Actual source code: aij.c
1: /*
2: Defines the basic matrix operations for the AIJ (compressed row)
3: matrix storage format.
4: */
6: #include <../src/mat/impls/aij/seq/aij.h>
7: #include <petscblaslapack.h>
8: #include <petscbt.h>
9: #include <petsc/private/kernels/blocktranspose.h>
11: /* defines MatSetValues_Seq_Hash(), MatAssemblyEnd_Seq_Hash(), MatSetUp_Seq_Hash() */
12: #define TYPE AIJ
13: #define TYPE_BS
14: #include "../src/mat/impls/aij/seq/seqhashmatsetvalues.h"
15: #include "../src/mat/impls/aij/seq/seqhashmat.h"
16: #undef TYPE
17: #undef TYPE_BS
19: PetscErrorCode MatSeqAIJSetTypeFromOptions(Mat A)
20: {
21: PetscBool flg;
22: char type[256];
24: PetscFunctionBegin;
25: PetscObjectOptionsBegin((PetscObject)A);
26: PetscCall(PetscOptionsFList("-mat_seqaij_type", "Matrix SeqAIJ type", "MatSeqAIJSetType", MatSeqAIJList, "seqaij", type, 256, &flg));
27: if (flg) PetscCall(MatSeqAIJSetType(A, type));
28: PetscOptionsEnd();
29: PetscFunctionReturn(PETSC_SUCCESS);
30: }
32: PetscErrorCode MatGetColumnReductions_SeqAIJ(Mat A, PetscInt type, PetscReal *reductions)
33: {
34: PetscInt i, m, n;
35: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
37: PetscFunctionBegin;
38: PetscCall(MatGetSize(A, &m, &n));
39: PetscCall(PetscArrayzero(reductions, n));
40: if (type == NORM_2) {
41: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i] * aij->a[i]);
42: } else if (type == NORM_1) {
43: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i]);
44: } else if (type == NORM_INFINITY) {
45: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]), reductions[aij->j[i]]);
46: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
47: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscRealPart(aij->a[i]);
48: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
49: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscImaginaryPart(aij->a[i]);
50: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Unknown reduction type");
52: if (type == NORM_2) {
53: for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
54: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
55: for (i = 0; i < n; i++) reductions[i] /= m;
56: }
57: PetscFunctionReturn(PETSC_SUCCESS);
58: }
60: PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A, IS *is)
61: {
62: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
63: PetscInt i, m = A->rmap->n, cnt = 0, bs = A->rmap->bs;
64: const PetscInt *jj = a->j, *ii = a->i;
65: PetscInt *rows;
67: PetscFunctionBegin;
68: for (i = 0; i < m; i++) {
69: if ((ii[i] != ii[i + 1]) && ((jj[ii[i]] < bs * (i / bs)) || (jj[ii[i + 1] - 1] > bs * ((i + bs) / bs) - 1))) cnt++;
70: }
71: PetscCall(PetscMalloc1(cnt, &rows));
72: cnt = 0;
73: for (i = 0; i < m; i++) {
74: if ((ii[i] != ii[i + 1]) && ((jj[ii[i]] < bs * (i / bs)) || (jj[ii[i + 1] - 1] > bs * ((i + bs) / bs) - 1))) {
75: rows[cnt] = i;
76: cnt++;
77: }
78: }
79: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, is));
80: PetscFunctionReturn(PETSC_SUCCESS);
81: }
83: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A, PetscInt *nrows, PetscInt **zrows)
84: {
85: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
86: const MatScalar *aa;
87: PetscInt i, m = A->rmap->n, cnt = 0;
88: const PetscInt *ii = a->i, *jj = a->j, *diag;
89: PetscInt *rows;
91: PetscFunctionBegin;
92: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
93: PetscCall(MatMarkDiagonal_SeqAIJ(A));
94: diag = a->diag;
95: for (i = 0; i < m; i++) {
96: if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) cnt++;
97: }
98: PetscCall(PetscMalloc1(cnt, &rows));
99: cnt = 0;
100: for (i = 0; i < m; i++) {
101: if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) rows[cnt++] = i;
102: }
103: *nrows = cnt;
104: *zrows = rows;
105: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
106: PetscFunctionReturn(PETSC_SUCCESS);
107: }
109: PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A, IS *zrows)
110: {
111: PetscInt nrows, *rows;
113: PetscFunctionBegin;
114: *zrows = NULL;
115: PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(A, &nrows, &rows));
116: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), nrows, rows, PETSC_OWN_POINTER, zrows));
117: PetscFunctionReturn(PETSC_SUCCESS);
118: }
120: PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A, IS *keptrows)
121: {
122: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
123: const MatScalar *aa;
124: PetscInt m = A->rmap->n, cnt = 0;
125: const PetscInt *ii;
126: PetscInt n, i, j, *rows;
128: PetscFunctionBegin;
129: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
130: *keptrows = NULL;
131: ii = a->i;
132: for (i = 0; i < m; i++) {
133: n = ii[i + 1] - ii[i];
134: if (!n) {
135: cnt++;
136: goto ok1;
137: }
138: for (j = ii[i]; j < ii[i + 1]; j++) {
139: if (aa[j] != 0.0) goto ok1;
140: }
141: cnt++;
142: ok1:;
143: }
144: if (!cnt) {
145: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
146: PetscFunctionReturn(PETSC_SUCCESS);
147: }
148: PetscCall(PetscMalloc1(A->rmap->n - cnt, &rows));
149: cnt = 0;
150: for (i = 0; i < m; i++) {
151: n = ii[i + 1] - ii[i];
152: if (!n) continue;
153: for (j = ii[i]; j < ii[i + 1]; j++) {
154: if (aa[j] != 0.0) {
155: rows[cnt++] = i;
156: break;
157: }
158: }
159: }
160: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
161: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, keptrows));
162: PetscFunctionReturn(PETSC_SUCCESS);
163: }
165: PetscErrorCode MatDiagonalSet_SeqAIJ(Mat Y, Vec D, InsertMode is)
166: {
167: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)Y->data;
168: PetscInt i, m = Y->rmap->n;
169: const PetscInt *diag;
170: MatScalar *aa;
171: const PetscScalar *v;
172: PetscBool missing;
174: PetscFunctionBegin;
175: if (Y->assembled) {
176: PetscCall(MatMissingDiagonal_SeqAIJ(Y, &missing, NULL));
177: if (!missing) {
178: diag = aij->diag;
179: PetscCall(VecGetArrayRead(D, &v));
180: PetscCall(MatSeqAIJGetArray(Y, &aa));
181: if (is == INSERT_VALUES) {
182: for (i = 0; i < m; i++) aa[diag[i]] = v[i];
183: } else {
184: for (i = 0; i < m; i++) aa[diag[i]] += v[i];
185: }
186: PetscCall(MatSeqAIJRestoreArray(Y, &aa));
187: PetscCall(VecRestoreArrayRead(D, &v));
188: PetscFunctionReturn(PETSC_SUCCESS);
189: }
190: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
191: }
192: PetscCall(MatDiagonalSet_Default(Y, D, is));
193: PetscFunctionReturn(PETSC_SUCCESS);
194: }
196: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
197: {
198: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
199: PetscInt i, ishift;
201: PetscFunctionBegin;
202: if (m) *m = A->rmap->n;
203: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
204: ishift = 0;
205: if (symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) {
206: PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, ishift, oshift, (PetscInt **)ia, (PetscInt **)ja));
207: } else if (oshift == 1) {
208: PetscInt *tia;
209: PetscInt nz = a->i[A->rmap->n];
210: /* malloc space and add 1 to i and j indices */
211: PetscCall(PetscMalloc1(A->rmap->n + 1, &tia));
212: for (i = 0; i < A->rmap->n + 1; i++) tia[i] = a->i[i] + 1;
213: *ia = tia;
214: if (ja) {
215: PetscInt *tja;
216: PetscCall(PetscMalloc1(nz + 1, &tja));
217: for (i = 0; i < nz; i++) tja[i] = a->j[i] + 1;
218: *ja = tja;
219: }
220: } else {
221: *ia = a->i;
222: if (ja) *ja = a->j;
223: }
224: PetscFunctionReturn(PETSC_SUCCESS);
225: }
227: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
228: {
229: PetscFunctionBegin;
230: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
231: if ((symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) || oshift == 1) {
232: PetscCall(PetscFree(*ia));
233: if (ja) PetscCall(PetscFree(*ja));
234: }
235: PetscFunctionReturn(PETSC_SUCCESS);
236: }
238: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
239: {
240: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
241: PetscInt i, *collengths, *cia, *cja, n = A->cmap->n, m = A->rmap->n;
242: PetscInt nz = a->i[m], row, *jj, mr, col;
244: PetscFunctionBegin;
245: *nn = n;
246: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
247: if (symmetric) {
248: PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, 0, oshift, (PetscInt **)ia, (PetscInt **)ja));
249: } else {
250: PetscCall(PetscCalloc1(n, &collengths));
251: PetscCall(PetscMalloc1(n + 1, &cia));
252: PetscCall(PetscMalloc1(nz, &cja));
253: jj = a->j;
254: for (i = 0; i < nz; i++) collengths[jj[i]]++;
255: cia[0] = oshift;
256: for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
257: PetscCall(PetscArrayzero(collengths, n));
258: jj = a->j;
259: for (row = 0; row < m; row++) {
260: mr = a->i[row + 1] - a->i[row];
261: for (i = 0; i < mr; i++) {
262: col = *jj++;
264: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
265: }
266: }
267: PetscCall(PetscFree(collengths));
268: *ia = cia;
269: *ja = cja;
270: }
271: PetscFunctionReturn(PETSC_SUCCESS);
272: }
274: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
275: {
276: PetscFunctionBegin;
277: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
279: PetscCall(PetscFree(*ia));
280: PetscCall(PetscFree(*ja));
281: PetscFunctionReturn(PETSC_SUCCESS);
282: }
284: /*
285: MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from
286: MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output
287: spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ()
288: */
289: PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
290: {
291: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
292: PetscInt i, *collengths, *cia, *cja, n = A->cmap->n, m = A->rmap->n;
293: PetscInt nz = a->i[m], row, mr, col, tmp;
294: PetscInt *cspidx;
295: const PetscInt *jj;
297: PetscFunctionBegin;
298: *nn = n;
299: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
301: PetscCall(PetscCalloc1(n, &collengths));
302: PetscCall(PetscMalloc1(n + 1, &cia));
303: PetscCall(PetscMalloc1(nz, &cja));
304: PetscCall(PetscMalloc1(nz, &cspidx));
305: jj = a->j;
306: for (i = 0; i < nz; i++) collengths[jj[i]]++;
307: cia[0] = oshift;
308: for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
309: PetscCall(PetscArrayzero(collengths, n));
310: jj = a->j;
311: for (row = 0; row < m; row++) {
312: mr = a->i[row + 1] - a->i[row];
313: for (i = 0; i < mr; i++) {
314: col = *jj++;
315: tmp = cia[col] + collengths[col]++ - oshift;
316: cspidx[tmp] = a->i[row] + i; /* index of a->j */
317: cja[tmp] = row + oshift;
318: }
319: }
320: PetscCall(PetscFree(collengths));
321: *ia = cia;
322: *ja = cja;
323: *spidx = cspidx;
324: PetscFunctionReturn(PETSC_SUCCESS);
325: }
327: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
328: {
329: PetscFunctionBegin;
330: PetscCall(MatRestoreColumnIJ_SeqAIJ(A, oshift, symmetric, inodecompressed, n, ia, ja, done));
331: PetscCall(PetscFree(*spidx));
332: PetscFunctionReturn(PETSC_SUCCESS);
333: }
335: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A, PetscInt row, const PetscScalar v[])
336: {
337: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
338: PetscInt *ai = a->i;
339: PetscScalar *aa;
341: PetscFunctionBegin;
342: PetscCall(MatSeqAIJGetArray(A, &aa));
343: PetscCall(PetscArraycpy(aa + ai[row], v, ai[row + 1] - ai[row]));
344: PetscCall(MatSeqAIJRestoreArray(A, &aa));
345: PetscFunctionReturn(PETSC_SUCCESS);
346: }
348: /*
349: MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions
351: - a single row of values is set with each call
352: - no row or column indices are negative or (in error) larger than the number of rows or columns
353: - the values are always added to the matrix, not set
354: - no new locations are introduced in the nonzero structure of the matrix
356: This does NOT assume the global column indices are sorted
358: */
360: #include <petsc/private/isimpl.h>
361: PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
362: {
363: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
364: PetscInt low, high, t, row, nrow, i, col, l;
365: const PetscInt *rp, *ai = a->i, *ailen = a->ilen, *aj = a->j;
366: PetscInt lastcol = -1;
367: MatScalar *ap, value, *aa;
368: const PetscInt *ridx = A->rmap->mapping->indices, *cidx = A->cmap->mapping->indices;
370: PetscFunctionBegin;
371: PetscCall(MatSeqAIJGetArray(A, &aa));
372: row = ridx[im[0]];
373: rp = aj + ai[row];
374: ap = aa + ai[row];
375: nrow = ailen[row];
376: low = 0;
377: high = nrow;
378: for (l = 0; l < n; l++) { /* loop over added columns */
379: col = cidx[in[l]];
380: value = v[l];
382: if (col <= lastcol) low = 0;
383: else high = nrow;
384: lastcol = col;
385: while (high - low > 5) {
386: t = (low + high) / 2;
387: if (rp[t] > col) high = t;
388: else low = t;
389: }
390: for (i = low; i < high; i++) {
391: if (rp[i] == col) {
392: ap[i] += value;
393: low = i + 1;
394: break;
395: }
396: }
397: }
398: PetscCall(MatSeqAIJRestoreArray(A, &aa));
399: return PETSC_SUCCESS;
400: }
402: PetscErrorCode MatSetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
403: {
404: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
405: PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
406: PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen;
407: PetscInt *aj = a->j, nonew = a->nonew, lastcol = -1;
408: MatScalar *ap = NULL, value = 0.0, *aa;
409: PetscBool ignorezeroentries = a->ignorezeroentries;
410: PetscBool roworiented = a->roworiented;
412: PetscFunctionBegin;
413: PetscCall(MatSeqAIJGetArray(A, &aa));
414: for (k = 0; k < m; k++) { /* loop over added rows */
415: row = im[k];
416: if (row < 0) continue;
417: PetscCheck(row < A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->n - 1);
418: rp = aj + ai[row];
419: if (!A->structure_only) ap = aa + ai[row];
420: rmax = imax[row];
421: nrow = ailen[row];
422: low = 0;
423: high = nrow;
424: for (l = 0; l < n; l++) { /* loop over added columns */
425: if (in[l] < 0) continue;
426: PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1);
427: col = in[l];
428: if (v && !A->structure_only) value = roworiented ? v[l + k * n] : v[k + l * m];
429: if (!A->structure_only && value == 0.0 && ignorezeroentries && is == ADD_VALUES && row != col) continue;
431: if (col <= lastcol) low = 0;
432: else high = nrow;
433: lastcol = col;
434: while (high - low > 5) {
435: t = (low + high) / 2;
436: if (rp[t] > col) high = t;
437: else low = t;
438: }
439: for (i = low; i < high; i++) {
440: if (rp[i] > col) break;
441: if (rp[i] == col) {
442: if (!A->structure_only) {
443: if (is == ADD_VALUES) {
444: ap[i] += value;
445: (void)PetscLogFlops(1.0);
446: } else ap[i] = value;
447: }
448: low = i + 1;
449: goto noinsert;
450: }
451: }
452: if (value == 0.0 && ignorezeroentries && row != col) goto noinsert;
453: if (nonew == 1) goto noinsert;
454: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at (%" PetscInt_FMT ",%" PetscInt_FMT ") in the matrix", row, col);
455: if (A->structure_only) {
456: MatSeqXAIJReallocateAIJ_structure_only(A, A->rmap->n, 1, nrow, row, col, rmax, ai, aj, rp, imax, nonew, MatScalar);
457: } else {
458: MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
459: }
460: N = nrow++ - 1;
461: a->nz++;
462: high++;
463: /* shift up all the later entries in this row */
464: PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
465: rp[i] = col;
466: if (!A->structure_only) {
467: PetscCall(PetscArraymove(ap + i + 1, ap + i, N - i + 1));
468: ap[i] = value;
469: }
470: low = i + 1;
471: A->nonzerostate++;
472: noinsert:;
473: }
474: ailen[row] = nrow;
475: }
476: PetscCall(MatSeqAIJRestoreArray(A, &aa));
477: PetscFunctionReturn(PETSC_SUCCESS);
478: }
480: PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
481: {
482: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
483: PetscInt *rp, k, row;
484: PetscInt *ai = a->i;
485: PetscInt *aj = a->j;
486: MatScalar *aa, *ap;
488: PetscFunctionBegin;
489: PetscCheck(!A->was_assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot call on assembled matrix.");
490: PetscCheck(m * n + a->nz <= a->maxnz, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of entries in matrix will be larger than maximum nonzeros allocated for %" PetscInt_FMT " in MatSeqAIJSetTotalPreallocation()", a->maxnz);
492: PetscCall(MatSeqAIJGetArray(A, &aa));
493: for (k = 0; k < m; k++) { /* loop over added rows */
494: row = im[k];
495: rp = aj + ai[row];
496: ap = aa + ai[row];
498: PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
499: if (!A->structure_only) {
500: if (v) {
501: PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
502: v += n;
503: } else {
504: PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
505: }
506: }
507: a->ilen[row] = n;
508: a->imax[row] = n;
509: a->i[row + 1] = a->i[row] + n;
510: a->nz += n;
511: }
512: PetscCall(MatSeqAIJRestoreArray(A, &aa));
513: PetscFunctionReturn(PETSC_SUCCESS);
514: }
516: /*@
517: MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix.
519: Input Parameters:
520: + A - the `MATSEQAIJ` matrix
521: - nztotal - bound on the number of nonzeros
523: Level: advanced
525: Notes:
526: This can be called if you will be provided the matrix row by row (from row zero) with sorted column indices for each row.
527: Simply call `MatSetValues()` after this call to provide the matrix entries in the usual manner. This matrix may be used
528: as always with multiple matrix assemblies.
