Actual source code: mpibaij.c
1: #include <../src/mat/impls/baij/mpi/mpibaij.h>
3: #include <petsc/private/hashseti.h>
4: #include <petscblaslapack.h>
5: #include <petscsf.h>
7: static PetscErrorCode MatDestroy_MPIBAIJ(Mat mat)
8: {
9: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
11: PetscFunctionBegin;
12: PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ",Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
13: PetscCall(MatStashDestroy_Private(&mat->stash));
14: PetscCall(MatStashDestroy_Private(&mat->bstash));
15: PetscCall(MatDestroy(&baij->A));
16: PetscCall(MatDestroy(&baij->B));
17: #if defined(PETSC_USE_CTABLE)
18: PetscCall(PetscHMapIDestroy(&baij->colmap));
19: #else
20: PetscCall(PetscFree(baij->colmap));
21: #endif
22: PetscCall(PetscFree(baij->garray));
23: PetscCall(VecDestroy(&baij->lvec));
24: PetscCall(VecScatterDestroy(&baij->Mvctx));
25: PetscCall(PetscFree2(baij->rowvalues, baij->rowindices));
26: PetscCall(PetscFree(baij->barray));
27: PetscCall(PetscFree2(baij->hd, baij->ht));
28: PetscCall(PetscFree(baij->rangebs));
29: PetscCall(PetscFree(mat->data));
31: PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
32: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
33: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
34: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIBAIJSetPreallocation_C", NULL));
35: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIBAIJSetPreallocationCSR_C", NULL));
36: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
37: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetHashTableFactor_C", NULL));
38: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_mpisbaij_C", NULL));
39: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_mpiadj_C", NULL));
40: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_mpiaij_C", NULL));
41: #if defined(PETSC_HAVE_HYPRE)
42: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_hypre_C", NULL));
43: #endif
44: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpibaij_is_C", NULL));
45: PetscFunctionReturn(PETSC_SUCCESS);
46: }
48: /* defines MatSetValues_MPI_Hash(), MatAssemblyBegin_MPI_Hash(), and MatAssemblyEnd_MPI_Hash() */
49: #define TYPE BAIJ
50: #include "../src/mat/impls/aij/mpi/mpihashmat.h"
51: #undef TYPE
53: #if defined(PETSC_HAVE_HYPRE)
54: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
55: #endif
57: static PetscErrorCode MatGetRowMaxAbs_MPIBAIJ(Mat A, Vec v, PetscInt idx[])
58: {
59: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
60: PetscInt i, *idxb = NULL, m = A->rmap->n, bs = A->cmap->bs;
61: PetscScalar *vv;
62: Vec vB, vA;
63: const PetscScalar *va, *vb;
65: PetscFunctionBegin;
66: PetscCall(MatCreateVecs(a->A, NULL, &vA));
67: PetscCall(MatGetRowMaxAbs(a->A, vA, idx));
69: PetscCall(VecGetArrayRead(vA, &va));
70: if (idx) {
71: for (i = 0; i < m; i++) {
72: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
73: }
74: }
76: PetscCall(MatCreateVecs(a->B, NULL, &vB));
77: PetscCall(PetscMalloc1(m, &idxb));
78: PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));
80: PetscCall(VecGetArrayWrite(v, &vv));
81: PetscCall(VecGetArrayRead(vB, &vb));
82: for (i = 0; i < m; i++) {
83: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
84: vv[i] = vb[i];
85: if (idx) idx[i] = bs * a->garray[idxb[i] / bs] + (idxb[i] % bs);
86: } else {
87: vv[i] = va[i];
88: if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > bs * a->garray[idxb[i] / bs] + (idxb[i] % bs)) idx[i] = bs * a->garray[idxb[i] / bs] + (idxb[i] % bs);
89: }
90: }
91: PetscCall(VecRestoreArrayWrite(v, &vv));
92: PetscCall(VecRestoreArrayRead(vA, &va));
93: PetscCall(VecRestoreArrayRead(vB, &vb));
94: PetscCall(PetscFree(idxb));
95: PetscCall(VecDestroy(&vA));
96: PetscCall(VecDestroy(&vB));
97: PetscFunctionReturn(PETSC_SUCCESS);
98: }
100: static PetscErrorCode MatGetRowSumAbs_MPIBAIJ(Mat A, Vec v)
101: {
102: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
103: Vec vB, vA;
105: PetscFunctionBegin;
106: PetscCall(MatCreateVecs(a->A, NULL, &vA));
107: PetscCall(MatGetRowSumAbs(a->A, vA));
108: PetscCall(MatCreateVecs(a->B, NULL, &vB));
109: PetscCall(MatGetRowSumAbs(a->B, vB));
110: PetscCall(VecAXPY(vA, 1.0, vB));
111: PetscCall(VecDestroy(&vB));
112: PetscCall(VecCopy(vA, v));
113: PetscCall(VecDestroy(&vA));
114: PetscFunctionReturn(PETSC_SUCCESS);
115: }
117: static PetscErrorCode MatStoreValues_MPIBAIJ(Mat mat)
118: {
119: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
121: PetscFunctionBegin;
122: PetscCall(MatStoreValues(aij->A));
123: PetscCall(MatStoreValues(aij->B));
124: PetscFunctionReturn(PETSC_SUCCESS);
125: }
127: static PetscErrorCode MatRetrieveValues_MPIBAIJ(Mat mat)
128: {
129: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
131: PetscFunctionBegin;
132: PetscCall(MatRetrieveValues(aij->A));
133: PetscCall(MatRetrieveValues(aij->B));
134: PetscFunctionReturn(PETSC_SUCCESS);
135: }
137: /*
138: Local utility routine that creates a mapping from the global column
139: number to the local number in the off-diagonal part of the local
140: storage of the matrix. This is done in a non scalable way since the
141: length of colmap equals the global matrix length.
142: */
143: PetscErrorCode MatCreateColmap_MPIBAIJ_Private(Mat mat)
144: {
145: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
146: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)baij->B->data;
147: PetscInt nbs = B->nbs, i, bs = mat->rmap->bs;
149: PetscFunctionBegin;
150: #if defined(PETSC_USE_CTABLE)
151: PetscCall(PetscHMapICreateWithSize(baij->nbs, &baij->colmap));
152: for (i = 0; i < nbs; i++) PetscCall(PetscHMapISet(baij->colmap, baij->garray[i] + 1, i * bs + 1));
153: #else
154: PetscCall(PetscCalloc1(baij->Nbs + 1, &baij->colmap));
155: for (i = 0; i < nbs; i++) baij->colmap[baij->garray[i]] = i * bs + 1;
156: #endif
157: PetscFunctionReturn(PETSC_SUCCESS);
158: }
160: #define MatSetValues_SeqBAIJ_A_Private(row, col, value, addv, orow, ocol) \
161: do { \
162: brow = row / bs; \
163: rp = PetscSafePointerPlusOffset(aj, ai[brow]); \
164: ap = PetscSafePointerPlusOffset(aa, bs2 * ai[brow]); \
165: rmax = aimax[brow]; \
166: nrow = ailen[brow]; \
167: bcol = col / bs; \
168: ridx = row % bs; \
169: cidx = col % bs; \
170: low = 0; \
171: high = nrow; \
172: while (high - low > 3) { \
173: t = (low + high) / 2; \
174: if (rp[t] > bcol) high = t; \
175: else low = t; \
176: } \
177: for (_i = low; _i < high; _i++) { \
178: if (rp[_i] > bcol) break; \
179: if (rp[_i] == bcol) { \
180: bap = ap + bs2 * _i + bs * cidx + ridx; \
181: if (addv == ADD_VALUES) *bap += value; \
182: else *bap = value; \
183: goto a_noinsert; \
184: } \
185: } \
186: if (a->nonew == 1) goto a_noinsert; \
187: PetscCheck(a->nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
188: MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, brow, bcol, rmax, aa, ai, aj, rp, ap, aimax, a->nonew, MatScalar); \
189: N = nrow++ - 1; \
190: /* shift up all the later entries in this row */ \
191: PetscCall(PetscArraymove(rp + _i + 1, rp + _i, N - _i + 1)); \
192: PetscCall(PetscArraymove(ap + bs2 * (_i + 1), ap + bs2 * _i, bs2 * (N - _i + 1))); \
193: PetscCall(PetscArrayzero(ap + bs2 * _i, bs2)); \
194: rp[_i] = bcol; \
195: ap[bs2 * _i + bs * cidx + ridx] = value; \
196: a_noinsert:; \
197: ailen[brow] = nrow; \
198: } while (0)
200: #define MatSetValues_SeqBAIJ_B_Private(row, col, value, addv, orow, ocol) \
201: do { \
202: brow = row / bs; \
203: rp = PetscSafePointerPlusOffset(bj, bi[brow]); \
204: ap = PetscSafePointerPlusOffset(ba, bs2 * bi[brow]); \
205: rmax = bimax[brow]; \
206: nrow = bilen[brow]; \
207: bcol = col / bs; \
208: ridx = row % bs; \
209: cidx = col % bs; \
210: low = 0; \
211: high = nrow; \
212: while (high - low > 3) { \
213: t = (low + high) / 2; \
214: if (rp[t] > bcol) high = t; \
215: else low = t; \
216: } \
217: for (_i = low; _i < high; _i++) { \
218: if (rp[_i] > bcol) break; \
219: if (rp[_i] == bcol) { \
220: bap = ap + bs2 * _i + bs * cidx + ridx; \
221: if (addv == ADD_VALUES) *bap += value; \
222: else *bap = value; \
223: goto b_noinsert; \
224: } \
225: } \
226: if (b->nonew == 1) goto b_noinsert; \
227: PetscCheck(b->nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
228: MatSeqXAIJReallocateAIJ(B, b->mbs, bs2, nrow, brow, bcol, rmax, ba, bi, bj, rp, ap, bimax, b->nonew, MatScalar); \
229: N = nrow++ - 1; \
230: /* shift up all the later entries in this row */ \
231: PetscCall(PetscArraymove(rp + _i + 1, rp + _i, N - _i + 1)); \
232: PetscCall(PetscArraymove(ap + bs2 * (_i + 1), ap + bs2 * _i, bs2 * (N - _i + 1))); \
233: PetscCall(PetscArrayzero(ap + bs2 * _i, bs2)); \
234: rp[_i] = bcol; \
235: ap[bs2 * _i + bs * cidx + ridx] = value; \
236: b_noinsert:; \
237: bilen[brow] = nrow; \
238: } while (0)
240: PetscErrorCode MatSetValues_MPIBAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
241: {
242: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
243: MatScalar value;
244: PetscBool roworiented = baij->roworiented;
245: PetscInt i, j, row, col;
246: PetscInt rstart_orig = mat->rmap->rstart;
247: PetscInt rend_orig = mat->rmap->rend, cstart_orig = mat->cmap->rstart;
248: PetscInt cend_orig = mat->cmap->rend, bs = mat->rmap->bs;
250: /* Some Variables required in the macro */
251: Mat A = baij->A;
252: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
253: PetscInt *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
254: MatScalar *aa = a->a;
256: Mat B = baij->B;
257: Mat_SeqBAIJ *b = (Mat_SeqBAIJ *)B->data;
258: PetscInt *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j;
259: MatScalar *ba = b->a;
261: PetscInt *rp, ii, nrow, _i, rmax, N, brow, bcol;
262: PetscInt low, high, t, ridx, cidx, bs2 = a->bs2;
263: MatScalar *ap, *bap;
265: PetscFunctionBegin;
266: for (i = 0; i < m; i++) {
267: if (im[i] < 0) continue;
268: PetscCheck(im[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], mat->rmap->N - 1);
269: if (im[i] >= rstart_orig && im[i] < rend_orig) {
270: row = im[i] - rstart_orig;
271: for (j = 0; j < n; j++) {
272: if (in[j] >= cstart_orig && in[j] < cend_orig) {
273: col = in[j] - cstart_orig;
274: if (roworiented) value = v[i * n + j];
275: else value = v[i + j * m];
276: MatSetValues_SeqBAIJ_A_Private(row, col, value, addv, im[i], in[j]);
277: } else if (in[j] < 0) {
278: continue;
279: } else {
280: PetscCheck(in[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
281: if (mat->was_assembled) {
282: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
283: #if defined(PETSC_USE_CTABLE)
284: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] / bs + 1, 0, &col));
285: col = col - 1;
286: #else
287: col = baij->colmap[in[j] / bs] - 1;
288: #endif
289: if (col < 0 && !((Mat_SeqBAIJ *)baij->B->data)->nonew) {
290: PetscCall(MatDisAssemble_MPIBAIJ(mat));
291: col = in[j];
292: /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
293: B = baij->B;
294: b = (Mat_SeqBAIJ *)B->data;
295: bimax = b->imax;
296: bi = b->i;
297: bilen = b->ilen;
298: bj = b->j;
299: ba = b->a;
300: } else {
301: PetscCheck(col >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]);
302: col += in[j] % bs;
303: }
304: } else col = in[j];
305: if (roworiented) value = v[i * n + j];
306: else value = v[i + j * m];
307: MatSetValues_SeqBAIJ_B_Private(row, col, value, addv, im[i], in[j]);
308: /* PetscCall(MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv)); */
309: }
310: }
311: } else {
312: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Setting off process row %" PetscInt_FMT " even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set", im[i]);
313: if (!baij->donotstash) {
314: mat->assembled = PETSC_FALSE;
315: if (roworiented) {
316: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, PETSC_FALSE));
317: } else {
318: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, PETSC_FALSE));
319: }
320: }
321: }
322: }
323: PetscFunctionReturn(PETSC_SUCCESS);
324: }
326: static inline PetscErrorCode MatSetValuesBlocked_SeqBAIJ_Inlined(Mat A, PetscInt row, PetscInt col, const PetscScalar v[], InsertMode is, PetscInt orow, PetscInt ocol)
327: {
328: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
329: PetscInt *rp, low, high, t, ii, jj, nrow, i, rmax, N;
330: PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen;
331: PetscInt *aj = a->j, nonew = a->nonew, bs2 = a->bs2, bs = A->rmap->bs;
332: PetscBool roworiented = a->roworiented;
333: const PetscScalar *value = v;
334: MatScalar *ap, *aa = a->a, *bap;
336: PetscFunctionBegin;
337: rp = aj + ai[row];
338: ap = aa + bs2 * ai[row];
339: rmax = imax[row];
340: nrow = ailen[row];
341: value = v;
342: low = 0;
343: high = nrow;
344: while (high - low > 7) {
345: t = (low + high) / 2;
346: if (rp[t] > col) high = t;
347: else low = t;
348: }
349: for (i = low; i < high; i++) {
350: if (rp[i] > col) break;
351: if (rp[i] == col) {
352: bap = ap + bs2 * i;
353: if (roworiented) {
354: if (is == ADD_VALUES) {
355: for (ii = 0; ii < bs; ii++) {
356: for (jj = ii; jj < bs2; jj += bs) bap[jj] += *value++;
357: }
358: } else {
359: for (ii = 0; ii < bs; ii++) {
360: for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
361: }
362: }
363: } else {
364: if (is == ADD_VALUES) {
365: for (ii = 0; ii < bs; ii++, value += bs) {
366: for (jj = 0; jj < bs; jj++) bap[jj] += value[jj];
367: bap += bs;
368: }
369: } else {
370: for (ii = 0; ii < bs; ii++, value += bs) {
371: for (jj = 0; jj < bs; jj++) bap[jj] = value[jj];
372: bap += bs;
373: }
374: }
375: }
376: goto noinsert2;
377: }
378: }
379: if (nonew == 1) goto noinsert2;
380: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new global block indexed nonzero block (%" PetscInt_FMT ", %" PetscInt_FMT ") in the matrix", orow, ocol);
381: MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
382: N = nrow++ - 1;
383: high++;
384: /* shift up all the later entries in this row */
385: PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
386: PetscCall(PetscArraymove(ap + bs2 * (i + 1), ap + bs2 * i, bs2 * (N - i + 1)));
387: rp[i] = col;
388: bap = ap + bs2 * i;
389: if (roworiented) {
390: for (ii = 0; ii < bs; ii++) {
391: for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
392: }
393: } else {
394: for (ii = 0; ii < bs; ii++) {
395: for (jj = 0; jj < bs; jj++) *bap++ = *value++;
396: }
397: }
398: noinsert2:;
399: ailen[row] = nrow;
400: PetscFunctionReturn(PETSC_SUCCESS);
401: }
403: /*
404: This routine should be optimized so that the block copy at ** Here a copy is required ** below is not needed
405: by passing additional stride information into the MatSetValuesBlocked_SeqBAIJ_Inlined() routine
406: */
407: static PetscErrorCode MatSetValuesBlocked_MPIBAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
408: {
409: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
410: const PetscScalar *value;
411: MatScalar *barray = baij->barray;
412: PetscBool roworiented = baij->roworiented;
413: PetscInt i, j, ii, jj, row, col, rstart = baij->rstartbs;
414: PetscInt rend = baij->rendbs, cstart = baij->cstartbs, stepval;
415: PetscInt cend = baij->cendbs, bs = mat->rmap->bs, bs2 = baij->bs2;
417: PetscFunctionBegin;
418: if (!barray) {
419: PetscCall(PetscMalloc1(bs2, &barray));
420: baij->barray = barray;
421: }
423: if (roworiented) stepval = (n - 1) * bs;
424: else stepval = (m - 1) * bs;
426: for (i = 0; i < m; i++) {
427: if (im[i] < 0) continue;
428: PetscCheck(im[i] < baij->Mbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Block indexed row too large %" PetscInt_FMT " max %" PetscInt_FMT, im[i], baij->Mbs - 1);
