1034: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1035: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1036: PetscCall(VecScatterView(baij->Mvctx, viewer));
1037: PetscFunctionReturn(PETSC_SUCCESS);
1038: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1039: PetscCall(PetscViewerASCIIPrintf(viewer, " block size is %" PetscInt_FMT "\n", bs));
1040: PetscFunctionReturn(PETSC_SUCCESS);
1041: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1042: PetscFunctionReturn(PETSC_SUCCESS);
1043: }
1044: }
1046: if (isdraw) {
1047: PetscDraw draw;
1048: PetscBool isnull;
1049: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1050: PetscCall(PetscDrawIsNull(draw, &isnull));
1051: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1052: }
1054: {
1055: /* assemble the entire matrix onto first processor. */
1056: Mat A;
1057: Mat_SeqBAIJ *Aloc;
1058: PetscInt M = mat->rmap->N, N = mat->cmap->N, *ai, *aj, col, i, j, k, *rvals, mbs = baij->mbs;
1059: MatScalar *a;
1060: const char *matname;
1062: /* Here we are creating a temporary matrix, so will assume MPIBAIJ is acceptable */
1063: /* Perhaps this should be the type of mat? */
1064: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &A));
1065: if (rank == 0) {
1066: PetscCall(MatSetSizes(A, M, N, M, N));
1067: } else {
1068: PetscCall(MatSetSizes(A, 0, 0, M, N));
1069: }
1070: PetscCall(MatSetType(A, MATMPIBAIJ));
1071: PetscCall(MatMPIBAIJSetPreallocation(A, mat->rmap->bs, 0, NULL, 0, NULL));
1072: PetscCall(MatSetOption(A, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_FALSE));
1074: /* copy over the A part */
1075: Aloc = (Mat_SeqBAIJ *)baij->A->data;
1076: ai = Aloc->i;
1077: aj = Aloc->j;
1078: a = Aloc->a;
1079: PetscCall(PetscMalloc1(bs, &rvals));
1081: for (i = 0; i < mbs; i++) {
1082: rvals[0] = bs * (baij->rstartbs + i);
1083: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1084: for (j = ai[i]; j < ai[i + 1]; j++) {
1085: col = (baij->cstartbs + aj[j]) * bs;
1086: for (k = 0; k < bs; k++) {
1087: PetscCall(MatSetValues_MPIBAIJ(A, bs, rvals, 1, &col, a, INSERT_VALUES));
1088: col++;
1089: a += bs;
1090: }
1091: }
1092: }
1093: /* copy over the B part */
1094: Aloc = (Mat_SeqBAIJ *)baij->B->data;
1095: ai = Aloc->i;
1096: aj = Aloc->j;
1097: a = Aloc->a;
1098: for (i = 0; i < mbs; i++) {
1099: rvals[0] = bs * (baij->rstartbs + i);
1100: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1101: for (j = ai[i]; j < ai[i + 1]; j++) {
1102: col = baij->garray[aj[j]] * bs;
1103: for (k = 0; k < bs; k++) {
1104: PetscCall(MatSetValues_MPIBAIJ(A, bs, rvals, 1, &col, a, INSERT_VALUES));
1105: col++;
1106: a += bs;
1107: }
1108: }
1109: }
1110: PetscCall(PetscFree(rvals));
1111: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1112: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1113: /*
1114: Everyone has to call to draw the matrix since the graphics waits are
1115: synchronized across all processors that share the PetscDraw object
1116: */
1117: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1118: if (((PetscObject)mat)->name) PetscCall(PetscObjectGetName((PetscObject)mat, &matname));
1119: if (rank == 0) {
1120: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)((Mat_MPIBAIJ *)A->data)->A, matname));
1121: PetscCall(MatView_SeqBAIJ(((Mat_MPIBAIJ *)A->data)->A, sviewer));
1122: }
1123: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1124: PetscCall(MatDestroy(&A));
1125: }
1126: PetscFunctionReturn(PETSC_SUCCESS);
1127: }
1129: /* Used for both MPIBAIJ and MPISBAIJ matrices */
1130: PetscErrorCode MatView_MPIBAIJ_Binary(Mat mat, PetscViewer viewer)
1131: {
1132: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
1133: Mat_SeqBAIJ *A = (Mat_SeqBAIJ *)aij->A->data;
1134: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)aij->B->data;
1135: const PetscInt *garray = aij->garray;
1136: PetscInt header[4], M, N, m, rs, cs, bs, cnt, i, j, ja, jb, k, l;
1137: PetscCount nz, hnz;
1138: PetscInt *rowlens, *colidxs;
1139: PetscScalar *matvals;
1140: PetscMPIInt rank;
1142: PetscFunctionBegin;
1143: PetscCall(PetscViewerSetUp(viewer));
1145: M = mat->rmap->N;
1146: N = mat->cmap->N;
1147: m = mat->rmap->n;
1148: rs = mat->rmap->rstart;
1149: cs = mat->cmap->rstart;
1150: bs = mat->rmap->bs;
1151: nz = bs * bs * (A->nz + B->nz);
1153: /* write matrix header */
1154: header[0] = MAT_FILE_CLASSID;
1155: header[1] = M;
1156: header[2] = N;
1157: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_COUNT, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1158: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1159: if (rank == 0) PetscCall(PetscIntCast(hnz, &header[3]));
1160: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1162: /* fill in and store row lengths */
1163: PetscCall(PetscMalloc1(m, &rowlens));
1164: for (cnt = 0, i = 0; i < A->mbs; i++)
1165: for (j = 0; j < bs; j++) rowlens[cnt++] = bs * (A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i]);
1166: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1167: PetscCall(PetscFree(rowlens));
1169: /* fill in and store column indices */
1170: PetscCall(PetscMalloc1(nz, &colidxs));
1171: for (cnt = 0, i = 0; i < A->mbs; i++) {
1172: for (k = 0; k < bs; k++) {
1173: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1174: if (garray[B->j[jb]] > cs / bs) break;
1175: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * garray[B->j[jb]] + l;
1176: }
1177: for (ja = A->i[i]; ja < A->i[i + 1]; ja++)
1178: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * A->j[ja] + l + cs;
1179: for (; jb < B->i[i + 1]; jb++)
1180: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * garray[B->j[jb]] + l;
1181: }
1182: }
1183: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscCount_FMT, cnt, nz);
1184: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DECIDE, PETSC_DECIDE, PETSC_INT));
1185: PetscCall(PetscFree(colidxs));
1187: /* fill in and store nonzero values */
1188: PetscCall(PetscMalloc1(nz, &matvals));
1189: for (cnt = 0, i = 0; i < A->mbs; i++) {
1190: for (k = 0; k < bs; k++) {
1191: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1192: if (garray[B->j[jb]] > cs / bs) break;
1193: for (l = 0; l < bs; l++) matvals[cnt++] = B->a[bs * (bs * jb + l) + k];
1194: }
1195: for (ja = A->i[i]; ja < A->i[i + 1]; ja++)
1196: for (l = 0; l < bs; l++) matvals[cnt++] = A->a[bs * (bs * ja + l) + k];
1197: for (; jb < B->i[i + 1]; jb++)
1198: for (l = 0; l < bs; l++) matvals[cnt++] = B->a[bs * (bs * jb + l) + k];
1199: }
1200: }
1201: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DECIDE, PETSC_DECIDE, PETSC_SCALAR));
1202: PetscCall(PetscFree(matvals));
1204: /* write block size option to the viewer's .info file */
1205: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1206: PetscFunctionReturn(PETSC_SUCCESS);
1207: }
1209: PetscErrorCode MatView_MPIBAIJ(Mat mat, PetscViewer viewer)
1210: {
1211: PetscBool isascii, isdraw, issocket, isbinary;
1213: PetscFunctionBegin;
1214: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1215: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1216: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1217: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1218: if (isascii || isdraw || issocket) 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: static 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: static 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: PetscCallMPI(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: PetscCallMPI(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_IGNORE_OFF_PROC_ENTRIES:
1484: a->donotstash = flg;
1485: break;
1486: case MAT_USE_HASH_TABLE:
1487: a->ht_flag = flg;
1488: a->ht_fact = 1.39;
1489: break;
1490: case MAT_SYMMETRIC:
1491: case MAT_STRUCTURALLY_SYMMETRIC:
1492: case MAT_HERMITIAN:
1493: case MAT_SYMMETRY_ETERNAL:
1494: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1495: case MAT_SPD_ETERNAL:
1496: /* if the diagonal matrix is square it inherits some of the properties above */
1497: if (a->A && A->rmap->n == A->cmap->n) PetscCall(MatSetOption(a->A, op, flg));
1498: break;
1499: default:
1500: break;
1501: }
1502: PetscFunctionReturn(PETSC_SUCCESS);
1503: }
1505: static PetscErrorCode MatTranspose_MPIBAIJ(Mat A, MatReuse reuse, Mat *matout)
1506: {
1507: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)A->data;
1508: Mat_SeqBAIJ *Aloc;
1509: Mat B;
1510: PetscInt M = A->rmap->N, N = A->cmap->N, *ai, *aj, i, *rvals, j, k, col;
1511: PetscInt bs = A->rmap->bs, mbs = baij->mbs;
1512: MatScalar *a;
1514: PetscFunctionBegin;
1515: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1516: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
1517: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1518: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1519: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1520: /* Do not know preallocation information, but must set block size */
1521: PetscCall(MatMPIBAIJSetPreallocation(B, A->rmap->bs, PETSC_DECIDE, NULL, PETSC_DECIDE, NULL));
1522: } else {
1523: B = *matout;
1524: }
1526: /* copy over the A part */
1527: Aloc = (Mat_SeqBAIJ *)baij->A->data;
1528: ai = Aloc->i;
1529: aj = Aloc->j;
1530: a = Aloc->a;
1531: PetscCall(PetscMalloc1(bs, &rvals));
1533: for (i = 0; i < mbs; i++) {
1534: rvals[0] = bs * (baij->rstartbs + i);
1535: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1536: for (j = ai[i]; j < ai[i + 1]; j++) {
1537: col = (baij->cstartbs + aj[j]) * bs;
1538: for (k = 0; k < bs; k++) {
1539: PetscCall(MatSetValues_MPIBAIJ(B, 1, &col, bs, rvals, a, INSERT_VALUES));
1541: col++;
1542: a += bs;
1543: }
1544: }
1545: }
1546: /* copy over the B part */
1547: Aloc = (Mat_SeqBAIJ *)baij->B->data;
1548: ai = Aloc->i;
1549: aj = Aloc->j;
1550: a = Aloc->a;
1551: for (i = 0; i < mbs; i++) {
1552: rvals[0] = bs * (baij->rstartbs + i);
1553: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1554: for (j = ai[i]; j < ai[i + 1]; j++) {
1555: col = baij->garray[aj[j]] * bs;
1556: for (k = 0; k < bs; k++) {
1557: PetscCall(MatSetValues_MPIBAIJ(B, 1, &col, bs, rvals, a, INSERT_VALUES));
1558: col++;
1559: a += bs;
1560: }
1561: }
1562: }
1563: PetscCall(PetscFree(rvals));
1564: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1565: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1567: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) *matout = B;
1568: else PetscCall(MatHeaderMerge(A, &B));
1569: PetscFunctionReturn(PETSC_SUCCESS);
1570: }
1572: static PetscErrorCode MatDiagonalScale_MPIBAIJ(Mat mat, Vec ll, Vec rr)
1573: {
1574: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1575: Mat a = baij->A, b = baij->B;
1576: PetscInt s1, s2, s3;
1578: PetscFunctionBegin;
1579: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1580: if (rr) {
1581: PetscCall(VecGetLocalSize(rr, &s1));
1582: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1583: /* Overlap communication with computation. */
1584: PetscCall(VecScatterBegin(baij->Mvctx, rr, baij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1585: }
1586: if (ll) {
1587: PetscCall(VecGetLocalSize(ll, &s1));
1588: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1589: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1590: }
1591: /* scale the diagonal block */
1592: PetscUseTypeMethod(a, diagonalscale, ll, rr);
1594: if (rr) {
1595: /* Do a scatter end and then right scale the off-diagonal block */
1596: PetscCall(VecScatterEnd(baij->Mvctx, rr, baij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1597: PetscUseTypeMethod(b, diagonalscale, NULL, baij->lvec);
1598: }
1599: PetscFunctionReturn(PETSC_SUCCESS);
1600: }
1602: static PetscErrorCode MatZeroRows_MPIBAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
1603: {
1604: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1605: PetscInt *lrows;
1606: PetscInt r, len;
1607: PetscBool cong;
1609: PetscFunctionBegin;
1610: /* get locally owned rows */
1611: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
1612: /* fix right-hand side if needed */
1613: if (x && b) {
1614: const PetscScalar *xx;
1615: PetscScalar *bb;
1617: PetscCall(VecGetArrayRead(x, &xx));
1618: PetscCall(VecGetArray(b, &bb));
1619: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
1620: PetscCall(VecRestoreArrayRead(x, &xx));
1621: PetscCall(VecRestoreArray(b, &bb));
1622: }
1624: /* actually zap the local rows */
1625: /*
1626: Zero the required rows. If the "diagonal block" of the matrix
1627: is square and the user wishes to set the diagonal we use separate
1628: code so that MatSetValues() is not called for each diagonal allocating
1629: new memory, thus calling lots of mallocs and slowing things down.