530: .seealso: [](chapter_matrices), `Mat`, `MatSetOption()`, `MAT_SORTED_FULL`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`
531: @*/
533: PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A, PetscInt nztotal)
534: {
535: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
537: PetscFunctionBegin;
538: PetscCall(PetscLayoutSetUp(A->rmap));
539: PetscCall(PetscLayoutSetUp(A->cmap));
540: a->maxnz = nztotal;
541: if (!a->imax) { PetscCall(PetscMalloc1(A->rmap->n, &a->imax)); }
542: if (!a->ilen) {
543: PetscCall(PetscMalloc1(A->rmap->n, &a->ilen));
544: } else {
545: PetscCall(PetscMemzero(a->ilen, A->rmap->n * sizeof(PetscInt)));
546: }
548: /* allocate the matrix space */
549: if (A->structure_only) {
550: PetscCall(PetscMalloc1(nztotal, &a->j));
551: PetscCall(PetscMalloc1(A->rmap->n + 1, &a->i));
552: } else {
553: PetscCall(PetscMalloc3(nztotal, &a->a, nztotal, &a->j, A->rmap->n + 1, &a->i));
554: }
555: a->i[0] = 0;
556: if (A->structure_only) {
557: a->singlemalloc = PETSC_FALSE;
558: a->free_a = PETSC_FALSE;
559: } else {
560: a->singlemalloc = PETSC_TRUE;
561: a->free_a = PETSC_TRUE;
562: }
563: a->free_ij = PETSC_TRUE;
564: A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
565: A->preallocated = PETSC_TRUE;
566: PetscFunctionReturn(PETSC_SUCCESS);
567: }
569: PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
570: {
571: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
572: PetscInt *rp, k, row;
573: PetscInt *ai = a->i, *ailen = a->ilen;
574: PetscInt *aj = a->j;
575: MatScalar *aa, *ap;
577: PetscFunctionBegin;
578: PetscCall(MatSeqAIJGetArray(A, &aa));
579: for (k = 0; k < m; k++) { /* loop over added rows */
580: row = im[k];
581: PetscCheck(n <= a->imax[row], PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Preallocation for row %" PetscInt_FMT " does not match number of columns provided", n);
582: rp = aj + ai[row];
583: ap = aa + ai[row];
584: if (!A->was_assembled) PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
585: if (!A->structure_only) {
586: if (v) {
587: PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
588: v += n;
589: } else {
590: PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
591: }
592: }
593: ailen[row] = n;
594: a->nz += n;
595: }
596: PetscCall(MatSeqAIJRestoreArray(A, &aa));
597: PetscFunctionReturn(PETSC_SUCCESS);
598: }
600: PetscErrorCode MatGetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[])
601: {
602: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
603: PetscInt *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j;
604: PetscInt *ai = a->i, *ailen = a->ilen;
605: const MatScalar *ap, *aa;
607: PetscFunctionBegin;
608: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
609: for (k = 0; k < m; k++) { /* loop over rows */
610: row = im[k];
611: if (row < 0) {
612: v += n;
613: continue;
614: } /* negative row */
615: PetscCheck(row < A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->n - 1);
616: rp = aj + ai[row];
617: ap = aa + ai[row];
618: nrow = ailen[row];
619: for (l = 0; l < n; l++) { /* loop over columns */
620: if (in[l] < 0) {
621: v++;
622: continue;
623: } /* negative column */
624: PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1);
625: col = in[l];
626: high = nrow;
627: low = 0; /* assume unsorted */
628: while (high - low > 5) {
629: t = (low + high) / 2;
630: if (rp[t] > col) high = t;
631: else low = t;
632: }
633: for (i = low; i < high; i++) {
634: if (rp[i] > col) break;
635: if (rp[i] == col) {
636: *v++ = ap[i];
637: goto finished;
638: }
639: }
640: *v++ = 0.0;
641: finished:;
642: }
643: }
644: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
645: PetscFunctionReturn(PETSC_SUCCESS);
646: }
648: PetscErrorCode MatView_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
649: {
650: Mat_SeqAIJ *A = (Mat_SeqAIJ *)mat->data;
651: const PetscScalar *av;
652: PetscInt header[4], M, N, m, nz, i;
653: PetscInt *rowlens;
655: PetscFunctionBegin;
656: PetscCall(PetscViewerSetUp(viewer));
658: M = mat->rmap->N;
659: N = mat->cmap->N;
660: m = mat->rmap->n;
661: nz = A->nz;
663: /* write matrix header */
664: header[0] = MAT_FILE_CLASSID;
665: header[1] = M;
666: header[2] = N;
667: header[3] = nz;
668: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
670: /* fill in and store row lengths */
671: PetscCall(PetscMalloc1(m, &rowlens));
672: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i];
673: PetscCall(PetscViewerBinaryWrite(viewer, rowlens, m, PETSC_INT));
674: PetscCall(PetscFree(rowlens));
675: /* store column indices */
676: PetscCall(PetscViewerBinaryWrite(viewer, A->j, nz, PETSC_INT));
677: /* store nonzero values */
678: PetscCall(MatSeqAIJGetArrayRead(mat, &av));
679: PetscCall(PetscViewerBinaryWrite(viewer, av, nz, PETSC_SCALAR));
680: PetscCall(MatSeqAIJRestoreArrayRead(mat, &av));
682: /* write block size option to the viewer's .info file */
683: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
684: PetscFunctionReturn(PETSC_SUCCESS);
685: }
687: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A, PetscViewer viewer)
688: {
689: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
690: PetscInt i, k, m = A->rmap->N;
692: PetscFunctionBegin;
693: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
694: for (i = 0; i < m; i++) {
695: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
696: for (k = a->i[i]; k < a->i[i + 1]; k++) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ") ", a->j[k]));
697: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
698: }
699: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
700: PetscFunctionReturn(PETSC_SUCCESS);
701: }
703: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat, PetscViewer);
705: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A, PetscViewer viewer)
706: {
707: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
708: const PetscScalar *av;
709: PetscInt i, j, m = A->rmap->n;
710: const char *name;
711: PetscViewerFormat format;
713: PetscFunctionBegin;
714: if (A->structure_only) {
715: PetscCall(MatView_SeqAIJ_ASCII_structonly(A, viewer));
716: PetscFunctionReturn(PETSC_SUCCESS);
717: }
719: PetscCall(PetscViewerGetFormat(viewer, &format));
720: if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscFunctionReturn(PETSC_SUCCESS);
722: /* trigger copy to CPU if needed */
723: PetscCall(MatSeqAIJGetArrayRead(A, &av));
724: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
725: if (format == PETSC_VIEWER_ASCII_MATLAB) {
726: PetscInt nofinalvalue = 0;
727: if (m && ((a->i[m] == a->i[m - 1]) || (a->j[a->nz - 1] != A->cmap->n - 1))) {
728: /* Need a dummy value to ensure the dimension of the matrix. */
729: nofinalvalue = 1;
730: }
731: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
732: PetscCall(PetscViewerASCIIPrintf(viewer, "%% Size = %" PetscInt_FMT " %" PetscInt_FMT " \n", m, A->cmap->n));
733: PetscCall(PetscViewerASCIIPrintf(viewer, "%% Nonzeros = %" PetscInt_FMT " \n", a->nz));
734: #if defined(PETSC_USE_COMPLEX)
735: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",4);\n", a->nz + nofinalvalue));
736: #else
737: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",3);\n", a->nz + nofinalvalue));
738: #endif
739: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = [\n"));
741: for (i = 0; i < m; i++) {
742: for (j = a->i[i]; j < a->i[i + 1]; j++) {
743: #if defined(PETSC_USE_COMPLEX)
744: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e %18.16e\n", i + 1, a->j[j] + 1, (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
745: #else
746: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", i + 1, a->j[j] + 1, (double)a->a[j]));
747: #endif
748: }
749: }
750: if (nofinalvalue) {
751: #if defined(PETSC_USE_COMPLEX)
752: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e %18.16e\n", m, A->cmap->n, 0., 0.));
753: #else
754: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", m, A->cmap->n, 0.0));
755: #endif
756: }
757: PetscCall(PetscObjectGetName((PetscObject)A, &name));
758: PetscCall(PetscViewerASCIIPrintf(viewer, "];\n %s = spconvert(zzz);\n", name));
759: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
760: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
761: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
762: for (i = 0; i < m; i++) {
763: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
764: for (j = a->i[i]; j < a->i[i + 1]; j++) {
765: #if defined(PETSC_USE_COMPLEX)
766: if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
767: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
768: } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
769: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j])));
770: } else if (PetscRealPart(a->a[j]) != 0.0) {
771: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
772: }
773: #else
774: if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
775: #endif
776: }
777: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
778: }
779: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
780: } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
781: PetscInt nzd = 0, fshift = 1, *sptr;
782: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
783: PetscCall(PetscMalloc1(m + 1, &sptr));
784: for (i = 0; i < m; i++) {
785: sptr[i] = nzd + 1;
786: for (j = a->i[i]; j < a->i[i + 1]; j++) {
787: if (a->j[j] >= i) {
788: #if defined(PETSC_USE_COMPLEX)
789: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
790: #else
791: if (a->a[j] != 0.0) nzd++;
792: #endif
793: }
794: }
795: }
796: sptr[m] = nzd + 1;
797: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n\n", m, nzd));
798: for (i = 0; i < m + 1; i += 6) {
799: if (i + 4 < m) {
800: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3], sptr[i + 4], sptr[i + 5]));
801: } else if (i + 3 < m) {
802: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3], sptr[i + 4]));
803: } else if (i + 2 < m) {
804: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3]));
805: } else if (i + 1 < m) {
806: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2]));
807: } else if (i < m) {
808: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1]));
809: } else {
810: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT "\n", sptr[i]));
811: }
812: }
813: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
814: PetscCall(PetscFree(sptr));
815: for (i = 0; i < m; i++) {
816: for (j = a->i[i]; j < a->i[i + 1]; j++) {
817: if (a->j[j] >= i) PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " ", a->j[j] + fshift));
818: }
819: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
820: }
821: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
822: for (i = 0; i < m; i++) {
823: for (j = a->i[i]; j < a->i[i + 1]; j++) {
824: if (a->j[j] >= i) {
825: #if defined(PETSC_USE_COMPLEX)
826: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " %18.16e %18.16e ", (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
827: #else
828: if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " %18.16e ", (double)a->a[j]));
829: #endif
830: }
831: }
832: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
833: }
834: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
835: } else if (format == PETSC_VIEWER_ASCII_DENSE) {
836: PetscInt cnt = 0, jcnt;
837: PetscScalar value;
838: #if defined(PETSC_USE_COMPLEX)
839: PetscBool realonly = PETSC_TRUE;
841: for (i = 0; i < a->i[m]; i++) {
842: if (PetscImaginaryPart(a->a[i]) != 0.0) {
843: realonly = PETSC_FALSE;
844: break;
845: }
846: }
847: #endif
849: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
850: for (i = 0; i < m; i++) {
851: jcnt = 0;
852: for (j = 0; j < A->cmap->n; j++) {
853: if (jcnt < a->i[i + 1] - a->i[i] && j == a->j[cnt]) {
854: value = a->a[cnt++];
855: jcnt++;
856: } else {
857: value = 0.0;
858: }
859: #if defined(PETSC_USE_COMPLEX)
860: if (realonly) {
861: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)PetscRealPart(value)));
862: } else {
863: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e+%7.5e i ", (double)PetscRealPart(value), (double)PetscImaginaryPart(value)));
864: }
865: #else
866: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)value));
867: #endif
868: }
869: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
870: }
871: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
872: } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
873: PetscInt fshift = 1;
874: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
875: #if defined(PETSC_USE_COMPLEX)
876: PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate complex general\n"));
877: #else
878: PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate real general\n"));
879: #endif
880: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", m, A->cmap->n, a->nz));
881: for (i = 0; i < m; i++) {
882: for (j = a->i[i]; j < a->i[i + 1]; j++) {
883: #if defined(PETSC_USE_COMPLEX)
884: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %g %g\n", i + fshift, a->j[j] + fshift, (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
885: #else
886: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %g\n", i + fshift, a->j[j] + fshift, (double)a->a[j]));
887: #endif
888: }
889: }
890: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
891: } else {
892: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
893: if (A->factortype) {
894: for (i = 0; i < m; i++) {
895: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
896: /* L part */
897: for (j = a->i[i]; j < a->i[i + 1]; j++) {
898: #if defined(PETSC_USE_COMPLEX)
899: if (PetscImaginaryPart(a->a[j]) > 0.0) {
900: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
901: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
902: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j]))));
903: } else {
904: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
905: }
906: #else
907: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
908: #endif
909: }
910: /* diagonal */
911: j = a->diag[i];
912: #if defined(PETSC_USE_COMPLEX)
913: if (PetscImaginaryPart(a->a[j]) > 0.0) {
914: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(1.0 / a->a[j]), (double)PetscImaginaryPart(1.0 / a->a[j])));
915: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
916: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(1.0 / a->a[j]), (double)(-PetscImaginaryPart(1.0 / a->a[j]))));
917: } else {
918: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(1.0 / a->a[j])));
919: }
920: #else
921: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)(1.0 / a->a[j])));
922: #endif
924: /* U part */
925: for (j = a->diag[i + 1] + 1; j < a->diag[i]; j++) {
926: #if defined(PETSC_USE_COMPLEX)
927: if (PetscImaginaryPart(a->a[j]) > 0.0) {
928: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
929: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
930: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j]))));
931: } else {
932: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
933: }
934: #else
935: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
936: #endif
937: }
938: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
939: }
940: } else {
941: for (i = 0; i < m; i++) {
942: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
943: for (j = a->i[i]; j < a->i[i + 1]; j++) {
944: #if defined(PETSC_USE_COMPLEX)
945: if (PetscImaginaryPart(a->a[j]) > 0.0) {
946: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
947: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
948: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j])));
949: } else {
950: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
951: }
952: #else
953: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
954: #endif
955: }
956: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
957: }
958: }
959: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
960: }
961: PetscCall(PetscViewerFlush(viewer));
962: PetscFunctionReturn(PETSC_SUCCESS);
963: }
965: #include <petscdraw.h>
966: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw, void *Aa)
967: {
968: Mat A = (Mat)Aa;
969: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
970: PetscInt i, j, m = A->rmap->n;
971: int color;
972: PetscReal xl, yl, xr, yr, x_l, x_r, y_l, y_r;
973: PetscViewer viewer;
974: PetscViewerFormat format;
975: const PetscScalar *aa;
977: PetscFunctionBegin;
978: PetscCall(PetscObjectQuery((PetscObject)A, "Zoomviewer", (PetscObject *)&viewer));
979: PetscCall(PetscViewerGetFormat(viewer, &format));
980: PetscCall(PetscDrawGetCoordinates(draw, &xl, &yl, &xr, &yr));
982: /* loop over matrix elements drawing boxes */
983: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
984: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
985: PetscDrawCollectiveBegin(draw);
986: /* Blue for negative, Cyan for zero and Red for positive */
987: color = PETSC_DRAW_BLUE;
988: for (i = 0; i < m; i++) {
989: y_l = m - i - 1.0;
990: y_r = y_l + 1.0;
991: for (j = a->i[i]; j < a->i[i + 1]; j++) {
992: x_l = a->j[j];
993: x_r = x_l + 1.0;
994: if (PetscRealPart(aa[j]) >= 0.) continue;
995: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
996: }
997: }
998: color = PETSC_DRAW_CYAN;
999: for (i = 0; i < m; i++) {
1000: y_l = m - i - 1.0;
1001: y_r = y_l + 1.0;
1002: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1003: x_l = a->j[j];
1004: x_r = x_l + 1.0;
1005: if (aa[j] != 0.) continue;
1006: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1007: }
1008: }
1009: color = PETSC_DRAW_RED;
1010: for (i = 0; i < m; i++) {
1011: y_l = m - i - 1.0;
1012: y_r = y_l + 1.0;
1013: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1014: x_l = a->j[j];
1015: x_r = x_l + 1.0;
1016: if (PetscRealPart(aa[j]) <= 0.) continue;
1017: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1018: }
1019: }
1020: PetscDrawCollectiveEnd(draw);
1021: } else {
1022: /* use contour shading to indicate magnitude of values */
1023: /* first determine max of all nonzero values */
1024: PetscReal minv = 0.0, maxv = 0.0;
1025: PetscInt nz = a->nz, count = 0;
1026: PetscDraw popup;
1028: for (i = 0; i < nz; i++) {
1029: if (PetscAbsScalar(aa[i]) > maxv) maxv = PetscAbsScalar(aa[i]);
1030: }
1031: if (minv >= maxv) maxv = minv + PETSC_SMALL;
1032: PetscCall(PetscDrawGetPopup(draw, &popup));
1033: PetscCall(PetscDrawScalePopup(popup, minv, maxv));
1035: PetscDrawCollectiveBegin(draw);
1036: for (i = 0; i < m; i++) {
1037: y_l = m - i - 1.0;
1038: y_r = y_l + 1.0;
1039: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1040: x_l = a->j[j];
1041: x_r = x_l + 1.0;
1042: color = PetscDrawRealToColor(PetscAbsScalar(aa[count]), minv, maxv);
1043: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1044: count++;
1045: }
1046: }
1047: PetscDrawCollectiveEnd(draw);
1048: }
1049: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1050: PetscFunctionReturn(PETSC_SUCCESS);
1051: }
1053: #include <petscdraw.h>
1054: PetscErrorCode MatView_SeqAIJ_Draw(Mat A, PetscViewer viewer)
1055: {
1056: PetscDraw draw;
1057: PetscReal xr, yr, xl, yl, h, w;
1058: PetscBool isnull;
1060: PetscFunctionBegin;
1061: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1062: PetscCall(PetscDrawIsNull(draw, &isnull));
1063: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1065: xr = A->cmap->n;
1066: yr = A->rmap->n;
1067: h = yr / 10.0;
1068: w = xr / 10.0;
1069: xr += w;
1070: yr += h;
1071: xl = -w;
1072: yl = -h;
1073: PetscCall(PetscDrawSetCoordinates(draw, xl, yl, xr, yr));
1074: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", (PetscObject)viewer));
1075: PetscCall(PetscDrawZoom(draw, MatView_SeqAIJ_Draw_Zoom, A));
1076: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", NULL));
1077: PetscCall(PetscDrawSave(draw));
1078: PetscFunctionReturn(PETSC_SUCCESS);
1079: }
1081: PetscErrorCode MatView_SeqAIJ(Mat A, PetscViewer viewer)
1082: {
1083: PetscBool iascii, isbinary, isdraw;
1085: PetscFunctionBegin;
1086: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1087: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1088: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1089: if (iascii) PetscCall(MatView_SeqAIJ_ASCII(A, viewer));
1090: else if (isbinary) PetscCall(MatView_SeqAIJ_Binary(A, viewer));
1091: else if (isdraw) PetscCall(MatView_SeqAIJ_Draw(A, viewer));
1092: PetscCall(MatView_SeqAIJ_Inode(A, viewer));
1093: PetscFunctionReturn(PETSC_SUCCESS);
1094: }
1096: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A, MatAssemblyType mode)
1097: {
1098: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1099: PetscInt fshift = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax;
1100: PetscInt m = A->rmap->n, *ip, N, *ailen = a->ilen, rmax = 0;
1101: MatScalar *aa = a->a, *ap;
1102: PetscReal ratio = 0.6;
1104: PetscFunctionBegin;
1105: if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
1106: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1107: if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) {
1108: /* we need to respect users asking to use or not the inodes routine in between matrix assemblies */
1109: PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1110: PetscFunctionReturn(PETSC_SUCCESS);
1111: }
1113: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
1114: for (i = 1; i < m; i++) {
1115: /* move each row back by the amount of empty slots (fshift) before it*/
1116: fshift += imax[i - 1] - ailen[i - 1];
1117: rmax = PetscMax(rmax, ailen[i]);
1118: if (fshift) {
1119: ip = aj + ai[i];
1120: ap = aa + ai[i];
1121: N = ailen[i];
1122: PetscCall(PetscArraymove(ip - fshift, ip, N));
1123: if (!A->structure_only) PetscCall(PetscArraymove(ap - fshift, ap, N));
1124: }
1125: ai[i] = ai[i - 1] + ailen[i - 1];
1126: }
1127: if (m) {
1128: fshift += imax[m - 1] - ailen[m - 1];
1129: ai[m] = ai[m - 1] + ailen[m - 1];
1130: }
1131: /* reset ilen and imax for each row */
1132: a->nonzerorowcnt = 0;
1133: if (A->structure_only) {
1134: PetscCall(PetscFree(a->imax));
1135: PetscCall(PetscFree(a->ilen));
1136: } else { /* !A->structure_only */
1137: for (i = 0; i < m; i++) {
1138: ailen[i] = imax[i] = ai[i + 1] - ai[i];
1139: a->nonzerorowcnt += ((ai[i + 1] - ai[i]) > 0);
1140: }
1141: }
1142: a->nz = ai[m];
1143: PetscCheck(!fshift || a->nounused != -1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unused space detected in matrix: %" PetscInt_FMT " X %" PetscInt_FMT ", %" PetscInt_FMT " unneeded", m, A->cmap->n, fshift);
1145: PetscCall(MatMarkDiagonal_SeqAIJ(A));
1146: PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT "; storage space: %" PetscInt_FMT " unneeded,%" PetscInt_FMT " used\n", m, A->cmap->n, fshift, a->nz));
1147: PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues() is %" PetscInt_FMT "\n", a->reallocs));
1148: PetscCall(PetscInfo(A, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", rmax));
1150: A->info.mallocs += a->reallocs;
1151: a->reallocs = 0;
1152: A->info.nz_unneeded = (PetscReal)fshift;
1153: a->rmax = rmax;
1155: if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, m, ratio));
1156: PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1157: PetscFunctionReturn(PETSC_SUCCESS);
1158: }
1160: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1161: {
1162: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1163: PetscInt i, nz = a->nz;
1164: MatScalar *aa;
1166: PetscFunctionBegin;
1167: PetscCall(MatSeqAIJGetArray(A, &aa));
1168: for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]);
1169: PetscCall(MatSeqAIJRestoreArray(A, &aa));
1170: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1171: PetscFunctionReturn(PETSC_SUCCESS);
1172: }
1174: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1175: {
1176: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1177: PetscInt i, nz = a->nz;
1178: MatScalar *aa;
1180: PetscFunctionBegin;
1181: PetscCall(MatSeqAIJGetArray(A, &aa));
1182: for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1183: PetscCall(MatSeqAIJRestoreArray(A, &aa));
1184: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1185: PetscFunctionReturn(PETSC_SUCCESS);
1186: }
1188: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1189: {
1190: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1191: MatScalar *aa;
1193: PetscFunctionBegin;
1194: PetscCall(MatSeqAIJGetArrayWrite(A, &aa));
1195: PetscCall(PetscArrayzero(aa, a->i[A->rmap->n]));
1196: PetscCall(MatSeqAIJRestoreArrayWrite(A, &aa));
1197: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1198: PetscFunctionReturn(PETSC_SUCCESS);
1199: }
1201: PETSC_INTERN PetscErrorCode MatResetPreallocationCOO_SeqAIJ(Mat A)
1202: {
1203: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1205: PetscFunctionBegin;
1206: PetscCall(PetscFree(a->perm));
1207: PetscCall(PetscFree(a->jmap));
1208: PetscFunctionReturn(PETSC_SUCCESS);
1209: }
1211: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1212: {
1213: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1215: PetscFunctionBegin;
1216: #if defined(PETSC_USE_LOG)
1217: PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->n, A->cmap->n, a->nz));
1218: #endif
1219: PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i));
1220: PetscCall(MatResetPreallocationCOO_SeqAIJ(A));
1221: PetscCall(ISDestroy(&a->row));
1222: PetscCall(ISDestroy(&a->col));
1223: PetscCall(PetscFree(a->diag));
1224: PetscCall(PetscFree(a->ibdiag));
1225: PetscCall(PetscFree(a->imax));
1226: PetscCall(PetscFree(a->ilen));
1227: PetscCall(PetscFree(a->ipre));
1228: PetscCall(PetscFree3(a->idiag, a->mdiag, a->ssor_work));
1229: PetscCall(PetscFree(a->solve_work));
1230: PetscCall(ISDestroy(&a->icol));
1231: PetscCall(PetscFree(a->saved_values));
1232: PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex));
1233: PetscCall(MatDestroy_SeqAIJ_Inode(A));
1234: PetscCall(PetscFree(A->data));
1236: /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
1237: That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
1238: that is hard to properly add this data to the MatProduct data. We free it here to avoid
1239: users reusing the matrix object with different data to incur in obscure segmentation faults
1240: due to different matrix sizes */
1241: PetscCall(PetscObjectCompose((PetscObject)A, "__PETSc__ab_dense", NULL));
1243: PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL));
1244: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEnginePut_C", NULL));
1245: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEngineGet_C", NULL));
1246: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetColumnIndices_C", NULL));
