Actual source code: mpibaij.c
1: #include <../src/mat/impls/baij/mpi/mpibaij.h>
3: #include <petsc/private/hashseti.h>
4: #include <petscblaslapack.h>
5: #include <petscsf.h>
7: static PetscErrorCode MatDestroy_MPIBAIJ(Mat mat)
8: {
9: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
11: PetscFunctionBegin;
12: PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ",Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
13: PetscCall(MatStashDestroy_Private(&mat->stash));
14: PetscCall(MatStashDestroy_Private(&mat->bstash));
15: PetscCall(MatDestroy(&baij->A));
16: PetscCall(MatDestroy(&baij->B));
17: #if defined(PETSC_USE_CTABLE)
18: PetscCall(PetscHMapIDestroy(&baij->colmap));
19: #else
20: PetscCall(PetscFree(baij->colmap));
21: #endif
22: PetscCall(PetscFree(baij->garray));
23: PetscCall(VecDestroy(&baij->lvec));
24: PetscCall(VecScatterDestroy(&baij->Mvctx));
25: PetscCall(PetscFree2(baij->rowvalues, baij->rowindices));
26: PetscCall(PetscFree(baij->barray));
27: PetscCall(PetscFree2(baij->hd, baij->ht));
28: PetscCall(PetscFree(baij->rangebs));
29: PetscCall(PetscFree(mat->data));
31: PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
32: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
33: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
34: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIBAIJSetPreallocation_C", NULL));
35: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIBAIJSetPreallocationCSR_C", NULL));
36: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
37: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetHashTableFactor_C", NULL));
38: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_mpisbaij_C", NULL));
39: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_mpiadj_C", NULL));
40: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_mpiaij_C", NULL));
41: #if defined(PETSC_HAVE_HYPRE)
42: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_hypre_C", NULL));
43: #endif
44: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_is_C", NULL));
45: PetscFunctionReturn(PETSC_SUCCESS);
46: }
48: /* defines MatSetValues_MPI_Hash(), MatAssemblyBegin_MPI_Hash(), and MatAssemblyEnd_MPI_Hash() */
49: #define TYPE BAIJ
50: #include "../src/mat/impls/aij/mpi/mpihashmat.h"
51: #undef TYPE
53: #if defined(PETSC_HAVE_HYPRE)
54: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
55: #endif
57: static PetscErrorCode MatGetRowMaxAbs_MPIBAIJ(Mat A, Vec v, PetscInt idx[])
58: {
59: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
60: PetscInt i, *idxb = NULL, m = A->rmap->n, bs = A->cmap->bs;
61: PetscScalar *va, *vv;
62: Vec vB, vA;
63: const PetscScalar *vb;
65: PetscFunctionBegin;
66: PetscCall(MatCreateVecs(a->A, NULL, &vA));
67: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
69: PetscCall(VecGetArrayWrite(vA, &va));
70: if (idx) {
71: for (i = 0; i < m; i++) {
72: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
73: }
74: }
76: PetscCall(MatCreateVecs(a->B, NULL, &vB));
77: PetscCall(PetscMalloc1(m, &idxb));
78: PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));
80: PetscCall(VecGetArrayWrite(v, &vv));
81: PetscCall(VecGetArrayRead(vB, &vb));
82: for (i = 0; i < m; i++) {
83: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
84: vv[i] = vb[i];
85: if (idx) idx[i] = bs * a->garray[idxb[i] / bs] + (idxb[i] % bs);
86: } else {
87: vv[i] = va[i];
88: if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > bs * a->garray[idxb[i] / bs] + (idxb[i] % bs)) idx[i] = bs * a->garray[idxb[i] / bs] + (idxb[i] % bs);
89: }
90: }
91: PetscCall(VecRestoreArrayWrite(vA, &vv));
92: PetscCall(VecRestoreArrayWrite(vA, &va));
93: PetscCall(VecRestoreArrayRead(vB, &vb));
94: PetscCall(PetscFree(idxb));
95: PetscCall(VecDestroy(&vA));
96: PetscCall(VecDestroy(&vB));
97: PetscFunctionReturn(PETSC_SUCCESS);
98: }
100: static PetscErrorCode MatGetRowSumAbs_MPIBAIJ(Mat A, Vec v)
101: {
102: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
103: Vec vB, vA;
105: PetscFunctionBegin;
106: PetscCall(MatCreateVecs(a->A, NULL, &vA));
107: PetscCall(MatGetRowSumAbs(a->A, vA));
108: PetscCall(MatCreateVecs(a->B, NULL, &vB));
109: PetscCall(MatGetRowSumAbs(a->B, vB));
110: PetscCall(VecAXPY(vA, 1.0, vB));
111: PetscCall(VecDestroy(&vB));
112: PetscCall(VecCopy(vA, v));
113: PetscCall(VecDestroy(&vA));
114: PetscFunctionReturn(PETSC_SUCCESS);
115: }
117: static PetscErrorCode MatStoreValues_MPIBAIJ(Mat mat)
118: {
119: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
121: PetscFunctionBegin;
122: PetscCall(MatStoreValues(aij->A));
123: PetscCall(MatStoreValues(aij->B));
124: PetscFunctionReturn(PETSC_SUCCESS);
125: }
127: static PetscErrorCode MatRetrieveValues_MPIBAIJ(Mat mat)
128: {
129: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
131: PetscFunctionBegin;
132: PetscCall(MatRetrieveValues(aij->A));
133: PetscCall(MatRetrieveValues(aij->B));
134: PetscFunctionReturn(PETSC_SUCCESS);
135: }
137: /*
138: Local utility routine that creates a mapping from the global column
139: number to the local number in the off-diagonal part of the local
140: storage of the matrix. This is done in a non scalable way since the
141: length of colmap equals the global matrix length.
142: */
143: PetscErrorCode MatCreateColmap_MPIBAIJ_Private(Mat mat)
144: {
145: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
146: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)baij->B->data;
147: PetscInt nbs = B->nbs, i, bs = mat->rmap->bs;
149: PetscFunctionBegin;
150: #if defined(PETSC_USE_CTABLE)
151: PetscCall(PetscHMapICreateWithSize(baij->nbs, &baij->colmap));
152: for (i = 0; i < nbs; i++) PetscCall(PetscHMapISet(baij->colmap, baij->garray[i] + 1, i * bs + 1));
153: #else
154: PetscCall(PetscCalloc1(baij->Nbs + 1, &baij->colmap));
155: for (i = 0; i < nbs; i++) baij->colmap[baij->garray[i]] = i * bs + 1;
156: #endif
157: PetscFunctionReturn(PETSC_SUCCESS);
158: }
160: #define MatSetValues_SeqBAIJ_A_Private(row, col, value, addv, orow, ocol) \
161: do { \
162: brow = row / bs; \
163: rp = PetscSafePointerPlusOffset(aj, ai[brow]); \
164: ap = PetscSafePointerPlusOffset(aa, bs2 * ai[brow]); \
165: rmax = aimax[brow]; \
166: nrow = ailen[brow]; \
167: bcol = col / bs; \
168: ridx = row % bs; \
169: cidx = col % bs; \
170: low = 0; \
171: high = nrow; \
172: while (high - low > 3) { \
173: t = (low + high) / 2; \
174: if (rp[t] > bcol) high = t; \
175: else low = t; \
176: } \
177: for (_i = low; _i < high; _i++) { \
178: if (rp[_i] > bcol) break; \
179: if (rp[_i] == bcol) { \
180: bap = ap + bs2 * _i + bs * cidx + ridx; \
181: if (addv == ADD_VALUES) *bap += value; \
182: else *bap = value; \
183: goto a_noinsert; \
184: } \
185: } \
186: if (a->nonew == 1) goto a_noinsert; \
187: PetscCheck(a->nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
188: MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, brow, bcol, rmax, aa, ai, aj, rp, ap, aimax, a->nonew, MatScalar); \
189: N = nrow++ - 1; \
190: /* shift up all the later entries in this row */ \
191: PetscCall(PetscArraymove(rp + _i + 1, rp + _i, N - _i + 1)); \
192: PetscCall(PetscArraymove(ap + bs2 * (_i + 1), ap + bs2 * _i, bs2 * (N - _i + 1))); \
193: PetscCall(PetscArrayzero(ap + bs2 * _i, bs2)); \
194: rp[_i] = bcol; \
195: ap[bs2 * _i + bs * cidx + ridx] = value; \
196: a_noinsert:; \
197: ailen[brow] = nrow; \
198: } while (0)
200: #define MatSetValues_SeqBAIJ_B_Private(row, col, value, addv, orow, ocol) \
201: do { \
202: brow = row / bs; \
203: rp = PetscSafePointerPlusOffset(bj, bi[brow]); \
204: ap = PetscSafePointerPlusOffset(ba, bs2 * bi[brow]); \
205: rmax = bimax[brow]; \
206: nrow = bilen[brow]; \
207: bcol = col / bs; \
208: ridx = row % bs; \
209: cidx = col % bs; \
210: low = 0; \
211: high = nrow; \
212: while (high - low > 3) { \
213: t = (low + high) / 2; \
214: if (rp[t] > bcol) high = t; \
215: else low = t; \
216: } \
217: for (_i = low; _i < high; _i++) { \
218: if (rp[_i] > bcol) break; \
219: if (rp[_i] == bcol) { \
220: bap = ap + bs2 * _i + bs * cidx + ridx; \
221: if (addv == ADD_VALUES) *bap += value; \
222: else *bap = value; \
223: goto b_noinsert; \
224: } \
225: } \
226: if (b->nonew == 1) goto b_noinsert; \
227: PetscCheck(b->nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
228: MatSeqXAIJReallocateAIJ(B, b->mbs, bs2, nrow, brow, bcol, rmax, ba, bi, bj, rp, ap, bimax, b->nonew, MatScalar); \
229: N = nrow++ - 1; \
230: /* shift up all the later entries in this row */ \
231: PetscCall(PetscArraymove(rp + _i + 1, rp + _i, N - _i + 1)); \
232: PetscCall(PetscArraymove(ap + bs2 * (_i + 1), ap + bs2 * _i, bs2 * (N - _i + 1))); \
233: PetscCall(PetscArrayzero(ap + bs2 * _i, bs2)); \
234: rp[_i] = bcol; \
235: ap[bs2 * _i + bs * cidx + ridx] = value; \
236: b_noinsert:; \
237: bilen[brow] = nrow; \
238: } while (0)
240: PetscErrorCode MatSetValues_MPIBAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
241: {
242: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
243: MatScalar value;
244: PetscBool roworiented = baij->roworiented;
245: PetscInt i, j, row, col;
246: PetscInt rstart_orig = mat->rmap->rstart;
247: PetscInt rend_orig = mat->rmap->rend, cstart_orig = mat->cmap->rstart;
248: PetscInt cend_orig = mat->cmap->rend, bs = mat->rmap->bs;
250: /* Some Variables required in the macro */
251: Mat A = baij->A;
252: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
253: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
254: MatScalar *aa = a->a;
256: Mat B = baij->B;
257: Mat_SeqBAIJ *b = (Mat_SeqBAIJ *)B->data;
258: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j;
259: MatScalar *ba = b->a;
261: PetscInt *rp, ii, nrow, _i, rmax, N, brow, bcol;
262: PetscInt low, high, t, ridx, cidx, bs2 = a->bs2;
263: MatScalar *ap, *bap;
265: PetscFunctionBegin;
266: for (i = 0; i < m; i++) {
267: if (im[i] < 0) continue;
268: PetscCheck(im[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], mat->rmap->N - 1);
269: if (im[i] >= rstart_orig && im[i] < rend_orig) {
270: row = im[i] - rstart_orig;
271: for (j = 0; j < n; j++) {
272: if (in[j] >= cstart_orig && in[j] < cend_orig) {
273: col = in[j] - cstart_orig;
274: if (roworiented) value = v[i * n + j];
275: else value = v[i + j * m];
276: MatSetValues_SeqBAIJ_A_Private(row, col, value, addv, im[i], in[j]);
277: } else if (in[j] < 0) {
278: continue;
279: } else {
280: PetscCheck(in[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
281: if (mat->was_assembled) {
282: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
283: #if defined(PETSC_USE_CTABLE)
284: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] / bs + 1, 0, &col));
285: col = col - 1;
286: #else
287: col = baij->colmap[in[j] / bs] - 1;
288: #endif
289: if (col < 0 && !((Mat_SeqBAIJ *)baij->B->data)->nonew) {
290: PetscCall(MatDisAssemble_MPIBAIJ(mat));
291: col = in[j];
292: /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
293: B = baij->B;
294: b = (Mat_SeqBAIJ *)B->data;
295: bimax = b->imax;
296: bi = b->i;
297: bilen = b->ilen;
298: bj = b->j;
299: ba = b->a;
300: } else {
301: PetscCheck(col >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]);
302: col += in[j] % bs;
303: }
304: } else col = in[j];
305: if (roworiented) value = v[i * n + j];
306: else value = v[i + j * m];
307: MatSetValues_SeqBAIJ_B_Private(row, col, value, addv, im[i], in[j]);
308: /* PetscCall(MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv)); */
309: }
310: }
311: } else {
312: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Setting off process row %" PetscInt_FMT " even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set", im[i]);
313: if (!baij->donotstash) {
314: mat->assembled = PETSC_FALSE;
315: if (roworiented) {
316: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, PETSC_FALSE));
317: } else {
318: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, PETSC_FALSE));
319: }
320: }
321: }
322: }
323: PetscFunctionReturn(PETSC_SUCCESS);
324: }
326: static inline PetscErrorCode MatSetValuesBlocked_SeqBAIJ_Inlined(Mat A, PetscInt row, PetscInt col, const PetscScalar v[], InsertMode is, PetscInt orow, PetscInt ocol)
327: {
328: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
329: PetscInt *rp, low, high, t, ii, jj, nrow, i, rmax, N;
330: PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen;
331: PetscInt *aj = a->j, nonew = a->nonew, bs2 = a->bs2, bs = A->rmap->bs;
332: PetscBool roworiented = a->roworiented;
333: const PetscScalar *value = v;
334: MatScalar *ap, *aa = a->a, *bap;
336: PetscFunctionBegin;
337: rp = aj + ai[row];
338: ap = aa + bs2 * ai[row];
339: rmax = imax[row];
340: nrow = ailen[row];
341: value = v;
342: low = 0;
343: high = nrow;
344: while (high - low > 7) {
345: t = (low + high) / 2;
346: if (rp[t] > col) high = t;
347: else low = t;
348: }
349: for (i = low; i < high; i++) {
350: if (rp[i] > col) break;
351: if (rp[i] == col) {
352: bap = ap + bs2 * i;
353: if (roworiented) {
354: if (is == ADD_VALUES) {
355: for (ii = 0; ii < bs; ii++) {
356: for (jj = ii; jj < bs2; jj += bs) bap[jj] += *value++;
357: }
358: } else {
359: for (ii = 0; ii < bs; ii++) {
360: for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
361: }
362: }
363: } else {
364: if (is == ADD_VALUES) {
365: for (ii = 0; ii < bs; ii++, value += bs) {
366: for (jj = 0; jj < bs; jj++) bap[jj] += value[jj];
367: bap += bs;
368: }
369: } else {
370: for (ii = 0; ii < bs; ii++, value += bs) {
371: for (jj = 0; jj < bs; jj++) bap[jj] = value[jj];
372: bap += bs;
373: }
374: }
375: }
376: goto noinsert2;
377: }
378: }
379: if (nonew == 1) goto noinsert2;
380: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new global block indexed nonzero block (%" PetscInt_FMT ", %" PetscInt_FMT ") in the matrix", orow, ocol);
381: MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
382: N = nrow++ - 1;
383: high++;
384: /* shift up all the later entries in this row */
385: PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
386: PetscCall(PetscArraymove(ap + bs2 * (i + 1), ap + bs2 * i, bs2 * (N - i + 1)));
387: rp[i] = col;
388: bap = ap + bs2 * i;
389: if (roworiented) {
390: for (ii = 0; ii < bs; ii++) {
391: for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
392: }
393: } else {
394: for (ii = 0; ii < bs; ii++) {
395: for (jj = 0; jj < bs; jj++) *bap++ = *value++;
396: }
397: }
398: noinsert2:;
399: ailen[row] = nrow;
400: PetscFunctionReturn(PETSC_SUCCESS);
