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: /* if the diagonal matrix is square it inherits some of the properties above */
1342: break;
1343: case MAT_FORCE_DIAGONAL_ENTRIES:
1344: case MAT_IGNORE_OFF_PROC_ENTRIES:
1345: case MAT_USE_HASH_TABLE:
1346: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1347: break;
1348: case MAT_USE_INODES:
1349: PetscCall(MatSetOption_SeqAIJ_Inode(A, MAT_USE_INODES, flg));
1350: break;
1351: case MAT_SUBMAT_SINGLEIS:
1352: A->submat_singleis = flg;
1353: break;
1354: case MAT_SORTED_FULL:
1355: if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
1356: else A->ops->setvalues = MatSetValues_SeqAIJ;
1357: break;
1358: case MAT_FORM_EXPLICIT_TRANSPOSE:
1359: A->form_explicit_transpose = flg;
1360: break;
1361: default:
1362: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1363: }
1364: PetscFunctionReturn(PETSC_SUCCESS);
1365: }
1367: static PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A, Vec v)
1368: {
1369: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1370: PetscInt i, j, n, *ai = a->i, *aj = a->j;
1371: PetscScalar *x;
1372: const PetscScalar *aa;
1374: PetscFunctionBegin;
1375: PetscCall(VecGetLocalSize(v, &n));
1376: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
1377: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1378: if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1379: PetscInt *diag = a->diag;
1380: PetscCall(VecGetArrayWrite(v, &x));
1381: for (i = 0; i < n; i++) x[i] = 1.0 / aa[diag[i]];
1382: PetscCall(VecRestoreArrayWrite(v, &x));
1383: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1384: PetscFunctionReturn(PETSC_SUCCESS);
1385: }
1387: PetscCall(VecGetArrayWrite(v, &x));
1388: for (i = 0; i < n; i++) {
1389: x[i] = 0.0;
1390: for (j = ai[i]; j < ai[i + 1]; j++) {
1391: if (aj[j] == i) {
1392: x[i] = aa[j];
1393: break;
1394: }
1395: }
1396: }
1397: PetscCall(VecRestoreArrayWrite(v, &x));
1398: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1399: PetscFunctionReturn(PETSC_SUCCESS);
1400: }
1402: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1403: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A, Vec xx, Vec zz, Vec yy)
1404: {
1405: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1406: const MatScalar *aa;
1407: PetscScalar *y;
1408: const PetscScalar *x;
1409: PetscInt m = A->rmap->n;
1410: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1411: const MatScalar *v;
1412: PetscScalar alpha;
1413: PetscInt n, i, j;
1414: const PetscInt *idx, *ii, *ridx = NULL;
1415: Mat_CompressedRow cprow = a->compressedrow;
1416: PetscBool usecprow = cprow.use;
1417: #endif
1419: PetscFunctionBegin;
1420: if (zz != yy) PetscCall(VecCopy(zz, yy));
1421: PetscCall(VecGetArrayRead(xx, &x));
1422: PetscCall(VecGetArray(yy, &y));
1423: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1425: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1426: fortranmulttransposeaddaij_(&m, x, a->i, a->j, aa, y);
1427: #else
1428: if (usecprow) {
1429: m = cprow.nrows;
1430: ii = cprow.i;
1431: ridx = cprow.rindex;
1432: } else {
1433: ii = a->i;
1434: }
1435: for (i = 0; i < m; i++) {
1436: idx = a->j + ii[i];
1437: v = aa + ii[i];
1438: n = ii[i + 1] - ii[i];
1439: if (usecprow) {
1440: alpha = x[ridx[i]];
1441: } else {
1442: alpha = x[i];
1443: }
1444: for (j = 0; j < n; j++) y[idx[j]] += alpha * v[j];
1445: }
1446: #endif
1447: PetscCall(PetscLogFlops(2.0 * a->nz));
1448: PetscCall(VecRestoreArrayRead(xx, &x));
1449: PetscCall(VecRestoreArray(yy, &y));
1450: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1451: PetscFunctionReturn(PETSC_SUCCESS);
1452: }
1454: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A, Vec xx, Vec yy)
1455: {
1456: PetscFunctionBegin;
1457: PetscCall(VecSet(yy, 0.0));
1458: PetscCall(MatMultTransposeAdd_SeqAIJ(A, xx, yy, yy));
1459: PetscFunctionReturn(PETSC_SUCCESS);
1460: }
1462: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1464: PetscErrorCode MatMult_SeqAIJ(Mat A, Vec xx, Vec yy)
1465: {
1466: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1467: PetscScalar *y;
1468: const PetscScalar *x;
1469: const MatScalar *a_a;
1470: PetscInt m = A->rmap->n;
1471: const PetscInt *ii, *ridx = NULL;
1472: PetscBool usecprow = a->compressedrow.use;
1474: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1475: #pragma disjoint(*x, *y, *aa)
1476: #endif
1478: PetscFunctionBegin;
1479: if (a->inode.use && a->inode.checked) {
1480: PetscCall(MatMult_SeqAIJ_Inode(A, xx, yy));
1481: PetscFunctionReturn(PETSC_SUCCESS);
1482: }
1483: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1484: PetscCall(VecGetArrayRead(xx, &x));
1485: PetscCall(VecGetArray(yy, &y));
1486: ii = a->i;
1487: if (usecprow) { /* use compressed row format */
1488: PetscCall(PetscArrayzero(y, m));
1489: m = a->compressedrow.nrows;
1490: ii = a->compressedrow.i;
1491: ridx = a->compressedrow.rindex;
1492: PetscPragmaUseOMPKernels(parallel for)
1493: for (PetscInt i = 0; i < m; i++) {
1494: PetscInt n = ii[i + 1] - ii[i];
1495: const PetscInt *aj = a->j + ii[i];
1496: const PetscScalar *aa = a_a + ii[i];
1497: PetscScalar sum = 0.0;
1498: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1499: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1500: y[*ridx++] = sum;
1501: }
1502: } else { /* do not use compressed row format */
1503: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1504: fortranmultaij_(&m, x, ii, a->j, a_a, y);
1505: #else
1506: PetscPragmaUseOMPKernels(parallel for)
1507: for (PetscInt i = 0; i < m; i++) {
1508: PetscInt n = ii[i + 1] - ii[i];
1509: const PetscInt *aj = a->j + ii[i];
1510: const PetscScalar *aa = a_a + ii[i];
1511: PetscScalar sum = 0.0;
1512: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1513: y[i] = sum;
1514: }
1515: #endif
1516: }
1517: PetscCall(PetscLogFlops(2.0 * a->nz - a->nonzerorowcnt));
1518: PetscCall(VecRestoreArrayRead(xx, &x));
1519: PetscCall(VecRestoreArray(yy, &y));
1520: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1521: PetscFunctionReturn(PETSC_SUCCESS);
1522: }
1524: // HACK!!!!! Used by src/mat/tests/ex170.c
1525: PETSC_EXTERN PetscErrorCode MatMultMax_SeqAIJ(Mat A, Vec xx, Vec yy)
1526: {
1527: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1528: PetscScalar *y;
1529: const PetscScalar *x;
1530: const MatScalar *aa, *a_a;
1531: PetscInt m = A->rmap->n;
1532: const PetscInt *aj, *ii, *ridx = NULL;
1533: PetscInt n, i, nonzerorow = 0;
1534: PetscScalar sum;
1535: PetscBool usecprow = a->compressedrow.use;
1537: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1538: #pragma disjoint(*x, *y, *aa)
1539: #endif
1541: PetscFunctionBegin;
1542: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1543: PetscCall(VecGetArrayRead(xx, &x));
1544: PetscCall(VecGetArray(yy, &y));
1545: if (usecprow) { /* use compressed row format */
1546: m = a->compressedrow.nrows;
1547: ii = a->compressedrow.i;
1548: ridx = a->compressedrow.rindex;
1549: for (i = 0; i < m; i++) {
1550: n = ii[i + 1] - ii[i];
1551: aj = a->j + ii[i];
1552: aa = a_a + ii[i];
1553: sum = 0.0;
1554: nonzerorow += (n > 0);
1555: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1556: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1557: y[*ridx++] = sum;
1558: }
1559: } else { /* do not use compressed row format */
1560: ii = a->i;
1561: for (i = 0; i < m; i++) {
1562: n = ii[i + 1] - ii[i];
1563: aj = a->j + ii[i];
1564: aa = a_a + ii[i];
1565: sum = 0.0;
1566: nonzerorow += (n > 0);
1567: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1568: y[i] = sum;
1569: }
1570: }
1571: PetscCall(PetscLogFlops(2.0 * a->nz - nonzerorow));
1572: PetscCall(VecRestoreArrayRead(xx, &x));
1573: PetscCall(VecRestoreArray(yy, &y));
1574: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1575: PetscFunctionReturn(PETSC_SUCCESS);
1576: }
1578: // HACK!!!!! Used by src/mat/tests/ex170.c
1579: PETSC_EXTERN PetscErrorCode MatMultAddMax_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1580: {
1581: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1582: PetscScalar *y, *z;
1583: const PetscScalar *x;
1584: const MatScalar *aa, *a_a;
1585: PetscInt m = A->rmap->n, *aj, *ii;
1586: PetscInt n, i, *ridx = NULL;
1587: PetscScalar sum;
1588: PetscBool usecprow = a->compressedrow.use;
1590: PetscFunctionBegin;
1591: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1592: PetscCall(VecGetArrayRead(xx, &x));
1593: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1594: if (usecprow) { /* use compressed row format */
1595: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1596: m = a->compressedrow.nrows;
1597: ii = a->compressedrow.i;
1598: ridx = a->compressedrow.rindex;
1599: for (i = 0; i < m; i++) {
1600: n = ii[i + 1] - ii[i];
1601: aj = a->j + ii[i];
1602: aa = a_a + ii[i];
1603: sum = y[*ridx];
1604: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1605: z[*ridx++] = sum;
1606: }
1607: } else { /* do not use compressed row format */
1608: ii = a->i;
1609: for (i = 0; i < m; i++) {
1610: n = ii[i + 1] - ii[i];
1611: aj = a->j + ii[i];
1612: aa = a_a + ii[i];
1613: sum = y[i];
1614: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1615: z[i] = sum;
1616: }
1617: }
1618: PetscCall(PetscLogFlops(2.0 * a->nz));
1619: PetscCall(VecRestoreArrayRead(xx, &x));
1620: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1621: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1622: PetscFunctionReturn(PETSC_SUCCESS);
1623: }
1625: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1626: PetscErrorCode MatMultAdd_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1627: {
1628: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1629: PetscScalar *y, *z;
1630: const PetscScalar *x;
1631: const MatScalar *a_a;
1632: const PetscInt *ii, *ridx = NULL;
1633: PetscInt m = A->rmap->n;
1634: PetscBool usecprow = a->compressedrow.use;
1636: PetscFunctionBegin;
1637: if (a->inode.use && a->inode.checked) {
1638: PetscCall(MatMultAdd_SeqAIJ_Inode(A, xx, yy, zz));
1639: PetscFunctionReturn(PETSC_SUCCESS);
1640: }
1641: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1642: PetscCall(VecGetArrayRead(xx, &x));
1643: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1644: if (usecprow) { /* use compressed row format */
1645: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1646: m = a->compressedrow.nrows;
1647: ii = a->compressedrow.i;
1648: ridx = a->compressedrow.rindex;
1649: for (PetscInt i = 0; i < m; i++) {
1650: PetscInt n = ii[i + 1] - ii[i];
1651: const PetscInt *aj = a->j + ii[i];
1652: const PetscScalar *aa = a_a + ii[i];
1653: PetscScalar sum = y[*ridx];
1654: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1655: z[*ridx++] = sum;
1656: }
1657: } else { /* do not use compressed row format */
1658: ii = a->i;
1659: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1660: fortranmultaddaij_(&m, x, ii, a->j, a_a, y, z);
1661: #else
1662: PetscPragmaUseOMPKernels(parallel for)
1663: for (PetscInt i = 0; i < m; i++) {
1664: PetscInt n = ii[i + 1] - ii[i];
1665: const PetscInt *aj = a->j + ii[i];
1666: const PetscScalar *aa = a_a + ii[i];
1667: PetscScalar sum = y[i];
1668: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1669: z[i] = sum;
1670: }
1671: #endif
1672: }
1673: PetscCall(PetscLogFlops(2.0 * a->nz));
1674: PetscCall(VecRestoreArrayRead(xx, &x));
1675: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1676: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1677: PetscFunctionReturn(PETSC_SUCCESS);
1678: }
1680: /*
1681: Adds diagonal pointers to sparse matrix nonzero structure.
1682: */
1683: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1684: {
1685: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1686: PetscInt i, j, m = A->rmap->n;
1687: PetscBool alreadySet = PETSC_TRUE;
1689: PetscFunctionBegin;
1690: if (!a->diag) {
1691: PetscCall(PetscMalloc1(m, &a->diag));
1692: alreadySet = PETSC_FALSE;
1693: }
1694: for (i = 0; i < A->rmap->n; i++) {
1695: /* If A's diagonal is already correctly set, this fast track enables cheap and repeated MatMarkDiagonal_SeqAIJ() calls */
1696: if (alreadySet) {
1697: PetscInt pos = a->diag[i];
1698: if (pos >= a->i[i] && pos < a->i[i + 1] && a->j[pos] == i) continue;
1699: }
1701: a->diag[i] = a->i[i + 1];
1702: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1703: if (a->j[j] == i) {
1704: a->diag[i] = j;
1705: break;
1706: }
1707: }
1708: }
1709: PetscFunctionReturn(PETSC_SUCCESS);
1710: }
1712: static PetscErrorCode MatShift_SeqAIJ(Mat A, PetscScalar v)
1713: {
1714: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1715: const PetscInt *diag = (const PetscInt *)a->diag;
1716: const PetscInt *ii = (const PetscInt *)a->i;
1717: PetscInt i, *mdiag = NULL;
1718: PetscInt cnt = 0; /* how many diagonals are missing */
1720: PetscFunctionBegin;
1721: if (!A->preallocated || !a->nz) {
1722: PetscCall(MatSeqAIJSetPreallocation(A, 1, NULL));
1723: PetscCall(MatShift_Basic(A, v));
1724: PetscFunctionReturn(PETSC_SUCCESS);
1725: }
1727: if (a->diagonaldense) {
1728: cnt = 0;
1729: } else {
1730: PetscCall(PetscCalloc1(A->rmap->n, &mdiag));
1731: for (i = 0; i < A->rmap->n; i++) {
1732: if (i < A->cmap->n && diag[i] >= ii[i + 1]) { /* 'out of range' rows never have diagonals */
1733: cnt++;
1734: mdiag[i] = 1;
1735: }
1736: }
1737: }
1738: if (!cnt) {
1739: PetscCall(MatShift_Basic(A, v));
1740: } else {
1741: PetscScalar *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */
1742: PetscInt *oldj = a->j, *oldi = a->i;
1743: PetscBool free_a = a->free_a, free_ij = a->free_ij;
1744: const PetscScalar *Aa;
1746: PetscCall(MatSeqAIJGetArrayRead(A, &Aa)); // sync the host
1747: PetscCall(MatSeqAIJRestoreArrayRead(A, &Aa));
1749: a->a = NULL;
1750: a->j = NULL;
1751: a->i = NULL;
1752: /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1753: for (i = 0; i < PetscMin(A->rmap->n, A->cmap->n); i++) a->imax[i] += mdiag[i];
1754: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, 0, a->imax));
1756: /* copy old values into new matrix data structure */
1757: for (i = 0; i < A->rmap->n; i++) {
1758: PetscCall(MatSetValues(A, 1, &i, a->imax[i] - mdiag[i], &oldj[oldi[i]], &olda[oldi[i]], ADD_VALUES));
1759: if (i < A->cmap->n) PetscCall(MatSetValue(A, i, i, v, ADD_VALUES));
1760: }
1761: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1762: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1763: if (free_a) PetscCall(PetscShmgetDeallocateArray((void **)&olda));
1764: if (free_ij) PetscCall(PetscShmgetDeallocateArray((void **)&oldj));
1765: if (free_ij) PetscCall(PetscShmgetDeallocateArray((void **)&oldi));
1766: }
1767: PetscCall(PetscFree(mdiag));
1768: a->diagonaldense = PETSC_TRUE;
1769: PetscFunctionReturn(PETSC_SUCCESS);
1770: }
1772: /*
1773: Checks for missing diagonals
1774: */
1775: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A, PetscBool *missing, PetscInt *d)
1776: {
1777: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1778: PetscInt *diag, *ii = a->i, i;
1780: PetscFunctionBegin;
1781: *missing = PETSC_FALSE;
1782: if (A->rmap->n > 0 && !ii) {
1783: *missing = PETSC_TRUE;
1784: if (d) *d = 0;
1785: PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n"));
1786: } else {
1787: PetscInt n;
1788: n = PetscMin(A->rmap->n, A->cmap->n);
1789: diag = a->diag;
1790: for (i = 0; i < n; i++) {
1791: if (diag[i] >= ii[i + 1]) {
1792: *missing = PETSC_TRUE;
1793: if (d) *d = i;
1794: PetscCall(PetscInfo(A, "Matrix is missing diagonal number %" PetscInt_FMT "\n", i));
1795: break;
1796: }
1797: }
1798: }
1799: PetscFunctionReturn(PETSC_SUCCESS);
1800: }
1802: #include <petscblaslapack.h>
1803: #include <petsc/private/kernels/blockinvert.h>
1805: /*
1806: Note that values is allocated externally by the PC and then passed into this routine
1807: */
1808: static PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
1809: {
1810: PetscInt n = A->rmap->n, i, ncnt = 0, *indx, j, bsizemax = 0, *v_pivots;
1811: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
1812: const PetscReal shift = 0.0;
1813: PetscInt ipvt[5];
1814: PetscCount flops = 0;
1815: PetscScalar work[25], *v_work;
1817: PetscFunctionBegin;
1818: allowzeropivot = PetscNot(A->erroriffailure);
1819: for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
1820: PetscCheck(ncnt == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Total blocksizes %" PetscInt_FMT " doesn't match number matrix rows %" PetscInt_FMT, ncnt, n);
1821: for (i = 0; i < nblocks; i++) bsizemax = PetscMax(bsizemax, bsizes[i]);
1822: PetscCall(PetscMalloc1(bsizemax, &indx));
1823: if (bsizemax > 7) PetscCall(PetscMalloc2(bsizemax, &v_work, bsizemax, &v_pivots));
1824: ncnt = 0;
1825: for (i = 0; i < nblocks; i++) {
1826: for (j = 0; j < bsizes[i]; j++) indx[j] = ncnt + j;
1827: PetscCall(MatGetValues(A, bsizes[i], indx, bsizes[i], indx, diag));
1828: switch (bsizes[i]) {
1829: case 1:
1830: *diag = 1.0 / (*diag);
1831: break;
1832: case 2:
1833: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
1834: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1835: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
1836: break;
1837: case 3:
1838: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
1839: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1840: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
1841: break;
1842: case 4:
1843: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
1844: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1845: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
1846: break;
1847: case 5:
1848: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
1849: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1850: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
1851: break;
1852: case 6:
1853: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
1854: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1855: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
1856: break;
1857: case 7:
1858: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
1859: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1860: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
1861: break;
1862: default:
1863: PetscCall(PetscKernel_A_gets_inverse_A(bsizes[i], diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
1864: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1865: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bsizes[i]));
1866: }
1867: ncnt += bsizes[i];
1868: diag += bsizes[i] * bsizes[i];
1869: flops += 2 * PetscPowInt64(bsizes[i], 3) / 3;
1870: }
1871: PetscCall(PetscLogFlops(flops));
1872: if (bsizemax > 7) PetscCall(PetscFree2(v_work, v_pivots));
1873: PetscCall(PetscFree(indx));
1874: PetscFunctionReturn(PETSC_SUCCESS);
1875: }
1877: /*
1878: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1879: */
1880: static PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A, PetscScalar omega, PetscScalar fshift)
1881: {
1882: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1883: PetscInt i, *diag, m = A->rmap->n;
1884: const MatScalar *v;
1885: PetscScalar *idiag, *mdiag;
1887: PetscFunctionBegin;
1888: if (a->idiagvalid) PetscFunctionReturn(PETSC_SUCCESS);
1889: PetscCall(MatMarkDiagonal_SeqAIJ(A));
1890: diag = a->diag;
1891: if (!a->idiag) { PetscCall(PetscMalloc3(m, &a->idiag, m, &a->mdiag, m, &a->ssor_work)); }
1893: mdiag = a->mdiag;
1894: idiag = a->idiag;
1895: PetscCall(MatSeqAIJGetArrayRead(A, &v));
1896: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1897: for (i = 0; i < m; i++) {
1898: mdiag[i] = v[diag[i]];
1899: if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1900: if (PetscRealPart(fshift)) {
1901: PetscCall(PetscInfo(A, "Zero diagonal on row %" PetscInt_FMT "\n", i));
1902: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1903: A->factorerror_zeropivot_value = 0.0;
1904: A->factorerror_zeropivot_row = i;
1905: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Zero diagonal on row %" PetscInt_FMT, i);
1906: }
1907: idiag[i] = 1.0 / v[diag[i]];
1908: }
1909: PetscCall(PetscLogFlops(m));
1910: } else {
1911: for (i = 0; i < m; i++) {
1912: mdiag[i] = v[diag[i]];
1913: idiag[i] = omega / (fshift + v[diag[i]]);
1914: }
1915: PetscCall(PetscLogFlops(2.0 * m));
1916: }
1917: a->idiagvalid = PETSC_TRUE;
1918: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
1919: PetscFunctionReturn(PETSC_SUCCESS);
1920: }
1922: PetscErrorCode MatSOR_SeqAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1923: {
1924: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1925: PetscScalar *x, d, sum, *t, scale;
1926: const MatScalar *v, *idiag = NULL, *mdiag, *aa;
1927: const PetscScalar *b, *bs, *xb, *ts;
1928: PetscInt n, m = A->rmap->n, i;
1929: const PetscInt *idx, *diag;
1931: PetscFunctionBegin;
1932: if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
1933: PetscCall(MatSOR_SeqAIJ_Inode(A, bb, omega, flag, fshift, its, lits, xx));
1934: PetscFunctionReturn(PETSC_SUCCESS);
1935: }
1936: its = its * lits;
1938: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1939: if (!a->idiagvalid) PetscCall(MatInvertDiagonal_SeqAIJ(A, omega, fshift));
1940: a->fshift = fshift;
1941: a->omega = omega;
1943: diag = a->diag;
1944: t = a->ssor_work;
1945: idiag = a->idiag;
1946: mdiag = a->mdiag;
1948: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1949: PetscCall(VecGetArray(xx, &x));
1950: PetscCall(VecGetArrayRead(bb, &b));
1951: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1952: if (flag == SOR_APPLY_UPPER) {
1953: /* apply (U + D/omega) to the vector */
1954: bs = b;
1955: for (i = 0; i < m; i++) {
1956: d = fshift + mdiag[i];
1957: n = a->i[i + 1] - diag[i] - 1;
1958: idx = a->j + diag[i] + 1;
1959: v = aa + diag[i] + 1;
1960: sum = b[i] * d / omega;
1961: PetscSparseDensePlusDot(sum, bs, v, idx, n);
1962: x[i] = sum;
1963: }
1964: PetscCall(VecRestoreArray(xx, &x));
1965: PetscCall(VecRestoreArrayRead(bb, &b));
1966: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1967: PetscCall(PetscLogFlops(a->nz));
1968: PetscFunctionReturn(PETSC_SUCCESS);
1969: }
1971: PetscCheck(flag != SOR_APPLY_LOWER, PETSC_COMM_SELF, PETSC_ERR_SUP, "SOR_APPLY_LOWER is not implemented");
1972: if (flag & SOR_EISENSTAT) {
1973: /* Let A = L + U + D; where L is lower triangular,
1974: U is upper triangular, E = D/omega; This routine applies
1976: (L + E)^{-1} A (U + E)^{-1}
1978: to a vector efficiently using Eisenstat's trick.