1631: */
1632: /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1633: PetscCall(MatZeroRows_SeqBAIJ(l->B, len, lrows, 0.0, NULL, NULL));
1634: PetscCall(MatHasCongruentLayouts(A, &cong));
1635: if ((diag != 0.0) && cong) {
1636: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, diag, NULL, NULL));
1637: } else if (diag != 0.0) {
1638: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, 0.0, NULL, NULL));
1639: 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");
1640: for (r = 0; r < len; ++r) {
1641: const PetscInt row = lrows[r] + A->rmap->rstart;
1642: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
1643: }
1644: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1645: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1646: } else {
1647: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, 0.0, NULL, NULL));
1648: }
1649: PetscCall(PetscFree(lrows));
1651: /* only change matrix nonzero state if pattern was allowed to be changed */
1652: if (!((Mat_SeqBAIJ *)l->A->data)->keepnonzeropattern || !((Mat_SeqBAIJ *)l->A->data)->nonew) {
1653: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1654: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1655: }
1656: PetscFunctionReturn(PETSC_SUCCESS);
1657: }
1659: static PetscErrorCode MatZeroRowsColumns_MPIBAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
1660: {
1661: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1662: PetscMPIInt n, p = 0;
1663: PetscInt i, j, k, r, len = 0, row, col, count;
1664: PetscInt *lrows, *owners = A->rmap->range;
1665: PetscSFNode *rrows;
1666: PetscSF sf;
1667: const PetscScalar *xx;
1668: PetscScalar *bb, *mask;
1669: Vec xmask, lmask;
1670: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)l->B->data;
1671: PetscInt bs = A->rmap->bs, bs2 = baij->bs2;
1672: PetscScalar *aa;
1674: PetscFunctionBegin;
1675: PetscCall(PetscMPIIntCast(A->rmap->n, &n));
1676: /* Create SF where leaves are input rows and roots are owned rows */
1677: PetscCall(PetscMalloc1(n, &lrows));
1678: for (r = 0; r < n; ++r) lrows[r] = -1;
1679: PetscCall(PetscMalloc1(N, &rrows));
1680: for (r = 0; r < N; ++r) {
1681: const PetscInt idx = rows[r];
1682: 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);
1683: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
1684: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
1685: }
1686: rrows[r].rank = p;
1687: rrows[r].index = rows[r] - owners[p];
1688: }
1689: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1690: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
1691: /* Collect flags for rows to be zeroed */
1692: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
1693: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
1694: PetscCall(PetscSFDestroy(&sf));
1695: /* Compress and put in row numbers */
1696: for (r = 0; r < n; ++r)
1697: if (lrows[r] >= 0) lrows[len++] = r;
1698: /* zero diagonal part of matrix */
1699: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
1700: /* handle off-diagonal part of matrix */
1701: PetscCall(MatCreateVecs(A, &xmask, NULL));
1702: PetscCall(VecDuplicate(l->lvec, &lmask));
1703: PetscCall(VecGetArray(xmask, &bb));
1704: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
1705: PetscCall(VecRestoreArray(xmask, &bb));
1706: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
1707: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
1708: PetscCall(VecDestroy(&xmask));
1709: if (x) {
1710: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
1711: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
1712: PetscCall(VecGetArrayRead(l->lvec, &xx));
1713: PetscCall(VecGetArray(b, &bb));
1714: }
1715: PetscCall(VecGetArray(lmask, &mask));
1716: /* remove zeroed rows of off-diagonal matrix */
1717: for (i = 0; i < len; ++i) {
1718: row = lrows[i];
1719: count = (baij->i[row / bs + 1] - baij->i[row / bs]) * bs;
1720: aa = baij->a + baij->i[row / bs] * bs2 + (row % bs);
1721: for (k = 0; k < count; ++k) {
1722: aa[0] = 0.0;
1723: aa += bs;
1724: }
1725: }
1726: /* loop over all elements of off process part of matrix zeroing removed columns*/
1727: for (i = 0; i < l->B->rmap->N; ++i) {
1728: row = i / bs;
1729: for (j = baij->i[row]; j < baij->i[row + 1]; ++j) {
1730: for (k = 0; k < bs; ++k) {
1731: col = bs * baij->j[j] + k;
1732: if (PetscAbsScalar(mask[col])) {
1733: aa = baij->a + j * bs2 + (i % bs) + bs * k;
1734: if (x) bb[i] -= aa[0] * xx[col];
1735: aa[0] = 0.0;
1736: }
1737: }
1738: }
1739: }
1740: if (x) {
1741: PetscCall(VecRestoreArray(b, &bb));
1742: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1743: }
1744: PetscCall(VecRestoreArray(lmask, &mask));
1745: PetscCall(VecDestroy(&lmask));
1746: PetscCall(PetscFree(lrows));
1748: /* only change matrix nonzero state if pattern was allowed to be changed */
1749: if (!((Mat_SeqBAIJ *)l->A->data)->nonew) {
1750: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1751: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1752: }
1753: PetscFunctionReturn(PETSC_SUCCESS);
1754: }
1756: static PetscErrorCode MatSetUnfactored_MPIBAIJ(Mat A)
1757: {
1758: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1760: PetscFunctionBegin;
1761: PetscCall(MatSetUnfactored(a->A));
1762: PetscFunctionReturn(PETSC_SUCCESS);
1763: }
1765: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat, MatDuplicateOption, Mat *);
1767: static PetscErrorCode MatEqual_MPIBAIJ(Mat A, Mat B, PetscBool *flag)
1768: {
1769: Mat_MPIBAIJ *matB = (Mat_MPIBAIJ *)B->data, *matA = (Mat_MPIBAIJ *)A->data;
1770: Mat a, b, c, d;
1771: PetscBool flg;
1773: PetscFunctionBegin;
1774: a = matA->A;
1775: b = matA->B;
1776: c = matB->A;
1777: d = matB->B;
1779: PetscCall(MatEqual(a, c, &flg));
1780: if (flg) PetscCall(MatEqual(b, d, &flg));
1781: PetscCallMPI(MPIU_Allreduce(&flg, flag, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
1782: PetscFunctionReturn(PETSC_SUCCESS);
1783: }
1785: static PetscErrorCode MatCopy_MPIBAIJ(Mat A, Mat B, MatStructure str)
1786: {
1787: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1788: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
1790: PetscFunctionBegin;
1791: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1792: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1793: PetscCall(MatCopy_Basic(A, B, str));
1794: } else {
1795: PetscCall(MatCopy(a->A, b->A, str));
1796: PetscCall(MatCopy(a->B, b->B, str));
1797: }
1798: PetscCall(PetscObjectStateIncrease((PetscObject)B));
1799: PetscFunctionReturn(PETSC_SUCCESS);
1800: }
1802: PetscErrorCode MatAXPYGetPreallocation_MPIBAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
1803: {
1804: PetscInt bs = Y->rmap->bs, m = Y->rmap->N / bs;
1805: Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data;
1806: Mat_SeqBAIJ *y = (Mat_SeqBAIJ *)Y->data;
1808: PetscFunctionBegin;
1809: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
1810: PetscFunctionReturn(PETSC_SUCCESS);
1811: }
1813: static PetscErrorCode MatAXPY_MPIBAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
1814: {
1815: Mat_MPIBAIJ *xx = (Mat_MPIBAIJ *)X->data, *yy = (Mat_MPIBAIJ *)Y->data;
1816: PetscBLASInt bnz, one = 1;
1817: Mat_SeqBAIJ *x, *y;
1818: PetscInt bs2 = Y->rmap->bs * Y->rmap->bs;
1820: PetscFunctionBegin;
1821: if (str == SAME_NONZERO_PATTERN) {
1822: PetscScalar alpha = a;
1823: x = (Mat_SeqBAIJ *)xx->A->data;
1824: y = (Mat_SeqBAIJ *)yy->A->data;
1825: PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz));
1826: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one));
1827: x = (Mat_SeqBAIJ *)xx->B->data;
1828: y = (Mat_SeqBAIJ *)yy->B->data;
1829: PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz));
1830: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one));
1831: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
1832: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
1833: PetscCall(MatAXPY_Basic(Y, a, X, str));
1834: } else {
1835: Mat B;
1836: PetscInt *nnz_d, *nnz_o, bs = Y->rmap->bs;
1837: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
1838: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
1839: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
1840: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
1841: PetscCall(MatSetSizes(B, Y->rmap->n, Y->cmap->n, Y->rmap->N, Y->cmap->N));
1842: PetscCall(MatSetBlockSizesFromMats(B, Y, Y));
1843: PetscCall(MatSetType(B, MATMPIBAIJ));
1844: PetscCall(MatAXPYGetPreallocation_SeqBAIJ(yy->A, xx->A, nnz_d));
1845: PetscCall(MatAXPYGetPreallocation_MPIBAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
1846: PetscCall(MatMPIBAIJSetPreallocation(B, bs, 0, nnz_d, 0, nnz_o));
1847: /* MatAXPY_BasicWithPreallocation() for BAIJ matrix is much slower than AIJ, even for bs=1 ! */
1848: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
1849: PetscCall(MatHeaderMerge(Y, &B));
1850: PetscCall(PetscFree(nnz_d));
1851: PetscCall(PetscFree(nnz_o));
1852: }
1853: PetscFunctionReturn(PETSC_SUCCESS);
1854: }
1856: static PetscErrorCode MatConjugate_MPIBAIJ(Mat mat)
1857: {
1858: PetscFunctionBegin;
1859: if (PetscDefined(USE_COMPLEX)) {
1860: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)mat->data;
1862: PetscCall(MatConjugate_SeqBAIJ(a->A));
1863: PetscCall(MatConjugate_SeqBAIJ(a->B));
1864: }
1865: PetscFunctionReturn(PETSC_SUCCESS);
1866: }
1868: static PetscErrorCode MatRealPart_MPIBAIJ(Mat A)
1869: {
1870: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1872: PetscFunctionBegin;
1873: PetscCall(MatRealPart(a->A));
1874: PetscCall(MatRealPart(a->B));
1875: PetscFunctionReturn(PETSC_SUCCESS);
1876: }
1878: static PetscErrorCode MatImaginaryPart_MPIBAIJ(Mat A)
1879: {
1880: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1882: PetscFunctionBegin;
1883: PetscCall(MatImaginaryPart(a->A));
1884: PetscCall(MatImaginaryPart(a->B));
1885: PetscFunctionReturn(PETSC_SUCCESS);
1886: }
1888: static PetscErrorCode MatCreateSubMatrix_MPIBAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
1889: {
1890: IS iscol_local;
1891: PetscInt csize;
1893: PetscFunctionBegin;
1894: PetscCall(ISGetLocalSize(iscol, &csize));
1895: if (call == MAT_REUSE_MATRIX) {
1896: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
1897: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
1898: } else {
1899: PetscCall(ISAllGather(iscol, &iscol_local));
1900: }
1901: PetscCall(MatCreateSubMatrix_MPIBAIJ_Private(mat, isrow, iscol_local, csize, call, newmat, PETSC_FALSE));
1902: if (call == MAT_INITIAL_MATRIX) {
1903: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
1904: PetscCall(ISDestroy(&iscol_local));
1905: }
1906: PetscFunctionReturn(PETSC_SUCCESS);
1907: }
1909: /*
1910: Not great since it makes two copies of the submatrix, first an SeqBAIJ
1911: in local and then by concatenating the local matrices the end result.
