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: static 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: static 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(PetscObjectComm((PetscObject)A), 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: static 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: static 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: static 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: static 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 = PetscSafePointerPlusOffset(aj, ai[row]);
419: if (!A->structure_only) ap = PetscSafePointerPlusOffset(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: noinsert:;
472: }
473: ailen[row] = nrow;
474: }
475: PetscCall(MatSeqAIJRestoreArray(A, &aa));
476: PetscFunctionReturn(PETSC_SUCCESS);
477: }
479: static PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
480: {
481: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
482: PetscInt *rp, k, row;
483: PetscInt *ai = a->i;
484: PetscInt *aj = a->j;
485: MatScalar *aa, *ap;
487: PetscFunctionBegin;
488: PetscCheck(!A->was_assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot call on assembled matrix.");
489: 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);
491: PetscCall(MatSeqAIJGetArray(A, &aa));
492: for (k = 0; k < m; k++) { /* loop over added rows */
493: row = im[k];
494: rp = aj + ai[row];
495: ap = PetscSafePointerPlusOffset(aa, ai[row]);
497: PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
498: if (!A->structure_only) {
499: if (v) {
500: PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
501: v += n;
502: } else {
503: PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
504: }
505: }
506: a->ilen[row] = n;
507: a->imax[row] = n;
508: a->i[row + 1] = a->i[row] + n;
509: a->nz += n;
510: }
511: PetscCall(MatSeqAIJRestoreArray(A, &aa));
512: PetscFunctionReturn(PETSC_SUCCESS);
513: }
515: /*@
516: MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix.
518: Input Parameters:
519: + A - the `MATSEQAIJ` matrix
520: - nztotal - bound on the number of nonzeros
522: Level: advanced
524: Notes:
525: This can be called if you will be provided the matrix row by row (from row zero) with sorted column indices for each row.
526: Simply call `MatSetValues()` after this call to provide the matrix entries in the usual manner. This matrix may be used
527: as always with multiple matrix assemblies.
529: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MAT_SORTED_FULL`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`
530: @*/
531: PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A, PetscInt nztotal)
532: {
533: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
535: PetscFunctionBegin;
536: PetscCall(PetscLayoutSetUp(A->rmap));
537: PetscCall(PetscLayoutSetUp(A->cmap));
538: a->maxnz = nztotal;
539: if (!a->imax) { PetscCall(PetscMalloc1(A->rmap->n, &a->imax)); }
540: if (!a->ilen) {
541: PetscCall(PetscMalloc1(A->rmap->n, &a->ilen));
542: } else {
543: PetscCall(PetscMemzero(a->ilen, A->rmap->n * sizeof(PetscInt)));
544: }
546: /* allocate the matrix space */
547: PetscCall(PetscShmgetAllocateArray(A->rmap->n + 1, sizeof(PetscInt), (void **)&a->i));
548: PetscCall(PetscShmgetAllocateArray(nztotal, sizeof(PetscInt), (void **)&a->j));
549: a->free_ij = PETSC_TRUE;
550: if (A->structure_only) {
551: a->free_a = PETSC_FALSE;
552: } else {
553: PetscCall(PetscShmgetAllocateArray(nztotal, sizeof(PetscScalar), (void **)&a->a));
554: a->free_a = PETSC_TRUE;
555: }
556: a->i[0] = 0;
557: A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
558: A->preallocated = PETSC_TRUE;
559: PetscFunctionReturn(PETSC_SUCCESS);
560: }
562: static PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
563: {
564: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
565: PetscInt *rp, k, row;
566: PetscInt *ai = a->i, *ailen = a->ilen;
567: PetscInt *aj = a->j;
568: MatScalar *aa, *ap;
570: PetscFunctionBegin;
571: PetscCall(MatSeqAIJGetArray(A, &aa));
572: for (k = 0; k < m; k++) { /* loop over added rows */
573: row = im[k];
574: 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);
575: rp = aj + ai[row];
576: ap = aa + ai[row];
577: if (!A->was_assembled) PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
578: if (!A->structure_only) {
579: if (v) {
580: PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
581: v += n;
582: } else {
583: PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
584: }
585: }
586: ailen[row] = n;
587: a->nz += n;
588: }
589: PetscCall(MatSeqAIJRestoreArray(A, &aa));
590: PetscFunctionReturn(PETSC_SUCCESS);
591: }
593: static PetscErrorCode MatGetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[])
594: {
595: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
596: PetscInt *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j;
597: PetscInt *ai = a->i, *ailen = a->ilen;
598: const MatScalar *ap, *aa;
600: PetscFunctionBegin;
601: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
602: for (k = 0; k < m; k++) { /* loop over rows */
603: row = im[k];
604: if (row < 0) {
605: v += n;
606: continue;
607: } /* negative row */
608: 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);
609: rp = PetscSafePointerPlusOffset(aj, ai[row]);
610: ap = PetscSafePointerPlusOffset(aa, ai[row]);
611: nrow = ailen[row];
612: for (l = 0; l < n; l++) { /* loop over columns */
613: if (in[l] < 0) {
614: v++;
615: continue;
616: } /* negative column */
617: 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);
618: col = in[l];
619: high = nrow;
620: low = 0; /* assume unsorted */
621: while (high - low > 5) {
622: t = (low + high) / 2;
623: if (rp[t] > col) high = t;
624: else low = t;
625: }
626: for (i = low; i < high; i++) {
627: if (rp[i] > col) break;
628: if (rp[i] == col) {
629: *v++ = ap[i];
630: goto finished;
631: }
632: }
633: *v++ = 0.0;
634: finished:;
635: }
636: }
637: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
638: PetscFunctionReturn(PETSC_SUCCESS);
639: }
641: static PetscErrorCode MatView_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
642: {
643: Mat_SeqAIJ *A = (Mat_SeqAIJ *)mat->data;
644: const PetscScalar *av;
645: PetscInt header[4], M, N, m, nz, i;
646: PetscInt *rowlens;
648: PetscFunctionBegin;
649: PetscCall(PetscViewerSetUp(viewer));
651: M = mat->rmap->N;
652: N = mat->cmap->N;
653: m = mat->rmap->n;
654: nz = A->nz;
656: /* write matrix header */
657: header[0] = MAT_FILE_CLASSID;
658: header[1] = M;
659: header[2] = N;
660: header[3] = nz;
661: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
663: /* fill in and store row lengths */
664: PetscCall(PetscMalloc1(m, &rowlens));
665: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i];
666: if (PetscDefined(USE_DEBUG)) {
667: PetscInt mnz = 0;
669: for (i = 0; i < m; i++) mnz += rowlens[i];
670: PetscCheck(nz == mnz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Row lens %" PetscInt_FMT " do not sum to nz %" PetscInt_FMT, mnz, nz);
671: }
672: PetscCall(PetscViewerBinaryWrite(viewer, rowlens, m, PETSC_INT));
673: PetscCall(PetscFree(rowlens));
674: /* store column indices */
675: PetscCall(PetscViewerBinaryWrite(viewer, A->j, nz, PETSC_INT));
676: /* store nonzero values */
677: PetscCall(MatSeqAIJGetArrayRead(mat, &av));
678: PetscCall(PetscViewerBinaryWrite(viewer, av, nz, PETSC_SCALAR));
679: PetscCall(MatSeqAIJRestoreArrayRead(mat, &av));
681: /* write block size option to the viewer's .info file */
682: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
683: PetscFunctionReturn(PETSC_SUCCESS);
684: }
686: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A, PetscViewer viewer)
687: {
688: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
689: PetscInt i, k, m = A->rmap->N;
691: PetscFunctionBegin;
692: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
693: for (i = 0; i < m; i++) {
694: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
695: for (k = a->i[i]; k < a->i[i + 1]; k++) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ") ", a->j[k]));
696: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
697: }
698: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
699: PetscFunctionReturn(PETSC_SUCCESS);
700: }
702: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat, PetscViewer);
704: static PetscErrorCode MatView_SeqAIJ_ASCII(Mat A, PetscViewer viewer)
705: {
706: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
707: const PetscScalar *av;
708: PetscInt i, j, m = A->rmap->n;
709: const char *name;
710: PetscViewerFormat format;
712: PetscFunctionBegin;
713: if (A->structure_only) {
714: PetscCall(MatView_SeqAIJ_ASCII_structonly(A, viewer));
715: PetscFunctionReturn(PETSC_SUCCESS);
716: }
718: PetscCall(PetscViewerGetFormat(viewer, &format));
719: // By petsc's rule, even PETSC_VIEWER_ASCII_INFO_DETAIL doesn't print matrix entries
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: static 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: static 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, n;
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);
1144: PetscCall(MatMarkDiagonal_SeqAIJ(A)); // since diagonal info is used a lot, it is helpful to set them up at the end of assembly
1145: a->diagonaldense = PETSC_TRUE;
1146: n = PetscMin(A->rmap->n, A->cmap->n);
1147: for (i = 0; i < n; i++) {
1148: if (a->diag[i] >= ai[i + 1]) {
1149: a->diagonaldense = PETSC_FALSE;
1150: break;
1151: }
1152: }
1153: 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));
1154: PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues() is %" PetscInt_FMT "\n", a->reallocs));
1155: PetscCall(PetscInfo(A, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", rmax));
1157: A->info.mallocs += a->reallocs;
1158: a->reallocs = 0;
1159: A->info.nz_unneeded = (PetscReal)fshift;
1160: a->rmax = rmax;
1162: if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, m, ratio));
1163: PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1164: PetscFunctionReturn(PETSC_SUCCESS);
1165: }
1167: static PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1168: {
1169: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1170: PetscInt i, nz = a->nz;
1171: MatScalar *aa;
1173: PetscFunctionBegin;
1174: PetscCall(MatSeqAIJGetArray(A, &aa));
1175: for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]);
1176: PetscCall(MatSeqAIJRestoreArray(A, &aa));
1177: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1178: PetscFunctionReturn(PETSC_SUCCESS);
1179: }
1181: static PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1182: {
1183: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1184: PetscInt i, nz = a->nz;
1185: MatScalar *aa;
1187: PetscFunctionBegin;
1188: PetscCall(MatSeqAIJGetArray(A, &aa));
1189: for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1190: PetscCall(MatSeqAIJRestoreArray(A, &aa));
1191: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1192: PetscFunctionReturn(PETSC_SUCCESS);
1193: }
1195: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1196: {
1197: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1198: MatScalar *aa;
1200: PetscFunctionBegin;
1201: PetscCall(MatSeqAIJGetArrayWrite(A, &aa));
1202: PetscCall(PetscArrayzero(aa, a->i[A->rmap->n]));
1203: PetscCall(MatSeqAIJRestoreArrayWrite(A, &aa));
1204: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1205: PetscFunctionReturn(PETSC_SUCCESS);
1206: }
1208: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1209: {
1210: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1212: PetscFunctionBegin;
1213: if (A->hash_active) {
1214: A->ops[0] = a->cops;
1215: PetscCall(PetscHMapIJVDestroy(&a->ht));
1216: PetscCall(PetscFree(a->dnz));
1217: A->hash_active = PETSC_FALSE;
1218: }
1220: PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->n, A->cmap->n, a->nz));
1221: PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i));
1222: PetscCall(ISDestroy(&a->row));
1223: PetscCall(ISDestroy(&a->col));
1224: PetscCall(PetscFree(a->diag));
1225: PetscCall(PetscFree(a->ibdiag));
1226: PetscCall(PetscFree(a->imax));
1227: PetscCall(PetscFree(a->ilen));
1228: PetscCall(PetscFree(a->ipre));
1229: PetscCall(PetscFree3(a->idiag, a->mdiag, a->ssor_work));
1230: PetscCall(PetscFree(a->solve_work));
1231: PetscCall(ISDestroy(&a->icol));
1232: PetscCall(PetscFree(a->saved_values));
1233: PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex));
1234: PetscCall(MatDestroy_SeqAIJ_Inode(A));
1235: PetscCall(PetscFree(A->data));
1237: /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
1238: That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
1239: that is hard to properly add this data to the MatProduct data. We free it here to avoid
1240: users reusing the matrix object with different data to incur in obscure segmentation faults
1241: due to different matrix sizes */
1242: PetscCall(PetscObjectCompose((PetscObject)A, "__PETSc__ab_dense", NULL));
1244: PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL));
1245: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEnginePut_C", NULL));
1246: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEngineGet_C", NULL));
1247: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetColumnIndices_C", NULL));
1248: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL));
1249: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL));
1250: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsbaij_C", NULL));
1251: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqbaij_C", NULL));
1252: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijperm_C", NULL));
1253: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijsell_C", NULL));
1254: #if defined(PETSC_HAVE_MKL_SPARSE)
1255: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijmkl_C", NULL));
1256: #endif
1257: #if defined(PETSC_HAVE_CUDA)
1258: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcusparse_C", NULL));
1259: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", NULL));
1260: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", NULL));
1261: #endif
1262: #if defined(PETSC_HAVE_HIP)
1263: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijhipsparse_C", NULL));
1264: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", NULL));
1265: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", NULL));
1266: #endif
1267: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
1268: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijkokkos_C", NULL));
1269: #endif
1270: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcrl_C", NULL));
1271: #if defined(PETSC_HAVE_ELEMENTAL)
1272: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_elemental_C", NULL));
1273: #endif
1274: #if defined(PETSC_HAVE_SCALAPACK)
1275: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_scalapack_C", NULL));
1276: #endif
1277: #if defined(PETSC_HAVE_HYPRE)
1278: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_hypre_C", NULL));
1279: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", NULL));
1280: #endif
1281: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqdense_C", NULL));
1282: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsell_C", NULL));
1283: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_is_C", NULL));
1284: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL));
1285: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsHermitianTranspose_C", NULL));
1286: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocation_C", NULL));
1287: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetPreallocation_C", NULL));
1288: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocationCSR_C", NULL));
1289: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatReorderForNonzeroDiagonal_C", NULL));
1290: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_is_seqaij_C", NULL));
1291: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqdense_seqaij_C", NULL));
1292: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaij_C", NULL));
1293: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJKron_C", NULL));
1294: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
1295: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
1296: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1297: /* these calls do not belong here: the subclasses Duplicate/Destroy are wrong */
1298: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijsell_seqaij_C", NULL));
1299: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijperm_seqaij_C", NULL));
1300: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijviennacl_C", NULL));
1301: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqdense_C", NULL));
1302: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqaij_C", NULL));
1303: PetscFunctionReturn(PETSC_SUCCESS);
1304: }
1306: PetscErrorCode MatSetOption_SeqAIJ(Mat A, MatOption op, PetscBool flg)
1307: {
1308: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1310: PetscFunctionBegin;
1311: switch (op) {
1312: case MAT_ROW_ORIENTED:
1313: a->roworiented = flg;
1314: break;
1315: case MAT_KEEP_NONZERO_PATTERN:
1316: a->keepnonzeropattern = flg;
1317: break;
1318: case MAT_NEW_NONZERO_LOCATIONS:
1319: a->nonew = (flg ? 0 : 1);
1320: break;
1321: case MAT_NEW_NONZERO_LOCATION_ERR:
1322: a->nonew = (flg ? -1 : 0);
1323: break;
1324: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1325: a->nonew = (flg ? -2 : 0);
1326: break;
1327: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1328: a->nounused = (flg ? -1 : 0);
1329: break;
1330: case MAT_IGNORE_ZERO_ENTRIES:
1331: a->ignorezeroentries = flg;
1332: break;
1333: case MAT_SPD:
1334: case MAT_SYMMETRIC:
1335: case MAT_STRUCTURALLY_SYMMETRIC:
1336: case MAT_HERMITIAN:
1337: case MAT_SYMMETRY_ETERNAL:
1338: case MAT_STRUCTURE_ONLY:
1339: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1340: case MAT_SPD_ETERNAL:
1341: break;
1342: case MAT_FORCE_DIAGONAL_ENTRIES:
1343: case MAT_IGNORE_OFF_PROC_ENTRIES:
1344: case MAT_USE_HASH_TABLE:
1345: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1346: break;
1347: case MAT_USE_INODES:
1348: PetscCall(MatSetOption_SeqAIJ_Inode(A, MAT_USE_INODES, flg));
1349: break;
1350: case MAT_SUBMAT_SINGLEIS:
1351: A->submat_singleis = flg;
1352: break;
1353: case MAT_SORTED_FULL:
1354: if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
1355: else A->ops->setvalues = MatSetValues_SeqAIJ;
1356: break;
1357: case MAT_FORM_EXPLICIT_TRANSPOSE:
1358: A->form_explicit_transpose = flg;
1359: break;
1360: default:
1361: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1362: }
1363: PetscFunctionReturn(PETSC_SUCCESS);
1364: }
1366: static PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A, Vec v)
1367: {
1368: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1369: PetscInt i, j, n, *ai = a->i, *aj = a->j;
1370: PetscScalar *x;
1371: const PetscScalar *aa;
1373: PetscFunctionBegin;
1374: PetscCall(VecGetLocalSize(v, &n));
1375: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
1376: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1377: if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1378: PetscInt *diag = a->diag;
1379: PetscCall(VecGetArrayWrite(v, &x));
1380: for (i = 0; i < n; i++) x[i] = 1.0 / aa[diag[i]];
1381: PetscCall(VecRestoreArrayWrite(v, &x));
1382: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1383: PetscFunctionReturn(PETSC_SUCCESS);
1384: }
1386: PetscCall(VecGetArrayWrite(v, &x));
1387: for (i = 0; i < n; i++) {
1388: x[i] = 0.0;
1389: for (j = ai[i]; j < ai[i + 1]; j++) {
1390: if (aj[j] == i) {
1391: x[i] = aa[j];
1392: break;
1393: }
1394: }
1395: }
1396: PetscCall(VecRestoreArrayWrite(v, &x));
1397: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1398: PetscFunctionReturn(PETSC_SUCCESS);
1399: }
1401: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1402: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A, Vec xx, Vec zz, Vec yy)
1403: {
1404: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1405: const MatScalar *aa;
1406: PetscScalar *y;
1407: const PetscScalar *x;