1979: */
1980: scale = (2.0 / omega) - 1.0;
1982: /* x = (E + U)^{-1} b */
1983: for (i = m - 1; i >= 0; i--) {
1984: n = a->i[i + 1] - diag[i] - 1;
1985: idx = a->j + diag[i] + 1;
1986: v = aa + diag[i] + 1;
1987: sum = b[i];
1988: PetscSparseDenseMinusDot(sum, x, v, idx, n);
1989: x[i] = sum * idiag[i];
1990: }
1992: /* t = b - (2*E - D)x */
1993: v = aa;
1994: for (i = 0; i < m; i++) t[i] = b[i] - scale * (v[*diag++]) * x[i];
1996: /* t = (E + L)^{-1}t */
1997: ts = t;
1998: diag = a->diag;
1999: for (i = 0; i < m; i++) {
2000: n = diag[i] - a->i[i];
2001: idx = a->j + a->i[i];
2002: v = aa + a->i[i];
2003: sum = t[i];
2004: PetscSparseDenseMinusDot(sum, ts, v, idx, n);
2005: t[i] = sum * idiag[i];
2006: /* x = x + t */
2007: x[i] += t[i];
2008: }
2010: PetscCall(PetscLogFlops(6.0 * m - 1 + 2.0 * a->nz));
2011: PetscCall(VecRestoreArray(xx, &x));
2012: PetscCall(VecRestoreArrayRead(bb, &b));
2013: PetscFunctionReturn(PETSC_SUCCESS);
2014: }
2015: if (flag & SOR_ZERO_INITIAL_GUESS) {
2016: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2017: for (i = 0; i < m; i++) {
2018: n = diag[i] - a->i[i];
2019: idx = a->j + a->i[i];
2020: v = aa + a->i[i];
2021: sum = b[i];
2022: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2023: t[i] = sum;
2024: x[i] = sum * idiag[i];
2025: }
2026: xb = t;
2027: PetscCall(PetscLogFlops(a->nz));
2028: } else xb = b;
2029: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2030: for (i = m - 1; i >= 0; i--) {
2031: n = a->i[i + 1] - diag[i] - 1;
2032: idx = a->j + diag[i] + 1;
2033: v = aa + diag[i] + 1;
2034: sum = xb[i];
2035: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2036: if (xb == b) {
2037: x[i] = sum * idiag[i];
2038: } else {
2039: x[i] = (1 - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2040: }
2041: }
2042: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2043: }
2044: its--;
2045: }
2046: while (its--) {
2047: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2048: for (i = 0; i < m; i++) {
2049: /* lower */
2050: n = diag[i] - a->i[i];
2051: idx = a->j + a->i[i];
2052: v = aa + a->i[i];
2053: sum = b[i];
2054: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2055: t[i] = sum; /* save application of the lower-triangular part */
2056: /* upper */
2057: n = a->i[i + 1] - diag[i] - 1;
2058: idx = a->j + diag[i] + 1;
2059: v = aa + diag[i] + 1;
2060: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2061: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2062: }
2063: xb = t;
2064: PetscCall(PetscLogFlops(2.0 * a->nz));
2065: } else xb = b;
2066: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2067: for (i = m - 1; i >= 0; i--) {
2068: sum = xb[i];
2069: if (xb == b) {
2070: /* whole matrix (no checkpointing available) */
2071: n = a->i[i + 1] - a->i[i];
2072: idx = a->j + a->i[i];
2073: v = aa + a->i[i];
2074: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2075: x[i] = (1. - omega) * x[i] + (sum + mdiag[i] * x[i]) * idiag[i];
2076: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
2077: n = a->i[i + 1] - diag[i] - 1;
2078: idx = a->j + diag[i] + 1;
2079: v = aa + diag[i] + 1;
2080: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2081: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2082: }
2083: }
2084: if (xb == b) {
2085: PetscCall(PetscLogFlops(2.0 * a->nz));
2086: } else {
2087: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2088: }
2089: }
2090: }
2091: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2092: PetscCall(VecRestoreArray(xx, &x));
2093: PetscCall(VecRestoreArrayRead(bb, &b));
2094: PetscFunctionReturn(PETSC_SUCCESS);
2095: }
2097: static PetscErrorCode MatGetInfo_SeqAIJ(Mat A, MatInfoType flag, MatInfo *info)
2098: {
2099: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2101: PetscFunctionBegin;
2102: info->block_size = 1.0;
2103: info->nz_allocated = a->maxnz;
2104: info->nz_used = a->nz;
2105: info->nz_unneeded = (a->maxnz - a->nz);
2106: info->assemblies = A->num_ass;
2107: info->mallocs = A->info.mallocs;
2108: info->memory = 0; /* REVIEW ME */
2109: if (A->factortype) {
2110: info->fill_ratio_given = A->info.fill_ratio_given;
2111: info->fill_ratio_needed = A->info.fill_ratio_needed;
2112: info->factor_mallocs = A->info.factor_mallocs;
2113: } else {
2114: info->fill_ratio_given = 0;
2115: info->fill_ratio_needed = 0;
2116: info->factor_mallocs = 0;
2117: }
2118: PetscFunctionReturn(PETSC_SUCCESS);
2119: }
2121: static PetscErrorCode MatZeroRows_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2122: {
2123: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2124: PetscInt i, m = A->rmap->n - 1;
2125: const PetscScalar *xx;
2126: PetscScalar *bb, *aa;
2127: PetscInt d = 0;
2129: PetscFunctionBegin;
2130: if (x && b) {
2131: PetscCall(VecGetArrayRead(x, &xx));
2132: PetscCall(VecGetArray(b, &bb));
2133: for (i = 0; i < N; i++) {
2134: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2135: if (rows[i] >= A->cmap->n) continue;
2136: bb[rows[i]] = diag * xx[rows[i]];
2137: }
2138: PetscCall(VecRestoreArrayRead(x, &xx));
2139: PetscCall(VecRestoreArray(b, &bb));
2140: }
2142: PetscCall(MatSeqAIJGetArray(A, &aa));
2143: if (a->keepnonzeropattern) {
2144: for (i = 0; i < N; i++) {
2145: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2146: PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));
2147: }
2148: if (diag != 0.0) {
2149: for (i = 0; i < N; i++) {
2150: d = rows[i];
2151: if (rows[i] >= A->cmap->n) continue;
2152: 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);
2153: }
2154: for (i = 0; i < N; i++) {
2155: if (rows[i] >= A->cmap->n) continue;
2156: aa[a->diag[rows[i]]] = diag;
2157: }
2158: }
2159: } else {
2160: if (diag != 0.0) {
2161: for (i = 0; i < N; i++) {
2162: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2163: if (a->ilen[rows[i]] > 0) {
2164: if (rows[i] >= A->cmap->n) {
2165: a->ilen[rows[i]] = 0;
2166: } else {
2167: a->ilen[rows[i]] = 1;
2168: aa[a->i[rows[i]]] = diag;
2169: a->j[a->i[rows[i]]] = rows[i];
2170: }
2171: } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2172: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2173: }
2174: }
2175: } else {
2176: for (i = 0; i < N; i++) {
2177: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2178: a->ilen[rows[i]] = 0;
2179: }
2180: }
2181: A->nonzerostate++;
2182: }
2183: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2184: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2185: PetscFunctionReturn(PETSC_SUCCESS);
2186: }
2188: static PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2189: {
2190: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2191: PetscInt i, j, m = A->rmap->n - 1, d = 0;
2192: PetscBool missing, *zeroed, vecs = PETSC_FALSE;
2193: const PetscScalar *xx;
2194: PetscScalar *bb, *aa;
2196: PetscFunctionBegin;
2197: if (!N) PetscFunctionReturn(PETSC_SUCCESS);
2198: PetscCall(MatSeqAIJGetArray(A, &aa));
2199: if (x && b) {
2200: PetscCall(VecGetArrayRead(x, &xx));
2201: PetscCall(VecGetArray(b, &bb));
2202: vecs = PETSC_TRUE;
2203: }
2204: PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
2205: for (i = 0; i < N; i++) {
2206: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2207: PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aa, a->i[rows[i]]), a->ilen[rows[i]]));
2209: zeroed[rows[i]] = PETSC_TRUE;
2210: }
2211: for (i = 0; i < A->rmap->n; i++) {
2212: if (!zeroed[i]) {
2213: for (j = a->i[i]; j < a->i[i + 1]; j++) {
2214: if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2215: if (vecs) bb[i] -= aa[j] * xx[a->j[j]];
2216: aa[j] = 0.0;
2217: }
2218: }
2219: } else if (vecs && i < A->cmap->N) bb[i] = diag * xx[i];
2220: }
2221: if (x && b) {
2222: PetscCall(VecRestoreArrayRead(x, &xx));
2223: PetscCall(VecRestoreArray(b, &bb));
2224: }
2225: PetscCall(PetscFree(zeroed));
2226: if (diag != 0.0) {
2227: PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, &d));
2228: if (missing) {
2229: for (i = 0; i < N; i++) {
2230: if (rows[i] >= A->cmap->N) continue;
2231: 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]);
2232: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2233: }
2234: } else {
2235: for (i = 0; i < N; i++) aa[a->diag[rows[i]]] = diag;
2236: }
2237: }
2238: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2239: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2240: PetscFunctionReturn(PETSC_SUCCESS);
2241: }
2243: PetscErrorCode MatGetRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2244: {
2245: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2246: const PetscScalar *aa;
2248: PetscFunctionBegin;
2249: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2250: *nz = a->i[row + 1] - a->i[row];
2251: if (v) *v = PetscSafePointerPlusOffset((PetscScalar *)aa, a->i[row]);
2252: if (idx) {
2253: if (*nz && a->j) *idx = a->j + a->i[row];
2254: else *idx = NULL;
2255: }
2256: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2257: PetscFunctionReturn(PETSC_SUCCESS);
2258: }
2260: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2261: {
2262: PetscFunctionBegin;
2263: PetscFunctionReturn(PETSC_SUCCESS);
2264: }
2266: static PetscErrorCode MatNorm_SeqAIJ(Mat A, NormType type, PetscReal *nrm)
2267: {
2268: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2269: const MatScalar *v;
2270: PetscReal sum = 0.0;
2271: PetscInt i, j;
2273: PetscFunctionBegin;
2274: PetscCall(MatSeqAIJGetArrayRead(A, &v));
2275: if (type == NORM_FROBENIUS) {
2276: #if defined(PETSC_USE_REAL___FP16)
2277: PetscBLASInt one = 1, nz = a->nz;
2278: PetscCallBLAS("BLASnrm2", *nrm = BLASnrm2_(&nz, v, &one));
2279: #else
2280: for (i = 0; i < a->nz; i++) {
2281: sum += PetscRealPart(PetscConj(*v) * (*v));
2282: v++;
2283: }
2284: *nrm = PetscSqrtReal(sum);
2285: #endif
2286: PetscCall(PetscLogFlops(2.0 * a->nz));
2287: } else if (type == NORM_1) {
2288: PetscReal *tmp;
2289: PetscInt *jj = a->j;
2290: PetscCall(PetscCalloc1(A->cmap->n + 1, &tmp));
2291: *nrm = 0.0;
2292: for (j = 0; j < a->nz; j++) {
2293: tmp[*jj++] += PetscAbsScalar(*v);
2294: v++;
2295: }
2296: for (j = 0; j < A->cmap->n; j++) {
2297: if (tmp[j] > *nrm) *nrm = tmp[j];
2298: }
2299: PetscCall(PetscFree(tmp));
2300: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2301: } else if (type == NORM_INFINITY) {
2302: *nrm = 0.0;
2303: for (j = 0; j < A->rmap->n; j++) {
2304: const PetscScalar *v2 = PetscSafePointerPlusOffset(v, a->i[j]);
2305: sum = 0.0;
2306: for (i = 0; i < a->i[j + 1] - a->i[j]; i++) {
2307: sum += PetscAbsScalar(*v2);
2308: v2++;
2309: }
2310: if (sum > *nrm) *nrm = sum;
2311: }
2312: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2313: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for two norm");
2314: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
2315: PetscFunctionReturn(PETSC_SUCCESS);
2316: }
2318: static PetscErrorCode MatIsTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2319: {
2320: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2321: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2322: const MatScalar *va, *vb;
2323: PetscInt ma, na, mb, nb, i;
2325: PetscFunctionBegin;
2326: PetscCall(MatGetSize(A, &ma, &na));
2327: PetscCall(MatGetSize(B, &mb, &nb));
2328: if (ma != nb || na != mb) {
2329: *f = PETSC_FALSE;
2330: PetscFunctionReturn(PETSC_SUCCESS);
2331: }
2332: PetscCall(MatSeqAIJGetArrayRead(A, &va));
2333: PetscCall(MatSeqAIJGetArrayRead(B, &vb));
2334: aii = aij->i;
2335: bii = bij->i;
2336: adx = aij->j;
2337: bdx = bij->j;
2338: PetscCall(PetscMalloc1(ma, &aptr));
2339: PetscCall(PetscMalloc1(mb, &bptr));
2340: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2341: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2343: *f = PETSC_TRUE;
2344: for (i = 0; i < ma; i++) {
2345: while (aptr[i] < aii[i + 1]) {
2346: PetscInt idc, idr;
2347: PetscScalar vc, vr;
2348: /* column/row index/value */
2349: idc = adx[aptr[i]];
2350: idr = bdx[bptr[idc]];
2351: vc = va[aptr[i]];
2352: vr = vb[bptr[idc]];
2353: if (i != idr || PetscAbsScalar(vc - vr) > tol) {
2354: *f = PETSC_FALSE;
2355: goto done;
2356: } else {
2357: aptr[i]++;
2358: if (B || i != idc) bptr[idc]++;
2359: }
2360: }
2361: }
2362: done:
2363: PetscCall(PetscFree(aptr));
2364: PetscCall(PetscFree(bptr));
2365: PetscCall(MatSeqAIJRestoreArrayRead(A, &va));
2366: PetscCall(MatSeqAIJRestoreArrayRead(B, &vb));
2367: PetscFunctionReturn(PETSC_SUCCESS);
2368: }
2370: static PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2371: {
2372: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2373: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2374: MatScalar *va, *vb;
2375: PetscInt ma, na, mb, nb, i;
2377: PetscFunctionBegin;
2378: PetscCall(MatGetSize(A, &ma, &na));
2379: PetscCall(MatGetSize(B, &mb, &nb));
2380: if (ma != nb || na != mb) {
2381: *f = PETSC_FALSE;
2382: PetscFunctionReturn(PETSC_SUCCESS);
2383: }
2384: aii = aij->i;
2385: bii = bij->i;
2386: adx = aij->j;
2387: bdx = bij->j;
2388: va = aij->a;
2389: vb = bij->a;
2390: PetscCall(PetscMalloc1(ma, &aptr));
2391: PetscCall(PetscMalloc1(mb, &bptr));
2392: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2393: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2395: *f = PETSC_TRUE;
2396: for (i = 0; i < ma; i++) {
2397: while (aptr[i] < aii[i + 1]) {
2398: PetscInt idc, idr;
2399: PetscScalar vc, vr;
2400: /* column/row index/value */
2401: idc = adx[aptr[i]];
2402: idr = bdx[bptr[idc]];
2403: vc = va[aptr[i]];
2404: vr = vb[bptr[idc]];
2405: if (i != idr || PetscAbsScalar(vc - PetscConj(vr)) > tol) {
2406: *f = PETSC_FALSE;
2407: goto done;
2408: } else {
2409: aptr[i]++;
2410: if (B || i != idc) bptr[idc]++;
2411: }
2412: }
2413: }
2414: done:
2415: PetscCall(PetscFree(aptr));
2416: PetscCall(PetscFree(bptr));
2417: PetscFunctionReturn(PETSC_SUCCESS);
2418: }
2420: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A, Vec ll, Vec rr)
2421: {
2422: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2423: const PetscScalar *l, *r;
2424: PetscScalar x;
2425: MatScalar *v;
2426: PetscInt i, j, m = A->rmap->n, n = A->cmap->n, M, nz = a->nz;
2427: const PetscInt *jj;
2429: PetscFunctionBegin;
2430: if (ll) {
2431: /* The local size is used so that VecMPI can be passed to this routine
2432: by MatDiagonalScale_MPIAIJ */
2433: PetscCall(VecGetLocalSize(ll, &m));
2434: PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length");
2435: PetscCall(VecGetArrayRead(ll, &l));
2436: PetscCall(MatSeqAIJGetArray(A, &v));
2437: for (i = 0; i < m; i++) {
2438: x = l[i];
2439: M = a->i[i + 1] - a->i[i];
2440: for (j = 0; j < M; j++) (*v++) *= x;
2441: }
2442: PetscCall(VecRestoreArrayRead(ll, &l));
2443: PetscCall(PetscLogFlops(nz));
2444: PetscCall(MatSeqAIJRestoreArray(A, &v));
2445: }
2446: if (rr) {
2447: PetscCall(VecGetLocalSize(rr, &n));
2448: PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length");
2449: PetscCall(VecGetArrayRead(rr, &r));
2450: PetscCall(MatSeqAIJGetArray(A, &v));
2451: jj = a->j;
2452: for (i = 0; i < nz; i++) (*v++) *= r[*jj++];
2453: PetscCall(MatSeqAIJRestoreArray(A, &v));
2454: PetscCall(VecRestoreArrayRead(rr, &r));
2455: PetscCall(PetscLogFlops(nz));
2456: }
2457: PetscCall(MatSeqAIJInvalidateDiagonal(A));
2458: PetscFunctionReturn(PETSC_SUCCESS);
2459: }
2461: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A, IS isrow, IS iscol, PetscInt csize, MatReuse scall, Mat *B)
2462: {
2463: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *c;
2464: PetscInt *smap, i, k, kstart, kend, oldcols = A->cmap->n, *lens;
2465: PetscInt row, mat_i, *mat_j, tcol, first, step, *mat_ilen, sum, lensi;
2466: const PetscInt *irow, *icol;
2467: const PetscScalar *aa;
2468: PetscInt nrows, ncols;
2469: PetscInt *starts, *j_new, *i_new, *aj = a->j, *ai = a->i, ii, *ailen = a->ilen;
2470: MatScalar *a_new, *mat_a, *c_a;
2471: Mat C;
2472: PetscBool stride;
2474: PetscFunctionBegin;
2475: PetscCall(ISGetIndices(isrow, &irow));
2476: PetscCall(ISGetLocalSize(isrow, &nrows));
2477: PetscCall(ISGetLocalSize(iscol, &ncols));
2479: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &stride));
2480: if (stride) {
2481: PetscCall(ISStrideGetInfo(iscol, &first, &step));
2482: } else {
2483: first = 0;
2484: step = 0;
2485: }
2486: if (stride && step == 1) {
2487: /* special case of contiguous rows */
2488: PetscCall(PetscMalloc2(nrows, &lens, nrows, &starts));
2489: /* loop over new rows determining lens and starting points */
2490: for (i = 0; i < nrows; i++) {
2491: kstart = ai[irow[i]];
2492: kend = kstart + ailen[irow[i]];
2493: starts[i] = kstart;
2494: for (k = kstart; k < kend; k++) {
2495: if (aj[k] >= first) {
2496: starts[i] = k;
2497: break;
2498: }
2499: }
2500: sum = 0;