1912: Writing it directly would be much like MatCreateSubMatrices_MPIBAIJ().
1913: This routine is used for BAIJ and SBAIJ matrices (unfortunate dependency).
1914: */
1915: PetscErrorCode MatCreateSubMatrix_MPIBAIJ_Private(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat, PetscBool sym)
1916: {
1917: PetscMPIInt rank, size;
1918: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs;
1919: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
1920: Mat M, Mreuse;
1921: MatScalar *vwork, *aa;
1922: MPI_Comm comm;
1923: IS isrow_new, iscol_new;
1924: Mat_SeqBAIJ *aij;
1926: PetscFunctionBegin;
1927: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
1928: PetscCallMPI(MPI_Comm_rank(comm, &rank));
1929: PetscCallMPI(MPI_Comm_size(comm, &size));
1930: /* The compression and expansion should be avoided. Doesn't point
1931: out errors, might change the indices, hence buggey */
1932: PetscCall(ISCompressIndicesGeneral(mat->rmap->N, mat->rmap->n, mat->rmap->bs, 1, &isrow, &isrow_new));
1933: if (isrow == iscol) {
1934: iscol_new = isrow_new;
1935: PetscCall(PetscObjectReference((PetscObject)iscol_new));
1936: } else PetscCall(ISCompressIndicesGeneral(mat->cmap->N, mat->cmap->n, mat->cmap->bs, 1, &iscol, &iscol_new));
1938: if (call == MAT_REUSE_MATRIX) {
1939: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
1940: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
1941: PetscCall(MatCreateSubMatrices_MPIBAIJ_local(mat, 1, &isrow_new, &iscol_new, MAT_REUSE_MATRIX, &Mreuse, sym));
1942: } else {
1943: PetscCall(MatCreateSubMatrices_MPIBAIJ_local(mat, 1, &isrow_new, &iscol_new, MAT_INITIAL_MATRIX, &Mreuse, sym));
1944: }
1945: PetscCall(ISDestroy(&isrow_new));
1946: PetscCall(ISDestroy(&iscol_new));
1947: /*
1948: m - number of local rows
1949: n - number of columns (same on all processors)
1950: rstart - first row in new global matrix generated
1951: */
1952: PetscCall(MatGetBlockSize(mat, &bs));
1953: PetscCall(MatGetSize(Mreuse, &m, &n));
1954: m = m / bs;
1955: n = n / bs;
1957: if (call == MAT_INITIAL_MATRIX) {
1958: aij = (Mat_SeqBAIJ *)Mreuse->data;
1959: ii = aij->i;
1960: jj = aij->j;
1962: /*
1963: Determine the number of non-zeros in the diagonal and off-diagonal
1964: portions of the matrix in order to do correct preallocation
1965: */
1967: /* first get start and end of "diagonal" columns */
1968: if (csize == PETSC_DECIDE) {
1969: PetscCall(ISGetSize(isrow, &mglobal));
1970: if (mglobal == n * bs) { /* square matrix */
1971: nlocal = m;
1972: } else {
1973: nlocal = n / size + ((n % size) > rank);
1974: }
1975: } else {
1976: nlocal = csize / bs;
1977: }
1978: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
1979: rstart = rend - nlocal;
1980: 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);
1982: /* next, compute all the lengths */
1983: PetscCall(PetscMalloc2(m + 1, &dlens, m + 1, &olens));
1984: for (i = 0; i < m; i++) {
1985: jend = ii[i + 1] - ii[i];
1986: olen = 0;
1987: dlen = 0;
1988: for (j = 0; j < jend; j++) {
1989: if (*jj < rstart || *jj >= rend) olen++;
1990: else dlen++;
1991: jj++;
1992: }
1993: olens[i] = olen;
1994: dlens[i] = dlen;
1995: }
1996: PetscCall(MatCreate(comm, &M));
1997: PetscCall(MatSetSizes(M, bs * m, bs * nlocal, PETSC_DECIDE, bs * n));
1998: PetscCall(MatSetType(M, sym ? ((PetscObject)mat)->type_name : MATMPIBAIJ));
1999: PetscCall(MatMPIBAIJSetPreallocation(M, bs, 0, dlens, 0, olens));
2000: PetscCall(MatMPISBAIJSetPreallocation(M, bs, 0, dlens, 0, olens));
2001: PetscCall(PetscFree2(dlens, olens));
2002: } else {
2003: PetscInt ml, nl;
2005: M = *newmat;
2006: PetscCall(MatGetLocalSize(M, &ml, &nl));
2007: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
2008: PetscCall(MatZeroEntries(M));
2009: /*
2010: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
2011: rather than the slower MatSetValues().
2012: */
2013: M->was_assembled = PETSC_TRUE;
2014: M->assembled = PETSC_FALSE;
2015: }
2016: PetscCall(MatSetOption(M, MAT_ROW_ORIENTED, PETSC_FALSE));
2017: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
2018: aij = (Mat_SeqBAIJ *)Mreuse->data;
2019: ii = aij->i;
2020: jj = aij->j;
2021: aa = aij->a;
2022: for (i = 0; i < m; i++) {
2023: row = rstart / bs + i;
2024: nz = ii[i + 1] - ii[i];
2025: cwork = jj;
2026: jj = PetscSafePointerPlusOffset(jj, nz);
2027: vwork = aa;
2028: aa = PetscSafePointerPlusOffset(aa, nz * bs * bs);
2029: PetscCall(MatSetValuesBlocked_MPIBAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
2030: }
2032: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
2033: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
2034: *newmat = M;
2036: /* save submatrix used in processor for next request */
2037: if (call == MAT_INITIAL_MATRIX) {
2038: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
2039: PetscCall(PetscObjectDereference((PetscObject)Mreuse));
2040: }
2041: PetscFunctionReturn(PETSC_SUCCESS);
2042: }
2044: static PetscErrorCode MatPermute_MPIBAIJ(Mat A, IS rowp, IS colp, Mat *B)
2045: {
2046: MPI_Comm comm, pcomm;
2047: PetscInt clocal_size, nrows;
2048: const PetscInt *rows;
2049: PetscMPIInt size;
2050: IS crowp, lcolp;
2052: PetscFunctionBegin;
2053: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
2054: /* make a collective version of 'rowp' */
2055: PetscCall(PetscObjectGetComm((PetscObject)rowp, &pcomm));
2056: if (pcomm == comm) {
2057: crowp = rowp;
2058: } else {
2059: PetscCall(ISGetSize(rowp, &nrows));
2060: PetscCall(ISGetIndices(rowp, &rows));
2061: PetscCall(ISCreateGeneral(comm, nrows, rows, PETSC_COPY_VALUES, &crowp));
2062: PetscCall(ISRestoreIndices(rowp, &rows));
2063: }
2064: PetscCall(ISSetPermutation(crowp));
2065: /* make a local version of 'colp' */
2066: PetscCall(PetscObjectGetComm((PetscObject)colp, &pcomm));
2067: PetscCallMPI(MPI_Comm_size(pcomm, &size));
2068: if (size == 1) {
2069: lcolp = colp;
2070: } else {
2071: PetscCall(ISAllGather(colp, &lcolp));
2072: }
2073: PetscCall(ISSetPermutation(lcolp));
2074: /* now we just get the submatrix */
2075: PetscCall(MatGetLocalSize(A, NULL, &clocal_size));
2076: PetscCall(MatCreateSubMatrix_MPIBAIJ_Private(A, crowp, lcolp, clocal_size, MAT_INITIAL_MATRIX, B, PETSC_FALSE));
2077: /* clean up */
2078: if (pcomm != comm) PetscCall(ISDestroy(&crowp));
2079: if (size > 1) PetscCall(ISDestroy(&lcolp));
2080: PetscFunctionReturn(PETSC_SUCCESS);
2081: }
2083: static PetscErrorCode MatGetGhosts_MPIBAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
2084: {
2085: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
2086: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)baij->B->data;
2088: PetscFunctionBegin;
2089: if (nghosts) *nghosts = B->nbs;
2090: if (ghosts) *ghosts = baij->garray;
2091: PetscFunctionReturn(PETSC_SUCCESS);
2092: }
2094: static PetscErrorCode MatGetSeqNonzeroStructure_MPIBAIJ(Mat A, Mat *newmat)
2095: {
2096: Mat B;
2097: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2098: Mat_SeqBAIJ *ad = (Mat_SeqBAIJ *)a->A->data, *bd = (Mat_SeqBAIJ *)a->B->data;
2099: Mat_SeqAIJ *b;
2100: PetscMPIInt size, rank, *recvcounts = NULL, *displs = NULL;
2101: PetscInt sendcount, i, *rstarts = A->rmap->range, n, cnt, j, bs = A->rmap->bs;
2102: PetscInt m, *garray = a->garray, *lens, *jsendbuf, *a_jsendbuf, *b_jsendbuf;
2104: PetscFunctionBegin;
2105: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
2106: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)A), &rank));
2108: /* Tell every processor the number of nonzeros per row */
2109: PetscCall(PetscMalloc1(A->rmap->N / bs, &lens));
2110: 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];
2111: PetscCall(PetscMalloc1(2 * size, &recvcounts));
2112: displs = recvcounts + size;
2113: for (i = 0; i < size; i++) {
2114: PetscCall(PetscMPIIntCast(A->rmap->range[i + 1] / bs - A->rmap->range[i] / bs, &recvcounts[i]));
2115: PetscCall(PetscMPIIntCast(A->rmap->range[i] / bs, &displs[i]));
2116: }
2117: PetscCallMPI(MPI_Allgatherv(MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, lens, recvcounts, displs, MPIU_INT, PetscObjectComm((PetscObject)A)));
2118: /* Create the sequential matrix of the same type as the local block diagonal */
2119: PetscCall(MatCreate(PETSC_COMM_SELF, &B));
2120: PetscCall(MatSetSizes(B, A->rmap->N / bs, A->cmap->N / bs, PETSC_DETERMINE, PETSC_DETERMINE));
2121: PetscCall(MatSetType(B, MATSEQAIJ));
2122: PetscCall(MatSeqAIJSetPreallocation(B, 0, lens));
2123: b = (Mat_SeqAIJ *)B->data;
2125: /* Copy my part of matrix column indices over */
2126: sendcount = ad->nz + bd->nz;
2127: jsendbuf = b->j + b->i[rstarts[rank] / bs];
2128: a_jsendbuf = ad->j;
2129: b_jsendbuf = bd->j;
2130: n = A->rmap->rend / bs - A->rmap->rstart / bs;
2131: cnt = 0;
2132: for (i = 0; i < n; i++) {
2133: /* put in lower diagonal portion */
2134: m = bd->i[i + 1] - bd->i[i];
2135: while (m > 0) {
2136: /* is it above diagonal (in bd (compressed) numbering) */
2137: if (garray[*b_jsendbuf] > A->rmap->rstart / bs + i) break;
2138: jsendbuf[cnt++] = garray[*b_jsendbuf++];
2139: m--;
2140: }
2142: /* put in diagonal portion */
2143: for (j = ad->i[i]; j < ad->i[i + 1]; j++) jsendbuf[cnt++] = A->rmap->rstart / bs + *a_jsendbuf++;
2145: /* put in upper diagonal portion */
2146: while (m-- > 0) jsendbuf[cnt++] = garray[*b_jsendbuf++];
2147: }
2148: PetscCheck(cnt == sendcount, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Corrupted PETSc matrix: nz given %" PetscInt_FMT " actual nz %" PetscInt_FMT, sendcount, cnt);
2150: /* Gather all column indices to all processors */
2151: for (i = 0; i < size; i++) {
2152: recvcounts[i] = 0;
2153: for (j = A->rmap->range[i] / bs; j < A->rmap->range[i + 1] / bs; j++) recvcounts[i] += lens[j];
2154: }
2155: displs[0] = 0;
2156: for (i = 1; i < size; i++) displs[i] = displs[i - 1] + recvcounts[i - 1];
2157: PetscCallMPI(MPI_Allgatherv(MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, b->j, recvcounts, displs, MPIU_INT, PetscObjectComm((PetscObject)A)));
2158: /* Assemble the matrix into usable form (note numerical values not yet set) */
2159: /* set the b->ilen (length of each row) values */
2160: PetscCall(PetscArraycpy(b->ilen, lens, A->rmap->N / bs));
2161: /* set the b->i indices */
2162: b->i[0] = 0;
2163: for (i = 1; i <= A->rmap->N / bs; i++) b->i[i] = b->i[i - 1] + lens[i - 1];
2164: PetscCall(PetscFree(lens));
2165: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2166: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2167: PetscCall(PetscFree(recvcounts));
2169: PetscCall(MatPropagateSymmetryOptions(A, B));
2170: *newmat = B;
2171: PetscFunctionReturn(PETSC_SUCCESS);
2172: }
2174: static PetscErrorCode MatSOR_MPIBAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
2175: {
2176: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)matin->data;
2177: Vec bb1 = NULL;
2179: PetscFunctionBegin;
2180: if (flag == SOR_APPLY_UPPER) {
