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: [](ch_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 (A->hash_active) {
1217: PetscCall(PetscMemcpy(&A->ops, &a->cops, sizeof(*(A->ops))));
1218: PetscCall(PetscHMapIJVDestroy(&a->ht));
1219: PetscCall(PetscFree(a->dnz));
1220: A->hash_active = PETSC_FALSE;
1221: }
1223: #if defined(PETSC_USE_LOG)
1224: PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->n, A->cmap->n, a->nz));
1225: #endif
1226: PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i));
1227: PetscCall(MatResetPreallocationCOO_SeqAIJ(A));
1228: PetscCall(ISDestroy(&a->row));
1229: PetscCall(ISDestroy(&a->col));
1230: PetscCall(PetscFree(a->diag));
1231: PetscCall(PetscFree(a->ibdiag));
1232: PetscCall(PetscFree(a->imax));
1233: PetscCall(PetscFree(a->ilen));
1234: PetscCall(PetscFree(a->ipre));
1235: PetscCall(PetscFree3(a->idiag, a->mdiag, a->ssor_work));
1236: PetscCall(PetscFree(a->solve_work));
1237: PetscCall(ISDestroy(&a->icol));
1238: PetscCall(PetscFree(a->saved_values));
1239: PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex));
1240: PetscCall(MatDestroy_SeqAIJ_Inode(A));
1241: PetscCall(PetscFree(A->data));
1243: /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
1244: That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
1245: that is hard to properly add this data to the MatProduct data. We free it here to avoid
1246: users reusing the matrix object with different data to incur in obscure segmentation faults
1247: due to different matrix sizes */
1248: PetscCall(PetscObjectCompose((PetscObject)A, "__PETSc__ab_dense", NULL));
1250: PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL));
1251: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEnginePut_C", NULL));
1252: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEngineGet_C", NULL));
1253: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetColumnIndices_C", NULL));
1254: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL));
1255: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL));
1256: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsbaij_C", NULL));
1257: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqbaij_C", NULL));
1258: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijperm_C", NULL));
1259: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijsell_C", NULL));
1260: #if defined(PETSC_HAVE_MKL_SPARSE)
1261: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijmkl_C", NULL));
1262: #endif
1263: #if defined(PETSC_HAVE_CUDA)
1264: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcusparse_C", NULL));
1265: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", NULL));
1266: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", NULL));
1267: #endif
1268: #if defined(PETSC_HAVE_HIP)
1269: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijhipsparse_C", NULL));
1270: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", NULL));
1271: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", NULL));
1272: #endif
1273: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
1274: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijkokkos_C", NULL));
1275: #endif
1276: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcrl_C", NULL));
1277: #if defined(PETSC_HAVE_ELEMENTAL)
1278: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_elemental_C", NULL));
1279: #endif
1280: #if defined(PETSC_HAVE_SCALAPACK)
1281: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_scalapack_C", NULL));
1282: #endif
1283: #if defined(PETSC_HAVE_HYPRE)
1284: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_hypre_C", NULL));
1285: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", NULL));
1286: #endif
1287: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqdense_C", NULL));
1288: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsell_C", NULL));
1289: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_is_C", NULL));
1290: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL));
1291: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsHermitianTranspose_C", NULL));
1292: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocation_C", NULL));
1293: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetPreallocation_C", NULL));
1294: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocationCSR_C", NULL));
1295: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatReorderForNonzeroDiagonal_C", NULL));
1296: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_is_seqaij_C", NULL));
1297: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqdense_seqaij_C", NULL));
1298: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaij_C", NULL));
1299: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJKron_C", NULL));
1300: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
1301: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
1302: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1303: /* these calls do not belong here: the subclasses Duplicate/Destroy are wrong */
1304: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijsell_seqaij_C", NULL));
1305: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijperm_seqaij_C", NULL));
1306: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijviennacl_C", NULL));
1307: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqdense_C", NULL));
1308: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqaij_C", NULL));
1309: PetscFunctionReturn(PETSC_SUCCESS);
1310: }
1312: PetscErrorCode MatSetOption_SeqAIJ(Mat A, MatOption op, PetscBool flg)
1313: {
1314: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1316: PetscFunctionBegin;
1317: switch (op) {
1318: case MAT_ROW_ORIENTED:
1319: a->roworiented = flg;
1320: break;
1321: case MAT_KEEP_NONZERO_PATTERN:
1322: a->keepnonzeropattern = flg;
1323: break;
1324: case MAT_NEW_NONZERO_LOCATIONS:
1325: a->nonew = (flg ? 0 : 1);
1326: break;
1327: case MAT_NEW_NONZERO_LOCATION_ERR:
1328: a->nonew = (flg ? -1 : 0);
1329: break;
1330: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1331: a->nonew = (flg ? -2 : 0);
1332: break;
1333: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1334: a->nounused = (flg ? -1 : 0);
1335: break;
1336: case MAT_IGNORE_ZERO_ENTRIES:
1337: a->ignorezeroentries = flg;
1338: break;
1339: case MAT_SPD:
1340: case MAT_SYMMETRIC:
1341: case MAT_STRUCTURALLY_SYMMETRIC:
1342: case MAT_HERMITIAN:
1343: case MAT_SYMMETRY_ETERNAL:
1344: case MAT_STRUCTURE_ONLY:
1345: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1346: case MAT_SPD_ETERNAL:
1347: /* if the diagonal matrix is square it inherits some of the properties above */
1348: break;
1349: case MAT_FORCE_DIAGONAL_ENTRIES:
1350: case MAT_IGNORE_OFF_PROC_ENTRIES:
1351: case MAT_USE_HASH_TABLE:
1352: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1353: break;
1354: case MAT_USE_INODES:
1355: PetscCall(MatSetOption_SeqAIJ_Inode(A, MAT_USE_INODES, flg));
1356: break;
1357: case MAT_SUBMAT_SINGLEIS:
1358: A->submat_singleis = flg;
1359: break;
1360: case MAT_SORTED_FULL:
1361: if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
1362: else A->ops->setvalues = MatSetValues_SeqAIJ;
1363: break;
1364: case MAT_FORM_EXPLICIT_TRANSPOSE:
1365: A->form_explicit_transpose = flg;
1366: break;
1367: default:
1368: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1369: }
1370: PetscFunctionReturn(PETSC_SUCCESS);
1371: }
1373: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A, Vec v)
1374: {
1375: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1376: PetscInt i, j, n, *ai = a->i, *aj = a->j;
1377: PetscScalar *x;
1378: const PetscScalar *aa;
1380: PetscFunctionBegin;
1381: PetscCall(VecGetLocalSize(v, &n));
1382: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
1383: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1384: if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1385: PetscInt *diag = a->diag;
1386: PetscCall(VecGetArrayWrite(v, &x));
1387: for (i = 0; i < n; i++) x[i] = 1.0 / aa[diag[i]];
1388: PetscCall(VecRestoreArrayWrite(v, &x));
1389: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1390: PetscFunctionReturn(PETSC_SUCCESS);
1391: }
1393: PetscCall(VecGetArrayWrite(v, &x));
1394: for (i = 0; i < n; i++) {
1395: x[i] = 0.0;
1396: for (j = ai[i]; j < ai[i + 1]; j++) {
1397: if (aj[j] == i) {
1398: x[i] = aa[j];
1399: break;
1400: }
1401: }
1402: }
1403: PetscCall(VecRestoreArrayWrite(v, &x));
1404: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1405: PetscFunctionReturn(PETSC_SUCCESS);
1406: }
1408: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1409: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A, Vec xx, Vec zz, Vec yy)
1410: {
1411: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1412: const MatScalar *aa;
1413: PetscScalar *y;
1414: const PetscScalar *x;
1415: PetscInt m = A->rmap->n;
1416: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1417: const MatScalar *v;
1418: PetscScalar alpha;
1419: PetscInt n, i, j;
1420: const PetscInt *idx, *ii, *ridx = NULL;
1421: Mat_CompressedRow cprow = a->compressedrow;
1422: PetscBool usecprow = cprow.use;
1423: #endif
1425: PetscFunctionBegin;
1426: if (zz != yy) PetscCall(VecCopy(zz, yy));
1427: PetscCall(VecGetArrayRead(xx, &x));
1428: PetscCall(VecGetArray(yy, &y));
1429: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1431: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1432: fortranmulttransposeaddaij_(&m, x, a->i, a->j, aa, y);
1433: #else
1434: if (usecprow) {
1435: m = cprow.nrows;
1436: ii = cprow.i;
1437: ridx = cprow.rindex;
1438: } else {
1439: ii = a->i;
1440: }
1441: for (i = 0; i < m; i++) {
1442: idx = a->j + ii[i];
1443: v = aa + ii[i];
1444: n = ii[i + 1] - ii[i];
1445: if (usecprow) {
1446: alpha = x[ridx[i]];
1447: } else {
1448: alpha = x[i];
1449: }
1450: for (j = 0; j < n; j++) y[idx[j]] += alpha * v[j];
1451: }
1452: #endif
1453: PetscCall(PetscLogFlops(2.0 * a->nz));
1454: PetscCall(VecRestoreArrayRead(xx, &x));
1455: PetscCall(VecRestoreArray(yy, &y));
1456: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1457: PetscFunctionReturn(PETSC_SUCCESS);
1458: }
1460: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A, Vec xx, Vec yy)
1461: {
1462: PetscFunctionBegin;
1463: PetscCall(VecSet(yy, 0.0));
1464: PetscCall(MatMultTransposeAdd_SeqAIJ(A, xx, yy, yy));
1465: PetscFunctionReturn(PETSC_SUCCESS);
1466: }
1468: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1470: PetscErrorCode MatMult_SeqAIJ(Mat A, Vec xx, Vec yy)
1471: {
1472: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1473: PetscScalar *y;
1474: const PetscScalar *x;
1475: const MatScalar *aa, *a_a;
1476: PetscInt m = A->rmap->n;
1477: const PetscInt *aj, *ii, *ridx = NULL;
1478: PetscInt n, i;
1479: PetscScalar sum;
1480: PetscBool usecprow = a->compressedrow.use;
1482: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1483: #pragma disjoint(*x, *y, *aa)
1484: #endif
1486: PetscFunctionBegin;
1487: if (a->inode.use && a->inode.checked) {
1488: PetscCall(MatMult_SeqAIJ_Inode(A, xx, yy));
1489: PetscFunctionReturn(PETSC_SUCCESS);
1490: }
1491: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1492: PetscCall(VecGetArrayRead(xx, &x));
1493: PetscCall(VecGetArray(yy, &y));
1494: ii = a->i;
1495: if (usecprow) { /* use compressed row format */
1496: PetscCall(PetscArrayzero(y, m));
1497: m = a->compressedrow.nrows;
1498: ii = a->compressedrow.i;
1499: ridx = a->compressedrow.rindex;
1500: for (i = 0; i < m; i++) {
1501: n = ii[i + 1] - ii[i];
1502: aj = a->j + ii[i];
1503: aa = a_a + ii[i];
1504: sum = 0.0;
1505: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1506: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1507: y[*ridx++] = sum;
1508: }
1509: } else { /* do not use compressed row format */
1510: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1511: aj = a->j;
1512: aa = a_a;
1513: fortranmultaij_(&m, x, ii, aj, aa, y);
1514: #else
1515: for (i = 0; i < m; i++) {
1516: n = ii[i + 1] - ii[i];
1517: aj = a->j + ii[i];
1518: aa = a_a + ii[i];
1519: sum = 0.0;
1520: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1521: y[i] = sum;
1522: }
1523: #endif
1524: }
1525: PetscCall(PetscLogFlops(2.0 * a->nz - a->nonzerorowcnt));
1526: PetscCall(VecRestoreArrayRead(xx, &x));
1527: PetscCall(VecRestoreArray(yy, &y));
1528: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1529: PetscFunctionReturn(PETSC_SUCCESS);
1530: }
1532: PetscErrorCode MatMultMax_SeqAIJ(Mat A, Vec xx, Vec yy)
1533: {
1534: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1535: PetscScalar *y;
1536: const PetscScalar *x;
1537: const MatScalar *aa, *a_a;
1538: PetscInt m = A->rmap->n;
1539: const PetscInt *aj, *ii, *ridx = NULL;
1540: PetscInt n, i, nonzerorow = 0;
1541: PetscScalar sum;
1542: PetscBool usecprow = a->compressedrow.use;
1544: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1545: #pragma disjoint(*x, *y, *aa)
1546: #endif
1548: PetscFunctionBegin;
1549: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1550: PetscCall(VecGetArrayRead(xx, &x));
1551: PetscCall(VecGetArray(yy, &y));
1552: if (usecprow) { /* use compressed row format */
1553: m = a->compressedrow.nrows;
1554: ii = a->compressedrow.i;
1555: ridx = a->compressedrow.rindex;
1556: for (i = 0; i < m; i++) {
1557: n = ii[i + 1] - ii[i];
1558: aj = a->j + ii[i];
1559: aa = a_a + ii[i];
1560: sum = 0.0;
1561: nonzerorow += (n > 0);
1562: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1563: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1564: y[*ridx++] = sum;
1565: }
1566: } else { /* do not use compressed row format */
1567: ii = a->i;
1568: for (i = 0; i < m; i++) {
1569: n = ii[i + 1] - ii[i];
1570: aj = a->j + ii[i];
1571: aa = a_a + ii[i];
1572: sum = 0.0;
1573: nonzerorow += (n > 0);
1574: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1575: y[i] = sum;
1576: }
1577: }
1578: PetscCall(PetscLogFlops(2.0 * a->nz - nonzerorow));
1579: PetscCall(VecRestoreArrayRead(xx, &x));
1580: PetscCall(VecRestoreArray(yy, &y));
1581: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1582: PetscFunctionReturn(PETSC_SUCCESS);
1583: }
1585: PetscErrorCode MatMultAddMax_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1586: {
1587: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1588: PetscScalar *y, *z;
1589: const PetscScalar *x;
1590: const MatScalar *aa, *a_a;
1591: PetscInt m = A->rmap->n, *aj, *ii;
1592: PetscInt n, i, *ridx = NULL;
1593: PetscScalar sum;
1594: PetscBool usecprow = a->compressedrow.use;
1596: PetscFunctionBegin;
1597: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1598: PetscCall(VecGetArrayRead(xx, &x));
1599: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1600: if (usecprow) { /* use compressed row format */
1601: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1602: m = a->compressedrow.nrows;
1603: ii = a->compressedrow.i;
1604: ridx = a->compressedrow.rindex;
1605: for (i = 0; i < m; i++) {
1606: n = ii[i + 1] - ii[i];
1607: aj = a->j + ii[i];
1608: aa = a_a + ii[i];
1609: sum = y[*ridx];
1610: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1611: z[*ridx++] = sum;
1612: }
1613: } else { /* do not use compressed row format */
1614: ii = a->i;
1615: for (i = 0; i < m; i++) {
1616: n = ii[i + 1] - ii[i];
1617: aj = a->j + ii[i];
1618: aa = a_a + ii[i];
1619: sum = y[i];
1620: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1621: z[i] = sum;
1622: }
1623: }
1624: PetscCall(PetscLogFlops(2.0 * a->nz));
1625: PetscCall(VecRestoreArrayRead(xx, &x));
1626: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1627: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1628: PetscFunctionReturn(PETSC_SUCCESS);
1629: }
1631: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1632: PetscErrorCode MatMultAdd_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1633: {
1634: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1635: PetscScalar *y, *z;
1636: const PetscScalar *x;
1637: const MatScalar *aa, *a_a;
1638: const PetscInt *aj, *ii, *ridx = NULL;
1639: PetscInt m = A->rmap->n, n, i;
1640: PetscScalar sum;
1641: PetscBool usecprow = a->compressedrow.use;
1643: PetscFunctionBegin;
1644: if (a->inode.use && a->inode.checked) {
1645: PetscCall(MatMultAdd_SeqAIJ_Inode(A, xx, yy, zz));
1646: PetscFunctionReturn(PETSC_SUCCESS);
1647: }
1648: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1649: PetscCall(VecGetArrayRead(xx, &x));
1650: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1651: if (usecprow) { /* use compressed row format */
1652: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1653: m = a->compressedrow.nrows;
1654: ii = a->compressedrow.i;
1655: ridx = a->compressedrow.rindex;
1656: for (i = 0; i < m; i++) {
1657: n = ii[i + 1] - ii[i];
1658: aj = a->j + ii[i];
1659: aa = a_a + ii[i];
1660: sum = y[*ridx];
1661: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1662: z[*ridx++] = sum;
1663: }
1664: } else { /* do not use compressed row format */
1665: ii = a->i;
1666: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1667: aj = a->j;
1668: aa = a_a;
1669: fortranmultaddaij_(&m, x, ii, aj, aa, y, z);
1670: #else
1671: for (i = 0; i < m; i++) {
1672: n = ii[i + 1] - ii[i];
1673: aj = a->j + ii[i];
1674: aa = a_a + ii[i];
1675: sum = y[i];
1676: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1677: z[i] = sum;
1678: }
1679: #endif
1680: }
1681: PetscCall(PetscLogFlops(2.0 * a->nz));
1682: PetscCall(VecRestoreArrayRead(xx, &x));
1683: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1684: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1685: PetscFunctionReturn(PETSC_SUCCESS);
1686: }
1688: /*
1689: Adds diagonal pointers to sparse matrix structure.