1247: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL));
1248: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL));
1249: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsbaij_C", NULL));
1250: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqbaij_C", NULL));
1251: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijperm_C", NULL));
1252: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijsell_C", NULL));
1253: #if defined(PETSC_HAVE_MKL_SPARSE)
1254: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijmkl_C", NULL));
1255: #endif
1256: #if defined(PETSC_HAVE_CUDA)
1257: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcusparse_C", NULL));
1258: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", NULL));
1259: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", NULL));
1260: #endif
1261: #if defined(PETSC_HAVE_HIP)
1262: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijhipsparse_C", NULL));
1263: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", NULL));
1264: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", NULL));
1265: #endif
1266: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
1267: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijkokkos_C", NULL));
1268: #endif
1269: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcrl_C", NULL));
1270: #if defined(PETSC_HAVE_ELEMENTAL)
1271: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_elemental_C", NULL));
1272: #endif
1273: #if defined(PETSC_HAVE_SCALAPACK)
1274: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_scalapack_C", NULL));
1275: #endif
1276: #if defined(PETSC_HAVE_HYPRE)
1277: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_hypre_C", NULL));
1278: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", NULL));
1279: #endif
1280: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqdense_C", NULL));
1281: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsell_C", NULL));
1282: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_is_C", NULL));
1283: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL));
1284: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsHermitianTranspose_C", NULL));
1285: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocation_C", NULL));
1286: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetPreallocation_C", NULL));
1287: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocationCSR_C", NULL));
1288: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatReorderForNonzeroDiagonal_C", NULL));
1289: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_is_seqaij_C", NULL));
1290: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqdense_seqaij_C", NULL));
1291: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaij_C", NULL));
1292: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJKron_C", NULL));
1293: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
1294: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
1295: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1296: /* these calls do not belong here: the subclasses Duplicate/Destroy are wrong */
1297: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijsell_seqaij_C", NULL));
1298: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijperm_seqaij_C", NULL));
1299: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijviennacl_C", NULL));
1300: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqdense_C", NULL));
1301: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqaij_C", NULL));
1302: PetscFunctionReturn(PETSC_SUCCESS);
1303: }
1305: PetscErrorCode MatSetOption_SeqAIJ(Mat A, MatOption op, PetscBool flg)
1306: {
1307: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1309: PetscFunctionBegin;
1310: switch (op) {
1311: case MAT_ROW_ORIENTED:
1312: a->roworiented = flg;
1313: break;
1314: case MAT_KEEP_NONZERO_PATTERN:
1315: a->keepnonzeropattern = flg;
1316: break;
1317: case MAT_NEW_NONZERO_LOCATIONS:
1318: a->nonew = (flg ? 0 : 1);
1319: break;
1320: case MAT_NEW_NONZERO_LOCATION_ERR:
1321: a->nonew = (flg ? -1 : 0);
1322: break;
1323: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1324: a->nonew = (flg ? -2 : 0);
1325: break;
1326: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1327: a->nounused = (flg ? -1 : 0);
1328: break;
1329: case MAT_IGNORE_ZERO_ENTRIES:
1330: a->ignorezeroentries = flg;
1331: break;
1332: case MAT_SPD:
1333: case MAT_SYMMETRIC:
1334: case MAT_STRUCTURALLY_SYMMETRIC:
1335: case MAT_HERMITIAN:
1336: case MAT_SYMMETRY_ETERNAL:
1337: case MAT_STRUCTURE_ONLY:
1338: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1339: case MAT_SPD_ETERNAL:
1340: /* if the diagonal matrix is square it inherits some of the properties above */
1341: break;
1342: case MAT_FORCE_DIAGONAL_ENTRIES:
1343: case MAT_IGNORE_OFF_PROC_ENTRIES:
1344: case MAT_USE_HASH_TABLE:
1345: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1346: break;
1347: case MAT_USE_INODES:
1348: PetscCall(MatSetOption_SeqAIJ_Inode(A, MAT_USE_INODES, flg));
1349: break;
1350: case MAT_SUBMAT_SINGLEIS:
1351: A->submat_singleis = flg;
1352: break;
1353: case MAT_SORTED_FULL:
1354: if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
1355: else A->ops->setvalues = MatSetValues_SeqAIJ;
1356: break;
1357: case MAT_FORM_EXPLICIT_TRANSPOSE:
1358: A->form_explicit_transpose = flg;
1359: break;
1360: default:
1361: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1362: }
1363: PetscFunctionReturn(PETSC_SUCCESS);
1364: }
1366: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A, Vec v)
1367: {
1368: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1369: PetscInt i, j, n, *ai = a->i, *aj = a->j;
1370: PetscScalar *x;
1371: const PetscScalar *aa;
1373: PetscFunctionBegin;
1374: PetscCall(VecGetLocalSize(v, &n));
1375: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
1376: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1377: if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1378: PetscInt *diag = a->diag;
1379: PetscCall(VecGetArrayWrite(v, &x));
1380: for (i = 0; i < n; i++) x[i] = 1.0 / aa[diag[i]];
1381: PetscCall(VecRestoreArrayWrite(v, &x));
1382: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1383: PetscFunctionReturn(PETSC_SUCCESS);
1384: }
1386: PetscCall(VecGetArrayWrite(v, &x));
1387: for (i = 0; i < n; i++) {
1388: x[i] = 0.0;
1389: for (j = ai[i]; j < ai[i + 1]; j++) {
1390: if (aj[j] == i) {
1391: x[i] = aa[j];
1392: break;
1393: }
1394: }
1395: }
1396: PetscCall(VecRestoreArrayWrite(v, &x));
1397: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1398: PetscFunctionReturn(PETSC_SUCCESS);
1399: }
1401: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1402: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A, Vec xx, Vec zz, Vec yy)
1403: {
1404: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1405: const MatScalar *aa;
1406: PetscScalar *y;
1407: const PetscScalar *x;
1408: PetscInt m = A->rmap->n;
1409: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1410: const MatScalar *v;
1411: PetscScalar alpha;
1412: PetscInt n, i, j;
1413: const PetscInt *idx, *ii, *ridx = NULL;
1414: Mat_CompressedRow cprow = a->compressedrow;
1415: PetscBool usecprow = cprow.use;
1416: #endif
1418: PetscFunctionBegin;
1419: if (zz != yy) PetscCall(VecCopy(zz, yy));
1420: PetscCall(VecGetArrayRead(xx, &x));
1421: PetscCall(VecGetArray(yy, &y));
1422: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1424: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1425: fortranmulttransposeaddaij_(&m, x, a->i, a->j, aa, y);
1426: #else
1427: if (usecprow) {
1428: m = cprow.nrows;
1429: ii = cprow.i;
1430: ridx = cprow.rindex;
1431: } else {
1432: ii = a->i;
1433: }
1434: for (i = 0; i < m; i++) {
1435: idx = a->j + ii[i];
1436: v = aa + ii[i];
1437: n = ii[i + 1] - ii[i];
1438: if (usecprow) {
1439: alpha = x[ridx[i]];
1440: } else {
1441: alpha = x[i];
1442: }
1443: for (j = 0; j < n; j++) y[idx[j]] += alpha * v[j];
1444: }
1445: #endif
1446: PetscCall(PetscLogFlops(2.0 * a->nz));
1447: PetscCall(VecRestoreArrayRead(xx, &x));
1448: PetscCall(VecRestoreArray(yy, &y));
1449: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1450: PetscFunctionReturn(PETSC_SUCCESS);
1451: }
1453: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A, Vec xx, Vec yy)
1454: {
1455: PetscFunctionBegin;
1456: PetscCall(VecSet(yy, 0.0));
1457: PetscCall(MatMultTransposeAdd_SeqAIJ(A, xx, yy, yy));
1458: PetscFunctionReturn(PETSC_SUCCESS);
1459: }
1461: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1463: PetscErrorCode MatMult_SeqAIJ(Mat A, Vec xx, Vec yy)
1464: {
1465: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1466: PetscScalar *y;
1467: const PetscScalar *x;
1468: const MatScalar *aa, *a_a;
1469: PetscInt m = A->rmap->n;
1470: const PetscInt *aj, *ii, *ridx = NULL;
1471: PetscInt n, i;
1472: PetscScalar sum;
1473: PetscBool usecprow = a->compressedrow.use;
1475: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1476: #pragma disjoint(*x, *y, *aa)
1477: #endif
1479: PetscFunctionBegin;
1480: if (a->inode.use && a->inode.checked) {
1481: PetscCall(MatMult_SeqAIJ_Inode(A, xx, yy));
1482: PetscFunctionReturn(PETSC_SUCCESS);
1483: }
1484: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1485: PetscCall(VecGetArrayRead(xx, &x));
1486: PetscCall(VecGetArray(yy, &y));
1487: ii = a->i;
1488: if (usecprow) { /* use compressed row format */
1489: PetscCall(PetscArrayzero(y, m));
1490: m = a->compressedrow.nrows;
1491: ii = a->compressedrow.i;
1492: ridx = a->compressedrow.rindex;
1493: for (i = 0; i < m; i++) {
1494: n = ii[i + 1] - ii[i];
1495: aj = a->j + ii[i];
1496: aa = a_a + ii[i];
1497: sum = 0.0;
1498: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1499: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1500: y[*ridx++] = sum;
1501: }
1502: } else { /* do not use compressed row format */
1503: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1504: aj = a->j;
1505: aa = a_a;
1506: fortranmultaij_(&m, x, ii, aj, aa, y);
1507: #else
1508: for (i = 0; i < m; i++) {
1509: n = ii[i + 1] - ii[i];
1510: aj = a->j + ii[i];
1511: aa = a_a + ii[i];
1512: sum = 0.0;
1513: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1514: y[i] = sum;
1515: }
1516: #endif
1517: }
1518: PetscCall(PetscLogFlops(2.0 * a->nz - a->nonzerorowcnt));
1519: PetscCall(VecRestoreArrayRead(xx, &x));
1520: PetscCall(VecRestoreArray(yy, &y));
1521: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1522: PetscFunctionReturn(PETSC_SUCCESS);
1523: }
1525: PetscErrorCode MatMultMax_SeqAIJ(Mat A, Vec xx, Vec yy)
1526: {
1527: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1528: PetscScalar *y;
1529: const PetscScalar *x;
1530: const MatScalar *aa, *a_a;
1531: PetscInt m = A->rmap->n;
1532: const PetscInt *aj, *ii, *ridx = NULL;
1533: PetscInt n, i, nonzerorow = 0;
1534: PetscScalar sum;
1535: PetscBool usecprow = a->compressedrow.use;
1537: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1538: #pragma disjoint(*x, *y, *aa)
1539: #endif
1541: PetscFunctionBegin;
1542: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1543: PetscCall(VecGetArrayRead(xx, &x));
1544: PetscCall(VecGetArray(yy, &y));
1545: if (usecprow) { /* use compressed row format */
1546: m = a->compressedrow.nrows;
1547: ii = a->compressedrow.i;
1548: ridx = a->compressedrow.rindex;
1549: for (i = 0; i < m; i++) {
1550: n = ii[i + 1] - ii[i];
1551: aj = a->j + ii[i];
1552: aa = a_a + ii[i];
1553: sum = 0.0;
1554: nonzerorow += (n > 0);
1555: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1556: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1557: y[*ridx++] = sum;
1558: }
1559: } else { /* do not use compressed row format */
1560: ii = a->i;
1561: for (i = 0; i < m; i++) {
1562: n = ii[i + 1] - ii[i];
1563: aj = a->j + ii[i];
1564: aa = a_a + ii[i];
1565: sum = 0.0;
1566: nonzerorow += (n > 0);
1567: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1568: y[i] = sum;
1569: }
1570: }
1571: PetscCall(PetscLogFlops(2.0 * a->nz - nonzerorow));
1572: PetscCall(VecRestoreArrayRead(xx, &x));
1573: PetscCall(VecRestoreArray(yy, &y));
1574: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1575: PetscFunctionReturn(PETSC_SUCCESS);
1576: }
1578: PetscErrorCode MatMultAddMax_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1579: {
1580: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1581: PetscScalar *y, *z;
1582: const PetscScalar *x;
1583: const MatScalar *aa, *a_a;
1584: PetscInt m = A->rmap->n, *aj, *ii;
1585: PetscInt n, i, *ridx = NULL;
1586: PetscScalar sum;
1587: PetscBool usecprow = a->compressedrow.use;
1589: PetscFunctionBegin;
1590: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1591: PetscCall(VecGetArrayRead(xx, &x));
1592: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1593: if (usecprow) { /* use compressed row format */
1594: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1595: m = a->compressedrow.nrows;
1596: ii = a->compressedrow.i;
1597: ridx = a->compressedrow.rindex;
1598: for (i = 0; i < m; i++) {
1599: n = ii[i + 1] - ii[i];
1600: aj = a->j + ii[i];
1601: aa = a_a + ii[i];
1602: sum = y[*ridx];
1603: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1604: z[*ridx++] = sum;
1605: }
1606: } else { /* do not use compressed row format */
1607: ii = a->i;
1608: for (i = 0; i < m; i++) {
1609: n = ii[i + 1] - ii[i];
1610: aj = a->j + ii[i];
1611: aa = a_a + ii[i];
1612: sum = y[i];
1613: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1614: z[i] = sum;
1615: }
1616: }
1617: PetscCall(PetscLogFlops(2.0 * a->nz));
1618: PetscCall(VecRestoreArrayRead(xx, &x));
1619: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1620: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1621: PetscFunctionReturn(PETSC_SUCCESS);
1622: }
1624: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1625: PetscErrorCode MatMultAdd_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1626: {
1627: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1628: PetscScalar *y, *z;
1629: const PetscScalar *x;
1630: const MatScalar *aa, *a_a;
1631: const PetscInt *aj, *ii, *ridx = NULL;
1632: PetscInt m = A->rmap->n, n, i;
1633: PetscScalar sum;
1634: PetscBool usecprow = a->compressedrow.use;
1636: PetscFunctionBegin;
1637: if (a->inode.use && a->inode.checked) {
1638: PetscCall(MatMultAdd_SeqAIJ_Inode(A, xx, yy, zz));
1639: PetscFunctionReturn(PETSC_SUCCESS);
1640: }
1641: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1642: PetscCall(VecGetArrayRead(xx, &x));
1643: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1644: if (usecprow) { /* use compressed row format */
1645: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1646: m = a->compressedrow.nrows;
1647: ii = a->compressedrow.i;
1648: ridx = a->compressedrow.rindex;
1649: for (i = 0; i < m; i++) {
1650: n = ii[i + 1] - ii[i];
1651: aj = a->j + ii[i];
1652: aa = a_a + ii[i];
1653: sum = y[*ridx];
1654: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1655: z[*ridx++] = sum;
1656: }
1657: } else { /* do not use compressed row format */
1658: ii = a->i;
1659: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1660: aj = a->j;
1661: aa = a_a;
1662: fortranmultaddaij_(&m, x, ii, aj, aa, y, z);
1663: #else
1664: for (i = 0; i < m; i++) {
1665: n = ii[i + 1] - ii[i];
1666: aj = a->j + ii[i];
1667: aa = a_a + ii[i];
1668: sum = y[i];
1669: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1670: z[i] = sum;
1671: }
1672: #endif
1673: }
1674: PetscCall(PetscLogFlops(2.0 * a->nz));
1675: PetscCall(VecRestoreArrayRead(xx, &x));
1676: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1677: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1678: PetscFunctionReturn(PETSC_SUCCESS);
1679: }
1681: /*
1682: Adds diagonal pointers to sparse matrix structure.
1683: */
1684: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1685: {
1686: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1687: PetscInt i, j, m = A->rmap->n;
1688: PetscBool alreadySet = PETSC_TRUE;
1690: PetscFunctionBegin;
1691: if (!a->diag) {
1692: PetscCall(PetscMalloc1(m, &a->diag));
1693: alreadySet = PETSC_FALSE;
1694: }
1695: for (i = 0; i < A->rmap->n; i++) {
1696: /* If A's diagonal is already correctly set, this fast track enables cheap and repeated MatMarkDiagonal_SeqAIJ() calls */
1697: if (alreadySet) {
1698: PetscInt pos = a->diag[i];
1699: if (pos >= a->i[i] && pos < a->i[i + 1] && a->j[pos] == i) continue;
1700: }
1702: a->diag[i] = a->i[i + 1];
1703: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1704: if (a->j[j] == i) {
1705: a->diag[i] = j;
1706: break;
1707: }
1708: }
1709: }
1710: PetscFunctionReturn(PETSC_SUCCESS);
1711: }
1713: PetscErrorCode MatShift_SeqAIJ(Mat A, PetscScalar v)
1714: {
1715: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1716: const PetscInt *diag = (const PetscInt *)a->diag;
1717: const PetscInt *ii = (const PetscInt *)a->i;
1718: PetscInt i, *mdiag = NULL;
1719: PetscInt cnt = 0; /* how many diagonals are missing */
1721: PetscFunctionBegin;
1722: if (!A->preallocated || !a->nz) {
1723: PetscCall(MatSeqAIJSetPreallocation(A, 1, NULL));
1724: PetscCall(MatShift_Basic(A, v));
1725: PetscFunctionReturn(PETSC_SUCCESS);
1726: }
1728: if (a->diagonaldense) {
1729: cnt = 0;
1730: } else {
1731: PetscCall(PetscCalloc1(A->rmap->n, &mdiag));
1732: for (i = 0; i < A->rmap->n; i++) {
1733: if (i < A->cmap->n && diag[i] >= ii[i + 1]) { /* 'out of range' rows never have diagonals */
1734: cnt++;
1735: mdiag[i] = 1;
1736: }
1737: }
1738: }
1739: if (!cnt) {
1740: PetscCall(MatShift_Basic(A, v));
1741: } else {
1742: PetscScalar *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */
1743: PetscInt *oldj = a->j, *oldi = a->i;
1744: PetscBool singlemalloc = a->singlemalloc, free_a = a->free_a, free_ij = a->free_ij;
1746: a->a = NULL;
1747: a->j = NULL;
1748: a->i = NULL;
1749: /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1750: for (i = 0; i < PetscMin(A->rmap->n, A->cmap->n); i++) a->imax[i] += mdiag[i];
1751: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, 0, a->imax));
1753: /* copy old values into new matrix data structure */
1754: for (i = 0; i < A->rmap->n; i++) {
1755: PetscCall(MatSetValues(A, 1, &i, a->imax[i] - mdiag[i], &oldj[oldi[i]], &olda[oldi[i]], ADD_VALUES));
1756: if (i < A->cmap->n) PetscCall(MatSetValue(A, i, i, v, ADD_VALUES));
1757: }
1758: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1759: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1760: if (singlemalloc) {
1761: PetscCall(PetscFree3(olda, oldj, oldi));
1762: } else {
1763: if (free_a) PetscCall(PetscFree(olda));
1764: if (free_ij) PetscCall(PetscFree(oldj));
1765: if (free_ij) PetscCall(PetscFree(oldi));
1766: }
1767: }
1768: PetscCall(PetscFree(mdiag));
1769: a->diagonaldense = PETSC_TRUE;
1770: PetscFunctionReturn(PETSC_SUCCESS);
1771: }
1773: /*
1774: Checks for missing diagonals
1775: */
1776: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A, PetscBool *missing, PetscInt *d)
1777: {
1778: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1779: PetscInt *diag, *ii = a->i, i;
1781: PetscFunctionBegin;
1782: *missing = PETSC_FALSE;
1783: if (A->rmap->n > 0 && !ii) {
1784: *missing = PETSC_TRUE;
1785: if (d) *d = 0;
1786: PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n"));
1787: } else {
1788: PetscInt n;
1789: n = PetscMin(A->rmap->n, A->cmap->n);
1790: diag = a->diag;
1791: for (i = 0; i < n; i++) {
1792: if (diag[i] >= ii[i + 1]) {
1793: *missing = PETSC_TRUE;
1794: if (d) *d = i;
1795: PetscCall(PetscInfo(A, "Matrix is missing diagonal number %" PetscInt_FMT "\n", i));
1796: break;
1797: }
1798: }
1799: }
1800: PetscFunctionReturn(PETSC_SUCCESS);
1801: }
1803: #include <petscblaslapack.h>
1804: #include <petsc/private/kernels/blockinvert.h>
1806: /*
1807: Note that values is allocated externally by the PC and then passed into this routine
1808: */
1809: PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
1810: {
1811: PetscInt n = A->rmap->n, i, ncnt = 0, *indx, j, bsizemax = 0, *v_pivots;
1812: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
1813: const PetscReal shift = 0.0;
1814: PetscInt ipvt[5];
1815: PetscCount flops = 0;
1816: PetscScalar work[25], *v_work;
1818: PetscFunctionBegin;
1819: allowzeropivot = PetscNot(A->erroriffailure);
1820: for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
1821: PetscCheck(ncnt == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Total blocksizes %" PetscInt_FMT " doesn't match number matrix rows %" PetscInt_FMT, ncnt, n);
1822: for (i = 0; i < nblocks; i++) bsizemax = PetscMax(bsizemax, bsizes[i]);
1823: PetscCall(PetscMalloc1(bsizemax, &indx));
1824: if (bsizemax > 7) PetscCall(PetscMalloc2(bsizemax, &v_work, bsizemax, &v_pivots));
1825: ncnt = 0;
1826: for (i = 0; i < nblocks; i++) {
1827: for (j = 0; j < bsizes[i]; j++) indx[j] = ncnt + j;
1828: PetscCall(MatGetValues(A, bsizes[i], indx, bsizes[i], indx, diag));
1829: switch (bsizes[i]) {
1830: case 1:
1831: *diag = 1.0 / (*diag);
1832: break;
1833: case 2:
1834: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
1835: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1836: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
1837: break;
1838: case 3:
1839: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
1840: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1841: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
1842: break;
1843: case 4:
1844: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
1845: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1846: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
1847: break;
1848: case 5:
1849: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
1850: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1851: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
1852: break;
1853: case 6:
1854: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
1855: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1856: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
1857: break;
1858: case 7:
1859: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
1860: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1861: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
1862: break;
1863: default:
1864: PetscCall(PetscKernel_A_gets_inverse_A(bsizes[i], diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
1865: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1866: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bsizes[i]));
1867: }
1868: ncnt += bsizes[i];
1869: diag += bsizes[i] * bsizes[i];
1870: flops += 2 * PetscPowInt(bsizes[i], 3) / 3;
1871: }
1872: PetscCall(PetscLogFlops(flops));
1873: if (bsizemax > 7) PetscCall(PetscFree2(v_work, v_pivots));
1874: PetscCall(PetscFree(indx));
1875: PetscFunctionReturn(PETSC_SUCCESS);
1876: }
1878: /*
1879: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1880: */
1881: PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A, PetscScalar omega, PetscScalar fshift)
1882: {
1883: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1884: PetscInt i, *diag, m = A->rmap->n;
1885: const MatScalar *v;
1886: PetscScalar *idiag, *mdiag;
1888: PetscFunctionBegin;
1889: if (a->idiagvalid) PetscFunctionReturn(PETSC_SUCCESS);
1890: PetscCall(MatMarkDiagonal_SeqAIJ(A));
1891: diag = a->diag;
1892: if (!a->idiag) { PetscCall(PetscMalloc3(m, &a->idiag, m, &a->mdiag, m, &a->ssor_work)); }
1894: mdiag = a->mdiag;
1895: idiag = a->idiag;
1896: PetscCall(MatSeqAIJGetArrayRead(A, &v));
1897: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1898: for (i = 0; i < m; i++) {
1899: mdiag[i] = v[diag[i]];
1900: if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1901: if (PetscRealPart(fshift)) {
1902: PetscCall(PetscInfo(A, "Zero diagonal on row %" PetscInt_FMT "\n", i));
1903: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1904: A->factorerror_zeropivot_value = 0.0;
1905: A->factorerror_zeropivot_row = i;
1906: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Zero diagonal on row %" PetscInt_FMT, i);
1907: }
1908: idiag[i] = 1.0 / v[diag[i]];
1909: }
1910: PetscCall(PetscLogFlops(m));
1911: } else {
1912: for (i = 0; i < m; i++) {
1913: mdiag[i] = v[diag[i]];
1914: idiag[i] = omega / (fshift + v[diag[i]]);
1915: }
1916: PetscCall(PetscLogFlops(2.0 * m));
1917: }
1918: a->idiagvalid = PETSC_TRUE;
1919: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
1920: PetscFunctionReturn(PETSC_SUCCESS);
1921: }
1923: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1924: PetscErrorCode MatSOR_SeqAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1925: {
1926: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1927: PetscScalar *x, d, sum, *t, scale;
1928: const MatScalar *v, *idiag = NULL, *mdiag, *aa;
1929: const PetscScalar *b, *bs, *xb, *ts;
1930: PetscInt n, m = A->rmap->n, i;
1931: const PetscInt *idx, *diag;
1933: PetscFunctionBegin;
1934: if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
1935: PetscCall(MatSOR_SeqAIJ_Inode(A, bb, omega, flag, fshift, its, lits, xx));
1936: PetscFunctionReturn(PETSC_SUCCESS);
1937: }
1938: its = its * lits;
1940: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1941: if (!a->idiagvalid) PetscCall(MatInvertDiagonal_SeqAIJ(A, omega, fshift));
1942: a->fshift = fshift;
1943: a->omega = omega;
1945: diag = a->diag;
1946: t = a->ssor_work;
1947: idiag = a->idiag;
1948: mdiag = a->mdiag;
1950: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1951: PetscCall(VecGetArray(xx, &x));
1952: PetscCall(VecGetArrayRead(bb, &b));
1953: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1954: if (flag == SOR_APPLY_UPPER) {
1955: /* apply (U + D/omega) to the vector */
1956: bs = b;
1957: for (i = 0; i < m; i++) {
1958: d = fshift + mdiag[i];
1959: n = a->i[i + 1] - diag[i] - 1;
1960: idx = a->j + diag[i] + 1;
1961: v = aa + diag[i] + 1;
1962: sum = b[i] * d / omega;
1963: PetscSparseDensePlusDot(sum, bs, v, idx, n);
1964: x[i] = sum;
1965: }
1966: PetscCall(VecRestoreArray(xx, &x));
1967: PetscCall(VecRestoreArrayRead(bb, &b));
1968: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1969: PetscCall(PetscLogFlops(a->nz));
1970: PetscFunctionReturn(PETSC_SUCCESS);
1971: }
1973: PetscCheck(flag != SOR_APPLY_LOWER, PETSC_COMM_SELF, PETSC_ERR_SUP, "SOR_APPLY_LOWER is not implemented");
1974: if (flag & SOR_EISENSTAT) {
1975: /* Let A = L + U + D; where L is lower triangular,
1976: U is upper triangular, E = D/omega; This routine applies
1978: (L + E)^{-1} A (U + E)^{-1}
1980: to a vector efficiently using Eisenstat's trick.