401: }
403: /*
404: This routine should be optimized so that the block copy at ** Here a copy is required ** below is not needed
405: by passing additional stride information into the MatSetValuesBlocked_SeqBAIJ_Inlined() routine
406: */
407: static PetscErrorCode MatSetValuesBlocked_MPIBAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
408: {
409: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
410: const PetscScalar *value;
411: MatScalar *barray = baij->barray;
412: PetscBool roworiented = baij->roworiented;
413: PetscInt i, j, ii, jj, row, col, rstart = baij->rstartbs;
414: PetscInt rend = baij->rendbs, cstart = baij->cstartbs, stepval;
415: PetscInt cend = baij->cendbs, bs = mat->rmap->bs, bs2 = baij->bs2;
417: PetscFunctionBegin;
418: if (!barray) {
419: PetscCall(PetscMalloc1(bs2, &barray));
420: baij->barray = barray;
421: }
423: if (roworiented) stepval = (n - 1) * bs;
424: else stepval = (m - 1) * bs;
426: for (i = 0; i < m; i++) {
427: if (im[i] < 0) continue;
428: PetscCheck(im[i] < baij->Mbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Block indexed row too large %" PetscInt_FMT " max %" PetscInt_FMT, im[i], baij->Mbs - 1);
429: if (im[i] >= rstart && im[i] < rend) {
430: row = im[i] - rstart;
431: for (j = 0; j < n; j++) {
432: /* If NumCol = 1 then a copy is not required */
433: if ((roworiented) && (n == 1)) {
434: barray = (MatScalar *)v + i * bs2;
435: } else if ((!roworiented) && (m == 1)) {
436: barray = (MatScalar *)v + j * bs2;
437: } else { /* Here a copy is required */
438: if (roworiented) {
439: value = v + (i * (stepval + bs) + j) * bs;
440: } else {
441: value = v + (j * (stepval + bs) + i) * bs;
442: }
443: for (ii = 0; ii < bs; ii++, value += bs + stepval) {
444: for (jj = 0; jj < bs; jj++) barray[jj] = value[jj];
445: barray += bs;
446: }
447: barray -= bs2;
448: }
450: if (in[j] >= cstart && in[j] < cend) {
451: col = in[j] - cstart;
452: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->A, row, col, barray, addv, im[i], in[j]));
453: } else if (in[j] < 0) {
454: continue;
455: } else {
456: PetscCheck(in[j] < baij->Nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Block indexed column too large %" PetscInt_FMT " max %" PetscInt_FMT, in[j], baij->Nbs - 1);
457: if (mat->was_assembled) {
458: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
460: #if defined(PETSC_USE_CTABLE)
461: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &col));
462: col = col < 1 ? -1 : (col - 1) / bs;
463: #else
464: col = baij->colmap[in[j]] < 1 ? -1 : (baij->colmap[in[j]] - 1) / bs;
465: #endif
466: if (col < 0 && !((Mat_SeqBAIJ *)baij->B->data)->nonew) {
467: PetscCall(MatDisAssemble_MPIBAIJ(mat));
468: col = in[j];
469: } else PetscCheck(col >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new blocked indexed nonzero block (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]);
470: } else col = in[j];
471: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->B, row, col, barray, addv, im[i], in[j]));
472: }
473: }
474: } else {
475: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Setting off process block indexed row %" PetscInt_FMT " even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set", im[i]);
476: if (!baij->donotstash) {
477: if (roworiented) {
478: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
479: } else {
480: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
481: }
482: }
483: }
484: }
485: PetscFunctionReturn(PETSC_SUCCESS);
486: }
488: #define HASH_KEY 0.6180339887
489: #define HASH(size, key, tmp) (tmp = (key) * HASH_KEY, (PetscInt)((size) * (tmp - (PetscInt)tmp)))
490: /* #define HASH(size,key) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
491: /* #define HASH(size,key,tmp) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
492: static PetscErrorCode MatSetValues_MPIBAIJ_HT(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
493: {
494: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
495: PetscBool roworiented = baij->roworiented;
496: PetscInt i, j, row, col;
497: PetscInt rstart_orig = mat->rmap->rstart;
498: PetscInt rend_orig = mat->rmap->rend, Nbs = baij->Nbs;
499: PetscInt h1, key, size = baij->ht_size, bs = mat->rmap->bs, *HT = baij->ht, idx;
500: PetscReal tmp;
501: MatScalar **HD = baij->hd, value;
502: PetscInt total_ct = baij->ht_total_ct, insert_ct = baij->ht_insert_ct;
504: PetscFunctionBegin;
505: for (i = 0; i < m; i++) {
506: if (PetscDefined(USE_DEBUG)) {
507: PetscCheck(im[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row");
508: PetscCheck(im[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], mat->rmap->N - 1);
509: }
510: row = im[i];
511: if (row >= rstart_orig && row < rend_orig) {
512: for (j = 0; j < n; j++) {
513: col = in[j];
514: if (roworiented) value = v[i * n + j];
515: else value = v[i + j * m];
516: /* Look up PetscInto the Hash Table */
517: key = (row / bs) * Nbs + (col / bs) + 1;
518: h1 = HASH(size, key, tmp);
520: idx = h1;
521: if (PetscDefined(USE_DEBUG)) {
522: insert_ct++;
523: total_ct++;
524: if (HT[idx] != key) {
525: for (idx = h1; (idx < size) && (HT[idx] != key); idx++, total_ct++);
526: if (idx == size) {
527: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++, total_ct++);
528: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
529: }
530: }
531: } else if (HT[idx] != key) {
532: for (idx = h1; (idx < size) && (HT[idx] != key); idx++);
533: if (idx == size) {
534: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++);
535: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
536: }
537: }
538: /* A HASH table entry is found, so insert the values at the correct address */
539: if (addv == ADD_VALUES) *(HD[idx] + (col % bs) * bs + (row % bs)) += value;
540: else *(HD[idx] + (col % bs) * bs + (row % bs)) = value;
541: }
542: } else if (!baij->donotstash) {
543: if (roworiented) {
544: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, PETSC_FALSE));
545: } else {
546: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, PETSC_FALSE));
547: }
548: }
549: }
550: if (PetscDefined(USE_DEBUG)) {
551: baij->ht_total_ct += total_ct;
552: baij->ht_insert_ct += insert_ct;
553: }
554: PetscFunctionReturn(PETSC_SUCCESS);
555: }
557: static PetscErrorCode MatSetValuesBlocked_MPIBAIJ_HT(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
558: {
559: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
560: PetscBool roworiented = baij->roworiented;
561: PetscInt i, j, ii, jj, row, col;
562: PetscInt rstart = baij->rstartbs;
563: PetscInt rend = mat->rmap->rend, stepval, bs = mat->rmap->bs, bs2 = baij->bs2, nbs2 = n * bs2;
564: PetscInt h1, key, size = baij->ht_size, idx, *HT = baij->ht, Nbs = baij->Nbs;
565: PetscReal tmp;
566: MatScalar **HD = baij->hd, *baij_a;
567: const PetscScalar *v_t, *value;
568: PetscInt total_ct = baij->ht_total_ct, insert_ct = baij->ht_insert_ct;
570: PetscFunctionBegin;
571: if (roworiented) stepval = (n - 1) * bs;
572: else stepval = (m - 1) * bs;
574: for (i = 0; i < m; i++) {
575: if (PetscDefined(USE_DEBUG)) {
576: PetscCheck(im[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row: %" PetscInt_FMT, im[i]);
577: PetscCheck(im[i] < baij->Mbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], baij->Mbs - 1);
578: }
579: row = im[i];
580: v_t = v + i * nbs2;
581: if (row >= rstart && row < rend) {
582: for (j = 0; j < n; j++) {
583: col = in[j];
585: /* Look up into the Hash Table */
586: key = row * Nbs + col + 1;
587: h1 = HASH(size, key, tmp);
589: idx = h1;
590: if (PetscDefined(USE_DEBUG)) {
591: total_ct++;
592: insert_ct++;
593: if (HT[idx] != key) {
594: for (idx = h1; (idx < size) && (HT[idx] != key); idx++, total_ct++);
595: if (idx == size) {
596: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++, total_ct++);
597: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
598: }
599: }
600: } else if (HT[idx] != key) {
601: for (idx = h1; (idx < size) && (HT[idx] != key); idx++);
602: if (idx == size) {
603: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++);
604: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
605: }
606: }
607: baij_a = HD[idx];
608: if (roworiented) {
609: /*value = v + i*(stepval+bs)*bs + j*bs;*/
610: /* value = v + (i*(stepval+bs)+j)*bs; */
611: value = v_t;
612: v_t += bs;
613: if (addv == ADD_VALUES) {
614: for (ii = 0; ii < bs; ii++, value += stepval) {
615: for (jj = ii; jj < bs2; jj += bs) baij_a[jj] += *value++;
616: }
617: } else {
618: for (ii = 0; ii < bs; ii++, value += stepval) {
619: for (jj = ii; jj < bs2; jj += bs) baij_a[jj] = *value++;
620: }
621: }
622: } else {
623: value = v + j * (stepval + bs) * bs + i * bs;
624: if (addv == ADD_VALUES) {
625: for (ii = 0; ii < bs; ii++, value += stepval, baij_a += bs) {
626: for (jj = 0; jj < bs; jj++) baij_a[jj] += *value++;
627: }
628: } else {
629: for (ii = 0; ii < bs; ii++, value += stepval, baij_a += bs) {
630: for (jj = 0; jj < bs; jj++) baij_a[jj] = *value++;
631: }
632: }
633: }
634: }
635: } else {
636: if (!baij->donotstash) {
637: if (roworiented) {
638: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
639: } else {
640: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
641: }
642: }
643: }
644: }
645: if (PetscDefined(USE_DEBUG)) {
646: baij->ht_total_ct += total_ct;
647: baij->ht_insert_ct += insert_ct;
648: }
649: PetscFunctionReturn(PETSC_SUCCESS);
650: }
652: static PetscErrorCode MatGetValues_MPIBAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
653: {
654: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
655: PetscInt bs = mat->rmap->bs, i, j, bsrstart = mat->rmap->rstart, bsrend = mat->rmap->rend;
656: PetscInt bscstart = mat->cmap->rstart, bscend = mat->cmap->rend, row, col, data;
658: PetscFunctionBegin;
659: for (i = 0; i < m; i++) {
660: if (idxm[i] < 0) continue; /* negative row */
661: PetscCheck(idxm[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, idxm[i], mat->rmap->N - 1);
662: if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
663: row = idxm[i] - bsrstart;
664: for (j = 0; j < n; j++) {
665: if (idxn[j] < 0) continue; /* negative column */
666: PetscCheck(idxn[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, idxn[j], mat->cmap->N - 1);
667: if (idxn[j] >= bscstart && idxn[j] < bscend) {
668: col = idxn[j] - bscstart;
669: PetscCall(MatGetValues_SeqBAIJ(baij->A, 1, &row, 1, &col, v + i * n + j));
670: } else {
671: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
672: #if defined(PETSC_USE_CTABLE)
673: PetscCall(PetscHMapIGetWithDefault(baij->colmap, idxn[j] / bs + 1, 0, &data));
674: data--;
675: #else
676: data = baij->colmap[idxn[j] / bs] - 1;
677: #endif
678: if ((data < 0) || (baij->garray[data / bs] != idxn[j] / bs)) *(v + i * n + j) = 0.0;
679: else {
680: col = data + idxn[j] % bs;
681: PetscCall(MatGetValues_SeqBAIJ(baij->B, 1, &row, 1, &col, v + i * n + j));
682: }
683: }
684: }
685: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local values currently supported");
686: }
687: PetscFunctionReturn(PETSC_SUCCESS);
688: }
690: static PetscErrorCode MatNorm_MPIBAIJ(Mat mat, NormType type, PetscReal *nrm)
691: {
692: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
693: Mat_SeqBAIJ *amat = (Mat_SeqBAIJ *)baij->A->data, *bmat = (Mat_SeqBAIJ *)baij->B->data;
694: PetscInt i, j, bs2 = baij->bs2, bs = baij->A->rmap->bs, nz, row, col;
695: PetscReal sum = 0.0;
696: MatScalar *v;
698: PetscFunctionBegin;
699: if (baij->size == 1) {
700: PetscCall(MatNorm(baij->A, type, nrm));
701: } else {
702: if (type == NORM_FROBENIUS) {
703: v = amat->a;
704: nz = amat->nz * bs2;
705: for (i = 0; i < nz; i++) {
706: sum += PetscRealPart(PetscConj(*v) * (*v));
707: v++;
708: }
709: v = bmat->a;
710: nz = bmat->nz * bs2;
711: for (i = 0; i < nz; i++) {
712: sum += PetscRealPart(PetscConj(*v) * (*v));
713: v++;
714: }
715: PetscCallMPI(MPIU_Allreduce(&sum, nrm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
716: *nrm = PetscSqrtReal(*nrm);
717: } else if (type == NORM_1) { /* max column sum */
718: PetscReal *tmp, *tmp2;
719: PetscInt *jj, *garray = baij->garray, cstart = baij->rstartbs;
720: PetscMPIInt iN;
722: PetscCall(PetscCalloc1(mat->cmap->N, &tmp));
723: PetscCall(PetscMalloc1(mat->cmap->N, &tmp2));
724: v = amat->a;
725: jj = amat->j;
726: for (i = 0; i < amat->nz; i++) {
727: for (j = 0; j < bs; j++) {
728: col = bs * (cstart + *jj) + j; /* column index */
729: for (row = 0; row < bs; row++) {
730: tmp[col] += PetscAbsScalar(*v);
731: v++;
732: }
733: }
734: jj++;
735: }
736: v = bmat->a;
737: jj = bmat->j;
738: for (i = 0; i < bmat->nz; i++) {
739: for (j = 0; j < bs; j++) {
740: col = bs * garray[*jj] + j;
741: for (row = 0; row < bs; row++) {
742: tmp[col] += PetscAbsScalar(*v);
743: v++;
744: }
745: }
746: jj++;
747: }
748: PetscCall(PetscMPIIntCast(mat->cmap->N, &iN));
749: PetscCallMPI(MPIU_Allreduce(tmp, tmp2, iN, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
750: *nrm = 0.0;
751: for (j = 0; j < mat->cmap->N; j++) {
752: if (tmp2[j] > *nrm) *nrm = tmp2[j];
753: }
754: PetscCall(PetscFree(tmp));
755: PetscCall(PetscFree(tmp2));
756: } else if (type == NORM_INFINITY) { /* max row sum */
757: PetscReal *sums;
758: PetscCall(PetscMalloc1(bs, &sums));
759: sum = 0.0;
760: for (j = 0; j < amat->mbs; j++) {
761: for (row = 0; row < bs; row++) sums[row] = 0.0;
762: v = amat->a + bs2 * amat->i[j];
763: nz = amat->i[j + 1] - amat->i[j];
764: for (i = 0; i < nz; i++) {
765: for (col = 0; col < bs; col++) {
766: for (row = 0; row < bs; row++) {
767: sums[row] += PetscAbsScalar(*v);
768: v++;
769: }
770: }
771: }
772: v = bmat->a + bs2 * bmat->i[j];
773: nz = bmat->i[j + 1] - bmat->i[j];
774: for (i = 0; i < nz; i++) {
775: for (col = 0; col < bs; col++) {
776: for (row = 0; row < bs; row++) {
777: sums[row] += PetscAbsScalar(*v);
778: v++;
779: }
780: }
781: }
782: for (row = 0; row < bs; row++) {
783: if (sums[row] > sum) sum = sums[row];
784: }
785: }
786: PetscCallMPI(MPIU_Allreduce(&sum, nrm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
787: PetscCall(PetscFree(sums));
788: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for this norm yet");
789: }
790: PetscFunctionReturn(PETSC_SUCCESS);
791: }
793: /*
794: Creates the hash table, and sets the table
795: This table is created only once.
796: If new entried need to be added to the matrix
797: then the hash table has to be destroyed and
798: recreated.