1690: */
1691: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1692: {
1693: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1694: PetscInt i, j, m = A->rmap->n;
1695: PetscBool alreadySet = PETSC_TRUE;
1697: PetscFunctionBegin;
1698: if (!a->diag) {
1699: PetscCall(PetscMalloc1(m, &a->diag));
1700: alreadySet = PETSC_FALSE;
1701: }
1702: for (i = 0; i < A->rmap->n; i++) {
1703: /* If A's diagonal is already correctly set, this fast track enables cheap and repeated MatMarkDiagonal_SeqAIJ() calls */
1704: if (alreadySet) {
1705: PetscInt pos = a->diag[i];
1706: if (pos >= a->i[i] && pos < a->i[i + 1] && a->j[pos] == i) continue;
1707: }
1709: a->diag[i] = a->i[i + 1];
1710: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1711: if (a->j[j] == i) {
1712: a->diag[i] = j;
1713: break;
1714: }
1715: }
1716: }
1717: PetscFunctionReturn(PETSC_SUCCESS);
1718: }
1720: PetscErrorCode MatShift_SeqAIJ(Mat A, PetscScalar v)
1721: {
1722: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1723: const PetscInt *diag = (const PetscInt *)a->diag;
1724: const PetscInt *ii = (const PetscInt *)a->i;
1725: PetscInt i, *mdiag = NULL;
1726: PetscInt cnt = 0; /* how many diagonals are missing */
1728: PetscFunctionBegin;
1729: if (!A->preallocated || !a->nz) {
1730: PetscCall(MatSeqAIJSetPreallocation(A, 1, NULL));
1731: PetscCall(MatShift_Basic(A, v));
1732: PetscFunctionReturn(PETSC_SUCCESS);
1733: }
1735: if (a->diagonaldense) {
1736: cnt = 0;
1737: } else {
1738: PetscCall(PetscCalloc1(A->rmap->n, &mdiag));
1739: for (i = 0; i < A->rmap->n; i++) {
1740: if (i < A->cmap->n && diag[i] >= ii[i + 1]) { /* 'out of range' rows never have diagonals */
1741: cnt++;
1742: mdiag[i] = 1;
1743: }
1744: }
1745: }
1746: if (!cnt) {
1747: PetscCall(MatShift_Basic(A, v));
1748: } else {
1749: PetscScalar *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */
1750: PetscInt *oldj = a->j, *oldi = a->i;
1751: PetscBool singlemalloc = a->singlemalloc, free_a = a->free_a, free_ij = a->free_ij;
1753: a->a = NULL;
1754: a->j = NULL;
1755: a->i = NULL;
1756: /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1757: for (i = 0; i < PetscMin(A->rmap->n, A->cmap->n); i++) a->imax[i] += mdiag[i];
1758: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, 0, a->imax));
1760: /* copy old values into new matrix data structure */
1761: for (i = 0; i < A->rmap->n; i++) {
1762: PetscCall(MatSetValues(A, 1, &i, a->imax[i] - mdiag[i], &oldj[oldi[i]], &olda[oldi[i]], ADD_VALUES));
1763: if (i < A->cmap->n) PetscCall(MatSetValue(A, i, i, v, ADD_VALUES));
1764: }
1765: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1766: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1767: if (singlemalloc) {
1768: PetscCall(PetscFree3(olda, oldj, oldi));
1769: } else {
1770: if (free_a) PetscCall(PetscFree(olda));
1771: if (free_ij) PetscCall(PetscFree(oldj));
1772: if (free_ij) PetscCall(PetscFree(oldi));
1773: }
1774: }
1775: PetscCall(PetscFree(mdiag));
1776: a->diagonaldense = PETSC_TRUE;
1777: PetscFunctionReturn(PETSC_SUCCESS);
1778: }
1780: /*
1781: Checks for missing diagonals
1782: */
1783: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A, PetscBool *missing, PetscInt *d)
1784: {
1785: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1786: PetscInt *diag, *ii = a->i, i;
1788: PetscFunctionBegin;
1789: *missing = PETSC_FALSE;
1790: if (A->rmap->n > 0 && !ii) {
1791: *missing = PETSC_TRUE;
1792: if (d) *d = 0;
1793: PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n"));
1794: } else {
1795: PetscInt n;
1796: n = PetscMin(A->rmap->n, A->cmap->n);
1797: diag = a->diag;
1798: for (i = 0; i < n; i++) {
1799: if (diag[i] >= ii[i + 1]) {
1800: *missing = PETSC_TRUE;
1801: if (d) *d = i;
1802: PetscCall(PetscInfo(A, "Matrix is missing diagonal number %" PetscInt_FMT "\n", i));
1803: break;
1804: }
1805: }
1806: }
1807: PetscFunctionReturn(PETSC_SUCCESS);
1808: }
1810: #include <petscblaslapack.h>
1811: #include <petsc/private/kernels/blockinvert.h>
1813: /*
1814: Note that values is allocated externally by the PC and then passed into this routine
1815: */
1816: PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
1817: {
1818: PetscInt n = A->rmap->n, i, ncnt = 0, *indx, j, bsizemax = 0, *v_pivots;
1819: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
1820: const PetscReal shift = 0.0;
1821: PetscInt ipvt[5];
1822: PetscCount flops = 0;
1823: PetscScalar work[25], *v_work;
1825: PetscFunctionBegin;
1826: allowzeropivot = PetscNot(A->erroriffailure);
1827: for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
1828: PetscCheck(ncnt == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Total blocksizes %" PetscInt_FMT " doesn't match number matrix rows %" PetscInt_FMT, ncnt, n);
1829: for (i = 0; i < nblocks; i++) bsizemax = PetscMax(bsizemax, bsizes[i]);
1830: PetscCall(PetscMalloc1(bsizemax, &indx));
1831: if (bsizemax > 7) PetscCall(PetscMalloc2(bsizemax, &v_work, bsizemax, &v_pivots));
1832: ncnt = 0;
1833: for (i = 0; i < nblocks; i++) {
1834: for (j = 0; j < bsizes[i]; j++) indx[j] = ncnt + j;
1835: PetscCall(MatGetValues(A, bsizes[i], indx, bsizes[i], indx, diag));
1836: switch (bsizes[i]) {
1837: case 1:
1838: *diag = 1.0 / (*diag);
1839: break;
1840: case 2:
1841: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
1842: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1843: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
1844: break;
1845: case 3:
1846: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
1847: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1848: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
1849: break;
1850: case 4:
1851: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
1852: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1853: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
1854: break;
1855: case 5:
1856: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
1857: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1858: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
1859: break;
1860: case 6:
1861: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
1862: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1863: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
1864: break;
1865: case 7:
1866: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
1867: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1868: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
1869: break;
1870: default:
1871: PetscCall(PetscKernel_A_gets_inverse_A(bsizes[i], diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
1872: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1873: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bsizes[i]));
1874: }
1875: ncnt += bsizes[i];
1876: diag += bsizes[i] * bsizes[i];
1877: flops += 2 * PetscPowInt(bsizes[i], 3) / 3;
1878: }
1879: PetscCall(PetscLogFlops(flops));
1880: if (bsizemax > 7) PetscCall(PetscFree2(v_work, v_pivots));
1881: PetscCall(PetscFree(indx));
1882: PetscFunctionReturn(PETSC_SUCCESS);
1883: }
1885: /*
1886: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1887: */
1888: PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A, PetscScalar omega, PetscScalar fshift)
1889: {
1890: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1891: PetscInt i, *diag, m = A->rmap->n;
1892: const MatScalar *v;
1893: PetscScalar *idiag, *mdiag;
1895: PetscFunctionBegin;
1896: if (a->idiagvalid) PetscFunctionReturn(PETSC_SUCCESS);
1897: PetscCall(MatMarkDiagonal_SeqAIJ(A));
1898: diag = a->diag;
1899: if (!a->idiag) { PetscCall(PetscMalloc3(m, &a->idiag, m, &a->mdiag, m, &a->ssor_work)); }
1901: mdiag = a->mdiag;
1902: idiag = a->idiag;
1903: PetscCall(MatSeqAIJGetArrayRead(A, &v));
1904: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1905: for (i = 0; i < m; i++) {
1906: mdiag[i] = v[diag[i]];
1907: if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1908: if (PetscRealPart(fshift)) {
1909: PetscCall(PetscInfo(A, "Zero diagonal on row %" PetscInt_FMT "\n", i));
1910: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1911: A->factorerror_zeropivot_value = 0.0;
1912: A->factorerror_zeropivot_row = i;
1913: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Zero diagonal on row %" PetscInt_FMT, i);
1914: }
1915: idiag[i] = 1.0 / v[diag[i]];
1916: }
1917: PetscCall(PetscLogFlops(m));
1918: } else {
1919: for (i = 0; i < m; i++) {
1920: mdiag[i] = v[diag[i]];
1921: idiag[i] = omega / (fshift + v[diag[i]]);
1922: }
1923: PetscCall(PetscLogFlops(2.0 * m));
1924: }
1925: a->idiagvalid = PETSC_TRUE;
1926: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
1927: PetscFunctionReturn(PETSC_SUCCESS);
1928: }
1930: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1931: PetscErrorCode MatSOR_SeqAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1932: {
1933: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1934: PetscScalar *x, d, sum, *t, scale;
1935: const MatScalar *v, *idiag = NULL, *mdiag, *aa;
1936: const PetscScalar *b, *bs, *xb, *ts;
1937: PetscInt n, m = A->rmap->n, i;
1938: const PetscInt *idx, *diag;
1940: PetscFunctionBegin;
1941: if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
1942: PetscCall(MatSOR_SeqAIJ_Inode(A, bb, omega, flag, fshift, its, lits, xx));
1943: PetscFunctionReturn(PETSC_SUCCESS);
1944: }
1945: its = its * lits;
1947: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1948: if (!a->idiagvalid) PetscCall(MatInvertDiagonal_SeqAIJ(A, omega, fshift));
1949: a->fshift = fshift;
1950: a->omega = omega;
1952: diag = a->diag;
1953: t = a->ssor_work;
1954: idiag = a->idiag;
1955: mdiag = a->mdiag;
1957: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1958: PetscCall(VecGetArray(xx, &x));
1959: PetscCall(VecGetArrayRead(bb, &b));
1960: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1961: if (flag == SOR_APPLY_UPPER) {
1962: /* apply (U + D/omega) to the vector */
1963: bs = b;
1964: for (i = 0; i < m; i++) {
1965: d = fshift + mdiag[i];
1966: n = a->i[i + 1] - diag[i] - 1;
1967: idx = a->j + diag[i] + 1;
1968: v = aa + diag[i] + 1;
1969: sum = b[i] * d / omega;
1970: PetscSparseDensePlusDot(sum, bs, v, idx, n);
1971: x[i] = sum;
1972: }
1973: PetscCall(VecRestoreArray(xx, &x));
1974: PetscCall(VecRestoreArrayRead(bb, &b));
1975: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1976: PetscCall(PetscLogFlops(a->nz));
1977: PetscFunctionReturn(PETSC_SUCCESS);
1978: }
1980: PetscCheck(flag != SOR_APPLY_LOWER, PETSC_COMM_SELF, PETSC_ERR_SUP, "SOR_APPLY_LOWER is not implemented");
1981: if (flag & SOR_EISENSTAT) {
1982: /* Let A = L + U + D; where L is lower triangular,
1983: U is upper triangular, E = D/omega; This routine applies
1985: (L + E)^{-1} A (U + E)^{-1}
1987: to a vector efficiently using Eisenstat's trick.
1988: */
1989: scale = (2.0 / omega) - 1.0;
1991: /* x = (E + U)^{-1} b */
1992: for (i = m - 1; i >= 0; i--) {
1993: n = a->i[i + 1] - diag[i] - 1;
1994: idx = a->j + diag[i] + 1;
1995: v = aa + diag[i] + 1;
1996: sum = b[i];
1997: PetscSparseDenseMinusDot(sum, x, v, idx, n);
1998: x[i] = sum * idiag[i];
1999: }
2001: /* t = b - (2*E - D)x */
2002: v = aa;
2003: for (i = 0; i < m; i++) t[i] = b[i] - scale * (v[*diag++]) * x[i];
2005: /* t = (E + L)^{-1}t */
2006: ts = t;
2007: diag = a->diag;
2008: for (i = 0; i < m; i++) {
2009: n = diag[i] - a->i[i];
2010: idx = a->j + a->i[i];
2011: v = aa + a->i[i];
2012: sum = t[i];
2013: PetscSparseDenseMinusDot(sum, ts, v, idx, n);
2014: t[i] = sum * idiag[i];
2015: /* x = x + t */
2016: x[i] += t[i];
2017: }
2019: PetscCall(PetscLogFlops(6.0 * m - 1 + 2.0 * a->nz));
2020: PetscCall(VecRestoreArray(xx, &x));
2021: PetscCall(VecRestoreArrayRead(bb, &b));
2022: PetscFunctionReturn(PETSC_SUCCESS);
2023: }
2024: if (flag & SOR_ZERO_INITIAL_GUESS) {
2025: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2026: for (i = 0; i < m; i++) {
2027: n = diag[i] - a->i[i];
2028: idx = a->j + a->i[i];
2029: v = aa + a->i[i];
2030: sum = b[i];
2031: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2032: t[i] = sum;
2033: x[i] = sum * idiag[i];
2034: }
2035: xb = t;
2036: PetscCall(PetscLogFlops(a->nz));
2037: } else xb = b;
2038: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2039: for (i = m - 1; i >= 0; i--) {
2040: n = a->i[i + 1] - diag[i] - 1;
2041: idx = a->j + diag[i] + 1;
2042: v = aa + diag[i] + 1;
2043: sum = xb[i];
2044: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2045: if (xb == b) {
2046: x[i] = sum * idiag[i];
2047: } else {
2048: x[i] = (1 - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2049: }
2050: }
2051: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2052: }
2053: its--;
2054: }
2055: while (its--) {
2056: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2057: for (i = 0; i < m; i++) {
2058: /* lower */
2059: n = diag[i] - a->i[i];
2060: idx = a->j + a->i[i];
2061: v = aa + a->i[i];
2062: sum = b[i];
2063: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2064: t[i] = sum; /* save application of the lower-triangular part */
2065: /* upper */
2066: n = a->i[i + 1] - diag[i] - 1;
2067: idx = a->j + diag[i] + 1;
2068: v = aa + diag[i] + 1;
2069: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2070: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2071: }
2072: xb = t;
2073: PetscCall(PetscLogFlops(2.0 * a->nz));
2074: } else xb = b;
2075: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2076: for (i = m - 1; i >= 0; i--) {
2077: sum = xb[i];
2078: if (xb == b) {
2079: /* whole matrix (no checkpointing available) */
2080: n = a->i[i + 1] - a->i[i];
2081: idx = a->j + a->i[i];
2082: v = aa + a->i[i];
2083: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2084: x[i] = (1. - omega) * x[i] + (sum + mdiag[i] * x[i]) * idiag[i];
2085: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
2086: n = a->i[i + 1] - diag[i] - 1;
2087: idx = a->j + diag[i] + 1;
2088: v = aa + diag[i] + 1;
2089: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2090: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2091: }
2092: }
2093: if (xb == b) {
2094: PetscCall(PetscLogFlops(2.0 * a->nz));
2095: } else {
2096: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2097: }
2098: }
2099: }
2100: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2101: PetscCall(VecRestoreArray(xx, &x));
2102: PetscCall(VecRestoreArrayRead(bb, &b));
2103: PetscFunctionReturn(PETSC_SUCCESS);
2104: }
2106: PetscErrorCode MatGetInfo_SeqAIJ(Mat A, MatInfoType flag, MatInfo *info)
2107: {
2108: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2110: PetscFunctionBegin;
2111: info->block_size = 1.0;
2112: info->nz_allocated = a->maxnz;
2113: info->nz_used = a->nz;
2114: info->nz_unneeded = (a->maxnz - a->nz);
2115: info->assemblies = A->num_ass;
2116: info->mallocs = A->info.mallocs;
2117: info->memory = 0; /* REVIEW ME */
2118: if (A->factortype) {
2119: info->fill_ratio_given = A->info.fill_ratio_given;
2120: info->fill_ratio_needed = A->info.fill_ratio_needed;
2121: info->factor_mallocs = A->info.factor_mallocs;
2122: } else {
2123: info->fill_ratio_given = 0;
2124: info->fill_ratio_needed = 0;
2125: info->factor_mallocs = 0;
2126: }
2127: PetscFunctionReturn(PETSC_SUCCESS);
2128: }
2130: PetscErrorCode MatZeroRows_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2131: {
2132: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2133: PetscInt i, m = A->rmap->n - 1;
2134: const PetscScalar *xx;
2135: PetscScalar *bb, *aa;
2136: PetscInt d = 0;
2138: PetscFunctionBegin;
2139: if (x && b) {
2140: PetscCall(VecGetArrayRead(x, &xx));
2141: PetscCall(VecGetArray(b, &bb));
2142: for (i = 0; i < N; i++) {
2143: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2144: if (rows[i] >= A->cmap->n) continue;
2145: bb[rows[i]] = diag * xx[rows[i]];
2146: }
2147: PetscCall(VecRestoreArrayRead(x, &xx));
2148: PetscCall(VecRestoreArray(b, &bb));
2149: }
2151: PetscCall(MatSeqAIJGetArray(A, &aa));
2152: if (a->keepnonzeropattern) {
2153: for (i = 0; i < N; i++) {
2154: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2155: PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));
2156: }
2157: if (diag != 0.0) {
2158: for (i = 0; i < N; i++) {
2159: d = rows[i];
2160: if (rows[i] >= A->cmap->n) continue;
2161: 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);
2162: }
2163: for (i = 0; i < N; i++) {
2164: if (rows[i] >= A->cmap->n) continue;
2165: aa[a->diag[rows[i]]] = diag;
2166: }
2167: }
2168: } else {
2169: if (diag != 0.0) {
2170: for (i = 0; i < N; i++) {
2171: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2172: if (a->ilen[rows[i]] > 0) {
2173: if (rows[i] >= A->cmap->n) {
2174: a->ilen[rows[i]] = 0;
2175: } else {
2176: a->ilen[rows[i]] = 1;
2177: aa[a->i[rows[i]]] = diag;
2178: a->j[a->i[rows[i]]] = rows[i];
2179: }
2180: } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2181: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2182: }
2183: }
2184: } else {
2185: for (i = 0; i < N; i++) {
2186: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2187: a->ilen[rows[i]] = 0;
2188: }
2189: }
2190: A->nonzerostate++;
2191: }
2192: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2193: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2194: PetscFunctionReturn(PETSC_SUCCESS);
2195: }
2197: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2198: {
2199: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2200: PetscInt i, j, m = A->rmap->n - 1, d = 0;
2201: PetscBool missing, *zeroed, vecs = PETSC_FALSE;
2202: const PetscScalar *xx;
2203: PetscScalar *bb, *aa;
2205: PetscFunctionBegin;
2206: if (!N) PetscFunctionReturn(PETSC_SUCCESS);
2207: PetscCall(MatSeqAIJGetArray(A, &aa));
2208: if (x && b) {
2209: PetscCall(VecGetArrayRead(x, &xx));
2210: PetscCall(VecGetArray(b, &bb));
2211: vecs = PETSC_TRUE;
2212: }
2213: PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
2214: for (i = 0; i < N; i++) {
2215: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2216: PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));
2218: zeroed[rows[i]] = PETSC_TRUE;
2219: }
2220: for (i = 0; i < A->rmap->n; i++) {
2221: if (!zeroed[i]) {
2222: for (j = a->i[i]; j < a->i[i + 1]; j++) {
2223: if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2224: if (vecs) bb[i] -= aa[j] * xx[a->j[j]];
2225: aa[j] = 0.0;
2226: }
2227: }
2228: } else if (vecs && i < A->cmap->N) bb[i] = diag * xx[i];
2229: }
2230: if (x && b) {
2231: PetscCall(VecRestoreArrayRead(x, &xx));
2232: PetscCall(VecRestoreArray(b, &bb));
2233: }
2234: PetscCall(PetscFree(zeroed));