2501: while (k < kend) {
2502: if (aj[k++] >= first + ncols) break;
2503: sum++;
2504: }
2505: lens[i] = sum;
2506: }
2507: /* create submatrix */
2508: if (scall == MAT_REUSE_MATRIX) {
2509: PetscInt n_cols, n_rows;
2510: PetscCall(MatGetSize(*B, &n_rows, &n_cols));
2511: PetscCheck(n_rows == nrows && n_cols == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Reused submatrix wrong size");
2512: PetscCall(MatZeroEntries(*B));
2513: C = *B;
2514: } else {
2515: PetscInt rbs, cbs;
2516: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2517: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2518: PetscCall(ISGetBlockSize(isrow, &rbs));
2519: PetscCall(ISGetBlockSize(iscol, &cbs));
2520: PetscCall(MatSetBlockSizes(C, rbs, cbs));
2521: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2522: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2523: }
2524: c = (Mat_SeqAIJ *)C->data;
2526: /* loop over rows inserting into submatrix */
2527: PetscCall(MatSeqAIJGetArrayWrite(C, &a_new)); // Not 'a_new = c->a-new', since that raw usage ignores offload state of C
2528: j_new = c->j;
2529: i_new = c->i;
2530: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2531: for (i = 0; i < nrows; i++) {
2532: ii = starts[i];
2533: lensi = lens[i];
2534: if (lensi) {
2535: for (k = 0; k < lensi; k++) *j_new++ = aj[ii + k] - first;
2536: PetscCall(PetscArraycpy(a_new, aa + starts[i], lensi));
2537: a_new += lensi;
2538: }
2539: i_new[i + 1] = i_new[i] + lensi;
2540: c->ilen[i] = lensi;
2541: }
2542: PetscCall(MatSeqAIJRestoreArrayWrite(C, &a_new)); // Set C's offload state properly
2543: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2544: PetscCall(PetscFree2(lens, starts));
2545: } else {
2546: PetscCall(ISGetIndices(iscol, &icol));
2547: PetscCall(PetscCalloc1(oldcols, &smap));
2548: PetscCall(PetscMalloc1(1 + nrows, &lens));
2549: for (i = 0; i < ncols; i++) {
2550: 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);
2551: smap[icol[i]] = i + 1;
2552: }
2554: /* determine lens of each row */
2555: for (i = 0; i < nrows; i++) {
2556: kstart = ai[irow[i]];
2557: kend = kstart + a->ilen[irow[i]];
2558: lens[i] = 0;
2559: for (k = kstart; k < kend; k++) {
2560: if (smap[aj[k]]) lens[i]++;
2561: }
2562: }
2563: /* Create and fill new matrix */
2564: if (scall == MAT_REUSE_MATRIX) {
2565: PetscBool equal;
2567: c = (Mat_SeqAIJ *)((*B)->data);
2568: PetscCheck((*B)->rmap->n == nrows && (*B)->cmap->n == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong size");
2569: PetscCall(PetscArraycmp(c->ilen, lens, (*B)->rmap->n, &equal));
2570: PetscCheck(equal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong number of nonzeros");
2571: PetscCall(PetscArrayzero(c->ilen, (*B)->rmap->n));
2572: C = *B;
2573: } else {
2574: PetscInt rbs, cbs;
2575: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2576: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2577: PetscCall(ISGetBlockSize(isrow, &rbs));
2578: PetscCall(ISGetBlockSize(iscol, &cbs));
2579: if (rbs > 1 || cbs > 1) PetscCall(MatSetBlockSizes(C, rbs, cbs));
2580: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2581: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2582: }
2583: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2585: c = (Mat_SeqAIJ *)C->data;
2586: PetscCall(MatSeqAIJGetArrayWrite(C, &c_a)); // Not 'c->a', since that raw usage ignores offload state of C
2587: for (i = 0; i < nrows; i++) {
2588: row = irow[i];
2589: kstart = ai[row];
2590: kend = kstart + a->ilen[row];
2591: mat_i = c->i[i];
2592: mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2593: mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2594: mat_ilen = c->ilen + i;
2595: for (k = kstart; k < kend; k++) {
2596: if ((tcol = smap[a->j[k]])) {
2597: *mat_j++ = tcol - 1;
2598: *mat_a++ = aa[k];
2599: (*mat_ilen)++;
2600: }
2601: }
2602: }
2603: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2604: /* Free work space */
2605: PetscCall(ISRestoreIndices(iscol, &icol));
2606: PetscCall(PetscFree(smap));
2607: PetscCall(PetscFree(lens));
2608: /* sort */
2609: for (i = 0; i < nrows; i++) {
2610: PetscInt ilen;
2612: mat_i = c->i[i];
2613: mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2614: mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2615: ilen = c->ilen[i];
2616: PetscCall(PetscSortIntWithScalarArray(ilen, mat_j, mat_a));
2617: }
2618: PetscCall(MatSeqAIJRestoreArrayWrite(C, &c_a));
2619: }
2620: #if defined(PETSC_HAVE_DEVICE)
2621: PetscCall(MatBindToCPU(C, A->boundtocpu));
2622: #endif
2623: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
2624: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
2626: PetscCall(ISRestoreIndices(isrow, &irow));
2627: *B = C;
2628: PetscFunctionReturn(PETSC_SUCCESS);
2629: }
2631: static PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
2632: {
2633: Mat B;
2635: PetscFunctionBegin;
2636: if (scall == MAT_INITIAL_MATRIX) {
2637: PetscCall(MatCreate(subComm, &B));
2638: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n));
2639: PetscCall(MatSetBlockSizesFromMats(B, mat, mat));
2640: PetscCall(MatSetType(B, MATSEQAIJ));
2641: PetscCall(MatDuplicateNoCreate_SeqAIJ(B, mat, MAT_COPY_VALUES, PETSC_TRUE));
2642: *subMat = B;
2643: } else {
2644: PetscCall(MatCopy_SeqAIJ(mat, *subMat, SAME_NONZERO_PATTERN));
2645: }
2646: PetscFunctionReturn(PETSC_SUCCESS);
2647: }
2649: static PetscErrorCode MatILUFactor_SeqAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
2650: {
2651: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2652: Mat outA;
2653: PetscBool row_identity, col_identity;
2655: PetscFunctionBegin;
2656: PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels=0 supported for in-place ilu");
2658: PetscCall(ISIdentity(row, &row_identity));
2659: PetscCall(ISIdentity(col, &col_identity));
2661: outA = inA;
2662: outA->factortype = MAT_FACTOR_LU;
2663: PetscCall(PetscFree(inA->solvertype));
2664: PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));
2666: PetscCall(PetscObjectReference((PetscObject)row));
2667: PetscCall(ISDestroy(&a->row));
2669: a->row = row;
2671: PetscCall(PetscObjectReference((PetscObject)col));
2672: PetscCall(ISDestroy(&a->col));
2674: a->col = col;
2676: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2677: PetscCall(ISDestroy(&a->icol));
2678: PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));
2680: if (!a->solve_work) { /* this matrix may have been factored before */
2681: PetscCall(PetscMalloc1(inA->rmap->n + 1, &a->solve_work));
2682: }
2684: PetscCall(MatMarkDiagonal_SeqAIJ(inA));
2685: if (row_identity && col_identity) {
2686: PetscCall(MatLUFactorNumeric_SeqAIJ_inplace(outA, inA, info));
2687: } else {
2688: PetscCall(MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA, inA, info));
2689: }
2690: PetscFunctionReturn(PETSC_SUCCESS);
2691: }
2693: PetscErrorCode MatScale_SeqAIJ(Mat inA, PetscScalar alpha)
2694: {
2695: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2696: PetscScalar *v;
2697: PetscBLASInt one = 1, bnz;
2699: PetscFunctionBegin;
2700: PetscCall(MatSeqAIJGetArray(inA, &v));
2701: PetscCall(PetscBLASIntCast(a->nz, &bnz));
2702: PetscCallBLAS("BLASscal", BLASscal_(&bnz, &alpha, v, &one));
2703: PetscCall(PetscLogFlops(a->nz));
2704: PetscCall(MatSeqAIJRestoreArray(inA, &v));
2705: PetscCall(MatSeqAIJInvalidateDiagonal(inA));
2706: PetscFunctionReturn(PETSC_SUCCESS);
2707: }
2709: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2710: {
2711: PetscInt i;
2713: PetscFunctionBegin;
2714: if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2715: PetscCall(PetscFree4(submatj->sbuf1, submatj->ptr, submatj->tmp, submatj->ctr));
2717: for (i = 0; i < submatj->nrqr; ++i) PetscCall(PetscFree(submatj->sbuf2[i]));
2718: PetscCall(PetscFree3(submatj->sbuf2, submatj->req_size, submatj->req_source1));
2720: if (submatj->rbuf1) {
2721: PetscCall(PetscFree(submatj->rbuf1[0]));
2722: PetscCall(PetscFree(submatj->rbuf1));
2723: }
2725: for (i = 0; i < submatj->nrqs; ++i) PetscCall(PetscFree(submatj->rbuf3[i]));
2726: PetscCall(PetscFree3(submatj->req_source2, submatj->rbuf2, submatj->rbuf3));
2727: PetscCall(PetscFree(submatj->pa));
2728: }
2730: #if defined(PETSC_USE_CTABLE)
2731: PetscCall(PetscHMapIDestroy(&submatj->rmap));
2732: if (submatj->cmap_loc) PetscCall(PetscFree(submatj->cmap_loc));
2733: PetscCall(PetscFree(submatj->rmap_loc));
2734: #else
2735: PetscCall(PetscFree(submatj->rmap));
2736: #endif
2738: if (!submatj->allcolumns) {
2739: #if defined(PETSC_USE_CTABLE)
2740: PetscCall(PetscHMapIDestroy((PetscHMapI *)&submatj->cmap));
2741: #else
2742: PetscCall(PetscFree(submatj->cmap));
2743: #endif
2744: }
2745: PetscCall(PetscFree(submatj->row2proc));
2747: PetscCall(PetscFree(submatj));
2748: PetscFunctionReturn(PETSC_SUCCESS);
2749: }
2751: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2752: {
2753: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data;
2754: Mat_SubSppt *submatj = c->submatis1;
2756: PetscFunctionBegin;
2757: PetscCall((*submatj->destroy)(C));
2758: PetscCall(MatDestroySubMatrix_Private(submatj));
2759: PetscFunctionReturn(PETSC_SUCCESS);
2760: }
2762: /* Note this has code duplication with MatDestroySubMatrices_SeqBAIJ() */
2763: static PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n, Mat *mat[])
2764: {
2765: PetscInt i;
2766: Mat C;
2767: Mat_SeqAIJ *c;
2768: Mat_SubSppt *submatj;
2770: PetscFunctionBegin;
2771: for (i = 0; i < n; i++) {
2772: C = (*mat)[i];
2773: c = (Mat_SeqAIJ *)C->data;
2774: submatj = c->submatis1;
2775: if (submatj) {
2776: if (--((PetscObject)C)->refct <= 0) {
2777: PetscCall(PetscFree(C->factorprefix));
2778: PetscCall((*submatj->destroy)(C));
2779: PetscCall(MatDestroySubMatrix_Private(submatj));
2780: PetscCall(PetscFree(C->defaultvectype));
2781: PetscCall(PetscFree(C->defaultrandtype));
2782: PetscCall(PetscLayoutDestroy(&C->rmap));
2783: PetscCall(PetscLayoutDestroy(&C->cmap));
2784: PetscCall(PetscHeaderDestroy(&C));
2785: }
2786: } else {
2787: PetscCall(MatDestroy(&C));
2788: }
2789: }
2791: /* Destroy Dummy submatrices created for reuse */
2792: PetscCall(MatDestroySubMatrices_Dummy(n, mat));
2794: PetscCall(PetscFree(*mat));
2795: PetscFunctionReturn(PETSC_SUCCESS);
2796: }
2798: static PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *B[])
2799: {
2800: PetscInt i;
2802: PetscFunctionBegin;
2803: if (scall == MAT_INITIAL_MATRIX) PetscCall(PetscCalloc1(n + 1, B));
2805: for (i = 0; i < n; i++) PetscCall(MatCreateSubMatrix_SeqAIJ(A, irow[i], icol[i], PETSC_DECIDE, scall, &(*B)[i]));
2806: PetscFunctionReturn(PETSC_SUCCESS);
2807: }
2809: static PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A, PetscInt is_max, IS is[], PetscInt ov)
2810: {
2811: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2812: PetscInt row, i, j, k, l, ll, m, n, *nidx, isz, val;
2813: const PetscInt *idx;
2814: PetscInt start, end, *ai, *aj, bs = (A->rmap->bs > 0 && A->rmap->bs == A->cmap->bs) ? A->rmap->bs : 1;
2815: PetscBT table;
2817: PetscFunctionBegin;
2818: m = A->rmap->n / bs;
2819: ai = a->i;
2820: aj = a->j;
2822: PetscCheck(ov >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "illegal negative overlap value used");
2824: PetscCall(PetscMalloc1(m + 1, &nidx));
2825: PetscCall(PetscBTCreate(m, &table));
2827: for (i = 0; i < is_max; i++) {
2828: /* Initialize the two local arrays */
2829: isz = 0;
2830: PetscCall(PetscBTMemzero(m, table));
2832: /* Extract the indices, assume there can be duplicate entries */
2833: PetscCall(ISGetIndices(is[i], &idx));
2834: PetscCall(ISGetLocalSize(is[i], &n));
2836: if (bs > 1) {
2837: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2838: for (j = 0; j < n; ++j) {
2839: if (!PetscBTLookupSet(table, idx[j] / bs)) nidx[isz++] = idx[j] / bs;
2840: }
2841: PetscCall(ISRestoreIndices(is[i], &idx));
2842: PetscCall(ISDestroy(&is[i]));
2844: k = 0;
2845: for (j = 0; j < ov; j++) { /* for each overlap */
2846: n = isz;
2847: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2848: for (ll = 0; ll < bs; ll++) {
2849: row = bs * nidx[k] + ll;
2850: start = ai[row];
2851: end = ai[row + 1];
2852: for (l = start; l < end; l++) {
2853: val = aj[l] / bs;
2854: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2855: }
2856: }
2857: }
2858: }
2859: PetscCall(ISCreateBlock(PETSC_COMM_SELF, bs, isz, nidx, PETSC_COPY_VALUES, is + i));
2860: } else {
2861: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2862: for (j = 0; j < n; ++j) {
2863: if (!PetscBTLookupSet(table, idx[j])) nidx[isz++] = idx[j];
2864: }
2865: PetscCall(ISRestoreIndices(is[i], &idx));
2866: PetscCall(ISDestroy(&is[i]));
2868: k = 0;
2869: for (j = 0; j < ov; j++) { /* for each overlap */
2870: n = isz;
2871: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2872: row = nidx[k];
2873: start = ai[row];
2874: end = ai[row + 1];
2875: for (l = start; l < end; l++) {
2876: val = aj[l];
2877: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2878: }
2879: }
2880: }
2881: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, isz, nidx, PETSC_COPY_VALUES, is + i));
2882: }
2883: }
2884: PetscCall(PetscBTDestroy(&table));
2885: PetscCall(PetscFree(nidx));
2886: PetscFunctionReturn(PETSC_SUCCESS);
2887: }
2889: static PetscErrorCode MatPermute_SeqAIJ(Mat A, IS rowp, IS colp, Mat *B)
2890: {
2891: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2892: PetscInt i, nz = 0, m = A->rmap->n, n = A->cmap->n;
2893: const PetscInt *row, *col;
2894: PetscInt *cnew, j, *lens;
2895: IS icolp, irowp;
2896: PetscInt *cwork = NULL;
2897: PetscScalar *vwork = NULL;
2899: PetscFunctionBegin;
2900: PetscCall(ISInvertPermutation(rowp, PETSC_DECIDE, &irowp));
2901: PetscCall(ISGetIndices(irowp, &row));
2902: PetscCall(ISInvertPermutation(colp, PETSC_DECIDE, &icolp));
2903: PetscCall(ISGetIndices(icolp, &col));
2905: /* determine lengths of permuted rows */
2906: PetscCall(PetscMalloc1(m + 1, &lens));
2907: for (i = 0; i < m; i++) lens[row[i]] = a->i[i + 1] - a->i[i];
2908: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2909: PetscCall(MatSetSizes(*B, m, n, m, n));
2910: PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2911: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
2912: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*B, 0, lens));
2913: PetscCall(PetscFree(lens));
2915: PetscCall(PetscMalloc1(n, &cnew));
2916: for (i = 0; i < m; i++) {
2917: PetscCall(MatGetRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2918: for (j = 0; j < nz; j++) cnew[j] = col[cwork[j]];
2919: PetscCall(MatSetValues_SeqAIJ(*B, 1, &row[i], nz, cnew, vwork, INSERT_VALUES));
2920: PetscCall(MatRestoreRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2921: }
2922: PetscCall(PetscFree(cnew));
2924: (*B)->assembled = PETSC_FALSE;
2926: #if defined(PETSC_HAVE_DEVICE)
2927: PetscCall(MatBindToCPU(*B, A->boundtocpu));
2928: #endif
2929: PetscCall(MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY));
2930: PetscCall(MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY));
2931: PetscCall(ISRestoreIndices(irowp, &row));
2932: PetscCall(ISRestoreIndices(icolp, &col));
2933: PetscCall(ISDestroy(&irowp));
2934: PetscCall(ISDestroy(&icolp));
2935: if (rowp == colp) PetscCall(MatPropagateSymmetryOptions(A, *B));
2936: PetscFunctionReturn(PETSC_SUCCESS);
2937: }
2939: PetscErrorCode MatCopy_SeqAIJ(Mat A, Mat B, MatStructure str)
2940: {
2941: PetscFunctionBegin;
2942: /* If the two matrices have the same copy implementation, use fast copy. */
2943: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2944: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2945: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2946: const PetscScalar *aa;
2948: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2949: 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]);
2950: PetscCall(PetscArraycpy(b->a, aa, a->i[A->rmap->n]));
2951: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2952: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2953: } else {
2954: PetscCall(MatCopy_Basic(A, B, str));
2955: }
2956: PetscFunctionReturn(PETSC_SUCCESS);
2957: }
2959: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A, PetscScalar *array[])
2960: {
2961: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2963: PetscFunctionBegin;
2964: *array = a->a;
2965: PetscFunctionReturn(PETSC_SUCCESS);
2966: }
2968: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A, PetscScalar *array[])
2969: {
2970: PetscFunctionBegin;
2971: *array = NULL;
2972: PetscFunctionReturn(PETSC_SUCCESS);
2973: }
2975: /*
2976: Computes the number of nonzeros per row needed for preallocation when X and Y
2977: have different nonzero structure.