429: if (im[i] >= rstart && im[i] < rend) {
430: row = im[i] - rstart;
431: for (j = 0; j < n; j++) {
432: /* If NumCol = 1 then a copy is not required */
433: if ((roworiented) && (n == 1)) {
434: barray = (MatScalar *)v + i * bs2;
435: } else if ((!roworiented) && (m == 1)) {
436: barray = (MatScalar *)v + j * bs2;
437: } else { /* Here a copy is required */
438: if (roworiented) {
439: value = v + (i * (stepval + bs) + j) * bs;
440: } else {
441: value = v + (j * (stepval + bs) + i) * bs;
442: }
443: for (ii = 0; ii < bs; ii++, value += bs + stepval) {
444: for (jj = 0; jj < bs; jj++) barray[jj] = value[jj];
445: barray += bs;
446: }
447: barray -= bs2;
448: }
450: if (in[j] >= cstart && in[j] < cend) {
451: col = in[j] - cstart;
452: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->A, row, col, barray, addv, im[i], in[j]));
453: } else if (in[j] < 0) {
454: continue;
455: } else {
456: PetscCheck(in[j] < baij->Nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Block indexed column too large %" PetscInt_FMT " max %" PetscInt_FMT, in[j], baij->Nbs - 1);
457: if (mat->was_assembled) {
458: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
460: #if defined(PETSC_USE_CTABLE)
461: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &col));
462: col = col < 1 ? -1 : (col - 1) / bs;
463: #else
464: col = baij->colmap[in[j]] < 1 ? -1 : (baij->colmap[in[j]] - 1) / bs;
465: #endif
466: if (col < 0 && !((Mat_SeqBAIJ *)baij->B->data)->nonew) {
467: PetscCall(MatDisAssemble_MPIBAIJ(mat));
468: col = in[j];
469: } else PetscCheck(col >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new blocked indexed nonzero block (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]);
470: } else col = in[j];
471: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->B, row, col, barray, addv, im[i], in[j]));
472: }
473: }
474: } else {
475: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Setting off process block indexed row %" PetscInt_FMT " even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set", im[i]);
476: if (!baij->donotstash) {
477: if (roworiented) {
478: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
479: } else {
480: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
481: }
482: }
483: }
484: }
485: PetscFunctionReturn(PETSC_SUCCESS);
486: }
488: #define HASH_KEY 0.6180339887
489: #define HASH(size, key, tmp) (tmp = (key) * HASH_KEY, (PetscInt)((size) * (tmp - (PetscInt)tmp)))
490: /* #define HASH(size,key) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
491: /* #define HASH(size,key,tmp) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
492: static PetscErrorCode MatSetValues_MPIBAIJ_HT(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
493: {
494: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
495: PetscBool roworiented = baij->roworiented;
496: PetscInt i, j, row, col;
497: PetscInt rstart_orig = mat->rmap->rstart;
498: PetscInt rend_orig = mat->rmap->rend, Nbs = baij->Nbs;
499: PetscInt h1, key, size = baij->ht_size, bs = mat->rmap->bs, *HT = baij->ht, idx;
500: PetscReal tmp;
501: MatScalar **HD = baij->hd, value;
502: PetscInt total_ct = baij->ht_total_ct, insert_ct = baij->ht_insert_ct;
504: PetscFunctionBegin;
505: for (i = 0; i < m; i++) {
506: if (PetscDefined(USE_DEBUG)) {
507: PetscCheck(im[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row");
508: PetscCheck(im[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], mat->rmap->N - 1);
509: }
510: row = im[i];
511: if (row >= rstart_orig && row < rend_orig) {
512: for (j = 0; j < n; j++) {
513: col = in[j];
514: if (roworiented) value = v[i * n + j];
515: else value = v[i + j * m];
516: /* Look up PetscInto the Hash Table */
517: key = (row / bs) * Nbs + (col / bs) + 1;
518: h1 = HASH(size, key, tmp);
520: idx = h1;
521: if (PetscDefined(USE_DEBUG)) {
522: insert_ct++;
523: total_ct++;
524: if (HT[idx] != key) {
525: for (idx = h1; (idx < size) && (HT[idx] != key); idx++, total_ct++);
526: if (idx == size) {
527: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++, total_ct++);
528: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
529: }
530: }
531: } else if (HT[idx] != key) {
532: for (idx = h1; (idx < size) && (HT[idx] != key); idx++);
533: if (idx == size) {
534: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++);
535: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
536: }
537: }
538: /* A HASH table entry is found, so insert the values at the correct address */
539: if (addv == ADD_VALUES) *(HD[idx] + (col % bs) * bs + (row % bs)) += value;
540: else *(HD[idx] + (col % bs) * bs + (row % bs)) = value;
541: }
542: } else if (!baij->donotstash) {
543: if (roworiented) {
544: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, PETSC_FALSE));
545: } else {
546: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, PETSC_FALSE));
547: }
548: }
549: }
550: if (PetscDefined(USE_DEBUG)) {
551: baij->ht_total_ct += total_ct;
552: baij->ht_insert_ct += insert_ct;
553: }
554: PetscFunctionReturn(PETSC_SUCCESS);
555: }
557: static PetscErrorCode MatSetValuesBlocked_MPIBAIJ_HT(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
558: {
559: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
560: PetscBool roworiented = baij->roworiented;
561: PetscInt i, j, ii, jj, row, col;
562: PetscInt rstart = baij->rstartbs;
563: PetscInt rend = mat->rmap->rend, stepval, bs = mat->rmap->bs, bs2 = baij->bs2, nbs2 = n * bs2;
564: PetscInt h1, key, size = baij->ht_size, idx, *HT = baij->ht, Nbs = baij->Nbs;
565: PetscReal tmp;
566: MatScalar **HD = baij->hd, *baij_a;
567: const PetscScalar *v_t, *value;
568: PetscInt total_ct = baij->ht_total_ct, insert_ct = baij->ht_insert_ct;
570: PetscFunctionBegin;
571: if (roworiented) stepval = (n - 1) * bs;
572: else stepval = (m - 1) * bs;
574: for (i = 0; i < m; i++) {
575: if (PetscDefined(USE_DEBUG)) {
576: PetscCheck(im[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row: %" PetscInt_FMT, im[i]);
577: PetscCheck(im[i] < baij->Mbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], baij->Mbs - 1);
578: }
579: row = im[i];
580: v_t = v + i * nbs2;
581: if (row >= rstart && row < rend) {
582: for (j = 0; j < n; j++) {
583: col = in[j];
585: /* Look up into the Hash Table */
586: key = row * Nbs + col + 1;
587: h1 = HASH(size, key, tmp);
589: idx = h1;
590: if (PetscDefined(USE_DEBUG)) {
591: total_ct++;
592: insert_ct++;
593: if (HT[idx] != key) {
594: for (idx = h1; (idx < size) && (HT[idx] != key); idx++, total_ct++);
595: if (idx == size) {
596: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++, total_ct++);
597: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
598: }
599: }
600: } else if (HT[idx] != key) {
601: for (idx = h1; (idx < size) && (HT[idx] != key); idx++);
602: if (idx == size) {
603: for (idx = 0; (idx < h1) && (HT[idx] != key); idx++);
604: PetscCheck(idx != h1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "(%" PetscInt_FMT ",%" PetscInt_FMT ") has no entry in the hash table", row, col);
605: }
606: }
607: baij_a = HD[idx];
608: if (roworiented) {
609: /*value = v + i*(stepval+bs)*bs + j*bs;*/
610: /* value = v + (i*(stepval+bs)+j)*bs; */
611: value = v_t;
612: v_t += bs;
613: if (addv == ADD_VALUES) {
614: for (ii = 0; ii < bs; ii++, value += stepval) {
615: for (jj = ii; jj < bs2; jj += bs) baij_a[jj] += *value++;
616: }
617: } else {
618: for (ii = 0; ii < bs; ii++, value += stepval) {
619: for (jj = ii; jj < bs2; jj += bs) baij_a[jj] = *value++;
620: }
621: }
622: } else {
623: value = v + j * (stepval + bs) * bs + i * bs;
624: if (addv == ADD_VALUES) {
625: for (ii = 0; ii < bs; ii++, value += stepval, baij_a += bs) {
626: for (jj = 0; jj < bs; jj++) baij_a[jj] += *value++;
627: }
628: } else {
629: for (ii = 0; ii < bs; ii++, value += stepval, baij_a += bs) {
630: for (jj = 0; jj < bs; jj++) baij_a[jj] = *value++;
631: }
632: }
633: }
634: }
635: } else {
636: if (!baij->donotstash) {
637: if (roworiented) {
638: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
639: } else {
640: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
641: }
642: }
643: }
644: }
645: if (PetscDefined(USE_DEBUG)) {
646: baij->ht_total_ct += total_ct;
647: baij->ht_insert_ct += insert_ct;
648: }
649: PetscFunctionReturn(PETSC_SUCCESS);
650: }
652: static PetscErrorCode MatGetValues_MPIBAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
653: {
654: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
655: PetscInt bs = mat->rmap->bs, i, j, bsrstart = mat->rmap->rstart, bsrend = mat->rmap->rend;
656: PetscInt bscstart = mat->cmap->rstart, bscend = mat->cmap->rend, row, col, data;
658: PetscFunctionBegin;
659: for (i = 0; i < m; i++) {
660: if (idxm[i] < 0) continue; /* negative row */
661: PetscCheck(idxm[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, idxm[i], mat->rmap->N - 1);
662: 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;
719: PetscInt *jj, *garray = baij->garray, cstart = baij->rstartbs;
721: PetscCall(PetscCalloc1(mat->cmap->N, &tmp));
722: v = amat->a;
723: jj = amat->j;
724: for (i = 0; i < amat->nz; i++) {
725: for (j = 0; j < bs; j++) {
726: col = bs * (cstart + *jj) + j; /* column index */
727: for (row = 0; row < bs; row++) {
728: tmp[col] += PetscAbsScalar(*v);
729: v++;
730: }
731: }
732: jj++;
733: }
734: v = bmat->a;
735: jj = bmat->j;
736: for (i = 0; i < bmat->nz; i++) {
737: for (j = 0; j < bs; j++) {
738: col = bs * garray[*jj] + j;
739: for (row = 0; row < bs; row++) {
740: tmp[col] += PetscAbsScalar(*v);
741: v++;
742: }
743: }
744: jj++;
745: }
746: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, tmp, mat->cmap->N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
747: *nrm = 0.0;
748: for (j = 0; j < mat->cmap->N; j++) {
749: if (tmp[j] > *nrm) *nrm = tmp[j];
750: }
751: PetscCall(PetscFree(tmp));
752: } else if (type == NORM_INFINITY) { /* max row sum */
753: PetscReal *sums;
754: PetscCall(PetscMalloc1(bs, &sums));
755: sum = 0.0;
756: for (j = 0; j < amat->mbs; j++) {
757: for (row = 0; row < bs; row++) sums[row] = 0.0;
758: v = amat->a + bs2 * amat->i[j];
759: nz = amat->i[j + 1] - amat->i[j];
760: for (i = 0; i < nz; i++) {
761: for (col = 0; col < bs; col++) {
762: for (row = 0; row < bs; row++) {
763: sums[row] += PetscAbsScalar(*v);
764: v++;
765: }
766: }
767: }
768: v = bmat->a + bs2 * bmat->i[j];
769: nz = bmat->i[j + 1] - bmat->i[j];
770: for (i = 0; i < nz; i++) {
771: for (col = 0; col < bs; col++) {
772: for (row = 0; row < bs; row++) {
773: sums[row] += PetscAbsScalar(*v);
774: v++;
775: }
776: }
777: }
778: for (row = 0; row < bs; row++) {
779: if (sums[row] > sum) sum = sums[row];
780: }
781: }
782: PetscCallMPI(MPIU_Allreduce(&sum, nrm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
783: PetscCall(PetscFree(sums));
784: } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for this norm yet");
785: }
786: PetscFunctionReturn(PETSC_SUCCESS);
787: }
789: /*
790: Creates the hash table, and sets the table
791: This table is created only once.
792: If new entried need to be added to the matrix
793: then the hash table has to be destroyed and
794: recreated.
795: */
796: static PetscErrorCode MatCreateHashTable_MPIBAIJ_Private(Mat mat, PetscReal factor)
797: {
798: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
799: Mat A = baij->A, B = baij->B;
800: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data, *b = (Mat_SeqBAIJ *)B->data;
801: PetscInt i, j, k, nz = a->nz + b->nz, h1, *ai = a->i, *aj = a->j, *bi = b->i, *bj = b->j;
802: PetscInt ht_size, bs2 = baij->bs2, rstart = baij->rstartbs;
803: PetscInt cstart = baij->cstartbs, *garray = baij->garray, row, col, Nbs = baij->Nbs;
804: PetscInt *HT, key;
805: MatScalar **HD;
806: PetscReal tmp;
807: #if defined(PETSC_USE_INFO)
808: PetscInt ct = 0, max = 0;
809: #endif
811: PetscFunctionBegin;
812: if (baij->ht) PetscFunctionReturn(PETSC_SUCCESS);
814: baij->ht_size = (PetscInt)(factor * nz);
815: ht_size = baij->ht_size;
817: /* Allocate Memory for Hash Table */
818: PetscCall(PetscCalloc2(ht_size, &baij->hd, ht_size, &baij->ht));
819: HD = baij->hd;
820: HT = baij->ht;
822: /* Loop Over A */
823: for (i = 0; i < a->mbs; i++) {
824: for (j = ai[i]; j < ai[i + 1]; j++) {
825: row = i + rstart;
826: col = aj[j] + cstart;
828: key = row * Nbs + col + 1;
829: h1 = HASH(ht_size, key, tmp);
830: for (k = 0; k < ht_size; k++) {
831: if (!HT[(h1 + k) % ht_size]) {
832: HT[(h1 + k) % ht_size] = key;
833: HD[(h1 + k) % ht_size] = a->a + j * bs2;
834: break;
835: #if defined(PETSC_USE_INFO)
836: } else {
837: ct++;
838: #endif
839: }
840: }
841: #if defined(PETSC_USE_INFO)
842: if (k > max) max = k;
843: #endif
844: }
845: }
846: /* Loop Over B */
847: for (i = 0; i < b->mbs; i++) {
848: for (j = bi[i]; j < bi[i + 1]; j++) {
849: row = i + rstart;
850: col = garray[bj[j]];
851: key = row * Nbs + col + 1;
852: h1 = HASH(ht_size, key, tmp);
853: for (k = 0; k < ht_size; k++) {
854: if (!HT[(h1 + k) % ht_size]) {
855: HT[(h1 + k) % ht_size] = key;
856: HD[(h1 + k) % ht_size] = b->a + j * bs2;
857: break;
858: #if defined(PETSC_USE_INFO)
859: } else {
860: ct++;
861: #endif
862: }
863: }
864: #if defined(PETSC_USE_INFO)
865: if (k > max) max = k;
866: #endif
867: }
868: }
870: /* Print Summary */
871: #if defined(PETSC_USE_INFO)
872: for (i = 0, j = 0; i < ht_size; i++) {
873: if (HT[i]) j++;
874: }
875: PetscCall(PetscInfo(mat, "Average Search = %5.2g,max search = %" PetscInt_FMT "\n", (!j) ? 0.0 : (double)(((PetscReal)(ct + j)) / j), max));
876: #endif
877: PetscFunctionReturn(PETSC_SUCCESS);
878: }
880: static PetscErrorCode MatAssemblyBegin_MPIBAIJ(Mat mat, MatAssemblyType mode)
881: {
882: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
883: PetscInt nstash, reallocs;
885: PetscFunctionBegin;
886: if (baij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
888: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
889: PetscCall(MatStashScatterBegin_Private(mat, &mat->bstash, baij->rangebs));
890: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
891: PetscCall(PetscInfo(mat, "Stash has %" PetscInt_FMT " entries,uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
892: PetscCall(MatStashGetInfo_Private(&mat->bstash, &nstash, &reallocs));
893: PetscCall(PetscInfo(mat, "Block-Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
894: PetscFunctionReturn(PETSC_SUCCESS);
895: }
897: static PetscErrorCode MatAssemblyEnd_MPIBAIJ(Mat mat, MatAssemblyType mode)
898: {
899: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
900: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)baij->A->data;
901: PetscInt i, j, rstart, ncols, flg, bs2 = baij->bs2;
902: PetscInt *row, *col;
903: PetscBool r1, r2, r3, other_disassembled;
904: MatScalar *val;
905: PetscMPIInt n;
907: PetscFunctionBegin;
908: /* do not use 'b=(Mat_SeqBAIJ*)baij->B->data' as B can be reset in disassembly */
909: if (!baij->donotstash && !mat->nooffprocentries) {
910: while (1) {
911: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
912: if (!flg) break;
914: for (i = 0; i < n;) {
915: /* Now identify the consecutive vals belonging to the same row */
916: for (j = i, rstart = row[j]; j < n; j++) {
917: if (row[j] != rstart) break;
918: }
919: if (j < n) ncols = j - i;
920: else ncols = n - i;
921: /* Now assemble all these values with a single function call */
922: PetscCall(MatSetValues_MPIBAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
923: i = j;
924: }
925: }