2235: if (diag != 0.0) {
2236: PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, &d));
2237: if (missing) {
2238: for (i = 0; i < N; i++) {
2239: if (rows[i] >= A->cmap->N) continue;
2240: 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]);
2241: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2242: }
2243: } else {
2244: for (i = 0; i < N; i++) aa[a->diag[rows[i]]] = diag;
2245: }
2246: }
2247: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2248: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2249: PetscFunctionReturn(PETSC_SUCCESS);
2250: }
2252: PetscErrorCode MatGetRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2253: {
2254: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2255: const PetscScalar *aa;
2256: PetscInt *itmp;
2258: PetscFunctionBegin;
2259: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2260: *nz = a->i[row + 1] - a->i[row];
2261: if (v) *v = (PetscScalar *)(aa + a->i[row]);
2262: if (idx) {
2263: itmp = a->j + a->i[row];
2264: if (*nz) *idx = itmp;
2265: else *idx = NULL;
2266: }
2267: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2268: PetscFunctionReturn(PETSC_SUCCESS);
2269: }
2271: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2272: {
2273: PetscFunctionBegin;
2274: if (nz) *nz = 0;
2275: if (idx) *idx = NULL;
2276: if (v) *v = NULL;
2277: PetscFunctionReturn(PETSC_SUCCESS);
2278: }
2280: PetscErrorCode MatNorm_SeqAIJ(Mat A, NormType type, PetscReal *nrm)
2281: {
2282: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2283: const MatScalar *v;
2284: PetscReal sum = 0.0;
2285: PetscInt i, j;
2287: PetscFunctionBegin;
2288: PetscCall(MatSeqAIJGetArrayRead(A, &v));
2289: if (type == NORM_FROBENIUS) {
2290: #if defined(PETSC_USE_REAL___FP16)
2291: PetscBLASInt one = 1, nz = a->nz;
2292: PetscCallBLAS("BLASnrm2", *nrm = BLASnrm2_(&nz, v, &one));
2293: #else
2294: for (i = 0; i < a->nz; i++) {
2295: sum += PetscRealPart(PetscConj(*v) * (*v));
2296: v++;
2297: }
2298: *nrm = PetscSqrtReal(sum);
2299: #endif
2300: PetscCall(PetscLogFlops(2.0 * a->nz));
2301: } else if (type == NORM_1) {
2302: PetscReal *tmp;
2303: PetscInt *jj = a->j;
2304: PetscCall(PetscCalloc1(A->cmap->n + 1, &tmp));
2305: *nrm = 0.0;
2306: for (j = 0; j < a->nz; j++) {
2307: tmp[*jj++] += PetscAbsScalar(*v);
2308: v++;
2309: }
2310: for (j = 0; j < A->cmap->n; j++) {
2311: if (tmp[j] > *nrm) *nrm = tmp[j];
2312: }
2313: PetscCall(PetscFree(tmp));
2314: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2315: } else if (type == NORM_INFINITY) {
2316: *nrm = 0.0;
2317: for (j = 0; j < A->rmap->n; j++) {
2318: const PetscScalar *v2 = v + a->i[j];
2319: sum = 0.0;
2320: for (i = 0; i < a->i[j + 1] - a->i[j]; i++) {
2321: sum += PetscAbsScalar(*v2);
2322: v2++;
2323: }
2324: if (sum > *nrm) *nrm = sum;
2325: }
2326: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2327: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for two norm");
2328: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
2329: PetscFunctionReturn(PETSC_SUCCESS);
2330: }
2332: PetscErrorCode MatIsTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2333: {
2334: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2335: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2336: const MatScalar *va, *vb;
2337: PetscInt ma, na, mb, nb, i;
2339: PetscFunctionBegin;
2340: PetscCall(MatGetSize(A, &ma, &na));
2341: PetscCall(MatGetSize(B, &mb, &nb));
2342: if (ma != nb || na != mb) {
2343: *f = PETSC_FALSE;
2344: PetscFunctionReturn(PETSC_SUCCESS);
2345: }
2346: PetscCall(MatSeqAIJGetArrayRead(A, &va));
2347: PetscCall(MatSeqAIJGetArrayRead(B, &vb));
2348: aii = aij->i;
2349: bii = bij->i;
2350: adx = aij->j;
2351: bdx = bij->j;
2352: PetscCall(PetscMalloc1(ma, &aptr));
2353: PetscCall(PetscMalloc1(mb, &bptr));
2354: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2355: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2357: *f = PETSC_TRUE;
2358: for (i = 0; i < ma; i++) {
2359: while (aptr[i] < aii[i + 1]) {
2360: PetscInt idc, idr;
2361: PetscScalar vc, vr;
2362: /* column/row index/value */
2363: idc = adx[aptr[i]];
2364: idr = bdx[bptr[idc]];
2365: vc = va[aptr[i]];
2366: vr = vb[bptr[idc]];
2367: if (i != idr || PetscAbsScalar(vc - vr) > tol) {
2368: *f = PETSC_FALSE;
2369: goto done;
2370: } else {
2371: aptr[i]++;
2372: if (B || i != idc) bptr[idc]++;
2373: }
2374: }
2375: }
2376: done:
2377: PetscCall(PetscFree(aptr));
2378: PetscCall(PetscFree(bptr));
2379: PetscCall(MatSeqAIJRestoreArrayRead(A, &va));
2380: PetscCall(MatSeqAIJRestoreArrayRead(B, &vb));
2381: PetscFunctionReturn(PETSC_SUCCESS);
2382: }
2384: PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2385: {
2386: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2387: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2388: MatScalar *va, *vb;
2389: PetscInt ma, na, mb, nb, i;
2391: PetscFunctionBegin;
2392: PetscCall(MatGetSize(A, &ma, &na));
2393: PetscCall(MatGetSize(B, &mb, &nb));
2394: if (ma != nb || na != mb) {
2395: *f = PETSC_FALSE;
2396: PetscFunctionReturn(PETSC_SUCCESS);
2397: }
2398: aii = aij->i;
2399: bii = bij->i;
2400: adx = aij->j;
2401: bdx = bij->j;
2402: va = aij->a;
2403: vb = bij->a;
2404: PetscCall(PetscMalloc1(ma, &aptr));
2405: PetscCall(PetscMalloc1(mb, &bptr));
2406: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2407: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2409: *f = PETSC_TRUE;
2410: for (i = 0; i < ma; i++) {
2411: while (aptr[i] < aii[i + 1]) {
2412: PetscInt idc, idr;
2413: PetscScalar vc, vr;
2414: /* column/row index/value */
2415: idc = adx[aptr[i]];
2416: idr = bdx[bptr[idc]];
2417: vc = va[aptr[i]];
2418: vr = vb[bptr[idc]];
2419: if (i != idr || PetscAbsScalar(vc - PetscConj(vr)) > tol) {
2420: *f = PETSC_FALSE;
2421: goto done;
2422: } else {
2423: aptr[i]++;
2424: if (B || i != idc) bptr[idc]++;
2425: }
2426: }
2427: }
2428: done:
2429: PetscCall(PetscFree(aptr));
2430: PetscCall(PetscFree(bptr));
2431: PetscFunctionReturn(PETSC_SUCCESS);
2432: }
2434: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A, PetscReal tol, PetscBool *f)
2435: {
2436: PetscFunctionBegin;
2437: PetscCall(MatIsTranspose_SeqAIJ(A, A, tol, f));
2438: PetscFunctionReturn(PETSC_SUCCESS);
2439: }
2441: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A, PetscReal tol, PetscBool *f)
2442: {
2443: PetscFunctionBegin;
2444: PetscCall(MatIsHermitianTranspose_SeqAIJ(A, A, tol, f));
2445: PetscFunctionReturn(PETSC_SUCCESS);
2446: }
2448: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A, Vec ll, Vec rr)
2449: {
2450: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2451: const PetscScalar *l, *r;
2452: PetscScalar x;
2453: MatScalar *v;
2454: PetscInt i, j, m = A->rmap->n, n = A->cmap->n, M, nz = a->nz;
2455: const PetscInt *jj;
2457: PetscFunctionBegin;
2458: if (ll) {
2459: /* The local size is used so that VecMPI can be passed to this routine
2460: by MatDiagonalScale_MPIAIJ */
2461: PetscCall(VecGetLocalSize(ll, &m));
2462: PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length");
2463: PetscCall(VecGetArrayRead(ll, &l));
2464: PetscCall(MatSeqAIJGetArray(A, &v));
2465: for (i = 0; i < m; i++) {
2466: x = l[i];
2467: M = a->i[i + 1] - a->i[i];
2468: for (j = 0; j < M; j++) (*v++) *= x;
2469: }
2470: PetscCall(VecRestoreArrayRead(ll, &l));
2471: PetscCall(PetscLogFlops(nz));
2472: PetscCall(MatSeqAIJRestoreArray(A, &v));
2473: }
2474: if (rr) {
2475: PetscCall(VecGetLocalSize(rr, &n));
2476: PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length");
2477: PetscCall(VecGetArrayRead(rr, &r));
2478: PetscCall(MatSeqAIJGetArray(A, &v));
2479: jj = a->j;
2480: for (i = 0; i < nz; i++) (*v++) *= r[*jj++];
2481: PetscCall(MatSeqAIJRestoreArray(A, &v));
2482: PetscCall(VecRestoreArrayRead(rr, &r));
2483: PetscCall(PetscLogFlops(nz));
2484: }
2485: PetscCall(MatSeqAIJInvalidateDiagonal(A));
2486: PetscFunctionReturn(PETSC_SUCCESS);
2487: }
2489: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A, IS isrow, IS iscol, PetscInt csize, MatReuse scall, Mat *B)
2490: {
2491: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *c;
2492: PetscInt *smap, i, k, kstart, kend, oldcols = A->cmap->n, *lens;
2493: PetscInt row, mat_i, *mat_j, tcol, first, step, *mat_ilen, sum, lensi;
2494: const PetscInt *irow, *icol;
2495: const PetscScalar *aa;
2496: PetscInt nrows, ncols;
2497: PetscInt *starts, *j_new, *i_new, *aj = a->j, *ai = a->i, ii, *ailen = a->ilen;
2498: MatScalar *a_new, *mat_a, *c_a;
2499: Mat C;
2500: PetscBool stride;
2502: PetscFunctionBegin;
2503: PetscCall(ISGetIndices(isrow, &irow));
2504: PetscCall(ISGetLocalSize(isrow, &nrows));
2505: PetscCall(ISGetLocalSize(iscol, &ncols));
2507: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &stride));
2508: if (stride) {
2509: PetscCall(ISStrideGetInfo(iscol, &first, &step));
2510: } else {
2511: first = 0;
2512: step = 0;
2513: }
2514: if (stride && step == 1) {
2515: /* special case of contiguous rows */
2516: PetscCall(PetscMalloc2(nrows, &lens, nrows, &starts));
2517: /* loop over new rows determining lens and starting points */
2518: for (i = 0; i < nrows; i++) {
2519: kstart = ai[irow[i]];
2520: kend = kstart + ailen[irow[i]];
2521: starts[i] = kstart;
2522: for (k = kstart; k < kend; k++) {
2523: if (aj[k] >= first) {
2524: starts[i] = k;
2525: break;
2526: }
2527: }
2528: sum = 0;
2529: while (k < kend) {
2530: if (aj[k++] >= first + ncols) break;
2531: sum++;
2532: }
2533: lens[i] = sum;
2534: }
2535: /* create submatrix */
2536: if (scall == MAT_REUSE_MATRIX) {
2537: PetscInt n_cols, n_rows;
2538: PetscCall(MatGetSize(*B, &n_rows, &n_cols));
2539: PetscCheck(n_rows == nrows && n_cols == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Reused submatrix wrong size");
2540: PetscCall(MatZeroEntries(*B));
2541: C = *B;
2542: } else {
2543: PetscInt rbs, cbs;
2544: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2545: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2546: PetscCall(ISGetBlockSize(isrow, &rbs));
2547: PetscCall(ISGetBlockSize(iscol, &cbs));
2548: PetscCall(MatSetBlockSizes(C, rbs, cbs));
2549: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2550: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2551: }
2552: c = (Mat_SeqAIJ *)C->data;
2554: /* loop over rows inserting into submatrix */
2555: PetscCall(MatSeqAIJGetArrayWrite(C, &a_new)); // Not 'a_new = c->a-new', since that raw usage ignores offload state of C
2556: j_new = c->j;
2557: i_new = c->i;
2558: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2559: for (i = 0; i < nrows; i++) {
2560: ii = starts[i];
2561: lensi = lens[i];
2562: for (k = 0; k < lensi; k++) *j_new++ = aj[ii + k] - first;
2563: PetscCall(PetscArraycpy(a_new, aa + starts[i], lensi));
2564: a_new += lensi;
2565: i_new[i + 1] = i_new[i] + lensi;
2566: c->ilen[i] = lensi;
2567: }
2568: PetscCall(MatSeqAIJRestoreArrayWrite(C, &a_new)); // Set C's offload state properly
2569: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2570: PetscCall(PetscFree2(lens, starts));
2571: } else {
2572: PetscCall(ISGetIndices(iscol, &icol));
2573: PetscCall(PetscCalloc1(oldcols, &smap));
2574: PetscCall(PetscMalloc1(1 + nrows, &lens));
2575: for (i = 0; i < ncols; i++) {
2576: 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);
2577: smap[icol[i]] = i + 1;
2578: }
2580: /* determine lens of each row */
2581: for (i = 0; i < nrows; i++) {
2582: kstart = ai[irow[i]];
2583: kend = kstart + a->ilen[irow[i]];
2584: lens[i] = 0;
2585: for (k = kstart; k < kend; k++) {
2586: if (smap[aj[k]]) lens[i]++;
2587: }
2588: }
2589: /* Create and fill new matrix */
2590: if (scall == MAT_REUSE_MATRIX) {
2591: PetscBool equal;
2593: c = (Mat_SeqAIJ *)((*B)->data);
2594: PetscCheck((*B)->rmap->n == nrows && (*B)->cmap->n == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong size");
2595: PetscCall(PetscArraycmp(c->ilen, lens, (*B)->rmap->n, &equal));
2596: PetscCheck(equal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong no of nonzeros");
2597: PetscCall(PetscArrayzero(c->ilen, (*B)->rmap->n));
2598: C = *B;
2599: } else {
2600: PetscInt rbs, cbs;
2601: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2602: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2603: PetscCall(ISGetBlockSize(isrow, &rbs));
2604: PetscCall(ISGetBlockSize(iscol, &cbs));
2605: PetscCall(MatSetBlockSizes(C, rbs, cbs));
2606: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2607: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2608: }
2609: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2611: c = (Mat_SeqAIJ *)(C->data);
2612: PetscCall(MatSeqAIJGetArrayWrite(C, &c_a)); // Not 'c->a', since that raw usage ignores offload state of C
2613: for (i = 0; i < nrows; i++) {
2614: row = irow[i];
2615: kstart = ai[row];
2616: kend = kstart + a->ilen[row];
2617: mat_i = c->i[i];
2618: mat_j = c->j + mat_i;
2619: mat_a = c_a + mat_i;
2620: mat_ilen = c->ilen + i;
2621: for (k = kstart; k < kend; k++) {
2622: if ((tcol = smap[a->j[k]])) {
2623: *mat_j++ = tcol - 1;
2624: *mat_a++ = aa[k];
2625: (*mat_ilen)++;
2626: }
2627: }
2628: }
2629: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2630: /* Free work space */
2631: PetscCall(ISRestoreIndices(iscol, &icol));
2632: PetscCall(PetscFree(smap));
2633: PetscCall(PetscFree(lens));
2634: /* sort */
2635: for (i = 0; i < nrows; i++) {
2636: PetscInt ilen;
2638: mat_i = c->i[i];
2639: mat_j = c->j + mat_i;
2640: mat_a = c_a + mat_i;
2641: ilen = c->ilen[i];
2642: PetscCall(PetscSortIntWithScalarArray(ilen, mat_j, mat_a));
2643: }
2644: PetscCall(MatSeqAIJRestoreArrayWrite(C, &c_a));
2645: }
2646: #if defined(PETSC_HAVE_DEVICE)
2647: PetscCall(MatBindToCPU(C, A->boundtocpu));
2648: #endif
2649: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
2650: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
2652: PetscCall(ISRestoreIndices(isrow, &irow));
2653: *B = C;
2654: PetscFunctionReturn(PETSC_SUCCESS);
2655: }
2657: PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
2658: {
2659: Mat B;
2661: PetscFunctionBegin;
2662: if (scall == MAT_INITIAL_MATRIX) {
2663: PetscCall(MatCreate(subComm, &B));
2664: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n));
2665: PetscCall(MatSetBlockSizesFromMats(B, mat, mat));
2666: PetscCall(MatSetType(B, MATSEQAIJ));
2667: PetscCall(MatDuplicateNoCreate_SeqAIJ(B, mat, MAT_COPY_VALUES, PETSC_TRUE));
2668: *subMat = B;
2669: } else {
2670: PetscCall(MatCopy_SeqAIJ(mat, *subMat, SAME_NONZERO_PATTERN));
2671: }
2672: PetscFunctionReturn(PETSC_SUCCESS);
2673: }
2675: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
2676: {
2677: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2678: Mat outA;
2679: PetscBool row_identity, col_identity;
2681: PetscFunctionBegin;
2682: PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels=0 supported for in-place ilu");
2684: PetscCall(ISIdentity(row, &row_identity));
2685: PetscCall(ISIdentity(col, &col_identity));
2687: outA = inA;
2688: outA->factortype = MAT_FACTOR_LU;
2689: PetscCall(PetscFree(inA->solvertype));
2690: PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));
2692: PetscCall(PetscObjectReference((PetscObject)row));
2693: PetscCall(ISDestroy(&a->row));
2695: a->row = row;
2697: PetscCall(PetscObjectReference((PetscObject)col));
2698: PetscCall(ISDestroy(&a->col));
2700: a->col = col;
2702: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2703: PetscCall(ISDestroy(&a->icol));
2704: PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));
2706: if (!a->solve_work) { /* this matrix may have been factored before */
2707: PetscCall(PetscMalloc1(inA->rmap->n + 1, &a->solve_work));
2708: }
2710: PetscCall(MatMarkDiagonal_SeqAIJ(inA));
2711: if (row_identity && col_identity) {
2712: PetscCall(MatLUFactorNumeric_SeqAIJ_inplace(outA, inA, info));
2713: } else {
2714: PetscCall(MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA, inA, info));
2715: }
2716: PetscFunctionReturn(PETSC_SUCCESS);
2717: }
2719: PetscErrorCode MatScale_SeqAIJ(Mat inA, PetscScalar alpha)
2720: {
2721: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2722: PetscScalar *v;
2723: PetscBLASInt one = 1, bnz;
2725: PetscFunctionBegin;
2726: PetscCall(MatSeqAIJGetArray(inA, &v));
2727: PetscCall(PetscBLASIntCast(a->nz, &bnz));
2728: PetscCallBLAS("BLASscal", BLASscal_(&bnz, &alpha, v, &one));
2729: PetscCall(PetscLogFlops(a->nz));
2730: PetscCall(MatSeqAIJRestoreArray(inA, &v));
2731: PetscCall(MatSeqAIJInvalidateDiagonal(inA));
2732: PetscFunctionReturn(PETSC_SUCCESS);
2733: }
2735: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2736: {
2737: PetscInt i;
2739: PetscFunctionBegin;
2740: if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2741: PetscCall(PetscFree4(submatj->sbuf1, submatj->ptr, submatj->tmp, submatj->ctr));
2743: for (i = 0; i < submatj->nrqr; ++i) PetscCall(PetscFree(submatj->sbuf2[i]));
2744: PetscCall(PetscFree3(submatj->sbuf2, submatj->req_size, submatj->req_source1));
2746: if (submatj->rbuf1) {
2747: PetscCall(PetscFree(submatj->rbuf1[0]));
2748: PetscCall(PetscFree(submatj->rbuf1));
2749: }
2751: for (i = 0; i < submatj->nrqs; ++i) PetscCall(PetscFree(submatj->rbuf3[i]));
2752: PetscCall(PetscFree3(submatj->req_source2, submatj->rbuf2, submatj->rbuf3));
2753: PetscCall(PetscFree(submatj->pa));
2754: }
2756: #if defined(PETSC_USE_CTABLE)
2757: PetscCall(PetscHMapIDestroy(&submatj->rmap));
2758: if (submatj->cmap_loc) PetscCall(PetscFree(submatj->cmap_loc));
2759: PetscCall(PetscFree(submatj->rmap_loc));
2760: #else
2761: PetscCall(PetscFree(submatj->rmap));
2762: #endif
2764: if (!submatj->allcolumns) {
2765: #if defined(PETSC_USE_CTABLE)
2766: PetscCall(PetscHMapIDestroy((PetscHMapI *)&submatj->cmap));
2767: #else
2768: PetscCall(PetscFree(submatj->cmap));
2769: #endif
2770: }
2771: PetscCall(PetscFree(submatj->row2proc));
2773: PetscCall(PetscFree(submatj));
2774: PetscFunctionReturn(PETSC_SUCCESS);
2775: }
2777: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2778: {
2779: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data;
2780: Mat_SubSppt *submatj = c->submatis1;
2782: PetscFunctionBegin;
2783: PetscCall((*submatj->destroy)(C));
2784: PetscCall(MatDestroySubMatrix_Private(submatj));
2785: PetscFunctionReturn(PETSC_SUCCESS);
2786: }
2788: /* Note this has code duplication with MatDestroySubMatrices_SeqBAIJ() */
2789: PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n, Mat *mat[])
2790: {
2791: PetscInt i;
2792: Mat C;
2793: Mat_SeqAIJ *c;
2794: Mat_SubSppt *submatj;