1981: */
1982: scale = (2.0 / omega) - 1.0;
1984: /* x = (E + U)^{-1} b */
1985: for (i = m - 1; i >= 0; i--) {
1986: n = a->i[i + 1] - diag[i] - 1;
1987: idx = a->j + diag[i] + 1;
1988: v = aa + diag[i] + 1;
1989: sum = b[i];
1990: PetscSparseDenseMinusDot(sum, x, v, idx, n);
1991: x[i] = sum * idiag[i];
1992: }
1994: /* t = b - (2*E - D)x */
1995: v = aa;
1996: for (i = 0; i < m; i++) t[i] = b[i] - scale * (v[*diag++]) * x[i];
1998: /* t = (E + L)^{-1}t */
1999: ts = t;
2000: diag = a->diag;
2001: for (i = 0; i < m; i++) {
2002: n = diag[i] - a->i[i];
2003: idx = a->j + a->i[i];
2004: v = aa + a->i[i];
2005: sum = t[i];
2006: PetscSparseDenseMinusDot(sum, ts, v, idx, n);
2007: t[i] = sum * idiag[i];
2008: /* x = x + t */
2009: x[i] += t[i];
2010: }
2012: PetscCall(PetscLogFlops(6.0 * m - 1 + 2.0 * a->nz));
2013: PetscCall(VecRestoreArray(xx, &x));
2014: PetscCall(VecRestoreArrayRead(bb, &b));
2015: PetscFunctionReturn(PETSC_SUCCESS);
2016: }
2017: if (flag & SOR_ZERO_INITIAL_GUESS) {
2018: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2019: for (i = 0; i < m; i++) {
2020: n = diag[i] - a->i[i];
2021: idx = a->j + a->i[i];
2022: v = aa + a->i[i];
2023: sum = b[i];
2024: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2025: t[i] = sum;
2026: x[i] = sum * idiag[i];
2027: }
2028: xb = t;
2029: PetscCall(PetscLogFlops(a->nz));
2030: } else xb = b;
2031: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2032: for (i = m - 1; i >= 0; i--) {
2033: n = a->i[i + 1] - diag[i] - 1;
2034: idx = a->j + diag[i] + 1;
2035: v = aa + diag[i] + 1;
2036: sum = xb[i];
2037: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2038: if (xb == b) {
2039: x[i] = sum * idiag[i];
2040: } else {
2041: x[i] = (1 - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2042: }
2043: }
2044: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2045: }
2046: its--;
2047: }
2048: while (its--) {
2049: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2050: for (i = 0; i < m; i++) {
2051: /* lower */
2052: n = diag[i] - a->i[i];
2053: idx = a->j + a->i[i];
2054: v = aa + a->i[i];
2055: sum = b[i];
2056: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2057: t[i] = sum; /* save application of the lower-triangular part */
2058: /* upper */
2059: n = a->i[i + 1] - diag[i] - 1;
2060: idx = a->j + diag[i] + 1;
2061: v = aa + diag[i] + 1;
2062: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2063: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2064: }
2065: xb = t;
2066: PetscCall(PetscLogFlops(2.0 * a->nz));
2067: } else xb = b;
2068: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2069: for (i = m - 1; i >= 0; i--) {
2070: sum = xb[i];
2071: if (xb == b) {
2072: /* whole matrix (no checkpointing available) */
2073: n = a->i[i + 1] - a->i[i];
2074: idx = a->j + a->i[i];
2075: v = aa + a->i[i];
2076: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2077: x[i] = (1. - omega) * x[i] + (sum + mdiag[i] * x[i]) * idiag[i];
2078: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
2079: n = a->i[i + 1] - diag[i] - 1;
2080: idx = a->j + diag[i] + 1;
2081: v = aa + diag[i] + 1;
2082: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2083: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2084: }
2085: }
2086: if (xb == b) {
2087: PetscCall(PetscLogFlops(2.0 * a->nz));
2088: } else {
2089: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2090: }
2091: }
2092: }
2093: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2094: PetscCall(VecRestoreArray(xx, &x));
2095: PetscCall(VecRestoreArrayRead(bb, &b));
2096: PetscFunctionReturn(PETSC_SUCCESS);
2097: }
2099: PetscErrorCode MatGetInfo_SeqAIJ(Mat A, MatInfoType flag, MatInfo *info)
2100: {
2101: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2103: PetscFunctionBegin;
2104: info->block_size = 1.0;
2105: info->nz_allocated = a->maxnz;
2106: info->nz_used = a->nz;
2107: info->nz_unneeded = (a->maxnz - a->nz);
2108: info->assemblies = A->num_ass;
2109: info->mallocs = A->info.mallocs;
2110: info->memory = 0; /* REVIEW ME */
2111: if (A->factortype) {
2112: info->fill_ratio_given = A->info.fill_ratio_given;
2113: info->fill_ratio_needed = A->info.fill_ratio_needed;
2114: info->factor_mallocs = A->info.factor_mallocs;
2115: } else {
2116: info->fill_ratio_given = 0;
2117: info->fill_ratio_needed = 0;
2118: info->factor_mallocs = 0;
2119: }
2120: PetscFunctionReturn(PETSC_SUCCESS);
2121: }
2123: PetscErrorCode MatZeroRows_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2124: {
2125: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2126: PetscInt i, m = A->rmap->n - 1;
2127: const PetscScalar *xx;
2128: PetscScalar *bb, *aa;
2129: PetscInt d = 0;
2131: PetscFunctionBegin;
2132: if (x && b) {
2133: PetscCall(VecGetArrayRead(x, &xx));
2134: PetscCall(VecGetArray(b, &bb));
2135: for (i = 0; i < N; i++) {
2136: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2137: if (rows[i] >= A->cmap->n) continue;
2138: bb[rows[i]] = diag * xx[rows[i]];
2139: }
2140: PetscCall(VecRestoreArrayRead(x, &xx));
2141: PetscCall(VecRestoreArray(b, &bb));
2142: }
2144: PetscCall(MatSeqAIJGetArray(A, &aa));
2145: if (a->keepnonzeropattern) {
2146: for (i = 0; i < N; i++) {
2147: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2148: PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));
2149: }
2150: if (diag != 0.0) {
2151: for (i = 0; i < N; i++) {
2152: d = rows[i];
2153: if (rows[i] >= A->cmap->n) continue;
2154: PetscCheck(a->diag[d] < a->i[d + 1], PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry in the zeroed row %" PetscInt_FMT, d);
2155: }
2156: for (i = 0; i < N; i++) {
2157: if (rows[i] >= A->cmap->n) continue;
2158: aa[a->diag[rows[i]]] = diag;
2159: }
2160: }
2161: } else {
2162: if (diag != 0.0) {
2163: for (i = 0; i < N; i++) {
2164: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2165: if (a->ilen[rows[i]] > 0) {
2166: if (rows[i] >= A->cmap->n) {
2167: a->ilen[rows[i]] = 0;
2168: } else {
2169: a->ilen[rows[i]] = 1;
2170: aa[a->i[rows[i]]] = diag;
2171: a->j[a->i[rows[i]]] = rows[i];
2172: }
2173: } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2174: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2175: }
2176: }
2177: } else {
2178: for (i = 0; i < N; i++) {
2179: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2180: a->ilen[rows[i]] = 0;
2181: }
2182: }
2183: A->nonzerostate++;
2184: }
2185: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2186: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2187: PetscFunctionReturn(PETSC_SUCCESS);
2188: }
2190: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2191: {
2192: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2193: PetscInt i, j, m = A->rmap->n - 1, d = 0;
2194: PetscBool missing, *zeroed, vecs = PETSC_FALSE;
2195: const PetscScalar *xx;
2196: PetscScalar *bb, *aa;
2198: PetscFunctionBegin;
2199: if (!N) PetscFunctionReturn(PETSC_SUCCESS);
2200: PetscCall(MatSeqAIJGetArray(A, &aa));
2201: if (x && b) {
2202: PetscCall(VecGetArrayRead(x, &xx));
2203: PetscCall(VecGetArray(b, &bb));
2204: vecs = PETSC_TRUE;
2205: }
2206: PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
2207: for (i = 0; i < N; i++) {
2208: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2209: PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));
2211: zeroed[rows[i]] = PETSC_TRUE;
2212: }
2213: for (i = 0; i < A->rmap->n; i++) {
2214: if (!zeroed[i]) {
2215: for (j = a->i[i]; j < a->i[i + 1]; j++) {
2216: if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2217: if (vecs) bb[i] -= aa[j] * xx[a->j[j]];
2218: aa[j] = 0.0;
2219: }
2220: }
2221: } else if (vecs && i < A->cmap->N) bb[i] = diag * xx[i];
2222: }
2223: if (x && b) {
2224: PetscCall(VecRestoreArrayRead(x, &xx));
2225: PetscCall(VecRestoreArray(b, &bb));
2226: }
2227: PetscCall(PetscFree(zeroed));
2228: if (diag != 0.0) {
2229: PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, &d));
2230: if (missing) {
2231: for (i = 0; i < N; i++) {
2232: if (rows[i] >= A->cmap->N) continue;
2233: PetscCheck(!a->nonew || rows[i] < d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry in row %" PetscInt_FMT " (%" PetscInt_FMT ")", d, rows[i]);
2234: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2235: }
2236: } else {
2237: for (i = 0; i < N; i++) aa[a->diag[rows[i]]] = diag;
2238: }
2239: }
2240: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2241: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2242: PetscFunctionReturn(PETSC_SUCCESS);
2243: }
2245: PetscErrorCode MatGetRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2246: {
2247: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2248: const PetscScalar *aa;
2249: PetscInt *itmp;
2251: PetscFunctionBegin;
2252: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2253: *nz = a->i[row + 1] - a->i[row];
2254: if (v) *v = (PetscScalar *)(aa + a->i[row]);
2255: if (idx) {
2256: itmp = a->j + a->i[row];
2257: if (*nz) *idx = itmp;
2258: else *idx = NULL;
2259: }
2260: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2261: PetscFunctionReturn(PETSC_SUCCESS);
2262: }
2264: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2265: {
2266: PetscFunctionBegin;
2267: if (nz) *nz = 0;
2268: if (idx) *idx = NULL;
2269: if (v) *v = NULL;
2270: PetscFunctionReturn(PETSC_SUCCESS);
2271: }
2273: PetscErrorCode MatNorm_SeqAIJ(Mat A, NormType type, PetscReal *nrm)
2274: {
2275: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2276: const MatScalar *v;
2277: PetscReal sum = 0.0;
2278: PetscInt i, j;
2280: PetscFunctionBegin;
2281: PetscCall(MatSeqAIJGetArrayRead(A, &v));
2282: if (type == NORM_FROBENIUS) {
2283: #if defined(PETSC_USE_REAL___FP16)
2284: PetscBLASInt one = 1, nz = a->nz;
2285: PetscCallBLAS("BLASnrm2", *nrm = BLASnrm2_(&nz, v, &one));
2286: #else
2287: for (i = 0; i < a->nz; i++) {
2288: sum += PetscRealPart(PetscConj(*v) * (*v));
2289: v++;
2290: }
2291: *nrm = PetscSqrtReal(sum);
2292: #endif
2293: PetscCall(PetscLogFlops(2.0 * a->nz));
2294: } else if (type == NORM_1) {
2295: PetscReal *tmp;
2296: PetscInt *jj = a->j;
2297: PetscCall(PetscCalloc1(A->cmap->n + 1, &tmp));
2298: *nrm = 0.0;
2299: for (j = 0; j < a->nz; j++) {
2300: tmp[*jj++] += PetscAbsScalar(*v);
2301: v++;
2302: }
2303: for (j = 0; j < A->cmap->n; j++) {
2304: if (tmp[j] > *nrm) *nrm = tmp[j];
2305: }
2306: PetscCall(PetscFree(tmp));
2307: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2308: } else if (type == NORM_INFINITY) {
2309: *nrm = 0.0;
2310: for (j = 0; j < A->rmap->n; j++) {
2311: const PetscScalar *v2 = v + a->i[j];
2312: sum = 0.0;
2313: for (i = 0; i < a->i[j + 1] - a->i[j]; i++) {
2314: sum += PetscAbsScalar(*v2);
2315: v2++;
2316: }
2317: if (sum > *nrm) *nrm = sum;
2318: }
2319: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2320: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for two norm");
2321: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
2322: PetscFunctionReturn(PETSC_SUCCESS);
2323: }
2325: PetscErrorCode MatIsTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2326: {
2327: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2328: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2329: const MatScalar *va, *vb;
2330: PetscInt ma, na, mb, nb, i;
2332: PetscFunctionBegin;
2333: PetscCall(MatGetSize(A, &ma, &na));
2334: PetscCall(MatGetSize(B, &mb, &nb));
2335: if (ma != nb || na != mb) {
2336: *f = PETSC_FALSE;
2337: PetscFunctionReturn(PETSC_SUCCESS);
2338: }
2339: PetscCall(MatSeqAIJGetArrayRead(A, &va));
2340: PetscCall(MatSeqAIJGetArrayRead(B, &vb));
2341: aii = aij->i;
2342: bii = bij->i;
2343: adx = aij->j;
2344: bdx = bij->j;
2345: PetscCall(PetscMalloc1(ma, &aptr));
2346: PetscCall(PetscMalloc1(mb, &bptr));
2347: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2348: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2350: *f = PETSC_TRUE;
2351: for (i = 0; i < ma; i++) {
2352: while (aptr[i] < aii[i + 1]) {
2353: PetscInt idc, idr;
2354: PetscScalar vc, vr;
2355: /* column/row index/value */
2356: idc = adx[aptr[i]];
2357: idr = bdx[bptr[idc]];
2358: vc = va[aptr[i]];
2359: vr = vb[bptr[idc]];
2360: if (i != idr || PetscAbsScalar(vc - vr) > tol) {
2361: *f = PETSC_FALSE;
2362: goto done;
2363: } else {
2364: aptr[i]++;
2365: if (B || i != idc) bptr[idc]++;
2366: }
2367: }
2368: }
2369: done:
2370: PetscCall(PetscFree(aptr));
2371: PetscCall(PetscFree(bptr));
2372: PetscCall(MatSeqAIJRestoreArrayRead(A, &va));
2373: PetscCall(MatSeqAIJRestoreArrayRead(B, &vb));
2374: PetscFunctionReturn(PETSC_SUCCESS);
2375: }
2377: PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2378: {
2379: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2380: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2381: MatScalar *va, *vb;
2382: PetscInt ma, na, mb, nb, i;
2384: PetscFunctionBegin;
2385: PetscCall(MatGetSize(A, &ma, &na));
2386: PetscCall(MatGetSize(B, &mb, &nb));
2387: if (ma != nb || na != mb) {
2388: *f = PETSC_FALSE;
2389: PetscFunctionReturn(PETSC_SUCCESS);
2390: }
2391: aii = aij->i;
2392: bii = bij->i;
2393: adx = aij->j;
2394: bdx = bij->j;
2395: va = aij->a;
2396: vb = bij->a;
2397: PetscCall(PetscMalloc1(ma, &aptr));
2398: PetscCall(PetscMalloc1(mb, &bptr));
2399: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2400: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2402: *f = PETSC_TRUE;
2403: for (i = 0; i < ma; i++) {
2404: while (aptr[i] < aii[i + 1]) {
2405: PetscInt idc, idr;
2406: PetscScalar vc, vr;
2407: /* column/row index/value */
2408: idc = adx[aptr[i]];
2409: idr = bdx[bptr[idc]];
2410: vc = va[aptr[i]];
2411: vr = vb[bptr[idc]];
2412: if (i != idr || PetscAbsScalar(vc - PetscConj(vr)) > tol) {
2413: *f = PETSC_FALSE;
2414: goto done;
2415: } else {
2416: aptr[i]++;
2417: if (B || i != idc) bptr[idc]++;
2418: }
2419: }
2420: }
2421: done:
2422: PetscCall(PetscFree(aptr));
2423: PetscCall(PetscFree(bptr));
2424: PetscFunctionReturn(PETSC_SUCCESS);
2425: }
2427: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A, PetscReal tol, PetscBool *f)
2428: {
2429: PetscFunctionBegin;
2430: PetscCall(MatIsTranspose_SeqAIJ(A, A, tol, f));
2431: PetscFunctionReturn(PETSC_SUCCESS);
2432: }
2434: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A, PetscReal tol, PetscBool *f)
2435: {
2436: PetscFunctionBegin;
2437: PetscCall(MatIsHermitianTranspose_SeqAIJ(A, A, tol, f));
2438: PetscFunctionReturn(PETSC_SUCCESS);
2439: }
2441: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A, Vec ll, Vec rr)
2442: {
2443: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2444: const PetscScalar *l, *r;
2445: PetscScalar x;
2446: MatScalar *v;
2447: PetscInt i, j, m = A->rmap->n, n = A->cmap->n, M, nz = a->nz;
2448: const PetscInt *jj;
2450: PetscFunctionBegin;
2451: if (ll) {
2452: /* The local size is used so that VecMPI can be passed to this routine
2453: by MatDiagonalScale_MPIAIJ */
2454: PetscCall(VecGetLocalSize(ll, &m));
2455: PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length");
2456: PetscCall(VecGetArrayRead(ll, &l));
2457: PetscCall(MatSeqAIJGetArray(A, &v));
2458: for (i = 0; i < m; i++) {
2459: x = l[i];
2460: M = a->i[i + 1] - a->i[i];
2461: for (j = 0; j < M; j++) (*v++) *= x;
2462: }
2463: PetscCall(VecRestoreArrayRead(ll, &l));
2464: PetscCall(PetscLogFlops(nz));
2465: PetscCall(MatSeqAIJRestoreArray(A, &v));
2466: }
2467: if (rr) {
2468: PetscCall(VecGetLocalSize(rr, &n));
2469: PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length");
2470: PetscCall(VecGetArrayRead(rr, &r));
2471: PetscCall(MatSeqAIJGetArray(A, &v));
2472: jj = a->j;
2473: for (i = 0; i < nz; i++) (*v++) *= r[*jj++];
2474: PetscCall(MatSeqAIJRestoreArray(A, &v));
2475: PetscCall(VecRestoreArrayRead(rr, &r));
2476: PetscCall(PetscLogFlops(nz));
2477: }
2478: PetscCall(MatSeqAIJInvalidateDiagonal(A));
2479: PetscFunctionReturn(PETSC_SUCCESS);
2480: }
2482: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A, IS isrow, IS iscol, PetscInt csize, MatReuse scall, Mat *B)
2483: {
2484: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *c;
2485: PetscInt *smap, i, k, kstart, kend, oldcols = A->cmap->n, *lens;
2486: PetscInt row, mat_i, *mat_j, tcol, first, step, *mat_ilen, sum, lensi;
2487: const PetscInt *irow, *icol;
2488: const PetscScalar *aa;
2489: PetscInt nrows, ncols;
2490: PetscInt *starts, *j_new, *i_new, *aj = a->j, *ai = a->i, ii, *ailen = a->ilen;
2491: MatScalar *a_new, *mat_a, *c_a;
2492: Mat C;
2493: PetscBool stride;
2495: PetscFunctionBegin;
2496: PetscCall(ISGetIndices(isrow, &irow));
2497: PetscCall(ISGetLocalSize(isrow, &nrows));
2498: PetscCall(ISGetLocalSize(iscol, &ncols));
2500: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &stride));
2501: if (stride) {
2502: PetscCall(ISStrideGetInfo(iscol, &first, &step));
2503: } else {
2504: first = 0;
2505: step = 0;
2506: }
2507: if (stride && step == 1) {
2508: /* special case of contiguous rows */
2509: PetscCall(PetscMalloc2(nrows, &lens, nrows, &starts));
2510: /* loop over new rows determining lens and starting points */
2511: for (i = 0; i < nrows; i++) {
2512: kstart = ai[irow[i]];
2513: kend = kstart + ailen[irow[i]];
2514: starts[i] = kstart;
2515: for (k = kstart; k < kend; k++) {
2516: if (aj[k] >= first) {
2517: starts[i] = k;
2518: break;
2519: }
2520: }
2521: sum = 0;
2522: while (k < kend) {
2523: if (aj[k++] >= first + ncols) break;
2524: sum++;
2525: }
2526: lens[i] = sum;
2527: }
2528: /* create submatrix */
2529: if (scall == MAT_REUSE_MATRIX) {
2530: PetscInt n_cols, n_rows;
2531: PetscCall(MatGetSize(*B, &n_rows, &n_cols));
2532: PetscCheck(n_rows == nrows && n_cols == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Reused submatrix wrong size");
2533: PetscCall(MatZeroEntries(*B));
2534: C = *B;
2535: } else {
2536: PetscInt rbs, cbs;
2537: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2538: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2539: PetscCall(ISGetBlockSize(isrow, &rbs));
2540: PetscCall(ISGetBlockSize(iscol, &cbs));
2541: PetscCall(MatSetBlockSizes(C, rbs, cbs));
2542: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2543: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2544: }
2545: c = (Mat_SeqAIJ *)C->data;
2547: /* loop over rows inserting into submatrix */
2548: PetscCall(MatSeqAIJGetArrayWrite(C, &a_new)); // Not 'a_new = c->a-new', since that raw usage ignores offload state of C
2549: j_new = c->j;
2550: i_new = c->i;
2551: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2552: for (i = 0; i < nrows; i++) {
2553: ii = starts[i];
2554: lensi = lens[i];
2555: for (k = 0; k < lensi; k++) *j_new++ = aj[ii + k] - first;
2556: PetscCall(PetscArraycpy(a_new, aa + starts[i], lensi));
2557: a_new += lensi;
2558: i_new[i + 1] = i_new[i] + lensi;
2559: c->ilen[i] = lensi;
2560: }
2561: PetscCall(MatSeqAIJRestoreArrayWrite(C, &a_new)); // Set C's offload state properly
2562: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2563: PetscCall(PetscFree2(lens, starts));
2564: } else {
2565: PetscCall(ISGetIndices(iscol, &icol));
2566: PetscCall(PetscCalloc1(oldcols, &smap));
2567: PetscCall(PetscMalloc1(1 + nrows, &lens));
2568: for (i = 0; i < ncols; i++) {
2569: PetscCheck(icol[i] < oldcols, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Requesting column beyond largest column icol[%" PetscInt_FMT "] %" PetscInt_FMT " >= A->cmap->n %" PetscInt_FMT, i, icol[i], oldcols);
2570: smap[icol[i]] = i + 1;
2571: }
2573: /* determine lens of each row */
2574: for (i = 0; i < nrows; i++) {
2575: kstart = ai[irow[i]];
2576: kend = kstart + a->ilen[irow[i]];
2577: lens[i] = 0;
2578: for (k = kstart; k < kend; k++) {
2579: if (smap[aj[k]]) lens[i]++;
2580: }
2581: }
2582: /* Create and fill new matrix */
2583: if (scall == MAT_REUSE_MATRIX) {
2584: PetscBool equal;
2586: c = (Mat_SeqAIJ *)((*B)->data);
2587: PetscCheck((*B)->rmap->n == nrows && (*B)->cmap->n == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong size");
2588: PetscCall(PetscArraycmp(c->ilen, lens, (*B)->rmap->n, &equal));
2589: PetscCheck(equal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong no of nonzeros");
2590: PetscCall(PetscArrayzero(c->ilen, (*B)->rmap->n));
2591: C = *B;
2592: } else {
2593: PetscInt rbs, cbs;
2594: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2595: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2596: PetscCall(ISGetBlockSize(isrow, &rbs));
2597: PetscCall(ISGetBlockSize(iscol, &cbs));
2598: PetscCall(MatSetBlockSizes(C, rbs, cbs));
2599: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2600: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2601: }
2602: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2604: c = (Mat_SeqAIJ *)(C->data);
2605: PetscCall(MatSeqAIJGetArrayWrite(C, &c_a)); // Not 'c->a', since that raw usage ignores offload state of C
2606: for (i = 0; i < nrows; i++) {
2607: row = irow[i];
2608: kstart = ai[row];
2609: kend = kstart + a->ilen[row];
2610: mat_i = c->i[i];
2611: mat_j = c->j + mat_i;
2612: mat_a = c_a + mat_i;
2613: mat_ilen = c->ilen + i;
2614: for (k = kstart; k < kend; k++) {
2615: if ((tcol = smap[a->j[k]])) {
2616: *mat_j++ = tcol - 1;
2617: *mat_a++ = aa[k];
2618: (*mat_ilen)++;
2619: }
2620: }
2621: }
2622: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2623: /* Free work space */
2624: PetscCall(ISRestoreIndices(iscol, &icol));
2625: PetscCall(PetscFree(smap));
2626: PetscCall(PetscFree(lens));
2627: /* sort */
2628: for (i = 0; i < nrows; i++) {
2629: PetscInt ilen;
2631: mat_i = c->i[i];
2632: mat_j = c->j + mat_i;
2633: mat_a = c_a + mat_i;
2634: ilen = c->ilen[i];
2635: PetscCall(PetscSortIntWithScalarArray(ilen, mat_j, mat_a));
2636: }
2637: PetscCall(MatSeqAIJRestoreArrayWrite(C, &c_a));
2638: }
2639: #if defined(PETSC_HAVE_DEVICE)
2640: PetscCall(MatBindToCPU(C, A->boundtocpu));
2641: #endif
2642: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
2643: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
2645: PetscCall(ISRestoreIndices(isrow, &irow));
2646: *B = C;
2647: PetscFunctionReturn(PETSC_SUCCESS);
2648: }
2650: PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
2651: {
2652: Mat B;
2654: PetscFunctionBegin;
2655: if (scall == MAT_INITIAL_MATRIX) {
2656: PetscCall(MatCreate(subComm, &B));
2657: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n));
2658: PetscCall(MatSetBlockSizesFromMats(B, mat, mat));
2659: PetscCall(MatSetType(B, MATSEQAIJ));
2660: PetscCall(MatDuplicateNoCreate_SeqAIJ(B, mat, MAT_COPY_VALUES, PETSC_TRUE));
2661: *subMat = B;
2662: } else {
2663: PetscCall(MatCopy_SeqAIJ(mat, *subMat, SAME_NONZERO_PATTERN));
2664: }
2665: PetscFunctionReturn(PETSC_SUCCESS);
2666: }
2668: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
2669: {
2670: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2671: Mat outA;
2672: PetscBool row_identity, col_identity;
2674: PetscFunctionBegin;
2675: PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels=0 supported for in-place ilu");
2677: PetscCall(ISIdentity(row, &row_identity));
2678: PetscCall(ISIdentity(col, &col_identity));
2680: outA = inA;
2681: outA->factortype = MAT_FACTOR_LU;
2682: PetscCall(PetscFree(inA->solvertype));
2683: PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));
2685: PetscCall(PetscObjectReference((PetscObject)row));
2686: PetscCall(ISDestroy(&a->row));
2688: a->row = row;
2690: PetscCall(PetscObjectReference((PetscObject)col));
2691: PetscCall(ISDestroy(&a->col));
2693: a->col = col;
2695: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2696: PetscCall(ISDestroy(&a->icol));
2697: PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));
2699: if (!a->solve_work) { /* this matrix may have been factored before */
2700: PetscCall(PetscMalloc1(inA->rmap->n + 1, &a->solve_work));
2701: }
2703: PetscCall(MatMarkDiagonal_SeqAIJ(inA));
2704: if (row_identity && col_identity) {
2705: PetscCall(MatLUFactorNumeric_SeqAIJ_inplace(outA, inA, info));
2706: } else {
2707: PetscCall(MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA, inA, info));
2708: }
2709: PetscFunctionReturn(PETSC_SUCCESS);
2710: }
2712: PetscErrorCode MatScale_SeqAIJ(Mat inA, PetscScalar alpha)
2713: {
2714: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2715: PetscScalar *v;
2716: PetscBLASInt one = 1, bnz;
2718: PetscFunctionBegin;
2719: PetscCall(MatSeqAIJGetArray(inA, &v));
2720: PetscCall(PetscBLASIntCast(a->nz, &bnz));
2721: PetscCallBLAS("BLASscal", BLASscal_(&bnz, &alpha, v, &one));
2722: PetscCall(PetscLogFlops(a->nz));
2723: PetscCall(MatSeqAIJRestoreArray(inA, &v));
2724: PetscCall(MatSeqAIJInvalidateDiagonal(inA));
2725: PetscFunctionReturn(PETSC_SUCCESS);
2726: }
2728: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2729: {
2730: PetscInt i;
2732: PetscFunctionBegin;
2733: if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2734: PetscCall(PetscFree4(submatj->sbuf1, submatj->ptr, submatj->tmp, submatj->ctr));
2736: for (i = 0; i < submatj->nrqr; ++i) PetscCall(PetscFree(submatj->sbuf2[i]));
2737: PetscCall(PetscFree3(submatj->sbuf2, submatj->req_size, submatj->req_source1));
2739: if (submatj->rbuf1) {
2740: PetscCall(PetscFree(submatj->rbuf1[0]));
2741: PetscCall(PetscFree(submatj->rbuf1));
2742: }
2744: for (i = 0; i < submatj->nrqs; ++i) PetscCall(PetscFree(submatj->rbuf3[i]));
2745: PetscCall(PetscFree3(submatj->req_source2, submatj->rbuf2, submatj->rbuf3));
2746: PetscCall(PetscFree(submatj->pa));
2747: }
2749: #if defined(PETSC_USE_CTABLE)
2750: PetscCall(PetscHMapIDestroy(&submatj->rmap));
2751: if (submatj->cmap_loc) PetscCall(PetscFree(submatj->cmap_loc));
2752: PetscCall(PetscFree(submatj->rmap_loc));
2753: #else
2754: PetscCall(PetscFree(submatj->rmap));
2755: #endif
2757: if (!submatj->allcolumns) {
2758: #if defined(PETSC_USE_CTABLE)
2759: PetscCall(PetscHMapIDestroy((PetscHMapI *)&submatj->cmap));
2760: #else
2761: PetscCall(PetscFree(submatj->cmap));
2762: #endif
2763: }
2764: PetscCall(PetscFree(submatj->row2proc));
2766: PetscCall(PetscFree(submatj));
2767: PetscFunctionReturn(PETSC_SUCCESS);
2768: }
2770: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2771: {
2772: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data;
2773: Mat_SubSppt *submatj = c->submatis1;
2775: PetscFunctionBegin;
2776: PetscCall((*submatj->destroy)(C));
2777: PetscCall(MatDestroySubMatrix_Private(submatj));
2778: PetscFunctionReturn(PETSC_SUCCESS);
2779: }
2781: /* Note this has code duplication with MatDestroySubMatrices_SeqBAIJ() */
2782: PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n, Mat *mat[])
2783: {
2784: PetscInt i;
2785: Mat C;
2786: Mat_SeqAIJ *c;
2787: Mat_SubSppt *submatj;
2789: PetscFunctionBegin;
2790: for (i = 0; i < n; i++) {
2791: C = (*mat)[i];
2792: c = (Mat_SeqAIJ *)C->data;
2793: submatj = c->submatis1;
2794: if (submatj) {
2795: if (--((PetscObject)C)->refct <= 0) {
2796: PetscCall(PetscFree(C->factorprefix));
2797: PetscCall((*submatj->destroy)(C));
2798: PetscCall(MatDestroySubMatrix_Private(submatj));
2799: PetscCall(PetscFree(C->defaultvectype));
2800: PetscCall(PetscFree(C->defaultrandtype));