2978: */
2979: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *yi, const PetscInt *yj, PetscInt *nnz)
2980: {
2981: PetscInt i, j, k, nzx, nzy;
2983: PetscFunctionBegin;
2984: /* Set the number of nonzeros in the new matrix */
2985: for (i = 0; i < m; i++) {
2986: const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2987: nzx = xi[i + 1] - xi[i];
2988: nzy = yi[i + 1] - yi[i];
2989: nnz[i] = 0;
2990: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2991: for (; k < nzy && yjj[k] < xjj[j]; k++) nnz[i]++; /* Catch up to X */
2992: if (k < nzy && yjj[k] == xjj[j]) k++; /* Skip duplicate */
2993: nnz[i]++;
2994: }
2995: for (; k < nzy; k++) nnz[i]++;
2996: }
2997: PetscFunctionReturn(PETSC_SUCCESS);
2998: }
3000: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y, Mat X, PetscInt *nnz)
3001: {
3002: PetscInt m = Y->rmap->N;
3003: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
3004: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
3006: PetscFunctionBegin;
3007: /* Set the number of nonzeros in the new matrix */
3008: PetscCall(MatAXPYGetPreallocation_SeqX_private(m, x->i, x->j, y->i, y->j, nnz));
3009: PetscFunctionReturn(PETSC_SUCCESS);
3010: }
3012: PetscErrorCode MatAXPY_SeqAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
3013: {
3014: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;
3016: PetscFunctionBegin;
3017: if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
3018: PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE;
3019: if (e) {
3020: PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e));
3021: if (e) {
3022: PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e));
3023: if (e) str = SAME_NONZERO_PATTERN;
3024: }
3025: }
3026: if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
3027: }
3028: if (str == SAME_NONZERO_PATTERN) {
3029: const PetscScalar *xa;
3030: PetscScalar *ya, alpha = a;
3031: PetscBLASInt one = 1, bnz;
3033: PetscCall(PetscBLASIntCast(x->nz, &bnz));
3034: PetscCall(MatSeqAIJGetArray(Y, &ya));
3035: PetscCall(MatSeqAIJGetArrayRead(X, &xa));
3036: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, xa, &one, ya, &one));
3037: PetscCall(MatSeqAIJRestoreArrayRead(X, &xa));
3038: PetscCall(MatSeqAIJRestoreArray(Y, &ya));
3039: PetscCall(PetscLogFlops(2.0 * bnz));
3040: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3041: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
3042: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
3043: PetscCall(MatAXPY_Basic(Y, a, X, str));
3044: } else {
3045: Mat B;
3046: PetscInt *nnz;
3047: PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
3048: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
3049: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
3050: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
3051: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
3052: PetscCall(MatAXPYGetPreallocation_SeqAIJ(Y, X, nnz));
3053: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
3054: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
3055: PetscCall(MatHeaderMerge(Y, &B));
3056: PetscCall(MatSeqAIJCheckInode(Y));
3057: PetscCall(PetscFree(nnz));
3058: }
3059: PetscFunctionReturn(PETSC_SUCCESS);
3060: }
3062: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
3063: {
3064: #if defined(PETSC_USE_COMPLEX)
3065: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3066: PetscInt i, nz;
3067: PetscScalar *a;
3069: PetscFunctionBegin;
3070: nz = aij->nz;
3071: PetscCall(MatSeqAIJGetArray(mat, &a));
3072: for (i = 0; i < nz; i++) a[i] = PetscConj(a[i]);
3073: PetscCall(MatSeqAIJRestoreArray(mat, &a));
3074: #else
3075: PetscFunctionBegin;
3076: #endif
3077: PetscFunctionReturn(PETSC_SUCCESS);
3078: }
3080: static PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3081: {
3082: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3083: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3084: PetscReal atmp;
3085: PetscScalar *x;
3086: const MatScalar *aa, *av;
3088: PetscFunctionBegin;
3089: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3090: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3091: aa = av;
3092: ai = a->i;
3093: aj = a->j;
3095: PetscCall(VecSet(v, 0.0));
3096: PetscCall(VecGetArrayWrite(v, &x));
3097: PetscCall(VecGetLocalSize(v, &n));
3098: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3099: for (i = 0; i < m; i++) {
3100: ncols = ai[1] - ai[0];
3101: ai++;
3102: for (j = 0; j < ncols; j++) {
3103: atmp = PetscAbsScalar(*aa);
3104: if (PetscAbsScalar(x[i]) < atmp) {
3105: x[i] = atmp;
3106: if (idx) idx[i] = *aj;
3107: }
3108: aa++;
3109: aj++;
3110: }
3111: }
3112: PetscCall(VecRestoreArrayWrite(v, &x));
3113: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3114: PetscFunctionReturn(PETSC_SUCCESS);
3115: }
3117: static PetscErrorCode MatGetRowSumAbs_SeqAIJ(Mat A, Vec v)
3118: {
3119: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3120: PetscInt i, j, m = A->rmap->n, *ai, ncols, n;
3121: PetscScalar *x;
3122: const MatScalar *aa, *av;
3124: PetscFunctionBegin;
3125: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3126: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3127: aa = av;
3128: ai = a->i;
3130: PetscCall(VecSet(v, 0.0));
3131: PetscCall(VecGetArrayWrite(v, &x));
3132: PetscCall(VecGetLocalSize(v, &n));
3133: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3134: for (i = 0; i < m; i++) {
3135: ncols = ai[1] - ai[0];
3136: ai++;
3137: for (j = 0; j < ncols; j++) {
3138: x[i] += PetscAbsScalar(*aa);
3139: aa++;
3140: }
3141: }
3142: PetscCall(VecRestoreArrayWrite(v, &x));
3143: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3144: PetscFunctionReturn(PETSC_SUCCESS);
3145: }
3147: static PetscErrorCode MatGetRowMax_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3148: {
3149: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3150: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3151: PetscScalar *x;
3152: const MatScalar *aa, *av;
3154: PetscFunctionBegin;
3155: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3156: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3157: aa = av;
3158: ai = a->i;
3159: aj = a->j;
3161: PetscCall(VecSet(v, 0.0));
3162: PetscCall(VecGetArrayWrite(v, &x));
3163: PetscCall(VecGetLocalSize(v, &n));
3164: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3165: for (i = 0; i < m; i++) {
3166: ncols = ai[1] - ai[0];
3167: ai++;
3168: if (ncols == A->cmap->n) { /* row is dense */
3169: x[i] = *aa;
3170: if (idx) idx[i] = 0;
3171: } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
3172: x[i] = 0.0;
3173: if (idx) {
3174: for (j = 0; j < ncols; j++) { /* find first implicit 0.0 in the row */
3175: if (aj[j] > j) {
3176: idx[i] = j;
3177: break;
3178: }
3179: }
3180: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3181: if (j == ncols && j < A->cmap->n) idx[i] = j;
3182: }
3183: }
3184: for (j = 0; j < ncols; j++) {
3185: if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {
3186: x[i] = *aa;
3187: if (idx) idx[i] = *aj;
3188: }
3189: aa++;
3190: aj++;
3191: }
3192: }
3193: PetscCall(VecRestoreArrayWrite(v, &x));
3194: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3195: PetscFunctionReturn(PETSC_SUCCESS);
3196: }
3198: static PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3199: {
3200: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3201: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3202: PetscScalar *x;
3203: const MatScalar *aa, *av;
3205: PetscFunctionBegin;
3206: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3207: aa = av;
3208: ai = a->i;
3209: aj = a->j;
3211: PetscCall(VecSet(v, 0.0));
3212: PetscCall(VecGetArrayWrite(v, &x));
3213: PetscCall(VecGetLocalSize(v, &n));
3214: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector, %" PetscInt_FMT " vs. %" PetscInt_FMT " rows", m, n);
3215: for (i = 0; i < m; i++) {
3216: ncols = ai[1] - ai[0];
3217: ai++;
3218: if (ncols == A->cmap->n) { /* row is dense */
3219: x[i] = *aa;
3220: if (idx) idx[i] = 0;
3221: } else { /* row is sparse so already KNOW minimum is 0.0 or higher */
3222: x[i] = 0.0;
3223: if (idx) { /* find first implicit 0.0 in the row */
3224: for (j = 0; j < ncols; j++) {
3225: if (aj[j] > j) {
3226: idx[i] = j;
3227: break;
3228: }
3229: }
3230: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3231: if (j == ncols && j < A->cmap->n) idx[i] = j;
3232: }
3233: }
3234: for (j = 0; j < ncols; j++) {
3235: if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {
3236: x[i] = *aa;
3237: if (idx) idx[i] = *aj;
3238: }
3239: aa++;
3240: aj++;
3241: }
3242: }
3243: PetscCall(VecRestoreArrayWrite(v, &x));
3244: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3245: PetscFunctionReturn(PETSC_SUCCESS);
3246: }
3248: static PetscErrorCode MatGetRowMin_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3249: {
3250: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3251: PetscInt i, j, m = A->rmap->n, ncols, n;
3252: const PetscInt *ai, *aj;
3253: PetscScalar *x;
3254: const MatScalar *aa, *av;
3256: PetscFunctionBegin;
3257: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3258: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3259: aa = av;
3260: ai = a->i;
3261: aj = a->j;
3263: PetscCall(VecSet(v, 0.0));
3264: PetscCall(VecGetArrayWrite(v, &x));
3265: PetscCall(VecGetLocalSize(v, &n));
3266: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3267: for (i = 0; i < m; i++) {
3268: ncols = ai[1] - ai[0];
3269: ai++;
3270: if (ncols == A->cmap->n) { /* row is dense */
3271: x[i] = *aa;
3272: if (idx) idx[i] = 0;
3273: } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
3274: x[i] = 0.0;
3275: if (idx) { /* find first implicit 0.0 in the row */
3276: for (j = 0; j < ncols; j++) {
3277: if (aj[j] > j) {
3278: idx[i] = j;
3279: break;
3280: }
3281: }
3282: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3283: if (j == ncols && j < A->cmap->n) idx[i] = j;
3284: }
3285: }
3286: for (j = 0; j < ncols; j++) {
3287: if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {
3288: x[i] = *aa;
3289: if (idx) idx[i] = *aj;
3290: }
3291: aa++;
3292: aj++;
3293: }
3294: }
3295: PetscCall(VecRestoreArrayWrite(v, &x));
3296: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3297: PetscFunctionReturn(PETSC_SUCCESS);
3298: }
3300: static PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A, const PetscScalar **values)
3301: {
3302: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3303: PetscInt i, bs = PetscAbs(A->rmap->bs), mbs = A->rmap->n / bs, ipvt[5], bs2 = bs * bs, *v_pivots, ij[7], *IJ, j;
3304: MatScalar *diag, work[25], *v_work;
3305: const PetscReal shift = 0.0;
3306: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
3308: PetscFunctionBegin;
3309: allowzeropivot = PetscNot(A->erroriffailure);
3310: if (a->ibdiagvalid) {
3311: if (values) *values = a->ibdiag;
3312: PetscFunctionReturn(PETSC_SUCCESS);
3313: }
3314: PetscCall(MatMarkDiagonal_SeqAIJ(A));
3315: if (!a->ibdiag) { PetscCall(PetscMalloc1(bs2 * mbs, &a->ibdiag)); }
3316: diag = a->ibdiag;
3317: if (values) *values = a->ibdiag;
3318: /* factor and invert each block */
3319: switch (bs) {
3320: case 1:
3321: for (i = 0; i < mbs; i++) {
3322: PetscCall(MatGetValues(A, 1, &i, 1, &i, diag + i));
3323: if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3324: if (allowzeropivot) {
3325: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3326: A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3327: A->factorerror_zeropivot_row = i;
3328: PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g\n", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON));
3329: } 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);
3330: }
3331: diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3332: }
3333: break;
3334: case 2:
3335: for (i = 0; i < mbs; i++) {
3336: ij[0] = 2 * i;
3337: ij[1] = 2 * i + 1;
3338: PetscCall(MatGetValues(A, 2, ij, 2, ij, diag));
3339: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
3340: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3341: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
3342: diag += 4;
3343: }
3344: break;
3345: case 3:
3346: for (i = 0; i < mbs; i++) {
3347: ij[0] = 3 * i;
3348: ij[1] = 3 * i + 1;
3349: ij[2] = 3 * i + 2;
3350: PetscCall(MatGetValues(A, 3, ij, 3, ij, diag));
3351: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
3352: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3353: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
3354: diag += 9;
3355: }
3356: break;
3357: case 4:
3358: for (i = 0; i < mbs; i++) {
3359: ij[0] = 4 * i;
3360: ij[1] = 4 * i + 1;
3361: ij[2] = 4 * i + 2;
3362: ij[3] = 4 * i + 3;
3363: PetscCall(MatGetValues(A, 4, ij, 4, ij, diag));
3364: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
3365: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3366: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
3367: diag += 16;
3368: }
3369: break;
3370: case 5:
3371: for (i = 0; i < mbs; i++) {
3372: ij[0] = 5 * i;
3373: ij[1] = 5 * i + 1;
3374: ij[2] = 5 * i + 2;
3375: ij[3] = 5 * i + 3;
3376: ij[4] = 5 * i + 4;
3377: PetscCall(MatGetValues(A, 5, ij, 5, ij, diag));
3378: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
3379: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3380: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
3381: diag += 25;
3382: }
3383: break;
3384: case 6:
3385: for (i = 0; i < mbs; i++) {
3386: ij[0] = 6 * i;
3387: ij[1] = 6 * i + 1;
3388: ij[2] = 6 * i + 2;
3389: ij[3] = 6 * i + 3;
3390: ij[4] = 6 * i + 4;
3391: ij[5] = 6 * i + 5;
3392: PetscCall(MatGetValues(A, 6, ij, 6, ij, diag));
3393: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
3394: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3395: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
3396: diag += 36;
3397: }
3398: break;
3399: case 7:
3400: for (i = 0; i < mbs; i++) {
3401: ij[0] = 7 * i;
3402: ij[1] = 7 * i + 1;
3403: ij[2] = 7 * i + 2;
3404: ij[3] = 7 * i + 3;
3405: ij[4] = 7 * i + 4;
3406: ij[5] = 7 * i + 5;
3407: ij[6] = 7 * i + 6;
3408: PetscCall(MatGetValues(A, 7, ij, 7, ij, diag));
3409: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
3410: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3411: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
3412: diag += 49;
3413: }
3414: break;
3415: default:
3416: PetscCall(PetscMalloc3(bs, &v_work, bs, &v_pivots, bs, &IJ));
3417: for (i = 0; i < mbs; i++) {
3418: for (j = 0; j < bs; j++) IJ[j] = bs * i + j;
3419: PetscCall(MatGetValues(A, bs, IJ, bs, IJ, diag));
3420: PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
3421: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3422: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bs));
3423: diag += bs2;
3424: }
3425: PetscCall(PetscFree3(v_work, v_pivots, IJ));
3426: }
3427: a->ibdiagvalid = PETSC_TRUE;
3428: PetscFunctionReturn(PETSC_SUCCESS);
3429: }
3431: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x, PetscRandom rctx)
3432: {
3433: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3434: PetscScalar a, *aa;
3435: PetscInt m, n, i, j, col;
3437: PetscFunctionBegin;
3438: if (!x->assembled) {
3439: PetscCall(MatGetSize(x, &m, &n));
3440: for (i = 0; i < m; i++) {
3441: for (j = 0; j < aij->imax[i]; j++) {
3442: PetscCall(PetscRandomGetValue(rctx, &a));
3443: col = (PetscInt)(n * PetscRealPart(a));
3444: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3445: }
3446: }
3447: } else {
3448: PetscCall(MatSeqAIJGetArrayWrite(x, &aa));
3449: for (i = 0; i < aij->nz; i++) PetscCall(PetscRandomGetValue(rctx, aa + i));
3450: PetscCall(MatSeqAIJRestoreArrayWrite(x, &aa));
3451: }
3452: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3453: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3454: PetscFunctionReturn(PETSC_SUCCESS);
3455: }
3457: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3458: PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x, PetscInt low, PetscInt high, PetscRandom rctx)
3459: {
3460: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3461: PetscScalar a;
3462: PetscInt m, n, i, j, col, nskip;
3464: PetscFunctionBegin;
3465: nskip = high - low;
3466: PetscCall(MatGetSize(x, &m, &n));
3467: n -= nskip; /* shrink number of columns where nonzeros can be set */
3468: for (i = 0; i < m; i++) {
3469: for (j = 0; j < aij->imax[i]; j++) {
3470: PetscCall(PetscRandomGetValue(rctx, &a));
3471: col = (PetscInt)(n * PetscRealPart(a));
3472: if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3473: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3474: }
3475: }
3476: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3477: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3478: PetscFunctionReturn(PETSC_SUCCESS);
3479: }
3481: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
3482: MatGetRow_SeqAIJ,
3483: MatRestoreRow_SeqAIJ,
3484: MatMult_SeqAIJ,
3485: /* 4*/ MatMultAdd_SeqAIJ,
3486: MatMultTranspose_SeqAIJ,
3487: MatMultTransposeAdd_SeqAIJ,
3488: NULL,
3489: NULL,
3490: NULL,
3491: /* 10*/ NULL,
3492: MatLUFactor_SeqAIJ,
3493: NULL,
3494: MatSOR_SeqAIJ,
3495: MatTranspose_SeqAIJ,
3496: /*1 5*/ MatGetInfo_SeqAIJ,
3497: MatEqual_SeqAIJ,
3498: MatGetDiagonal_SeqAIJ,
3499: MatDiagonalScale_SeqAIJ,
3500: MatNorm_SeqAIJ,
3501: /* 20*/ NULL,
3502: MatAssemblyEnd_SeqAIJ,
3503: MatSetOption_SeqAIJ,
3504: MatZeroEntries_SeqAIJ,
3505: /* 24*/ MatZeroRows_SeqAIJ,
3506: NULL,
3507: NULL,
3508: NULL,
3509: NULL,
3510: /* 29*/ MatSetUp_Seq_Hash,
3511: NULL,
3512: NULL,
3513: NULL,
3514: NULL,
3515: /* 34*/ MatDuplicate_SeqAIJ,
3516: NULL,
3517: NULL,
3518: MatILUFactor_SeqAIJ,
3519: NULL,