926: PetscCall(MatStashScatterEnd_Private(&mat->stash));
927: /* Now process the block-stash. Since the values are stashed column-oriented,
928: set the row-oriented flag to column-oriented, and after MatSetValues()
929: restore the original flags */
930: r1 = baij->roworiented;
931: r2 = a->roworiented;
932: r3 = ((Mat_SeqBAIJ *)baij->B->data)->roworiented;
934: baij->roworiented = PETSC_FALSE;
935: a->roworiented = PETSC_FALSE;
936: ((Mat_SeqBAIJ *)baij->B->data)->roworiented = PETSC_FALSE;
937: while (1) {
938: PetscCall(MatStashScatterGetMesg_Private(&mat->bstash, &n, &row, &col, &val, &flg));
939: if (!flg) break;
941: for (i = 0; i < n;) {
942: /* Now identify the consecutive vals belonging to the same row */
943: for (j = i, rstart = row[j]; j < n; j++) {
944: if (row[j] != rstart) break;
945: }
946: if (j < n) ncols = j - i;
947: else ncols = n - i;
948: PetscCall(MatSetValuesBlocked_MPIBAIJ(mat, 1, row + i, ncols, col + i, val + i * bs2, mat->insertmode));
949: i = j;
950: }
951: }
952: PetscCall(MatStashScatterEnd_Private(&mat->bstash));
954: baij->roworiented = r1;
955: a->roworiented = r2;
956: ((Mat_SeqBAIJ *)baij->B->data)->roworiented = r3;
957: }
959: PetscCall(MatAssemblyBegin(baij->A, mode));
960: PetscCall(MatAssemblyEnd(baij->A, mode));
962: /* determine if any processor has disassembled, if so we must
963: also disassemble ourselves, in order that we may reassemble. */
964: /*
965: if nonzero structure of submatrix B cannot change then we know that
966: no processor disassembled thus we can skip this stuff
967: */
968: if (!((Mat_SeqBAIJ *)baij->B->data)->nonew) {
969: PetscCallMPI(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
970: if (mat->was_assembled && !other_disassembled) PetscCall(MatDisAssemble_MPIBAIJ(mat));
971: }
973: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIBAIJ(mat));
974: PetscCall(MatAssemblyBegin(baij->B, mode));
975: PetscCall(MatAssemblyEnd(baij->B, mode));
977: #if defined(PETSC_USE_INFO)
978: if (baij->ht && mode == MAT_FINAL_ASSEMBLY) {
979: PetscCall(PetscInfo(mat, "Average Hash Table Search in MatSetValues = %5.2f\n", (double)((PetscReal)baij->ht_total_ct) / baij->ht_insert_ct));
981: baij->ht_total_ct = 0;
982: baij->ht_insert_ct = 0;
983: }
984: #endif
985: if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
986: PetscCall(MatCreateHashTable_MPIBAIJ_Private(mat, baij->ht_fact));
988: mat->ops->setvalues = MatSetValues_MPIBAIJ_HT;
989: mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
990: }
992: PetscCall(PetscFree2(baij->rowvalues, baij->rowindices));
994: baij->rowvalues = NULL;
996: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
997: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqBAIJ *)baij->A->data)->nonew) {
998: PetscObjectState state = baij->A->nonzerostate + baij->B->nonzerostate;
999: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
1000: }
1001: PetscFunctionReturn(PETSC_SUCCESS);
1002: }
1004: extern PetscErrorCode MatView_SeqBAIJ(Mat, PetscViewer);
1005: #include <petscdraw.h>
1006: static PetscErrorCode MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1007: {
1008: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1009: PetscMPIInt rank = baij->rank;
1010: PetscInt bs = mat->rmap->bs;
1011: PetscBool iascii, isdraw;
1012: PetscViewer sviewer;
1013: PetscViewerFormat format;
1015: PetscFunctionBegin;
1016: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1017: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1018: if (iascii) {
1019: PetscCall(PetscViewerGetFormat(viewer, &format));
1020: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1021: MatInfo info;
1022: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1023: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1024: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1025: 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,
1026: mat->rmap->bs, info.memory));
1027: PetscCall(MatGetInfo(baij->A, MAT_LOCAL, &info));
1028: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1029: PetscCall(MatGetInfo(baij->B, MAT_LOCAL, &info));
1030: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1031: PetscCall(PetscViewerFlush(viewer));
1032: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1033: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1034: PetscCall(VecScatterView(baij->Mvctx, viewer));
1035: PetscFunctionReturn(PETSC_SUCCESS);
1036: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1037: PetscCall(PetscViewerASCIIPrintf(viewer, " block size is %" PetscInt_FMT "\n", bs));
1038: PetscFunctionReturn(PETSC_SUCCESS);
1039: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1040: PetscFunctionReturn(PETSC_SUCCESS);
1041: }
1042: }
1044: if (isdraw) {
1045: PetscDraw draw;
1046: PetscBool isnull;
1047: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1048: PetscCall(PetscDrawIsNull(draw, &isnull));
1049: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1050: }
1052: {
1053: /* assemble the entire matrix onto first processor. */
1054: Mat A;
1055: Mat_SeqBAIJ *Aloc;
1056: PetscInt M = mat->rmap->N, N = mat->cmap->N, *ai, *aj, col, i, j, k, *rvals, mbs = baij->mbs;
1057: MatScalar *a;
1058: const char *matname;
1060: /* Here we are creating a temporary matrix, so will assume MPIBAIJ is acceptable */
1061: /* Perhaps this should be the type of mat? */
1062: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &A));
1063: if (rank == 0) {
1064: PetscCall(MatSetSizes(A, M, N, M, N));
1065: } else {
1066: PetscCall(MatSetSizes(A, 0, 0, M, N));
1067: }
1068: PetscCall(MatSetType(A, MATMPIBAIJ));
1069: PetscCall(MatMPIBAIJSetPreallocation(A, mat->rmap->bs, 0, NULL, 0, NULL));
1070: PetscCall(MatSetOption(A, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_FALSE));
1072: /* copy over the A part */
1073: Aloc = (Mat_SeqBAIJ *)baij->A->data;
1074: ai = Aloc->i;
1075: aj = Aloc->j;
1076: a = Aloc->a;
1077: PetscCall(PetscMalloc1(bs, &rvals));
1079: for (i = 0; i < mbs; i++) {
1080: rvals[0] = bs * (baij->rstartbs + i);
1081: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1082: for (j = ai[i]; j < ai[i + 1]; j++) {
1083: col = (baij->cstartbs + aj[j]) * bs;
1084: for (k = 0; k < bs; k++) {
1085: PetscCall(MatSetValues_MPIBAIJ(A, bs, rvals, 1, &col, a, INSERT_VALUES));
1086: col++;
1087: a += bs;
1088: }
1089: }
1090: }
1091: /* copy over the B part */
1092: Aloc = (Mat_SeqBAIJ *)baij->B->data;
1093: ai = Aloc->i;
1094: aj = Aloc->j;
1095: a = Aloc->a;
1096: for (i = 0; i < mbs; i++) {
1097: rvals[0] = bs * (baij->rstartbs + i);
1098: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1099: for (j = ai[i]; j < ai[i + 1]; j++) {
1100: col = baij->garray[aj[j]] * bs;
1101: for (k = 0; k < bs; k++) {
1102: PetscCall(MatSetValues_MPIBAIJ(A, bs, rvals, 1, &col, a, INSERT_VALUES));
1103: col++;
1104: a += bs;
1105: }
1106: }
1107: }
1108: PetscCall(PetscFree(rvals));
1109: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1110: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1111: /*
1112: Everyone has to call to draw the matrix since the graphics waits are
1113: synchronized across all processors that share the PetscDraw object
1114: */
1115: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1116: if (((PetscObject)mat)->name) PetscCall(PetscObjectGetName((PetscObject)mat, &matname));
1117: if (rank == 0) {
1118: if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)((Mat_MPIBAIJ *)A->data)->A, matname));
1119: PetscCall(MatView_SeqBAIJ(((Mat_MPIBAIJ *)A->data)->A, sviewer));
1120: }
1121: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1122: PetscCall(MatDestroy(&A));
1123: }
1124: PetscFunctionReturn(PETSC_SUCCESS);
1125: }
1127: /* Used for both MPIBAIJ and MPISBAIJ matrices */
1128: PetscErrorCode MatView_MPIBAIJ_Binary(Mat mat, PetscViewer viewer)
1129: {
1130: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)mat->data;
1131: Mat_SeqBAIJ *A = (Mat_SeqBAIJ *)aij->A->data;
1132: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)aij->B->data;
1133: const PetscInt *garray = aij->garray;
1134: PetscInt header[4], M, N, m, rs, cs, bs, cnt, i, j, ja, jb, k, l;
1135: PetscCount nz, hnz;
1136: PetscInt *rowlens, *colidxs;
1137: PetscScalar *matvals;
1138: PetscMPIInt rank;
1140: PetscFunctionBegin;
1141: PetscCall(PetscViewerSetUp(viewer));
1143: M = mat->rmap->N;
1144: N = mat->cmap->N;
1145: m = mat->rmap->n;
1146: rs = mat->rmap->rstart;
1147: cs = mat->cmap->rstart;
1148: bs = mat->rmap->bs;
1149: nz = bs * bs * (A->nz + B->nz);
1151: /* write matrix header */
1152: header[0] = MAT_FILE_CLASSID;
1153: header[1] = M;
1154: header[2] = N;
1155: PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_COUNT, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1156: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1157: if (rank == 0) PetscCall(PetscIntCast(hnz, &header[3]));
1158: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1160: /* fill in and store row lengths */
1161: PetscCall(PetscMalloc1(m, &rowlens));
1162: for (cnt = 0, i = 0; i < A->mbs; i++)
1163: for (j = 0; j < bs; j++) rowlens[cnt++] = bs * (A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i]);
1164: PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1165: PetscCall(PetscFree(rowlens));
1167: /* fill in and store column indices */
1168: PetscCall(PetscMalloc1(nz, &colidxs));
1169: for (cnt = 0, i = 0; i < A->mbs; i++) {
1170: for (k = 0; k < bs; k++) {
1171: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1172: if (garray[B->j[jb]] > cs / bs) break;
1173: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * garray[B->j[jb]] + l;
1174: }
1175: for (ja = A->i[i]; ja < A->i[i + 1]; ja++)
1176: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * A->j[ja] + l + cs;
1177: for (; jb < B->i[i + 1]; jb++)
1178: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * garray[B->j[jb]] + l;
1179: }
1180: }
1181: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscCount_FMT, cnt, nz);
1182: PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DECIDE, PETSC_DECIDE, PETSC_INT));
1183: PetscCall(PetscFree(colidxs));
1185: /* fill in and store nonzero values */
1186: PetscCall(PetscMalloc1(nz, &matvals));
1187: for (cnt = 0, i = 0; i < A->mbs; i++) {
1188: for (k = 0; k < bs; k++) {
1189: for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1190: if (garray[B->j[jb]] > cs / bs) break;
1191: for (l = 0; l < bs; l++) matvals[cnt++] = B->a[bs * (bs * jb + l) + k];
1192: }
1193: for (ja = A->i[i]; ja < A->i[i + 1]; ja++)
1194: for (l = 0; l < bs; l++) matvals[cnt++] = A->a[bs * (bs * ja + l) + k];
1195: for (; jb < B->i[i + 1]; jb++)
1196: for (l = 0; l < bs; l++) matvals[cnt++] = B->a[bs * (bs * jb + l) + k];
1197: }
1198: }
1199: PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DECIDE, PETSC_DECIDE, PETSC_SCALAR));
1200: PetscCall(PetscFree(matvals));
1202: /* write block size option to the viewer's .info file */
1203: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1204: PetscFunctionReturn(PETSC_SUCCESS);
1205: }
1207: PetscErrorCode MatView_MPIBAIJ(Mat mat, PetscViewer viewer)
1208: {
1209: PetscBool iascii, isdraw, issocket, isbinary;
1211: PetscFunctionBegin;
1212: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1213: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1214: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1215: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1216: if (iascii || isdraw || issocket) {
1217: PetscCall(MatView_MPIBAIJ_ASCIIorDraworSocket(mat, viewer));
1218: } else if (isbinary) PetscCall(MatView_MPIBAIJ_Binary(mat, viewer));
1219: PetscFunctionReturn(PETSC_SUCCESS);
1220: }
1222: static PetscErrorCode MatMult_MPIBAIJ(Mat A, Vec xx, Vec yy)
1223: {
1224: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1225: PetscInt nt;
1227: PetscFunctionBegin;
1228: PetscCall(VecGetLocalSize(xx, &nt));
1229: PetscCheck(nt == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A and xx");
1230: PetscCall(VecGetLocalSize(yy, &nt));
1231: PetscCheck(nt == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A and yy");
1232: PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1233: PetscCall((*a->A->ops->mult)(a->A, xx, yy));
1234: PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1235: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, yy, yy));
1236: PetscFunctionReturn(PETSC_SUCCESS);
1237: }
1239: static PetscErrorCode MatMultAdd_MPIBAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1240: {
1241: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1243: PetscFunctionBegin;
1244: PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1245: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1246: PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1247: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1248: PetscFunctionReturn(PETSC_SUCCESS);
1249: }
1251: static PetscErrorCode MatMultTranspose_MPIBAIJ(Mat A, Vec xx, Vec yy)
1252: {
1253: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1255: PetscFunctionBegin;
1256: /* do nondiagonal part */
1257: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1258: /* do local part */
1259: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1260: /* add partial results together */
1261: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1262: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1263: PetscFunctionReturn(PETSC_SUCCESS);
1264: }
1266: static PetscErrorCode MatMultTransposeAdd_MPIBAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1267: {
1268: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1270: PetscFunctionBegin;
1271: /* do nondiagonal part */
1272: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1273: /* do local part */
1274: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1275: /* add partial results together */
1276: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1277: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1278: PetscFunctionReturn(PETSC_SUCCESS);
1279: }
1281: /*
1282: This only works correctly for square matrices where the subblock A->A is the
1283: diagonal block
1284: */
1285: static PetscErrorCode MatGetDiagonal_MPIBAIJ(Mat A, Vec v)
1286: {
1287: PetscFunctionBegin;
1288: PetscCheck(A->rmap->N == A->cmap->N, PETSC_COMM_SELF, PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1289: PetscCall(MatGetDiagonal(((Mat_MPIBAIJ *)A->data)->A, v));
1290: PetscFunctionReturn(PETSC_SUCCESS);
1291: }
1293: static PetscErrorCode MatScale_MPIBAIJ(Mat A, PetscScalar aa)
1294: {
1295: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1297: PetscFunctionBegin;
1298: PetscCall(MatScale(a->A, aa));
1299: PetscCall(MatScale(a->B, aa));
1300: PetscFunctionReturn(PETSC_SUCCESS);
1301: }
1303: PetscErrorCode MatGetRow_MPIBAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1304: {
1305: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)matin->data;
1306: PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1307: PetscInt bs = matin->rmap->bs, bs2 = mat->bs2, i, *cworkA, *cworkB, **pcA, **pcB;
1308: PetscInt nztot, nzA, nzB, lrow, brstart = matin->rmap->rstart, brend = matin->rmap->rend;
1309: PetscInt *cmap, *idx_p, cstart = mat->cstartbs;
1311: PetscFunctionBegin;
1312: PetscCheck(row >= brstart && row < brend, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local rows");
1313: PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1314: mat->getrowactive = PETSC_TRUE;
1316: if (!mat->rowvalues && (idx || v)) {
1317: /*
1318: allocate enough space to hold information from the longest row.