799: */
800: static PetscErrorCode MatCreateHashTable_MPIBAIJ_Private(Mat mat, PetscReal factor)
801: {
802: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
803: Mat A = baij->A, B = baij->B;
804: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data, *b = (Mat_SeqBAIJ *)B->data;
805: PetscInt i, j, k, nz = a->nz + b->nz, h1, *ai = a->i, *aj = a->j, *bi = b->i, *bj = b->j;
806: PetscInt ht_size, bs2 = baij->bs2, rstart = baij->rstartbs;
807: PetscInt cstart = baij->cstartbs, *garray = baij->garray, row, col, Nbs = baij->Nbs;
808: PetscInt *HT, key;
809: MatScalar **HD;
810: PetscReal tmp;
811: #if defined(PETSC_USE_INFO)
812: PetscInt ct = 0, max = 0;
813: #endif
815: PetscFunctionBegin;
816: if (baij->ht) PetscFunctionReturn(PETSC_SUCCESS);
818: baij->ht_size = (PetscInt)(factor * nz);
819: ht_size = baij->ht_size;
821: /* Allocate Memory for Hash Table */
822: PetscCall(PetscCalloc2(ht_size, &baij->hd, ht_size, &baij->ht));
823: HD = baij->hd;
824: HT = baij->ht;
826: /* Loop Over A */
827: for (i = 0; i < a->mbs; i++) {
828: for (j = ai[i]; j < ai[i + 1]; j++) {
829: row = i + rstart;
830: col = aj[j] + cstart;
832: key = row * Nbs + col + 1;
833: h1 = HASH(ht_size, key, tmp);
834: for (k = 0; k < ht_size; k++) {
835: if (!HT[(h1 + k) % ht_size]) {
836: HT[(h1 + k) % ht_size] = key;
837: HD[(h1 + k) % ht_size] = a->a + j * bs2;
838: break;
839: #if defined(PETSC_USE_INFO)
840: } else {
841: ct++;
842: #endif
843: }
844: }
845: #if defined(PETSC_USE_INFO)
846: if (k > max) max = k;
847: #endif
848: }
849: }
850: /* Loop Over B */
851: for (i = 0; i < b->mbs; i++) {
852: for (j = bi[i]; j < bi[i + 1]; j++) {
853: row = i + rstart;
854: col = garray[bj[j]];
855: key = row * Nbs + col + 1;
856: h1 = HASH(ht_size, key, tmp);
857: for (k = 0; k < ht_size; k++) {
858: if (!HT[(h1 + k) % ht_size]) {
859: HT[(h1 + k) % ht_size] = key;
860: HD[(h1 + k) % ht_size] = b->a + j * bs2;
861: break;
862: #if defined(PETSC_USE_INFO)
863: } else {
864: ct++;
865: #endif
866: }
867: }
868: #if defined(PETSC_USE_INFO)
869: if (k > max) max = k;
870: #endif
871: }
872: }
874: /* Print Summary */
875: #if defined(PETSC_USE_INFO)
876: for (i = 0, j = 0; i < ht_size; i++) {
877: if (HT[i]) j++;
878: }
879: PetscCall(PetscInfo(mat, "Average Search = %5.2g,max search = %" PetscInt_FMT "\n", (!j) ? 0.0 : (double)(((PetscReal)(ct + j)) / j), max));
880: #endif
881: PetscFunctionReturn(PETSC_SUCCESS);
882: }
884: static PetscErrorCode MatAssemblyBegin_MPIBAIJ(Mat mat, MatAssemblyType mode)
885: {
886: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
887: PetscInt nstash, reallocs;
889: PetscFunctionBegin;
890: if (baij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
892: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
893: PetscCall(MatStashScatterBegin_Private(mat, &mat->bstash, baij->rangebs));
894: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
895: PetscCall(PetscInfo(mat, "Stash has %" PetscInt_FMT " entries,uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
896: PetscCall(MatStashGetInfo_Private(&mat->bstash, &nstash, &reallocs));
897: PetscCall(PetscInfo(mat, "Block-Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
898: PetscFunctionReturn(PETSC_SUCCESS);
899: }
901: static PetscErrorCode MatAssemblyEnd_MPIBAIJ(Mat mat, MatAssemblyType mode)
902: {
903: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
904: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)baij->A->data;
905: PetscInt i, j, rstart, ncols, flg, bs2 = baij->bs2;
906: PetscInt *row, *col;
907: PetscBool r1, r2, r3, other_disassembled;
908: MatScalar *val;
909: PetscMPIInt n;
911: PetscFunctionBegin;
912: /* do not use 'b=(Mat_SeqBAIJ*)baij->B->data' as B can be reset in disassembly */
913: if (!baij->donotstash && !mat->nooffprocentries) {
914: while (1) {
915: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
916: if (!flg) break;
918: for (i = 0; i < n;) {
919: /* Now identify the consecutive vals belonging to the same row */
920: for (j = i, rstart = row[j]; j < n; j++) {
921: if (row[j] != rstart) break;
922: }
923: if (j < n) ncols = j - i;
924: else ncols = n - i;
925: /* Now assemble all these values with a single function call */
926: PetscCall(MatSetValues_MPIBAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
927: i = j;
928: }
929: }
930: PetscCall(MatStashScatterEnd_Private(&mat->stash));
931: /* Now process the block-stash. Since the values are stashed column-oriented,
932: set the row-oriented flag to column-oriented, and after MatSetValues()
933: restore the original flags */
934: r1 = baij->roworiented;
935: r2 = a->roworiented;
936: r3 = ((Mat_SeqBAIJ *)baij->B->data)->roworiented;
938: baij->roworiented = PETSC_FALSE;
939: a->roworiented = PETSC_FALSE;
940: ((Mat_SeqBAIJ *)baij->B->data)->roworiented = PETSC_FALSE;
941: while (1) {
942: PetscCall(MatStashScatterGetMesg_Private(&mat->bstash, &n, &row, &col, &val, &flg));
943: if (!flg) break;
945: for (i = 0; i < n;) {
946: /* Now identify the consecutive vals belonging to the same row */
947: for (j = i, rstart = row[j]; j < n; j++) {
948: if (row[j] != rstart) break;
949: }
950: if (j < n) ncols = j - i;
951: else ncols = n - i;
952: PetscCall(MatSetValuesBlocked_MPIBAIJ(mat, 1, row + i, ncols, col + i, val + i * bs2, mat->insertmode));
953: i = j;
954: }
955: }
956: PetscCall(MatStashScatterEnd_Private(&mat->bstash));
958: baij->roworiented = r1;
959: a->roworiented = r2;
960: ((Mat_SeqBAIJ *)baij->B->data)->roworiented = r3;
961: }
963: PetscCall(MatAssemblyBegin(baij->A, mode));
964: PetscCall(MatAssemblyEnd(baij->A, mode));
966: /* determine if any processor has disassembled, if so we must
967: also disassemble ourselves, in order that we may reassemble. */
968: /*
969: if nonzero structure of submatrix B cannot change then we know that
970: no processor disassembled thus we can skip this stuff
971: */
972: if (!((Mat_SeqBAIJ *)baij->B->data)->nonew) {
973: PetscCallMPI(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
974: if (mat->was_assembled && !other_disassembled) PetscCall(MatDisAssemble_MPIBAIJ(mat));
975: }
977: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIBAIJ(mat));
978: PetscCall(MatAssemblyBegin(baij->B, mode));
979: PetscCall(MatAssemblyEnd(baij->B, mode));
981: #if defined(PETSC_USE_INFO)
982: if (baij->ht && mode == MAT_FINAL_ASSEMBLY) {
983: PetscCall(PetscInfo(mat, "Average Hash Table Search in MatSetValues = %5.2f\n", (double)((PetscReal)baij->ht_total_ct) / baij->ht_insert_ct));
985: baij->ht_total_ct = 0;
986: baij->ht_insert_ct = 0;
987: }
988: #endif
989: if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
990: PetscCall(MatCreateHashTable_MPIBAIJ_Private(mat, baij->ht_fact));
992: mat->ops->setvalues = MatSetValues_MPIBAIJ_HT;
993: mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
994: }
996: PetscCall(PetscFree2(baij->rowvalues, baij->rowindices));
998: baij->rowvalues = NULL;
1000: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
1001: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqBAIJ *)baij->A->data)->nonew) {
1002: PetscObjectState state = baij->A->nonzerostate + baij->B->nonzerostate;
1003: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
1004: }
1005: PetscFunctionReturn(PETSC_SUCCESS);
1006: }
1008: extern PetscErrorCode MatView_SeqBAIJ(Mat, PetscViewer);
1009: #include <petscdraw.h>
1010: static PetscErrorCode MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1011: {
1012: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1013: PetscMPIInt rank = baij->rank;
1014: PetscInt bs = mat->rmap->bs;
1015: PetscBool iascii, isdraw;
1016: PetscViewer sviewer;
1017: PetscViewerFormat format;
1019: PetscFunctionBegin;
1020: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1021: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1022: if (iascii) {
1023: PetscCall(PetscViewerGetFormat(viewer, &format));
1024: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1025: MatInfo info;
1026: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1027: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1028: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1029: 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,
1030: mat->rmap->bs, info.memory));
1031: PetscCall(MatGetInfo(baij->A, MAT_LOCAL, &info));
1032: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1033: PetscCall(MatGetInfo(baij->B, MAT_LOCAL, &info));
1034: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1035: PetscCall(PetscViewerFlush(viewer));
1036: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1037: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1038: PetscCall(VecScatterView(baij->Mvctx, viewer));
1039: PetscFunctionReturn(PETSC_SUCCESS);
1040: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1041: PetscCall(PetscViewerASCIIPrintf(viewer, " block size is %" PetscInt_FMT "\n", bs));
1042: PetscFunctionReturn(PETSC_SUCCESS);
1043: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1044: PetscFunctionReturn(PETSC_SUCCESS);
1045: }
1046: }
1048: if (isdraw) {
1049: PetscDraw draw;
1050: PetscBool isnull;
1051: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1052: PetscCall(PetscDrawIsNull(draw, &isnull));
1053: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1054: }
1056: {
1057: /* assemble the entire matrix onto first processor. */
1058: Mat A;
1059: Mat_SeqBAIJ *Aloc;
1060: PetscInt M = mat->rmap->N, N = mat->cmap->N, *ai, *aj, col, i, j, k, *rvals, mbs = baij->mbs;
1061: MatScalar *a;
1062: const char *matname;
1064: /* Here we are creating a temporary matrix, so will assume MPIBAIJ is acceptable */
1065: /* Perhaps this should be the type of mat? */
1066: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &A));
1067: if (rank == 0) {
1068: PetscCall(MatSetSizes(A, M, N, M, N));
1069: } else {
1070: PetscCall(MatSetSizes(A, 0, 0, M, N));
1071: }
1072: PetscCall(MatSetType(A, MATMPIBAIJ));
1073: PetscCall(MatMPIBAIJSetPreallocation(A, mat->rmap->bs, 0, NULL, 0, NULL));
1074: PetscCall(MatSetOption(A, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_FALSE));
1076: /* copy over the A part */
1077: Aloc = (Mat_SeqBAIJ *)baij->A->data;
1078: ai = Aloc->i;
1079: aj = Aloc->j;
1080: a = Aloc->a;
1081: PetscCall(PetscMalloc1(bs, &rvals));
1083: for (i = 0; i < mbs; i++) {
1084: rvals[0] = bs * (baij->rstartbs + i);
1085: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1086: for (j = ai[i]; j < ai[i + 1]; j++) {
1087: col = (baij->cstartbs + aj[j]) * bs;
1088: for (k = 0; k < bs; k++) {
1089: PetscCall(MatSetValues_MPIBAIJ(A, bs, rvals, 1, &col, a, INSERT_VALUES));
1090: col++;
1091: a += bs;
1092: }
1093: }
1094: }
1095: /* copy over the B part */
1096: Aloc = (Mat_SeqBAIJ *)baij->B->data;
1097: ai = Aloc->i;
1098: aj = Aloc->j;
1099: a = Aloc->a;
1100: for (i = 0; i < mbs; i++) {
1101: rvals[0] = bs * (baij->rstartbs + i);
1102: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1103: for (j = ai[i]; j < ai[i + 1]; j++) {
1104: col = baij->garray[aj[j]] * bs;
1105: for (k = 0; k < bs; k++) {
1106: PetscCall(MatSetValues_MPIBAIJ(A, bs, rvals, 1, &col, a, INSERT_VALUES));
1107: col++;
1108: a += bs;
1109: }
1110: }
1111: }
1112: PetscCall(PetscFree(rvals));
1113: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1114: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1115: /*
1116: Everyone has to call to draw the matrix since the graphics waits are
1117: synchronized across all processors that share the PetscDraw object
1118: */
1119: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1120: if (((PetscObject)mat)->name) PetscCall(PetscObjectGetName((PetscObject)mat, &matname));
1121: if (rank == 0) {
1122: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)((Mat_MPIBAIJ *)A->data)->A, matname));
1123: PetscCall(MatView_SeqBAIJ(((Mat_MPIBAIJ *)A->data)->A, sviewer));
1124: }
1125: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1126: PetscCall(MatDestroy(&A));
1127: }
1128: PetscFunctionReturn(PETSC_SUCCESS);
1129: }
1131: /* Used for both MPIBAIJ and MPISBAIJ matrices */
1132: PetscErrorCode MatView_MPIBAIJ_Binary(Mat mat, PetscViewer viewer)
1133: {
1134: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
1135: Mat_SeqBAIJ *A = (Mat_SeqBAIJ *)aij->A->data;
1136: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)aij->B->data;
1137: const PetscInt *garray = aij->garray;
1138: PetscInt header[4], M, N, m, rs, cs, bs, cnt, i, j, ja, jb, k, l;
1139: PetscCount nz, hnz;
1140: PetscInt *rowlens, *colidxs;
1141: PetscScalar *matvals;
1142: PetscMPIInt rank;
1144: PetscFunctionBegin;
1145: PetscCall(PetscViewerSetUp(viewer));
1147: M = mat->rmap->N;
1148: N = mat->cmap->N;
1149: m = mat->rmap->n;
1150: rs = mat->rmap->rstart;
1151: cs = mat->cmap->rstart;
1152: bs = mat->rmap->bs;
1153: nz = bs * bs * (A->nz + B->nz);
1155: /* write matrix header */
1156: header[0] = MAT_FILE_CLASSID;
1157: header[1] = M;
1158: header[2] = N;
1159: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_COUNT, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1160: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1161: if (rank == 0) PetscCall(PetscIntCast(hnz, &header[3]));
1162: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1164: /* fill in and store row lengths */
1165: PetscCall(PetscMalloc1(m, &rowlens));
1166: for (cnt = 0, i = 0; i < A->mbs; i++)
1167: for (j = 0; j < bs; j++) rowlens[cnt++] = bs * (A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i]);
1168: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1169: PetscCall(PetscFree(rowlens));
1171: /* fill in and store column indices */
1172: PetscCall(PetscMalloc1(nz, &colidxs));
1173: for (cnt = 0, i = 0; i < A->mbs; i++) {
1174: for (k = 0; k < bs; k++) {
1175: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1176: if (garray[B->j[jb]] > cs / bs) break;
1177: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * garray[B->j[jb]] + l;
1178: }
1179: for (ja = A->i[i]; ja < A->i[i + 1]; ja++)
1180: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * A->j[ja] + l + cs;
1181: for (; jb < B->i[i + 1]; jb++)
1182: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * garray[B->j[jb]] + l;
1183: }
1184: }
1185: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscCount_FMT, cnt, nz);
1186: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DECIDE, PETSC_DECIDE, PETSC_INT));
1187: PetscCall(PetscFree(colidxs));
1189: /* fill in and store nonzero values */
1190: PetscCall(PetscMalloc1(nz, &matvals));
1191: for (cnt = 0, i = 0; i < A->mbs; i++) {
1192: for (k = 0; k < bs; k++) {
1193: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1194: if (garray[B->j[jb]] > cs / bs) break;
1195: for (l = 0; l < bs; l++) matvals[cnt++] = B->a[bs * (bs * jb + l) + k];
1196: }
1197: for (ja = A->i[i]; ja < A->i[i + 1]; ja++)
1198: for (l = 0; l < bs; l++) matvals[cnt++] = A->a[bs * (bs * ja + l) + k];
1199: for (; jb < B->i[i + 1]; jb++)
1200: for (l = 0; l < bs; l++) matvals[cnt++] = B->a[bs * (bs * jb + l) + k];
1201: }
1202: }
1203: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DECIDE, PETSC_DECIDE, PETSC_SCALAR));
1204: PetscCall(PetscFree(matvals));
1206: /* write block size option to the viewer's .info file */
1207: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1208: PetscFunctionReturn(PETSC_SUCCESS);
1209: }
1211: PetscErrorCode MatView_MPIBAIJ(Mat mat, PetscViewer viewer)
1212: {
1213: PetscBool iascii, isdraw, issocket, isbinary;
1215: PetscFunctionBegin;
1216: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1217: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1218: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1219: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1220: if (iascii || isdraw || issocket) {
1221: PetscCall(MatView_MPIBAIJ_ASCIIorDraworSocket(mat, viewer));
1222: } else if (isbinary) PetscCall(MatView_MPIBAIJ_Binary(mat, viewer));
1223: PetscFunctionReturn(PETSC_SUCCESS);
1224: }
1226: static PetscErrorCode MatMult_MPIBAIJ(Mat A, Vec xx, Vec yy)
1227: {
1228: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1229: PetscInt nt;
1231: PetscFunctionBegin;
1232: PetscCall(VecGetLocalSize(xx, &nt));
1233: PetscCheck(nt == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A and xx");
1234: PetscCall(VecGetLocalSize(yy, &nt));
1235: PetscCheck(nt == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A and yy");
1236: PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1237: PetscCall((*a->A->ops->mult)(a->A, xx, yy));
1238: PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1239: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, yy, yy));
1240: PetscFunctionReturn(PETSC_SUCCESS);
1241: }
1243: static PetscErrorCode MatMultAdd_MPIBAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1244: {
1245: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1247: PetscFunctionBegin;
1248: PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1249: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1250: PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1251: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1252: PetscFunctionReturn(PETSC_SUCCESS);
1253: }
1255: static PetscErrorCode MatMultTranspose_MPIBAIJ(Mat A, Vec xx, Vec yy)
1256: {
1257: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1259: PetscFunctionBegin;
1260: /* do nondiagonal part */
1261: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1262: /* do local part */
1263: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1264: /* add partial results together */
1265: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1266: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1267: PetscFunctionReturn(PETSC_SUCCESS);
1268: }
1270: static PetscErrorCode MatMultTransposeAdd_MPIBAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1271: {
1272: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1274: PetscFunctionBegin;
1275: /* do nondiagonal part */
1276: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1277: /* do local part */
1278: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1279: /* add partial results together */
1280: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1281: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1282: PetscFunctionReturn(PETSC_SUCCESS);
1283: }
1285: /*
1286: This only works correctly for square matrices where the subblock A->A is the
1287: diagonal block
1288: */
1289: static PetscErrorCode MatGetDiagonal_MPIBAIJ(Mat A, Vec v)
1290: {
1291: PetscFunctionBegin;
1292: PetscCheck(A->rmap->N == A->cmap->N, PETSC_COMM_SELF, PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1293: PetscCall(MatGetDiagonal(((Mat_MPIBAIJ *)A->data)->A, v));
1294: PetscFunctionReturn(PETSC_SUCCESS);
1295: }
1297: static PetscErrorCode MatScale_MPIBAIJ(Mat A, PetscScalar aa)
1298: {
1299: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1301: PetscFunctionBegin;
1302: PetscCall(MatScale(a->A, aa));
1303: PetscCall(MatScale(a->B, aa));
1304: PetscFunctionReturn(PETSC_SUCCESS);
1305: }
1307: PetscErrorCode MatGetRow_MPIBAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1308: {
1309: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)matin->data;
1310: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1311: PetscInt bs = matin->rmap->bs, bs2 = mat->bs2, i, *cworkA, *cworkB, **pcA, **pcB;
1312: PetscInt nztot, nzA, nzB, lrow, brstart = matin->rmap->rstart, brend = matin->rmap->rend;
1313: PetscInt *cmap, *idx_p, cstart = mat->cstartbs;
1315: PetscFunctionBegin;
1316: PetscCheck(row >= brstart && row < brend, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local rows");
1317: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1318: mat->getrowactive = PETSC_TRUE;
1320: if (!mat->rowvalues && (idx || v)) {
1321: /*
1322: allocate enough space to hold information from the longest row.