2181: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2182: PetscFunctionReturn(PETSC_SUCCESS);
2183: }
2185: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS) PetscCall(VecDuplicate(bb, &bb1));
2187: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
2188: if (flag & SOR_ZERO_INITIAL_GUESS) {
2189: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2190: its--;
2191: }
2193: while (its--) {
2194: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2195: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2197: /* update rhs: bb1 = bb - B*x */
2198: PetscCall(VecScale(mat->lvec, -1.0));
2199: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2201: /* local sweep */
2202: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
2203: }
2204: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
2205: if (flag & SOR_ZERO_INITIAL_GUESS) {
2206: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2207: its--;
2208: }
2209: while (its--) {
2210: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2211: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2213: /* update rhs: bb1 = bb - B*x */
2214: PetscCall(VecScale(mat->lvec, -1.0));
2215: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2217: /* local sweep */
2218: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
2219: }
2220: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
2221: if (flag & SOR_ZERO_INITIAL_GUESS) {
2222: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2223: its--;
2224: }
2225: while (its--) {
2226: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2227: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2229: /* update rhs: bb1 = bb - B*x */
2230: PetscCall(VecScale(mat->lvec, -1.0));
2231: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2233: /* local sweep */
2234: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
2235: }
2236: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel version of SOR requested not supported");
2238: PetscCall(VecDestroy(&bb1));
2239: PetscFunctionReturn(PETSC_SUCCESS);
2240: }
2242: static PetscErrorCode MatGetColumnReductions_MPIBAIJ(Mat A, PetscInt type, PetscReal *reductions)
2243: {
2244: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)A->data;
2245: PetscInt m, N, i, *garray = aij->garray;
2246: PetscInt ib, jb, bs = A->rmap->bs;
2247: Mat_SeqBAIJ *a_aij = (Mat_SeqBAIJ *)aij->A->data;
2248: MatScalar *a_val = a_aij->a;
2249: Mat_SeqBAIJ *b_aij = (Mat_SeqBAIJ *)aij->B->data;
2250: MatScalar *b_val = b_aij->a;
2251: PetscReal *work;
2253: PetscFunctionBegin;
2254: PetscCall(MatGetSize(A, &m, &N));
2255: PetscCall(PetscCalloc1(N, &work));
2256: if (type == NORM_2) {
2257: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2258: for (jb = 0; jb < bs; jb++) {
2259: for (ib = 0; ib < bs; ib++) {
2260: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val * *a_val);
2261: a_val++;
2262: }
2263: }
2264: }
2265: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2266: for (jb = 0; jb < bs; jb++) {
2267: for (ib = 0; ib < bs; ib++) {
2268: work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val * *b_val);
2269: b_val++;
2270: }
2271: }
2272: }
2273: } else if (type == NORM_1) {
2274: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2275: for (jb = 0; jb < bs; jb++) {
2276: for (ib = 0; ib < bs; ib++) {
2277: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val);
2278: a_val++;
2279: }
2280: }
2281: }
2282: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2283: for (jb = 0; jb < bs; jb++) {
2284: for (ib = 0; ib < bs; ib++) {
2285: work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val);
2286: b_val++;
2287: }
2288: }
2289: }
2290: } else if (type == NORM_INFINITY) {
2291: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2292: for (jb = 0; jb < bs; jb++) {
2293: for (ib = 0; ib < bs; ib++) {
2294: PetscInt col = A->cmap->rstart + a_aij->j[i] * bs + jb;
2295: work[col] = PetscMax(PetscAbsScalar(*a_val), work[col]);
2296: a_val++;
2297: }
2298: }
2299: }
2300: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2301: for (jb = 0; jb < bs; jb++) {
2302: for (ib = 0; ib < bs; ib++) {
2303: PetscInt col = garray[b_aij->j[i]] * bs + jb;
2304: work[col] = PetscMax(PetscAbsScalar(*b_val), work[col]);
2305: b_val++;
2306: }
2307: }
2308: }
2309: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
2310: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2311: for (jb = 0; jb < bs; jb++) {
2312: for (ib = 0; ib < bs; ib++) {
2313: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscRealPart(*a_val);
2314: a_val++;
2315: }
2316: }
2317: }
2318: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2319: for (jb = 0; jb < bs; jb++) {
2320: for (ib = 0; ib < bs; ib++) {
2321: work[garray[b_aij->j[i]] * bs + jb] += PetscRealPart(*b_val);
2322: b_val++;
2323: }
2324: }
2325: }
2326: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
2327: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2328: for (jb = 0; jb < bs; jb++) {
2329: for (ib = 0; ib < bs; ib++) {
2330: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscImaginaryPart(*a_val);
2331: a_val++;
2332: }
2333: }
2334: }
2335: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2336: for (jb = 0; jb < bs; jb++) {
2337: for (ib = 0; ib < bs; ib++) {
2338: work[garray[b_aij->j[i]] * bs + jb] += PetscImaginaryPart(*b_val);
2339: b_val++;
2340: }
2341: }
2342: }
2343: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
2344: if (type == NORM_INFINITY) {
2345: PetscCallMPI(MPIU_Allreduce(work, reductions, N, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
2346: } else {
2347: PetscCallMPI(MPIU_Allreduce(work, reductions, N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
2348: }
2349: PetscCall(PetscFree(work));
2350: if (type == NORM_2) {
2351: for (i = 0; i < N; i++) reductions[i] = PetscSqrtReal(reductions[i]);
2352: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
2353: for (i = 0; i < N; i++) reductions[i] /= m;
2354: }
2355: PetscFunctionReturn(PETSC_SUCCESS);
2356: }
2358: static PetscErrorCode MatInvertBlockDiagonal_MPIBAIJ(Mat A, const PetscScalar **values)
2359: {
2360: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2362: PetscFunctionBegin;
2363: PetscCall(MatInvertBlockDiagonal(a->A, values));
2364: A->factorerrortype = a->A->factorerrortype;
2365: A->factorerror_zeropivot_value = a->A->factorerror_zeropivot_value;
2366: A->factorerror_zeropivot_row = a->A->factorerror_zeropivot_row;
2367: PetscFunctionReturn(PETSC_SUCCESS);
2368: }
2370: static PetscErrorCode MatShift_MPIBAIJ(Mat Y, PetscScalar a)
2371: {
2372: Mat_MPIBAIJ *maij = (Mat_MPIBAIJ *)Y->data;
2373: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)maij->A->data;
2375: PetscFunctionBegin;
2376: if (!Y->preallocated) {
2377: PetscCall(MatMPIBAIJSetPreallocation(Y, Y->rmap->bs, 1, NULL, 0, NULL));
2378: } else if (!aij->nz) {
2379: PetscInt nonew = aij->nonew;
2380: PetscCall(MatSeqBAIJSetPreallocation(maij->A, Y->rmap->bs, 1, NULL));
2381: aij->nonew = nonew;
2382: }
2383: PetscCall(MatShift_Basic(Y, a));
2384: PetscFunctionReturn(PETSC_SUCCESS);
2385: }
2387: static PetscErrorCode MatGetDiagonalBlock_MPIBAIJ(Mat A, Mat *a)
2388: {
2389: PetscFunctionBegin;
2390: *a = ((Mat_MPIBAIJ *)A->data)->A;
2391: PetscFunctionReturn(PETSC_SUCCESS);
2392: }
2394: static PetscErrorCode MatEliminateZeros_MPIBAIJ(Mat A, PetscBool keep)
2395: {
2396: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2398: PetscFunctionBegin;
2399: PetscCall(MatEliminateZeros_SeqBAIJ(a->A, keep)); // possibly keep zero diagonal coefficients
2400: PetscCall(MatEliminateZeros_SeqBAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2401: PetscFunctionReturn(PETSC_SUCCESS);
2402: }
2404: static struct _MatOps MatOps_Values = {MatSetValues_MPIBAIJ,
2405: MatGetRow_MPIBAIJ,
2406: MatRestoreRow_MPIBAIJ,
2407: MatMult_MPIBAIJ,
2408: /* 4*/ MatMultAdd_MPIBAIJ,
2409: MatMultTranspose_MPIBAIJ,
2410: MatMultTransposeAdd_MPIBAIJ,
2411: NULL,
2412: NULL,
2413: NULL,
2414: /*10*/ NULL,
2415: NULL,
2416: NULL,
2417: MatSOR_MPIBAIJ,
2418: MatTranspose_MPIBAIJ,
2419: /*15*/ MatGetInfo_MPIBAIJ,
2420: MatEqual_MPIBAIJ,
2421: MatGetDiagonal_MPIBAIJ,
2422: MatDiagonalScale_MPIBAIJ,
2423: MatNorm_MPIBAIJ,
2424: /*20*/ MatAssemblyBegin_MPIBAIJ,
2425: MatAssemblyEnd_MPIBAIJ,
2426: MatSetOption_MPIBAIJ,
2427: MatZeroEntries_MPIBAIJ,
2428: /*24*/ MatZeroRows_MPIBAIJ,
2429: NULL,
2430: NULL,
2431: NULL,
2432: NULL,
2433: /*29*/ MatSetUp_MPI_Hash,
2434: NULL,
2435: NULL,
2436: MatGetDiagonalBlock_MPIBAIJ,
2437: NULL,
2438: /*34*/ MatDuplicate_MPIBAIJ,
2439: NULL,
2440: NULL,
2441: NULL,
2442: NULL,
2443: /*39*/ MatAXPY_MPIBAIJ,
2444: MatCreateSubMatrices_MPIBAIJ,
2445: MatIncreaseOverlap_MPIBAIJ,
2446: MatGetValues_MPIBAIJ,
2447: MatCopy_MPIBAIJ,
2448: /*44*/ NULL,
2449: MatScale_MPIBAIJ,
2450: MatShift_MPIBAIJ,
2451: NULL,
2452: MatZeroRowsColumns_MPIBAIJ,
2453: /*49*/ NULL,
2454: NULL,
2455: NULL,
2456: NULL,
2457: NULL,
2458: /*54*/ MatFDColoringCreate_MPIXAIJ,
2459: NULL,
2460: MatSetUnfactored_MPIBAIJ,
2461: MatPermute_MPIBAIJ,
2462: MatSetValuesBlocked_MPIBAIJ,
2463: /*59*/ MatCreateSubMatrix_MPIBAIJ,
2464: MatDestroy_MPIBAIJ,
2465: MatView_MPIBAIJ,
2466: NULL,
2467: NULL,
2468: /*64*/ NULL,
2469: NULL,
2470: NULL,
2471: NULL,
2472: MatGetRowMaxAbs_MPIBAIJ,
2473: /*69*/ NULL,
2474: NULL,
2475: NULL,
2476: MatFDColoringApply_BAIJ,
2477: NULL,
2478: /*74*/ NULL,
2479: NULL,
2480: NULL,
2481: NULL,
2482: MatLoad_MPIBAIJ,
2483: /*79*/ NULL,
2484: NULL,
2485: NULL,
2486: NULL,
2487: NULL,
2488: /*84*/ NULL,
2489: NULL,
2490: NULL,
2491: NULL,
2492: NULL,
2493: /*89*/ NULL,
2494: NULL,
2495: NULL,
2496: NULL,
2497: MatConjugate_MPIBAIJ,
2498: /*94*/ NULL,
2499: NULL,
2500: MatRealPart_MPIBAIJ,
2501: MatImaginaryPart_MPIBAIJ,
2502: NULL,
2503: /*99*/ NULL,
2504: NULL,
2505: NULL,
2506: NULL,
2507: NULL,
2508: /*104*/ MatGetSeqNonzeroStructure_MPIBAIJ,
2509: NULL,
2510: MatGetGhosts_MPIBAIJ,
2511: NULL,
2512: NULL,
2513: /*109*/ NULL,
2514: NULL,
2515: NULL,
2516: NULL,
2517: MatGetMultiProcBlock_MPIBAIJ,
2518: /*114*/ NULL,
2519: MatGetColumnReductions_MPIBAIJ,
2520: MatInvertBlockDiagonal_MPIBAIJ,
2521: NULL,
2522: NULL,
2523: /*119*/ NULL,
2524: NULL,
2525: NULL,
2526: NULL,
2527: NULL,
2528: /*124*/ NULL,
2529: NULL,
2530: MatSetBlockSizes_Default,
2531: NULL,
2532: MatFDColoringSetUp_MPIXAIJ,
2533: /*129*/ NULL,
2534: MatCreateMPIMatConcatenateSeqMat_MPIBAIJ,
2535: NULL,
2536: NULL,
2537: NULL,
2538: /*134*/ NULL,
2539: NULL,
2540: MatEliminateZeros_MPIBAIJ,
2541: MatGetRowSumAbs_MPIBAIJ,
2542: NULL,
2543: /*139*/ NULL,
2544: NULL,
2545: MatCopyHashToXAIJ_MPI_Hash,
2546: NULL,
2547: NULL};
2549: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
2550: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