1319: */
1320: Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ *)mat->A->data, *Ba = (Mat_SeqBAIJ *)mat->B->data;
1321: PetscInt max = 1, mbs = mat->mbs, tmp;
1322: for (i = 0; i < mbs; i++) {
1323: tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1324: if (max < tmp) max = tmp;
1325: }
1326: PetscCall(PetscMalloc2(max * bs2, &mat->rowvalues, max * bs2, &mat->rowindices));
1327: }
1328: lrow = row - brstart;
1330: pvA = &vworkA;
1331: pcA = &cworkA;
1332: pvB = &vworkB;
1333: pcB = &cworkB;
1334: if (!v) {
1335: pvA = NULL;
1336: pvB = NULL;
1337: }
1338: if (!idx) {
1339: pcA = NULL;
1340: if (!v) pcB = NULL;
1341: }
1342: PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1343: PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1344: nztot = nzA + nzB;
1346: cmap = mat->garray;
1347: if (v || idx) {
1348: if (nztot) {
1349: /* Sort by increasing column numbers, assuming A and B already sorted */
1350: PetscInt imark = -1;
1351: if (v) {
1352: *v = v_p = mat->rowvalues;
1353: for (i = 0; i < nzB; i++) {
1354: if (cmap[cworkB[i] / bs] < cstart) v_p[i] = vworkB[i];
1355: else break;
1356: }
1357: imark = i;
1358: for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1359: for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1360: }
1361: if (idx) {
1362: *idx = idx_p = mat->rowindices;
1363: if (imark > -1) {
1364: for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i] / bs] * bs + cworkB[i] % bs;
1365: } else {
1366: for (i = 0; i < nzB; i++) {
1367: if (cmap[cworkB[i] / bs] < cstart) idx_p[i] = cmap[cworkB[i] / bs] * bs + cworkB[i] % bs;
1368: else break;
1369: }
1370: imark = i;
1371: }
1372: for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart * bs + cworkA[i];
1373: for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i] / bs] * bs + cworkB[i] % bs;
1374: }
1375: } else {
1376: if (idx) *idx = NULL;
1377: if (v) *v = NULL;
1378: }
1379: }
1380: *nz = nztot;
1381: PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1382: PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1383: PetscFunctionReturn(PETSC_SUCCESS);
1384: }
1386: PetscErrorCode MatRestoreRow_MPIBAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1387: {
1388: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1390: PetscFunctionBegin;
1391: PetscCheck(baij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow not called");
1392: baij->getrowactive = PETSC_FALSE;
1393: PetscFunctionReturn(PETSC_SUCCESS);
1394: }
1396: static PetscErrorCode MatZeroEntries_MPIBAIJ(Mat A)
1397: {
1398: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1400: PetscFunctionBegin;
1401: PetscCall(MatZeroEntries(l->A));
1402: PetscCall(MatZeroEntries(l->B));
1403: PetscFunctionReturn(PETSC_SUCCESS);
1404: }
1406: static PetscErrorCode MatGetInfo_MPIBAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1407: {
1408: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)matin->data;
1409: Mat A = a->A, B = a->B;
1410: PetscLogDouble isend[5], irecv[5];
1412: PetscFunctionBegin;
1413: info->block_size = (PetscReal)matin->rmap->bs;
1415: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
1417: isend[0] = info->nz_used;
1418: isend[1] = info->nz_allocated;
1419: isend[2] = info->nz_unneeded;
1420: isend[3] = info->memory;
1421: isend[4] = info->mallocs;
1423: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
1425: isend[0] += info->nz_used;
1426: isend[1] += info->nz_allocated;
1427: isend[2] += info->nz_unneeded;
1428: isend[3] += info->memory;
1429: isend[4] += info->mallocs;
1431: if (flag == MAT_LOCAL) {
1432: info->nz_used = isend[0];
1433: info->nz_allocated = isend[1];
1434: info->nz_unneeded = isend[2];
1435: info->memory = isend[3];
1436: info->mallocs = isend[4];
1437: } else if (flag == MAT_GLOBAL_MAX) {
1438: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
1440: info->nz_used = irecv[0];
1441: info->nz_allocated = irecv[1];
1442: info->nz_unneeded = irecv[2];
1443: info->memory = irecv[3];
1444: info->mallocs = irecv[4];
1445: } else if (flag == MAT_GLOBAL_SUM) {
1446: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
1448: info->nz_used = irecv[0];
1449: info->nz_allocated = irecv[1];
1450: info->nz_unneeded = irecv[2];
1451: info->memory = irecv[3];
1452: info->mallocs = irecv[4];
1453: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_ARG_WRONG, "Unknown MatInfoType argument %d", (int)flag);
1454: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1455: info->fill_ratio_needed = 0;
1456: info->factor_mallocs = 0;
1457: PetscFunctionReturn(PETSC_SUCCESS);
1458: }
1460: static PetscErrorCode MatSetOption_MPIBAIJ(Mat A, MatOption op, PetscBool flg)
1461: {
1462: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1464: PetscFunctionBegin;
1465: switch (op) {
1466: case MAT_NEW_NONZERO_LOCATIONS:
1467: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1468: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1469: case MAT_KEEP_NONZERO_PATTERN:
1470: case MAT_NEW_NONZERO_LOCATION_ERR:
1471: MatCheckPreallocated(A, 1);
1472: PetscCall(MatSetOption(a->A, op, flg));
1473: PetscCall(MatSetOption(a->B, op, flg));
1474: break;
1475: case MAT_ROW_ORIENTED:
1476: MatCheckPreallocated(A, 1);
1477: a->roworiented = flg;
1479: PetscCall(MatSetOption(a->A, op, flg));
1480: PetscCall(MatSetOption(a->B, op, flg));
1481: break;
1482: case MAT_IGNORE_OFF_PROC_ENTRIES:
1483: a->donotstash = flg;
1484: break;
1485: case MAT_USE_HASH_TABLE:
1486: a->ht_flag = flg;
1487: a->ht_fact = 1.39;
1488: break;
1489: case MAT_SYMMETRIC:
1490: case MAT_STRUCTURALLY_SYMMETRIC:
1491: case MAT_HERMITIAN:
1492: case MAT_SYMMETRY_ETERNAL:
1493: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1494: case MAT_SPD_ETERNAL:
1495: /* if the diagonal matrix is square it inherits some of the properties above */
1496: if (a->A && A->rmap->n == A->cmap->n) PetscCall(MatSetOption(a->A, op, flg));
1497: break;
1498: default:
1499: break;
1500: }
1501: PetscFunctionReturn(PETSC_SUCCESS);
1502: }
1504: static PetscErrorCode MatTranspose_MPIBAIJ(Mat A, MatReuse reuse, Mat *matout)
1505: {
1506: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)A->data;
1507: Mat_SeqBAIJ *Aloc;
1508: Mat B;
1509: PetscInt M = A->rmap->N, N = A->cmap->N, *ai, *aj, i, *rvals, j, k, col;
1510: PetscInt bs = A->rmap->bs, mbs = baij->mbs;
1511: MatScalar *a;
1513: PetscFunctionBegin;
1514: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1515: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
1516: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1517: PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1518: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1519: /* Do not know preallocation information, but must set block size */
1520: PetscCall(MatMPIBAIJSetPreallocation(B, A->rmap->bs, PETSC_DECIDE, NULL, PETSC_DECIDE, NULL));
1521: } else {
1522: B = *matout;
1523: }
1525: /* copy over the A part */
1526: Aloc = (Mat_SeqBAIJ *)baij->A->data;
1527: ai = Aloc->i;
1528: aj = Aloc->j;
1529: a = Aloc->a;
1530: PetscCall(PetscMalloc1(bs, &rvals));
1532: for (i = 0; i < mbs; i++) {
1533: rvals[0] = bs * (baij->rstartbs + i);
1534: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1535: for (j = ai[i]; j < ai[i + 1]; j++) {
1536: col = (baij->cstartbs + aj[j]) * bs;
1537: for (k = 0; k < bs; k++) {
1538: PetscCall(MatSetValues_MPIBAIJ(B, 1, &col, bs, rvals, a, INSERT_VALUES));
1540: col++;
1541: a += bs;
1542: }
1543: }
1544: }
1545: /* copy over the B part */
1546: Aloc = (Mat_SeqBAIJ *)baij->B->data;
1547: ai = Aloc->i;
1548: aj = Aloc->j;
1549: a = Aloc->a;
1550: for (i = 0; i < mbs; i++) {
1551: rvals[0] = bs * (baij->rstartbs + i);
1552: for (j = 1; j < bs; j++) rvals[j] = rvals[j - 1] + 1;
1553: for (j = ai[i]; j < ai[i + 1]; j++) {
1554: col = baij->garray[aj[j]] * bs;
1555: for (k = 0; k < bs; k++) {
1556: PetscCall(MatSetValues_MPIBAIJ(B, 1, &col, bs, rvals, a, INSERT_VALUES));
1557: col++;
1558: a += bs;
1559: }
1560: }
1561: }
1562: PetscCall(PetscFree(rvals));
1563: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1564: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1566: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) *matout = B;
1567: else PetscCall(MatHeaderMerge(A, &B));
1568: PetscFunctionReturn(PETSC_SUCCESS);
1569: }
1571: static PetscErrorCode MatDiagonalScale_MPIBAIJ(Mat mat, Vec ll, Vec rr)
1572: {
1573: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
1574: Mat a = baij->A, b = baij->B;
1575: PetscInt s1, s2, s3;
1577: PetscFunctionBegin;
1578: PetscCall(MatGetLocalSize(mat, &s2, &s3));
1579: if (rr) {
1580: PetscCall(VecGetLocalSize(rr, &s1));
1581: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1582: /* Overlap communication with computation. */
1583: PetscCall(VecScatterBegin(baij->Mvctx, rr, baij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1584: }
1585: if (ll) {
1586: PetscCall(VecGetLocalSize(ll, &s1));
1587: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1588: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1589: }
1590: /* scale the diagonal block */
1591: PetscUseTypeMethod(a, diagonalscale, ll, rr);
1593: if (rr) {
1594: /* Do a scatter end and then right scale the off-diagonal block */
1595: PetscCall(VecScatterEnd(baij->Mvctx, rr, baij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1596: PetscUseTypeMethod(b, diagonalscale, NULL, baij->lvec);
1597: }
1598: PetscFunctionReturn(PETSC_SUCCESS);
1599: }
1601: static PetscErrorCode MatZeroRows_MPIBAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
1602: {
1603: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1604: PetscInt *lrows;
1605: PetscInt r, len;
1606: PetscBool cong;
1608: PetscFunctionBegin;
1609: /* get locally owned rows */
1610: PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
1611: /* fix right-hand side if needed */
1612: if (x && b) {
1613: const PetscScalar *xx;
1614: PetscScalar *bb;
1616: PetscCall(VecGetArrayRead(x, &xx));
1617: PetscCall(VecGetArray(b, &bb));
1618: for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
1619: PetscCall(VecRestoreArrayRead(x, &xx));
1620: PetscCall(VecRestoreArray(b, &bb));
1621: }
1623: /* actually zap the local rows */
1624: /*
1625: Zero the required rows. If the "diagonal block" of the matrix
1626: is square and the user wishes to set the diagonal we use separate
1627: code so that MatSetValues() is not called for each diagonal allocating
1628: new memory, thus calling lots of mallocs and slowing things down.
1630: */
1631: /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1632: PetscCall(MatZeroRows_SeqBAIJ(l->B, len, lrows, 0.0, NULL, NULL));
1633: PetscCall(MatHasCongruentLayouts(A, &cong));
1634: if ((diag != 0.0) && cong) {
1635: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, diag, NULL, NULL));
1636: } else if (diag != 0.0) {
1637: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, 0.0, NULL, NULL));
1638: 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");
1639: for (r = 0; r < len; ++r) {
1640: const PetscInt row = lrows[r] + A->rmap->rstart;
1641: PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
1642: }
1643: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1644: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1645: } else {
1646: PetscCall(MatZeroRows_SeqBAIJ(l->A, len, lrows, 0.0, NULL, NULL));
1647: }
1648: PetscCall(PetscFree(lrows));
1650: /* only change matrix nonzero state if pattern was allowed to be changed */
1651: if (!((Mat_SeqBAIJ *)l->A->data)->keepnonzeropattern || !((Mat_SeqBAIJ *)l->A->data)->nonew) {
1652: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1653: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1654: }
1655: PetscFunctionReturn(PETSC_SUCCESS);
1656: }
1658: static PetscErrorCode MatZeroRowsColumns_MPIBAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
1659: {
1660: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *)A->data;
1661: PetscMPIInt n, p = 0;
1662: PetscInt i, j, k, r, len = 0, row, col, count;
1663: PetscInt *lrows, *owners = A->rmap->range;
1664: PetscSFNode *rrows;
1665: PetscSF sf;
1666: const PetscScalar *xx;
1667: PetscScalar *bb, *mask;
1668: Vec xmask, lmask;
1669: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)l->B->data;
1670: PetscInt bs = A->rmap->bs, bs2 = baij->bs2;
1671: PetscScalar *aa;
1673: PetscFunctionBegin;
1674: PetscCall(PetscMPIIntCast(A->rmap->n, &n));
1675: /* Create SF where leaves are input rows and roots are owned rows */
1676: PetscCall(PetscMalloc1(n, &lrows));
1677: for (r = 0; r < n; ++r) lrows[r] = -1;
1678: PetscCall(PetscMalloc1(N, &rrows));
1679: for (r = 0; r < N; ++r) {
1680: const PetscInt idx = rows[r];
1681: 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);
1682: if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
1683: PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
1684: }
1685: rrows[r].rank = p;
1686: rrows[r].index = rows[r] - owners[p];
1687: }
1688: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1689: PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
1690: /* Collect flags for rows to be zeroed */
1691: PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
1692: PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
1693: PetscCall(PetscSFDestroy(&sf));
1694: /* Compress and put in row numbers */
1695: for (r = 0; r < n; ++r)
1696: if (lrows[r] >= 0) lrows[len++] = r;
1697: /* zero diagonal part of matrix */
1698: PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
1699: /* handle off-diagonal part of matrix */
1700: PetscCall(MatCreateVecs(A, &xmask, NULL));
1701: PetscCall(VecDuplicate(l->lvec, &lmask));
1702: PetscCall(VecGetArray(xmask, &bb));
1703: for (i = 0; i < len; i++) bb[lrows[i]] = 1;
1704: PetscCall(VecRestoreArray(xmask, &bb));
1705: PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
1706: PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
1707: PetscCall(VecDestroy(&xmask));
1708: if (x) {
1709: PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
1710: PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
1711: PetscCall(VecGetArrayRead(l->lvec, &xx));
1712: PetscCall(VecGetArray(b, &bb));
1713: }
1714: PetscCall(VecGetArray(lmask, &mask));
1715: /* remove zeroed rows of off-diagonal matrix */
1716: for (i = 0; i < len; ++i) {
1717: row = lrows[i];
1718: count = (baij->i[row / bs + 1] - baij->i[row / bs]) * bs;
1719: aa = baij->a + baij->i[row / bs] * bs2 + (row % bs);
1720: for (k = 0; k < count; ++k) {
1721: aa[0] = 0.0;
1722: aa += bs;
1723: }
1724: }
1725: /* loop over all elements of off process part of matrix zeroing removed columns*/
1726: for (i = 0; i < l->B->rmap->N; ++i) {
1727: row = i / bs;
1728: for (j = baij->i[row]; j < baij->i[row + 1]; ++j) {
1729: for (k = 0; k < bs; ++k) {
1730: col = bs * baij->j[j] + k;
1731: if (PetscAbsScalar(mask[col])) {
1732: aa = baij->a + j * bs2 + (i % bs) + bs * k;
1733: if (x) bb[i] -= aa[0] * xx[col];
1734: aa[0] = 0.0;
1735: }
1736: }
1737: }
1738: }
1739: if (x) {
1740: PetscCall(VecRestoreArray(b, &bb));
1741: PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1742: }
1743: PetscCall(VecRestoreArray(lmask, &mask));
1744: PetscCall(VecDestroy(&lmask));
1745: PetscCall(PetscFree(lrows));
1747: /* only change matrix nonzero state if pattern was allowed to be changed */
1748: if (!((Mat_SeqBAIJ *)l->A->data)->nonew) {
1749: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1750: PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1751: }
1752: PetscFunctionReturn(PETSC_SUCCESS);
1753: }
1755: static PetscErrorCode MatSetUnfactored_MPIBAIJ(Mat A)
1756: {
1757: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1759: PetscFunctionBegin;
1760: PetscCall(MatSetUnfactored(a->A));
1761: PetscFunctionReturn(PETSC_SUCCESS);
1762: }
1764: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat, MatDuplicateOption, Mat *);
1766: static PetscErrorCode MatEqual_MPIBAIJ(Mat A, Mat B, PetscBool *flag)
1767: {
1768: Mat_MPIBAIJ *matB = (Mat_MPIBAIJ *)B->data, *matA = (Mat_MPIBAIJ *)A->data;
1769: Mat a, b, c, d;
1770: PetscBool flg;
1772: PetscFunctionBegin;
1773: a = matA->A;
1774: b = matA->B;
1775: c = matB->A;
1776: d = matB->B;
1778: PetscCall(MatEqual(a, c, &flg));
1779: if (flg) PetscCall(MatEqual(b, d, &flg));
1780: PetscCallMPI(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
1781: PetscFunctionReturn(PETSC_SUCCESS);
1782: }
1784: static PetscErrorCode MatCopy_MPIBAIJ(Mat A, Mat B, MatStructure str)
1785: {
1786: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1787: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
1789: PetscFunctionBegin;
1790: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1791: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1792: PetscCall(MatCopy_Basic(A, B, str));
1793: } else {
1794: PetscCall(MatCopy(a->A, b->A, str));
1795: PetscCall(MatCopy(a->B, b->B, str));
1796: }
1797: PetscCall(PetscObjectStateIncrease((PetscObject)B));
1798: PetscFunctionReturn(PETSC_SUCCESS);
1799: }
1801: PetscErrorCode MatAXPYGetPreallocation_MPIBAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
1802: {
1803: PetscInt bs = Y->rmap->bs, m = Y->rmap->N / bs;
1804: Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data;
1805: Mat_SeqBAIJ *y = (Mat_SeqBAIJ *)Y->data;
1807: PetscFunctionBegin;
1808: PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
1809: PetscFunctionReturn(PETSC_SUCCESS);
1810: }
1812: static PetscErrorCode MatAXPY_MPIBAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
1813: {
1814: Mat_MPIBAIJ *xx = (Mat_MPIBAIJ *)X->data, *yy = (Mat_MPIBAIJ *)Y->data;
1815: PetscBLASInt bnz, one = 1;
1816: Mat_SeqBAIJ *x, *y;
1817: PetscInt bs2 = Y->rmap->bs * Y->rmap->bs;
1819: PetscFunctionBegin;
1820: if (str == SAME_NONZERO_PATTERN) {
1821: PetscScalar alpha = a;
1822: x = (Mat_SeqBAIJ *)xx->A->data;
1823: y = (Mat_SeqBAIJ *)yy->A->data;
1824: PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz));
1825: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one));
1826: x = (Mat_SeqBAIJ *)xx->B->data;
1827: y = (Mat_SeqBAIJ *)yy->B->data;
1828: PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz));
1829: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one));
1830: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
1831: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
1832: PetscCall(MatAXPY_Basic(Y, a, X, str));
1833: } else {
1834: Mat B;
1835: PetscInt *nnz_d, *nnz_o, bs = Y->rmap->bs;
1836: PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
1837: PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
1838: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
1839: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
1840: PetscCall(MatSetSizes(B, Y->rmap->n, Y->cmap->n, Y->rmap->N, Y->cmap->N));
1841: PetscCall(MatSetBlockSizesFromMats(B, Y, Y));
1842: PetscCall(MatSetType(B, MATMPIBAIJ));
1843: PetscCall(MatAXPYGetPreallocation_SeqBAIJ(yy->A, xx->A, nnz_d));
1844: PetscCall(MatAXPYGetPreallocation_MPIBAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
1845: PetscCall(MatMPIBAIJSetPreallocation(B, bs, 0, nnz_d, 0, nnz_o));
1846: /* MatAXPY_BasicWithPreallocation() for BAIJ matrix is much slower than AIJ, even for bs=1 ! */
1847: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
1848: PetscCall(MatHeaderMerge(Y, &B));
1849: PetscCall(PetscFree(nnz_d));
1850: PetscCall(PetscFree(nnz_o));
1851: }
1852: PetscFunctionReturn(PETSC_SUCCESS);
1853: }
1855: static PetscErrorCode MatConjugate_MPIBAIJ(Mat mat)
1856: {
1857: PetscFunctionBegin;
1858: if (PetscDefined(USE_COMPLEX)) {
1859: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)mat->data;
1861: PetscCall(MatConjugate_SeqBAIJ(a->A));
1862: PetscCall(MatConjugate_SeqBAIJ(a->B));
1863: }
1864: PetscFunctionReturn(PETSC_SUCCESS);
1865: }
1867: static PetscErrorCode MatRealPart_MPIBAIJ(Mat A)
1868: {
1869: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1871: PetscFunctionBegin;
1872: PetscCall(MatRealPart(a->A));
1873: PetscCall(MatRealPart(a->B));
1874: PetscFunctionReturn(PETSC_SUCCESS);
1875: }
1877: static PetscErrorCode MatImaginaryPart_MPIBAIJ(Mat A)
1878: {
1879: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
1881: PetscFunctionBegin;
1882: PetscCall(MatImaginaryPart(a->A));
1883: PetscCall(MatImaginaryPart(a->B));
1884: PetscFunctionReturn(PETSC_SUCCESS);
1885: }
1887: static PetscErrorCode MatCreateSubMatrix_MPIBAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
1888: {
1889: IS iscol_local;
1890: PetscInt csize;
1892: PetscFunctionBegin;
1893: PetscCall(ISGetLocalSize(iscol, &csize));
1894: if (call == MAT_REUSE_MATRIX) {
1895: PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
1896: PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
1897: } else {
1898: PetscCall(ISAllGather(iscol, &iscol_local));
1899: }
1900: PetscCall(MatCreateSubMatrix_MPIBAIJ_Private(mat, isrow, iscol_local, csize, call, newmat, PETSC_FALSE));
1901: if (call == MAT_INITIAL_MATRIX) {
1902: PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
1903: PetscCall(ISDestroy(&iscol_local));
1904: }
1905: PetscFunctionReturn(PETSC_SUCCESS);
1906: }
1908: /*
1909: Not great since it makes two copies of the submatrix, first an SeqBAIJ
1910: in local and then by concatenating the local matrices the end result.