1408: PetscInt m = A->rmap->n;
1409: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1410: const MatScalar *v;
1411: PetscScalar alpha;
1412: PetscInt n, i, j;
1413: const PetscInt *idx, *ii, *ridx = NULL;
1414: Mat_CompressedRow cprow = a->compressedrow;
1415: PetscBool usecprow = cprow.use;
1416: #endif
1418: PetscFunctionBegin;
1419: if (zz != yy) PetscCall(VecCopy(zz, yy));
1420: PetscCall(VecGetArrayRead(xx, &x));
1421: PetscCall(VecGetArray(yy, &y));
1422: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1424: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1425: fortranmulttransposeaddaij_(&m, x, a->i, a->j, aa, y);
1426: #else
1427: if (usecprow) {
1428: m = cprow.nrows;
1429: ii = cprow.i;
1430: ridx = cprow.rindex;
1431: } else {
1432: ii = a->i;
1433: }
1434: for (i = 0; i < m; i++) {
1435: idx = a->j + ii[i];
1436: v = aa + ii[i];
1437: n = ii[i + 1] - ii[i];
1438: if (usecprow) {
1439: alpha = x[ridx[i]];
1440: } else {
1441: alpha = x[i];
1442: }
1443: for (j = 0; j < n; j++) y[idx[j]] += alpha * v[j];
1444: }
1445: #endif
1446: PetscCall(PetscLogFlops(2.0 * a->nz));
1447: PetscCall(VecRestoreArrayRead(xx, &x));
1448: PetscCall(VecRestoreArray(yy, &y));
1449: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1450: PetscFunctionReturn(PETSC_SUCCESS);
1451: }
1453: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A, Vec xx, Vec yy)
1454: {
1455: PetscFunctionBegin;
1456: PetscCall(VecSet(yy, 0.0));
1457: PetscCall(MatMultTransposeAdd_SeqAIJ(A, xx, yy, yy));
1458: PetscFunctionReturn(PETSC_SUCCESS);
1459: }
1461: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1463: PetscErrorCode MatMult_SeqAIJ(Mat A, Vec xx, Vec yy)
1464: {
1465: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1466: PetscScalar *y;
1467: const PetscScalar *x;
1468: const MatScalar *a_a;
1469: PetscInt m = A->rmap->n;
1470: const PetscInt *ii, *ridx = NULL;
1471: PetscBool usecprow = a->compressedrow.use;
1473: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1474: #pragma disjoint(*x, *y, *aa)
1475: #endif
1477: PetscFunctionBegin;
1478: if (a->inode.use && a->inode.checked) {
1479: PetscCall(MatMult_SeqAIJ_Inode(A, xx, yy));
1480: PetscFunctionReturn(PETSC_SUCCESS);
1481: }
1482: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1483: PetscCall(VecGetArrayRead(xx, &x));
1484: PetscCall(VecGetArray(yy, &y));
1485: ii = a->i;
1486: if (usecprow) { /* use compressed row format */
1487: PetscCall(PetscArrayzero(y, m));
1488: m = a->compressedrow.nrows;
1489: ii = a->compressedrow.i;
1490: ridx = a->compressedrow.rindex;
1491: PetscPragmaUseOMPKernels(parallel for)
1492: for (PetscInt i = 0; i < m; i++) {
1493: PetscInt n = ii[i + 1] - ii[i];
1494: const PetscInt *aj = a->j + ii[i];
1495: const PetscScalar *aa = a_a + ii[i];
1496: PetscScalar sum = 0.0;
1497: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1498: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1499: y[*ridx++] = sum;
1500: }
1501: } else { /* do not use compressed row format */
1502: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1503: fortranmultaij_(&m, x, ii, a->j, a_a, y);
1504: #else
1505: PetscPragmaUseOMPKernels(parallel for)
1506: for (PetscInt i = 0; i < m; i++) {
1507: PetscInt n = ii[i + 1] - ii[i];
1508: const PetscInt *aj = a->j + ii[i];
1509: const PetscScalar *aa = a_a + ii[i];
1510: PetscScalar sum = 0.0;
1511: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1512: y[i] = sum;
1513: }
1514: #endif
1515: }
1516: PetscCall(PetscLogFlops(2.0 * a->nz - a->nonzerorowcnt));
1517: PetscCall(VecRestoreArrayRead(xx, &x));
1518: PetscCall(VecRestoreArray(yy, &y));
1519: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1520: PetscFunctionReturn(PETSC_SUCCESS);
1521: }
1523: // HACK!!!!! Used by src/mat/tests/ex170.c
1524: PETSC_EXTERN PetscErrorCode MatMultMax_SeqAIJ(Mat A, Vec xx, Vec yy)
1525: {
1526: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1527: PetscScalar *y;
1528: const PetscScalar *x;
1529: const MatScalar *aa, *a_a;
1530: PetscInt m = A->rmap->n;
1531: const PetscInt *aj, *ii, *ridx = NULL;
1532: PetscInt n, i, nonzerorow = 0;
1533: PetscScalar sum;
1534: PetscBool usecprow = a->compressedrow.use;
1536: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1537: #pragma disjoint(*x, *y, *aa)
1538: #endif
1540: PetscFunctionBegin;
1541: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1542: PetscCall(VecGetArrayRead(xx, &x));
1543: PetscCall(VecGetArray(yy, &y));
1544: if (usecprow) { /* use compressed row format */
1545: m = a->compressedrow.nrows;
1546: ii = a->compressedrow.i;
1547: ridx = a->compressedrow.rindex;
1548: for (i = 0; i < m; i++) {
1549: n = ii[i + 1] - ii[i];
1550: aj = a->j + ii[i];
1551: aa = a_a + ii[i];
1552: sum = 0.0;
1553: nonzerorow += (n > 0);
1554: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1555: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1556: y[*ridx++] = sum;
1557: }
1558: } else { /* do not use compressed row format */
1559: ii = a->i;
1560: for (i = 0; i < m; i++) {
1561: n = ii[i + 1] - ii[i];
1562: aj = a->j + ii[i];
1563: aa = a_a + ii[i];
1564: sum = 0.0;
1565: nonzerorow += (n > 0);
1566: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1567: y[i] = sum;
1568: }
1569: }
1570: PetscCall(PetscLogFlops(2.0 * a->nz - nonzerorow));
1571: PetscCall(VecRestoreArrayRead(xx, &x));
1572: PetscCall(VecRestoreArray(yy, &y));
1573: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1574: PetscFunctionReturn(PETSC_SUCCESS);
1575: }
1577: // HACK!!!!! Used by src/mat/tests/ex170.c
1578: PETSC_EXTERN PetscErrorCode MatMultAddMax_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1579: {
1580: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1581: PetscScalar *y, *z;
1582: const PetscScalar *x;
1583: const MatScalar *aa, *a_a;
1584: PetscInt m = A->rmap->n, *aj, *ii;
1585: PetscInt n, i, *ridx = NULL;
1586: PetscScalar sum;
1587: PetscBool usecprow = a->compressedrow.use;
1589: PetscFunctionBegin;
1590: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1591: PetscCall(VecGetArrayRead(xx, &x));
1592: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1593: if (usecprow) { /* use compressed row format */
1594: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1595: m = a->compressedrow.nrows;
1596: ii = a->compressedrow.i;
1597: ridx = a->compressedrow.rindex;
1598: for (i = 0; i < m; i++) {
1599: n = ii[i + 1] - ii[i];
1600: aj = a->j + ii[i];
1601: aa = a_a + ii[i];
1602: sum = y[*ridx];
1603: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1604: z[*ridx++] = sum;
1605: }
1606: } else { /* do not use compressed row format */
1607: ii = a->i;
1608: for (i = 0; i < m; i++) {
1609: n = ii[i + 1] - ii[i];
1610: aj = a->j + ii[i];
1611: aa = a_a + ii[i];
1612: sum = y[i];
1613: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1614: z[i] = sum;
1615: }
1616: }
1617: PetscCall(PetscLogFlops(2.0 * a->nz));
1618: PetscCall(VecRestoreArrayRead(xx, &x));
1619: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1620: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1621: PetscFunctionReturn(PETSC_SUCCESS);
1622: }
1624: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1625: PetscErrorCode MatMultAdd_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1626: {
1627: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1628: PetscScalar *y, *z;
1629: const PetscScalar *x;
1630: const MatScalar *a_a;
1631: const PetscInt *ii, *ridx = NULL;
1632: PetscInt m = A->rmap->n;
1633: PetscBool usecprow = a->compressedrow.use;
1635: PetscFunctionBegin;
1636: if (a->inode.use && a->inode.checked) {
1637: PetscCall(MatMultAdd_SeqAIJ_Inode(A, xx, yy, zz));
1638: PetscFunctionReturn(PETSC_SUCCESS);
1639: }
1640: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1641: PetscCall(VecGetArrayRead(xx, &x));
1642: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1643: if (usecprow) { /* use compressed row format */
1644: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1645: m = a->compressedrow.nrows;
1646: ii = a->compressedrow.i;
1647: ridx = a->compressedrow.rindex;
1648: for (PetscInt i = 0; i < m; i++) {
1649: PetscInt n = ii[i + 1] - ii[i];
1650: const PetscInt *aj = a->j + ii[i];
1651: const PetscScalar *aa = a_a + ii[i];
1652: PetscScalar sum = y[*ridx];
1653: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1654: z[*ridx++] = sum;
1655: }
1656: } else { /* do not use compressed row format */
1657: ii = a->i;
1658: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1659: fortranmultaddaij_(&m, x, ii, a->j, a_a, y, z);
1660: #else
1661: PetscPragmaUseOMPKernels(parallel for)
1662: for (PetscInt i = 0; i < m; i++) {
1663: PetscInt n = ii[i + 1] - ii[i];
1664: const PetscInt *aj = a->j + ii[i];
1665: const PetscScalar *aa = a_a + ii[i];
1666: PetscScalar sum = y[i];
1667: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1668: z[i] = sum;
1669: }
1670: #endif
1671: }
1672: PetscCall(PetscLogFlops(2.0 * a->nz));
1673: PetscCall(VecRestoreArrayRead(xx, &x));
1674: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1675: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1676: PetscFunctionReturn(PETSC_SUCCESS);
1677: }
1679: /*
1680: Adds diagonal pointers to sparse matrix nonzero structure.
1681: */
1682: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1683: {
1684: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1685: PetscInt i, j, m = A->rmap->n;
1686: PetscBool alreadySet = PETSC_TRUE;
1688: PetscFunctionBegin;
1689: if (!a->diag) {
1690: PetscCall(PetscMalloc1(m, &a->diag));
1691: alreadySet = PETSC_FALSE;
1692: }
1693: for (i = 0; i < A->rmap->n; i++) {
1694: /* If A's diagonal is already correctly set, this fast track enables cheap and repeated MatMarkDiagonal_SeqAIJ() calls */
1695: if (alreadySet) {
1696: PetscInt pos = a->diag[i];
1697: if (pos >= a->i[i] && pos < a->i[i + 1] && a->j[pos] == i) continue;
1698: }
1700: a->diag[i] = a->i[i + 1];
1701: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1702: if (a->j[j] == i) {
1703: a->diag[i] = j;
1704: break;
1705: }
1706: }
1707: }
1708: PetscFunctionReturn(PETSC_SUCCESS);
1709: }
1711: static PetscErrorCode MatShift_SeqAIJ(Mat A, PetscScalar v)
1712: {
1713: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1714: const PetscInt *diag = (const PetscInt *)a->diag;
1715: const PetscInt *ii = (const PetscInt *)a->i;
1716: PetscInt i, *mdiag = NULL;
1717: PetscInt cnt = 0; /* how many diagonals are missing */
1719: PetscFunctionBegin;
1720: if (!A->preallocated || !a->nz) {
1721: PetscCall(MatSeqAIJSetPreallocation(A, 1, NULL));
1722: PetscCall(MatShift_Basic(A, v));
1723: PetscFunctionReturn(PETSC_SUCCESS);
1724: }
1726: if (a->diagonaldense) {
1727: cnt = 0;
1728: } else {
1729: PetscCall(PetscCalloc1(A->rmap->n, &mdiag));
1730: for (i = 0; i < A->rmap->n; i++) {
1731: if (i < A->cmap->n && diag[i] >= ii[i + 1]) { /* 'out of range' rows never have diagonals */
1732: cnt++;
1733: mdiag[i] = 1;
1734: }
1735: }
1736: }
1737: if (!cnt) {
1738: PetscCall(MatShift_Basic(A, v));
1739: } else {
1740: PetscScalar *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */
1741: PetscInt *oldj = a->j, *oldi = a->i;
1742: PetscBool free_a = a->free_a, free_ij = a->free_ij;
1743: const PetscScalar *Aa;
1745: PetscCall(MatSeqAIJGetArrayRead(A, &Aa)); // sync the host
1746: PetscCall(MatSeqAIJRestoreArrayRead(A, &Aa));
1748: a->a = NULL;
1749: a->j = NULL;
1750: a->i = NULL;
1751: /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1752: for (i = 0; i < PetscMin(A->rmap->n, A->cmap->n); i++) a->imax[i] += mdiag[i];
1753: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, 0, a->imax));
1755: /* copy old values into new matrix data structure */
1756: for (i = 0; i < A->rmap->n; i++) {
1757: PetscCall(MatSetValues(A, 1, &i, a->imax[i] - mdiag[i], &oldj[oldi[i]], &olda[oldi[i]], ADD_VALUES));
1758: if (i < A->cmap->n) PetscCall(MatSetValue(A, i, i, v, ADD_VALUES));
1759: }
1760: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1761: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1762: if (free_a) PetscCall(PetscShmgetDeallocateArray((void **)&olda));
1763: if (free_ij) PetscCall(PetscShmgetDeallocateArray((void **)&oldj));
1764: if (free_ij) PetscCall(PetscShmgetDeallocateArray((void **)&oldi));
1765: }
1766: PetscCall(PetscFree(mdiag));
1767: a->diagonaldense = PETSC_TRUE;
1768: PetscFunctionReturn(PETSC_SUCCESS);
1769: }
1771: /*
1772: Checks for missing diagonals
1773: */
1774: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A, PetscBool *missing, PetscInt *d)
1775: {
1776: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1777: PetscInt *diag, *ii = a->i, i;
1779: PetscFunctionBegin;
1780: *missing = PETSC_FALSE;
1781: if (A->rmap->n > 0 && !ii) {
1782: *missing = PETSC_TRUE;
1783: if (d) *d = 0;
1784: PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n"));
1785: } else {
1786: PetscInt n;
1787: n = PetscMin(A->rmap->n, A->cmap->n);
1788: diag = a->diag;
1789: for (i = 0; i < n; i++) {
1790: if (diag[i] >= ii[i + 1]) {
1791: *missing = PETSC_TRUE;
1792: if (d) *d = i;
1793: PetscCall(PetscInfo(A, "Matrix is missing diagonal number %" PetscInt_FMT "\n", i));
1794: break;
1795: }
1796: }
1797: }
1798: PetscFunctionReturn(PETSC_SUCCESS);
1799: }
1801: #include <petscblaslapack.h>
1802: #include <petsc/private/kernels/blockinvert.h>
1804: /*
1805: Note that values is allocated externally by the PC and then passed into this routine
1806: */
1807: static PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
1808: {
1809: PetscInt n = A->rmap->n, i, ncnt = 0, *indx, j, bsizemax = 0, *v_pivots;
1810: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
1811: const PetscReal shift = 0.0;
1812: PetscInt ipvt[5];
1813: PetscCount flops = 0;
1814: PetscScalar work[25], *v_work;
1816: PetscFunctionBegin;
1817: allowzeropivot = PetscNot(A->erroriffailure);
1818: for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
1819: PetscCheck(ncnt == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Total blocksizes %" PetscInt_FMT " doesn't match number matrix rows %" PetscInt_FMT, ncnt, n);
1820: for (i = 0; i < nblocks; i++) bsizemax = PetscMax(bsizemax, bsizes[i]);
1821: PetscCall(PetscMalloc1(bsizemax, &indx));
1822: if (bsizemax > 7) PetscCall(PetscMalloc2(bsizemax, &v_work, bsizemax, &v_pivots));
1823: ncnt = 0;
1824: for (i = 0; i < nblocks; i++) {
1825: for (j = 0; j < bsizes[i]; j++) indx[j] = ncnt + j;
1826: PetscCall(MatGetValues(A, bsizes[i], indx, bsizes[i], indx, diag));
1827: switch (bsizes[i]) {
1828: case 1:
1829: *diag = 1.0 / (*diag);
1830: break;
1831: case 2:
1832: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
1833: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1834: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
1835: break;
1836: case 3:
1837: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
1838: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1839: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
1840: break;
1841: case 4:
1842: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
1843: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1844: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
1845: break;
1846: case 5:
1847: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
1848: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1849: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
1850: break;
1851: case 6:
1852: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
1853: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1854: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
1855: break;
1856: case 7:
1857: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
1858: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1859: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
1860: break;
1861: default:
1862: PetscCall(PetscKernel_A_gets_inverse_A(bsizes[i], diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
1863: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1864: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bsizes[i]));
1865: }
1866: ncnt += bsizes[i];
1867: diag += bsizes[i] * bsizes[i];
1868: flops += 2 * PetscPowInt64(bsizes[i], 3) / 3;
1869: }
1870: PetscCall(PetscLogFlops(flops));
1871: if (bsizemax > 7) PetscCall(PetscFree2(v_work, v_pivots));
1872: PetscCall(PetscFree(indx));
1873: PetscFunctionReturn(PETSC_SUCCESS);
1874: }
1876: /*
1877: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1878: */
1879: static PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A, PetscScalar omega, PetscScalar fshift)
1880: {
1881: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1882: PetscInt i, *diag, m = A->rmap->n;
1883: const MatScalar *v;
1884: PetscScalar *idiag, *mdiag;
1886: PetscFunctionBegin;
1887: if (a->idiagvalid) PetscFunctionReturn(PETSC_SUCCESS);
1888: PetscCall(MatMarkDiagonal_SeqAIJ(A));
1889: diag = a->diag;
1890: if (!a->idiag) { PetscCall(PetscMalloc3(m, &a->idiag, m, &a->mdiag, m, &a->ssor_work)); }
1892: mdiag = a->mdiag;
1893: idiag = a->idiag;
1894: PetscCall(MatSeqAIJGetArrayRead(A, &v));
1895: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1896: for (i = 0; i < m; i++) {
1897: mdiag[i] = v[diag[i]];
1898: if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1899: if (PetscRealPart(fshift)) {
1900: PetscCall(PetscInfo(A, "Zero diagonal on row %" PetscInt_FMT "\n", i));
1901: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1902: A->factorerror_zeropivot_value = 0.0;
1903: A->factorerror_zeropivot_row = i;
1904: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Zero diagonal on row %" PetscInt_FMT, i);
1905: }
1906: idiag[i] = 1.0 / v[diag[i]];
1907: }
1908: PetscCall(PetscLogFlops(m));
1909: } else {
1910: for (i = 0; i < m; i++) {
1911: mdiag[i] = v[diag[i]];
1912: idiag[i] = omega / (fshift + v[diag[i]]);
1913: }
1914: PetscCall(PetscLogFlops(2.0 * m));
1915: }
1916: a->idiagvalid = PETSC_TRUE;
1917: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
1918: PetscFunctionReturn(PETSC_SUCCESS);
1919: }
1921: PetscErrorCode MatSOR_SeqAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1922: {
1923: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1924: PetscScalar *x, d, sum, *t, scale;
1925: const MatScalar *v, *idiag = NULL, *mdiag, *aa;
1926: const PetscScalar *b, *bs, *xb, *ts;
1927: PetscInt n, m = A->rmap->n, i;
1928: const PetscInt *idx, *diag;
1930: PetscFunctionBegin;
1931: if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
1932: PetscCall(MatSOR_SeqAIJ_Inode(A, bb, omega, flag, fshift, its, lits, xx));
1933: PetscFunctionReturn(PETSC_SUCCESS);
1934: }
1935: its = its * lits;
1937: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1938: if (!a->idiagvalid) PetscCall(MatInvertDiagonal_SeqAIJ(A, omega, fshift));
1939: a->fshift = fshift;
1940: a->omega = omega;
1942: diag = a->diag;
1943: t = a->ssor_work;
1944: idiag = a->idiag;
1945: mdiag = a->mdiag;
1947: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1948: PetscCall(VecGetArray(xx, &x));
1949: PetscCall(VecGetArrayRead(bb, &b));
1950: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1951: if (flag == SOR_APPLY_UPPER) {
1952: /* apply (U + D/omega) to the vector */
1953: bs = b;
1954: for (i = 0; i < m; i++) {
1955: d = fshift + mdiag[i];
1956: n = a->i[i + 1] - diag[i] - 1;
1957: idx = a->j + diag[i] + 1;
1958: v = aa + diag[i] + 1;
1959: sum = b[i] * d / omega;
1960: PetscSparseDensePlusDot(sum, bs, v, idx, n);
1961: x[i] = sum;
1962: }
1963: PetscCall(VecRestoreArray(xx, &x));
1964: PetscCall(VecRestoreArrayRead(bb, &b));
1965: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1966: PetscCall(PetscLogFlops(a->nz));
1967: PetscFunctionReturn(PETSC_SUCCESS);
1968: }
1970: PetscCheck(flag != SOR_APPLY_LOWER, PETSC_COMM_SELF, PETSC_ERR_SUP, "SOR_APPLY_LOWER is not implemented");
1971: if (flag & SOR_EISENSTAT) {
1972: /* Let A = L + U + D; where L is lower triangular,
1973: U is upper triangular, E = D/omega; This routine applies
1975: (L + E)^{-1} A (U + E)^{-1}
1977: to a vector efficiently using Eisenstat's trick.