2552: static PetscErrorCode MatMPIBAIJSetPreallocationCSR_MPIBAIJ(Mat B, PetscInt bs, const PetscInt ii[], const PetscInt jj[], const PetscScalar V[])
2553: {
2554: PetscInt m, rstart, cstart, cend;
2555: PetscInt i, j, dlen, olen, nz, nz_max = 0, *d_nnz = NULL, *o_nnz = NULL;
2556: const PetscInt *JJ = NULL;
2557: PetscScalar *values = NULL;
2558: PetscBool roworiented = ((Mat_MPIBAIJ *)B->data)->roworiented;
2559: PetscBool nooffprocentries;
2561: PetscFunctionBegin;
2562: PetscCall(PetscLayoutSetBlockSize(B->rmap, bs));
2563: PetscCall(PetscLayoutSetBlockSize(B->cmap, bs));
2564: PetscCall(PetscLayoutSetUp(B->rmap));
2565: PetscCall(PetscLayoutSetUp(B->cmap));
2566: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
2567: m = B->rmap->n / bs;
2568: rstart = B->rmap->rstart / bs;
2569: cstart = B->cmap->rstart / bs;
2570: cend = B->cmap->rend / bs;
2572: PetscCheck(!ii[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "ii[0] must be 0 but it is %" PetscInt_FMT, ii[0]);
2573: PetscCall(PetscMalloc2(m, &d_nnz, m, &o_nnz));
2574: for (i = 0; i < m; i++) {
2575: nz = ii[i + 1] - ii[i];
2576: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
2577: nz_max = PetscMax(nz_max, nz);
2578: dlen = 0;
2579: olen = 0;
2580: JJ = jj + ii[i];
2581: for (j = 0; j < nz; j++) {
2582: if (*JJ < cstart || *JJ >= cend) olen++;
2583: else dlen++;
2584: JJ++;
2585: }
2586: d_nnz[i] = dlen;
2587: o_nnz[i] = olen;
2588: }
2589: PetscCall(MatMPIBAIJSetPreallocation(B, bs, 0, d_nnz, 0, o_nnz));
2590: PetscCall(PetscFree2(d_nnz, o_nnz));
2592: values = (PetscScalar *)V;
2593: if (!values) PetscCall(PetscCalloc1(bs * bs * nz_max, &values));
2594: for (i = 0; i < m; i++) {
2595: PetscInt row = i + rstart;
2596: PetscInt ncols = ii[i + 1] - ii[i];
2597: const PetscInt *icols = jj + ii[i];
2598: if (bs == 1 || !roworiented) { /* block ordering matches the non-nested layout of MatSetValues so we can insert entire rows */
2599: const PetscScalar *svals = values + (V ? (bs * bs * ii[i]) : 0);
2600: PetscCall(MatSetValuesBlocked_MPIBAIJ(B, 1, &row, ncols, icols, svals, INSERT_VALUES));
2601: } else { /* block ordering does not match so we can only insert one block at a time. */
2602: PetscInt j;
2603: for (j = 0; j < ncols; j++) {
2604: const PetscScalar *svals = values + (V ? (bs * bs * (ii[i] + j)) : 0);
2605: PetscCall(MatSetValuesBlocked_MPIBAIJ(B, 1, &row, 1, &icols[j], svals, INSERT_VALUES));
2606: }
2607: }
2608: }
2610: if (!V) PetscCall(PetscFree(values));
2611: nooffprocentries = B->nooffprocentries;
2612: B->nooffprocentries = PETSC_TRUE;
2613: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2614: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2615: B->nooffprocentries = nooffprocentries;
2617: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
2618: PetscFunctionReturn(PETSC_SUCCESS);
2619: }
2621: /*@C
2622: MatMPIBAIJSetPreallocationCSR - Creates a sparse parallel matrix in `MATBAIJ` format using the given nonzero structure and (optional) numerical values
2624: Collective
2626: Input Parameters:
2627: + B - the matrix
2628: . bs - the block size
2629: . i - the indices into `j` for the start of each local row (starts with zero)
2630: . j - the column indices for each local row (starts with zero) these must be sorted for each row
2631: - v - optional values in the matrix, use `NULL` if not provided
2633: Level: advanced
2635: Notes:
2636: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
2637: thus you CANNOT change the matrix entries by changing the values of `v` after you have
2638: called this routine.
2640: The order of the entries in values is specified by the `MatOption` `MAT_ROW_ORIENTED`. For example, C programs
2641: 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
2642: over rows within a block and the last index is over columns within a block row. Fortran programs will likely set
2643: `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
2644: block column and the second index is over columns within a block.
2646: 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
2648: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIBAIJSetPreallocation()`, `MatCreateAIJ()`, `MATMPIAIJ`, `MatCreateMPIBAIJWithArrays()`, `MATMPIBAIJ`
2649: @*/
2650: PetscErrorCode MatMPIBAIJSetPreallocationCSR(Mat B, PetscInt bs, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
2651: {
2652: PetscFunctionBegin;
2656: PetscTryMethod(B, "MatMPIBAIJSetPreallocationCSR_C", (Mat, PetscInt, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, bs, i, j, v));
2657: PetscFunctionReturn(PETSC_SUCCESS);
2658: }
2660: PetscErrorCode MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B, PetscInt bs, PetscInt d_nz, const PetscInt *d_nnz, PetscInt o_nz, const PetscInt *o_nnz)
2661: {
2662: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
2663: PetscInt i;
2664: PetscMPIInt size;
2666: PetscFunctionBegin;
2667: if (B->hash_active) {
2668: B->ops[0] = b->cops;
2669: B->hash_active = PETSC_FALSE;
2670: }
2671: if (!B->preallocated) PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), bs, &B->bstash));
2672: PetscCall(MatSetBlockSize(B, bs));
2673: PetscCall(PetscLayoutSetUp(B->rmap));
2674: PetscCall(PetscLayoutSetUp(B->cmap));
2675: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
2677: if (d_nnz) {
2678: 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]);
2679: }
2680: if (o_nnz) {
2681: 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]);
2682: }
2684: b->bs2 = bs * bs;
2685: b->mbs = B->rmap->n / bs;
2686: b->nbs = B->cmap->n / bs;
2687: b->Mbs = B->rmap->N / bs;
2688: b->Nbs = B->cmap->N / bs;
2690: for (i = 0; i <= b->size; i++) b->rangebs[i] = B->rmap->range[i] / bs;
2691: b->rstartbs = B->rmap->rstart / bs;
2692: b->rendbs = B->rmap->rend / bs;
2693: b->cstartbs = B->cmap->rstart / bs;
2694: b->cendbs = B->cmap->rend / bs;
2696: #if defined(PETSC_USE_CTABLE)
2697: PetscCall(PetscHMapIDestroy(&b->colmap));
2698: #else
2699: PetscCall(PetscFree(b->colmap));
2700: #endif
2701: PetscCall(PetscFree(b->garray));
2702: PetscCall(VecDestroy(&b->lvec));
2703: PetscCall(VecScatterDestroy(&b->Mvctx));
2705: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2707: MatSeqXAIJGetOptions_Private(b->B);
2708: PetscCall(MatDestroy(&b->B));
2709: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2710: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2711: PetscCall(MatSetType(b->B, MATSEQBAIJ));
2712: MatSeqXAIJRestoreOptions_Private(b->B);
2714: MatSeqXAIJGetOptions_Private(b->A);
2715: PetscCall(MatDestroy(&b->A));
2716: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2717: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2718: PetscCall(MatSetType(b->A, MATSEQBAIJ));
2719: MatSeqXAIJRestoreOptions_Private(b->A);
2721: PetscCall(MatSeqBAIJSetPreallocation(b->A, bs, d_nz, d_nnz));
2722: PetscCall(MatSeqBAIJSetPreallocation(b->B, bs, o_nz, o_nnz));
2723: B->preallocated = PETSC_TRUE;
2724: B->was_assembled = PETSC_FALSE;
2725: B->assembled = PETSC_FALSE;
2726: PetscFunctionReturn(PETSC_SUCCESS);
2727: }
2729: extern PetscErrorCode MatDiagonalScaleLocal_MPIBAIJ(Mat, Vec);
2730: extern PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat, PetscReal);
2732: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAdj(Mat B, MatType newtype, MatReuse reuse, Mat *adj)
2733: {
2734: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
2735: Mat_SeqBAIJ *d = (Mat_SeqBAIJ *)b->A->data, *o = (Mat_SeqBAIJ *)b->B->data;
2736: PetscInt M = B->rmap->n / B->rmap->bs, i, *ii, *jj, cnt, j, k, rstart = B->rmap->rstart / B->rmap->bs;
2737: const PetscInt *id = d->i, *jd = d->j, *io = o->i, *jo = o->j, *garray = b->garray;
2739: PetscFunctionBegin;
2740: PetscCall(PetscMalloc1(M + 1, &ii));
2741: ii[0] = 0;
2742: for (i = 0; i < M; i++) {
2743: 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]);
2744: 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]);
2745: ii[i + 1] = ii[i] + id[i + 1] - id[i] + io[i + 1] - io[i];
2746: /* remove one from count of matrix has diagonal */
2747: for (j = id[i]; j < id[i + 1]; j++) {
2748: if (jd[j] == i) {
2749: ii[i + 1]--;
2750: break;
2751: }
2752: }
2753: }
2754: PetscCall(PetscMalloc1(ii[M], &jj));
2755: cnt = 0;
2756: for (i = 0; i < M; i++) {
2757: for (j = io[i]; j < io[i + 1]; j++) {
2758: if (garray[jo[j]] > rstart) break;
2759: jj[cnt++] = garray[jo[j]];
2760: }
2761: for (k = id[i]; k < id[i + 1]; k++) {
2762: if (jd[k] != i) jj[cnt++] = rstart + jd[k];
2763: }
2764: for (; j < io[i + 1]; j++) jj[cnt++] = garray[jo[j]];
2765: }
2766: PetscCall(MatCreateMPIAdj(PetscObjectComm((PetscObject)B), M, B->cmap->N / B->rmap->bs, ii, jj, NULL, adj));
2767: PetscFunctionReturn(PETSC_SUCCESS);
2768: }
2770: #include <../src/mat/impls/aij/mpi/mpiaij.h>
2772: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat, MatType, MatReuse, Mat *);
2774: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAIJ(Mat A, MatType newtype, MatReuse reuse, Mat *newmat)
2775: {
2776: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2777: Mat_MPIAIJ *b;
2778: Mat B;
2780: PetscFunctionBegin;
2781: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Matrix must be assembled");
2783: if (reuse == MAT_REUSE_MATRIX) {
2784: B = *newmat;
2785: } else {
2786: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
2787: PetscCall(MatSetType(B, MATMPIAIJ));
2788: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
2789: PetscCall(MatSetBlockSizes(B, A->rmap->bs, A->cmap->bs));
2790: PetscCall(MatSeqAIJSetPreallocation(B, 0, NULL));
2791: PetscCall(MatMPIAIJSetPreallocation(B, 0, NULL, 0, NULL));
2792: }
2793: b = (Mat_MPIAIJ *)B->data;
2795: if (reuse == MAT_REUSE_MATRIX) {
2796: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_REUSE_MATRIX, &b->A));
2797: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_REUSE_MATRIX, &b->B));
2798: } else {
2799: PetscInt *garray = a->garray;
2800: Mat_SeqAIJ *bB;
2801: PetscInt bs, nnz;
2802: PetscCall(MatDestroy(&b->A));
2803: PetscCall(MatDestroy(&b->B));
2804: /* just clear out the data structure */
2805: PetscCall(MatDisAssemble_MPIAIJ(B, PETSC_FALSE));
2806: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->A));
2807: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->B));
2809: /* Global numbering for b->B columns */
2810: bB = (Mat_SeqAIJ *)b->B->data;
2811: bs = A->rmap->bs;
2812: nnz = bB->i[A->rmap->n];
2813: for (PetscInt k = 0; k < nnz; k++) {
2814: PetscInt bj = bB->j[k] / bs;
2815: PetscInt br = bB->j[k] % bs;
2816: bB->j[k] = garray[bj] * bs + br;
2817: }
2818: }
2819: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2820: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2822: if (reuse == MAT_INPLACE_MATRIX) {
2823: PetscCall(MatHeaderReplace(A, &B));
2824: } else {
2825: *newmat = B;
2826: }
2827: PetscFunctionReturn(PETSC_SUCCESS);
2828: }
2830: /*MC
2831: MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.