2796: PetscFunctionBegin;
2797: for (i = 0; i < n; i++) {
2798: C = (*mat)[i];
2799: c = (Mat_SeqAIJ *)C->data;
2800: submatj = c->submatis1;
2801: if (submatj) {
2802: if (--((PetscObject)C)->refct <= 0) {
2803: PetscCall(PetscFree(C->factorprefix));
2804: PetscCall((*submatj->destroy)(C));
2805: PetscCall(MatDestroySubMatrix_Private(submatj));
2806: PetscCall(PetscFree(C->defaultvectype));
2807: PetscCall(PetscFree(C->defaultrandtype));
2808: PetscCall(PetscLayoutDestroy(&C->rmap));
2809: PetscCall(PetscLayoutDestroy(&C->cmap));
2810: PetscCall(PetscHeaderDestroy(&C));
2811: }
2812: } else {
2813: PetscCall(MatDestroy(&C));
2814: }
2815: }
2817: /* Destroy Dummy submatrices created for reuse */
2818: PetscCall(MatDestroySubMatrices_Dummy(n, mat));
2820: PetscCall(PetscFree(*mat));
2821: PetscFunctionReturn(PETSC_SUCCESS);
2822: }
2824: PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *B[])
2825: {
2826: PetscInt i;
2828: PetscFunctionBegin;
2829: if (scall == MAT_INITIAL_MATRIX) PetscCall(PetscCalloc1(n + 1, B));
2831: for (i = 0; i < n; i++) PetscCall(MatCreateSubMatrix_SeqAIJ(A, irow[i], icol[i], PETSC_DECIDE, scall, &(*B)[i]));
2832: PetscFunctionReturn(PETSC_SUCCESS);
2833: }
2835: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A, PetscInt is_max, IS is[], PetscInt ov)
2836: {
2837: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2838: PetscInt row, i, j, k, l, ll, m, n, *nidx, isz, val;
2839: const PetscInt *idx;
2840: PetscInt start, end, *ai, *aj, bs = (A->rmap->bs > 0 && A->rmap->bs == A->cmap->bs) ? A->rmap->bs : 1;
2841: PetscBT table;
2843: PetscFunctionBegin;
2844: m = A->rmap->n / bs;
2845: ai = a->i;
2846: aj = a->j;
2848: PetscCheck(ov >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "illegal negative overlap value used");
2850: PetscCall(PetscMalloc1(m + 1, &nidx));
2851: PetscCall(PetscBTCreate(m, &table));
2853: for (i = 0; i < is_max; i++) {
2854: /* Initialize the two local arrays */
2855: isz = 0;
2856: PetscCall(PetscBTMemzero(m, table));
2858: /* Extract the indices, assume there can be duplicate entries */
2859: PetscCall(ISGetIndices(is[i], &idx));
2860: PetscCall(ISGetLocalSize(is[i], &n));
2862: if (bs > 1) {
2863: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2864: for (j = 0; j < n; ++j) {
2865: if (!PetscBTLookupSet(table, idx[j] / bs)) nidx[isz++] = idx[j] / bs;
2866: }
2867: PetscCall(ISRestoreIndices(is[i], &idx));
2868: PetscCall(ISDestroy(&is[i]));
2870: k = 0;
2871: for (j = 0; j < ov; j++) { /* for each overlap */
2872: n = isz;
2873: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2874: for (ll = 0; ll < bs; ll++) {
2875: row = bs * nidx[k] + ll;
2876: start = ai[row];
2877: end = ai[row + 1];
2878: for (l = start; l < end; l++) {
2879: val = aj[l] / bs;
2880: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2881: }
2882: }
2883: }
2884: }
2885: PetscCall(ISCreateBlock(PETSC_COMM_SELF, bs, isz, nidx, PETSC_COPY_VALUES, (is + i)));
2886: } else {
2887: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2888: for (j = 0; j < n; ++j) {
2889: if (!PetscBTLookupSet(table, idx[j])) nidx[isz++] = idx[j];
2890: }
2891: PetscCall(ISRestoreIndices(is[i], &idx));
2892: PetscCall(ISDestroy(&is[i]));
2894: k = 0;
2895: for (j = 0; j < ov; j++) { /* for each overlap */
2896: n = isz;
2897: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2898: row = nidx[k];
2899: start = ai[row];
2900: end = ai[row + 1];
2901: for (l = start; l < end; l++) {
2902: val = aj[l];
2903: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2904: }
2905: }
2906: }
2907: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, isz, nidx, PETSC_COPY_VALUES, (is + i)));
2908: }
2909: }
2910: PetscCall(PetscBTDestroy(&table));
2911: PetscCall(PetscFree(nidx));
2912: PetscFunctionReturn(PETSC_SUCCESS);
2913: }
2915: PetscErrorCode MatPermute_SeqAIJ(Mat A, IS rowp, IS colp, Mat *B)
2916: {
2917: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2918: PetscInt i, nz = 0, m = A->rmap->n, n = A->cmap->n;
2919: const PetscInt *row, *col;
2920: PetscInt *cnew, j, *lens;
2921: IS icolp, irowp;
2922: PetscInt *cwork = NULL;
2923: PetscScalar *vwork = NULL;
2925: PetscFunctionBegin;
2926: PetscCall(ISInvertPermutation(rowp, PETSC_DECIDE, &irowp));
2927: PetscCall(ISGetIndices(irowp, &row));
2928: PetscCall(ISInvertPermutation(colp, PETSC_DECIDE, &icolp));
2929: PetscCall(ISGetIndices(icolp, &col));
2931: /* determine lengths of permuted rows */
2932: PetscCall(PetscMalloc1(m + 1, &lens));
2933: for (i = 0; i < m; i++) lens[row[i]] = a->i[i + 1] - a->i[i];
2934: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2935: PetscCall(MatSetSizes(*B, m, n, m, n));
2936: PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2937: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
2938: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*B, 0, lens));
2939: PetscCall(PetscFree(lens));
2941: PetscCall(PetscMalloc1(n, &cnew));
2942: for (i = 0; i < m; i++) {
2943: PetscCall(MatGetRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2944: for (j = 0; j < nz; j++) cnew[j] = col[cwork[j]];
2945: PetscCall(MatSetValues_SeqAIJ(*B, 1, &row[i], nz, cnew, vwork, INSERT_VALUES));
2946: PetscCall(MatRestoreRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2947: }
2948: PetscCall(PetscFree(cnew));
2950: (*B)->assembled = PETSC_FALSE;
2952: #if defined(PETSC_HAVE_DEVICE)
2953: PetscCall(MatBindToCPU(*B, A->boundtocpu));
2954: #endif
2955: PetscCall(MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY));
2956: PetscCall(MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY));
2957: PetscCall(ISRestoreIndices(irowp, &row));
2958: PetscCall(ISRestoreIndices(icolp, &col));
2959: PetscCall(ISDestroy(&irowp));
2960: PetscCall(ISDestroy(&icolp));
2961: if (rowp == colp) PetscCall(MatPropagateSymmetryOptions(A, *B));
2962: PetscFunctionReturn(PETSC_SUCCESS);
2963: }
2965: PetscErrorCode MatCopy_SeqAIJ(Mat A, Mat B, MatStructure str)
2966: {
2967: PetscFunctionBegin;
2968: /* If the two matrices have the same copy implementation, use fast copy. */
2969: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2970: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2971: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2972: const PetscScalar *aa;
2974: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2975: 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]);
2976: PetscCall(PetscArraycpy(b->a, aa, a->i[A->rmap->n]));
2977: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2978: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2979: } else {
2980: PetscCall(MatCopy_Basic(A, B, str));
2981: }
2982: PetscFunctionReturn(PETSC_SUCCESS);
2983: }
2985: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A, PetscScalar *array[])
2986: {
2987: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2989: PetscFunctionBegin;
2990: *array = a->a;
2991: PetscFunctionReturn(PETSC_SUCCESS);
2992: }
2994: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A, PetscScalar *array[])
2995: {
2996: PetscFunctionBegin;
2997: *array = NULL;
2998: PetscFunctionReturn(PETSC_SUCCESS);
2999: }
3001: /*
3002: Computes the number of nonzeros per row needed for preallocation when X and Y
3003: have different nonzero structure.
3004: */
3005: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *yi, const PetscInt *yj, PetscInt *nnz)
3006: {
3007: PetscInt i, j, k, nzx, nzy;
3009: PetscFunctionBegin;
3010: /* Set the number of nonzeros in the new matrix */
3011: for (i = 0; i < m; i++) {
3012: const PetscInt *xjj = xj + xi[i], *yjj = yj + yi[i];
3013: nzx = xi[i + 1] - xi[i];
3014: nzy = yi[i + 1] - yi[i];
3015: nnz[i] = 0;
3016: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
3017: for (; k < nzy && yjj[k] < xjj[j]; k++) nnz[i]++; /* Catch up to X */
3018: if (k < nzy && yjj[k] == xjj[j]) k++; /* Skip duplicate */
3019: nnz[i]++;
3020: }
3021: for (; k < nzy; k++) nnz[i]++;
3022: }
3023: PetscFunctionReturn(PETSC_SUCCESS);
3024: }
3026: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y, Mat X, PetscInt *nnz)
3027: {
3028: PetscInt m = Y->rmap->N;
3029: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
3030: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
3032: PetscFunctionBegin;
3033: /* Set the number of nonzeros in the new matrix */
3034: PetscCall(MatAXPYGetPreallocation_SeqX_private(m, x->i, x->j, y->i, y->j, nnz));
3035: PetscFunctionReturn(PETSC_SUCCESS);
3036: }
3038: PetscErrorCode MatAXPY_SeqAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
3039: {
3040: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;
3042: PetscFunctionBegin;
3043: if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
3044: PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE;
3045: if (e) {
3046: PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e));
3047: if (e) {
3048: PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e));
3049: if (e) str = SAME_NONZERO_PATTERN;
3050: }
3051: }
3052: if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
3053: }
3054: if (str == SAME_NONZERO_PATTERN) {
3055: const PetscScalar *xa;
3056: PetscScalar *ya, alpha = a;
3057: PetscBLASInt one = 1, bnz;
3059: PetscCall(PetscBLASIntCast(x->nz, &bnz));
3060: PetscCall(MatSeqAIJGetArray(Y, &ya));
3061: PetscCall(MatSeqAIJGetArrayRead(X, &xa));
3062: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, xa, &one, ya, &one));
3063: PetscCall(MatSeqAIJRestoreArrayRead(X, &xa));
3064: PetscCall(MatSeqAIJRestoreArray(Y, &ya));
3065: PetscCall(PetscLogFlops(2.0 * bnz));
3066: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3067: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
3068: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
3069: PetscCall(MatAXPY_Basic(Y, a, X, str));
3070: } else {
3071: Mat B;
3072: PetscInt *nnz;
3073: PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
3074: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
3075: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
3076: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
3077: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
3078: PetscCall(MatAXPYGetPreallocation_SeqAIJ(Y, X, nnz));
3079: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
3080: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
3081: PetscCall(MatHeaderMerge(Y, &B));
3082: PetscCall(MatSeqAIJCheckInode(Y));
3083: PetscCall(PetscFree(nnz));
3084: }
3085: PetscFunctionReturn(PETSC_SUCCESS);
3086: }
3088: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
3089: {
3090: #if defined(PETSC_USE_COMPLEX)
3091: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3092: PetscInt i, nz;
3093: PetscScalar *a;
3095: PetscFunctionBegin;
3096: nz = aij->nz;
3097: PetscCall(MatSeqAIJGetArray(mat, &a));
3098: for (i = 0; i < nz; i++) a[i] = PetscConj(a[i]);
3099: PetscCall(MatSeqAIJRestoreArray(mat, &a));
3100: #else
3101: PetscFunctionBegin;
3102: #endif
3103: PetscFunctionReturn(PETSC_SUCCESS);
3104: }
3106: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3107: {
3108: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3109: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3110: PetscReal atmp;
3111: PetscScalar *x;
3112: const MatScalar *aa, *av;
3114: PetscFunctionBegin;
3115: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3116: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3117: aa = av;
3118: ai = a->i;
3119: aj = a->j;
3121: PetscCall(VecSet(v, 0.0));
3122: PetscCall(VecGetArrayWrite(v, &x));
3123: PetscCall(VecGetLocalSize(v, &n));
3124: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3125: for (i = 0; i < m; i++) {
3126: ncols = ai[1] - ai[0];
3127: ai++;
3128: for (j = 0; j < ncols; j++) {
3129: atmp = PetscAbsScalar(*aa);
3130: if (PetscAbsScalar(x[i]) < atmp) {
3131: x[i] = atmp;
3132: if (idx) idx[i] = *aj;
3133: }
3134: aa++;
3135: aj++;
3136: }
3137: }
3138: PetscCall(VecRestoreArrayWrite(v, &x));
3139: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3140: PetscFunctionReturn(PETSC_SUCCESS);
3141: }
3143: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3144: {
3145: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3146: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3147: PetscScalar *x;
3148: const MatScalar *aa, *av;
3150: PetscFunctionBegin;
3151: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3152: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3153: aa = av;
3154: ai = a->i;
3155: aj = a->j;
3157: PetscCall(VecSet(v, 0.0));
3158: PetscCall(VecGetArrayWrite(v, &x));
3159: PetscCall(VecGetLocalSize(v, &n));
3160: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3161: for (i = 0; i < m; i++) {
3162: ncols = ai[1] - ai[0];
3163: ai++;
3164: if (ncols == A->cmap->n) { /* row is dense */
3165: x[i] = *aa;
3166: if (idx) idx[i] = 0;
3167: } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
3168: x[i] = 0.0;
3169: if (idx) {
3170: for (j = 0; j < ncols; j++) { /* find first implicit 0.0 in the row */
3171: if (aj[j] > j) {
3172: idx[i] = j;
3173: break;
3174: }
3175: }
3176: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3177: if (j == ncols && j < A->cmap->n) idx[i] = j;
3178: }
3179: }
3180: for (j = 0; j < ncols; j++) {
3181: if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {
3182: x[i] = *aa;
3183: if (idx) idx[i] = *aj;
3184: }
3185: aa++;
3186: aj++;
3187: }
3188: }
3189: PetscCall(VecRestoreArrayWrite(v, &x));
3190: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3191: PetscFunctionReturn(PETSC_SUCCESS);
3192: }
3194: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3195: {
3196: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3197: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3198: PetscScalar *x;
3199: const MatScalar *aa, *av;
3201: PetscFunctionBegin;
3202: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3203: aa = av;
3204: ai = a->i;
3205: aj = a->j;
3207: PetscCall(VecSet(v, 0.0));
3208: PetscCall(VecGetArrayWrite(v, &x));
3209: PetscCall(VecGetLocalSize(v, &n));
3210: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector, %" PetscInt_FMT " vs. %" PetscInt_FMT " rows", m, n);
3211: for (i = 0; i < m; i++) {
3212: ncols = ai[1] - ai[0];
3213: ai++;
3214: if (ncols == A->cmap->n) { /* row is dense */
3215: x[i] = *aa;
3216: if (idx) idx[i] = 0;
3217: } else { /* row is sparse so already KNOW minimum is 0.0 or higher */
3218: x[i] = 0.0;
3219: if (idx) { /* find first implicit 0.0 in the row */
3220: for (j = 0; j < ncols; j++) {
3221: if (aj[j] > j) {
3222: idx[i] = j;
3223: break;
3224: }
3225: }
3226: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3227: if (j == ncols && j < A->cmap->n) idx[i] = j;
3228: }
3229: }
3230: for (j = 0; j < ncols; j++) {
3231: if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {
3232: x[i] = *aa;
3233: if (idx) idx[i] = *aj;
3234: }
3235: aa++;
3236: aj++;
3237: }
3238: }
3239: PetscCall(VecRestoreArrayWrite(v, &x));
3240: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3241: PetscFunctionReturn(PETSC_SUCCESS);
3242: }
3244: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3245: {
3246: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3247: PetscInt i, j, m = A->rmap->n, ncols, n;
3248: const PetscInt *ai, *aj;
3249: PetscScalar *x;
3250: const MatScalar *aa, *av;
3252: PetscFunctionBegin;
3253: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3254: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3255: aa = av;
3256: ai = a->i;
3257: aj = a->j;
3259: PetscCall(VecSet(v, 0.0));
3260: PetscCall(VecGetArrayWrite(v, &x));
3261: PetscCall(VecGetLocalSize(v, &n));
3262: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3263: for (i = 0; i < m; i++) {
3264: ncols = ai[1] - ai[0];
3265: ai++;
3266: if (ncols == A->cmap->n) { /* row is dense */
3267: x[i] = *aa;
3268: if (idx) idx[i] = 0;
3269: } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
3270: x[i] = 0.0;
3271: if (idx) { /* find first implicit 0.0 in the row */
3272: for (j = 0; j < ncols; j++) {
3273: if (aj[j] > j) {
3274: idx[i] = j;
3275: break;
3276: }
3277: }
3278: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3279: if (j == ncols && j < A->cmap->n) idx[i] = j;
3280: }
3281: }
3282: for (j = 0; j < ncols; j++) {
3283: if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {
3284: x[i] = *aa;
3285: if (idx) idx[i] = *aj;
3286: }
3287: aa++;
3288: aj++;
3289: }
3290: }
3291: PetscCall(VecRestoreArrayWrite(v, &x));
3292: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3293: PetscFunctionReturn(PETSC_SUCCESS);
3294: }
3296: PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A, const PetscScalar **values)
3297: {
3298: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3299: PetscInt i, bs = PetscAbs(A->rmap->bs), mbs = A->rmap->n / bs, ipvt[5], bs2 = bs * bs, *v_pivots, ij[7], *IJ, j;
3300: MatScalar *diag, work[25], *v_work;
3301: const PetscReal shift = 0.0;
3302: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
3304: PetscFunctionBegin;
3305: allowzeropivot = PetscNot(A->erroriffailure);
3306: if (a->ibdiagvalid) {
3307: if (values) *values = a->ibdiag;
3308: PetscFunctionReturn(PETSC_SUCCESS);
3309: }
3310: PetscCall(MatMarkDiagonal_SeqAIJ(A));
3311: if (!a->ibdiag) { PetscCall(PetscMalloc1(bs2 * mbs, &a->ibdiag)); }
3312: diag = a->ibdiag;
3313: if (values) *values = a->ibdiag;
3314: /* factor and invert each block */
3315: switch (bs) {
3316: case 1:
3317: for (i = 0; i < mbs; i++) {
3318: PetscCall(MatGetValues(A, 1, &i, 1, &i, diag + i));
3319: if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3320: if (allowzeropivot) {
3321: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3322: A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3323: A->factorerror_zeropivot_row = i;
3324: PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g\n", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON));
3325: } 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);
3326: }
3327: diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3328: }
3329: break;
3330: case 2:
3331: for (i = 0; i < mbs; i++) {
3332: ij[0] = 2 * i;
3333: ij[1] = 2 * i + 1;
3334: PetscCall(MatGetValues(A, 2, ij, 2, ij, diag));
3335: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
3336: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3337: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
3338: diag += 4;
3339: }
3340: break;
3341: case 3:
3342: for (i = 0; i < mbs; i++) {
3343: ij[0] = 3 * i;
3344: ij[1] = 3 * i + 1;
3345: ij[2] = 3 * i + 2;