3520: /* 39*/ MatAXPY_SeqAIJ,
3521: MatCreateSubMatrices_SeqAIJ,
3522: MatIncreaseOverlap_SeqAIJ,
3523: MatGetValues_SeqAIJ,
3524: MatCopy_SeqAIJ,
3525: /* 44*/ MatGetRowMax_SeqAIJ,
3526: MatScale_SeqAIJ,
3527: MatShift_SeqAIJ,
3528: MatDiagonalSet_SeqAIJ,
3529: MatZeroRowsColumns_SeqAIJ,
3530: /* 49*/ MatSetRandom_SeqAIJ,
3531: MatGetRowIJ_SeqAIJ,
3532: MatRestoreRowIJ_SeqAIJ,
3533: MatGetColumnIJ_SeqAIJ,
3534: MatRestoreColumnIJ_SeqAIJ,
3535: /* 54*/ MatFDColoringCreate_SeqXAIJ,
3536: NULL,
3537: NULL,
3538: MatPermute_SeqAIJ,
3539: NULL,
3540: /* 59*/ NULL,
3541: MatDestroy_SeqAIJ,
3542: MatView_SeqAIJ,
3543: NULL,
3544: NULL,
3545: /* 64*/ NULL,
3546: MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3547: NULL,
3548: NULL,
3549: NULL,
3550: /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3551: MatGetRowMinAbs_SeqAIJ,
3552: NULL,
3553: NULL,
3554: NULL,
3555: /* 74*/ NULL,
3556: MatFDColoringApply_AIJ,
3557: NULL,
3558: NULL,
3559: NULL,
3560: /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3561: NULL,
3562: NULL,
3563: NULL,
3564: MatLoad_SeqAIJ,
3565: /* 84*/ NULL,
3566: NULL,
3567: NULL,
3568: NULL,
3569: NULL,
3570: /* 89*/ NULL,
3571: NULL,
3572: MatMatMultNumeric_SeqAIJ_SeqAIJ,
3573: NULL,
3574: NULL,
3575: /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3576: NULL,
3577: NULL,
3578: MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3579: NULL,
3580: /* 99*/ MatProductSetFromOptions_SeqAIJ,
3581: NULL,
3582: NULL,
3583: MatConjugate_SeqAIJ,
3584: NULL,
3585: /*104*/ MatSetValuesRow_SeqAIJ,
3586: MatRealPart_SeqAIJ,
3587: MatImaginaryPart_SeqAIJ,
3588: NULL,
3589: NULL,
3590: /*109*/ MatMatSolve_SeqAIJ,
3591: NULL,
3592: MatGetRowMin_SeqAIJ,
3593: NULL,
3594: MatMissingDiagonal_SeqAIJ,
3595: /*114*/ NULL,
3596: NULL,
3597: NULL,
3598: NULL,
3599: NULL,
3600: /*119*/ NULL,
3601: NULL,
3602: NULL,
3603: NULL,
3604: MatGetMultiProcBlock_SeqAIJ,
3605: /*124*/ MatFindNonzeroRows_SeqAIJ,
3606: MatGetColumnReductions_SeqAIJ,
3607: MatInvertBlockDiagonal_SeqAIJ,
3608: MatInvertVariableBlockDiagonal_SeqAIJ,
3609: NULL,
3610: /*129*/ NULL,
3611: NULL,
3612: NULL,
3613: MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3614: MatTransposeColoringCreate_SeqAIJ,
3615: /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3616: MatTransColoringApplyDenToSp_SeqAIJ,
3617: NULL,
3618: NULL,
3619: MatRARtNumeric_SeqAIJ_SeqAIJ,
3620: /*139*/ NULL,
3621: NULL,
3622: NULL,
3623: MatFDColoringSetUp_SeqXAIJ,
3624: MatFindOffBlockDiagonalEntries_SeqAIJ,
3625: MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3626: /*145*/ MatDestroySubMatrices_SeqAIJ,
3627: NULL,
3628: NULL,
3629: MatCreateGraph_Simple_AIJ,
3630: NULL,
3631: /*150*/ MatTransposeSymbolic_SeqAIJ,
3632: MatEliminateZeros_SeqAIJ,
3633: MatGetRowSumAbs_SeqAIJ,
3634: NULL,
3635: NULL,
3636: NULL};
3638: static PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat, PetscInt *indices)
3639: {
3640: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3641: PetscInt i, nz, n;
3643: PetscFunctionBegin;
3644: nz = aij->maxnz;
3645: n = mat->rmap->n;
3646: for (i = 0; i < nz; i++) aij->j[i] = indices[i];
3647: aij->nz = nz;
3648: for (i = 0; i < n; i++) aij->ilen[i] = aij->imax[i];
3649: PetscFunctionReturn(PETSC_SUCCESS);
3650: }
3652: /*
3653: * Given a sparse matrix with global column indices, compact it by using a local column space.
3654: * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3655: */
3656: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3657: {
3658: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3659: PetscHMapI gid1_lid1;
3660: PetscHashIter tpos;
3661: PetscInt gid, lid, i, ec, nz = aij->nz;
3662: PetscInt *garray, *jj = aij->j;
3664: PetscFunctionBegin;
3666: PetscAssertPointer(mapping, 2);
3667: /* use a table */
3668: PetscCall(PetscHMapICreateWithSize(mat->rmap->n, &gid1_lid1));
3669: ec = 0;
3670: for (i = 0; i < nz; i++) {
3671: PetscInt data, gid1 = jj[i] + 1;
3672: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &data));
3673: if (!data) {
3674: /* one based table */
3675: PetscCall(PetscHMapISet(gid1_lid1, gid1, ++ec));
3676: }
3677: }
3678: /* form array of columns we need */
3679: PetscCall(PetscMalloc1(ec, &garray));
3680: PetscHashIterBegin(gid1_lid1, tpos);
3681: while (!PetscHashIterAtEnd(gid1_lid1, tpos)) {
3682: PetscHashIterGetKey(gid1_lid1, tpos, gid);
3683: PetscHashIterGetVal(gid1_lid1, tpos, lid);
3684: PetscHashIterNext(gid1_lid1, tpos);
3685: gid--;
3686: lid--;
3687: garray[lid] = gid;
3688: }
3689: PetscCall(PetscSortInt(ec, garray)); /* sort, and rebuild */
3690: PetscCall(PetscHMapIClear(gid1_lid1));
3691: for (i = 0; i < ec; i++) PetscCall(PetscHMapISet(gid1_lid1, garray[i] + 1, i + 1));
3692: /* compact out the extra columns in B */
3693: for (i = 0; i < nz; i++) {
3694: PetscInt gid1 = jj[i] + 1;
3695: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &lid));
3696: lid--;
3697: jj[i] = lid;
3698: }
3699: PetscCall(PetscLayoutDestroy(&mat->cmap));
3700: PetscCall(PetscHMapIDestroy(&gid1_lid1));
3701: PetscCall(PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat), ec, ec, 1, &mat->cmap));
3702: PetscCall(ISLocalToGlobalMappingCreate(PETSC_COMM_SELF, mat->cmap->bs, mat->cmap->n, garray, PETSC_OWN_POINTER, mapping));
3703: PetscCall(ISLocalToGlobalMappingSetType(*mapping, ISLOCALTOGLOBALMAPPINGHASH));
3704: PetscFunctionReturn(PETSC_SUCCESS);
3705: }
3707: /*@
3708: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3709: in the matrix.
3711: Input Parameters:
3712: + mat - the `MATSEQAIJ` matrix
3713: - indices - the column indices
3715: Level: advanced
3717: Notes:
3718: This can be called if you have precomputed the nonzero structure of the
3719: matrix and want to provide it to the matrix object to improve the performance
3720: of the `MatSetValues()` operation.
3722: You MUST have set the correct numbers of nonzeros per row in the call to
3723: `MatCreateSeqAIJ()`, and the columns indices MUST be sorted.
3725: MUST be called before any calls to `MatSetValues()`
3727: The indices should start with zero, not one.
3729: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`
3730: @*/
3731: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat, PetscInt *indices)
3732: {
3733: PetscFunctionBegin;
3735: PetscAssertPointer(indices, 2);
3736: PetscUseMethod(mat, "MatSeqAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices));
3737: PetscFunctionReturn(PETSC_SUCCESS);
3738: }
3740: static PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3741: {
3742: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3743: size_t nz = aij->i[mat->rmap->n];
3745: PetscFunctionBegin;
3746: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3748: /* allocate space for values if not already there */
3749: if (!aij->saved_values) { PetscCall(PetscMalloc1(nz + 1, &aij->saved_values)); }
3751: /* copy values over */
3752: PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
3753: PetscFunctionReturn(PETSC_SUCCESS);
3754: }
3756: /*@
3757: MatStoreValues - Stashes a copy of the matrix values; this allows reusing of the linear part of a Jacobian, while recomputing only the
3758: nonlinear portion.
3760: Logically Collect
3762: Input Parameter:
3763: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3765: Level: advanced
3767: Example Usage:
3768: .vb
3769: Using SNES
3770: Create Jacobian matrix
3771: Set linear terms into matrix
3772: Apply boundary conditions to matrix, at this time matrix must have
3773: final nonzero structure (i.e. setting the nonlinear terms and applying
3774: boundary conditions again will not change the nonzero structure
3775: MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3776: MatStoreValues(mat);
3777: Call SNESSetJacobian() with matrix
3778: In your Jacobian routine
3779: MatRetrieveValues(mat);
3780: Set nonlinear terms in matrix
3782: Without `SNESSolve()`, i.e. when you handle nonlinear solve yourself:
3783: // build linear portion of Jacobian
3784: MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3785: MatStoreValues(mat);
3786: loop over nonlinear iterations
3787: MatRetrieveValues(mat);
3788: // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3789: // call MatAssemblyBegin/End() on matrix
3790: Solve linear system with Jacobian
3791: endloop
3792: .ve
3794: Notes:
3795: Matrix must already be assembled before calling this routine
3796: Must set the matrix option `MatSetOption`(mat,`MAT_NEW_NONZERO_LOCATIONS`,`PETSC_FALSE`); before
3797: calling this routine.
3799: When this is called multiple times it overwrites the previous set of stored values
3800: and does not allocated additional space.
3802: .seealso: [](ch_matrices), `Mat`, `MatRetrieveValues()`
3803: @*/
3804: PetscErrorCode MatStoreValues(Mat mat)
3805: {
3806: PetscFunctionBegin;
3808: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3809: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3810: PetscUseMethod(mat, "MatStoreValues_C", (Mat), (mat));
3811: PetscFunctionReturn(PETSC_SUCCESS);
3812: }
3814: static PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3815: {
3816: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3817: PetscInt nz = aij->i[mat->rmap->n];
3819: PetscFunctionBegin;
3820: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3821: PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");
3822: /* copy values over */
3823: PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
3824: PetscFunctionReturn(PETSC_SUCCESS);
3825: }
3827: /*@
3828: MatRetrieveValues - Retrieves the copy of the matrix values that was stored with `MatStoreValues()`
3830: Logically Collect
3832: Input Parameter:
3833: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3835: Level: advanced
3837: .seealso: [](ch_matrices), `Mat`, `MatStoreValues()`
3838: @*/
3839: PetscErrorCode MatRetrieveValues(Mat mat)
3840: {
3841: PetscFunctionBegin;
3843: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3844: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3845: PetscUseMethod(mat, "MatRetrieveValues_C", (Mat), (mat));
3846: PetscFunctionReturn(PETSC_SUCCESS);
3847: }
3849: /*@
3850: MatCreateSeqAIJ - Creates a sparse matrix in `MATSEQAIJ` (compressed row) format
3851: (the default parallel PETSc format). For good matrix assembly performance
3852: the user should preallocate the matrix storage by setting the parameter `nz`
3853: (or the array `nnz`).
3855: Collective
3857: Input Parameters:
3858: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3859: . m - number of rows
3860: . n - number of columns
3861: . nz - number of nonzeros per row (same for all rows)
3862: - nnz - array containing the number of nonzeros in the various rows
3863: (possibly different for each row) or NULL
3865: Output Parameter:
3866: . A - the matrix
3868: Options Database Keys:
3869: + -mat_no_inode - Do not use inodes
3870: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3872: Level: intermediate
3874: Notes:
3875: It is recommend to use `MatCreateFromOptions()` instead of this routine
3877: If `nnz` is given then `nz` is ignored
3879: The `MATSEQAIJ` format, also called
3880: compressed row storage, is fully compatible with standard Fortran
3881: storage. That is, the stored row and column indices can begin at
3882: either one (as in Fortran) or zero.
3884: Specify the preallocated storage with either `nz` or `nnz` (not both).
3885: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3886: allocation.
3888: By default, this format uses inodes (identical nodes) when possible, to
3889: improve numerical efficiency of matrix-vector products and solves. We
3890: search for consecutive rows with the same nonzero structure, thereby
3891: reusing matrix information to achieve increased efficiency.
3893: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`
3894: @*/
3895: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3896: {
3897: PetscFunctionBegin;
3898: PetscCall(MatCreate(comm, A));
3899: PetscCall(MatSetSizes(*A, m, n, m, n));
3900: PetscCall(MatSetType(*A, MATSEQAIJ));
3901: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz));
3902: PetscFunctionReturn(PETSC_SUCCESS);
3903: }
3905: /*@
3906: MatSeqAIJSetPreallocation - For good matrix assembly performance
3907: the user should preallocate the matrix storage by setting the parameter nz
3908: (or the array nnz). By setting these parameters accurately, performance
3909: during matrix assembly can be increased by more than a factor of 50.
3911: Collective
3913: Input Parameters:
3914: + B - The matrix
3915: . nz - number of nonzeros per row (same for all rows)
3916: - nnz - array containing the number of nonzeros in the various rows
3917: (possibly different for each row) or NULL
3919: Options Database Keys:
3920: + -mat_no_inode - Do not use inodes
3921: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3923: Level: intermediate
3925: Notes:
3926: If `nnz` is given then `nz` is ignored
3928: The `MATSEQAIJ` format also called
3929: compressed row storage, is fully compatible with standard Fortran
3930: storage. That is, the stored row and column indices can begin at
3931: either one (as in Fortran) or zero. See the users' manual for details.
3933: Specify the preallocated storage with either `nz` or `nnz` (not both).
3934: Set nz = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3935: allocation.
3937: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3938: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3939: You can also run with the option -info and look for messages with the string
3940: malloc in them to see if additional memory allocation was needed.
3942: Developer Notes:
3943: Use nz of `MAT_SKIP_ALLOCATION` to not allocate any space for the matrix
3944: entries or columns indices
3946: By default, this format uses inodes (identical nodes) when possible, to
3947: improve numerical efficiency of matrix-vector products and solves. We
3948: search for consecutive rows with the same nonzero structure, thereby
3949: reusing matrix information to achieve increased efficiency.
3951: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatGetInfo()`,
3952: `MatSeqAIJSetTotalPreallocation()`
3953: @*/
3954: PetscErrorCode MatSeqAIJSetPreallocation(Mat B, PetscInt nz, const PetscInt nnz[])
3955: {
3956: PetscFunctionBegin;
3959: PetscTryMethod(B, "MatSeqAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[]), (B, nz, nnz));
3960: PetscFunctionReturn(PETSC_SUCCESS);
3961: }
3963: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B, PetscInt nz, const PetscInt *nnz)
3964: {
3965: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
3966: PetscBool skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;
3967: PetscInt i;
3969: PetscFunctionBegin;
3970: if (B->hash_active) {
3971: B->ops[0] = b->cops;
3972: PetscCall(PetscHMapIJVDestroy(&b->ht));
3973: PetscCall(PetscFree(b->dnz));
3974: B->hash_active = PETSC_FALSE;
3975: }
3976: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3977: if (nz == MAT_SKIP_ALLOCATION) {
3978: skipallocation = PETSC_TRUE;
3979: nz = 0;
3980: }
3981: PetscCall(PetscLayoutSetUp(B->rmap));
3982: PetscCall(PetscLayoutSetUp(B->cmap));
3984: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3985: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
3986: if (nnz) {
3987: for (i = 0; i < B->rmap->n; i++) {
3988: 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]);
3989: 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);
3990: }
3991: }
3993: B->preallocated = PETSC_TRUE;
3994: if (!skipallocation) {
3995: if (!b->imax) { PetscCall(PetscMalloc1(B->rmap->n, &b->imax)); }
3996: if (!b->ilen) {
3997: /* b->ilen will count nonzeros in each row so far. */
3998: PetscCall(PetscCalloc1(B->rmap->n, &b->ilen));
3999: } else {
4000: PetscCall(PetscMemzero(b->ilen, B->rmap->n * sizeof(PetscInt)));
4001: }
4002: if (!b->ipre) PetscCall(PetscMalloc1(B->rmap->n, &b->ipre));
4003: if (!nnz) {
4004: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
4005: else if (nz < 0) nz = 1;
4006: nz = PetscMin(nz, B->cmap->n);
4007: for (i = 0; i < B->rmap->n; i++) b->imax[i] = nz;
4008: PetscCall(PetscIntMultError(nz, B->rmap->n, &nz));
4009: } else {
4010: PetscInt64 nz64 = 0;
4011: for (i = 0; i < B->rmap->n; i++) {
4012: b->imax[i] = nnz[i];
4013: nz64 += nnz[i];
4014: }
4015: PetscCall(PetscIntCast(nz64, &nz));
4016: }
4018: /* allocate the matrix space */
4019: PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
4020: PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscInt), (void **)&b->j));
4021: PetscCall(PetscShmgetAllocateArray(B->rmap->n + 1, sizeof(PetscInt), (void **)&b->i));
4022: b->free_ij = PETSC_TRUE;
4023: if (B->structure_only) {
4024: b->free_a = PETSC_FALSE;
4025: } else {
4026: PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscScalar), (void **)&b->a));
4027: b->free_a = PETSC_TRUE;
4028: }
4029: b->i[0] = 0;
4030: for (i = 1; i < B->rmap->n + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];
4031: } else {
4032: b->free_a = PETSC_FALSE;
4033: b->free_ij = PETSC_FALSE;
4034: }
4036: if (b->ipre && nnz != b->ipre && b->imax) {
4037: /* reserve user-requested sparsity */
4038: PetscCall(PetscArraycpy(b->ipre, b->imax, B->rmap->n));
4039: }
4041: b->nz = 0;
4042: b->maxnz = nz;
4043: B->info.nz_unneeded = (double)b->maxnz;
4044: if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
4045: B->was_assembled = PETSC_FALSE;
4046: B->assembled = PETSC_FALSE;
4047: /* We simply deem preallocation has changed nonzero state. Updating the state
4048: will give clients (like AIJKokkos) a chance to know something has happened.