2833: Options Database Keys:
2834: + -mat_type mpibaij - sets the matrix type to `MATMPIBAIJ` during a call to `MatSetFromOptions()`
2835: . -mat_block_size <bs> - set the blocksize used to store the matrix
2836: . -mat_baij_mult_version version - indicate the version of the matrix-vector product to use (0 often indicates using BLAS)
2837: - -mat_use_hash_table <fact> - set hash table factor
2839: Level: beginner
2841: Note:
2842: `MatSetOption(A, MAT_STRUCTURE_ONLY, PETSC_TRUE)` may be called for this matrix type. In this no
2843: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
2845: .seealso: `Mat`, `MATBAIJ`, `MATSEQBAIJ`, `MatCreateBAIJ`
2846: M*/
2848: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIBSTRM(Mat, MatType, MatReuse, Mat *);
2850: PETSC_EXTERN PetscErrorCode MatCreate_MPIBAIJ(Mat B)
2851: {
2852: Mat_MPIBAIJ *b;
2853: PetscBool flg = PETSC_FALSE;
2855: PetscFunctionBegin;
2856: PetscCall(PetscNew(&b));
2857: B->data = (void *)b;
2858: B->ops[0] = MatOps_Values;
2859: B->assembled = PETSC_FALSE;
2861: B->insertmode = NOT_SET_VALUES;
2862: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
2863: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &b->size));
2865: /* build local table of row and column ownerships */
2866: PetscCall(PetscMalloc1(b->size + 1, &b->rangebs));
2868: /* build cache for off array entries formed */
2869: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
2871: b->donotstash = PETSC_FALSE;
2872: b->colmap = NULL;
2873: b->garray = NULL;
2874: b->roworiented = PETSC_TRUE;
2876: /* stuff used in block assembly */
2877: b->barray = NULL;
2879: /* stuff used for matrix vector multiply */
2880: b->lvec = NULL;
2881: b->Mvctx = NULL;
2883: /* stuff for MatGetRow() */
2884: b->rowindices = NULL;
2885: b->rowvalues = NULL;
2886: b->getrowactive = PETSC_FALSE;
2888: /* hash table stuff */
2889: b->ht = NULL;
2890: b->hd = NULL;
2891: b->ht_size = 0;
2892: b->ht_flag = PETSC_FALSE;
2893: b->ht_fact = 0;
2894: b->ht_total_ct = 0;
2895: b->ht_insert_ct = 0;
2897: /* stuff for MatCreateSubMatrices_MPIBAIJ_local() */
2898: b->ijonly = PETSC_FALSE;
2900: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpiadj_C", MatConvert_MPIBAIJ_MPIAdj));
2901: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpiaij_C", MatConvert_MPIBAIJ_MPIAIJ));
2902: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpisbaij_C", MatConvert_MPIBAIJ_MPISBAIJ));
2903: #if defined(PETSC_HAVE_HYPRE)
2904: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_hypre_C", MatConvert_AIJ_HYPRE));
2905: #endif
2906: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIBAIJ));
2907: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIBAIJ));
2908: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIBAIJSetPreallocation_C", MatMPIBAIJSetPreallocation_MPIBAIJ));
2909: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIBAIJSetPreallocationCSR_C", MatMPIBAIJSetPreallocationCSR_MPIBAIJ));
2910: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIBAIJ));
2911: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetHashTableFactor_C", MatSetHashTableFactor_MPIBAIJ));
2912: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_is_C", MatConvert_XAIJ_IS));
2913: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIBAIJ));
2915: PetscOptionsBegin(PetscObjectComm((PetscObject)B), NULL, "Options for loading MPIBAIJ matrix 1", "Mat");
2916: PetscCall(PetscOptionsName("-mat_use_hash_table", "Use hash table to save time in constructing matrix", "MatSetOption", &flg));
2917: if (flg) {
2918: PetscReal fact = 1.39;
2919: PetscCall(MatSetOption(B, MAT_USE_HASH_TABLE, PETSC_TRUE));
2920: PetscCall(PetscOptionsReal("-mat_use_hash_table", "Use hash table factor", "MatMPIBAIJSetHashTableFactor", fact, &fact, NULL));
2921: if (fact <= 1.0) fact = 1.39;
2922: PetscCall(MatMPIBAIJSetHashTableFactor(B, fact));
2923: PetscCall(PetscInfo(B, "Hash table Factor used %5.2g\n", (double)fact));
2924: }
2925: PetscOptionsEnd();
2926: PetscFunctionReturn(PETSC_SUCCESS);
2927: }
2929: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
2930: /*MC
2931: MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.
2933: This matrix type is identical to `MATSEQBAIJ` when constructed with a single process communicator,
2934: and `MATMPIBAIJ` otherwise.
2936: Options Database Keys:
2937: . -mat_type baij - sets the matrix type to `MATBAIJ` during a call to `MatSetFromOptions()`
2939: Level: beginner
2941: .seealso: `Mat`, `MatCreateBAIJ()`, `MATSEQBAIJ`, `MATMPIBAIJ`, `MatMPIBAIJSetPreallocation()`, `MatMPIBAIJSetPreallocationCSR()`
2942: M*/
2944: /*@
2945: MatMPIBAIJSetPreallocation - Allocates memory for a sparse parallel matrix in `MATMPIBAIJ` format
2946: (block compressed row).
2948: Collective
2950: Input Parameters:
2951: + B - the matrix
2952: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
2953: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
2954: . d_nz - number of block nonzeros per block row in diagonal portion of local
2955: submatrix (same for all local rows)
2956: . d_nnz - array containing the number of block nonzeros in the various block rows
2957: of the in diagonal portion of the local (possibly different for each block
2958: row) or `NULL`. If you plan to factor the matrix you must leave room for the diagonal entry and
2959: set it even if it is zero.
2960: . o_nz - number of block nonzeros per block row in the off-diagonal portion of local
2961: submatrix (same for all local rows).
2962: - o_nnz - array containing the number of nonzeros in the various block rows of the
2963: off-diagonal portion of the local submatrix (possibly different for
2964: each block row) or `NULL`.
2966: If the *_nnz parameter is given then the *_nz parameter is ignored
2968: Options Database Keys:
2969: + -mat_block_size - size of the blocks to use
2970: - -mat_use_hash_table <fact> - set hash table factor
2972: Level: intermediate
2974: Notes:
2975: For good matrix assembly performance
2976: the user should preallocate the matrix storage by setting the parameters
2977: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`). By setting these parameters accurately,
2978: performance can be increased by more than a factor of 50.
2980: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one processor
2981: than it must be used on all processors that share the object for that argument.
2983: Storage Information:
2984: For a square global matrix we define each processor's diagonal portion
2985: to be its local rows and the corresponding columns (a square submatrix);
2986: each processor's off-diagonal portion encompasses the remainder of the
2987: local matrix (a rectangular submatrix).
2989: The user can specify preallocated storage for the diagonal part of
2990: the local submatrix with either `d_nz` or `d_nnz` (not both). Set
2991: `d_nz` = `PETSC_DEFAULT` and `d_nnz` = `NULL` for PETSc to control dynamic
2992: memory allocation. Likewise, specify preallocated storage for the
2993: off-diagonal part of the local submatrix with `o_nz` or `o_nnz` (not both).
2995: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2996: the figure below we depict these three local rows and all columns (0-11).
2998: .vb
2999: 0 1 2 3 4 5 6 7 8 9 10 11
3000: --------------------------
3001: row 3 |o o o d d d o o o o o o
3002: row 4 |o o o d d d o o o o o o
3003: row 5 |o o o d d d o o o o o o
3004: --------------------------
3005: .ve
3007: Thus, any entries in the d locations are stored in the d (diagonal)
3008: submatrix, and any entries in the o locations are stored in the
3009: o (off-diagonal) submatrix. Note that the d and the o submatrices are
3010: stored simply in the `MATSEQBAIJ` format for compressed row storage.
3012: Now `d_nz` should indicate the number of block nonzeros per row in the d matrix,
3013: and `o_nz` should indicate the number of block nonzeros per row in the o matrix.
3014: In general, for PDE problems in which most nonzeros are near the diagonal,
3015: one expects `d_nz` >> `o_nz`.
3017: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3018: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3019: You can also run with the option `-info` and look for messages with the string
3020: malloc in them to see if additional memory allocation was needed.
3022: .seealso: `Mat`, `MATMPIBAIJ`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`, `MatMPIBAIJSetPreallocationCSR()`, `PetscSplitOwnership()`
3023: @*/
3024: PetscErrorCode MatMPIBAIJSetPreallocation(Mat B, PetscInt bs, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
3025: {
3026: PetscFunctionBegin;
3030: PetscTryMethod(B, "MatMPIBAIJSetPreallocation_C", (Mat, PetscInt, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, bs, d_nz, d_nnz, o_nz, o_nnz));
3031: PetscFunctionReturn(PETSC_SUCCESS);
3032: }
3034: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
3035: /*@
3036: MatCreateBAIJ - Creates a sparse parallel matrix in `MATBAIJ` format
3037: (block compressed row).