3346: PetscCall(MatGetValues(A, 3, ij, 3, ij, diag));
3347: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
3348: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3349: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
3350: diag += 9;
3351: }
3352: break;
3353: case 4:
3354: for (i = 0; i < mbs; i++) {
3355: ij[0] = 4 * i;
3356: ij[1] = 4 * i + 1;
3357: ij[2] = 4 * i + 2;
3358: ij[3] = 4 * i + 3;
3359: PetscCall(MatGetValues(A, 4, ij, 4, ij, diag));
3360: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
3361: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3362: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
3363: diag += 16;
3364: }
3365: break;
3366: case 5:
3367: for (i = 0; i < mbs; i++) {
3368: ij[0] = 5 * i;
3369: ij[1] = 5 * i + 1;
3370: ij[2] = 5 * i + 2;
3371: ij[3] = 5 * i + 3;
3372: ij[4] = 5 * i + 4;
3373: PetscCall(MatGetValues(A, 5, ij, 5, ij, diag));
3374: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
3375: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3376: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
3377: diag += 25;
3378: }
3379: break;
3380: case 6:
3381: for (i = 0; i < mbs; i++) {
3382: ij[0] = 6 * i;
3383: ij[1] = 6 * i + 1;
3384: ij[2] = 6 * i + 2;
3385: ij[3] = 6 * i + 3;
3386: ij[4] = 6 * i + 4;
3387: ij[5] = 6 * i + 5;
3388: PetscCall(MatGetValues(A, 6, ij, 6, ij, diag));
3389: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
3390: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3391: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
3392: diag += 36;
3393: }
3394: break;
3395: case 7:
3396: for (i = 0; i < mbs; i++) {
3397: ij[0] = 7 * i;
3398: ij[1] = 7 * i + 1;
3399: ij[2] = 7 * i + 2;
3400: ij[3] = 7 * i + 3;
3401: ij[4] = 7 * i + 4;
3402: ij[5] = 7 * i + 5;
3403: ij[6] = 7 * i + 6;
3404: PetscCall(MatGetValues(A, 7, ij, 7, ij, diag));
3405: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
3406: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3407: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
3408: diag += 49;
3409: }
3410: break;
3411: default:
3412: PetscCall(PetscMalloc3(bs, &v_work, bs, &v_pivots, bs, &IJ));
3413: for (i = 0; i < mbs; i++) {
3414: for (j = 0; j < bs; j++) IJ[j] = bs * i + j;
3415: PetscCall(MatGetValues(A, bs, IJ, bs, IJ, diag));
3416: PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
3417: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3418: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bs));
3419: diag += bs2;
3420: }
3421: PetscCall(PetscFree3(v_work, v_pivots, IJ));
3422: }
3423: a->ibdiagvalid = PETSC_TRUE;
3424: PetscFunctionReturn(PETSC_SUCCESS);
3425: }
3427: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x, PetscRandom rctx)
3428: {
3429: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3430: PetscScalar a, *aa;
3431: PetscInt m, n, i, j, col;
3433: PetscFunctionBegin;
3434: if (!x->assembled) {
3435: PetscCall(MatGetSize(x, &m, &n));
3436: for (i = 0; i < m; i++) {
3437: for (j = 0; j < aij->imax[i]; j++) {
3438: PetscCall(PetscRandomGetValue(rctx, &a));
3439: col = (PetscInt)(n * PetscRealPart(a));
3440: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3441: }
3442: }
3443: } else {
3444: PetscCall(MatSeqAIJGetArrayWrite(x, &aa));
3445: for (i = 0; i < aij->nz; i++) PetscCall(PetscRandomGetValue(rctx, aa + i));
3446: PetscCall(MatSeqAIJRestoreArrayWrite(x, &aa));
3447: }
3448: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3449: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3450: PetscFunctionReturn(PETSC_SUCCESS);
3451: }
3453: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3454: PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x, PetscInt low, PetscInt high, PetscRandom rctx)
3455: {
3456: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3457: PetscScalar a;
3458: PetscInt m, n, i, j, col, nskip;
3460: PetscFunctionBegin;
3461: nskip = high - low;
3462: PetscCall(MatGetSize(x, &m, &n));
3463: n -= nskip; /* shrink number of columns where nonzeros can be set */
3464: for (i = 0; i < m; i++) {
3465: for (j = 0; j < aij->imax[i]; j++) {
3466: PetscCall(PetscRandomGetValue(rctx, &a));
3467: col = (PetscInt)(n * PetscRealPart(a));
3468: if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3469: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3470: }
3471: }
3472: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3473: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3474: PetscFunctionReturn(PETSC_SUCCESS);
3475: }
3477: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
3478: MatGetRow_SeqAIJ,
3479: MatRestoreRow_SeqAIJ,
3480: MatMult_SeqAIJ,
3481: /* 4*/ MatMultAdd_SeqAIJ,
3482: MatMultTranspose_SeqAIJ,
3483: MatMultTransposeAdd_SeqAIJ,
3484: NULL,
3485: NULL,
3486: NULL,
3487: /* 10*/ NULL,
3488: MatLUFactor_SeqAIJ,
3489: NULL,
3490: MatSOR_SeqAIJ,
3491: MatTranspose_SeqAIJ,
3492: /*1 5*/ MatGetInfo_SeqAIJ,
3493: MatEqual_SeqAIJ,
3494: MatGetDiagonal_SeqAIJ,
3495: MatDiagonalScale_SeqAIJ,
3496: MatNorm_SeqAIJ,
3497: /* 20*/ NULL,
3498: MatAssemblyEnd_SeqAIJ,
3499: MatSetOption_SeqAIJ,
3500: MatZeroEntries_SeqAIJ,
3501: /* 24*/ MatZeroRows_SeqAIJ,
3502: NULL,
3503: NULL,
3504: NULL,
3505: NULL,
3506: /* 29*/ MatSetUp_Seq_Hash,
3507: NULL,
3508: NULL,
3509: NULL,
3510: NULL,
3511: /* 34*/ MatDuplicate_SeqAIJ,
3512: NULL,
3513: NULL,
3514: MatILUFactor_SeqAIJ,
3515: NULL,
3516: /* 39*/ MatAXPY_SeqAIJ,
3517: MatCreateSubMatrices_SeqAIJ,
3518: MatIncreaseOverlap_SeqAIJ,
3519: MatGetValues_SeqAIJ,
3520: MatCopy_SeqAIJ,
3521: /* 44*/ MatGetRowMax_SeqAIJ,
3522: MatScale_SeqAIJ,
3523: MatShift_SeqAIJ,
3524: MatDiagonalSet_SeqAIJ,
3525: MatZeroRowsColumns_SeqAIJ,
3526: /* 49*/ MatSetRandom_SeqAIJ,
3527: MatGetRowIJ_SeqAIJ,
3528: MatRestoreRowIJ_SeqAIJ,
3529: MatGetColumnIJ_SeqAIJ,
3530: MatRestoreColumnIJ_SeqAIJ,
3531: /* 54*/ MatFDColoringCreate_SeqXAIJ,
3532: NULL,
3533: NULL,
3534: MatPermute_SeqAIJ,
3535: NULL,
3536: /* 59*/ NULL,
3537: MatDestroy_SeqAIJ,
3538: MatView_SeqAIJ,
3539: NULL,
3540: NULL,
3541: /* 64*/ NULL,
3542: MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3543: NULL,
3544: NULL,
3545: NULL,
3546: /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3547: MatGetRowMinAbs_SeqAIJ,
3548: NULL,
3549: NULL,
3550: NULL,
3551: /* 74*/ NULL,
3552: MatFDColoringApply_AIJ,
3553: NULL,
3554: NULL,
3555: NULL,
3556: /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3557: NULL,
3558: NULL,
3559: NULL,
3560: MatLoad_SeqAIJ,
3561: /* 84*/ MatIsSymmetric_SeqAIJ,
3562: MatIsHermitian_SeqAIJ,
3563: NULL,
3564: NULL,
3565: NULL,
3566: /* 89*/ NULL,
3567: NULL,
3568: MatMatMultNumeric_SeqAIJ_SeqAIJ,
3569: NULL,
3570: NULL,
3571: /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3572: NULL,
3573: NULL,
3574: MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3575: NULL,
3576: /* 99*/ MatProductSetFromOptions_SeqAIJ,
3577: NULL,
3578: NULL,
3579: MatConjugate_SeqAIJ,
3580: NULL,
3581: /*104*/ MatSetValuesRow_SeqAIJ,
3582: MatRealPart_SeqAIJ,
3583: MatImaginaryPart_SeqAIJ,
3584: NULL,
3585: NULL,
3586: /*109*/ MatMatSolve_SeqAIJ,
3587: NULL,
3588: MatGetRowMin_SeqAIJ,
3589: NULL,
3590: MatMissingDiagonal_SeqAIJ,
3591: /*114*/ NULL,
3592: NULL,
3593: NULL,
3594: NULL,
3595: NULL,
3596: /*119*/ NULL,
3597: NULL,
3598: NULL,
3599: NULL,
3600: MatGetMultiProcBlock_SeqAIJ,
3601: /*124*/ MatFindNonzeroRows_SeqAIJ,
3602: MatGetColumnReductions_SeqAIJ,
3603: MatInvertBlockDiagonal_SeqAIJ,
3604: MatInvertVariableBlockDiagonal_SeqAIJ,
3605: NULL,
3606: /*129*/ NULL,
3607: NULL,
3608: NULL,
3609: MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3610: MatTransposeColoringCreate_SeqAIJ,
3611: /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3612: MatTransColoringApplyDenToSp_SeqAIJ,
3613: NULL,
3614: NULL,
3615: MatRARtNumeric_SeqAIJ_SeqAIJ,
3616: /*139*/ NULL,
3617: NULL,
3618: NULL,
3619: MatFDColoringSetUp_SeqXAIJ,
3620: MatFindOffBlockDiagonalEntries_SeqAIJ,
3621: MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3622: /*145*/ MatDestroySubMatrices_SeqAIJ,
3623: NULL,
3624: NULL,
3625: MatCreateGraph_Simple_AIJ,
3626: NULL,
3627: /*150*/ MatTransposeSymbolic_SeqAIJ,
3628: MatEliminateZeros_SeqAIJ};
3630: PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat, PetscInt *indices)
3631: {
3632: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3633: PetscInt i, nz, n;
3635: PetscFunctionBegin;
3636: nz = aij->maxnz;
3637: n = mat->rmap->n;
3638: for (i = 0; i < nz; i++) aij->j[i] = indices[i];
3639: aij->nz = nz;
3640: for (i = 0; i < n; i++) aij->ilen[i] = aij->imax[i];
3641: PetscFunctionReturn(PETSC_SUCCESS);
3642: }
3644: /*
3645: * Given a sparse matrix with global column indices, compact it by using a local column space.
3646: * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3647: */
3648: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3649: {
3650: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3651: PetscHMapI gid1_lid1;
3652: PetscHashIter tpos;
3653: PetscInt gid, lid, i, ec, nz = aij->nz;
3654: PetscInt *garray, *jj = aij->j;
3656: PetscFunctionBegin;
3659: /* use a table */
3660: PetscCall(PetscHMapICreateWithSize(mat->rmap->n, &gid1_lid1));
3661: ec = 0;
3662: for (i = 0; i < nz; i++) {
3663: PetscInt data, gid1 = jj[i] + 1;
3664: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &data));
3665: if (!data) {
3666: /* one based table */
3667: PetscCall(PetscHMapISet(gid1_lid1, gid1, ++ec));
3668: }
3669: }
3670: /* form array of columns we need */
3671: PetscCall(PetscMalloc1(ec, &garray));
3672: PetscHashIterBegin(gid1_lid1, tpos);
3673: while (!PetscHashIterAtEnd(gid1_lid1, tpos)) {
3674: PetscHashIterGetKey(gid1_lid1, tpos, gid);
3675: PetscHashIterGetVal(gid1_lid1, tpos, lid);
3676: PetscHashIterNext(gid1_lid1, tpos);
3677: gid--;
3678: lid--;
3679: garray[lid] = gid;
3680: }
3681: PetscCall(PetscSortInt(ec, garray)); /* sort, and rebuild */
3682: PetscCall(PetscHMapIClear(gid1_lid1));
3683: for (i = 0; i < ec; i++) PetscCall(PetscHMapISet(gid1_lid1, garray[i] + 1, i + 1));
3684: /* compact out the extra columns in B */
3685: for (i = 0; i < nz; i++) {
3686: PetscInt gid1 = jj[i] + 1;
3687: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &lid));
3688: lid--;
3689: jj[i] = lid;
3690: }
3691: PetscCall(PetscLayoutDestroy(&mat->cmap));
3692: PetscCall(PetscHMapIDestroy(&gid1_lid1));
3693: PetscCall(PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat), ec, ec, 1, &mat->cmap));
3694: PetscCall(ISLocalToGlobalMappingCreate(PETSC_COMM_SELF, mat->cmap->bs, mat->cmap->n, garray, PETSC_OWN_POINTER, mapping));
3695: PetscCall(ISLocalToGlobalMappingSetType(*mapping, ISLOCALTOGLOBALMAPPINGHASH));
3696: PetscFunctionReturn(PETSC_SUCCESS);
3697: }
3699: /*@
3700: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3701: in the matrix.
3703: Input Parameters:
3704: + mat - the `MATSEQAIJ` matrix
3705: - indices - the column indices
3707: Level: advanced
3709: Notes:
3710: This can be called if you have precomputed the nonzero structure of the
3711: matrix and want to provide it to the matrix object to improve the performance
3712: of the `MatSetValues()` operation.
3714: You MUST have set the correct numbers of nonzeros per row in the call to
3715: `MatCreateSeqAIJ()`, and the columns indices MUST be sorted.
3717: MUST be called before any calls to `MatSetValues()`
3719: The indices should start with zero, not one.
3721: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`
3722: @*/
3723: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat, PetscInt *indices)
3724: {
3725: PetscFunctionBegin;
3728: PetscUseMethod(mat, "MatSeqAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices));
3729: PetscFunctionReturn(PETSC_SUCCESS);
3730: }
3732: PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3733: {
3734: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3735: size_t nz = aij->i[mat->rmap->n];
3737: PetscFunctionBegin;
3738: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3740: /* allocate space for values if not already there */
3741: if (!aij->saved_values) { PetscCall(PetscMalloc1(nz + 1, &aij->saved_values)); }
3743: /* copy values over */
3744: PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
3745: PetscFunctionReturn(PETSC_SUCCESS);
3746: }
3748: /*@
3749: MatStoreValues - Stashes a copy of the matrix values; this allows reusing of the linear part of a Jacobian, while recomputing only the
3750: nonlinear portion.
3752: Logically Collect
3754: Input Parameter:
3755: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3757: Level: advanced
3759: Usage:
3760: .vb
3761: Using SNES
3762: Create Jacobian matrix
3763: Set linear terms into matrix
3764: Apply boundary conditions to matrix, at this time matrix must have
3765: final nonzero structure (i.e. setting the nonlinear terms and applying
3766: boundary conditions again will not change the nonzero structure
3767: MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3768: MatStoreValues(mat);
3769: Call SNESSetJacobian() with matrix
3770: In your Jacobian routine
3771: MatRetrieveValues(mat);
3772: Set nonlinear terms in matrix
3774: Without `SNESSolve()`, i.e. when you handle nonlinear solve yourself:
3775: // build linear portion of Jacobian
3776: MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3777: MatStoreValues(mat);
3778: loop over nonlinear iterations
3779: MatRetrieveValues(mat);
3780: // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3781: // call MatAssemblyBegin/End() on matrix
3782: Solve linear system with Jacobian
3783: endloop
3784: .ve
3786: Notes:
3787: Matrix must already be assembled before calling this routine
3788: Must set the matrix option `MatSetOption`(mat,`MAT_NEW_NONZERO_LOCATIONS`,`PETSC_FALSE`); before
3789: calling this routine.
3791: When this is called multiple times it overwrites the previous set of stored values
3792: and does not allocated additional space.
3794: .seealso: [](ch_matrices), `Mat`, `Mat`, `MatRetrieveValues()`
3795: @*/
3796: PetscErrorCode MatStoreValues(Mat mat)
3797: {
3798: PetscFunctionBegin;
3800: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3801: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3802: PetscUseMethod(mat, "MatStoreValues_C", (Mat), (mat));
3803: PetscFunctionReturn(PETSC_SUCCESS);
3804: }
3806: PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3807: {
3808: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3809: PetscInt nz = aij->i[mat->rmap->n];
3811: PetscFunctionBegin;
3812: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3813: PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");
3814: /* copy values over */
3815: PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
3816: PetscFunctionReturn(PETSC_SUCCESS);
3817: }
3819: /*@
3820: MatRetrieveValues - Retrieves the copy of the matrix values that was stored with `MatStoreValues()`
3822: Logically Collect
3824: Input Parameter:
3825: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3827: Level: advanced
3829: .seealso: [](ch_matrices), `Mat`, `MatStoreValues()`
3830: @*/
3831: PetscErrorCode MatRetrieveValues(Mat mat)
3832: {
3833: PetscFunctionBegin;
3835: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3836: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3837: PetscUseMethod(mat, "MatRetrieveValues_C", (Mat), (mat));
3838: PetscFunctionReturn(PETSC_SUCCESS);
3839: }
3841: /*@C
3842: MatCreateSeqAIJ - Creates a sparse matrix in `MATSEQAIJ` (compressed row) format
3843: (the default parallel PETSc format). For good matrix assembly performance
3844: the user should preallocate the matrix storage by setting the parameter `nz`
3845: (or the array `nnz`).
3847: Collective
3849: Input Parameters:
3850: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3851: . m - number of rows
3852: . n - number of columns
3853: . nz - number of nonzeros per row (same for all rows)
3854: - nnz - array containing the number of nonzeros in the various rows
3855: (possibly different for each row) or NULL
3857: Output Parameter:
3858: . A - the matrix
3860: Options Database Keys:
3861: + -mat_no_inode - Do not use inodes
3862: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3864: Level: intermediate
3866: Notes:
3867: If `nnz` is given then `nz` is ignored
3869: The `MATSEQAIJ` format, also called
3870: compressed row storage, is fully compatible with standard Fortran
3871: storage. That is, the stored row and column indices can begin at
3872: either one (as in Fortran) or zero.
3874: Specify the preallocated storage with either `nz` or `nnz` (not both).
3875: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3876: allocation.
3878: By default, this format uses inodes (identical nodes) when possible, to
3879: improve numerical efficiency of matrix-vector products and solves. We
3880: search for consecutive rows with the same nonzero structure, thereby
3881: reusing matrix information to achieve increased efficiency.
3883: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`
3884: @*/
3885: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3886: {
3887: PetscFunctionBegin;
3888: PetscCall(MatCreate(comm, A));
3889: PetscCall(MatSetSizes(*A, m, n, m, n));
3890: PetscCall(MatSetType(*A, MATSEQAIJ));
3891: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz));
3892: PetscFunctionReturn(PETSC_SUCCESS);
3893: }
3895: /*@C
3896: MatSeqAIJSetPreallocation - For good matrix assembly performance
3897: the user should preallocate the matrix storage by setting the parameter nz
3898: (or the array nnz). By setting these parameters accurately, performance
3899: during matrix assembly can be increased by more than a factor of 50.
3901: Collective
3903: Input Parameters:
3904: + B - The matrix
3905: . nz - number of nonzeros per row (same for all rows)
3906: - nnz - array containing the number of nonzeros in the various rows
3907: (possibly different for each row) or NULL
3909: Options Database Keys:
3910: + -mat_no_inode - Do not use inodes
3911: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3913: Level: intermediate
3915: Notes:
3916: If `nnz` is given then `nz` is ignored
3918: The `MATSEQAIJ` format also called
3919: compressed row storage, is fully compatible with standard Fortran
3920: storage. That is, the stored row and column indices can begin at
3921: either one (as in Fortran) or zero. See the users' manual for details.
3923: Specify the preallocated storage with either `nz` or `nnz` (not both).
3924: Set nz = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3925: allocation.