2801: PetscCall(PetscLayoutDestroy(&C->rmap));
2802: PetscCall(PetscLayoutDestroy(&C->cmap));
2803: PetscCall(PetscHeaderDestroy(&C));
2804: }
2805: } else {
2806: PetscCall(MatDestroy(&C));
2807: }
2808: }
2810: /* Destroy Dummy submatrices created for reuse */
2811: PetscCall(MatDestroySubMatrices_Dummy(n, mat));
2813: PetscCall(PetscFree(*mat));
2814: PetscFunctionReturn(PETSC_SUCCESS);
2815: }
2817: PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *B[])
2818: {
2819: PetscInt i;
2821: PetscFunctionBegin;
2822: if (scall == MAT_INITIAL_MATRIX) PetscCall(PetscCalloc1(n + 1, B));
2824: for (i = 0; i < n; i++) PetscCall(MatCreateSubMatrix_SeqAIJ(A, irow[i], icol[i], PETSC_DECIDE, scall, &(*B)[i]));
2825: PetscFunctionReturn(PETSC_SUCCESS);
2826: }
2828: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A, PetscInt is_max, IS is[], PetscInt ov)
2829: {
2830: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2831: PetscInt row, i, j, k, l, ll, m, n, *nidx, isz, val;
2832: const PetscInt *idx;
2833: PetscInt start, end, *ai, *aj, bs = (A->rmap->bs > 0 && A->rmap->bs == A->cmap->bs) ? A->rmap->bs : 1;
2834: PetscBT table;
2836: PetscFunctionBegin;
2837: m = A->rmap->n / bs;
2838: ai = a->i;
2839: aj = a->j;
2841: PetscCheck(ov >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "illegal negative overlap value used");
2843: PetscCall(PetscMalloc1(m + 1, &nidx));
2844: PetscCall(PetscBTCreate(m, &table));
2846: for (i = 0; i < is_max; i++) {
2847: /* Initialize the two local arrays */
2848: isz = 0;
2849: PetscCall(PetscBTMemzero(m, table));
2851: /* Extract the indices, assume there can be duplicate entries */
2852: PetscCall(ISGetIndices(is[i], &idx));
2853: PetscCall(ISGetLocalSize(is[i], &n));
2855: if (bs > 1) {
2856: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2857: for (j = 0; j < n; ++j) {
2858: if (!PetscBTLookupSet(table, idx[j] / bs)) nidx[isz++] = idx[j] / bs;
2859: }
2860: PetscCall(ISRestoreIndices(is[i], &idx));
2861: PetscCall(ISDestroy(&is[i]));
2863: k = 0;
2864: for (j = 0; j < ov; j++) { /* for each overlap */
2865: n = isz;
2866: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2867: for (ll = 0; ll < bs; ll++) {
2868: row = bs * nidx[k] + ll;
2869: start = ai[row];
2870: end = ai[row + 1];
2871: for (l = start; l < end; l++) {
2872: val = aj[l] / bs;
2873: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2874: }
2875: }
2876: }
2877: }
2878: PetscCall(ISCreateBlock(PETSC_COMM_SELF, bs, isz, nidx, PETSC_COPY_VALUES, (is + i)));
2879: } else {
2880: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2881: for (j = 0; j < n; ++j) {
2882: if (!PetscBTLookupSet(table, idx[j])) nidx[isz++] = idx[j];
2883: }
2884: PetscCall(ISRestoreIndices(is[i], &idx));
2885: PetscCall(ISDestroy(&is[i]));
2887: k = 0;
2888: for (j = 0; j < ov; j++) { /* for each overlap */
2889: n = isz;
2890: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2891: row = nidx[k];
2892: start = ai[row];
2893: end = ai[row + 1];
2894: for (l = start; l < end; l++) {
2895: val = aj[l];
2896: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2897: }
2898: }
2899: }
2900: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, isz, nidx, PETSC_COPY_VALUES, (is + i)));
2901: }
2902: }
2903: PetscCall(PetscBTDestroy(&table));
2904: PetscCall(PetscFree(nidx));
2905: PetscFunctionReturn(PETSC_SUCCESS);
2906: }
2908: PetscErrorCode MatPermute_SeqAIJ(Mat A, IS rowp, IS colp, Mat *B)
2909: {
2910: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2911: PetscInt i, nz = 0, m = A->rmap->n, n = A->cmap->n;
2912: const PetscInt *row, *col;
2913: PetscInt *cnew, j, *lens;
2914: IS icolp, irowp;
2915: PetscInt *cwork = NULL;
2916: PetscScalar *vwork = NULL;
2918: PetscFunctionBegin;
2919: PetscCall(ISInvertPermutation(rowp, PETSC_DECIDE, &irowp));
2920: PetscCall(ISGetIndices(irowp, &row));
2921: PetscCall(ISInvertPermutation(colp, PETSC_DECIDE, &icolp));
2922: PetscCall(ISGetIndices(icolp, &col));
2924: /* determine lengths of permuted rows */
2925: PetscCall(PetscMalloc1(m + 1, &lens));
2926: for (i = 0; i < m; i++) lens[row[i]] = a->i[i + 1] - a->i[i];
2927: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2928: PetscCall(MatSetSizes(*B, m, n, m, n));
2929: PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2930: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
2931: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*B, 0, lens));
2932: PetscCall(PetscFree(lens));
2934: PetscCall(PetscMalloc1(n, &cnew));
2935: for (i = 0; i < m; i++) {
2936: PetscCall(MatGetRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2937: for (j = 0; j < nz; j++) cnew[j] = col[cwork[j]];
2938: PetscCall(MatSetValues_SeqAIJ(*B, 1, &row[i], nz, cnew, vwork, INSERT_VALUES));
2939: PetscCall(MatRestoreRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2940: }
2941: PetscCall(PetscFree(cnew));
2943: (*B)->assembled = PETSC_FALSE;
2945: #if defined(PETSC_HAVE_DEVICE)
2946: PetscCall(MatBindToCPU(*B, A->boundtocpu));
2947: #endif
2948: PetscCall(MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY));
2949: PetscCall(MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY));
2950: PetscCall(ISRestoreIndices(irowp, &row));
2951: PetscCall(ISRestoreIndices(icolp, &col));
2952: PetscCall(ISDestroy(&irowp));
2953: PetscCall(ISDestroy(&icolp));
2954: if (rowp == colp) PetscCall(MatPropagateSymmetryOptions(A, *B));
2955: PetscFunctionReturn(PETSC_SUCCESS);
2956: }
2958: PetscErrorCode MatCopy_SeqAIJ(Mat A, Mat B, MatStructure str)
2959: {
2960: PetscFunctionBegin;
2961: /* If the two matrices have the same copy implementation, use fast copy. */
2962: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2963: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2964: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2965: const PetscScalar *aa;
2967: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2968: PetscCheck(a->i[A->rmap->n] == b->i[B->rmap->n], PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Number of nonzeros in two matrices are different %" PetscInt_FMT " != %" PetscInt_FMT, a->i[A->rmap->n], b->i[B->rmap->n]);
2969: PetscCall(PetscArraycpy(b->a, aa, a->i[A->rmap->n]));
2970: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2971: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2972: } else {
2973: PetscCall(MatCopy_Basic(A, B, str));
2974: }
2975: PetscFunctionReturn(PETSC_SUCCESS);
2976: }
2978: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A, PetscScalar *array[])
2979: {
2980: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2982: PetscFunctionBegin;
2983: *array = a->a;
2984: PetscFunctionReturn(PETSC_SUCCESS);
2985: }
2987: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A, PetscScalar *array[])
2988: {
2989: PetscFunctionBegin;
2990: *array = NULL;
2991: PetscFunctionReturn(PETSC_SUCCESS);
2992: }
2994: /*
2995: Computes the number of nonzeros per row needed for preallocation when X and Y
2996: have different nonzero structure.
2997: */
2998: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *yi, const PetscInt *yj, PetscInt *nnz)
2999: {
3000: PetscInt i, j, k, nzx, nzy;
3002: PetscFunctionBegin;
3003: /* Set the number of nonzeros in the new matrix */
3004: for (i = 0; i < m; i++) {
3005: const PetscInt *xjj = xj + xi[i], *yjj = yj + yi[i];
3006: nzx = xi[i + 1] - xi[i];
3007: nzy = yi[i + 1] - yi[i];
3008: nnz[i] = 0;
3009: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
3010: for (; k < nzy && yjj[k] < xjj[j]; k++) nnz[i]++; /* Catch up to X */
3011: if (k < nzy && yjj[k] == xjj[j]) k++; /* Skip duplicate */
3012: nnz[i]++;
3013: }
3014: for (; k < nzy; k++) nnz[i]++;
3015: }
3016: PetscFunctionReturn(PETSC_SUCCESS);
3017: }
3019: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y, Mat X, PetscInt *nnz)
3020: {
3021: PetscInt m = Y->rmap->N;
3022: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
3023: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
3025: PetscFunctionBegin;
3026: /* Set the number of nonzeros in the new matrix */
3027: PetscCall(MatAXPYGetPreallocation_SeqX_private(m, x->i, x->j, y->i, y->j, nnz));
3028: PetscFunctionReturn(PETSC_SUCCESS);
3029: }
3031: PetscErrorCode MatAXPY_SeqAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
3032: {
3033: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;
3035: PetscFunctionBegin;
3036: if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
3037: PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE;
3038: if (e) {
3039: PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e));
3040: if (e) {
3041: PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e));
3042: if (e) str = SAME_NONZERO_PATTERN;
3043: }
3044: }
3045: if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
3046: }
3047: if (str == SAME_NONZERO_PATTERN) {
3048: const PetscScalar *xa;
3049: PetscScalar *ya, alpha = a;
3050: PetscBLASInt one = 1, bnz;
3052: PetscCall(PetscBLASIntCast(x->nz, &bnz));
3053: PetscCall(MatSeqAIJGetArray(Y, &ya));
3054: PetscCall(MatSeqAIJGetArrayRead(X, &xa));
3055: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, xa, &one, ya, &one));
3056: PetscCall(MatSeqAIJRestoreArrayRead(X, &xa));
3057: PetscCall(MatSeqAIJRestoreArray(Y, &ya));
3058: PetscCall(PetscLogFlops(2.0 * bnz));
3059: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3060: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
3061: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
3062: PetscCall(MatAXPY_Basic(Y, a, X, str));
3063: } else {
3064: Mat B;
3065: PetscInt *nnz;
3066: PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
3067: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
3068: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
3069: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
3070: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
3071: PetscCall(MatAXPYGetPreallocation_SeqAIJ(Y, X, nnz));
3072: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
3073: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
3074: PetscCall(MatHeaderMerge(Y, &B));
3075: PetscCall(MatSeqAIJCheckInode(Y));
3076: PetscCall(PetscFree(nnz));
3077: }
3078: PetscFunctionReturn(PETSC_SUCCESS);
3079: }
3081: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
3082: {
3083: #if defined(PETSC_USE_COMPLEX)
3084: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3085: PetscInt i, nz;
3086: PetscScalar *a;
3088: PetscFunctionBegin;
3089: nz = aij->nz;
3090: PetscCall(MatSeqAIJGetArray(mat, &a));
3091: for (i = 0; i < nz; i++) a[i] = PetscConj(a[i]);
3092: PetscCall(MatSeqAIJRestoreArray(mat, &a));
3093: #else
3094: PetscFunctionBegin;
3095: #endif
3096: PetscFunctionReturn(PETSC_SUCCESS);
3097: }
3099: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3100: {
3101: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3102: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3103: PetscReal atmp;
3104: PetscScalar *x;
3105: const MatScalar *aa, *av;
3107: PetscFunctionBegin;
3108: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3109: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3110: aa = av;
3111: ai = a->i;
3112: aj = a->j;
3114: PetscCall(VecSet(v, 0.0));
3115: PetscCall(VecGetArrayWrite(v, &x));
3116: PetscCall(VecGetLocalSize(v, &n));
3117: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3118: for (i = 0; i < m; i++) {
3119: ncols = ai[1] - ai[0];
3120: ai++;
3121: for (j = 0; j < ncols; j++) {
3122: atmp = PetscAbsScalar(*aa);
3123: if (PetscAbsScalar(x[i]) < atmp) {
3124: x[i] = atmp;
3125: if (idx) idx[i] = *aj;
3126: }
3127: aa++;
3128: aj++;
3129: }
3130: }
3131: PetscCall(VecRestoreArrayWrite(v, &x));
3132: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3133: PetscFunctionReturn(PETSC_SUCCESS);
3134: }
3136: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3137: {
3138: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3139: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3140: PetscScalar *x;
3141: const MatScalar *aa, *av;
3143: PetscFunctionBegin;
3144: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3145: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3146: aa = av;
3147: ai = a->i;
3148: aj = a->j;
3150: PetscCall(VecSet(v, 0.0));
3151: PetscCall(VecGetArrayWrite(v, &x));
3152: PetscCall(VecGetLocalSize(v, &n));
3153: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3154: for (i = 0; i < m; i++) {
3155: ncols = ai[1] - ai[0];
3156: ai++;
3157: if (ncols == A->cmap->n) { /* row is dense */
3158: x[i] = *aa;
3159: if (idx) idx[i] = 0;
3160: } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
3161: x[i] = 0.0;
3162: if (idx) {
3163: for (j = 0; j < ncols; j++) { /* find first implicit 0.0 in the row */
3164: if (aj[j] > j) {
3165: idx[i] = j;
3166: break;
3167: }
3168: }
3169: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3170: if (j == ncols && j < A->cmap->n) idx[i] = j;
3171: }
3172: }
3173: for (j = 0; j < ncols; j++) {
3174: if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {
3175: x[i] = *aa;
3176: if (idx) idx[i] = *aj;
3177: }
3178: aa++;
3179: aj++;
3180: }
3181: }
3182: PetscCall(VecRestoreArrayWrite(v, &x));
3183: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3184: PetscFunctionReturn(PETSC_SUCCESS);
3185: }
3187: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3188: {
3189: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3190: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3191: PetscScalar *x;
3192: const MatScalar *aa, *av;
3194: PetscFunctionBegin;
3195: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3196: aa = av;
3197: ai = a->i;
3198: aj = a->j;
3200: PetscCall(VecSet(v, 0.0));
3201: PetscCall(VecGetArrayWrite(v, &x));
3202: PetscCall(VecGetLocalSize(v, &n));
3203: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector, %" PetscInt_FMT " vs. %" PetscInt_FMT " rows", m, n);
3204: for (i = 0; i < m; i++) {
3205: ncols = ai[1] - ai[0];
3206: ai++;
3207: if (ncols == A->cmap->n) { /* row is dense */
3208: x[i] = *aa;
3209: if (idx) idx[i] = 0;
3210: } else { /* row is sparse so already KNOW minimum is 0.0 or higher */
3211: x[i] = 0.0;
3212: if (idx) { /* find first implicit 0.0 in the row */
3213: for (j = 0; j < ncols; j++) {
3214: if (aj[j] > j) {
3215: idx[i] = j;
3216: break;
3217: }
3218: }
3219: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3220: if (j == ncols && j < A->cmap->n) idx[i] = j;
3221: }
3222: }
3223: for (j = 0; j < ncols; j++) {
3224: if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {
3225: x[i] = *aa;
3226: if (idx) idx[i] = *aj;
3227: }
3228: aa++;
3229: aj++;
3230: }
3231: }
3232: PetscCall(VecRestoreArrayWrite(v, &x));
3233: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3234: PetscFunctionReturn(PETSC_SUCCESS);
3235: }
3237: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3238: {
3239: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3240: PetscInt i, j, m = A->rmap->n, ncols, n;
3241: const PetscInt *ai, *aj;
3242: PetscScalar *x;
3243: const MatScalar *aa, *av;
3245: PetscFunctionBegin;
3246: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3247: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3248: aa = av;
3249: ai = a->i;
3250: aj = a->j;
3252: PetscCall(VecSet(v, 0.0));
3253: PetscCall(VecGetArrayWrite(v, &x));
3254: PetscCall(VecGetLocalSize(v, &n));
3255: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3256: for (i = 0; i < m; i++) {
3257: ncols = ai[1] - ai[0];
3258: ai++;
3259: if (ncols == A->cmap->n) { /* row is dense */
3260: x[i] = *aa;
3261: if (idx) idx[i] = 0;
3262: } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
3263: x[i] = 0.0;
3264: if (idx) { /* find first implicit 0.0 in the row */
3265: for (j = 0; j < ncols; j++) {
3266: if (aj[j] > j) {
3267: idx[i] = j;
3268: break;
3269: }
3270: }
3271: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3272: if (j == ncols && j < A->cmap->n) idx[i] = j;
3273: }
3274: }
3275: for (j = 0; j < ncols; j++) {
3276: if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {
3277: x[i] = *aa;
3278: if (idx) idx[i] = *aj;
3279: }
3280: aa++;
3281: aj++;
3282: }
3283: }
3284: PetscCall(VecRestoreArrayWrite(v, &x));
3285: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3286: PetscFunctionReturn(PETSC_SUCCESS);
3287: }
3289: PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A, const PetscScalar **values)
3290: {
3291: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3292: PetscInt i, bs = PetscAbs(A->rmap->bs), mbs = A->rmap->n / bs, ipvt[5], bs2 = bs * bs, *v_pivots, ij[7], *IJ, j;
3293: MatScalar *diag, work[25], *v_work;
3294: const PetscReal shift = 0.0;
3295: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
3297: PetscFunctionBegin;
3298: allowzeropivot = PetscNot(A->erroriffailure);
3299: if (a->ibdiagvalid) {
3300: if (values) *values = a->ibdiag;
3301: PetscFunctionReturn(PETSC_SUCCESS);
3302: }
3303: PetscCall(MatMarkDiagonal_SeqAIJ(A));
3304: if (!a->ibdiag) { PetscCall(PetscMalloc1(bs2 * mbs, &a->ibdiag)); }
3305: diag = a->ibdiag;
3306: if (values) *values = a->ibdiag;
3307: /* factor and invert each block */
3308: switch (bs) {
3309: case 1:
3310: for (i = 0; i < mbs; i++) {
3311: PetscCall(MatGetValues(A, 1, &i, 1, &i, diag + i));
3312: if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3313: if (allowzeropivot) {
3314: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3315: A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3316: A->factorerror_zeropivot_row = i;
3317: PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g\n", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON));
3318: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_MAT_LU_ZRPVT, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON);
3319: }
3320: diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3321: }
3322: break;
3323: case 2:
3324: for (i = 0; i < mbs; i++) {
3325: ij[0] = 2 * i;
3326: ij[1] = 2 * i + 1;
3327: PetscCall(MatGetValues(A, 2, ij, 2, ij, diag));
3328: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
3329: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3330: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
3331: diag += 4;
3332: }
3333: break;
3334: case 3:
3335: for (i = 0; i < mbs; i++) {
3336: ij[0] = 3 * i;
3337: ij[1] = 3 * i + 1;
3338: ij[2] = 3 * i + 2;
3339: PetscCall(MatGetValues(A, 3, ij, 3, ij, diag));
3340: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
3341: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3342: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
3343: diag += 9;
3344: }
3345: break;
3346: case 4:
3347: for (i = 0; i < mbs; i++) {
3348: ij[0] = 4 * i;
3349: ij[1] = 4 * i + 1;
3350: ij[2] = 4 * i + 2;
3351: ij[3] = 4 * i + 3;
3352: PetscCall(MatGetValues(A, 4, ij, 4, ij, diag));
3353: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
3354: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3355: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
3356: diag += 16;
3357: }
3358: break;
3359: case 5:
3360: for (i = 0; i < mbs; i++) {
3361: ij[0] = 5 * i;
3362: ij[1] = 5 * i + 1;
3363: ij[2] = 5 * i + 2;
3364: ij[3] = 5 * i + 3;
3365: ij[4] = 5 * i + 4;
3366: PetscCall(MatGetValues(A, 5, ij, 5, ij, diag));
3367: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
3368: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3369: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
3370: diag += 25;
3371: }
3372: break;
3373: case 6:
3374: for (i = 0; i < mbs; i++) {
3375: ij[0] = 6 * i;
3376: ij[1] = 6 * i + 1;
3377: ij[2] = 6 * i + 2;
3378: ij[3] = 6 * i + 3;
3379: ij[4] = 6 * i + 4;
3380: ij[5] = 6 * i + 5;
3381: PetscCall(MatGetValues(A, 6, ij, 6, ij, diag));
3382: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
3383: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3384: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
3385: diag += 36;
3386: }
3387: break;
3388: case 7:
3389: for (i = 0; i < mbs; i++) {
3390: ij[0] = 7 * i;
3391: ij[1] = 7 * i + 1;
3392: ij[2] = 7 * i + 2;
3393: ij[3] = 7 * i + 3;
3394: ij[4] = 7 * i + 4;
3395: ij[5] = 7 * i + 5;
3396: ij[6] = 7 * i + 6;
3397: PetscCall(MatGetValues(A, 7, ij, 7, ij, diag));
3398: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
3399: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3400: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
3401: diag += 49;
3402: }
3403: break;
3404: default:
3405: PetscCall(PetscMalloc3(bs, &v_work, bs, &v_pivots, bs, &IJ));
3406: for (i = 0; i < mbs; i++) {
3407: for (j = 0; j < bs; j++) IJ[j] = bs * i + j;
3408: PetscCall(MatGetValues(A, bs, IJ, bs, IJ, diag));
3409: PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
3410: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3411: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bs));
3412: diag += bs2;
3413: }
3414: PetscCall(PetscFree3(v_work, v_pivots, IJ));
3415: }
3416: a->ibdiagvalid = PETSC_TRUE;
3417: PetscFunctionReturn(PETSC_SUCCESS);
3418: }
3420: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x, PetscRandom rctx)
3421: {
3422: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3423: PetscScalar a, *aa;
3424: PetscInt m, n, i, j, col;
3426: PetscFunctionBegin;
3427: if (!x->assembled) {
3428: PetscCall(MatGetSize(x, &m, &n));
3429: for (i = 0; i < m; i++) {
3430: for (j = 0; j < aij->imax[i]; j++) {
3431: PetscCall(PetscRandomGetValue(rctx, &a));
3432: col = (PetscInt)(n * PetscRealPart(a));
3433: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3434: }
3435: }
3436: } else {
3437: PetscCall(MatSeqAIJGetArrayWrite(x, &aa));
3438: for (i = 0; i < aij->nz; i++) PetscCall(PetscRandomGetValue(rctx, aa + i));
3439: PetscCall(MatSeqAIJRestoreArrayWrite(x, &aa));
3440: }
3441: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3442: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3443: PetscFunctionReturn(PETSC_SUCCESS);
3444: }
3446: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3447: PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x, PetscInt low, PetscInt high, PetscRandom rctx)
3448: {
3449: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3450: PetscScalar a;
3451: PetscInt m, n, i, j, col, nskip;
3453: PetscFunctionBegin;
3454: nskip = high - low;
3455: PetscCall(MatGetSize(x, &m, &n));
3456: n -= nskip; /* shrink number of columns where nonzeros can be set */
3457: for (i = 0; i < m; i++) {
3458: for (j = 0; j < aij->imax[i]; j++) {
3459: PetscCall(PetscRandomGetValue(rctx, &a));
3460: col = (PetscInt)(n * PetscRealPart(a));
3461: if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3462: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3463: }
3464: }
3465: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3466: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3467: PetscFunctionReturn(PETSC_SUCCESS);
3468: }
3470: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
3471: MatGetRow_SeqAIJ,
3472: MatRestoreRow_SeqAIJ,
3473: MatMult_SeqAIJ,
3474: /* 4*/ MatMultAdd_SeqAIJ,
3475: MatMultTranspose_SeqAIJ,
3476: MatMultTransposeAdd_SeqAIJ,
3477: NULL,
3478: NULL,
3479: NULL,
3480: /* 10*/ NULL,
3481: MatLUFactor_SeqAIJ,
3482: NULL,
3483: MatSOR_SeqAIJ,
3484: MatTranspose_SeqAIJ,
3485: /*1 5*/ MatGetInfo_SeqAIJ,
3486: MatEqual_SeqAIJ,
3487: MatGetDiagonal_SeqAIJ,
3488: MatDiagonalScale_SeqAIJ,
3489: MatNorm_SeqAIJ,
3490: /* 20*/ NULL,
3491: MatAssemblyEnd_SeqAIJ,
3492: MatSetOption_SeqAIJ,
3493: MatZeroEntries_SeqAIJ,
3494: /* 24*/ MatZeroRows_SeqAIJ,
3495: NULL,
3496: NULL,
3497: NULL,
3498: NULL,
3499: /* 29*/ MatSetUp_Seq_Hash,
3500: NULL,
3501: NULL,
3502: NULL,
3503: NULL,
3504: /* 34*/ MatDuplicate_SeqAIJ,
3505: NULL,
3506: NULL,
3507: MatILUFactor_SeqAIJ,
3508: NULL,
3509: /* 39*/ MatAXPY_SeqAIJ,
3510: MatCreateSubMatrices_SeqAIJ,
3511: MatIncreaseOverlap_SeqAIJ,
3512: MatGetValues_SeqAIJ,
3513: MatCopy_SeqAIJ,
3514: /* 44*/ MatGetRowMax_SeqAIJ,
3515: MatScale_SeqAIJ,
3516: MatShift_SeqAIJ,
3517: MatDiagonalSet_SeqAIJ,
3518: MatZeroRowsColumns_SeqAIJ,
3519: /* 49*/ MatSetRandom_SeqAIJ,
3520: MatGetRowIJ_SeqAIJ,
3521: MatRestoreRowIJ_SeqAIJ,
3522: MatGetColumnIJ_SeqAIJ,
3523: MatRestoreColumnIJ_SeqAIJ,
3524: /* 54*/ MatFDColoringCreate_SeqXAIJ,
3525: NULL,
3526: NULL,
3527: MatPermute_SeqAIJ,
3528: NULL,
3529: /* 59*/ NULL,
3530: MatDestroy_SeqAIJ,
3531: MatView_SeqAIJ,
3532: NULL,
3533: NULL,
3534: /* 64*/ NULL,
3535: MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3536: NULL,
3537: NULL,
3538: NULL,
3539: /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3540: MatGetRowMinAbs_SeqAIJ,
3541: NULL,
3542: NULL,
3543: NULL,
3544: /* 74*/ NULL,
3545: MatFDColoringApply_AIJ,
3546: NULL,
3547: NULL,
3548: NULL,
3549: /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3550: NULL,
3551: NULL,
3552: NULL,
3553: MatLoad_SeqAIJ,
3554: /* 84*/ MatIsSymmetric_SeqAIJ,
3555: MatIsHermitian_SeqAIJ,
3556: NULL,
3557: NULL,
3558: NULL,
3559: /* 89*/ NULL,
3560: NULL,
3561: MatMatMultNumeric_SeqAIJ_SeqAIJ,
3562: NULL,
3563: NULL,
3564: /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3565: NULL,
3566: NULL,
3567: MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3568: NULL,
3569: /* 99*/ MatProductSetFromOptions_SeqAIJ,
3570: NULL,
3571: NULL,
3572: MatConjugate_SeqAIJ,
3573: NULL,
3574: /*104*/ MatSetValuesRow_SeqAIJ,
3575: MatRealPart_SeqAIJ,
3576: MatImaginaryPart_SeqAIJ,
3577: NULL,
3578: NULL,
3579: /*109*/ MatMatSolve_SeqAIJ,
3580: NULL,
3581: MatGetRowMin_SeqAIJ,
3582: NULL,
3583: MatMissingDiagonal_SeqAIJ,
3584: /*114*/ NULL,
3585: NULL,
3586: NULL,
3587: NULL,
3588: NULL,
3589: /*119*/ NULL,
3590: NULL,
3591: NULL,
3592: NULL,
3593: MatGetMultiProcBlock_SeqAIJ,
3594: /*124*/ MatFindNonzeroRows_SeqAIJ,
3595: MatGetColumnReductions_SeqAIJ,
3596: MatInvertBlockDiagonal_SeqAIJ,
3597: MatInvertVariableBlockDiagonal_SeqAIJ,
3598: NULL,
3599: /*129*/ NULL,
3600: NULL,
3601: NULL,
3602: MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3603: MatTransposeColoringCreate_SeqAIJ,
3604: /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3605: MatTransColoringApplyDenToSp_SeqAIJ,
3606: NULL,
3607: NULL,
3608: MatRARtNumeric_SeqAIJ_SeqAIJ,
3609: /*139*/ NULL,
3610: NULL,
3611: NULL,
3612: MatFDColoringSetUp_SeqXAIJ,
3613: MatFindOffBlockDiagonalEntries_SeqAIJ,
3614: MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3615: /*145*/ MatDestroySubMatrices_SeqAIJ,
3616: NULL,
3617: NULL,
3618: MatCreateGraph_Simple_AIJ,
3619: NULL,
3620: /*150*/ MatTransposeSymbolic_SeqAIJ,
3621: MatEliminateZeros_SeqAIJ};
3623: PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat, PetscInt *indices)
3624: {
3625: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3626: PetscInt i, nz, n;
3628: PetscFunctionBegin;
3629: nz = aij->maxnz;
3630: n = mat->rmap->n;
3631: for (i = 0; i < nz; i++) aij->j[i] = indices[i];
3632: aij->nz = nz;
3633: for (i = 0; i < n; i++) aij->ilen[i] = aij->imax[i];
3634: PetscFunctionReturn(PETSC_SUCCESS);
3635: }
3637: /*
3638: * Given a sparse matrix with global column indices, compact it by using a local column space.