1978: */
1979: scale = (2.0 / omega) - 1.0;
1981: /* x = (E + U)^{-1} b */
1982: for (i = m - 1; i >= 0; i--) {
1983: n = a->i[i + 1] - diag[i] - 1;
1984: idx = a->j + diag[i] + 1;
1985: v = aa + diag[i] + 1;
1986: sum = b[i];
1987: PetscSparseDenseMinusDot(sum, x, v, idx, n);
1988: x[i] = sum * idiag[i];
1989: }
1991: /* t = b - (2*E - D)x */
1992: v = aa;
1993: for (i = 0; i < m; i++) t[i] = b[i] - scale * (v[*diag++]) * x[i];
1995: /* t = (E + L)^{-1}t */
1996: ts = t;
1997: diag = a->diag;
1998: for (i = 0; i < m; i++) {
1999: n = diag[i] - a->i[i];
2000: idx = a->j + a->i[i];
2001: v = aa + a->i[i];
2002: sum = t[i];
2003: PetscSparseDenseMinusDot(sum, ts, v, idx, n);
2004: t[i] = sum * idiag[i];
2005: /* x = x + t */
2006: x[i] += t[i];
2007: }
2009: PetscCall(PetscLogFlops(6.0 * m - 1 + 2.0 * a->nz));
2010: PetscCall(VecRestoreArray(xx, &x));
2011: PetscCall(VecRestoreArrayRead(bb, &b));
2012: PetscFunctionReturn(PETSC_SUCCESS);
2013: }
2014: if (flag & SOR_ZERO_INITIAL_GUESS) {
2015: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2016: for (i = 0; i < m; i++) {
2017: n = diag[i] - a->i[i];
2018: idx = a->j + a->i[i];
2019: v = aa + a->i[i];
2020: sum = b[i];
2021: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2022: t[i] = sum;
2023: x[i] = sum * idiag[i];
2024: }
2025: xb = t;
2026: PetscCall(PetscLogFlops(a->nz));
2027: } else xb = b;
2028: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2029: for (i = m - 1; i >= 0; i--) {
2030: n = a->i[i + 1] - diag[i] - 1;
2031: idx = a->j + diag[i] + 1;
2032: v = aa + diag[i] + 1;
2033: sum = xb[i];
2034: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2035: if (xb == b) {
2036: x[i] = sum * idiag[i];
2037: } else {
2038: x[i] = (1 - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2039: }
2040: }
2041: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2042: }
2043: its--;
2044: }
2045: while (its--) {
2046: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2047: for (i = 0; i < m; i++) {
2048: /* lower */
2049: n = diag[i] - a->i[i];
2050: idx = a->j + a->i[i];
2051: v = aa + a->i[i];
2052: sum = b[i];
2053: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2054: t[i] = sum; /* save application of the lower-triangular part */
2055: /* upper */
2056: n = a->i[i + 1] - diag[i] - 1;
2057: idx = a->j + diag[i] + 1;
2058: v = aa + diag[i] + 1;
2059: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2060: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2061: }
2062: xb = t;
2063: PetscCall(PetscLogFlops(2.0 * a->nz));
2064: } else xb = b;
2065: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2066: for (i = m - 1; i >= 0; i--) {
2067: sum = xb[i];
2068: if (xb == b) {
2069: /* whole matrix (no checkpointing available) */
2070: n = a->i[i + 1] - a->i[i];
2071: idx = a->j + a->i[i];
2072: v = aa + a->i[i];
2073: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2074: x[i] = (1. - omega) * x[i] + (sum + mdiag[i] * x[i]) * idiag[i];
2075: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
2076: n = a->i[i + 1] - diag[i] - 1;
2077: idx = a->j + diag[i] + 1;
2078: v = aa + diag[i] + 1;
2079: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2080: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2081: }
2082: }
2083: if (xb == b) {
2084: PetscCall(PetscLogFlops(2.0 * a->nz));
2085: } else {
2086: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2087: }
2088: }
2089: }
2090: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2091: PetscCall(VecRestoreArray(xx, &x));
2092: PetscCall(VecRestoreArrayRead(bb, &b));
2093: PetscFunctionReturn(PETSC_SUCCESS);
2094: }
2096: static PetscErrorCode MatGetInfo_SeqAIJ(Mat A, MatInfoType flag, MatInfo *info)
2097: {
2098: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2100: PetscFunctionBegin;
2101: info->block_size = 1.0;
2102: info->nz_allocated = a->maxnz;
2103: info->nz_used = a->nz;
2104: info->nz_unneeded = (a->maxnz - a->nz);
2105: info->assemblies = A->num_ass;
2106: info->mallocs = A->info.mallocs;
2107: info->memory = 0; /* REVIEW ME */
2108: if (A->factortype) {
2109: info->fill_ratio_given = A->info.fill_ratio_given;
2110: info->fill_ratio_needed = A->info.fill_ratio_needed;
2111: info->factor_mallocs = A->info.factor_mallocs;
2112: } else {
2113: info->fill_ratio_given = 0;
2114: info->fill_ratio_needed = 0;
2115: info->factor_mallocs = 0;
2116: }
2117: PetscFunctionReturn(PETSC_SUCCESS);
2118: }
2120: static PetscErrorCode MatZeroRows_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2121: {
2122: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2123: PetscInt i, m = A->rmap->n - 1;
2124: const PetscScalar *xx;
2125: PetscScalar *bb, *aa;
2126: PetscInt d = 0;
2128: PetscFunctionBegin;
2129: if (x && b) {
2130: PetscCall(VecGetArrayRead(x, &xx));
2131: PetscCall(VecGetArray(b, &bb));
2132: for (i = 0; i < N; i++) {
2133: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2134: if (rows[i] >= A->cmap->n) continue;
2135: bb[rows[i]] = diag * xx[rows[i]];
2136: }
2137: PetscCall(VecRestoreArrayRead(x, &xx));
2138: PetscCall(VecRestoreArray(b, &bb));
2139: }
2141: PetscCall(MatSeqAIJGetArray(A, &aa));
2142: if (a->keepnonzeropattern) {
2143: for (i = 0; i < N; i++) {
2144: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2145: PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));
2146: }
2147: if (diag != 0.0) {
2148: for (i = 0; i < N; i++) {
2149: d = rows[i];
2150: if (rows[i] >= A->cmap->n) continue;
2151: 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);
2152: }
2153: for (i = 0; i < N; i++) {
2154: if (rows[i] >= A->cmap->n) continue;
2155: aa[a->diag[rows[i]]] = diag;
2156: }
2157: }
2158: } else {
2159: if (diag != 0.0) {
2160: for (i = 0; i < N; i++) {
2161: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2162: if (a->ilen[rows[i]] > 0) {
2163: if (rows[i] >= A->cmap->n) {
2164: a->ilen[rows[i]] = 0;
2165: } else {
2166: a->ilen[rows[i]] = 1;
2167: aa[a->i[rows[i]]] = diag;
2168: a->j[a->i[rows[i]]] = rows[i];
2169: }
2170: } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2171: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2172: }
2173: }
2174: } else {
2175: for (i = 0; i < N; i++) {
2176: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2177: a->ilen[rows[i]] = 0;
2178: }
2179: }
2180: A->nonzerostate++;
2181: }
2182: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2183: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2184: PetscFunctionReturn(PETSC_SUCCESS);
2185: }
2187: static PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2188: {
2189: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2190: PetscInt i, j, m = A->rmap->n - 1, d = 0;
2191: PetscBool missing, *zeroed, vecs = PETSC_FALSE;
2192: const PetscScalar *xx;
2193: PetscScalar *bb, *aa;
2195: PetscFunctionBegin;
2196: if (!N) PetscFunctionReturn(PETSC_SUCCESS);
2197: PetscCall(MatSeqAIJGetArray(A, &aa));
2198: if (x && b) {
2199: PetscCall(VecGetArrayRead(x, &xx));
2200: PetscCall(VecGetArray(b, &bb));
2201: vecs = PETSC_TRUE;
2202: }
2203: PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
2204: for (i = 0; i < N; i++) {
2205: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2206: PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aa, a->i[rows[i]]), a->ilen[rows[i]]));
2208: zeroed[rows[i]] = PETSC_TRUE;
2209: }
2210: for (i = 0; i < A->rmap->n; i++) {
2211: if (!zeroed[i]) {
2212: for (j = a->i[i]; j < a->i[i + 1]; j++) {
2213: if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2214: if (vecs) bb[i] -= aa[j] * xx[a->j[j]];
2215: aa[j] = 0.0;
2216: }
2217: }
2218: } else if (vecs && i < A->cmap->N) bb[i] = diag * xx[i];
2219: }
2220: if (x && b) {
2221: PetscCall(VecRestoreArrayRead(x, &xx));
2222: PetscCall(VecRestoreArray(b, &bb));
2223: }
2224: PetscCall(PetscFree(zeroed));
2225: if (diag != 0.0) {
2226: PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, &d));
2227: if (missing) {
2228: for (i = 0; i < N; i++) {
2229: if (rows[i] >= A->cmap->N) continue;
2230: 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]);
2231: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2232: }
2233: } else {
2234: for (i = 0; i < N; i++) aa[a->diag[rows[i]]] = diag;
2235: }
2236: }
2237: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2238: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2239: PetscFunctionReturn(PETSC_SUCCESS);
2240: }
2242: PetscErrorCode MatGetRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2243: {
2244: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2245: const PetscScalar *aa;
2247: PetscFunctionBegin;
2248: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2249: *nz = a->i[row + 1] - a->i[row];
2250: if (v) *v = PetscSafePointerPlusOffset((PetscScalar *)aa, a->i[row]);
2251: if (idx) {
2252: if (*nz && a->j) *idx = a->j + a->i[row];
2253: else *idx = NULL;
2254: }
2255: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2256: PetscFunctionReturn(PETSC_SUCCESS);
2257: }
2259: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2260: {
2261: PetscFunctionBegin;
2262: PetscFunctionReturn(PETSC_SUCCESS);
2263: }
2265: static PetscErrorCode MatNorm_SeqAIJ(Mat A, NormType type, PetscReal *nrm)
2266: {
2267: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2268: const MatScalar *v;
2269: PetscReal sum = 0.0;
2270: PetscInt i, j;
2272: PetscFunctionBegin;
2273: PetscCall(MatSeqAIJGetArrayRead(A, &v));
2274: if (type == NORM_FROBENIUS) {
2275: #if defined(PETSC_USE_REAL___FP16)
2276: PetscBLASInt one = 1, nz = a->nz;
2277: PetscCallBLAS("BLASnrm2", *nrm = BLASnrm2_(&nz, v, &one));
2278: #else
2279: for (i = 0; i < a->nz; i++) {
2280: sum += PetscRealPart(PetscConj(*v) * (*v));
2281: v++;
2282: }
2283: *nrm = PetscSqrtReal(sum);
2284: #endif
2285: PetscCall(PetscLogFlops(2.0 * a->nz));
2286: } else if (type == NORM_1) {
2287: PetscReal *tmp;
2288: PetscInt *jj = a->j;
2289: PetscCall(PetscCalloc1(A->cmap->n + 1, &tmp));
2290: *nrm = 0.0;
2291: for (j = 0; j < a->nz; j++) {
2292: tmp[*jj++] += PetscAbsScalar(*v);
2293: v++;
2294: }
2295: for (j = 0; j < A->cmap->n; j++) {
2296: if (tmp[j] > *nrm) *nrm = tmp[j];
2297: }
2298: PetscCall(PetscFree(tmp));
2299: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2300: } else if (type == NORM_INFINITY) {
2301: *nrm = 0.0;
2302: for (j = 0; j < A->rmap->n; j++) {
2303: const PetscScalar *v2 = PetscSafePointerPlusOffset(v, a->i[j]);
2304: sum = 0.0;
2305: for (i = 0; i < a->i[j + 1] - a->i[j]; i++) {
2306: sum += PetscAbsScalar(*v2);
2307: v2++;
2308: }
2309: if (sum > *nrm) *nrm = sum;
2310: }
2311: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2312: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for two norm");
2313: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
2314: PetscFunctionReturn(PETSC_SUCCESS);
2315: }
2317: static PetscErrorCode MatIsTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2318: {
2319: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2320: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2321: const MatScalar *va, *vb;
2322: PetscInt ma, na, mb, nb, i;
2324: PetscFunctionBegin;
2325: PetscCall(MatGetSize(A, &ma, &na));
2326: PetscCall(MatGetSize(B, &mb, &nb));
2327: if (ma != nb || na != mb) {
2328: *f = PETSC_FALSE;
2329: PetscFunctionReturn(PETSC_SUCCESS);
2330: }
2331: PetscCall(MatSeqAIJGetArrayRead(A, &va));
2332: PetscCall(MatSeqAIJGetArrayRead(B, &vb));
2333: aii = aij->i;
2334: bii = bij->i;
2335: adx = aij->j;
2336: bdx = bij->j;
2337: PetscCall(PetscMalloc1(ma, &aptr));
2338: PetscCall(PetscMalloc1(mb, &bptr));
2339: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2340: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2342: *f = PETSC_TRUE;
2343: for (i = 0; i < ma; i++) {
2344: while (aptr[i] < aii[i + 1]) {
2345: PetscInt idc, idr;
2346: PetscScalar vc, vr;
2347: /* column/row index/value */
2348: idc = adx[aptr[i]];
2349: idr = bdx[bptr[idc]];
2350: vc = va[aptr[i]];
2351: vr = vb[bptr[idc]];
2352: if (i != idr || PetscAbsScalar(vc - vr) > tol) {
2353: *f = PETSC_FALSE;
2354: goto done;
2355: } else {
2356: aptr[i]++;
2357: if (B || i != idc) bptr[idc]++;
2358: }
2359: }
2360: }
2361: done:
2362: PetscCall(PetscFree(aptr));
2363: PetscCall(PetscFree(bptr));
2364: PetscCall(MatSeqAIJRestoreArrayRead(A, &va));
2365: PetscCall(MatSeqAIJRestoreArrayRead(B, &vb));
2366: PetscFunctionReturn(PETSC_SUCCESS);
2367: }
2369: static PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2370: {
2371: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2372: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2373: MatScalar *va, *vb;
2374: PetscInt ma, na, mb, nb, i;
2376: PetscFunctionBegin;
2377: PetscCall(MatGetSize(A, &ma, &na));
2378: PetscCall(MatGetSize(B, &mb, &nb));
2379: if (ma != nb || na != mb) {
2380: *f = PETSC_FALSE;
2381: PetscFunctionReturn(PETSC_SUCCESS);
2382: }
2383: aii = aij->i;
2384: bii = bij->i;
2385: adx = aij->j;
2386: bdx = bij->j;
2387: va = aij->a;
2388: vb = bij->a;
2389: PetscCall(PetscMalloc1(ma, &aptr));
2390: PetscCall(PetscMalloc1(mb, &bptr));
2391: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2392: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2394: *f = PETSC_TRUE;
2395: for (i = 0; i < ma; i++) {
2396: while (aptr[i] < aii[i + 1]) {
2397: PetscInt idc, idr;
2398: PetscScalar vc, vr;
2399: /* column/row index/value */
2400: idc = adx[aptr[i]];
2401: idr = bdx[bptr[idc]];
2402: vc = va[aptr[i]];
2403: vr = vb[bptr[idc]];
2404: if (i != idr || PetscAbsScalar(vc - PetscConj(vr)) > tol) {
2405: *f = PETSC_FALSE;
2406: goto done;
2407: } else {
2408: aptr[i]++;
2409: if (B || i != idc) bptr[idc]++;
2410: }
2411: }
2412: }
2413: done:
2414: PetscCall(PetscFree(aptr));
2415: PetscCall(PetscFree(bptr));
2416: PetscFunctionReturn(PETSC_SUCCESS);
2417: }
2419: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A, Vec ll, Vec rr)
2420: {
2421: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2422: const PetscScalar *l, *r;
2423: PetscScalar x;
2424: MatScalar *v;
2425: PetscInt i, j, m = A->rmap->n, n = A->cmap->n, M, nz = a->nz;
2426: const PetscInt *jj;
2428: PetscFunctionBegin;
2429: if (ll) {
2430: /* The local size is used so that VecMPI can be passed to this routine
2431: by MatDiagonalScale_MPIAIJ */
2432: PetscCall(VecGetLocalSize(ll, &m));
2433: PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length");
2434: PetscCall(VecGetArrayRead(ll, &l));
2435: PetscCall(MatSeqAIJGetArray(A, &v));
2436: for (i = 0; i < m; i++) {
2437: x = l[i];
2438: M = a->i[i + 1] - a->i[i];
2439: for (j = 0; j < M; j++) (*v++) *= x;
2440: }
2441: PetscCall(VecRestoreArrayRead(ll, &l));
2442: PetscCall(PetscLogFlops(nz));
2443: PetscCall(MatSeqAIJRestoreArray(A, &v));
2444: }
2445: if (rr) {
2446: PetscCall(VecGetLocalSize(rr, &n));
2447: PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length");
2448: PetscCall(VecGetArrayRead(rr, &r));
2449: PetscCall(MatSeqAIJGetArray(A, &v));
2450: jj = a->j;
2451: for (i = 0; i < nz; i++) (*v++) *= r[*jj++];
2452: PetscCall(MatSeqAIJRestoreArray(A, &v));
2453: PetscCall(VecRestoreArrayRead(rr, &r));
2454: PetscCall(PetscLogFlops(nz));
2455: }
2456: PetscCall(MatSeqAIJInvalidateDiagonal(A));
2457: PetscFunctionReturn(PETSC_SUCCESS);
2458: }
2460: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A, IS isrow, IS iscol, PetscInt csize, MatReuse scall, Mat *B)
2461: {
2462: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *c;
2463: PetscInt *smap, i, k, kstart, kend, oldcols = A->cmap->n, *lens;
2464: PetscInt row, mat_i, *mat_j, tcol, first, step, *mat_ilen, sum, lensi;
2465: const PetscInt *irow, *icol;
2466: const PetscScalar *aa;
2467: PetscInt nrows, ncols;
2468: PetscInt *starts, *j_new, *i_new, *aj = a->j, *ai = a->i, ii, *ailen = a->ilen;
2469: MatScalar *a_new, *mat_a, *c_a;
2470: Mat C;
2471: PetscBool stride;
2473: PetscFunctionBegin;
2474: PetscCall(ISGetIndices(isrow, &irow));
2475: PetscCall(ISGetLocalSize(isrow, &nrows));
2476: PetscCall(ISGetLocalSize(iscol, &ncols));
2478: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &stride));
2479: if (stride) {
2480: PetscCall(ISStrideGetInfo(iscol, &first, &step));
2481: } else {
2482: first = 0;
2483: step = 0;
2484: }
2485: if (stride && step == 1) {
2486: /* special case of contiguous rows */
2487: PetscCall(PetscMalloc2(nrows, &lens, nrows, &starts));
2488: /* loop over new rows determining lens and starting points */
2489: for (i = 0; i < nrows; i++) {
2490: kstart = ai[irow[i]];
2491: kend = kstart + ailen[irow[i]];
2492: starts[i] = kstart;
2493: for (k = kstart; k < kend; k++) {
2494: if (aj[k] >= first) {
2495: starts[i] = k;
2496: break;
2497: }
2498: }
2499: sum = 0;