3039: Collective
3041: Input Parameters:
3042: + comm - MPI communicator
3043: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
3044: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
3045: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
3046: This value should be the same as the local size used in creating the
3047: y vector for the matrix-vector product y = Ax.
3048: . n - number of local columns (or `PETSC_DECIDE` to have calculated if N is given)
3049: This value should be the same as the local size used in creating the
3050: x vector for the matrix-vector product y = Ax.
3051: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
3052: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
3053: . d_nz - number of nonzero blocks per block row in diagonal portion of local
3054: submatrix (same for all local rows)
3055: . d_nnz - array containing the number of nonzero blocks in the various block rows
3056: of the in diagonal portion of the local (possibly different for each block
3057: row) or NULL. If you plan to factor the matrix you must leave room for the diagonal entry
3058: and set it even if it is zero.
3059: . o_nz - number of nonzero blocks per block row in the off-diagonal portion of local
3060: submatrix (same for all local rows).
3061: - o_nnz - array containing the number of nonzero blocks in the various block rows of the
3062: off-diagonal portion of the local submatrix (possibly different for
3063: each block row) or NULL.
3065: Output Parameter:
3066: . A - the matrix
3068: Options Database Keys:
3069: + -mat_block_size - size of the blocks to use
3070: - -mat_use_hash_table <fact> - set hash table factor
3072: Level: intermediate
3074: Notes:
3075: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
3076: MatXXXXSetPreallocation() paradigm instead of this routine directly.
3077: [MatXXXXSetPreallocation() is, for example, `MatSeqBAIJSetPreallocation()`]
3079: For good matrix assembly performance
3080: the user should preallocate the matrix storage by setting the parameters
3081: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`). By setting these parameters accurately,
3082: performance can be increased by more than a factor of 50.
3084: If the *_nnz parameter is given then the *_nz parameter is ignored
3086: A nonzero block is any block that as 1 or more nonzeros in it
3088: The user MUST specify either the local or global matrix dimensions
3089: (possibly both).
3091: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one processor
3092: than it must be used on all processors that share the object for that argument.
3094: If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by
3095: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`.
3097: Storage Information:
3098: For a square global matrix we define each processor's diagonal portion
3099: to be its local rows and the corresponding columns (a square submatrix);
3100: each processor's off-diagonal portion encompasses the remainder of the
3101: local matrix (a rectangular submatrix).
3103: The user can specify preallocated storage for the diagonal part of
3104: the local submatrix with either d_nz or d_nnz (not both). Set
3105: `d_nz` = `PETSC_DEFAULT` and `d_nnz` = `NULL` for PETSc to control dynamic
3106: memory allocation. Likewise, specify preallocated storage for the
3107: off-diagonal part of the local submatrix with `o_nz` or `o_nnz` (not both).
3109: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
3110: the figure below we depict these three local rows and all columns (0-11).
3112: .vb
3113: 0 1 2 3 4 5 6 7 8 9 10 11
3114: --------------------------
3115: row 3 |o o o d d d o o o o o o
3116: row 4 |o o o d d d o o o o o o
3117: row 5 |o o o d d d o o o o o o
3118: --------------------------
3119: .ve
3121: Thus, any entries in the d locations are stored in the d (diagonal)
3122: submatrix, and any entries in the o locations are stored in the
3123: o (off-diagonal) submatrix. Note that the d and the o submatrices are
3124: stored simply in the `MATSEQBAIJ` format for compressed row storage.
3126: Now `d_nz` should indicate the number of block nonzeros per row in the d matrix,
3127: and `o_nz` should indicate the number of block nonzeros per row in the o matrix.
3128: In general, for PDE problems in which most nonzeros are near the diagonal,
3129: one expects `d_nz` >> `o_nz`.
3131: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatMPIBAIJSetPreallocation()`, `MatMPIBAIJSetPreallocationCSR()`,
3132: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`
3133: @*/
3134: 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)
3135: {
3136: PetscMPIInt size;
3138: PetscFunctionBegin;
3139: PetscCall(MatCreate(comm, A));
3140: PetscCall(MatSetSizes(*A, m, n, M, N));
3141: PetscCallMPI(MPI_Comm_size(comm, &size));
3142: if (size > 1) {
3143: PetscCall(MatSetType(*A, MATMPIBAIJ));
3144: PetscCall(MatMPIBAIJSetPreallocation(*A, bs, d_nz, d_nnz, o_nz, o_nnz));
3145: } else {
3146: PetscCall(MatSetType(*A, MATSEQBAIJ));
3147: PetscCall(MatSeqBAIJSetPreallocation(*A, bs, d_nz, d_nnz));
3148: }
3149: PetscFunctionReturn(PETSC_SUCCESS);
3150: }
3152: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
3153: {
3154: Mat mat;
3155: Mat_MPIBAIJ *a, *oldmat = (Mat_MPIBAIJ *)matin->data;
3156: PetscInt len = 0;
3158: PetscFunctionBegin;
3159: *newmat = NULL;
3160: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
3161: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
3162: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
3164: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
3165: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
3166: if (matin->hash_active) {
3167: PetscCall(MatSetUp(mat));
3168: } else {
3169: mat->factortype = matin->factortype;
3170: mat->preallocated = PETSC_TRUE;
3171: mat->assembled = PETSC_TRUE;
3172: mat->insertmode = NOT_SET_VALUES;
3174: a = (Mat_MPIBAIJ *)mat->data;
3175: mat->rmap->bs = matin->rmap->bs;
3176: a->bs2 = oldmat->bs2;
3177: a->mbs = oldmat->mbs;
3178: a->nbs = oldmat->nbs;
3179: a->Mbs = oldmat->Mbs;
3180: a->Nbs = oldmat->Nbs;
3182: a->size = oldmat->size;
3183: a->rank = oldmat->rank;
3184: a->donotstash = oldmat->donotstash;
3185: a->roworiented = oldmat->roworiented;
3186: a->rowindices = NULL;
3187: a->rowvalues = NULL;
3188: a->getrowactive = PETSC_FALSE;
3189: a->barray = NULL;
3190: a->rstartbs = oldmat->rstartbs;
3191: a->rendbs = oldmat->rendbs;
3192: a->cstartbs = oldmat->cstartbs;
3193: a->cendbs = oldmat->cendbs;
3195: /* hash table stuff */
3196: a->ht = NULL;
3197: a->hd = NULL;
3198: a->ht_size = 0;
3199: a->ht_flag = oldmat->ht_flag;
3200: a->ht_fact = oldmat->ht_fact;
3201: a->ht_total_ct = 0;
3202: a->ht_insert_ct = 0;
3204: PetscCall(PetscArraycpy(a->rangebs, oldmat->rangebs, a->size + 1));
3205: if (oldmat->colmap) {
3206: #if defined(PETSC_USE_CTABLE)
3207: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3208: #else
3209: PetscCall(PetscMalloc1(a->Nbs, &a->colmap));
3210: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, a->Nbs));
3211: #endif
3212: } else a->colmap = NULL;
3214: if (oldmat->garray && (len = ((Mat_SeqBAIJ *)oldmat->B->data)->nbs)) {
3215: PetscCall(PetscMalloc1(len, &a->garray));
3216: PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3217: } else a->garray = NULL;
3219: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)matin), matin->rmap->bs, &mat->bstash));
3220: PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3221: PetscCall(VecScatterCopy(oldmat->Mvctx, &a->Mvctx));
3223: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3224: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3225: }
3226: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3227: *newmat = mat;
3228: PetscFunctionReturn(PETSC_SUCCESS);
3229: }
3231: /* Used for both MPIBAIJ and MPISBAIJ matrices */
3232: PetscErrorCode MatLoad_MPIBAIJ_Binary(Mat mat, PetscViewer viewer)
3233: {
3234: PetscInt header[4], M, N, nz, bs, m, n, mbs, nbs, rows, cols, sum, i, j, k;
3235: PetscInt *rowidxs, *colidxs, rs, cs, ce;
3236: PetscScalar *matvals;
3238: PetscFunctionBegin;
3239: PetscCall(PetscViewerSetUp(viewer));
3241: /* read in matrix header */
3242: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3243: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3244: M = header[1];
3245: N = header[2];
3246: nz = header[3];
3247: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3248: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3249: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIBAIJ");
3251: /* set block sizes from the viewer's .info file */
3252: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3253: /* set local sizes if not set already */
3254: if (mat->rmap->n < 0 && M == N) mat->rmap->n = mat->cmap->n;
3255: if (mat->cmap->n < 0 && M == N) mat->cmap->n = mat->rmap->n;
3256: /* set global sizes if not set already */
3257: if (mat->rmap->N < 0) mat->rmap->N = M;
3258: if (mat->cmap->N < 0) mat->cmap->N = N;
3259: PetscCall(PetscLayoutSetUp(mat->rmap));
3260: PetscCall(PetscLayoutSetUp(mat->cmap));
3262: /* check if the matrix sizes are correct */
3263: PetscCall(MatGetSize(mat, &rows, &cols));
3264: 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);
3265: PetscCall(MatGetBlockSize(mat, &bs));
3266: PetscCall(MatGetLocalSize(mat, &m, &n));
3267: PetscCall(PetscLayoutGetRange(mat->rmap, &rs, NULL));
3268: PetscCall(PetscLayoutGetRange(mat->cmap, &cs, &ce));
3269: mbs = m / bs;
3270: nbs = n / bs;
3272: /* read in row lengths and build row indices */
3273: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3274: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3275: rowidxs[0] = 0;
3276: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3277: PetscCallMPI(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3278: 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);
3280: /* read in column indices and matrix values */
3281: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3282: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3283: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3285: { /* preallocate matrix storage */
3286: PetscBT bt; /* helper bit set to count diagonal nonzeros */
3287: PetscHSetI ht; /* helper hash set to count off-diagonal nonzeros */
3288: PetscBool sbaij, done;
3289: PetscInt *d_nnz, *o_nnz;
3291: PetscCall(PetscBTCreate(nbs, &bt));
3292: PetscCall(PetscHSetICreate(&ht));
3293: PetscCall(PetscCalloc2(mbs, &d_nnz, mbs, &o_nnz));
3294: PetscCall(PetscObjectTypeCompare((PetscObject)mat, MATMPISBAIJ, &sbaij));
3295: for (i = 0; i < mbs; i++) {
3296: PetscCall(PetscBTMemzero(nbs, bt));
3297: PetscCall(PetscHSetIClear(ht));
3298: for (k = 0; k < bs; k++) {
3299: PetscInt row = bs * i + k;
3300: for (j = rowidxs[row]; j < rowidxs[row + 1]; j++) {
3301: PetscInt col = colidxs[j];
3302: if (!sbaij || col >= row) {
3303: if (col >= cs && col < ce) {
3304: if (!PetscBTLookupSet(bt, (col - cs) / bs)) d_nnz[i]++;
3305: } else {
3306: PetscCall(PetscHSetIQueryAdd(ht, col / bs, &done));
3307: if (done) o_nnz[i]++;
3308: }
3309: }
3310: }
3311: }
3312: }
3313: PetscCall(PetscBTDestroy(&bt));
3314: PetscCall(PetscHSetIDestroy(&ht));
3315: PetscCall(MatMPIBAIJSetPreallocation(mat, bs, 0, d_nnz, 0, o_nnz));
3316: PetscCall(MatMPISBAIJSetPreallocation(mat, bs, 0, d_nnz, 0, o_nnz));
3317: PetscCall(PetscFree2(d_nnz, o_nnz));
3318: }
3320: /* store matrix values */
3321: for (i = 0; i < m; i++) {
3322: PetscInt row = rs + i, s = rowidxs[i], e = rowidxs[i + 1];
3323: PetscUseTypeMethod(mat, setvalues, 1, &row, e - s, colidxs + s, matvals + s, INSERT_VALUES);
3324: }
3326: PetscCall(PetscFree(rowidxs));
3327: PetscCall(PetscFree2(colidxs, matvals));
3328: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
3329: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
3330: PetscFunctionReturn(PETSC_SUCCESS);
3331: }
3333: PetscErrorCode MatLoad_MPIBAIJ(Mat mat, PetscViewer viewer)
3334: {
3335: PetscBool isbinary;
3337: PetscFunctionBegin;
3338: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3339: 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);
3340: PetscCall(MatLoad_MPIBAIJ_Binary(mat, viewer));
3341: PetscFunctionReturn(PETSC_SUCCESS);
3342: }
3344: /*@
3345: MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the matrices hash table
3347: Input Parameters:
3348: + mat - the matrix
3349: - fact - factor
3351: Options Database Key:
3352: . -mat_use_hash_table <fact> - provide the factor
3354: Level: advanced
3356: .seealso: `Mat`, `MATMPIBAIJ`, `MatSetOption()`
3357: @*/
3358: PetscErrorCode MatMPIBAIJSetHashTableFactor(Mat mat, PetscReal fact)
3359: {
3360: PetscFunctionBegin;
3361: PetscTryMethod(mat, "MatSetHashTableFactor_C", (Mat, PetscReal), (mat, fact));
3362: PetscFunctionReturn(PETSC_SUCCESS);
3363: }
3365: PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat mat, PetscReal fact)
3366: {
3367: Mat_MPIBAIJ *baij;
3369: PetscFunctionBegin;
3370: baij = (Mat_MPIBAIJ *)mat->data;
3371: baij->ht_fact = fact;
3372: PetscFunctionReturn(PETSC_SUCCESS);
3373: }
3375: PetscErrorCode MatMPIBAIJGetSeqBAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
3376: {
3377: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
3378: PetscBool flg;
3380: PetscFunctionBegin;
3381: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIBAIJ, &flg));
3382: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIBAIJ matrix as input");
3383: if (Ad) *Ad = a->A;
3384: if (Ao) *Ao = a->B;
3385: if (colmap) *colmap = a->garray;
3386: PetscFunctionReturn(PETSC_SUCCESS);
3387: }
3389: /*
3390: Special version for direct calls from Fortran (to eliminate two function call overheads
3391: */
3392: #if defined(PETSC_HAVE_FORTRAN_CAPS)
3393: #define matmpibaijsetvaluesblocked_ MATMPIBAIJSETVALUESBLOCKED
3394: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
3395: #define matmpibaijsetvaluesblocked_ matmpibaijsetvaluesblocked
3396: #endif
3398: // PetscClangLinter pragma disable: -fdoc-synopsis-matching-symbol-name
3399: /*@C
3400: MatMPIBAIJSetValuesBlocked - Direct Fortran call to replace call to `MatSetValuesBlocked()`
3402: Collective
3404: Input Parameters:
3405: + matin - the matrix
3406: . min - number of input rows
3407: . im - input rows
3408: . nin - number of input columns
3409: . in - input columns
3410: . v - numerical values input
3411: - addvin - `INSERT_VALUES` or `ADD_VALUES`
3413: Level: advanced
3415: Developer Notes:
3416: This has a complete copy of `MatSetValuesBlocked_MPIBAIJ()` which is terrible code un-reuse.