3927: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3928: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3929: You can also run with the option -info and look for messages with the string
3930: malloc in them to see if additional memory allocation was needed.
3932: Developer Notes:
3933: Use nz of `MAT_SKIP_ALLOCATION` to not allocate any space for the matrix
3934: entries or columns indices
3936: By default, this format uses inodes (identical nodes) when possible, to
3937: improve numerical efficiency of matrix-vector products and solves. We
3938: search for consecutive rows with the same nonzero structure, thereby
3939: reusing matrix information to achieve increased efficiency.
3941: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatGetInfo()`,
3942: `MatSeqAIJSetTotalPreallocation()`
3943: @*/
3944: PetscErrorCode MatSeqAIJSetPreallocation(Mat B, PetscInt nz, const PetscInt nnz[])
3945: {
3946: PetscFunctionBegin;
3949: PetscTryMethod(B, "MatSeqAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[]), (B, nz, nnz));
3950: PetscFunctionReturn(PETSC_SUCCESS);
3951: }
3953: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B, PetscInt nz, const PetscInt *nnz)
3954: {
3955: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
3956: PetscBool skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;
3957: PetscInt i;
3959: PetscFunctionBegin;
3960: if (B->hash_active) {
3961: PetscCall(PetscMemcpy(&B->ops, &b->cops, sizeof(*(B->ops))));
3962: PetscCall(PetscHMapIJVDestroy(&b->ht));
3963: PetscCall(PetscFree(b->dnz));
3964: B->hash_active = PETSC_FALSE;
3965: }
3966: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3967: if (nz == MAT_SKIP_ALLOCATION) {
3968: skipallocation = PETSC_TRUE;
3969: nz = 0;
3970: }
3971: PetscCall(PetscLayoutSetUp(B->rmap));
3972: PetscCall(PetscLayoutSetUp(B->cmap));
3974: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3975: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
3976: if (PetscUnlikelyDebug(nnz)) {
3977: for (i = 0; i < B->rmap->n; i++) {
3978: 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]);
3979: 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);
3980: }
3981: }
3983: B->preallocated = PETSC_TRUE;
3984: if (!skipallocation) {
3985: if (!b->imax) { PetscCall(PetscMalloc1(B->rmap->n, &b->imax)); }
3986: if (!b->ilen) {
3987: /* b->ilen will count nonzeros in each row so far. */
3988: PetscCall(PetscCalloc1(B->rmap->n, &b->ilen));
3989: } else {
3990: PetscCall(PetscMemzero(b->ilen, B->rmap->n * sizeof(PetscInt)));
3991: }
3992: if (!b->ipre) PetscCall(PetscMalloc1(B->rmap->n, &b->ipre));
3993: if (!nnz) {
3994: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3995: else if (nz < 0) nz = 1;
3996: nz = PetscMin(nz, B->cmap->n);
3997: for (i = 0; i < B->rmap->n; i++) b->imax[i] = nz;
3998: nz = nz * B->rmap->n;
3999: } else {
4000: PetscInt64 nz64 = 0;
4001: for (i = 0; i < B->rmap->n; i++) {
4002: b->imax[i] = nnz[i];
4003: nz64 += nnz[i];
4004: }
4005: PetscCall(PetscIntCast(nz64, &nz));
4006: }
4008: /* allocate the matrix space */
4009: /* FIXME: should B's old memory be unlogged? */
4010: PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
4011: if (B->structure_only) {
4012: PetscCall(PetscMalloc1(nz, &b->j));
4013: PetscCall(PetscMalloc1(B->rmap->n + 1, &b->i));
4014: } else {
4015: PetscCall(PetscMalloc3(nz, &b->a, nz, &b->j, B->rmap->n + 1, &b->i));
4016: }
4017: b->i[0] = 0;
4018: for (i = 1; i < B->rmap->n + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];
4019: if (B->structure_only) {
4020: b->singlemalloc = PETSC_FALSE;
4021: b->free_a = PETSC_FALSE;
4022: } else {
4023: b->singlemalloc = PETSC_TRUE;
4024: b->free_a = PETSC_TRUE;
4025: }
4026: b->free_ij = PETSC_TRUE;
4027: } else {
4028: b->free_a = PETSC_FALSE;
4029: b->free_ij = PETSC_FALSE;
4030: }
4032: if (b->ipre && nnz != b->ipre && b->imax) {
4033: /* reserve user-requested sparsity */
4034: PetscCall(PetscArraycpy(b->ipre, b->imax, B->rmap->n));
4035: }
4037: b->nz = 0;
4038: b->maxnz = nz;
4039: B->info.nz_unneeded = (double)b->maxnz;
4040: if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
4041: B->was_assembled = PETSC_FALSE;
4042: B->assembled = PETSC_FALSE;
4043: /* We simply deem preallocation has changed nonzero state. Updating the state
4044: will give clients (like AIJKokkos) a chance to know something has happened.
4045: */
4046: B->nonzerostate++;
4047: PetscFunctionReturn(PETSC_SUCCESS);
4048: }
4050: PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
4051: {
4052: Mat_SeqAIJ *a;
4053: PetscInt i;
4054: PetscBool skipreset;
4056: PetscFunctionBegin;
4059: /* Check local size. If zero, then return */
4060: if (!A->rmap->n) PetscFunctionReturn(PETSC_SUCCESS);
4062: a = (Mat_SeqAIJ *)A->data;
4063: /* if no saved info, we error out */
4064: PetscCheck(a->ipre, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "No saved preallocation info ");
4066: PetscCheck(a->i && a->imax && a->ilen, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "Memory info is incomplete, and can not reset preallocation ");
4068: PetscCall(PetscArraycmp(a->ipre, a->ilen, A->rmap->n, &skipreset));
4069: if (!skipreset) {
4070: PetscCall(PetscArraycpy(a->imax, a->ipre, A->rmap->n));
4071: PetscCall(PetscArrayzero(a->ilen, A->rmap->n));
4072: a->i[0] = 0;
4073: for (i = 1; i < A->rmap->n + 1; i++) a->i[i] = a->i[i - 1] + a->imax[i - 1];
4074: A->preallocated = PETSC_TRUE;
4075: a->nz = 0;
4076: a->maxnz = a->i[A->rmap->n];
4077: A->info.nz_unneeded = (double)a->maxnz;
4078: A->was_assembled = PETSC_FALSE;
4079: A->assembled = PETSC_FALSE;
4080: }
4081: PetscFunctionReturn(PETSC_SUCCESS);
4082: }
4084: /*@
4085: MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in `MATSEQAIJ` format.
4087: Input Parameters:
4088: + B - the matrix
4089: . i - the indices into j for the start of each row (starts with zero)
4090: . j - the column indices for each row (starts with zero) these must be sorted for each row
4091: - v - optional values in the matrix
4093: Level: developer
4095: Notes:
4096: The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqAIJWithArrays()`
4098: This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
4099: structure will be the union of all the previous nonzero structures.
4101: Developer Notes:
4102: 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
4103: then just copies the `v` values directly with `PetscMemcpy()`.
4105: This routine could also take a `PetscCopyMode` argument to allow sharing the values instead of always copying them.
4107: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MatResetPreallocation()`
4108: @*/
4109: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4110: {
4111: PetscFunctionBegin;
4114: PetscTryMethod(B, "MatSeqAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4115: PetscFunctionReturn(PETSC_SUCCESS);
4116: }
4118: PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4119: {
4120: PetscInt i;
4121: PetscInt m, n;
4122: PetscInt nz;
4123: PetscInt *nnz;
4125: PetscFunctionBegin;
4126: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);
4128: PetscCall(PetscLayoutSetUp(B->rmap));
4129: PetscCall(PetscLayoutSetUp(B->cmap));
4131: PetscCall(MatGetSize(B, &m, &n));
4132: PetscCall(PetscMalloc1(m + 1, &nnz));
4133: for (i = 0; i < m; i++) {
4134: nz = Ii[i + 1] - Ii[i];
4135: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
4136: nnz[i] = nz;
4137: }
4138: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
4139: PetscCall(PetscFree(nnz));
4141: 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));
4143: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4144: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4146: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
4147: PetscFunctionReturn(PETSC_SUCCESS);
4148: }
4150: /*@
4151: MatSeqAIJKron - Computes `C`, the Kronecker product of `A` and `B`.
4153: Input Parameters:
4154: + A - left-hand side matrix
4155: . B - right-hand side matrix
4156: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
4158: Output Parameter:
4159: . C - Kronecker product of `A` and `B`
4161: Level: intermediate
4163: Note:
4164: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the product matrix has not changed from that last call to `MatSeqAIJKron()`.
4166: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATKAIJ`, `MatReuse`
4167: @*/
4168: PetscErrorCode MatSeqAIJKron(Mat A, Mat B, MatReuse reuse, Mat *C)
4169: {
4170: PetscFunctionBegin;
4176: if (reuse == MAT_REUSE_MATRIX) {
4179: }
4180: PetscTryMethod(A, "MatSeqAIJKron_C", (Mat, Mat, MatReuse, Mat *), (A, B, reuse, C));
4181: PetscFunctionReturn(PETSC_SUCCESS);
4182: }
4184: PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A, Mat B, MatReuse reuse, Mat *C)
4185: {
4186: Mat newmat;
4187: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
4188: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
4189: PetscScalar *v;
4190: const PetscScalar *aa, *ba;
4191: 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;
4192: PetscBool flg;
4194: PetscFunctionBegin;
4195: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4196: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4197: PetscCheck(!B->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4198: PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4199: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJ, &flg));
4200: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatType %s", ((PetscObject)B)->type_name);
4201: PetscCheck(reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatReuse %d", (int)reuse);
4202: if (reuse == MAT_INITIAL_MATRIX) {
4203: PetscCall(PetscMalloc2(am * bm + 1, &i, a->i[am] * b->i[bm], &j));
4204: PetscCall(MatCreate(PETSC_COMM_SELF, &newmat));
4205: PetscCall(MatSetSizes(newmat, am * bm, an * bn, am * bm, an * bn));
4206: PetscCall(MatSetType(newmat, MATAIJ));
4207: i[0] = 0;
4208: for (m = 0; m < am; ++m) {
4209: for (p = 0; p < bm; ++p) {
4210: i[m * bm + p + 1] = i[m * bm + p] + (a->i[m + 1] - a->i[m]) * (b->i[p + 1] - b->i[p]);
4211: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4212: for (q = b->i[p]; q < b->i[p + 1]; ++q) j[nnz++] = a->j[n] * bn + b->j[q];
4213: }
4214: }
4215: }
4216: PetscCall(MatSeqAIJSetPreallocationCSR(newmat, i, j, NULL));
4217: *C = newmat;
4218: PetscCall(PetscFree2(i, j));
4219: nnz = 0;
4220: }
4221: PetscCall(MatSeqAIJGetArray(*C, &v));
4222: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4223: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
4224: for (m = 0; m < am; ++m) {
4225: for (p = 0; p < bm; ++p) {
4226: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4227: for (q = b->i[p]; q < b->i[p + 1]; ++q) v[nnz++] = aa[n] * ba[q];
4228: }
4229: }
4230: }
4231: PetscCall(MatSeqAIJRestoreArray(*C, &v));
4232: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
4233: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
4234: PetscFunctionReturn(PETSC_SUCCESS);
4235: }
4237: #include <../src/mat/impls/dense/seq/dense.h>
4238: #include <petsc/private/kernels/petscaxpy.h>
4240: /*
4241: Computes (B'*A')' since computing B*A directly is untenable
4243: n p p
4244: [ ] [ ] [ ]
4245: m [ A ] * n [ B ] = m [ C ]
4246: [ ] [ ] [ ]
4248: */
4249: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A, Mat B, Mat C)
4250: {
4251: Mat_SeqDense *sub_a = (Mat_SeqDense *)A->data;
4252: Mat_SeqAIJ *sub_b = (Mat_SeqAIJ *)B->data;
4253: Mat_SeqDense *sub_c = (Mat_SeqDense *)C->data;
4254: PetscInt i, j, n, m, q, p;
4255: const PetscInt *ii, *idx;
4256: const PetscScalar *b, *a, *a_q;
4257: PetscScalar *c, *c_q;
4258: PetscInt clda = sub_c->lda;
4259: PetscInt alda = sub_a->lda;
4261: PetscFunctionBegin;
4262: m = A->rmap->n;
4263: n = A->cmap->n;
4264: p = B->cmap->n;
4265: a = sub_a->v;
4266: b = sub_b->a;
4267: c = sub_c->v;
4268: if (clda == m) {
4269: PetscCall(PetscArrayzero(c, m * p));
4270: } else {
4271: for (j = 0; j < p; j++)
4272: for (i = 0; i < m; i++) c[j * clda + i] = 0.0;
4273: }
4274: ii = sub_b->i;
4275: idx = sub_b->j;
4276: for (i = 0; i < n; i++) {
4277: q = ii[i + 1] - ii[i];
4278: while (q-- > 0) {
4279: c_q = c + clda * (*idx);
4280: a_q = a + alda * i;
4281: PetscKernelAXPY(c_q, *b, a_q, m);
4282: idx++;
4283: b++;
4284: }
4285: }
4286: PetscFunctionReturn(PETSC_SUCCESS);
4287: }
4289: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A, Mat B, PetscReal fill, Mat C)
4290: {
4291: PetscInt m = A->rmap->n, n = B->cmap->n;
4292: PetscBool cisdense;
4294: PetscFunctionBegin;
4295: 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);
4296: PetscCall(MatSetSizes(C, m, n, m, n));
4297: PetscCall(MatSetBlockSizesFromMats(C, A, B));
4298: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATSEQDENSE, MATSEQDENSECUDA, MATSEQDENSEHIP, ""));
4299: if (!cisdense) PetscCall(MatSetType(C, MATDENSE));
4300: PetscCall(MatSetUp(C));
4302: C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4303: PetscFunctionReturn(PETSC_SUCCESS);
4304: }
4306: /*MC
4307: MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4308: based on compressed sparse row format.
4310: Options Database Key:
4311: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()
4313: Level: beginner
4315: Notes:
4316: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
4317: in this case the values associated with the rows and columns one passes in are set to zero
4318: in the matrix
4320: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
4321: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
4323: Developer Note:
4324: It would be nice if all matrix formats supported passing `NULL` in for the numerical values
4326: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MatSetFromOptions()`, `MatSetType()`, `MatCreate()`, `MatType`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4327: M*/
4329: /*MC
4330: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
4332: This matrix type is identical to `MATSEQAIJ` when constructed with a single process communicator,
4333: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
4334: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4335: for communicators controlling multiple processes. It is recommended that you call both of
4336: the above preallocation routines for simplicity.
4338: Options Database Key:
4339: . -mat_type aij - sets the matrix type to "aij" during a call to `MatSetFromOptions()`
4341: Level: beginner
4343: Note:
4344: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, and also automatically switches over to use inodes when
4345: enough exist.
4347: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4348: M*/
4350: /*MC
4351: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
4353: Options Database Key:
4354: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to `MatSetFromOptions()`
4356: Level: beginner
4358: Note:
4359: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
4360: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
4361: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4362: for communicators controlling multiple processes. It is recommended that you call both of
4363: the above preallocation routines for simplicity.
4365: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
4366: M*/
4368: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
4369: #if defined(PETSC_HAVE_ELEMENTAL)
4370: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
4371: #endif
4372: #if defined(PETSC_HAVE_SCALAPACK)
4373: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
4374: #endif
4375: #if defined(PETSC_HAVE_HYPRE)
4376: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A, MatType, MatReuse, Mat *);
4377: #endif
4379: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat, MatType, MatReuse, Mat *);
4380: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
4381: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
4383: /*@C
4384: MatSeqAIJGetArray - gives read/write access to the array where the data for a `MATSEQAIJ` matrix is stored
4386: Not Collective
4388: Input Parameter:
4389: . mat - a `MATSEQAIJ` matrix
4391: Output Parameter:
4392: . array - pointer to the data
4394: Level: intermediate
4396: Fortran Note:
4397: `MatSeqAIJGetArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJGetArrayF90()`
4399: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4400: @*/
4401: PetscErrorCode MatSeqAIJGetArray(Mat A, PetscScalar **array)
4402: {
4403: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4405: PetscFunctionBegin;
4406: if (aij->ops->getarray) {
4407: PetscCall((*aij->ops->getarray)(A, array));
4408: } else {
4409: *array = aij->a;
4410: }
4411: PetscFunctionReturn(PETSC_SUCCESS);
4412: }
4414: /*@C
4415: MatSeqAIJRestoreArray - returns access to the array where the data for a `MATSEQAIJ` matrix is stored obtained by `MatSeqAIJGetArray()`
4417: Not Collective
4419: Input Parameters:
4420: + mat - a `MATSEQAIJ` matrix
4421: - array - pointer to the data
4423: Level: intermediate
4425: Fortran Note:
4426: `MatSeqAIJRestoreArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJRestoreArrayF90()`
4428: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayF90()`
4429: @*/
4430: PetscErrorCode MatSeqAIJRestoreArray(Mat A, PetscScalar **array)
4431: {
4432: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4434: PetscFunctionBegin;
4435: if (aij->ops->restorearray) {
4436: PetscCall((*aij->ops->restorearray)(A, array));
4437: } else {
4438: *array = NULL;
4439: }
4440: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4441: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4442: PetscFunctionReturn(PETSC_SUCCESS);
4443: }
4445: /*@C
4446: MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4448: Not Collective; No Fortran Support
4450: Input Parameter:
4451: . mat - a `MATSEQAIJ` matrix
4453: Output Parameter:
4454: . array - pointer to the data
4456: Level: intermediate
4458: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4459: @*/
4460: PetscErrorCode MatSeqAIJGetArrayRead(Mat A, const PetscScalar **array)
4461: {
4462: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4464: PetscFunctionBegin;
4465: if (aij->ops->getarrayread) {
4466: PetscCall((*aij->ops->getarrayread)(A, array));
4467: } else {
4468: *array = aij->a;
4469: }
4470: PetscFunctionReturn(PETSC_SUCCESS);
4471: }
4473: /*@C
4474: MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJGetArrayRead()`
4476: Not Collective; No Fortran Support
4478: Input Parameter:
4479: . mat - a `MATSEQAIJ` matrix
4481: Output Parameter:
4482: . array - pointer to the data
4484: Level: intermediate
4486: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4487: @*/
4488: PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A, const PetscScalar **array)
4489: {
4490: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4492: PetscFunctionBegin;
4493: if (aij->ops->restorearrayread) {
4494: PetscCall((*aij->ops->restorearrayread)(A, array));
4495: } else {
4496: *array = NULL;
4497: }
4498: PetscFunctionReturn(PETSC_SUCCESS);
4499: }
4501: /*@C
4502: MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4504: Not Collective; No Fortran Support
4506: Input Parameter:
4507: . mat - a `MATSEQAIJ` matrix
4509: Output Parameter:
4510: . array - pointer to the data
4512: Level: intermediate
4514: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4515: @*/
4516: PetscErrorCode MatSeqAIJGetArrayWrite(Mat A, PetscScalar **array)
4517: {
4518: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4520: PetscFunctionBegin;
4521: if (aij->ops->getarraywrite) {
4522: PetscCall((*aij->ops->getarraywrite)(A, array));
4523: } else {
4524: *array = aij->a;
4525: }
4526: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4527: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4528: PetscFunctionReturn(PETSC_SUCCESS);
4529: }
4531: /*@C
4532: MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead
4534: Not Collective; No Fortran Support
4536: Input Parameter:
4537: . mat - a MATSEQAIJ matrix
4539: Output Parameter:
4540: . array - pointer to the data
4542: Level: intermediate
4544: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4545: @*/
4546: PetscErrorCode MatSeqAIJRestoreArrayWrite(Mat A, PetscScalar **array)
4547: {
4548: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4550: PetscFunctionBegin;
4551: if (aij->ops->restorearraywrite) {
4552: PetscCall((*aij->ops->restorearraywrite)(A, array));
4553: } else {
4554: *array = NULL;
4555: }
4556: PetscFunctionReturn(PETSC_SUCCESS);
4557: }
4559: /*@C
4560: MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the `MATSEQAIJ` matrix
4562: Not Collective; No Fortran Support
4564: Input Parameter:
4565: . mat - a matrix of type `MATSEQAIJ` or its subclasses
4567: Output Parameters:
4568: + i - row map array of the matrix
4569: . j - column index array of the matrix
4570: . a - data array of the matrix
4571: - memtype - memory type of the arrays
4573: Level: Developer
4575: Notes:
4576: Any of the output parameters can be `NULL`, in which case the corresponding value is not returned.