1323: */
1324: Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ *)mat->A->data, *Ba = (Mat_SeqBAIJ *)mat->B->data;
1325: PetscInt max = 1, mbs = mat->mbs, tmp;
1326: for (i = 0; i < mbs; i++) {
1327: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1328: if (max < tmp) max = tmp;
1329: }
1330: PetscCall(PetscMalloc2(max * bs2, &mat->rowvalues, max * bs2, &mat->rowindices));
1331: }
1332: lrow = row - brstart;
1334: pvA = &vworkA;
1335: pcA = &cworkA;
1336: pvB = &vworkB;
1337: pcB = &cworkB;
1338: if (!v) {
1339: pvA = NULL;
1340: pvB = NULL;
1341: }
1342: if (!idx) {
1343: pcA = NULL;
1344: if (!v) pcB = NULL;
1345: }
1346: PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1347: PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1348: nztot = nzA + nzB;
1350: cmap = mat->garray;
1351: if (v || idx) {
1352: if (nztot) {
1353: /* Sort by increasing column numbers, assuming A and B already sorted */
1354: PetscInt imark = -1;
1355: if (v) {
1356: *v = v_p = mat->rowvalues;
1357: for (i = 0; i < nzB; i++) {
1358: if (cmap[cworkB[i] / bs] < cstart) v_p[i] = vworkB[i];
1359: else break;
1360: }
1361: imark = i;
1362: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1363: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1364: }
1365: if (idx) {
1366: *idx = idx_p = mat->rowindices;
1367: if (imark > -1) {
1368: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i] / bs] * bs + cworkB[i] % bs;
1369: } else {
1370: for (i = 0; i < nzB; i++) {
1371: if (cmap[cworkB[i] / bs] < cstart) idx_p[i] = cmap[cworkB[i] / bs] * bs + cworkB[i] % bs;
1372: else break;
1373: }
1374: imark = i;
1375: }
1376: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart * bs + cworkA[i];
1377: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i] / bs] * bs + cworkB[i] % bs;
1378: }
1379: } else {
1380: if (idx) *idx = NULL;
1381: if (v) *v = NULL;
1382: }
1383: }
1384: *nz = nztot;
1385: PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1386: PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1387: PetscFunctionReturn(PETSC_SUCCESS);
1388: }
1390: PetscErrorCode MatRestoreRow_MPIBAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1391: {
1392: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1394: PetscFunctionBegin;
1395: PetscCheck(baij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow not called");
1396: baij->getrowactive = PETSC_FALSE;
1397: PetscFunctionReturn(PETSC_SUCCESS);
1398: }
1400: static PetscErrorCode MatZeroEntries_MPIBAIJ(Mat A)
1401: {
1402: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1404: PetscFunctionBegin;
1405: PetscCall(MatZeroEntries(l->A));
1406: PetscCall(MatZeroEntries(l->B));
1407: PetscFunctionReturn(PETSC_SUCCESS);
1408: }
1410: static PetscErrorCode MatGetInfo_MPIBAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1411: {
1412: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)matin->data;
1413: Mat A = a->A, B = a->B;
1414: PetscLogDouble isend[5], irecv[5];
1416: PetscFunctionBegin;
1417: info->block_size = (PetscReal)matin->rmap->bs;
1419: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1421: isend[0] = info->nz_used;
1422: isend[1] = info->nz_allocated;
1423: isend[2] = info->nz_unneeded;
1424: isend[3] = info->memory;
1425: isend[4] = info->mallocs;
1427: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1429: isend[0] += info->nz_used;
1430: isend[1] += info->nz_allocated;
1431: isend[2] += info->nz_unneeded;
1432: isend[3] += info->memory;
1433: isend[4] += info->mallocs;
1435: if (flag == MAT_LOCAL) {
1436: info->nz_used = isend[0];
1437: info->nz_allocated = isend[1];
1438: info->nz_unneeded = isend[2];
1439: info->memory = isend[3];
1440: info->mallocs = isend[4];
1441: } else if (flag == MAT_GLOBAL_MAX) {
1442: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1444: info->nz_used = irecv[0];
1445: info->nz_allocated = irecv[1];
1446: info->nz_unneeded = irecv[2];
1447: info->memory = irecv[3];
1448: info->mallocs = irecv[4];
1449: } else if (flag == MAT_GLOBAL_SUM) {
1450: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1452: info->nz_used = irecv[0];
1453: info->nz_allocated = irecv[1];
1454: info->nz_unneeded = irecv[2];
1455: info->memory = irecv[3];
1456: info->mallocs = irecv[4];
1457: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_ARG_WRONG, "Unknown MatInfoType argument %d", (int)flag);
1458: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1459: info->fill_ratio_needed = 0;
1460: info->factor_mallocs = 0;
1461: PetscFunctionReturn(PETSC_SUCCESS);
1462: }
1464: static PetscErrorCode MatSetOption_MPIBAIJ(Mat A, MatOption op, PetscBool flg)
1465: {
1466: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1468: PetscFunctionBegin;
1469: switch (op) {
1470: case MAT_NEW_NONZERO_LOCATIONS:
1471: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1472: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1473: case MAT_KEEP_NONZERO_PATTERN:
1474: case MAT_NEW_NONZERO_LOCATION_ERR:
1475: MatCheckPreallocated(A, 1);
1476: PetscCall(MatSetOption(a->A, op, flg));
1477: PetscCall(MatSetOption(a->B, op, flg));
1478: break;
1479: case MAT_ROW_ORIENTED:
1480: MatCheckPreallocated(A, 1);
1481: a->roworiented = flg;
1483: PetscCall(MatSetOption(a->A, op, flg));
1484: PetscCall(MatSetOption(a->B, op, flg));
1485: break;
1486: case MAT_FORCE_DIAGONAL_ENTRIES:
1487: case MAT_SORTED_FULL:
1488: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1489: break;
1490: case MAT_IGNORE_OFF_PROC_ENTRIES:
1491: a->donotstash = flg;
1492: break;
1493: case MAT_USE_HASH_TABLE:
1494: a->ht_flag = flg;
1495: a->ht_fact = 1.39;
1496: break;
1497: case MAT_SYMMETRIC:
1498: case MAT_STRUCTURALLY_SYMMETRIC:
1499: case MAT_HERMITIAN:
1500: case MAT_SUBMAT_SINGLEIS:
1501: case MAT_SYMMETRY_ETERNAL:
1502: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1503: case MAT_SPD_ETERNAL:
1504: /* if the diagonal matrix is square it inherits some of the properties above */
1505: break;
1506: default:
1507: SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "unknown option %d", op);
1508: }
1509: PetscFunctionReturn(PETSC_SUCCESS);
1510: }
1512: static PetscErrorCode MatTranspose_MPIBAIJ(Mat A, MatReuse reuse, Mat *matout)
1513: {
1514: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)A->data;
1515: Mat_SeqBAIJ *Aloc;
1516: Mat B;
1517: PetscInt M = A->rmap->N, N = A->cmap->N, *ai, *aj, i, *rvals, j, k, col;
1518: PetscInt bs = A->rmap->bs, mbs = baij->mbs;
1519: MatScalar *a;
1521: PetscFunctionBegin;
1522: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1523: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
1524: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1525: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1526: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1527: /* Do not know preallocation information, but must set block size */
1528: PetscCall(MatMPIBAIJSetPreallocation(B, A->rmap->bs, PETSC_DECIDE, NULL, PETSC_DECIDE, NULL));
1529: } else {
1530: B = *matout;
1531: }
1533: /* copy over the A part */
1534: Aloc = (Mat_SeqBAIJ *)baij->A->data;
1535: ai = Aloc->i;
1536: aj = Aloc->j;
1537: a = Aloc->a;
1538: PetscCall(PetscMalloc1(bs, &rvals));
1540: for (i = 0; i < mbs; i++) {
1541: rvals[0] = bs * (baij->rstartbs + i);
1542: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1543: for (j = ai[i]; j < ai[i + 1]; j++) {
1544: col = (baij->cstartbs + aj[j]) * bs;
1545: for (k = 0; k < bs; k++) {
1546: PetscCall(MatSetValues_MPIBAIJ(B, 1, &col, bs, rvals, a, INSERT_VALUES));
1548: col++;
1549: a += bs;
1550: }
1551: }
1552: }
1553: /* copy over the B part */
1554: Aloc = (Mat_SeqBAIJ *)baij->B->data;
1555: ai = Aloc->i;
1556: aj = Aloc->j;
1557: a = Aloc->a;
1558: for (i = 0; i < mbs; i++) {
1559: rvals[0] = bs * (baij->rstartbs + i);
1560: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1561: for (j = ai[i]; j < ai[i + 1]; j++) {
1562: col = baij->garray[aj[j]] * bs;
1563: for (k = 0; k < bs; k++) {
1564: PetscCall(MatSetValues_MPIBAIJ(B, 1, &col, bs, rvals, a, INSERT_VALUES));
1565: col++;
1566: a += bs;
1567: }
1568: }
1569: }
1570: PetscCall(PetscFree(rvals));
1571: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1572: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1574: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) *matout = B;
1575: else PetscCall(MatHeaderMerge(A, &B));
1576: PetscFunctionReturn(PETSC_SUCCESS);
1577: }
1579: static PetscErrorCode MatDiagonalScale_MPIBAIJ(Mat mat, Vec ll, Vec rr)
1580: {
1581: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1582: Mat a = baij->A, b = baij->B;
1583: PetscInt s1, s2, s3;
1585: PetscFunctionBegin;
1586: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1587: if (rr) {
1588: PetscCall(VecGetLocalSize(rr, &s1));
1589: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1590: /* Overlap communication with computation. */
1591: PetscCall(VecScatterBegin(baij->Mvctx, rr, baij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1592: }
1593: if (ll) {
1594: PetscCall(VecGetLocalSize(ll, &s1));
1595: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1596: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1597: }
1598: /* scale the diagonal block */
1599: PetscUseTypeMethod(a, diagonalscale, ll, rr);
1601: if (rr) {
1602: /* Do a scatter end and then right scale the off-diagonal block */
1603: PetscCall(VecScatterEnd(baij->Mvctx, rr, baij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1604: PetscUseTypeMethod(b, diagonalscale, NULL, baij->lvec);
1605: }
1606: PetscFunctionReturn(PETSC_SUCCESS);
1607: }
1609: static PetscErrorCode MatZeroRows_MPIBAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
1610: {
1611: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1612: PetscInt *lrows;
1613: PetscInt r, len;
1614: PetscBool cong;
1616: PetscFunctionBegin;
1617: /* get locally owned rows */
1618: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
1619: /* fix right-hand side if needed */
1620: if (x && b) {
1621: const PetscScalar *xx;
1622: PetscScalar *bb;
1624: PetscCall(VecGetArrayRead(x, &xx));
1625: PetscCall(VecGetArray(b, &bb));
1626: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
1627: PetscCall(VecRestoreArrayRead(x, &xx));
1628: PetscCall(VecRestoreArray(b, &bb));
1629: }
1631: /* actually zap the local rows */
1632: /*
1633: Zero the required rows. If the "diagonal block" of the matrix
1634: is square and the user wishes to set the diagonal we use separate
1635: code so that MatSetValues() is not called for each diagonal allocating
1636: new memory, thus calling lots of mallocs and slowing things down.
1638: */
1639: /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1640: PetscCall(MatZeroRows_SeqBAIJ(l->B, len, lrows, 0.0, NULL, NULL));
1641: PetscCall(MatHasCongruentLayouts(A, &cong));
1642: if ((diag != 0.0) && cong) {
1643: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, diag, NULL, NULL));
1644: } else if (diag != 0.0) {
1645: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, 0.0, NULL, NULL));
1646: 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");
1647: for (r = 0; r < len; ++r) {
1648: const PetscInt row = lrows[r] + A->rmap->rstart;
1649: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
1650: }
1651: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1652: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1653: } else {
1654: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, 0.0, NULL, NULL));
1655: }
1656: PetscCall(PetscFree(lrows));
1658: /* only change matrix nonzero state if pattern was allowed to be changed */
1659: if (!((Mat_SeqBAIJ *)l->A->data)->keepnonzeropattern || !((Mat_SeqBAIJ *)l->A->data)->nonew) {
1660: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1661: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1662: }
1663: PetscFunctionReturn(PETSC_SUCCESS);
1664: }
1666: static PetscErrorCode MatZeroRowsColumns_MPIBAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
1667: {
1668: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1669: PetscMPIInt n, p = 0;
1670: PetscInt i, j, k, r, len = 0, row, col, count;
1671: PetscInt *lrows, *owners = A->rmap->range;
1672: PetscSFNode *rrows;
1673: PetscSF sf;
1674: const PetscScalar *xx;
1675: PetscScalar *bb, *mask;
1676: Vec xmask, lmask;
1677: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)l->B->data;
1678: PetscInt bs = A->rmap->bs, bs2 = baij->bs2;
1679: PetscScalar *aa;
1681: PetscFunctionBegin;
1682: PetscCall(PetscMPIIntCast(A->rmap->n, &n));
1683: /* Create SF where leaves are input rows and roots are owned rows */
1684: PetscCall(PetscMalloc1(n, &lrows));
1685: for (r = 0; r < n; ++r) lrows[r] = -1;
1686: PetscCall(PetscMalloc1(N, &rrows));
1687: for (r = 0; r < N; ++r) {
1688: const PetscInt idx = rows[r];
1689: 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);
1690: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
1691: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
1692: }
1693: rrows[r].rank = p;
1694: rrows[r].index = rows[r] - owners[p];
1695: }
1696: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1697: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
1698: /* Collect flags for rows to be zeroed */
1699: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
1700: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
1701: PetscCall(PetscSFDestroy(&sf));
1702: /* Compress and put in row numbers */
1703: for (r = 0; r < n; ++r)
1704: if (lrows[r] >= 0) lrows[len++] = r;
1705: /* zero diagonal part of matrix */
1706: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
1707: /* handle off-diagonal part of matrix */
1708: PetscCall(MatCreateVecs(A, &xmask, NULL));
1709: PetscCall(VecDuplicate(l->lvec, &lmask));
1710: PetscCall(VecGetArray(xmask, &bb));
1711: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
1712: PetscCall(VecRestoreArray(xmask, &bb));
1713: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
1714: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
1715: PetscCall(VecDestroy(&xmask));
1716: if (x) {
1717: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
1718: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
1719: PetscCall(VecGetArrayRead(l->lvec, &xx));
1720: PetscCall(VecGetArray(b, &bb));
1721: }
1722: PetscCall(VecGetArray(lmask, &mask));
1723: /* remove zeroed rows of off-diagonal matrix */
1724: for (i = 0; i < len; ++i) {
1725: row = lrows[i];
1726: count = (baij->i[row / bs + 1] - baij->i[row / bs]) * bs;
1727: aa = baij->a + baij->i[row / bs] * bs2 + (row % bs);
1728: for (k = 0; k < count; ++k) {
1729: aa[0] = 0.0;
1730: aa += bs;
1731: }
1732: }
1733: /* loop over all elements of off process part of matrix zeroing removed columns*/
1734: for (i = 0; i < l->B->rmap->N; ++i) {
1735: row = i / bs;
1736: for (j = baij->i[row]; j < baij->i[row + 1]; ++j) {
1737: for (k = 0; k < bs; ++k) {
1738: col = bs * baij->j[j] + k;
1739: if (PetscAbsScalar(mask[col])) {
1740: aa = baij->a + j * bs2 + (i % bs) + bs * k;
1741: if (x) bb[i] -= aa[0] * xx[col];
1742: aa[0] = 0.0;
1743: }
1744: }
1745: }
1746: }
1747: if (x) {
1748: PetscCall(VecRestoreArray(b, &bb));
1749: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1750: }
1751: PetscCall(VecRestoreArray(lmask, &mask));
1752: PetscCall(VecDestroy(&lmask));
1753: PetscCall(PetscFree(lrows));
1755: /* only change matrix nonzero state if pattern was allowed to be changed */
1756: if (!((Mat_SeqBAIJ *)l->A->data)->nonew) {
1757: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1758: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1759: }
1760: PetscFunctionReturn(PETSC_SUCCESS);
1761: }
1763: static PetscErrorCode MatSetUnfactored_MPIBAIJ(Mat A)
1764: {
1765: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1767: PetscFunctionBegin;
1768: PetscCall(MatSetUnfactored(a->A));
1769: PetscFunctionReturn(PETSC_SUCCESS);
1770: }
1772: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat, MatDuplicateOption, Mat *);
1774: static PetscErrorCode MatEqual_MPIBAIJ(Mat A, Mat B, PetscBool *flag)
1775: {
1776: Mat_MPIBAIJ *matB = (Mat_MPIBAIJ *)B->data, *matA = (Mat_MPIBAIJ *)A->data;
1777: Mat a, b, c, d;
1778: PetscBool flg;
1780: PetscFunctionBegin;
1781: a = matA->A;
1782: b = matA->B;
1783: c = matB->A;
1784: d = matB->B;
1786: PetscCall(MatEqual(a, c, &flg));
1787: if (flg) PetscCall(MatEqual(b, d, &flg));
1788: PetscCallMPI(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
1789: PetscFunctionReturn(PETSC_SUCCESS);
1790: }
1792: static PetscErrorCode MatCopy_MPIBAIJ(Mat A, Mat B, MatStructure str)
1793: {
1794: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1795: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
1797: PetscFunctionBegin;
1798: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1799: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1800: PetscCall(MatCopy_Basic(A, B, str));
1801: } else {
1802: PetscCall(MatCopy(a->A, b->A, str));
1803: PetscCall(MatCopy(a->B, b->B, str));
1804: }
1805: PetscCall(PetscObjectStateIncrease((PetscObject)B));
1806: PetscFunctionReturn(PETSC_SUCCESS);
1807: }
1809: PetscErrorCode MatAXPYGetPreallocation_MPIBAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
1810: {
1811: PetscInt bs = Y->rmap->bs, m = Y->rmap->N / bs;
1812: Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data;
1813: Mat_SeqBAIJ *y = (Mat_SeqBAIJ *)Y->data;
1815: PetscFunctionBegin;
1816: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
1817: PetscFunctionReturn(PETSC_SUCCESS);
1818: }
1820: static PetscErrorCode MatAXPY_MPIBAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
1821: {
1822: Mat_MPIBAIJ *xx = (Mat_MPIBAIJ *)X->data, *yy = (Mat_MPIBAIJ *)Y->data;
1823: PetscBLASInt bnz, one = 1;
1824: Mat_SeqBAIJ *x, *y;
1825: PetscInt bs2 = Y->rmap->bs * Y->rmap->bs;
1827: PetscFunctionBegin;
1828: if (str == SAME_NONZERO_PATTERN) {
1829: PetscScalar alpha = a;
1830: x = (Mat_SeqBAIJ *)xx->A->data;
1831: y = (Mat_SeqBAIJ *)yy->A->data;
1832: PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz));
1833: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one));
1834: x = (Mat_SeqBAIJ *)xx->B->data;
1835: y = (Mat_SeqBAIJ *)yy->B->data;
1836: PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz));
1837: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one));
1838: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
1839: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
1840: PetscCall(MatAXPY_Basic(Y, a, X, str));
1841: } else {
1842: Mat B;
1843: PetscInt *nnz_d, *nnz_o, bs = Y->rmap->bs;
1844: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
1845: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
1846: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
1847: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
1848: PetscCall(MatSetSizes(B, Y->rmap->n, Y->cmap->n, Y->rmap->N, Y->cmap->N));
1849: PetscCall(MatSetBlockSizesFromMats(B, Y, Y));
1850: PetscCall(MatSetType(B, MATMPIBAIJ));
1851: PetscCall(MatAXPYGetPreallocation_SeqBAIJ(yy->A, xx->A, nnz_d));
1852: PetscCall(MatAXPYGetPreallocation_MPIBAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
1853: PetscCall(MatMPIBAIJSetPreallocation(B, bs, 0, nnz_d, 0, nnz_o));
1854: /* MatAXPY_BasicWithPreallocation() for BAIJ matrix is much slower than AIJ, even for bs=1 ! */
1855: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
1856: PetscCall(MatHeaderMerge(Y, &B));
1857: PetscCall(PetscFree(nnz_d));
1858: PetscCall(PetscFree(nnz_o));
1859: }
1860: PetscFunctionReturn(PETSC_SUCCESS);
1861: }
1863: static PetscErrorCode MatConjugate_MPIBAIJ(Mat mat)
1864: {
1865: PetscFunctionBegin;
1866: if (PetscDefined(USE_COMPLEX)) {
1867: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)mat->data;
1869: PetscCall(MatConjugate_SeqBAIJ(a->A));
1870: PetscCall(MatConjugate_SeqBAIJ(a->B));
1871: }
1872: PetscFunctionReturn(PETSC_SUCCESS);
1873: }
1875: static PetscErrorCode MatRealPart_MPIBAIJ(Mat A)
1876: {
1877: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1879: PetscFunctionBegin;
1880: PetscCall(MatRealPart(a->A));
1881: PetscCall(MatRealPart(a->B));
1882: PetscFunctionReturn(PETSC_SUCCESS);
1883: }
1885: static PetscErrorCode MatImaginaryPart_MPIBAIJ(Mat A)
1886: {
1887: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1889: PetscFunctionBegin;
1890: PetscCall(MatImaginaryPart(a->A));
1891: PetscCall(MatImaginaryPart(a->B));
1892: PetscFunctionReturn(PETSC_SUCCESS);
1893: }
1895: static PetscErrorCode MatCreateSubMatrix_MPIBAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
1896: {
1897: IS iscol_local;
1898: PetscInt csize;
1900: PetscFunctionBegin;
1901: PetscCall(ISGetLocalSize(iscol, &csize));
1902: if (call == MAT_REUSE_MATRIX) {
1903: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
1904: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
1905: } else {
1906: PetscCall(ISAllGather(iscol, &iscol_local));
1907: }
1908: PetscCall(MatCreateSubMatrix_MPIBAIJ_Private(mat, isrow, iscol_local, csize, call, newmat, PETSC_FALSE));
1909: if (call == MAT_INITIAL_MATRIX) {
1910: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
1911: PetscCall(ISDestroy(&iscol_local));
1912: }
1913: PetscFunctionReturn(PETSC_SUCCESS);
1914: }
1916: /*
1917: Not great since it makes two copies of the submatrix, first an SeqBAIJ
1918: in local and then by concatenating the local matrices the end result.