3639: * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3640: */
3641: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3642: {
3643: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3644: PetscHMapI gid1_lid1;
3645: PetscHashIter tpos;
3646: PetscInt gid, lid, i, ec, nz = aij->nz;
3647: PetscInt *garray, *jj = aij->j;
3649: PetscFunctionBegin;
3652: /* use a table */
3653: PetscCall(PetscHMapICreateWithSize(mat->rmap->n, &gid1_lid1));
3654: ec = 0;
3655: for (i = 0; i < nz; i++) {
3656: PetscInt data, gid1 = jj[i] + 1;
3657: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &data));
3658: if (!data) {
3659: /* one based table */
3660: PetscCall(PetscHMapISet(gid1_lid1, gid1, ++ec));
3661: }
3662: }
3663: /* form array of columns we need */
3664: PetscCall(PetscMalloc1(ec, &garray));
3665: PetscHashIterBegin(gid1_lid1, tpos);
3666: while (!PetscHashIterAtEnd(gid1_lid1, tpos)) {
3667: PetscHashIterGetKey(gid1_lid1, tpos, gid);
3668: PetscHashIterGetVal(gid1_lid1, tpos, lid);
3669: PetscHashIterNext(gid1_lid1, tpos);
3670: gid--;
3671: lid--;
3672: garray[lid] = gid;
3673: }
3674: PetscCall(PetscSortInt(ec, garray)); /* sort, and rebuild */
3675: PetscCall(PetscHMapIClear(gid1_lid1));
3676: for (i = 0; i < ec; i++) PetscCall(PetscHMapISet(gid1_lid1, garray[i] + 1, i + 1));
3677: /* compact out the extra columns in B */
3678: for (i = 0; i < nz; i++) {
3679: PetscInt gid1 = jj[i] + 1;
3680: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &lid));
3681: lid--;
3682: jj[i] = lid;
3683: }
3684: PetscCall(PetscLayoutDestroy(&mat->cmap));
3685: PetscCall(PetscHMapIDestroy(&gid1_lid1));
3686: PetscCall(PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat), ec, ec, 1, &mat->cmap));
3687: PetscCall(ISLocalToGlobalMappingCreate(PETSC_COMM_SELF, mat->cmap->bs, mat->cmap->n, garray, PETSC_OWN_POINTER, mapping));
3688: PetscCall(ISLocalToGlobalMappingSetType(*mapping, ISLOCALTOGLOBALMAPPINGHASH));
3689: PetscFunctionReturn(PETSC_SUCCESS);
3690: }
3692: /*@
3693: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3694: in the matrix.
3696: Input Parameters:
3697: + mat - the `MATSEQAIJ` matrix
3698: - indices - the column indices
3700: Level: advanced
3702: Notes:
3703: This can be called if you have precomputed the nonzero structure of the
3704: matrix and want to provide it to the matrix object to improve the performance
3705: of the `MatSetValues()` operation.
3707: You MUST have set the correct numbers of nonzeros per row in the call to
3708: `MatCreateSeqAIJ()`, and the columns indices MUST be sorted.
3710: MUST be called before any calls to `MatSetValues()`
3712: The indices should start with zero, not one.
3714: .seealso: [](chapter_matrices), `Mat`, `MATSEQAIJ`
3715: @*/
3716: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat, PetscInt *indices)
3717: {
3718: PetscFunctionBegin;
3721: PetscUseMethod(mat, "MatSeqAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices));
3722: PetscFunctionReturn(PETSC_SUCCESS);
3723: }
3725: PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3726: {
3727: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3728: size_t nz = aij->i[mat->rmap->n];
3730: PetscFunctionBegin;
3731: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3733: /* allocate space for values if not already there */
3734: if (!aij->saved_values) { PetscCall(PetscMalloc1(nz + 1, &aij->saved_values)); }
3736: /* copy values over */
3737: PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
3738: PetscFunctionReturn(PETSC_SUCCESS);
3739: }
3741: /*@
3742: MatStoreValues - Stashes a copy of the matrix values; this allows, for
3743: example, reuse of the linear part of a Jacobian, while recomputing the
3744: nonlinear portion.
3746: Logically Collect
3748: Input Parameter:
3749: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3751: Level: advanced
3753: Usage:
3754: .vb
3755: Using SNES
3756: Create Jacobian matrix
3757: Set linear terms into matrix
3758: Apply boundary conditions to matrix, at this time matrix must have
3759: final nonzero structure (i.e. setting the nonlinear terms and applying
3760: boundary conditions again will not change the nonzero structure
3761: MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3762: MatStoreValues(mat);
3763: Call SNESSetJacobian() with matrix
3764: In your Jacobian routine
3765: MatRetrieveValues(mat);
3766: Set nonlinear terms in matrix
3768: Without `SNESSolve()`, i.e. when you handle nonlinear solve yourself:
3769: // build linear portion of Jacobian
3770: MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3771: MatStoreValues(mat);
3772: loop over nonlinear iterations
3773: MatRetrieveValues(mat);
3774: // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3775: // call MatAssemblyBegin/End() on matrix
3776: Solve linear system with Jacobian
3777: endloop
3778: .ve
3780: Notes:
3781: Matrix must already be assembled before calling this routine
3782: Must set the matrix option `MatSetOption`(mat,`MAT_NEW_NONZERO_LOCATIONS`,`PETSC_FALSE`); before
3783: calling this routine.
3785: When this is called multiple times it overwrites the previous set of stored values
3786: and does not allocated additional space.
3788: .seealso: [](chapter_matrices), `Mat`, `Mat`, `MatRetrieveValues()`
3789: @*/
3790: PetscErrorCode MatStoreValues(Mat mat)
3791: {
3792: PetscFunctionBegin;
3794: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3795: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3796: PetscUseMethod(mat, "MatStoreValues_C", (Mat), (mat));
3797: PetscFunctionReturn(PETSC_SUCCESS);
3798: }
3800: PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3801: {
3802: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3803: PetscInt nz = aij->i[mat->rmap->n];
3805: PetscFunctionBegin;
3806: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3807: PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");
3808: /* copy values over */
3809: PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
3810: PetscFunctionReturn(PETSC_SUCCESS);
3811: }
3813: /*@
3814: MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
3815: example, reuse of the linear part of a Jacobian, while recomputing the
3816: nonlinear portion.
3818: Logically Collect
3820: Input Parameters:
3821: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3823: Level: advanced
3825: .seealso: [](chapter_matrices), `Mat`, `MatStoreValues()`
3826: @*/
3827: PetscErrorCode MatRetrieveValues(Mat mat)
3828: {
3829: PetscFunctionBegin;
3831: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3832: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3833: PetscUseMethod(mat, "MatRetrieveValues_C", (Mat), (mat));
3834: PetscFunctionReturn(PETSC_SUCCESS);
3835: }
3837: /*@C
3838: MatCreateSeqAIJ - Creates a sparse matrix in `MATSEQAIJ` (compressed row) format
3839: (the default parallel PETSc format). For good matrix assembly performance
3840: the user should preallocate the matrix storage by setting the parameter nz
3841: (or the array nnz). By setting these parameters accurately, performance
3842: during matrix assembly can be increased by more than a factor of 50.
3844: Collective
3846: Input Parameters:
3847: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3848: . m - number of rows
3849: . n - number of columns
3850: . nz - number of nonzeros per row (same for all rows)
3851: - nnz - array containing the number of nonzeros in the various rows
3852: (possibly different for each row) or NULL
3854: Output Parameter:
3855: . A - the matrix
3857: Options Database Keys:
3858: + -mat_no_inode - Do not use inodes
3859: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3861: Level: intermediate
3863: Notes:
3864: If `nnz` is given then `nz` is ignored
3866: The `MATSEQAIJ` format, also called
3867: compressed row storage, is fully compatible with standard Fortran
3868: storage. That is, the stored row and column indices can begin at
3869: either one (as in Fortran) or zero.
3871: Specify the preallocated storage with either nz or nnz (not both).
3872: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3873: allocation. For large problems you MUST preallocate memory or you
3874: will get TERRIBLE performance, see the users' manual chapter on matrices.
3876: By default, this format uses inodes (identical nodes) when possible, to
3877: improve numerical efficiency of matrix-vector products and solves. We
3878: search for consecutive rows with the same nonzero structure, thereby
3879: reusing matrix information to achieve increased efficiency.
3881: .seealso: [](chapter_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`
3882: @*/
3883: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3884: {
3885: PetscFunctionBegin;
3886: PetscCall(MatCreate(comm, A));
3887: PetscCall(MatSetSizes(*A, m, n, m, n));
3888: PetscCall(MatSetType(*A, MATSEQAIJ));
3889: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz));
3890: PetscFunctionReturn(PETSC_SUCCESS);
3891: }
3893: /*@C
3894: MatSeqAIJSetPreallocation - For good matrix assembly performance
3895: the user should preallocate the matrix storage by setting the parameter nz
3896: (or the array nnz). By setting these parameters accurately, performance
3897: during matrix assembly can be increased by more than a factor of 50.
3899: Collective
3901: Input Parameters:
3902: + B - The matrix
3903: . nz - number of nonzeros per row (same for all rows)
3904: - nnz - array containing the number of nonzeros in the various rows
3905: (possibly different for each row) or NULL
3907: Options Database Keys:
3908: + -mat_no_inode - Do not use inodes
3909: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3911: Level: intermediate
3913: Notes:
3914: If `nnz` is given then `nz` is ignored
3916: The `MATSEQAIJ` format also called
3917: compressed row storage, is fully compatible with standard Fortran
3918: storage. That is, the stored row and column indices can begin at
3919: either one (as in Fortran) or zero. See the users' manual for details.
3921: Specify the preallocated storage with either `nz` or `nnz` (not both).
3922: Set nz = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3923: allocation.
3925: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3926: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3927: You can also run with the option -info and look for messages with the string
3928: malloc in them to see if additional memory allocation was needed.
3930: Developer Notes:
3931: Use nz of `MAT_SKIP_ALLOCATION` to not allocate any space for the matrix
3932: entries or columns indices
3934: By default, this format uses inodes (identical nodes) when possible, to
3935: improve numerical efficiency of matrix-vector products and solves. We
3936: search for consecutive rows with the same nonzero structure, thereby
3937: reusing matrix information to achieve increased efficiency.
3939: .seealso: [](chapter_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatGetInfo()`,
3940: `MatSeqAIJSetTotalPreallocation()`
3941: @*/
3942: PetscErrorCode MatSeqAIJSetPreallocation(Mat B, PetscInt nz, const PetscInt nnz[])
3943: {
3944: PetscFunctionBegin;
3947: PetscTryMethod(B, "MatSeqAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[]), (B, nz, nnz));
3948: PetscFunctionReturn(PETSC_SUCCESS);
3949: }
3951: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B, PetscInt nz, const PetscInt *nnz)
3952: {
3953: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
3954: PetscBool skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;
3955: PetscInt i;
3957: PetscFunctionBegin;
3958: if (B->hash_active) {
3959: PetscCall(PetscMemcpy(&B->ops, &b->cops, sizeof(*(B->ops))));
3960: PetscCall(PetscHMapIJVDestroy(&b->ht));
3961: PetscCall(PetscFree(b->dnz));
3962: B->hash_active = PETSC_FALSE;
3963: }
3964: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3965: if (nz == MAT_SKIP_ALLOCATION) {
3966: skipallocation = PETSC_TRUE;
3967: nz = 0;
3968: }
3969: PetscCall(PetscLayoutSetUp(B->rmap));
3970: PetscCall(PetscLayoutSetUp(B->cmap));
3972: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3973: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
3974: if (PetscUnlikelyDebug(nnz)) {
3975: for (i = 0; i < B->rmap->n; i++) {
3976: PetscCheck(nnz[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be less than 0: local row %" PetscInt_FMT " value %" PetscInt_FMT, i, nnz[i]);
3977: PetscCheck(nnz[i] <= B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be greater than row length: local row %" PetscInt_FMT " value %" PetscInt_FMT " rowlength %" PetscInt_FMT, i, nnz[i], B->cmap->n);
3978: }
3979: }
3981: B->preallocated = PETSC_TRUE;
3982: if (!skipallocation) {
3983: if (!b->imax) { PetscCall(PetscMalloc1(B->rmap->n, &b->imax)); }
3984: if (!b->ilen) {
3985: /* b->ilen will count nonzeros in each row so far. */
3986: PetscCall(PetscCalloc1(B->rmap->n, &b->ilen));
3987: } else {
3988: PetscCall(PetscMemzero(b->ilen, B->rmap->n * sizeof(PetscInt)));
3989: }
3990: if (!b->ipre) PetscCall(PetscMalloc1(B->rmap->n, &b->ipre));
3991: if (!nnz) {
3992: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3993: else if (nz < 0) nz = 1;
3994: nz = PetscMin(nz, B->cmap->n);
3995: for (i = 0; i < B->rmap->n; i++) b->imax[i] = nz;
3996: nz = nz * B->rmap->n;
3997: } else {
3998: PetscInt64 nz64 = 0;
3999: for (i = 0; i < B->rmap->n; i++) {
4000: b->imax[i] = nnz[i];
4001: nz64 += nnz[i];
4002: }
4003: PetscCall(PetscIntCast(nz64, &nz));
4004: }
4006: /* allocate the matrix space */
4007: /* FIXME: should B's old memory be unlogged? */
4008: PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
4009: if (B->structure_only) {
4010: PetscCall(PetscMalloc1(nz, &b->j));
4011: PetscCall(PetscMalloc1(B->rmap->n + 1, &b->i));
4012: } else {
4013: PetscCall(PetscMalloc3(nz, &b->a, nz, &b->j, B->rmap->n + 1, &b->i));
4014: }
4015: b->i[0] = 0;
4016: for (i = 1; i < B->rmap->n + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];
4017: if (B->structure_only) {
4018: b->singlemalloc = PETSC_FALSE;
4019: b->free_a = PETSC_FALSE;
4020: } else {
4021: b->singlemalloc = PETSC_TRUE;
4022: b->free_a = PETSC_TRUE;
4023: }
4024: b->free_ij = PETSC_TRUE;
4025: } else {
4026: b->free_a = PETSC_FALSE;
4027: b->free_ij = PETSC_FALSE;
4028: }
4030: if (b->ipre && nnz != b->ipre && b->imax) {
4031: /* reserve user-requested sparsity */
4032: PetscCall(PetscArraycpy(b->ipre, b->imax, B->rmap->n));
4033: }
4035: b->nz = 0;
4036: b->maxnz = nz;
4037: B->info.nz_unneeded = (double)b->maxnz;
4038: if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
4039: B->was_assembled = PETSC_FALSE;
4040: B->assembled = PETSC_FALSE;
4041: /* We simply deem preallocation has changed nonzero state. Updating the state
4042: will give clients (like AIJKokkos) a chance to know something has happened.
4043: */
4044: B->nonzerostate++;
4045: PetscFunctionReturn(PETSC_SUCCESS);
4046: }
4048: PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
4049: {
4050: Mat_SeqAIJ *a;
4051: PetscInt i;
4053: PetscFunctionBegin;
4056: /* Check local size. If zero, then return */
4057: if (!A->rmap->n) PetscFunctionReturn(PETSC_SUCCESS);
4059: a = (Mat_SeqAIJ *)A->data;
4060: /* if no saved info, we error out */
4061: PetscCheck(a->ipre, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "No saved preallocation info ");
4063: PetscCheck(a->i && a->j && a->a && a->imax && a->ilen, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "Memory info is incomplete, and can not reset preallocation ");
4065: PetscCall(PetscArraycpy(a->imax, a->ipre, A->rmap->n));
4066: PetscCall(PetscArrayzero(a->ilen, A->rmap->n));
4067: a->i[0] = 0;
4068: for (i = 1; i < A->rmap->n + 1; i++) a->i[i] = a->i[i - 1] + a->imax[i - 1];
4069: A->preallocated = PETSC_TRUE;
4070: a->nz = 0;
4071: a->maxnz = a->i[A->rmap->n];
4072: A->info.nz_unneeded = (double)a->maxnz;
4073: A->was_assembled = PETSC_FALSE;
4074: A->assembled = PETSC_FALSE;
4075: PetscFunctionReturn(PETSC_SUCCESS);
4076: }
4078: /*@
4079: MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in `MATSEQAIJ` format.
4081: Input Parameters:
4082: + B - the matrix
4083: . i - the indices into j for the start of each row (starts with zero)
4084: . j - the column indices for each row (starts with zero) these must be sorted for each row
4085: - v - optional values in the matrix
4087: Level: developer
4089: Notes:
4090: The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqAIJWithArrays()`
4092: This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
4093: structure will be the union of all the previous nonzero structures.
4095: Developer Notes:
4096: An optimization could be added to the implementation where it checks if the `i`, and `j` are identical to the current `i` and `j` and
4097: then just copies the `v` values directly with `PetscMemcpy()`.
4099: This routine could also take a `PetscCopyMode` argument to allow sharing the values instead of always copying them.
4101: .seealso: [](chapter_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MatResetPreallocation()`
4102: @*/
4103: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4104: {
4105: PetscFunctionBegin;
4108: PetscTryMethod(B, "MatSeqAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4109: PetscFunctionReturn(PETSC_SUCCESS);
4110: }
4112: PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4113: {
4114: PetscInt i;
4115: PetscInt m, n;
4116: PetscInt nz;
4117: PetscInt *nnz;
4119: PetscFunctionBegin;
4120: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);
4122: PetscCall(PetscLayoutSetUp(B->rmap));
4123: PetscCall(PetscLayoutSetUp(B->cmap));
4125: PetscCall(MatGetSize(B, &m, &n));
4126: PetscCall(PetscMalloc1(m + 1, &nnz));
4127: for (i = 0; i < m; i++) {
4128: nz = Ii[i + 1] - Ii[i];
4129: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
4130: nnz[i] = nz;
4131: }
4132: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
4133: PetscCall(PetscFree(nnz));
4135: for (i = 0; i < m; i++) PetscCall(MatSetValues_SeqAIJ(B, 1, &i, Ii[i + 1] - Ii[i], J + Ii[i], v ? v + Ii[i] : NULL, INSERT_VALUES));
4137: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4138: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4140: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
4141: PetscFunctionReturn(PETSC_SUCCESS);
4142: }
4144: /*@
4145: MatSeqAIJKron - Computes `C`, the Kronecker product of `A` and `B`.
4147: Input Parameters:
4148: + A - left-hand side matrix
4149: . B - right-hand side matrix
4150: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
4152: Output Parameter:
4153: . C - Kronecker product of `A` and `B`
4155: Level: intermediate
4157: Note:
4158: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the product matrix has not changed from that last call to `MatSeqAIJKron()`.