2500: while (k < kend) {
2501: if (aj[k++] >= first + ncols) break;
2502: sum++;
2503: }
2504: lens[i] = sum;
2505: }
2506: /* create submatrix */
2507: if (scall == MAT_REUSE_MATRIX) {
2508: PetscInt n_cols, n_rows;
2509: PetscCall(MatGetSize(*B, &n_rows, &n_cols));
2510: PetscCheck(n_rows == nrows && n_cols == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Reused submatrix wrong size");
2511: PetscCall(MatZeroEntries(*B));
2512: C = *B;
2513: } else {
2514: PetscInt rbs, cbs;
2515: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2516: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2517: PetscCall(ISGetBlockSize(isrow, &rbs));
2518: PetscCall(ISGetBlockSize(iscol, &cbs));
2519: PetscCall(MatSetBlockSizes(C, rbs, cbs));
2520: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2521: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2522: }
2523: c = (Mat_SeqAIJ *)C->data;
2525: /* loop over rows inserting into submatrix */
2526: PetscCall(MatSeqAIJGetArrayWrite(C, &a_new)); // Not 'a_new = c->a-new', since that raw usage ignores offload state of C
2527: j_new = c->j;
2528: i_new = c->i;
2529: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2530: for (i = 0; i < nrows; i++) {
2531: ii = starts[i];
2532: lensi = lens[i];
2533: if (lensi) {
2534: for (k = 0; k < lensi; k++) *j_new++ = aj[ii + k] - first;
2535: PetscCall(PetscArraycpy(a_new, aa + starts[i], lensi));
2536: a_new += lensi;
2537: }
2538: i_new[i + 1] = i_new[i] + lensi;
2539: c->ilen[i] = lensi;
2540: }
2541: PetscCall(MatSeqAIJRestoreArrayWrite(C, &a_new)); // Set C's offload state properly
2542: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2543: PetscCall(PetscFree2(lens, starts));
2544: } else {
2545: PetscCall(ISGetIndices(iscol, &icol));
2546: PetscCall(PetscCalloc1(oldcols, &smap));
2547: PetscCall(PetscMalloc1(1 + nrows, &lens));
2548: for (i = 0; i < ncols; i++) {
2549: 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);
2550: smap[icol[i]] = i + 1;
2551: }
2553: /* determine lens of each row */
2554: for (i = 0; i < nrows; i++) {
2555: kstart = ai[irow[i]];
2556: kend = kstart + a->ilen[irow[i]];
2557: lens[i] = 0;
2558: for (k = kstart; k < kend; k++) {
2559: if (smap[aj[k]]) lens[i]++;
2560: }
2561: }
2562: /* Create and fill new matrix */
2563: if (scall == MAT_REUSE_MATRIX) {
2564: PetscBool equal;
2566: c = (Mat_SeqAIJ *)((*B)->data);
2567: PetscCheck((*B)->rmap->n == nrows && (*B)->cmap->n == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong size");
2568: PetscCall(PetscArraycmp(c->ilen, lens, (*B)->rmap->n, &equal));
2569: PetscCheck(equal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong number of nonzeros");
2570: PetscCall(PetscArrayzero(c->ilen, (*B)->rmap->n));
2571: C = *B;
2572: } else {
2573: PetscInt rbs, cbs;
2574: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2575: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2576: PetscCall(ISGetBlockSize(isrow, &rbs));
2577: PetscCall(ISGetBlockSize(iscol, &cbs));
2578: if (rbs > 1 || cbs > 1) PetscCall(MatSetBlockSizes(C, rbs, cbs));
2579: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2580: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2581: }
2582: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2584: c = (Mat_SeqAIJ *)C->data;
2585: PetscCall(MatSeqAIJGetArrayWrite(C, &c_a)); // Not 'c->a', since that raw usage ignores offload state of C
2586: for (i = 0; i < nrows; i++) {
2587: row = irow[i];
2588: kstart = ai[row];
2589: kend = kstart + a->ilen[row];
2590: mat_i = c->i[i];
2591: mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2592: mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2593: mat_ilen = c->ilen + i;
2594: for (k = kstart; k < kend; k++) {
2595: if ((tcol = smap[a->j[k]])) {
2596: *mat_j++ = tcol - 1;
2597: *mat_a++ = aa[k];
2598: (*mat_ilen)++;
2599: }
2600: }
2601: }
2602: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2603: /* Free work space */
2604: PetscCall(ISRestoreIndices(iscol, &icol));
2605: PetscCall(PetscFree(smap));
2606: PetscCall(PetscFree(lens));
2607: /* sort */
2608: for (i = 0; i < nrows; i++) {
2609: PetscInt ilen;
2611: mat_i = c->i[i];
2612: mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2613: mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2614: ilen = c->ilen[i];
2615: PetscCall(PetscSortIntWithScalarArray(ilen, mat_j, mat_a));
2616: }
2617: PetscCall(MatSeqAIJRestoreArrayWrite(C, &c_a));
2618: }
2619: #if defined(PETSC_HAVE_DEVICE)
2620: PetscCall(MatBindToCPU(C, A->boundtocpu));
2621: #endif
2622: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
2623: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
2625: PetscCall(ISRestoreIndices(isrow, &irow));
2626: *B = C;
2627: PetscFunctionReturn(PETSC_SUCCESS);
2628: }
2630: static PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
2631: {
2632: Mat B;
2634: PetscFunctionBegin;
2635: if (scall == MAT_INITIAL_MATRIX) {
2636: PetscCall(MatCreate(subComm, &B));
2637: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n));
2638: PetscCall(MatSetBlockSizesFromMats(B, mat, mat));
2639: PetscCall(MatSetType(B, MATSEQAIJ));
2640: PetscCall(MatDuplicateNoCreate_SeqAIJ(B, mat, MAT_COPY_VALUES, PETSC_TRUE));
2641: *subMat = B;
2642: } else {
2643: PetscCall(MatCopy_SeqAIJ(mat, *subMat, SAME_NONZERO_PATTERN));
2644: }
2645: PetscFunctionReturn(PETSC_SUCCESS);
2646: }
2648: static PetscErrorCode MatILUFactor_SeqAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
2649: {
2650: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2651: Mat outA;
2652: PetscBool row_identity, col_identity;
2654: PetscFunctionBegin;
2655: PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels=0 supported for in-place ilu");
2657: PetscCall(ISIdentity(row, &row_identity));
2658: PetscCall(ISIdentity(col, &col_identity));
2660: outA = inA;
2661: outA->factortype = MAT_FACTOR_LU;
2662: PetscCall(PetscFree(inA->solvertype));
2663: PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));
2665: PetscCall(PetscObjectReference((PetscObject)row));
2666: PetscCall(ISDestroy(&a->row));
2668: a->row = row;
2670: PetscCall(PetscObjectReference((PetscObject)col));
2671: PetscCall(ISDestroy(&a->col));
2673: a->col = col;
2675: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2676: PetscCall(ISDestroy(&a->icol));
2677: PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));
2679: if (!a->solve_work) { /* this matrix may have been factored before */
2680: PetscCall(PetscMalloc1(inA->rmap->n + 1, &a->solve_work));
2681: }
2683: PetscCall(MatMarkDiagonal_SeqAIJ(inA));
2684: if (row_identity && col_identity) {
2685: PetscCall(MatLUFactorNumeric_SeqAIJ_inplace(outA, inA, info));
2686: } else {
2687: PetscCall(MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA, inA, info));
2688: }
2689: PetscFunctionReturn(PETSC_SUCCESS);
2690: }
2692: PetscErrorCode MatScale_SeqAIJ(Mat inA, PetscScalar alpha)
2693: {
2694: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2695: PetscScalar *v;
2696: PetscBLASInt one = 1, bnz;
2698: PetscFunctionBegin;
2699: PetscCall(MatSeqAIJGetArray(inA, &v));
2700: PetscCall(PetscBLASIntCast(a->nz, &bnz));
2701: PetscCallBLAS("BLASscal", BLASscal_(&bnz, &alpha, v, &one));
2702: PetscCall(PetscLogFlops(a->nz));
2703: PetscCall(MatSeqAIJRestoreArray(inA, &v));
2704: PetscCall(MatSeqAIJInvalidateDiagonal(inA));
2705: PetscFunctionReturn(PETSC_SUCCESS);
2706: }
2708: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2709: {
2710: PetscInt i;
2712: PetscFunctionBegin;
2713: if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2714: PetscCall(PetscFree4(submatj->sbuf1, submatj->ptr, submatj->tmp, submatj->ctr));
2716: for (i = 0; i < submatj->nrqr; ++i) PetscCall(PetscFree(submatj->sbuf2[i]));
2717: PetscCall(PetscFree3(submatj->sbuf2, submatj->req_size, submatj->req_source1));
2719: if (submatj->rbuf1) {
2720: PetscCall(PetscFree(submatj->rbuf1[0]));
2721: PetscCall(PetscFree(submatj->rbuf1));
2722: }
2724: for (i = 0; i < submatj->nrqs; ++i) PetscCall(PetscFree(submatj->rbuf3[i]));
2725: PetscCall(PetscFree3(submatj->req_source2, submatj->rbuf2, submatj->rbuf3));
2726: PetscCall(PetscFree(submatj->pa));
2727: }
2729: #if defined(PETSC_USE_CTABLE)
2730: PetscCall(PetscHMapIDestroy(&submatj->rmap));
2731: if (submatj->cmap_loc) PetscCall(PetscFree(submatj->cmap_loc));
2732: PetscCall(PetscFree(submatj->rmap_loc));
2733: #else
2734: PetscCall(PetscFree(submatj->rmap));
2735: #endif
2737: if (!submatj->allcolumns) {
2738: #if defined(PETSC_USE_CTABLE)
2739: PetscCall(PetscHMapIDestroy((PetscHMapI *)&submatj->cmap));
2740: #else
2741: PetscCall(PetscFree(submatj->cmap));
2742: #endif
2743: }
2744: PetscCall(PetscFree(submatj->row2proc));
2746: PetscCall(PetscFree(submatj));
2747: PetscFunctionReturn(PETSC_SUCCESS);
2748: }
2750: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2751: {
2752: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data;
2753: Mat_SubSppt *submatj = c->submatis1;
2755: PetscFunctionBegin;
2756: PetscCall((*submatj->destroy)(C));
2757: PetscCall(MatDestroySubMatrix_Private(submatj));
2758: PetscFunctionReturn(PETSC_SUCCESS);
2759: }
2761: /* Note this has code duplication with MatDestroySubMatrices_SeqBAIJ() */
2762: static PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n, Mat *mat[])
2763: {
2764: PetscInt i;
2765: Mat C;
2766: Mat_SeqAIJ *c;
2767: Mat_SubSppt *submatj;
2769: PetscFunctionBegin;
2770: for (i = 0; i < n; i++) {
2771: C = (*mat)[i];
2772: c = (Mat_SeqAIJ *)C->data;
2773: submatj = c->submatis1;
2774: if (submatj) {
2775: if (--((PetscObject)C)->refct <= 0) {
2776: PetscCall(PetscFree(C->factorprefix));
2777: PetscCall((*submatj->destroy)(C));
2778: PetscCall(MatDestroySubMatrix_Private(submatj));
2779: PetscCall(PetscFree(C->defaultvectype));
2780: PetscCall(PetscFree(C->defaultrandtype));
2781: PetscCall(PetscLayoutDestroy(&C->rmap));
2782: PetscCall(PetscLayoutDestroy(&C->cmap));
2783: PetscCall(PetscHeaderDestroy(&C));
2784: }
2785: } else {
2786: PetscCall(MatDestroy(&C));
2787: }
2788: }
2790: /* Destroy Dummy submatrices created for reuse */
2791: PetscCall(MatDestroySubMatrices_Dummy(n, mat));
2793: PetscCall(PetscFree(*mat));
2794: PetscFunctionReturn(PETSC_SUCCESS);
2795: }
2797: static PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *B[])
2798: {
2799: PetscInt i;
2801: PetscFunctionBegin;
2802: if (scall == MAT_INITIAL_MATRIX) PetscCall(PetscCalloc1(n + 1, B));
2804: for (i = 0; i < n; i++) PetscCall(MatCreateSubMatrix_SeqAIJ(A, irow[i], icol[i], PETSC_DECIDE, scall, &(*B)[i]));
2805: PetscFunctionReturn(PETSC_SUCCESS);
2806: }
2808: static PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A, PetscInt is_max, IS is[], PetscInt ov)
2809: {
2810: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2811: PetscInt row, i, j, k, l, ll, m, n, *nidx, isz, val;
2812: const PetscInt *idx;
2813: PetscInt start, end, *ai, *aj, bs = (A->rmap->bs > 0 && A->rmap->bs == A->cmap->bs) ? A->rmap->bs : 1;
2814: PetscBT table;
2816: PetscFunctionBegin;
2817: m = A->rmap->n / bs;
2818: ai = a->i;
2819: aj = a->j;
2821: PetscCheck(ov >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "illegal negative overlap value used");
2823: PetscCall(PetscMalloc1(m + 1, &nidx));
2824: PetscCall(PetscBTCreate(m, &table));
2826: for (i = 0; i < is_max; i++) {
2827: /* Initialize the two local arrays */
2828: isz = 0;
2829: PetscCall(PetscBTMemzero(m, table));
2831: /* Extract the indices, assume there can be duplicate entries */
2832: PetscCall(ISGetIndices(is[i], &idx));
2833: PetscCall(ISGetLocalSize(is[i], &n));
2835: if (bs > 1) {
2836: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2837: for (j = 0; j < n; ++j) {
2838: if (!PetscBTLookupSet(table, idx[j] / bs)) nidx[isz++] = idx[j] / bs;
2839: }
2840: PetscCall(ISRestoreIndices(is[i], &idx));
2841: PetscCall(ISDestroy(&is[i]));
2843: k = 0;
2844: for (j = 0; j < ov; j++) { /* for each overlap */
2845: n = isz;
2846: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2847: for (ll = 0; ll < bs; ll++) {
2848: row = bs * nidx[k] + ll;
2849: start = ai[row];
2850: end = ai[row + 1];
2851: for (l = start; l < end; l++) {
2852: val = aj[l] / bs;
2853: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2854: }
2855: }
2856: }
2857: }
2858: PetscCall(ISCreateBlock(PETSC_COMM_SELF, bs, isz, nidx, PETSC_COPY_VALUES, is + i));
2859: } else {
2860: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2861: for (j = 0; j < n; ++j) {
2862: if (!PetscBTLookupSet(table, idx[j])) nidx[isz++] = idx[j];
2863: }
2864: PetscCall(ISRestoreIndices(is[i], &idx));
2865: PetscCall(ISDestroy(&is[i]));
2867: k = 0;
2868: for (j = 0; j < ov; j++) { /* for each overlap */
2869: n = isz;
2870: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2871: row = nidx[k];
2872: start = ai[row];
2873: end = ai[row + 1];
2874: for (l = start; l < end; l++) {
2875: val = aj[l];
2876: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2877: }
2878: }
2879: }
2880: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, isz, nidx, PETSC_COPY_VALUES, is + i));
2881: }
2882: }
2883: PetscCall(PetscBTDestroy(&table));
2884: PetscCall(PetscFree(nidx));
2885: PetscFunctionReturn(PETSC_SUCCESS);
2886: }
2888: static PetscErrorCode MatPermute_SeqAIJ(Mat A, IS rowp, IS colp, Mat *B)
2889: {
2890: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2891: PetscInt i, nz = 0, m = A->rmap->n, n = A->cmap->n;
2892: const PetscInt *row, *col;
2893: PetscInt *cnew, j, *lens;
2894: IS icolp, irowp;
2895: PetscInt *cwork = NULL;
2896: PetscScalar *vwork = NULL;
2898: PetscFunctionBegin;
2899: PetscCall(ISInvertPermutation(rowp, PETSC_DECIDE, &irowp));
2900: PetscCall(ISGetIndices(irowp, &row));
2901: PetscCall(ISInvertPermutation(colp, PETSC_DECIDE, &icolp));
2902: PetscCall(ISGetIndices(icolp, &col));
2904: /* determine lengths of permuted rows */
2905: PetscCall(PetscMalloc1(m + 1, &lens));
2906: for (i = 0; i < m; i++) lens[row[i]] = a->i[i + 1] - a->i[i];
2907: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2908: PetscCall(MatSetSizes(*B, m, n, m, n));
2909: PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2910: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
2911: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*B, 0, lens));
2912: PetscCall(PetscFree(lens));
2914: PetscCall(PetscMalloc1(n, &cnew));
2915: for (i = 0; i < m; i++) {
2916: PetscCall(MatGetRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2917: for (j = 0; j < nz; j++) cnew[j] = col[cwork[j]];
2918: PetscCall(MatSetValues_SeqAIJ(*B, 1, &row[i], nz, cnew, vwork, INSERT_VALUES));
2919: PetscCall(MatRestoreRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2920: }
2921: PetscCall(PetscFree(cnew));
2923: (*B)->assembled = PETSC_FALSE;
2925: #if defined(PETSC_HAVE_DEVICE)
2926: PetscCall(MatBindToCPU(*B, A->boundtocpu));
2927: #endif
2928: PetscCall(MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY));
2929: PetscCall(MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY));
2930: PetscCall(ISRestoreIndices(irowp, &row));
2931: PetscCall(ISRestoreIndices(icolp, &col));
2932: PetscCall(ISDestroy(&irowp));
2933: PetscCall(ISDestroy(&icolp));
2934: if (rowp == colp) PetscCall(MatPropagateSymmetryOptions(A, *B));
2935: PetscFunctionReturn(PETSC_SUCCESS);
2936: }
2938: PetscErrorCode MatCopy_SeqAIJ(Mat A, Mat B, MatStructure str)
2939: {
2940: PetscFunctionBegin;
2941: /* If the two matrices have the same copy implementation, use fast copy. */
2942: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2943: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2944: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2945: const PetscScalar *aa;
2947: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2948: 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]);
2949: PetscCall(PetscArraycpy(b->a, aa, a->i[A->rmap->n]));
2950: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2951: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2952: } else {
2953: PetscCall(MatCopy_Basic(A, B, str));
2954: }
2955: PetscFunctionReturn(PETSC_SUCCESS);
2956: }
2958: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A, PetscScalar *array[])
2959: {
2960: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2962: PetscFunctionBegin;
2963: *array = a->a;
2964: PetscFunctionReturn(PETSC_SUCCESS);
2965: }
2967: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A, PetscScalar *array[])
2968: {
2969: PetscFunctionBegin;
2970: *array = NULL;
2971: PetscFunctionReturn(PETSC_SUCCESS);
2972: }
2974: /*
2975: Computes the number of nonzeros per row needed for preallocation when X and Y
2976: have different nonzero structure.