4577: If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host.
4579: One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix.
4580: If the matrix is assembled, the data array `a` is guaranteed to have the latest values of the matrix.
4582: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4583: @*/
4584: PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat, const PetscInt **i, const PetscInt **j, PetscScalar **a, PetscMemType *mtype)
4585: {
4586: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
4588: PetscFunctionBegin;
4589: PetscCheck(mat->preallocated, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "matrix is not preallocated");
4590: if (aij->ops->getcsrandmemtype) {
4591: PetscCall((*aij->ops->getcsrandmemtype)(mat, i, j, a, mtype));
4592: } else {
4593: if (i) *i = aij->i;
4594: if (j) *j = aij->j;
4595: if (a) *a = aij->a;
4596: if (mtype) *mtype = PETSC_MEMTYPE_HOST;
4597: }
4598: PetscFunctionReturn(PETSC_SUCCESS);
4599: }
4601: /*@C
4602: MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row
4604: Not Collective
4606: Input Parameter:
4607: . mat - a `MATSEQAIJ` matrix
4609: Output Parameter:
4610: . nz - the maximum number of nonzeros in any row
4612: Level: intermediate
4614: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4615: @*/
4616: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A, PetscInt *nz)
4617: {
4618: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4620: PetscFunctionBegin;
4621: *nz = aij->rmax;
4622: PetscFunctionReturn(PETSC_SUCCESS);
4623: }
4625: PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
4626: {
4627: MPI_Comm comm;
4628: PetscInt *i, *j;
4629: PetscInt M, N, row;
4630: PetscCount k, p, q, nneg, nnz, start, end; /* Index the coo array, so use PetscCount as their type */
4631: PetscInt *Ai; /* Change to PetscCount once we use it for row pointers */
4632: PetscInt *Aj;
4633: PetscScalar *Aa;
4634: Mat_SeqAIJ *seqaij = (Mat_SeqAIJ *)(mat->data);
4635: MatType rtype;
4636: PetscCount *perm, *jmap;
4638: PetscFunctionBegin;
4639: PetscCall(MatResetPreallocationCOO_SeqAIJ(mat));
4640: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
4641: PetscCall(MatGetSize(mat, &M, &N));
4642: i = coo_i;
4643: j = coo_j;
4644: PetscCall(PetscMalloc1(coo_n, &perm));
4645: for (k = 0; k < coo_n; k++) { /* Ignore entries with negative row or col indices */
4646: if (j[k] < 0) i[k] = -1;
4647: perm[k] = k;
4648: }
4650: /* Sort by row */
4651: PetscCall(PetscSortIntWithIntCountArrayPair(coo_n, i, j, perm));
4652: for (k = 0; k < coo_n; k++) {
4653: if (i[k] >= 0) break;
4654: } /* Advance k to the first row with a non-negative index */
4655: nneg = k;
4656: 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 */
4657: nnz = 0; /* Total number of unique nonzeros to be counted */
4658: jmap++; /* Inc jmap by 1 for convenience */
4660: PetscCall(PetscCalloc1(M + 1, &Ai)); /* CSR of A */
4661: PetscCall(PetscMalloc1(coo_n - nneg, &Aj)); /* We have at most coo_n-nneg unique nonzeros */
4663: /* In each row, sort by column, then unique column indices to get row length */
4664: Ai++; /* Inc by 1 for convenience */
4665: q = 0; /* q-th unique nonzero, with q starting from 0 */
4666: while (k < coo_n) {
4667: row = i[k];
4668: start = k; /* [start,end) indices for this row */
4669: while (k < coo_n && i[k] == row) k++;
4670: end = k;
4671: PetscCall(PetscSortIntWithCountArray(end - start, j + start, perm + start));
4672: /* Find number of unique col entries in this row */
4673: Aj[q] = j[start]; /* Log the first nonzero in this row */
4674: jmap[q] = 1; /* Number of repeats of this nozero entry */
4675: Ai[row] = 1;
4676: nnz++;
4678: for (p = start + 1; p < end; p++) { /* Scan remaining nonzero in this row */
4679: if (j[p] != j[p - 1]) { /* Meet a new nonzero */
4680: q++;
4681: jmap[q] = 1;
4682: Aj[q] = j[p];
4683: Ai[row]++;
4684: nnz++;
4685: } else {
4686: jmap[q]++;
4687: }
4688: }
4689: q++; /* Move to next row and thus next unique nonzero */
4690: }
4692: Ai--; /* Back to the beginning of Ai[] */
4693: for (k = 0; k < M; k++) Ai[k + 1] += Ai[k];
4694: jmap--; /* Back to the beginning of jmap[] */
4695: jmap[0] = 0;
4696: for (k = 0; k < nnz; k++) jmap[k + 1] += jmap[k];
4697: if (nnz < coo_n - nneg) { /* Realloc with actual number of unique nonzeros */
4698: PetscCount *jmap_new;
4699: PetscInt *Aj_new;
4701: PetscCall(PetscMalloc1(nnz + 1, &jmap_new));
4702: PetscCall(PetscArraycpy(jmap_new, jmap, nnz + 1));
4703: PetscCall(PetscFree(jmap));
4704: jmap = jmap_new;
4706: PetscCall(PetscMalloc1(nnz, &Aj_new));
4707: PetscCall(PetscArraycpy(Aj_new, Aj, nnz));
4708: PetscCall(PetscFree(Aj));
4709: Aj = Aj_new;
4710: }
4712: if (nneg) { /* Discard heading entries with negative indices in perm[], as we'll access it from index 0 in MatSetValuesCOO */
4713: PetscCount *perm_new;
4715: PetscCall(PetscMalloc1(coo_n - nneg, &perm_new));
4716: PetscCall(PetscArraycpy(perm_new, perm + nneg, coo_n - nneg));
4717: PetscCall(PetscFree(perm));
4718: perm = perm_new;
4719: }
4721: PetscCall(MatGetRootType_Private(mat, &rtype));
4722: PetscCall(PetscCalloc1(nnz, &Aa)); /* Zero the matrix */
4723: PetscCall(MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF, M, N, Ai, Aj, Aa, rtype, mat));
4725: seqaij->singlemalloc = PETSC_FALSE; /* Ai, Aj and Aa are not allocated in one big malloc */
4726: seqaij->free_a = seqaij->free_ij = PETSC_TRUE; /* Let newmat own Ai, Aj and Aa */
4727: /* Record COO fields */
4728: seqaij->coo_n = coo_n;
4729: seqaij->Atot = coo_n - nneg; /* Annz is seqaij->nz, so no need to record that again */
4730: seqaij->jmap = jmap; /* of length nnz+1 */
4731: seqaij->perm = perm;
4732: PetscFunctionReturn(PETSC_SUCCESS);
4733: }
4735: static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A, const PetscScalar v[], InsertMode imode)
4736: {
4737: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)A->data;
4738: PetscCount i, j, Annz = aseq->nz;
4739: PetscCount *perm = aseq->perm, *jmap = aseq->jmap;
4740: PetscScalar *Aa;
4742: PetscFunctionBegin;
4743: PetscCall(MatSeqAIJGetArray(A, &Aa));
4744: for (i = 0; i < Annz; i++) {
4745: PetscScalar sum = 0.0;
4746: for (j = jmap[i]; j < jmap[i + 1]; j++) sum += v[perm[j]];
4747: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
4748: }
4749: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
4750: PetscFunctionReturn(PETSC_SUCCESS);
4751: }
4753: #if defined(PETSC_HAVE_CUDA)
4754: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
4755: #endif
4756: #if defined(PETSC_HAVE_HIP)
4757: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
4758: #endif
4759: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4760: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat, MatType, MatReuse, Mat *);
4761: #endif
4763: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4764: {
4765: Mat_SeqAIJ *b;
4766: PetscMPIInt size;
4768: PetscFunctionBegin;
4769: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
4770: PetscCheck(size <= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Comm must be of size 1");
4772: PetscCall(PetscNew(&b));
4774: B->data = (void *)b;
4776: PetscCall(PetscMemcpy(B->ops, &MatOps_Values, sizeof(struct _MatOps)));
4777: if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
4779: b->row = NULL;
4780: b->col = NULL;
4781: b->icol = NULL;
4782: b->reallocs = 0;
4783: b->ignorezeroentries = PETSC_FALSE;
4784: b->roworiented = PETSC_TRUE;
4785: b->nonew = 0;
4786: b->diag = NULL;
4787: b->solve_work = NULL;
4788: B->spptr = NULL;
4789: b->saved_values = NULL;
4790: b->idiag = NULL;
4791: b->mdiag = NULL;
4792: b->ssor_work = NULL;
4793: b->omega = 1.0;
4794: b->fshift = 0.0;
4795: b->idiagvalid = PETSC_FALSE;
4796: b->ibdiagvalid = PETSC_FALSE;
4797: b->keepnonzeropattern = PETSC_FALSE;
4799: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4800: #if defined(PETSC_HAVE_MATLAB)
4801: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEnginePut_C", MatlabEnginePut_SeqAIJ));
4802: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEngineGet_C", MatlabEngineGet_SeqAIJ));
4803: #endif
4804: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetColumnIndices_C", MatSeqAIJSetColumnIndices_SeqAIJ));
4805: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqAIJ));
4806: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqAIJ));
4807: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsbaij_C", MatConvert_SeqAIJ_SeqSBAIJ));
4808: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqbaij_C", MatConvert_SeqAIJ_SeqBAIJ));
4809: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijperm_C", MatConvert_SeqAIJ_SeqAIJPERM));
4810: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijsell_C", MatConvert_SeqAIJ_SeqAIJSELL));
4811: #if defined(PETSC_HAVE_MKL_SPARSE)
4812: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijmkl_C", MatConvert_SeqAIJ_SeqAIJMKL));
4813: #endif
4814: #if defined(PETSC_HAVE_CUDA)
4815: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcusparse_C", MatConvert_SeqAIJ_SeqAIJCUSPARSE));
4816: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4817: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJ));
4818: #endif
4819: #if defined(PETSC_HAVE_HIP)
4820: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijhipsparse_C", MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
4821: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4822: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", MatProductSetFromOptions_SeqAIJ));
4823: #endif
4824: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4825: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijkokkos_C", MatConvert_SeqAIJ_SeqAIJKokkos));
4826: #endif
4827: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcrl_C", MatConvert_SeqAIJ_SeqAIJCRL));
4828: #if defined(PETSC_HAVE_ELEMENTAL)
4829: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_elemental_C", MatConvert_SeqAIJ_Elemental));
4830: #endif
4831: #if defined(PETSC_HAVE_SCALAPACK)
4832: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
4833: #endif
4834: #if defined(PETSC_HAVE_HYPRE)
4835: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_hypre_C", MatConvert_AIJ_HYPRE));
4836: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
4837: #endif
4838: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqdense_C", MatConvert_SeqAIJ_SeqDense));
4839: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsell_C", MatConvert_SeqAIJ_SeqSELL));
4840: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_is_C", MatConvert_XAIJ_IS));
4841: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqAIJ));
4842: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsHermitianTranspose_C", MatIsTranspose_SeqAIJ));
4843: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocation_C", MatSeqAIJSetPreallocation_SeqAIJ));
4844: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_SeqAIJ));
4845: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocationCSR_C", MatSeqAIJSetPreallocationCSR_SeqAIJ));
4846: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatReorderForNonzeroDiagonal_C", MatReorderForNonzeroDiagonal_SeqAIJ));
4847: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_seqaij_C", MatProductSetFromOptions_IS_XAIJ));
4848: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqdense_seqaij_C", MatProductSetFromOptions_SeqDense_SeqAIJ));
4849: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4850: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJKron_C", MatSeqAIJKron_SeqAIJ));
4851: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJ));
4852: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJ));
4853: PetscCall(MatCreate_SeqAIJ_Inode(B));
4854: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4855: PetscCall(MatSeqAIJSetTypeFromOptions(B)); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4856: PetscFunctionReturn(PETSC_SUCCESS);
4857: }
4859: /*
4860: Given a matrix generated with MatGetFactor() duplicates all the information in A into C
4861: */
4862: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
4863: {
4864: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data, *a = (Mat_SeqAIJ *)A->data;
4865: PetscInt m = A->rmap->n, i;
4867: PetscFunctionBegin;
4868: PetscCheck(A->assembled || cpvalues == MAT_DO_NOT_COPY_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");
4870: C->factortype = A->factortype;
4871: c->row = NULL;
4872: c->col = NULL;
4873: c->icol = NULL;
4874: c->reallocs = 0;
4876: C->assembled = A->assembled;
4878: if (A->preallocated) {
4879: PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
4880: PetscCall(PetscLayoutReference(A->cmap, &C->cmap));
4882: if (!A->hash_active) {
4883: PetscCall(PetscMalloc1(m, &c->imax));
4884: PetscCall(PetscMemcpy(c->imax, a->imax, m * sizeof(PetscInt)));
4885: PetscCall(PetscMalloc1(m, &c->ilen));
4886: PetscCall(PetscMemcpy(c->ilen, a->ilen, m * sizeof(PetscInt)));
4888: /* allocate the matrix space */
4889: if (mallocmatspace) {
4890: PetscCall(PetscMalloc3(a->i[m], &c->a, a->i[m], &c->j, m + 1, &c->i));
4892: c->singlemalloc = PETSC_TRUE;
4894: PetscCall(PetscArraycpy(c->i, a->i, m + 1));
4895: if (m > 0) {
4896: PetscCall(PetscArraycpy(c->j, a->j, a->i[m]));
4897: if (cpvalues == MAT_COPY_VALUES) {
4898: const PetscScalar *aa;
4900: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4901: PetscCall(PetscArraycpy(c->a, aa, a->i[m]));
4902: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4903: } else {
4904: PetscCall(PetscArrayzero(c->a, a->i[m]));
4905: }
4906: }
4907: }
4908: C->preallocated = PETSC_TRUE;
4909: } else {
4910: PetscCheck(mallocmatspace, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot malloc matrix memory from a non-preallocated matrix");
4911: PetscCall(MatSetUp(C));
4912: }
4914: c->ignorezeroentries = a->ignorezeroentries;
4915: c->roworiented = a->roworiented;
4916: c->nonew = a->nonew;
4917: if (a->diag) {
4918: PetscCall(PetscMalloc1(m + 1, &c->diag));
4919: PetscCall(PetscMemcpy(c->diag, a->diag, m * sizeof(PetscInt)));
4920: } else c->diag = NULL;
4922: c->solve_work = NULL;
4923: c->saved_values = NULL;
4924: c->idiag = NULL;
4925: c->ssor_work = NULL;
4926: c->keepnonzeropattern = a->keepnonzeropattern;
4927: c->free_a = PETSC_TRUE;
4928: c->free_ij = PETSC_TRUE;
4930: c->rmax = a->rmax;
4931: c->nz = a->nz;
4932: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
4934: c->compressedrow.use = a->compressedrow.use;
4935: c->compressedrow.nrows = a->compressedrow.nrows;
4936: if (a->compressedrow.use) {
4937: i = a->compressedrow.nrows;
4938: PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i, &c->compressedrow.rindex));
4939: PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
4940: PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
4941: } else {
4942: c->compressedrow.use = PETSC_FALSE;
4943: c->compressedrow.i = NULL;
4944: c->compressedrow.rindex = NULL;
4945: }
4946: c->nonzerorowcnt = a->nonzerorowcnt;
4947: C->nonzerostate = A->nonzerostate;
4949: PetscCall(MatDuplicate_SeqAIJ_Inode(A, cpvalues, &C));
4950: }
4951: PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
4952: PetscFunctionReturn(PETSC_SUCCESS);
4953: }
4955: PetscErrorCode MatDuplicate_SeqAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
4956: {
4957: PetscFunctionBegin;
4958: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
4959: PetscCall(MatSetSizes(*B, A->rmap->n, A->cmap->n, A->rmap->n, A->cmap->n));
4960: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) PetscCall(MatSetBlockSizesFromMats(*B, A, A));
4961: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
4962: PetscCall(MatDuplicateNoCreate_SeqAIJ(*B, A, cpvalues, PETSC_TRUE));
4963: PetscFunctionReturn(PETSC_SUCCESS);
4964: }
4966: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4967: {
4968: PetscBool isbinary, ishdf5;
4970: PetscFunctionBegin;
4973: /* force binary viewer to load .info file if it has not yet done so */
4974: PetscCall(PetscViewerSetUp(viewer));
4975: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
4976: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
4977: if (isbinary) {
4978: PetscCall(MatLoad_SeqAIJ_Binary(newMat, viewer));
4979: } else if (ishdf5) {
4980: #if defined(PETSC_HAVE_HDF5)
4981: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
4982: #else
4983: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
4984: #endif
4985: } else {
4986: 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);
4987: }
4988: PetscFunctionReturn(PETSC_SUCCESS);
4989: }
4991: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
4992: {
4993: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->data;
4994: PetscInt header[4], *rowlens, M, N, nz, sum, rows, cols, i;
4996: PetscFunctionBegin;
4997: PetscCall(PetscViewerSetUp(viewer));
4999: /* read in matrix header */
5000: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
5001: PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
5002: M = header[1];
5003: N = header[2];
5004: nz = header[3];
5005: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
5006: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
5007: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqAIJ");
5009: /* set block sizes from the viewer's .info file */
5010: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
5011: /* set local and global sizes if not set already */
5012: if (mat->rmap->n < 0) mat->rmap->n = M;
5013: if (mat->cmap->n < 0) mat->cmap->n = N;
5014: if (mat->rmap->N < 0) mat->rmap->N = M;
5015: if (mat->cmap->N < 0) mat->cmap->N = N;
5016: PetscCall(PetscLayoutSetUp(mat->rmap));
5017: PetscCall(PetscLayoutSetUp(mat->cmap));
5019: /* check if the matrix sizes are correct */
5020: PetscCall(MatGetSize(mat, &rows, &cols));
5021: 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);
5023: /* read in row lengths */
5024: PetscCall(PetscMalloc1(M, &rowlens));
5025: PetscCall(PetscViewerBinaryRead(viewer, rowlens, M, NULL, PETSC_INT));
5026: /* check if sum(rowlens) is same as nz */
5027: sum = 0;
5028: for (i = 0; i < M; i++) sum += rowlens[i];
5029: 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);
5030: /* preallocate and check sizes */
5031: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(mat, 0, rowlens));
5032: PetscCall(MatGetSize(mat, &rows, &cols));
5033: 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);
5034: /* store row lengths */
5035: PetscCall(PetscArraycpy(a->ilen, rowlens, M));
5036: PetscCall(PetscFree(rowlens));
5038: /* fill in "i" row pointers */
5039: a->i[0] = 0;
5040: for (i = 0; i < M; i++) a->i[i + 1] = a->i[i] + a->ilen[i];
5041: /* read in "j" column indices */
5042: PetscCall(PetscViewerBinaryRead(viewer, a->j, nz, NULL, PETSC_INT));
5043: /* read in "a" nonzero values */
5044: PetscCall(PetscViewerBinaryRead(viewer, a->a, nz, NULL, PETSC_SCALAR));
5046: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
5047: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
5048: PetscFunctionReturn(PETSC_SUCCESS);
5049: }
5051: PetscErrorCode MatEqual_SeqAIJ(Mat A, Mat B, PetscBool *flg)
5052: {
5053: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data;
5054: const PetscScalar *aa, *ba;
5055: #if defined(PETSC_USE_COMPLEX)
5056: PetscInt k;
5057: #endif
5059: PetscFunctionBegin;
5060: /* If the matrix dimensions are not equal,or no of nonzeros */
5061: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) || (a->nz != b->nz)) {
5062: *flg = PETSC_FALSE;
5063: PetscFunctionReturn(PETSC_SUCCESS);
5064: }
5066: /* if the a->i are the same */
5067: PetscCall(PetscArraycmp(a->i, b->i, A->rmap->n + 1, flg));
5068: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5070: /* if a->j are the same */
5071: PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg));
5072: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5074: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5075: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
5076: /* if a->a are the same */
5077: #if defined(PETSC_USE_COMPLEX)
5078: for (k = 0; k < a->nz; k++) {
5079: if (PetscRealPart(aa[k]) != PetscRealPart(ba[k]) || PetscImaginaryPart(aa[k]) != PetscImaginaryPart(ba[k])) {
5080: *flg = PETSC_FALSE;
5081: PetscFunctionReturn(PETSC_SUCCESS);
5082: }
5083: }
5084: #else
5085: PetscCall(PetscArraycmp(aa, ba, a->nz, flg));
5086: #endif
5087: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
5088: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
5089: PetscFunctionReturn(PETSC_SUCCESS);
5090: }
5092: /*@
5093: MatCreateSeqAIJWithArrays - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in CSR format)
5094: provided by the user.