1919: Writing it directly would be much like MatCreateSubMatrices_MPIBAIJ().
1920: This routine is used for BAIJ and SBAIJ matrices (unfortunate dependency).
1921: */
1922: PetscErrorCode MatCreateSubMatrix_MPIBAIJ_Private(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat, PetscBool sym)
1923: {
1924: PetscMPIInt rank, size;
1925: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs;
1926: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
1927: Mat M, Mreuse;
1928: MatScalar *vwork, *aa;
1929: MPI_Comm comm;
1930: IS isrow_new, iscol_new;
1931: Mat_SeqBAIJ *aij;
1933: PetscFunctionBegin;
1934: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
1935: PetscCallMPI(MPI_Comm_rank(comm, &rank));
1936: PetscCallMPI(MPI_Comm_size(comm, &size));
1937: /* The compression and expansion should be avoided. Doesn't point
1938: out errors, might change the indices, hence buggey */
1939: PetscCall(ISCompressIndicesGeneral(mat->rmap->N, mat->rmap->n, mat->rmap->bs, 1, &isrow, &isrow_new));
1940: if (isrow == iscol) {
1941: iscol_new = isrow_new;
1942: PetscCall(PetscObjectReference((PetscObject)iscol_new));
1943: } else PetscCall(ISCompressIndicesGeneral(mat->cmap->N, mat->cmap->n, mat->cmap->bs, 1, &iscol, &iscol_new));
1945: if (call == MAT_REUSE_MATRIX) {
1946: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
1947: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
1948: PetscCall(MatCreateSubMatrices_MPIBAIJ_local(mat, 1, &isrow_new, &iscol_new, MAT_REUSE_MATRIX, &Mreuse, sym));
1949: } else {
1950: PetscCall(MatCreateSubMatrices_MPIBAIJ_local(mat, 1, &isrow_new, &iscol_new, MAT_INITIAL_MATRIX, &Mreuse, sym));
1951: }
1952: PetscCall(ISDestroy(&isrow_new));
1953: PetscCall(ISDestroy(&iscol_new));
1954: /*
1955: m - number of local rows
1956: n - number of columns (same on all processors)
1957: rstart - first row in new global matrix generated
1958: */
1959: PetscCall(MatGetBlockSize(mat, &bs));
1960: PetscCall(MatGetSize(Mreuse, &m, &n));
1961: m = m / bs;
1962: n = n / bs;
1964: if (call == MAT_INITIAL_MATRIX) {
1965: aij = (Mat_SeqBAIJ *)Mreuse->data;
1966: ii = aij->i;
1967: jj = aij->j;
1969: /*
1970: Determine the number of non-zeros in the diagonal and off-diagonal
1971: portions of the matrix in order to do correct preallocation
1972: */
1974: /* first get start and end of "diagonal" columns */
1975: if (csize == PETSC_DECIDE) {
1976: PetscCall(ISGetSize(isrow, &mglobal));
1977: if (mglobal == n * bs) { /* square matrix */
1978: nlocal = m;
1979: } else {
1980: nlocal = n / size + ((n % size) > rank);
1981: }
1982: } else {
1983: nlocal = csize / bs;
1984: }
1985: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
1986: rstart = rend - nlocal;
1987: 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);
1989: /* next, compute all the lengths */
1990: PetscCall(PetscMalloc2(m + 1, &dlens, m + 1, &olens));
1991: for (i = 0; i < m; i++) {
1992: jend = ii[i + 1] - ii[i];
1993: olen = 0;
1994: dlen = 0;
1995: for (j = 0; j < jend; j++) {
1996: if (*jj < rstart || *jj >= rend) olen++;
1997: else dlen++;
1998: jj++;
1999: }
2000: olens[i] = olen;
2001: dlens[i] = dlen;
2002: }
2003: PetscCall(MatCreate(comm, &M));
2004: PetscCall(MatSetSizes(M, bs * m, bs * nlocal, PETSC_DECIDE, bs * n));
2005: PetscCall(MatSetType(M, sym ? ((PetscObject)mat)->type_name : MATMPIBAIJ));
2006: PetscCall(MatMPIBAIJSetPreallocation(M, bs, 0, dlens, 0, olens));
2007: PetscCall(MatMPISBAIJSetPreallocation(M, bs, 0, dlens, 0, olens));
2008: PetscCall(PetscFree2(dlens, olens));
2009: } else {
2010: PetscInt ml, nl;
2012: M = *newmat;
2013: PetscCall(MatGetLocalSize(M, &ml, &nl));
2014: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
2015: PetscCall(MatZeroEntries(M));
2016: /*
2017: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
2018: rather than the slower MatSetValues().
2019: */
2020: M->was_assembled = PETSC_TRUE;
2021: M->assembled = PETSC_FALSE;
2022: }
2023: PetscCall(MatSetOption(M, MAT_ROW_ORIENTED, PETSC_FALSE));
2024: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
2025: aij = (Mat_SeqBAIJ *)Mreuse->data;
2026: ii = aij->i;
2027: jj = aij->j;
2028: aa = aij->a;
2029: for (i = 0; i < m; i++) {
2030: row = rstart / bs + i;
2031: nz = ii[i + 1] - ii[i];
2032: cwork = jj;
2033: jj = PetscSafePointerPlusOffset(jj, nz);
2034: vwork = aa;
2035: aa = PetscSafePointerPlusOffset(aa, nz * bs * bs);
2036: PetscCall(MatSetValuesBlocked_MPIBAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
2037: }
2039: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
2040: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
2041: *newmat = M;
2043: /* save submatrix used in processor for next request */
2044: if (call == MAT_INITIAL_MATRIX) {
2045: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
2046: PetscCall(PetscObjectDereference((PetscObject)Mreuse));
2047: }
2048: PetscFunctionReturn(PETSC_SUCCESS);
2049: }
2051: static PetscErrorCode MatPermute_MPIBAIJ(Mat A, IS rowp, IS colp, Mat *B)
2052: {
2053: MPI_Comm comm, pcomm;
2054: PetscInt clocal_size, nrows;
2055: const PetscInt *rows;
2056: PetscMPIInt size;
2057: IS crowp, lcolp;
2059: PetscFunctionBegin;
2060: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
2061: /* make a collective version of 'rowp' */
2062: PetscCall(PetscObjectGetComm((PetscObject)rowp, &pcomm));
2063: if (pcomm == comm) {
2064: crowp = rowp;
2065: } else {
2066: PetscCall(ISGetSize(rowp, &nrows));
2067: PetscCall(ISGetIndices(rowp, &rows));
2068: PetscCall(ISCreateGeneral(comm, nrows, rows, PETSC_COPY_VALUES, &crowp));
2069: PetscCall(ISRestoreIndices(rowp, &rows));
2070: }
2071: PetscCall(ISSetPermutation(crowp));
2072: /* make a local version of 'colp' */
2073: PetscCall(PetscObjectGetComm((PetscObject)colp, &pcomm));
2074: PetscCallMPI(MPI_Comm_size(pcomm, &size));
2075: if (size == 1) {
2076: lcolp = colp;
2077: } else {
2078: PetscCall(ISAllGather(colp, &lcolp));
2079: }
2080: PetscCall(ISSetPermutation(lcolp));
2081: /* now we just get the submatrix */
2082: PetscCall(MatGetLocalSize(A, NULL, &clocal_size));
2083: PetscCall(MatCreateSubMatrix_MPIBAIJ_Private(A, crowp, lcolp, clocal_size, MAT_INITIAL_MATRIX, B, PETSC_FALSE));
2084: /* clean up */
2085: if (pcomm != comm) PetscCall(ISDestroy(&crowp));
2086: if (size > 1) PetscCall(ISDestroy(&lcolp));
2087: PetscFunctionReturn(PETSC_SUCCESS);
2088: }
2090: static PetscErrorCode MatGetGhosts_MPIBAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
2091: {
2092: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
2093: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)baij->B->data;
2095: PetscFunctionBegin;
2096: if (nghosts) *nghosts = B->nbs;
2097: if (ghosts) *ghosts = baij->garray;
2098: PetscFunctionReturn(PETSC_SUCCESS);
2099: }
2101: static PetscErrorCode MatGetSeqNonzeroStructure_MPIBAIJ(Mat A, Mat *newmat)
2102: {
2103: Mat B;
2104: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2105: Mat_SeqBAIJ *ad = (Mat_SeqBAIJ *)a->A->data, *bd = (Mat_SeqBAIJ *)a->B->data;
2106: Mat_SeqAIJ *b;
2107: PetscMPIInt size, rank, *recvcounts = NULL, *displs = NULL;
2108: PetscInt sendcount, i, *rstarts = A->rmap->range, n, cnt, j, bs = A->rmap->bs;
2109: PetscInt m, *garray = a->garray, *lens, *jsendbuf, *a_jsendbuf, *b_jsendbuf;
2111: PetscFunctionBegin;
2112: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
2113: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)A), &rank));
2115: /* Tell every processor the number of nonzeros per row */
2116: PetscCall(PetscMalloc1(A->rmap->N / bs, &lens));
2117: 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];
2118: PetscCall(PetscMalloc1(2 * size, &recvcounts));
2119: displs = recvcounts + size;
2120: for (i = 0; i < size; i++) {
2121: PetscCall(PetscMPIIntCast(A->rmap->range[i + 1] / bs - A->rmap->range[i] / bs, &recvcounts[i]));
2122: PetscCall(PetscMPIIntCast(A->rmap->range[i] / bs, &displs[i]));
2123: }
2124: PetscCallMPI(MPI_Allgatherv(MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, lens, recvcounts, displs, MPIU_INT, PetscObjectComm((PetscObject)A)));
2125: /* Create the sequential matrix of the same type as the local block diagonal */
2126: PetscCall(MatCreate(PETSC_COMM_SELF, &B));
2127: PetscCall(MatSetSizes(B, A->rmap->N / bs, A->cmap->N / bs, PETSC_DETERMINE, PETSC_DETERMINE));
2128: PetscCall(MatSetType(B, MATSEQAIJ));
2129: PetscCall(MatSeqAIJSetPreallocation(B, 0, lens));
2130: b = (Mat_SeqAIJ *)B->data;
2132: /* Copy my part of matrix column indices over */
2133: sendcount = ad->nz + bd->nz;
2134: jsendbuf = b->j + b->i[rstarts[rank] / bs];
2135: a_jsendbuf = ad->j;
2136: b_jsendbuf = bd->j;
2137: n = A->rmap->rend / bs - A->rmap->rstart / bs;
2138: cnt = 0;
2139: for (i = 0; i < n; i++) {
2140: /* put in lower diagonal portion */
2141: m = bd->i[i + 1] - bd->i[i];
2142: while (m > 0) {
2143: /* is it above diagonal (in bd (compressed) numbering) */
2144: if (garray[*b_jsendbuf] > A->rmap->rstart / bs + i) break;
2145: jsendbuf[cnt++] = garray[*b_jsendbuf++];
2146: m--;
2147: }
2149: /* put in diagonal portion */
2150: for (j = ad->i[i]; j < ad->i[i + 1]; j++) jsendbuf[cnt++] = A->rmap->rstart / bs + *a_jsendbuf++;
2152: /* put in upper diagonal portion */
2153: while (m-- > 0) jsendbuf[cnt++] = garray[*b_jsendbuf++];
2154: }
2155: PetscCheck(cnt == sendcount, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Corrupted PETSc matrix: nz given %" PetscInt_FMT " actual nz %" PetscInt_FMT, sendcount, cnt);
2157: /* Gather all column indices to all processors */
2158: for (i = 0; i < size; i++) {
2159: recvcounts[i] = 0;
2160: for (j = A->rmap->range[i] / bs; j < A->rmap->range[i + 1] / bs; j++) recvcounts[i] += lens[j];
2161: }
2162: displs[0] = 0;
2163: for (i = 1; i < size; i++) displs[i] = displs[i - 1] + recvcounts[i - 1];
2164: PetscCallMPI(MPI_Allgatherv(MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, b->j, recvcounts, displs, MPIU_INT, PetscObjectComm((PetscObject)A)));
2165: /* Assemble the matrix into usable form (note numerical values not yet set) */
2166: /* set the b->ilen (length of each row) values */
2167: PetscCall(PetscArraycpy(b->ilen, lens, A->rmap->N / bs));
2168: /* set the b->i indices */
2169: b->i[0] = 0;
2170: for (i = 1; i <= A->rmap->N / bs; i++) b->i[i] = b->i[i - 1] + lens[i - 1];
2171: PetscCall(PetscFree(lens));
2172: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2173: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2174: PetscCall(PetscFree(recvcounts));
2176: PetscCall(MatPropagateSymmetryOptions(A, B));
2177: *newmat = B;
2178: PetscFunctionReturn(PETSC_SUCCESS);
2179: }
2181: static PetscErrorCode MatSOR_MPIBAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
2182: {
2183: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)matin->data;
2184: Vec bb1 = NULL;
2186: PetscFunctionBegin;
2187: if (flag == SOR_APPLY_UPPER) {
2188: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2189: PetscFunctionReturn(PETSC_SUCCESS);
2190: }
2192: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS) PetscCall(VecDuplicate(bb, &bb1));
2194: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
2195: if (flag & SOR_ZERO_INITIAL_GUESS) {
2196: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2197: its--;
2198: }
2200: while (its--) {
2201: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2202: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2204: /* update rhs: bb1 = bb - B*x */
2205: PetscCall(VecScale(mat->lvec, -1.0));
2206: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2208: /* local sweep */
2209: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
2210: }
2211: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
2212: if (flag & SOR_ZERO_INITIAL_GUESS) {
2213: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2214: its--;
2215: }
2216: while (its--) {
2217: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2218: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2220: /* update rhs: bb1 = bb - B*x */
2221: PetscCall(VecScale(mat->lvec, -1.0));
2222: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2224: /* local sweep */
2225: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
2226: }
2227: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
2228: if (flag & SOR_ZERO_INITIAL_GUESS) {
2229: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2230: its--;
2231: }
2232: while (its--) {
2233: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2234: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2236: /* update rhs: bb1 = bb - B*x */
2237: PetscCall(VecScale(mat->lvec, -1.0));
2238: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2240: /* local sweep */
2241: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
2242: }
2243: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel version of SOR requested not supported");
2245: PetscCall(VecDestroy(&bb1));
2246: PetscFunctionReturn(PETSC_SUCCESS);
2247: }
2249: static PetscErrorCode MatGetColumnReductions_MPIBAIJ(Mat A, PetscInt type, PetscReal *reductions)
2250: {
2251: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)A->data;
2252: PetscInt m, N, i, *garray = aij->garray;
2253: PetscInt ib, jb, bs = A->rmap->bs;