4049: */
4050: B->nonzerostate++;
4051: PetscFunctionReturn(PETSC_SUCCESS);
4052: }
4054: static PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
4055: {
4056: Mat_SeqAIJ *a;
4057: PetscInt i;
4058: PetscBool skipreset;
4060: PetscFunctionBegin;
4063: /* Check local size. If zero, then return */
4064: if (!A->rmap->n) PetscFunctionReturn(PETSC_SUCCESS);
4066: a = (Mat_SeqAIJ *)A->data;
4067: /* if no saved info, we error out */
4068: PetscCheck(a->ipre, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "No saved preallocation info ");
4070: PetscCheck(a->i && a->imax && a->ilen, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "Memory info is incomplete, and can not reset preallocation ");
4072: PetscCall(PetscArraycmp(a->ipre, a->ilen, A->rmap->n, &skipreset));
4073: if (!skipreset) {
4074: PetscCall(PetscArraycpy(a->imax, a->ipre, A->rmap->n));
4075: PetscCall(PetscArrayzero(a->ilen, A->rmap->n));
4076: a->i[0] = 0;
4077: for (i = 1; i < A->rmap->n + 1; i++) a->i[i] = a->i[i - 1] + a->imax[i - 1];
4078: A->preallocated = PETSC_TRUE;
4079: a->nz = 0;
4080: a->maxnz = a->i[A->rmap->n];
4081: A->info.nz_unneeded = (double)a->maxnz;
4082: A->was_assembled = PETSC_FALSE;
4083: A->assembled = PETSC_FALSE;
4084: }
4085: PetscFunctionReturn(PETSC_SUCCESS);
4086: }
4088: /*@
4089: MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in `MATSEQAIJ` format.
4091: Input Parameters:
4092: + B - the matrix
4093: . i - the indices into `j` for the start of each row (indices start with zero)
4094: . j - the column indices for each row (indices start with zero) these must be sorted for each row
4095: - v - optional values in the matrix, use `NULL` if not provided
4097: Level: developer
4099: Notes:
4100: The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqAIJWithArrays()`
4102: This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
4103: structure will be the union of all the previous nonzero structures.
4105: Developer Notes:
4106: 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
4107: then just copies the `v` values directly with `PetscMemcpy()`.
4109: This routine could also take a `PetscCopyMode` argument to allow sharing the values instead of always copying them.
4111: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`, `MATSEQAIJ`, `MatResetPreallocation()`
4112: @*/
4113: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4114: {
4115: PetscFunctionBegin;
4118: PetscTryMethod(B, "MatSeqAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4119: PetscFunctionReturn(PETSC_SUCCESS);
4120: }
4122: static PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4123: {
4124: PetscInt i;
4125: PetscInt m, n;
4126: PetscInt nz;
4127: PetscInt *nnz;
4129: PetscFunctionBegin;
4130: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);
4132: PetscCall(PetscLayoutSetUp(B->rmap));
4133: PetscCall(PetscLayoutSetUp(B->cmap));
4135: PetscCall(MatGetSize(B, &m, &n));
4136: PetscCall(PetscMalloc1(m + 1, &nnz));
4137: for (i = 0; i < m; i++) {
4138: nz = Ii[i + 1] - Ii[i];
4139: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
4140: nnz[i] = nz;
4141: }
4142: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
4143: PetscCall(PetscFree(nnz));
4145: 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));
4147: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4148: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4150: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
4151: PetscFunctionReturn(PETSC_SUCCESS);
4152: }
4154: /*@
4155: MatSeqAIJKron - Computes `C`, the Kronecker product of `A` and `B`.
4157: Input Parameters:
4158: + A - left-hand side matrix
4159: . B - right-hand side matrix
4160: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
4162: Output Parameter:
4163: . C - Kronecker product of `A` and `B`
4165: Level: intermediate
4167: Note:
4168: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the product matrix has not changed from that last call to `MatSeqAIJKron()`.
4170: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATKAIJ`, `MatReuse`
4171: @*/
4172: PetscErrorCode MatSeqAIJKron(Mat A, Mat B, MatReuse reuse, Mat *C)
4173: {
4174: PetscFunctionBegin;
4179: PetscAssertPointer(C, 4);
4180: if (reuse == MAT_REUSE_MATRIX) {
4183: }
4184: PetscTryMethod(A, "MatSeqAIJKron_C", (Mat, Mat, MatReuse, Mat *), (A, B, reuse, C));
4185: PetscFunctionReturn(PETSC_SUCCESS);
4186: }
4188: static PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A, Mat B, MatReuse reuse, Mat *C)
4189: {
4190: Mat newmat;
4191: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
4192: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
4193: PetscScalar *v;
4194: const PetscScalar *aa, *ba;
4195: 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;
4196: PetscBool flg;
4198: PetscFunctionBegin;
4199: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4200: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4201: PetscCheck(!B->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4202: PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4203: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJ, &flg));
4204: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatType %s", ((PetscObject)B)->type_name);
4205: PetscCheck(reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatReuse %d", (int)reuse);
4206: if (reuse == MAT_INITIAL_MATRIX) {
4207: PetscCall(PetscMalloc2(am * bm + 1, &i, a->i[am] * b->i[bm], &j));
4208: PetscCall(MatCreate(PETSC_COMM_SELF, &newmat));
4209: PetscCall(MatSetSizes(newmat, am * bm, an * bn, am * bm, an * bn));
4210: PetscCall(MatSetType(newmat, MATAIJ));
4211: i[0] = 0;
4212: for (m = 0; m < am; ++m) {
4213: for (p = 0; p < bm; ++p) {
4214: i[m * bm + p + 1] = i[m * bm + p] + (a->i[m + 1] - a->i[m]) * (b->i[p + 1] - b->i[p]);
4215: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4216: for (q = b->i[p]; q < b->i[p + 1]; ++q) j[nnz++] = a->j[n] * bn + b->j[q];
4217: }
4218: }
4219: }
4220: PetscCall(MatSeqAIJSetPreallocationCSR(newmat, i, j, NULL));
4221: *C = newmat;
4222: PetscCall(PetscFree2(i, j));
4223: nnz = 0;
4224: }
4225: PetscCall(MatSeqAIJGetArray(*C, &v));
4226: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4227: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
4228: for (m = 0; m < am; ++m) {
4229: for (p = 0; p < bm; ++p) {
4230: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4231: for (q = b->i[p]; q < b->i[p + 1]; ++q) v[nnz++] = aa[n] * ba[q];
4232: }
4233: }
4234: }
4235: PetscCall(MatSeqAIJRestoreArray(*C, &v));
4236: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
4237: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
4238: PetscFunctionReturn(PETSC_SUCCESS);
4239: }
4241: #include <../src/mat/impls/dense/seq/dense.h>
4242: #include <petsc/private/kernels/petscaxpy.h>
4244: /*
4245: Computes (B'*A')' since computing B*A directly is untenable
4247: n p p
4248: [ ] [ ] [ ]
4249: m [ A ] * n [ B ] = m [ C ]
4250: [ ] [ ] [ ]
4252: */
4253: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A, Mat B, Mat C)
4254: {
4255: Mat_SeqDense *sub_a = (Mat_SeqDense *)A->data;
4256: Mat_SeqAIJ *sub_b = (Mat_SeqAIJ *)B->data;
4257: Mat_SeqDense *sub_c = (Mat_SeqDense *)C->data;
4258: PetscInt i, j, n, m, q, p;
4259: const PetscInt *ii, *idx;
4260: const PetscScalar *b, *a, *a_q;
4261: PetscScalar *c, *c_q;
4262: PetscInt clda = sub_c->lda;
4263: PetscInt alda = sub_a->lda;
4265: PetscFunctionBegin;
4266: m = A->rmap->n;
4267: n = A->cmap->n;
4268: p = B->cmap->n;
4269: a = sub_a->v;
4270: b = sub_b->a;
4271: c = sub_c->v;
4272: if (clda == m) {
4273: PetscCall(PetscArrayzero(c, m * p));
4274: } else {
4275: for (j = 0; j < p; j++)
4276: for (i = 0; i < m; i++) c[j * clda + i] = 0.0;
4277: }
4278: ii = sub_b->i;
4279: idx = sub_b->j;
4280: for (i = 0; i < n; i++) {
4281: q = ii[i + 1] - ii[i];
4282: while (q-- > 0) {
4283: c_q = c + clda * (*idx);
4284: a_q = a + alda * i;
4285: PetscKernelAXPY(c_q, *b, a_q, m);
4286: idx++;
4287: b++;
4288: }
4289: }
4290: PetscFunctionReturn(PETSC_SUCCESS);
4291: }
4293: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A, Mat B, PetscReal fill, Mat C)
4294: {
4295: PetscInt m = A->rmap->n, n = B->cmap->n;
4296: PetscBool cisdense;
4298: PetscFunctionBegin;
4299: 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);
4300: PetscCall(MatSetSizes(C, m, n, m, n));
4301: PetscCall(MatSetBlockSizesFromMats(C, A, B));
4302: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATSEQDENSE, MATSEQDENSECUDA, MATSEQDENSEHIP, ""));
4303: if (!cisdense) PetscCall(MatSetType(C, MATDENSE));
4304: PetscCall(MatSetUp(C));
4306: C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4307: PetscFunctionReturn(PETSC_SUCCESS);
4308: }
4310: /*MC
4311: MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4312: based on compressed sparse row format.
4314: Options Database Key:
4315: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()
4317: Level: beginner
4319: Notes:
4320: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
4321: in this case the values associated with the rows and columns one passes in are set to zero
4322: in the matrix
4324: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
4325: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
4327: Developer Note:
4328: It would be nice if all matrix formats supported passing `NULL` in for the numerical values
4330: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MatSetFromOptions()`, `MatSetType()`, `MatCreate()`, `MatType`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4331: M*/
4333: /*MC
4334: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
4336: This matrix type is identical to `MATSEQAIJ` when constructed with a single process communicator,
4337: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
4338: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4339: for communicators controlling multiple processes. It is recommended that you call both of
4340: the above preallocation routines for simplicity.
4342: Options Database Key:
4343: . -mat_type aij - sets the matrix type to "aij" during a call to `MatSetFromOptions()`
4345: Level: beginner
4347: Note:
4348: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, and also automatically switches over to use inodes when
4349: enough exist.
4351: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4352: M*/
4354: /*MC
4355: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
4357: Options Database Key:
4358: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to `MatSetFromOptions()`
4360: Level: beginner
4362: Note:
4363: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
4364: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
4365: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4366: for communicators controlling multiple processes. It is recommended that you call both of
4367: the above preallocation routines for simplicity.
4369: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
4370: M*/
4372: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
4373: #if defined(PETSC_HAVE_ELEMENTAL)
4374: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
4375: #endif
4376: #if defined(PETSC_HAVE_SCALAPACK)
4377: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
4378: #endif
4379: #if defined(PETSC_HAVE_HYPRE)
4380: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A, MatType, MatReuse, Mat *);
4381: #endif
4383: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat, MatType, MatReuse, Mat *);
4384: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
4385: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
4387: /*@C
4388: MatSeqAIJGetArray - gives read/write access to the array where the data for a `MATSEQAIJ` matrix is stored
4390: Not Collective
4392: Input Parameter:
4393: . A - a `MATSEQAIJ` matrix
4395: Output Parameter:
4396: . array - pointer to the data
4398: Level: intermediate
4400: Fortran Notes:
4401: `MatSeqAIJGetArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJGetArrayF90()`
4403: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4404: @*/
4405: PetscErrorCode MatSeqAIJGetArray(Mat A, PetscScalar *array[])
4406: {
4407: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4409: PetscFunctionBegin;
4410: if (aij->ops->getarray) {
4411: PetscCall((*aij->ops->getarray)(A, array));
4412: } else {
4413: *array = aij->a;
4414: }
4415: PetscFunctionReturn(PETSC_SUCCESS);
4416: }
4418: /*@C
4419: MatSeqAIJRestoreArray - returns access to the array where the data for a `MATSEQAIJ` matrix is stored obtained by `MatSeqAIJGetArray()`
4421: Not Collective
4423: Input Parameters:
4424: + A - a `MATSEQAIJ` matrix
4425: - array - pointer to the data
4427: Level: intermediate
4429: Fortran Notes:
4430: `MatSeqAIJRestoreArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJRestoreArrayF90()`
4432: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayF90()`
4433: @*/
4434: PetscErrorCode MatSeqAIJRestoreArray(Mat A, PetscScalar *array[])
4435: {
4436: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4438: PetscFunctionBegin;
4439: if (aij->ops->restorearray) {
4440: PetscCall((*aij->ops->restorearray)(A, array));
4441: } else {
4442: *array = NULL;
4443: }
4444: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4445: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4446: PetscFunctionReturn(PETSC_SUCCESS);
4447: }
4449: /*@C
4450: MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4452: Not Collective; No Fortran Support
4454: Input Parameter:
4455: . A - a `MATSEQAIJ` matrix
4457: Output Parameter:
4458: . array - pointer to the data
4460: Level: intermediate
4462: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4463: @*/
4464: PetscErrorCode MatSeqAIJGetArrayRead(Mat A, const PetscScalar *array[])
4465: {
4466: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4468: PetscFunctionBegin;
4469: if (aij->ops->getarrayread) {
4470: PetscCall((*aij->ops->getarrayread)(A, array));
4471: } else {
4472: *array = aij->a;
4473: }
4474: PetscFunctionReturn(PETSC_SUCCESS);
4475: }
4477: /*@C
4478: MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJGetArrayRead()`
4480: Not Collective; No Fortran Support
4482: Input Parameter:
4483: . A - a `MATSEQAIJ` matrix
4485: Output Parameter:
4486: . array - pointer to the data
4488: Level: intermediate
4490: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4491: @*/
4492: PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A, const PetscScalar *array[])
4493: {
4494: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4496: PetscFunctionBegin;
4497: if (aij->ops->restorearrayread) {
4498: PetscCall((*aij->ops->restorearrayread)(A, array));
4499: } else {
4500: *array = NULL;
4501: }
4502: PetscFunctionReturn(PETSC_SUCCESS);
4503: }
4505: /*@C
4506: MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4508: Not Collective; No Fortran Support
4510: Input Parameter:
4511: . A - a `MATSEQAIJ` matrix
4513: Output Parameter:
4514: . array - pointer to the data
4516: Level: intermediate
4518: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4519: @*/
4520: PetscErrorCode MatSeqAIJGetArrayWrite(Mat A, PetscScalar *array[])
4521: {
4522: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4524: PetscFunctionBegin;
4525: if (aij->ops->getarraywrite) {
4526: PetscCall((*aij->ops->getarraywrite)(A, array));
4527: } else {
4528: *array = aij->a;
4529: }
4530: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4531: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4532: PetscFunctionReturn(PETSC_SUCCESS);
4533: }
4535: /*@C
4536: MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead
4538: Not Collective; No Fortran Support
4540: Input Parameter:
4541: . A - a MATSEQAIJ matrix
4543: Output Parameter:
4544: . array - pointer to the data
4546: Level: intermediate
4548: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4549: @*/
4550: PetscErrorCode MatSeqAIJRestoreArrayWrite(Mat A, PetscScalar *array[])
4551: {
4552: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4554: PetscFunctionBegin;
4555: if (aij->ops->restorearraywrite) {
4556: PetscCall((*aij->ops->restorearraywrite)(A, array));
4557: } else {
4558: *array = NULL;
4559: }
4560: PetscFunctionReturn(PETSC_SUCCESS);
4561: }
4563: /*@C
4564: MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the `MATSEQAIJ` matrix
4566: Not Collective; No Fortran Support
4568: Input Parameter:
4569: . mat - a matrix of type `MATSEQAIJ` or its subclasses
4571: Output Parameters:
4572: + i - row map array of the matrix
4573: . j - column index array of the matrix
4574: . a - data array of the matrix
4575: - mtype - memory type of the arrays
4577: Level: developer
4579: Notes:
4580: Any of the output parameters can be `NULL`, in which case the corresponding value is not returned.
4581: If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host.
4583: One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix.
4584: If the matrix is assembled, the data array `a` is guaranteed to have the latest values of the matrix.
4586: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4587: @*/
4588: PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat, const PetscInt *i[], const PetscInt *j[], PetscScalar *a[], PetscMemType *mtype)
4589: {
4590: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
4592: PetscFunctionBegin;
4593: PetscCheck(mat->preallocated, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "matrix is not preallocated");
4594: if (aij->ops->getcsrandmemtype) {
4595: PetscCall((*aij->ops->getcsrandmemtype)(mat, i, j, a, mtype));
4596: } else {
4597: if (i) *i = aij->i;
4598: if (j) *j = aij->j;
4599: if (a) *a = aij->a;
4600: if (mtype) *mtype = PETSC_MEMTYPE_HOST;
4601: }
4602: PetscFunctionReturn(PETSC_SUCCESS);
4603: }
4605: /*@
4606: MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row
4608: Not Collective
4610: Input Parameter:
4611: . A - a `MATSEQAIJ` matrix
4613: Output Parameter:
4614: . nz - the maximum number of nonzeros in any row
4616: Level: intermediate
4618: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4619: @*/
4620: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A, PetscInt *nz)
4621: {
4622: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4624: PetscFunctionBegin;
4625: *nz = aij->rmax;
4626: PetscFunctionReturn(PETSC_SUCCESS);
4627: }
4629: static PetscErrorCode MatCOOStructDestroy_SeqAIJ(void *data)
4630: {
4631: MatCOOStruct_SeqAIJ *coo = (MatCOOStruct_SeqAIJ *)data;
4633: PetscFunctionBegin;
4634: PetscCall(PetscFree(coo->perm));
4635: PetscCall(PetscFree(coo->jmap));
4636: PetscCall(PetscFree(coo));
4637: PetscFunctionReturn(PETSC_SUCCESS);
4638: }
4640: PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
4641: {
4642: MPI_Comm comm;
4643: PetscInt *i, *j;
4644: PetscInt M, N, row, iprev;
4645: PetscCount k, p, q, nneg, nnz, start, end; /* Index the coo array, so use PetscCount as their type */
4646: PetscInt *Ai; /* Change to PetscCount once we use it for row pointers */
4647: PetscInt *Aj;
4648: PetscScalar *Aa;
4649: Mat_SeqAIJ *seqaij = (Mat_SeqAIJ *)mat->data;
4650: MatType rtype;
4651: PetscCount *perm, *jmap;
4652: MatCOOStruct_SeqAIJ *coo;
4653: PetscBool isorted;
4654: PetscBool hypre;
4655: const char *name;
4657: PetscFunctionBegin;
4658: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
4659: PetscCall(MatGetSize(mat, &M, &N));
4660: i = coo_i;
4661: j = coo_j;
4662: PetscCall(PetscMalloc1(coo_n, &perm));
4664: /* 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) */
4665: isorted = PETSC_TRUE;
4666: iprev = PETSC_INT_MIN;
4667: for (k = 0; k < coo_n; k++) {
4668: if (j[k] < 0) i[k] = -1;
4669: if (isorted) {
4670: if (i[k] < iprev) isorted = PETSC_FALSE;
4671: else iprev = i[k];
4672: }
4673: perm[k] = k;
4674: }
4676: /* Sort by row if not already */
4677: if (!isorted) PetscCall(PetscSortIntWithIntCountArrayPair(coo_n, i, j, perm));
4679: /* Advance k to the first row with a non-negative index */
4680: for (k = 0; k < coo_n; k++)
4681: if (i[k] >= 0) break;
4682: nneg = k;
4683: 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 */
4684: nnz = 0; /* Total number of unique nonzeros to be counted */
4685: jmap++; /* Inc jmap by 1 for convenience */
4687: PetscCall(PetscShmgetAllocateArray(M + 1, sizeof(PetscInt), (void **)&Ai)); /* CSR of A */
4688: PetscCall(PetscArrayzero(Ai, M + 1));
4689: PetscCall(PetscShmgetAllocateArray(coo_n - nneg, sizeof(PetscInt), (void **)&Aj)); /* We have at most coo_n-nneg unique nonzeros */
4691: PetscCall(PetscObjectGetName((PetscObject)mat, &name));
4692: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", name, &hypre));
4694: /* In each row, sort by column, then unique column indices to get row length */
4695: Ai++; /* Inc by 1 for convenience */
4696: q = 0; /* q-th unique nonzero, with q starting from 0 */
4697: while (k < coo_n) {
4698: PetscBool strictly_sorted; // this row is strictly sorted?