3418: .seealso: `Mat`, `MatSetValuesBlocked()`
3419: @*/
3420: PETSC_EXTERN PetscErrorCode matmpibaijsetvaluesblocked_(Mat *matin, PetscInt *min, const PetscInt im[], PetscInt *nin, const PetscInt in[], const MatScalar v[], InsertMode *addvin)
3421: {
3422: /* convert input arguments to C version */
3423: Mat mat = *matin;
3424: PetscInt m = *min, n = *nin;
3425: InsertMode addv = *addvin;
3427: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
3428: const MatScalar *value;
3429: MatScalar *barray = baij->barray;
3430: PetscBool roworiented = baij->roworiented;
3431: PetscInt i, j, ii, jj, row, col, rstart = baij->rstartbs;
3432: PetscInt rend = baij->rendbs, cstart = baij->cstartbs, stepval;
3433: PetscInt cend = baij->cendbs, bs = mat->rmap->bs, bs2 = baij->bs2;
3435: PetscFunctionBegin;
3436: /* tasks normally handled by MatSetValuesBlocked() */
3437: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
3438: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
3439: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3440: if (mat->assembled) {
3441: mat->was_assembled = PETSC_TRUE;
3442: mat->assembled = PETSC_FALSE;
3443: }
3444: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
3446: if (!barray) {
3447: PetscCall(PetscMalloc1(bs2, &barray));
3448: baij->barray = barray;
3449: }
3451: if (roworiented) stepval = (n - 1) * bs;
3452: else stepval = (m - 1) * bs;
3454: for (i = 0; i < m; i++) {
3455: if (im[i] < 0) continue;
3456: 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);
3457: if (im[i] >= rstart && im[i] < rend) {
3458: row = im[i] - rstart;
3459: for (j = 0; j < n; j++) {
3460: /* If NumCol = 1 then a copy is not required */
3461: if ((roworiented) && (n == 1)) {
3462: barray = (MatScalar *)v + i * bs2;
3463: } else if ((!roworiented) && (m == 1)) {
3464: barray = (MatScalar *)v + j * bs2;
3465: } else { /* Here a copy is required */
3466: if (roworiented) {
3467: value = v + i * (stepval + bs) * bs + j * bs;
3468: } else {
3469: value = v + j * (stepval + bs) * bs + i * bs;
3470: }
3471: for (ii = 0; ii < bs; ii++, value += stepval) {
3472: for (jj = 0; jj < bs; jj++) *barray++ = *value++;
3473: }
3474: barray -= bs2;
3475: }
3477: if (in[j] >= cstart && in[j] < cend) {
3478: col = in[j] - cstart;
3479: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->A, row, col, barray, addv, im[i], in[j]));
3480: } else if (in[j] < 0) {
3481: continue;
3482: } else {
3483: 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);
3484: if (mat->was_assembled) {
3485: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
3487: #if defined(PETSC_USE_DEBUG)
3488: #if defined(PETSC_USE_CTABLE)
3489: {
3490: PetscInt data;
3491: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &data));
3492: PetscCheck((data - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
3493: }
3494: #else
3495: PetscCheck((baij->colmap[in[j]] - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
3496: #endif
3497: #endif
3498: #if defined(PETSC_USE_CTABLE)
3499: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &col));
3500: col = (col - 1) / bs;
3501: #else
3502: col = (baij->colmap[in[j]] - 1) / bs;
3503: #endif
3504: if (col < 0 && !((Mat_SeqBAIJ *)baij->A->data)->nonew) {
3505: PetscCall(MatDisAssemble_MPIBAIJ(mat));
3506: col = in[j];
3507: }
3508: } else col = in[j];
3509: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->B, row, col, barray, addv, im[i], in[j]));
3510: }
3511: }
3512: } else {
3513: if (!baij->donotstash) {
3514: if (roworiented) {
3515: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
3516: } else {
3517: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
3518: }
3519: }
3520: }
3521: }
3523: /* task normally handled by MatSetValuesBlocked() */
3524: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
3525: PetscFunctionReturn(PETSC_SUCCESS);
3526: }
3528: /*@
3529: MatCreateMPIBAIJWithArrays - creates a `MATMPIBAIJ` matrix using arrays that contain in standard block CSR format for the local rows.
3531: Collective
3533: Input Parameters:
3534: + comm - MPI communicator
3535: . bs - the block size, only a block size of 1 is supported
3536: . m - number of local rows (Cannot be `PETSC_DECIDE`)
3537: . n - This value should be the same as the local size used in creating the
3538: x vector for the matrix-vector product $ y = Ax $. (or `PETSC_DECIDE` to have
3539: calculated if `N` is given) For square matrices `n` is almost always `m`.
3540: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
3541: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
3542: . 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
3543: . j - column indices
3544: - a - matrix values
3546: Output Parameter:
3547: . mat - the matrix
3549: Level: intermediate
3551: Notes:
3552: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
3553: thus you CANNOT change the matrix entries by changing the values of a[] after you have
3554: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3556: The order of the entries in values is the same as the block compressed sparse row storage format; that is, it is
3557: the same as a three dimensional array in Fortran values(bs,bs,nnz) that contains the first column of the first
3558: block, followed by the second column of the first block etc etc. That is, the blocks are contiguous in memory
3559: with column-major ordering within blocks.
3561: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
3563: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
3564: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`
3565: @*/
3566: 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)
3567: {
3568: PetscFunctionBegin;
3569: PetscCheck(!i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
3570: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
3571: PetscCall(MatCreate(comm, mat));
3572: PetscCall(MatSetSizes(*mat, m, n, M, N));
3573: PetscCall(MatSetType(*mat, MATMPIBAIJ));
3574: PetscCall(MatSetBlockSize(*mat, bs));
3575: PetscCall(MatSetUp(*mat));
3576: PetscCall(MatSetOption(*mat, MAT_ROW_ORIENTED, PETSC_FALSE));
3577: PetscCall(MatMPIBAIJSetPreallocationCSR(*mat, bs, i, j, a));
3578: PetscCall(MatSetOption(*mat, MAT_ROW_ORIENTED, PETSC_TRUE));
3579: PetscFunctionReturn(PETSC_SUCCESS);
3580: }
3582: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIBAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
3583: {
3584: PetscInt m, N, i, rstart, nnz, Ii, bs, cbs;
3585: PetscInt *indx;
3586: PetscScalar *values;
3588: PetscFunctionBegin;
3589: PetscCall(MatGetSize(inmat, &m, &N));
3590: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
3591: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)inmat->data;
3592: PetscInt *dnz, *onz, mbs, Nbs, nbs;
3593: PetscInt *bindx, rmax = a->rmax, j;
3594: PetscMPIInt rank, size;
3596: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
3597: mbs = m / bs;
3598: Nbs = N / cbs;
3599: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnershipBlock(comm, cbs, &n, &N));
3600: nbs = n / cbs;
3602: PetscCall(PetscMalloc1(rmax, &bindx));
3603: MatPreallocateBegin(comm, mbs, nbs, dnz, onz); /* inline function, output __end and __rstart are used below */
3605: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3606: PetscCallMPI(MPI_Comm_rank(comm, &size));
3607: if (rank == size - 1) {
3608: /* Check sum(nbs) = Nbs */
3609: PetscCheck(__end == Nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local block columns %" PetscInt_FMT " != global block columns %" PetscInt_FMT, __end, Nbs);
3610: }
3612: rstart = __rstart; /* block rstart of *outmat; see inline function MatPreallocateBegin */
3613: for (i = 0; i < mbs; i++) {
3614: PetscCall(MatGetRow_SeqBAIJ(inmat, i * bs, &nnz, &indx, NULL)); /* non-blocked nnz and indx */
3615: nnz = nnz / bs;
3616: for (j = 0; j < nnz; j++) bindx[j] = indx[j * bs] / bs;
3617: PetscCall(MatPreallocateSet(i + rstart, nnz, bindx, dnz, onz));
3618: PetscCall(MatRestoreRow_SeqBAIJ(inmat, i * bs, &nnz, &indx, NULL));
3619: }
3620: PetscCall(PetscFree(bindx));
3622: PetscCall(MatCreate(comm, outmat));
3623: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
3624: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
3625: PetscCall(MatSetType(*outmat, MATBAIJ));
3626: PetscCall(MatSeqBAIJSetPreallocation(*outmat, bs, 0, dnz));
3627: PetscCall(MatMPIBAIJSetPreallocation(*outmat, bs, 0, dnz, 0, onz));
3628: MatPreallocateEnd(dnz, onz);
3629: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3630: }
3632: /* numeric phase */
3633: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
3634: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
3636: for (i = 0; i < m; i++) {
3637: PetscCall(MatGetRow_SeqBAIJ(inmat, i, &nnz, &indx, &values));
3638: Ii = i + rstart;
3639: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
3640: PetscCall(MatRestoreRow_SeqBAIJ(inmat, i, &nnz, &indx, &values));
3641: }
3642: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
3643: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
3644: PetscFunctionReturn(PETSC_SUCCESS);
3645: }