5096: Collective
5098: Input Parameters:
5099: + comm - must be an MPI communicator of size 1
5100: . m - number of rows
5101: . n - number of columns
5102: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
5103: . j - column indices
5104: - a - matrix values
5106: Output Parameter:
5107: . mat - the matrix
5109: Level: intermediate
5111: Notes:
5112: The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
5113: once the matrix is destroyed and not before
5115: You cannot set new nonzero locations into this matrix, that will generate an error.
5117: The `i` and `j` indices are 0 based
5119: The format which is used for the sparse matrix input, is equivalent to a
5120: row-major ordering.. i.e for the following matrix, the input data expected is
5121: as shown
5122: .vb
5123: 1 0 0
5124: 2 0 3
5125: 4 5 6
5127: i = {0,1,3,6} [size = nrow+1 = 3+1]
5128: j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row
5129: v = {1,2,3,4,5,6} [size = 6]
5130: .ve
5132: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateMPIAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`
5133: @*/
5134: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
5135: {
5136: PetscInt ii;
5137: Mat_SeqAIJ *aij;
5138: PetscInt jj;
5140: PetscFunctionBegin;
5141: PetscCheck(m <= 0 || i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
5142: PetscCall(MatCreate(comm, mat));
5143: PetscCall(MatSetSizes(*mat, m, n, m, n));
5144: /* PetscCall(MatSetBlockSizes(*mat,,)); */
5145: PetscCall(MatSetType(*mat, MATSEQAIJ));
5146: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, MAT_SKIP_ALLOCATION, NULL));
5147: aij = (Mat_SeqAIJ *)(*mat)->data;
5148: PetscCall(PetscMalloc1(m, &aij->imax));
5149: PetscCall(PetscMalloc1(m, &aij->ilen));
5151: aij->i = i;
5152: aij->j = j;
5153: aij->a = a;
5154: aij->singlemalloc = PETSC_FALSE;
5155: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
5156: aij->free_a = PETSC_FALSE;
5157: aij->free_ij = PETSC_FALSE;
5159: for (ii = 0, aij->nonzerorowcnt = 0, aij->rmax = 0; ii < m; ii++) {
5160: aij->ilen[ii] = aij->imax[ii] = i[ii + 1] - i[ii];
5161: if (PetscDefined(USE_DEBUG)) {
5162: 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]);
5163: for (jj = i[ii] + 1; jj < i[ii + 1]; jj++) {
5164: 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);
5165: 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);
5166: }
5167: }
5168: }
5169: if (PetscDefined(USE_DEBUG)) {
5170: for (ii = 0; ii < aij->i[m]; ii++) {
5171: PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
5172: 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]);
5173: }
5174: }
5176: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5177: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5178: PetscFunctionReturn(PETSC_SUCCESS);
5179: }
5181: /*@
5182: MatCreateSeqAIJFromTriple - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in COO format)
5183: provided by the user.
5185: Collective
5187: Input Parameters:
5188: + comm - must be an MPI communicator of size 1
5189: . m - number of rows
5190: . n - number of columns
5191: . i - row indices
5192: . j - column indices
5193: . a - matrix values
5194: . nz - number of nonzeros
5195: - idx - if the `i` and `j` indices start with 1 use `PETSC_TRUE` otherwise use `PETSC_FALSE`
5197: Output Parameter:
5198: . mat - the matrix
5200: Level: intermediate
5202: Example:
5203: For the following matrix, the input data expected is as shown (using 0 based indexing)
5204: .vb
5205: 1 0 0
5206: 2 0 3
5207: 4 5 6
5209: i = {0,1,1,2,2,2}
5210: j = {0,0,2,0,1,2}
5211: v = {1,2,3,4,5,6}
5212: .ve
5213: Note:
5214: Instead of using this function, users should also consider `MatSetPreallocationCOO()` and `MatSetValuesCOO()`, which allow repeated or remote entries,
5215: and are particularly useful in iterative applications.
5217: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateSeqAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`, `MatSetValuesCOO()`, `MatSetPreallocationCOO()`
5218: @*/
5219: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat, PetscInt nz, PetscBool idx)
5220: {
5221: PetscInt ii, *nnz, one = 1, row, col;
5223: PetscFunctionBegin;
5224: PetscCall(PetscCalloc1(m, &nnz));
5225: for (ii = 0; ii < nz; ii++) nnz[i[ii] - !!idx] += 1;
5226: PetscCall(MatCreate(comm, mat));
5227: PetscCall(MatSetSizes(*mat, m, n, m, n));
5228: PetscCall(MatSetType(*mat, MATSEQAIJ));
5229: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, 0, nnz));
5230: for (ii = 0; ii < nz; ii++) {
5231: if (idx) {
5232: row = i[ii] - 1;
5233: col = j[ii] - 1;
5234: } else {
5235: row = i[ii];
5236: col = j[ii];
5237: }
5238: PetscCall(MatSetValues(*mat, one, &row, one, &col, &a[ii], ADD_VALUES));
5239: }
5240: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5241: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5242: PetscCall(PetscFree(nnz));
5243: PetscFunctionReturn(PETSC_SUCCESS);
5244: }
5246: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
5247: {
5248: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5250: PetscFunctionBegin;
5251: a->idiagvalid = PETSC_FALSE;
5252: a->ibdiagvalid = PETSC_FALSE;
5254: PetscCall(MatSeqAIJInvalidateDiagonal_Inode(A));
5255: PetscFunctionReturn(PETSC_SUCCESS);
5256: }
5258: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
5259: {
5260: PetscFunctionBegin;
5261: PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm, inmat, n, scall, outmat));
5262: PetscFunctionReturn(PETSC_SUCCESS);
5263: }
5265: /*
5266: Permute A into C's *local* index space using rowemb,colemb.
5267: The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
5268: of [0,m), colemb is in [0,n).
5269: If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
5270: */
5271: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C, IS rowemb, IS colemb, MatStructure pattern, Mat B)
5272: {
5273: /* If making this function public, change the error returned in this function away from _PLIB. */
5274: Mat_SeqAIJ *Baij;
5275: PetscBool seqaij;
5276: PetscInt m, n, *nz, i, j, count;
5277: PetscScalar v;
5278: const PetscInt *rowindices, *colindices;
5280: PetscFunctionBegin;
5281: if (!B) PetscFunctionReturn(PETSC_SUCCESS);
5282: /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
5283: PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQAIJ, &seqaij));
5284: PetscCheck(seqaij, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is of wrong type");
5285: if (rowemb) {
5286: PetscCall(ISGetLocalSize(rowemb, &m));
5287: 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);
5288: } else {
5289: PetscCheck(C->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is row-incompatible with the target matrix");
5290: }
5291: if (colemb) {
5292: PetscCall(ISGetLocalSize(colemb, &n));
5293: 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);
5294: } else {
5295: PetscCheck(C->cmap->n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is col-incompatible with the target matrix");
5296: }
5298: Baij = (Mat_SeqAIJ *)(B->data);
5299: if (pattern == DIFFERENT_NONZERO_PATTERN) {
5300: PetscCall(PetscMalloc1(B->rmap->n, &nz));
5301: for (i = 0; i < B->rmap->n; i++) nz[i] = Baij->i[i + 1] - Baij->i[i];
5302: PetscCall(MatSeqAIJSetPreallocation(C, 0, nz));
5303: PetscCall(PetscFree(nz));
5304: }
5305: if (pattern == SUBSET_NONZERO_PATTERN) PetscCall(MatZeroEntries(C));
5306: count = 0;
5307: rowindices = NULL;
5308: colindices = NULL;
5309: if (rowemb) PetscCall(ISGetIndices(rowemb, &rowindices));
5310: if (colemb) PetscCall(ISGetIndices(colemb, &colindices));
5311: for (i = 0; i < B->rmap->n; i++) {
5312: PetscInt row;
5313: row = i;
5314: if (rowindices) row = rowindices[i];
5315: for (j = Baij->i[i]; j < Baij->i[i + 1]; j++) {
5316: PetscInt col;
5317: col = Baij->j[count];
5318: if (colindices) col = colindices[col];
5319: v = Baij->a[count];
5320: PetscCall(MatSetValues(C, 1, &row, 1, &col, &v, INSERT_VALUES));
5321: ++count;
5322: }
5323: }
5324: /* FIXME: set C's nonzerostate correctly. */
5325: /* Assembly for C is necessary. */
5326: C->preallocated = PETSC_TRUE;
5327: C->assembled = PETSC_TRUE;
5328: C->was_assembled = PETSC_FALSE;
5329: PetscFunctionReturn(PETSC_SUCCESS);
5330: }
5332: PetscErrorCode MatEliminateZeros_SeqAIJ(Mat A)
5333: {
5334: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5335: MatScalar *aa = a->a;
5336: PetscInt m = A->rmap->n, fshift = 0, fshift_prev = 0, i, k;
5337: PetscInt *ailen = a->ilen, *imax = a->imax, *ai = a->i, *aj = a->j, rmax = 0;
5339: PetscFunctionBegin;
5340: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix");
5341: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
5342: for (i = 1; i <= m; i++) {
5343: /* move each nonzero entry back by the amount of zero slots (fshift) before it*/
5344: for (k = ai[i - 1]; k < ai[i]; k++) {
5345: if (aa[k] == 0 && aj[k] != i - 1) fshift++;
5346: else {
5347: if (aa[k] == 0 && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal zero at row %" PetscInt_FMT "\n", i - 1));
5348: aa[k - fshift] = aa[k];
5349: aj[k - fshift] = aj[k];
5350: }
5351: }
5352: ai[i - 1] -= fshift_prev; // safe to update ai[i-1] now since it will not be used in the next iteration
5353: fshift_prev = fshift;
5354: /* reset ilen and imax for each row */
5355: ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1];
5356: a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0);
5357: rmax = PetscMax(rmax, ailen[i - 1]);
5358: }
5359: if (m) {
5360: ai[m] -= fshift;
5361: a->nz = ai[m];
5362: }
5363: 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));
5364: A->nonzerostate -= fshift;
5365: A->info.nz_unneeded += (PetscReal)fshift;
5366: a->rmax = rmax;
5367: if (a->inode.use && a->inode.checked) PetscCall(MatSeqAIJCheckInode(A));
5368: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
5369: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
5370: PetscFunctionReturn(PETSC_SUCCESS);
5371: }
5373: PetscFunctionList MatSeqAIJList = NULL;
5375: /*@C
5376: MatSeqAIJSetType - Converts a `MATSEQAIJ` matrix to a subtype
5378: Collective
5380: Input Parameters:
5381: + mat - the matrix object
5382: - matype - matrix type
5384: Options Database Key:
5385: . -mat_seqaij_type <method> - for example seqaijcrl
5387: Level: intermediate
5389: .seealso: [](ch_matrices), `Mat`, `PCSetType()`, `VecSetType()`, `MatCreate()`, `MatType`, `Mat`
5390: @*/
5391: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
5392: {
5393: PetscBool sametype;
5394: PetscErrorCode (*r)(Mat, MatType, MatReuse, Mat *);
5396: PetscFunctionBegin;
5398: PetscCall(PetscObjectTypeCompare((PetscObject)mat, matype, &sametype));
5399: if (sametype) PetscFunctionReturn(PETSC_SUCCESS);
5401: PetscCall(PetscFunctionListFind(MatSeqAIJList, matype, &r));
5402: PetscCheck(r, PETSC_COMM_SELF, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown Mat type given: %s", matype);
5403: PetscCall((*r)(mat, matype, MAT_INPLACE_MATRIX, &mat));
5404: PetscFunctionReturn(PETSC_SUCCESS);
5405: }
5407: /*@C
5408: MatSeqAIJRegister - - Adds a new sub-matrix type for sequential `MATSEQAIJ` matrices
5410: Not Collective
5412: Input Parameters:
5413: + name - name of a new user-defined matrix type, for example `MATSEQAIJCRL`
5414: - function - routine to convert to subtype
5416: Level: advanced
5418: Notes:
5419: `MatSeqAIJRegister()` may be called multiple times to add several user-defined solvers.
5421: Then, your matrix can be chosen with the procedural interface at runtime via the option
5422: $ -mat_seqaij_type my_mat
5424: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRegisterAll()`
5425: @*/
5426: PetscErrorCode MatSeqAIJRegister(const char sname[], PetscErrorCode (*function)(Mat, MatType, MatReuse, Mat *))
5427: {
5428: PetscFunctionBegin;
5429: PetscCall(MatInitializePackage());
5430: PetscCall(PetscFunctionListAdd(&MatSeqAIJList, sname, function));
5431: PetscFunctionReturn(PETSC_SUCCESS);
5432: }
5434: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;
5436: /*@C
5437: MatSeqAIJRegisterAll - Registers all of the matrix subtypes of `MATSSEQAIJ`
5439: Not Collective
5441: Level: advanced
5443: Note:
5444: This registers the versions of `MATSEQAIJ` for GPUs
5446: .seealso: [](ch_matrices), `Mat`, `MatRegisterAll()`, `MatSeqAIJRegister()`
5447: @*/
5448: PetscErrorCode MatSeqAIJRegisterAll(void)
5449: {
5450: PetscFunctionBegin;
5451: if (MatSeqAIJRegisterAllCalled) PetscFunctionReturn(PETSC_SUCCESS);
5452: MatSeqAIJRegisterAllCalled = PETSC_TRUE;
5454: PetscCall(MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL));
5455: PetscCall(MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM));
5456: PetscCall(MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL));
5457: #if defined(PETSC_HAVE_MKL_SPARSE)
5458: PetscCall(MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL));
5459: #endif
5460: #if defined(PETSC_HAVE_CUDA)
5461: PetscCall(MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE));
5462: #endif
5463: #if defined(PETSC_HAVE_HIP)
5464: PetscCall(MatSeqAIJRegister(MATSEQAIJHIPSPARSE, MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
5465: #endif
5466: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
5467: PetscCall(MatSeqAIJRegister(MATSEQAIJKOKKOS, MatConvert_SeqAIJ_SeqAIJKokkos));
5468: #endif
5469: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5470: PetscCall(MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL));
5471: #endif
5472: PetscFunctionReturn(PETSC_SUCCESS);
5473: }
5475: /*
5476: Special version for direct calls from Fortran
5477: */
5478: #include <petsc/private/fortranimpl.h>
5479: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5480: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5481: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5482: #define matsetvaluesseqaij_ matsetvaluesseqaij
5483: #endif
5485: /* Change these macros so can be used in void function */
5487: /* Change these macros so can be used in void function */
5488: /* Identical to PetscCallVoid, except it assigns to *_ierr */
5489: #undef PetscCall
5490: #define PetscCall(...) \
5491: do { \
5492: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
5493: if (PetscUnlikely(ierr_msv_mpiaij)) { \
5494: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
5495: return; \
5496: } \
5497: } while (0)
5499: #undef SETERRQ
5500: #define SETERRQ(comm, ierr, ...) \
5501: do { \
5502: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
5503: return; \
5504: } while (0)
5506: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[], InsertMode *isis, PetscErrorCode *_ierr)
5507: {
5508: Mat A = *AA;
5509: PetscInt m = *mm, n = *nn;
5510: InsertMode is = *isis;
5511: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5512: PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
5513: PetscInt *imax, *ai, *ailen;
5514: PetscInt *aj, nonew = a->nonew, lastcol = -1;
5515: MatScalar *ap, value, *aa;
5516: PetscBool ignorezeroentries = a->ignorezeroentries;
5517: PetscBool roworiented = a->roworiented;
5519: PetscFunctionBegin;
5520: MatCheckPreallocated(A, 1);
5521: imax = a->imax;
5522: ai = a->i;
5523: ailen = a->ilen;
5524: aj = a->j;
5525: aa = a->a;
5527: for (k = 0; k < m; k++) { /* loop over added rows */
5528: row = im[k];
5529: if (row < 0) continue;
5530: PetscCheck(row < A->rmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Row too large");
5531: rp = aj + ai[row];
5532: ap = aa + ai[row];
5533: rmax = imax[row];
5534: nrow = ailen[row];
5535: low = 0;
5536: high = nrow;
5537: for (l = 0; l < n; l++) { /* loop over added columns */
5538: if (in[l] < 0) continue;
5539: PetscCheck(in[l] < A->cmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Column too large");
5540: col = in[l];
5541: if (roworiented) value = v[l + k * n];
5542: else value = v[k + l * m];
5544: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
5546: if (col <= lastcol) low = 0;
5547: else high = nrow;
5548: lastcol = col;
5549: while (high - low > 5) {
5550: t = (low + high) / 2;
5551: if (rp[t] > col) high = t;
5552: else low = t;
5553: }
5554: for (i = low; i < high; i++) {
5555: if (rp[i] > col) break;
5556: if (rp[i] == col) {
5557: if (is == ADD_VALUES) ap[i] += value;
5558: else ap[i] = value;
5559: goto noinsert;
5560: }
5561: }
5562: if (value == 0.0 && ignorezeroentries) goto noinsert;
5563: if (nonew == 1) goto noinsert;
5564: PetscCheck(nonew != -1, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero in the matrix");
5565: MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
5566: N = nrow++ - 1;
5567: a->nz++;
5568: high++;
5569: /* shift up all the later entries in this row */
5570: for (ii = N; ii >= i; ii--) {
5571: rp[ii + 1] = rp[ii];
5572: ap[ii + 1] = ap[ii];
5573: }
5574: rp[i] = col;
5575: ap[i] = value;
5576: A->nonzerostate++;
5577: noinsert:;
5578: low = i + 1;
5579: }
5580: ailen[row] = nrow;
5581: }
5582: PetscFunctionReturnVoid();
5583: }
5584: /* Undefining these here since they were redefined from their original definition above! No
5585: * other PETSc functions should be defined past this point, as it is impossible to recover the
5586: * original definitions */
5587: #undef PetscCall
5588: #undef SETERRQ