2977: */
2978: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *yi, const PetscInt *yj, PetscInt *nnz)
2979: {
2980: PetscInt i, j, k, nzx, nzy;
2982: PetscFunctionBegin;
2983: /* Set the number of nonzeros in the new matrix */
2984: for (i = 0; i < m; i++) {
2985: const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2986: nzx = xi[i + 1] - xi[i];
2987: nzy = yi[i + 1] - yi[i];
2988: nnz[i] = 0;
2989: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2990: for (; k < nzy && yjj[k] < xjj[j]; k++) nnz[i]++; /* Catch up to X */
2991: if (k < nzy && yjj[k] == xjj[j]) k++; /* Skip duplicate */
2992: nnz[i]++;
2993: }
2994: for (; k < nzy; k++) nnz[i]++;
2995: }
2996: PetscFunctionReturn(PETSC_SUCCESS);
2997: }
2999: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y, Mat X, PetscInt *nnz)
3000: {
3001: PetscInt m = Y->rmap->N;
3002: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
3003: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
3005: PetscFunctionBegin;
3006: /* Set the number of nonzeros in the new matrix */
3007: PetscCall(MatAXPYGetPreallocation_SeqX_private(m, x->i, x->j, y->i, y->j, nnz));
3008: PetscFunctionReturn(PETSC_SUCCESS);
3009: }
3011: PetscErrorCode MatAXPY_SeqAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
3012: {
3013: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;
3015: PetscFunctionBegin;
3016: if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
3017: PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE;
3018: if (e) {
3019: PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e));
3020: if (e) {
3021: PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e));
3022: if (e) str = SAME_NONZERO_PATTERN;
3023: }
3024: }
3025: if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
3026: }
3027: if (str == SAME_NONZERO_PATTERN) {
3028: const PetscScalar *xa;
3029: PetscScalar *ya, alpha = a;
3030: PetscBLASInt one = 1, bnz;
3032: PetscCall(PetscBLASIntCast(x->nz, &bnz));
3033: PetscCall(MatSeqAIJGetArray(Y, &ya));
3034: PetscCall(MatSeqAIJGetArrayRead(X, &xa));
3035: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, xa, &one, ya, &one));
3036: PetscCall(MatSeqAIJRestoreArrayRead(X, &xa));
3037: PetscCall(MatSeqAIJRestoreArray(Y, &ya));
3038: PetscCall(PetscLogFlops(2.0 * bnz));
3039: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3040: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
3041: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
3042: PetscCall(MatAXPY_Basic(Y, a, X, str));
3043: } else {
3044: Mat B;
3045: PetscInt *nnz;
3046: PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
3047: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
3048: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
3049: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
3050: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
3051: PetscCall(MatAXPYGetPreallocation_SeqAIJ(Y, X, nnz));
3052: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
3053: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
3054: PetscCall(MatHeaderMerge(Y, &B));
3055: PetscCall(MatSeqAIJCheckInode(Y));
3056: PetscCall(PetscFree(nnz));
3057: }
3058: PetscFunctionReturn(PETSC_SUCCESS);
3059: }
3061: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
3062: {
3063: #if defined(PETSC_USE_COMPLEX)
3064: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3065: PetscInt i, nz;
3066: PetscScalar *a;
3068: PetscFunctionBegin;
3069: nz = aij->nz;
3070: PetscCall(MatSeqAIJGetArray(mat, &a));
3071: for (i = 0; i < nz; i++) a[i] = PetscConj(a[i]);
3072: PetscCall(MatSeqAIJRestoreArray(mat, &a));
3073: #else
3074: PetscFunctionBegin;
3075: #endif
3076: PetscFunctionReturn(PETSC_SUCCESS);
3077: }
3079: static PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3080: {
3081: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3082: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3083: PetscReal atmp;
3084: PetscScalar *x;
3085: const MatScalar *aa, *av;
3087: PetscFunctionBegin;
3088: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3089: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3090: aa = av;
3091: ai = a->i;
3092: aj = a->j;
3094: PetscCall(VecGetArrayWrite(v, &x));
3095: PetscCall(VecGetLocalSize(v, &n));
3096: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3097: for (i = 0; i < m; i++) {
3098: ncols = ai[1] - ai[0];
3099: ai++;
3100: x[i] = 0;
3101: for (j = 0; j < ncols; j++) {
3102: atmp = PetscAbsScalar(*aa);
3103: if (PetscAbsScalar(x[i]) < atmp) {
3104: x[i] = atmp;
3105: if (idx) idx[i] = *aj;
3106: }
3107: aa++;
3108: aj++;
3109: }
3110: }
3111: PetscCall(VecRestoreArrayWrite(v, &x));
3112: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3113: PetscFunctionReturn(PETSC_SUCCESS);
3114: }
3116: static PetscErrorCode MatGetRowSumAbs_SeqAIJ(Mat A, Vec v)
3117: {
3118: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3119: PetscInt i, j, m = A->rmap->n, *ai, ncols, n;
3120: PetscScalar *x;
3121: const MatScalar *aa, *av;
3123: PetscFunctionBegin;
3124: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3125: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3126: aa = av;
3127: ai = a->i;
3129: PetscCall(VecGetArrayWrite(v, &x));
3130: PetscCall(VecGetLocalSize(v, &n));
3131: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3132: for (i = 0; i < m; i++) {
3133: ncols = ai[1] - ai[0];
3134: ai++;
3135: x[i] = 0;
3136: for (j = 0; j < ncols; j++) {
3137: x[i] += PetscAbsScalar(*aa);
3138: aa++;
3139: }
3140: }
3141: PetscCall(VecRestoreArrayWrite(v, &x));
3142: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3143: PetscFunctionReturn(PETSC_SUCCESS);
3144: }
3146: static PetscErrorCode MatGetRowMax_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3147: {
3148: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3149: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3150: PetscScalar *x;
3151: const MatScalar *aa, *av;
3153: PetscFunctionBegin;
3154: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3155: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3156: aa = av;
3157: ai = a->i;
3158: aj = a->j;
3160: PetscCall(VecGetArrayWrite(v, &x));
3161: PetscCall(VecGetLocalSize(v, &n));
3162: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3163: for (i = 0; i < m; i++) {
3164: ncols = ai[1] - ai[0];
3165: ai++;
3166: if (ncols == A->cmap->n) { /* row is dense */
3167: x[i] = *aa;
3168: if (idx) idx[i] = 0;
3169: } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
3170: x[i] = 0.0;
3171: if (idx) {
3172: for (j = 0; j < ncols; j++) { /* find first implicit 0.0 in the row */
3173: if (aj[j] > j) {
3174: idx[i] = j;
3175: break;
3176: }
3177: }
3178: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3179: if (j == ncols && j < A->cmap->n) idx[i] = j;
3180: }
3181: }
3182: for (j = 0; j < ncols; j++) {
3183: if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {
3184: x[i] = *aa;
3185: if (idx) idx[i] = *aj;
3186: }
3187: aa++;
3188: aj++;
3189: }
3190: }
3191: PetscCall(VecRestoreArrayWrite(v, &x));
3192: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3193: PetscFunctionReturn(PETSC_SUCCESS);
3194: }
3196: static PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3197: {
3198: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3199: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3200: PetscScalar *x;
3201: const MatScalar *aa, *av;
3203: PetscFunctionBegin;
3204: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3205: aa = av;
3206: ai = a->i;
3207: aj = a->j;
3209: PetscCall(VecGetArrayWrite(v, &x));
3210: PetscCall(VecGetLocalSize(v, &n));
3211: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector, %" PetscInt_FMT " vs. %" PetscInt_FMT " rows", m, n);
3212: for (i = 0; i < m; i++) {
3213: ncols = ai[1] - ai[0];
3214: ai++;
3215: if (ncols == A->cmap->n) { /* row is dense */
3216: x[i] = *aa;
3217: if (idx) idx[i] = 0;
3218: } else { /* row is sparse so already KNOW minimum is 0.0 or higher */
3219: x[i] = 0.0;
3220: if (idx) { /* find first implicit 0.0 in the row */
3221: for (j = 0; j < ncols; j++) {
3222: if (aj[j] > j) {
3223: idx[i] = j;
3224: break;
3225: }
3226: }
3227: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3228: if (j == ncols && j < A->cmap->n) idx[i] = j;
3229: }
3230: }
3231: for (j = 0; j < ncols; j++) {
3232: if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {
3233: x[i] = *aa;
3234: if (idx) idx[i] = *aj;
3235: }
3236: aa++;
3237: aj++;
3238: }
3239: }
3240: PetscCall(VecRestoreArrayWrite(v, &x));
3241: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3242: PetscFunctionReturn(PETSC_SUCCESS);
3243: }
3245: static PetscErrorCode MatGetRowMin_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3246: {
3247: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3248: PetscInt i, j, m = A->rmap->n, ncols, n;
3249: const PetscInt *ai, *aj;
3250: PetscScalar *x;
3251: const MatScalar *aa, *av;
3253: PetscFunctionBegin;
3254: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3255: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3256: aa = av;
3257: ai = a->i;
3258: aj = a->j;
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: static 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*/ NULL,
3562: NULL,
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,
3629: MatGetRowSumAbs_SeqAIJ,
3630: NULL,
3631: NULL,
3632: NULL};
3634: static PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat, PetscInt *indices)
3635: {
3636: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3637: PetscInt i, nz, n;
3639: PetscFunctionBegin;
3640: nz = aij->maxnz;
3641: n = mat->rmap->n;
3642: for (i = 0; i < nz; i++) aij->j[i] = indices[i];
3643: aij->nz = nz;
3644: for (i = 0; i < n; i++) aij->ilen[i] = aij->imax[i];
3645: PetscFunctionReturn(PETSC_SUCCESS);
3646: }
3648: /*
3649: * Given a sparse matrix with global column indices, compact it by using a local column space.
3650: * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3651: */
3652: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3653: {
3654: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3655: PetscHMapI gid1_lid1;
3656: PetscHashIter tpos;
3657: PetscInt gid, lid, i, ec, nz = aij->nz;
3658: PetscInt *garray, *jj = aij->j;
3660: PetscFunctionBegin;
3662: PetscAssertPointer(mapping, 2);
3663: /* use a table */
3664: PetscCall(PetscHMapICreateWithSize(mat->rmap->n, &gid1_lid1));
3665: ec = 0;
3666: for (i = 0; i < nz; i++) {
3667: PetscInt data, gid1 = jj[i] + 1;
3668: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &data));
3669: if (!data) {
3670: /* one based table */
3671: PetscCall(PetscHMapISet(gid1_lid1, gid1, ++ec));
3672: }
3673: }
3674: /* form array of columns we need */
3675: PetscCall(PetscMalloc1(ec, &garray));
3676: PetscHashIterBegin(gid1_lid1, tpos);
3677: while (!PetscHashIterAtEnd(gid1_lid1, tpos)) {
3678: PetscHashIterGetKey(gid1_lid1, tpos, gid);
3679: PetscHashIterGetVal(gid1_lid1, tpos, lid);
3680: PetscHashIterNext(gid1_lid1, tpos);
3681: gid--;
3682: lid--;
3683: garray[lid] = gid;
3684: }
3685: PetscCall(PetscSortInt(ec, garray)); /* sort, and rebuild */
3686: PetscCall(PetscHMapIClear(gid1_lid1));
3687: for (i = 0; i < ec; i++) PetscCall(PetscHMapISet(gid1_lid1, garray[i] + 1, i + 1));
3688: /* compact out the extra columns in B */
3689: for (i = 0; i < nz; i++) {
3690: PetscInt gid1 = jj[i] + 1;
3691: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &lid));
3692: lid--;
3693: jj[i] = lid;
3694: }
3695: PetscCall(PetscLayoutDestroy(&mat->cmap));
3696: PetscCall(PetscHMapIDestroy(&gid1_lid1));
3697: PetscCall(PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat), ec, ec, 1, &mat->cmap));
3698: PetscCall(ISLocalToGlobalMappingCreate(PETSC_COMM_SELF, mat->cmap->bs, mat->cmap->n, garray, PETSC_OWN_POINTER, mapping));
3699: PetscCall(ISLocalToGlobalMappingSetType(*mapping, ISLOCALTOGLOBALMAPPINGHASH));
3700: PetscFunctionReturn(PETSC_SUCCESS);
3701: }
3703: /*@
3704: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3705: in the matrix.
3707: Input Parameters:
3708: + mat - the `MATSEQAIJ` matrix
3709: - indices - the column indices
3711: Level: advanced
3713: Notes:
3714: This can be called if you have precomputed the nonzero structure of the
3715: matrix and want to provide it to the matrix object to improve the performance
3716: of the `MatSetValues()` operation.
3718: You MUST have set the correct numbers of nonzeros per row in the call to
3719: `MatCreateSeqAIJ()`, and the columns indices MUST be sorted.
3721: MUST be called before any calls to `MatSetValues()`
3723: The indices should start with zero, not one.
3725: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`
3726: @*/
3727: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat, PetscInt *indices)
3728: {
3729: PetscFunctionBegin;
3731: PetscAssertPointer(indices, 2);
3732: PetscUseMethod(mat, "MatSeqAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices));
3733: PetscFunctionReturn(PETSC_SUCCESS);
3734: }
3736: static PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3737: {
3738: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3739: size_t nz = aij->i[mat->rmap->n];
3741: PetscFunctionBegin;
3742: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3744: /* allocate space for values if not already there */
3745: if (!aij->saved_values) { PetscCall(PetscMalloc1(nz + 1, &aij->saved_values)); }
3747: /* copy values over */
3748: PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
3749: PetscFunctionReturn(PETSC_SUCCESS);
3750: }
3752: /*@
3753: MatStoreValues - Stashes a copy of the matrix values; this allows reusing of the linear part of a Jacobian, while recomputing only the
3754: nonlinear portion.
3756: Logically Collect
3758: Input Parameter:
3759: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3761: Level: advanced
3763: Example Usage:
3764: .vb
3765: Using SNES
3766: Create Jacobian matrix
3767: Set linear terms into matrix
3768: Apply boundary conditions to matrix, at this time matrix must have
3769: final nonzero structure (i.e. setting the nonlinear terms and applying
3770: boundary conditions again will not change the nonzero structure
3771: MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3772: MatStoreValues(mat);
3773: Call SNESSetJacobian() with matrix
3774: In your Jacobian routine
3775: MatRetrieveValues(mat);
3776: Set nonlinear terms in matrix
3778: Without `SNESSolve()`, i.e. when you handle nonlinear solve yourself:
3779: // build linear portion of Jacobian
3780: MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3781: MatStoreValues(mat);
3782: loop over nonlinear iterations
3783: MatRetrieveValues(mat);
3784: // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3785: // call MatAssemblyBegin/End() on matrix
3786: Solve linear system with Jacobian
3787: endloop
3788: .ve
3790: Notes:
3791: Matrix must already be assembled before calling this routine
3792: Must set the matrix option `MatSetOption`(mat,`MAT_NEW_NONZERO_LOCATIONS`,`PETSC_FALSE`); before
3793: calling this routine.
3795: When this is called multiple times it overwrites the previous set of stored values
3796: and does not allocated additional space.
3798: .seealso: [](ch_matrices), `Mat`, `MatRetrieveValues()`
3799: @*/
3800: PetscErrorCode MatStoreValues(Mat mat)
3801: {
3802: PetscFunctionBegin;
3804: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3805: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3806: PetscUseMethod(mat, "MatStoreValues_C", (Mat), (mat));
3807: PetscFunctionReturn(PETSC_SUCCESS);
3808: }
3810: static PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3811: {
3812: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3813: PetscInt nz = aij->i[mat->rmap->n];
3815: PetscFunctionBegin;
3816: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3817: PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");
3818: /* copy values over */
3819: PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
3820: PetscFunctionReturn(PETSC_SUCCESS);
3821: }
3823: /*@
3824: MatRetrieveValues - Retrieves the copy of the matrix values that was stored with `MatStoreValues()`
3826: Logically Collect
3828: Input Parameter:
3829: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3831: Level: advanced
3833: .seealso: [](ch_matrices), `Mat`, `MatStoreValues()`
3834: @*/
3835: PetscErrorCode MatRetrieveValues(Mat mat)
3836: {
3837: PetscFunctionBegin;
3839: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3840: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3841: PetscUseMethod(mat, "MatRetrieveValues_C", (Mat), (mat));
3842: PetscFunctionReturn(PETSC_SUCCESS);
3843: }
3845: /*@
3846: MatCreateSeqAIJ - Creates a sparse matrix in `MATSEQAIJ` (compressed row) format
3847: (the default parallel PETSc format). For good matrix assembly performance
3848: the user should preallocate the matrix storage by setting the parameter `nz`
3849: (or the array `nnz`).
3851: Collective
3853: Input Parameters:
3854: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3855: . m - number of rows
3856: . n - number of columns
3857: . nz - number of nonzeros per row (same for all rows)
3858: - nnz - array containing the number of nonzeros in the various rows
3859: (possibly different for each row) or NULL
3861: Output Parameter:
3862: . A - the matrix
3864: Options Database Keys:
3865: + -mat_no_inode - Do not use inodes
3866: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3868: Level: intermediate
3870: Notes:
3871: It is recommend to use `MatCreateFromOptions()` instead of this routine
3873: If `nnz` is given then `nz` is ignored
3875: The `MATSEQAIJ` format, also called
3876: compressed row storage, is fully compatible with standard Fortran
3877: storage. That is, the stored row and column indices can begin at
3878: either one (as in Fortran) or zero.
3880: Specify the preallocated storage with either `nz` or `nnz` (not both).
3881: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3882: allocation.
3884: By default, this format uses inodes (identical nodes) when possible, to
3885: improve numerical efficiency of matrix-vector products and solves. We
3886: search for consecutive rows with the same nonzero structure, thereby
3887: reusing matrix information to achieve increased efficiency.
3889: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`
3890: @*/
3891: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3892: {
3893: PetscFunctionBegin;
3894: PetscCall(MatCreate(comm, A));
3895: PetscCall(MatSetSizes(*A, m, n, m, n));
3896: PetscCall(MatSetType(*A, MATSEQAIJ));
3897: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz));
3898: PetscFunctionReturn(PETSC_SUCCESS);
3899: }
3901: /*@
3902: MatSeqAIJSetPreallocation - For good matrix assembly performance
3903: the user should preallocate the matrix storage by setting the parameter nz
3904: (or the array nnz). By setting these parameters accurately, performance
3905: during matrix assembly can be increased by more than a factor of 50.
3907: Collective
3909: Input Parameters:
3910: + B - The matrix
3911: . nz - number of nonzeros per row (same for all rows)
3912: - nnz - array containing the number of nonzeros in the various rows
3913: (possibly different for each row) or NULL
3915: Options Database Keys:
3916: + -mat_no_inode - Do not use inodes
3917: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3919: Level: intermediate
3921: Notes:
3922: If `nnz` is given then `nz` is ignored
3924: The `MATSEQAIJ` format also called
3925: compressed row storage, is fully compatible with standard Fortran
3926: storage. That is, the stored row and column indices can begin at
3927: either one (as in Fortran) or zero. See the users' manual for details.
3929: Specify the preallocated storage with either `nz` or `nnz` (not both).
3930: Set nz = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3931: allocation.
3933: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3934: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3935: You can also run with the option -info and look for messages with the string
3936: malloc in them to see if additional memory allocation was needed.
3938: Developer Notes:
3939: Use nz of `MAT_SKIP_ALLOCATION` to not allocate any space for the matrix
3940: entries or columns indices
3942: By default, this format uses inodes (identical nodes) when possible, to
3943: improve numerical efficiency of matrix-vector products and solves. We
3944: search for consecutive rows with the same nonzero structure, thereby
3945: reusing matrix information to achieve increased efficiency.