4699: PetscInt jprev;
4701: /* get [start,end) indices for this row; also check if cols in this row are strictly sorted */
4702: row = i[k];
4703: start = k;
4704: jprev = PETSC_INT_MIN;
4705: strictly_sorted = PETSC_TRUE;
4706: while (k < coo_n && i[k] == row) {
4707: if (strictly_sorted) {
4708: if (j[k] <= jprev) strictly_sorted = PETSC_FALSE;
4709: else jprev = j[k];
4710: }
4711: k++;
4712: }
4713: end = k;
4715: /* hack for HYPRE: swap min column to diag so that diagonal values will go first */
4716: if (hypre) {
4717: PetscInt minj = PETSC_INT_MAX;
4718: PetscBool hasdiag = PETSC_FALSE;
4720: if (strictly_sorted) { // fast path to swap the first and the diag
4721: PetscCount tmp;
4722: for (p = start; p < end; p++) {
4723: if (j[p] == row && p != start) {
4724: j[p] = j[start];
4725: j[start] = row;
4726: tmp = perm[start];
4727: perm[start] = perm[p];
4728: perm[p] = tmp;
4729: break;
4730: }
4731: }
4732: } else {
4733: for (p = start; p < end; p++) {
4734: hasdiag = (PetscBool)(hasdiag || (j[p] == row));
4735: minj = PetscMin(minj, j[p]);
4736: }
4738: if (hasdiag) {
4739: for (p = start; p < end; p++) {
4740: if (j[p] == minj) j[p] = row;
4741: else if (j[p] == row) j[p] = minj;
4742: }
4743: }
4744: }
4745: }
4746: // sort by columns in a row
4747: if (!strictly_sorted) PetscCall(PetscSortIntWithCountArray(end - start, j + start, perm + start));
4749: if (strictly_sorted) { // fast path to set Aj[], jmap[], Ai[], nnz, q
4750: for (p = start; p < end; p++, q++) {
4751: Aj[q] = j[p];
4752: jmap[q] = 1;
4753: }
4754: PetscCall(PetscIntCast(end - start, Ai + row));
4755: nnz += Ai[row]; // q is already advanced
4756: } else {
4757: /* Find number of unique col entries in this row */
4758: Aj[q] = j[start]; /* Log the first nonzero in this row */
4759: jmap[q] = 1; /* Number of repeats of this nonzero entry */
4760: Ai[row] = 1;
4761: nnz++;
4763: for (p = start + 1; p < end; p++) { /* Scan remaining nonzero in this row */
4764: if (j[p] != j[p - 1]) { /* Meet a new nonzero */
4765: q++;
4766: jmap[q] = 1;
4767: Aj[q] = j[p];
4768: Ai[row]++;
4769: nnz++;
4770: } else {
4771: jmap[q]++;
4772: }
4773: }
4774: q++; /* Move to next row and thus next unique nonzero */
4775: }
4776: }
4778: Ai--; /* Back to the beginning of Ai[] */
4779: for (k = 0; k < M; k++) Ai[k + 1] += Ai[k];
4780: jmap--; // Back to the beginning of jmap[]
4781: jmap[0] = 0;
4782: for (k = 0; k < nnz; k++) jmap[k + 1] += jmap[k];
4784: if (nnz < coo_n - nneg) { /* Reallocate with actual number of unique nonzeros */
4785: PetscCount *jmap_new;
4786: PetscInt *Aj_new;
4788: PetscCall(PetscMalloc1(nnz + 1, &jmap_new));
4789: PetscCall(PetscArraycpy(jmap_new, jmap, nnz + 1));
4790: PetscCall(PetscFree(jmap));
4791: jmap = jmap_new;
4793: PetscCall(PetscShmgetAllocateArray(nnz, sizeof(PetscInt), (void **)&Aj_new));
4794: PetscCall(PetscArraycpy(Aj_new, Aj, nnz));
4795: PetscCall(PetscShmgetDeallocateArray((void **)&Aj));
4796: Aj = Aj_new;
4797: }
4799: if (nneg) { /* Discard heading entries with negative indices in perm[], as we'll access it from index 0 in MatSetValuesCOO */
4800: PetscCount *perm_new;
4802: PetscCall(PetscMalloc1(coo_n - nneg, &perm_new));
4803: PetscCall(PetscArraycpy(perm_new, perm + nneg, coo_n - nneg));
4804: PetscCall(PetscFree(perm));
4805: perm = perm_new;
4806: }
4808: PetscCall(MatGetRootType_Private(mat, &rtype));
4809: PetscCall(PetscShmgetAllocateArray(nnz, sizeof(PetscScalar), (void **)&Aa));
4810: PetscCall(PetscArrayzero(Aa, nnz));
4811: PetscCall(MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF, M, N, Ai, Aj, Aa, rtype, mat));
4813: seqaij->free_a = seqaij->free_ij = PETSC_TRUE; /* Let newmat own Ai, Aj and Aa */
4815: // Put the COO struct in a container and then attach that to the matrix
4816: PetscCall(PetscMalloc1(1, &coo));
4817: PetscCall(PetscIntCast(nnz, &coo->nz));
4818: coo->n = coo_n;
4819: coo->Atot = coo_n - nneg; // Annz is seqaij->nz, so no need to record that again
4820: coo->jmap = jmap; // of length nnz+1
4821: coo->perm = perm;
4822: PetscCall(PetscObjectContainerCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", coo, MatCOOStructDestroy_SeqAIJ));
4823: PetscFunctionReturn(PETSC_SUCCESS);
4824: }
4826: static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A, const PetscScalar v[], InsertMode imode)
4827: {
4828: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)A->data;
4829: PetscCount i, j, Annz = aseq->nz;
4830: PetscCount *perm, *jmap;
4831: PetscScalar *Aa;
4832: PetscContainer container;
4833: MatCOOStruct_SeqAIJ *coo;
4835: PetscFunctionBegin;
4836: PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
4837: PetscCheck(container, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
4838: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
4839: perm = coo->perm;
4840: jmap = coo->jmap;
4841: PetscCall(MatSeqAIJGetArray(A, &Aa));
4842: for (i = 0; i < Annz; i++) {
4843: PetscScalar sum = 0.0;
4844: for (j = jmap[i]; j < jmap[i + 1]; j++) sum += v[perm[j]];
4845: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
4846: }
4847: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
4848: PetscFunctionReturn(PETSC_SUCCESS);
4849: }
4851: #if defined(PETSC_HAVE_CUDA)
4852: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
4853: #endif
4854: #if defined(PETSC_HAVE_HIP)
4855: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
4856: #endif
4857: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4858: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat, MatType, MatReuse, Mat *);
4859: #endif
4861: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4862: {
4863: Mat_SeqAIJ *b;
4864: PetscMPIInt size;
4866: PetscFunctionBegin;
4867: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
4868: PetscCheck(size <= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Comm must be of size 1");
4870: PetscCall(PetscNew(&b));
4872: B->data = (void *)b;
4873: B->ops[0] = MatOps_Values;
4874: if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
4876: b->row = NULL;
4877: b->col = NULL;
4878: b->icol = NULL;
4879: b->reallocs = 0;
4880: b->ignorezeroentries = PETSC_FALSE;
4881: b->roworiented = PETSC_TRUE;
4882: b->nonew = 0;
4883: b->diag = NULL;
4884: b->solve_work = NULL;
4885: B->spptr = NULL;
4886: b->saved_values = NULL;
4887: b->idiag = NULL;
4888: b->mdiag = NULL;
4889: b->ssor_work = NULL;
4890: b->omega = 1.0;
4891: b->fshift = 0.0;
4892: b->idiagvalid = PETSC_FALSE;
4893: b->ibdiagvalid = PETSC_FALSE;
4894: b->keepnonzeropattern = PETSC_FALSE;
4896: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4897: #if defined(PETSC_HAVE_MATLAB)
4898: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEnginePut_C", MatlabEnginePut_SeqAIJ));
4899: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEngineGet_C", MatlabEngineGet_SeqAIJ));
4900: #endif
4901: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetColumnIndices_C", MatSeqAIJSetColumnIndices_SeqAIJ));
4902: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqAIJ));
4903: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqAIJ));
4904: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsbaij_C", MatConvert_SeqAIJ_SeqSBAIJ));
4905: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqbaij_C", MatConvert_SeqAIJ_SeqBAIJ));
4906: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijperm_C", MatConvert_SeqAIJ_SeqAIJPERM));
4907: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijsell_C", MatConvert_SeqAIJ_SeqAIJSELL));
4908: #if defined(PETSC_HAVE_MKL_SPARSE)
4909: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijmkl_C", MatConvert_SeqAIJ_SeqAIJMKL));
4910: #endif
4911: #if defined(PETSC_HAVE_CUDA)
4912: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcusparse_C", MatConvert_SeqAIJ_SeqAIJCUSPARSE));
4913: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4914: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJ));
4915: #endif
4916: #if defined(PETSC_HAVE_HIP)
4917: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijhipsparse_C", MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
4918: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4919: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", MatProductSetFromOptions_SeqAIJ));
4920: #endif
4921: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4922: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijkokkos_C", MatConvert_SeqAIJ_SeqAIJKokkos));
4923: #endif
4924: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcrl_C", MatConvert_SeqAIJ_SeqAIJCRL));
4925: #if defined(PETSC_HAVE_ELEMENTAL)
4926: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_elemental_C", MatConvert_SeqAIJ_Elemental));
4927: #endif
4928: #if defined(PETSC_HAVE_SCALAPACK)
4929: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
4930: #endif
4931: #if defined(PETSC_HAVE_HYPRE)
4932: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_hypre_C", MatConvert_AIJ_HYPRE));
4933: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
4934: #endif
4935: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqdense_C", MatConvert_SeqAIJ_SeqDense));
4936: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsell_C", MatConvert_SeqAIJ_SeqSELL));
4937: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_is_C", MatConvert_XAIJ_IS));
4938: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqAIJ));
4939: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsHermitianTranspose_C", MatIsHermitianTranspose_SeqAIJ));
4940: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocation_C", MatSeqAIJSetPreallocation_SeqAIJ));
4941: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_SeqAIJ));
4942: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocationCSR_C", MatSeqAIJSetPreallocationCSR_SeqAIJ));
4943: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatReorderForNonzeroDiagonal_C", MatReorderForNonzeroDiagonal_SeqAIJ));
4944: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_seqaij_C", MatProductSetFromOptions_IS_XAIJ));
4945: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqdense_seqaij_C", MatProductSetFromOptions_SeqDense_SeqAIJ));
4946: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4947: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJKron_C", MatSeqAIJKron_SeqAIJ));
4948: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJ));
4949: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJ));
4950: PetscCall(MatCreate_SeqAIJ_Inode(B));
4951: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4952: PetscCall(MatSeqAIJSetTypeFromOptions(B)); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4953: PetscFunctionReturn(PETSC_SUCCESS);
4954: }
4956: /*
4957: Given a matrix generated with MatGetFactor() duplicates all the information in A into C
4958: */
4959: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
4960: {
4961: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data, *a = (Mat_SeqAIJ *)A->data;
4962: PetscInt m = A->rmap->n, i;
4964: PetscFunctionBegin;
4965: PetscCheck(A->assembled || cpvalues == MAT_DO_NOT_COPY_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");
4967: C->factortype = A->factortype;
4968: c->row = NULL;
4969: c->col = NULL;
4970: c->icol = NULL;
4971: c->reallocs = 0;
4972: c->diagonaldense = a->diagonaldense;
4974: C->assembled = A->assembled;
4976: if (A->preallocated) {
4977: PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
4978: PetscCall(PetscLayoutReference(A->cmap, &C->cmap));
4980: if (!A->hash_active) {
4981: PetscCall(PetscMalloc1(m, &c->imax));
4982: PetscCall(PetscMemcpy(c->imax, a->imax, m * sizeof(PetscInt)));
4983: PetscCall(PetscMalloc1(m, &c->ilen));
4984: PetscCall(PetscMemcpy(c->ilen, a->ilen, m * sizeof(PetscInt)));
4986: /* allocate the matrix space */
4987: if (mallocmatspace) {
4988: PetscCall(PetscShmgetAllocateArray(a->i[m], sizeof(PetscScalar), (void **)&c->a));
4989: PetscCall(PetscShmgetAllocateArray(a->i[m], sizeof(PetscInt), (void **)&c->j));
4990: PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&c->i));
4991: PetscCall(PetscArraycpy(c->i, a->i, m + 1));
4992: c->free_a = PETSC_TRUE;
4993: c->free_ij = PETSC_TRUE;
4994: if (m > 0) {
4995: PetscCall(PetscArraycpy(c->j, a->j, a->i[m]));
4996: if (cpvalues == MAT_COPY_VALUES) {
4997: const PetscScalar *aa;
4999: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5000: PetscCall(PetscArraycpy(c->a, aa, a->i[m]));
5001: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5002: } else {
5003: PetscCall(PetscArrayzero(c->a, a->i[m]));
5004: }
5005: }
5006: }
5007: C->preallocated = PETSC_TRUE;
5008: } else {
5009: PetscCheck(mallocmatspace, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot malloc matrix memory from a non-preallocated matrix");
5010: PetscCall(MatSetUp(C));
5011: }
5013: c->ignorezeroentries = a->ignorezeroentries;
5014: c->roworiented = a->roworiented;
5015: c->nonew = a->nonew;
5016: if (a->diag) {
5017: PetscCall(PetscMalloc1(m + 1, &c->diag));
5018: PetscCall(PetscMemcpy(c->diag, a->diag, m * sizeof(PetscInt)));
5019: } else c->diag = NULL;
5021: c->solve_work = NULL;
5022: c->saved_values = NULL;
5023: c->idiag = NULL;
5024: c->ssor_work = NULL;
5025: c->keepnonzeropattern = a->keepnonzeropattern;
5027: c->rmax = a->rmax;
5028: c->nz = a->nz;
5029: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
5031: c->compressedrow.use = a->compressedrow.use;
5032: c->compressedrow.nrows = a->compressedrow.nrows;
5033: if (a->compressedrow.use) {
5034: i = a->compressedrow.nrows;
5035: PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i, &c->compressedrow.rindex));
5036: PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
5037: PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
5038: } else {
5039: c->compressedrow.use = PETSC_FALSE;
5040: c->compressedrow.i = NULL;
5041: c->compressedrow.rindex = NULL;
5042: }
5043: c->nonzerorowcnt = a->nonzerorowcnt;
5044: C->nonzerostate = A->nonzerostate;
5046: PetscCall(MatDuplicate_SeqAIJ_Inode(A, cpvalues, &C));
5047: }
5048: PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
5049: PetscFunctionReturn(PETSC_SUCCESS);
5050: }
5052: PetscErrorCode MatDuplicate_SeqAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
5053: {
5054: PetscFunctionBegin;
5055: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
5056: PetscCall(MatSetSizes(*B, A->rmap->n, A->cmap->n, A->rmap->n, A->cmap->n));
5057: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) PetscCall(MatSetBlockSizesFromMats(*B, A, A));
5058: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
5059: PetscCall(MatDuplicateNoCreate_SeqAIJ(*B, A, cpvalues, PETSC_TRUE));
5060: PetscFunctionReturn(PETSC_SUCCESS);
5061: }
5063: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
5064: {
5065: PetscBool isbinary, ishdf5;
5067: PetscFunctionBegin;
5070: /* force binary viewer to load .info file if it has not yet done so */
5071: PetscCall(PetscViewerSetUp(viewer));
5072: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
5073: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
5074: if (isbinary) {
5075: PetscCall(MatLoad_SeqAIJ_Binary(newMat, viewer));
5076: } else if (ishdf5) {
5077: #if defined(PETSC_HAVE_HDF5)
5078: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
5079: #else
5080: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
5081: #endif
5082: } else {
5083: 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);
5084: }
5085: PetscFunctionReturn(PETSC_SUCCESS);
5086: }
5088: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
5089: {
5090: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->data;
5091: PetscInt header[4], *rowlens, M, N, nz, sum, rows, cols, i;
5093: PetscFunctionBegin;
5094: PetscCall(PetscViewerSetUp(viewer));
5096: /* read in matrix header */
5097: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
5098: PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
5099: M = header[1];
5100: N = header[2];
5101: nz = header[3];
5102: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
5103: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
5104: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqAIJ");
5106: /* set block sizes from the viewer's .info file */
5107: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
5108: /* set local and global sizes if not set already */
5109: if (mat->rmap->n < 0) mat->rmap->n = M;
5110: if (mat->cmap->n < 0) mat->cmap->n = N;
5111: if (mat->rmap->N < 0) mat->rmap->N = M;
5112: if (mat->cmap->N < 0) mat->cmap->N = N;
5113: PetscCall(PetscLayoutSetUp(mat->rmap));
5114: PetscCall(PetscLayoutSetUp(mat->cmap));
5116: /* check if the matrix sizes are correct */
5117: PetscCall(MatGetSize(mat, &rows, &cols));
5118: 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);
5120: /* read in row lengths */
5121: PetscCall(PetscMalloc1(M, &rowlens));
5122: PetscCall(PetscViewerBinaryRead(viewer, rowlens, M, NULL, PETSC_INT));
5123: /* check if sum(rowlens) is same as nz */
5124: sum = 0;
5125: for (i = 0; i < M; i++) sum += rowlens[i];
5126: 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);
5127: /* preallocate and check sizes */
5128: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(mat, 0, rowlens));
5129: PetscCall(MatGetSize(mat, &rows, &cols));
5130: 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);
5131: /* store row lengths */
5132: PetscCall(PetscArraycpy(a->ilen, rowlens, M));
5133: PetscCall(PetscFree(rowlens));
5135: /* fill in "i" row pointers */
5136: a->i[0] = 0;
5137: for (i = 0; i < M; i++) a->i[i + 1] = a->i[i] + a->ilen[i];
5138: /* read in "j" column indices */
5139: PetscCall(PetscViewerBinaryRead(viewer, a->j, nz, NULL, PETSC_INT));
5140: /* read in "a" nonzero values */
5141: PetscCall(PetscViewerBinaryRead(viewer, a->a, nz, NULL, PETSC_SCALAR));
5143: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
5144: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
5145: PetscFunctionReturn(PETSC_SUCCESS);
5146: }
5148: PetscErrorCode MatEqual_SeqAIJ(Mat A, Mat B, PetscBool *flg)
5149: {
5150: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data;
5151: const PetscScalar *aa, *ba;
5152: #if defined(PETSC_USE_COMPLEX)
5153: PetscInt k;
5154: #endif
5156: PetscFunctionBegin;
5157: /* If the matrix dimensions are not equal,or no of nonzeros */
5158: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) || (a->nz != b->nz)) {
5159: *flg = PETSC_FALSE;
5160: PetscFunctionReturn(PETSC_SUCCESS);
5161: }
5163: /* if the a->i are the same */
5164: PetscCall(PetscArraycmp(a->i, b->i, A->rmap->n + 1, flg));
5165: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5167: /* if a->j are the same */
5168: PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg));
5169: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5171: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5172: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
5173: /* if a->a are the same */
5174: #if defined(PETSC_USE_COMPLEX)
5175: for (k = 0; k < a->nz; k++) {
5176: if (PetscRealPart(aa[k]) != PetscRealPart(ba[k]) || PetscImaginaryPart(aa[k]) != PetscImaginaryPart(ba[k])) {
5177: *flg = PETSC_FALSE;
5178: PetscFunctionReturn(PETSC_SUCCESS);
5179: }
5180: }
5181: #else
5182: PetscCall(PetscArraycmp(aa, ba, a->nz, flg));
5183: #endif
5184: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
5185: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
5186: PetscFunctionReturn(PETSC_SUCCESS);
5187: }
5189: /*@
5190: MatCreateSeqAIJWithArrays - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in CSR format)
5191: provided by the user.