4160: .seealso: [](chapter_matrices), `Mat`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATKAIJ`, `MatReuse`
4161: @*/
4162: PetscErrorCode MatSeqAIJKron(Mat A, Mat B, MatReuse reuse, Mat *C)
4163: {
4164: PetscFunctionBegin;
4170: if (reuse == MAT_REUSE_MATRIX) {
4173: }
4174: PetscTryMethod(A, "MatSeqAIJKron_C", (Mat, Mat, MatReuse, Mat *), (A, B, reuse, C));
4175: PetscFunctionReturn(PETSC_SUCCESS);
4176: }
4178: PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A, Mat B, MatReuse reuse, Mat *C)
4179: {
4180: Mat newmat;
4181: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
4182: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
4183: PetscScalar *v;
4184: const PetscScalar *aa, *ba;
4185: PetscInt *i, *j, m, n, p, q, nnz = 0, am = A->rmap->n, bm = B->rmap->n, an = A->cmap->n, bn = B->cmap->n;
4186: PetscBool flg;
4188: PetscFunctionBegin;
4189: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4190: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4191: PetscCheck(!B->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4192: PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4193: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJ, &flg));
4194: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatType %s", ((PetscObject)B)->type_name);
4195: PetscCheck(reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatReuse %d", (int)reuse);
4196: if (reuse == MAT_INITIAL_MATRIX) {
4197: PetscCall(PetscMalloc2(am * bm + 1, &i, a->i[am] * b->i[bm], &j));
4198: PetscCall(MatCreate(PETSC_COMM_SELF, &newmat));
4199: PetscCall(MatSetSizes(newmat, am * bm, an * bn, am * bm, an * bn));
4200: PetscCall(MatSetType(newmat, MATAIJ));
4201: i[0] = 0;
4202: for (m = 0; m < am; ++m) {
4203: for (p = 0; p < bm; ++p) {
4204: i[m * bm + p + 1] = i[m * bm + p] + (a->i[m + 1] - a->i[m]) * (b->i[p + 1] - b->i[p]);
4205: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4206: for (q = b->i[p]; q < b->i[p + 1]; ++q) j[nnz++] = a->j[n] * bn + b->j[q];
4207: }
4208: }
4209: }
4210: PetscCall(MatSeqAIJSetPreallocationCSR(newmat, i, j, NULL));
4211: *C = newmat;
4212: PetscCall(PetscFree2(i, j));
4213: nnz = 0;
4214: }
4215: PetscCall(MatSeqAIJGetArray(*C, &v));
4216: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4217: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
4218: for (m = 0; m < am; ++m) {
4219: for (p = 0; p < bm; ++p) {
4220: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4221: for (q = b->i[p]; q < b->i[p + 1]; ++q) v[nnz++] = aa[n] * ba[q];
4222: }
4223: }
4224: }
4225: PetscCall(MatSeqAIJRestoreArray(*C, &v));
4226: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
4227: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
4228: PetscFunctionReturn(PETSC_SUCCESS);
4229: }
4231: #include <../src/mat/impls/dense/seq/dense.h>
4232: #include <petsc/private/kernels/petscaxpy.h>
4234: /*
4235: Computes (B'*A')' since computing B*A directly is untenable
4237: n p p
4238: [ ] [ ] [ ]
4239: m [ A ] * n [ B ] = m [ C ]
4240: [ ] [ ] [ ]
4242: */
4243: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A, Mat B, Mat C)
4244: {
4245: Mat_SeqDense *sub_a = (Mat_SeqDense *)A->data;
4246: Mat_SeqAIJ *sub_b = (Mat_SeqAIJ *)B->data;
4247: Mat_SeqDense *sub_c = (Mat_SeqDense *)C->data;
4248: PetscInt i, j, n, m, q, p;
4249: const PetscInt *ii, *idx;
4250: const PetscScalar *b, *a, *a_q;
4251: PetscScalar *c, *c_q;
4252: PetscInt clda = sub_c->lda;
4253: PetscInt alda = sub_a->lda;
4255: PetscFunctionBegin;
4256: m = A->rmap->n;
4257: n = A->cmap->n;
4258: p = B->cmap->n;
4259: a = sub_a->v;
4260: b = sub_b->a;
4261: c = sub_c->v;
4262: if (clda == m) {
4263: PetscCall(PetscArrayzero(c, m * p));
4264: } else {
4265: for (j = 0; j < p; j++)
4266: for (i = 0; i < m; i++) c[j * clda + i] = 0.0;
4267: }
4268: ii = sub_b->i;
4269: idx = sub_b->j;
4270: for (i = 0; i < n; i++) {
4271: q = ii[i + 1] - ii[i];
4272: while (q-- > 0) {
4273: c_q = c + clda * (*idx);
4274: a_q = a + alda * i;
4275: PetscKernelAXPY(c_q, *b, a_q, m);
4276: idx++;
4277: b++;
4278: }
4279: }
4280: PetscFunctionReturn(PETSC_SUCCESS);
4281: }
4283: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A, Mat B, PetscReal fill, Mat C)
4284: {
4285: PetscInt m = A->rmap->n, n = B->cmap->n;
4286: PetscBool cisdense;
4288: PetscFunctionBegin;
4289: PetscCheck(A->cmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "A->cmap->n %" PetscInt_FMT " != B->rmap->n %" PetscInt_FMT, A->cmap->n, B->rmap->n);
4290: PetscCall(MatSetSizes(C, m, n, m, n));
4291: PetscCall(MatSetBlockSizesFromMats(C, A, B));
4292: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATSEQDENSE, MATSEQDENSECUDA, MATSEQDENSEHIP, ""));
4293: if (!cisdense) PetscCall(MatSetType(C, MATDENSE));
4294: PetscCall(MatSetUp(C));
4296: C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4297: PetscFunctionReturn(PETSC_SUCCESS);
4298: }
4300: /*MC
4301: MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4302: based on compressed sparse row format.
4304: Options Database Key:
4305: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()
4307: Level: beginner
4309: Notes:
4310: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
4311: in this case the values associated with the rows and columns one passes in are set to zero
4312: in the matrix
4314: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
4315: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
4317: Developer Note:
4318: It would be nice if all matrix formats supported passing `NULL` in for the numerical values
4320: .seealso: [](chapter_matrices), `Mat`, `MatCreateSeqAIJ()`, `MatSetFromOptions()`, `MatSetType()`, `MatCreate()`, `MatType`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4321: M*/
4323: /*MC
4324: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
4326: This matrix type is identical to `MATSEQAIJ` when constructed with a single process communicator,
4327: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
4328: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4329: for communicators controlling multiple processes. It is recommended that you call both of
4330: the above preallocation routines for simplicity.
4332: Options Database Key:
4333: . -mat_type aij - sets the matrix type to "aij" during a call to `MatSetFromOptions()`
4335: Level: beginner
4337: Note:
4338: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, and also automatically switches over to use inodes when
4339: enough exist.
4341: .seealso: [](chapter_matrices), `Mat`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4342: M*/
4344: /*MC
4345: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
4347: Options Database Key:
4348: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to `MatSetFromOptions()`
4350: Level: beginner
4352: Note:
4353: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
4354: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
4355: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4356: for communicators controlling multiple processes. It is recommended that you call both of
4357: the above preallocation routines for simplicity.
4359: .seealso: [](chapter_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
4360: M*/
4362: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
4363: #if defined(PETSC_HAVE_ELEMENTAL)
4364: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
4365: #endif
4366: #if defined(PETSC_HAVE_SCALAPACK)
4367: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
4368: #endif
4369: #if defined(PETSC_HAVE_HYPRE)
4370: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A, MatType, MatReuse, Mat *);
4371: #endif
4373: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat, MatType, MatReuse, Mat *);
4374: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
4375: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
4377: /*@C
4378: MatSeqAIJGetArray - gives read/write access to the array where the data for a `MATSEQAIJ` matrix is stored
4380: Not Collective
4382: Input Parameter:
4383: . mat - a `MATSEQAIJ` matrix
4385: Output Parameter:
4386: . array - pointer to the data
4388: Level: intermediate
4390: Fortran Note:
4391: `MatSeqAIJGetArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJGetArrayF90()`
4393: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4394: @*/
4395: PetscErrorCode MatSeqAIJGetArray(Mat A, PetscScalar **array)
4396: {
4397: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4399: PetscFunctionBegin;
4400: if (aij->ops->getarray) {
4401: PetscCall((*aij->ops->getarray)(A, array));
4402: } else {
4403: *array = aij->a;
4404: }
4405: PetscFunctionReturn(PETSC_SUCCESS);
4406: }
4408: /*@C
4409: MatSeqAIJRestoreArray - returns access to the array where the data for a `MATSEQAIJ` matrix is stored obtained by `MatSeqAIJGetArray()`
4411: Not Collective
4413: Input Parameters:
4414: + mat - a `MATSEQAIJ` matrix
4415: - array - pointer to the data
4417: Level: intermediate
4419: Fortran Note:
4420: `MatSeqAIJRestoreArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJRestoreArrayF90()`
4422: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayF90()`
4423: @*/
4424: PetscErrorCode MatSeqAIJRestoreArray(Mat A, PetscScalar **array)
4425: {
4426: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4428: PetscFunctionBegin;
4429: if (aij->ops->restorearray) {
4430: PetscCall((*aij->ops->restorearray)(A, array));
4431: } else {
4432: *array = NULL;
4433: }
4434: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4435: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4436: PetscFunctionReturn(PETSC_SUCCESS);
4437: }
4439: /*@C
4440: MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4442: Not Collective; No Fortran Support
4444: Input Parameter:
4445: . mat - a `MATSEQAIJ` matrix
4447: Output Parameter:
4448: . array - pointer to the data
4450: Level: intermediate
4452: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4453: @*/
4454: PetscErrorCode MatSeqAIJGetArrayRead(Mat A, const PetscScalar **array)
4455: {
4456: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4458: PetscFunctionBegin;
4459: if (aij->ops->getarrayread) {
4460: PetscCall((*aij->ops->getarrayread)(A, array));
4461: } else {
4462: *array = aij->a;
4463: }
4464: PetscFunctionReturn(PETSC_SUCCESS);
4465: }
4467: /*@C
4468: MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJGetArrayRead()`
4470: Not Collective; No Fortran Support
4472: Input Parameter:
4473: . mat - a `MATSEQAIJ` matrix
4475: Output Parameter:
4476: . array - pointer to the data
4478: Level: intermediate
4480: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4481: @*/
4482: PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A, const PetscScalar **array)
4483: {
4484: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4486: PetscFunctionBegin;
4487: if (aij->ops->restorearrayread) {
4488: PetscCall((*aij->ops->restorearrayread)(A, array));
4489: } else {
4490: *array = NULL;
4491: }
4492: PetscFunctionReturn(PETSC_SUCCESS);
4493: }
4495: /*@C
4496: MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4498: Not Collective; No Fortran Support
4500: Input Parameter:
4501: . mat - a `MATSEQAIJ` matrix
4503: Output Parameter:
4504: . array - pointer to the data
4506: Level: intermediate
4508: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4509: @*/
4510: PetscErrorCode MatSeqAIJGetArrayWrite(Mat A, PetscScalar **array)
4511: {
4512: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4514: PetscFunctionBegin;
4515: if (aij->ops->getarraywrite) {
4516: PetscCall((*aij->ops->getarraywrite)(A, array));
4517: } else {
4518: *array = aij->a;
4519: }
4520: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4521: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4522: PetscFunctionReturn(PETSC_SUCCESS);
4523: }
4525: /*@C
4526: MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead
4528: Not Collective; No Fortran Support
4530: Input Parameter:
4531: . mat - a MATSEQAIJ matrix
4533: Output Parameter:
4534: . array - pointer to the data
4536: Level: intermediate
4538: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4539: @*/
4540: PetscErrorCode MatSeqAIJRestoreArrayWrite(Mat A, PetscScalar **array)
4541: {
4542: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4544: PetscFunctionBegin;
4545: if (aij->ops->restorearraywrite) {
4546: PetscCall((*aij->ops->restorearraywrite)(A, array));
4547: } else {
4548: *array = NULL;
4549: }
4550: PetscFunctionReturn(PETSC_SUCCESS);
4551: }
4553: /*@C
4554: MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the `MATSEQAIJ` matrix
4556: Not Collective; No Fortran Support
4558: Input Parameter:
4559: . mat - a matrix of type `MATSEQAIJ` or its subclasses
4561: Output Parameters:
4562: + i - row map array of the matrix
4563: . j - column index array of the matrix
4564: . a - data array of the matrix
4565: - memtype - memory type of the arrays
4567: Level: Developer
4569: Notes:
4570: Any of the output parameters can be `NULL`, in which case the corresponding value is not returned.
4571: If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host.
4573: One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix.
4574: If the matrix is assembled, the data array `a` is guaranteed to have the latest values of the matrix.
4576: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4577: @*/
4578: PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat, const PetscInt **i, const PetscInt **j, PetscScalar **a, PetscMemType *mtype)
4579: {
4580: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
4582: PetscFunctionBegin;
4583: PetscCheck(mat->preallocated, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "matrix is not preallocated");
4584: if (aij->ops->getcsrandmemtype) {
4585: PetscCall((*aij->ops->getcsrandmemtype)(mat, i, j, a, mtype));
4586: } else {
4587: if (i) *i = aij->i;
4588: if (j) *j = aij->j;
4589: if (a) *a = aij->a;
4590: if (mtype) *mtype = PETSC_MEMTYPE_HOST;
4591: }
4592: PetscFunctionReturn(PETSC_SUCCESS);
4593: }
4595: /*@C
4596: MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row
4598: Not Collective
4600: Input Parameter:
4601: . mat - a `MATSEQAIJ` matrix
4603: Output Parameter:
4604: . nz - the maximum number of nonzeros in any row
4606: Level: intermediate
4608: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4609: @*/
4610: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A, PetscInt *nz)
4611: {
4612: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4614: PetscFunctionBegin;
4615: *nz = aij->rmax;
4616: PetscFunctionReturn(PETSC_SUCCESS);
4617: }
4619: PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
4620: {
4621: MPI_Comm comm;
4622: PetscInt *i, *j;
4623: PetscInt M, N, row;
4624: PetscCount k, p, q, nneg, nnz, start, end; /* Index the coo array, so use PetscCount as their type */
4625: PetscInt *Ai; /* Change to PetscCount once we use it for row pointers */
4626: PetscInt *Aj;
4627: PetscScalar *Aa;
4628: Mat_SeqAIJ *seqaij = (Mat_SeqAIJ *)(mat->data);
4629: MatType rtype;
4630: PetscCount *perm, *jmap;
4632: PetscFunctionBegin;
4633: PetscCall(MatResetPreallocationCOO_SeqAIJ(mat));
4634: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
4635: PetscCall(MatGetSize(mat, &M, &N));
4636: i = coo_i;
4637: j = coo_j;
4638: PetscCall(PetscMalloc1(coo_n, &perm));
4639: for (k = 0; k < coo_n; k++) { /* Ignore entries with negative row or col indices */
4640: if (j[k] < 0) i[k] = -1;
4641: perm[k] = k;
4642: }
4644: /* Sort by row */
4645: PetscCall(PetscSortIntWithIntCountArrayPair(coo_n, i, j, perm));
4646: for (k = 0; k < coo_n; k++) {
4647: if (i[k] >= 0) break;
4648: } /* Advance k to the first row with a non-negative index */
4649: nneg = k;
4650: PetscCall(PetscMalloc1(coo_n - nneg + 1, &jmap)); /* +1 to make a CSR-like data structure. jmap[i] originally is the number of repeats for i-th nonzero */
4651: nnz = 0; /* Total number of unique nonzeros to be counted */
4652: jmap++; /* Inc jmap by 1 for convenience */
4654: PetscCall(PetscCalloc1(M + 1, &Ai)); /* CSR of A */
4655: PetscCall(PetscMalloc1(coo_n - nneg, &Aj)); /* We have at most coo_n-nneg unique nonzeros */
4657: /* In each row, sort by column, then unique column indices to get row length */
4658: Ai++; /* Inc by 1 for convenience */
4659: q = 0; /* q-th unique nonzero, with q starting from 0 */
4660: while (k < coo_n) {
4661: row = i[k];
4662: start = k; /* [start,end) indices for this row */
4663: while (k < coo_n && i[k] == row) k++;
4664: end = k;
4665: PetscCall(PetscSortIntWithCountArray(end - start, j + start, perm + start));
4666: /* Find number of unique col entries in this row */
4667: Aj[q] = j[start]; /* Log the first nonzero in this row */
4668: jmap[q] = 1; /* Number of repeats of this nozero entry */
4669: Ai[row] = 1;
4670: nnz++;
4672: for (p = start + 1; p < end; p++) { /* Scan remaining nonzero in this row */
4673: if (j[p] != j[p - 1]) { /* Meet a new nonzero */
4674: q++;
4675: jmap[q] = 1;
4676: Aj[q] = j[p];
4677: Ai[row]++;
4678: nnz++;
4679: } else {
4680: jmap[q]++;
4681: }
4682: }
4683: q++; /* Move to next row and thus next unique nonzero */
4684: }
4686: Ai--; /* Back to the beginning of Ai[] */
4687: for (k = 0; k < M; k++) Ai[k + 1] += Ai[k];
4688: jmap--; /* Back to the beginning of jmap[] */
4689: jmap[0] = 0;
4690: for (k = 0; k < nnz; k++) jmap[k + 1] += jmap[k];
4691: if (nnz < coo_n - nneg) { /* Realloc with actual number of unique nonzeros */
4692: PetscCount *jmap_new;
4693: PetscInt *Aj_new;
4695: PetscCall(PetscMalloc1(nnz + 1, &jmap_new));
4696: PetscCall(PetscArraycpy(jmap_new, jmap, nnz + 1));
4697: PetscCall(PetscFree(jmap));
4698: jmap = jmap_new;
4700: PetscCall(PetscMalloc1(nnz, &Aj_new));
4701: PetscCall(PetscArraycpy(Aj_new, Aj, nnz));
4702: PetscCall(PetscFree(Aj));
4703: Aj = Aj_new;
4704: }
4706: if (nneg) { /* Discard heading entries with negative indices in perm[], as we'll access it from index 0 in MatSetValuesCOO */
4707: PetscCount *perm_new;
4709: PetscCall(PetscMalloc1(coo_n - nneg, &perm_new));
4710: PetscCall(PetscArraycpy(perm_new, perm + nneg, coo_n - nneg));
4711: PetscCall(PetscFree(perm));
4712: perm = perm_new;
4713: }
4715: PetscCall(MatGetRootType_Private(mat, &rtype));
4716: PetscCall(PetscCalloc1(nnz, &Aa)); /* Zero the matrix */
4717: PetscCall(MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF, M, N, Ai, Aj, Aa, rtype, mat));
4719: seqaij->singlemalloc = PETSC_FALSE; /* Ai, Aj and Aa are not allocated in one big malloc */
4720: seqaij->free_a = seqaij->free_ij = PETSC_TRUE; /* Let newmat own Ai, Aj and Aa */
4721: /* Record COO fields */
4722: seqaij->coo_n = coo_n;
4723: seqaij->Atot = coo_n - nneg; /* Annz is seqaij->nz, so no need to record that again */
4724: seqaij->jmap = jmap; /* of length nnz+1 */
4725: seqaij->perm = perm;
4726: PetscFunctionReturn(PETSC_SUCCESS);
4727: }
4729: static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A, const PetscScalar v[], InsertMode imode)
4730: {
4731: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)A->data;
4732: PetscCount i, j, Annz = aseq->nz;
4733: PetscCount *perm = aseq->perm, *jmap = aseq->jmap;
4734: PetscScalar *Aa;
4736: PetscFunctionBegin;
4737: PetscCall(MatSeqAIJGetArray(A, &Aa));
4738: for (i = 0; i < Annz; i++) {
4739: PetscScalar sum = 0.0;
4740: for (j = jmap[i]; j < jmap[i + 1]; j++) sum += v[perm[j]];
4741: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
4742: }
4743: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
4744: PetscFunctionReturn(PETSC_SUCCESS);
4745: }
4747: #if defined(PETSC_HAVE_CUDA)
4748: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
4749: #endif
4750: #if defined(PETSC_HAVE_HIP)
4751: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
4752: #endif
4753: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4754: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat, MatType, MatReuse, Mat *);
4755: #endif
4757: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4758: {
4759: Mat_SeqAIJ *b;
4760: PetscMPIInt size;
4762: PetscFunctionBegin;
4763: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
4764: PetscCheck(size <= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Comm must be of size 1");
4766: PetscCall(PetscNew(&b));
4768: B->data = (void *)b;
4770: PetscCall(PetscMemcpy(B->ops, &MatOps_Values, sizeof(struct _MatOps)));
4771: if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
4773: b->row = NULL;
4774: b->col = NULL;
4775: b->icol = NULL;
4776: b->reallocs = 0;
4777: b->ignorezeroentries = PETSC_FALSE;
4778: b->roworiented = PETSC_TRUE;
4779: b->nonew = 0;
4780: b->diag = NULL;
4781: b->solve_work = NULL;
4782: B->spptr = NULL;
4783: b->saved_values = NULL;
4784: b->idiag = NULL;
4785: b->mdiag = NULL;
4786: b->ssor_work = NULL;
4787: b->omega = 1.0;
4788: b->fshift = 0.0;
4789: b->idiagvalid = PETSC_FALSE;
4790: b->ibdiagvalid = PETSC_FALSE;
4791: b->keepnonzeropattern = PETSC_FALSE;
4793: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4794: #if defined(PETSC_HAVE_MATLAB)
4795: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEnginePut_C", MatlabEnginePut_SeqAIJ));
4796: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEngineGet_C", MatlabEngineGet_SeqAIJ));
4797: #endif
4798: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetColumnIndices_C", MatSeqAIJSetColumnIndices_SeqAIJ));
4799: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqAIJ));
4800: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqAIJ));
4801: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsbaij_C", MatConvert_SeqAIJ_SeqSBAIJ));
4802: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqbaij_C", MatConvert_SeqAIJ_SeqBAIJ));
4803: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijperm_C", MatConvert_SeqAIJ_SeqAIJPERM));
4804: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijsell_C", MatConvert_SeqAIJ_SeqAIJSELL));
4805: #if defined(PETSC_HAVE_MKL_SPARSE)
4806: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijmkl_C", MatConvert_SeqAIJ_SeqAIJMKL));
4807: #endif
4808: #if defined(PETSC_HAVE_CUDA)
4809: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcusparse_C", MatConvert_SeqAIJ_SeqAIJCUSPARSE));
4810: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4811: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJ));
4812: #endif
4813: #if defined(PETSC_HAVE_HIP)
4814: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijhipsparse_C", MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
4815: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4816: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", MatProductSetFromOptions_SeqAIJ));
4817: #endif
4818: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4819: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijkokkos_C", MatConvert_SeqAIJ_SeqAIJKokkos));
4820: #endif
4821: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcrl_C", MatConvert_SeqAIJ_SeqAIJCRL));
4822: #if defined(PETSC_HAVE_ELEMENTAL)
4823: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_elemental_C", MatConvert_SeqAIJ_Elemental));
4824: #endif
4825: #if defined(PETSC_HAVE_SCALAPACK)
4826: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
4827: #endif
4828: #if defined(PETSC_HAVE_HYPRE)
4829: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_hypre_C", MatConvert_AIJ_HYPRE));
4830: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
4831: #endif
4832: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqdense_C", MatConvert_SeqAIJ_SeqDense));
4833: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsell_C", MatConvert_SeqAIJ_SeqSELL));
4834: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_is_C", MatConvert_XAIJ_IS));
4835: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqAIJ));
4836: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsHermitianTranspose_C", MatIsTranspose_SeqAIJ));
4837: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocation_C", MatSeqAIJSetPreallocation_SeqAIJ));
4838: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_SeqAIJ));
4839: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocationCSR_C", MatSeqAIJSetPreallocationCSR_SeqAIJ));
4840: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatReorderForNonzeroDiagonal_C", MatReorderForNonzeroDiagonal_SeqAIJ));
4841: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_seqaij_C", MatProductSetFromOptions_IS_XAIJ));
4842: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqdense_seqaij_C", MatProductSetFromOptions_SeqDense_SeqAIJ));
4843: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4844: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJKron_C", MatSeqAIJKron_SeqAIJ));
4845: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJ));
4846: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJ));
4847: PetscCall(MatCreate_SeqAIJ_Inode(B));
4848: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4849: PetscCall(MatSeqAIJSetTypeFromOptions(B)); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4850: PetscFunctionReturn(PETSC_SUCCESS);
4851: }
4853: /*
4854: Given a matrix generated with MatGetFactor() duplicates all the information in A into C
4855: */
4856: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
4857: {
4858: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data, *a = (Mat_SeqAIJ *)A->data;
4859: PetscInt m = A->rmap->n, i;
4861: PetscFunctionBegin;
4862: PetscCheck(A->assembled || cpvalues == MAT_DO_NOT_COPY_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");
4864: C->factortype = A->factortype;
4865: c->row = NULL;
4866: c->col = NULL;
4867: c->icol = NULL;
4868: c->reallocs = 0;
4870: C->assembled = A->assembled;
4872: if (A->preallocated) {
4873: PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
4874: PetscCall(PetscLayoutReference(A->cmap, &C->cmap));
4876: if (!A->hash_active) {
4877: PetscCall(PetscMalloc1(m, &c->imax));
4878: PetscCall(PetscMemcpy(c->imax, a->imax, m * sizeof(PetscInt)));
4879: PetscCall(PetscMalloc1(m, &c->ilen));
4880: PetscCall(PetscMemcpy(c->ilen, a->ilen, m * sizeof(PetscInt)));
4882: /* allocate the matrix space */
4883: if (mallocmatspace) {
4884: PetscCall(PetscMalloc3(a->i[m], &c->a, a->i[m], &c->j, m + 1, &c->i));
4886: c->singlemalloc = PETSC_TRUE;
4888: PetscCall(PetscArraycpy(c->i, a->i, m + 1));
4889: if (m > 0) {
4890: PetscCall(PetscArraycpy(c->j, a->j, a->i[m]));
4891: if (cpvalues == MAT_COPY_VALUES) {
4892: const PetscScalar *aa;
4894: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4895: PetscCall(PetscArraycpy(c->a, aa, a->i[m]));
4896: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4897: } else {
4898: PetscCall(PetscArrayzero(c->a, a->i[m]));
4899: }
4900: }
4901: }
4902: C->preallocated = PETSC_TRUE;
4903: } else {
4904: PetscCheck(mallocmatspace, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot malloc matrix memory from a non-preallocated matrix");
4905: PetscCall(MatSetUp(C));
4906: }
4908: c->ignorezeroentries = a->ignorezeroentries;
4909: c->roworiented = a->roworiented;
4910: c->nonew = a->nonew;
4911: if (a->diag) {
4912: PetscCall(PetscMalloc1(m + 1, &c->diag));
4913: PetscCall(PetscMemcpy(c->diag, a->diag, m * sizeof(PetscInt)));
4914: } else c->diag = NULL;
4916: c->solve_work = NULL;
4917: c->saved_values = NULL;
4918: c->idiag = NULL;
4919: c->ssor_work = NULL;
4920: c->keepnonzeropattern = a->keepnonzeropattern;
4921: c->free_a = PETSC_TRUE;
4922: c->free_ij = PETSC_TRUE;
4924: c->rmax = a->rmax;
4925: c->nz = a->nz;
4926: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
4928: c->compressedrow.use = a->compressedrow.use;
4929: c->compressedrow.nrows = a->compressedrow.nrows;
4930: if (a->compressedrow.use) {
4931: i = a->compressedrow.nrows;
4932: PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i, &c->compressedrow.rindex));
4933: PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
4934: PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
4935: } else {
4936: c->compressedrow.use = PETSC_FALSE;
4937: c->compressedrow.i = NULL;
4938: c->compressedrow.rindex = NULL;
4939: }
4940: c->nonzerorowcnt = a->nonzerorowcnt;
4941: C->nonzerostate = A->nonzerostate;
4943: PetscCall(MatDuplicate_SeqAIJ_Inode(A, cpvalues, &C));
4944: }
4945: PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
4946: PetscFunctionReturn(PETSC_SUCCESS);
4947: }
4949: PetscErrorCode MatDuplicate_SeqAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
4950: {
4951: PetscFunctionBegin;
4952: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
4953: PetscCall(MatSetSizes(*B, A->rmap->n, A->cmap->n, A->rmap->n, A->cmap->n));
4954: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) PetscCall(MatSetBlockSizesFromMats(*B, A, A));
4955: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
4956: PetscCall(MatDuplicateNoCreate_SeqAIJ(*B, A, cpvalues, PETSC_TRUE));
4957: PetscFunctionReturn(PETSC_SUCCESS);
4958: }
4960: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4961: {
4962: PetscBool isbinary, ishdf5;
4964: PetscFunctionBegin;
4967: /* force binary viewer to load .info file if it has not yet done so */
4968: PetscCall(PetscViewerSetUp(viewer));
4969: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
4970: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
4971: if (isbinary) {
4972: PetscCall(MatLoad_SeqAIJ_Binary(newMat, viewer));
4973: } else if (ishdf5) {
4974: #if defined(PETSC_HAVE_HDF5)
4975: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
4976: #else
4977: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
4978: #endif
4979: } else {
4980: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "Viewer type %s not yet supported for reading %s matrices", ((PetscObject)viewer)->type_name, ((PetscObject)newMat)->type_name);
4981: }
4982: PetscFunctionReturn(PETSC_SUCCESS);
4983: }
4985: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
4986: {
4987: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->data;
4988: PetscInt header[4], *rowlens, M, N, nz, sum, rows, cols, i;
4990: PetscFunctionBegin;
4991: PetscCall(PetscViewerSetUp(viewer));
4993: /* read in matrix header */
4994: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
4995: PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
4996: M = header[1];
4997: N = header[2];
4998: nz = header[3];
4999: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
5000: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
5001: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqAIJ");
5003: /* set block sizes from the viewer's .info file */
5004: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
5005: /* set local and global sizes if not set already */
5006: if (mat->rmap->n < 0) mat->rmap->n = M;
5007: if (mat->cmap->n < 0) mat->cmap->n = N;
5008: if (mat->rmap->N < 0) mat->rmap->N = M;
5009: if (mat->cmap->N < 0) mat->cmap->N = N;
5010: PetscCall(PetscLayoutSetUp(mat->rmap));
5011: PetscCall(PetscLayoutSetUp(mat->cmap));
5013: /* check if the matrix sizes are correct */
5014: PetscCall(MatGetSize(mat, &rows, &cols));
5015: 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);
5017: /* read in row lengths */
5018: PetscCall(PetscMalloc1(M, &rowlens));
5019: PetscCall(PetscViewerBinaryRead(viewer, rowlens, M, NULL, PETSC_INT));
5020: /* check if sum(rowlens) is same as nz */
5021: sum = 0;
5022: for (i = 0; i < M; i++) sum += rowlens[i];
5023: PetscCheck(sum == nz, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Inconsistent matrix data in file: nonzeros = %" PetscInt_FMT ", sum-row-lengths = %" PetscInt_FMT, nz, sum);
5024: /* preallocate and check sizes */
5025: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(mat, 0, rowlens));
5026: PetscCall(MatGetSize(mat, &rows, &cols));
5027: PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols);
5028: /* store row lengths */
5029: PetscCall(PetscArraycpy(a->ilen, rowlens, M));
5030: PetscCall(PetscFree(rowlens));
5032: /* fill in "i" row pointers */
5033: a->i[0] = 0;
5034: for (i = 0; i < M; i++) a->i[i + 1] = a->i[i] + a->ilen[i];
5035: /* read in "j" column indices */
5036: PetscCall(PetscViewerBinaryRead(viewer, a->j, nz, NULL, PETSC_INT));
5037: /* read in "a" nonzero values */
5038: PetscCall(PetscViewerBinaryRead(viewer, a->a, nz, NULL, PETSC_SCALAR));
5040: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
5041: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
5042: PetscFunctionReturn(PETSC_SUCCESS);
5043: }
5045: PetscErrorCode MatEqual_SeqAIJ(Mat A, Mat B, PetscBool *flg)
5046: {
5047: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data;
5048: const PetscScalar *aa, *ba;
5049: #if defined(PETSC_USE_COMPLEX)
5050: PetscInt k;
5051: #endif
5053: PetscFunctionBegin;
5054: /* If the matrix dimensions are not equal,or no of nonzeros */
5055: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) || (a->nz != b->nz)) {
5056: *flg = PETSC_FALSE;
5057: PetscFunctionReturn(PETSC_SUCCESS);
5058: }
5060: /* if the a->i are the same */
5061: PetscCall(PetscArraycmp(a->i, b->i, A->rmap->n + 1, flg));
5062: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5064: /* if a->j are the same */
5065: PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg));
5066: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5068: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5069: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
5070: /* if a->a are the same */
5071: #if defined(PETSC_USE_COMPLEX)
5072: for (k = 0; k < a->nz; k++) {
5073: if (PetscRealPart(aa[k]) != PetscRealPart(ba[k]) || PetscImaginaryPart(aa[k]) != PetscImaginaryPart(ba[k])) {
5074: *flg = PETSC_FALSE;
5075: PetscFunctionReturn(PETSC_SUCCESS);
5076: }
5077: }
5078: #else
5079: PetscCall(PetscArraycmp(aa, ba, a->nz, flg));
5080: #endif
5081: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
5082: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
5083: PetscFunctionReturn(PETSC_SUCCESS);
5084: }
5086: /*@
5087: MatCreateSeqAIJWithArrays - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in CSR format)
5088: provided by the user.