2254: Mat_SeqBAIJ *a_aij = (Mat_SeqBAIJ *)aij->A->data;
2255: MatScalar *a_val = a_aij->a;
2256: Mat_SeqBAIJ *b_aij = (Mat_SeqBAIJ *)aij->B->data;
2257: MatScalar *b_val = b_aij->a;
2258: PetscReal *work;
2259: PetscMPIInt iN;
2261: PetscFunctionBegin;
2262: PetscCall(MatGetSize(A, &m, &N));
2263: PetscCall(PetscCalloc1(N, &work));
2264: if (type == NORM_2) {
2265: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2266: for (jb = 0; jb < bs; jb++) {
2267: for (ib = 0; ib < bs; ib++) {
2268: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val * *a_val);
2269: a_val++;
2270: }
2271: }
2272: }
2273: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2274: for (jb = 0; jb < bs; jb++) {
2275: for (ib = 0; ib < bs; ib++) {
2276: work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val * *b_val);
2277: b_val++;
2278: }
2279: }
2280: }
2281: } else if (type == NORM_1) {
2282: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2283: for (jb = 0; jb < bs; jb++) {
2284: for (ib = 0; ib < bs; ib++) {
2285: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val);
2286: a_val++;
2287: }
2288: }
2289: }
2290: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2291: for (jb = 0; jb < bs; jb++) {
2292: for (ib = 0; ib < bs; ib++) {
2293: work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val);
2294: b_val++;
2295: }
2296: }
2297: }
2298: } else if (type == NORM_INFINITY) {
2299: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2300: for (jb = 0; jb < bs; jb++) {
2301: for (ib = 0; ib < bs; ib++) {
2302: PetscInt col = A->cmap->rstart + a_aij->j[i] * bs + jb;
2303: work[col] = PetscMax(PetscAbsScalar(*a_val), work[col]);
2304: a_val++;
2305: }
2306: }
2307: }
2308: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2309: for (jb = 0; jb < bs; jb++) {
2310: for (ib = 0; ib < bs; ib++) {
2311: PetscInt col = garray[b_aij->j[i]] * bs + jb;
2312: work[col] = PetscMax(PetscAbsScalar(*b_val), work[col]);
2313: b_val++;
2314: }
2315: }
2316: }
2317: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
2318: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2319: for (jb = 0; jb < bs; jb++) {
2320: for (ib = 0; ib < bs; ib++) {
2321: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscRealPart(*a_val);
2322: a_val++;
2323: }
2324: }
2325: }
2326: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2327: for (jb = 0; jb < bs; jb++) {
2328: for (ib = 0; ib < bs; ib++) {
2329: work[garray[b_aij->j[i]] * bs + jb] += PetscRealPart(*b_val);
2330: b_val++;
2331: }
2332: }
2333: }
2334: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
2335: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2336: for (jb = 0; jb < bs; jb++) {
2337: for (ib = 0; ib < bs; ib++) {
2338: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscImaginaryPart(*a_val);
2339: a_val++;
2340: }
2341: }
2342: }
2343: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2344: for (jb = 0; jb < bs; jb++) {
2345: for (ib = 0; ib < bs; ib++) {
2346: work[garray[b_aij->j[i]] * bs + jb] += PetscImaginaryPart(*b_val);
2347: b_val++;
2348: }
2349: }
2350: }
2351: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
2352: PetscCall(PetscMPIIntCast(N, &iN));
2353: if (type == NORM_INFINITY) {
2354: PetscCallMPI(MPIU_Allreduce(work, reductions, iN, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
2355: } else {
2356: PetscCallMPI(MPIU_Allreduce(work, reductions, iN, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
2357: }
2358: PetscCall(PetscFree(work));
2359: if (type == NORM_2) {
2360: for (i = 0; i < N; i++) reductions[i] = PetscSqrtReal(reductions[i]);
2361: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
2362: for (i = 0; i < N; i++) reductions[i] /= m;
2363: }
2364: PetscFunctionReturn(PETSC_SUCCESS);
2365: }
2367: static PetscErrorCode MatInvertBlockDiagonal_MPIBAIJ(Mat A, const PetscScalar **values)
2368: {
2369: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2371: PetscFunctionBegin;
2372: PetscCall(MatInvertBlockDiagonal(a->A, values));
2373: A->factorerrortype = a->A->factorerrortype;
2374: A->factorerror_zeropivot_value = a->A->factorerror_zeropivot_value;
2375: A->factorerror_zeropivot_row = a->A->factorerror_zeropivot_row;
2376: PetscFunctionReturn(PETSC_SUCCESS);
2377: }
2379: static PetscErrorCode MatShift_MPIBAIJ(Mat Y, PetscScalar a)
2380: {
2381: Mat_MPIBAIJ *maij = (Mat_MPIBAIJ *)Y->data;
2382: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)maij->A->data;
2384: PetscFunctionBegin;
2385: if (!Y->preallocated) {
2386: PetscCall(MatMPIBAIJSetPreallocation(Y, Y->rmap->bs, 1, NULL, 0, NULL));
2387: } else if (!aij->nz) {
2388: PetscInt nonew = aij->nonew;
2389: PetscCall(MatSeqBAIJSetPreallocation(maij->A, Y->rmap->bs, 1, NULL));
2390: aij->nonew = nonew;
2391: }
2392: PetscCall(MatShift_Basic(Y, a));
2393: PetscFunctionReturn(PETSC_SUCCESS);
2394: }
2396: static PetscErrorCode MatMissingDiagonal_MPIBAIJ(Mat A, PetscBool *missing, PetscInt *d)
2397: {
2398: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2400: PetscFunctionBegin;
2401: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2402: PetscCall(MatMissingDiagonal(a->A, missing, d));
2403: if (d) {
2404: PetscInt rstart;
2405: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2406: *d += rstart / A->rmap->bs;
2407: }
2408: PetscFunctionReturn(PETSC_SUCCESS);
2409: }
2411: static PetscErrorCode MatGetDiagonalBlock_MPIBAIJ(Mat A, Mat *a)
2412: {
2413: PetscFunctionBegin;
2414: *a = ((Mat_MPIBAIJ *)A->data)->A;
2415: PetscFunctionReturn(PETSC_SUCCESS);
2416: }
2418: static PetscErrorCode MatEliminateZeros_MPIBAIJ(Mat A, PetscBool keep)
2419: {
2420: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2422: PetscFunctionBegin;
2423: PetscCall(MatEliminateZeros_SeqBAIJ(a->A, keep)); // possibly keep zero diagonal coefficients
2424: PetscCall(MatEliminateZeros_SeqBAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2425: PetscFunctionReturn(PETSC_SUCCESS);
2426: }
2428: static struct _MatOps MatOps_Values = {MatSetValues_MPIBAIJ,
2429: MatGetRow_MPIBAIJ,
2430: MatRestoreRow_MPIBAIJ,
2431: MatMult_MPIBAIJ,
2432: /* 4*/ MatMultAdd_MPIBAIJ,
2433: MatMultTranspose_MPIBAIJ,
2434: MatMultTransposeAdd_MPIBAIJ,
2435: NULL,
2436: NULL,
2437: NULL,
2438: /*10*/ NULL,
2439: NULL,
2440: NULL,
2441: MatSOR_MPIBAIJ,
2442: MatTranspose_MPIBAIJ,
2443: /*15*/ MatGetInfo_MPIBAIJ,
2444: MatEqual_MPIBAIJ,
2445: MatGetDiagonal_MPIBAIJ,
2446: MatDiagonalScale_MPIBAIJ,
2447: MatNorm_MPIBAIJ,
2448: /*20*/ MatAssemblyBegin_MPIBAIJ,
2449: MatAssemblyEnd_MPIBAIJ,
2450: MatSetOption_MPIBAIJ,
2451: MatZeroEntries_MPIBAIJ,
2452: /*24*/ MatZeroRows_MPIBAIJ,
2453: NULL,
2454: NULL,
2455: NULL,
2456: NULL,
2457: /*29*/ MatSetUp_MPI_Hash,
2458: NULL,
2459: NULL,
2460: MatGetDiagonalBlock_MPIBAIJ,
2461: NULL,
2462: /*34*/ MatDuplicate_MPIBAIJ,
2463: NULL,
2464: NULL,
2465: NULL,
2466: NULL,
2467: /*39*/ MatAXPY_MPIBAIJ,
2468: MatCreateSubMatrices_MPIBAIJ,
2469: MatIncreaseOverlap_MPIBAIJ,
2470: MatGetValues_MPIBAIJ,
2471: MatCopy_MPIBAIJ,
2472: /*44*/ NULL,
2473: MatScale_MPIBAIJ,
2474: MatShift_MPIBAIJ,
2475: NULL,
2476: MatZeroRowsColumns_MPIBAIJ,
2477: /*49*/ NULL,
2478: NULL,
2479: NULL,
2480: NULL,
2481: NULL,
2482: /*54*/ MatFDColoringCreate_MPIXAIJ,
2483: NULL,
2484: MatSetUnfactored_MPIBAIJ,
2485: MatPermute_MPIBAIJ,
2486: MatSetValuesBlocked_MPIBAIJ,
2487: /*59*/ MatCreateSubMatrix_MPIBAIJ,
2488: MatDestroy_MPIBAIJ,
2489: MatView_MPIBAIJ,
2490: NULL,
2491: NULL,
2492: /*64*/ NULL,
2493: NULL,
2494: NULL,
2495: NULL,
2496: NULL,
2497: /*69*/ MatGetRowMaxAbs_MPIBAIJ,
2498: NULL,
2499: NULL,
2500: NULL,
2501: NULL,
2502: /*74*/ NULL,
2503: MatFDColoringApply_BAIJ,
2504: NULL,
2505: NULL,
2506: NULL,
2507: /*79*/ NULL,
2508: NULL,
2509: NULL,
2510: NULL,
2511: MatLoad_MPIBAIJ,
2512: /*84*/ NULL,
2513: NULL,
2514: NULL,
2515: NULL,
2516: NULL,
2517: /*89*/ NULL,
2518: NULL,
2519: NULL,
2520: NULL,
2521: NULL,
2522: /*94*/ NULL,
2523: NULL,
2524: NULL,
2525: NULL,
2526: NULL,
2527: /*99*/ NULL,
2528: NULL,
2529: NULL,
2530: MatConjugate_MPIBAIJ,
2531: NULL,
2532: /*104*/ NULL,
2533: MatRealPart_MPIBAIJ,
2534: MatImaginaryPart_MPIBAIJ,
2535: NULL,
2536: NULL,
2537: /*109*/ NULL,
2538: NULL,
2539: NULL,
2540: NULL,
2541: MatMissingDiagonal_MPIBAIJ,
2542: /*114*/ MatGetSeqNonzeroStructure_MPIBAIJ,
2543: NULL,
2544: MatGetGhosts_MPIBAIJ,
2545: NULL,
2546: NULL,
2547: /*119*/ NULL,
2548: NULL,
2549: NULL,
2550: NULL,
2551: MatGetMultiProcBlock_MPIBAIJ,
2552: /*124*/ NULL,
2553: MatGetColumnReductions_MPIBAIJ,
2554: MatInvertBlockDiagonal_MPIBAIJ,
2555: NULL,
2556: NULL,
2557: /*129*/ NULL,
2558: NULL,
2559: NULL,
2560: NULL,
2561: NULL,
2562: /*134*/ NULL,
2563: NULL,
2564: NULL,
2565: NULL,
2566: NULL,
2567: /*139*/ MatSetBlockSizes_Default,
2568: NULL,
2569: NULL,
2570: MatFDColoringSetUp_MPIXAIJ,
2571: NULL,
2572: /*144*/ MatCreateMPIMatConcatenateSeqMat_MPIBAIJ,
2573: NULL,
2574: NULL,
2575: NULL,
2576: NULL,
2577: NULL,
2578: /*150*/ NULL,
2579: MatEliminateZeros_MPIBAIJ,
2580: MatGetRowSumAbs_MPIBAIJ,
2581: NULL,
2582: NULL,
2583: /*155*/ NULL,
2584: MatCopyHashToXAIJ_MPI_Hash};
2586: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
2587: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
2589: static PetscErrorCode MatMPIBAIJSetPreallocationCSR_MPIBAIJ(Mat B, PetscInt bs, const PetscInt ii[], const PetscInt jj[], const PetscScalar V[])
2590: {
2591: PetscInt m, rstart, cstart, cend;
2592: PetscInt i, j, dlen, olen, nz, nz_max = 0, *d_nnz = NULL, *o_nnz = NULL;
2593: const PetscInt *JJ = NULL;
2594: PetscScalar *values = NULL;
2595: PetscBool roworiented = ((Mat_MPIBAIJ *)B->data)->roworiented;
2596: PetscBool nooffprocentries;
2598: PetscFunctionBegin;
2599: PetscCall(PetscLayoutSetBlockSize(B->rmap, bs));
2600: PetscCall(PetscLayoutSetBlockSize(B->cmap, bs));
2601: PetscCall(PetscLayoutSetUp(B->rmap));
2602: PetscCall(PetscLayoutSetUp(B->cmap));
2603: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
2604: m = B->rmap->n / bs;
2605: rstart = B->rmap->rstart / bs;
2606: cstart = B->cmap->rstart / bs;
2607: cend = B->cmap->rend / bs;
2609: PetscCheck(!ii[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "ii[0] must be 0 but it is %" PetscInt_FMT, ii[0]);
2610: PetscCall(PetscMalloc2(m, &d_nnz, m, &o_nnz));
2611: for (i = 0; i < m; i++) {
2612: nz = ii[i + 1] - ii[i];
2613: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
2614: nz_max = PetscMax(nz_max, nz);
2615: dlen = 0;
2616: olen = 0;
2617: JJ = jj + ii[i];
2618: for (j = 0; j < nz; j++) {
2619: if (*JJ < cstart || *JJ >= cend) olen++;
2620: else dlen++;
2621: JJ++;
2622: }
2623: d_nnz[i] = dlen;
2624: o_nnz[i] = olen;
2625: }
2626: PetscCall(MatMPIBAIJSetPreallocation(B, bs, 0, d_nnz, 0, o_nnz));
2627: PetscCall(PetscFree2(d_nnz, o_nnz));
2629: values = (PetscScalar *)V;
2630: if (!values) PetscCall(PetscCalloc1(bs * bs * nz_max, &values));
2631: for (i = 0; i < m; i++) {
2632: PetscInt row = i + rstart;
2633: PetscInt ncols = ii[i + 1] - ii[i];
2634: const PetscInt *icols = jj + ii[i];
2635: if (bs == 1 || !roworiented) { /* block ordering matches the non-nested layout of MatSetValues so we can insert entire rows */
2636: const PetscScalar *svals = values + (V ? (bs * bs * ii[i]) : 0);
2637: PetscCall(MatSetValuesBlocked_MPIBAIJ(B, 1, &row, ncols, icols, svals, INSERT_VALUES));
2638: } else { /* block ordering does not match so we can only insert one block at a time. */
2639: PetscInt j;
2640: for (j = 0; j < ncols; j++) {
2641: const PetscScalar *svals = values + (V ? (bs * bs * (ii[i] + j)) : 0);
2642: PetscCall(MatSetValuesBlocked_MPIBAIJ(B, 1, &row, 1, &icols[j], svals, INSERT_VALUES));
2643: }
2644: }
2645: }
2647: if (!V) PetscCall(PetscFree(values));
2648: nooffprocentries = B->nooffprocentries;
2649: B->nooffprocentries = PETSC_TRUE;
2650: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2651: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2652: B->nooffprocentries = nooffprocentries;
2654: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
2655: PetscFunctionReturn(PETSC_SUCCESS);
2656: }
2658: /*@C
2659: MatMPIBAIJSetPreallocationCSR - Creates a sparse parallel matrix in `MATBAIJ` format using the given nonzero structure and (optional) numerical values
2661: Collective
2663: Input Parameters:
2664: + B - the matrix
2665: . bs - the block size
2666: . i - the indices into `j` for the start of each local row (starts with zero)
2667: . j - the column indices for each local row (starts with zero) these must be sorted for each row
2668: - v - optional values in the matrix, use `NULL` if not provided
2670: Level: advanced
2672: Notes:
2673: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
2674: thus you CANNOT change the matrix entries by changing the values of `v` after you have
2675: called this routine.
2677: The order of the entries in values is specified by the `MatOption` `MAT_ROW_ORIENTED`. For example, C programs
2678: 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
2679: over rows within a block and the last index is over columns within a block row. Fortran programs will likely set
2680: `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
2681: block column and the second index is over columns within a block.