3947: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatGetInfo()`,
3948: `MatSeqAIJSetTotalPreallocation()`
3949: @*/
3950: PetscErrorCode MatSeqAIJSetPreallocation(Mat B, PetscInt nz, const PetscInt nnz[])
3951: {
3952: PetscFunctionBegin;
3955: PetscTryMethod(B, "MatSeqAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[]), (B, nz, nnz));
3956: PetscFunctionReturn(PETSC_SUCCESS);
3957: }
3959: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B, PetscInt nz, const PetscInt *nnz)
3960: {
3961: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
3962: PetscBool skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;
3963: PetscInt i;
3965: PetscFunctionBegin;
3966: if (B->hash_active) {
3967: B->ops[0] = b->cops;
3968: PetscCall(PetscHMapIJVDestroy(&b->ht));
3969: PetscCall(PetscFree(b->dnz));
3970: B->hash_active = PETSC_FALSE;
3971: }
3972: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3973: if (nz == MAT_SKIP_ALLOCATION) {
3974: skipallocation = PETSC_TRUE;
3975: nz = 0;
3976: }
3977: PetscCall(PetscLayoutSetUp(B->rmap));
3978: PetscCall(PetscLayoutSetUp(B->cmap));
3980: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3981: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
3982: if (nnz) {
3983: for (i = 0; i < B->rmap->n; i++) {
3984: 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]);
3985: 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);
3986: }
3987: }
3989: B->preallocated = PETSC_TRUE;
3990: if (!skipallocation) {
3991: if (!b->imax) { PetscCall(PetscMalloc1(B->rmap->n, &b->imax)); }
3992: if (!b->ilen) {
3993: /* b->ilen will count nonzeros in each row so far. */
3994: PetscCall(PetscCalloc1(B->rmap->n, &b->ilen));
3995: } else {
3996: PetscCall(PetscMemzero(b->ilen, B->rmap->n * sizeof(PetscInt)));
3997: }
3998: if (!b->ipre) PetscCall(PetscMalloc1(B->rmap->n, &b->ipre));
3999: if (!nnz) {
4000: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
4001: else if (nz < 0) nz = 1;
4002: nz = PetscMin(nz, B->cmap->n);
4003: for (i = 0; i < B->rmap->n; i++) b->imax[i] = nz;
4004: PetscCall(PetscIntMultError(nz, B->rmap->n, &nz));
4005: } else {
4006: PetscInt64 nz64 = 0;
4007: for (i = 0; i < B->rmap->n; i++) {
4008: b->imax[i] = nnz[i];
4009: nz64 += nnz[i];
4010: }
4011: PetscCall(PetscIntCast(nz64, &nz));
4012: }
4014: /* allocate the matrix space */
4015: PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
4016: PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscInt), (void **)&b->j));
4017: PetscCall(PetscShmgetAllocateArray(B->rmap->n + 1, sizeof(PetscInt), (void **)&b->i));
4018: b->free_ij = PETSC_TRUE;
4019: if (B->structure_only) {
4020: b->free_a = PETSC_FALSE;
4021: } else {
4022: PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscScalar), (void **)&b->a));
4023: b->free_a = PETSC_TRUE;
4024: }
4025: b->i[0] = 0;
4026: for (i = 1; i < B->rmap->n + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];
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: static 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: A->nonzerostate++;
4081: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
4082: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4083: }
4084: PetscFunctionReturn(PETSC_SUCCESS);
4085: }
4087: /*@
4088: MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in `MATSEQAIJ` format.
4090: Input Parameters:
4091: + B - the matrix
4092: . i - the indices into `j` for the start of each row (indices start with zero)
4093: . j - the column indices for each row (indices start with zero) these must be sorted for each row
4094: - v - optional values in the matrix, use `NULL` if not provided
4096: Level: developer
4098: Notes:
4099: The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqAIJWithArrays()`
4101: This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
4102: structure will be the union of all the previous nonzero structures.
4104: Developer Notes:
4105: 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
4106: then just copies the `v` values directly with `PetscMemcpy()`.
4108: This routine could also take a `PetscCopyMode` argument to allow sharing the values instead of always copying them.
4110: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`, `MATSEQAIJ`, `MatResetPreallocation()`
4111: @*/
4112: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4113: {
4114: PetscFunctionBegin;
4117: PetscTryMethod(B, "MatSeqAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4118: PetscFunctionReturn(PETSC_SUCCESS);
4119: }
4121: static PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4122: {
4123: PetscInt i;
4124: PetscInt m, n;
4125: PetscInt nz;
4126: PetscInt *nnz;
4128: PetscFunctionBegin;
4129: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);
4131: PetscCall(PetscLayoutSetUp(B->rmap));
4132: PetscCall(PetscLayoutSetUp(B->cmap));
4134: PetscCall(MatGetSize(B, &m, &n));
4135: PetscCall(PetscMalloc1(m + 1, &nnz));
4136: for (i = 0; i < m; i++) {
4137: nz = Ii[i + 1] - Ii[i];
4138: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
4139: nnz[i] = nz;
4140: }
4141: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
4142: PetscCall(PetscFree(nnz));
4144: for (i = 0; i < m; i++) PetscCall(MatSetValues_SeqAIJ(B, 1, &i, Ii[i + 1] - Ii[i], J + Ii[i], PetscSafePointerPlusOffset(v, Ii[i]), INSERT_VALUES));
4146: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4147: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4149: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
4150: PetscFunctionReturn(PETSC_SUCCESS);
4151: }
4153: /*@
4154: MatSeqAIJKron - Computes `C`, the Kronecker product of `A` and `B`.
4156: Input Parameters:
4157: + A - left-hand side matrix
4158: . B - right-hand side matrix
4159: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
4161: Output Parameter:
4162: . C - Kronecker product of `A` and `B`
4164: Level: intermediate
4166: Note:
4167: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the product matrix has not changed from that last call to `MatSeqAIJKron()`.
4169: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATKAIJ`, `MatReuse`
4170: @*/
4171: PetscErrorCode MatSeqAIJKron(Mat A, Mat B, MatReuse reuse, Mat *C)
4172: {
4173: PetscFunctionBegin;
4178: PetscAssertPointer(C, 4);
4179: if (reuse == MAT_REUSE_MATRIX) {
4182: }
4183: PetscTryMethod(A, "MatSeqAIJKron_C", (Mat, Mat, MatReuse, Mat *), (A, B, reuse, C));
4184: PetscFunctionReturn(PETSC_SUCCESS);
4185: }
4187: static PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A, Mat B, MatReuse reuse, Mat *C)
4188: {
4189: Mat newmat;
4190: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
4191: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
4192: PetscScalar *v;
4193: const PetscScalar *aa, *ba;
4194: 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;
4195: PetscBool flg;
4197: PetscFunctionBegin;
4198: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4199: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4200: PetscCheck(!B->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4201: PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4202: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJ, &flg));
4203: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatType %s", ((PetscObject)B)->type_name);
4204: PetscCheck(reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatReuse %d", (int)reuse);
4205: if (reuse == MAT_INITIAL_MATRIX) {
4206: PetscCall(PetscMalloc2(am * bm + 1, &i, a->i[am] * b->i[bm], &j));
4207: PetscCall(MatCreate(PETSC_COMM_SELF, &newmat));
4208: PetscCall(MatSetSizes(newmat, am * bm, an * bn, am * bm, an * bn));
4209: PetscCall(MatSetType(newmat, MATAIJ));
4210: i[0] = 0;
4211: for (m = 0; m < am; ++m) {
4212: for (p = 0; p < bm; ++p) {
4213: i[m * bm + p + 1] = i[m * bm + p] + (a->i[m + 1] - a->i[m]) * (b->i[p + 1] - b->i[p]);
4214: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4215: for (q = b->i[p]; q < b->i[p + 1]; ++q) j[nnz++] = a->j[n] * bn + b->j[q];
4216: }
4217: }
4218: }
4219: PetscCall(MatSeqAIJSetPreallocationCSR(newmat, i, j, NULL));
4220: *C = newmat;
4221: PetscCall(PetscFree2(i, j));
4222: nnz = 0;
4223: }
4224: PetscCall(MatSeqAIJGetArray(*C, &v));
4225: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4226: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
4227: for (m = 0; m < am; ++m) {
4228: for (p = 0; p < bm; ++p) {
4229: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4230: for (q = b->i[p]; q < b->i[p + 1]; ++q) v[nnz++] = aa[n] * ba[q];
4231: }
4232: }
4233: }
4234: PetscCall(MatSeqAIJRestoreArray(*C, &v));
4235: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
4236: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
4237: PetscFunctionReturn(PETSC_SUCCESS);
4238: }
4240: #include <../src/mat/impls/dense/seq/dense.h>
4241: #include <petsc/private/kernels/petscaxpy.h>
4243: /*
4244: Computes (B'*A')' since computing B*A directly is untenable
4246: n p p
4247: [ ] [ ] [ ]
4248: m [ A ] * n [ B ] = m [ C ]
4249: [ ] [ ] [ ]
4251: */
4252: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A, Mat B, Mat C)
4253: {
4254: Mat_SeqDense *sub_a = (Mat_SeqDense *)A->data;
4255: Mat_SeqAIJ *sub_b = (Mat_SeqAIJ *)B->data;
4256: Mat_SeqDense *sub_c = (Mat_SeqDense *)C->data;
4257: PetscInt i, j, n, m, q, p;
4258: const PetscInt *ii, *idx;
4259: const PetscScalar *b, *a, *a_q;
4260: PetscScalar *c, *c_q;
4261: PetscInt clda = sub_c->lda;
4262: PetscInt alda = sub_a->lda;
4264: PetscFunctionBegin;
4265: m = A->rmap->n;
4266: n = A->cmap->n;
4267: p = B->cmap->n;
4268: a = sub_a->v;
4269: b = sub_b->a;
4270: c = sub_c->v;
4271: if (clda == m) {
4272: PetscCall(PetscArrayzero(c, m * p));
4273: } else {
4274: for (j = 0; j < p; j++)
4275: for (i = 0; i < m; i++) c[j * clda + i] = 0.0;
4276: }
4277: ii = sub_b->i;
4278: idx = sub_b->j;
4279: for (i = 0; i < n; i++) {
4280: q = ii[i + 1] - ii[i];
4281: while (q-- > 0) {
4282: c_q = c + clda * (*idx);
4283: a_q = a + alda * i;
4284: PetscKernelAXPY(c_q, *b, a_q, m);
4285: idx++;
4286: b++;
4287: }
4288: }
4289: PetscFunctionReturn(PETSC_SUCCESS);
4290: }
4292: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A, Mat B, PetscReal fill, Mat C)
4293: {
4294: PetscInt m = A->rmap->n, n = B->cmap->n;
4295: PetscBool cisdense;
4297: PetscFunctionBegin;
4298: 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);
4299: PetscCall(MatSetSizes(C, m, n, m, n));
4300: PetscCall(MatSetBlockSizesFromMats(C, A, B));
4301: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATSEQDENSE, MATSEQDENSECUDA, MATSEQDENSEHIP, ""));
4302: if (!cisdense) PetscCall(MatSetType(C, MATDENSE));
4303: PetscCall(MatSetUp(C));
4305: C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4306: PetscFunctionReturn(PETSC_SUCCESS);
4307: }
4309: /*MC
4310: MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4311: based on compressed sparse row format.
4313: Options Database Key:
4314: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()
4316: Level: beginner
4318: Notes:
4319: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
4320: in this case the values associated with the rows and columns one passes in are set to zero
4321: in the matrix
4323: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
4324: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
4326: Developer Note:
4327: It would be nice if all matrix formats supported passing `NULL` in for the numerical values
4329: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MatSetFromOptions()`, `MatSetType()`, `MatCreate()`, `MatType`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4330: M*/
4332: /*MC
4333: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
4335: This matrix type is identical to `MATSEQAIJ` when constructed with a single process communicator,
4336: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
4337: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4338: for communicators controlling multiple processes. It is recommended that you call both of
4339: the above preallocation routines for simplicity.
4341: Options Database Key:
4342: . -mat_type aij - sets the matrix type to "aij" during a call to `MatSetFromOptions()`
4344: Level: beginner
4346: Note:
4347: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, and also automatically switches over to use inodes when
4348: enough exist.
4350: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4351: M*/
4353: /*MC
4354: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
4356: Options Database Key:
4357: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to `MatSetFromOptions()`
4359: Level: beginner
4361: Note:
4362: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
4363: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
4364: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4365: for communicators controlling multiple processes. It is recommended that you call both of
4366: the above preallocation routines for simplicity.
4368: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
4369: M*/
4371: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
4372: #if defined(PETSC_HAVE_ELEMENTAL)
4373: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
4374: #endif
4375: #if defined(PETSC_HAVE_SCALAPACK)
4376: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
4377: #endif
4378: #if defined(PETSC_HAVE_HYPRE)
4379: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A, MatType, MatReuse, Mat *);
4380: #endif
4382: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat, MatType, MatReuse, Mat *);
4383: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
4384: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
4386: /*@C
4387: MatSeqAIJGetArray - gives read/write access to the array where the data for a `MATSEQAIJ` matrix is stored
4389: Not Collective
4391: Input Parameter:
4392: . A - a `MATSEQAIJ` matrix
4394: Output Parameter:
4395: . array - pointer to the data
4397: Level: intermediate
4399: Fortran Notes:
4400: `MatSeqAIJGetArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJGetArrayF90()`
4402: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4403: @*/
4404: PetscErrorCode MatSeqAIJGetArray(Mat A, PetscScalar *array[])
4405: {
4406: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4408: PetscFunctionBegin;
4409: if (aij->ops->getarray) {
4410: PetscCall((*aij->ops->getarray)(A, array));
4411: } else {
4412: *array = aij->a;
4413: }
4414: PetscFunctionReturn(PETSC_SUCCESS);
4415: }
4417: /*@C
4418: MatSeqAIJRestoreArray - returns access to the array where the data for a `MATSEQAIJ` matrix is stored obtained by `MatSeqAIJGetArray()`
4420: Not Collective
4422: Input Parameters:
4423: + A - a `MATSEQAIJ` matrix
4424: - array - pointer to the data
4426: Level: intermediate
4428: Fortran Notes:
4429: `MatSeqAIJRestoreArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJRestoreArrayF90()`
4431: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayF90()`
4432: @*/
4433: PetscErrorCode MatSeqAIJRestoreArray(Mat A, PetscScalar *array[])
4434: {
4435: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4437: PetscFunctionBegin;
4438: if (aij->ops->restorearray) {
4439: PetscCall((*aij->ops->restorearray)(A, array));
4440: } else {
4441: *array = NULL;
4442: }
4443: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4444: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4445: PetscFunctionReturn(PETSC_SUCCESS);
4446: }
4448: /*@C
4449: MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4451: Not Collective; No Fortran Support
4453: Input Parameter:
4454: . A - a `MATSEQAIJ` matrix
4456: Output Parameter:
4457: . array - pointer to the data
4459: Level: intermediate
4461: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4462: @*/
4463: PetscErrorCode MatSeqAIJGetArrayRead(Mat A, const PetscScalar *array[])
4464: {
4465: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4467: PetscFunctionBegin;
4468: if (aij->ops->getarrayread) {
4469: PetscCall((*aij->ops->getarrayread)(A, array));
4470: } else {
4471: *array = aij->a;
4472: }
4473: PetscFunctionReturn(PETSC_SUCCESS);
4474: }
4476: /*@C
4477: MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJGetArrayRead()`
4479: Not Collective; No Fortran Support
4481: Input Parameter:
4482: . A - a `MATSEQAIJ` matrix
4484: Output Parameter:
4485: . array - pointer to the data
4487: Level: intermediate
4489: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4490: @*/
4491: PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A, const PetscScalar *array[])
4492: {
4493: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4495: PetscFunctionBegin;
4496: if (aij->ops->restorearrayread) {
4497: PetscCall((*aij->ops->restorearrayread)(A, array));
4498: } else {
4499: *array = NULL;
4500: }
4501: PetscFunctionReturn(PETSC_SUCCESS);
4502: }
4504: /*@C
4505: MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4507: Not Collective; No Fortran Support
4509: Input Parameter:
4510: . A - a `MATSEQAIJ` matrix
4512: Output Parameter:
4513: . array - pointer to the data
4515: Level: intermediate
4517: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4518: @*/
4519: PetscErrorCode MatSeqAIJGetArrayWrite(Mat A, PetscScalar *array[])
4520: {
4521: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4523: PetscFunctionBegin;
4524: if (aij->ops->getarraywrite) {
4525: PetscCall((*aij->ops->getarraywrite)(A, array));
4526: } else {
4527: *array = aij->a;
4528: }
4529: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4530: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4531: PetscFunctionReturn(PETSC_SUCCESS);
4532: }
4534: /*@C
4535: MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead
4537: Not Collective; No Fortran Support
4539: Input Parameter:
4540: . A - a MATSEQAIJ matrix
4542: Output Parameter:
4543: . array - pointer to the data
4545: Level: intermediate
4547: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4548: @*/
4549: PetscErrorCode MatSeqAIJRestoreArrayWrite(Mat A, PetscScalar *array[])
4550: {
4551: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4553: PetscFunctionBegin;
4554: if (aij->ops->restorearraywrite) {
4555: PetscCall((*aij->ops->restorearraywrite)(A, array));
4556: } else {
4557: *array = NULL;
4558: }
4559: PetscFunctionReturn(PETSC_SUCCESS);
4560: }
4562: /*@C
4563: MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the `MATSEQAIJ` matrix
4565: Not Collective; No Fortran Support
4567: Input Parameter:
4568: . mat - a matrix of type `MATSEQAIJ` or its subclasses
4570: Output Parameters:
4571: + i - row map array of the matrix
4572: . j - column index array of the matrix
4573: . a - data array of the matrix
4574: - mtype - memory type of the arrays
4576: Level: developer
4578: Notes:
4579: Any of the output parameters can be `NULL`, in which case the corresponding value is not returned.
4580: If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host.
4582: One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix.
4583: If the matrix is assembled, the data array `a` is guaranteed to have the latest values of the matrix.
4585: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4586: @*/
4587: PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat, const PetscInt *i[], const PetscInt *j[], PetscScalar *a[], PetscMemType *mtype)
4588: {
4589: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
4591: PetscFunctionBegin;
4592: PetscCheck(mat->preallocated, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "matrix is not preallocated");
4593: if (aij->ops->getcsrandmemtype) {
4594: PetscCall((*aij->ops->getcsrandmemtype)(mat, i, j, a, mtype));
4595: } else {
4596: if (i) *i = aij->i;
4597: if (j) *j = aij->j;
4598: if (a) *a = aij->a;
4599: if (mtype) *mtype = PETSC_MEMTYPE_HOST;
4600: }
4601: PetscFunctionReturn(PETSC_SUCCESS);
4602: }
4604: /*@
4605: MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row
4607: Not Collective
4609: Input Parameter:
4610: . A - a `MATSEQAIJ` matrix
4612: Output Parameter:
4613: . nz - the maximum number of nonzeros in any row
4615: Level: intermediate
4617: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4618: @*/
4619: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A, PetscInt *nz)
4620: {
4621: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4623: PetscFunctionBegin;
4624: *nz = aij->rmax;
4625: PetscFunctionReturn(PETSC_SUCCESS);
4626: }
4628: static PetscErrorCode MatCOOStructDestroy_SeqAIJ(void *data)
4629: {
4630: MatCOOStruct_SeqAIJ *coo = (MatCOOStruct_SeqAIJ *)data;
4632: PetscFunctionBegin;
4633: PetscCall(PetscFree(coo->perm));
4634: PetscCall(PetscFree(coo->jmap));
4635: PetscCall(PetscFree(coo));
4636: PetscFunctionReturn(PETSC_SUCCESS);
4637: }
4639: PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
4640: {
4641: MPI_Comm comm;
4642: PetscInt *i, *j;
4643: PetscInt M, N, row, iprev;
4644: PetscCount k, p, q, nneg, nnz, start, end; /* Index the coo array, so use PetscCount as their type */
4645: PetscInt *Ai; /* Change to PetscCount once we use it for row pointers */
4646: PetscInt *Aj;
4647: PetscScalar *Aa;
4648: Mat_SeqAIJ *seqaij = (Mat_SeqAIJ *)mat->data;
4649: MatType rtype;
4650: PetscCount *perm, *jmap;
4651: MatCOOStruct_SeqAIJ *coo;
4652: PetscBool isorted;
4653: PetscBool hypre;
4654: const char *name;
4656: PetscFunctionBegin;
4657: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
4658: PetscCall(MatGetSize(mat, &M, &N));
4659: i = coo_i;
4660: j = coo_j;
4661: PetscCall(PetscMalloc1(coo_n, &perm));
4663: /* Ignore entries with negative row or col indices; at the same time, check if i[] is already sorted (e.g., MatConvert_AlJ_HYPRE results in this case) */
4664: isorted = PETSC_TRUE;
4665: iprev = PETSC_INT_MIN;
4666: for (k = 0; k < coo_n; k++) {
4667: if (j[k] < 0) i[k] = -1;
4668: if (isorted) {
4669: if (i[k] < iprev) isorted = PETSC_FALSE;
4670: else iprev = i[k];
4671: }
4672: perm[k] = k;
4673: }
4675: /* Sort by row if not already */
4676: if (!isorted) PetscCall(PetscSortIntWithIntCountArrayPair(coo_n, i, j, perm));
4678: /* Advance k to the first row with a non-negative index */
4679: for (k = 0; k < coo_n; k++)
4680: if (i[k] >= 0) break;
4681: nneg = k;
4682: 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 */
4683: nnz = 0; /* Total number of unique nonzeros to be counted */
4684: jmap++; /* Inc jmap by 1 for convenience */
4686: PetscCall(PetscShmgetAllocateArray(M + 1, sizeof(PetscInt), (void **)&Ai)); /* CSR of A */
4687: PetscCall(PetscArrayzero(Ai, M + 1));
4688: PetscCall(PetscShmgetAllocateArray(coo_n - nneg, sizeof(PetscInt), (void **)&Aj)); /* We have at most coo_n-nneg unique nonzeros */
4690: PetscCall(PetscObjectGetName((PetscObject)mat, &name));
4691: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", name, &hypre));
4693: /* In each row, sort by column, then unique column indices to get row length */
4694: Ai++; /* Inc by 1 for convenience */
4695: q = 0; /* q-th unique nonzero, with q starting from 0 */
4696: while (k < coo_n) {
4697: PetscBool strictly_sorted; // this row is strictly sorted?