5193: Collective
5195: Input Parameters:
5196: + comm - must be an MPI communicator of size 1
5197: . m - number of rows
5198: . n - number of columns
5199: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
5200: . j - column indices
5201: - a - matrix values
5203: Output Parameter:
5204: . mat - the matrix
5206: Level: intermediate
5208: Notes:
5209: The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
5210: once the matrix is destroyed and not before
5212: You cannot set new nonzero locations into this matrix, that will generate an error.
5214: The `i` and `j` indices are 0 based
5216: The format which is used for the sparse matrix input, is equivalent to a
5217: row-major ordering.. i.e for the following matrix, the input data expected is
5218: as shown
5219: .vb
5220: 1 0 0
5221: 2 0 3
5222: 4 5 6
5224: i = {0,1,3,6} [size = nrow+1 = 3+1]
5225: j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row
5226: v = {1,2,3,4,5,6} [size = 6]
5227: .ve
5229: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateMPIAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`
5230: @*/
5231: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
5232: {
5233: PetscInt ii;
5234: Mat_SeqAIJ *aij;
5235: PetscInt jj;
5237: PetscFunctionBegin;
5238: PetscCheck(m <= 0 || i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
5239: PetscCall(MatCreate(comm, mat));
5240: PetscCall(MatSetSizes(*mat, m, n, m, n));
5241: /* PetscCall(MatSetBlockSizes(*mat,,)); */
5242: PetscCall(MatSetType(*mat, MATSEQAIJ));
5243: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, MAT_SKIP_ALLOCATION, NULL));
5244: aij = (Mat_SeqAIJ *)(*mat)->data;
5245: PetscCall(PetscMalloc1(m, &aij->imax));
5246: PetscCall(PetscMalloc1(m, &aij->ilen));
5248: aij->i = i;
5249: aij->j = j;
5250: aij->a = a;
5251: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
5252: aij->free_a = PETSC_FALSE;
5253: aij->free_ij = PETSC_FALSE;
5255: for (ii = 0, aij->nonzerorowcnt = 0, aij->rmax = 0; ii < m; ii++) {
5256: aij->ilen[ii] = aij->imax[ii] = i[ii + 1] - i[ii];
5257: if (PetscDefined(USE_DEBUG)) {
5258: 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]);
5259: for (jj = i[ii] + 1; jj < i[ii + 1]; jj++) {
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 not sorted", jj - i[ii], j[jj], ii);
5261: 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);
5262: }
5263: }
5264: }
5265: if (PetscDefined(USE_DEBUG)) {
5266: for (ii = 0; ii < aij->i[m]; ii++) {
5267: PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
5268: 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);
5269: }
5270: }
5272: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5273: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5274: PetscFunctionReturn(PETSC_SUCCESS);
5275: }
5277: /*@
5278: MatCreateSeqAIJFromTriple - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in COO format)
5279: provided by the user.
5281: Collective
5283: Input Parameters:
5284: + comm - must be an MPI communicator of size 1
5285: . m - number of rows
5286: . n - number of columns
5287: . i - row indices
5288: . j - column indices
5289: . a - matrix values
5290: . nz - number of nonzeros
5291: - idx - if the `i` and `j` indices start with 1 use `PETSC_TRUE` otherwise use `PETSC_FALSE`
5293: Output Parameter:
5294: . mat - the matrix
5296: Level: intermediate
5298: Example:
5299: For the following matrix, the input data expected is as shown (using 0 based indexing)
5300: .vb
5301: 1 0 0
5302: 2 0 3
5303: 4 5 6
5305: i = {0,1,1,2,2,2}
5306: j = {0,0,2,0,1,2}
5307: v = {1,2,3,4,5,6}
5308: .ve
5310: Note:
5311: Instead of using this function, users should also consider `MatSetPreallocationCOO()` and `MatSetValuesCOO()`, which allow repeated or remote entries,
5312: and are particularly useful in iterative applications.
5314: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateSeqAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`, `MatSetValuesCOO()`, `MatSetPreallocationCOO()`
5315: @*/
5316: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat, PetscInt nz, PetscBool idx)
5317: {
5318: PetscInt ii, *nnz, one = 1, row, col;
5320: PetscFunctionBegin;
5321: PetscCall(PetscCalloc1(m, &nnz));
5322: for (ii = 0; ii < nz; ii++) nnz[i[ii] - !!idx] += 1;
5323: PetscCall(MatCreate(comm, mat));
5324: PetscCall(MatSetSizes(*mat, m, n, m, n));
5325: PetscCall(MatSetType(*mat, MATSEQAIJ));
5326: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, 0, nnz));
5327: for (ii = 0; ii < nz; ii++) {
5328: if (idx) {
5329: row = i[ii] - 1;
5330: col = j[ii] - 1;
5331: } else {
5332: row = i[ii];
5333: col = j[ii];
5334: }
5335: PetscCall(MatSetValues(*mat, one, &row, one, &col, &a[ii], ADD_VALUES));
5336: }
5337: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5338: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5339: PetscCall(PetscFree(nnz));
5340: PetscFunctionReturn(PETSC_SUCCESS);
5341: }
5343: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
5344: {
5345: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5347: PetscFunctionBegin;
5348: a->idiagvalid = PETSC_FALSE;
5349: a->ibdiagvalid = PETSC_FALSE;
5351: PetscCall(MatSeqAIJInvalidateDiagonal_Inode(A));
5352: PetscFunctionReturn(PETSC_SUCCESS);
5353: }
5355: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
5356: {
5357: PetscFunctionBegin;
5358: PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm, inmat, n, scall, outmat));
5359: PetscFunctionReturn(PETSC_SUCCESS);
5360: }
5362: /*
5363: Permute A into C's *local* index space using rowemb,colemb.
5364: The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
5365: of [0,m), colemb is in [0,n).
5366: If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
5367: */
5368: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C, IS rowemb, IS colemb, MatStructure pattern, Mat B)
5369: {
5370: /* If making this function public, change the error returned in this function away from _PLIB. */
5371: Mat_SeqAIJ *Baij;
5372: PetscBool seqaij;
5373: PetscInt m, n, *nz, i, j, count;
5374: PetscScalar v;
5375: const PetscInt *rowindices, *colindices;
5377: PetscFunctionBegin;
5378: if (!B) PetscFunctionReturn(PETSC_SUCCESS);
5379: /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
5380: PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQAIJ, &seqaij));
5381: PetscCheck(seqaij, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is of wrong type");
5382: if (rowemb) {
5383: PetscCall(ISGetLocalSize(rowemb, &m));
5384: 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);
5385: } else {
5386: PetscCheck(C->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is row-incompatible with the target matrix");
5387: }
5388: if (colemb) {
5389: PetscCall(ISGetLocalSize(colemb, &n));
5390: 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);
5391: } else {
5392: PetscCheck(C->cmap->n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is col-incompatible with the target matrix");
5393: }
5395: Baij = (Mat_SeqAIJ *)B->data;
5396: if (pattern == DIFFERENT_NONZERO_PATTERN) {
5397: PetscCall(PetscMalloc1(B->rmap->n, &nz));
5398: for (i = 0; i < B->rmap->n; i++) nz[i] = Baij->i[i + 1] - Baij->i[i];
5399: PetscCall(MatSeqAIJSetPreallocation(C, 0, nz));
5400: PetscCall(PetscFree(nz));
5401: }
5402: if (pattern == SUBSET_NONZERO_PATTERN) PetscCall(MatZeroEntries(C));
5403: count = 0;
5404: rowindices = NULL;
5405: colindices = NULL;
5406: if (rowemb) PetscCall(ISGetIndices(rowemb, &rowindices));
5407: if (colemb) PetscCall(ISGetIndices(colemb, &colindices));
5408: for (i = 0; i < B->rmap->n; i++) {
5409: PetscInt row;
5410: row = i;
5411: if (rowindices) row = rowindices[i];
5412: for (j = Baij->i[i]; j < Baij->i[i + 1]; j++) {
5413: PetscInt col;
5414: col = Baij->j[count];
5415: if (colindices) col = colindices[col];
5416: v = Baij->a[count];
5417: PetscCall(MatSetValues(C, 1, &row, 1, &col, &v, INSERT_VALUES));
5418: ++count;
5419: }
5420: }
5421: /* FIXME: set C's nonzerostate correctly. */
5422: /* Assembly for C is necessary. */
5423: C->preallocated = PETSC_TRUE;
5424: C->assembled = PETSC_TRUE;
5425: C->was_assembled = PETSC_FALSE;
5426: PetscFunctionReturn(PETSC_SUCCESS);
5427: }
5429: PetscErrorCode MatEliminateZeros_SeqAIJ(Mat A, PetscBool keep)
5430: {
5431: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5432: MatScalar *aa = a->a;
5433: PetscInt m = A->rmap->n, fshift = 0, fshift_prev = 0, i, k;
5434: PetscInt *ailen = a->ilen, *imax = a->imax, *ai = a->i, *aj = a->j, rmax = 0;
5436: PetscFunctionBegin;
5437: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix");
5438: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
5439: for (i = 1; i <= m; i++) {
5440: /* move each nonzero entry back by the amount of zero slots (fshift) before it*/
5441: for (k = ai[i - 1]; k < ai[i]; k++) {
5442: if (aa[k] == 0 && (aj[k] != i - 1 || !keep)) fshift++;
5443: else {
5444: if (aa[k] == 0 && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal zero at row %" PetscInt_FMT "\n", i - 1));
5445: aa[k - fshift] = aa[k];
5446: aj[k - fshift] = aj[k];
5447: }
5448: }
5449: ai[i - 1] -= fshift_prev; // safe to update ai[i-1] now since it will not be used in the next iteration
5450: fshift_prev = fshift;
5451: /* reset ilen and imax for each row */
5452: ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1];
5453: a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0);
5454: rmax = PetscMax(rmax, ailen[i - 1]);
5455: }
5456: if (fshift) {
5457: if (m) {
5458: ai[m] -= fshift;
5459: a->nz = ai[m];
5460: }
5461: 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));
5462: A->nonzerostate++;
5463: A->info.nz_unneeded += (PetscReal)fshift;
5464: a->rmax = rmax;
5465: if (a->inode.use && a->inode.checked) PetscCall(MatSeqAIJCheckInode(A));
5466: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
5467: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
5468: }
5469: PetscFunctionReturn(PETSC_SUCCESS);
5470: }
5472: PetscFunctionList MatSeqAIJList = NULL;
5474: /*@
5475: MatSeqAIJSetType - Converts a `MATSEQAIJ` matrix to a subtype
5477: Collective
5479: Input Parameters:
5480: + mat - the matrix object
5481: - matype - matrix type
5483: Options Database Key:
5484: . -mat_seqaij_type <method> - for example seqaijcrl
5486: Level: intermediate
5488: .seealso: [](ch_matrices), `Mat`, `PCSetType()`, `VecSetType()`, `MatCreate()`, `MatType`
5489: @*/
5490: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
5491: {
5492: PetscBool sametype;
5493: PetscErrorCode (*r)(Mat, MatType, MatReuse, Mat *);
5495: PetscFunctionBegin;
5497: PetscCall(PetscObjectTypeCompare((PetscObject)mat, matype, &sametype));
5498: if (sametype) PetscFunctionReturn(PETSC_SUCCESS);
5500: PetscCall(PetscFunctionListFind(MatSeqAIJList, matype, &r));
5501: PetscCheck(r, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown Mat type given: %s", matype);
5502: PetscCall((*r)(mat, matype, MAT_INPLACE_MATRIX, &mat));
5503: PetscFunctionReturn(PETSC_SUCCESS);
5504: }
5506: /*@C
5507: MatSeqAIJRegister - - Adds a new sub-matrix type for sequential `MATSEQAIJ` matrices
5509: Not Collective, No Fortran Support
5511: Input Parameters:
5512: + sname - name of a new user-defined matrix type, for example `MATSEQAIJCRL`
5513: - function - routine to convert to subtype
5515: Level: advanced
5517: Notes:
5518: `MatSeqAIJRegister()` may be called multiple times to add several user-defined solvers.
5520: Then, your matrix can be chosen with the procedural interface at runtime via the option
5521: $ -mat_seqaij_type my_mat
5523: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRegisterAll()`
5524: @*/
5525: PetscErrorCode MatSeqAIJRegister(const char sname[], PetscErrorCode (*function)(Mat, MatType, MatReuse, Mat *))
5526: {
5527: PetscFunctionBegin;
5528: PetscCall(MatInitializePackage());
5529: PetscCall(PetscFunctionListAdd(&MatSeqAIJList, sname, function));
5530: PetscFunctionReturn(PETSC_SUCCESS);
5531: }
5533: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;
5535: /*@C
5536: MatSeqAIJRegisterAll - Registers all of the matrix subtypes of `MATSSEQAIJ`
5538: Not Collective
5540: Level: advanced
5542: Note:
5543: This registers the versions of `MATSEQAIJ` for GPUs
5545: .seealso: [](ch_matrices), `Mat`, `MatRegisterAll()`, `MatSeqAIJRegister()`
5546: @*/
5547: PetscErrorCode MatSeqAIJRegisterAll(void)
5548: {
5549: PetscFunctionBegin;
5550: if (MatSeqAIJRegisterAllCalled) PetscFunctionReturn(PETSC_SUCCESS);
5551: MatSeqAIJRegisterAllCalled = PETSC_TRUE;
5553: PetscCall(MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL));
5554: PetscCall(MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM));
5555: PetscCall(MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL));
5556: #if defined(PETSC_HAVE_MKL_SPARSE)
5557: PetscCall(MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL));
5558: #endif
5559: #if defined(PETSC_HAVE_CUDA)
5560: PetscCall(MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE));
5561: #endif
5562: #if defined(PETSC_HAVE_HIP)
5563: PetscCall(MatSeqAIJRegister(MATSEQAIJHIPSPARSE, MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
5564: #endif
5565: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
5566: PetscCall(MatSeqAIJRegister(MATSEQAIJKOKKOS, MatConvert_SeqAIJ_SeqAIJKokkos));
5567: #endif
5568: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5569: PetscCall(MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL));
5570: #endif
5571: PetscFunctionReturn(PETSC_SUCCESS);
5572: }
5574: /*
5575: Special version for direct calls from Fortran
5576: */
5577: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5578: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5579: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5580: #define matsetvaluesseqaij_ matsetvaluesseqaij
5581: #endif
5583: /* Change these macros so can be used in void function */
5585: /* Change these macros so can be used in void function */
5586: /* Identical to PetscCallVoid, except it assigns to *_ierr */
5587: #undef PetscCall
5588: #define PetscCall(...) \
5589: do { \
5590: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
5591: if (PetscUnlikely(ierr_msv_mpiaij)) { \
5592: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
5593: return; \
5594: } \
5595: } while (0)
5597: #undef SETERRQ
5598: #define SETERRQ(comm, ierr, ...) \
5599: do { \
5600: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
5601: return; \
5602: } while (0)
5604: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[], InsertMode *isis, PetscErrorCode *_ierr)
5605: {
5606: Mat A = *AA;
5607: PetscInt m = *mm, n = *nn;
5608: InsertMode is = *isis;
5609: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5610: PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
5611: PetscInt *imax, *ai, *ailen;
5612: PetscInt *aj, nonew = a->nonew, lastcol = -1;
5613: MatScalar *ap, value, *aa;
5614: PetscBool ignorezeroentries = a->ignorezeroentries;
5615: PetscBool roworiented = a->roworiented;
5617: PetscFunctionBegin;
5618: MatCheckPreallocated(A, 1);
5619: imax = a->imax;
5620: ai = a->i;
5621: ailen = a->ilen;
5622: aj = a->j;
5623: aa = a->a;
5625: for (k = 0; k < m; k++) { /* loop over added rows */
5626: row = im[k];
5627: if (row < 0) continue;
5628: PetscCheck(row < A->rmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Row too large");
5629: rp = aj + ai[row];
5630: ap = aa + ai[row];
5631: rmax = imax[row];
5632: nrow = ailen[row];
5633: low = 0;
5634: high = nrow;
5635: for (l = 0; l < n; l++) { /* loop over added columns */
5636: if (in[l] < 0) continue;
5637: PetscCheck(in[l] < A->cmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Column too large");
5638: col = in[l];
5639: if (roworiented) value = v[l + k * n];
5640: else value = v[k + l * m];
5642: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
5644: if (col <= lastcol) low = 0;
5645: else high = nrow;
5646: lastcol = col;
5647: while (high - low > 5) {
5648: t = (low + high) / 2;
5649: if (rp[t] > col) high = t;
5650: else low = t;
5651: }
5652: for (i = low; i < high; i++) {
5653: if (rp[i] > col) break;
5654: if (rp[i] == col) {
5655: if (is == ADD_VALUES) ap[i] += value;
5656: else ap[i] = value;
5657: goto noinsert;
5658: }
5659: }
5660: if (value == 0.0 && ignorezeroentries) goto noinsert;
5661: if (nonew == 1) goto noinsert;
5662: PetscCheck(nonew != -1, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero in the matrix");
5663: MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
5664: N = nrow++ - 1;
5665: a->nz++;
5666: high++;
5667: /* shift up all the later entries in this row */
5668: for (ii = N; ii >= i; ii--) {
5669: rp[ii + 1] = rp[ii];
5670: ap[ii + 1] = ap[ii];
5671: }
5672: rp[i] = col;
5673: ap[i] = value;
5674: noinsert:;
5675: low = i + 1;
5676: }
5677: ailen[row] = nrow;
5678: }
5679: PetscFunctionReturnVoid();
5680: }
5681: /* Undefining these here since they were redefined from their original definition above! No
5682: * other PETSc functions should be defined past this point, as it is impossible to recover the
5683: * original definitions */
5684: #undef PetscCall
5685: #undef SETERRQ