2683: 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
2685: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIBAIJSetPreallocation()`, `MatCreateAIJ()`, `MATMPIAIJ`, `MatCreateMPIBAIJWithArrays()`, `MATMPIBAIJ`
2686: @*/
2687: PetscErrorCode MatMPIBAIJSetPreallocationCSR(Mat B, PetscInt bs, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
2688: {
2689: PetscFunctionBegin;
2693: PetscTryMethod(B, "MatMPIBAIJSetPreallocationCSR_C", (Mat, PetscInt, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, bs, i, j, v));
2694: PetscFunctionReturn(PETSC_SUCCESS);
2695: }
2697: PetscErrorCode MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B, PetscInt bs, PetscInt d_nz, const PetscInt *d_nnz, PetscInt o_nz, const PetscInt *o_nnz)
2698: {
2699: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
2700: PetscInt i;
2701: PetscMPIInt size;
2703: PetscFunctionBegin;
2704: if (B->hash_active) {
2705: B->ops[0] = b->cops;
2706: B->hash_active = PETSC_FALSE;
2707: }
2708: if (!B->preallocated) PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), bs, &B->bstash));
2709: PetscCall(MatSetBlockSize(B, PetscAbs(bs)));
2710: PetscCall(PetscLayoutSetUp(B->rmap));
2711: PetscCall(PetscLayoutSetUp(B->cmap));
2712: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
2714: if (d_nnz) {
2715: 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]);
2716: }
2717: if (o_nnz) {
2718: 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]);
2719: }
2721: b->bs2 = bs * bs;
2722: b->mbs = B->rmap->n / bs;
2723: b->nbs = B->cmap->n / bs;
2724: b->Mbs = B->rmap->N / bs;
2725: b->Nbs = B->cmap->N / bs;
2727: for (i = 0; i <= b->size; i++) b->rangebs[i] = B->rmap->range[i] / bs;
2728: b->rstartbs = B->rmap->rstart / bs;
2729: b->rendbs = B->rmap->rend / bs;
2730: b->cstartbs = B->cmap->rstart / bs;
2731: b->cendbs = B->cmap->rend / bs;
2733: #if defined(PETSC_USE_CTABLE)
2734: PetscCall(PetscHMapIDestroy(&b->colmap));
2735: #else
2736: PetscCall(PetscFree(b->colmap));
2737: #endif
2738: PetscCall(PetscFree(b->garray));
2739: PetscCall(VecDestroy(&b->lvec));
2740: PetscCall(VecScatterDestroy(&b->Mvctx));
2742: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2744: MatSeqXAIJGetOptions_Private(b->B);
2745: PetscCall(MatDestroy(&b->B));
2746: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2747: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2748: PetscCall(MatSetType(b->B, MATSEQBAIJ));
2749: MatSeqXAIJRestoreOptions_Private(b->B);
2751: MatSeqXAIJGetOptions_Private(b->A);
2752: PetscCall(MatDestroy(&b->A));
2753: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2754: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2755: PetscCall(MatSetType(b->A, MATSEQBAIJ));
2756: MatSeqXAIJRestoreOptions_Private(b->A);
2758: PetscCall(MatSeqBAIJSetPreallocation(b->A, bs, d_nz, d_nnz));
2759: PetscCall(MatSeqBAIJSetPreallocation(b->B, bs, o_nz, o_nnz));
2760: B->preallocated = PETSC_TRUE;
2761: B->was_assembled = PETSC_FALSE;
2762: B->assembled = PETSC_FALSE;
2763: PetscFunctionReturn(PETSC_SUCCESS);
2764: }
2766: extern PetscErrorCode MatDiagonalScaleLocal_MPIBAIJ(Mat, Vec);
2767: extern PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat, PetscReal);
2769: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAdj(Mat B, MatType newtype, MatReuse reuse, Mat *adj)
2770: {
2771: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
2772: Mat_SeqBAIJ *d = (Mat_SeqBAIJ *)b->A->data, *o = (Mat_SeqBAIJ *)b->B->data;
2773: PetscInt M = B->rmap->n / B->rmap->bs, i, *ii, *jj, cnt, j, k, rstart = B->rmap->rstart / B->rmap->bs;
2774: const PetscInt *id = d->i, *jd = d->j, *io = o->i, *jo = o->j, *garray = b->garray;
2776: PetscFunctionBegin;
2777: PetscCall(PetscMalloc1(M + 1, &ii));
2778: ii[0] = 0;
2779: for (i = 0; i < M; i++) {
2780: 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]);
2781: 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]);
2782: ii[i + 1] = ii[i] + id[i + 1] - id[i] + io[i + 1] - io[i];
2783: /* remove one from count of matrix has diagonal */
2784: for (j = id[i]; j < id[i + 1]; j++) {
2785: if (jd[j] == i) {
2786: ii[i + 1]--;
2787: break;
2788: }
2789: }
2790: }
2791: PetscCall(PetscMalloc1(ii[M], &jj));
2792: cnt = 0;
2793: for (i = 0; i < M; i++) {
2794: for (j = io[i]; j < io[i + 1]; j++) {
2795: if (garray[jo[j]] > rstart) break;
2796: jj[cnt++] = garray[jo[j]];
2797: }
2798: for (k = id[i]; k < id[i + 1]; k++) {
2799: if (jd[k] != i) jj[cnt++] = rstart + jd[k];
2800: }
2801: for (; j < io[i + 1]; j++) jj[cnt++] = garray[jo[j]];
2802: }
2803: PetscCall(MatCreateMPIAdj(PetscObjectComm((PetscObject)B), M, B->cmap->N / B->rmap->bs, ii, jj, NULL, adj));
2804: PetscFunctionReturn(PETSC_SUCCESS);
2805: }
2807: #include <../src/mat/impls/aij/mpi/mpiaij.h>
2809: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat, MatType, MatReuse, Mat *);
2811: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAIJ(Mat A, MatType newtype, MatReuse reuse, Mat *newmat)
2812: {
2813: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2814: Mat_MPIAIJ *b;
2815: Mat B;
2817: PetscFunctionBegin;
2818: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Matrix must be assembled");
2820: if (reuse == MAT_REUSE_MATRIX) {
2821: B = *newmat;
2822: } else {
2823: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
2824: PetscCall(MatSetType(B, MATMPIAIJ));
2825: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
2826: PetscCall(MatSetBlockSizes(B, A->rmap->bs, A->cmap->bs));
2827: PetscCall(MatSeqAIJSetPreallocation(B, 0, NULL));
2828: PetscCall(MatMPIAIJSetPreallocation(B, 0, NULL, 0, NULL));
2829: }
2830: b = (Mat_MPIAIJ *)B->data;
2832: if (reuse == MAT_REUSE_MATRIX) {
2833: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_REUSE_MATRIX, &b->A));
2834: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_REUSE_MATRIX, &b->B));
2835: } else {
2836: PetscInt *garray = a->garray;
2837: Mat_SeqAIJ *bB;
2838: PetscInt bs, nnz;
2839: PetscCall(MatDestroy(&b->A));
2840: PetscCall(MatDestroy(&b->B));
2841: /* just clear out the data structure */
2842: PetscCall(MatDisAssemble_MPIAIJ(B));
2843: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->A));
2844: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->B));
2846: /* Global numbering for b->B columns */
2847: bB = (Mat_SeqAIJ *)b->B->data;
2848: bs = A->rmap->bs;
2849: nnz = bB->i[A->rmap->n];
2850: for (PetscInt k = 0; k < nnz; k++) {
2851: PetscInt bj = bB->j[k] / bs;
2852: PetscInt br = bB->j[k] % bs;
2853: bB->j[k] = garray[bj] * bs + br;
2854: }
2855: }
2856: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2857: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2859: if (reuse == MAT_INPLACE_MATRIX) {
2860: PetscCall(MatHeaderReplace(A, &B));
2861: } else {
2862: *newmat = B;
2863: }
2864: PetscFunctionReturn(PETSC_SUCCESS);
2865: }
2867: /*MC
2868: MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.
2870: Options Database Keys:
2871: + -mat_type mpibaij - sets the matrix type to `MATMPIBAIJ` during a call to `MatSetFromOptions()`
2872: . -mat_block_size <bs> - set the blocksize used to store the matrix
2873: . -mat_baij_mult_version version - indicate the version of the matrix-vector product to use (0 often indicates using BLAS)
2874: - -mat_use_hash_table <fact> - set hash table factor
2876: Level: beginner
2878: Note:
2879: `MatSetOption(A, MAT_STRUCTURE_ONLY, PETSC_TRUE)` may be called for this matrix type. In this no
2880: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
2882: .seealso: `Mat`, `MATBAIJ`, `MATSEQBAIJ`, `MatCreateBAIJ`
2883: M*/
2885: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIBSTRM(Mat, MatType, MatReuse, Mat *);
2887: PETSC_EXTERN PetscErrorCode MatCreate_MPIBAIJ(Mat B)
2888: {
2889: Mat_MPIBAIJ *b;
2890: PetscBool flg = PETSC_FALSE;
2892: PetscFunctionBegin;
2893: PetscCall(PetscNew(&b));
2894: B->data = (void *)b;
2895: B->ops[0] = MatOps_Values;
2896: B->assembled = PETSC_FALSE;
2898: B->insertmode = NOT_SET_VALUES;
2899: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
2900: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &b->size));
2902: /* build local table of row and column ownerships */
2903: PetscCall(PetscMalloc1(b->size + 1, &b->rangebs));
2905: /* build cache for off array entries formed */
2906: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
2908: b->donotstash = PETSC_FALSE;
2909: b->colmap = NULL;
2910: b->garray = NULL;
2911: b->roworiented = PETSC_TRUE;
2913: /* stuff used in block assembly */
2914: b->barray = NULL;
2916: /* stuff used for matrix vector multiply */
2917: b->lvec = NULL;
2918: b->Mvctx = NULL;
2920: /* stuff for MatGetRow() */
2921: b->rowindices = NULL;
2922: b->rowvalues = NULL;
2923: b->getrowactive = PETSC_FALSE;
2925: /* hash table stuff */
2926: b->ht = NULL;
2927: b->hd = NULL;
2928: b->ht_size = 0;
2929: b->ht_flag = PETSC_FALSE;
2930: b->ht_fact = 0;
2931: b->ht_total_ct = 0;
2932: b->ht_insert_ct = 0;
2934: /* stuff for MatCreateSubMatrices_MPIBAIJ_local() */
2935: b->ijonly = PETSC_FALSE;
2937: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpiadj_C", MatConvert_MPIBAIJ_MPIAdj));
2938: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpiaij_C", MatConvert_MPIBAIJ_MPIAIJ));
2939: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpisbaij_C", MatConvert_MPIBAIJ_MPISBAIJ));
2940: #if defined(PETSC_HAVE_HYPRE)
2941: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_hypre_C", MatConvert_AIJ_HYPRE));
2942: #endif
2943: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIBAIJ));
2944: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIBAIJ));
2945: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIBAIJSetPreallocation_C", MatMPIBAIJSetPreallocation_MPIBAIJ));
2946: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIBAIJSetPreallocationCSR_C", MatMPIBAIJSetPreallocationCSR_MPIBAIJ));
2947: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIBAIJ));
2948: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetHashTableFactor_C", MatSetHashTableFactor_MPIBAIJ));
2949: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_is_C", MatConvert_XAIJ_IS));
2950: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIBAIJ));
2952: PetscOptionsBegin(PetscObjectComm((PetscObject)B), NULL, "Options for loading MPIBAIJ matrix 1", "Mat");
2953: PetscCall(PetscOptionsName("-mat_use_hash_table", "Use hash table to save time in constructing matrix", "MatSetOption", &flg));
2954: if (flg) {
2955: PetscReal fact = 1.39;
2956: PetscCall(MatSetOption(B, MAT_USE_HASH_TABLE, PETSC_TRUE));
2957: PetscCall(PetscOptionsReal("-mat_use_hash_table", "Use hash table factor", "MatMPIBAIJSetHashTableFactor", fact, &fact, NULL));
2958: if (fact <= 1.0) fact = 1.39;
2959: PetscCall(MatMPIBAIJSetHashTableFactor(B, fact));
2960: PetscCall(PetscInfo(B, "Hash table Factor used %5.2g\n", (double)fact));
2961: }
2962: PetscOptionsEnd();
2963: PetscFunctionReturn(PETSC_SUCCESS);
2964: }
2966: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
2967: /*MC
2968: MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.
2970: This matrix type is identical to `MATSEQBAIJ` when constructed with a single process communicator,
2971: and `MATMPIBAIJ` otherwise.
2973: Options Database Keys:
2974: . -mat_type baij - sets the matrix type to `MATBAIJ` during a call to `MatSetFromOptions()`
2976: Level: beginner
2978: .seealso: `Mat`, `MatCreateBAIJ()`, `MATSEQBAIJ`, `MATMPIBAIJ`, `MatMPIBAIJSetPreallocation()`, `MatMPIBAIJSetPreallocationCSR()`
2979: M*/
2981: /*@
2982: MatMPIBAIJSetPreallocation - Allocates memory for a sparse parallel matrix in `MATMPIBAIJ` format
2983: (block compressed row).
2985: Collective
2987: Input Parameters:
2988: + B - the matrix
2989: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
2990: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
2991: . d_nz - number of block nonzeros per block row in diagonal portion of local
2992: submatrix (same for all local rows)
2993: . d_nnz - array containing the number of block nonzeros in the various block rows
2994: of the in diagonal portion of the local (possibly different for each block
2995: row) or `NULL`. If you plan to factor the matrix you must leave room for the diagonal entry and
2996: set it even if it is zero.
2997: . o_nz - number of block nonzeros per block row in the off-diagonal portion of local
2998: submatrix (same for all local rows).
2999: - o_nnz - array containing the number of nonzeros in the various block rows of the
3000: off-diagonal portion of the local submatrix (possibly different for
3001: each block row) or `NULL`.
3003: If the *_nnz parameter is given then the *_nz parameter is ignored
3005: Options Database Keys:
3006: + -mat_block_size - size of the blocks to use
3007: - -mat_use_hash_table <fact> - set hash table factor
3009: Level: intermediate
3011: Notes:
3012: For good matrix assembly performance
3013: the user should preallocate the matrix storage by setting the parameters
3014: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`). By setting these parameters accurately,
3015: performance can be increased by more than a factor of 50.
3017: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one processor
3018: than it must be used on all processors that share the object for that argument.
3020: Storage Information:
3021: For a square global matrix we define each processor's diagonal portion
3022: to be its local rows and the corresponding columns (a square submatrix);
3023: each processor's off-diagonal portion encompasses the remainder of the
3024: local matrix (a rectangular submatrix).
3026: The user can specify preallocated storage for the diagonal part of
3027: the local submatrix with either `d_nz` or `d_nnz` (not both). Set
3028: `d_nz` = `PETSC_DEFAULT` and `d_nnz` = `NULL` for PETSc to control dynamic
3029: memory allocation. Likewise, specify preallocated storage for the
3030: off-diagonal part of the local submatrix with `o_nz` or `o_nnz` (not both).
3032: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
3033: the figure below we depict these three local rows and all columns (0-11).
3035: .vb
3036: 0 1 2 3 4 5 6 7 8 9 10 11
3037: --------------------------
3038: row 3 |o o o d d d o o o o o o
3039: row 4 |o o o d d d o o o o o o
3040: row 5 |o o o d d d o o o o o o
3041: --------------------------
3042: .ve
3044: Thus, any entries in the d locations are stored in the d (diagonal)
3045: submatrix, and any entries in the o locations are stored in the
3046: o (off-diagonal) submatrix. Note that the d and the o submatrices are
3047: stored simply in the `MATSEQBAIJ` format for compressed row storage.
3049: Now `d_nz` should indicate the number of block nonzeros per row in the d matrix,
3050: and `o_nz` should indicate the number of block nonzeros per row in the o matrix.
3051: In general, for PDE problems in which most nonzeros are near the diagonal,
3052: one expects `d_nz` >> `o_nz`.
3054: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3055: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3056: You can also run with the option `-info` and look for messages with the string
3057: malloc in them to see if additional memory allocation was needed.
3059: .seealso: `Mat`, `MATMPIBAIJ`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`, `MatMPIBAIJSetPreallocationCSR()`, `PetscSplitOwnership()`
3060: @*/
3061: PetscErrorCode MatMPIBAIJSetPreallocation(Mat B, PetscInt bs, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
3062: {
3063: PetscFunctionBegin;
3067: PetscTryMethod(B, "MatMPIBAIJSetPreallocation_C", (Mat, PetscInt, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, bs, d_nz, d_nnz, o_nz, o_nnz));
3068: PetscFunctionReturn(PETSC_SUCCESS);
3069: }
3071: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
3072: /*@
3073: MatCreateBAIJ - Creates a sparse parallel matrix in `MATBAIJ` format
3074: (block compressed row).
3076: Collective
3078: Input Parameters:
3079: + comm - MPI communicator
3080: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
3081: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
3082: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
3083: This value should be the same as the local size used in creating the
3084: y vector for the matrix-vector product y = Ax.
3085: . n - number of local columns (or `PETSC_DECIDE` to have calculated if N is given)
3086: This value should be the same as the local size used in creating the
3087: x vector for the matrix-vector product y = Ax.
3088: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
3089: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
3090: . d_nz - number of nonzero blocks per block row in diagonal portion of local
3091: submatrix (same for all local rows)
3092: . d_nnz - array containing the number of nonzero blocks in the various block rows
3093: of the in diagonal portion of the local (possibly different for each block
3094: row) or NULL. If you plan to factor the matrix you must leave room for the diagonal entry
3095: and set it even if it is zero.
3096: . o_nz - number of nonzero blocks per block row in the off-diagonal portion of local
3097: submatrix (same for all local rows).
3098: - o_nnz - array containing the number of nonzero blocks in the various block rows of the
3099: off-diagonal portion of the local submatrix (possibly different for
3100: each block row) or NULL.
3102: Output Parameter:
3103: . A - the matrix
3105: Options Database Keys:
3106: + -mat_block_size - size of the blocks to use
3107: - -mat_use_hash_table <fact> - set hash table factor
3109: Level: intermediate
3111: Notes:
3112: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
3113: MatXXXXSetPreallocation() paradigm instead of this routine directly.
3114: [MatXXXXSetPreallocation() is, for example, `MatSeqBAIJSetPreallocation()`]
3116: For good matrix assembly performance
3117: the user should preallocate the matrix storage by setting the parameters
3118: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`). By setting these parameters accurately,
3119: performance can be increased by more than a factor of 50.
3121: If the *_nnz parameter is given then the *_nz parameter is ignored
3123: A nonzero block is any block that as 1 or more nonzeros in it
3125: The user MUST specify either the local or global matrix dimensions
3126: (possibly both).
3128: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one processor
3129: than it must be used on all processors that share the object for that argument.
3131: If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by
3132: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`.
3134: Storage Information:
3135: For a square global matrix we define each processor's diagonal portion
3136: to be its local rows and the corresponding columns (a square submatrix);
3137: each processor's off-diagonal portion encompasses the remainder of the
3138: local matrix (a rectangular submatrix).
3140: The user can specify preallocated storage for the diagonal part of
3141: the local submatrix with either d_nz or d_nnz (not both). Set
3142: `d_nz` = `PETSC_DEFAULT` and `d_nnz` = `NULL` for PETSc to control dynamic
3143: memory allocation. Likewise, specify preallocated storage for the
3144: off-diagonal part of the local submatrix with `o_nz` or `o_nnz` (not both).
3146: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
3147: the figure below we depict these three local rows and all columns (0-11).
3149: .vb
3150: 0 1 2 3 4 5 6 7 8 9 10 11
3151: --------------------------
3152: row 3 |o o o d d d o o o o o o
3153: row 4 |o o o d d d o o o o o o
3154: row 5 |o o o d d d o o o o o o
3155: --------------------------
3156: .ve
3158: Thus, any entries in the d locations are stored in the d (diagonal)
3159: submatrix, and any entries in the o locations are stored in the
3160: o (off-diagonal) submatrix. Note that the d and the o submatrices are
3161: stored simply in the `MATSEQBAIJ` format for compressed row storage.
3163: Now `d_nz` should indicate the number of block nonzeros per row in the d matrix,
3164: and `o_nz` should indicate the number of block nonzeros per row in the o matrix.
3165: In general, for PDE problems in which most nonzeros are near the diagonal,
3166: one expects `d_nz` >> `o_nz`.