4698: PetscInt jprev;
4700: /* get [start,end) indices for this row; also check if cols in this row are strictly sorted */
4701: row = i[k];
4702: start = k;
4703: jprev = PETSC_INT_MIN;
4704: strictly_sorted = PETSC_TRUE;
4705: while (k < coo_n && i[k] == row) {
4706: if (strictly_sorted) {
4707: if (j[k] <= jprev) strictly_sorted = PETSC_FALSE;
4708: else jprev = j[k];
4709: }
4710: k++;
4711: }
4712: end = k;
4714: /* hack for HYPRE: swap min column to diag so that diagonal values will go first */
4715: if (hypre) {
4716: PetscInt minj = PETSC_INT_MAX;
4717: PetscBool hasdiag = PETSC_FALSE;
4719: if (strictly_sorted) { // fast path to swap the first and the diag
4720: PetscCount tmp;
4721: for (p = start; p < end; p++) {
4722: if (j[p] == row && p != start) {
4723: j[p] = j[start];
4724: j[start] = row;
4725: tmp = perm[start];
4726: perm[start] = perm[p];
4727: perm[p] = tmp;
4728: break;
4729: }
4730: }
4731: } else {
4732: for (p = start; p < end; p++) {
4733: hasdiag = (PetscBool)(hasdiag || (j[p] == row));
4734: minj = PetscMin(minj, j[p]);
4735: }
4737: if (hasdiag) {
4738: for (p = start; p < end; p++) {
4739: if (j[p] == minj) j[p] = row;
4740: else if (j[p] == row) j[p] = minj;
4741: }
4742: }
4743: }
4744: }
4745: // sort by columns in a row
4746: if (!strictly_sorted) PetscCall(PetscSortIntWithCountArray(end - start, j + start, perm + start));
4748: if (strictly_sorted) { // fast path to set Aj[], jmap[], Ai[], nnz, q
4749: for (p = start; p < end; p++, q++) {
4750: Aj[q] = j[p];
4751: jmap[q] = 1;
4752: }
4753: PetscCall(PetscIntCast(end - start, Ai + row));
4754: nnz += Ai[row]; // q is already advanced
4755: } else {
4756: /* Find number of unique col entries in this row */
4757: Aj[q] = j[start]; /* Log the first nonzero in this row */
4758: jmap[q] = 1; /* Number of repeats of this nonzero entry */
4759: Ai[row] = 1;
4760: nnz++;
4762: for (p = start + 1; p < end; p++) { /* Scan remaining nonzero in this row */
4763: if (j[p] != j[p - 1]) { /* Meet a new nonzero */
4764: q++;
4765: jmap[q] = 1;
4766: Aj[q] = j[p];
4767: Ai[row]++;
4768: nnz++;
4769: } else {
4770: jmap[q]++;
4771: }
4772: }
4773: q++; /* Move to next row and thus next unique nonzero */
4774: }
4775: }
4777: Ai--; /* Back to the beginning of Ai[] */
4778: for (k = 0; k < M; k++) Ai[k + 1] += Ai[k];
4779: jmap--; // Back to the beginning of jmap[]
4780: jmap[0] = 0;
4781: for (k = 0; k < nnz; k++) jmap[k + 1] += jmap[k];
4783: if (nnz < coo_n - nneg) { /* Reallocate with actual number of unique nonzeros */
4784: PetscCount *jmap_new;
4785: PetscInt *Aj_new;
4787: PetscCall(PetscMalloc1(nnz + 1, &jmap_new));
4788: PetscCall(PetscArraycpy(jmap_new, jmap, nnz + 1));
4789: PetscCall(PetscFree(jmap));
4790: jmap = jmap_new;
4792: PetscCall(PetscShmgetAllocateArray(nnz, sizeof(PetscInt), (void **)&Aj_new));
4793: PetscCall(PetscArraycpy(Aj_new, Aj, nnz));
4794: PetscCall(PetscShmgetDeallocateArray((void **)&Aj));
4795: Aj = Aj_new;
4796: }
4798: if (nneg) { /* Discard heading entries with negative indices in perm[], as we'll access it from index 0 in MatSetValuesCOO */
4799: PetscCount *perm_new;
4801: PetscCall(PetscMalloc1(coo_n - nneg, &perm_new));
4802: PetscCall(PetscArraycpy(perm_new, perm + nneg, coo_n - nneg));
4803: PetscCall(PetscFree(perm));
4804: perm = perm_new;
4805: }
4807: PetscCall(MatGetRootType_Private(mat, &rtype));
4808: PetscCall(PetscShmgetAllocateArray(nnz, sizeof(PetscScalar), (void **)&Aa));
4809: PetscCall(PetscArrayzero(Aa, nnz));
4810: PetscCall(MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF, M, N, Ai, Aj, Aa, rtype, mat));
4812: seqaij->free_a = seqaij->free_ij = PETSC_TRUE; /* Let newmat own Ai, Aj and Aa */
4814: // Put the COO struct in a container and then attach that to the matrix
4815: PetscCall(PetscMalloc1(1, &coo));
4816: PetscCall(PetscIntCast(nnz, &coo->nz));
4817: coo->n = coo_n;
4818: coo->Atot = coo_n - nneg; // Annz is seqaij->nz, so no need to record that again
4819: coo->jmap = jmap; // of length nnz+1
4820: coo->perm = perm;
4821: PetscCall(PetscObjectContainerCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", coo, MatCOOStructDestroy_SeqAIJ));
4822: PetscFunctionReturn(PETSC_SUCCESS);
4823: }
4825: static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A, const PetscScalar v[], InsertMode imode)
4826: {
4827: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)A->data;
4828: PetscCount i, j, Annz = aseq->nz;
4829: PetscCount *perm, *jmap;
4830: PetscScalar *Aa;
4831: PetscContainer container;
4832: MatCOOStruct_SeqAIJ *coo;
4834: PetscFunctionBegin;
4835: PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
4836: PetscCheck(container, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
4837: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
4838: perm = coo->perm;
4839: jmap = coo->jmap;
4840: PetscCall(MatSeqAIJGetArray(A, &Aa));
4841: for (i = 0; i < Annz; i++) {
4842: PetscScalar sum = 0.0;
4843: for (j = jmap[i]; j < jmap[i + 1]; j++) sum += v[perm[j]];
4844: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
4845: }
4846: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
4847: PetscFunctionReturn(PETSC_SUCCESS);
4848: }
4850: #if defined(PETSC_HAVE_CUDA)
4851: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
4852: #endif
4853: #if defined(PETSC_HAVE_HIP)
4854: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
4855: #endif
4856: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4857: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat, MatType, MatReuse, Mat *);
4858: #endif
4860: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4861: {
4862: Mat_SeqAIJ *b;
4863: PetscMPIInt size;
4865: PetscFunctionBegin;
4866: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
4867: PetscCheck(size <= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Comm must be of size 1");
4869: PetscCall(PetscNew(&b));
4871: B->data = (void *)b;
4872: B->ops[0] = MatOps_Values;
4873: if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
4875: b->row = NULL;
4876: b->col = NULL;
4877: b->icol = NULL;
4878: b->reallocs = 0;
4879: b->ignorezeroentries = PETSC_FALSE;
4880: b->roworiented = PETSC_TRUE;
4881: b->nonew = 0;
4882: b->diag = NULL;
4883: b->solve_work = NULL;
4884: B->spptr = NULL;
4885: b->saved_values = NULL;
4886: b->idiag = NULL;
4887: b->mdiag = NULL;
4888: b->ssor_work = NULL;
4889: b->omega = 1.0;
4890: b->fshift = 0.0;
4891: b->idiagvalid = PETSC_FALSE;
4892: b->ibdiagvalid = PETSC_FALSE;
4893: b->keepnonzeropattern = PETSC_FALSE;
4895: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4896: #if defined(PETSC_HAVE_MATLAB)
4897: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEnginePut_C", MatlabEnginePut_SeqAIJ));
4898: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEngineGet_C", MatlabEngineGet_SeqAIJ));
4899: #endif
4900: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetColumnIndices_C", MatSeqAIJSetColumnIndices_SeqAIJ));
4901: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqAIJ));
4902: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqAIJ));
4903: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsbaij_C", MatConvert_SeqAIJ_SeqSBAIJ));
4904: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqbaij_C", MatConvert_SeqAIJ_SeqBAIJ));
4905: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijperm_C", MatConvert_SeqAIJ_SeqAIJPERM));
4906: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijsell_C", MatConvert_SeqAIJ_SeqAIJSELL));
4907: #if defined(PETSC_HAVE_MKL_SPARSE)
4908: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijmkl_C", MatConvert_SeqAIJ_SeqAIJMKL));
4909: #endif
4910: #if defined(PETSC_HAVE_CUDA)
4911: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcusparse_C", MatConvert_SeqAIJ_SeqAIJCUSPARSE));
4912: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4913: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJ));
4914: #endif
4915: #if defined(PETSC_HAVE_HIP)
4916: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijhipsparse_C", MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
4917: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4918: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", MatProductSetFromOptions_SeqAIJ));
4919: #endif
4920: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4921: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijkokkos_C", MatConvert_SeqAIJ_SeqAIJKokkos));
4922: #endif
4923: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcrl_C", MatConvert_SeqAIJ_SeqAIJCRL));
4924: #if defined(PETSC_HAVE_ELEMENTAL)
4925: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_elemental_C", MatConvert_SeqAIJ_Elemental));
4926: #endif
4927: #if defined(PETSC_HAVE_SCALAPACK)
4928: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
4929: #endif
4930: #if defined(PETSC_HAVE_HYPRE)
4931: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_hypre_C", MatConvert_AIJ_HYPRE));
4932: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
4933: #endif
4934: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqdense_C", MatConvert_SeqAIJ_SeqDense));
4935: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsell_C", MatConvert_SeqAIJ_SeqSELL));
4936: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_is_C", MatConvert_XAIJ_IS));
4937: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqAIJ));
4938: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsHermitianTranspose_C", MatIsHermitianTranspose_SeqAIJ));
4939: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocation_C", MatSeqAIJSetPreallocation_SeqAIJ));
4940: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_SeqAIJ));
4941: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocationCSR_C", MatSeqAIJSetPreallocationCSR_SeqAIJ));
4942: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatReorderForNonzeroDiagonal_C", MatReorderForNonzeroDiagonal_SeqAIJ));
4943: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_seqaij_C", MatProductSetFromOptions_IS_XAIJ));
4944: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqdense_seqaij_C", MatProductSetFromOptions_SeqDense_SeqAIJ));
4945: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4946: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJKron_C", MatSeqAIJKron_SeqAIJ));
4947: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJ));
4948: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJ));
4949: PetscCall(MatCreate_SeqAIJ_Inode(B));
4950: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4951: PetscCall(MatSeqAIJSetTypeFromOptions(B)); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4952: PetscFunctionReturn(PETSC_SUCCESS);
4953: }
4955: /*
4956: Given a matrix generated with MatGetFactor() duplicates all the information in A into C
4957: */
4958: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
4959: {
4960: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data, *a = (Mat_SeqAIJ *)A->data;
4961: PetscInt m = A->rmap->n, i;
4963: PetscFunctionBegin;
4964: PetscCheck(A->assembled || cpvalues == MAT_DO_NOT_COPY_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");
4966: C->factortype = A->factortype;
4967: c->row = NULL;
4968: c->col = NULL;
4969: c->icol = NULL;
4970: c->reallocs = 0;
4971: c->diagonaldense = a->diagonaldense;
4973: C->assembled = A->assembled;
4975: if (A->preallocated) {
4976: PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
4977: PetscCall(PetscLayoutReference(A->cmap, &C->cmap));
4979: if (!A->hash_active) {
4980: PetscCall(PetscMalloc1(m, &c->imax));
4981: PetscCall(PetscMemcpy(c->imax, a->imax, m * sizeof(PetscInt)));
4982: PetscCall(PetscMalloc1(m, &c->ilen));
4983: PetscCall(PetscMemcpy(c->ilen, a->ilen, m * sizeof(PetscInt)));
4985: /* allocate the matrix space */
4986: if (mallocmatspace) {
4987: PetscCall(PetscShmgetAllocateArray(a->i[m], sizeof(PetscScalar), (void **)&c->a));
4988: PetscCall(PetscShmgetAllocateArray(a->i[m], sizeof(PetscInt), (void **)&c->j));
4989: PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&c->i));
4990: PetscCall(PetscArraycpy(c->i, a->i, m + 1));
4991: c->free_a = PETSC_TRUE;
4992: c->free_ij = PETSC_TRUE;
4993: if (m > 0) {
4994: PetscCall(PetscArraycpy(c->j, a->j, a->i[m]));
4995: if (cpvalues == MAT_COPY_VALUES) {
4996: const PetscScalar *aa;
4998: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4999: PetscCall(PetscArraycpy(c->a, aa, a->i[m]));
5000: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5001: } else {
5002: PetscCall(PetscArrayzero(c->a, a->i[m]));
5003: }
5004: }
5005: }
5006: C->preallocated = PETSC_TRUE;
5007: } else {
5008: PetscCheck(mallocmatspace, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot malloc matrix memory from a non-preallocated matrix");
5009: PetscCall(MatSetUp(C));
5010: }
5012: c->ignorezeroentries = a->ignorezeroentries;
5013: c->roworiented = a->roworiented;
5014: c->nonew = a->nonew;
5015: if (a->diag) {
5016: PetscCall(PetscMalloc1(m + 1, &c->diag));
5017: PetscCall(PetscMemcpy(c->diag, a->diag, m * sizeof(PetscInt)));
5018: } else c->diag = NULL;
5020: c->solve_work = NULL;
5021: c->saved_values = NULL;
5022: c->idiag = NULL;
5023: c->ssor_work = NULL;
5024: c->keepnonzeropattern = a->keepnonzeropattern;
5026: c->rmax = a->rmax;
5027: c->nz = a->nz;
5028: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
5030: c->compressedrow.use = a->compressedrow.use;
5031: c->compressedrow.nrows = a->compressedrow.nrows;
5032: if (a->compressedrow.use) {
5033: i = a->compressedrow.nrows;
5034: PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i, &c->compressedrow.rindex));
5035: PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
5036: PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
5037: } else {
5038: c->compressedrow.use = PETSC_FALSE;
5039: c->compressedrow.i = NULL;
5040: c->compressedrow.rindex = NULL;
5041: }
5042: c->nonzerorowcnt = a->nonzerorowcnt;
5043: C->nonzerostate = A->nonzerostate;
5045: PetscCall(MatDuplicate_SeqAIJ_Inode(A, cpvalues, &C));
5046: }
5047: PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
5048: PetscFunctionReturn(PETSC_SUCCESS);
5049: }
5051: PetscErrorCode MatDuplicate_SeqAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
5052: {
5053: PetscFunctionBegin;
5054: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
5055: PetscCall(MatSetSizes(*B, A->rmap->n, A->cmap->n, A->rmap->n, A->cmap->n));
5056: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) PetscCall(MatSetBlockSizesFromMats(*B, A, A));
5057: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
5058: PetscCall(MatDuplicateNoCreate_SeqAIJ(*B, A, cpvalues, PETSC_TRUE));
5059: PetscFunctionReturn(PETSC_SUCCESS);
5060: }
5062: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
5063: {
5064: PetscBool isbinary, ishdf5;
5066: PetscFunctionBegin;
5069: /* force binary viewer to load .info file if it has not yet done so */
5070: PetscCall(PetscViewerSetUp(viewer));
5071: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
5072: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
5073: if (isbinary) {
5074: PetscCall(MatLoad_SeqAIJ_Binary(newMat, viewer));
5075: } else if (ishdf5) {
5076: #if defined(PETSC_HAVE_HDF5)
5077: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
5078: #else
5079: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
5080: #endif
5081: } else {
5082: 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);
5083: }
5084: PetscFunctionReturn(PETSC_SUCCESS);
5085: }
5087: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
5088: {
5089: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->data;
5090: PetscInt header[4], *rowlens, M, N, nz, sum, rows, cols, i;
5092: PetscFunctionBegin;
5093: PetscCall(PetscViewerSetUp(viewer));
5095: /* read in matrix header */
5096: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
5097: PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
5098: M = header[1];
5099: N = header[2];
5100: nz = header[3];
5101: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
5102: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
5103: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqAIJ");
5105: /* set block sizes from the viewer's .info file */
5106: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
5107: /* set local and global sizes if not set already */
5108: if (mat->rmap->n < 0) mat->rmap->n = M;
5109: if (mat->cmap->n < 0) mat->cmap->n = N;
5110: if (mat->rmap->N < 0) mat->rmap->N = M;
5111: if (mat->cmap->N < 0) mat->cmap->N = N;
5112: PetscCall(PetscLayoutSetUp(mat->rmap));
5113: PetscCall(PetscLayoutSetUp(mat->cmap));
5115: /* check if the matrix sizes are correct */
5116: PetscCall(MatGetSize(mat, &rows, &cols));
5117: 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);
5119: /* read in row lengths */
5120: PetscCall(PetscMalloc1(M, &rowlens));
5121: PetscCall(PetscViewerBinaryRead(viewer, rowlens, M, NULL, PETSC_INT));
5122: /* check if sum(rowlens) is same as nz */
5123: sum = 0;
5124: for (i = 0; i < M; i++) sum += rowlens[i];
5125: 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);
5126: /* preallocate and check sizes */
5127: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(mat, 0, rowlens));
5128: PetscCall(MatGetSize(mat, &rows, &cols));
5129: 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);
5130: /* store row lengths */
5131: PetscCall(PetscArraycpy(a->ilen, rowlens, M));
5132: PetscCall(PetscFree(rowlens));
5134: /* fill in "i" row pointers */
5135: a->i[0] = 0;
5136: for (i = 0; i < M; i++) a->i[i + 1] = a->i[i] + a->ilen[i];
5137: /* read in "j" column indices */
5138: PetscCall(PetscViewerBinaryRead(viewer, a->j, nz, NULL, PETSC_INT));
5139: /* read in "a" nonzero values */
5140: PetscCall(PetscViewerBinaryRead(viewer, a->a, nz, NULL, PETSC_SCALAR));
5142: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
5143: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
5144: PetscFunctionReturn(PETSC_SUCCESS);
5145: }
5147: PetscErrorCode MatEqual_SeqAIJ(Mat A, Mat B, PetscBool *flg)
5148: {
5149: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data;
5150: const PetscScalar *aa, *ba;
5151: #if defined(PETSC_USE_COMPLEX)
5152: PetscInt k;
5153: #endif
5155: PetscFunctionBegin;
5156: /* If the matrix dimensions are not equal,or no of nonzeros */
5157: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) || (a->nz != b->nz)) {
5158: *flg = PETSC_FALSE;
5159: PetscFunctionReturn(PETSC_SUCCESS);
5160: }
5162: /* if the a->i are the same */
5163: PetscCall(PetscArraycmp(a->i, b->i, A->rmap->n + 1, flg));
5164: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5166: /* if a->j are the same */
5167: PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg));
5168: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5170: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5171: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
5172: /* if a->a are the same */
5173: #if defined(PETSC_USE_COMPLEX)
5174: for (k = 0; k < a->nz; k++) {
5175: if (PetscRealPart(aa[k]) != PetscRealPart(ba[k]) || PetscImaginaryPart(aa[k]) != PetscImaginaryPart(ba[k])) {
5176: *flg = PETSC_FALSE;
5177: PetscFunctionReturn(PETSC_SUCCESS);
5178: }
5179: }
5180: #else
5181: PetscCall(PetscArraycmp(aa, ba, a->nz, flg));
5182: #endif
5183: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
5184: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
5185: PetscFunctionReturn(PETSC_SUCCESS);
5186: }
5188: /*@
5189: MatCreateSeqAIJWithArrays - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in CSR format)
5190: provided by the user.