5090: Collective
5092: Input Parameters:
5093: + comm - must be an MPI communicator of size 1
5094: . m - number of rows
5095: . n - number of columns
5096: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
5097: . j - column indices
5098: - a - matrix values
5100: Output Parameter:
5101: . mat - the matrix
5103: Level: intermediate
5105: Notes:
5106: The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
5107: once the matrix is destroyed and not before
5109: You cannot set new nonzero locations into this matrix, that will generate an error.
5111: The `i` and `j` indices are 0 based
5113: The format which is used for the sparse matrix input, is equivalent to a
5114: row-major ordering.. i.e for the following matrix, the input data expected is
5115: as shown
5116: .vb
5117: 1 0 0
5118: 2 0 3
5119: 4 5 6
5121: i = {0,1,3,6} [size = nrow+1 = 3+1]
5122: j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row
5123: v = {1,2,3,4,5,6} [size = 6]
5124: .ve
5126: .seealso: [](chapter_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateMPIAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`
5127: @*/
5128: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
5129: {
5130: PetscInt ii;
5131: Mat_SeqAIJ *aij;
5132: PetscInt jj;
5134: PetscFunctionBegin;
5135: PetscCheck(m <= 0 || i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
5136: PetscCall(MatCreate(comm, mat));
5137: PetscCall(MatSetSizes(*mat, m, n, m, n));
5138: /* PetscCall(MatSetBlockSizes(*mat,,)); */
5139: PetscCall(MatSetType(*mat, MATSEQAIJ));
5140: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, MAT_SKIP_ALLOCATION, NULL));
5141: aij = (Mat_SeqAIJ *)(*mat)->data;
5142: PetscCall(PetscMalloc1(m, &aij->imax));
5143: PetscCall(PetscMalloc1(m, &aij->ilen));
5145: aij->i = i;
5146: aij->j = j;
5147: aij->a = a;
5148: aij->singlemalloc = PETSC_FALSE;
5149: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
5150: aij->free_a = PETSC_FALSE;
5151: aij->free_ij = PETSC_FALSE;
5153: for (ii = 0, aij->nonzerorowcnt = 0, aij->rmax = 0; ii < m; ii++) {
5154: aij->ilen[ii] = aij->imax[ii] = i[ii + 1] - i[ii];
5155: if (PetscDefined(USE_DEBUG)) {
5156: PetscCheck(i[ii + 1] - i[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row length in i (row indices) row = %" PetscInt_FMT " length = %" PetscInt_FMT, ii, i[ii + 1] - i[ii]);
5157: for (jj = i[ii] + 1; jj < i[ii + 1]; jj++) {
5158: PetscCheck(j[jj] >= j[jj - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is not sorted", jj - i[ii], j[jj], ii);
5159: PetscCheck(j[jj] != j[jj - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is identical to previous entry", jj - i[ii], j[jj], ii);
5160: }
5161: }
5162: }
5163: if (PetscDefined(USE_DEBUG)) {
5164: for (ii = 0; ii < aij->i[m]; ii++) {
5165: PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
5166: PetscCheck(j[ii] <= n - 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index to large at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
5167: }
5168: }
5170: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5171: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5172: PetscFunctionReturn(PETSC_SUCCESS);
5173: }
5175: /*@
5176: MatCreateSeqAIJFromTriple - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in COO format)
5177: provided by the user.
5179: Collective
5181: Input Parameters:
5182: + comm - must be an MPI communicator of size 1
5183: . m - number of rows
5184: . n - number of columns
5185: . i - row indices
5186: . j - column indices
5187: . a - matrix values
5188: . nz - number of nonzeros
5189: - idx - if the `i` and `j` indices start with 1 use `PETSC_TRUE` otherwise use `PETSC_FALSE`
5191: Output Parameter:
5192: . mat - the matrix
5194: Level: intermediate
5196: Example:
5197: For the following matrix, the input data expected is as shown (using 0 based indexing)
5198: .vb
5199: 1 0 0
5200: 2 0 3
5201: 4 5 6
5203: i = {0,1,1,2,2,2}
5204: j = {0,0,2,0,1,2}
5205: v = {1,2,3,4,5,6}
5206: .ve
5207: Note:
5208: Instead of using this function, users should also consider `MatSetPreallocationCOO()` and `MatSetValuesCOO()`, which allow repeated or remote entries,
5209: and are particularly useful in iterative applications.
5211: .seealso: [](chapter_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateSeqAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`, `MatSetValuesCOO()`, `MatSetPreallocationCOO()`
5212: @*/
5213: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat, PetscInt nz, PetscBool idx)
5214: {
5215: PetscInt ii, *nnz, one = 1, row, col;
5217: PetscFunctionBegin;
5218: PetscCall(PetscCalloc1(m, &nnz));
5219: for (ii = 0; ii < nz; ii++) nnz[i[ii] - !!idx] += 1;
5220: PetscCall(MatCreate(comm, mat));
5221: PetscCall(MatSetSizes(*mat, m, n, m, n));
5222: PetscCall(MatSetType(*mat, MATSEQAIJ));
5223: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, 0, nnz));
5224: for (ii = 0; ii < nz; ii++) {
5225: if (idx) {
5226: row = i[ii] - 1;
5227: col = j[ii] - 1;
5228: } else {
5229: row = i[ii];
5230: col = j[ii];
5231: }
5232: PetscCall(MatSetValues(*mat, one, &row, one, &col, &a[ii], ADD_VALUES));
5233: }
5234: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5235: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5236: PetscCall(PetscFree(nnz));
5237: PetscFunctionReturn(PETSC_SUCCESS);
5238: }
5240: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
5241: {
5242: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5244: PetscFunctionBegin;
5245: a->idiagvalid = PETSC_FALSE;
5246: a->ibdiagvalid = PETSC_FALSE;
5248: PetscCall(MatSeqAIJInvalidateDiagonal_Inode(A));
5249: PetscFunctionReturn(PETSC_SUCCESS);
5250: }
5252: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
5253: {
5254: PetscFunctionBegin;
5255: PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm, inmat, n, scall, outmat));
5256: PetscFunctionReturn(PETSC_SUCCESS);
5257: }
5259: /*
5260: Permute A into C's *local* index space using rowemb,colemb.
5261: The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
5262: of [0,m), colemb is in [0,n).
5263: If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
5264: */
5265: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C, IS rowemb, IS colemb, MatStructure pattern, Mat B)
5266: {
5267: /* If making this function public, change the error returned in this function away from _PLIB. */
5268: Mat_SeqAIJ *Baij;
5269: PetscBool seqaij;
5270: PetscInt m, n, *nz, i, j, count;
5271: PetscScalar v;
5272: const PetscInt *rowindices, *colindices;
5274: PetscFunctionBegin;
5275: if (!B) PetscFunctionReturn(PETSC_SUCCESS);
5276: /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
5277: PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQAIJ, &seqaij));
5278: PetscCheck(seqaij, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is of wrong type");
5279: if (rowemb) {
5280: PetscCall(ISGetLocalSize(rowemb, &m));
5281: PetscCheck(m == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Row IS of size %" PetscInt_FMT " is incompatible with matrix row size %" PetscInt_FMT, m, B->rmap->n);
5282: } else {
5283: PetscCheck(C->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is row-incompatible with the target matrix");
5284: }
5285: if (colemb) {
5286: PetscCall(ISGetLocalSize(colemb, &n));
5287: PetscCheck(n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Diag col IS of size %" PetscInt_FMT " is incompatible with input matrix col size %" PetscInt_FMT, n, B->cmap->n);
5288: } else {
5289: PetscCheck(C->cmap->n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is col-incompatible with the target matrix");
5290: }
5292: Baij = (Mat_SeqAIJ *)(B->data);
5293: if (pattern == DIFFERENT_NONZERO_PATTERN) {
5294: PetscCall(PetscMalloc1(B->rmap->n, &nz));
5295: for (i = 0; i < B->rmap->n; i++) nz[i] = Baij->i[i + 1] - Baij->i[i];
5296: PetscCall(MatSeqAIJSetPreallocation(C, 0, nz));
5297: PetscCall(PetscFree(nz));
5298: }
5299: if (pattern == SUBSET_NONZERO_PATTERN) PetscCall(MatZeroEntries(C));
5300: count = 0;
5301: rowindices = NULL;
5302: colindices = NULL;
5303: if (rowemb) PetscCall(ISGetIndices(rowemb, &rowindices));
5304: if (colemb) PetscCall(ISGetIndices(colemb, &colindices));
5305: for (i = 0; i < B->rmap->n; i++) {
5306: PetscInt row;
5307: row = i;
5308: if (rowindices) row = rowindices[i];
5309: for (j = Baij->i[i]; j < Baij->i[i + 1]; j++) {
5310: PetscInt col;
5311: col = Baij->j[count];
5312: if (colindices) col = colindices[col];
5313: v = Baij->a[count];
5314: PetscCall(MatSetValues(C, 1, &row, 1, &col, &v, INSERT_VALUES));
5315: ++count;
5316: }
5317: }
5318: /* FIXME: set C's nonzerostate correctly. */
5319: /* Assembly for C is necessary. */
5320: C->preallocated = PETSC_TRUE;
5321: C->assembled = PETSC_TRUE;
5322: C->was_assembled = PETSC_FALSE;
5323: PetscFunctionReturn(PETSC_SUCCESS);
5324: }
5326: PetscErrorCode MatEliminateZeros_SeqAIJ(Mat A)
5327: {
5328: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5329: MatScalar *aa = a->a;
5330: PetscInt m = A->rmap->n, fshift = 0, fshift_prev = 0, i, k;
5331: PetscInt *ailen = a->ilen, *imax = a->imax, *ai = a->i, *aj = a->j, rmax = 0;
5333: PetscFunctionBegin;
5334: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix");
5335: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
5336: for (i = 1; i <= m; i++) {
5337: /* move each nonzero entry back by the amount of zero slots (fshift) before it*/
5338: for (k = ai[i - 1]; k < ai[i]; k++) {
5339: if (aa[k] == 0 && aj[k] != i - 1) fshift++;
5340: else {
5341: if (aa[k] == 0 && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal zero at row %" PetscInt_FMT "\n", i - 1));
5342: aa[k - fshift] = aa[k];
5343: aj[k - fshift] = aj[k];
5344: }
5345: }
5346: ai[i - 1] -= fshift_prev; // safe to update ai[i-1] now since it will not be used in the next iteration
5347: fshift_prev = fshift;
5348: /* reset ilen and imax for each row */
5349: ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1];
5350: a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0);
5351: rmax = PetscMax(rmax, ailen[i - 1]);
5352: }
5353: if (m) {
5354: ai[m] -= fshift;
5355: a->nz = ai[m];
5356: }
5357: PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT "; zeros eliminated: %" PetscInt_FMT "; nonzeros left: %" PetscInt_FMT "\n", m, A->cmap->n, fshift, a->nz));
5358: A->nonzerostate -= fshift;
5359: A->info.nz_unneeded += (PetscReal)fshift;
5360: a->rmax = rmax;
5361: if (a->inode.use && a->inode.checked) PetscCall(MatSeqAIJCheckInode(A));
5362: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
5363: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
5364: PetscFunctionReturn(PETSC_SUCCESS);
5365: }
5367: PetscFunctionList MatSeqAIJList = NULL;
5369: /*@C
5370: MatSeqAIJSetType - Converts a `MATSEQAIJ` matrix to a subtype
5372: Collective
5374: Input Parameters:
5375: + mat - the matrix object
5376: - matype - matrix type
5378: Options Database Key:
5379: . -mat_seqaij_type <method> - for example seqaijcrl
5381: Level: intermediate
5383: .seealso: [](chapter_matrices), `Mat`, `PCSetType()`, `VecSetType()`, `MatCreate()`, `MatType`, `Mat`
5384: @*/
5385: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
5386: {
5387: PetscBool sametype;
5388: PetscErrorCode (*r)(Mat, MatType, MatReuse, Mat *);
5390: PetscFunctionBegin;
5392: PetscCall(PetscObjectTypeCompare((PetscObject)mat, matype, &sametype));
5393: if (sametype) PetscFunctionReturn(PETSC_SUCCESS);
5395: PetscCall(PetscFunctionListFind(MatSeqAIJList, matype, &r));
5396: PetscCheck(r, PETSC_COMM_SELF, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown Mat type given: %s", matype);
5397: PetscCall((*r)(mat, matype, MAT_INPLACE_MATRIX, &mat));
5398: PetscFunctionReturn(PETSC_SUCCESS);
5399: }
5401: /*@C
5402: MatSeqAIJRegister - - Adds a new sub-matrix type for sequential `MATSEQAIJ` matrices
5404: Not Collective
5406: Input Parameters:
5407: + name - name of a new user-defined matrix type, for example `MATSEQAIJCRL`
5408: - function - routine to convert to subtype
5410: Level: advanced
5412: Notes:
5413: `MatSeqAIJRegister()` may be called multiple times to add several user-defined solvers.
5415: Then, your matrix can be chosen with the procedural interface at runtime via the option
5416: $ -mat_seqaij_type my_mat
5418: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJRegisterAll()`
5419: @*/
5420: PetscErrorCode MatSeqAIJRegister(const char sname[], PetscErrorCode (*function)(Mat, MatType, MatReuse, Mat *))
5421: {
5422: PetscFunctionBegin;
5423: PetscCall(MatInitializePackage());
5424: PetscCall(PetscFunctionListAdd(&MatSeqAIJList, sname, function));
5425: PetscFunctionReturn(PETSC_SUCCESS);
5426: }
5428: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;
5430: /*@C
5431: MatSeqAIJRegisterAll - Registers all of the matrix subtypes of `MATSSEQAIJ`
5433: Not Collective
5435: Level: advanced
5437: Note:
5438: This registers the versions of `MATSEQAIJ` for GPUs
5440: .seealso: [](chapter_matrices), `Mat`, `MatRegisterAll()`, `MatSeqAIJRegister()`
5441: @*/
5442: PetscErrorCode MatSeqAIJRegisterAll(void)
5443: {
5444: PetscFunctionBegin;
5445: if (MatSeqAIJRegisterAllCalled) PetscFunctionReturn(PETSC_SUCCESS);
5446: MatSeqAIJRegisterAllCalled = PETSC_TRUE;
5448: PetscCall(MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL));
5449: PetscCall(MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM));
5450: PetscCall(MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL));
5451: #if defined(PETSC_HAVE_MKL_SPARSE)
5452: PetscCall(MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL));
5453: #endif
5454: #if defined(PETSC_HAVE_CUDA)
5455: PetscCall(MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE));
5456: #endif
5457: #if defined(PETSC_HAVE_HIP)
5458: PetscCall(MatSeqAIJRegister(MATSEQAIJHIPSPARSE, MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
5459: #endif
5460: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
5461: PetscCall(MatSeqAIJRegister(MATSEQAIJKOKKOS, MatConvert_SeqAIJ_SeqAIJKokkos));
5462: #endif
5463: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5464: PetscCall(MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL));
5465: #endif
5466: PetscFunctionReturn(PETSC_SUCCESS);
5467: }
5469: /*
5470: Special version for direct calls from Fortran
5471: */
5472: #include <petsc/private/fortranimpl.h>
5473: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5474: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5475: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5476: #define matsetvaluesseqaij_ matsetvaluesseqaij
5477: #endif
5479: /* Change these macros so can be used in void function */
5481: /* Change these macros so can be used in void function */
5482: /* Identical to PetscCallVoid, except it assigns to *_ierr */
5483: #undef PetscCall
5484: #define PetscCall(...) \
5485: do { \
5486: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
5487: if (PetscUnlikely(ierr_msv_mpiaij)) { \
5488: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
5489: return; \
5490: } \
5491: } while (0)
5493: #undef SETERRQ
5494: #define SETERRQ(comm, ierr, ...) \
5495: do { \
5496: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
5497: return; \
5498: } while (0)
5500: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[], InsertMode *isis, PetscErrorCode *_ierr)
5501: {
5502: Mat A = *AA;
5503: PetscInt m = *mm, n = *nn;
5504: InsertMode is = *isis;
5505: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5506: PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
5507: PetscInt *imax, *ai, *ailen;
5508: PetscInt *aj, nonew = a->nonew, lastcol = -1;
5509: MatScalar *ap, value, *aa;
5510: PetscBool ignorezeroentries = a->ignorezeroentries;
5511: PetscBool roworiented = a->roworiented;
5513: PetscFunctionBegin;
5514: MatCheckPreallocated(A, 1);
5515: imax = a->imax;
5516: ai = a->i;
5517: ailen = a->ilen;
5518: aj = a->j;
5519: aa = a->a;
5521: for (k = 0; k < m; k++) { /* loop over added rows */
5522: row = im[k];
5523: if (row < 0) continue;
5524: PetscCheck(row < A->rmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Row too large");
5525: rp = aj + ai[row];
5526: ap = aa + ai[row];
5527: rmax = imax[row];
5528: nrow = ailen[row];
5529: low = 0;
5530: high = nrow;
5531: for (l = 0; l < n; l++) { /* loop over added columns */
5532: if (in[l] < 0) continue;
5533: PetscCheck(in[l] < A->cmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Column too large");
5534: col = in[l];
5535: if (roworiented) value = v[l + k * n];
5536: else value = v[k + l * m];
5538: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
5540: if (col <= lastcol) low = 0;
5541: else high = nrow;
5542: lastcol = col;
5543: while (high - low > 5) {
5544: t = (low + high) / 2;
5545: if (rp[t] > col) high = t;
5546: else low = t;
5547: }
5548: for (i = low; i < high; i++) {
5549: if (rp[i] > col) break;
5550: if (rp[i] == col) {
5551: if (is == ADD_VALUES) ap[i] += value;
5552: else ap[i] = value;
5553: goto noinsert;
5554: }
5555: }
5556: if (value == 0.0 && ignorezeroentries) goto noinsert;
5557: if (nonew == 1) goto noinsert;
5558: PetscCheck(nonew != -1, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero in the matrix");
5559: MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
5560: N = nrow++ - 1;
5561: a->nz++;
5562: high++;
5563: /* shift up all the later entries in this row */
5564: for (ii = N; ii >= i; ii--) {
5565: rp[ii + 1] = rp[ii];
5566: ap[ii + 1] = ap[ii];
5567: }
5568: rp[i] = col;
5569: ap[i] = value;
5570: A->nonzerostate++;
5571: noinsert:;
5572: low = i + 1;
5573: }
5574: ailen[row] = nrow;
5575: }
5576: PetscFunctionReturnVoid();
5577: }
5578: /* Undefining these here since they were redefined from their original definition above! No
5579: * other PETSc functions should be defined past this point, as it is impossible to recover the
5580: * original definitions */
5581: #undef PetscCall
5582: #undef SETERRQ