3168: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatMPIBAIJSetPreallocation()`, `MatMPIBAIJSetPreallocationCSR()`,
3169: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`
3170: @*/
3171: 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)
3172: {
3173: PetscMPIInt size;
3175: PetscFunctionBegin;
3176: PetscCall(MatCreate(comm, A));
3177: PetscCall(MatSetSizes(*A, m, n, M, N));
3178: PetscCallMPI(MPI_Comm_size(comm, &size));
3179: if (size > 1) {
3180: PetscCall(MatSetType(*A, MATMPIBAIJ));
3181: PetscCall(MatMPIBAIJSetPreallocation(*A, bs, d_nz, d_nnz, o_nz, o_nnz));
3182: } else {
3183: PetscCall(MatSetType(*A, MATSEQBAIJ));
3184: PetscCall(MatSeqBAIJSetPreallocation(*A, bs, d_nz, d_nnz));
3185: }
3186: PetscFunctionReturn(PETSC_SUCCESS);
3187: }
3189: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
3190: {
3191: Mat mat;
3192: Mat_MPIBAIJ *a, *oldmat = (Mat_MPIBAIJ *)matin->data;
3193: PetscInt len = 0;
3195: PetscFunctionBegin;
3196: *newmat = NULL;
3197: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
3198: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
3199: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
3201: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
3202: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
3203: if (matin->hash_active) {
3204: PetscCall(MatSetUp(mat));
3205: } else {
3206: mat->factortype = matin->factortype;
3207: mat->preallocated = PETSC_TRUE;
3208: mat->assembled = PETSC_TRUE;
3209: mat->insertmode = NOT_SET_VALUES;
3211: a = (Mat_MPIBAIJ *)mat->data;
3212: mat->rmap->bs = matin->rmap->bs;
3213: a->bs2 = oldmat->bs2;
3214: a->mbs = oldmat->mbs;
3215: a->nbs = oldmat->nbs;
3216: a->Mbs = oldmat->Mbs;
3217: a->Nbs = oldmat->Nbs;
3219: a->size = oldmat->size;
3220: a->rank = oldmat->rank;
3221: a->donotstash = oldmat->donotstash;
3222: a->roworiented = oldmat->roworiented;
3223: a->rowindices = NULL;
3224: a->rowvalues = NULL;
3225: a->getrowactive = PETSC_FALSE;
3226: a->barray = NULL;
3227: a->rstartbs = oldmat->rstartbs;
3228: a->rendbs = oldmat->rendbs;
3229: a->cstartbs = oldmat->cstartbs;
3230: a->cendbs = oldmat->cendbs;
3232: /* hash table stuff */
3233: a->ht = NULL;
3234: a->hd = NULL;
3235: a->ht_size = 0;
3236: a->ht_flag = oldmat->ht_flag;
3237: a->ht_fact = oldmat->ht_fact;
3238: a->ht_total_ct = 0;
3239: a->ht_insert_ct = 0;
3241: PetscCall(PetscArraycpy(a->rangebs, oldmat->rangebs, a->size + 1));
3242: if (oldmat->colmap) {
3243: #if defined(PETSC_USE_CTABLE)
3244: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3245: #else
3246: PetscCall(PetscMalloc1(a->Nbs, &a->colmap));
3247: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, a->Nbs));
3248: #endif
3249: } else a->colmap = NULL;
3251: if (oldmat->garray && (len = ((Mat_SeqBAIJ *)oldmat->B->data)->nbs)) {
3252: PetscCall(PetscMalloc1(len, &a->garray));
3253: PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3254: } else a->garray = NULL;
3256: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)matin), matin->rmap->bs, &mat->bstash));
3257: PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3258: PetscCall(VecScatterCopy(oldmat->Mvctx, &a->Mvctx));
3260: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3261: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3262: }
3263: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3264: *newmat = mat;
3265: PetscFunctionReturn(PETSC_SUCCESS);
3266: }
3268: /* Used for both MPIBAIJ and MPISBAIJ matrices */
3269: PetscErrorCode MatLoad_MPIBAIJ_Binary(Mat mat, PetscViewer viewer)
3270: {
3271: PetscInt header[4], M, N, nz, bs, m, n, mbs, nbs, rows, cols, sum, i, j, k;
3272: PetscInt *rowidxs, *colidxs, rs, cs, ce;
3273: PetscScalar *matvals;
3275: PetscFunctionBegin;
3276: PetscCall(PetscViewerSetUp(viewer));
3278: /* read in matrix header */
3279: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3280: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3281: M = header[1];
3282: N = header[2];
3283: nz = header[3];
3284: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3285: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3286: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIBAIJ");
3288: /* set block sizes from the viewer's .info file */
3289: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3290: /* set local sizes if not set already */
3291: if (mat->rmap->n < 0 && M == N) mat->rmap->n = mat->cmap->n;
3292: if (mat->cmap->n < 0 && M == N) mat->cmap->n = mat->rmap->n;
3293: /* set global sizes if not set already */
3294: if (mat->rmap->N < 0) mat->rmap->N = M;
3295: if (mat->cmap->N < 0) mat->cmap->N = N;
3296: PetscCall(PetscLayoutSetUp(mat->rmap));
3297: PetscCall(PetscLayoutSetUp(mat->cmap));
3299: /* check if the matrix sizes are correct */
3300: PetscCall(MatGetSize(mat, &rows, &cols));
3301: 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);
3302: PetscCall(MatGetBlockSize(mat, &bs));
3303: PetscCall(MatGetLocalSize(mat, &m, &n));
3304: PetscCall(PetscLayoutGetRange(mat->rmap, &rs, NULL));
3305: PetscCall(PetscLayoutGetRange(mat->cmap, &cs, &ce));
3306: mbs = m / bs;
3307: nbs = n / bs;
3309: /* read in row lengths and build row indices */
3310: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3311: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3312: rowidxs[0] = 0;
3313: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3314: PetscCallMPI(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3315: 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);
3317: /* read in column indices and matrix values */
3318: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3319: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3320: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3322: { /* preallocate matrix storage */
3323: PetscBT bt; /* helper bit set to count diagonal nonzeros */
3324: PetscHSetI ht; /* helper hash set to count off-diagonal nonzeros */
3325: PetscBool sbaij, done;
3326: PetscInt *d_nnz, *o_nnz;
3328: PetscCall(PetscBTCreate(nbs, &bt));
3329: PetscCall(PetscHSetICreate(&ht));
3330: PetscCall(PetscCalloc2(mbs, &d_nnz, mbs, &o_nnz));
3331: PetscCall(PetscObjectTypeCompare((PetscObject)mat, MATMPISBAIJ, &sbaij));
3332: for (i = 0; i < mbs; i++) {
3333: PetscCall(PetscBTMemzero(nbs, bt));
3334: PetscCall(PetscHSetIClear(ht));
3335: for (k = 0; k < bs; k++) {
3336: PetscInt row = bs * i + k;
3337: for (j = rowidxs[row]; j < rowidxs[row + 1]; j++) {
3338: PetscInt col = colidxs[j];
3339: if (!sbaij || col >= row) {
3340: if (col >= cs && col < ce) {
3341: if (!PetscBTLookupSet(bt, (col - cs) / bs)) d_nnz[i]++;
3342: } else {
3343: PetscCall(PetscHSetIQueryAdd(ht, col / bs, &done));
3344: if (done) o_nnz[i]++;
3345: }
3346: }
3347: }
3348: }
3349: }
3350: PetscCall(PetscBTDestroy(&bt));
3351: PetscCall(PetscHSetIDestroy(&ht));
3352: PetscCall(MatMPIBAIJSetPreallocation(mat, bs, 0, d_nnz, 0, o_nnz));
3353: PetscCall(MatMPISBAIJSetPreallocation(mat, bs, 0, d_nnz, 0, o_nnz));
3354: PetscCall(PetscFree2(d_nnz, o_nnz));
3355: }
3357: /* store matrix values */
3358: for (i = 0; i < m; i++) {
3359: PetscInt row = rs + i, s = rowidxs[i], e = rowidxs[i + 1];
3360: PetscUseTypeMethod(mat, setvalues, 1, &row, e - s, colidxs + s, matvals + s, INSERT_VALUES);
3361: }
3363: PetscCall(PetscFree(rowidxs));
3364: PetscCall(PetscFree2(colidxs, matvals));
3365: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
3366: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
3367: PetscFunctionReturn(PETSC_SUCCESS);
3368: }
3370: PetscErrorCode MatLoad_MPIBAIJ(Mat mat, PetscViewer viewer)
3371: {
3372: PetscBool isbinary;
3374: PetscFunctionBegin;
3375: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3376: 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);
3377: PetscCall(MatLoad_MPIBAIJ_Binary(mat, viewer));
3378: PetscFunctionReturn(PETSC_SUCCESS);
3379: }
3381: /*@
3382: MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the matrices hash table
3384: Input Parameters:
3385: + mat - the matrix
3386: - fact - factor
3388: Options Database Key:
3389: . -mat_use_hash_table <fact> - provide the factor
3391: Level: advanced
3393: .seealso: `Mat`, `MATMPIBAIJ`, `MatSetOption()`
3394: @*/
3395: PetscErrorCode MatMPIBAIJSetHashTableFactor(Mat mat, PetscReal fact)
3396: {
3397: PetscFunctionBegin;
3398: PetscTryMethod(mat, "MatSetHashTableFactor_C", (Mat, PetscReal), (mat, fact));
3399: PetscFunctionReturn(PETSC_SUCCESS);
3400: }
3402: PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat mat, PetscReal fact)
3403: {
3404: Mat_MPIBAIJ *baij;
3406: PetscFunctionBegin;
3407: baij = (Mat_MPIBAIJ *)mat->data;
3408: baij->ht_fact = fact;
3409: PetscFunctionReturn(PETSC_SUCCESS);
3410: }
3412: PetscErrorCode MatMPIBAIJGetSeqBAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
3413: {
3414: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
3415: PetscBool flg;
3417: PetscFunctionBegin;
3418: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIBAIJ, &flg));
3419: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIBAIJ matrix as input");
3420: if (Ad) *Ad = a->A;
3421: if (Ao) *Ao = a->B;
3422: if (colmap) *colmap = a->garray;
3423: PetscFunctionReturn(PETSC_SUCCESS);
3424: }
3426: /*
3427: Special version for direct calls from Fortran (to eliminate two function call overheads
3428: */
3429: #if defined(PETSC_HAVE_FORTRAN_CAPS)
3430: #define matmpibaijsetvaluesblocked_ MATMPIBAIJSETVALUESBLOCKED
3431: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
3432: #define matmpibaijsetvaluesblocked_ matmpibaijsetvaluesblocked
3433: #endif
3435: // PetscClangLinter pragma disable: -fdoc-synopsis-matching-symbol-name
3436: /*@C
3437: MatMPIBAIJSetValuesBlocked - Direct Fortran call to replace call to `MatSetValuesBlocked()`
3439: Collective
3441: Input Parameters:
3442: + matin - the matrix
3443: . min - number of input rows
3444: . im - input rows
3445: . nin - number of input columns
3446: . in - input columns
3447: . v - numerical values input
3448: - addvin - `INSERT_VALUES` or `ADD_VALUES`
3450: Level: advanced
3452: Developer Notes:
3453: This has a complete copy of `MatSetValuesBlocked_MPIBAIJ()` which is terrible code un-reuse.
3455: .seealso: `Mat`, `MatSetValuesBlocked()`
3456: @*/
3457: PETSC_EXTERN PetscErrorCode matmpibaijsetvaluesblocked_(Mat *matin, PetscInt *min, const PetscInt im[], PetscInt *nin, const PetscInt in[], const MatScalar v[], InsertMode *addvin)
3458: {
3459: /* convert input arguments to C version */
3460: Mat mat = *matin;
3461: PetscInt m = *min, n = *nin;
3462: InsertMode addv = *addvin;
3464: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
3465: const MatScalar *value;
3466: MatScalar *barray = baij->barray;
3467: PetscBool roworiented = baij->roworiented;
3468: PetscInt i, j, ii, jj, row, col, rstart = baij->rstartbs;
3469: PetscInt rend = baij->rendbs, cstart = baij->cstartbs, stepval;
3470: PetscInt cend = baij->cendbs, bs = mat->rmap->bs, bs2 = baij->bs2;
3472: PetscFunctionBegin;
3473: /* tasks normally handled by MatSetValuesBlocked() */
3474: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
3475: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
3476: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3477: if (mat->assembled) {
3478: mat->was_assembled = PETSC_TRUE;
3479: mat->assembled = PETSC_FALSE;
3480: }
3481: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
3483: if (!barray) {
3484: PetscCall(PetscMalloc1(bs2, &barray));
3485: baij->barray = barray;
3486: }
3488: if (roworiented) stepval = (n - 1) * bs;
3489: else stepval = (m - 1) * bs;
3491: for (i = 0; i < m; i++) {
3492: if (im[i] < 0) continue;
3493: 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);
3494: if (im[i] >= rstart && im[i] < rend) {
3495: row = im[i] - rstart;
3496: for (j = 0; j < n; j++) {
3497: /* If NumCol = 1 then a copy is not required */
3498: if ((roworiented) && (n == 1)) {
3499: barray = (MatScalar *)v + i * bs2;
3500: } else if ((!roworiented) && (m == 1)) {
3501: barray = (MatScalar *)v + j * bs2;
3502: } else { /* Here a copy is required */
3503: if (roworiented) {
3504: value = v + i * (stepval + bs) * bs + j * bs;
3505: } else {
3506: value = v + j * (stepval + bs) * bs + i * bs;
3507: }
3508: for (ii = 0; ii < bs; ii++, value += stepval) {
3509: for (jj = 0; jj < bs; jj++) *barray++ = *value++;
3510: }
3511: barray -= bs2;
3512: }
3514: if (in[j] >= cstart && in[j] < cend) {
3515: col = in[j] - cstart;
3516: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->A, row, col, barray, addv, im[i], in[j]));
3517: } else if (in[j] < 0) {
3518: continue;
3519: } else {
3520: 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);
3521: if (mat->was_assembled) {
3522: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
3524: #if defined(PETSC_USE_DEBUG)
3525: #if defined(PETSC_USE_CTABLE)
3526: {
3527: PetscInt data;
3528: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &data));
3529: PetscCheck((data - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
3530: }
3531: #else
3532: PetscCheck((baij->colmap[in[j]] - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
3533: #endif
3534: #endif
3535: #if defined(PETSC_USE_CTABLE)
3536: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &col));
3537: col = (col - 1) / bs;
3538: #else
3539: col = (baij->colmap[in[j]] - 1) / bs;
3540: #endif
3541: if (col < 0 && !((Mat_SeqBAIJ *)baij->A->data)->nonew) {
3542: PetscCall(MatDisAssemble_MPIBAIJ(mat));
3543: col = in[j];
3544: }
3545: } else col = in[j];
3546: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->B, row, col, barray, addv, im[i], in[j]));
3547: }
3548: }
3549: } else {
3550: if (!baij->donotstash) {
3551: if (roworiented) {
3552: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
3553: } else {
3554: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
3555: }
3556: }
3557: }
3558: }
3560: /* task normally handled by MatSetValuesBlocked() */
3561: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
3562: PetscFunctionReturn(PETSC_SUCCESS);
3563: }
3565: /*@
3566: MatCreateMPIBAIJWithArrays - creates a `MATMPIBAIJ` matrix using arrays that contain in standard block CSR format for the local rows.
3568: Collective
3570: Input Parameters:
3571: + comm - MPI communicator
3572: . bs - the block size, only a block size of 1 is supported
3573: . m - number of local rows (Cannot be `PETSC_DECIDE`)
3574: . n - This value should be the same as the local size used in creating the
3575: x vector for the matrix-vector product $ y = Ax $. (or `PETSC_DECIDE` to have
3576: calculated if `N` is given) For square matrices `n` is almost always `m`.
3577: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
3578: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
3579: . 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
3580: . j - column indices
3581: - a - matrix values
3583: Output Parameter:
3584: . mat - the matrix
3586: Level: intermediate
3588: Notes:
3589: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
3590: thus you CANNOT change the matrix entries by changing the values of a[] after you have
3591: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3593: The order of the entries in values is the same as the block compressed sparse row storage format; that is, it is
3594: the same as a three dimensional array in Fortran values(bs,bs,nnz) that contains the first column of the first
3595: block, followed by the second column of the first block etc etc. That is, the blocks are contiguous in memory
3596: with column-major ordering within blocks.
3598: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
3600: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
3601: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`
3602: @*/
3603: 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)
3604: {
3605: PetscFunctionBegin;
3606: PetscCheck(!i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
3607: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
3608: PetscCall(MatCreate(comm, mat));
3609: PetscCall(MatSetSizes(*mat, m, n, M, N));
3610: PetscCall(MatSetType(*mat, MATMPIBAIJ));
3611: PetscCall(MatSetBlockSize(*mat, bs));
3612: PetscCall(MatSetUp(*mat));
3613: PetscCall(MatSetOption(*mat, MAT_ROW_ORIENTED, PETSC_FALSE));
3614: PetscCall(MatMPIBAIJSetPreallocationCSR(*mat, bs, i, j, a));
3615: PetscCall(MatSetOption(*mat, MAT_ROW_ORIENTED, PETSC_TRUE));
3616: PetscFunctionReturn(PETSC_SUCCESS);
3617: }
3619: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIBAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
3620: {
3621: PetscInt m, N, i, rstart, nnz, Ii, bs, cbs;
3622: PetscInt *indx;
3623: PetscScalar *values;
3625: PetscFunctionBegin;
3626: PetscCall(MatGetSize(inmat, &m, &N));
3627: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
3628: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)inmat->data;
3629: PetscInt *dnz, *onz, mbs, Nbs, nbs;
3630: PetscInt *bindx, rmax = a->rmax, j;
3631: PetscMPIInt rank, size;
3633: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
3634: mbs = m / bs;
3635: Nbs = N / cbs;
3636: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnershipBlock(comm, cbs, &n, &N));
3637: nbs = n / cbs;
3639: PetscCall(PetscMalloc1(rmax, &bindx));
3640: MatPreallocateBegin(comm, mbs, nbs, dnz, onz); /* inline function, output __end and __rstart are used below */
3642: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3643: PetscCallMPI(MPI_Comm_rank(comm, &size));
3644: if (rank == size - 1) {
3645: /* Check sum(nbs) = Nbs */
3646: PetscCheck(__end == Nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local block columns %" PetscInt_FMT " != global block columns %" PetscInt_FMT, __end, Nbs);
3647: }
3649: rstart = __rstart; /* block rstart of *outmat; see inline function MatPreallocateBegin */
3650: for (i = 0; i < mbs; i++) {
3651: PetscCall(MatGetRow_SeqBAIJ(inmat, i * bs, &nnz, &indx, NULL)); /* non-blocked nnz and indx */
3652: nnz = nnz / bs;
3653: for (j = 0; j < nnz; j++) bindx[j] = indx[j * bs] / bs;
3654: PetscCall(MatPreallocateSet(i + rstart, nnz, bindx, dnz, onz));
3655: PetscCall(MatRestoreRow_SeqBAIJ(inmat, i * bs, &nnz, &indx, NULL));
3656: }
3657: PetscCall(PetscFree(bindx));
3659: PetscCall(MatCreate(comm, outmat));
3660: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
3661: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
3662: PetscCall(MatSetType(*outmat, MATBAIJ));
3663: PetscCall(MatSeqBAIJSetPreallocation(*outmat, bs, 0, dnz));
3664: PetscCall(MatMPIBAIJSetPreallocation(*outmat, bs, 0, dnz, 0, onz));
3665: MatPreallocateEnd(dnz, onz);
3666: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3667: }
3669: /* numeric phase */
3670: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
3671: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
3673: for (i = 0; i < m; i++) {
3674: PetscCall(MatGetRow_SeqBAIJ(inmat, i, &nnz, &indx, &values));
3675: Ii = i + rstart;
3676: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
3677: PetscCall(MatRestoreRow_SeqBAIJ(inmat, i, &nnz, &indx, &values));
3678: }
3679: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
3680: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
3681: PetscFunctionReturn(PETSC_SUCCESS);
3682: }