1911: Writing it directly would be much like MatCreateSubMatrices_MPIBAIJ().
1912: This routine is used for BAIJ and SBAIJ matrices (unfortunate dependency).
1913: */
1914: PetscErrorCode MatCreateSubMatrix_MPIBAIJ_Private(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat, PetscBool sym)
1915: {
1916: PetscMPIInt rank, size;
1917: PetscInt i, m, n, rstart, row, rend, nz, *cwork, j, bs;
1918: PetscInt *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
1919: Mat M, Mreuse;
1920: MatScalar *vwork, *aa;
1921: MPI_Comm comm;
1922: IS isrow_new, iscol_new;
1923: Mat_SeqBAIJ *aij;
1925: PetscFunctionBegin;
1926: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
1927: PetscCallMPI(MPI_Comm_rank(comm, &rank));
1928: PetscCallMPI(MPI_Comm_size(comm, &size));
1929: /* The compression and expansion should be avoided. Doesn't point
1930: out errors, might change the indices, hence buggey */
1931: PetscCall(ISCompressIndicesGeneral(mat->rmap->N, mat->rmap->n, mat->rmap->bs, 1, &isrow, &isrow_new));
1932: if (isrow == iscol) {
1933: iscol_new = isrow_new;
1934: PetscCall(PetscObjectReference((PetscObject)iscol_new));
1935: } else PetscCall(ISCompressIndicesGeneral(mat->cmap->N, mat->cmap->n, mat->cmap->bs, 1, &iscol, &iscol_new));
1937: if (call == MAT_REUSE_MATRIX) {
1938: PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
1939: PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
1940: PetscCall(MatCreateSubMatrices_MPIBAIJ_local(mat, 1, &isrow_new, &iscol_new, MAT_REUSE_MATRIX, &Mreuse, sym));
1941: } else {
1942: PetscCall(MatCreateSubMatrices_MPIBAIJ_local(mat, 1, &isrow_new, &iscol_new, MAT_INITIAL_MATRIX, &Mreuse, sym));
1943: }
1944: PetscCall(ISDestroy(&isrow_new));
1945: PetscCall(ISDestroy(&iscol_new));
1946: /*
1947: m - number of local rows
1948: n - number of columns (same on all processors)
1949: rstart - first row in new global matrix generated
1950: */
1951: PetscCall(MatGetBlockSize(mat, &bs));
1952: PetscCall(MatGetSize(Mreuse, &m, &n));
1953: m = m / bs;
1954: n = n / bs;
1956: if (call == MAT_INITIAL_MATRIX) {
1957: aij = (Mat_SeqBAIJ *)Mreuse->data;
1958: ii = aij->i;
1959: jj = aij->j;
1961: /*
1962: Determine the number of non-zeros in the diagonal and off-diagonal
1963: portions of the matrix in order to do correct preallocation
1964: */
1966: /* first get start and end of "diagonal" columns */
1967: if (csize == PETSC_DECIDE) {
1968: PetscCall(ISGetSize(isrow, &mglobal));
1969: if (mglobal == n * bs) { /* square matrix */
1970: nlocal = m;
1971: } else {
1972: nlocal = n / size + ((n % size) > rank);
1973: }
1974: } else {
1975: nlocal = csize / bs;
1976: }
1977: PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
1978: rstart = rend - nlocal;
1979: 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);
1981: /* next, compute all the lengths */
1982: PetscCall(PetscMalloc2(m + 1, &dlens, m + 1, &olens));
1983: for (i = 0; i < m; i++) {
1984: jend = ii[i + 1] - ii[i];
1985: olen = 0;
1986: dlen = 0;
1987: for (j = 0; j < jend; j++) {
1988: if (*jj < rstart || *jj >= rend) olen++;
1989: else dlen++;
1990: jj++;
1991: }
1992: olens[i] = olen;
1993: dlens[i] = dlen;
1994: }
1995: PetscCall(MatCreate(comm, &M));
1996: PetscCall(MatSetSizes(M, bs * m, bs * nlocal, PETSC_DECIDE, bs * n));
1997: PetscCall(MatSetType(M, sym ? ((PetscObject)mat)->type_name : MATMPIBAIJ));
1998: PetscCall(MatMPIBAIJSetPreallocation(M, bs, 0, dlens, 0, olens));
1999: PetscCall(MatMPISBAIJSetPreallocation(M, bs, 0, dlens, 0, olens));
2000: PetscCall(PetscFree2(dlens, olens));
2001: } else {
2002: PetscInt ml, nl;
2004: M = *newmat;
2005: PetscCall(MatGetLocalSize(M, &ml, &nl));
2006: PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
2007: PetscCall(MatZeroEntries(M));
2008: /*
2009: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
2010: rather than the slower MatSetValues().
2011: */
2012: M->was_assembled = PETSC_TRUE;
2013: M->assembled = PETSC_FALSE;
2014: }
2015: PetscCall(MatSetOption(M, MAT_ROW_ORIENTED, PETSC_FALSE));
2016: PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
2017: aij = (Mat_SeqBAIJ *)Mreuse->data;
2018: ii = aij->i;
2019: jj = aij->j;
2020: aa = aij->a;
2021: for (i = 0; i < m; i++) {
2022: row = rstart / bs + i;
2023: nz = ii[i + 1] - ii[i];
2024: cwork = jj;
2025: jj = PetscSafePointerPlusOffset(jj, nz);
2026: vwork = aa;
2027: aa = PetscSafePointerPlusOffset(aa, nz * bs * bs);
2028: PetscCall(MatSetValuesBlocked_MPIBAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
2029: }
2031: PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
2032: PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
2033: *newmat = M;
2035: /* save submatrix used in processor for next request */
2036: if (call == MAT_INITIAL_MATRIX) {
2037: PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
2038: PetscCall(PetscObjectDereference((PetscObject)Mreuse));
2039: }
2040: PetscFunctionReturn(PETSC_SUCCESS);
2041: }
2043: static PetscErrorCode MatPermute_MPIBAIJ(Mat A, IS rowp, IS colp, Mat *B)
2044: {
2045: MPI_Comm comm, pcomm;
2046: PetscInt clocal_size, nrows;
2047: const PetscInt *rows;
2048: PetscMPIInt size;
2049: IS crowp, lcolp;
2051: PetscFunctionBegin;
2052: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
2053: /* make a collective version of 'rowp' */
2054: PetscCall(PetscObjectGetComm((PetscObject)rowp, &pcomm));
2055: if (pcomm == comm) {
2056: crowp = rowp;
2057: } else {
2058: PetscCall(ISGetSize(rowp, &nrows));
2059: PetscCall(ISGetIndices(rowp, &rows));
2060: PetscCall(ISCreateGeneral(comm, nrows, rows, PETSC_COPY_VALUES, &crowp));
2061: PetscCall(ISRestoreIndices(rowp, &rows));
2062: }
2063: PetscCall(ISSetPermutation(crowp));
2064: /* make a local version of 'colp' */
2065: PetscCall(PetscObjectGetComm((PetscObject)colp, &pcomm));
2066: PetscCallMPI(MPI_Comm_size(pcomm, &size));
2067: if (size == 1) {
2068: lcolp = colp;
2069: } else {
2070: PetscCall(ISAllGather(colp, &lcolp));
2071: }
2072: PetscCall(ISSetPermutation(lcolp));
2073: /* now we just get the submatrix */
2074: PetscCall(MatGetLocalSize(A, NULL, &clocal_size));
2075: PetscCall(MatCreateSubMatrix_MPIBAIJ_Private(A, crowp, lcolp, clocal_size, MAT_INITIAL_MATRIX, B, PETSC_FALSE));
2076: /* clean up */
2077: if (pcomm != comm) PetscCall(ISDestroy(&crowp));
2078: if (size > 1) PetscCall(ISDestroy(&lcolp));
2079: PetscFunctionReturn(PETSC_SUCCESS);
2080: }
2082: static PetscErrorCode MatGetGhosts_MPIBAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
2083: {
2084: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
2085: Mat_SeqBAIJ *B = (Mat_SeqBAIJ *)baij->B->data;
2087: PetscFunctionBegin;
2088: if (nghosts) *nghosts = B->nbs;
2089: if (ghosts) *ghosts = baij->garray;
2090: PetscFunctionReturn(PETSC_SUCCESS);
2091: }
2093: static PetscErrorCode MatGetSeqNonzeroStructure_MPIBAIJ(Mat A, Mat *newmat)
2094: {
2095: Mat B;
2096: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2097: Mat_SeqBAIJ *ad = (Mat_SeqBAIJ *)a->A->data, *bd = (Mat_SeqBAIJ *)a->B->data;
2098: Mat_SeqAIJ *b;
2099: PetscMPIInt size, rank, *recvcounts = NULL, *displs = NULL;
2100: PetscInt sendcount, i, *rstarts = A->rmap->range, n, cnt, j, bs = A->rmap->bs;
2101: PetscInt m, *garray = a->garray, *lens, *jsendbuf, *a_jsendbuf, *b_jsendbuf;
2103: PetscFunctionBegin;
2104: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
2105: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)A), &rank));
2107: /* Tell every processor the number of nonzeros per row */
2108: PetscCall(PetscMalloc1(A->rmap->N / bs, &lens));
2109: 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];
2110: PetscCall(PetscMalloc1(2 * size, &recvcounts));
2111: displs = recvcounts + size;
2112: for (i = 0; i < size; i++) {
2113: PetscCall(PetscMPIIntCast(A->rmap->range[i + 1] / bs - A->rmap->range[i] / bs, &recvcounts[i]));
2114: PetscCall(PetscMPIIntCast(A->rmap->range[i] / bs, &displs[i]));
2115: }
2116: PetscCallMPI(MPI_Allgatherv(MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, lens, recvcounts, displs, MPIU_INT, PetscObjectComm((PetscObject)A)));
2117: /* Create the sequential matrix of the same type as the local block diagonal */
2118: PetscCall(MatCreate(PETSC_COMM_SELF, &B));
2119: PetscCall(MatSetSizes(B, A->rmap->N / bs, A->cmap->N / bs, PETSC_DETERMINE, PETSC_DETERMINE));
2120: PetscCall(MatSetType(B, MATSEQAIJ));
2121: PetscCall(MatSeqAIJSetPreallocation(B, 0, lens));
2122: b = (Mat_SeqAIJ *)B->data;
2124: /* Copy my part of matrix column indices over */
2125: sendcount = ad->nz + bd->nz;
2126: jsendbuf = b->j + b->i[rstarts[rank] / bs];
2127: a_jsendbuf = ad->j;
2128: b_jsendbuf = bd->j;
2129: n = A->rmap->rend / bs - A->rmap->rstart / bs;
2130: cnt = 0;
2131: for (i = 0; i < n; i++) {
2132: /* put in lower diagonal portion */
2133: m = bd->i[i + 1] - bd->i[i];
2134: while (m > 0) {
2135: /* is it above diagonal (in bd (compressed) numbering) */
2136: if (garray[*b_jsendbuf] > A->rmap->rstart / bs + i) break;
2137: jsendbuf[cnt++] = garray[*b_jsendbuf++];
2138: m--;
2139: }
2141: /* put in diagonal portion */
2142: for (j = ad->i[i]; j < ad->i[i + 1]; j++) jsendbuf[cnt++] = A->rmap->rstart / bs + *a_jsendbuf++;
2144: /* put in upper diagonal portion */
2145: while (m-- > 0) jsendbuf[cnt++] = garray[*b_jsendbuf++];
2146: }
2147: PetscCheck(cnt == sendcount, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Corrupted PETSc matrix: nz given %" PetscInt_FMT " actual nz %" PetscInt_FMT, sendcount, cnt);
2149: /* Gather all column indices to all processors */
2150: for (i = 0; i < size; i++) {
2151: recvcounts[i] = 0;
2152: for (j = A->rmap->range[i] / bs; j < A->rmap->range[i + 1] / bs; j++) recvcounts[i] += lens[j];
2153: }
2154: displs[0] = 0;
2155: for (i = 1; i < size; i++) displs[i] = displs[i - 1] + recvcounts[i - 1];
2156: PetscCallMPI(MPI_Allgatherv(MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, b->j, recvcounts, displs, MPIU_INT, PetscObjectComm((PetscObject)A)));
2157: /* Assemble the matrix into usable form (note numerical values not yet set) */
2158: /* set the b->ilen (length of each row) values */
2159: PetscCall(PetscArraycpy(b->ilen, lens, A->rmap->N / bs));
2160: /* set the b->i indices */
2161: b->i[0] = 0;
2162: for (i = 1; i <= A->rmap->N / bs; i++) b->i[i] = b->i[i - 1] + lens[i - 1];
2163: PetscCall(PetscFree(lens));
2164: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2165: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2166: PetscCall(PetscFree(recvcounts));
2168: PetscCall(MatPropagateSymmetryOptions(A, B));
2169: *newmat = B;
2170: PetscFunctionReturn(PETSC_SUCCESS);
2171: }
2173: static PetscErrorCode MatSOR_MPIBAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
2174: {
2175: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ *)matin->data;
2176: Vec bb1 = NULL;
2178: PetscFunctionBegin;
2179: if (flag == SOR_APPLY_UPPER) {
2180: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2181: PetscFunctionReturn(PETSC_SUCCESS);
2182: }
2184: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS) PetscCall(VecDuplicate(bb, &bb1));
2186: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
2187: if (flag & SOR_ZERO_INITIAL_GUESS) {
2188: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2189: its--;
2190: }
2192: while (its--) {
2193: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2194: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2196: /* update rhs: bb1 = bb - B*x */
2197: PetscCall(VecScale(mat->lvec, -1.0));
2198: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2200: /* local sweep */
2201: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
2202: }
2203: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
2204: if (flag & SOR_ZERO_INITIAL_GUESS) {
2205: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2206: its--;
2207: }
2208: while (its--) {
2209: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2210: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2212: /* update rhs: bb1 = bb - B*x */
2213: PetscCall(VecScale(mat->lvec, -1.0));
2214: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2216: /* local sweep */
2217: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
2218: }
2219: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
2220: if (flag & SOR_ZERO_INITIAL_GUESS) {
2221: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
2222: its--;
2223: }
2224: while (its--) {
2225: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2226: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
2228: /* update rhs: bb1 = bb - B*x */
2229: PetscCall(VecScale(mat->lvec, -1.0));
2230: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
2232: /* local sweep */
2233: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
2234: }
2235: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel version of SOR requested not supported");
2237: PetscCall(VecDestroy(&bb1));
2238: PetscFunctionReturn(PETSC_SUCCESS);
2239: }
2241: static PetscErrorCode MatGetColumnReductions_MPIBAIJ(Mat A, PetscInt type, PetscReal *reductions)
2242: {
2243: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ *)A->data;
2244: PetscInt m, N, i, *garray = aij->garray;
2245: PetscInt ib, jb, bs = A->rmap->bs;
2246: Mat_SeqBAIJ *a_aij = (Mat_SeqBAIJ *)aij->A->data;
2247: MatScalar *a_val = a_aij->a;
2248: Mat_SeqBAIJ *b_aij = (Mat_SeqBAIJ *)aij->B->data;
2249: MatScalar *b_val = b_aij->a;
2250: PetscReal *work;
2252: PetscFunctionBegin;
2253: PetscCall(MatGetSize(A, &m, &N));
2254: PetscCall(PetscCalloc1(N, &work));
2255: if (type == NORM_2) {
2256: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2257: for (jb = 0; jb < bs; jb++) {
2258: for (ib = 0; ib < bs; ib++) {
2259: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val * *a_val);
2260: a_val++;
2261: }
2262: }
2263: }
2264: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2265: for (jb = 0; jb < bs; jb++) {
2266: for (ib = 0; ib < bs; ib++) {
2267: work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val * *b_val);
2268: b_val++;
2269: }
2270: }
2271: }
2272: } else if (type == NORM_1) {
2273: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2274: for (jb = 0; jb < bs; jb++) {
2275: for (ib = 0; ib < bs; ib++) {
2276: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val);
2277: a_val++;
2278: }
2279: }
2280: }
2281: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2282: for (jb = 0; jb < bs; jb++) {
2283: for (ib = 0; ib < bs; ib++) {
2284: work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val);
2285: b_val++;
2286: }
2287: }
2288: }
2289: } else if (type == NORM_INFINITY) {
2290: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2291: for (jb = 0; jb < bs; jb++) {
2292: for (ib = 0; ib < bs; ib++) {
2293: PetscInt col = A->cmap->rstart + a_aij->j[i] * bs + jb;
2294: work[col] = PetscMax(PetscAbsScalar(*a_val), work[col]);
2295: a_val++;
2296: }
2297: }
2298: }
2299: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2300: for (jb = 0; jb < bs; jb++) {
2301: for (ib = 0; ib < bs; ib++) {
2302: PetscInt col = garray[b_aij->j[i]] * bs + jb;
2303: work[col] = PetscMax(PetscAbsScalar(*b_val), work[col]);
2304: b_val++;
2305: }
2306: }
2307: }
2308: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
2309: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2310: for (jb = 0; jb < bs; jb++) {
2311: for (ib = 0; ib < bs; ib++) {
2312: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscRealPart(*a_val);
2313: a_val++;
2314: }
2315: }
2316: }
2317: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2318: for (jb = 0; jb < bs; jb++) {
2319: for (ib = 0; ib < bs; ib++) {
2320: work[garray[b_aij->j[i]] * bs + jb] += PetscRealPart(*b_val);
2321: b_val++;
2322: }
2323: }
2324: }
2325: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
2326: for (i = a_aij->i[0]; i < a_aij->i[aij->A->rmap->n / bs]; i++) {
2327: for (jb = 0; jb < bs; jb++) {
2328: for (ib = 0; ib < bs; ib++) {
2329: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscImaginaryPart(*a_val);
2330: a_val++;
2331: }
2332: }
2333: }
2334: for (i = b_aij->i[0]; i < b_aij->i[aij->B->rmap->n / bs]; i++) {
2335: for (jb = 0; jb < bs; jb++) {
2336: for (ib = 0; ib < bs; ib++) {
2337: work[garray[b_aij->j[i]] * bs + jb] += PetscImaginaryPart(*b_val);
2338: b_val++;
2339: }
2340: }
2341: }
2342: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
2343: if (type == NORM_INFINITY) {
2344: PetscCallMPI(MPIU_Allreduce(work, reductions, N, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
2345: } else {
2346: PetscCallMPI(MPIU_Allreduce(work, reductions, N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
2347: }
2348: PetscCall(PetscFree(work));
2349: if (type == NORM_2) {