5192: Collective
5194: Input Parameters:
5195: + comm - must be an MPI communicator of size 1
5196: . m - number of rows
5197: . n - number of columns
5198: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
5199: . j - column indices
5200: - a - matrix values
5202: Output Parameter:
5203: . mat - the matrix
5205: Level: intermediate
5207: Notes:
5208: The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
5209: once the matrix is destroyed and not before
5211: You cannot set new nonzero locations into this matrix, that will generate an error.
5213: The `i` and `j` indices are 0 based
5215: The format which is used for the sparse matrix input, is equivalent to a
5216: row-major ordering.. i.e for the following matrix, the input data expected is
5217: as shown
5218: .vb
5219: 1 0 0
5220: 2 0 3
5221: 4 5 6
5223: i = {0,1,3,6} [size = nrow+1 = 3+1]
5224: j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row
5225: v = {1,2,3,4,5,6} [size = 6]
5226: .ve
5228: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateMPIAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`
5229: @*/
5230: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
5231: {
5232: PetscInt ii;
5233: Mat_SeqAIJ *aij;
5234: PetscInt jj;
5236: PetscFunctionBegin;
5237: PetscCheck(m <= 0 || i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
5238: PetscCall(MatCreate(comm, mat));
5239: PetscCall(MatSetSizes(*mat, m, n, m, n));
5240: /* PetscCall(MatSetBlockSizes(*mat,,)); */
5241: PetscCall(MatSetType(*mat, MATSEQAIJ));
5242: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, MAT_SKIP_ALLOCATION, NULL));
5243: aij = (Mat_SeqAIJ *)(*mat)->data;
5244: PetscCall(PetscMalloc1(m, &aij->imax));
5245: PetscCall(PetscMalloc1(m, &aij->ilen));
5247: aij->i = i;
5248: aij->j = j;
5249: aij->a = a;
5250: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
5251: aij->free_a = PETSC_FALSE;
5252: aij->free_ij = PETSC_FALSE;
5254: for (ii = 0, aij->nonzerorowcnt = 0, aij->rmax = 0; ii < m; ii++) {
5255: aij->ilen[ii] = aij->imax[ii] = i[ii + 1] - i[ii];
5256: if (PetscDefined(USE_DEBUG)) {
5257: 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]);
5258: for (jj = i[ii] + 1; jj < i[ii + 1]; jj++) {
5259: 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);
5260: 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);
5261: }
5262: }
5263: }
5264: if (PetscDefined(USE_DEBUG)) {
5265: for (ii = 0; ii < aij->i[m]; ii++) {
5266: PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
5267: PetscCheck(j[ii] <= n - 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index to large at location = %" PetscInt_FMT " index = %" PetscInt_FMT " last column = %" PetscInt_FMT, ii, j[ii], n - 1);
5268: }
5269: }
5271: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5272: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5273: PetscFunctionReturn(PETSC_SUCCESS);
5274: }
5276: /*@
5277: MatCreateSeqAIJFromTriple - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in COO format)
5278: provided by the user.
5280: Collective
5282: Input Parameters:
5283: + comm - must be an MPI communicator of size 1
5284: . m - number of rows
5285: . n - number of columns
5286: . i - row indices
5287: . j - column indices
5288: . a - matrix values
5289: . nz - number of nonzeros
5290: - idx - if the `i` and `j` indices start with 1 use `PETSC_TRUE` otherwise use `PETSC_FALSE`
5292: Output Parameter:
5293: . mat - the matrix
5295: Level: intermediate
5297: Example:
5298: For the following matrix, the input data expected is as shown (using 0 based indexing)
5299: .vb
5300: 1 0 0
5301: 2 0 3
5302: 4 5 6
5304: i = {0,1,1,2,2,2}
5305: j = {0,0,2,0,1,2}
5306: v = {1,2,3,4,5,6}
5307: .ve
5309: Note:
5310: Instead of using this function, users should also consider `MatSetPreallocationCOO()` and `MatSetValuesCOO()`, which allow repeated or remote entries,
5311: and are particularly useful in iterative applications.
5313: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateSeqAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`, `MatSetValuesCOO()`, `MatSetPreallocationCOO()`
5314: @*/
5315: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat, PetscInt nz, PetscBool idx)
5316: {
5317: PetscInt ii, *nnz, one = 1, row, col;
5319: PetscFunctionBegin;
5320: PetscCall(PetscCalloc1(m, &nnz));
5321: for (ii = 0; ii < nz; ii++) nnz[i[ii] - !!idx] += 1;
5322: PetscCall(MatCreate(comm, mat));
5323: PetscCall(MatSetSizes(*mat, m, n, m, n));
5324: PetscCall(MatSetType(*mat, MATSEQAIJ));
5325: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, 0, nnz));
5326: for (ii = 0; ii < nz; ii++) {
5327: if (idx) {
5328: row = i[ii] - 1;
5329: col = j[ii] - 1;
5330: } else {
5331: row = i[ii];
5332: col = j[ii];
5333: }
5334: PetscCall(MatSetValues(*mat, one, &row, one, &col, &a[ii], ADD_VALUES));
5335: }
5336: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5337: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5338: PetscCall(PetscFree(nnz));
5339: PetscFunctionReturn(PETSC_SUCCESS);
5340: }
5342: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
5343: {
5344: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5346: PetscFunctionBegin;
5347: a->idiagvalid = PETSC_FALSE;
5348: a->ibdiagvalid = PETSC_FALSE;
5350: PetscCall(MatSeqAIJInvalidateDiagonal_Inode(A));
5351: PetscFunctionReturn(PETSC_SUCCESS);
5352: }
5354: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
5355: {
5356: PetscFunctionBegin;
5357: PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm, inmat, n, scall, outmat));
5358: PetscFunctionReturn(PETSC_SUCCESS);
5359: }
5361: /*
5362: Permute A into C's *local* index space using rowemb,colemb.
5363: The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
5364: of [0,m), colemb is in [0,n).
5365: If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
5366: */
5367: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C, IS rowemb, IS colemb, MatStructure pattern, Mat B)
5368: {
5369: /* If making this function public, change the error returned in this function away from _PLIB. */
5370: Mat_SeqAIJ *Baij;
5371: PetscBool seqaij;
5372: PetscInt m, n, *nz, i, j, count;
5373: PetscScalar v;
5374: const PetscInt *rowindices, *colindices;
5376: PetscFunctionBegin;
5377: if (!B) PetscFunctionReturn(PETSC_SUCCESS);
5378: /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
5379: PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQAIJ, &seqaij));
5380: PetscCheck(seqaij, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is of wrong type");
5381: if (rowemb) {
5382: PetscCall(ISGetLocalSize(rowemb, &m));
5383: 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);
5384: } else {
5385: PetscCheck(C->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is row-incompatible with the target matrix");
5386: }
5387: if (colemb) {
5388: PetscCall(ISGetLocalSize(colemb, &n));
5389: 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);
5390: } else {
5391: PetscCheck(C->cmap->n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is col-incompatible with the target matrix");
5392: }
5394: Baij = (Mat_SeqAIJ *)B->data;
5395: if (pattern == DIFFERENT_NONZERO_PATTERN) {
5396: PetscCall(PetscMalloc1(B->rmap->n, &nz));
5397: for (i = 0; i < B->rmap->n; i++) nz[i] = Baij->i[i + 1] - Baij->i[i];
5398: PetscCall(MatSeqAIJSetPreallocation(C, 0, nz));
5399: PetscCall(PetscFree(nz));
5400: }
5401: if (pattern == SUBSET_NONZERO_PATTERN) PetscCall(MatZeroEntries(C));
5402: count = 0;
5403: rowindices = NULL;
5404: colindices = NULL;
5405: if (rowemb) PetscCall(ISGetIndices(rowemb, &rowindices));
5406: if (colemb) PetscCall(ISGetIndices(colemb, &colindices));
5407: for (i = 0; i < B->rmap->n; i++) {
5408: PetscInt row;
5409: row = i;
5410: if (rowindices) row = rowindices[i];
5411: for (j = Baij->i[i]; j < Baij->i[i + 1]; j++) {
5412: PetscInt col;
5413: col = Baij->j[count];
5414: if (colindices) col = colindices[col];
5415: v = Baij->a[count];
5416: PetscCall(MatSetValues(C, 1, &row, 1, &col, &v, INSERT_VALUES));
5417: ++count;
5418: }
5419: }
5420: /* FIXME: set C's nonzerostate correctly. */
5421: /* Assembly for C is necessary. */
5422: C->preallocated = PETSC_TRUE;
5423: C->assembled = PETSC_TRUE;
5424: C->was_assembled = PETSC_FALSE;
5425: PetscFunctionReturn(PETSC_SUCCESS);
5426: }
5428: PetscErrorCode MatEliminateZeros_SeqAIJ(Mat A, PetscBool keep)
5429: {
5430: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5431: MatScalar *aa = a->a;
5432: PetscInt m = A->rmap->n, fshift = 0, fshift_prev = 0, i, k;
5433: PetscInt *ailen = a->ilen, *imax = a->imax, *ai = a->i, *aj = a->j, rmax = 0;
5435: PetscFunctionBegin;
5436: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix");
5437: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
5438: for (i = 1; i <= m; i++) {
5439: /* move each nonzero entry back by the amount of zero slots (fshift) before it*/
5440: for (k = ai[i - 1]; k < ai[i]; k++) {
5441: if (aa[k] == 0 && (aj[k] != i - 1 || !keep)) fshift++;
5442: else {
5443: if (aa[k] == 0 && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal zero at row %" PetscInt_FMT "\n", i - 1));
5444: aa[k - fshift] = aa[k];
5445: aj[k - fshift] = aj[k];
5446: }
5447: }
5448: ai[i - 1] -= fshift_prev; // safe to update ai[i-1] now since it will not be used in the next iteration
5449: fshift_prev = fshift;
5450: /* reset ilen and imax for each row */
5451: ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1];
5452: a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0);
5453: rmax = PetscMax(rmax, ailen[i - 1]);
5454: }
5455: if (fshift) {
5456: if (m) {
5457: ai[m] -= fshift;
5458: a->nz = ai[m];
5459: }
5460: 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));
5461: A->nonzerostate++;
5462: A->info.nz_unneeded += (PetscReal)fshift;
5463: a->rmax = rmax;
5464: if (a->inode.use && a->inode.checked) PetscCall(MatSeqAIJCheckInode(A));
5465: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
5466: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
5467: }
5468: PetscFunctionReturn(PETSC_SUCCESS);
5469: }
5471: PetscFunctionList MatSeqAIJList = NULL;
5473: /*@
5474: MatSeqAIJSetType - Converts a `MATSEQAIJ` matrix to a subtype
5476: Collective
5478: Input Parameters:
5479: + mat - the matrix object
5480: - matype - matrix type
5482: Options Database Key:
5483: . -mat_seqaij_type <method> - for example seqaijcrl
5485: Level: intermediate
5487: .seealso: [](ch_matrices), `Mat`, `PCSetType()`, `VecSetType()`, `MatCreate()`, `MatType`
5488: @*/
5489: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
5490: {
5491: PetscBool sametype;
5492: PetscErrorCode (*r)(Mat, MatType, MatReuse, Mat *);
5494: PetscFunctionBegin;
5496: PetscCall(PetscObjectTypeCompare((PetscObject)mat, matype, &sametype));
5497: if (sametype) PetscFunctionReturn(PETSC_SUCCESS);
5499: PetscCall(PetscFunctionListFind(MatSeqAIJList, matype, &r));
5500: PetscCheck(r, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown Mat type given: %s", matype);
5501: PetscCall((*r)(mat, matype, MAT_INPLACE_MATRIX, &mat));
5502: PetscFunctionReturn(PETSC_SUCCESS);
5503: }
5505: /*@C
5506: MatSeqAIJRegister - - Adds a new sub-matrix type for sequential `MATSEQAIJ` matrices
5508: Not Collective, No Fortran Support
5510: Input Parameters:
5511: + sname - name of a new user-defined matrix type, for example `MATSEQAIJCRL`
5512: - function - routine to convert to subtype
5514: Level: advanced
5516: Notes:
5517: `MatSeqAIJRegister()` may be called multiple times to add several user-defined solvers.
5519: Then, your matrix can be chosen with the procedural interface at runtime via the option
5520: $ -mat_seqaij_type my_mat
5522: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRegisterAll()`
5523: @*/
5524: PetscErrorCode MatSeqAIJRegister(const char sname[], PetscErrorCode (*function)(Mat, MatType, MatReuse, Mat *))
5525: {
5526: PetscFunctionBegin;
5527: PetscCall(MatInitializePackage());
5528: PetscCall(PetscFunctionListAdd(&MatSeqAIJList, sname, function));
5529: PetscFunctionReturn(PETSC_SUCCESS);
5530: }
5532: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;
5534: /*@C
5535: MatSeqAIJRegisterAll - Registers all of the matrix subtypes of `MATSSEQAIJ`
5537: Not Collective
5539: Level: advanced
5541: Note:
5542: This registers the versions of `MATSEQAIJ` for GPUs
5544: .seealso: [](ch_matrices), `Mat`, `MatRegisterAll()`, `MatSeqAIJRegister()`
5545: @*/
5546: PetscErrorCode MatSeqAIJRegisterAll(void)
5547: {
5548: PetscFunctionBegin;
5549: if (MatSeqAIJRegisterAllCalled) PetscFunctionReturn(PETSC_SUCCESS);
5550: MatSeqAIJRegisterAllCalled = PETSC_TRUE;
5552: PetscCall(MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL));
5553: PetscCall(MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM));
5554: PetscCall(MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL));
5555: #if defined(PETSC_HAVE_MKL_SPARSE)
5556: PetscCall(MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL));
5557: #endif
5558: #if defined(PETSC_HAVE_CUDA)
5559: PetscCall(MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE));
5560: #endif
5561: #if defined(PETSC_HAVE_HIP)
5562: PetscCall(MatSeqAIJRegister(MATSEQAIJHIPSPARSE, MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
5563: #endif
5564: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
5565: PetscCall(MatSeqAIJRegister(MATSEQAIJKOKKOS, MatConvert_SeqAIJ_SeqAIJKokkos));
5566: #endif
5567: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5568: PetscCall(MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL));
5569: #endif
5570: PetscFunctionReturn(PETSC_SUCCESS);
5571: }
5573: /*
5574: Special version for direct calls from Fortran
5575: */
5576: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5577: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5578: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5579: #define matsetvaluesseqaij_ matsetvaluesseqaij
5580: #endif
5582: /* Change these macros so can be used in void function */
5584: /* Change these macros so can be used in void function */
5585: /* Identical to PetscCallVoid, except it assigns to *_ierr */
5586: #undef PetscCall
5587: #define PetscCall(...) \
5588: do { \
5589: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
5590: if (PetscUnlikely(ierr_msv_mpiaij)) { \
5591: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
5592: return; \
5593: } \
5594: } while (0)
5596: #undef SETERRQ
5597: #define SETERRQ(comm, ierr, ...) \
5598: do { \
5599: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
5600: return; \
5601: } while (0)
5603: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[], InsertMode *isis, PetscErrorCode *_ierr)
5604: {
5605: Mat A = *AA;
5606: PetscInt m = *mm, n = *nn;
5607: InsertMode is = *isis;
5608: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5609: PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
5610: PetscInt *imax, *ai, *ailen;
5611: PetscInt *aj, nonew = a->nonew, lastcol = -1;
5612: MatScalar *ap, value, *aa;
5613: PetscBool ignorezeroentries = a->ignorezeroentries;
5614: PetscBool roworiented = a->roworiented;
5616: PetscFunctionBegin;
5617: MatCheckPreallocated(A, 1);
5618: imax = a->imax;
5619: ai = a->i;
5620: ailen = a->ilen;
5621: aj = a->j;
5622: aa = a->a;
5624: for (k = 0; k < m; k++) { /* loop over added rows */
5625: row = im[k];
5626: if (row < 0) continue;
5627: PetscCheck(row < A->rmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Row too large");
5628: rp = aj + ai[row];
5629: ap = aa + ai[row];
5630: rmax = imax[row];
5631: nrow = ailen[row];
5632: low = 0;
5633: high = nrow;
5634: for (l = 0; l < n; l++) { /* loop over added columns */
5635: if (in[l] < 0) continue;
5636: PetscCheck(in[l] < A->cmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Column too large");
5637: col = in[l];
5638: if (roworiented) value = v[l + k * n];
5639: else value = v[k + l * m];
5641: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
5643: if (col <= lastcol) low = 0;
5644: else high = nrow;
5645: lastcol = col;
5646: while (high - low > 5) {
5647: t = (low + high) / 2;
5648: if (rp[t] > col) high = t;
5649: else low = t;
5650: }
5651: for (i = low; i < high; i++) {
5652: if (rp[i] > col) break;
5653: if (rp[i] == col) {
5654: if (is == ADD_VALUES) ap[i] += value;
5655: else ap[i] = value;
5656: goto noinsert;
5657: }
5658: }
5659: if (value == 0.0 && ignorezeroentries) goto noinsert;
5660: if (nonew == 1) goto noinsert;
5661: PetscCheck(nonew != -1, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero in the matrix");
5662: MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
5663: N = nrow++ - 1;
5664: a->nz++;
5665: high++;
5666: /* shift up all the later entries in this row */
5667: for (ii = N; ii >= i; ii--) {
5668: rp[ii + 1] = rp[ii];
5669: ap[ii + 1] = ap[ii];
5670: }
5671: rp[i] = col;
5672: ap[i] = value;
5673: noinsert:;
5674: low = i + 1;
5675: }
5676: ailen[row] = nrow;
5677: }
5678: PetscFunctionReturnVoid();
5679: }
5680: /* Undefining these here since they were redefined from their original definition above! No
5681: * other PETSc functions should be defined past this point, as it is impossible to recover the
5682: * original definitions */
5683: #undef PetscCall
5684: #undef SETERRQ