2350: for (i = 0; i < N; i++) reductions[i] = PetscSqrtReal(reductions[i]);
2351: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
2352: for (i = 0; i < N; i++) reductions[i] /= m;
2353: }
2354: PetscFunctionReturn(PETSC_SUCCESS);
2355: }
2357: static PetscErrorCode MatInvertBlockDiagonal_MPIBAIJ(Mat A, const PetscScalar **values)
2358: {
2359: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2361: PetscFunctionBegin;
2362: PetscCall(MatInvertBlockDiagonal(a->A, values));
2363: A->factorerrortype = a->A->factorerrortype;
2364: A->factorerror_zeropivot_value = a->A->factorerror_zeropivot_value;
2365: A->factorerror_zeropivot_row = a->A->factorerror_zeropivot_row;
2366: PetscFunctionReturn(PETSC_SUCCESS);
2367: }
2369: static PetscErrorCode MatShift_MPIBAIJ(Mat Y, PetscScalar a)
2370: {
2371: Mat_MPIBAIJ *maij = (Mat_MPIBAIJ *)Y->data;
2372: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)maij->A->data;
2374: PetscFunctionBegin;
2375: if (!Y->preallocated) {
2376: PetscCall(MatMPIBAIJSetPreallocation(Y, Y->rmap->bs, 1, NULL, 0, NULL));
2377: } else if (!aij->nz) {
2378: PetscInt nonew = aij->nonew;
2379: PetscCall(MatSeqBAIJSetPreallocation(maij->A, Y->rmap->bs, 1, NULL));
2380: aij->nonew = nonew;
2381: }
2382: PetscCall(MatShift_Basic(Y, a));
2383: PetscFunctionReturn(PETSC_SUCCESS);
2384: }
2386: static PetscErrorCode MatMissingDiagonal_MPIBAIJ(Mat A, PetscBool *missing, PetscInt *d)
2387: {
2388: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2390: PetscFunctionBegin;
2391: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2392: PetscCall(MatMissingDiagonal(a->A, missing, d));
2393: if (d) {
2394: PetscInt rstart;
2395: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2396: *d += rstart / A->rmap->bs;
2397: }
2398: PetscFunctionReturn(PETSC_SUCCESS);
2399: }
2401: static PetscErrorCode MatGetDiagonalBlock_MPIBAIJ(Mat A, Mat *a)
2402: {
2403: PetscFunctionBegin;
2404: *a = ((Mat_MPIBAIJ *)A->data)->A;
2405: PetscFunctionReturn(PETSC_SUCCESS);
2406: }
2408: static PetscErrorCode MatEliminateZeros_MPIBAIJ(Mat A, PetscBool keep)
2409: {
2410: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2412: PetscFunctionBegin;
2413: PetscCall(MatEliminateZeros_SeqBAIJ(a->A, keep)); // possibly keep zero diagonal coefficients
2414: PetscCall(MatEliminateZeros_SeqBAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2415: PetscFunctionReturn(PETSC_SUCCESS);
2416: }
2418: static struct _MatOps MatOps_Values = {MatSetValues_MPIBAIJ,
2419: MatGetRow_MPIBAIJ,
2420: MatRestoreRow_MPIBAIJ,
2421: MatMult_MPIBAIJ,
2422: /* 4*/ MatMultAdd_MPIBAIJ,
2423: MatMultTranspose_MPIBAIJ,
2424: MatMultTransposeAdd_MPIBAIJ,
2425: NULL,
2426: NULL,
2427: NULL,
2428: /*10*/ NULL,
2429: NULL,
2430: NULL,
2431: MatSOR_MPIBAIJ,
2432: MatTranspose_MPIBAIJ,
2433: /*15*/ MatGetInfo_MPIBAIJ,
2434: MatEqual_MPIBAIJ,
2435: MatGetDiagonal_MPIBAIJ,
2436: MatDiagonalScale_MPIBAIJ,
2437: MatNorm_MPIBAIJ,
2438: /*20*/ MatAssemblyBegin_MPIBAIJ,
2439: MatAssemblyEnd_MPIBAIJ,
2440: MatSetOption_MPIBAIJ,
2441: MatZeroEntries_MPIBAIJ,
2442: /*24*/ MatZeroRows_MPIBAIJ,
2443: NULL,
2444: NULL,
2445: NULL,
2446: NULL,
2447: /*29*/ MatSetUp_MPI_Hash,
2448: NULL,
2449: NULL,
2450: MatGetDiagonalBlock_MPIBAIJ,
2451: NULL,
2452: /*34*/ MatDuplicate_MPIBAIJ,
2453: NULL,
2454: NULL,
2455: NULL,
2456: NULL,
2457: /*39*/ MatAXPY_MPIBAIJ,
2458: MatCreateSubMatrices_MPIBAIJ,
2459: MatIncreaseOverlap_MPIBAIJ,
2460: MatGetValues_MPIBAIJ,
2461: MatCopy_MPIBAIJ,
2462: /*44*/ NULL,
2463: MatScale_MPIBAIJ,
2464: MatShift_MPIBAIJ,
2465: NULL,
2466: MatZeroRowsColumns_MPIBAIJ,
2467: /*49*/ NULL,
2468: NULL,
2469: NULL,
2470: NULL,
2471: NULL,
2472: /*54*/ MatFDColoringCreate_MPIXAIJ,
2473: NULL,
2474: MatSetUnfactored_MPIBAIJ,
2475: MatPermute_MPIBAIJ,
2476: MatSetValuesBlocked_MPIBAIJ,
2477: /*59*/ MatCreateSubMatrix_MPIBAIJ,
2478: MatDestroy_MPIBAIJ,
2479: MatView_MPIBAIJ,
2480: NULL,
2481: NULL,
2482: /*64*/ NULL,
2483: NULL,
2484: NULL,
2485: NULL,
2486: NULL,
2487: /*69*/ MatGetRowMaxAbs_MPIBAIJ,
2488: NULL,
2489: NULL,
2490: NULL,
2491: NULL,
2492: /*74*/ NULL,
2493: MatFDColoringApply_BAIJ,
2494: NULL,
2495: NULL,
2496: NULL,
2497: /*79*/ NULL,
2498: NULL,
2499: NULL,
2500: NULL,
2501: MatLoad_MPIBAIJ,
2502: /*84*/ NULL,
2503: NULL,
2504: NULL,
2505: NULL,
2506: NULL,
2507: /*89*/ NULL,
2508: NULL,
2509: NULL,
2510: NULL,
2511: NULL,
2512: /*94*/ NULL,
2513: NULL,
2514: NULL,
2515: NULL,
2516: NULL,
2517: /*99*/ NULL,
2518: NULL,
2519: NULL,
2520: MatConjugate_MPIBAIJ,
2521: NULL,
2522: /*104*/ NULL,
2523: MatRealPart_MPIBAIJ,
2524: MatImaginaryPart_MPIBAIJ,
2525: NULL,
2526: NULL,
2527: /*109*/ NULL,
2528: NULL,
2529: NULL,
2530: NULL,
2531: MatMissingDiagonal_MPIBAIJ,
2532: /*114*/ MatGetSeqNonzeroStructure_MPIBAIJ,
2533: NULL,
2534: MatGetGhosts_MPIBAIJ,
2535: NULL,
2536: NULL,
2537: /*119*/ NULL,
2538: NULL,
2539: NULL,
2540: NULL,
2541: MatGetMultiProcBlock_MPIBAIJ,
2542: /*124*/ NULL,
2543: MatGetColumnReductions_MPIBAIJ,
2544: MatInvertBlockDiagonal_MPIBAIJ,
2545: NULL,
2546: NULL,
2547: /*129*/ NULL,
2548: NULL,
2549: NULL,
2550: NULL,
2551: NULL,
2552: /*134*/ NULL,
2553: NULL,
2554: NULL,
2555: NULL,
2556: NULL,
2557: /*139*/ MatSetBlockSizes_Default,
2558: NULL,
2559: NULL,
2560: MatFDColoringSetUp_MPIXAIJ,
2561: NULL,
2562: /*144*/ MatCreateMPIMatConcatenateSeqMat_MPIBAIJ,
2563: NULL,
2564: NULL,
2565: NULL,
2566: NULL,
2567: NULL,
2568: /*150*/ NULL,
2569: MatEliminateZeros_MPIBAIJ,
2570: MatGetRowSumAbs_MPIBAIJ,
2571: NULL,
2572: NULL,
2573: /*155*/ NULL,
2574: MatCopyHashToXAIJ_MPI_Hash};
2576: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
2577: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
2579: static PetscErrorCode MatMPIBAIJSetPreallocationCSR_MPIBAIJ(Mat B, PetscInt bs, const PetscInt ii[], const PetscInt jj[], const PetscScalar V[])
2580: {
2581: PetscInt m, rstart, cstart, cend;
2582: PetscInt i, j, dlen, olen, nz, nz_max = 0, *d_nnz = NULL, *o_nnz = NULL;
2583: const PetscInt *JJ = NULL;
2584: PetscScalar *values = NULL;
2585: PetscBool roworiented = ((Mat_MPIBAIJ *)B->data)->roworiented;
2586: PetscBool nooffprocentries;
2588: PetscFunctionBegin;
2589: PetscCall(PetscLayoutSetBlockSize(B->rmap, bs));
2590: PetscCall(PetscLayoutSetBlockSize(B->cmap, bs));
2591: PetscCall(PetscLayoutSetUp(B->rmap));
2592: PetscCall(PetscLayoutSetUp(B->cmap));
2593: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
2594: m = B->rmap->n / bs;
2595: rstart = B->rmap->rstart / bs;
2596: cstart = B->cmap->rstart / bs;
2597: cend = B->cmap->rend / bs;
2599: PetscCheck(!ii[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "ii[0] must be 0 but it is %" PetscInt_FMT, ii[0]);
2600: PetscCall(PetscMalloc2(m, &d_nnz, m, &o_nnz));
2601: for (i = 0; i < m; i++) {
2602: nz = ii[i + 1] - ii[i];
2603: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
2604: nz_max = PetscMax(nz_max, nz);
2605: dlen = 0;
2606: olen = 0;
2607: JJ = jj + ii[i];
2608: for (j = 0; j < nz; j++) {
2609: if (*JJ < cstart || *JJ >= cend) olen++;
2610: else dlen++;
2611: JJ++;
2612: }
2613: d_nnz[i] = dlen;
2614: o_nnz[i] = olen;
2615: }
2616: PetscCall(MatMPIBAIJSetPreallocation(B, bs, 0, d_nnz, 0, o_nnz));
2617: PetscCall(PetscFree2(d_nnz, o_nnz));
2619: values = (PetscScalar *)V;
2620: if (!values) PetscCall(PetscCalloc1(bs * bs * nz_max, &values));
2621: for (i = 0; i < m; i++) {
2622: PetscInt row = i + rstart;
2623: PetscInt ncols = ii[i + 1] - ii[i];
2624: const PetscInt *icols = jj + ii[i];
2625: if (bs == 1 || !roworiented) { /* block ordering matches the non-nested layout of MatSetValues so we can insert entire rows */
2626: const PetscScalar *svals = values + (V ? (bs * bs * ii[i]) : 0);
2627: PetscCall(MatSetValuesBlocked_MPIBAIJ(B, 1, &row, ncols, icols, svals, INSERT_VALUES));
2628: } else { /* block ordering does not match so we can only insert one block at a time. */
2629: PetscInt j;
2630: for (j = 0; j < ncols; j++) {
2631: const PetscScalar *svals = values + (V ? (bs * bs * (ii[i] + j)) : 0);
2632: PetscCall(MatSetValuesBlocked_MPIBAIJ(B, 1, &row, 1, &icols[j], svals, INSERT_VALUES));
2633: }
2634: }
2635: }
2637: if (!V) PetscCall(PetscFree(values));
2638: nooffprocentries = B->nooffprocentries;
2639: B->nooffprocentries = PETSC_TRUE;
2640: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2641: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2642: B->nooffprocentries = nooffprocentries;
2644: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
2645: PetscFunctionReturn(PETSC_SUCCESS);
2646: }
2648: /*@C
2649: MatMPIBAIJSetPreallocationCSR - Creates a sparse parallel matrix in `MATBAIJ` format using the given nonzero structure and (optional) numerical values
2651: Collective
2653: Input Parameters:
2654: + B - the matrix
2655: . bs - the block size
2656: . i - the indices into `j` for the start of each local row (starts with zero)
2657: . j - the column indices for each local row (starts with zero) these must be sorted for each row
2658: - v - optional values in the matrix, use `NULL` if not provided
2660: Level: advanced
2662: Notes:
2663: The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
2664: thus you CANNOT change the matrix entries by changing the values of `v` after you have
2665: called this routine.
2667: The order of the entries in values is specified by the `MatOption` `MAT_ROW_ORIENTED`. For example, C programs
2668: 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
2669: over rows within a block and the last index is over columns within a block row. Fortran programs will likely set
2670: `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
2671: block column and the second index is over columns within a block.
2673: 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
2675: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIBAIJSetPreallocation()`, `MatCreateAIJ()`, `MATMPIAIJ`, `MatCreateMPIBAIJWithArrays()`, `MATMPIBAIJ`
2676: @*/
2677: PetscErrorCode MatMPIBAIJSetPreallocationCSR(Mat B, PetscInt bs, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
2678: {
2679: PetscFunctionBegin;
2683: PetscTryMethod(B, "MatMPIBAIJSetPreallocationCSR_C", (Mat, PetscInt, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, bs, i, j, v));
2684: PetscFunctionReturn(PETSC_SUCCESS);
2685: }
2687: PetscErrorCode MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B, PetscInt bs, PetscInt d_nz, const PetscInt *d_nnz, PetscInt o_nz, const PetscInt *o_nnz)
2688: {
2689: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
2690: PetscInt i;
2691: PetscMPIInt size;
2693: PetscFunctionBegin;
2694: if (B->hash_active) {
2695: B->ops[0] = b->cops;
2696: B->hash_active = PETSC_FALSE;
2697: }
2698: if (!B->preallocated) PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), bs, &B->bstash));
2699: PetscCall(MatSetBlockSize(B, PetscAbs(bs)));
2700: PetscCall(PetscLayoutSetUp(B->rmap));
2701: PetscCall(PetscLayoutSetUp(B->cmap));
2702: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
2704: if (d_nnz) {
2705: 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]);
2706: }
2707: if (o_nnz) {
2708: 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]);
2709: }
2711: b->bs2 = bs * bs;
2712: b->mbs = B->rmap->n / bs;
2713: b->nbs = B->cmap->n / bs;
2714: b->Mbs = B->rmap->N / bs;
2715: b->Nbs = B->cmap->N / bs;
2717: for (i = 0; i <= b->size; i++) b->rangebs[i] = B->rmap->range[i] / bs;
2718: b->rstartbs = B->rmap->rstart / bs;
2719: b->rendbs = B->rmap->rend / bs;
2720: b->cstartbs = B->cmap->rstart / bs;
2721: b->cendbs = B->cmap->rend / bs;
2723: #if defined(PETSC_USE_CTABLE)
2724: PetscCall(PetscHMapIDestroy(&b->colmap));
2725: #else
2726: PetscCall(PetscFree(b->colmap));
2727: #endif
2728: PetscCall(PetscFree(b->garray));
2729: PetscCall(VecDestroy(&b->lvec));
2730: PetscCall(VecScatterDestroy(&b->Mvctx));
2732: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2734: MatSeqXAIJGetOptions_Private(b->B);
2735: PetscCall(MatDestroy(&b->B));
2736: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2737: PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2738: PetscCall(MatSetType(b->B, MATSEQBAIJ));
2739: MatSeqXAIJRestoreOptions_Private(b->B);
2741: MatSeqXAIJGetOptions_Private(b->A);
2742: PetscCall(MatDestroy(&b->A));
2743: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2744: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2745: PetscCall(MatSetType(b->A, MATSEQBAIJ));
2746: MatSeqXAIJRestoreOptions_Private(b->A);
2748: PetscCall(MatSeqBAIJSetPreallocation(b->A, bs, d_nz, d_nnz));
2749: PetscCall(MatSeqBAIJSetPreallocation(b->B, bs, o_nz, o_nnz));
2750: B->preallocated = PETSC_TRUE;
2751: B->was_assembled = PETSC_FALSE;
2752: B->assembled = PETSC_FALSE;
2753: PetscFunctionReturn(PETSC_SUCCESS);
2754: }
2756: extern PetscErrorCode MatDiagonalScaleLocal_MPIBAIJ(Mat, Vec);
2757: extern PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat, PetscReal);
2759: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAdj(Mat B, MatType newtype, MatReuse reuse, Mat *adj)
2760: {
2761: Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
2762: Mat_SeqBAIJ *d = (Mat_SeqBAIJ *)b->A->data, *o = (Mat_SeqBAIJ *)b->B->data;
2763: PetscInt M = B->rmap->n / B->rmap->bs, i, *ii, *jj, cnt, j, k, rstart = B->rmap->rstart / B->rmap->bs;
2764: const PetscInt *id = d->i, *jd = d->j, *io = o->i, *jo = o->j, *garray = b->garray;
2766: PetscFunctionBegin;
2767: PetscCall(PetscMalloc1(M + 1, &ii));
2768: ii[0] = 0;
2769: for (i = 0; i < M; i++) {
2770: 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]);
2771: 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]);
2772: ii[i + 1] = ii[i] + id[i + 1] - id[i] + io[i + 1] - io[i];
2773: /* remove one from count of matrix has diagonal */
2774: for (j = id[i]; j < id[i + 1]; j++) {
2775: if (jd[j] == i) {
2776: ii[i + 1]--;
2777: break;
2778: }
2779: }
2780: }
2781: PetscCall(PetscMalloc1(ii[M], &jj));
2782: cnt = 0;
2783: for (i = 0; i < M; i++) {
2784: for (j = io[i]; j < io[i + 1]; j++) {
2785: if (garray[jo[j]] > rstart) break;
2786: jj[cnt++] = garray[jo[j]];
2787: }
2788: for (k = id[i]; k < id[i + 1]; k++) {
2789: if (jd[k] != i) jj[cnt++] = rstart + jd[k];
2790: }
2791: for (; j < io[i + 1]; j++) jj[cnt++] = garray[jo[j]];
2792: }
2793: PetscCall(MatCreateMPIAdj(PetscObjectComm((PetscObject)B), M, B->cmap->N / B->rmap->bs, ii, jj, NULL, adj));
2794: PetscFunctionReturn(PETSC_SUCCESS);
2795: }
2797: #include <../src/mat/impls/aij/mpi/mpiaij.h>
2799: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat, MatType, MatReuse, Mat *);
2801: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAIJ(Mat A, MatType newtype, MatReuse reuse, Mat *newmat)
2802: {
2803: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
2804: Mat_MPIAIJ *b;
2805: Mat B;
2807: PetscFunctionBegin;
2808: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Matrix must be assembled");
2810: if (reuse == MAT_REUSE_MATRIX) {
2811: B = *newmat;
2812: } else {
2813: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
2814: PetscCall(MatSetType(B, MATMPIAIJ));
2815: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
2816: PetscCall(MatSetBlockSizes(B, A->rmap->bs, A->cmap->bs));
2817: PetscCall(MatSeqAIJSetPreallocation(B, 0, NULL));
2818: PetscCall(MatMPIAIJSetPreallocation(B, 0, NULL, 0, NULL));
2819: }
2820: b = (Mat_MPIAIJ *)B->data;
2822: if (reuse == MAT_REUSE_MATRIX) {
2823: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_REUSE_MATRIX, &b->A));
2824: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_REUSE_MATRIX, &b->B));
2825: } else {
2826: PetscInt *garray = a->garray;
2827: Mat_SeqAIJ *bB;
2828: PetscInt bs, nnz;
2829: PetscCall(MatDestroy(&b->A));
2830: PetscCall(MatDestroy(&b->B));
2831: /* just clear out the data structure */
2832: PetscCall(MatDisAssemble_MPIAIJ(B, PETSC_FALSE));
2833: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->A));
2834: PetscCall(MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->B));
2836: /* Global numbering for b->B columns */
2837: bB = (Mat_SeqAIJ *)b->B->data;
2838: bs = A->rmap->bs;
2839: nnz = bB->i[A->rmap->n];
2840: for (PetscInt k = 0; k < nnz; k++) {
2841: PetscInt bj = bB->j[k] / bs;
2842: PetscInt br = bB->j[k] % bs;
2843: bB->j[k] = garray[bj] * bs + br;
2844: }
2845: }
2846: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
2847: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
2849: if (reuse == MAT_INPLACE_MATRIX) {
2850: PetscCall(MatHeaderReplace(A, &B));
2851: } else {
2852: *newmat = B;
2853: }
2854: PetscFunctionReturn(PETSC_SUCCESS);
2855: }
2857: /*MC
2858: MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.
2860: Options Database Keys:
2861: + -mat_type mpibaij - sets the matrix type to `MATMPIBAIJ` during a call to `MatSetFromOptions()`
2862: . -mat_block_size <bs> - set the blocksize used to store the matrix
2863: . -mat_baij_mult_version version - indicate the version of the matrix-vector product to use (0 often indicates using BLAS)
2864: - -mat_use_hash_table <fact> - set hash table factor
2866: Level: beginner
2868: Note:
2869: `MatSetOption(A, MAT_STRUCTURE_ONLY, PETSC_TRUE)` may be called for this matrix type. In this no
2870: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
2872: .seealso: `Mat`, `MATBAIJ`, `MATSEQBAIJ`, `MatCreateBAIJ`
2873: M*/
2875: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIBSTRM(Mat, MatType, MatReuse, Mat *);
2877: PETSC_EXTERN PetscErrorCode MatCreate_MPIBAIJ(Mat B)
2878: {
2879: Mat_MPIBAIJ *b;
2880: PetscBool flg = PETSC_FALSE;
2882: PetscFunctionBegin;
2883: PetscCall(PetscNew(&b));
2884: B->data = (void *)b;
2885: B->ops[0] = MatOps_Values;
2886: B->assembled = PETSC_FALSE;
2888: B->insertmode = NOT_SET_VALUES;
2889: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
2890: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &b->size));
2892: /* build local table of row and column ownerships */
2893: PetscCall(PetscMalloc1(b->size + 1, &b->rangebs));
2895: /* build cache for off array entries formed */
2896: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
2898: b->donotstash = PETSC_FALSE;
2899: b->colmap = NULL;
2900: b->garray = NULL;
2901: b->roworiented = PETSC_TRUE;
2903: /* stuff used in block assembly */
2904: b->barray = NULL;
2906: /* stuff used for matrix vector multiply */
2907: b->lvec = NULL;
2908: b->Mvctx = NULL;
2910: /* stuff for MatGetRow() */
2911: b->rowindices = NULL;
2912: b->rowvalues = NULL;
2913: b->getrowactive = PETSC_FALSE;
2915: /* hash table stuff */
2916: b->ht = NULL;
2917: b->hd = NULL;
2918: b->ht_size = 0;
2919: b->ht_flag = PETSC_FALSE;
2920: b->ht_fact = 0;
2921: b->ht_total_ct = 0;
2922: b->ht_insert_ct = 0;
2924: /* stuff for MatCreateSubMatrices_MPIBAIJ_local() */
2925: b->ijonly = PETSC_FALSE;
2927: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpiadj_C", MatConvert_MPIBAIJ_MPIAdj));
2928: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpiaij_C", MatConvert_MPIBAIJ_MPIAIJ));
2929: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_mpisbaij_C", MatConvert_MPIBAIJ_MPISBAIJ));
2930: #if defined(PETSC_HAVE_HYPRE)
2931: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_hypre_C", MatConvert_AIJ_HYPRE));
2932: #endif
2933: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIBAIJ));
2934: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIBAIJ));
2935: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIBAIJSetPreallocation_C", MatMPIBAIJSetPreallocation_MPIBAIJ));
2936: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIBAIJSetPreallocationCSR_C", MatMPIBAIJSetPreallocationCSR_MPIBAIJ));
2937: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIBAIJ));
2938: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetHashTableFactor_C", MatSetHashTableFactor_MPIBAIJ));
2939: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpibaij_is_C", MatConvert_XAIJ_IS));
2940: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIBAIJ));
2942: PetscOptionsBegin(PetscObjectComm((PetscObject)B), NULL, "Options for loading MPIBAIJ matrix 1", "Mat");
2943: PetscCall(PetscOptionsName("-mat_use_hash_table", "Use hash table to save time in constructing matrix", "MatSetOption", &flg));
2944: if (flg) {
2945: PetscReal fact = 1.39;
2946: PetscCall(MatSetOption(B, MAT_USE_HASH_TABLE, PETSC_TRUE));
2947: PetscCall(PetscOptionsReal("-mat_use_hash_table", "Use hash table factor", "MatMPIBAIJSetHashTableFactor", fact, &fact, NULL));
2948: if (fact <= 1.0) fact = 1.39;
2949: PetscCall(MatMPIBAIJSetHashTableFactor(B, fact));
2950: PetscCall(PetscInfo(B, "Hash table Factor used %5.2g\n", (double)fact));
2951: }
2952: PetscOptionsEnd();
2953: PetscFunctionReturn(PETSC_SUCCESS);
2954: }
2956: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
2957: /*MC
2958: MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.
2960: This matrix type is identical to `MATSEQBAIJ` when constructed with a single process communicator,
2961: and `MATMPIBAIJ` otherwise.
2963: Options Database Keys:
2964: . -mat_type baij - sets the matrix type to `MATBAIJ` during a call to `MatSetFromOptions()`
2966: Level: beginner
2968: .seealso: `Mat`, `MatCreateBAIJ()`, `MATSEQBAIJ`, `MATMPIBAIJ`, `MatMPIBAIJSetPreallocation()`, `MatMPIBAIJSetPreallocationCSR()`
2969: M*/
2971: /*@
2972: MatMPIBAIJSetPreallocation - Allocates memory for a sparse parallel matrix in `MATMPIBAIJ` format
2973: (block compressed row).
2975: Collective
2977: Input Parameters:
2978: + B - the matrix
2979: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
2980: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
2981: . d_nz - number of block nonzeros per block row in diagonal portion of local
2982: submatrix (same for all local rows)
2983: . d_nnz - array containing the number of block nonzeros in the various block rows
2984: of the in diagonal portion of the local (possibly different for each block
2985: row) or `NULL`. If you plan to factor the matrix you must leave room for the diagonal entry and
2986: set it even if it is zero.
2987: . o_nz - number of block nonzeros per block row in the off-diagonal portion of local
2988: submatrix (same for all local rows).
2989: - o_nnz - array containing the number of nonzeros in the various block rows of the
2990: off-diagonal portion of the local submatrix (possibly different for
2991: each block row) or `NULL`.
2993: If the *_nnz parameter is given then the *_nz parameter is ignored
2995: Options Database Keys:
2996: + -mat_block_size - size of the blocks to use
2997: - -mat_use_hash_table <fact> - set hash table factor
2999: Level: intermediate
3001: Notes:
3002: For good matrix assembly performance
3003: the user should preallocate the matrix storage by setting the parameters
3004: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`). By setting these parameters accurately,
3005: performance can be increased by more than a factor of 50.
3007: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one processor
3008: than it must be used on all processors that share the object for that argument.
3010: Storage Information:
3011: For a square global matrix we define each processor's diagonal portion
3012: to be its local rows and the corresponding columns (a square submatrix);
3013: each processor's off-diagonal portion encompasses the remainder of the
3014: local matrix (a rectangular submatrix).
3016: The user can specify preallocated storage for the diagonal part of
3017: the local submatrix with either `d_nz` or `d_nnz` (not both). Set
3018: `d_nz` = `PETSC_DEFAULT` and `d_nnz` = `NULL` for PETSc to control dynamic
3019: memory allocation. Likewise, specify preallocated storage for the
3020: off-diagonal part of the local submatrix with `o_nz` or `o_nnz` (not both).
3022: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
3023: the figure below we depict these three local rows and all columns (0-11).
3025: .vb
3026: 0 1 2 3 4 5 6 7 8 9 10 11
3027: --------------------------
3028: row 3 |o o o d d d o o o o o o
3029: row 4 |o o o d d d o o o o o o
3030: row 5 |o o o d d d o o o o o o
3031: --------------------------
3032: .ve
3034: Thus, any entries in the d locations are stored in the d (diagonal)
3035: submatrix, and any entries in the o locations are stored in the
3036: o (off-diagonal) submatrix. Note that the d and the o submatrices are
3037: stored simply in the `MATSEQBAIJ` format for compressed row storage.
3039: Now `d_nz` should indicate the number of block nonzeros per row in the d matrix,
3040: and `o_nz` should indicate the number of block nonzeros per row in the o matrix.
3041: In general, for PDE problems in which most nonzeros are near the diagonal,
3042: one expects `d_nz` >> `o_nz`.
3044: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3045: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3046: You can also run with the option `-info` and look for messages with the string
3047: malloc in them to see if additional memory allocation was needed.
3049: .seealso: `Mat`, `MATMPIBAIJ`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`, `MatMPIBAIJSetPreallocationCSR()`, `PetscSplitOwnership()`
3050: @*/
3051: PetscErrorCode MatMPIBAIJSetPreallocation(Mat B, PetscInt bs, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
3052: {
3053: PetscFunctionBegin;
3057: PetscTryMethod(B, "MatMPIBAIJSetPreallocation_C", (Mat, PetscInt, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, bs, d_nz, d_nnz, o_nz, o_nnz));
3058: PetscFunctionReturn(PETSC_SUCCESS);
3059: }
3061: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
3062: /*@
3063: MatCreateBAIJ - Creates a sparse parallel matrix in `MATBAIJ` format
3064: (block compressed row).
3066: Collective
3068: Input Parameters:
3069: + comm - MPI communicator
3070: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
3071: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
3072: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
3073: This value should be the same as the local size used in creating the
3074: y vector for the matrix-vector product y = Ax.
3075: . n - number of local columns (or `PETSC_DECIDE` to have calculated if N is given)
3076: This value should be the same as the local size used in creating the
3077: x vector for the matrix-vector product y = Ax.
3078: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
3079: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
3080: . d_nz - number of nonzero blocks per block row in diagonal portion of local
3081: submatrix (same for all local rows)
3082: . d_nnz - array containing the number of nonzero blocks in the various block rows
3083: of the in diagonal portion of the local (possibly different for each block
3084: row) or NULL. If you plan to factor the matrix you must leave room for the diagonal entry
3085: and set it even if it is zero.
3086: . o_nz - number of nonzero blocks per block row in the off-diagonal portion of local
3087: submatrix (same for all local rows).
3088: - o_nnz - array containing the number of nonzero blocks in the various block rows of the
3089: off-diagonal portion of the local submatrix (possibly different for
3090: each block row) or NULL.
3092: Output Parameter:
3093: . A - the matrix
3095: Options Database Keys:
3096: + -mat_block_size - size of the blocks to use
3097: - -mat_use_hash_table <fact> - set hash table factor
3099: Level: intermediate
3101: Notes:
3102: It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
3103: MatXXXXSetPreallocation() paradigm instead of this routine directly.
3104: [MatXXXXSetPreallocation() is, for example, `MatSeqBAIJSetPreallocation()`]
3106: For good matrix assembly performance
3107: the user should preallocate the matrix storage by setting the parameters
3108: `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`). By setting these parameters accurately,
3109: performance can be increased by more than a factor of 50.
3111: If the *_nnz parameter is given then the *_nz parameter is ignored
3113: A nonzero block is any block that as 1 or more nonzeros in it
3115: The user MUST specify either the local or global matrix dimensions
3116: (possibly both).
3118: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one processor
3119: than it must be used on all processors that share the object for that argument.
3121: If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by
3122: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`.
3124: Storage Information:
3125: For a square global matrix we define each processor's diagonal portion
3126: to be its local rows and the corresponding columns (a square submatrix);
3127: each processor's off-diagonal portion encompasses the remainder of the
3128: local matrix (a rectangular submatrix).
3130: The user can specify preallocated storage for the diagonal part of
3131: the local submatrix with either d_nz or d_nnz (not both). Set
3132: `d_nz` = `PETSC_DEFAULT` and `d_nnz` = `NULL` for PETSc to control dynamic
3133: memory allocation. Likewise, specify preallocated storage for the
3134: off-diagonal part of the local submatrix with `o_nz` or `o_nnz` (not both).
3136: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
3137: the figure below we depict these three local rows and all columns (0-11).
3139: .vb
3140: 0 1 2 3 4 5 6 7 8 9 10 11
3141: --------------------------
3142: row 3 |o o o d d d o o o o o o
3143: row 4 |o o o d d d o o o o o o
3144: row 5 |o o o d d d o o o o o o
3145: --------------------------
3146: .ve
3148: Thus, any entries in the d locations are stored in the d (diagonal)
3149: submatrix, and any entries in the o locations are stored in the
3150: o (off-diagonal) submatrix. Note that the d and the o submatrices are
3151: stored simply in the `MATSEQBAIJ` format for compressed row storage.
3153: Now `d_nz` should indicate the number of block nonzeros per row in the d matrix,
3154: and `o_nz` should indicate the number of block nonzeros per row in the o matrix.
3155: In general, for PDE problems in which most nonzeros are near the diagonal,
3156: one expects `d_nz` >> `o_nz`.
3158: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatMPIBAIJSetPreallocation()`, `MatMPIBAIJSetPreallocationCSR()`,
3159: `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`
3160: @*/
3161: 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)
3162: {
3163: PetscMPIInt size;
3165: PetscFunctionBegin;
3166: PetscCall(MatCreate(comm, A));
3167: PetscCall(MatSetSizes(*A, m, n, M, N));
3168: PetscCallMPI(MPI_Comm_size(comm, &size));
3169: if (size > 1) {
3170: PetscCall(MatSetType(*A, MATMPIBAIJ));
3171: PetscCall(MatMPIBAIJSetPreallocation(*A, bs, d_nz, d_nnz, o_nz, o_nnz));
3172: } else {
3173: PetscCall(MatSetType(*A, MATSEQBAIJ));
3174: PetscCall(MatSeqBAIJSetPreallocation(*A, bs, d_nz, d_nnz));
3175: }
3176: PetscFunctionReturn(PETSC_SUCCESS);
3177: }
3179: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
3180: {
3181: Mat mat;
3182: Mat_MPIBAIJ *a, *oldmat = (Mat_MPIBAIJ *)matin->data;
3183: PetscInt len = 0;
3185: PetscFunctionBegin;
3186: *newmat = NULL;
3187: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
3188: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
3189: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
3191: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
3192: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
3193: if (matin->hash_active) {
3194: PetscCall(MatSetUp(mat));
3195: } else {
3196: mat->factortype = matin->factortype;
3197: mat->preallocated = PETSC_TRUE;
3198: mat->assembled = PETSC_TRUE;
3199: mat->insertmode = NOT_SET_VALUES;
3201: a = (Mat_MPIBAIJ *)mat->data;
3202: mat->rmap->bs = matin->rmap->bs;
3203: a->bs2 = oldmat->bs2;
3204: a->mbs = oldmat->mbs;
3205: a->nbs = oldmat->nbs;
3206: a->Mbs = oldmat->Mbs;
3207: a->Nbs = oldmat->Nbs;
3209: a->size = oldmat->size;
3210: a->rank = oldmat->rank;
3211: a->donotstash = oldmat->donotstash;
3212: a->roworiented = oldmat->roworiented;
3213: a->rowindices = NULL;
3214: a->rowvalues = NULL;
3215: a->getrowactive = PETSC_FALSE;
3216: a->barray = NULL;
3217: a->rstartbs = oldmat->rstartbs;
3218: a->rendbs = oldmat->rendbs;
3219: a->cstartbs = oldmat->cstartbs;
3220: a->cendbs = oldmat->cendbs;
3222: /* hash table stuff */
3223: a->ht = NULL;
3224: a->hd = NULL;
3225: a->ht_size = 0;
3226: a->ht_flag = oldmat->ht_flag;
3227: a->ht_fact = oldmat->ht_fact;
3228: a->ht_total_ct = 0;
3229: a->ht_insert_ct = 0;
3231: PetscCall(PetscArraycpy(a->rangebs, oldmat->rangebs, a->size + 1));
3232: if (oldmat->colmap) {
3233: #if defined(PETSC_USE_CTABLE)
3234: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3235: #else
3236: PetscCall(PetscMalloc1(a->Nbs, &a->colmap));
3237: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, a->Nbs));
3238: #endif
3239: } else a->colmap = NULL;
3241: if (oldmat->garray && (len = ((Mat_SeqBAIJ *)oldmat->B->data)->nbs)) {
3242: PetscCall(PetscMalloc1(len, &a->garray));
3243: PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3244: } else a->garray = NULL;
3246: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)matin), matin->rmap->bs, &mat->bstash));
3247: PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3248: PetscCall(VecScatterCopy(oldmat->Mvctx, &a->Mvctx));
3250: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3251: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3252: }
3253: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3254: *newmat = mat;
3255: PetscFunctionReturn(PETSC_SUCCESS);
3256: }
3258: /* Used for both MPIBAIJ and MPISBAIJ matrices */
3259: PetscErrorCode MatLoad_MPIBAIJ_Binary(Mat mat, PetscViewer viewer)
3260: {
3261: PetscInt header[4], M, N, nz, bs, m, n, mbs, nbs, rows, cols, sum, i, j, k;
3262: PetscInt *rowidxs, *colidxs, rs, cs, ce;
3263: PetscScalar *matvals;
3265: PetscFunctionBegin;
3266: PetscCall(PetscViewerSetUp(viewer));
3268: /* read in matrix header */
3269: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3270: PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3271: M = header[1];
3272: N = header[2];
3273: nz = header[3];
3274: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3275: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3276: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIBAIJ");
3278: /* set block sizes from the viewer's .info file */
3279: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3280: /* set local sizes if not set already */
3281: if (mat->rmap->n < 0 && M == N) mat->rmap->n = mat->cmap->n;
3282: if (mat->cmap->n < 0 && M == N) mat->cmap->n = mat->rmap->n;
3283: /* set global sizes if not set already */
3284: if (mat->rmap->N < 0) mat->rmap->N = M;
3285: if (mat->cmap->N < 0) mat->cmap->N = N;
3286: PetscCall(PetscLayoutSetUp(mat->rmap));
3287: PetscCall(PetscLayoutSetUp(mat->cmap));
3289: /* check if the matrix sizes are correct */
3290: PetscCall(MatGetSize(mat, &rows, &cols));
3291: 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);
3292: PetscCall(MatGetBlockSize(mat, &bs));
3293: PetscCall(MatGetLocalSize(mat, &m, &n));
3294: PetscCall(PetscLayoutGetRange(mat->rmap, &rs, NULL));
3295: PetscCall(PetscLayoutGetRange(mat->cmap, &cs, &ce));
3296: mbs = m / bs;
3297: nbs = n / bs;
3299: /* read in row lengths and build row indices */
3300: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3301: PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3302: rowidxs[0] = 0;
3303: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3304: PetscCallMPI(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3305: 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);
3307: /* read in column indices and matrix values */
3308: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3309: PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3310: PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3312: { /* preallocate matrix storage */
3313: PetscBT bt; /* helper bit set to count diagonal nonzeros */
3314: PetscHSetI ht; /* helper hash set to count off-diagonal nonzeros */
3315: PetscBool sbaij, done;
3316: PetscInt *d_nnz, *o_nnz;
3318: PetscCall(PetscBTCreate(nbs, &bt));
3319: PetscCall(PetscHSetICreate(&ht));
3320: PetscCall(PetscCalloc2(mbs, &d_nnz, mbs, &o_nnz));
3321: PetscCall(PetscObjectTypeCompare((PetscObject)mat, MATMPISBAIJ, &sbaij));
3322: for (i = 0; i < mbs; i++) {
3323: PetscCall(PetscBTMemzero(nbs, bt));
3324: PetscCall(PetscHSetIClear(ht));
3325: for (k = 0; k < bs; k++) {
3326: PetscInt row = bs * i + k;
3327: for (j = rowidxs[row]; j < rowidxs[row + 1]; j++) {
3328: PetscInt col = colidxs[j];
3329: if (!sbaij || col >= row) {
3330: if (col >= cs && col < ce) {
3331: if (!PetscBTLookupSet(bt, (col - cs) / bs)) d_nnz[i]++;
3332: } else {
3333: PetscCall(PetscHSetIQueryAdd(ht, col / bs, &done));
3334: if (done) o_nnz[i]++;
3335: }
3336: }
3337: }
3338: }
3339: }
3340: PetscCall(PetscBTDestroy(&bt));
3341: PetscCall(PetscHSetIDestroy(&ht));
3342: PetscCall(MatMPIBAIJSetPreallocation(mat, bs, 0, d_nnz, 0, o_nnz));
3343: PetscCall(MatMPISBAIJSetPreallocation(mat, bs, 0, d_nnz, 0, o_nnz));
3344: PetscCall(PetscFree2(d_nnz, o_nnz));
3345: }
3347: /* store matrix values */
3348: for (i = 0; i < m; i++) {
3349: PetscInt row = rs + i, s = rowidxs[i], e = rowidxs[i + 1];
3350: PetscUseTypeMethod(mat, setvalues, 1, &row, e - s, colidxs + s, matvals + s, INSERT_VALUES);
3351: }
3353: PetscCall(PetscFree(rowidxs));
3354: PetscCall(PetscFree2(colidxs, matvals));
3355: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
3356: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
3357: PetscFunctionReturn(PETSC_SUCCESS);
3358: }
3360: PetscErrorCode MatLoad_MPIBAIJ(Mat mat, PetscViewer viewer)
3361: {
3362: PetscBool isbinary;
3364: PetscFunctionBegin;
3365: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3366: 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);
3367: PetscCall(MatLoad_MPIBAIJ_Binary(mat, viewer));
3368: PetscFunctionReturn(PETSC_SUCCESS);
3369: }
3371: /*@
3372: MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the matrices hash table
3374: Input Parameters:
3375: + mat - the matrix
3376: - fact - factor
3378: Options Database Key:
3379: . -mat_use_hash_table <fact> - provide the factor
3381: Level: advanced
3383: .seealso: `Mat`, `MATMPIBAIJ`, `MatSetOption()`
3384: @*/
3385: PetscErrorCode MatMPIBAIJSetHashTableFactor(Mat mat, PetscReal fact)
3386: {
3387: PetscFunctionBegin;
3388: PetscTryMethod(mat, "MatSetHashTableFactor_C", (Mat, PetscReal), (mat, fact));
3389: PetscFunctionReturn(PETSC_SUCCESS);
3390: }
3392: PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat mat, PetscReal fact)
3393: {
3394: Mat_MPIBAIJ *baij;
3396: PetscFunctionBegin;
3397: baij = (Mat_MPIBAIJ *)mat->data;
3398: baij->ht_fact = fact;
3399: PetscFunctionReturn(PETSC_SUCCESS);
3400: }
3402: PetscErrorCode MatMPIBAIJGetSeqBAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
3403: {
3404: Mat_MPIBAIJ *a = (Mat_MPIBAIJ *)A->data;
3405: PetscBool flg;
3407: PetscFunctionBegin;
3408: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIBAIJ, &flg));
3409: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIBAIJ matrix as input");
3410: if (Ad) *Ad = a->A;
3411: if (Ao) *Ao = a->B;
3412: if (colmap) *colmap = a->garray;
3413: PetscFunctionReturn(PETSC_SUCCESS);
3414: }
3416: /*
3417: Special version for direct calls from Fortran (to eliminate two function call overheads
3418: */
3419: #if defined(PETSC_HAVE_FORTRAN_CAPS)
3420: #define matmpibaijsetvaluesblocked_ MATMPIBAIJSETVALUESBLOCKED
3421: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
3422: #define matmpibaijsetvaluesblocked_ matmpibaijsetvaluesblocked
3423: #endif
3425: // PetscClangLinter pragma disable: -fdoc-synopsis-matching-symbol-name
3426: /*@C
3427: MatMPIBAIJSetValuesBlocked - Direct Fortran call to replace call to `MatSetValuesBlocked()`
3429: Collective
3431: Input Parameters:
3432: + matin - the matrix
3433: . min - number of input rows
3434: . im - input rows
3435: . nin - number of input columns
3436: . in - input columns
3437: . v - numerical values input
3438: - addvin - `INSERT_VALUES` or `ADD_VALUES`
3440: Level: advanced
3442: Developer Notes:
3443: This has a complete copy of `MatSetValuesBlocked_MPIBAIJ()` which is terrible code un-reuse.
3445: .seealso: `Mat`, `MatSetValuesBlocked()`
3446: @*/
3447: PETSC_EXTERN PetscErrorCode matmpibaijsetvaluesblocked_(Mat *matin, PetscInt *min, const PetscInt im[], PetscInt *nin, const PetscInt in[], const MatScalar v[], InsertMode *addvin)
3448: {
3449: /* convert input arguments to C version */
3450: Mat mat = *matin;
3451: PetscInt m = *min, n = *nin;
3452: InsertMode addv = *addvin;
3454: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ *)mat->data;
3455: const MatScalar *value;
3456: MatScalar *barray = baij->barray;
3457: PetscBool roworiented = baij->roworiented;
3458: PetscInt i, j, ii, jj, row, col, rstart = baij->rstartbs;
3459: PetscInt rend = baij->rendbs, cstart = baij->cstartbs, stepval;
3460: PetscInt cend = baij->cendbs, bs = mat->rmap->bs, bs2 = baij->bs2;
3462: PetscFunctionBegin;
3463: /* tasks normally handled by MatSetValuesBlocked() */
3464: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
3465: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
3466: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3467: if (mat->assembled) {
3468: mat->was_assembled = PETSC_TRUE;
3469: mat->assembled = PETSC_FALSE;
3470: }
3471: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
3473: if (!barray) {
3474: PetscCall(PetscMalloc1(bs2, &barray));
3475: baij->barray = barray;
3476: }
3478: if (roworiented) stepval = (n - 1) * bs;
3479: else stepval = (m - 1) * bs;
3481: for (i = 0; i < m; i++) {
3482: if (im[i] < 0) continue;
3483: 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);
3484: if (im[i] >= rstart && im[i] < rend) {
3485: row = im[i] - rstart;
3486: for (j = 0; j < n; j++) {
3487: /* If NumCol = 1 then a copy is not required */
3488: if ((roworiented) && (n == 1)) {
3489: barray = (MatScalar *)v + i * bs2;
3490: } else if ((!roworiented) && (m == 1)) {
3491: barray = (MatScalar *)v + j * bs2;
3492: } else { /* Here a copy is required */
3493: if (roworiented) {
3494: value = v + i * (stepval + bs) * bs + j * bs;
3495: } else {
3496: value = v + j * (stepval + bs) * bs + i * bs;
3497: }
3498: for (ii = 0; ii < bs; ii++, value += stepval) {
3499: for (jj = 0; jj < bs; jj++) *barray++ = *value++;
3500: }
3501: barray -= bs2;
3502: }
3504: if (in[j] >= cstart && in[j] < cend) {
3505: col = in[j] - cstart;
3506: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->A, row, col, barray, addv, im[i], in[j]));
3507: } else if (in[j] < 0) {
3508: continue;
3509: } else {
3510: 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);
3511: if (mat->was_assembled) {
3512: if (!baij->colmap) PetscCall(MatCreateColmap_MPIBAIJ_Private(mat));
3514: #if defined(PETSC_USE_DEBUG)
3515: #if defined(PETSC_USE_CTABLE)
3516: {
3517: PetscInt data;
3518: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &data));
3519: PetscCheck((data - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
3520: }
3521: #else
3522: PetscCheck((baij->colmap[in[j]] - 1) % bs == 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Incorrect colmap");
3523: #endif
3524: #endif
3525: #if defined(PETSC_USE_CTABLE)
3526: PetscCall(PetscHMapIGetWithDefault(baij->colmap, in[j] + 1, 0, &col));
3527: col = (col - 1) / bs;
3528: #else
3529: col = (baij->colmap[in[j]] - 1) / bs;
3530: #endif
3531: if (col < 0 && !((Mat_SeqBAIJ *)baij->A->data)->nonew) {
3532: PetscCall(MatDisAssemble_MPIBAIJ(mat));
3533: col = in[j];
3534: }
3535: } else col = in[j];
3536: PetscCall(MatSetValuesBlocked_SeqBAIJ_Inlined(baij->B, row, col, barray, addv, im[i], in[j]));
3537: }
3538: }
3539: } else {
3540: if (!baij->donotstash) {
3541: if (roworiented) {
3542: PetscCall(MatStashValuesRowBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
3543: } else {
3544: PetscCall(MatStashValuesColBlocked_Private(&mat->bstash, im[i], n, in, v, m, n, i));
3545: }
3546: }
3547: }
3548: }
3550: /* task normally handled by MatSetValuesBlocked() */
3551: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
3552: PetscFunctionReturn(PETSC_SUCCESS);
3553: }
3555: /*@
3556: MatCreateMPIBAIJWithArrays - creates a `MATMPIBAIJ` matrix using arrays that contain in standard block CSR format for the local rows.
3558: Collective
3560: Input Parameters:
3561: + comm - MPI communicator
3562: . bs - the block size, only a block size of 1 is supported
3563: . m - number of local rows (Cannot be `PETSC_DECIDE`)
3564: . n - This value should be the same as the local size used in creating the
3565: x vector for the matrix-vector product $ y = Ax $. (or `PETSC_DECIDE` to have
3566: calculated if `N` is given) For square matrices `n` is almost always `m`.
3567: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
3568: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
3569: . 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
3570: . j - column indices
3571: - a - matrix values
3573: Output Parameter:
3574: . mat - the matrix
3576: Level: intermediate
3578: Notes:
3579: The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
3580: thus you CANNOT change the matrix entries by changing the values of a[] after you have
3581: called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.
3583: The order of the entries in values is the same as the block compressed sparse row storage format; that is, it is
3584: the same as a three dimensional array in Fortran values(bs,bs,nnz) that contains the first column of the first
3585: block, followed by the second column of the first block etc etc. That is, the blocks are contiguous in memory
3586: with column-major ordering within blocks.
3588: The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.
3590: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
3591: `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`
3592: @*/
3593: 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)
3594: {
3595: PetscFunctionBegin;
3596: PetscCheck(!i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
3597: PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
3598: PetscCall(MatCreate(comm, mat));
3599: PetscCall(MatSetSizes(*mat, m, n, M, N));
3600: PetscCall(MatSetType(*mat, MATMPIBAIJ));
3601: PetscCall(MatSetBlockSize(*mat, bs));
3602: PetscCall(MatSetUp(*mat));
3603: PetscCall(MatSetOption(*mat, MAT_ROW_ORIENTED, PETSC_FALSE));
3604: PetscCall(MatMPIBAIJSetPreallocationCSR(*mat, bs, i, j, a));
3605: PetscCall(MatSetOption(*mat, MAT_ROW_ORIENTED, PETSC_TRUE));
3606: PetscFunctionReturn(PETSC_SUCCESS);
3607: }
3609: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIBAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
3610: {
3611: PetscInt m, N, i, rstart, nnz, Ii, bs, cbs;
3612: PetscInt *indx;
3613: PetscScalar *values;
3615: PetscFunctionBegin;
3616: PetscCall(MatGetSize(inmat, &m, &N));
3617: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
3618: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)inmat->data;
3619: PetscInt *dnz, *onz, mbs, Nbs, nbs;
3620: PetscInt *bindx, rmax = a->rmax, j;
3621: PetscMPIInt rank, size;
3623: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
3624: mbs = m / bs;
3625: Nbs = N / cbs;
3626: if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnershipBlock(comm, cbs, &n, &N));
3627: nbs = n / cbs;
3629: PetscCall(PetscMalloc1(rmax, &bindx));
3630: MatPreallocateBegin(comm, mbs, nbs, dnz, onz); /* inline function, output __end and __rstart are used below */
3632: PetscCallMPI(MPI_Comm_rank(comm, &rank));
3633: PetscCallMPI(MPI_Comm_rank(comm, &size));
3634: if (rank == size - 1) {
3635: /* Check sum(nbs) = Nbs */
3636: PetscCheck(__end == Nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local block columns %" PetscInt_FMT " != global block columns %" PetscInt_FMT, __end, Nbs);
3637: }
3639: rstart = __rstart; /* block rstart of *outmat; see inline function MatPreallocateBegin */
3640: for (i = 0; i < mbs; i++) {
3641: PetscCall(MatGetRow_SeqBAIJ(inmat, i * bs, &nnz, &indx, NULL)); /* non-blocked nnz and indx */
3642: nnz = nnz / bs;
3643: for (j = 0; j < nnz; j++) bindx[j] = indx[j * bs] / bs;
3644: PetscCall(MatPreallocateSet(i + rstart, nnz, bindx, dnz, onz));
3645: PetscCall(MatRestoreRow_SeqBAIJ(inmat, i * bs, &nnz, &indx, NULL));
3646: }
3647: PetscCall(PetscFree(bindx));
3649: PetscCall(MatCreate(comm, outmat));
3650: PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
3651: PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
3652: PetscCall(MatSetType(*outmat, MATBAIJ));
3653: PetscCall(MatSeqBAIJSetPreallocation(*outmat, bs, 0, dnz));
3654: PetscCall(MatMPIBAIJSetPreallocation(*outmat, bs, 0, dnz, 0, onz));
3655: MatPreallocateEnd(dnz, onz);
3656: PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3657: }
3659: /* numeric phase */
3660: PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
3661: PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
3663: for (i = 0; i < m; i++) {
3664: PetscCall(MatGetRow_SeqBAIJ(inmat, i, &nnz, &indx, &values));
3665: Ii = i + rstart;
3666: PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
3667: PetscCall(MatRestoreRow_SeqBAIJ(inmat, i, &nnz, &indx, &values));
3668: }
3669: PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
3670: PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
3671: PetscFunctionReturn(PETSC_SUCCESS);
3672: }